4a - Sentinel-1 GRD Batch - Subset.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "<font face=\"Calibri\" size=\"2\"> <i>Open SAR Toolkit - Tutorial 4a, version 1.1, Nobember 2019. Andreas Vollrath, ESA/ESRIN phi-lab</i>\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](auxiliary/header_image.PNG)\n",
    "\n",
    "--------\n",
    "\n",
    "# OST Tutorial IV - A\n",
    "## How to do a batch processing of Sentinel-1 GRD data subset\n",
    "\n",
    "--------\n",
    "\n",
    "**Short description**\n",
    "\n",
    "This notebook shows f OST for the batch processing of GRD data using the *Sentinel1_GRDBatch* class. This is a subclass of the *Sentinel1* class, and thus inherits all the functionalities of the *Generic* and the *Sentinel1* classes presented in Tutorial II. In addition, functions for the generation of calibrated backscatter products are provided. Within the example a subset area will be produced, thus avoiding the overload of processing the full images.\n",
    "\n",
    "--------\n",
    "\n",
    "**Requirements**\n",
    "\n",
    "- a PC/Mac with at least 16GB of RAM\n",
    "- about 15GB of free disk space\n",
    "- a Copernicus Open Data Hub user account, valid for at least 7 days (https://scihub.copernicus.eu)\n",
    "--------\n",
    "\n",
    "**NOTE:** all cells that have an * after its number can be executed without changing any code. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1* - Import of Libraries\n",
    "\n",
    "In this step we import some standard python libraries for OS independent path handling as well as the *Sentinel1_GRDBatch* class thta handles the full workflow from search, download and processing of multiple GRD scenes. In addition, the OST helper module *vector* is loaded to create an AOI based on Point coordinates, and the *raster* module for creating a time-series animation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "from ost import Sentinel1Batch as Sen1Batch\n",
    "from ost.helpers import vector, raster\n",
    "\n",
    "# Setup logging so we can see what could go wrong\n",
    "import logging\n",
    "from ost.log import set_log_level\n",
    "set_log_level(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2* - Set up the project \n",
    "\n",
    "Here you going to initialize the *Sentinel1_GRDBatch* class by determining the project folder, the AOI and the start and end date. Since you should be already familiar with the *search* and *refine* functions, we execute them within the same cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:55:57,636 WARNING root Project directory already exists.No data has been deleted at this point butmake sure you really want to use this folder.\n",
      "2020-02-20 09:55:57,647 DEBUG ost.helpers.scihub If you do not have a Copernicus Scihub user account go to: https://scihub.copernicus.eu and register\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " Your Copernicus Scihub Username: suprd\n",
      " Your Copernicus Scihub Password: ·········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:04,513 DEBUG ost.s1_core.search INFO: writing inventory data to shape file: /home/jovyan/Tutorial_4a/inventory/full_inventory.shp\n",
      "2020-02-20 09:56:04,596 WARNING fiona._env Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "2020-02-20 09:56:04,597 WARNING fiona._env Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "2020-02-20 09:56:04,597 WARNING fiona._env Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "2020-02-20 09:56:04,597 WARNING fiona._env Normalized/laundered field name: 'relativeorbitnumber' to 'relativeor'\n",
      "2020-02-20 09:56:04,598 WARNING fiona._env Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "2020-02-20 09:56:04,598 WARNING fiona._env Normalized/laundered field name: 'producttype' to 'producttyp'\n",
      "2020-02-20 09:56:04,598 WARNING fiona._env Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "2020-02-20 09:56:04,599 WARNING fiona._env Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "2020-02-20 09:56:04,599 WARNING fiona._env Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "2020-02-20 09:56:04,599 WARNING fiona._env Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "2020-02-20 09:56:04,600 WARNING fiona._env Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "2020-02-20 09:56:04,600 WARNING fiona._env Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "2020-02-20 09:56:04,600 WARNING fiona._env Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "2020-02-20 09:56:04,601 WARNING fiona._env Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "2020-02-20 09:56:04,601 WARNING fiona._env Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "2020-02-20 09:56:04,601 WARNING fiona._env Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "2020-02-20 09:56:04,737 DEBUG ost.s1_core.refine INFO: Coverage analysis for DESCENDING tracks in VV VH polarisation.\n",
      "2020-02-20 09:56:04,740 DEBUG ost.s1_core.refine INFO: 15 frames for DESCENDING tracks in VV VH polarisation.\n",
      "2020-02-20 09:56:04,752 DEBUG ost.s1_core.refine INFO: 15 frames remain after double entry removal\n",
      "2020-02-20 09:56:04,769 DEBUG ost.s1_core.refine 2\n",
      "2020-02-20 09:56:04,771 DEBUG ost.s1_core.refine INFO: excluding track 124\n",
      "2020-02-20 09:56:04,774 DEBUG ost.s1_core.refine INFO: excluding track 22\n",
      "2020-02-20 09:56:04,776 DEBUG ost.s1_core.refine INFO: 6 frames remain after non-AOI overlap\n",
      "2020-02-20 09:56:04,776 DEBUG ost.s1_core.refine INFO: All tracks fully overlap the AOI. Not removing anything\n",
      "2020-02-20 09:56:04,800 DEBUG ost.s1_core.refine INFO: 6 frames remain after removal of non-full AOI crossing\n",
      "2020-02-20 09:56:05,005 WARNING fiona._env Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "2020-02-20 09:56:05,005 WARNING fiona._env Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "2020-02-20 09:56:05,006 WARNING fiona._env Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "2020-02-20 09:56:05,006 WARNING fiona._env Normalized/laundered field name: 'relativeorbit' to 'relativeor'\n",
      "2020-02-20 09:56:05,006 WARNING fiona._env Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "2020-02-20 09:56:05,007 WARNING fiona._env Normalized/laundered field name: 'product_type' to 'product_ty'\n",
      "2020-02-20 09:56:05,007 WARNING fiona._env Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "2020-02-20 09:56:05,007 WARNING fiona._env Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "2020-02-20 09:56:05,008 WARNING fiona._env Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "2020-02-20 09:56:05,008 WARNING fiona._env Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "2020-02-20 09:56:05,008 WARNING fiona._env Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "2020-02-20 09:56:05,009 WARNING fiona._env Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "2020-02-20 09:56:05,009 WARNING fiona._env Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "2020-02-20 09:56:05,009 WARNING fiona._env Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "2020-02-20 09:56:05,010 WARNING fiona._env Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "2020-02-20 09:56:05,010 WARNING fiona._env Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "2020-02-20 09:56:05,010 WARNING fiona._env Normalized/laundered field name: 'download_path' to 'download_p'\n",
      "2020-02-20 09:56:05,036 DEBUG ost.s1_core.refine INFO: Found 6 full coverage mosaics.\n",
      "2020-02-20 09:56:05,037 DEBUG ost.s1_core.refine INFO: Coverage analysis for ASCENDING tracks in VV VH polarisation.\n",
      "2020-02-20 09:56:05,039 DEBUG ost.s1_core.refine INFO: 11 frames for ASCENDING tracks in VV VH polarisation.\n",
      "2020-02-20 09:56:05,050 DEBUG ost.s1_core.refine INFO: 10 frames remain after double entry removal\n",
      "2020-02-20 09:56:05,067 DEBUG ost.s1_core.refine 2\n",
      "2020-02-20 09:56:05,070 DEBUG ost.s1_core.refine INFO: excluding track 73\n",
      "2020-02-20 09:56:05,072 DEBUG ost.s1_core.refine INFO: excluding track 146\n",
      "2020-02-20 09:56:05,074 DEBUG ost.s1_core.refine INFO: 5 frames remain after non-AOI overlap\n",
      "2020-02-20 09:56:05,074 DEBUG ost.s1_core.refine INFO: All tracks fully overlap the AOI. Not removing anything\n",
      "2020-02-20 09:56:05,095 DEBUG ost.s1_core.refine INFO: 5 frames remain after removal of non-full AOI crossing\n",
      "2020-02-20 09:56:05,320 WARNING fiona._env Normalized/laundered field name: 'polarisationmode' to 'polarisati'\n",
      "2020-02-20 09:56:05,320 WARNING fiona._env Normalized/laundered field name: 'orbitdirection' to 'orbitdirec'\n",
      "2020-02-20 09:56:05,321 WARNING fiona._env Normalized/laundered field name: 'acquisitiondate' to 'acquisitio'\n",
      "2020-02-20 09:56:05,321 WARNING fiona._env Normalized/laundered field name: 'relativeorbit' to 'relativeor'\n",
      "2020-02-20 09:56:05,321 WARNING fiona._env Normalized/laundered field name: 'orbitnumber' to 'orbitnumbe'\n",
      "2020-02-20 09:56:05,322 WARNING fiona._env Normalized/laundered field name: 'product_type' to 'product_ty'\n",
      "2020-02-20 09:56:05,322 WARNING fiona._env Normalized/laundered field name: 'slicenumber' to 'slicenumbe'\n",
      "2020-02-20 09:56:05,323 WARNING fiona._env Normalized/laundered field name: 'beginposition' to 'beginposit'\n",
      "2020-02-20 09:56:05,323 WARNING fiona._env Normalized/laundered field name: 'endposition' to 'endpositio'\n",
      "2020-02-20 09:56:05,323 WARNING fiona._env Normalized/laundered field name: 'lastrelativeorbitnumber' to 'lastrelati'\n",
      "2020-02-20 09:56:05,324 WARNING fiona._env Normalized/laundered field name: 'lastorbitnumber' to 'lastorbitn'\n",
      "2020-02-20 09:56:05,324 WARNING fiona._env Normalized/laundered field name: 'platformidentifier' to 'platformid'\n",
      "2020-02-20 09:56:05,324 WARNING fiona._env Normalized/laundered field name: 'missiondatatakeid' to 'missiondat'\n",
      "2020-02-20 09:56:05,324 WARNING fiona._env Normalized/laundered field name: 'swathidentifier' to 'swathident'\n",
      "2020-02-20 09:56:05,325 WARNING fiona._env Normalized/laundered field name: 'ingestiondate' to 'ingestiond'\n",
      "2020-02-20 09:56:05,325 WARNING fiona._env Normalized/laundered field name: 'sensoroperationalmode' to 'sensoroper'\n",
      "2020-02-20 09:56:05,325 WARNING fiona._env Normalized/laundered field name: 'download_path' to 'download_p'\n",
      "2020-02-20 09:56:05,350 DEBUG ost.s1_core.refine INFO: Found 5 full coverage mosaics.\n",
      "2020-02-20 09:56:05,350 DEBUG ost.project --------------------------------------------\n",
      "2020-02-20 09:56:05,351 DEBUG ost.project Summing up the info about mosaics\n",
      "2020-02-20 09:56:05,351 DEBUG ost.project --------------------------------------------\n",
      "2020-02-20 09:56:05,351 DEBUG ost.project \n",
      "2020-02-20 09:56:05,352 DEBUG ost.project 6 mosaics for mosaic key DESCENDING_VVVH\n",
      "2020-02-20 09:56:05,352 DEBUG ost.project \n",
      "2020-02-20 09:56:05,352 DEBUG ost.project 5 mosaics for mosaic key ASCENDING_VVVH\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "from pprint import pprint\n",
    "\n",
    "from ost import Sentinel1Batch as Sen1Batch\n",
    "from ost.helpers import vector, raster\n",
    "\n",
    "# Setup logging so we can see what could go wrong\n",
    "import logging\n",
    "from ost.log import set_log_level\n",
    "set_log_level(logging.DEBUG)\n",
    "\n",
    "# define a project directory\n",
    "home = str(Path.home())\n",
    "# create a processing directory\n",
    "project_dir = join(home, 'Tutorial_4a')\n",
    "\n",
    "# Vienna lat lon\n",
    "lat = 48.20849\n",
    "lon= 16.37208\n",
    "\n",
    "# define aoi with a 2 point coordinates and create a buffer\n",
    "lat, lon = '47.25', '14'\n",
    "aoi = vector.latlon_to_wkt(lat, lon, buffer_meter=10000, envelope=True)\n",
    "\n",
    "#define the start and end date\n",
    "start = '2020-01-15'\n",
    "end = '2020-02-15'\n",
    "\n",
    "# initialize the class to s1_grd instance\n",
    "s1_grd = Sen1Batch(\n",
    "    project_dir=project_dir,\n",
    "    aoi = aoi,\n",
    "    start = start,\n",
    "    end = end,\n",
    "    product_type='GRD',\n",
    "    polarisation='VV,VH',\n",
    "    ard_type='OST',\n",
    "    processing_dir=join(project_dir, 'processing'),\n",
    "    download_dir=join(project_dir,'download')\n",
    "    \n",
    ")\n",
    "\n",
    "# trigger the search\n",
    "s1_grd.search()\n",
    "\n",
    "# optional: once you did the search the first time, you can load \n",
    "# the full inventory uncommenting the follwoing 2 lines\n",
    "# and commenting the search command\n",
    "#s1_grd.inventory_file = join(s1_grd.inventory_dir, 'full.inventory.shp')\n",
    "#s1_grd.read_inventory()\n",
    "\n",
    "# do the refinement\n",
    "s1_grd.refine()\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### 3* - Plot refined data inventory\n",
    "\n",
    "Here you will visualize the resultant dataframes from the refined search inventory based on the product key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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NpVIpXV1d6fb2NqgYAIAZlUgkGOwNAACAJzXxkiSZTKpUKrF6BADwUxKJhAaDQdAxAAAAMEcmWpJEIhG9fv2a1SMAgJ+WSqWYYwUAAIAnNdGSxDAMHRwcaDgcTvKyAIA5YxiGIpGIGo1G0FEAAAAwR8xJXmwwGFCQAAB+WiwWk+d5cl036CgAAACYIxMtSQAAeAqJRIKZVgAAAHhylCQAgJmTSqXU6/WCjgEAAIA5Q0kCAJg5iUSCeSQAAAB4cpQkAICZEg6HZRiGbNsOOgoAAADmDCUJAGCmJBIJOY4TdAwAAADMIUoSAMBMSaVSrCIBAADAs6AkAQDMlGQyqXa7HXQMAAAAzCFKEgDAzDBNU5ZlqdVqBR0FAAAAc4iSBAAwM+LxuFzXled5QUcBAADAHKIkAQDMjEQioeFwGHQMAAAAzClKEgDAzEilUup2u0HHAAAAwJyiJAEAzIx4PK5GoxF0DAAAAMwpShIAwEyIRqPyfZ/tNgAAAHg2lCQAgJmQSCQ0Go2CjgEAAIA5RkkCAJgJqVRKtm0HHQMAAABzjJIEADATEomEWq1W0DEAAAAwxyhJAABTLxQKKRQKqd1uBx0FAAAAc4ySBAAw9RKJhBzHCToGAAAA5hwlCQBg6iUSCQ0Gg6BjAAAAYM5ZQQcAAOBbUqmUGo1G0DFehL29PRmGIcdxNBgMZNu22u02Ry8DAIAXgZIEADDVDMNQNBpVs9kMOsrcW19fl+M4qlQqisViisViyuVyWl5eliS5rivXdcflSbfb5cQhAAAwVyhJAABTLRaLyfM8ZpI8s0gkokwmo/fv32swGKjb7d7776FQSNFodFyeZDIZLS4uyjCM8Z/PcDhUv99XtVqlPAEAADOJkgQAMNUSiYRGo1HQMeZeqVRSo9H44uwX13XV6/XU6/Xuvd80zXvlSTKZ1NramkKhkBKJhFzX1XA4HK88+bx8AQAAmCaUJACAqZZKpX71whxPK51OKxKJ6Pj4+Ls/1vM82bZ9b+WIbdsyTVPZbFaxWEzRaFSJREL5fF6WZY237YxGI/X7/XF54nneE35VAAAA34+SBAAw1eLxuK6vr4OOMddWV1dVqVTkuu6TfU7P89Tv99Xv9++9/27GzN2PeDyuTCajcDgsz/PG5cndlp92u015AgAAJoaSBAAwtcLhsEzTZCXJM1pcXJTv+7q9vZ3I9Xzff7A8kfSr8mR5eVnr6+vj8sRxHPX7ffV6PXU6HebUAACAJ0dJAgCYWuFwWJK0tbWlm5sbtdvtgBPNF9M0tbCwoNPT06CjSJIGg8GDM1Eikci98mRxcVGrq6vyfV+e541Xnti2rU6nw3HFAADgh1GSAACmVq/X08HBgfL5vNbX1+X7vtrttiqVCqsInkCpVFKv15v6YarD4VDD4fBXJVk4HL5XnhQKBa2srEjSeOXJp8cVP7R6BQAA4FOUJACAqTYcDlWpVFSpVJRKpVQsFrW3t6fRaKTb21vVarWgI86kaDSqZDKpg4ODoKP8sNFopNFopE6nc+/9lmXdK09yuZyWlpZkGMZ4aOxgMFC/31en0+G4YgAAMEZJAgCYGZ1OR51OR6FQSLlcToVCQUtLS7JtW5VKhRe736FUKqlWq83l1hTHceQ4zq9WyIRCoXF5EovFlE6nVSwWZZrmeO7JXXnCccUAALxMlCQAgJnjuq5qtZpqtZpisZgKhYI2NzfleZ6azaaq1SononxFLpdTOBx+cacGua6rXq/3q0HApmneK0+SyaQKhYJCodB45clwOBwPjeW4YgAA5hclCQBgpvX7fV1eXqpcLo9XBvzyyy8aDAaq1WpqNBpBR5w6KysrKpfLvND/yPM82bb9q5VInx5XHIvFxlt3LMuS67rjobF3K086nQ6/pwAAzDhKEgDAXPB9X61WS61WS+FwWLlcTsvLy1pZWVG321WlUpnLrSXfa2VlRY7jUB49wqfHFTebzfH7DcMYn7hzV56k02mFw2GOKwYAYMZRkgAA5s5oNNL19bWur6+VSCRULBa1u7s7Lgeq1WrQEQMRCoWUz+d1fHwcdJSZ5vv++LjiVqt17799Xp58elzxpyfu3JUno9EooK8CAAA8hJIEADDX7mZQmKapbDarQqGghYUF2batm5ubXx0rO89KpRKnuTyzbx1XHIvFFIvFVCwW9erVKzmOo3fv3gWUFgAAfI6SBADwIniep3q9rnq9rkgkonw+r/X1dfm+r3a7rUqlMtdbIuLxuBKJBC/IA/LQccWGYeiP/uiPFIlE2AoGAMCUoCQBALw4w+FQlUpFlUpFqVRKxWJRe3t7Go1Gur29Va1WCzrikyuVSrq+vp7rImjW+L6vXq+nhYUFXV5eBh0HAACIkgQA8MJ1Oh11Oh2FQiHlcjkVCgUtLS3Jtm1VKpW52JpSLBZlmqZubm6CjoLPNBoNLS0tBR0DAAB8REkCAIAk13VVq9VUq9UUi8VUKBS0ubkpz/PUbDZVrVZn9njXxcVFXV5eyvf9oKPgM+12W6urqzJNc2bvLwAA5gklCQAAn+n3+7q8vFS5XFY6nVaxWNQvv/yiwWCgWq02U8fnrq2taTgc/uoUFkwH13U1HA5VKBRY6QMAwBSgJAEA4At831er1VKr1ZJlWcrn81peXtbKyoq63a4qlcpUD9y0LEvZbFaHh4dBR8FXNBoNZbNZShIAAKYAJQkAAI/gOI6ur691fX2tRCKhQqGg3d1dOY6jRqOharUadMRf2djYULPZVL/fDzoKvqLdbmtxcTHoGAAAQJQkAAB8t16vp16vJ9M0lclkVCwWtbCwoH6/r+vra7Xb7aAjKpVKKRqN6uTkJOgo+IbBYCDP85TJZNgWBQBAwChJAAD4QZ7nqdFoqNFoKBKJKJ/Pa21tTdKH1QGVSiWwI3fX1tZUrVblum4g18f3abVayufzlCQAAASMkgQAgCcwHA5VqVRUqVSUSqVULBa1t7en0Wik29tb1Wq1iWVZWFiQ7/sTvSZ+TqvVUqlUCjoGAAAvHiUJAABPrNPpqNPpKBQKKZvNqlgsamlpSbZtq1KpyLbtZ7u2aZpaXFzU2dnZs10DT6/b7co0TUWjUQ0Gg6DjAADwYlGSAADwTFzX1e3trW5vbxWLxZTP57W5uSnP89RsNlWtVuV53pNec21tTbZtq9PpPOnnxfPrdDpaWFjQxcVF0FEAAHixKEkAAJiAfr+vcrmsq6srpdNpFYtF/fLLLxoMBqrVamo0Gj99jUgkonQ6rYODgydIjElrNptaXl4OOgYAAC8aJQkAABPk+75arZZarZYsy1I+n9fy8rJevXqlTqejSqWi4XD4Q597Y2ND9Xr9hz8ewWq321pbW5Npmk++wggAADwOJQkAAAFxHEfX19e6vr5WIpFQoVDQ7u6uHMdRo9FQtVp99OfKZrMKh8OqVCrPmBjPyfM89ft9LSwsfNefPQAAeDqUJAAATIFer6deryfTNJXJZFQsFrWwsKB+v6/r62u12+2vfvyrV690dXXFCoQZ12w2lc/nKUkAAAgIJQkAAFPE8zw1Gg01Gg1FIhHl83mtra1J+rAdo1KpyHGcex+zvLws13VVr9eDiIwn1G63mUsCAECAKEkAAJhSw+FQlUpFlUpFqVRKhUJBe3t7Go1Gur29Va1Wk2maKhQKOjk5CTounsBwOJTjOMrlck8yzBcAAHwfShIAAGZAp9NRp9NRKBRSNptVsVjU0tKSPM9Tt9tVr9cLOiKeyN2WG0oSAAAmj5IEAIAZ4rqubm9vdXt7q1gspmw2q1qtFnQsPKF2u618Ph90DAAAXiRKEgAAZlS/31e/3w86Bp5Yr9eTYRiKx+OybTvoOAAAvChm0AEAAABwX7vdVrFYDDoGAAAvDiUJAADAlGm1Wkomk0HHAADgxaEkAQAAmDJ3Q3oti53RAABMEiUJAADAlPE8T7Zta2FhIegoAAC8KJQkAAAAU6jZbCqdTgcdAwCAF4WSBAAAYAq1222Fw+GgYwAA8KJQkgAAAEyh0Wik0WikQqEQdBQAAF4MShIAAIAp1Ww2lcvlgo4BAMCLQUkCAAAwpVqtlqLRaNAxAAB4MShJAAAAplS/35fv+0omk0FHAQDgRaAkAQAAmGLtdlvFYjHoGAAAvAiUJAAAAFOs2WwqHo8HHQMAgBeBkgQAAGCKdbtdhUIhRSKRoKMAADD3KEkAAACmmO/76vV6WlhYCDoKAABzj5IEAABgyjUaDaVSqaBjAAAw9yhJAAAAply73ZZlWTJN/ukGAMBz4m9aAACAKee6robDoQqFQtBRAACYa5QkAAAAM6DRaCibzQYdAwCAuUZJAgAAMAPa7TYn3AAA8MwoSQAAAGbAYDCQ53nK5XJBRwEAYG5ZQQcAAADA49RqNa2urmptbU2+78vzvPHPnufJdV25rivHcTQcDjUajTQcDjUcDuV5XtDxAQCYepQkAAAAM+Lm5kY3NzeSpFAoJMuyZFnW+O1QKKRwOKxIJKJEIqFQKKRQKCTTNOX7/vjHXanieZ4cx5HjOBqNRuNSZTAYyHXdgL9aAAAmj5IEAABgBt2tGhkMBo96vGmaXyxVLMtSNBodvy8UCknSvZUqvu/fW6nyaalyt2oFAIBZ96iSxDCMY0ltSa4kx/f9v2UYRkHSX0jalHQs6T/0fb/+PDEBAADwMzzPGxcaj2EYxr1C5dO3w+GwEonEvVLFMIwvbgFyHEfNZlOj0UjhcHi8WgUAgGnzPStJ/q7v+zef/PofSvrffd//R4Zh/MOPv/6vnjQdAAAAAuH7/ni1yGMYhjEuTB4qVSKRiOLxuHK53K+2AH1arHy+WuWu2LkbXAsAwHP6me02/56kv/Px7f9F0v8hShIAAIAXyff98XyTh1aJ2LYtSapWq+P3fVqqfFqu3BUrsVhs/JivbQFyHEeu645LldFopMFgIMdxJvPFAwDmxmNLEl/SvzAMw5f0577v/07Ssu/7ZUnyfb9sGMbStz6J53njvyAx/fr9ftARMGW4J/AQ7gt8jnsCD3mK+8I0zXulyUOrVqLR6Hho7de2AN2VK3crVgaDAXNVJoznCjyE+wKfm/Q98diS5G/7vn/5sQj5K8Mwfv/YCxiG8VtJv5WklZWVH4gIAAAAaFxy/MgWoLsS5dMBtp8WKo/dAnT3Yzgc8mIOAObQo0oS3/cvP/5cNQzjLyX9qaSKYRivPq4ieSWp+oWP/Z2k30nSb37zGz8ejz9NckwMf2b4HPcEHsJ9gc9xT+AhQd0XdzNWJH11aOzn23/ufo7FYuOTgO7eL0mHh4cMof1JPFfgIdwX+Nyk7olvliSGYSQlmb7vtz++/fck/XeS/omk/1jSP/r48z9+zqAAAADAc7tbNfIYi4uL2t7e1sHBAVt1AGBOPGYlybKkvzQM4+7x/6vv+//MMIz/R9L/ZhjGfyLpVNKfPV9MAAAAYLpcX1/Lsizt7Oxof3//0eUKAGB6fbMk8X3/UNKfPPD+mqR/+zlCAQAAALOgXC4rFAppd3dX+/v7HFMMADPODDoAAAAAMMvOz89l27Z2d3eDjgIA+EmUJAAAAMBPOjs702g0oigBgBlHSQIAAAD8JN/3dXx8LN/3tb29HXQcAMAPoiQBAAAAnoDv+zo6OlIoFNLm5mbQcQAAP4CSBAAAAHginufp8PBQ0WhUpVIp6DgAgO9ESQIAAAA8Idd1dXh4qGQyqVevXgUdBwDwHShJAAAAgCc2Go10eHioXC6npaWloOMAAB6JkgQAAAB4BsPhUEdHRyoWiyoWi0HHAQA8AiUJAAAA8Ez6/b5OTk60vLysXC4XdBwAwDdQkgAAAADPqNfr6fT0VKurq0qn00HHAQB8BSUJAAAA8Mw6nY7Oz89VKpWUTCaDjgMA+AJKEgAAAGACWq2WyuWyNjY2FIvFgo4DAHgAJQkAAAAwIfV6XdfX19ra2lIkEgk6DgDgM5QkAAAAwATd3Nzo9vZW29vbsiwr6DgAgE9QkgAAAAATVqlU1G63tbu7K9Pkn+QAMC14RgYAAAACcHFxoV6vp93d3aCjAAA+oiQBAAAAAnJ6eqrhcKg3b94EHQUAIEoSAAAAIFAnJydyXVc7OztBRwGAF4+SBAAAAAiQ7/s6Pj6WYRja3NwMOg6gES/ZAAAgAElEQVQAvGiUJAAAAEDAPM/T0dGRIpGINjY2go4DAC8WJQkAAAAwBVzX1eHhoRKJhNbW1oKOAwAvEiUJAAAAMCUcx9Hh4aEymYyWl5eDjgMALw4lCQAAADBFhsOhjo6OVCgUtLCwEHQcAHhRKEkAAACAKdPv93V8fKylpSXl8/mg4wDAi0FJAgAAAEwh27Z1cnKiV69eKZPJBB0HAF4EShIAAABgSnW7XZ2fn2t9fV3JZDLoOAAw9yhJAAAAgCnWarV0eXmpjY0NxePxoOMAwFyjJAEAAACmXKPRULVa1ebmpiKRSNBxAGBuUZIAAAAAM6BWq6lWq2l7e1uWZQUdBwDmEiUJAAAAMCOq1aqazaZ2d3dlmvxTHgCeGs+sAAAAwAwpl8vqdDp68+YNRQkAPDGeVQEAAIAZc35+rn6/r52dnaCjAMBcoSQBAAAAZtDp6alc19Xu7m7QUQBgblCSAAAAADPI930dHx9Lkra2toINAwBzgpIEAAAAmFGe5+no6EjhcFivX78OOg4AzDxKEgAAAGCGua6rw8NDxWIxra+vBx0HAGYaJQkAAAAw4xzH0eHhoVKplFZWVoKOAwAzi5IEAAAAmAOj0UhHR0fK5/NaXFwMOg4AzCRKEgAAAGBODAYDHR8fa2FhQcViMeg4ADBzKEkAAACAOWLbtk5PT7W8vKxsNht0HACYKZQkAAAAwJzpdrs6OzvT2tqa0ul00HEAYGZQkgAAAABzqN1u6/LyUuvr60okEkHHAYCZQEkCAAAAzKlGo6FKpaLXr18rGo0GHQcAph4lCQAAADDHbm9vVavVtL29rXA4HHQcAJhqlCQAAADAnKtWq2o0GtrZ2VEoFAo6DgBMLUoSAAAA4AUol8vqdDra3d2VafIyAAAewrMjAAAA8EKcn5/Ltm3t7u4GHQUAphIlCQAAAPCCnJ2daTQaUZQAwAMoSQAAAIAXxPd9HR8fy/d9bW9vBx0HAKYKJQkAAADwwvi+r6OjI4VCIW1ubgYdBwCmBiUJAAAA8AJ5nqfDw0NFo1GVSqWg4wDAVKAkAQAAAF4o13V1eHioZDKpV69eBR1naqXTaaXT6aBjAJgAK+gAAAAAAIIzGo10dHSk7e1tua6rarUadKSpkE6nVSwWFY/H5fu+DMOQbds6Pj4OOhqAZ0RJAgAAALxwg8FAR0dH2trakuu6qtVqQUcKRCaTUbFYVCwWk+/7ajabqlQqsm1blmWpVCrpl19+0dnZmbrdbtBxATwDShIAAAAA6vf7Ojk50ebmplzXVaPRCDrSRGSzWRUKBcViMXmep2azqaurK9m2fe9xjuPo6OhIhUJBr1+/VrPZ1MXFRUCpATwXShIAAAAAkqRer6fT01NtbGzIdV212+2gIz2LXC6nfD4/LkYajYbK5bL6/f43P/b29lbdblcbGxva29vT8fGxhsPhBFIDmARKEgAAAABjnU5HFxcXKpVKOjk5mZttJblcToVCQdFo9LuLkc8NBgMdHBxoeXlZu7u7qlarurm5eYbUACaNkgQAAADAPc1mU6ZpamNjQ0dHRz9UJEyDT4uRuy1EFxcXGgwGP/25fd/X1dWVWq2WNjY2lM1mdXR0JM/zniA5gKBQkgAAAAD4lXq9LsuytLW1pffv38/MlpJCoaBcLvcsxchDer2e3r17p7W1Nb19+1aXl5dqNpvPci0Az4+SBAAAAMCDrq+vFQqFtL29rYODAzmOE3SkBxUKBeXzeUWjUTmOo3q9/qzFyOc8z9PZ2Zmy2axWV1eVy+V0cnIykWsDeFqUJAAAAAC+6OrqSqFQSDs7O9rf35+a7STFYnG8YmQ0GqnRaKjZbAa64qXZbKrX642PCj45OfnVKTkAphslCQAAADAlDMOQZVkyDOOL//053v+txzWbTSUSCb1580Z/+MMfHnzsJHxejNTrdbVaranaCjQajXR4eKhisaitrS3V63WVy+WgYwF4JEoSAAAAYAqYpqnd3V2FQqGvPs73/Z+6zo9+vO/7MgxDb9680f7+/k9l+B4LCwvK5XKKRCIaDofjFSOj0WhiGX5ErVZTp9PR69evlUqldHx8PPWZAVCSAAAAAIEzDEObm5tyHEfv3r0LOs4XGYah7e1t7ezs6P37989yDdM0VSwWlc1mx8XI3YqRWSsZBoOB9vf3tbKyMj4quFarBR0LwFdQkgAAAAABW1tbk2VZU12QSB9WkxwdHWlnZ0ebm5s6Pj5+ks87T8XI53zfV7lcVqvVUqlUUjab1fHx8dTMdgFwHyUJAAAAEKDFxUWlUqmpL0jueJ6nw8ND7ezsaGNjQ6enpz/0eUzTVKFQ0OrqqsLhsAaDger1uprN5tSeovMzut2u9vf3x0cFn5+fq91uBx0LwGcoSQAAAICAZLNZLSws6PDwcKZWFriuO15Rsrq6qsvLy0d9nGmaWlhYUDabVb/f12AwUK1WU6vVmsti5HOu6+r09FS5XE7r6+vqdrs/XDIBeB6UJAAAAEAAEomE1tbWdHZ2psFgEHSc73Z3isvOzo5c11WlUnnwcaZpanFxUZlMRuFwWP1+X7VaTZVKRY7jKB6PTzh58BqNhrrdrjY2NvT27VudnJyo3+8HHQuAKEkAAACAiYtEInr9+rWq1epMb7kYDoc6OjrS1taWXNfVzc2NpIeLkZubG7VaLbmuK0kvYuXI14xGI71//16Li4va3t4eF0cAgkVJAgAAAExQKBTS1taWWq3WuFSYZf1+XycnJ9rc3FQ8Hlc8HpdlWbJt+1fFCH7t+vpa7XZbGxsbymQyOjo6evEFEhAkShIAAABgQu6O+h0Oh7q4uAg6zpPp9Xo6OTnRwsKCrq+vKUa+U7/f1/7+vl69eqU3b96oUqno9vY26Fh4QqZpamlpSclkUoPBQO12W81mM+hYeAAlCQAAADAh6+vrMk1T79+/DzrKk+t2u+p2u0HHmFm+7+vy8vLeUcEnJyczNdAXv1YsFpXP5xWJRGTbthqNhuLxuJaXl7W2tibXdTUcDtXr9dRsNplNMwUoSQAAAIAJuPsu8qwc9YtgdDodvXv3TqVSSXt7ezo/P1en0wk6Fr5DOp3WwsKCYrGYHMfR7e3tg0dbm6apeDyuRCKhZDKpQqEg6cMpSLZtq9PpqNlsUpRNGCUJAAAA8MxyuZyKxaLev3/PCx58k+u6Oj4+Vj6f18bGhlqtls7Pz4OOha+IRqPjIlT6cILR5eXlV0+u8jxvvALr+vpa0oehznfFycLCglZXV+W6rkajkWzbVrPZZMXWM6MkAQAAAJ5RMpnU6uqqTk9PNRwOg46DGVKv1+8dFXx8fDyTx0XPK9M0tby8rHQ6Lcuy1G63dXZ29lMlxnA41HA4HM8rMQxDsVhsvNqkVCrJNE05jqPBYKBut6tGo8Gw3ydESQIAAAA8k2g0qo2NDVUqFbZM4IcMh0MdHBxoaWlJOzs7urm5UbVaDTrWi/b5nJFKpaJWqyXf95/8Wr7vy7Zt2batWq0mSbIsa7zaJJvNamlpSb7vj1ebtNtttVqtJ8/yUlCSAAAAAM8gFAppc3NTjUZj/OIG+FHVavVXRwVzgtDkPDRnpNFoBPJn4DiO2u222u22KpWKpA+F7N1qk5WVFa2vrzMU9gdRkgAAAABPzDAMbW1taTAYqFwuBx0Hc8K2be3v72t1dVV7e3sql8tqNBpBx5pbsVhMi4uL4zkj9Xr9m3NGgjIYDDQYDFSv1yXdHwqbSqVe/FBY0zQf/VhKEgAAAOCJlUolGYah4+PjoKNgzniep/Pzc2UyGa2trSmXy3GfPaFQKKSlpSVlMhmFQiG1Wq2fnjMShK8NhU0mk3M9FNayLGUyGSWTScViMYVCIYVCocd//DNmAwAAAF6clZUVJRIJjvrFs2q1Wur1eiqVSvrll19m8oX8NFlYWFA+n1c4HJZt2yqXy2q3288yZyQoXxsKm0qlZnIobDgcHhci0WhUlmXJMIzxNqPr62v1+/3vWv1DSQIAAAA8kXw+r3w+z1G/mAjHcXR0dKRCoaDXr1+Pj53F42QymfGckeFwqFqtpmaz+WJmvczaUNhIJKJsNqtEIqFIJDIuRAaDgXq9nlqtlmzb/untUJQkAAAAwBNIpVJ69eqVTk5OOOoXE3V7ezs+Knhvb0/Hx8fcg18Qi8W0tLSkRCIh6cOckfPzc36/PnpoKGwsFhtv0/l8KGy321Wz2XzyOS2xWEyZTEbxeFzD4VChUEiZTObeChfbtp/lz42SBAAAAPhJ0WhUpVJJ5XKZLQ8IxGAw0MHBgZaXl7W7u6tqtaqbm5ugY00Fy7K0uLg4njPSbDZ1enqqXq8XdLSZ0O/31e/3vzgUtlgsSvpQsPT7/e8eChuPx5VOp5VMJhUOh2VZlnzfHxcitVpN/X5fljWZ+oKSBAAAAPgJlmVpa2tL9Xp9/CICCILv+7q6ulKr1dLGxoay2ayOjo5e7NavxcVF5XI5hcNh9Xq9uZwzEoTvHQp7txWm2+0qmUwqlUqNt8yEQiH5vq9+v69utyvbttXv9zUajcbXs21bkihJAAAAgGlnGIY2Nzdl27aurq6CjgNIknq9nt69e6e1tTW9fftWl5eX42Gd8y6bzapYLL7YOSNBecxQ2FAoJNd1x6tN7gqRaRsMS0kCAAAA/KCNjQ1J0snJScBJgPs8z9PZ2Zmy2axWV1eVy+Xm9j6Nx+NaXFwczxm5vb1lzkjAHhoKaxjGTKzioSQBAAAAvuHuu6J33xmNx+OKRCJyHEcHBwdBxwO+qNls3jsq+OTkZLx9YZZZlqWlpSWl02nmjMyIWShIJEoSAAAA4J5QKDQuRJLJpGKxmMLhsFzXleM4GgwGur29VavVmrpl4sBDRqORDg8PtbCwoK2tLd3e3s7s9jDmjOC5UZIAAADgxQqHw+PjLROJhGKxmEzTHA8c7Pf7urq6UqfTebHDLzE/bm5u1Ol0tLGxoXQ6raOjo5ko+pgzgkmiJAEAAMCLEI1GFY/Hx4VINBqV9OHYyrsTGK6vrznCF3Ot3+9rf39fKysrevPmjarV6nhmxDSJx+NaWlpSIpGQ53mq1+vMGcFEUJIAAABgrpim+eD8EM/zxttlms2mWq0WL7jwIvm+P96msr6+rmw2q+Pj48BXS1mWpeXlZaVSKWWzWbVaLR0fH8/FDBXMDkoSAAAAzLR4PK5kMjmeH2JZ1nh+SL/f1/X1tVqtVuAvAIFp0+l0tL+/r/X1de3t7eni4kLtdnuiGUzT1MLCgrLZrMLhsCqViiqVisrlMnNGEAhKEgAAAMysUCikra0t9ft92bat29vbib/IA2aZ67o6OTlRLpfT+vq6Op2Ozs7Onv26uVxOxWJR0WhUg8FANzc3ajab4+1u8Xj82TMAD6EkAQAAwMxKp9NyHEeHh4dBRwFmWqPRULfb1cbGht6+fauTkxP1+/0nvUYikdDS0pLi8bg8z9Pt7a0ajYZGo9GTXgf4GZQkAAAAmFmZTEadTifoGMBcGI1Gev/+vRYXF7W9va1araZKpfJTn/PTOSOmaarZbKpSqTBnBFOLkgQAAAAzK5VKsYoEeGLX19fqdDoqlUrKZDLffVTw3ZyRXC4ny7LU7XZ1eXmpTqfDnBFMPUoSAAAAzKS7o0GfeksAAMm2be3v72t1dVVv3rzR1dWV6vX6Vz/m8zkj19fXajabDE3GTKEkAQAAwExKp9MaDAZBxwDmlu/7uri4UKvVGh8VfHp6eq/0SCaTWlxcZM4I5gYlCQAAAGZSJpPRzc1N0DGAuddut/Xu3TuVSiXt7e3p6upKqVRKqVRKhmGo0WgwZwRzg5IEAAAAM8eyLIXD4W8u/wfwNFzX1fHxsfL5vFZWVtTr9XR+fs7gZMwdShIAAADMnHQ6zXJ+IAD1ep1yEnPNDDoAAAAA8L0ymYy63W7QMQAAc2aiJYlhGJO8HAAAAOZUMplkHgkA4MlNtCSJRqOKRqOTvCQAAADmTDKZlOd5Gg6HQUcBAMyZiZYkrutqZ2dHuVxukpcFAADAHEmn0+r3+0HHAADMoYmWJKPRSGdnZ3r16pXW19fZfgMAAIDvlslk1Gg0go4BAJhDEx/c2m63tb+/r3g8rt3dXUUikUlHAAAAwIwKh8OyLIuSBADwLAI53cZxHO3v78u2be3s7CiTyQQRAwAAADMmlUpx9C8A4NkEegTw+fm5yuWy1tbW9OrVK7bfAAAA4Kuy2aw6nU7QMQAAcyrQkkSSGo2G9vf3lU6ntbOzo3A4HHQkAAAATCHDMJRIJFSr1YKOAgCYU4GXJNKH7Tfv3r3TcDjU7u6uUqlU0JEAAAAwZRKJhFzX5ehfAMCzmYqS5M7p6akqlYpKpZKWl5eDjgMAAIApkslkOPoXAPCspqokkaTb21u9f/9euVxO29vbsiwr6EgAAACYAhz9CwB4blNXkkjScDjUH/7wB3mep93dXSUSiaAjAQAAIEDhcFihUEjNZjPoKACAOTaVJcmd4+Nj3dzcaHNzU4uLi0HHAQAAQEDS6TSzSAAAz27q97Lc3Nyo2+3q9evXSiaTOjs7k+u6QccCAADABHH0LwBgEqZ6Jckd27b1+9//XqZp6s2bN4rH40FHAgAAwIQYhqF4PK6bm5ugowAA5txMlCR3Dg8PdXt7q62tLRUKhaDjAAAAYAKSyaRc15XjOEFHAQDMuanfbvO5arWqbrerUqmkVCql8/NzeZ4XdCwAAAA8k3Q6Ldu2g44BAHgBZmolyZ1ut6t3794pHA5rd3dX0Wg06EgAAAB4Jhz9CwCYlJksSSTJ8zy9f/9erVZLOzs7yuVyQUcCAADAE4tEIgqFQmq1WkFHAQC8ADO33eZzV1dX6na7Wl9fVyqV0sXFhXzfDzoWAAAAngBH/wIAJmlmV5J8qt1u6927d4rH49rd3VUkEgk6EoBndPddxXlRbg111hyq0hmqYbvqDV05HHUO4AWzLEuZTEZra2taWlpiFQkAYGJmfiXJHdd1tb+/r7W1Ne3s7Oji4oK/UIE5YFmW4vG4EomEksmkYrGYfN+XYRhqt9u6vb1Vt9sNOuYP831fV11HfzN/+m9ObrBMQ5YpRUKGwiFTkdCHtyOmqXBIioQ0V2URgJfLsiwlk0mlUimlUimFQiE5jiPbtnV5ealmsxl0RADACzE3Jcmdi4sLdTodra2tKZlM6urqiu03wIwwTVOxWGxciMTjcZmmKcdxNBwO1el0dH5+ruFwqEgkouXlZW1sbMjzPNVqNTUajZk7HrI39PSlA7ocz5fjSX3Hl/Twg6yQoYhpfCxPDFmWFAmZH34dkqIWJQqA6fO1UqRcLvONLgBAYB5dkhiGEZL0ryRd+L7/9w3D+BNJ/5OklKRjSf+R7/tT8Tdas9lUt9vV9va2ksmkTk5ONBqNgo4F4DOxWEzxeFzJZFKJRELhcFiu62o4HMq2bd3c3HxxlchwONTZ2ZkkKZfLaWFhQUtLS+p2u6rVaup0OpP8Un5YZ/RzR5g7ri/H9dX7wlOcISlsGQqHDIXND6tRwiEpFgp9XJkiWaxGAfDMwuGwwuGwksmklpeXx6VIr9fT5eWl2u120BEBAJD0fStJ/lNJ/0ZS5uOv/2dJ/7nv+/+nYRj/QNJ/Iem/fuJ8P8xxHL17904bGxva3d3V2dnZzLxoAuZROBy+t20mGo3K932NRiP1+31VKhW12215X1pW8RWNRkONRkOWZWl5eVnr6+uSpNvbW9Xr9akuSTvDnytJvsWXNHR8DX+1GuVvVtyEDEPhkBQOGYqETIVNKWqxrQfAj7srRO5WipimqXq9Pt4+QykCAJhWjypJDMNYl/TvSPrvJf1nH9/9VtK//Pj2X0n65/pGSeJ5nmzb/rGkP+gPf/iDstmslpaWJEnX19cTvf4s6/f7QUfAlHnsPWGapuLx+HjrTCwWk2EY420zFxcXarfbz7I15u4f3ul0WoVCQSsrK+r3+6rX61P5j/JWuy/HC3ZLoCNp8I3HWKahsPlhRUrENGR9LFUs05BGfYXZ1oNP8PfHy3NXhKdSKSUSCZmmqdFopE6no3K5rE6nM74vYrFYwGkxLXiuwEO4L/C5Sd8Tj11J8j9I+i8lpT9537+W9O9K+seS/kxS6aEPNAzjt5J+K0krKys/HPRnNJtN9Xo9bWxsKJFI6OLiYubmFgDTyjAMRaNRxWKx8WDVUCgk13U1Go1k27aur68n/uTWbrfVbrdlmuZ4K87KyoqazebUrC4ZOa6GARckj/VhPoov+4GnTnc4kqGRojF93NZjKGx9GDob/ThwNmyyrQeYN5ZlaWlp6V4pcrdSpNfrBR0PAIAf8s2SxDCMvy+p6vv+/2sYxt/55D/9A0n/o2EY/42kfyLpwQPsfd//nf5/9u4kNrK0XxP6875njDgxOjxGOu2sr6qudPVx0ZWQWNALEEIsGuglAqm33Q1C6gsIIV0JIbFgg1jw9Yq+ArHpHYIVGzboLliwAMEGUVlVWemsTKedjgg7HGeeWcTgsNPpOeLE8PykUmaVh3jLQ9jnOf8B+BsA+OMf/5iXSqVnH/qpPn/+jDdv3uAv/uIv8PHjx6XeiDFPRX7OaPFomobNzc1J24yu65NAJAgCDAaDryo2hBCFfh2NAxPLsrC9vY3Xr18jDEN0u10MBoPChjt7XgJVX4lN7ACAXDUQAYhyALdkUIoA1Fs29OhT81HY1rNa+PNjtW1tbaHZbOL4+Pir36nu+tzz64Ju4tcE3YZfF3TTvL4mHlJJ8ncA/D0hxN8FYAKoCSH+WZ7nfx/Avw4AQog/w7AdZ+EdHR2h1Wrh8PAQnU6H7TdEd7i5fjeOY+R5jlKphCAI0O12cXl5+aQ5IkVwXRfv37+HlBJbW1vY3d1Fu91Gv9/H+fk5wvC+ppOXNet5JIsmzXOkCRDeMR9FlWIUnshJW4+hSmhCQFcBU2OIQrQo6vU6er0ebzoREdFKuTckyfP8rwH8NQCMKkn+kzzP/74QYjvP8zMhhATwn2G46WYpjH+gv3nzBpZl4ePHj0jTtOhjERXqvvW7tm3j5OQEcRwvfbKfZRm+fPmCL1++oFQqYWdnB3/4wx8QxzF6vd7cgh/vmZttVtF47bEf3/6cLHC19ng4aFZAU4fVKMZoc4/OahSimVMUBbqu4+LiouijEBERvajHbLe56d8VQvwHo7//zwD+hxc4z9wEQYCffvoJ3333HX788Ud8+PBh7kNliYqkqiqq1eqj1u8uwhyPl+b7Po6OjgAMS8e3trawt7eHy8tLnJ+fz+x5Ic0yBAxJHi0HEKc54jS/0dJzVY0icbX2eLqtx2BbD9GLqVQqSJJkaSoJiYiIHupRIUme538L4G9Hf/8TgD+9/JHm6/3799ja2sJ3332HL1++oNfrFX0kopmr1+tot9tIkgS+7z9r/e4qGbfgmaaJ7e1tvHnzBmmaotvtot/vv+jHx41yLMnM1qWTYdjS89C2Hm3y96tQpaQzRCG6S71eZ5sNERGtpOdUkqyMTqcD13VxcHAAy7Lw6dOntb9YpNUkpUS73Ua1WsXx8TEGg0HRR1pIQRDg999/BwC0Wi20Wi3s7u7Ctm30er0X2drghmzxK9Jz2nr0qYGzROuqUqngt99+K/oYREREL44hyYjnefj555+vtd9wRzetEtM0cXh4iDRN8fbtWwaBD9Tr9dDr9aDrOnZ2dnB4eIgsy9Dr9XBxcfHkeUZOwjKSRca2HqJvK5VKyLKMvycREdFKYkgyJcsyvHv3Dru7u/jDH/6Ak5MTDiSjldBqtbCzs4Pz83Ocnp4WfZylFEURPn78CABoNBrY3NzE9vY2XNdFr9eD4zgPfl95nsOLWEmy7NjWQ+uqVqvNfRsYERHRvDAkucXp6Skcx8Hr169hWRaOj4+R57zrS8tHURS8fv0apmni6OjoRdpECOj3++j3+1BVFTs7O9jf3wcAnJ+f4+Li4t4Bt36cIWEhz1pgWw+tolqthm63W/QxiIiIZoIhyTc4joO3b9/i+++/x48//oijoyNEUVT0sYgezLIsvH79erLJiV5ekiQ4Pj4GMBxiuLm5ic3NTXieh16vB9u2b307J2JCQkNs66Flo6oqNE1jpS0REa0shiR3yLIMv/zyC169eoXvv/8enz9/xuXlZdHHIrrX7u4ums0mzs7OuLFpTi4vL3F5eQkpJXZ2dvDq1SsIISbVJdMhq8fVv/QIbOuhRVKtVldyHTwREdEYQ5IHOD4+hm3bePXqFSzLwsnJCdtvaCHpuo6DgwNIKfHu3TtWPxUgyzKcnJzg5OQElUoF29vbaLVaCIIAvV4Pg8EAbsznD3pZ97X1ABhVorCth56nVqtx9S8REa00hiQPNBgM4Hke/vCHP+D777/Hhw8feCeFFkq9Xke73YZt2/j06VPRxyEM2/Ycx4GUEtvb29jd3UVrcxvv/Y9IBjYSPofQHLGth56qVCqhVquhVqtB0zT8+uuvRR+JiIhoZhiSPEKSJPj5559xcHCAH374AZ8+ffrmzAGieZFSot1uo1qt4vj4GIPBoOgj0Q1ZluH09BSnp6cIoEHTSmi320jieFhZ4rqsTqPCsa2HxoQQqFQqk2Akz3OEYYhut8tZJEREtPIYkjzB77//jkajgf39fVxcXHClKhXGNE0cHh4iTVO8ffsWWcZZF4uue+niizOcbdRoNNBo1LHRasFzXVxeDhDHbJGixfXQth4Rh9AUAStU2NazJFRVRbVaRb1eR7lcRpqm8DwPv//+O9triIhorTAkeaJ+vw/P8/Ddd99N5pRwvSrNU6vVws7ODnq9Hr58+VL0ceiBpjfbjFcJa5qO5kYTu3u7yNIUl7YNz3EYetFSitMcSZIBCeDm4yoUtvUsIsMwUKvVUK/Xoes64jiG4zj49OkTkiS5/x0QERGtIIYkzxBFEQaDAer1Og4PD5EkCTqdDi4vL1k6TzOjKApev34N0zRxdHTEcKQ+E8gAACAASURBVG6JZHkO/5bNNnEc4WwUdFVrNdSrVWw0m/A8D/ZggDAM531Uopl5ibYeU5MQQsz76EtPCIFyuYx6vY5arQYhBMIwxMXFBS4uLhjMEhERgSHJs1SrVTQajckWka2tLezs7GBvbw+9Xg/n5+e8E0MvyrIsvH79GkEQ4Keffir6OPRIXpQhuyc/tQcD2IMBVE1Do9HA9s4OkGfoXw7guQ7SlBcxtPq4reflKIqCarWKWq2GSqWCNE3h+/5kcx8RERFdx5DkiXRdx/7+Pk5OTiZrVjudDjqdDizLwu7uLjY3N2HbNrrdLnzfL/jEtOx2d3fRbDZxdnaGXq9X9HHoCezo2xd8NyVxjG6nAwCoVKuoVatoNpvwfR+ObfM5hdYet/V8m67rkxs5hmEgSRI4joNff/2Vq+GJiIjuwZDkCYQQODw8hG3b6Pf7X73cdV28e/cOqqpib28Pb968QRzHk1YcosfQdR0HBweQUk6qlmg5ufHT2vAc24Zj21BUBc1mE5ubmwCGq8kd10GaPDx8IVoX69bWUy6XJ/NFFEVBGIYYDAbo9XpsoyEiInoEhiRP0G63AQCfPn268/WSJMHHjx8BANvb29jd3b3WipOmvLChu9XrdbTbbdi2fe/XGy0+L3ze93yapOh2ugAAq2KhXquh3mggDALYts35NESPtMxtPVJKVCoV1Ot1VCoV5HmOIAhwenrKGzJERETPwJDkkRqNBmq1Gn755ZdHvd3Z2RnOzs5Qq9Wwvb2Nra0tDAYDdDodDmWkr0gp0W63Ua1WcXx8jMFgUPSR6Jn8OMUtM1ufzHVcuI4LRZFoNppotVpobW7CsW3Yjo0k5jwkopewSG09mqZN2mhM00SSJPA8D0dHR2zBIyIieiEMSR7BMAy02218/PjxyQNZB4MBBoMBdF3H3t4evv/+ewRBgE6nwwFqBAAwTROHh4dI0xRv375lmfSKcKPZfB7TNEO31wN6vcnWimqthjiKMBhcwvN8btsimqFZt/WUSiXUajXUajVomoYoimDbNn7//XcOhyciIpoBhiQPJKXE4eEhLi4uXiTMiKIIHz58gJQS29vbePXqFfI8R7fb5Rq+NdZqtbCzs4Ner4cvo5WwtBpmFZJM8zwPnudBSolGo4GNjQ1stCQ818HlwEYSx/e/E6ICWVYZhmEiy7LhPwCQ58jzDHmWI8/z0cty5MgB5MizDHmOhf65+ai2HlWiWS1jo1HDZqMGXVMgsuFcs9vmoBEREdHLYkjyQPv7+0jTFCcnJy/6frMsw+npKU5PT9FoNCZrhPv9PrrdLod0rglFUXBwcADDMHB0dMTZEivIeeLQ1qfIsgzn5+c4Pz+HaZpoNBpot9tI4giDgQ3XdVldQgtH1TS0NrcQBAEEhjcnhsUVAkIICIGpaguB0QuHL5t6PzmAPMsAjEOV4Z95niMbhSyYhC3Df3Jg9DrFhDGKqsAolWGVyzBME0mW4sNFiP/3998QBAGA9d7WQ0RENE8MSR5gY2MD5XIZP//880wfp9/vo9/vwzRN7O3t4YcffoDv++h0OnAcZ6aPTcWxLAuvX79GEAT46aefij4OzUCcpAheciDJI4wHOQLDmUqNRh0brRY818Xl5QBxzCCWFsP21hY810W3233y+5BSQioKBAQURUJRJISQEFJCkXL4ciEAKaCoKlQhIMU4hPlGGAMAo5dN/ZdJsII8m4QxwPDP6TAmzzOkaTYJacZhDPIcqqrBsiyomoY0SRAEPrrH3Vs3Vq3bth4iIqKiMCS5R6lUwu7uLo6OjuZWyhsEAd6/fw8pJXZ3d7G/v48syyatOLwDvBqEENjZ2UGz2cTZ2Rl6vV7RR6IZeerq35c2DmJ1XUez2cTu3i6yNMWlbcNznIVuV6DVVq3VoCjyWQEJgEllCADMsrtMSgkhBaRQ7g1jpFShqLeHMWmawXYc2C80nHuZt/UQEREtCoYkd1AUBYeHh+h0OoW0P2RZhs+fP+Pz589otVrY3Ny81ooTc77A0tJ1HQcHB5BS4t27d2yrWnHzmEfyGFEUTWbe1Go1NGpVbDSb8DwX9sDmxq1HUlQFilSgacMfqRyW+ziqpg7D4iWaw5RlGZABKdKZhjGzsEjbeoiIiBYRQ5I7HBwcIAxDdDqdoo+CXq+HXq+HUqmEvb09/Pjjj3Bdt7AAh56uXq+j3W7Dtm18+vSp6OPQHCxKJcltxhu3VE1Ds9nE9s4O8izF5cCG5zpI08UKeOZtOgCRigpNVaGooz8VBVLKyQyMPM8ghOCw3EfabG3C9/3J7A0q1ku09RiqgJRy3kcnIiJ6EQxJvmF7exuGYSzcjAjf9/Hbb79BURTs7e3h8PAQSZKg0+ng8vKSdy8XmJQS7XYb1WoVx8fHGLxQeTUttjzP4d5R+r4okjhG5+wMAFCp1VCvVtFsNuH7PuzBYCUvYG8LQFRNg6ootwYgWZYhTlJkaYJBECAOQ8Rx/FWbkmEYaDabaLfbk1XMrssw+zaVSgWapuH048eij0KP8NS2njxOoSmAoqds6yEiooXFkOQWlmVhc3MTv/32W9FH+aY0TSdVCOONOHt7ezg/P0ev10OSJPe8B5on0zRxeHiINE3x9u1bzn5YI16UYdk+3c5gAGcwgKIqaDab2NraApBjMLDhuM6tQyUXjaqpkEJOAhBdU6Go1wOQLM+vDdZM0hRJHMPzPERhiCRJnvS9GoYhTk9Pr69i3mjBtm3Yjr0UH795UBSJ5sYGznvPm0NCi+m2tp5k1Fqq2pJtPUREtLAYktygqioODg7w5cuXpblz2ul00Ol0YFkWdnd30WoNfxnvdrvwfb/o4629VquFnZ0d9Hq9yRwIWh9OQVttXkKapOh2hhewlUoFtVoN9UYDYRDAtu3CWv1UTYUiFaiq8nUAoqqQQkwCkPEgzzhJrgUg85gDNL2KuVwuo9Goo1avIwgC2IPB2j8/b7Q2EUcRq2zW1EPbelQJGCrbeoiIaH4YktxweHgIz/OWctOI67p49+4dVFXF3t4e3rx5gziOJ604NF+KouDg4ACGYeDo6IizY9aUs2BDW5/KcRw4jjO8+99ootVqodVqwXEcXPQ6L1K9JoSYtMCMgxBNU6GqGlRVhVSUqQAkG65azaYqQFwXURQt5CBkz/Pged7w49fcwObmJoAcl5cDuGs4+6VcLsM0TRxzLhPdYdzWE9xRfaUqAsZUW4+qAIaqQJOAoXJbDxERPR5Dkil7e3tQVRXv3r0r+ijPkiQJPo76u7e3t7G7u4u9vT30ej2cn58jTVnqPWuWZeH169cIgmDh5trQfHkrEpKMpWmGbq8H9Hool8uo1+vY2d1FHEYIk+Sbm12eE4BEUQTXdRGGEeJ48QKQx0jTbLLmdlyd02g24XkeBpeXCxnwvDQpJVqtFi76fbYe0rMlaY7kgdt6NCmhK4CmACbbeoiI6BsYkoxUq1U0Go2lD0huOjs7w9nZGWq1Gra3t7G1tYXBYIBOp8M1nzMghMDOzs5wneXZ2VJWJNHLiZIUUbq6w5TH1RFZEqFWq2GjtYmNloTvugAARVXvDEDiJFmpAOSxxtU5qqai0WhiZ3cXWZri0rbhOc7KBggbGxtI0wQOh1fTHDymrUdXxHBjz422HgYpRETrhSEJAF3Xsb+/j5OTk5W9izde86nrOvb29vD9998jCAJ0Oh3Ytl308VaCrus4ODiAlBLv3r1b2a8lejg7Wt2AZFqWZej3+3C8AKZpolqrIc/zSQAShCFX4d4hiRN0R6vmq7UaGrUqNppNeK6L/uXlSn3sDMNAqVzG58/HRR+FaOKqredmkHJFHW3r0Ud/qupwBfIwWBm2+BAR0WpY+5BECIHDw0PYto1+v1/0cWYuiiJ8+PABALC7u4tXr14hz3N0u11cXFys7J3LWavX62i327Bte7J1iGhV5pE8RhAESzP0ehHZgwHswQCapmNjo4m9vT2kSYzLwQCe6y31mnchBLa3tzC4vOSGH1o647Ye7xuZ5bitR71lW48xavFRWY1CRLQU1j4kabfbALCWF7anp6c4PT1Fo9GYrBHu9/vodrusgnggKSXa7Taq1SqOj48xYPk4TfGWeLMNFSuOo8k2rEajgY1mExsbLbiOg4FtL2V1SaPRQJ7nHCROK2m6rcd9QFuPpsjRTBS29RARLZq1DkkajQZqtRp++eWXoo9SqH6/j36/D9M0sbe3hx9++AG+7+PDhw9wR7MF6GumaeLw8BBpmuLt27eswqFr0ixDwJCEXsD0c3Sj0UC73UYUhhgMBkuzNUvTdFRrNXz+/LnooxAVhm09RETLYW1DEsMw0G638fHjxxdZXbkKgiDA+/fvIaWcbMTJ8xxBEKDPLQTXtFot7OzsoNfrTe72Ek1zoxzZ8nZG0AIKggCnp6eQUqLRaKDV2kCr1YJt27Ade6FbWLa3t+A5zlJWwBDN03PaesbbetjWQ0T0PGsZkkgpcXh4iIuLCw4tvUWWZfj8+TN830ej0cDh4eG1Vpx4jX/JVRQFBwcHMAwDR0dHS3MXl+bPDRf3gpWWW5ZlOD8/x/n5OSxruIa5Vq8jCAIMLi8XbiZMrVaDlGK4OpqInuWhbT2KBAy29RARPclahiT7+/tI0xQnJydFH2Xh9ft9hGGIUqmE3d1d/Pjjj3BdF91ud+1acSzLwuvXrxEEAX766aeij0MLzklYRkKz57oeXNeDoipoNpvY2toCkKN/OYDnOkjTYisAVU1Do9nEl9PTQs9BtE7GbT1frz2+Mm7r0RQBQwqoCqCrEpoUMFS29RDRelu7kGRjYwPlchk///xz0UdZKr7v4/3791AUBXt7ezg4OECSJOh0Ori8vFzqjQv3EUJgZ2cHzWYTZ2dn6PFuKN0iz3MEcQZdGVareRErSWh+0iRFt9MFAFSqVdRrNTSbTXieh8HlZWHDuLc2N+F5HsIwLOTxieh247Ye3NHWoyrDEEWf/Mm2HiJaD2sVkoyrIY6Ojjhf44nSNJ1sAtrc3MTOzg729vZwfn6OXq+3cvNddF3HwcEBpJR49+4dt/7QN3lRip+6w6+PNMtwbCdQhIA2ukOnSAFdAooANEVCCFHwiWlVObYNx7ahahqazSZ2dneRpelojbA7t59/llWBqqqs2iRaQhmAKM0RpTmu1w1f/Z6niFFFiiKgK8NWHoNtPUS0AtYmJFEUBYeHh+h0Opwj8UK63S663S4sy8Lu7u5kgGC324Xv+0Uf79nq9Tra7TZs217LFdH0OE50VU1lx9mozPnbFVbDVZACmsQwTFGv1kMOgxT+YknPk8QxOmdnAIZzQRr1GjY2NuA5DvqDwUyHqCqKgnqjzso7ohWW5kA6mo/ykLae4Z8YVaUMZ6UQES2itQlJDg4OEIYhOp1O0UdZOa7r4t27d1BVFXt7e3jz5g3iOEan08FgMFi6VhwpJdrtNqrVKo6PjzEYDIo+Ei0BZ2rdbxDf/zU/7BnPMRmxeaMbQSKBqojRAL5heKJJTIIVVQpIKV/uf4BW2mAwwGAwgK7raDabaLfbSOJoVF3ivfjzdLO5gTiMeFOCaM3d19aTRgE0KVAqj9p6pICuAvpUWw9vGhDRvK1FSLK9vQ3DMDhsc8aSJMHHjx8BDD/m4zXCvV4P5+fnSNPFn9FgmiYODw+Rpinevn3Ltix6kCzP4U2FJP4DQpJ73yeuSp2/ZbzBYLqt56o6BdA5eI9uiKJosra82Wxio9nExkYLzmiNcBI/v2XSssrQDR2nJyeQqv7s90dEqysHEGU5sih7UFvP9LYeg209RDQjKx+SWJaFzc1N/Pbbb0UfZa2cnZ3h7OwM1WoVOzs72NrawmAwQLfbXbj1lGOtVgs7Ozvo9XqTiwiih4jSHPFom02UpEiy+VRPjTcYfLutJ7nW1qPeEqRw8N76uri4wMXFBUzTRLPZRLVWQxSGGAwGT64AkVJiY6OFs9MTZFkG1joR0XPd19ZjqAL/3E5p/gcjopW10iGJqqo4ODjAly9fFvbCfNXZtg3btqHrOvb29vDdd98hiqJJK84iUBQFBwcHMAwD79+/X4l5KjRfTpRNYopoTgHJQ33V1nODFFdByqStRxmFKoJtPesgCAKcnJxASolms4FWq4VWq4XBYADHdZAmD68C3GhtIE0TuK4zwxMTEV2pcrgJEb2wlQ5JDg8P4XkeB8ctgCiK8OHDBwDA7u4u2u022u02Op0OLi4uCmtrqVQqeP36NXzfZzsWPZk7te7XTxYrJLlPlj+srUeVAqoYbelRRy0+CiAFWC2wIrIsQ693jl7vHJZVRr3eQL3RgO/7sAeDe282mKaJUqmMz5+P53RiIiKgXmJIQjRPuq6jVCrBNE2Uy2XEcTy8seI4KzOqYGVDkr29Paiqinfv3hV9FLrh9PQUp6enaDQa2Nraws7ODvr9Prrd7txW7AohsLOzg2azibOzMwZp9GR5nsONrn4ghC8wj2TRjKtRJm4MmRVJNAxRDGWyoWd6yKwi2C++bFzXg+t6UFQFzWYTW1tbyPMMlwMb7i2/BAkhsLW1icHl5aMqT4iInkMRAjVdFH0MopUkpYRhGCiVSpN/dF1HnudI0xRhGMJ1Xei6jt3dXaiqiiiKcHl5Cdu2l7qTYyVDkmq1ikajwYBkwfX7ffT7fZimid3dXfzwww/wfR+dTgeOM7tSbV3XcXBwACkl3r17N7dghlZTnOaIRvPlsjxHeEdFxqrKAcRZjjj+9t0DVaZfDZlVpIDOIbMLLU1SdDtdAEClVkO9WkWz2YTnuRhcDibPn81mE1mW4/LyssjjEtGaqRiSLaFEL0BV1VFFaAnlchmmaUJVVaRpiiRJEAQBer0ebNtGktw+5H3YtttEvV7H5uYmAMBxnEmVyTIs8RhbuZBE13Xs7+/j5OSEF79LIggCHB0dQUqJnZ0d7O/vI89zdDod9Pv9Fy3bqtfraLfbsG0bnz59erH3S+vLjTKk+Xhoa4YFG0myMB47ZFYRApqKSWWKIrgGsmjOYABnMICqaWg2m9jZ3UWWpnAcB5VqFZ8/fy76iES0ZmomAxKixxpXh4zbZQzDgBACaZoiiiIEQYDz83PYtv2o9zts2+1NKvRLpRI2Njaws7ODV69eIYoiDAYD2La98DMgVyokEULg8PAQtm2j3+8XfRx6pCzLcHJygpOTE2xsbGBzcxO7u7u4uLhAt9tFHMdPft9SSrTbbVSrVRwfHy/M0Fhaft7UDBJvyeaRLJqvhszeaOuRSKAqXw+ZVcVVew/vKM5eEsfonJ0BGAbPtVoNjm0jecZzNBHRYwkAdZ3P+UTfIqWcVIdMt8tkWYYkSRCGIWzbxvHxMcIwvP8dPpLv+zg+Pp6cpdlsolarYWNjA0IIuK47CU0WrcpkpUKSdrsNAKwQWAHn5+c4Pz9HqVTC7u4ufvzxR7iui263C9d1H/W+TNPE4eEh0jTF27dvV2agEC0GL7x6Ug/vaDeh58vw8CGzymgzjzZq69HY1jMTumEgjmOcn58XfRQiWjOmLmBofE4nAgBN075ql5FSXmuXGW8XLeJa6FtVJtvb22i325Phr7Ztw/O8uZ/vppUJSRqNBmq1Gn755Zeij0IvyPd9vH//HlJK7O3t4eDgAEmSoNvtot/vI8/vvnPfarWws7ODXq+HL1++zOnUtC7SLIc3dcHu396iSXP01ZDZGwSSURWKmFSfqMr1IbMq23oeZFyuyzYbIipC3eBzNa0fIcSt7TLjYapxHMPzPHQ6nUffWJ6n6SoTANjY2EC9Xkez2YSU8tosk2/NQJmllQhJDMNAu93Gx48fC/kg0uxlWYbj42McHx9jc3MT29vb2N3dxfn5OXq93lefd0VRcHBwAMMw8P79+4Xve6PlFMQpklFIEibpZDYJLa4ct7T13CDF1XyU8UwUVRmFKgLQFAkhuE1he3sLl/0+22yIqBA1nSEJrTZFUWCaJkzThGVZME0TmqYhTVOkaYogCNDv92Hb9tLP4hx3EQDDLoCNjQ1sbW1NqkxOTk5mutjjpqUPSaSUODw8xMXFxaOHy9By6na76Ha7sCwLu7u7aLVacBwHnU4Hvu+jUqng9evX8H0fP/30U9HHpRXmTq37XcetNqsqyx/e1qOK0ZYedTRsVgGkAPQVr0bZ2NjgNhsiKoymCFQMziOh1WNZFra3t2Ga5mSYahzHCIIAp6encBxn5UcHBEFwrUq12WyiVCqhXq+jXq9fm2UyqwKJpQ9J9vf3kaYpTk5Oij4KzZnrunj37h1UVcXe3h7evHmDNE2hKArOzs4mPW9Es+JEVz+kAg5tXStftfXcmHcmpobMjqtRxu0947YeZUmDFFXTUKlWccqfu0RUkKqusKKPVo5lWTg8PES/38fp6Skr4UcuLi4mH4vxeuHNzU3s7e0hSZJrs0zuG8XwUEsdkmxsbKBcLuPnn38u+ihUoCRJ8PHjRwDDGSSrUHJGiy/Lc3hTg1qDmCEJXckBxGmO+M5qlBTKeN3xaDaKIgX0BR8yu721Bdfh8ywRFafO1b+0YsYByenpKYeh3yGKomtVJo1GY/KPlBKe5+Hy8hKO4zxrM+rShiTjrSdHR0crX3JED8fqEZqX4QXw8O9JmrLdhh5tWI0CDCOVW19jUomijcMU9aoaRQpAm3M1SrVWg6IouLjoz/VxiYjGBICqwSoSWh0MSJ6u3++j3x/+TqLrOlqt1rUqE9u2MRgMHl1lspQhiaIoODw8RKfTWYgVQUS0frwoQzZ6sg0Xa7U7rZCvhszeaOuRU209yritZ2rIrCoFpHyZO65SSjQaDfS6Xd6cIKLCWLqce0BMNCsMSF5OFEXXRnDU63U0Gg3s7+9DUZRHtS8tZUhycHCAMAzR6XSKPgoRrSk7ukpG/IQXjFSMDA8fMquMQhNt1NajPbKtZ2trC0EQ8OYEERWqxlYbWhHjgOTLly8MSGbg8vJyMmBe0zS0Wq0Hv+3ShSTb29swDINbS4ioUNObbTi0lRbZV0NmbxBIRlUoYjJcVlUwnJMyqkipVqvQDQOfPx/P8eRERF9rmKwioeU3HZBwXMDsxXGM09PTB7/+UoUklmVhc3MTv/32W9FHIaI1lmQ5wlEwkuc5wtlsHyOaixy3tPXcsGuo+PDrZ0RBMNnWo0619WiK5KYJIpo5QxUoaQxJaLmVy2UcHBywxWaBLU1IoqoqDg4O8OXLFwTBt36NIyKaPS9OkY7uzIfJ1WwSolVUq9cQxyn6tnvn603WHQtApAl0FdDydNjeU8CQWSJaPTWDzyO03MrlMltslsDShCSHh4fwPI/lSERUOCe6CkU8ttrQClMVFaZpote9/2fvtbaeJAMiANFVmZUYDZnVpofMSkxt7xkOZici+pY6W21oiTEgWR5LEZLs7e1BVVW8e/eu6KMQEcENrwa1hjFDElpdjWYDnuMhTZ+/winHeHX2XUNmUyjjdcej2SiKFNAfOWSWiFaPIgSqOtv6aDmVy2W8efOGLTZLYuFDkmq1ikajwYCEiBZCmuXXttkEd1zwES2zilUBALju3W02L2lYjQIMI5VbX2PS1qONwxT1ejWKymoUopVUMeSLrTQnmicGJMtnoUMSXdexv7+Pk5MTRFFU9HGIiBDEKZJRMBIl6Z1bQ4iWlZQSJauE/kUf+TcDi2J8NWQ2vP5yKa6ClElbz9SQWVUKXmgRLaE6V//SEmKLzXJa2JBECIHDw0PYto1+v1/0cYiIAAxX/44vGVlFQqvKNEzkaY44jos+yqNlORClOaI723pGIcooNNEUTLb2KBLQWY1CtFAEgJrOkISWyzggOTs741zNJbOwIUm73QYAfPr0qeCTEBFdcaKpVhsObaUVFQQ+KrUKpBArub3p2pDZWwgkUKSApgioYjgbZThodrT+mENmieaqpAsYXP1LS4QByXJbyJCk0WigVqvhl19+KfooREQTeX5jHgmHttKKyvIceZZDN3QEQXj/G6yYHPcHKYpMh2HJePWxxGh7zzBE0RQJIThkkugl1Ln6l5bIOCDpdDoMSJbUwoUkhmGg3W7j48ePSJLk/jcgIpqTOM0nG03TLLuznJ9o2YVRBN0w1jIkeYg0y5ECwAPaesazUMZDZtXRkFmN1ShED1LV+b1Cy2E6IOl2u0Ufh55ooUISKSUODw9xcXEB27aLPg4R0TVulE1aD4Jk0cZZEr2swPdRr9eLPsZS+6oa5eaQWSRQlRtDZuX1bT1s66F1pykCVZPfB7T4SqUSA5IVsVAhyf7+PtI0xcnJSdFHISL6ihtPzyPJ7nhNouUXRREgAFVRkaSs7JyFDA8ZMptCGa87lsP5KIoU0Echiq7y4pFWW41VJLQESqUS3rx5w4BkRSxMSLKxsYFyuYyff/656KMQEd3KnRra6nNoK62BNE6h6zoSnyFJUYbVKAC+Wbt2tfJ4XIWiKterUVRWo9ASq3H1Ly04BiSrp9CQREqJWq2GZrOJUqmEo6MjZBnvzhLR4kmzfLLyN8+vZpMQrbIwDFEqmfB8r+ij0B3GbT3BN14uxVWQMmnrmRoyq0oBKXkhSotHCoE6QxJaYAxIVtPcQxIhBCqVCprNJiqVCpIkwWAwwIcPHxiQENHCCuIUySgkiZIM6QquRSW6yfc9WFVrZVcBr4ssf0hbz9WQWUUK6OqoxUcBpAB0VqNQASxNQGGARwuKAcnqmmtIous6/vzP/xxpmsJxHPz666/DnmciogXnRFcXF/4da0GJVkmW58jSbG1XAa+T+4bMiqkhs6oYt/RgOCdlVJHCIbP00thqQ4uKAclqm3slyfv37+H7/rwflojoWZzpoa0xQxJaH1HMVcA0nIgSpzniO6pRFJlOKlHGq47VqbYeXu7SYzW41YYWEAOS1TfXkCSKIgYkRLR0sjyHNxWS+AxJaI34no9Gs1H0MWgJpFmOFAC+FaQk0TA80ZXJLBRNxSRQUQSgsRqFRgxVt7/ExgAAIABJREFUwNT49UCLZRyQdLtdBiQrbGG22xARLaoozREn43kk6fWSdKIVF8cxAK4CppeRZDmS6RXqNwqU5FRbz2TIrLy+rYdtPeuhzioSWjDTAUmn0yn6ODRDDEmIiO7hRNlk+WbEgITWUBqnMAwDiceQhGYrw0OGzKZQ5Giw7C0rj3WVF9eroG7w80iLgwHJemFIQkR0DzdKJ3/3E4YktH6CIECpVILruUUfhWg0ZBYAbn8+Fkgmc1HGVSg3gxSV1SgLTRECFV0UfQwiAAxI1hFDEiKiO+R5Dje6Kg0POY+E1lAQ+KjUKpBSIGM1FS24HFfbeoJvvI4UydXa41uGzKpSQHL1bGGqhuTHnxaCaZoMSNYQQxIiojvEaY5o1GGQ5TnCO0rAiVbVZBWwbiAIvnXZSbQ8svwhbT3jlcfDjT26OmrxUQApAJ3VKDNT5+pfWgCmaeK7775jQLKGGJIQEd3BjTKk+XhoawbeRKd1dbUKmCEJrYdxNcrEjSGzYmrIrCrGLT0YzkkZVaRwyOzjCQA1gyEJFYsByXpjSEJEdAd3avWvx3kktMY810Nzo1n0MYgWRo5htWF8RzWKItNJJcptbT0cMvu1si74caFCjWeQ9Ho9BiRriiEJEdEdvGvzSLI7XpNotSVJAuSAqqrDvxPRvdIsRwoA3wxSrmajaOONPSomgYoiAG3NqlFq3GpDc6YoCiqVCqrVKiqVCoQQ6PV6ODs7K/poVBCGJERE35BmOfypX2x9XhfSmkvjBKZhwkmcoo9CtDK+GjJ7o61HTrX1KKPwZLy1Z7ytZ5XaehiS0KwJIWBZFqrVKqrV6iT89zwPHz9+hOtyk9u6Y0hCRPQNQZwiGYUkYZJOZpMQrasgDFEqleC4DEmI5iXDQ4bMpsMQZbSZR1OGLT7jEGVZ2ld0RaDCkIRmoFQqoVKpoFarwTAMpGmKIAhwdnaGfr9f9PFowTAkISL6Bndq3S+32hBxFTDRovpqyOwNAslkLoo2mouyiENma3rxZ6DVoOv6JBQpl8vIsgxRFGEwGOD8/BxpmhZ9RFpgDEmIiL7BmZpH4se8IFxU//S//acIw8XZuGIYJv7Rv/ePij7GTIxXARu6CT/wiz4OET1Qjlvaem64b8ispkgIIWZ6zlqJIQk9jaqqsCwLtVoNlmVBCIE4juG6Lj5//owoioo+Ii0RhiRERLfI8hze1KDWkJttFlYYBvjHf/VXRR9j4p/86U9FH2GmojiCbhoMSYhWzP1DZjEZMjsOU3R1NGxWAaQA9GdUoyhCoG7MNoSh1SGlhGVZk4GrnCtCL4khCRHRLYZrHYd/T9KU7TZEI57jYWNzo+hjEFEBvmrruTFkVkwNmVWFgKrgwW09li4gpZzh6WnZlcvlSQuNruucK0Izw5CEiOgWXpQhGw1qDdm2SjSRpAnyLOcqYCL6So7xTYZv31iYbutR0gSKBPQ8xU6ZVSR0nWEYk0qR8VyRMAzR7/dxfn6OLMvufydET8CQhIjoFnZ0lYz4CX8IE01L4wQl04TtcMsNET3Otbae8c/XJME/v60XeayVoqoqarUaGo0GSqUSBoMBTk5OFj7YVlUVlUoFzWZzsqI3jmM4jsO5IjRXDEmIiG4xvdkm4DwSomv8MIRVLjMkIaIXYWkCzTJDkufQdR3VahXNZhO6riOOYwwGAxwfH6PdbuPP/uzP8OXLF5yfnyPPF+P3mvFckWq1imq1CkVRkCQJvnz5guPj44U5J60fhiRERDckWT4Z1JrnOcLFvvFCNHe+76Faq0BKyXJnInq2TUuFItlu81iGYUwqRjRNQxRFuLy8RK/Xu/bc/P79e1iWhf39fbRaLRwfHxcy2FQIgVKp9NVcEd/3cXp6isvLSwCA7w8Hg5dKpbmfkQhgSEJE9BUvTpGOBtOFydVsEiK6MlwFzC03RPR8r2pa0UdYGqVSCfV6HfV6HVJKhGGIXq+H8/PzO9/OdV28ffsWW1tbODg4gOd5+Pz5M+I4nul5x3NFarUaSqXSZK7IxcUFLi4uGLTTQmJIQkR0gxNdhSIeW22IbhVFIVcBE9GzqUJg2+IlyV0sy5oEI3meIwzDa5UXj9HpdNDr9XBwcIAff/wR3W4XnU7nxVpbNE2DZVmo1WqwLAsAEEURHMfBp0+fZh7KEL0EPiMREd3ghld3NcKYIQnRbVzXQ2uzVfQxiGjJNUoCpnb7WuB1JYRApVJBo9FAtVpFlmXwPA+///77i7TJZFmGo6MjlEolvH79GhsbGzg+PoZt249+X7fNFYnjGK7r4sOHD/A879nnJZo3hiRERFPSLL+2zSa4Y40h0TpL0xR5lkPTNN4ZJKIn265wYCswDBuq1SoajQYsy0KapnBdF+/fv5/M6Hhpvu/j559/RqvVwv7+PoIgwPHx8Z1bZIQQKJfLk9W8uq4jSRL4vo+TkxMMBoOZnJVonhiSEBFNCeIUySgYiZIUScaQhOhb0jiBaRgMSYjoSSSAXUsWfYzCKIpybVVvkiRwHAe//vrrXNfd9no99Ho9vH79Gj/88APOz89xdnY2mRdimuZkrohpmpwrQiuPIQkR0RQ3zjGORVhFQnQ3PwxglS2uAiaiJynrEpaxXpcjmqZNghFjFDLbto2PHz8iSYpdp/fx40cYhoGDgwM0Gg34vo9yuQxgOFfEtm38/vvvhZ+TaNbW61mJiOgeTjTVasOhrUR38n0f1VqVq4CJ6Em2LAkpVn/170NX9S6CMAzxyy+/oNFooFwu4+joaGbtPkSLiiEJEdFInt+YR8KhrUT3ypIMhmHwl2gierRta3VX/5ZKpUkwMl7Ve35+jl6vV/TRHqTf76Pf7xd9DKJCMCQhIhqJ0xzRqII0zTJEbLchulcUhzAMkyEJET2KqQpsllfrUmS8qrdWq0EIgSAI8OXLF4YNREtmtZ6ZiIiewY0yZPkwGAmSq9kkRPRt41XAAgL8riGih2qVFKjKcg9tHa/qrdfrqFarw4pU38enT5/gcFYT0dJiSEJENOLG0/NIFqtHmGhRTVYB69pctzEQ0XLbq2rAEgarUkpUKhU0Gg1UKhWkaQrP8zi7g2iFMCQhIhpxp4a2+hzaSvRgSZzAMAyGJET0IIoA2lUVyJZjffhtq3pd1537ql4img+GJEREANIsn6z8zfOr2SREdL8gCGBVLNi2XfRRiGgJNEyJkq4gCBY3JNE0DdVqFY1GA6ZpIkmShVnVS0SzxZCEiAhAEKdIRiFJlGRIc1aSED2UH/io1qtQFAVpmhZ9HCJacNuWBrGAq391XZ9UjOi6jiiKMBgMcHR0tHCreolodhiSEBEBcKKrUMTPGJAQPVaWZNB1nT35RHQnAWC/rhR9jAnTNFGv11Gv16GqKsIwxMXFxdKs6iWil8eQhIgIgDM1tNWPGJIQPVYYhzC5CpiI7lHWBBolvdgzlMuTYGS8qvfs7IyreokIAEMSIiJkeQ7v2mYbhiREj+W7PjY2N1CxKvAG0WSdNhHRtE1LhSLn22ojhIBlWajX66jVasjzHEEQ4Pj4mLOUiOgrDEmIaO1FaY44Gc8jSZGw3Wbp/JM//anoI0wYhln0EQqhaRryLIdhGDA2NhD4Afw4ZVhCRNfs17WZvW8hBBRFgaIoUFUVqqqiVquhWq0iyzK4rosPHz7A87yZnYGIlh9DEiJae06UYXwZFzEgWTr/+K/+qugjEADN0BBGIQaXA2giQ7VSRblRhud48H2PYQkRQZMC29bDQhIpJVRVnYQe4+BjOgCZfrmUEkII5HmOLMuQ5znyPIfrunj37h3CMJzx/x0RrQqGJES09tzoahuHz1YboifRFBW+HwAA4jjG+cU59DBGrVpDucKwhGidSSkghMRezcBmsz4JN8IwhKIoaDQa18IQKeUk5Bj/k2UZsixDmqZI0xRxHMPzPMRxjDiOEUURV/MS0YtgSEJEay3Pc7jR1TySMOYFHNFTSEVBkl6/QImiCN1ed7hWcxSWuLaLIPAZlhAtISkEhJQQEJCKHFVvSEghIFUJKRUoEJCKAiEFxNTskTzL8Zd/2MDeXmMSeFxeXiJJEvi+fy3siOOYK3eJqDAMSYhorcVpjmh0XZflOcKUF25ETyGkQBLHt75sHJYYhoFqpQqrasG1Xfi+jxz8niNaBLquQ1VUKIqEUCQUoQwrQKQcBh5iGHjkeY7xt22e5cjzDFmWI80z5FmKMBlVfGQpkiSZhB0SQNzw8LN/NnnM8TasUqk01/9XIqK7MCQhorXmRhnSfDy0NQNHkhA9nq7ryLP83uqQMAwRhiHDEqIFoygKGs0GkihBlufI8hRJEiNNs+Hf4xRZ9rxBzBVDomrObmgrEdFLYUhCRGvNnVr963EeCdGTGLqOJH74LIBrYUmVYQlR0cpWGUkY47x/MbPH2KmokGK+q3+JiJ6CIQkRrTXv2jwS9j8TPYWmaoi+0WpzF4YlRMUTEDBNExfnswtIAKBd5WUHES0HPlsR0dpKsxz+1AwSn0PxiZ5EqgqSwHvy24/DEtM0UGEbDtFcmaYJ5JjpZhhTFdh64OpfIqKiMSQhorUVxCmSUUgSJulkNgkRPY6QAvEj2m2+JQhCBMFUWFKx4DguAoYlRDNTLpfge/5MH2OzrEBV5Ewfg4jopTAkIaK15U6t++VWG6KnURQFQgikafpi7/MqLDFRqVRQYVhCNBOKokDRVDjn5zN9nL0Kq0iIaHkwJCGiteVMzSPxY154ET2FoRtIX6CK5DZBECAIAoYlRDNStsqIw8fPE3oMVQB7nEdCREuEz1hEtJayPIc3Nag1YEhC9CSqriFOZnuRNQ5LSmYJVsViWEL0AgQESqUSznuzrSKpmxIlXZnpYxARvaQHNwcKIRQhxP8thPhfRv/+l0KI/0MI8f8IIf5PIcS/OLtjEhG9rDjNEY+6A5I0RZTxQovoKTRFfZF5JA/hBz663S4c20GlYmFzaxOlUgkCXCtK9FimaSLP8pkObAWAHUuH4OpfIloij5mg9FcA/r+pf/+vAPwXeZ7/JYD/fPTvRERLwYsyZKNBreHLjVIgWjtSkYij2VaS3OQHPjqdzigsqTAsIXqCcrk884GtAsCrOge2EtFyeVC7jRBiH8C/AeC/BPAfj/5zDqA2+nsdwOf73k+WZfD92T4Z08sJgqDoI9CCWaWviZ4dIYmG6YjrJ0CS3fMW9E1JWPQJqCBSCORxgDT0gOm2lzl9TfhOCN/pwzRMWBULZd2C67or9Vy1UvhcsTAUKYE0hHPZm+njlFQBUxjf/P2f36t0G35d0E3z/pp46EyS/wbAfwqgOvXf/kMA/6sQ4r/GsCLlX7rtDYUQ/xDAPwSA3d3dp5+UiOgFedObbRK22hA9haZryLKs8LkgQRggCIcDXi3LgmVZcB0XQchftIluUy6XkMyhTa5VViDZakNES+bekEQI8W8COMvz/P8SQvwrUy/69wH8R3me/09CiH8bwH8P4F+7+fZ5nv8NgL8BgD/+8Y95qVR6kYPT/PBzRjct+9dEkuXIFEAVOfI8RyQkoDIoeTbVKPoENGeGVUUqlW9/7uf8NREkOYJLB6VSGdWNTVQBOLYDP2AV60Lhc0WhBARK9Y3hwNYZfy4Ot8oole5/jGX/vYJmg18XdNO8viYeUknydwD8PSHE3wVgAqgJIf4ZgH8LwzklAPA/AvjvZnNEIqKX5cUp0tGg1jC5mk1CRI+jKtpClkX7vgff91AqlVGpVlCpVhiWEI2YpokszWY+sFWXAtsVbaaPQUQ0C/dOUsrz/K/zPN/P8/wNgH8HwP+W5/nfx3AGyb88erV/FcAvMzslEdELcqKrUMRjqw3RkymqgiSdz2abp/B9D51OB67jolKtYGtrCyWTdyZpvZXLZQT+7MPNZklCVzi0lYiWz0NnktzmHwD4kxBCBRBgNHeEiGjRueHVkNYwZkhC9FRCCiTJfDfbPIXne/B8D+WpyhLbtheyCoZollRFhVQlnHNn5o/Vrukzfwwioll4VEiS5/nfAvjb0d//dwD/wssfiYhodtIshz+1ySZIGZIQPYWqqsjzHFm2PN9D02FJtVpFpVKB4zgMS2htlK0Skmj21V9SAK9qz7kXS0RUHD57EdFaCeIUySgYiZIUyRJd4BEtEtMwkc7hYmsWGJbQOhIQMEslnHfPZ/5YNV3C0pWZPw4R0SwwJCGiteLGV8tKWUVC9HSqpiFe4HkkD3F7WGIjCMKij0b04iYDW+fwfbtdVSG4+peIlhRDEiJaK0401WrDoa1ET6YqCoIV2RYzDkussoVqtYZKJWdYQitnXgNbAaBd4SUGES0vPoMR0drI8xvzSDi0lejJhCIQx4s/tPUxXM+F67kMS2jlzHNgq6kKbFpc/UtEy4shCRGtjTjNMR6hkGYZIrbbED2JlBJCCKRpWvRRZuKrsMTK4bgMS2h5lSvluQxsBYAtS4HK1b9EtMT4DEZEa8ONMmT5MBgJkqvZJET0OIZhIJuqylpVruei0+kgCAJUqzVstjZhGEbRxyJ6FCkETNOEbdtzebxdVpEQ0ZJjJQkRrQ03np5HsvoXeESzoukaoiQq+hhz47gOHNdBxaqgVqshz3LYjo0wZGUJLT5jjgNbVQHsVXl5QUTLjc9iRLQ23KmhrT6HthI9maZo8PzVGNr6GAxLaBmVS2X43ny+XxsliRJX/xLRkmNIQkRrIc3yycrfPL+aTUJEjycViSRZraGtj3EzLMnSDLZjI4rWp7qGlsN4YKvruXN5vB1L5+pfIlp6DEmIaC34cYpkFJJESYY0ZyUJ0VMJKZAmTBrHYUm1UkG9UUeWMCyh4kkpoCgqVEVFyTTnNrBVAGjXOO6QiJYfQxIiWgtudBWK+BkDEqKn0nUdeZZPhiATYDsObIdhCc2PFAJSUaCqwzBE0VRoo6oRAMizHHmWI0lTDAaXczmTpUs0y/pcHouIaJYYkhDRWnCmhrb6ES/uiJ7KMAzEMatIbnNbWOL7PsIoXNl1yTQ7UghIqUDVFCjyehAihJgEIWmaIs0SuEGIKI4K+1rbLiuQbLUhohXAkISIVl6W5/CubbZhSEL0VJqiImSFxJ3GYYlVtlAqmbCqFpADQRggDELEUQwuIScAEBCQioSqqlCkAlUfByHKVRCS58PtNFkCz/cRLWjo1q5z9S8RrQaGJES08qI0R5yM55GkSNhuQ/RkUlWQ+KwkeQjXcycDMw3DgFkqoV6vQ0iBOIyHoUkYIsu4knzVKYoCRVGgKipUTYGqaFBUBUIK5PmwImQchPh+gCiOkCzR3B9DEdiuMCQhotXAkISIVp4TZZN7thEDEqJnGW62WZ6Lt0URhuFkVbCiKChbZVhlC9VaFVmSIQgCBEGAJOXHdlmNgxBlFIRooxaZcRCCHMjSFHGaIgxDxG68MnNrNsoKdIVDW4loNTAkIaKV50ZXZck+W22InkxRFABYyFL/ZZKmKeyBDRs2AKBUKqNkGChZJQCjQCUIEUcRB+QuGCnlsCJEVa+GpqoKpCK/CkKiOEbkuUjieOU/j3usIiGiFcKQhIhWWp7ncKOrUvYwXu1fVIlmydANpBza+uJ834PvewCG24PMkolarQYhBZIogR8ECzuHYhVJORyYqqnaMBDRNKiKcm1zTJZmSLMMSRLDDzxE4foGWlIAr2q8pCCi1cFnNCJaaXGaIxpd02V5jjBdz19iiV6CZuiIkrjoY6y0KIomLRhSSpRLZZRLJVRrFWRphjAMEQQB4pifh+e42hwzHpiqQZUKFG1YLTW9QjfNEoRBgDDi/Jjb1HSJsq4UfQwiohfDkISIVpobZUjz8dDWDBxJQvR0mqLCDcKij7E2siyD4zpwXAcAYJomSmYJjUYDQgqEYYRwVGWS8cntK+PNMZqmQorx5hjtqxW6WZoON8cUvEJ3We1UNQiu/iWiFcKQhIhWmju1+tfjPBKiZxFSsIKhQOPhrsBwPoxllVGpVCCVGtI4he/7CNesLeebK3SV0eaY8QrdJEWSDz9GURRxQO4LelVjFQkRrRaGJES00rxr80hYJk30VFIICCnW6gJ8kaVpisHABmBDCgHTLME0TVhVC8iBMAwQBCHiKEaO5Q+IpzfHaCu4QndZlTSBjRKHthLRamFIQkQrK81y+FMzSHz+vkz0ZLphIEuzlbjgXjVZnsPzPXhTw19L5TLq9fqw+ieMEYQBwnCxZ2pcBSHKZHOMuiYrdJfVZlmBytW/RLRiGJIQ0coK4hTJKCQJk3Qym4SIHk83dERstVkKN4e/WpYFq2yhWqsiSzKEYQA/CAqptJBSQtG0UQiiQFE1qF+t0M2QZCniOIbne1yFvMBecfUvEa0ghiREtLLcqXW/3GpD9DyaosL3g6KPQY+UZRls24YNGwBQKpVQMkyYzRIggDCMEIXBi66wvblCV9GGc0KQDof+Cs2crNBN0wRB4CGKIg6fXTKqENipMiQhotXDkISIVpYzNY/Ej/nLN9FzSEXhsMsV4Ps+fN8HAKiqirJVRqVShaxLJFGCYLTq9r7ZM1crdBUoUp0EITdX6KajzTFuECL0BsN2H/X/Z+9OXiPPwzy/f35L7HsotOdSmZWVmUXTxmAGDMPAePBpphmDD16gwT41NtgMDEND/wEDvhjm3Ic5DRj74Jvv7aNNz83NNFW5KFP7Fvv6W32QIkrKlFKh9feLiPcLREkRWh5VhkKKTzzf50k9+veJx1fOGEonOGoDYP4QkgCYS0EYqn9hUOuQkAS4F8M05Hkct5knnuep3WpLOgs9stmcMpm08sW8wiA8m2MyHJ11hhgXNsfY1tUrdAd9Oa0frNCN8TwU3N5aPsnqXwBziZAEwFxy/VDu+d/pnu/LoY0buLNEInH2YJifo7kVhKG6va66vbO30+mU0umsSqX02Qrdi5tjWKG78AxJ6wW6SADMJ0ISAHOp7wST8/UjNpYC95JKpuS7PCheJMPhSMPhKOoyEFP5pKlKNhl1GQDwKIiAAcyljvNHMjLwaPEG7iORSMjlqA2AcytZSyZHbQDMKUISAHPp4maboccRAeA+LNsiJAEwsV5iqw2A+UVIAmDueEGo0XkwEoahRpwSAO7FMA25HLcBICltG1rJE5IAmF/MJAEwd/quL/98wOTI+2M2CXCRbdln0wfPjbc03LSt4brrL19sXHjtB5/P+O6VK96aro4f1f3dVd9e8KM3DUOGYdy4EhbAYqhkLCUtnmcFML8ISQDMna7zRyjS56gNrpDJZFQsFRUEgXSHm0h4U/D2g6t//LFXX3fth1xzxW2/QnjFpRfr7HV6P/iMABbJeoEuEgDzjZAEwNzpjf4Y1DoiJMEVBoOBMumMJKnRaFwZEgAALjMNabPAwwcA841eOQBzxQ/CS9tsGNqK69QbdZmmqWK5FHUpADATSilT2aQVdRkA8KgISQDMlaHry/PPghHH8+UFhCS4Xr1+qmQioUKxEHUpABB7K/nEjXObAGDWEZIAmCs994+DE0OfgAQ/FoShTk9PlU6nlc1koy4HAGLLkPS8RBcJgPlHSAJgrnQdjtrgdoIgUOO0oVwhN5lTAgC4LJMwVE4ztBXA/CMkATA3wvCbeSQuIQmm4/me2q22CqWCkslk1OUAQOwsZS3ZrP4FsAC4pwMwN1w/lOOdve4HgRyO2+AWRqOROu2OSuWSbIvtDQBw0TNW/wJYEIQkAOZGzwkUhGfByNBjqStubzAYaNAbqLJUkWVx9h4AJClhGlot0GUHYDEQkgCYGz334jyS4AfvCVyv2+tqNBqpWq3KNNniAADltKGUzf0hgMVASAJgbvQuDG0dMLQV99ButeW7nirlqgzxwADAYlvLJ1n9C2BhEJIAmAt+EE5W/obhH7NJgLuqNxuSpHK5HHElABAdQ9JagYcMABYH93gA5sLA9eWdhySOF8gP6STB/Z3WT2UnbBVLxahLAYBI5JOmKlnmkQBYHIQkAOZCz/kjFBkEBCR4OKenJ0olU8rn8lGXAgBPbiVry+SoDYAFQkgCYC50LwxtHTiEJHg4QRCqXq8rk8sok8lEXQ4APKnNEivRASwWQhIAMy8IQ/UvbbYhJMHD8n1frWZLhWJBqVQq6nIA4EmkbUO1fCLqMgDgSRGSAJh5jh/K9cbzSHx5HLfBI3AcR51WR6VySbbNM6sA5l8lYylp8XABwGLhXg/AzOs6gcaxiENAgkc0GA7UbXdVqVZkWVbU5QDAo1ov0EUCYPEQkgCYeT3Hn7w+4KgNHll/0NdwOFS1WpVp8msUwHyyDGmzQNccgMXDX3cAZloYhuo5f8wjGbmEJHh8nXZHruupWqmy9QHAXCqmTGWTdMwBWDyEJABmmuuHcryz14Mw1MgnJMHTaDYbCsNA5XJFhghKAMyX1UJSBiEwgAVESAJgpvWcQH44HtoaiJEkeEqn9bpMy1ShVIy6FAB4MIakZ0UeJgBYTNz7AZhpvQurfw1DqmZNlVKmsklDKdOQxbNgeGT101OlkkkVioWoSwGAB5FJGCqnGdoKYDExjQnATOtfmEeSsi2l7O/PT3u+Ly+QvPDseI4bhPKDUK4v+aFYGYx7CcJQ9Xpd1aWqfNdXf9CPuiQAuJdazpLN6l8AC4qQBMDM8oNQgylmkNiWpSuyk4kwDOUGgTxf8gLJCQL5wVl44vpnXye4/sMB+b6vRr2hSrWiIAw0HA6jLgkA7uxZni4SAIuLkATAzBq6vrwHGNRqGIaSlqU/hvh/n6i4vn8Wopx3nlzsRvHCsyAFi83zPLVbbZXKJQVBIMdxoi4JAG4tYRpaKSSjLgMAIkNIAmBm9Z5w3W/CspT4QTeK7/vnYYnkBOF5F0p43pFCN8qiGI1G6ra7KpVLapw25Ple1CUBwK1UM4ZSNvO8ACwuQhIAM6vrxCd2sCxr0n+SveZ9LnajuOdBiheEk2M+4y09mG39QV+WZapSraher8v3/ahLAoCpreVZ/QtgsRHssjNWAAAgAElEQVSSAJhJQRiq78YnJJnGNN0obnA2TNb9phvFPe9GIUaZDZ1uV4ZlqVKpqF4/VcBxLAAzwDSktcIPflEBwAIgJAEwk1z/bB7IPLEsS9YNf5te7EZxgkC+L7lhKJ9ulNhpt1paXl5WKpnWYDiIuhwAuFE+YaqUYWgrgMVGSAJgJvWdQMECBgKXu1G+T1TG3SjjAbPjbhTWHT+9dDolGWLTDYCZsZKzZXLUBsCCIyQBMJM6zpy1kTyQabpRnHE3SiC54Xk3SnDejRLSjfJQ8vmCup2eOCQFYFZsFHloAADcEwKYSU+52Wbe3LTu2PP9swAl+H7dMd0o08mkMzIMQ8MBx2wAzIa0bWg5z1EbACAkATBzXD/QyOOB+mOxLUu2JaWvuT4MQ7lBIO+8A2U0MhUEkmcarDs+ly/k1e126SIBMDOWspYSlhl1GQAQOUISADNn4AXy6WaIjGEYl7pRPPPsV4mdTE7e5+KA2XE3ymTdcai5/vfLZM6WQA/oIgEwQ1bpIgEASYQkAGZQ15nfB9jzYpp1x2cByvfrjme9GyWfz6nb6UZdBgBMzTKkzQIPCwBAIiQBMIN6o1l9+Iwxy7JkSUr94H0udqNMgpQg3uuOs5mswjBk5S+AmVJKm8omb5j6DQALgpAEwEzxg1ADj5BkEUzTjeIGZ8NkL3ajeBe6UZ46Rsnlc+p02k/8VQHgflbySRms/gUASYQkAGbM0PXl+fHrIMDTm2bdsev7cr9ddxw+TjdKPpdXGIQaDkcP9jkB4LEZkp4VGdgKAGOEJABmSs9lXwimd7kb5ftEZdyNMh4wO+5Gucu640w2o3abLhIAsyWbNFVOM7QVAMYISQDMlK7DURs8nGm6UZzxbJSL3Sjj2SjhWTdKIZ9X4AcajegiATBblrKmbFb/AsAEIQmAmRGGzCPB07u47viqbhTX97W+UdP+0bGMrHUWoAR360YBgKf2jNW/AHAJIQmAmeH6oRwv6iqAy9ZWlpW0pEToqZL5/tdqEATywlDeeQeKG4TnnSnhzK87BjDbkqah1WIy6jIAIFYISQDMjJ4TKIjh2lcstmwur6PDw2uvN01TSUk/2q55cd3xeNWxNw5TwrMgBQAeWjVrKmmx1QYALiIkATAzei7PtyNearWaXMe59yySu6479i+sO+anA8BtreUSrP4FgG8QkgCYGT2GtiJGTNNUJpvV4cHBo3+tadcdjztPzo70hI+27hjA7DMNab14wx0LACwgQhIAM8EPQg19HuQhPqpLVTmjkRzHiboUSXfvRvEudKPwEwYsjnzCVIHVvwDwHUISADNh4PryCEkQE6ZpKpvNaX9vP+pSpnbndcd0owBzaa2QkMlRGwD4DiEJgJnQdXhwhvhYqtU0GAzkuvHoInkoN6079nz/rPMklDw/lMO6Y2BmcdQGAK5GSAJgJjC0FXFh2ZYymYz29vaiLuXJ2ZYl+wePq8IwlBsEGg08+aHk2+ZZN8qFtcf8JAPRy9iGalmO2gDAVQhJAMReEIbqE5IgJmpLNfX7fXmuG3UpsWMYhpKWJfO8HcVOfv8g7Kp1x3SjAE9rKWspYZlRlwEAsURIAiD2HD+U6/HACdGzE7ZS6fRCdpE8lGkGzHqh5AdnHSgu646BB7dWoIsEAK5DSAIg9rpOwNYNxMJSdUn9Xo8ukkdkWdYV01Auu6obxZsc6WHALPAjtmlog5AEAK5FSAIg9nqOH3UJgOxE4ryLZDfqUhbeXdcd040CSKW0oUyCozYAcB1CEgCxFoaheg4PZxC95VpNvW5HnutFXQpuMM2644vdKE7AumMsjtV8UgarfwHgWoQkAGLN9UM5PCZFxBKJpBLJpI6Oj6IuBQ/kcjfK94nKdd0o3oVuFGIUzBpD0kaBLhIA+BFCEgCx1nMCntFF5JaXl9TtdOR7HP1aFNN0ozjjbpRAckO6URB/uaSpcpp5JADwI4QkAGKtx+pfRCyZTMpOJNU6PIy6FMRM0rKU/EE3iuf7Z50n4VlXHOuOEbVa1pTN6l8A+CFCEgCx1mceCSJWq9XUbrXk+9wWcTu2Zcn+QTdKGIZyg2DSjeIEwflw2fMghQGzeGCbebpIAOAmhCQAYssPQg18nmlFdNLptCzbVrvdjroUzCHDMG7sRrlq3fG4G8ULz+4ngWmkLEMrxWTUZQBA7BGSAIitoevLIyRBhJaWltRutRQEPJ+PaEyz7vgsLJEc1h3jByoZU0mLrTYAcBNCEgCx1XMJSBCdbDYr07LU6XSiLgW4lmVZk/6T7DXvc7EbZbypxwvCyTEfBswuhrVcgtW/ADAFQhIAsdVhHgkiVK1W1Gw26SLBzJumG+Wqdcd+ILmBFNCNMvNMQ9oo3rCuCQAgiZAEQEyFYag+m20QkVw+J8Mw1aWLBAtgmnXHru+fzUFh3fFMKiRN5Vn9CwBTISQBEEuOf9YKDkShWqmo0Wop5IEfIOnbbpTvE5XrulFYdxwPq/mETI7aAMBUCEkAxFLPCRTwABURyOfzkgz16CIBpjZNN8pg4MnzpdC2L3ejeOeberjPfxSGpM0SR20AYFqEJABiqevQRoJoVCplNRoNukiABzbuRrGTlq7qRvF8/+w4T/D9umO6Ue4ubRuqZjhqAwDTIiQBEEtstkEUCsWiJKnb7UZcCbB4bMuSbUnpa64Pw1BuEEy28jhBcDZc1g9Zd/wDSzlLCcuMugwAmBmEJABix/UDjTxCEjy9cqmkeqMedRkArmAYhpKWpeQPZqNcXHf8bTeKF54FKYtmvUAXCQDcBiEJgNgZeMFC/iGLaJVKJYVhoF63F3UpAO5omnXHZwHK9+uO57EbxTYNbRSSUZcBADOFkARA7HQdAhI8vWKxqHr9NOoyADwiy7JkSUr94H0udqNMgpRgNtcdl9OG0jZbbQDgNghJAMSO6weyTINuEjyZcrmsIAjU6/WjLgVAxKbpRrlq3bF/PnQ2iFE3ymo+KYPVvwBwK4QkAGLnZTmll+WzTQeOL418aeT7cn3J8c9CFMcP5fmhiFHwEArFok6Oj6MuA8AMmGbdsXv+O8sLdHnd8RN2oxiS1gsMbAWA2yIkARBb400HWUlXDegLgrOwZORJznmg4vpnGw9cf7w2khgFP1atVuV7rgaDQdSlAJgTl7tRvv/9dV03ykOuO84nTZXTDG0FgNsiJAEws0zTVNqUzv4GvPppPccbhyehRkEgx5McP5TjB+fP8hGiLLpcPq/jo6OoywCwQKbpRnHGs1G+7Ubxzjf13PAkwFLWks3qXwC4NUISAHMtaVtK/uCeLgjO1g0750d6nItHeoJQrseRnnlWW1qS57oaDodRlwIAl9xn3bEfSs8K/JkPAHfBvSeAhWaapjJJKSPpum6UoXvejRKcdaA4ruSEf3SjMGB2NpmmqUwup6PDw6hLAYBb+9GAWdOQlln9CwB3QkgCADdIJyz96Fi3e6ED5eKAWcf35Xpnz/ARo8RPdakq13E0Go2iLgUAHlQmYSplc9QGAO6CkAQA7mn8bF5O0nUDZsdHehgwGw+maSqTyepg/yDqUgDgwRWTBCQAcFeEJADwyKY50jMeMOv4oUZ+INeTnIABs4+lurSk0Wgk13WiLgUAHlwxTUgCAHdFSAIAMXDTgFn/vANldKEbZXQ+YNZlwOytWJapbDarvb29qEsBgAeXsAxlkzeszgEAXIuQBABmgGVZyljXd6OEYaiRF0wGzI68826UMJTjBRzpuWCptqx+vy/PdaMuBQAeXCFpyjSMqMsAgJlFSAIAc8AwjDsNmD3rRjkbMOsuwJEey7aUTqfpIgEwt8oZukgA4D4ISQBgQdxnwKzjh/LmoBultlRTv9+jiwTAXLINQ3mGtgLAvRCSAAAkTTdgduSNw5M/Bsz2DEuOH0imEesBs3YioVQ6rb293ahLAYBHkUkaSliEJABwH4QkAICppWxLqW9+cwzSviQpk8nIu/ZIT/QDZmtLS+r3evJcL6IKAOBxldIctQGA+yIkAQA8GNuyZFtSVtJNA2Yd/+x4j+c//oBZO5FQMpXS8cnxg39uAIgDw5CKKUISALgvQhIAwJO5PGD26j/mvx0w6/iS44Vn3Sj+3QbMLtdq6nU78j3/ft8AAMRU2jKUttlqAwD3RUgCAIiVuw6YHfmBnCCU54UKLrx/MplUIpnU0dHhE30HAPD08mlLBqt/AeDeCEkAADNl2gGzo/O1xmubL9Rod5VPGHJMQ44v+TEeMAsAd1FKMbAVAB4CIQkAYO6MB8zmcjm9WCvpt/ah8kupyfUXB8wOzwfMOjEZMAsAt2WbhnIJQhIAeAiEJACAubW+vq7T01P5/uVZJLcZMDtee+z50igI5Prhow2YBYC7yKdM2az+BYAHMXVIYhiGJelvJe2GYfhnhmH875LenV9dltQMw/A/foQaAQC4tXw+r0QioZOTk1t/7J0GzHqS458NmHV8yeNID4AnUkoSkADAQ7lNJ8m/kPQfJBUlKQzD/3p8hWEY/6uk1sOWBgDA3a2vr+v4+FhBENz8zndwlwGz49XHVw2YBYC7MA2pkGb1LwA8lKlCEsMwnkn6Z5L+taR/+c11hqT/StI/efDqAAC4g2KxKNu2dXp6GlkNtx0w6/iBXE9yglCj83XHDJgFcJNswlTKppMEAB7KtJ0k/0bSX0oqXHHdP5J0GIbh7zd9kiAINBgMblEeojQcDqMuATHDbQJXiePtYn19XZ8+fVK/34+6lBslJCUMKWvru9/Knh/IDc5WHA/9QN75664fyAtCuX48B8z6zijqEhBD3C4eRyphazCI4z3BzeL4+wPR43aBbz31beLGkMQwjD+TdBSG4b83DOMfX/Eu/62k/+0HH/8Xkv5CktbW1u5YJgAA0ykWizJNU63W7J8CHQ+YzSSk4jUDZh0/OJ+H8keg4p4PmPUCBswC8y6fMqIuAQDmyjSdJP9Q0j83DOOfSkpLKhqG8e/CMPxzwzBsSf+lpP/kug8Ow/CvJf21JP3Jn/xJmMlkHqBsPCX+zfAtbhO4SlxuF69fv9bR0ZHS6XTUpTyJ7A3XXzdg1jk/0vOYA2bt5GL8G+B2uF08nKRlqFpMyzRmOyiJy+8PxAu3C3zrqW4TN4YkYRj+laS/kqTzTpJ/FYbhn59f/Z9L+vswDHcerUIAAKZUrVYlSY1GI+JK4uMuA2ZH50d6GDALxFs+ac58QAIAcXOb7TZX+W/0g6M2AAA8pZWVFR0cHERdxkyZZsDs0D0LTxgwC8RLOcNWGwB4aLcKScIw/BtJf3Ph7f/+YcsBAOBuarWagiBQs9mMupS5k05YSieuv9670IEy8n31+rZcX5Jtyg1CuV48B8wCs8w2DOWTbLUBgId2304SAABioVaraW9vL+oyFtJ4wOzZfBRLA8uXJGUyZ7MnrjrS4/qScz5g1vUZMAvcViZpKGERkgDAQyMkAQDMvJWVFfm+r3a7HXUpuMI0R3pc39doPFQ2CJ50wCwwi0ppjtoAwGMgJAEAzLxqtard3d2oy8A9jAfMXsf/dkvP+evjAbMc6cEiMQypmCIkAYDHQEgCAJhpq6ur8jxPnU4n6lLwiCzLUsa6/YDZUfBHNwoDZjEv0pahtM1WGwB4DIQkAICZVq1Wtb29HXUZiIGbBsy633SjuBe7UbxQLiEKZkQhbclg9S8APApCEgDAzFpfX5fjOOp2u1GXghkwPtKTk3RVNwoDZjEriikGtgLAYyEkAQDMrHK5rC9fvkRdBubENANmnfMBs64fanR+pMcJGDCLp5MwDeUShCQA8FgISQAAM2lzc1PD4VD9fj/qUrBAkpal5JQDZofnR3pG50d6XAbM4gHkUqZsVv8CwKMhJAEAzBzTNFUsFrW1tRV1KcAlFwfMlqYYMDvyzrtRwlCOF3CkBzcqJQlIAOAxEZIAAGbOxsaGBoOBBoNB1KUAt3anAbPeH0d6GDC7uEzjbGgrAODxEJIAAGbKuIvk06dPUZcCPIr7DJh1/LMjPcFTF40nkU2YStl0kgDAYyIkAQDMlGfPnqnb7Wo4HEZdChCJaQbMjrxxeMKA2XlS4KgNADw6QhIAwMywbVv5fF4fP36MuhQg1lK2pdQP/sobD5gdXTjSw4DZ+CumCUkA4LERkgAAZsbz5881GAzk+37UpQAz7eKA2au6UcLwbKjs2ZGes+M9ns+A2SglLUPZH61WAgA8CEISAMBMWF1dVSaTURAEevv2rQzDUBiGl16CIJj81/d9eZ43+a/neXJdV67rynGcqL8dINYMw7gwYPbqB+bfDZj1pJEfnnWjMGD2weWTpkzDiLoMAJh7hCQAgNh78eKFstmsPn78qNFoNLncsixZliXTNCevj19s25ZlWUqlUspms5fe76qApdvtand3N8LvEpgttx0w2+3bcn0pTJhy/FAeA2ZvpZyhiwQAngIhCQAg1l6/fi3btvXx40e5rnvpOt/373z05mKwYtu2Xrx4oUajoX6//xBlAwvv2wGzeevsZzWTSU/eZ+T5GnlnXScOA2avZRuG8gxtBYAnQUgCAIgl0zT15s0beZ6nDx8+KAge9jnnIAgUBMEkeDk+PtazZ8/022+/PejXAXC92wyYHZ4PmHUWcMBsJmkoYRGSAMBTICQBAMROIpHQzz//rH6/r+3tbYVPMCDy5ORES0tLqlarqtfrj/71ANxs6gGznuQEfwyYHQWBXD+cmwGzpTRHbQDgqRCSAABiJZPJ6KefflKj0dDBwcGTfd0wDLW3t6eNjQ1CEmBG3HfArDMDR3oMQyqmCEkA4KkQkgAAYqNYLGpzc1OHh4eRBBXtdlvLy8va3NxkiCswJ247YPbsOM9ZN0ocBsymLUNpm602APBUCEkAALFQq9W0srKinZ0dtdvtyOrY3d3V69evdXh4KM/zIqsDwNP4dsDsVaIcMFtIn23kAgA8DUISAEDk1tfXVS6XtbW1Ffl2meFwqFarpRcvXujTp0+R1gIgHm4aMOtdONLz0ANmiykGtgLAUyIkAQBE6uXLl8pkMvr06ZNGo1HU5UiSDg4O9PbtW+VyOfV6vajLARBztmXJtqSspOsGzA7d4LsjPc4NA2YTlqFcgpAEAJ4SIQkAIDI///yzTNPUhw8fYnW0xfd9HR0daXNzk5XAAO7NMAxlktYPj/S4F9YdO74vx5OSpiGb1b8A8KQISQAAT840Tb1580au6+rz588KgijHIl6tXq+rVqupVqvp5OQk6nIAzLmbBswCAJ4G0TQA4Eklk0m9fftW/X4/tgGJ9MdK4OXl5ahLAQAAwBMhJAEAPJlcLqeff/5ZjUZDOzs7UZdzo06no+FwqGfPnkVdCgAAAJ4AIQkA4EmUy2W9fPlSBwcHOjw8jLqcqe3t7alYLCqZTEZdCgAAAB4ZIQkA4NEtLy9rfX1d29vbajQaUZdzK6PRSM1mU8+fP4+6FAAAADwyQhIAwKPa3NxUrVbT1taWOp1O1OXcyeHhoZLJpAqFQtSlAAAA4BERkgAAHs1PP/2kfD6vjx8/ajAYRF3Onfm+r8PDQ21sbERdCgAAAB4RIQkA4FG8efNGtm3rw4cPchwn6nLurV6vKwxDtt0AAADMMUISAMCDsm1b7969k+d5+vjxo3zfj7qkB7O3t6darSbT5NcnAADAPOKvPADAg0mlUnrz5o263a62trYUhmHUJT2obrerwWDASmAAAIA5RUgCAHgQ+Xxer1+/1unpqXZ3d6Mu59Hs7e0pn8+zEhgAAGAO2VEXAAB4WqZpXnqxLGvyumEYk7cNw7h0+cW3x59HOuuuMAxDS0tL2tvbU7PZjPLbe3SO46jRaOjFixf68OFD1OUAAADgARGSAMATuC6UuCmYuPjf8eeRNAktxi/jy6b5rySFYTg5ChOGoYIgmFw2frl4WRAECoJAvu9PXh9f1+12FQSBWq2WhsPhE/zfjN7h4aHevXunYrGodrsddTkAAAB4IIQkAObWj4KJb0OKHwUTFy8bv32bYMIwjO8CiKtevg0lwjC8MpS4KtS4zdsPbbzaN5PJPPjnjqsgCHRwcKCNjQ1CEgAAgDlCSALgyZmmqVqtJsuyrjzGcTGUuEunxGMEE/cNKTB/Go2GarWaVldXdXh4GHU5AAAAeACEJACeTCqV0sbGhjKZjIbDoTzPmwQKF1//UYBx3dsEE4jC7u6uXr58qePjYwVBEHU5AAAAuCdCEgCPrlAoaHV1VclkUs1mU7u7u3IcJ+qygHvr9/vq9/t6/vy5vnz5EnU5AAAAuCdCEgCPZmlpSbVaTaZp6uTkRPV6Xb7vR10W8KD29vb0yy+/KJ1OL8zgWgAAgHlFSALgQZmmqdXVVZVKJQVBoMPDQ7VaLY6/YG65rqvT01M9f/5cv//+e9TlAAAA4B6eNCQZb5fg3DYwfxKJhNbX15XP5zUYDLSzs6Nutxt1WcCTOD4+VqVSUblcVrPZjLocAAAA3NGThiSJREK//vqrgiCQ7/sajUYaDAbqdDqTFZIAZksul9Pa2ppSqZQ6nY4+ffrEkQMsnPFK4LW1NUISAACAGfakIclwONTf/d3fKZVKKZ1OK51OK5fLqVqtyjRN+b4vz/M0HA7V6/XU7Xbled5TlghgSuVyWSsrK7IsS/V6XV++fOHnFQut2WyqVqtpfX1d+/v7UZcDAACAO4hkJsloNNJoNFKr1ZpcZlnWJDjJZDJaXl7WxsaGwjCU7/tyHEeDwUDdble9Xi+KsgFIWl5e1rNnzxSGoY6Pj9VsNjlCB5zb3d3Vq1evdHR0xJBiAACAGRSbwa2+76vX630XgCSTyUlwkslkVKlUJnNNXNfVaDRSr9dTp9PhWWzgkViWpfX1dUlnQyr39vbUbrcjrgqIn/ER0ufPn2trayvqcgAAAHBLsQlJruM4jhzHufSAzDTNS10nS0tLWl9fn3SduK6rfr9P1wlwT+l0Wuvr68pkMur1etra2tJwOFQmk4m6NCC29vf39fbtW2UyGeZtAQAAzJjYhyRXCYJA/X5f/X7/0uXjrpN0Oq1sNqtKpSLLsi7NOhkMBmq323JdN6LqgfgrFApaW1tTIpFQs9nU7u6uHMdhICswBc/zdHJyoufPn+u3336LuhwAAADcwkyGJNe5rutkPCh2HJysrq5e6joZDofqdrvqdDoRVg9Er1araWlpSYZh6OTkRI1Gg7kKwB0cHx+rWq2qWq2qXq9HXQ4AAACmNFchyVWCINBgMNBgMFCj0ZhcnkgkLnWdbGxsyLbtSdfJaDRSv99Xp9OR4zgRfgfA4zJNU2trayqVSvJ9X4eHh2q1WgrDMOrSgJkVhqH29va0sbFBSAIAADBD5j4kuY7runJd91L3iGEYl2adlMtlra6uStJkw86466TX67HRAzMtmUxqfX1duVxOg8FAX79+ZYYP8IDa7baWl5e1ubmp3d3dqMsBAADAFBY2JLlKGIa37jrxff9S18loNIrwOwBulsvltLa2plQqpXa7rY8fP3K7BR7J7u6uXr9+rcPDQzawAQAAzABCkilc13UynnWSyWRUKpW0srIiSd/NOul2u3SdIHKVSkXLy8uyLEv1el1fvnzhQRvwyIbDodrttp4/f67Pnz9HXQ4AAABuQEhyR2EYajgcajgcqtlsTi63bftS18l4Q8jFrpPBYKBOp8OmEDyJ1dVVVSoVhWGo4+NjNRoN5o0AT+jg4EBv375VLpfjSBsAAEDMEZI8MM/zJt0jYxe7TtLptAqFgmq1mgzDmHSdjEYj9Xo9tdttuk5wb7Zta319Xfl8Xo7jaHd3l+1NQEQ8z9PR0ZE2NzdZCQwAABBzhCRP4GLXyUWWZV3qOllZWdHm5qaCIJDneXIcZ9J1MhgMIqoesySdTmt9fV2ZTEbdbldbW1vcdoAYOD091dLSkmq1mk5OTqIuBwAAANcgJImQ7/vq9Xrq9Xo6PT2dXH5x1kkul9PS0tKk62S8nvjbFyy2YrGo1dVVJRIJNRoN7ezsyHXdqMsCcG68EvjZs2eEJAAAADFGSBJD4+Cj1WpNLrvYdbK+vq7hcCjTNGVZlkzTVBiGCoJAYRjK9/1L3Sjj4zzD4ZBBnXNmeXlZ1WpVhmHo5ORE9Xqd41pATA2HQwVBoDdv3mh3d5cuLwAAgBgiJJkR464T6ex8+4cPHy5db9v2dy+JREKpVEq5XE62bcuyLBmGMQlTgiCYDJQdByqO49CdEnOmaWptbU2lUkme5+nw8FCtVothrEAMJRIJlUollctlJZPJSXD96tUr+b6vZrOp4+Njwk0AAICYICSZMaVSSf1+/7vLPc+bqkvEMIzvgpTxf7PZrIrF4iRQabfbCsNQuVxuEqiMv864O4VA5ekkk0ltbGwom81qMBjo69evbMoAYuiqYKTRaFw6VmkYhorFomq1mpaWljQcDnV0dHRp6DcAAACeHiHJjMlkMpdWDt9WGIZyXXeqeRWj0Ui2bU+Ck3GYkkgklE6nValUvjvuM34ZByrjrzUcDjUajTjucwe5XE5ra2tKpVJqt9v6+PEjwRQQM9MEIxeFYahWq6VWq6VkMqlKpaLnz58rDEO1220dHh7K9/0n/i4AAABASDJjEonElZ0kjyEIAjmOM1W3wn2P+4zDlIvHfRa9/bxarapWq8myLNXrdX358oWQCYiR2wYj13EcR4eHhzo8PFShUNDS0pLevXsnx3F0cnJyr2AcAAAAt0NIMkNM05Rpmt+tEo6DxzjuE4bhpQ6VRTnus7q6qkqlojAMdXx8rEajwbwRICYSiYSKxaIqlcq9gpHrdDoddTod2batcrms1dVVra+vq9vt6vDwUI7jPMjXAQAAwNUISWZIuVyW4zgz/YD5Nsd9TNOchCjzftzHtm2tr68rn8/LcRzt7u6q0+lEXRYAPX4wchXP83RycqKTkxNls1ktLS3pzZs38jxPjUZDx8fHj/a1AQAAFhkhyQzJ5/MLNagzCIKpO/U7uuAAACAASURBVEWuOupj23bsj/tkMhmtra0pk8mo2+1qa2uLtaBADFwVjDSbTZ2cnDx5Lf1+X/1+X6ZpqlQqaWlpSbVaTcPhUIeHh092BBMAAGAREJLMkHQ6zdn0a9zluM/FLpVkMqlsNqtSqSTLsibHfb4NVMZfZ7wueTgc3qn9vVgsanV1VYlEQo1GQzs7O1N11wB4PHEKRq4SBIEajYYajYZSqZSq1apevnypIAjUarV0dHS08LOcAAAA7ouQZIbYtk2XwT1dPO5z0/9Ly7Ku7FAZP5Aad6fcdNznYndKpVJRtVqVYRg6OTlRvV7nQQ0QobgHI9cZjUba39/XwcHBZNjr+/fvNRqNdHx8rHa7HXWJAAAAM4mQZEZkMpnJA3w8jfFRnIc87uO6rg4ODtRqtZ7gOwBwlVkNRq4yXhncbreVSCRUqVS0sbGhjY0NdbtdHRwcxHYWEwAAwFN5/fr11O9LSDIjSqUS585jbNrjPgCiMU/ByHVc19XR0ZGOjo6Uz+dVrVb19u1bua6rer3+qINmAQAA4urFixey7emjD0KSGZHNZmmfBoBbWIRg5DrdblfdbleWZalcLmtpaUkrKyvq9/s6PDyM5Sp5AACAh7a2tqZsNquPHz9O/TGEJDMimUzSSQIAN1jkYOQqvu/r9PRUp6enymQyqlarev36tTzPU7PZ1NHRUdQlAgAAPIqlpSVVKhV9+vTpVmMrCElmgGmasiyLoa0AcIWrgpFWq6Xj4+OoS4uVwWCg3d1d7e/vq1gsTlYJDwYDHR0dLdSKeQAAMN8KhYJWV1f15cuXqWZMXkRIMgOKxaIcx1EYhlGXAgCxQDByd0EQqNlsqtlsKplMqlqt6sWLFwqCQO12W4eHh2zdAgAAMyuTyejZs2fa3d2905NAhCQzoFAo8AwfgIVHMPLwHMfRwcHBd6uEHcfRycmJms1m1CUCAABMLZFI6KefftLR0dGdN4oSksyAdDrNuXEAC+m6YOT09JRuhwfW6XTU6XRk27YqlYrW1ta0srKinZ0dZmIBAIDYM01TP//8s5rN5r22+hGSzADbtplHAmBhEIxEy/M8HR8f6+TkRLVaTT/99JMGg4G2t7dZdQ4AAGLrzZs36vf72t/fv9fnISSJuXQ6LemsJRoA5pVt2yqVSgQjMRKGoY6Pj9VoNLS+vq63b9+q2Wxqb28v6tIAAAAu+fnnn+V5nra3t+/9uQhJYq5UKtFFAmAuEYzMhvEfHJlMRhsbG3r//r2Ojo5Ur9ejLg0AAEAvX76UaZr6+PHjgyw7ISSJuVwup06nE3UZAPAgxsFIuVxWKpUiGJkhg8FAHz9+VKlU0vr6upaWlrSzs0OQDwAAIrO+vq5MJqMPHz482N+ShCQxl0gkGJgHYKYRjMyXVquldrut5eVlvXr1SoPBQF+/fpXv+1GXBgAAFsjS0pLK5bI+ffr0oHPTCElizDRNWZbFs3QAZg7ByHwLw1BHR0dqNBpaW1ubzCu576A0AACAaRSLRa2urmpra0uj0ehBPzchSYwVCgV5nscDCgAzgWBk8biuO5lXsrm5qffv3+vg4IBwHwAAPJpsNqtnz55pZ2fnUU5dEJLEWKFQUK/Xi7oMALiWbduqVCoqFouq1WoEIwtqMBjow4cPKpfLWltbUzqdpqsEAAA8qGw2q3K5rFKppIODA7Xb7Uf5OoQkMZbJZHR8fBx1GQBwybcdI6enp+p0Ojo5OSEYWXDNZlPtdlvZbFYvXryQZVn6+vUrtwsAAHBrxWJRxWJRnucpkUgon8+r1+tpb29PrVbr0b4uIUmM2bZNyzKAWLBtW8ViUZVK5bujNOOOt0wmE3GViIMgCHR8fKxms6nXr1/r3bt3ajQaOjg4iLo0AAAQU6ZpqlKpKJ/PK51Oy7IseZ6nXq+n09NTDQYD2fbTxBeEJDGVTCZlGMaDD6EBgGn9KBihMwA3cV1XX79+VTab1ebmpsrlsg4ODtRsNqMuDQAARCyZTKpcLiufzyuZTMqyLI1GI3W7XdXrdfX7/cnmvHHjACHJgiuXy3SRAHhyVwUj7XZbnz9/JhjBnfT7ff3+++8ql8taX1/X8vKytre3NRwOoy4NAAA8kfE8kWw2q0QiIcMwNBgM1Ol01Ov1NBgMFIZh1GVKIiSJrVwux9BWAE+CYARPYTyvZHl5Wa9fv1av19P29ja3MQAA5tB4nkgmk1EikVAYhur1epMukTg/WUJIElPJZJKhrQAezbfBiOu6arVaBCN4VEEQ6PDwUPV6XRsbG3r37p3q9boODw+jLg0AANzRj+aJHB8fq9fryXXdqMucGiFJTFmW9Sg7nwEsLoIRxIXruvry5YtyuZw2NjZUqVS0v7//qJPqAQDAw7jNPJFZREgSQ8ViUb7v86AFwL0RjCDOer2efv/9d1UqFW1sbEzmlTC0HACA+MjlciqVSjMxT+QhEJLEUKFQYB4JgDsjGMGsaTQaarVaWllZ0c8//8y8EgAAIlQqlVQoFGZynshDICSJoUwmo9PT06jLADBDLMtSqVQiGMHMCoJABwcHqtfrWl9f17t373R6eqqjo6OoSwMAYG7N2zyRh0BIEkOJRIJ5JABu5dWrVzJNU81mk2AEM81xnMm8ks3Nzcm8kna7HXVpAADMvHmfJ/IQCEliZnzGi/PYAKZlmqaSyaT+/u//nnAEc6PX6+m3335TtVrV5ubmZF6J4zhRlwYAwMxYtHkiD4GQJGbK5TIBCYBbyWazDHvG3KrX62o2m1pdXdWbN2/U6XS0u7vL7R0AgCt8O08kCAL1+/2FmSfyEAhJYiaXy6nb7UZdBoAZksvlCFcx14Ig0P7+vk5PT7WxsaF3797p5OREx8fHUZcGAEBkmCfyOAhJYiaVSjG0FcCtFAoFNRqNqMsAHp3jONra2lI+n9fGxoaq1ar29vbU6XSiLg0AgEfHPJGnQUgSM5ZlMbQVwNQMw1AymSQkwULpdruTeSXPnj2T4zjMKwEAzB3miUSDkCRGCoWCfN8n/QMwtWw2qyAImM+AhVSv19VqtS7NK9ne3o66LAAA7oR5IvFASBIjxWJRg8Eg6jIAzJBsNss8Eiw03/e1t7c3mVfy66+/Mq8EABB7zBOJL0KSGMlkMqrX61GXAWCGFAoFhj0DkkajkT5//qxCoTCZV7K7u8vPB4C5lcvllEwm5bquHMeR53l0lsYY80RmByFJjNi2zTwSALeSTqf19evXqMsAYqPT6UzmlTx//lyj0Ujb29s8GwdgLuRyOdVqNWUyGUmS53myLEumaco0TYVhOHkJgmDy3yAI5Pu+PM+bvIzDFdd1CVceAfNEZhchSUzYti3TNDlnBmBqmUxGQRDI87yoSwFiJQxDnZ6eqtlsam1tTb/88ova7bZ2dnaiLg0Abi2TyahWqymXy8kwDLVaLR0fH1/55KppmrIs69KLbduXXs9ms5euN01TkiahysWAxff9yd8a42Bl/OI4DuHKBaVSScViUel0mnkiM46QJCZKpRJzBQDcSjab5dlx4Ad839fu7q5OTk60ubmpX3/9VcfHxzo5OYm6NAD4oWQyqZWVFeVyOVmWpXa7re3tbfV6vR9+3Lhr5LZ/H1wVrlwMVmzbViaTUT6f/2G4clPniuu6Go1GMx+uME9kvhGSxEQ+n7/xTg8ALioUCtxvAFMYjUb69OnTd/NK+PkBECeJRELLy8sqFAqyLEudTkd7e3vqdruPfizjruGKYRhXdqx8G65cDFYsy5p8zavClVarJc/zlE6nv+teiap7lnkii4WQJCZSqZQajUbUZQCYIdlsVvv7+1GXAcyM8bySpaUlvXjxQqPRSF+/fuXIGoDI2LatWq2mYrEo27bV6/W0v7+vTqczE/MqwjCcdIrcpiv+YrjybcgyGo1kWZbS6fTksvHMFcMwLh0H+rZz5WL3ysVjQbe9n2eeyGIjJIkJhrYCuI1UKqUwDDmmB9xSGIY6OTlRo9HQ+vq63r59q1arpd3d3ahLA7AgTNPU8vKySqXS5DHA4eGhOp3OzB9DmdbFcOVbg8FAktRqtb677qpw5dvOlVQqdWnuysVw5duAZRys+L4vy7KUSqVk2zbzRBYcIUkM5HI5hi8CuJVcLsdZV+AefN/Xzs6O0um0NjY29P79ex0fH+v09DTq0gDMIdM0VavVVCqVlEgkNBgMdHR0pHa7vTDByEP4UbjyI4ZhyDTN744EjV8SicRkY9CnT58IRRYcIUkMlEolukgA3Eo+n+d+A3gAw+FQnz59UrFY1MbGhpaWlphXAuDB1Go1lctlJZNJjUYjnZ6eqtVqMb/iiV3sGrmOaZra3NzUq1evtLOzo06n84QVIk4ISWIgk8mo2WxGXQaAGZLL5fTly5eoywDmRrvdVqfTUa1W08uXLzUYDLS9vU2XJ4Bbq1arqlQqSqVSchxHjUZjMowU8RUEgba3t1Uul/X8+XNWxy8wQpIYSCQSPCMMYGrJZFKSuN8AHlgYhjo+Plaj0dDa2prevn2rZrOpvb29qEsDEHPlclnValXpdFqu606CEY7Gzp5ms6l+v6+XL1/q7du32trakuM4UZeFJ0RIErHxpGbOvQGYVjab5dko4BF5njeZV7K5uan379/r6OhI9Xo96tIAxEixWNTS0pLS6bSCIFCj0dDu7i5D1eeA4zj68OGD1tbW9PPPP+vo6IiZVQuEkCRi5XJZjuOwQgrA1PL5/GTyO4DHMxwO9fHjR5VKJa2vr6tWq2l7e5ufP2CB5XI5LS8vK5PJKAxDNRoN7e/v84TnHArDcLKO+fnz5yoUCtra2oq6LDwBQpKI5fN5hsMBuJVcLkf7P/CEWq2W2u22lpeX9erVK/X7fW1vbzN4EVgQ2WxWtVpN2WxW0tl9wuHhIYHpguh2u/rtt9/04sULvX//Xl++fOHffs4RkkQsnU4ztBXA1Mar65i4DjytMAwnR27W19f17t27yTPIAOZPOp3W8vKycrmcDMNQu93W169fmQe2oHzf1+fPn7W0tKRXr17p9PRUh4eHUZeFR0JIEjHbtkkiAUyNeSRAtDzP0/b2tjKZzGReyeHhoRqNRtSlAbinZDKplZUV5XI5WZY12W7S7XajLg0xcXp6ql6vp5cvX6pQKOjz5890Fc4hQpIIjc8yMvUawLSYRwLEw2Aw0IcPHy7NK9nZ2eHnE5gxtm1rZWVFhUJh0qm5t7enbrfLzEBcaTgc6vfff9fGxobevn2rvb09tVqtqMvCAyIkiVC5XKZlD8Ct5PN5HR0dRV0GgHNXzSv5+vWrgiCIujQA17BtW8vLyyoUCrJtW71ebzKgk2AE0wiCQDs7OyqVStrY2FCxWNT29nbUZeGBEJJEKJPJqN1uR10GgBlhWZZs22aOERAz43kljUbj0rySg4ODqEsDcM40TS0vL6tUKk2Oux8dHandbhNq4s5arZb6/b5evnypd+/e6fPnz3IcJ+qycE+EJBFKJpN0kgCYWjab5dwrEGOu6+rr16/KZrPa2NjQ+/fvdXBwQLAJRMQ0TdVqNZVKJSUSCQ2HQx0fH6vdbvP7FA/GdV19+PBBq6urevPmjY6OjnRychJ1WbgHQpKImKYpy7I4uwxgarlcTsPhMOoyANyg3+/rw4cPKpfLWltb0/Lysra3t/n5BZ7I0tKSKpWKksmkRqORTk9P1Wq1CEbwqA4PD9XtdvX8+XMVCgV9+fKFLqUZNXVIYhiGJelvJe2GYfhn55f9z5L+J0mepP8rDMO/fJQq51CxWJTjOJx7BDC1fD6ver0edRkAptRsNifzSl6/fs28EuARVatVVSoVpVIpOY6jRqOhdrvNggQ8qV6vp99//13Pnz/X27dvtb29rV6vF3VZuKXbdJL8C0n/QVJRkgzD+M8k/ReS/qMwDEeGYaw8Qn1zazAYKJFIKJFIcOcN4EamaSqZTNK2D8yYIAh0eHioer2ujY0NvXv3TvV6XYeHh1GXBsy8crmsarWqVCol3/fVaDTUarWYCYFI+b6vra0tVatVvXz5Uo1GQ/v7+1GXhVuYKiQxDOOZpH8m6V9L+pfnF/+Pkv6XMAxHkhSG4Y3rFoIg4HjJufGwqEKhoN3d3ajLuRJtwfgWt4noZLNZNZvNWD4bwe0C3+I28b3BYKB2u61MJqO1tTVtbm5OhkYuCm4X+NZdbhP5fH4SjIxDyHa7TTAyR+blvmJ3d1fHx8d69uyZ1tbWtL29Lc/zoi5rJj31bcKc8v3+jaS/lHSxP/StpH9kGMb/YxjG/20Yxj+46gMNw/gLwzD+1jCMv+UZ0Mt2d3eVzWaVTqejLgVAzGWzWbrOgDkwGAz0+fNnHR8fa3V1VT/99JOSyWTUZQGxls1m9fz5c/3yyy9aW1vTcDjU169f9fHjR52cnBCQILYcx9Hnz581GAz06tUrFYvFqEvCFG7sJDEM488kHYVh+O8Nw/jH33xsRdJ/KukfSPo/DMN4HX4zZCMMw7+W9NeS9Cd/8idhJpN5qNrngu/7+uWXX/Thw4eoS7kW/2b4FreJp7e2tjZ5Fjqu4lwbosFt4nqO42hvb08rKyv60z/9U/V6PW1vby/EvBJuF/jWVbeJTCaj5eVlZbNZGYahZrOp09PTSVe6aZrclubcPP37tlothWGoN2/eqN/v68uXL1GXNJOe6jYxzXGbfyjpnxuG8U8lpSUVDcP4d5J2JP2f56HI/2sYRiCpJun40aqdQ/v7+3r//r1KpZJarVbU5QCIIcMwlEql1Gg0oi4FwAMKgkAHBweq1+taX19nXgkWXjqd1vLysnK5nAzDULvdZvAl5ka73dZgMNCLFy/07t07ffnyZW6OFs2bG0OSMAz/StJfSdJ5J8m/CsPwzw3D+B8k/RNJf2MYxltJSUkshL6Do6Mjra+vq91us+0GwHfS6bSCIOAcKzCnHMfRly9flMvltLm5qUqlor29vYWaV4LFlUgktLm5qXw+L8uy1Ol0tLOzo263G3VpwINzXVcfP37UysqKXr9+rePjYx0f02MQN7fZbvOtfyvp3xqG8f9JciT9d98etcF06vW6arWaarUaPyQAvpPL5ThvDSyAXq+n3377TZVKRZubm1pZWdHXr1/5+cfcsW1by8vLk9cNw9De3p663S5PGGIhHB0dqdvt6sWLFyoUCtra2lqI45az4lYhSRiGfyPpb85fdyT9+cOXtJh2d3f18uVL1et1+b4fdTkAYqRQKNBqDCyQ8RrT1dVVvXnzRt1uVzs7O/wBjZlmWZZWVlZUKBRk27b6/f6kY4QlBlhE/X5fv/32m549e6Z3795pe3ubDqqYuE8nCR5Qr9fTcDjU2tpabFcCA4hGJpPhfgFYMEEQaH9/X6enp9rY2ND79+/luq4Gg4FarZY6nU7UJQI3Mk1TtVpNpVJJiURCg8Fgsvo6CILJEFZgUQVBoK9fv6pSqejFixdqtVr8zRcDhCQx8vXrV719+1YnJycajUZRlwMgBtLptMIwpN0eWFCO42hra0vJZFK5XE65XE4bGxuyLEue52k0GqnT6ajZbNJpglgwTVNLS0sql8tKJBIaDoc6OTlRu92mWxq4RqPRUL/f14sXL/T27Vt9+vSJWXQRIiSJEc/z1G63tbGxoc+fP0ddDoAYyGazBCQA5DiOHMeZbLmyLGsSmlSrVa2vr8v3fTmOo263q2azyX0HntTS0pIqlYqSyaRGo5FOT0/Vbrd5oAdMaTQa6cOHD1pbW9Mvv/yig4MDNhtGhJAkZnZ2dvTrr78qn89zJg2ACoWC+v1+1GUAiBnf99VutycbcAzDUDabVTabVT6fV61WUxiGcl1X/X5frVaL2UZ4cJVKRdVqValUSq7rTubpuK4bdWnATArDUPv7++p2u3r27JlKpZK2traiLmvhEJLE0MnJiTY2NvTbb79FXQqAiGWzWR0dHUVdBoCYC8NQvV5PvV5vsikvnU5PQpPnz5/LNE15nqfBYKBOpzOZCwHcRqlUUrVaVTqdlu/7k2CEziXg4XQ6Hf3+++968eKF3r17p69fvzLD5wkRksTQ8fGxqtWqKpUKLVbAAksmk5LEL0UAdzIcDjUcDlWv1yVJiURC2WxWuVxOKysr2tjYuHREp9FocDQCVyoWi1paWlI6nVYQBGo2m9rb22OGHvCIPM/Tp0+ftLy8rFevXunk5IQnzp4IIUlM7e/va3NzU61Wi2d5gAWVy+V4wALgwbiuq1arpVarJelswOY4NCkWi1peXlYQBJMjOs1mk5B2gZmmqdXVVZVKJZmmKcdx9PnzZw2Hw6hLAxbK8fGxut2uXrx4oULh/2/vzmJje9f0oD/fqnl2lWseXZ53dwMHOCAkiIAoJCiJGgJqBimDlAuikKAkyqA0IAh3qCE0iIugTjo6EaAoQTSQRLmJEkK4IEGnQ4c+9P9sj1Vll8s1z3PV+rjwrnVsb3tv27tcq4bnJ1nb2y5Xvdteu1zrWe/3fi5cXl7y/PCdMSRZUq1WC8FgEIFAAMViUe9yiEgHTqeT80iI6N2oqopOp4NOp6O91rDZbNoSnZ2dHQDQlujcn4FC68vlciEYDMJisaDX6yGfz8NqtcLn87FzhEgn/X4fp6eniMViODo6wvX1NbeCf0cMSZbY9fU1dnd3UavVOACLaAM5HA5cXV3pXQYRbZB+v49+v49qtQrgbtnfrNskEokgHo9jOp1iMBhou+hwW9fVpygKwuEw3G43AKBWqyGXy2mvP9vtNra2thAMBnnxjkgnqqri6uoKW1tbiMfjaLfbuL6+1rustcSQZIkNBgP0ej1EIhHkcjm9yyGiBTKZTFAUhbtREJGuZlsPNxoNAHdbD89CE6/Xi1AoBFVVMRqN0O120Wg02G2wQp7qGnnu6nQul8Pe3h5/xkQ6azQa6PV6SCaTODw8RCaT4eDkOWNIsuRyuRyOjo5gs9m4Lphog9jtds4jIaKlM51O0W63tRNpIcSDJTrb29va1sOzJTpsCV8uX+saec5wOESz2UQ8Hsf5+fkiSiWiZ4xGI5yfnyMUCmFvbw+lUknrAKRvx5Bkyc0miMdiMZydneldDhEtCOeRENEqkFKi1+uh1+uhUqkAACwWixaaRKNRGAwGTCYTVCoVdDodTCYTDh3UwWu6Rp6Tz+dxfHzMHRiJloCUEre3t+h0OkgkEnC5XMhkMnqXtRYYkqyAQqGA4+NjeDwebSI9Ea03h8OB29tbvcsgInq14XCI4XConUQbjUbY7XZIKeHz+bC9vY3JZPJgiQ5bxd/HW7tGviSfz2vzENjxSKS/TqeDk5MTJJNJHB8fI5fL8ULbN2JIsiJKpRIikQharRaklHqXQ0TvyGAwwGg0chcJIloLk8kErVZLWzZcKpW0JToulwt+v19botPr9dBsNjmP6RvNtnT+lq6R57TbbQwGA0SjUc7MI1oS0+kUl5eX2N7exs7ODmq1Gi+2fQOGJCuiVqvB7/fD7/ejXC7rXQ4RvSOHw8Grc0S0tlRVRbfbRbfb1V7TWK1WbYlOIpGAoija1sPtdlsbHEvPe6prJJvNvsvvk2w2i6OjIzidTnQ6nbnfPxG9TbVaRbfbRSqVgtPpxOXlJXcgewOGJCskn88jlUqhVqvxYCdaYw6HA4PBQO8yiIgWZjAYYDAYoFarAbjb4WsWmgSDQUSjUUynUwyHQ23rYYbJdx53jVxfX797cKGqKkqlEuLxOD5+/MguZ6IlMhgMcHp6img0isPDQ9zc3HBkwysxJFkh3W4Xg8EA4XAY+Xxe73KIaI4cDgdcLhfcbjeMRiNbmIloo43HYzSbTe2FvaIo2tbDHo8HwWAQqqpiPB5rc002KVxeZNfIc6rVKnw+H0KhENv6iZaMqqq4vr6Gx+NBNBqFx+Pha8tXYEiyYnK5HA4PD1GpVLhHPdEKMxgMWijidDqhqioGgwFKpRLbyomIHlFVFZ1OB51OB8ViEUIIbYmOy+VCOp0GAG2JTrPZXMuth/XoGvmSbDaL/f39jQupiFZFs9lEr9dDKpXC0dERLi8vOSj7BRiSrJjZ8LNoNIrLy0u9yyGiV7BarXC5XPB4PDCbzZhMJuh0Ojg/P2foSUT0ClJK9Pt99Pt9VKtVAIDZbH5y6+HZEp16vb6SWw8vQ9fIc0ajERqNBuLxOM7OzvQuh4ieMB6PcXZ2hlAohP39fZRKJW3LdnoaQ5IVdH19jQ8fPnBYFtGSE0LA6XTC7XbD5XJBCKFti7mqL9aJiJbVaDTSTtqBu4692RIdr9eLcDiM6XSK0WikzTVZ5iuqy9Y18pybmxscHx/D5/NpM2WIaPkUi0V0Oh0kEgm4XC5ks1m+Fn0GQ5IVValUEI1GcXJyoncpRHSPyWTSukVsNhum0yl6vR6urq64pSUR0QJNp1O0221t2Y0QAjabDQ6HA06n88HWw/1+H41GQ/fnaYPBgFAotJRdI19yfX2NZDKJVqu19LUSbbJut4vT01PE43EcHh7y9ekzGJKsqHK5DJ/PB6/Xi3q9rnc5RBvNbrfD7XZrQ1dHoxHa7Taurq74YpGIaElIKdHr9dDr9bSthy0WixaaPLX1cKvVWsiV1lXpGnlOp9NBr9dDLBZDNpvVuxwi+oLpdIpsNgufz4dUKoV6vY5CoaB3WUuFIckKKxQKiMViaDabbJUiWiCDwQCn0wmPx8Ohq0REK2w4HGI4HGrLRIxGozbXJBAIaFsPz5bo1Ov1uYXfq9o18pxcLoejoyO4XK61HJpLtG5qtRq63S5SqRScTicuLy9X9vln3hiSrLBWq4VgMIhAIIBisah3OURrzWKxwO12fzZ09eLighP9iYjWxGxAfqvVAnA3NHW2RMflciEQCGhbD/d6PTQaDfT7/Vc9xqp3jTxHVVWUSiXEYjGcnJzwAh7RChgOhzg9PUU0GsXBwQEKhQIv+IEhycq7vr7G7u4uarUaxuOx3uUQrY3nhq42Gg3UajW++CMi2gCqqqLbJCzBeAAAIABJREFU7aLb7aJUKgG426lstkRnZ2cHQghtrkm73Uaz2fzsftata+Q51WoVXq8XoVCI7ftEK0JKiXw+j3a7jVgsBo/Hs/HL5hiSrLjBYIBer4dIJIJcLqd3OUQrjUNXiYjoawaDAQaDgbb1sMlkgsPhgMPhQCgUQiwWw3Q6xWAwQL/fh8vlWruukS/J5XLY399HvV5npyXRCmm1Wuj3+0gmkzg6OkI2m93Y/8MMSdbAbA2ozWZ7dcsn0aaz2+1aMDIbutrpdDh0lYiIXmQ8HqPRaHy29fBstkmr1ZrrLJNlNxqNUK/XkUgkcHp6qnc5RPQK4/EY5+fnCAaD2N3dRblc1gZdbxKGJGtAVVU0Gg3EYjGcnZ3pXQ7RUntq6OpwOOTQVSIimovHWw9vokKhgKOjI2xvb2sdN0S0OkqlEjqdDpLJJFwuFzKZzEYtNWdIsiYKhQKOj4/h8XieXAtLtMksFovWLWKxWDCZTNDtdjl0lYiI6J1cX18jlUqh1Wpxbh7RCur1ejg5OUE8HsfR0RGurq7WfrngDEOSNVIqlRCJRNBqtSCl1LscIl0JIRCJROB2uyGEwGg0QqvVQrVa3agknIiISA/dbhe9Xg+xWAyZTEbvcojoDVRVRS6Xg9frRTKZRDab3Yg5fQsNSWw2G/b399Hv99Hv97XBVzxhmY9arQa/3w+/37+Ra8eI7jOZTNja2sLV1dVGtzwTERHpZTY3z+12a9sqE9HqqdfrsFqtCAQCDEneg9VqhdVqhcfjgZQSiqJoE8B7vZ4WnIxGo0WXthby+TxSqRRqtRqm06ne5RDpSkrJgISIiEgnqqqiWCwiGo2i0+nwwijRCms0GtjZ2dG7jIXQbbmNoig/KcJohNPphMPhgKqqEEIAuJuO3e/3tfBkOBzyyfUrut0uBoMBwuEw8vm83uUQ6YZLzoiIiPRXq9Xg8/kQDodxc3OjdzlE9Eb9fh+qqm7EDMylmkkihIDBYND+brVaYbFYHnSdTCaTz7pOOAzqoVwuh8PDQ1QqFQyHQ73LISIiIqINls1mcXBwgHq9jn6/r3c5RPRG9Xod29vbDEn0JoTQOkuAuzkDJpNJ27pz9rnhcPjZrJNNvZI8mUzQarUQjUZxeXmpdzlEutjU//9ERETLZjweo1arIR6P4/T0VO9yiOiNGo0G/H6/3mW8u6UPSZ7zuOvEZrNps04AfNZ1MgtPJpOJXiUv1PX1NT58+ACn07kxWzUR3SelfBCwEhERkX5ub2/h8Xjg9/tRqVT0LoeI3mA0GmE8HmN7exvValXvct7NyoYkT3kcnHyp6+R+cDIcDtfyqnOlUkE0GsXJyYnepRARERHRhru+vkYqlUKz2eRyeaIVVavV4PV6GZKsuue6TmZDYBVFwXg8/mzWyap3nZTLZfh8Pni9XtTrdb3LIVqodQw+iYiIVlm320W320U8HueScKIV1Ww2EQqFoCjK2m6qshEhyVMeBydmsxlmsxlOp1Nr05dSfjbrZNW6TgqFAmKxGJrN5toexERERES0GrLZLD58+AC3280hrkQraDbSIhAIoFgs6l3Ou9jYkOQ597cmBgC73Q6bzfZZ18n9rYkHgwGm06ke5X5Vq9VCMBhc64OY6CmcSUJERLScZhfxyuUyL+IRraB6vb7W55cMSV7gua4Tl8v1WdfJ41kny+D6+hq7u7uo1Wpc/0lEREREumo0Gtje3kYwGMTt7a3e5RDRKzWbTUQiERiNxpUfUfEUhiTf4CVdJ6PR6LOtiRfddTKbtRKJRJDL5Rb62ER6WaVlcURERJsmm80iEomg2WzqXQoRvZKqquj1eggEAigUCnqXM3cMSebscdeJxWKBxWKB2+3Wuk5UVf2s62Q0Gr1rXblcDkdHR7DZbFz/uQBOpxNGoxHNZpMn6zrichsiIqLlNJlMUK/XEYlEcHt7y9dLRCumVqshEokwJKG3u991oigKjEYj7Ha71nUihHhy1sm81mmqqopGo4FYLIazs7O53Cc9zev1IhKJYDqdIhwOo1wuo1ar8Ze/Dvg9JyIiWl7lchlutxt+vx/lclnvcojoFdrtNuLxOCwWy9KMmZgXhiQ6+lrXiaIomE6nGA6H6Ha7WnDy1q6TQqGA4+NjeDwetja+E7/fj0AggGw2i263C4/Hg1AohGAwiEqlgmq1ygFlC7bO25MRERGtunw+j5/+6Z9Gs9l8985qIpofKSXa7TaCwSCurq70LmeuGJIsoftdJ0aj8cmuk6dmnbzkRLBUKiESiaDVavEq+5yFw2F4vV5cXl5iMBgAuBtq1Gw24XK5EA6H4ff7UavVUKlUlnZHpHXCY5yIiGi5DQYDdDodxGIxXF5e6l0OEb1CvV5HPB7Xu4y5Y0iyIh53nVitVlgsFng8ngddJ7MhrbPw5PFuNrVaDX6/n22NcxaPx+F0OnF2dvbkDkLtdhvtdht2ux2RSATb29uo1+sol8trORF6mbCThIiIaLnlcjl8+PCB3c5EK6bT6UAIAYfDgW63q3c5c8OQZIUJIR4MpjQajXA6nXA4HFBVVfvcaDR6EJzc3NwgmUyiVquxm+EbCSGQTCZhtVpxcnLy1ZPxXq+H8/NzWK1WRKNRHB4eotlsolQqcXvmd8LhrURERMuvUCggGo2i0+nw9SnRCmk0GggEAgxJaLk913WytbWldZ1IKREIBLg3/TdQFAU7OzswGAw4PT19VbfCYDDAxcUFzGYzYrEYDg4O0G63USqV1m7wkZ5mO0oRERHRcms0GvD5fIhEIri+vta7HCJ6oUajgZ2dHb3LmCvl6zehdSCE0GadSCnRbDZRq9V0rmp1GQwG7O3tQQjx6oDkvtFohMvLS5ycnEBRFOzt7SGVSsFqtc654s3FkISIiGg15HI5uFwu2O12vUshohfq9/tQVRUej0fvUuaGnSQbQEoJVVUxnU5RqVTQaDQ4o+EbmEwm7O7uYjgcIpPJzOU+J5MJstksFEVBLBZDOp3GYDBAsVhEr9eby2NsInaSEBERrY7JZIJqtYp4PI7T01MOYCdaEY1GA9vb22szU4ghyRqbBSHtdhvVapUn23NgsViQTqfR7XbfZasrVVVxdXUFRVEQiUSQSqUwGo1QLBbR6XTm/nibgCEJERHR6iiVStja2kIgEECpVNK7HCJ6gVlIsi4YkqyZWdeIqqqoVquo1+scfjUnNpsNOzs7qNfr7z7LRVVV5PN55PN5hMNhJBIJjMdjlEoltFqtd33sdTKbwUNERESr4+rqCul0Go1GA6PRSO9yiOgrhsMhxuMxtre3Ua1W9S7nmzEkWROzrpFut4tqtcqugzlzOp1IJBIol8uoVCoLfezb21vc3t4iGAwiGo0iHA6jWCyuTTsbERER0X39fh/tdhvxeBwXFxd6l0NEL1Cv1+H1ehmSkL5mXSNSStRqNdRqNUwmE73LWjtutxvxeByFQgH1el23OkqlEkqlEvx+P8LhMMLhMEqlEhqNBtfsfgE7SYiIiFbP1dUVjo+PsbW1hUajoXc5RPQVzWYTwWAQiqKs/PxLhiQraHbQ9ft9VCoVtNttnStaX16vF5FIBFdXV0vzfa5UKqhUKvB6vQgGgwiFQiiXy6jVagxLHuH3g4iIaHUVCgVEo1G0220uHydacuPxGMPhEH6/f+XnCTEkWSGzXw6zrpHxeKxzRestEAjA7/cjm82i2+3qXc5n6vU66vU6PB4PQqEQgsEgKpUKqtXqyqe388TBrURERKup2Wxie3sb0Wj0XQbmE9F81Wo1hiT0/qSUkFJiOByiUqmg1Wrx6vgCRCIRbG1t4eLiAsPhUO9yvqjZbKLZbMLlciEcDsPv96NWq6FSqWz8VRduAUxERLTastksDg8P4XQ6OXOPaMm1Wi1EIhEYjcaVHgPBkGRJTadTCCFQr9dRq9WW/kR9nSQSCTgcDpyenq7Uf+52u412uw273Y5IJILt7W3U63WUy+WV+nfME0MSIiKi1TadTlEsFpFMJpdq+TMRfW46naLX6yEQCKBQKOhdzpsxJFkisyGs4/EYlUoFzWaTXSMLJIRAKpWC2WzGycnJyi5Z6fV6OD8/h9VqRTQaxeHhIZrNJsrl8kZuo8eQhIiIaLXVajWoqopEIqH7IH0i+rJarYZwOMyQhL6NqqoQQkAIgVarhW63CyklnE4nVFXV3qbTqfY+zZeiKEin01AUBScnJ3qXMxeDwQAXFxcwm82IxWLY399Hu91GqVTamM4kdpIQERGth0ajgfF4jGQyCaPRiHK5rHdJRPSE2fbdZrN5ZS/QMiTR0XQ6hZQS3W4XQggoigKz2QyLxQJFUbTgZPYG4MH7s86TxyHKdDp98P79oOWpwGV2P5vKaDQinU5jOp3i9PRU73LmbjQa4fLyEkajEbFYDHt7e+h2uygWixgMBnqX9+4YkhAREa2HbreLi4sLpNNpmEwm3Nzc6F0SET0ipUS73UYwGMT19bXe5bwJQ5IFm4Ua4/EYpVIJrVbrTfejKAqMRqP2ZjAYYDQaoSiK9r7BYNDCl/t/AngygLkfuEgp0Ww2oaoq7Ha7Fry8NHRZlW4Xk8mE3d1dDAYDZLNZvct5V5PJBNlsFoqiIBaLIZ1OYzAYoFgsotfr6V3eu9jk8I+IiGgdDYdDnJ2dYW9vD6lUCrlcjr/viZZMvV5HPB7Xu4w3Y0iyYKqqIp/PvzkcuX8/o9Fori1MRqMRJpNJC1gGgwEMBgNMJhMMBgMsFosWtjzV6fK442W2M89zocpToctzIcx7dLtYLBak02l0Op2VTTnfQlVVXF1dQVEURCIRpFIpjEYjFIvFtZsaL6WEoih6l0FERERzNJlMcHp6ir29Pezu7iKTyWz8jn5Ey6TT6UAIAbvdvpIXYxmSLJgQYmkPlMlk8mAXlH6/D+CutfEtZh0t97td7r+ZTCaYzebPOl6eWl70VLfLW0OX6XQKi8WCVCqFWq2GYrH4jd+51TQL7PL5PMLhMBKJhNbl9Nz3dLYFtdVqffL7vIxXcrjchoiIaP2oqorT01Ps7u5ib28Pl5eXGI/HepdFRJ80m00EAoGV7NZnSLJgQoiNSbpnJ9jzGhI6W2J0fznR/fBFURSYTKYXd7uUSiVUKpW51Lbqbm9vcXt7i62trc8CLZPJpH0/jUYjhBBwu93Pfl9nQdaX5uU8F149FYB9i2UMbYiIiGh+Li4ukEwmtaBkU4bTEy27er2OnZ0dvct4E4YkOuCJ29vMlhjR+2k0Gl/8/Ky7yGazPXubx8u2Zm+zmTnzWrb1kkHF9+fwEBER0XrK5XIIh8PY3d1FNptd2q5tok3S7/chpYTb7f7mUROLxpBkwVZloCnRWz1etvWtHg8nfqp76EvLtqrV6txqISIiouV0e3uL8XiMnZ0dXF9fr9xJGdE6qtfr2N7eXrn/jwxJFowhCdHrzDt0ISIiovVUrVYxmUwQj8dxe3uLWq2md0lEG63RaMDn8+ldxqtx24cFY0hCRERERPQ+ms0mMpkMQqEQQqGQ3uUQbbThcIjJZLJyQQlDkgXblKGtRERERER66PV6OD8/h9frRTwe17scoo1Wr9fh9Xr1LuNVGJIsGJcNEBERERG9r9FohJOTEzgcDuzs7HCQO5FOms0mLBYLrFar3qW8GEOSBWMnCRERERHR+1NVFR8/foTRaMTe3h4MBoPeJRFtnPF4jGq1it3dXRwdHSEUCkFRljuGWO7q1oyUkp0kREREREQLdHZ2hslkgr29PZhMJr3LIdo4xWIR3333HYrFIpxOJ46Pj7G7uwuXy6V3aU/i7jYLJKVkJwkRERER0YJlMhnE43Hs7e0hk8lgMBjoXRLRRpFSotFooNFowGw2azODpJRot9soFotL01DATpIFY0hCRERERLR419fXqNfrSKfTcDgcepdDtLFGo5HWXZLP52EymXB4eIj9/f2lGPLKTpIFYicJEREREZF+isUixuMxUqkU8vk8ms2m3iURbbR2u412uw2DwQCv14tgMIhwOIxut4tisYjhcLjwmhiSLBhDEiIiIiIi/dRqNYzHY8TjcRiNRlSrVb1LItp40+kUlUoFlUoFdrsdPp8Pe3t7mEwmyOfzqFQqC6uFIcmCMSQhIiIiItJXu91GJpPBzs4OTCYTbm9v9S6JiD7p9Xro9XpQFAUejwdOpxM+nw9GoxGlUgndbvddH58hyYKpqqp3CUREREREG6/f7+Ps7Ezb9eb6+hpSSr3LIqJPVFVFvV5Hv9+H2WxGLBZDMpmEqqpotVooFovvcn7Nwa0LJIRgJwkRERER0ZIYj8c4OTmBzWbDzs4OFIWnR0TLaDQa4fb2Ft999x1ubm5gsVhwfHyM/f19bG1tzfWx+CywQAxJiIiIiIiWi6qqODk5gaIo2Nvbg9HIZnuiZTZbLndycoJms4lQKITj42MkEgmYzeZvvn+GJAvE9j0iIiIiouV0fn6O8XiMvb29uZxoEdH7mkwmKJfL+PjxI3K5HABgf38fh4eHCAQCb75fxqQLxHkkRERERETLK5PJIBqNYm9vD5lMBv1+X++SiOgFHg973d7eht/vx2AwQLFYRK/Xe/F9MSRZIIYkRERERETL7ebmBuPxGDs7O7i6ukKn09G7JCJ6odmw13q9DovFAp/Ph1Qq9apzcYYkC8R5JEREREREy69cLmM8HiOZTOLm5gaNRkPvkojolYbDIQqFAm5vb+FyuV78dQxJFoghCRERERHRamg0GlpQYjKZUC6X9S6JiN5ASolWq/Xi23Nw6wIxJCEiIiIiWh3dbheXl5fw+/2IRqN6l0NEC8CQZIEmk4neJRARERER0SsMBgOcnp7C5XIhmUxCCKF3SUT0jhiSLIiUkiEJEREREdEKmkwmOD09hcViQTqdhqLwNIpoXfF/94JIKbnchoiIiIhoRamqitPTUwDA3t4ejEaOdyRaRwxJFoQhCRERERHR6ru4uMBwOMT+/j4sFove5RDRnDEkWaDX7M1MRERERETLKZfLodlsYnd3FzabTe9yiGiOGJIsEDtJiIiIiIjWQ6FQQLlcRjqdhsvl0rscIpoThiQLxJCEiIiIiGh9VCoV5PN5JBIJeL1evcshojngtKEFEUIwJCEiIiIiWjPNZhOTyQTJZBJGoxHlclnvkojoGzAkWRCGJG8XCoXg8/nQ6/XQbrfR7XYxHA71Loto48z+L5ZKJVSrVb3LoTkzGo0wGo0wGAzan/ffFEV58CaEePat0+kAAOx2OyaTCQaDAbrdLtrtNiaTic7/UiKi+et2u5hMJnA6nQxJiFYcQ5IFklLqXcLKcTgc2N7ehqIocLlccDgckFJCCPEgNBkMBnqXSrT2fD4fKpUKAoEAfD4fstksRqOR3mVtpMeBxux9RVEeBBoGg0ELLmbBBoDPQg3g7neUlBKqqn72Np1OMZlMMJ1On/z849t2u10AwNbWFmw2G+x2O7a3txGJRCClxGQywXA41IITHkdEtOoODg4wmUyQyWT0LoWIvhFDkgXhzjavZzAYkEgktBfwAKAoPxmj43Q6YbfbtdCk3+9roUm/39ejZKK1FYlEMBqNUC6XUa1WEQqFsL+/j1qthsvLS73LW2rPBRr3Qw0hhPbn/S4N4HWBxizEeEmgcf/z8w7xZ/c3GAwwGAxQr9e1z1ksFthsNthsNmxtbSEUCkFKiel0iuFwqD2X83mciFbFwcEBxuMxstksL4oSrQGGJAvCkOT14vH4gyufT7kfmjgcDthsNi00GQwGD0IT/tIierutrS3kcjkAd89nhUIBjUYDiUQCu7u7uLm50bnC5aMoCvb29mAymbQg4qmQYjwePxtoPPXxVX8uGw6HGA6HaDQa2sfMZjOsVitsNpvWQThbpjoajR6E4EREy0JRFOzv72M0GjEgIVojDEkWhPNIXsfr9cJutz8IQV7i/u3tdjusViv8fj+EEBgOh9qL7F6vx19kRC8UjUa1pRH39ft9nJ6ewmazIZlMQkqJ6+trnapcLoqi4ODgAIPBAGdnZ3y++YrRaITRaIRWq6V9zGg0PghOtra2oCgKptMpxuOxFoS3220dKyeiTTULSIbDIXK5HJ/nidYIQ5IFYSfJy5nNZkQikVcHJE+5fx82mw1Wq1W7QjkcDtHpdLTQhD8joqd5PB5ks9knPyelRLVaRavVwtHREY6Pj5HP5zf6xNVgMGB/fx/9fl/rvqHXm0wm6HQ66HQ62hBEg8EAq9UKq9UKh8OBaDQKo9GozUzp9/vodrtotVp8TieidzMLwvk8T7SeGJIsCKf5v1wymXy3+56t+wd+Epr4fD4IITAajbQX5AxNiO7Mukh6vd4Xbzcej3F+fg6v14t4PK69cNy0/0dGoxH7+/vodDrsqnkHs6Gw3W5X22FJURRtzondbkcgEEAsFsN0OsV0OsVgMECv19O26CQi+hazgKTX6+Hq6krvcojoHTAkWRAut3mZUCgEs9k8ly6Sl7gfmlitVpjNZni9XiiK8llowp8hbRpFUb7YRfKUer2OdruNWCyGo6Mj3N7ePhjauc7MZjP29vbQaDRQKBT0LmdjqKqKfr+Pfr+PWq0G4O653WKxwGq1wm63w+v1fjYgdhaccGcdInopBuFEm4EhyQJIKTEej/UuY+nNtohcVEDylPuPbbFYYDabsbW1BSHEg9bvbrfL0ITWXiQSeVEXyWOTyQTZbBZutxvRaFTbLnidr+JbLBbs7u6iXq/j9vZW73I2npRS21nn8YDY2c46LpcLgUAAwN0xOxqNtK3lubMOET02C0ja7Tby+bze5RDRO2JIsgCzHQ3oeYqiIJlMfnEnGz3c7zSZdZl4PB5t14X7ock6nwDS5lEUBW63G5lM5s330Wq10Ol0EIlEcHBwgEqlos2WWCc2mw07Oztr++9bJ7MBsc1mU/uYyWT64s46swGxnU5Hx8qJSE+zgKTVanE3N6INwJBkQdh18GWxWOyr2/0ug/uhiaIo2Nragtvt1l5Qd7tdLTRh9xCtskgkgsFg8M1X1FVVRT6fR6PRQDwe17YSHg6Hc6pUXw6HA8lkEuVyGZVKRe9y6A3G4zHG4/GDYcMGg0HrOLHb7YjFYjAYDNqA2PvBCS+CEK03k8mEvb09NJtNLqUk2hAMSRZgtgaanubxeOByuXRdZvNWj0OT2b9FCAFVVdHtdtFut9Hr9bjunVbGPLpIHut2uzg5OUEwGNTmdqz61Tin04lEIrFRc1c2xf1OwRlFUbSOE7vdjnA4DJPJ9CA46XQ63FmHaI3MZk1xKSXRZmFIsiAMSZ5mMpkQjUZXMiB5yuPQxO12w+l0aqHJbL17t9tlaEJLKxqNzqWL5DEpJYrFIprNJuLxOI6OjnB9fY1utzvXx1kEt9uNeDyOm5ubBzMvaH3NnsN7vZ62s44QQtuS+P7OOqqqasFJr9dDq9VidyHRipkFJLVaDcViUe9yiGiBGJIsiM1mW8kTgfe2jHNI5ump0MThcEAIASnlg9BkXZYf0GpTFAUul2uuXSSPDQYDnJ2dYXt7G6lUCt1u91U76Ohta2sL0WgU19fXaLVaepdDOpJSajvr3O8mmm1JbLPZsLW19dnOOv1+H61WC4PBQMfqieg5VqsV6XQa1WoVpVJJ73KIaMEYkiyAwWBAMBjEaDTiC+p7AoHAQrf7XRaz0AQAXC4XHA4HpJQAgH6/r4UmfPFMeohGo9pJ33urVqtotVqIx+M4Pj5GoVB4MFBzGfl8PoTDYVxdXT2YYUF033A4xHA4/GxnndlyHafTqQ2InUwmGI/H6PV62kwrItLPLCDhMG6izcWQZEEURUE8Hkcmk3n1dprryGazIRAIbFxA8pT73wOn0wm73Q4pJYQQD0ITbklJ723WRXJ5ebmwxxyPx7i8vITH43mwXfAyznTw+/0IBoPIZrM8kaVXm+2sc/9iidFohM1mg9VqhcPhgNfrhaIomE6nGI/H2oBYBnJEizHbrYzDuIk2G0OSBRJCIJVK4fz8fKPnUSzrdr/L4n5o4nA4YLPZtNBk9oJ5FprMOlCI5mHWRaJHF1Oz2USn00E0GsXR0RFKpZI292EZBAIB+P1+Bt00V5PJRAtBZlesDQbDgwGx0WgURqPxQXDS7XY5IJZozux2O1KpFIrFImq1mt7lEJGOGJIskBACiqIgnU7j7OxsY4e5RqNRGAwGhiQvdD80sdvtsFqt8Pv9EEJgOByi1WppwwQZmtBbzWbmXFxc6FbDdDrF1dUVnE4n4vG41lWid6gcDofh9XpxeXnJZXD07mbbyd/vVprtrDMbEBsMBhGLxbSddYbDoRacTCYTHasnWk2z7dy5WxkRAQxJFm42yDOdTuPi4mLjrgK53W643W4us/kG9793szZtVVW10GS2bSVDE3qNWCy2NLNwOp0OTk5OEAqFsL+/j1qtptvWi9FoFB6PBxcXFxyuTLq5v7PO7Aq3EEIbEGu327V5OVJKLTiZ7ayjd9BItMxmAUmhUOBuZUQEgCGJLhRFgdlsRjKZfNcdJJaN0WhELBZjQDJn93fQmYUmPp8PQgiMRiNteU6v19u4UI5eZjaLRM8uksdUVdVesCYSCbjdblxdXS10Nk88HofT6dz4JZK0nKSUGAwGGAwGn+2sM+s48Xg8CAaD2s46o9FI21WNc66I7gboJxIJbudORA8wJNGJoiiw2+2IxWLI5/N6l7MQnEOyGPdDE6vVCovFooUm4/FY6zTpdrsMTQjAcnWRPNbv93F6egq/3490Oo1Wq4Xr6+t3f9xkMgmbzYbz83OMx+N3fzyieZntrHN/pyiTyaRtSexwOLSddWZzTu5vR093r9Fmb7Ol0rMLPI8/LoTQ3p99fvaxp94APLj9/Y/df43UbDa59ew7c7vdiMfjyOfzS7+zGhEtFkMSHSmKAo/Hg9FotPZbjPn9flgsFnaR6OB+aGKxWGA2m7G1tQUhBLcxJa2L5Pz8XO9SniVFKKG8AAAT4klEQVSlRLlcRrPZRCKRwPHxMfL5/Lsduzs7OzCbzTg/P+d8B1oL4/EY4/H4s5117g+I3dra+mxnnU6n8+BrvuZr4YLJZAJwt5Pb/dvMPv+t4cL9kOGpj33tNkIIbZnq/T8fv//U37/0Np1Ov/j5x/elKAr8fj98Ph/q9TqKxeKLfwb0Mh6PB7FYDNfX1686xoloMzAk0ZmiKAgEAhiPx2vb5me1WhEMBhmQLIlZaCKlhNVqZUiy4eLxOLrd7krM2xiNRjg/P4fX60U8Hke/30cul5trR9Tu7i4MBgPOz883drg2bYbJZKJ1Fs7MdtaZbUkcDoeRSCQ++z/21nBhdjLqdDrnEi68NKz42m0e17sM6vU6XC4XQqGQFpboNZtp3WxtbSEajfJCERE9iyHJElAUBdFoFOPxeO1aXYUQXGazpO53mNBmMhgM2syNVVKv19FutxGLxXB0dDS33Qj29vYghMDFxQUDEtpI93fWmW3BLYSA2WyeS7gwm4Nis9ne5x+wZmbbQzudToRCIXz48AGNRgOFQkHv0laW1+tFJBJBLpd7EBASEd3HkGRJKIqCZDK5dltMRqNRGI1GhiRLiiHJZovH4+h0OivRRfLYZDJBNpuF2+1GNBrVtgt+6/KY/f19SCk3ctcxoi+RUq7kc8Q6mXX8OBwOLSxptVooFAp8vnqF2Q5QDEiI6Gu4/mGJKIqCnZ0dbc3uqnO5XPB4PFxms8QYkmwuo9EIh8Ox8mvdW60WTk5OMBgMcHBwgEAg8Or7ODg4wHQ6ZUBCREut2+3i4uIC2WwWZrMZR0dHiMfjfJ31Aj6fD6FQCNlslgEJEX0Vn1WXyGwgWTqdXvlfeEajkb+4VwBDks0Vi8VWtovkMVVVkc/nkcvl4PP5cHBwAIvF8tWvUxQFh4eHGI/HyGQySzWPgIjoOb1eD5eXl8hkMjAajTg+PkYikeBrrmf4/X4tIFm3Ze1E9D74bLpkFEWB0WjEzs7OSi9RSSQSK13/pmBIspnWpYvksW63i5OTE7RaLezt7SEajT57W0VRcHBwgMFgwICEiFZSv99HJpPBxcUFFEXB8fExkskkf7ff4/f7EQgEkMlk0Ov19C6HiFbEi0MSIYRBCPH/CCH+xqe//xkhRF4I8Wuf3n77+5W5WRRFgdVqRSKR0LuUN/H5fLBarbyisQL4M9pMsVgM7XZ7LbpIHpNSolgs4uLiAna7HUdHR3A4HA9uYzQacXh4iG63i1wup1OlRETzMRgMkM1mtSHcR0dHSKVSMBo3e/RgMBjUApLZ0GAiopd4zRnSHwHw3aOP/aKU8nuf3v7mHOvaeIqiaNvvrRKLxYJwOMyrGCuCIcnmWdcukscGgwHOzs5QqVSQSqWQSqUAACaTCfv7+2i1Wri+vta5SiKi+RkOh8jlcjg/P4eqqjg8PMTOzs5GhiWhUAjb29u4vLxkQEJEr/aiMyQhRBzA7wDwF963HLrPYDDA5/PB5/PpXcqLcLvf1cOQZPPE43G0222MRiO9S1mIarWK09NTKIqCDx8+YH9/H41GAzc3N3qXRkT0LobDIa6urnB2dobJZKKFJWazWe/SFiIcDsPn863djpFEtDgvjZb/awB/CoDr0cf/sBDi9wL4IYA/LqWsf+lOVFVFu91+fZUbzuFwoNlsLnwa92t/sQSDQQwGg405+VoHUspXXWHhi43VZjQaMZ1OcXl5ifF4PLf7Xfbjot/v4zd+4zfgdrthMBhQr3/xVxXNwbIfE6QPHheL1e/30Ww2YTKZsL29jVAohMFggEKhMNffAd9i3sdEMBiEwWDAj370I74eXWF8rqDHFn1MfPUyshDidwIoSSl/9dGn/hyAPQDfA1AA8Gef+fp/TwjxQyHEDyuVyrfWu5EURUE0GoXVatW7lGc5HA5sbW2xM2EFsfNnc0QiEXQ6naV5cbxorVaLAQkRbZzxeIzb21tcXl5iOBwinU4jlUq9aBewVRIKheB2u5HNZhmQENE3eUknyT8P4Gc/DWa1AnALIf4HKeXvnt1ACPHnAfyNp75YSvlLAH4JAL7//e9Ll+txMwq9hJQSP/VTP4Xz8/OFP/HbbLYvft5gMODg4GAj17yuOlVV4XA4MJ1OX/V1XzsmaPkYjUYEg0GcnZ2928+PxwU9xmOCnsLjQj+tVgu9Xg+BQAA/8zM/g+FwiJubG93ndnzrMRGLxeByuXBxcQGj0cjXpGuCzxX02KKOia9e9pdS/ryUMi6l3AHw7wD4O1LK3y2EiNy72e8C8KN3qpFwd7VfURSk0+mlG4oaj8fZQbKipJRLdzzR+4jH42i1Wry6RkS04SaTCQqFAj5+/IhOp4N0Oo39/X3Y7Xa9S3uTRCIBl8uly4VEIlpP3xKz/oIQ4nsAJIAMgD8wl4roWUIIGAwGpNNpnJ+fQ0qpd0nwer2w2+0MSVYYf3brz2QywW634/T0VO9SiIhoSUynUxSLRVQqFfj9fqRSKUwmE9zc3KDb7epd3oskk0nYbDacn59v7FJSIpq/V4UkUsq/C+Dvfnr/97xDPfQViqLAbDYjlUohk8noWovZbEYkEuFJ9opjJ8n6m3WR8AUkERE9NgtLyuUy/H4/kskkJpMJbm9vl3rDhVQqBavVivPzc0wmE73LIaI1wrPbFaQoCux2O2KxmG41cLvf9cGQa72ZzWbYbDYUi0W9SyEioiWmqipKpRI+fvyIer2OeDyOw8NDuN1uvUv7zM7ODiwWCwMSInoXnGq0ohRFgcfjwWg0QrlcXvjjh0IhmEwmhiRrgJ0k6y0Wi6HZbLKLhIiIXkRVVVQqFVSrVfh8PsRiMYTDYRSLRTSbTb3Lw87ODsxmM87Pz189eJ6I6CUYkqwwRVEQCAQwHo/RaDQW9rh2ux0+n48dCGtgNhCY1tOsi+T6+lrvUoiIaMVIKVGtVlGr1eD1ehGJRLSwZJGvO+9Lp9MwGo0MSIjoXTEkWXGKoiAajWI8Hi9kyJaiKFxms0Zmw4BpPbGLhIiIvpWUErVaDfV6HVtbWwiHwwiFQiiXy6jVagurY3d3F4qi4OLiggEJEb0rXkJeA7Pgwmq1vvtjzbb7ZUiyHhiSrK9ZF0mpVNK7FCIiWgNSStTrdfz4xz9GsVhEIBDA8fExtre33/2x9/b2GJAQ0cIwJFkTiqJgZ2cHJpPp3R5ja2sLDoeDyzPWDEOS9RSLxdBoNNhFQkREc9doNPDx40cUCgVsb2/j+PgYgUDgXR5rf38fAHBxcQFVVd/lMYiI7uPZ7pqYzZZIp9PvEmKYTCZEo1GeUK8h/kzXD7tIiIhoEZrNJk5OTnBzcwOv14vj42MEg8G53f/+/j5UVcXl5SUDEiJaGM4kWSOKosBoNGJnZweXl5eQUs7tvpPJ5Nzui5YLQ5L1E4/H0Wg0uC0iEREtRKvVQqvVgtvtRigUgs/nQ71e/6bt5w8ODjCZTJDJZOb6mpaI6GvYSbJmFEWB1WpFPB6f230Gg0GYzWYus1lTDEnWi8VigdVqZRcJEREtXKvVwunpKfL5PFwuFz58+IBwOPzq+2FAQkR64lnvGlIUBU6n802/lB6zWq3w+/08kV5jDL/WSywWQ71eZxcJERHppt1u4+zsDFdXV3A4HPjw4QMikchXv05RFBweHmI8HjMgISLdcLnNmjIYDPD5fBiNRm/enk1RFMRiMe5ks+YYkqyPWRdJLpfTuxQiIiJ0Oh10Oh04HA6EQiF8+PABrVYLhULhsxkjiqJgf38fw+EQuVyOAQkR6YZnR2tMURSEw2G4XK43fX04HIbBYGBIsub4810f7CIhIqJl1O12cXFxgWw2C7PZjKOjI8Tjce1CjaIoODg4wGAwQDabZUBCRLpiJ8maUxQFiUQCl5eX6Pf7L/46t9sNp9PJLoMNwJ/xerBarbBarchms3qXQkRE9KRer4fLy0vYbDaEQiHs7++j0+kgHA5jOBzi9vYWZrNZu/39Cznv+f7jC0av/drxeIxGo/HFfzsRrQ6GJBtACIGdnR2cn59jNBp99fYmkwmxWAy9Xm8B1dEyEELwqs2Km3WRTKdTvUshIiL6on6/j0wmA1VVEYlEYDAYYLFYsLe39+B2X3tt8tTnpZRagPHc1z/++HP385Kvs1qtGA6HDEmI1ghDkg0ghICiKEin0zg7O/vqSVQikeASjA0ipYSiKDy5XmE2mw0WiwWZTEbvUoiIiF5sOBwik8l801bBejIajTg8PMTNzY3epRDRHLHPfkMIIWAwGJBOp78YgAQCAVitVi7B2CBSSu5etOKi0ShqtRqDLiIiogVKJBJotVoYDAZ6l0JEc8Qz4Q2iKArMZjNSqdSTn7darQgEAgxINhB/5qtr1kVSLpf1LoWIiGhj2O122Gw23N7e6l0KEc0Zz4w2jKIosNvtiMViDz4uhEAymeQymw3FTpLVFY1GUa1W2UVCRES0QPF4HOVymTvKEa0hhiQbSFEUuN1uBAIB7WPRaBRGo5EhyYZiJ8lqslgssFgsqFQqepdCRES0MXw+HxRF4e9fojXFM6MNZTAYEAgE4PF44HK54PF4eKK8wdhJsprG4zFUVUUwGNS7FCIioo0RDAZRKBS4MyDRmuLuNhtMURTEYjFtdxPaTLPdj2j1qKqKs7Mz7O/vQ1EU5PN5vUsiIiJaa9FoFOPxGM1mU+9SiOid8MxowymKwhPkDTfb+YhW02QywdnZGZxOJxKJhN7lEBERrS2j0YitrS1u+Uu05nh2TJxDsuHYSbL6JpMJTk9PYbfbkUql+H+aiIjoHcTjcbTbbfT7fb1LIaJ3xDMjIoLRyJV3q05VVZyensJisTAoISIimjObzQa73Y5CoaB3KUT0zhiSEBGX26wJVVVxcnICk8mEdDrNDiEiIqI5icfjqFQq3PKXaAOIRU5lFkKUAWQX9oA0D34A3N+M7uMxQU/hcUGP8Zigp/C4oMd4TNBTeFzQY/M4JlJSysDXbrTQkIRWjxDih1LK7+tdBy0PHhP0FB4X9BiPCXoKjwt6jMcEPYXHBT22yGOCvdhERERERERERGBIQkREREREREQEgCEJfd0v6V0ALR0eE/QUHhf0GI8JegqPC3qMxwQ9hccFPbawY4IzSYiIiIiIiIiIwE4SIiIiIiIiIiIADEnoEyHEXxRClIQQP3ric39CCCGFEH49aiP9PHVcCCH+jBAiL4T4tU9vv13PGmmxnnuuEEL8B0KIj0KI/08I8Qt61Uf6eOa54q/ce57ICCF+Tc8aabGeOSa+J4T4+5+OiR8KIf5ZPWukxXvmuPgnhBD/lxDi14UQf10I4dazRlosIURCCPG/CyG++/Qa4o98+rhPCPG3hBCnn/706l0rLc4Xjouf+/R3VQjxbjvdMCShmR8A+Fcff1AIkQDwrwDILbogWgo/wBPHBYBflFJ+79Pb31xwTaSvH+DRMSGE+JcB/GsA/nEp5U8D+C91qIv09QM8Oi6klP/27HkCwP8M4Ff0KIx08wN8/vvjFwD8Z5+Oif/k099ps/wAnx8XfwHAn5ZS/mMA/hcAf3LRRZGuJgD+uJTyA4B/DsAfEkL8FIA/DeBvSykPAPztT3+nzfHccfEjAP8GgL/3ng/OkIQAAFLKvweg9sSnfhHAnwLA4TUb6AvHBW2oZ46JPwjgP5dSDj/dprTwwkhXX3quEEIIAP8WgL+80KJIV88cExLArEvAA+BmoUWR7p45Lo7wkxOevwXg31xoUaQrKWVBSvkPP73fBvAdgBjuLr78pU83+0sA/nV9KiQ9PHdcSCm/k1J+fO/HZ0hCzxJC/CyAvJTyH+ldCy2dPyyE+H8/tc2y/ZEOAfwmIcQ/EEL8H0KIf0bvgmip/CYARSnlqd6FkO7+KID/QghxhbuOs5/XuR5aDj8C8LOf3v85AAkdayEdCSF2APyTAP4BgJCUsgDcnTADCOpXGenp0XGxEAxJ6ElCCDuA/wh37bBE9/05AHsAvgegAODP6lsOLQEjAC/u2iH/JIC/+ql7gAgA/l2wi4Tu/EEAf0xKmQDwxwD8ss710HL4/bhrpf9VAC4AI53rIR0IIZy4W5r5R6WULb3roeWg13HBkISeswcgDeAfCSEyAOIA/qEQIqxrVaQ7KWVRSjmVUqoA/jwADt6jawC/Iu/83wBUABz0TBBCGHG3dviv6F0LLYXfh5/MpvmfwN8fBEBK+WMp5W+VUv7TuAtUz/WuiRZLCGHC3Ynw/yilnD1HFIUQkU+fjwDgUt4N88xxsRAMSehJUspfl1IGpZQ7Usod3J0E/VNSyludSyOdzX5hffK7cNcmS5vtfwXwmwFACHEIwAygomtFtCx+C4AfSymv9S6ElsINgH/x0/u/GQCXYBGEEMFPfyoA/mMA/52+FdEifeo8/WUA30kp/6t7n/pruAtW8enP/23RtZF+vnBcLObxpeQ8TgKEEH8ZwL+Eu6u/RQD/qZTyl+99PgPg+1JKnvhskKeOi09//x7uBvBlAPyB2ZpRWn/PHBP/PYC/iLvjYgTgT0gp/45eNdLiPfc7RAjxAwB/X0rJk54N88xzxUcA/w3ulugNAPz7Uspf1atGWrxnjgsngD/06Sa/AuDnJU9QNoYQ4l8A8H8C+HXcdaICwH+Iu/kTfxVAEne7bP6clJKbCWyILxwXFgD/LYAAgAaAX5NS/ra5Pz6fg4iIiIiIiIiIuNyGiIiIiIiIiAgAQxIiIiIiIiIiIgAMSYiIiIiIiIiIADAkISIiIiIiIiICwJCEiIiIiIiIiAgAQxIiIiIiIiIiIgAMSYiIiIiIiIiIADAkISIiIiIiIiICAPz/3j2t/Mj5AwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1368x1368 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---------------------------------------------------\n",
    "# for plotting purposes we use this iPython magic\n",
    "%matplotlib inline\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (19, 19)\n",
    "#---------------------------------------------------\n",
    "\n",
    "# search command\n",
    "key = 'DESCENDING_VVVH'\n",
    "# we plot the full Inventory on a map\n",
    "s1_grd.plot_inventory(s1_grd.refined_inventory_dict[key], transparency=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4* - Download of GRD scenes\n",
    "\n",
    "As already shown in Tutorial II, you will download the scenes based on the refined inventory dataframe for the respective produckt key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:17,734 DEBUG ost.project INFO: One or more of your scenes need to be downloaded.\n",
      "2020-02-20 09:56:17,734 DEBUG ost.s1_core.s1_dl Select the server from where you want to download:\n",
      "2020-02-20 09:56:17,735 DEBUG ost.s1_core.s1_dl (1) Copernicus Apihub (ESA, rolling archive)\n",
      "2020-02-20 09:56:17,735 DEBUG ost.s1_core.s1_dl (2) Alaska Satellite Facility (NASA, full archive)\n",
      "2020-02-20 09:56:17,735 DEBUG ost.s1_core.s1_dl (3) PEPS (CNES, 1 year rolling archive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Type 1, 2 or 3:  2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:18,638 DEBUG ost.s1_core.s1_dl Please provide username for the selected server\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Username: suprd\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:19,976 DEBUG ost.s1_core.s1_dl Please provide password for the selected server\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Password: ·········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:36,979 DEBUG ost.helpers.asf INFO: Downloading scene to: /home/jovyan/Tutorial_4a/download/SAR/GRD/2020/01/29/S1A_IW_GRDH_1SDV_20200129T165058_20200129T165123_031018_03901B_2431.zip\n",
      "2020-02-20 09:56:40,487 DEBUG ost.helpers.asf Product S1A_IW_GRDH_1SDV_20200129T165058_20200129T165123_031018_03901B_2431.zip missing from the archive, continuing.\n",
      "2020-02-20 09:56:40,618 DEBUG ost.helpers.asf Some products are missing from the archive: ['S1A_IW_GRDH_1SDV_20200129T165058_20200129T165123_031018_03901B_2431.zip']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "print(len(s1_grd.inventory))\n",
    "s1_grd.download()\n",
    "print(len(s1_grd.inventory))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5* - Set ARD parameters\n",
    "\n",
    "Similar to the *Sentinel1-Scene* class (Tutorial I and III), the *Sentinel1-GRDBatch* class handles the defintion of ARD types in a hierarchical dictionary structure. You can use the same types and steps to customize as for the *Sentinel1-Scene* class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'OST', 'resolution': 20, 'border_noise': True, 'product_type': 'GTCgamma', 'speckle_filter': False, 'ls_mask_create': False, 'to_db': False, 'polarisation': 'VV,VH,HH,HV', 'dem': 'SRTM 1Sec HGT', 'resampling': 'CUBIC_CONVOLUTION', 'mt_speckle_filter': True, 'to_db_mt': True, 'dtype_output': 'float32', 'apply_ls_mask': False, 'create_ls_mask': False, 'metrics': ['avg', 'max', 'min', 'std', 'cov', 'count'], 'remove_outliers': True}\n"
     ]
    }
   ],
   "source": [
    "# single scene ARD parameters\n",
    "s1_grd.set_ard_parameters('OST')\n",
    "s1_grd.ard_parameters['resolution'] = 20\n",
    "# s1_grd.ard_parameters['single ARD']['product type'] = 'GTCgamma'\n",
    "# s1_grd.ard_parameters['single ARD']['create ls mask'] = False\n",
    "\n",
    "# time-series ARD\n",
    "# s1_grd.ard_parameters['time-series ARD']['remove mt speckle'] = False\n",
    "\n",
    "# in case we want to use an external dem\n",
    "#s1_grd.set_external_dem('path/to/externaldem')\n",
    "\n",
    "print(s1_grd.ard_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6* - Run the batch routine\n",
    "\n",
    "To process all the data, including time-series and timescans is as easy as one command. All the complexity is handled behind, and you just have to wait, since processing can take quite a while.\n",
    "\n",
    "**Note** that as a last argument we submit the AOI (given as WKT) for subsetting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-02-20 09:56:46,678 DEBUG ost.project INFO: Starting OST ARD processing for GRD\n",
      "2020-02-20 09:56:46,721 DEBUG ost.s1_core.batch Acquisition from 20200215 of track 124already processed\n",
      "2020-02-20 09:56:46,733 DEBUG ost.s1_core.batch Acquisition from 20200209 of track 124already processed\n",
      "2020-02-20 09:56:46,744 DEBUG ost.s1_core.batch Acquisition from 20200203 of track 124already processed\n",
      "2020-02-20 09:56:46,762 DEBUG ost.s1_core.batch Acquisition from 20200128 of track 124already processed\n",
      "2020-02-20 09:56:46,773 DEBUG ost.s1_core.batch Acquisition from 20200122 of track 124already processed\n",
      "2020-02-20 09:56:46,787 DEBUG ost.s1_core.batch Acquisition from 20200116 of track 124already processed\n",
      "2020-02-20 09:56:46,802 DEBUG ost.s1_core.batch Acquisition from 20200214 of track 22already processed\n",
      "2020-02-20 09:56:46,815 DEBUG ost.s1_core.batch Acquisition from 20200208 of track 22already processed\n",
      "2020-02-20 09:56:46,825 DEBUG ost.s1_core.batch Acquisition from 20200202 of track 22already processed\n",
      "2020-02-20 09:56:46,837 DEBUG ost.s1_core.batch Acquisition from 20200127 of track 22already processed\n",
      "2020-02-20 09:56:46,850 DEBUG ost.s1_core.batch Acquisition from 20200121 of track 22already processed\n",
      "2020-02-20 09:56:46,863 DEBUG ost.s1_core.batch Acquisition from 20200115 of track 22already processed\n",
      "2020-02-20 09:56:46,877 DEBUG ost.s1_core.batch Acquisition from 20200211 of track 73already processed\n",
      "2020-02-20 09:56:46,889 DEBUG ost.s1_core.batch Acquisition from 20200205 of track 73already processed\n",
      "2020-02-20 09:56:46,902 DEBUG ost.s1_core.batch Acquisition from 20200130 of track 73already processed\n",
      "2020-02-20 09:56:46,915 DEBUG ost.s1_core.batch Acquisition from 20200124 of track 73already processed\n",
      "2020-02-20 09:56:46,927 DEBUG ost.s1_core.batch Acquisition from 20200118 of track 73already processed\n",
      "2020-02-20 09:56:46,943 DEBUG ost.s1_core.batch Acquisition from 20200210 of track 146already processed\n",
      "2020-02-20 09:56:46,955 DEBUG ost.s1_core.batch Acquisition from 20200129 of track 146already processed\n",
      "2020-02-20 09:56:46,966 DEBUG ost.s1_core.batch Acquisition from 20200204 of track 146already processed\n",
      "2020-02-20 09:56:46,977 DEBUG ost.s1_core.batch Acquisition from 20200123 of track 146already processed\n",
      "2020-02-20 09:56:46,988 DEBUG ost.s1_core.batch Acquisition from 20200117 of track 146already processed\n",
      "2020-02-20 09:56:46,988 DEBUG ost.project INFO:OST ARD processing for GRD DONE!\n",
      "2020-02-20 09:56:47,116 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 124\n",
      "2020-02-20 09:56:52,887 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:04.737416\n",
      "2020-02-20 09:56:53,185 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 22\n",
      "2020-02-20 09:56:58,779 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:05.154851\n",
      "2020-02-20 09:56:58,858 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 73\n",
      "2020-02-20 09:57:03,051 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.815552\n",
      "2020-02-20 09:57:03,122 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 146\n",
      "2020-02-20 09:57:07,207 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.649385\n",
      "2020-02-20 09:57:07,232 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:07,241 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:07,272 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:07,282 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:07,312 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:07,323 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:07,349 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:07,358 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:07,373 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 124\n",
      "2020-02-20 09:57:10,817 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.117599\n",
      "2020-02-20 09:57:10,838 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 22\n",
      "2020-02-20 09:57:14,563 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.448224\n",
      "2020-02-20 09:57:14,583 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 73\n",
      "2020-02-20 09:57:17,808 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.926412\n",
      "2020-02-20 09:57:17,829 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 146\n",
      "2020-02-20 09:57:20,810 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.742105\n",
      "2020-02-20 09:57:20,830 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:20,839 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:20,865 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:20,873 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:20,896 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:20,904 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:20,927 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:20,934 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:21,007 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 124\n",
      "2020-02-20 09:57:24,393 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.110983\n",
      "2020-02-20 09:57:24,415 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 22\n",
      "2020-02-20 09:57:28,146 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.446284\n",
      "2020-02-20 09:57:28,167 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 73\n",
      "2020-02-20 09:57:31,303 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.904149\n",
      "2020-02-20 09:57:31,319 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 146\n",
      "2020-02-20 09:57:34,294 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.750313\n",
      "2020-02-20 09:57:34,318 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:34,327 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:34,357 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:34,367 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:34,394 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:34,402 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:34,426 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:34,434 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:34,448 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 124\n",
      "2020-02-20 09:57:37,868 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.137409\n",
      "2020-02-20 09:57:37,890 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 22\n",
      "2020-02-20 09:57:41,620 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.456212\n",
      "2020-02-20 09:57:41,638 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 73\n",
      "2020-02-20 09:57:44,760 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.908557\n",
      "2020-02-20 09:57:44,780 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 146\n",
      "2020-02-20 09:57:47,868 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.853039\n",
      "2020-02-20 09:57:47,890 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:47,898 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:47,926 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:47,935 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:47,960 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:47,968 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:47,991 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VV polarisation already processed\n",
      "2020-02-20 09:57:47,998 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VH polarisation already processed\n",
      "2020-02-20 09:57:48,013 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 124\n",
      "2020-02-20 09:57:51,363 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.093337\n",
      "2020-02-20 09:57:51,384 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 22\n",
      "2020-02-20 09:57:55,061 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:03.420466\n",
      "2020-02-20 09:57:55,079 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 73\n",
      "2020-02-20 09:57:58,193 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.899904\n",
      "2020-02-20 09:57:58,209 DEBUG ost.s1_core.batch INFO: Creating common extent mask for track 146\n",
      "2020-02-20 09:58:01,147 DEBUG ost.helpers.utils INFO: Time elapsed: 0:00:02.718072\n",
      "2020-02-20 09:58:01,167 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VV polarisation already processed\n",
      "2020-02-20 09:58:01,174 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 124 for TC in VH polarisation already processed\n",
      "2020-02-20 09:58:01,201 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VV polarisation already processed\n",
      "2020-02-20 09:58:01,208 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 22 for TC in VH polarisation already processed\n",
      "2020-02-20 09:58:01,230 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VV polarisation already processed\n",
      "2020-02-20 09:58:01,236 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 73 for TC in VH polarisation already processed\n",
      "2020-02-20 09:58:01,258 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VV polarisation already processed\n",
      "2020-02-20 09:58:01,263 DEBUG ost.multitemporal.ard_to_ts INFO: Timeseries of 146 for TC in VH polarisation already processed\n",
      "2020-02-20 09:58:01,291 DEBUG ost.s1_core.batch INFO: Entering track 124.\n",
      "2020-02-20 09:58:01,302 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 124.\n",
      "2020-02-20 09:58:12,321 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 124.\n",
      "2020-02-20 09:58:23,307 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 124\n",
      "2020-02-20 09:58:23,310 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 124\n",
      "2020-02-20 09:58:23,310 DEBUG ost.s1_core.batch INFO: Entering track 22.\n",
      "2020-02-20 09:58:23,321 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 22.\n",
      "2020-02-20 09:58:34,195 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 22.\n",
      "2020-02-20 09:58:45,016 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 22\n",
      "2020-02-20 09:58:45,019 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 22\n",
      "2020-02-20 09:58:45,019 DEBUG ost.s1_core.batch INFO: Entering track 73.\n",
      "2020-02-20 09:58:45,031 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 73.\n",
      "2020-02-20 09:58:55,771 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 73.\n",
      "2020-02-20 09:59:06,350 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 73\n",
      "2020-02-20 09:59:06,353 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 73\n",
      "2020-02-20 09:59:06,353 DEBUG ost.s1_core.batch INFO: Entering track 146.\n",
      "2020-02-20 09:59:06,365 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 146.\n",
      "2020-02-20 09:59:16,821 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 146.\n",
      "2020-02-20 09:59:27,156 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 146\n",
      "2020-02-20 09:59:27,159 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 146\n",
      "2020-02-20 09:59:27,170 DEBUG ost.s1_core.batch INFO: Entering track 124.\n",
      "2020-02-20 09:59:27,175 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 124.\n",
      "2020-02-20 09:59:37,988 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 124.\n",
      "2020-02-20 09:59:48,476 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 124\n",
      "2020-02-20 09:59:48,478 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 124\n",
      "2020-02-20 09:59:48,478 DEBUG ost.s1_core.batch INFO: Entering track 22.\n",
      "2020-02-20 09:59:48,484 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 22.\n",
      "2020-02-20 09:59:59,527 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 22.\n",
      "2020-02-20 10:00:10,170 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 22\n",
      "2020-02-20 10:00:10,172 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 22\n",
      "2020-02-20 10:00:10,172 DEBUG ost.s1_core.batch INFO: Entering track 73.\n",
      "2020-02-20 10:00:10,179 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 73.\n",
      "2020-02-20 10:00:20,835 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 73.\n",
      "2020-02-20 10:00:32,635 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 73\n",
      "2020-02-20 10:00:32,637 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 73\n",
      "2020-02-20 10:00:32,637 DEBUG ost.s1_core.batch INFO: Entering track 146.\n",
      "2020-02-20 10:00:32,645 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 146.\n",
      "2020-02-20 10:00:42,731 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 146.\n",
      "2020-02-20 10:01:05,766 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 146\n",
      "2020-02-20 10:01:05,897 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 146\n",
      "2020-02-20 10:01:07,115 DEBUG ost.s1_core.batch INFO: Entering track 124.\n",
      "2020-02-20 10:01:07,627 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 124.\n",
      "2020-02-20 10:01:29,588 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 124.\n",
      "2020-02-20 10:01:40,690 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 124\n",
      "2020-02-20 10:01:40,692 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 124\n",
      "2020-02-20 10:01:40,692 DEBUG ost.s1_core.batch INFO: Entering track 22.\n",
      "2020-02-20 10:01:40,698 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 22.\n",
      "2020-02-20 10:01:56,000 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 22.\n",
      "2020-02-20 10:02:06,755 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 22\n",
      "2020-02-20 10:02:06,756 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 22\n",
      "2020-02-20 10:02:06,756 DEBUG ost.s1_core.batch INFO: Entering track 73.\n",
      "2020-02-20 10:02:06,763 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 73.\n",
      "2020-02-20 10:02:32,641 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 73.\n",
      "2020-02-20 10:02:52,159 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 73\n",
      "2020-02-20 10:02:52,352 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 73\n",
      "2020-02-20 10:02:52,353 DEBUG ost.s1_core.batch INFO: Entering track 146.\n",
      "2020-02-20 10:02:53,029 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 146.\n",
      "2020-02-20 10:03:11,717 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 146.\n",
      "2020-02-20 10:03:35,049 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 146\n",
      "2020-02-20 10:03:35,090 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 146\n",
      "2020-02-20 10:03:35,373 DEBUG ost.s1_core.batch INFO: Entering track 124.\n",
      "2020-02-20 10:03:35,377 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 124.\n",
      "2020-02-20 10:03:46,505 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 124.\n",
      "2020-02-20 10:04:02,481 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 124\n",
      "2020-02-20 10:04:02,483 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 124\n",
      "2020-02-20 10:04:02,483 DEBUG ost.s1_core.batch INFO: Entering track 22.\n",
      "2020-02-20 10:04:02,488 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 22.\n",
      "2020-02-20 10:04:16,055 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 22.\n",
      "2020-02-20 10:04:33,615 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 22\n",
      "2020-02-20 10:04:33,616 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 22\n",
      "2020-02-20 10:04:33,616 DEBUG ost.s1_core.batch INFO: Entering track 73.\n",
      "2020-02-20 10:04:33,621 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 73.\n",
      "2020-02-20 10:04:48,795 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 73.\n",
      "2020-02-20 10:04:59,741 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 73\n",
      "2020-02-20 10:04:59,742 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 73\n",
      "2020-02-20 10:04:59,743 DEBUG ost.s1_core.batch INFO: Entering track 146.\n",
      "2020-02-20 10:04:59,750 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 146.\n",
      "2020-02-20 10:05:18,130 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 146.\n",
      "2020-02-20 10:05:30,903 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 146\n",
      "2020-02-20 10:05:30,904 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 146\n",
      "2020-02-20 10:05:30,912 DEBUG ost.s1_core.batch INFO: Entering track 124.\n",
      "2020-02-20 10:05:30,917 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 124.\n",
      "2020-02-20 10:05:42,193 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 124.\n",
      "2020-02-20 10:05:56,605 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 124\n",
      "2020-02-20 10:05:56,606 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 124\n",
      "2020-02-20 10:05:56,607 DEBUG ost.s1_core.batch INFO: Entering track 22.\n",
      "2020-02-20 10:05:56,612 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 22.\n",
      "2020-02-20 10:06:07,997 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 22.\n",
      "2020-02-20 10:06:20,714 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 22\n",
      "2020-02-20 10:06:20,715 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 22\n",
      "2020-02-20 10:06:20,716 DEBUG ost.s1_core.batch INFO: Entering track 73.\n",
      "2020-02-20 10:06:20,722 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 73.\n",
      "2020-02-20 10:06:31,156 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 73.\n",
      "2020-02-20 10:06:41,668 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 73\n",
      "2020-02-20 10:06:41,669 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 73\n",
      "2020-02-20 10:06:41,670 DEBUG ost.s1_core.batch INFO: Entering track 146.\n",
      "2020-02-20 10:06:41,677 DEBUG ost.s1_core.batch INFO: Processing Timescans of VV for track 146.\n",
      "2020-02-20 10:06:52,234 DEBUG ost.s1_core.batch INFO: Processing Timescans of VH for track 146.\n",
      "2020-02-20 10:07:04,553 DEBUG ost.s1_core.batch The HH polarisation is not availible in track 146\n",
      "2020-02-20 10:07:04,554 DEBUG ost.s1_core.batch The HV polarisation is not availible in track 146\n"
     ]
    }
   ],
   "source": [
    "# RAW SNAP ARD processing\n",
    "s1_grd.to_ard(    \n",
    "    overwrite=False, \n",
    "    subset=s1_grd.aoi\n",
    ")\n",
    "\n",
    "# Create Multitemporal outputs\n",
    "s1_grd.create_timeseries()\n",
    "s1_grd.create_timescan()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7* - Create a time-series animation\n",
    "\n",
    "For interactive presentations it is nice to have animated \"movies\". The following command allows you to create animated time-series of oyur processed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the animation\n",
    "s1_grd.create_timeseries_animations(\n",
    "    shrink_factor=5,\n",
    "    duration=1,\n",
    "    add_dates=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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