sentinel1_autodownload.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "colab_type": "code",
    "id": "_CIx4yeZCvbH",
    "outputId": "dce598cb-15e3-4c91-ac16-48874630e332"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting rasterio\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/be/e5/7052a3eef72af7e883a280d8dff64f4ea44cb92ec25ffb1d00ce27bc1a12/rasterio-1.1.2-cp36-cp36m-manylinux1_x86_64.whl (18.0MB)\n",
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      "\u001b[?25hCollecting affine\n",
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      "Collecting snuggs>=1.4.1\n",
      "  Downloading https://files.pythonhosted.org/packages/cc/0e/d27d6e806d6c0d1a2cfdc5d1f088e42339a0a54a09c3343f7f81ec8947ea/snuggs-1.4.7-py3-none-any.whl\n",
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      "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from rasterio) (1.17.5)\n",
      "Collecting cligj>=0.5\n",
      "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
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      "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.6/dist-packages (from snuggs>=1.4.1->rasterio) (2.4.6)\n",
      "Installing collected packages: affine, snuggs, click-plugins, cligj, rasterio\n",
      "Successfully installed affine-2.3.0 click-plugins-1.1.1 cligj-0.5.0 rasterio-1.1.2 snuggs-1.4.7\n",
      "Collecting geopandas\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/0c/e6c99e561b03482220f00443f610ccf4dce9b50f4b1093d735f93c6fc8c6/geopandas-0.6.2-py2.py3-none-any.whl (919kB)\n",
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      "\u001b[?25hRequirement already satisfied: pandas>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from geopandas) (0.25.3)\n",
      "Collecting fiona\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/f7/9899f8a9a2e38601472fe1079ce5088f58833221c8b8507d8b5eafd5404a/Fiona-1.8.13-cp36-cp36m-manylinux1_x86_64.whl (11.8MB)\n",
      "\u001b[K     |████████████████████████████████| 11.8MB 322kB/s \n",
      "\u001b[?25hRequirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.6.4.post2)\n",
      "Collecting pyproj\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d6/70/eedc98cd52b86de24a1589c762612a98bea26cde649ffdd60c1db396cce8/pyproj-2.4.2.post1-cp36-cp36m-manylinux2010_x86_64.whl (10.1MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (1.17.5)\n",
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      "Collecting munch\n",
      "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (0.5.0)\n",
      "Installing collected packages: munch, fiona, pyproj, geopandas\n",
      "Successfully installed fiona-1.8.13 geopandas-0.6.2 munch-2.5.0 pyproj-2.4.2.post1\n",
      "Collecting sentinelsat\n",
      "  Downloading https://files.pythonhosted.org/packages/5c/79/c2ac7b71dd13db95a9b83865bbbc7f1e4359c2b141bedad21b0e181fa06e/sentinelsat-0.13-py2.py3-none-any.whl\n",
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      "Collecting html2text\n",
      "  Downloading https://files.pythonhosted.org/packages/49/21/eb38d335ab15fc13564a5e971c1403707fb3a037292f246fa82e17208794/html2text-2019.9.26-py3-none-any.whl\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from sentinelsat) (2.21.0)\n",
      "Collecting geojson>=2\n",
      "  Downloading https://files.pythonhosted.org/packages/e4/8d/9e28e9af95739e6d2d2f8d4bef0b3432da40b7c3588fbad4298c1be09e48/geojson-2.5.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->sentinelsat) (1.24.3)\n",
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      "Installing collected packages: geomet, html2text, geojson, sentinelsat\n",
      "Successfully installed geojson-2.5.0 geomet-0.2.1.post1 html2text-2019.9.26 sentinelsat-0.13\n"
     ]
    }
   ],
   "source": [
    "!pip install rasterio\n",
    "!pip install geopandas\n",
    "!pip install sentinelsat\n",
    "import os, sys\n",
    "import numpy as np\n",
    "import rasterio\n",
    "from sentinelsat.sentinel import SentinelAPI, read_geojson, geojson_to_wkt\n",
    "from datetime import date\n",
    "import getpass\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "fWDhAzR_CvbT",
    "outputId": "60c8bf52-22e0-4a3e-86df-e0da96354fc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel Hub Password: ··········\n"
     ]
    }
   ],
   "source": [
    "# connect to the API\n",
    "user = 'Username' # enter the username\n",
    "# user = input('Sentinel Hub Username: ')\n",
    "pw = getpass.getpass('Sentinel Hub Password: ')\n",
    "api = SentinelAPI(user, pw, 'https://scihub.copernicus.eu/dhus')\n",
    "\n",
    "# download single scene by known product id\n",
    "#api.download(<product_id>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "MQSdqPXqCvbb",
    "outputId": "7fd3fe83-a7dc-4b47-b87a-8425e7789661"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sentinelsat.sentinel.SentinelAPI at 0x7f4298d35fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UZw2s4rDCvbi"
   },
   "source": [
    "## specify a geometry from http://geojson.io ... this is near Denver "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T90WRhKUCvbm"
   },
   "outputs": [],
   "source": [
    "gj_geom = {\n",
    "\"type\": \"FeatureCollection\",\n",
    "\"name\": \"SELECT\",\n",
    "\"crs\": { \"type\": \"name\", \"properties\": { \"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\" } },\n",
    "\"features\": [\n",
    "{ \"type\": \"Feature\", \"properties\": { }, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 78.790282313505003, 14.722851905653091, 0.0 ], [ 78.78944315111346, 14.723002082692179, 0.0 ], [ 78.789509498945648, 14.724354341456941, 0.0 ], [ 78.790479227929708, 14.724338990131089, 0.0 ], [ 78.790282313505003, 14.722851905653091, 0.0 ] ] ] } }\n",
    "]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3QohckRTCvbq"
   },
   "source": [
    "## Execute a search query. for all keyword options, see lower section of https://scihub.copernicus.eu/twiki/do/view/SciHubUserGuide/3FullTextSearch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "nNf147V3Cvbs",
    "outputId": "bb2ad2f1-3829-4cac-df7b-980ad4d100f2"
   },
   "outputs": [
    {
     "ename": "SentinelAPIError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSentinelAPIError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-ce1fd5fb5134>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m                      \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'20191201'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2019\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                      \u001b[0mplatformname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Sentinel-1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m                      polarizationmode = 'VV')\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sentinelsat/sentinel.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, area, date, raw, area_relation, order_by, limit, offset, **keywords)\u001b[0m\n\u001b[1;32m    145\u001b[0m         \"\"\"\n\u001b[1;32m    146\u001b[0m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marea_reltion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkeywords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m         self.logger.debug(\"Running query: orderby=%s, limit%s, offset=%s, query=%s\",\n\u001b[1;32m    149\u001b[0m                           \u001b[0morder_by\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sentinelsat/sentinel.py\u001b[0m in \u001b[0;36m_load_query\u001b[0;34m(self, query, order_by, limit, offset)\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m \u001b[0mtching\u001b[0m \u001b[0ma\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \"\"\"\n\u001b[0m\u001b[1;32m    273\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'order_by'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'limit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'offset'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m            \u001b[0;31m# Allow these function arguments to be included for compatibility with query(),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sentinelsat/sentinel.py\u001b[0m in \u001b[0;36m_load_subquery\u001b[0;34m(self, query, order_by, limit, offset)\u001b[0m\n\u001b[1;32m    314\u001b[0m , limit, offset)\n\u001b[1;32m    315\u001b[0m         response = self.session.post(url, {'q': query}, ath=self.session.auth,\n\u001b[0;32m--> 316\u001b[0;31m                                     \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Conten-Type'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'application/x-www-form-urlencoded; charset=UTF-8'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m                                    timeout=slf.timeout)\n\u001b[1;32m    318\u001b[0m         \u001b[0m_check_scihub_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSentinelAPIError\u001b[0m: HTTP status 200 OK: Invalid query string. Check the parameters and format."
     ]
    }
   ],
   "source": [
    "# search by polygon, time, and Hub query keywords... \n",
    "# 7/24/2016 is date of DG strip which was used for impervious surface mapping\n",
    "# 6/1/2018 - 7/31/2019 are containement dates for durango 416 fire\n",
    "# 12/4/2017 - 1/12/2018 are containment dates for Thomas fire\n",
    "\n",
    "footprint = geojson_to_wkt(gj_geom)\n",
    "products_S2 = api.query(footprint,\n",
    "                     date = ('20191201', date(2019, 12, 15)),\n",
    "                     platformname = 'Sentinel-2',\n",
    "                     cloudcoverpercentage = (0, 30))\n",
    " \n",
    "# products_S1 = api.query(footprint,\n",
    "#                      date = ('20191201', date(2019, 12, 15)),\n",
    "#                      platformname = 'Sentinel-1', \n",
    "#                      polarizationmode = 'VV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3zNwRUHyamlM"
   },
   "outputs": [],
   "source": [
    "products_S1 = api.query(footprint,\n",
    "                         date = ('20191201', date(2019, 12, 15)), \n",
    "                        producttype='SLC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "nw-mio6Aayx4",
    "outputId": "744b9bf4-85be-46b5-c302-43cee2340d9b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('4ad1ad42-193c-4dbd-b6a9-00b38820cca4',\n",
       "              {'acquisitiontype': 'NOMINAL',\n",
       "               'beginposition': datetime.datetime(2019, 12, 14, 0, 39, 35, 761000),\n",
       "               'endposition': datetime.datetime(2019, 12, 14, 0, 40, 2, 723000),\n",
       "               'filename': 'S1A_IW_SLC__1SDV_20191214T003935_20191214T004002_030337_03786D_0B42.SAFE',\n",
       "               'footprint': 'MULTIPOLYGON (((78.808105 13.698874, 79.143959 15.329204, 76.817497 15.767323, 76.499695 14.140973, 78.808105 13.698874)))',\n",
       "               'format': 'SAFE',\n",
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       "               'identifier': 'S1A_IW_SLC__1SDV_20191214T003935_20191214T004002_030337_03786D_0B42',\n",
       "               'ingestiondate': datetime.datetime(2019, 12, 14, 5, 47, 0, 476000),\n",
       "               'instrumentname': 'Synthetic Aperture Radar (C-band)',\n",
       "               'instrumentshortname': 'SAR-C SAR',\n",
       "               'lastorbitnumber': 30337,\n",
       "               'lastrelativeorbitnumber': 165,\n",
       "               'link': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('4ad1ad42-193c-4dbd-b6a9-00b38820cca4')/$value\",\n",
       "               'link_alternative': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('4ad1ad42-193c-4dbd-b6a9-00b38820cca4')/\",\n",
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       "               'missiondatatakeid': 227437,\n",
       "               'orbitdirection': 'DESCENDING',\n",
       "               'orbitnumber': 30337,\n",
       "               'platformidentifier': '2014-016A',\n",
       "               'platformname': 'Sentinel-1',\n",
       "               'polarisationmode': 'VV VH',\n",
       "               'productclass': 'S',\n",
       "               'producttype': 'SLC',\n",
       "               'relativeorbitnumber': 165,\n",
       "               'sensoroperationalmode': 'IW',\n",
       "               'size': '7.27 GB',\n",
       "               'slicenumber': 20,\n",
       "               'status': 'ARCHIVED',\n",
       "               'summary': 'Date: 2019-12-14T00:39:35.761Z, Instrument: SAR-C SAR, Mode: VV VH, Satellite: Sentinel-1, Size: 7.27 GB',\n",
       "               'swathidentifier': 'IW1 IW2 IW3',\n",
       "               'timeliness': 'Fast-24h',\n",
       "               'title': 'S1A_IW_SLC__1SDV_20191214T003935_20191214T004002_030337_03786D_0B42',\n",
       "               'uuid': '4ad1ad42-193c-4dbd-b6a9-00b38820cca4'}),\n",
       "             ('d159aaa7-f7dd-440d-9e70-f78dc1cd963e',\n",
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       "               'endposition': datetime.datetime(2019, 12, 9, 0, 31, 55, 891000),\n",
       "               'filename': 'S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2.SAFE',\n",
       "               'footprint': 'MULTIPOLYGON (((80.786095 13.291933, 81.133827 14.98076, 78.82402 15.417462, 78.494339 13.732757, 80.786095 13.291933)))',\n",
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       "               'identifier': 'S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2',\n",
       "               'ingestiondate': datetime.datetime(2019, 12, 9, 8, 35, 15, 674000),\n",
       "               'instrumentname': 'Synthetic Aperture Radar (C-band)',\n",
       "               'instrumentshortname': 'SAR-C SAR',\n",
       "               'lastorbitnumber': 30264,\n",
       "               'lastrelativeorbitnumber': 92,\n",
       "               'link': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d159aaa7-f7dd-440d-9e70-f78dc1cd963e')/$value\",\n",
       "               'link_alternative': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d159aaa7-f7dd-440d-9e70-f78dc1cd963e')/\",\n",
       "               'link_icon': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d159aaa7-f7dd-440d-9e70-f78dc1cd963e')/Products('Quicklook')/$value\",\n",
       "               'missiondatatakeid': 226770,\n",
       "               'orbitdirection': 'DESCENDING',\n",
       "               'orbitnumber': 30264,\n",
       "               'platformidentifier': '2014-016A',\n",
       "               'platformname': 'Sentinel-1',\n",
       "               'polarisationmode': 'VV VH',\n",
       "               'productclass': 'S',\n",
       "               'producttype': 'SLC',\n",
       "               'relativeorbitnumber': 92,\n",
       "               'sensoroperationalmode': 'IW',\n",
       "               'size': '7.36 GB',\n",
       "               'slicenumber': 13,\n",
       "               'status': 'ARCHIVED',\n",
       "               'summary': 'Date: 2019-12-09T00:31:27.962Z, Instrument: SAR-C SAR, Mode: VV VH, Satellite: Sentinel-1, Size: 7.36 GB',\n",
       "               'swathidentifier': 'IW1 IW2 IW3',\n",
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       "               'title': 'S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2',\n",
       "               'uuid': 'd159aaa7-f7dd-440d-9e70-f78dc1cd963e'}),\n",
       "             ('c05af45d-46b1-494c-988a-2e482b5b4cc4',\n",
       "              {'acquisitiontype': 'NOMINAL',\n",
       "               'beginposition': datetime.datetime(2019, 12, 8, 0, 38, 57, 953000),\n",
       "               'endposition': datetime.datetime(2019, 12, 8, 0, 39, 24, 909000),\n",
       "               'filename': 'S1B_IW_SLC__1SDV_20191208T003857_20191208T003924_019266_024600_17A5.SAFE',\n",
       "               'footprint': 'MULTIPOLYGON (((78.761421 13.474153, 79.097099 15.104148, 76.789635 15.539832, 76.471565 13.913797, 78.761421 13.474153)))',\n",
       "               'format': 'SAFE',\n",
       "               'gmlfootprint': '<gml:Polygon srsName=\"http://www.opengis.net/gml/srs/epsg.xml#4326\" xmlns:gml=\"http://www.opengis.net/gml\">\\n   <gml:outerBoundaryIs>\\n      <gml:LinearRing>\\n         <gml:coordinates>13.474153,78.761421 13.913797,76.471565 15.539832,76.789635 15.104148,79.097099 13.474153,78.761421</gml:coordinates>\\n      </gml:LinearRing>\\n   </gml:outerBoundaryIs>\\n</gml:Polygon>',\n",
       "               'identifier': 'S1B_IW_SLC__1SDV_20191208T003857_20191208T003924_019266_024600_17A5',\n",
       "               'ingestiondate': datetime.datetime(2019, 12, 8, 4, 31, 47, 226000),\n",
       "               'instrumentname': 'Synthetic Aperture Radar (C-band)',\n",
       "               'instrumentshortname': 'SAR-C SAR',\n",
       "               'lastorbitnumber': 19266,\n",
       "               'lastrelativeorbitnumber': 165,\n",
       "               'link': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('c05af45d-46b1-494c-988a-2e482b5b4cc4')/$value\",\n",
       "               'link_alternative': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('c05af45d-46b1-494c-988a-2e482b5b4cc4')/\",\n",
       "               'link_icon': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('c05af45d-46b1-494c-988a-2e482b5b4cc4')/Products('Quicklook')/$value\",\n",
       "               'missiondatatakeid': 148992,\n",
       "               'orbitdirection': 'DESCENDING',\n",
       "               'orbitnumber': 19266,\n",
       "               'platformidentifier': '2016-025A',\n",
       "               'platformname': 'Sentinel-1',\n",
       "               'polarisationmode': 'VV VH',\n",
       "               'productclass': 'S',\n",
       "               'producttype': 'SLC',\n",
       "               'relativeorbitnumber': 165,\n",
       "               'sensoroperationalmode': 'IW',\n",
       "               'size': '7.06 GB',\n",
       "               'slicenumber': 12,\n",
       "               'status': 'ARCHIVED',\n",
       "               'summary': 'Date: 2019-12-08T00:38:57.953Z, Instrument: SAR-C SAR, Mode: VV VH, Satellite: Sentinel-1, Size: 7.06 GB',\n",
       "               'swathidentifier': 'IW1 IW2 IW3',\n",
       "               'timeliness': 'Fast-24h',\n",
       "               'title': 'S1B_IW_SLC__1SDV_20191208T003857_20191208T003924_019266_024600_17A5',\n",
       "               'uuid': 'c05af45d-46b1-494c-988a-2e482b5b4cc4'}),\n",
       "             ('5e75fe94-b134-48b6-bf39-90769a362615',\n",
       "              {'acquisitiontype': 'NOMINAL',\n",
       "               'beginposition': datetime.datetime(2019, 12, 3, 0, 30, 49, 151000),\n",
       "               'endposition': datetime.datetime(2019, 12, 3, 0, 31, 16, 108000),\n",
       "               'filename': 'S1B_IW_SLC__1SDV_20191203T003049_20191203T003116_019193_0243C9_7B53.SAFE',\n",
       "               'footprint': 'MULTIPOLYGON (((80.763535 13.183815, 81.099113 14.813874, 78.794464 15.250401, 78.476097 13.624321, 80.763535 13.183815)))',\n",
       "               'format': 'SAFE',\n",
       "               'gmlfootprint': '<gml:Polygon srsName=\"http://www.opengis.net/gml/srs/epsg.xml#4326\" xmlns:gml=\"http://www.opengis.net/gml\">\\n   <gml:outerBoundaryIs>\\n      <gml:LinearRing>\\n         <gml:coordinates>13.183815,80.763535 13.624321,78.476097 15.250401,78.794464 14.813874,81.099113 13.183815,80.763535</gml:coordinates>\\n      </gml:LinearRing>\\n   </gml:outerBoundaryIs>\\n</gml:Polygon>',\n",
       "               'identifier': 'S1B_IW_SLC__1SDV_20191203T003049_20191203T003116_019193_0243C9_7B53',\n",
       "               'ingestiondate': datetime.datetime(2019, 12, 3, 4, 32, 21, 393000),\n",
       "               'instrumentname': 'Synthetic Aperture Radar (C-band)',\n",
       "               'instrumentshortname': 'SAR-C SAR',\n",
       "               'lastorbitnumber': 19193,\n",
       "               'lastrelativeorbitnumber': 92,\n",
       "               'link': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('5e75fe94-b134-48b6-bf39-90769a362615')/$value\",\n",
       "               'link_alternative': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('5e75fe94-b134-48b6-bf39-90769a362615')/\",\n",
       "               'link_icon': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('5e75fe94-b134-48b6-bf39-90769a362615')/Products('Quicklook')/$value\",\n",
       "               'missiondatatakeid': 148425,\n",
       "               'orbitdirection': 'DESCENDING',\n",
       "               'orbitnumber': 19193,\n",
       "               'platformidentifier': '2016-025A',\n",
       "               'platformname': 'Sentinel-1',\n",
       "               'polarisationmode': 'VV VH',\n",
       "               'productclass': 'S',\n",
       "               'producttype': 'SLC',\n",
       "               'relativeorbitnumber': 92,\n",
       "               'sensoroperationalmode': 'IW',\n",
       "               'size': '7.04 GB',\n",
       "               'slicenumber': 12,\n",
       "               'status': 'ARCHIVED',\n",
       "               'summary': 'Date: 2019-12-03T00:30:49.151Z, Instrument: SAR-C SAR, Mode: VV VH, Satellite: Sentinel-1, Size: 7.04 GB',\n",
       "               'swathidentifier': 'IW1 IW2 IW3',\n",
       "               'timeliness': 'Fast-24h',\n",
       "               'title': 'S1B_IW_SLC__1SDV_20191203T003049_20191203T003116_019193_0243C9_7B53',\n",
       "               'uuid': '5e75fe94-b134-48b6-bf39-90769a362615'}),\n",
       "             ('d59375f7-071a-476c-babf-edeb5901f023',\n",
       "              {'acquisitiontype': 'NOMINAL',\n",
       "               'beginposition': datetime.datetime(2019, 12, 2, 0, 39, 36, 135000),\n",
       "               'endposition': datetime.datetime(2019, 12, 2, 0, 40, 3, 95000),\n",
       "               'filename': 'S1A_IW_SLC__1SDV_20191202T003936_20191202T004003_030162_037260_F819.SAFE',\n",
       "               'footprint': 'MULTIPOLYGON (((78.808365 13.69875, 79.144218 15.32895, 76.817741 15.767107, 76.499939 14.140885, 78.808365 13.69875)))',\n",
       "               'format': 'SAFE',\n",
       "               'gmlfootprint': '<gml:Polygon srsName=\"http://www.opengis.net/gml/srs/epsg.xml#4326\" xmlns:gml=\"http://www.opengis.net/gml\">\\n   <gml:outerBoundaryIs>\\n      <gml:LinearRing>\\n         <gml:coordinates>13.698750,78.808365 14.140885,76.499939 15.767107,76.817741 15.328950,79.144218 13.698750,78.808365</gml:coordinates>\\n      </gml:LinearRing>\\n   </gml:outerBoundaryIs>\\n</gml:Polygon>',\n",
       "               'identifier': 'S1A_IW_SLC__1SDV_20191202T003936_20191202T004003_030162_037260_F819',\n",
       "               'ingestiondate': datetime.datetime(2019, 12, 2, 5, 49, 42, 31000),\n",
       "               'instrumentname': 'Synthetic Aperture Radar (C-band)',\n",
       "               'instrumentshortname': 'SAR-C SAR',\n",
       "               'lastorbitnumber': 30162,\n",
       "               'lastrelativeorbitnumber': 165,\n",
       "               'link': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d59375f7-071a-476c-babf-edeb5901f023')/$value\",\n",
       "               'link_alternative': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d59375f7-071a-476c-babf-edeb5901f023')/\",\n",
       "               'link_icon': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d59375f7-071a-476c-babf-edeb5901f023')/Products('Quicklook')/$value\",\n",
       "               'missiondatatakeid': 225888,\n",
       "               'orbitdirection': 'DESCENDING',\n",
       "               'orbitnumber': 30162,\n",
       "               'platformidentifier': '2014-016A',\n",
       "               'platformname': 'Sentinel-1',\n",
       "               'polarisationmode': 'VV VH',\n",
       "               'productclass': 'S',\n",
       "               'producttype': 'SLC',\n",
       "               'relativeorbitnumber': 165,\n",
       "               'sensoroperationalmode': 'IW',\n",
       "               'size': '7.27 GB',\n",
       "               'slicenumber': 20,\n",
       "               'status': 'ARCHIVED',\n",
       "               'summary': 'Date: 2019-12-02T00:39:36.135Z, Instrument: SAR-C SAR, Mode: VV VH, Satellite: Sentinel-1, Size: 7.27 GB',\n",
       "               'swathidentifier': 'IW1 IW2 IW3',\n",
       "               'timeliness': 'Fast-24h',\n",
       "               'title': 'S1A_IW_SLC__1SDV_20191202T003936_20191202T004003_030162_037260_F819',\n",
       "               'uuid': 'd59375f7-071a-476c-babf-edeb5901f023'})])"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fYM3IeAWCvby"
   },
   "outputs": [],
   "source": [
    "s1_items = list(products_S1.items())\n",
    "s1_items[0]\n",
    "\n",
    "# should also be able to convert to pandas\n",
    "s1_res_df = api.to_geodataframe(products_S1)\n",
    "s2_res_df = api.to_geodataframe(products_S2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "-yOJ5BscCvb2",
    "outputId": "a9f9aa58-74c7-4b11-fd8a-876d16f094ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 33), (1, 32))"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# s1_res_df.plot()\n",
    "# plt.show()\n",
    "s1_res_df.shape, s2_res_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "colab_type": "code",
    "id": "3ZRe2IUmCvb7",
    "outputId": "d6241020-9051-4093-dedf-9c8807f9f4ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel-1 results\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>link_alternative</th>\n",
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       "      <th>summary</th>\n",
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       "      <th>relativeorbitnumber</th>\n",
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       "      <th>4ad1ad42-193c-4dbd-b6a9-00b38820cca4</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-14T00:39:35.761Z, Instrument: SA...</td>\n",
       "      <td>2019-12-14 00:39:35.761</td>\n",
       "      <td>2019-12-14 00:40:02.723</td>\n",
       "      <td>2019-12-14 05:47:00.476</td>\n",
       "      <td>227437</td>\n",
       "      <td>20</td>\n",
       "      <td>30337</td>\n",
       "      <td>30337</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2014-016A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.27 GB</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...</td>\n",
       "      <td>4ad1ad42-193c-4dbd-b6a9-00b38820cca4</td>\n",
       "      <td>MULTIPOLYGON (((78.80810 13.69887, 79.14396 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d159aaa7-f7dd-440d-9e70-f78dc1cd963e</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-09T00:31:27.962Z, Instrument: SA...</td>\n",
       "      <td>2019-12-09 00:31:27.962</td>\n",
       "      <td>2019-12-09 00:31:55.891</td>\n",
       "      <td>2019-12-09 08:35:15.674</td>\n",
       "      <td>226770</td>\n",
       "      <td>13</td>\n",
       "      <td>30264</td>\n",
       "      <td>30264</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2014-016A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.36 GB</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...</td>\n",
       "      <td>d159aaa7-f7dd-440d-9e70-f78dc1cd963e</td>\n",
       "      <td>MULTIPOLYGON (((80.78610 13.29193, 81.13383 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c05af45d-46b1-494c-988a-2e482b5b4cc4</th>\n",
       "      <td>S1B_IW_SLC__1SDV_20191208T003857_20191208T0039...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-08T00:38:57.953Z, Instrument: SA...</td>\n",
       "      <td>2019-12-08 00:38:57.953</td>\n",
       "      <td>2019-12-08 00:39:24.909</td>\n",
       "      <td>2019-12-08 04:31:47.226</td>\n",
       "      <td>148992</td>\n",
       "      <td>12</td>\n",
       "      <td>19266</td>\n",
       "      <td>19266</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2016-025A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191208T003857_20191208T0039...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.06 GB</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191208T003857_20191208T0039...</td>\n",
       "      <td>c05af45d-46b1-494c-988a-2e482b5b4cc4</td>\n",
       "      <td>MULTIPOLYGON (((78.76142 13.47415, 79.09710 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5e75fe94-b134-48b6-bf39-90769a362615</th>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-03T00:30:49.151Z, Instrument: SA...</td>\n",
       "      <td>2019-12-03 00:30:49.151</td>\n",
       "      <td>2019-12-03 00:31:16.108</td>\n",
       "      <td>2019-12-03 04:32:21.393</td>\n",
       "      <td>148425</td>\n",
       "      <td>12</td>\n",
       "      <td>19193</td>\n",
       "      <td>19193</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2016-025A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.04 GB</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>5e75fe94-b134-48b6-bf39-90769a362615</td>\n",
       "      <td>MULTIPOLYGON (((80.76354 13.18381, 81.09911 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d59375f7-071a-476c-babf-edeb5901f023</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-02T00:39:36.135Z, Instrument: SA...</td>\n",
       "      <td>2019-12-02 00:39:36.135</td>\n",
       "      <td>2019-12-02 00:40:03.095</td>\n",
       "      <td>2019-12-02 05:49:42.031</td>\n",
       "      <td>225888</td>\n",
       "      <td>20</td>\n",
       "      <td>30162</td>\n",
       "      <td>30162</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2014-016A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.27 GB</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>d59375f7-071a-476c-babf-edeb5901f023</td>\n",
       "      <td>MULTIPOLYGON (((78.80836 13.69875, 79.14422 15...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  title  ...                                           geometry\n",
       "4ad1ad42-193c-4dbd-b6a9-00b38820cca4  S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...  ...  MULTIPOLYGON (((78.80810 13.69887, 79.14396 15...\n",
       "d159aaa7-f7dd-440d-9e70-f78dc1cd963e  S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...  ...  MULTIPOLYGON (((80.78610 13.29193, 81.13383 14...\n",
       "c05af45d-46b1-494c-988a-2e482b5b4cc4  S1B_IW_SLC__1SDV_20191208T003857_20191208T0039...  ...  MULTIPOLYGON (((78.76142 13.47415, 79.09710 15...\n",
       "5e75fe94-b134-48b6-bf39-90769a362615  S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...  ...  MULTIPOLYGON (((80.76354 13.18381, 81.09911 14...\n",
       "d59375f7-071a-476c-babf-edeb5901f023  S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...  ...  MULTIPOLYGON (((78.80836 13.69875, 79.14422 15...\n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sentinel-1 results')\n",
    "s1_res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "id": "vVbeV-EFCvcA",
    "outputId": "6c71f589-0ce9-4d46-f23f-53b7626f283d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel-2 results\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>link_alternative</th>\n",
       "      <th>link_icon</th>\n",
       "      <th>summary</th>\n",
       "      <th>datatakesensingstart</th>\n",
       "      <th>beginposition</th>\n",
       "      <th>endposition</th>\n",
       "      <th>ingestiondate</th>\n",
       "      <th>orbitnumber</th>\n",
       "      <th>relativeorbitnumber</th>\n",
       "      <th>cloudcoverpercentage</th>\n",
       "      <th>sensoroperationalmode</th>\n",
       "      <th>tileid</th>\n",
       "      <th>hv_order_tileid</th>\n",
       "      <th>format</th>\n",
       "      <th>processingbaseline</th>\n",
       "      <th>platformname</th>\n",
       "      <th>filename</th>\n",
       "      <th>instrumentname</th>\n",
       "      <th>instrumentshortname</th>\n",
       "      <th>size</th>\n",
       "      <th>s2datatakeid</th>\n",
       "      <th>producttype</th>\n",
       "      <th>platformidentifier</th>\n",
       "      <th>orbitdirection</th>\n",
       "      <th>platformserialidentifier</th>\n",
       "      <th>processinglevel</th>\n",
       "      <th>identifier</th>\n",
       "      <th>level1cpdiidentifier</th>\n",
       "      <th>uuid</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5949b107-6d3e-4be8-a4b9-f685974b0dc4</th>\n",
       "      <td>S2A_MSIL1C_20191210T051211_N0208_R019_T43PHS_2...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-10T05:12:11.024Z, Instrument: MS...</td>\n",
       "      <td>2019-12-10 05:12:11.024</td>\n",
       "      <td>2019-12-10 05:12:11.024</td>\n",
       "      <td>2019-12-10 05:12:11.024</td>\n",
       "      <td>2019-12-10 11:00:13.221</td>\n",
       "      <td>23325</td>\n",
       "      <td>19</td>\n",
       "      <td>27.0693</td>\n",
       "      <td>INS-NOBS</td>\n",
       "      <td>43PHS</td>\n",
       "      <td>PS43H</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>02.08</td>\n",
       "      <td>Sentinel-2</td>\n",
       "      <td>S2A_MSIL1C_20191210T051211_N0208_R019_T43PHS_2...</td>\n",
       "      <td>Multi-Spectral Instrument</td>\n",
       "      <td>MSI</td>\n",
       "      <td>817.45 MB</td>\n",
       "      <td>GS2A_20191210T051211_023325_N02.08</td>\n",
       "      <td>S2MSI1C</td>\n",
       "      <td>2015-028A</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>Sentinel-2A</td>\n",
       "      <td>Level-1C</td>\n",
       "      <td>S2A_MSIL1C_20191210T051211_N0208_R019_T43PHS_2...</td>\n",
       "      <td>S2A_OPER_MSI_L1C_TL_EPAE_20191210T081559_A0233...</td>\n",
       "      <td>5949b107-6d3e-4be8-a4b9-f685974b0dc4</td>\n",
       "      <td>MULTIPOLYGON (((78.79801 14.35393, 78.81538 15...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  title  ...                                           geometry\n",
       "5949b107-6d3e-4be8-a4b9-f685974b0dc4  S2A_MSIL1C_20191210T051211_N0208_R019_T43PHS_2...  ...  MULTIPOLYGON (((78.79801 14.35393, 78.81538 15...\n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sentinel-2 results')\n",
    "s2_res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 182
    },
    "colab_type": "code",
    "id": "7zek5tb8CvcI",
    "outputId": "76020463-e7fb-4352-8fc7-d03bc6599b8a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1.0000\n",
       "mean     27.0693\n",
       "std          NaN\n",
       "min      27.0693\n",
       "25%      27.0693\n",
       "50%      27.0693\n",
       "75%      27.0693\n",
       "max      27.0693\n",
       "Name: cloudcoverpercentage, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2_res_df.describe()['cloudcoverpercentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "LDK462mpEw7G",
    "outputId": "6d70e80f-1d8a-4a2a-92f6-15a1edfa9f42"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missiondatatakeid</th>\n",
       "      <th>slicenumber</th>\n",
       "      <th>orbitnumber</th>\n",
       "      <th>lastorbitnumber</th>\n",
       "      <th>relativeorbitnumber</th>\n",
       "      <th>lastrelativeorbitnumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>195502.400000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>25844.400000</td>\n",
       "      <td>25844.400000</td>\n",
       "      <td>135.800000</td>\n",
       "      <td>135.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42720.794331</td>\n",
       "      <td>4.219005</td>\n",
       "      <td>6038.924929</td>\n",
       "      <td>6038.924929</td>\n",
       "      <td>39.983747</td>\n",
       "      <td>39.983747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>148425.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>19193.000000</td>\n",
       "      <td>19193.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>148992.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>19266.000000</td>\n",
       "      <td>19266.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>225888.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>30162.000000</td>\n",
       "      <td>30162.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>226770.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30264.000000</td>\n",
       "      <td>30264.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>227437.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30337.000000</td>\n",
       "      <td>30337.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       missiondatatakeid  ...  lastrelativeorbitnumber\n",
       "count           5.000000  ...                 5.000000\n",
       "mean       195502.400000  ...               135.800000\n",
       "std         42720.794331  ...                39.983747\n",
       "min        148425.000000  ...                92.000000\n",
       "25%        148992.000000  ...                92.000000\n",
       "50%        225888.000000  ...               165.000000\n",
       "75%        226770.000000  ...               165.000000\n",
       "max        227437.000000  ...               165.000000\n",
       "\n",
       "[8 rows x 6 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1_res_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ndnega-2CvcO",
    "outputId": "214248c4-ad4c-4bd0-dcbc-cafb44aa3791"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 producttype:  SLC\n"
     ]
    }
   ],
   "source": [
    "## subset Sentinel-1 results by 'producttype'\n",
    "prod_groups_list = list(s1_res_df.groupby('producttype'))\n",
    "for i, item in enumerate(prod_groups_list):\n",
    "    print(i, 'producttype: ', item[0])\n",
    "    \n",
    "# GRD_df = prod_groups_list[0][1]\n",
    "# RAW_df = prod_groups_list[1][1]\n",
    "SLC_df = prod_groups_list[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "colab": {},
    "colab_type": "code",
    "id": "FjEGlKbbCvcS",
    "outputId": "75ba2af3-76dd-436f-a233-2773883edbc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel-1 GRD products\n"
     ]
    },
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      "text/plain": [
       "                                                                                  title  \\\n",
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       "\n",
       "                                                                                   link  \\\n",
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       "\n",
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       "\n",
       "                                                                                summary  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df  Date: 2016-07-08T00:53:43.908Z, Instrument: SA...   \n",
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       "\n",
       "                                               ingestiondate  \\\n",
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       "\n",
       "                                               beginposition  \\\n",
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       "\n",
       "                                                 endposition  \\\n",
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       "f0b0aa7f-c9bb-4878-bc56-1700a0e6ce26 2016-07-13 01:02:15.187   \n",
       "\n",
       "                                      missiondatatakeid  orbitnumber  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df              76297        12050   \n",
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       "\n",
       "                                                            ...                          \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df                        ...                           \n",
       "8b6fa6ca-89e4-4337-95fb-c79f54a97a7c                        ...                           \n",
       "c1e9f6bd-b8dd-4b9f-942a-ee0c9d1e2872                        ...                           \n",
       "f0b0aa7f-c9bb-4878-bc56-1700a0e6ce26                        ...                           \n",
       "\n",
       "                                      orbitdirection  polarisationmode  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df       ASCENDING                VV   \n",
       "8b6fa6ca-89e4-4337-95fb-c79f54a97a7c       ASCENDING                VV   \n",
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       "f0b0aa7f-c9bb-4878-bc56-1700a0e6ce26       ASCENDING                VV   \n",
       "\n",
       "                                      productclass  producttype platformname  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df             S          GRD   Sentinel-1   \n",
       "8b6fa6ca-89e4-4337-95fb-c79f54a97a7c             S          GRD   Sentinel-1   \n",
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       "f0b0aa7f-c9bb-4878-bc56-1700a0e6ce26             S          GRD   Sentinel-1   \n",
       "\n",
       "                                           size    status processed  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df  813.42 MB  ARCHIVED         F   \n",
       "8b6fa6ca-89e4-4337-95fb-c79f54a97a7c  824.82 MB  ARCHIVED         F   \n",
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       "f0b0aa7f-c9bb-4878-bc56-1700a0e6ce26   824.5 MB  ARCHIVED         F   \n",
       "\n",
       "                                     productconsolidation  \\\n",
       "82ffc6cd-eaec-42eb-80cf-c98d21fc77df                  NaN   \n",
       "8b6fa6ca-89e4-4337-95fb-c79f54a97a7c                  NaN   \n",
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       "\n",
       "                                                                               geometry  \n",
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       "\n",
       "[4 rows x 34 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print('Sentinel-1 GRD products')\n",
    "# GRD_df.head()"
   ]
  },
  {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel-1 RAW products\n"
     ]
    },
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       "      <td>2016-07-08 05:34:51.900</td>\n",
       "      <td>2016-07-08 00:53:40.208</td>\n",
       "      <td>2016-07-08 00:54:12.608</td>\n",
       "      <td>76297</td>\n",
       "      <td>12050</td>\n",
       "      <td>...</td>\n",
       "      <td>ASCENDING</td>\n",
       "      <td>VV</td>\n",
       "      <td>S</td>\n",
       "      <td>RAW</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>757.22 MB</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>F</td>\n",
       "      <td>SLICE</td>\n",
       "      <td>POLYGON ((-102.7161 38.1846, -103.1164 40.1393...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f4b44b31-d283-4704-84e6-ff9730b3be97</th>\n",
       "      <td>S1A_IW_RAW__0SSV_20160718T130906_20160718T1309...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
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       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2016-07-18T13:09:06.093Z, Instrument: SA...</td>\n",
       "      <td>2016-07-19 04:01:17.895</td>\n",
       "      <td>2016-07-18 13:09:06.093</td>\n",
       "      <td>2016-07-18 13:09:38.493</td>\n",
       "      <td>77574</td>\n",
       "      <td>12203</td>\n",
       "      <td>...</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>VV</td>\n",
       "      <td>S</td>\n",
       "      <td>RAW</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>760.25 MB</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>F</td>\n",
       "      <td>SLICE</td>\n",
       "      <td>POLYGON ((-107.1698 41.3891, -107.5743 39.4348...</td>\n",
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       "    <tr>\n",
       "      <th>f84826e7-7369-46a9-b83f-12d9b9bf5399</th>\n",
       "      <td>S1A_IW_RAW__0SSV_20160708T005405_20160708T0054...</td>\n",
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       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2016-07-08T00:54:05.209Z, Instrument: SA...</td>\n",
       "      <td>2016-07-08 05:37:05.044</td>\n",
       "      <td>2016-07-08 00:54:05.209</td>\n",
       "      <td>2016-07-08 00:54:27.445</td>\n",
       "      <td>76297</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  title  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  S1A_IW_RAW__0SSV_20160713T010211_20160713T0102...   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  S1A_IW_RAW__0SSV_20160708T005340_20160708T0054...   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  S1A_IW_RAW__0SSV_20160718T130906_20160718T1309...   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  S1A_IW_RAW__0SSV_20160708T005405_20160708T0054...   \n",
       "\n",
       "                                                                                   link  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "\n",
       "                                                                       link_alternative  \\\n",
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       "3422b3f2-bfff-4b07-8268-a285f571448c  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "\n",
       "                                                                              link_icon  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  https://scihub.copernicus.eu/dhus/odata/v1/Pro...   \n",
       "\n",
       "                                                                                summary  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  Date: 2016-07-13T01:02:11.49Z, Instrument: SAR...   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  Date: 2016-07-08T00:53:40.208Z, Instrument: SA...   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  Date: 2016-07-18T13:09:06.093Z, Instrument: SA...   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  Date: 2016-07-08T00:54:05.209Z, Instrument: SA...   \n",
       "\n",
       "                                               ingestiondate  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3 2016-07-13 12:16:42.138   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c 2016-07-08 05:34:51.900   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97 2016-07-19 04:01:17.895   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399 2016-07-08 05:37:05.044   \n",
       "\n",
       "                                               beginposition  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3 2016-07-13 01:02:11.490   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c 2016-07-08 00:53:40.208   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97 2016-07-18 13:09:06.093   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399 2016-07-08 00:54:05.209   \n",
       "\n",
       "                                                 endposition  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3 2016-07-13 01:02:43.890   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c 2016-07-08 00:54:12.608   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97 2016-07-18 13:09:38.493   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399 2016-07-08 00:54:27.445   \n",
       "\n",
       "                                      missiondatatakeid  orbitnumber  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3              76904        12123   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c              76297        12050   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97              77574        12203   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399              76297        12050   \n",
       "\n",
       "                                                            ...                          \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3                        ...                           \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c                        ...                           \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97                        ...                           \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399                        ...                           \n",
       "\n",
       "                                      orbitdirection  polarisationmode  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3       ASCENDING                VV   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c       ASCENDING                VV   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97      DESCENDING                VV   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399       ASCENDING                VV   \n",
       "\n",
       "                                      productclass  producttype platformname  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3             S          RAW   Sentinel-1   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c             S          RAW   Sentinel-1   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97             S          RAW   Sentinel-1   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399             S          RAW   Sentinel-1   \n",
       "\n",
       "                                           size    status processed  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  766.09 MB  ARCHIVED         F   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  757.22 MB  ARCHIVED         F   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  760.25 MB  ARCHIVED         F   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  511.26 MB  ARCHIVED         F   \n",
       "\n",
       "                                     productconsolidation  \\\n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3                SLICE   \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c                SLICE   \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97                SLICE   \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399                SLICE   \n",
       "\n",
       "                                                                               geometry  \n",
       "11a2c49a-7e57-46c5-8997-564c4b9c05b3  POLYGON ((-104.9884 39.2381, -105.3924 41.1925...  \n",
       "3422b3f2-bfff-4b07-8268-a285f571448c  POLYGON ((-102.7161 38.1846, -103.1164 40.1393...  \n",
       "f4b44b31-d283-4704-84e6-ff9730b3be97  POLYGON ((-107.1698 41.3891, -107.5743 39.4348...  \n",
       "f84826e7-7369-46a9-b83f-12d9b9bf5399  POLYGON ((-103.0244 39.693, -103.302 41.0342, ...  \n",
       "\n",
       "[4 rows x 34 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print('Sentinel-1 RAW products')\n",
    "# RAW_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "colab_type": "code",
    "id": "IFjrgxPpCvca",
    "outputId": "ea13cdde-e17f-45b3-89dd-75e5feff5801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentinel-1 SLC products\n"
     ]
    },
    {
     "data": {
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       "      <th>4ad1ad42-193c-4dbd-b6a9-00b38820cca4</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-14T00:39:35.761Z, Instrument: SA...</td>\n",
       "      <td>2019-12-14 00:39:35.761</td>\n",
       "      <td>2019-12-14 00:40:02.723</td>\n",
       "      <td>2019-12-14 05:47:00.476</td>\n",
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       "      <td>30337</td>\n",
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       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
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       "      <td>VV VH</td>\n",
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       "      <td>ARCHIVED</td>\n",
       "      <td>7.27 GB</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...</td>\n",
       "      <td>4ad1ad42-193c-4dbd-b6a9-00b38820cca4</td>\n",
       "      <td>MULTIPOLYGON (((78.80810 13.69887, 79.14396 15...</td>\n",
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       "    <tr>\n",
       "      <th>d159aaa7-f7dd-440d-9e70-f78dc1cd963e</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-09T00:31:27.962Z, Instrument: SA...</td>\n",
       "      <td>2019-12-09 00:31:27.962</td>\n",
       "      <td>2019-12-09 00:31:55.891</td>\n",
       "      <td>2019-12-09 08:35:15.674</td>\n",
       "      <td>226770</td>\n",
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       "      <td>VV VH</td>\n",
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       "      <td>7.36 GB</td>\n",
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       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-08T00:38:57.953Z, Instrument: SA...</td>\n",
       "      <td>2019-12-08 00:38:57.953</td>\n",
       "      <td>2019-12-08 00:39:24.909</td>\n",
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       "      <td>7.06 GB</td>\n",
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       "      <td>MULTIPOLYGON (((78.76142 13.47415, 79.09710 15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5e75fe94-b134-48b6-bf39-90769a362615</th>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-03T00:30:49.151Z, Instrument: SA...</td>\n",
       "      <td>2019-12-03 00:30:49.151</td>\n",
       "      <td>2019-12-03 00:31:16.108</td>\n",
       "      <td>2019-12-03 04:32:21.393</td>\n",
       "      <td>148425</td>\n",
       "      <td>12</td>\n",
       "      <td>19193</td>\n",
       "      <td>19193</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2016-025A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.04 GB</td>\n",
       "      <td>S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...</td>\n",
       "      <td>5e75fe94-b134-48b6-bf39-90769a362615</td>\n",
       "      <td>MULTIPOLYGON (((80.76354 13.18381, 81.09911 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d59375f7-071a-476c-babf-edeb5901f023</th>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>https://scihub.copernicus.eu/dhus/odata/v1/Pro...</td>\n",
       "      <td>Date: 2019-12-02T00:39:36.135Z, Instrument: SA...</td>\n",
       "      <td>2019-12-02 00:39:36.135</td>\n",
       "      <td>2019-12-02 00:40:03.095</td>\n",
       "      <td>2019-12-02 05:49:42.031</td>\n",
       "      <td>225888</td>\n",
       "      <td>20</td>\n",
       "      <td>30162</td>\n",
       "      <td>30162</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>IW</td>\n",
       "      <td>IW1 IW2 IW3</td>\n",
       "      <td>DESCENDING</td>\n",
       "      <td>SLC</td>\n",
       "      <td>Fast-24h</td>\n",
       "      <td>Sentinel-1</td>\n",
       "      <td>2014-016A</td>\n",
       "      <td>Synthetic Aperture Radar (C-band)</td>\n",
       "      <td>SAR-C SAR</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>SAFE</td>\n",
       "      <td>S</td>\n",
       "      <td>VV VH</td>\n",
       "      <td>NOMINAL</td>\n",
       "      <td>ARCHIVED</td>\n",
       "      <td>7.27 GB</td>\n",
       "      <td>S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...</td>\n",
       "      <td>d59375f7-071a-476c-babf-edeb5901f023</td>\n",
       "      <td>MULTIPOLYGON (((78.80836 13.69875, 79.14422 15...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  title  ...                                           geometry\n",
       "4ad1ad42-193c-4dbd-b6a9-00b38820cca4  S1A_IW_SLC__1SDV_20191214T003935_20191214T0040...  ...  MULTIPOLYGON (((78.80810 13.69887, 79.14396 15...\n",
       "d159aaa7-f7dd-440d-9e70-f78dc1cd963e  S1A_IW_SLC__1SDV_20191209T003127_20191209T0031...  ...  MULTIPOLYGON (((80.78610 13.29193, 81.13383 14...\n",
       "c05af45d-46b1-494c-988a-2e482b5b4cc4  S1B_IW_SLC__1SDV_20191208T003857_20191208T0039...  ...  MULTIPOLYGON (((78.76142 13.47415, 79.09710 15...\n",
       "5e75fe94-b134-48b6-bf39-90769a362615  S1B_IW_SLC__1SDV_20191203T003049_20191203T0031...  ...  MULTIPOLYGON (((80.76354 13.18381, 81.09911 14...\n",
       "d59375f7-071a-476c-babf-edeb5901f023  S1A_IW_SLC__1SDV_20191202T003936_20191202T0040...  ...  MULTIPOLYGON (((78.80836 13.69875, 79.14422 15...\n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sentinel-1 SLC products')\n",
    "SLC_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "id": "GLyovKuICvce",
    "outputId": "745e9ce0-47af-4c8d-8697-e979f7c4a56a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading SLC product\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|██████████| 4.72G/4.72G [04:21<00:00, 18.1MB/s]\n",
      "MD5 checksumming: 100%|██████████| 4.72G/4.72G [00:26<00:00, 178MB/s]\n"
     ]
    }
   ],
   "source": [
    "# download a couple scenes by uuid\n",
    "# help(api.download)\n",
    "SLC_uuid = SLC_df['uuid'][1]\n",
    "SLC_title = SLC_df['title'][1]\n",
    "if not os.path.exists(SLC_title + '.zip'):\n",
    "    print('Downloading SLC product')\n",
    "    SLC_dl = api.download(SLC_uuid)\n",
    "\n",
    "# same for GRD product\n",
    "GRD_uuid = GRD_df['uuid'][1]\n",
    "GRD_title = GRD_df['title'][1]\n",
    "if not os.path.exists(GRD_title + '.zip'):\n",
    "    print('Downloading GRD product')\n",
    "    GRD_dl = api.download(GRD_uuid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oUmNopsfCvcl"
   },
   "outputs": [],
   "source": [
    "## you can get the path by assigning the download call to a variable \n",
    "import zipfile\n",
    "for zippath in (SLC_dl['path'], GRD_dl['path']):\n",
    "    zip_ref = zipfile.ZipFile(zippath, 'r')\n",
    "    zip_ref.extractall(os.path.join('.', 's1_files'))\n",
    "    zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eaZgq6N7Cvcq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "colab_type": "code",
    "id": "L9gG9fcMCvcu",
    "outputId": "377ecb88-a01a-43ed-8314-9ee22784e0a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Creation Date': datetime.datetime(2019, 12, 9, 8, 35, 46, 286000),\n",
       " 'Ingestion Date': datetime.datetime(2019, 12, 9, 8, 35, 15, 674000),\n",
       " 'Online': True,\n",
       " 'date': datetime.datetime(2019, 12, 9, 0, 31, 27, 962000),\n",
       " 'downloaded_bytes': 4720292537,\n",
       " 'footprint': 'POLYGON((80.786095 13.291933,78.494339 13.732757,78.824020 15.417462,81.133827 14.980760,80.786095 13.291933))',\n",
       " 'id': 'd159aaa7-f7dd-440d-9e70-f78dc1cd963e',\n",
       " 'md5': 'EF90868F0B54FC5DCE1169E40833AA90',\n",
       " 'path': './S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2.zip',\n",
       " 'size': 4720292537,\n",
       " 'title': 'S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2',\n",
       " 'url': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('d159aaa7-f7dd-440d-9e70-f78dc1cd963e')/$value\"}"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SLC_dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 741
    },
    "colab_type": "code",
    "id": "OXVqVqmuCvcz",
    "outputId": "90379cfd-f0d9-4d45-8213-81b2d3406b2d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/rasterio/__init__.py:219: NotGeoreferencedWarning: Dataset has no geotransform set. The identity matrix may be returned.\n",
      "  s = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'SAFE', 'dtype': 'complex', 'nodata': None, 'width': 24178, 'height': 15100, 'count': 2, 'crs': None, 'transform': Affine(1.0, 0.0, 0.0,\n",
      "       0.0, 1.0, 0.0), 'tiled': False}\n",
      "(1, 21013)\n",
      "(5500, 5000)\n",
      "0.0 83.0 5.129790072727273\n"
     ]
    },
    {
     "data": {
      "image/png": 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yut4qi+N2BvFvMnSZUy7Fk0ajuF90X7X7LhFe6xDF6vKTMTrcZB2TUrt/Bt7D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3sblK/QeniCmBDCSM3tDyWgnKQIhK4RyLW2lnZ4bQP6bA7GC92k3LK5Qce6oKuY3UUzivxgs+R8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8jjQyuxx4GSA9RvmA0nIXzS/fRj1yWSojp58bRP14H/xskL9rDmkSDDX5l8TTI6gvorxFZcRD\nZq+FzL96PaJaAxA9CUAJ2OqoJtHMELGbWlAlPFaorN0ohMQbjhKLZfztONRXXkf2+QURQUmOvsLQ\ngnASnPel3uoJbZ/afdLHLFTW0vKGlZ+rQ6ZaafBEtRN9cizpYxr1W4KKa+xuonM+8+xtsm2BVHzu\nNBqPHUVrwEfi7cDZNx8/ImNVHwlyYQAEbtHupL2Cj+JdVaI5NtXAxCFHGFUzR8nJVu+ryDkIR1F+\nJqD84Vab1AmN9HODJAk/SvlAPldeggROKw+eRGWYyAPdbEtalIpra0ZcInigRdhj2JP7lOlouFqZ\n+kEOzTvpmDkSrD58nEj66jS22j0lxF8mBH3l0MDc0gd3I6nLN6scHZ5VC4jP7Em6qSQa+fJ+lO4/\nB7tMbRGlu+vw+3ygQUuC0gMTMoNlNi6mPqyxfug8LR1Ykii3tRfxJ14hvqVlNHNXHz4O9wuHI7Nr\no+Ci9dkz8sDanxxD5mhA8dq41oX1wwUSsZVWUhI/c0SLnBarrHg95PDsskFUH7WgF9bl4U2NUUNz\n+rnBCE92ZtOAaTMJnCfn7ZqneuRYIzqBhqG0XZQBAHyHSOHyu+PCswTQMpsVWvzfeEMcXmXYg/pv\nlHxl8GX6uUEs/LnXRWewHdbgZmNTe/PaUPCZzf1UcDBRHLfisAlrp6C7Y6i+SuPQliuQEOe8L/TA\nbrYl3Fa8ZJWIpkA8WfbgBbQ+fk7ygdWlCrkt/aLnCATOBECkWjlFzeWJFZjOwoSNpfvPwZnQpE1o\n+gV5PM0ecz5fTKG0/gIST48g+eChabf5gWyWQM4PC5jzipmXNWqupqu+OuRJs2xlxAPuOUm5khAv\nt7YomkidoJCdk6sAoJado9D9jtNInrJQe+gU3D3kHDAZl+UC5wDa2QbYRHzgHXI+sn8rcBKNAjUp\nw6Z8RW5rL7RrITYRR2n9BXoIn1qK2rdHoTzAu+800j8KpLOqSxXUPErgN19YAm25iN1OUQM3Erc+\nY/oEB4LlVbOH4Bne538CAKhvOCOfCcXMeABKBILyfX2Rh9q6SUr0G2iB/PappZGeOjZ2XmzVh49T\ngvzX7iTYg4lgkidpjNPxxYeXt42CC+1SRTS2ax45lmenVqHssiPLSjfWV6u0AAANjUlEQVTbQvom\nilAzR4mVoPLQCaFMxg8GkN+eJfmr1xyTt/RR3jAuXQHKs1G8t4xmHyQfaD0zDLWgivpgTCZEXY5L\nyoDzVZyPzO0NyPCyDwRo4XAk2M4hzxznmaMW/B/3TvmcCyLe538CL6VkOTgnFlOze11h62gnxVZc\nUxeF3p5PHURuS79Qs6aP0VImd8BG6tkl8p6XUoKNkd4xs/Znzm/15PWk2nKAKkWVYdKC45Cab8bM\nmIXqJ0vi7Nw8nbbSypZUvRoLY5HQnscIEMareXsZlQdPyo1dX+TBe3QM6fsPEV7nuCWf5Q7YyOxI\n4cTm1UQ78sAYbcdwL5VHPShXQ7U0NSwbMQd+GC78xU1IvxbwQPGNz3kRv+FMq12Y35YXXiskfJTW\nXwj4mB4/InAPIErVwpY+ZknPXfYbL9Ayyiyjw1Fo2PmnjxF4MeyoeAlX3jCO+OEEmv/ruwHv+1HL\n9PL5UvK3GqTvx5Fm+aYgsswesshxmmVw8a4qmn1AdUhRyuB2szw2eohhdtXYhndQXFNH6ghQe+gU\niqubaKytIL4vg8ymgcDpO4qqgxUb+NNhuUZs1imjXmx0/oDAcXH0pON0vBydtaPY3WwLiTfiInJx\n2aYA35rd60pbRzupWEMhPhmQhBXXFeEuJJlr5tgWytjXFpHs014H9rtEa4FXFki3O9/k7DishoPY\nY2/B+v5CePODRCvPXPlteXIkv7MKlWFP5Mrzu5KRXBgvZ8JJWbvsyBjf+dO1SB+gHIlQwjyxQpaG\nfDPXl+pI0yosH9lvvCAVruwhS5pZlWfD+v7CCPGdX2ii1RejRO2X9sM7Eco5mQ6XxARRllhLz0qF\nM+xswtVLXpJxP2OYyQEIVFxye4Nt8LXgzxoFF6oei0RO2UNW1PkPecEDjiBnVlxNiXBvsIHsVsJu\n5bb2yrZj5hI0RlvI7CD4QPXh44RsL5N4Rvq5QVFQDvM1Zb+bDZRnCi5qv34HKiu0OUcmejeRFwB4\n+xZGpOSL64pofWZC7gPfAvQ5onhpPn5Eoh1earITbhRc+YyddmOYlqTuX10jqs7Z5xfAO0n3il12\nkHp2CRJPj8AbnBsGBLFuTuryzU9ooUrh/EHT8IIDNPukjxHvtMwwJzLC68OKHdohKaL0MQv2O5Qj\nSm4ukGNZe1qwPZxoZbMaDqzf2ROZ1cJI5rCV/y01m/qZ6JJmyZe3Q49THsO7roLc1l6o/+MgrPPk\nbPI7UwGR2X+8FvntWcpNmZmfIx4/A+CJFfBSxGVVeegEUs8uISWX7VmUR00U59HSqvLgSUl+Jw8G\nXfoA4C6kZXP6xRRiNRVJKmfGLIlqrIYDN0eRhWMeXtWinFpighhR/bwv6sRAgOOxGo5RhdFwnr5a\nEOrlUQ9q6yBVQg1gtXltU/oXmy8skW21/uYaxF7vA4ZK5PhNJbNRiKG2WMkxFdcVRYHHTxE41y+m\nYA8UgYxh3ZwMmriZWYEt9fWXZNztIFMA8D8Z9Bhy5BOrBUtsN9tC886iLI95Wa6apIQTn3RkIrRu\nPUXbMbktq6YRqwDOZ98R2Ex5wzgqD50gUZFsC7VH3kXjsaOUa5vD3j0dm93rSlsHDOHiJku2DeNE\nJJelRDffLNUhKu9nn6eoqXl7OZpL2lxA7dujaNxVgv1OQiIMXlK4WRVhu5RlkOUHnefX1QgI6tkB\nL9XaGqxGwF658Av/A5mNiyWa4O1VRjxphq4Meyitv4DUCS0y6d5gE7F8jVDvtxxD8d4y0XVMOrCe\nXCaOxdqXQ275e4gfC5YjtUfeRWNthR7wvY6wJfCDVls3ieRJf0qiurZYIb/+TbzxzY/DT2mkX06I\no4rt6ZPvZ/Yr+DZFGG6PEmiCSrpI7Ekju09JMzU7Wp5AvEQL3kfPk0z9gz+Gl1SIj5HuXu2RdyVK\naRViiB+nSNfPauCfvyusl83Hj5AicZ+PWMh3EHA2WiyoPHSCiO/MktZaUCKcE0tRrb8QAXfyOUo8\nPRIhRWSoSfHesrB+RjBPk3Tf8XvCb1ZwUVpPMu4cHXPU1+xpwRpLYeKRdaguNRXBu6rIbekX/cDY\n+RgSe4KkvNWgymbymWHkd6YCdodpnOgHMYIg6Fm9LmVKqU8ppQ4rpd5WSv3mJX/wPq2jnVSspRCr\nUW5JMEgw7I2rm1DNYGYjUUeidglbdf0knF85LIhna+iCzGKsscbLKC9B6/78zhRKd9eDUvjBDHTS\nOB4rmEHDyzXrFD0owFS6WxUqcHFZP789i+zzC1AfiMG/sSSJ2/zuOPI70nAmfNgPHwm20dMQtZGw\ntQwsorSyhfpgTLiHqL0mwPvkt2eD/IflojmksfQe4jVix8hqvwxS9Pq8QFSgRvJX+d1xVNY2iPkg\nNBZ2+jyBMPAzt7UXjYIrJHTVIS+SgE/ut6kNaV0RlWFPKm3Fe4kaxevR0IZqJ78tT31xhioGgCy/\nuRfPLpNTqa9uRHiYACCzK/hbQKOPHUXjsaPiZPWqs9KDZ9WJrYAjS+XZOLfj+ggTBCtTM5Nn8oiS\nJXB+dxzaOPnivWVc8wu7hIYYgGCuAKB8k0b1DRqDXXaQ/h4l9OuPjqG4tiaR7HS5xA9sc6BgrJSy\nAPwnECHmDQAeVErdMHeD7HAnBeMPONEN0MPGuYBWIRYpcVeHFPR1FLXwDJcY9wkI+vwCSrAaTAsQ\nJbXnCIhZFVMngv6t+u01KYO3I8bFQjJJ/ig1nXIUGO6/4+ZoZKgXMLMzIdQeAFHCFO+qAhYieCJv\nsCk3ND+omTELMVcJEtnNtpB+kdSQnQmFpulFzR6iwoA+HZqpaxratSKlcz4vzq8clnFz5Gi/5whD\nadrIffEyuD1fVVzdlAe9nVEgvyON+qNjxCf15DK5Hrw8BCD85e7JvIh35vZS64td9CPiDZWVJiLa\nk0Z+d1yuKacJWGYrvz0b6eVzvlNAaSVFrIkJG7FRqpSywjFAzBm1R96FzgZCDfZXX4O2g+9YR4lO\npbTSh33BHPtKKs4UVzfhvjAkeTZ3mUknnDPL6XcTct8lzmjhinKzLejrJiMcXjJBH5ujapvS0LY7\nq9cl7DYAb2ut39FaNwH8fyCdgzmzjm4wVkqVAMwdy9fcWwHAxCW/dWWsk8cGdPb4OnlsALBEaz3/\ncjaglPou6DhnY0kA4ZzBM1rrZ8x2fh7Ap7TW/4v5/y8BuF1r/auXM76wdQA56EXt8JXsvr6UKaV2\nd+r4OnlsQGePr5PHNlemtb6oUlMnWWcv97rWta51ur1v7YL3a10n1bWude1y7FUAS5VSVyul4gA+\nB9I5mDPr9OXeM1d6AJewTh5fJ48N6OzxdfLYOsq01q5S6ldBoioWgG9qrd+4xM/el3V04rxrXeta\n17rLva51rWsdbV0n1bWuda2jrWOd1E8baj/DPr+plBpXSh0IvdevlPp7pdQR82+feV8ppf7IjG+f\nUmpV6DcPm+8fUUo9PN2+PuD4hpRSP1RKvamUekMp9W87ZYxKqaRS6hWl1OtmbP+nef9qpdTLZgx/\nbpKrUEolzP/fNp8Ph7b1JfP+YaXUJy93bKHtWkqpvUqpv+20sXXtIqa17rgXKAF3FMA1AOIAXgdw\nw89gvx8DsArAgdB7vwfgN83fvwng/zZ/fwbA34HYotcAeNm83w8SR+0H0Gf+7puj8Q0AWGX+zgF4\nC9SKcMXHaPaRNX87AF42+/wWgM+Z9/8zgH9t/n4MwH82f38OwJ+bv28w1zsB4GpzH1hzdP5+DcCf\nAfhb8/+OGVv3dZHrdqUHMO2ggDsAfC/0/y8B+NLPaN/DbU7qMIAB8/cACGAKAP8FwIPt3wPwIID/\nEno/8r05HutfA/hEp40RQBrAHgC3g5Dbdvt1BVWD7jB/2+Z7qv1ah793mWMaBLAdwD0A/tbsqyPG\n1n1d/NWpy72rABwL/f+4ee9K2EKtNXN1nALJzgMzj/FnMnazBFkJilg6YoxmOfUjAOMA/h4UaVzQ\nWnMDWHg/Mgbz+SSAeT+tsQH4QwC/AekIxbwOGlvXLmKd6qQ60jRNn1ccs6GUygL4NoB/p7Uuhj+7\nkmPUWnta61tAUcttAEavxDjaTSn1cwDGtdavXemxdO39W6c6qZ861P592Gml1AAAmH/HzfszjfGn\nOnallANyUP9Na725E8eotb4A4IegJVSvUopBw+H9yBjM5z0Azv6UxvZPANyvlBoDdenfA+AbHTK2\nrl3COtVJ/dSh9u/DtgDg6tfDoDwQv/95U0FbA2DSLLm+B2CdUqrPVNnWmfcu25RSCsCzAA5qrX+/\nk8aolJqvlOo1f6dAubKDIGf18zOMjcf88wB+YKLALQA+ZypsVwNYCuCVyxmb1vpLWutBrfUw6F76\ngdb6X3TC2Lo2C7vSSbGZXqDK1FugvMa//xntcxOAkwBaoHzDI6BcxHYARwB8H0C/+a4CkX0dBbAf\nwOrQdn4FwNvm9S/ncHxrQUu5fQB+ZF6f6YQxArgJwF4ztgMAfsu8fw3oQX4bwPMAEub9pPn/2+bz\na0Lb+vdmzIcBfHqOr/HdCKp7HTW27mv6V7ctpmtd61pHW6cu97rWta51DUDXSXWta13rcOs6qa51\nrWsdbV0n1bWuda2jreukuta1rnW0dZ1U17rWtY62rpPqWte61tH2/wMVX6+OQHM2/QAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'SAFE', 'dtype': 'complex', 'nodata': None, 'width': 24178, 'height': 15100, 'count': 2, 'crs': None, 'transform': Affine(1.0, 0.0, 0.0,\n",
      "       0.0, 1.0, 0.0), 'tiled': False}\n",
      "(1, 21013)\n",
      "(5500, 5000)\n",
      "0.0 83.0 5.129790072727273\n"
     ]
    },
    {
     "data": {
      "image/png": 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yut4qi+N2BvFvMnSZUy7Fk0ajuF90X7X7LhFe6xDF6vKTMTrcZB2TUrt/Bt7D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3sblK/QeniCmBDCSM3tDyWgnKQIhK4RyLW2lnZ4bQP6bA7GC92k3LK5Qce6oKuY3UUzivxgs+R8\n5p7TgBXQGMuXb5EZNOuWsnTO+HwZ+AlA0ime6eNrFuQjZ8ayVkyo52cUIcbDGhaP/XDa6/dFda44\n3MSqBagPR2rScZ6l4t4SlKeReYmK1FaN1JcZstL6xTdQ2dyge+0pZF6yEnQuRsUQwGz1Qy0CDofU\nyK0+H95cTtSL43Ur5RNJYWtDm0QywgYKAGIaDdeScyiXIBzUZnQP+POsGrGPOgccVLZWqNTw2aMU\nHfsWjMOXns18Z3aNBWFJjBG6C+ejAKrfAHSD6w/MyC7uHHCkdZwuW/CzFOoTt7gF1TJEcog5gwBi\n0syPh5JHMdI3cyolcky1NRGuqHjMgt/nihM1fve2KK0KW/NcL8nv7o9wQiNugr2R2R5zN5yTyCs1\nH2HB2DEzKJNn7Kr3lRM0HvVhSkOr95Wl5sXXjgdW+cHNj5uJ1zCUIljRhl8K4D32CsypNLybm3Lc\n8Xk8Bn4C5Ci45tL8k3V0LOHMmTOapXuw3EX9321C0LZCipkg0e30HnsFhTGi2eX3A1EHkgUwWn1J\n+THRVdw5EHXuwvlKP6sl5QQoKuf70h5U0PNpOX7WXGw+dJowV3t7xGGyQwx+9g1UNnSIaufzd4gj\nyk1oAquOhlisB8Ia6DELXkHJOuq80hsxrR6z4DkGstNa1jaj53M7ByRy9Ls0vD9aA4AccXy9LoVd\nc1JLaAsn352DBeR+nmDE5pfWJkZV/JvrUeu4qITwTJsestNaJuXjmBPmXzLH81JsNXzC8ywUXuDa\nSnWdB6+gZOHlf/JV+dzUdx3oAwPwjlOK0LqvLHUy5dODap6jeot19xtwDuVowPil6yRaYzaB4lGb\nunTrI66s6jqK/KrbL4juHRBym08mxQ68YjiUHXKHF8ZMGGPFxGvqwwR0ra9WQg1c2dxAc2WSoA9W\nIM4pXbbQ/szGRGpo/MZxFPf1RE2JAXqo3a/fiPzvj6L9yDjyR0OWzZnongFhDW00LddI7vXhDDrd\nkcNq9fmiVszWeHBq0eAtO04AQrnSLnmwTuSJyfSnKOXNvUypHDuLyoYOavefFfAvfw8zj1o1G9b/\n9iLUmYxcu+r2CzKlwMIO1XUetE3X3jmSgrl1EulJgh243x0A/nwI1mtpWPPkBPxMeB9KkdxXY9CH\n+fgY/v8wpQDDDC7r5922q9pJGXPhaESIDyqMmSjuLaGypRYRyT1+Au2h6EIax4siVpmZChIhfbzT\nw6MIAA+M2tCtcHTjqI3MSYVMd7Rg+H2FPb0SPTFTKEARklFTUDVyHs2HTqPx4BQxL57Usjtq04NV\n1fD7XIIwrPPgTfQQx/l9F0gp5lBOukTBWAm1+8/CLgeJGgmroADkUIr7l4UMofR3libP7e9KXNPa\nGiJZiztqHsfQpofGrRHDpzY9wQQVj1moryeGBuYKL/7GswuYQQ2onibag/Qe89U8Cnt6kbmLmDoL\nYwSwNY4Xk1zrsZk/+V4u6IfyVXF59vqO6QR/lXOwcElZLDap/exfJqKxzEJaHSE++XbJk3SWSf8y\nM0HEzjBMayf3vCUjTNQRtoRtwjmUg1UJ0F5O58/ipY01Gl4hRNPPAd7PvYHOoydR3dSRbiR3aFVX\n1E3k38edZTwCvVK7Fkktgemih8xTQ7LLsSpJ3Ir7ehIFxWBNFZXNDXqIXUhEwpEDDx0DyWHNzFQg\nc4LMTeQ99gqcAw4VW8PFygIJC+WinEM5ogAJh3qL+3rEMbolOj7hBmoSDUd1hChduHaS39tDANMV\n0UJtfPIcjU2cs6VGwmkec2cDELWb+LlVNjVJyy/GIspF+foQKeYCQHE0imoCS0uNhGcVi/t6SEkl\n7aK9XAlXeKfLRecWOtbUPEEYKpuaSL8GqavU7j8LPU1jNbU1PtxuA/CTy64dIx+MM6YCSEBFmG0B\nALy1dL7OAYdqeHF2jJ0DiXMWEdVtFyM213ETRs1MFMQ5tfWWAe2bonXCvPCFE1FEJywapYg9gpk2\ntUko+SAURjVbGnZZw+8i0kFtk8PUJtHv5HetpDVmKxlfAqKaG28gQJK//srsWk1qSUwFpN7B3D38\nkDsHnGiyfXMVRj2kxtjXQ4IJkyZqv/5B4cbmoVGAcDULqX8BQL3cTYt7AaK3srWCTjc93Iw4D0yg\nvjo5Z1bZ3IC3LFRdeXEF3FkHfobYIuOYGOaSyu0coG7irRDZ8fnjA6S31h+JgmrTQ+YlC3q6QGmO\np2REiJ1sftdK2HMBObqQaxuIKI79x08QQ8KeXkLXf/4OAJGis65FTio7rZHftZJm5gZ9OEdSaG+u\nSrqa3dVH6QzDPmJMD+y4qyNugn+9vWUeRp0cUGBpqN5G4jozCyUr5yiXhDaB5Bygn4nWAJ974wMh\nt9g5W9hMGw9OwX/8xCJZsTg2imETtfvPJo6leMyC/f0upF+gqCozS+m3rlFt0jmcQfbpVUgNRbCf\neITLEWpjjZZ6VrvkwfhhngQkSgFy3yiKo65uv4DWmS6kZqnux6UEgJxTvBnB+n1LZdec1BJYYCPk\naKKhWQa1Vbc06EYjUkrpdLkSCTUGfRT+8DsAIsyUFbKuVjZ0ZKqfcTsAsVPGQYQMDHVGsxLeV+8r\nC2bFbCr5frZWH42OqM0zSN1yVmpBhbGIasQ/WyQcjRnAOJMmSuI2gUvNzz8HZzSLTpcrD6H15VtQ\n2dSEek8F9R3TiQXM59f+xDlY/3gdpYzDvjCBpj/5qtRqmg+dRu3+sySB9RsvUSfyZRqC1cOkvZfb\nOQDrRB7m0FxE9Lehg8wJU9LVxoNTl5Swx3Q+KZMeSj7F75G+rgPjD1ajM+Qi/4NkM6S67aI4Pu7Q\nFvb0RpCOA07ie/nhzUwFgm3L7e6VSCc/biaUYQCSnDLbtogiOKNZmG1b5LLMNmGkOh+oAOkwipoi\nznh1XYMompe5cH92WsQsikfthNJ0Y1CF9Uo3cd8rWyvUxHgtDbWyhur2CzTrN24if/eENIm06Qm+\nLY74N8smjDeyl5wseKf2NgaM31W7qp0UlIa+joZlWXnYK1Do2/xwNfHSePrFC5vFHQFa+Fxz4Dmq\n4sEcaj9VE5yQfG2sKFvZ1CTgZSgGWh1xkX16lXBYXcrqQ748oEz+Xx1xYb1hw+ytUoS3Y1peU39g\nBrW1FJVxgZzNe4zI/DntYuMITrs0htPZOkeQgbCj5YxmEdhJ+l42Px0O6FoBte5f7ob5pbXkpG+u\nC1VxnHE0fo3jqr8MP+BCNEDRjnYXL63qOg/pT74qXTiGdkjzIYxInIMFZGYJMc/RizuZJHbkTl9t\njS+EcfUd09RE+eJ6wjGVIudT3L9MpLCq9xCYt7XeC7FORnTdDuUk7eLrXh+m+1k8GlHU8BhPdYRq\ni7wxiPbh5+9Aa8AQCAkAWDVNc6ThxuUVXMCke5SQJAs7u6kJO9IXXK1FpGMpTF3DSS2NmeFcW3XE\nhU42g4jGNSbvwzc5M2tKDajz6EnoleQIUk+sRvsmGo9p9ZGTgEO0tYwTyo+byD69iupWD8wkv29V\nB8pT9FB21xMwgnhbnHdUfqDNmZSMOlQ2tqBXtBKv5xTSbCqZ7s/v7heJq3itpbr9AjnMkPcoXbak\nq8QIcAE5hq14vlY8oc/mHM4gqKfR2DYP/645iS7rQz463XQeogAcRhVBniJL749vls/Rpgf7q5FM\nPEDRlXsnbSKchsf/Vjwa0j6fLkCbUfNBoAdbaomZTH85wQMY8Q9AIAkABPbhHEnBL/nI/9zLkg5z\nU8C9syqsn9r0BKbCnGJ83Vrvc5Ng1dCBEsA3kP9PzUVOtTriwvs9gsRwym1+/rnwc40ELQ+vkSgV\nNAicfIk0rn1TdE0XXqOlMGUGl/XzbttV7aT8rJbohxdcbjJS3+Adh/muAaLG8AqGqHhU91FdpvPo\nScHDxDtHQDj/VSfUeRxkySbSQyFeKl68N9s2vN0Rm0Fj0Ef5oa0RMHRTE4jVkVTLQOpU5HGr95Xh\nHCzIQ5LftRLtj5WhGymhM+ZIKDVvo31bRx4Whlfkd61E6onVwvnO1wmgB4pb8PUhXx6GysYWzNXn\nKd0Mnw+JzkJNQedggY65HUZNh1YAlkZu+1gi9fJ/ZpqQ+OOR2AGj8/0MoPLR3GJhzIRfCijCWBVF\nX/GHNC5m0di3BjoVckn1+VH6tKA72C7RfKRZJl4pe+f1ML+0FsVRckytPh/eCh/WG5HDMNu2oP3Z\n0oeKiX+nZoiNk5sxANC4kwrm8ZGZ7P9yjK5FOknE5xWSTg9INmwMPwQnL2D44EkGP+1GysmHkoo8\nV2bXCudLYoar4L1Skn/nT2p4f3PjoteZsxFlC1NmGCxzFVNG4S6PMZZciO2hIIEyZ2qNhQ9OPP1h\ntkQ/7cL4ZZLj5tcPfeoggKiVXdnUpDGLlxRwIZMQkshPmPCHmwnH62c1go3nYX35FhLQDB1Y5kia\nuNzDh4VVnes7ptF59CTyLynUbtfiZFLzkYQ7nzuzVCrfgreCHhZ2OG5BywOifEsUUziSMnKdRUh6\nxnUBVIxO8LMfKtLDlycBgeJRG/poL8x5Uo/muldq3o7oTwCk/2Y5cs+kSYnm08fRXKlk/IajOx6m\nBsjhFI9SahQUqHHSengC/uMnEvfVOpGXjSk1T+ndQvR6nOccoAidz6l4jDT6WP6Lrx3zURWPWcJz\nb9UiAju+t/E1xZtIXMIeiMZl8nt7iOts3paNMy4YesV2Ld1bGgtsDXx4Rnb42hofnUdPJibBzbYt\nCw+I0iwe0o2nS36GEMDBmipRYITdL7scpoeffb/AFYI9N0bF6SMppM8EornH0AI2XmiZmOKucyiH\nTr8B/xSlOpUtNWrRh2oxQLjrMgd7ONzsHHCQ+uNVpBry2CvwHz8hDqZxd1u+HyCUe7tE6GaGC5hN\nhfzufkkJi3tLUL4Fu0zI6Hbo4w1PQfnR9WKYhZ8mUdHiaCpiyJwsUrRpBYtAk7U1Pop7S7AqQcKp\n53etlOJwZWML/kAHeL0LetNZAaPGrx9vMs6hHOo7pqHnM0hN0OfZNZW43jxU7pYUFfvDmUZ2Oo1B\nPwEtMI920TpwOrImmO+puK9HaGScIynpEPJMZ9yq6zxUtlZQva9M+LeYfmJ+gpDslc0NqpvaVCyv\nr1ZolwyJ5vy0i8Ke3sT5OAccSUGFtfT+s6jdrqPRonDNeM7SPbLXnNQSmfVaWoZPeeHV1kYt2fTZ\nIEHmZpwh1HK8kC4jLi2N3PMWzHPkiLgOw5P9qS9+X+oUrEwLALhAJHpuyUhgrhZaXF2muqkD5Wr4\n956R3/FMWfVDkVN1DuUEU1XZ3BB64XjdyqrR+QvpXIhSNh4hlHt1nQfrDUq3rFpAKi/MXfWeCorf\nowHW6vYLQp3ip120S4aMosTPyZxJAe1Iaaa6/QIJCdw8Lw0M51AuEnnYNg/rjIn84chJtc5QtKHt\nkOJkyEd7y7w8nPEaFndYnSMpwYip6xqobGwlIjW27OthujupoDbNwGxpucfOwYIQ5wGhQ7+xArdk\nCB10fdgnSMRoFsHt8xS57O4nyp45oHqPi3Q5iJRgQotDABqDvpxLap4wbG5BEw3yzgHYcwGK+3pQ\nOAGkywHUKK3d1BOrF423VLc0YH5pLcyplIzupMuWKFDnd60UoCs71yu1aywIS2RmxQRCUUuAdhfe\n8etDPnQmzO/NgEjcnhyGrqWEVheg1I4f7nbJQ1DqSPoChMRoZQtGk6KE1Lyd3OUOZ6T93ulyoVyN\ni//mw8g8NYT87n6ii/Uj5HaQDR2IR6BH6yKgT/TAGc3C7TZg+MkHFKYmlHbJS8ghxdMm3l3TZQvO\n4Qx0igruXGcCqK7B6RbX51LzNqWym5riUAp7eiXN8gquPPDMoABQvSeOcwLo+2prfBgVigr8gTaC\n7oD+P+TkqmxuSHrDG4DZAvD/sffuUXZU153w79Tjvu/tbqkldUtqq6ERSEiAG4Qt4k6UsRLZn5XI\nHiUkYcIMM2GGmZDlldieOI4/vnzEcfwlzvKQjDPMDCvKhIQ1SoLDxEoU27IFlqMxAgTiIaHGoqFx\nC6klXanV9123Huf7Y5+9q6pbYAGdoDCctXpJffs+6lad2mefvX+PAp1/dyaS7CTVEDDF/9qGLjor\njZ5ckAAAIABJREFUY1oOAPH0Sw4VUCbdXk0BJ8klRDZMBTWvP4A6lYslh81orAkBjzKX8mEHne1V\n8dNzG0pqVrXNDanxNdaEKWcaHvy+2g4kyAYl4oi2hxXsaZJ8tr3Ylp1LCvw69e9fRG2sLfpR3T7A\nPm74hYM0/yoHMwvmFgO8g4KUUiqnlHpcKfWMUuqIUuo3zeOXKaUeU0q9qJT6C6VUxjyeNb+/aP4+\nnHivXzePv6CU+tAP+uywHNHkT9QRkk7D0d6VBBA8V0CYU8hecQbeWBP+H5PuUGncBkIrxdNLYXkO\n5JA7HsGtammLJydyadyWgFbZU5FiZt8ffgfYNoXmLSdhzVgSdLQdpGgOAGB9v4jm9nOojbWR39Un\nlkdAXAgVFPNBkmNhUF/yOJyGi8xjJdSN5dFc5D0L+anQiU0g9iU4ei9TZsOI+eT5YEslnYFAAsqH\nHQkUhSkbmsGHoz7sqkUCc8MxJmvu+RXvvB4f3SvYqNWS42G+oFwLc0442LDoXPIxHqwFJs/l85mg\nxyTfu7suLrJX9lRioOwij7p264md0BwhvXieA1zL4xofvy+TqpPWZ+WH+lHZWyLO37Wn5Njyk5pc\noA/kJNMsTtqw2wkLs4MZZKsxXKKyr0Bd500tCZRJqewFGUrDsi/u5+0eF5NJeQA+qLW+DsB7AXxY\nKbURwO8CuEdrfQWAGQC3m+ffDmDGPH6PeR6UUlcD+DkA6wB8GMC9SqnXrwIqKuSyA4hf0lBjcY2q\nffsrhFA2bVJ2/Y0+fQQAED02iPq2c2itjvlnQFx8DVaQyH5r15rUas0BgvFNlb0l6OUtZM8YesvO\npSlNcQDiAzd31a9tqcEyK7++8SwyX16NMw9Tu7q2qZXqUgVXtpF/mIr6Sa0nnSMTgu77G+ltkqmB\n5XesgvUXK0jl8+u0PSlO2ARZMG33+vZq3AH9u7gZUZiyBUjovOoKJCB6eqmcM6sJ+P8sLignTUAv\nBC5kFYTsuKkpVen7W3OSAFa35POUhCqEg13aPh+KlQO40yuYJlOY1h2yyZItUSMtp5zMSvWSNpUQ\nQpJcqY91UwGPAcM8ku5CrPYp2VQu5n/Wt1eFz8fdUBZoDEo+usM+otN0bayn+oi2ZCSU2UgCoMAY\nrvLkGNg8pLhvAS3WzXjHZFKaBveKXfOjAXwQ5AwBpM3/kqaAXwGwWSmlzON/rrX2tNYvA3gRwPt+\n0OdHjkaYV/BLVMz1+615NYr69ioyfxXbepV3LULwGzfAMWmyyO4+tkJkdouTtlhaR58+ItbdACSo\nAMbqyDga82Rv3noihgUkQKC5g9nU+8jjhn5iV6nWxU4g/P48WPUz2UXUJ0ri1svbXn7/9pZZsuve\nclzMTlWPR/rqzxD4sXmNjrOd2Yy45SQ/U+ASJmMtPbiUgJH786g+sEEE2eSY7xsmCosJChw8JKPZ\nUyH5FxOAW6uVFPLZHiuJecrMUnbS/mCdaCWG3lQ5mJGAWF8fIFgRyycDRkcrB7Q/WBfNq8L9K0Wm\nBTBa7tX4vNVHfXjruqIewYRvlrJJvj//rkZNDamVyPz259F5fhDO0WLqHHR7fKl9RUeWyPu0/2g9\nrKV0vurbqyhO0jmImlmCjiSy/eZwKGagAC0qtS21eQvgWxnvuJqUUspWSj0NsqX5JoAJAOe11jxz\njwNYYf6/AsAUAJi/zwJYnHz8Aq9JftYdbEJYPRPCbVBtJ39CIzzRM/fpKD6wXPhaXOSsbzsH53NP\norallkr7rc3HBSs1FxTHxU8gDmq0wsZgvGS3x/7T95DEi6kxqLYFPWvkO4bDlN46bwFZRZKL+ip0\nYr2oMC6uN7+ZAEtePSOBrPvsAIqTNtp/uUZW/9pYG2FeCRO/NtaG3YFsBwvPx9+ntrkhAZ7PCzsP\nJ4MlrjlL52FlF9nfOZCGXty/Ep07JhFVIsFlcfDgOlZtC8nfMjKda1Z8DECc3VT256UxIk7UBgZQ\n29BF9fYtcqwqsdvhrKs5TAuN/aW1RCe67TipJvzpe1DZnxcnZICgKgBEYE8v7oqZBUvZcMAsPrA8\n5dlIBxjba9XG2uh+/BiCtU3qrJoAX5iKQaLNW08gN01g0Z6bJlDb1IL1xXVy3JZP8s9OXUudkLej\nyUwwtjCLUtnmWxoKgNIX9/M2j4sKUlrrUGv9XgArQdnPmn+oA9Ja38cmhP1LbPileCtiL62nRO7L\nD/UDY6cIh7Q/j+DKNnVUEu3n5MWO9pKDLMtqJAffKNyy59cGJZKwBcjCqTBFlBq1fTJlrhn2R1Dv\nqcn7debUjHgwtSJbdWAFStDXvI1zv9sn21UAaF9mwTlPx+zf9iqCkkL06SNCPq3sLVFDIK+RJbiW\nQDQq+/NijMmDqTMs4dLtIYkZ1mlKNh04aEd9kfAcGdrBGLGoQm4sbIUeXB6rIiQNB+aizgGD4r6J\ngJGRo2GfcWE925OCOfTv2EPdtweWwz6TcAWeg2cqrDkpC0BpnGSaW++Ns7/KgVwKquI/PIzWapXK\nEMuHXOSO2oQlGzs1TxaFKS1C4Dao9mQxm/Fp7eWKJJMdYikwZzO/fEaYBGr/UlFSAGgRjN4To0uT\nmCrnnqtIOWPbm/YzmDfeUZkUD+Px/giAmwD0KqU4tVgJgB0gXwUwBADm7z0AziYfv8BrLjhUpGIu\n1ICfkuRojoTQV8/IjaSLAbpfuQL1befQuPk06SkdyKWyn/btr9BN+tfLZBXl7ZrXb3S4r55B8ZhO\n1Zf0plOCeAZou6cdCgRBScnWMVnYFKJykopi7JnsL60VNHF91J9nowRA8DqM/en2kOsLT+jihA1v\niUJtc4Mm97dXonGtpoxiwHzWWFs6Z6UHl8rWNT+p0d4yG6sWGH5c89YT6W1dIrsqHtNETv7yajkv\n+R2r4C2jxkRngOp3zAzo9vipDKz1obqQe/n7MYq7vGuR0JP0lbPw1vip4j4AWow2dlIPJQnI7U1N\nUU+V4r2xlmdKEhBDU9wldSmM2x4JC9ZHfegTJeBEMeUsnXJUNhzA8u7eWKfqMAVHlhd2ahEKk9SM\ncRoabkPBuuaMcDa9/gCwKUMS6MSeCjLTppv5vbzMIRU6gpkDsIBbPr1gbjH/0ONiuntLlFK95v95\nAD8O4CgoWP20eVrS/C9pCvjTAB42Nsu7APyc6f5dBmA1gMdf97ODeFKx0y5z5sqHXGlFA0Z4/5+/\nCAASNJqjIfTOy+hmMY7Atc0NOIM10kfKqRQeyj1pySTv9sR8LudVVwT62f8seTPrxIJbenCpTKTS\nuI3m/7xaVt7ahi5NbgPQTEmEHHKR37GK6hWG/DxXyK3b40uWyNZKdG4U2re/QmDMTx2V7UvqXGbj\nmlqU12TffVl8+VkHC4gbDJ0BAkU2h0NY540M7sePyXlxh88iP6kRPb00tRiIKKABjjK6O3c8Qv0m\nH9YX16Xki9XSFioHcrQQrLSQmQZyT5ss4n7KfrGfao4qdMgoYX8eze3nUnAK23PFDiqpKZ7MPgSa\nMkjcQq8/gDsTobndaIltrwrYFzBQgeccyQTZMJWDv9Nw4fdrqiE1HZz9rR+C8728BDht03Xj5oR8\nZxNvk/W1+noyt0UCIsOCf3PrZQsx3kmZ1CCAR5RSzwJ4AsA3tdZ/C+DXAHxSKfUiqOa0wzx/B4DF\n5vFPwvjCa62PgMwCnwfwdQC/pLV+XdBHlNGwGjbKu3tTFkZWzdhe59MnkMXTmsMhMl9ejezpiBQQ\nR32hxxQnbGHUcw2EJ0D72zHlpnyI8FOZWVcca7iVzQGq9OBSUuf8CnH3bM9Fd8uMTCTtAvjsc6nt\nQLSGbo7sU6StJNnKVJlItPcNU6a2fL55ZWV/XiAEKY3wEvG7KgdyYkwxd9S3nRMkNWOLkrU3p+Gi\nO0T+gs2/u4ogABO23LxJbBlblHc2eKivD2C997Sck/Jhwv9wbSYzG0uhdFZaKD/qIvr0EXj9QSzZ\na4w18sfIXaW+PoC3zgTZD1I9hnma2g7kOiRF/wDKPKJ1swh6VIoOk9w+Vg7kCNV+I2XOlf15ZF7I\nSmAtH3JhjZdhfXUFctPECQwHu8K9DIfbkpWq0EH+GOD3WQiLtB0s/cF3pO5W3DkYg1fdNCyCg2US\n6MvXEiagcUmi8LzR3Drkpt7jrQylAGVd3M/bPS6mu/es1nrUGP+t11p/zjz+ktb6fVrrK7TWN2ut\nPfN4x/x+hfn7S4n3+m2t9YjW+iqt9dcu5gDD/hDd9zeQ31dEYYoyKN4eNUdCacMDgPU/huMX/qtJ\ntJfHmZYw0UdC2B+gugrMfc5BpXB9XNdXZ/LIfLdCq6BZzZKtbACCHNZ30lcUCRQzkkRTyTQepYyg\ntqWGwpSOoQxmtecuXZSP4ReV/XmilfB7TWmRswVoC9K+/RUgUFBn8lJoLe/ujTlhOwdRG2tfcCXm\n2pvz9aWwZ4gvp3t92DO23LxJyRE+Ztb9Tko0cxeUb07rL1ZI5y3M+tC98eeHOYXyrkWkm7TSIoNS\nE1xYzrc1FAo2iUfmOJmRRm6ajAwAQU9cZ8rvWEXmGsZ0ATABcaUnKqO6GKC5Mcal1Ud9ROvqcK86\nHW/vR+Ljb46E6G6ZibPSvigl9wLEhqV8HWyPFgZlvnqSDcFb7WQDhbemtU0tgUoUJ23ofLSgelLv\nqML52zWsDgUZrz9Afet5eEutefWb+vaqMP3dj7wkzih+iYi25cOOyHJwhpEsDFcOZoRig2wU64Zv\nqQFDddJKn7bnTQ5GqgMQ6/NkDSdpDlGctEVdNGnRNLfDWDmQSxX9C98oiwxwUKIbCoBkkIWd5rnL\nqdhaG2uLTrsKHXhjTVEBsNZU5UZPdvZ4MFFZn6LVe67rMtVX4mDhrevCGi+L8Btfp+KknXJAYaJ3\nt4f89OrrY9xR4TBhuZjTRppNcTu+tpFkbXIno5RKJTo2dI5sygtPpu3XOUCp0IFfoxoBb8142BPU\nTNB2gOjIkpQ8r9OgLm842EV49/UyH5jyUtlTiUXy2kq2bYXv5pGf1DSPOumCu3hDmuvWWEPb6KQi\nBdezClM6dY2Swn1JIPJCjHfSdu/tG1bMcE+OZL0AoBWneIhUCVgNM6kU0BymOhavbLlpkngt7+5F\nfZSci/HcYhH954nZ/vblKH87l8qgkoEpeySD8kP9sjImn3fmK9fL/4OSSt1IXHxOdonav3oTKQzc\nfJqCWpGyq9y0LZbqHKDtGdpqBB+mTC4ZuOvbzslWMDL3SrePxNeYRMs3DWd9+R2riKi8axHCKsE4\nxLJpyihTGn85Pgdef4DWjzVhBQrtGCoGq6EQlMkezPnrZWjddlwyDM4QuPOZZBLwCPtj8ww6d5YE\ncz6/ejaLriEeJLd1QAyqLT/qQt/5kmScyQWkuYm2i+XdvcBN5DrMixvXtmADmV9/TuZRYUoDNhlq\ntIaUbPXU96hLyjUlNRGbTKTci+8blkUToCw8ufDpUwVkn8mlYDCF+1fGwOPSQktkvivVsiAjcqnu\nwzdBxojcMcE22bqtbUzbZs8d7FAMAJkXsvCGFOpbz5PT7BSpQDLOiqWBg0+8EAMWTV0j851elHf3\nIjPrQi/uor69Cu0S/QGIQX0M2GSIAB8nAORGj8NpUOu++MByFI5p9P7aE9QQ2N0LPLoU3T9dI/Kx\nDFmQLG9DF+VDVqrTlyTC6kza0inzV8ukGxn2hyk1TID0oBihzpZKXOhNEoLLh2jLE5SNl6FPVvJc\nLwEoywuzPsIiKVGw3DIAgTjwzcnngzXoq7dvoXOSqKmxYF/y3LZ+rPmaJO/GmhC5p13oYlxzswIV\ny/B6MRq9vvU8rDbZkvHiBtB3ag6HKHwrhm+oTkxf0jbJ5eh8bN5hewYTVi3JdSocjudh545JciTi\netPeUooY397UFJULHt7PnaLsbV8hVcNbkKHwzunuva1DaTRuPi160ayr0xwJ5YKp0IEKjHeZmXys\n7ZO9d0TAcwBhjpKcNB6tIcq0apsbKEzZEnCSozbWpsAUWqhvPU+a6utJh7s5EkJ1DDE2KaQHAD3x\nlinzGInIsWpmZU9FdMszU2TdXd96Hmr0NNxfeIGeY4Kj1x+g9NWYK5a0F2+OhBJMbc9F5CZsoja1\n0P2pU+Ko0xwOUX7CETNLIK5xFe5fie77G3KuOChwba0+6qeCTOGwDQSWkTgxNlYzFDDjGkucATRv\nPSHXik05Obh2BkL079hDr3HC1Ha58KQrMILC/SvnCcTNu1abWpKB5HesIh10812UT0FdHGTWB8ic\nJHI5i8sxFoq3iUnsGA8VOrAaCYjGAapZtm47Llu02sYOipO2KMhmT0WIipRVIbSAbDJj9CWLy8wa\nT8a6RusGP1WnW9Bh6Yv7eZvHpR2kEmPuJMk+RbUTbQepbZY4upQ0OudKiD59BOXdvbKC8lYid5Do\nCJW9JVT2luRGZHNM9lOT9z3kIjtFQbNyMCO1o+CHCZ1d29BF7r5hedwbJsZ/bUOMx6pvPZ+qrdS2\n1GgST1BRNPldvX5qR9fG2vKeSVKx03ATWxEbhftXInffMNz7V8CtKoFsACYb+V4+/tyNHcCjbKr4\nwHIh7LZuOy5ZUpj1JeB6n9kYn/dJKhBn7x0BmpTBdlfGGLHahq4EzNy0HUvGhA4K969E5lkKju5M\nRFvP02kBwsL9K0nbfDTB69vUQrRuFpV9BWIWsDP0ZLpWmLxejJFzltSBx5fGiPBxJTCH5PmujbXh\nrjslj6l91OAo71pEwTQB/i1O2Cjvz6C7Mp53LEhXfqhfCt0AZWDNW09QXbBNulidOyYRXtlEONWb\navzwee/2+AhzpHM2t9GxUDipf0q0GEUQpktzbFhd0I/92RBRMI5n0b3Ki+kUBhgJ0ERizlt91BfP\nN94uOQ0Xhacd8oS7dwTZ618VraLOSouwPnmd6sYRncFCZiaCXbUQ9UXzCt1OI97KlMbtCxY2ky14\n7nIBVC/irQyvoGHWl/dhF5POQCii//x+uWMK3nD0uiur03AR5jUyMxBhPKFemBtOL+6isRYofzuH\n2uYGKUd+axnsK87JTa1CQsZzPauzOhZi42Ov7KnAu75F23CbHFN4e5isl809R9l7R+B8aBJWjTp7\nzY0J8m7oIP/AACLPkY5nYcoGHh4U1DtA2/DaWJuCQyKI5+4bltdlZl1Y7ShtLZaYP3J8Dy6FDmzY\ny2flWBipzgh6IEavFyds4ZIWdw4KIVpqh547r+FSGrdhVQlom9+xSnYHyfdKusTIuTJ1wNK4Dfe9\nLz6ptd7wmhf/Isa1S5bov/noRy/qucM7drzlz3sr45LOpIJ6DjofiVtHZ4C2JMVJWzIUAFCLqFbQ\nXm0m35YanHuuigutLtA0WCfvzgmgaV4XKIR5Db9fw6pZJKDPHbBvLUOY1+Qw8uwyhHsILM/peGV/\nPlVI5roRPwcwUjBH7Vh5c09FdI2CEt0ArHrArH6+icOsD+c8ZQdJ5QOCRGiEOUK6M9iRt028BWRX\nHZa2SRajmxt9qqcZzXbOfCyfVv2U0ulRSOscgZXaJnJwrW2pwW5reMuoyK3tgDJHlvo1HdYk+Jav\nRXOEkOrB5V5cQ/riOqqpfewUOndMklciGytsOkkdP9P8YBG+9pbZVC3Svy0mMzA9h68dy9lwFsNS\nv42bT6N5y0noCgWo7L0jci6YnlWciHl6zZFYu8r/SJUWglG6rskGBX8GQH6NtTGSi44++qrUGvm9\nwruvJ4rSHLNTr5+C/sJ1+N4tnC/IsJe00FgTpnSdzzwzLBmNZBKm7lE4bCMcp4kXtGlLwJPFnYli\n7zsDyKuvpzpPUCJog3WkRyZW89YTIunauo1UBkrjtL0Ic8DJh24AALR/9SYEZZWqjbGsRm1jJ03l\nWBT/P3vvCFrrw5RJAYvrAYRr0jmkiKc8VNOB8z8Jf9T9qVOiSFDZV0D50TTQM5l1JB1xWR89+Vyr\nmRZzAyBSI9mqg87aMNXOL0zZ0sw4/19vQFDy5VplfvwlKYCrU9S1SgZKDjTZe0dQ3t2bqqNFnz6C\n4qQtGSjO5URJojkcwjkP0UjnABqUYp5g9t6RlK67nDffoPafVcQbNEJ3dkfLuS9O2oj6DMj3I5Py\n2qSD8IXMZblGCRgBwn0jANI6WZW9pfh9hkOEOcJ05f5khQTY7H98jpgKxoAjM+vKNayP+vO2h29l\nLGSQMiIEh5RSf2t+v6De3JsZl3SQYnPQMK+kDrH0lx6lC2poLswBK07YCF/uExxS4V8+nyq+2h2i\naTDimoGY3uZZkleZsCVglB5cGge0PTHdkAulkaNR/E/7kZl1seT2x1E8ZKPwl0tii6UttdRnV/YV\nYH9prWwvCvevhHfnBLKnIngvJCRmknIdt5wEEosm30ROw0UwsRj+ba8az7soBrdu9BFO9cJqK2Tv\nHRHoAEBZnVPXImEMANlncgh6lBxTdzAW8Kvsz6e+Q/ZFJwVUZeoRb1cyX3icOIRfo/Ol2pZw05pj\nnlwrgIrZnYEQmRcz8O6cEPvz5Ha6ORzCmjEMgk1tEcmzPRdWNSOFcJY04XPnNFzKljG/0Fz4bl6y\nHRaUgw1pKgCANWOJKufc7b0KyYgh+zPfI6/GhF9gZpb4f+yCzcqk7LCcmY2lbXiwrA+fQ4Z5cLYU\nlHxEdlxnLU7YCG+cwYIMteCWVr8MosvxeC29uTc8LukgBaVR+98jyDxWQufRVch8eTVpVQ+EQnPR\nv7uWBOxHQrR+5gzVbz5/HVQX6K4MpQDbHA6Re85B54vXUhDaRW4cLADXHAnlZmpuP0er1mEH7oo4\nc+AgwzcyO/PCs9A5YXS6zeTl59qeCwQWCmtiDSfnKgJWhkXa8jAvTLhb5n0YdJgcQclHuHVaAIi8\njVGhg8IxDbyPjA54K6VtkiXW2RDN/3yduCwDSGmtW4UuZRnGdbf52HDKHCEJ8agcyKXssSoHcnDu\nuYokkI39eH3UJ2pSNRJxO20H0qW1PVfoH3MHdzQ5+BaOEV0nv6uPCvpjbclQdSZGulcOZpD7WtpE\nNJkp1rbURACxciAH77evhXpiMezZeBvaHdKCLp9LcrYCZXBSIVrrSdOcwZ/dHh9WTwf2tadSAnV8\nPBzgy7sWSYYE0LwsjVMNqnQU80ZQ8lHfXpXFNcwvDF5qIQvnSqmVALYC+CPzu8Jr68294XFJBykV\nKeCzzyG8to7wjkk4H3spRbIFQMHKLHhugwq26q5nEPQoaEel6jm1sTaczz1J3aNtFIjYPKFyIIfI\n3DTBb10LgIJGtK6eUjIo7+6VVjVAK3dtUwvObceQu2+Yul0sJ7NzEO5MBN0hE092I2GfNfami66d\nBQCpc+Wmqe7RnSTKCKORk8huIAZB5l+OUH7CQXtYIfjG8Lzz6P5dP/zBCM7nnqTP2Rd3RtkphzNL\nq0HCcM5tx5DfsUpssWzPhd2hLJMDBG+No6kK8leeIk2uTTEpuvvxY+gMWuj2WSjv7oX3mY1xTeUP\nr4B3RSDnks+x03Bl+14+RNgjdTaTyn5L47YEstZQKBiiqESUleA3bpBjSMoUV/aWUltxmQu8Df3C\nNcg+Q3robHcPwATbGHrBuCptB1JTzE3bwKIOWlcDenkrVne9+TTKh2Lgbn3bOVEo5cf4nNTXp40f\neAtY3DkoW24uKbz18YZqUv2s8WZ+7pjzZr8P4NMAOO1ajNfWm3vD45Lu7o3ekNXf/vs4IHDA4a4J\nd714QqQMDi4wLtRtudDfbM9F5GjpagFIddjmDu6ARY7+gc8BkHpe8thf6/gu9Hiy6zb38eR5AmLJ\nD/6cCx3b6x138m8qdGC31bzW+A96nwt9nwtdvwtdB37O3ON4re/yWt/zQsc699pd6H0v9nv9oM95\nrff/QZ/FXVT+W1/u5bfcbbtu6WL9tZ/ZelHPXfFf/uw1P08p9RMAPqK1vlMp9aMA/iOAfw3ggNnq\nQSk1BOBrWuv1b+ZYL+lMCgAyM2ab8KySlUd0fB41/KajEDnYCw2urYRZX8TzeQVnoTQuzAoR1njQ\nJR09+O9JVr3tuShM6hT/K/XZjFSfoc+3O0SxYLE7/n58fMWJRMfOfB/x/zsQg5+yZzRlULsWpbYl\nyeCX1ONmlDQP4Svy97hAMZhfl6z3MJI9WWDnYHjB77+vkLaVNzpMLN0LAOW9hdQ5YOsoPqeZWTrH\nuWlbtm/yWrNV5mvM35OJy/z8uSwEZhWUnlXyOaxrxRmrqBBMktghq5/ymFuYZ+Bw0tI+MxP76gFA\n6au9QtOp7CtQVrivQFnepC0LmZDLDVWHv1vymr3VsUDbvQ8A2KaUmgTw56Bt3h/gtfXm3vC45IOU\n3nkZAEIuswIkYKgwrBe0PqDC71UeCZiN2ynQmzcc2zYJovr2V8iEwA6gnu8TjFSUmAPFSRvWN6hD\nxajhbo+ftvjen4U9mUflQE7oJjyRyrsWxVQKV8G55yqqXRwpi9gdb90kANlUHwJiXhqbf7J2NkCc\ntu4g2SbljqYnrtNwUd6fkdZ9cmKz/bz1VB/U81QvKT2rULjsDICYq5cc3hDdOLnjkQR5NnwAgNzJ\nuLjK54mHXtyVhkJm1oVyTY1wIwVk+0tr0Xl/OxWAgThgA0BmTx/w6DI4f79YvA/5WOvbzqF82EF3\nJTkJc7CqbzsH/dCwsBOSKgOVAznqtu0rCPCysqcCtdxokJt6mm4YNYOREN0+C/roIoS5OOAl5Wkq\neyqiqGp3tBTdnYaGXtKWLWp9exWFSU1KDsUA7css6IoPvaxDHNP9pNhQfE7BOWVDz3FrWTAIwgIV\nzrXWv661Xqm1HgYZrTystf55vLbe3Bs/1Et9u/fId+OtbBJQ59xzlagVlsZt6ENL0bzlZKx+acCQ\nblXBG1LIHY3xLZUDOUS9Ppqr45pVbtqG3TH65MZJtnKQUMXS2jaAOgbu8chWHTgNDWvGgvJstNaH\nqe1QYcqG81JWgk5u2kbmuA39/QpUqSudu2SqX5iy4ZyKEdvZqkMoaV+hPkp1uaQBZkoGdw7aNgSL\nAAAgAElEQVSwsbK3hGhFWyY4gxEZ4DkXdGl7LooHXISrPPh9FjInI3ltccKWgJ783NK4DTVRRjRT\ngA6tFPQhCSTl9+cMj79HeXcv4Dmp88GjOGHDPp6dR2fiz2WMmtcfENVoUxvKTwvE5aZtuFUF/7sr\nhUcXlHxyrf5CWnuRj1d//jqou55JgS7lOXPOWfJYkiM3bUM/NCwAYiC9Vc9WHThfXyrmHiIbNGWL\n1BDPiWzVQfZIBvaHjrz17d6yxXrPv/iBrnIAgIHf33lRn8fbPa31TyilLgdlVosAHAJwK8s5vdFx\nSWdSdsNCZU8l1ZqX8e9Iw8n2XFjfL8JeTHIlndVxd6YzECJ8coCQ24ksRJ/N0QqWSJ+TLXC/jzh+\n3nAEv49OESlHmm7OLSdT5gXZydiTrraxgzCvxacPoOIua3/zZ9U2dNH9kfPoXtuUjKH8BFFxnHuu\ngv1sGZglB5PSuA23SmL9M//rWlQO5FIuuEI0fmA5yaEYG3AeHKC44aBPGMMKEzii8fnUjNqmFlpD\nymig03tV9ufh91silZts3TfWEIYqKUUD0I117IsfShWE7Q5SagClB5cSZ3FOgBIhuG8MQ1fSDi7J\nz63sLaHbZ5oY5vXM90xeX+1oQaEL+foLj8ux8BaNO4XqrmcAUNatQkecprNVJxWgBEBrqJWZ2bhQ\n3hkI4d05gWiK8FLl3b0SoMqHHeidl8WwmSfj72Y/W05t0SsHcsh8p/eCgfrNDPUPAObUWn9ba/0T\n5v8X1Jt7M+OSDlJhKRIJDID4duXdvQQiNBZWxQPUUUq6DLOVeG7ahrPuNJrDIbJ7e2IJlk1Nqg95\nLoJes8VJdPByR8m6PKleGbxgQKIlnyyXhkOh3IjcixnlQxb5yB0it9/gN24Q7W8ACO++XpxMOgMh\nWsO0YnaHCRAYfOIF1LeeJz3yPT04+yc3ihW887knZUVOaZBP2IIWtz0X3lJLvpPgbgzPsPsj58lM\nggnKiawwGQS4zhf2hyLHy7CL2oauKCQwjsf2SO7EvjbmwHn9AZZdeRzROdJiKu9aJMDH4s5BqNAh\nPmTCKZotr4Qx8PPfF6Iy38hMwgVIEjozA2RmIpQf6petYmNNiPB/r5RzxedQrtNh4hPy/GI4QG7a\nEKf35wUbBdAW0usPkJmm79z97PsErwdAzCy6Pb6cm+IDy0li+Ax1Abvvb6C4P0v2WusDdD9+DCok\nUG0yAEXr6sjMkmZZadyGPptDeOPMBVVX39R4p7nFvJ2DC6O258K7rgMsbxJOxRBN9dkCae9MxbUq\ngFZD96SFqGTau9uryJ7RKNy/UlbRMOvD/tYytIZCWE/1Sb2ltrGTsgNnyyz7eAbOPVelNLPDPAnt\nz62BOQ0XndVEq+HWvwia3f0UwSGMphSvllF+/uWIrp/Bov/wBAAKwPkdq1DZVyBD0G+kbbNU6JC0\n7ckIuePpwFl8YLlYsHv9gSG9xhOQi8wiq2IK68HlHvn+cX3sUAxg5N/r26vksnPv5UAQCxOyEKD7\nK8+TuWiFZHjZ/ML/SJVML768OoXMby9XKe5a97+sETKvAHdzSIFLvf4A1oHFYoLqLaNzyfU9v89C\nZV9BUOxOg2SK7RtipnFumnh1mRey6KwNEawiXbJgbROloxAoQXcAwGWz6P3FJ4HPPpdqGIjM8eev\no2t9xTmiD/3kJACS+glH2nJtuIHQGlbIVsl8I3vvCFqmROH1B4iKJCsT5lVqQXmr4x0jH/x2DhUq\ntD9ImuBh1ocKNNS5LMoP9QtgkoFukUsdu8x3K7HfW3+UqhF0BkJEHzoZF2RnXdiDNanh1LeeR3GC\n0MNJO/CovysW3vrOl6TIW3xguTj+8s1dPkz1qaRdN0Ddp2TQ4OKt1XxtZrsKHdgTedFuz03b4trc\nvv2VVN3HabjInyCVBoQAHl2WFnozN0+wLD4GlWDsdAZidxSuyfkPD8cZgVnB66O+ABiTHb769iqC\nT7yAcEn8na3/MQznPDkX220tgSg7NpkqzifNMJJcRcluP/sc3Cp5C9oemTXwd+YOXHILPPv1teIU\nwzc1b2H5GhSeJj6h3x8Hauc8Yelqm1qpbKg1RBSc+qi5XrOabL72G5dnk0nyQqlCB+quZ+h3jzIy\nq0ldvvr2KuzjGTmfHPytwMgeezay605S4Fpviu+vuub76nlNjbcy3uXuLcRQ6eKnt0QRStqJyNE4\nUavy+yy0b38F9W3n4FYjyqLMvZ/sbiVvxm6PDz2bSxWZmyMhcgdjTWwA8PsV9LIOKgdyCLM+zv0d\ntZrVhlMojc/vhjWHQ9hVK7W18B4fSj0n6qUtqfUqATjLu3uRMV0ybkcz+ZfPgZ12dEp10jJ/tQz2\noR75DuGPnYJ7krabyXoQa2cBRHZ1Gq60xEvj1OViFUjvzglBRydXcObo5R8YQFQhrabipEGqj6QL\nx52VFsI/ugL2rBLJnPqoL4TazkpLbnLri+sQ5TWKOwdRPuxIQCnv7kV7WJHDT9ZH/uUIkWNkTEz3\ni7fjAKDueibVgWXLKQFHTtgIV3ZhPdsT+wIeduZtB4G0JRkXxpVP15jPCc+f1lCY8lBsDYWoj3UB\nR9MW2Wxna2NtARGLPdiuPiJrb27EDRYDi6lt7AjKv/kXV2NBhgKUFV3Uz9s9LukgpecIbpX3Z2gC\nDNWh7YA6U7t74TTSAvXMrLcNYVanu9uo7KnEVlk3zkiw4y1ecGU7FXi8/gB4qSKZgPO5J1HetUjs\nr5I3Jtc36qM+svuLMgmf33M9ihO2BIP2ZRbpiBs+lyp1JevTHbpB+aaS2tFwmGrVJ4vUrduOp7ah\ndhPig+ddR8ctCg8TRcIitRUK40okkvlGCv9sRDKUoBSbSzgNVzLN1lCI+stLySVlYwd21RazBnFc\nNvZXjeleykS2nicT0ISxAtM+yofIRaY5EsK65oxkOE7DBVY10uDR5xZLIGCZFuv9FDAqe0spzqF0\n6nK0yAFUiG8NKdQPr6DvtHMQUV5TJr4nTatJZr98fRprSD20OGkTxcrUSQGSP2Yun+1RwAqX+KmF\nIjlXGH2u1p5D8VBcsihMpTuFUof8hefnvc+bGxrKurift3tc0kHKrtkp4GRtU8vokic0vQ05lQev\nhLlpG61hheIxneKZAcThaqwh9c7WUAi8RIVR3uK1huLAwwGNAwB322DwIxzs5oLsVEhUGL653vvv\nvgmdMV0ml7YfDCgtTFHRlLFWrR+jTiXbePm/eZ3YRNVvDMRX7kKDAYApp5ojdNPmTMbSeWEZGmsM\nTGI2g9rmhtTjbI+0wXk1Z2BlZV8BQYkyoNxzDuwvrYV991MIShbhwTZ0JQgA6Swv/3uPyv/L+zNw\n6loAnTzaq2OsGDvOJFvySapL4+bTckwA0HxxGazzrpB4GbaRm7YR9tBN1kmQp52+FtwGUa4Kh0n7\nvjlCZOlk15TPx9zzq0KHNKeGDVVneROqGzsa29MulK8FVd8cCVF77DJ5j6R0MNfI6utpe1c5kEO3\njxQpeO4nsznn7xdjwca7hfO3PnQ+Qm2snWqnd9/fIBOFhG13svaS3Utbns4AkWutV+lC505G8yZc\nd4CKoVEtJzfp3BHN5tD5Gvnqkf+fT7pIqygDSq6unPGUxm2UH3XjgAaI2w0Qy/FGHyUQLtc+woEY\n6Q7EW13nc0+iMxCi+5UriFT811dSATnhzAJQ3Wuuk7IKHUTLqPtrVdpkdnkjWXdV9pZEMvn8E5cL\ndCOJcG+siZ1cGBhaG2sLyz8o+RLAGS3dGQjnQREAyhB0IyOLQW1TC5WD5GYTlAi+oUIKgNlqbPzg\nDSlk/+9nib+4axEq+wpw6jqu59z5vXnuxpzBssJmKtO+5ST8EqHIgxVxsOTsOWn0EWZJHiXJFdR2\ngNZTtH0vH3IRVSKxnWfepdefpsBkyh1ZQDsbPHTeT9vR5jBJEZGpbBSbgXyvR7aszWGSkc7MuvOC\n6Jsd/5SUOS/tIHWWakPWgcXI71glZpyqY6W2Nty1AgBcNktSv/eSng+L2zeHw3nWU90+QJ3Jw/vY\nmZROUtKSqbtlBm6FagKc4UR5Ldu6JO1C27T9bKwJ0bwxiAOaGYVjOpVhZF+c42IyRxqEBz8n89Pk\n0Jz7qe/J85kgDAA6F0mHiIN4+YlYn5sNDCLjfcdbzdZQCPvup2LlSYNNYziDILyP6ddEPKvQQeTo\nODjMMXvgZkR963lB0Jcf6k9BGQCCPRQ/8DKRhs01BCjj8PoDtD9YR21TSwIdKajG7jd8PYKSLyJ+\nFxp2W6HbR3XLzntNAHvcSD+b9+Z/69urCCf7cPaOH4+dhA2QuD7qI+hRsL+0lpRLB8gLsrhzMNX1\ni/7NpPyfYTI8WkOUkfGOoPTgUjS2xngxZi6IvtZCDAUyCL2In7d7XNJByip1kfnyagQ/fDaViidl\ngwFIpy/z5dUETDxRhPORydQE5pGf1LDMQ4WdSxGeLUK7hpNnthvuI4tQGjcOvCcjsjA/UULwe1S0\n5O5SturAapPBZWcgLc4nxOROfIq9IQV7aYOKzLt7acVdQvZPc2khfDNkvryaMpNPjqE1FCK/Y5UU\ntjkg8mgPK2QnLYSDXTS21sS8AKCJHznkC9dcnZ55yUBEXywA/NgRhm+oJGp8brOAgYcCkpyh1/E5\nEWWICRttgwtrv0JbF97OcNteVBb+1ffltZ0B2gplT8XiheWH+mVbyN+RxeJy0/brypqw8UFngAr4\nKnRgXUPUIM5+k91La90ZXPYLf4+MCRS5+4bjLvGUQvipo6ht6NJ59kjJQXBWhn+XDEy88Niem1KE\nBWg7W0iYgdS3nUNhyn7NRezNDb3QelL/YOOSDlJRXiN7+ZlY15zJurMuwruvl2wqs4cwTu2zZXg/\nWQVCi+gck/GqXj7sIHffMMmvGMtr/yfPoHnLSeRfjqD8uEWurpiRbSKjiOvbq1B3PQOnQXSb4iSh\nwNvDCq0PEUwiqFioHYpVGwCIUmjlYIYmaaWLMEfbP9tzYQUKQY9C54l4+1A+5MLuUFbC7flldzwq\nAmmyws8xvQxKPjqrtehItSeWyHtag3Xk/mQFrFPZNM2iShlQ8gZit5XyrkWE8fnSWqnNcUCxnumL\nLbruG55XI0vW93iwJlLWOB8Hn3iBaCzm5uO2PaP5JeCZAFq/MUD07diLTi2lzDY/ScadvIhx8GFZ\nk+Rx8ygfIq/DJGGZOYp87fMntFhV1Ud9NEdDhH8zDMA4TdsGbzWaMJ69+TT02GkC1o61qUM7R0sd\nMLit/XlEjo63ioZXCUAAvgAF8aBiXbCT/JbGuzWptz5UCOgRCgCVgxnZo0d/fDnsTKz10942A7W4\nYwq5ceqvsyZ9/movvCEF98ZXUdw5KI65PBprQmSm4yJpezVtE8uHnVRRHiBbIqbQSPEZdMN3e3yo\nu54hO62qWUGNgWdwZCmKEzbO/d06dAZCcUVmcm728jMIfuMGRBVS2tSuQn5SU9b1UL9xkLFk5S3u\nHJQbI7lltNqR4MiSW5LaWBvWjx6H/+ISyTy8fkLVJ7clSe9CXsHDTx1F9J4mbWNNQImunxFX5s4d\nkwhP9Mh7cFbIGQl3wQrjBtrwvwiS0PjlH0Hrh9qo7KnIMdmz5HnnPt4jwcmtUvbkNhTcq06bueHA\nWxNneAI2fahfgqcg1nNkrslQh8rBDNS5LPK/9yjCYkzRkUBdjANs7sElYoZROKbh3TmByt4SwTKG\n4+vfvOWkENvZcYivC3f2KgeJ5hTeTcaxtbG21PEAIHN9vDUtTGpR6GBRvaAXsGoLdMuqfzo1qUue\nYPzkb11FtIdZspSKSjHDnJ1CeDCVIlt10O2j1dFpcFaixcWE8S48OYV2cwGXDiDW82GiKd/I+V19\nUGvPCdcr7COKS/mQhbAvRGelNY/o+oP0hJLaQdbvr0b4qaNUnO2LgBDzcEg85mpOJd1QClN2KqO5\nEDkWoK1Y0KPgNDSCEoELvc9sRHHZjAQ8p+GiMK6EeJ3swPFnChF7kpDh9iNLBVOU/Ozy7l7JBpPk\n8SSJ+UIjN23DPWkBL/cIRqmyr4DaJhKbcxsqlRkmz035kIuwP0qdDzn391wFy4ng/uRLsKsxuZth\nDEzM5sHXUqAiu1fCGZhF7dAqqLueEXcddyaC1VCp7XJysIMMm0rYMzbC/nAeY8B6Lwn0LYSe1OiK\nXr3vF3/4op7b8//87btuMa83wpPUqs09lpd0vLI/LwhwHhxgctN2KjvImIKrPZEnJcUdq2I8kqMF\nfgDQynshvZ7ccSpIhz9tiKDHqL3cuPk0dQhNC563WfVR6va4M1EqQJXG7dcNUOXdvcieoszKblOd\no7xrEWVW+wZTLsxscMnSKmLYabSoahu61AU1MIvk99L5SMjGDNas7KmQimWbFgFxXf6dAxKgAODs\nZz6A2oZYfsX+0/fEX6CTljBpDofIHLdTQND6qI/iA8upnvbhhmwTuz2xXG9zJAZVlh/qlxoUD+c8\nvU8ShBuuMh3MQME128kkXorPP2PHpn5uvp1T8IkX0P34MXj9VmqLljRYSJ7Hwk46h1GeJK3bt79C\nROm7nkFu2ob/zCARtJ/qmxegKntLUr5g37/mCAFFaxu6wjLg7mfz1hMy/xdivNvdW8AR/tgp5Hes\nitnxIVC/yUd0roDw7utJC2hfgQrQZisHxKRh5xTdxN51HWrzDszK5PD7lUj32p4rllJJOAITVb3+\nYF4BubI/DxVo+NMU6DitdxtUNLUfWZqivDTWGM2jvaVUDYefU996XjIILuazXVfrtuPU9TEBtjlC\nx9RerqD8uF7nrEu0qF+qyLEmIRI6R7UTlsHt9vhyfltDYQrWMFfYbend+1O/Z276PmzPFSNTPmf8\n2sbDq1F6cGmqMNy89YRY3Nc2teTGzyR0qZyawQ9tr5I0zbgtKg+NNWEKPwcQDgqghkVzhLJkwUuZ\n921fFtNp1v3iNwV0yYGdBy8sKnTkuDlQitzN3hIwRkTq7EuJ82GCq/3IUpF36U7TVpixUdmqQ0yC\nVfNrVXO7p81rNJyXqMvd7Vlot5h3WHfvYi1rlFJZ8/uL5u/Diff4dfP4C0qpHyhmY3UULB/xxe7x\nYZ8k1Lk1VIN991MkpZKlSdj6GerOMCKaV0SdAcKcQuPm0wivrcvN5PUHklKHWR/ei1RoThakuVuU\nHLlp050zdumZq06LrVTzk2PE+3pvgOYtJ1P6QAB1arj1zxOfmf184xWmbORORvME9oKZggSa8qEY\n0Wy9mkf9JgMrSGzxuLHAEiMZYzTC8jPdHj9lCsDvyyaUAMTRmEfmO2k8WX3Uh1OPV9vK/jzCovk3\npxB9+gh1q45poeCkXHy+tFaCh06c5siF1H548Uh5Aho9+vyOVeJkXJiyRTk1mbl0Bi2qad1P2mRB\nyUe40oNnzDhbtx2/YIeVMWtcHAfo2lf2luBd15G5Ux/1BS7BAoTJ7DGzkk58OEKyPNlnjOV7mHYI\nSl6H/L4iKgdyZK5h3KYzs24qe3xLQ70zu3sXa1lzO4AZ8/g95nlQSl0NUu5bB+DDAO5VSr1uqyLK\n6Xm1g9pYmzhOZrJ2ewgEWJiKdcM54+EAYR1YjMJjtBq1l1OWk7tvmIigYcyfcioUEJIrHtsM8eBi\neqqz5jloXKvJx+4/7Uf5ULpOUxqnmhAXtHPTtnTQCvevRH2sS7WSwS6cBgVYvgFK47Zkdp07JqXO\nVh+NQZedDR60HUggFCS9GarYlSI5EGcpAG2Hsy8ZNU9jEQ7EK3qY9dH+1ZvirclH6Xsnb2ivP0D+\nWHx9OgO0fdEuROlBdSxol9ySveEYWGv/2xdjU9BEUdhbolDb3ECYI4ZAEt4BQLBe0UdflXPVGqIC\nc/PGIJVpFQ/Q+cpcS4oHtufC77dSPoFWzZJGCz+HR/L6dwZC6NncPLus7LVUCuB5mQw+vFiERVr0\neJGa60Xol6ibWD7kEpNibQhviBxquj3+PAXWtzzeSd29N2hZ81HzO8zfN5vnfxTAn2utPa31ywBe\nBPC+1z24Dq3iUp/gzGN3Tyq4ZO8dkYnKnKnU+wzWUdvcoG7JswrwbHTumERmhnTSpUNjgH/NTSRn\nO7fzpUIHjbU0gcuHY/eW2pYa4YRcOl6+0Rm/JW4g26vo9vg4+8Wb5D3ta0/RZ7jUiuaaFECfowIF\nb4xa7dIxNM7BgJFENkHZW2oRHSdhjZWtOvCu66ByMCPb0WQAaw0ZB+GSj6hF3afkFs/2XJQ+f1Ay\nNVkIhuMtUTLTSF4n0nTXgoIPsz7qP9qB9dUVovnE2RyfN/7s8hOOvAdA8A4grjMxOdrrn69tz7ZX\nfCzc+WutJ3qUUyfIReVADlaT6nhRJSLT1Yb7moYY5cMOFfsT2QxnUP4LS+XzWCtKXmf01pkgrkIn\nFXRlXk9RrZMljcMcdbLLu3tR3rUI4YC/cBAEhXccd++NWNasADAFAObvs+b58vgFXnPBEeXoBHV7\nCLMTjfej+MBytLfMUlfFsMS9Oydke9LtSW8/ACCYINBgYVLTzVQMZFvDHZXMrCs3S5j1UyBIHtoO\nEP1/62hrOKQu2CED4ppMssOUmXVlMg5ufRoARBEAMDrtU9TV4m5RmPURPjkQB6H+GHMTZmkiJ1d/\nNhRIivcLBWVDF9GKdtqe63BM4wGMa7NLr+GAFmZ9FA+4Yp3OdRy/j7ZQpa+XUDicsKMC0Hr4Cslk\ntJvW2rLbCu46quVYxgzhQs4z/L1sz4X+/HXI7itTBp3wPuTsTc9SVlfZW5pnN99ebb7r7l7kj1HT\nw+snc4faxg4aa0i+x6pZgGej8FhWzi3Xf8oP9RMkw5CkeRTuX4nMyhmS+jUlCZ2PEObSQFBvU51g\nLzmaT9oOcOaem0STvT7qG95f3BjqDFoofKuIzE+/iPrW86hvO0cqCK/T9XzD452SSRnLmtNa6yf/\nEY4HSqk72N+reiahLrDtHDonetG89YRM6mSRMal0wLypyp4KbevGqD7g7SIOXmt9iPrX1gCIO0m5\nowQenCtVzAVmgCa6uusZonzMYcvn7hsW3qB9MpMqvpcPO1C+RutqoPvZ90m6H2b9VH2GM5wkDMK5\nilbt4iQRh8uHCBmfrTrw/+ZyqHO0jS0fiu3V5UY2CG6uMSVxTrlpG+o8fS+/P647hVnKMNi2PVt1\noBd3BVjKmJ38rj5E4/2kIGoCen6S6k7FTROSySS1tgDAaUSAQ4tLI2FQwxkUB1XlUcMjcjTUXc+g\nvu0cHZsJQqUHl8YB3mynapsbcm55+6tdyjbrW89D5yNSBJ2wU1teyzdYss0NoKcrelnRdTPkAt3T\niSVdEoXr1m3H0f2hmmR5AKDalvBGc9N2KtNVu1cK4LTnc48BdoTahi4qBzMoPkG657lpG1G/yTq3\nnqcyQcKtZ26n882PhZcP/ocaF5NJfQBvzLLmVQBDAGD+3gPgbPLxC7xGhtb6Pq31Bq31hmX5mGdX\n2VMRUisjkiv7CjHrfziUtF+FDkq7K4Adwd4yBTy+lBQQ73yJaiSPZVH4lyR5Udw5KHUtp+Ei7NGo\nHMyIeqTTIPxOturIKqrtQCYbb7v8216VbUDnGlpxGUFcXx/AfWQRCs8D+f/3EAC6KcsP9UPnEk4r\nk8SwT94IHACaw+TG7A9G0KcLxG1bMQM4kWB/YJIkPme5/3BE3sc6lUVhyhaJ385ACG00rTJT6eJ4\nd5gyidqmlmirByUyzCxO0ns0bj4tWu886usDhHmN+mhE/L/dvfH2bXcvKnsqREnq2MJ15OH1k+ce\nP7+2sSMW8ABtq8q7FsVW5decTd2w5cMkYpeslan9Sym7LFHmlOyMsvYTAHEwLh+iTC3M041pHemR\nGhKft8ZHz6c4hdz1FX30UR/OPVfRnOiz0Fofyny1P3BcFq+g5Mt8qm3oonljIJZnLAXDGa428jZB\nyX/N7P1NDUtf3M/bPH5gkHoTljW7zO8wf39YE2J0F4CfM92/ywCsBpC26ZgzooKG+zhlJwJB+MI1\noi/ureuivVwRJuihfjE7cBsKamnLCMYp4KbTqG1qIcz6sEJacSVrMYGlfNgxIEoyc+h+/Bh1vmYi\nFKbs2OxgwpaVjVUDClM2Ct8oy3Ez5kdvPy5bHX096YtzrcPrD4i4aqREmB6i7UCsleaO8hNUtO/+\nEJ0Lb/MsARibDuxDPZIlCYZp9wDdXAdyxljBKBps6FJ2sZ40uTBVlizM9lxB1OvPXye2VwCgz+UR\nlNKGpEkZ3ML9K4XDV9tSI8xQQH559a3nMfPdKxD+zTBqY21kXkxjmPjcZKaNlPGkjeyLMfDVu3MC\n+spZWRQIUmF0z6dsE0gVghf6SVVg3Bb3IPhpcCdfu6hCeDGuH/H7sdcgZ2iVgxmoey8nF6L/PILu\nv6etHQcrFqTjwbiy7GnCydlVG94Shc5KiwKT0UXnecQeiQBgHaFr0bzlpATVqBhv7+d2IN/sUO/A\nmtSFxq8B+KRS6kVQzWmHeXwHgMXm8U8C+AwAaK2PAPhLAM8D+DqAX9Jav+4GW1sa9W3nUisXPvuc\nFDeDskLJ9Bvr26viy9ft8dEd9kU1ITmB5k7W7OlYz4e6cCrVHWwNhbAOxBo+9skMaWN7MULaPtST\nOk7bcxHZFIjCPLnXsBAfj8pBkpBl9dCkS8pc2REZHl2uzHd6hYZTnLQx++0rhT8IxMJvrduOw52J\nTPczzngq+/OxvdeeCnB5TWodYdaXLFLd9Qy8zbEFfPuDdTgNLcfPdZfctI1wlYfWbcfjOo7JUqK8\nBe2YYLJ0Ft6dEwCA5o0xzIFNNQGgtZr0zb1+C/Ubg9R2uLEmFGBtcTJGhHd20tZduwr2DdOwZxWU\n2Tp5w9E8uy7AbEN7FPSNZ0mR9EAOtQ3d2LxibXydAAo8xQeWQ9/5ktT9uBYa9cbqGsnsLsxTJ7m2\ngbTSC49RNovPPgcAItqYP5EgExusXPJ6Od/Ly+L8mnPjzYx3SiaVHBdjWaO17pjfrw31xaoAACAA\nSURBVDB/fynx+t/WWo9ora/SWn/tBx6cp1DZW4L++e/LjQNQ8Cjuz6LwjTJm/vK6lLY0d1DsDtDe\nNpN6vyQOpXIgh8qeCrp9Fv1/bwmNNaFMGJ7MxQlbfOFy9w1LraWQEEjUV9KNzDeg6DEdzIgG+lwk\ne21DF81bTsbk6QuI2JV3LUoVYKVLtXVWitvaBtxfeR7FA7GNEgu/FaZimo84uxioA29Fu9c2U52o\nzKyL6N9MxnZd5urVxtokejccisRK6au9KE7YKTuwmaeJYC0qnUeJiF3e3Qu1fVI+p/icEjUJNtVk\n5Hy26iDMG0hJM/7+LE/s9QcC3gSA4kcoc1E+bU2jkkb09FIT7GLgLX8uYOqEAaHrVaBQ29hBbpp4\nctlqnME1r9ForSEeXfPWE3EW2SR+ZmNNiO6ghfwxQsIDsaqD1x8IvxAAuuvaIo7H10qP1AkTNgcd\nz557AAAnQmk7TbiFyqSAdxHnCzKirEZtc4MyI6MGwO3b2uYGvE119P6Lp9EaCs0NG0CbWJCymJpL\nIIXJVuwI2Sp58kUr2ijvWhQL0JnJ2BwJSUB/63l07piUSRIdWSLv1TU3DB+bCh1kqxHC/lBcWJJF\n/rnONkAcgJI1Hm9T/YI0Gjm2MY8Q4qciILBIFyoBv3BeiSe+BPKaC3uGIBjlQ27qxqrsLUmhm4Nb\nfdRPBXcg1vSub69C+WlV0kXbTKbbpJuxO+yLJx/TlgC6+bmgzh1U9dRiFKZsqt9N6hR8oPxQv3Tq\nAAB/eEX8fyMr4/UTPqo5PF90LxzsSnAFSDaHJXs4SPN39vrjDC53nLZs2g7S12YdYc8KU1SE9wcj\ngotcDrSGSF+qNG4jM0VMiPLuXnp/mzqMPM8aa4z57Cx93lyEf/mhftQ2tcT7kJsOb3lcbGfv3SD1\nxoe2A7KJMgVtvvlrmxskTDcUymTim5lxQ+XDaSG22uYGENJqrw8ug7epjvav3iTPYVAnb8VK4yTf\nm5u2EbYyMqFYjEzbAawm/ev3WbCe6oP9rWUpKWIgzmq8/oB4bAmbKJY6Lk7YyB2LcWJJKERyVPbn\noTMQErYyN175kCvAV/5MlikO+8MY/vDcYtgedbMYS8byJDyy+6jeNrfzycVo6/vF+Jxu6MYyw8dJ\nMaKytyRif50BMoIoHoiR7tyEsAbr5E94xQzx2M5lScCw6iC8cSbVJeQmChDj0uwvrZ3H5+Tg3BwJ\nUd9eTUFV5srIJLdqTJS2Z+j5ST2nyr4CnK8b7uPRIlpDFGjq6wO4MxFKRwH3579HwXnUh1VpI1pX\nN0RvhaikybwigXnihgAHPrdBqg3qPTWqa3bSnduFGP8n1KT+UUbjl38E5V2L6Cdxk7CAPRNtgXQn\nDIjBj5W9JQIrljT8kqmpcCfQp25W89YTyB21sezDzwnAsLalJgGvOBGLxXUGQnTumER20pq38gW9\n5pheokwjuaIHa5vzgKm46ZTcZEmKTnMkRFQKpfbExp5ALFLHoEW+2ZQfZyc6HxEA0JigZqsxCr05\nTETW3H3D0NefFeqHCh3k/mQFcg8uSXn6cQGZMw7eejMMg4xQ57fG7YkiBYmebuo8NEdCdK/yEOYo\ncHNQrI21ifrxtTWkKjHcRn3Upw5aXzxVmZoC0MIR3n09sveOIPqVY3I9OBPiLl5lf566khdwhAGo\nhBD1pbMUZ1ZLUHBOxY0FFAP5PryA6c9fZ2qgZLCROR7L1NS3nofXb0GFQPaMhv1KlgQPl8XfKXlM\njJzXV88AASHj+dy8lv3ZGx4K5LRzMT9v87ikg5QKFEp/8B3Ut52D/75Z4JXYcSOzp08K0tkX4wJu\ncgj40egD2a/Egm/8WHO1EnG82sYO1Q0SVBjZNj66DG4jLtQDtKplHivBeraHuF97KugMhLDGy0KK\n5cFyKUJSHo23kxwIk44i5d29UBNlqanYjywVfe8w6wvTPzkYSlDZW6It6OU1eT6/D2cWQclH9LOv\nwj5j0M7jJGPTvv0VdG4+A7svdtNNaoCXdy2CuuVl5HesgsoGEtwkmzGZW2bWBRZ14JyyxaA1eZNl\njuRRPEALQfs3R+E+aRx+e3yUr5tCbUMX6ilqWJDNVqzy0BkI48yxbcG++ynq/tmByCnXRyMJtPVR\nn2pDZouY3Vemra6BCvD5bg6HqYWQz00SRMrdUebScXac/7eH0bj5tBxnVCJCt/j8HaZMTDsKrRt8\n0cvP3TdMXeST869nZ6WFcEmM3cs951wQ+Ppmx7s1qQUY2tFS8GS+nHi0DdWQe9pF45d/RDIorilk\nZt1UIdK7gnAyzY2+FKJrG7qiB8S+ZsVJW4B4Qjsxk7B56wkoX8dyMQczdJO4EaGBR8gOqrK3RMd5\nyE3VoS6kXwTQFsp+JUs3dU9XrLXrW88Ljqayt4TmLSeJnLypReTiM3FGxltBDhS1zQ3kjpF+kW4k\n7J0OuanjsNoRusN+SnuqNE70DVXsStDnulBYBFQPmQy0b38F+spZqdcAEKIvEBNtmRbU/ez7YmDn\nF65B/Uc74pCT+cLjKV0p/n/j5tMoH3YQni3KtbDLnRSivz4a8+84UwKAzH9fheBjp+Rxu61li1jf\ndo7kb+6krkBxwpbPVGczktXMfouCGDdjkt0792+W0IKzKD335Nw2bJFycRouWutjGaHCYRuND9P2\nrnPHJJxGbGKb7OrxtpkBqMmt7IKMd2tSCzOaN6a7M1y09a4wMh9/8B1y0vjyalmluz0+wvF+IRDb\n7VgetzAVu8CycQJgvOMmEqRU0wXqXtuUx5Kk0mBZCL+kJd3nbCha0SaDyBdpK8SOMcnuJGC2LAcz\noirZ7fER9lOmxXw0bvM3xzx5XfkwZS0idezZaG6n7Rh/r/O/+EG01piMYut5QT7DTU+4zkAIt6pS\ndQ6+Wbrr2nHn0QR8xqYBhsc4R1bErsY3WGdt7FqTrTrIfOFxeV3ml8YFi5RsqVf2FaT+xaO+PhBF\ngeamNm2hR+LFiCzJ6XemQxUnbXh3ThA/72AGxeeUdMuS2S0HVPtkHMj5WgBA8bbDIvusQkeQ6ZlZ\nF7iJAmDYN79BA4BI1EMU+IIS4fM6AyTV0x32UX405pgyRAOIgaWFKQIr85xeUDoMfeo7E4Lwdozi\nASJs1sbaqTZ9djwWRauNtZG7cUqQwgBlPoVJDf27a6VoHGZ9WEfK1LHbWyJBuQQXq7a5gfIh0heq\nbeiisq8g0rE8lE+fEeZpe3TqX/9f9Hms0922EB1ZAh3E26rGmhDdjx8jh5RDrkADksGB5WgBE2wC\nkpkp/OUSqSsxf6yypyIZJVsgAZAaR+9/fRh2Jz4XmUniqSUtxQHKTLxHLqMCsRfrwXd7fGSeLUqG\nxgEPgOCkSl+NaT98flpr4sw32Unt9hnxOmOhzpCM8MkB+r5J1LqpfwlqftqWonv2dJQKMlY7EtgH\nAAGCdgbjWmFtQ5ccaT5zI3LT1GV1GvG20/Zc4W8mB2dTYb/R9zINm8IUNTQ4aIj+1+evE7YCQAta\n6wayyyofduJt/rZzohLBC6HAJMyiVZwgE4nappaICgokZE4N9E2Pf0LywZd8kIJNWBan4coqDgDh\nyi4Fme1VFHcOpm5WgDBGzZEQxZ8lfIlsc5Y3UdlTgV7kpWyxRO9plPSFAFpV8ztWpZw7YllcmmDL\n/uRr84TIrPeeltW/NG7LTRy5QOPauP3Mrwvvvh72V+hGZLMC6fb8JGWOYdZHlDfCblc2JaPkm61y\nMCMGB7bnpiY+ZyuZI3GmWNlbQvSeJpRNyqXKpxtTnIG31BC1DJCx5IszT3M4RHj39QjruVhET4wd\nyCeRbdsBCrjaJnS9dFnNVpi3Yxyca5ta8n24K5j08GsNka58bppIzkmHHlZ/YP367JGMBEzbc5H/\nvUcp8I7bCPMa2VORqB1wgd/2Yv1x7v41h8kuLZklhX1psGV5dy+cX31e1B74XAUl8iuEnyZZsyls\na41OcTzDvljMj69fZmBW4AvZqiNdxQUZ72ZSCzRyNIGDktkGGZxScziE6jHdPMMhY9kQp+GKU0oy\nTWa9pNqW2jwOVGsozpiSE6p9+yvyHk7DpUkV0vHwauttnpX/q3PZVMZivZqXm7gzEKL8qCuZQX17\nleyM7n5KWPSF78bCd/y9uV3NUAe+qcsP9acKtWHO1F8SoGTO1oqTtmSKAFD/0Q6aqxUZC5jCtNWO\nCO7AgncfOyVKomzKUNmfJ4++nzqVWhQ4E+SgxPir5gjZcHHQYv5ZZX9+Pvq/6oiyBUB292xLnxzO\neUjjgAO+toMUTKO2qSXCeGGWrhU7V2dmIA4txQkbqHSh9i1D8TkF++6n6Hwl/BFrG7rCSHBeylJ3\nso9MMfI7VqG+9bxkRQCgcrSQJfl8yYK3/7MnyAGm5KP7/obMu+ZILCPM50NfPQOVi30AX8tH8I2O\n/1NoMf8og5HT2SqtzlwEB2IHXGats+9bUPIRfOIFYuQnsCj17dXU5KsczKSckMtPEBiTJUD4OTyC\nkg+VC2QS8U3mlzSsdkSwhc0NOH+/mKAPeypAGAM97S+tRTjVKxOt/FA/dAapIn1tSw3l3b2pAje3\nq8/9txtTK3h9e1UK+401IaK8JXWWzKyL4DduEAoRO+VycGaOHUDQCICCaPYlwKoaIONjeVESLUzZ\nsGeVFG+DElFy5srSWEd6UJy00dq1BrlpIta2b38l9jq8f+U8ffripC38yKTMMeOPrFfz8jnFnYPw\nHx5OLT7C5zOFdrleKwzyf18hFRAZrFkaJ2mc2sYOqX6aZkpxwob3sTMxOdq8Z2NNKE2E3PEIuePk\nyajCWKq4fNhBbUsNqpMGFOembWTvHZHgpG2CgCQ1y+ZmbNkjGar7FY2qR6L7uyBjATIppdSQUuoR\npdTzSqkjSqlfNo8vUkp9Uyl1zPzb97pv9HqH+WZf+I81WNXS6w/MCqqRORml6hicugNxNw4wMrHG\nsihbdVDcOZhaiWobuimn2HCJLyJw/P5zwXPCwk8MK1AISpZkV+qKGaqFbKmh9UMsvmYh/NTRtCnB\n9iqirw2RlfeuRQivpGAhXb39eXQ/+z5YX1yH7F8vQfZ3DqDbZ6UCr17eksDF8iNWm6SHe37haTiN\nKBaY67Mko0kGO0bsZ6uO2F8BgPat1HOS2u4Asfw5WLCmfH3bOYR7hmDd+T0qzH+3jwLVckWZyGBt\n3jnsDFopVYK5KPva5gaBO0MHygnhfGQSAESRIakflqT4qP2UWTU3xoFbBPnG+1OFfz4v2qZFiCWd\nM7OuSNoAcU2oO2gJ0DN7xlCpvrRWsmh2I+KtXWbShdo+CXs6Btd27phMMQS84QjqfEIJwgTE2sYO\nSs8qhCML2d1bMMR5AOBTWuurAWwE8EtGhfczAPZqrVcD2Gt+f1Pjkg9S1nlqsRemqOhpNWxEjw0K\nMpvxNFxc5m4bF9kzR0jv2uuPu0ScHdmeS9uvCdpSJMF1zA1L1gzmjsIUKSKE2Tidzz3nAFPUoUri\nrfT3Kxd8v84dk6TJve0c7GlXgmNm1kX3ii4yX3gc0aePSHDltjQP75HLhFjN2VfmSF4wOsJlnKEM\n0W5CJIoBqo9kZk0GOBtPyOIDy1OwgCRYk1U6kxZUQcUCwvg75bmOd9mswDqsagbRe5qo7C2lttTC\nDBiNMUGc6TBTACAVVVXo0lbffL8w60MFOrVo2Z6L4s5BBD9+Rt6fQbGCTzPns7I/L/zDzGx8/vXi\nLpxZ6gp3rokhKVwrstoRdI4WR+7OhZ86GteOGi6pL5wvEHxgY4eySc+iOueULd6Lci37A9TG2vOk\niQtTNlpXQ4QGF2QoLEgmpbU+qbV+yvy/DpIYX4G0Qm9SufcNj0s6SDHxk2kTAGU29g3TJBNsbkAd\nULAo7O6Jhd82tUhWONG+B4gnxt2q4iGDPbEB6/vFWJd8XwHF/VnSOC9RJjXXnaQ4YaeyC96ihINd\nRDWqm2US/ObouhmaYGE80SoHckS1ucknIOjaUKyMco/l4VbJgh1fuEa2pZwpionnKBX5vSVxTaS2\nuSH+gp1By8AWSA0gLAJ2R0sNr337K7D+ggRSWRW0MGWjdctpeU7y5gbmZzrNkRC5x/Kp4MkqAsnX\n1cbatGXa3BB9KgCyLbc9VyzYGV8VlHw5x/WbfMEXaTtImYDO/vF75drnjkdo3nISmen4GC9041cO\nZsgwg2uOtSg2V1gfwFtmwfvMRoKHJEjjAGGnVIMWKPeRRang4fUHyE9qhM8uQ2HNSRJB3LUI9rNl\n6MVdqT92BsJ5xGI+H8nRXk7yOHOvw1seF59J9bMQpfm544JvR6YrowAeA7BMa83bhmkAy97sYV7S\nQUo7cRRP1mi6AwQWDP77lQD+//bePcqu8roT/H33nHPf99ZDV48SVVZBISiBBEjIIGiNwYDlh2J6\nFh11TJqYdJjFdNNhuh1PMrHbK8l4vOhMvEji0M10k8ErpNXRxMRKokSOrVh2RKtBgJCMJJCEEC4s\noUep9Kj7fpxzvvljf3ufc25VSQUqW5esu9e6S6X7OOc7r/3tb+/f/v0ocV5a7sL76HmoPdSLlj1k\nwblnDKkf+5HO8TAq2PsxLZNj52NRqpS7qvBGakgcoopiYsImCMTuuFR/+MYOL1Hy2/KwzjioPHSC\nlk9vBMBAlgdnIGF+e1boa1MntJDtu9kWEcLNrxFGaMQDvrxfqFh4nOrpa2i7J3Oy/3Yrj3rEafTy\nVUiPaSG785IK8BA4skfHABg572wMqmmUUpLR7eZ3RxlHrYYTVPgWVyP71pZLSffQ8pvN+51VVGE1\nuRY+n16iJTASfngTEzaV8TcXYNWUjIfpn9nSX9wnrAqVEcIj+SFISnzSiUBJuBJnl33kd6aQfX6B\nSKjzsbnZFuZ/eh850Y2LqXUntPxv7DHs1yvORkClrFZjFcpovjsPxbU1tG6bhK7GxQn7DqZsL3uI\nJluGHXAe9mJajZdj7yNxPsFElOb1zJRtKZUF8G0A/05rXQx/ZvjkPnAGvuMVjH/4It0ITExvX8AU\nRoHqkIfMxsWwrj2HE5tuw+INr6J0RwvZ72ahku6UHEh+Rxr1W1qRZGrylAVtK6jNg4jfcBreYDOS\n+Ayr8rb6YvJbfp8ts3ExWp89A+e8L2yhnFsAMK2CcRjxfTHLHbCBt3rh30wNuDMpOLPi7kxKwO1C\nA2Hq3rAiMVMOh7cNBI3Q4fM/ZaybCxHBgvD3RBF4exbFe8tIPD0SwTtdzNrHHlY+BgJVZB6j8mxk\njmjEypaQ/blXRZuL45MOnAkfLSNx1X49+JrltvTDPZODc+NpVFaS+CszO0ynfE1OXwWUyJ6N3KsE\nYOVthvfFhZP8riQqK71pxSAAzImC8eprM/qVr4/O6rvWA3suuj+llAPgbwF8T2v9++a9wwDu1lqf\nVEoNAPgHrfX1M23jYtbRkRRbZtMArLcyaMxXU9oH2EGxJlv2Gy+guLaG3PY0GmsrxI7QFlK7Y/1I\n7k5E3iPJcA23kqAHf+dCAV8CkJ6wyp/dQHTChuCufRlReegEmj0t0bYLOygARGsMkzcZs4SyFqCl\naO6AHUmmAgFKurTchfoIEdSx7h/nX4Ag3+T/aIFUrvK7klM4iETtmNtJahqJPZTDs+oa+R1pxH7v\nRsTeS0lFiatziYmgHy15ypLoI3fAjixTwg4qvysJFfbBhlqFJ4/EqiiLdPqYFTn34TwdwzB4u+0T\nDefRrHcTsBoOnKevpnvmXFLwdNUhD9aTy2A9SWvSya+thtujkDhmJuyKHamypfl9y4e96gQa1xLV\nb/2bN1B3gR2wVTCrKFNaNwrk3FgjEUW6FwWA+8qQXL/iuiJFV2vqco3C53Q6zrEPbnOTODdKUM8C\nOMgOyliYofdhBMy979s+FE7KGroAXSYCOZYM4pkwvy0P91PjkQc7t9dB6d6qUJ2IYKZRqq0+fJxm\n8BB6l2fe5KffBmB69ZoEvszvjotwZeYXCRzKM7/kl7ZnI8sJfnjsshNxEqX7z0mPW2XYQ21pUFlq\nncmRasybAeLZLjvwDROKXXakTM7QBi/RgrWUGBIqI5TDqTx0ArH3KN9TXFOfwubIjb5MeVMdor7D\nzFEL2lHwBhuUrF/bgH8VRWrcrmHVApqR+iIP3ud/IlTE6UNT5W5zewntzsowAKJLo13JKeOrDnlS\nZWWKFGY7FUGEvY5ADNjC+LDGjSRmwDxkxXVFohM2D7r3xYNw/sVbAIDU118iOuflRkXmrip8w5bB\ny9vE0yMorb+A1oCP0u/Tkh9f3o/SSkrc8zW3DPSgMexT0/nOFLyVk0FUlAiiqvyONBqPHYW1oCTX\nRY7POEkpyBitxjm1uQFz/hMAvwTgHqXUj8zrMwB+F8AnlFJHANxn/v/BhvlBf/iztOa1TeECKq1s\nRTT29OIq0fRyYnNXkppHLRf6dFochPXkMlg5Q5kbal1IHyOsT2WY2AhqV8fE4VVGCPjXGPahTTBW\n+9Yoks8My00Ubur1/0NAg8uWPqTkIWRnFCuHBAb+ar4kvRO3HZNtNnsINsAUxiyJDgDJP7kqso/i\n6qYkjYXxsb8R4YUKJ/6bPS20/vCGiPQVAOg45fq4tO4lWnB7ATyxQkrrbj5GuKIRYui06ho6Sw+e\nPj39TJ/flkd1qYpMCtUhAnl686cuadjZKy+gl8nsckQ6i88RfyZEetyaZPo1BfVuIrH0c4OAGxPd\nxPqigCQws3Gx6AACAHYupAhnSZmcjpmU6os8FD6zPzLe+HGK+pRnAy8tkEbi+mAMxbU1oZlhR5M7\nQMowAjEwEWWzJ1Bh5mKBXab+RK4+Tkcz84FMYU4iKa31Tq210lrfpLW+xby+o7U+q7W+V2u9VGt9\nn9b63EU3dLGhdnJO6taP5PX2IwXktvai9V6vlOsBoDHaQvzlLFRPg/qgNi4G7jgN63iCQJ47U6LA\nAURzIuG8y3TGURX/JvzbcA4hPunA+3+vjRCwNX5zDRK/uwsA3ZSMn+IxNPuC/4e3Fc615PYSIZ1d\ndoRGN78tj8pdtRnzFECQ68ntdaDOJaArcTSP96Hx2FG6wU/68F8ekLaf8O/UBWfaLnur4SCz10J1\nuQe77BOtDVP62j7KL18t9L/JUxacCRXFKnk2Mpv7CWi5uYDWneeD3NamAVgLSvBGaqgOUVI8nLPj\n8865q/bzBFCVVbUwpdl5pmuc35GGPpsGEoGGXn5bHs2bKmbJH/1dbkt/pH0KiObBEhM2mn20fGvP\nj4W/m9+RBtwY6qsbiNV8OU+8v8ymAcBTERxfeN/h8zInOamlaf3KH143q+9aP/f6Ze/vcqyjIyl/\nPkUD/mgJ8VUnkDtgwy80AU+h2WdAj0bhI3bLOKGHmQf8FheJcZ9m+4aDM38QqAaHZ+R2aAEAySex\nY/L/bojoX3amoC0tVLuJYxrp0ZOSU8jtdcRBAbT8SB+wBBekmkH4ntvaS9JPptG53UEBJIEecwkE\n6Z3NILNfSbULmFqqllK90ZDzb5xEfJBwEM2eFpoDMXFQ+R1pqb7pg/1o7CMkPueC+HitupGXMpTC\nxTV12vZVNdRvaYmD4qZpdZY6AJjMjuW/socs+KuinPPKJjhCoxBDbi+dc225yGwaiOC+woWP9oS/\njgPn/vTWKecj5gVLNZaUSp6yUFpLUXlp/QWBdRTXFcVxJo/7gjq3y07EQfF55ygXQETRJnlEkbTW\nxsXUUL03cFrFu6pChc0snkBwP1QePInKQyci8JTy+qJoR2Y201jnVMHY8mf1utLW0ZFUuLoHTK3k\nKM9Gbmc8slZvj37iTy1F8/EjyG3tJdL7DIRimEnOqksJh2L/wfVIfvptKDcaDQCQ5Hx7JUd5NlIn\ntERIPDNOV8VrH3+48scCpVzVmUnqG5hdNTB7iJauqS19aJ3JyXJFeTayBxFZTqQPWIFi8yFiIA2a\nhm14SYXUmJ5yTsLHmBmzENvTJwnzxATp+ZVWtiQiyG3ph1pcnnbsmU0DETQ+R098zsLnncCSGo1C\nzJD16Vnjh3Jb+uGtnERjQQy+rZE5ouV6hyu1+e1ZuNcR62ni6RHEEq7gm6Y71wCkgshGkvV+pEoc\nNv21m6G+8nokL9d+bXk5Gr4X5iSSui6lX33q2kt/EUDsUwe6kdRMFmupSEe9IJwNL7i2XHnIOY9h\n7SWdvuqQod59/Agty24qQblKZmeWtAYA5zlyhKm73yFe6tADzPvz7qOOfee1XIQBNLtPwUspJI8T\n+tguB3znACIJ/falQPN6ApoylUdxTR3p72ciSOTcln7JqyUmaIaf7iFvj6rqg/QQ1u4/D+eeMTk/\nqRMa5WUQWaj0j6gkzj2N5VFqgmZrFFxY9akOKnyMjP5m+SvOyTSHaAJUSVealMMcU7m9TsCuEHJQ\nAIRv3S76QkYImELFt5bIgz/53WURB8U5G87NTRGRuP+cgCN5/CxxL8wDjMB/K0WQhUd+Au/nT8h2\nw/2eAKlnl0c9uAujzkg7kKQ/gCmN0sl/9YYUFpgtg8ViWfiWZe75nps7BWPQ0z+b1xW2DhjCzKZa\n9CCorYOR9ysPnZAbk8GC2b/upcT6/efkgeObjmk3WoWAGoNlsEvLXdgfO0aS3vkgtE2eojYSljKK\n76MSG6uesJVWtuD91xGoN/uITuX5a0TwIbNxsURFXM1hNRmAIAP5nSk0iVYJzS/fhtL6C0Sta8L6\n0v3nAJsAqY0CgTvDFL25zQVkD1GCPb8tLw7NS2kkzmh4CeKz4geyOkTFADenoF6dJ3mo1mephSSz\naUCIBtkSYzGiuDV0ubmtvfLQhKMpdsLOhKIE8At0TYrrov16dpmAmKpCApzttM/sWJjDqTJMmLD8\nzhSqN1BlNX3MQswjOS85F1v6hZ9eoujQxJAZs5DZNID0twKlH/VWD+JPLY0o7TR7WkSjY/tAxY7Q\nNVeHPKnQ8r3E7A+S5B8zVT3Oh74ZOBeGrsQnzTk4kxJBC6esUF1KS0ZvSSPAnWyg+wAAIABJREFU\ncmXciHrPnFmXmfPyTddMM6dBcE9njMD2V50PZLQfOiGRRW6vI0T8QkF8wA4khmCSrpbhG9+4WGZu\nVbGR3adkP9PlA+KTDtwvHJZ8i/foGErLidiNk6BWHUg8O4j6LS3UFgdLKabm4K58Zq/0Tubh9qhA\nrWZ1E8U1dbnBvQQBDltZjbO7r0F51IBG1xVl2ZD+1nzhbmepLYAeoMSeNLwnR6GWnRP2UoZr6LXj\niBsnL/id1U3Ez1PTcnFdEfBiQWTzD8lpc2OxCmGlGAoRPnfp14j4r7i2FlnGsfNuXxLn9jpAw0L1\nFiqEMBtn4piGXfZFqNW/cRL5XUkR6Wg+HggzWA0HsT19qDx4EtbSswEH2FBJYAqJ8SBHWHpgAsW7\nqmjcXBdQbPg4rVMO/GKQz+SKKGCqjMnoRFZa2SLOerPsllzVuqJg9rj9xs/qgABxjAgQ59zmqHfv\nZ2Ed7aSUQ7OXl6BEsGBWpmEnrAx78nluSz9iHkVZfINUX/2IgOrKyyAcPSwSyjdF5aETshQsriW1\nknDrBhsDAUXF+BQ1Qjvn/YhMFfMm1R8dg5eEJIiBgLOIVW4BSvZ6951G4m1bWBEAeoAZ9Jg7YIsq\nLjs295oGUeUaZ+B+4gz8XrM8PqJRW6xMNGZagL68H6XlLlInNHLb04LGrg55sG+n/bjn0xQVhFpb\nrEY0mQyPcnGZo1ZE2KA+GJNlExCwhua3Z0XyPb8tL1GyNBG3EQiSBJcPb5jESXN7HXnQy8sIEpDe\nSkv8WI3gHp6BpYUjNC/RAq67gPikg+qoRnX9pLA+8H1THfKgK3HU7inJkq5RcKHjBu4S8sV+b4t4\nzEzPYWXHiDh1IMqeweOQamIbuLZ4bznixMNQClYtDrNWzJmpWb6usH2oEucA5QR8h9RTdNOCff1E\nwPft2ZRP8dQU4Nt05eFLGYFGFWGlCkGeKn7eh+8A8X2ZIMKyIAyiVl0jcSgo6WeOWogVY5FQPfXs\nEtQeeVcS5ESmFwOGSlDnEvJw87j5+2GbqR0jvyuJ0kddqCevl+pbeyk9t9dBc0hDuToCG8jvTMEb\nbALezLza3C4zXXEgM2bB/c4w3Ed+ArukYW1dhOrDxyUaUZ4dOZ/THQcXFBhSwRafdGD9xWI414/D\nLzTh9hIS3pqgc5vbXACuuxCJRICgRSd5yoL1wwVwP3EGjcL0kAEgaK251LkOH7/9B9fD/cLhoBjy\nxAqRU5dzY84BtzNxkny68xh+z2o41DQ9QnnW9OCRy0+cj6b0K3989ay+a33sYDdxPpMpT9FSzZR+\nWTuuvshD5cGThBwPoZXTmxbAXdIkgQbD0ph+blCWA0JiZmbr8BKSowXl2QL+syYs02gbTCcMrqwv\nIpR27oAN63hCcFVutoXEnjT0eEZoQCojHvy+KD+3Xn+cRCBNm0f1vgr8GycFPqC/djOAIFKrPfIu\ncgdsaoY122Ftt/AyJP3cIBrXEpEay49bDQeqvxYwi4KWIFZNT3nwimtrpAAcclD57VmJDHNbe0Ot\nSTqixpzfkYabVbAyDWEHYMiDcIFbRJvDkVP2UECTIm0fDQOUNf2HfIytrIb/T99DY99i+Bnq42ws\nIAeVPGUJRXEY7JvfkUbpgQmiVJ6vgNvGEX+ZrntiZyBqyqwI2ecXoHZXEMGyiSiFuU8IjmIqmxsX\nI/3Rn0QUl/Mfo86FcHsNnwOevIqrm8gespA5omWbbEKEtzOF1I99yUW2t2FdlnVzUpdvyjOYH3Ph\njzz22UionJiwkdtcCFRqHzqB6n+9AQAQ66kTw+XDxylxeSAoL3OZPJzMrd5ZI16p72alMbU5SLMe\nQwrCxg6P1V5yB2x4KZPXqdtEaHcyB7y0kBpGh70I4LDVFxNnJ0nWlxbKdtVXXgeACMdReRlQX+Gi\nVTD8RYWAWpktdvvJ4DiNaEPyuE/cVOd9qIXmITJLO/6eOKFpqkfFe8uY+HPCIoUjjNievogas7eE\nqpXsmMIUO3wsfK2YyqU8Ss692UPXOf7UUuhyPKI8DFBPITu4xmNHhe+ck9POyam3Mm8TAJKbC4i5\nClXDQmA1SDqL+wSrQx68gofyhnERsWDOp3BBQtcNId3aGqwnlyG3uUC5x6SHxjXBvr1jVFzQB6OV\nQACRe6k8GtwXkWbxZ4Zpf+MZlEc9wspdBMj7/m2W+ahuTurips21TExQ4vcj3/rLSOTUKBDBf3WI\n8E65zQUJsfX8WuSBKi13qUK0PStRCoAQpxHNVsxXFD9vZk9PEcbKdKy3k5+Ft88RV+3ofMp9bRiH\n33Akp8A3Z5jCGKCbM3eAqoiJN+IyW2fGLJKANwEYK604P6QbPywhxc6lPOpFQIzopxaW+iJP6EkA\nAmiKUOdkACDFj3uE8RIgeazc1l7k//fdU/IiDDngvAw8yDJKxhXKMXlJkiXPbS5EljfWpAKeWEEI\n+0fH0LrzfIQxof18hwsB0ny9kpbE+d3xSEO58oiV1Rq6ILCD2l0VKu2bHlBrIiYUy1x5zGwakEkE\nCVfuuzDXu/fFgzLheX0kRyYc/A+epOLLtecjhHx8Hdsts2kAub1OIDJh6HNq6yZF5WZObbb5qG5O\n6uK26uak/sErwQOtPBvZv+6NdNgDxB6glp2jiOCADfflq1B75F2ZIRmkF85xhMGD3F0fzmNMS6li\n8g1MuYFGDHoyCd2wUd4wHvkNgzzDlCDpY5bkT4Ao5QnnJzJHLVhnnClNt+3GtB7tdCzt487vjqO5\nZzGcO4+jVdDQtoI9qRGrKWhboz4Ym759JNR+xJ38uKYIr4ful+qQN2XfnF/jnEs4jzNdI3Hq2SVw\nhs+icXMdyo0uPRnMmdvai/Knysgc0dM+qEKjsteBV/AFSpB+E1PwU14SSG/tIUCpGUtucwHV9ZNS\nMfUKHtSr8+D+T2dFJ0/11+Q8AFEKm/Df7dZOY5Pb0g8MlQKeeQPmzB2w0RhSJCU2Q56M7xWr4SDf\n89bl56SWJfUrfzI8q+9aaw53c1IzGS+H87uSSD83SEhp46B4xkqesqBXnYW2NVVrlgfIYPs0zWDO\nBDWb+tlAF63y4EnJSfFNwduMTzrIHNGRqlZuc0GWDtnvZuHNp6VE464S9KqzlJQNdfonzgQOKrNx\nsTTLhh+cluEcz4wFXEcsDBA2xlcBoWXAYupP9ExaRWhvLRfWk8skaiut9BG/6RRUnWADibdtUcVl\n5svwEjr5zDAJC6ytRahsW7dNorSccEvcMMtNxqKyY7jGWdbKzdLx2WUH9WVTH+T6L79HqPJTRknH\nRF3xSUcYVUvrL1BrzWgUAsD5K3bIrQFfzreXCBgsORLj8n7pgQmgEdz2pQcmyEFty+PYn9wJLwlU\nHjiH+OEEVYfvPwdvsIn4i3kRWW0OxCRavFiEY79HfOX53XFqdzKEh3yt1Fdep4KGgazwOKczxmKF\nVWku2z4ky71ZRVJKqTEAJRCLtau1Xq2U6gfw5wCGAYwB+Oda6/OGX+YbAD4DoArgl5kDWSn1MICv\nmM1+TWv9HC5i4epee4RgNRz4tp62KmLVFLSDKZ+3t5pc7P8zbb99X+2fh7ehPOp2n00u4WJNz9Pt\nJzFhw81Ft92ugzfd7y62TR6vVZ/6sFzq3PH2wmNojyytmpJjDH//UsyTM+1rpuOa7pxzhNJeNfMS\nrRnHEr4HLlYdnumYRdfvIi1O0x3nxb5vlx3kCnMQSd2Q1C9v/MisvmvfevnVxMux9xNJfdzQMPBg\nZ1KD+DSApeb1KID/BwCMU/ttALcDuA3Ab78fmRu+AYRL3OQXwu/x90T8oN2BmAAlvI2whf8fbptg\nPT/+XRgR3W580/PnYafHxhGB8mxBozNtbnuC3mo4iLkqqqLs2RF6mplspgdZCNim+T5zaYe/y8fV\nfpyRbZooMiyVFT5/pKgTdVAAKe20j63dZpswtsuOHANvh881H1PYkYYdFH8WpkQO3wNeFNoUsfAx\nt1vY8fD/28Gv7ccZvofCxwDgouwd79s+JDmpy1nuzaQG8U8B/Kkm2wWg19CHfhLA32utz2mtzwP4\newCfutgOrMmg9YLkybV0g7Npy0X2IOVQwglVtnDSkm/U1MZFyB6ypjwQ+Z0pAmIeIPkrFgjQlov0\niynEz1OSuJXVlB87RK0RnLzN70zBLjuCUg8D9FQrxDBpYAe5V6nlIrtPofRrd0bGwlU+L9GKPCws\nVc6W29IfWerNhg87zG45k4VFQrn1JmzhY6v9+h2ybMzvjiO/Kylkf/ntWaQ3LcDkb9wR9J/9Q/DE\np48QqwQvY71EawrYsV1hR1tEccLXkCcNFphITNhQ5llmIr4wsDN8Ltm5ur91q5AR5nZGmVxTzy6R\nnBU7jPQxYrcIM4iGYQlcjfMSpIBjPTNMrVe2JpiKub5hXvT2Y5Rr8e2FyB6yRJZtTkzhH13vngaw\nTSn1WkgpYiY1iKsAHAv99rh5b6b3I6aUepRVKU43m3QTnge8go/Ynj4pa+cOBGKJ3muLUFxbo5lw\nUkmDLicbuZ0EoIRz7PaTKI96yLxqy3u0IYVYvk5YmwdPIn1IoVWgvjX014PeOSMIoI7mUFp/IVJC\nTm3robzDUQvu94Zlv242IOvz/26Ivr+mTvielS0s/CjhaoR94FAgbJo8RRiezJgl1LtspfvP0TFO\n2HIjt7cQcTWTH6ZwMru9by7czF3dMhrkcYCIKm9lxJMHZt7/9oocT3F1E5WVlOsqrqnDv6qGykMn\nkP3GC9C2Qvb5BSjdHeTcGNfkXBXIZ7Xn5LhKmzlqjm9XkhSXJ+IEr+iha93sAxqFWMBksCsJ78g8\nIbgDopFaaTlF3YmnR2B/9TXpEWTR2eQpC7nNBclxFlc3JZKpDhHNTDjPyFJf+R1pEVhl8x4dQ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8jjQyuxx4GSA9RvmA0nIXzS/fRj1yWSojp58bRP14H/xskL9rDmkSDDX5l8TTI6gvorxFZcRD\nZq+FzL96PaJaAxA9CUAJ2OqoJtHMELGbWlAlPFaorN0ohMQbjhKLZfztONRXXkf2+QURQUmOvsLQ\ngnASnPel3uoJbZ/afdLHLFTW0vKGlZ+rQ6ZaafBEtRN9cizpYxr1W4KKa+xuonM+8+xtsm2BVHzu\nNBqPHUVrwEfi7cDZNx8/ImNVHwlyYQAEbtHupL2Cj+JdVaI5NtXAxCFHGFUzR8nJVu+ryDkIR1F+\nJqD84Vab1AmN9HODJAk/SvlAPldeggROKw+eRGWYyAPdbEtalIpra0ZcInigRdhj2JP7lOlouFqZ\n+kEOzTvpmDkSrD58nEj66jS22j0lxF8mBH3l0MDc0gd3I6nLN6scHZ5VC4jP7Em6qSQa+fJ+lO4/\nB7tMbRGlu+vw+3ygQUuC0gMTMoNlNi6mPqyxfug8LR1Ykii3tRfxJ14hvqVlNHNXHz4O9wuHI7Nr\no+Ci9dkz8sDanxxD5mhA8dq41oX1wwUSsZVWUhI/c0SLnBarrHg95PDsskFUH7WgF9bl4U2NUUNz\n+rnBCE92ZtOAaTMJnCfn7ZqneuRYIzqBhqG0XZQBAHyHSOHyu+PCswTQMpsVWvzfeEMcXmXYg/pv\nlHxl8GX6uUEs/LnXRWewHdbgZmNTe/PaUPCZzf1UcDBRHLfisAlrp6C7Y6i+SuPQliuQEOe8L/TA\nbrYl3Fa8ZJWIpkA8WfbgBbQ+fk7ygdWlCrkt/aLnCATOBECkWjlFzeWJFZjOwoSNpfvPwZnQpE1o\n+gV5PM0ecz5fTKG0/gIST48g+eChabf5gWyWQM4PC5jzipmXNWqupqu+OuRJs2xlxAPuOUm5khAv\nt7YomkidoJCdk6sAoJado9D9jtNInrJQe+gU3D3kHDAZl+UC5wDa2QbYRHzgHXI+sn8rcBKNAjUp\nw6Z8RW5rL7RrITYRR2n9BXoIn1qK2rdHoTzAu+800j8KpLOqSxXUPErgN19YAm25iN1OUQM3Erc+\nY/oEB4LlVbOH4Bne538CAKhvOCOfCcXMeABKBILyfX2Rh9q6SUr0G2iB/PappZGeOjZ2XmzVh49T\ngvzX7iTYg4lgkidpjNPxxYeXt42CC+1SRTS2ax45lmenVqHssiPLSjfWV6u0AAANjUlEQVTbQvom\nilAzR4mVoPLQCaFMxg8GkN+eJfmr1xyTt/RR3jAuXQHKs1G8t4xmHyQfaD0zDLWgivpgTCZEXY5L\nyoDzVZyPzO0NyPCyDwRo4XAk2M4hzxznmaMW/B/3TvmcCyLe538CL6VkOTgnFlOze11h62gnxVZc\nUxeF3p5PHURuS79Qs6aP0VImd8BG6tkl8p6XUoKNkd4xs/Znzm/15PWk2nKAKkWVYdKC45Cab8bM\nmIXqJ0vi7Nw8nbbSypZUvRoLY5HQnscIEMareXsZlQdPyo1dX+TBe3QM6fsPEV7nuCWf5Q7YyOxI\n4cTm1UQ78sAYbcdwL5VHPShXQ7U0NSwbMQd+GC78xU1IvxbwQPGNz3kRv+FMq12Y35YXXiskfJTW\nXwj4mB4/InAPIErVwpY+ZknPXfYbL9Ayyiyjw1Fo2PmnjxF4MeyoeAlX3jCO+OEEmv/ruwHv+1HL\n9PL5UvK3GqTvx5Fm+aYgsswesshxmmVw8a4qmn1AdUhRyuB2szw2eohhdtXYhndQXFNH6ghQe+gU\niqubaKytIL4vg8ymgcDpO4qqgxUb+NNhuUZs1imjXmx0/oDAcXH0pON0vBydtaPY3WwLiTfiInJx\n2aYA35rd60pbRzupWEMhPhmQhBXXFeEuJJlr5tgWytjXFpHs014H9rtEa4FXFki3O9/k7DishoPY\nY2/B+v5CePODRCvPXPlteXIkv7MKlWFP5Mrzu5KRXBgvZ8JJWbvsyBjf+dO1SB+gHIlQwjyxQpaG\nfDPXl+pI0yosH9lvvCAVruwhS5pZlWfD+v7CCPGdX2ii1RejRO2X9sM7Eco5mQ6XxARRllhLz0qF\nM+xswtVLXpJxP2OYyQEIVFxye4Nt8LXgzxoFF6oei0RO2UNW1PkPecEDjiBnVlxNiXBvsIHsVsJu\n5bb2yrZj5hI0RlvI7CD4QPXh44RsL5N4Rvq5QVFQDvM1Zb+bDZRnCi5qv34HKiu0OUcmejeRFwB4\n+xZGpOSL64pofWZC7gPfAvQ5onhpPn5Eoh1earITbhRc+YyddmOYlqTuX10jqs7Z5xfAO0n3il12\nkHp2CRJPj8AbnBsGBLFuTuryzU9ooUrh/EHT8IIDNPukjxHvtMwwJzLC68OKHdohKaL0MQv2O5Qj\nSm4ukGNZe1qwPZxoZbMaDqzf2ROZ1cJI5rCV/y01m/qZ6JJmyZe3Q49THsO7roLc1l6o/+MgrPPk\nbPI7UwGR2X+8FvntWcpNmZmfIx4/A+CJFfBSxGVVeegEUs8uISWX7VmUR00U59HSqvLgSUl+Jw8G\nXfoA4C6kZXP6xRRiNRVJKmfGLIlqrIYDN0eRhWMeXtWinFpighhR/bwv6sRAgOOxGo5RhdFwnr5a\nEOrlUQ9q6yBVQg1gtXltU/oXmy8skW21/uYaxF7vA4ZK5PhNJbNRiKG2WMkxFdcVRYHHTxE41y+m\nYA8UgYxh3ZwMmriZWYEt9fWXZNztIFMA8D8Z9Bhy5BOrBUtsN9tC886iLI95Wa6apIQTn3RkIrRu\nPUXbMbktq6YRqwDOZ98R2Ex5wzgqD50gUZFsC7VH3kXjsaOUa5vD3j0dm93rSlsHDOHiJku2DeNE\nJJelRDffLNUhKu9nn6eoqXl7OZpL2lxA7dujaNxVgv1OQiIMXlK4WRVhu5RlkOUHnefX1QgI6tkB\nL9XaGqxGwF658Av/A5mNiyWa4O1VRjxphq4Meyitv4DUCS0y6d5gE7F8jVDvtxxD8d4y0XVMOrCe\nXCaOxdqXQ275e4gfC5YjtUfeRWNthR7wvY6wJfCDVls3ieRJf0qiurZYIb/+TbzxzY/DT2mkX06I\no4rt6ZPvZ/Yr+DZFGG6PEmiCSrpI7Ekju09JMzU7Wp5AvEQL3kfPk0z9gz+Gl1SIj5HuXu2RdyVK\naRViiB+nSNfPauCfvyusl83Hj5AicZ+PWMh3EHA2WiyoPHSCiO/MktZaUCKcE0tRrb8QAXfyOUo8\nPRIhRWSoSfHesrB+RjBPk3Tf8XvCb1ZwUVpPMu4cHXPU1+xpwRpLYeKRdaguNRXBu6rIbekX/cDY\n+RgSe4KkvNWgymbymWHkd6YCdodpnOgHMYIg6Fm9LmVKqU8ppQ4rpd5WSv3mJX/wPq2jnVSspRCr\nUW5JMEgw7I2rm1DNYGYjUUeidglbdf0knF85LIhna+iCzGKsscbLKC9B6/78zhRKd9eDUvjBDHTS\nOB4rmEHDyzXrFD0owFS6WxUqcHFZP789i+zzC1AfiMG/sSSJ2/zuOPI70nAmfNgPHwm20dMQtZGw\ntQwsorSyhfpgTLiHqL0mwPvkt2eD/IflojmksfQe4jVix8hqvwxS9Pq8QFSgRvJX+d1xVNY2iPkg\nNBZ2+jyBMPAzt7UXjYIrJHTVIS+SgE/ut6kNaV0RlWFPKm3Fe4kaxevR0IZqJ78tT31xhioGgCy/\nuRfPLpNTqa9uRHiYACCzK/hbQKOPHUXjsaPiZPWqs9KDZ9WJrYAjS+XZOLfj+ggTBCtTM5Nn8oiS\nJXB+dxzaOPnivWVc8wu7hIYYgGCuAKB8k0b1DRqDXXaQ/h4l9OuPjqG4tiaR7HS5xA9sc6BgrJSy\nAPwnECHmDQAeVErdMHeD7HAnBeMPONEN0MPGuYBWIRYpcVeHFPR1FLXwDJcY9wkI+vwCSrAaTAsQ\nJbXnCIhZFVMngv6t+u01KYO3I8bFQjJJ/ig1nXIUGO6/4+ZoZKgXMLMzIdQeAFHCFO+qAhYieCJv\nsCk3ND+omTELMVcJEtnNtpB+kdSQnQmFpulFzR6iwoA+HZqpaxratSKlcz4vzq8clnFz5Gi/5whD\nadrIffEyuD1fVVzdlAe9nVEgvyON+qNjxCf15DK5Hrw8BCD85e7JvIh35vZS64td9CPiDZWVJiLa\nk0Z+d1yuKacJWGYrvz0b6eVzvlNAaSVFrIkJG7FRqpSywjFAzBm1R96FzgZCDfZXX4O2g+9YR4lO\npbTSh33BHPtKKs4UVzfhvjAkeTZ3mUknnDPL6XcTct8lzmjhinKzLejrJiMcXjJBH5ujapvS0LY7\nq9cl7DYAb2ut39FaNwH8fyCdgzmzjm4wVkqVAMwdy9fcWwHAxCW/dWWsk8cGdPb4OnlsALBEaz3/\ncjaglPou6DhnY0kA4ZzBM1rrZ8x2fh7Ap7TW/4v5/y8BuF1r/auXM76wdQA56EXt8JXsvr6UKaV2\nd+r4OnlsQGePr5PHNlemtb6oUlMnWWcv97rWta51ur1v7YL3a10n1bWude1y7FUAS5VSVyul4gA+\nB9I5mDPr9OXeM1d6AJewTh5fJ48N6OzxdfLYOsq01q5S6ldBoioWgG9qrd+4xM/el3V04rxrXeta\n17rLva51rWsdbV0n1bWuda2jrWOd1E8baj/DPr+plBpXSh0IvdevlPp7pdQR82+feV8ppf7IjG+f\nUmpV6DcPm+8fUUo9PN2+PuD4hpRSP1RKvamUekMp9W87ZYxKqaRS6hWl1OtmbP+nef9qpdTLZgx/\nbpKrUEolzP/fNp8Ph7b1JfP+YaXUJy93bKHtWkqpvUqpv+20sXXtIqa17rgXKAF3FMA1AOIAXgdw\nw89gvx8DsArAgdB7vwfgN83fvwng/zZ/fwbA34HYotcAeNm83w8SR+0H0Gf+7puj8Q0AWGX+zgF4\nC9SKcMXHaPaRNX87AF42+/wWgM+Z9/8zgH9t/n4MwH82f38OwJ+bv28w1zsB4GpzH1hzdP5+DcCf\nAfhb8/+OGVv3dZHrdqUHMO2ggDsAfC/0/y8B+NLPaN/DbU7qMIAB8/cACGAKAP8FwIPt3wPwIID/\nEno/8r05HutfA/hEp40RQBrAHgC3g5Dbdvt1BVWD7jB/2+Z7qv1ah793mWMaBLAdwD0A/tbsqyPG\n1n1d/NWpy72rABwL/f+4ee9K2EKtNXN1nALJzgMzj/FnMnazBFkJilg6YoxmOfUjAOMA/h4UaVzQ\nWnMDWHg/Mgbz+SSAeT+tsQH4QwC/AekIxbwOGlvXLmKd6qQ60jRNn1ccs6GUygL4NoB/p7Uuhj+7\nkmPUWnta61tAUcttAEavxDjaTSn1cwDGtdavXemxdO39W6c6qZ861P592Gml1AAAmH/HzfszjfGn\nOnallANyUP9Na725E8eotb4A4IegJVSvUopBw+H9yBjM5z0Azv6UxvZPANyvlBoDdenfA+AbHTK2\nrl3COtVJ/dSh9u/DtgDg6tfDoDwQv/95U0FbA2DSLLm+B2CdUqrPVNnWmfcu25RSCsCzAA5qrX+/\nk8aolJqvlOo1f6dAubKDIGf18zOMjcf88wB+YKLALQA+ZypsVwNYCuCVyxmb1vpLWutBrfUw6F76\ngdb6X3TC2Lo2C7vSSbGZXqDK1FugvMa//xntcxOAkwBaoHzDI6BcxHYARwB8H0C/+a4CkX0dBbAf\nwOrQdn4FwNvm9S/ncHxrQUu5fQB+ZF6f6YQxArgJwF4ztgMAfsu8fw3oQX4bwPMAEub9pPn/2+bz\na0Lb+vdmzIcBfHqOr/HdCKp7HTW27mv6V7ctpmtd61pHW6cu97rWta51DUDXSXWta13rcOs6qa51\nrWsdbV0n1bWuda2jreukuta1rnW0dZ1U17rWtY62rpPqWte61tH2/wMVX6+OQHM2/QAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## open the file with rasterio\n",
    "# SLC_fi = os.path.join('s1_files', SLC_dl['title'] + '.SAFE', 'manifest.safe')\n",
    "SLC_fi = '/content/s1_files/S1A_IW_SLC__1SDV_20191209T003127_20191209T003155_030264_0375D2_F3F2.SAFE/manifest.safe'\n",
    "# s1_filepath = r\"C:\\Projects\\sentinel_api\\s1_files\\S1A_IW_RAW__0SSV_20161216T130058_20161216T130130_014405_0175A1_975B.SAFE\\s1a-iw-raw-s-vv-20161216t130058-20161216t130130-014405-0175a1.dat\"\n",
    "# s1_filepath = r\"C:/Projects/sentinel_api/s1_files/S1A_IW_GRDH_1SDV_20160830T010152_20160830T010217_012823_01439D_86AC.SAFE/manifest.safe\"\n",
    "\n",
    "for prd in (SLC_title, GRD_title):\n",
    "    SLC_fi = os.path.join('s1_files', prd + '.SAFE', 'manifest.safe')\n",
    "    with rasterio.open(SLC_fi, 'r') as src:\n",
    "        print(src.profile)\n",
    "        #arr = src.read() # careful... this could kill the kernel\n",
    "        #print(arr.shape)\n",
    "\n",
    "    with rasterio.open(SLC_fi, 'r') as src:    \n",
    "        for ji, window in src.block_windows(1):\n",
    "            r = src.read(1, window=window)\n",
    "            print(r.shape)\n",
    "            break\n",
    "\n",
    "    with rasterio.open(SLC_fi, 'r') as src:\n",
    "        r = src.read(1, window=((7000,12500), (7500,12500)))\n",
    "        print(r.shape)\n",
    "\n",
    "    if 'SLC' in prd:\n",
    "        arr = np.abs(r)\n",
    "        print(arr.min(), arr.max(), arr.mean())\n",
    "        plt.imshow(arr, cmap='plasma_r', vmin=arr.min(), vmax=arr.mean() + 100)\n",
    "        plt.colorbar()\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(r.min(), r.max(), r.mean())\n",
    "        print('clipping to mean + 100')\n",
    "        plt.imshow(r, cmap='plasma_r', vmin=r.min(), vmax=r.mean() + 100)\n",
    "        plt.colorbar()\n",
    "        plt.show()\n",
    "        \n",
    "    \n",
    "\n",
    "# !gdalinfo $SLC_fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eP19e3tECvc3",
    "outputId": "deb742cf-7531-4d07-fc9d-541ecd0c0744"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.  +0.j, 0. +79.j, 0.  +0.j, ..., 0. +88.j, 0.+163.j, 0. +95.j],\n",
       "        [0.  +0.j, 0. +65.j, 0.  +0.j, ..., 0. +55.j, 0. +45.j, 0.  +0.j],\n",
       "        [0. +11.j, 0. +18.j, 0.  +0.j, ..., 0. +61.j, 0. +36.j, 0.  +0.j],\n",
       "        ...,\n",
       "        [0.  +0.j, 0.  +0.j, 0.  +0.j, ..., 0.  +0.j, 0.  +0.j, 0.  +0.j],\n",
       "        [0.  +0.j, 0. +18.j, 0. +27.j, ..., 0.  +0.j, 0. +34.j, 0. +71.j],\n",
       "        [0. +37.j, 0. +60.j, 0. +20.j, ..., 0. +50.j, 0.  +0.j, 0.  +0.j]]),\n",
       " <matplotlib.image.AxesImage at 0x201b1af35f8>)"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201d3b073c8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 246
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "qnK8iYVxCvc7",
    "outputId": "7de8a003-a839-4d24-b1de-1194266b225f"
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-8cf0b52bc465>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msub_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_arr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_arr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "sub_arr = arr[:, 100:200, 100:200]\n",
    "plt.imshow(sub_arr[0])\n",
    "plt.show()\n",
    "plt.imshow(sub_arr[1])\n",
    "plt.show()\n",
    "plt.imshow(sub_arr[0] - sub_arr[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "GulfYUIaCvdB",
    "outputId": "6978a96a-6c55-41c9-d6f3-77bb4491f305"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method query in module sentinelsat.sentinel:\n",
      "\n",
      "query(area=None, date=None, raw=None, area_relation='Intersects', order_by=None, limit=None, offset=0, **keywords) method of sentinelsat.sentinel.SentinelAPI instance\n",
      "    Query the OpenSearch API with the coordinates of an area, a date interval\n",
      "    and any other search keywords accepted by the API.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    area : str, optional\n",
      "        The area of interest formatted as a Well-Known Text string.\n",
      "    date : tuple of (str or datetime) or str, optional\n",
      "        A time interval filter based on the Sensing Start Time of the products.\n",
      "        Expects a tuple of (start, end), e.g. (\"NOW-1DAY\", \"NOW\").\n",
      "        The timestamps can be either a Python datetime or a string in one of the\n",
      "        following formats:\n",
      "    \n",
      "            - yyyyMMdd\n",
      "            - yyyy-MM-ddThh:mm:ss.SSSZ (ISO-8601)\n",
      "            - yyyy-MM-ddThh:mm:ssZ\n",
      "            - NOW\n",
      "            - NOW-<n>DAY(S) (or HOUR(S), MONTH(S), etc.)\n",
      "            - NOW+<n>DAY(S)\n",
      "            - yyyy-MM-ddThh:mm:ssZ-<n>DAY(S)\n",
      "            - NOW/DAY (or HOUR, MONTH etc.) - rounds the value to the given unit\n",
      "    \n",
      "        Alternatively, an already fully formatted string such as \"[NOW-1DAY TO NOW]\" can be\n",
      "        used as well.\n",
      "    raw : str, optional\n",
      "        Additional query text that will be appended to the query.\n",
      "    area_relation : {'Intersects', 'Contains', 'IsWithin'}, optional\n",
      "        What relation to use for testing the AOI. Case insensitive.\n",
      "    \n",
      "            - Intersects: true if the AOI and the footprint intersect (default)\n",
      "            - Contains: true if the AOI is inside the footprint\n",
      "            - IsWithin: true if the footprint is inside the AOI\n",
      "    \n",
      "    order_by: str, optional\n",
      "        A comma-separated list of fields to order by (on server side).\n",
      "        Prefix the field name by '+' or '-' to sort in ascending or descending order,\n",
      "        respectively. Ascending order is used if prefix is omitted.\n",
      "        Example: \"cloudcoverpercentage, -beginposition\".\n",
      "    limit: int, optional\n",
      "        Maximum number of products returned. Defaults to no limit.\n",
      "    offset: int, optional\n",
      "        The number of results to skip. Defaults to 0.\n",
      "    **keywords\n",
      "        Additional keywords can be used to specify other query parameters,\n",
      "        e.g. `relativeorbitnumber=70`.\n",
      "        See https://scihub.copernicus.eu/twiki/do/view/SciHubUserGuide/3FullTextSearch\n",
      "        for a full list.\n",
      "    \n",
      "    \n",
      "    Range values can be passed as two-element tuples, e.g. `cloudcoverpercentage=(0, 30)`.\n",
      "    `None` can be used in range values for one-sided ranges, e.g. `orbitnumber=(16302, None)`.\n",
      "    Ranges with no bounds (`orbitnumber=(None, None)`) will not be included in the query.\n",
      "    \n",
      "    The time interval formats accepted by the `date` parameter can also be used with\n",
      "    any other parameters that expect time intervals (that is: 'beginposition', 'endposition',\n",
      "    'date', 'creationdate', and 'ingestiondate').\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    dict[string, dict]\n",
      "        Products returned by the query as a dictionary with the product ID as the key and\n",
      "        the product's attributes (a dictionary) as the value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(api.query)"
   ]
  },
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