examples/data-block-examples/esa-sentinel5p-l3-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ESA Sentinel-5P L3 (GeoTIFF)\n",
    "\n",
    "Sentinel-5P L3 (GeoTIFF) provides clipped Level-3 (daily composites) images as Cloud Optimized GeoTIFFs (COGs) from the Sentinel-5P mission. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere.\n",
    "\n",
    "In the example, the workflow, the area of interest and the workflow parameters are defined. After running the job, the results are downloaded and visualized. For more information, refer to the block's [UP42 Marketplace page](https://marketplace.up42.com/block/70919851-c563-4e5e-9393-029cf10377c7) and [Documentation](https://docs.up42.com/up42-blocks/data/esa-s5p-l3-gtiff.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geojson\n",
    "import up42\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "elder-wilderness",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-15 11:36:54,716 - Authentication with UP42 successful!\n",
      "2021-03-15 11:36:55,311 - Initialized Project(name: Blocks, project_id: 810e8039-cb5c-445b-b08d-53b0dde1bde0, description: , createdAt: 2020-07-13T14:32:49.406Z)\n",
      "2021-03-15 11:36:55,312 - Getting existing workflows in project ...\n",
      "2021-03-15 11:36:55,633 - Got 60 workflows for project 810e8039-cb5c-445b-b08d-53b0dde1bde0.\n",
      "100%|██████████| 60/60 [00:20<00:00,  2.94it/s]\n",
      "2021-03-15 11:37:16,034 - Using existing workflow: esa-s5p-workflow - 1ecf3424-4dae-44a2-ac6b-79311dbc8089\n"
     ]
    }
   ],
   "source": [
    "up42.authenticate(project_id=\"12345\", project_api_key=\"12345\")\n",
    "project = up42.initialize_project()\n",
    "\n",
    "# Construct workflow\n",
    "workflow = project.create_workflow(name=\"esa-s5p-workflow\", use_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "overall-charm",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-15 11:37:19,104 - Added tasks to workflow: [{'name': 'esa-s5p-l3-gtiff:1', 'parentName': None, 'blockId': '70919851-c563-4e5e-9393-029cf10377c7'}]\n"
     ]
    }
   ],
   "source": [
    "input_tasks = [\"esa-s5p-l3-gtiff\"]\n",
    "workflow.add_workflow_tasks(input_tasks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the aoi \n",
    "barcelona_example_aoi = {\"type\":\"Feature\",\"properties\":{}, \n",
    "      \"geometry\":{\"type\": \"Polygon\",\"coordinates\": [\n",
    "        [[2.109314,41.394249],\n",
    "         [2.241211,41.391234],\n",
    "         [2.241211,41.327326],\n",
    "         [2.083707,41.326001],\n",
    "         [2.109314,41.394249]]]}}\n",
    "# other options include to define aoi up42.draw_aoi(), up42.read_vector_file(), FeatureCollection, GeoDataFrame etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-15 11:37:19,464 - Got 1 tasks/blocks in workflow 1ecf3424-4dae-44a2-ac6b-79311dbc8089.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'esa-s5p-l3-gtiff:1': {'bbox': {'type': 'array', 'default': None},\n",
       "  'time': {'type': 'dateRange',\n",
       "   'default': '2020-05-01T00:00:00+00:00/2021-12-31T23:59:59+00:00'},\n",
       "  'layer': {'type': 'string'},\n",
       "  'qa_mask': {'type': 'string', 'default': 'nomask'},\n",
       "  'contains': {'type': 'geometry'},\n",
       "  'intersects': {'type': 'geometry'},\n",
       "  'geojson_url': {'type': 'string', 'default': None},\n",
       "  'fail_on_missing': {'type': 'boolean', 'default': False}}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get input parameters\n",
    "workflow.get_parameters_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "polish-tract",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define input parameters of the workflow to run it.\n",
    "input_parameters = workflow.construct_parameters(geometry=barcelona_example_aoi, \n",
    "                                                 geometry_operation=\"intersects\", \n",
    "                                                 start_date=\"2019-09-01\",\n",
    "                                                 end_date=\"2019-09-10\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "south-district",
   "metadata": {},
   "source": [
    "In the above code cell, we created a basic input parameters. However, the block requires additional parameters as per workflow.get_parameters_info(). We will add those to our parameter configuration in the following steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "specialized-petite",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_block_additional_params = {\"layer\": \"L3__CH4___\",\n",
    "                                \"geojson_url\": None,\n",
    "                                \"qa_mask\": \"nomask\",}\n",
    "# update input parameters with additional params\n",
    "input_parameters['esa-s5p-l3-gtiff:1'] = {**input_parameters['esa-s5p-l3-gtiff:1'], **data_block_additional_params}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "induced-football",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'esa-s5p-l3-gtiff:1': {'time': '2019-09-01T00:00:00Z/2019-09-10T23:59:59Z', 'qa_mask': 'nomask', 'fail_on_missing': False, 'intersects': {'type': 'Polygon', 'coordinates': (((2.109314, 41.394249), (2.241211, 41.391234), (2.241211, 41.327326), (2.083707, 41.326001), (2.109314, 41.394249)),)}, 'layer': 'L3__CH4___', 'geojson_url': None}}\n"
     ]
    }
   ],
   "source": [
    "# check if everything is as expected\n",
    "print(input_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "representative-peeing",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-15 11:37:24,722 - Estimated: 1-1 Credits, Duration: 0-0 min.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'esa-s5p-l3-gtiff:1': {'blockConsumption': {'resources': {'unit': 'SQUARE_KM',\n",
       "    'min': 890.336938,\n",
       "    'max': 890.336938},\n",
       "   'credit': {'min': 0, 'max': 0}},\n",
       "  'machineConsumption': {'duration': {'min': 0, 'max': 0},\n",
       "   'credit': {'min': 1, 'max': 1}}}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Price estimation\n",
    "workflow.estimate_job(input_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "finite-technique",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Run the actual job.\n",
    "job = workflow.run_job(input_parameters, track_status=True)\n",
    "\n",
    "job.download_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "empirical-reaction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7/oRSykG99NuVet17einlO6WU55dBH3LZfiul3LedQ2e3Y/zIUsqlKmabyWRyqRmSfDjJJMlbUy+iXpvk9CR3aOmTJF9JsqM3/HZv+d1tntcmeUpvfd9JctUl235Dm/c9SV7T/r87ySEt/clJTkw9Wd7R0i9Mcv2W/rtJ3pbk2CQvS3JBm+d+g+3ctU3/fvv3yAV52tnm2bHi/lu2f7rf9+wkX+jNf5kF6/y/bb5vtfx8p43fuaX/eJIv9fbZJMnmdRzzA5N8pC33jiTHt333rSRXWGH5E3r7ck9v+jV7+TmgTbvNYN+8rHccj9iHctsdpy+349/to0e19KMG2/2vlv7ZdWxjd2p5fm6Sj7blv5fkGguWuXeb7529aQvzkuT6g/Tn9PZjdx5uz/S8elb73ZMkx7X0rW1894K83S3Jx5O8c9a8bR3nJnl9avDmjDbfK1bdJy2fp7bj/IokF7V5/nQ/1llXSPL5Xhk8ccXy8uUkL0jypt7+vVtLP6Dtm0mSNybZ1f7/nt46Hpd6Ln+ipe0cbONv2vSvpZbJL7bx3+nNs6zOe12b9sG2j89ux+SI3m//VpvnhCQfaP9/cW8bXd77Zepv99f+N/xwh2xQm5x6Y3CSZLJgnoVtbpJrt7L7vFZ+u3r5uN46jm9ltMvn9nXsmzu0Zb7Um3aXwb7ozrHTk1y+zdOdE59s+Tqnn+8529qrLp8zX1eXbGnjC9vvfSwbD8m03vvBNnvH/XuDffGAQfqyunuvem1JfjYl+WqvvL2i/f9jmfYBuvXuS9v1iMHv+lJb5jkLlunWe2Fqvf++Nv7F/VQWHzLI08faOt6yH47z4YN1H5PpuXbvFc/FpW3akjwsbItWKZNz1ru55fXtqf3irj37QG+efeoDZ7VyebvU/skZSY5LrVMnSR7YS7+g/eadSf67pT+jt52HtWlfaOs4ty1zk8FvfuJgH23a1zJiuPiGbOB18jrzuTO969esVgfuSvKi9rs+NUxfYZsLz6MVlv9sm/+XVph37rV8llxrJ9nWpr1pcJyu1dLvkOTdbfnjU+u7Sdo15T7ka+H+zZI4R2++leuQdRz3SeZcJ/TSt230+dfL09xrvxWOb5e+a8H6V2rz9qU8tPRrJvluL31HL22f+4+Z9gWuu8K8y86bped3kv9JrdvOa2lbe2lL401Jjmj7+futvHbnyeNa+nXb9i9K8uLUwPkkyetX3W9J7tXG97Tf2vWNLlXXxhuegXUUwsPbDv5uktKbfmiSy/UK36ITcnf/BEpypUwrwN9csNyWNs9pmQZX/jNzKrQkpRWMuQ1Rpp3Nf+xNu0qSb6YGzNbktaXfIfVi66wkz0/ykux9wv9akne1/fS9JP/RS1u2f27V+//m3r655YJlTmnz/HYbv/+s7aR2orv1bR6kXbv9lq+2/famJDdtaTdpy5yX5OA27cQ27a+WlJn7pjZa27uTdcZv64a99kuSJ7W0V/amPTTJZ9oxOC+183WPJfnoyspD2vgTMwhkDOY/uaX/WW/ai9r+OS+18npbkpvNOXaHJTmzreO35mxj24x9sH2VvAzS/zR7X3Ad36Y9sY0/pI2/o41vbeO7k/x1apD3q7OOZ5Iju3kH06+R5Oozfs/3Vt0nrWxdpjfPzpb+mt60Gyd5VWog9pzU8n7EsnLbW/55Sb6R5GkZdDKW1DcH9sZ3tWWfMtgnH2njB2ZaX2wdrGtHZgRikry/Tb/vYJ0f7eVhbp2X2nnqAvJHDLb11DZ+dBt/be8YnJN6Tl63/9uW7RPDJW/IxrbJm3vz3Tu1I3lqkif0z53BMnu1uYP0v2zpewVpM21ztg+mH5Tkz1ODQWentt+PyLSO6w87Z6y3Cyb9a29aF9S8axs/IQsuHjOnLk/th/xjaif2K0nu00vf0pY9JQva7yQ/k+S9mV5gfD3Jv6XVCQuOz43a/njEcJu94757wfLL6u7tbfwlqRcSZ6XeTPvlBev89bbMF3rThr9/v7Vdbf6rZHqD4GcW5K3b7ye38Zu28QvS+j1jy+KMbR2S2p5NkvxGm3Zw6oXQN1KfINqTer5cq7fcNdu+/mLqBdYnk9x6xvrv1tb99XnlJINzMSu0aan1zVOSfK5t//OpF8dL26JVyuScfP5Ykhv0xrf2lj18lXOot+ym3rKb11kuu2P+V238l9PrFyX5lzb+vMG2zk99g+Kg1KctJ2nnVpJ/yqBear/vosE+2rRoHxkuOUM2sE1u8y7qLy+8fs066sCWfsuWfmGm14aXS/L41EDwWanBtyN7yyw8j9q0n0vty5+a2u68t6232y8/GBbsh2XX8guvtTNt07eteNyf0uZ//pL5FuZr2f4dpM+Mc2Sddcgqxz1LrhMyvUZ6VOpDSWel3hTcvErZnLG+fW4Ps+Dab9nx7aW/MzVecHpqe/d7LX3VNu83UgP232v5fFJaPbBiOS2pN6o+ntnxplOypO1blIcMzqcs6BOueN6cmCXnd2/5PRlcM2eFeNNwP6c+dDdJrSsOS/InbfztLf2ATAPIt1plv6W+VT9J8sg2vqWNfyfJQavUCZeEYcMzsHJG6wnfPaV4SpJ/Te2UXr43zyS14jg7tXN7fNYGoX5wArWD/tu9Anr7Bdu+X7/AtGlPnnGy3SXJUzO9s/ef6XWwM3368+WpledX0rvz1wry51Irjx/ktaVtSu00TFJP+NelVvz9BvpXer/ndakBs4+vun8Gv/mnMq3k581zUKZ3n45o027exvcM5t3Uy9vm3vTLpXYGLkp9GvX5qRX6t5JcOcn12jIXJbl1ageqe/pmbmOaepfp9NSAw9Z+nto6/r2Xnx1pwd/e8odlWgH9Qm/605K8sv3bPYlzbhY8lZ3kHu039Z9g/nZmX5z9QpfXJIf1pr87yQvbdrvy9ck527tCpnfnbjNnnttk+nRs9zTDXVbJSy+9pAbbJ0nu05t++9TG5Dvt9345tcHvAibd8bgwtcN5fKZ3R+822MaRWa3h+aM23yf2YZ+8qKU/vY1fLdOLsg+m3kn8cGplv7DctuXv3pb99UwDIieOqPu6p8j/po0/Kr2LyTbtVW3a0YNld2RwEdmmd8HiJye5TGogqjsmB2dJnZd6wdIds99KPV+6O7XvbPMfl14j2aZ9KGvrtV2ZXhDtSa3b9jovDJe8IRvbJm/uzfe51Pq8C0L+aW++hW1um2dHW/47La93n7G9E9u6tw+mP6ZNP7v9tpel1iPXzzQw3D2pe9SM33BBGzb3ph/dzsNPpr4hck7bT9eZsy9m1uWZnsPnpF7sdHX1JLUOW9p+p3bQ39nycWymT4Q8bEm5ODn1SZMD+9scHPfvp5afU5O8OslPzVnfXnV3pvXphalPvL6xjZ+V5CfmrKe72Phe6lMm18z0idQu0Lnf2q42/z+0tLcuOZeunOTTbb3Hp16QXZTkn/a1LM5YvrvR8JlMn5A9NLVfdVxqH+ODbZ43tPTLtfxNUp/YeXZqu/SbM9b/1jbfPwymzz0Xs6RNS60bureZvpJaHt+S5M+yWlu0tEyuWOf9aqZ9okOyf/rAq5TL7g2jO7bxH++ta1OmweL/Sg2K968FfinJdTItt6Wt4zfbtFN66/xiKzeb++tfb9tg2JghG9smL+ovb8ry69eV6sDUm35Pz/TJwSf00l7cpn2gbf+bbR1bVzyPbpp6PTdJrW+em9rP35QaMP1eSzshmf/mcBZcy8+Yd69r7UwDjKe3Y/U/Sf58sFz35kj3BPN3k9xuSflYmq9F+7elz41zZEQdsspxz5LrhF76eW0d3VtP719WNufkaZ/awyy59lt2fHvpF6a2WV1f8qLUm/6rtHl3buOntt/x7uzdxi4sD0n+ou3TLdn7jYNV+o8L85Dp9ekktQ++1w3xBeVm1nmz8PweLL+nTd/am7Y03pRpub9vb7mubtuSabD480mumlqndP3XP1hxv3UPOrw8taz9Ye93LH3S+5IybHgG1pXZ+tj4nt6OnqTeIbp1S/9aagPz7NSGc5Lkv3rL7x4s2w2vSetoz9lu92rZa3vTHt2mvaQ3bXtvneck+busvYu8dbDd45P8eEv7w1bofn6Q1yPbePcK7Gcy7SB2r5x3J/xJbfyY3jYP7v1/4f7pzXdYphVBv/He0RuOSq20u99ypTbP9XvT+k+HbupN39ybfs9MLxq6dXevIf1xm+clmX3c3tTS+6+kPiS1U/SO1A7WIRkEmNsym7v1zDnmez2Z26ZfPsnvp3bon9zbj0fN2kdt2rUzbQC74eXdPhusv3ui7UmD6T/Z8vT41Ma9W881BvMd1FvHS3vT1+yjNm1bm2/XnH0wMy+99O5Jpa9l7Y2UK2baIHbD27rj3jse3880GPvkbr8MtnFkm757wfl5w9QG7MK0QMAq+2Qwzz1SG5VvJ7l2m/bQTDskBwzWt7Dcpp4bp6a9opqRAeZMn6r8TJIfa9Oe2ab171S/oE17/GD5HW36zsH0u2bayA2Ha2SFOi/1e3Szlv90S+9e//+r3jre1e2jNv7aNjwza1/FvNp69pNhY4ZsXJu8uTfvzdu0P2/j7+/Nt3Ww3h+0ub15+unvTfLTM7Z3Ykvf3ptWMr2Yv3tv+sGDbe+e8xu6N2ReMZh+s0yfcOiGpyW5bEtfqS5PDcBN0gJ9bb3d+rZkxfY7yS+m9mX+NdPgYdf2rvmkUpv26LZfrjfYv1t6eftAahtxbKb15ucy+BxX5rdn29u0D/amfahN+5Ps/Smn27R1dfXPcHhWW8f+bLv6TwrfrTd91j47IMkjs7ZO/mxmvAGXEWVxsHz3VtKfDqbfILW9eUKmNwfPaeu+R6Zt/eXmrT/TMnZOkqsM0rp9t9e5mCVtWpKf7a336sPtZ3lbtLRMrlDX/USmQYUHt2n7ow+8Srnsgl636i3TzXOj1D7mqXPWcVTqU5mTJGf0tvv/tWnf6O3vr6ReVG/uLb9pTNtg2JghG9cmL+ovr3L9ulIdmLXXUl/ONKh3lTbtwtRrpB29ebs+67Lz6N/a/1/d296Bmd6I6/bN1jY+/OTV4VlyLT/4LfOutX8/NdD67NQHmrp98ke9efrn6CT1QZef3Nd8zdu/vfTtvfQ1cY4sqUMysu3LkuuEXp6f3MavnGkA9iZZXDaHn8bsbnqOag+zwrXfsuObaX/um5m2ca9q0/6ljS9r8/6jjXef4Xh6G78o06DlophTd7Plb9v4zqw9X5e2fcvy0Jbp5t+8jjpu3nmz8PwerGNPeudyb/qyeNP/tPH+Z4W6By/uknqD99Nz1vHwFffbLTP97M1w+PmNbmNWPk4bnYF1Z7ju/Dundhi/2Xb4a1paP5h7w94B6e5udCfQa1MvCv4+yf/pLzdnm7Oe5tvRP9l60w9KvcPU3Un5vUF6SX1Coft+zrPb9Lenvo5+Uhu6wvX+1Er5b9v463rrenE/D5l+p+V35/yOhfunTb9Kpq/PP2uwTL+Q78zsOzFb2viewbY39Zbd3Jv+13NOokmmr6mW1EDjP6U+zdJdmL9wcFwnqQ3NEe3/H2778j1t/Ptt/KpZEGDO/CdzD8n0zuhw+PNZ+6hN676H97Akl830lZmXDba7ObOfaLtBphePw+Hmvfkul2mFflKSQ3tpa/ZRm7atP75KXgbzzHtS6aVt+tPa7+3K7vta+tY2/rXeMg9s094zWNeRbfruOXm4dWpjfkF6dxRX2Se9ee7flj81a4Mg3XE6dsYyC8tt6udZJqnBiZMybXC+keS5K9Z129syn0sLerfps572OrFNO3qwjh3plcVB2o1bmXxUbz9fkFrHrlTnpV6kbm/HuDvO725ps55gPiVrOzH9+uWQTMvpzDrMcMkbsjFt8ubeurqnZ7qnJb4+mHdmmzuY58czvYHymRnp3fm1vTftKhn8nsEyWzOn7sr8N2QOzPR7rEe1+V6dXkc6K9blqU9AT5Lcq40f2ltuS1Zov1MvHmfVcR9q6Tv709u0L6R2uLu+TJf+zrRPWAzKxaZM+zu3601f1J5tb9Nf1JvW9Ycel72DmdvaPAcn+b3Up30fmPpW0CTJY1r6/my7+k8K93/vrH32oG47qRfnt2/jZ6UFsvelLPbmm/eG1C9k/g3HH8+0vXvjkvPyuW2+mW1c5vd/F7Zpmd7Q/dSCbS9qi5aWySW/63qp7fAkyd/3pu9zH3jFcjl8Mqu/rk1t2pVSH7B4TOoTdN2Nm1/J2ieYu2DZkW3aKW18ktpPOSnTvt0k9UbxzZbtI8MlZ8jGtMmL+surXL+upw68TOoT+BemXtcdkXotMKv+mmT6+YWF51Gm7c3fzfmN3b7Z2sa3D7azOUuu5XvrWnStXQbbfVzm1L9tPd0buW/e13zN27+D9JlxjiypQzKy7cuS64RMA8z9AHx38+TOWVw2tw321dbsQ3uYFa79lh3fXp7+a8Y8/b9hs6jN6+JBs4abLisPqUH/i1Lb6pMy/XTb51teVuk/LsxDr8xMsmKAOYvPm6XtZG/ePd3xHkxfFm+a9QRzt64tbfxySR6Q5LGp/cC3t/QHrLLf2rRrpd7geHRq4Lo7PjdYZT9dEoYNz8DKGa0dsDsMph3ddvjbU5+6699J6jecP9Gm7W7jR65z27doy30n7SIn07sn3R8pucJgme6VzUfNSe8u3Lpv++1acCJuz/qeYN7R285B7d9V9s8Rmb4W89gV9013N/GebfwB/d/Vm29Tb3ube9O7C4eTs/dF5xXb//tPx1420wvnmX/0KLO/sdwfNmdxgHnek7lb2vTvp77KWDKtQI9esI+6V7fv1Ma74N1HB/N1FdkJc8r5h1Ibt6v2fsuWNs/hmX5K4bis8J2eLA4wz8xLL33Rk0rdKyXduXGnNn5GG9/a24+jn2BOvXA6o+XhN2ekL90nmZ6HX8jgFe2s9gTzzHKb2d9F7Ya9fstguwdkeqf3gxk8zZvp61cfa2XwwEz/kNSdBvPuaNN3DqYPnzzrLvC7+miVOq9/bhyYaYeyq/P+oo3/Rxu/Quod5otSL9gvl94T+FnbcfydRfvIsPFDNrZN3txb183btD9v491rkcva3GH67Vr6St+9zdqnRo/sTe/a3K3zzvdM35A5eTD9yr3fdZ02rTs3X7tgf2zLoC7PkieY27SF7XemN4T+XzvHuz8OesqCvOzubWc4bEsNhF2xN/+mTDvPt23TFtbdWfIE84K89eusq2T61GfXNu/Ptqt7Uvgh8/LTm7d7cq77Y4KHpn5yaZLedwfHlsXeePc0+BMH03e06Sel1su36R2zTVn7xNZlZ60/a783fbPB+pediwvbtKx9gvlqw+1neVu0OwvK5JJjsyU1OHBhkgfNSN+nPvCK5bK7yfTXbbz7BMYXe8e/fyy6cntmart7UGowYZLpk6zdJ1W6Mjdv/0wyuAg3XDKHbGybvK9PMC+sA1Ov/fp/m+TQ1E8MdHVEd5PtvPQC0m2fdH88bdl51OXhxN7yB/Ty3O2buedDllzLt3mOyIJr7ez9Ga8uuPj6Nj6sT3+3/zvG5GvZ/p2z3WGcY911yArHfel1QvbhCeY5edqRke1hVrj2W+H4dutY9ATzsjavu1nyZ4NtXXewz+aVh+0L0ne1dSzrPy7Mw6DMbF6hjll23iw8vwfz7smMcpkl8abUP2I8SfK0Nn6DTNvaw2as40apddJFmb5B9f+3d+/x0l11ffg/3yQEKyABEiyFwBM1gJEK1IhY2hrrDSINWKwSpBKlpLVSifrTH1KKEfVXqYhIjUIU+gjlKhQMggSFRCzlFiAghEYDJJAgEMhFIUAIrN8fa41nMjm3rJyc8+Th/X699uucfZm91177MjOfWbNmq3o7LNfPFRaZ0cXZ4oO+A2nY8wJsu6C9JU9L/yGXF6Z/tWDRp86TxgX56fSuB5a/+vNnS+u4OBNPnOOxiz4O35L+yW5LfwG8CF8+k/4Vot/N2hu7L2Xtl3vfkP7Jx+JXtBdN+Z+0wfauV9b00GpxQSz6sFp08v7Mscyif7jFjfG5WfvRlO3Uz6Ll1CVZ+XrpJvXyqPGYxa9hXjnGH7K0zP6s9W/b0r+Cuj/9SeA26Z+ItfQ3lM8e+3ZN1j4lfuHYnzOz1rL4Xdnih4aWtn/CeMxVS9P2LcqzzvKLG/WTV6bfLWv9hr1yHOfFE+Fpm2x/8ST8wXH8Fy0Kln/he7lF2+oLxMWLh8+Oejt/qS7vN5ZZhH9XpN8AF8fuwZuU65QsPVlspyxLyyxaKv3+OvOek7Unx+dkreXP4slzcTzW68dy8aND9x77+sas3bz3Z61V+zdlrV+jN2fl62DbqZOs3dRbesu1xfynjPnL/XadN/blvPQ3nFuetyt1cnpWXrhuclwWb/y+NOp5Ua7F1+EPydqT3uuz9onq25bW8fBRXxeMeReN8X835j80/Tz6vaydn9dmqY+9bH3Pe1r6NXBm1s7JjyzV/+2W6u/lWevH7KVL1+AX0r/at/zVt49npcWK4cAbsofPydm8D+afGsts+pw7rsm/HNfFc7N2X3790nb+3Zi/CLvOH+MPH/MX1+rih4tenOR/jnknZOlNxdI6l78h8+h19m3xAvo9o94W/T5u+AvSWT9g/vExbdEH86JF4/LzxqbP31m7N1ww1rGYf/6NOFar2zxhHKs/yvW7yHh/1lqjb3XvPj1r98jVPpg37F4n/U3VK8bxXnRf8cdL82/yc9dYbtFS+Mqs8/sF65Rr8Rx/bXqgvugy4VNZ+yrp9Lm4dM0svpV0j5Xt/8LS9hb9ji6O2xG5fp+T/3fUzZ9n6YPdrPU3/Wfr7N9W1+Kmz2m5fh/MH02/Lv4ka9f6ps9FW52TmxyXO2UtZPnLrP9V6pv0Gnib5+WD0l/vfyb9/Fi8Vv+JpXvxpeO4vyhrQf//s7SOJ2XtTerzxzG4Lis/TLzO/fWInXi+MNz8Q/b2OXmz18vbef+66T0w/d77sfSvsP9u1j4M/GTWutpZfAPlg+mvKf8w/fXk6WP+VtfRch/M5446+kDWviWwqJsTbkS93KA+s8V77bHt96bfD5a7UHj0mP8/0r8Z+/vpz0GLe9SZs+XaZv1umnOsrH9ftnEP2cZx35ct3idk4z6Y35n+emvDc3ODMt3k58OldZ2elfd+2zi+pyzV7f/J9ftgvt9YZqv3Xydm7fXfi9NfI5+XpR+T3eo8XZm/P0vX65i21evHLcuwVK/7tnHObnXdbHp9j2WePsq6yG5eN8bvPeZvmjdl7Xy8Lr1LmMV58LSlbVyY/jy8XCe/fSPq7T5j24vz40tjv37o5njeuLmGPS/AtgvaE/1npLdSuTL9SeCi9B+nOmwckFem3yC/MC6WZ2XpBeZWF9AW2z9ynHhXpYdIr89Sny7prUouG9u+PP0G8tCl+U8ZJ8zn0p8M3pPe/H3dTyPWK2t6X4jvG9t/SdZ+aXL5gj8x/c3ZVelvTBetB7dTP22D4ZQt6uan0sO2a8fF9uMr8zda774xf9+4GC8d9fPB9It78ZWtJ466vTb9SeXZWWoBtY1jd8LY3lVL0/YtyrGy7H2ydkO8QciV5D+OMlydfoM/dyx/2ibbv0v6C/pF3X8kvY+w5a+oPn6s57x1Hn9o+guJvx119MNLdXi/lfNldTh9k3KdMpY5d2X6hmVZuhYWb2DWe3Nyu/RPpS8Z+/ux9Jv9ooXE4nhcnP5Vo8vHMj+/zjFbHS7eYv7yebVpnWTjT2gvXirH4peH/yY3/FXsfdnkvF2pk8W2XrWN83X/BuU6d2mZY9JD38+kt1x7ea7/Kf9G+7Z/zL9/+guca7L2i8sPWinHVve8x6S3/P5C+puWF2XpV5aXtnPuqJ8r0p8wF31J3y79BfwHx/yPj7r+pht7fzbs/pA9fE7O9d+8PHpcg59Kf/F46Fhm0+fc9A9h3pV+X11cv8/M9VvXbnQtnr5UB6dl7Xn5E1n7gOqErNxPxvSHjukfy/p95N5r1NsnRp1+MP3reoduUh+n5Ib3iEPSv173yfT71/IHavdbWm7D5+8kx6W/Ifh8esC3CBHPvxHHavW56m7p95WPjPUugrHlX2i/OJvX++lj/CXpzy2fTQ8CvneLsjxr1OsXx/Z/LdfvJ/cmP3eN5RZvCJ9+I+rpp9ODoWvS76d/muv/kNH+Lepkw3NxzH/6WP4G30pKD6ZeOerxr7PWYqZlLVw5ehynxXH7QNZaw94qa8Hoer+DsOXr32z9nHbHcfw+NI7NhzL6ts42nos2Oye3eZ9ZHU7YzjW0sr3VYd92zsuxzA+P8+PascwTs3Yvu3X69XnVqIO/TPLYlccfmn4fWbyWfs96x2qd/T7ixtybDXs3ZO/fJ2/2enk77183vAem/6jXuWP6teM8flmW3oOk38f+a/q95vNjmVcmeeDSMhteR2P+A8d2P5V+P3xr1j7kW9TNCTeiTm5QnxvcC1rWunL6d+ldRlyVfi98V67/lfxT0z+Q/Wz6/fID4xjfoAvA7ZZrm/X7qmySc6ysf1+2eQ/Z4rhv+T4ha+/Dfyn9RwevGdOWW8pueG6uU56b9Hy4sq7Ts/LebxvH95TxmDelP29fPY7XcpedWz7npb/Gfet4/FVjm6dt9zxdmb8/K9frmL7Vc9+mZcjKc+EW58mm1802r++LN1jHCWP+lnlT+g/znjfq/uPjGC3/3tkrsnYd/XX6N/8O2W69pf/m1tvTr+3Pp3+YdNJW9XOgDYsXJwAAAAAAcKMcttcFOFBU1R3TW1msuqK19tTdLg9w8KqqR6X367XqRa21t+92eeBA4zkZuLm5z8D2uFaAm1NVPXO96a2103a3JNxUWjAPVbUv/esGqy5pre3b3dIAB7Oq2p/+FadVP9Za27+7pYEDj+dk4ObmPgPb41oBbk5VtW4o2Vqr3S4LN42AGQAAAACAKYfsdQEAAAAAALhlEjADAAAAADBl0x/5e1HdS/8ZAHxFe1S7cM/7/6qfeKDn413wwmdfuddFAGADB8LzsffHAHyl2+j5WAtmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYMphe10AAGBzL3z2lXtdBOAW5kf+wx32ugiwox611wUAADakBTMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFOqtbbXZQAAAAAA4BZIC2YAAAAAAKYImAEAAAAAmCJgBgAAAABgioAZAAAAAIApAmYAAAAAAKYImAEAAAAAmCJgBgAAAABgioAZAAAAAIApAmYAAAAAAKYImAEAAAAAmCJgPghV1b6qamM4Yq/LQ1dVJ4xjcvFel2VVVX1NVf1RVf3dKOPj97pMAAAAABz4bhEBc1V9XVX9r6r6ZFV9vqourarXVdXXj/ltneG3lx5/8cq8T1XV2VV1/Da2faeqekFVXVlVnx3bvdfS/DtU1e9U1UdH2d5eVd9xI/bt9A3K36pq31jmV6vqPVV19RjeVFX/7EbW4clV9Y6qunase/8Gy926qt67EwH1NvdtX1W9YunYXlRVT6mqGvNrjF805n9yLL9vm2V4ztI2H740/dx1yvS+bZT9yKVl7lRVZ1bVx6vqC1V1SVX95Gx9bXN/qqp+tqouXKqvn1iaf8omdX7CJqv+D0lOSvLpJM9Kcn5VPaqq/qKqLq+qa6rqfVX12KVtHTnOxU+P8+qyqtpfVXdcKfMPjHPvc+P8/d9VdYebuq9jmT8d58S1VfWJcW4cszR/02tng/Pg7z8EqKojqup/jm1fM7bx0qq6+9I6Nqrv/Vvt4yb7fkxVvbz6vevzo27PXK63qjpknKeXjvPv/Ko6cWn+XavqtVV1xaJM62zn26vqnFE3V1TV89c5fj9ZVR8c27iwqh6zMv8x1e8Z11S/D/5iVR2yNP+rquq/j+P0uap6c1V929L8W1fVb1XVR8Y2PrbeeQQAAAAcmA7b6wJs0yuTfHOSNyb5qyR3S/IvktwlyQfHMpclefnSY/5infX8cZIPJ/mOJN+b5Fur6t6ttU9usu0XJvm+JG9NcnmSf5Xk7Kq6Z2vt2jH/IUneneT1SU4e8+/VWrtkG/v21iS/tTR+77G9vx3bS5IfSXL12L8HJPnnSV47yv6xbWwj6fV3XZKLknzjJsv96hbzb4zt7Nv+9OPxf5O8OsmjkvxSko8m+R9JHjPGP59e1/80yb9OcuR43Iaq6qFJTk3f743O9eXy/c0681+R5NKl8c+Ndd86yRuS3DfJXyb5oyRfm+QbNivTDvi5JE9LL+sfJHlwkt+pqitaay9NckGuv0//KMm/SfLl9HN/I/ccf5/fWntKkoyAdF+Ss5PcOcn3JPn9qvpka+3VSb46yW2TnDXW/wPpx6vG31TVyUlelOQLSV6V5DPp5/BXJ7nyJu5r0u8Fr0s/Lg/NDc+Nra6dlyc5f2mb353km9KvkyQ5Iskj0+8n5yR5WJIfSj/O3zKWWa7vJDklye2X1jHjHmN/zkm/7/2bJI9LcqckjxjL/HySX0xycZKXJPnhJGdV1X1ba+9Pr4djk5yXfuyup6q+Lv2edZskLx3L/9skR6Xf01JVj0zy2+nX64vTP4TYX1Ufb62dXVU/lH4NX5216/P09Ov1aWNTz0zy75O8L/2a+eEkf1pVX9da+1SSX0jyU+nnwx+MbV/vPAIAAAAOYK21A3pIcsckLT18qKXpt07y1eP/luTcTdZx8Vjm4WP8TmO8JXnYJo+731jm00kOH9P+Ykw7Jcnt0oO1luQeY/4zx/h/X1rPSUnenh6sXpLkNxZlX2ebrxiPf8bStG9Z+v+26SFdS/Kvx7TDk/zuqKOL0kPVxf4dsbL+Rfn2r7PtE8b+PGWjx6/zmJu6bx8d0x46xl8+xn9pjP/yGH/5GH/oGL9ki3IdleQTSZ63evzH/HP76b/h408fjzlhg/mPHfPfmOSQbZ7LJ4zHXJwenl6e/sHIzy4tc1h6cPiBJJ9ND4xPXZr/jrGOx4zxh4/xv9xgm78x5v+vTcq1f+l4L4YT0s//Q1frLMmzNljPz4z5bxrjleQjW9TjYUmekB4+XjOO2VMm9/Vfj/kf2e61s/L4w5N8bMw/aUz7miTHrnMMW5I7rrOOfzzmfT7JUUvTT0zyv9Ov0b9N8potzpV/lOQuS+OnjPX+7VK9fWpM+5aVa2X/yrrutyjzyvSfHNPPGeOHJLliZZ3nj/FHrJz3547xPxzjv7iyrStGGe+c5NokX0py57HMC8Yyp6+MP32MP36M//l2riuDwWAwGAwGg8FgMBgMezvcErrI+Lv0UOiIJO+uqmdU7+rgsNbaNUvLfdv4ivbfVO/S4i7rrWx8dfuEpUmf2mTb9x9/39t6a+WktwZMepCyCE6S5Fuq6rbprR+T3rI1VfV96a1bj0lviX1pehB3xjpl25feQvJL6d0UJElaa+9cXizJrcb/i5a1/zm9i4OW5E3p4eiNUlW3T289+PIkz9/mY27yvqUHoF9O8utV9ftJvj89qN4/5r8gPXT8/qp6bpJfT2+R/KtbFO/30s+bJ2yxD1dW1VVV9Yaq+tZ1FnnVOK/eU1WPWpr+XePvYUk+PLoYeHVtr+uOo5P8aHrL2zsneXpV/asx75fTW35Wesvfr0rynKVuCT4//t6vqr4qyT8Z48dV1eK8WOzbbdMDwST5zU3K8/r0QDtJ3pbeIvfS1tr5rbUvLS13+Pi73KI7VfXMqnpekieP8i22dezY188l+fmq+szoamK5G5FfSv/Q4+vSP4D48/SW7tve1+pdqDwn/bz6Uvo5kmRb186yH07/VsRF6d92SGvtb1trf71OHVydfn6tOm38fWFr7fJRvu9J8pokD0ryliT/K71l+IZaax9rrS23qF+t+6PTPyj7cpJ3jWnL96btWNTvParqzkmOS2/NnCT3rarDktxnZd2r21is4z5V9dVJFt0O3SHJ3dPvh7dKD/0/ucE6np1+n//xcRz/3/QPG/7+OAIAAAAHrgO+i4zW2her9/t6Znpoe98kP53kE1X1r1pr70j/Cv2fpwc+D03y6CRfn/517WWvXBl/dXrgs5GvHX+Xg6TPjr//sLX2hap6RnqL01esPPYfjr8/Nf6+O7314ntGuR5TVT+5EpL/pySHprc2vXi1MCPw2Z8eNr2stfb2MetHxt8ntNZeMMLKszbZr/WcMbb9H9Jbba5u+1HpXQwkSVprp+3Qvr0hvYuJ+2YtWHxNko+P/z+SXrf/McmPL23v7aNcD07vOmHhqektWR+a5F+01v6uenfOq/4uPUS8LMm3J/mX6V2bHNda+3h6iP2m9K479qV3qfLCqvp0a+3s9O4Ekh4aviS9i4mHpnfZcP8NyrXw5STf2Vr7VFV9Kj2U/NGq+uP01ptJ8n/Sz7X3pQf4P5H+AcDTRnlPy1qYmfTWp0elt8Bd+LH0rhre1Vr7i1Ffd0xvob7wutbai6rqe9O7Rnlda+301cqqqp8Z270oPRBcthzivy3JheP/RR39g/QA+WXpXcj8dlVdlv7hxOIc+pHW2ivHthYh8Hb39cfTu5RIelD+rqzY5NpZbz+e1Vr78jrr+Nr07iKS5ElLHzot5h+V3sVL0kPz9db7hJV93FL1Pt9/Nf28+fkxeXFvuqa11sb/f39v2uaq/zA9zD02/UOcZf8w/fgdOsYX98DFNm4/Qv/fTO+y4wfHsLqOTe+h4+8F6R9yPCL92xdJ7xrkfQEAAAAOfHvdhHq7Q3pLzu9L8ivpYUhLctaYt9x1xr2y9hX2u4xpF4/xV6cHIk9O7+ezttjmj2XpK+Rj2jPHtGcuTfvu9FbD/+8YWpI3j3nvXyrP6nCfpXXcNslVY/o/X6csX50evLb0YPTWS/M+N6Y/YJ06OGJlPYvy71+Z3tL7t/7j9NB38fjXpX/tf/9y2Xdi39LDq8vG9EeN5f5ojP+3sczTxvgfjfknj/GPjcefvrLdfenh1KfHvvxxemvIlt7lwqPWOWcOXzpHTl6dP8ZfPOY/Z4y/cIy/ZowftVSGu29QrhMWZV9a778f096yso7V4VNLj/nGJE9Mb/378DH/uiRftbRMJfnrMe/fLk3ft7Le08f0/cvjK/u+2JcPJrn7BtfK7dOvzZbkr8e0Y5e2861j2hlj/Hkr+3uXDda75b6O5W6THsK39A87brOda2dpmX8+5l+V5LbrzP/6sf8tyZM3KOt/GfPfsDJ9cZ2cPHHf+9b0rlSuy+gqZEw/ZqzzSxldtCzVz/kr67jfop43uK88Lsn/l97/8jlj2celfwB53Ri/x8q6rlpax9Hp31z4lfQPVRbX27FJvnP8/+Gl5U8b0141xl86xs9I/zBicQ99+42tL4PBYDAYDAaDwWAwGAy7PxzwXWRU1a2q6p+11j7fWju7tfbkJP91zL5dVf2j9FBiPautEJ/bWvvp1tqvtNb+pLXWttj8+ePvfcePuiU98El6a91U1eGttT9rvdXn09NbuibJn42/F4+/T2it1WJI8vWtteUWeovWpu9so7Xpwmh1+mfp/bg+P70v4S8sLXLZ+Huv8feemXNsehcV/3Jp2vcluVNr7ZSV8u/Evt0hva/ZJHlLa+0zWavzxQ8NLrocOX/Mf+sYv0t6eH768rZbbx1d6X13f/8YFufH8UnuOb7Kv24XKlk7Z75+i/nv3WB+knxmg3ItHFVVi9a9i1bbl6Z317Jo3Xnfpfo8ZJQ9VXWr1toHWmu/1lr7xax14/Lm1tqiu4Kkt6b+hvTW/YsfxEtr7eKVcp2+0U5U1SFV9TvpPyT37iQPaq19ZGn+7ZbWe3V6iJskx4wWupek9ze8bh2N/V20bP22pfUetp19rarb1mie3lr7bPqPCCa9O507j3Vsde0snDb+/v44z5br4X5J3pwezv/H1tqvrD64qg5PD7iTG3ZH8uGN9nEzo2uNN6Z/sPKI1tofLM3+aHo/x4dk7ccGr3dv2qbrWmu/11p7UvoHMP80Pdx9Y2vtuvRwPFn79sLq/e+w9O5UnjHuzXdJv94uSW/tfkGSLya5+2gBvl45F9f4ea21z2V8OyE792OjAAAAwM3ogO8iI/3H/P6iqj6QHnJdk+QHxrw/TQ90f6Oq3pje+vChY94bWmurX/u+UVpr766qP03yPUnOHd0Z/NP0cOdFY7FfrqpvSfKh9BDmvmP+b435Z6SHW0+rqm9Pb238zen9px6TJCMkW3QV8Mx1ivLq9K4Crhz7+PSRq72utfa6UZb/kuS3quqEsb3rGf1WPzxrQdE/q6r9Sf53a+33l0LjRX/Ji1DsDq21qzaoopu0b613EfFX6YH4q6rq7en94Cb9B9GSHux9f5Kfrqq7Zi2c+r+ttU+vV6jW2gkr+35xehcKP9Bae9XYvwvHOXNJet3eI71l/BvGw/60qj6e3n3H3dOD9i+nd4eR9C5bnpjkwVX1wqyF+n/cWrti/er6e4ckOaeqzk/yyDHtBa21VlVnpHeD8PqqenV6uPjA9C5gTknyfVX1K+lh4N3Tz/8vJnnSyjZOG39/p6105XAj/HJ6aPrl9GvvieO8u6i19ttJfraqHpHknektaRfX3htba19Mev/M6V1yPL+q3pLeAv1L6X0Ut6p61ij7C6vqFen99X4pvZubrfb1B5P8l7Hea9Kv06R30XHx+H+ra2e1f/D/vlwBVXWn9Lr/mvQuG+419ilJfru1dtH4f9F/819nLWhfeFb6OfyEqvqG9PPsW9OvlXVV1Telt7Y+PL27lO+squ8cs5/aWruiqn4jveuMP6yqNyX5oSz1QT0+xHh6+octi/XuT5LW2ilj0l9W1TvT+5N/2NjeGa21D475T0tvrX9GVX3/WCZJfm38vXeSV47t32HMb0l+fnyA94mxzccleUNVvW+U8zNZ627kzekh869V1QPTvxGSrN0DAAAAgAPZXjeh3mpID8GfkbV+fj+f3jLuqWPefdL7Vv5Yki+kB0vPSnLHpXVcnB56PHxi+0emByxXpYdYr09y76X5j0kPY7+Q3i3Di5IcvbKOh6e3vL16rOftSU5bmv/QrHX7cKt1yrAo/+pw+ph/6yTPGev+cHp/x4tljhjLnL7BOvavs719q4/fpH5u6r7daxy/T4xj+8H0YPPQpeP/K2P655N8cix/7I04htc7/klul/4jgB9MD8U/Ptb5TUuPeVJ6C8u/G/v15iTfv7Leb03yF2Mdl6X3TbxhfWWti4yLk/xcetcHH0sP4xbL3Co9YL5grPcT6d2UPGTMv3966+lr0ls7n5Pesnh5O/cZ2/lckiO3WUf7l8+plWmrw7lLx/5d6a2UPzfq85npH0osX7//Nb0l9WfTw+ITV+aflh7eXjP29ynb2df0D3vekrX7wiVJnpulbjyyxbUzlnn6mPbyLa6F1eGEpeXOG9Mev0H9njjOoatGfb1mi+Nxwibb3TeWOTT9WrksPSB+T5KHbqfsS8u8Iv2+dW16OP7zGV1uLC3zU+kfoF2b3o3Ojy/Nu2v6Nf+ZcQzeluSklcf/g/QPoy4fy/yfJN++NP926WHzJen30Y+l9zf+tTf2fm0wGAwGg8FgMBgMBoNh94dqbateIgAAAAAA4IZuCV1k3KxGH61PWWfWFa21p+52eYCvDKO7jMevM2vRBQkAAADAAe8rvgXzSn/Dyy5pre3b3dIAXylGf+nnrDPrz9tKP+LcclTV89K7Bvpka+0+68yv9D76T0zv/uWU1tq7dreUAAAAsHO+4gNmANgpVfUv0vukfv4GAfOJ6f3kn5jk25L8Vmvt23a3lAAAALBzDtnrAgDAwaK19qYkV2yyyMPSw+fWWntrkiOq6i67UzoAAADYeQJmANg9d03y0aXxS8c0AAAAuEXa9Ef+XlT30n8GAF/RHtUurL3YblWdmuTUJLnNbW7zLfe+9733ohgAcEB45zvf+anW2lF7XQ4A4IY2DZgBgB11WZKjl8bvNqbdQGvtzCRnJsnxxx/fzjvvvJu/dABwgKqqS/a6DADA+nSRAQC756wkP1rdA5Nc3Vr7m70uFAAAAMzSghkAdkhVvTjJCUmOrKpLk/xiklslSWvt2Ulem+TEJBcluSbJj+1NSQEAAGBnCJgBYIe01k7eYn5L8pO7VBwAAAC42ekiAwAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmAAAAAACmCJgBAAAAAJgiYAYAAAAAYIqAGQAAAACAKQJmANhBVfXgqrqwqi6qqieuM//uVXVOVb27qt5bVSfuRTkBAABgJwiYAWCHVNWhSc5I8pAkxyU5uaqOW1nsyUle1lq7f5JHJvmd3S0lAAAA7BwBMwDsnAckuai19qHW2rVJXpLkYSvLtCRfM/6/fZKP7WL5AAAAYEcJmAFg59w1yUeXxi8d05adnuTRVXVpktcm+U/rraiqTq2q86rqvMsvv/zmKCsAAADcZAJmANhdJyfZ31q7W5ITk7ygqm7wfNxaO7O1dnxr7fijjjpq1wsJAAAA2yFgBoCdc1mSo5fG7zamLXtskpclSWvtLUm+KsmRu1I6AAAA2GECZgDYOe9IcmxVHVNVh6f/iN9ZK8t8JMl3JUlVfWN6wKwPDAAAAG6RBMwAsENaa9cleXySs5N8IMnLWmvvr6qnVtVJY7GfTfK4qnpPkhcnOaW11vamxAAAAHDTHLbXBQCAg0lr7bXpP963PO0pS/9fkORBu10uAAAAuDlowQwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAO6iqHlxVF1bVRVX1xA2W+aGquqCq3l9VL9rtMgIAAMBOOWyvCwAAB4uqOjTJGUm+J8mlSd5RVWe11i5YWubYJL+Q5EGttSur6s57U1oAAAC46bRgBoCd84AkF7XWPtRauzbJS5I8bGWZxyU5o7V2ZZK01j65y2UEAACAHSNgBoCdc9ckH10av3RMW3bPJPesqjdX1Vur6sHrraiqTq2q86rqvMsvv/xmKi4AAADcNAJmANhdhyU5NskJSU5O8ntVdcTqQq21M1trx7fWjj/qqKN2t4QAAACwTQJmANg5lyU5emn8bmPaskuTnNVa+2Jr7cNJ/io9cAYAAIBbHAEzAOycdyQ5tqqOqarDkzwyyVkry7wqvfVyqurI9C4zPrSLZQQAAIAdI2AGgB3SWrsuyeOTnJ3kA0le1lp7f1U9tapOGoudneTTVXVBknOS/Fxr7dN7U2IAAAC4aQ7b6wIAwMGktfbaJK9dmfaUpf9bkp8ZAwAAANyiacEMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMAAAAAMAUATMAAAAAAFMEzAAAAAAATBEwAwAAAAAwRcAMADuoqh5cVRdW1UVV9cRNlntEVbWqOn43ywcAAAA7ScAMADukqg5NckaShyQ5LsnJVXXcOsvdLskTkrxtd0sIAAAAO0vADAA75wFJLmqtfai1dm2SlyR52DrL/XKSpyX5/G4WDgAAAHaagBkAds5dk3x0afzSMe3vVdU/SXJ0a+01m62oqk6tqvOq6rzLL79850sKAAAAO0DADAC7pKoOSfKMJD+71bKttTNba8e31o4/6qijbv7CAQAAwAQBMwDsnMuSHL00frcxbeF2Se6T5NyqujjJA5Oc5Yf+AAAAuKUSMAPAznlHkmOr6piqOjzJI5OctZjZWru6tXZka21fa21fkrcmOam1dt7eFBcAAABuGgEzAOyQ1tp1SR6f5OwkH0jystba+6vqqVV10t6WDgAAAHbeYXtdAAA4mLTWXpvktSvTnrLBsifsRpkAAADg5qIFMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQBMwAAAAAAUwTMAAAAAABMETADAAAAADBFwAwAAAAAwBQ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      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "job.plot_results(figsize=(20,20), bands=[1], cmap=\"RdYlGn\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}