{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "numerical-beads",
   "metadata": {},
   "source": [
    "# OneAtlas WorldDEM Full Resolution Block - Data Access Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WorldDEM™ Streaming Service is a Digital Surface Model (DSM) with unprecedented quality, accuracy and coverage. The resolution of the data is 12 meters and it is in GeoTIFF format to represent the surface of the Earth including buildings, infrastructure and vegetation.\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/1cdf4786-f524-41d5-9d88-b877c2a2bb4f) and [Documentation](https://docs.up42.com/up42-blocks/data/oneatlas-worlddem-12m.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import up42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:34:46,295 - Authentication with UP42 successful!\n",
      "2021-03-11 14:34:47,159 - Initialized Project(name: datablock marathon, project_id: 76f9c5f3-8f3a-4add-986b-149da06d2983, description: , createdAt: 2021-03-11T09:25:48.206469Z)\n"
     ]
    }
   ],
   "source": [
    "# authenticate and intialize the project\n",
    "up42.authenticate(project_id=\"1234\", project_api_key=\"abcd\")\n",
    "project = up42.initialize_project()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "israeli-kingdom",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:34:54,355 - Getting existing workflows in project ...\n",
      "2021-03-11 14:34:54,764 - Got 4 workflows for project 76f9c5f3-8f3a-4add-986b-149da06d2983.\n",
      "100%|██████████| 4/4 [00:01<00:00,  2.52it/s]\n",
      "2021-03-11 14:34:56,361 - Using existing workflow: worlddem12-data-access - 74eb64ad-8b83-4b29-b0ea-7968ec7a0deb\n"
     ]
    }
   ],
   "source": [
    "# Construct workflow\n",
    "workflow = project.create_workflow(name=\"worlddem12-data-access\", use_existing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "juvenile-dragon",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:35:02,196 - Added tasks to workflow: [{'name': 'oneatlas-worlddem-12m:1', 'parentName': None, 'blockId': '1cdf4786-f524-41d5-9d88-b877c2a2bb4f'}]\n"
     ]
    }
   ],
   "source": [
    "# construct parameters\n",
    "input_tasks = [\"oneatlas-worlddem-12m\"]\n",
    "workflow.add_workflow_tasks(input_tasks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set aoi\n",
    "aoi = {\"type\": \"FeatureCollection\",\"features\": [{\"type\": \"Feature\",\"properties\": {},\n",
    "      \"geometry\": {\"type\": \"Polygon\",\"coordinates\": [[[13.368789,52.495375],\n",
    "                                                      [13.383853,52.495375],\n",
    "                                                      [13.383853,52.502715],\n",
    "                                                      [13.368789,52.502715],\n",
    "                                                      [13.368789,52.495375]]]}}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the aoi and input parameters of the workflow to run it.\n",
    "input_parameters = workflow.construct_parameters(geometry=aoi, \n",
    "                                                 geometry_operation=\"bbox\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "hundred-orlando",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:35:10,909 - Estimated: 416-416 Credits, Duration: 0-0 min.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'oneatlas-worlddem-12m:1': {'blockConsumption': {'resources': {'unit': 'SQUARE_KM',\n",
       "    'min': 0.83,\n",
       "    'max': 0.83},\n",
       "   'credit': {'min': 415, 'max': 415}},\n",
       "  'machineConsumption': {'duration': {'min': 0, 'max': 0},\n",
       "   'credit': {'min': 1, 'max': 1}}}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Price estimation\n",
    "workflow.estimate_job(input_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hybrid-newark",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:35:10,919 - +++++++++++++++++++++++++++++++++\n",
      "2021-03-11 14:35:10,920 - Running this job as Test Query...\n",
      "2021-03-11 14:35:10,921 - +++++++++++++++++++++++++++++++++\n",
      "2021-03-11 14:35:10,922 - Selected input_parameters: {'oneatlas-worlddem-12m:1': {'bbox': [13.368789, 52.495375, 13.383853, 52.502715]}, 'config': {'mode': 'DRY_RUN'}}\n",
      "2021-03-11 14:35:12,345 - Created and running new job: ac372a5f-cdef-40d1-ab97-ebb477789b8e.\n",
      "2021-03-11 14:35:12,891 - Tracking job status continuously, reporting every 30 seconds...\n",
      "2021-03-11 14:35:29,641 - Job finished successfully! - ac372a5f-cdef-40d1-ab97-ebb477789b8e\n"
     ]
    }
   ],
   "source": [
    "# Run a test job to query data availability and check the configuration.\n",
    "test_job = workflow.test_job(input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "chronic-times",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-03-11 14:35:34,649 - Selected input_parameters: {'oneatlas-worlddem-12m:1': {'bbox': [13.368789, 52.495375, 13.383853, 52.502715]}}\n",
      "2021-03-11 14:35:38,253 - Created and running new job: 387385a4-0d9a-43a6-8b50-77aaf7946b6e.\n",
      "2021-03-11 14:35:38,795 - Tracking job status continuously, reporting every 30 seconds...\n",
      "2021-03-11 14:36:01,031 - Job finished successfully! - 387385a4-0d9a-43a6-8b50-77aaf7946b6e\n"
     ]
    }
   ],
   "source": [
    "# Run the actual job.\n",
    "job = workflow.run_job(input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job.download_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8p/fUCiv/o1avWPmTwKnKQuT7K/Uj9o0t0Pf+K+5vWPnHgd/6Z9769Yk9f+YPW/kT0HUrLyr/P6v86+SVereVf/x6zsxWrLxgP/MNnKd7LZx+wH7vAv1xK//Y90TlmL5pZn/bzBr2/Jk/aeWF56KVf3x991+U/1Hx+n/Cyn8tYNnKC/9ftN+9QJfno/Jn/ncrL9pXrPwv2n/evv6v9rdYeRF7tXK+x6z8jYAHIsfldOXPjVfe79XK+62p1D9i5UX0dOV1X6+Mq317ejxW2Zepyvt7zsrfNHiPlb+hMlvpfdPKi9ThPdvu/ivu1yrn/pqZfXhP/futvKBfs/I3bv7KW147eEzezvVp5ei+3f4LVv7mzwNvOUZ9Vh7f81b+ZsGnLDD27N7j7M9Z+d8WmKuMmZdtz7XIF1988cVX3l9FqVQyAAAAAADwrcXfQQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADOxXxR/7sR8L/gtyKf+43MzMTLDW3t7u7ru0dM8oXTMzGx8fd/ddX18P1hoaGoK1mJ2dcCTp2tqau29jY2Owpo5RzL2jacu6u7vdfYeGhoK1pqYmV0+1r2Zmq6urrr4vvPCCrHvH79mzZ2V9cnLS1Te23fLysqtvbe29YpvLNjc3XT3NzG7duhWsdXZ2uvuqceQdY2Z6HlxZWXH3raurC9YOHjzo7qvOzfb2trtva2trsFZfX+/uq8ZDyv6Ojo4Ga+q9xHR0dARrar6PqampCdbUfK+oMWbmv2eq+6WZ/14R66ueK5S+vr6k1w3Zty/8+UfKWFBz9uzsrLuv2ic1/mLU+T5w4IC7rxr3Gxsb7r7K/Py8e1v1zLG4uOjuq+arlOOrnhVTnnvVs5n3WjPTz17e5+krV67Iuve6aGlpkfXY82tI7Hx7z9vW1pas9/b2uvqq/U0ZC+p6unz5sruvur+nPOc88cQT9zzhfIIOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZEDmoKuMuuPHj7tfVGUAPvPMM+6+KrcxJVNPZWqm5MGr/EqVoRqjjm8sz1BR+asLCwvuvur4ejOfYxnJ3lzMWO7lkSNHXH1jOajevPJYJnksCzlEHd+Ua0JlSabkq6tMZ5VtGbN/f3gK9WaDmulrIiVDWZ0b71gw09fTa6+95u47NTUVrKWcNzXOUuaylZWVYK2pqcndVx3f/v5+d1/Fm6Fcrbm3ublZ1r2ZxLG+3nGm5vSUPG31bJAy59y9ezdY854zM7PJyclgra2tzd1XjbPYGFRmZmaCtZ6eHnff2tpaVy1GPfemnDdFZY7HvPHGG8FaLB/cS83LSmzO9uaVx55lvH1jOefe4xvL+PaOB3Wdqusw5tq1a8FayjWs7gWrq6vuviF8gg4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkQOagd3V1BWsqpzfm3LlzwZrKdIw5f/58sNbd3e3uqzIAU7LvVCahN0/bTGcopuyv6uvNzDXT2Y3eTOJYVvTs7Kyrb7VyOsfGxmTdm30dy4NNyV8NScm2Vdd/ytgdGhoK1lLyv1XWcUpe+fHjx4O1lJzura2tYC0lv17dK1Lyv9W2KcdXzVcq2z5meno6WFNjJaaxsTFYU7nYSmzce6+3zs7OpLqXN/s6ljnsHQ/qvFy9etXV00yPXW8WvJkeY2reiKnGPcbMbG5uLlhLmRvU+Ey5V1SLGp/e/G8z/dzb3Nzs7vuBD3wgWEs5b2pd4V2vxJ73vOuV2Jyzvr7u6hvbznsdx65hlQ/u7TsxMeHqaabXe7GseKVaz6chfIIOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZEAGfKpM1/n5efeLqmzGlHz106dPB2spGbQq4zeFykm9ffu2u6/KB03J8VY5ySnnTWULqjxo5bXXXpN173GI7Y83FzOWT+nNM43lV3ozftX7nJqacvU00xnqKfnqKjs45fpubW0N1lLyf1VOakpfdQwPHz7s7qvyyl9//XV3X5WLG8uSVZaXl4O12NyhqPxVb1asmc4k9mbmzszMyLp3bohlGVcrk3h2dtbVN5Yj781mVpnZ3mNrpjOo29vb3X0vX74crKXkf6vxqa7DGHUc1HyfQj0DxahxlnJ8a2pqgjWVbR+j9lfN9zGf/exng7WUe5vaJ29fdWzN4nNHiJrPzfzjNzZHesdD7N41PDzs6vviiy8Ga2tra66eZnq9lzLnKCnPIyF8gg4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZ2K+KfX194Q33y02lhoYGVy2ms7MzWKupqXH3bWxsDNZqa2vdfdfW1oK1mZkZd9/t7e1gLeU4nDx5Mljb3Nx0952bmwvWrl696uoZ2x/v8b1+/bqsF0Xh6ruzsyPr+/b5vpe2vr6eVA9R+xt7L8rKykqwduDAAXdfdU28+uqr7r79/f3Bmpo/Y0ZHR4O1pqYmd191/c/Pz7v73rx5M1jr7e11921paQnW7ty54+6r5u33v//97r4jIyPB2uDgoLvv4uJisOade2P3cO/4VWPBTN/3lKmpKVn3zr0dHR2yrs6pouac2GsqPT09wVp7e7u7r7rWVldX3X1/67d+K1hLmSPVc5l3LJiZ1dfXu14zRh3DlP1V99ulpSV3XzWWxsfH3X27u7uDNe/ziJk+b95nko2NDVn3roO2trZc28XExlHs/YTErv/nn3/e1Vc9hy8vL7t6xqSsVdTxTXk+DeETdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADRalUChafe+65YDElx0/loKpM7BiV+ZiSzawyc1NyxVXOn8pBjlFZkimZz3V1dcGayqCMURmLanwqal9T+qpsW7P8cjFVrriZPxdTZaSmXGvq+Hqz4M10RmVsrCgqU3NoaMjdV0m51rq6uoK1lJxulQ/a3Nzs7js5Oel6zZgrV64EaxcuXHD3vXv3brD24IMPuvuqMeq9Z6r7mpn/3hY73xMTE66+KqfbzD+nx+ZIbwa4yjpva2tz9TRLG/eKei5Lec6ZnZ0N1lKe9xoaGoK1lHtFU1NTsJZyr1DXW8r+quspJZtZXW+x5yBlamoqWPM+P5np8+a9hmPj0/vcFjvf3nu8ur+b+e/x8/Pzsj42Nubqq45vynNkY2NjsOZ95jXTc2RKvvqTTz55z0mdT9ABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADMjQQZXjG8skVVR24LVr19x9L168GKx5cxDNdPZdSiapyitPydS7fft2sKbeS4w6b7EsWUVli3pzR2PnRR17JZZf6R0P1cokjuV0eq9jtb8LCwuunmZ6f1Oyg9X+qszMGJW9HDunirr+U7Ji1ThKuYZV/mrKvUJl2371q19191X7FMuSVVQ2c8p5U/nq3vzv2NzgzYttb2+X9dbWVlffWPayt29s3HvPm8qgTrm/Ly4uBmspc6SaB2PZ9oq6hr25zGZ6/KYcX3WPV9d3jLofpGQznz59OlhLyVdXz4re3GszfQ9KmXvVGB0fH3f1jF373rkhNvd6c9Bj91pv31gefGxu9ki5X6Zsq6i51/uMrvAJOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABGRanMp2fffZZ94t2dnYGaypnLkbl4npzr810FndK9p3KB03J8VSZmin5gN3d3cHa1NSUu6/ap6WlJVfPWG6r9/jGciS9eZApmdlK7H2qMaio63R+ft7V00xfTyljV2Ukp8wNq6urwVpKxq/KM03Jbb9582aw5s1INdPXW8r+qizp97znPe6+165dC9YGBgbcfdU8eOXKFXdfdS/e2dlx9YxlJKv7nhLLivb2VbnMZv4saTXGzPxzs8pe9p4zM7OOjo5gLeX5SeXXp+R/q+Ob8vyksqTVvJwiljOtqOOwsrLi7queOSYnJ919FTVWYtT94NChQ+6+ajx4z9vExISsLywsuPrG5irvs07sejp8+LCrb+w4tLW1ufqq58+UOVJd/ymZ7er4xu6nHnyCDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGRAhu1duHChKi96586dYG1ubs7dt6+vL1gbHR1191W5raVSyd23WlmdZ86cCdbu3r3r7qsyAFXOdMzy8rLrNZVYdq03Bz2WdejNWIz1VTnISizje3p62tVXXRM9PT2unmZ6LMSy7RW1T9evX3f3Vfuk5qMYNQ+qrO2YpaWlYO2BBx5w91Xb9vb2uvuqbFvv2DXT4+Hy5cvuvkpKhrLKSfbeR7q6umTde12oa9jMP/fGsoHHxsZcfevr62Xdm/msso7VuI5R99qzZ8+6+6ptP//5z7v7qnt4yvOTykn25kib6bzyauV/q1rM+Ph4Vfqq6ynlOJw+fTpYS5kjOzo6grXPfOYzrp6x566U46B4r4tYzvmXv/xlV1+1ZjMzO3nypKuvmnu99wkzs8nJyWAtJQddnW91j/biE3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAzIscmRkJLxhQs6kykmdmZlx91XZwdXK21S1FLFsVkXleK6trbn7qlxnlYsdo7IFFxYWXD1feeUVWffmbcZy2b155bEcdG9ubmwceXPm1f6mZPyq/VH5vzHq+vceAzO9TyqLM0ZdE97cazOzU6dOBWv9/f3uvmqfvvCFL7j7qizpwcFBd1+VHZxy3tR4+LVf+zV33yNHjgRr3ntFLCPZe8+MzYFTU1Ouvs3NzbLuzXyOzSverGN1v1xfX3f1NDPr7e0N1i5fvuzu+9RTTwVrsXxlReUZx+57SrXuQV1dXa5ajBpH6tk1Rl1Po6Oj7r7qOk4Zv6pvyj1IjdHPfvazrp6xZwPvs0Nsu6Wlpar09VJzTsrrqrHgfZaObZuyVlHUs4oXn6ADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABvar4vXr16vyomtra8HanTt33H3r6+uDtc7OTnff7e3tYC0lnH5hYcH1mjFTU1PBWsr+NjY2BmvqvcSo89ba2urq2dfXJ+tNTU2uvrHzsrW15eobOy/Nzc1V6buzs+Pqu76+Hqx59zXlNWPUeFhdXXX33b8/PIW2tLS4+6p5cGVlxd1X7VNK34sXLwZro6Oj7r5Hjx51b6uo4/Dwww+7+6rzdvLkSXdfxTuXqXnXzOyxxx5z9Y3dC0qlkqvvvn368wTv9VZXVyfrHR0drr4NDQ3B2tLSkqunmdnY2FiwVq1r7ciRI+6+1Zp77969G6wtLi66+6rxsLm56e67vLzsqsWoe3zsmlFqa2uDNe9zg5k+5+rZNeZzn/tcsBa7xkOKopB17/PeyMiIrJ87d87V9+rVq7Ieey4O6e/vl3X1HKRMTk4Ga977mpn/fMd0d3cHa7Gx4sEn6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGZHidynycn593v6jKM+7q6nL3VdmMKRl1KuMvpa/KUPTmK5rpnMmU/b106VKwpjJfY9ra2oK12dlZV8/Y8fPmwdfU1Mi6N3c0lkmckl+vePMi1XGIHSNFXWspOegqdzQl21aN+xs3brj7NjY2Bmutra3uvur4dnZ2uvueOXMmWEvJfFbbqszxlL7Nzc3uvmfPng3WYlmyyi//8i8Ha9682Nh23kzilZUVWT927JirbyxfPfa6IcePH5f19vZ2V191D0rJvT516lSw5s2YNzNbW1sL1lLmdJVJnvK8NzMzE6yl5H+r45ByH1a54in3tpaWlmDNm09tpp8VDxw44O6rrtMrV664+6p98ua2x56PvNdx7Hn5sccec/WNnRfvvS0296p8cEVdEwMDA66eZnpu8N4nzMwuX74crKlnNi8+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAyIEMSVVZfSo6nyrZWuXgxKrMwJYtX5Zleu3bN3VdlVKbkV6qsTm/utZnO+Zubm3P3VbmE29vbrp6x4+fNvo7lq3rz62PnRWWdpvT15rqq7VKuYZVXmpLxq8ZnSq6493zHqON78OBBd98TJ04Eayk53ep6S8k67uvrC9ZUHnTMM888E6x55wYzfc88d+6cu6+6purr6109Vc6xmf+8xTKHvZnaPT09sv61r33N1TeW8evNDlbXROzYK4cPHw7WhoeH3X3Vc1nK856ay+bn591979y5E6ylPD+p55GUuUGdt5T9TckkV9QY9V7DZjrrPOU5Xc2D3ms49iyzurrq6hubc5qamlx929raZP3YsWOuvrHnT+/4Vc9lKdfEoUOHgrXXX3/d3VddEyn56iF8gg4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkQAbNPfroo8Hak08+6X7R7u7uYC0lV1hlZnvzns3MfumXfilYU9mWMWfPng3WFhcX3X1VVndKlrTK+UvJxaxWvno1xPJrvTnzse3Gx8ddfTs6OmTdmzuqridvNqiZzpFOyVdX28ay7RWVQZuS46m0t7e7t7148WKwdvXqVXdf9V5TzttHP/rRYO3ll19291U5tGo+ilFj6eMf/7i7rzpvn/vc51w9Y/cCb75y7Hx7M7Vj+zs2NubqG8t7V88rijp+k5OTrp5mZhcuXAjWHnzwQXdflaHsHWNmOr/em09tZnbz5s1grb+/3913aGgoWEuZG9Qc6R27ZvoYqvkz5nd+53eCtZT8evWM39vb6+47MTERrHnvQbHnPe89fnNzU9a9c6S61szMBgcHXX1VxryZvhYVdRxS1hRqjKlnzJhTp04Fa9V43uMTdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADMrhN5eJeu3bN/aIqi8+bvWqmsw5TMvV++Id/OFhLySu/c+dOsBbLX1RUXuzOzo67b1NTk+s1U3jPW2w7leOtqHzFt/O6IWtra7LuzeqOZZJ7c9vVWEhRrXGkxn1KDro63ynXsMqv397edvdV4yE2thV1HLxjzMzsK1/5SrCmssxjVlZWgrWUnPlqjTOV1f3www+7eqrcYDP/vbilpUXW33jjDVffWIbvhz/8YVffK1euyLp6DlLU80jsGCl3794N1s6cOePuq67/gYEBd191rzh27Ji777PPPhuspeSrj46OBmspOd3Dw8PB2sGDB919Z2ZmgjU1z8W85z3vCdampqbcfW/cuBGsxeYkRZ0b73NvLPfeex2rPG0z/z2ztbVV1r3PV7Hc+9jrVuM1lddffz1Yiz0TK+vr68FaLNveg0/QAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAzIHPTx8fFg7dChQ+4XVfnA9fX17r4pGd/evtPT0+6+Khc3Jbdd5a9+K45RjMq+jOVQenkzfmMZ1N6M6tj79GYoLy0tyXpK9rX3NRWVmZtjXrm61lJyMdW2al6OUTme586dc/ft7u4O1k6ePOnuu7a2Fqy99NJL7r6HDx8O1vr6+tx9VS5uyv6q68KboTw3Nyfry8vLrr6xrGjv/sZycY8ePerq++Uvf1nWveNXzSsLCwuunmY6c/j69evuvvfff3+wljKXffKTnwzWUu7v/f39wVrKNazea8pxUOc85Xmvq6srWEu5t6nM7La2Nnffd7/73a7XjJmcnAzWbt686eoZyzn35rbHcs7375dLsyA1Fsz8z3sbGxuu7WLU81PK2L17926wlrJmU/fhhoYGd98QPkEHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADOxXxStXrgRrKSHyXV1dwZoKgo9ZWVkJ1mpqatx9L168WJW++/eHD39dXZ2777594e+7HDhwoCp9t7e33X13dnbc23p7qmOvlEolWfdeFxMTE7Kurhmlvb1d1ldXV119Nzc3g7WUa7i+vj5YSxm7Gxsb7m2VtbW1YC3lGlb729zc7O7b2Njo3lZRx+Hu3bvuvhcuXAjWlpeX3X3VvJ1y3hYWFoK1kZERd98HH3wwWFP3J2VqakrW1TWu3L59W9a99wo1N5iZXb9+3dX30UcflfVnn33W1Vftb8p1ODs7G6yljLEnn3wyWFPjOmZwcDBYS5nTGxoagrU333zT3Xd4eDhY6+/vd/dV4yF2LSotLS3BWsrxVc9IsWvRK2VOV+PMe/9/7bXXZN37/B87frW1ta6+seO3tbXl6ht7n95xpu4FKWNXPdcePHjQ3Xd0dDRYW19fd/cN4RN0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMyDFplOqdkxaosvvn5eXdflQeZkg+qsvpUFmeMymZMOb4qA7xaedCxfHBF5R1WI1/RzH98Y9nA3uzgWM7krVu3XH1bW1tlvRq5mN4MTzM956RcaysrK8GaynuNUfu0b5//+58zMzPBmjf32sxsaWkpWFPzZ4zKAFW5zTHVyitXebEpmc/qekrJSZ2eng7Wzpw54+r5zDPPyLr3umhqapJ1bwb42NiYrK+trbn6qvxkM3/WscrMPXTokKunmT4vKfnqqm9HR4e7r5oHU3LF1X1mbm7O3Vfdg1JyulXfrq4ud1/1rOO9v5vpseS9Jsx0RnXKPfPy5cvBWux5MCT2POd91oldp97x0NPTI+ve60I9j5j575lqjrxy5Yqrp5meyyYmJtx9VX79wMCAu28In6ADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGZBBpyrzzZs5ala9LF6VxZ2yvyrrsFp58Ck5qWqfVJ5ujDpvKRm/KZmaISoL3kznYiuxHHlvLqY6tmbxPPOQ2HHw5qSqMZaSZapy75ubm919VY5nyvhTmZqxPGjl/PnzwVrsnCoqX1XNnylSsnjVdeHNtjXT5zxlf69duxaspWRUT05OBmtXr1519Yxdpyq3WYldT97zFpsjvfeg9vZ2WffOZ2fPng3WVBZ0jNq2r6/P3VeNz5S5QWUkp8xlvb29wVosv1pR4yE2BhV1XaQcBzU+U/qqZ52UuUyNh7GxMXffhoaGYM0758RyxdVrKrF1jnecxdY5sbkuJLZu8D5DqXuMWn/GqPfpfZY202M3Ze0awifoAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZkDvqjjz4arM3Pz7tfdHl5OVhLyUFU+5SSD6qymRcXF9191XFI6atyfFP6qszClOxrlaGojpGyf78c2u6s41gerDd/Nba/3kziWOajNx9UXRMp2avq+MYy6BU1PlNyr9V5q62tdfcdGhoK1lLmXrW/3mvNTJ/zlDx4lTPb0dHh7qtyxcfHx919u7u7g7Xf/u3fdvc9cuRIsFZXV+fqGZtTvH1T7gVKLGfWmy0eu/692cEqy3hwcNDV08xsYmIiWEu5htV8lTKXdXZ2BmspeeUvv/xysKbuTzFq3Kfkf6v3mjJHqv1NOQ4qU/vOnTvuvuo+HnsO+m8t9szhXa/EzsvMzIyrb+x68j5HxvLevc9mc3NzwVrKmq2trS1YS7mG1fyq5mUvPkEHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMiBDB1VmdkoOosrU6+npcfdVeZAqnzZG7e/Vq1fdfQcGBoK1lCxpleva3Nzs7qtyaFWucIzK4vbmr8ayeFX2uhLLzPX2jeV/erOFVZapWdo4C0nJzFVjIWWMqfOSkhWrzps3R9rM7Pjx48Ha888/7+6rjkNKBq06b7ExqKhtU8busWPHgjWVQR+jcnF7e3vdfdV5U8deiWXierNtY32996DYs4F3f9fX12X9xIkTrr4qF3d6etrV00xnZqvs9Rh1Xurr69191f0g5XlEXf/VmtNTjoPKX/aOXTOz0dHRYK29vd3dVz3vpRzfjo6OYG1kZMTdVx3DkydPuno+8MADsv7ss8+6+h49elTWY7njIT/7sz8r694c9Nj5bm1tdfVVc3rKGFtZWQnW5ufn3X3V83/KnB7CJ+gAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABmTw7e3bt4M1b+5dbNuUDOW5ublgTWWSxqg84ytXrrj7qpxJb562mT6GKXmbap82NzfdfVU2oze3MXb8vBnKsRzUWKaul/c4xDKSveNM5UyWSiVXTzN9TaT0VTny6r3EqLkhZS5T1+mhQ4fcfdX+plzDKn9ZHfsYlePb3d3t7jszMxOsqSzzGJV1njKnqzx473URmwO94yH2bOCdy2L7W63j4M3N7e/vD9ampqZcPc30nO7NTzbT59t7vzTT4z4lV/zhhx8O1tSza4waRynPT4uLi8Fayr1CZUmr+2mMOm8pGdXqOMSeVxSVXz87O+vqGVs3eO/FsXH/pS99ydU3dvy8c+Rv/MZvyPpHP/pRV9+mpqZgLWXOUZnk6t4fo65/731N4RN0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyMB+VWxtba3Kiy4vLwdr+/b5v2dw4cKFYG1wcNDdd2trK1gbHh52911ZWQnWdnZ23H1rampcrxlz9+7dYC1lf+vq6oK1jY0NV89SqSTr6hgp9fX1Sa8bsr29Leve47u6uirramwrjY2NwVrsvSjq+KXMDWtra8Ha/v1yGpSam5uDtba2NndfNUcePHjQ3be7uztYU+8lxa/8yq+4tz106FCwpsZgzNjYWLA2MzPj7js7OxusFUXh7qvmHe+cE5sbYvWQlpYWWffOOeo+YWbW29vr6hubr65everqq97n0tKSq6eZHmMPPvigu68637FzqqjrtKmpyd1XHV91jGLUs0HKNdzQ0BCspRwH9ZyuXjNG3Rc3NzfdfdX1Njo66u6reOeco0ePyvrIyIirb+we823f9m2uvhcvXpR17z3+u77ru2Tde6+Ynp4O1mpra109Y33VvT9FR0fHN70nn6ADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGZABwAcOHAjW7ty5435Rlek8Nzfn7qsyKtfX1919+/r6gjV1jGJUzmRKlrR6rynZ9iqTPCUXU2V1xrJvQ2KZ2d589RjveYtlGXszn2PHT+WDK2rspuRTq/1NuSbU/qbkq6uszpRsW5WTmjKXqbzSlIzf5557LlhLmdMvXLgQrC0sLLj7qlxx7zVhpseDN6fbzKy/vz9Y885l8/Pzsv7II4+4+sYyqL2Z2rHscG/fxcVFWfdmKI+Pjwdrjz/+uKunmc46TrmvVeuaUHNZ7L6n3Lp1K1hL2d/29vZgLWWOVPnqqhajjmHKeFheXg7WUuZ0lR2ujn1K30OHDrl6xs5LT0+Pq++NGzdk3ZvVHcttv3btmqtv7FkmNjeHqPGpxl8Ktd5I4b3/KHyCDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGRA5qCvrKwEa95sUDOdfaeyOGPUtvfff7+7r8qvjWWoKoODg8FaSo6nynx84YUX3H1V7mjKeVP51t7s61j+tzf7OjbuVd62EsuD9Y6HWA6qN2deScnp3tnZCdZUxnSMOm8p2bYqqzMlp1udbzUvx6j97erqcvdVubhqnotR+d+bm5vuvirfdmJiwt1XZYCn7O/k5GSw1tra6urZ3d0t6ypvW1HXsJn/uohl/HoztWP5td6+6npSmc0x6t4Vy6BX1LzS0dHh7qvG/dTUlLuv4r0Pm+m5IaXv6upqsJZyD1L3Nu9zjpnZ5cuXg7VLly65+54+fTpYS7lXnDhxIlj7whe+4Op5/fp1Wfeet9h58d7j79y5I+u3b9929Y3NgbG5OUTlq6c8R6r5qrm52d1XbZvyfBrCJ+gAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABmSo45UrV4K14eFh94uqPMgUS0tLwdrrr7/u7nvt2rVgTeUKxxw7dixYS8mZf+WVV4K1lGOvss5j2beKyklVmaRKLP/bu7+x7VJyRxVvFm8sm9Gb66qOg3dfzXT+d8rYVXmlKfmVqq937JqZzczMVKWvOt83btxw91WZ2u3t7e6+09PTwVpKTurJkyeDtZSManV8UzKUX3vttWDtwIEDrp6xPNiHHnrI1Vdltpvpc6qMjo7Kuvf4qjnHzOzUqVOuvg0NDcFayv1dZZ3H3ouiMpL7+/vdfXt6eoI19UwRo45hXV2du696Hok9V3ilPDeo6189w8eo516VXx3T1tYWrKXMkeq51/tMEls3eOfeRx55RNbVNa6o5wYzszfeeMPV98iRI7JejefplLlBXU/nzp1z91XPXp2dne6+IXyCDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGRAhg4ODAwEa/Pz8+4XVdnBKfmKKgM0JRdTZV82Nja6+6q8zZT9VfnAKps1pqOjI1hLyV/d3t4O1rzHIZZP7z1vsbzHWAZwSCzzUR17pbW1VdZV9q2i9ndpacnV00xn23ozR8101nlKrrjK20zJtlW5rXfv3nX3VeOhpaXF3Vedm5TjMDg4GKyp+0jMwsJCsLa6uuruq/Lg+/r63H1VZrl3/MbmHO/cOzw8LOsf/OAHXX3VOTMzGxsbc/U9f/68rHvzeNWzTEoOurrXxuZ75fTp08FaSv63uh/E7tOKGvcpz2XqvabMDWq+SslmVvubMh7UOFPPmDEqi/vpp59291X3oBs3brh6xuZs7/GNPYd7c9tjz17evur56e3UQ1Ruu7rnxVy9ejVYe/jhh9191bo35XkkhE/QAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAzIHHSVg5qSB6ky87yZo2Y6V1zl6caoPEj1mjEqt/WRRx5x933ooYeCtZTzprJFUzIAVTajt29su1ieudeZM2dc28Vycb05k7Ht+vv7XX2VWF6xoo5DSqaryuJOydtU19P29ra7b2dnZ7CWMpddunQpWEvZX5WhnjI3qHuQyr2N6e3tDdZSMpTVeDhy5Ii7b3t7e7D2zDPPuHru3y9v/+4c9He84x2y7r2Oh4aGZH1iYsLVN3a+vdnBKjM35VpT+5MyxlQevJqPUoyMjLi37erqCtZSjq96Lku5t6k5Uj1jxqhM7ZT9VVLuQeo4qHMa09HREaw99thjrp7j4+Oy7r1XVOt6ij1Hep//Y3NgLNc9ZH19PVhLeUY/ceJEsLaxseHuq6h7tBefoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAG9qviAw88UJUXPXjwYLB2/vx5d9/R0dFgbW5uzt13377w9zF6enrcfVdWVoK1yclJd9/19fVgrbm52d1XHYft7W1337q6umBNHaMURVFUpe/GxoZru9h5UcdeUWMhpW+pVArWGhoaXD3NzJqamoK1lHOmxtjm5mZV+jY2Nrr7tra2urdVBgcHg7XV1dWqvGZNTY17W3XOU87b0tJSsDY7O+vuq8avd24wM9va2grWvud7vsfV8+mnn5Z1770tdl7Ue1Fi88rjjz/u6qvOmZnZzMyMq++LL74YrO3fLx+9pIGBgWCts7PT3VfNOWreiOnu7g7WnnvuOXffM2fOBGs7Ozvuvsry8rJ7W3V8U+aG6enpqvRV5zxl/Kp5JeV5emJiIli7du2aq2ds3TA8POzqe+XKFVn33ovVc5mZ//jGrn917BU1jt58801XTzOzlpaWYC1ljJ04cSJYu++++9x9Q/gEHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMiADDNUWZIpGbQLCwvBmjcj1czs+PHjwZrKvY1RGd8XL15091XZ1yrHL6atrS1YSzkOY2NjVelbjczSWE5nLC8yJJYr3tXV5eob482Z9+acx6jrv76+3t23trY2WEvJV1fXREqmq5rLvPnJZvqa6OjocPdV+3Tq1Cl332qNB5VR7b2GzcwuX74crKWct5T3qqi595Of/KSrZ3t7u6yvrKy4+saeDbzjN5ZJrHKmlXe+852y/uu//uuuvidPngzW6urqXD1Tt1Uee+yxYK0oCndfNac//PDD7r5qjvTeL2N9a2pq3H3VvJLS98CBA8Gauu/FqGeZlPFw9+5d97aKetbx5qA3NjbKujf/W10TZvG5LiR2XrzjIdZ3aGjI1ffFF18M1lKepdVYGB8fd/dVzyNPPPGEu+8P/uAP3vP3+QQdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyIAMAB4dHQ3WUnImVVZsSia2yr5LyW1U2ewp2cxqf1VGeopq5YOmZLOq4+vNqI7llXvHWSx72ZtfOTs7K+vejN/Y8VPHXqlWvvry8nKwtra2VpXXTMmuVsc3dk4VtU8bGxvuvur4Xr161d1XZWp7x5iZziz1ZtCa6flB5QrHqOzwV1991d1Xza/e4xu7x6QcXyWWARyicsXN/Lm5LS0tsv593/d9rr6f/vSng7U333zT1dNMj7Genh53X5XFnZIdfP/99wdr3/7t3+7ue+nSpWAtZc5Rz0gpz09q3n7hhRfcfdX47e3tdfdVzzKDg4Puvp2dncFa7LlNUVnn3vMWe+bwzpHd3d2y7n3uXVxclHV1n1Zizxze597Tp08Ha977hJkeR5ubm+6+ytTU1De9J5+gAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABmQYXsqL86bp2emM36vX7/u7qtyUlPy1VWmZiwXW1H5lSrrNEZtu7S05O6rsuRTsgXVePBmEqdkGStjY2OyrrJklVgWtzfHM5YH6z1ODQ0NwVrK2FV9UywsLARrKXNOf39/sKYyx2NUzrzK/41ROckpefBNTU3BWspxuHDhQrCWMlbU3JuSoazyymOZuoqaXxsbG109Y1m83nvb5OSkrLe2trr6Hjp0qCp9Y/dENbaVEydOBGtqnMSo7OXnn3/e3bejoyNYGxgYcPcdHR0N1lKey9ScntJX3WtT5gY1X33wgx90971582awlpIlrY5vynOkknJ8VQa495lkaGjIuztS7Lx48+vf9773ybp6hldi+fTe514158ReU7l161awdubMGXff8+fPB2ve+4TCJ+gAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABgqVdfrFL34xWBwcHHS/6E/91E8Fa319fe6+Kls0JZt5ZmamKn1VtmhKrrjatlr5lSn279/vqimxTGeVmanEsoy9OZOxrGhvnmks59ybx6tyZlMyaNXx9eY9m+nxkHKtqfOysbHh7qsyc1WWeYzaX++1ZqbH78jIiLuvOm+xsa2o/U3JV6/WeFBjVGW6K/v26e/Pe+8Vw8PDsv7AAw9Upe8rr7zi6hubVx566CFXX5WnPT097eppZjY2Nhasee9rZmZzc3PBmsoVjnnjjTeCtdnZWXdfNe67urrcfdX17817NtN55SlzmbrfpuSgq31qbm5291XnTa1HYtT8GpvrQmLPBt7jELvHePPgr1y5IutPPvmkq+973/teWVdznaLO940bN1w9zcwWFhaCtVOnTrn7qrHrPWdmZp/+9KfvuXDgE3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAzL4VmVffuUrX3G/aGtra7Cm8utiVOZjStbx+vp6sJaSt6n2yZvbaKazUFPyK1VmdizHW0nJBw6J5Wl680G9eY8xTU1Nsp6SsVgNKg82JctUHd+UY6DGbsrcoI6DN2PeTF+nKhs8pW9KZu7ExESw1t3d7e5bFPeMBzWztEzi9vb2YE3dn2JUdvDKyoq7r8rN9s5JsfNy3333ufoePXpU1lUetPLmm2/Ken9/v6vv6dOnk+ohah5Mub9fvXo1WFNZ5jEjIyPBWsr9Xc1XKfnfKpNYXd8xao5MmXPu3Lnj3tYrZX+rdd7Us87q6qq7r5oHVVa8t6eZ2eTkpKvv4OCgrHvvFU899ZSst7W1ufrG3udXv/pVV993vvOdwVpPT4+rp1n8edpLzYPXrl37pr8en6ADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABvar4vnz54O1qakp94tubW0Fa+3t7e6+CwsLwdqBAwfcfWtqaoK1paUld9+WlpZgbWZmxt13377w913275en3N23rq7O3Vfx9lX7amZWW1vr6ru+vi7rpVLJ1Tc2jurr6119Y8fBOx7UNVwUhaunmb7+vefMTI8jNW/EqOO3ubnp7qt4x4KZ2crKSrB2+/Ztd9+dnZ1gLWXOaW1tDdaamprcfRsbG4O1zs5Od1/1Xqenp9195+bmgjXv9Ra7nrzjTI0xM7OjR4+6+j700EOy7h0PR44ckXXvs8Pq6mqwpq6XmBMnTgRrKXOZmtNnZ2fdfdU96KmnnnL3VeN3Y2PD3VeNX/XMFqPGUcp4aGhoCNZ6enrcfdV4WFxcdPedn58P1ra3t9191b3CO0fGnp+8Ytep9zjcf//9su6d0/v6+mT98OHDrr7qPpwyl6WsIxX1fK/mZS8+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAyIANqn3/++aq8qMrUS8m+UzmIKbniKr8yJTNX5Q6n5HiqrM5YjreisrpTctBVXrQ3+zolV1SJ5WKq7FAllpntzZKO5V56cz7V+0zJDlW5mCm54irHN2WsLC8vB2vesWCmz3fKNTw+Ph6speTXq7khJbe9q6vLva2iznnKa05NTQVrKfc2dT9QOchKbLtq9fWOMzV2zcwGBgZcfWP59N55R927vPO5mc5XVxnTMadOnQrW1PUdo47Ds88+W5W+KXOOOjcqBzlG3dtSnvdqamqCtZRxtra2Fqyl3CvUc3rKeVPjwZtfPzExIevePPibN2/Kuvd5Ova87M3qjr1PNVaUu3fvBmspWeZzc3PBWltbm7uvuhd475cKn6ADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGZAhiR/4wAeCtVdeeeWbvjNmZk1NTe5tDx8+HKzF8gwVlfGXkjuqMiq9uY1mOqMyJb9SZQenZFSrbFGVM63E8iC9+aCxvEdvpna19jfW15sz39zcHKyl5HSrPNhqZdseOHDA3XdlZSVYS9lflZmrcpBj1LlJmRt6e3uDtZTjq47Dvn3+7y+rfUo5DjMzM8GaNzPXTL9XdS0q1chtNYvfC7a3t1191bVmZjY2NubqGxtH3vGr+nqPgZm+X3rzk83MJicng7XZ2Vl3XzX3nj592t1Xjd+U56fR0dFgzZv3nLqtsrW1FaypnOkYdXy9zw1m+h6/tLTk7qvGmfc4pOTIK7F1jsqKV7q6umR9enra1Tc2dr33THWdpsyR1Zp71f6mPI8Ee37TOwIAAAAAgG8YC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADMiQP5Wp+Z3f+Z3uF7148aLrNWNUtuDQ0JC7r8qDvH37truvysX15mmb6ezGmpoad1+VH5iSF6mOr/c4xHIbq5VJWi0q+1ZRmaNm8TzOEJV7mZIjrTKdU3KbVQ5qypyj8kpTctCrlVHd1tYWrKXMOUrKnKO2TcniVZnZExMT7r4qCzWl75EjR4K148ePu3qePXtW1n/xF3/R1Vftq5k/LzaW4Xvo0CFX3/b2dlmfn5939VX51N7sejN9TXR3d7v7qjkyJZ96bm4uWDt48KC7740bN1yvGaOec7z3YTOzwcHBYE09C8asrKwEa977u5m+V/T397v7qmfFlOfplPtMyMLCgqx3dHS4+sayuL1Z3bFnjs7OTlff3t5eWb9w4YKrb7XmMnWtHT161N33+eefD9ZSnkdC+AQdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyIAMr1aZjym5mCkZoMr4+HiwlpLxq3LbUzKfVQZgSq642jYl/1vl/KXkgyqxHMoQb45kTCxn0pvxG8tQ9Pat1nlJGZ+K2t+UbNupqalgzZsNaqav4QMHDrj7rq6uBmvr6+vuvmreTpkj1XjwZsWa6fGQkjP/4IMPBmu/+qu/6u6rxLJkFXV8vfm/zz33nKx7596XXnpJ1r3ZzK2trbLufSaJ5ZyrvG1FnbNjx465epqZzczMBGsp2cFqHL373e92933mmWeCtVi2vaLuid6xa6bnnJQ5Ut0PUu7Tp0+fDtZSjkNdXV2wlpKv/sYbbwRrDQ0N7r5qPHif09vb22XdOx7e8573yPrm5qar7+Lioqx7j29sjvTe29Sco9ZzMV1dXcFayvNTf39/sHbfffe5+4bwCToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARlmrHIQt7a23C+qsrjffPNNd9+hoaFgLSW3cWBgIFhLyYNXWccpuZjq3KRkM6v3mpJtv7GxEax5879jvPmVsf3xXhfqWjPzZx3Hxr03E1Jdw8vLy66eZnrcx3KQlZaWFve2ijoOsQxVRZ23xsZGd1+VB5tyfNV1kZI7qsbD9evX3X29r5licnLSve329naw5r0HxTKSveettrZW1tV8r4yNjcm6NztYXcNm/rljdHQ0WIsdI0WNz8HBQXff119/PVibmJhw911ZWQnWUp4j1fFNeW5Q82DKHKnm7bm5OXdflfmc8lymznnKc6TKi75165a7rzfrPIV3PMTep/e6OHTokKw///zzrr6xY7t/v1xKuqh1V4yac6amptx91XN4yv09hE/QAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMyXX7fvvD6fWRkxP2i3d3dwVpHR4e774EDB4K1zs5Od9/Nzc1gra2tzd13Y2MjWJuZmXH33draCtYmJibcfdUx3L9fDiVpe3s7WCuKwtWzVCrJuhrbSmx/vH1rampkXR0jZXl5Wdbr6upcfcfGxoK1nZ0dV08zs8bGxmBtZWXF3VdpaWlxbxsbZ17r6+vBWuycKup8e8eYmdnCwkKwpuajmP7+/mAt5dhfuHAhWGtvb3f3Ve/1Xe96l7uvut/Ozc25ejY1Ncm69zqOjaP6+npX39h2fX19rr6xe5eak5QTJ04EaynX2tLSUrB248YNd9/V1dVgbW1tzd1XPeeoWowaD95zZqav/5MnT7r7qvmqtbXV3Vfdv7761a+6+6pj6J1zzMyOHDkSrE1NTbn7qn1qaGhw9RwdHZV177oi9j4PHjzo6hu7J9bW1rr6xuZIb1+1tlLruRh1fNWzSox6LvPefxQ+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAy4A6vVvl1MSrH05uRaqazDr351GZpWZ1eKfnqKTnUispmT8l1VWPJm9Mdy1D0HqNY3mO1jr33+MauU3VOU/p6qTz4WFa8oq6nlPcyPz8frHnHrpmeI73nzMyst7c3WJuennb3VVm8Kcfh9u3bwdqLL77o7nv06NFgLWXuVTm0KVnSKm/Xm0Ebe5/q2Cve/YmJza3j4+OuvimZ2YrKDv7a177m7qvuBUVRuPsuLy8Ha8ePH3f3VfNrynODyiuvVg76K6+84u47MTERrL33ve9191XH8OzZs+6+s7OzwVpPT4+77+LiYrA2NDRUlb7ejOrJyUlZb2pqcvWN5d571yuxOVCNbSWWr+59dlDPDSnXcEdHR7CmxnWMumemzGUhfIIOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZEDmoJ87dy5YU/l1MUeOHAnWVJZhjMokTMk6VvnqKVR2YEr2usqhTcmoVRn1KcdX5dt6c8VjOeje8RvLUIzlRYbE3qc33za2nXc8VCvbVm2bkoOupORTq7zSubk5d191XlS+coy6hru6utx9q5EBaqavp2PHjrn7qozVlHub6uudG8z0/cB7Da+vr8u6N4s3tt3W1parb11dnax7c3Nj58V7HavjmzLG1JyeMsaUkZER97Yq/7u/v9/dVx1f79g10+PMm3ttZvbII48EaymZz+p+kDIvqzG6vLzs7ru6uhqsxXLHFbVPS0tLrp7qfmkWn0NDFhYWZD32/BoSWzdcvHjR1Tf2zOF9Tlf3gpS8cjXGvPefGG/GvMIn6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGZLidykE8ePCg+0VVnnFKHqTKB03JV1dSso5VZmEsf1GpVs5fyj4pKrPUm68Yy8z15orHshm9WYix3FZvrmssF9c7VhoaGoI1lZEeo/I2vdmgZjqvNCUHPSXPWFF5uylZvOo4ePO0zfTcUK2c+aGhIXfftra2YC02dyjqfpCS69rc3BysqWtRiWUDe6/jWDawyqhVYuNTPVek9PWOB3UvmJmZcfU0i2cde6lrOCVffXBwMFhLeS/qftDT0+Pu+6lPfSpYS3m2evzxx4O1lZUVd191z0w5b0pKzvybb74ZrKU8O6jM99HRUVfP2HrE+/wfm1O8c061nv1jc6T3maSzszNY82bXm5ldu3YtWEtZY6rxGcu29+ATdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADMgddZVTGsk4VlSWXkhWrshlTMpQnJyeDtampKXdflV+ZkjOpcv5SsvpUpqY3V9zMn1+rxLKXvfmgsZxzb99Y3rv3+Kps0LfzuiHq+k85nynjXlHHL2VuULmjsWOvqGvYe87MzC5fvhys3Xfffe6+atynZLOqbObe3l53XzX3plDnJiXXtRrjLJZH7s34jc2B3ustds68mdqxXFzv+FVZ5ynPOSqTOGWMxe5tXmocxcag0tHREaylXN8qm3lxcdHdV+V/t7W1ufu+9tprwdqJEyfcfdV1mnKPV+dmeXnZ3fehhx4K1iYmJtx9ldhzZoi6r5n5x29DQ4Nru5hYzrn3HqSOn/dZ2kyv2U6ePOnuq+7val3rxSfoAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZk2F53d3d4w4ScyZs3bwZrKbmYdXV1wVpKFu/8/HywlpIHqbIFU47v2NhYsJaSM62Ob7Xyq73ZgtXan1gepHd/Va64mT/7OiUzW1EZlSn536pvSi6muv69GfNmZsPDw8Ha6Oiou686hil5m6dPnw7WVL5yjMqvTZnL1LbeDFozs+bm5mBNzXMxKos7ln2rDA4Oul5TiWUDe8dZLIvXm9Ubm3u9Od4LCwuy7p1D1T3oW5VXrKjx2dLS4u774IMPBmsvv/yyu6/KQU/JmW9sbAzWqpF1bGZ25coV97bq/pVyL1ZURnrMn//zfz5Y+8QnPuHu+/TTTwdr3uPQ09Mj6945PSXvXbl7966se591Yu/Te89U9/DW1lZXTzOzD3zgA8GaWs/FqHx1NR958Qk6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZGC/Km5ubgZrc3Nz7hddXl4O1mpra9196+vrg7X9++VbldRxWF9fd/etqakJ1g4cOODu29jYGKxtb2+7++7s7ARr6hjFqG337avO95C8x2FjY0PWt7a2XH1j47NUKrn6xo6f9zgUReF+TUVdE7FjrywsLARrfX197r7z8/PBWmtrq7uvOi/eMRbjHWNmen/VfBSjrovm5mZ3X/Vel5aW3H3b29uDtZR7ppojvfc2dQ2b+Y9vS0uLrPf29rr61tXVybq6/ys3b96Ude/+Kmtra+5tm5qagjU1f8aoefull15y9+3q6grW1HuJUc9IKfegtra2YE3N9zE3btwI1j70oQ+5+6rr/9q1a+6+aoyurKy4+7766qvB2nd/93e7+z755JPB2vPPP+/uq3ivt9g6x3t8U9ZPiromzPzroJmZmWAt5X556NChYC1ljlT3zJTnkRA+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAyIANUV1dXg7WUPG0lJf9bZdtOTU25+6pswZT9VbnOKcc3JWNVUTnoKbmj1civj2X8TkxMuPrGMhS9OZTq2Kb0jY2j2OuGqHFfjTxIs7QcdJVP7c1PjknJ8VQ6Ojrc26o5PSVfXV2nKXOkyv9W7yVGvdfx8XF334aGhmDtO77jO9x9X3nllWCts7PT1fPd7363rHvvI7HrSV2LSixf3TvOYuNePVco6l7hPQZm+ryorO0YdQ3HMugVdQ9KOQ79/f3B2uXLl919VdZ5ypxz7NixYM2bI22mz433+clMzysqvzpmbGwsWLtz5467r9pfb654LPf+9OnTrr5NTU2y/uijj7r6fuUrX5F1deyV5eVlWe/t7XX1VfsTm+8VdR8eHh5291VzesocGcIn6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGZEiiypKbnp72v6jIZkzJ01Z9UzL1VA7q4uJiVfqmHAclJUs6li3upfJrvRnVsbzSnp4eV99YXmksJz0kJYM6hfe6OHjwYLCWkm2rcq/Pnz/v7qtyR71Z8Gb6Gk45p2qfFhYW3H3V8W1tbXX3VfeKvr4+d1+VvzoyMuLuW1tbG6yljAc1R05OTrr7qnnQO85i79M7l8W2887pU1NTsu7NkvZmJMeoe0y17qVnzpxxb3v16tVgzXvOzPQ4UxnpMdeuXQvWUvLg1fhV81yMyg5/4okn3H2bm5uDNXV/ijl06FCwlpKvrsZSyvFVc/rHPvYxV8/Y3Op9/o89I8WeX0OWlpaSXjdkcHBQ1tXYVqo1FtSaYmBgwN33wQcfDNaeffZZd98QPkEHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMiDDDFVGpcqZi1HZgd7sVTOzxsbGYC0l/3ttbS1YS8nxVLmNKq84RmU3puRXqmz2lPNWV1cXrHnzYmO5rd7MXHXOzPyZurG8TXWMlJMnT8q6umYUdb5Txq7Kr/UegxiVkR6jcmZT5hw1r6TkdKv32tnZ6e6rMr7PnTvn7qvuMynHoaury/WaMWo8eLNizfS8482oVvO5mX+OjN0LvHnwsXnFOz94c+RjZmdng7WUHHQ1FlKeR1TOfOy+p3R3dwdr3vuPmdnt27eDtWo956QcX3VdpPRV4z7lvKl5RZ3TlL4q2z5GnXPv3BAbn8vLy66+sbnXO0fGssObm5tdfWNz5LFjx1x91TiKZa8r6v4+Pz/v7tvX1xesTUxMuPuG8Ak6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAF3DnpKzmRra2uwFsvx82ppaXFvqzJLVdZpjHqvKRnKKm+zWsc3JTu4ra0tWPNm8cZyZr25mLHjd/PmTVffWAb1wYMHXX3VtWbmH78qg/bOnTuunmY6RzolO1jleHqzTM107v3q6qq7r5p7U7Jt1TwYy2ZVzp49G6yNjIy4+y4tLQVrsbGtqHzbtbU1d9+xsbFgLSXruL29PVjzzumxa7+np8fVd3p62rVdTFNTk6yPj4+7+sayjr3HV93DU+YGdU2kZLqr853SV2USe+/DZvo6XVhYcPet1v6quffQoUPuvirXeWBgwN1X7dPQ0JC77+XLl4O1W7duufuq8etdr8TGkXpeUWLPMt7n6eHhYVmPzXUhsfWI99nh8OHDwVrKNayeOVKeny5duhSsfehDH3L3DeETdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADMhwwJRdXvqjIJEzJmVR9vXmFZmYdHR3BWrWyeL3532Y6J1VlpMdsbm4Ga9U6b83Nza6esdxWb/63yiM28+egx3ImvRnKKrfRzGxqasrVV80NKddata5hNXZTcttVpmZ9fb27r8pJTbnWqrW/anzGrhlF7W9Knqm6/r152mY6mznl+Kqx753LYrniXrGMb28mcayvN78+ljnsvRere5B3X83MVlZWgjV1749Rc7rK8I5Rzzk7OzvuvtPT08FaSk63uta8Y9dMz4N9fX3uvouLi8GaN/fazKyzszNYSxm/ah48evSou6+63ryZ2rH3qbLXldg90fvcq3LFzcyWl5ddfVWuuFn8+TVkbm4uWEuZy1KuU6WrqytYq8Z6mU/QAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMyzX1hYSG8YZWC4NVrxlRrn0qlUrDW1NTk7qve6+rqqrvvvn3h77tsbm66+25vb7u39aqtrXVtFzt+RVG4+s7Pz8u6dwyura3J+qVLl1x9V1ZWZL2mpsbVV40x7zkzM9vZ2QnWvOcsxnsMYtuur6+7+/b29gZrzc3N7r719fXBWmdnp7vvU089FawdO3bM3TdlvlLUWEoZv4uLi8GaumZi1HHo6upy9YyNo9jcEdLR0SHr3uO7sbHh2i7mwIEDsu6976kxlvKssrS0FKzV1dW5+1brmlD34pTjcOTIkWBNzXMxs7OzwVrK8VXPkePj4+6+c3NzwdrU1JS7r7ou1H06Ro3f/v5+d191fI8fP+7qeefOHfdrKrE523uviI372PsJiT0jeZ9J1HF4/fXXXT1jfWPP2srt27eDtb6+PnffED5BBwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADIgQyhV1mFKJmm18spVXmlKfqXKg2xoaHD3VZm5KZnPKrMw5dirvltbW+6+altvlnR3d7f7NZVYHqQ3HzSW9+zNJI7ldHpzPFNyUBV1raWMXZVtm5JX3tTUFKylZPGq6z9lbmhtbQ3W2tvb3X0//vGPB2tf+9rX3H2VWN62ou4HKlc4RmXJx+YOReW6Dg8Pu3qqsWDmz6CPHT+Vi63E7rWxOd/LO9ep97m8vOzdHZnjm5Lxq8auN+fYTO+TN+/ZTOegz8/Pu/sqKdewOobPPvusu6/ap5R72/T0dLCWcq9QxsbG3NuqZ7pjx465evb09Hh3R4o9y1y+fNnVt7e3V9bVc5CinsvM/M866j58//33u3qa6WckNa5j1HiIHSMPPkEHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMiDD+FTW4cLCgvtFVYZ6Snawym315j2b6XxFb1asmc4ATcltV9nMKcdBZTemHAeVk+rN8VxaWpJ1b45nLDvYm+MZO37e6y2WzejNbtze3g7WUsaC2jYlB722tjZYS7kmGhsbg7VYbrOi3qt6LzEqO1zNGzGnTp0K1t7xjne4+37qU58K1lLyytU9aHBw0N1X7ZO6j8Soc+693mJzqzfz+fbt27Le0tLi6hvLT19cXHT1jWUde/uq+9rKyoqrp5nOZY/dnxSVg97V1eXuq8aRN2PeTF/DKc9Pap9ScsXb2tqCtZT9VddFyv5evHgxWEu5F6s8+JQs6Wo8O8zMzMh6LHc8JPZsMDU15eobe649evSoq++lS5dk/datW66+6ppIGbvqevKeMzO9Pk3Z3xA+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAyIMMBVa6gyjKMUX1VtmWMymb2Zrqa6bxyVYtR+ZUqHzBGZT6m5K+qbPHl5WV3X5V96c0HVfnUZmbj4+OuvrHs5aIoXH1j23lz22OZ2d4MZbW/3mNgpq8nldMbo46DyiuOURnq1cqK9Y4FM32+vdeEmdn8/HywljIeVDb79evX3X3VeEjJklbGxsbc26p7Wyx33NPTzH/eYuPIey9W15pZfK4Lic2BV69edfVV96BYprui5sGUXHF1vlP2V+1TyvOeeuY4d+6cu6/KqD5x4oS7rzI8POze9umnnw7WUvLV1dybMqerc55yz1TPkVtbW66eHR0dSfWQWM65914xMTEh6z09Pa6+aiyY+XPbP//5zwdrhw8fdvU009eT9z5hZra4uBispVwTIXyCDgAAAABABligAwAAAACQARboAAAAAABkgAU6AAAAAAAZYIEOAAAAAEAGWKADAAAAAJABFugAAAAAAGRAhg6qDMX19XX3i6r81ZQsOZVvpzISY1SeaUruqMo6VlmcMSqzVOUVx6i86JRsQXUMvcdXZbab+bOOY+fFm+MZy+KN5a+HxM6L9/iqXFGVZR6j9iclr7xac4M6bykZtGp/1XwUo8Z9yv6urKwEa/X19e6+Bw8eDNbefPNNd191D0o5vmosffGLX3T3VdniLS0trp6xbNtY7nhI7HryZhLH5vTY+wmJZRkPDQ25+qos45Q5UmXxqmeKGLVP3nNmpu+JKc97V65cCda8Y8FMX/8px7e/vz9YGxkZcfft6uoK1mpqatx91f6qeTlmZmYmWEu5V7S1tQVrar5XYs9Hd+/edfVV90uz+PNgiMqYN/M/98auUzUnKer4erPVzfS4V9dLzOzsbLA2PT3t7vvhD3/4nr/PJ+gAAAAAAGSABToAAAAAABlggQ4AAAAAQAZYoAMAAAAAkAEW6AAAAAAAZIAFOgAAAAAAGWCBDgAAAABABmRocyx31Etl/Hlz+sx0fmVKrni1so5VXuzy8rK7r8pB9+ZBmuks1JQMQJV9qbItU6hjpFQrXz2WZezNV4/leHrzeNX+pIwxxZsNahbPg/fyZkXHqPe6vr7u7puSZ+ztm5Jfr663lL4DAwPBmjdX3Mzs9ddfD9YGBwfdfdU1FbvGQ2JzmfeaiWUve8dvLCPZm18fyw6uxvHt6+tz9TTTxzflfqmekVJy20dHR4O1lKxjdU2oZ6sYNR7m5ubcfcfGxoK1GzduuPv29vYGayn7+/LLLwdrKfnq1cqZV/cD79xw8eJFWfdmasfuXd3d3a6+4+Pjru1iYuun4eFhV191vlPWgiMjI8GaN7veTO/v0aNH3X1D+AQdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMrBfFUulUrC2s7PjftGiKIK1ffv83zOor68P1vbvl29VWlhYCNba29vdfZW1tTX3tpubm8HagQMH3H1ra2uDNXXsY9S58R6H7e1tWfeOs7q6Otd2MeqaMDPr7u529Z2dnXVtlyLlGlZjV9VSpOyv2lZdLzHqmkjZ39g481L3AzV/xszNzQVrAwMD7r5NTU3BWso4O3XqVLB2+PBhd9/R0dFgbWZmxtUz9j6998zY+fbeg7xzYMyNGzdk3Xu9NTQ0BGux+5PS0tISrHV1dbn7qrlhenra3XdlZSVYU8coRo3P5eVld181N6yurrr7quu0v7/f3bdaz9NqLKXM6eq8bW1tufueP38+WOvp6XH1vO+++2Tdez+Nvc/Ozk5X39j+NjY2uvrG7jHe53R1D0qZc9T+Njc3u/uq55G2tjZ33xA+QQcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAyIINOVbZtSl6hymZUmZkxKnc4JQddZZaOj4+7+6r9TTm+aluVbR+zuLgYrKXkg6tz7s0djWXtqqzTamwXEzsv3usiNu43NjZcfdXckJL/XVNTU5W+6vil5F6r45tyrVUrK1ZdT+qcxqhxtLS05O6rMku9ma4xKdnBd+/eDdZScl1VrrN3PMSufW/Gr7qGzczW19ddfVUGrZl/fohlh7/wwguuvu3t7cFayhhTxzd27BV1z+zr63P3Vc9PsXxlpb6+PlhL2V/1TJdyfNX9IOVeoa7jlHx1dW5ScuYHBweDNXXNxKj7QUtLyze9p5l/7j1y5Iisx+akEHWtmfkzwG/duiXr3vlsaGgoWLt06ZKrp5k+bylj7MUXXwzWYvcn5WMf+9g9f59P0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMyJBklcWrMihjVO5wSrZtQ0NDsFatbObR0VF337a2Nve2ijq+sXxwRZ3zWP6i0tnZGazNz8+7esbyyk+ePOnqG8sVvX79uqtvLCPZmzMZy0FPyV+tBvU+vbnMZjoHNSW/UmVqpmTmKq2tre5tBwYGgrUrV664+6ps69i1qKj7QWxsKyqbPSW3XV3/al5O4R1nsQzfuro6V99Y5rB3Lovdw1dXV11919bWZD2WWRyiznfK/VKdt8XFRXdfNY6mpqbcfdX+phwH9fyUMveqbVPmnJ2dnWDt8OHD7r5qvuru7nb3VeN+YmLC3ffs2bPBWkr2tXqv3nvmysqKrHvnstj6yftMop6lzfzXWyw/3fvsoNZPKWNXzemvvPKKu686b+r69uITdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADMtRRZd+lZMWqzFyVZR6j8itTctBVjq8399pMZxKm5HgePHgwWIvlbSsq8zklA1BlTXZ1dbl6xvJKb9265eoby8z1ZtvHMpJnZmZcfWPnJZaFHOLN/4xReaXenOOYlLxNlTt64MABd1+VQauywWOWl5eDNW/utZnZxsZGsJYy96rcUXUfifHmzMaUSqWq9FXXmzfbNpbF29PT4+obMzs769ouNpd5j31sLvPe29T1r66XFPPz8+5tx8bGgjXvOTPTxzc2BhX1jKTeS4w63977pZmet1Oee9Vzesr+9vf3B2uPPvqou+/Q0FCw5p3LzMwuX74crMVyvEPu3r0r6y0tLa6+sedT7z2zKApZj72fkNhzrXe9oubslOc9dQ3HnuEV9ayY0jeET9ABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADOgwPsGb/2emM/5SMn4XFxeDNZX/G6NyPAcGBtx9VWZhtTJ+U3Ix1TGM5S8qKgNUZdArsQxFb85kLL/Wm90Yy+L15kzGxpG3rzrfKVmmExMT7m0VlTPvza43M1taWgrWvPnJZjrPOCUfVI3flNx2dc5T+qqc5JS5TOXiXrx40d1X7VNKHrwav6qmxO613vG7srIi697zFssyrkYOrZl//KrzkjJHqucc73xuZjY5ORmspYxddQ3H7nvKzMxMsLawsODuq5451GvGVGsuU5nara2t7r7qOHjnHDOdUZ/SV10XKesKxfvsEMu9b29vd/WN5ZzfuXPH1VcdWzOz4eFhV98rV64Ea97sejO9Vunv73f37ezsDNZS5pwQPkEHAAAAACADLNABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMiBz0FPydpVSqRSspWQHq77ePG0znSWdkn1XrZxUlUObkmeq8mBj2bdKXV1dsObNxY5l16pMUiWWK65ysZVYvro3JzUln97bNyWLt1r56mqMpVB9U15zdnY2WEvJe1b5qtU6byl91XWhcptjVB5syvFV2a2xa1xRY1/lICux9xnL1PXy7m/s3hXLFg6JZSR7x5k63ynPOdXKe1dzQ8rcq85bSu612idvjrSZ2dzcXLAWe65Qbt26FazduHHD3Ve9146ODnff27dvB2spzxXq+vc+75npZ7qbN2+6enZ1dcm69zjEcsW9a68nnnhC1r334vvvv1/WR0ZGXH3V87S6DmPU83LK/KmO37593/zPu/kEHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADKwXxVLpVKwVltb635Rte2BAwfcfYuiCNaWl5fdfVdXV4O1uro6d1/1XlOO7+3bt4O1mpoad9/t7e1grampyd23sbExWOvt7XX1nJqaknXveVtbW5P1jY0NV9/YuPeOBzV2U6hrbd8+//f91PHb2dlx992/PzzVpVwTatv29nZ3X3UMFxYWsuurtu3s7HT33draCtbu3Lnj7qvOm5qPYtTxXVxcdPetr68P1hoaGlw9Nzc3Zd17z0y5hyszMzOy7p3rYvcYb181Zzc3N7t6mpkNDAwEa7Ozs+6+6lpraWlx91XX0/z8vLuvGmfq2TVGzVdLS0vuvkNDQ8Gauj/FvPTSS8FaV1dXVfqm7K/ap5T9VfvkfXaIPct4x0PsOvU+77W2tsq69x4/Pj4u697joO5dsddU2tragrWUuVfNOd5nf4VP0AEAAAAAyAALdAAAAAAAMsACHQAAAACADLBABwAAAAAgAyzQAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMyDBDlR2Ykkms8jZVvnKKlP2dm5sL1jo6Otx9VaZ2LPNVUVl91Tq+Kdm3KpNcHXsltp03ozqWT+nNV1c5x2b+LN5Y9rJ3PKgc6ZQMWpWLub297e5brWtCHYeUXEyVZ3ro0CF3XzXuvWPXTM9Xly5dcvf9VtwrUvLr1X1GvZcYlVnuzSSOHT/veIj1Vfc9ZXR0VNb7+/tdfVdWVmS9GnO6N+c4tm3sPqKozOeUa03dg7xjwUxfaylzWVNTU7AWGyvK8vJysPb000+7+6png5Tn3oMHD1alr7pXtLS0uPvef//9wdrw8LCr5xe+8AVZ9x4HNZ+bmd26dasqfdfX1119Y8903vlB3RPVdRij5jL1jBmj5t6XX37Z3TeET9ABAAAAAMgAC3QAAAAAADLAAh0AAAAAgAywQAcAAAAAIAMs0AEAAAAAyAALdAAAAAAAMsACHQAAAACADBQpmcUAAAAAAOCbg0/QAQAAAADIAAt0AAAAAAAywAIdAAAAAIAMsEAHAAAAACADLNABAAAAAMgAC3QAAAAAADLw/wMvnONLiCdDQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "job.plot_results(bands=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numeric-homeless",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "up42-py-dev",
   "language": "python",
   "name": "up42-py-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}