gwdm_aquifermapping (pdsi compare version).ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/BYU-Hydroinformatics/gwdm-notebooks/blob/main/gwdm_aquifermapping%20(pdsi%20compare%20version).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLC55BdBCaKl"
      },
      "source": [
        "# Install Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "AyApLXOWCSDZ"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Installing packages**\n",
        "%%capture\n",
        "#!pip install rasterio #DOESN'T SEEM TO BE USED\n",
        "!pip install rioxarray\n",
        "!pip install gstools\n",
        "#!pip install pyod #DOESN'T SEEM TO BE USED. IT IS FOR OUTLIER DETECTION\n",
        "!pip install netCDF4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04I7TAGFCkIP"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Importing packages**\n",
        "import calendar\n",
        "import copy\n",
        "import datetime\n",
        "import math\n",
        "import os\n",
        "import shutil\n",
        "import tempfile\n",
        "import time\n",
        "import urllib\n",
        "from pathlib import Path\n",
        "from timeit import default_timer as timer\n",
        "from urllib import request\n",
        "from xml.etree import cElementTree as ET\n",
        "\n",
        "import geopandas as gpd\n",
        "import gstools as gs\n",
        "import netCDF4\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import requests\n",
        "import xarray\n",
        "import rioxarray\n",
        "from scipy import interpolate\n",
        "from shapely import wkt\n",
        "from shapely.geometry import mapping\n",
        "from google.colab import files\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import ipywidgets as widgets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhGS6o2bEhG6"
      },
      "source": [
        "# Uploading Aquifer Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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            "c56cff97045749c5a5f08a28a76fa6e0",
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        "outputId": "1478903a-79e8-4712-828f-c3e0d00373b0"
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              "\n",
              "     <input type=\"file\" id=\"files-44a84456-f608-4241-8ee3-1cc9aeafe028\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-44a84456-f608-4241-8ee3-1cc9aeafe028\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
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              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
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              "          100 :\n",
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              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving deadsea2.geojson to deadsea2.geojson\n",
            "\n",
            "Please select the appropriate headers for your file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Aquifer ID', options=('GW_CODE', 'NAME', 'HECTARES', 'KM2', 'Brakish_W…"
            ],
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Uploading Aquifer File**\n",
        "upload_aquifers = files.upload()\n",
        "aquifers = gpd.GeoDataFrame.from_file(list(upload_aquifers.keys())[0])\n",
        "\n",
        "aList1 = list(aquifers.columns)\n",
        "#aList1.append('NA')\n",
        "a_ID = widgets.Dropdown(options=aList1, description = \"Aquifer ID\")\n",
        "a_name = widgets.Dropdown(options=aList1, description = \"Aquifer Name\")\n",
        "\n",
        "a1Items = [a_ID, a_name]\n",
        "print(\"\\nPlease select the appropriate headers for your file\")\n",
        "widgets.GridBox(a1Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tADsIEeHkbr-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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            "dde6e03f146343ddb673063f08e4e2be",
            "7e7bd6c657334d4a9292659815d53517"
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        "outputId": "42c779f2-3ed0-4581-d41c-d4e08305873f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Please select the aquifer you would like to map\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Aquifer Name', options=('Dead Sea GW',), value='Dead Sea GW'),), layou…"
            ],
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              "version_major": 2,
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              "model_id": "2ee65f596c5742f582114933f07608c2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Select the Aquifer you would like to Map**\n",
        "aList2 = list(aquifers[a_name.value])\n",
        "aquifer_name = widgets.Dropdown(options=aList2, description = \"Aquifer Name\")\n",
        "\n",
        "a2Items = [aquifer_name]\n",
        "print(\"\\nPlease select the aquifer you would like to map\")\n",
        "widgets.GridBox(a2Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  GW_CODE         NAME    HECTARES   KM2   Brakish_Wa    Salinity_R  \\\n",
              "0      DS  Dead Sea GW  687351.699  6874  9 to 12 MCM  1000 to 1700   \n",
              "\n",
              "   AquiferID                                           geometry  \n",
              "0          8  MULTIPOLYGON (((35.96199 31.91493, 35.95569 31...  "
            ],
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              "      <th></th>\n",
              "      <th>GW_CODE</th>\n",
              "      <th>NAME</th>\n",
              "      <th>HECTARES</th>\n",
              "      <th>KM2</th>\n",
              "      <th>Brakish_Wa</th>\n",
              "      <th>Salinity_R</th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>DS</td>\n",
              "      <td>Dead Sea GW</td>\n",
              "      <td>687351.699</td>\n",
              "      <td>6874</td>\n",
              "      <td>9 to 12 MCM</td>\n",
              "      <td>1000 to 1700</td>\n",
              "      <td>8</td>\n",
              "      <td>MULTIPOLYGON (((35.96199 31.91493, 35.95569 31...</td>\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('aquifer')\"\n",
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35.4965510387874 31.199704817534712, 35.49568383479347 31.199594669550514, 35.49461871928545 31.199829461486388, 35.493153115839135 31.200009124301747, 35.49248888206353 31.200356240450006, 35.491625824719414 31.200876349418483, 35.490978236908816 31.20128988847718)))\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#@markdown ### **Filter and Display Aquifer Information**\n",
        "aquifer_index = aquifers[aquifers[a_name.value]==aquifer_name.value].index.values\n",
        "aquifer = aquifers[aquifers[a_name.value].isin([aquifer_name.value])]\n",
        "aquifer_ID = list(aquifer[a_ID.value])\n",
        "aquifer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtMFJ3UFDY8T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267,
          "referenced_widgets": [
            "32bb87aa3c7643fbb0600575bb923db4",
            "11af51e9c8324184b06baefc2000ec8f",
            "6c8d061f22e645938c95ff4e9abcad98",
            "5f22553b014841bd90cd042d496fce53",
            "a70ba024d5b04c17bda27c453766d2de",
            "cbe59ef44c934b8a84f02608cbb49bd6",
            "c7a35f6cbf5f42a58a4c5c1f46770650",
            "a8c67fb50fcb4321a1b4c148cc542d2f",
            "ffb418b72fd14926a7c22aed3da1ad90",
            "9b686f468b0f4e879583a5312fa29228",
            "614fc525143540e8aea88a226cb247b8",
            "8e29be6293eb42f6bb3bb43b870ffcb2",
            "344ba82452dd44cc8a4f54698fd1e3a1",
            "a6d9e7146da74647aaad55628449c5c2",
            "541cde7d17f644f2beda5e99c98f488b",
            "3cde34a30d124ab38a4107c0ffd439fa",
            "ecef40193a844486a9ff656faf1f6225"
          ]
        },
        "outputId": "a2a7768a-7832-4dc2-cf40-b02f15869790"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-61e9507f-cf8e-4670-b9db-912b60e6fdc1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-61e9507f-cf8e-4670-b9db-912b60e6fdc1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving deadsea_wells.csv to deadsea_wells.csv\n",
            "\n",
            "Please select the appropriate headers for your file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Well ID', options=('AquiferID', 'Well_ID', 'Well_Name', 'lat_dec', 'lo…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32bb87aa3c7643fbb0600575bb923db4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Uploading wells file**\n",
        "upload_wells = files.upload()\n",
        "wells = pd.read_csv(list(upload_wells.keys())[0])\n",
        "\n",
        "wList = list(wells.columns)\n",
        "wList.append('NA')\n",
        "w_well_ID = widgets.Dropdown(options=wList, description = \"Well ID\")\n",
        "w_well_name = widgets.Dropdown(options=wList, description = \"Well Name\")\n",
        "w_lat = widgets.Dropdown(options=wList, description = \"Latitude\")\n",
        "w_long = widgets.Dropdown(options=wList, description = \"Longitude\")\n",
        "w_aquifer_ID = widgets.Dropdown(options=wList, description = \"Aquifer ID\")\n",
        "\n",
        "wItems = [w_well_ID, w_well_name, w_lat, w_long, w_aquifer_ID]\n",
        "print(\"\\nPlease select the appropriate headers for your file\")\n",
        "widgets.GridBox(wItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mWepWYSG7ynf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c6e6609d-2414-4dba-9c1a-d82f1f8c25cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AquiferID  Well_ID Well_Name    lat_dec    lon_dec     GSE  \\\n",
              "0          8        9    CA1006  31.265351  35.519902  -342.0   \n",
              "1          8       10    CA1010  31.135761  35.533139  -317.5   \n",
              "2          8       11    CA1011  31.202619  35.532498  -375.1   \n",
              "3          8       12    CA1038  31.046755  35.466592  -376.0   \n",
              "4          8       13    CA1095  31.024057  35.486789  -336.5   \n",
              "5          8        1    CA3043  31.750003  35.593839  -399.8   \n",
              "6          8       14    CC1014  31.696520  35.807080   725.6   \n",
              "7          8       15    CC1015  31.707859  35.810405   736.2   \n",
              "8          8       16    CD1021  31.021006  36.012778   841.1   \n",
              "9          8       17    CD1075  31.776217  35.931920   749.9   \n",
              "10         8       18    CD1097  31.558877  35.735745   344.0   \n",
              "11         8       19    CD1106  31.289381  36.034699   783.0   \n",
              "12         8        2    CD1126  31.357977  36.079998   766.6   \n",
              "13         8       20    CD1132  31.362440  36.081650   760.0   \n",
              "14         8       21    CD1137  31.586803  36.014427   696.6   \n",
              "15         8       22    CD1152  31.211950  35.823230   831.1   \n",
              "16         8       23    CD1174  31.123645  36.010241   823.4   \n",
              "17         8       24    CD1182  31.009222  36.035934   856.7   \n",
              "18         8       25    CD1207  31.165776  35.737651   976.0   \n",
              "19         8       26    CD1212  31.632453  36.093550   707.9   \n",
              "20         8        3    CD1213  31.478607  35.973060   762.1   \n",
              "21         8        4    CD3125  31.534353  35.884396   667.9   \n",
              "22         8       27    CD3133  31.563590  35.731270     0.0   \n",
              "23         8       28    CD3307  31.372780  36.077180     0.0   \n",
              "24         8        5    CD3448  31.082370  36.021645   818.5   \n",
              "25         8       29    CD3469  30.996049  35.940721   901.4   \n",
              "26         8       30    CD3497  31.546768  35.873368   657.0   \n",
              "27         8        6    CD3504  31.551130  35.872681   649.0   \n",
              "28         8       31    CD3508  31.144693  35.879363   870.1   \n",
              "29         8       32    CD3530  31.468133  36.024398   731.0   \n",
              "30         8       33    CD3684  31.268320  36.060640     0.0   \n",
              "31         8       34    CD3685  31.232200  36.046700     0.0   \n",
              "32         8       35    CD3688  31.526300  36.051000     0.0   \n",
              "33         8       36    CF1074  30.833422  36.029070   889.3   \n",
              "34         8       37    CF1078  30.610413  35.822486  1032.2   \n",
              "35         8       38    CF3020  30.815857  35.754081  1110.0   \n",
              "36         8       39    CF3042  30.787877  35.934187   860.1   \n",
              "37         8       40    DA1033  31.028241  35.478376  -342.0   \n",
              "38         8        7      JDW2  30.644843  35.826603  1011.2   \n",
              "39         8        8        Q4  31.201908  36.036972   810.5   \n",
              "\n",
              "                     geometry  \n",
              "0   POINT (35.51990 31.26535)  \n",
              "1   POINT (35.53314 31.13576)  \n",
              "2   POINT (35.53250 31.20262)  \n",
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              "5   POINT (35.59384 31.75000)  \n",
              "6   POINT (35.80708 31.69652)  \n",
              "7   POINT (35.81041 31.70786)  \n",
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              "9   POINT (35.93192 31.77622)  \n",
              "10  POINT (35.73575 31.55888)  \n",
              "11  POINT (36.03470 31.28938)  \n",
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              "13  POINT (36.08165 31.36244)  \n",
              "14  POINT (36.01443 31.58680)  \n",
              "15  POINT (35.82323 31.21195)  \n",
              "16  POINT (36.01024 31.12364)  \n",
              "17  POINT (36.03593 31.00922)  \n",
              "18  POINT (35.73765 31.16578)  \n",
              "19  POINT (36.09355 31.63245)  \n",
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              "21  POINT (35.88440 31.53435)  \n",
              "22  POINT (35.73127 31.56359)  \n",
              "23  POINT (36.07718 31.37278)  \n",
              "24  POINT (36.02165 31.08237)  \n",
              "25  POINT (35.94072 30.99605)  \n",
              "26  POINT (35.87337 31.54677)  \n",
              "27  POINT (35.87268 31.55113)  \n",
              "28  POINT (35.87936 31.14469)  \n",
              "29  POINT (36.02440 31.46813)  \n",
              "30  POINT (36.06064 31.26832)  \n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>Well_ID</th>\n",
              "      <th>Well_Name</th>\n",
              "      <th>lat_dec</th>\n",
              "      <th>lon_dec</th>\n",
              "      <th>GSE</th>\n",
              "      <th>geometry</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>CA1006</td>\n",
              "      <td>31.265351</td>\n",
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              "      <th>1</th>\n",
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              "      <td>35.533139</td>\n",
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              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>CA1011</td>\n",
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              "      <td>-375.1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>CA1038</td>\n",
              "      <td>31.046755</td>\n",
              "      <td>35.466592</td>\n",
              "      <td>-376.0</td>\n",
              "      <td>POINT (35.46659 31.04676)</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>13</td>\n",
              "      <td>CA1095</td>\n",
              "      <td>31.024057</td>\n",
              "      <td>35.486789</td>\n",
              "      <td>-336.5</td>\n",
              "      <td>POINT (35.48679 31.02406)</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>CA3043</td>\n",
              "      <td>31.750003</td>\n",
              "      <td>35.593839</td>\n",
              "      <td>-399.8</td>\n",
              "      <td>POINT (35.59384 31.75000)</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>CC1014</td>\n",
              "      <td>31.696520</td>\n",
              "      <td>35.807080</td>\n",
              "      <td>725.6</td>\n",
              "      <td>POINT (35.80708 31.69652)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>CC1015</td>\n",
              "      <td>31.707859</td>\n",
              "      <td>35.810405</td>\n",
              "      <td>736.2</td>\n",
              "      <td>POINT (35.81041 31.70786)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>CD1021</td>\n",
              "      <td>31.021006</td>\n",
              "      <td>36.012778</td>\n",
              "      <td>841.1</td>\n",
              "      <td>POINT (36.01278 31.02101)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>17</td>\n",
              "      <td>CD1075</td>\n",
              "      <td>31.776217</td>\n",
              "      <td>35.931920</td>\n",
              "      <td>749.9</td>\n",
              "      <td>POINT (35.93192 31.77622)</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>CD1097</td>\n",
              "      <td>31.558877</td>\n",
              "      <td>35.735745</td>\n",
              "      <td>344.0</td>\n",
              "      <td>POINT (35.73575 31.55888)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>19</td>\n",
              "      <td>CD1106</td>\n",
              "      <td>31.289381</td>\n",
              "      <td>36.034699</td>\n",
              "      <td>783.0</td>\n",
              "      <td>POINT (36.03470 31.28938)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CD1126</td>\n",
              "      <td>31.357977</td>\n",
              "      <td>36.079998</td>\n",
              "      <td>766.6</td>\n",
              "      <td>POINT (36.08000 31.35798)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>CD1132</td>\n",
              "      <td>31.362440</td>\n",
              "      <td>36.081650</td>\n",
              "      <td>760.0</td>\n",
              "      <td>POINT (36.08165 31.36244)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8</td>\n",
              "      <td>21</td>\n",
              "      <td>CD1137</td>\n",
              "      <td>31.586803</td>\n",
              "      <td>36.014427</td>\n",
              "      <td>696.6</td>\n",
              "      <td>POINT (36.01443 31.58680)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>CD1152</td>\n",
              "      <td>31.211950</td>\n",
              "      <td>35.823230</td>\n",
              "      <td>831.1</td>\n",
              "      <td>POINT (35.82323 31.21195)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>8</td>\n",
              "      <td>23</td>\n",
              "      <td>CD1174</td>\n",
              "      <td>31.123645</td>\n",
              "      <td>36.010241</td>\n",
              "      <td>823.4</td>\n",
              "      <td>POINT (36.01024 31.12364)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>CD1182</td>\n",
              "      <td>31.009222</td>\n",
              "      <td>36.035934</td>\n",
              "      <td>856.7</td>\n",
              "      <td>POINT (36.03593 31.00922)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
              "      <td>CD1207</td>\n",
              "      <td>31.165776</td>\n",
              "      <td>35.737651</td>\n",
              "      <td>976.0</td>\n",
              "      <td>POINT (35.73765 31.16578)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "      <td>26</td>\n",
              "      <td>CD1212</td>\n",
              "      <td>31.632453</td>\n",
              "      <td>36.093550</td>\n",
              "      <td>707.9</td>\n",
              "      <td>POINT (36.09355 31.63245)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>CD1213</td>\n",
              "      <td>31.478607</td>\n",
              "      <td>35.973060</td>\n",
              "      <td>762.1</td>\n",
              "      <td>POINT (35.97306 31.47861)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>CD3125</td>\n",
              "      <td>31.534353</td>\n",
              "      <td>35.884396</td>\n",
              "      <td>667.9</td>\n",
              "      <td>POINT (35.88440 31.53435)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8</td>\n",
              "      <td>27</td>\n",
              "      <td>CD3133</td>\n",
              "      <td>31.563590</td>\n",
              "      <td>35.731270</td>\n",
              "      <td>0.0</td>\n",
              "      <td>POINT (35.73127 31.56359)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>CD3307</td>\n",
              "      <td>31.372780</td>\n",
              "      <td>36.077180</td>\n",
              "      <td>0.0</td>\n",
              "      <td>POINT (36.07718 31.37278)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>CD3448</td>\n",
              "      <td>31.082370</td>\n",
              "      <td>36.021645</td>\n",
              "      <td>818.5</td>\n",
              "      <td>POINT (36.02165 31.08237)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8</td>\n",
              "      <td>29</td>\n",
              "      <td>CD3469</td>\n",
              "      <td>30.996049</td>\n",
              "      <td>35.940721</td>\n",
              "      <td>901.4</td>\n",
              "      <td>POINT (35.94072 30.99605)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>CD3497</td>\n",
              "      <td>31.546768</td>\n",
              "      <td>35.873368</td>\n",
              "      <td>657.0</td>\n",
              "      <td>POINT (35.87337 31.54677)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>CD3504</td>\n",
              "      <td>31.551130</td>\n",
              "      <td>35.872681</td>\n",
              "      <td>649.0</td>\n",
              "      <td>POINT (35.87268 31.55113)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
              "      <td>31</td>\n",
              "      <td>CD3508</td>\n",
              "      <td>31.144693</td>\n",
              "      <td>35.879363</td>\n",
              "      <td>870.1</td>\n",
              "      <td>POINT (35.87936 31.14469)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>CD3530</td>\n",
              "      <td>31.468133</td>\n",
              "      <td>36.024398</td>\n",
              "      <td>731.0</td>\n",
              "      <td>POINT (36.02440 31.46813)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>8</td>\n",
              "      <td>33</td>\n",
              "      <td>CD3684</td>\n",
              "      <td>31.268320</td>\n",
              "      <td>36.060640</td>\n",
              "      <td>0.0</td>\n",
              "      <td>POINT (36.06064 31.26832)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>8</td>\n",
              "      <td>34</td>\n",
              "      <td>CD3685</td>\n",
              "      <td>31.232200</td>\n",
              "      <td>36.046700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>POINT (36.04670 31.23220)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8</td>\n",
              "      <td>35</td>\n",
              "      <td>CD3688</td>\n",
              "      <td>31.526300</td>\n",
              "      <td>36.051000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>POINT (36.05100 31.52630)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>8</td>\n",
              "      <td>36</td>\n",
              "      <td>CF1074</td>\n",
              "      <td>30.833422</td>\n",
              "      <td>36.029070</td>\n",
              "      <td>889.3</td>\n",
              "      <td>POINT (36.02907 30.83342)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>8</td>\n",
              "      <td>37</td>\n",
              "      <td>CF1078</td>\n",
              "      <td>30.610413</td>\n",
              "      <td>35.822486</td>\n",
              "      <td>1032.2</td>\n",
              "      <td>POINT (35.82249 30.61041)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>8</td>\n",
              "      <td>38</td>\n",
              "      <td>CF3020</td>\n",
              "      <td>30.815857</td>\n",
              "      <td>35.754081</td>\n",
              "      <td>1110.0</td>\n",
              "      <td>POINT (35.75408 30.81586)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>8</td>\n",
              "      <td>39</td>\n",
              "      <td>CF3042</td>\n",
              "      <td>30.787877</td>\n",
              "      <td>35.934187</td>\n",
              "      <td>860.1</td>\n",
              "      <td>POINT (35.93419 30.78788)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>8</td>\n",
              "      <td>40</td>\n",
              "      <td>DA1033</td>\n",
              "      <td>31.028241</td>\n",
              "      <td>35.478376</td>\n",
              "      <td>-342.0</td>\n",
              "      <td>POINT (35.47838 31.02824)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>JDW2</td>\n",
              "      <td>30.644843</td>\n",
              "      <td>35.826603</td>\n",
              "      <td>1011.2</td>\n",
              "      <td>POINT (35.82660 30.64484)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>Q4</td>\n",
              "      <td>31.201908</td>\n",
              "      <td>36.036972</td>\n",
              "      <td>810.5</td>\n",
              "      <td>POINT (36.03697 31.20191)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7586d9eb-4286-4453-80ac-c2178ac4068c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('wells_gdf')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7586d9eb-4286-4453-80ac-c2178ac4068c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('wells_gdf');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wells_gdf",
              "summary": "{\n  \"name\": \"wells_gdf\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Well_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 1,\n        \"max\": 40,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Well_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"CD1212\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3040835496833587,\n        \"min\": 30.6104133966,\n        \"max\": 31.776216951,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          31.6324532772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19561535939745336,\n        \"min\": 35.466591631,\n        \"max\": 36.0935502282,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          36.0935502282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 492.1913278117331,\n        \"min\": -399.8,\n        \"max\": 1110.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          649.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"POINT (36.0935502282 31.6324532772)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#@markdown ### **Formatting wells dataframe**\n",
        "#To avoid type mismatch, convert the aquifer_id found from the aquifer file and the aquiferid column\n",
        "#in the wells dataframe to both be strings\n",
        "aquifer_ID = [str(item) for item in aquifer_ID]\n",
        "if (w_aquifer_ID.value !=\"NA\"):\n",
        "  wells[w_aquifer_ID.value] = wells[w_aquifer_ID.value].astype(str)\n",
        "\n",
        "wells_gdf = gpd.GeoDataFrame(wells, geometry=gpd.points_from_xy(wells[w_long.value], wells[w_lat.value]))\n",
        "if (w_aquifer_ID.value !=\"NA\"):\n",
        "  wells_gdf = wells_gdf[wells_gdf[w_aquifer_ID.value].isin(aquifer_ID)]\n",
        "wells_gdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MGDfk069Dxo7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "a3871879-62e1-428f-a5eb-a0ed7110d67f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Plotting aquifer and wells**\n",
        "fig, ax = plt.subplots()\n",
        "aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax)\n",
        "wells_gdf.plot(ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MFTl7W_ID-QV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235,
          "referenced_widgets": [
            "29811bd24c8c47aab962ab131934fd43",
            "8ef8f6e2674744ca82487fad112c3f5a",
            "403a8bf07894454984f27b24e66c817b",
            "2ce040bdb119450dbed5495087b2865a",
            "824bf965400240bc85418bba56c81f57",
            "0b37f560af574b88896426ba3b251359",
            "94358df0586342a8a014677313f8e932",
            "be1ca56ad43142a096397e356e7d21e9",
            "2ef8dc906258406d983684f5898dcc1f",
            "7bc808780869451daab734a10212694c",
            "78f14cc8a9e64c0d9574820a7c3a41e0",
            "1e035d34bce547a8adba4d673e509de2",
            "8ddc0eadcf88464ea17d8b90158b059c",
            "22e805e7f7cc459e976fab3c70af20dc"
          ]
        },
        "outputId": "5d2e9e47-37f2-4fc2-8a86-80e4b321ac27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-200eab74-3ebf-4ac9-9d1a-b4725a56c41c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-200eab74-3ebf-4ac9-9d1a-b4725a56c41c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving deadsea_ts.csv to deadsea_ts.csv\n",
            "\n",
            "Please select the appropriate headers for your file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Well ID', options=('AquiferID', 'WellName', 'WellID', 'DateString', 'W…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29811bd24c8c47aab962ab131934fd43"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Uploading timeseries**\n",
        "upload_timeseries = files.upload()\n",
        "measurements = pd.read_csv(list(upload_timeseries.keys())[0])\n",
        "\n",
        "tList = list(measurements.columns)\n",
        "tList.append('NA')\n",
        "ts_well_ID = widgets.Dropdown(options=tList, description = \"Well ID\")\n",
        "ts_date = widgets.Dropdown(options=tList, description = \"Date\")\n",
        "ts_measurement = widgets.Dropdown(options=tList, description = \"Measurement\")\n",
        "ts_aquifer_ID = widgets.Dropdown(options=tList, description = \"Aquifer ID\")\n",
        "\n",
        "tItems = [ts_well_ID, ts_date, ts_measurement, ts_aquifer_ID]\n",
        "print(\"\\nPlease select the appropriate headers for your file\")\n",
        "widgets.GridBox(tItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UvCf3wWq9EfI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "ecb7617e-987a-465a-8cbb-8fb52e6934e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-9ffa11407012>:9: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
            "  measurements[ts_date.value] = pd.to_datetime(measurements[ts_date.value], infer_datetime_format=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     AquiferID          WellName  WellID DateString  WaterTableElev\n",
              "0            8      MAZRA_A NO 1       9 1996-04-25         -370.45\n",
              "1            8      MAZRA_A NO 1       9 1996-05-27         -370.67\n",
              "2            8      MAZRA_A NO 1       9 1996-06-24         -370.89\n",
              "3            8      MAZRA_A NO 1       9 1996-07-21         -370.96\n",
              "4            8      MAZRA_A NO 1       9 1996-08-25         -371.01\n",
              "...        ...               ...     ...        ...             ...\n",
              "5863         8  Jurf Deep Well 2       7 2021-10-25          488.63\n",
              "5864         8  Jurf Deep Well 2       7 2022-09-26          488.55\n",
              "5865         8       Qtraneh EX4       8 2020-02-23          386.77\n",
              "5866         8       Qtraneh EX4       8 2021-12-06          386.52\n",
              "5867         8       Qtraneh EX4       8 2022-09-28          386.45\n",
              "\n",
              "[5868 rows x 5 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>WellName</th>\n",
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              "      <th>DateString</th>\n",
              "      <th>WaterTableElev</th>\n",
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              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>MAZRA_A NO 1</td>\n",
              "      <td>9</td>\n",
              "      <td>1996-04-25</td>\n",
              "      <td>-370.45</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>MAZRA_A NO 1</td>\n",
              "      <td>9</td>\n",
              "      <td>1996-05-27</td>\n",
              "      <td>-370.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>MAZRA_A NO 1</td>\n",
              "      <td>9</td>\n",
              "      <td>1996-06-24</td>\n",
              "      <td>-370.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>MAZRA_A NO 1</td>\n",
              "      <td>9</td>\n",
              "      <td>1996-07-21</td>\n",
              "      <td>-370.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>MAZRA_A NO 1</td>\n",
              "      <td>9</td>\n",
              "      <td>1996-08-25</td>\n",
              "      <td>-371.01</td>\n",
              "    </tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863</th>\n",
              "      <td>8</td>\n",
              "      <td>Jurf Deep Well 2</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-10-25</td>\n",
              "      <td>488.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5864</th>\n",
              "      <td>8</td>\n",
              "      <td>Jurf Deep Well 2</td>\n",
              "      <td>7</td>\n",
              "      <td>2022-09-26</td>\n",
              "      <td>488.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5865</th>\n",
              "      <td>8</td>\n",
              "      <td>Qtraneh EX4</td>\n",
              "      <td>8</td>\n",
              "      <td>2020-02-23</td>\n",
              "      <td>386.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5866</th>\n",
              "      <td>8</td>\n",
              "      <td>Qtraneh EX4</td>\n",
              "      <td>8</td>\n",
              "      <td>2021-12-06</td>\n",
              "      <td>386.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5867</th>\n",
              "      <td>8</td>\n",
              "      <td>Qtraneh EX4</td>\n",
              "      <td>8</td>\n",
              "      <td>2022-09-28</td>\n",
              "      <td>386.45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5868 rows × 5 columns</p>\n",
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              "        document.querySelector('#id_ef89932e-24fc-4b16-958d-c91619664756 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('measurements');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "measurements",
              "summary": "{\n  \"name\": \"measurements\",\n  \"rows\": 5868,\n  \"fields\": [\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WellName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"Jurf Deep Well 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WellID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1,\n        \"max\": 40,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DateString\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1972-07-01 00:00:00\",\n        \"max\": \"2023-04-27 00:00:00\",\n        \"num_unique_values\": 2687,\n        \"samples\": [\n          \"2006-03-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WaterTableElev\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 514.7507232867664,\n        \"min\": -1738.85,\n        \"max\": 1009.92,\n        \"num_unique_values\": 3901,\n        \"samples\": [\n          530.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#@markdown ## **Formatting Timeseries**\n",
        "#@markdown #### Run this cell to coerce datatypes, convert dates, and filter for the specific aquifer\n",
        "\n",
        "#To avoid type mismatch datatypes\n",
        "if (ts_aquifer_ID.value !=\"NA\"):\n",
        "  measurements[ts_aquifer_ID.value] = measurements[ts_aquifer_ID.value].astype(str)\n",
        "\n",
        "#converting date format\n",
        "measurements[ts_date.value] = pd.to_datetime(measurements[ts_date.value], infer_datetime_format=True)\n",
        "\n",
        "#filtering by aquifer if aquiferIDs are included in the dataset\n",
        "if (ts_aquifer_ID.value !=\"NA\"):\n",
        "  measurements = measurements[measurements[ts_aquifer_ID.value].isin(aquifer_ID)]\n",
        "measurements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RUoMfX_7rUiT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d925f96f-20f1-4849-b315-d25c7c14f2ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33  unique well IDs\n",
            "33  wells with measurements\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Dropping measurements that don't have corresponding wells**\n",
        "no_uniquewells = len(measurements[ts_well_ID.value].unique())\n",
        "measurements = measurements[measurements[ts_well_ID.value].isin(wells_gdf[w_well_ID.value])].reset_index(drop=True)\n",
        "no_uniquewellsref = len(measurements[ts_well_ID.value].unique())\n",
        "print(no_uniquewells, ' unique well IDs')\n",
        "print(no_uniquewellsref, ' wells with measurements')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5oSloAWjEOb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2658cbd5-b72a-4912-e455-b7d3eba33b9e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[35.398332886716226, 30.563510619412916, 36.12764297914039, 31.918859774480342]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#@markdown ### **Defining aquifer bounds**\n",
        "bbox = aquifer.bounds.values[0].tolist()\n",
        "bbox"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hvdh4c0JGPEc"
      },
      "source": [
        "# Accessing Data from Server"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JPbXu32uGfMs"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Defining functions for this section**\n",
        "from io import StringIO\n",
        "\n",
        "def get_pdsi_df(aquifer_gdf):\n",
        "    data_dir = \"/content\"\n",
        "    nc_file = os.path.join(data_dir, \"pdsi.nc4\")\n",
        "    pdsi_ds = xarray.open_dataset(nc_file, decode_times=False)\n",
        "    units, _, reference_date = pdsi_ds.time.attrs[\"units\"].split(\"since\")\n",
        "    pdsi_ds[\"time\"] = pd.date_range(\n",
        "        start=reference_date, periods=pdsi_ds.sizes[\"time\"], freq=\"MS\"\n",
        "    )\n",
        "    ds = pdsi_ds[\"pdsi_filled\"].to_dataset()\n",
        "    ds[\"pdsi_filled\"] = ds[\"pdsi_filled\"].rio.write_crs(\"epsg:4326\")\n",
        "    ds.rio.set_spatial_dims(x_dim=\"lon\", y_dim=\"lat\", inplace=True)\n",
        "    # aquifer_geom = wkt.loads(aquifer_obj[0])\n",
        "    # aquifer_gdf = gpd.GeoDataFrame(\n",
        "    #     {\"name\": [\"random\"], \"geometry\": [aquifer_geom]}, crs=\"EPSG:4326\"\n",
        "    # )\n",
        "    clipped_ds = ds.rio.clip(\n",
        "        aquifer_gdf.geometry.apply(mapping),\n",
        "        aquifer_gdf.crs,\n",
        "        drop=True,\n",
        "        all_touched=True,\n",
        "    ).drop(\"spatial_ref\")\n",
        "    filled_df = (\n",
        "        clipped_ds.to_dataframe()\n",
        "        .reset_index()\n",
        "        .groupby(\"time\")[\"pdsi_filled\"]\n",
        "        .mean()\n",
        "        .reset_index()\n",
        "        .set_index(\"time\")\n",
        "    )\n",
        "    return filled_df\n",
        "\n",
        "def get_time_bounds(url):\n",
        "    # gio's new get_time_bounds\n",
        "    # This function returns the first and last available time\n",
        "    # from a url of a getcapabilities page located on a Thredds Server\n",
        "    f = urllib.request.urlopen(url)\n",
        "    tree = ET.parse(f)\n",
        "    root = tree.getroot()\n",
        "    # These lines of code find the time dimension information for the netcdf on the Thredds server\n",
        "    dim = root.findall(\".//{http://www.opengis.net/wms}Dimension\")\n",
        "    dim = dim[0].text\n",
        "    times = dim.split(\",\")\n",
        "    timemin = re.sub(r\"[\\n\\t\\s]*\", \"\", times[0])\n",
        "    timemax = re.sub(r\"[\\n\\t\\s]*\", \"\", times[-1])\n",
        "    timemax=timemax.split(\"/\")[0] ### ------> this is the minor change\n",
        "    # timemax=timemax.split(\"/\")[1] ### ------> uncoment this if you want the latest\n",
        "\n",
        "    # timemin and timemax are the first and last available times on the specified url\n",
        "    return timemin, timemax\n",
        "\n",
        "def get_thredds_value(server, layer, bbox):\n",
        "    # gio's get_thredds_value\n",
        "    # This function returns a pandas dataframe of the timeseries values of a specific layer\n",
        "    # at a specific latitude and longitude from a file on a Thredds server\n",
        "    # server: the url of the netcdf desired netcdf file on the Thredds server to read\n",
        "    # layer: the name of the layer to extract timeseries information from for the netcdf file\n",
        "    # bbox: an array of lat/lon values defining the bounding box\n",
        "    # returns df: a pandas dataframe of the timeseries for the lat/lon at the center of the bbox for the layer in the server netcdf file\n",
        "    # calls the getTimeBounds function to get the first and last available times for the netcdf file on the server\n",
        "    time_min, time_max = get_time_bounds(\n",
        "        server + \"?service=WMS&version=1.3.0&request=GetCapabilities\"\n",
        "    )\n",
        "    print(time_min)\n",
        "    print(time_max)\n",
        "    # These lines properly format a url request for the timeseries of a speific layer from a netcdf on\n",
        "    # a Thredds server\n",
        "    server = f\"{server}?service=WMS&version=1.3.0&request=GetTimeseries&CRS=CRS:84&QUERY_LAYERS={layer}\"\n",
        "    server = f\"{server}&X=0&Y=0&I=0&J=0&BBOX={bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}&LAYER={layer}\"\n",
        "    server = f\"{server}&WIDTH=1&Height=1&INFO_FORMAT=text/csv&STYLES=raster/default\"\n",
        "    server = f\"{server}&TIME={time_min}/{time_max}\"\n",
        "    print(server)\n",
        "    df = pd.read_csv(server,skiprows=2)\n",
        "    df = df.rename(columns={'Model-Calculated Monthly Mean Soil Moisture (mm)': f'{layer}'})\n",
        "    df[layer] = df[layer].replace(\"none\", np.nan).astype(float)\n",
        "\n",
        "\n",
        "    df = df.rename(columns={'Time (UTC)': 'time'})\n",
        "    df['time'] = df['time'].apply(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%dT%H:%M:%S.%fZ\"))\n",
        "    df = df.set_index('time')\n",
        "\n",
        "    return df\n",
        "\n",
        "def sat_resample(gldas_df):\n",
        "    # resamples the data from both datasets to a monthly value,\n",
        "    # uses the mean of all measurements in a month\n",
        "    # first resample to daily values, then take the start of the month\n",
        "\n",
        "    # Gio's addition Convert the index to a DatetimeIndex\n",
        "    gldas_df.index = pd.to_datetime(gldas_df.index)\n",
        "\n",
        "    # Resample. D is daily, mean averages any values in a given day, if no data in that day, gives NaN\n",
        "    gldas_df = gldas_df.resample(\"D\").mean()\n",
        "\n",
        "    # Fill in NaN values using PCHIP interpolation\n",
        "    gldas_df.interpolate(method=\"pchip\", inplace=True, limit_area=\"inside\")\n",
        "\n",
        "    # Resample to monthly values. MS means \"month start\" or to the start of the month, this is the interpolated value\n",
        "    gldas_df = gldas_df.resample(\"MS\").first()\n",
        "\n",
        "    return gldas_df\n",
        "\n",
        "def sat_rolling_window(gldas_df):\n",
        "    years = [1, 3, 5, 10]\n",
        "    names = list(gldas_df.columns)\n",
        "    new_names = copy.deepcopy(\n",
        "        names\n",
        "    )  # names is a list of the varibiles in the data frame, need to unlink for loop\n",
        "    # This loop adds the yearly, 3-year, 5-year, and 10-year rolling averages of each variable to the dataframe\n",
        "    # rolling averages uses the preceding time to compute the last value,\n",
        "    # e.g., using the preceding 5 years of data to get todays\n",
        "    for name in names:\n",
        "        for year in years:\n",
        "            new = name + \"_yr\" + str(year).zfill(2)\n",
        "            gldas_df[new] = gldas_df[name].rolling(year * 12).mean()\n",
        "            new_names.append(new)\n",
        "    return gldas_df, new_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0wgNQGlGVXL",
        "outputId": "d2b7c289-3867-477c-f6ad-612094f6af7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            pdsi_filled\n",
            "time                   \n",
            "1948-01-01    -2.235825\n",
            "1948-02-01    -2.349896\n",
            "1948-03-01    -1.048602\n",
            "1948-04-01    -1.489158\n",
            "1948-05-01    -1.657278\n",
            "...                 ...\n",
            "2020-08-01     0.441064\n",
            "2020-09-01     0.377285\n",
            "2020-10-01    -0.067582\n",
            "2020-11-01     0.526363\n",
            "2020-12-01    -0.046666\n",
            "\n",
            "[876 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Downloading PDSI data from server**\n",
        "pdsi_response = requests.get(\"https://github.com/BYU-Hydroinformatics/gwdm-data/blob/main/pdsi.nc4?raw=true\")\n",
        "with open(\"pdsi.nc4\", \"wb\") as binary_file:\n",
        "    # Write bytes to file\n",
        "    binary_file.write(pdsi_response.content)\n",
        "pdsi_df = get_pdsi_df(aquifer)\n",
        "print(pdsi_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bf5EpzZLG791",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b46280d-d8ba-4aba-f935-3eeda6725a64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1948-01-01T00:00:00.000Z\n",
            "2023-12-01T00:00:00.000Z\n",
            "https://apps.geoglows.org/thredds/wms/geoglows_data/soilw.mon.mean.v2.nc?service=WMS&version=1.3.0&request=GetTimeseries&CRS=CRS:84&QUERY_LAYERS=soilw&X=0&Y=0&I=0&J=0&BBOX=35.398332886716226,30.563510619412916,36.12764297914039,31.918859774480342&LAYER=soilw&WIDTH=1&Height=1&INFO_FORMAT=text/csv&STYLES=raster/default&TIME=1948-01-01T00:00:00.000Z/2023-12-01T00:00:00.000Z\n",
            "                 soilw\n",
            "time                  \n",
            "1948-01-01  220.360140\n",
            "1948-02-01  224.065520\n",
            "1948-03-01  241.779020\n",
            "1948-04-01  236.698290\n",
            "1948-05-01  194.676800\n",
            "...                ...\n",
            "2023-08-01   42.785736\n",
            "2023-09-01   32.274204\n",
            "2023-10-01   26.108322\n",
            "2023-11-01   28.802877\n",
            "2023-12-01   38.053270\n",
            "\n",
            "[912 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Downloading soil moisture data from server**\n",
        "# SERVER2 = \"https://tethyswa.servirglobal.net/thredds/wms/testAll/psl/soilw.mon.mean.nc\"\n",
        "SERVER2 = \"https://apps.geoglows.org/thredds/wms/geoglows_data/soilw.mon.mean.v2.nc\"\n",
        "# SERVER2 = \"https://www.psl.noaa.gov/thredds/wms/Datasets/cpcsoil/soilw.mon.mean.nc\"\n",
        "LAYER2 = \"soilw\"  # name of data column to be returned\n",
        "soilw_df = get_thredds_value(SERVER2, LAYER2, bbox)\n",
        "print(soilw_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ncaaUXSHyW_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23057313-d770-412e-febe-56a68537428b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            pdsi_filled       soilw\n",
            "time                               \n",
            "1948-01-01    -2.235825  220.360140\n",
            "1948-02-01    -2.349896  224.065520\n",
            "1948-03-01    -1.048602  241.779020\n",
            "1948-04-01    -1.489158  236.698290\n",
            "1948-05-01    -1.657278  194.676800\n",
            "...                 ...         ...\n",
            "2020-08-01     0.441064   46.401447\n",
            "2020-09-01     0.377285   34.657433\n",
            "2020-10-01    -0.067582   27.250402\n",
            "2020-11-01     0.526363   33.309814\n",
            "2020-12-01    -0.046666   45.034546\n",
            "\n",
            "[876 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Resampling to monthly values**\n",
        "gldas_df = pd.concat([pdsi_df, soilw_df], join=\"outer\", axis=1) # Combine PDSI and GLDAS\n",
        "gldas_df.dropna(inplace=True)\n",
        "gldas_df = sat_resample(gldas_df) # Resample to monthly values - not sure this is necessary\n",
        "print(gldas_df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ### **Adding rolling averages of both PDSI and GLDAS**\n",
        "gldas_df, names = sat_rolling_window(gldas_df)\n",
        "print(gldas_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1X4UcneD_Zxk",
        "outputId": "e52ef0ce-57c0-469e-9033-0ac1da1b65f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            pdsi_filled       soilw  pdsi_filled_yr01  pdsi_filled_yr03  \\\n",
            "time                                                                      \n",
            "1948-01-01    -2.235825  220.360140               NaN               NaN   \n",
            "1948-02-01    -2.349896  224.065520               NaN               NaN   \n",
            "1948-03-01    -1.048602  241.779020               NaN               NaN   \n",
            "1948-04-01    -1.489158  236.698290               NaN               NaN   \n",
            "1948-05-01    -1.657278  194.676800               NaN               NaN   \n",
            "...                 ...         ...               ...               ...   \n",
            "2020-08-01     0.441064   46.401447          0.289350          0.402944   \n",
            "2020-09-01     0.377285   34.657433          0.296164          0.398049   \n",
            "2020-10-01    -0.067582   27.250402          0.246256          0.399757   \n",
            "2020-11-01     0.526363   33.309814          0.311918          0.432957   \n",
            "2020-12-01    -0.046666   45.034546          0.308598          0.479281   \n",
            "\n",
            "            pdsi_filled_yr05  pdsi_filled_yr10  soilw_yr01  soilw_yr03  \\\n",
            "time                                                                     \n",
            "1948-01-01               NaN               NaN         NaN         NaN   \n",
            "1948-02-01               NaN               NaN         NaN         NaN   \n",
            "1948-03-01               NaN               NaN         NaN         NaN   \n",
            "1948-04-01               NaN               NaN         NaN         NaN   \n",
            "1948-05-01               NaN               NaN         NaN         NaN   \n",
            "...                      ...               ...         ...         ...   \n",
            "2020-08-01          0.342270          0.393477   68.909950   53.232461   \n",
            "2020-09-01          0.329494          0.391777   69.702681   53.740149   \n",
            "2020-10-01          0.285570          0.395180   70.065005   54.121347   \n",
            "2020-11-01          0.251674          0.414111   71.008794   54.633740   \n",
            "2020-12-01          0.251512          0.428396   71.911787   55.364030   \n",
            "\n",
            "            soilw_yr05  soilw_yr10  \n",
            "time                                \n",
            "1948-01-01         NaN         NaN  \n",
            "1948-02-01         NaN         NaN  \n",
            "1948-03-01         NaN         NaN  \n",
            "1948-04-01         NaN         NaN  \n",
            "1948-05-01         NaN         NaN  \n",
            "...                ...         ...  \n",
            "2020-08-01   51.440527   47.330621  \n",
            "2020-09-01   51.581199   47.487943  \n",
            "2020-10-01   51.444789   47.609440  \n",
            "2020-11-01   51.227535   47.799482  \n",
            "2020-12-01   51.148924   48.042531  \n",
            "\n",
            "[876 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQDvbJCQIaoB"
      },
      "source": [
        "# Temporal Interpolation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQ5d6JG8I00d"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Setting parameters for temporal interpolation**\n",
        "min_samples = 5 #@param {type:\"integer\"}\n",
        "gap_size = \"3650\" #@param {type:\"string\"}\n",
        "pad = 180 #@param {type:\"integer\"}\n",
        "spacing = \"1MS\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fQE3F7EuI77v"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Defining functions for this section**\n",
        "\n",
        "def extract_well_data(name, well_df, min_samples=0, ts_col=\"ts_value\", date_col=\"date\"):\n",
        "    if len(well_df) >= min_samples:\n",
        "        # if (well_df['date'].min() < start_date) and (well_df['date'].max() > end_date):\n",
        "        # elevation = well_df['gse'].unique()[0]\n",
        "        df = pd.DataFrame(\n",
        "            index=well_df[date_col].values,\n",
        "            data=well_df[ts_col].values,\n",
        "            columns=[name],\n",
        "        )\n",
        "        df = df[np.logical_not(df.index.duplicated())]\n",
        "        return df\n",
        "\n",
        "def interp_well(wells_df, gap_size, pad, spacing):\n",
        "    well_interp_df = pd.DataFrame()\n",
        "    # create a time index to interpolate over - cover entire range\n",
        "    interp_index: pd.DatetimeIndex = pd.date_range(\n",
        "        start=min(wells_df.index), freq=spacing, end=max(wells_df.index)\n",
        "    )\n",
        "    # loop over each well, interpolate data using pchip\n",
        "    for well in wells_df:\n",
        "        temp_df = wells_df[well].dropna()  # available data for a well\n",
        "        x_index = temp_df.index.astype(\"int\")  # dates for available data\n",
        "        x_diff = temp_df.index.to_series().diff()  # data gap sizes\n",
        "        fit2 = interpolate.pchip(x_index, temp_df)  # pchip fit to data\n",
        "        ynew = fit2(interp_index.astype(\"int\"))  # interpolated data on full range\n",
        "        interp_df = pd.DataFrame(ynew, index=interp_index, columns=[well])\n",
        "\n",
        "        # replace data in gaps of > 1 year with nans\n",
        "        gaps = np.where(x_diff > gap_size)  # list of indexes where gaps are large\n",
        "        for g in gaps[0]:\n",
        "            start = x_diff.index[g - 1] + datetime.timedelta(days=pad)\n",
        "            end = x_diff.index[g] - datetime.timedelta(days=pad)\n",
        "            interp_df[start:end] = np.nan\n",
        "        beg_meas_date = x_diff.index[0]  # date of 1st measured point\n",
        "        end_meas_date = temp_df.index[-1]  # date of last measured point\n",
        "\n",
        "        mask1 = (\n",
        "            interp_df.index < beg_meas_date\n",
        "        )  # locations of data before 1st measured point\n",
        "\n",
        "        interp_df[mask1] = np.nan  # blank out data before 1st measured point\n",
        "\n",
        "        mask2 = (\n",
        "            interp_df.index >= end_meas_date\n",
        "        )  # locations of data after the last measured point\n",
        "\n",
        "        interp_df[mask2] = np.nan  # blank data from last measured point\n",
        "\n",
        "        # add the interp_df data to the full data frame\n",
        "\n",
        "        well_interp_df = pd.concat(\n",
        "            [well_interp_df, interp_df], join=\"outer\", axis=1, sort=False\n",
        "        )\n",
        "    return well_interp_df\n",
        "\n",
        "def norm_training_data(in_df, ref_df):\n",
        "    norm_in_df = (in_df - ref_df.min().values) / (\n",
        "        ref_df.max().values - ref_df.min().values\n",
        "    )  # use values as df sometimes goofs\n",
        "    return norm_in_df\n",
        "\n",
        "def input_to_hidden(x, Win, b):\n",
        "    # setup matrixes\n",
        "    a = np.dot(x, Win) + b\n",
        "    a = np.maximum(a, 0, a)  # relu\n",
        "    return a\n",
        "\n",
        "def predict(in_values, W_in, b, W_out):\n",
        "    x = input_to_hidden(in_values, W_in, b)\n",
        "    y = np.dot(x, W_out)\n",
        "    return y\n",
        "\n",
        "def impute_data(comb_df, well_names, names):\n",
        "    # for out test set we will impute everything\n",
        "    imputed_df = pd.DataFrame(index=comb_df.index)\n",
        "\n",
        "    for well in well_names:  # list of the wells in the aquifer\n",
        "        train_nona_df = comb_df.dropna(\n",
        "            subset=[well]\n",
        "        )  # drop any rows with na in well (measured) data\n",
        "        labels_df = train_nona_df[\n",
        "            well\n",
        "        ]  # measured data used as \"labels\" or truth in training\n",
        "        tx_df = train_nona_df[\n",
        "            names\n",
        "        ]  # data we will predict with only over the test period\n",
        "        all_tx_df = comb_df[names]  # data over the full period, will use for imputation\n",
        "\n",
        "        tx = tx_df.values  # convert to an array\n",
        "        x1 = np.column_stack(np.ones(tx.shape[0])).T  # bias vector of 1's\n",
        "        tx = np.hstack((tx, x1))  # training matrix\n",
        "        ty = labels_df.values\n",
        "        input_length = tx.shape[1]\n",
        "        hidden_units = 500\n",
        "        lamb_value = 100\n",
        "        W_in = np.random.normal(size=[input_length, hidden_units])\n",
        "        b = np.random.normal(size=[hidden_units])\n",
        "\n",
        "        # now do the matrix multiplication\n",
        "        X = input_to_hidden(\n",
        "            tx, W_in, b\n",
        "        )  # setup matrix for multiplication, it is a function\n",
        "        I = np.identity(X.shape[1])\n",
        "        I[X.shape[1] - 1, X.shape[1] - 1] = 0\n",
        "        I[X.shape[1] - 2, X.shape[1] - 2] = 0\n",
        "        W_out = np.linalg.lstsq(X.T.dot(X) + lamb_value * I, X.T.dot(ty), rcond=-1)[0]\n",
        "        all_tx_values = all_tx_df.values\n",
        "        a1 = np.column_stack(np.ones(all_tx_values.shape[0])).T\n",
        "        all_tx_values = np.hstack((all_tx_values, a1))\n",
        "        predict_values = predict(all_tx_values, W_in, b, W_out)  # it is a function\n",
        "        #pre_name = f\"{well}_imputed\"\n",
        "        pre_name = f\"{well}\"\n",
        "        imputed_df[pre_name] = pd.Series(predict_values, index=imputed_df.index)\n",
        "\n",
        "    return imputed_df\n",
        "\n",
        "def renorm_data(in_df, ref_df):\n",
        "    assert in_df.shape[1] == ref_df.shape[1], \"must have same # of columns\"\n",
        "    renorm_df = (\n",
        "        in_df * (ref_df.max().values - ref_df.min().values)\n",
        "    ) + ref_df.min().values\n",
        "    return renorm_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MAc1hoUEI3wM"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Collect well data that qualifies for use in interpolation**\n",
        "wells_df = pd.concat([\n",
        "        extract_well_data(name, group, min_samples, ts_measurement.value, ts_date.value)\n",
        "        for name, group in measurements.groupby(ts_well_ID.value)],\n",
        "    axis=1, sort=False,)\n",
        "wells_qual = wells_gdf[wells_gdf[w_well_ID.value].isin(wells_df.columns.values)].reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2i764sqEJI1F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6af013ad-312b-49e7-f3c7-d9ae6841b855"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33  wells with measurements\n",
            "4  wells dropped\n",
            "29  qualified for use in interpolation\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Cleaning up wells dataframe**\n",
        "wells_df.drop_duplicates(inplace=True)\n",
        "wells_df[wells_df == 0] = np.nan\n",
        "# wells_df.to_csv(\"wells_one.csv\")\n",
        "wells_df.dropna(thresh=min_samples, axis=1, inplace=True)\n",
        "no_qualifiedwells = len(wells_df.columns.values)\n",
        "no_droppedwells = no_uniquewells - no_qualifiedwells\n",
        "print(no_uniquewellsref,  ' wells with measurements')\n",
        "print(no_droppedwells,  ' wells dropped')\n",
        "print(no_qualifiedwells,  ' qualified for use in interpolation')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1,2)\n",
        "aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax[0])\n",
        "aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax[1])\n",
        "wells_gdf.plot(ax=ax[0],  markersize = 30, marker = 'o')\n",
        "wells_qual.plot(ax=ax[1],  markersize = 30, marker = 'o')"
      ],
      "metadata": {
        "id": "LmXcDkzBhgJe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "ecf67cc6-b5d8-4256-eaa6-ff4d91d28684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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Ae/ak9tmJonGjUiXgk0/oojIhAYiKkud5bKARwvyFqrGxsUhISEB6ejoCAwMRHh6OKVOmoEePHgCA//znP3jvvfceeNyyZcvw/PPPAwDq1auH559/Hv/5z38AADk5OXjnnXewdu1aZGZmok6dOnj22Wfx7rvvwsuCMf3s7GwEBgYiKysLWmcYg/7pJ6pytnIl8PTT0p777FnaPjc5mf7AHzxIgaBVK7raaNIEqF2b5lRcv04f7Fu3gCpVKClp146SEHd3ShqSk+kP4z//0AqFM2eA3Fx6Lnd3oHlzIDIS6NoV6NFD0X0xOsxMQEZ2foW319L6YNc0GT+oa9ZQArVli122L1bL50KtsUMtvx/J6PVUEO36dfpc2nIBcr/CQmDbNmD3bpqbkJhI85uqVKHPd8uWdGFQuTLd9+JF+hKCiiy1bk2JSLVqdL6cHOpp3b6dLjhSU+n+hh6lKlXo/p06Ua9D+/aAh0xX/SYoHjeEoPo5hYXUO2OHHiFLPhsWJRJq5lQBISuL/vi2b09deXK/aS5eBDZtomJPBw8Cp0+XVlXz8KAgULUqcO0aJRUAXQ3UqUMJQ2YmEBBAH/pmzSiYNGxI5asbN7brlYMpDeLWP3BVcS83DXAmvq98DRACiIig3+vff8v+f+tUnwsZON3vZ+FC4JVX6A9+587yPldREZCURFfJe/bQkMPFizQUAVAiULcuXaScOQMYltoGBdHQRFoa9UA0bkwXMY0a0XLNRo3oYqZOHfsOoRiheNwAKHHr1o0uMgcPlve5YNlnQ5n0jhn3zjv0h3z+fPt8kIKDaXb2vV3C+fn0Iff3L9ttmZVFVyOHDwOXL9MVT8eO9Ab39JS/rTYKCvAxemURFCBz0qPR0LBOTAzw++/UNcyYFK5coZVaL70kfxIBUDL8+OP0ZaDX08oQD4+yvSFC0FCnodcyN5cuUPr2tfvwpjUUjxsA9ehGR9PfhwED1LPhGDiRUJ/z5wFDid/Tp4HgYOTpirB0x1ks352GzJx8BAX4YFhECGI71Zdvgo+PT/k9CYGBQPfu9OWAhkWEYM7mlHKvLtw0dLvsevakQP/vfwO9eztmsS2mPvHxlOh7eJQMOdg9dri5Ue/k/TQa6mVo0kT657QDVcQNgHYIjYigibQjRtjnOc3AEUxtatSgyXgtWgBRUcjbtgNDFydhzuYUZGTnQy9KZwsPXZyEPF2R0i12KLGd6iOsthZu93X0uGmAsNpaxHayw9WRRkNB/+BBWvfOmBQGDqQJef/7HzBwIPLuFHDskIgq4gZAw92DBlGvRH7FPST2xomE2lSqRCssDh4EHnkESxf8bHK2MDOfn7cHVr4ciUnRTVBL6wM3DU2UmhTdRN4lXPfr2JHGOePiSosEMWaLrl2pJPvatcD27Vi6ZD3HDomoJm4AdBFy+TLw6af2e04TeLKlmq1fjw7rryFDW73Cu8g+W5jJ58wZWgXzxhu03bEMnPJzISGn/P0IAXTvjg4tX0SGX8VLujl2OLDJk4HFi4GTJ2mOmwws+Wxwj4Sa9emDTG01o3fJzFFP9xazUIMGwFtvAbNn03wYxqSg0QD/+x8yK1U2ejeOHQ5s+nSai/LGG0q3BAAnEuqm0SBIa3wduF1mCzP5TJ1KFT1ff13pljBn8tBDCAqsZPQuHDscWGAg8NFHVGdo+3alW8OJhNoNiwh5YIKPgV1nCzN5GObErFtH68QZk8iwiBC4VTByzbHDCfzrX1Qc8PXXaThLQZxIqFxsp/oIc7sDt/v2D7D7bGEmn6efpmJe06cr3RLmRGI71UfY9fMPJBMcO5yEmxv1Suzbp/geHJxIqJyftwdW5iVhUsqfys8WZvLQaGgflO3bqdgXYxLw0+ix8ts3MKl6HscOZ9WtG60A++wzRZvB7yQH4HfjKiZcTcWEaZ8r3RQml/79gZo1qa5E27ZKt4Y5g1u34FeYjwmNvDBhEK/OcFqjRwPPP09LQuvUUaQJ3COhdt9+C/z8M/9xcXYeHlTlcssWpVvCnMHNm1Ty3s2NdupkzstQZn/rVsWawD0Savfyy7ROWKY6A0pSpPS3mrVtS6Vvi4tVVUefOaAffgDWrwe++84h9rKwBMeN+1SvDoSGAocOAcOHK9IE7pFQuz59aL2wsxTLuStPV8Tle+9Xvz5tE5yRoXRLmKOLiaGjk+3jwnGjAvXq0W6qCnGud5mzuXSJJt/5Gq8l4YiW7jjL5XvvFxREx8xMZdvBHJ9hFn8l47UkHA3HjQoEBQFXryr29JxIqNmOHbQb6OLFSrdEct/vPl/uTnoABYXlu5XLrhVT7W4V0+vXlW0Hc3zffgv06EHbTTuR5bvTOG6Up1o1ReMGJxJq9tBDSrdAFnm6IlzJ1hm9j0uW761Vi46XLinbDub4goNpno2mgmp2DspUXHDJuAHQao2LFxV7ek4k1OzMGTpWM77fhqMxp/vRJcv3VqpEfwBOnFC6JcyRCQGcPet0cQMAKnkZn0xp6nan1aQJcO2aYr0SnEio1aVLtCFL375A7dpKt0ZS5nQ/umz53tatgb17lW4Fc2SzZtEM/thYpVvC7KV1azru26fI03MioVbvvUfdkl9/rXRLJGdO96PLlu/t1AnYtYtWbzBmqStXKHZMmUJVD52MqVUZLrtqo3FjoEYNxTbw4kRCjXQ64McfqYZE9epKt0ZypoYtggK8XXM9OAB07w7k5VEywZilVq8Giopoe3onVFNrPHaYut1paTSUOG7erMjTcyKhRn//DWRllVYsczKmdjR9rkOofRukJq1bA1WrAps2Kd0S5ojWrwciI+k95IR4N2QjevakoY0bN+z+1JxIqFGjRpRh/vKL0i2RRWyn+girrX0gIPCuhKCZ9j16AH/8oXRLmCNq0gTYs4f2XXBCHDuM6NUL0OsV6ZXgREKNQkJo6Wd2ttItkYWftwdWvhyJSdFNeFfC8sTEUCGya9eUbglzNI89BhQUOO0cG44dRgQHA2FhilyEuPBvXcVu3KA1wY89pnRLZOPn7YEJUY0xIaqx0k1Rn169aAnfpk3AsGFKt4Y5kgMHqKZAqPMOD3LsMCImBlixguKHHWuIcI+EGm3bRsfISGXbwZRRuzbQqhUPbzDLbdsGdOyodCuYUmJiaFjr8GG7Pi0nEmq0axf9MalXT+mWMKXExFAiodcr3RLmKHQ64J9/OJFwZZ07U2G7DRvs+rScSKjRhQs0aYq5rr59aRMehQrMMAd0/TqQn081BZhr8vYGoqNp9Y4dcSKhRpUrU2EZ5roMS/jWrVO6JcxRVK5MR9491rX160clBOxYLpsTCTVKSCgtecpck4cH0Lt36XbQjJliWPbHscO19e1LQ6J2nGPFiYQaeXgAGRk085a5nDxdET5POIUO9YeiQc8P0OGDTfg84ZTrlv9l5vG4uwgvI0PZdjDF5OmK8PnxPHSY+D0aHNKiw8wEu8QOjRDO8dcqOzsbgYGByMrKglarVbo5tlm5EnjmGeDUKSpOxVxGnq4IQxcn4Vh6NvT3fDINBXcsXSvvVJ8LGTjV70evBzp0oOWfP/+sdGuYnSkZO7hHQo3q1KFjQYGy7WB2t3TH2QcCAQDoBXAsPdusLdiZi3Jzo9VeTlqMihmnZOzgREKNzp+nY4gL1413Uct3pz0QCAz0wrwt2JkLO38eqFtX6VYwBSgZOziRUKNTp2hLWH9/pVvC7MzUFuvmbMHOXFRxMZCaCjRooHRLmAKUjB2cSKjNvn3A558DXboo3RKmANNbrLvoNsnMuKIi4LXXgNxcjh0uSsnYwYmEmqSkUGWyhg2BefOUbg1TAG+TzKzyxhvAggXAf/8LREQo3RqmACVjBycSanLgAHDnDvD770CtWkq3himg3G2ShYCbELxNMqvY338Dw4cDkycr3RKmkApjhx22WLcokVi4cCHCw8Oh1Wqh1WoRGRmJDffU9F6yZAm6du0KrVYLjUaDW7dumXXeS5cu4bnnnkO1atXg6+uLhx9+GPtcsTSwYV8FLy9l28EUU+42yYV5mHR1r0Nvk8yxQ2bFxRw3XFy5sSPnOiY18pI9dlh05uDgYMyaNQuNGzeGEALffPMNBgwYgAMHDqBFixa4ffs2YmJiEBMTg7i4OLPOefPmTTz22GPo1q0bNmzYgBo1auDUqVOoUqWKVS/IoX33He0nHxiodEuYgh7YJnniRCBpE+D9nrINswHHDhkdOUK9mRMmKN0SprAysSM/nzbw+uILQO4LEGGjKlWqiC+//LLMz7Zu3SoAiJs3b5p8/JQpU0SnTp1sbYbIysoSAERWVpbN51JEbq4Q7u5CzJ+vdEuY2nz+uRBeXkIUFVn8UDV/LtQQO9T8+zHbhx8KERAgREGB0i1hahMSIsTUqVY91JLPhtVzJIqLi7FixQrk5eUhMjLS6kRm3bp1ePTRR/HUU08hKCgIrVu3xhdffGH1+RzWpk3UPdmzp9ItYWrTpAkVJ0tzjhoSHDsk9vvvQLdugKen0i1hatOkCU3il5nF/R2HDx9GZGQk8vPz4e/vj7Vr1yIsLMzqBpw5cwYLFy7E5MmTMW3aNOzduxcTJkyAl5cXRo4cWeHjdDoddDpdyb+zs7OtboMqfPIJzbbmLYAdTp6uCEt3nMXy3WnIzMlHUIAPhkWEILZTfWnGJZs2peOJE0B9x51sqYbY4XRxIykJ2LkTWLVK6ZYwK9gldmzdavt5TLC4R6Jp06ZITk7G7t27MXbsWIwcORLHjh2zugF6vR5t2rTBzJkz0bp1a4wePRqjRo3CokWLjD4uPj4egYGBJV91HbmaW1YWBYOXXlK6JcxChvr2czanICM7H3oBZGTnY87mFAxdnCTNZjkhIYCvLyUSDkwNscOp4gYAbNgAVKkCDB6sdEuYhewSO5o1owKHRfJu2mVxIuHl5YVGjRqhbdu2iI+PR6tWrTB37lyrG1C7du0HrkqaN2+ONBPduHFxccjKyir5unDhgtVtUNzJk3TkJZ8Oxy717d3c6Mri+HHbz6UgNcQOp4obACWXQUH0HmEOxS6xo3lz2nvlzBnbz2WEze8+vV5fpqvQUo899hhOGv6Q3pWSkoLQ0FCjj/P29i5ZSmb4clgzZtAfih49lG4Js5Dd6tuHhQE2XL2rkRKxw6niRkoKDWlMnKh0S5gV7BI7DIm2zLHDokQiLi4O27dvx7lz53D48GHExcUhMTERw4cPBwBkZGQgOTkZp0+fBkBjosnJybhx40bJOaKiojB//vySf7/22mvYtWsXZs6cidOnT2P58uVYsmQJxo0bJ8Xrcwx79lDXpLe30i1hFrJbfXtDIiEqiDwqx7FDBnv30vGZZ5RtB7OKXWJHrVpA5crqSiQyMzMxYsQING3aFFFRUdi7dy82btyIHnevpBctWoTWrVtj1KhRAIDOnTujdevWWLduXck5UlNTce3atZJ/t2vXDmvXrsUPP/yAli1b4oMPPsCcOXNKAozTy8igrzZtlG4Js4Ld6tuHhQE3b9J7xQFx7JDBP/8AoaE0R4I5HLvEDo3GLr2ZGiEc9BLnPtnZ2QgMDERWVpZjdVfu2gVERgIHDwLh4Uq3xijZZxg7oM8TTmHO5pRyuyjdNMCk6CalhaVscfIkTZzavBmIijL7YQ77ubATh/79DBlCm3Rt3Kh0S0zi2PEgu8WOUaNoM8gDByx6mCWfDZ6hozSfu1lnvrq3h65ohvGnf6ag2yeJyMxWd/vlUm59e0D6+vYNG1KdAAefcMkk5OtLe/OoHMeO8tktdoSF0YWIYQsGGXAiobSqVel4z1iwGlU0wxgAMnN06DdvhzTLlRxMufXttT6YFN1E2vr2Hh5UY+S+yYXMhVWtqvq4AXDsqIjdYkfTppRwyrhCyTX7lNQkIICOeXnKtsMEYzOMAQoIS3eclaYrzgxq6ip9YG8MuTRoAJyVYEkYcw4BAaqPG4D6Yoea2CV2NGxIx7NnaU6NDLhHQmn+/jQh5uZNpVtilDkziCVb6mhCnq4ITy1Kwmd/lu0q/ezPFDy1SKJCLmpUqxaQmal0K5haaLWqjxuAumKHSzLUJ5IxdnAioTRPT5p1ffWq0i0xypwZxJItdTRhYWIqjqVn4/6LHAEq5LIwMdUu7bA7X1/Vz6VhdhQURFVxZa5aaCs1xQ6XZId5eJxIqEGlSqpf1jcsIsTkfSRb6mjC1zvP2XS7wyoqorkSjAEUNwCOHcy44mI6yhg7OJFQg6goIDFR6VYYFdupPoICKi6YpYF5AUMKuSaGLnJ1Rfg84ZTzDXHcvl36x4Oxbt3oyLGDGWOYRyNj7OBEQg1q1ABycpRuhVF+3h747dVO5QYEDYAWdSRcriQBSTe+UYvs7NLJuYxVq0Z7bHDsYMYY3h8yxg7uJ1WDK1eAmjWVboVJQVofbH2jq+KrJfy9PUz2Sty78Y0aZoNLssokKwuoXl3ehjLHcf061Qbg2OHUbI4dWVl0DAyUrY38v6cGaWmyLcuRmt2WOhrxfMd6mL/1tMn7GTa+UTqRMBTkuXctvWG74E1HM8xfM56dXbqUizHDLqchjjEsoIbY4WgkiR12SCR4aEMN6tYFzp1TuhUOY2zXhmheKwAa03dVxWxwybYLzsnhoQ1WKjiYjufPK9sOJhtJYkduLh39/aVv4F2cSKhBgwayVh1zNn7eHlg9tiNe69HkgfKy91PDbHDJtgvOzZU1GDAHU7MmLQnm2OG0JIkdnEi4CJ6NbzFDN+mk6IqTCTeNOmaDS7ZdcF4e4OcnQYuY08jP59jhxCSJHYZVGzLGDk4k1KByZRraKChQuiUOx24b39hAsu2Cb9/mRIKV0mho3DslRemWMJlIEjvy8qgolZt8f+45kVCDfv2o2NB33yndEodjt41vbDAsIsT2XpOiIko0+eqT3at/f2DFCkoymdORJHbYocebEwk1aNUKePJJYMYMpVvikAzDHLumReFMfF/smhaFCVGNVZFEABL1mhi2i+ZEgt1r+nQgPR34+mulW8JkIEns4ETChfTpQ8MbhokxzGlI0mui09HRu+IKgcwFNWwINGsGHD2qdEuYDCSLHTLHDXVcsjHgsccAIYCffgJGjlS6NUxiNq+hLyyko6endI1izqFjR+DXX4FPPqFVHMypSBI7ZI4b3COhFk2aAE88QcMbooL1Psx18XuCVeT114GLF3mOFauYxpyqO9bjREJNJk4ETp2iqwvG7mXYuU/lW0YzBTRrBvTqBcydW9pzxZiBh4fs7wtOJNSkWzegRw9g9GggM1Pp1ri8vLu7iHaYmYAGcevRYWaCcruKGorJ8BwaVp4PPwROngTeflvpljC18fMrrSUhE04k1ESjodnXOTnA/PlKt8alGWrcz9mcgozsfOhFaY17RXYV9fWlteDXrtn3eZljaNsWmDaNeiX4IoTdq3p1iht6vWxPwYmE2tSpAzz9NLBgAXDa9MZUTB7W1riXrRdDo6G9FdLMLKfNXM8rrwDFxZRQMGYQHExDG1euyPYUnEio0ccfU7XLN95QuiUuy5oa97L3YjRuzFUMWcVq1aILkKVLgb//Vro1TC0aNaLjqVOyPQUnEmoiBPDPP8DPP1M3VEaG0i1yWdbUuJdsl8+KtGwJHDpk2zmYc8rKAtavB06coH9z7GAGTZoAXl7AwYOyPQUnEmqyZAmNdY4eTUMcixcr3SKXZU2Ne8l2+azIo4/S0IaMXZTMAQkBtGtHpfa//pqWgw4cqHSrmFp4egLh4cDevbI9BScSamKYke/rC3z6KZXOZoqwpsa9ZLt8ViQyko47d9p2HuZchCiNHV260NCou7uybWLqEhkp63AXJxJqMmkSsH07EBAAfPml0q1xadbUuJdsl8+K1K0LhIYC27bZdh7mXNzcgAMHaJLlzz/zJG32oM6dgTNnqHCZDDiRUBN3dyo4dPVq6dUnU4Q1Ne4l2anPlO7dgYQE28/DnEuNGsDhw0DNmkC9ekq3hqlNly50lCl28F4bavPaa0Dr1sC//qV0S1yepTXuYzvVx6ajGQ9MuLRopz5ToqOBZcuAy5dpHg1jAP2B+PVX4PvvaWIdY/eqUYP+rvz5pyx7OXGPhNpkZtJOoLw5k8ORZKc+U3r2pJoSGzfafi7mPAxFqPr2VbYdTL1iYihuyFCYinsk1KZlS1oCyhySzTv1mVK9OtC+PS31e+EFeZ6DOZ6WLen4zz9Uap+x+/XpA8THA3v2AB06SHpq7pFQm/BwqpnPWEX69QM2bQIKCpRuCVOLFi2op4oLlrGKdOgAVK1KFyES40RCbfbuBR56SOlWMDXr25f2Y9mxQ+mWMLU4cICWgfK8GVYRDw8a3uBEwsklJtLyzzFjlG4JU7NHHgFq1wY2bFC6JUwtpk0DQkKA3r2VbglTs969KelMT5f0tJxIqImhPsATTyjbDqZuGg1tN795s9ItYWqRlERj4B487Y0Z0aMHHbdskfS0nEioScjdOgO8YoOZ0rUr1c7PylK6JUwNQkM5bjDTatYEmjcH/vpL0tNyIqEm165RVUteB85Mad+exsQPHFC6JUwNrl6lWgGMmRIRAezbJ+kpOZFQkzt3AD8/pVvBHEGTJlQJ1bDbI3Nt+flApUpKt4I5grAwihuigh0GrWBRIrFw4UKEh4dDq9VCq9UiMjISG+6Z8LVkyRJ07doVWq0WGo0Gt27dsqgxs2bNgkajwaRJkyx6nNPQaGQpFsKckKcnzdCXqXa+1Dh2yEyjkfQPA3NioaFAXp6kw6IWJRLBwcGYNWsW9u/fj3379qF79+4YMGAAjh49CgC4ffs2YmJiMG3aNIsbsnfvXixevBjh4eEWP9YppKdT1bG6dZVuCXMU1aoBN24o3QqzcOyQSXExsG4dkJ0NBAcr3RrmCKpWpePNm9KdU9ioSpUq4ssvvyzzs61btwoA4ubNm2adIycnRzRu3Fj8+eefokuXLmLixIkWtyMrK0sAEFlZWRY/VlG5uUI884wQGo0QPj5C/Pmn0i1ijiIyUojnnzd6FzV/LtQQO9T8+zEpIUGIkBAhACG6dhUiP1/pFjFHsGMHvWeOHjV6N0s+G1bPkSguLsaKFSuQl5eHSBt3qhw3bhz69u2L6Ohosx+j0+mQnZ1d5sshTZgArFgBzJ0LpKXRpkyMmcPNzSGHwpSMHU4TN86cAYYMoZLp27bRUmBvb6VbxRyBuzsdJYwdFi86Pnz4MCIjI5Gfnw9/f3+sXbsWYWFhVjdgxYoV+Oeff7B3716LHhcfH4/33nvP6udVhWvXgK++AubMAV59VenWMEcjBCUTDkINscMp4gYAfPEF1YxISAAqV1a6NcyRGBIIjUayU1ochZo2bYrk5GTs3r0bY8eOxciRI3Hs2DGrnvzChQuYOHEivv/+e/j4+Fj02Li4OGRlZZV8Xbhwwao2KOrSJTraeFXGXFRRkUMVIFJD7HCKuAHQJNvmzTmJYJYrKqKjhLHD4jN5eXmhUaNGAIC2bdti7969mDt3LhYvXmzxk+/fvx+ZmZlo06ZNyc+Ki4uxfft2zJ8/HzqdDu6Gbpj7eHt7w9vRu/KqV6fj0aNUF4AxSxQWOlQRIjXEDqeIGwDFjg0baLJlBTGSsXIZEgkJY4fNKYler4dOp7PqsVFRUTh8+HCZn73wwgto1qwZpkyZUmES4TTq1AEefpgCAm8J7bLydEVYuuMslu9OQ2ZOPoICfDAsIgSxnerDz9vIR7SoyKH/iHDssEHv3jQkun8/X4S4KJviBiBp7LAokYiLi0Pv3r0REhKCnJwcLF++HImJidi4cSMAICMjAxkZGTh9+jQAGhMNCAhASEgIqt5dchIVFYVBgwZh/PjxCAgIQMuWLcs8h5+fH6pVq/bAz52SRkNLthxkCR+TXp6uCEMXJ+FYejb0d8sAZGTnY87mFGw6moGVL0dWHBQcaI4Exw6JGZZ6cuxwSTbFDcMcCQljh0VnyszMxIgRI9C0aVNERUVh79692LhxI3rc3Qhk0aJFaN26NUaNGgUA6Ny5M1q3bo1169aVnCM1NRXXrl2T7AU4tMOHqTfiySeVbglTyNIdZ8sEAwO9AI5ezsbCxFTjJ3CQIkQcOyT28ce0b0KnTkq3hCnAWNw4lp6NpTvOVvxgCSdZlpxSCAeJRCZkZ2cjMDAQWVlZ0Gq19m/AnTu08mLdOuoymjnT9HDFW28By5YBly871Fg3k06HmQnIyM6v8HYPNw0OTu9Z/tVF27bUrb1wYYWPV/xzoXKq+P38/TcwbhyQmgo8+iiwfDltE1+R27dpX41p04B//9t+7WSqYSpu1NL6YNe0qPJv3LIFiIoCTp8GGjas8ByWfDYco1/UEYwfD3z/PTB6NNCxIzBqFHD8uPHHbNkC9O3LSYQLy8ypOBgAQJFeVHx14etLCSxzXGlpQK9etE/GtGnAsWPA2LHGH7N7NyUTAwfapYlMfUzFDaO3+/rSUcLYwYmEFI4fp56F2bOBDz+khKJuXSA21njXc34+EBhov3Yy1QkKML108fvd58u/wd8fyMmRuEXMrj78kAL7xo1AXBzw2WfAL78Aa9ZU/Jj8u38kOHa4LFNxw02jQZ6uqPwbAwLoKGHs4ETCVtnZwHPP0UYoL71EP/PxoaQiKQk4a2SsqnJlnizl4oZFhJi8T2Z2BSsbtFp6/zHH9PPPVFgqLq40uD/7LA1Xff99xY8z1I7g2OGyhkWEwNhMh2JjPZmGYQoJYwcnEra4fp2WYaWm0hXEvYVxOnemo7F936tX52Dg4mI71Td5nwrnRnEi4bh++gl4+mngqaeAe3cs1WiALl1Mxw2A4g9zSbGd6sPdreJUQgBYvjut/BsNiQT3SKhAQgLQogVw6BB1S7ZuXfb2oCD6MjZPwsODCsowl2V0vfddFY6O+fvTdsDMceh0NAfiySeBPn2o5+H+ZXgPP0xzJ3Jzyz+HoSIhxw6X5eftgeL7l2zcp8J5En5+dKzo/WUFTiSskZAAxMRQIpGcDEREPHifwsLSwh8VKSjgiZYMQQHGKy3WqOh2b2/6w8QcgxDUA2HYX2f16vI//6YmwRUW0pFjh0urqTU+T6LCeRSenpS8Shg7OJGwxqJFlET88UfFy2e++IL2ex80qOLz+PryFSXDcx1CKxzv1Ny9vVyenpSMMsdw/jzw668UPyZOLH+vg6Ii4JNP6ELF37/88xiGUG/flq+tTPWGRYSgotENN42J+VeenqUJqQQ4kbBUcTGwfTt90Cu6IrhxA3jvPZo4FR5e8bnq1AHS0+VpJ3MYsZ3qo0Ud7QNBwU0DtKijrXgehcTBgMksMZGO/fpVfJ8FC4BTp4AZMyq+T506dLx8WbKmMccT26k+wmqXHzfCahuJG4DkFyGcSFhq504gMxPo37/i+8yaRd2Tn3xi/Fx37vBkOQY/bw+sfDkSk6KboJbWB24aKigzKbqJ8VK3Pj48tOFI1q4FOnSgYlLlycoCpk+nORT3bEb2gJwcKnrHscOlWR03AMljh+PsQawWq1dTIKho6+/iYuDLL4GXXzZene7UKRr+mDZNnnYyh+Ln7YEJUY0xIaqx+Q+qVImHxhzFrVvA5s3Au+9WfJ9Vq2gCnKlqlf/+Ny0X5YJULs+quAFQ7JBwaIx7JCxx6RKweDFdMVS04cmZMzQ34u4eAhX66SeaLMclbpm1/PzoqsLUpF6mvI8/puPIkRXfZ+9emnv10EMV30evp4uZ0aOB+qaXDjNWLj8/XrWhmGXL6IM8eXLF90m7u3bX1If8t9+o3rmP6cqGjJXLsIyLJ92pW14e8PXXtPdOrVoV3+/8edNxY88e4OpVWjrKmLX8/CTtzeREwphFi4B69egqoU0b4J13aA8NY6VpL16kY4iRGbPnz9NciyFDJG0uczEy1MxnEsjIAAYPpiHQxx4DGjSg+Q9jxhh/3MWLxuMGAPzwA9Wneewx6drLXI/E+/TwHImKPPsssGIFMHw4ZW9FRcDrr9PPjcnJoRmx3vet/ReCxkj37aOtwz09eftwZhtDbxZPuFSPAweA6GjAy4t6IDIyaBO/0aOBxibGsXNzy1/yee0aLRs9ehRYupRK8Ze3dJQxc0lcg4bfjeU5dYqSiA8+oDkMluzfnptbWoL0XvPm0dpxPz+gWTNa3lXROnHGzOHuTkeeI6EeCxfSRUJSEvVmWiIn58HYkZcHPP44cOIEna9jR+CVV6RqLXNVHh6Sxg1OJO535w4NX9SqRTXwLUkiDPT6sv++ehWYMoWuShYurHiiJmOWMNTO5veTOiQk0Dyqd96xPIkAKNbcXw/9k0+Ac+eop+ORRyRoJGOgv1ESxg3XTiQKC6mw1NWrtIOnry+wZAltwrVpk3U9BtWr01KvggLq3szJASZMoCAxYwYHfSYdQ0EZLy9l2+GKNm8GRoyg3oJevYCDB2lOVVQU7eZpjerVgStXSv+dlESltGNjOYlg0ioslDRuuHYi8d57wJYttFPnu+/SL7d7d+Dbbx/chMtcDRrQVcXAgbQM9J9/KIlYtqx01z7m9PJ0RVi64yyW705DZk4+ggJ8MCwiBLGd6pu1UZdZDMu3DKs3mH1cv05JhLs7cPgw8OOP9NmeMoWGQq3dA6N+fZpMef487eFz7hzQti0NsTImpdxcSeOG6yYShw4B8fH0IX37berq0ettn8TUsSMwdCjNwG7YkGZvP/MMULeuNO1mqpenK8LQxUk4lp4NwwZ9Gdn5mLM5BZuOZpiuOmeuW7coSQ0IsP1czHxvvgnk5wNHjlC56sJCihvWDIPea+JEikm5ubRHT8eOdEHCEyuZ1G7eBCpXlux0rvsOnTIFaNSIjgANOUgx7ODjQxM1mctauuNsmSTCQC+AY+nZWLrjrOWV6Mpz7RpQrRoPl9nToUNUE2LBgtI9L6TahTMmhr4Yk9u1a5L2kLtmBEpNpZ0733mHt+Jlklu+O+2BJMJAL+h2SWRkUE0BZj8LF1LlyZdeUroljFlHp6PezJo1JTulayYSy5bRMqvBg5VuCXNCmTn5Nt1utvR04/u5MGndvk29jc89xxcgzHEZdpyWMHa43tBGYSEVdXnuOdq4REZ2mXDHVCcowAcZ2RUnC9X9vfF5winb3xeXL9M8HGYfq1bRldyoUbI+DccNVh7J3heGRMIwNCcB1+uR2LiRuoTtEAyGLk7CnM0pyMjOh16UTrgbujgJeTouIuSshkWEwK2CeXeGH0vyvkhPlzQYMBOWLaPlnQ0ayPYUHDdYeSR9X8jQI+F6icQPPwBhYbKvyzZnwh1zTrGd6iOstvaBZMJNA9QI8Ma1XJ0074urV2k/Bya/S5eA7dupJ1NGHDdYeSR9X2Rm0gTtqlUla59rJRJ37gDr1wNPPSX7U9ltwh1THT9vD6x8ORKTopugltYHbhqgltYHk6KbQKOBNO+LwkJaJihhMGBGrFlDdSMGDJD1aThusPJI+r64dYuWfnJlSyutW0e78A0fLvtT2W3CHVMlP28PTIhq/MAyzzmbU4w+zuz3hWHrcC5GZR/ffgv07QtUqSLr03DcYOWR9H0hcTEqwNV6JDZsoCENU7vwSSAowMem25lzkux9Ydhwh1cPyO/qVdq11w49mRw3WHkkfV8UF0seN1wrkUhMBLp0sctTGZtw56ah25nrkex9YaiieP8GcUx6iYl0tEPs4LjByiPp+6K8zeFs5DqJRGEh1bAPD7fL0xmbcBdWW4vYTvXt0g6mLpK9LwxXFLyFuPxSU2kuSnCw7E/FcYOVR9L3hacn/T2UkOskEoaJJQkJNNYZHg5ERNBQR9WqQGAg8OijVOtep7P56YxNuJNsrwXmcCR7X/j60tEwV4LJR6MB8vJoY7/27YFWrYDISCA0lArb1akDPPkk8PffNj8Vxw1WHknfF76+tPBAQq7zrjQE3OXLKWHo0oW2+Pb2pk213NxoK+Dp02lS5po1Nq+zrWjCHXMtFRWSSXi9i/V/GDw86L1r2AGUyef2bbq4+Ogj2pAvIIAmbXfpQvsV3LoF/Por7SL82WfAq6/atIEXxw1mIEtxMn9/+tsnIddJJPz9S79ftIi25y3PxIm0417HjnSFwQV/mA1k3Qk0MJD+iDF5hdwdf27dmlZvlOe992hX0IkTaZ3+hx/ar33MKckWOwIDgYIC6pUw9GzayHWGNjQaoEkT+v7SpYrv1749sGsXjSF17gwkJ9ulecw5yVpgqFo14MYN2xrITDMUr9u9u+L7uLsDn34KfPwxMGMGMGkSbTXOmJVkix2G2jMSxg7XSSQAGscEjAcEgK5Atm+ntbbt2wMrV8rfNuaUZC0w5O5OS7mYvO6doG2qB+iNN4C5c4H/+z+gUyfg5k1Zm8acl2yxw92djhLGDtdMJGbONN4rAVA9/b17aUz0X/8Cvv9e/vYxpyNbgaHiYuDsWaBuXesez8zn6Ql060bfT51q/L4aDTBhAl2snD0LxMTQ3j6MWUi22JGaSnOsatWy7vHlcK1E4pFHaJ8NgOZBmOp69PICvvoKePZZqrEfFyf5+ltG8nRF+DzhFDrMTECDuPXoMDMBnyeccvhNimQrMHTyJK0kqGiuD5PWjBl0XLzYvIuK1q1pg8C0NIo7R47I2jxXxrHDQvv2AQ8/TH/fJOJaiYRGQ4kBQL/Ml182nRh4egJff01jn7NmAWPHchEgiTnzjoeyFRj65x86tmlj3eOZZSIjaXUXQBcVe/aYfsyjjwIHDgA1awKPP256SJVZjGOHFQ4ckPwCxLUSCYBqR3z0EX3/7bfAf/9r+jEaDc3IXrYMWLKEhjuuXbPoaZ01a5aCs+54mKcrQkGRHm7lLAW0ucDQ8ePAQw/R5jvMPhYvpt85QLVoTA2PAtR9vH070KIFEB1N860s6NXkuGGcs8YOQKbiZHo99Wa2aCFNIw1tsuTOCxcuRHh4OLRaLbRaLSIjI7Fhw4aS25csWYKuXbtCq9VCo9HglhlL0+Lj49GuXTsEBAQgKCgIAwcOxMmTJy1+IRZ54w36UAOUIPz+u3mPe/55YNUq2rMjLAyYM4eW0ZjgzFmzFJxxx0PD//n/JZ5G0X0vzsNNg1e6NrJt6ee5czSPx0E4RezQaoHVq+n7a9doeNScwj6BgTTMERVFvRoxMaU9SkZw3DDNGWOHgSzFya5coSF9iWOHRYlEcHAwZs2ahf3792Pfvn3o3r07BgwYgKNHjwIAbt++jZiYGEybNs3sc27btg3jxo3Drl278Oeff6KwsBA9e/ZEXl6eZa/EEm5uwI8/lm7e1bcvcOyYeY8dMgQ4fRro148SkshIk0tEnTlrloIz7nhY0f85AOiFgJeHm21VCi9fLr06dgBOEzs6dAC++Ya+37cPiI01r4fBzw/4+Wf6SksD2rUD3n7baBVdjhumOWPsuJehONmuaVE4E98Xu6ZFYUJUY+tjh6EXTeJy7xohbJs9WLVqVcyePRuxsbElP0tMTES3bt1w8+ZNVLaw6/Xq1asICgrCtm3b0LlzZ7Mfl52djcDAQGRlZUGr1Zr3oHPngJYtadJaUBB1FxvW2Jpj/35g5EjqKvr3v4EpU6jaYE4OVb+7W5a7w8wEZGRX/IaupfXBrmlR5j+vk3HG34/sr6lZM6BPH6pdYIRVnws7UUPssPr3M2MGJQIAldU3tZrjXoWFtHJs5ky6mFm0CHjsMUoq9HqgUiUAzvm5kBr/jiz066/AE09QQmGi2KIlnw2r50gUFxdjxYoVyMvLQ2RkpLWneUBWVhYACjLG6HQ6ZGdnl/myWL16wM6d9H1mJjB4sGWbILVtS8nEW29RYAkMpDW6lSsDNWoAL74IpKU5fdZsK2fc8VD2/3MzAoFaKRk7JIkbADBtGjB+PH0fF0cB2lyenlSKf/duqiz4+ON09PWlCrzh4cDSpRw3zOCMsUNWly/TBW5QkKSntTiROHz4MPz9/eHt7Y0xY8Zg7dq1CDMsqbSRXq/HpEmT8Nhjj6Fly5ZG7xsfH4/AwMCSr7rWrqcPDwc2b6bvt20DXn/dssd7e1MScfw4MHs2Tcj64Qdg3Dhg/XqgRQsEmdj63eplPE7CGXc8lG3pFkBFjnJzHa6GhBpih2RxQ6OhfTUGDaJ/P/EEcHeYxmyPPELJxNq11DvxzTfAF18AjRoBL72EIJ3x/RBcPW4Azhk7ZHX+PF2AeEi7O4bFiUTTpk2RnJyM3bt3Y+zYsRg5ciSOmTu/wIRx48bhyJEjWLFihcn7xsXFISsrq+TrwoUL1j9xVBStyACAzz8HvvzS8nM0akR19kePpglV778PnDoFdO2KYVuXww3ljyBx1uycOx7KeqV0+DAdmze3/hwKUEPskDRueHhQTYmmTenfffoA169bdg43N5q0OXkyMGIEzblYswb4808MS/4DbqL8peYcN4gzxg5ZHTkiS9yweY5EdHQ0GjZsiMWLF5f8zJpxzvHjx+OXX37B9u3bUb++5VmkzWPBQgC9e9PsaoBmVbdubfl57ldYiLxnn8NQ/444Vqsh9Cj962LImvkN73zK23AHkOj//N//BhYuBK5eLS13WwE1z5FQQ+yQ5Pdz4EBpPY/evYHffiuZH2WLvJ27MXTpHhyrXg96Ten5OG4wq+Tn05DGm28C77xj8u52mSNhoNfroTMy89gUIQTGjx+PtWvXYsuWLVYlEZLQaKjwlGFZTJs2FKht5ekJv5XLsdLjGCb99T1quRdx1uwCZLtSKiyk9+lTT5lMItTOaWJH69Y0JAHQ0vD//EeS0/p1jMDKV7ti0oFfUOv2TY4bzDY//kgLAYYOlf7cwgJTp04V27ZtE2fPnhWHDh0SU6dOFRqNRmzatEkIIUR6ero4cOCA+OKLLwQAsX37dnHgwAFx/fr1knN0795dzJs3r+TfY8eOFYGBgSIxMVGkp6eXfN2+fduSpomsrCwBQGRlZVn0uAdcvSqEv78QgBCRkUIUFdl2PoPiYiFGjhSiShUhbtyQ5pwKyc0vFHM3p4iIGZtF/am/iYgZm8XczSkiN79Q6aY5v+XL6b156JBZd5fsc2EjtcYOSX8/y5bR/w0gxIYNtp/P4MwZIfz8hHjzTenOyVyLXi9E69ZC9Opl9kMs+WxYlEi8+OKLIjQ0VHh5eYkaNWqIqKiokkAghBDTp08XAB74WrZsWcl9QkNDxfTp00sbUM7973+MOSQNCCdOlAaETz+1/XwGFy4I4eEhxH//K9057Sw3v1D0nbtd1J/6mwidUvpVf+pvou/c7ZxMyK1jRyG6dzf77mpJJNQaOyT//bz+emnsuHZNmnMKQUlEYKAQCv8/Mge1c6fFCa4lnw2b50ioheRjwd98Q5UsAZrpGiLRxKZnnwUOHbJ8hrdKfJ5wCnM2p5RbaMlNA0yKboIJUY3t3zBXcPIk1Y/48Uca2jCDmudIqIHkv5+iIlpNk5EBvPBC6d4+tkpLA+rXp4ngL7wgzTmZ6xg1ilYnpqaaPX/HrnMknNZzz5VusxoXJ91569ShpXsOyplL0qrejz9SobP+/ZVuCauIhwfwf/9H3y9bZlYpbLPUqEHnlrNqJ3NORUW0EujZZyWZBFweTiQq4u5OdSEAYPlyqoJpq5wcWi7Wt6/t51IIF8lR0K+/0j4NPlw/QNUGDqSKuUBpDLHV11/TRNvevaU5H3MdO3cCN26U1jyRAScSxgwdSjXygdJZ2da6dAno0oWSibfesr1tCpG10JIVXGZ3xBs3aG8H/kOifhoNMGkSfb9iheW1Je73xRd0vpEjgYYNbW0dgwvFDQD480+genXJtw6/FycSxnh60pp9APjf/6w/z44dlERcvkxbCterJ0nzlKCmkrQutTvi9u00ha97d6VbwswxfHjp91u2WHeO/HyKPy+/DAwbRntyMJu5VNwAgK1bga5dZRvWADiRMO2ll+iYlgbcreVvtnPnaJLL44/TGOeOHbJmhfagppK0LrU74l9/0YTf0FClW8LM4eNDm/gBlicSej2NabdqRUMj779Pkza9vaVvpwtyqbiRnw/s3QtYsAGmNbiiiSk1apR+n5gIDBhg/P6ffUbd0FevUpdkYCDw+efIix2NpUlpWP5jAjJz8hEU4INhESGI7VTfoQrLGAotLd1xFst3pyn6WsyZ+Ok0K0h27gQ6dlS6FcwSERF0/O47qkRqzOnTtE9PYCBNqj18mK4i165FXsMmWLrltOKfN2fhUnFj/36goIB2l5URvwvN8fTT9OHeudN4IpGTQzXzAbp6fPtt4I03kOfl+0C5ZENX2qajGQ5Xpc7P2wMTohor/mFzmYmfBQVUhvmZZ5RuCbPEo4/SMS8PuHaNxqkr8t13wCefANWqAS1aUC9Gt27lllp35NihBi4TN4DSHWYffljWp+GhDXPUrEnH48eN38+wJfG6dVR74r33gIAA1+pKsyO1TfyUzZEjgE4HtGundEuYJe7dqtmc2NGkCSUc27YB3boBcLFueDtxmbgB0ATt1q1pvp+MOJEwh6EYR0aG8fvVrk1XHUlJZX7MtRfkoYaJn3aZ/Z2cTCsBWrWS7pxMfvfOaUhPN37f8HDaLfi+/X04dkhPDXEDsGPskGLzSRM4kTCHIZG4fdv4/dzcaHne6tVAcXHJj12qK82OlJ74abfZ3ydO0Eofw1Jk5jgMO5iaih2GZb0//VTmxxw7pKd03ADsFDuKiig5DQuz/VwmcCJhjjt36Fhkxn/u+PH0n7dmTcmPXKorzY5k22HTTHbrdj53jsojM8dTWEjHey4sylWrFvDkk8DHH5eJMxw7pKd03ADsFDsuX6b3kh1iB8/SMcfNm3Q0Zx1u+/ZAVBQt2Ro8GHB3x7CIEKP7U9iz9oKzUXLip91mf2dkSLfXC7OfwsLSktbmxI6pU2l5+HffleynwbFDHkpPGLdL7DAMxdeubdt5zMA9Eua4cYOOhm5KU2bOpAlyd5d8qaErjUnPbt3ON27QbH7mWAwXIIB5saNNG6qmGxdXUrOGY4dzskvsMFRUtUPs4ETCHKdP0/Ghh8y7f/v2VIgqLg44eVIVXWlMenbrds7KovoCzLEY4gZAm/WZ45NPqBdj3DhACI4dTsouscOwitAOsYPfhaYIUbrld6NG5j/uv/+lglQxMcCpU4p3pTHp2a3bOTubdv1kjsUQNwDzY0dwMO0eOmIE0KMHMHIkxw4nZJfYYUgk/P1tP5cJ3CNhyqVLpf8h5pa3vnGjdOvxe8dJmVOxS7ezEPT+sUMwYBIzJBL16pnfvZyQAHz0EX1/8aIszWLKs0vsyM0FKlWSdY8NA04kTDl4sPT7Dh2M37eoCFiwAGjcuLRSXWoqd0s7Kbt0OxcU0Ix/XvrpeJKT6WgolW3MmTM0OTs6muZT7NtXumEgczp2iR12vADhoQ1Tdu+mY9Om1O1YkWPHqITxkSNAbCzw4YelFTGZ05K929nQm8WJhGMpLqYKlQANURgzaxYwfTpVwvzhB5pwqamgYhJzGnaJHZUqyXPu+3AiYYohkbhbsrZCH31EQxp79zr8Dp/MOnm6Iuk3MzPUMLFTQGASOXas9HtjsSMzk4ZBR48GPv2UE0YmnTt3OJFQBSGATZvo+yeeMH7fzExKIDiJcEmyba6Uf3cZmA8XHnIo+/bRMSQEaNCg4vsZSmI/9xwnEUxa+fl2ixs8R8KY8+dLv+/e3fR9ufqgy5KtUp1OR8d7921g6mfYb+e554zf79w5OhpLNhizhk5nt7jBiYQxO3bQMSqq/P+QggJg61bglVeAkydpdjZzSbJtrmRIJLy8rHs8U8YXX9Dx6afLvz09HViyBHjnHbpqvHenUMakUFBgt7jBQxvG/OtfdHzvPTreuEGrOPbvpwTir7+AnBygbl1gyhQa52QuSbZKddwj4XjuXekVHk4TL1NTgQMHqKciIYEmZbu50UXKL7/Ivs0zc0E6nd2GNjiRqEhqaun3nTqVvc3XF4iMpNr4vXrRNq12WKvL1CsowAcZ2RUnC1ZXqjNMtvT1te7xzP4WLy79/v64EBpKyYMhdlSvbt+2Mddx5w4nEopbsYJmvH77LfVECEH1IMLDqU6EB//qWCnZKtXx8k/HUlQE/Pgj9WZGR9OENzc3GvZ85BFOHJj95OWZv62DjfivYUU2bgQefxwYMkTpljAHENupPjYdzXhgwqXNlerubt7ERc0cxD//0GZJI0ZQIsGYUrKygLAwuzwV98dXpFs34M8/gcREpVvCHIBslequXaPuSR7acAyNG9NV4HvvUXl8xpRy7Zrddg3mHomKvPMOrdp45hngwAHkVa0hfbEh5lRkqVR36RLtHMmVDh1DlSo0tNGlCzBtGvI+jOe4weyvqAjIyDB/11kb8Tu5Ih4ewPLlQOvWyBs+AkMHvit9sSHGTDlxwrJdZ5nyOnYEZs9G3ltxGKrtgmO3NRw3mH2dOUPJhJ1iBw9tGFOzJrBiBZYW1MCxy1nSFxtizJjiYlou2K6d0i1hlpo4EUtHxOFYjuC4wexvxw7qxbRTpWVOJEzp3BnLH38KepTftWxTsSHGjFm/nsY5BwxQuiXMUhoNltePhL6CZeEcN5hshACWLQM6dwaqVrXLU3IiYYZMGK8OdiU7H3m6Iju1hrkEnY6KnHXpwj0SDiozt8Do7RnZ+fg84RTHDiatn3+mHok33rDbU3IiYQZTxYQEgBbTN6L9jM0cGJg0/vtf4PRpYN48pVvCrGROEbJP/0xBq/c24ZONJzluMNvl5QGvvgr06wf07Wu3p+VEwgzDIkLgZsak+cwcHeZsTsHQxUkcFFxInq4InyecQoeZCWgQtx4dZibYllDm5NC29OPHAw8/LG1jmd2YGzeK9AILtp7muOGCJI8dixfTTtSff27XlV6cSJghtlN9hNXWmnVfnkjlWgzbh8/ZnIKM7HzoRenMfKv/MKxeTcnE5MnSN5jZjSFuuGlA49ZGCHDccDWyxI6lS4Enn7T7TtScSJjBUGzI3PyOJ1K5Dlm2D1+7FnjsMdoMjjmse4uUmXN1yHHDtUgeO06eBI4do9pHdsaLmM3k5+2BmlrjGzPdy+rdHplDMbV9+JLtZywrRqTTAVu2AG+/LV+jmd0YipQt351mVuzguOE6TMWOOZtTMGdzivlFzDZupG3Do6LkabAR3CNhAXPHPAEbdntkDsVU4M/VFVnWbZmURBOmevWSobVMKebGDo4brsNU7NALWDbcsWkT7VStwAZ/FiUSCxcuRHh4OLRaLbRaLSIjI7Fhw4aS25csWYKuXbtCq9VCo9Hg1q1bZp13wYIFqFevHnx8fBAREYE9e/ZY9CLspcyYpxFuQo9habuBRYuoTClzWpYGfpPdlhs3AjVqAK1aSdA69eDYQbHDWOhw0+sx7NBGWrb3559UkIw5LUtih8m4odMBW7cCPXtK1DrLWJRIBAcHY9asWdi/fz/27duH7t27Y8CAATh69CgA4Pbt24iJicG0adPMPufKlSsxefJkTJ8+Hf/88w9atWqFXr16ITMz07JXYgf3jnnW1HqXex83CISJXMQe/oOW4YSEUD2AAuNrypn9STFj2pJeKgOjY+Hr1wMxMbT1tBPh2EGxY1y3RvAo5w3jpgHCvAsQq7kMrFxJfxDCwqgeAFMdJWKH0biRmAjcvg307m3+CSWkEcLEdGITqlatitmzZyM2NrbkZ4mJiejWrRtu3ryJypUrG318REQE2rVrh/nz5wMA9Ho96tati1dffRVTp041ux3Z2dkIDAxEVlYWtFrzVljYKk9XZHxDnps3gQULgPffp8lza9cCJn4fzD4MM6Yr2vbb3H0QKjqPKW4a4Ez8feu8z5wBGjakTZ+eesr8kxmhxOfCXGqIHaqMG0IAe/YAr78O7N4NfPklMHKkXdrGTFMydpQbNwDglVeA338Hzp6VbNmnJZ8NqydbFhcXY9WqVcjLy0NkZKRV5ygoKMD+/fsRFxdX8jM3NzdER0cjKSnJ6GN1Oh10Ol3Jv7Ozs61qgy1M7vZYpQpNmuvShcocd+wIrFoFtGhh34ayB5gzY9qcXTwNV5r3/2HI1RUh18jVSbndmj/8AFSqpNhVhb0oGTscIm5oNEBEBHVVv/IK8PzzwPHjwAcfAJ6edm0re5CcscNwnoqUGzcKCmjJ+IgRiu0SbHH/6eHDh+Hv7w9vb2+MGTMGa9euRVhYmFVPfu3aNRQXF6NmzZplfl6zZk1kmJhbEB8fj8DAwJKvumpeKvf44zSJzrCJyuefm1xXzuRlasa0JcvwDH8Ydk2Lwpn4vtg1LQqjOzeosNvSTUPdmmXcuUO9V88+C/j7m/3cjkQNscOh4oanJ7BkCTB7NvDJJ3QhkpKidKtcnpyxY1J0E8viBgB89x1w9SpwT8+evVmcSDRt2hTJycnYvXs3xo4di5EjR+LYsWNytM2ouLg4ZGVllXxduHDB7m2wSNOmwL59wOjRwMSJwLhxSrfIpZmaMW3rMryKJuYauj9jO91XMObbb4ErVwALhvMcjRpih8PFDY2GJl8mJQHZ2bTvyoEDSrfKpckZOyyOG0JQFdwhQ4Dmza1+XltZnEh4eXmhUaNGaNu2LeLj49GqVSvMnTvXqievXr063N3dceXKlTI/v3LlCmrVqmX0sd7e3iUzwA1fqufrS70R8+cDCxdSdyVThKkZ07Yuw7t3Ym4trQ/cNEAtrQ8mRTcpfwzVMMGuUSObnlfN1BA7HDJuAJRA7NsHhIYC77yjdGtcmpyxw+K48c8/wKlTwNixVj+nFGyeGq7X68uMOVrCy8sLbdu2RUJCQpnzJSQkWD126hBGjqQrDRPzQJh8jM2YrrAL0ULlDXlMiGr8YDDIy6PZ+XbcZEcNOHZYKCCAyh8nJfHQqILkjh1mxw2Aakf4+9OW4QqyKJGIi4vD9u3bce7cORw+fBhxcXFITEzE8OHDAQAZGRlITk7G6dOnAdCYaHJyMm7cuFFyjqioqJJZ1gAwefJkfPHFF/jmm29w/PhxjB07Fnl5eXjhhRekeH3q5O9PdQK2bFG6JS7L4i5EOf31F1BYqEhFOnvh2CGRxx4DbtwADh5UuiUuS1WxY/Nmmsyv9CRcYYEXX3xRhIaGCi8vL1GjRg0RFRUlNm3aVHL79OnTBWj/mTJfy5YtK7lPaGiomD59epnzzps3T4SEhAgvLy/Rvn17sWvXLkuaJYQQIisrSwAQWVlZFj9WEe++K0RgoBA6ndItcVm5+YVi7uYUETFjs6g/9TcRMWOzmLs5ReTmF9q3Ia+9JsRDDwmh10t+arV8LtQaO9Ty+zGbTkdx4913lW6JS1NF7MjNFcLbW4jPPpPl9JZ8NmyuI6EWal4vX66DB4FHHgH++IPLIbsyIWheRHQ0bQEsMYf7XNiZQ/5+/vUvih+HDindEqakNWtokmVKCtDY9HJTS1ny2XCu8nmOJDycdne8p0wwc0F791IhqiefVLolzFH07w8cPgxcvKh0S5iSVqygIXIZkghLcSKhFI0G6NGDJss4R6cQs8bXXwN16gDduyvdEuYounen+PHnn0q3hCnlxg3gl1+A555TuiUAOJFQ1pNP0hLQNWuUbglTwu3bwPffAy+8ALi7K90a5iiqVwe6dQM++wzIyVG6NUwJ330H6PVUzVIFOJFQUkwMJRNPPQUMGkRlTjkwuI6ffqIiQwpWpGMO6tNPgfPngSZNqK5EcjL3bLqSpUtp24WgIKVbAoATCWVpNDTOtXgxBYWnnqKrjYEDae4EBwbntnIllU+vb8flYsw5tGpFycMTTwDz5gGtWwMNGtDePpcvK906Jqfjx2mOjEp6IwBOJJTn7g6MGkUVys6cAWbNAi5cAPr0oTXjvF7cORUV0aZM/fsr3RLmqOrXp4uQzEyaa9WrFyUVDRsC774L5NtW5p2p1ObNgJcXzbFTCU4k1KR+feC116gU7p9/0jBHu3a0aY9er3TrmJRSU2mOxKOPKt0S5ugMf1QWLQLS0mj78VmzgPbtuQy/Mzp0CGjZkrZcUAlOJNRIo6G6Avv3U2Lx1lvA4MFAVpbSLWNSMXQ/Bwcr2w7mXAIDgQ8/pNhRXEwXIj/+qHSrmJQuX6bSASrCiYSaeXnRzm7r1gGJiXT1um+f0q1iUsjNpWNAgLLtYM7p4YeBPXtoDsXQocCrr9JW9czx5eaqLm5wIuEI+venBCIwEOjQAZgwAUhPV7pVzBaGoSqPcjbiYUwKfn60vHjePOCLLyi5WL2aeiqY4youVt1ycU4kHEWjRrTr38yZwDffAPXqAc88Q6s7ioqUbh2zlNvdjx7/3zE5aTTA+PG0wqNRI1oZ1qQJDX+cO6d065g13N1VlwxyIuFIPD1pvsT580B8PC0B6tOHxtnfeIP2pWeOwc+Pjnl5yraDuYZmzWhfn127aMlxfDxN7u7SBfj2W17h4UgqVVJd3OBEwhFVrgxMngwcOUKTqp55hkotN21K3589q3QLmSnVqtHx6lVl28FcS0QExYorV6g6oqcnMHIk1aBYuFB1V7qsHNWrqy5ucCLhyDQaoE0bYM4c2sBn0SJgxw6gRQtgwQIuaKVmhiJUp08r2w7mmvz9aZ+GzZuBEydo+ei4cUDHjvyeVLv69VXX+8yJhLPw8QFGj6ag8OKLNC46YgRQUKB0y1h5tFratW/nTqVbwlxd06Y07+rvv4GbN2l1GG8Ipl5t21KPkop6njmRcDb+/sD8+cAPP9D68YEDefxTrfr0oQ3bONljahAZSdvaR0YCffsCP/+sdItYebp1owvHFSuUbkkJTiSc1TPPAOvXUxnmYcN47FONRo+m8sZffaV0SxgjgYFUt2bgQODpp4GEBKVbxO6n1QLDhwNz55bWo1EYJxLOLDoaWLWK9q2fNk3p1rD7hYVRQPjPf1Q3C5u5ME9Pqj/RvTswZIjqxuMZaMfXmzdpF1gV4ETC2fXrB3z8MX399pvSrWH3e/994No1mknPmFp4etLQaM2aVHtCp1O6RexeoaHAmDHUK6GCiqWcSLiCyZNpPH7UKN6vQ23q16du5AULeGM2pi5aLW11f+wYFcJj6jJhAvVKLF+udEs4kXAJGg1tN5yTQxXtmLpMmkS7NK5dq3RLGCvrkUeoCN5HH3ElTLVp2JD2UvnoI6CwUNGmcCLhKoKDgTffpLr7ly4p3Rp2r06dgF69gKlTuQuZqc/UqVQE7/33lW4Ju99771Hdj0WLFG0GJxKu5LXXqLzqRx8p3RJ2v08/pSu+//5X6ZYwVpa/PyUT334LnDmjdGvYvVq1Al56iSZfXrmiWDM4kXAlWi11o3/xBS07ZOoRFkb/N7yZElOj0aOBqlVp0jZTl/h42sjrzTcVawInEq5m/Hh6082bp3RL2P2mT6dg/dprSreEsbIqVaJE17BPB1OPatWol/m776g6qQI4kXA1VasCsbE0psYVL9XF3x+YPZsqCm7dqnRrGCtr7Fi6CFm8WOmWsPu9+CKVNp84UZHVX5xIuKJXXqHaBVwCV32eeQZo354KiPGma0xNqlShAmpffslLldXGzY3mV+3fr0hc50TCFTVtCnToQGvEmbpoNFTpctcu4K+/lG4NY2WNGAFcuEDvT6YunTvTPhwKTKbnRMJV9e4NJCbyVa8axcRQssddyExtIiNpPw4eelOnSZOAPXuAAwfs+rScSLiq1q2BW7e4poQaaTR05ffzz6oof8tYCXd3WnJ45IjSLWHl6dMHqF7d7juDciLhqurWpWN6urLtYOUbNAi4fZt6jRhTk+Bg4PJlpVvByuPhAfTvb/d9lTzs+mxMPfz96ZiTo2w7nFCerghLd5zF8t1pyMzJR1CAD4ZFhCC2U334eZv5kWvWjJK9TZtoGIoxtQgIUM321c5GktjRqxewbBn1Nj/0kLwNvot7JFyVRqN0C5xSnq4IQxcnYc7mFGRk50MvgIzsfMzZnIKhi5OQpysy70QaDdCzJ7Bxo7wNZsxSGg3PrZKBZLEjOpr+j+wYOziRcFV5eXT081O2HU5m6Y6zOJaeDf19cVYvgGPp2Vi646z5J+vThzbzOnVK2kYyZou8PI4bMpAsdlSrRqvy1q2TvpEV4ETCVRlKZNeooWw7nMzy3WkPBAIDvaDbzRYTQwFbBdsEM1YiM5PjhgwkjR1PPQVs2ADcuCFN40zgRMJVpabSDOzgYJtPlacrwucJp9BhZgIaxK1Hh5kJ+DzhlPldcU4kM8d4tVBTt5dRqRIwdCiNdxYX29gyxiSSmgrUq2fzaThulCVp7Bg2jIqG/e9/NrbKPJxIuKqjR4FGjQAvL5tOI9m4npMICvCx6fYHvPwycP488PvvNrSKMYncvk07gIaF2XQajhsPkjR21KxJK78WLrTLfBZOJFxVcjKtB7eRpHMCnMCwiBC4VTCP1U1Dt1ukfXuqob9woe2NY8xWR47Qle4jj9h0Go4bD5I8drzyCnDihF2WkHMi4YqEAA4dsjkYABKP6zmB2E71EVZb+0BAcNMAYbW1iO1U3/KTjh4N/PEHFw9jyjt4kPZ1aNHCptNw3HiQ5LGjSxegSRNg6VLpGlkBixKJhQsXIjw8HFqtFlqtFpGRkdiwYUPJ7fn5+Rg3bhyqVasGf39/DBkyBFdMbDmbm5uL8ePHIzg4GL6+vggLC8OiRYusezXMPOnpQFaWzd2TgMTjek7Az9sDK1+OxKToJqil9YGbBqil9cGk6CZY+XKk+WvB7/X004CnJ/DTT9I32E44djiJ48eBhg0BX1+bTsNx40GSxw6NBnjuOeCXXwCdTp5G32VRy4KDgzFr1iw0btwYQgh88803GDBgAA4cOIAWLVrgtddew/r167Fq1SoEBgZi/PjxGDx4MP42skf65MmTsWXLFvzvf/9DvXr1sGnTJrzyyiuoU6cOnnjiCZtfICvHuXN0bNDA5lMFBfggI7viD73FcwKcgJ+3ByZENcaEqMbSnDAwEOjUCUhIACZMkOacdsaxw0mcOwfUt6JX7T4cN8oneezo2xd4913af+Pxx6U5Zzks6pHo378/+vTpg8aNG6NJkyaYMWMG/P39sWvXLmRlZWHp0qX49NNP0b17d7Rt2xbLli3Dzp07scvITnE7d+7EyJEj0bVrV9SrVw+jR49Gq1atsGfPHptfHKuAYelnrVo2n0rycT1Wvvbt7b4Rj5Q4djiJK1c4bjiS8HCaUJ+cLOvTWD1Hori4GCtWrEBeXh4iIyOxf/9+FBYWIjo6uuQ+zZo1Q0hICJKSkio8T8eOHbFu3TpcunQJQghs3boVKSkp6Nmzp9Hn1+l0yM7OLvPFzGT4XWm1Np9KljkBMnPIZWcNGgAXLwJFKm6jmZSMHRw3bJSdTT1kNnLEuAE4YOzw8ABCQ0t7oWVicSJx+PBh+Pv7w9vbG2PGjMHatWsRFhaGjIwMeHl5oXLlymXuX7NmTWRkZFR4vnnz5iEsLAzBwcHw8vJCTEwMFixYgM6dOxttR3x8PAIDA0u+6ho2oWKm5efT+JmNSz8BmeYEyMhhl51Vr06TZG/dUrolVlND7OC4YaP8fMDH9mEHR4sbgAPHjho1gOvXZX0Ki/+3mjZtiuTkZGRlZWH16tUYOXIktm3bZnUD5s2bh127dmHdunUIDQ3F9u3bMW7cONSpU6fMFcr94uLiMHny5JJ/Z2dnc1AwV3ExFaOSaL8Nycf1ZGTOsjNVvg7DVeCtW5RUOCA1xA6OGzbS6yl2SMCR4gbg4LEjK0vWp7A4kfDy8kKjRo0AAG3btsXevXsxd+5cDB06FAUFBbh161aZK4srV66gVgVjanfu3MG0adOwdu1a9O3bFwAQHh6O5ORkfPLJJ0YTCW9vb3h7e1vafAa49KY75iw7U2UwMJQkzsykQmIOSA2xg+OGBDh2PED1sUPm/XpsriOh1+uh0+nQtm1beHp6IiEhoeS2kydPIi0tDZGRkeU+trCwEIWFhXBzK9sMd3d36PV6W5vGKuLu7rIllx122VmjRjTeuX+/0i2RDMcOB8Sxw+rbFdO8OXD4MFBQINtTWJRIxMXFYfv27Th37hwOHz6MuLg4JCYmYvjw4QgMDERsbCwmT56MrVu3Yv/+/XjhhRcQGRmJDh06lJyjWbNmWLt2LQBAq9WiS5cuePPNN5GYmIizZ8/i66+/xrfffotBgwZJ+0pZKSFcdhtxyUtY24uvL9C1K60Jd0AcO5wExw6rb1dM795Abi6wdatsT2HR0EZmZiZGjBiB9PR0BAYGIjw8HBs3bkSPHj0AAJ999hnc3NwwZMgQ6HQ69OrVC//3f/9X5hwnT55E1j3jNStWrEBcXByGDx+OGzduIDQ0FDNmzMCYMWMkeHmsXLduSbJiwxENiwjBnM0p5XZRqn7ZWb9+wFtv0ZWFBBNl7Yljh5PIyuLY4WixIzycNmdMSAB69ZLlKTRCOMeAV3Z2NgIDA5GVlQWti77RzTZ6NBUokXltsRoZZl7fP2nKsOxMrTPGAVCZ7N69aSlXaKhZD+HPhXH8+7FAVhZQuTLtKDl8uNKtsTuHjh3R0UDVqsCPP5r9EEs+G7zXhis6cECSfTYckSMuOyvhxh9XpiBDQTQJNvtzRA4dO2QejlLxK2eyKC6miTfPPad0SxTjaMvOSly7Rsdq1ZRtB3NNyclUQ6JZM6VbohiHjR3Xr8u62osvcVzNpUu0gUuTJkq3hFnq4kUan/b3V7olzBWdOUMbdnnw9afDuXgRqFNHttNzIuFqDCWB76siyBzApUs0aYoxJWRnc9xwRAUFwNWrgIyF1ziRcDWennQsLFS2Hcxy1687bFVL5gQ8PDhuOCJDeWwZh0Q5kXA1hj9EV68q2w5muawsSTZMYswqNWqU7hzMHIdhybSMsYMTCVdTtSrg5yf7bnBMBmfPAg89pHQrmKsKCXGaHWhdiiHWyxg7OJFwNRoNTZhKTVW6JcwSJ08CR48CJnbFZUw2DRtSEpGWpnRLmCXWrKEkokED2Z6CEwlXVK8ecP680q1glvjgA5p1zeWfmVLq1aMjxw7Hcf488M03wPjxstaS4ETCFdWoUVqTgKnfsWPA8uXAO+/QOn7GlBAUREeOHY7jgw9opc348bI+DScSrqhyZdpvgzmG2bOpa/LFF5VuCXNlAQF05NjhGNLTqTfizTdlrz3DiYQrqlULuHwZ4O2W1e/WLWDFCmDcOIfbqIs5GXd36pW4dEnpljBzLFtGMWPUKNmfihMJV9S2LW0ra6idz9Rr9WoqKDNihNItYYxix7ZtSreCmSIE8O23wODBdlkyzomEK+rUiSbuzZ+vdEuYKStXAl27ylreljGzPfMMkJgIHDmidEuYMQcP0kqvYcPs8nScSLgiT08aN/vuO6pNwNTp2jVg61bg6aeVbglj5JlnaPXGhx8q3RJmzOrVQJUqtH24HXAi4apGj6YNoJYsUbolrCJr11IXJS/5ZGrh5QVMnkx/qK5cUbo1rDxCAD/+CAwcWLolgsw4kXBVlSoBTz5JxUqYOq1YQcMahmV3jKnBsGH0x2rdOqVbwspz4ABw6pRdezI5kXBlUVFASgrXz1ejM2doWONf/1K6JYyVVa0a0KYNsGOH0i1h5fnqK6BmTbsNawCcSLi2hx+m44kTyraDPWj2bNoXhedHMDV6+GGOG2p05Qot+3z5Zdqt1U44kXBlhpUAPNapLvv20dyVuDgagmJMberUATIylG4Fu9/kyVT9duJEuz6t/VIWpj6GjLWwUNl2sFJXr1IvROvWwIQJSreGsfJ5eHDcUJvFi6mU/nffUW+mHXEi4coMFeq4RoE63L4N9O9Pxy1b7DbjmjGLXbrEcUNNfv8deOUV2lPjuefs/vScSLgyw3bAISHKtoNRufIRI4DDh4Ht20t3WmRMjdLSOG6oxcGDwNChQL9+wJw5ijSB50i4sosX6fjQQ8q2gwHTp9NS3OXLqQwxY2p28SJQt67SrWCZmcATTwCNG1PscHdXpBncI+HKLl2iLcW9vZVuiWtbvZoqBcbHAwMGKN0axky7eJEvQJRWWEi1gHQ6qunh56dYUziRcGWXLnEwUNrx48Dzz1PX5JQpSreGMdNyc4HsbI4dSnvzTWDXLqo3ExysaFM4kXBlFy4o/gZ0abm5dEURGgp8+SWg0SjdIsZMMwyJcuxQzqpVwNy5wOefA489pnRrOJFwaWlpwOOPK92KEnm6IizdcRbLd6chMycfQQE+GBYRgthO9eHn7WRvVb0eiI0Fzp8H9u4F/P2VbhFj5uFJ2so6cgR48UXqxRw/XunWAODJlq5LCCrDXL++0i0BQEnE0MVJmLM5BRnZ+dALICM7H3M2p2Do4iTk6YqUbqJ0hKCCMatWAd9+CzRvrnSLGDNfaipN6uMeCfs7fRqIiQEaNlRVLyYnEq7q0iUgLw9o0kTplgAAlu44i2Pp2dCLsj/XC+BYejaW7nCS7c51OlrmOX8+sGgRMHiw0i1izDInT9IfMq5zYl9799Iwhr8/sGGDqnoxOZFwVUeO0LFlS2Xbcdfy3WkPJBEGekG3O7xr14AePWiL3x9+oK3cGXM0R44ALVoo3QrXsno10KUL0KAB8NdfQO3aSreoDE4kXNWBA4BWq5rCR5k5+TbdrnqpqUCHDrTR0datwDPPKN0ixiwnBMWORx5RuiWuY/Zs4KmnaGn4li20ZF9lOJFwVf/8Q8HATR1vgaAAH5tuV7WTJ2lSq7s7sHs30LGj0i1izDrnzwM3btA24kxeQlChurfeAv79byo45eurdKvKpY6/Isz+kpNVFQyGRYTArYJ5Q24aut0hHToEdO0KVKlCpa9VMrmVMascPEjH1q2VbYezE4J2/33/fWDWLCpYp5KJleXhRMIVFRbSig0VrRaI7VQfYbW1DyQTbhogrLYWsZ0c7A+wEMCyZdT7ULs2DWfUrKl0qxizzcmTNCTKG3bJJyODhjE++gj49FOHKFTHiYQrun6d6hioaMKOn7cHVr4ciUnRTVBL6wM3DVBL64NJ0U2w8uVIx6ojcfw4EB1Na72ffJJ6IoKClG4VY7bLzARq1VL11bHDKi4GFi6kC7xdu4BffwVee03pVpnFgaIzk4xhXkRxsbLtuIdTFKMqKgJmzKCv0FBaohUTo3SrGJOOmxvHDTmcOAGMHAns2UOF6mbNAqpXV7pVZuMeCVdUvTp1Tx4+rHRLADhJMaqsLKB3b+CDD6gr8vBhTiKY82nYkCZcZmcr3RLniBsAsH498OijFEP+/psKTTlQEgFYmEgsXLgQ4eHh0Gq10Gq1iIyMxIYNG0puz8/Px7hx41CtWjX4+/tjyJAhuHLlisnzHj9+HE888QQCAwPh5+eHdu3aIS3NCeoGqJWbG9C/P7B0KRWlUpjDF6M6fpwKxezbB/z5JyUTPg68ykQGHDucRJ8+NP9n0SKlW+L4caO4GPjvf2kb8B49gP37HXZFl0WJRHBwMGbNmoX9+/dj37596N69OwYMGICjR48CAF577TX8+uuvWLVqFbZt24bLly9jsInKfampqejUqROaNWuGxMREHDp0CO+88w58OBDL6913gatXgUGDaNxTQQ5bjOrsWeCVV4BWrSgo7NwJdOumdKtUiWOHk6hbF3j1VVqO+PXXlFQoxGHjRnEx8NNPQNu2wBtvAK+/TgWnFNwG3FYaIWx7J1StWhWzZ8/Gk08+iRo1amD58uV48sknAQAnTpxA8+bNkZSUhA4dOpT7+GeeeQaenp747rvvbGkGsrOzERgYiKysLGi1WpvO5TK2bKFCJ0VFFBzGj6eJVHbWIG59hQEBoJUbZ+L72q9Bply4ALz9NvD997Ss8/XXae8MFa7xVvPnQg2xQ82/H9UqLARefplWJXXoAEydCvTrR3VS7Mjh4oYQlDC88w6tfunWjZZ1qrQXwpLPhtVzJIqLi7FixQrk5eUhMjIS+/fvR2FhIaKjo0vu06xZM4SEhCApKancc+j1eqxfvx5NmjRBr169EBQUhIiICPz8888mn1+n0yE7O7vMF7NQ9+40ySc2FpgzhyYIjh1LPRV25DDFqPR6YMECmlW9cSMtzTp/ngKpCpMItVIydnDckICnJ/DVV8CmTTRMOnAgfSZWrrRrD4XDxA2Adkzt0wd4+mmaZ7JrF13IqTSJsJTFicThw4fh7+8Pb29vjBkzBmvXrkVYWBgyMjLg5eWFypUrl7l/zZo1kZGRUe65MjMzkZubi1mzZiEmJgabNm3CoEGDMHjwYGzbts1oO+Lj4xEYGFjyVbduXUtfCgOo3OqnnwIXLwLvvUfB4OGHqZveTlRfjOr2bWDFCqBdO+q1ee45ICUFmDABqFRJ2bY5EDXEDo4bEurRgyYH7toFNGtGZd+HDwfy7VPOXvVxAwBOnaLJ182bU3G6X3+lyZUREUq3TFIWJxJNmzZFcnIydu/ejbFjx2LkyJE4duyYVU+u1+sBAAMGDMBrr72GRx55BFOnTkW/fv2wyMRknri4OGRlZZV8Xbhwwao2sLsqV6Yr6+PHaUfQ6Gi64rADVRajOn0a+Pxz6rKtXh149lla6bJjB000425wi6khdnDckEFEBLBuHSXba9fSRO7cXNmfVpVxQ6ej3srJk2ljsyZNgMWL6aLj+HGKJ07I4oW2Xl5eaNSoEQCgbdu22Lt3L+bOnYuhQ4eioKAAt27dKnNlceXKFdSqYNy9evXq8PDwQFhYWJmfN2/eHDt27DDaDm9vb3h7e1vafGZKzZr0QXjySXrTz5tHu1TKWIDGUIxK1vXgQgC3bgE3b1JBrvR0mmR6+zbdrtFQELhwgapQHj4MeHnRaoz//IcmpTZubHs7XJgaYgfHDRkNHUpzrPr1o50qf/pJ1k0B7RI3AKCggGLGzZsUM9LT6fuiIoobej0thz10iC6+srOBhx4CevakFVy9ezv90KfNv2m9Xg+dToe2bdvC09MTCQkJGDJkCADg5MmTSEtLQ2RkZLmP9fLyQrt27XDy5MkyP09JSUFoaKitTWPW8vUFfv4ZmDQJGDMG2LYNWLIE8PeX7Sn9vD0wIaoxJkRZ8Mc6Lw9ISKCroNRUat9DD9FXYCCQk0Mf7oMHaS5DYeGD5/D1pSRDCMDbm6p9RkRQ8tCrl0PPpFY7jh1OqEsX2uZ60CDaj+Prr6ncs0ysihsAxYs1a2iewp07VHn2oYcoEfL0pHjxzz80h+zatQcf7+kJeHhQEuHmBgQE0IXG5MnA4MFAy5YuVf3TokQiLi4OvXv3RkhICHJycrB8+XIkJiZi48aNCAwMRGxsLCZPnoyqVatCq9Xi1VdfRWRkZJlZ182aNUN8fDwGDRoEAHjzzTcxdOhQdO7cGd26dcMff/yBX3/9FYmJiZK+UGYhT0+aWNi5M/DSSzQG+q9/Ubdl27b0R1cJGRnAjz/SWOP27XS10KIF7WSam0ubkf3+OxV38fMDwsKozQ0bUrCoWhWoVo0CRo0adp9p7qo4driQRx6hP8IvvUQTMXv1okmG0dG0fFSJP7B6PSU4P/1E8SE1lWq9dO9O+4ZkZtJFR2YmXXDUqUOJUK9eQHAwxYyqVSlm1K5NFy0ulCiYJCzw4osvitDQUOHl5SVq1KghoqKixKZNm0puv3PnjnjllVdElSpVRKVKlcSgQYNEenp6mXMAEMuWLSvzs6VLl4pGjRoJHx8f0apVK/Hzzz9b0iwhhBBZWVkCgMjKyrL4scyEo0eFeOklIapVo2t3Dw8hGjcWonNnIfr0EWLgQCGGDxfizTeF+OEHITIzpW/DxYtCjBwphLu7EJ6eQvTsKcRnnwlx4oT0z+VE1PK5UGvsUMvvxynp9UIsWUJxQqOh2FG1qhBt2gjRo4cQ/fsL8eSTFFs++kiInTuFKCyUvg2rVwvRrBk9f3CwEGPGCPHzz0Lk5Ej7XE7Gks+GzXUk1ILXg9tBURFw4ABVcDx9GrhyhXoBdDqaa5CWBpw7R/etU4cy9+rVKaNv2JC6/ho2pGWmVaqYn9H/8AMNsfj40Na6I0fS45lJ/Lkwjn8/dnLtGq3wOHKE4sSNGzSkUFBA3588SbHEx4fiQ40aNF8rNJTiRuPGNN+ibl2au2SOmzepV2TNGpqnMHUq8Pjj3JNgJks+G5xIMGmlp9NkxRMnKNG4epUmMJ46RcMNBoGBNKM5PJyWVXbsSEMUbvcsJCospDHH+fNp1cSCBZxAWIg/F8bx70clioroAmXXLko0rl2jYcyzZ+nLsFGYRgOEhNCQZevWVBCrY0caerjX4cM0N+PWLZrfdbfQGTMfJxIcENRHCJr5fOYM9VqcPUvJRnIyfeiLi2kJakQEBYemTWnzmu3bgblzqVAWX0lYjD8XxvHvxwEUFtLkx7Nn6Xj6NPVs7NtHFysAzeGKjKTNr/R66rls1IgmYsu4csSZWfLZcKB9VplD02homKN6daB9+7K35eXR9rl//02FsBYsoCuSypVpKWr37oo0mTGmAp6elBTcXTpcQghKLHbsoNixezfw3XfUuzFgAJWw51VXdsGJBFOenx/VnTdseCUEzZ728aEhEMYYu59GQ70N9epRtVmA5lykp9PwB/dg2g0nEkx9NBqaaMUYY5bw8qIJmsyurN60izHGGGOMEwnGGGOMWY0TCcYYY4xZjRMJxhhjjFmNEwnGGGOMWY0TCcYYY4xZjRMJxhhjjFmNEwnGGGOMWY0TCcYYY4xZjRMJxhhjjFmNEwnGGGOMWY0TCcYYY4xZjRMJxhhjjFnNaXb/FEIAALKzsxVuCWPqYfg8GD4frCyOG4yVz5LY4TSJRE5ODgCgbt26CreEMfXJyclBYGCg0s1QHY4bjBlnTuzQCCe5VNHr9bh8+TICAgKg0WgUa0d2djbq1q2LCxcuQKvVKtYOe+PXrc7XLYRATk4O6tSpAzc3Hsm8H8cNZfHrVu/rtiR2OE2PhJubG4KDg5VuRgmtVqvaN4ic+HWrD/dEVIzjhjrw61Ync2MHX6IwxhhjzGqcSDDGGGPMapxISMzb2xvTp0+Ht7e30k2xK37drvW6mbRc9X3Er9s5XrfTTLZkjDHGmP1xjwRjjDHGrMaJBGOMMcasxokEY4wxxqzGiQRjjDHGrMaJxF0LFy5EeHh4SYGQyMhIbNiwoeT2rl27QqPRlPkaM2aM0XM+//zzDzwmJiamzH1u3LiB4cOHQ6vVonLlyoiNjUVubq4sr7E8crzu++9v+Jo9e3bJferVq/fA7bNmzZLtdd7P1OsGgKSkJHTv3h1+fn7QarXo3Lkz7ty5Y/S8CxYsQL169eDj44OIiAjs2bOnzO35+fkYN24cqlWrBn9/fwwZMgRXrlyR/PUx++HYwbHD5WOHYEIIIdatWyfWr18vUlJSxMmTJ8W0adOEp6enOHLkiBBCiC5duohRo0aJ9PT0kq+srCyj5xw5cqSIiYkp85gbN26UuU9MTIxo1aqV2LVrl/jrr79Eo0aNxLPPPivb67yfHK/73vump6eLr776Smg0GpGamlpyn9DQUPH++++XuV9ubq6sr/Vepl73zp07hVarFfHx8eLIkSPixIkTYuXKlSI/P7/Cc65YsUJ4eXmJr776Shw9elSMGjVKVK5cWVy5cqXkPmPGjBF169YVCQkJYt++faJDhw6iY8eOsr9eJh+OHRw7XD12cCJhRJUqVcSXX34phKAPxcSJEy16/MiRI8WAAQMqvP3YsWMCgNi7d2/JzzZs2CA0Go24dOmSNU2WhK2v+34DBgwQ3bt3L/Oz0NBQ8dlnn9l0Xqnd+7ojIiLE22+/bdHj27dvL8aNG1fy7+LiYlGnTh0RHx8vhBDi1q1bwtPTU6xatarkPsePHxcARFJSkgSvgKkFxw6OHZZw9NjBQxvlKC4uxooVK5CXl4fIyMiSn3///feoXr06WrZsibi4ONy+fdvkuRITExEUFISmTZti7NixuH79esltSUlJqFy5Mh599NGSn0VHR8PNzQ27d++W9kWZQcrXbXDlyhWsX78esbGxD9w2a9YsVKtWDa1bt8bs2bNRVFQkyeuw1P2vOzMzE7t370ZQUBA6duyImjVrokuXLtixY0eF5ygoKMD+/fsRHR1d8jM3NzdER0cjKSkJALB//34UFhaWuU+zZs0QEhJSch/m2Dh2cOxwxdjhNJt2SeHw4cOIjIxEfn4+/P39sXbtWoSFhQEAhg0bhtDQUNSpUweHDh3ClClTcPLkSaxZs6bC88XExGDw4MGoX78+UlNTMW3aNPTu3RtJSUlwd3dHRkYGgoKCyjzGw8MDVatWRUZGhqyv9V5Sv+57ffPNNwgICMDgwYPL/HzChAlo06YNqlatip07dyIuLg7p6en49NNPJX99Fanode/atQsA8J///AeffPIJHnnkEXz77beIiorCkSNH0Lhx4wfOde3aNRQXF6NmzZplfl6zZk2cOHECAJCRkQEvLy9Urlz5gfvY8/+bSY9jB8cOl44dSneJqIlOpxOnTp0S+/btE1OnThXVq1cXR48eLfe+CQkJAoA4ffq02edPTU0VAMTmzZuFEELMmDFDNGnS5IH71ahRQ/zf//2fdS/CCnK+7qZNm4rx48ebvN/SpUuFh4eH0XFEqVX0uv/++28BQMTFxZW5/8MPPyymTp1a7rkuXbokAIidO3eW+fmbb74p2rdvL4QQ4vvvvxdeXl4PPLZdu3birbfekuhVMSVw7ODY4cqxg4c27uHl5YVGjRqhbdu2iI+PR6tWrTB37txy7xsREQEAOH36tNnnb9CgAapXr17ymFq1aiEzM7PMfYqKinDjxg3UqlXLyldhOble919//YWTJ0/ipZdeMnnfiIgIFBUV4dy5cxa13RYVve7atWsDQMmVlUHz5s2RlpZW7rmqV68Od3f3B2ZRX7lypeT/slatWigoKMCtW7cqvA9zTBw7OHa4cuzgRMIIvV4PnU5X7m3JyckAUPLGMcfFixdx/fr1ksdERkbi1q1b2L9/f8l9tmzZAr1eX/KhU4JUr3vp0qVo27YtWrVqZfK+ycnJcHNze6C71p4Mr7tevXqoU6cOTp48Web2lJQUhIaGlvtYLy8vtG3bFgkJCWXOl5CQUDJm3LZtW3h6epa5z8mTJ5GWllZmXJk5Po4dD+LY4cSxQ+kuEbWYOnWq2LZtmzh79qw4dOiQmDp1qtBoNGLTpk3i9OnT4v333xf79u0TZ8+eFb/88oto0KCB6Ny5c5lzNG3aVKxZs0YIIUROTo544403RFJSkjh79qzYvHmzaNOmjWjcuHGZLriYmBjRunVrsXv3brFjxw7RuHFjuy7hkvp1G2RlZYlKlSqJhQsXPvCcO3fuFJ999plITk4Wqamp4n//+5+oUaOGGDFihKyv9V7GXrcQQnz22WdCq9WKVatWiVOnTom3335b+Pj4lOmW7d69u5g3b17Jv1esWCG8vb3F119/LY4dOyZGjx4tKleuLDIyMkruM2bMGBESEiK2bNki9u3bJyIjI0VkZKTdXjeTHscOjh2uHjs4kbjrxRdfFKGhocLLy0vUqFFDREVFlbwx0tLSROfOnUXVqlWFt7e3aNSokXjzzTcfWBMNQCxbtkwIIcTt27dFz549RY0aNYSnp6cIDQ0Vo0aNKvPGEEKI69evi2effVb4+/sLrVYrXnjhBZGTk2OX1yyE9K/bYPHixcLX11fcunXrgefcv3+/iIiIEIGBgcLHx0c0b95czJw5065jnMZet0F8fLwIDg4WlSpVEpGRkeKvv/4qc3toaKiYPn16mZ/NmzdPhISECC8vL9G+fXuxa9euMrffuXNHvPLKK6JKlSqiUqVKYtCgQSI9PV2W18jsg2MHxw5Xjx28jThjjDHGrMZzJBhjjDFmNU4kGGOMMWY1TiQYY4wxZjVOJBhjjDFmNU4kGGOMMWY1TiQYY4wxZjVOJBhjjDFmNU4kGGOMMWY1TiQYY4wxZjVOJBhjjDFmNU4kGGOMMWY1TiQYY4wxZrX/BxxurwVsRGULAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KRiecuiHJObc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6662db4-54f4-4e0d-80f1-91cb11703162"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            1           2           9           10          11          12  \\\n",
            "1972-07-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-08-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-09-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-10-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-11-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "...         ..         ...         ...         ...         ...         ...   \n",
            "2022-12-01 NaN  634.049268 -382.160151 -369.020174 -394.141030 -393.667876   \n",
            "2023-01-01 NaN  633.485247 -381.745248 -368.600535 -394.125786 -393.539244   \n",
            "2023-02-01 NaN         NaN -381.102402 -368.072763 -394.099002 -393.265714   \n",
            "2023-03-01 NaN         NaN -380.287850 -367.515338 -394.062188 -392.853863   \n",
            "2023-04-01 NaN         NaN -379.084980 -366.822338 -394.004459 -392.170874   \n",
            "\n",
            "                    13         14          15          16  ...          29  \\\n",
            "1972-07-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-08-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-09-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-10-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-11-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "...                ...        ...         ...         ...  ...         ...   \n",
            "2022-12-01 -375.514171  525.04917  514.658580  742.864235  ...  779.430326   \n",
            "2023-01-01 -375.554709        NaN  514.533702  743.063141  ...  779.480518   \n",
            "2023-02-01 -375.618650        NaN  513.949452  743.341043  ...  779.565738   \n",
            "2023-03-01 -375.694054        NaN  513.104639         NaN  ...         NaN   \n",
            "2023-04-01 -375.794348        NaN         NaN         NaN  ...         NaN   \n",
            "\n",
            "            31          33          34          35          36          37  \\\n",
            "1972-07-01 NaN         NaN         NaN         NaN         NaN  986.820000   \n",
            "1972-08-01 NaN         NaN         NaN         NaN         NaN  986.770000   \n",
            "1972-09-01 NaN         NaN         NaN         NaN         NaN  986.720000   \n",
            "1972-10-01 NaN         NaN         NaN         NaN         NaN  986.702086   \n",
            "1972-11-01 NaN         NaN         NaN         NaN         NaN  986.690000   \n",
            "...         ..         ...         ...         ...         ...         ...   \n",
            "2022-12-01 NaN -162.375190 -138.724248 -222.516347  782.812866  930.766420   \n",
            "2023-01-01 NaN -162.469035 -138.529856 -221.425948  782.732320  932.332471   \n",
            "2023-02-01 NaN         NaN         NaN         NaN  782.638230  934.217521   \n",
            "2023-03-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "2023-04-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "\n",
            "            38          39          40  \n",
            "1972-07-01 NaN         NaN         NaN  \n",
            "1972-08-01 NaN         NaN         NaN  \n",
            "1972-09-01 NaN         NaN         NaN  \n",
            "1972-10-01 NaN         NaN         NaN  \n",
            "1972-11-01 NaN         NaN         NaN  \n",
            "...         ..         ...         ...  \n",
            "2022-12-01 NaN  783.436119 -372.388994  \n",
            "2023-01-01 NaN  783.489577 -372.382655  \n",
            "2023-02-01 NaN  783.576110 -372.374394  \n",
            "2023-03-01 NaN         NaN -372.365087  \n",
            "2023-04-01 NaN         NaN -372.352527  \n",
            "\n",
            "[610 rows x 29 columns]\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Running simple pchip temporal interpolation**\n",
        "well_interp_df = interp_well(wells_df, gap_size, pad, spacing)\n",
        "well_interp_df.dropna(thresh=min_samples, axis=1, inplace=True)\n",
        "print(well_interp_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mOXbYEhnKHM2"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Running machine learning algorithm to fill gaps**\n",
        "# combine the  data from the wells and the satellite observations  to a single dataframe (combined_df)\n",
        "# this will have a row for every measurement (on the start of the month) a column for each well,\n",
        "# and a column for pdsi and soilw and their rolling averages, and potentially offsets\n",
        "combined_df = pd.concat(\n",
        "    [well_interp_df, gldas_df], join=\"outer\", axis=1, sort=False\n",
        ")\n",
        "combined_df.dropna(\n",
        "    subset=names, inplace=True\n",
        ")  # drop rows where there are no satellite data\n",
        "combined_df.dropna(how=\"all\", axis=1, inplace=True)\n",
        "\n",
        "norm_df = norm_training_data(combined_df, combined_df)\n",
        "norm_df.dropna(how=\"all\", axis=1, inplace=True)\n",
        "well_names = [col for col in well_interp_df.columns if col in norm_df.columns]\n",
        "imputed_norm_df = impute_data(norm_df, well_names, names)\n",
        "ref_df = combined_df[well_names]\n",
        "imputed_df = renorm_data(imputed_norm_df, ref_df)\n",
        "\n",
        "imputed_well_names = imputed_df.columns  # create a list of well names\n",
        "loc_well_names = [(strg.replace(\"_imputed\", \"\")) for strg in imputed_well_names]  # strip off \"_imputed\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(well_interp_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjrHar8VNzc0",
        "outputId": "cb58e6c0-5c6b-4d79-986a-2c0e1c0f3760"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             1           2           9          10          11          12  \\\n",
            "1972-07-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-08-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-09-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-10-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "1972-11-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "...         ..         ...         ...         ...         ...         ...   \n",
            "2022-12-01 NaN  634.049268 -382.160151 -369.020174 -394.141030 -393.667876   \n",
            "2023-01-01 NaN  633.485247 -381.745248 -368.600535 -394.125786 -393.539244   \n",
            "2023-02-01 NaN         NaN -381.102402 -368.072763 -394.099002 -393.265714   \n",
            "2023-03-01 NaN         NaN -380.287850 -367.515338 -394.062188 -392.853863   \n",
            "2023-04-01 NaN         NaN -379.084980 -366.822338 -394.004459 -392.170874   \n",
            "\n",
            "                    13         14          15          16  ...          29  \\\n",
            "1972-07-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-08-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-09-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-10-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "1972-11-01         NaN        NaN         NaN         NaN  ...         NaN   \n",
            "...                ...        ...         ...         ...  ...         ...   \n",
            "2022-12-01 -375.514171  525.04917  514.658580  742.864235  ...  779.430326   \n",
            "2023-01-01 -375.554709        NaN  514.533702  743.063141  ...  779.480518   \n",
            "2023-02-01 -375.618650        NaN  513.949452  743.341043  ...  779.565738   \n",
            "2023-03-01 -375.694054        NaN  513.104639         NaN  ...         NaN   \n",
            "2023-04-01 -375.794348        NaN         NaN         NaN  ...         NaN   \n",
            "\n",
            "            31          33          34          35          36          37  \\\n",
            "1972-07-01 NaN         NaN         NaN         NaN         NaN  986.820000   \n",
            "1972-08-01 NaN         NaN         NaN         NaN         NaN  986.770000   \n",
            "1972-09-01 NaN         NaN         NaN         NaN         NaN  986.720000   \n",
            "1972-10-01 NaN         NaN         NaN         NaN         NaN  986.702086   \n",
            "1972-11-01 NaN         NaN         NaN         NaN         NaN  986.690000   \n",
            "...         ..         ...         ...         ...         ...         ...   \n",
            "2022-12-01 NaN -162.375190 -138.724248 -222.516347  782.812866  930.766420   \n",
            "2023-01-01 NaN -162.469035 -138.529856 -221.425948  782.732320  932.332471   \n",
            "2023-02-01 NaN         NaN         NaN         NaN  782.638230  934.217521   \n",
            "2023-03-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "2023-04-01 NaN         NaN         NaN         NaN         NaN         NaN   \n",
            "\n",
            "            38          39          40  \n",
            "1972-07-01 NaN         NaN         NaN  \n",
            "1972-08-01 NaN         NaN         NaN  \n",
            "1972-09-01 NaN         NaN         NaN  \n",
            "1972-10-01 NaN         NaN         NaN  \n",
            "1972-11-01 NaN         NaN         NaN  \n",
            "...         ..         ...         ...  \n",
            "2022-12-01 NaN  783.436119 -372.388994  \n",
            "2023-01-01 NaN  783.489577 -372.382655  \n",
            "2023-02-01 NaN  783.576110 -372.374394  \n",
            "2023-03-01 NaN         NaN -372.365087  \n",
            "2023-04-01 NaN         NaN -372.352527  \n",
            "\n",
            "[610 rows x 29 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ### **Repeat machine learning without PDSI dataset (JONES EDIT)**\n",
        "\n",
        "# Make a copy of norm_df\n",
        "norm_df2 = norm_df.copy()\n",
        "\n",
        "# Drop the PDSI columns\n",
        "norm_df2.drop(columns=['pdsi_filled', 'pdsi_filled_yr01', 'pdsi_filled_yr03', 'pdsi_filled_yr05', 'pdsi_filled_yr10'], inplace=True)\n",
        "names2 = ['soilw', 'soilw_yr01', 'soilw_yr03', 'soilw_yr05', 'soilw_yr10']\n",
        "\n",
        "# Impute data\n",
        "imputed_norm_df2 = impute_data(norm_df2, well_names, names2)\n",
        "imputed_df2 = renorm_data(imputed_norm_df2, ref_df)\n",
        "\n",
        "print(imputed_df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzyN1zOmLzF5",
        "outputId": "fe833361-30cc-4107-c575-d0659ab787bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     1           2           9          10          11  \\\n",
            "1957-12-01 -352.417355  635.090839 -366.471667 -377.488823 -388.455766   \n",
            "1958-01-01 -352.165274  635.001683 -366.914893 -376.877824 -388.411806   \n",
            "1958-02-01 -352.091864  634.980965 -367.246942 -376.586794 -388.352145   \n",
            "1958-03-01 -352.280750  635.136750 -367.319192 -377.097738 -388.360496   \n",
            "1958-04-01 -352.391804  635.334118 -367.352728 -377.884163 -388.432989   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2020-08-01 -356.035934  636.694499 -373.629425 -377.798164 -390.851339   \n",
            "2020-09-01 -356.327773  636.696578 -373.610285 -377.889048 -390.867636   \n",
            "2020-10-01 -356.510676  636.700167 -373.603688 -378.030548 -390.887086   \n",
            "2020-11-01 -356.532831  636.639621 -373.696577 -378.068522 -390.881388   \n",
            "2020-12-01 -356.469116  636.554727 -373.822663 -378.064894 -390.848264   \n",
            "\n",
            "                    12          13          14          15          16  ...  \\\n",
            "1957-12-01 -379.239822 -379.573827  511.337305  510.656993  740.372763  ...   \n",
            "1958-01-01 -379.045967 -379.903226  511.497576  511.370447  740.336011  ...   \n",
            "1958-02-01 -378.957553 -379.962998  511.625231  511.690147  740.544601  ...   \n",
            "1958-03-01 -379.326213 -379.288253  512.049227  511.376745  741.338038  ...   \n",
            "1958-04-01 -379.727028 -378.356685  512.648515  511.251288  742.190744  ...   \n",
            "...                ...         ...         ...         ...         ...  ...   \n",
            "2020-08-01 -387.103387 -370.006356  527.154795  513.905028  749.691969  ...   \n",
            "2020-09-01 -387.148393 -369.992582  527.112913  513.459251  749.745683  ...   \n",
            "2020-10-01 -387.162215 -369.933677  527.139702  513.218624  749.779350  ...   \n",
            "2020-11-01 -387.004963 -370.017674  527.154481  513.424399  749.633125  ...   \n",
            "2020-12-01 -386.745920 -370.177997  527.116236  513.779601  749.430948  ...   \n",
            "\n",
            "                    29          31          33          34          35  \\\n",
            "1957-12-01  775.655573  419.402158 -161.404016 -137.515135 -216.529537   \n",
            "1958-01-01  775.187401  418.766633 -161.409008 -137.560314 -216.613585   \n",
            "1958-02-01  775.004916  433.244915 -161.411240 -137.564532 -216.672116   \n",
            "1958-03-01  775.616456  451.460608 -161.408167 -137.459760 -216.778416   \n",
            "1958-04-01  776.436532  475.647734 -161.405366 -137.332969 -216.948669   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2020-08-01  785.473050   72.000431 -161.415771 -136.521510 -219.228745   \n",
            "2020-09-01  785.500425   74.853209 -161.413644 -136.514151 -219.149014   \n",
            "2020-10-01  785.551910   78.744632 -161.412411 -136.507805 -219.103623   \n",
            "2020-11-01  785.422929   87.370558 -161.413941 -136.533426 -219.084794   \n",
            "2020-12-01  785.185758  101.494655 -161.416432 -136.570903 -219.071235   \n",
            "\n",
            "                    36          37           38          39          40  \n",
            "1957-12-01  793.981880  978.501614  1008.946120  775.099406 -362.783394  \n",
            "1958-01-01  793.424005  978.110598  1008.901644  774.525728 -363.250827  \n",
            "1958-02-01  793.263761  977.914320  1008.854598  774.519560 -363.536457  \n",
            "1958-03-01  793.967234  977.994976  1008.818531  776.025977 -363.562494  \n",
            "1958-04-01  795.073015  978.287485  1008.776796  778.011287 -363.468136  \n",
            "...                ...         ...          ...         ...         ...  \n",
            "2020-08-01  795.595191  959.483956  1008.489793  794.309346 -371.774977  \n",
            "2020-09-01  795.686534  959.756343  1008.496132  794.329543 -371.785854  \n",
            "2020-10-01  795.834118  960.022414  1008.500159  794.435050 -371.769599  \n",
            "2020-11-01  795.865953  959.836271  1008.502505  794.218744 -371.745769  \n",
            "2020-12-01  795.849872  959.535074  1008.500194  793.850082 -371.702966  \n",
            "\n",
            "[757 rows x 29 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NcVDS0wWDmrb"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Merging measurement, pchip, and machine learning dataframes**\n",
        "#formating column names to match so that the dataframes can be combined\n",
        "well_interp_columns = []\n",
        "for i in range(len(well_interp_df.columns)) : well_interp_columns.append(str(well_interp_df.columns[i]))\n",
        "well_interp_df.columns = well_interp_columns\n",
        "\n",
        "imputed_columns = []\n",
        "for i in range(len(imputed_df.columns)) : imputed_columns.append(str(imputed_df.columns[i]))\n",
        "imputed_df.columns = imputed_columns\n",
        "\n",
        "#replacing the machine learning values with pchip values when available\n",
        "replaced_df = well_interp_df.combine_first(imputed_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ### **Create a second merged set using non-PDSI results (JONES EDIT)**\n",
        "replaced_df2 = well_interp_df.combine_first(imputed_df2)\n",
        "print(replaced_df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztX6hoyuWDqF",
        "outputId": "5e2cb351-edaa-43b0-be1a-0ca04b6c9cd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     1           2           9          10          11  \\\n",
            "1957-12-01 -352.417355  635.090839 -366.471667 -377.488823 -388.455766   \n",
            "1958-01-01 -352.165274  635.001683 -366.914893 -376.877824 -388.411806   \n",
            "1958-02-01 -352.091864  634.980965 -367.246942 -376.586794 -388.352145   \n",
            "1958-03-01 -352.280750  635.136750 -367.319192 -377.097738 -388.360496   \n",
            "1958-04-01 -352.391804  635.334118 -367.352728 -377.884163 -388.432989   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2022-12-01         NaN  634.049268 -382.160151 -369.020174 -394.141030   \n",
            "2023-01-01         NaN  633.485247 -381.745248 -368.600535 -394.125786   \n",
            "2023-02-01         NaN         NaN -381.102402 -368.072763 -394.099002   \n",
            "2023-03-01         NaN         NaN -380.287850 -367.515338 -394.062188   \n",
            "2023-04-01         NaN         NaN -379.084980 -366.822338 -394.004459   \n",
            "\n",
            "                    12          13          14          15          16  ...  \\\n",
            "1957-12-01 -379.239822 -379.573827  511.337305  510.656993  740.372763  ...   \n",
            "1958-01-01 -379.045967 -379.903226  511.497576  511.370447  740.336011  ...   \n",
            "1958-02-01 -378.957553 -379.962998  511.625231  511.690147  740.544601  ...   \n",
            "1958-03-01 -379.326213 -379.288253  512.049227  511.376745  741.338038  ...   \n",
            "1958-04-01 -379.727028 -378.356685  512.648515  511.251288  742.190744  ...   \n",
            "...                ...         ...         ...         ...         ...  ...   \n",
            "2022-12-01 -393.667876 -375.514171  525.049170  514.658580  742.864235  ...   \n",
            "2023-01-01 -393.539244 -375.554709         NaN  514.533702  743.063141  ...   \n",
            "2023-02-01 -393.265714 -375.618650         NaN  513.949452  743.341043  ...   \n",
            "2023-03-01 -392.853863 -375.694054         NaN  513.104639         NaN  ...   \n",
            "2023-04-01 -392.170874 -375.794348         NaN         NaN         NaN  ...   \n",
            "\n",
            "                    29          31          33          34          35  \\\n",
            "1957-12-01  775.655573  419.402158 -161.404016 -137.515135 -216.529537   \n",
            "1958-01-01  775.187401  418.766633 -161.409008 -137.560314 -216.613585   \n",
            "1958-02-01  775.004916  433.244915 -161.411240 -137.564532 -216.672116   \n",
            "1958-03-01  775.616456  451.460608 -161.408167 -137.459760 -216.778416   \n",
            "1958-04-01  776.436532  475.647734 -161.405366 -137.332969 -216.948669   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2022-12-01  779.430326         NaN -162.375190 -138.724248 -222.516347   \n",
            "2023-01-01  779.480518         NaN -162.469035 -138.529856 -221.425948   \n",
            "2023-02-01  779.565738         NaN         NaN         NaN         NaN   \n",
            "2023-03-01         NaN         NaN         NaN         NaN         NaN   \n",
            "2023-04-01         NaN         NaN         NaN         NaN         NaN   \n",
            "\n",
            "                    36          37           38          39          40  \n",
            "1957-12-01  793.981880  978.501614  1008.946120  775.099406 -362.783394  \n",
            "1958-01-01  793.424005  978.110598  1008.901644  774.525728 -363.250827  \n",
            "1958-02-01  793.263761  977.914320  1008.854598  774.519560 -363.536457  \n",
            "1958-03-01  793.967234  977.994976  1008.818531  776.025977 -363.562494  \n",
            "1958-04-01  795.073015  978.287485  1008.776796  778.011287 -363.468136  \n",
            "...                ...         ...          ...         ...         ...  \n",
            "2022-12-01  782.812866  930.766420          NaN  783.436119 -372.388994  \n",
            "2023-01-01  782.732320  932.332471          NaN  783.489577 -372.382655  \n",
            "2023-02-01  782.638230  934.217521          NaN  783.576110 -372.374394  \n",
            "2023-03-01         NaN         NaN          NaN         NaN -372.365087  \n",
            "2023-04-01         NaN         NaN          NaN         NaN -372.352527  \n",
            "\n",
            "[785 rows x 29 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(imputed_df2.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fqlMWR3Vcc7",
        "outputId": "6232da19-9b93-4d50-933e-561e49336990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['1', '2', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18',\n",
            "       '21', '22', '23', '24', '26', '27', '28', '29', '31', '33', '34', '35',\n",
            "       '36', '37', '38', '39', '40'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Rei8iiYcSVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "a98fc97f84544780a9ec327a99502ed3",
            "a804844d6d1648719726f10fe711862f",
            "6a1d0ac8486f41d5b020293b3e1b4864",
            "754baf2ab29749c6903af30fac223d02",
            "209cd7d9d43d4396b9432fac9ed2a681"
          ]
        },
        "outputId": "fea438cc-0e59-4ff6-8a50-654a64b2388c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Select the well index you wish to plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Well Index', options=('1', '2', '9', '10', '11', '12', '13', '14', '15…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a98fc97f84544780a9ec327a99502ed3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Run Cell and Select the Well you Wish to Plot**\n",
        "pList = imputed_df.columns\n",
        "p_index = widgets.Dropdown(options=pList, description = \"Well Index\")\n",
        "\n",
        "pItems = [p_index]\n",
        "print(\"\\nSelect the well index you wish to plot\")\n",
        "widgets.GridBox(pItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11LdQpfzWhH5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "outputId": "a06a9e96-1e54-4d42-fac8-7f7aa3488c4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ac802532dd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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tzWqzqveM3to2f5v2fraXkLwTObT4kI4sPSJbgE1TfjfF4+dPGOrcT16wpUCGYbT7aC6gqyneUyzJubvN5m9rt+tEpkfK6mdVQ02DjuUeU3hKeLtdCwAAAABgPlcXecbUDPkH+3fINeMHOfeed4Vx61VFVdr1n13a9Z9dOvztYdlr7Y2+bvWzKqZvjCJ7RCoiLUIhsSEKigqSxWpR/qZ8bXl9iz679zN1i++m/lf3N+lZoDMgJAcAqKG2QQcWHZAk9b/K3F8sLBaLZr44U3kb81S0q0jvXPWOu5NbkroldOvwcTq9L3OG5Ps+26dpj03r0Guj/Wx4aYMkaeSckYpIjfD4+eMGxclitaiqqEqV+ZUKSwrz+DWArqw8s1ySFJHu+e/fU1n9rIrsEamSfSUq2V9CSA4AAAAAndz+hc6QvNeMXh12zfjBJ0PyzthsYa+3a9/n+7Tp5U3at3CfDLvh/lpEWoR6XNhDqZNSlTw22XkzfEDTN8MbhiGrv1WbXt6kf9/0b92z5R7F9ovtqKeBToaQHACg7NXZqq+qV7eEbu2617W5AkIDdPt3t2vlUyu1/oX1qi6pltXPqowLMnTh/114xl+S2kvvGb1lsVpUuK1Q5Znlikhr30AG7a+hpsH9gmf4bcPb5Rr+wf6K6Rejol1Fyt+cT0gOeFjZkTJJ7R+SS86R666QPGNqRrtfDwAAAABgjrrKOmUuy5Qk9bm0Y/aRS1JM3xhZ/ayqrajVsZzOM8WsrrJOG17aoFV/XaVjOcfcn08amaSB1w9Uvyv7KXZAbLNvCrBYLJr5wkyVHSrTocWHtOqpVbripSvaq3x0coTkAAAd+uaQJKnHhT285i7FkNgQTXtsms77zXnK25CnhKEJCo4ONqeWmBCljE9R1sosHfrmkIb/aLgpdcBzDi4+qLrKOoWnhCtpVFK7Xaf7qO4q2lWk7NXZHfrCCugKKjIrJKlDblxy7SUv3lfc7tcCAAAAAJjn0JJDstfZFdkjUtF9ojvsurYAm6L7RKtoV5EKtxf6fEheVVSlNc+u0dpn16qmtEaSFBIXouE/Gq4Rd45oU/e31c+qqb+fqkOLD2nLG1t04Z8uVLe4bh6qHF2J1ewCAADmO/zNYUnOkNzbBIYFKmNqhmkBuUviSGeH/dFdnX8vUFew+8PdkqR+V/dr1xtDUienSpL7DmQAnuPqJI9Mj2z3a7lC8tL9pe1+LQAAAACAeVz7yHvP6N3hzUSukeuFOwo79LqeVFdZp8W/Xayn05/Wd3/4TjWlNYruE60rXr5CD2U9pIufvNgj49FTJzpHs9tr7Vr/wnoPVI6uiJAcALq4uuN1yl6dLUnqcZH3heTeIqZvjCSpeA9dhL7OYXdoz4I9kqT+V/dv12uln5cuybnSwF5vb9drAV2Neyd5B3aSl+wvafdrAQAAAADMc2DRAUnOkLyjxQ2Kk+TcS+5rDIehLW9s0bN9n9XyR5ervqpeiSMSdd171+n+Xfdr5J0j5RfoueHWFotF4x8eL0la9/w6NdQ0eOzc6DoYtw4AbbTt7W1a9LNF6pbQTbH9YtXv6n4acvMQWazeMbb8XDKXZcrR4FBkRqSiekSZXY7Xct3hSEju+7JWZqnqaJWCooKUPiW9Xa8V2z9WwdHBqi6pVt7GPKWMS2nX6wFdhaPB4d5l1tEhuWEYXrOaBAAAAADgOWWHy1R6oFQWm0UZUzM6/Prxg5yd5Ed3+lZInr06W1/89AvlrM2RJEX1jNLFf7lY/a/u366vnwfOGqiv075WeWa5tr29TSNuH9Fu10LnRCc5ALRBVXGVFv5koY4XHlfhtkLt/PdOfXjLh/rnyH/q4OKDZpfXLK595BkXZphbiJeL6efsJC85UCJHg8PkatAWrlHrfWf2lc3f1q7XslgtSpucJknKXM7IdcBTKnIqZDgMWf2tCk0MbffrRfaIlMVmUX1VvTucBwAAAAB0Lq73c5PHJiswPLDDrx8UFSRJqj9e3+HXbq11L6zTq5NeVc7aHAWEBuiixy/SfTvv04BrBrT7DeZWP6tG3TNKkrTtzW3tei10ToTkANAGS/5niapLqhU/JF4/+OwHmvI/UxQYEaiCLQV685I3tfvj3Y2Ory6t1ppn1yhrZZZJFZ/OFZL3vKinyZV4t4jUCPkF+8lR71DZ4TKzy0EbHPzK+YKn31X9OuR6aeedCMnZSw54jHvUempEh0xusfnbFNXTOW2leC8TRQAAAACgMzq0+MT7pNPMeZ/U6ueM7Bx272/QMQxDX/3qK31+3+cyHIYG3zRYD+x7QJN/PdmjY9XPZfBNgyVJh789rMqCyg67LjoHQnIAaKWCrQXa8OIGSdKlz1yqPpf10QW/v0APHnhQg28eLMNh6IObP1DWyizlrs/V1498rafTn9YXD36heefN07LHlslwGKY+B9cIaEnKuCDD1Fq8ncVqUUwfZzd50Z4ik6tBa9Udr3OPrEqdkNoh1zy1k9wwzP2eBzqLjtxH7uJau8G/AQAAAADQ+RiG4W4m6nFRD1NqcIfkPjDFcv/C/Vr555WSpAv+eIGunX9th0x6+76oHlFKHpssw2Fo5793dvj14dsIyQGglRb/ZrEMh6GB1w9stKMmJCZE17xxjfpc1kcN1Q16ddKrmjtmrlY8vkJ1x+oUlhwmw2Hom998ow9u/sDU0Cx7TbZkOEeJhyWFmVaHr4jp6wzJ2Uvuu/I358twGApNClVY9475O580Mkl+wX6qLq5W0W7CNcATyo+cCMnTOy4kj+7r3EtOJzkAAAAAdD5HdxzV8YLj8gv2U8r4FFNqsNp8JyR3BdKj7hmlKb+b0u6j1c9m0I2DJEk73t1hWg3wTYTkANAKDTUNOvi1c2Tz+f97/mlft/pZdd2716n7mO6SpMDwQPWd2Vc3fnijHsp8SDNfmimrv1U73tuhnDU5HVr7qQq2FkiSkkYkmVaDL3HtJaeL0HflbXBOTug+unuHXdMWYFPKOOeLK0auA55hZid5yd6SDrsmAAAAAKBjuN7rTT8vvUPHhZ/KVzrJHXaH9n66V5I06PpBJlcjDbrBWUPmskxVZFeYXA18CSE5ALRC9ups2WvtCk0KVdzAuCaPCQgN0I+W/kj37bhPvyr5lW7+5Gb1v7q/LFaLRt09Sv2udO5DPvDVgY4svZHCrYWSpPih8abV4EtcITkBie/KXZ8rSUoa1bE3hqRNcY5cd73gAtA27pC8AzvJXdNEuFEKAAAAADof1z5ys0atS74TkmevzlbV0SoFRQYp7bw0s8tReEq4e93hjvfpJkfzEZIDQCscWuL8pSljasZZR8n4B/srbmCce1TOqXpd0kuSdPAr80IzVyd5wtAE02rwJeyj9X2ukLwjO8klqe/lfSVJ+7/Yr4bahg69NtAZucetd2AnuetGqbJDZbLX2TvsugAAAACA9uVocOjw0sOSpJ7TeppWhyskN+zmredsjj0f75Ek9bm8j2z+NpOrcWLkOlqDkBwAWuHIt0ckSRkXZLT6HD0vdv7Clb0qW7XHaj1RVovY6+zu/ciE5M3j6iKszKtUbUXH/3+GtqmrrHP/ne8+qmND8u6juys0KVR1x+p0+NvDHXptoLMxDMOUceuhiaEKCA2Q4TBUcoCJIgAAAADQWeSsy1HdsToFRwcrcXiiaXX4Sie5KyTvd1U/kys5acCsAZKknDU5OpZ7zORq4CsIyQGgheqr65W9OluSs5O8taJ6RCmqV5TzTkUTQrOi3UVyNDgUFBmk8JTwDr++LwqKDFK3+G6SpCPLjujjOz/W/i/2m1wVmitvU55kSGHJYQpNDO3Qa1usFvW9wtlNvmfBng69NtDZ1JTVqK6yTlLHhuQWi8XdTV68t7jDrgsAAAAAaF+uUesZF2TIYj3z1ND2ZrE5r+3NIXnR7iIV7y2WLcCm3jN6m12OW1hSmFLGp0iSdn+82+Rq4CsIyQGghbJXZcteZ1dY9zBF945u07lc3eRmjFw/ddT62UbGozFXQPLBTR9o86ub9eUvvjS5IjSXWaPWXfpd6by7du+CvTIM7x6bBXgz16j1kLgQ+Qf7d+i1XRNFivcQkgMAAABAZ3Hwa+d7s2buI5d8o5Pc1fzR48IeCgwLNLmaxvpd7Xzvbc9HNKigeQjJAaCF3PvILzj7PvLm6HWxeXvJXSF5/JD4Dr+2L3OF5K4uxqM7jqq6pNrMktBMeRvyJElJo5JMuX7Pi3rKP8RfFdkVyt+Ub0oNQGdgxqh1FzrJAQAAAKBzqa+qV/Yq59TQnheZt49c8o2QPGtlliSp1/ReJldyugHXOEeuH/rmkGrKakyuBr6AkBwAWsi9j7wNo9ZdelzYQxarRUW7i1SeVd7m87XEqZ3kaL7Y/rGSJFuATSGxIZJO/nKIMzMMQ9vf3a55U+Zp3QvrZDg6vpPa7E5yvyA/9wsIxj4BrecKySPTIzv82nSSAwAAAEDnkrk8U/Y6u8JTwhXdp21TQ9vKF0Jy13vKZu5uP5OYvjGKHRArR4NDez/ba3Y58AGE5ADQAvVV9cpec2If+QUZbT5fUGSQkscmSzo51qejFG4rlERI3lJDZw9V/2v66/p/X+8e4XNk2RGTq/I+jgaHXh73sp6IekIf3/mx3r/+fX1w0wfKXJapz+/7XG9c9IZKD5V2WD11lXXuUKv7KHNCcqnxyHUArVO8z/m9HJHe8Z3ksf2cN0rRSQ4AAAAAncPBxc73ZHtO62n6SkpXSG44DK9c1VdbUauyQ2WSvHc6af9r+kti5Dqah5AcAFqgYFuBHPUOdYvvpqieUR45Z/r56ZLkHuvTEaqKqnQs95gkKX6wd/5C461CE0N1439uVL8r+iltcpokKWs5neTft/uj3cpZm6OashptfnWzdn2wS1Y/q4beOlT+If46/O1hvXvNux1WT9mRMknOG1O6xXfrsOt+X5/L+0gWKX9zvo7lHTOtDsCX5azJkWTOVAhXV8HxwuOMbgMAAACATuDQYudqTbP3kUuS1XYysjPs3heSF253Nl2FJYcpJCbE5Gqa1v9qZ0i+b+E+1VfXm1wNvB0hOQC0gHtE+bAEj91ZmDzO2UnuetO/IxRscz6PqF5RCggN6LDrdjaukDxnXQ6/dH3PmmfWSJKG3jJUI+eM1KAbB+muNXfpmjeu0T1b7pHFZlHBlgL32OT2VpFdIUkKTw3vkOudSbe4bu5O9gNfHjC1FsAXNdQ2KH9TviQpZXxKh18/MCxQYclhkqSjO492+PUBAAAAAJ5TXVKtvI15kpxrMc3m6iSXvHPkuus95YQh3juZtPvo7opIi1D98XrtX7jf7HLg5QjJAaAF2mOPd8o455v8hdsLVVdZ57Hzno37eXjxLzS+IKpnlEKTQuWodyh3Xa7Z5XiNvE15ylyWKaufVRc9fpGu+OcVuu6d65Q0MkmSFN072h0UH156uENqqsg6EZKnmBuSS1KvGc695Ae+ICQHWipvY57sdXZ1i++myB6RptTg2ruWu4Gf+wAAAADgyw5/e1gypNgBsQrrHmZ2Od4fkp94Tzl+qPdOJrVYLBp4/UBJ0o73dphcDbwdITkAtEDh1hN7vId5LlwO6x6m8NRwGQ5Dues75g33/I3OLjxPPo+uyGKxuLvJM5dnmlyN91j77FpJ0sDrBio8uelQOn2qc83A4SWHO6Qmdye5F4TkvWf0luTsJHfYve8FD+DNslc7V5OkjE8xbVeca8x73oY8U64PAAAAAPCMw98eliRlXJBhah0u3h6Su98b92ADWXsYdOMgSdLeT/aqvso7p3/WVtSqobbB7DK6PEJyAGgmwzDapZNcOjky1vXmf3vLXuO8jmvUO1rPHZIv63wh+aZ5m7Tg7gXN3rtbmV+pja9s1Lb52yRJYx8ce8Zje1zgHGHlejHS3rxl3LrknB4RFBmk6pJqJhAALZSz2rmaJHm8ef9+JY1yTsUgJAcAAAAA3+Z6X8r1PpXZGoXkXtZY0Z7vjXta99HdFZkRqfqqeu1buM/schoxHIa2vLFFz/V7Tqv+usrscro8QnIAaKaK7ArVlNXI6mdVbP9Yj567I/eS15TVqHhPsfO6YwjJ28oVkmetzPK6X17boqqoSp/d+5k2vbxJb1z0hqqKqyRJ9dX1qi6tVlVxlYp2F+nAlwf03f99p5fHvaynkp7SJ3d9InutXSkTUs66Lzh1UqosNovKDpWp7EhZuz8fbxq3bvWzqufFPSVJ+79gNxLQEqd2kpvFtS7i6M6jqjveMWtSAAAAAACeVVVcpcJtzs7o9CnpJlfjZLFapBND07ytk7w8s1y1FbWy+lsV28+z7417msVi0cAbnCPXd7630+RqTsrdkKtXJ7+qj277SJX5ldr5/k4ZDsPssrq0FofkOTk5uuWWWxQTE6Pg4GANGTJE69evd3/dYrE0+fHnP//ZfUxJSYlmz56t8PBwRUZG6s4771RlZaVnnhEAtBPXnXKx/WPlF+jn0XO79pJnr86WYbTvP4yuke5RPaMUEhvSrtfqChKGJsgv2E+1FbUq2V9idjkes/GVjbLX2iU5dwDPHTNXzw98Xo92e1RPRj+pP8f+Wc8PeF5vTn9TS/57iXLWOm/w6D66u6b+fqpu/uTms45CDgwLdI8sPrL0SLs/H1cneURqRLtfqzlcI9cJyYHmO5Z7TOWZ5bJYLabe5BXWPUyhSaEyHIYKthSYVgcAAAAAoPWOfOd8PypuYJy6xXczuZqTrDZnbOdtIfmp743bAmwmV3Nug244MXL9072m3+BeVVSlT378ieaOmavsVdkKCA3QtCem6a7VdzlvjIBpWpTylJaWatKkSbrgggu0cOFCxcXFad++fYqKinIfk5fXeOzgwoULdeedd2rWrFnuz82ePVt5eXn66quvVF9fr9tvv11z5szR/Pnz2/h0AKD9tOc4maSRSbL6WVWZX6mKrApFpLVfkOcKM5PH0kXuCVY/q+IHxyt3Xa4KthR4/Z2UzeGwO7T+H84b4CY/MlmbX9usskNlpx0XEBagyPRIRfeJVu9Le6vv5X0V1j2s2dfJuCBDOWtydHjJYQ374TBPld+k8qxySd7RSS5Jvab3kuT8fqwsqFRoQqjJFQHez9VFHj8kXgGhAabW0n1Ud+39dK9yN+QqdWKqqbUAAAAAAFrO1bSRfr53dJG7WP2scjQ4vC4kd3Xde/uodZekkUmK6hml0oOl2vWfXRp2a/u+99iUquIqrfrrKq19Zq3qKp1B/ZDZQ3Txkxe36D1UtJ8WheRPPPGEUlNTNW/ePPfnevRovKshMTGx0X9//PHHuuCCC9Szp3Os6K5du/TFF19o3bp1Gj16tCTp2Wef1WWXXaa//OUv6t69e6ueCAC0t8Ktzl8E4ofGe/zc/iH+ShiaoLyNecpek92+IfmJke7sI/echGEJyl2Xq/wt+e67FH3Z3k/3qjyzXMExwTr/f87XqB+P0s73dyp2QKy6j+6ukJgQyXLyztbWypiaoRWPr2j3veS1FbWqO+b8RdRbQvLw5HAlj01WztocbX9nu8b/dLzZJQFeL2tVliRzR627JI1O0t5P9ypvPXvJAQAAAMAXud6PypiaYWod3+faS+5tIbmv7CN3sVgsGn7HcC353RKtfWatht4y9KxTLz3p6M6jWvvcWm15Y4vqj9dLcob2M/4+w726E96hRe9uL1iwQKNHj9b111+v+Ph4jRgxQnPnzj3j8QUFBfrss8905513uj+3atUqRUZGugNySZo2bZqsVqvWrFnT5Hlqa2tVUVHR6AMAOlp7/yKQMtH5pv+Od3a0y/klyTAMZa9xduLRSe45icOcN4i5bqTwdeueWydJGnn3SPkF+SkyPVITfzFRfS/vq9CEUFn9rG0OyCUpdYKz+7LscJmqS6rbfL4zcY1aD4oMMr379FRDbx0qSdr6r60mVwK03fGjx7XuhXWqKqpqt2u47vJPnWR+57ZrL3nuhlyTKwEAAAAAtFR1SbX7vV5v7CSXJMPuXbuqfS0kl6RRc0bJFmhT7vpc93S69lK0p0jf/ek7/XPkP/WPQf/Q+hfWq/54vRKHJ+rGj27U3evvJiD3Qi16h/vgwYN64YUX1KdPHy1atEj33nuvHnzwQb3++utNHv/6668rLCxM1157rftz+fn5io9v3IXp5+en6Oho5efnN3mexx57TBEREe6P1FTz3xgD0LU01DSoaE+RpPb7RWD0PaMli7TrP7vcv3R4WkV2hY4XHJfVz6rEEYnnfgCaJWGY8+9E/pam/x3zJWWHy3Tw64OyWC3Ov5PtKDA8UBHpzqkJBdvab6+vt41adxl04yBZ/azK25Cno7uOml0O0Gr11fV6c/qb+vy+z/Xq5FdVkeP5G1prymuUt8HZtd3jgh7nOLr9JY1KkiQV7SoyfbcZAAAAAKBljiw7IhnO/dretgLPGzvJHXaHSvaXSJJiB/jOqslucd005AdDJElr/t50k25r1VfV6+DXB7X4t4v1wpAX9Hz/57Xkd0uUvylfFqtF/a/urx8u/qHmbJyj/lf177AudrRMi0Jyh8OhkSNH6tFHH9WIESM0Z84c3X333XrxxRebPP7VV1/V7NmzFRQU1KYiH3nkEZWXl7s/srKy2nQ+AGipo7uOyrAbCo4Jbrd9IfGD4jXwuoGSpO/+77t2uYZrH3nC0AT5B/u3yzW6IteNExVZFe3aEd0RDi89LMk5zjgyPbLdr5cwxPln59pr1B5cneThqd4VkneL66beM3pLkra+STc5fNfCBxYqf5PzJqHiPcWad948lR4q9eg1jnx3RIbDUHSfaK+44SUsKUxh3cNkOAzlb/b9G6QAAAAAoCtxjVpPn+pdXeSSZLE5w1RvCskrsirkqHfIFmDzitfkLTHuwXGSpJ3/3ul+j7A16qvqdXDxQX3z399o3nnz9Hjk4/rXxf/S8keXq3B7oax+VvWe0VtXvHyFflHwC9344Y3qcWEPwnEv16KQPCkpSQMHDmz0uQEDBigzM/O0Y5ctW6Y9e/borrvuavT5xMREFRY2fiO8oaFBJSUlp+0zdwkMDFR4eHijDwDoSIXbT+wjHxzfrv+wTfnvKZKc/2gX7vB8aOgaK9N9bHePn7srC4oIUmRGpCS12xSAjpK53Plvetp5HTP+J36Ic7qM63usPVRknQjJvfCXeNfI9W1vbpPh8K4xWkBzbPnXFm16ZZMsVouumneVonpFqexQmd648A0dyzvmsescXnJYkpRxQYbHztlWrm5y1w1oAAAAAADf4Frn5W37yCXv7CR3dZFH9YzyyArGjpQ4PFHpU9Jl2I0WNabVVzcRik/7l5b93zJlLs+Uo96h8JRwDb11qK5+/Wr9ovAXmr1wtkbeOVIhsSHt+IzgSX4tOXjSpEnas2dPo8/t3btX6emn323zyiuvaNSoURo2bFijz0+YMEFlZWXasGGDRo0aJUn65ptv5HA4NG7cuJbWDwAdonhvsSTnCJ72lDAkQQNmDdCuD3Zp+aPLde1b1577QS1w8KuDksT+k3aQMDRBZYfLlL8l3yt/wW6urOXOaS0d9XfEHZJ3wU5ySep7RV8FhgeqPLNcR7474tN/d9A1rXpqlSRpyv9M0fAfDVevS3rptfNfU8n+Er116Vv60dIfKSiibVOlpJMhuTeMWnfJmJqhvZ/s1e4Pd2vCQxPMLgcAAAAA0AzVpdXuiWAZ52eYW0wTvDkkj+4dbXIlrXPe787TkUuOaMM/N6j7mO4aeefI046x19mVvTpbh745pEPfHFLOmhzZ6+yNjglPCVfGBRnKmOr8iOwRSae4j2tRSP7QQw9p4sSJevTRR3XDDTdo7dq1eumll/TSSy81Oq6iokLvv/++nnrqqdPOMWDAAM2YMcM9pr2+vl4/+clPdNNNN6l7dzobAXinkr3OXwRi+sa0+7Um/XqSdn2wS7s/2q366nqPjUWvyKlQwZYCySL1nt7bI+fESQnDErRnwR7nn7GPOn70uIp2F0mSUiemdsg13ePWtxfKMIx2+cXSHZJ7YSe5f7C/Bt04SBvnbtSaZ9YQksOnFO0pUsGWAln9rBr3gPNm17DuYbpl0S16ZeIrKthSoHevflezv5gtv8AWvexopKq46uQbGF7UST7w+oH68udfKnN5piqyK7zyZwwAAAAAoLHMZZmSIcX0i1FoonftI5dOCcnt3heSR/WOMrmS1ul1cS9N/cNUffs/3+qzez9T/fF6hSWHqf54vYp2FylvQ56OLDuihuqGRo8LSw5Tjwt6KH1qujKmZiiqZxSheCfTonerxowZow8//FCPPPKI/vCHP6hHjx56+umnNXv27EbHvfPOOzIMQzfffHOT53nrrbf0k5/8RBdddJGsVqtmzZqlZ555pvXPAgDaWdEeZ3DYESF599HdFZEWofLMch1afEh9Z/b1yHn3L9wvSUoZl8LIl3aQMMwZ9vpySJ610tlFHjcwTsHRwR1yzZh+MbL6W1VbUavyzPJ22YPuGrcekRrh8XN7wvifjdfGuRu1+6PdKt5XrJg+7f9zBvCEHe/tkCT1vLhno58ZUT2jNHvhbL12/ms6/O1hfXjrh5r19qxWj2RzjcGLGxin0ATveQMjIjVCaZPTlLk8Uzve30E3OQAAAAD4gMNLD0vyzlHrknd2kpceKJXku53kkjTld1NUuK1QO9/fqS9++kWTx3SL76YeF/ZQxoXOTvHo3tGE4p1ci1s6Zs6cqZkzZ571mDlz5mjOnDln/Hp0dLTmz5/f0ksDgCkMh6GSfSc6yfu1f3hlsVjU98q+WvfcOu3+eLfHQvJ9n+2TJPW+jC7y9pA4LFGSVLijUI4Gh/sXWl/i2keeOrljusglyeZvU2z/WBVuK1ThtsL2Ccm9uJNccgZ/fS7ro32f79Pqp1fr8ucvN7skoJGcdTn6/L7PlTgyUSPvHKnuY7rLYrFox7vOkHzQDYNOe0zSiCTd9NFNenPGm9r5/k51S+imS5+5tFUvLg8tOSTJu7rIXQbdOMgZkr9LSA4AAAAAvuDIt84bsdPPP32NsDdw3WDuTSG5e9x6L98NyS0Wi65+7WpF9ohU4bZC1VbUyi/QTzH9YxQ3ME4ZUzMUNzCOULyL8b138AGggx3LPab6qnpZ/ayKzIjskGv2u7KfJGnvJ3tlOIw2n6+htkEHv3buI+97uWdCdzQW1TNKAaEBstfa3ZMHfE1H7yN3cY1cL9jm+S782opa1VbUSvLekFySJvzCGa5tnrdZVcVVJleDrsIwzv3vS0Ntgz689UPlrs/Vxpc26uVxL+utGW8pc0Wmju44KluATf2v7t/kY3tc2EPX/OsaSdK659Zp+WPLW1Xjoa8Puc/nbQZeN1AWq0U5a3JUeqjU7HIAAAAAAGdRU1bj1fvIJe/rJDcchkoO+PZOchf/EH9d/MTFmv35bN2x/A79cPEPdfnzl2vs/WMVPyiegLwLav1yQADoIor3FktyhqA2f1uHXDPj/AwFhgfqeMFx5azNUcr4lDadL3N5puoq6xSaGKrE4YkeqhKnslgtShyeqMzlmcpZm6P4QfFml9Qi9VX1yt2QK6njQ/L4Ic4/q8JthR4/t6uLPCgySAGhAR4/v6dkTM1Q0sgk5W3M0/oX1mvK76aYXRI6sfrqen18+8fa+e+dCgwPVLe4borqFaWYvjHqltBNgWGBzruoL8jQ8seWq3hPsboldFOvi3tpx/s7dODLAzr87WFJUq/pvRQUGXTGaw2+cbAq8yu16GeL9M1vv1FoYqhG3DGi2bVmr85W0e4i+QX5eWVIHpoYqowLMnRo8SHteHeHJv/XZLNLAgAAAACcQebyTBkOQzF9YxTWPczscprkbSH5sbxjaqhukMVmUUS6d64yBFqLTnIAOIeO3EfuYguwqfelzrHouz/e3ebz7fv8xKj1S3vLYuWOuPbiGlOeuSzT5EpaLmddjhz1DoV1D+uwiQkuHRGSe3MXueQc+TTh585u8rXPrlVDTYPJFaGzqimv0ZvT39SOd3fIsBuqKa1R8d5i7V+4X2v+vkbf/OYbLXxgod646A29Mv4VLXt0mSTp0mcv1TX/ukZ3rb5LYclhstfZJTnHjZ/L+J+O16RfT5IkfTLnE+39dG+z693w4gb3dc4WxpvJ9WfgGj8PAAAAAPBOrhu+vXXUunQyJDfsbZ8u6gmuUeuRGZEd1kAGdBRCcgA4B1cneUfsIz9Vv6ucI9f3fLynzefav3C/JKnPZX3afC6cWfoU5y/YR747YnIlLefaR542Oa3DRwu5xq0X7S5yB2+eUp5VLkkKT/XukFySBl4/UOGp4TpeeFxb39xqdjnohOx1dv1r2r+UuSxTgeGBumXRLbpvx3364eIfauY/Z2rCLyZo+B3DNeDaAfIL9lPOWufNM31n9tXA6wZKkhKHJ+rutXcrdVKqYvvHqv9VTY9a/76LHrtIw24bJsNu6P0b3lfWqqxzPqa6pFrb390uSRp97+jWP/F2NuDaAbL6WZW/Od9n120AAAAAQFdwZKl37yOXvK+T3L2P3MdHrQNNYdw6AJxDyV7nLwId2UkuSX0u7SOrv1VFu4pUuL1Q8YNbN767tqJWRbucb9pnTM3wYIX4vtSJqZJFKj1QqmO5x7x2bFNTXPvIXd3wHSk8NVyBEYGqLa9V0Z4id2juCb7SSS5JNn+bxv10nL76xVda9ddVGnHHCCY/wKM2v75ZuetzFRwdrFu/vlVJI5IkSXED404bZV6ZX6nljztHrV/+wuWNbp4J6x6m25fdLknNvqnGYrHoirlXqOpolfZ9vk9vz3xbty+/XXED4s5ar73WrsThiUoem9zSp9thQmJC1PPintq/cL92vLtD5//P+WaXBAAAAAD4npryGuVtzJPk3e+RWmzO19neEpKXHiiVJEX1ijK5EsDz6CQHgHNwd5J3cEgeFBnk7vze8q8trT5P/uZ8Sc4gMiQ2xCO1oWlBEUHune9HlvlON7nD7lDWSmdI3tH7yCVneOa6CcTTI9crsk6E5D7QSS5Jo+4epcDwQBXtKtK+hfvMLgediL3eruWPLpckTfnvKe6A/ExCE0M14+kZmr1wdpM3mVgslhZPnbD523Tde9cpeVyyqkuq9cZFb+jorqONjqmtqNXSPyzV4t8u1pq/r5EkjbpnVIdPuGipwTcNliRtf3u7DMM7RuIBAAAAAE7KWpElw2Eoune0wpO9930iOsmBjkNIDgBnYa+zq/SQ8265jg7JJWnorUMlSdve2ibD0bo33fM2Oe+QPFcgAs9IO88ZMvvSXvLC7YWqrahVQFiAR7u4W8K1l7xgW4FHz+tLneSSFBgeqJFzRkqSvv2fb1VdWm1yRegstryxRWWHy9QtoZtGzRllWh0B3QL0g09/oPjB8arMq9Rr57+mgq3O73vDMPTRjz7St//7rZY/ulzlR8oVEBqgIT8YYlq9zdXvqn6yBdpUtLvI4zf7AAAAAADazhf2kUuE5EBHIiQHgLMoPVgqw24oIDRAoUmhHX79vjP7KigySMdyjrl/kWup/E3OTvLEkYkerAxn4ot7yV37yFMnpLp/Ee9ornDe453kPhaSS9L4n45XYHig8jbm6eVxL6toNzuO0Tb2eruW/WmZJGniLyfKP8Tf1HpCYkN025LblDQySVVHq/Ta+a9pz4I9Wvf8Ou3+cLes/laNvm+0Rv14lK577zoFhgWaWm9zBEUEqc+lzukvrj3qAAAAAADv4dpH7s2j1iXvCskNwyAkR6dGSA4AZ3HqqHUzRr36Bfpp4A0DJUlb/7W1Vedw7dqhk7xjuMaVF24v9JkuYDP3kbu4Osnba9x6RGqER8/bnsJTwnX7stsVkR6hkn0lenn8y9r/xX6zy4IP2/rmVpUdKlNIXIhG3zPa7HIkOYPyHy7+oVInpqqmrEbvXPWOvvjZF5Kki/98sS5//nLNfHGmO3j2BYNuGiRJ2vHODkauAwAAAIAXqT1Wq9wNuZJ8qJPcbn5IXnW0SnXH6iSLFNWDneTofAjJAeAszNpHfqphtw6TJO38907VV9W36LENNQ06utO57zVxBJ3kHSE0IVQx/WIkw7nryBe4OsnN2Efu4tpJXp5ZrpryGo+cs7aiVrUVtZJ8q5NckhKGJujudXcr7bw01ZbXav7l87Xqb6sI3tBijgZHoy7ygG4BJld0UlBkkG5bcpvGPzxekmTYDfW7sp/GPTjO5Mpap+/MvvIP8VfpwVLlbcgzuxwAAAAAwAlZK7Jk2A1F9Yzy+kYKq817OsnLDpdJksKTw+UX5GduMUA7ICQHgLNwheTRfcwbJ5M6MVWRGZGqq6zTzn/vbNFjC7YVyLAbCokN8bmQ0Je59pK3dkR+RyrPLFdFdoWsflYlj002rY7gqGD339HC7Z7pJneNWg+KDFJAqPcEg83VLa6bfvj1DzXirhEyHIa+fPhL7Xy/ZT8DgK1vbVXpgVKFxIZozH1jzC7nNLYAm6Y/NV23LLpFkx+ZrKtfv9qUyS2eENAtQH2v6CtJ2v5O80euVxVX6bP7PnPvZgcAAAAAeJav7COXvGvcuiskj8yINLUOoL0QkgPAWZRnlkuSIntEmlaDxWrRyLtHSpLWPLOmRZ2k7n3kIxJ9NnTwRb0u7iVJ2vf5PpMrOTdXF3nSyCTTO0w9PXLdF/eRf58twKYrXrpCYx8cK0na/NpmcwuCTzm1i3zCLyaY/j1+Nr0u6aWLHr1IQZFBZpfSJoNvGixJ2vHuDhmO5v17ve75dVr/wnr9+6Z/e8U4PQAAAADobHxlH7nkXSF56aFSSYTk6LwIyQHgLLwlZBt590jZAm3K25Cn7NXZzX5c3ibnuFdGrXesXpf0ktXPqqJdRSo5UGJ2OWeVucIZkqdOMm8fuYsrJC/Y5pluSvf3b6rvhuSSZLFYNOZeZwfwwa8P+syue5hv+zvbVbKvRMHRwV7ZRd4Z9Z7RW4HhgarIrlDWqizVHa/TvoX7ZK+3n/ExOWtzJElFu4q0/e3md6ADAAAAAM6trrJOOeucr7voJG8ZVyd5RIZ3j6gHWouQHADOwhWymb2rpltcNw35wRBJ0tpn1zb7cfkbnZ3kSSOT2qUuNC0oMsg9cn3vp3tNrubsCrc6u7aTRpn/dyRhSIIkz3WSl2c5J0GYfZOLJ8T2j1X84Hg56h3a8/Ees8uBj3D9ezH+4fEKDAs0uZquwS/IT/2v7i9JWv/Cer12/muaf9l8fXz7x00ebxiGctfluv976e+XnjVQBwAAAAC0TNZK5z7yyIxIRaZHml3OOblCcsPe/Gmi7aX88Ikpq3SSo5MiJAeAM6g9Vqva8lpJUlhymMnVSGMfcI5b3vn+Th3LPXbO4x0NDvd+06QR5gegXY1rL+2+T7135LphGCrc4Qyk4wfHm1xN43HrLVkrcCadpZPcZeD1AyVJO97bYXIl8AUF2wqUszZHVj+rRt09yuxyupRBNw2SJG17a5vyNuS5//eeBXtkr7Pruz99p1V/WyXJ+XPqeOFxWWwWhcSGqGR/id669C29f8P7Wv30atOeAwAAAAB0Fr60j1ySLH7OlZne1ElOSI7OipAcAM7AFbAFRgR6RQde0ogkpZ2XJkeDQyv+vOKcxx/ddVQNNQ0KCA1QdO/oDqgQp+o70xmSH156WLUVtSZX07TKvErVlNbIYrMotl+s2eUotn+sLDaLaspqdCzn3DeCnEtFlnesS/AUV0h+8OuDKjtcpu/+7zt9/V9fK3tNtkduKkDnsumVTZKkflf2U7f4biZX07X0nNZTwdHBkqRuCd00ZLZzEsynP/5Ur1/4upb8bom+fPhLFe8rdneRJwxJ0ORHJkuSDi0+pJ3v79SihxZp2/xt5jwJAAAAAOgkfGkfuSRZbd4xbt0wDEJydHqE5ABwBt6yj/xU5/32PEnS2mfWKm9j3lmPPbzksCQpZXyKLFZLe5eG74npE6OYvjFy1Dt04MsDZpfTpMLtzi7ymD4x8gvyM7kayS/QTzF9YyR5Zi+5N34Pt0XcgDjFDYqTo96h5/o9pyX/vUQrnlihV8a/ormj56qmrMbsEuElGmobtPVfWyVJI+4cYXI1XY/N36aLHr9IPaf11I+W/khXvnylYvvHqjK/UlkrstzH7f5wt3LXO0PypNFJGvfTcbr69as14+8zNOy2YZKkT+/5VCUHSkx5HgAAAADg6+qO1ylnre/sI5e8Zyf58cLjaqhpkCzmryIF2gshOQCcgbfsIz9V7+m9NfimwTIchj65+5Oz/rJ0aPEhSVKPaT06qjx8T5+ZfSRJez/xzr3krpA8blCcyZWclDDUuZfcdZNHW3jj93BbDbrBOcbZXmdXbP9YDb5psPy7+StvY55WPHnuCRPoGnZ/tFvVJdUKSw5Tr+m9zC6nSxp19yjd+tWtiu0XK78gP1312lUKCA1Q/JB4TfzlREnSrg92uTvJk8cky2qzatgPh2ncg+N05ctXKu28NNUdq9MHN3/AnnIAAAAAaIXsVdlyNDgUkRbhM93Q3hKSu7rIw5PDZQuwmVoL0F4IyQHgDFyjmsNSzN9HfqrpT09XUGSQ8jbmafXfm95X6mhwuPft9LyoZwdWh1P1v7q/JGnH+ztUVVRlcjWn86Z95C6Dbx4sSVr77Fp3yN0atcdqVVvuHHPfWTrJJWnsA2M1/EfDNf3p6bpn6z2a9fYsXfvWtZKk1U+v1rG8to+ph+/b+oazi3z47cPdY9pgrpRxKfp53s91z5Z7NOHhCZJFylmbo8wVmZKk7mO6Nzre6mfVtW9dq6CoIOWuy9X6F9abUTYAAAAA+LRT95FbLL4xadMdktu9IyT3lZsLgNbgXTMAOANvHdUcmhCqaU9OkyR9/euvtWfBntOOyV2fq9qKWgVFBSlxRGJHl4gT0ianKXFEohqqG7TuH+vMLuc0R7cfleRdIXm/K/sp7bw0NdQ06JvfftPq87i+f4MigxQQGuCp8kwXHBWsq+ZdpfE/HS+bv/Mu3n5X9lPKhBQ1VDdo6R+WmlwhzFZTXqMDXzlXPAydPdTkanCqgNAAWSwWhSaGKm1SmiSpobpBtkBbkz+HI1IjNO1x57/3S3+/VNWl1R1aLwAAAAD4Ol/bRy55Xyc5ITk6M0JyADgDbw3JJWnkXSM17IfDZNgNvX/D++67Il0OLj4oSepxQQ+6CE1ksVg06VeTJDk7o+ur6k2u6CTDYXhlJ7nFYtH0v06XJG15Y4vyNua16jyuSRDe+P3raRaLxR2kbZy7UcX7ik2uCGba9/k+Oeodiu0fq9j+sWaXgzPof21/9/9OHJ7ovunl+0bcMUJxA+NUXVKtZY8u66jyAAAAAMDn1VfVK3tNtiTf2UcuSRabs+Pda0LyHpGm1gG0J5ITADgDbw7JLRaLrnzlSvW7sp/stXa9Me0NvTn9TW1+bbMcDQ4d+pp95N5i4HUDFZkRqaqiKm1+bbPZ5biVHSlT/fF62QJsiu4dbXY5jXQf3V1DZg+RJH37/75t1Tnc37+p3vf92x7Sp6Sr96W9ZdgNr5xagI6z+z+7JTUOYeF9BlwzwP2/u4/ufsbjrH5WXfyXiyVJa59Zq9KDpe1eGwAAAAB0Btmrs+WodygsOUxRPaPMLqfZvKWTvPxwuSQ6ydG5EZIDwBm4QraI1AiTK2ma1c+q6969Tn2v6CvDbujAlwf08e0f69VJryprZZYk9pF7A6ufVRN+PkGStPIvK9VQ22ByRU6F251d5LH9Y92/fHsTVwf+ocWHZK+3t/jxFTnO79+w5DCP1uXNRt8zWpK08/2dMhyGydXADPXV9dr3+T5J0oBrB5zjaJgpMiPSvYc8dWLqWY/tPaO3el7cU/Y6uz667aNW/UwEAAAAgK7GNXkzY2qGz+wjl7wnJC895LxJm5AcnZn3vSsOAF6g7nidakprJHlnJ7mLX5Cfbl5ws36y9ye64I8XKDAiUDlrc2Svsys8JVzRfbyrQ7irGn77cHWL76ayQ2Va/Mhis8uRJB3d4X37yE8VPzheIbEhqq+qV+763BY/vjKvUpIUltR1QvJe03spMCJQx3KOKXNFptnlwAQHvjyg+qp6RaRFKGlkktnl4Byu+dc1mvH3GRp046CzHmexWHTZ85cpMDxQmcsz9dWvvuqgCgEAAADAd7n2kfvSqHXpZEhu2M1rgDAMQ+VH6CRH50dIDgBNcHWRB4QFKDA80ORqzi2mT4ym/G6K7t12r3pc5Byx3u+qfj51l2RnFtAtQFe+cqUkafXfVmvfwn0mV3SykzxucJzJlTTNYrUofYrzRYzrzt+WOJZ7TJIUmhTqybK8ml+gn/pf7RyxvePdHSZXAzOcOmqdn//eL7ZfrMY9OE5W27lfksX0idHVb1wtSVrz9Bptm7+tnasDAAAAAN9VX12v7NXOfeQZUzPMLaaFvKGT/HjBcTXUNMhitXh1AxnQVoTkANAEb95HfjYRqRG69ctbdff6u3XJXy4xuxycou/Mvhr7wFhJ0ke3feQeB24WV0jurZ3kkpQ+1RmSH/n2SIsf6+4k7951OskluTtSd/57pxx2c8dyoWM5Ghza88keSYxa76z6X9Vfk/7LuYri49s/1oGvDphcEQAAAAB4p+zV2bLX2RWaFKro3r41adN1I7WZIXnZ4TJJzjWGtgCbaXUA7Y2QHACa4O37yM/GYrWo+6ju8gvyM7sUfM/FT16shKEJqjpapX9d/C9VFVWZUofhMFS8p1iSFDfAOzvJpZN3+mYuz2zxDt5jec5O8q40bl2Sek7rqeDoYB0vOO4eK4auIWtVlmpKaxQcHXzOHdfwXRf+34UaeN1A2evsevfqd5W1KsvskgAAAADA67jeE/G1feSSd3SSu0JyRq2jsyMkB4AmVGQ5Q/KwlK4VsKF9+QX56aaPb1JYcpiKdhXpX5f8SzVlNR1ex7G8Y86RSTaLItK990aQ+EHxCo4JbvFecsNhqDLf2UnelcatS5LN36b+154Yuf4eI9e7kn2fO9c49J7Ru1nju+GbrDarrnnzGvWa3kv1VfV6c/qb2v/FfrPLAgAAAACv4lrd52v7yCVCcqAj8Q4aADTBV8etw/tFZkTqh1//UCFxIcrflK9FDy3q8BpKD5S6a7H5e+/IpFP3krekK7qquEqOeucLidCErhWSSydHbe9fuF+GYZhcDTrK/s+dQWnvy3qbXAnam1+gn2744AZlTM1Q3bE6zb98vlY/vVqGg+93AAAAAGioafDZfeTSyZDcsJv3Go+QHF0FITkANOFYjnNUc3gyITk8L7Z/rG78z42SpC3/2uL+xbOjlOwvkSRF9/L+nUyuFzOuO4Cbw7WPPCQupEvuTUqfki6rv1XlmeXuGyLQuVVkV6hga4FkkXpPJyTvCgK6BWj2F7M1/PbhMhyGFj20SM/2fVYrn1rZ4vUUAAAAANCZZK/Jlr3Wrm4J3RTTN8bsclqMTnKg4xCSA0ATjhcelyR1S+hmciXorNImp6nntJ4y7IZWPLmiQ69dcsAZkkf1iurQ67bGqXvJG2oamvWYrrqP3CWgW4B7J/WBrw6YXA06wr6FzlHrKeNTFBIbYnI16Ch+gX668pUrNf1v0xUYEajSA6X66hdfafFvFptdGgAAAACYxpf3kUuSxeas2StC8h6RptUAdARCcgBowvGjJ0LyOEJytJ/zfnueJGnTq5vcwW5LGIah6pJqlR4qbdGYXVd3cXRv7+8kjx8cr7DkMNUfr9fBrw826zGuTvKuto/8VD2n9ZQkHfr6kMmVoCO4Rq33uayPyZWgo1ksFo3/2Xg9nPOwLnz0QknS5lc3q6G2eTcVAQAAAEBnk7UiS5KUdl6ayZW0jtmd5IZhqPxIuSQ6ydH5EZIDQBPcneTxhORoP+nnpyt1YqrstXZ998fvWrQ/evnjy/Wn4D/pyZgn9UzPZ/T8wOe1Ye6GZnVbu8at+0InucVqce/Y3vXBrmY9pqt3kktSz4tPhOTfHJLDbt6dx2h/DbUN7htICMm7roBuAZr0q0kKSw5TdUm19izYY3ZJAAAAANDhHHaHsladCMknEZK3RmV+pRpqGmSxWhSewipSdG4tDslzcnJ0yy23KCYmRsHBwRoyZIjWr1/f6Jhdu3bpyiuvVEREhLp166YxY8YoMzPT/fWamhrdf//9iomJUWhoqGbNmqWCgoK2PxsA8ID66nrVH6+X5NxpDLQXi8Wi837n7CZf/8J6zZs8T3kb8875OHudXcsfXy57rXPvrNXPquI9xfp0zqd6ddKrqimrOevj3Z3kPrCTXJIGzHKG5Ls/3t2sXbvHcp0heVfuJO8+qrsCIwJVU1ajvA3n/jsF35W9Klt1lXXqltBNicMTzS4HJrLarBp22zBJ0uZ5m80tBgAAAABMcHTHUdUdq1NAaIDiB8ebXU6rmB2Su0ath6eEy+ZvM6UGoKO0KCQvLS3VpEmT5O/vr4ULF2rnzp166qmnFBV1shPtwIEDmjx5svr3769vv/1WW7du1X//938rKCjIfcxDDz2kTz75RO+//76WLl2q3NxcXXvttZ57VgDQBlVHqyRJVn+rAsMDTa4GnV3vGb118V8uln83f2WtzNKrk19V6cHSsz7mwFcHVFteq9CkUP22+rf6VcmvNP1v0xUcE6y8jXl6+4q3VV9V3+Rjq0uq3SF6VE/v7ySXnPvbu8V3U01pjQ4vOXzO413j1sO6d91OcqufVT0u7CFJzR5TD9+UuyFXkvP7xGL1vV1r8KzhPxouSTqw6IAqcirMLQYAAAAAOljmCmezZsr4FHfY7GvcIblJkwHd+8gZtY4uoEU/JZ544gmlpqZq3rx5Gjt2rHr06KFLLrlEvXr1ch/z29/+VpdddpmefPJJjRgxQr169dKVV16p+HjnXTvl5eV65ZVX9Ne//lUXXnihRo0apXnz5mnlypVavXq1Z58dALTCqfvILRYCB7Qvi8WiiT+fqJ/s+YmSxyWrobpBK/684qyP2fn+TknODmu/ID8FhgVq/M/G64df/1CBEYHKXJ6p92a912TXtWvUelj3MPmH+Hv+CbUDq82q/tf0lyTt/PfOcx7PTnIn115yQvLOLX9TviQpcQRd5JBi+sQobXKaDIehLW9sMbscAAAAAOhQ2SuzJUmpk1JNrqT1rDbv6CQnJEdX0KKQfMGCBRo9erSuv/56xcfHa8SIEZo7d6776w6HQ5999pn69u2r6dOnKz4+XuPGjdNHH33kPmbDhg2qr6/XtGnT3J/r37+/0tLStGrVqiavW1tbq4qKikYfANBe2EcOM4Qnh+viJy+WJG1+dbN7r/b32evs2v3RbknSoOsHNfpa4vBE/eCzH8gv2E/7v9ivL3/+5WmPLzngDMmje/vGqHUX98j1j3af805adpI7ufaSZy7LVEU2vzv5AkeD45zrEr4vf/OJkJxR6zhh+B3DJUlr/r5G5Znl5hYDAAAAAB3I1UmeOtGHQ3IvGbcekRFhyvWBjtSikPzgwYN64YUX1KdPHy1atEj33nuvHnzwQb3++uuSpMLCQlVWVurxxx/XjBkz9OWXX+qaa67Rtddeq6VLl0qS8vPzFRAQoMjIyEbnTkhIUH5+fpPXfeyxxxQREeH+SE313R9wALyfa9w6+8jR0dLOS1PqpFTZ6+xa9dembxw7ddR6U3fFpk1K06z5syRJa59dqw1zNzT6uquTPKqXb4xad8mYmqGgqCBVHa3S4W8Pn/E4wzDYSX5CTJ8YpU9Jl6PBcca/T/AOhmFo+zvb9XTG03oi6gnNmzJPG1/Z2OQ0iFPVV9eraHeRJEJynDT4xsGKGxSn4wXH9dalb6m6tNrskgAAAACg3R3LO6ayQ2WyWC1KGZ9idjmtZnZIXn7YebM1neToCloUkjscDo0cOVKPPvqoRowYoTlz5ujuu+/Wiy++6P66JF111VV66KGHNHz4cP3Xf/2XZs6c6T6mNR555BGVl5e7P7Kyslp9LgA4l1PHrQMdyWKx6LzfnCdJWv/CelWXnB5snDpq3TV+6fv6X91fF/zxAknS5/d/rszlme6vlR5w7jv3tZDc5m/TwOsHSpI2z9t8xuNqympkr3UGi129k1ySJj8yWZK04aUNqiquMrkaNMVeZ9f8y+brg5s/0LEc5w0emcsy9cldn+idK99R3fG6Mz62cHuhDLuhkLgQhXXn7zuc/EP8NXvhbIV1D9PRnUf13rXvyXAYZpcFAAAAAO0qa6UzN4ofEq/A8ECTq2k9s0Nyxq2jK2lRSJ6UlKSBAwc2+tyAAQOUmel88z02NlZ+fn5nPSYxMVF1dXUqKytrdExBQYESE5vugAkMDFR4eHijDwBoL3SSw0y9L+2thGEJqj9ef9o+WcMwtGfBHkmnj1r/vvN+e54GXj9QjnqH3r32XffIXVdI7mvj1iVp5F0jJTn3kp+pM9K1jzwoKkh+QX4dVpu36jW9lxJHJKr+eL3WPrvW7HLQhI0vb9T+L/bLL8hPU38/VQ/sf0AXPnqhe23CGxe+oaqipm9wOHXUusVi6cCq4e0iUiM0e+Fs+QX76fC3h91/VwAAAACgs8pa4QzJfXnUunQyJDfsHX+zs+EwVHakTBIhObqGFoXkkyZN0p49exp9bu/evUpPT5ckBQQEaMyYMWc9ZtSoUfL399fixYvdX9+zZ48yMzM1YcKEVj0JAPAkdpLDTBaLRcN/NFyStO+zfY2+VplXqZrSmmaNjbJYLLpq3lVKHJ6oqqNVeueqd1RdUu0etx7dy/dC8u6juyt+SLzstXZtm7+tyWPYR96YxWJxd5OveWaNaspbtu8a7au+ul7L/rRMknTxXy7W+f9zvqJ7Reu8R87Tbd/cpuDoYOWszdHrF77e5CQA9pHjbBKGJqjnRT0lSQe/PmhyNQAAAADQvrJXZUvy/ZDcYnPeBG9GJ3llQaXstXZZbBZFpLKTHJ1fi0Lyhx56SKtXr9ajjz6q/fv3a/78+XrppZd0//33u4/55S9/qXfffVdz587V/v379dxzz+mTTz7RfffdJ0mKiIjQnXfeqYcfflhLlizRhg0bdPvtt2vChAkaP368Z58dALQCneQwW5/L+kiSDi89rLrKk6OWXQF3ZEakbAG2c54noFuAbvr4JoXEhSh/c76ejHlSlfnOTmtfG7cuOQPfEXeOkCRtemVTk8ewj/x0A64doNj+saoprdGXv/jS7HJwig3/3KBjuccUnhrunpTgkjI+Rbcvv12hSaEq3Faot2a8ddpNDvmbToTkIwjJ0bQe03pIIiQHAAAA0Lk5GhzuG8mTxyWbXE3bmDlu3TVqPTwl3F0H0Jm16G/5mDFj9OGHH+rtt9/W4MGD9cc//lFPP/20Zs+e7T7mmmuu0Ysvvqgnn3xSQ4YM0csvv6wPPvhAkydPdh/zt7/9TTNnztSsWbM0ZcoUJSYm6j//+Y/nnhUAtAE7yWG26D7RiuoVJUe9QwcXnww23F3gLRiVHpEWoZs+vqnRY2L6xSg4KthzBXegobcMlS3ApvxN+crblOf+/MZXNurb//ete6w8neQnWW1WXf7i5ZKkTS9v0v5F+02uCJJUd7xOyx9bLkk6/3/Ol1/g6esB4gbE6Ydf/1AhsSHKXZ+rf037l/tFv8PuUMHWAkl0kuPMek5zdpJnLstUQ02DydUAAAAAQPs4uvOoGmoaFBge6JPTE0/VkpC87nid+71sT2AfObqaFi/rnDlzpmbOnHnWY+644w7dcccdZ/x6UFCQnn/+eT3//PMtvTwAtDs6yWE2i8WiPpf10dpn12rf5/vU/6r+kk4Jyfu07Jf91AmpemDfA6ouqVbR7iKf3EfuEhITov7X9NeOd3dowz83aOaLM1V6sFSf3P2JZMi9hzy0O53kp8o4P0NjHxyrtc+s1Sd3faJ7t9+roIggs8vq0na8t0PHC48rqmeUht027IzHxQ2M061f3arXL3xduetz9c+R/9SIO0do4HUDVX+8Xn7BforpG9OBlcOXxA2MU2hiqCrzK5W1Mks9LuxhdkkAAAAA4HG563MlSUmjkmSxWkyupm1aEpK/dv5rKt5TrJ8e/qlCYtr+XnbZoTJJhOToOpiXAADf4+4kZyc5TNT70t6SpP2f75dhGJKkkn0t7yQ/VXB0sFInpvr83+3R946WJG15fYuqiqq09rm1kvOPyN0pSSf56S569CJF9YpSRXaF/vOD/5gytgsnHfzKOSViyC1DZPM/+/qExOGJumfzPRp802DJcE4EeGvGW5Kce6etNn6lR9MsFou7m5yR6wAAAAA6q1NDcl/nDsnt537fpmhXkeoq65SzNscj13Z1kkeks48cXQPvqAHAKRpqGlR3zLkDmnHrMFPG1Az5BfmpIrtChdsLJbVu3HpnlD4lXUmjktRQ06AVT65w7yef/JvJCggNkCRF9fS9nevtLaBbgGbNnyW/YD/t+3yfPr3nU/cNGOhYhmHo0OJDkqSeF/Vs1mMi0iI06+1Zun357eo1vZf788ljfXvXGtofe8kBAAAAdHZ5G5wr+bqP7m5yJW3Xkk7yhlpns0jhtkKPXLv0YKkk3ldD10FIDgCncHWRW/2tCowINLkadGX+wf7usbj7Pt8nwzAIyU+wWCya8PMJkqSVf16p2opaxfSN0YV/vFB3rblLl79wufpc1sfkKr1T8thkXffOdbJYLdr0yiYt/s1ignITFG4v1PHC4/IP8VfK+JQWPTZtUppu+eIW/Xjzj3XxXy7W+f9zfjtVic7CdSNG7vpcVZdWm1wNAAAAAHiWvd6u/C35kjpJSG5rXkjusDtk2J3v6XgsJD/gDMl9fa870FyE5ABwCvc+8tgQWSy+vb8Gvq/3Zc6R6wcWHdDxwuOqq6yTLFJkj0hzC/MCA68bqPDUcPd/j31wrCxWi+IGxmn0PaPdd93idP2u7KfLX7hckrTi8RX69MefMnq9g7m6yNOnpMsWcPZR62eSOCxRE38+USGxbd85hs4tPCVcsf1jJUM6vOSw2eUAAAAAgEcd3XFU9lq7giKDOkUHdHM7ye11dvf/LthW0Obr2uvsKs8slyRF9fL9P0egOXgHGQBOwT5yeBNXJ3n2qmwd3XlUknPksl+gn5lleQWbv03jfzZekhQYEajhtw03tyAfM2rOKF3+wuWyWC3aOHej3r3mXdVW1JpdVpfhGnvd46IeJleCriJtSpokeWxPHQAAAAB4i1P3kXeGpqdmh+S1J0PyozuPyl5vP8vR51Z2pEyGw5B/iL9CE0PbdC7AVxCSA8ApjheeCMnZRw4vENs/ViFxIWqoadD2t7dLYtT6qUbfO1pjHxyrq+Zd5d5FjuYbfc9oXf/v62ULtGnvp3v18viXVby32OyyOj17vV1Hlh6RJPWc1rx95EBbJY9x7q53vXkEAAAAAJ3FqSF5Z+AKyQ27cdYVead2kjvqHW1+T8c1aj2qZ1SnuNkAaA5CcgA4hXvcehzja2E+i8Wi9CnpkqTt7xCSf59/sL8u/fulGnDNALNL8VkDrhmg27+7XWHJYSraVaS5Y+eq9GCp2WV1ajlrc1RXWaeQ2BAlDE0wuxx0Ea69fLnrc8/6JgsAAAAA+Jq8DXmSOsc+ckmNVggajjO/fmuobWj0323dS15yoEQSo9bRtRCSA8ApXOPWCcnhLVwhed2xOkmE5PC85LHJmrN+jhKHJ6q2vFabX99sdkmdmmsfeY8Le8hi5c5sdIy4QXGyBdpUW17r7g4AAAAAAF9nr7OrYKtzH3f3UZ0jJLfYTr5XcLaR66d2kktt30vu7iQnJEcXQkgOAKdwdZKzkxzewhWSuxCSoz2EJoZq/MPOHe97PtpjcjWdW9bKLElS+tT0cxwJeI7N36bE4YmSGLkOAAAAoPM4uvOo7HV2BUYEKrJHpNnleMSpneRnDclrG4fkhVvb1knuCsmje/HeI7oOQnIAOAU7yeFt4ofEKygyyP3fhORoL30v7yuLzaKCrQWMXG9H+ZvzJXWeO9zhO1z7+QjJAQAAAHQW+Vucr7EThyV2mj3azQ7JPdxJzrh1dEWE5ABwCnaSw9tYbValTU5z/ze/qKK9BEcHK+P8DEnS7o92m1tMJ1VZUKnjBcdlsVoUPzje7HLQxbj287n29QEAAACAryvY4gyGE4YlmFyJ5zTaSW4/907ywIhASVL5kXLVVtS26pqGYbgbJugkR1dCSA4Ap3DtJKeTHN4kbYozJA9PCZd/sL/J1aAz63d1P0nOkNxwGNr72V6VHqKr3FNcL96j+0TLP4TvZXQsV0ieuyFXhuPMb7QAAAAAgK9wvc52rZfqDCzWlu0kD00IVVhymCSpcHvrRq5X5lWqobpBFptFEekRrToH4IsIyQHgFOwkhzcaOGugAsMD1e+qfmaXgk6u/1X9JUmZyzP12tTX9PbMt/Xete+ZXFXn4Rq1njis87x4h++IGxAnv2A/1R2rU/G+YrPLAQAAAIA2MQzDPW69M3WSWywWWWzOoLw5O8ltATYlDHE+f9efR0u5Rq1HpEXI5m9r1TkAX0RIDgAnNNQ2uEfSMG4d3iSqZ5R+XfprXfbcZWaXgk4uIi1CSSOTJEPKXJYpyRnsVhVXmVxZ59AZx8DBd1j9rEoawV5yAAAAAJ3Dsdxjqi6ulsVmUfygzrXSzDVyvTmd5LZAmxJHOG/Gb+16rdIDjFpH10RIDgAnuLrIrX5WBUUGmVwN0Nipo5aA9jRyzkhJUvqUdEVmREqSslZmmVhR5+G6o7szjYGDb0kaTUgOAAAAoHNw3Yge2y9WfkF+JlfjWc0JyV07yW0BNnUfc2K91rrWvdZzdZJH9Ypq1eMBX0VIDgAnuPaRh8SGyGIhkATQNY3+8Wg9nPuwbltym3pe3FOSc/w62qahpkFFu4sk0UkO8yQMdf7dK97NuHUAAAAAvq0zjlp3aVYn+Ylx636BfkoekyxJKtxRqPqq+hZfz9VJTkiOroaQHABOYB85ADiFJYXJYrUobXKaJClrOZ3kbVW4o1CG3VBwTLDCuoeZXQ66qNDEUEnS8cLjJlcCAAAAAG3TmVeauUNye/PGrYclhyk0MVSG3VDeppaPXGfcOroqQnIAOMHdSc4+cgCQJKVOSpUk5azLUX11y+9ExkmuF++JwxKZVgLThCYQkgMAAADoHE59nd3ZWG0tG7dusVhOjlxv4XotwzBUsp9x6+iaCMkB4ATXG8bd4ugkBwBJiuoZpdDEUDnqHewwbiP3GLjhne8Od/gO17Sc44XHZRiGydUAAAAAQOvUV9ereK9zjVSn7iQ/27j1upPj1iW1ei/58YLjqi6plsVqUUzfmNaUC/gsQnIAOME1bp1OcgBwslhOjlxnL3nbdOY73OE7XL/j2Ovsqq2oNbkaAAAAAGidwu2FMhyGQuJC3GulOpOW7CS3BdgkSd1Hty4kL9xeKEmK7h0t/2D/FtcK+DJCcgA4wTVunZ3kAHBS6mTnyHX2kreeYRjK33yik7wT3uEO3+Ef7K+AsABJjFwHAAAA4LsKtnbulWYt6SS3BTpD8uQxyZKk4r3Fqimrafa1XCF5/OD4VtUK+DJCcgA4gU5yADidu5N8RaYMB+OZW6M8s1y15bWy+lsVNyDO7HLQxZ06ch0AAAAAfJE72B3SOYNdV0hu2M/8PsypO8klKSQ2RJEZkZKk3A3N7yYv3OH8s4wbxPsV6HoIyQHgBHaSA8DpEoclyr+bv2rLa1W0u8jscnySa9R63IA494tXwCyE5AAAAAB83dHtRyV13u5ni83ZHd+STnKpdXvJO/ufJXA2hOQAcAKd5ABwOquf1b1H2zUyHC2Tv8X555Y4nH3kMB8hOQAAAABf19m7n1uzk1w6GZLnrM1p1nUMw2DcOro0QnIAOIGd5ADQtIThzj3aeZvyTK7ENxVsdnaSs48c3oCQHAAAAIAvqyquUmVepSQpbmDXDcld49b9Av3cn0udmCpJylzWvJV55Znlqqusk9Xfqug+0W0pGfBJhOQAIOd4mtryWkmMWweA70sakSTpZNiLlnF1khOSwxsQkgMAAADwZUd3OMeDR2ZEKjAs0ORq2kezOsmbGLeePCZZ/t38VVVU5e4QPxvXMbH9Y2XzZz0cuh5CcgCQVFXkHLVusVkUFBlkcjUA4F1cY8LzN+fLME7eiXzom0N6+8q3tfODnWaV5vVqj9Wq9ECpJLnH1gNmcofkBYTkAAAAAHyPK9jtrKPWpVNCcnvLxq3bAmxKPy9dkvM9m3Nx3XDAqHV0VYTkAKCT3VQhsSGyWC0mVwMA3iV+cLwsNouqiqp0LOeYqkur9cEPPtAbF72hvZ/s1cc/+lgVORVml+mVCrc5X7yHdQ9TSGyIydUAdJIDAAAA8G1dYYd2SzrJTx23LkkZF2ZIkg4tPndI3hVuOADOhpAcAMQ+cgA4G78gP8UNcL5gyt+cr4UPLNT2t7fLYrUoNClUdZV1+vpXX5tcpXfK3+wcte7qxgfMRkgOAAAAwJd1he5nq60ZIXkTneSS1OPCHpKkw0sPn/XxUte44QA4G0JyAJBUddQ5bp195ADQNFfIu+uDXdr+9nZJ0q1f36offPoDySJtm79NR747YmaJXol95PA2hOQAAAAAfJVhGF0i2G3tTnLJ+f5NUFSQ6o7VKXdD7hkf77A7dHRn57/hADgbQnIA0MlO8pA4RuECQFMSRzhD8s2vbZbhMNRrei/1uKCHkkYmadSPR0mSPv/J5zIcxtlO0+UUbCmQREgO7+EKyauLq8/ZVQAAAAAA3qQyv1LVJdWyWC2K7R9rdjntpjkheUNtg6TTO8mtNqsypmZIOvte8tIDpbLX2uUX7KeoHlFtrBjwTYTkAKBTdpITkgNAk74/Lvy835zn/t8X/t+FCowIVOG2Qu39bG9Hl+a1HHaHeyd54jDGrcM7BMcEy2K1SJKqiqpMrgYAAAAAms/VRR7dO1p+QX7nONp3uUJyw37mRoQz7SSXTo5cP/jVwTM+/sgy5zTApBFJ7teIQFdDSA4AOmXcOjvJAaBJp4bkqRNTlXZemvu/Q2JC3N3kq55a1eG1eavyI+Wqr6qXLdCm6D7RZpcDSHJ2FYTEOm8KZOQ6AAAAAF/SFfaRS80ct36GneSS1PvS3pKkI0uP6FjusSYff/BLZ4De85KebaoV8GUtDslzcnJ0yy23KCYmRsHBwRoyZIjWr1/v/vqPfvQjWSyWRh8zZsxodI6SkhLNnj1b4eHhioyM1J133qnKysq2PxsAaCV2kgPA2QVHByumb4wkafJvJstiaXyX8bgHxsnqZ9WRpUfOuvOqKyk9WCpJiuoZJauNe1PhPdhLDgAAAMAXuTrJ4wbHmVxJ+7LYnO+5tGYnuSRF94pW6qRUGQ5DW9/cetrXHXaHDnx1QJLU65JenigZ8EktereutLRUkyZNkr+/vxYuXKidO3fqqaeeUlRU430FM2bMUF5envvj7bffbvT12bNna8eOHfrqq6/06aef6rvvvtOcOXPa/mwAoJXYSQ4A53bDBzfopo9vUt/L+572tfCUcA26cZAkafVfV3d0aV6p9NCJkJzdXvAyhOQAAAAAfJErJI8fRCe5ayd5U+PWJWnYbcMkSVte3yLDaDy2PW9DnmpKaxQYEajkMcmeKBnwSS0KyZ944gmlpqZq3rx5Gjt2rHr06KFLLrlEvXo1vtMkMDBQiYmJ7o9TQ/Rdu3bpiy++0Msvv6xx48Zp8uTJevbZZ/XOO+8oN5euIwDmcL1JTCc5AJxZ/OB49buy3xm/PuHhCZKkHe/tUHlWeUeV5bVcneSRPSLNLQT4HkJyAAAAAL7GMAzGrZ/ibOPWJWnQDYPkF+SnozuPKm9DXqOvHfjS2UXec1pP97WArqhFf/sXLFig0aNH6/rrr1d8fLxGjBihuXPnnnbct99+q/j4ePXr10/33nuviouL3V9btWqVIiMjNXr0aPfnpk2bJqvVqjVr1jR53draWlVUVDT6AABPYic5ALRd0sgkZUzNkKPBobXPrjW7HNOVHSqTREgO7xMSz05yAAAAAL6lPLNcdZV1svpbFd0n2uxy2pU7JLe3bty6JAVFBKn/1f0lSZtf39zoa66QnFHr6P34FXUAAHRQSURBVOpaFJIfPHhQL7zwgvr06aNFixbp3nvv1YMPPqjXX3/dfcyMGTP0xhtvaPHixXriiSe0dOlSXXrppbLbnd+w+fn5io9vfJePn5+foqOjlZ+f3+R1H3vsMUVERLg/UlNTW/o8AeCM7PV21ZTVSGLcOgC01fiHx0uSNry0QbXHak2uxlyukJxx6/A2dJIDAAAA8DWuUeux/WNl8286GO4sWjJu/Uyd5NLJkevb529XTbnz/e/ailplr8qWJPW8uKdH6gV8VYtCcofDoZEjR+rRRx/ViBEjNGfOHN1999168cUX3cfcdNNNuvLKKzVkyBBdffXV+vTTT7Vu3Tp9++23rS7ykUceUXl5ufsjKyur1ecCgO+rKnJ2kVtsFgVHBZtcDQD4tr6X91VM3xjVltdq06ubzC7HVO6d5D0JyeFdCMkBAAAA+Jquso9ccr5PLZ1j3PqJTvIz7SSXnCF4ZI9IVZdU66PbPpLhMLT1ra1yNDgU3Seam/rR5bUoJE9KStLAgQMbfW7AgAHKzMw842N69uyp2NhY7d+/X5KUmJiowsLCRsc0NDSopKREiYmJTZ4jMDBQ4eHhjT4AwFNcbxCHxITIYrWYXA0A+DaL1aLxDzm7ydc8veasL+jOpDK/Uh/96CP9c8Q/tebZNaqrrPN0me2urrLOvcqDcevwNmFJYZKkiizWWAEAAADwDUe3O/eRxw2OM7mS9ueJneSSZLVZdd2718kWYNOej/fotamv6fP7PpckDbx+4BkfB3QVLQrJJ02apD179jT63N69e5Wenn7Gx2RnZ6u4uFhJSUmSpAkTJqisrEwbNmxwH/PNN9/I4XBo3LhxLSkHADzCHZIzah0APGLYD4cpOCZYZYfLtOvDXc1+nKPBodV/X63n+j2nLa9vUf7mfH3x4Bf6W+rflLnizDdleiNXF3lQVJCCIoJMrgZoLLq3c39f8b5iGYZhcjUAAAAAcG6FO050kg/u/J3kzQrJz7GT3CV5TLIu+8dlkqTMZc73Vib8fIKm/r+pHqgU8G0tCskfeughrV69Wo8++qj279+v+fPn66WXXtL9998vSaqsrNQvf/lLrV69WocPH9bixYt11VVXqXfv3po+fbokZ+f5jBkzdPfdd2vt2rVasWKFfvKTn+imm25S9+7dPf8MAeAc8jbkSZJi+saYXAkAdA7+If4ac98YSdLyx5Y3K4TLWpWll0a/pEU/W6Tailp1H9Nd056YpqheUaopq9HS/7e0vcv2KPaRw5tF9YySxWpR/fF6VeZXml0OAAAAAJyVw+7Q0Z3OTnJCcslwGO6vna2T3GXknSM1+ZHJikiP0PXvX69L/nJJp9/rDjRHi0LyMWPG6MMPP9Tbb7+twYMH649//KOefvppzZ49W5Jks9m0detWXXnllerbt6/uvPNOjRo1SsuWLVNgYKD7PG+99Zb69++viy66SJdddpkmT56sl156ybPPDACa6cjSI5Kk9PPPPBUDANAy4x4cJ/8Qf+Vvytf+hfvPemzO2hy9NuU1FWwpUFBUkGb+c6buXHWnJv1qkm798lZJ0qFvDvlUmMc+cngzW4BNEekRkqSSfSUmVwMAAAAAZ1d6oFT2Wrv8gv26xM3orpDcsDfddODqIpfOvpP8VBc9epF+dvhnGngdY9YBl+Z995xi5syZmjlzZpNfCw4O1qJFi855jujoaM2fP7+llwYAj3M0OJS53DlmJuP8DHOLAYBOJCQ2RKPvHa1VT63Sd3/8Tr0v7S2LxXLacY4Ghz6951M5GhzqfWlvXfPGNQqJPbn+IqpnlJLHJStnTY52vLdD4x70jfU8pQedITn7yOGtYvrGqOxQmYr3Fit9CjcKAgAAAPBehdudo9bjBsbJYj39vYXOxmo7eyd5Q22D+3+fa9w6gDNrUSc5AHQ2eZvyVFdZp6DIIMUP6fyjegCgI038xUTZAm3KXp2tw0sON3nM2ufXKn9TvoKignT1a1c3CshdBt88WJK0/e3t7VmuR7nGrROSw1tF9zm5lxwAAAAAvFlX2kcunXvcur32ZCc5Y9OB1iMkB9ClHfnOOWo97bw09x16AADPCE0M1ci7R0qSFj20SMcLjzf6ekVOhZb8bokkadrj09QtvluT5xl0wyBZrBZlr852jzH3duwkh7eL6RMjiXHrAAAAALzf0e3OfeRxg+JMrqRjnDMkPzFu3epv7RKd9UB7IREC0KW595EzZhQA2sXkX09WcEywCrYW6JWJr6hk/8lA7rs/fqe6yjqljE/RyLtGnvEcYUlhyrggQ5K0/R3v7yY3DMM9bp2d5PBWMX2dIXnxXjrJAQAAAHi3gm0Fkugkd3GNW7cF0EUOtAUhOZrl6K6jOpZ3zOwyAI9y2B3KXObcR55+PiE5ALSH8JRw3bHiDkVmRKr0QKnmnTdPxwuPq7KgUptf2yxJuujxi8555/OQHwyRJG16ZdMZXyR6i6qjVaqvqpcsUkR6hNnlAE1yjVsv2V8iw2GYXA0AAAAANK32WK2KdhdJkhKHJ5pcTcdwheSGvenXaq5Ocr9Avw6rCeiMCMlxThXZFfrn8H/q+QHP68iyI2aXA3hM4bZC1ZTVKCAsQEkjkswuBwA6rdh+sbpz1Z2KHRCryvxKfXbvZ1rzzBrZa+1KHpfcrGkeg24YpOCYYJUeKNXOf+/sgKpbzzUSPqx7GC9Y4bUi0yNl9bPKXmtXeVa52eUAAAAAQJNy1+VKhvMm9LCkMLPL6RAWm7OR4Fw7yekkB9qGkBzndPjbw7LX2VVbXqs3L3lTez7ZY3ZJgEccXnpYkpQ2Kc19dx4AoH2EJoZq1vxZsvpZtes/u7TyzyslSZN+PUkWy7n3ZwWEBmjcT8dJkpY9ukyG4b2dr+wjhy+w+lkV1cv5d5S95AAAAAC8VdaqLElSyvgUkyvpOM3dSW4LJCQH2oJUCOeUtdL5j1BAaIAaahr03qz3dOQ7Osrh+w5+dVCS3HtuAQDtK3F4oqb89xRJkqPeoZi+Mep3Zb9mP37sT8YqIDRAhdsKte+zfe1VZpuVHSmTJEVmRJpaB3AuMX1O7CXfx15yAAAAAN4pZ3WOJELyU7GTHPAMQnKcU/aqbEnSFXOv0IBrB8hR79C717yrkv10nMB3NdQ26PCSw5KkXtN7mVsMAHQhkx+ZrKRRzhUXk/5rkqy25v86GhwVrNH3jZYkLfuT93aTV2RXSJLCU8NNrgQ4O9de8uK9hOQAAAAAvI9hGMpe7cwnCMlPYic54BmE5Dir2mO1KthaIElKn5Kua/51jbqP6a7qkmrNv3y+qkuqTa4QaJ3M5Zmqr6pXaGKoEoYmmF0OAHQZNn+bbv3qVt3y5S0a/qPhLX78hIcmyC/IT9mrs7X7o92eL9ADjmUfkySFpxCSw7vF9HV2kjNuHQAAAIA3Kj1YqqqiKtkCbEockWh2OR3GHZLbz7GTnHHrQJsQkuOsctbmyHAYikiPUFj3MPmH+OvmBTcrIi1CxXuL9d5177nvWgJ8yf4v9kuSes/o3axduAAAzwmOClavi3u16udvaGKoJvx8giTpq1985R4x5k3Ks8olEZLD+7k6yQnJAQAAAHgjVxd54ojELtU17Zq6d86d5IxbB9qEkBxn5Rq1njox1f250MRQ3fzJzQoIDdDhJYf16b2feu24U+BMDiw6IIlR6wDgiyb/12SFJoWq9GCpVj+92uxyTsO4dfgK107y0oOlstdz4ysAAAAA79IVR61Lzd9J3pVuHADaAyE5ziprZZYkKWVC43+EEoYm6Lp3r5PFatHmVzdrxZMrzCgPaJWKnAoVbiuULFLPi3uaXQ4AoIUCQgM07YlpkqRl/7dMZUfKzC3oFPY6u44XHJdEJzm8X3hKuKz+VjkaHDqWe8zscgAAAACgkZzVOZIIyb/PPW6dTnKgTQjJcUaGw2iyk9ylz2V9NOPvMyRJix9ZrF0f7urQ+oDWOvCls4s8eUyyQmJCTK4GANAaQ2cPVcr4FNVV1mn+5fNVU1bTrMfVHqvV3DFz9UzvZ/TxHR9r65tbVXus1mN1VeQ4u8htgTaFxPJvDLybxWpReLLzZg7XBAQAAAAAMFvB1gJ9+YsvlbcpT9LpTXyd3TlD8jp2kgOeQEiOMyraU6Sashr5h/grYWhCk8eM/clYjbl/zP9v777jqyrs/4+/z829N3uTQSAJU/YGAQEXCLj3RK17VGtrW2v91tb211ZrW6tttVqstbaKAxVUFBFFBQHZM+wNIQuyk5vkjvP743ovBAIk5ObejNfz8biP3Nx7xucEcnLv/ZzP5yOZ0qybZylvdV6QowSapqa0Ruv+s06S1HMqrdYBoK0yLIaueecaxWbEqiinSG9f9Xaj5pNvmrlJB1ceVMnOEq19da1m3TJLf077s2bfNlu15d5keWVBpV6f8rqW/mVpk+Pyt1rvGndaM9eBYPONBSjfT5IcAAAAQGg5ih2afdtsvTTkJS19ZqlMt6mMkRmKz4oPdWhB5UuSm+6Gx9z6Pv+gkhxoHpLkOCFfq/UuZ3ZRmO3EJ9upz01Vz8k95ax26vWpr6swpzBYIQKNZpqmNszYoOf7Pq+9C/fKCDPU/+r+oQ4LANAM8Znxuunjm2SPsWvPl3v01mVvnbKifP3/1kuSht4+VOMeHafkM5Llcri07rV1mnPvHJmmqTn3ztHOz3bq80c/b3Ir96OT5EBb4Pu/SiU5AAAAgFDKXZGrF/q9oHWvrZMMqe+VfXX9rOt1x+I7OtxF6I2tJGcmOdA8JMlxQgXrCyRJnUd0PulyFqtF17xzjdKHpau6qFqvnfcaiXK0Koe2HtL/Lvif3p/2vqoKqpTcJ1m3zL/lhB0SAABtR/rQdF33/nWyRdm087Od+teYf2nTu5u0bc42Hdp6qN6yZfvKtOerPZKkc399rib9YZIe2PKAps2dJiPM0Ma3NmrmtTO19YOtkrxvRr956psmxUOSHG2Nr5K8bH9ZiCMBAAAA0FGZHlMf3f2Rqgqr1KlfJ92x+A5d//716ntF3w5ZLW2EeS8KYCY50LJIkuOEinKKJEkpA1JOuWxEfIRu/fxWf6L81fGvauNbG1s6ROCkTNPUyn+u1EuDX9LuL3bLGmHVeb87T/etu0/dz+se6vAAAAHS84KeumPxHYrLjNPhrYc189qZevPSN/XS4JdUuPHIhXvr3/BWkXc7t5u/VZthGOo1tZfO/935kqTN722WJPW7qp8kac2/16hsX+OTh76W1b7EI9Da+S7oqDhQEeJIAAAAAHRUG9/aqIJ1BQqPC9ftC29X5tjMUIcUUswkB4KDJDlOyJckTx2Q2qjlI5Midevnt6rL6C6qKa3Reze+p3evf1eHthw69cpAgLlqXPrwrg/18X0fy13nVs8pPfX9nO/r7F+cTRsaAGiH0oem6+4Vd2vwzYPVdUxXxWXGyV3n1kd3fyTTY8o0TX+r9cG3DD5u/XE/G6cek3pIkjJGZuiat69R9/O7y+P0aNFTixodB5XkaGviM70XjFBJDgAAACAU3HVuffnLLyVJZ/3sLEV1igpxRKF3qiS5fyY5SXKgWUiSo0GOYocq8yslSSn9T11J7hOZFKnbF92us391towwQznv5OiFfi/ov5P+q83vbz7hSR0IJNM09d5N72ntv9fKsBia+IeJmjZ3mhJ7JIY6NABAC4pJi9GV/7tSdy69U3cuuVP2GLsOfHtAK15coZy3c3Ro8yFZI6zqf03/49Y1LIaueecaTXp6kq6ffb0sVovOeeIcSdKaV9Y0OoFIkhxtDTPJAQAAAITSqumrVLKrRDHpMRrzozGhDqdV8CfJ3aeoJKfdOtAsJMnRIN9M8fjseNlj7E1aN8wWpvN+c57u+vYu9bm8jwyLod1f7NY7V7+jv3b/qza9t6klQgb8Fv9xsbbM2qIwe5hu+uQmjX90vAzDCHVYAIAgiusap4lPTZQkzX1wrt678T1JUv9r+is8LrzBdSITIzXuZ+MU18WbNMw+O1vdzu0mj7Pxs8l97dZ91blAa+cbDVCZX+n/oAUAAAAAgmX1y6slSWf/8mzZo5uWi2ivTtlu/buZ5HRMBZqHJDka1NRW6w3JGJmhG2bfoId2PaTxj41XVKcolR8o1+zvzZajxBGoUIF6di/YrQX/t0CSdOHfL1SvKb1CHBEAIFRG3j9SXcd2lSTZY+0a8/AYXfj8hU3axtHV5KeqtHXXuVVZ4O3EQyU52orolGhZbBbJlCrymEsOAAAAILiqiqokyf/+HZIlrJHt1qkkB5qFJDka5KskTxnQ+FbrJ5KQnaCJT07UwwceVuqgVDmrnP6rw4BAqimr0fs3vy/TY2robUM1/O7hoQ4JABBCljCLbvr4Jl0781o9vP9hTfnLFEXERzRpG93O7absc7LlrnPrmz+cvJq8Iq9CMr1vUpmhhrbCsBhHWq7vp+U6AAAAgOCqq6yTJIXHNtz1rSM6VSW5p877ODPJgeYhSY4G+SrJmzKP/FSs4VaN/fFYSdKyvy2jnSMCbsHjC1SZV6mk3km66B8X0WIdAKDIxEj1v6Z/k5PjR/NVk69+efVJZ5P7EoxxXeNkWPgbhLaDueQAAAAAQsE0TX+S3B5Lq3WfUyXJqSQHAoMkORrkT5IHoJL8aANvHKiY9BhV5FYoZ2ZOQLeNji13ea5WvLBCknTxixfLFmkLcUQAgPbi6Gry+T+df8LlfAlGWq2jrYnPjJekk14EAgAAAACB5qxySqb3PpXkR5xyJnkdM8mBQCBJjuNUH6pWVaF3DkhKv8Amya3hVo16cJQkaekzS2WaZkC3j47J4/Zozr1zJFMafPNg9ZjYI9QhAQDaEcMwNPW5qTLCDOW8k6Mdn+5ocDmS5GirYrvGSqKSHAAAAEBw+arIDYshayQJXx9fktx0N5w/cdd6k+RUkgPNQ5Icx/HNI0/oliB7TOBbnIy8b6SsEVblr8lX0aaiEy5XfqCcD+rQKLu/2K38tfkKjw/X5GcmhzocAEA7lD40XaN/OFqS9MkDn8jpcB63jK8KNy6TJDnaFl8lOTPJAQAAAARTbUWtJMkeY2d05lGMMO/P4lSV5MwkB5qHJDmO40tcB7rVuk9UcpQyRmVIkvJW5TW4TG15rV4a+pJe6P+CDm873CJxoP3Y+OZGSd52/tGp0SGOBgDQXp3763MV1zVOJbtK9NUTX9V7zjRN7fx0pyQppX/LvIYCWgozyQEAAACEQl0F88gb0tiZ5LRbB5qHJDmO01LzyI+WPixdkpS3puEk+eZZm+U47FBdRZ3eu/E9/5VRwLFcNS5tfn+zJGnQjYNCHA0AoD0Ljw3Xhc9fKEla8qcl2vnZTv9zexfu1eFth2WPsavfVf1CFSJwWnzdD6gkBwAAABBMvkpy5pHX19iZ5LRbB5qHJDmO40uSpw5IbbF9dB7WWZJUsLagwec3vLHBfz9vdZ6++MUXLRYL2rbtc7ertrxWsV1ilTU+K9ThAADaub6X99WI+0ZIkmbdMkuV+ZWSpNXTV0uSBt40sEXG1QAtyVdJXllQycWpAAAAAILGN5Oc99H1nTJJXku7dSAQSJLjONnnZKvHpB5KH5reYvvwVZLnr82XaZr1nqs4WKHdX+yWJE15dookaemfl2rWrbNovY7j+FqtD7h+gAwLc2sAAC1vyl+mKG1wmqoKq/TONe+oeEexNr23SZI04u4RIY4OaLrolGhvBYLpfS0OAAAAAMFAu/WG+ZLkkmR6zOOep5IcCAyS5DjOub8+V7fMv0Vpg9NabB8p/VMUZg9TTWmNSveU1ntu41sbZXpMZZ6VqTE/GqNxj46TJK3/33q90O8FLXh8gTzuhq+gQsdSW1GrbR9tk0SrdQBA8Ngibbrm7Wtkj7Vr/+L9enHwi3LXupU+LF2dR3QOdXhAkxkWQ7FdYiVJZfvLQhwNAAAAgI6CdusNs4QdSd01VE3OTHIgMEiSIyTCbGFKHeht556/Jr/ec75W64Nu9iY9J/1hku5afpfOuOQMmR5Ti36/SDMumqHqw9XBDRqtzvaPt8tV41JSrySSEgCAoOrUt5Pu+OYOxWfHy+XwvjkdfvdwGQZdTdA2JWQnSJLK9pIkBwAAABAcVJI37OhK8oaS5P5261SSA81Ckhwh42u5nrcmz/9Y6d5S5a3Ok8Vq0YBrB/gf7zKqi2786EZdNeMqWSOt2vnZTr00+CVtm7Ot3jZN09ThbYfldjJLsSPY+dlOSVKfy/uQlAAABF3a4DTdveJu9ZraS+lD0zV42uBQhwSctoQeCZKkkl0loQ0EAAAAQIfBTPKGnTJJXsdMciAQ6MWAkPHPJT+qkvzAtwe8zw1NV1SnqOPWGXTjIKUOSNXMa2fq8LbDevPSN9X/mv4a9eAoxXaO1dwfzNXOz3YqfVi6rnvvOiV2TwzOwSDoTNPUrs93SZJ6XNAjxNEAADqq6JRoTZs7LdRhAM2W2MP7upkkOQAAAIBg8bVbp5K8vlMlyWm3DgRGkyvJc3NzdfPNNys5OVmRkZEaNGiQVq5c2eCy9913nwzD0HPPPVfv8eLiYk2bNk1xcXFKSEjQnXfeqcrKytM6ALRdnYd522MfnSTPXZYrSeoyussJ10sbnKZ7196rsT8dK8NiaNO7m/Taua/p+T7P+yuL89fka/qI6f7v0f4c3nZY5fvLFWYPU/aE7FCHAwAA0KaRJAcAAO2Nx+XRmlfX6JMffKL/Tf6f3rjoDX1494f68okvtWr6Km35YIu2z92uPV/t8VdlAgguX7t1ZpLXZ4Qd6ZrqcZ+kkpx260CzNOkyk5KSEo0bN07nnXee5s6dq5SUFG3fvl2JicdX686aNUvffvutMjIyjntu2rRpysvL0/z58+V0OnX77bfrnnvu0YwZM07/SNDmpA1Okwyp4mCFqgqrFJ0ardzl3yXJzzxxklySbJE2Tf7TZA2eNlgr/rFCG9/cqLrKOvWa2kvjfj5On//sc+Uuz9Wbl72pW7+4VVnjsoJxSAgiXxV55rhM2aJsIY4GAACgbUvqmSSJJDkAAGg/5v5wrlb+o+HirmONemCULnr+ohaOCMCx/DPJabdej2EYMiyGTI+p6qJqRadE13veP5OcdutAszQpSf70008rMzNTr776qv+x7t27H7dcbm6ufvCDH2jevHm6+OKL6z23efNmffrpp1qxYoVGjhwpSfr73/+uiy66SH/+858bTKrX1taqtrbW/315eXlTwkYrZY+xK/mMZB3eelh5q/PUfWJ35a3yzic/WSX50dKHpuvS6Zdqyl+mqPpwteKz4mUYhm5beJveve5dbf1wq966/C3d9e1dSuqV1JKHgyDbNZ9W6wAAAIHiqySvyK2Qq8YlawRt+wAAQNtVuLFQq15aJUk686EzlT40XabHVMXBClXkem9VRVWqKqhS6Z5SFW8vDnHEQMfkn0lOu/XjdB3TVfuX7Nd7N72nO765w38hgekx/S3YqSQHmqdJ7dY//PBDjRw5Utdee61SU1M1bNgwvfzyy/WW8Xg8uuWWW/TII49owIABx21j6dKlSkhI8CfIJWnSpEmyWCxatmxZg/t96qmnFB8f779lZmY2JWy0Yplnef8tt3ywRYUbC+WqcSk8PlzJvZObtB17jF0J2QkyDG8bEmu4VVfNuEoZIzPkOOzQGxe9obqquoDHj9DwuDza8+UeSVKPSSTJAQAAmisyOdL/wVTpntLQBgMAANAMpmlq3sPzZHpM9bu6ny7864UadvswDb9zuM755Tm65KVLdONHN+qub+/SpKcnSZJcNa4QRw10TL6Z5LRbP95VM65SdGq0CtYV6P1p7/vbrh89HoKZ5EDzNClJvmvXLr344ovq3bu35s2bp/vvv18PPfSQXnvtNf8yTz/9tKxWqx566KEGt5Gfn6/U1NR6j1mtViUlJSk/P7/BdR577DGVlZX5b/v3729K2GjFBt00SJKU81aO9i7cK8nbat2wGCdbrVHs0Xbd+NGNiu0Sq+Ltxcp5O6fZ20TrcHDlQdWW1yoiMUKdh3cOdTgAAABtnmEYzCUHAADtwvaPt2vX57sUZg/TBX+84KTLWiO9CSanwxmM0AAcw99unUry4yRkJ+iGD25QWHiYtn64VXMfmivTNOslyakkB5qnSUlyj8ej4cOH68knn9SwYcN0zz336O6779ZLL70kSVq1apX++te/6j//+Y+/ojcQwsPDFRcXV++G9qHbed0U1zVONaU1Wvz0YkmnnkfeFDHpMRr1wChJ0tr/rA3YdhFaO+fvlCR1P7+7LGFNOo0BAADgBEiSAwCA9uCbp76RJI3+0Wj/65sTsUXaJEkuB5XkQCj4KsmZSd6wrmO66sr/XikZ0sp/rNSCxxfIVXvkfEWSHGieJmWXOnfurP79+9d7rF+/ftq3b58kadGiRSosLFRWVpasVqusVqv27t2rn/zkJ+rWrZskKT09XYWFhfW24XK5VFxcrPT09GYcCtoiS5hFg272VpNX5lVKavw88sYacusQGRZD+xbtU/EO5gu1B/sXe7tJdD+/e4gjAQAAaD9IkgMAgLaubF+Z9i/ZLxnSmB+NOeXyVJIDoeWbSU679RMbcN0AXfLSJZKkb578RvN+NE+SZLFaAtKRF+jImpQkHzdunLZu3VrvsW3btik7O1uSdMstt2j9+vVau3at/5aRkaFHHnlE8+Z5f3HHjh2r0tJSrVq1yr+NBQsWyOPxaPTo0c09HrRBQ24ZUu/7QFaSS1Jclzj1uMA7t3rta2sDum2ERsH6Akmi1ToAAEAAkSQHAABtXc473nGL2WdnK7Zz7CmXp5IcCC3arTfOiHtGaNLTkyRJG2ZskCSFhVNFDjRXk5LkDz/8sL799ls9+eST2rFjh2bMmKHp06frgQcekCQlJydr4MCB9W42m03p6enq06ePJG/l+dSpU3X33Xdr+fLlWrx4sR588EHdcMMNysjICPwRotVL6Z+ijJHef/v47HjFpMUEfB9DbxsqSVr32jqZHjPg20fwVBVV+bsOpA5MDXE0AAAA7QdJcgAA0NblvO1Nkg+4fkCjlrdGUEkOhIppmlSSN8G4n43TtLnTFJ8VL0kKj+NnBjRXk5Lko0aN0qxZs/Tmm29q4MCB+u1vf6vnnntO06ZNa9JO33jjDfXt21cTJ07URRddpPHjx2v69OlN2gbal+H3DJckdZ/YMu2z+17RV+Hx4SrfX67dC3a3yD4QHL4q8sSeicyqAQAACKCjk+SmyYWlAACgbSneWayDKw/KsBjqf3X/U6+gI+3WXTVUkgPB5nK4/AVtfM7bOL2m9tL9G+/X+b8/Xxe9cFGowwHaPGtTV7jkkkt0ySWXNHr5PXv2HPdYUlKSZsyY0dRdox0bftdwJfZI9FeUB5o1wqqBNwzUqn+u0oYZG9RjUo8W2Q9ani9Jnj4kPcSRAAAAtC/x2fGSITmrnKouqlZ0anSoQwIAAGg0X6v17ud3b/TrmKPbrZumKcNgvi8QLLUVtd47hmSLtoU2mDYkPDZcE/5vQqjDANqFJlWSAy3FMAz1mNhDEfERLbaPgTcOlCRtfn+zXLVcHdpWFa4vlCSlDqbVOgAAQCBZw62K6xoniZbrAACg7Wlqq3XpSCW5JLlr3QGPCcCJ+eeRx9i5QAVASJAkR4eRPSFbsV1iVVtWqx1zd4Q6HJwmXyV52uC0EEcCAADQ/jCXHAAAtEUlu0tUsK5ARpihvlf2bfR6vkpyibnkQLD5KslptQ4gVEiSo8MwLIb/StKNb24McTQ4HR6XR4U53kpykuQAAACBR5IcAAC0Rds/2S5JyhqXpajkqEavZ7FZZFi8FawuB50ngWCqq/RWkofHhoc4EgAdFUlydCiDbhwkSdr60Vb/H2G0HYe3HZa71i1btE2J3RNDHQ4AAEC740uSF+8oDnEkAAAAjbf9Y2+SvPfFvZu0nmEYskZ4W667akiSA8Hkb7ceSyU5gNAgSY4OpfOIzkrqlSSXw6WtH24NdThoIn+r9UFp/qt8AQAAEDjJZyRLkg5vPRziSAAAABrHWe3Uni/3SGp6klw6MpecdutAcPnarVNJDiBUSJKjQzEMQwNvHChJ2vze5hBHg6byJclTB6eGOBIAAID2KbnPd0nybSTJAQBA27B7wW65alyKz45XSv+UJq/vm0tOu3UguPyV5MwkBxAiJMnR4XQ/v7skKW91XogjaTmrX1mtuT+cK2d1+7oC1pckTx+SHuJIAAAA2qfk3t4kuaPYoepD1SGOBgAA4NS2fbxNkreK3DCa3nmQSnIgNHzjUGm3DiBUSJKjw0kd5K1CLt1T6m/p0p6U55br4/s+1vK/LdfM62bK7XSHOqSAKdpUJElKGdD0q4IBAABwarYom+Kz4iVJh7YeCnE0AAAAJ2ea5pF55Bc1vdW6RCU5ECq+z+ZJkgMIFZLk6HCikqMU0zlGklS4sTDE0QTein+skMflkSRt/3i7PrjtA5keM8RRNZ+7zq2yvWWSjszKBAAAQOAxlxwAALQVRTlFKt9fLmuEVd3P635a26CSHAgNX7t1ZpIDCBWS5OiQ0galSZIKN7SvJLnT4dSqf66SJI38/khZrBZtmLFBy59fHuLImq9kV4lMjyl7jF0x6TGhDgcAAKDdYi45AABoK3yt1ruf3122KNtpbcMa4U2Su2qoJAeCyV9JzkxyACFCkhwdkq/lesGGghBHElgb3tggx2GH4rPjdeHfLtSU56ZIkj7/+edt/kPOw9u98Sf1Sjqt+VIAAABoHH+SnEpyAADQyvlbrV98eq3WJdqtA6HirPR2b6DdOoBQIUmODsmXJG9PleSmaerb576VJJ35gzNlCbNo1P2j1H1id7kcLs2+bbY8bk+Iozx9xTuKJUlJvZNCHAkAAED71qlPJ0nMJAcAAK2bo8Sh/Uv2Szr9eeQS7daBUPFVktNuHUCokCRHh3R0u3XTbPvzuiUpf02+inKKZIuyafidwyVJhsXQ5f++XPZYuw4sPaAV/1gR4ihPX/H275LkvUiSAwAAtCTfTPLiHcVt+iJLAADQvu2ct1Om21RK/xQldEs47e1QSQ6Ehm8mOZXkAEKFJDk6pE79OsmwGHIUO1SZVxnqcAIib3WeJKnr2K6KSIjwPx6fFa9Jf5gkSVr656VyO90hia+5/ElyKskBAABaVHxWvKwRVnmcHpXuKQ11OAAAAA0KRKt1iUpyIFTqKr9LkjOTHECIkCRHh2SLtPmTre1lLnn+unxJUtqQtOOeG3bHMEWnRqtsX5k2vbsp2KEFhG8meXLv5BBHAgAA0L4ZFsP/Wpm55AAAoDXyuD3aPjewSXIqyYHgot06gFAjSY4O6+iW6+1B4XrvcaQPST/uOWuEVaMeGCXJW03e1lrMu2pdKttXJolKcgAAgGDwtVxnLjkAAGiNcpfnynHYofD4cGWeldmsbVkjvkuS15AkB4KJdusAQo0kOTqs1EGpktpHktw0zZNWkkvSqO+PkjXCqrzVedr79d5ghtdsJbtKJNPbeic6NTrU4QAAALR7yX28SfLD26gkBwAArc/m9zZLknpN7aUwW1iztuWbSU67dSC4fJXktFsHECokydFh+ZLk7aHdevn+ctWW1cpitahT304NLhPVKUpDbhsiSVr6zNJghtdsR88jNwwjxNEAAAC0f536eF9THt5CkhwAALQuHrdHG9/cKEkaeMPAZm+PdutA8Jmm6Z9JTrt1AKFCkhwdlq/detGmIrnr3CGOpnl8VeSd+nWSNdx6wuXGPjxWMqRtc7bp0Ja20zqzeIc3Sc48cgAAgOBIGZAiyXtBaVsb1QMAANq3vV/vVcXBCkUkRKjXhb2avT1fJTlJciB4XDUumW7v+wzarQMIFZLk6LASeyQqKiVK7lq3Diw7EOpwmqVgnbcavqF55EdLPiNZfS7rI0la+pe2U01+eLu3gimxV2KIIwEAAOgYUgekyggz5DjsUEVuRajDAQAA8Fv/xnpJUv9r+5+0WKSxfJXktFsHgsc3j1yS7NEkyQGEBklydFiGxVCPiT0kSbs+3xXiaJqnYL03SZ46OPWUy479yVhJ0rr/rlNVYVWLxhUovnbrVJIDAAAEhzXCqpR+3mryvDV5IY4GAADAy1Xj8s8jHzRtUEC26a8kr6GSHAiWmtIaSZIt2ibDwnhNAKFBkhwdWvdJ3SVJuz/fHeJImqexleSSlDU+S13O7CJ3rVsr/rGipUMLiKNnkgMAACA40od5X1vmr80PcSQAAABe2z/ZrtqyWsVlxil7QnZAtmmNYCY5EGwHVx6UJKX0TwlxJAA6MpLk6NB6TPJWkh9YdkC15bUhjub0OKud/nbkaUPSTrm8YRj+avIVL6xQXVXdKdYILVeNS2X7yyRRSQ4AABBM6UO9SfKCtQUhjgQAAMBr9b9WS5IG3jgwYNWntFsHgm/f4n2SpMxxmSGOBEBHRpIcHVpCdoKSeiXJdJva8/WeUIdzWgo3FkqmFJ0arZi0mEat0++qfkrsmajqQ9Va9rdlLRxh85TuLZVMyR5jV1RKVKjDAQAA6DB8leS0WwcAAK3B/qX7tWPuDhlhhkbcPSJg2/W3W6eSHAia/d/sl+TtegoAoUKSHB1e94neluttdS55/jpv+8vGVJH7WKwWnfubcyVJS/64RI4SRwtEFhhle71V5PHZ8TIM5tMAAAAEi2+UT+nuUv/MQAAAgGBx17k187qZ+uIXX8hd59aXj38pSRp621Al9QrcSD4qyYHgqimtUcEGb7eqrHEkyQGEDklydHi+luu7v2ibc8kLNxRKktIGNz5JLkkDbxiolAEpqimt0ZI/L2mJ0AKibJ83SZ6QnRDaQAAAADqYyKRIxWfFSzpyYWYgmR5TO+btUMXBihMvY5rKW5MnVw2VXQAAdDQHlh3Qppmb9M2T3+ifw/6p3Qt2K8wepnN+dU5A90MlORBc+5ful0wpqVeSYtIb1xkVAFoCSXJ0eN3O6yYZUlFOkSrzK0MdTpMV5RRJklIHpjZpPUuYRef/7nxJ0rK/LlNF3ok/nAwlX5I8LisuxJEAAAB0PL6W6/lrA58k//JXX+qNqW/o2axn9ealb2rL7C1yO93+503T1NyH5mr68On6+IGPA75/AADQulUVVPnvF23yfv41/J7h/ov4AsVXSc5FeUBw7F9Mq3UArQNJcnR4UclRSumfIkk6uOpgiKNpusKN3krylAEpTV63z+V91GV0FzmrnPrkgU9kmmagw2s2f7v1AL8BAgAAwKmlD/UmyQvWFgR0uyW7SrTkT95uRqbb1LY52/T2lW/r2a7P6tOHP9XO+Ts1/5H5WvH8CknS+v+tb7UXdQIAgJZRVehNkncZ3UUZozKU0D1BE/5vQsD3Y42g3ToQTPu+2SdJyhyfGeJIAHR0JMkBHWlVXrA+sB/+tbTqQ9X+Nwwp/ZqeJDcMQ5e8dIksVou2zNqinLdzAh1is9FuHQAAIHR8leR5a/ICut3PfvqZ3HVu9bighx7Y/IDO+tlZik6LVlVhlZY9t0yvT35dS59ZKkmK6Rwjj9OjVf9cFdAYAABA61ZZ4O34mD4sXXcvv1sP7XhIsZ1jA76fo9utt8YCEqA9cde5lbssVxLzyAGEHklyQFLakO+S5OvaVpK8MMdbRZ7QPUH2GPtpbSN9aLomPO69CveTBz/xvwFpLXxJcirJAQAAgs9XSV6UUyRXbWBakG77eJu2zNoiI8zQlGenqFPfTrrg6Qv08P6Hdf3s6zX09qGKzYiVYTF04fMXaupzUyVJK19cGbAYAABA6+crDIlOjZYkGRajRfbja7cuSe5a90mWBNBceavz5KpxKTI5Usl9kkMdDoAOznrqRYD2r61WkvtaracOaNo88mNNeGyCtszaooJ1BZp962zd9MlNsoSF/hoa02OqbP93SfJskuQAAADBFp8Vr8jkSDkOO5S/Nl9dR3c97W3t+HSHvnnqG+1duFeSNOr7o+q9jg2zhanv5X3V9/K+Mk1Tziqn7DF2uZ1uxXaJVUVuhXLeydGQW4Y0+7gAAEDr55tJ7kuStxRfJbnkbbnua78OIPB2L9gtScqekC3DaJkLXwCgsUKfBQNagfQh3gqZw1sPt6n5Q0U5RZKklIFNb7V+tDB7mK7835WyRlq187Od+vJXXwYivGarzK+Ux+mREWa0SDstAAAAnJxhGMo8yzsrcP+S/ae9nfWvr9cbF72hvQv3yggzNOD6ATr/d+efdL++TklhtjCN+v4oSdK3f/lWHrfntOMAAABth6+SPCYtpkX3Y7FZ/FXqrhq61gAtacfcHZKknlN6hjgSACBJDkjyzjmMTI6U6TFVtKko1OE0WqAqySUpbVCaLnvlMknSN09+o03vbWr2NpurdG+pJCmuS5wsVk5XAAAAoeBPki8+vSR5zjs5mv292ZIpDbl1iH6050e65q1rFB4X3uhtjLhnhOwxduWvzdfyvy8/rTgAAEDbcmy79ZZiGIa/5brLQZIcaCk1pTXav9T7nqLX1F4hjgYATiNJnpubq5tvvlnJycmKjIzUoEGDtHLlSv/zv/71r9W3b19FR0crMTFRkyZN0rJly+pto7i4WNOmTVNcXJwSEhJ05513qrKydc1BRsdiGIa/mrytzCU3TfNIJfmA5lWS+wy6cZDGPDxGkvTeje9p3f/WBWS7p8s/j5xW6wAAACGTOe5Iktw0zSatm7cmT+9Pe1+mx9TQO4bq8lcvV1zXuCbHENUpShf8+QJJ0hf/94UObz/c5G0AAIC2JVjt1iX5W6y3pQ6TQFuz6/NdMt2mOvXtpIRuCaEOBwCaliQvKSnRuHHjZLPZNHfuXG3atEnPPPOMEhMT/cucccYZev7557VhwwZ988036tatmyZPnqyioiPVudOmTVNOTo7mz5+vOXPmaOHChbrnnnsCd1TAaUgb4p1Lnr8uP8SRNE5VQZUcxQ4ZFkOd+nYK2HYv+OMFGnDdAHmcHs2+dbYW/m5hwLbdVP4keRZJcgAAgFDJGJkhi82iyvxKle4pbdK6C3+7UB6XR2dceoYunX6pv5Xp6Rhxzwj1mNRDLodLs2+drZLdJae9LQAA0Lq5alyqLa+VJEWntXyS3DeXnEpyoOXs+PS7VutTabUOoHVoUpL86aefVmZmpl599VWdeeaZ6t69uyZPnqyePY+c1G666SZNmjRJPXr00IABA/SXv/xF5eXlWr9+vSRp8+bN+vTTT/Wvf/1Lo0eP1vjx4/X3v/9db731lg4ePBjYowOaIG2wN0leuL4wxJE0jq/VemLPRP8L+UCwWC26+s2rNe7RcZKkL3/5pfZ9sy9g22+Ksr0kyQEAAELNFmlT5+GdJTVtLnlhTqG2zNoiSZr0h0myhDVvfI5hGLr0X5fKHmPXgW8P6O+9/q53rn5HVUVVzdouAABofXx/3y1WiyISIlp8f75261SSAy3DNE1/kpxW6wBaiyZ9SvHhhx9q5MiRuvbaa5Wamqphw4bp5ZdfPuHydXV1mj59uuLj4zVkyBBJ0tKlS5WQkKCRI0f6l5s0aZIsFstxbdl9amtrVV5eXu8GBNrRleRNbSMZCoU5gZtHfizDYmjSHyZp2J3DJEkLHl/Q6J/J4e2H9fZVb2vf4uYn1mm3DgAA0Dqczlzyb576RpLU76p+SukfmPFACdkJuvWLW9VzSk+ZHlOb39+sT3/4aUC2DQAAWo+jW60bxul3omksKsmBllW4sVAVuRWyRlrV7ZxuoQ4HACQ1MUm+a9cuvfjii+rdu7fmzZun+++/Xw899JBee+21esvNmTNHMTExioiI0LPPPqv58+erUydvO+j8/HylptZP6lmtViUlJSk/v+E210899ZTi4+P9t8zMzKaEDTRKSr8UGWGGakpqVJFbEZIYnNXORl+xmr/a+/uSMjAwHzg25JwnzlGYPUx7v96r3Qt2N2qdhf9vobbM2qK3Lnurye04j0W7dQAAgNbBP5e8kZXkB5Yd0MY3N0qSxv/f+IDG0uXMLrr505t128LbJEkb39yo/LVtY2RSoGyZvUV/7/13LX12aZu4wBcAgKaqKvwuSR6EVuvSkUpyVw1JcqAl7JjrrSLvdm43WSOsIY4GALyalCT3eDwaPny4nnzySQ0bNkz33HOP7r77br300kv1ljvvvPO0du1aLVmyRFOnTtV1112nwsLTb2H92GOPqayszH/bv7/x1QtAY1kjrP7Z3sGcS+6qdWnxHxfr1Qmv6g/xf9DTiU9rxiUztOrlVarMr2xwnSV/XqJ1/10nSeo6pmuLxRafGa8R946QJH35+Jen/ACurrJOm9/fLElyFDv0ztXvNKtNla/dekJ2wmlvAwAAAM3nqyQv3FDonw96ImtfW6v/nPMfmR5TZ1xyhjJGZLRITNkTsjXwhoGSpAW/WNAi+2iNKvMr9cHtH6h4R7E++/Fnmn3rbFrDAgDaHX+SPDU4SXJfJTl/U4GWsfk972fGvS6k1TqA1qNJSfLOnTurf//+9R7r16+f9u2r31Y5OjpavXr10pgxY/TKK6/IarXqlVdekSSlp6cflzB3uVwqLi5Wenp6g/sNDw9XXFxcvRvQEnxzyQvWFwRlfx63R7NunqXPH/1c+77ZJ4/LI3etW9s/3q4598zRMxnP6JWxr+izRz7Thjc3aNX0VXr3hnc1/5H5kqQxPx6j3hf1btEYxz82XtZIqw58e0Dr/7f+pMtumb1Fzmqn4rPiFdUpSnmr8zT3B3NPa7+15bWqKa2RJMVl8jsPAAAQSrGdY5XQPUGmx9SBbw8c97xpmtq9YLdmXDJDH9z2gdy1bp1xyRm68vUrWzSuc//fuTLCDG3/ZLv2fdP8cT9twdyH5qqmtEZxmXEywgytf329/nv+f+UodoQ6NAAAAqaywFs4EpMWE5T9+SpbabcOBF7RpiLlLs+VEWZowHUDQh0OAPg1KUk+btw4bd26td5j27ZtU3Z29knX83g8qq31VhuMHTtWpaWlWrVqlf/5BQsWyOPxaPTo0U0JBwi41IHeUQBFOUUtvi/TNDXv4Xna9O4mWWwWTXluih7a+ZDu33i/zvvdecoYlSGZ0oFvD2jpn5fq/Zve15x75yjn7RxJ0gV/ukBTnpnS4nOZYjvH6qxHzpIkfXjXhydtu77+dW8SfegdQ3X1W1dLhrTmlTXKmZnT5P2W7fdWkUckRig8Nvw0IgcAAEAg+WYHbvt4W73HK/Iq9Nq5r+m/E/+r7R9vlwzv2J4bPrhBEfERLRpTcu9kDbtzmCRp0e8Xtei+WoMtH2zRppmbZIQZuuGDG3TLZ7coIjFCB749oFfPflXlueWhDhEAgIDwVZJHpUYFZX++dutUkiOYHCUOvT/tfa39z9pQh9KifMd3xsVnBO3CFwBojCYNf3j44Yd11lln6cknn9R1112n5cuXa/r06Zo+fbokqaqqSr///e912WWXqXPnzjp06JBeeOEF5ebm6tprr5XkrTyfOnWqv0270+nUgw8+qBtuuEEZGS3Thg9orJQB3vnewUiSr355tZb/fbkk6cr/XamB1w/0P5c6IFVn/+JsleeWa+dnO3Vw5UHlr8lXRHyEUgenqveFvdXt3G4tHqPPOb86R4c2HdKmdzfprSve0h2L71DaoLR6y1TmV2rX/F2SpMHTBiupV5LGPzZe3zz5jebcM0ddx3RVfGbjZ4vTah0AAKB16XtVX639z1ptfnezpj47VYbFUO7yXL195duqOFgha6RVQ28bqtE/HK1OfToFLa6xD4/V6umrtXvBbjmrnbJF2YK272CqOFihj+7+SJJ01k/PUudhnSVJty+6Xa9Pfl1FOUV6ceCL6ntlX/W9oq+6jukatBa1wWB6TFUVVqmqqEqOw96q+bDwMEWnRiuhW4IsYU2qAQAAtHLVhdWSgt9unUpyBNO8h+dpw4wN2vTeJnU7t5sSuiWEOqSA87g8/u6kQ28fGtpgAOAYTUqSjxo1SrNmzdJjjz2m//f//p+6d++u5557TtOmTZMkhYWFacuWLXrttdd06NAhJScna9SoUVq0aJEGDDjSRuONN97Qgw8+qIkTJ8pisejqq6/W3/72t8AeGXAaUgd4K8kPbTkkj9vTYh+0uGpc+urXX0mSJj41sV6C/GhxXeI07PZhGnb7sBaJo7EsYRZd+b8rVVVUpb1f79XC/7dQ1868tt4yG9/aKNNjquuYrkrqlSRJOvfX52rX/F06uOKgZt86W7d+casMS+Mq30t2l0iS4rMan1gHAABAy+k5uafC48JVcbBC+5fsV3RatP5z7n/kcrjUqV8n3fDBDUrunRz0uJL7JCs+K15l+8q05+s96n1hy44jCgWP26P3p72v6qJqpQ1O0zlPnON/LnVAqu5YfIfeuOgNHdp8SGtfXau1r66VJMV2iVXnYZ2VPjxdnfp0UlxmnOK6ximuS5zC7GEhOppTc1Y7lbcmT7nLc3VwxUEVbihU8c7iEyYurBFWpQ5MVbfzuilrfJYcJQ6V7S1TYo9EnXHJGYpIaFxHA7fTrarCKu97FvPICCjDYsgeY1d0WrSikoNT0QgAHV3Q261TSY4g2zFvh9a9tk6S5K51a8EvFuiqN64KcVSBt2PeDlXmVyqqU1SLjw0FgKZqUpJcki655BJdcsklDT4XERGh999//5TbSEpK0owZM5q6a6DFJXRPkDXCKleNSyW7SlrsQ741r65RZV6l4rrGaeyPx7bIPgLNGmHVxKcm6t9n/Vu7F+yW6TH9Ce89X+3R1//va0nSoJsH+dcJs4Xpqjeu0j+H/VN7vtqjVdNXaeR9Ixu1v8INhZKkTv2DV4UEAACAE7OGW9Xn8j5a/7/1ypmZo5KdJXI5XMqakKWb5tyk8LjQjMgxDEM9JvfQmn+t0c7PdrbLJPnXv/lae77aI1u0Tde8c42/2s0noVuC7t9wv/Yt2qdN727Srs936fC2w6rIrVBFboW2zdl23DajOkXJHmOXNdIqW5RNtiibYjvHKmVgirqf311Z47KCcmzOaqcKNxYqb02eDq446E2K5xTKdJvHLWtYDEV1ilJkUqQMiyGnw6nKvEq5alw6uPKgDq48qCV/WlJvHYvNopT+KfI4PTJNU5GJkbLH2uWscqq2ola2SJsiEiNUXVStgg0Fcte6TxpvdFq0UgekKmVgilIHpCp1YKpSBqTIVeNS8fZiVRV52wOHx4ar65iussfYA/fDCjLTNOVyuFRXWSePy6Pw+HDZomzyOD1y1bhki7LJYqWCH0DL8LVbD1YluS9J7qqhkhwtr7a8VnPumSNJ6n1Rb23/ZLs2zNigMQ+PUcbI9tVtd+2/10ryfmbcmi/SBNAxNTlJDrRnljCLOvXrpPw1+SrKKWqRJLnb6dbipxdLksY9Oq5NvTjoMqqL7LF2OYodyl+br87DO2vDjA2a/b3Z8rg86jqmq4beNrTeOsm9kzXxyYn69Ief6vNHP9cZl56huC5xp9xXwboCSVL6kPSWOBQAAACchv7X9tf6/63X6pdXy+VwyWK16LJ/XRayBLlPrym9tOZfa7Trs10hjSPQTNPUot8v0sLfLpQkXfLPS07Yyt4SZlG3c7v5xzLVVdYpf12+8lbnKX91vkr3lKpsf5nKD5TLXetW9aFqVR+qPn5D70hf/eor9bmsjyY/M9nfJeqEMXpM5czM0erpq2WNtCqxR6Ki06IVkRChiPgIhceHyx5j9ydWK/MrVZ5brsNbDit/Xb6KtxfL9ByfEI9Jj1GXM7soY1SG0od5K+Hjs+MVZqv//snj9qh0d6lyl+dq1+e7lLcqT9Gp0YrrGqfc5bkq2lTkf2/RGEbYd52vTCk8LlwRCREyPabqKuvkKHaoqqBKuwt2a/eC3afclsVqUcaoDMVmxCo8Ltx/MyyG6qrqVFNao4rcClUVVMnj9pxwG/GZ8YrvFq/UAalKH5qu2IxYGWGGLFaLLGEWWawW7/e++43s3uVxe7Rz3k7lrshVdVG193aoWlVFVf77HtcxcRnen42PPdbuvWjDkOzRdiX1SlJsl1hVF1Wr4mCFwuPDldA9QWmD09Rrai8ln5Eswzg+PtNjSoYafA5AxxTsJDnt1tHSPC6PNxn+xgZt/2S76irrlNAtQde8fY0+/v7HWv+/9frono80+oejlT40XQndEhQR37huOK3VgW8PaPP7myVJw+4IbadUAGgISXLgGKkDUpW/Jl+FOYXqe0XfgG9/wxsbVLa3TNFp0Rp2Z9t6cWCxWtTtnG7aNmebdn2xS8l9kvXx/R/L4/Jo4I0Ddfm/L5c14vjTyqgHRmnDGxuUuzxXnz70qa5777qT7sf0mCrY8F2SfChJcgAAgNbC13K9trxWkjT6h6OVfEbwW6wfq/v53WVYDBVtKlL5gXLFdT31RZltwac//FTL/75ckjThFxM0eNrgRq9rj7Era1zWcRXhpmmq+lC1KvMr5ax2yuVwyVntlLPaqdI9pcpblaecmTna+uFW7fh0hy76x0UafufwBveRuzxXc+6bo/w1+ad/kJKiUqKUPiRdGaMylDEqQ11GdVFsl9hGJUwtYRYl9UpSUq8kDbpp0HHPH9p6SCW7SmSLtMk0TdWU1Ki2olb2aLu3orzaKUexQ+Gx4eo8orMSeySecL91lXUq2lykwo2FKtxYqKIc7/2K3ArJkBKyExSbESsZUvmBcpXtLdOBpQea9bORpLxVeU1ex5c4t0ZYvcn++IgjifrvKsJ3zd+l8gPljdqeEWZ4q/uPuZ6hrqJOdRV1kqQqValkV8nxK3/p/TJP8xSZFCkZ3kSBx+mRx+WR2+mWTG/Vf0xajMLjw1VdVK2a0hpFpUQptnOst+tBhFVh4WGyhltljbDKYrfIGm5VmD3M/3iYPUzWSKvCY8Nlj7XLHmP33w+P9V6wYY+1yxZlIyEPtGKmxzySJE8LUiV5BO3WERg7Pt2hhb9dKIvN4v27E+O9oGzXF7tUvv/I3934rHhdNeMq2WPsOv/352vTu5uUvyZfH9z2gX+Z8PhwxWfFKz4rXkm9k7zdbPqnKKV/SqPHyTSX6TFVV1Wn2vJa1VV6/+YbFkPxmfENfg7s43F59PH9H0uShnxviNIGpQUlXgBoCpLkwDFSBqRIkg5tOtQi21/6zFJJ0tifjD2uTWJb0H1id22bs027v9it6JRo1ZbXKrFHoq56/aoTVixYwiy69OVLNX3EdG1+f7NyZuZowLUDTriP4p3FclY5ZY20Kqn3yStXAAAAEDxHt1yPTo3W2b88O9QhSZIikyKVMSpDuctytXP+Tg27vW1djNqQ0r2l3gS5IV34twt15oNnBmS7hmEoOiVa0SknTjqc/auzNe9H87Tzs5366K6PdGjzIU16epIsYUdaa2//ZLveueYduRwu2WPtGvvjsYrNiFXJrhJVH6pWbVmtaspqVFNaI2eVUxabN6EZnRqt2C6xSuyRqLQhaUofkq6Y9JabN9upT6cTVt83lT3Gri6juqjLqC71Hq8tr/UmZ4/5oLhkd4kOfHvAm5gvr/XOOC+rkekxvcnbuHDFZsQqtnOsLLaG25a7alwq31+u4p3FKlxfqPy1+XKUOBpsR380j8sjubwzTmvLaut9KH+0yORI9bmsj2K7xCqqU5SiU6IVlRLlvx+RECFblLdS3FnlVF1lnT9Z7axyqvpwtdy1bu8FCKU1Kt5RrMq8SkWnRSu2c6wcJQ6V7CrR/m/2a8/Xe+Qodpw4ZqfHm7Q/6roC38iAgDN0wgR6eGy4bDE22aOPSsxHWGWLtCkyKdL/8/GNLJDpvfjE9HgvIrBYLbLYLAqzhSnMHiYjzCAhDzTR0ee5k/29CiR/u3UqydEMhRsLNfPamf5k8rGiOkVpyG1DNOC6AcoYmeH/+xCfGa87Ft+hdf9dp/zV3uItx2GHastqVbih0D+W8mixGbFK6Z+iTv071UueRyZFNinmmtIaFeZ4LwAs3OC9CLCqsMr/2qW2ova4i+QkyRZlU49JPdT3qr4acN2A4z7nXv7CcuWvzVdEYoQu+NMFTYoJAIKFJDlwDF+SvDDn+BcfzVW6t1SFGwtlhBkaflfD1RitXfeJ3SVJ+xbt83/AMeyuYads6Zc2OE3jfj5Oi363SB/f97GyxmV5qywakL/WW4mSOjC13gdxAAAACL1xj45T8Y5inf342a2qBWTPyT29SfJ57SNJXlXgraCLz4wPWIK8sVL6pWjap9P09W++1te/+VpLn1mqw1sP+6ud1vx7jT6+z9tRqteFvXTlf69UVKeooMbYmpxo3EBi90Qldk9skX36krKm2/RWZbs99e57XN7vnQ6n6irqVFN2JFFfW+b9mtQ7SX2v6CtreOM+GvJVw/nYIm3H/bt3O6fbCdevq6xTya4SbxL5qESy776z2qnK/ErVltUqKiVKEfERqj5UrYq8CjmrnHLVuuSqccld6/Z+rXPLXeeWq/a7+7Xe753V3mOuq6xTbUWt6iq++1rpfUymJLN+FXxL8x+rzXvsRx/30fePfa6h722RNsVkxCiua5zCY8NljbD6b0aY4f/5uGpd8jg9skXbFJkYqTB7mEzTVJg9TDHpMYpOifa3529si34czzTNej9zyTt6wBZla/TP1VXrUk3pUb+jR92cVU7/OIWqoipVHKxQTXGN6irr5K5z+y/k8Lg89X4PfL8XkvdCssikSIXZvrto47t/86PvW8Is9R73/Z7aY+yK6hSlmLQYJfb0ntOCMTbQ9zcwIiEiaGMKabfeNnjcHlUVVKm2otb/f9wWZat3O3Y0S0syTVMep0c1pTUqzy3XzGu8CfJu53bTyPtH+v/21FXWKbFHovpeeeK/u52HdVbnYZ3939dV1qlsf5nK9pWpdE+pDm89rKKcIn/npIqDFao4WKFdn9cfNxST7v19jUmLUXRatKJTvTeL1SIZkuOwQxUHK1S8o1iFGwqb1FUmPDZcMuT9e1vl1NYPt2rrh1v12U8+07A7h+mMi89Q8hnJWvXyKi3+g3fc6KQ/TAraxS4A0FQkyYFjpA5IlSQd2nJIHrcnoEnanfN2SpK6ju6qyMSmXdXXWqQOTFV0arSqCqt0cMVBGWHGcXPIT+ScX56jHZ/sUN7qPH1w+weaNndag28afTMD04bQhgcAAKC1SR2QqjuX3BnqMI7Tc3JPLfztQu36fJdMj9nmkz6+lvbh8aGZ924Yhs799bnq1LeTPrj9A22bs03/PuvfskXblLssV5I0+ObBuuzflwX1w2h4GYY3kaUwBS2B1Fz2GLvSBp/kPV6y96KQoyV0SwhoDKbH9CbRG0qgH3PfWe30Jz7dNW7VVXnn0lcf8s5vryqqksd58pntPh6n5/hlW5GIhAjFdolVSr8UdTu/mzLPyvR3ErBGWoNaCX+y+fSmx5Tb6U0EVxysUOmeUrnr3IpMipQt2iaP09u+313n9t7/7kIKi82iqOQohceH+z/jqSmtUfXhajmKHXIcdshV6/JeaGAY/nEJpXtLVX6gXIbFmxiyWC1yOo4aU+Hw/h9p6N9c8rbvtsfYFZEYoajkKEUmRyoqOUqGxfAntyoOVpy0w0JrY1gMxWfHK7l3srcDRcqRDhTHfrVHey+qOfr3zlXr8o9MsEZ6RyQ09G8d7Fbr0lGV5DUkyUPF4/KoMr9S5QfKvb+H+8u8yeADFf7vKw5WnLKbisVqOS5xbouyKcwe5k3ufve7e/TN4/L4Lw7xX5x01AVKFqtFhmEcWdfhXc/pcB53DkjonqBr371WUcnNu4DQHmNXSr8UpfRLOe65mrIaHdp8SEWbilSYU6hDm7z3y/aVqTK/UpX5lU3aV1zXOKUOSlXqQO8tLjPuyJiW726+c6TkvTigYF2Btn64VWteWaOyfWVa8sclWvLHJfW2231i9zZbKAagYyBJDhwjoVuCbFE2OaudKtlZEtAZi74kec8pPQO2zWAzDEPdz++ujW9tlCT1ubSPYjs3XBF+rDB7mK58/UpNHz5dOz/bqWV/W6YxPxpz3HK+JHn6EOaRAwAAoHG6nNlF1kirHIcdOrTlkFL6H/+BYltSU1Yj6cRVysEy8IaBSuyRqLcuf0uFG73dtmzRNk34vwka//Pxbf5iBHQshsXwV8Q3t82/aXor+Q2LUS+p60vkHpew/e4x3xz2Y+eyn+j7Y5+rq6pTRa43YXRshb3H5Tkynz08TGG2MNVV1ammpEZup1uGxZDL4VJlfqW3Jf93akq9oxGKcoq06d1N9Y4zzB6miIQIRSRGKDIpUtYIq7drwVHdC2orav0X9hyXYDomyeR7zhZtU3RqtLfCOTxMHqdHuctydeDbA3LVuGSPsctis9RLeJuekyfGWhtf8q36ULWKtxeffGFDCo8NPy4pZYuyyfR4f86RyZGKzfCORrDH2hVmC/NfyOFr7x8W7v1qDbf6uwc4ih1yFDu8HSa+60Bherw3j9vjv+/vTvHdv63b6VZdRZ23o8N3VafOKqdKd5eqdHfpKY/fGmmVYTHkrDr5jO+IhAj/CIOoFO+ttsz7/yk6NXhJcl8leVuYSe47/3hcnuNu7jr3kXPQUfcdJd7q4epD1f7tGIb3/OW773F7vBeBOJzeC4UcR5LBhsXwdy4IC/denOU47FBNaY2s4VbZom2yRXtHVfi+WiOscte5VVtRq6KcIhVu8HbXTOqZpKhOUf6xLDUlNXIUO1RZUHnKBLjkPZeHx4d7LxIzvf9mziqn/xzhcXn83RiCxpCikqOUfEayLpl+SbMT5KcSER+hrmO6quuYrvUer62o9Vaa7y9XZUGlqgqqVFlQKcchh//3PTIxUjEZMUrITvAmxgekNnm+uWEYSh+arvSh6ZrwfxO09aOtynk7R3u+3KOqwiqlDUnT+MfGq//V/XmtCKBVI0kOHMOwGOrUr5PyVuWpMKcwYElyj8ujXV9429+05SS55L0K0JckH353064GTOmXogv+fIHmPjhX8x+Zr4yRGcoan1Vvmfx13nbrVJIDAACgscLsYeo6uqv2fLVHexftbfNJct8Hu62hpX2XM7voruV36dMffqq4rnGa8H8TWnSOONAWGIYhw3r8B/+GxfC20g3t9S0nZXpM74U4prd1cXVRtcoPlCt3ea52L9itgnUFqimt8Sb869yqKqzyV/YGy6mSW/ZYuxK6JcgaYVVNSY3qquqOJIqPuvkS7Y7DDtWU1fiTaBHxEf7Kbl/y3+lwynSbiu0Sq/jseCVkJyg+K14yvPF4nB5ZI63eqtRIW/37EVZZbJZ6CTtf9bSj2OGtWj/s/Wq6TcVmxHpvXWIV2zlWEQkRrT6RZJqmKvMrVby9WMU7ilWZX6mqoipVFx3pruD76q51H9e23LAYCrOHedvSH5UH9V2kUbzj+AsJGluUEQiBnklumqbyVuVp76K93gsMKp0686EzlTEiQ7Xltfr4+x/rwNID9S6MMT2mwuPCFZno/T8pw/t5Yl1FnX82tLPKWe8il7boZBeNWKwWxXaJVVzXuPq3zCP3Y9JivK3Dj+Jre+6s/u53r6rOf993c9e6642o8N8irbKEWbzjSk5w8ZLv38caafX/zvvvR1plj7YfF1MohMeGq+vortLo4O3TYrWo35X91O/KfjJNU9WHqhXVKSqoXUgA4HSRJAcakDogVXmr8lSUU6R+V/YLyDZzl+eqtqxWEYkRyhiZEZBthkqvC3vJFm1TfFb8aSX8R31/lPYt2qect3M089qZumf1Pf43Po5ih8r3e2fhnLQVHwAAAHCMrAlZ2vPVHu1btE8j7x0Z6nCaxd9uPcSV5D7xmfG6/v3rQx0GgAAwLEa9EXDRKdFK6Z+inpN76uzHz5bkTTjVVXqr0B0l3mpRR7FD7jr3kRnW383KtsfaFR4X7q1EPTq55Ls5j6l0/a5CuaqwSo4Sh79KPH1IujLHZSqqU5TqKuvkcXr8ie4we5h/rnuYPaxe299WxfDOJPe1Gm9PDMNQbGdvUj/77OwTLuf7v1NdVC3TNBUeGy57jN3fut+XzHTVeNvW15TWeBPsh6pVfahalfmVqsitUG1Zrcb+dGzQjs8a4f2YvLmV5KZpauVLK7Xi+RUq2lRU77mcd3J04fMXatVLq3Rw5cEG13ccdjSqUv9Ejv2d8X0fHhuu2C6xik6NrtcyW+Z3X/XdRT4NXAhijbDK9Jj+rhW+Cx0ikyIVkRAht9M7n7quss6bnK7yJqndNW5vd4tIq5J7J/uLYUp2lshR4vB2qUiIUGRipCISIxSb4Y3vdEZfGobhvzimqVXRCBzDMJg/DqBNIUkONCBlgLfqpGB9QcC2uWPeDklSj0k9AjrnPBTiusTpgc0PeK+SPM0Xrpe9cpm31dLGQr1x4Ru65q1r1KlvJ//PPKFbQquomgEAAEDbkTXB26Fo36J9IY6k+XytZkM1kxxAx2YY3jnc4bHh3mpqoJGO/r9zoud9yczwuHDFpMeoU99OQY7yeL52682pJDc9pj59+FMt/9tySd7Ee88pPZXSP0V5q/O0c95OfXTXR5KkyORIXf7q5YrtHOsfR+DrWlBTUuO/eMQI8/48fRej2KPt3sT30SMNfDOzW3k3Ap/u53UPdQgAAEgiSQ40qMuZXSR5P1wzTTMgVye3h3nkR4vPbN6bZHu0Xde9f51eGfuKCtYV6J/D/qkzHzrzSBU5rdYBAADQRJljM2WEGSrbV6ayfWVtOrHT2irJAQBoz3zt1k+3ktzj8ujj73+s1S+vliRNfGqiRt4/0l8A4nF79NlPP9Oy55Yptkusbpl/i1L6te3RMAAAtHUkyYEGdB3TVWHhYarMr9ThbYfVqU/zrmh1O93KW5UnSep+PldL+iT3TtZ9a+/Th3d9qJ3zdmrJH5f4n0sfmh7CyAAAANAW2WPs6jy8sw6uOKi9i/Zq8LTBoQ7ptNWU1UgiSQ4AQDD4KskrDlboo3s/OjILu0ucIpMjFZnkvflGCxwtd0Wu5twzR/lr82VYDF3+6uUacuuQestYwiya+uxUDb1tqBKyE2gJDgBAK0CSHGiANcKqzLGZ2vPVHu35ck+zk+Sle0rlcXlkjbQqITshMEG2E3Fd4zRt7jRteGODdn+xWzVlNbJYLRp+1/BQhwYAAIA2KGtClg6uOKh9i/a16SR5XXmdJNqtAwAQDHFd42SEGXI5XFo9ffUJlzPCDNlj7LJF2mRYDDkdTtWUeC9si0iM0GX/ukz9rup3wvXTh1AUAgBAa0GSHDiBbud18yfJR943slnbKt5eLElK6pXUZuYDBZNhGBp882ANvrntfogJAACA1iF7Qra+/cu3bX4uOZXkAAAET3RqtO5be58OLDug8gPlKt9frvID5ao4WCFHsUOOYodcDpdMt6naslrVltXWW3/QtEGa8pcpik6NDtERAACApiJJDpxAt/O6SU9Ie77a0+y55Ie3H5bkbS8OAAAAoOVkjc+SJBVtKlL14WpFJUeFOKLT45tJ7ptlCgAAWlbqwFSlDkw94fNOh1OOYofqKuvkqnHJ9JiyRdoUmRRJchwAgDaIJDlwAl3O7CJrpFVVhVU6tPmQUvqnnPa2/JXkvZMCFR4AAACABkR1ilKnvp10aMsh5S7LVe+Leoc6pNPiq1CjkhwAgNbBFmmTrYst1GEAAIAAsYQ6AKC1soZblXlWpiRp95e7m7UtkuQAAABA8HQe3lmSlLcmL8SRnD5fJTkzyQEAAAAACDyS5MBJdDuvmyRpz5d7mrUd2q0DAAAAwZM+PF2SlL8mP8SRnD5/kpxKcgAAAAAAAo4kOXAS3c/rLsk7l9zj9pzWNly1LpXtLZNEJTkAAAAQDJ2HfVdJvrptVpKbHlO1FcwkBwAAAACgpZAkB04iY1SGIhIi5Djs0P4l+09rGyW7SmR6TNlj7IpJjwlwhAAAAACOlT7MW0leurtUjhJHiKNpurrKOsn03qeSHAAAAACAwCNJDpxEmC1MfS7rI0na/N7m09rG0fPIDcMIWGwAAAAAGhaZGKmEbgmSpPy1ba/lek1ZjSTJYrMoLDwsxNEAAAAAAND+kCQHTqHvVX0lSZvf3yzTNJu8PvPIAQAAgODrPNzbcr0tziX3zSOPiI/gQlsAAAAAAFoASXLgFHpO7ilbtE3l+8uVt6rpMw2PriQHAAAAEBzpw70t19viXPLaMm+SnFbrAAA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          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Plots measurments, pchip, ml timeseries to show complete temporal interpolation**\n",
        "well_no = imputed_df.columns.get_loc(p_index.value)\n",
        "\n",
        "#formatting dates for plotting\n",
        "imputed_date =  pd.to_datetime(imputed_df.index.values)\n",
        "interp_date =  pd.to_datetime(well_interp_df.index.values)\n",
        "wells_date =  pd.to_datetime(wells_df.index.values)\n",
        "replaced_date =  pd.to_datetime(replaced_df.index.values)\n",
        "\n",
        "#ignoring NANs for plotting\n",
        "well_interp_df.dropna()\n",
        "wells_df.dropna()\n",
        "\n",
        "#plotting the three dataframes: measurements, pchip, and machine learning\n",
        "fig, ax = plt.subplots(2, 1, figsize=(25,10))\n",
        "#ax.set_xlim([datetime.date(2001, 1, 26), datetime.date(2021, 2, 1)])\n",
        "ax[0].plot(imputed_date, imputed_df[imputed_df.columns[well_no]]) #machine learning (blue)\n",
        "ax[0].plot(interp_date, well_interp_df[well_interp_df.columns[well_no]], color = 'red') #pchip\n",
        "ax[0].plot(wells_date, wells_df[wells_df.columns[well_no]], marker = \"o\", linestyle = 'none', color = 'green') #measurements\n",
        "ax[1].plot(replaced_date, replaced_df[replaced_df.columns[well_no]], color = 'purple')\n",
        "\n",
        "ax[0].plot(imputed_date, imputed_df2[imputed_df2.columns[well_no]]) # ml NO PDSI (orange)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "wells_df.dropna()\n",
        "fig, ax = plt.subplots(1, 1, figsize=(25,5))\n",
        "ax.plot(wells_date, wells_df, \"o\") #measurements\n",
        "ax.plot(interp_date, well_interp_df) #pchip"
      ],
      "metadata": {
        "id": "W6RS71etVkKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "id": "2DkkusTYQHUe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "d7238b22-4f42-4181-94f8-a5d40b42379c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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//yRC07Asi6++7/+SnZnC+QDzf/gLBIKegSFe9ZG/Q7hLstR/CCKcMagmSPhuAlJafO0D73HzadQ/vGwB4XXDamZoiNf/07+jaXpVD+E8J1961x+SnT7aIo9W141khpxnbmHqaGic3uER3vJvXwCoPK9LjZfnsVr+2j8WRcGvX7tpg4jSDouJ79Gu3se6Hfy0q1832qFdXviuP+OGr/zHkucZ1g5Ryux9Th1vWhn/u+0cdyLRqm6ONcfD0erJwFK085P5uVE8+RhNruVZK19zvNU4IfnZQWfLPVV/iiCO0au+JyQy4rscheLJyfJ+PobfvBWAo1+4P1Lc5IYBCo/ORIrfCX3PW8/c/z62aDmert1iMWVupstSyfWjaRpC0zrK43hQLhWxTWfZ9ZpX076LimHYPRQeneXolx6ovee7752MvGlrZTa0EMJZQcpX/0HbkQghjvss6rCVrurpe8F65n7U+vk5XuWJWg6/foVdMxz+3L2V7xqaCO7Lx7uNFMEsiz3dlyvvf//7efe7q8tieDPdFd1j/44HKsYCPb4JI/W0JcnHMzfEW28tEglLwo8/7f9y8Gzivd2R3SzPH/6rf4B+zpLn2QpTwtf+4lZfyMtJ9i9e5lXv+7V79SqSA83jfum9N/tCXtkyfq2+nfLbTfPpFFPCF979q5C7ryXZuCBHx3gvV8q2c0wMQJjRvmTBZ9/5cwAs+3d8cb1NduodNuqdO6T7f0B4jfNHNf9iCb72oV8SS6ZaluXyN25hZE3jw314zzzXXbW91nFC1H559ZwbyoU8pfJzifcGOTU0OkoUi/DNv/kVyUwvpfwCxeJFxHpswIagmWENHh61FIvO/VhPQ8KA2AJvEZZq/Cj790gKBfjeP/2adF8/uflZisWnEusJKCeAlBTy8INP/IaegQHnS7io++7uK9fJW4c5ZVtz78qF6SK3XP1oVWP/7wWfo4kAFqanKBTOxkiFO+/kc3DNZ2+hd3iEhaNHKOTPwEjVxqlPUzkPdA5q78XAT790C2bxNIzkpiaxGtxnfWGi7oZoPBeCfF7wo3/7DZnBIaee/HWUPyugzGHHujApAdvZC1e6/Rc7JH4dgS9ow+vvrmsfQY9vDbkrue5L1wOgx89okCXr4jan2f0oz0mAPFk/tjn1VuuwFtTOQXk26nDXT+5Fi21ozDfgtHlglLDW9Xf9Vd8n1e/06eu/9H2EsTogWhQHsGb9r+480vMYzSEw6LryUnwRToHN864Lb3g+JHf86EdALFr6iPlf/6XPtvp4USieMBwp7CNnzpHSexucZMH5bhsUvpzoho5SSiQSgWgpS0pJzprnSGEfQNP6WwrCynus2sorv4AlKfeJ0OeicKzLcCzbf6nzqTdSPFH6xFKj9cWdsWy+u/tiKxRR0fsTpE91JjLMDaSwZsP3Wdb640wV97NwywNk+gYw+lPYc+HxO0HrjzM/OA0pDVEIMn9GwytXs/2do2xb5Se5YQDRY0DODjTMBiEBmZQcye+FB/cFbmeWPnW4Zd03K6PEbqscJwKxeALi7aUxNiVYGHy8aT3q/QnSG2v7RerUIWIDyZbpEusX+ZJ/kSTW96P3x1vqmXnaKrK/3L9syxO1HH79Euv7MQYSy7ZMiu5w3IzuK1asAGBiYqJmpvvExERlufkVK1YwOVm7F4RpmkxNTVXSr1ixgomJiZo43rUXp55EIkEikehKORTBLMxMV86lPY9d3lcboa0fLMc2bv/4ClKZXvIL88xOHDomevWNjpHoyVDMLjB32F2KpVJHAcYa37Fq1AqKH5bOu6w3DFXPdSOGpmnYto1lmi3K1Hi+FC8/Go2+VMKMWBxN133v3asv4uvfxTe8pJeOfNtuXNrmRPtxLRr6TXNsy0sXCxAWMc9o0SrMHbWAhZbxzJIVGF4uWUwdyEbOTzOCPwvCOLrfBJwxTI+f0lba48nBXSXAGT/0eOultvY/VAQmWsZL98VbGt2LuTIP3nwwipoAGImzW8Z57J4C4Hx2GMnzIsvuFkZrv5CusGd7EWisOyN5zrFRYInxRtVGxxPFscaS8P1/uce9uoLEcXbwU4RjAUgqTnmyYaWj+vOg61ZxGh2ymqdfpMNCiIOEbIjXbv5BThFB3xfr08i6NE64rL8f4GzY9H6Qri3ar/mMyk4dcSC4TbskuyNHqXCHnF/O7GNNj/fdpfrq1zFCUwn3I7z7laUw67ISwd9Rw37BNFwHTSoKuvb5aHmae7o553UlCJIrJZqAA7mdrEtvRAjQhUBz02u+snjjwZ1Tt4DmDBK3Tt3CtqFnA50ZBoPqRCLR6h0zXV29GEKIGr2EL7xBdphaAcbMIH1q8hGCu446Tn0Xjb0kskE0SjyvfqPG7fS3YjtpTwSDbzv1tlzzaeZMEjXPdvVaSgeW49FvBl/k/HY++tUdxzRfhcJj4IUbKgbIgReeEtoXJXDbgR/zyF/fUQk7dew8tvU8G2Tr94DeuBAWz7v/mz0/4LGP3cPq9GnO51VdGiml96UhfPwRoqZcQUTZ7i0ofn9hyNVLNuoVpCtw8+7vsf9jD9fI8+clNMHAC0/hyFd3NHzGe+UJKsnACzfwyG2/bqscT2S8emw2ngb1i07THWui6qkZ2rIuTyf1faK0kWJxHLfl5aWUrFq1ij/5kz/hPe95D+DMOB8bG+Oqq67i1a9+NTt27GDLli3cfvvtnHee88L92muv5corr2Tfvn2sWrWKT3/60/z5n/85ExMTxGKO0egDH/gA3/nOd3jwwQcj6RZ1WQBFdPY+cC/f/Gjw/hjLnVf+xcdYe8ZZx7QMxyPPViyFTr/9ob8B4Ft/9UFfaNBrLMkrPvhXrDvjLPZuv5dv/dWfR9a3UzorZ+TXdrz8Ax9l9WZ3Nqg76u7bfj/f+bu/DE4TtEZ+0/zb08cL+613vRck/PCT/+B7KVjvqOH7E83uu9c1+wA1OgFc8rtvYvTk+tmfjYyd3Eci1egbVsyVmdzj7l9aeQcuGxwspJQc2f04v/j6l+t0CHJMqJ4/7RWvYWj1Wo7s3sOt3/8OoIHQnGMgnbx4Dglvc5axg6P7tue/mMGVq5k+uJ87//fqhvv11+c89wX0j6+s1l9IVuPr+1h92mBTDfLzJXbcfLBmGcuKn4wr2Ls1OznB/Tde52oS3mc3P+My+kbGmDs8yY5f/twXJWo/bxavFe0aQILu1Xj11FxL3/mZlz2H/rEV1NTRxAQP/Px6N07Iq2dRd10TR+Dvt6JyHmUcCQtrFh5RVpg1I7J8L043vro69VQZpyrOa/6/xRjCOilfh+3T8oVq9X5m0N3CYnqqhdzGZ8sb2Vs/h8Hpo93zghb/+dYq/XI3YCgUCoWiOZ6hX/g+l4K+GdVf16yOVXeUSCxZxhAx/ydfQ9pufYKEfMuryc+1URD2zTn022/914UIOrS6V/9Vzru2bJOcNUeP0YvhOnM36CXqdRYNZai2Z22Y5p6UrCKz5SP0x0YwtITf5yVQx/p8w8oFtUZq//2SXWSmNMlgfIyYlgiU7x08GfXlDNNLSruyrKz/flh4fX6B9z0ZnuNMhHT+az0Tp/eZqxFCMH/TXuwFEwEYAnoHk6TOHiF/z+GaGXp6f4KBF24gtXUEgPz9R5j+zk7sXNDkjQDqvnbr/YnAfJqlWdRPhJC0Wtp5F1FTjrB8IuYfJDOsvJHqoQVLLXvghc57nZmrdwXKaasOF0F9H/TI33+kQTc7BTc//l325x6uF8Pq9GlsG342aaO5baBg5dDRienBE/qy5hx3Hb2+Jo8g2Vlzjj0LOzgpszkwT0/OeX/wilCjc6ttw/xbjAXFD9KrYOUQQEJPNy1TWF47b7mZOz7z7Uhyvbbbl324rXI8Wcjff4TpH+yqWYVB648z+MJTGvp7N9Ida4Ke0aDnOWq840Un+nVapnZXtVB0l2WxvPzCwgKPPPJI5fqxxx7j7rvvZmhoiHXr1vGud72Lv/7rv2bjxo2sX7+eD33oQ6xatapimN+8eTNXXnklb33rW/nMZz5DuVzmHe94B69+9atZtWoVAK95zWv4yEc+wpvf/Gbe9773cf/99/OJT3yCf/7nf17KoilasHrzGWSGRpZ8P9xO9z8Oo3d4hNWbnSVwj1UZjkeex0On3uER1mxxjM6ZoWGfvMZv273DI6w9YytCE6zZsrVl/n59O6WzckaZ5eXot+7MrQ0fgiedvZXMUH9wnov8ERJ1T/eNFzqOCpmBRNf7XVAReodHOOeKcxf1hSCRjrH29KFIcdedMcgdP/q3tvZ0v/BF29A0nVPPG+W+6z91wuzpfvGrznf3QF/Jjps+3fKZeeZrzu/aF7NUb5xtV5wUKa5tr2PXrZ9pqd/lb3qfW56TeezOzx7zcTHV10d+bm5J8+gdHuGy130gYE/3dey6rXkdLZc93RUnFpe/y3mR8c2Pfvw4a7Lc6abRv5mTRBQTSDu6+MIqh+47LNRcN4jotO4i6FJXd8FmjCATjndocr8hfm3cal4B8WtWGYpaxghtUW/dah4hNCx4PlEzM1sL2R3pFT1NyjXambJM3lzwhWcwRAxdxLCkiSnLFMwsSaMHQ8TRhIElLSxZomDlSOo96CKG7oabskzRypHQ0xgihiYMbGlhSseg5KUH3DQmINCFXg3XGuMUrXxF/4SeqsgpWQUA4nrKJ6NWnqdD2S7iOMvplaMuEgihu3P9Pcc5iR203ZG/JoWGcP+TFUc/UWPjEG6I3w3Q7yzruApKX1i1farf71svg7/UePlH+0UWNZIA4lht+MweO45r5i3QgUEWTIiuZyfliQErmSpDdT2lbhKkUwxYzZFS1DzbLVfw6m7h4UvMQh6++UhD8NoNfbzwT85DaIL+K9dTfGwWe76E1hsnsb6/dnnjrSMktwxT2DVD6dFZAOLr+xCawF4oI3piCAH2QhmtN078pD5Ku+ca5PnzaZXGf+2P651bcyWsbBk9E0fLBMuyZouVOHpfvLK0r7+sYXFb5e/lFSQzqLyt6iGsPN79g4/MYuqC2IoeSkIg1/ZRPpRF5k20tEFsvIeSJmBtH+WJrGMUT2jc+v3/gaJFyS4ggJiWJKYlKNslSnaeRDrN+Ve8FD0dJ7aih8PevIQXnUr5UBY7W8YqmOipGFraYMNlayPVYVAZopzvuvsIImUQG0uzULAQd9SulgvAc06mPJlDFkxEUufGH3yZQtlGi53aEPVg2ebHh35Kb2yYhJak6NZDXIsT0xKYdpGinWW2NAlSMpAYZSg+DuDWUYGilWWmNOl8jurVFQMPFKc5eOB/GIiPkdDTFK0cM6UJJJL7Z+9nIDFOUk8T15KUrAIFO8tM0bk/e9XXGVp3JprQqp+9AqRtc/2X/guh9eMfe7xJKQA/u+qrjJ96jvNuRVpc/6X/BNFTib8/v48D+77MUGIlKT1D3lpgqngAkAwnVpHUeyhYWY4W9rvfGuKEffL+7KovsPasbVz/pf8gmzvCgdwuhpOrXbnzHHW3xVk3dgZXvuldGH1JEuv7kdj87O2fa2w7Hzd8+XOccsFTnnTGxX3Zh/nZ3s+TyiUr7ZOfLfCs7FvZSLhhttN0x5rU1hHipw9w4Of3Ujg6T3K4l7FLzkI3jI7iHS860a+TNO2uaqE4fizpTPcbb7yRyy67rCH89a9/PVdddRVSSj784Q/zuc99jpmZGS6++GI+9alPcdpp1WVxp6ameMc73sHVV1+Npmm8/OUv55Of/CSZTHWf33vvvZe3v/3t3HbbbYyMjPDOd76T973vfZH1VDPdl4ZW3nbd4EXvdmYmdyufVh6AS8HxyLMVS6GTX+ZiPTFbxe+Upar7ZvotZZ7Q/Nlop026qdex/iLQTtm63ffbHaM6HdOO1zPTKd0eA7pN7/AIl7zuLfzwn/9uSfNZzNhw/gtfxu1Xf2epVItMK+N/t53jTiSWm2NE7/AIb/m3LwDw+be/+bg7+D1RWIp2fjI/NwqF4kSmWw5LfueXgPDQo3PeuN1WkB5BDjTN4gTJ6pTG8tU6EbVyCoqiW114Q9mCdRIBYY3XzZ2UGo/1etQ7NdWnr3dmclcc86b94/+LoF9LHVsTPCcfmvfRkH7XUAeN6Rpao2Udd6Zbun+AWCJZu3KcBKTrkOPZ7aRk7eYhnvOmxU10UBw7vv33tzPx2NI6kEfh7Z951pLK//c//Nny9kdSNCClDdgY8RhGTEfTBbZtUZibQeLcAxtk7bnEYtXGU+kZHEDXBbqhocU0dMP90wW6ex1L6Jx56ZrjWs5u0Ol7veX+PtBPVCPycjc2d6JfJ9tCnCjt+kQmqh35mC0vv5xRRvelI2gACaJ3eIRNT38mD/7qpkgvgXuHR7js9W+rMRguJp96eZ2UASCZcTZILSzMdz3PdmRHKXNY3E7rod1yBslrN/9m8TslLJ9WdRjWPlH0a6ePtSLKs9FOm9TL7rS/LUVbtUOUsnVjDAiT127+7YwBx/uZ6ZRujAFLhX9ZtG7kmcz0dm1s8KcN6yf+vJaaVsb/bjvHnUgsF8cIj+PhaPVkYCna+cn83CgUCoVCoTg2LHaLPsXy5bqrtjN1IBt6P8gUUMguMDc54QsJdkzpGx0jmckE74xXx6s/eGHrSIvge/98Z8Xo3kwfr7y5uVmm9++rvdl01RbP8cdzCPKm9vvC8K8pFObk1MzRrFVc5zqWTKLpRtUpBrBNC7Ncaple03UEAikldsVZ+PivWLNUxJM6b/2XS463GhUWpgsUsmU0XUPzHAZ0ge5ea4Zw7vlWDLFtq6WjvOdU75/932m640FUI/JyNzZ3ol+7aU6kdn2io4zubaCM7kuLt9fE/NRR8nOzpPr66RkYBAG52dma/Sf8+1Kk+/orcfznYftVdJpPlP0v2tELCJS9mDzbkR1WV0FxstPTlbrqHRruuB46LWc36mQpPkzC8mlVfghun3bzDGoff1/u9Nlot03a6UuLfcaOBVH0bidtWPtEaYdO2w3a62PLsR38dHssfvSO29jxyxs6XhY+yBgedfzPzc9x45f/I9BAfsoFT+nK2BCUNuj+rttuaTDG18/GbXd2bn38Vsb/Tp3jajMVzd+eRNAzyr3FzlRut246JcjxJgqdOlo1q5dOdWnVpsnePrZeenlkJ8x26aStw9K0audmeUV1UOu0/xzLVRbC8mrmKAZ05ODYLL9247RLx33eR1S9osYL0qnVWL/Ysd+ft2WWKRcKteFL/AwrFAqFojuol/OKevY+cC/f/OgHWsY7kZ01opZxORJU7+22WfTyh62gEnRee33x77yeX/73V+qcGRpXL3nB/3kv4xtOw7YlBx9+mGs/9+/OPaEhvBVO3Gtv650LX/RK+sdWYZkSy7R9f8617V7rhsalrz09QjmPDb/4xsPce8O+lvGEAM01yAtsirl5d5a/DVi+2f8VtwuG164j2dODEAIhoJjLMvHYI9SsCtOwSoxk7Rln0jMwiBBU0grNOaK5jiRu+Pj6Pk5/6sqW+t/5k92U8mZlu5EamQiE5uSFAKTklu9+g2LO20qqXldIZnq4+DWv4xdf+zKF7Fw1ni8OSFK9vTznrW9HaFolPy8ff/m88iR7Ygyvrq6g7WdmIkepYNakdxxUvDbyhzsrN3zzIx8gNzvlK4d/iwdJZnCQ1/zNP6HpeiXNV//s/5Kd8f9W8nkPCY3M4Ape8r6/xixKinmTQ488xq0/+CFCpEBLgyxh5n/eoP+JPD6fKCijexsoo7tCoVAoFE8MWjlgteu00kney8HJoV6XlZtO5+BDDwZeB9VJvVNWffooxv8ojifNnFdWbjqd/Tu2s3f7fQhg9eYzEJrWMk1Qufz3/H3DX7agPhOU12LqplVdh/XVIMebsL7cTr9u5gzUrC79ujR71qS02bfjAQSw5owzWX36lhqZ9ffXbjkzshNmu+dR+n2zNPX9ptUz0Kr+ojqotVsXYTpHGQcXU6dB9ePvr/Xla+Xg6MnzVu+I8rwuVfk7ff466X/txAuq42Zj/WLG/rD23fvAfZUxOugZ9tLNHp7kwV/dGOoU14nDVLN7S+VktRzohgNIFI5VXS0HJ6HlIu/JiKrD48fxnhmoWH48GWZSRinjciSs3ttts6Uuf+/wCG/65Of4wjvf1lY/eqL3vd98bxfbbz6IbdnYpsS2JJZlnzBbI2x66gouf8OWlvG+/IFfsTBVPAYaLY61mwd50R+fG3jvB5+4i707po+xRu0j7XmKs59vCH/+//lTNl+0fFZ5eCKijO5toIzuCoVCoVAoFAqFQqFQLA2dOvl0y5kqitNGtxxSOl21KupqStDcAaRVvu06lC22PO06dhxLJ6FO22Up66jb/Wk55dNO+wetYJXq62PzxZexYdsF7H/wAe665moKCwuV+8fbiN+t1UT8tLsVYyuW03ZjiuXHcl/GuRuciNtsNav3dttsKcu/mOXAnwx9rx7bls7sfEtWDPKWezzw8INc8+l/xZnxryOEcwTh/AkAjYte+bsMrzkJKZ1tFI7s3cNvvvONSjzhxXcSuGkF5175IgbGVyGlRNpOWucPpO3O1pbOPyNretlw7mjL8tz2o8coZMtOOtvZBsHTy3+NLZmZnGD/jgcq+tTo5/vrGxtn7vBkYxlqygaDq9aQzDg2PWnLar7gK5+jwIpT+nnW720OLMO1X3iAg4/MODKA6oR1n6xKIFhlk3KpWKdb49FzFolqhZWyTCIdo6c/TTxlYFt5Dj1yH9LOI2UO7CxW6f6GdGqm+9KjjO5toIzuCoVCoVAoFAqFQqFQKBQKxfKg3W2WjqdjTbtOJFH0bWe7wGO1spfiiU+UrcNOdMLKGNXBJWybnfo40HwboFZ5Rq33dtss6hZWzbaGaqVnJ/3oydD3ovJE39M96lYHl77uLdz4lf9oGe94GZs72ZajNk29MwF4Wwj405wo7fpkQBnd20AZ3RUKhUKhUCgUCoVCoVAoFAqFQvFkZjltm7ZUtNrqKMzBJWybHW8rtHa2AQrbXilsO6tOytMqfhSnn3r5UZ15OulHT4a+F5VOZ/+fCKsGRDUid7JdwbGkE2N4pwb0E6Fdnwwoo3sbKKO7QqFQKBQKhUKhUCgUCoVCoVAoFAqF4njT6ez/E2HVgKhG5OVubD6W2ymcCO36REcZ3dtAGd0VCoVCoVAoFAqFQqFQKBQKhUKhUCgUy4FOZ/+fCKsGRDUiL3dj87HcTuFEaNcnMsro3gbK6K5QKBQKhUKhUCgUCoVCoVAoFAqFQqFQLD1RjcjL3distlN4cqCM7m2gjO4KhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKh8BPVjqwdQ50UCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoXhCoYzuCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVB0iDK6KxQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhULRIcrorlAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFApFhxjHWwGFQqFQKBQKhUKhaAfblhzcOUN2rkg6E0cKyM+XQs97+hKMn9LPxK7ZjtIszBTIz5dJ9cbJDCRYuXEATROB+kTNqz5eT58jF2irbFF07ulrT04z+X4dvTySPTEK2eqxvp7arZ/cbLFGrievVdmWovydtouXzt9PFCcm7Y439f243T4Yli6oL7bTz7xyBD0b/jyjPGudjiNLkc8T7VmzbZvdu3ezsLBAJpNh7dq17N27l/n5ebLZLD09PfT09CCEIJvNkk6n2zpvJXNhYaHjfDKZDCeddBJAyzL09vYuumx+XRcjr506Ccunt7eXk046CU3Tatqw0zyj1O1StIlXhvq+GFV+O2Wpryt/eNhz0Up+u/03TJd2+pg/vFU5ojzrYfUdpWxR8m7WrlJK9uzZg5SS9evXs27dukCd6vMJah+vP4X1vXbruVl7SinZvXs3MzMzDAwMVHTfvXs3u3fvDixPszqv7xe7du3ivvvuo1gsctJJJ3H++eezf//+0DL4x46FhQVyuRwzMzOVNhBCMDAwUKkff52ffPLJDXXW7vNyIrPYMj4Z6kihUCx/hJRSHm8ljjdzc3P09/czOztLX1/f8VZHoVAoFAqF4glNvQEu6GW5bUsOPDTNvp3TCAkrNw4gNFFjWDu0c6Zyf9WmQVafNgg0N4yFvfCvj1+vV6dGl24aRf06hekTNU47xt4wma3yqq/rsPpst64fv+8ID98yQWGh3Fa/EwLa/eUTlqZnIMEzXrWRU84dY9ddk/ziGzvJzhTbzqs+XiJtgIBi1mxP0Qg6d4t2dOwZSLDxgjF23jbZUf3UEyXdUpS/03ZJ9sQ47anjbDhrtCNHjHYcAYKe62bXnRpEvWfZyzvqvbAxrtnz30znKGNpWPp2jOGdjjdLRVBfTPbE2PiUMfqGUqFlarccSz2OdDsf71lbv3VkyRySjoVjwmP7HuHmO35OvpD11ZGg26/tBALJ0jRwIp5ESkmp7BvzQ/LrdtkWI6+dtGHl6e3tY/3qU3nk8YfI+dqwXTlhBNVtFGJGHCFEpDbp6+vjyiuvBOCaa65hbm5uSfTqSWVYt+IU9hzaRTa/UBP+/N96PmecsaXyebDjwe3c/sCvKJRykeW3g9duOx9/qObZCyKsn9TXp1ePW7ZsARo/22bMg/zkJ7X1263nsieVYdvmp7Np4+kNn7UPPrgjcru2IkpfSaVSAOTz+UpYlHIuxbgXmE8TXfr6+ti6dSu33347pVJpyXXxMHQDXTcolgoNutx///019dzX18cVV1zJgLGybUe85cj27dsb+lL9s9SN9FHeRUShW3KWA5aU/GZmgcmSyVjc4KkDGXRxYpZFoVhKotqRldEdZXRXKBQKhUKhWAzNjCT1xqFH7z3c8OI/kTY469lrOP9569E0wa67JrnhPx+kmGvPwGUkNHRDa8sw1uyFv2dYBRqMqothMUaGMCNmu3HaNfaGyYySlz9uWH0uVV0vNec8Zy13/3Tv8VZDcQLQqSNGGEHPbH3aVted6tJsvOjEQaHZ899M5+PlhKE4Mel2X+i2vGLiCHMD213hvhuy7robLIVMv2yIVoZu67EYee2kbVYejyiy2tU3qG47TRelTaLq14leYXXlhp910jOYezjJVH5/8HPRTdpptzb78itf+UoSxZGaz7bKs14fv1vPg1uevpktJIojleDS4DSzifu609fr73VLZjtxusGxyqcdmj1P9foGtLXfMfhEYvv27Xzzm98MLqNwnqVmhveo6XfeOckN33iI8mz1XUQndRbkdL1c677V7P8fHZ7hgzv3c7BYrZNVhs7/WcizqlhkcGiAc5++BcPQj4f6CsWyQhnd20AZ3RUKhUKhUCg6I8os36hGmESPweanr1RGTMUJgzLoKRQKhWKxSCRTo7dga6WKsSAl46ywBwLihl+F3W/1MRU00zM4jWxxP1xeK5lykYYvsajP4u5ZOmWduKAaqz8LU102re/wtPX1HzVtJZ3PaNVSV9F5fkiQQQ0nQbPj9B89l9nhu2qeC9FhWwWnqmuogEhRcgvUyS1WKpkmte+MShyJZGbobmyt3Fon90og0NHQvKP0naOhI2raSeJ8NxXSoGf2VEBgI5nrfxBLKyOFRCKxkb7/wp/buiIFB4qgwM7GiUj5Rqaz1Mflq/0iHD6EHWfg6NnuzH0n2iWv3cRJW4ebijQGkp3pGhFroW6FgLqZ096lLSX/+ol/I1/IBY4pSEile/g/f/xOx1hcVw9S2vzLxz9BLp9rGFOk+0863cO6C17C4W/tcfIO0PfK398ayWC+665Jrvns/aH3o8rplPoJD2On9nPrXDZwlnqr2f8/OjzDW+5/vKbGL35wD1sP3g9atf10meDiCy/lshc8bcnK1Ypi2eR7P7uL6alZBof6ecmzziURW9zO2WXL4prtD3F4do7R/j6u3LIJXWgdrWDQiX7HKo2ieyijexsoo7tCoVAoFApF+7T6wbmkSJtkYYp0bgLDKiCkjZA2SBuBREjn9VHltYmk5pVUJRAqVlPhD6tE8d/z7vteShIst/p7X9Yd3TQ1X8Fr4zSmDdCrjtYvm1u/co+KaPrzIVjnWv2ipQ9PE54+XLeQtm9Dt5p2jqBbw09zWW1ngay8BfKfO9f+uA1CguUH9qdGFlWGEJ0a+39znZzy2dVnVgik0Jw/3KMQvtxFzUHWaOXdq39662o/9H5VtsSnh9Ar594r7xOVxRqyji8ntPIRiV7G6G3ZTr11v45l15cj7X552nsulqIf1sq09AKF1ERNWH9qLVvWvGwJ8lYoFAqFAgae734z1nUyF13Udfn7/uwXXZfZLWwpG/6kJukbSyMMgdCh5ym9GAMxJ4H3O0TCDz5xN7l5b1a47/NcOL+KkkO9vPKvLkXP9CC6/J0oaKb+Qlrjf89N8+CaOAArEzH+euNq1h8+4Mz+D+EVr3wlvzcja2a4X/zgHrYeurOhaN6PqEsuvOK4GN7/41s3su++X4PmWw3PTrDmzKfxlt++tCOZ//mb27n/hutJFavbX5SMJGPZjcSmBythUVYw6ES/Y5VG0V2U0b0NlNFdoVAoFAqFoj1sW/KVD9xcmeGeyk2y8tCv3buOkcp5qV01bnnXFWMbEikMbM1ACoFh5jHMHDEz75yXc2h22TVK2hVjpECSKE6j28tjj12FQqFQKBSKbqL1ryOx9bedi8qL76CX97Uv/BtDRUDUIOelEJkBslvfX2Q+x+U15WLyDHb08sJFTVhdmtBwv9ww+c1k1dd3nU5BfUGE5NdMVljfDJPl76NCoxOktKNGjBKpgzRB6SLECYoUJZ20kbYJ7p+0Ld+5CdJyI2q+fiMcY5/QfH8ChA5CC7jnORmG0NRuGDw2NA4JIULC+vOi6VDOorLvNPEiMl1E0oWr3+GIyKQ5/fY7OhcUwnI2ukche8NfY8/u6VyApqFlMuiZDFpvL/rAQO3fYPXcqIQNovX2IrTG8fGbNz7G5NcfAwLt4Xz7ogwProk7I4CUvP2O6ylnF9D1EolklmRigWQySyyexzBKxJOSfUO9pMmSJsu87COzV2d+fpDZ2RWYZqJWAQmaTPCBv3jvMV1q/j++dSP77r/RuQgo+Jqtl7ZtcP7P39zOI9f8sEGkJ7N+ew4IX8GgE/2OVRpF91FG9zZQRneFQqFQKBSK9tj/0DTf++e7KteDUzs4995/O6Y62MIglx6jHOupnSErNGc2rPsyL3D2a7OZr4Ezan1xhP9esxm10WfqVmfdNcrtbNZg1DSdzrIMS+fXOyx+eJ7hZQ1O365OnejVrk6t9JKVPuY6p0DNy+nqdYg+gQaUcH0qfTxCOWp+GLYysITq0kIPqMwm94wrgurMd2/FiiaauWnq77VegcJLGyRbSM+5x9PDco8n8s/lE1n3E5el6TMdGoKaEH057uNXnvaWDO9+HUVlsW1u6UVKiaO1Mheh59DQXhKJfOON49k1o6RpIiv0m0OnZepAh7ZldTlN0+ehIx06+H7ZdR3aTxJaD8ul7QSggdScI0Ii9aBw99htPduJa4OwAMs9msI5hvkLVL5jheTVbOGlJtfCL9/7s0HYzhFbVOLI6lfmakIvTAuKU/sn/df1abyfbG7+NXr4wmrvi8b43rUX37vnvy/AToBMgDR8+gR18KBHNcxHxkfLR9y7n9Q5+wfbW0QOxpKS38wsBC5t/ujLXubrO/7v4aLmOrtQIJufqyicSOTR9XI1rfDa31cg/7XwXfvChS99NQ0INBA6QouBiCM0A/QYQsRAiyO0OEKLYR69H2nmqWwhIQVSgm1Jtz0F0opTbVzn+4BmldCI6BwUhKah9/dXjPD6wADTqVPosTcjbRspbWzpHSW2bSNFGVsvsNBTwqBMXJZI2AWEZiEqD5JXCRJkdWOJIKQUlMtJSsU0tl1rYB/sHyLTl/Yqt8ZZSfj+BRh+wzOJrxkFoHzwKEe+/ItqDOE1nPAlETXNbEt47NHHfQ+R//enl6/OKaducJeAr33vQiWfajewgR0P70SzLbfrCHydwz3ViRcHfYIkRkxj3ZkjPlUFtm2zY/tDOIOor/5qLgRbtm52tkbAWWXhgfu3U2mTAAM6aJxx5hZ03UljWZL773ugkk9JWNw6nKtNJxO8/0N/qpaaX2Ki2pFVKygUCoVCoVAo2iY7V6y5LiaH2Lv6Mup/dDo4Yc4PX1lZuhkEQppotomQNqaRxjRSlI0UppHCNNLYetw1UorKEaAY76OQHEFqx87LWqGoR+3prlAoFIrFErSn+2IYX9FPMrkQOX7bBv6mOgbLartYHWzS3r1ydKkM0HY5mrgltpmgk/oLu9G9LzqhbVRndBBSJ14cppQ8gsQOUK7NNmpVBtkscX0e7Tg3acRLQzVhpcSUW6ZwOYt+XqRjWHSMRl6+00jhN7y1mXfQDenlHVHjE+w7c8uS1dxsLFzVONe84DV9qs6jQfjSC+H7ne2FuVoaZtr9mew9LxJj0KAvFcOz/ktvpTlvGzZpI7ExjN6m+oXxo8MzfHDn/prlyb2lzV8wOsCG73wnkpy9O47y3f/6bOUz8LTTfsXwyF5AumVyymroulsX1bIcz05VnFvBY9f8VeMNKXnxH21mfIWBvTCPPT+PNT+PNTPL/oe+TPHwbrSsQCyAlpWIBRuxYMF8GVGwwLaxpqexpqfhsccASJ9skjjngpY6DXsnAmj2miSKQ0bC/QvAnm+pCgAL+x5kyDW6L+zZjjUz3CJFIycNDLaMYx0Gq2WsKhsz57atB0B5b4Csvm0t05l1CyZs6j2vdZrHwfRdn+5LYxWOcit3V28KQBT53s/u4lVXtO4niqVHGd0VCoVCoVAoFG3T01f7CyyXHmfnxlccJ20UiuPD2Zev5e6fBvz6VigUCoUiIgJBZu5U5ga2N1p52rBneUwc2tg8QgcyI1OZWRghv27rsRh57aRtVh6PKLLa1TeobjtNF6UMUfPqRK+wunLDe2e2kCyOUEwccZ6LduW3QzvtFrUvuzKN3BYG52uXKA4tU7eeBzfv+uWRO6rLZjpF6Svt9L2o+UYkSERDWJN8mnWLpRxCQ+ssSJmQtpbAXErwzA+ex9OGl2ZF3x8dnuEt9z/e0A0OFcu85f7H+Y+tJ/OC0YFIslZvGmLYOp3D2r0g4eGHL4KH3ZtuBmPmWfzBX73Unc1cyz89sp/8Z3+Bmd7uLhbmc0xwvaNjhU3o5jCZgqw4L/idGBA2uYTFLzftxUzHmR4dJiZM/vSkfrb1GthWHssuYpo5bvnfh8AsYJd6GnSRgDEQZ9WZKx1dx2uXI9+15utMTx9tSFehDFoWtKxwj6AtCIzsbcSyexDSQMhY7ZEYiBjSTlEWKYp6HGnrJEomUurIylIb1eURhLuqWjbdQ0E491OlMulyDgnoukksVnRWHPA5jYiwnh8S3Dv0ssq5NpAim/1+taKaCZFN8oKAZRzqrhv8WERw3MCHWYToh+txH55vo8Yt9PR08GSH3au7b4lcY1xgemo2MFxx7FFGd4VCoVAoFApF26zcOEDPQKKyp7siOlJK94fu0sn3CMunXgcvzbHSKzwvd7m+JdBDSrvt/UPDZrJnBhNc/MqNnHLuGCs29POLbzxMdqbUMl0r+ckeAwkUs2ZomlYsdf+KJcE2bSyzdV1Ku4Rtz6HpAwix+J+ekerVt0Jgt+hGuywNtW+Kgp7r2r7QvdfE3e1nsqZdm5ehNl2r8iz18/BE5Vit4qHap0qiOELfzBYW+h7B1kutEyxThG0476vFchsvlx7NTpDIj1JMTS5JG3Zat+2k0+wEPXOnAJCN2Bc70Susrrz8E8URJMfmuViKdtPsBPeuOoN83yqe/4s5JLJiQEoUR+id2RK5fjvJO+PWoZ94l/ON0ldMYRCzj894EMno3oSFRIpHRlex5cDjJOx25tAuEqkh0GrqrGAkGZgdodTkeakkd4+3XdjH+4Y6m8HeCktKPrhzf+DXba+OP7RzP1eO9FeWmm+Gpgmu/O2L+f5VZsOz7vXnK95wcaDB3ZKS/5yYZuCsDbzwdq2mL0oJmuXU0dXnnwzAK37lrAIT6NNw9GouenwnAPM9fVx/0fP5i8Q6frF2HdLKY1lZTLvIz4Z7ufDuWRA26dGHQFggdWzbQFoGv942wGvzjxPTE+h6BsPIVH6DbjrtLymVprDtIrZdwLaLWHYB2ypWwiw33Lace7vzee4/NMspEyWqzgJQWhiiOLuK0txqSvPjSBHjK5f1snsshpCS1/7mJ2RKBQLngEtJOpVkyxtfzVu3O9OwhWXztpuuAVFddUcIDSFsdyV4CTLOJb//Jp423O+rPek7p7qSAmAYXjzo3XgOyX86OVCXgECuOzrLl356J+fuv6OhzerZeN6zeeGlZzeV53Hdgzu5938D9nOvi9s7s5lYuc+N54Q9581nsGJDtew/vOleHrnrZ5WUYTqecu6lvOAZZwHwo1/cy667bqzRKTzNmZU0j97986qGUkCh8fkeHOpvCFMcH5TRXaFQKBQKhULRNpomeMarNnLNZ++n4RWGlDVG06gGXS+eWbidWHIriFSTuI0GVGkXARuhhaeLIqcqb45y7kYAYunLEFq3XlwsxvCVZXi1ydH9BtDoXe+In8csPoSR2AwiE00HmXeuQ+pc2nOuzNNr6iEsPEwvf30i/HLmKeduqNwTonVdSzuLVXoQu/yoEyDSIHMh51ls8wCasQpET5N4tWmkdQihrwDRg9DSSDsPcoE8RezSW4Ex7NIjFGa+QGk+4cquTRdVPjIL5TkASoW+Stpkbz+rNp3L/ofupDg/11Y5qzpnI5Q3Wp0UpvcDoBmrQWQQWgpp5xuOyAVscz9eX3Pi97g6HkQzVjatH093v7zg9sjW6Nd2+YUAUk3LHNQuzepKGBswEpsRmrffYXSnj8jx7Bwg6sa75lNk2zVwhuni5R00Xkjb2Ue6rXHYzlLOOS+s6seGZuNltPJ0x9GgvfGmsR+3+wyG9m9rllj6UvT4RmxrGiGSbde1VXoQac81yPePcf5ntNvjSG0+ixuvgp610LJ34Hh1LOWBY4yLHx6mHJ/F1kpodhy9mMFKLGBpRaRWRrNjCDvu6KCVEXYM7ByzyWlirCZd8oXTeK7ZcYxSH2Z8LlCmrZXC82lxrtlxYiXnha+/DEH5aXYCrdSH7QsfGN1F3FsWX5ggdccBRFgI6dS1c+4sMSyFCe61kDrCSiKNAlKYlTRCuuvrahaOMatOnmahSR1hJZB6AVuYoPnSevm4YRoamh1D6sVKXE0KBBrCjqOJRwCJpZWQwnbzEUhsx5AmBQgbbANR7sESFhY2CBMNzTXMSic/ACErZdTsOEiBrZeQwgJ3n10ppJOPFJU8nf1kbSedlQAEll5EYiGkgWYlsPUitrAc3aSOkAZlkWLXthkkglMOlonbBWxhIWxnJqR0jUtI4ehoG2iWMwZZegHb3RxcSB2k4ZbXvXbTC9tANy1k7z6kFJh60TFKWzF0E/L9j7JveDfrJ4vELKedRoSNZZSRouzIQENIo7avgNsmZqVPSHDz1yr9wN/2wtbRLAutbzcIiWXkkMJEoLnt4PQlDc3tY3EwCtii7MrSK3Vs60WksDA1nUfHM4z1/5pTdz/I4MYCWuxkhIhXnvWCIZD9GqPzWQzbKY9fRrW/29Vz7Gr/9/q1sGvPbR3dSsLoZMP4UjQE+QGddTMmMVkIzEcCtl4AQLeSdTp5/c05L2tJHt02DcCGQyVitiPTq4ODQ0kmhzKMzJlsmFhwyil1NCtJIV5ktgf6FyBmOTI9w6WQBsJMIvWi+yy7dVBpb9vX9517JR2O9pcxdYuiEWc23cNcsofeQo7+fBYpBTOpDLPJDH3FHCOzJVYfgVQxiW3ksYVJWdfZMxojm7IpGQZzyRgDYjcT62368wuML0xhYJJNpjja209PMU/MsihrOv05i5XTJbeMWqVNvPqQwkZqtY4Hmm2gWUnw17mdRLcSICTFeIHDA5LZjIEZT2Av7OHUI2V0mXfHB4FNDCF2ELPd7QqEpGxIJgY13rr1LZEM3h7TM7exc+ffOP3eMwZWDrWG1axp8Z6FIrotsTUouxOp3c3gHAqSn98cJ6Vr7melcI+a+93NHe+ExvDwJZxy7v/hxVzKTd9YRXz8v0mP7AJbJ6YnGFr1IAv69dx7n5e+Ku9wyeIlxSzJoRxcnmbjEQtdlpE446wpkuwZ3ctTew3KUjDzPMlJEyYpWUIYRTSjiBUvIY2jJO0pNMNGizl/Fxu3IIrwq1/W1tUrh4Fnh9fleuDWW2rDHON7b91fX92xl3hilFhNeB9H5uHj0/t4572z9OVl4Ldab3WDPSPOcyKF4OZTz+K522+l/t2MZ5S2H3mATY8/yH9s3VLZJuD+lVvZeujOytfnykx5t/kfWXE2Hxhf01bf8tA0g0R8pHVEXMeOxw8zsXojW3ftqnEEaCi4TPCiZz078l7m9ooEU/LX9JQKwb8QpOPswezJmG4MKSXJjOCkzVtqnD9e9KyV/O2v7omg3/Mq+r3oWav521/dFyHN831p1vK3v3qgZZqXPOvcSHWgWHqErJ9y8iRkbm6O/v5+Zmdn6etbmmVXFAqFQqFQKJYr0rLI3X4H5uHDGKOjpM49h/xdd1eu0+efh9AbNwXbecvN/PBfv9lgkK5/ER1kHJJ2AYRW8wLKM3Lb5UdwjHRrEMYa5yWauc+JVGNYW11z34tTNe6198K/0XjpGQyh1mi4OKNl50aGbIgRMzzOBY8dJWZJ58UDgNCRWCBLIBKAcy1xZxS4Yd5+gBIdsJDSt6KBlgQMJ50s+MJTbloL582LDsJASgswkTJfbWtwyo7hys9VdHZk9eD4B5vuC9MY1V3NDCQm0qszn0ynDMG7bdavQtfwyiJgJTnpvEHC0gSPjg02yDz/hS/j9quj7VfYjFSxzOrp+YoegfrV6FWNVxNG3Q1Rfy8kfviKfNWwShzRMp5EOEf3hYwtRKV+5SJntR7JpGpfHC1b6h0Nojh9tBPP7/wQlEcWWdrPulkBxIES0ppB6AOVa4facyHiSFkGirXxtTg2FrYoYdoz2JpAGqPYWhpp9GBRxqaAaU9iaQLhM9jaMu/qL5AiDThlkaIHcPSW/rE2tiZCvUUZS8PTdz72Lg/0xPlYxdvx97PWZVp+5egOUT6fu+OQ1E2Hge7lU21XW2gcHTsPKz6AVpx1whK153pxhpHpCWYGT2I+qVFMJOkpSLIJQbxUIJO3MUiQTwi0kpPu6OgGdp60jmI8RU9Rkk06A3r9eS4ukUILDy/YznVCYz6tsW9IsGZKkslZ9BQlL1r796xP3de9rqFQKBRPUrZs+SdWrnhJ5Trs9355YgJraoqF0QM8kvqP46LrihUv5Ywt/w8A25bc/pvfZ75w/XHRpR5n0raGEGl0LU3RTjBbkKRzmmOQtg2k7Tj9oNkIYVFOFenrm0aTZaTszmoWhfwQubk1GCUdKQ2QAmnrSKmB7RjGj/YY5OIGNo4DTbwMozOHsIwY4DmACbBtRDGPVjbRYzE2bDsfhODAbJ6HDx/A0FIM5qed3/oCBDagc7RngHj+MJedfx6apoGUlUkTVeuis6S/971+05Y3k0yNumU4zI57PxupvIdLZX4xswBScvKhQ8S0+tUyqt9lbeKcfeYmx5lDABWnDOEcXecOx5FN8K2JabTsAuumJlx9nd+tTiEEEkkiP4puORMcpO387tTEYzz1Zc9B03RnBQBNYNuS7199I3G96JPj1rPzw5e5ch9bzzqHc08fw9B1LGnzX1//MQm96GTvGfadJWNAQr6c5k1vfDm6poG0MC2TL3/1u6SMHE4T6kxPr66pimwpw19/9I9JxGKR6ljRGVHtyMrojjK6KxQKhUKhePIyd+21THzsbzEPHaoGahrYduXSWLGC8Q+8n77nPrcSZtsWn3/7m1mYOkJz41KYcSgsrJOvprrzJ5yjELXXCB1RieP8CHPwG+7qw0SIYc+/u1h9etfQjIEQMRAGYLh5urRY7qx6Xb9Um/R+9VfC6mcd1N8HyWkHj2DY0lkSTTptKqTEmf3krkDg3nOOVO5VTNfSrluxu7ZuKsZh4f1g9NWHeyrr68pvGBcalhbD1uJYetw9xrA1A9CQQiDRnDRuHsItq3fE09c9VsIr57YvDXXX9XEbj5YGO8cH6+rbdsos7WqY+yd9NeH0Ga/fidr+4NJTKHLykVlfDflnmFTzrNRiTT+qD/e3VGs5Ugik0JFCQwoNhFZzLXGPQvcZzN1Wrsj15VmnW2Ncn24yIF6L8m1fNVznQNHsmarXhbq2wVdnAc9cg+ygMaP+PGJc0Txd9V/NHUscJxZRGduC0tdeaxLWHZ1zr+r7OoB/TPD3+aCwuvu+MScwzD+eQF3c4Pv+MQdfmtp+FNBGbt8S7rgokO6knuq9yhgpPPmiTrYTVyArL94cOe69ynrr1Xycc+GL45XfPy660t0xT7rtLoVTFuHmKbzkAkxdw9Q1NFsSNy1ito0UYAkNU9MwNYGla9iaFtj7qeQTEN7gbCSQQsMWTr+SQscWBmg6Eh0pDPfZ10HTkN42EbJ2rHNGNxshQZO2U39SorlHf7vX9jEqnzGV/uHrF17d4I7jNaWoG39q+wbu7N+651pWx8X6cdqf1u+6JetrudkrtBb+QDLgzB/iOStV+oz7+Wl758LXy91r2/vsdd/V2vXp3Wunr9luX3PbzO2Dmls3mtd/3bS2K88WAksIDg+vIJvuwSgVkQKyqQwL6R5ipQKpYoGxI/s4NLqGhXQv+VSaRCFHTz7LisN7OTi2joV0L9l0mlQ+RyY/z4rJvRwYX8dCuo9cuocLxm9lOOXsdWvpBprpzIw1tTh6WSKljiUSaKZESIGpJbFEHM2U6KZNsligEE9j6TqmrqPbJrplgZRYho5uOS/tTd3AsMqVfHTLJFnIkU+mMPUYlq6jWSa2YaCZJrppAhJTj2GYJqn8ArlUhrIeo2zE0M0yMbNMT3Yeb9hZ6MlQjsWJlcsgbMxYDMN0jC+mESNuFektzpLP9GAaBqbu5GWYno4xtHLZjR9HM0sIJGYsjmkYGGaZmFUCbCwjjm459y1XH6HZmIaTZ2/O+X6x0JPBjBnEzDKZ/DzZtKOjJy9ulujJLTj93+3L2XSvc9+rL08+EtOIEbPKpPPO6gS5dA9lLYZlGOhWGcN05ghahoFhmaQKWfLJHve6TLqUrey/XEymkLqGZlkkSnnfFsLOOFdIpLA0A81tQ1s30CwLpHDCbSfc0mJYmoZmW057C4ml604/QFba1N/2qUKOfMJtB82Rq5etSjuYbl+MeW2f7KVsxCi7/SFmlkjnF8ilnPBKX6gZIWsffSlgId3HfI/zDro3O0dPdp5supdyLI7htX0shlF26tvfVjHT6xsxYmaJntw8Cz29LPT0IYBMdo6e/Lyrk5NON8vEzTLp3Dy5dLPwGIYr34tjmGUMs4xw8zTMMpm6Mi709NbEj/ni2AiyqX7KWhqjjDsDOYWtGWh2GcMy3fZxnj0h7Eo/8tpNt0pOH0q5z7jhPOOWrmNYZWKWSaq4QD6ZxjR095kvY+saujSd9istUEimMA0nTTJfoJBMO+2s6Vi6Y6xK53KkCjmQAikF2USGhVQfUkIqNw9SYBoGMatMT24OIZ3vDk77xRzdzDJxq+yOB32U4olKN4iXihiW6T53noxqH5EI5tN9lIwEsXKZzMIs2FDWE4w+63nMX/e/mGYJpMCWgtneQYqxJLFSkf7ZaeLxNC/8w0/R23saEO33vtUrKa+TFR0A9MFBBl/9agCm//vrHLUGefyk57spqr/1pNupf741xZ7RWKXP/9XG1WzNpACJlLbvaFevkSQS4/T1bq3osnfXb5if9Qyi0o3vOpF7Bl1pI5HszxfY84vriLtjpBSCbE+vOz6V6MnPYMY0hGYRt3MgLLK9KYp6Ei1vkZmZxy4lsMtJ7PIIVukUpLUeKYeozspvj7IG5//V+Vw83IdlFTGteSxzHtP9K5tzleuyOYdpOve9c+eveg42M7uewaE7Xte2Lsebl/3ZGlae7PTDg7sf5jt/u+84a7QECKcvQ3Xpf/fLK4iqSb0a5joyiNpwUZfOkVeNI20NbAPbNtATcxzoecTJy05QKAhGd/+S1PjvcMUbn8Mp544du/I/yVBG9zZQRneFQqFQKBRPRuauvZb9f/yu5i+QoWJMXf2Jf6kY3vc+cC/f/OgHqlGM1cTTz/O9cG5lkAoKb5am3lCgUTWwKxQKhUKhUCgUishIz1Dgp4WBqctbKhw3ahwm3WOd407tPaeuhO+81pnScZKTwnOW8zmf+pzDamV7Bs6AsKB4+F2dmsfzHLn8rek44Iiqz5nwHCKbtXnwb0S/444jQVaL4zrJSqGDiDnOWiLGibFK0RIR4Bjt9QnPIFdb0zLkHDRdx3adT2STuIlUD7qhI0slrGyWqtOn5yrhurYJgWHmueg3H2peBOCBzW/ENNJUnwXpOnM5z0U+Dteem0IgGdIEf3nKSjQpkZYFtmsot2zH2C8EQ7/3u4F5XfO5+9l1Z+MWCcuSilOpO0ZISTp3iKeP76R/9RAilUJLp92/nsq53teL1tuL3tuLlskgjOCl0aWU/GryAF/7yg1s2D+E6cYzbFlxlItZsuI0qkmJZnuPubu6XShen4gDulMWaeM4O7ljmXTHMimorIbnjSOeBCGDxQIvf/8WxtasQloW+66/kR9+31kRT/iWaA96FSUBy5YNQ5CojL1OrEpvFo6DROCI5Y1zlcfuxB+LRHyOPemdaKUsAwfuRHc/02I9z0ePn86Vv79VGd6XiKh2ZLWnu0KhUCgUCsWTEGlZTHzsbxt+5ewZgSP9AT9EBNz9lY+walMMoevs3Xcve0dzld88adtktXX8nRedpdJNZ09JLGycvQAlpnvPqsSsP8qGcP8LEF89CX9o7YsOJ28TW5SRlCv54/uB2PCCq2HGq7MMmnTPvB+61Zmv7jJpDdfB95rel7X3ICgf76++rPV1U/8iMEq9OfVsizI2ZWxhuvXmnUuqbeF/ORykHzVhjeVyj7IxbmC8AJn+dqivUyHdP1+9Ote+WRpItz9K77e/rzz+XlDvgCICYwXH9Z01vFTw3/e/5rMr9SuxQXjXdsN9GiQE5F/Xx0VEnZvKrNxt5dAjfHGb5eGFi7o0Qc8KNL7aDHqtE/aSNCStDI5XK9Fyxy/vzxnbGl+4VxF1+dXWafCz3lgHBITVy6iLLwXOjHB/LQtEQNzqM+XKE8LXZ139BL4+HNZO1XuVPF0dao0egsayNdZJJX2TuAJ4whiePKSFkFbl6JybvnM3HNuXyKmXyst734t8b0Y/npEH4Vah/2V/NY53Xb9ySmM7VO/JJvdC21n471EXN+D6idbOTwCE7fRLTVoI2+uvNpq0qKxiUfPiPajf+O6JoDAvri+9K6Ox/1InV4TEJUCvgPTHss89mft3UNmDvlLU0fGstQiy2xETVVybLhWRqTXxt4eQFppVQrNNROXbTO3nRWVlJaEFfMZoNfdPCGq+j3hhzqHderQloMX8IgIpFYGiBegQb/4bPcRUWRcHtu74Ust4F95dPZ9oJi8WCzW6J3sMMoOJwHv1lItFmJoOyQQkGrZmIDUDWxhILcBhX9oYVoF4aY6BmZ0MTT1I7/weNGkipLc6mu2e112H6GUCRyOVwEFLp9H6+tB7M2i9fWi9GfTePqyZGUbuupP/k80hgYdPfTn7R4bIpuJopsHIggBhOOV0VyzDXSXOsiYo5a6pWWVJ4jngUHmmjN6Xo8XWYZr7KWe/7asX94nzHETcbrLp7e/ligsvqKvr4JoQyWTNSguXuuH6+Djjf/on9F5+eaCMQtni6X/5U7LYwc+PlPRYC/zuvq+jIXnFn/8Va7dsrY0ToNMvjszx9o/fTLbx07xynrEWeM2+b1IZXXz5v/hPPsiqTVvwft5ff8ed7Pzcv1TuSyH49sqXkdPTCFH7W1IAQkKPtHnxgW8Bgh+s+C3yeqoxHoCEjBBc9bancuuOHTz+jS8CzndyMBm152hA9ACSX35zJ+vPHq3Zf15xbFEz3VEz3RUKhUKhUDz5yN5yK3te//qG8M9doXHdtvZfYMTNJP0FZ7+uBl/9Gu/nYONT1TYoG+OIxjS2sLGFiaWZWO7RFlb33iYpFAqFQtGMinNB1ZnHc7Zp6yVL/Qyhpll2//WNLey2dHhSU/1i0nAr5HVzY0hgVUf78tLowETgjK2oukTPI2I8hDvzkepMW+/PDdMkYDubriBBd2VrXjzbFx+Q0kYKx9nIM3AI6uL58tH8YUH3gvRx88d/HSSfWhma7VxX/tzZhZrvkfLk+Vec9X/lFQH3qlsweHFFxWHTydvv4FeNU5XnOTFpNferMkT1WoganSpbwfjaQNSVwf/dvbKLB/5yydBrz5Dgfw5sIZEaWAJszdsWwVdH/vQB9Vfpc5UwUTlW20SgSYHu1p/TXk496rbXFwTCFmg4952tFXC24NC8bR5056hpriOldJw9hLtctu+6st2IZ7CRINCcDYcqjpogpFZpG60S5n6e2ALDBsMSxE3QpajKw9HNKXbVWcSpEkkhDqYOZd3ph8myJFGS7jMinfoWsrKVRINNi3qEWw+O3EJMkI8LCgknD1N3hKSLNukCxCwTYZcQsoywS+i2c6x13vJJDxxn6giIUzYE0z2C2R4dWxP0FCSZPMQs3zjlczKuPi+OY6y3zYujg2NJ854Dr39puNvWeOml6xhItbOKGtlQ2UNaVrcKsTTIpnSyCUEx5v3edvpbzHRmLMcsgWE5+humJGZBzBLOdi04/QUpfWOL3+nQOdi+Lbm8X9Gew6+ztZRXkd63CptE8UhFdc84i+9oaTqmkaxuY1KzlUl1O5N8QiOdjpE0dKTm9EsphHOu+c51ned84erGBm2TvQ/cy23v/CNfWak7F5V6cYZ5gbdNU8VBUNbvF+4ve5wLX/x6hlY4xszpg1nuunY3wieRSh9yt9axy2y5YIhMysbO5Rr/slns+Xms+XlkPr/oOug23iZM3XitMvbBP2fybz5Gy5UVW/Crs87jJ099ZmVLncXSzVdGokum1d//7n+x+vAE9a4c3lb1YSbcR0cHeGjVMIheEv1vRrifCy/5v+eyetNgV3RTVFEz3RUKhUKhUCgUoZiHDweGj8xJNhwM/kIvgNiategD/VgzMxw9Munb49lGhviz19jT68No9qMn6stqcL7WRvtqG5pfG7+X2pXROGO2vZ963bCHtCsj+IV6CKHlboMuyIhqLGguIxwZcuW9ILY0ia05e8DbmsQWshJmC+eFbuVFr/eCt4l+TVVfxNuC1lUSHmMpV+RbKtnN67F5bSzq0WtSnu68ngmQW5Nn0FjTpG1bCe+wfZaqDhcrW3ZjYK1KqxyP23PbtL+Fp9ZbJW7Cch0POnZOiNzfospv/OITVK6levaeeDxZKqJVOV1Dj+JJh25JEmVIlqgeXXuhxDGGz2RgpgcsPWiwka5DAYTNTF16lmp2utU6yhMV1yDvGIk7bdcov6OLHcpuJKbFeE4X5KzefAY/3nQqC1NHQuNkhoYBmJ862vanSGbsJZz8h79bmTHcZ0t+vu9msjPhdZEZTLD+w0+vpDmaP8pCeQFNaBhCQxc6mtDQhIYwbcRCDrJZxEIee34Bkc1hz81x+B//CTm/UNG5EO8n27MKzTbR7LL7Z/pm5NsgbdeZxLu2ENL0OZi0pptP6GNf+jTltPs72IaBXPsyTE3nH3/3bcz2PrEnyr7mJ98HnMX9a/AcbELSFWPO2BdLX1oxuANkZwrdVlHRBsrorlAoFAqFQvEkxBgdDQx/2a8lL/t1+EuLdV/+CD1PuZDsLbdy67/+EXeeNO7c8P/A97xw68PaeQkQJGMpqNerXT27kWe7aT1ayZB1F0HxpWuUa0edY1FHS0LldVR0wsrqtsP6wzM8Njrgiu+gHwX1vyBZHidkvXeRpeh7UWR2YzzqtE+0o1Or8eyYjG88eexixxE9cT5W8fbjrcaJRbf7/zL7LAzcEThs5qqou65zIguU7x9K6s8a5FXzafap2xg/olNQhw5NjWVbvINZs3jtOHxFddZprz7D7y5NfTbJL2p91gdELPuS12c7Tk01fra140Tn9emP1/y77Noa3UL0aqFLbR1F/+4cVgbNWI9lPhaqS61eTfJrowydP8OdjUOd97no41A8vYWTd/2Sx9ZcQMnaFapLPH06F7zwAsr79jPzP98Kza3p+NVQPsHeNZdhGsnGyM40XIy44KzLT6p0+WaLK2td2hpA03SGX/BU5v/zalcV//Pm5D/8gqcBMP+fV7tfU6N1Dj1xPpe/6UU1S3RrmuAZr9rINZ+9PzTdxa/cWJPm8/d9nq/t+FrkMlV4B4CBsN0VKmQOqTnPUfhzUv95LnDMf264t2S8DTqiIrtm5RabygoYtsB1Jqe6AkIdzWqzbMwihWN+FFKSCNlivpmMfOpMZnv70Mw5Bo5+s0lMn7xOvBk7mQDQ7Htgm/L+/mXTGGbA9gctdNiwP8V5h16IHt9YE64dfAxY2SSlYilRRneFQqFQKBSKJyHp88/DWLECc2Ki1qgXhhAY4+Okzz+vkn5Nqhd2T7B91QiFePVrZcy0QAjKRvCPhii0kiE0DWkHL0+4XEj19bH54svYcM557P+TPyW/MEeiZDKYLTDdk6QQMygZGnHTJlF2pqlM9vVwYDBDKRb8NT1ZNlk5s8DBgd6aOg+OW2bLAWc3ufo28uhGW0WlWZvV34tZNqun5hibzQJQihnE3TqKeh4rWyxkVpFPpEkX86yd2MVMJsnkihEOrRghP1/dBy1WthjIFZhNJ0Lr3k+ybLLlwFFWzGYZzBVD67ddgtqjnTY/5nRqYF1mxqpWHMvnJCrJssnmA0eJmxbFmEGiXB1baq4zKcojQyTe+Abu+tZ/UQia+RZGM6eTkPaLmWakuoqbsHq+h0RC8HhijoIe8vK1SV7Jkslg3uBIRlDWfZ9jT2DDf7JksvnAEZLiTg71pdnfm6O0JMNCB05KiiUiWlvUGxEETZI1t84eQ7rdzxYjrxu6LKfnpkNdRIZY6lIAyvkbQS50U6mwTKldFSKDHtuElruTshG1DGHlPQ59zK3DdXuvY3/vQhvf3Y5V/+lSXbnlHDl6D4fSU0iZDY2jxzdilR+hnLuhrk8dq3YLo1k+y+h5Fhli6ct4/ttfjHzP73BgV5F7Tn1t4zPq1vfz3/ESTj17BXKrxSP/eFP03/tNkUyOHOH+M97q5tXoIDz24hInrxph29g29KC905cAy7b4TP7bJLbN8ZTtQ/QUqs9bNmlx25ZpCrlvgyAwTsmAmJVASP/M9RSpoedyxdtezCnnjjXkecq5Y1z5+1v5xTd21sx4zwwmuPiVGxvSxPU4mVgGW9rY0saSFlJK5xhl/TFNuOs4SJzd4heB226W5q0NsfSf/8KubjtTNqjOuPeOsRhC15FSVupD2jam7TgZzQ88HYB4/ja0wk1uMaL1Z4Go7K3ubD0iHbmB6y4KVydBSpMIXUcTAk03EG69mZZJzszVtJuGRiaeIaEnKGVzzJq6bxl8z1NBVPIREpJMAVBgBBtHnzmRhFjKSeZ6vegINvRAIakxmZvElCY9eUm6POquOKmRij291uAuJYniNMN6KlIdKZYGtac7ak93hUKhUCgUT07mrr2W/X/8Luei2VdC90fD6k/8C33PfW5DeglMpRMVQ89Q1lnKaqre+ONee4bRYoDRueTJyBVByoqMuOn8LOx58xsZfeYlrNx0Oo987avs/8yniZctkDKSMdafZ7JsMpgrMu3T3X8dNyPI9ek1fPEzQEBudpbMwCCrN5+B5r5wiFzXOL/LpurqquSrWxESp75sXly/TMfQrxO3LEYvfy6xb3+3Us9BTgD15W1oS7f86Te9AU5aR6qvn56BQRCQnZ4mPzdLqq+f3qFhVm46nYMPPcjCzDTpvv6auvLfywwM0rv3AAf/77sj1VdkfP04c/mz2b/jARZmphGP7ML+u/9X2SstqN8WgupWiIpuUoiaZyCov7eqy7Bnp1mbd+KM0K3zsPpJBPTH+ue7Vf14aWOmTdn/vDbJr0a/uEG8bDHy9j8iX8iT/cKXAp9lv2Faf/azmP7VL4mbVuN4VNcui6mrdvpEs3j+5zuUurF75ppruP/P39+y3pvVd7KJjvV1FdZO3hgvXN1s2+b+D36AYkwnXopWH2HPRjv9q9lY143zTvMpxg1Kus7gxRdjXvcz576hB7Z77dgeLa9CPMZsZpzklm2Iu+9EANlEimIsTrJcoqeYZ83Eo+wb38CCLzxVzCOAfCJFMuQ8VcyRWThAOaZXPh9ilz+b8nXXV8oR9JzKU08hPzcLu/eQ++JVjXGXeLyKMn5EeT660Z/845+ZWEFc9lIS8xjFQ5XxsFk+EsFM/2qkliJWzlPWQWppLCMNdo5UMUtm4SAz/auw3XBp50BLEzNzSDtLWRcIkSRVLDRt81wiXUkn7Bz9swfIZlaSS/QEhmcT6Zp+tnriMXaefCHZRApLs+jNTpMq5lr2M++8W/224pjXkyAfM5jtHQeRaln++vw9GYWYQSkxTjnWA3YOwwIzlq459+plYsV5TAxtwtJshJYCkcHZGziPJF13bgHFSriUOaSda0iH6EHoK5HWQaS9EBhHyiRCiBoZQuslZaY47dHvAvDImb9HtngYZDZAl9rzMF1q72cRWrpy3ygdZmh2D3ODz6AQK4DMguhxdNj1HUaP3MNUT5KJofUcGjsbMz5cSSvKU/Rkd9G3cAQZG2R6+FkUYoWa8gqtt3U9kCZemqNvbjtzfWdQjmdCyyW0XoQ2hrQnQ+X59R87ck/oGD3Tfwp7Vl5I2RANssPqu1Ud+8szPvFzDAv2r7qUUkxrWSdh+TfrY/5y2gi2n/IMDg2dVunLXn8aP3wnk2MXUEwOIqWNbe6vtHVQfUap52b1o0kDTaSxtBi2tQ/seYTWi2asRegrKmX0519f5869caQ9gbQXsMwFkJMgTTRjNVrsTJCOfraVddJIgdCSIFJIO18JQySQMg/2Ap4RXwiB0PoQ+hrns9ze7xj79NWBz2WmdxWXvP5sTjl3rPKbcnLkbB7e8DLyRq7m2XnapYNsfWN14fZ2foM24Put4zE5cjY7T/1tisnBSliiMMWOke9w7Zn3ATCeHufPLvwzLj/p8vbya4ZtQX7a+SvMQXEWCrM8Nnkv377vi/TaNr2WzWhB0GNBTLMQukVSSpJSkpK2c2471zaCMoKSBmUhEFaMmOjDjg2RGDuZ9PgqRGYU0iPQuwIG1jl/qcHK92vblhzcOUN2rkhPX4KVGwdqZrhHQUpZMcbb2Fi2hS1tFm6/nT1v/0Ns4Zt1rsHRwS08dNrvOIlDVgsQNoFOEZsf+hojU9sBWP3JT5Ledi677zvCDV95yEtZTYJEcxeaF1JUVgi4/PWbOfXcFYCzWkGNQVs4BmgvLHfrbex5/etb1sG6L3+ZnqdcWBM2/5tf87m//Q6f3HwpxWethJhG/NbDaNOlii/MOx+8kbe9/2X0PvVpFYO954AoQhx2b/rBp7ntC4/yr6dfWl/kGrl/8IHfbtDput3X8e4b393gKOHl+fFLP87TDvXxmY99q6X8W8/7MQAX3vH8lnEvePMG3jH96Uq+W3bb/NG1ZzZ1gNn6wOc5/5/e3VAGxeKJakdWRneU0V2hUCgUCsWTl7lrr2XiY3+LeehQNVDTwDfr2FixgvEPvL/G4N4svTYwAIA9MxMqMwgvH6BBZpgOgfq3IEo+QfeCytWsburpRNcg3fte8HzmfvTjjuX4dY6qU1i+7ZS/XYJ0W0z5u9KPA+RBY1+J0t89GWH12lE56/Otu25VprB03eh3kYg4TrTSpb6tm/XzyM9DmG5d0rkdQuVFGLvnrr2Wg3/x4cY+EPBitSPa6Pst2yhE1jHrj8eJTsZooKO67/Rzqd3nMGw8j/y53u7zF2Hsi/q51o3P7qYsYmxZljzRynMMkAhmBk6lGO8jXpoHoBTvrTlPlObon9nFzMCpTA9sBAQDMzsRyIa4QefFeB9l9zpRmqV/ZhezA6dQiPfXhA/MPEJsxTjjH3g/mcufwyP/fR2Hvv599MN7qzLLC44jW4D8WGmBcjxDvDRPvDQHQlCKZSr6zw6cQjHeR6I0x8DMI46xVNOZ6dtQCR/iCAJZ87z668iftv6+vzzx0hxSSEobJUZyDuYNzAMrSBQXauq1okdAO5ija9HOOh/73juIHd4dWG/x0lygvHbavVI/g6dSjLVuQ3+e9f3En39YvTTrE/7yh+Xp5RNbMV4zjga1kz7Qj0QwxUhDfwvrh15dFGI+HeM2AiiUREU/a3Qt4696MQAT3/g+xuE9DMw8AtBQ7voyJkpzDCeziLf+GfLUreTmSszd9zDZm39F/767GJh5pKJbMd5LNrMWS08wWNrPmsO3MMMQhXg/pXgvptGDBGJmlnhpgVI8g9U/Ts9FF9G39VQKWZOFqQKlg4coPHA/5PMkC1MMzOxEy2SYzpyMLBQrz7RX78mUzspXv4hTf+fyGmOu97lUPjRRqe90xmDTH7+GgSui/c5ZzG9zfzsX9Dm+/dRHufX0qn5+I2So4d22IHcUFiYhe9g5zx5xjrkj1fPsEec6Pw1yGXyGxHurBvjR0+A5H12SbKRl8cizLw9cpSDI8cGbZS9tmxs/9WuKsf7KvURhio2PfJuxI/dUVi889frrELoOhVn2/uZudvzk18QKMyS1eZLaPBoldGEjcP4sPc/RzG7OXLeR4US/0362BdIC2/RdO/O2kTZS2hQe2A6mb0154TsIEDGD+Ib17sx36aaV5M0cct8BfrTy2bxz258zWjjKd3/9dsrEKMk4A7kcmdxRxFOfwlD/OkgNQLIfku4x4NrSYlz57Sv4i/93gFtW/RZfPu0S5n0rgPVZNq9/+CZeNHt7tX5cLNviiv+5goncRGB7CQTj6XH+9yU/4rHnXMEP+i/gqtOe2SD/dQ/fxFMO/JC3/5EOUvLvn7a4bdUL+M/TnkFOlwgkGpJBq8xrdt3M8+bv5AOvFxwpHkEAhwxnu4F//5SFlTibRwIcYDbu+h9W6YcayqDoDsro3gbK6K5QKBQKheLJjLQscrffgXn4MMboKKlzzyF/192V6/T55zX9wl6f3luCPkymPjwMSMzDR7CmpjCGhipL13v5BMkM08Ef15NtHZ1adD5RytWqbtrVtdm5P792yqyPjgbKqNepPDHRMk077dINwvJrtx670Y+byWv2DLXbnp22szE+3vDsBj3LQWVqJWex/S5qG7UzToTpEtbWQf28fjxoFs/TLSx8MTq3Oo8ir52xW1oW2VtvI3fLLQCkn3Ih6fPOayhft9ow6vMYVp6wNlvMeLqU54t9DsL6ZKs67bTuFzOetvMcdvq53qwvRHkuW31+R9Gr1efkYp7ndsrWaZ6L6bfa0CDz9g4K8/tJ9q6hV9uEPTXTujyHDmLtuhMjYWOsOYnUc15N/qdfp7z3cayjhzBGxtFXrAUhsCb2o4+t6ujcPLgPqwBGfy96yoLCLFYxhj6+etGyrUP70BNlSA5g5gTW3AJGEvQVIbJXrIHxM7CmZtCHBuDQA1gT+6KVoahhnLINfWwcaZXJX/89ZGEePW4TG1tkXbl6mYePVNokrAzGqnWkn/86RCzuGFB234yc3U/urnsw8wbG6mpbmgf2NMjWYxZWSWD092JkBKmzzyX/0KOU9+8NbKd6eV7+ALkff8XJo83yamMrmI/t52jpbqYGdmNq+cqzHLd7WXvwHIbTFzIf209hbi/JvnX0Dl+BPT1XbbdDezFSJulzz0b0r0auOp/cNV/rTv8N6Vddf07a7b9t9mV/X5HlUmB7VdpT08nddivmA79ELzwG2cNYshf9lPNB0wP7Yat+UdNX6z/L/DqvWIMc3Uz+9juRto0+OEBseARjbIT0SAmRPwyZcTjp6aDplbJ4baAPjWGV9EqfTp9/Iay90OkP+3Y7z+6Gc9DlYchNY5UMjM1PJ33Bhc4E1N03w8JEJQ9ZLpH7r49i7t+Nsfok0q/5C6d+fvwVSnsfZdZ8CHs4RXJwPSue+wG0ePAS0bZZ4tCt/0Vhdg/J/nWsuPA1aEY8MC5Q00bGqnWkrngt+Xvuqxmr9VVrK+1ujAzV1E/9M6ANj/H++eu5eUUWqXnLZksGbJthy2LEstmgZ3jfljegZY+4xvVJ57gw6RjSOzGiJ/oh2ecYUhN9zAibXx65l3lNY17TWNA0spqgIAQFTSMv3HMhKGiCvBCUhOMWEJOSmHSPSP7yvD/l9MRw1dCfPez8zR2EmT2O/n5GNsE7bm2/DBEJW6XAqTXBf730XM57xht5yinn1cyyn/nJtWz/8L+7ziWOM5VuWCQGTBL9JsMvfCrxZBYmtzuODcuY39/8F3x/7Nn8/t5v8JFHP7UoWbYWYxoLLEFvFqQU5I0UZRFDs2zS5azjgDXSSyqVdFYSMBIgJQUzz9H8YYQEjepi8RqyJixjpNEtC7vgrPwlNQ0pnHia23JSQJsLIgAwpwkuOmktABc+ZPOe79jYCOb8jk6zuxDIhhUqFd1DGd3bQBndFQqFQqFQKBRPJCzb4s7JOzmcO8xoepRtY9sAOg7z9uWrl3v2yNncc+SewPQT2Qmmi9MMJgYZ7xmviTucGnaWci9M1ZzX5xelfGHpg+pgqfYXbFYvYWX1zo/kj7RdT1HOW7VdfVu32yat2iCK7Pp0Xtnr+06zftmqncP0bFX/Yf03SvhoejSwXH4d/Pn643dal836eDvPQtS47T5fzfpLq/rx+kBUPdrte4sdjxSKpaTVGLbY85HUMBviFmb5CEZshEdLOpO5w0wXpxm29pCc/h7SnKoqpPVi9pxH39AzuODk12DoAUae7T+Aa94HcweqYULryMAigZn+GMW4IF5yXl+W6s4TJcnAbLn1lhvHgr5VsPUVcP+3a8u/1GlbkRoEBOSnWkZtqUd9W7YjO4guypscjvPwqT0UEyHjtvsKPGZKyrHq0syJEpymP4PR+37DjDhCsb5fddh/2+ZY5ROFKP0xII4F3JlMcFjXGbUstsWH0be+Auuu/+ROmWNC15nWNQYtm1HLQgJTus6w73zUsji7WOaeRKwqR6QBwZ0yWyv7yr+HLS+q6hQ0/vStAn+8sDhRn7/6dhIalrSr5U4Msm3Ty9Ae+HZNf+rNx7lTszjab9CvS07LlhieNSHew2MrLPauTmHW98uxNzK27YM12U9O/oSHd36UYrE6cz2RWMFpG/+CsbErGvV1yyvnDlTH09ggrH8mpb03EZ91nrViXFDuGyC24ikkHrsVslOVMbZ/1mS+V8d0rYtZU7BTj5Eo2Qy5RvYhy8JozL1ZRULPiLOEe88IpIer1+lh6Bn23RuB9BDosRoJ3gzkydxE4PoSAhhLjYOAydxkw9LgThxnlvI1L7+m+Xe+Ug5m9zkG+JndoMdh2++1VWLu+pqTfmAt9K+B/rXQtxpiycDoc9dey+6PfhjjyEwl7EgvXPUcjds2ObpWVhSQEnJTcPQR8jd9j/zP/oeYPkNywCTWY4XrlB4m2zPMrdm9zGoas5pGQROYCGzAEmAhsAW8cvNrOal/PWg6CB00w3euO8+G0NwlzwUIQe7uu5n51rexpmecieyAPjjIwKteTc9559XEdY4aD04/xF/f9i/ceMp/YOpJvviZ97LlwCNoumQhI/nFOYL9K+DNo0/hpJXnQ2HW/ZtxjvmZ2uvlMq52gInjaDGvaVx60ppK+IUP2bzhpzYj89W4S7kKocJBGd3bQBndFQqFQqFQKBRPFK7bfR1/d+vf1Sx/1h939lCfLc62Hebtywc0yNWEhu37ERuUPixuGGOpMX77tN9mXd+6QKNXUPn8jKfHef765/Pjx35cE2dJ9hcM0SdqWYNYTNp6mrVds7YKk+PVXas2aEe2n7Cyh/XLVu3cSs92dGg3vN04YSymnSC4rcKehahx25EZFt9PlPqJqkenfS+MwcQgv7Xht7hk7SWRnWSiOFS0cx7VQaNdxybPmBvmMNINJ6FO62KpnWG6kX+nbdtO2b74wBf56vavMlvqTn+u56yUyUsHygwa1deCCxbctKADguf1OfvFh2yNyoIlWIhvIZVaQyoxxkDmZM7NprnrF+9iJqGRT2ikijZ9RQtt3mK6zrDmPx+yLKxenfmETqboGAeygzFiwzrSZ4QKI1ayGJ8okiraxMo2iRADfafnxbigHNOayvYbaaM4C7RKW/DlaZQkO+Mx5nz1s5DQ6Y1Qt975EZ+xc9yyOLtQ5J5kosYIOm5ZbCsU0akaT+uNpLarX78uOTVb4vGixqSuk+036NMkPbpElCULlmBgtoxd167zCb3SNwaKFuceznHXaJrZhE5/0eKCwzkEVcNtWLlGLIuTEzaP9MSZsQTFPp3eFYZrJA93wZBSNsaRjinOtkA3fPsamzbxQ2WMAyVm+2P0umWbzeEYfd26Wkx7+wmq86A2aVYvnbb9aJ3Ru1WeQXFuSKf4YaaHad9qIeOmyfMXsvxvJk2mR6NPl8xZgl1FDdnEVUaTEtvXRv2W039m62T/2dEZLv+tzzoG9e0/gG++zm0RP66cV37FOQbGcTCB2+r6o+5rX8cAXWbW94w+rBlc15/mIDpzlsauosbF8TIvGywh4tXxK2c5xrOMz64ryzaGAMtoHOe8vnrGwBtYcd5fAI7B/b773x5axjO3/nut4d2tk8nhWFOHFGFLkkWbZMEiWbBJFZ1jsmCRKtokinZkx6ZpTeOIrnNU11i78nxWj58DmbHqX8+YM/M/PQx6e2b6IK775d/y7ke+BoD09Rnhmrw+fuprYe0FvPvGdztxfHUXaSn8bvKVl8CjNzSG94w6jh+pIcfxKD0EqSHsZD//cu/niE/MkSgIckmYGAJDwIBlM2TbrCHGszMnI47ucozMIcjelYixLTC+Bca2wNhmGN4IiYzjvPD1ZzJZmq2pQw8hJeOJAa551c87ckZtd7U8y7Z46tXvZW/f76GXJ7joN+9maAGmM7BjrbN//Lhlcc3eg+iv/Eqt401D5hJKC9yz5+d89Mb3YEiJLsGwJRsmJf15SS4Fj48KbE3wp0enOb1crhGx/alv4a/2XI2zAL7jjGADCNxzZ1OPv3nGxzhz9OxKtvn7HuDR3Xfzqd1f4+FVAkvTHBluOk+elJJPjb+L9dawWz8XIIw4t03cwZuufXNo0YQt2bxX8men/D6nnfrUJV+FUKGM7m2hjO4KhUKhUCgUiicC1+2+jnff+O5AL/5OEe5Ol8eLeiNqp+VbipcqS1Hf3aRbbeevO2DZlxngDWe8gaseuGrZ6rkU1PfxsP4Z9CxEjduOzGZyOy1fKz2ONd10klksUXUJc1hZyjwXy1I4w3Qj/6WUvdSfvWelTN447Oyz2sQ+2Ta2rC6batka08UBALKW5L68zs6i88I5o0uylvPCeVPS5qyU6RqiZI1O9Xtji7r7lfhevLp0PvNp03SVYFFrBpQAUlRejINwJ0v7wnwqxks2Y4eLTI4mKCWqRrTGctQjSZQsxiZLTI7FKdUYxoLrwGPBhruyOjsKOhLoN2wyAuYlzFuCR13jpkCyIWHTp0vmLceIkjFg3m2HPh3mLEE2Z3PFQo6fZFKukdSRtyBh2JA8NW0yaIAQjh4L7gRKvyHR09HrD836mL/PAMyacM2UzmGMiq6eft751pTNeWmT3ia2BdOMMT8/wvz8MLlcP7FYkWRygWRygXR6lmRyvt7u7ugeoGu9jp5zykwOfkfkiQ8ZlOOtnUP81DuKPKDF+Id4Lz09Ov26TUaDedtpw0HL4qJykV/FEkzpek1dRDnv12WNPKftRdN0rfKcswQLWZvnLeQqBnW/3p7xWUo4K2Xx0sESgz77qleHjxaDZT9agA1Jqkb6gtMApyRlJezRgkAi+PiCzeV/cDd88uwms9QF9K50Hr65AzWOEp4TxPWrMsyfnKTPp2fRgriUCKOuszTp1AsW9LjdoVUfc8IkIkSelDBnQd/Gf+fZJz+HX/7qmTUz3GtKKASJxAouevrPEUJ3vEj+ZSuTsSPct6W3MXMf4xMFtj60EFomcGY8F5I6haRGIeEcizGNYlzj27k4vzHjTOk6pi+PL17xRS5YcUFTuYvCLeN15jR/NzzIhFFtvBWmyfuOznC5MQjvuo/r9t7Q4Ky5Ir2C9134vmNjcAe4/Ytw8B53xvxe51jOdjeP/rUwfAoMn+oY1sfOgLHT3VVEQrAtrvvUmbw743TcQOeFBZvL/+g+Z0b7MeBNd97Mj2fTpOZ+Qmbmq436TB7h8lzBcVZ4V2u9qqsihKx4IKVryD9ArSSB1buSKwZ1JjXCnRJsuOZ1d6LXbfNgmSWu+Mq2ztIusTOEon2U0b0NlNFdoVAoFAqFQnGi4/2QXKwRZbnhGfP+3yX/j3+47R8WVb7IywdG4Ila32EIBGOpMRCcEGVeTsbQY4nXx3/00h/x/O8+P7St/M8C0LQvdyLTm0ndzWckqh4KhaI5AslfrCwwoMuWBvdSKcGe3WczNbWGZGqOvr7D9PdN0j8wgaY1jrF+W9RUYYA/vemjS1ACxRMJQfPPas+4X43vp/FehiJbjQk2aEeb7ptrSo1ZmWBGJpmVKWbc8wIGIOqejeZOE/U6+uMHOVjUpq2T1cTZpsF5oyHfJnKa5NNax+B8bAllCXFN4rkc+GVZQElCSgvOq1FH33WdXdszknlBwpW/YAlMG9YlBpz9twNkN6wooEMprmF7TjreEhMhzj41KgpZE7fmXkA/0fxmF1E19Ulf/BiSlLRISZMUFilpkZQWBhJNSne/aImBszd0GLYzZxZdCApamiIx7FKOoqFhCoElNCwEFsK5RmC5x/XyIO83v8kMKabpYUIbZEIMMiEGOCQGOCQGmaEHKbSG/iCl0xZ7S1XtBBDT4mwZ3hLoUGBogm/8/tMCy/FP1z7Er3c132u8IrIwBxMPVOp0QdMwhbNPe8b2zc4f3wrJfiSShfICZbtMTIvRG8vUdLQPvmAzZ60ZaJp3V5ES8tOO8X3+kLOtRm7KOeanOXD4AR6avJuELYkjsQBTCExgVteY1nSmdJ1LznoD27a8EoY2QCzFJ6/fyY6Dcw3ZNY43bkD2MDz+SyYMnQfjMQq+iEkp2VwqMW5asP4Zzqx8CGzX+pAPvmAzY33O0vmTcwX+5sc7auL5ZYi6k5v6YF8CMo/eA5OPV+KlpcVZhRKrLXc1HiRsvALRt6JGQNV5r1r2ffP7+NWBX/k0dHus+5w+I59nnWn6dKo66O0xDG5Kp5zoPgc+4R4vy+U5edOLnG0DKnkLmN3L7ge/y/XptDvGiUpa7/jcbJbTtv42xsjJxDSNmC4wdI2YBg/d8EH+O2WDKGP07ajqdpycIZ7sKKN7Gyiju0KhUCgUCoXiROe2Q7fxpp+8qSbsvUen6bFtdHCWUgN06by08ZZW05EY7lGXYPuWSbOF8xLFO/eWUgvytJZ1x7DzoB8ffnlh8WJanJJdiizb+zEctHTlRauezkhqxI3Q7OdQyD0pOVo4yq8P/LomuJntot2JhJ3IaiePsHYKuo4SL6juq/eaLPMaId8oabsZt+34bTZus/pYlB6++OeMncPdk3e3jH/Z2ssA+NneG1rGPXfsXO6avKu1zHXPYjw9xkTuMD/bc33L+NDYZ5qxbWwbd0zeGT0B7faVLk77XYQe7cZvW3bb/XbpOBbPxFLIbpd26nwpyzmoSy7scV4qHy6Ocdv0RQzEpty/aXRhItE4Whrh3tnzKMtEg4y4VmBd+jHWpnbTH5uhx5hHF7XG05yZ5PrdlwToKBquJWBhYIs4Eq3y59wTle8J3mxzL1xgI6SFY0qy3c90yQIpbrTPxkKjIA0KMkYRY0mf72Y8Q7uXQaobr7aa+R5k9As06AkbDYmOjYZdOXrnABY6ZfdO/X3dNdI1p/G+E6JRXRy31feWOiNdXTmC4gTLCdbOxsASCSwtQVmkK9YWTZbRZQlNlp0+JXRsYWARAxE8M11It8ZkCV2WnT9KdfpF0VXW9XGvluyK4bRVb4xWJ53VW7dlOHG8fuXvY055hfcnnHjeU+w86TYJyiQpERcmptQoY1DGoOQeLfSGr+z1z3Pjdav4zUvpxbcRlIlhYWCKWJ0cUSmtJQxMDCwMLBHDxECKE9MQVpaa8/vP/ZMIbOkcH7JG2W6taC2kCXFd4+G/eV7gvbd/7U5+dN/BRcnvlK+++SlcvHHkuOQdRNDv+yDqVxT4vS/cwi92HllK1SLxs/dcwobRDACPHl7gWf/088hpi08fQ/bGiN1xBP1IcalUPGEQWoHMpr+sXDsrOUxzeS4Pr/+h4xChWHKU0b0NlNFdoVAoFAqFQnGi8+NHf8z7fvG+mrBf7N7HgP3km+2rUCgUCoWiNbdyFj/m2U3jrGCSS/gNWdLsZSW7OJkFeupiSTLkSJEnTYEEJXTHRIXPDIWB6YZblIlRIE6OFLtZzRxL/T5OomkWQtjOUbPRhHvULETlno0mbGxbo1xOYpoJbFtHSgFSw3aPUmqAjRDSmfkpJEK45inh/Tm7tqZKFoa0XCNj1QipueYs59quGCDrw8Li1srrblyJwHFn0MmSZoEeZunlMENMMYBdtwCv19ZeWs8cLysSqeTgN25qWCQokaBEnBIJyu7RH1YiTpkYptuDdGboY5JhJhmhRO2SvKfyGJdwC2sJNtzZCKboZ5IRJhlhghEmGWaKAapztmvpZ45hpuklSy8L6NiUiFEkzgJp5smwQNrt7bpbd8H7VQ8zxWoOMcZR4m7Z/H8xtw781+0tXn/8KBDnEKPM0keWFFnSlIhhomNioGP5ylpf9lLNecztT36Xm/qjfyZq82NtWnDcHyx0isSZYoApBiptaKIzT4YJRjnCYGi/6Ba9zDPEDEPMMsQMg8ySJk/M1SZWGUvNwNnuFhqzWpo5PUUxvYGDa17MjN3D/KM/5GhfCmnrYGs1R2lr4B6lrYOlQYRyxoYPER9tXOL+xnmDXUWdwcQgL9v4Us5y97YOQgDPPSPYcH/Xnmkm5got9ZASmNwBN36seTwEXPp+Z6n1Flxw8hCjvY2OZseLlkuiE7yK2/U7Jjgw66tDGe74BCCP7IJbPtM8DgIufJuzdH2jyEDXnVdsW0N/OgbAbK7Mt+7YW5vGm2jupvaubSn5mFygDLzj5i8wlJ+u6lDB9zSf+zrkwEk1eoTJloCUNgezB8mWc6QLC6x46KcVNzJ/PrUjhveZBocMg5wQpKVkzLTctALOeBmyf3VtfrP7Yfv33M9AmNAN8pogZUtGLbviilU+7QWY6XHKlk3ZlpiWTXn6AOVDD2CiE6fMO/o+wWFdZ9Sy2FYoVj+FX/4FOPMVAS2g6DbK6N4GyuiuUCgUCoVCoTjRCfKEf93sHDEp3WUEwXKXo3OWEqwuT+ddW0JUljHUcGbFe+eaO1Pev6yjh7fKYLPZSo0/kUPCAn6dNO7NGiwjMC+fbp7oV276bdb2rg1J4Q8Ol75nfi/feOjrldCos8Obhbc7Ay9s1mSrH3hB9RMcr35eYoiMDvOKKiNY7tL9jG1Hl7b1bkPtxayOcNHqi/nV/l+2TPOSU18MwPce+X4L2ZKLVl1UtyRjMC8+5cWszqxi/8IBfrCruVxHdns8feXT+fXBmyPHb6fO22VJ+0pbcZf2tU5burSpynKpw3Zpt86X07jSr0vOTDkbcWetNBOlFUybw0yVh5gxB5EINGETE2U2pXdwWupBNF8mthQcKq3k0cKpTJRWMm/1YspYm1o3omExEjtMWs+REAVimmO414X7h+0enWtNWJTsBAtWhgUrQ1nGHWOn1CkLg3wshmUZlM0EZjnB0vYIxfHEEGVGYocZjU2wPvkoY/HJwHheLw7rCabUmTEHmS4PMWUOM2UOMV0eImdnuqKnwO7YeGuIMoYwMUSZmCi7Dh5QNQNRcfionHvhonqu4TiV6MIZA6SsulrYsjonvXIuPXcMd8nmikay9lxAyY4zaw7wRHzWDFEmpedI6TkM4a6cIAWasCrtktTzpN04zjFLSs/jbKHgayXp1LsXFhNlDM2q5OWvPQkUbbg+GyOrafzWaAxbFDB1gaVDOaZRimuUDQFCa9jTffo/tnDnqSZRsW0Ny4phmjGn7aVTTv8xEc+RTDXu/14afwcjwxezbWzbsdtf2t3TnbmDBP8CEpH3/F6uXLf7Ot5947uBqgEZ3KXLgY9f+vHF70m/zOpxoljm7JsfQAMev/t3ic/uWzq9opS9d6XzYHZSP4up28d+AV/+rdZlUDPdjxnK6N4GyuiuUCgUCoVCoTjRaeUJ/0RgMDHITHGm4/ItxZ7uT+T69uPf0/1EKLMmNKSUy17PblO/73mUmTFApFk07cj07+nerf4SVQ+FQtGcdvZ0j4KUUC4nKZVSlMsJTDOBZRnO7HBbx7Z1bFvDlt65M7NS0yx0o4xhlMhkpujrm0TXrdYZdqSjoFxO1ORv27pPJ61WX6k5jgfxArFYAV0vV2au+4+4yy07xihRMUpB7bV3TsWQ6V3702vOtS+sej/knqwaRUPv0xg3+L5Wc42QaMJGCJtYrEg8nieeyJFOzZJKz5JI5JBSq9RppW6lcA2/Xv+SVaOvcA21omoctiwdy4r5/gwsM4YZEGbbOppmoWk2sXienp4ZenqmSaXmm+5v7rFgQY9r867ZL1w2hvkpl+PksgMUihlKxRTFUhqkQNfL6LpJLF4gEc8Ri+fRdbOyYkJlVYXKtaRcjjM/P8L83AiFQgbLNrAto1pOy3DDnOsT0YCdSGRJpeaIxQrE4gUMveSUX7OQtl5TTsuKueWvqwMrhm0bvtm0rTYx6BwhbJLJBZLJeRKJHJpmomkWRqxY6WPxeL4r42U7eGX/0tE49+adVRM++7Q/IL/vn70YvtiOcmdu/XfGxq6oynjge/xqz7soxrXwDt4FRRP6IBddcqtj7D/WbP8BfPN1njK+G255X/kV2PKiY61VV7lu93X83a1/x0RuohK2Ir2C9134vsUb3D2WUT3eMrPAi+96hLXJOLf1P7L0ekUpO3SuR6d1u8ycIRTK6N4WyuiuUCgUCoXCj2VLbn1sikOzeaayJYYyCVb0JTnvpEHu2D3N5HyBkZ4ECDiyUKycT84VKvHHMtX7Y73VtPUyL1w/hK6Jmnwn5wuM9dbeq9fNr4M/33o9PTlAoOygPMPihtVTfX00S9MsXTsy2pXXqr28tvCXPYpunZSlVTt32g+OLBTZW7iFzz704ZZ11g6C6q6U/vNjiWds+9ML/pQ/+fmfALSth6f775z0QS5Z/ey22yoo3g17rw+cefBE5fc3fQQEfPbB7vaxbuLN+njDGW/gqgeuAp4cbQONM17amRkTNW67s23C4ndavlZ6KBSKaJyVMnnjcAlYOjvMkxZJ57bBxaRVBDJvwR1ZnfsLBruKGmemLF46UGbQqH52hBnjjzdSgm3rVcO0bVSM0p5DhhPPNUhL6sKoiec5WngOJ0Ddlgh2xSFC1G2X0LCkhmxch0rTLNdA3Xpp8MVQa4h3ynrO2V9mYOAC2PEj5E8/CPMHq3XRuwqe/RFmVoxw9z1v8+lruw40dtfbPciRIzxMIgIUWLDgm9OOwd3v2Hj0yHU8vPOjFIvVJd4TiZWctvFDNQZ3j8k7/5r7pr8EyM47uAxJ645ZZ279VGDex4ztP4Br3gdzB6phfavhyr874Q3uHpZtcefknRzOHWY0Pbo0Kwosk3r8xsEp/vjBPTxjMMO3zjn12OgVJY/F6NFp2mXkDKFQRve2UEZ3hUKhUCgUHtfcf5CPXL2dg7ONLws0AXaH35zC0q7sT/LhF24BaMjXu3fl1pUtdWuW14C7j9ZMrlwj+0Vnr+QH9xyskRcW169HFF2C0rRTBoAVfQl+58J1nDzSE2p8bkdeK4LKHkZY/TWLH6Wdg8rTbj8weu8nMX41Wmy2EmabaQA0I1eNaKWxZW1YYDxzgKcPvImz1gzwnT3/zqTPw16gIfHtGR8gMzRum/zB6R/lDy54CTfsvb7B09/PYHyMM/qfyf2zP2emdLiavzlA7tBvYc5vbZpPs7YKinff9C/5z52fROozlXvVWV7ts5i09djlfooTLwSI1idayPHqLqiP+YmLDDFdI2vOtaWvJjRs2dhHgnQV5gDPXP0cHpy/qaYvDMZHuXLl73PZ2suZ1+/kH277+9C+EkRoP3Vn/UXVud04YfQYfdhSkrfmW8YdT6/gvRe8l15rW8XB65B1Gz85+Fmmfc9C2MyYoFk04+kVvGTtH1KY3QIInnbKMFnjroZ67YuN8ppT3slbz3txg3Paz/ddz/8e/GzN8+gnSv0E6Xzt4z/lr3/9tzVlG4gPIIVkthjcNxUKRZWzUiavHCyRafHO3ntrWJCQarEydphdRnF8abdd2onfbJZ4FDn1cbrRhzyd/nfO4KdzsYbtewSSUxI2fbpkzhIVY3zY86D6dTit6kZ6jiRLVIcSSCZWVpdUB2d26O6bYWECMuNw0tNB05HS4lc3P5NicYKgWaOtVjyIrJMnWu9B2NlKeNZyvmP6+5jUeohrMcrmTCVswYKbFqp9N8ixUUqLmZnbKBYnSSTGGBi4oOks88mJH/Pwg39B0ZrusFQaBHw/bmbsP+aEtLuiTZZBPf79owf5590T/N6qYf5x09pjp1eUPBajR6dpl4kzhEIZ3dtCGd0VCoVCoVCAY8z8w6/euWzmzHm/9z/9u9sAjptufj08w3ArXerTwOLrN8j4vJzaqxnuu6bQewBve+Z6PnfTYw3xOusHNnr6MYQxjzR7sXLrARYR5rxl14SNSPnvnYSe3o1mzGM3pJ9F07NIK8MLtmzi+7+Jo6V3u2l7AIEwFmrOtdgRYoO3osWqxlq/wddzdvj+Pfs5XN5RkTXUk2DbeoM7H7M4fHg13ouhsHI0o1lbhcerz8upF08/gYC6slbP5xBuPUmzryFtUD1FO68vc6s+EVVOUJn9ZWxHdm0+Mn8SIrUbYczW1Ul4v+xP6yzwcGA7D6RjSGzf/Xbqf5a+TInz16zhZ/cXMX3h/vh2/iS0Bp0zNeUaiW3idZdK1o/bDCSGePDQHHtmDnE4d5TRnmHW9K5ACMn++cOsyowhhOTXj+/m9kctZqbWRG6nDKch0AIch2wGhvZywQaDi9Zv4FVnXsLde+cCVzg5MLPAfUfvRhjz5PI9XHN7itl87XLP6bhOzCCw3sOd02r7S08ixqWbk7zm/DM5e+RsvnHfL9gzd6hS/n1zE6Dn6I31g9lPLxsZ6U1XViX56fZDrlNMriI3Ywzy0tOfwbNOH2Pn/H3s88nz1+3++cOs6R3j9JV9zBSnGE4NI6VkqlA9v2nfTfzw0R8yXWz+cnoxDhXdJqou4+kVXHnylfzgkR/VOCwsZZ6LJSyfdsO7nf9Syq4P74/385rTX8O28W01fXWx5yOpYYZyv2H//i9jmsHOKsIYojT4UgaGL8PK7uDxgz+gt/QAPVqAk5R0nD+7SdbSSA9exrYNbwCgVDpCPD4CwJEjP+PQxPcpl6e6m2mHJBIrGR/7LSYmr66ZhbrUaZvhf+sbxZjYykhab2RdcD8igozVUfpDfZxuGD6nTcF3Z2KVJbmDWJFewfPWP48fP/bjihOZQPKcvjKXZEx6fOVZin4dxIlm3I9mUBesW/sWDhz8FqbPsNwdgpdUb8bk5E+47/63u1e1s0YlkpItSGitfgkEG6Arkoxhtmz6S8bHrqgxjPf2bePOybs4OvUb+g3JaSMXMjz0FIBKvPtn9vL3932HQ7nJirxuLSPuN9R7Y6h/PC0WJymXp4jFh0nERyv3E4kx+vu3MTt7J4XCoUqcZGK8pbFfoeiEP9q+m+9MTPPBDSt5x0njx1ud5cEycIZQKKN7Wyiju0KhUCgUCsuWXPz3P+vKjOluIoDxvgQgODR3/HQTwIr+JD//08u45B9viFRPXppfvu9ZAIuuX7/x+TlbVixpe2nA2egMIziK5D4sznSvp9wXNEN15168s9DZhvMD6C4sZN39MDkaMNBEPil3X8d8OTDOMIIBBDNIjoTk5Y9zNED3IN3890aa5HEUyT04b17r6+4s9IqssDjVvG20+EGGdJMpy8AurWQIrZLW/4qrvp3895finkLRKQLHsSbq6hhLSZTVUI4V6bhO3AhyFAhnIB3riq5DPTFees5qnnX6eOB2E0Npo8F473cGePSQzi8f6GVe7qxziJgLccCodZjIJGNcuCHGU046uakzwJH8EaaL0wwmBhlNjwYaTkfTo2wdOou//OmP+Z97tkd0zihWHKT8+gbpFVR+YfezbfQcdi1s5+bHHuW2R8ssFMxAp5b+xCCvv/BMLjh5KJIx2F/m8Z5xzh45m3uO3MNEdqLj8KC6O5o/Snm2TD6fp2gU2bRmE0KISPmHtUW3y7aky8j6MC2T3zz4M2YXDtLXM87mlb2Y5aOhMyhNq8Rtj/8XMwuPkS8eJpUYYyBzMueufQV37f12JTxNgVj+ToiwWodFhkNsRUs+BSEkM/kDlKwx1g29jFUDfaHbDnmGJL8xyG8wCjIutXseZozyx/XXVSvjVpS0/vIYxjA7Z3Yyl9tLJrUagOzMzRi5O1rWrTCG0fueRmn2l2jWTMt2aGn81wcxM0+jN3MKpw6fz+PlGJPZCbLzd9OnWfToIPQ+FmyDgYHzsXMPMZ/fV9F7PrcnsM/MZvfQ37OOk3tG2LXrbyM5HsRig9D7NBaMk5kvL9CnQ196HVpqE9PFmdDn0f9MeUs2+5/BkdQwVnYH8/l99KbXIpIbmZv4BrH5m8BeCNFliBXjL2ZkxPndE9bezR1Fmhtzlxut+op/FrSUFo89/in27v1SqINPLbV1YRgDCKiZEd7pLOv/z957x1lylQfaz6mqGzuH6enJGo3iaBRQRELCYAskbMDYxrK9yMisDbta410s48V4F4PWnwlmbeO1Mawj2AJsDGuQDAgQQQIkkMQojUbSKIx6Uk/3dO6+uarO90eFWzff22GS3md+PZVOnfOeU/HWmyYnv94wPPvg0Kt5YO/7KEx/FeWWIywFx3fduutrFNBWbAC7NLtqiujjEkZcEE5ifvrH+9i9kOVvLziD14/0n2hxBCFElO4dIEp3QRAEQRAeeH6aX/mbH1as00mTwsvXRVYE00ge2+o3KT8vW7lsdJuusy6yTSlPM2P4U39ZV6+rR7BaB386nKo666rLqep1gSx+3dpf3jKQ5uBcrjLfZFhOVa7z69ox3IWB4tmJxfptVcnUcDsKTEUqaXLR1gF+ODYDhkKbyhsfw9uug/lgcKuOhXKr29TeNx1/eUDDdtcg7edC1ICjvOB+rvKLKsJ521DYCkoGlFTlsm0oHMDE65ehvU9IhqZi3tQQczWWhphbOW9pjeVCLLKsARflyRGRx/H/bKWwlcZVKlznRI6PgScLfvsxDZaribtB+xDT5WXT1SSC9l0dyuKisQ1FwVAUTVgwNEVDEQ/a0l6+ZSNywhT9A5H0x1fh1WOgMLTG1GD5Y2L6cln+uqx2+ZZbYp92OF8bvF7HGHIVpvbGYFpp7lQltKF4jYqRVga2AtuAeaX5rrLRCq4yYiT8sbENxaLS7FYO2oDzlUVMqTCtakFrnsZhApdBrUiiKGjNDJohrUgCeTTTOpJZOnL9omBAGcSUIm+CNhSGoSgakDFV5bUUXk/VK6uuieDEjF7DVF5H4TyRfcLruixb9PquKBO93zga5Whw3Mi8rpp3G643HM1OTAaUYkbBU5bGsRTEDLRlgKXAMjxZot1XXkDNEeWNew44pvxx1n4bdvO2le2XcauDy/pDo0AnTEiY6KThzVuGdx8xCe8t2lB+ey6q5EIpmOrKZad+OytFG3jjFTPCsaoJyeDqshz22sgRyqOA4N4bff4Ex2AN2z7dqTYGmFzIM5MpMtidYKS7bBQQNRB4cSrLZ380xsRi4USL35R3/dRZbF/XXSF7o/lGaWU6wXE1D+6fqUh3ELQzNvYi9z74CDq7AAryxBhKx7jiiitIDY6GYz7am+SybQP8eGw2TNsQrF+pfNUyrkaf22mneiweenGGT93/InO5sjHLYFeMn714I5sH0iseh6hCvzs1glKwmJ0M5/cc3M+3n3X58fg23wSxPkHaoa2D6brXxErGr974tHPtrfY520qm6jaH0hZu8VEWM0dDY4lS8VhdT9R2DAFaKf+TifX09F7OQy/ONxyr1bg2OpX1eNFIrk5laTS29byJV8OApB2jkU7n2zlX6o1Ju8e2PBYTzM662PZ2uru76es7Rql0bMXHv1V49k7DtwuCsHrs/P4TzJQc7rn8HHb1pE+0OIIQIkr3DhCluyAIgiAIX370MP/tnx+tWOemTYrXjZ4YgQRBEITVQwcK+sjPX0NBvHXKgY6IKr6L3jRUyhf9dXkblXe89b4BhLYUOukp/920he6y0GkLHTcgULSvRI6cg8rZqKKLsn2lvD/FdgmtTBQQN9EJzwBBp305UqZn4GQZZWV7M+yIgUbRRRUdVMH1ZMjanjx5x5OxxReJIPpEn2lwNKl4KqFwIwYS67UiDaS0Io8mpzWmrem3NYWiQzLrsCHnMud/+qiOIrLa8/1K0WMazBowg8YquQzr5ddXL0JJdWST6ggi4wnFkTO60EkTM2uTytgMFDULBuxXnsXXFsMkrRQ518W0NQMljbtQwnJ0U7miEUCikUHalb2TPttxk53nDLHzso0kjuXITGcpLhSJ9yboGU5z4VWbcA4u4swXcDIlzO44Rk+Mw840d987zjNjeZKOrhinTuWeQvMkDhfUifZyOGXw9h0jXD7UjZl3mFOa9EASgMxsnmnt0puMsc4wOXNbP89OLrIY6cOhks1f7ZtgZKkU1t2sz12DXt3ZuQLpgSSF0TRT2WJLI4ZvPTXBlx49wkymWHFNDbdxfCqOt6qfPmIwqXjHFYNcf94IR51unh47QkIXyOfzPDEN947lmM87tTuuAcNpk9fu6GLbYJrEZIle26FrMMnioMXRhRzr+1IoFEfns+H8fS/Mcu+LqyPjQDrGz79sE68+dx1HJyY4Opdhw0AX60fWM50ttVToj/R4xg2f+O7z/MMP9lcYQrRDfyrG215xBre+6qwaA4lODAeAugr/F6eyfO7BAy2jbwXGQ9fvHF0z45lgrH48NtuWscRaGrUIa8PevXu5++67WVgop53q7e3lxhtvZOfOnSdQMkE4cbiuy9jYGEtLS3R3d7Nt2zYMY5V/z5xAuRZth7O/9wQAz113Id1W+8YuJ+vYwMktm9A+onTvAFG6C4IgCIJQ19Ndge7ycwFGvU9DL8xI4Wovb6Iem6q9faLeqW7goV7phV2umNqFUGlS5akameo66yq3q1AWVeFd6zVw06Wb+fzDh/z2qtqmXC7slIL//rrzcDX8728+U9GmjrYffABTqpw62lD+GPrrtO+l7mheuWOI+5455il0/HW45e3hV+F6421U9ddfVgpuUynSBrhKYQflC/O4k3v86pQnng5EV56nOp5XduC1bgDKsVFuCbRGo0AZYBiAQlfNa8PAMQwcpXDDeSOctw3lrfP/vNzJKjJVGMrA1AYmCgOF5Wrfc1z7cmk/YIA3dQ0DlIFrmNimiWNaFE0D2zQpWRYl06CkoOR7ZYfe/BFPfg3EXUi4EHc1Cd9L34w4Y1efsjq8Pmo/epa98j3vdEd5bQXe6rZSOEp7U1+moIwBWK7Xz8Az3vIjA5i6aptb9qYPIgkE86b2xi68JJV/SfrXchhZAA1KhYEvHb8736HEAe1yhja4QVtlj33tj5Ojw/EKogb8nS7wuK80q/Fejo5b9Jqhajnira6r10PtPUbjRZWgdn1NVI4gioTpTb15IzKv6swbxE2FZRrk/W8lwZinbU2PrZmxHRZLruc9buuKdjdhcA0mnn7Qu86Ce5ICvmvYHDJp2LanIFbtKatd7SmAC75iuOR69xD/fqJc/z5sKnTMQMdUhde5jvmKcXONP+QHivSokjzyXNKGgpjX/+NG9NkUPd862b/kH38/koC33quvyzLoNk0WY4pMbPnja7qaTTnNcMFloKgZLOqK6bq8y3DBUzwnHJr42sJ8DPZ3mezv9owAJhMGx5KKSX9+qY6cfUXNSMFlx6LLWUsuZy06nLXksj6/OlEB5nFxgeluizs3xfjClhjFZZyPlqu5fMbhumM22zIug0VNf1GTiERCMTUcw+WblLieGAPKYDKp2Ndjsq/XYCxtcDhtMJ5UbMhrfvvpAi+bW5ky00Fj1ntmdLh+0pf7NcQYaXqUV9ZOJ6yk7llcvk6J+7GB5gYNgfL8WizeRbKj/pfbKQGqoYHCPCV2o2rS0XRicNFO2pxmqW5mcLkIi18kTl+kj0EffoBd1/iinjHJavRhOf3cg8OuNWi/3fEcSxm4ChaypZpxWE76osneGO97405es3O0JqoD1FfuB9u/ufcof3jnXkYWSnXTHzUyiGmUGmigK8bPN4gkEo3msJKIB8crksfpzt69e/n85z/fcPtNN90kinfhJcfJaoiymnI9sZjlNQ/vYyhm8eS1u06IDKvNcmUTRf3JhyjdO0CU7oIgCIIgnAo53ScW8o30ccdFjmhO96PzrWUJ9onmdG9nv3b4zK9fxbu/8Niq1QfwMkz+gq6a9cfyh/j2+GdWqZVTC0OZGJgYykAFZgZKEfwjOh/ZFqDrHJ3yzw9d8390H+WbOCilMDBQykBh+LIYoUxKGRgYlcq+qp84daRo2XeF8vqvLExl+vNmeZ7oem9eKYMlZfC/KfIe1UUfhrfe74fWLi4uWrtoXFzt4qJZQPNR8mhUlYFChdVIxIjBWz8dHyRjdbfsy2qQtrOomk/Y0WNduU4p+Du6GPSNQKLbgnGY0g6/SQZwvfQLuCjtYqL5sE7Sh/aNVnR5zNC42mE+MmaV7VeOm8YLDb+Q7GUmPVypGHe1531ecldF8RkovXU8UMSboae6DpT0CQOdNNEps1I5brue93vB8bzAM75HeMEJPebbDRkfDUWvYwYkTb9N33PeUl5Yf9+IgCDNQWCrVHTLBgg525Ml53jKfkeHUxxd4aWuwTPQsCIGEL5RQoXnfMqbEq+6btugy9aM5F3W5TXrCi7dtqedd/1qXMDxUzcsxhQzccWBtEHe6qyduKNJupB0PCW8VlAwIO/X2y5K66rruJKekuaMjMuGnMvGnMuGnPanLhvynoFONUUFR1OK8aTBeMrgaErxYpfB7gGTmUT5nLpk1uZVEzaH0gb7uw0ypiLuGyIFaUQsrSmYiiVLMZVQjKdaf8hTfuoRQ0PB9IzUWvGzh4pcNe2QN6Bgek8Jyzc+Gsl7fR4peEZR9fBM12rbWc76ggELMcVCzGAhppj3/0oGJKqOe9LVdNnQV3TpK0Fct9dOJ3Tah5Uwj0tKKaYTiumEwXTcnyYUWVNRMDyDuoTrGUelHei2NWlb021r1uc1G3IuPTYNJWvXECFrwmRCMZUwmEp45990wmA+5m2PPjWUb7iWsjVdDgwVNNsyLtuynhGN26HxQyPji+j6XsNgNq6Yiyvm/PPD8I2+BoveWAwU1yaVxmoYc6yknVbGKZ3KN4vLNygyZ2qOOmWl/KaYYhb4YckOlfiB8QPAKxJxLikoXkuMgQZGIo1k6cQgJTo/67+FrkbEjlZGMAfTBm+8eAO7tMUQBl0DCcCPZNHfer57IMm5o72obAmdtnjm6CJLs/mwTBBtozthUZovMKAMuvqTnHnGAM9OVpatqXswxUVXb8bqNMoOoF1NYf887mIRoydOYnsfQM061cTgwHVdPvaxj1UoqKrp7e3lXe96lyighJcMTz75BN/4xseJx3MUiynm50eImomeKEOUsoGMS1/fZI18ncp15+Qc73jyRS7rTXHHmbNtpXg4HkY6y005sdzx8RT1X0Wp58J9tD6LG2/86bC8pME4/ojSvQNE6S4IgiAIAsDde8a59Y7dJ1SxrauWAT5x86UA3HrHbqAddeHqEpXjxl0bwnFqJkv1PkBb+7UjS6DI/+beo6s6Jtdj8QFqc4YtlmZ4au6H9RXIRLw8q0qYysRUMQxloNG4vtLQUx7qcOoGy3W2hUpaNFpXlqm/n79c8bE+mAuUoYHiU+Pi4GonlE04tfj20E/wZO/x+bhy86HPMVCaOy5trQW7ey/mB0PXnGgxQjRUKrpPoCzHg+HCFJvyR/x+q9DARFuG92cYuKZREeHkp4nT5yqSLqRdRW9Rk9SeOY6navf/+dE+gvtcebl8D5xNGBxLGCzGDBZjioW4ESpe52IGc3GDmaSiZBhl+ZRnwKF9A6NAea6VYrgAWzMu6wswVNQMFzSDeZvBfImefBHLKaFcG0c75CyTxZjBdNLgcDrGwS6TA10Wh7tMHNPE9aOauMpAGwqN4bWrFAMlzUgeHEOxZHle9HMxhaFdTMchXiqQKOaJFwskSgW68jm2L5Z4+Xyci7IpYkYCwzdOioyYbyBlRIyovH4fTBn8YCTGQ0MxppIG0wmDuYTZUrEeczVnLbqcs+hw5pLLppwXPeBLm2P825Z4W+dIEI1g+5LD9ozLGZG/bruz862o4Oleg+d6TPZ3GYx1eVEJjiWNstFE8CmsA8OPgYKn6N2acTln0eXCOYdzFl1iTV5CNLBkQcbyjBvm4oojScWRtMFcTJEzFXnTU+h225pu2zNGCBS8hvb/AEOX15vB1IVeW9NX9CKIGL4seTOIvqA4nDZ4scv7O5ZcuYKqy9aM+kYiG/Iuo/50qKCJ+VFvXAU5U5E1YSphcDitOJwyvL+0Ym6VUnv0ljTbMg7bMprNWZf1eZfRvGY07zLSwHAlY2pm4orZuMFM3IvUsL/b4IUuL0rDbFxRaCNSRMLR9Ja8ce8uwfq8y4a8y8acZ5wQjE+qjUAPLjATVxxJKQ6nFeNJk/GUN2ZHUwY5E4p+5KG4bxiScjQpx5vvsmGkUHlMNua8CB6NRrqeYYcGcqZmyT9fM5ZnaBQsL1lE5r1tGcs73r0lTV8RhoplA6KNOe941DsOAdWK83kvlhD9LaIxlBRkLE3ONHBU+TrpKXkGIyf7c7Xdfp4o5nE5tDHF0MUjQHPFvG27PP7AIdxnZ1l3IIMZSdWgUpYXjSdbvpFrA4pJE9KelY1TcjFjRjjv4jC7MEkCCxMDB5eib0ARj6wb3jRCKl3+7eZmS55RoKkw/LoBkmf10/PKzRX9a2UcoLpiKAXuUqkt44F69QXbSkWHp7/6PMXpHPGhFGffeCaPHJ5ncj7HpiUHM2czgyZ9Zj9X7hhCu5rHHzjE0kxuRQYQx4Nov1VXDLSmuN8zlkjs6CNxZn9TAwuhPSYmvsbuR95DPJ4BwNWKJycvYN+Bi3GW+lhvLNLf5xmiaFRNVJG1iqoRGMjEYnvYcdZDJBLZcFuhkOb5566gVNrVkYHMX4xN8EcvjPNK40H+k/ORcH0iMco5Z/8BIyM31JVhLY10Jie/zr5n/xeFwtGW8tSTrdPx2bt3L9/61v9uuM9P/dS7GR4+uCyZhJUhSvcOEKW7IAiCIAgBd+8Z5/a79tb1eG+Uy7IdGu27oS/J+9/gKc2q2w22RZXWjWRr1la//9FhLlvOC7mhL8kbL97AnY+NV9TXqGxUjnZkqbdPJ32oRyNF/nLrq+a6RIIPFRIrrudUpayEd31FvON7ZXvzGkLFvj/nK/yDZULlf0BU6QWBbkNFtlcvq3LIbN+AwI0YG9R6iZeXa2n+caGVB6HGxQnGQTs42vbHxsahen0w702nsBnQVMmtPe983zM/8NxXvub1AC7zfj+i/u6eJ7GuXQ881H8Zz3ed2bQf1bR7H6su9yuH/qVG6V7pl1+7rtkIB2NQxEvl4GLgKk/hmMCgPxifqkgHobIQKsaspu3IuAE80302j/Vd3LrjNf2Bd7xyO//y8KGK+6LQGRcu7OFV09870WIIyyZivKVUZE30/7JRV9RP2TEUOcvLEaO094f2zS78SBZUXa9+ZZWLutL7uVIuD43nee8oQqMW5belXNebai/1SoBn7GCiDRPXMHFNE8ewcA0v3YptmJQsE8fw/lzDN5YwDBzDBMP0Igj4aUsMXU5PUlSaonLQaAzXxXAdb6oj8/4fCt8Aw8QxgrQzvkyGb5jhb3MNMyzrmp7BhhOW86ZaGX4bDmakbdNxsFyHdMkhaTukbIeE7WBpP4KM8vpZskyKpknRMimYFvmYxVzCZCkR88bCtLBNb5xAhYYimiDFSWC8UjYSMV0H07ExHAfLtUmVHHqKNt1Fm66SS9rxvNnD4xkJpeEoRdGyKFoW8wmLY6kY00kLx4ph+/IEMmnDoH9+mvVTR1g/c4xcup/pdZuZHtrITDJOSdU/BqbreNFWXAflusQdh56iQ3fJwXIVrmliGwbzCZPZhNde9LiUj1f5HHENkx4bRgsGw0VN0iFUQC9asBhTTMW9KBMNU0JEP9t2GB3EcssRCgaLfpQLrSkpX4FeoVD31jlroJxZl69UxPfYXsSMhENoqGH5xholQ5M3FXMxzwAi+jfvK/mzlqLURM6EoxkqeClEhgouQ0UvqsaGnMv6gqa7pEk7mrQNacc7Lu2qYlxgIYZnrOVHQsiZnqFLyfAiMazPu6wreEYRCbd+2pJoZKdTlSmlObopxejhHMO6fj9Ohn4untXLvsuHQwVkce80c3c9jzNfDMsYaS+lnJutb+VVb7vZF6f/DTsAmL3zedyFSH29cQbeuIOnd48zuHe2wrDEQfMDbM7DrIm2cZ/l8BOOyTpdXj+lNNnrNnDNT5+9kmFYdXJ7pmr6XY1KWwz+/Nmkdg0fR8nq47i6aYqLThXU9errRLm9eO9B7Jkm3zD8W38ud5CZ2e+HOdqmcoM8N7edolv+bmHhMKIybNiwgaeP5cmWPMMXBaRjJi/b2s+mgXRdT4XBXzwnnLenc8x/c6xGhlrZvA2ZTIYjR56kt/dYZGNkDLRicWGYDRt30tXVVfk8a1D3/+hd5Eu9CW4Z38+vTez3i5YL9/ddSiI5Gi5nMhn27w/K1RUWgDN37KC7uypCXPXh8p+xXVeOktjm6QgnJ7/OE3t+E9BY+QGGnv/ZyL6awcFrSaW2VD6f/dmFhQVefPEhBgYPRdrS3kutL+z0zGbOPPPl9PX3Eby8PvHEPfT2vQBops75Qrkn/j7HJi9hZP1jdXrsNXLhro+L4n2NOGWU7h/4wAe4/fbbK9ade+65PP300wDk83l+53d+h3/+53+mUChwww038Fd/9VesX78+LH/gwAFuvfVWvvOd79Dd3c0tt9zChz70ISzLaksGUboLgiAIghAl+AF1dD7XMs9fNEdfo5x/Iz3lfavrjP44a+eHW7RMvXar5Wz1g3IlPz7rydJOnsJG+0XnX5zK8rkHD3B0obERQif1tTpeo71Jrtg2wOQfP4SzUDyFP3+d/mi8fOoKImHLo9sbf9wLfvjUO77NtnVK1+vOIPO1Fzva5+hPb+Vwr7WiHKHtnOet7mPN7lnBfe1bT03wpUePMJMpf2SLOG0DjdM1aK0jijn4LTI84oeSDRT9jfatZv/1G/nvD73Y0OimnhFRJ0TvOY6r+ctvP8c//GA/c7lyfe0YMQRGTn993/41jVSy0v62S1fcJGYZddtpNB5bswc4f+mZ0DAiUIRCYCyhfaWqt34IxQ7tKWqDa1pHlLSV64is0xXrKvbXtcvlMn5tumo5bJMm+2q/7yamsjBVDEtZmMrCUCYVUUsqjIjKaROiUU2q+9iMmJEgpuJYRsKbN+IYyqTg5DjmHgNf0Rwqtaum8qwThOOHEzGKCA04fKOFwDjCszs0QBlYjoNpF1BOqfJaVQYoEwwLDBNtWLi+wUHJipGPWRQsk7wZMdhQXgSNYFkrI4yyoQDlG4Mo7WK4GsN1iLmauOMQc1ws18UKpq6LWfWHUjhWHNuKkY8lyCTiLCbiLMZj2AYVkUIgcvdR/rxvcBm963kGMm4dg5XIOt+gQyuFbcUoWXFKsRglM4ZtWdhWDLvOvBMxTvGmipg2iGlFwlXEtQEYmIaBRuHgYuOQNzRLpgbt1srmeuNnWzGK8QTFWPAXRxsWm3Oa7RmXzVlNf0nTX/T/Spr+okt/SdNbal/5Xw+NZ8wxmfQMFkoGOMozaBjw2xooNo880Alu5Dmy2kr16kgM9d7vvag3tfNR2b5Ake9g87j/rvkziQS/5xtZVxttVa+janv1Nl21FJXNDZfryV3PQLnxeAbrD77y5FG85/ZMMXXHU1T3uxrtj9zwzee3pXhvFjFgJQSG+hPzeS7201kUEiZPGi4zuebOBs3qa+Yw0YrJjz9K8eDi8jq0imz+8HXhfPHQIpN/+eiJEwb4z5eneHjI4vbHc/zMeCRCRuS6aT5XHX8w2FZeW36zr7++7407SF4wiNYODz/8ixSKx0ArYpkNbH7kt+vUE62vdpkG26sxqgxaFfDiK34f5SvpkwvbsQo96OQ8xcFnMC3b21aBIpEY5RXX3Cuh5teAU0rp/oUvfIF77rknXGdZFsPD3o341ltv5Stf+Qqf+tSn6Ovr453vfCeGYfCDH/wAAMdxuOSSSxgdHeWjH/0o4+PjvPWtb+Xtb387H/zgB9uSQZTugiAIgiAIJx8rtR5fDrk9U0zf8dSatnEqUu9DU731q6m4bkb6uk1kvne4blsVH2zXWI56mH0J1v/u5Ux89KEKD5pW+4y+54pTKvxi9fV52bYBHto/wwMvTAGKq7YNsOVfn8fM2A3PHafL4vAvncVUtlip6J/NcuG/HyCRb5xrPRgzF5oa/9BgezsGCq0Mn6IyNzOECupZTmSOwa4YP3vxRjYPpGsMupr1NzCWODSX48tVBhLtREMJGO1N8EtXbMFxvav96h1DvPzMoZp22jHqeOjFGT51/4sVRguN2m7X8OJ0p1q57xmtBNEymt8v/jb5rXYbiUx1xTpV4ZFU+TkzOokux3Q5godC+cYUgYIt6gVU5bcedYzSmsAMIW8qMokkS4kUuVgC11DYhueBHngXazxFldIuPbks6xdn6M1ny/WraDtV4+Z73qM1SruReX88tBt651ds0962ghnDNo2yJ3ykrZjjEHds4q6NoQk9wEOZIukLAlnK7VbLUX85PE5R+YLtQVqEsM3IcsQbvbJ9t24/m86Hp0Cd+eBNQSm0MsrtBvO+UrPxA7usUlNag+tWyFmW10W5Zbl0LE4x1U2uqxezmCeRWSCRXfTFqTP+/hhVjxfKP26a8rjWjI8uyxfI1ag7wksSjfKNLYzK6BUquuyl8lDKxPDv82aogqF8XkWMwxw0rvKWXDwFu/aVM1HjNo3CMYOIEJ6hhqksrwVlYioTCyOM2hHTEHMhrr10BkFUgi5bY6jKtFXeXP0zvqSgYIKt/JQtQAyDpLZIuSax0FjNihiumRjh1PRvcxFjtArDNI0b3AfrtO+imUNzO1m/jOZ9OklvWL6s+grkMJXlG87FMA0rNKYzVH1ziObGvs23dbLeRTOjYNcfXnvCQ81rV7P//3sAK2vXNYCuKY+mlLbY/j+vbvpbJ7dnqiYCQRBNYCWe8kGKu+uweBfJmugCHyPPfX76gkC6aGS9RvXVqjpb73vnnXdy4MABANycHb6Q11MRA7iujW2XFfNZO43bIKoEgAUk8d6Jok/hvH+v6El6791vvuR19Kc8HVjvq7aE+79wzx7+9ft3ElUXagXKNCoqDc5t13XQuvwOX0/J6L0FWCgVMazStf0Oru9/uuq1LCXTvOmRexldmG3YV8Fj0+YnOfPM3TXrL33ZZxgYePkJkOj05pRSun/pS1/i0Ucfrdk2Pz/PunXr+OxnP8ub3/xmAJ5++mnOP/98HnjgAV7+8pfzta99jde//vUcOXIk9H7/5Cc/yXve8x6OHTtGPN46f5go3QVBEARBEISAej/4a9x4l8tq1bMWbTUr32hb1fq6oRmXUaYRZl+C/jecSWrXcIMPM952YNW2GWmrYajJegz5nhydGHAMten9carRagya9Xsl+57M1Iuk0kqRvlJjo2aGAo2U9c0MD1ZTloaGEQt5Lv7KwYZGG0Jrgvy39bx56nnmaKXxk3f4U42Di6vKy16mXR165SeJk9AxksRI6BhxrLY+wHeCjUNGFVhUOf8vz6LKUcLBz0YPlJN1RDLXY2qDGCYxbWJhEsPEwED7ffOTqOAoN9LvoI8arcr9dSPKk3JyEIXSqmpdIA3ldZEy0SkQjq0O2lHR5fJYu+iqbW44r6it30Bhak+RZmFiaRPTV+JVyk552d+v4jxQ5XknIotTdV64EfkdXL9eA0MH8hiVU12ej45FdBw01WMRHBcdSuJWldE146Qj42FE5Ii0ryuPaVSuQFbt99nzePZ67KjIPC62isxrx0tFg4OpFYYLplaY2j8vXD8HeWCUokArA4XGcg1vH2ViGDGUYfpnunf1lrSNTQlb29i6hK1LONrG1naY5sbBS2ugIkYBlcY0UYOBqGGK8g16lO9RT7i9wkAjasgR3EVcF6UdzzDCdVGuQ+3Lna7zvqfrziqUrwz2/ymTmD8fUxYxYiSIESPmKfV0EdstUvLHxtElHNcbFzdMBWR7SqJo5BF02cCpxsjFcwn3jDE8j3gV9p86xiuKhGtguWC7RWzdnvGlcPKjMEKlvFJl7WO9CDkafCOI6rg1gWFUsKTC6A+hURRV0/D88subBoZRaUBVNh7yJEUphuIxui0TlOLVv/YOtuy8sGn/7n/0Ue793Kf9KiqvfZRRThGmFNrWuIvBuR25LwT386ihmxFDx9PoWBexc0ax1g+hunuguxfV3YcR834X2hMZso94IcqDyEdXTjuct+hdg0M3n8+/DSqWHCdsyR+2quNUniqlcF3Nn37zGUZyLj+rY2jKkdIUgZEDfIkS+3Q5JHtfKsZ7X3ceyqhMluZq+OBXnmI+V6LS0KxMX1ecd994Hjaaoqspui5FrSm5mkef3MvMwoJngKOMimnlvGeE4wQGhkFUDr8NXdXx8jlSHgnTdejNZRjILjK8NM+ZU0dCg8R3vvOdocNrQG7PFE9/5kd8OfEQJwpHGfzNdW8ApXjr/V8jXSocdxkCo1ZvqtHa9ybXBoYbIzryURMkI2GhLKNif9fNY9ternnlxjBL6cjbIoCLE1/CSnZjGCm01hSLGRxnCVDeI8mNoZy4X7r8nhOwY/AYG3fdXdOPC3b+GaOjb1zFkRHgFFO6f/SjH6Wvr49kMsnVV1/Nhz70IbZu3cq3v/1tfuqnforZ2Vn6+/vDfbZt28a73vUufvu3f5s/+IM/4M4776xQ2u/fv58zzzyT3bt387KXvaymzUKhQKFQvmgXFhbYsmWLKN0FQRAEQRAEgJrQdvFtvRTHFnAXi6iuGEqBu1SqmA/KFfbPU3xhHoD49l6UoSq2t6pnufPOQhEnU8LsjmP21m8rWsborpW9nmzRbc58oWEbQQhAoOHYtVOm2fhWhxhsFoJwNbfl6+R+pMpQIGoQEFDXgIPm+5xuNDOOaNXvlewrnPpI5JH2aeZNt5KyrepYaT2dtLcanoLLlbvTdjphLetu1B4cn+N2MnO8x/1EEBiWtKKeMchqjUNopIFLEZuCKpFXnlek6RtemARe3WXDCBPPYOZEHgs3YmDh9aWWeiGCLUzieIpErTW2LmK7Jc9IxE8p4urovGcEUMJl0dQsmS5L/jRjuDgGBApYz0DKG5OEVsQdSLrlv5QNfSWI60AR5CvitA6NMbLKYdF0yBgOOeVQUA5FHEpKUzSgZHge6kWDcLlgQN7Aj+ThR33wB0XVHZkyhva85xVeGhmtHbR2sBwb0/+zbDtcrpw6vkGMd74E/1AKRylcpXAV/nytItLQnnNuEbA1WCh6MLB0UJ9//msNro0O/0q+oUb7RrcnM2/67+9jx2VXNS3zhW9/m7H/+6fHSaIyuUSKTLqbTKqbbKob24p5URkMi8vmNOcvKi/yQzLGHdsTzDuun9rcM4Kp+aP+Ovzzo7zdKBs11NsPyttqththSotjQ6PNO3iScYbW/PJIN287ayt9yXJOeO1qjn7kQQrzORZUDqiOHqEwu+Ose/uFFb/FFxYeY+9TvwN4Bgkvzm9lsdRNT3yJ7X1jnmGk0uy64M/p6/N0dNWRmqLLj089ypvHeknoPF+99xCxQn/Fc0BF/jd746z/b5eiDMUzzzzDl770pYb9/vmf/3nOO+88lFIVSvF681FmZ3/I7kfeAlpx5n1/glUYaPhcqhe5Lti/e+IyNj72Tl/6yLcMXECRfJPFupdfA8D09AM8+tjNfoH67QbGoyVKuIkFDr3yPZ61SgTxdF8bThml+9e+9jWWlpY499xzGR8f5/bbb+fw4cPs2bOHu+66i7e97W0VCnKAK6+8kle/+tV85CMf4R3veAdjY2N8/etfD7dns1m6urr46le/yute97qaNuvlkQdE6S4IgiAIgiAIQl2aGWI0yzkY3a+VEcHpykryM65Vbkfh1KDTiBYvVbKUMBQkdaxlWdtaBAVWqWfZ7ZUS0yyM/ojeoy8nVhhcdj3t4qAx63zk7HR9NjbPC33Pceb82aRL7X/70TgoavNiNmqnEzrtw0oI8gIXzRwJJ72qdQespdJ6tRTlrfImV68/3hwvxf9yjVNOJsOEk0mW0x0HOJhWPN1nMpY2MDVYGixX+1NIuOW88f1Fb5p2qpV3Xl0ZCxZjiiVLMR9THE4bHEgbHEwrDqQNDqcNCuYJPrbaxXIcLLtEulAkVbJJlYokbI2lNRaKiaRB3jJ8pSwEBhJ9Rc1ZSy47ljTDRU2PDUmH0DW7HPq7nD4mGka/HIEBL/KC8uYXz+1j4xl9flAGLwaL1i46SK/hL5+VTrA+FkNrl5HtO0j39jXt6hOHj/C1h39crsP1jU20L5suyzR9LEPXTCESgjyIFFGOpxOsV3YRo5TFLGRxVZ60m8fILGJkFlG+1/qpykz3EP9w4621GzRctLGXbQNp4oYirhQxwyChFDFDVayLK3/ZXxc3DBKGIqYU+eefZu+f/K+mMkSVelGFdEUZFZRV4bzy/8wwbYL2Lsx6RKpUphEuK9/kxnULuFrhaDPyFPUMbUzDwVQaw0zWqbZW1uc27+Dzr/kV1k0f5Te+8NcNBCpjxE0vbxbgui62XarIhqSUwrIsNp59Dr/4vnIa6gf+3+d58M4v4qIwDEXcNL3fl0E0E8MIjZaKxaOUSglSpe5wjENlPSpMjWGpGKkzBokPdBOLJ7Dicax4nPHJf2bgxeuIOT1YKoZpxMJUGq72DLtUyqD3Z85Auy52scDTT30Uu5jHWBwmPX4pji5hYHDFulo9J8DXt3+R/NBTnDPwPIZCcrqvIe0q3a3jKFNdokrxiy66iKuuuopt27bx+c9/nlQqtSZtvve97+W2224LlwNPd0EQBEEQBEEQhHooQ5Hc0V+xrnq53f1eaqxkDGT8Xtqkdg2T3DnU0PAi2BaNwhGN4rHaEUWq592kwb59z5CfyZAYSDPQ309xPk/CjaFSBjmjSCKRxMhrcqpQMZ9KphnuHsTsjtfUbS8WmcpMk8vmSboWKq/JqxLxgRQKRWE2Q2mxQKwnSXK4ix1XnQPAwR/tY3HiMbL6Lhx3DqvYhx1fALQ3n5gnN/CMN7az52IVqra3Mx/UoTRT53yR1OzZjesp9GMWe3HiC9iJ+fbbKfRjFHs5VEoxjUu2fx/rMptxMutQhQFIznHEtfiXY2exuTTMMIp+FHNoptAcsqb4pZHn2GiUGMBgsH8/TnKW3MAzxJTmoFbN+x+VOzlHrn8fqblzsPLl9aXEHI9ql+TsuQxqg6TdxRwupKZQgJMbZlpr0rE8wwqG+p/jcGa0og9GepKhLd+la/5srHzrPqvUMRQwMHMBZ82fQ8Lu6uh6chIzTJ73WZZGdnv9z1cfH4VV7AnHofvYJfSOX4PVkYHCWipBXahj/NB4PRSNAgpNzC1/8M/HFuoaXziJmeNqTNKYxv1ZTRodKzsxw/zoj+g6ci2pusd+deWrlqOzaAzHZ6zWglMt6oQJnJHVnFGVcqnVNR+EV4+WMYFeG3rtiIJ2plLj5wKTScXBtMHBtMFkUpEzFTnT26O35Cmye0suvSVNXwkSTlnlVzQUSxYcteBjZgFtGWApboglyMcNFmOKggEFQ1E0/anh7Zc3PS9/rQxsy8C2YuSTjQ2VUrbm/AWHC+ZcLph3uGDeYTRf7tuq5nT/+bXJ6X7hpo1cuGljW2UfeHaKxN89yTAqDNXeDI1mMqEp/voFXH22F6lKa00+s0R2bpbph/dz7J6nyTsZPw2E/4cTpswAiO/oxRxIorXrGQEEf65bNg4I573pzFKBg2OznOUno6k2bgiNCdAcwmHB367QnDGUpitmVLSTLdocncuh0CzZKeI/nq7b5/dfdjZX7xhqazwbcWgizd4WZeqdQTVldFC2Nr1HEMmjKdF97Hqaee+Mtepp7R1wUbh2e2Hip5PdAPTPT7eXnqNOtdVjYjs2xXw+XL57zzhf+O7TnJ/LAN69pnlMCxOwyTDXWp6nx+qsTHKENkL2/1V0ocf/A3gEgJiKN1S6P7D/ddyz/zUMJGb5lfP+H7/2U/9JFO4nmBOudK+mv7+fc845h+eee47XvOY1FItF5ubmKsLLT0xMMDrqhe8YHR3lwQcfrKhjYmIi3FaPRCJBIpGou00QBEEQBEEQBEEQhJODZoYXJ4NRxqXnrU2qg27Wd7zPGa84HzgfrX+RubmHKBQmicc9+YrFqXC+UJikVJohFh8iEV9Xs73RfHS/ZGI9fX2XMj+/u2E7y20zKLsxNoBdmiUWfzW53AGOHPlnCoWjAKwHLtYm+2a3M5vvY7HYzbb4Epck531PH+8rcSw2yMD6n2V4+Ccbtjk39xAHD30a255vOLa5wadr1p0HMLwvXB6p2j4KWFY/Wzb/Gmec8eEGY/VrAExNfZujE1+mpzQTfmZl3TP+jAHRD+Pb7+HFVoYD/rxl9TCUfBW9G3aR2ryOlH0z8fi7mh7PfP5ouD5u9eMegoXxPUznv4ttL7QwUHiW1NzZFQp9nXYYGb6BhDuK1ev1zl5YbDlfnF/AXcpjdqcoxo9y2P17jIneSmOBqFFE9VjUMzLx19UYXyTmsUeOMTr6RnoHh3EPQWF2Pmw/1ufJtDjxZP1xWMl8G0YedmIey+qh29jFkrtnee3XtPMcqbmzasdz4BkSyVE2/Nyr0IfzjD/3JdyM22C8FwG3roFNu0Yb1UrGgplHoSuiMRhdMdKXrCNxXj9LS09Tml8gPtBH//lXMPfUbvIzc7hLeYxMNzyTglzTJk8K5n1NVv8ponSvRxAdopnifbn9VGhG8zCad7hixqlrpBCNThHN1R3d9j/IYUZUaiVyfJhUTfnq+jVgKy+0ftFU5A1NwVCUTE85XzC8dSMFzfYlF6NB+0Hd9QxLqtc3Gs9gffa6DWuicO+UK3cMcVva4d1ZCxfdVPEe9PUf0w5/GlFCK6VIdfeQ6u6hq9BL+oet+zV804Udv/M5ruYdf/Qdfj3TOhLQb5HhERwUMNqX5IPv+UnMquhajqu59iPf5uh8vo56m3DfK7ev3HBrwznncuvffAaAqWPf4fkX/jcPHR7mH558C/imLOAp1Xdh8EfUMwyplHLgP5xHbFsv980s8k+Hp9i9mPXl1pw/7/LKyRLXHnMYLOk6e8P7yfIffu58XnWO98bjuJo3f/J+JhcKdUp7jPQk+Nf/fHXFWDaKuz1zdAFmsuTYwM9sfkeDXpTXDtx0LomtlZGb6tVtxbzjf/eecW69YzeJxE5+vOlMAgOL4Oz7Hz99LtfsGA6NOhzX5e2ffpjhpRK3kaTi6vXCUOBoO/xLXTsCfRZ2sYBdLFAqFDj0zASpIws42sbWJRzXxtElHO14qRMwUMogsaEHqz+FacU4ltd8/8UFBlScG4w0pooTM+L1Bw2Y9qWaLfTzV4/9OhddeBE3Vr+UCseVEx5evpqlpSW2bt3KBz7wAW655RbWrVvH5z73OX7hF34BgGeeeYbzzjuPBx54gJe//OV87Wtf4/Wvfz3j4+OMjHhn01//9V/zu7/7u0xOTralXG83LIAgCIIgCIIgCIIgCIJw/NHaCY0JEomRukriqEI/kRihv/+Ktrx9onW3MiBod76T9qMyRPvTSBnejrFEp+23K190jOop7ufmHmZ29ocADAy8nIGBq1ZFhlbtd2JAspKxWo1zpZHcsdggS0vPkM8fJJncTHf3eZRKMxXyddJ+o3ai1091PdVj0ai9dq7BmDXU0IDBXlgk1t+L2qgpji1gFXpR3Qp7ZJpEYh3puXNxF+0VpaWpjFJihgr7WF8v3d3noTNOxfqo8UdQxl20Vz2SCt0xHtMOR+dz6ENLDGHQNeB9v87OFUj3t57vHkhy7mgvKltCpy2eObrI0mw+LJOZzTOtXboTFqX5AgPKoKs/yZlnDPDsZGXZaN1Tj06yeTzflpJ8SmmObkoxejjHsC6Xn8HlG5T4ATZjKU+dtS3nci0WryXGAK0VrNUpNuZ8w6P+yL4TuPwf8qTjJm8vxhip2vbn5Lmvjg/rK7F4F8mK8vXqj9Ko/T/H86Ctri+Q7Vc2D3L+oVxFXxw0P8DmPMyafb5nOfyEY7JOl9dPKU32ug1c89Nn15XtRHD3nnE+d8fj/Leqflczh8tHyfMrN1/Ejbs21C0T5BNvli6oXr7stmV9/Agjn322oWe+i+YYml9kKVTufuLmSxvKGyhuoV549+b7roTgXnz3k5N87N4YE4tlY7xNvUn+yU4Ryzp1r1wNWHXG8NGFLH996Bh3Ts5iBx7x2jMk2Zp12ZzVxFyNVpBB8yVKpJMWv3LlVlBwZC7HV544WuteHl1Wip86f4SRniSur7TWvtO9jiwbCu47tsBRx8GYLfD6BejWYGqFpTWmBkOD6f/FEib9P7kVy1CYSmEphakI5w28eVOVDWLe84XHmFkq+gdOe+keFGAolIK+rji///qduEBRa56dXOIffzSGUvCLRoK4UjhGEFrek0lpr4pY3KTnus1e26ocwv8L9zzHzxS8O4Dh98EADK2xXMJUHYPXbyO9sRtDw23/8ggzi0WU1vyxTjHsKkwUjvJEdxS4ypsaeYd35hdD08zA8OP7dYxGhJVzyuR0f/e7380b3vAGtm3bxpEjR3j/+9/Po48+yt69e1m3bh233norX/3qV/nUpz5Fb28vv/VbvwXA/fffD4DjOFxyySVs3LiRP/7jP+bo0aP86q/+Kr/xG7/BBz/4wWZNh4jSXRAEQRAEQRAEQRAEQRAEQXgpY9sujz9wiKWZXGOl/2CKi67ejGUZleUHkhRG00xli4z0lD1+H9w/w+RinuFUnMRElmwDpX92rsDBQom/2jfByFKJIRTTaB7zQ1dfjBmuexwHF0/Jiav51y8/jcqUwvLNAmcbVXVV1z/jq1MHm7QfbaO6vsneGO97405u3LWBUtHh6a8+T3E6R3woxdk3nskjh+eZnM+xacnBzNnMoEmf2c+VO4bQrg7HMzrOJxt37xnnD+/cy8hCKRwzE28cAHbjMBEZh2bk9kwxfcdTDbcP3Xw+qV3Ljyx0/1efZct9400jItyHzYa+JO9/Q2t5794zzu137WV8vhy2vN19VwPH1eE1FVxnxb3Tyx7Do4US/++xQ/zboRmeGJCw5KcSZ7yQ4eizczXrP/f2l684xYFQyymjdP/lX/5l7rvvPqanp1m3bh3XXnstf/RHf8SOHTsAyOfz/M7v/A6f+9znKBQK3HDDDfzVX/1VRej4sbExbr31Vr773e/S1dXFLbfcwoc//GEsq73o+aJ0FwRBEARBEARBEARBEF5KuK7L2NgYS0tLdHd3s23bNgzj5FPuCILw0qJaqTibKfKHX2mu5Az2OTKb5dFDc4Bi62CK80Z7+c4zk3zp0SPMZNrIE90AQ4HbRIsy2BXj5y7ZxPU7R7ly++BLwsu0+jhdtm2AH4/NViiD2x2H3J4p5u56vsLj3exL0P+GM1ekcA9YevwYU19+DitTjn5g9MWZvGKEseF4x/LWU3yf6GO+0jG89659FB4+ylyPxYG0wZGUwaLyDEwO68DERPOT545w9kgP43M57nzsSLmCqJpRlx3ef/GyzWwdTGOgQudyCJzMvSVXaz724gRLrotxKIMquGxRBpcqi4ShsJWXaGfJAHdjF30bunG0xtbgaI2Dxnbx13nLWoOjvegSM5kiYzNZ3wVdleXVeBe29rz8z1/fw6a+FDFDMZ8pcf+zUyitwdVs0gaXuyZdgde5giU0eksPg5t7PC90rXG1Z9AxNpPloRdnGVEGu5SJi7ePo7xc9yXDm1/qj9M1kKSkNTPZIofn86EXvlYQNxRdqNDLX/miL+BSOpTB2r9Ucyz//Jcv4Wcv2dTymAudccoo3U8GROkuCIIgCIIgCMJLjROtbKluf8uWLRw8eJClpSXS6TRKKZaWlshkMqTTabLZLF1dXfT09KyZrIFMi4uLZDKZsL1Atur1x2PMouMUjEswJsF8cPyANTmm9Y7V2NgYY2NjaK3Zvn07W7durVl3xhlniAJPEJrQzvVdfT/s6uqiq6ur5vpvdq3Va2ffvn08/vjjZLPZsFwqleLKK69k27ZtDeVoJFOze2W99dE+dFJ3o7HqdHxajX2j+lrdhw3DWPZx7XSsXmqGEo3eGzo5rzo5r6vfPVqd39XH+3gdn0bn2+l2fqxUyRndfyBh8fi+55mcmWNksI+Lzj2L2bzNcFcCFBxbzGFmpinksuS0xUA6zqZugyMZTT7Rx9EjR0hSIk+M0U2bSObn2Nyj6I28GzZ7z613v+j0HbOT9/h6ZYGG502z997ofis5x+rJpFBheohOU0u0QzT9RFC/Rtftz0rH90Rdd/X62O4YPvD8NG/5mx82jOgQEHhRt5vjvp1Q51przrj3cQpak7j3KCrvRZeoF0Xivt/rPHT6A89P8yt/88OW5aIe4vX614k80TbrpbUIUlTc+vbLwzYbyVkvOkezaB7i6b42tKtHbs8VXBAEQRAEQRAEQTht2Lt3L3fffTcLCwvhut7eXm688UZ27tzZdN92PyI22/eZZ56pUfYopWjXJjydTnPRRRdx7rnnrujDVrQv09PT7N69u2JMWsnW09PDZZddxuDg4LKV8c0+lmezWb7+9a/XlamaVCoFQC6Xq1h31VVX8cpXvrLjMWp2rKr53ve+V3ddKpXiDW94Q8tzajl0YoxwuigchFOfdu85nRLcE88+++yaa6Gecr0RuVyOe++9d9lyNLpXdnJ/77Tudqh+vtV7Bq4Gvb297Nq1iz179iy77k772e6z+1Rltd4bVoPlnN/13ldc12X//v01hmvtvktB+8/o1Xpfaoe1VjyahlqREinYf+/evdz9Ne/6N4CpMXj4+R9x4403ct6O87jvvvt45kc/qnifAnjYn1Yf76lHvOWH/OVG94Hq/VrdL5pd2528x9crW++dMUqjc7refsu5B63kd8hKUIYiuaO/pRz1jksn43si78vVfeyEK7cPsr4vyaMtlOhB2gjTULz/DTu59Y7dGGhGjEVSlMgRY9LtQeNtb0dBPlNyKPjnnCp4uek1nnf7I5Rz1X/ijRcvK6LAldsH2dCXbGkgEPStun/LkSfa5n3YfJ+lmhQZI1VtNpIzaBe8qBuNHn31+iEcf8TTHfF0FwRBEARBEAThpcPevXv5/Oc/33D7TTfd1PAjUb0PS9Uf5hp5Snai+OmEZh+UG30Adl2X++67jx/V+ai62jI1M0pYK8VPNfF4nEsvvbTlGK3VsXrzm99MV1dXRx/im3l+Pf30022P22oqHNqJznA8Ig8IK+NEeKSt9T1HaI+bbroJoOkz8FTlVa961bIMrE5GOjH6OpUIlHmPPPJIy/tAI2X9SsZlJYZ4zWh0f+tE8Xi87sut3oHj8TjF4vJD0K8F1e/lnbzHtyq7mjT7/RBlJb9DVpPljk0n43u8+rKa3L1nnFvv2A1QofQNlcw3X1qTt/6zX/8Bjz9wL0nK106eOBdd/RP8hxte0Va7exazXP/wPtbFLT7aN8TtdzVPJ7EcltO3YL/lyrOcNlvt845Xbuev79vfcT+ElSPh5TtAlO6CIAiCIAiCILwUcF2Xj33sYxXKyoSOsd7to6BsipSI9yR5+2/9Z8yEhVJly/21+nCX0nEG3W4sDAwMTAwMrTAw0GhKysHGYVHlmFWZ8heFOkQ/8NZTaKfTaTZv3syLL77Y8qOq0oounaCbJN06iaEVrtK4aOZUhhm11FSWsJ4qo4R0Os2FF15IoVDg0UcfbV1BFA0mBjFMYtrCwsDFk8lWDjmKLWUKvPOHhoaYmprioYceWp5CQ4OBwsLEwsBBU6BUt/3qMUgkElx88cWcf/75dUOYzszMNPQATqVSy1ZaNvMGbjV/4MABHnzwwYq2m3k3xuNxlFIUCoWm7Z/uyvjlhs1dK5588km+8pWvVJzzvb29vPa1rw0NQ6IhppeWlsJjHsjc6XHbu3cvd911V3vnbXA6rUFa2Li26NFJEjpGnBgJbZEgRlxbuGhKyqaEg4sb3utc3PAeE6x3cClhU1QORWxsnJXLG7m3aTQa/Kk3b2KQ1gm6dAITAwcXR7neNPrnr9NVfmzKF7C7u9s7touL/hNHYWhvanrZZjHxnj/Bc8hAYXhJYMP/g2kwNrpinDSuqrMuKKlq13nec/7cCsbydPB6b9sYTUOKOL06TUJbKP84hVNdXlZAXpXIqSJ5it5ZXnUcyseyxTGIPPu8Z7Hp5RL231VsXLRa3U/twbvLoUOHVsUAITDEW41nUTv3t5tuuonzzjuv5lkQpAKoZ+gXfVdZreekYzt88mMfp7SQJ44V3mdKOCyoXOPj5t+fqlFASifo1Sm6dRITw7/zRP+n4n6kKu4jKvSetXFwlIuNi+OfR8H9LNWd5q1veytGzEIbmk/+9f9ldnEOFx2e79Fzv7e7l99426+jbZc7Pv1P5DM57/1ae/c5M3JlhLJobz6QwcYho/IsqBxum+dzb28v73rXu1pGaKj+HbKceqrRQW7ucIX/X806b8ZxXD7+8Y+zuLBA+QlRJrqmelt3Tw+3/pdbwVB8/ON/yfzCQnguVe+wnL6cDHSiZF4tw4NvTM3z1if2c1FPim9cfu6K00k0YrkK9JXIs5w2W+2zEkMAYfmI0r0DROkuCIIgCIIgCMJLgf379/PpT3+6Yt0mZ5DXlV5WW9hUGGkLszuO0RPnqQP7mC8tkVVFsirPosqzpPIUsTtTEmgY1f3scNazwR2gX3e1vWuWAkeMWQ4bMxwxZ8ioQt1y11xzDffff39HMg3pbnY4o6xze+nWSbpIYNT5yBqQo8i4McuMscSCyjKvcsyopRV9bFda0a2T9OkUfTpNb/iX8hVlZlOZbByWVJ4ptcij1n7mjJV9nE/oGJvcQUbcXr99i7i2SJMgrRPEMCvKu7jkKTFpzLPPHOegMd1yPFYrPHBcW+GH7yI2WVUko/KUlLPiutea1Q7/eyLy+3YaMeFEGCB84xvf6Oy+0IJmfQBCj9Qf/rA2N2e3m/SuLd3HiNsXKpNN39jIoaz48JQgjrdOlRUyNg62csioAkvhPTlHFu++2K+7GHH7wjYGOrjXdoKLpoSN7WcXbaa8qFxfnoth1lVqvRQJFL86ogQuK+Zdb6q8MX/MepEXzMmaOn7iJ34ijDazmtdVs+gxzdLONIoKUs+4qVl6g5SOc4YzwhnuOobdHhLEVtynuv2MKOGBGoV+KxxcitjMqyyzRoYFlaVACVs5FP3rtoRDCZuSP79c4xVTG2xxhzjTWc96tw/Lv5YcXA4bMxwwpzhoTFFQdsu6Whmm1YvW044xZiqVIhaLrSiqT7sGJVpr3KUSpaMZShNZ7Mks9nQOeyaPPVeg0SuJjcO0WmRR5TD9MUzoGF06QZp403ev0xkXTYY8tnLr3JN0jfJ+cGCAmBUDN6IIdzW4Gq3BsW0KuTxEriUVTsuYpln57GiqRD+5cH0VvOsbo6aHekgOdWMNJLCGUlhDSayhFOZgEiNutqzvRNGOknk1jSg+fXiK9+w7xI3DvXzqwjPXNPrFWin0V7vNVvuciH681BGleweI0l0QBEEQBEEQhJcCTzzxBF/84hcr1o26/VxZOiv0eEz4PuftUsRmUeVY8pXwnvInR0YVyKkiOYqkiNPjphjS3ZzjbGRQd4f7a99zvIiNowKPMxfH/5gXwySuLQZ0F1aVkndOZZhUC8waS8wq78P2ksqjDVorcjUM6x62uMOc6YwwEJEpwMEN++TgoFBY2mRY99YonAHylDhsTHPImGHOyLCk8k29zw2t2OgOsMEdYMTtY53ureljI4rYOLihEiBG5QdKF5enzSM8au0nq9oLldrtJn0FXS/r3X6GdU9DhVkUF11XEZGlwH5zkgPGFOPGbNveUnXRkCBGb2CQ4Kbo1anQKCFJvM4umgPGFI9bY0yo+VX1Hja1p9RYbY/kdtMlVCtCAqVWq7DDjby6WylX6rEWIaCXY4DQjiLw2LFj3HfffSuWrx0a5srVsF73caG9lW3uuraureXg+l7fMayabVkK5FWJIjYFVaJAiaKyMTCIac9zN+rdHXiBl/8MTG0Qw2ppBLTa5CmRVQVsHAyMcnQU7RkrWBEPznZxIs+bUNGqAoVrpae6RqNV4M3o/a9Qvkd8oJCtHC+la9cZful2lLftsNc8xI+sZ3GU27BMs+u+lZI8uKbqpfVoN3f1so2rNIzoXjY7Q2x2h1ineyuOr0azqPLkKYYKQS/KQPmYed7AnhFZijhJHS9HMwjVhcvDi/rg1H0Gd0oJm1mVYc7IMKHmOWhONXx2G1qxyR3kTGc929x1xOtc69Vy7jcmedo6zFE1t6LnVlQx/+Uvf7l83DUM6m62uevY4gyhwXsfMqc5phZXzfs/8JrVWuMsFD2l+mSW0rGcp2SfyOBmmxsYFChRULanKNaKBFbd+2U7BJGYFlUe239PhOohLp8ZtZE8NCpy/7K0Gd7PDAws7U2TVhylFTitx9HFRZkG2lTki/kwCkhwrwsiPQSSuKFkXhSH4FnQrVMtzy1hZZh9caz1XcRG08TWd5E8ewCzt/Z9djmsttJa2y72VI7SRAb7WA5nvsji0TmmD02GUT8UKoz6kVcl5lWWeZXl8tdew9Yrz8ZINj6fPvTCOH8+NsHbNg3zq/ZC3efNiYzkstLxXM3jcSLSJAkeonTvAFG6C4IgCIIgCILwUqCep3sNGm75D7/KlvWbcDM2zmKRA0+9wFM/3kNaJ0jrOGmdoEenSNVRdLZDCYfnzaMcMKY4asxRbMMDy9CKEd3HJmeQje4g63RvXaWFRpPFV/irEgWKfoh67xN8SsdJk2DA7SJNItzPxuGAMcUBc4oFlfM9RusrzA2tWKf7GHX7fAVwmgHdVdfrLvA+X1Q5sqrgebnh0KfTbHaHaj5o2n6Y0wX/Q1Uwn1NFisrxPUoddJVchh8Ov1enOd/ZxBnuSLhtXmWZVPMsqlwkJHM5tPGI7mOjO0ivTtXIP6MWOWLMhrIXVImsKpLzlXe2H8zZQJEkTpdOsN0Z4WxnQ8X5UcRmylhgWi0xo5ZwlEOlryuhUUNgaJEkTkrH6NJJz9u/hVdjFs/j18IkrRMkI+Un1TwTxpxnnGHkwnDUNg45VaRUz8tQQxyLdW4vo24/o24/XSRJ6RgxLP9ju+ddjP+h2sSgoErkKVHylZmmNnBxmTUyzKolpoxF8qrUtC9QqShrpdherYgBUaq9HpeWltpKAVAjmx+a1/Cv2EKb0TGC/MPXXnttTQqCIER9PUVgO6GY17m9bHD72W9Msmj4oTl9Q5xunQyVInlVZF5l2/ISbdoXHee60vlsdYfDdeNqlqPGHJPGPPMqG16bCoWpjdBj1apSxJjaS+lg+R+5u/wUGD3aT4XhK8JL2BwzFphU80waC0wa822dd22jwcJXwPvpLuqdgdWh3ustBfe2Ek5N6OUgjHszpXK0umbK7EonyZWFcl8xvqzRQM81837I5+DaiW7f5gxzibMdgCm1wA9iz3DMWJ4ncSsl+UrSeiyHbjfJOc4GznY20EPlc2lCzfOiOclhYya8blaC0oEne9Q4Ikg94J0g9ZT5Nk6lIZl/nwuuzYSO0a+7GHC76NEp4mFamHJY+hhWU2W9d+3Os6jyZFWBPp1mxO1jvdtX8TxcJMcL5gQHzCnyvkd9WifY6gyzzV1XYeg4qzI8bR7mWXO8rXevduh3u3hF6Vw26IG620u+F/kxY8Ez8tGV53tZ0WthosioIku+8aahFSYmcf+53mt1cebwVuxjOXSxQSQbBdZQitj6NNb6NNZwCmswyZGlY/zjv36m1gBAQ59OM6x7SOtEmCagpGwyquC//9h172+rkl6jDW655Ra2b9/O/hde4I5P3xGmxAiNTahMj3DLLbcAtH7nb4afwqFHpzC1QlXdnwLc8IkJr7nhNYxu2IAyFBgKlHc/CeYPHznCl+/8ckUEj4pnhG/Y9OZfeDNbtm719o9sq1xB1byKTsrr6pQfGxvjM5/5TM0xjcpS/azS4e6Kt978q6A1n/nMZyPPrMpnl6ENEsR4w0/9NOu6BrCn817Ehekc9lQena+9/obfdgHJcwe9cS3YZB+ewBrtIrY+jdnd/m+uemk62o4W4WpPTj9aRGkiQ+loFnsq50UtWC4KYuu7iG/rIb6tl8S2XszBZJjK7LeeGuNfj87yG90m1le+2LCadsPVryYrGc/V2H+t6hI6R5TuHSBKd0EQBEEQBEEQXgosNwxgI2W9qY1Q0dPth/bu8efTvnI7CHO6pPIsqCwHjWmeNcdXHPY7ri1G3X4GdTcDbjcD2vuwXc8DvRFFbC/8qjHFi+bkimRSWjGie9niDLPe7aNHp0iTaOnNmKHAIXOaCeUp3+ZUdlU+4I66/VxR2sF63d/2Pg5u+GF80pjniDFLrk0v+WqUVmx2B9nmrmOrM1xh4LASlsizYHiGCIFBgjfNYVcdvz43zYXOVs5yRltGECjhhOF/HdxQcd/J+dQJk2qeMfMY+41JFozVV2aldZyUTrCk8hQorb1SIFRYp8ioPBlVYNDt5mxnA9vc4YrxX1BZxowpDhjHmDGW2lbCV7NcReBZzijXlc4Pw5kfNqZZUDm2OsN0kay7T54SOd/wxFPE5JlTWeZUhrwqhTnOcxRrFDrbnHVcVzqPJHEcXJ4zx3nCPMickem80y1QGlIkiGuTeZWtMc55qdPT04NSakVhrk8mNjmDvKp0QWjgdNCY4jFrjCm1gL1CZXQrDK1IECOhYxgoisqmiF2TckZpGNDdYd71RgYGMW2yTvey3u2riDxTxOaQMe39mTNkG6SVOWXxjVe6dJIB3c2g28Vmd4gR3dd0tywFXjAnecE8yqRaaHoPHXJ7ON/ZxA5nfejRbeNw1JgLjQLnVJZpY5El8m3fjy1tcIm9nYucrRgY2DgcNmZ40TgGwBZ3iE3u4JqlAcDwlOvWujSxkRTWiOctHBtJoWK1z+523oFPRqLv5Z28xwPHtb8nMqd7p6zkXOhkfJv1xc2WvAgNRzOUjmawJ7IM/tK5mH3e+3JhbIFjn3gsLG90xYitTxMb7fKMSQaTmP0JrP4kKlauv90c627B9gwBpnPedDLrK9mzYNd/fqiE6bW/LoXVn2CmtMi37v9OmCZDo0ODwBRx+tw0fTrN9u6NqIXa31hGT4zEtl7iZ/Rxi7nI/fk8P/Pik2wZe3ZZY7oWrDRn/WrlvF/tuoTlIUr3DhCluyAIgiAIgiAILxWW84N92R+nNMQw63pmrwkaksTo0SmSOkZSx0n6vlOW9j7x51WRrCqypPJMqvmVhTxvQeB93q1T9OgkaRKhJ3cRmwPmFMdafCxfKXHteWqP6D5Sflhd0w9XGngfz6kMh40ZjhpzNYrrVcEPOzvk9jCkexjQaQxt+OdE2a8o8B4s4VBSngd6niIZVWBB5UJP/U5J6ThbnWEGtGecEeTPNrThedW3CJ+6SI5xY46jxixzRpY8RfKqRFJ751qXTqCVpuR7/cexSGovXYOtglDfJoNudyhDlHE1y9PWYV40jrXVP++8SqK8ofXDXnvj2KtTXGBvYZu7LjT4KOEwrzK+1/MC08YicyqzKue+qQ22uyNcYG9hnV7e95QCJRZUjsPGNGOrfE2Y2mC924ejXBZVnvPsjVzqnAl4ESD6dLqifBGbWbUEeB5raZ2gu4EivhE5//ywtEEcK1Q4TalFvht7ck2U7UJ73HTTTQBt5aE+VUjrBJfZZ3K2M1oR7j9DnnkjGjElF6Z8yaliRx66pjbY4A6Ehm6DbnfD66KEw6xaYtbIkNLxGq/sdtBoDhsz7DPHGWvzvni6kdJxNjtDDOguunWSLp1kSeXCqBVTaqHj96qYNjnLGeU8ZxNDuqdumTyeEVEQaaNAyY8cVOSIMcuYeYwcRXY6m7nI3hYafLxoHOOHsX0sqXxFfSr0Iu9lyO3Bwqjwyg6mJT/ahYsO35vSxL0oF7iUsL0oO6rAhVe/jB1XnI81mERZnSne2s1DfzJR/V7eyXv88exvuwq/k0VxuNyx6WR8V9KX4sFFFr5zkNJEBmcm3zSHvdEdw+xPYPbGeeqFZ1gq5bzfXv52ExWmEes104zEB3EzTSLfWIan4F/vGbJYfvh7sy8eeqZDZ0YUOmNTHFugMLZAcWyB4uGlinQJP39tFwe6DP7swVnOmlsM7zsOkZQvyrtnXHbFZQwND4FpoEwvkoIyFZgKZRj+1I9yoBTKoBz1QFVuQ1G77E9dNH/393/P4tJi+GslGtlBo+np6eG/vPM3MU2zog6Ul+pstYxMThaDlZc6onTvAFG6C4IgCIIgCILwUmI5oenW+sPdaobHXu1Q22sRunu1SaVSOI5Dsbg8z/SXMpY2SOkECbzQv6Y2cJRLlgIZVVh1hU9ax9nqrGObO8wmdyhUjru4nrehWqyIMGBiENMWCSwvnYFOtZVLO0exYQoIF9dTwlGMpBxww9yv0eUgrLJG++HyFSniDLu9DOiuUBYbhxm15KWhIE4Rm+fNCZ41xz0lPxoTg03uIFuddWxyB+p6lucocsyYZ0otMmtkKGJj+15UJWxKyiGhY/TpNP2+EUVKx0npOHlVYk5lWFQ5NrgDDfMdP2a+yEPW83RrL4x1ghgHjWnGjdma421pg16dJqFjxH0jjR43Rb/u8tZjEdONc5y7uDxuHmC39cKaGvksl7hlgnYptpEruBWN7pWrcQ9dSR296QQ3XjTCzrN3gFLs3fccdz8+yUJ2db2me9MJdm3uZs+hpWXXvdx+9rgpLnHOYJszTLKN1C9Bao0CNg6On4fXxcbFUQ5KKyxMkjrGOt1bN1qIRlPARqOJYTaMKFLEC9MdVbIG/1zfaMhBM6uWwpQL7aRCON7P5sDQ6UTLsWI0DOkeBt3uMDrRoO5iQHeHEUCaUcIOPeYXVJYfWs9ywJxaa6lDglDry8V7B/4aCwuL4bpU3CJXbC/cfk06BirPi950gl0XX8qeJ/dUtFG9X6Ny4fYm7+X1+tCofN3+plLglBr2WSmod0qn4iYog1yhfH1WtOs6MHY/LE1A93rYdg0YtfeFTuRfSxr9Htq1axd79uxp63fSmvSlahzdDVdhTxUoHc1SmvS84u3ZAs5cHl1c/juq0WV50SKGUljDqTC3vDmY9BTRbbBcwwNdcigeWqIwtkBhbJ4rNpXIm4p/+94SW7Kn0P20CZXqeWpSKgTLiWQC0zLLBgC+MYGyfAMC0yBfKnD46GFcNFlV5Aexp+u2udL7o9AcUbp3gCjdBUEQBEE4KXEd2P89GPu+98a+/TrYejUc/FHtD9l6Zc+41qtn7H5YHIfMMehaB10j3i/pzDFID3vzSxPtb+/ZUP8HdPTHYbBftI7MMU/mLVeV+9Buueof7e201c78atVbPT6B7NFxj65vVH+r49CqL/XabVVPO23W+2gSjFWjPrbb9+q67SI89Dcw+yIMnAFXvB2sqo/H7Z5rKzm2zc7VoO7g+qp3PdZrq3q/xXGvTG4OULXXeKeyNTqWDc4Nt2uUMbWJpUyW7u5utm3bVraMr/fRDNh735e4+0dPsZBbnTyg6USMi7b0cO6OM9hy2Q0cPHyEpaUl0qkkamIvS7MTZBbnSXf3kjW6mVMDPLFnT8M8zQHXXLCV+5880LE8yWSSl5+7gcFYgcziPF09ffQMjbLlZddz8JF7WJyZIEOark0XMDM3x+4f/5iFxdoPpatFbzrBa3cN06WXWKKbdN8QCkVmYZp072A43z0wwrYrXgfAff/+WX705Bi50iorimOKi7YOcO6ZW9iyrp+xgy8yNqfQ/ZvZvv1Mtm7exNiDX2HsmSe8x9C5F5Ht28EXvvj/VlWOgOOdY3itSOsE5zgbONfeWJO7uBm271UP5RzWBgoblxfMCZ40DzJrZDC0olsnGdQ9jLi9rHN7GdTdqxrud5EcT1mH2WceCRVlSvtKiBbfbL0UFSmGdQ9bnWE2u0MtIw90SoY8rvK8Jx1cfmg9yzPWkVVtAwgjbaR1goSOYSuHIjY5VWyYOzm45wwlShXXdDj/4sOkD3wHVVxgibR3/yFHF0soFPvYzuOcT5Z03fqjpMlwEU9zNi94dZOmmwzbOAzAGJtYoos0mXB7q/moTD0ssYWjHGSURbparg/60GndjWSsNz7RPlZnnHdRTfvcqD5vu0GGVMV+0XZa1d3pGFa3f4BN3MvVfk9qL7KEtujVXljfXj+8b7dOkiJOSsdChWknLJHnsDnNlFpkxlhiTmVqUkNE7zeDbhdFZXPUmGNaLdXm0V4mwXl8Li8s67zq5LxOkydLMnJsjnCQjSzSTYZU03Oz3rXZywK7eIZH2EWu5n7vQhsK71bjElzf+9jObnZR7CC1i6EV/boLAxUaXSWIkdJx+nSarc4w63U/BopFleMRcz/PmkcbHttk3EKZsVV9VvemE7zrxrMwekYbv+e3+k20727cx/6VsVyCJbrCa/dpdnA3r2KBchSAFFmu5FG2cSS8zqPH21v2zotoXUaqH1cbjOVT5XKJHAf1CEtFt2656vvmth6N8boPw8431g7E3jtxv/Z7jC2qcruNytcryxFA171PRftUlilPD4vl50X6IpYu+o90n/sT5d8Pe++Eu98DC5Hna+9GuPEj7cnUrL9riOu6jI2NsbS0VPF7qNH6Gla7Lx2Mo9YanbNDBfzBp17kyUefIKativRILpqCKoXvJFdcfzXnXXUhRnJ13rf2fuPT3P3AkyxEogf1qgw3Xr2Lna+9peX+cyWb876/B4Dfv/c++h2LlI6T1IE5o/Jjc3npSM7avoOuVBfa0eBqtOOCo9Gu9qaO62u8Ndr1puGyxstNH13WOixTLq9xHRfHcfxEKLRl8Ho8WCTHvyTvr7vtF37hF7jwwguPs0QvHUTp3gGidBcEQRAE4aRj751w13+F3GzVhipb+t6NsOvN8Mg/1ZaNd4MZh9zM6stX/cOv3o/DRijD/zXTYbmgTWi/rXZYi3ob9bHdvq92u6tB9Jg3O97L6XtQ96GH4IG/rCynDLj6nfDaP/SWOznXVnpsm8mcGgBU5fUVXI97vtC4rXr7VTZK0/iB7cjWSfl6H3HqjXFE7qgyIfj4+H0u40e8rM5H5FqiH8wrFCHtHK/ejbgXvJmxx+7lmWxf3Q/aN3IvO3mOvfGLubt4FQt01dZTRYosV/Eor+RhDOqEV693P9r1ZtwnvsjYogo/Ss7RyxOcVyGTwkW3/EjjjcFPxPawrfRMU0VR407UHqNnOLOOQk7TbkzhNFku4qnaYxWl2XMoNcg3Bm7m/iNGkzYbyVN/fa/KcOM5Sc478sXwA2c9pUknysjlszIlSQUa0iQYcrsZ0j3EtfchVAE2LkVlU8JhUeWYNTJkKSw/BLvf1oDbRQwLCwNTe+kGwr/IcjT/shfm06WIw4yxyDFjgcxKZKnC0Ip1updht4cht5ce7SWIiPk5QmOYxLDCkPlzKsuSypFVXkj3lI7Tr7vo0ylmVYYXzAkm1LznPaS9kPEng7d5y3tOB7Sj4O34fiKc9OzlLL7Gq1ikfpjwZnjRPeKkiBPTXoQPCwNLe1MTE9f3ereVw5TyUlLUv87bf6Ysl7aeRSch1e9LUaOM/WxijC2ekRqH2MphDrKxwXO7Pg3fpyLt38eVbb+ftUNSx+jVaY6phYbK9vL97UGejl/E54uv9rd0cp5Un1deWzfx7+zkOW9Vs/fGZf4manTMThwKbvrH2vf0z7+V2t8L/nhFyzcsuwpyRdtaFZnqlD3ZWe2+rLC+/fv38+lPf7plM6vqDe3L7ELVtXPEu3baGIOnlnK8+qFnGLBM3v7QN9c+hHqbERnqjqcOjoaniFcobn7LW9i2bZt32NyyIv/A2AE+//nPh0p7hfL3Ly8r4Gd/9k1s3rTJMxpwNdrRaNstGxA4monxCX5w3/cw/Ghcz5lH63ZNPN3XFlG6d4Ao3QVBEARBOKnYeyd8/ldPtBRt4H8EgDX6MV+nvTVpY63qPd3wf+xf81tw/1+wumPWxjG45r/C5is6PNfk2LaHauOjWWtWrvjp/Hi1+jjaTPm8Vh/xa40SjrT8kB41FlhtovJM089uLqzw4oqSIsMVPMY65lb1Y/OTnMVX+Km6fW9klNDDApexh0HmKj2/OpCpufHB8gkUCtfyUOjdFnifPlil3EiTYTNHOcSGNTYAaJe1V5CtOcHhX5VurMV41BPQW/dydp9yisMTjdbetexi4KJQaGI4qAaHzXNWU/4+lYVUxXzls0JH/oL2vDCw3jRov/46hdaqot2gLhMXCwdD+VNcLFxMHEzc8M9q0qcoJW2SIUmWBArNQ1zMA1yG4d9JDXSdelbjPG98XteuXw28ul/FA7ySByuuF0cr8sTJkGJJJ8mQIkOCjE5RwsLGwMHExsTR3vNlnZpjizrGqPKMHm1/u41JSZu4keeQCqdBm940GNdgfXlKZN/abQqXBCUSlIhjtzzOzd9dmivaO61v5Wh6WOTn+EbD97y9nFXjQV6PoG9JCjXvKvXfk06u9/zgXuVd5UbFvSS4JzjhvBHeK1wMtC5vC/YF7zxS3SMYv3YXyjC9Ev9wIywexVDar8mb6mDf7lHc//h1717496/DXToW3q+AivtbvftceerJnKLABerFOuet8gwf/uuj8H8urjGWdbRiggEKOk6xeyPFX/kiBdul+C9vw8mUjY919RWUHoKf+yQYZsu0DdH84i8/c5CEVT/FRSfsOTxP0fGeHoZSfopuf+rPGwYo10Xd8SaMpQkA/+gROSZAzwjGr38dZZgo5dfndTOcV0phKFDahU9cjbE4Hj5BykfBq0/1bsD4b4+G9amqg3Lc8367DnxsVxPDdv8cedcTdZXaAd+aXuAtj7/ABd1J/qLL9cPV1zO8UQ3D1bdNB5EEVjqeq3k8JKf7yYEo3TtAlO6CIAiCIJw0uA782QWwOM4+dxOPumcdl2Yrf+y2KhugINnn/RbKza2ozijNPmDVlFEV0lTt36ye9j6SVbddW+fq1tNsv8r9G29bLRoev4jnSKtj3K5UJi6m/zG6/AHaCZe9D0kmdA2jM8dqZNQV89Gj7H1UcjCwMf15hau9ZYdG3reVkicp0qOy9JDDwIXIR/mgvegnER22Xb3OqCgbbKemPOHH+2ZUnBNNroXoutr1/geNrmH4xU/Dv74VMs3zctaTq3pd9bFvuV23rrN1m/XqKOOimGCYHElSFFhPbT9bye3RaV8rr/RJhsiSJE+CJEVS5FnHTMVxaTVeK5FDA8cYJEeSBAUUnvIiSYFhZjuUo/1zQaOYYoAcSQrESVAkSYFB5pimnzwJCiSI++ujskTPj87Pi/L6aQbIkyBOAVAUiROngInGVhZJ8t48FilyGGhKxEmTxURTJE6aHCNMl2WruI69D9VHGSZPkmTk2AbnX54kvSwyrBaZU/3sU2c3UYB0oihrt+yqaqzbIk2GC3mafhbrRoNYGcvtj7dfinyFkUSKLFfoR1nPMabpY55ucqTQGjZyFFC8wFaeZoe33m87+hyKk0djkCcRbkvqHJeyh41MUMKiFCr7gvnIOixK2ltnY1L0p4Fy0MYKP8Ybyo2od/x1/of/svI3otzR5eUsCeZ0N/N0USQWKiijyshWo14fVVGm+hqp97ymzjodkb+2BZcYdug1HO1j68giJy8qopBPUKKbHGlVoECMjE6yRIpCG/nao8p8U7mYaCy/bks5kXnXfzuKnD8qei55fyYuhtIkKJIngau9sU5QYIQpDrOeHMlwfdTwoHzuGRXbwu1V+4TGEloTpwRoX5EeXB/mKX+MAwV8ghIJVSJBkQQlkhT95ej2IkXi4Ad9j1OioGPkift/MRZ1mgW6yOr6oeRj2HSrHL1k6VdLpFWRXpVniDlSFJlV/YwziqNiWDjE/PMk5p8b9Q05oJ73udawRIoMyZp7SpAKYIk0cUok/HtlTidJUmAd036tngL6KOvIkgzfCarfoaOK4eh5BpWGOgViFIiR13F/3hu7rE7472NxbAxc7f1mCJTmwe+H8E/7hhyY2NXbVjktysnG/7Tu4Desr9bfeMMH4eu/X7FKa/jZ4h/yuN5xHKTzeOh/XM+6nvbTKTTiFR/+NofnTp30RZ4hQNlAQGuNdl1A84rYi5xpVkZXa6a0fmp8gVv+/kFMQ2EohWkof5466xRGcRFz8kmM8FnjXXWmf5/rVVn6yNB76S/Qu/Es+lIx+lIxBtJx768rRnfC4jPjM7z7mYNcP9TLHdYe9n7+9hpDnV4WuZHvsvOm9y8/EsIyIgns/can+fz9L1SWg7COm645s2kI/ZXuv1Z1CcujXT3y6f1EEARBEARBONUI8jwD97kX8f/ZJ7nH+9KJFkB4SVE80QKcxhSBv34I+K0TLYkgvKToJsuQWiCtStjEMZQijkNMOcSx/am3HMP7iKqrFFaBj5vrKwEyOk5eG3TpDCmyuJgUiKHQFLE4xhBFbVXWE06pqhcSyiatSp4KRQXKPO2pHpQOFbyWLjHMDL0s4mBQIIHCJYvmW5xfodBYJEEJiwRFYjhkVJpZ+vAyZpaVfKavTvXWaX9doAjypmmVp0Tc/9BbVhza2qCARUFbFLRJAYuitihg4WrPO1Nplzm6PcWLjlHE4pNctyZKvX/j0lWv86WKxvAVkcefwH8+OP8Ck7qoorpsZkdEeWf412jjT7EagxIGJSzyJJinu6GFQww7VA5WE1xnfqXVjawy5652hRGah0RXuHSR9/6UN41T8iIHKM9408LxjKH0AIf0OpaqDH4s7Cojz7IBiDel7rpAgnrrq8tHj1FgkJMPcq2v+fGpqneZ9Su/F8q/Fys0Fg7dKsu93IqJg4PJhB4gS3K1pD4tUUSMGSLzRuTZFqjTQq/zWBptxHCdEtou1PVQr7gnmTHvPuXkqfaajnpP11unVPletqDTzNDLU+6Wxh2afbFm1ST9ocK9m6wX5SHdQ9yAeOYIFnbVmFSNUf9WSPV783UMPgIX0qgrqWWsjjHhxv4khkE5zbfW3qhqcCPzupRHFxarjKzrGF+bcf+q0TV1rAZBunK3okLvnhM1RO3t7eXGG29s6iVesF0mFwsdSnCeL0iTIg8VgCfrboqZCs7ph61pnnx+mv/06F7SpdcQVzZFioCimywxNc39nMOP/9+XiS9djGmaWL4BQDg1FaZh1KyPmYb33vrvfwruxppjFRoH3vUX0HMtWvnHy3XRj3yGi9wUP+RSsqRC074eFvkJfkTfY99k+uU3kUrGSFomRvQ8dB127vkwN5FuYERwLzv35OD6m5tGAlj1uoQ1RzzdEU93QRAEQRBOIp74Anzx1wH4unM5/+K8alnVLOcnZ2fe0u2VXZ7fWXnPag/R6LpGH7iq55vtU7ceXdt2q3rqtV27rVnbjT7W1Zat50FWK0PzsW92rJtva0Yrv7f624PPTYEnuo2Bo83wg3Ep8nE66tlfz9u/8ih46wNPL0PpMKxr1Aq/OixnPS/ZHAmWdIpFUrgYtR+nwvbLy+UPZtUfsyJlVXk5Wh9UhiisJ1v9c6P5eduyjkQvFBbQqJrjVbtMi+2t7xGr3UZzP/f6MrVu42SQYXXHvqZ8nZysqz0uLWXocFw6laf2OFRuid6DHAyciHLYwYzMV4Yfjl6z1X665e1lmYLlDAkm9CCLJ0W4eaEZMWyqlSLR+3y14qRCiaHKShRP+WcT96cWDnFlh0rBOHZYxjOwsH2P08h8pHyg+q32ZncwQm92A9f3hC/L6MntlU5SpF8tMcASSVX0I8yUI80E5ZZD9XkfrKvwY1eVZertUza4cMOxdVEUsSgSQ6PqHoeo8jsalaJWwuCdqVpx7obtR491O+HfWxENQV2+v5h+SHR/XhsUiJMhSUYnSagSXeTpVjm6ydFFnphyAHC1ouR735b8yAgF4qE3dODhmyPOATYwp3tw0RRIsKC7MHBIkWOOXvIkMHBJkGde9/iKYY2FHXr0WlSen9Hz1qL8lyWFg0la5RlmNnKO+/ur6P7lOi3fyMjCrt0e2ceLFFPo6JhoDVkSkWuyvZD+K0VrKGKFx6OgPW/rWg9s/y/ikV0u73mhJ1WRJN5fghJJVaSXDL0qSxf5us/TAnEWdYoF0szqHiYYYEIPsqDTXqoCnfBTFnjnWxbPA7yeQUe7mJFjF6RPCO8xqhzZSlVcd2U1clT5XKmkDu6v5SDsRp16osYxQUSBZFVEgRQFusj7979ydIjob4UYDpay/egQTs2f6Z+v3hVYfkZEjcOi95Vl30du+XfYfh3s/x58+vXtlYf2yjbhc/area/9dq43fszfxv+kfqE6nu7fd3Zxc+n3OVMd4duJd3cuU9Dfk5lOjkWDvmity0rzF76H/qc3Rc7yaqNI/yn6y/+Mu/XloTGA61cQzAf1OY7D0vQETiFLd3c327Ztaxl2PFd0eGFqCdcFR2scV+MGU1dXrQPn6JO43/lQ+f3Df4652iBPnAXSLOgu5s/+ORasIeZzJeayJeayRWayRfIl71lbuqAfZ3MX1rMLWC8sdnIUTjripkEiZpCMmd5zaulAeO9xlfeMV/47WGjgun4XOtFbadQROb5ag5Nfojh7CBuTdczxvxN/Vz+92qlw7ZzCiKe7IAiCIAjCqUj3+nD2BvNhbjAfPoHCCILwkqLORzNBENaOJZ3kqB7kqB5kXA8yTzcLOsUiaZZI+SGD08zoXhZJk9Px8od+FVVilJWlCUqMqhk2qSkG1WKN0sNSTpVy1fWVwP6ycn01oMsxhsiSIq9NZulhUg+QIQUaFA5JvLDXDkYoQ0VdvnKlbPCkKxQaCh2GUy9iUdKWr0y1mKafHEnQLnFKfpj3BAXi4QdlhaYU2b+oY5SwKOBNUxQYUIsMsEi/ytDPEv1qiT4yDKhFelSOLl+BmVYF0uTpokBKedMkRYw6RimCsBKUIowUAU6DQu3X54V9t6HKc7R+HY+2X/FpiFLQRadenKvTbgLbP04cz+wey0ZryBOvTG+BFYZYj6bFCAxFDKUZYY71apa0Ov7jfHqivHzT267xFrdd4y0vjFPf4LmqfNOyrelXXli7Od3duK0r3g4P/GVFO8/qTQCcrQ51KFNV2ZOZTo9FvRJ+SHgA88xXQN/61vWd+4r2PZmH6h23xqTiJhds7Gt/h/OG4dHfbC3zW/+yrsy5osNstsg7njnAw9kcN28pcMGBv48YKFUbJPnGSBsux+kexXE1tuMZAtiu60+95ZJTuWwXcjj5RV+qqNtClVF8egBiaW9dKQvZ6dBAOZqiLkg/USRGkVjYp6LjUnRcFvPBMzkSJaLRZXjUAWbbGPCNAJSw2M6h+kWWJtqoR1hrROkuCIIgCIJwMrHtGujZEIaYP7lRnqyKFf2YF04xIjndjy8G9Iz614aca6tL449mpy3Kjxt5uvdTOKnpVnnOUkc4iyMnWpQGvNC6iCAIQl0U5WdsdF44FVAKUhRJVed2OgUMBk47bvxwWWFpmHDjR/y81NXXlaot37Bse/SRAWCOrqotkbaseE07+/RmAM5WRzqQqY78JzOdHovjXd/xYIUyp+ImqXiKBX+/n9nWxXU/uqd1u6//d9h+ZWey7v8efPotrcv9asRTfP/34NM3t9zF+dW7KGy5hnzJJV9y/D+X3NiPKXz198kT942YYgRB7SuiI73qvaj1O1HKGzXDN8Yw/BUKiE0+ifXN3yOGTaqZ8VjEiUc4cYjSXRAEQRAE4WTCMOF1fwyfP8lzuQe87iPedAU/5ttnrT7eyUfB9vB/OF/9Trj/L1jdcWpj3K95J2y+osNzTY5t2zT4aHZ8OR5tVp/Hcl4IgiCsGo0M81bDYO+EGf3VYS1lOV79XHY7bb5b9W703i0A7n4PLKyBcdFaj1W75/PJdG6+pDhB7/ntHO/UoDfNzXS+LqB3k3cN7Xxj5fqdb4Sb/rH2ugquuWj5RmXrjlftugHleQbXeLpXt1XVznOu7+nelYE3/WN7MtWT/2RntftyKo7NKsg8XvCMezZsvWTtIiEsJzJBm/uY219B2jBJx6s2j74SmVJw2wAAiidJREFUHphr3earf6K1McXZQ/DQwukRJeIlgOR0R3K6C4IgCIJwErL3Trjrv0KuOsxU1Y/h3k2w6xfgkX+qLRvvBjNe/0f8Sqn+CLD3zvY/qLX7Yaq6XNAmrO7Hu7Wody0/Oi+n3dUgesybHe/l9D2o+9BDnqd1xYdME67+TXjtH3rLnZxrKz22zWSu95EsuB73fKFxW80+rnmN0tZHw06PdaPy9T7o1RvjZnJ3Iks75wE0Pl7tjHEzmVudx+2ev53I0Y78y60vSstzy2e1jldAs+dQahBednPzfnV6z1iNsVotTkVFS7vnibB86o3xSt8L0sNw4Zuhfyt0rYOuEc8VNXPM27bS+aUJb7nTuqP79WyALVfBwR95UWlarW/VTqO6lyZWtw+djkn3+uX1ZyVjuBp1N+pD9XhOPw+7P9X4/trsWZ0ehotugnN/2vvwHygRXAfG7m987JYzv9rHod36q8/FZmO50vN0393w+OchO1U7xmff0Hk79epbCavy3tuA6ntjs3c6aP99LkqzsVzJ9d+9vqz4Cs77dtZVn2fRa6ge0esqqKtR+eqyW67ylse+7/3s2H4dbL265jweP3aMq/8tRcxU7Pv1blRmsnlbroN+8Qdc8qkF5ouKr/7WNezcNLBy+U92Vrsvp+LYLFPmRdvh7O89AcDz111I176v+AbgUNdz/qZ/XL7xwd47O697Ofus5v5rVZewLNrVI4vSHVG6C4IgCIJwkuI6XkirRj+Goz9m6pU941qvnuof8fU+8LT6AFT9QbDej6h2Pqi1+2Gqulz1D7fV+ni3WvV2+tF5LT4YL/fjYztt1vvhHIxVOx/Wm/W9um67CA/9Dcy+CANneCHPrSqz8XbPtZUc22bnarMPZ8H1WK+teh/XliYgNweohh+82pat0w+EzT7o1ftwUu9+0kpB0K7SpNHxaiRvve3tfvCsdx5Xfzhv9YG92bFudhzq7bfc+lqdW50oWoI20kOQnW5f+dPOc6jVNdGJkq7eObCaSpzl3us7PV7N5g88AA/+3zpGdy2oVjDUUybUuy5WIlczpUZ1++2OSyftrMToYbnytfteER3jdu8t7T7PBGGtafUeU+/dSs7TtWEtlXmr9Ruq3ffe5b6Dt/NO1877XKP3F6EhuaLD+X9wNwB7br+B7oTVcp/JxTxX/tG3MBTs/V83kozJGAuN2ZfJ88oHn6bPMnnmugu9lfUMoxtFfuiU5dS9UnlWsz9rOTZCS0Tp3gGidBcEQRAEQRAEQRAE4aRgpUZCa6VMaKTUaKWYW612VsN7WpQtgiAIgtAWWmvOfd/dFG2X77/n1WweSLfc5/7npvgPf/sjtg938Z13v2rthRROae6dWeSXHnue87qSfPfK88ob1tKoazl1r1Se1eyPGLydMNrVI7c2TxIEQRAEQRAEQRAEQRCOD4bpRQrolOXs0wn15FqLNjtpZ637LAiCIAgvUZRS9KdiTC4WmMuW2NwgUnyUfRNeHvizRrpblBQEOJz387knYpUblvsu3A7LqXul8qxmf9ZybIRVwTjRAgiCIAiCIAiCIAiCIAiCIAiCIAgnD/1pTxk6nyu1Vf7ZySUAzlkvSnehNQd9pfuWZLxFSUE4dRCluyAIgiAIgiAIgiAIgiAIgiAIghDSn/KUoXPZNpXuE57S/eyRnjWTSTh9OFTwlO6bRekunEaI0l0QBEEQBEEQBEEQBEEQBEEQBEEICTzd53LFlmW11uyblPDyQvsczInSXTj9EKW7IAiCIAiCIAiCIAiCIAiCIAiCEBIq3dvwdJ/OFJnLllBKlO5CewSe7hJeXjidEKW7IAiCIAiCIAiCIAiCIAiCIAiCENKfDsLLt/Z03zfheblvHUyTjJlrKpdw6mO7mvGCZ8yxORk7wdIIwuohSndBEARBEARBEARBEARBEARBEAQhpC/Vvqf7c5OSz11on/FiCUdDTCnWx0XpLpw+iNJdEARBEARBEARBEARBEARBEARBCCnndG+tdA883c9eL6HlhdYcynvREzYlYxhKnWBpBGH1EKW7IAiCIAiCIAiCIAiCIAiCIAiCENKf8sLLz7fh6f7sRODpLkp3oTWB0n1zQvK5C6cXonQXBEEQBEEQBEEQBEEQBEEQBEEQQgJP99k2crpPLhYA2NSfWlOZhNODg77SfUtKlO7C6YUo3QVBEARBEARBEARBEARBEARBEISQTsLLF20XgETMXFOZhNMD8XQXTldE6S4IgiAIgiAIgiAIgiAIgiAIgiCE9KfL4eW11k3LlhxP6R4zJT+30JpQ6Z4UpbtweiFKd0EQBEEQBEEQBEEQBEEQBEEQBCGkP+V5uhcdl1zJaVo2ULrHTVE5Ca0Jw8uL0l04zZA7oCAIgiAIgiAIgiAIgiAIgiAIghCSjpuh5/pctnmI+ZLjecLHROkutMDVmsN573zanIydYGkEYXWRO6AgCIIgCIIgCIIgCIIgCIIgCIIQopSiL+V5IrdSuheD8PKWqJyE5hwr2hS1xgA2SE534TRD7oCCIAiCIAiCIAiCIAiCIAiCIAhCBf1pzxN5LltsWEZrLTndhbYJQstvSMSIGXK+CKcXonQXBEEQBEEQBEEQBEEQBEEQBEEQKgjyus/lGnu6O65Ge9HlJae70JJDks9dOI2RO6AgCIIgCIIgCIIgCIIgCIIgCIJQQX+6dXj5IJ87SE53oTWBp/tmUboLpyFyBxQEQRAEQRAEQRAEQRAEQRAEQRAqCMPL5xqHlw/yuYMo3YXWHBRPd+E0Ru6AgiAIgiAIgiAIgiAIgiAIgiAIQgVBePn5pp7uUaW75OgWmnNIPN2F0xhRuguCIAiCIAiCIAiCIAiCIAiCIAgVhJ7ubSjd46aBUqJ0F5oj4eWF0xlRuguCIAiCIAiCIAiCIAiCIAiCIAgV9Pk53WezjcPLl2wvp7t4uQut0FpzKO8ZcEh4eeF0RJTugiAIgiAIgiAIgiAIgiAIgiAIQgVBePm5XGNP9yCne8wSdZPQnJmSQ871zpeNidgJlkYQVh+5CwqCIAiCIAiCIAiCIAiCIAiCIAgVBOHl28npHjNF3SQ058VcAYCRuEVSzhfhNETOakEQBEEQBEEQBEEQBEEQBEEQBKGCAT+8/FyuSXj5SE53QWjG7oUsABd2p0+wJIKwNshdUBAEQRAEQRAEQRAEQRAEQRAEQaigLwgv35anu+R0F5rz0EIGgCv6ROkunJ6I0l0QBEEQBEEQBEEQBEEQBEEQBEGoIAgvX7Bd8iWnbpmirQEJLy+05uF5T+l+eV/XCZZEENYGuQsKgiAIgiAIgiAIgiAIgiAIgiAIFXQnLEzD82CfzdYPMV+UnO5CGxzOFzlSKGEqeFmPeLoLpydyFxQEQRAEQRAEQRAEQRAEQRAEQRAqUErR3yLEfMn2le6WqJuExjzke7lf0JWiyzJPsDSCsDbIXVAQBEEQBEEQBEEQBEEQBEEQBEGooS/dQunue7rHJae70ISHFyS0vHD6I0p3QRAEQRAEQRAEQRAEQRAEQRAEoYbA030+J+HlheUTeLpfIUp34TRG7oKCIAiCIAiCIAiCIAiCIAiCIAhCDQPpONDM010DonQXGpN1XJ5cygHi6S6c3pxWd8GPf/zjnHHGGSSTSa666ioefPDBEy2SIAiCIAiCIAiCIAiCIAiCIAgvIRxX88Dz03z50cM88Pw0jqtPtEjLJggvP9sivLwo3VeX5Z5DJ9O5F8jy8d1j2BpG4xabE7ETJo8grDXWiRZgtfiXf/kXbrvtNj75yU9y1VVX8bGPfYwbbriBZ555hpGRkRMtniAIgiAIgiAIgiAIgiAIwksKx9U8uH+GycU8Iz1Jrtw+iGlI3mfh9ObuPePcftdexufz4boNfUne/4ad3LhrwwmUbHn0JDw10lKhRU53S67t1WK559DJdO5FZbG3d8M5fSyMZ/j6k0dPyetAENrhtDE9+tM//VPe/va387a3vY2dO3fyyU9+knQ6zd///d/XlC0UCiwsLFT8CYIgCIIgCIIgCIIgCIIgnAhOJs/E1eKrj49zxR/dw6/8zQ/5b//8KL/yNz/kFR/+Fn9+z77Tqp+CEOXuPePcesfuCqUnwNH5PLfesZu794yfIMmWj2l4aiRft15D0RZP99VkuefQyXTuVcvi9nspCkrHcqfsdSAI7XBaeLoXi0V+/OMf8973vjdcZxgG119/PQ888EBN+Q996EPcfvvtx1NEQRAEQRAEQRAEQRAEQRCEGk4mz8TV4kNf3cv/vW9/zfqjCwX+7J5nw+UNfUne9zPnM9CVEG944ZTHcTW337WXeqYkGlDA7Xft5TU7R0+pc9wyPVkdt77WXXK6rx7LPYdOpnMvKotWoLtjodJdzRXhOMoiCMeb00LpPjU1heM4rF+/vmL9+vXrefrpp2vKv/e97+W2224LlxcWFtiyZcuayykIgiAIgiAIgiAIgiAIghAQeANWK0oCz8RP3HzpKad4/+rjR+oq3HXcgJKLinR2fD7Pf/nsIxXlTnWDA+H0wNGaPUs55ksOs7bNfMlhznaYKznM2TYZxyXnuOTc8nQ2bzN+UR/QB3gKRwCUP6NgP7Dre08Qs9pTUO/qTvHZi3esev86IVCMNvJ0l5zu9fnwC+N8dny6o31Ktsvsy/qblql3Di13v7WgQpaYAYFi3dGohRIa797/4P4Zrt4xtKayCMLx5rRQundKIpEgkUicaDEEQRAEQRAEQRAEQRAEQXiJUu2ZqE2Fc0Y3uBpcjXLh3T98nvmhOEnTIKEM4oYibigShj+vvPmYoUgoRV/MJGGcOMWX42r+55f3hMvaVDjrUzib0+iBBMaxPPHdzZVQp7LBgXD64GjNDQ/v63zHdGuVy6zrQrGBBru6bMnpXIZVxlStPN39nO6meC1HWbQdJot25zsmzJZF6p5Dy91vLYjKUnQxFkuYhzMVRleTi/na/QThFOe0ULoPDw9jmiYTExMV6ycmJhgdHT1BUgmCIAiCIAiCIAiCIAiCINTnwf0zlbl3Ywb2Wb0VZaaA//b0wbbr/IddZ/C6df0rlu1LE7P8r+ePhMv1VGll593y1mLJYfLSQbSpwDKgSgnnrkvi9sUw5ksN2z6Vw3ALpw9xw2BHKkHMUPRbJv0xkz7Loj9m0m+Z9FgmKcMgZRqkDEXKNHh+fInb73wSAlOa0KKG8oyGj/zCRVy8pb8tOZIn0IgmIPR01/WCl0NRPN3r8s5tI7xlY2ee3I8dnOM9X3y8Zbnqc2i5+60FUVmUrSHv1H2GjPQk11QOQTgRnBZK93g8zmWXXca3vvUt3vSmNwHgui7f+ta3eOc733lihRMEQRAEQRAEQRAEQRAEQaiixsvP0ZgHM2CANpQXklcpzt3YQ19XnKLWFF1NwXUpupqS1hRcTdF1Kfrz8VVS0GVdlyOFxorxplR5+qqsjXkog9sbxx1NYW/rJv74bLjd7bK8EMSuRhVdVN6R8MPCScEPXn5+R+Wv6+/hb+98iqPz+bq5tRUw2pfkpnNPLWOScnj5+kr3ku3ndF/jsOWnGhsScTZ0GHD53HOT/B9zb8fn0HL3WwvaleXK7YNrKocgnAhOC6U7wG233cYtt9zC5ZdfzpVXXsnHPvYxMpkMb3vb2060aIIgCIIgCIIgCIIgCIIgCBVUe/mpkkts71xNuQ9efnZbimfdwAt1Odww1Mc3Lk959Yb1R9oKp5Urnzg8z/u+vMcLkV/SKNsFW6MAt8eiOJrCHU3h7lvAyDuUzunF2d5T0bb13ALW84uAhB8WTi1MQ/H+N+zk1jt2o6BC4RioOd//hp2nlMId2lC6i6f7qrHcc+hkOvdOJlkE4Xhz2ijdf+mXfoljx47xB3/wBxw9epRLLrmEu+++m/Xr159o0QRBEARBEARBEARBEARBECq4cvsgG/qSq+YNqNTqKTCG4hZD8c4/HV/ck+aTX36K8TrKcmPRxpjK4w4ncbZ14+bsUOGusjZaASnLC7FfcrEOZCT8sHDKceOuDXzi5ku5/a69FekjRvuSvP8NO7lx14YTKN3yCJSjdgulu+R0Xx2Wew6dTOfeySSLIBxPThulO8A73/lOCScvCIIgCIIgCIIgCIIgCMJJz+noDRjtUz31nDm25Cndt6S98PmAtW8ea/8SAPaOHuyzerHP62PAMiX8sHBKcuOuDbxm5ygP7p9hcjHPSI9nPHMqXctRLF9ut4HSXXK6rz7LPYdOpnPvZJJFEI4Xp5XSXRAEQRAEQRAEQRAEQRAE4VThdPQGbNSn7oSJs2Azt1hC98QAMA9lMH2FO4D5/CI6ZuBs62bm7B52L2a5oq/ruPdBEFaKaai20kKcChiqlae7n9NdlO6rynLPoZPp3DuZZBGE44Eo3QVBEARBEARBEARBEARBEE4Qp6M3YL0+XbZtgFf+8XdYemGR0sWDGFN5rL1zRHupgC3jBdadM8RDhQK/+8xBvnn5ucRO4bEQhFMdy2yR0932Pd0tUboLgvDSRpTugiAIgiAIgiAIgiAIgiAIJ5DT0Ruwuk8PPD/N0YU85gKohQlU1qaeKv1P3nwx55/Rz7U/eoqnM3n+7tAx/vPWkeMnuCAIFQSe7g2V7n54+YR4uguC8BJH7oKCIAiCIAiCIAiCIAiCIAjCmjK5WA41bzRQuANMZQoMxiz+55kbAfjoi0cZLxSPg4SCINQjyOneSOke5nS3JCKFIAgvbUTpLgiCIAiCIAiCIAiCIAiCIKwpIz3Jjsr98oZBLu9Nk3Fc3v/ckbUUTRCEJhiB0l0393SXnO6CILzUkfDygiAIgiAIgiAIgiAIgiAIwppy5fZBNvQlOTqfp57qTgGjfV4+e/BCWn/4nM289uF93Dk5x1s3LnLtQM9xlVkQhNae7iXHWy9Kd6Eax3XYPbmbY9ljrEuv49KRSzEN87jXcSLbcFyHhyce5sHxB0HBFeuv4IrRK9qu/3j0X1g9ROkuCIIgCIIgCIIgCIIgCIIgrCmmoXj/G3Zy6x27UVCheA+CUr//DTsxjXKI6l09aX5t0zB/f3iK//XcEe6+/Jwwv7QgCMcHs6XS3fN0j4vSXYhwz9g9fPjBDzORnQjXrU+v5/eu/D2u33b9cavjRLZxz9g9fOD+DzBfnA/X/TV/TV+ijw9c/YGW9R+P/guri9wFBUEQBEEQBEEQBEEQBEEQhDXnxl0b+MTNlzLaVxlqfrQvySduvpQbd22o2ee2M0bpNg0eX8rx5cm54ySpIAgBgdLdbpTT3Zbw8kIl94zdw23fva1CWQwwmZ3ktu/exj1j9xyXOk5kG/eM3cNvf/e3KxTuAfOFeX77u7/dtP7j0X9h9RFPd0EQBEEQBEEQBEEQBEEQBOG4cOOuDbxm5ygP7p9hcjHPSI8XUj7q4R5lOG7xzq0jfHj/UT74wjg/va6PhCHKPUE4XgTh5d0Wnu4xU6JQCF449A8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          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZlgqfszLNZI"
      },
      "source": [
        "# Spatial Interpolation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lFIun_lQlTdv"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Setting Parameters for Spatial Interpolation**\n",
        "raster_extent = \"aquifer\" #@param [\"aquifer\", \"wells\"]\n",
        "raster_interval = 12 #@param {type:\"slider\", min:1, max:24, step:1}\n",
        "output_file = \"results.nc\" #@param {type:\"string\"}\n",
        "aquifer_name = \"dead_sea\" #@param {type:\"string\"}\n",
        "units = \"Cubic Meter\" #@param [\"acre-ft\", \"cubic-ft\", \"Cubic Meter\"]\n",
        "\n",
        "#This should be the average storage coefficient for the aquifer\n",
        "storage_coefficient = 0.08 #@param\n",
        "\n",
        "start_date = 2002 #@param {type:\"integer\"}\n",
        "end_date = 2019 #@param {type:\"integer\"}\n",
        "#let user pick the entire date\n",
        "#then if they pick an inappropriate date, give them a suggestion for the date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37y1_38NMNWv"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Defining functions for this section**\n",
        "\n",
        "def create_grid_coords(x_c, y_c, x_steps, bbox, raster_extent):\n",
        "    # create grid coordinates fro kriging, make x and y steps the same\n",
        "    # x_steps is the number of cells in the x-direction\n",
        "    min_x = min_y = max_x = max_y = None\n",
        "    if raster_extent == \"aquifer\":\n",
        "        min_x, min_y, max_x, max_y = bbox\n",
        "    elif raster_extent == \"wells\":\n",
        "        min_x, max_x = min(x_c), max(x_c)\n",
        "        min_y, max_y = min(y_c), max(y_c)\n",
        "\n",
        "    n_bin = np.absolute((max_x - min_x) / x_steps)  # determine step size (positive)\n",
        "    # make grid 10 bin steps bigger than date, will give 110 steps in x-direction\n",
        "    grid_x = np.arange(\n",
        "        min_x - 5 * n_bin, max_x + 5 * n_bin, n_bin\n",
        "    )  # make grid 10 steps bigger than data\n",
        "    grid_y = np.arange(\n",
        "        min_y - 5 * n_bin, max_y + 5 * n_bin, n_bin\n",
        "    )  # make grid 10 steps bigger than data\n",
        "\n",
        "    return grid_x, grid_y\n",
        "\n",
        "def krig_field_generate(var_fitted, x_c, y_c, values, grid_x, grid_y):\n",
        "    # use GSTools to krig  the well data, need coords and value for each well\n",
        "    # use model variogram paramters generated by GSTools\n",
        "    # fast - is faster the variogram fitting\n",
        "    krig_map = gs.krige.Ordinary(var_fitted, cond_pos=[x_c, y_c], cond_val=values)\n",
        "    krig_map.structured([grid_x, grid_y], chunk_size=1000)  # krig_map.field is the numpy array of values\n",
        "    return krig_map\n",
        "\n",
        "def fit_model_var(x_c, y_c, values, bbox, raster_extent):\n",
        "    # fit the model varigrom to the experimental variogram\n",
        "    min_x = min_y = max_x = max_y = None\n",
        "    if raster_extent == \"aquifer\":\n",
        "        min_x, min_y, max_x, max_y = bbox\n",
        "    elif raster_extent == \"wells\":\n",
        "        min_x, max_x = min(x_c), max(x_c)\n",
        "        min_y, max_y = min(y_c), max(y_c)\n",
        "\n",
        "    # first get the coords and determine distances\n",
        "    x_delta = max_x - min_x  # distance across x coords\n",
        "    y_delta = max_y - min_y  # distance across y coords\n",
        "    max_dist = (\n",
        "        np.sqrt(np.square(x_delta + y_delta)) / 4\n",
        "    )  # assume correlated over 1/4 of distance\n",
        "    data_var = np.var(values)\n",
        "    data_std = np.std(values)\n",
        "    fit_var = gs.Stable(dim=2, var=data_var, len_scale=max_dist, nugget=data_std)\n",
        "    return fit_var\n",
        "\n",
        "def generate_nc_file(\n",
        "    file_name, grid_x, grid_y, years_df, x_coords, y_coords, bbox, raster_extent\n",
        "):\n",
        "    temp_dir = tempfile.mkdtemp()\n",
        "    file_path = os.path.join(temp_dir, file_name)\n",
        "    h = netCDF4.Dataset(file_path, \"w\", format=\"NETCDF4\")\n",
        "    lat_len = len(grid_y)\n",
        "    lon_len = len(grid_x)\n",
        "    time_dim = h.createDimension(\"time\", 0)\n",
        "    lat = h.createDimension(\"lat\", lat_len)\n",
        "    lon = h.createDimension(\"lon\", lon_len)\n",
        "    latitude = h.createVariable(\"lat\", np.float64, (\"lat\"))\n",
        "    longitude = h.createVariable(\"lon\", np.float64, (\"lon\"))\n",
        "    time_dim = h.createVariable(\"time\", np.float64, (\"time\"), fill_value=\"NaN\")\n",
        "    ts_value = h.createVariable(\n",
        "        \"tsvalue\", np.float64, (\"time\", \"lon\", \"lat\"), fill_value=-9999\n",
        "    )\n",
        "    latitude.long_name = \"Latitude\"\n",
        "    latitude.units = \"degrees_north\"\n",
        "    latitude.axis = \"Y\"\n",
        "    longitude.long_name = \"Longitude\"\n",
        "    longitude.units = \"degrees_east\"\n",
        "    longitude.axis = \"X\"\n",
        "    time_dim.axis = \"T\"\n",
        "    time_dim.units = \"days since 0001-01-01 00:00:00 UTC\"\n",
        "\n",
        "    latitude[:] = grid_y[:]\n",
        "    longitude[:] = grid_x[:]\n",
        "\n",
        "    time_counter = 0\n",
        "    for measurement in years_df:\n",
        "        # loop through the data\n",
        "        values = years_df[measurement].values\n",
        "\n",
        "        beg_time = timer()  # time the kriging method including variogram fitting\n",
        "        # fit the model variogram to the experimental variogram\n",
        "        var_fitted = fit_model_var(\n",
        "            x_coords, y_coords, values, bbox, raster_extent\n",
        "        )  # fit variogram\n",
        "        krig_map = krig_field_generate(\n",
        "            var_fitted, x_coords, y_coords, values, grid_x, grid_y\n",
        "        )  # krig data\n",
        "        # krig_map.field provides the 2D array of values\n",
        "        end_time = timer()\n",
        "        time_dim[time_counter] = measurement.toordinal()\n",
        "        ts_value[time_counter, :, :] = krig_map.field\n",
        "        time_counter += 1\n",
        "\n",
        "    h.close()\n",
        "    return Path(file_path)\n",
        "\n",
        "def earth_radius(lat):\n",
        "    \"\"\"\n",
        "    calculate radius of Earth assuming oblate spheroid\n",
        "    defined by WGS84\n",
        "    Input\n",
        "    ---------\n",
        "    lat: vector or latitudes in degrees\n",
        "    Output\n",
        "    ----------\n",
        "    r: vector of radius in meters\n",
        "    Notes\n",
        "    -----------\n",
        "    WGS84: https://earth-info.nga.mil/GandG/publications/tr8350.2/tr8350.2-a/Chapter%203.pdf\n",
        "    Taken from: https://gist.github.com/lgloege/3fdb1ed83b002d68d8944539a797b0bc\n",
        "    \"\"\"\n",
        "    from numpy import deg2rad\n",
        "\n",
        "    # define oblate spheroid from WGS84\n",
        "    a = 6378137\n",
        "    b = 6356752.3142\n",
        "    e2 = 1 - (b ** 2 / a ** 2)\n",
        "\n",
        "    # convert from geodecic to geocentric\n",
        "    # see equation 3-110 in WGS84\n",
        "    lat = deg2rad(lat)\n",
        "    lat_gc = np.arctan((1 - e2) * np.tan(lat))\n",
        "\n",
        "    # radius equation\n",
        "    # see equation 3-107 in WGS84\n",
        "    r = (a * (1 - e2) ** 0.5) / (1 - (e2 * np.cos(lat_gc) ** 2)) ** 0.5\n",
        "\n",
        "    return r\n",
        "\n",
        "def calculate_aquifer_area(imputed_raster, units):\n",
        "    y_res = abs(\n",
        "        round(imputed_raster[\"lat\"].values[0] - imputed_raster[\"lat\"].values[1], 7)\n",
        "    )  # this assumes all cells will be the same\n",
        "    # size in one dimension (all cells will have same x-component)\n",
        "    x_res = abs(\n",
        "        round(imputed_raster[\"lon\"].values[0] - imputed_raster[\"lon\"].values[1], 7)\n",
        "    )\n",
        "    area = 0\n",
        "    # Loop through each y row\n",
        "    for y in range(imputed_raster.lat.size):\n",
        "        # Define the upper and lower bounds of the row\n",
        "        cur_lat_max = math.radians(imputed_raster[\"lat\"].values[y] + (y_res / 2))\n",
        "        cur_lat_min = math.radians(imputed_raster[\"lat\"].values[y] - (y_res / 2))\n",
        "\n",
        "        # Count how many cells in each row are in aquifer (i.e. and, therefore, not nan)\n",
        "        x_count = np.count_nonzero(~np.isnan(imputed_raster.tsvalue[0, :, y]))\n",
        "\n",
        "        # Area calculated based on the equation found here:\n",
        "        # https://www.pmel.noaa.gov/maillists/tmap/ferret_users/fu_2004/msg00023.html\n",
        "        #     (pi/180) * R^2 * |lon1-lon2| * |sin(lat1)-sin(lat2)|\n",
        "        radius = earth_radius(imputed_raster[\"lat\"].values[y])\n",
        "        if units == \"acre-ft\":\n",
        "            area_factor = 1 / 4046.8564224\n",
        "        elif units == \"cubic-ft\":\n",
        "            area_factor = 10.764\n",
        "        else:\n",
        "            area_factor = 1\n",
        "        area += (\n",
        "            radius ** 2\n",
        "            * area_factor\n",
        "            * math.radians(x_res * x_count)\n",
        "            * abs((math.sin(cur_lat_min) - math.sin(cur_lat_max)))\n",
        "        )\n",
        "\n",
        "    return area"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wcBFa86OLMF2"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Setting up grid for nc file**\n",
        "coords_df = wells_gdf[wells_gdf[w_well_ID.value].astype(str).isin(loc_well_names)]\n",
        "x_coords = coords_df[w_long.value].values\n",
        "y_coords = coords_df[w_lat.value].values\n",
        "\n",
        "x_steps = 150  # steps in x-direction, number of y-steps will be computed with same spacing, adds 10%\n",
        "grid_x, grid_y = create_grid_coords(\n",
        "    x_coords, y_coords, x_steps, bbox, raster_extent\n",
        ")  # coordinates for x and y axis - not full grid\n",
        "replaced_df = replaced_df[\n",
        "    (replaced_df.index >= f\"04-01-{start_date-1}\")\n",
        "    & (replaced_df.index <= f\"12-31-{end_date+1}\")\n",
        "]\n",
        "# skip_month = 48  # take data every nth month (skip_months), e.g., 60 = every 5 years\n",
        "years_df = replaced_df.iloc[\n",
        "    ::raster_interval\n",
        "].T  # extract every nth month of data and transpose array\n",
        "\n",
        "file_name = f\"{aquifer_name}_{time.time()}.nc\"\n",
        "# setup a netcdf file to store the time series of rasters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "auxwnaz0MhaP"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Generate nc file using kriging**\n",
        "nc_file_path = generate_nc_file(file_name, grid_x, grid_y, years_df, x_coords, y_coords, bbox, raster_extent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e26WwYjhNNFe"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Assigning dimensions and clipping nc file**\n",
        "file_path = nc_file_path\n",
        "temp_dir = file_path.parent.absolute()\n",
        "interp_nc = xarray.open_dataset(file_path)\n",
        "interp_nc.rio.set_spatial_dims(x_dim=\"lon\", y_dim=\"lat\", inplace=True)\n",
        "interp_nc.rio.write_crs(\"epsg:4326\", inplace=True)\n",
        "clipped_nc = interp_nc.rio.clip(aquifer.geometry.apply(mapping), crs=4326, drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RqTj_U06NlRE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18bac219-bab3-42be-a98d-7bd137b859e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6884411607.249258 Cubic Meter\n"
          ]
        }
      ],
      "source": [
        "#@markdown ### **Calculating drawdown**\n",
        "area = calculate_aquifer_area(clipped_nc, units)\n",
        "if units == \"acre-ft\":\n",
        "    vol_unit = \"Acre Feet\"\n",
        "elif units == \"cubic-ft\":\n",
        "    vol_unit = \"Cubic Feet\"\n",
        "else:\n",
        "    vol_unit = \"Cubic Meters\"\n",
        "# Calculate total drawdown volume at each time step\n",
        "drawdown_grid = np.zeros(\n",
        "    (clipped_nc.time.size, clipped_nc.lon.size, clipped_nc.lat.size)\n",
        ")\n",
        "drawdown_volume = np.zeros(clipped_nc.time.size)\n",
        "for t in range(clipped_nc.time.size):\n",
        "    # Calculate drawdown at time t by subtracting original WTE at time 0\n",
        "    drawdown_grid[t, :, :] = (\n",
        "        clipped_nc[\"tsvalue\"][t, :, :] - clipped_nc[\"tsvalue\"][0, :, :]\n",
        "    )\n",
        "    # Average drawdown across entire aquifer x storage_coefficient x area of aquifer\n",
        "    drawdown_volume[t] = np.nanmean(\n",
        "        drawdown_grid[t, :, :] * storage_coefficient * area\n",
        "    )\n",
        "print(area, units)\n",
        "clipped_nc.attrs = {\"area\": area, \"area_units\": units}\n",
        "clipped_nc[\"drawdown\"] = ([\"time\", \"lon\", \"lat\"], drawdown_grid)\n",
        "clipped_nc[\"volume\"] = ([\"time\"], drawdown_volume, {\"units\": vol_unit})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sw8LQkQUOCsX"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Saving to notebook**\n",
        "clipped_nc.to_netcdf(aquifer_name + \"_\"+ output_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJQqMoirIt8-"
      },
      "source": [
        "# Visualizing Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JSKToMJ5HDJ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "fe6002cd-4f56-4cf9-e7c0-77670efea001"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'dead_sea Drawdown Curve')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Plotting the Drawdown Curve**\n",
        "nc = xarray.open_dataset('/content/'+aquifer_name + \"_\"+ output_file)\n",
        "time = nc.variables['time'][:]\n",
        "volume = nc.variables['volume'][:]\n",
        "ts = nc.variables['tsvalue'][:]\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(25,5))\n",
        "ax.plot(time, volume)\n",
        "plt.xlabel(\"Date (years)\")\n",
        "plt.ylabel(\"Volume (\"+ vol_unit+\")\")\n",
        "plt.title(aquifer_name + \" Drawdown Curve\")\n",
        "### don't clip it so we can see the whole curve from machine learning"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Export drawdown curve to CSV\n",
        "drawdown_df = pd.DataFrame({'Time': time, 'Volume': volume})\n",
        "drawdown_df.to_csv(aquifer_name + \"_\" + \"drawdown.csv\")"
      ],
      "metadata": {
        "id": "X9UWqEfIINb2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install basemap\n",
        "from mpl_toolkits.basemap import Basemap\n",
        "import matplotlib.pyplot as plt\n",
        "from os import path\n",
        "import glob\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "I2m7vkhpXuMB",
        "outputId": "11d4e48e-7538-4427-bae2-964882a3b7eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: basemap in /usr/local/lib/python3.10/dist-packages (1.4.1)\n",
            "Requirement already satisfied: basemap-data<1.4,>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from basemap) (1.3.2)\n",
            "Requirement already satisfied: pyshp<2.4,>=1.2 in /usr/local/lib/python3.10/dist-packages (from basemap) (2.3.1)\n",
            "Requirement already satisfied: matplotlib<3.9,>=1.5 in /usr/local/lib/python3.10/dist-packages (from basemap) (3.7.1)\n",
            "Requirement already satisfied: pyproj<3.7.0,>=1.9.3 in /usr/local/lib/python3.10/dist-packages (from basemap) (3.6.1)\n",
            "Requirement already satisfied: packaging<24.0,>=16.0 in /usr/local/lib/python3.10/dist-packages (from basemap) (23.2)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.10/dist-packages (from basemap) (1.25.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<3.9,>=1.5->basemap) (2.8.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from pyproj<3.7.0,>=1.9.3->basemap) (2024.2.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib<3.9,>=1.5->basemap) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "leftbound = bbox[0]\n",
        "bottombound = bbox[1]\n",
        "rightbound = bbox[2]\n",
        "topbound = bbox[3]\n",
        "Frame_Cushion = 0.25"
      ],
      "metadata": {
        "id": "J0_gvTEGeMgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Set up the time index for your GIF\n",
        "starttime = np.datetime64(f\"{start_date+1}-04-01\", 'M')\n",
        "endtime = np.datetime64(f\"{end_date-1}-12-31\", 'M')\n",
        "startindex, endindex = -1, -1\n",
        "for i in range(len(nc['time'][:])):\n",
        "  check = np.datetime64(nc.coords['time'].values[i], 'M')\n",
        "  if starttime < check and startindex == -1:\n",
        "    startindex = i-1\n",
        "  if endtime <= check and endindex == -1:\n",
        "    endindex = i"
      ],
      "metadata": {
        "id": "ZCDKuRweO5ON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "if path.exists('/content/images_gif') == False:\n",
        "  os.mkdir('/content/images_gif')\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax)\n",
        "m = Basemap(projection='cyl', llcrnrlon=leftbound-Frame_Cushion, llcrnrlat=bottombound-Frame_Cushion, urcrnrlon=rightbound+Frame_Cushion, urcrnrlat=topbound+Frame_Cushion, resolution='i', epsg=4326)\n",
        "EsriBasemaps = \"World_Topo_Map\" #@param [\"NatGeo_World_Map\", \"USA_Topo_Maps\", \"World_Imagery\", \"World_Physical_Map\", \"World_Shaded_Relief\", \"World_Street_Map\", \"World_Terrain_Base\", \"World_Topo_Map\"]\n",
        "m.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service=EsriBasemaps, xpixels = 400, dpi = 69,verbose = True)\n",
        "\n",
        "for i in range(startindex,endindex+1):\n",
        "  im = plt.imshow(np.rot90(ts[i,:,:]), cmap=\"Spectral\",extent=(leftbound, rightbound, bottombound, topbound) ,vmin=np.amin(ts),vmax=np.amax(ts))\n",
        "  plt.title(aquifer_name.capitalize()+\" \"+str(nc.coords['time'].values[i])[:10], pad = 15)\n",
        "  if i==startindex:\n",
        "    cbar = plt.colorbar()\n",
        "    cbar.set_label(ts_measurement.value+\" in \"+units, labelpad = 20)\n",
        "  plt.savefig(f\"/content/images_gif/image{str(nc.coords['time'].values[i])[:10]}.jpg\")"
      ],
      "metadata": {
        "cellView": "code",
        "id": "9HFQb2dbVS6O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frame_duration=300\n",
        "frames = []\n",
        "imgs=glob.glob(f\"/content/images_gif/*.jpg\")\n",
        "imgs.sort()\n",
        "for i in imgs:\n",
        "    new_frame = Image.open(i)\n",
        "    frames.append(new_frame)\n",
        "    frames[0].save(aquifer_name + '_animation.gif', format='GIF', append_images=frames[1:],save_all=True, duration = frame_duration, loop=0)"
      ],
      "metadata": {
        "id": "GkNIO8kOV4De"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "62f7bce75ff84a04a4874c7f5b504bbb": {
          "model_module": "@jupyter-widgets/controls",
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