gwdm_aquifermapping (orig pdsi version).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BYU-Hydroinformatics/gwdm-notebooks/blob/main/gwdm_aquifermapping%20(orig%20pdsi%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": null,
      "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": {
        "id": "JH0Vj3NcCxFp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "11cb624caed74ba0bc39440073977440",
            "d2081366e0be484591a2c1a24b544ead",
            "fecb45ac485a4d0f98f198725e95c571",
            "b993a6c853334e1db65dfd2379edd515",
            "44f808aa0a1c4e0f96dfc427bf5e2e1d",
            "33c0a349af3549db98ec7c83391264ef",
            "c11e4c75d7a546b3a31e469504cf2a8b",
            "a819de381aa6473b83b570be28b5ab5b"
          ]
        },
        "outputId": "54e666f7-a69d-4b47-c78b-966a5644803e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8d82ba7d-186e-4735-815b-7d4a756e9bcc\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8d82ba7d-186e-4735-815b-7d4a756e9bcc\">\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 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…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11cb624caed74ba0bc39440073977440"
            }
          },
          "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": [
            "cb8939132bc94b6d995d54cd77440d86",
            "f21e4807b2884c79bf1c2e6a863fa4ee",
            "175a9daf5179408ba7a7636742cb0196",
            "7108764e9819482ea85618b1b2edb34c",
            "6a904ba49f2443e997c3bcbc640f2f38"
          ]
        },
        "outputId": "a9175d2a-e3b5-400c-ed12-a9b0b14818d1"
      },
      "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…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb8939132bc94b6d995d54cd77440d86"
            }
          },
          "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)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y6cTAFF1j8tV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "1af68618-5dd4-47fa-e408-ac46cc4a64dc"
      },
      "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...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72ffc418-dddc-4655-a328-70c4efc95f0e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72ffc418-dddc-4655-a328-70c4efc95f0e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-72ffc418-dddc-4655-a328-70c4efc95f0e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72ffc418-dddc-4655-a328-70c4efc95f0e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_a80f9c4f-46e4-49c0-bd0d-66e8193c92cb\">\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",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\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('aquifer')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a80f9c4f-46e4-49c0-bd0d-66e8193c92cb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('aquifer');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "aquifer",
              "summary": "{\n  \"name\": \"aquifer\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"GW_CODE\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"DS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Dead Sea GW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HECTARES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 687351.699,\n        \"max\": 687351.699,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          687351.699\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6874,\n        \"max\": 6874,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brakish_Wa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"9 to 12 MCM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salinity_R\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1000 to 1700\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"MULTIPOLYGON (((35.9619876410522 31.91493093288515, 35.955685049096346 31.906041888439436, 35.95238286548591 31.899439843797634, 35.95113512768701 31.894117829674602, 35.95157031963792 31.88985245278281, 35.95331616386433 31.886417423784575, 35.95600080442277 31.883589247224855, 35.9592531311437 31.88114274692166, 35.96270041004276 31.878852759476853, 35.966035525861386 31.8765347117804, 35.969204525503876 31.874169576008043, 35.97221777968892 31.87177605028234, 35.975085642374495 31.869374522184856, 35.97781846921841 31.86698537790155, 35.980426287032955 31.864628999701267, 35.98291946015606 31.862325207651395, 35.98530802354651 31.860093818102264, 35.987605070378024 31.857946778616686, 35.98983170904833 31.855857202692196, 35.992011769897644 31.85379033155229, 35.99416973199798 31.85171197340268, 35.996328766440236 31.849586233265857, 35.998512681006524 31.847378910585913, 36.000745944214444 31.84505524448173, 36.00305203999043 31.842579337091752, 36.00544905807772 31.8399251117408, 36.00793293407032 31.837100141492517, 36.010493908104515 31.834119847593097, 36.01312155329182 31.831000492404574, 36.01580644755168 31.827757219371442, 36.01853849640977 31.82440657684695, 36.02130795330343 31.820963707420997, 36.0241050629343 31.817444881927237, 36.02691745045716 31.813864661075407, 36.02972585170493 31.81023389044053, 36.03250870242766 31.806562836303012, 36.03524411885325 31.8028612007762, 36.03791087903587 31.799138974717135, 36.040487105682324 31.795406145846957, 36.04295158615811 31.791672708529067, 36.04528212529926 31.787948369413694, 36.047466775368335 31.784241503232426, 36.04952865946384 31.780551443433925, 36.05150048763066 31.77687533747281, 36.05341528866258 31.773210896531367, 36.05530476917203 31.769555538025568, 36.05720227720625 31.765906971301938, 36.059139844407106 31.76226204840779, 36.06115014843949 31.758618469657335, 36.063251652699726 31.754976366208517, 36.065404687282474 31.751343336891257, 36.0675550347376 31.747731105540442, 36.06964916348322 31.74414971355679, 36.07163321278521 31.740610049153123, 36.0734526711755 31.737122999116277, 36.075054354817894 31.733699462507428, 36.07638443367389 31.73034977184188, 36.077406581301375 31.727082413089292, 36.07815747595029 31.72389342937392, 36.07869161147206 31.720777297511017, 36.07906347872054 31.717727928220935, 36.07932789575814 31.714738669310865, 36.079539005330204 31.711803708400968, 36.079750945794466 31.708916950624335, 36.08001883771356 31.7060723076693, 36.080385935065166 31.703262760127938, 36.08085033689984 31.700479555651196, 36.08139993430976 31.697712741273808, 36.082021630749495 31.694952356997742, 36.08270364950441 31.692188452357623, 36.08343289094727 31.689411631341027, 36.08419659175556 31.686611936832392, 36.08498297853651 31.683779419026457, 36.08578091342933 31.680906669706513, 36.08658971318732 31.6779959386629, 36.087410655729954 31.6750511808248, 36.08824434889169 31.672077473810795, 36.089091743798 31.66907848832291, 36.08995476656733 31.66605931127638, 36.09083403602701 31.66302389282663, 36.09173049514799 31.65997674913837, 36.09264312620381 31.656920691314998, 36.09356305532552 31.65385311964667, 36.094478465989084 31.650769159027526, 36.0953778697849 31.647664218872126, 36.09625010640653 31.644533993094864, 36.09708369014439 31.64137389175707, 36.09786680457771 31.638179604749965, 36.09858862852064 31.634946265443215, 36.09924164992074 31.63166762118743, 36.0998375415098 31.628329379310262, 36.10039194304709 31.624915583282387, 36.10092082116397 31.62141027846854, 36.10143948647304 31.61779694155885, 36.101964226906226 31.6140599013429, 36.10251034469454 31.61018291565366, 36.10309412424486 31.60615003068344, 36.10372578212911 31.601959625406497, 36.10439421686282 31.597669968848805, 36.10508324967473 31.593354234327524, 36.10577704444413 31.589084752505084, 36.10645910122412 31.584934977514152, 36.10711293071997 31.58097780040677, 36.10772304148903 31.577285555939696, 36.10827294774041 31.573931699917186, 36.108749355722466 31.570965751407222, 36.10915333363851 31.568345720271363, 36.109487818692024 31.566005669807907, 36.10975705666575 31.56388023578486, 36.109964632648634 31.56190404913205, 36.11011412999482 31.560011740540297, 36.11020880429786 31.55813765637153, 36.11025289515939 31.55621643148543, 36.11025051696977 31.554195947769838, 36.110207589158215 31.552077091640733, 36.110131873039315 31.549874855858754, 36.11002949171336 31.54760337625431, 36.10990788160756 31.545277079609136, 36.10977348582087 31.54291094962161, 36.109633742056914 31.540519131270667, 36.10949542373709 31.538116328737175, 36.10936404028011 31.53571160003285, 36.109239370884744 31.533293386806367, 36.10911992895755 31.530844766333807, 36.10900423074665 31.528348534007396, 36.108890792684335 31.52578748519046, 36.10877746800077 31.523144974381857, 36.10866375799559 31.520404085560635, 36.10854719524314 31.517547325318716, 36.10842852312115 31.51456567864547, 36.10831510458623 31.51148090064145, 36.10821686445531 31.508322092917748, 36.10814273653904 31.505118632172344, 36.10810264026024 31.501899901965654, 36.10810551213535 31.498694715343287, 36.108161592737645 31.495533021869477, 36.108279487499054 31.492443632273257, 36.10846720699394 31.4894485880808, 36.10872251983397 31.486539699226153, 36.10904194315262 31.48370200187183, 36.10942167362226 31.480919684252576, 36.10985757412632 31.478177495987484, 36.1103458393864 31.475459907062305, 36.11088266429882 31.472751387431103, 36.111464241204224 31.47003668888866, 36.112086733437096 31.46730366367314, 36.112746175214056 31.464553975132517, 36.11343857225305 31.461792387125605, 36.1141592732336 31.459023659077708, 36.114904937489854 31.45625312325169, 36.1156712464167 31.453485259658333, 36.116454202991655 31.45072539624924, 36.11724948994435 31.44797801325992, 36.11805245405755 31.445248434384713, 36.11885417684608 31.442541108722065, 36.11964573055908 31.43986161306495, 36.12041721286826 31.437214390298543, 36.12115937231443 31.434604733623985, 36.121862635067316 31.43203737051231, 36.12251775190146 31.42951759459617, 36.123114822397454 31.42705013149092, 36.12364663387728 31.424633523824472, 36.12411246081593 31.422240143433392, 36.12451294365405 31.41983673367756, 36.12484938724113 31.41738919661175, 36.12512243593234 31.414863711531535, 36.125333722351655 31.4122261823832, 36.12548421646021 31.40944307222779, 36.1255745622599 31.406480559886297, 36.125607916427604 31.403317525630857, 36.12559649393696 31.399984213097646, 36.1255537038845 31.396523840485916, 36.1254932797883 31.392979910118687, 36.12542928704665 31.389395362880865, 36.125375131878506 31.385813135291297, 36.12534519900067 31.382276734338387, 36.12535288810753 31.378829378539603, 36.12540772746124 31.37550636759165, 36.12550373207597 31.37231499002888, 36.12563006825215 31.369254045154925, 36.1257758987795 31.366322896038124, 36.125931047517604 31.363520628376072, 36.126085008555414 31.360846607572068, 36.12622662023932 31.35830019465629, 36.126346041025975 31.355880195858955, 36.126435139628526 31.35357810004074, 36.12649987948438 31.35135279281988, 36.12654827377293 31.349154435189252, 36.12658931476278 31.346933758415833, 36.126631334255265 31.34464177110021, 36.126683001772854 31.342228356498257, 36.12675231594715 31.339644802648355, 36.12684859797632 31.336841278853502, 36.12697577126455 31.33378342143091, 36.12712310412777 31.33049511762203, 36.127275786675106 31.327015448964918, 36.127419016570705 31.323382933438165, 36.12753798899537 31.319636652938165, 36.127617576217446 31.315815405459716, 36.12764297914039 31.3119582732232, 36.127599406575136 31.308103774808107, 36.127477017213096 31.304287925179274, 36.127285444405196 31.30053531316975, 36.127039930824274 31.296867182762405, 36.12675439823854 31.293305332851176, 36.12644441214879 31.289871009731467, 36.12612421991683 31.286585732827007, 36.12580872297394 31.28347102611611, 36.12551249235781 31.280548411494618, 36.125248223577266 31.277828968556424, 36.12501783988127 31.275281421365577, 36.124821058019116 31.27286461300923, 36.12465825936181 31.27053654531537, 36.12452948974504 31.268256063419766, 36.12443381856135 31.265981160121495, 36.12437162020923 31.263670682611146, 36.12434294862482 31.26128263017213, 36.12434672868728 31.25879078001121, 36.124379720823555 31.25622611761556, 36.124438208057576 31.25363597470443, 36.124518161373274 31.251065989637805, 36.12461521149651 31.248563207988678, 36.12472631002656 31.246173838669616, 36.12484676636487 31.243944361470948, 36.124972872387644 31.241921544777362, 36.12509975273076 31.240136363472402, 36.125218491101116 31.238559441323456, 36.12531899065098 31.23714701786359, 36.12539149045976 31.235854489205654, 36.12542622714677 31.234637533299487, 36.12541310947986 31.233451825832237, 36.12534171854749 31.232253040201886, 36.125202948140505 31.230996858542703, 36.12499059357831 31.22964462015924, 36.124714643250925 31.228180606367964, 36.1243896261156 31.22659476685797, 36.124029417239655 31.224876765071066, 36.12364887254017 31.223016553057178, 36.1232618605633 31.221004358210784, 36.12288258462213 31.21882956465613, 36.12252556446506 31.21648268633771, 36.122200966971945 31.213963792056656, 36.12190478055374 31.211316829287988, 36.121628626723115 31.208596991480114, 36.12136413507816 31.205858626475013, 36.121103261038925 31.203156366229507, 36.1208379579146 31.2005451246014, 36.120560182006486 31.1980795336004, 36.120261236639976 31.195813938923667, 36.119933876444534 31.193787474070433, 36.119574417493645 31.191972770797648, 36.11917964112004 31.190327523780145, 36.11874600638917 31.188808861607587, 36.11827030342259 31.187373633108027, 36.11774964835181 31.185978971095707, 36.117180499880945 31.184582285684307, 36.116559976158385 31.18314070944973, 36.11588646651152 31.18161589372177, 36.115165738343535 31.179987299046825, 36.114405807597784 31.17823947537906, 36.11361403991607 31.176356404581053, 36.112798781995515 31.174322357240968, 36.11196739708115 31.17212159738611, 36.11112757622174 31.169738391438273, 36.110287340715594 31.167156726352378, 36.10945393825794 31.164373268988566, 36.108635479343704 31.16143289548333, 36.10783930112706 31.158392880062546, 36.10707241279709 31.155310213041034, 36.10634312926304 31.15224217582981, 36.10565877956916 31.149246043281597, 36.10502669314536 31.14637880854738, 36.10445419754302 31.14369746491903, 36.103947119624664 31.14124405523907, 36.1035013934555 31.138996846770105, 36.10311112526042 31.13691943564762, 36.102770428194226 31.13497485408512, 36.10247341680352 31.133126134146092, 36.10221387910685 31.131336305502355, 36.10198625681082 31.129568684119487, 36.10178400939177 31.127786579075813, 36.10160250770202 31.12595923229658, 36.101438201867204 31.12408069954827, 36.101289447550435 31.12215153325198, 36.101153287296796 31.12017255862252, 36.10102775728896 31.118143480168786, 36.10091055040396 31.11606569136366, 36.10079937549739 31.113938894432316, 36.10069160345494 31.11176391682016, 36.10058620650899 31.10954554353737, 36.10048298426703 31.107305196955878, 36.10038333208364 31.105068820795584, 36.100288975353045 31.102862079205078, 36.10019999613852 31.100711188775772, 36.10011844714211 31.098641815989783, 36.100045070647205 31.096679618286814, 36.09998126136436 31.09485053954177, 36.0999275419906 31.093168678121263, 36.09988160787527 31.091600191056113, 36.09984126527353 31.090099396607762, 36.099804323245095 31.088620331118758, 36.09976793330336 31.08711730822889, 36.099729902179135 31.085544646073288, 36.09968738171305 31.08385665820705, 36.09963817897829 31.082007662671185, 36.09957964462995 31.079965787051407, 36.099509260210795 31.07775525654932, 36.0994240536027 31.07541382412038, 36.09932137261913 31.072980090651633, 36.09919825108639 31.070491245426595, 36.09905203477405 31.06798617128807, 36.098880080179924 31.06550262361387, 36.09867941644775 31.063078355541062, 36.098446795379964 31.06074576231522, 36.09817561751391 31.05851043600492, 36.097859001153004 31.056373174441738, 36.09749040290635 31.05433365011766, 36.09706261954115 31.052392094598623, 36.09656877809572 31.05054845967808, 36.096002003102285 31.04880297882373, 36.09535509440842 31.047155601110553, 36.09462579968684 31.04560236323557, 36.09382903839323 31.04412363519588, 36.09298336487118 31.042696147918274, 36.09210865035497 31.04129579622189, 36.09122345089239 31.039899030010286, 36.090347631688225 31.03848230881851, 36.089499423462804 31.037021799361625, 36.08869901532955 31.03549424630201, 36.087959364815234 31.03387944488235, 36.08727429130901 31.032173124294495, 36.08663071113064 31.03037406590716, 36.0860168399692 31.028482187488628, 36.085419922983235 31.026496272160195, 36.08482785556781 31.024415671165503, 36.084228536805156 31.022239453662205, 36.083608879678515 31.01996724543814, 36.082960638204895 31.017606035422546, 36.082288690885285 31.0151939134069, 36.08160143976244 31.012776887237955, 36.08090695222809 31.010401526400972, 36.08021395619232 31.008113559571804, 36.07953051764693 31.00595927475709, 36.07886503061666 31.00398468058662, 36.07822588470184 31.002236067741144, 36.07761967421467 31.000741954910147, 36.077043846647676 30.99946060397507, 36.07649437385652 30.99833363439224, 36.07596724291414 30.99730125613002, 36.07545777186487 30.996305365679763, 36.07496227801214 30.995286175157325, 36.07447641207207 30.99418530133821, 36.07399616133718 30.992943517611938, 36.073517365706316 30.99151710050407, 36.073037569676934 30.98992351001985, 36.0725548312734 30.9881948683841, 36.07206719995851 30.986364143437505, 36.071572078536676 30.98446345271567, 36.07106817269798 30.98252548682865, 36.07055255184593 30.98058292471559, 36.07002359903496 30.978667890886797, 36.06947942933317 30.976806306214126, 36.068919091241455 30.974995062885863, 36.06834202837806 30.973224008476574, 36.06774735201714 30.97148355195026, 36.06713450352096 30.969763822708085, 36.066502602823036 30.96805438402882, 36.06585174332315 30.96634565183111, 36.06518038585247 30.96462774840701, 36.06449057334705 30.962892513109992, 36.06379342558447 30.9611403074656, 36.06310234021215 30.95937263719658, 36.06243070291417 30.95759213580139, 36.061791576685295 30.955800870921323, 36.06119835600555 30.954000349019783, 36.06066475040233 30.95219320661327, 36.06020414944445 30.95038123236177, 36.05982568999179 30.94856618430623, 36.059524799746825 30.94674690251242, 36.05929264979799 30.944922759924534, 36.059120422852835 30.943092001805336, 36.05899929132155 30.94125400080101, 36.05892076898899 30.939406722441383, 36.05887570255047 30.937549536864253, 36.0588559286239 30.935680975627292, 36.05885424626318 30.93380155050657, 36.058871572365284 30.93191775175415, 36.05891109897586 30.930037495538336, 36.05897568546585 30.92816925941336, 36.05906819651544 30.926320957163068, 36.05919181815406 30.92450106866884, 36.05934909056752 30.92271722343011, 36.05954319685436 30.920978183147938, 36.059776693517065 30.919289885977232, 36.06005087303516 30.917653750533038, 36.06036705801231 30.916068094714255, 36.06072557661094 30.91453263869364, 36.06112807300592 30.913046266379375, 36.061575537536 30.91160785690745, 36.06206862821816 30.91021685076001, 36.06260833551962 30.90887212695299, 36.06319402085664 30.907571988932556, 36.06381494706339 30.906311002913036, 36.0644587511334 30.905082877919092, 36.06511339474707 30.903881607432577, 36.06576586478502 30.90270061439229, 36.06640445342262 30.90153361324378, 36.06701647255433 30.90037431164851, 36.06758955867117 30.89921670171736, 36.06811824564342 30.898051442239066, 36.06862430895543 30.89685782868921, 36.069136740786576 30.89561238865158, 36.06968452858896 30.894291931067595, 36.070296665658155 30.892872418659802, 36.07100181036437 30.89133037488468, 36.07182894206982 30.889642606523854, 36.072806714842756 30.887785353126727, 36.07395321979112 30.885745208151313, 36.07524171376843 30.883548475823673, 36.07663457159344 30.881231814272972, 36.07809384875749 30.87883159887279, 36.0795822625848 30.876383929443247, 36.08106220416871 30.87392546910556, 36.08249541714778 30.871492878273965, 36.083844960999954 30.869122546489134, 36.08508052380301 30.86684160853697, 36.086204810874634 30.864641629807714, 36.087227146237176 30.86250520069488, 36.08815783740765 30.86041435405578, 36.089006852656865 30.858352247434247, 36.08978450117766 30.856300348864714, 36.09050041864341 30.854242094585892, 36.09116458456086 30.852158949562824, 36.09178889788325 30.850036620336592, 36.09239074185036 30.84786818073172, 36.09299007746251 30.845650386841545, 36.09360556493261 30.84337913961841, 36.09425748813357 30.841051196595394, 36.0949648299426 30.838662460115717, 36.09574689510251 30.836209116146744, 36.09662331004512 30.833687634170808, 36.09760451835413 30.831098084227595, 36.098665208897515 30.828456357599464, 36.09977153684289 30.825783079841955, 36.10088967236006 30.823097750163132, 36.10198512924018 30.820420710212872, 36.10302408445759 30.81777174363509, 36.10397238512609 30.81517147864547, 36.10479622222122 30.812639137225307, 36.10547029464607 30.810192590701025, 36.10600501436675 30.807837550997057, 36.10641898176557 30.80557696631039, 36.106731445347876 30.803414070529183, 36.10696132474574 30.80135209480784, 36.10712786400946 30.799394272170375, 36.10724965729271 30.797543267151553, 36.10734594490667 30.79580231235385, 36.10743435874732 30.794171810236588, 36.107525456906316 30.792639992946132, 36.107629172085744 30.79119198734137, 36.10775510983358 30.789812917989885, 36.107912216949025 30.788488468708305, 36.108110748333694 30.787204050249578, 36.10835933095564 30.785944498336594, 36.10866854507681 30.784695225683613, 36.109041139563644 30.78344102763273, 36.10945834805833 30.782162888075707, 36.10989487250957 30.78084202852162, 36.110325752588444 30.779458545468373, 36.11072537090337 30.7779930950825, 36.111069090777804 30.77642634049478, 36.11133195320413 30.7747386609425, 36.111488014096494 30.77291127476799, 36.111521380073356 30.770933361884296, 36.111447810067084 30.768831810557423, 36.11129179672218 30.7666423072732, 36.11107848374417 30.764400542885586, 36.11083203328025 30.76214220165522, 36.11057725528256 30.759903254312036, 36.110338630706096 30.75771966954855, 36.110140972164736 30.755626572781146, 36.110000017910586 30.75365169823769, 36.1098975533083 30.751787592643144, 36.109807609437475 30.750018292638725, 36.10970290879356 30.74832838969591, 36.10955749155417 30.746701356535514, 36.10934408383691 30.745121784424803, 36.1090367214776 30.74357399138336, 36.10860846805046 30.74204172458012, 36.10804056057104 30.74050737671549, 36.10734531757284 30.738945094534404, 36.10654257631971 30.737327666415304, 36.10565283690712 30.73562675779365, 36.104696260139455 30.733815441649863, 36.10369236187945 30.731865941256842, 36.10266131311926 30.729750202966443, 36.101623603028756 30.727441021601837, 36.100595700659554 30.724922441283177, 36.099579922989165 30.722226897487193, 36.09857521331219 30.719398642698447, 36.09758083949092 30.716481931645145, 36.096595417814456 30.71352073267496, 36.09561821033414 30.710559582425116, 36.09464783027899 30.707642449261403, 36.09368386550637 30.704813590218304, 36.09271558628909 30.702103941152416, 36.091695256865115 30.69948949058827, 36.09056613435735 30.6969331951671, 36.089271154366536 30.694398289062338, 36.08775359681663 30.69184687846095, 36.085956413327054 30.68924219132154, 36.08382257035714 30.686546887211605, 36.08129536910619 30.68372390394549, 36.076482296039096 30.678127136614954, 36.07253293290604 30.673369918347902, 36.06930364237457 30.66934023391276, 36.066650522689926 30.66592744816082, 36.06442908828939 30.663019773606955, 36.062496193479745 30.66050626302284, 36.06070807581163 30.658275671795792, 36.05892067750387 30.656216180637156, 36.057020752542236 30.65423535392624, 36.055016645051836 30.652313513339394, 36.05294684697863 30.65044924092794, 36.05085049095719 30.648641971370914, 36.04876573941105 30.64689000710354, 36.0467310698713 30.64519250098502, 36.044785612089605 30.643548331053292, 36.042967841422396 30.64195637273343, 36.04130287036951 30.640414841670374, 36.03976268603239 30.63892015544064, 36.03830592036768 30.637467786156133, 36.03689054809676 30.636054044781513, 36.03547586460585 30.634673840578227, 36.03401985014354 30.633323480907503, 36.032481147815695 30.63199843041782, 36.030818400422234 30.63069443361848, 36.02900393825836 30.629407897882327, 36.027068097979736 30.628137350714997, 36.02505425016283 30.62688169988081, 36.02300673250867 30.62564070853148, 36.02096923971369 30.624413010074196, 36.01898578177933 30.623198088703248, 36.01710036933073 30.621995149317822, 36.01535635899369 30.620803394256182, 36.01378405949676 30.61962277531166, 36.01235506822609 30.618457031313227, 36.01102761524816 30.617310362265666, 36.00975927786659 30.616187243157345, 36.008507635265595 30.61509214723087, 36.00723092309442 30.614029269154646, 36.00588672109857 30.613003360813256, 36.00443294212123 30.61201861099629, 36.00283991044569 30.61107676365702, 36.00112759142879 30.610169790519173, 35.99932934337769 30.609286665232794, 35.99747721751328 30.608416634967224, 35.995604568161696 30.607548958591398, 35.9937440974256 30.606672891848227, 35.99192883576277 30.605777412952335, 35.990190835487155 30.604851494381514, 35.988552319614975 30.603889108714114, 35.986989733619495 30.60289881680899, 35.985468711185575 30.601894732727207, 35.98395522116271 30.600890125737614, 35.982414255438236 30.599898255942158, 35.980812104125356 30.59893295570014, 35.97911408898529 30.598007202390672, 35.977285523260015 30.597134817081663, 35.97530544417982 30.59632606103926, 35.97320582938534 30.595576949245633, 35.971031715810454 30.59488106367511, 35.96882814223528 30.594231707205, 35.966640478389856 30.593621624486268, 35.96451310697054 30.593044401128413, 35.96249171949765 30.59249307212134, 35.960621023620924 30.59196123110174, 35.958931049125795 30.591443766793006, 35.957391098042386 30.590946643981347, 35.955955469695354 30.590477115445697, 35.9545781322832 30.590042992793293, 35.95321305459472 30.589652085559667, 35.95181486051092 30.58931192465701, 35.950336870818184 30.58903002859527, 35.94873402930926 30.588814490487668, 35.94697468645881 30.588668998118273, 35.945088275606636 30.588583351415657, 35.94311795119229 30.58854407933465, 35.941107522970384 30.58853743660846, 35.93910080465032 30.588549398738692, 35.937141604901925 30.588566507709327, 35.935273410676594 30.588575023605262, 35.933540035715865 30.58856121154022, 35.9319702729679 30.58851431719794, 35.9305334626343 30.588437197298656, 35.92918392918968 30.58833512119494, 35.92787599074164 30.58821391984837, 35.9265642941997 30.58807914308216, 35.925203483381495 30.58793662075754, 35.92374755358011 30.587791893567257, 35.92215114865593 30.58765078722785, 35.92038360209662 30.587516708716752, 35.91847526197369 30.587385672936513, 35.91647083721766 30.587251559534746, 35.91441568857021 30.58710825602401, 35.912354528455865 30.58694936538933, 35.91033271170217 30.586769344493707, 35.908395276737174 30.586561804642372, 35.9065869361047 30.58632035698224, 35.90493833559275 30.586043571584334, 35.9034186918098 30.585745292211914, 35.90198283152244 30.585444032548185, 35.90058492936303 30.585158298568057, 35.89918046908165 30.58490604138192, 35.897723618034085 30.58470632670824, 35.89616888125853 30.584577093378737, 35.89447075627017 30.58453684160913, 35.89260142131949 30.584596324893788, 35.89059886794776 30.58473781548593, 35.88851942941048 30.584935285926996, 35.88641812831109 30.58516354674685, 35.884350647742856 30.58539685383558, 35.88237267340367 30.585609466671432, 35.88054021276719 30.58577621459055, 35.878908304209915 30.585871357629408, 35.87751399691075 30.58587520560314, 35.87632042679067 30.585791960515138, 35.8752711059107 30.585632137277095, 35.874310206829826 30.585405408293127, 35.87338156631858 30.585122286857636, 35.87242935678503 30.584792441761124, 35.87139774134788 30.584426385783537, 35.87023023862181 30.584034060475755, 35.86888472906962 30.583623273802807, 35.86737264806176 30.58319158201029, 35.86571980196371 30.582733564683405, 35.86395101330742 30.582244357478697, 35.86209175297835 30.581719384579056, 35.860167501651965 30.581153225765554, 35.858203085109075 30.58054073839348, 35.85622429977505 30.57987735353885, 35.85423596023885 30.57914168799374, 35.85216119968981 30.57824849676581, 35.84990314580368 30.57709684963831, 35.84736495238499 30.575584963791215, 35.844449459223476 30.573611890549188, 35.84105987093179 30.57107582754497, 35.83709974643849 30.56787580701223, 35.83200705729318 30.563510619412916, 35.83062236936166 30.56369165504155, 35.828343999800765 30.56404566957856, 35.82596047786443 30.56447425181924, 35.82344764885934 30.56498084369153, 35.820828160029905 30.56555070052199, 35.81813580986288 30.566162977920804, 35.8154047083739 30.566798246511244, 35.81266800831021 30.567435661586046, 35.8099598256508 30.56805579953871, 35.80731429300831 30.56863811219218, 35.804764565194084 30.569162327116914, 35.80233720891407 30.56961431596701, 35.80002694673168 30.569999957318338, 35.79782060964182 30.570330976530382, 35.79570567160782 30.570619667480592, 35.793668634824336 30.570877750239514, 35.79169697745737 30.571116952853284, 35.78977785077951 30.571348999454145, 35.78789775002125 30.57158588915024, 35.78604614299268 30.57183626494455, 35.78422046852906 30.57209643955783, 35.78241982248968 30.572360485754405, 35.780643963192716 30.57262163670809, 35.778891997946275 30.572873119505225, 35.77716334973714 30.573109009828396, 35.775457778263416 30.573322540771983, 35.7737740664094 30.573506936267936, 35.77211290258984 30.573659384649353, 35.770477755171655 30.573789222076023, 35.768873359490314 30.57390861576528, 35.76730411368239 30.574030575658668, 35.7657747479068 30.574167551197863, 35.76428998803171 30.574332273858122, 35.7628542370355 30.57453719032403, 35.76147222305145 30.57479475048494, 35.76013863940015 30.57511223515628, 35.75881100242597 30.57547402054958, 35.75743713108569 30.57585846922808, 35.75596386393161 30.576244214113256, 35.75433966885217 30.576609901061918, 35.7525117044079 30.576934724273084, 35.75042747429618 30.577196467987434, 35.748035114491366 30.57737460923106, 35.745294955586544 30.577463394886397, 35.74221894298359 30.577522680308903, 35.73883184306343 30.577628228995373, 35.73515874077006 30.57785552564377, 35.73122437869241 30.578280334112804, 35.727053810410965 30.578978422297123, 35.72267141810584 30.580025834912274, 35.714707140234076 30.582648173269373, 35.71457059507185 30.58284895245218, 35.71405326706212 30.583389623347298, 35.71334598868618 30.58400397336745, 35.71244884724486 30.584709759637114, 35.71138826555728 30.58550413734284, 35.710197911730276 30.586378413415815, 35.708910477382155 30.587323885941483, 35.707560273147735 30.588332715725958, 35.70617967499784 30.58939563629348, 35.704802345224806 30.590504804745382, 35.70346164087716 30.591650967139472, 35.70217917705083 30.592825882072873, 35.70093261186867 30.59401889935685, 35.69968755155263 30.595218966249135, 35.69840992335335 30.59641531375538, 35.69706500559146 30.59759688315572, 35.69561904936324 30.59875290579613, 35.69403797918091 30.599872608060927, 35.69228739696884 30.600944929193336, 35.69034814343985 30.601964596429966, 35.68826072725099 30.602948357317935, 35.68608056316401 30.603919312770735, 35.68386307744106 30.60489972154311, 35.681664014638656 30.60591241307054, 35.67953847530224 30.60697965024765, 35.67754252659341 30.608124554937397, 35.67573094855301 30.60936911154821, 35.67414783457228 30.61073040640537, 35.672791343667264 30.61220025427606, 35.67164635247047 30.613764695943246, 35.670699701653824 30.61540922677713, 35.669936921503336 30.617119891300153, 35.6693441944134 30.6188827395238, 35.66890705484541 30.620683532027165, 35.668612014652744 30.6225080385572, 35.668444887307786 30.62434540384386, 35.66839293851868 30.626197753971628, 35.66844404275942 30.628070321670503, 35.66858542183868 30.629968332756974, 35.66880527069308 30.631897304673906, 35.669090813352916 30.6338621810983, 35.66942959128023 30.63586847269821, 35.66980915277401 30.637921126457673, 35.670218299261016 30.640028766777675, 35.67064960120697 30.642210486475527, 35.671096882860816 30.64448905576173, 35.671553979808444 30.646886399329173, 35.672014719755715 30.649425005618966, 35.67247260411534 30.65212735980025, 35.67292178588859 30.65501595369779, 35.6733557731843 30.658112708828103, 35.67377047424888 30.661430832450666, 35.67417244367214 30.664943893182382, 35.67456998537867 30.668616738719052, 35.67497108947527 30.672413367871695, 35.67538406536389 30.67629834645478, 35.67581787946098 30.680235965034512, 35.67628051713399 30.68419078609483, 35.67678062072181 30.68812709684855, 35.67674534354031 30.691016396689438, 35.67658864730169 30.693742660034818, 35.67632552611864 30.6963060384384, 35.67597228525305 30.698706414730474, 35.67554326785179 30.700944215622005, 35.675054453859474 30.70301960260922, 35.67452084282757 30.70493300929332, 35.67395776979716 30.706684309111804, 35.67337954278503 30.708277029881444, 35.6727937626604 30.709728162170705, 35.67220735107951 30.711056662977622, 35.67162688416634 30.712282895331416, 35.671058953180854 30.713426094953395, 35.67050981138991 30.714506339882284, 35.669986368048775 30.71554343303106, 35.669495209799145 30.716556892217557, 35.669041645128786 30.71756424906994, 35.66862718318138 30.718574539837817, 35.66825172881305 30.719594811160793, 35.66791518677497 30.720632109635076, 35.667618125778354 30.721692642927383, 35.667359794141205 30.722783732742368, 35.66714108291495 30.72391187184808, 35.666961566643415 30.725084385198052, 35.66681987198783 30.726306332914092, 35.66670852705192 30.727575383679365, 35.66661777969285 30.72888890093623, 35.66653887541215 30.730242848832273, 35.6664623990221 30.73163374850698, 35.66637925724049 30.733058406246563, 35.666279707561 30.73451333978193, 35.66615498574329 30.735995076800478, 35.66599861102753 30.737500168280917, 35.6658148545194 30.739026402634433, 35.66561124671352 30.740571883504646, 35.66539564874598 30.742134436075634, 35.66517461315931 30.74371215408938, 35.66495632838163 30.745302866193388, 35.66474767811685 30.74690438774887, 35.66455651779717 30.748515107892484, 35.66438940194186 30.75013312060743, 35.664253165445764 30.75175990542534, 35.66415333829328 30.753396928531746, 35.66409577333349 30.75504594130072, 35.66408631996515 30.756708976936725, 35.66413116212409 30.758387508255534, 35.66423615786014 30.76008300467098, 35.66440715795936 30.7617974992057, 35.66464579402169 30.763531290244693, 35.66493909572137 30.765278325079095, 35.66526855865061 30.76703136683854, 35.66561666005301 30.768782906936394, 35.6659655455441 30.77052571543426, 35.66629801990588 30.772252005533574, 35.66659589577536 30.77395482610409, 35.66684131862274 30.775626665774535, 35.66702124790415 30.777265982064176, 35.66714158859613 30.778894258908807, 35.66721272739895 30.78053950931684, 35.667244410423635 30.78222889399022, 35.66724702949819 30.78399014393599, 35.667230977005985 30.78585099012263, 35.667206327171336 30.787838596466404, 35.66718380003331 30.78998069723758, 35.66717294199536 30.79229543057821, 35.667182193866935 30.794762586890293, 35.667219802530745 30.797352370519263, 35.667294012152134 30.800035267699936, 35.66741307191977 30.80278148284835, 35.66758490561135 30.80556121704557, 35.66781776063483 30.808344956528376, 35.6681198852499 30.81110318746506, 35.668494548953895 30.813812264602692, 35.6689270160313 30.816475419549445, 35.669397238925555 30.81910203119021, 35.66988614764073 30.821701488578892, 35.670374013186134 30.82428345614892, 35.67084078572276 30.826857031535432, 35.67126738934885 30.82943160442419, 35.67163375869682 30.83201711835276, 35.67192415341107 30.834617641498994, 35.67213779206755 30.8372182264284, 35.672277551672046 30.839799170865234, 35.672345986784855 30.842340487254972, 35.67234630571697 30.844822194617795, 35.6722813907551 30.847224308576976, 35.672153471562254 30.84952683804238, 35.67196640807588 30.851710090385463, 35.67172167399544 30.8537616775731, 35.671418362432156 30.855699912497155, 35.67105546605433 30.85755043587183, 35.67063131619878 30.859339445297746, 35.6701439248335 30.861092571204352, 35.66959227576572 30.86283601756144, 35.66897470643714 30.864595417814193, 35.668289553411405 30.866396405226286, 35.66753752461532 30.86825843573752, 35.666727564624146 30.870172297539085, 35.66587066861865 30.872122316713238, 35.66497816377047 30.874092541138445, 35.6640610442082 30.876067579359386, 35.66313031335023 30.878031476590408, 35.66219730294358 30.879968281776886, 35.66127334241055 30.881862326093923, 35.66036446783791 30.883702678571666, 35.65946011515255 30.88549712345531, 35.65854442793072 30.88725790019768, 35.657602197807215 30.88899753632397, 35.656617884119214 30.890728837311702, 35.655575952403176 30.892464044391478, 35.654460862609824 30.89421568000727, 35.65325674608729 30.895996262510273, 35.65195237070688 30.897813847409097, 35.65055078879866 30.899660006762428, 35.64905904622229 30.901521280191965, 35.64748450910398 30.90338477457372, 35.64583356836347 30.905237304980005, 35.64411359696396 30.907065696945146, 35.642331638663194 30.908857054763335, 35.6404950696908 30.910598204660587, 35.638607317687224 30.912277905066247, 35.63666156298568 30.913892981631427, 35.63464670457232 30.915442469599856, 35.63255230684174 30.916924564777364, 35.6303679220692 30.918338307661926, 35.628083106403345 30.91968245602903, 35.62568741197317 30.920956048529938, 35.6231700760974 30.922157273804082, 35.620529110652356 30.92328807684544, 35.61779538902026 30.924361744751117, 35.61500824422203 30.925394475876235, 35.612207327954216 30.926403039004597, 35.6094319808179 30.927403075483433, 35.60672152681983 30.92841135734463, 35.60411529772861 30.92944409616632, 35.60165262521214 30.93051750648991, 35.59936176616815 30.931644867343696, 35.597224409500754 30.932826272122554, 35.595211168653954 30.93405887393671, 35.593292329383324 30.93533982008188, 35.591438499337634 30.936666541176372, 35.58962029738601 30.938035620490794, 35.58780767488792 30.939444477936664, 35.585971893746574 30.94089026408641, 35.58408687977693 30.9423667743733, 35.582142438866626 30.943855291581986, 35.580133001152774 30.94533404775788, 35.578052013224834 30.946781545664557, 35.575893593939405 30.94817516745295, 35.57365151631761 30.94949370037518, 35.57131956795409 30.950715085688987, 35.56889186165184 30.951817549538195, 35.56637251183992 30.952788729675092, 35.563805617789214 30.953654758961093, 35.56124593047923 30.954451193415274, 35.5587475447331 30.955213586042202, 35.55636519866504 30.95597806481121, 35.5541529869411 30.956779908660554, 35.55216597123835 30.95765525603918, 35.55045792175862 30.958639105850853, 35.549066024802116 30.959760071800602, 35.54795697013584 30.961014973013764, 35.54708020905891 30.96239423143994, 35.546384228299225 30.963887127906816, 35.54581848588133 30.96548351580125, 35.54533211071608 30.96717324338306, 35.544873910434234 30.968945590523393, 35.5443936535665 30.970790974502776, 35.543851970682404 30.97269514177878, 35.54326102358651 30.97463229575615, 35.542643501950266 30.97657281203425, 35.5420237335388 30.978487085097537, 35.54142507188456 30.98034521716322, 35.540870860305205 30.982118156453588, 35.540384784316835 30.983776009866112, 35.53999052279578 30.985289448294917, 35.53970049299806 30.986638317830593, 35.53948008850304 30.98783970597146, 35.539284732593025 30.98892073487081, 35.53906821882427 30.989907944205346, 35.53878530715099 30.99082872975659, 35.53839109989623 30.991709363158694, 35.53783937618571 30.992576945923503, 35.53708522587658 30.993458311138838, 35.53609754910781 30.9943756539331, 35.53490441705867 30.995332949964396, 35.533548364527036 30.9963298283751, 35.5320722590634 30.997365641278137, 35.53051831110406 30.99844001677851, 35.528929064165595 30.99955230674949, 35.527347386430755 31.000702150685846, 35.52581582089092 31.001889186521115, 35.52436421347133 31.003108963663234, 35.522972234577324 31.00434293195837, 35.521606196743015 31.00556872380013, 35.52023273935731 31.006763973851843, 35.518818824389946 31.00790660090819, 35.51733142241954 31.008973958661866, 35.515736514070944 31.009943951612364, 35.514001071014555 31.010793929993316, 35.512103365858856 31.011511236873513, 35.51006560047599 31.012120642670485, 35.507921292780814 31.012655786709857, 35.50570428177225 31.01315059573674, 35.50344806381499 31.013639840245784, 35.50118648177285 31.014156887617727, 35.49895335836702 31.014736234767877, 35.496781868234955 31.015411809636905, 35.4946984845176 31.016206471722864, 35.49270091913544 31.017098776717475, 35.490778542239354 31.018056753946087, 35.48892172595142 31.01904731623251, 35.48712049545875 31.020038781084754, 35.48536424578529 31.02099804874543, 35.48364366513173 31.02189316146403, 35.48194847524287 31.022691304102874, 35.48027179005646 31.02337294842804, 35.47862099731878 31.02396805969311, 35.4770062225551 31.024519601399174, 35.475437915680175 31.02507054126921, 35.47392620563612 31.02566327991587, 35.47248186002534 31.02634107141318, 35.47111499434343 31.027146880607926, 35.46983606311006 31.028122830996047, 35.46865179016833 31.02929860018341, 35.46755604370131 31.03064790476485, 35.46653898242478 31.03213088764105, 35.465590439733106 31.033707687458936, 35.464701227854675 31.03533873596065, 35.46386086465947 31.036983603675736, 35.46305983277543 31.038602999741872, 35.462288297333636 31.040157065575016, 35.46151054591416 31.041647914769964, 35.460588607163075 31.043248664113097, 35.459357958036314 31.04517438949468, 35.457654705253766 31.047639883629994, 35.45531425062259 31.050860198468342, 35.4521725762805 31.05505065269403, 35.44806524029836 31.060426245939954, 35.44349957553232 31.06633313491906, 35.44567349863207 31.073246226483448, 35.44961580318537 31.082997657649365, 35.45198550735275 31.09493517974094, 35.45413161927733 31.104885403202044, 35.456470669690454 31.11880760169663, 35.45729707782018 31.131167696576576, 35.4565782346301 31.143950736965547, 35.45435625593131 31.15451025416848, 35.451120810640866 31.16122938270303, 35.45064597141784 31.162660324752423, 35.45030784620753 31.163461259590836, 35.4499026368579 31.164376387268927, 35.44922783124421 31.165806335282632, 35.44848586739529 31.167293254171728, 35.44774448040495 31.168722957871726, 35.44707016091948 31.170038182303024, 35.44646263572445 31.17129642425906, 35.44612476958294 31.172097348828565, 35.445585517141446 31.173126692650055, 35.44504652758421 31.17409882002551, 35.44450753125094 31.175070662835164, 35.44363371833778 31.176270441729972, 35.44229119828171 31.177926813314944, 35.44114998600965 31.1792975393449, 35.439873597108004 31.180954116712364, 35.43893372945583 31.182096410122483, 35.43799468191427 31.183066773698716, 35.436451948474264 31.18466485206619, 35.433637567471 31.187174532959407, 35.43175606774036 31.18968814029753, 35.431351698754135 31.190374061715087, 35.43074472745393 31.191517510197777, 35.43013892229166 31.192489319661806, 35.42966740506549 31.193289416399914, 35.428727004109135 31.194488838071855, 35.427921162711414 31.195459895154244, 35.427048138370296 31.196487627535188, 35.42583992454667 31.19785767106775, 35.424833118227184 31.1989423834266, 35.42355828134379 31.20031215541318, 35.42261740395023 31.201511522242207, 35.42134249530954 31.202881551307232, 35.41986542520697 31.20453686802623, 35.418322359710814 31.20613470591754, 35.417180161498656 31.20756240329449, 35.41583819877035 31.20893211550872, 35.41436034301419 31.210702068183664, 35.413285486385014 31.211958059674362, 35.412345280156806 31.213042919878095, 35.41127069761867 31.214298896322124, 35.41053048092876 31.215384509742012, 35.40938682543252 31.216984043398956, 35.40817563410679 31.218697729509298, 35.40716690018222 31.2200686296994, 35.40609153491866 31.221382046644692, 35.40494742900063 31.223038746722715, 35.404003332952726 31.224696497355783, 35.40299015423495 31.22669752730291, 35.40231416856939 31.22812717718088, 35.40190571415965 31.229386230399527, 35.40116002122319 31.231273865144715, 35.40075123281118 31.232590125266263, 35.40034182490413 31.23402080768423, 35.40006805423608 31.235108464116106, 35.399793031793735 31.236368033567143, 35.3995861405155 31.23739845558105, 35.39937863069704 31.23854358286146, 35.399101726586686 31.24008976926183, 35.39882728831665 31.241234628944774, 35.39861851119754 31.24255166749441, 35.39840942229521 31.24392591723883, 35.398332886716226 31.245415678279834, 35.39838736581779 31.247249512965237, 35.398579076541886 31.24851120082061, 35.39856851953532 31.250115647054468, 35.39848886864274 31.25206365755985, 35.398476747764256 31.25389751000836, 35.398667552990474 31.255330832666534, 35.39912194959804 31.25716652418959, 35.39964391823931 31.258945554160203, 35.400096182500775 31.261125073269856, 35.400747375008415 31.263534790392068, 35.40153396862054 31.265601195968706, 35.40212323992407 31.267265772303286, 35.40284513315738 31.269045572794454, 35.403497990675625 31.271225870332593, 35.40395471816562 31.272775213146602, 35.40474145010042 31.274841601181866, 35.40539529143131 31.27685025442332, 35.406182714621146 31.278859426764193, 35.40710062239276 31.28127043232146, 35.408019813694594 31.283509236891078, 35.40900475557585 31.285920492485012, 35.40972600228023 31.287872175869232, 35.41057746095443 31.290282620067458, 35.41163008546676 31.292579413171826, 35.4127512074684 31.29459011741429, 35.414202491052066 31.297117837864626, 35.415656294315475 31.299301716623773, 35.418168456907495 31.302865815721564, 35.41929256371385 31.304475147595127, 35.4194237874152 31.304657703189534, 35.4201860806771 31.304514842041623, 35.42163961477777 31.304178349197976, 35.42229571707914 31.303926227409644, 35.42307717424453 31.303498068919815, 35.42373294006118 31.30324593546286, 35.42550649954815 31.302611446144546, 35.42636259553213 31.302352724558897, 35.42736302219079 31.30231813676754, 35.42782940432219 31.30230211875938, 35.42836278744154 31.302283814952165, 35.429632242149424 31.302297300848508, 35.43010428407044 31.302395774857313, 35.43097604165889 31.302480225181895, 35.43213315831967 31.30295652843132, 35.432602543781165 31.302997463498848, 35.43362899605169 31.303535582379137, 35.434311748670964 31.303855879372126, 35.43527154599918 31.304396276820743, 35.435956647228885 31.304774091359644, 35.43686080121496 31.30554548416326, 35.437348889400745 31.305987420985485, 35.438116719962466 31.30670585696687, 35.43875119988681 31.307429439117385, 35.439519048059715 31.30814814908656, 35.44037495744068 31.309322827043154, 35.44086842611652 31.309879247587325, 35.44123356029266 31.31055474785559, 35.44158833639963 31.31100125889025, 35.44195615745087 31.31173372357404, 35.4422656472602 31.31264051235307, 35.44242841427496 31.313265650637472, 35.442521500574934 31.313835831946687, 35.442678924005826 31.314346473838466, 35.442841367975944 31.314971607662553, 35.44313215913536 31.315477658287605, 35.44342529902837 31.316040952383055, 35.443851812856394 31.316599372478123, 35.44533488847713 31.316892343790137, 35.44568135728936 31.31716681468313, 35.44602815175301 31.31744157044572, 35.44638565121123 31.31794531736519, 35.44680415842798 31.31833226609773, 35.447153964868974 31.318664270979852, 35.44750579829252 31.319053233273966, 35.44846615396937 31.319593542309814, 35.44915677325634 31.32008578687715, 35.44969796624596 31.320238855867423, 35.450306170378255 31.320389909280404, 35.45098909793514 31.320710404384933, 35.45192773447219 31.320792418845823, 35.452853302812876 31.32058819052676, 35.453968198340874 31.320148342360916, 35.454698733913894 31.320065495347432, 35.45589638945582 31.31996681653137, 35.45689935600403 31.31998924841966, 35.457904673283295 31.320069198336775, 35.4588412849167 31.32009392207365, 35.45958556401752 31.32029729097102, 35.46039619937873 31.320498629190197, 35.461140485367665 31.320701989268624, 35.46169243901989 31.321084282120275, 35.46265526246715 31.32168145037862, 35.46354905336837 31.322223673681318, 35.46397302427512 31.32272506499381, 35.464469060682184 31.323338644515758, 35.46504287264988 31.324178629471547, 35.46519473106014 31.324574747682306, 35.46528291275412 31.32503070617873, 35.4654404808362 31.325541042097377, 35.465517469246485 31.325937960163234, 35.46561652958313 31.326452391189633, 35.46590478989313 31.326901146526726, 35.46621960239693 31.327922376482938, 35.466523997622225 31.32871461802703, 35.466958765277106 31.329444992062555, 35.46731878155017 31.330005928845008, 35.46782596425766 31.330848210343266, 35.46834357924942 31.331919759426018, 35.46863960779772 31.332540248939136, 35.46894908733508 31.333446977156687, 35.469243091232435 31.334010225349544, 35.46956301635958 31.33514565795828, 35.469811523880445 31.336169192643577, 35.47012102514202 31.3370759182861, 35.47035307267827 31.33775596109894, 35.470537586270034 31.338839051396395, 35.47098050077017 31.339741156660306, 35.471207178585445 31.34030670244352, 35.47153285593004 31.341556630014875, 35.471772996878414 31.342408413869972, 35.472109777157776 31.34388761688941, 35.472414267935086 31.344679845808436, 35.472523708500454 31.3455934961357, 35.47263315533299 31.346506864514442, 35.47287364527178 31.347358650289852, 35.47323107342739 31.34786204275048, 35.473593885390294 31.348480211579542, 35.474287867055644 31.349029581424197, 35.47464530432602 31.349533252060812, 35.47582477765263 31.350466883946563, 35.47639043205731 31.351135358012158, 35.477492784304054 31.35184230041628, 35.47894184425137 31.352823902536773, 35.48012139693136 31.35375777892391, 35.48126321977275 31.353889895704857, 35.4819731422351 31.354782708519814, 35.4830301252747 31.355949898084035, 35.48407936201221 31.356945622252486, 35.48527219040865 31.358165406658333, 35.48660690477797 31.35955229335057, 35.48751706097614 31.360438126274406, 35.48801884825754 31.36116583595405, 35.488510161466834 31.36166483903147, 35.48887582603548 31.362340215847365, 35.48950328292172 31.36289182649113, 35.48987977245618 31.363795909315026, 35.49067548470383 31.365087092086082, 35.49097445957401 31.365764782548332, 35.491428504621425 31.3668955323225, 35.49179183857816 31.36751337607148, 35.49230720883634 31.36852759006918, 35.492958734540906 31.369594409437592, 35.49351683733654 31.370091074727913, 35.494296424554484 31.371038469981816, 35.49477667180084 31.371308456997888, 35.49561792073693 31.372139032433672, 35.496714916844574 31.37273160261992, 35.49772929476132 31.37298272338884, 35.499068836595725 31.37305075572573, 35.499677470716485 31.373201305412387, 35.50075012613152 31.373278614460965, 35.5013533418397 31.3733149430916, 35.50188171095914 31.373181569370008, 35.50241007422198 31.37304847522168, 35.50279957495149 31.37280525259545, 35.503663929687676 31.37271777020428, 35.50417906188844 31.372298434844605, 35.505203237043304 31.37134497296983, 35.50577661205261 31.37075155772574, 35.506080713223774 31.370110204256022, 35.506696387625745 31.36899894773657, 35.507250749112536 31.368004792167216, 35.50755483077136 31.36736343434327, 35.50786401045743 31.36683628507823, 35.50831475843591 31.366476502891857, 35.50868250320271 31.36577556222362, 35.509192146852364 31.365241709279857, 35.50974645876586 31.36424754084047, 35.510114188114024 31.363546313423114, 35.51025765774625 31.3623371304716, 35.5105454107545 31.361352278321412, 35.51078544789046 31.36077048793731, 35.51091804326493 31.35933231297699, 35.51126130655989 31.358115852451334, 35.51147417700018 31.35696158401341, 35.51156448199187 31.35604096575387, 35.51173202839795 31.35534700365931, 35.51188599431201 31.354366803077866, 35.51204300063252 31.35344385348838, 35.51209037133832 31.353040785756097, 35.51210852563594 31.352008001507727, 35.512078981546956 31.35137828120257, 35.512030099814226 31.3503478234763, 35.51176785173218 31.349038122577525, 35.51139935285053 31.34830555847689, 35.51093429007414 31.346945598999394, 35.51058209366962 31.346556518867317, 35.510157430224375 31.346055831959795, 35.50955660402049 31.344643283382077, 35.509112966202395 31.343741295792366, 35.50860534426492 31.342899165293602, 35.508159338218995 31.34193993079005, 35.50758807943644 31.341157095061682, 35.50693928020997 31.34014759417181, 35.506506859927136 31.339474874982802, 35.50614353234313 31.338856788879895, 35.5057084160201 31.338126537676807, 35.505190339969865 31.33705512581551, 35.50482193565 31.336322544572948, 35.504517193777154 31.33553038381773, 35.50399881166191 31.33445924559592, 35.50369169988682 31.33360983907772, 35.50336562100049 31.332359697214454, 35.503191907796044 31.331505636617422, 35.50307914160943 31.330534749985326, 35.502833317108205 31.329568801341498, 35.50264064361099 31.328314005075082, 35.50249570069184 31.326655865535702, 35.50242828048378 31.325224954559086, 35.50217705943359 31.32414422698517, 35.50208056635754 31.323516826299088, 35.502040251966 31.322658111815123, 35.5019278325692 31.32168722739816, 35.501873644397165 31.320542269359837, 35.50179301999448 31.31882483959219, 35.50180584549709 31.31767755896613, 35.50167717664758 31.316363186254186, 35.5016124787162 31.31498952120606, 35.50156371309238 31.313959058039796, 35.50149631225613 31.31252786277028, 35.501500681569546 31.311208833427216, 35.50158831144123 31.310230969548254, 35.5015875878359 31.30879744752038, 35.50160820861474 31.307821905425957, 35.50176792730363 31.306956210687975, 35.50199629963491 31.30471163234459, 35.50215297060489 31.303788684596064, 35.50228828953505 31.302407755893285, 35.50245070046432 31.30159930716985, 35.502644552456495 31.300044306486388, 35.503220206090965 31.29950784944216, 35.504078794438186 31.29930584977543, 35.50514520107969 31.29926831861382, 35.50641736699812 31.299338574093838, 35.507950468440036 31.299284717253602, 35.50950528052307 31.299688826475396, 35.51064716247218 31.29982067107132, 35.51172443143883 31.300012359435264, 35.51273808677773 31.30026305917206, 35.5136153513617 31.300461727935073, 35.51522090503991 31.3005199521075, 35.51584236695639 31.3009566493238, 35.51754961993764 31.301756731946387, 35.51836839522538 31.30212944753174, 35.519198384661365 31.30273115387302, 35.51988416113022 31.3031082527917, 35.52080500611994 31.304222560048935, 35.52150951599834 31.30500066478326, 35.522160970364695 31.306067342639135, 35.52269509034592 31.307482175706937, 35.52321050204028 31.308495993037926, 35.52366262123234 31.30956968329302, 35.52381470728002 31.30996574001041, 35.524383531223926 31.310691261300605, 35.52500782113262 31.311185446627626, 35.525603574934095 31.312483156643726, 35.5265881993244 31.313538117447948, 35.52742619098953 31.314311232495605, 35.52789568444375 31.314352103890794, 35.52889863266414 31.314373971739013, 35.53017103317223 31.31444371671748, 35.531262149776126 31.314921486646124, 35.53227056585753 31.315057542350953, 35.533342601574375 31.31513455980329, 35.534150973688924 31.31527790985231, 35.534887203384166 31.31530938420274, 35.53549520120887 31.315460033531277, 35.53609807342174 31.31549590621861, 35.53656484740886 31.315479498314694, 35.537109226205175 31.315689735796823, 35.53752264999362 31.31596162263094, 35.53789156721539 31.316694121353535, 35.53824810666487 31.318573917174906, 35.53892956239374 31.320270238994166, 35.539461603467096 31.321627765400986, 35.53991877874181 31.322815610295535, 35.540464510953775 31.32445937189876, 35.54095690877759 31.326391712672724, 35.54141991433529 31.327694333713456, 35.541703078077944 31.32802843451975, 35.54198450968969 31.32973853807972, 35.54246361991098 31.33138463836064, 35.54308127495146 31.333140534525594, 35.543949657325825 31.334543273819655, 35.54443807892166 31.3349848181192, 35.54522064644212 31.335989445182165, 35.545640309579284 31.336375811261973, 35.545903002287304 31.33768545881508, 35.545882490378375 31.338661013897713, 35.545742255518014 31.33992778565698, 35.54560202051184 31.34119427509836, 35.54553669420417 31.342629876960157, 35.54550829587207 31.343433691934475, 35.545351635528185 31.344356694485615, 35.5451610973276 31.345969015872857, 35.54502084222184 31.347235503011763, 35.54504682115024 31.349184265183215, 35.54498420715977 31.350677393983943, 35.545062884678714 31.35233756596241, 35.545087834355655 31.352852797676746, 35.54514904766923 31.355545772542413, 35.54507001258523 31.35669541474031, 35.545082308357124 31.358357936679194, 35.54500633276962 31.35956482946061, 35.54486331221642 31.360774065658198, 35.54474183884637 31.36244155932718, 35.54458819565035 31.363421808282883, 35.54452555560988 31.36491493314274, 35.54450774727017 31.36594773083756, 35.54446258418107 31.366408053866845, 35.54440540890807 31.368015672750275, 35.54448473487304 31.369675848291276, 35.54466161500986 31.370587109192495, 35.54490281255074 31.371438778927867, 35.545614504007034 31.373764503069363, 35.54599463737058 31.37472597052768, 35.54630498859627 31.375632533749044, 35.546767953406224 31.37693513177039, 35.547142307523444 31.37778209768532, 35.54779726606501 31.378905623571857, 35.54828932182133 31.37940441257598, 35.54882179682471 31.380761902383895, 35.549142817575884 31.381897444949182, 35.549405663924254 31.38320679988739, 35.54926264682105 31.384416319113882, 35.54910626683079 31.385339323288516, 35.548771263413975 31.386727348460926, 35.548473194179415 31.387483307141398, 35.54824973813996 31.388408654438976, 35.54772407781949 31.38859946564032, 35.54741368026222 31.390502429062984, 35.547195679790526 31.391542268054888, 35.54716967215195 31.39240332098734, 35.54708820354849 31.393495714318828, 35.547033445900546 31.395160572186967, 35.54706661917476 31.395847542514677, 35.5471850349611 31.396932888002585, 35.547415363789014 31.39755556355623, 35.5477501037241 31.398977343947983, 35.54797770311769 31.399542771375668, 35.54856606994176 31.400668642719513, 35.549196543493174 31.401277220130183, 35.54969080029101 31.40183324562806, 35.550041277445764 31.40216498662633, 35.5506238772862 31.403176351305504, 35.55099288191777 31.403908812551247, 35.5518715205023 31.404107252344566, 35.55271356839336 31.404937473430213, 35.553730010113874 31.40536631415827, 35.55462323537058 31.405730065589474, 35.55564603204207 31.406152761903638, 35.55645214631313 31.40623874344479, 35.55720007015878 31.40649909963304, 35.558017164289765 31.406814059924, 35.558762353102495 31.407017160128795, 35.55953741264562 31.407849688584477, 35.56030457453201 31.40851075690571, 35.56095165001893 31.40946276214655, 35.56137685717728 31.40996357479341, 35.5616847138048 31.410812856890633, 35.56173656002084 31.411900547212593, 35.56191399880763 31.412811789626794, 35.56201337569076 31.41349639520727, 35.56201927228463 31.416420696431146, 35.56201535368049 31.41773972906006, 35.56195841009797 31.419347351454064, 35.561906955849054 31.42106946732674, 35.562098145798565 31.42226666184413, 35.562332621828865 31.42438008171757, 35.562448489791834 31.42540816788894, 35.56252781192295 31.427068325309857, 35.56248851676795 31.427643149739126, 35.56217517545483 31.429489174073055, 35.56189320750149 31.430588917772056, 35.561725870769756 31.43128294476811, 35.561961535305 31.432020086514065, 35.56225331104906 31.43252588907136, 35.56307064559644 31.432840823216047, 35.56308987436985 31.43324155137176, 35.563411878608456 31.434377067136804, 35.563797580893976 31.435452975951243, 35.56425006342624 31.436526246020357, 35.5648498286132 31.43788131835173, 35.56534161512083 31.43838004214161, 35.56586801368607 31.440998967571787, 35.56586138089924 31.442261032018084, 35.5658578289148 31.443580064862843, 35.56562888950896 31.444390942218874, 35.56537439821451 31.446062876842944, 35.56474913866931 31.448378938691953, 35.56428911963065 31.449943446947938, 35.56440419920848 31.452347806136775, 35.56452011179688 31.453375887805137, 35.56448323040071 31.4540079536419, 35.564658010581944 31.45486194293743, 35.56502731210953 31.45559436744027, 35.56584793135522 31.455966539437778, 35.566508787634305 31.457204751093087, 35.56714557650919 31.457927465103364, 35.56765672500298 31.458826908734856, 35.56817646231685 31.45989809411809, 35.568473524351056 31.460518373621365, 35.56861196475474 31.461060788278896, 35.56872031247281 31.46148420823484, 35.56894542438139 31.461992354774676, 35.56904764066808 31.462734199535028, 35.568634813389 31.46389591412049, 35.568408920250334 31.464764043459986, 35.567637716747285 31.46542204307282, 35.5665993437383 31.466089745623485, 35.565480428419455 31.466473280280706, 35.564681705388 31.466559076733745, 35.564016563415464 31.46663987620197, 35.56355453505719 31.466770854352827, 35.56308975407024 31.4668448659824, 35.56177626537473 31.46735020985785, 35.56167531252791 31.468041873335213, 35.56170002895607 31.46855709282196, 35.56166070104021 31.469131912718243, 35.5617546261202 31.469702021208366, 35.562116047405745 31.47026271741332, 35.56308926482649 31.47103092418749, 35.563968228684885 31.47122901318138, 35.565581912959786 31.471401184278122, 35.566330379035826 31.47166121912705, 35.56726790011633 31.471685189846422, 35.56821674753395 31.471938425420785, 35.569162853742235 31.47213412574364, 35.57005009220715 31.472504195141582, 35.57120759242593 31.47292207412066, 35.57195025343573 31.473067580110513, 35.57245084243787 31.473738023851297, 35.57287330692331 31.474181553815484, 35.57329609992038 31.474625367924926, 35.573810146215685 31.47558203570623, 35.573926180282776 31.476610109175205, 35.57369997148368 31.477478243086065, 35.57334324581702 31.478408346028463, 35.57297274673645 31.479052215194045, 35.572543369903165 31.479870459982813, 35.571535141875636 31.481167908428333, 35.570510050733176 31.482121583552566, 35.56955756625041 31.483187670473143, 35.56918591288727 31.485207795666824, 35.568742724292754 31.4857395111063, 35.5678448549354 31.487950507761088, 35.56735991797775 31.48899979566481, 35.567183921717366 31.489522078827193, 35.56654061257415 31.490060857626446, 35.56565480653872 31.49112455454104, 35.56496907719826 31.49218089407055, 35.5640918052389 31.493416321523764, 35.56353723989958 31.49441070010745, 35.56300672510527 31.495920567773137, 35.56243565148079 31.496571462172327, 35.56210861957918 31.49813123199793, 35.56154850678487 31.49901138887053, 35.56101277253946 31.50040647465207, 35.560460547606986 31.501458079292274, 35.559836329381426 31.502397825961662, 35.559172407411374 31.50391209880038, 35.55861886361141 31.506339953341044, 35.558238626974436 31.508188572735868, 35.55813663507755 31.510256489341348, 35.557968775900655 31.51095049426684, 35.557525726866345 31.51148216690677, 35.55704328754188 31.51258865111806, 35.55617328324914 31.513995742956, 35.55556232344444 31.515221408944686, 35.554954414143594 31.516504603060365, 35.554600083048726 31.5174918844515, 35.55414327361658 31.517737312152907, 35.553968533173 31.51969307202653, 35.55376702504345 31.521076376050928, 35.55373825107408 31.52188016798748, 35.553651196782425 31.522858332422373, 35.553724562575844 31.5244039725277, 35.55376294580484 31.525205419253304, 35.55388154481097 31.526290462813922, 35.55390896240256 31.526862924618275, 35.55389663207699 31.528010474690355, 35.55386785464948 31.52881426578216, 35.55370540860423 31.52962275351952, 35.55348774907223 31.53066258110229, 35.553344485980105 31.53187179093287, 35.55307028705257 31.533142942347997, 35.55279608000855 31.534414092706466, 35.552872507421554 31.536016698462664, 35.55292151982732 31.537047124666376, 35.55308535256893 31.537672136136415, 35.55332666424907 31.538523780123363, 35.55337601086312 31.5395542096555, 35.553494612380725 31.540639533430678, 35.553535407149944 31.541498220759557, 35.553562823259185 31.54207068139102, 35.553670133115126 31.542927016825388, 35.55379214057529 31.54406959345564, 35.553974853614314 31.545095322250827, 35.553804149156385 31.545732069217486, 35.55378117185894 31.54665035369845, 35.55355491189393 31.547518435807824, 35.553325576859315 31.548329267559055, 35.55312364700113 31.54971284052914, 35.55243184206757 31.550654594363795, 35.552272408076355 31.55152032578952, 35.552257305547535 31.55261034293843, 35.55215923521963 31.553359234785482, 35.552163315877124 31.5548499733399, 35.55219861758143 31.555594167147, 35.552186252430154 31.556741429520528, 35.55199557892846 31.558353703545677, 35.55184401772156 31.559391166988387, 35.55171985831711 31.561001089201408, 35.55162724887306 31.561864752915675, 35.5515915336999 31.56393029110979, 35.55154792273645 31.56582409466737, 35.55137573780102 31.567837078291724, 35.55131019450754 31.569272913635945, 35.55119181559046 31.57099732765015, 35.551112564022375 31.572146932896064, 35.55097192512807 31.573413375460706, 35.55089025663587 31.574505737904218, 35.550955690912375 31.575879633656264, 35.551004685046735 31.576910054370263, 35.55128168880538 31.57850589493517, 35.55139755630987 31.579533969232966, 35.55181620714286 31.58129684993198, 35.55206067333676 31.582205743192112, 35.5522543885248 31.583460453183083, 35.5523897916973 31.584888968374944, 35.552411391220176 31.585346930498027, 35.552516641080906 31.586146024164638, 35.55294497194125 31.586704398054245, 35.553438784176016 31.588636382005802, 35.55387020266594 31.589252002500228, 35.55436817448833 31.589865270581033, 35.555088731316495 31.59092917743896, 35.555466678413964 31.59183336535817, 35.555732819716326 31.593200213629743, 35.55577396740617 31.59405889841752, 35.55547796958263 31.594872077165455, 35.55552152885376 31.595788003702374, 35.55606618209656 31.597374441750393, 35.556399227975504 31.598738940125354, 35.55692470387398 31.59992465549389, 35.55721386834961 31.6003729404846, 35.55750336046102 31.60082151036997, 35.55801239353702 31.601663748101394, 35.558398642884086 31.602739664762947, 35.55904321118793 31.605010688729653, 35.55943223457612 31.606143566240846, 35.559604449956176 31.6069403043619, 35.559921101387566 31.60796132093512, 35.56003708739272 31.608989386742635, 35.56027862016608 31.609841015805003, 35.56088482661983 31.611310593730433, 35.56131331768201 31.611868942362673, 35.56184716900405 31.613226093857012, 35.562131226690965 31.613560163262207, 35.562455838278915 31.614752906748404, 35.562658314958384 31.616179341019613, 35.56274041184144 31.6178967007619, 35.56266398614309 31.61910355349481, 35.562542638713026 31.62077072028215, 35.56250321392617 31.62134552543114, 35.56260824899409 31.622144606519644, 35.562914316796835 31.62293663789008, 35.56375527400155 31.623709571670027, 35.56404728487362 31.624215369928578, 35.565367578545334 31.625200530603973, 35.566406859854006 31.625909430691483, 35.567103389932406 31.626458353412747, 35.56787993114962 31.627290856500217, 35.56842616529821 31.62750100750834, 35.56887678372479 31.62851729571341, 35.5693002243936 31.62896113283098, 35.56962530737812 31.630153581478343, 35.569880735148075 31.631291420452417, 35.56996657678339 31.631689779316787, 35.57015818603873 31.632887221309076, 35.570382168730504 31.634771599036096, 35.57067339874612 31.63665362486393, 35.570722944450644 31.637684039609056, 35.57109453515615 31.639849931787587, 35.57114926205731 31.640994832853874, 35.57115909808554 31.64260005057339, 35.57106342594952 31.64421057521347, 35.571009147669486 31.645071007204717, 35.57064034979739 31.64577207479308, 35.56983096936744 31.64706235909363, 35.56932039167559 31.64759639870751, 35.56889048416166 31.648414308264243, 35.56833740080339 31.649466181780465, 35.56813299537551 31.65079223411094, 35.56846087498698 31.652042210543915, 35.568975776359295 31.65299861775911, 35.56950169119691 31.65418428540889, 35.569880010507255 31.655088433734182, 35.56992371524995 31.65600435202486, 35.56976972704706 31.6569845811684, 35.56955123194983 31.658024405204277, 35.56937676063744 31.65998015005455, 35.56941220519551 31.660724332509538, 35.56943146826424 31.66112504808602, 35.569405817972566 31.66198607797336, 35.569525033106444 31.663071380567548, 35.56968608275498 31.663639119949522, 35.57008920908946 31.66505847306472, 35.57044435745265 31.666880608896673, 35.57081085020907 31.668932290973558, 35.57134513815091 31.67028912294969, 35.57169756936454 31.67205429195856, 35.571800628543635 31.672796125709468, 35.572083384773734 31.67450640321985, 35.57248383436045 31.675868503923883, 35.572887048965704 31.677287848376174, 35.57308669895436 31.678657012693787, 35.57304697056236 31.679231813582987, 35.57283153752489 31.680328606053035, 35.572041999478834 31.680643371295325, 35.57155018586549 31.681578122717582, 35.571058357794854 31.682513153615016, 35.57080710121723 31.68286603932138, 35.57079521360502 31.684013291333176, 35.571134240082905 31.685492238663976, 35.57137362101137 31.686286604496946, 35.57188597408319 31.68718575657689, 35.57245858754939 31.6893445716931, 35.57323482943899 31.69155356404253, 35.574122974022295 31.69329960076092, 35.57521784437185 31.695153338862823, 35.576349395851175 31.69637473735821, 35.577419532203244 31.69771325495492, 35.57860045170832 31.69996503701169, 35.57909254104164 31.70183995898029, 35.57923915298093 31.7034976942773, 35.57925184972221 31.705160145128858, 35.578994274086874 31.706774785974748, 35.578719811886145 31.70804595390987, 35.57865174491653 31.70942453763555, 35.578703813391094 31.71051218263182, 35.57892400942552 31.710905822420752, 35.57975759205616 31.711506933650206, 35.58031228748989 31.711888776683313, 35.58095359630116 31.71266897485601, 35.58158355619761 31.713219624267765, 35.581957020724246 31.71400925351775, 35.58207300547118 31.715037290133544, 35.5819435265035 31.716533005637533, 35.58170634148366 31.717172136140874, 35.58110255249287 31.718569590941286, 35.58029815920731 31.71997440223719, 35.58007133304541 31.720842217961156, 35.580257358277194 31.72192542676959, 35.580798654324695 31.72202076794154, 35.58157916793273 31.72291073254672, 35.58247432519349 31.723394980613584, 35.583093643227095 31.723717213064138, 35.58399709553414 31.724373466286078, 35.584783186850466 31.725377619699174, 35.58522889238969 31.726279365079332, 35.58532348681666 31.72684944952233, 35.585493344123904 31.72758890664363, 35.58565081156949 31.729475606909972, 35.58574264881366 31.729988446449877, 35.58583347432524 31.731877510494, 35.58582969853179 31.73319649032893, 35.58603515143597 31.734680120699515, 35.586162582135344 31.735936852021865, 35.586311778387376 31.73765209840703, 35.58653759655444 31.738159933271962, 35.58705897868413 31.73923104857161, 35.58744054616557 31.740192392596818, 35.58776066275999 31.74127058602556, 35.58813395374597 31.74206019496919, 35.58822581440868 31.742573032496324, 35.58844612181903 31.74296665729312, 35.588601882513586 31.743419602758614, 35.58862675504569 31.743935083834636, 35.588547735172476 31.745084409633783, 35.58823766386758 31.745611700161987, 35.587751168167166 31.74666098827166, 35.58731816454358 31.74742169469371, 35.58687409722537 31.747953703797588, 35.586555392331206 31.748308971027345, 35.585980100930776 31.748902660892, 35.58506984440006 31.749508148701914, 35.58375485872804 31.750070823920076, 35.58310425975176 31.750495407974466, 35.58251546079053 31.75080286301539, 35.58166906262962 31.75134900027464, 35.5805688113333 31.752190505942142, 35.579851189426044 31.752617430003667, 35.57940981717612 31.75320637321494, 35.57902440099446 31.753563980606046, 35.57856290882452 31.755128430501223, 35.578188182301254 31.755715008475352, 35.57761278699203 31.756308938772804, 35.577263193694684 31.75741071590883, 35.57690222974454 31.758283792613966, 35.57639933009793 31.758989285873717, 35.57602765986056 31.759633104237338, 35.57558865516175 31.76027927249984, 35.57514996975996 31.76092572446987, 35.57476968598126 31.761397802016585, 35.574322727588466 31.761872236118478, 35.5736692520546 31.7622392414823, 35.572881765017556 31.762611231928847, 35.57182660059827 31.762992350701985, 35.57110852532358 31.76341921749707, 35.5697798277119 31.763695521428833, 35.56904280717838 31.763721388151122, 35.567470577953046 31.76402369311908, 35.5669822487892 31.764138009759563, 35.56694377723718 31.76415138926463, 35.57098152553777 31.77081750793744, 35.57550245228973 31.777964344585346, 35.578973764312494 31.783118887993517, 35.58156229740174 31.786611413899337, 35.58343470994637 31.78877300814342, 35.58475777645756 31.7899344527479, 35.58569801125298 31.790426511654182, 35.58642296177261 31.790579669118593, 35.58707230667574 31.790670264818438, 35.58767754556075 31.790753606307653, 35.588243614213624 31.7908305941822, 35.58877512281678 31.790901843481098, 35.5892770157816 31.79096769098072, 35.58975389060412 31.79102959689369, 35.590210366110554 31.791087612382807, 35.590651048386064 31.791142633913104, 35.59108054780303 31.791195276086363, 35.591499189979075 31.791245824438906, 35.5919073047899 31.79129428270681, 35.59230522211362 31.79134065461937, 35.59269294190442 31.79138494027099, 35.59307079829129 31.791426861587574, 35.59343910841824 31.791467267662558, 35.593797880740766 31.791505594990877, 35.59414744514687 31.791541847276612, 35.59448812304163 31.791576591805565, 35.594819592951325 31.791609261438264, 35.59514283614273 31.791640430698862, 35.59545719697373 31.791669810613442, 35.5957636609599 31.79169769390171, 35.59606190238701 31.791723795206824, 35.5963525768479 31.791748403617248, 35.59663535439233 31.79177151556623, 35.59691089485532 31.791793138340537, 35.59717886405393 31.791813550163926, 35.59744025599001 31.791832480137685, 35.5976944107882 31.791849921071325, 35.597942313984554 31.79186616564571, 35.59818363140463 31.79188149207704, 35.59841870142673 31.791895340427757, 35.59864784550455 31.791908277933114, 35.59887139778592 31.79192002644297, 35.59908902832988 31.791930582374835, 35.5993013885272 31.791940516569415, 35.59950848682565 31.79194926546453, 35.59971064891728 31.79195711448989, 35.59990787479409 31.79196406366884, 35.60010082008152 31.791970402036544, 35.600289154844695 31.79197612600379, 35.60047321323214 31.791980957404654, 35.6006536550962 31.791984903472823, 35.600830142054356 31.79198852420511, 35.601003008256065 31.79199154143016, 35.601172579404356 31.79199424056342, 35.6013388639403 31.791996058029117, 35.601502179124175 31.79199784282875, 35.601662863326524 31.791999034989864, 35.60182091232349 31.791999916316836, 35.60197665604275 31.792000490423614, 35.60213009026297 31.792001039113156, 35.60228122342156 31.79200099880475, 35.602431036867785 31.79200094390471, 35.60257887074173 31.79200086721241, 35.60272505919045 31.79200049054242, 35.60286992792506 31.79200009929686, 35.603013806875246 31.79199969708185, 35.6031563661109 31.791999280299482, 35.603297605631795 31.79199884895424, 35.60343818951307 31.791998128456683, 35.603577449463366 31.791997675198246, 35.60371605377289 31.79199693279321, 35.603853998226136 31.79199618303851, 35.60399095289295 31.79199542233722, 35.60412724770324 31.791994654291244, 35.60426321679987 31.79199360070617, 35.604398521826454 31.791992821574656, 35.60453317120838 31.79199175330924, 35.60466749066268 31.791990681304043, 35.60480148018932 31.79198960556014, 35.604935139788246 31.79198852607851, 35.60506847366993 31.79198716106534, 35.60520147762302 31.791985792316513, 35.60533448157765 31.7919844234286, 35.60546748974307 31.79198277260664, 35.60560016797901 31.79198111805073, 35.60573284621565 31.791979463356363, 35.60586552866105 31.791977526728562, 35.605998211106346 31.791975589962302, 35.60613122768902 31.791973374855985, 35.60626457420106 31.791971163203574, 35.6063979207122 31.791968951411302, 35.606531601358796 31.79196646127642, 35.60666561613966 31.791963692797957, 35.60679996084833 31.79196092776987, 35.60693496961996 31.791957887987355, 35.607070638249006 31.791954855243397, 35.607206645213154 31.791951262354946, 35.60734297789919 31.79194795470632, 35.60748030877855 31.791944094090873, 35.607618629443316 31.79194024409571, 35.60775728003281 31.791936397539093, 35.60789692881153 31.791931998007257, 35.60803756737407 31.79192760908736, 35.608178869991136 31.79192294539264, 35.60832149652216 31.791918014096495, 35.60846478290499 31.791913089814837, 35.608609393200105 31.791907897924208, 35.60875533160547 31.791902156624733, 35.60890225979038 31.791896425916175, 35.60905051188363 31.791890427585596, 35.60920008788381 31.791884161628367, 35.60935098778956 31.79187762803978, 35.609503207402476 31.79187110861044, 35.60965708504595 31.791864043329063, 35.60981228659071 31.791856710401134, 35.609969141965635 31.791849113405217, 35.61012765116921 31.791841252335168, 35.610287814199914 31.79183312718474, 35.61044963105617 31.791824737947614, 35.61061343166667 31.791816088199326, 35.61077888609956 31.791807174350694, 35.610946324283425 31.79179799997619, 35.61111575040871 31.79178828327234, 35.61128715608894 31.79177858782234, 35.61146021977578 31.791768346447256, 35.61163559713585 31.7917578480942, 35.61181295823706 31.791747089174898, 35.61199197314731 31.79173606610272, 35.612173635844265 31.791724507808198, 35.612356948156986 31.79171296713952, 35.612542248390994 31.79170088407477, 35.612729528168124 31.79168882219682, 35.612918461744634 31.79167649612521, 35.613109383234644 31.791663627631976, 35.613301950147864 31.79165105852086, 35.61349617504042 31.79163794339679, 35.61369204953988 31.791624845843828, 35.613889577829646 31.79161148405842, 35.61408809586448 31.791598132683184, 35.61428826350425 31.79158479885803, 35.61448975500047 31.791571197207404, 35.61469223623989 31.791557605949723, 35.614895707222345 31.79154402508031, 35.61510049787756 31.791530458164527, 35.61530594834547 31.791516898056784, 35.61551238855585 31.791503348322927, 35.615719484400564 31.791490087185807, 35.61592724423633 31.791476551049797, 35.61613565553064 31.79146358530033, 35.61634440088594 31.791450340978567, 35.6165537977034 31.791437667038046, 35.616763198651945 31.791424710955088, 35.61697325106634 31.791412325249183, 35.61718296933471 31.79140021742662, 35.6173930174915 31.791388112821107, 35.617603057262194 31.791376571460166, 35.6178130969941 31.791365029751123, 35.61802279841529 31.791354047724305, 35.61823216570009 31.79134334358499, 35.61844152461034 31.791333202692933, 35.61865021945965 31.791323336129782, 35.61885825024994 31.791313743898943, 35.6190659427449 31.79130471136013, 35.61927263708899 31.791296231396498, 35.61947899314532 31.791288311130565, 35.619684025294696 31.791280658092727, 35.619888389232855 31.791273561201503, 35.620091750869065 31.79126729869976, 35.62029378860748 31.791261303442308, 35.6204948240524 31.791256142585244, 35.620694531442524 31.79125153078099, 35.62089290661895 31.791247749832703, 35.62108995374872 31.791244517949988, 35.621285012975704 31.791241828030877, 35.621478735841535 31.791240250785602, 35.621670804903175 31.79123893727659, 35.6218612076838 31.79123873290223, 35.622049618418934 31.79123935232108, 35.62223604127151 31.79124051374517, 35.622420137998986 31.791242777226106, 35.62260257262447 31.79124586807856, 35.62278268529237 31.791249779209355, 35.62296014192065 31.791254788875754, 35.62313560653051 31.79126062239065, 35.62330841926561 31.791267272664562, 35.62347890590521 31.791275025053995, 35.62364674067818 31.791283594223327, 35.623811915280804 31.791293543777435, 35.623974438025165 31.791304310132546, 35.6241339748313 31.79131617154931, 35.62429118556423 31.791329135131775, 35.62444508458818 31.791342908453654, 35.62459632346417 31.7913580622126, 35.62474457641722 31.791374311075838, 35.62489017338137 31.791391658598634, 35.62503311850979 31.79140982299295, 35.62517340350551 31.791429367862378, 35.625311036670674 31.791449729621203, 35.62544600970971 31.7914714718727, 35.625578330922735 31.79149403103078, 35.625708656025886 31.791517695986432, 35.6258363210117 31.791542741458557, 35.62596166825604 31.79156832560513, 35.62608501525057 31.791595297366868, 35.62620637029137 31.79162309315488, 35.62632572923376 31.791651994772295, 35.62644309207984 31.791682002224782, 35.626558462976895 31.791712833720506, 35.626672501790296 31.791744496345267, 35.62678454451221 31.79177726482029, 35.626895251009365 31.79181114623023, 35.627004625428015 31.791845858781073, 35.627112337836614 31.791881398937225, 35.627218714025645 31.791918052039232, 35.62732408807321 31.791955539831502, 35.62742845998088 31.79199386231652, 35.62753182975002 31.792033019496838, 35.62763419324002 31.792073293172454, 35.627735558736575 31.79211411975069, 35.62783624789112 31.792156066366328, 35.62793659478143 31.792198854760382, 35.62803594368209 31.792242196062755, 35.62813494618105 31.79228666094387, 35.628233606421574 31.792331967607, 35.62833192854576 31.79237783425533, 35.62843023835009 31.79242454622301, 35.62852820590203 31.792472099974756, 35.62862616113897 31.792520499046514, 35.62872410406349 31.792569743438392, 35.62882236875245 31.792619554887526, 35.628920621134014 31.79267021165646, 35.62901952522091 31.792721439015953, 35.629118417005856 31.792773511694385, 35.62921796050181 31.792826154961546, 35.62931815157465 31.7928796506137, 35.62941899436504 31.79293371685162, 35.62952081467663 31.792988639004584, 35.62962328671284 31.79304413173987, 35.62972674041432 31.793100198588423, 35.62983117578477 31.79315683954789, 35.62993658869275 31.7932143364138, 35.63004331321617 31.793272410917613, 35.63015134935952 31.79333106305613, 35.630260697127405 31.79339029282614, 35.630371686463334 31.793450103755173, 35.63048398743359 31.79351049230851, 35.630598259921555 31.793571465543284, 35.63071417399389 31.793633019924382, 35.63083172965616 31.79369515544745, 35.63095126098629 31.793757593839523, 35.63107275972581 31.79382089869095, 35.63119623414539 31.793884506400495, 35.63132168011933 31.793948698760428, 35.63144943172605 31.79401319749521, 35.631579480711046 31.79407856619395, 35.63171150953159 31.79414395592856, 35.631845835745075 31.794210215613944, 35.63198279756087 31.79427678517379, 35.63212173510174 31.794343657547103, 35.632262974187675 31.794411118051542, 35.632406184883905 31.794479163153984, 35.63255170126816 31.794547514575235, 35.63269918927693 31.794616450580722, 35.632848653044206 31.794685689365668, 35.63300009257612 31.794755230923293, 35.63315350375309 31.794825357044807, 35.633308890707994 31.79489578592515, 35.633465919377606 31.794966795832433, 35.63362492383815 31.79503810848512, 35.63378524008159 31.79510999862996, 35.63394720218311 31.79518218798563, 35.63411081014827 31.795254676546016, 35.634233764783815 31.7953089750236, 35.65144819338446 31.788631447465924, 35.653861514205346 31.787792434434113, 35.65587722653557 31.786927216820327, 35.65756533982937 31.78605543136559, 35.658995524930695 31.785197268300923, 35.660237463590285 31.784372067403154, 35.661360823826726 31.783600010015796, 35.66243594396372 31.782900435971236, 35.6635318332481 31.78229323237266, 35.66470506055786 31.78179138990072, 35.665956819684176 31.78138082670434, 35.66727355107743 31.781040824737214, 35.668643348038444 31.78075040172423, 35.670053317277976 31.78048828371298, 35.6714905524144 31.78023404265186, 35.67294248384945 31.779966690827226, 35.67439654078341 31.779665241054317, 35.675845023765994 31.779314112129594, 35.67730106242295 31.778917949348397, 35.678782663119705 31.778486520938117, 35.680308158411584 31.77802987948994, 35.68189588510211 31.777557794885304, 35.68356417629837 31.7770803178286, 35.68533070568808 31.776607491168672, 35.68721414895613 31.77614852131508, 35.68922392579225 31.775713927779503, 35.69133351934807 31.775312452675937, 35.693508489146346 31.774953322476904, 35.69571308456069 31.774645188855864, 35.697913197845864 31.774397286336068, 35.70007373278999 31.77421884177953, 35.7021592681292 31.77411879928135, 35.704135046824035 31.774105830040067, 35.705976278623446 31.774183634640746, 35.707698765023295 31.77433123993388, 35.709327942119884 31.774522695364798, 35.710890233754895 31.774732058740263, 35.71241042440973 31.774932524384315, 35.713915268623 31.77509786882845, 35.71542986620799 31.775202132549953, 35.716980308953445 31.77521908289233, 35.71858565875966 31.775129743570954, 35.720237819146696 31.774946714646603, 35.72192199638354 31.774689857756357, 35.72362372415546 31.774379320420575, 35.725328537703724 31.774035250963795, 35.72702132183151 31.77367722835351, 35.728687941131675 31.77332568765845, 35.730313279875205 31.773000491293704, 35.731885231004156 31.77271871374814, 35.73339875352375 31.772487353407527, 35.73485214367743 31.772310623304936, 35.7362427195749 31.772191881272654, 35.73756877371915 31.7721356221649, 35.73882695683446 31.77214576094815, 35.74001589883719 31.772226232080655, 35.741133239893934 31.772380960229494, 35.74218556088038 31.772611421173952, 35.74321787914787 31.77290678428317, 35.74428513499317 31.773254341756765, 35.74544128320137 31.773641092434513, 35.74674061274085 31.774053754491923, 35.74823774302192 31.774479046742492, 35.74998729014371 31.774903966478846, 35.75204321441545 31.77531521844855, 35.75444356226487 31.775705266028346, 35.75716011251116 31.776087626585525, 35.76014906871038 31.776481021793362, 35.76336532021261 31.776903880974096, 35.76676440325552 31.777375770268048, 35.77030220485491 31.77791485378472, 35.77393394329567 31.778540139150905, 35.77761516520961 31.779270923822956, 35.78130318621025 31.78011750793378, 35.784961645044625 31.781060662227674, 35.78855561592688 31.782071874060183, 35.79205080478704 31.783124331637982, 35.795412273550276 31.784189529515288, 35.79860506369411 31.785240092719125, 35.80159520839718 31.78624809470987, 35.804347412792445 31.78718588067388, 35.80683748109721 31.78803492020309, 35.809083355215364 31.78881033340101, 35.81111373968591 31.789537484192692, 35.81295669390832 31.790240881989323, 35.81464127041378 31.790945042887067, 35.816195204707405 31.791674468634337, 35.81764754740209 31.792454235024685, 35.819026368623604 31.793308843508903, 35.82035981536075 31.794256876001715, 35.82167336886903 31.795292931290447, 35.822991272856626 31.796405113144637, 35.82433908790765 31.79758181827531, 35.825741058980014 31.798811148401107, 35.82722241470376 31.800081776845825, 35.82880772817937 31.80138208781137, 35.83052223688498 31.802700188289762, 35.83237995197648 31.804024644952854, 35.83435628436017 31.80534282638457, 35.83641541150303 31.806642845793633, 35.83852217121972 31.807912542588603, 35.8406414016473 31.809139476531513, 35.84273694424724 31.81031148251744, 35.84477428768937 31.811416412636017, 35.84671726790428 31.812442106218153, 35.84854119341986 31.813382708491197, 35.85026000438958 31.814258079252493, 35.85189879914226 31.81509353249335, 35.85348199814629 31.815916065797477, 35.85503437892786 31.816750705689447, 35.85658004077848 31.81762416252665, 35.85814375611536 31.818562305956142, 35.85975063365172 31.819590725567465, 35.86141530557746 31.82072815208858, 35.863115173178436 31.821960571951497, 35.86481847592363 31.823267127070032, 35.866492796705025 31.82462639150067, 35.8681063750583 31.826016946762547, 35.86962678340696 31.82741765185299, 35.87102192765503 31.828806806358838, 35.872260027005666 31.83016384110085, 35.873317189858234 31.83147079368406, 35.874205944122664 31.83272834235836, 35.87494604752328 31.83394004637683, 35.875558569712894 31.835110321058224, 35.87606392846162 31.83624301147805, 35.87648253952825 31.83734224392935, 35.87683481654198 31.838412426075543, 35.87714184473197 31.83945712557223, 35.8774203745589 31.84048353588858, 35.87767515702794 31.84150832811062, 35.87790794511283 31.84255040180739, 35.878119807995304 31.84363062348588, 35.87831250921356 31.844767047189283, 35.87848745518724 31.845979978615432, 35.87864606599423 31.84728859614436, 35.87878974823534 31.848713205413635, 35.878919659688144 31.85026734541072, 35.879035173514325 31.851948192080613, 35.87913473310356 31.853747839892133, 35.87921778569647 31.855657264695086, 35.879282767428045 31.857669124591432, 35.879328794903024 31.859774392529683, 35.87935496420241 31.86196573238246, 35.87935973109845 31.864234111305166, 35.87934351217596 31.866572204723198, 35.87931104420034 31.868970471083234, 35.87926870207582 31.871420510595886, 35.879222861378175 31.873913923502617, 35.879178907598714 31.87644230140259, 35.87914389179463 31.87899612311119, 35.87912319409517 31.881567543939855, 35.879123536796676 31.8841470398777, 35.879148670271825 31.886725060894815, 35.879193528681064 31.88928352423241, 35.87925105692915 31.89180489341968, 35.87931422627385 31.894269377440626, 35.87937565651874 31.896658873326956, 35.87942831740781 31.898953590105442, 35.8794651508948 31.901135991379178, 35.879478795154235 31.903186283308635, 35.87946343117081 31.905093704749966, 35.87941984502215 31.906875173630148, 35.87935005628316 31.908554946720702, 35.879255740764364 31.910158405137715, 35.87913958590805 31.91170924773424, 35.87900293729457 31.913232852669815, 35.87884814522916 31.91475347948871, 35.87867689915768 31.91629538192067, 35.8784916307626 31.917876055815693, 35.87837072172846 31.918859774480342, 35.90606517983577 31.91735108455285, 35.92736999410976 31.916277289501814, 35.94324374900006 31.915565543041247, 35.95459926526525 31.91513966502969, 35.9619876410522 31.91493093288515), (35.490978236908816 31.20128988847718, 35.488719424488146 31.20264911725222, 35.487608778034186 31.203146855170765, 35.486714134434486 31.203980832265977, 35.48586987820736 31.204469271125028, 35.485161197648765 31.205010027592127, 35.48445283244604 31.20555106549966, 35.48321734633412 31.20622487081236, 35.48230611927923 31.206715599864342, 35.48056944964599 31.208095183871343, 35.479727826315134 31.208640546886954, 35.47914453190953 31.209005173004275, 35.47843341264005 31.20948864476965, 35.47771689715369 31.209857614737118, 35.476736633559966 31.210293372980118, 35.47543678503135 31.21102694136235, 35.47432597412689 31.21152455851298, 35.473742646367434 31.2118888769181, 35.47270919699246 31.21261287885361, 35.4721924619323 31.21297515828882, 35.47167034571725 31.213222656134636, 35.47135074843658 31.213520463111678, 35.47036503904215 31.21384167284952, 35.46914784155088 31.214916354552642, 35.46811668565323 31.215697561897386, 35.46729104998032 31.216586612646193, 35.466585190407315 31.217184512012647, 35.465820766125496 31.21795674479639, 35.46339354127928 31.220277458059552, 35.461275775172965 31.222071355023036, 35.46045505964399 31.223074850621547, 35.45974642494402 31.223615460174756, 35.458856706215066 31.224564005818777, 35.45775101821925 31.225175968112186, 35.45434587431266 31.227989343644172, 35.45308045011708 31.229466902977403, 35.45187324386994 31.2307703962905, 35.45078887031424 31.23184000161178, 35.45001616488916 31.2324403847011, 35.449240448995994 31.232983228376572, 35.44833447561072 31.233588201724867, 35.44749277192105 31.234133342096822, 35.44633064992776 31.234976489372627, 35.44517953500259 31.23604862385032, 35.444142932471216 31.236715129109406, 35.44256247816689 31.238604798809575, 35.44097427093378 31.240322704595485, 35.440014576604156 31.24121614963535, 35.437501454641854 31.243137889164032, 35.436286177902836 31.244269478869693, 35.43525709783476 31.245107930077133, 35.43447885419355 31.245593432812978, 35.433447408701376 31.24637434198711, 35.432661466892526 31.246688373100486, 35.43128081968238 31.24713751222438, 35.430291972838006 31.247400866671878, 35.42884167745796 31.247795016693033, 35.42638984144685 31.248166238874067, 35.42585675873826 31.248184826560163, 35.425123776237854 31.24820999353643, 35.42391668701623 31.248079706933108, 35.42211787309024 31.248141731594853, 35.42091312239917 31.248068661468903, 35.420172161337796 31.247922054540684, 35.419231627300256 31.24778230158142, 35.41794961631785 31.24748249437243, 35.41708070799711 31.247455216763353, 35.41614251351753 31.24737268601934, 35.415204644286995 31.24729043429847, 35.41482864641238 31.24750989155722, 35.41421834887188 31.24745702007454, 35.41261638858525 31.247557969816583, 35.41161469198148 31.247664029819624, 35.41068020685085 31.24771341146937, 35.40721465475376 31.24751202797266, 35.4055527392048 31.247039145902043, 35.40382869510655 31.24622217973127, 35.40224134442486 31.245119675901993, 35.40184593608262 31.24471511769925, 35.40146513799391 31.243106952242062, 35.401819967526585 31.241333143673938, 35.40183043970384 31.24047362635846, 35.402205230914745 31.237037702018203, 35.40228736023562 31.235777431897407, 35.4024928052169 31.235320836812228, 35.40284917273986 31.23343232771492, 35.40306191167688 31.232402534913604, 35.40368551245941 31.23045898253546, 35.40402630825436 31.229831563484378, 35.40429893901349 31.229318015104013, 35.4049073113863 31.228635273421823, 35.405313103530034 31.228180301058902, 35.40619976267985 31.22652577915776, 35.41120722965245 31.220435464922073, 35.421354889362 31.208141100604642, 35.43724874064175 31.18878648115186, 35.43799583941843 31.187588885144454, 35.442526600474515 31.18200923972999, 35.4459793228096 31.17739507485196, 35.446316021500756 31.177111063136387, 35.44943837806977 31.172207585479494, 35.451884453687626 31.16815784906966, 35.45249812955695 31.1669594064143, 35.45304553715966 31.165702668744874, 35.45373056101949 31.16410342506069, 35.45433747275323 31.16283358097676, 35.45482256582525 31.161819614657954, 35.45529555155503 31.16125027356878, 35.45620506989389 31.157474649023218, 35.45717685587523 31.154100841666185, 35.45752975922652 31.15249930191798, 35.45892189927654 31.147294459446265, 35.45948170498174 31.144949013244922, 35.459979782975694 31.142201743103215, 35.46026503598036 31.14054188679316, 35.46037858990979 31.139532772593363, 35.460561682830765 31.13790777719378, 35.460717189154046 31.135960005746348, 35.460818698043774 31.132865832315865, 35.46083069323946 31.13183408712067, 35.46091756044492 31.13005799523732, 35.46087648680426 31.12782235936995, 35.46089862622253 31.125873562198805, 35.46091239544596 31.124669902491284, 35.46085695893794 31.12369513552931, 35.46075056984492 31.121344285880895, 35.4605078957112 31.1192787483592, 35.46032632133491 31.117672523113058, 35.460267601338224 31.116984085993014, 35.46021158074493 31.1160665285772, 35.460025208809625 31.114861332442167, 35.45959322570627 31.111877494088816, 35.45889642923087 31.108719412639978, 35.45833175240789 31.105862799619498, 35.458204221160614 31.105217511790805, 35.45782021205997 31.103838586979588, 35.45711486898816 31.101482850049205, 35.45667311580854 31.099358557951565, 35.45589553169869 31.09648868485906, 35.45545898153343 31.094878000463794, 35.45526489943454 31.094360444227693, 35.45488037126408 31.093039003722794, 35.45423563000472 31.091199496983567, 35.45314716518032 31.087407593631298, 35.4528201521187 31.08688929175196, 35.452597340740695 31.08614449034195, 35.452115044849 31.084533247904123, 35.45179288475896 31.083613631505372, 35.451536876370334 31.082694523236935, 35.45074996557271 31.081656321658752, 35.45042902453947 31.08062199910241, 35.45010596844071 31.079759585662124, 35.449584376849984 31.078780923612207, 35.44939068324124 31.078263363343765, 35.44913231753135 31.077573375208264, 35.44907069132255 31.077171831017818, 35.44901053850994 31.076598087305197, 35.449553825501134 31.075685441307908, 35.450958844421265 31.075066369550864, 35.4528240753188 31.074966893932963, 35.455421424048474 31.074873584875476, 35.46008168511277 31.074911303081468, 35.46088060774537 31.074917696014065, 35.462810106911085 31.07522133794696, 35.470448089522186 31.07665650025657, 35.472904877457474 31.077249488185547, 35.477219010347845 31.078430254034547, 35.47848159374039 31.078669450631324, 35.48060489785172 31.079317004274674, 35.484122316963834 31.080319078460374, 35.48631102290023 31.08108145928179, 35.48803584769567 31.081668427127177, 35.48989217410685 31.08237049607614, 35.49048879774776 31.082604349076803, 35.49134672924851 31.083298988778697, 35.49160598926367 31.083931690527628, 35.49179124798677 31.085251276952125, 35.492146373434196 31.08926660772666, 35.492766305876366 31.093398326396315, 35.49277653326469 31.09683294650998, 35.496348985495835 31.099054116791716, 35.51209857556281 31.108169393945676, 35.51470334022182 31.10936705621536, 35.51326519169544 31.111100645088705, 35.51218283967317 31.112639591093092, 35.51177693503675 31.11320964638138, 35.50941227074153 31.11628632602269, 35.508340209262485 31.118061273944846, 35.507241544119886 31.119879875561033, 35.506359952544706 31.121305799340124, 35.504386627323036 31.124957943303635, 35.50326308185923 31.12743605835249, 35.502884712713325 31.12827022349545, 35.50118194041796 31.131638220451563, 35.50090719628379 31.13238112767566, 35.500765794875626 31.13312501494332, 35.500006090257614 31.135583068519757, 35.49931195612536 31.138099094159877, 35.49910516070351 31.138727784467452, 35.498955333753166 31.140216531056634, 35.49893886026345 31.141706260113803, 35.49900048130955 31.142165281628092, 35.49924086071512 31.144516539101012, 35.49942627160975 31.145838364229814, 35.502976445043245 31.145522363336998, 35.50437400411697 31.145342910752, 35.50563973952273 31.14533639317722, 35.50637382067921 31.145504401526637, 35.50744172490663 31.145785336816278, 35.508845045503 31.14635086312821, 35.510252763354636 31.147603604524807, 35.51138639958518 31.14776941910128, 35.51265815114826 31.148622299799133, 35.514194520278316 31.149129825234528, 35.51560023303504 31.150096396752886, 35.51660253148747 31.15049206834464, 35.51787436730293 31.15134489978968, 35.5192800572519 31.152253932033762, 35.52135510676974 31.15356069728464, 35.52256016097128 31.15441384802101, 35.52342955853427 31.154867423142825, 35.524368398833744 31.155664247367113, 35.52584470584701 31.15714619603555, 35.52625920473508 31.157813294553236, 35.52651891072118 31.158231153863778, 35.52752101468454 31.158626740341695, 35.52839262127336 31.159366962252527, 35.529464628044934 31.160163291962487, 35.530067608602025 31.160618587011808, 35.53067372154536 31.161531941632727, 35.53164828571509 31.163234181003926, 35.53191999591939 31.16374799936453, 35.5326020513553 31.165290196758335, 35.53314864330293 31.166604237333445, 35.533565076398304 31.168262927500837, 35.533841580927195 31.16923538652193, 35.5338560073566 31.170667974958782, 35.53379672095998 31.171413392794562, 35.533024616930966 31.173323276137513, 35.53283035603544 31.174126635255067, 35.532368261854714 31.174759247436445, 35.53210690069453 31.175448535223715, 35.53145084370778 31.176884504212605, 35.530928736452175 31.178434453497932, 35.53013661370356 31.179527534216945, 35.529875325669785 31.18027403518279, 35.529546518970605 31.180848677777316, 35.529023063814634 31.18222694831031, 35.52803078433514 31.1832636203258, 35.52743548956476 31.183897233101412, 35.52657319608868 31.184474811362456, 35.52584305205331 31.18507136537643, 35.52544784246437 31.185569700516105, 35.52491983265049 31.18626015835078, 35.52412363140249 31.18678040969225, 35.523263519263224 31.187701860026653, 35.52233597893486 31.18850894370734, 35.521414423984844 31.189203626217022, 35.52101093770601 31.189662109592284, 35.520084107902676 31.19052668375955, 35.51962057137222 31.19098757564318, 35.519091218686185 31.191506057157767, 35.51850825628649 31.192161751984962, 35.51816529356676 31.192542561874635, 35.5173706976582 31.193291658266276, 35.51664180873594 31.193925940443144, 35.515978963010234 31.194445131632236, 35.5154509286545 31.195192764688944, 35.51419082948188 31.19605910384454, 35.513261867021995 31.196694448739326, 35.51219933984032 31.197273005147014, 35.51173521949955 31.19761942484147, 35.510339173561796 31.19819976300818, 35.50987504645258 31.19854589377434, 35.50901209171941 31.199066404921133, 35.507681240010605 31.199417185816806, 35.50693369677491 31.199538492151994, 35.50608368321962 31.199712163381097, 35.50535135372312 31.199887753249477, 35.50435356882419 31.200179494690595, 35.50355416026981 31.20024099064039, 35.502487728201594 31.200246681128284, 35.501619854992974 31.20013656520887, 35.500486445835335 31.20008509097769, 35.49921915383697 31.199977371952038, 35.49801958648201 31.19998373468945, 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": [
            "931a205d8c154f7593d6eae8dd02706d",
            "da9ec42e982f49ff9f38c733587bdfda",
            "5eba91c101e84c8dae7ed655824f6dcf",
            "777781364ce54217946e4ce9da08ba02",
            "9b20e29a01714274a0664833d95e1c63",
            "c5079682c7c244d99bd04efeb98e07d9",
            "3739d8a05eca4b308552fe96532f2286",
            "b431870fb29e442d973d89465f52e49f",
            "da0a4934e8a94a728f5f702da2e12ad7",
            "854ead141d8343f89704655980bf9d15",
            "497029d140aa4136911d0c2e00beb59a",
            "452a216037364013b507d16bfc1e6254",
            "8f07d6b05c7b4b1fa1d905b986e080d3",
            "b46451c5b7c24d62ab2441105ad64b4c",
            "b6eebd04695c45a0b311c52ce9e4daa2",
            "33beee6422a449fc8c0f8c9c5127d3f7",
            "6051898686284a6099b1993a5c3a4864"
          ]
        },
        "outputId": "15888bdb-6422-4d34-f5b0-3a36691a6d29"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-209836c5-41c0-41c6-aa29-fcede1f5e887\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-209836c5-41c0-41c6-aa29-fcede1f5e887\">\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": "931a205d8c154f7593d6eae8dd02706d"
            }
          },
          "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": "959468aa-9d26-4aff-88a3-71550a027df6"
      },
      "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",
              "3   POINT (35.46659 31.04676)  \n",
              "4   POINT (35.48679 31.02406)  \n",
              "5   POINT (35.59384 31.75000)  \n",
              "6   POINT (35.80708 31.69652)  \n",
              "7   POINT (35.81041 31.70786)  \n",
              "8   POINT (36.01278 31.02101)  \n",
              "9   POINT (35.93192 31.77622)  \n",
              "10  POINT (35.73575 31.55888)  \n",
              "11  POINT (36.03470 31.28938)  \n",
              "12  POINT (36.08000 31.35798)  \n",
              "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",
              "20  POINT (35.97306 31.47861)  \n",
              "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",
              "31  POINT (36.04670 31.23220)  \n",
              "32  POINT (36.05100 31.52630)  \n",
              "33  POINT (36.02907 30.83342)  \n",
              "34  POINT (35.82249 30.61041)  \n",
              "35  POINT (35.75408 30.81586)  \n",
              "36  POINT (35.93419 30.78788)  \n",
              "37  POINT (35.47838 31.02824)  \n",
              "38  POINT (35.82660 30.64484)  \n",
              "39  POINT (36.03697 31.20191)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43cd5db8-6365-4d1d-9b40-fa59f5e6ffa1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </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",
              "      <td>35.519902</td>\n",
              "      <td>-342.0</td>\n",
              "      <td>POINT (35.51990 31.26535)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>CA1010</td>\n",
              "      <td>31.135761</td>\n",
              "      <td>35.533139</td>\n",
              "      <td>-317.5</td>\n",
              "      <td>POINT (35.53314 31.13576)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>CA1011</td>\n",
              "      <td>31.202619</td>\n",
              "      <td>35.532498</td>\n",
              "      <td>-375.1</td>\n",
              "      <td>POINT (35.53250 31.20262)</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43cd5db8-6365-4d1d-9b40-fa59f5e6ffa1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-43cd5db8-6365-4d1d-9b40-fa59f5e6ffa1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-43cd5db8-6365-4d1d-9b40-fa59f5e6ffa1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-79476d6b-5362-442f-b70f-968ec82e8955\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79476d6b-5362-442f-b70f-968ec82e8955')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79476d6b-5362-442f-b70f-968ec82e8955 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f8ee56f0-2c64-4255-974f-147f4fef5a78\">\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",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\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",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f8ee56f0-2c64-4255-974f-147f4fef5a78 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",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </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": "0c11d74b-2646-43ae-9246-fa3be4b68baa"
      },
      "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": [
            "5d85337c54d1490fb93f722433d39a85",
            "8fb05cdd27ff44c08ba183bfdb072f3e",
            "1bc3dfbea8d445f9afed3363c34ccc14",
            "0257c9ecb0194641af612f48653e9711",
            "630303e225c34370a16fbbf16d052673",
            "8076824a024944c497308e5d8ef67759",
            "b24babca571c466bb581221dfd33596b",
            "56cea5198726492e858cc1d05a5030e6",
            "0ffb85d02f904006a35a2cb071a1df4e",
            "17e89cc488ca46d7a7217d5c04616e16",
            "cc0347a796e2479a8edc6441f8b9cbe3",
            "6600cb8070524dd6b4274dca65a5eba1",
            "2a91b3e457e6471e87dd82de724fe093",
            "4bf856b065f94a779af56eb2dd96f6e3"
          ]
        },
        "outputId": "89ccf0e0-bc95-4d6d-d723-f7d0178de6f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9e1918cf-0de4-4d5f-a8c2-2fbb59b18b04\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9e1918cf-0de4-4d5f-a8c2-2fbb59b18b04\">\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": "5d85337c54d1490fb93f722433d39a85"
            }
          },
          "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": "a22b65bb-6cc0-424e-91cd-afd4d2046f43"
      },
      "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": [
              "\n",
              "  <div id=\"df-3f047b03-071f-44ef-b383-c3df501c1ade\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>WellName</th>\n",
              "      <th>WellID</th>\n",
              "      <th>DateString</th>\n",
              "      <th>WaterTableElev</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    <tr>\n",
              "      <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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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",
              "</table>\n",
              "<p>5868 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f047b03-071f-44ef-b383-c3df501c1ade')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f047b03-071f-44ef-b383-c3df501c1ade button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f047b03-071f-44ef-b383-c3df501c1ade');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b3c456a0-84ab-495a-8510-9342956be3ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3c456a0-84ab-495a-8510-9342956be3ee')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b3c456a0-84ab-495a-8510-9342956be3ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a16652a0-4792-4114-85f8-d3c59653e783\">\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",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\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('measurements')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a16652a0-4792-4114-85f8-d3c59653e783 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": "4fdee3ab-fdd6-4831-beef-699ed53d308c"
      },
      "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": "b790391a-12f5-43c1-bb0f-3e9d5d5bc22b"
      },
      "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",
        "    print(gldas_df)\n",
        "    gldas_df.index = pd.to_datetime(gldas_df.index)  # Gio's addition Convert the index to a DatetimeIndex\n",
        "\n",
        "    gldas_df = gldas_df.resample(\"D\").mean()\n",
        "    # D is daily, mean averages any values in a given day, if no data in that day, gives NaN\n",
        "\n",
        "    gldas_df.interpolate(method=\"pchip\", inplace=True, limit_area=\"inside\")\n",
        "\n",
        "    gldas_df = gldas_df.resample(\"MS\").first()\n",
        "    # MS means \"month start\" or to the start of the month, this is the interpolated value\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": "5ab37fa3-8df2-4c1a-e99a-39a91874a818",
        "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": "ba164757-9aa6-4778-f23d-78117f64105d"
      },
      "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": "1c22a972-79c3-44e5-b049-fbbb12ad2016"
      },
      "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 the data grid size and adding rolling average**\n",
        "gldas_df = pd.concat([pdsi_df, soilw_df], join=\"outer\", axis=1)\n",
        "gldas_df.dropna(inplace=True)\n",
        "gldas_df = sat_resample(gldas_df)\n",
        "gldas_df, names = sat_rolling_window(gldas_df)"
      ]
    },
    {
      "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": "1c1b0b9b-aa58-4592-fbe6-00690f13df7a"
      },
      "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": "0708d0c8-9429-476d-d60e-62c6dbcbe4ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KRiecuiHJObc"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "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",
      "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",
      "execution_count": null,
      "metadata": {
        "id": "5Rei8iiYcSVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "113b7b4c7c7243c9b3083136bdc62258",
            "b031fd1f4302478b9f5d406fd39aee5a",
            "35b16339822c45878a9b8077b7c96358",
            "f173a5d33ef444f6b43605ea321d7641",
            "c86e748c168745ceabf6f1e6573a4b3d"
          ]
        },
        "outputId": "9dfd97b5-f4a9-44d5-c1bb-36185c1e4462"
      },
      "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": "113b7b4c7c7243c9b3083136bdc62258"
            }
          },
          "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": 630
        },
        "outputId": "b4fa68c6-1a67-4262-f2b5-34397889027a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78d5b69fc040>]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "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\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')"
      ]
    },
    {
      "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": 301
        },
        "outputId": "50e1c231-814d-4dc4-c945-7107adf9bb64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "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>"
            ],
            "image/png": "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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": {
        "11cb624caed74ba0bc39440073977440": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "GridBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "GridBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "GridBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d2081366e0be484591a2c1a24b544ead",
              "IPY_MODEL_fecb45ac485a4d0f98f198725e95c571"
            ],
            "layout": "IPY_MODEL_b993a6c853334e1db65dfd2379edd515"
          }
        },
        "d2081366e0be484591a2c1a24b544ead": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "GW_CODE",
              "NAME",
              "HECTARES",
              "KM2",
              "Brakish_Wa",
              "Salinity_R",
              "AquiferID",
              "geometry"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Aquifer ID",
            "description_tooltip": null,
            "disabled": false,
            "index": 6,
            "layout": "IPY_MODEL_44f808aa0a1c4e0f96dfc427bf5e2e1d",
            "style": "IPY_MODEL_33c0a349af3549db98ec7c83391264ef"
          }
        },
        "fecb45ac485a4d0f98f198725e95c571": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "GW_CODE",
              "NAME",
              "HECTARES",
              "KM2",
              "Brakish_Wa",
              "Salinity_R",
              "AquiferID",
              "geometry"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Aquifer Name",
            "description_tooltip": null,
            "disabled": false,
            "index": 1,
            "layout": "IPY_MODEL_c11e4c75d7a546b3a31e469504cf2a8b",
            "style": "IPY_MODEL_a819de381aa6473b83b570be28b5ab5b"
          }
        },
        "b993a6c853334e1db65dfd2379edd515": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": "repeat(1, 550px)",
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "44f808aa0a1c4e0f96dfc427bf5e2e1d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "33c0a349af3549db98ec7c83391264ef": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c11e4c75d7a546b3a31e469504cf2a8b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a819de381aa6473b83b570be28b5ab5b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cb8939132bc94b6d995d54cd77440d86": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "GridBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "GridBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "GridBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f21e4807b2884c79bf1c2e6a863fa4ee"
            ],
            "layout": "IPY_MODEL_175a9daf5179408ba7a7636742cb0196"
          }
        },
        "f21e4807b2884c79bf1c2e6a863fa4ee": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "Dead Sea GW"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Aquifer Name",
            "description_tooltip": null,
            "disabled": false,
            "index": 0,
            "layout": "IPY_MODEL_7108764e9819482ea85618b1b2edb34c",
            "style": "IPY_MODEL_6a904ba49f2443e997c3bcbc640f2f38"
          }
        },
        "175a9daf5179408ba7a7636742cb0196": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": "repeat(1, 550px)",
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7108764e9819482ea85618b1b2edb34c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6a904ba49f2443e997c3bcbc640f2f38": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "931a205d8c154f7593d6eae8dd02706d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "GridBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "GridBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "GridBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_da9ec42e982f49ff9f38c733587bdfda",
              "IPY_MODEL_5eba91c101e84c8dae7ed655824f6dcf",
              "IPY_MODEL_777781364ce54217946e4ce9da08ba02",
              "IPY_MODEL_9b20e29a01714274a0664833d95e1c63",
              "IPY_MODEL_c5079682c7c244d99bd04efeb98e07d9"
            ],
            "layout": "IPY_MODEL_3739d8a05eca4b308552fe96532f2286"
          }
        },
        "da9ec42e982f49ff9f38c733587bdfda": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "Well_ID",
              "Well_Name",
              "lat_dec",
              "lon_dec",
              "GSE",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Well ID",
            "description_tooltip": null,
            "disabled": false,
            "index": 1,
            "layout": "IPY_MODEL_b431870fb29e442d973d89465f52e49f",
            "style": "IPY_MODEL_da0a4934e8a94a728f5f702da2e12ad7"
          }
        },
        "5eba91c101e84c8dae7ed655824f6dcf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "Well_ID",
              "Well_Name",
              "lat_dec",
              "lon_dec",
              "GSE",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Well Name",
            "description_tooltip": null,
            "disabled": false,
            "index": 2,
            "layout": "IPY_MODEL_854ead141d8343f89704655980bf9d15",
            "style": "IPY_MODEL_497029d140aa4136911d0c2e00beb59a"
          }
        },
        "777781364ce54217946e4ce9da08ba02": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "Well_ID",
              "Well_Name",
              "lat_dec",
              "lon_dec",
              "GSE",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Latitude",
            "description_tooltip": null,
            "disabled": false,
            "index": 3,
            "layout": "IPY_MODEL_452a216037364013b507d16bfc1e6254",
            "style": "IPY_MODEL_8f07d6b05c7b4b1fa1d905b986e080d3"
          }
        },
        "9b20e29a01714274a0664833d95e1c63": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "Well_ID",
              "Well_Name",
              "lat_dec",
              "lon_dec",
              "GSE",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Longitude",
            "description_tooltip": null,
            "disabled": false,
            "index": 4,
            "layout": "IPY_MODEL_b46451c5b7c24d62ab2441105ad64b4c",
            "style": "IPY_MODEL_b6eebd04695c45a0b311c52ce9e4daa2"
          }
        },
        "c5079682c7c244d99bd04efeb98e07d9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "Well_ID",
              "Well_Name",
              "lat_dec",
              "lon_dec",
              "GSE",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Aquifer ID",
            "description_tooltip": null,
            "disabled": false,
            "index": 0,
            "layout": "IPY_MODEL_33beee6422a449fc8c0f8c9c5127d3f7",
            "style": "IPY_MODEL_6051898686284a6099b1993a5c3a4864"
          }
        },
        "3739d8a05eca4b308552fe96532f2286": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": "repeat(1, 550px)",
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b431870fb29e442d973d89465f52e49f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "da0a4934e8a94a728f5f702da2e12ad7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "854ead141d8343f89704655980bf9d15": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "497029d140aa4136911d0c2e00beb59a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "452a216037364013b507d16bfc1e6254": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8f07d6b05c7b4b1fa1d905b986e080d3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b46451c5b7c24d62ab2441105ad64b4c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b6eebd04695c45a0b311c52ce9e4daa2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "33beee6422a449fc8c0f8c9c5127d3f7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6051898686284a6099b1993a5c3a4864": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5d85337c54d1490fb93f722433d39a85": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "GridBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "GridBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "GridBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8fb05cdd27ff44c08ba183bfdb072f3e",
              "IPY_MODEL_1bc3dfbea8d445f9afed3363c34ccc14",
              "IPY_MODEL_0257c9ecb0194641af612f48653e9711",
              "IPY_MODEL_630303e225c34370a16fbbf16d052673"
            ],
            "layout": "IPY_MODEL_8076824a024944c497308e5d8ef67759"
          }
        },
        "8fb05cdd27ff44c08ba183bfdb072f3e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "WellName",
              "WellID",
              "DateString",
              "WaterTableElev",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Well ID",
            "description_tooltip": null,
            "disabled": false,
            "index": 2,
            "layout": "IPY_MODEL_b24babca571c466bb581221dfd33596b",
            "style": "IPY_MODEL_56cea5198726492e858cc1d05a5030e6"
          }
        },
        "1bc3dfbea8d445f9afed3363c34ccc14": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "WellName",
              "WellID",
              "DateString",
              "WaterTableElev",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Date",
            "description_tooltip": null,
            "disabled": false,
            "index": 3,
            "layout": "IPY_MODEL_0ffb85d02f904006a35a2cb071a1df4e",
            "style": "IPY_MODEL_17e89cc488ca46d7a7217d5c04616e16"
          }
        },
        "0257c9ecb0194641af612f48653e9711": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "WellName",
              "WellID",
              "DateString",
              "WaterTableElev",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Measurement",
            "description_tooltip": null,
            "disabled": false,
            "index": 4,
            "layout": "IPY_MODEL_cc0347a796e2479a8edc6441f8b9cbe3",
            "style": "IPY_MODEL_6600cb8070524dd6b4274dca65a5eba1"
          }
        },
        "630303e225c34370a16fbbf16d052673": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "AquiferID",
              "WellName",
              "WellID",
              "DateString",
              "WaterTableElev",
              "NA"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Aquifer ID",
            "description_tooltip": null,
            "disabled": false,
            "index": 0,
            "layout": "IPY_MODEL_2a91b3e457e6471e87dd82de724fe093",
            "style": "IPY_MODEL_4bf856b065f94a779af56eb2dd96f6e3"
          }
        },
        "8076824a024944c497308e5d8ef67759": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": "repeat(1, 550px)",
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b24babca571c466bb581221dfd33596b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "56cea5198726492e858cc1d05a5030e6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0ffb85d02f904006a35a2cb071a1df4e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "17e89cc488ca46d7a7217d5c04616e16": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cc0347a796e2479a8edc6441f8b9cbe3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6600cb8070524dd6b4274dca65a5eba1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2a91b3e457e6471e87dd82de724fe093": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4bf856b065f94a779af56eb2dd96f6e3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "113b7b4c7c7243c9b3083136bdc62258": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "GridBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "GridBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "GridBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_b031fd1f4302478b9f5d406fd39aee5a"
            ],
            "layout": "IPY_MODEL_35b16339822c45878a9b8077b7c96358"
          }
        },
        "b031fd1f4302478b9f5d406fd39aee5a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DropdownModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DropdownModel",
            "_options_labels": [
              "1",
              "2",
              "9",
              "10",
              "11",
              "12",
              "13",
              "14",
              "15",
              "16",
              "17",
              "18",
              "21",
              "22",
              "23",
              "24",
              "26",
              "27",
              "28",
              "29",
              "31",
              "33",
              "34",
              "35",
              "36",
              "37",
              "38",
              "39",
              "40"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Well Index",
            "description_tooltip": null,
            "disabled": false,
            "index": 16,
            "layout": "IPY_MODEL_f173a5d33ef444f6b43605ea321d7641",
            "style": "IPY_MODEL_c86e748c168745ceabf6f1e6573a4b3d"
          }
        },
        "35b16339822c45878a9b8077b7c96358": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": "repeat(1, 550px)",
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f173a5d33ef444f6b43605ea321d7641": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c86e748c168745ceabf6f1e6573a4b3d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}