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  "cells": [
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        "<a href=\"https://colab.research.google.com/github/BYU-Hydroinformatics/gwdm-notebooks/blob/main/nwis_file_retriever.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
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      },
      "source": [
        "# **NWIS File Retriever**\n",
        "\n",
        "### This notebook retrieves data from the USGS NWIS site. Data from every state / territory in the United States can be retrieved.\n",
        "\n",
        "##### The user defines the parameters in the third cell and then all cells should be run. This will generate two files:\n",
        "1. a wells file with all of the wells that meet the given parameters and fall within the aquifers provided\n",
        "2. a time series file with all time series entries from the specified wells\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BW7JiFvlc6DO"
      },
      "source": [
        "## **Instructions:**\n",
        "*This is a Google Colab Notebook, shared as a Github Gist. That means that you can perform operations within this notebook and download your files once you're done. Once you restart the session (e.g. refresh the page) your changes will not be saved. However, you can save a copy of this notebook using the \"File\" tab.*\n",
        "* To use this tool, follow the instructions in the right side of each cell.\n",
        "* **To run each cell, press the play button on the left. Occasionally, the cell will require additional input after pressing the play button.**\n",
        "* If an error appears, double check that all input options are correct.\n",
        "* After all cells have been run, csv files will be generated, which can be downloaded from the \"Files\" window on the left"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmHjKMJAj3us"
      },
      "source": [
        "#@markdown ### **Run this cell to install dependencies**\n",
        "# Install dependencies\n",
        "%%capture\n",
        "!pip install geopandas\n",
        "!pip install pygeos"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJW1e_vuE1F3"
      },
      "source": [
        "#@markdown ### **Run this cell to load necessary packages**\n",
        "# Load packages\n",
        "%%capture\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import datetime\n",
        "from google.colab import files\n",
        "import ipywidgets as widgets\n",
        "import sys"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IArR8pdKwcJl"
      },
      "source": [
        "### **Define the parameters:**\n",
        "1. The 2-letter state id (e.g. ut)\n",
        "2. The water level parameter\n",
        "3. The start and end dates\n",
        "4. Use of an aquifers file\n",
        "  * In order to load data into the GWDM app, it is recommended to assign aquifer IDs and names to each well.\n",
        "  * A single aquifers file is normally sufficient; however, if you would like to reclassify wells that fall outside the aquifers, you can add a secondary aquifers file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1CnIlVNdjTM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234,
          "referenced_widgets": [
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            "53180a51f07943df921b5ee49e75faab",
            "b157b3b597e04da78a93477384072fbc"
          ]
        },
        "outputId": "d3c34d04-c579-413a-a420-d8ea27617b28"
      },
      "source": [
        "#@markdown ### **Run this cell to define parameters**\n",
        "#@markdown ## **-> Follow the instructions on the output after running the cell <-**\n",
        "#@markdown ##### * State IDs are 2-letter ANSI IDs: https://www2.census.gov/geo/docs/reference/state.txt\n",
        "#@markdown ##### * Generally, water levels will be specified in feet below ground surface (for use in places like Utah). For coastal and island areas, some water levels are measured in feet above a particular datum (For use in places like Guam)\n",
        "#@markdown ##### * If you would like to reclassify wells that fall outside of your aquifers file using a secondary aquifers file, select the appropriate option\n",
        "\n",
        "mystate_widget = widgets.Text( placeholder='(e.g. ut for Utah)', description='State ID', disabled=False )\n",
        "lvl_ground_widget = widgets.Dropdown(options=[\"Ft below ground surface\", \"Ft above datum\"], layout={'width': 'max-content'})\n",
        "startDate_widget = widgets.DatePicker(description='Start Date')\n",
        "endDate_widget = widgets.DatePicker(description='End Date')\n",
        "addAq_widget = widgets.Dropdown(options=[\"1) Add an aquifers file\", \"2) Add a primary aquifers file and a secondary aquifers file\", \"3) Don't add aquifers file (all wells from state will be retrieved)\"], description = \"Aquifers?\")\n",
        "\n",
        "def saveAqHeaders(button):\n",
        "  global mystate, lvl_ground, startDate, endDate, addAq\n",
        "  mystate, lvl_ground, startDate, endDate, addAq = mystate_widget.value, lvl_ground_widget.value, startDate_widget.value, endDate_widget.value, int(addAq_widget.value[0])\n",
        "  print(f'\\rState=\"{mystate}\", Datum=\"{lvl_ground}\", Start Date={startDate}, End Date={endDate}, Aquifer Selection Option={addAq}.   ONCE OPTIONS ARE SATISFACTORY, PROCEED TO NEXT CELL', end='')\n",
        "\n",
        "button = widgets.Button(description='Save', disabled=False)\n",
        "button.on_click(saveAqHeaders)\n",
        "# Print the widgets so that the user can specify the headers\n",
        "wItems = [ mystate_widget, startDate_widget, widgets.HBox([widgets.Label(value=\"Water Level Datum\"), lvl_ground_widget]), endDate_widget, addAq_widget ]\n",
        "print(\"\\nPLEASE CHOOSE YOUR PARAMETERS:\\n\")\n",
        "display(widgets.GridBox(wItems, layout=widgets.Layout(grid_template_columns=\"repeat(2, 450px)\")))\n",
        "button\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "PLEASE CHOOSE YOUR PARAMETERS:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Text(value='', description='State ID', placeholder='(e.g. ut for Utah)'), DatePicker(value=N…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a82cf0a2ff94995bf63054f988362bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Button(description='Save', style=ButtonStyle())"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52685dfacbb54e07bf6632e8143e06ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rState=\"UT\", Datum=\"Ft below ground surface\", Start Date=1900-01-01, End Date=2023-12-31, Aquifer Selection Option=1.   ONCE OPTIONS ARE SATISFACTORY, PROCEED TO NEXT CELL"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TQPyLWoispQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325,
          "referenced_widgets": [
            "cebace50ec974eb28de91c00b6f011f4",
            "6b0579199975480389ee98353cff2d2a",
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          ]
        },
        "outputId": "92e0ed94-ac81-4916-e673-bb103a898f2d"
      },
      "source": [
        "#@markdown ### **Run this cell to load the aquifers file**\n",
        "#@markdown ##### *Follow the instructions on the output after running the cell.*\n",
        "#@markdown ##### * If using shapefile, zip the files and load the zip. You may need to zip files directly, rather than zipping the folder that they reside in.\n",
        "\n",
        "# Create Aquifers GeoDataframe and define the coordinate system to be WGS84 (this ensures consistency between datasets)\n",
        "if addAq_widget.value[0] == '3':\n",
        "  print(\"'DO NOT ADD AQUIFERS FILE' OPTION SELECTED - NO AQUIFERS FILE ADDED\\n\")\n",
        "  sys.exit(\"'DO NOT ADD AQUIFERS FILE' option selected - No aquifers file added. PROCEED TO NEXT CELL.\")\n",
        "\n",
        "def addAqFile(aqNum):\n",
        "  uploaded = files.upload()\n",
        "  aq_file = ''.join(uploaded)\n",
        "  aq_gdf = gpd.read_file(aq_file)\n",
        "  aq_gdf.crs = ('epsg:4326')\n",
        "  aList = aq_gdf.columns\n",
        "  Aquifer_Name_widget = widgets.Dropdown(options=aList, layout={'width': 'max-content'}, description = aqNum + \" Name\")\n",
        "  AquiferID_widget = widgets.Dropdown(options=aList, layout={'width': 'max-content'}, description = aqNum + \" ID\")\n",
        "  return aq_gdf, Aquifer_Name_widget, AquiferID_widget\n",
        "\n",
        "def saveAqHeaders(button):\n",
        "  global Aquifer_Name, AquiferID, Aquifer_Name2, AquiferID2\n",
        "  if 'aq_gdf' in globals():\n",
        "    Aquifer_Name = Aquifer_Name_widget.value\n",
        "    AquiferID =  AquiferID_widget.value\n",
        "    print(f'\\rAquifer Name header = {Aquifer_Name}, Aquifer ID header = {AquiferID}')\n",
        "  if 'aq_gdf2' in globals():\n",
        "    Aquifer_Name2 = Aquifer_Name2_widget.value\n",
        "    AquiferID2 =  AquiferID2_widget.value\n",
        "    print(f'\\rSecondary Aquifer Name header = {Aquifer_Name2}, Secondary Aquifer ID header = {AquiferID2}', end='')\n",
        "\n",
        "\n",
        "button = widgets.Button(description='Save', disabled=False)\n",
        "button.on_click(saveAqHeaders)\n",
        "\n",
        "print(\"PLEASE UPLOAD YOUR AQUIFERS FILE: (Load shapefiles as zip files)\\n(If error occurs, try disabling ad blocker)\\n\")\n",
        "print(\"If the upload widget isn't working, you can upload the file manually to the 'files' pane on the left.\\nCreate a GPD gdf from your file called aq_gdf. Assign the appropriate column names to Aquifer_ID and Aquifer_Name\")\n",
        "aq_gdf, Aquifer_Name_widget, AquiferID_widget = addAqFile('Aquifer')\n",
        "widgetList = [widgets.VBox([Aquifer_Name_widget, AquiferID_widget])]\n",
        "\n",
        "if int(addAq_widget.value[0]) == 2:\n",
        "  print(\"\\nPLEASE UPLOAD YOUR SECONDARY AQUIFERS FILE: (Load shapefiles as zip files)\\n\")\n",
        "  aq_gdf2, Aquifer_Name2_widget, AquiferID2_widget = addAqFile('2nd Aq')\n",
        "  widgetList = [widgets.VBox([Aquifer_Name_widget, AquiferID_widget]), widgets.VBox([Aquifer_Name2_widget, AquiferID2_widget])]\n",
        "\n",
        "# Set up the widgets for the headers\n",
        "print(\"\\nPLEASE SPECIFY YOUR HEADERS:\\n\")\n",
        "display(widgets.GridBox(widgetList, layout=widgets.Layout(grid_template_columns=\"repeat(3, 300px)\")))\n",
        "button\n"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLEASE UPLOAD YOUR AQUIFERS FILE: (Load shapefiles as zip files)\n",
            "(If error occurs, try disabling ad blocker)\n",
            "\n",
            "If the upload widget isn't working, you can upload the file manually to the 'files' pane on the left.\n",
            "Create a GPD gdf from your file called aq_gdf. Assign the appropriate column names to Aquifer_ID and Aquifer_Name\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-26e4a814-50f2-4970-8b72-aab510261915\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-26e4a814-50f2-4970-8b72-aab510261915\">\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 gls_basin.zip to gls_basin (1).zip\n",
            "\n",
            "PLEASE SPECIFY YOUR HEADERS:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(VBox(children=(Dropdown(description='Aquifer Name', layout=Layout(width='max-content'), opti…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cebace50ec974eb28de91c00b6f011f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Button(description='Save', style=ButtonStyle())"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "876b6a515a5b47c999ace825299a7c2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rAquifer Name header = NAME, Aquifer ID header = BASIN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If you need to upload your file manually, uncomment the cells below and type in the proper file name, aquifer name column, and aquifer id column. Otherwise, continue to next cell\n",
        "# aq_gdf = gpd.read_file(aq_file)\n",
        "# Aquifer_Name = 'Basin_Name'\n",
        "# Aquifer_ID = 'OBJECTID'"
      ],
      "metadata": {
        "id": "DO1YdnXffAf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISnWlSXalHvr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d1ac492-81e0-434c-8f87-12e5b49ebec8"
      },
      "source": [
        "#@markdown ### **Run this cell to validate values from dropdown menus**\n",
        "\n",
        "if type(startDate) != datetime.date or startDate > endDate:\n",
        "  print(\"*ERROR* You need a start date that is before the end date\\n\\n\")\n",
        "else:\n",
        "  startDateStr, endDateStr = str(startDate), str(endDate)\n",
        "  if lvl_ground == \"Ft below ground surface\":\n",
        "    valueCol = \"lev_va\"\n",
        "    tsurl = \"https://waterservices.usgs.gov/nwis/gwlevels/?format=rdb&stateCd=\"+mystate+\"&startDT=\"+startDateStr+\"&endDT=\"+endDateStr+\"&parameterCd=72019&siteStatus=all\"\n",
        "  else:\n",
        "    valueCol = \"sl_lev_va\"\n",
        "    tsurl = \"https://waterservices.usgs.gov/nwis/gwlevels/?format=rdb&stateCd=\"+mystate+\"&startDT=\"+startDateStr+\"&endDT=\"+endDateStr+\"&parameterCd=72229,72150&siteStatus=all\"\n",
        "try:\n",
        "  ts_df = pd.read_csv(tsurl, sep='\\t', header=0, comment='#', usecols=[1,3,4,6,7], low_memory=False).drop(0)\n",
        "  if len(ts_df) < 1:\n",
        "    sys.exit('THERE ARE NO WELLS WITH THE GIVEN PARAMETERS')\n",
        "except: print(\"*ERROR* Check that your state ID is an approved ANSI two letter state ID: https://www2.census.gov/geo/docs/reference/state.txt\")\n",
        "print(\"Success!\")"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4F2uSzWNxZwl"
      },
      "source": [
        "## **Now run all remaining cells**\n",
        "*If you run into a urlopen error, try re-running the script. Sometimes the url isn't available.*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1zbElYqifYK"
      },
      "source": [
        "# Define the background map function, which will be used later on\n",
        "\n",
        "def background_map(aquifers, secondaryAq = 1, wells = 1, wells_in_aquifer = 1):\n",
        "  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Background map is part of geopandas library\n",
        "  ax = world.plot(color='white', edgecolor='black')\n",
        "  minx, miny, maxx, maxy = aquifers.total_bounds\n",
        "  if type(secondaryAq) == gpd.geodataframe.GeoDataFrame:\n",
        "    secondaryAq.plot(ax=ax, color='lightsteelblue') # L Blue Polygons = secondary aquifers\n",
        "  aquifers.plot(ax=ax, color='royalblue') # Dark Blue Polygons = secondary aquifers\n",
        "  if type(wells) == gpd.geodataframe.GeoDataFrame:\n",
        "    wells.plot(ax=ax, color='red', markersize=15) # Red dots = wells outside of aquifers\n",
        "    minx, miny, maxx, maxy = wells.total_bounds\n",
        "  if type(wells_in_aquifer) == gpd.geodataframe.GeoDataFrame:\n",
        "    wells_in_aquifer.plot(ax=ax, color='greenyellow', markersize=20) # Green dots = wells inside of aquifers\n",
        "  ax.set_xlim(minx, maxx)\n",
        "  ax.set_ylim(miny, maxy)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "QSg8J1JPE1GK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "9f70838e-9b86-40f6-cb36-8fabd1b10c0b"
      },
      "source": [
        "# Create a background map (this will plot on a map at the bottom so that the user can check that it looks right)\n",
        "    # This background map is part of the geopandas library. The aquifers shapefile / geojson plots on top\n",
        "print('Royal Blue = Primary Aquifers \\nLight Blue = Secondary Aquifers (if applicable)')\n",
        "if addAq == 2 and 'aq_gdf2' in globals(): background_map(aq_gdf, aq_gdf2)\n",
        "elif addAq == 1: background_map(aq_gdf)\n",
        "else: print(\"\\nNo aquifers file added (wells will not be assigned an aquifer ID or name\\nIf you wanted to assign aquifer IDs/names, go back to the parameters)\")"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Royal Blue = Primary Aquifers \n",
            "Light Blue = Secondary Aquifers (if applicable)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-390a7af34891>:4: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
            "  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Background map is part of geopandas library\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UG_jrIeBE1Gg"
      },
      "source": [
        "# Re-import the time series and store in a dataframe. Rename and format the columns\n",
        "ts_df = ts_df.rename(columns=({'site_no':'Well_ID', 'lev_dt':'Date', 'lev_tm':'Time', valueCol:'GW_measurement'}))\n",
        "ts_df['GW_measurement'] = pd.to_numeric(ts_df['GW_measurement'])\n",
        "ts_df = ts_df[['Well_ID', 'Date', 'GW_measurement']]"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKU6eL0yE1G4"
      },
      "source": [
        "# Next we obtain the Wells from NWIS #\n",
        "\n",
        "# Import the well data from NWIS. Clip the file and read to a Pandas Dataframe\n",
        "wellsurl = \"https://waterservices.usgs.gov/nwis/site/?format=rdb&stateCd=\"+mystate+\"&siteType=GW&siteStatus=all\"\n",
        "w_df = pd.read_csv(wellsurl, comment='#', sep='\\t', dtype=str)\n",
        "\n",
        "# Drop unneccessary data, rename columns, and set data types\n",
        "w_df = w_df.drop([0])\n",
        "w_df = w_df.drop(['agency_cd', 'site_tp_cd', 'coord_acy_cd', 'dec_coord_datum_cd', 'alt_acy_va', 'alt_datum_cd', 'huc_cd'], axis=1)\n",
        "w_df = w_df.rename(columns=({'site_no':'Well_ID', 'station_nm':'Well_Name', 'dec_lat_va':'lat_dec', 'dec_long_va':'long_dec', 'alt_va':'GSE'}))\n",
        "w_df['long_dec'], w_df['lat_dec'] = pd.to_numeric(w_df['long_dec']), pd.to_numeric(w_df['lat_dec'])\n",
        "\n",
        "# Compare to the Time series csv to extract only wells that actually have data in the specified year range (this allows for a smaller dataframe)\n",
        "wells_df = pd.merge(w_df, ts_df[\"Well_ID\"], on=['Well_ID'])\n",
        "wells_df = wells_df.drop_duplicates(subset = 'Well_ID')"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oM2uUJ7HE1G9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fdc1193-2569-4a00-d9cf-e0e3421ca39a"
      },
      "source": [
        "# Clip the wells with data in the date range to those that fall within the aquifers. #\n",
        "\n",
        "if addAq < 3:\n",
        "  # Create a GeoDataFrame from the wells dataframe\n",
        "  wells_gdf = gpd.GeoDataFrame(wells_df, geometry=gpd.points_from_xy(wells_df.long_dec, wells_df.lat_dec))\n",
        "  # Ensure that its Coordinate Reference System is WGS84 (ensures consistency between dataframes)\n",
        "  wells_gdf.crs = ('epsg:4326')\n",
        "\n",
        "  # Merge the wells and aquifers GeoDataFrames and drop the NAN values\n",
        "      # This will give us the wells that fall within the aquifers\n",
        "  wells_in_aq_gdf = gpd.sjoin(wells_gdf, aq_gdf, how='left', op='intersects')\n",
        "  wells_in_aq_gdf = wells_in_aq_gdf.dropna(subset=[Aquifer_Name])\n",
        "  wells_in_aq_gdf = wells_in_aq_gdf.rename(columns=({AquiferID:'AquiferID', Aquifer_Name:'Aquifer_Name'}))\n",
        "\n",
        "  # Drop the unneccessary columns and set proper data types\n",
        "  wells_in_aq_df = wells_in_aq_gdf.loc[:,('Well_ID', 'Well_Name', 'lat_dec', 'long_dec', 'GSE', 'AquiferID', 'Aquifer_Name')]\n",
        "  wells_in_aq_df.loc[:,'AquiferID'] = wells_in_aq_df.loc[:,'AquiferID'].astype(int)\n",
        "  wells_in_aq_df.loc[:, 'GSE'] = wells_in_aq_df.loc[:, 'GSE'].astype(float)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py:3473: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n",
            "  if (await self.run_code(code, result,  async_=asy)):\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUswVkhGE1Hc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "f7cbcb0e-8460-45d6-ecf1-2edcb11c108c"
      },
      "source": [
        "# Create a plot that shows the wells that fall within the aquifers\n",
        "if addAq < 3:\n",
        "  wDrop = len(wells_gdf) - len(wells_in_aq_gdf)\n",
        "  print(\"Map:\\n - Green dots are wells inside the primary aquifer(s)\\n - Red dots are wells outside primary aquifer(s)\\n\\n%d wells fell outside the primary aquifer(s) and will be dropped or reclassified (if applicable)\" %wDrop)\n",
        "  if addAq_widget.value[0] == '2' and 'aq_gdf2' in globals():\n",
        "    background_map(aq_gdf, aq_gdf2, wells=wells_gdf, wells_in_aquifer=wells_in_aq_gdf)\n",
        "  else:\n",
        "    background_map(aq_gdf, wells=wells_gdf, wells_in_aquifer=wells_in_aq_gdf)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Map:\n",
            " - Green dots are wells inside the primary aquifer(s)\n",
            " - Red dots are wells outside primary aquifer(s)\n",
            "\n",
            "7784 wells fell outside the primary aquifer(s) and will be dropped or reclassified (if applicable)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-390a7af34891>:4: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
            "  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) # Background map is part of geopandas library\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add state column to well file (OPTIONAL) - add a \"State\" column to the ts_df and populate with state code.\n",
        "# This is useful when an aquifer spans multiple states and you need to combine the files.\n",
        "wells_in_aq_df['State'] = mystate"
      ],
      "metadata": {
        "id": "rTDRN9C_cl0z"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yHo_ex5E1Hn"
      },
      "source": [
        "# Save the wells file as a csv file\n",
        "if addAq < 3:\n",
        "  wellsFileName = mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_wells_with_aquifers.csv'\n",
        "  wells_in_aq_df.to_csv (wellsFileName, index = None, header=True)\n",
        "else:\n",
        "  wellsFileName = mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_wells.csv'\n",
        "  wells_df.to_csv (wellsFileName, index = None, header=True)\n",
        "\n",
        "# Save reclassified wells, if applicable\n",
        "if addAq == 2 and wDrop > 0  and 'aq_gdf2' in globals():\n",
        "  wls_out_aq_gdf = wells_gdf[~wells_gdf.isin(wells_in_aq_gdf)].dropna()\n",
        "  wells_in_aq_gdf2 = gpd.sjoin(wls_out_aq_gdf, aq_gdf2[[AquiferID2, Aquifer_Name2, \"geometry\"]], how='left', op='within')\n",
        "  wells_in_aq_gdf2 = wells_in_aq_gdf2.dropna(subset=[AquiferID2])\n",
        "  wells_in_aq_gdf2 = wells_in_aq_gdf2.drop('index_right', axis=1)\n",
        "  if len(wells_in_aq_gdf2) > 0:\n",
        "    # Save the reclassified wells to a csv file\n",
        "    wells_in_aq_gdf2.to_csv (mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_wells_with_secondary_aquifers.csv', index = None, header=True)\n"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyeq17kjE1Hr"
      },
      "source": [
        "# Clean up columns in time series dataframe.\n",
        "if addAq < 3:\n",
        "  # Merge ts_df with wells_in_aq_gdf to assign the aquifer id and name to each time series entry. Then drop unnecessary columns.\n",
        "  ts_with_aq_df = ts_df.merge(wells_in_aq_df,on=['Well_ID'])\n",
        "\n",
        "  # Recalculate the WTE using the GSE attribute of each well\n",
        "  ts_with_aq_df['GSE'] = pd.to_numeric(ts_with_aq_df['GSE'])\n",
        "  if lvl_ground == \"Ft below ground surface\":\n",
        "      ts_with_aq_df['GW_measurement'] = ts_with_aq_df['GW_measurement'].abs()\n",
        "      ts_with_aq_df['WTE'] = ts_with_aq_df.GSE - ts_with_aq_df.GW_measurement\n",
        "  else:\n",
        "      ts_with_aq_df['WTE'] = ts_with_aq_df['GW_measurement']\n",
        "  ts_with_aq_df = ts_with_aq_df[['AquiferID', 'Well_ID', 'Date', 'WTE']]"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleanup time series dataframe (OPTIONAL)\n",
        "# 1. delete rows in the ts_df where the WTE field is empty.\n",
        "ts_with_aq_df.dropna(subset=['WTE'], inplace=True)\n",
        "# 2. Round the WTE values to 2 decimal places to get rid of noise that shows up in the resulting CSV file\n",
        "# for some of the entries (4216.0000000000001 for example).\n",
        "ts_with_aq_df[\"WTE\"] = ts_with_aq_df[\"WTE\"].round(2)"
      ],
      "metadata": {
        "id": "tdbHtkeHlFgz"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix incomplete dates\n",
        "# 1. Find entries with missing MM-DD in Date field and append \"06-01\"\n",
        "\n",
        "# Count number of entries with where length of 'Date' column is less than 5 (YYYY only).\n",
        "date_lengths = ts_with_aq_df['Date'].str.len()\n",
        "count = date_lengths.lt(5).sum()\n",
        "print(f'There are {count} entries with missing MM-DD')\n",
        "\n",
        "# Fix entries by appending \"06-01\"\n",
        "ts_with_aq_df.loc[ts_with_aq_df['Date'].str.len() < 5, 'Date'] = ts_with_aq_df.loc[ts_with_aq_df['Date'].str.len() < 5, 'Date'] + '-06-01'\n",
        "\n",
        "# 2. Find entries with missing -DD in Date field and append \"-01\"\n",
        "\n",
        "# Count number of entries with where length of 'Date' column is less than 8.\n",
        "date_lengths = ts_with_aq_df['Date'].str.len()\n",
        "count = date_lengths.lt(8).sum()\n",
        "print(f'There are {count} entries with missing -DD')\n",
        "\n",
        "# Fix entries by appending \"-01\"\n",
        "ts_with_aq_df.loc[ts_with_aq_df['Date'].str.len() < 8, 'Date'] = ts_with_aq_df.loc[ts_with_aq_df['Date'].str.len() < 8, 'Date'] + '-01'"
      ],
      "metadata": {
        "id": "nXpDfcZse_fB",
        "outputId": "40beb9d9-9a79-4df3-b95f-2a2dcc2d9f8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 0 entries with missing MM-DD\n",
            "There are 0 entries with missing -DD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add state column to time series file (OPTIONAL) - add a \"State\" column to the ts_with_aq_df and populate with state code.\n",
        "# This is useful when an aquifer spans multiple states and you need to combine the files.\n",
        "ts_with_aq_df['State'] = mystate"
      ],
      "metadata": {
        "id": "as_yJlkQpgf7"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucZELM8JE1Hx"
      },
      "source": [
        "# Save the time series dataframe to a csv file\n",
        "if addAq < 3:\n",
        "  tsFileName = mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_TS_with_aquifers.csv'\n",
        "  ts_with_aq_df.to_csv (tsFileName, index = None, header=True)\n",
        "else:\n",
        "  tsFileName = mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_TS.csv'\n",
        "  ts_df.to_csv (tsFileName, index = None, header=True)\n",
        "\n",
        "if 'wells_in_aq_gdf2' in globals():\n",
        "  if len(wells_in_aq_gdf2) > 0:\n",
        "    ts_with_aq_df2 = ts_df.merge(wells_in_aq_gdf2,on=['Well_ID'])\n",
        "    # Recalculate the WTE using the GSE attribute of each well\n",
        "    ts_with_aq_df2['GSE'] = pd.to_numeric(ts_with_aq_df2['GSE'])\n",
        "    if lvl_ground == \"Ft below ground surface\":\n",
        "        ts_with_aq_df2['GW_measurement'] = ts_with_aq_df2['GW_measurement'].abs()\n",
        "        ts_with_aq_df2['WTE'] = ts_with_aq_df2.GSE - ts_with_aq_df2.GW_measurement\n",
        "    else:\n",
        "        ts_with_aq_df2['WTE'] = ts_with_aq_df2['GW_measurement']\n",
        "    ts_with_aq_df2 = ts_with_aq_df2[['AquiferID', 'Well_ID', 'Date', 'WTE']]\n",
        "    # Save csv file\n",
        "    ts_with_aq_df2.to_csv (mystate+'_'+str(startDate.year)+'-'+str(endDate.year)+'_TS_with_secondary_aquifers.csv', index = None, header=True)"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taI_OUXAh3-q"
      },
      "source": [
        "## **Retrieve Files**\n",
        "#### Check the 'Files' panel on the left for your new files (hit the refresh button if they don't appear immediately) or run the cell below\n",
        "![files_panel.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "fRsgKHugHPwl",
        "outputId": "803bc29d-3ed9-4b00-fcf2-da37074e7c4e"
      },
      "source": [
        "files.download(wellsFileName)\n",
        "files.download(tsFileName)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f9a6fe58-ad69-48fa-bda9-385892746ad9\", \"UT_1900-2023_wells_with_aquifers.csv\", 644065)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1f1c2aa3-6526-4588-87da-b1d53dd24ded\", \"UT_1900-2023_TS_with_aquifers.csv\", 6686567)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}