GroundwaterDepths.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "5eef11a5e62b469ebe588a31214cee42": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_93ea0e47ab7649c7854abd6f1e4a5469",
              "IPY_MODEL_a1274ff6832540fdb13ee8e0a29b5162"
            ],
            "layout": "IPY_MODEL_6150565b8b044abf874a97406f48c994"
          }
        },
        "93ea0e47ab7649c7854abd6f1e4a5469": {
          "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": [
              "B24F0005",
              "B24F0028",
              "B24F0029",
              "B24F0030",
              "B24F0043",
              "B24F0045",
              "B24F0047",
              "B24F0049",
              "B24F0065",
              "B24F0069",
              "B24F0086",
              "B24F0088",
              "B24F0089",
              "B24F0090",
              "B24F0091",
              "B24F0098",
              "B24F0099",
              "B24F0100",
              "B24F0101",
              "B24F0102",
              "B24F0103",
              "B24F0104",
              "B24F0105",
              "B24F0106",
              "B24F0107",
              "B24F0108",
              "B24F0109",
              "B24F0110",
              "B24F0111",
              "B24F0112",
              "B24F0113",
              "B24F0114",
              "B24F0115",
              "B24F0116",
              "B24F0117",
              "B24F0118",
              "B24F0119",
              "B24F0120",
              "B24F0121",
              "B24F0122",
              "B24F0123",
              "B24F0124",
              "B24F0125",
              "B24F0126",
              "B24F0127",
              "B24F0128",
              "B24F0150",
              "B24F0151",
              "B24F0165",
              "B24F0168",
              "B24F0169",
              "B24F0172",
              "B24F0173",
              "B24H0060",
              "B24H0194",
              "B24H0257",
              "B24H0371",
              "B24H0711",
              "B24H1244",
              "B24H1282",
              "B24H1307",
              "B25A0235",
              "B25A0274",
              "B25A0278",
              "B25A0283",
              "B25A0373",
              "B25A0379",
              "B25A0406",
              "B25A0407",
              "B25A0537",
              "B25A0538",
              "B25A0540",
              "B25A0541",
              "B25A0543",
              "B25A0549",
              "B25A0554",
              "B25A0556",
              "B25A0557",
              "B25A0799",
              "B25A0827",
              "B25A0828",
              "B25A0838",
              "B25A0839",
              "B25A0857",
              "B25A0939",
              "B25A0941",
              "B25A0943",
              "B25A0944",
              "B25A1212",
              "B25A1214",
              "B25A1248",
              "B25A1256",
              "B25A1257",
              "B25A1269",
              "B25A1320",
              "B25A1321",
              "B25A1322",
              "B25A1361",
              "B25A1362",
              "B25A1395",
              "B25A1505",
              "B25A1506",
              "B25A1507",
              "B25A1508",
              "B25A1512",
              "B25A1513",
              "B25A1514",
              "B25A1515",
              "B25A1516",
              "B25A1517",
              "B25A1518",
              "B25A1519",
              "B25A1523",
              "B25A1524",
              "B25A1525",
              "B25A1526",
              "B25A1527",
              "B25A1528",
              "B25A1529",
              "B25A1530",
              "B25A1531",
              "B25A1532",
              "B25A1533",
              "B25A1534",
              "B25A1535",
              "B25A1536",
              "B25A1537",
              "B25A1538",
              "B25A1539",
              "B25A1540",
              "B25A1541",
              "B25A1542",
              "B25A1543",
              "B25A1544",
              "B25A1545",
              "B25A1546",
              "B25A1547",
              "B25A1548",
              "B25A1549",
              "B25A1550",
              "B25A1558",
              "B25A1559",
              "B25A1560",
              "B25A1561",
              "B25A1562",
              "B25A1563",
              "B25A1564",
              "B25A1565",
              "B25A1566",
              "B25A1567",
              "B25A1568",
              "B25A1569",
              "B25A1570",
              "B25A1571",
              "B25A1572",
              "B25A1573",
              "B25A1575",
              "B25A1576",
              "B25A1580",
              "B25A1581",
              "B25A1582",
              "B25A1583",
              "B25A1589",
              "B25A1590",
              "B25A1591",
              "B25A1592",
              "B25A1593",
              "B25A1594",
              "B25A1595",
              "B25A1596",
              "B25A1597",
              "B25A1598",
              "B25A1599",
              "B25A1600",
              "B25A1601",
              "B25A1602",
              "B25A1603",
              "B25A1611",
              "B25A1612",
              "B25A1613",
              "B25A1623",
              "B25A1624",
              "B25A1625",
              "B25A1626",
              "B25A1627",
              "B25A1628",
              "B25A1629",
              "B25A1635",
              "B25A1636",
              "B25A1637",
              "B25A1638",
              "B25A1639",
              "B25A1640",
              "B25A1641",
              "B25A1642",
              "B25A1643",
              "B25A1647",
              "B25A1650",
              "B25A1651",
              "B25A1652",
              "B25A1653",
              "B25A1654",
              "B25A1655",
              "B25A1656",
              "B25A1657",
              "B25A1658",
              "B25A1659",
              "B25A1660",
              "B25A1661",
              "B25A1662",
              "B25A1663",
              "B25A1664",
              "B25A1665",
              "B25A1666",
              "B25A1667",
              "B25A1668",
              "B25A1669",
              "B25A1670",
              "B25A1671",
              "B25A1672",
              "B25A1673",
              "B25A1674",
              "B25A1675",
              "B25A1676",
              "B25A1677",
              "B25A1678",
              "B25A1679",
              "B25A1680",
              "B25A1681",
              "B25A1682",
              "B25A1683",
              "B25A1684",
              "B25A1685",
              "B25A1686",
              "B25A1687",
              "B25A1688",
              "B25A1689",
              "B25A1690",
              "B25A1692",
              "B25A1693",
              "B25A2877",
              "B25A3862",
              "B25A3863",
              "B25A3864",
              "B25A3865",
              "B25A3866",
              "B25A3867",
              "B25A3868",
              "B25A3869",
              "B25A3870",
              "B25A3871",
              "B25A3872",
              "B25A3873",
              "B25A3874",
              "B25A3875",
              "B25A3876",
              "B25A3877",
              "B25A3878",
              "B25A3879",
              "B25A3880",
              "B25A3881",
              "B25A3882",
              "B25A3883",
              "B25A3884",
              "B25A3885",
              "B25A3886",
              "B25A3887",
              "B25A3888",
              "B25A3889",
              "B25A3890",
              "B25A3891",
              "B25A3892",
              "B25A3893",
              "B25A3894",
              "B25A3895",
              "B25A3896",
              "B25A3897",
              "B25A3898",
              "B25A3899",
              "B25A3900",
              "B25A3901",
              "B25A3902",
              "B25A3903",
              "B25A3904",
              "B25A3905",
              "B25A3906",
              "B25A3907",
              "B25A3908",
              "B25A3909",
              "B25A3910",
              "B25A3911",
              "B25B0054",
              "B25B0055",
              "B25B0056",
              "B25B0249",
              "B25B0250",
              "B25B0287",
              "B25B0289",
              "B25B0290",
              "B25B0319",
              "B25B0320",
              "B25B0321",
              "B25B0322",
              "B25B0323",
              "B25B0330",
              "B25B0337",
              "B25B0357",
              "B25B0358",
              "B25B0370",
              "B25B0375",
              "B25B0392",
              "B25B0407",
              "B25B0408",
              "B25B0409",
              "B25B0420",
              "B25B0429",
              "B25B0430",
              "B25B0530",
              "B25B0628",
              "B25B0908",
              "B25B0909",
              "B25B0970",
              "B25B0976",
              "B25B0993",
              "B25B0994",
              "B25B0995",
              "B25B0996",
              "B25B0997",
              "B25B0998",
              "B25B0999",
              "B25B1000",
              "B25B1001",
              "B25B1002",
              "B25B1003",
              "B25B1004",
              "B25B1005",
              "B25B1006",
              "B25B1007",
              "B25B1008",
              "B25B1009",
              "B25B1010",
              "B25B1011",
              "B25B1012",
              "B25B1013",
              "B25B1014",
              "B25B1015",
              "B25B1016",
              "B25B1017",
              "B25B1018",
              "B25B1019",
              "B25B1020",
              "B25B1021",
              "B25B1023",
              "B25B1027",
              "B25B1028",
              "B25B1029",
              "B25B1030",
              "B25B1031",
              "B25B1032",
              "B25B1033",
              "B25B1034",
              "B25B1035",
              "B25B1036",
              "B25B1037",
              "B25B1038",
              "B25B1039",
              "B25B1040",
              "B25B1049",
              "B25B1050",
              "B25B1051",
              "B25B2142",
              "B25B2771",
              "B25B2772",
              "B25B2773",
              "B25B2776",
              "B25B2777",
              "B25B2778",
              "B25B2779",
              "B25B2780",
              "B25B2781",
              "B25B2782",
              "B25B2783",
              "B25B2784",
              "B25B2785",
              "B25B2786",
              "B25B2787",
              "B25B2788",
              "B25B2789",
              "B25B2790",
              "B25B2791",
              "B25B2792",
              "B25B2793",
              "B25B2795",
              "B25B2796",
              "B25B2798",
              "B25B2799",
              "B25B2800",
              "B25B2802",
              "B25B2804",
              "B25B2806",
              "B25B2807",
              "B25B2808",
              "B25C0002",
              "B25C0203",
              "B25C0209",
              "B25C0215",
              "B25C0322",
              "B25C0331",
              "B25C0335",
              "B25C0349",
              "B25C0351",
              "B25C0352",
              "B25C0376",
              "B25C0387",
              "B25C0388",
              "B25C0393",
              "B25C0395",
              "B25C0397",
              "B25C0402",
              "B25C0403",
              "B25C0404",
              "B25C0405",
              "B25C0406",
              "B25C0407",
              "B25C0408",
              "B25C0409",
              "B25C0410",
              "B25C0411",
              "B25C0412",
              "B25C0413",
              "B25C0414",
              "B25C0415",
              "B25C0476",
              "B25C1166",
              "B25C1503",
              "B25C1504",
              "B25C1522",
              "B25C1523",
              "B25C1524",
              "B25C1525",
              "B25C1526",
              "B25C1527",
              "B25C1528",
              "B25C1529",
              "B25C1530",
              "B25C1531",
              "B25C1532",
              "B25C1533",
              "B25C1534",
              "B25C1535",
              "B25C1552",
              "B25D0138",
              "B25D0222",
              "B25D0282",
              "B25D0294",
              "B25D0582",
              "B25D0586",
              "B25D0587",
              "B25D0604",
              "B25D0605",
              "B25D0606",
              "B25D0607",
              "B25D0608",
              "B25D0609",
              "B25D0610",
              "B25D0611",
              "B25D0612",
              "B25D0613",
              "B25D0638",
              "B25E0171",
              "B25E0185",
              "B25E0186",
              "B25E0187",
              "B25E0250",
              "B25E0274",
              "B25E0275",
              "B25E0276",
              "B25E0277",
              "B25E0320",
              "B25E0340",
              "B25E0341",
              "B25E0342",
              "B25E0344",
              "B25E0346",
              "B25E0347",
              "B25E0348",
              "B25E0349",
              "B25E0823",
              "B25E0867",
              "B25E0884",
              "B25E0885",
              "B25E0929",
              "B25E0930",
              "B25E0931",
              "B25E0933",
              "B25E0938",
              "B25E0939",
              "B25E0940",
              "B25E0941",
              "B25E0942",
              "B25E0943",
              "B25E0944",
              "B25E0945",
              "B25E0946",
              "B25E0947",
              "B25E0948",
              "B25E0949",
              "B25E0953",
              "B25E0954",
              "B25E0968",
              "B25E0989",
              "B25E0990",
              "B25E0991",
              "B25E0992",
              "B25E0993",
              "B25E0994",
              "B25E0995",
              "B25E0996",
              "B25E0997",
              "B25E0998",
              "B25E0999",
              "B25E1000",
              "B25E1001",
              "B25E1002",
              "B25E1003",
              "B25E1004",
              "B25E1005",
              "B25E1006",
              "B25E1007",
              "B25E1008",
              "B25E1009",
              "B25E1010",
              "B25E1011",
              "B25E1012",
              "B25E1013",
              "B25E1014",
              "B25E1015",
              "B25E1016",
              "B25E1017",
              "B25E1018",
              "B25E1019",
              "B25E1020",
              "B25E1021",
              "B25E1022",
              "B25E1023",
              "B25E1024",
              "B25E1025",
              "B25E2092",
              "B25F0055",
              "B25F0057",
              "B25F0066",
              "B25F0078",
              "B25F0079",
              "B25F0136",
              "B25F0325",
              "B25F0326",
              "B25F0327",
              "B25F0328",
              "B25F0329",
              "B25G0519"
            ],
            "_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": 10,
            "layout": "IPY_MODEL_e29ec62233254a6bbeea1f9625c98b20",
            "style": "IPY_MODEL_f6bf8beb2ed84aa795e6bd4869a18452"
          }
        },
        "a1274ff6832540fdb13ee8e0a29b5162": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_46d7df20ae154825916d38d24eb84191",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 864x432 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "6150565b8b044abf874a97406f48c994": {
          "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
          }
        },
        "e29ec62233254a6bbeea1f9625c98b20": {
          "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
          }
        },
        "f6bf8beb2ed84aa795e6bd4869a18452": {
          "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": ""
          }
        },
        "46d7df20ae154825916d38d24eb84191": {
          "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
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "G4kkQTXuMB2C"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from scipy import interpolate\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import pandas as pd\n",
        "import datetime\n",
        "\n",
        "\n",
        "# import ipywidgets\n",
        "from ipywidgets import *"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mpl.rcParams['figure.facecolor'] = 'white'"
      ],
      "metadata": {
        "id": "fl0ueT8PMMYP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_depths = pd.read_pickle(r'all_depths_v1.pkl.gz')"
      ],
      "metadata": {
        "id": "vV9Un4oGMakM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_depths1 = all_depths.loc[(slice(None),1),:].reset_index(1, drop=True)\n",
        "all_depths1_permonth = all_depths1.reset_index(0).groupby('well').resample('MS').mean()"
      ],
      "metadata": {
        "id": "vxuZyCVuM9_d"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# first define the dimensions of the grid for the depth surface\n",
        "x_min = all_depths.x.min()\n",
        "x_max = all_depths.x.max()\n",
        "y_min = all_depths.y.min()\n",
        "y_max = all_depths.y.max()\n",
        "\n",
        "cellsize = 100\n",
        "vector_x = np.arange(x_min-2*cellsize, x_max+3*cellsize, cellsize)\n",
        "vector_y = np.arange(y_min-2*cellsize, y_max+3*cellsize, cellsize)\n",
        "grid_x, grid_y = np.meshgrid(vector_x,vector_y)\n",
        "extent1 = (vector_x.min(),vector_x.max(), vector_y.min(), vector_y.max())"
      ],
      "metadata": {
        "id": "gNTuKX9DNP6V"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_surface(date='1996-01-01', method='nearest', add_wells=True):\n",
        "    # selection and gridding\n",
        "    dfa = all_depths1_permonth.loc[(slice(None),date),:].dropna()\n",
        "    points = dfa[['x','y']]\n",
        "    values = dfa.depth\n",
        "    grid1 = interpolate.griddata(points, values, (grid_x, np.flipud(grid_y)), method=method)\n",
        "    # plotting\n",
        "    fig,ax = plt.subplots(figsize=(16,12))\n",
        "    plt.imshow(grid1,extent=extent1,origin='upper', cmap='coolwarm_r',\n",
        "               vmin=all_depths1.depth.quantile(0.04) ,\n",
        "               vmax=all_depths1.depth.quantile(0.96))  # fix the depth range to fix the colormap\n",
        "    if add_wells: \n",
        "        dfa.plot.scatter(x='x', y='y', c='none', s=50,edgecolors='k', ax=ax, colorbar=False)\n",
        "    # \n",
        "    plt.colorbar(shrink=0.5)\n",
        "    ax.set_xlabel('')\n",
        "    ax.set_ylabel('')\n",
        "    ax.set_title(date)\n",
        "    plt.show() # this is necessary to ensure the same plot is updated as we change the date with the cursor"
      ],
      "metadata": {
        "id": "9OmHkCmKNYl6"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a list of dates (every first day of the month from 1955 to 2015)\n",
        "dates = [datetime.date((1955, 1, 1)) for y in range(1955,2016) for m in range(1,13) ]\n",
        "# create a widget with a slider for the data\n",
        "dateWidget = SelectionSlider(\n",
        "                options=dates,\n",
        "                description='Date')\n",
        "# make the slider wider\n",
        "dateWidget.layout.width = '500px'\n",
        "# make a couple of radio buttons for the method selection\n",
        "methodWidget = RadioButtons(\n",
        "            options=['nearest', 'linear'], value='nearest',\n",
        "            description='Interpolation:')\n",
        "# run widget with the interact function (note how the function automatically creates a tick box for the add_wells parameter)\n",
        "interact(plot_surface, date=dateWidget, method=methodWidget)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "TrRvAaHJNbpG",
        "outputId": "9ab3100b-4eab-4f19-b4b7-a6ab4366674d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-8c2d6b480327>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# create a list of dates (every first day of the month from 1955 to 2015)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1955\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1955\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2016\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# create a widget with a slider for the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m dateWidget = SelectionSlider(\n\u001b[1;32m      5\u001b[0m                 \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-13-8c2d6b480327>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# create a list of dates (every first day of the month from 1955 to 2015)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1955\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1955\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2016\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# create a widget with a slider for the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m dateWidget = SelectionSlider(\n\u001b[1;32m      5\u001b[0m                 \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: an integer is required (got type tuple)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_depth_vs_time(well='B24F0005'):\n",
        "    # selection \n",
        "    dfa = all_depths1_permonth.loc[well]\n",
        "    \n",
        "    # plot\n",
        "    fig,ax = plt.subplots(figsize=(12,6))\n",
        "    ax.plot(dfa.depth)\n",
        "    ax.set_xlabel('')\n",
        "    ax.set_ylabel('mean depth (cm w.r.t. NAP)')\n",
        "    ax.set_xlim(pd.to_datetime('1950'), pd.to_datetime('2017'))\n",
        "    ax.set_ylim(-750,750)\n",
        "    ax.xaxis.set_major_locator(mdates.YearLocator(10))\n",
        "    ax.grid()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "I8udNicxNxlc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a list of all the wells\n",
        "well_list = all_depths1.index.get_level_values(0).unique().tolist()\n",
        "# create a widget with a dropdown menu\n",
        "wellWidget = Dropdown(options=well_list, value=well_list[0],\n",
        "                description='Well ID')\n",
        "\n",
        "# run widget with the interact function\n",
        "interact(plot_depth_vs_time, well=wellWidget)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
            "5eef11a5e62b469ebe588a31214cee42",
            "93ea0e47ab7649c7854abd6f1e4a5469",
            "a1274ff6832540fdb13ee8e0a29b5162",
            "6150565b8b044abf874a97406f48c994",
            "e29ec62233254a6bbeea1f9625c98b20",
            "f6bf8beb2ed84aa795e6bd4869a18452",
            "46d7df20ae154825916d38d24eb84191"
          ]
        },
        "id": "aBcld4hiNz2m",
        "outputId": "a1d8fb7d-ae71-4541-c38b-7976152c9679"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='Well ID', options=('B24F0005', 'B24F0028', 'B24F0029', 'B24F0030',…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5eef11a5e62b469ebe588a31214cee42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.plot_depth_vs_time(well='B24F0005')>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}