Visualising_Time_Series_Data.ipynb

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  "metadata": {
    "colab": {
      "name": "Visualising_Time_Series_Data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1QAIdrd96BdotyrLkgOgP7jLOA93IONND",
      "authorship_tag": "ABX9TyM618kAkB4xh/+9PQKP5xiq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Max-FM/SPRINT-Colombia/blob/main/Visualising_Time_Series_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSbZMzk_GZFA"
      },
      "source": [
        "# Visualising Time Series Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95uhcVyOTaby"
      },
      "source": [
        "## Installing/Upgrading Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDureWO-w3Zx"
      },
      "source": [
        "%%capture\n",
        "\n",
        "!pip install plotly --upgrade\n",
        "\n",
        "# Installing Orca - required to save to png using Google Colab.\n",
        "!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca\n",
        "!chmod +x /usr/local/bin/orca\n",
        "!apt-get install xvfb libgtk2.0-0 libgconf-2-4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-tmGMG6Tm1-"
      },
      "source": [
        "##Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aboDmsOCumFK"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.graph_objs as go\n",
        "from plotly.subplots import make_subplots"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euNNcpD3m56Y"
      },
      "source": [
        "## Importing Table of Disaster Dates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfFQ1PA_Atsl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fef99c5c-04a6-416a-839c-d75534c564ef"
      },
      "source": [
        "disaster_dates = pd.read_csv('/content/drive/Shared drives/Colombia SPRINT/Test Districts/disaster_dates.csv', index_col=0)\n",
        "\n",
        "disaster_dates"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Disaster</th>\n",
              "      <th>Start_Datetime</th>\n",
              "      <th>End_Datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drought</td>\n",
              "      <td>1998-01-01</td>\n",
              "      <td>1999-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Flood</td>\n",
              "      <td>1999-01-10</td>\n",
              "      <td>1999-05-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Earthquake</td>\n",
              "      <td>1999-01-25</td>\n",
              "      <td>1999-01-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Flood</td>\n",
              "      <td>1999-10-28</td>\n",
              "      <td>1999-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2000-05-18</td>\n",
              "      <td>2000-05-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Wildfire</td>\n",
              "      <td>2001-08-01</td>\n",
              "      <td>2001-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2002-01-01</td>\n",
              "      <td>2003-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2002-04-24</td>\n",
              "      <td>2002-04-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2003-08-01</td>\n",
              "      <td>2003-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2004-01-01</td>\n",
              "      <td>2004-06-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2004-01-01</td>\n",
              "      <td>2005-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2005-04-12</td>\n",
              "      <td>2005-05-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2005-09-15</td>\n",
              "      <td>2005-11-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2006-01-01</td>\n",
              "      <td>2006-04-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2006-01-01</td>\n",
              "      <td>2007-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2007-10-20</td>\n",
              "      <td>2007-10-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2008-01-01</td>\n",
              "      <td>2008-05-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2008-11-16</td>\n",
              "      <td>2009-01-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2009-01-01</td>\n",
              "      <td>2010-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Wildfire</td>\n",
              "      <td>2010-01-01</td>\n",
              "      <td>2010-04-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2010-10-30</td>\n",
              "      <td>2011-01-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2011-02-10</td>\n",
              "      <td>2011-06-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2011-09-01</td>\n",
              "      <td>2011-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2012-03-15</td>\n",
              "      <td>2012-05-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Earthquake</td>\n",
              "      <td>2013-02-09</td>\n",
              "      <td>2013-02-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2013-09-15</td>\n",
              "      <td>2013-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2015-08-01</td>\n",
              "      <td>2016-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Storm</td>\n",
              "      <td>2016-09-20</td>\n",
              "      <td>2016-09-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2017-03-17</td>\n",
              "      <td>2017-05-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2017-12-01</td>\n",
              "      <td>2018-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Drought</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2019-02-20</td>\n",
              "      <td>2019-02-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Flood</td>\n",
              "      <td>2020-06-10</td>\n",
              "      <td>2020-07-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Disaster Start_Datetime End_Datetime\n",
              "0      Drought     1998-01-01   1999-01-01\n",
              "1        Flood     1999-01-10   1999-05-19\n",
              "2   Earthquake     1999-01-25   1999-01-26\n",
              "3        Flood     1999-10-28   1999-12-31\n",
              "4        Flood     2000-05-18   2000-05-24\n",
              "5     Wildfire     2001-08-01   2001-09-01\n",
              "6      Drought     2002-01-01   2003-01-01\n",
              "7        Flood     2002-04-24   2002-04-29\n",
              "8        Flood     2003-08-01   2003-12-01\n",
              "9        Flood     2004-01-01   2004-06-28\n",
              "10     Drought     2004-01-01   2005-01-01\n",
              "11       Flood     2005-04-12   2005-05-07\n",
              "12       Flood     2005-09-15   2005-11-17\n",
              "13       Flood     2006-01-01   2006-04-27\n",
              "14     Drought     2006-01-01   2007-01-01\n",
              "15       Flood     2007-10-20   2007-10-26\n",
              "16       Flood     2008-01-01   2008-05-19\n",
              "17       Flood     2008-11-16   2009-01-12\n",
              "18     Drought     2009-01-01   2010-01-01\n",
              "19    Wildfire     2010-01-01   2010-04-06\n",
              "20       Flood     2010-10-30   2011-01-12\n",
              "21       Flood     2011-02-10   2011-06-05\n",
              "22       Flood     2011-09-01   2011-12-31\n",
              "23       Flood     2012-03-15   2012-05-14\n",
              "24  Earthquake     2013-02-09   2013-02-09\n",
              "25       Flood     2013-09-15   2013-12-01\n",
              "26     Drought     2015-08-01   2016-02-01\n",
              "27       Storm     2016-09-20   2016-09-23\n",
              "28       Flood     2017-03-17   2017-05-16\n",
              "29       Flood     2017-12-01   2018-01-07\n",
              "30     Drought     2018-01-01   2020-01-01\n",
              "31       Flood     2019-02-20   2019-02-26\n",
              "32       Flood     2020-06-10   2020-07-10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWdp81_IWnKE"
      },
      "source": [
        "## Define Import Functions for CSV Data\n",
        "\n",
        "A series of functions to import CSV data aquired from the [Colombia Farms Time Series Data](https://colab.research.google.com/drive/1-_YUqAHe102ILURNULWhR1eBCMyB050-?usp=sharing) IPython notebook. I haven't made a generalied import function as each the datasets are not formatted uniformly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7Lh7FTt7YfM"
      },
      "source": [
        "def import_MODIS_TERRA_AQUA(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "\n",
        "    # Imports and concatinates NDVI/EVI data for both the MODIS TERRA and AQUA\n",
        "    # satellites.\n",
        "    satellites = ['TERRA', 'AQUA']\n",
        "    \n",
        "    if farm:\n",
        "        df_list = [pd.read_csv(f'{directory}/{farm}_MODIS_16D_{satellite}_VEG.csv', index_col=0) for satellite in satellites]\n",
        "    else:\n",
        "        df_list = [pd.read_csv(f'{directory}/{district}_MODIS_16D_{satellite}_VEG.csv', index_col=0) for satellite in satellites]\n",
        "\n",
        "    df = pd.concat(df_list)\n",
        "\n",
        "    # Convert timestap to datetime object.\n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "\n",
        "    # Rescaling data columns from large integers to correct floating point value.\n",
        "    columns_of_interest = ['EVI_median', 'EVI_stdDev','NDVI_median', 'NDVI_stdDev']\n",
        "    df[columns_of_interest] *= 0.0001\n",
        "\n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    df = df.sort_index()\n",
        "    \n",
        "    return df\n",
        "\n",
        "def import_MODIS_COMBINED(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "        df = pd.read_csv(f'{directory}/{farm}_MODIS_16D_COMBINED_NDVI.csv', index_col=0)\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "        df = pd.read_csv(f'{directory}/{district}_MODIS_16D_COMBINED_NDVI.csv', index_col=0)\n",
        "\n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "    \n",
        "    return df\n",
        "\n",
        "def import_SENTINEL_1_RVI(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "        df = pd.read_csv(f'{directory}/{farm}_SENTINEL_1_RVI.csv', index_col=0)\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "        df = pd.read_csv(f'{directory}/{district}_SENTINEL_1_RVI.csv', index_col=0)\n",
        "\n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "\n",
        "    df = df.sort_index()\n",
        "    \n",
        "    return df\n",
        "\n",
        "def import_CHIRPS(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "        df = pd.read_csv(f'{directory}/{farm}_CHIRPS_DAILY.csv', index_col=0)\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "        df = pd.read_csv(f'{directory}/{district}_CHIRPS_DAILY.csv', index_col=0)\n",
        "\n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "    \n",
        "    return df\n",
        "\n",
        "def import_PERSIANN(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "        df = pd.read_csv(f'{directory}/{farm}_PERSIANN_DAILY.csv', index_col=0)\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "        df = pd.read_csv(f'{directory}/{district}_PERSIANN_DAILY.csv', index_col=0)\n",
        "    \n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "    \n",
        "    return df\n",
        "\n",
        "def import_TerraClimate(district, farm=None):\n",
        "    if farm:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/Farms/CSV Data/{farm}'\n",
        "        df = pd.read_csv(f'{directory}/{farm}_TERRA_CLIMATE_MONTHLY.csv', index_col=0)\n",
        "    else:\n",
        "        directory = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district}/CSV Data/'\n",
        "        df = pd.read_csv(f'{directory}/{district}_TERRA_CLIMATE_MONTHLY.csv', index_col=0)\n",
        "\n",
        "    df['Timestamp'] = df['Timestamp'].apply(pd.to_datetime)\n",
        "    df.index = df['Timestamp']\n",
        "\n",
        "    # Rescaling values from large integers into correct floating point values.\n",
        "    columns_1 = ['aet_median', 'aet_stdDev', \n",
        "                 'def_median', 'def_stdDev', \n",
        "                 'pet_median', 'pet_stdDev', \n",
        "                 'soil_median', 'soil_stdDev', \n",
        "                 'srad_median', 'srad_stdDev', \n",
        "                 'tmmn_median', 'tmmn_stdDev', \n",
        "                 'tmmx_median', 'tmmx_stdDev']\n",
        "    \n",
        "    df[columns_1] *= 0.1\n",
        "    \n",
        "    columns_2 = ['vpd_median', 'vpd_stdDev',\n",
        "                 'vs_median', 'vs_stdDev']\n",
        "    df[columns_2] *= 0.01\n",
        "\n",
        "    # In the documentation for TerraClimate monthly the pdsi scaling relation is\n",
        "    # given as 0.01, however this puts the min/max values to be between \n",
        "    # approximately -40 and 40 whereas is should be between -4 and 4. So have\n",
        "    # set the scaling factor to 0.001 instead.\n",
        "    columns_3 = ['pdsi_median', 'pdsi_stdDev',\n",
        "                 'vap_median', 'vap_stdDev']\n",
        "    df[columns_3] *= 0.001\n",
        "    \n",
        "    df = calculate_time_series_derivative(df, 'median')\n",
        "\n",
        "    return df\n",
        "\n",
        "# Calculates the time derivative for an aggregated quantity.\n",
        "def calculate_time_series_derivative(dataframe, aggregation):\n",
        "    filtered = dataframe.filter(like=f'{aggregation}', axis=1)\n",
        "\n",
        "    for column_name, column_data in filtered.iteritems():\n",
        "        dataframe[f'{column_name}_dt'] = pd.Series(np.gradient(column_data.values), column_data.index, name='slope')\n",
        "\n",
        "    return dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2znVou3FY7mk"
      },
      "source": [
        "## Define Disaster Shading Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3eNchK5Y7Ld"
      },
      "source": [
        "def add_disaster_shading(fig, disaster_dates):    \n",
        "    ### Shading plot background to correspond to a disaster. ###\n",
        "    disasterColourDict = {'Drought': 'orange',\n",
        "                          'Earthquake': 'brown',\n",
        "                          'Flood': 'darkturquoise', \n",
        "                          'Wildfire': 'maroon',\n",
        "                          'Storm': 'grey'}\n",
        "\n",
        "    # Generating a shape to be used as background shading for each disaster \n",
        "    # using list comprehension.\n",
        "    shapes=[\n",
        "            dict(type=\"rect\",\n",
        "                yref=\"paper\",\n",
        "                x0=disaster['Start_Datetime'],\n",
        "                y0=0,\n",
        "                x1=disaster['End_Datetime'],\n",
        "                y1=1,\n",
        "                fillcolor=disasterColourDict[disaster['Disaster']],\n",
        "                opacity=0.7,\n",
        "                layer=\"below\",\n",
        "                line_width=0.2) \\\n",
        "                for index, disaster in disaster_dates.iterrows()\n",
        "            ]\n",
        "\n",
        "    # Add shapes to figure.\n",
        "    fig.update_layout(shapes=shapes)\n",
        "\n",
        "    return fig"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfBONrt__2QH"
      },
      "source": [
        "## Define Timeseries Plotting Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esWPNBW89oDE"
      },
      "source": [
        "def plot_interactive_time_series(district, farm=None):\n",
        "    MODIS_AQUA_TERRA_df = import_MODIS_TERRA_AQUA(district, farm)\n",
        "    CHIRPS_df = import_CHIRPS(district, farm)\n",
        "    PERSIANN_df = import_PERSIANN(district, farm)\n",
        "    TerraClimate_df = import_TerraClimate(district, farm)\n",
        "    SENTINEL_1_RVI_df = import_SENTINEL_1_RVI(district, farm)\n",
        "\n",
        "    fig = make_subplots(rows=3, cols=1, \n",
        "                        shared_xaxes=True, \n",
        "                        vertical_spacing=0.02)\n",
        "\n",
        "    ## Plotting TERRA and AQUA datssets seperately - commented out for now.\n",
        "    # nameDict ={'MODIS_16D_AQUA_VEG': 'Modis Aqua 250m 16 Day NDVI',\n",
        "    #            'MODIS_16D_TERRA_VEG': 'Modis Terra 250m 16 Day NDVI'}\n",
        "\n",
        "    # # Grouping NDVI data by satellitle (TERRA and AQUA) and plotting.\n",
        "    # for collection_name, collection in MODIS_AQUA_TERRA_df.groupby('Image_Collection'):\n",
        "    #     # Define error dictionary.\n",
        "    #     NDVI_error_dict = dict(type='data',\n",
        "    #                            array=collection['NDVI_stdDev'],\n",
        "    #                            visible=True)\n",
        "    #     # Create scatter plot.\n",
        "    #     NDVI_scatter = go.Scatter(\n",
        "    #                               x=collection['Timestamp'], \n",
        "    #                               y=collection['NDVI_median'], \n",
        "    #                               error_y=NDVI_error_dict, \n",
        "    #                               name=nameDict[collection_name], \n",
        "    #                               mode='lines+markers'\n",
        "    #                               )\n",
        "    #     # Add scatter plot to figure.\n",
        "    #     fig.add_trace(NDVI_scatter, row=1, col=1)\n",
        "\n",
        "    \n",
        "    # Plotting MODIS AQUA and TERRA NDVI as a single timeseries and taking the \n",
        "    # monthly rolling median and mean.\n",
        "    \n",
        "    # Create scatter plot.\n",
        "    NDVI_scatter_rolling_median = go.Scatter(\n",
        "                                             x=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').median().index, \n",
        "                                             y=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').median(), \n",
        "                                             name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Median', \n",
        "                                             mode='lines+markers'\n",
        "                                            )\n",
        "    \n",
        "    # Add scatter plot to figure.\n",
        "    fig.add_trace(NDVI_scatter_rolling_median, row=1, col=1)\n",
        "\n",
        "    NDVI_scatter_rolling_mean = go.Scatter(\n",
        "                                           x=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').mean().index, \n",
        "                                           y=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').mean(), \n",
        "                                           name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Mean', \n",
        "                                           mode='lines+markers'\n",
        "                                         )\n",
        "    \n",
        "    fig.add_trace(NDVI_scatter_rolling_mean, row=1, col=1)\n",
        "\n",
        "    ### Plotting Sentinel 1 RVI ###\n",
        "    \n",
        "    ## RVI time-series data is unfortunately rubbish and so this part \n",
        "    ## has been commented out for now. If uncommented, this will need it's own\n",
        "    ## subplot so update the 'make_subplots' and 'add_trace' calls accordingly.\n",
        "\n",
        "    # RVI_scatter_rolling_median = go.Scatter(\n",
        "    #                                         x=SENTINEL_1_RVI_df.index,#['RVI_median'].resample('1D').median().index, \n",
        "    #                                         y=SENTINEL_1_RVI_df['RVI_median'],#.resample('1D').median(),\n",
        "    #                                         marker_color='saddlebrown', \n",
        "    #                                         name='Sentinel 1 Radar Vegetation Index', \n",
        "    #                                         mode='lines+markers'\n",
        "    #                                      )\n",
        "    \n",
        "    # fig.add_trace(RVI_scatter_rolling_median, row=2, col=1)\n",
        "\n",
        "    ### Plotting CHIRPS Precipitation data ###\n",
        "    CHIRPS_scatter = go.Scatter(\n",
        "                                x=CHIRPS_df['precipitation_median'].resample('14D').median().index, \n",
        "                                y=CHIRPS_df['precipitation_median'].resample('14D').median(), \n",
        "                                marker_color='green',\n",
        "                                name='CHIRPS Precipitation - 14 Day Rolling Median (mm)', \n",
        "                                mode='lines+markers'\n",
        "                                )\n",
        "\n",
        "    fig.add_trace(CHIRPS_scatter, row=2, col=1)\n",
        "\n",
        "    ### Plotting PERSIANN Precipitation data ###\n",
        "    PERSIANN_scatter = go.Scatter(\n",
        "                                  x=PERSIANN_df['precipitation_median'].resample('14D').median().index, \n",
        "                                  y=PERSIANN_df['precipitation_median'].resample('14D').median(), \n",
        "                                  marker_color='black',\n",
        "                                  name='PERSIANN Precipitation - 14 Day Rolling Median (mm)', \n",
        "                                  mode='lines+markers'\n",
        "                                  )\n",
        "  \n",
        "    fig.add_trace(PERSIANN_scatter, row=2, col=1)\n",
        "\n",
        "    ### Plotting TerraClimate PDSI data ###\n",
        "    TerraClimate_PDSI_Scatter = go.Scatter(\n",
        "                                           x=TerraClimate_df.index, \n",
        "                                           y=TerraClimate_df['pdsi_median'], \n",
        "                                           marker_color='purple',\n",
        "                                           name='TerraClimate PDSI', \n",
        "                                           mode='lines+markers'\n",
        "                                          )\n",
        "  \n",
        "    fig.add_trace(TerraClimate_PDSI_Scatter, row=3, col=1)\n",
        "\n",
        "    # Adds background shading to plots to represent disaster events.\n",
        "    fig = add_disaster_shading(fig, disaster_dates)\n",
        "\n",
        "    ### Updating axes, title and legend. ###\n",
        "    # Fixing y-axis range.\n",
        "    fig.update_yaxes(fixedrange=True)  \n",
        "    # Adding title.\n",
        "    if farm:\n",
        "        fig.update_layout(title=f\"{district}: {farm.replace('_', ' ')}\")\n",
        "    else:\n",
        "        fig.update_layout(title=f\"{district}\")\n",
        "    # Updating legend layout.\n",
        "    fig.update_layout(legend=dict(\n",
        "                                  orientation=\"h\"\n",
        "                                 ))        \n",
        "\n",
        "    return fig\n",
        "\n",
        "def add_district_level_NDVI(fig, district):\n",
        "    MODIS_AQUA_TERRA_df = import_MODIS_TERRA_AQUA(district)\n",
        "\n",
        "    NDVI_scatter_rolling_median = go.Scatter(\n",
        "                                            x=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').median().index, \n",
        "                                            y=MODIS_AQUA_TERRA_df['NDVI_median'].resample('1M').median(), \n",
        "                                            marker_color = 'grey',\n",
        "                                            name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Median (District Level)', \n",
        "                                            mode='lines+markers'\n",
        "                                            )\n",
        "    \n",
        "    fig.add_trace(NDVI_scatter_rolling_median, row=1, col=1)\n",
        "\n",
        "    return fig"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMtgMqCyXc6z"
      },
      "source": [
        "## Define Timeseries Gradient Plotting Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpndDY0bXbq-"
      },
      "source": [
        "def plot_interactive_time_series_gradient(district, farm=None):\n",
        "    MODIS_AQUA_TERRA_df = import_MODIS_TERRA_AQUA(district, farm)\n",
        "    CHIRPS_df = import_CHIRPS(district, farm)\n",
        "    PERSIANN_df = import_PERSIANN(district, farm)\n",
        "    TerraClimate_df = import_TerraClimate(district, farm)\n",
        "\n",
        "    fig = make_subplots(rows=3, cols=1, \n",
        "                        shared_xaxes=True, \n",
        "                        vertical_spacing=0.02,\n",
        "                        y_title='$\\partial/\\partial t$')\n",
        "\n",
        "    ## Plotting TERRA and AQUA datssets seperately - commented out for now.\n",
        "    # nameDict ={'MODIS_16D_AQUA_VEG': 'Modis Aqua 250m 16 Day NDVI',\n",
        "    #            'MODIS_16D_TERRA_VEG': 'Modis Terra 250m 16 Day NDVI'}\n",
        "\n",
        "    # # Grouping NDVI data by satellitle (TERRA and AQUA) and plotting.\n",
        "    # for collection_name, collection in MODIS_AQUA_TERRA_df.groupby('Image_Collection'):\n",
        "    #     # Define error dictionary.\n",
        "    #     NDVI_error_dict = dict(type='data',\n",
        "    #                            array=collection['NDVI_stdDev'],\n",
        "    #                            visible=True)\n",
        "    #     # Create scatter plot.\n",
        "    #     NDVI_scatter = go.Scatter(\n",
        "    #                               x=collection['Timestamp'], \n",
        "    #                               y=collection['NDVI_median'], \n",
        "    #                               error_y=NDVI_error_dict, \n",
        "    #                               name=nameDict[collection_name], \n",
        "    #                               mode='lines+markers'\n",
        "    #                               )\n",
        "    #     # Add scatter plot to figure.\n",
        "    #     fig.add_trace(NDVI_scatter, row=1, col=1)\n",
        "\n",
        "    \n",
        "    # Plotting MODIS AQUA and TERRA NDVI as a single timeseries and taking the \n",
        "    # monthly rolling median and mean.\n",
        "    \n",
        "    # Create scatter plot.\n",
        "    NDVI_scatter_rolling_median = go.Scatter(\n",
        "                                             x=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').median().index, \n",
        "                                             y=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').median(), \n",
        "                                             name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Median', \n",
        "                                             mode='lines+markers'\n",
        "                                            )\n",
        "    \n",
        "    # Add scatter plot to figure.\n",
        "    fig.add_trace(NDVI_scatter_rolling_median, row=1, col=1)\n",
        "\n",
        "    NDVI_scatter_rolling_mean = go.Scatter(\n",
        "                                           x=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').mean().index, \n",
        "                                           y=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').mean(), \n",
        "                                           name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Mean', \n",
        "                                           mode='lines+markers'\n",
        "                                         )\n",
        "    \n",
        "    fig.add_trace(NDVI_scatter_rolling_mean, row=1, col=1)\n",
        "\n",
        "    ### Plotting CHIRPS Precipitation data ###\n",
        "    CHIRPS_scatter = go.Scatter(\n",
        "                                x=CHIRPS_df['precipitation_median_dt'].resample('14D').median().index, \n",
        "                                y=CHIRPS_df['precipitation_median_dt'].resample('14D').median(), \n",
        "                                marker_color='green',\n",
        "                                name='CHIRPS Precipitation - 14 Day Rolling Median (mm)', \n",
        "                                mode='lines+markers'\n",
        "                                )\n",
        "\n",
        "    fig.add_trace(CHIRPS_scatter, row=2, col=1)\n",
        "\n",
        "    ### Plotting PERSIANN Precipitation data ###\n",
        "    PERSIANN_scatter = go.Scatter(\n",
        "                                  x=PERSIANN_df['precipitation_median_dt'].resample('14D').median().index, \n",
        "                                  y=PERSIANN_df['precipitation_median_dt'].resample('14D').median(), \n",
        "                                  marker_color='black',\n",
        "                                  name='PERSIANN Precipitation - 14 Day Rolling Median (mm)', \n",
        "                                  mode='lines+markers'\n",
        "                                  )\n",
        "  \n",
        "    fig.add_trace(PERSIANN_scatter, row=2, col=1)\n",
        "\n",
        "    ### Plotting TerraClimate PDSI data ###\n",
        "    TerraClimate_PDSI_Scatter = go.Scatter(\n",
        "                                           x=TerraClimate_df.index, \n",
        "                                           y=TerraClimate_df['pdsi_median_dt'], \n",
        "                                           marker_color='purple',\n",
        "                                           name='TerraClimate PDSI', \n",
        "                                           mode='lines+markers'\n",
        "                                          )\n",
        "  \n",
        "    fig.add_trace(TerraClimate_PDSI_Scatter, row=3, col=1)\n",
        "\n",
        "\n",
        "    # Adds background shading to plots to represent disaster events.\n",
        "    fig = add_disaster_shading(fig, disaster_dates)\n",
        "\n",
        "    ### Updating axes, title and legend. ###\n",
        "    # Fixing y-axis range.\n",
        "    fig.update_yaxes(fixedrange=True)  \n",
        "    # Adding title.\n",
        "    if farm:\n",
        "        fig.update_layout(title=f\"{district}: {farm.replace('_', ' ')}\")\n",
        "    else:\n",
        "        fig.update_layout(title=f\"{district}\")\n",
        "    # Updating legend layout.\n",
        "    fig.update_layout(legend=dict(\n",
        "                                  orientation=\"h\"\n",
        "                                 ))        \n",
        "\n",
        "    return fig\n",
        "\n",
        "def add_district_level_NDVI_gradient(fig, district):\n",
        "    MODIS_AQUA_TERRA_df = import_MODIS_TERRA_AQUA(district)\n",
        "\n",
        "    NDVI_scatter_rolling_median = go.Scatter(\n",
        "                                            x=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').median().index, \n",
        "                                            y=MODIS_AQUA_TERRA_df['NDVI_median_dt'].resample('1M').median(), \n",
        "                                            marker_color = 'grey',\n",
        "                                            name='MODIS 250m 16 Day NDVI (Terra & Aqua) - 1 Month Rolling Median (District Level)', \n",
        "                                            mode='lines+markers'\n",
        "                                            )\n",
        "    \n",
        "    fig.add_trace(NDVI_scatter_rolling_median, row=1, col=1)\n",
        "\n",
        "    return fig"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4DXYt-MT1TG"
      },
      "source": [
        "## Creating Test Interactive Plots using Plotly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KVFo6o9vfKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5330d8df-41f9-4b9a-b9a3-c94224d2ba8a"
      },
      "source": [
        "district = 'Versailles'\n",
        "farm = 'Barcelona'\n",
        "\n",
        "fig_data_district = plot_interactive_time_series(district)\n",
        "fig_data_farm = plot_interactive_time_series(district, farm)\n",
        "fig_data_farm = add_district_level_NDVI(fig_data_farm, district)\n",
        "\n",
        "fig_gradient_district = plot_interactive_time_series_gradient(district)\n",
        "fig_gradient_farm = plot_interactive_time_series_gradient(district, farm)\n",
        "fig_gradient_farm = add_district_level_NDVI_gradient(fig_gradient_farm, district)\n",
        "\n",
        "\n",
        "display(fig_data_district, fig_data_farm, fig_gradient_district, fig_gradient_farm)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Versailles: Barcelona\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"matches\": \"x3\", \"showticklabels\": false}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x3\", \"showticklabels\": false}, \"xaxis3\": {\"anchor\": \"y3\", \"domain\": [0.0, 1.0]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.68, 1.0], \"fixedrange\": true}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.34, 0.66], 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              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spqkU5KgeuPV"
      },
      "source": [
        "## Creating Interactive Time Series Visualisations for Each District\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "785gAUeNeuPW",
        "outputId": "49f2d282-3897-4f55-e61c-8d57ad0b7ada"
      },
      "source": [
        "for district_name in ['Dosquebradas', 'Versailles']:\n",
        "\n",
        "    display(district_name)\n",
        "\n",
        "    fig_data = plot_interactive_time_series(district_name)\n",
        "    fig_gradient = plot_interactive_time_series_gradient(district_name)\n",
        "\n",
        "    filepath_data = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/{district_name}_time_series.html'\n",
        "    filepath_gradient = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/{district_name}_time_series_gradient.html'\n",
        "\n",
        "    fig_data.write_html(filepath_data)\n",
        "    fig_gradient.write_html(filepath_gradient)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles'"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-i50tkCWeuPZ"
      },
      "source": [
        "## Creating Static Plots for Each District By Year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "UH6qgN_FeuPZ",
        "outputId": "75e3447b-7999-4914-a9b8-3b20ec8b0928"
      },
      "source": [
        "years = np.arange(2000, 2021)\n",
        "\n",
        "for district_name in ['Dosquebradas', 'Versailles']:\n",
        "    display(district_name)\n",
        "    \n",
        "    fig_data = plot_interactive_time_series(district_name)\n",
        "    fig_gradient = plot_interactive_time_series_gradient(district_name)\n",
        "\n",
        "\n",
        "    for year in years:\n",
        "        fig_data.update_xaxes(\n",
        "                         range=[f'{year}-01-01',f'{year+1}-01-01'],  # sets the range of xaxis\n",
        "                         constrain=\"domain\",  # meanwhile compresses the xaxis by decreasing its \"domain\"\n",
        "                        )\n",
        "        \n",
        "        fig_gradient.update_xaxes(\n",
        "                         range=[f'{year}-01-01',f'{year+1}-01-01'],  # sets the range of xaxis\n",
        "                         constrain=\"domain\",  # meanwhile compresses the xaxis by decreasing its \"domain\"\n",
        "                        )\n",
        "        \n",
        "        filepath_data = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Yearly Static Plots/{district_name}_{year}.png'\n",
        "        filepath_gradient = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Yearly Static Plots/{district_name}_{year}_gradient.png'\n",
        "\n",
        "        fig_data.write_image(filepath_data)\n",
        "        fig_gradient.write_image(filepath_gradient)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles'"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2ybqpnmT4HO"
      },
      "source": [
        "## Creating Interactive Time Series Visualisations for Each Farm\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iw9rBO4LUbxx"
      },
      "source": [
        "farm_list = [\n",
        "             'Dosquebradas_Central_N_Farm_C', \n",
        "             'Dosquebradas_Dos_Cosechas_C',\n",
        "             'Dosquebradas_Fincas_campesinas_C',\n",
        "             'Dosquebradas_La_Union_C',\n",
        "             'Dosquebradas_Las_Camillas_C',\n",
        "             'Dosquebradas_Minas_del_Socorro_C',\n",
        "             'Dosquebradas_NE_area_C',\n",
        "             'Dosquebradas_N_Farm_C',\n",
        "             'Dosquebradas_New_coffee_farm_C',\n",
        "             'Dosquebradas_Santa_Ana_Alto_C',\n",
        "             'Dosquebradas_Santa_Ana_Bajo_La_Union_C',\n",
        "             'Versailles_Barcelona',\n",
        "             'Versailles_El_Chalet',\n",
        "             'Versailles_Guyabel',\n",
        "             'Versailles_La_Primavera',\n",
        "             'Versailles_La_Rivera',\n",
        "             'Versailles_Miraflores',\n",
        "             'Versailles_Predio_Valery',\n",
        "             'Versailles_Roberto',\n",
        "             'Versailles_San_Fernando',\n",
        "             'Versailles_Villa_Emanuel'\n",
        "            ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3oVwDja-LBd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "35b1e86a-0107-4d42-9f8f-30d50cbef1fa"
      },
      "source": [
        "for farm in farm_list:\n",
        "    district_name = farm.partition('_')[0]\n",
        "    farm_name = farm.partition('_')[-1]\n",
        "\n",
        "    display(farm)\n",
        "\n",
        "    fig_data = plot_interactive_time_series(district_name, farm_name)\n",
        "    fig_data = add_district_level_NDVI(fig_data, district_name)\n",
        "\n",
        "    fig_gradient = plot_interactive_time_series_gradient(district_name, farm_name)\n",
        "    fig_gradient = add_district_level_NDVI_gradient(fig_gradient, district)\n",
        "\n",
        "    filepath_data = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Farms/Interactive Plots/{farm_name}.html'\n",
        "    filepath_gradient = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Farms/Interactive Plots/{farm_name}_gradient.html'\n",
        "\n",
        "    fig_data.write_html(filepath_data)\n",
        "    fig_gradient.write_html(filepath_gradient)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Central_N_Farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Dos_Cosechas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Fincas_campesinas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_La_Union_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Las_Camillas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Minas_del_Socorro_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_NE_area_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_N_Farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_New_coffee_farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Santa_Ana_Alto_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Santa_Ana_Bajo_La_Union_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Barcelona'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_El_Chalet'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Guyabel'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_La_Primavera'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_La_Rivera'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Miraflores'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Predio_Valery'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Roberto'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_San_Fernando'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Villa_Emanuel'"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_gJj2lB5B1q"
      },
      "source": [
        "## Creating Static Plots for Each Farm By Year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1uoJC7x5fTp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "da5e0140-51da-4a96-92e6-779c1f5c6c65"
      },
      "source": [
        "years = np.arange(2000, 2021)\n",
        "\n",
        "for farm in farm_list:\n",
        "    district_name = farm.partition('_')[0]\n",
        "    farm_name = farm.partition('_')[-1]\n",
        "\n",
        "    display(farm)\n",
        "    \n",
        "    fig_data = plot_interactive_time_series(district_name, farm_name)\n",
        "    fig_data = add_district_level_NDVI(fig_data, district_name)\n",
        "\n",
        "    fig_gradient = plot_interactive_time_series_gradient(district_name, farm_name)\n",
        "    fig_gradient = add_district_level_NDVI_gradient(fig_gradient, district)\n",
        "\n",
        "\n",
        "    for year in years:\n",
        "        fig_data.update_xaxes(\n",
        "                         range=[f'{year}-01-01',f'{year+1}-01-01'],  # sets the range of xaxis\n",
        "                         constrain=\"domain\",  # meanwhile compresses the xaxis by decreasing its \"domain\"\n",
        "                        )\n",
        "        \n",
        "        fig_gradient.update_xaxes(\n",
        "                         range=[f'{year}-01-01',f'{year+1}-01-01'],  # sets the range of xaxis\n",
        "                         constrain=\"domain\",  # meanwhile compresses the xaxis by decreasing its \"domain\"\n",
        "                        )\n",
        "\n",
        "        filepath_data = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Farms/Yearly Static Plots/{farm_name}/{farm_name}_{year}.png'\n",
        "        filepath_gradient = f'/content/drive/Shared drives/Colombia SPRINT/Test Districts/{district_name}/Farms/Yearly Static Plots/{farm_name}/{farm_name}_{year}_gradient.png'\n",
        "\n",
        "        fig_data.write_image(filepath_data)\n",
        "        fig_gradient.write_image(filepath_gradient)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Central_N_Farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Dos_Cosechas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Fincas_campesinas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_La_Union_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Las_Camillas_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_Minas_del_Socorro_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_NE_area_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_N_Farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dosquebradas_New_coffee_farm_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "'Dosquebradas_Santa_Ana_Alto_C'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Barcelona'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_El_Chalet'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Guyabel'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_La_Primavera'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_La_Rivera'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Miraflores'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Predio_Valery'"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Versailles_Roberto'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "'Versailles_San_Fernando'"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "'Versailles_Villa_Emanuel'"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}