GeoPandasObsMap.ipynb

geopandasobsmap.ipynb

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  "metadata": {
    "colab": {
      "name": "GeoPandasObsMap.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/gist/barronh/309be7163b06017120324957088ebae2/geopandasobsmap.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuBusJgYFufY",
        "colab_type": "text"
      },
      "source": [
        "# Overview on Making Maps for Air Quality\n",
        "## Example IMPROVE Visibility\n",
        "\n",
        "    author: Barron H. Henderson\n",
        "    date: 2019-09-24\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-oDsLb8h0k0",
        "colab_type": "text"
      },
      "source": [
        "## Install Prerequisites\n",
        "\n",
        "At the time of writing, colab already includes most libraries used here (pandas, matplotlib), but does not include geopandas. Luckily, you can easily install with pip. See command below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zd31Vdd7wfx",
        "colab_type": "code",
        "outputId": "d64254d6-2640-4332-c1e5-db01c2132733",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Install geopandas using pip\n",
        "!pip install geopandas"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.6/dist-packages (0.5.1)\n",
            "Requirement already satisfied: pyproj in /usr/local/lib/python3.6/dist-packages (from geopandas) (2.4.0)\n",
            "Requirement already satisfied: fiona in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.8.6)\n",
            "Requirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.6.4.post2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from geopandas) (0.24.2)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (2.3.2)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.1.1)\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (7.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.12.0)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (0.5.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (19.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas) (2.5.3)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas) (1.16.5)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HK-yz0pVi5ha",
        "colab_type": "text"
      },
      "source": [
        "# Get Data\n",
        "\n",
        "This example uses two files from the IMPROVE website. The first is a summary of impairement data for each monitor. The second is metadata (longitude, latitude, elevation) for each site, which helps us in the plotting step.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhU9_dkH7zO7",
        "colab_type": "code",
        "outputId": "a8b2aeba-4c8a-406e-9f0d-6fdde1d8878e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Download data file from IMPROVE Website\n",
        "!wget http://vista.cira.colostate.edu/DataWarehouse/IMPROVE/Data/SummaryData/RHR_2017/sia_impairment_group_means_5_19.csv\n",
        "# Download site metadata file from IMPROVE Website\n",
        "!wget http://vista.cira.colostate.edu/improve/wp-content/uploads/2018/12/IMPROVE_sites_Oct2017.xlsx"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-09-24 19:16:50--  http://vista.cira.colostate.edu/DataWarehouse/IMPROVE/Data/SummaryData/RHR_2017/sia_impairment_group_means_5_19.csv\n",
            "Resolving vista.cira.colostate.edu (vista.cira.colostate.edu)... 129.82.109.67\n",
            "Connecting to vista.cira.colostate.edu (vista.cira.colostate.edu)|129.82.109.67|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7817783 (7.5M) [application/octet-stream]\n",
            "Saving to: ‘sia_impairment_group_means_5_19.csv.2’\n",
            "\n",
            "sia_impairment_grou 100%[===================>]   7.46M  3.41MB/s    in 2.2s    \n",
            "\n",
            "2019-09-24 19:16:52 (3.41 MB/s) - ‘sia_impairment_group_means_5_19.csv.2’ saved [7817783/7817783]\n",
            "\n",
            "--2019-09-24 19:16:54--  http://vista.cira.colostate.edu/improve/wp-content/uploads/2018/12/IMPROVE_sites_Oct2017.xlsx\n",
            "Resolving vista.cira.colostate.edu (vista.cira.colostate.edu)... 129.82.109.67\n",
            "Connecting to vista.cira.colostate.edu (vista.cira.colostate.edu)|129.82.109.67|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 22874 (22K) [application/vnd.openxmlformats-officedocument.spreadsheetml.sheet]\n",
            "Saving to: ‘IMPROVE_sites_Oct2017.xlsx.1’\n",
            "\n",
            "IMPROVE_sites_Oct20 100%[===================>]  22.34K  98.5KB/s    in 0.2s    \n",
            "\n",
            "2019-09-24 19:16:54 (98.5 KB/s) - ‘IMPROVE_sites_Oct2017.xlsx.1’ saved [22874/22874]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTEQJNLxiJLI",
        "colab_type": "text"
      },
      "source": [
        "# Load Libraries\n",
        "\n",
        "    matplotlib : python plotting library used extensively!\n",
        "    pandas : python library for data frames\n",
        "    geopands : python library for georeferenced data\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fujAzNgk7zZL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the pyplot module of matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# Pandas for reading non-georeferenced data\n",
        "import pandas as pd\n",
        "# geopandas for georeferencing\n",
        "import geopandas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CW-x40mfjhxX",
        "colab_type": "text"
      },
      "source": [
        "# Load Data\n",
        "\n",
        "The first step is to open the data and meta data separately and then understand what is in each. We use read_csv and read_excel to open each file. Then we use head to view the first 5 rows of each data frame. This helps understnad the columns and their meaning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nnTk9As73xo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Open data file as a data frame\n",
        "datadf = pd.read_csv('sia_impairment_group_means_5_19.csv')\n",
        "# Open metadata file as a data frame\n",
        "metadf = pd.read_excel('IMPROVE_sites_Oct2017.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmmvZPdz8I9t",
        "colab_type": "code",
        "outputId": "0b3adf54-7428-4247-f2f4-3ec51b116b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "datadf.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site</th>\n",
              "      <th>year</th>\n",
              "      <th>impairment_Group</th>\n",
              "      <th>EAmm_SO4</th>\n",
              "      <th>EAmm_NO3</th>\n",
              "      <th>EOMC</th>\n",
              "      <th>ELAC</th>\n",
              "      <th>ESoil</th>\n",
              "      <th>ECM</th>\n",
              "      <th>ESea_Salt</th>\n",
              "      <th>episodic_eomc</th>\n",
              "      <th>episodic_elac</th>\n",
              "      <th>episodic_esoil</th>\n",
              "      <th>episodic_ecm</th>\n",
              "      <th>routine_eamm_so4</th>\n",
              "      <th>routine_eamm_no3</th>\n",
              "      <th>routine_eomc</th>\n",
              "      <th>routine_elac</th>\n",
              "      <th>routine_esoil</th>\n",
              "      <th>routine_ecm</th>\n",
              "      <th>routine_esea_salt</th>\n",
              "      <th>ss_rayleigh</th>\n",
              "      <th>anthro_eamm_so4</th>\n",
              "      <th>anthro_eamm_no3</th>\n",
              "      <th>anthro_eomc</th>\n",
              "      <th>anthro_elac</th>\n",
              "      <th>anthro_esoil</th>\n",
              "      <th>anthro_ecm</th>\n",
              "      <th>episodic_bext</th>\n",
              "      <th>routine_bext</th>\n",
              "      <th>anthro_bext</th>\n",
              "      <th>episodic_routine_bext</th>\n",
              "      <th>f_anthro</th>\n",
              "      <th>TBext</th>\n",
              "      <th>routine_dv</th>\n",
              "      <th>haze_dv</th>\n",
              "      <th>episodic_routine_dv</th>\n",
              "      <th>impairment</th>\n",
              "      <th>good_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>1990</td>\n",
              "      <td>10</td>\n",
              "      <td>9.597295</td>\n",
              "      <td>1.671101</td>\n",
              "      <td>3.587307</td>\n",
              "      <td>2.849937</td>\n",
              "      <td>0.135075</td>\n",
              "      <td>5.337357</td>\n",
              "      <td>0.884668</td>\n",
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              "      <td>2.960141</td>\n",
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              "      <td>3.172182</td>\n",
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              "      <td>0.094342</td>\n",
              "      <td>1.528048</td>\n",
              "      <td>0.884668</td>\n",
              "      <td>12</td>\n",
              "      <td>9.061365</td>\n",
              "      <td>1.376117</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.890142</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.849168</td>\n",
              "      <td>4.236760</td>\n",
              "      <td>18.649189</td>\n",
              "      <td>13.176791</td>\n",
              "      <td>22.885949</td>\n",
              "      <td>0.352565</td>\n",
              "      <td>36.062740</td>\n",
              "      <td>6.148580</td>\n",
              "      <td>12.194677</td>\n",
              "      <td>7.754473</td>\n",
              "      <td>4.440204</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>1990</td>\n",
              "      <td>30</td>\n",
              "      <td>20.176452</td>\n",
              "      <td>2.868535</td>\n",
              "      <td>6.518862</td>\n",
              "      <td>3.049133</td>\n",
              "      <td>0.210913</td>\n",
              "      <td>4.120946</td>\n",
              "      <td>0.864524</td>\n",
              "      <td>0.923669</td>\n",
              "      <td>0.194679</td>\n",
              "      <td>0.039818</td>\n",
              "      <td>1.598339</td>\n",
              "      <td>1.126689</td>\n",
              "      <td>0.506356</td>\n",
              "      <td>5.595193</td>\n",
              "      <td>0.195581</td>\n",
              "      <td>0.171095</td>\n",
              "      <td>1.621504</td>\n",
              "      <td>0.864524</td>\n",
              "      <td>12</td>\n",
              "      <td>19.049763</td>\n",
              "      <td>2.362179</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.658874</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.901103</td>\n",
              "      <td>2.756504</td>\n",
              "      <td>22.080942</td>\n",
              "      <td>24.971918</td>\n",
              "      <td>24.837447</td>\n",
              "      <td>0.499752</td>\n",
              "      <td>49.809365</td>\n",
              "      <td>7.789261</td>\n",
              "      <td>15.755190</td>\n",
              "      <td>8.812371</td>\n",
              "      <td>6.942819</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>1990</td>\n",
              "      <td>50</td>\n",
              "      <td>24.427646</td>\n",
              "      <td>3.335768</td>\n",
              "      <td>3.896989</td>\n",
              "      <td>2.696000</td>\n",
              "      <td>0.161202</td>\n",
              "      <td>2.259215</td>\n",
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              "      <td>1.364084</td>\n",
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              "      <td>3.502058</td>\n",
              "      <td>0.167543</td>\n",
              "      <td>0.144997</td>\n",
              "      <td>1.104297</td>\n",
              "      <td>1.011468</td>\n",
              "      <td>12</td>\n",
              "      <td>23.063562</td>\n",
              "      <td>2.746936</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.277708</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.613681</td>\n",
              "      <td>1.203123</td>\n",
              "      <td>19.883279</td>\n",
              "      <td>28.701886</td>\n",
              "      <td>21.086402</td>\n",
              "      <td>0.577101</td>\n",
              "      <td>49.788288</td>\n",
              "      <td>6.763389</td>\n",
              "      <td>15.857289</td>\n",
              "      <td>7.241422</td>\n",
              "      <td>8.615867</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>1990</td>\n",
              "      <td>70</td>\n",
              "      <td>49.643454</td>\n",
              "      <td>7.435414</td>\n",
              "      <td>9.601873</td>\n",
              "      <td>5.265733</td>\n",
              "      <td>0.292785</td>\n",
              "      <td>3.726004</td>\n",
              "      <td>1.354326</td>\n",
              "      <td>3.898735</td>\n",
              "      <td>2.010782</td>\n",
              "      <td>0.056047</td>\n",
              "      <td>2.003671</td>\n",
              "      <td>2.772180</td>\n",
              "      <td>1.312505</td>\n",
              "      <td>5.703138</td>\n",
              "      <td>0.223022</td>\n",
              "      <td>0.236737</td>\n",
              "      <td>1.107097</td>\n",
              "      <td>1.354326</td>\n",
              "      <td>12</td>\n",
              "      <td>46.871274</td>\n",
              "      <td>6.122908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.031929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.615236</td>\n",
              "      <td>7.969236</td>\n",
              "      <td>24.709006</td>\n",
              "      <td>56.641348</td>\n",
              "      <td>32.678242</td>\n",
              "      <td>0.633067</td>\n",
              "      <td>89.319590</td>\n",
              "      <td>8.892884</td>\n",
              "      <td>20.839208</td>\n",
              "      <td>10.806925</td>\n",
              "      <td>10.032283</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>1990</td>\n",
              "      <td>90</td>\n",
              "      <td>77.240788</td>\n",
              "      <td>11.023324</td>\n",
              "      <td>9.450994</td>\n",
              "      <td>6.362824</td>\n",
              "      <td>0.239531</td>\n",
              "      <td>2.860645</td>\n",
              "      <td>1.640068</td>\n",
              "      <td>3.867441</td>\n",
              "      <td>2.344458</td>\n",
              "      <td>0.049628</td>\n",
              "      <td>1.154741</td>\n",
              "      <td>4.313265</td>\n",
              "      <td>1.945846</td>\n",
              "      <td>5.583553</td>\n",
              "      <td>0.275330</td>\n",
              "      <td>0.189903</td>\n",
              "      <td>1.096536</td>\n",
              "      <td>1.640068</td>\n",
              "      <td>12</td>\n",
              "      <td>72.927523</td>\n",
              "      <td>9.077478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.743036</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609368</td>\n",
              "      <td>7.416267</td>\n",
              "      <td>27.044501</td>\n",
              "      <td>86.357405</td>\n",
              "      <td>34.460768</td>\n",
              "      <td>0.701214</td>\n",
              "      <td>120.818173</td>\n",
              "      <td>9.855044</td>\n",
              "      <td>24.130989</td>\n",
              "      <td>11.905216</td>\n",
              "      <td>12.225774</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    site  year  impairment_Group  ...  episodic_routine_dv  impairment  good_year\n",
              "0  ACAD1  1990                10  ...             7.754473    4.440204          1\n",
              "1  ACAD1  1990                30  ...             8.812371    6.942819          1\n",
              "2  ACAD1  1990                50  ...             7.241422    8.615867          1\n",
              "3  ACAD1  1990                70  ...            10.806925   10.032283          1\n",
              "4  ACAD1  1990                90  ...            11.905216   12.225774          1\n",
              "\n",
              "[5 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMk6HC8Mj4RH",
        "colab_type": "code",
        "outputId": "db09d3b7-dabb-4ee5-f73c-a751fe8d9246",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "metadf.head()"
      ],
      "execution_count": 8,
      "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>Name</th>\n",
              "      <th>SiteName</th>\n",
              "      <th>StartDate</th>\n",
              "      <th>AffiliationCode</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Elevation (m)</th>\n",
              "      <th>LocationState</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ACAD1</td>\n",
              "      <td>Acadia</td>\n",
              "      <td>1988-03-02</td>\n",
              "      <td>NPS</td>\n",
              "      <td>44.380000</td>\n",
              "      <td>-68.260000</td>\n",
              "      <td>157.330000</td>\n",
              "      <td>ME</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AGTI1</td>\n",
              "      <td>Agua Tibia</td>\n",
              "      <td>2000-11-15</td>\n",
              "      <td>FS</td>\n",
              "      <td>33.463646</td>\n",
              "      <td>-116.970625</td>\n",
              "      <td>507.500000</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ATLA1</td>\n",
              "      <td>Atlanta</td>\n",
              "      <td>2004-04-30</td>\n",
              "      <td>STN</td>\n",
              "      <td>33.687963</td>\n",
              "      <td>-84.290291</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>GA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BADL1</td>\n",
              "      <td>Badlands</td>\n",
              "      <td>1988-03-02</td>\n",
              "      <td>NPS</td>\n",
              "      <td>43.743500</td>\n",
              "      <td>-101.941167</td>\n",
              "      <td>736.000000</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BALD1</td>\n",
              "      <td>Mount Baldy</td>\n",
              "      <td>2000-02-29</td>\n",
              "      <td>FS</td>\n",
              "      <td>34.058441</td>\n",
              "      <td>-109.440564</td>\n",
              "      <td>2508.666667</td>\n",
              "      <td>AZ</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name     SiteName  StartDate  ...   Longitude  Elevation (m)  LocationState\n",
              "0  ACAD1       Acadia 1988-03-02  ...  -68.260000     157.330000             ME\n",
              "1  AGTI1   Agua Tibia 2000-11-15  ... -116.970625     507.500000             CA\n",
              "2  ATLA1      Atlanta 2004-04-30  ...  -84.290291     243.000000             GA\n",
              "3  BADL1     Badlands 1988-03-02  ... -101.941167     736.000000             SD\n",
              "4  BALD1  Mount Baldy 2000-02-29  ... -109.440564    2508.666667             AZ\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZkFq1OHjmVG",
        "colab_type": "text"
      },
      "source": [
        "# Using Columns as Indices\n",
        "\n",
        "The metadata dataframe has a row for each monitor, while the data has a row for each monitor, year, impairment group. Based on this knowledge, we set the index for each data frame. We also see all the data available in each data frame. For this excercise, we will plot total extinction (TBext). You can modify it to plot whatever you want."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZb3ST1jAmMi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the site, year, and impairment_Group as the index\n",
        "datadf.set_index(['site', 'year', 'impairment_Group'], inplace=True)\n",
        "# Set the \"Name\" as an index\n",
        "metadf.set_index('Name', inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCACGdPjkheV",
        "colab_type": "text"
      },
      "source": [
        "# Subsetting\n",
        "\n",
        "For this exercise, we're going to use a cross section (xs) of the data. We're going to choose a single year (2016) anda single impairment group (90). The data currently has a three level index (0: site, 1: year, 2: impairment_Group), so we can use the cross section function xs twice to get just the data we want. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVrmJnu0-nfF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Take just the 90th percentile group (aka most impaired)\n",
        "mimpdf = datadf.xs(90, level=2)\n",
        "# Take just the year 2016\n",
        "yyyydf = mimpdf.xs(2016, level=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKDKWrn_lgE2",
        "colab_type": "text"
      },
      "source": [
        "# Joining metadata with data\n",
        "\n",
        "The data we have is not currently associated with the location metadata. To connect the two, we use the join methodology. Join is similar to the SQL join statement.\n",
        "\n",
        "To confirm the data looks right, we filter for the total extinction column and location. Confirm by looking at the ACAD1 site in Acadia National Park. The longitude and latitude should be Eastish and Northish."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNyY7Q1llhwH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Combine the metadata with the data\n",
        "plotdf = yyyydf.join(metadf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idO38pBw_F05",
        "colab_type": "code",
        "outputId": "37bbc046-aa31-437a-e81b-5dc1f858d38e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "# View example data\n",
        "plotdf.filter(['Longitude', 'Latitude', 'TBext']).head()"
      ],
      "execution_count": 12,
      "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>Longitude</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>TBext</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>site</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ACAD1</th>\n",
              "      <td>-68.260000</td>\n",
              "      <td>44.380000</td>\n",
              "      <td>40.338074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGTI1</th>\n",
              "      <td>-116.970625</td>\n",
              "      <td>33.463646</td>\n",
              "      <td>51.945919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BADL1</th>\n",
              "      <td>-101.941167</td>\n",
              "      <td>43.743500</td>\n",
              "      <td>34.155968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BALA1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.130472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BALD1</th>\n",
              "      <td>-109.440564</td>\n",
              "      <td>34.058441</td>\n",
              "      <td>19.778096</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Longitude   Latitude      TBext\n",
              "site                                   \n",
              "ACAD1  -68.260000  44.380000  40.338074\n",
              "AGTI1 -116.970625  33.463646  51.945919\n",
              "BADL1 -101.941167  43.743500  34.155968\n",
              "BALA1         NaN        NaN  37.130472\n",
              "BALD1 -109.440564  34.058441  19.778096"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ko1FA3Zmcny",
        "colab_type": "text"
      },
      "source": [
        "# Preparing a Lamber Conformal Projection\n",
        "\n",
        "The latitude and longitude that we currently have is basically WGS84 data. When we plot obs data with model data, it is often useful to have them on the same projection. For that purpose, we are going to define a `PROJ4` definition of the EPA 12US2 domain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gJp4RM25RqZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setup a EPA 12US2 domain in PROJ4 language\n",
        "proj4string = '+proj=lcc +lon_0=-97 +lat_0=40 +lat_1=33 +lat_2=45 +a=6370000 +b=6370000 +x_0=2412000 +y_0=1620000 +to_meter=12000 +no_defs'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVwKhRhgmyR4",
        "colab_type": "text"
      },
      "source": [
        "# Georeferencing\n",
        "\n",
        "Here we create a georeferenced DataFrame by creating point geometry from the Longitude and Latitude fields. Because these are not projected coordinates, we set an intiial projection of epsg:4326. Then, we convert the coordinate reference system to the 12US2 projected space using `to_crs`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-oHvyEs5DQ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use geopandas to make the data a point dataframe\n",
        "gdf = geopandas.GeoDataFrame(\n",
        "    plotdf,\n",
        "    geometry=geopandas.points_from_xy(plotdf.Longitude, plotdf.Latitude),\n",
        "    crs = {'init': 'epsg:4326'}\n",
        ")\n",
        "# Convert point dataframe to 12US2\n",
        "gdf = gdf.to_crs(proj4string);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZvud1mdnScD",
        "colab_type": "text"
      },
      "source": [
        "# Geopolitical Borders\n",
        "\n",
        "What would a map be without some geopolitical borders? So, we are going to use the built-in natural earth country borders. Only include data within the bounding box defined by our data (bbox=gdf) They are stored in their own projection and we convert those to the 12US2 space."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsKQg3Hw73eJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get a low resolution natural earth that includes boundaries and convert to 12US2\n",
        "world = geopandas.read_file(\n",
        "    geopandas.datasets.get_path('naturalearth_lowres'),\n",
        "    bbox=gdf\n",
        ").to_crs(proj4string)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88bbARFCnoaz",
        "colab_type": "text"
      },
      "source": [
        "# Making a Figure\n",
        "\n",
        "1. Make figure with a 1x1 grid of axes.\n",
        "2. Put the world country borders on the map.\n",
        "3. Plot the TBext column\n",
        "4. Grab the collection of dots\n",
        "5. Make a colorbar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYg8wRYr8BQG",
        "colab_type": "code",
        "outputId": "06dffc51-2758-401f-eb65-ea71e689c308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "# Open a new figure for plotting\n",
        "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
        "# Add world to the default axis (new)\n",
        "world.plot(facecolor='white', edgecolor='black', ax=ax)\n",
        "# Add data to existing axis\n",
        "gdf.plot(c=plotdf.TBext, label='Ext', ax=ax)\n",
        "dots = ax.collections[-1]\n",
        "cbar = fig.colorbar(dots, label='Mm**-1')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8yYsXLwDQ1NSkVKlS3L9/nzFjxry3NSYuLo59+/bRt29fevTowbhx43BycmL1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7aGpAT7zBmjwcSWZXswGp5vNX/r8u2UsZ506JLZ7LRoiiThINGbmYQEFSawcD7Uqc7+BaBRKWnz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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFO_5hpOoQL0",
        "colab_type": "text"
      },
      "source": [
        "# Beautify the Map\n",
        "\n",
        "1. Set the x and y limits to the rows (0, 246) and columns (0, 396).\n",
        "2. Row/Col ticks are distracting, so remove them.\n",
        "3. Use `fig` to rereference the figure and show it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unAZ5WLF9rxu",
        "colab_type": "code",
        "outputId": "41bfce43-a558-416c-daa6-9e035f70ee3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "source": [
        "# Clip to 12US2\n",
        "ax.set_xlim(0, 396)\n",
        "ax.set_ylim(0, 246)\n",
        "# Remove spatial ticks\n",
        "ax.set_xticks([])\n",
        "ax.set_yticks([])\n",
        "fig"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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X15ewsDB+//13PvnkE9zd3fHw8KBp1UFcOHUDXayB6EgdBr2Rw3uD+HXV4SyL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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8p39HOy5oxyJ",
        "colab_type": "text"
      },
      "source": [
        "# More Beautifying\n",
        "\n",
        "1. Grab the colorbar axis, so you can replace the colorbar\n",
        "2. Change the color normalizer (norm) of the dots to be a log scale\n",
        "3. Put the colorbar on the old colorbar axis (cax)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewTdxEnBC2xG",
        "colab_type": "code",
        "outputId": "11cc6fd5-3a4e-4503-f824-a8ebc89faee6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "# Get the axis where the colobar is stored\n",
        "cax = cbar.ax\n",
        "cax.cla()\n",
        "\n",
        "# Update color scale\n",
        "dots.set_norm(plt.matplotlib.colors.LogNorm(vmin=20, vmax=100))\n",
        "cbar = fig.colorbar(dots, cax=cax, label='Mm$^{-1}$')\n",
        "fig"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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X77zZKpPtV4vjx4/b9VqFZUMq9kO4rcK04CiKQnR0NHPmzCnxufqN60qXQa04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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVIEJISRFZ8-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}