new_maps.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.1",
      "mimetype": "text/x-python",
      "file_extension": ".py",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "nbconvert_exporter": "python"
    },
    "colab": {
      "name": "new_maps.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vitorsr/ccd/blob/master/new_maps.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qdDEt6FRQ0u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !apt-get update -qqq && apt-get dist-upgrade -qqq -y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y0-IbttPYSg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "763eaffe-f1ed-4939-8970-3dff5a300757"
      },
      "source": [
        "# https://github.com/googlecolab/colabtools/issues/85#issuecomment-372532728\n",
        "!apt-get install libproj-dev proj-data proj-bin -qqq -y\n",
        "!apt-get install libgeos-dev -qqq -y\n",
        "!pip install -q -U cython\n",
        "!pip install -q -U cartopy\n",
        "!pip install -q -U geopandas\n",
        "!pip install -q -U geoplot"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package libproj-dev:amd64.\n",
            "(Reading database ... 132681 files and directories currently installed.)\n",
            "Preparing to unpack .../libproj-dev_4.9.3-2_amd64.deb ...\n",
            "Unpacking libproj-dev:amd64 (4.9.3-2) ...\n",
            "Selecting previously unselected package proj-bin.\n",
            "Preparing to unpack .../proj-bin_4.9.3-2_amd64.deb ...\n",
            "Unpacking proj-bin (4.9.3-2) ...\n",
            "Setting up libproj-dev:amd64 (4.9.3-2) ...\n",
            "Setting up proj-bin (4.9.3-2) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Selecting previously unselected package libgeos-dev.\n",
            "(Reading database ... 132714 files and directories currently installed.)\n",
            "Preparing to unpack .../libgeos-dev_3.6.2-1build2_amd64.deb ...\n",
            "Unpacking libgeos-dev (3.6.2-1build2) ...\n",
            "Setting up libgeos-dev (3.6.2-1build2) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "\u001b[K     |████████████████████████████████| 8.9MB 9.1MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 225kB 49.4MB/s \n",
            "\u001b[?25h  Building wheel for cartopy (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 921kB 9.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 11.9MB 199kB/s \n",
            "\u001b[K     |████████████████████████████████| 10.1MB 44.4MB/s \n",
            "\u001b[?25h  Building wheel for munch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 15.1MB 162kB/s \n",
            "\u001b[?25h  Building wheel for mapclassify (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Goln-ckpZk7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://gadm.org/about.html\n",
        "!wget -q \"https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_BRA_shp.zip\"\n",
        "!unzip -qq -u gadm36_BRA_shp.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zn8PTPwFG4ZJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import geopandas as gpd\n",
        "import geoplot as gplt\n",
        "\n",
        "br = gpd.read_file('gadm36_BRA_1.shp')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXwO37oDTz3T",
        "colab_type": "code",
        "outputId": "7b5157c0-b3a1-4fbe-c6c4-f8d68bc4f7ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# pca_vals = pd.read_csv(\"https://raw.githubusercontent.com/vitorsr/ccd/master/data/pca_values_08-19.csv?token=AJW2VYKF6JXIF7CPJPIX4YC5X3H4E\")\n",
        "\n",
        "pca_vals = pd.read_csv(\"https://raw.githubusercontent.com/vitorsr/ccd/master/data/pc1_2019.csv?token=AJW2VYLHX2G6H76T3YWGEQ25X5MEW\")\n",
        "pca_vals.head(1)"
      ],
      "execution_count": 5,
      "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>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>82098</td>\n",
              "      <td>-0.519376</td>\n",
              "      <td>2.422612</td>\n",
              "      <td>0.173939</td>\n",
              "      <td>-51.116667</td>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0     id         0         1         2        lon   lat\n",
              "0           0  82098 -0.519376  2.422612  0.173939 -51.116667 -0.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "846eUuiLSH7j",
        "colab_type": "code",
        "outputId": "458c5076-996c-4c7b-ba78-10e9794880ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "(np.min(pca_vals[\"0\"].to_numpy()[:]), np.max(pca_vals[\"0\"].to_numpy()[:]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-3.4831080515443564, 4.534872295930258)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dbQ81YyVN0c",
        "colab_type": "code",
        "outputId": "6b77e461-284a-471d-d82a-4813b61b81e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "import cartopy.crs as ccrs\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.cm import ScalarMappable\n",
        "from matplotlib.colors import Normalize\n",
        "\n",
        "lons = pca_vals[\"lon\"].to_numpy()[:]\n",
        "lats = pca_vals[\"lat\"].to_numpy()[:]\n",
        "data = pca_vals[\"0\"].to_numpy()[:] # 0th princomp\n",
        "\n",
        "fig_pc = plt.figure()\n",
        "\n",
        "ax = plt.axes(projection=ccrs.Miller())\n",
        "\n",
        "lb = -5; ub = 5; lv = 20 # lower bound, upper bound, levels\n",
        "\n",
        "plt.tricontourf(lons, lats, data, np.linspace(lb, ub, lv),\n",
        "                cmap=\"coolwarm\", extend=\"both\")\n",
        "\n",
        "gplt.polyplot(br, edgecolor=\"dimgray\", linewidth=0.5, zorder=1, ax=ax)\n",
        "\n",
        "plt.colorbar(ScalarMappable(norm=Normalize(lb, ub), cmap=\"coolwarm\"), ax=ax)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "fig_pc.savefig(\"pca_princomp1_20190819.png\", dpi=300)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F6P2twh9iKHfYpz5i9sWHr9E5IZk92zfVee+NN97A19e5QCt4Ba+I2PpHbnSpbdC0uYqI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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEyc84cAZMgn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metadata = pd.read_csv(\"https://onedrive.live.com/download?cid=BCBADB0836C97273&resid=BCBADB0836C97273%21115089&authkey=AN3WuZnSKh9jgC8\")\n",
        "temp_aug = pd.read_csv(\"https://raw.githubusercontent.com/vitorsr/ccd/master/data/baseline.csv?token=AJW2VYMFUCGYYQMVEQAMNTC5X5MLE\")\n",
        "temp_vals = pd.merge(metadata, temp_aug.groupby(by=\"id\").mean(), on=\"id\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuPFSbPqe_7a",
        "colab_type": "code",
        "outputId": "a5270921-43a8-45d3-c90d-448e29c19bb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "temp_vals.head(1)"
      ],
      "execution_count": 11,
      "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>id</th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "      <th>alt</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>uf</th>\n",
              "      <th>time_zone</th>\n",
              "      <th>offset_utc</th>\n",
              "      <th>time_zone.1</th>\n",
              "      <th>offset_utc.1</th>\n",
              "      <th>prec</th>\n",
              "      <th>tair</th>\n",
              "      <th>tw</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>urmax</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>wd</th>\n",
              "      <th>wsmax</th>\n",
              "      <th>n</th>\n",
              "      <th>cc</th>\n",
              "      <th>evap</th>\n",
              "      <th>ur</th>\n",
              "      <th>ws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>82915</td>\n",
              "      <td>-67.8</td>\n",
              "      <td>-9.966667</td>\n",
              "      <td>160.0</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>1.736536</td>\n",
              "      <td>25.67877</td>\n",
              "      <td>22.235053</td>\n",
              "      <td>32.977355</td>\n",
              "      <td>18.284184</td>\n",
              "      <td>76.200715</td>\n",
              "      <td>995.277863</td>\n",
              "      <td>1012.995128</td>\n",
              "      <td>10.004215</td>\n",
              "      <td>1.072976</td>\n",
              "      <td>6.372858</td>\n",
              "      <td>5.134233</td>\n",
              "      <td>3.471002</td>\n",
              "      <td>77.147346</td>\n",
              "      <td>1.073483</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id   lon       lat    alt  ...        cc      evap         ur        ws\n",
              "0  82915 -67.8 -9.966667  160.0  ...  5.134233  3.471002  77.147346  1.073483\n",
              "\n",
              "[1 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyFSD3tpXbzs",
        "colab_type": "code",
        "outputId": "097bc704-8109-4234-c845-b8ac1843bcb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "temp_vals = temp_vals[temp_vals[\"tair\"] == temp_vals[\"tair\"]]\n",
        "(np.min(temp_vals[\"tair\"].to_numpy()[:]), np.max(temp_vals[\"tair\"].to_numpy()[:]))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13.094847427330537, 30.7841906905677)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqBx99DoJyEE",
        "colab_type": "code",
        "outputId": "f58b830d-9881-4c2e-87d2-0f7eef8aa127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "lons = temp_vals[\"lon\"].to_numpy()[:]\n",
        "lats = temp_vals[\"lat\"].to_numpy()[:]\n",
        "data = temp_vals[\"tair\"].to_numpy()[:]\n",
        "\n",
        "fig_tair = plt.figure()\n",
        "\n",
        "ax = plt.axes(projection=ccrs.Miller())\n",
        "\n",
        "lb = 13; ub = 31; lv = 20 # lower bound, upper bound, levels\n",
        "\n",
        "plt.tricontourf(lons, lats, data, np.linspace(lb, ub, lv),\n",
        "                cmap=\"coolwarm\", extend=\"both\")\n",
        "\n",
        "gplt.polyplot(br, edgecolor=\"dimgray\", linewidth=0.5, zorder=1, ax=ax)\n",
        "\n",
        "plt.colorbar(ScalarMappable(norm=Normalize(lb, ub), cmap=\"coolwarm\"), ax=ax)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "fig_tair.savefig(\"tair_baseline.png\", dpi=300)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XOmS+mOPjgy4df23VM0pWaA7E/jAL5WDM10ddEnPNqbjvB8YSfyur5Hxu8AfvRQ16Dkkm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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}