pypsa_gridtracer_costs_raster_v1.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyNNbTThcFvvC8c0sX+NXgu1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Cesare-Caputo/PyPSA-gridtracer/blob/main/pypsa_gridtracer_costs_raster_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#setup"
      ],
      "metadata": {
        "id": "EoKKQkw0Hl2I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nN6LesCWGrXz",
        "outputId": "4ce0413c-cdc5-47dc-9279-1d6eec851121"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting geopandas\n",
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            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n",
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            "Installing collected packages: rtree\n",
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            "Collecting rasterio\n",
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            "Successfully installed affine-2.3.1 rasterio-1.2.10 snuggs-1.4.7\n"
          ]
        }
      ],
      "source": [
        "!pip install geopandas\n",
        "!pip install folium\n",
        "!pip install rtree\n",
        "!pip install rasterio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDQYk2tuHBzV",
        "outputId": "56379ecf-8556-4201-d7b6-799e092f7cb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##helper functions\n"
      ],
      "metadata": {
        "id": "JCyt-CvSHSz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from osgeo import osr, gdal\n",
        "import numpy as np\n",
        "from gdalconst import *\n",
        "from shapely.geometry import Polygon, Point\n",
        "import geopandas as gpd\n",
        "import folium\n",
        "import os\n",
        "import rtree\n",
        "from tqdm import tqdm\n",
        "# import pygeos\n",
        "\n",
        "\n",
        "def make_polygon_list(path, resume=None):\n",
        "    \"\"\"\n",
        "    Creates GeoDataFrame, checks the tif files in a desired dir\n",
        "    and adds their geometries (with respective file name) as row\n",
        "    to GeoDataFrame\n",
        "\n",
        "    Parameters\n",
        "    -----------\n",
        "\n",
        "    path : (str)\n",
        "        directory with tif files\n",
        "\n",
        "    resume : (gpd.GeoDataFrame or None)\n",
        "        if None: initializes new GeoDataFrame\n",
        "        else: appends to given one\n",
        "\n",
        "    Returns\n",
        "    ----------\n",
        "    coverage : gpd.GeoDataFrame\n",
        "        df with geometries of tif files\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    tif_files = []\n",
        "\n",
        "    # iterate over all .tif files in desired dir\n",
        "    for filename in os.listdir(path):\n",
        "        if filename.endswith(\".tif\"):\n",
        "            tif_files.append(os.path.join(path, filename))\n",
        "\n",
        "    tif_files = [os.path.join(path, tif_file) for tif_file in tif_files]\n",
        "\n",
        "    # add data to existing dataframe if desired\n",
        "    if resume is None:\n",
        "        coverage = gpd.GeoDataFrame({\"filename\": [], \"geometry\": []}).set_crs(epsg=4326)\n",
        "    else:\n",
        "        coverage = resume.set_crs(epsg=4326)\n",
        "\n",
        "    # extract geometries of .tif files and add to geodataframe\n",
        "    for file in tqdm(tif_files):\n",
        "        info = gdal.Info(file, format=\"json\")\n",
        "        corners = info[\"cornerCoordinates\"]\n",
        "        poly = Polygon((\n",
        "                    corners[\"upperLeft\"],\n",
        "                    corners[\"upperRight\"],\n",
        "                    corners[\"lowerRight\"],\n",
        "                    corners[\"lowerLeft\"]\n",
        "                        ))\n",
        "        coverage = coverage.append({\"filename\": file, \"geometry\": poly}, ignore_index=True)\n",
        "\n",
        "    return coverage\n"
      ],
      "metadata": {
        "id": "kIWZriGnHdrJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#explore GH coverage"
      ],
      "metadata": {
        "id": "UxlHoHguHx8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base = '/content/drive/MyDrive/PyPSA_Africa_images/maxar'\n",
        "path_GH = os.path.join(base, 'ghana/raw')\n",
        "coverage=None\n",
        "coverage = make_polygon_list(path_GH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7ScgkWHH0Ax",
        "outputId": "e3c1900a-dd96-449c-8f88-8262f84b42a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 52/52 [00:51<00:00,  1.01it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "map_start = coverage.dissolve().centroid\n",
        "map_start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvK-5Jr5Iik-",
        "outputId": "66963e75-7507-441e-a4f0-e6cb7d3ee90a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
            "\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    POINT (-1.87253 9.45276)\n",
              "dtype: geometry"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import folium\n",
        "\n",
        "fn = 'gh_tif_coverage.geojson'\n",
        "coverage.to_file(fn, driver='GeoJSON')\n",
        "\n",
        "m = folium.Map(\n",
        "    location=[-1.87, 9.45],\n",
        "    tiles=\"cartodbpositron\",\n",
        "    zoom_start=5,\n",
        ")\n",
        "\n",
        "folium.GeoJson(fn, name=\"geojson\").add_to(m)\n",
        "\n",
        "m"
      ],
      "metadata": {
        "id": "cmgMsHE_JGGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "towers_GH = os.path.join(base, \"ghana/raw/GH_raw_towers.geojson\")\n",
        "towers_GH = gpd.read_file(towers_GH)"
      ],
      "metadata": {
        "id": "CRT2YN3mJqCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hits_GH = gpd.sjoin(towers_GH, coverage, how=\"inner\").set_crs(epsg=4326)\n",
        "print(len(hits_GH))\n",
        "\n",
        "hits_GH = hits_GH.drop([\"index_right\"], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbyUgj1nJtld",
        "outputId": "b194281c-12b4-4169-a9fb-96802a8977cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hits_GH"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cHBSKfdxgKun",
        "outputId": "251dc7a8-7c63-4809-d8e7-bff76bab91f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                 id tags.power tags.material tags.height tags.structure  \\\n",
              "3      3.063970e+09      tower          None        None           None   \n",
              "4      3.063970e+09      tower          None        None           None   \n",
              "157    6.299637e+09      tower          None        None           None   \n",
              "237    3.063969e+09      tower          None        None           None   \n",
              "405    3.063969e+09      tower          None        None           None   \n",
              "...             ...        ...           ...         ...            ...   \n",
              "11342  2.622444e+09      tower          None        None           None   \n",
              "11397  2.622444e+09      tower          None        None           None   \n",
              "11449  2.622276e+09      tower          None        None           None   \n",
              "1578   2.622444e+09      tower          None        None           None   \n",
              "4724   2.622444e+09      tower          None        None           None   \n",
              "\n",
              "      tags.line_management  Type Country                  geometry  \\\n",
              "3                     None  Node      GH  POINT (-2.34855 8.85587)   \n",
              "4                     None  Node      GH  POINT (-2.41069 8.92856)   \n",
              "157                   None  Node      GH  POINT (-2.30691 8.69095)   \n",
              "237                   None  Node      GH  POINT (-2.33556 8.81249)   \n",
              "405                   None  Node      GH  POINT (-2.32309 8.75970)   \n",
              "...                    ...   ...     ...                       ...   \n",
              "11342                 None  Node      GH  POINT (-1.27925 9.11115)   \n",
              "11397                 None  Node      GH  POINT (-1.33825 9.01048)   \n",
              "11449                 None  Node      GH  POINT (-1.30005 9.07561)   \n",
              "1578                  None  Node      GH  POINT (-1.32736 9.02910)   \n",
              "4724                  None  Node      GH  POINT (-1.32917 9.02600)   \n",
              "\n",
              "                                                filename  \n",
              "3      /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "4      /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "157    /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "237    /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "405    /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "...                                                  ...  \n",
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              "11397  /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "11449  /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "1578   /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
              "4724   /content/drive/MyDrive/PyPSA_Africa_images/max...  \n",
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              "      <td>3.063969e+09</td>\n",
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              "      <td>None</td>\n",
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              "      <td>None</td>\n",
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              "      <td>GH</td>\n",
              "      <td>POINT (-1.30005 9.07561)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
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              "      <td>None</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hits_GH.filename.unique()\n",
        "max_imgs = 0\n",
        "max_tif = None\n",
        "for tif in hits_GH.filename.unique():\n",
        "    # print(tif)\n",
        "    curr = hits_GH.loc[hits_GH['filename'] == tif]\n",
        "    # print(curr)\n",
        "    if len(curr) > max_imgs:\n",
        "        max_imgs = len(curr)\n",
        "        max_tif = tif\n",
        "\n",
        "print('max images')\n",
        "print(max_imgs)\n",
        "print('file')\n",
        "print(max_tif)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJQRRVX7KIC1",
        "outputId": "8beae483-5907-4370-e2b0-9ca2c1b368f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max images\n",
            "380\n",
            "file\n",
            "/content/drive/MyDrive/PyPSA_Africa_images/maxar/ghana/raw/10500100103B0300.tif\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets_df = hits_GH.loc[hits_GH['filename'] == max_tif]\n",
        "targets_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uO6woFqTKjMa",
        "outputId": "fcc070cf-d64d-481d-9d5e-ab764ccab162"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "               id tags.power tags.material tags.height tags.structure  \\\n",
              "18   6.299636e+09      tower          None        None           None   \n",
              "26   3.063969e+09      tower          None        None           None   \n",
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              "294  6.299915e+09      tower          None        None           None   \n",
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              "26                  None  Node      GH  POINT (-2.14358 8.43817)   \n",
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              "108                 None  Node      GH  POINT (-2.13402 8.23473)   \n",
              "294                 None  Node      GH  POINT (-2.05582 8.20089)   \n",
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              "                                              filename  \n",
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              "        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>tags.power</th>\n",
              "      <th>tags.material</th>\n",
              "      <th>tags.height</th>\n",
              "      <th>tags.structure</th>\n",
              "      <th>tags.line_management</th>\n",
              "      <th>Type</th>\n",
              "      <th>Country</th>\n",
              "      <th>geometry</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6.299636e+09</td>\n",
              "      <td>tower</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Node</td>\n",
              "      <td>GH</td>\n",
              "      <td>POINT (-2.14599 8.44320)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>3.063969e+09</td>\n",
              "      <td>tower</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Node</td>\n",
              "      <td>GH</td>\n",
              "      <td>POINT (-2.14358 8.43817)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>3.063969e+09</td>\n",
              "      <td>tower</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Node</td>\n",
              "      <td>GH</td>\n",
              "      <td>POINT (-2.06821 8.20735)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>3.063969e+09</td>\n",
              "      <td>tower</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Node</td>\n",
              "      <td>GH</td>\n",
              "      <td>POINT (-2.13402 8.23473)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>6.299915e+09</td>\n",
              "      <td>tower</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Node</td>\n",
              "      <td>GH</td>\n",
              "      <td>POINT (-2.05582 8.20089)</td>\n",
              "      <td>/content/drive/MyDrive/PyPSA_Africa_images/max...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16adcb1d-9bb1-44b5-bb5b-293b72036345')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-16adcb1d-9bb1-44b5-bb5b-293b72036345 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16adcb1d-9bb1-44b5-bb5b-293b72036345');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "_, ax = plt.subplots(1, 1, figsize=(16, 9))\n",
        "targets_df.plot(ax=ax)\n",
        "coverage.plot(ax=ax, alpha=0.1)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WpLV5StjfxlG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aoi = coverage.loc[coverage['filename'] == max_tif]\n",
        "aoi.total_bounds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yvglo2YsNkgv",
        "outputId": "953d9940-9659-40d0-db3d-632cd8b8505e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.1770943,  7.9835061, -1.9918943,  9.0126198])"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "_, ax = plt.subplots(1, 1, figsize=(16, 9))\n",
        "targets_df.plot(ax=ax)\n",
        "aoi.plot(ax=ax, alpha=0.1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "OkdOPrDCKOqG",
        "outputId": "923b5761-eb1f-4618-a6f9-0b40628399f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#GF utils"
      ],
      "metadata": {
        "id": "T-mPolaOmrp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "import geopandas as gpd\n",
        "import rasterio\n",
        "from rasterio.mask import mask\n",
        "\n",
        "\n",
        "def save_raster(path, raster, affine, crs=None, nodata=0):\n",
        "    \"\"\"Save a raster to the specified file.\n",
        "    Parameters\n",
        "    ----------\n",
        "    file : str\n",
        "        Output file path\n",
        "    raster : numpy.array\n",
        "        2D numpy array containing raster values\n",
        "    affine: affine.Affine\n",
        "        Affine transformation for the raster\n",
        "    crs: str, proj.Proj, optional (default EPSG4326)\n",
        "        CRS for the raster\n",
        "    \"\"\"\n",
        "\n",
        "    path = Path(path)\n",
        "    if not path.parents[0].exists():\n",
        "        path.parents[0].mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "    if not crs:\n",
        "        crs = \"+proj=latlong\"\n",
        "\n",
        "    filtered_out = rasterio.open(\n",
        "        path,\n",
        "        \"w\",\n",
        "        driver=\"GTiff\",\n",
        "        height=raster.shape[0],\n",
        "        width=raster.shape[1],\n",
        "        count=1,\n",
        "        dtype=raster.dtype,\n",
        "        crs=crs,\n",
        "        transform=affine,\n",
        "        nodata=nodata,\n",
        "    )\n",
        "    filtered_out.write(raster, 1)\n",
        "    filtered_out.close()\n",
        "\n",
        "def estimate_mem_use(targets, costs):\n",
        "    \"\"\"Estimate memory usage in GB, probably not very accurate.\n",
        "    Parameters\n",
        "    ----------\n",
        "    targets : numpy array\n",
        "        2D array of targets.\n",
        "    costs : numpy array\n",
        "        2D array of costs.\n",
        "    Returns\n",
        "    -------\n",
        "    est_mem : float\n",
        "        Estimated memory requirement in GB.\n",
        "    \"\"\"\n",
        "\n",
        "    # make sure these match the ones used in optimise below\n",
        "    visited = np.zeros_like(targets, dtype=np.int8)\n",
        "    dist = np.full_like(costs, np.nan, dtype=np.float32)\n",
        "    prev = np.full_like(costs, np.nan, dtype=object)\n",
        "\n",
        "    est_mem_arr = [targets, costs, visited, dist, prev]\n",
        "    est_mem = len(pickle.dumps(est_mem_arr, -1))\n",
        "\n",
        "    return est_mem / 1e9"
      ],
      "metadata": {
        "id": "A4YKVqfdM-Md"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from shapely import geometry\n",
        "import shapely\n",
        "# note that these are for now specific to extrating tower data\n",
        "def gdf_2_gridded_conversion_2(gdf, n_cells):\n",
        "    xmin, ymin, xmax, ymax= gdf.total_bounds\n",
        "  # how many cells for range of x values investigated - can be customized for y if needed\n",
        "  #n_cells=30\n",
        "    cell_size = (xmax-xmin)/n_cells\n",
        "    # projection of the grid\n",
        "    crs = \"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs\"\n",
        "    # create the cells in a loop\n",
        "    grid_cells = []\n",
        "    for x0 in np.arange(xmin, xmax+cell_size, cell_size ):\n",
        "        for y0 in np.arange(ymin, ymax+cell_size, cell_size):\n",
        "            # bounds\n",
        "            x1 = x0-cell_size\n",
        "            y1 = y0+cell_size\n",
        "            grid_cells.append( shapely.geometry.box(x0, y0, x1, y1)  )\n",
        "    cell = gpd.GeoDataFrame(grid_cells, columns=['geometry'],\n",
        "                                    crs=crs)\n",
        "\n",
        "\n",
        "    merged = gpd.sjoin(gdf, cell, how='left', op='within')\n",
        "\n",
        "    #clean_merge = merged.drop(labels =['tags.power', 'tags.material', 'tags.height', 'tags.structure',\n",
        "        #'tags.line_management', 'Type', 'Country'], axis = 1)\n",
        "\n",
        "    #clean_merge = merged.drop(labels =['tags.power', 'Type', 'Country'], axis = 1)\n",
        "    #clean_merge = merged.loc[:, ['id', 'geometry', 'index_right']]\n",
        "\n",
        "    clean_merge = merged\n",
        "    ## removing nan which can cause error\n",
        "    merged2 = clean_merge.dropna(axis = 0, how = 'any')\n",
        "\n",
        "    return merged2 , cell\n",
        "\n",
        "def gdf_2_binary_gridded_conversion_2(gdf, n_cells):\n",
        "    merged2, cell = gdf_2_gridded_conversion_2(gdf, n_cells)\n",
        "\n",
        "    # make a simple count variable that we can sum\n",
        "    merged2['n_twr']=1\n",
        "    # Compute stats per grid cell -- aggregate fires to grid cells with dissolve\n",
        "    dissolve = merged2.dissolve(by=\"index_right\", aggfunc=\"count\")\n",
        "    # put this into cell\n",
        "    cell.loc[dissolve.index, 'n_twr'] = dissolve.n_twr.values\n",
        "\n",
        "\n",
        "    return cell\n",
        "\n",
        "def return_idx_twr_gridded(cell, n_cells): #cell here is df containing both shapely polygon grids and original tower point markers , ncells to number of cells in x coordinate system for now\n",
        "  # total area for the grid\n",
        "  gdf = cell\n",
        "  xmin, ymin, xmax, ymax= gdf.total_bounds\n",
        "  cell_size = (xmax-xmin)/n_cells\n",
        "\n",
        "  gps = []\n",
        "  #find location in grid ####\n",
        "  for i in range(len(cell)):\n",
        "    if cell[\"n_twr\"][i] ==1:\n",
        "      #print(i)\n",
        "      twr_grid_cnt = list(cell.iloc[i].geometry.centroid.coords)\n",
        "      yic = twr_grid_cnt[0][1]\n",
        "      xic = twr_grid_cnt[0][0]\n",
        "  #### now retrieve integer indexing #####\n",
        "      yi = int((yic- ymin) / cell_size)\n",
        "      xi = int((xic- xmin) / cell_size)\n",
        "\n",
        "      gps.append((yi,xi))\n",
        "  return gps\n",
        "\n",
        "\n",
        "def pts_df_to_2d_target_array(cell, n_cells):\n",
        "\n",
        "  gdf = cell\n",
        "  xmin, ymin, xmax, ymax= gdf.total_bounds\n",
        "  cell_size = (xmax-xmin)/n_cells\n",
        "\n",
        "  # define number of x and y cells used for indexing\n",
        "  nxcell = int((xmax - xmin) / cell_size)\n",
        "  nycell = int((ymax - ymin) / cell_size)\n",
        "\n",
        "  twr_pts_idx = return_idx_twr_gridded(cell, n_cells)\n",
        "\n",
        "  points = twr_pts_idx\n",
        "  targets = np.zeros((int(nycell+1), int(nxcell+1)))\n",
        "  targets[tuple(np.array(points).T)] = 1\n",
        "  #targets[tuple(np.array(points))] = 1\n",
        "  #equal costs for all cells - excluding road info etc\n",
        "  #costs = np.ones((int(nycell+1), int(nxcell+1)))\n",
        "\n",
        "  ## flip to be consistent with original gridded\n",
        "  t = np.flip(targets, 0)\n",
        "  #c = np.flip(costs, 0)\n",
        "\n",
        "  return t"
      ],
      "metadata": {
        "id": "MOQi1-UymDXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#load inferred towers over same areas"
      ],
      "metadata": {
        "id": "GapnzSp4lnLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/detected_gh_twr_test_1000_batch'\n",
        "inferred_gh_twrs = gpd.read_file(path)\n",
        "inferred_gh_twrs.head()"
      ],
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        "id": "Oft6sge1lp6Z",
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        "path = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/detected_gh_twr_test_1000_batch_rev'\n",
        "inferred_gh_twrs_rev = gpd.read_file(path)\n",
        "inferred_gh_twrs_rev.head()"
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08f5e792-110e-40d5-bc0a-3193c12c467a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08f5e792-110e-40d5-bc0a-3193c12c467a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "_, ax = plt.subplots(1, 1, figsize=(16, 9))\n",
        "inferred_gh_twrs.plot(ax=ax)\n",
        "inferred_gh_twrs_rev.plot(ax=ax)\n",
        "aoi.plot(ax=ax, alpha=0.1)\n",
        "targets_df.plot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "haxmzi5TfXNo",
        "outputId": "98d229e5-c675-4e38-ac56-9cf4748f53bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "clearly there is too much noise to infer anything here, proceed first with filtering by score and then outlier removal"
      ],
      "metadata": {
        "id": "i4ASUtTqp8po"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#old"
      ],
      "metadata": {
        "id": "KxBELedkLQCK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from math import sqrt\n",
        "import json\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "from scipy import signal\n",
        "\n",
        "import fiona\n",
        "import rasterio\n",
        "from rasterio.mask import mask\n",
        "from rasterio.features import rasterize\n",
        "from rasterio import Affine\n",
        "from rasterio.warp import reproject, Resampling\n",
        "\n",
        "#import geopandas as gpd\n",
        "\n",
        "# open files\n",
        "base_dir = '/content/drive/My Drive/PyPSA_Africa_images'\n",
        "tif = '10500100103B0300.tif'\n",
        "in_dir = os.path.join(base_dir, 'maxar/ghana/raw')\n",
        "fn =  os.path.join(in_dir,tif )\n",
        "#ds = gdal.Open(fn)\n",
        "#info = gdal.Info(fn, format=\"json\")\n",
        "#bands = [ds.GetRasterBand(i) for i in range(1, 4)]\n",
        "\n",
        "# extract relevant geographical data\n",
        "#transform = info[\"geoTransform\"]\n",
        "#affine = transform\n",
        "\n",
        "rst = rasterio.open(fn)\n",
        "print(rst.shape)\n",
        "print(rst.transform)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ghkde6_eeAyh",
        "outputId": "0767e8b1-5cf8-475a-9b3e-d3e1e83d46bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(231295, 41624)\n",
            "| 0.00, 0.00,-2.18|\n",
            "| 0.00,-0.00, 9.01|\n",
            "| 0.00, 0.00, 1.00|\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "twrs = inferred_gh_twrs\n",
        "\n",
        "pixel_size = .01\n",
        "\n",
        "min_x, min_y, max_x, max_y = bounds = aoi['geometry'].total_bounds\n",
        "w_px = math.ceil((max_x - min_x) / pixel_size)\n",
        "h_px = math.ceil((max_y - min_y) / pixel_size)\n",
        "print(w_px)\n",
        "print(h_px)\n",
        "\n",
        "\n",
        "\n",
        "#shape = w_px, h_px\n",
        "shape = h_px, w_px # y determines rows, x determines columns\n",
        "#transform = rasterio.transform.from_bounds(*twrs['geometry'].total_bounds, *shape)\n",
        "transform = rasterio.transform.from_bounds(*bounds, *shape)\n",
        "\n",
        "\n",
        "#twrs = inferred_gh_twrs\n",
        "\n",
        "#twr_for_raster = [(row.geometry, row.weight) for _, row in roads.iterrows()]\n",
        "#roads_for_raster = ((geometry, weight) for _, row in zip(roads.geometry, roads.weight))\n",
        "#roads_for_raster = [(row.geometry, row.weight) for _, row in zip(roads.iterrows())]\n",
        "# create tuples of geometry, value pairs, where value is the attribute value you want to burn\n",
        "\n",
        "\n",
        "geom_value = ((geom,value) for geom, value in zip(twrs.geometry, twrs.score))\n",
        "#shapes = ((geom,value) for geom, value in zip(roads.iterrows()\n",
        "\n",
        "twr_raster = rasterize(\n",
        "    geom_value,\n",
        "    out_shape=shape,\n",
        "    fill=0,\n",
        "    default_value=0,\n",
        "    all_touched=True,\n",
        "    transform= transform,\n",
        ")\n",
        "\n",
        "twr_raster\n",
        "plt.imshow(twr_raster, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "M8YxRNPEmNCQ",
        "outputId": "398a7b92-f886-4d06-950e-e6cd1f32c24a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19\n",
            "103\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0418131fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flip_r_t = np.flip(twr_raster, 0)\n",
        "#flip_r_t\n",
        "plt.imshow(flip_r_t, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "Z8SnyDcLr2IO",
        "outputId": "3949aea6-4112-4ecf-ce0d-1597d5a91eda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0418590e50>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "twrs = inferred_gh_twrs_rev\n",
        "\n",
        "pixel_size = .01\n",
        "\n",
        "min_x, min_y, max_x, max_y = bounds = aoi['geometry'].total_bounds\n",
        "w_px = math.ceil((max_x - min_x) / pixel_size)\n",
        "h_px = math.ceil((max_y - min_y) / pixel_size)\n",
        "print(w_px)\n",
        "print(h_px)\n",
        "\n",
        "\n",
        "\n",
        "shape = h_px, w_px\n",
        "#transform = rasterio.transform.from_bounds(*twrs['geometry'].total_bounds, *shape)\n",
        "transform = rasterio.transform.from_bounds(*bounds, *shape)\n",
        "\n",
        "\n",
        "#twrs = inferred_gh_twrs\n",
        "\n",
        "#twr_for_raster = [(row.geometry, row.weight) for _, row in roads.iterrows()]\n",
        "#roads_for_raster = ((geometry, weight) for _, row in zip(roads.geometry, roads.weight))\n",
        "#roads_for_raster = [(row.geometry, row.weight) for _, row in zip(roads.iterrows())]\n",
        "# create tuples of geometry, value pairs, where value is the attribute value you want to burn\n",
        "\n",
        "\n",
        "geom_value = ((geom,value) for geom, value in zip(twrs.geometry, twrs.score))\n",
        "#shapes = ((geom,value) for geom, value in zip(roads.iterrows()\n",
        "\n",
        "twr_raster = rasterize(\n",
        "    geom_value,\n",
        "    out_shape=shape,\n",
        "    fill=0,\n",
        "    default_value=0,\n",
        "    all_touched=True,\n",
        "    transform= transform,\n",
        ")\n",
        "\n",
        "twr_raster\n",
        "plt.imshow(twr_raster, cmap='viridis')"
      ],
      "metadata": {
        "id": "GVvDlDIcqaUb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import rasterio\n",
        "from rasterio.enums import Resampling\n",
        "\n",
        "base_dir = '/content/drive/My Drive/PyPSA_Africa_images'\n",
        "tif = '10500100103B0300.tif'\n",
        "in_dir = os.path.join(base_dir, 'maxar/ghana/raw')\n",
        "fn =  os.path.join(in_dir,tif )\n",
        "\n",
        "upscale_factor = .001 # need this otherwise colab crashes\n",
        "\n",
        "with rasterio.open(fn) as dataset:\n",
        "\n",
        "    # resample data to target shape\n",
        "    data = dataset.read(\n",
        "        out_shape=(\n",
        "            dataset.count,\n",
        "            int(dataset.height * upscale_factor),\n",
        "            int(dataset.width * upscale_factor)\n",
        "        ),\n",
        "        resampling=Resampling.bilinear\n",
        "    )\n",
        "\n",
        "    # scale image transform\n",
        "    transform = dataset.transform * dataset.transform.scale(\n",
        "        (dataset.width / data.shape[-1]),\n",
        "        (dataset.height / data.shape[-2])\n",
        "    )\n",
        "\n",
        "shape = data.shape[1] , data.shape[2]\n",
        "\n",
        "\n",
        "twrs = inferred_gh_twrs_rev\n",
        "\n",
        "geom_value = ((geom,value) for geom, value in zip(twrs.geometry, twrs.score))\n",
        "\n",
        "\n",
        "\n",
        "twr_raster = rasterize(\n",
        "    geom_value,\n",
        "    out_shape=shape,\n",
        "    fill=0,\n",
        "    default_value=0,\n",
        "    all_touched=True,\n",
        "    transform= transform,\n",
        ")\n",
        "\n",
        "twr_raster\n",
        "plt.imshow(twr_raster, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "9rJQDx3swu3-",
        "outputId": "e1bba7ab-426a-49cc-f7f6-3927aa833212"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f03f477a5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JQeKI4tg0VE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#rasterize towers"
      ],
      "metadata": {
        "id": "ennQTAiHLch7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import os\n",
        "from math import sqrt\n",
        "import json\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "from scipy import signal\n",
        "\n",
        "import fiona\n",
        "import rasterio\n",
        "from rasterio.mask import mask\n",
        "from rasterio.features import rasterize\n",
        "from rasterio import Affine\n",
        "from rasterio.warp import reproject, Resampling\n",
        "\n",
        "def rasterize_inferred_twrs(tif, twrs, upscale_factor = .001, threshold = .1 ):\n",
        "      \"\"\"Convert the supplied tif file and inferred towers df to output an array of potential towers for grid reconstruction by algorithm.\n",
        "    Parameters\n",
        "    ----------\n",
        "    tifs : str, Path\n",
        "        Path to TIF file we want to use as base to rasterize\n",
        "    twrs : GeoPandas.GeoDataframe, containing at least geometry and score colums\n",
        "    upscale_factor: factor by which to downsample original raster file so we dont run out of memory (default=.001 for testing for now)\n",
        "    treshold: float, optional, default of .1, accuracy score below which tower instances not considered once resampled to new upscaled size\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    twr_thresh : numpy array\n",
        "        Array of cells of value 0 (no tower) or 1 (tower).\n",
        "    tranform : affine.Affine\n",
        "        Affine raster transformation for the returned array to be used in other parts.\n",
        "    \"\"\"\n",
        "\n",
        "      fn = tif\n",
        "      with rasterio.open(fn) as dataset:\n",
        "\n",
        "          # resample data to target shape\n",
        "          data = dataset.read(\n",
        "              out_shape=(\n",
        "                  dataset.count,\n",
        "                  int(dataset.height * upscale_factor),\n",
        "                  int(dataset.width * upscale_factor)\n",
        "              ),\n",
        "              resampling=Resampling.bilinear\n",
        "          )\n",
        "\n",
        "          # scale image transform\n",
        "          transform = dataset.transform * dataset.transform.scale(\n",
        "              (dataset.width / data.shape[-1]),\n",
        "              (dataset.height / data.shape[-2])\n",
        "          )\n",
        "\n",
        "      shape = data.shape[1] , data.shape[2]\n",
        "\n",
        "      geom_value = ((geom,value) for geom, value in zip(twrs.geometry, twrs.score))\n",
        "\n",
        "\n",
        "\n",
        "      twr_raster = rasterize(\n",
        "          geom_value,\n",
        "          out_shape=shape,\n",
        "          fill=0,\n",
        "          default_value=0,\n",
        "          all_touched=True,\n",
        "          transform= transform,\n",
        "      )\n",
        "\n",
        "      #twr_thresh = np.empty_like(twr_thresh)\n",
        "      #twr_thresh[:] = twr_raster[:]\n",
        "      twr_thresh = twr_raster.copy()\n",
        "\n",
        "      twr_thresh[twr_raster < threshold] = 0\n",
        "      twr_thresh[twr_raster >= threshold] = 1\n",
        "\n",
        "      return twr_thresh , transform\n"
      ],
      "metadata": {
        "id": "vUbiJOuUhfDi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#try this to see how it connects them, here is where updated cost raster important to follow lines\n",
        "twr_raster_clean, new_aff = rasterize_inferred_twrs(tif = fn, twrs = combined_twr_df, upscale_factor = .001, threshold = .9)\n",
        "plt.imshow(twr_raster_clean, cmap='viridis')\n",
        "#print(np.amin(twr_raster))\n",
        "target_list = np.argwhere(twr_raster_clean == 1.0)\n",
        "print(len(target_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "TUSAv2XYbMVG",
        "outputId": "45c4efad-dd0f-48d8-b414-3019008add89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "356\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twr_raster_clean, new_aff = rasterize_inferred_twrs(tif = fn, twrs = inferred_gh_twrs_rev, upscale_factor = .001, threshold = .9)\n",
        "plt.imshow(twr_raster_clean, cmap='viridis')\n",
        "#print(np.amin(twr_raster))\n",
        "target_list = np.argwhere(twr_raster_clean == 1.0)\n",
        "print(len(target_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "1BPZWJ9n1WAn",
        "outputId": "cc828e86-7573-489c-cee1-e1cf228c4740"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets_out = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/outputs/targets_filtered.tif'\n",
        "save_raster(targets_out, twr_raster_clean, new_aff)"
      ],
      "metadata": {
        "id": "KlZVnTDfMkV6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roads_in ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/inputs/gh_full_osm.gpkg'\n",
        "roads_out ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/outputs/roads.tif'"
      ],
      "metadata": {
        "id": "WQPNYMvxYNl6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#extract roads and osm data"
      ],
      "metadata": {
        "id": "oO3qJ1n-Kzma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from math import sqrt\n",
        "import json\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "from scipy import signal\n",
        "\n",
        "import fiona\n",
        "import rasterio\n",
        "from rasterio.mask import mask\n",
        "from rasterio.features import rasterize\n",
        "from rasterio import Affine\n",
        "from rasterio.warp import reproject, Resampling\n",
        "\n",
        "import geopandas as gpd\n",
        "\n",
        "#roads_in ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/inputs/gh_roads_coverage.gpkg'\n",
        "#roads_in ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/inputs/gh_full_roads.gpkg'\n",
        "roads_in ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/inputs/gh_full_osm.gpkg'\n",
        "roads_out ='/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/outputs/roads.tif'"
      ],
      "metadata": {
        "id": "B83v8L_PbH6V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rasterize_road_osm_data(roads_in, aoi_in, twr_raster_in):\n",
        "    \"\"\"Prepare a roads feature layer for use in algorithm.\n",
        "    Parameters\n",
        "    ----------\n",
        "    roads_in : str, Path\n",
        "        Path to a roads feature layer. This implementation is specific to\n",
        "        OSM data and won't assign proper weights to other data inputs.\n",
        "    aoi_in : str, Path or GeoDataFrame\n",
        "        AOI to clip roads.\n",
        "    twr_raster_in:  Path to a raster file, only used for correct shape and\n",
        "        affine of roads raster.\n",
        "            Returns\n",
        "    -------\n",
        "    roads_raster : numpy array\n",
        "        Roads as a raster array with the value being the cost of traversing.\n",
        "    affine : affine.Affine\n",
        "        Affine raster transformation for the new raster (same as ntl_in).\n",
        "    \"\"\"\n",
        "    twr_rst = rasterio.open(twr_raster_in)\n",
        "    shape = twr_rst.shape\n",
        "    affine = twr_rst.transform\n",
        "\n",
        "    if isinstance(aoi_in, gpd.GeoDataFrame):\n",
        "        aoi = aoi_in\n",
        "    else:\n",
        "        aoi = gpd.read_file(aoi_in)\n",
        "\n",
        "    aoi = aoi.set_crs(epsg=4326)\n",
        "    roads_masked = gpd.read_file(roads_in, mask = aoi)\n",
        "    roads = gpd.sjoin(roads_masked, aoi, how=\"inner\", op=\"intersects\")\n",
        "    roads = roads[roads_masked.columns]\n",
        "\n",
        "    roads[\"weight\"] = 1\n",
        "    roads.loc[roads[\"highway\"] == \"motorway\", \"weight\"] = 1 / 10\n",
        "    roads.loc[roads[\"highway\"] == \"trunk\", \"weight\"] = 1 / 9\n",
        "    roads.loc[roads[\"highway\"] == \"primary\", \"weight\"] = 1 / 8\n",
        "    roads.loc[roads[\"highway\"] == \"secondary\", \"weight\"] = 1 / 7\n",
        "    roads.loc[roads[\"highway\"] == \"tertiary\", \"weight\"] = 1 / 6\n",
        "    roads.loc[roads[\"highway\"] == \"unclassified\", \"weight\"] = 1 / 5\n",
        "    roads.loc[roads[\"highway\"] == \"residential\", \"weight\"] = 1 / 4\n",
        "    roads.loc[roads[\"highway\"] == \"service\", \"weight\"] = 1 / 3\n",
        "    roads.loc[roads[\"highway\"] == \"trailhead\", \"weight\"] = 1 / 2\n",
        "\n",
        "    # Power lines get weight 0\n",
        "    # Note that this means both transportation AND power categories should be included when importing from OSM\n",
        "    if \"power\" in roads:\n",
        "        roads.loc[roads[\"power\"] == \"line\", \"weight\"] = 0\n",
        "\n",
        "    roads = roads[roads.weight != 1]\n",
        "\n",
        "    # sort by weight descending so that lower weight (bigger roads) are\n",
        "    # processed last and overwrite higher weight roads\n",
        "    roads = roads.sort_values(by=\"weight\", ascending=False)\n",
        "\n",
        "    geom_value = ((geom,value) for geom, value in zip(roads.geometry, roads.weight))\n",
        "\n",
        "    roads_raster = rasterize(\n",
        "        geom_value,\n",
        "        out_shape=shape,\n",
        "        fill=1,\n",
        "        default_value=0,\n",
        "        all_touched=True,\n",
        "        transform= transform,\n",
        "    )\n",
        "\n",
        "    return roads_raster, affine"
      ],
      "metadata": {
        "id": "jvmWzdwPL9HW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##cost estimations"
      ],
      "metadata": {
        "id": "kQ9HtvTGu74H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "note that here the lines should show once we import full osm data"
      ],
      "metadata": {
        "id": "cQ5cT9DPQ8xK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roads_raster, affine = rasterize_road_osm_data(roads_in, aoi, targets_out)\n",
        "plt.imshow(roads_raster, cmap='viridis', vmin=0, vmax=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "l8WngMHIPTtw",
        "outputId": "1dcb439a-86df-4e43-8027-dcf4455273f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f03eae888d0>"
            ]
          },
          "metadata": {},
          "execution_count": 275
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "save_raster(roads_out, roads_raster, affine, nodata=-1)"
      ],
      "metadata": {
        "id": "7WeNoYdRayui"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#load computed targets and costs"
      ],
      "metadata": {
        "id": "hXGjUpB5RpDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_targets_costs(targets_in, costs_in):\n",
        "    \"\"\"Load the targets and costs arrays from the given file paths.\n",
        "    Parameters\n",
        "    ----------\n",
        "    targets_in : str\n",
        "        Path for targets raster.\n",
        "    costs_in : str\n",
        "        Path for costs raster.\n",
        "    Returns\n",
        "    -------\n",
        "    targets : numpy array\n",
        "        2D array of targets\n",
        "    costs: numpy array\n",
        "        2D array of costs\n",
        "    start: tuple\n",
        "        Two-element tuple with row, col of starting point.\n",
        "    affine : affine.Affine\n",
        "        Affine transformation for the rasters.\n",
        "    \"\"\"\n",
        "\n",
        "    targets_ra = rasterio.open(targets_in)\n",
        "    affine = targets_ra.transform\n",
        "    targets = targets_ra.read(1)\n",
        "\n",
        "    costs_ra = rasterio.open(costs_in)\n",
        "    costs = costs_ra.read(1)\n",
        "\n",
        "    target_list = np.argwhere(targets == 1.0)\n",
        "    start = tuple(target_list[0].tolist())\n",
        "\n",
        "    targets = targets.astype(np.int8)\n",
        "    costs = costs.astype(np.float16)\n",
        "\n",
        "    return targets, costs, start, affine"
      ],
      "metadata": {
        "id": "hIFjXLtyRkkk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#optimise distances"
      ],
      "metadata": {
        "id": "UlK5YDdPSL-3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "from math import sqrt\n",
        "from heapq import heapify, heappush, heappop\n",
        "import pickle\n",
        "\n",
        "import numpy as np\n",
        "import rasterio\n",
        "from IPython.display import display, Markdown\n",
        "from skimage.morphology import skeletonize\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "from skimage.morphology import skeletonize\n",
        "import shapely.wkt\n",
        "from shapely.geometry import Point, LineString\n",
        "import rasterio\n",
        "from rasterio.features import rasterize\n",
        "from rasterio.transform import xy\n",
        "\n",
        "def optimise(\n",
        "    targets,\n",
        "    costs,\n",
        "    start,\n",
        "    jupyter=False,\n",
        "    animate=False,\n",
        "    affine=None,\n",
        "    animate_path=None,\n",
        "    silent=False,\n",
        "):\n",
        "    \"\"\"Run the Dijkstra algorithm for the supplied arrays.\n",
        "    Parameters\n",
        "    ----------\n",
        "    targets : numpy array\n",
        "        2D array of targets.\n",
        "    costs : numpy array\n",
        "        2D array of costs.\n",
        "    start : tuple\n",
        "        Two-element tuple with row, col of starting point.\n",
        "    jupyter : boolean, optional (default False)\n",
        "        Whether the code is being run from a Jupyter Notebook.\n",
        "    Returns\n",
        "    -------\n",
        "    dist : numpy array\n",
        "        2D array with the distance (in cells) of each point from a 'found'\n",
        "        on-grid point. Values of 0 imply that cell is part of an MV grid line.\n",
        "    \"\"\"\n",
        "\n",
        "    max_i = costs.shape[0]\n",
        "    max_j = costs.shape[1]\n",
        "\n",
        "    visited = np.zeros_like(targets, dtype=np.int8)\n",
        "    dist = np.full_like(costs, np.nan, dtype=np.float32)\n",
        "\n",
        "    # want to set this to dtype='int32, int32'\n",
        "    # but then the if type(prev_loc) == tuple check will break\n",
        "    # becuas it gets instantiated with tuples\n",
        "    prev = np.full_like(costs, np.nan, dtype=object)\n",
        "\n",
        "    dist[start] = 0\n",
        "\n",
        "    #       dist, loc\n",
        "    queue = [[0, start]]\n",
        "    heapify(queue)\n",
        "\n",
        "    def zero_and_heap_path(loc):\n",
        "        \"\"\"Zero the location's distance value and follow upstream doing same.\n",
        "        Parameters\n",
        "        ----------\n",
        "        loc : tuple\n",
        "            row, col of current point.\n",
        "        \"\"\"\n",
        "\n",
        "        if not dist[loc] == 0:\n",
        "            dist[loc] = 0\n",
        "            visited[loc] = 1\n",
        "\n",
        "            heappush(queue, [0, loc])\n",
        "            prev_loc = prev[loc]\n",
        "\n",
        "            if type(prev_loc) == tuple:\n",
        "                zero_and_heap_path(prev_loc)\n",
        "\n",
        "    counter = 0\n",
        "    progress = 0\n",
        "    max_cells = targets.shape[0] * targets.shape[1]\n",
        "    if jupyter:\n",
        "        handle = display(Markdown(\"\"), display_id=True)\n",
        "\n",
        "    while len(queue):\n",
        "        current = heappop(queue)\n",
        "        current_loc = current[1]\n",
        "        current_i = current_loc[0]\n",
        "        current_j = current_loc[1]\n",
        "        current_dist = dist[current_loc]\n",
        "\n",
        "        for x in range(-1, 2):\n",
        "            for y in range(-1, 2):\n",
        "                next_i = current_i + x\n",
        "                next_j = current_j + y\n",
        "                next_loc = (next_i, next_j)\n",
        "\n",
        "                # ensure we're within bounds\n",
        "                if next_i < 0 or next_j < 0 or next_i >= max_i or next_j >= max_j:\n",
        "                    continue\n",
        "\n",
        "                # ensure we're not looking at the same spot\n",
        "                if next_loc == current_loc:\n",
        "                    continue\n",
        "\n",
        "                # skip if we've already set dist to 0\n",
        "                if dist[next_loc] == 0:\n",
        "                    continue\n",
        "\n",
        "                # if the location is connected\n",
        "                if targets[next_loc]:\n",
        "                    prev[next_loc] = current_loc\n",
        "                    zero_and_heap_path(next_loc)\n",
        "\n",
        "                # otherwise it's a normal queue cell\n",
        "                else:\n",
        "                    dist_add = costs[next_loc]\n",
        "                    if x == 0 or y == 0:  # if this cell is  up/down/left/right\n",
        "                        dist_add *= 1\n",
        "                    else:  # or if it's diagonal\n",
        "                        dist_add *= sqrt(2)\n",
        "\n",
        "                    next_dist = current_dist + dist_add\n",
        "\n",
        "                    if visited[next_loc]:\n",
        "                        if next_dist < dist[next_loc]:\n",
        "                            dist[next_loc] = next_dist\n",
        "                            prev[next_loc] = current_loc\n",
        "                            heappush(queue, [next_dist, next_loc])\n",
        "\n",
        "                    else:\n",
        "                        heappush(queue, [next_dist, next_loc])\n",
        "                        visited[next_loc] = 1\n",
        "                        dist[next_loc] = next_dist\n",
        "                        prev[next_loc] = current_loc\n",
        "\n",
        "                        counter += 1\n",
        "                        progress_new = 100 * counter / max_cells\n",
        "                        if int(progress_new) > int(progress):\n",
        "                            progress = progress_new\n",
        "                            message = f\"{progress:.2f} %\"\n",
        "                            if jupyter:\n",
        "                                handle.update(message)\n",
        "                            elif not silent:\n",
        "                                print(message)\n",
        "                            if animate:\n",
        "                                i = int(progress)\n",
        "                                path = os.path.join(animate_path, f\"arr{i:03d}.tif\")\n",
        "                                save_raster(path, dist, affine)\n",
        "\n",
        "    return dist\n",
        "\n",
        "def threshold(dists_in, cutoff=0.5):\n",
        "    \"\"\"Convert distance array into binary array of connected locations.\n",
        "    Parameters\n",
        "    ----------\n",
        "    dists_in : path-like or numpy array\n",
        "        2D array output from gridfinder algorithm.\n",
        "    cutoff : float, optional (default 0.5.)\n",
        "        Cutoff value below which consider the cells to be grid.\n",
        "    Returns\n",
        "    -------\n",
        "    guess : numpy array\n",
        "        Binary representation of input array.\n",
        "    affine: affine.Affine\n",
        "        Affine transformation for raster.\n",
        "    \"\"\"\n",
        "    if isinstance(dists_in, (str, Path)):\n",
        "        dists_rd = rasterio.open(dists_in)\n",
        "        dists_r = dists_rd.read(1)\n",
        "        affine = dists_rd.transform\n",
        "\n",
        "        guess = dists_r.copy()\n",
        "        guess[dists_r > cutoff] = 0\n",
        "        guess[dists_r <= cutoff] = 1\n",
        "\n",
        "        return guess, affine\n",
        "\n",
        "    elif isinstance(dists_in, np.ndarray):\n",
        "        guess = dists_in.copy()\n",
        "        guess[dists_in > cutoff] = 0\n",
        "        guess[dists_in <= cutoff] = 1\n",
        "\n",
        "        return guess\n",
        "\n",
        "    else:\n",
        "        raise ValueError\n",
        "\n",
        "\n",
        "def thin(guess_in):\n",
        "    \"\"\"\n",
        "    Use scikit-image skeletonize to 'thin' the guess raster.\n",
        "    Parameters\n",
        "    ----------\n",
        "    guess_in : path-like or 2D array\n",
        "        Output from threshold().\n",
        "    Returns\n",
        "    -------\n",
        "    guess_skel : numpy array\n",
        "        Thinned version.\n",
        "    affine : Affine\n",
        "        Only if path-like supplied.\n",
        "    \"\"\"\n",
        "\n",
        "    if isinstance(guess_in, (str, Path)):\n",
        "        guess_rd = rasterio.open(guess_in)\n",
        "        guess_arr = guess_rd.read(1)\n",
        "        affine = guess_rd.transform\n",
        "\n",
        "        guess_skel = skeletonize(guess_arr)\n",
        "        guess_skel = guess_skel.astype(\"int32\")\n",
        "\n",
        "        return guess_skel, affine\n",
        "\n",
        "    elif isinstance(guess_in, np.ndarray):\n",
        "        guess_skel = skeletonize(guess_in)\n",
        "        guess_skel = guess_skel.astype(\"int32\")\n",
        "\n",
        "        return guess_skel\n",
        "\n",
        "    else:\n",
        "        raise ValueError\n",
        "\n",
        "\n",
        "def raster_to_lines(guess_skel_in):\n",
        "    \"\"\"\n",
        "    Convert thinned raster to linestring geometry.\n",
        "    Parameters\n",
        "    ----------\n",
        "    guess_skel_in : path-like\n",
        "        Output from thin().\n",
        "    Returns\n",
        "    -------\n",
        "    guess_gdf : GeoDataFrame\n",
        "        Converted to geometry.\n",
        "    \"\"\"\n",
        "\n",
        "    rast = rasterio.open(guess_skel_in)\n",
        "    arr = rast.read(1)\n",
        "    affine = rast.transform\n",
        "\n",
        "    max_row = arr.shape[0]\n",
        "    max_col = arr.shape[1]\n",
        "    lines = []\n",
        "\n",
        "    for row in range(0, max_row):\n",
        "        for col in range(0, max_col):\n",
        "            loc = (row, col)\n",
        "            if arr[loc] == 1:\n",
        "                for i in range(-1, 2):\n",
        "                    for j in range(-1, 2):\n",
        "                        next_row = row + i\n",
        "                        next_col = col + j\n",
        "                        next_loc = (next_row, next_col)\n",
        "\n",
        "                        # ensure we're within bounds\n",
        "                        # ensure we're not looking at the same spot\n",
        "                        if (\n",
        "                            next_row < 0\n",
        "                            or next_col < 0\n",
        "                            or next_row >= max_row\n",
        "                            or next_col >= max_col\n",
        "                            or next_loc == loc\n",
        "                        ):\n",
        "                            continue\n",
        "\n",
        "                        if arr[next_loc] == 1:\n",
        "                            line = (loc, next_loc)\n",
        "                            rev = (line[1], line[0])\n",
        "                            if line not in lines and rev not in lines:\n",
        "                                lines.append(line)\n",
        "\n",
        "    real_lines = []\n",
        "    for line in lines:\n",
        "        real = (xy(affine, line[0][0], line[0][1]), xy(affine, line[1][0], line[1][1]))\n",
        "        real_lines.append(real)\n",
        "\n",
        "    shapes = []\n",
        "    for line in real_lines:\n",
        "        shapes.append(LineString([Point(line[0]), Point(line[1])]).wkt)\n",
        "\n",
        "    guess_gdf = pd.DataFrame(shapes)\n",
        "    geometry = guess_gdf[0].map(shapely.wkt.loads)\n",
        "    guess_gdf = guess_gdf.drop(0, axis=1)\n",
        "    guess_gdf = gpd.GeoDataFrame(guess_gdf, crs=rast.crs, geometry=geometry)\n",
        "\n",
        "    guess_gdf[\"same\"] = 0\n",
        "    guess_gdf = guess_gdf.dissolve(by=\"same\")\n",
        "    guess_gdf = guess_gdf.to_crs(epsg=4326)\n",
        "\n",
        "    return guess_gdf"
      ],
      "metadata": {
        "id": "P-HdEwjkSLRJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#results"
      ],
      "metadata": {
        "id": "7-DllVOvW8_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "targets, costs, start, affine = get_targets_costs(targets_out, roads_out)\n"
      ],
      "metadata": {
        "id": "mTMyiEtLam0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dist = optimise(targets, costs, start,\n",
        "                   jupyter=True,\n",
        "                   animate=False,\n",
        "                   affine=affine)\n",
        "\n",
        "plt.imshow(dist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "zEdP9r4jSaSP",
        "outputId": "23267241-54de-4680-8188-5a3bfc57b572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'98.00 %'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f03f3f04550>"
            ]
          },
          "metadata": {},
          "execution_count": 278
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "guess_out = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/outputs/guess.tif'\n",
        "guess = threshold(dist, cutoff = .5)\n",
        "save_raster(guess_out, guess, affine)\n",
        "guess_skel = thin(guess)\n",
        "print('Got guess')\n",
        "plt.imshow(guess, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "ZjNp2rF5TSJD",
        "outputId": "ca56a285-6f66-4aa3-836f-ed0e60e85d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Got guess\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f03f3b58350>"
            ]
          },
          "metadata": {},
          "execution_count": 279
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "guess_skel_out = '/content/drive/MyDrive/PyPSA_Africa_images/models/outlier_detection/outputs/guess_skel.tif'\n",
        "guess_skel = thin(guess)\n",
        "save_raster(guess_skel_out, guess_skel, affine)"
      ],
      "metadata": {
        "id": "qcxgtIerSs_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "guess_gdf = raster_to_lines(guess_skel_out)"
      ],
      "metadata": {
        "id": "qzCJuBKiU4vJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "_, ax = plt.subplots(1, 1, figsize=(16, 9))\n",
        "inferred_gh_twrs.plot(ax=ax)\n",
        "inferred_gh_twrs_rev.plot(ax=ax)\n",
        "aoi.plot(ax=ax, alpha=0.1)\n",
        "guess_gdf.plot(ax = ax, alpha = .9)\n",
        "targets_df.plot(ax=ax, alpha = .1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "OtvKegpmWIp4",
        "outputId": "e806478a-7623-4b63-fbb6-04169b1f0159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#note now build method with also optional outolier detection"
      ],
      "metadata": {
        "id": "9fpuCzW9bpgK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#folium"
      ],
      "metadata": {
        "id": "dvl5N6vJV-Fe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "minx, miny, maxx, maxy = list(guess_gdf.bounds.iloc[0])\n",
        "bounds = ((miny, minx), (maxy, maxx))\n",
        "\n",
        "m = folium.Map(control_scale=True)\n",
        "m.fit_bounds(bounds)\n",
        "folium.GeoJson(guess_gdf).add_to(m)\n",
        "m"
      ],
      "metadata": {
        "id": "lxGgsGvZVpJd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "minx, miny, maxx, maxy = list(guess_gdf.bounds.iloc[0])\n",
        "bounds = ((miny, minx), (maxy, maxx))\n",
        "\n",
        "m = folium.Map(control_scale=True)\n",
        "m.fit_bounds(bounds)\n",
        "folium.GeoJson(guess_gdf).add_to(m)\n",
        "m"
      ],
      "metadata": {
        "id": "pMcZklCgVSYh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}