scenario_modeling_v2.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/edwardoughton/UK_Fixed_Broadband_Availability/blob/master/scenario_modeling_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mapclassify --upgrade\n",
        "!pip install contextily"
      ],
      "metadata": {
        "id": "allZraaeajdu"
      },
      "id": "allZraaeajdu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !cp -r /content/drive/MyDrive/spwio/data .\n",
        "# !tar -cvzf data.rar data\n",
        "\n",
        "# !cp data.rar /content/drive/MyDrive/spwio/data\n",
        "\n",
        "# Download data\n",
        "!wget -O data.rar https://zenodo.org/records/10586809/files/data.rar?download=1\n",
        "\n",
        "!tar -xvzf data.rar"
      ],
      "metadata": {
        "id": "mdJ6d7uHAiyp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49b4f89d-bd4a-427f-aaf9-1d7f3014e508"
      },
      "id": "mdJ6d7uHAiyp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-01-30 13:55:51--  https://zenodo.org/records/10586809/files/data.rar?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.79.172, 188.184.103.159, 188.184.98.238, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.79.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1156013605 (1.1G) [application/octet-stream]\n",
            "Saving to: ‘data.rar’\n",
            "\n",
            "data.rar            100%[===================>]   1.08G  23.7MB/s    in 48s     \n",
            "\n",
            "2024-01-30 13:56:41 (22.8 MB/s) - ‘data.rar’ saved [1156013605/1156013605]\n",
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            "data/NERC Map/\n",
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            "data/SAGDP/SAGDP8N_DC_2017_2022.csv\n",
            "data/SAGDP/SAGDP5N_RI_2017_2022.csv\n",
            "data/SAGDP/SAGDP11N_SC_2018_2022.csv\n",
            "data/SAGDP/SAGDP8N_ME_2017_2022.csv\n",
            "data/SAGDP/SAGDP6N_WI_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_LA_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_CT_2017_2022.csv\n",
            "data/SAGDP/SAGDP11N_GA_2018_2022.csv\n",
            "data/SAGDP/SAGDP2N_AK_2017_2022.csv\n",
            "data/SAGDP/SAGDP2N_WY_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_NE_2017_2022.csv\n",
            "data/SAGDP/SAGDP4N_MS_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_VA_2017_2022.csv\n",
            "data/SAGDP/SAGDP7N_KS_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_NENG_2017_2022.csv\n",
            "data/SAGDP/SAGDP4N_AZ_2017_2022.csv\n",
            "data/SAGDP/SAGDP4N_NJ_2017_2022.csv\n",
            "data/SAGDP/SAGDP6N_NE_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_GLAK_2017_2022.csv\n",
            "data/SAGDP/SAGDP3N_VA_2017_2022.csv\n",
            "data/SAGDP/SAGDP11N_NY_2018_2022.csv\n",
            "data/SAGDP/SAGDP2N_WA_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_MO_2017_2022.csv\n",
            "data/SAGDP/SAGDP2N_MI_2017_2022.csv\n",
            "data/SAGDP/SAGDP3N_NM_2017_2022.csv\n",
            "data/SAGDP/SAGDP2N_CA_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_SWST_2017_2022.csv\n",
            "data/SAGDP/SAGDP11N_RI_2018_2022.csv\n",
            "data/SAGDP/SAGDP6N_LA_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_NH_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_VT_2017_2022.csv\n",
            "data/SAGDP/SAGDP7N_MS_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N_TX_2017_2022.csv\n",
            "data/SAGDP/SAGDP5N_VT_2017_2022.csv\n",
            "data/SAGDP/SAGDP8N_VA_2017_2022.csv\n",
            "data/SAGDP/SAGDP9N__definition.xml\n",
            "data/grid_data.csv\n",
            "data/grid_data.json\n",
            "data/pop_2020_state.csv\n"
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        }
      ]
    },
    {
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      "execution_count": null,
      "id": "1d99911b-64d2-402a-af73-96465ceb201a",
      "metadata": {
        "tags": [],
        "id": "1d99911b-64d2-402a-af73-96465ceb201a"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import os\n",
        "import glob\n",
        "import json\n",
        "import warnings\n",
        "import csv\n",
        "import tqdm\n",
        "import time\n",
        "import shutil\n",
        "import string\n",
        "import math\n",
        "import gc\n",
        "import json\n",
        "from itertools import combinations, product, islice\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from multiprocessing import Pool, Lock, cpu_count\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "import matplotlib.gridspec as gridspec\n",
        "import matplotlib.colors as colors\n",
        "import geopandas as gpd\n",
        "# import contextily as ctx\n",
        "\n",
        "# prompt: suppress warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5b4abae2-f328-4534-ad27-50cccdd2e323",
      "metadata": {
        "tags": [],
        "id": "5b4abae2-f328-4534-ad27-50cccdd2e323"
      },
      "outputs": [],
      "source": [
        "# # configure data path\n",
        "# current_path = os.path.dirname(os.path.abspath(\"__file__\"))\n",
        "# data_path = os.path.join(current_path, \"..\", \"data\")\n",
        "\n",
        "# data_path = os.path.normpath(data_path)\n",
        "\n",
        "working_dir = \"/content\"\n",
        "data_path = os.path.join(working_dir, \"data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c732d59-6aae-4ef9-8faa-e85cbacca9d4",
      "metadata": {
        "tags": [],
        "id": "2c732d59-6aae-4ef9-8faa-e85cbacca9d4"
      },
      "outputs": [],
      "source": [
        "# map op naics industries\n",
        "\n",
        "# NAICs Industries\n",
        "NAICSIndustries = {\n",
        "    \"11\": \"Agriculture, Forestry, Fishing and Hunting\",\n",
        "    \"21\": \"Mining, Quarrying, and Oil and Gas Extraction\",\n",
        "    \"22\": \"Utilities\",\n",
        "    \"23\": \"Construction\",\n",
        "    \"31\": \"Manufacturing\",\n",
        "    \"42\": \"Wholesale Trade\",\n",
        "    \"44\": \"Retail Trade\",\n",
        "    \"48\": \"Transportation and Warehousing\",\n",
        "    \"51\": \"Information\",\n",
        "    \"52\": \"Finance and Insurance\",\n",
        "    \"53\": \"Real Estate and Rental and Leasing\",\n",
        "    \"54\": \"Professional, Scientific, and Technical Services\",\n",
        "    \"55\": \"Management of Companies and Enterprises\",\n",
        "    \"56\": \"Administrative and Support and Waste ...\",\n",
        "    \"61\": \"Educational Services\",\n",
        "    \"62\": \"Health Care and Social Assistance\",\n",
        "    \"71\": \"Arts, Entertainment, and Recreation\",\n",
        "    \"72\": \"Accommodation and Food Services\",\n",
        "    \"81\": \"Other Services (except Public Administration)\",\n",
        "    \"UNCLFD\": \"Unclassified\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c3ea5fe-1f93-4d7f-84b8-c94c719f76d9",
      "metadata": {
        "tags": [],
        "id": "1c3ea5fe-1f93-4d7f-84b8-c94c719f76d9"
      },
      "outputs": [],
      "source": [
        "# read the business and economic data at zcta and state levels\n",
        "data_zcta_path = os.path.join(data_path, \"NAICS_EST_GDP2022_ZCTA.csv\")\n",
        "population_df_path = os.path.join(data_path, \"2020_decennial_census_at_ZCTA_level.csv\")\n",
        "\n",
        "# Reading data\n",
        "data_zcta = pd.read_csv(data_zcta_path)\n",
        "population_df = pd.read_csv(population_df_path)\n",
        "\n",
        "# Merging and aggregating data\n",
        "unique_zcta_regions = data_zcta[[\"ZCTA\", \"REGIONS\", \"STABBR\"]].drop_duplicates()\n",
        "regions_pop_df = unique_zcta_regions.merge(population_df, on=[\"ZCTA\", \"STABBR\"])\n",
        "total_pop_regions = regions_pop_df.groupby(\"REGIONS\")[\"POP20\"].sum().reset_index()\n",
        "\n",
        "sum_total_pop = total_pop_regions.POP20.sum()\n",
        "total_pop_regions[\"POP20Ratio\"] = total_pop_regions.POP20 / sum_total_pop\n",
        "total_pop_regions = total_pop_regions[total_pop_regions.REGIONS != \"NotOrder1000\"]\n",
        "\n",
        "# import the grid regions\n",
        "ferc_gdf = gpd.read_file(os.path.join(data_path, \"nerc_gdf.geojson\"))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_pop_regions.REGIONS.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkJ8FbCxV9YC",
        "outputId": "fbd07d78-afdf-4a2d-c7be-fb5d9a7f7d3b"
      },
      "id": "FkJ8FbCxV9YC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['CAISO', 'ERCOT', 'FRCC', 'ISONE', 'MISO', 'NYISO',\n",
              "       'NorthernGridConnected', 'NorthernGridUnconnected', 'PJM', 'SCRTP',\n",
              "       'SERTP', 'SPP', 'WestConnect', 'WestConnectNonEnrolled'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b51f5539-2b57-44f2-a109-e979f845aa67",
      "metadata": {
        "id": "b51f5539-2b57-44f2-a109-e979f845aa67"
      },
      "source": [
        "### Business counts and GDP contribution by NAICs Classification\n",
        "In our calculation, we assume uniform every person/business contribute equally to State GDP."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ee81e0c-0b99-4eaf-8e87-798792527c5a",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3ee81e0c-0b99-4eaf-8e87-798792527c5a",
        "outputId": "781da355-1c4f-46c2-a617-92272044e23c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  REGIONS NAICS      EST    GDP2022  DailyGDP   ESTPerc\n",
              "0   CAISO    11   1254.0   26.55421  0.072751  0.000157\n",
              "1   CAISO    21    257.0    6.40774  0.017555  0.000032\n",
              "2   CAISO    22    552.0   36.87954  0.101040  0.000069\n",
              "3   CAISO    23  75608.0   99.96392  0.273874  0.009465\n",
              "4   CAISO    31  33696.0  335.19854  0.918352  0.004218"
            ],
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-340c74e7-9aef-4868-9901-62813e373e5c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "# zcta\n",
        "df_business = (\n",
        "    data_zcta.groupby([\"REGIONS\", \"NAICS\"])[[\"EST\", \"GDP2022\"]].sum().reset_index()\n",
        ")\n",
        "\n",
        "df_business.GDP2022 = df_business.GDP2022 / 1000\n",
        "sum_total_gdp = df_business.GDP2022.sum() * 1 / 365\n",
        "\n",
        "total_est_by_naics = df_business.groupby(\"NAICS\")[\"EST\"].sum().sum()\n",
        "\n",
        "df_business[\"DailyGDP\"] = df_business.GDP2022.map(lambda x: (x / 365))\n",
        "df_business[\"ESTPerc\"] = df_business.apply(\n",
        "    lambda row: row[\"EST\"] / total_est_by_naics, axis=1\n",
        ")\n",
        "\n",
        "# Exclude Non-order 1000 region\n",
        "df_business = df_business[df_business.REGIONS != \"NotOrder1000\"]\n",
        "df_business.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ccbafe4-b11c-48a7-98f7-f9b2fbf3c62a",
      "metadata": {
        "tags": [],
        "id": "4ccbafe4-b11c-48a7-98f7-f9b2fbf3c62a"
      },
      "outputs": [],
      "source": [
        "# Export the business data as a dictionary\n",
        "nested_dict = {}\n",
        "region_pop = total_pop_regions.set_index(\"REGIONS\")[[\"POP20\", \"POP20Ratio\"]]\n",
        "\n",
        "for _, row in df_business.iterrows():\n",
        "    region = row[\"REGIONS\"]\n",
        "    naics = row[\"NAICS\"]\n",
        "\n",
        "    if region not in nested_dict:\n",
        "        nested_dict[region] = {\n",
        "            \"NAICS\": {},\n",
        "            \"POP20\": region_pop.loc[region][\"POP20\"],\n",
        "            \"POP20Ratio\": region_pop.loc[region][\"POP20Ratio\"],\n",
        "        }\n",
        "\n",
        "    nested_dict[region][\"NAICS\"][naics] = {\n",
        "        \"EST\": row[\"EST\"],\n",
        "        \"ESTPerc\": row[\"ESTPerc\"],\n",
        "        \"DAILYGDP\": row[\"DailyGDP\"],\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4929f949-858d-451b-a3c8-228708d84c22",
      "metadata": {
        "id": "4929f949-858d-451b-a3c8-228708d84c22"
      },
      "source": [
        "## Estimates of the number of businesses and population without power"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3e917dc-bb8b-402f-bfb2-b44b2daf6c64",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "f3e917dc-bb8b-402f-bfb2-b44b2daf6c64",
        "outputId": "0aa4331e-7f7c-46b2-bf41-c3b01a5db7e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       CAISO  ERCOT  FRCC  ISONE  MISO  NYISO  NorthernGridConnected  \\\n",
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              "...      ...    ...   ...    ...   ...    ...                    ...   \n",
              "16379      1      1     1      1     1      1                      1   \n",
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              "\n",
              "       WestConnectNonEnrolled  \n",
              "0                           0  \n",
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              "      const quickchartButtonEl =\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "# Regions and their possible states (0 or 1)\n",
        "# states denote all regions with or without power\n",
        "regions = df_business.REGIONS.unique()\n",
        "\n",
        "# 0 with power in the entire block, 1 without power\n",
        "states = [0, 1]\n",
        "\n",
        "# Generate all possible combinations\n",
        "combinations = list(product(states, repeat=len(regions)))\n",
        "df_combinations = pd.DataFrame(combinations, columns=list(df_business.REGIONS.unique()))\n",
        "df_combinations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15f6128e-aa4d-4b97-87c5-43f5543ae355",
      "metadata": {
        "tags": [],
        "id": "15f6128e-aa4d-4b97-87c5-43f5543ae355"
      },
      "outputs": [],
      "source": [
        "df_bus_sum = (\n",
        "    df_business.groupby(\"REGIONS\")[[\"EST\", \"DailyGDP\", \"ESTPerc\"]].sum().reset_index()\n",
        ")\n",
        "df_bus_po = df_bus_sum.merge(total_pop_regions, on=\"REGIONS\")\n",
        "df_bus_po[\"DailyGDPPerc\"] = df_bus_po[\"DailyGDP\"] / sum_total_gdp\n",
        "df_index = df_bus_po.set_index(\"REGIONS\")\n",
        "\n",
        "#  save the grid regions quant figures\n",
        "df_index.to_csv(os.path.join(data_path, \"grid_data.csv\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d826d699-3da4-4c44-9a92-0a29f5e07b20",
      "metadata": {
        "tags": [],
        "id": "d826d699-3da4-4c44-9a92-0a29f5e07b20"
      },
      "outputs": [],
      "source": [
        "# Save the grid data\n",
        "df_index.to_json(os.path.join(data_path, \"grid_data.json\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d67466cb-1dfe-4cd7-9c6b-0c46214c2e1b",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "d67466cb-1dfe-4cd7-9c6b-0c46214c2e1b",
        "outputId": "eaac954f-51db-456e-efb2-c4fcc9d6a49c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "REGIONS              CAISO         ERCOT          FRCC         ISONE  \\\n",
              "EST           9.302580e+05  5.713920e+05  5.847680e+05  3.663680e+05   \n",
              "DailyGDP      8.322560e+00  4.735475e+00  3.196764e+00  2.912896e+00   \n",
              "ESTPerc       1.164573e-01  7.153152e-02  7.320604e-02  4.586494e-02   \n",
              "POP20         3.666968e+07  2.575638e+07  2.026976e+07  1.416044e+07   \n",
              "POP20Ratio    1.126283e-01  7.910886e-02  6.225710e-02  4.349276e-02   \n",
              "DailyGDPPerc  1.402378e-01  7.979429e-02  5.386652e-02  4.908325e-02   \n",
              "\n",
              "REGIONS               MISO         NYISO  NorthernGridConnected  \\\n",
              "EST           1.051459e+06  5.859790e+05           4.633720e+05   \n",
              "DailyGDP      7.118827e+00  5.261589e+00           3.180750e+00   \n",
              "ESTPerc       1.316302e-01  7.335764e-02           5.800869e-02   \n",
              "POP20         4.465623e+07  2.199486e+07           1.609561e+07   \n",
              "POP20Ratio    1.371584e-01  6.755562e-02           4.943651e-02   \n",
              "DailyGDPPerc  1.199545e-01  8.865949e-02           5.359667e-02   \n",
              "\n",
              "REGIONS       NorthernGridUnconnected           PJM         SCRTP  \\\n",
              "EST                      1.114560e+05  1.550176e+06  6.614400e+04   \n",
              "DailyGDP                 8.352190e-01  1.179109e+01  3.845126e-01   \n",
              "ESTPerc                  1.395297e-02  1.940637e-01  8.280447e-03   \n",
              "POP20                    4.593872e+06  6.574212e+07  2.695532e+06   \n",
              "POP20Ratio               1.410975e-02  2.019222e-01  8.279131e-03   \n",
              "DailyGDPPerc             1.407371e-02  1.986837e-01  6.479161e-03   \n",
              "\n",
              "REGIONS              SERTP           SPP   WestConnect  WestConnectNonEnrolled  \n",
              "EST           8.915730e+05  3.596550e+05  2.398830e+05            1.647360e+05  \n",
              "DailyGDP      6.132122e+00  2.335112e+00  1.633155e+00            1.101187e+00  \n",
              "ESTPerc       1.116144e-01  4.502455e-02  3.003051e-02            2.062300e-02  \n",
              "POP20         3.995916e+07  1.434230e+07  9.947333e+06            6.191785e+06  \n",
              "POP20Ratio    1.227317e-01  4.405134e-02  3.055251e-02            1.901762e-02  \n",
              "DailyGDPPerc  1.033282e-01  3.934740e-02  2.751919e-02            1.855537e-02  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd818697-82f8-4b27-966d-594fdd52576f\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>REGIONS</th>\n",
              "      <th>CAISO</th>\n",
              "      <th>ERCOT</th>\n",
              "      <th>FRCC</th>\n",
              "      <th>ISONE</th>\n",
              "      <th>MISO</th>\n",
              "      <th>NYISO</th>\n",
              "      <th>NorthernGridConnected</th>\n",
              "      <th>NorthernGridUnconnected</th>\n",
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              "      <th>WestConnectNonEnrolled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>EST</th>\n",
              "      <td>9.302580e+05</td>\n",
              "      <td>5.713920e+05</td>\n",
              "      <td>5.847680e+05</td>\n",
              "      <td>3.663680e+05</td>\n",
              "      <td>1.051459e+06</td>\n",
              "      <td>5.859790e+05</td>\n",
              "      <td>4.633720e+05</td>\n",
              "      <td>1.114560e+05</td>\n",
              "      <td>1.550176e+06</td>\n",
              "      <td>6.614400e+04</td>\n",
              "      <td>8.915730e+05</td>\n",
              "      <td>3.596550e+05</td>\n",
              "      <td>2.398830e+05</td>\n",
              "      <td>1.647360e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DailyGDP</th>\n",
              "      <td>8.322560e+00</td>\n",
              "      <td>4.735475e+00</td>\n",
              "      <td>3.196764e+00</td>\n",
              "      <td>2.912896e+00</td>\n",
              "      <td>7.118827e+00</td>\n",
              "      <td>5.261589e+00</td>\n",
              "      <td>3.180750e+00</td>\n",
              "      <td>8.352190e-01</td>\n",
              "      <td>1.179109e+01</td>\n",
              "      <td>3.845126e-01</td>\n",
              "      <td>6.132122e+00</td>\n",
              "      <td>2.335112e+00</td>\n",
              "      <td>1.633155e+00</td>\n",
              "      <td>1.101187e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESTPerc</th>\n",
              "      <td>1.164573e-01</td>\n",
              "      <td>7.153152e-02</td>\n",
              "      <td>7.320604e-02</td>\n",
              "      <td>4.586494e-02</td>\n",
              "      <td>1.316302e-01</td>\n",
              "      <td>7.335764e-02</td>\n",
              "      <td>5.800869e-02</td>\n",
              "      <td>1.395297e-02</td>\n",
              "      <td>1.940637e-01</td>\n",
              "      <td>8.280447e-03</td>\n",
              "      <td>1.116144e-01</td>\n",
              "      <td>4.502455e-02</td>\n",
              "      <td>3.003051e-02</td>\n",
              "      <td>2.062300e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POP20</th>\n",
              "      <td>3.666968e+07</td>\n",
              "      <td>2.575638e+07</td>\n",
              "      <td>2.026976e+07</td>\n",
              "      <td>1.416044e+07</td>\n",
              "      <td>4.465623e+07</td>\n",
              "      <td>2.199486e+07</td>\n",
              "      <td>1.609561e+07</td>\n",
              "      <td>4.593872e+06</td>\n",
              "      <td>6.574212e+07</td>\n",
              "      <td>2.695532e+06</td>\n",
              "      <td>3.995916e+07</td>\n",
              "      <td>1.434230e+07</td>\n",
              "      <td>9.947333e+06</td>\n",
              "      <td>6.191785e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POP20Ratio</th>\n",
              "      <td>1.126283e-01</td>\n",
              "      <td>7.910886e-02</td>\n",
              "      <td>6.225710e-02</td>\n",
              "      <td>4.349276e-02</td>\n",
              "      <td>1.371584e-01</td>\n",
              "      <td>6.755562e-02</td>\n",
              "      <td>4.943651e-02</td>\n",
              "      <td>1.410975e-02</td>\n",
              "      <td>2.019222e-01</td>\n",
              "      <td>8.279131e-03</td>\n",
              "      <td>1.227317e-01</td>\n",
              "      <td>4.405134e-02</td>\n",
              "      <td>3.055251e-02</td>\n",
              "      <td>1.901762e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DailyGDPPerc</th>\n",
              "      <td>1.402378e-01</td>\n",
              "      <td>7.979429e-02</td>\n",
              "      <td>5.386652e-02</td>\n",
              "      <td>4.908325e-02</td>\n",
              "      <td>1.199545e-01</td>\n",
              "      <td>8.865949e-02</td>\n",
              "      <td>5.359667e-02</td>\n",
              "      <td>1.407371e-02</td>\n",
              "      <td>1.986837e-01</td>\n",
              "      <td>6.479161e-03</td>\n",
              "      <td>1.033282e-01</td>\n",
              "      <td>3.934740e-02</td>\n",
              "      <td>2.751919e-02</td>\n",
              "      <td>1.855537e-02</td>\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b90bc572-c2de-4356-bd5f-7bf486798dda button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_73d6f2f3-50aa-4a0f-8cef-a9dfaa794dee\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('transposed_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_73d6f2f3-50aa-4a0f-8cef-a9dfaa794dee button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('transposed_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "# transpose the business df and cast on the combinatioons df\n",
        "transposed_df = df_index.T\n",
        "transposed_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b9b3933-8cde-477a-9e81-7712ee9b61b9",
      "metadata": {
        "id": "5b9b3933-8cde-477a-9e81-7712ee9b61b9"
      },
      "source": [
        "The business dataframe consist of total number of the employed, establishments, and population in grid regions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d0925edd-75f4-4c68-b6ed-7d018693da6b",
      "metadata": {
        "tags": [],
        "id": "d0925edd-75f4-4c68-b6ed-7d018693da6b"
      },
      "outputs": [],
      "source": [
        "# broadcast the emp, population, and establishment on the combination df\n",
        "array_transposed = transposed_df.to_numpy()\n",
        "array_comb = df_combinations.to_numpy()\n",
        "\n",
        "result_array = array_transposed[:, np.newaxis, :] * array_comb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scenario_dfs = []\n",
        "\n",
        "daily_gdp_df = transposed_df[transposed_df.index == \"DailyGDP\"]\n",
        "\n",
        "for i in range(1, len(regions)):\n",
        "\n",
        "  daily_gdp_array = daily_gdp_df.to_numpy()\n",
        "\n",
        "  df_filtered = df_combinations.loc[df_combinations.iloc[:, :].sum(axis=1) == i]\n",
        "  df_filtered_array = df_filtered.to_numpy()\n",
        "\n",
        "  df_filtered_result = pd.DataFrame(daily_gdp_array * df_filtered_array, columns=list(regions))\n",
        "\n",
        "  df_filtered_result[\"GDPSum\"] = df_filtered_result.sum(axis=1)\n",
        "  df_filtered_result = df_filtered_result.sort_values(by=\"GDPSum\", ascending=True)\n",
        "  scenario_dfs.append(df_filtered_result)\n",
        "\n"
      ],
      "metadata": {
        "id": "8VgV9zHZQrcG"
      },
      "id": "8VgV9zHZQrcG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scenarios where a single grid region is out of power\n",
        "\n",
        "# Est without power\n",
        "scenario_dfs[0][\"GDPSum\"].hist()\n",
        "\n",
        "scenario_dfs[0][\"GDPSum\"].quantile(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KAP-kUXJV0vX",
        "outputId": "7790e457-aa3c-4a78-8b3d-f70e871bf649"
      },
      "id": "KAP-kUXJV0vX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.1887572602739724"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combinations stats\n",
        "\n",
        "gdp_means = []\n",
        "gdp_variances = []\n",
        "num_combinations = []\n",
        "\n",
        "# Iterate over each scenario dataframe\n",
        "for scenario_df in scenario_dfs:\n",
        "    # Calculate the average GDP for the current scenario\n",
        "    gdp_means.append(scenario_df['GDPSum'].mean())\n",
        "\n",
        "    # Calculate the variance of GDP for the current scenario\n",
        "    gdp_variances.append(scenario_df['GDPSum'].var())\n",
        "\n",
        "    # Count the number of combinations (rows) in the current scenario\n",
        "    num_combinations.append(len(scenario_df))\n",
        "\n",
        "# Create a DataFrame to hold the results\n",
        "scenario_stats_df = pd.DataFrame({\n",
        "    'Scenario': np.arange(1, len(scenario_dfs) + 1),\n",
        "    'Average_GDP': gdp_means,\n",
        "    'GDP_Variance': gdp_variances,\n",
        "    'Number_of_Combinations': num_combinations\n",
        "})\n",
        "\n",
        "# Display the DataFrame\n",
        "print(scenario_stats_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMX9ZzMjCYd8",
        "outputId": "24702bf4-54b1-4551-91cb-1426e9ed8ed6"
      },
      "id": "TMX9ZzMjCYd8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Scenario  Average_GDP  GDP_Variance  Number_of_Combinations\n",
            "0          1     4.210090     10.579685                      14\n",
            "1          2     8.420180     18.338120                      91\n",
            "2          3    12.630270     25.006527                     364\n",
            "3          4    16.840360     30.257898                    1001\n",
            "4          5    21.050450     34.023124                    2002\n",
            "5          6    25.260540     36.285288                    3003\n",
            "6          7    29.470630     37.039689                    3432\n",
            "7          8    33.680720     36.285288                    3003\n",
            "8          9    37.890810     34.023124                    2002\n",
            "9         10    42.100900     30.257898                    1001\n",
            "10        11    46.310991     25.006527                     364\n",
            "11        12    50.521081     18.338120                      91\n",
            "12        13    54.731171     10.579685                      14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Define the quantiles\n",
        "quantiles = [0.01, 0.1, 0.5]\n",
        "quantile_labels = ['1%', '10%', '50%']\n",
        "\n",
        "# Grid Failures\n",
        "grid_failures = [3, 4, 6, 8] # 3, 4, or 6 regions fail\n",
        "\n",
        "# Loop over each DataFrame in scenario_dfs\n",
        "for i in grid_failures:\n",
        "\n",
        "    df_scenario = scenario_dfs[i-1]\n",
        "\n",
        "    # Rank the data\n",
        "    df_scenario['Rank'] = df_scenario['GDPSum'].rank(ascending=False)\n",
        "\n",
        "    # Calculate cumulative frequency 'Fi'\n",
        "    n = len(df_scenario)\n",
        "    df_scenario['Fi'] = df_scenario['Rank'] / (n + 1)\n",
        "\n",
        "    # Sort by 'GDPSum' for plotting\n",
        "    df_scenario = df_scenario.sort_values('GDPSum', ascending=True)\n",
        "\n",
        "    # Plot the cumulative frequency curve\n",
        "    plt.plot(df_scenario['GDPSum'], df_scenario['Fi'], linestyle='-', label=f'{i}')\n",
        "\n",
        "# Set the y-axis to log scale\n",
        "plt.yscale('log')\n",
        "\n",
        "# Add horizontal lines for each quantile value of Fi and label them\n",
        "for value, label in zip(quantiles, quantile_labels):\n",
        "  plt.axhline(y=value, color='k', linestyle='--')\n",
        "  plt.text(x=max(df_scenario['GDPSum']), y=value, s=f' {label} CP', color='k', va='bottom', ha='right')\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Total GDP Imapact')\n",
        "plt.ylabel('Cumulative Frequency')\n",
        "plt.title('F-N Cumulative Frequency Curves for Multiple Grid Failure Regions')\n",
        "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=4, frameon=False)\n",
        "\n",
        "# Add a text object to act as a legend title\n",
        "plt.text(x=0.1, y=-0.1, s='Number of\\n Regions', ha='center', va='top', transform=plt.gca().transAxes,  weight='bold')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "TBTAZO3gSK75",
        "outputId": "397d46cb-ae61-4462-b52b-0adc42fe6a64"
      },
      "id": "TBTAZO3gSK75",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scenario_percentile_dfs = []\n",
        "\n",
        "for i in grid_failures:\n",
        "\n",
        "    df_scenario = scenario_dfs[i-1]\n",
        "\n",
        "    # Calculate Fi rank\n",
        "    df_scenario['Fi'] = df_scenario['GDPSum'].rank(ascending=False) / len(df_scenario)\n",
        "    df_scenario_sorted = df_scenario.sort_values('Fi', ascending=False)\n",
        "\n",
        "    # List to hold rows for the current scenario\n",
        "    percentile_rows = []\n",
        "\n",
        "    for percentile in quantiles:\n",
        "        # Calculate the row corresponding to the percentile based on Fi\n",
        "        value = df_scenario_sorted['Fi'].quantile(percentile)\n",
        "        # Get the row with the Fi value closest to the calculated percentile value\n",
        "        row = df_scenario_sorted.iloc[(df_scenario_sorted['Fi']-value).abs().argsort()[:1]]\n",
        "        # row[\"Probability\"] = percentile\n",
        "        percentile_rows.append(row)\n",
        "\n",
        "    scenario_percentile_df = pd.concat(percentile_rows)\n",
        "    # Drop the Fi and Rank\n",
        "    scenario_percentile_df.drop([\"Rank\", \"Fi\"], axis=1, inplace=True)\n",
        "    # scenario_percentile_df[\"Size\"] = i\n",
        "    scenario_percentile_dfs.append(scenario_percentile_df)"
      ],
      "metadata": {
        "id": "9ndzJZ3asRef"
      },
      "id": "9ndzJZ3asRef",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_transformed_data(df, quantiles=quantiles, nested_dict=nested_dict, df_index=df_index):\n",
        "    \"\"\"\n",
        "    Transforms the input DataFrame and calculates various metrics for GDP and establishments.\n",
        "\n",
        "    Parameters:\n",
        "    df (DataFrame): The input DataFrame.\n",
        "    quantiles (list): List of quantile values.\n",
        "    nested_dict (dict): Nested dictionary containing NAICS data.\n",
        "    df_index (DataFrame): DataFrame containing index data such as DailyGDP and POP20.\n",
        "\n",
        "    Returns:\n",
        "    tuple: A dictionary containing grouped region and NAICS data, and population data.\n",
        "    \"\"\"\n",
        "    # Transpose and rename columns\n",
        "    percentiles_transformed = df.T\n",
        "    percentiles_transformed.columns = quantiles\n",
        "    percentiles_transformed.drop(\"GDPSum\", inplace=True)\n",
        "\n",
        "    # Initialize data lists\n",
        "    new_rows_gdp, new_rows_est = [], []\n",
        "    col_names = [f\"perc{int(i*100)}\" if i != 0 else \"perc0\" for i in quantiles]\n",
        "    new_col_names = [\"REGIONS\", \"NAICS\"] + col_names\n",
        "\n",
        "    # Process each region\n",
        "    for reg, row in percentiles_transformed.iterrows():\n",
        "        non_zero_indices = [i for i, value in enumerate(row) if value != 0]\n",
        "\n",
        "        for naics, data in nested_dict[reg][\"NAICS\"].items():\n",
        "            new_row_gdp = [reg, naics] + [data[\"DAILYGDP\"] if i in non_zero_indices else 0 for i in range(len(row))]\n",
        "            new_row_est = [reg, naics] + [data[\"EST\"] if i in non_zero_indices else 0 for i in range(len(row))]\n",
        "\n",
        "            new_rows_gdp.append(new_row_gdp)\n",
        "            new_rows_est.append(new_row_est)\n",
        "\n",
        "    # Create DataFrames\n",
        "    df_gdp = pd.DataFrame(new_rows_gdp, columns=new_col_names)\n",
        "    df_est = pd.DataFrame(new_rows_est, columns=new_col_names)\n",
        "\n",
        "    # Group by regions and NAICS\n",
        "    grouped_regions_est = df_est.groupby(\"REGIONS\").sum()\n",
        "    grouped_naics_est = df_est.groupby(\"NAICS\").sum()\n",
        "    df_gdp_naics = df_gdp.groupby(\"NAICS\").sum()\n",
        "    df_gdp_regions = df_gdp.groupby(\"REGIONS\").sum()\n",
        "\n",
        "    # Calculate percentages of daily output\n",
        "    gdp_loss_est_df_perc = (df_gdp_naics / df_index.DailyGDP.sum()).round(2)\n",
        "\n",
        "    # Establishments without power\n",
        "    est_binary = grouped_regions_est.ne(0).astype(int)\n",
        "\n",
        "    # Population without power\n",
        "    pop_est_df = (est_binary.apply(lambda row: row * df_index.loc[row.name, \"POP20\"], axis=1) / 1000000).round(2)\n",
        "    pop_est_df_perc = (pop_est_df / (df_index.POP20.sum()/1e6)).round(2)\n",
        "\n",
        "    return {\"regions_est\": grouped_regions_est, \"gdp_by_naics\": df_gdp_naics, \"gdp_by_regions\": df_gdp_regions, \"pop\": pop_est_df }\n"
      ],
      "metadata": {
        "id": "ikZ679MG0s03"
      },
      "id": "ikZ679MG0s03",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "us_crs = 'epsg:4269'\n",
        "ncols = len(scenario_percentile_dfs)\n",
        "nrows = len(quantiles)\n",
        "\n",
        "fig = plt.figure(figsize=(12, 8))\n",
        "gs = gridspec.GridSpec(nrows, ncols, hspace=0, wspace=0)\n",
        "\n",
        "axes = []\n",
        "for i in range(nrows):\n",
        "    for j in range(ncols):\n",
        "        ax = fig.add_subplot(gs[i, j])\n",
        "        axes.append(ax)\n",
        "\n",
        "cmap = plt.cm.viridis\n",
        "cmap.set_under('#cccccc')\n",
        "\n",
        "for k, ax in enumerate(axes):\n",
        "    i = k // ncols\n",
        "    j = k % ncols\n",
        "\n",
        "    scenario_df = scenario_percentile_dfs[j]\n",
        "    quantile = quantiles[i]\n",
        "\n",
        "    # Transform data for the current scenario\n",
        "    data = get_transformed_data(scenario_df)\n",
        "    merged_gdf = ferc_gdf.merge(data[\"gdp_by_regions\"], on=\"REGIONS\")\n",
        "\n",
        "    gdf_copy = merged_gdf.copy()\n",
        "    perc_column = f\"perc{int(quantile*100)}\"\n",
        "    gdf_copy = gdf_copy.dropna(subset=[perc_column])\n",
        "\n",
        "    # Plot each regionpop\n",
        "    gdf_copy.to_crs(us_crs).plot(\n",
        "        perc_column,\n",
        "        cmap=cmap,\n",
        "        ax=ax,\n",
        "        vmin=0.1\n",
        "    )\n",
        "\n",
        "    # Add scenario label on the bottom left of each subplot\n",
        "    ax.text(0.05, 0.1, f'S{k + 1}', transform=ax.transAxes, fontsize=10, verticalalignment='bottom', horizontalalignment='left')\n",
        "\n",
        "    # Add percentile label on the left of the first column\n",
        "    if j == 0:\n",
        "        ax.text(-0.2, 0.5, f'{int(quantile * 100)}% CP', transform=ax.transAxes, fontsize=10, va='center')\n",
        "\n",
        "    # Add grid failure label on the top center of the first row\n",
        "    if i == 0:\n",
        "        ax.set_title(f\"Grid Region Count ({grid_failures[j]})\", loc='center')\n",
        "\n",
        "    ax.set_facecolor('grey')\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "# Create a common colorbar\n",
        "norm = colors.Normalize(vmin=0.1, vmax=gdf_copy[perc_column].max())\n",
        "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
        "sm.set_array([])\n",
        "cbar_ax = fig.add_axes([0.25, 0.0, 0.4, 0.02])\n",
        "fig.colorbar(sm, cax=cbar_ax, orientation='horizontal')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "l_LBOzD-t6UO",
        "outputId": "74691978-a111-452d-dd99-8d860f4ff9df"
      },
      "id": "l_LBOzD-t6UO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 13 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Indirect Impact Models"
      ],
      "metadata": {
        "id": "sCiyTno_VHXN"
      },
      "id": "sCiyTno_VHXN"
    },
    {
      "cell_type": "code",
      "source": [
        "# Read CSV files\n",
        "TechnicalCoefficients = pd.read_csv(os.path.join(data_path, \"IOdata\", \"IndByComs.csv\"))\n",
        "grossOutput2022 = pd.read_csv(os.path.join(data_path, \"IOdata\", \"output_2022.csv\")).iloc[:, 1] / 1000\n",
        "consumption = pd.read_csv(os.path.join(data_path, \"IOdata\", \"consumption.csv\")).iloc[:, 2] / (365 * 1.2)\n",
        "\n",
        "Industries = pd.read_csv(os.path.join(data_path, \"IOdata\", \"Gross.csv\")).iloc[:, 0]\n",
        "Industries.iloc[68] = \"State Government Enterprises\"\n",
        "\n",
        "# Leontief matrix calculation\n",
        "L = TechnicalCoefficients.to_numpy()\n",
        "Identity = np.eye(69)\n",
        "A = Identity - np.linalg.inv(L)\n",
        "GrossDiag = np.diag(grossOutput2022)\n",
        "GrossDiagInv = np.linalg.inv(GrossDiag)\n",
        "BMatrix = np.dot(np.dot(GrossDiagInv, A), GrossDiag)\n",
        "\n",
        "# Ghosh model calculation\n",
        "Ghosh = np.linalg.inv(Identity - BMatrix)\n",
        "\n",
        "# Read GDP shocks and population scenario data\n",
        "GDPShocks = pd.read_csv(os.path.join(data_path, \"IOdata\", \"GDP_shocks_est.csv\"))\n",
        "pop_scenario = pd.read_csv(os.path.join(data_path, \"IOdata\", \"pop_est.csv\"))\n",
        "\n",
        "# Grid data calculations\n",
        "grid_data = pd.read_csv(os.path.join(data_path, \"IOdata\", \"grid_data.csv\"))\n",
        "total_pop = grid_data['POP20'].sum() / 1e6  # total population in millions\n",
        "daily_gdp = grid_data[\"DailyGDP\"].sum()"
      ],
      "metadata": {
        "id": "BhbmXhlbVNLU"
      },
      "id": "BhbmXhlbVNLU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# expand and compress the shocks using 2022 gross output values\n",
        "def get_value_added(GDPShocks, i):\n",
        "\n",
        "    # Give a second tier NAICS shocks\n",
        "    VA = np.zeros(69)  # Initialize VA with zeros\n",
        "\n",
        "    # NAICS 11 - Farms\n",
        "    VA[0] = GDPShocks.iloc[0, i + 1] * 0.887\n",
        "    VA[1] = GDPShocks.iloc[0, i + 1] * 0.113\n",
        "\n",
        "    # NAICS 21 - Oil and Gas\n",
        "    VA[2] = GDPShocks.iloc[1, i + 1] * 0.725\n",
        "    VA[3] = GDPShocks.iloc[1, i + 1] * 0.173\n",
        "    VA[4] = GDPShocks.iloc[1, i + 1] * 0.102\n",
        "\n",
        "    # NAICS 22\n",
        "    VA[5] = GDPShocks.iloc[2, i + 1]\n",
        "\n",
        "    # NAICS 23\n",
        "    VA[6] = GDPShocks.iloc[3, i + 1]\n",
        "\n",
        "    # NAICS 31\n",
        "    NAICS_31_multipliers = [0.0238, 0.0232, 0.0415, 0.0623, 0.0656, 0.0637, 0.0228,\n",
        "                            0.1160, 0.0487, 0.0126, 0.0289, 0.1667, 0.0083, 0.0036,\n",
        "                            0.0304, 0.0125, 0.0962, 0.1341, 0.0391]\n",
        "\n",
        "    for idx, multiplier in enumerate(NAICS_31_multipliers):\n",
        "        VA[7 + idx] = GDPShocks.iloc[4, i + 1] * multiplier\n",
        "\n",
        "    # NAICS 42\n",
        "    VA[26] = GDPShocks.iloc[5, i + 1]\n",
        "\n",
        "    # NAICS 44\n",
        "    VA[27] = GDPShocks.iloc[6, i + 1] * 0.1570\n",
        "    VA[28] = GDPShocks.iloc[6, i + 1] * 0.1270\n",
        "    VA[29] = GDPShocks.iloc[6, i + 1] * 0.1205\n",
        "    VA[30] = GDPShocks.iloc[6, i + 1] * 0.5956\n",
        "\n",
        "    # NAICS 48\n",
        "    VA[31] = GDPShocks.iloc[7, i + 1] * 0.1488\n",
        "    VA[32] = GDPShocks.iloc[7, i + 1] * 0.0549\n",
        "    VA[33] = GDPShocks.iloc[7, i + 1] * 0.0350\n",
        "    VA[34] = GDPShocks.iloc[7, i + 1] * 0.3224\n",
        "    VA[35] = GDPShocks.iloc[7, i + 1] * 0.0484\n",
        "    VA[36] = GDPShocks.iloc[7, i + 1] * 0.0403\n",
        "    VA[37] = GDPShocks.iloc[7, i + 1] * 0.2289\n",
        "    VA[38] = GDPShocks.iloc[7, i + 1] * 0.1213\n",
        "\n",
        "    # NAICS 51\n",
        "    VA[39] = GDPShocks.iloc[8, i + 1] * 0.2211\n",
        "    VA[40] = GDPShocks.iloc[8, i + 1] * 0.0757\n",
        "    VA[41] = GDPShocks.iloc[8, i + 1] * 0.4139\n",
        "    VA[42] = GDPShocks.iloc[8, i + 1] * 0.2904\n",
        "\n",
        "    # NAICS 52\n",
        "    VA[43] = GDPShocks.iloc[9, i + 1] * 0.3105\n",
        "    VA[44] = GDPShocks.iloc[9, i + 1] * 0.2282\n",
        "    VA[45] = GDPShocks.iloc[9, i + 1] * 0.4056\n",
        "    VA[46] = GDPShocks.iloc[9, i + 1] * 0.0557\n",
        "\n",
        "    # NAICS 53\n",
        "    VA[47] = GDPShocks.iloc[10, i + 1] * 0.7963\n",
        "    VA[48] = GDPShocks.iloc[10, i + 1] * 0.2037\n",
        "\n",
        "    # NAICS 54\n",
        "    VA[49] = GDPShocks.iloc[11, i + 1] * 0.1480\n",
        "    VA[50] = GDPShocks.iloc[11, i + 1] * 0.1936\n",
        "    VA[51] = GDPShocks.iloc[11, i + 1] * 0.6584\n",
        "\n",
        "    # NAICS 55\n",
        "    VA[52] = GDPShocks.iloc[12, i + 1]\n",
        "\n",
        "    # NAICS 56\n",
        "    VA[53] = GDPShocks.iloc[13, i + 1] * 0.9073\n",
        "    VA[54] = GDPShocks.iloc[13, i + 1] * 0.0927\n",
        "\n",
        "    # NAICS 61\n",
        "    VA[55] = GDPShocks.iloc[14, i + 1]\n",
        "\n",
        "    # NAICS 62\n",
        "    VA[56] = GDPShocks.iloc[15, i + 1] * 0.4396\n",
        "    VA[57] = GDPShocks.iloc[15, i + 1] * 0.3722\n",
        "    VA[58] = GDPShocks.iloc[15, i + 1] * 0.0949\n",
        "    VA[59] = GDPShocks.iloc[15, i + 1] * 0.0934\n",
        "\n",
        "    # NAICS 71\n",
        "    VA[60] = GDPShocks.iloc[16, i + 1] * 0.5709\n",
        "    VA[61] = GDPShocks.iloc[16, i + 1] * 0.4291\n",
        "\n",
        "    # NAICS 72\n",
        "    VA[62] = GDPShocks.iloc[17, i + 1] * 0.2011\n",
        "    VA[63] = GDPShocks.iloc[17, i + 1] * 0.7989\n",
        "\n",
        "    # NAICS 81\n",
        "    VA[64] = GDPShocks.iloc[18, i + 1] * 0.2011\n",
        "\n",
        "    # NAICS UNCLFD\n",
        "    VA[65] = GDPShocks.iloc[19, i + 1] * 0.1537\n",
        "    VA[66] = GDPShocks.iloc[19, i + 1] * 0.0248\n",
        "    VA[67] = GDPShocks.iloc[19, i + 1] * 0.7197\n",
        "    VA[68] = GDPShocks.iloc[19, i + 1] * 0.1017\n",
        "\n",
        "    return VA\n",
        "\n",
        "\n",
        "def condense_output(Output, LeonOutput):\n",
        "\n",
        "    # Initialize arrays for condensed output\n",
        "  CondensedOutput = np.zeros(20)\n",
        "  CondensedLeonOutput = np.zeros(20)\n",
        "\n",
        "  # Aggregate values by NAICS codes\n",
        "  # NAICS 11 - Farms\n",
        "  CondensedOutput[0] = Output[[0, 1]].sum()\n",
        "  CondensedLeonOutput[0] = LeonOutput[[0, 1]].sum()\n",
        "\n",
        "  # NAICS 21 - Mining, Quarrying, and Oil and Gas Extraction\n",
        "  CondensedOutput[1] = Output[[2, 3, 4]].sum()\n",
        "  CondensedLeonOutput[1] = LeonOutput[[2, 3, 4]].sum()\n",
        "\n",
        "  # NAICS 22 - Utilities\n",
        "  CondensedOutput[2] = Output[5].sum()\n",
        "  CondensedLeonOutput[2] = LeonOutput[5].sum()\n",
        "\n",
        "  # NAICS 23 - Construction\n",
        "  CondensedOutput[3] = Output[6].sum()\n",
        "  CondensedLeonOutput[3] = LeonOutput[6].sum()\n",
        "\n",
        "  # NAICS 31-33 - Manufacturing\n",
        "  CondensedOutput[4] = Output[7:26].sum()\n",
        "  CondensedLeonOutput[4] = LeonOutput[7:26].sum()\n",
        "\n",
        "  # NAICS 42 - Wholesale Trade\n",
        "  CondensedOutput[5] = Output[26].sum()\n",
        "  CondensedLeonOutput[5] = LeonOutput[26].sum()\n",
        "\n",
        "  # NAICS 44-45 - Retail Trade\n",
        "  CondensedOutput[6] = Output[27:31].sum()\n",
        "  CondensedLeonOutput[6] = LeonOutput[27:31].sum()\n",
        "\n",
        "  # NAICS 48-49 - Transportation and Warehousing\n",
        "  CondensedOutput[7] = Output[31:39].sum()\n",
        "  CondensedLeonOutput[7] = LeonOutput[31:39].sum()\n",
        "\n",
        "  # NAICS 51 - Information\n",
        "  CondensedOutput[8] = Output[39:43].sum()\n",
        "  CondensedLeonOutput[8] = LeonOutput[39:43].sum()\n",
        "\n",
        "  # NAICS 52 - Finance and Insurance\n",
        "  CondensedOutput[9] = Output[43:47].sum()\n",
        "  CondensedLeonOutput[9] = LeonOutput[43:47].sum()\n",
        "\n",
        "  # NAICS 53 - Real Estate and Rental and Leasing\n",
        "  CondensedOutput[10] = Output[[47, 48]].sum()\n",
        "  CondensedLeonOutput[10] = LeonOutput[[47, 48]].sum()\n",
        "\n",
        "  # NAICS 54 - Professional, Scientific, and Technical Services\n",
        "  CondensedOutput[11] = Output[49:52].sum()\n",
        "  CondensedLeonOutput[11] = LeonOutput[49:52].sum()\n",
        "\n",
        "  # NAICS 55 - Management of Companies and Enterprises\n",
        "  CondensedOutput[12] = Output[52].sum()\n",
        "  CondensedLeonOutput[12] = LeonOutput[52].sum()\n",
        "\n",
        "  # NAICS 56 - Administrative and Support and Waste Management and Remediation Services\n",
        "  CondensedOutput[13] = Output[[53, 54]].sum()\n",
        "  CondensedLeonOutput[13] = LeonOutput[[53, 54]].sum()\n",
        "\n",
        "  # NAICS 61 - Educational Services\n",
        "  CondensedOutput[14] = Output[55].sum()\n",
        "  CondensedLeonOutput[14] = LeonOutput[55].sum()\n",
        "\n",
        "  # NAICS 62 - Health Care and Social Assistance\n",
        "  CondensedOutput[15] = Output[56:60].sum()\n",
        "  CondensedLeonOutput[15] = LeonOutput[56:60].sum()\n",
        "\n",
        "  # NAICS 71 - Arts, Entertainment, and Recreation\n",
        "  CondensedOutput[16] = Output[[60, 61]].sum()\n",
        "  CondensedLeonOutput[16] = LeonOutput[[60, 61]].sum()\n",
        "\n",
        "  # NAICS 72 - Accommodation and Food Services\n",
        "  CondensedOutput[17] = Output[[62, 63]].sum()\n",
        "  CondensedLeonOutput[17] = LeonOutput[[62, 63]].sum()\n",
        "\n",
        "  # NAICS 81 - Other Services (except Public Administration)\n",
        "  CondensedOutput[18] = Output[64].sum()\n",
        "  CondensedLeonOutput[18] = LeonOutput[64].sum()\n",
        "\n",
        "  # NAICS UNCLFD - Unclassified\n",
        "  CondensedOutput[19] = Output[65:69].sum()\n",
        "  CondensedLeonOutput[19] = LeonOutput[65:69].sum()\n",
        "\n",
        "  return CondensedOutput, CondensedLeonOutput"
      ],
      "metadata": {
        "id": "gWhKGqlNVNP2"
      },
      "id": "gWhKGqlNVNP2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run Ghosh model to get indirect supply impacts\n",
        "\n",
        "impact_dfs = []\n",
        "percentile = 100\n",
        "scenario_counter = 0  # Scenario counter\n",
        "\n",
        "for i, scenario_df in tqdm.tqdm(enumerate(scenario_percentile_dfs)):\n",
        "    extent = grid_failures[i]\n",
        "\n",
        "    soc_econ_df = get_transformed_data(scenario_df)\n",
        "    GDPShocks = soc_econ_df[\"gdp_by_naics\"] # GDP by NAICS\n",
        "    pop_df = soc_econ_df[\"pop\"] # popualtion within the affected regions\n",
        "\n",
        "    for quantile_index, j in enumerate(quantiles):\n",
        "        scenario_counter += 1\n",
        "        perc_column = f\"perc{int(j * percentile)}\"\n",
        "        pop = pop_df[perc_column].sum()\n",
        "\n",
        "        try:\n",
        "            VA = get_value_added(GDPShocks, quantile_index - 1)\n",
        "        except IndexError:\n",
        "            print(f\"IndexError for quantile_index: {quantile_index}, DataFrame size: {GDPShocks.shape}\")\n",
        "            continue\n",
        "\n",
        "        # Apply Ghosh and Leontief models\n",
        "        VAm = VA.reshape(1, -1)\n",
        "        Lem = VA.reshape(-1, 1)\n",
        "        Output = (np.dot(VAm, Ghosh) - VAm).flatten()\n",
        "        LeonOutput = np.dot(L, Lem) - Lem\n",
        "\n",
        "        condensed_output, condensed_leon_output = condense_output(Output, LeonOutput)\n",
        "\n",
        "        # Prepare 'direct_impact_df'\n",
        "        direct_impact_df = GDPShocks.copy()\n",
        "        direct_impact_df[\"NAICS\"] = direct_impact_df.index\n",
        "        direct_impact_df[\"NAICSIndustries\"] = direct_impact_df[\"NAICS\"].map(NAICSIndustries)\n",
        "\n",
        "        # Create and concatenate data frames\n",
        "        concat_df = pd.concat([\n",
        "            pd.DataFrame({'Scenario': f's{scenario_counter}', 'Type': 'Supply', 'CP': f\"{int(j * percentile)} %\", 'NAICSIndustries': NAICSIndustries.values(), 'Output': condensed_output}),\n",
        "            # pd.DataFrame({'Scenario': f's{scenario_counter}', 'Type': 'Leon', 'NAICSIndustries': NAICSIndustries.values(), 'Output': condensed_leon_output}),\n",
        "            direct_impact_df.assign(Output=direct_impact_df[perc_column], CP= f\"{int(j * percentile)} %\", Type='Direct', Scenario=f's{scenario_counter}')[['Scenario', 'Type', 'CP', 'NAICSIndustries', 'Output']]\n",
        "        ], ignore_index=True)\n",
        "\n",
        "        concat_df[\"Extent\"] = extent\n",
        "        # concat_df[\"Population\"] = pop\n",
        "        impact_dfs.append(concat_df)\n",
        "\n",
        "impact_df = pd.concat(impact_dfs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-x-KfM_EVNTo",
        "outputId": "cd6920bf-0350-47d6-8642-bdfa4cd007c4"
      },
      "id": "-x-KfM_EVNTo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "4it [00:00, 10.17it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "impact_df.groupby([\"Scenario\", \"Type\", \"CP\"])[\"Output\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isChtj6Y9_go",
        "outputId": "a1cf4c9e-589d-42fd-c1cf-cc1c0e64e5de"
      },
      "id": "isChtj6Y9_go",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Scenario  Type    CP  \n",
              "s1        Direct  1 %     24.849126\n",
              "          Supply  1 %     21.925498\n",
              "s10       Direct  1 %     46.519953\n",
              "          Supply  1 %     40.507701\n",
              "s11       Direct  10 %    41.517263\n",
              "          Supply  10 %    36.175964\n",
              "s12       Direct  50 %    33.811673\n",
              "          Supply  50 %    29.458092\n",
              "s2        Direct  10 %    19.716271\n",
              "          Supply  10 %    17.019462\n",
              "s3        Direct  50 %    12.228930\n",
              "          Supply  50 %    10.474961\n",
              "s4        Direct  1 %     29.777514\n",
              "          Supply  1 %     26.139490\n",
              "s5        Direct  10 %    24.300727\n",
              "          Supply  10 %    20.821449\n",
              "s6        Direct  50 %    16.494335\n",
              "          Supply  50 %    14.820562\n",
              "s7        Direct  1 %     38.612607\n",
              "          Supply  1 %     33.435641\n",
              "s8        Direct  10 %    33.315705\n",
              "          Supply  10 %    28.877077\n",
              "s9        Direct  50 %    25.129588\n",
              "          Supply  50 %    21.792718\n",
              "Name: Output, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "impact_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9T4Iq1B1HXTP",
        "outputId": "5cef8592-ebe5-4c27-f8e0-229a65c849f7"
      },
      "id": "9T4Iq1B1HXTP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Scenario    Type    CP                                NAICSIndustries  \\\n",
              "0        s1  Supply   1 %     Agriculture, Forestry, Fishing and Hunting   \n",
              "1        s1  Supply   1 %  Mining, Quarrying, and Oil and Gas Extraction   \n",
              "2        s1  Supply   1 %                                      Utilities   \n",
              "3        s1  Supply   1 %                                   Construction   \n",
              "4        s1  Supply   1 %                                  Manufacturing   \n",
              "..      ...     ...   ...                                            ...   \n",
              "35      s12  Direct  50 %              Health Care and Social Assistance   \n",
              "36      s12  Direct  50 %            Arts, Entertainment, and Recreation   \n",
              "37      s12  Direct  50 %                Accommodation and Food Services   \n",
              "38      s12  Direct  50 %  Other Services (except Public Administration)   \n",
              "39      s12  Direct  50 %                                   Unclassified   \n",
              "\n",
              "      Output  Extent  Population  \n",
              "0   0.351363       3      128.17  \n",
              "1   0.345398       3      128.17  \n",
              "2   0.311044       3      128.17  \n",
              "3   1.055674       3      128.17  \n",
              "4   4.400469       3      128.17  \n",
              "..       ...     ...         ...  \n",
              "35  2.562602       8      179.84  \n",
              "36  0.372552       8      179.84  \n",
              "37  0.983874       8      179.84  \n",
              "38  0.671853       8      179.84  \n",
              "39  3.612616       8      179.84  \n",
              "\n",
              "[480 rows x 7 columns]"
            ],
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              "\n",
              "  <div id=\"df-7c7a2096-2dfc-4234-9bcb-1ed5dc9038ec\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Scenario</th>\n",
              "      <th>Type</th>\n",
              "      <th>CP</th>\n",
              "      <th>NAICSIndustries</th>\n",
              "      <th>Output</th>\n",
              "      <th>Extent</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>s1</td>\n",
              "      <td>Supply</td>\n",
              "      <td>1 %</td>\n",
              "      <td>Agriculture, Forestry, Fishing and Hunting</td>\n",
              "      <td>0.351363</td>\n",
              "      <td>3</td>\n",
              "      <td>128.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s1</td>\n",
              "      <td>Supply</td>\n",
              "      <td>1 %</td>\n",
              "      <td>Mining, Quarrying, and Oil and Gas Extraction</td>\n",
              "      <td>0.345398</td>\n",
              "      <td>3</td>\n",
              "      <td>128.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>s1</td>\n",
              "      <td>Supply</td>\n",
              "      <td>1 %</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>0.311044</td>\n",
              "      <td>3</td>\n",
              "      <td>128.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s1</td>\n",
              "      <td>Supply</td>\n",
              "      <td>1 %</td>\n",
              "      <td>Construction</td>\n",
              "      <td>1.055674</td>\n",
              "      <td>3</td>\n",
              "      <td>128.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>s1</td>\n",
              "      <td>Supply</td>\n",
              "      <td>1 %</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>4.400469</td>\n",
              "      <td>3</td>\n",
              "      <td>128.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>s12</td>\n",
              "      <td>Direct</td>\n",
              "      <td>50 %</td>\n",
              "      <td>Health Care and Social Assistance</td>\n",
              "      <td>2.562602</td>\n",
              "      <td>8</td>\n",
              "      <td>179.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>s12</td>\n",
              "      <td>Direct</td>\n",
              "      <td>50 %</td>\n",
              "      <td>Arts, Entertainment, and Recreation</td>\n",
              "      <td>0.372552</td>\n",
              "      <td>8</td>\n",
              "      <td>179.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>s12</td>\n",
              "      <td>Direct</td>\n",
              "      <td>50 %</td>\n",
              "      <td>Accommodation and Food Services</td>\n",
              "      <td>0.983874</td>\n",
              "      <td>8</td>\n",
              "      <td>179.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>s12</td>\n",
              "      <td>Direct</td>\n",
              "      <td>50 %</td>\n",
              "      <td>Other Services (except Public Administration)</td>\n",
              "      <td>0.671853</td>\n",
              "      <td>8</td>\n",
              "      <td>179.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>s12</td>\n",
              "      <td>Direct</td>\n",
              "      <td>50 %</td>\n",
              "      <td>Unclassified</td>\n",
              "      <td>3.612616</td>\n",
              "      <td>8</td>\n",
              "      <td>179.84</td>\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7c7a2096-2dfc-4234-9bcb-1ed5dc9038ec 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-7c7a2096-2dfc-4234-9bcb-1ed5dc9038ec');\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",
              "\n",
              "\n",
              "<div id=\"df-540cb44f-d7bf-4aff-a167-f7f0cd7eb99b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-540cb44f-d7bf-4aff-a167-f7f0cd7eb99b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-540cb44f-d7bf-4aff-a167-f7f0cd7eb99b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cbf21b70-5ef4-4670-a5e0-972f7d0975c3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('impact_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cbf21b70-5ef4-4670-a5e0-972f7d0975c3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('impact_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eHZrVYlJHgrX"
      },
      "id": "eHZrVYlJHgrX",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:spaceweather]",
      "language": "python",
      "name": "conda-env-spaceweather-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.0"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}