{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calif equity\n",
    "## Looking at collaboration components \n",
    "Date: 2024-12-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## set directory\n",
    "import os\n",
    "os.chdir('/home/dadams/Repos/california_equity_git')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data_raw/cci_programs_data.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Project IDNumber', 'Reporting Cycle Name', 'Agency Name',\n",
       "       'Program Name', 'Program Description', 'Sub Program Name',\n",
       "       'Record Type', 'Project Name', 'Project Type', 'Project Description',\n",
       "       ...\n",
       "       'Net Density  DUA', 'Applicants  Assisted', 'Invasive Cover 12 Months',\n",
       "       'Invasive Cover 36 Months', 'Project Acreage', 'IS IAE',\n",
       "       'Intermediary Admin Expenses Calc', 'PRIMARY_FUNDING_RECIPIENT_TYPE',\n",
       "       'TRIBAL AFFILIATION', 'PROJECT PARTNERS'],\n",
       "      dtype='object', length=127)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "def debug_shapefile(shapefile_path):\n",
    "    \"\"\"\n",
    "    Debug shapefile reading issues by checking file existence and required components.\n",
    "    \n",
    "    Args:\n",
    "        shapefile_path (str): Path to the .shp file\n",
    "    \n",
    "    Returns:\n",
    "        dict: Dictionary containing debug information\n",
    "    \"\"\"\n",
    "    base_path = Path(shapefile_path).parent\n",
    "    file_name = Path(shapefile_path).stem\n",
    "    \n",
    "    # Required shapefile components\n",
    "    required_extensions = ['.shp', '.shx', '.dbf']\n",
    "    optional_extensions = ['.prj', '.cpg', '.sbn', '.sbx']\n",
    "    \n",
    "    debug_info = {\n",
    "        'file_exists': os.path.exists(shapefile_path),\n",
    "        'parent_dir_exists': os.path.exists(base_path),\n",
    "        'components': {},\n",
    "        'file_sizes': {},\n",
    "        'readable': False\n",
    "    }\n",
    "    \n",
    "    # Check for all component files\n",
    "    for ext in required_extensions + optional_extensions:\n",
    "        full_path = base_path / f\"{file_name}{ext}\"\n",
    "        exists = full_path.exists()\n",
    "        debug_info['components'][ext] = exists\n",
    "        if exists:\n",
    "            debug_info['file_sizes'][ext] = os.path.getsize(full_path)\n",
    "    \n",
    "    # Try reading with explicit driver\n",
    "    try:\n",
    "        gdf = gpd.read_file(shapefile_path, driver='ESRI Shapefile')\n",
    "        debug_info['readable'] = True\n",
    "        debug_info['num_features'] = len(gdf)\n",
    "    except Exception as e:\n",
    "        debug_info['error'] = str(e)\n",
    "    \n",
    "    return debug_info\n",
    "\n",
    "# Usage example\n",
    "shapefile_path = \"/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp\"\n",
    "debug_results = debug_shapefile(shapefile_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking .shp file: True\n",
      "File permissions: 644\n",
      "Checking .shx file: True\n",
      "File permissions: 644\n",
      "Checking .dbf file: True\n",
      "File permissions: 644\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "# Define base path and file name\n",
    "base_path = '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape'\n",
    "file_name = 'CES4 Final Shapefile'\n",
    "\n",
    "# Check for existence of all required files\n",
    "required_files = ['.shp', '.shx', '.dbf']\n",
    "for ext in required_files:\n",
    "    full_path = os.path.join(base_path, file_name + ext)\n",
    "    print(f\"Checking {ext} file: {os.path.exists(full_path)}\")\n",
    "    if os.path.exists(full_path):\n",
    "        print(f\"File permissions: {oct(os.stat(full_path).st_mode)[-3:]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approach 1 failed: '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp' not recognized as being in a supported file format. It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.\n",
      "Approach 2 failed: '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp' not recognized as being in a supported file format. It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.\n",
      "Directory contents: ['CES4 Final Shapefile.sbx', 'CES4 Final Shapefile.shp', 'CES4 Final Shapefile.sbn', 'CES4 Final Shapefile.shp.xml', 'CES4 Final Shapefile.shx', 'CES4 Final Shapefile.prj', 'CES4 Final Shapefile.cpg', 'CES4 Final Shapefile.dbf']\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "# Approach 1: Using absolute path with normalized separators\n",
    "shapefile_path = Path(base_path) / f\"{file_name}.shp\"\n",
    "try:\n",
    "    gdf = gpd.read_file(shapefile_path)\n",
    "except Exception as e:\n",
    "    print(f\"Approach 1 failed: {e}\")\n",
    "\n",
    "# Approach 2: Using explicit ESRI Shapefile driver\n",
    "try:\n",
    "    gdf = gpd.read_file(shapefile_path, driver='ESRI Shapefile')\n",
    "except Exception as e:\n",
    "    print(f\"Approach 2 failed: {e}\")\n",
    "\n",
    "# Approach 3: Check if the directory is readable\n",
    "try:\n",
    "    print(f\"Directory contents: {os.listdir(base_path)}\")\n",
    "except Exception as e:\n",
    "    print(f\"Cannot list directory: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeoPandas version: 1.0.1\n",
      "Fiona version: 1.10.1\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import fiona\n",
    "print(f\"GeoPandas version: {gpd.__version__}\")\n",
    "print(f\"Fiona version: {fiona.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "DataSourceError",
     "evalue": "'/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp' not recognized as being in a supported file format. It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mDataSourceError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Load the shapefile\u001b[39;00m\n\u001b[1;32m      4\u001b[0m shapefile_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m gdf \u001b[38;5;241m=\u001b[39m \u001b[43mgpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshapefile_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Print the head of the GeoDataFrame\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(gdf\u001b[38;5;241m.\u001b[39mhead())\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/geopandas/io/file.py:294\u001b[0m, in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, columns, rows, engine, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m             from_bytes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyogrio\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read_file_pyogrio\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiona\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mapi\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_file_like(filename):\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/geopandas/io/file.py:547\u001b[0m, in \u001b[0;36m_read_file_pyogrio\u001b[0;34m(path_or_bytes, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[1;32m    538\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    539\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minclude_fields\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore_fields\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keywords are deprecated, and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    540\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwill be removed in a future release. You can use the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m keyword \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    543\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m    544\u001b[0m     )\n\u001b[1;32m    545\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minclude_fields\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 547\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpyogrio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pyogrio/geopandas.py:265\u001b[0m, in \u001b[0;36mread_dataframe\u001b[0;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_arrow:\n\u001b[1;32m    261\u001b[0m     \u001b[38;5;66;03m# For arrow, datetimes are read as is.\u001b[39;00m\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;66;03m# For numpy IO, datetimes are read as string values to preserve timezone info\u001b[39;00m\n\u001b[1;32m    263\u001b[0m     \u001b[38;5;66;03m# as numpy does not directly support timezones.\u001b[39;00m\n\u001b[1;32m    264\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatetime_as_string\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mread_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mread_geometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgdal_force_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    276\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    279\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    280\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfid_as_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    281\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    282\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_arrow:\n\u001b[1;32m    285\u001b[0m     meta, table \u001b[38;5;241m=\u001b[39m result\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pyogrio/raw.py:198\u001b[0m, in \u001b[0;36mread\u001b[0;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Read OGR data source into numpy arrays.\u001b[39;00m\n\u001b[1;32m     60\u001b[0m \n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mIMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    194\u001b[0m \n\u001b[1;32m    195\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    196\u001b[0m dataset_kwargs \u001b[38;5;241m=\u001b[39m _preprocess_options_key_value(kwargs) \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m--> 198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mogr_read\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_vsi_path_or_buffer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mread_geometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mread_geometry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_features\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    207\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    208\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_mask_to_wkb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    211\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m    \u001b[49m\u001b[43msql_dialect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msql_dialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_fids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_fids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdatetime_as_string\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatetime_as_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pyogrio/_io.pyx:1240\u001b[0m, in \u001b[0;36mpyogrio._io.ogr_read\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pyogrio/_io.pyx:216\u001b[0m, in \u001b[0;36mpyogrio._io.ogr_open\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mDataSourceError\u001b[0m: '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp' not recognized as being in a supported file format. It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'."
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "# Load the shapefile\n",
    "shapefile_path = '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp'\n",
    "gdf = gpd.read_file(shapefile_path)\n",
    "\n",
    "# Print the head of the GeoDataFrame\n",
    "print(gdf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total GGRF Funding: $8.13B\n",
      "Number of projects: 131428\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Basic cleaning\n",
    "data['Date Operational'] = pd.to_datetime(data['Date Operational'])\n",
    "data = data[\n",
    "    (data['Date Operational'] >= '2010-01-01') & \n",
    "    (data['Date Operational'] <= '2024-11-01')\n",
    "].copy()\n",
    "\n",
    "# Remove rows with no GGRF funding\n",
    "data = data.dropna(subset=['Total Program GGRFFunding'])\n",
    "\n",
    "# Add derived columns\n",
    "data['Year'] = data['Date Operational'].dt.year\n",
    "data['is_multi_county'] = data['County'].str.contains(',', na=False)\n",
    "data['partnership_size'] = data['County'].str.count(',').fillna(0) + 1\n",
    "\n",
    "# Quick validation\n",
    "print(f\"Total GGRF Funding: ${data['Total Program GGRFFunding'].sum()/1e9:.2f}B\")\n",
    "print(f\"Number of projects: {len(data)}\")"
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
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