data_code/PVWatts_output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fde2043a-524d-491b-96a1-3a283fe0c082",
   "metadata": {},
   "source": [
    "# Download US power output PV Watts (version 8) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6cf0c7-9ee4-4705-89f0-7abf1cfe3bf4",
   "metadata": {},
   "source": [
    "## Documentation: https://developer.nrel.gov/docs/api-key/\n",
    "## https://pvwatts.nrel.gov/downloads/pvwattsv5.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb118c6d-e239-46ab-9288-67972c425cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "652db7b4-be7b-40e6-976f-c104871c66ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "hrh_apikey1 = 'GNoTfD5IZWwIEz24zB5Wn0aEhDvNJSep5bwapzTI'\n",
    "hrh_apikey2 = 'bupvdoNsjaox9hQeCD6yufHWNf4cnM5OTNLlEBlZ'\n",
    "hrh_apikey3 = 'wfaZg9uNJwCc9zJfiCXTEDnmloDEk0o6EATrdC6A'\n",
    "hrh_apikey4 = '7iehBlUn3NW7rEEQYvMvB2xsinpbFQAnYGnrFoiy'\n",
    "hrh_apikey5 =  'HGHXaFdKeMcknyEsmXnFMnL4aqgZK5HuZ5AV0tyG'\n",
    "hrh_apikey6 = 'vPgwOy4OgMoOpnYtBsK5PyfEMwqD78HXCK5u1sNx'\n",
    "data_path   = '/Users/hrh/Desktop/AI/erdos/rapower_data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8cb94826-3d2d-4c27-adba-d4814712f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 1. Define PVWatts parameters and your API key\n",
    "# parameters = {\n",
    "#     'format': 'json',\n",
    "#     'system_capacity': 1000,\n",
    "#     'module_type': 0,\n",
    "#     'losses': 14.08,\n",
    "#     'array_type': 1,\n",
    "#     'tilt': 40,\n",
    "#     'azimuth': 180,\n",
    "#     'lat': 40.0099983215332,\n",
    "#     'lon':  -105.2600021362305,\n",
    "#     'dataset': 'nsrdb',\n",
    "#     'radius': 25,\n",
    "#     'timeframe': 'monthly',\n",
    "#     'api_key': hrh_apikey1  # Replace with your API key\n",
    "# }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86700974-2dbb-4995-a126-a27a026c756b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# url = 'https://developer.nrel.gov/api/pvwatts/v8'\n",
    "\n",
    "# # 2. Send request\n",
    "# response = requests.get(url, params=parameters)\n",
    "# data = response.json()\n",
    "\n",
    "# # 3. Convert the output data to a DataFrame\n",
    "# df = pd.DataFrame(data['outputs'])\n",
    "\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e691043-5fe0-4934-8ba8-8cc8d9b6afb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Try to get data for California on a grid with lat_delta and lon_delta resolution\n",
    "# lat_delta = 0.25\n",
    "# lon_delta = 0.25\n",
    "\n",
    "# # #CONUS box\n",
    "# # top = 49.3457868 # north lat\n",
    "# # left = -124.7844079 # west long\n",
    "# # right = -66.9513812 # east long\n",
    "# # bottom =  24.7433195 # south lat\n",
    "\n",
    "# #California box\n",
    "# top    = 42.009518\n",
    "# bottom = 32.534156\n",
    "# left   = -124.409591 \n",
    "# right  =  -114.131211\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6047904-7fe7-414b-8853-e9388fe21848",
   "metadata": {},
   "source": [
    "### Threshold is a hyperparameter and will be applied to all variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "930d8a0f-7ef5-4bd0-8f7e-d888d7f64806",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_lb = 0.05 #lb = lower bound, applied to the feature ac_annual\n",
    "thresh_ub = 0.95 #ub = upper bound, applied to the features elevation and slope (in this notebook)\n",
    "thresh_name = '95'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c251c7f9-b83c-4dff-abec-4afd3d163cf4",
   "metadata": {},
   "source": [
    "## Define list of lat lon coordinates (either for CONUS or California)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29c6fc9-a8a5-45ea-9a78-4f8817fe0cfb",
   "metadata": {},
   "source": [
    "<font color ='red'> PVWatts API access currently has a limit of 1000 queries per hour. So, data has to be downloaded one chunk (size<1000) at a time </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f713c92-c93c-43ff-9566-b080b5c348ed",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Longitude</th>\n",
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       "      <th>land_cover</th>\n",
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      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "0       36.177601 -120.109001                   1         1.0    0.0   \n",
       "1       33.265202 -116.328003                   1         1.0    1.0   \n",
       "2       34.823002 -118.348000                   1         1.0    0.0   \n",
       "3       34.707401 -118.305000                   1         1.0    0.0   \n",
       "4       34.708599 -118.274002                   1         1.0    0.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101697  34.277993 -114.160164                   0         1.0    3.0   \n",
       "101698  34.297547 -114.160164                   0         1.0   31.0   \n",
       "101699  34.317101 -114.160164                   0         1.0   20.0   \n",
       "101700  34.258439 -114.138592                   0         0.0    0.0   \n",
       "101701  34.277993 -114.138592                   0         1.0    4.0   \n",
       "\n",
       "        elevation  \n",
       "0           119.0  \n",
       "1           164.0  \n",
       "2           768.0  \n",
       "3           748.0  \n",
       "4           731.0  \n",
       "...           ...  \n",
       "101697      201.0  \n",
       "101698      228.0  \n",
       "101699      148.0  \n",
       "101700      112.0  \n",
       "101701      117.0  \n",
       "\n",
       "[101702 rows x 6 columns]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Define a list of latitudes and longitudes for CONUS\n",
    "all_data = []\n",
    "##### Read lats and lons CA_grid_dataframe #############\n",
    "#data_path2     = '/Users/hrh/Desktop/AI/erdos/SolarFarmPrediction/data/'\n",
    "#ca_gridpath    = data_path2 + 'threshold_90percentile/CA_data_version_11.csv'\n",
    "data_path2     = '/Users/hrh/Desktop/AI/erdos/SolarFarmPrediction/new_data/'\n",
    "ca_gridpath   = data_path2 + 'CA_data_version_2.csv'\n",
    "#df_main             = pd.read_csv(ca_gridpath)\n",
    "#ca_grid0            = df_main.loc[df_main['State']=='CA']\n",
    "ca_grid0             = pd.read_csv(ca_gridpath)\n",
    "ca_grid0"
   ]
  },
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   "execution_count": 29,
   "id": "d4a0f0f5-cbf4-429d-bf5d-e59a1c802047",
   "metadata": {},
   "outputs": [
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       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
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       "      <td>20.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-114.138592</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>117.0</td>\n",
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      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "0       36.177601 -120.109001                   1         1.0    0.0   \n",
       "1       33.265202 -116.328003                   1         1.0    1.0   \n",
       "2       34.823002 -118.348000                   1         1.0    0.0   \n",
       "3       34.707401 -118.305000                   1         1.0    0.0   \n",
       "4       34.708599 -118.274002                   1         1.0    0.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101697  34.277993 -114.160164                   0         1.0    3.0   \n",
       "101698  34.297547 -114.160164                   0         1.0   31.0   \n",
       "101699  34.317101 -114.160164                   0         1.0   20.0   \n",
       "101700  34.258439 -114.138592                   0         0.0    0.0   \n",
       "101701  34.277993 -114.138592                   0         1.0    4.0   \n",
       "\n",
       "        elevation  \n",
       "0           119.0  \n",
       "1           164.0  \n",
       "2           768.0  \n",
       "3           748.0  \n",
       "4           731.0  \n",
       "...           ...  \n",
       "101697      201.0  \n",
       "101698      228.0  \n",
       "101699      148.0  \n",
       "101700      112.0  \n",
       "101701      117.0  \n",
       "\n",
       "[101643 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Remove rows where there is no data for land_cover,slope or elevation\n",
    "#ca_grid = ca_grid0.dropna(subset = ['slope','elevation','land_cover'])\n",
    "ca_grid = ca_grid0.dropna(subset = ['slope','elevation'])\n",
    "ca_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1faf9c6d-664e-432a-bb04-f09a9381f90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "101697    False\n",
       "101698    False\n",
       "101699    False\n",
       "101700    False\n",
       "101701    False\n",
       "Name: slope, Length: 101643, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if it worked\n",
    "ca_grid['slope'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "037add87-1a9f-4653-8134-9b0a2920f43b",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.177601</td>\n",
       "      <td>-120.109001</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.265202</td>\n",
       "      <td>-116.328003</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.823002</td>\n",
       "      <td>-118.348000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.707401</td>\n",
       "      <td>-118.305000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.708599</td>\n",
       "      <td>-118.274002</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101697</th>\n",
       "      <td>34.277993</td>\n",
       "      <td>-114.160164</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101698</th>\n",
       "      <td>34.297547</td>\n",
       "      <td>-114.160164</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101699</th>\n",
       "      <td>34.317101</td>\n",
       "      <td>-114.160164</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101700</th>\n",
       "      <td>34.258439</td>\n",
       "      <td>-114.138592</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101701</th>\n",
       "      <td>34.277993</td>\n",
       "      <td>-114.138592</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101643 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "0       36.177601 -120.109001                   1         1.0    0.0   \n",
       "1       33.265202 -116.328003                   1         1.0    1.0   \n",
       "2       34.823002 -118.348000                   1         1.0    0.0   \n",
       "3       34.707401 -118.305000                   1         1.0    0.0   \n",
       "4       34.708599 -118.274002                   1         1.0    0.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101697  34.277993 -114.160164                   0         1.0    3.0   \n",
       "101698  34.297547 -114.160164                   0         1.0   31.0   \n",
       "101699  34.317101 -114.160164                   0         1.0   20.0   \n",
       "101700  34.258439 -114.138592                   0         0.0    0.0   \n",
       "101701  34.277993 -114.138592                   0         1.0    4.0   \n",
       "\n",
       "        elevation  \n",
       "0           119.0  \n",
       "1           164.0  \n",
       "2           768.0  \n",
       "3           748.0  \n",
       "4           731.0  \n",
       "...           ...  \n",
       "101697      201.0  \n",
       "101698      228.0  \n",
       "101699      148.0  \n",
       "101700      112.0  \n",
       "101701      117.0  \n",
       "\n",
       "[101643 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ca_grid_usable      = ca_grid.loc[ca_grid['land_cover']==1.0]\n",
    "ca_coords           = list(zip(ca_grid['Latitude'], ca_grid['Longitude']))\n",
    "#Check if ca_coords are within california grid box\n",
    "ca_lats = [item[0] for item in ca_coords] \n",
    "ca_lons = [item[1] for item in ca_coords]\n",
    "(min(ca_lats),max(ca_lats)),(min(ca_lons),max(ca_lons))\n",
    "ca_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ce5497-6953-46e5-b3af-0915bcce7f14",
   "metadata": {},
   "source": [
    "### Reduce the grid points to those with solar farms and a a few sites with no solar farms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7860d6ac-205d-443a-acc9-498ebda95b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.177601</td>\n",
       "      <td>-120.109001</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.265202</td>\n",
       "      <td>-116.328003</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>164.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>748.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>-118.274002</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>34.564400</td>\n",
       "      <td>-117.694000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>914.0</td>\n",
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       "    <tr>\n",
       "      <th>467</th>\n",
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       "      <td>-121.650002</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>102.0</td>\n",
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       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>34.687302</td>\n",
       "      <td>-118.320000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>32.660400</td>\n",
       "      <td>-115.629997</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>34.915401</td>\n",
       "      <td>-118.355003</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>899.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>471 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation\n",
       "0    36.177601 -120.109001                   1         1.0    0.0      119.0\n",
       "1    33.265202 -116.328003                   1         1.0    1.0      164.0\n",
       "2    34.823002 -118.348000                   1         1.0    0.0      768.0\n",
       "3    34.707401 -118.305000                   1         1.0    0.0      748.0\n",
       "4    34.708599 -118.274002                   1         1.0    0.0      731.0\n",
       "..         ...         ...                 ...         ...    ...        ...\n",
       "466  34.564400 -117.694000                   1         1.0    1.0      914.0\n",
       "467  39.651600 -121.650002                   1         1.0    3.0      102.0\n",
       "468  34.687302 -118.320000                   1         1.0    1.0      772.0\n",
       "469  32.660400 -115.629997                   1         1.0    0.0       -3.0\n",
       "470  34.915401 -118.355003                   1         1.0    2.0      899.0\n",
       "\n",
       "[471 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_farms = ca_grid.loc[ca_grid['Solar_farm_present']==1]\n",
    "solar_farms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256c0f8c-1da6-46bc-9b1f-f12425554459",
   "metadata": {},
   "source": [
    "### Let us pick locations where only one feature is restricting:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199da342-866a-48f7-aaff-76ce29547eca",
   "metadata": {},
   "source": [
    "#### 1) Slope 2) Elevation 3) Land Cover"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912b1ea6-dd13-48af-a2d3-ec40b320544b",
   "metadata": {},
   "source": [
    "### The thresholds will be set at thresh_ub percentile of the feature set of existing solar farm locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07ac3cc9-dc38-4670-827c-c4765f99131f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 915.0\n"
     ]
    }
   ],
   "source": [
    "slope_threshold       = solar_farms['slope'].quantile(thresh_ub)\n",
    "elevation_threshold   = solar_farms['elevation'].quantile(thresh_ub)\n",
    "print(slope_threshold,elevation_threshold)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fca65b9a-abb2-4dc1-b3dd-52336565426e",
   "metadata": {},
   "outputs": [
    {
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       "        7., 10., 15.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(solar_farms['slope'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91134fdd-6c3e-4e71-af00-62ec3b2483d0",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>40.476647</td>\n",
       "      <td>-124.385556</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>69.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>101695</th>\n",
       "      <td>34.317101</td>\n",
       "      <td>-114.181737</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>370.0</td>\n",
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       "    <tr>\n",
       "      <th>101697</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201.0</td>\n",
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       "    <tr>\n",
       "      <th>101698</th>\n",
       "      <td>34.297547</td>\n",
       "      <td>-114.160164</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>228.0</td>\n",
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       "    <tr>\n",
       "      <th>101699</th>\n",
       "      <td>34.317101</td>\n",
       "      <td>-114.160164</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>148.0</td>\n",
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       "    <tr>\n",
       "      <th>101701</th>\n",
       "      <td>34.277993</td>\n",
       "      <td>-114.138592</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43906 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "505     40.437539 -124.407129                   0         1.0   22.0   \n",
       "529     40.417985 -124.385556                   0         1.0   16.0   \n",
       "530     40.437539 -124.385556                   0         1.0   22.0   \n",
       "531     40.457093 -124.385556                   0         1.0    8.0   \n",
       "532     40.476647 -124.385556                   0         1.0    8.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101695  34.317101 -114.181737                   0         1.0   33.0   \n",
       "101697  34.277993 -114.160164                   0         1.0    3.0   \n",
       "101698  34.297547 -114.160164                   0         1.0   31.0   \n",
       "101699  34.317101 -114.160164                   0         1.0   20.0   \n",
       "101701  34.277993 -114.138592                   0         1.0    4.0   \n",
       "\n",
       "        elevation  \n",
       "505          76.0  \n",
       "529         270.0  \n",
       "530         218.0  \n",
       "531         193.0  \n",
       "532          69.0  \n",
       "...           ...  \n",
       "101695      370.0  \n",
       "101697      201.0  \n",
       "101698      228.0  \n",
       "101699      148.0  \n",
       "101701      117.0  \n",
       "\n",
       "[43906 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_slope = ca_grid.loc[(ca_grid['Solar_farm_present']!=1) & (ca_grid['slope']>=slope_threshold) &(ca_grid['land_cover']!=0)]\n",
    "high_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c35689d-a825-4c3d-a3ce-eac1cb28945d",
   "metadata": {},
   "outputs": [
    {
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       "<p>5170 rows × 6 columns</p>\n",
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      "text/plain": [
       "        Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation\n",
       "2474   41.923652 -123.889387                   0         1.0    2.0     1011.0\n",
       "3445   40.593972 -123.738380                   0         1.0    2.0     1140.0\n",
       "5425   40.241998 -123.479509                   0         1.0    0.0     1512.0\n",
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       "5949   40.281106 -123.414791                   0         1.0    2.0      955.0\n",
       "...          ...         ...                 ...         ...    ...        ...\n",
       "98789  35.099266 -115.023067                   0         1.0    0.0     1037.0\n",
       "98790  35.118820 -115.023067                   0         1.0    1.0     1053.0\n",
       "98791  35.138374 -115.023067                   0         1.0    0.0     1071.0\n",
       "98920  35.040604 -115.001494                   0         1.0    2.0      954.0\n",
       "98921  35.060158 -115.001494                   0         1.0    2.0      976.0\n",
       "\n",
       "[5170 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_elevation = ca_grid.loc[(ca_grid['Solar_farm_present']!=1) & (ca_grid['elevation']>=elevation_threshold) & (ca_grid['slope']<slope_threshold) & (ca_grid['land_cover']!=0)]\n",
    "high_elevation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12d56b76-c2e7-421c-949d-9ab28da3bc5d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "471     40.417985 -124.471847                   0         0.0    0.0   \n",
       "472     40.437539 -124.471847                   0         0.0    0.0   \n",
       "473     40.457093 -124.471847                   0         0.0    0.0   \n",
       "474     40.398431 -124.450274                   0         0.0    0.0   \n",
       "475     40.417985 -124.450274                   0         0.0    0.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101577  34.551751 -114.397462                   0         0.0    0.0   \n",
       "101626  34.434426 -114.332745                   0         0.0    0.0   \n",
       "101690  34.356210 -114.203309                   0         0.0    0.0   \n",
       "101696  34.336656 -114.181737                   0         0.0    0.0   \n",
       "101700  34.258439 -114.138592                   0         0.0    0.0   \n",
       "\n",
       "        elevation  \n",
       "471           0.0  \n",
       "472           0.0  \n",
       "473           0.0  \n",
       "474           0.0  \n",
       "475           0.0  \n",
       "...           ...  \n",
       "101577      137.0  \n",
       "101626      137.0  \n",
       "101690      138.0  \n",
       "101696      138.0  \n",
       "101700      112.0  \n",
       "\n",
       "[4809 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land_cover = ca_grid.loc[(ca_grid['Solar_farm_present']!=1) & (ca_grid['elevation'] < elevation_threshold) & (ca_grid['slope']<slope_threshold) & (ca_grid['land_cover']==0)]\n",
    "land_cover"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f376c0a-70c0-4159-99a3-0c3999d41fb1",
   "metadata": {},
   "source": [
    "### Let us pick locations with low elevation, low slope and no land cover restrictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "299b1dc8-8246-4594-8090-f9e76ddccf9a",
   "metadata": {},
   "outputs": [
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       "      <td>147.0</td>\n",
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       "    <tr>\n",
       "      <th>101643</th>\n",
       "      <td>34.219331</td>\n",
       "      <td>-114.289600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101665</th>\n",
       "      <td>34.180223</td>\n",
       "      <td>-114.246454</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101666</th>\n",
       "      <td>34.199777</td>\n",
       "      <td>-114.246454</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101668</th>\n",
       "      <td>34.238885</td>\n",
       "      <td>-114.246454</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24842 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "559     40.437539 -124.363984                   0         1.0    1.0   \n",
       "620     40.300660 -124.320839                   0         1.0    1.0   \n",
       "635     40.593972 -124.320839                   0         1.0    1.0   \n",
       "636     40.613526 -124.320839                   0         1.0    0.0   \n",
       "658     40.320215 -124.299266                   0         1.0    2.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "101641  34.180223 -114.289600                   0         1.0    1.0   \n",
       "101643  34.219331 -114.289600                   0         1.0    1.0   \n",
       "101665  34.180223 -114.246454                   0         1.0    1.0   \n",
       "101666  34.199777 -114.246454                   0         1.0    2.0   \n",
       "101668  34.238885 -114.246454                   0         1.0    2.0   \n",
       "\n",
       "        elevation  \n",
       "559         379.0  \n",
       "620           8.0  \n",
       "635           2.0  \n",
       "636           1.0  \n",
       "658          20.0  \n",
       "...           ...  \n",
       "101641      147.0  \n",
       "101643      257.0  \n",
       "101665      112.0  \n",
       "101666      146.0  \n",
       "101668      250.0  \n",
       "\n",
       "[24842 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## I am calling these points rad_limited because they are not infeasible due to any other feature considered so far.\n",
    "rad_limited = ca_grid.loc[(ca_grid['Solar_farm_present']!=1) & (ca_grid['elevation'] < elevation_threshold) & (ca_grid['slope']<slope_threshold) & (ca_grid['land_cover']!=0)]\n",
    "rad_limited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef13b31c-764f-4baa-8594-c221a97daad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2129"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Number of locations to be sampled from rad_limited , 800 for each feature slope,elevation and land_cover ##############\n",
    "radlim_size = 5000 - (len(solar_farms) + 2400)\n",
    "radlim_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6210d97c-9673-4a82-acf7-5477a51e0d2c",
   "metadata": {},
   "source": [
    "### Sample points from the dataframes to have a list of 5000 locations and set the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2bb2d7fc-c0b3-4e1e-97ee-9ea55f74006f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41212</th>\n",
       "      <td>40.359323</td>\n",
       "      <td>-120.631931</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45033</th>\n",
       "      <td>40.378877</td>\n",
       "      <td>-120.373061</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36915</th>\n",
       "      <td>40.985054</td>\n",
       "      <td>-120.890802</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64144</th>\n",
       "      <td>38.051937</td>\n",
       "      <td>-118.862982</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72990</th>\n",
       "      <td>36.565825</td>\n",
       "      <td>-117.978507</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>elevation</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",
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       "    <tr>\n",
       "      <th>41261</th>\n",
       "      <td>41.317474</td>\n",
       "      <td>-120.631931</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82857</th>\n",
       "      <td>34.610413</td>\n",
       "      <td>-116.964597</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27678</th>\n",
       "      <td>41.102379</td>\n",
       "      <td>-121.494834</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40456</th>\n",
       "      <td>40.554864</td>\n",
       "      <td>-120.675076</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73373</th>\n",
       "      <td>36.037864</td>\n",
       "      <td>-117.935362</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "41212  40.359323 -120.631931                   0         1.0    2.0   \n",
       "45033  40.378877 -120.373061                   0         1.0    1.0   \n",
       "36915  40.985054 -120.890802                   0         1.0    0.0   \n",
       "64144  38.051937 -118.862982                   0         1.0    1.0   \n",
       "72990  36.565825 -117.978507                   0         1.0    0.0   \n",
       "...          ...         ...                 ...         ...    ...   \n",
       "41261  41.317474 -120.631931                   0         1.0    0.0   \n",
       "82857  34.610413 -116.964597                   0         1.0    2.0   \n",
       "27678  41.102379 -121.494834                   0         1.0    0.0   \n",
       "40456  40.554864 -120.675076                   0         1.0    2.0   \n",
       "73373  36.037864 -117.935362                   0         1.0    0.0   \n",
       "\n",
       "       elevation feature_violated  \n",
       "41212     1303.0        elevation  \n",
       "45033     1233.0        elevation  \n",
       "36915     1720.0        elevation  \n",
       "64144     1986.0        elevation  \n",
       "72990     1117.0        elevation  \n",
       "...          ...              ...  \n",
       "41261     1496.0        elevation  \n",
       "82857      986.0        elevation  \n",
       "27678     1012.0        elevation  \n",
       "40456     1521.0        elevation  \n",
       "73373     1023.0        elevation  \n",
       "\n",
       "[800 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Add extra column called fearture violated \n",
    "sample_high_elevation                       = high_elevation.sample(800,random_state=42)\n",
    "sample_high_elevation['Solar_farm_present'] = 0\n",
    "sample_high_elevation['feature_violated']   = 'elevation'\n",
    "sample_high_elevation  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a31722e2-698f-4a63-9f07-eab731751749",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <th>479</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>43240</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
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       "    <tr>\n",
       "      <th>39076</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101314</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
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       "    <tr>\n",
       "      <th>16382</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "15871   38.130154 -122.379308                   0         0.0    0.0   \n",
       "40559   35.079712 -120.653504                   0         0.0    0.0   \n",
       "5863    38.599452 -123.414791                   0         0.0    0.0   \n",
       "10071   38.012829 -122.940195                   0         0.0    0.0   \n",
       "479     40.496202 -124.450274                   0         0.0    0.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "43240   34.414872 -120.502496                   0         0.0    0.0   \n",
       "10743   39.029643 -122.875477                   0         0.0    0.0   \n",
       "39076   35.157928 -120.739794                   0         0.0    0.0   \n",
       "101314  33.613153 -114.526898                   0         0.0    2.0   \n",
       "16382   38.208370 -122.336163                   0         0.0    1.0   \n",
       "\n",
       "        elevation feature_violated  \n",
       "15871         0.0       land_cover  \n",
       "40559         0.0       land_cover  \n",
       "5863          0.0       land_cover  \n",
       "10071         0.0       land_cover  \n",
       "479           0.0       land_cover  \n",
       "...           ...              ...  \n",
       "43240         0.0       land_cover  \n",
       "10743       404.0       land_cover  \n",
       "39076         0.0       land_cover  \n",
       "101314       83.0       land_cover  \n",
       "16382         1.0       land_cover  \n",
       "\n",
       "[800 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_land_cover                       = land_cover.sample(800, random_state=42)\n",
    "sample_land_cover['Solar_farm_present'] = 0\n",
    "sample_land_cover['feature_violated']   = 'land_cover'\n",
    "sample_land_cover      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ac2ff526-d6cb-45cd-8cf0-6bfb1fef2cfb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-122.616607</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96524</th>\n",
       "      <td>35.177482</td>\n",
       "      <td>-115.368227</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83372</th>\n",
       "      <td>36.135634</td>\n",
       "      <td>-116.921452</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>slope</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>68523</th>\n",
       "      <td>37.074232</td>\n",
       "      <td>-118.431531</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28061</th>\n",
       "      <td>36.252959</td>\n",
       "      <td>-121.451689</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74704</th>\n",
       "      <td>34.180223</td>\n",
       "      <td>-117.784354</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65120</th>\n",
       "      <td>36.057418</td>\n",
       "      <td>-118.755119</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7078</th>\n",
       "      <td>41.434799</td>\n",
       "      <td>-123.285356</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>slope</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "80378  34.297547 -117.201895                   0         1.0    6.0   \n",
       "71904  35.392578 -118.086370                   0         1.0    4.0   \n",
       "13268  38.638561 -122.616607                   0         1.0   25.0   \n",
       "96524  35.177482 -115.368227                   0         1.0   19.0   \n",
       "83372  36.135634 -116.921452                   0         1.0    6.0   \n",
       "...          ...         ...                 ...         ...    ...   \n",
       "68523  37.074232 -118.431531                   0         1.0   31.0   \n",
       "28061  36.252959 -121.451689                   0         1.0   21.0   \n",
       "74704  34.180223 -117.784354                   0         1.0   13.0   \n",
       "65120  36.057418 -118.755119                   0         1.0   37.0   \n",
       "7078   41.434799 -123.285356                   0         1.0   28.0   \n",
       "\n",
       "       elevation feature_violated  \n",
       "80378     1480.0            slope  \n",
       "71904     1166.0            slope  \n",
       "13268      553.0            slope  \n",
       "96524     1780.0            slope  \n",
       "83372      217.0            slope  \n",
       "...          ...              ...  \n",
       "68523     3335.0            slope  \n",
       "28061      504.0            slope  \n",
       "74704      646.0            slope  \n",
       "65120      614.0            slope  \n",
       "7078       778.0            slope  \n",
       "\n",
       "[800 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_high_slope                       = high_slope.sample(800, random_state=42)\n",
    "sample_high_slope['Solar_farm_present'] = 0\n",
    "sample_high_slope['feature_violated']   = 'slope'\n",
    "sample_high_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "55eeae2e-27d5-4308-a1f3-1b8ad3e3db3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_rad_limited = rad_limited.sample(radlim_size,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b427b3-4f98-468c-b03c-ed9fcca8ff67",
   "metadata": {},
   "source": [
    "### Combine data frames "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f937b44d-f789-452c-83d5-b56275463545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30111</th>\n",
       "      <td>38.638561</td>\n",
       "      <td>-121.322253</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27893</th>\n",
       "      <td>39.146968</td>\n",
       "      <td>-121.473261</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93767</th>\n",
       "      <td>33.632707</td>\n",
       "      <td>-115.734961</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94863</th>\n",
       "      <td>32.850543</td>\n",
       "      <td>-115.583953</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70383</th>\n",
       "      <td>33.886911</td>\n",
       "      <td>-118.237378</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</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>43240</th>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10743</th>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39076</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101314</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16382</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "30111   38.638561 -121.322253                   0         1.0    2.0   \n",
       "27893   39.146968 -121.473261                   0         1.0    0.0   \n",
       "93767   33.632707 -115.734961                   0         1.0    2.0   \n",
       "94863   32.850543 -115.583953                   0         1.0    0.0   \n",
       "70383   33.886911 -118.237378                   0         1.0    1.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "43240   34.414872 -120.502496                   0         0.0    0.0   \n",
       "10743   39.029643 -122.875477                   0         0.0    0.0   \n",
       "39076   35.157928 -120.739794                   0         0.0    0.0   \n",
       "101314  33.613153 -114.526898                   0         0.0    2.0   \n",
       "16382   38.208370 -122.336163                   0         0.0    1.0   \n",
       "\n",
       "        elevation feature_violated  \n",
       "30111        37.0              NaN  \n",
       "27893        27.0              NaN  \n",
       "93767       561.0              NaN  \n",
       "94863       -18.0              NaN  \n",
       "70383        27.0              NaN  \n",
       "...           ...              ...  \n",
       "43240         0.0       land_cover  \n",
       "10743       404.0       land_cover  \n",
       "39076         0.0       land_cover  \n",
       "101314       83.0       land_cover  \n",
       "16382         1.0       land_cover  \n",
       "\n",
       "[5000 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_grid_sampled = pd.concat([sample_rad_limited,sample_high_elevation,sample_high_slope,solar_farms,sample_land_cover])\n",
    "ca_grid_sampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfbdc042-cfd4-4933-86fe-b392406b68ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41212</th>\n",
       "      <td>40.359323</td>\n",
       "      <td>-120.631931</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45033</th>\n",
       "      <td>40.378877</td>\n",
       "      <td>-120.373061</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36915</th>\n",
       "      <td>40.985054</td>\n",
       "      <td>-120.890802</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64144</th>\n",
       "      <td>38.051937</td>\n",
       "      <td>-118.862982</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72990</th>\n",
       "      <td>36.565825</td>\n",
       "      <td>-117.978507</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>elevation</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>41261</th>\n",
       "      <td>41.317474</td>\n",
       "      <td>-120.631931</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82857</th>\n",
       "      <td>34.610413</td>\n",
       "      <td>-116.964597</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27678</th>\n",
       "      <td>41.102379</td>\n",
       "      <td>-121.494834</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40456</th>\n",
       "      <td>40.554864</td>\n",
       "      <td>-120.675076</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73373</th>\n",
       "      <td>36.037864</td>\n",
       "      <td>-117.935362</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>elevation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "41212  40.359323 -120.631931                   0         1.0    2.0   \n",
       "45033  40.378877 -120.373061                   0         1.0    1.0   \n",
       "36915  40.985054 -120.890802                   0         1.0    0.0   \n",
       "64144  38.051937 -118.862982                   0         1.0    1.0   \n",
       "72990  36.565825 -117.978507                   0         1.0    0.0   \n",
       "...          ...         ...                 ...         ...    ...   \n",
       "41261  41.317474 -120.631931                   0         1.0    0.0   \n",
       "82857  34.610413 -116.964597                   0         1.0    2.0   \n",
       "27678  41.102379 -121.494834                   0         1.0    0.0   \n",
       "40456  40.554864 -120.675076                   0         1.0    2.0   \n",
       "73373  36.037864 -117.935362                   0         1.0    0.0   \n",
       "\n",
       "       elevation feature_violated  \n",
       "41212     1303.0        elevation  \n",
       "45033     1233.0        elevation  \n",
       "36915     1720.0        elevation  \n",
       "64144     1986.0        elevation  \n",
       "72990     1117.0        elevation  \n",
       "...          ...              ...  \n",
       "41261     1496.0        elevation  \n",
       "82857      986.0        elevation  \n",
       "27678     1012.0        elevation  \n",
       "40456     1521.0        elevation  \n",
       "73373     1023.0        elevation  \n",
       "\n",
       "[800 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### Check #############\n",
    "ca_grid_sampled.loc[ca_grid_sampled['feature_violated']=='elevation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35dbaf2f-22fc-416b-b415-063c90e4d46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Get lat and lons \n",
    "ca_coords_sample           = list(zip(ca_grid_sampled['Latitude'], ca_grid_sampled['Longitude']))\n",
    "len(ca_coords_sample )\n",
    "########\n",
    "ca_coords_sample1 = ca_coords_sample[0:1000]\n",
    "ca_coords_sample2 = ca_coords_sample[1000:2000]\n",
    "ca_coords_sample3 = ca_coords_sample[2000:3000]\n",
    "ca_coords_sample4 = ca_coords_sample[3000:4000]\n",
    "ca_coords_sample5 = ca_coords_sample[4000:]\n",
    "len(ca_coords_sample3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4e6951e5-41f7-4e56-a5b9-1563fc64f8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             ac_monthly  \\\n",
      "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
      "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
      "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
      "3     [142250.309592729, 145540.4466112001, 169827.4...   \n",
      "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
      "...                                                 ...   \n",
      "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
      "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
      "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
      "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
      "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
      "\n",
      "                                            poa_monthly  \\\n",
      "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
      "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
      "2     [176.9045521906041, 169.7386789021357, 221.042...   \n",
      "3     [179.1055874454835, 186.1198841765338, 223.688...   \n",
      "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
      "...                                                 ...   \n",
      "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
      "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
      "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
      "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
      "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
      "\n",
      "                                         solrad_monthly  \\\n",
      "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
      "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
      "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
      "3     [5.777599595015598, 6.647138720590493, 7.21576...   \n",
      "4     [4.968825442673403, 5.542526271012572, 6.17385...   \n",
      "...                                                 ...   \n",
      "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
      "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
      "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
      "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
      "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
      "\n",
      "                                             dc_monthly     ac_annual  \\\n",
      "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
      "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
      "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
      "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
      "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
      "...                                                 ...           ...   \n",
      "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
      "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
      "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
      "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
      "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
      "\n",
      "      solrad_annual  capacity_factor            latitude            longitude  \n",
      "0          5.732396        18.249219   38.63856071966661  -121.32225320345118  \n",
      "1          5.648400        17.663854    39.1469676944991   -121.4732611026316  \n",
      "2          6.640421        20.951948  33.632707429008306  -115.73496093377555  \n",
      "3          6.666664        20.579213   32.85054285234295  -115.58395303459514  \n",
      "4          5.956900        19.084820   33.88691091642455   -118.2373775487654  \n",
      "...             ...              ...                 ...                  ...  \n",
      "4995       5.789799        19.202002  34.414872005673665  -120.50249603647174  \n",
      "4996       5.817975        17.838677    39.0296430079993  -122.87547730930694  \n",
      "4997       5.749070        18.751075   35.15792835350576  -120.73979416375526  \n",
      "4998       6.543961        20.400983   33.61315331459167   -114.5268977403322  \n",
      "4999       5.545259        17.808299   38.20837020250067  -122.33616338366258  \n",
      "\n",
      "[5000 rows x 9 columns]\n",
      "CPU times: user 31.5 s, sys: 3.3 s, total: 34.8 s\n",
      "Wall time: 12min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.DataFrame()\n",
    "for (lat,lon) in ca_coords_sample5:\n",
    "        \n",
    "        parameters = {\n",
    "            'format': 'json',\n",
    "            'system_capacity': 1000, #1000kW = 1 MW, 1 MW or greater is considered utility-scale\n",
    "            'module_type': 0,       # 0- Standard module, 1- Permium, 2-Thin film\n",
    "            'losses': 14,           # Losses in percentage\n",
    "            'array_type': 0,        # Open Rack: Also known as ground mount.\n",
    "            'tilt': 40,\n",
    "            'azimuth': 180,         # This means that the solar array is facing South in the Northeren Hemisphere\n",
    "            'lat': lat,\n",
    "            'lon': lon,\n",
    "            'dataset': 'nsrdb',      #tmy2 is 1960-1990, tmy3 is 1990-2005\n",
    "            'radius': 100,            # 0-Pick the station nearest to the given (lat,lon),e.g: 50 -50 miles\n",
    "            'timeframe': 'monthly',\n",
    "            'api_key': hrh_apikey5\n",
    "        }\n",
    "\n",
    "        url = 'https://developer.nrel.gov/api/pvwatts/v8'\n",
    "        response = requests.get(url, params=parameters)\n",
    "        data = response.json()\n",
    "             \n",
    "        # Extract and append relevant data to the all_data list\n",
    "        # Extract relevant data\n",
    "        #print('Processing (lat,lon):',lat,lon,'\\n')\n",
    "        \n",
    "        # Check if 'outputs' is in the data\n",
    "        if 'outputs' not in data:\n",
    "            print(f\"No data available for latitude {lat} and longitude {lon}\")\n",
    "            continue\n",
    "\n",
    "        output_data = data['outputs']\n",
    "        input_data  = data['inputs']\n",
    "\n",
    "        output_data['latitude']  = input_data['lat']\n",
    "        output_data['longitude'] = input_data['lon']\n",
    "        all_data.append(output_data)\n",
    "        \n",
    "        \n",
    "# Convert all_data to a DataFrame\n",
    "df = pd.DataFrame(all_data)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ba65b4f1-efb5-4ed8-9162-3139ec7ad918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>capacity_factor</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[84883.06914835521, 110107.3007904001, 142457....</td>\n",
       "      <td>[104.6620668695758, 137.671252552197, 181.5298...</td>\n",
       "      <td>[3.376195705470185, 4.916830448292751, 5.85580...</td>\n",
       "      <td>[89177.54404926489, 115410.2864073284, 149285....</td>\n",
       "      <td>1.598632e+06</td>\n",
       "      <td>5.732396</td>\n",
       "      <td>18.249219</td>\n",
       "      <td>38.63856071966661</td>\n",
       "      <td>-121.32225320345118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
       "      <td>[3.208356493401135, 4.782236841787659, 5.14776...</td>\n",
       "      <td>[82933.84836181221, 111212.6071361726, 130119....</td>\n",
       "      <td>1.547354e+06</td>\n",
       "      <td>5.648400</td>\n",
       "      <td>17.663854</td>\n",
       "      <td>39.1469676944991</td>\n",
       "      <td>-121.4732611026316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[142033.6075897373, 135669.0721331215, 169827....</td>\n",
       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
       "      <td>[5.706598457761423, 6.062095675076274, 7.13041...</td>\n",
       "      <td>[148606.6602928677, 142527.3934126461, 177911....</td>\n",
       "      <td>1.835391e+06</td>\n",
       "      <td>6.640421</td>\n",
       "      <td>20.951948</td>\n",
       "      <td>33.632707429008306</td>\n",
       "      <td>-115.73496093377555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[142250.309592729, 145540.4466112001, 169827.4...</td>\n",
       "      <td>[179.1055874454835, 186.1198841765338, 223.688...</td>\n",
       "      <td>[5.777599595015598, 6.647138720590493, 7.21576...</td>\n",
       "      <td>[148922.2969679596, 152201.7710591838, 177685....</td>\n",
       "      <td>1.802739e+06</td>\n",
       "      <td>6.666664</td>\n",
       "      <td>20.579213</td>\n",
       "      <td>32.85054285234295</td>\n",
       "      <td>-115.58395303459514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[123194.0549525465, 121968.199271841, 148417.1...</td>\n",
       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>19.084820</td>\n",
       "      <td>33.88691091642455</td>\n",
       "      <td>-118.2373775487654</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>19.202002</td>\n",
       "      <td>34.414872005673665</td>\n",
       "      <td>-120.50249603647174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>17.838677</td>\n",
       "      <td>39.0296430079993</td>\n",
       "      <td>-122.87547730930694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>18.751075</td>\n",
       "      <td>35.15792835350576</td>\n",
       "      <td>-120.73979416375526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>20.400983</td>\n",
       "      <td>33.61315331459167</td>\n",
       "      <td>-114.5268977403322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>17.808299</td>\n",
       "      <td>38.20837020250067</td>\n",
       "      <td>-122.33616338366258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             ac_monthly  \\\n",
       "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
       "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
       "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
       "3     [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "2     [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "3     [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "3     [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "4     [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
       "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
       "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
       "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
       "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
       "...                                                 ...           ...   \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      solrad_annual  capacity_factor            latitude            longitude  \n",
       "0          5.732396        18.249219   38.63856071966661  -121.32225320345118  \n",
       "1          5.648400        17.663854    39.1469676944991   -121.4732611026316  \n",
       "2          6.640421        20.951948  33.632707429008306  -115.73496093377555  \n",
       "3          6.666664        20.579213   32.85054285234295  -115.58395303459514  \n",
       "4          5.956900        19.084820   33.88691091642455   -118.2373775487654  \n",
       "...             ...              ...                 ...                  ...  \n",
       "4995       5.789799        19.202002  34.414872005673665  -120.50249603647174  \n",
       "4996       5.817975        17.838677    39.0296430079993  -122.87547730930694  \n",
       "4997       5.749070        18.751075   35.15792835350576  -120.73979416375526  \n",
       "4998       6.543961        20.400983   33.61315331459167   -114.5268977403322  \n",
       "4999       5.545259        17.808299   38.20837020250067  -122.33616338366258  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f151e72c-76ee-475d-9176-96af50f41e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/hrh/Desktop/AI/erdos/SolarFarmPrediction/new_data/threshold_95percentile/\n"
     ]
    }
   ],
   "source": [
    "# Save file in the appropriate threshold folder\n",
    "threshold_path = data_path2 + 'threshold_'+thresh_name+'percentile/'\n",
    "print(threshold_path)\n",
    "df.to_csv(threshold_path +'nsrdb_monthly_chunk5.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc243eaa-aa43-4443-8fbb-189940cc5c1f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>capacity_factor</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>[84883.06914835521, 110107.3007904001, 142457....</td>\n",
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       "      <td>38.638561</td>\n",
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       "      <th>1</th>\n",
       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
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       "      <th>2</th>\n",
       "      <td>[142033.6075897373, 135669.0721331215, 169827....</td>\n",
       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
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       "      <td>[123194.0549525465, 121968.199271841, 148417.1...</td>\n",
       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>19.084820</td>\n",
       "      <td>33.886911</td>\n",
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       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
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       "      <td>19.202002</td>\n",
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       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
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       "      <td>17.838677</td>\n",
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       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>17.808299</td>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
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       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             ac_monthly  \\\n",
       "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
       "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
       "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
       "3     [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "2     [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "3     [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "3     [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "4     [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
       "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
       "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
       "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
       "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
       "...                                                 ...           ...   \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      solrad_annual  capacity_factor   latitude   longitude  \n",
       "0          5.732396        18.249219  38.638561 -121.322253  \n",
       "1          5.648400        17.663854  39.146968 -121.473261  \n",
       "2          6.640421        20.951948  33.632707 -115.734961  \n",
       "3          6.666664        20.579213  32.850543 -115.583953  \n",
       "4          5.956900        19.084820  33.886911 -118.237378  \n",
       "...             ...              ...        ...         ...  \n",
       "4995       5.789799        19.202002  34.414872 -120.502496  \n",
       "4996       5.817975        17.838677  39.029643 -122.875477  \n",
       "4997       5.749070        18.751075  35.157928 -120.739794  \n",
       "4998       6.543961        20.400983  33.613153 -114.526898  \n",
       "4999       5.545259        17.808299  38.208370 -122.336163  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check = pd.read_csv(threshold_path +'nsrdb_monthly_chunk5.csv')\n",
    "df_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a1f015d0-eabf-499c-ac6e-56c7040d5947",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df0 = df.drop_duplicates(subset=['latitude','longitude'])\n",
    "# df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b5d5f492-3519-4ceb-aee1-2ea31d856a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_check.to_csv(threshold_path +'nsrdb_monthly.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f379d9b5-8c7b-4880-80a4-eea048697f20",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
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       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
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       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
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       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>19.084820</td>\n",
       "      <td>33.886911</td>\n",
       "      <td>-118.237378</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>19.202002</td>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>17.838677</td>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>18.751075</td>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>20.400983</td>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>17.808299</td>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             ac_monthly  \\\n",
       "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
       "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
       "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
       "3     [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "2     [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "3     [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "3     [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "4     [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \\\n",
       "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
       "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
       "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
       "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
       "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
       "...                                                 ...           ...   \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "      solrad_annual  capacity_factor   latitude   longitude  \n",
       "0          5.732396        18.249219  38.638561 -121.322253  \n",
       "1          5.648400        17.663854  39.146968 -121.473261  \n",
       "2          6.640421        20.951948  33.632707 -115.734961  \n",
       "3          6.666664        20.579213  32.850543 -115.583953  \n",
       "4          5.956900        19.084820  33.886911 -118.237378  \n",
       "...             ...              ...        ...         ...  \n",
       "4995       5.789799        19.202002  34.414872 -120.502496  \n",
       "4996       5.817975        17.838677  39.029643 -122.875477  \n",
       "4997       5.749070        18.751075  35.157928 -120.739794  \n",
       "4998       6.543961        20.400983  33.613153 -114.526898  \n",
       "4999       5.545259        17.808299  38.208370 -122.336163  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ca_rad1 = pd.read_csv(data_path2+'nsrdb_monthly_chunk1.csv')\n",
    "ca_rad = pd.read_csv(threshold_path +'nsrdb_monthly.csv')\n",
    "ca_rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88212246-c763-4573-8996-a50dba6fe7f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert DataFrame to GeoDataFrame\n",
    "geometry       = [Point(xy) for xy in zip(ca_rad['longitude'], ca_rad['latitude'])]\n",
    "geo_df_rad     = gpd.GeoDataFrame(ca_rad, geometry=geometry,crs = \"EPSG:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "65a2d4f1-4f27-4451-9658-db4aed0f7f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the California shapefile\n",
    "ca_shapfile_path = data_path+ 'ca_state_boundary/CA_State_TIGER2016.shp'\n",
    "ca_shape         = gpd.read_file(ca_shapfile_path)\n",
    "ca_shape         = ca_shape.to_crs(\"EPSG:4326\")\n",
    "print(ca_shape.crs)\n",
    "\n",
    "\n",
    "# Plot the county boundaries\n",
    "fig, ax = plt.subplots()\n",
    "ca_shape.plot(ax=ax, color='white', edgecolor='black')\n",
    "\n",
    "# Plot solar_rad data on top of the county boundaries\n",
    "geo_df_rad.plot(ax=ax, column='solrad_annual', legend=True, markersize=5, cmap='RdBu_r',  missing_kwds={\n",
    "        \"color\": \"white\",\n",
    "        \"edgecolor\": \"red\",\n",
    "        \"hatch\": \"///\",\n",
    "        \"label\": \"Missing values\",\n",
    "    })\n",
    "\n",
    "\n",
    "# geo_df_rad.plot(ax=ax, column='solrad_annual', legend=True, markersize=50, cmap='OrRd', alpha=0.5)\n",
    "\n",
    "# Add titles and labels as needed\n",
    "plt.title(\"Annual solar radiation\")\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8af8c095-e280-4299-bb5a-ceb28ed7529a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the California shapefile\n",
    "ca_shapfile_path = data_path+ 'ca_state_boundary/CA_State_TIGER2016.shp'\n",
    "ca_shape         = gpd.read_file(ca_shapfile_path)\n",
    "ca_shape         = ca_shape.to_crs(\"EPSG:4326\")\n",
    "print(ca_shape.crs)\n",
    "\n",
    "\n",
    "# Plot the county boundaries\n",
    "fig, ax = plt.subplots()\n",
    "ca_shape.plot(ax=ax, color='white', edgecolor='black')\n",
    "\n",
    "# Plot solar_rad data on top of the county boundaries\n",
    "geo_df_rad.plot(ax=ax, column='ac_annual', legend=True, markersize=5, cmap='RdBu_r',  missing_kwds={\n",
    "        \"color\": \"white\",\n",
    "        \"edgecolor\": \"red\",\n",
    "        \"hatch\": \"///\",\n",
    "        \"label\": \"Missing values\",\n",
    "    })\n",
    "\n",
    "\n",
    "# geo_df_rad.plot(ax=ax, column='solrad_annual', legend=True, markersize=50, cmap='OrRd', alpha=0.5)\n",
    "\n",
    "# Add titles and labels as needed\n",
    "plt.title(\"Annual solar radiation\")\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61e9956-d83a-4518-bb50-c1a343df91ec",
   "metadata": {},
   "source": [
    "## Add data to big dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cd4e07-10a3-46e6-adf5-25ef7605b910",
   "metadata": {},
   "source": [
    "#### Find threshold for ac_annual. Exclude the bottom thresh_lb * 100 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b54a785-4519-4e5a-9de2-506ecbd3a2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1457208.376409882"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_annual_threshold = geo_df_rad['ac_annual'].quantile(q=thresh_lb)\n",
    "ac_annual_threshold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7187947e-2c46-43bb-829a-f87bb7f41647",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure both GeoDataFrames have the same CRS\n",
    "# Convert ca_features to a GeoDataFrame\n",
    "gdf_ca_features = gpd.GeoDataFrame(\n",
    "    ca_grid_sampled, \n",
    "    geometry=gpd.points_from_xy(ca_grid_sampled.Longitude, ca_grid_sampled.Latitude),\n",
    "    crs=\"EPSG:4326\"\n",
    ")\n",
    "\n",
    "# Ensure ca_rad is a GeoDataFrame with the same CRS\n",
    "gdf_ca_rad = gpd.GeoDataFrame(\n",
    "    ca_rad,\n",
    "    geometry=gpd.points_from_xy(ca_rad.longitude, ca_rad.latitude),\n",
    "    crs=\"EPSG:4326\"\n",
    ")\n",
    "\n",
    "# Reproject to a suitable projected CRS for accurate distance calculations\n",
    "gdf_ca_features = gdf_ca_features.to_crs(\"EPSG:3310\")\n",
    "gdf_ca_rad      = gdf_ca_rad.to_crs(\"EPSG:3310\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c8c4b7d3-0666-4450-92b0-1f489bc4c268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30111</th>\n",
       "      <td>38.638561</td>\n",
       "      <td>-121.322253</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>POINT (-114965.109 69966.408)</td>\n",
       "      <td>0</td>\n",
       "      <td>5.732396</td>\n",
       "      <td>[84883.06914835521, 110107.3007904001, 142457....</td>\n",
       "      <td>[104.6620668695758, 137.671252552197, 181.5298...</td>\n",
       "      <td>[3.376195705470185, 4.916830448292751, 5.85580...</td>\n",
       "      <td>[89177.54404926489, 115410.2864073284, 149285....</td>\n",
       "      <td>1.598632e+06</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27893</th>\n",
       "      <td>39.146968</td>\n",
       "      <td>-121.473261</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>5.648400</td>\n",
       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
       "      <td>[3.208356493401135, 4.782236841787659, 5.14776...</td>\n",
       "      <td>[82933.84836181221, 111212.6071361726, 130119....</td>\n",
       "      <td>1.547354e+06</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>93767</th>\n",
       "      <td>33.632707</td>\n",
       "      <td>-115.734961</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>561.0</td>\n",
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       "      <td>POINT (395723.453 -477996.803)</td>\n",
       "      <td>2</td>\n",
       "      <td>6.640421</td>\n",
       "      <td>[142033.6075897373, 135669.0721331215, 169827....</td>\n",
       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
       "      <td>[5.706598457761423, 6.062095675076274, 7.13041...</td>\n",
       "      <td>[148606.6602928677, 142527.3934126461, 177911....</td>\n",
       "      <td>1.835391e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94863</th>\n",
       "      <td>32.850543</td>\n",
       "      <td>-115.583953</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (413760.377 -563940.891)</td>\n",
       "      <td>3</td>\n",
       "      <td>6.666664</td>\n",
       "      <td>[142250.309592729, 145540.4466112001, 169827.4...</td>\n",
       "      <td>[179.1055874454835, 186.1198841765338, 223.688...</td>\n",
       "      <td>[5.777599595015598, 6.647138720590493, 7.21576...</td>\n",
       "      <td>[148922.2969679596, 152201.7710591838, 177685....</td>\n",
       "      <td>1.802739e+06</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>70383</th>\n",
       "      <td>33.886911</td>\n",
       "      <td>-118.237378</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (163063.336 -457194.260)</td>\n",
       "      <td>4</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>[123194.0549525465, 121968.199271841, 148417.1...</td>\n",
       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>43240</th>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-46178.591 -400015.656)</td>\n",
       "      <td>4995</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10743</th>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-248663.872 116405.088)</td>\n",
       "      <td>4996</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39076</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-67341.976 -317389.303)</td>\n",
       "      <td>4997</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101314</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (507825.659 -474405.559)</td>\n",
       "      <td>4998</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16382</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-204285.396 23861.207)</td>\n",
       "      <td>4999</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "30111   38.638561 -121.322253                   0         1.0    2.0   \n",
       "27893   39.146968 -121.473261                   0         1.0    0.0   \n",
       "93767   33.632707 -115.734961                   0         1.0    2.0   \n",
       "94863   32.850543 -115.583953                   0         1.0    0.0   \n",
       "70383   33.886911 -118.237378                   0         1.0    1.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "43240   34.414872 -120.502496                   0         0.0    0.0   \n",
       "10743   39.029643 -122.875477                   0         0.0    0.0   \n",
       "39076   35.157928 -120.739794                   0         0.0    0.0   \n",
       "101314  33.613153 -114.526898                   0         0.0    2.0   \n",
       "16382   38.208370 -122.336163                   0         0.0    1.0   \n",
       "\n",
       "        elevation feature_violated                        geometry  \\\n",
       "30111        37.0              NaN   POINT (-114965.109 69966.408)   \n",
       "27893        27.0              NaN  POINT (-127215.578 126661.719)   \n",
       "93767       561.0              NaN  POINT (395723.453 -477996.803)   \n",
       "94863       -18.0              NaN  POINT (413760.377 -563940.891)   \n",
       "70383        27.0              NaN  POINT (163063.336 -457194.260)   \n",
       "...           ...              ...                             ...   \n",
       "43240         0.0       land_cover  POINT (-46178.591 -400015.656)   \n",
       "10743       404.0       land_cover  POINT (-248663.872 116405.088)   \n",
       "39076         0.0       land_cover  POINT (-67341.976 -317389.303)   \n",
       "101314       83.0       land_cover  POINT (507825.659 -474405.559)   \n",
       "16382         1.0       land_cover   POINT (-204285.396 23861.207)   \n",
       "\n",
       "        index_right  solrad_annual  \\\n",
       "30111             0       5.732396   \n",
       "27893             1       5.648400   \n",
       "93767             2       6.640421   \n",
       "94863             3       6.666664   \n",
       "70383             4       5.956900   \n",
       "...             ...            ...   \n",
       "43240          4995       5.789799   \n",
       "10743          4996       5.817975   \n",
       "39076          4997       5.749070   \n",
       "101314         4998       6.543961   \n",
       "16382          4999       5.545259   \n",
       "\n",
       "                                               ac_monthly  \\\n",
       "30111   [84883.06914835521, 110107.3007904001, 142457....   \n",
       "27893   [78866.77428118252, 106080.1644945415, 124079....   \n",
       "93767   [142033.6075897373, 135669.0721331215, 169827....   \n",
       "94863   [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "70383   [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                   ...   \n",
       "43240   [134294.313590256, 118366.24124287, 149754.498...   \n",
       "10743   [86549.00961829678, 108134.5256253014, 127573....   \n",
       "39076   [113917.7039648926, 111119.0424322115, 148308....   \n",
       "101314  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "16382   [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                              poa_monthly  \\\n",
       "30111   [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "27893   [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "93767   [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "94863   [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "70383   [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                   ...   \n",
       "43240   [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "10743   [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "39076   [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "101314  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "16382   [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                           solrad_monthly  \\\n",
       "30111   [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "27893   [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "93767   [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "94863   [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "70383   [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                   ...   \n",
       "43240   [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "10743   [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "39076   [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "101314  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "16382   [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                               dc_monthly     ac_annual  \\\n",
       "30111   [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06   \n",
       "27893   [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06   \n",
       "93767   [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06   \n",
       "94863   [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06   \n",
       "70383   [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06   \n",
       "...                                                   ...           ...   \n",
       "43240   [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06   \n",
       "10743   [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06   \n",
       "39076   [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06   \n",
       "101314  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06   \n",
       "16382   [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06   \n",
       "\n",
       "        distance  \n",
       "30111        0.0  \n",
       "27893        0.0  \n",
       "93767        0.0  \n",
       "94863        0.0  \n",
       "70383        0.0  \n",
       "...          ...  \n",
       "43240        0.0  \n",
       "10743        0.0  \n",
       "39076        0.0  \n",
       "101314       0.0  \n",
       "16382        0.0  \n",
       "\n",
       "[5000 rows x 16 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the spatial join to find the nearest neighbors and get their 'elevation'\n",
    "ca_features_with_solar_data = gpd.sjoin_nearest(gdf_ca_features, gdf_ca_rad[['geometry', 'solrad_annual','ac_monthly', 'poa_monthly', 'solrad_monthly','dc_monthly','ac_annual']],\\\n",
    "                                                how='left', distance_col='distance')\n",
    "ca_features_with_solar_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "194e9c11-77f0-47bc-ac2d-5bcd7a2cf25b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
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       "<p>5000 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "30111   38.638561 -121.322253                   0         1.0    2.0   \n",
       "27893   39.146968 -121.473261                   0         1.0    0.0   \n",
       "93767   33.632707 -115.734961                   0         1.0    2.0   \n",
       "94863   32.850543 -115.583953                   0         1.0    0.0   \n",
       "70383   33.886911 -118.237378                   0         1.0    1.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "43240   34.414872 -120.502496                   0         0.0    0.0   \n",
       "10743   39.029643 -122.875477                   0         0.0    0.0   \n",
       "39076   35.157928 -120.739794                   0         0.0    0.0   \n",
       "101314  33.613153 -114.526898                   0         0.0    2.0   \n",
       "16382   38.208370 -122.336163                   0         0.0    1.0   \n",
       "\n",
       "        elevation feature_violated                     geometry  \\\n",
       "30111        37.0              NaN  POINT (-121.32225 38.63856)   \n",
       "27893        27.0              NaN  POINT (-121.47326 39.14697)   \n",
       "93767       561.0              NaN  POINT (-115.73496 33.63271)   \n",
       "94863       -18.0              NaN  POINT (-115.58395 32.85054)   \n",
       "70383        27.0              NaN  POINT (-118.23738 33.88691)   \n",
       "...           ...              ...                          ...   \n",
       "43240         0.0       land_cover  POINT (-120.50250 34.41487)   \n",
       "10743       404.0       land_cover  POINT (-122.87548 39.02964)   \n",
       "39076         0.0       land_cover  POINT (-120.73979 35.15793)   \n",
       "101314       83.0       land_cover  POINT (-114.52690 33.61315)   \n",
       "16382         1.0       land_cover  POINT (-122.33616 38.20837)   \n",
       "\n",
       "        solrad_annual                                         ac_monthly  \\\n",
       "30111        5.732396  [84883.06914835521, 110107.3007904001, 142457....   \n",
       "27893        5.648400  [78866.77428118252, 106080.1644945415, 124079....   \n",
       "93767        6.640421  [142033.6075897373, 135669.0721331215, 169827....   \n",
       "94863        6.666664  [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "70383        5.956900  [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...               ...                                                ...   \n",
       "43240        5.789799  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "10743        5.817975  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "39076        5.749070  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "101314       6.543961  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "16382        5.545259  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                              poa_monthly  \\\n",
       "30111   [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "27893   [99.45905129543517, 133.9026315700545, 159.580...   \n",
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       "94863   [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "70383   [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                   ...   \n",
       "43240   [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "10743   [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "39076   [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "101314  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "16382   [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                           solrad_monthly  \\\n",
       "30111   [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "27893   [3.208356493401135, 4.782236841787659, 5.14776...   \n",
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       "...                                                   ...   \n",
       "43240   [5.329879132134445, 5.185863067001377, 6.07044...   \n",
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       "101314  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "16382   [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                               dc_monthly     ac_annual  \n",
       "30111   [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06  \n",
       "27893   [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06  \n",
       "93767   [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06  \n",
       "94863   [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06  \n",
       "70383   [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06  \n",
       "...                                                   ...           ...  \n",
       "43240   [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06  \n",
       "10743   [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06  \n",
       "39076   [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06  \n",
       "101314  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06  \n",
       "16382   [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06  \n",
       "\n",
       "[5000 rows x 14 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# If you don't need the distance information, drop the 'distance' column\n",
    "ca_features_with_solar_data.drop(columns=['index_right', 'distance',], inplace=True)\n",
    "\n",
    "# Optionally, project back to geographic CRS if needed for other purposes\n",
    "ca_features_with_solar_data = ca_features_with_solar_data.to_crs(\"EPSG:4326\")\n",
    "\n",
    "ca_features_with_solar_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e39c50ed-5f73-4757-82cb-06ebbf7509df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>geometry</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30111</th>\n",
       "      <td>38.638561</td>\n",
       "      <td>-121.322253</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-121.32225 38.63856)</td>\n",
       "      <td>5.732396</td>\n",
       "      <td>[84883.06914835521, 110107.3007904001, 142457....</td>\n",
       "      <td>[104.6620668695758, 137.671252552197, 181.5298...</td>\n",
       "      <td>[3.376195705470185, 4.916830448292751, 5.85580...</td>\n",
       "      <td>[89177.54404926489, 115410.2864073284, 149285....</td>\n",
       "      <td>1.598632e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27893</th>\n",
       "      <td>39.146968</td>\n",
       "      <td>-121.473261</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-121.47326 39.14697)</td>\n",
       "      <td>5.648400</td>\n",
       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
       "      <td>[3.208356493401135, 4.782236841787659, 5.14776...</td>\n",
       "      <td>[82933.84836181221, 111212.6071361726, 130119....</td>\n",
       "      <td>1.547354e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93767</th>\n",
       "      <td>33.632707</td>\n",
       "      <td>-115.734961</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-115.73496 33.63271)</td>\n",
       "      <td>6.640421</td>\n",
       "      <td>[142033.6075897373, 135669.0721331215, 169827....</td>\n",
       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
       "      <td>[5.706598457761423, 6.062095675076274, 7.13041...</td>\n",
       "      <td>[148606.6602928677, 142527.3934126461, 177911....</td>\n",
       "      <td>1.835391e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94863</th>\n",
       "      <td>32.850543</td>\n",
       "      <td>-115.583953</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-115.58395 32.85054)</td>\n",
       "      <td>6.666664</td>\n",
       "      <td>[142250.309592729, 145540.4466112001, 169827.4...</td>\n",
       "      <td>[179.1055874454835, 186.1198841765338, 223.688...</td>\n",
       "      <td>[5.777599595015598, 6.647138720590493, 7.21576...</td>\n",
       "      <td>[148922.2969679596, 152201.7710591838, 177685....</td>\n",
       "      <td>1.802739e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70383</th>\n",
       "      <td>33.886911</td>\n",
       "      <td>-118.237378</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-118.23738 33.88691)</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>[123194.0549525465, 121968.199271841, 148417.1...</td>\n",
       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</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",
       "      <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>43240</th>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-120.50250 34.41487)</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10743</th>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-122.87548 39.02964)</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39076</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-120.73979 35.15793)</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101314</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-114.52690 33.61315)</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16382</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>POINT (-122.33616 38.20837)</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude  Solar_farm_present  land_cover  slope  \\\n",
       "30111   38.638561 -121.322253                   0         1.0    2.0   \n",
       "27893   39.146968 -121.473261                   0         1.0    0.0   \n",
       "93767   33.632707 -115.734961                   0         1.0    2.0   \n",
       "94863   32.850543 -115.583953                   0         1.0    0.0   \n",
       "70383   33.886911 -118.237378                   0         1.0    1.0   \n",
       "...           ...         ...                 ...         ...    ...   \n",
       "43240   34.414872 -120.502496                   0         0.0    0.0   \n",
       "10743   39.029643 -122.875477                   0         0.0    0.0   \n",
       "39076   35.157928 -120.739794                   0         0.0    0.0   \n",
       "101314  33.613153 -114.526898                   0         0.0    2.0   \n",
       "16382   38.208370 -122.336163                   0         0.0    1.0   \n",
       "\n",
       "        elevation feature_violated                     geometry  \\\n",
       "30111        37.0              NaN  POINT (-121.32225 38.63856)   \n",
       "27893        27.0              NaN  POINT (-121.47326 39.14697)   \n",
       "93767       561.0              NaN  POINT (-115.73496 33.63271)   \n",
       "94863       -18.0              NaN  POINT (-115.58395 32.85054)   \n",
       "70383        27.0              NaN  POINT (-118.23738 33.88691)   \n",
       "...           ...              ...                          ...   \n",
       "43240         0.0       land_cover  POINT (-120.50250 34.41487)   \n",
       "10743       404.0       land_cover  POINT (-122.87548 39.02964)   \n",
       "39076         0.0       land_cover  POINT (-120.73979 35.15793)   \n",
       "101314       83.0       land_cover  POINT (-114.52690 33.61315)   \n",
       "16382         1.0       land_cover  POINT (-122.33616 38.20837)   \n",
       "\n",
       "        solrad_annual                                         ac_monthly  \\\n",
       "30111        5.732396  [84883.06914835521, 110107.3007904001, 142457....   \n",
       "27893        5.648400  [78866.77428118252, 106080.1644945415, 124079....   \n",
       "93767        6.640421  [142033.6075897373, 135669.0721331215, 169827....   \n",
       "94863        6.666664  [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "70383        5.956900  [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...               ...                                                ...   \n",
       "43240        5.789799  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "10743        5.817975  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "39076        5.749070  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "101314       6.543961  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "16382        5.545259  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                              poa_monthly  \\\n",
       "30111   [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "27893   [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "93767   [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "94863   [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "70383   [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                   ...   \n",
       "43240   [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "10743   [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "39076   [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "101314  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "16382   [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                           solrad_monthly  \\\n",
       "30111   [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "27893   [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "93767   [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "94863   [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "70383   [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                   ...   \n",
       "43240   [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "10743   [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "39076   [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "101314  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "16382   [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                               dc_monthly     ac_annual  \n",
       "30111   [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06  \n",
       "27893   [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06  \n",
       "93767   [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06  \n",
       "94863   [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06  \n",
       "70383   [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06  \n",
       "...                                                   ...           ...  \n",
       "43240   [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06  \n",
       "10743   [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06  \n",
       "39076   [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06  \n",
       "101314  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06  \n",
       "16382   [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06  \n",
       "\n",
       "[5000 rows x 14 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Set value where ac_annual is below threshold\n",
    "ac_annual_low   = (ca_features_with_solar_data['ac_annual']<=ac_annual_threshold) & (ca_features_with_solar_data['Solar_farm_present']!=1) \\\n",
    "& (ca_features_with_solar_data['elevation'] < elevation_threshold) \\\n",
    "& (ca_features_with_solar_data['slope']<slope_threshold) & (ca_features_with_solar_data['land_cover']!=0)\n",
    "\n",
    "ca_features_with_solar_data.loc[ac_annual_low,'Solar_farm_present']=0\n",
    "ca_features_with_solar_data.loc[ac_annual_low,'feature_violated']='ac_annual'\n",
    "ca_features_with_solar_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "477a928a-5669-4143-acb9-a60b616d3013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>geometry</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [Latitude, Longitude, Solar_farm_present, land_cover, slope, elevation, feature_violated, geometry, solrad_annual, ac_monthly, poa_monthly, solrad_monthly, dc_monthly, ac_annual]\n",
       "Index: []"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_features_with_solar_data.loc[ca_features_with_solar_data['Solar_farm_present'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70773afb-cbe4-48d6-a793-34c09cc3f93f",
   "metadata": {},
   "source": [
    "### Finally, save this data as a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "93c924b7-9920-41f2-a1b0-ebb9eb41627f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_features_with_solar_data.drop(columns='geometry').to_csv(threshold_path +'ca_data_version_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f4fb0d12-cd05-472a-bef3-8bc365a0f4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.638561</td>\n",
       "      <td>-121.322253</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.732396</td>\n",
       "      <td>[84883.06914835521, 110107.3007904001, 142457....</td>\n",
       "      <td>[104.6620668695758, 137.671252552197, 181.5298...</td>\n",
       "      <td>[3.376195705470185, 4.916830448292751, 5.85580...</td>\n",
       "      <td>[89177.54404926489, 115410.2864073284, 149285....</td>\n",
       "      <td>1.598632e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.146968</td>\n",
       "      <td>-121.473261</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.648400</td>\n",
       "      <td>[78866.77428118252, 106080.1644945415, 124079....</td>\n",
       "      <td>[99.45905129543517, 133.9026315700545, 159.580...</td>\n",
       "      <td>[3.208356493401135, 4.782236841787659, 5.14776...</td>\n",
       "      <td>[82933.84836181221, 111212.6071361726, 130119....</td>\n",
       "      <td>1.547354e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.632707</td>\n",
       "      <td>-115.734961</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.640421</td>\n",
       "      <td>[142033.6075897373, 135669.0721331215, 169827....</td>\n",
       "      <td>[176.9045521906041, 169.7386789021357, 221.042...</td>\n",
       "      <td>[5.706598457761423, 6.062095675076274, 7.13041...</td>\n",
       "      <td>[148606.6602928677, 142527.3934126461, 177911....</td>\n",
       "      <td>1.835391e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.850543</td>\n",
       "      <td>-115.583953</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.666664</td>\n",
       "      <td>[142250.309592729, 145540.4466112001, 169827.4...</td>\n",
       "      <td>[179.1055874454835, 186.1198841765338, 223.688...</td>\n",
       "      <td>[5.777599595015598, 6.647138720590493, 7.21576...</td>\n",
       "      <td>[148922.2969679596, 152201.7710591838, 177685....</td>\n",
       "      <td>1.802739e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.886911</td>\n",
       "      <td>-118.237378</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.956900</td>\n",
       "      <td>[123194.0549525465, 121968.199271841, 148417.1...</td>\n",
       "      <td>[154.0335887228755, 155.190735588352, 191.3896...</td>\n",
       "      <td>[4.968825442673403, 5.542526271012572, 6.17385...</td>\n",
       "      <td>[128993.2732027951, 127672.1091778474, 155507....</td>\n",
       "      <td>1.671830e+06</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>34.414872</td>\n",
       "      <td>-120.502496</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.789799</td>\n",
       "      <td>[134294.313590256, 118366.24124287, 149754.498...</td>\n",
       "      <td>[165.2262530961678, 145.2041658760386, 188.183...</td>\n",
       "      <td>[5.329879132134445, 5.185863067001377, 6.07044...</td>\n",
       "      <td>[140523.2672581649, 124183.2297143774, 156954....</td>\n",
       "      <td>1.682095e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>39.029643</td>\n",
       "      <td>-122.875477</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.817975</td>\n",
       "      <td>[86549.00961829678, 108134.5256253014, 127573....</td>\n",
       "      <td>[108.6456708032853, 140.6383844081999, 167.668...</td>\n",
       "      <td>[3.504699058170492, 5.022799443149995, 5.40866...</td>\n",
       "      <td>[90875.1327292863, 113331.488332072, 133713.53...</td>\n",
       "      <td>1.562668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>35.157928</td>\n",
       "      <td>-120.739794</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.749070</td>\n",
       "      <td>[113917.7039648926, 111119.0424322115, 148308....</td>\n",
       "      <td>[139.9636361902664, 138.7895862621193, 186.264...</td>\n",
       "      <td>[4.514956006137627, 4.956770937932831, 6.00852...</td>\n",
       "      <td>[119359.1192386094, 116425.779373396, 155596.2...</td>\n",
       "      <td>1.642594e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>33.613153</td>\n",
       "      <td>-114.526898</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>6.543961</td>\n",
       "      <td>[139332.6846621154, 142410.7072380826, 166391....</td>\n",
       "      <td>[177.3764960027499, 181.0924057287071, 217.431...</td>\n",
       "      <td>[5.721822451701609, 6.467585918882397, 7.01391...</td>\n",
       "      <td>[145754.6466105258, 148919.70615459, 174081.44...</td>\n",
       "      <td>1.787126e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>38.208370</td>\n",
       "      <td>-122.336163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>land_cover</td>\n",
       "      <td>5.545259</td>\n",
       "      <td>[93175.08528784048, 103355.3357037224, 129479....</td>\n",
       "      <td>[113.3388156294827, 129.7969652194793, 164.969...</td>\n",
       "      <td>[3.656090826757508, 4.63560590069569, 5.321594...</td>\n",
       "      <td>[97740.49794099826, 108382.4085881702, 135709....</td>\n",
       "      <td>1.560007e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation  \\\n",
       "0     38.638561 -121.322253                   0         1.0    2.0       37.0   \n",
       "1     39.146968 -121.473261                   0         1.0    0.0       27.0   \n",
       "2     33.632707 -115.734961                   0         1.0    2.0      561.0   \n",
       "3     32.850543 -115.583953                   0         1.0    0.0      -18.0   \n",
       "4     33.886911 -118.237378                   0         1.0    1.0       27.0   \n",
       "...         ...         ...                 ...         ...    ...        ...   \n",
       "4995  34.414872 -120.502496                   0         0.0    0.0        0.0   \n",
       "4996  39.029643 -122.875477                   0         0.0    0.0      404.0   \n",
       "4997  35.157928 -120.739794                   0         0.0    0.0        0.0   \n",
       "4998  33.613153 -114.526898                   0         0.0    2.0       83.0   \n",
       "4999  38.208370 -122.336163                   0         0.0    1.0        1.0   \n",
       "\n",
       "     feature_violated  solrad_annual  \\\n",
       "0                 NaN       5.732396   \n",
       "1                 NaN       5.648400   \n",
       "2                 NaN       6.640421   \n",
       "3                 NaN       6.666664   \n",
       "4                 NaN       5.956900   \n",
       "...               ...            ...   \n",
       "4995       land_cover       5.789799   \n",
       "4996       land_cover       5.817975   \n",
       "4997       land_cover       5.749070   \n",
       "4998       land_cover       6.543961   \n",
       "4999       land_cover       5.545259   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "0     [84883.06914835521, 110107.3007904001, 142457....   \n",
       "1     [78866.77428118252, 106080.1644945415, 124079....   \n",
       "2     [142033.6075897373, 135669.0721331215, 169827....   \n",
       "3     [142250.309592729, 145540.4466112001, 169827.4...   \n",
       "4     [123194.0549525465, 121968.199271841, 148417.1...   \n",
       "...                                                 ...   \n",
       "4995  [134294.313590256, 118366.24124287, 149754.498...   \n",
       "4996  [86549.00961829678, 108134.5256253014, 127573....   \n",
       "4997  [113917.7039648926, 111119.0424322115, 148308....   \n",
       "4998  [139332.6846621154, 142410.7072380826, 166391....   \n",
       "4999  [93175.08528784048, 103355.3357037224, 129479....   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "0     [104.6620668695758, 137.671252552197, 181.5298...   \n",
       "1     [99.45905129543517, 133.9026315700545, 159.580...   \n",
       "2     [176.9045521906041, 169.7386789021357, 221.042...   \n",
       "3     [179.1055874454835, 186.1198841765338, 223.688...   \n",
       "4     [154.0335887228755, 155.190735588352, 191.3896...   \n",
       "...                                                 ...   \n",
       "4995  [165.2262530961678, 145.2041658760386, 188.183...   \n",
       "4996  [108.6456708032853, 140.6383844081999, 167.668...   \n",
       "4997  [139.9636361902664, 138.7895862621193, 186.264...   \n",
       "4998  [177.3764960027499, 181.0924057287071, 217.431...   \n",
       "4999  [113.3388156294827, 129.7969652194793, 164.969...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "0     [3.376195705470185, 4.916830448292751, 5.85580...   \n",
       "1     [3.208356493401135, 4.782236841787659, 5.14776...   \n",
       "2     [5.706598457761423, 6.062095675076274, 7.13041...   \n",
       "3     [5.777599595015598, 6.647138720590493, 7.21576...   \n",
       "4     [4.968825442673403, 5.542526271012572, 6.17385...   \n",
       "...                                                 ...   \n",
       "4995  [5.329879132134445, 5.185863067001377, 6.07044...   \n",
       "4996  [3.504699058170492, 5.022799443149995, 5.40866...   \n",
       "4997  [4.514956006137627, 4.956770937932831, 6.00852...   \n",
       "4998  [5.721822451701609, 6.467585918882397, 7.01391...   \n",
       "4999  [3.656090826757508, 4.63560590069569, 5.321594...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \n",
       "0     [89177.54404926489, 115410.2864073284, 149285....  1.598632e+06  \n",
       "1     [82933.84836181221, 111212.6071361726, 130119....  1.547354e+06  \n",
       "2     [148606.6602928677, 142527.3934126461, 177911....  1.835391e+06  \n",
       "3     [148922.2969679596, 152201.7710591838, 177685....  1.802739e+06  \n",
       "4     [128993.2732027951, 127672.1091778474, 155507....  1.671830e+06  \n",
       "...                                                 ...           ...  \n",
       "4995  [140523.2672581649, 124183.2297143774, 156954....  1.682095e+06  \n",
       "4996  [90875.1327292863, 113331.488332072, 133713.53...  1.562668e+06  \n",
       "4997  [119359.1192386094, 116425.779373396, 155596.2...  1.642594e+06  \n",
       "4998  [145754.6466105258, 148919.70615459, 174081.44...  1.787126e+06  \n",
       "4999  [97740.49794099826, 108382.4085881702, 135709....  1.560007e+06  \n",
       "\n",
       "[5000 rows x 13 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_features_with_solar_data_check = pd.read_csv(threshold_path +'ca_data_version_3.csv')\n",
    "ca_features_with_solar_data_check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3d20e3fe-4fed-4e93-b7f2-fcbbbe224c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Solar_farm_present</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>slope</th>\n",
       "      <th>elevation</th>\n",
       "      <th>feature_violated</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
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       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>40.359323</td>\n",
       "      <td>-120.631931</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.800638</td>\n",
       "      <td>[103351.3635653951, 100500.2908635284, 144547....</td>\n",
       "      <td>[125.4477454750849, 123.7115319965838, 181.497...</td>\n",
       "      <td>[4.046701466938223, 4.418268999877993, 5.85474...</td>\n",
       "      <td>[108398.8087866204, 106465.7720028536, 152476....</td>\n",
       "      <td>1.622821e+06</td>\n",
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       "      <th>2130</th>\n",
       "      <td>40.378877</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1233.0</td>\n",
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       "      <td>5.837898</td>\n",
       "      <td>[100852.3082413978, 106494.0689454024, 147919....</td>\n",
       "      <td>[122.2390688486897, 131.6625069065885, 186.520...</td>\n",
       "      <td>[3.943195769312569, 4.702232389521017, 6.01678...</td>\n",
       "      <td>[105809.8420581117, 111629.9004175349, 155353....</td>\n",
       "      <td>1.630034e+06</td>\n",
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       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>40.985054</td>\n",
       "      <td>-120.890802</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.669324</td>\n",
       "      <td>[99722.61500801894, 109183.9367829212, 133277....</td>\n",
       "      <td>[117.9534011360403, 133.9159953835858, 165.822...</td>\n",
       "      <td>[3.804948423743235, 4.782714120842349, 5.34911...</td>\n",
       "      <td>[104649.9178305522, 116216.5982579751, 140785....</td>\n",
       "      <td>1.616480e+06</td>\n",
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       "      <th>2132</th>\n",
       "      <td>38.051937</td>\n",
       "      <td>-118.862982</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>6.289559</td>\n",
       "      <td>[115897.8822387333, 135441.9611633163, 167457....</td>\n",
       "      <td>[140.6816164551205, 165.7430776421407, 209.935...</td>\n",
       "      <td>[4.538116659842596, 5.919395630076456, 6.77210...</td>\n",
       "      <td>[122718.5112051391, 144540.066174457, 178085.4...</td>\n",
       "      <td>1.789784e+06</td>\n",
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       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>36.565825</td>\n",
       "      <td>-117.978507</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>6.658378</td>\n",
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       "      <td>[5.347496628437249, 6.157192458740993, 7.37047...</td>\n",
       "      <td>[141770.0925763517, 145643.535554575, 186725.2...</td>\n",
       "      <td>1.854907e+06</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>2924</th>\n",
       "      <td>41.317474</td>\n",
       "      <td>-120.631931</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.609309</td>\n",
       "      <td>[100112.9104320916, 107205.1854075825, 140681....</td>\n",
       "      <td>[118.4070288637792, 131.0064007636165, 174.189...</td>\n",
       "      <td>[3.819581576250942, 4.678800027272017, 5.61903...</td>\n",
       "      <td>[105089.1222966419, 113679.7550695216, 147897....</td>\n",
       "      <td>1.603751e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>34.610413</td>\n",
       "      <td>-116.964597</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>6.668318</td>\n",
       "      <td>[149162.238403233, 137938.623497123, 168315.74...</td>\n",
       "      <td>[183.102841582347, 171.9837657402796, 215.6461...</td>\n",
       "      <td>[5.906543276849902, 6.14227734786713, 6.956327...</td>\n",
       "      <td>[156760.9127243934, 145359.581589314, 176819.0...</td>\n",
       "      <td>1.861036e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>41.102379</td>\n",
       "      <td>-121.494834</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.724211</td>\n",
       "      <td>[101296.5085341618, 100270.9269323083, 141406....</td>\n",
       "      <td>[121.719166605095, 123.1725192125627, 176.5572...</td>\n",
       "      <td>[3.926424729196611, 4.399018543305812, 5.69539...</td>\n",
       "      <td>[106281.6886924296, 105161.6483851474, 148172....</td>\n",
       "      <td>1.612814e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>40.554864</td>\n",
       "      <td>-120.675076</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>5.856129</td>\n",
       "      <td>[106954.1577568898, 107414.3357440706, 150720....</td>\n",
       "      <td>[129.3927268200678, 131.8458939746107, 189.530...</td>\n",
       "      <td>[4.173958929679606, 4.708781927664667, 6.11389...</td>\n",
       "      <td>[112152.811041611, 112683.570282788, 158883.24...</td>\n",
       "      <td>1.647369e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>36.037864</td>\n",
       "      <td>-117.935362</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>elevation</td>\n",
       "      <td>6.645987</td>\n",
       "      <td>[131803.6371486934, 138895.017146305, 167836.1...</td>\n",
       "      <td>[164.8286815828727, 174.9319882140985, 214.166...</td>\n",
       "      <td>[5.317054244608797, 6.247571007646376, 6.90859...</td>\n",
       "      <td>[138112.8328342221, 145491.4584694412, 176480....</td>\n",
       "      <td>1.838535e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude   Longitude  Solar_farm_present  land_cover  slope  elevation  \\\n",
       "2129  40.359323 -120.631931                   0         1.0    2.0     1303.0   \n",
       "2130  40.378877 -120.373061                   0         1.0    1.0     1233.0   \n",
       "2131  40.985054 -120.890802                   0         1.0    0.0     1720.0   \n",
       "2132  38.051937 -118.862982                   0         1.0    1.0     1986.0   \n",
       "2133  36.565825 -117.978507                   0         1.0    0.0     1117.0   \n",
       "...         ...         ...                 ...         ...    ...        ...   \n",
       "2924  41.317474 -120.631931                   0         1.0    0.0     1496.0   \n",
       "2925  34.610413 -116.964597                   0         1.0    2.0      986.0   \n",
       "2926  41.102379 -121.494834                   0         1.0    0.0     1012.0   \n",
       "2927  40.554864 -120.675076                   0         1.0    2.0     1521.0   \n",
       "2928  36.037864 -117.935362                   0         1.0    0.0     1023.0   \n",
       "\n",
       "     feature_violated  solrad_annual  \\\n",
       "2129        elevation       5.800638   \n",
       "2130        elevation       5.837898   \n",
       "2131        elevation       5.669324   \n",
       "2132        elevation       6.289559   \n",
       "2133        elevation       6.658378   \n",
       "...               ...            ...   \n",
       "2924        elevation       5.609309   \n",
       "2925        elevation       6.668318   \n",
       "2926        elevation       5.724211   \n",
       "2927        elevation       5.856129   \n",
       "2928        elevation       6.645987   \n",
       "\n",
       "                                             ac_monthly  \\\n",
       "2129  [103351.3635653951, 100500.2908635284, 144547....   \n",
       "2130  [100852.3082413978, 106494.0689454024, 147919....   \n",
       "2131  [99722.61500801894, 109183.9367829212, 133277....   \n",
       "2132  [115897.8822387333, 135441.9611633163, 167457....   \n",
       "2133  [134765.4734673611, 138704.715076888, 178166.1...   \n",
       "...                                                 ...   \n",
       "2924  [100112.9104320916, 107205.1854075825, 140681....   \n",
       "2925  [149162.238403233, 137938.623497123, 168315.74...   \n",
       "2926  [101296.5085341618, 100270.9269323083, 141406....   \n",
       "2927  [106954.1577568898, 107414.3357440706, 150720....   \n",
       "2928  [131803.6371486934, 138895.017146305, 167836.1...   \n",
       "\n",
       "                                            poa_monthly  \\\n",
       "2129  [125.4477454750849, 123.7115319965838, 181.497...   \n",
       "2130  [122.2390688486897, 131.6625069065885, 186.520...   \n",
       "2131  [117.9534011360403, 133.9159953835858, 165.822...   \n",
       "2132  [140.6816164551205, 165.7430776421407, 209.935...   \n",
       "2133  [165.7723954815547, 172.4013888447478, 228.484...   \n",
       "...                                                 ...   \n",
       "2924  [118.4070288637792, 131.0064007636165, 174.189...   \n",
       "2925  [183.102841582347, 171.9837657402796, 215.6461...   \n",
       "2926  [121.719166605095, 123.1725192125627, 176.5572...   \n",
       "2927  [129.3927268200678, 131.8458939746107, 189.530...   \n",
       "2928  [164.8286815828727, 174.9319882140985, 214.166...   \n",
       "\n",
       "                                         solrad_monthly  \\\n",
       "2129  [4.046701466938223, 4.418268999877993, 5.85474...   \n",
       "2130  [3.943195769312569, 4.702232389521017, 6.01678...   \n",
       "2131  [3.804948423743235, 4.782714120842349, 5.34911...   \n",
       "2132  [4.538116659842596, 5.919395630076456, 6.77210...   \n",
       "2133  [5.347496628437249, 6.157192458740993, 7.37047...   \n",
       "...                                                 ...   \n",
       "2924  [3.819581576250942, 4.678800027272017, 5.61903...   \n",
       "2925  [5.906543276849902, 6.14227734786713, 6.956327...   \n",
       "2926  [3.926424729196611, 4.399018543305812, 5.69539...   \n",
       "2927  [4.173958929679606, 4.708781927664667, 6.11389...   \n",
       "2928  [5.317054244608797, 6.247571007646376, 6.90859...   \n",
       "\n",
       "                                             dc_monthly     ac_annual  \n",
       "2129  [108398.8087866204, 106465.7720028536, 152476....  1.622821e+06  \n",
       "2130  [105809.8420581117, 111629.9004175349, 155353....  1.630034e+06  \n",
       "2131  [104649.9178305522, 116216.5982579751, 140785....  1.616480e+06  \n",
       "2132  [122718.5112051391, 144540.066174457, 178085.4...  1.789784e+06  \n",
       "2133  [141770.0925763517, 145643.535554575, 186725.2...  1.854907e+06  \n",
       "...                                                 ...           ...  \n",
       "2924  [105089.1222966419, 113679.7550695216, 147897....  1.603751e+06  \n",
       "2925  [156760.9127243934, 145359.581589314, 176819.0...  1.861036e+06  \n",
       "2926  [106281.6886924296, 105161.6483851474, 148172....  1.612814e+06  \n",
       "2927  [112152.811041611, 112683.570282788, 158883.24...  1.647369e+06  \n",
       "2928  [138112.8328342221, 145491.4584694412, 176480....  1.838535e+06  \n",
       "\n",
       "[800 rows x 13 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_features_with_solar_data_check.loc[(ca_features_with_solar_data_check['feature_violated']=='elevation')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ff7e572d-a0af-490f-80bf-d780fc217ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              NaN\n",
       "1              NaN\n",
       "2              NaN\n",
       "3              NaN\n",
       "4              NaN\n",
       "           ...    \n",
       "4995    land_cover\n",
       "4996    land_cover\n",
       "4997    land_cover\n",
       "4998    land_cover\n",
       "4999    land_cover\n",
       "Name: feature_violated, Length: 5000, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_features_with_solar_data_check['feature_violated']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32cf235-1db4-461a-813e-527f6e8b7474",
   "metadata": {},
   "source": [
    "### Make a function to fetch solar data for a location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffe7a394-4761-48a4-a414-ee48c2b08c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_solar_data(latitude, longitude, radius_for_climate_data=100):\n",
    "    # Returns a dataframe with solar data. \n",
    "    # In order to use this function you have to import requests and pandas\n",
    "\n",
    "    # API Key\n",
    "    apikey = 'GNoTfD5IZWwIEz24zB5Wn0aEhDvNJSep5bwapzTI'\n",
    "    parameters = {\n",
    "        'format': 'json',\n",
    "        'system_capacity': 1000, # 1000kW = 1 MW, 1 MW or greater is considered utility-scale\n",
    "        'module_type': 0,       # 0- Standard module, 1- Premium, 2-Thin film\n",
    "        'losses': 14,           # Losses in percentage\n",
    "        'array_type': 0,        # Open Rack: Also known as ground mount.\n",
    "        'tilt': 40,\n",
    "        'azimuth': 180,         # This means that the solar array is facing South in the Northern Hemisphere\n",
    "        'lat': latitude,\n",
    "        'lon': longitude,\n",
    "        'dataset': 'nsrdb',     # tmy2 is 1960-1990, tmy3 is 1990-2005\n",
    "        'radius': radius_for_climate_data, # 0-Pick the station nearest to the given (lat,lon), e.g: 50 - 50 miles\n",
    "        'timeframe': 'monthly',\n",
    "        'api_key': apikey\n",
    "    }\n",
    "\n",
    "    url = 'https://developer.nrel.gov/api/pvwatts/v8'\n",
    "    response = requests.get(url, params=parameters)\n",
    "    data = response.json()\n",
    "\n",
    "    # Check if 'outputs' is in the data\n",
    "    if 'outputs' not in data:\n",
    "        print(f\"No data available for latitude {latitude} and longitude {longitude}\")\n",
    "        return pd.DataFrame() # Return an empty DataFrame if no data is available\n",
    "\n",
    "    output_data = data['outputs']\n",
    "    input_data  = data['inputs']\n",
    "\n",
    "    output_data['latitude']  = input_data['lat']\n",
    "    output_data['longitude'] = input_data['lon']\n",
    "\n",
    "    # Convert output_data to a DataFrame\n",
    "    df = pd.DataFrame([output_data])\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0353d59-368e-4f4f-96f0-546c4a45c177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_monthly</th>\n",
       "      <th>poa_monthly</th>\n",
       "      <th>solrad_monthly</th>\n",
       "      <th>dc_monthly</th>\n",
       "      <th>ac_annual</th>\n",
       "      <th>solrad_annual</th>\n",
       "      <th>capacity_factor</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[118764.6470668778, 120396.5739028037, 144716....</td>\n",
       "      <td>[142.5673628519303, 147.0683124880903, 181.586...</td>\n",
       "      <td>[4.598947188771946, 5.252439731717511, 5.85764...</td>\n",
       "      <td>[124653.1675803279, 127474.83090856, 152769.83...</td>\n",
       "      <td>1.500850e+06</td>\n",
       "      <td>5.276205</td>\n",
       "      <td>17.132995</td>\n",
       "      <td>40.0099983215332</td>\n",
       "      <td>-105.2600021362305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          ac_monthly  \\\n",
       "0  [118764.6470668778, 120396.5739028037, 144716....   \n",
       "\n",
       "                                         poa_monthly  \\\n",
       "0  [142.5673628519303, 147.0683124880903, 181.586...   \n",
       "\n",
       "                                      solrad_monthly  \\\n",
       "0  [4.598947188771946, 5.252439731717511, 5.85764...   \n",
       "\n",
       "                                          dc_monthly     ac_annual  \\\n",
       "0  [124653.1675803279, 127474.83090856, 152769.83...  1.500850e+06   \n",
       "\n",
       "   solrad_annual  capacity_factor          latitude           longitude  \n",
       "0       5.276205        17.132995  40.0099983215332  -105.2600021362305  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the fucntion\n",
    "lat_test = 40.0099983215332\n",
    "lon_test =  -105.2600021362305\n",
    "get_solar_data(lat_test,lon_test,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea8ca46d-3f21-4587-b6b2-d33df88abbf7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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