{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "653aa78b37452833",
   "metadata": {},
   "source": [
    "# Download NLDAS Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90147672",
   "metadata": {},
   "source": [
    "## Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d2f51fac865ad22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-30T01:41:48.189386Z",
     "start_time": "2025-08-30T01:41:48.096723Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import configparser\n",
    "import pathlib\n",
    "#import micromet\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbede0dcffafdabd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-30T01:41:51.597107Z",
     "start_time": "2025-08-30T01:41:50.549026Z"
    }
   },
   "outputs": [],
   "source": [
    "sys.path.append(\"../../src\")\n",
    "import fluxfootprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "57c5a8cb143a714f",
   "metadata": {},
   "outputs": [],
   "source": [
    "spath = \"../../secrets/config.ini\"\n",
    "sconfig = configparser.ConfigParser()\n",
    "sconfig.read(spath)\n",
    "\n",
    "ed_pass = sconfig['NLDAS']['pw']\n",
    "ed_user = sconfig['NLDAS']['user']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca7927e",
   "metadata": {},
   "source": [
    "To use this part of the module, you must register with the [NASA Earthdata Login Page](https://urs.earthdata.nasa.gov/home). The example below downloads hourly data from the site for North America."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d400b2cfe9af59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLDAS_data\\2021_01_01_00.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_01.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_02.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_03.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_04.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_05.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_06.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_07.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_08.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_09.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_10.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_11.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_12.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_13.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_14.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_15.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_16.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_17.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_18.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_19.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_20.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_21.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_22.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_01_23.nc already exists, not overwriting.\n",
      "NLDAS_data\\2021_01_02_00.nc already exists, not overwriting.\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "#nldas_out_dir = Path('C:/Users/paulinkenbrandt/Documents/GitHub/MicroMet/Notebooks/Footprints')\n",
    "\n",
    "\n",
    "for date in pd.date_range(start='2021-01-01', end='2021-01-02',freq='h'):\n",
    "    hour = date.hour\n",
    "    fluxfootprints.download_nldas(date,\n",
    "                           hour,\n",
    "                           ed_user,\n",
    "                           ed_pass,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dad3a970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 5MB\n",
      "Dimensions:      (lon: 464, lat: 224, time: 1, bnds: 2)\n",
      "Coordinates:\n",
      "  * lon          (lon) float32 2kB -124.9 -124.8 -124.7 ... -67.31 -67.19 -67.06\n",
      "  * lat          (lat) float32 896B 25.06 25.19 25.31 ... 52.69 52.81 52.94\n",
      "  * time         (time) datetime64[ns] 8B 2021-01-01\n",
      "Dimensions without coordinates: bnds\n",
      "Data variables:\n",
      "    time_bnds    (time, bnds) datetime64[ns] 16B ...\n",
      "    Tair         (time, lat, lon) float32 416kB ...\n",
      "    Qair         (time, lat, lon) float32 416kB ...\n",
      "    PSurf        (time, lat, lon) float32 416kB ...\n",
      "    Wind_E       (time, lat, lon) float32 416kB ...\n",
      "    Wind_N       (time, lat, lon) float32 416kB ...\n",
      "    LWdown       (time, lat, lon) float32 416kB ...\n",
      "    CRainf_frac  (time, lat, lon) float32 416kB ...\n",
      "    CAPE         (time, lat, lon) float32 416kB ...\n",
      "    PotEvap      (time, lat, lon) float32 416kB ...\n",
      "    Rainf        (time, lat, lon) float32 416kB ...\n",
      "    SWdown       (time, lat, lon) float32 416kB ...\n",
      "Attributes: (12/17)\n",
      "    missing_value:          -9999.0\n",
      "    time_definition:        hourly\n",
      "    shortname:              NLDAS_FORA0125_H_2.0\n",
      "    title:                  NLDAS Primary Forcing Data L4 Hourly 0.125 x 0.12...\n",
      "    version:                2.0\n",
      "    institution:            NASA GSFC\n",
      "    ...                     ...\n",
      "    websites:               https://ldas.gsfc.nasa.gov/nldas/ ; https://lis.g...\n",
      "    MAP_PROJECTION:         EQUIDISTANT CYLINDRICAL\n",
      "    SOUTH_WEST_CORNER_LAT:  25.0625\n",
      "    SOUTH_WEST_CORNER_LON:  -124.9375\n",
      "    DX:                     0.125\n",
      "    DY:                     0.125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x20454720550>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the NetCDF file\n",
    "ds = xr.open_dataset(\"./NLDAS_data/2021_01_01_00.nc\")\n",
    "\n",
    "# Inspect the dataset structure\n",
    "print(ds)\n",
    "\n",
    "var = ds['PotEvap']\n",
    "\n",
    "# Plot a 2D slice (e.g., first time step if it has a time dimension)\n",
    "var.isel(time=0).plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e686129",
   "metadata": {},
   "source": [
    "This function clips the downloaded data to the boundaries of Utah (USA), and lumps the data into efficient file formats labeled by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ad1f6bc067b1cc46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021\n",
      "NLDAS_data\\2021_01_01_00.nc\n",
      "NLDAS_data\\2021_01_01_01.nc\n",
      "NLDAS_data\\2021_01_01_02.nc\n",
      "NLDAS_data\\2021_01_01_03.nc\n",
      "NLDAS_data\\2021_01_01_04.nc\n",
      "NLDAS_data\\2021_01_01_05.nc\n",
      "NLDAS_data\\2021_01_01_06.nc\n",
      "NLDAS_data\\2021_01_01_07.nc\n",
      "NLDAS_data\\2021_01_01_08.nc\n",
      "NLDAS_data\\2021_01_01_09.nc\n",
      "NLDAS_data\\2021_01_01_10.nc\n",
      "NLDAS_data\\2021_01_01_11.nc\n",
      "NLDAS_data\\2021_01_01_12.nc\n",
      "NLDAS_data\\2021_01_01_13.nc\n",
      "NLDAS_data\\2021_01_01_14.nc\n",
      "NLDAS_data\\2021_01_01_15.nc\n",
      "NLDAS_data\\2021_01_01_16.nc\n",
      "NLDAS_data\\2021_01_01_17.nc\n",
      "NLDAS_data\\2021_01_01_18.nc\n",
      "NLDAS_data\\2021_01_01_19.nc\n",
      "NLDAS_data\\2021_01_01_20.nc\n",
      "NLDAS_data\\2021_01_01_21.nc\n",
      "NLDAS_data\\2021_01_01_22.nc\n",
      "NLDAS_data\\2021_01_01_23.nc\n",
      "NLDAS_data\\2021_01_02_00.nc\n"
     ]
    },
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\2021_utah_merged.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\2021_utah_merged.nc',), 'a', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '3d76a718-13bf-4c88-98bc-43cf7e6189ef']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mfluxfootprints\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclip_to_utah_merge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./NLDAS_data/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myears\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2021\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\Documents\\GitHub\\footprints\\docs\\notebooks\\../../src\\fluxfootprints\\volk.py:707\u001b[0m, in \u001b[0;36mclip_to_utah_merge\u001b[1;34m(file_dir, years, output_dir)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;66;03m# Save as NetCDF using a compatible format (default for xarray in this environment)\u001b[39;00m\n\u001b[0;32m    706\u001b[0m netcdf_output_path \u001b[38;5;241m=\u001b[39m output_dir \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00myear\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_utah_merged.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 707\u001b[0m \u001b[43mds_merged\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnetcdf_output_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;66;03m# Convert to Pandas DataFrame for Parquet format\u001b[39;00m\n\u001b[0;32m    710\u001b[0m df_parquet \u001b[38;5;241m=\u001b[39m ds_merged\u001b[38;5;241m.\u001b[39mto_dataframe()\u001b[38;5;241m.\u001b[39mreset_index()\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\core\\dataset.py:2380\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[1;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf, auto_complex)\u001b[0m\n\u001b[0;32m   2377\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m   2378\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[1;32m-> 2380\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[0;32m   2381\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2382\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2383\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2384\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2385\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2386\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2387\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2388\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2389\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2390\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   2391\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2392\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauto_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\api.py:1911\u001b[0m, in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf, auto_complex)\u001b[0m\n\u001b[0;32m   1908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m auto_complex \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1909\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_complex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m auto_complex\n\u001b[1;32m-> 1911\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mstore_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1913\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unlimited_dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1914\u001b[0m     unlimited_dims \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mencoding\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munlimited_dims\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:452\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[1;34m(cls, filename, mode, format, group, clobber, diskless, persist, auto_complex, lock, lock_maker, autoclose)\u001b[0m\n\u001b[0;32m    448\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_complex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m auto_complex\n\u001b[0;32m    449\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[0;32m    450\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[0;32m    451\u001b[0m )\n\u001b[1;32m--> 452\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:393\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[1;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[0;32m    391\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[0;32m    392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m--> 393\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[0;32m    394\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[0;32m    395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:461\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m    460\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 461\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:455\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    454\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 455\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[0;32m    456\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[0;32m    457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\contextlib.py:141\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[0;32m    140\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 141\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    142\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[0;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[1;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2521\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2158\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\2021_utah_merged.nc'"
     ]
    }
   ],
   "source": [
    "fluxfootprints.clip_to_utah_merge(file_dir=\"./NLDAS_data/\", years = [2021])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c93ff2e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 2MB\n",
      "Dimensions:      (time: 25, bnds: 2, lat: 40, lon: 40)\n",
      "Coordinates:\n",
      "  * lon          (lon) float32 160B -113.9 -113.8 -113.7 ... -109.2 -109.1\n",
      "  * lat          (lat) float32 160B 37.06 37.19 37.31 ... 41.69 41.81 41.94\n",
      "  * time         (time) datetime64[ns] 200B 2021-01-01 ... 2021-01-02\n",
      "Dimensions without coordinates: bnds\n",
      "Data variables:\n",
      "    time_bnds    (time, bnds) datetime64[ns] 400B ...\n",
      "    Tair         (time, lat, lon) float32 160kB ...\n",
      "    Qair         (time, lat, lon) float32 160kB ...\n",
      "    PSurf        (time, lat, lon) float32 160kB ...\n",
      "    Wind_E       (time, lat, lon) float32 160kB ...\n",
      "    Wind_N       (time, lat, lon) float32 160kB ...\n",
      "    LWdown       (time, lat, lon) float32 160kB ...\n",
      "    CRainf_frac  (time, lat, lon) float32 160kB ...\n",
      "    CAPE         (time, lat, lon) float32 160kB ...\n",
      "    PotEvap      (time, lat, lon) float32 160kB ...\n",
      "    Rainf        (time, lat, lon) float32 160kB ...\n",
      "    SWdown       (time, lat, lon) float32 160kB ...\n",
      "Attributes: (12/17)\n",
      "    missing_value:          -9999.0\n",
      "    time_definition:        hourly\n",
      "    shortname:              NLDAS_FORA0125_H_2.0\n",
      "    title:                  NLDAS Primary Forcing Data L4 Hourly 0.125 x 0.12...\n",
      "    version:                2.0\n",
      "    institution:            NASA GSFC\n",
      "    ...                     ...\n",
      "    websites:               https://ldas.gsfc.nasa.gov/nldas/ ; https://lis.g...\n",
      "    MAP_PROJECTION:         EQUIDISTANT CYLINDRICAL\n",
      "    SOUTH_WEST_CORNER_LAT:  25.0625\n",
      "    SOUTH_WEST_CORNER_LON:  -124.9375\n",
      "    DX:                     0.125\n",
      "    DY:                     0.125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x20451b034d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the NetCDF file\n",
    "ds = xr.open_dataset(\"2021_utah_merged.nc\")\n",
    "\n",
    "# Inspect the dataset structure\n",
    "print(ds)\n",
    "\n",
    "var = ds['PotEvap']\n",
    "\n",
    "# Plot a 2D slice (e.g., first time step if it has a time dimension)\n",
    "var.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a457b7a5",
   "metadata": {},
   "source": [
    "This .nc file includes a variable called ETo (reference evapotranspiration), defined over four dimensions: elevation, time, lat, and lon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2b0929a53d47d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021\n"
     ]
    },
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\output\\\\2021_with_eto.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\output\\\\2021_with_eto.nc',), 'a', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'd70bee1a-0c6c-4870-81ee-eba444716d9d']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mfluxfootprints\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalc_hourly_ffp_xr\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_data_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[43m                            \u001b[49m\u001b[43myears\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2021\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m                            \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./output/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\Documents\\GitHub\\footprints\\docs\\notebooks\\../../src\\fluxfootprints\\volk.py:1054\u001b[0m, in \u001b[0;36mcalc_hourly_ffp_xr\u001b[1;34m(input_data_dir, years, output_dir)\u001b[0m\n\u001b[0;32m   1051\u001b[0m ds \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39massign(ETr\u001b[38;5;241m=\u001b[39metr_da)\n\u001b[0;32m   1053\u001b[0m \u001b[38;5;66;03m# Save the modified dataset (Optional)\u001b[39;00m\n\u001b[1;32m-> 1054\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43myear\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_with_eto.nc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\core\\dataset.py:2380\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[1;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf, auto_complex)\u001b[0m\n\u001b[0;32m   2377\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m   2378\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[1;32m-> 2380\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[0;32m   2381\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2382\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2383\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2384\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2385\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2386\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2387\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2388\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2389\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2390\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   2391\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2392\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauto_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\api.py:1911\u001b[0m, in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf, auto_complex)\u001b[0m\n\u001b[0;32m   1908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m auto_complex \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1909\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_complex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m auto_complex\n\u001b[1;32m-> 1911\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mstore_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1913\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unlimited_dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1914\u001b[0m     unlimited_dims \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mencoding\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munlimited_dims\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:452\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[1;34m(cls, filename, mode, format, group, clobber, diskless, persist, auto_complex, lock, lock_maker, autoclose)\u001b[0m\n\u001b[0;32m    448\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_complex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m auto_complex\n\u001b[0;32m    449\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[0;32m    450\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[0;32m    451\u001b[0m )\n\u001b[1;32m--> 452\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:393\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[1;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[0;32m    391\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[0;32m    392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m--> 393\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[0;32m    394\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[0;32m    395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:461\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m    460\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 461\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:455\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    454\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 455\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[0;32m    456\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[0;32m    457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\contextlib.py:141\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[0;32m    140\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 141\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    142\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[0;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[1;32mc:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\backends\\file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[1;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2521\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2158\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'c:\\\\Users\\\\paulinkenbrandt\\\\Documents\\\\GitHub\\\\footprints\\\\docs\\\\notebooks\\\\output\\\\2021_with_eto.nc'"
     ]
    }
   ],
   "source": [
    "fluxfootprints.calc_hourly_ffp_xr(input_data_dir=\".\",\n",
    "                            years = [2021],\n",
    "                            output_dir=\"./output/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60476e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the NetCDF file\n",
    "ds = xr.open_dataset(\"./output/2021_with_eto.nc\")\n",
    "\n",
    "# Target location and elevation\n",
    "target_lat = 39.5\n",
    "target_lon = -111.8\n",
    "target_elev = 1500\n",
    "\n",
    "# Find nearest indices\n",
    "lat_idx = ds.lat.sel(lat=target_lat, method=\"nearest\")\n",
    "lon_idx = ds.lon.sel(lon=target_lon, method=\"nearest\")\n",
    "elev_idx = ds.elevation.sel(elevation=target_elev, method=\"nearest\")\n",
    "\n",
    "# Extract the time series\n",
    "eto_timeseries = ds.ETo.sel(lat=lat_idx, lon=lon_idx, elevation=elev_idx)\n",
    "\n",
    "# Plot the time series\n",
    "eto_timeseries.plot()\n",
    "plt.title(f\"ETo Time Series at {float(lat_idx):.2f}, {float(lon_idx):.2f}, {int(elev_idx)} m\")\n",
    "plt.ylabel(\"ETo (units)\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0877e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose time and elevation\n",
    "time_point = \"2021-01-01T18:00\"\n",
    "elev_idx = ds.elevation.sel(elevation=1500, method=\"nearest\")\n",
    "\n",
    "# Extract the raster\n",
    "eto_raster = ds.ETo.sel(time=time_point, elevation=elev_idx)\n",
    "\n",
    "# Plot raster\n",
    "eto_raster.plot()\n",
    "plt.title(f\"ETo Raster at {time_point}, Elevation {int(elev_idx)} m\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ee7f5b5e7b1f372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US-UTE\n",
      "US-UTW\n"
     ]
    }
   ],
   "source": [
    "fluxfootprints.extract_nldas_xr_to_df([2021],\n",
    "                                      config_path=\"../../data/station_config/\", \n",
    "                                      input_path=\"./output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "541d8b9726c25e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path = pathlib.Path(\"./output/\")\n",
    "eto_df = pd.read_parquet(output_path / 'nldas_all.parquet')\n",
    "normed_df = fluxfootprints.normalize_eto_df(eto_df)\n",
    "normed_df.to_parquet(output_path / 'nldas_all_normed.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26833d8c92de997",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path = pathlib.Path(\"./output/\")\n",
    "\n",
    "gpds = {}\n",
    "\n",
    "for raster in output_path.glob('*weighted.tif'):\n",
    "\n",
    "    date = pd.to_datetime(raster.stem.split('_')[0])\n",
    "    gpds[f\"{date:%Y-%m-%d}\"] = micromet.outline_valid_cells(raster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4d8d44005fe57b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "footprints = pd.concat(gpds)\n",
    "footprints['stationid'] = 'US-UTW'\n",
    "footprints = footprints.reset_index().rename(columns={'level_0':'date'}).drop(['level_1'],axis=1)\n",
    "footprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28a74202570e7df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import configparser\n",
    "config = configparser.ConfigParser()\n",
    "\n",
    "config.read('../../secrets/config.ini')\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "import urllib.parse\n",
    "host = config['DEFAULT']['ip']\n",
    "pw = config['DEFAULT']['pw']\n",
    "user = config['DEFAULT']['login']\n",
    "\n",
    "encoded_password = urllib.parse.quote_plus(pw)\n",
    "\n",
    "def postconn_et(encoded_password, host='localhost',user=user,port='5432',db='groundwater', schema = 'groundwater'):\n",
    "    connection_text = \"postgresql+psycopg2://{:}:{:}@{:}:{:}/{:}?gssencmode=disable\".format(user,encoded_password,host,port,db)\n",
    "    return create_engine(connection_text, connect_args={'options': '-csearch_path={}'.format(schema)})\n",
    "\n",
    "\n",
    "engine = postconn_et(encoded_password, host=host, user=user)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "629582d94385acfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "footprints.to_file(\"daily_footprint_areas.geojson\",driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77f2c26da1cedd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Export to PostGIS table\n",
    "table_name = \"daily_footprint_areas\"\n",
    "footprints.to_postgis(\n",
    "    name=table_name,\n",
    "    con=engine,\n",
    "    if_exists=\"replace\",   # or \"append\"\n",
    "    index=False,           # do not write pandas index as a column\n",
    "    #geom_col='geometry',     # use a custom geometry column name if desired\n",
    ")\n",
    "\n",
    "print(f\"GeoDataFrame successfully exported to '{table_name}' in database.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b53ef4757b3bb48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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