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 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b497c5a",
   "metadata": {},
   "source": [
    "# Generate footprint climatology and export results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1af91e",
   "metadata": {},
   "source": [
    "## Import Supporting Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90cc2bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import configparser"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718d7599",
   "metadata": {},
   "source": [
    "## Import Fluxfootprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4571e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(0, os.path.abspath(\"../../src\"))  # adjust path as needed\n",
    "\n",
    "from fluxfootprints import (\n",
    "    build_climatology,\n",
    "    summarize_periods, export_contours_gpkg, export_rasters_geotiff\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a1bfc5",
   "metadata": {},
   "source": [
    "## 1. Load site config and half-hourly data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17aa36d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_path = \"G:/Shared drives/UGS_Flux/Data_Processing/config/US-UTV.ini\"\n",
    "cfg = configparser.ConfigParser(interpolation=None)  \n",
    "cfg.read(config_path)\n",
    "lat = cfg.getfloat(\"METADATA\", \"station_latitude\")\n",
    "lon = cfg.getfloat(\"METADATA\", \"station_longitude\")\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"G:/Shared drives/UGS_Flux/Data_Processing/config/station_data/US-UTV_HH_202309271630_202507221300.csv\")\n",
    "df['datetime_start'] = pd.to_datetime(df['datetime_start'], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "df = df.set_index('datetime_start')\n",
    "df = df.sort_index()\n",
    "# Instrument & canopy heights (site-level constants if not in file)\n",
    "df[\"instr_height_m\"] = 3.66          # set your tower’s measurement height here (m)\n",
    "df[\"canopy_height_m\"] = 0.2         # set your vegetation height here (m)\n",
    "df[\"Z0_roughness\"] = 0.123 * df[\"canopy_height_m\"]  # roughness length ≈ 0.123*h_c\n",
    "df['date_time'] = df.index\n",
    "df['LE'] = df['LE_1_1_1']\n",
    "df['H'] = df['H_1_1_1']\n",
    "df['Rn'] = df['NETRAD_1_1_1']\n",
    "df['G'] = df['G_1_1_A']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602efecc",
   "metadata": {},
   "source": [
    "## 2. Build the footprint climatology object (computes f_2d per timestep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b314e918",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "clim = build_climatology(\n",
    "    df,\n",
    "    dx=5.0, dy=5.0,\n",
    "    domain=(-300, 300, -300, 300),   # 500 m domain at 5 m resolution\n",
    "    inst_height=3.66,\n",
    "    crop_height=0.5,\n",
    "    atm_bound_height=2000.0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e021b427",
   "metadata": {},
   "source": [
    "## 3. Summarize to daily & monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc7917dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\paulinkenbrandt\\.conda\\envs\\py313\\Lib\\site-packages\\xarray\\core\\computation.py:824: RuntimeWarning: overflow encountered in exp\n",
      "  result_data = func(*input_data)\n"
     ]
    }
   ],
   "source": [
    "summ = summarize_periods(\n",
    "    clim, df,\n",
    "    et_source=\"LE\",        # convert LE W/m² → ET mm/hr for weighting\n",
    "    daily=True,\n",
    "    monthly=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba55dc59",
   "metadata": {},
   "source": [
    "## 4. Export 50% and 80% footprint contours to a GeoPackage + CSV stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4e95f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('ffp_geotiffs')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_contours_gpkg(\n",
    "    clim, summ, df,\n",
    "    station_lat=lat,\n",
    "    station_lon=lon,\n",
    "    gpkg_path=\"US-CRT_footprints.gpkg\",\n",
    "    crs_out=\"auto\",            # auto-choose UTM zone for station\n",
    "    levels=[0.9],         # multiple contour levels\n",
    "    stats_csv=\"US-CRT_footprints_stats.csv\",\n",
    "    centroid_out=26912,        # NAD83 / UTM zone 12N\n",
    "    contour_method=\"skimage\",  # or \"rasterio\" / \"auto\"\n",
    "    rn_col=None, h_col=None,   # optionally set explicit column names\n",
    "    le_col=None, g_col=None    # (auto-detects if left None)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a080cdcf",
   "metadata": {},
   "source": [
    "## 5. Export footprint fields as GeoTIFF rasters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13bce00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "export_rasters_geotiff(\n",
    "    clim, summ,\n",
    "    station_lat=lat,\n",
    "    station_lon=lon,\n",
    "    out_dir=\"ffp_geotiffs\",\n",
    "    crs_out=\"auto\",\n",
    "    which=(\"daily_mean\", \"monthly_mean\", \"daily_etw\", \"monthly_etw\"),\n",
    "    prefix=\"US-UTV\"\n",
    ")"
   ]
  }
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