{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo of USGS-MAP-gwmodels\n",
    "This page demonstrates some of the core features of USGS-MAP-gwmodel obs, applied to the Mississippi Delta inset model area. Specifically:\n",
    "\n",
    "* setup of model layering and property zones by combining voxel-based hydrogeologic facies derived from an airborne electromagnetic (AEM) survey (Burton and others, 2020) with traditional raster surfaces denoting hydrogeologic contacts (Hart and others, 2008)\n",
    "* culling of NetCDF-based infiltration data (Westenbroek and others, 2020) to the model area, and aggregation to monthly values\n",
    "* preprocessing of streamflow timeseries for use as specified inflows to the SFR package, or streamflow observations\n",
    "* preprocessing of head observations\n",
    "* preprocessing of water use data, including:\n",
    "    * estimates of agricultural water use (Wilson, 2020)\n",
    "    * non-agricultulral water use data from the USGS\n",
    "\n",
    "[Cited References](https://aleaf.github.io/usgs-map-gwmodels/references.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mfsetup.grid import MFsetupGrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define some global inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_path = Path('../mapgwm/tests/data')\n",
    "output_folder = Path('output')\n",
    "output_folder.mkdir(exist_ok=True)  # make the output folder if it doesn't exist\n",
    "\n",
    "# ms delta geographic extent\n",
    "delta_extent = test_data_path / 'extents/ms_delta.shp'\n",
    "\n",
    "# simulation start date\n",
    "start_date='1998-04-01'\n",
    "\n",
    "# model grid for the MS Delta inset model\n",
    "ncol=270\n",
    "nrow=605\n",
    "dxy=500.\n",
    "delr = np.ones(ncol) * dxy\n",
    "delc = np.ones(nrow) * dxy\n",
    "delta_inset_model_grid = MFsetupGrid(delc, delr, top=None, botm=None,\n",
    "                                     lenuni=2, epsg=5070,\n",
    "                                     xoff=434955, yoff=1040285, angrot=0.0)\n",
    "\n",
    "# shapefile with polygons to group observations by geographic area\n",
    "geographic_groups = [test_data_path / 'extents/CompositeHydrographArea.shp',\n",
    "                     test_data_path / 'extents/MAP_generalized_regions.shp'\n",
    "                     ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the model layering and property zones\n",
    "from AEM-based voxel data and raster surfaces of hydrogeologic contacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean dem values for each 1 km gridcell, elevation units of feet\n",
    "dem_means_raster = test_data_path / 'rasters/dem_mean_elevs_1000.tif'\n",
    "\n",
    "# AEM electrical resistivity-based facies classes from tempest and resolve surveys\n",
    "# (All facies classes version)\n",
    "facies_classes_netcdf = test_data_path / 'netcdf/RSTM_res_fac_depth_15m.nc'\n",
    "\n",
    "# Original MERAS framework (1 mi resolution), elevation units of feet\n",
    "framework_rasters = [\n",
    "    test_data_path / 'rasters/vkbg_surf.tif',  # Vicksburg-Jackson Group (top)\n",
    "    test_data_path / 'rasters/ucaq_surf.tif',  # Upper Claiborne aquifer (top)\n",
    "    test_data_path / 'rasters/mccu_surf.tif',  # Middle Claiborne confining unit (t\n",
    "    test_data_path / 'rasters/mcaq_surf.tif',  # Middle Claiborne aquifer (top)\n",
    "    test_data_path / 'rasters/lccu_surf.tif',  # Lower Claiborne confining unit (to\n",
    "    test_data_path / 'rasters/lcaq_surf.tif',  # Lower Claiborne aquifer (top)\n",
    "    test_data_path / 'rasters/mwaq_surf.tif',  # Middle Wilcox aquifer (top)\n",
    "    test_data_path / 'rasters/lwaq_surf.tif',  # Lower Wilcox aquifer (top)\n",
    "    test_data_path / 'rasters/mdwy_surf.tif',  # Midway confining unit (top)\n",
    "]\n",
    "framework_unit_names = [\n",
    "    'Undifferentiated sediments\\nabove the Vicksburg',\n",
    "    'Vicksburg-Jackson Group',\n",
    "    'Upper Claiborne aquifer',\n",
    "    'Middle Claiborne confining unit',\n",
    "    'Middle Claiborne aquifer',\n",
    "    'Lower Claiborne confining unit',\n",
    "    'Lower Claiborne aquifer',\n",
    "    'Middle Wilcox aquifer',\n",
    "    'Lower Wilcox aquifer'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading data from ../mapgwm/tests/data/rasters/dem_mean_elevs_1000.tif...\n",
      "finished in 0.06s\n",
      "reading data from ../mapgwm/tests/data/rasters/vkbg_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/ucaq_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/mccu_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/mcaq_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/lccu_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/lcaq_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/mwaq_surf.tif...\n",
      "finished in 0.03s\n",
      "reading data from ../mapgwm/tests/data/rasters/lwaq_surf.tif...\n",
      "finished in 0.02s\n",
      "reading data from ../mapgwm/tests/data/rasters/mdwy_surf.tif...\n",
      "finished in 0.03s\n",
      "computing cell thicknesses...\n",
      "finished in 6.46s\n",
      "\n",
      "computing cell thicknesses...\n",
      "finished in 14.16s\n",
      "\n",
      "wrote output/botm_array/model_top.tif\n",
      "wrote output/botm_array/botm0.tif\n",
      "wrote output/botm_array/botm1.tif\n",
      "wrote output/botm_array/botm2.tif\n",
      "wrote output/botm_array/botm3.tif\n",
      "wrote output/botm_array/botm4.tif\n",
      "wrote output/botm_array/botm5.tif\n",
      "wrote output/botm_array/botm6.tif\n",
      "wrote output/botm_array/botm7.tif\n",
      "wrote output/botm_array/botm8.tif\n",
      "wrote output/botm_array/botm9.tif\n",
      "wrote output/botm_array/botm10.tif\n",
      "wrote output/botm_array/botm11.tif\n",
      "wrote output/botm_array/botm12.tif\n",
      "wrote output/botm_array/botm13.tif\n",
      "wrote output/botm_array/botm14.tif\n",
      "wrote output/botm_array/botm15.tif\n",
      "wrote output/botm_array/botm16.tif\n",
      "wrote output/botm_array/botm17.tif\n",
      "wrote output/botm_array/botm18.tif\n",
      "wrote output/botm_array/botm19.tif\n",
      "wrote output/botm_array/botm20.tif\n",
      "wrote output/botm_array/botm21.tif\n",
      "wrote output/botm_array/botm22.tif\n",
      "wrote output/botm_array/botm23.tif\n",
      "wrote output/botm_array/botm24.tif\n",
      "wrote output/botm_array/botm25.tif\n",
      "wrote output/botm_array/botm26.tif\n",
      "wrote output/botm_array/botm27.tif\n",
      "wrote output/botm_array/botm28.tif\n",
      "wrote output/botm_array/botm29.tif\n",
      "wrote output/botm_array/botm30.tif\n",
      "wrote output/zones/rasters/res_fac0.tif\n",
      "wrote output/zones/rasters/res_fac1.tif\n",
      "wrote output/zones/rasters/res_fac2.tif\n",
      "wrote output/zones/rasters/res_fac3.tif\n",
      "wrote output/zones/rasters/res_fac4.tif\n",
      "wrote output/zones/rasters/res_fac5.tif\n",
      "wrote output/zones/rasters/res_fac6.tif\n",
      "wrote output/zones/rasters/res_fac7.tif\n",
      "wrote output/zones/rasters/res_fac8.tif\n",
      "wrote output/zones/rasters/res_fac9.tif\n",
      "wrote output/zones/rasters/res_fac10.tif\n",
      "wrote output/zones/rasters/res_fac11.tif\n",
      "wrote output/zones/rasters/res_fac12.tif\n",
      "wrote output/zones/rasters/res_fac13.tif\n",
      "wrote output/zones/rasters/res_fac14.tif\n",
      "wrote output/zones/rasters/res_fac15.tif\n",
      "wrote output/zones/rasters/res_fac16.tif\n",
      "wrote output/zones/rasters/res_fac17.tif\n",
      "wrote output/zones/rasters/res_fac18.tif\n",
      "wrote output/zones/rasters/res_fac19.tif\n",
      "wrote output/zones/rasters/res_fac20.tif\n",
      "wrote output/zones/rasters/res_fac21.tif\n",
      "wrote output/zones/rasters/res_fac22.tif\n",
      "wrote output/zones/rasters/res_fac23.tif\n"
     ]
    }
   ],
   "source": [
    "from mapgwm.framework import setup_model_layers, plot_slice\n",
    "\n",
    "layers, zone_array = setup_model_layers(dem_means_raster,\n",
    "                                        facies_classes_netcdf,\n",
    "                                        framework_rasters,\n",
    "                                        delta_inset_model_grid,\n",
    "                                        facies_class_variable='fac_a',  # variable in facies_classes_netcdf with facies zones\n",
    "                                        dem_elevation_units='feet',\n",
    "                                        framework_raster_elevation_units='feet',\n",
    "                                        model_length_units='meters', output_folder=output_folder,\n",
    "                                        framework_unit_names=framework_unit_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a cross section slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "framework_unit_labels = dict(zip(range(13, 32), framework_unit_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Row 100'}, xlabel='Column in model', ylabel='Elevation, in meters'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_slice(layers, property_data=zone_array,\n",
    "           row=100, column=slice(None),\n",
    "           voxel_start_layer=0, voxel_zones=np.arange(1, 21), cmap='copper',\n",
    "           voxel_cmap='viridis', unit_labels=framework_unit_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess recharge\n",
    "Chop the SWB source data down to the model area and aggregate daily values to monthly means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using dask...\n",
      "opening ../mapgwm/tests/data/swb/delta_swb_summer2018.nc...\n",
      "culling to bounding box: (434955.0, 1040285.0, 569955.0, 1342785.0)\n",
      "Aggregating to monthly values...\n",
      "wrote output/swb_monthly_means.nc\n",
      "took 0.18s\n",
      "\n",
      "checking compressed file\n",
      "finished in 0.24s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from mapgwm.swb import get_monthly_means\n",
    "\n",
    "data_file = test_data_path / 'swb/delta_swb_summer2018.nc'\n",
    "\n",
    "# output\n",
    "outputfile = output_folder / 'swb_monthly_means.nc'\n",
    "\n",
    "# get the monthly means\n",
    "get_monthly_means(data_file, outfile=outputfile,\n",
    "                  filter=delta_inset_model_grid.bounds, check_results=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with xr.open_dataset(outputfile) as ds:\n",
    "    fig, ax = plt.subplots(figsize=(11, 8.5))\n",
    "    ds['net_infiltration'].sel(time='2018-08').plot(ax=ax, vmax=ds['net_infiltration'].quantile(0.95))\n",
    "    ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess streamflow data for specified inflows to the SFR package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.swflows import preprocess_flows\n",
    "\n",
    "streamflow_data_file = test_data_path / 'swflows/13Feb2020_rf_output_with_site_numbers.csv'\n",
    "streamflow_data = pd.read_csv(streamflow_data_file)\n",
    "\n",
    "# only include flows that are inflows (exclude observations)\n",
    "include_line_ids = set(streamflow_data.loc[streamflow_data.group == 'meras-inflows', 'comid'])\n",
    "\n",
    "outfile = output_folder / 'processed_inflows.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>site_no</th>\n",
       "      <th>comid</th>\n",
       "      <th>datetime</th>\n",
       "      <th>name</th>\n",
       "      <th>group</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>predicted_bf</th>\n",
       "      <th>predicted_total_flow</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07277700</td>\n",
       "      <td>15251676</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>HICKAHALA CREEK NR SENATOBIA, MS</td>\n",
       "      <td>meras-observations</td>\n",
       "      <td>-89.7839</td>\n",
       "      <td>34.6394</td>\n",
       "      <td>82.1099</td>\n",
       "      <td>107.1910</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07277700</td>\n",
       "      <td>15251676</td>\n",
       "      <td>2007-03-01</td>\n",
       "      <td>HICKAHALA CREEK NR SENATOBIA, MS</td>\n",
       "      <td>meras-observations</td>\n",
       "      <td>-89.7839</td>\n",
       "      <td>34.6394</td>\n",
       "      <td>75.3402</td>\n",
       "      <td>85.2905</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07277700</td>\n",
       "      <td>15251676</td>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>HICKAHALA CREEK NR SENATOBIA, MS</td>\n",
       "      <td>meras-observations</td>\n",
       "      <td>-89.7839</td>\n",
       "      <td>34.6394</td>\n",
       "      <td>46.6768</td>\n",
       "      <td>80.6870</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07277700</td>\n",
       "      <td>15251676</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>HICKAHALA CREEK NR SENATOBIA, MS</td>\n",
       "      <td>meras-observations</td>\n",
       "      <td>-89.7839</td>\n",
       "      <td>34.6394</td>\n",
       "      <td>41.8452</td>\n",
       "      <td>49.2666</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07277700</td>\n",
       "      <td>15251676</td>\n",
       "      <td>2007-06-01</td>\n",
       "      <td>HICKAHALA CREEK NR SENATOBIA, MS</td>\n",
       "      <td>meras-observations</td>\n",
       "      <td>-89.7839</td>\n",
       "      <td>34.6394</td>\n",
       "      <td>37.1356</td>\n",
       "      <td>47.5411</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    site_no     comid    datetime                              name  \\\n",
       "0  07277700  15251676  2007-02-01  HICKAHALA CREEK NR SENATOBIA, MS   \n",
       "1  07277700  15251676  2007-03-01  HICKAHALA CREEK NR SENATOBIA, MS   \n",
       "2  07277700  15251676  2007-04-01  HICKAHALA CREEK NR SENATOBIA, MS   \n",
       "3  07277700  15251676  2007-05-01  HICKAHALA CREEK NR SENATOBIA, MS   \n",
       "4  07277700  15251676  2007-06-01  HICKAHALA CREEK NR SENATOBIA, MS   \n",
       "\n",
       "                group        X        Y  predicted_bf  predicted_total_flow  \\\n",
       "0  meras-observations -89.7839  34.6394       82.1099              107.1910   \n",
       "1  meras-observations -89.7839  34.6394       75.3402               85.2905   \n",
       "2  meras-observations -89.7839  34.6394       46.6768               80.6870   \n",
       "3  meras-observations -89.7839  34.6394       41.8452               49.2666   \n",
       "4  meras-observations -89.7839  34.6394       37.1356               47.5411   \n",
       "\n",
       "    category  \n",
       "0  estimated  \n",
       "1  estimated  \n",
       "2  estimated  \n",
       "3  estimated  \n",
       "4  estimated  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streamflow_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing output/processed_inflows_info.shp... Done\n",
      "writing output/processed_inflows_info.csv\n",
      "writing output/processed_inflows.csv\n"
     ]
    }
   ],
   "source": [
    "data, metadata = preprocess_flows(streamflow_data, flow_data_columns=['predicted_total_flow', 'predicted_bf'],\n",
    "                                  include_line_ids=include_line_ids,\n",
    "                                  start_date=start_date,\n",
    "                                  datetime_col='datetime', \n",
    "                                  site_no_col='site_no',\n",
    "                                  line_id_col='comid',\n",
    "                                  x_coord_col='X',\n",
    "                                  y_coord_col='Y',\n",
    "                                  outfile=outfile\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>site_no</th>\n",
       "      <th>line_id</th>\n",
       "      <th>datetime</th>\n",
       "      <th>predicted_total_flow</th>\n",
       "      <th>predicted_bf</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-02-01</th>\n",
       "      <td>none</td>\n",
       "      <td>19274790</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>5.175779e+06</td>\n",
       "      <td>4.458664e+06</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-01</th>\n",
       "      <td>none</td>\n",
       "      <td>19274790</td>\n",
       "      <td>2007-03-01</td>\n",
       "      <td>2.497954e+06</td>\n",
       "      <td>1.873441e+06</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>none</td>\n",
       "      <td>19274790</td>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>3.820059e+06</td>\n",
       "      <td>2.017639e+06</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-01</th>\n",
       "      <td>none</td>\n",
       "      <td>19274790</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>2.154971e+06</td>\n",
       "      <td>1.052037e+06</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-01</th>\n",
       "      <td>none</td>\n",
       "      <td>19274790</td>\n",
       "      <td>2007-06-01</td>\n",
       "      <td>1.437397e+06</td>\n",
       "      <td>5.956531e+05</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           site_no   line_id    datetime  predicted_total_flow  predicted_bf  \\\n",
       "datetime                                                                       \n",
       "2007-02-01    none  19274790  2007-02-01          5.175779e+06  4.458664e+06   \n",
       "2007-03-01    none  19274790  2007-03-01          2.497954e+06  1.873441e+06   \n",
       "2007-04-01    none  19274790  2007-04-01          3.820059e+06  2.017639e+06   \n",
       "2007-05-01    none  19274790  2007-05-01          2.154971e+06  1.052037e+06   \n",
       "2007-06-01    none  19274790  2007-06-01          1.437397e+06  5.956531e+05   \n",
       "\n",
       "            category  \n",
       "datetime              \n",
       "2007-02-01  measured  \n",
       "2007-03-01  measured  \n",
       "2007-04-01  measured  \n",
       "2007-05-01  measured  \n",
       "2007-06-01  measured  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>start_dt</th>\n",
       "      <th>site_no</th>\n",
       "      <th>end_dt</th>\n",
       "      <th>n</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>line_id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>obsprefix</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>none</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>438600.380812</td>\n",
       "      <td>1.105540e+06</td>\n",
       "      <td>19274790</td>\n",
       "      <td>Tensas River</td>\n",
       "      <td>POINT (438600.3808115814 1105539.758365622)</td>\n",
       "      <td>none</td>\n",
       "      <td>fluxes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_dt site_no      end_dt    n              x             y  \\\n",
       "site_no                                                                     \n",
       "none     2007-02-01    none  2018-12-01  143  438600.380812  1.105540e+06   \n",
       "\n",
       "          line_id          name                                     geometry  \\\n",
       "site_no                                                                        \n",
       "none     19274790  Tensas River  POINT (438600.3808115814 1105539.758365622)   \n",
       "\n",
       "        obsprefix   group  \n",
       "site_no                    \n",
       "none         none  fluxes  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess streamflow data for SFR package observations\n",
    "* Unlike the inflows, which are referenced by NHDPlus COMID, these all have site numbers\n",
    "* Additionally, we want to fill gaps in measured flow data with statistical estimates\n",
    "* The ``data`` DataFrame outputs have the time series; \n",
    "* The ``metadata`` DataFrame outputs have site information that can be input to **``modflow-setup``** to set up the observation input for MODFLOW-6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only include flows that are inflows (exclude observations)\n",
    "include_sites = set(streamflow_data.loc[streamflow_data.group == 'meras-observations', 'site_no'])\n",
    "\n",
    "outfile = output_folder / 'processed_flow_obs.csv'\n",
    "\n",
    "# rename these columns, if present\n",
    "column_renames = {'predicted_total_flow': 'qtotal_m3d',\n",
    "                  'predicted_bf': 'qbase_m3d',\n",
    "                  'qbase_cfs': 'qbase_m3d',\n",
    "                  'q_cfs': 'qtotal_m3d',\n",
    "                  'station_nm': 'name'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess the statistical estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 7 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/MAP_generalized_regions.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/CompositeHydrographArea.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n"
     ]
    }
   ],
   "source": [
    "est_data, est_metadata = preprocess_flows(streamflow_data, flow_data_columns=['predicted_total_flow', 'predicted_bf'],\n",
    "                                          include_sites=include_sites,\n",
    "                                          start_date=start_date,\n",
    "                                          active_area=delta_extent,\n",
    "                                          datetime_col='datetime', \n",
    "                                          site_no_col='site_no',\n",
    "                                          line_id_col='comid',\n",
    "                                          x_coord_col='X',\n",
    "                                          y_coord_col='Y',\n",
    "                                          geographic_groups=geographic_groups,\n",
    "                                          geographic_groups_col='obsgroup',\n",
    "                                          flow_qualifier_column='category',\n",
    "                                          source_crs=4269,\n",
    "                                          dest_crs=5070,\n",
    "                                          source_volume_units='ft3',\n",
    "                                          source_time_units='s',\n",
    "                                          dest_volume_units='m3',\n",
    "                                          dest_time_units='d',\n",
    "                                          column_renames=column_renames,\n",
    "                                          outfile=None\n",
    "                                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>site_no</th>\n",
       "      <th>line_id</th>\n",
       "      <th>datetime</th>\n",
       "      <th>qtotal_m3d</th>\n",
       "      <th>qbase_m3d</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-02-01</th>\n",
       "      <td>07288847</td>\n",
       "      <td>17929890</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>659171.062935</td>\n",
       "      <td>564867.808532</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-01</th>\n",
       "      <td>07288847</td>\n",
       "      <td>17929890</td>\n",
       "      <td>2007-03-01</td>\n",
       "      <td>316339.771464</td>\n",
       "      <td>278767.710811</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>07288847</td>\n",
       "      <td>17929890</td>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>328301.079306</td>\n",
       "      <td>179244.687481</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-01</th>\n",
       "      <td>07288847</td>\n",
       "      <td>17929890</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>632789.638827</td>\n",
       "      <td>216849.532247</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-01</th>\n",
       "      <td>07288847</td>\n",
       "      <td>17929890</td>\n",
       "      <td>2007-06-01</td>\n",
       "      <td>250184.368712</td>\n",
       "      <td>130957.604569</td>\n",
       "      <td>estimated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             site_no   line_id    datetime     qtotal_m3d      qbase_m3d  \\\n",
       "datetime                                                                   \n",
       "2007-02-01  07288847  17929890  2007-02-01  659171.062935  564867.808532   \n",
       "2007-03-01  07288847  17929890  2007-03-01  316339.771464  278767.710811   \n",
       "2007-04-01  07288847  17929890  2007-04-01  328301.079306  179244.687481   \n",
       "2007-05-01  07288847  17929890  2007-05-01  632789.638827  216849.532247   \n",
       "2007-06-01  07288847  17929890  2007-06-01  250184.368712  130957.604569   \n",
       "\n",
       "             category  \n",
       "datetime               \n",
       "2007-02-01  estimated  \n",
       "2007-03-01  estimated  \n",
       "2007-04-01  estimated  \n",
       "2007-05-01  estimated  \n",
       "2007-06-01  estimated  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_dt</th>\n",
       "      <th>site_no</th>\n",
       "      <th>end_dt</th>\n",
       "      <th>n</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>line_id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>obsprefix</th>\n",
       "      <th>group</th>\n",
       "      <th>geo_group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07288000</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>07288000</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>496047.363537</td>\n",
       "      <td>1.262247e+06</td>\n",
       "      <td>17951955</td>\n",
       "      <td>Big Sunflower River</td>\n",
       "      <td>POINT (496047.3635373298 1262247.29440906)</td>\n",
       "      <td>07288000</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288280</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>07288280</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>489208.729363</td>\n",
       "      <td>1.239410e+06</td>\n",
       "      <td>17958105</td>\n",
       "      <td>BIG SUNFLOWER RIVER NR MERIGOLD, MS</td>\n",
       "      <td>POINT (489208.7293628419 1239410.03140523)</td>\n",
       "      <td>07288280</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288555</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>07288555</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>509254.529536</td>\n",
       "      <td>1.233598e+06</td>\n",
       "      <td>17958153</td>\n",
       "      <td>QUIVER RIVER SOUTHEAST RULEVILLE, MS</td>\n",
       "      <td>POINT (509254.5295362755 1233597.934250077)</td>\n",
       "      <td>07288555</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288650</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>07288650</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>472484.774612</td>\n",
       "      <td>1.196944e+06</td>\n",
       "      <td>17963095</td>\n",
       "      <td>BOGUE PHALIA NR LELAND, MS</td>\n",
       "      <td>POINT (472484.7746117859 1196943.873882911)</td>\n",
       "      <td>07288650</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288700</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>07288700</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>143</td>\n",
       "      <td>492594.661169</td>\n",
       "      <td>1.195806e+06</td>\n",
       "      <td>17970237</td>\n",
       "      <td>Big Sunflower River</td>\n",
       "      <td>POINT (492594.6611692713 1195806.16802324)</td>\n",
       "      <td>07288700</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            start_dt   site_no      end_dt    n              x             y  \\\n",
       "site_no                                                                        \n",
       "07288000  2007-02-01  07288000  2018-12-01  143  496047.363537  1.262247e+06   \n",
       "07288280  2007-02-01  07288280  2018-12-01  143  489208.729363  1.239410e+06   \n",
       "07288555  2007-02-01  07288555  2018-12-01  143  509254.529536  1.233598e+06   \n",
       "07288650  2007-02-01  07288650  2018-12-01  143  472484.774612  1.196944e+06   \n",
       "07288700  2007-02-01  07288700  2018-12-01  143  492594.661169  1.195806e+06   \n",
       "\n",
       "           line_id                                  name  \\\n",
       "site_no                                                    \n",
       "07288000  17951955                   Big Sunflower River   \n",
       "07288280  17958105   BIG SUNFLOWER RIVER NR MERIGOLD, MS   \n",
       "07288555  17958153  QUIVER RIVER SOUTHEAST RULEVILLE, MS   \n",
       "07288650  17963095            BOGUE PHALIA NR LELAND, MS   \n",
       "07288700  17970237                   Big Sunflower River   \n",
       "\n",
       "                                             geometry obsprefix   group  \\\n",
       "site_no                                                                   \n",
       "07288000   POINT (496047.3635373298 1262247.29440906)  07288000  fluxes   \n",
       "07288280   POINT (489208.7293628419 1239410.03140523)  07288280  fluxes   \n",
       "07288555  POINT (509254.5295362755 1233597.934250077)  07288555  fluxes   \n",
       "07288650  POINT (472484.7746117859 1196943.873882911)  07288650  fluxes   \n",
       "07288700   POINT (492594.6611692713 1195806.16802324)  07288700  fluxes   \n",
       "\n",
       "         geo_group  \n",
       "site_no             \n",
       "07288000     delta  \n",
       "07288280     delta  \n",
       "07288555     delta  \n",
       "07288650     delta  \n",
       "07288700     delta  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess the measured values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 40 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/MAP_generalized_regions.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/CompositeHydrographArea.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n"
     ]
    }
   ],
   "source": [
    "nwis_streamflow_data = test_data_path / 'swflows/nwis_dvs.csv'\n",
    "nwis_streamflow_metadata = test_data_path / 'swflows/nwis_dv_sites.csv'\n",
    "\n",
    "meas_data, meas_metadata = preprocess_flows(nwis_streamflow_data, nwis_streamflow_metadata,\n",
    "                                            flow_data_columns=['q_cfs', 'qbase_cfs'],\n",
    "                                            include_sites=include_sites,\n",
    "                                            start_date=start_date,\n",
    "                                            active_area=delta_extent,\n",
    "                                            datetime_col='datetime', \n",
    "                                            site_no_col='site_no',\n",
    "                                            line_id_col='comid',\n",
    "                                            x_coord_col='dec_long_va',\n",
    "                                            y_coord_col='dec_lat_va',\n",
    "                                            geographic_groups=geographic_groups,\n",
    "                                            geographic_groups_col='obsgroup',\n",
    "                                            source_crs=4269,\n",
    "                                            dest_crs=5070,\n",
    "                                            source_volume_units='ft3',\n",
    "                                            source_time_units='s',\n",
    "                                            dest_volume_units='m3',\n",
    "                                            dest_time_units='d',\n",
    "                                            column_renames=column_renames,\n",
    "                                            outfile=None\n",
    "                                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>qtotal_m3d</th>\n",
       "      <th>qbase_m3d</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-20</th>\n",
       "      <td>07280400</td>\n",
       "      <td>2008-01-20</td>\n",
       "      <td>93459.185840</td>\n",
       "      <td>93459.185840</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-21</th>\n",
       "      <td>07280400</td>\n",
       "      <td>2008-01-21</td>\n",
       "      <td>90278.637631</td>\n",
       "      <td>90278.637631</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-22</th>\n",
       "      <td>07280400</td>\n",
       "      <td>2008-01-22</td>\n",
       "      <td>153155.629151</td>\n",
       "      <td>92176.690939</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-23</th>\n",
       "      <td>07280400</td>\n",
       "      <td>2008-01-23</td>\n",
       "      <td>214809.332899</td>\n",
       "      <td>94114.868086</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-24</th>\n",
       "      <td>07280400</td>\n",
       "      <td>2008-01-24</td>\n",
       "      <td>144347.957187</td>\n",
       "      <td>96093.658387</td>\n",
       "      <td>measured</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             site_no    datetime     qtotal_m3d     qbase_m3d  category\n",
       "datetime                                                               \n",
       "2008-01-20  07280400  2008-01-20   93459.185840  93459.185840  measured\n",
       "2008-01-21  07280400  2008-01-21   90278.637631  90278.637631  measured\n",
       "2008-01-22  07280400  2008-01-22  153155.629151  92176.690939  measured\n",
       "2008-01-23  07280400  2008-01-23  214809.332899  94114.868086  measured\n",
       "2008-01-24  07280400  2008-01-24  144347.957187  96093.658387  measured"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>start_dt</th>\n",
       "      <th>end_dt</th>\n",
       "      <th>n</th>\n",
       "      <th>obsprefix</th>\n",
       "      <th>group</th>\n",
       "      <th>geo_group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07280400</th>\n",
       "      <td>07280400</td>\n",
       "      <td>TILLATOBA CREEK AT CHARLESTON, MS</td>\n",
       "      <td>ST</td>\n",
       "      <td>1.231964e+06</td>\n",
       "      <td>543849.348583</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1976-08-18</td>\n",
       "      <td>2015-09-08</td>\n",
       "      <td>132.0</td>\n",
       "      <td>POINT (543849.3485826917 1231964.199194193)</td>\n",
       "      <td>2008-01-20</td>\n",
       "      <td>2009-09-22</td>\n",
       "      <td>612.0</td>\n",
       "      <td>07280400</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288000</th>\n",
       "      <td>07288000</td>\n",
       "      <td>BIG SUNFLOWER RIVER AT CLARKSDALE, MS</td>\n",
       "      <td>ST</td>\n",
       "      <td>1.251190e+06</td>\n",
       "      <td>495901.606421</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>NAD27</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-05-18</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>29.0</td>\n",
       "      <td>POINT (495901.606420672 1251190.481277433)</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>2020-09-20</td>\n",
       "      <td>884.0</td>\n",
       "      <td>07288000</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288280</th>\n",
       "      <td>07288280</td>\n",
       "      <td>BIG SUNFLOWER RIVER NR MERIGOLD, MS</td>\n",
       "      <td>ST</td>\n",
       "      <td>1.209990e+06</td>\n",
       "      <td>489521.298812</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1992-10-06</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>266.0</td>\n",
       "      <td>POINT (489521.2988124544 1209990.152655879)</td>\n",
       "      <td>2008-01-08</td>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>4633.0</td>\n",
       "      <td>07288280</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288650</th>\n",
       "      <td>07288650</td>\n",
       "      <td>BOGUE PHALIA NR LELAND, MS</td>\n",
       "      <td>ST</td>\n",
       "      <td>1.160478e+06</td>\n",
       "      <td>475848.051335</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1953-11-10</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>233.0</td>\n",
       "      <td>POINT (475848.0513348849 1160478.108369947)</td>\n",
       "      <td>2008-01-08</td>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>4592.0</td>\n",
       "      <td>07288650</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07288700</th>\n",
       "      <td>07288700</td>\n",
       "      <td>BIG SUNFLOWER RIVER NR ANGUILLA, MS</td>\n",
       "      <td>ST</td>\n",
       "      <td>1.113472e+06</td>\n",
       "      <td>484902.570251</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>NAD27</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1980-07-23</td>\n",
       "      <td>2011-02-15</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (484902.5702511699 1113472.33857191)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07288700</td>\n",
       "      <td>fluxes</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           site_no                                   name site_tp_cd  \\\n",
       "site_no                                                                \n",
       "07280400  07280400      TILLATOBA CREEK AT CHARLESTON, MS         ST   \n",
       "07288000  07288000  BIG SUNFLOWER RIVER AT CLARKSDALE, MS         ST   \n",
       "07288280  07288280    BIG SUNFLOWER RIVER NR MERIGOLD, MS         ST   \n",
       "07288650  07288650             BOGUE PHALIA NR LELAND, MS         ST   \n",
       "07288700  07288700    BIG SUNFLOWER RIVER NR ANGUILLA, MS         ST   \n",
       "\n",
       "                     y              x coord_meth_cd coord_acy_cd  \\\n",
       "site_no                                                            \n",
       "07280400  1.231964e+06  543849.348583             G            5   \n",
       "07288000  1.251190e+06  495901.606421             M            F   \n",
       "07288280  1.209990e+06  489521.298812             G            5   \n",
       "07288650  1.160478e+06  475848.051335             G            5   \n",
       "07288700  1.113472e+06  484902.570251             M            M   \n",
       "\n",
       "         coord_datum_cd dec_coord_datum_cd  district_cd  ...  sv_begin_date  \\\n",
       "site_no                                                  ...                  \n",
       "07280400          NAD83              NAD83           28  ...     1976-08-18   \n",
       "07288000          NAD27              NAD83           28  ...     1995-05-18   \n",
       "07288280          NAD83              NAD83           28  ...     1992-10-06   \n",
       "07288650          NAD83              NAD83           28  ...     1953-11-10   \n",
       "07288700          NAD27              NAD83           28  ...     1980-07-23   \n",
       "\n",
       "          sv_end_date sv_count_nu  \\\n",
       "site_no                             \n",
       "07280400   2015-09-08       132.0   \n",
       "07288000   2020-08-11        29.0   \n",
       "07288280   2020-06-30       266.0   \n",
       "07288650   2020-06-30       233.0   \n",
       "07288700   2011-02-15        35.0   \n",
       "\n",
       "                                             geometry    start_dt      end_dt  \\\n",
       "site_no                                                                         \n",
       "07280400  POINT (543849.3485826917 1231964.199194193)  2008-01-20  2009-09-22   \n",
       "07288000   POINT (495901.606420672 1251190.481277433)  2017-10-02  2020-09-20   \n",
       "07288280  POINT (489521.2988124544 1209990.152655879)  2008-01-08  2020-09-18   \n",
       "07288650  POINT (475848.0513348849 1160478.108369947)  2008-01-08  2020-09-22   \n",
       "07288700   POINT (484902.5702511699 1113472.33857191)         NaN         NaN   \n",
       "\n",
       "               n obsprefix   group geo_group  \n",
       "site_no                                       \n",
       "07280400   612.0  07280400  fluxes     delta  \n",
       "07288000   884.0  07288000  fluxes     delta  \n",
       "07288280  4633.0  07288280  fluxes     delta  \n",
       "07288650  4592.0  07288650  fluxes     delta  \n",
       "07288700     NaN  07288700  fluxes     delta  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the measured and estimated observations into one dataset\n",
    "* Resample both datasets to a continuous monthly frequency (labeled with the start date of each month)\n",
    "* Prioritize measured data when available; fill gaps with statistical estimates\n",
    "* Once the model has been run, the observation output from the model can be fed to **``modflow-obs``** along with the  ``combined_obs`` timeseries (below), to set up **``PEST``** observations, or create observation output to be read by **``PEST``**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.swflows import combine_measured_estimated_values\n",
    "\n",
    "combined_obs = combine_measured_estimated_values(meas_data, est_data,\n",
    "                                                 measured_values_data_col='qbase_m3d', \n",
    "                                                 estimated_values_data_col='qbase_m3d',\n",
    "                                                 resample_freq='MS', how='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>site_no</th>\n",
       "      <th>line_id</th>\n",
       "      <th>datetime</th>\n",
       "      <th>category</th>\n",
       "      <th>est_qtotal_m3d</th>\n",
       "      <th>est_qbase_m3d</th>\n",
       "      <th>meas_qtotal_m3d</th>\n",
       "      <th>meas_qbase_m3d</th>\n",
       "      <th>obsval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07280400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>measured</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.219044e+05</td>\n",
       "      <td>99816.041527</td>\n",
       "      <td>99816.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07280400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>measured</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.343203e+05</td>\n",
       "      <td>130662.640359</td>\n",
       "      <td>130662.640359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07280400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>measured</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.228016e+05</td>\n",
       "      <td>112159.341365</td>\n",
       "      <td>112159.341365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07280400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>measured</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.080310e+06</td>\n",
       "      <td>197271.496485</td>\n",
       "      <td>197271.496485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07280400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-05-01</td>\n",
       "      <td>measured</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.351547e+05</td>\n",
       "      <td>165388.483203</td>\n",
       "      <td>165388.483203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    site_no  line_id   datetime  category  est_qtotal_m3d  est_qbase_m3d  \\\n",
       "0  07280400      NaN 2008-01-01  measured             NaN            NaN   \n",
       "1  07280400      NaN 2008-02-01  measured             NaN            NaN   \n",
       "2  07280400      NaN 2008-03-01  measured             NaN            NaN   \n",
       "3  07280400      NaN 2008-04-01  measured             NaN            NaN   \n",
       "4  07280400      NaN 2008-05-01  measured             NaN            NaN   \n",
       "\n",
       "   meas_qtotal_m3d  meas_qbase_m3d         obsval  \n",
       "0     2.219044e+05    99816.041527   99816.041527  \n",
       "1     3.343203e+05   130662.640359  130662.640359  \n",
       "2     5.228016e+05   112159.341365  112159.341365  \n",
       "3     1.080310e+06   197271.496485  197271.496485  \n",
       "4     6.351547e+05   165388.483203  165388.483203  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot data for a site of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Flow, in m$^3$/day')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "site_no = '07288280'\n",
    "site_data = combined_obs.loc[combined_obs.site_no == site_no].copy()\n",
    "site_data.index = site_data.datetime\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 8.5))\n",
    "ax = site_data[['meas_qtotal_m3d','meas_qbase_m3d']].plot(ax=ax)\n",
    "ax = site_data[['est_qtotal_m3d','est_qbase_m3d']].plot(alpha=0.3, ax=ax, color=['C0', 'C1'])\n",
    "site_data.obsval.plot(ax=ax, c='k')\n",
    "ax.set_title(meas_metadata.loc[site_no, 'name'])\n",
    "ax.legend()\n",
    "ax.set_ylabel('Flow, in m$^3$/day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess head observations\n",
    "Preprocess head observation data, from the [visGWDB program](https://doi.org/10.5066/P9W004O6)\n",
    "* reproject to the model CRS\n",
    "* cull data to a `start_date` and optionally, a polygon or set of polygons defining the model area\n",
    "* length units are converted to those of the groundwater model. Open intervals for the wells are\n",
    "  converted from depths to elevations\n",
    "* missing open intervals are filled based on well bottom depths (if availabile) and the median open\n",
    "  interval length for the dataset.\n",
    "* Wells are categorized based on the quality of the open interval information (see the documentation\n",
    "  for :func:`mapgwm.headobs.fill_well_open_intervals`).\n",
    "* Prefixes for observation names (with an optional length limit) that identify the location are generated\n",
    "* Similar to with flux observations, assign preliminary observation groups based on geographic areas defined by the\n",
    "* The ``data`` DataFrame outputs have the time series; \n",
    "* The ``metadata`` DataFrame outputs have site information that can be input to **``modflow-setup``** to set up the observation input for MODFLOW-6.\n",
    "* Once the model has been run, the observation output from the model can be fed to **``modflow-obs``** along with the  ``data`` timeseries (below), to set up **``PEST``** observations, or create observation output to be read by **``PEST``**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.headobs import preprocess_headobs, get_data\n",
    "\n",
    "data_file = test_data_path / 'headobs/GW_monthly_stats1990-01-01_2019-12-31.txt'\n",
    "metadata_file = test_data_path / 'headobs/GW_monthly_meta1990-01-01_2019-12-31.txt'\n",
    "\n",
    "# output\n",
    "outputfile = output_folder / 'preprocessed_monthly_headobs.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with 10 unique wells\n",
      "culling 13 wells with only data prior to start date of 1998-04-01 00:00:00\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 2 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "50% of wells (n=4) have incomplete open interval information. Filling screen bottoms with well bottoms where available, and estimating screen tops from median open interval length.\n",
      "12% of wells (n=1) still missing open interval information; assigned to category 4.\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/MAP_generalized_regions.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/CompositeHydrographArea.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "writing output/preprocessed_monthly_headobs_info.shp... Done\n",
      "writing output/preprocessed_monthly_headobs_info.csv\n",
      "writing output/preprocessed_monthly_headobs.csv\n"
     ]
    }
   ],
   "source": [
    "# read the data\n",
    "data_orig, metadata_orig = get_data(data_file, metadata_file)\n",
    "\n",
    "# do the preprocessing\n",
    "data, metadata = preprocess_headobs(data_orig, metadata_orig,\n",
    "                                    head_data_columns=['head', 'last_head'],\n",
    "                                    data_length_units='feet',\n",
    "                                    active_area=delta_extent,\n",
    "                                    source_crs=4269, dest_crs=5070,\n",
    "                                    start_date=start_date,\n",
    "                                    geographic_groups=geographic_groups,\n",
    "                                    geographic_groups_col='obsgroup',\n",
    "                                    outfile=outputfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>head</th>\n",
       "      <th>last_head</th>\n",
       "      <th>head_std</th>\n",
       "      <th>n</th>\n",
       "      <th>obsprefix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USGS:332915091025901</td>\n",
       "      <td>2003-07-01</td>\n",
       "      <td>37.185600</td>\n",
       "      <td>37.185600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USGS:332915091025901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USGS:333411090482201</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>28.096464</td>\n",
       "      <td>28.096464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USGS:333411090482201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>USGS:333411090482201</td>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>27.736800</td>\n",
       "      <td>27.736800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USGS:333411090482201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USGS:333411090482201</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>26.560272</td>\n",
       "      <td>26.560272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USGS:333411090482201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>USGS:333411090482201</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>26.484072</td>\n",
       "      <td>26.484072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USGS:333411090482201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                site_no   datetime       head  last_head  head_std  n  \\\n",
       "4  USGS:332915091025901 2003-07-01  37.185600  37.185600       NaN  1   \n",
       "5  USGS:333411090482201 2008-03-01  28.096464  28.096464       NaN  1   \n",
       "6  USGS:333411090482201 2010-03-01  27.736800  27.736800       NaN  1   \n",
       "7  USGS:333411090482201 2015-03-01  26.560272  26.560272       NaN  1   \n",
       "8  USGS:333411090482201 2019-03-01  26.484072  26.484072       NaN  1   \n",
       "\n",
       "              obsprefix  \n",
       "4  USGS:332915091025901  \n",
       "5  USGS:333411090482201  \n",
       "6  USGS:333411090482201  \n",
       "7  USGS:333411090482201  \n",
       "8  USGS:333411090482201  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>head_std</th>\n",
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       "      <th>n</th>\n",
       "      <th>aqfr_cd</th>\n",
       "      <th>nat_aqfr_cd</th>\n",
       "      <th>screen_botm</th>\n",
       "      <th>screen_top</th>\n",
       "      <th>well_depth</th>\n",
       "      <th>well_el</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>USGS:323340091085201</th>\n",
       "      <td>19.50720</td>\n",
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       "      <td>19.50720</td>\n",
       "      <td>1</td>\n",
       "      <td>112MRVA</td>\n",
       "      <td>N100MSRVVL</td>\n",
       "      <td>-88.177600</td>\n",
       "      <td>-75.985600</td>\n",
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       "      <td>2</td>\n",
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       "      <th>USGS:325121091093801</th>\n",
       "      <td>29.87040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.87040</td>\n",
       "      <td>1</td>\n",
       "      <td>112MRVA</td>\n",
       "      <td>N100MSRVVL</td>\n",
       "      <td>-58.605600</td>\n",
       "      <td>-46.413600</td>\n",
       "      <td>90.0</td>\n",
       "      <td>31.394400</td>\n",
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       "      <td>450055.327781</td>\n",
       "      <td>1.098664e+06</td>\n",
       "      <td>POINT (450055.3277808698 1098664.367787851)</td>\n",
       "      <td>-58.605600</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USGS:325121091093801</td>\n",
       "      <td>heads</td>\n",
       "      <td>boeuf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USGS:332915091025901</th>\n",
       "      <td>37.18560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.18560</td>\n",
       "      <td>1</td>\n",
       "      <td>112MRVA</td>\n",
       "      <td>N100MSRVVL</td>\n",
       "      <td>6.705600</td>\n",
       "      <td>15.240000</td>\n",
       "      <td>108.0</td>\n",
       "      <td>39.624000</td>\n",
       "      <td>...</td>\n",
       "      <td>456688.018145</td>\n",
       "      <td>1.169617e+06</td>\n",
       "      <td>POINT (456688.0181453624 1169616.855672613)</td>\n",
       "      <td>-68.376000</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7056</td>\n",
       "      <td>15.24</td>\n",
       "      <td>USGS:332915091025901</td>\n",
       "      <td>heads</td>\n",
       "      <td>delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USGS:333411090482201</th>\n",
       "      <td>27.60726</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.60726</td>\n",
       "      <td>40</td>\n",
       "      <td>112MRVA</td>\n",
       "      <td>N100MSRVVL</td>\n",
       "      <td>-81.262968</td>\n",
       "      <td>-69.070968</td>\n",
       "      <td>120.0</td>\n",
       "      <td>38.737032</td>\n",
       "      <td>...</td>\n",
       "      <td>478637.876037</td>\n",
       "      <td>1.179983e+06</td>\n",
       "      <td>POINT (478637.8760365412 1179983.321431023)</td>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USGS:333411090482201</td>\n",
       "      <td>heads</td>\n",
       "      <td>delta</td>\n",
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       "    <tr>\n",
       "      <th>USGS:333633090230701</th>\n",
       "      <td>23.77440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.77440</td>\n",
       "      <td>1</td>\n",
       "      <td>112MRVA</td>\n",
       "      <td>N100MSRVVL</td>\n",
       "      <td>3.048000</td>\n",
       "      <td>15.240000</td>\n",
       "      <td>115.0</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>517102.401121</td>\n",
       "      <td>1.186603e+06</td>\n",
       "      <td>POINT (517102.4011205511 1186603.416371753)</td>\n",
       "      <td>-76.900000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0480</td>\n",
       "      <td>15.24</td>\n",
       "      <td>USGS:333633090230701</td>\n",
       "      <td>heads</td>\n",
       "      <td>delta_cha</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          head  head_std  last_head   n  aqfr_cd nat_aqfr_cd  \\\n",
       "site_no                                                                        \n",
       "USGS:323340091085201  19.50720       NaN   19.50720   1  112MRVA  N100MSRVVL   \n",
       "USGS:325121091093801  29.87040       NaN   29.87040   1  112MRVA  N100MSRVVL   \n",
       "USGS:332915091025901  37.18560       NaN   37.18560   1  112MRVA  N100MSRVVL   \n",
       "USGS:333411090482201  27.60726       NaN   27.60726  40  112MRVA  N100MSRVVL   \n",
       "USGS:333633090230701  23.77440       NaN   23.77440   1  112MRVA  N100MSRVVL   \n",
       "\n",
       "                      screen_botm  screen_top  well_depth    well_el  ...  \\\n",
       "site_no                                                               ...   \n",
       "USGS:323340091085201   -88.177600  -75.985600       115.0  26.822400  ...   \n",
       "USGS:325121091093801   -58.605600  -46.413600        90.0  31.394400  ...   \n",
       "USGS:332915091025901     6.705600   15.240000       108.0  39.624000  ...   \n",
       "USGS:333411090482201   -81.262968  -69.070968       120.0  38.737032  ...   \n",
       "USGS:333633090230701     3.048000   15.240000       115.0  38.100000  ...   \n",
       "\n",
       "                                  x             y  \\\n",
       "site_no                                             \n",
       "USGS:323340091085201  452922.427646  1.065894e+06   \n",
       "USGS:325121091093801  450055.327781  1.098664e+06   \n",
       "USGS:332915091025901  456688.018145  1.169617e+06   \n",
       "USGS:333411090482201  478637.876037  1.179983e+06   \n",
       "USGS:333633090230701  517102.401121  1.186603e+06   \n",
       "\n",
       "                                                         geometry  well_botm  \\\n",
       "site_no                                                                        \n",
       "USGS:323340091085201  POINT (452922.4276462687 1065893.772968342) -88.177600   \n",
       "USGS:325121091093801  POINT (450055.3277808698 1098664.367787851) -58.605600   \n",
       "USGS:332915091025901  POINT (456688.0181453624 1169616.855672613) -68.376000   \n",
       "USGS:333411090482201  POINT (478637.8760365412 1179983.321431023) -81.262968   \n",
       "USGS:333633090230701  POINT (517102.4011205511 1186603.416371753) -76.900000   \n",
       "\n",
       "                     category  orig_scbot  orig_sctop             obsprefix  \\\n",
       "site_no                                                                       \n",
       "USGS:323340091085201        2         NaN         NaN  USGS:323340091085201   \n",
       "USGS:325121091093801        2         NaN         NaN  USGS:325121091093801   \n",
       "USGS:332915091025901        3      6.7056       15.24  USGS:332915091025901   \n",
       "USGS:333411090482201        2         NaN         NaN  USGS:333411090482201   \n",
       "USGS:333633090230701        3      3.0480       15.24  USGS:333633090230701   \n",
       "\n",
       "                      group  geo_group  \n",
       "site_no                                 \n",
       "USGS:323340091085201  heads      boeuf  \n",
       "USGS:325121091093801  heads      boeuf  \n",
       "USGS:332915091025901  heads      delta  \n",
       "USGS:333411090482201  heads      delta  \n",
       "USGS:333633090230701  heads  delta_cha  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess ag. water use from the IWUM\n",
    "* read agricultural water use estimates from the Irrigation Water Use Model (IWUM; Wilson, 2020)\n",
    "* cull data to the model area\n",
    "* convert rates from cumulative volumes per time period to cubic meters per day\n",
    "* assign open intervals based on raster surfaces of the estimated production zone for agricultural groundwater use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.iwum import preprocess_iwum_pumping\n",
    "\n",
    "iwum_output = test_data_path / 'iwum/irr_wu_1km_1999_to_2017.nc'\n",
    "production_zone_top_raster = test_data_path / 'iwum/est_prod_zone_top.tif'\n",
    "production_zone_botm_raster = test_data_path / 'iwum/est_prod_zone_botm.tif'\n",
    "\n",
    "outfile = output_folder / 'iwum_m3.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading data from ../mapgwm/tests/data/iwum/est_prod_zone_top.tif...\n",
      "finished in 0.40s\n",
      "reading data from ../mapgwm/tests/data/iwum/est_prod_zone_botm.tif...\n",
      "finished in 0.40s\n",
      "Reset screen top and bottom to mean elevation at 9 locations where screen top was < screen bottom\n",
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 14086 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "wrote output/iwum_m3.csv\n"
     ]
    }
   ],
   "source": [
    "ag_wu = preprocess_iwum_pumping(iwum_output,\n",
    "                                start_date=None,\n",
    "                                end_date=None,\n",
    "                                active_area=delta_extent,\n",
    "                                estimated_production_zone_top=production_zone_top_raster,\n",
    "                                estimated_production_zone_botm=production_zone_botm_raster,\n",
    "                                flux_variable='value',\n",
    "                                nc_crs=5070,\n",
    "                                nc_length_units='meters',\n",
    "                                estimated_production_surface_units='meters',\n",
    "                                model_length_units='meters',\n",
    "                                outfile=outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>start_datetime</th>\n",
       "      <th>end_datetime</th>\n",
       "      <th>q</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>iwum_179</td>\n",
       "      <td>477955.0</td>\n",
       "      <td>1041285.0</td>\n",
       "      <td>14.093952</td>\n",
       "      <td>6.803136</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-2043.222778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>iwum_314</td>\n",
       "      <td>476955.0</td>\n",
       "      <td>1042285.0</td>\n",
       "      <td>13.795248</td>\n",
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       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-2093.868652</td>\n",
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       "      <th>315</th>\n",
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       "      <td>13.722096</td>\n",
       "      <td>6.352032</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-1526.927734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>iwum_443</td>\n",
       "      <td>469955.0</td>\n",
       "      <td>1043285.0</td>\n",
       "      <td>11.457432</td>\n",
       "      <td>3.328416</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-1283.890991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>iwum_444</td>\n",
       "      <td>470955.0</td>\n",
       "      <td>1043285.0</td>\n",
       "      <td>11.884152</td>\n",
       "      <td>3.947160</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-1245.565308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      site_no         x          y  screen_top  screen_botm start_datetime  \\\n",
       "179  iwum_179  477955.0  1041285.0   14.093952     6.803136     2017-06-01   \n",
       "314  iwum_314  476955.0  1042285.0   13.795248     6.400800     2017-06-01   \n",
       "315  iwum_315  477955.0  1042285.0   13.722096     6.352032     2017-06-01   \n",
       "443  iwum_443  469955.0  1043285.0   11.457432     3.328416     2017-06-01   \n",
       "444  iwum_444  470955.0  1043285.0   11.884152     3.947160     2017-06-01   \n",
       "\n",
       "    end_datetime            q  \n",
       "179   2017-06-30 -2043.222778  \n",
       "314   2017-06-30 -2093.868652  \n",
       "315   2017-06-30 -1526.927734  \n",
       "443   2017-06-30 -1283.890991  \n",
       "444   2017-06-30 -1245.565308  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag_wu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot average pumping at each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "site_means = ag_wu.groupby('site_no').mean()\n",
    "ax = site_means.plot(kind='scatter', x='x', y='y', c='q', s=1,\n",
    "                     cmap='viridis_r', vmin=site_means.q.quantile(0.1))\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess non-ag. water use\n",
    "Including data from\n",
    "* the USGS Site-Specific Water Use Database (SWUDS)\n",
    "* USGS data on water use for thermo-electric power generation\n",
    "\n",
    "\n",
    "### SWUDs data\n",
    "* SWUDs data are assumed to come in an Excel spreadsheet, that may have incomplete information on well screen intervals\n",
    "* similar to other preprocessing methods, the data are reprojected and optionally culled to an area of interest\n",
    "* if input data do not have information on the well screen intervals, screen tops and bottoms are sampled from raster surfaces bounding estimated production zones; well depth is used to discriminate between zones\n",
    "* data are resampled to continous monthly values between a specified start and end date- typically bracketing the time range that the pumping should be simulated in the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.swuds import preprocess_swuds\n",
    "\n",
    "swuds_input = test_data_path / 'swuds/withdrawals_test_dataset.xlsx'\n",
    "worksheet = 'LMG-withdrawals-2000-2018'\n",
    "outfile = output_folder / 'preprocessed_swuds_data.csv'\n",
    "\n",
    "dem = test_data_path / 'rasters/dem_mean_elevs.tif'\n",
    "\n",
    "# raster defining the tops and bottoms of non-agricultural public supply production intervals\n",
    "# from Knierem\n",
    "production_zones = {'mrva': (test_data_path / 'swuds/rasters/pz_MRVA_top.tif',\n",
    "                             test_data_path / 'swuds/rasters/pz_MRVA_bot.tif',\n",
    "                             ),\n",
    "                    'mcaq': (test_data_path / 'swuds/rasters/pz_MCAQP_top.tif',\n",
    "                             test_data_path / 'swuds/rasters/pz_MCAQP_bot.tif',\n",
    "                             'feet'),\n",
    "                    'lcaq': (test_data_path / 'swuds/rasters/pz_LCAQP_top.tif',\n",
    "                             test_data_path / 'swuds/rasters/pz_LCAQP_bot.tif')\n",
    "                    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 831 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "reading data from ../mapgwm/tests/data/rasters/dem_mean_elevs.tif...\n",
      "finished in 0.26s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MRVA_top.tif...\n",
      "finished in 0.01s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MRVA_bot.tif...\n",
      "finished in 0.01s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MCAQP_top.tif...\n",
      "finished in 0.01s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MCAQP_bot.tif...\n",
      "finished in 0.01s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_LCAQP_top.tif...\n",
      "finished in 0.01s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_LCAQP_bot.tif...\n",
      "finished in 0.01s\n",
      "processed SWUDS data written to output/preprocessed_swuds_data.csv and in dataframe attribute\n",
      "writing output/preprocessed_swuds_data.shp... Done\n"
     ]
    }
   ],
   "source": [
    "swuds_results = preprocess_swuds(swuds_input, worksheet,\n",
    "                                 dem=dem, dem_units='feet',\n",
    "                                 start_date=None,\n",
    "                                 end_date=None,\n",
    "                                 active_area=test_data_path / 'extents/ms_delta.shp',\n",
    "                                 production_zones=production_zones,\n",
    "                                 source_crs=4269,\n",
    "                                 dest_crs=5070,\n",
    "                                 data_length_units='feet',\n",
    "                                 data_volume_units='mgal',\n",
    "                                 model_length_units='meters',\n",
    "                                 outfile=outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `mapgwm.swuds.Swuds` object is returned, which has a dataframe attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>site_no</th>\n",
       "      <th>q</th>\n",
       "      <th>q_monthly</th>\n",
       "      <th>month</th>\n",
       "      <th>well_elev</th>\n",
       "      <th>depth</th>\n",
       "      <th>screen_bot</th>\n",
       "      <th>screen_top</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>start_datetime</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>322431090530201</td>\n",
       "      <td>20895.471597</td>\n",
       "      <td>5.52</td>\n",
       "      <td>JAN_VAL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>37.1856</td>\n",
       "      <td>47.8144</td>\n",
       "      <td>67.8144</td>\n",
       "      <td>478430.626345</td>\n",
       "      <td>1.050261e+06</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>POINT (478430.6263445358 1050260.766381889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>322431090530201</td>\n",
       "      <td>20895.471597</td>\n",
       "      <td>5.52</td>\n",
       "      <td>FEB_VAL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>37.1856</td>\n",
       "      <td>47.8144</td>\n",
       "      <td>67.8144</td>\n",
       "      <td>478430.626345</td>\n",
       "      <td>1.050261e+06</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>POINT (478430.6263445358 1050260.766381889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>322431090530201</td>\n",
       "      <td>20895.471597</td>\n",
       "      <td>5.52</td>\n",
       "      <td>MAR_VAL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>37.1856</td>\n",
       "      <td>47.8144</td>\n",
       "      <td>67.8144</td>\n",
       "      <td>478430.626345</td>\n",
       "      <td>1.050261e+06</td>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>POINT (478430.6263445358 1050260.766381889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>322431090530201</td>\n",
       "      <td>20895.471597</td>\n",
       "      <td>5.52</td>\n",
       "      <td>APR_VAL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>37.1856</td>\n",
       "      <td>47.8144</td>\n",
       "      <td>67.8144</td>\n",
       "      <td>478430.626345</td>\n",
       "      <td>1.050261e+06</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>POINT (478430.6263445358 1050260.766381889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>322431090530201</td>\n",
       "      <td>20895.471597</td>\n",
       "      <td>5.52</td>\n",
       "      <td>MAY_VAL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>37.1856</td>\n",
       "      <td>47.8144</td>\n",
       "      <td>67.8144</td>\n",
       "      <td>478430.626345</td>\n",
       "      <td>1.050261e+06</td>\n",
       "      <td>2010-05-01</td>\n",
       "      <td>POINT (478430.6263445358 1050260.766381889)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    site_no             q  q_monthly    month  well_elev  \\\n",
       "2010-01-01  322431090530201  20895.471597       5.52  JAN_VAL       85.0   \n",
       "2010-02-01  322431090530201  20895.471597       5.52  FEB_VAL       85.0   \n",
       "2010-03-01  322431090530201  20895.471597       5.52  MAR_VAL       85.0   \n",
       "2010-04-01  322431090530201  20895.471597       5.52  APR_VAL       85.0   \n",
       "2010-05-01  322431090530201  20895.471597       5.52  MAY_VAL       85.0   \n",
       "\n",
       "              depth  screen_bot  screen_top              x             y  \\\n",
       "2010-01-01  37.1856     47.8144     67.8144  478430.626345  1.050261e+06   \n",
       "2010-02-01  37.1856     47.8144     67.8144  478430.626345  1.050261e+06   \n",
       "2010-03-01  37.1856     47.8144     67.8144  478430.626345  1.050261e+06   \n",
       "2010-04-01  37.1856     47.8144     67.8144  478430.626345  1.050261e+06   \n",
       "2010-05-01  37.1856     47.8144     67.8144  478430.626345  1.050261e+06   \n",
       "\n",
       "           start_datetime                                     geometry  \n",
       "2010-01-01     2010-01-01  POINT (478430.6263445358 1050260.766381889)  \n",
       "2010-02-01     2010-02-01  POINT (478430.6263445358 1050260.766381889)  \n",
       "2010-03-01     2010-03-01  POINT (478430.6263445358 1050260.766381889)  \n",
       "2010-04-01     2010-04-01  POINT (478430.6263445358 1050260.766381889)  \n",
       "2010-05-01     2010-05-01  POINT (478430.6263445358 1050260.766381889)  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swuds_results.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water use for thermoelectric power generation\n",
    "* similar to the SWUDs data, data for water use related to TE power generation are assumed to come in a spreadsheet\n",
    "* however, the column names might different between different datasets. \n",
    "* therefore, the first step is to read in the data using a function that harmonizes the column names, and then combine the data using ``DataFrame.append()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mapgwm.te_wateruse import read_te_water_use_spreadsheet, preprocess_te_wateruse\n",
    "\n",
    "df2010 = read_te_water_use_spreadsheet(test_data_path / 'swuds/2010_Thermo_Model_Estimates.xlsx',\n",
    "                                       date='2010',\n",
    "                                       x_coord_col='Longitude, decimal degrees',\n",
    "                                       y_coord_col='Latitude, decimal degrees',\n",
    "                                       q_col='USGS-Estimated Annual Withdrawal (Mgal/d)',\n",
    "                                       site_no_col='PLANT CODE',\n",
    "                                       site_name_col='PLANT NAME',\n",
    "                                       source_name_col='NAME OF WATER SOURCE',\n",
    "                                       source_code_col='USGS WATER SOURCE CODE',\n",
    "                                       sheet_name='Report_table_UPDATED', skiprows=2)\n",
    "\n",
    "df2015 = read_te_water_use_spreadsheet(test_data_path / 'swuds/2015_TE_Model_Estimates_lat.long_COMIDs.xlsx',\n",
    "                                   date='2015',\n",
    "                                   x_coord_col='LONGITUDE',\n",
    "                                   y_coord_col='LATITUDE',\n",
    "                                   q_col='WITHDRAWAL',\n",
    "                                   site_no_col='EIA_PLANT_ID',\n",
    "                                   site_name_col='PLANT_NAME',\n",
    "                                   source_name_col='NAME_OF_WATER_SOURCE',\n",
    "                                   source_code_col='WATER_SOURCE_CODE',\n",
    "                                   sheet_name='2015_ANNUAL_WD_CU', skiprows=0)\n",
    "df = df2010.append(df2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then run the data through a similar function to that used for SWUDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../mapgwm/tests/data/extents/ms_delta.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "Culling 405 sites outside of the model area defined by ['../mapgwm/tests/data/extents/ms_delta.shp'].\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MCAQP_top.tif...\n",
      "finished in 0.17s\n",
      "reading data from ../mapgwm/tests/data/swuds/rasters/pz_MCAQP_bot.tif...\n",
      "finished in 0.16s\n",
      "wrote output/preprocessed_te_data.csv\n",
      "writing output/preprocessed_te_data.shp... Done\n"
     ]
    }
   ],
   "source": [
    "outfile = output_folder / 'preprocessed_te_data.csv'\n",
    "\n",
    "estimated_production_zone_top = test_data_path / 'swuds/rasters/pz_MCAQP_top.tif'\n",
    "estimated_production_zone_botm = test_data_path / 'swuds/rasters/pz_MCAQP_bot.tif'\n",
    "\n",
    "te_results = preprocess_te_wateruse(df, #dem=dem, dem_units='feet',\n",
    "                                    start_date='2008-01-01',\n",
    "                                    end_date='2017-12-31',\n",
    "                                    active_area=test_data_path /'extents/ms_delta.shp',\n",
    "                                    estimated_production_zone_top=estimated_production_zone_top,\n",
    "                                    estimated_production_zone_botm=estimated_production_zone_botm,\n",
    "                                    estimated_production_surface_units='feet',\n",
    "                                    source_crs=4269,\n",
    "                                    dest_crs=5070,\n",
    "                                    data_volume_units='mgal',\n",
    "                                    model_length_units='meters',\n",
    "                                    outfile=outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>site_no</th>\n",
       "      <th>start_datetime</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>screen_top</th>\n",
       "      <th>screen_botm</th>\n",
       "      <th>q</th>\n",
       "      <th>geometry</th>\n",
       "      <th>source_name</th>\n",
       "      <th>site_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>2059</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>497139.150871</td>\n",
       "      <td>1.249816e+06</td>\n",
       "      <td>-147.409514</td>\n",
       "      <td>-162.649514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (497139.1508711056 1249816.468462982)</td>\n",
       "      <td>Wells</td>\n",
       "      <td>L L Wilkins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-01</th>\n",
       "      <td>2059</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>497139.150871</td>\n",
       "      <td>1.249816e+06</td>\n",
       "      <td>-147.409514</td>\n",
       "      <td>-162.649514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (497139.1508711056 1249816.468462982)</td>\n",
       "      <td>Wells</td>\n",
       "      <td>L L Wilkins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-01</th>\n",
       "      <td>2059</td>\n",
       "      <td>2008-03-01</td>\n",
       "      <td>497139.150871</td>\n",
       "      <td>1.249816e+06</td>\n",
       "      <td>-147.409514</td>\n",
       "      <td>-162.649514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (497139.1508711056 1249816.468462982)</td>\n",
       "      <td>Wells</td>\n",
       "      <td>L L Wilkins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>2059</td>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>497139.150871</td>\n",
       "      <td>1.249816e+06</td>\n",
       "      <td>-147.409514</td>\n",
       "      <td>-162.649514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (497139.1508711056 1249816.468462982)</td>\n",
       "      <td>Wells</td>\n",
       "      <td>L L Wilkins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-01</th>\n",
       "      <td>2059</td>\n",
       "      <td>2008-05-01</td>\n",
       "      <td>497139.150871</td>\n",
       "      <td>1.249816e+06</td>\n",
       "      <td>-147.409514</td>\n",
       "      <td>-162.649514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (497139.1508711056 1249816.468462982)</td>\n",
       "      <td>Wells</td>\n",
       "      <td>L L Wilkins</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            site_no start_datetime              x             y  screen_top  \\\n",
       "2008-01-01     2059     2008-01-01  497139.150871  1.249816e+06 -147.409514   \n",
       "2008-02-01     2059     2008-02-01  497139.150871  1.249816e+06 -147.409514   \n",
       "2008-03-01     2059     2008-03-01  497139.150871  1.249816e+06 -147.409514   \n",
       "2008-04-01     2059     2008-04-01  497139.150871  1.249816e+06 -147.409514   \n",
       "2008-05-01     2059     2008-05-01  497139.150871  1.249816e+06 -147.409514   \n",
       "\n",
       "            screen_botm    q                                     geometry  \\\n",
       "2008-01-01  -162.649514  0.0  POINT (497139.1508711056 1249816.468462982)   \n",
       "2008-02-01  -162.649514  0.0  POINT (497139.1508711056 1249816.468462982)   \n",
       "2008-03-01  -162.649514  0.0  POINT (497139.1508711056 1249816.468462982)   \n",
       "2008-04-01  -162.649514  0.0  POINT (497139.1508711056 1249816.468462982)   \n",
       "2008-05-01  -162.649514  0.0  POINT (497139.1508711056 1249816.468462982)   \n",
       "\n",
       "           source_name    site_name  \n",
       "2008-01-01       Wells  L L Wilkins  \n",
       "2008-02-01       Wells  L L Wilkins  \n",
       "2008-03-01       Wells  L L Wilkins  \n",
       "2008-04-01       Wells  L L Wilkins  \n",
       "2008-05-01       Wells  L L Wilkins  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the locations of SWUDs and TE water use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pumpage, in m$^3$/day')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11, 8.5))\n",
    "ax = swuds_results.df.plot.scatter(x='x', y='y', c='q_monthly', \n",
    "                                   cmap='viridis', \n",
    "                                   vmax=swuds_results.df.q.quantile(0.9),\n",
    "                                   norm=mpl.colors.LogNorm(), ax=ax)\n",
    "te_results.plot.scatter(x='x', y='y', c='r', ax=ax, label='TE sites')\n",
    "ax.legend()\n",
    "ax.set_aspect(1)\n",
    "cax = fig.get_axes()[1]\n",
    "#and we can modify it, i.e.:\n",
    "cax.set_ylabel('Pumpage, in m$^3$/day')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}