{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pleasant Lake Example\n",
    "This example is a simplified version of the Pleasant Lake model published by Fienen et al (2021). The goal of the Pleasant Lake model, part of the Central Sands Lake Study, was to address connections between groundwater abstraction and ecological function of a lake in central Wisconsin, USA (WDNR 2021; Figure 1). This required modeling at multiple scales. Fine discretization was needed near the lake for accurate simulation of water levels and groundwater-lake flux. A large model domain was also needed, to simulate farfield water-use activity (chiefly irrigated agriculture) in order to delineate a limit of connection, as well as to incorporate distant hydrologic boundaries. Adopting a fine enough discretization for the lake detail throughout the farfield would have resulted in a model with more cells than could be practically managed. To mitigate this, three models were combined: a large regional model built with MODFLOW-NWT (Niswonger et al. 2011), an intermediate MODFLOW 6 model inset within the regional model to simulate the irrigated agriculture area, and a refined MODFLOW 6 inset model (nested within the intermediate model) to simulate the lake. Regional groundwater flow and the effects of distant boundaries were simulated with the MODFLOW-NWT model, which was coupled sequentially (one-way) to the MODFLOW 6 models through time-varying specified head boundaries along the intermediate MODFLOW 6 model perimeter. The two MODFLOW 6 models were coupled dynamically (both ways) within the groundwater flow solution, allowing for feedback between the models. Estimates of groundwater recharge for the MODFLOW models were provided by a soil water balance code (SWB; Westenbroek et al 2018) simulation that could consider alternative assumptions of climate and land use. Net infiltration estimates from the SWB model were conveyed to the Recharge Packages in the MODFLOW models using the NetCDF functionality in Modflow-setup. \n",
    "\n",
    "<img src=\"https://github.com/aleaf/modflow-setup/blob/develop/docs/source/_static/fig1_pleasant_lake_model.jpg?raw=true\" align=\"center\">\n",
    "\n",
    "**Figure 1**: The full Pleasant Lake model domain with location map, showing the relationship between the regional, intermediate and LGR inset models, as well as the irrigation wells considered.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Most details of the Fienen et al (2021) model are included here, but to reduce file sizes and execution time, a smaller Modflow-6 simulation domain is used. The smaller domain is for illustration purposes only, and is not intended to be adequate for representing pumping impacts or providing a sufficient distance to the perimeter boundaries.\n",
    "\n",
    "\n",
    "* **[View the configuration file(s)](https://doi-usgs.github.io/modflow-setup/latest/config-file-gallery.html#pleasant-lake-test-case)** \n",
    "* **[Run this example](https://mybinder.org/v2/gh/usgs/modflow-setup/develop?urlpath=lab/tree/examples)  on  [Binder](https://mybinder.org/)**\n",
    "\n",
    "#### Model details\n",
    "* MODFLOW-6 simulation with a dynamically linked parent model and local grid refinement (LGR) inset model\n",
    "* LGR parent model is itself a Telescopic Mesh Refinment (TMR) inset from a regional MODFLOW-NWT model\n",
    "* Layer 1 in the regional model is subdivided evenly into two layers in the LGR models (``botm: from_parent: 0: -0.5``). The other layers are mapped explicitly between the TMR parent and LGR models.\n",
    "* starting heads for the LGR parent models were resampled from the regional model binary output\n",
    "* rch, npf, sto, and wel input copied from the regional model\n",
    "* SFR package constructed from an NHDPlus v2 dataset (path to NHDPlus files in the same structure as the `downloads from the NHDPlus website`_)\n",
    "* head observations from csv files with different column names\n",
    "* LGR inset extent based on a buffer distance around a feature of interest\n",
    "* LGR inset dis, ic, npf, sto and rch packages copied from LGR parent\n",
    "* Lake package created from polygon features, bathymetry raster, stage-area-volume file and climate data from `PRISM`_.\n",
    "* Lake package observations are set up automatically (output file for each lake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:42.046148Z",
     "iopub.status.busy": "2025-03-17T22:05:42.045987Z",
     "iopub.status.idle": "2025-03-17T22:05:43.206683Z",
     "shell.execute_reply": "2025-03-17T22:05:43.206225Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.interpolate import griddata\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import patheffects\n",
    "import flopy\n",
    "import flopy.utils.binaryfile as bf\n",
    "from mfsetup import MF6model\n",
    "from mfsetup.discretization import cellids_to_kij\n",
    "from gisutils import df2shp\n",
    "import mfexport\n",
    "from mfexport.utils import get_water_table\n",
    "\n",
    "wd = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just make a model grid\n",
    "Oftentimes at the start of a modeling project, we want to quickly test different grid resolutions and extents before attempting to build the model. We can do this with Modflow-setup by creating a model instance and then running the ``setup_grid()`` method. A model grid instance is created from the ``setup_grid:`` block in the configuration file. \n",
    "\n",
    "Note: ``%%capture`` in the block below is just to suppress printing of ``stdout`` for display of this notebook in the modflow-setup documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:43.208823Z",
     "iopub.status.busy": "2025-03-17T22:05:43.208331Z",
     "iopub.status.idle": "2025-03-17T22:05:43.857860Z",
     "shell.execute_reply": "2025-03-17T22:05:43.857420Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "m = MF6model(cfg='pleasant_lgr_parent.yml')\n",
    "m.setup_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this model has local-grid refinement, it actually consists of two models: a parent built from ``pleasant_lgr_parent.yml``, and an inset built from ``pleasant_lgr_inset.yml``, which is referenced within ``pleasant_lgr_parent.yml``. The two sub-models are connected and solved simulataneously within the same MODFLOW 6 simulation. A model grid is made for each sub-model. The model grids are instances of the ``MFsetupGrid`` grid class, a subclass of the Flopy ``StructuredGrid`` class with some added functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:43.859793Z",
     "iopub.status.busy": "2025-03-17T22:05:43.859625Z",
     "iopub.status.idle": "2025-03-17T22:05:43.865436Z",
     "shell.execute_reply": "2025-03-17T22:05:43.864899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5 layer(s), 25 row(s), 25 column(s)\n",
       "delr: [200.00...200.00] undefined\n",
       "delc: [200.00...200.00] undefined\n",
       "CRS: EPSG:3070\n",
       "length units: meters\n",
       "xll: 553400.0; yll: 387800.0; rotation: 0.0\n",
       "Bounds: (np.float64(553400.0), np.float64(558400.0), np.float64(387800.0), np.float64(392800.0))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.modelgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:43.892502Z",
     "iopub.status.busy": "2025-03-17T22:05:43.892310Z",
     "iopub.status.idle": "2025-03-17T22:05:43.896866Z",
     "shell.execute_reply": "2025-03-17T22:05:43.896318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75 row(s), 85 column(s)\n",
       "delr: [40.00...40.00] undefined\n",
       "delc: [40.00...40.00] undefined\n",
       "CRS: EPSG:3070\n",
       "length units: meters\n",
       "xll: 554200.0; yll: 388800.0; rotation: 0\n",
       "Bounds: (np.float64(554200.0), np.float64(557600.0), np.float64(388800.0), np.float64(391800.0))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.inset['plsnt_lgr_inset'].modelgrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working directory gottcha\n",
    "Currently, to facilitate working with external files in Flopy, **Modflow-setup changes the working directory to the model workspace**. In the context of a flat script that only builds the model, this is fine, but in a notebook or other workflows, this can potentially cause confusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:43.898383Z",
     "iopub.status.busy": "2025-03-17T22:05:43.898221Z",
     "iopub.status.idle": "2025-03-17T22:05:43.901708Z",
     "shell.execute_reply": "2025-03-17T22:05:43.901217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/runner/work/modflow-setup/modflow-setup/examples/pleasant_lgr'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write shapefiles of the inset and parent modelgrids\n",
    "A shapefile of the grid bounding box is written by default on creation of the model grid, to the location specified by ``output_files: grid_file:`` in the ``setup_grid:`` block (default is ``<model workspace>/postproc/shps/``). A shapefile of the grid cells as polygon features can be written as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:43.903463Z",
     "iopub.status.busy": "2025-03-17T22:05:43.903073Z",
     "iopub.status.idle": "2025-03-17T22:05:44.577917Z",
     "shell.execute_reply": "2025-03-17T22:05:44.577376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating shapely Polygons of grid cells...\n",
      "finished in 0.01s\n",
      "\n",
      "writing postproc/shps/plsnt_lgr_parent_grid.shp... Done\n",
      "creating shapely Polygons of grid cells...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished in 0.16s\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing postproc/shps/plsnt_lgr_inset_grid.shp... Done\n"
     ]
    }
   ],
   "source": [
    "m.modelgrid.write_shapefile('postproc/shps/plsnt_lgr_parent_grid.shp')\n",
    "m.inset['plsnt_lgr_inset'].modelgrid.write_shapefile('postproc/shps/plsnt_lgr_inset_grid.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Change the working directory back to the notebook location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:44.579626Z",
     "iopub.status.busy": "2025-03-17T22:05:44.579333Z",
     "iopub.status.idle": "2025-03-17T22:05:44.582077Z",
     "shell.execute_reply": "2025-03-17T22:05:44.581564Z"
    }
   },
   "outputs": [],
   "source": [
    "os.chdir(wd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the whole model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:44.583828Z",
     "iopub.status.busy": "2025-03-17T22:05:44.583429Z",
     "iopub.status.idle": "2025-03-17T22:05:53.264013Z",
     "shell.execute_reply": "2025-03-17T22:05:53.263504Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "m = MF6model.setup_from_yaml('pleasant_lgr_parent.yml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a ``MF6model`` instance (subclass of ``flopy.mf6.ModflowGwf``) is returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.265880Z",
     "iopub.status.busy": "2025-03-17T22:05:53.265720Z",
     "iopub.status.idle": "2025-03-17T22:05:53.272686Z",
     "shell.execute_reply": "2025-03-17T22:05:53.272248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pleasant Lake test case version 0.1.post75+gc7f9d7f\n",
       "Parent model: /home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/pleasant\n",
       "5 layer(s), 25 row(s), 25 column(s)\n",
       "delr: [200.00...200.00] undefined\n",
       "delc: [200.00...200.00] undefined\n",
       "CRS: EPSG:3070\n",
       "length units: meters\n",
       "xll: 553400.0; yll: 387800.0; rotation: 0.0\n",
       "Bounds: (np.float64(553400.0), np.float64(558400.0), np.float64(387800.0), np.float64(392800.0))\n",
       "Packages: dis ic npf sto rcha_0 oc chd_0 obs_0 sfr_0 wel_0 obs_1 obs_2\n",
       "13 period(s):\n",
       " per start_datetime end_datetime  perlen  steady  nstp\n",
       "   0     2012-01-01   2012-01-01     1.0    True     1\n",
       "   1     2012-01-01   2012-02-01    31.0   False     1\n",
       "   2     2012-02-01   2012-03-01    29.0   False     1\n",
       "   ...\n",
       "  12     2012-12-01   2013-01-01    31.0   False     1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "information from the configuration file is stored in an attached ``cfg`` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.274204Z",
     "iopub.status.busy": "2025-03-17T22:05:53.274053Z",
     "iopub.status.idle": "2025-03-17T22:05:53.277929Z",
     "shell.execute_reply": "2025-03-17T22:05:53.277407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['metadata', 'simulation', 'model', 'parent', 'postprocessing', 'setup_grid', 'dis', 'tdis', 'ic', 'npf', 'sto', 'rch', 'sfr', 'high_k_lakes', 'lak', 'mvr', 'chd', 'drn', 'ghb', 'riv', 'wel', 'oc', 'obs', 'ims', 'mfsetup_options', 'filename', 'maw', 'external_files', 'intermediate_data', 'grid'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.cfg.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the ``cfg`` dictionary contains both information from the configuration file, and MODFLOW input (such as external text file arrays) that was developed from the original source data. Internally in Modflow-setup, MODFLOW input in ``cfg`` is fed to the various Flopy object constructors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.279610Z",
     "iopub.status.busy": "2025-03-17T22:05:53.279293Z",
     "iopub.status.idle": "2025-03-17T22:05:53.283428Z",
     "shell.execute_reply": "2025-03-17T22:05:53.282953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {'remake_top': True,\n",
       "             'options': {'length_units': 'meters'},\n",
       "             'dimensions': {'nlay': 5, 'nrow': 25, 'ncol': 25},\n",
       "             'griddata': {'top': [{'filename': './external/plsnt_lgr_parent_top.dat'}],\n",
       "              'botm': [{'filename': './external/plsnt_lgr_parent_botm_000.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_botm_001.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_botm_002.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_botm_003.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_botm_004.dat'}],\n",
       "              'idomain': [{'filename': './external/plsnt_lgr_parent_idomain_000.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_idomain_001.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_idomain_002.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_idomain_003.dat'},\n",
       "               {'filename': './external/plsnt_lgr_parent_idomain_004.dat'}]},\n",
       "             'top_filename_fmt': 'top.dat',\n",
       "             'botm_filename_fmt': 'botm_{:03d}.dat',\n",
       "             'idomain_filename_fmt': 'idomain_{:03d}.dat',\n",
       "             'minimum_layer_thickness': 1,\n",
       "             'drop_thin_cells': True,\n",
       "             'source_data': {'top': {'filename': '/home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/source_data/rasters/dem40m.tif',\n",
       "               'elevation_units': 'meters'},\n",
       "              'botm': {'filenames': {1: '/home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/source_data/rasters/botm0.tif',\n",
       "                2: '/home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/source_data/rasters/botm1.tif',\n",
       "                3: '/home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/source_data/rasters/botm2.tif',\n",
       "                4: '/home/runner/work/modflow-setup/modflow-setup/examples/data/pleasant/source_data/rasters/botm3.tif'}}},\n",
       "             'nlay': 4})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.cfg['dis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inset LGR model is attached to the parent within an ``inset`` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.284991Z",
     "iopub.status.busy": "2025-03-17T22:05:53.284841Z",
     "iopub.status.idle": "2025-03-17T22:05:53.291598Z",
     "shell.execute_reply": "2025-03-17T22:05:53.291119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.6.0.post75+gc7f9d7f\n",
       " Parent model: ./plsnt_lgr_parent\n",
       " 5 layer(s), 75 row(s), 85 column(s)\n",
       " delr: [40.00...40.00] undefined\n",
       " delc: [40.00...40.00] undefined\n",
       " CRS: EPSG:3070\n",
       " length units: meters\n",
       " xll: 554200.0; yll: 388800.0; rotation: 0\n",
       " Bounds: (np.float64(554200.0), np.float64(557600.0), np.float64(388800.0), np.float64(391800.0))\n",
       " Packages: dis ic npf sto rcha_0 oc sfr_0 lak_0 obs_0 obs_1\n",
       " 13 period(s):\n",
       "  per start_datetime end_datetime  perlen  steady  nstp\n",
       "    0     2012-01-01   2012-01-01     1.0    True     1\n",
       "    1     2012-01-01   2012-02-01    31.0   False     1\n",
       "    2     2012-02-01   2012-03-01    29.0   False     1\n",
       "    ...\n",
       "   12     2012-12-01   2013-01-01    31.0   False     1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.inset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the inset and parent model grids with Lake Package connections by layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.293307Z",
     "iopub.status.busy": "2025-03-17T22:05:53.292932Z",
     "iopub.status.idle": "2025-03-17T22:05:53.464783Z",
     "shell.execute_reply": "2025-03-17T22:05:53.464280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f925e1d70e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inset = m.inset['plsnt_lgr_inset']\n",
    "\n",
    "l, r, b, t = m.modelgrid.extent\n",
    "layer = 0\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "parent_mv = flopy.plot.PlotMapView(model=m, ax=ax, layer=layer)\n",
    "inset_mv = flopy.plot.PlotMapView(model=inset, ax=ax, layer=layer)\n",
    "\n",
    "vconn = inset.lak.connectiondata.array[inset.lak.connectiondata.array['claktype'] == 'vertical']\n",
    "k, i, j = cellids_to_kij(vconn['cellid'])\n",
    "lakeconnections = np.zeros((inset.nrow, inset.ncol))\n",
    "lakeconnections[i, j] = np.array(k+1)\n",
    "lakeconnections = np.ma.masked_array(lakeconnections, mask=lakeconnections == 0)\n",
    "qmi = inset_mv.plot_array(lakeconnections)\n",
    "\n",
    "#inset_mv.plot_bc('LAK', color='navy')\n",
    "#parent_mv.plot_bc('WEL_0', color='red')\n",
    "\n",
    "lcp = parent_mv.plot_grid(lw=0.5, ax=ax)\n",
    "lci = inset_mv.plot_grid(lw=0.5)\n",
    "ax.set_ylim(b, t)\n",
    "ax.set_xlim(l, r)\n",
    "ax.set_aspect(1)\n",
    "plt.colorbar(qmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a cross section of the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.466545Z",
     "iopub.status.busy": "2025-03-17T22:05:53.466270Z",
     "iopub.status.idle": "2025-03-17T22:05:53.748884Z",
     "shell.execute_reply": "2025-03-17T22:05:53.748354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 5))\n",
    "xs_line = [(553000, 390200), (558000, 390200)]\n",
    "xs = flopy.plot.PlotCrossSection(model=m,\n",
    "                                line={\"line\": xs_line}, ax=ax,\n",
    "                                geographic_coords=True)\n",
    "lc = xs.plot_grid(zorder=4)\n",
    "xs2 = flopy.plot.PlotCrossSection(model=inset,\n",
    "                                line={\"line\": xs_line}, ax=ax,\n",
    "                                geographic_coords=True)\n",
    "lc = xs2.plot_grid(zorder=4)\n",
    "ax.set_ylim(190, 400)\n",
    "plt.savefig('../../docs/source/_static/pleasant_lgr_xsection.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### write the MODFLOW input files\n",
    "(just like you would for a Flopy model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:53.750669Z",
     "iopub.status.busy": "2025-03-17T22:05:53.750352Z",
     "iopub.status.idle": "2025-03-17T22:05:54.438903Z",
     "shell.execute_reply": "2025-03-17T22:05:54.438436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing solution package ims_-1...\n",
      "  writing package pleasant_lgr.gwfgwf...\n",
      "  writing package pleasant_lgr.mvr...\n",
      "  writing model plsnt_lgr_parent...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package sto...\n",
      "    writing package rcha_0...\n",
      "    writing package oc...\n",
      "    writing package chd_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'chd', 'dimensions') changed to 269 based on size of stress_period_data\n",
      "    writing package obs_0...\n",
      "    writing package sfr_0...\n",
      "    writing package wel_0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 2 based on size of stress_period_data\n",
      "    writing package obs_1...\n",
      "    writing package obs_2...\n",
      "  writing model plsnt_lgr_inset...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package sto...\n",
      "    writing package rcha_0...\n",
      "    writing package oc...\n",
      "    writing package sfr_0...\n",
      "    writing package lak_0...\n",
      "    writing package obs_0...\n",
      "    writing package obs_1...\n",
      "SFRmaker v. 0.11.3.post39+g85c5b4f\n",
      "\n",
      "Running Flopy v. 3.10.0.dev2 diagnostics...\n",
      "passed.\n",
      "\n",
      "Checking for continuity in segment and reach numbering...\n",
      "passed.\n",
      "\n",
      "Checking for increasing segment numbers in downstream direction...\n",
      "passed.\n",
      "\n",
      "Checking for circular routing...\n",
      "passed.\n",
      "\n",
      "Checking reach connections for proximity...\n",
      "2 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "1 2\n",
      "\n",
      "2 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "1 2\n",
      "\n",
      "\n",
      "Checking for model cells with multiple non-zero SFR conductances...\n",
      "passed.\n",
      "\n",
      "Checking for streambed tops of less than -10...\n",
      "passed.\n",
      "\n",
      "Checking for streambed tops of greater than 15000...\n",
      "passed.\n",
      "\n",
      "Checking segment_data for downstream rises in streambed elevation...\n",
      "Segment elevup and elevdn not specified for nstrm=-16 and isfropt=1\n",
      "passed.\n",
      "\n",
      "Checking reach_data for downstream rises in streambed elevation...\n",
      "7 reaches encountered with strtop < strtop of downstream reach.\n",
      "Elevation rises:\n",
      "k i j iseg ireach strtop strtopdn d_strtop reachID diff\n",
      "4 20 15 2 1 290.66015625 292.6883850097656 2.028228759765625 5 -2.028228759765625\n",
      "4 21 15 2 2 290.66015625 292.11285400390625 1.45269775390625 6 -1.45269775390625\n",
      "2 24 20 2 10 288.81939697265625 290.1935119628906 1.374114990234375 14 -1.374114990234375\n",
      "2 24 19 2 9 289.3415832519531 290.66015625 1.318572998046875 13 -1.318572998046875\n",
      "2 24 18 2 8 289.4702453613281 290.66015625 1.189910888671875 12 -1.189910888671875\n",
      "1 24 21 2 11 288.60980224609375 289.4702453613281 0.860443115234375 15 -0.860443115234375\n",
      "4 23 18 2 7 290.1935119628906 290.66015625 0.466644287109375 11 -0.466644287109375\n",
      "\n",
      "\n",
      "Checking reach_data for inconsistencies between streambed elevations and the model grid...\n",
      "passed.\n",
      "\n",
      "Checking segment_data for inconsistencies between segment end elevations and the model grid...\n",
      "Segment elevup and elevdn not specified for nstrm=-16 and isfropt=1\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of less than 0.0001...\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of greater than 1.0...\n",
      "passed.\n",
      "\n",
      "wrote plsnt_lgr_parent_SFR.chk\n",
      "wrote plsnt_lgr_parent.sfr.obs\n",
      "converting reach and segment data to package data...\n",
      "wrote /home/runner/work/modflow-setup/modflow-setup/examples/pleasant_lgr/external/plsnt_lgr_parent_packagedata.dat\n",
      "wrote /home/runner/work/modflow-setup/modflow-setup/examples/pleasant_lgr/plsnt_lgr_parent.sfr\n",
      "SFRmaker v. 0.11.3.post39+g85c5b4f\n",
      "\n",
      "Running Flopy v. 3.10.0.dev2 diagnostics...\n",
      "passed.\n",
      "\n",
      "Checking for continuity in segment and reach numbering...\n",
      "passed.\n",
      "\n",
      "Checking for increasing segment numbers in downstream direction...\n",
      "passed.\n",
      "\n",
      "Checking for circular routing...\n",
      "passed.\n",
      "\n",
      "Checking reach connections for proximity...\n",
      "0 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "\n",
      "\n",
      "0 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "\n",
      "\n",
      "\n",
      "Checking for model cells with multiple non-zero SFR conductances...\n",
      "1 model cells with multiple non-zero SFR conductances found.\n",
      "This may lead to circular routing between collocated reaches.\n",
      "Nodes with overlapping conductances:\n",
      "k\ti\tj\tiseg\tireach\trchlen\tstrthick\tstrhc1\n",
      "0\t27\t71\t1\t12\t13.225701332092285\t1.0\t1.0\n",
      "0\t27\t71\t1\t13\t26.684703826904297\t1.0\t1.0\n",
      "\n",
      "Checking for streambed tops of less than -10...\n",
      "isfropt setting of 1,2 or 3 requires strtop information!\n",
      "\n",
      "\n",
      "Checking for streambed tops of greater than 15000...\n",
      "isfropt setting of 1,2 or 3 requires strtop information!\n",
      "\n",
      "\n",
      "Checking segment_data for downstream rises in streambed elevation...\n",
      "Segment elevup and elevdn not specified for nstrm=-41 and isfropt=1\n",
      "passed.\n",
      "\n",
      "Checking reach_data for downstream rises in streambed elevation...\n",
      "passed.\n",
      "\n",
      "Checking reach_data for inconsistencies between streambed elevations and the model grid...\n",
      "passed.\n",
      "\n",
      "Checking segment_data for inconsistencies between segment end elevations and the model grid...\n",
      "Segment elevup and elevdn not specified for nstrm=-41 and isfropt=1\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of less than 0.0001...\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of greater than 1.0...\n",
      "passed.\n",
      "\n",
      "wrote plsnt_lgr_inset_SFR.chk\n",
      "converting reach and segment data to package data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote /home/runner/work/modflow-setup/modflow-setup/examples/pleasant_lgr/external/plsnt_lgr_inset_packagedata.dat\n",
      "wrote /home/runner/work/modflow-setup/modflow-setup/examples/pleasant_lgr/plsnt_lgr_inset.sfr\n"
     ]
    }
   ],
   "source": [
    "m.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the model\n",
    "\n",
    "**Note:** Running the model through Flopy (as below) requires specification of the MODFLOW executable. In Flopy, the executable is specified via the ``exe_name`` argument to the simulation constructor for MODFLOW 6, or model constructor for previous MODFLOW versions. Similarly, in Modflow-setup, the ``exe_name`` is specified in the ``simulation:`` or ``model:`` block of the [configuration file](https://doi-usgs.github.io/modflow-setup/latest/config-file-gallery.html#pleasant-lake-test-case). This example assumes that a MODFLOW 6 executable with the name \"mf6\" either resides in the model workspace, or is included in the system path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:54.440747Z",
     "iopub.status.busy": "2025-03-17T22:05:54.440417Z",
     "iopub.status.idle": "2025-03-17T22:05:58.289272Z",
     "shell.execute_reply": "2025-03-17T22:05:58.288855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: ../../../../../.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                   VERSION 6.3.0 release candidate 03/08/2022\n",
      "                               ***DEVELOP MODE***\n",
      "\n",
      "   MODFLOW 6 compiled Mar 08 2022 20:13:10 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.5.0\n",
      "                             Build 20211109_000000\n",
      "\n",
      "This software is preliminary or provisional and is subject to \n",
      "revision. It is being provided to meet the need for timely best \n",
      "science. The software has not received final approval by the U.S. \n",
      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
      "by the USGS or the U.S. Government as to the functionality of the \n",
      "software and related material nor shall the fact of release \n",
      "constitute any such warranty. The software is provided on the \n",
      "condition that neither the USGS nor the U.S. Government shall be held \n",
      "liable for any damages resulting from the authorized or unauthorized \n",
      "use of the software.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2025/03/17 22:05:54\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     1    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:     1\n",
      "    Solving:  Stress period:     3    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:     1\n",
      "    Solving:  Stress period:     5    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     6    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     7    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     8    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     9    Time step:     1\n",
      "    Solving:  Stress period:    10    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    11    Time step:     1\n",
      "    Solving:  Stress period:    12    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    13    Time step:     1\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2025/03/17 22:05:58\n",
      " Elapsed run time:  3.840 Seconds\n",
      " \n",
      " Normal termination of simulation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.simulation.run_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the head results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:58.291130Z",
     "iopub.status.busy": "2025-03-17T22:05:58.290721Z",
     "iopub.status.idle": "2025-03-17T22:05:58.303996Z",
     "shell.execute_reply": "2025-03-17T22:05:58.303638Z"
    }
   },
   "outputs": [],
   "source": [
    "tmr_parent_headsobj = bf.HeadFile('../data/pleasant/pleasant.hds')\n",
    "lgr_parent_headsobj = bf.HeadFile('plsnt_lgr_parent.hds')\n",
    "lgr_inset_headsobj = bf.HeadFile('plsnt_lgr_inset.hds')\n",
    "\n",
    "# read the head results for the last stress period\n",
    "kper = 12\n",
    "lgr_parent_hds = lgr_parent_headsobj.get_data(kstpkper=(0, kper))\n",
    "lgr_inset_hds = lgr_inset_headsobj.get_data(kstpkper=(0, kper))\n",
    "\n",
    "# Get the water table elevation from the 3D head results\n",
    "inset_wt = get_water_table(lgr_inset_hds, nodata=1e30)\n",
    "parent_wt = get_water_table(lgr_parent_hds, nodata=1e30)\n",
    "\n",
    "# put in the lake level (not included in head output)\n",
    "lake_results = pd.read_csv('lake1.obs.csv')\n",
    "stage = lake_results['STAGE'][kper]\n",
    "inset_wt[inset.lakarr[0] == 1] = stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First combine the parent and inset model head results\n",
    "(into a single grid at the inset model resolution; for a nicer looking plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:58.305519Z",
     "iopub.status.busy": "2025-03-17T22:05:58.305352Z",
     "iopub.status.idle": "2025-03-17T22:05:58.377516Z",
     "shell.execute_reply": "2025-03-17T22:05:58.377076Z"
    }
   },
   "outputs": [],
   "source": [
    "# make the single grid\n",
    "l, b, r, t = m.modelgrid.bounds\n",
    "xi = np.arange(l, r, 40)\n",
    "yi = np.arange(b, t, 40)[::-1]\n",
    "Xi, Yi = np.meshgrid(xi, yi)\n",
    "\n",
    "# make a single set of points\n",
    "# including both parent and inset cell centers\n",
    "# and water table values\n",
    "x = m.modelgrid.xcellcenters[~parent_wt.mask]\n",
    "y = m.modelgrid.ycellcenters[~parent_wt.mask]\n",
    "x = np.append(x, inset.modelgrid.xcellcenters[~inset_wt.mask])\n",
    "y = np.append(y, inset.modelgrid.ycellcenters[~inset_wt.mask])\n",
    "z = parent_wt[~parent_wt.mask].data\n",
    "z = np.append(z, inset_wt[~inset_wt.mask].data)\n",
    "\n",
    "# interpolate the results from the points\n",
    "# onto the single inset resolution grid\n",
    "results = griddata((x, y), z, (Xi, Yi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the plot\n",
    "* include the parent and inset model grids\n",
    "* show the head contours for the combined parent/inset simulation\n",
    "* show SFR boundary condition cells in green\n",
    "* show the lakebed leakance zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:58.379714Z",
     "iopub.status.busy": "2025-03-17T22:05:58.379195Z",
     "iopub.status.idle": "2025-03-17T22:05:59.441752Z",
     "shell.execute_reply": "2025-03-17T22:05:59.441234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 650x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['axes.labelsize'] = 8\n",
    "plt.rcParams['xtick.labelsize'] = 8\n",
    "plt.rcParams['ytick.labelsize'] = 8\n",
    "\n",
    "layer = 0\n",
    "fig, ax = plt.subplots(figsize=(6.5, 6.5))\n",
    "# create Flopy plot objects\n",
    "parent_mv = flopy.plot.PlotMapView(model=m, ax=ax, layer=layer)\n",
    "inset_mv = flopy.plot.PlotMapView(model=inset, ax=ax, layer=layer)\n",
    "\n",
    "# plot boundary condition cells from Modflow-setup array properties\n",
    "inset_bcs = np.ma.masked_array(inset._isbc2d, mask=inset._isbc2d==0)\n",
    "parent_bcs = np.ma.masked_array(m._isbc2d, mask=m._isbc2d==0)\n",
    "parent_mv.plot_array(parent_bcs, vmin=0, vmax=5)\n",
    "inset_mv.plot_array(inset_bcs, vmin=0, vmax=5)\n",
    "\n",
    "#bdlknc_values = inset.lak.connectiondata.array['bedleak']\n",
    "conn = inset.lak.connectiondata.array\n",
    "k, i, j = cellids_to_kij(conn['cellid'])\n",
    "bdlknc = np.zeros((inset.nlay, inset.nrow, inset.ncol))\n",
    "bdlknc[k, i, j] = conn['bedleak']\n",
    "bdlknc = np.max(bdlknc, axis=0)\n",
    "bdlknc = np.ma.masked_array(bdlknc, mask=bdlknc == 0)\n",
    "inset_mv.plot_array(bdlknc, cmap='Blues', zorder=200)\n",
    "\n",
    "# contour the combined inset/parent head results\n",
    "levels = np.arange(290, 315, 2)\n",
    "ctr = ax.contour(Xi, Yi, results, levels=levels, colors='b', zorder=10)\n",
    "labels = ax.clabel(ctr, inline=True, fontsize=8, inline_spacing=10)\n",
    "plt.setp(labels, path_effects=[\n",
    "    patheffects.withStroke(linewidth=3, foreground=\"w\")])\n",
    "\n",
    "# plot the grid cell edges\n",
    "lcp = parent_mv.plot_grid(lw=0.5, ax=ax)\n",
    "lci = inset_mv.plot_grid(lw=0.5)\n",
    "\n",
    "ax.set_ylim(b, t)\n",
    "ax.set_xlim(l, r)\n",
    "ax.set_aspect(1)\n",
    "ax.set_ylabel('Northing, in Wisconsin Transverse Mercator (meters)')\n",
    "ax.set_xlabel('Easting, in Wisconsin Transverse Mercator (meters)')\n",
    "\n",
    "ax.text(555600, 390450, 'Pleasant\\nLake', ha='left', va='top', color='DarkBlue', \n",
    "        fontsize=10, fontstyle='italic', family='Serif', zorder=202)\n",
    "txt = ax.text(556400, 391000, 'Chaffee Creek (SFR)', ha='left', va='top', color='DarkGreen', \n",
    "        fontsize=10, fontstyle='italic', family='Serif', zorder=20)\n",
    "txt.set_path_effects([patheffects.withStroke(linewidth=5, foreground='w')])\n",
    "txt = ax.text(556700, 388900, 'Tagatz\\nCreek (SFR)', ha='left', va='top', color='DarkGreen', \n",
    "        fontsize=10, fontstyle='italic', family='Serif', zorder=20)\n",
    "txt.set_path_effects([patheffects.withStroke(linewidth=5, foreground='w')])\n",
    "\n",
    "txt = ax.annotate(\"Littoral zone\",\n",
    "            xy=(555450, 390100), xycoords='data',\n",
    "            xytext=(555050,390100), textcoords='data',\n",
    "                  ha='right',\n",
    "            arrowprops=dict(arrowstyle=\"-|>\",\n",
    "                            connectionstyle=\"arc3\", fc='k'),\n",
    "            path_effects=[patheffects.withStroke(linewidth=4, foreground='w')],\n",
    "            zorder=203\n",
    "            )\n",
    "txt.arrow_patch.set_path_effects([\n",
    "    patheffects.Stroke(linewidth=2, foreground=\"w\"),\n",
    "    patheffects.Normal()])\n",
    "\n",
    "txt = ax.annotate(\"Profundal zone\",\n",
    "            xy=(555600, 390100), xycoords='data',\n",
    "            xytext=(555800,389500), textcoords='data',\n",
    "                  ha='right',\n",
    "            arrowprops=dict(arrowstyle=\"-|>\",\n",
    "                            connectionstyle=\"arc3\", fc='k'),\n",
    "            path_effects=[patheffects.withStroke(linewidth=4, foreground='w')],\n",
    "            zorder=203\n",
    "            )\n",
    "txt.arrow_patch.set_path_effects([\n",
    "    patheffects.Stroke(linewidth=2, foreground=\"w\"),\n",
    "    patheffects.Normal()])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('postproc/pdfs/figure_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use [Modflow-export](https://github.com/aleaf/modflow-export) to export the modflow input to PDFs, rasters and shapefiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:05:59.443358Z",
     "iopub.status.busy": "2025-03-17T22:05:59.443196Z",
     "iopub.status.idle": "2025-03-17T22:06:24.678126Z",
     "shell.execute_reply": "2025-03-17T22:06:24.677682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating postproc/plsnt_lgr_parent/pdfs...\n",
      "creating postproc/plsnt_lgr_parent/shps...\n",
      "creating postproc/plsnt_lgr_parent/rasters...\n",
      "\n",
      "dis package...\n",
      "wrote postproc/plsnt_lgr_parent/rasters/thickness_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/thickness_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/thickness_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/thickness_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/thickness_lay4.tif\n",
      "top:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/top.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botm:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/botm_lay4.tif\n",
      "idomain:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/idomain_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/idomain_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/idomain_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/idomain_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/idomain_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ic package...\n",
      "strt:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay4.tif\n",
      "\n",
      "npf package...\n",
      "icelltype:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k33:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k33_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k33_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/k33_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k33_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/k33_lay4.tif\n",
      "\n",
      "sto package...\n",
      "iconvert:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/iconvert_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/iconvert_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/iconvert_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/iconvert_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/iconvert_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ss:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay4.tif\n",
      "sy:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "rcha_0 package...\n",
      "irch:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/irch_per0.tif\n",
      "recharge:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per4.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per5.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per6.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per7.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per8.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per9.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per10.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per11.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per12.tif\n",
      "\n",
      "chd_0 package...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/shps/chd0_stress_period_data.shp\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "head:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boundname:\n",
      "Warning, variable: boundname\n",
      "Export of non-period data from transientlists not implemented!\n",
      "\n",
      "obs_0 package...\n",
      "skipped, not implemented yet\n",
      "\n",
      "sfr_0 package...\n",
      "wrote postproc/plsnt_lgr_parent/shps/plsnt_lgr_parent.sfr.shp\n",
      "\n",
      "wel_0 package...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp\n",
      "q:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boundname:\n",
      "Warning, variable: boundname\n",
      "Export of non-period data from transientlists not implemented!\n",
      "\n",
      "obs_1 package...\n",
      "skipped, not implemented yet\n",
      "\n",
      "obs_2 package...\n",
      "wrote postproc/plsnt_lgr_parent/shps/obs2_stress_period_data.shp\n",
      "creating postproc/plsnt_lgr_inset/pdfs...\n",
      "creating postproc/plsnt_lgr_inset/shps...\n",
      "creating postproc/plsnt_lgr_inset/rasters...\n",
      "\n",
      "dis package...\n",
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/top.tif\n",
      "botm:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/botm_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/botm_lay1.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/botm_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/botm_lay3.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/botm_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idomain:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/idomain_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/idomain_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/idomain_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/idomain_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/idomain_lay4.tif\n",
      "\n",
      "ic package...\n",
      "strt:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/strt_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/strt_lay1.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/strt_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/strt_lay3.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/strt_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "npf package...\n",
      "icelltype:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay4.tif\n",
      "k:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/k_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/k_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k_lay4.tif\n",
      "k33:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k33_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/k33_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k33_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/k33_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/k33_lay4.tif\n",
      "\n",
      "sto package...\n",
      "iconvert:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay1.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay3.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/iconvert_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ss:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/ss_lay4.tif\n",
      "sy:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/sy_lay0.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/sy_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/sy_lay2.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/sy_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/sy_lay4.tif\n",
      "\n",
      "rcha_0 package...\n",
      "irch:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/irch_per0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recharge:\n",
      "wrote postproc/plsnt_lgr_inset/rasters/recharge_per0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "sfr_0 package...\n",
      "wrote postproc/plsnt_lgr_inset/shps/plsnt_lgr_inset.sfr.shp\n",
      "\n",
      "lak_0 package...\n",
      "skipping lak0.perioddata; efficient export not implemented\n",
      "\n",
      "obs_0 package...\n",
      "skipped, not implemented yet\n",
      "\n",
      "obs_1 package...\n",
      "wrote postproc/plsnt_lgr_inset/shps/obs1_stress_period_data.shp\n"
     ]
    }
   ],
   "source": [
    "for model in m, inset:\n",
    "    mfexport.export(model, model.modelgrid, output_path=f'postproc/{model.name}/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modflow-export can also create a summary table of the model inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-17T22:06:24.679737Z",
     "iopub.status.busy": "2025-03-17T22:06:24.679557Z",
     "iopub.status.idle": "2025-03-17T22:06:26.318650Z",
     "shell.execute_reply": "2025-03-17T22:06:26.318223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summarizing plsnt_lgr_parent input...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped, not implemented yet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped, not implemented yet\n",
      "summarizing plsnt_lgr_inset input...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped, not implemented yet\n"
     ]
    }
   ],
   "source": [
    "for model in m, inset:\n",
    "    mfexport.summarize(model, output_path=f'postproc/{model.name}/')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}