{
 "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": "2024-11-14T14:19:53.280176Z",
     "iopub.status.busy": "2024-11-14T14:19:53.279787Z",
     "iopub.status.idle": "2024-11-14T14:19:54.576377Z",
     "shell.execute_reply": "2024-11-14T14:19:54.575823Z"
    }
   },
   "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": "2024-11-14T14:19:54.578641Z",
     "iopub.status.busy": "2024-11-14T14:19:54.578150Z",
     "iopub.status.idle": "2024-11-14T14:19:55.232729Z",
     "shell.execute_reply": "2024-11-14T14:19:55.232225Z"
    }
   },
   "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": "2024-11-14T14:19:55.234816Z",
     "iopub.status.busy": "2024-11-14T14:19:55.234467Z",
     "iopub.status.idle": "2024-11-14T14:19:55.240436Z",
     "shell.execute_reply": "2024-11-14T14:19:55.239939Z"
    }
   },
   "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": "2024-11-14T14:19:55.268268Z",
     "iopub.status.busy": "2024-11-14T14:19:55.267892Z",
     "iopub.status.idle": "2024-11-14T14:19:55.272237Z",
     "shell.execute_reply": "2024-11-14T14:19:55.271825Z"
    }
   },
   "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": "2024-11-14T14:19:55.273739Z",
     "iopub.status.busy": "2024-11-14T14:19:55.273572Z",
     "iopub.status.idle": "2024-11-14T14:19:55.276998Z",
     "shell.execute_reply": "2024-11-14T14:19:55.276598Z"
    }
   },
   "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": "2024-11-14T14:19:55.278595Z",
     "iopub.status.busy": "2024-11-14T14:19:55.278268Z",
     "iopub.status.idle": "2024-11-14T14:19:56.183067Z",
     "shell.execute_reply": "2024-11-14T14:19:56.182533Z"
    }
   },
   "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 0.19s\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": "2024-11-14T14:19:56.184712Z",
     "iopub.status.busy": "2024-11-14T14:19:56.184533Z",
     "iopub.status.idle": "2024-11-14T14:19:56.187403Z",
     "shell.execute_reply": "2024-11-14T14:19:56.186816Z"
    }
   },
   "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": "2024-11-14T14:19:56.188974Z",
     "iopub.status.busy": "2024-11-14T14:19:56.188695Z",
     "iopub.status.idle": "2024-11-14T14:20:05.596542Z",
     "shell.execute_reply": "2024-11-14T14:20:05.595968Z"
    }
   },
   "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": "2024-11-14T14:20:05.598645Z",
     "iopub.status.busy": "2024-11-14T14:20:05.598456Z",
     "iopub.status.idle": "2024-11-14T14:20:05.605966Z",
     "shell.execute_reply": "2024-11-14T14:20:05.605474Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pleasant Lake test case version 0.1.post58+g45e95f4\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": "2024-11-14T14:20:05.607780Z",
     "iopub.status.busy": "2024-11-14T14:20:05.607398Z",
     "iopub.status.idle": "2024-11-14T14:20:05.610788Z",
     "shell.execute_reply": "2024-11-14T14:20:05.610393Z"
    }
   },
   "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": "2024-11-14T14:20:05.612342Z",
     "iopub.status.busy": "2024-11-14T14:20:05.612173Z",
     "iopub.status.idle": "2024-11-14T14:20:05.616264Z",
     "shell.execute_reply": "2024-11-14T14:20:05.615789Z"
    }
   },
   "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": "2024-11-14T14:20:05.617946Z",
     "iopub.status.busy": "2024-11-14T14:20:05.617574Z",
     "iopub.status.idle": "2024-11-14T14:20:05.624786Z",
     "shell.execute_reply": "2024-11-14T14:20:05.624355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'plsnt_lgr_inset': plsnt_lgr_inset model version 0.5.0.post58+g45e95f4\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": "2024-11-14T14:20:05.626677Z",
     "iopub.status.busy": "2024-11-14T14:20:05.626261Z",
     "iopub.status.idle": "2024-11-14T14:20:05.808626Z",
     "shell.execute_reply": "2024-11-14T14:20:05.808129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f45612fcf80>"
      ]
     },
     "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": "2024-11-14T14:20:05.810462Z",
     "iopub.status.busy": "2024-11-14T14:20:05.810125Z",
     "iopub.status.idle": "2024-11-14T14:20:06.096206Z",
     "shell.execute_reply": "2024-11-14T14:20:06.095717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X71ltLMmubIB7JCUlUY3icW7dXQsAvsxWeLR69WqtXr1ahw8fliQNGzZMP//5zzV16lRJkmEYWrZsmX73u9+prq5OY8aM0W9/+1sNGzbMd47m5mYtXrxYzz//vE6fPq2JEyfqySefVEZGhnPPyuWsfph2I5bcobNYAgc7rLxOuvXnoSuW74W6Is+LjSUBBK+j15PWY1b7vlk5FwDAO2yFRxkZGXr00Uc1ZMgQSdK6det066236t1339WwYcP02GOP6Te/+Y2efvppXXLJJfrFL36hyZMn68CBA4qNjZUkLViwQH/5y19UUFCggQMHatGiRbrppptUXl6uHj16OP8MXczKh2k38mq1FLyBJXD4Mi//PDixfI+KPACh0Lp7sZXXU6uvuV6svgQAmLMVHt18881+tx9++GGtXr1aO3bs0GWXXaYnnnhC999/v6ZNmybpi3ApJSVFGzZs0L333quGhgatWbNGzz77rCZNmiRJeu6555SZmak333xT1113nUNPC0AkYgkcviwSfh6oyAPgBr1795ZkrdrTat83L1dfovsIttqOnz90J53ueXT+/Hm9+OKLOnnypHJzc1VVVaWamhpNmTLFNyYmJkbjxo3Ttm3bdO+996q8vFwtLS1+Y9LT05Wdna1t27YRHgGwJFQfuHlD4A1O/jy4dUmukxVYTgZkgfpwsCMb4A5Wl5oFej0tKyuz9JprZZzT1ZcETOHh5DLGUJ7r0KFDkpyrtrNaRee2fwcCMthhOzzau3evcnNzdebMGV144YUqLCzUZZddpm3btkmSUlJS/ManpKToww8/lCTV1NQoOjpaAwYMaDOmpqbG9DGbm5vV3Nzsu93Y2Gh32nCYF19EvDhnBMepD9xUJ0UGKx+KvPjmye4ucE4EZHZ2ZWNHNiB87CxHk7xZfelUwCTxQfqrOnqudsIXq+PCca4RI0b4WrJ8VVdU0bn138HKuNbXE3RftsOjSy+9VLt371Z9fb1eeukl3XPPPSotLfUdj4qK8htvGEab+74q0Jj8/HwtW7bM7768vDy7U4cD2CkOkcLLDZnhvEgIE61WAzjxocjKrmzsyAaEn5XlaJK3qy8lZwMmyb39mpxaYuVkVU5H4UvrYwUKYJxcEmn3XEOGDAm62s5KFZ3b/x2snOv48eMdvh92I96jO8t2eBQdHe17gcjJydHOnTu1YsUK/fSnP5X0RXXRl3doOX78uK8aKTU1VWfPnlVdXZ1f9dHx48c1duxY08dcsmSJFi5c6Lvd2NioNWvW2J06HMBOcYg0Xm7IDOd0hzCxKwIydmUDuk5tbW2H4aud7duthsteFqoP+F3RrynUS6ycrMoJFL60Pl4ol0TaOZdTwjH3UJ2L4gG06nTPo1aGYai5uVlZWVlKTU1VcXGxRo4cKUk6e/asSktL9ctf/lKSNHr0aPXq1UvFxcWaPn26JKm6ulr79u3TY489ZvoYMTExiomJCXaqcBA7xaE7iISGzLDHrb2FnGAnICNkB8KrtrZWq1atsjS2vr6eENcit/ZrCtUSq66oykHko3gArWyFR/fdd5+mTp2qzMxMnThxQgUFBdqyZYuKiooUFRWlBQsW6JFHHtHQoUM1dOhQPfLII+rbt6/uvvtuSVJ8fLxmz56tRYsWaeDAgUpISNDixYs1fPhw3+5rAOA27IgFKTy9hbqClb9UAgiv1oqj2267TUlJSe2OqaysVElJiT744AOdO3eu3TH8Tuk6Tr43COUSq3BU5SAyUDwAW+HRsWPHNHPmTFVXVys+Pl4jRoxQUVGRJk+eLEn6yU9+otOnT+uHP/yh6urqNGbMGL3xxhuKjY31nWP58uXq2bOnpk+frtOnT2vixIl6+umn1aNHD2efGQCEGEvguodQ9hYC0L0lJSWZVhV99NFHkrzdq607IMwBws+L77uqq6vDPYU2bIVHgfoMRUVFKS8vr8Nm1r1799bKlSu1cuVKOw8NAJ7GErjuIxKab1vd2hdA+Fhphu32SkgA6EqR0K/JTYLueQQAsI4lcJHPy823vd7XCeiOWIoKAO3zcr8mz1ceAZHAix+ovThnBIclcN7nxebbXdHXKdCOUNHR0Ro4cKC9iQIAAFjkxX5NrdWlbkJ4hG4jEsoW+Ss/pK5ZAkfAFFpub77tRP+NhoYGSdLLL78c8PHmzZtHgAQAAOBihEfoNrxctkjPArTHiSVw9FgKL68337YyrwkTJmjo0KHtjvnkk09UWFjo21kKgL/KykrT6j2CfQBAKBEeodvxYtmiRM8CdF6gn3l6LLmXW5tv25lX7969TXeLArqj2tragIFp605qJSUlAc/n5LVvJRC22lDfTefi9xMABI/wCPAQ3hihq9BjyZ3sNN8OZWWiW+cFuF1tba1WrVplefyYMWN00UUXtXvMyWvszJkzkqy9zlv9XeDGc3X0+0nqmuALznNjMMnPA7oDwiPAA+y8qWNpEZzWFT2W+Dm1x8puSuF44+rULk+BmmpLNNZGZGitOLrtttuUlJRkOq6yslIlJSW66KKLHLnGAl3/x48fl2QtELbaq81N57Lz+0lyNviy8gcVtwYdbjvXoUOHJLkzmHTrzwPgJMIjwANau+2ztAjh5ESPJX5OnefWndussNNUW6KxNtwv0JK01qA0KSmpw+WcVgJVK+z88al1XoHCKqu92qyEy6E8V6DfT5KzwZedwMqtQYdbzzVixAgNGTKk3WOhDibd/vNgtdoOsILwCPAQlhbBC/g5DS2379zWkZaWFkkdN9WW/tVY++OPP+7wgznVSeFjpY/PqVOn1Ldv3w7HuPX/oZXn19DQoI0bN1o6X319fUh6gVn545PkzteHrhDq4MvqH1TcFnS4/VxDhgxxVTDpxp8Hu9V2vN+CFYRHQIQI9fbtEr9MYB9L4LqOW3dus/J4AwYM6PCDtJ0KpenTp6t///4djrESYlgZ4+S5Qv14Tp7LTmhihdv+H9p9fpMnT1ZWVla7x1qXo33wwQc6d+6c6TnsXItWrjGrrw9wntsqsLxcPdY6zstC+W9qpdrOzvut1mWDXsJnFWcRHgERJtTbt0v8tQL2sQQu9EK9c5uTj2elQqmqqkrFxcV64YUXOjFbOMFKaOLl/4cdPT/pX88xNjbWNAxt3UXNiWWm9EMEEIgT77cOHjyoiooK7dmzR3v27OmKaXY5XgOdQXgEdFOh/msFVUzoDCeXwHX3Nw52dkhzIpDrih3ZOqpQau0RE2gJnJUQw8oYJ88V6sfrqnN1FJq0/v/x8v/Djp7fl+ffEbvLyDq6Fu00uY705WgAghPoc0FFRUXA1y034jXQWYRHAEyFo4qpu3/Ah3V2e/109CGsO4WXoQ7knNwpzoklcHZCjBMnTnQYCJw4cSLgOKfGeOFcTu0E5dZ/Byd/TgNdF3aqiqw0uQaAYFn5XOBGvAY6h/AIQNCc3OGED/iwK9DPX6iXa3lZqJtv2w2rnNxZpqPXEzvbQVsd59Zdi9x4Li/P3eq4QL1DqCoCALgN4RGAkHDyAz5L4GBHVyyfinShar5tN6xyssrRymtNR9tBW51XuHYtGjx4sBISEkzP9dlnn+nDDz901W5KkbITlJWfU6u9Q6gqAoDgefGzQXV1dbin0AbhEQBXsPIhkkbeCEawy6f4mfkXp6u5QrmzjJ2wINB20FbnFcoP+K3NxT/88EN9+OGHAccH2n4+HLy+E5STP6cE2gDQeXYrnNExwiMAruKmRt6EBd0HS9vs6S7VXE70uLGzBM7JZVGBqqbsvE6GeumXE0sPrYwJ57m6w5bkABBuViuc3YjKIwBwQKgbebNMLvJ1lzDEaU5Vc4U6pLE6LlRhTlcssbJSNRXodTLUS7+6YumhW38eCKEBIHS82Oy7dXdONyE8AhCxgq1iYplc9+Om5UdeZ6eaK5QVN+HqlxMozHF6iZVVblv65fTSQ7eeixAaAOA1hEcAujU3LZOTCJm8wGqVTHdnp5rLrX2KwhHmIPJ7HvHzAADwIsIjAAgg1MvkJKqY3Mhu00WWpXyBD+UAAADeR3gEAA4JdRUT4URo2d1WnmUpAAAAiBSERwAQQk5UMbWGE5988on27t1reh4qlLqG1SoZK42iAQAAAC8gPAIAF+oooLC7fIolcKFl5/8P1WMAAADwAsIjAPAYq8unnGrkTcBkj51G0SxtAwAAgBcQHgGAR4W6kTc7xdlDc2cAAABECsIjAIhwwTbyZqe4rhPo34N/LwAAALgB4REAgJ3iQsxu36ru/u8FAACA8CI8AgBYwk5xzrHat4reSAAQ+axWoVrZxTPYMeE8FwB3IzwCADiKneKssxLIlZWV8aYbAALwYmhy6NAhSdZ/J1oZ59SYcJzLam9FficC4UF4BAAIma7YKS6Sl3TZCdsi+d8BQPfmVADj1tBkxIgRGjJkiOnx1ipUK5W9wY4Jx7ns9lYkiALCg/AIABByLIGzxkrYxtI2AG7kRPWOnWCoowDGraFJ65ghQ4ZYqkK1sounE2PCcS4rvRXdGETZrUQDvIzwCADgWiyB+4KVN/C8cQUQrFAHPlZfv60EQ4ECGLeGJlaef3fh5SCqO7wXAQiPAACexBK4L7BzG4BA3Br4WP2AbyUYAr4sVEGU1Z9lO+9FrC65A0KN8AgA4GlOLoHz4tIvuzu3dbTMjzekkcNtjYHZvalzgv33stuQOdSBj9UP+EA4OVWJFui9iN0ld1QxIdRshUf5+fl6+eWX9Y9//EN9+vTR2LFj9ctf/lKXXnqpb0xUVFS73/vYY4/pv//7vyVJ48ePV2lpqd/xO+64QwUFBXbnDwCAJZG+dCDQ86P5tvt5efcmJ8/l1t2bQhnIOV0JZLUhM4EP0LWcWHJHFRPCxVZ4VFpaqrlz5+qKK67QuXPndP/992vKlCl677331K9fP0lSdXW13/e8/vrrmj17tm6//Xa/++fMmaOHHnrId9uLf+0FAMAr7DTfjuQm5HaEKizoisAnVLs3OXkut+/eFI5ALthKILsNmQG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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": "2024-11-14T14:20:06.097876Z",
     "iopub.status.busy": "2024-11-14T14:20:06.097707Z",
     "iopub.status.idle": "2024-11-14T14:20:06.791540Z",
     "shell.execute_reply": "2024-11-14T14:20:06.791025Z"
    }
   },
   "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.post35+g0f15d42\n",
      "\n",
      "Running Flopy v. 3.9.0.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.post35+g0f15d42\n",
      "\n",
      "Running Flopy v. 3.9.0.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": "2024-11-14T14:20:06.793401Z",
     "iopub.status.busy": "2024-11-14T14:20:06.793003Z",
     "iopub.status.idle": "2024-11-14T14:20:10.964839Z",
     "shell.execute_reply": "2024-11-14T14:20:10.964346Z"
    }
   },
   "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): 2024/11/14 14:20:06\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     5    Time step:     1\n",
      "    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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    10    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    11    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    12    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:    13    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/11/14 14:20:10\n",
      " Elapsed run time:  4.163 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": "2024-11-14T14:20:10.966645Z",
     "iopub.status.busy": "2024-11-14T14:20:10.966259Z",
     "iopub.status.idle": "2024-11-14T14:20:10.980259Z",
     "shell.execute_reply": "2024-11-14T14:20:10.979869Z"
    }
   },
   "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": "2024-11-14T14:20:10.982109Z",
     "iopub.status.busy": "2024-11-14T14:20:10.981708Z",
     "iopub.status.idle": "2024-11-14T14:20:11.057103Z",
     "shell.execute_reply": "2024-11-14T14:20:11.056610Z"
    }
   },
   "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": "2024-11-14T14:20:11.059126Z",
     "iopub.status.busy": "2024-11-14T14:20:11.058946Z",
     "iopub.status.idle": "2024-11-14T14:20:12.250266Z",
     "shell.execute_reply": "2024-11-14T14:20:12.249758Z"
    }
   },
   "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": "2024-11-14T14:20:12.252323Z",
     "iopub.status.busy": "2024-11-14T14:20:12.251964Z",
     "iopub.status.idle": "2024-11-14T14:20:39.140746Z",
     "shell.execute_reply": "2024-11-14T14:20:39.140223Z"
    }
   },
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/strt_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "npf package...\n",
      "icelltype:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/icelltype_lay4.tif\n",
      "k:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/k_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/ss_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sy:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay0.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay1.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay2.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay3.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/sy_lay4.tif\n",
      "\n",
      "rcha_0 package...\n",
      "irch:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/irch_per0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recharge:\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per0.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per1.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per3.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per5.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per6.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per7.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per8.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per9.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per10.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per11.tif\n",
      "wrote postproc/plsnt_lgr_parent/rasters/recharge_per12.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "chd_0 package...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing postproc/plsnt_lgr_parent/shps/chd0_stress_period_data.shp... Done\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",
      "writing postproc/plsnt_lgr_parent/shps/plsnt_lgr_parent.sfr.shp... Done\n",
      "\n",
      "wel_0 package...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing postproc/plsnt_lgr_parent/shps/wel0_stress_period_data.shp... Done\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "writing postproc/plsnt_lgr_parent/shps/obs2_stress_period_data.shp... Done\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay1.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/thickness_lay3.tif\n",
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay1.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay2.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay3.tif\n",
      "wrote postproc/plsnt_lgr_inset/rasters/icelltype_lay4.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k:\n",
      "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",
      "writing postproc/plsnt_lgr_inset/shps/plsnt_lgr_inset.sfr.shp... Done\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",
      "writing postproc/plsnt_lgr_inset/shps/obs1_stress_period_data.shp... Done\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": "2024-11-14T14:20:39.142433Z",
     "iopub.status.busy": "2024-11-14T14:20:39.142255Z",
     "iopub.status.idle": "2024-11-14T14:20:40.749401Z",
     "shell.execute_reply": "2024-11-14T14:20:40.748923Z"
    }
   },
   "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}/')"
   ]
  }
 ],
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