{
 "cells": [
  {
   "attachments": {
    "ex01a.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ex01a.png](attachment:ex01a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02: Building and post-processing a MODFLOW 6 model\n",
    "\n",
    "A MODFLOW 6 model will be developed of the domain shown above. This model simulation is based on example 1 in [Pollock, D.W., 2016, User guide for MODPATH Version 7—A particle-tracking model for MODFLOW: U.S. Geological Survey Open-File Report 2016–1086, 35 p., http://dx.doi.org/10.3133/ofr20161086](https://doi.org/10.3133/ofr20161086).\n",
    "\n",
    "The model domain will be discretized into 3 layers, 21 rows, and 20 columns. A constant value of 500 ft will be specified for `delr` and `delc`. The top (`TOP`) of the model should be set to 400 ft and the bottom of the three layers should be set to 220 ft, 200 ft, and 0 ft, respectively. The model has one steady-state stress period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import flopy\n",
    "from flopy.plot import styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get started lets install MODFLOW 6, other MODFLOW-based executables (MODFLOW-2005, MT3DMS, _etc._), and utilities programs used by FloPy (gridgen and triangle) in the Miniforge class environment (`pyclass`) using FloPy `get-modflow` functionality (`flopy.utils.get_modflow()`). Remember that `Shift-Tab` can be used to see the `docstrings` for a Python function, method, or function. Press `Shift-Tab` after the opening parenthesis in `flopy.utils.get_modflow()` below to see the `docstrings` for the function and determine the required (`args`) and optional arguments (`kwaargs`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flopy.utils.get_modflow(\":python\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before creating any of the MODFLOW 6 FloPy objects you should define the simulation workspace (`ws`) where the model files are and the simulation name (`name`). The `ws` should be set to `'data/ex01b'` and `name` should be set to `ex01b`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = \"../temp/ex01b\"\n",
    "name = \"ex01b\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a simulation object, a temporal discretization object, and a iterative model solution object using `flopy.mf6.MFSimulation()`, `flopy.mf6.ModflowTdis()`, and `flopy.mf6.ModflowIms()`, respectively. Set the `sim_name` to `name` and `sim_ws` to `ws` in the simulation object. Use default values for all temporal discretization and iterative model solution variables. Make sure to include the simulation object (`sim`) as the first variable in the temporal discretization and iterative model solution objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create simulation (sim = flopy.mf6.MFSimulation())\n",
    "sim = flopy.mf6.MFSimulation(sim_name=name, sim_ws=ws)\n",
    "\n",
    "# create tdis package (tdis = flopy.mf6.ModflowTdis(sim))\n",
    "tdis = flopy.mf6.ModflowTdis(sim)\n",
    "\n",
    "# create iterative model solution (ims = flopy.mf6.ModflowIms(sim))\n",
    "ims = flopy.mf6.ModflowIms(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the groundwater flow model object (`gwf`) using `flopy.mf6.ModflowGwf()`. Make sure to include the simulation object (`sim`) as the first variable in the groundwater flow model object and set `modelname` to `name`. Use `Shift-Tab` to see the optional variables that can be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gwf = flopy.mf6.ModflowGwf(sim, modelname=name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the discretization package using `flopy.mf6.ModflowGwfdis()`. Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `DIS` package (`flopy.mf6.ModflowGwfdis()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-dis.html).\n",
    "\n",
    "FloPy can accommodate all of the options for specifying array data for a model. `CONSTANT` values for a variable can be specified by using a `float` or `int` python variable (as is done below for `DELR`, `DELC`, and `TOP`). `LAYERED` data can be specified by using a list or `CONSTANT` values for each layer (as is done below for `BOTM` data) or a list of numpy arrays or lists. Three-Dimensional data can be specified using a three-dimensional numpy array (with a shape of `(nlay, nrow, ncol)`) for this example. More information on how to specify array data can be found in the [FloPy ReadTheDocs](https://flopy.readthedocs.io/en/latest/Notebooks/mf6_data_tutorial07.html#MODFLOW-6:-Working-with-MODFLOW-Grid-Array-Data). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlay, nrow, ncol = 3, 21, 20\n",
    "delr = delc = 500.0\n",
    "top = 400.0\n",
    "botm = [220, 200, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis = flopy.mf6.ModflowGwfdis(\n",
    "    gwf,\n",
    "    nlay=nlay,\n",
    "    nrow=nrow,\n",
    "    ncol=ncol,\n",
    "    delr=delr,\n",
    "    delc=delc,\n",
    "    top=top,\n",
    "    botm=botm,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`flopy.plot.PlotMapView()` and `flopy.plot.PlotCrossSection()` can be used to confirm that the discretization is correctly defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x174c2ec30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.PlotMapView(model=gwf)\n",
    "mm.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PatchCollection at 0x174c53a40>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = flopy.plot.PlotCrossSection(model=gwf, line={\"row\": 10})\n",
    "xs.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the initial conditions (IC) package\n",
    "\n",
    "Create the initial conditions package (`IC`) using `flopy.mf6.ModflowGwfic()` and set the initial head (`strt`) to 320. Default values can be used for the rest of the initial conditions package input. Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `IC` package (`flopy.mf6.ModflowGwfic()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-ic.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic = flopy.mf6.ModflowGwfic(gwf, strt=320.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the node property flow (NPF) package\n",
    "\n",
    "The hydraulic properties for the model are defined in the image above and are specified in the node property flow package (`NPF`) using `flopy.mf6.ModflowGwfnpf()`. The first layer should be convertible (unconfined) and the remaining two layers will be non-convertible so `icelltype` should be `[1, 0, 0]`. The variable `save_specific_discharge` should be set to `True` so that specific discharge data are saved to the cell-by-cell file and can be used to plot discharge. Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `NPF` package (`flopy.mf6.ModflowGwfic()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-npf.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "kh = [50, 0.01, 200]\n",
    "kv = [10, 0.01, 20]\n",
    "icelltype = [1, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "npf = flopy.mf6.ModflowGwfnpf(\n",
    "    gwf, save_specific_discharge=True, icelltype=icelltype, k=kh, k33=kv\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the recharge package\n",
    "\n",
    "The recharge rate is defined in the image above. Use the `flopy.mf6.ModflowGwfrcha()` method to specify recharge data using arrays. Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `RCH` package (`flopy.mf6.ModflowGwfrcha()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-rcha.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rch = flopy.mf6.ModflowGwfrcha(gwf, recharge=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the well package\n",
    "\n",
    "The well is located in layer 3, row 11, column 10. The pumping rate is defined in the image above. Use the `flopy.mf6.ModflowGwfwel()` method to specify well data for the well package (`WEL`). Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `WEL` package (`flopy.mf6.ModflowGwfwel()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-wel.html).\n",
    "\n",
    "`stress_period_data` for list-based stress packages (for example, `WEL`, `DRN`, `RIV`, and `GHB`) is specified as a dictionary with the zero-based stress-period number as the key and a list of tuples, with the tuple containing the data required for each stress entry. For example, each tuple for the `WEL` package includes a zero-based cellid and the well rate `(cellid, q)`. For this example, the zero-based cellid for `WEL` package can be a tuple with the `(layer, row, column)` for the well or three integers separated by a comma `layer, row, column`. More information on how to specify `stress_period_data` for list based stress packages can be found in the [FloPy ReadTheDocs](https://flopy.readthedocs.io/en/latest/Notebooks/mf6_data_tutorial06.html#Adding-Stress-Period-List-Data). \n",
    "\n",
    "An example of a `stress_period_data` tuple for the `WEL` package is\n",
    "\n",
    "```python\n",
    "# (layer, row, column, q)\n",
    "(0, 0, 0, -1e5)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wel_spd = {0: [[(2, 10, 9), -150000]]}\n",
    "wel = flopy.mf6.ModflowGwfwel(\n",
    "    gwf, print_input=True, stress_period_data=wel_spd\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the river package\n",
    "\n",
    "The river is located in layer 1 and column 20 in every row in the model. The river stage stage and bottom are at 320 and 318, respectively; the river conductance is 1e5. Use the `flopy.mf6.ModflowGwfriv()` method to specify well data for the river package (`RIV`). Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `RIV` package (`flopy.mf6.ModflowGwfriv()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-riv.html).\n",
    "\n",
    "An example of a `stress_period_data` tuple for the `RIV` package is\n",
    "\n",
    "```python\n",
    "# (layer, row, column, stage, cond, rbot)\n",
    "(0, 0, 0, 320., 1e5, 318.)\n",
    "```\n",
    "\n",
    "**HINT**: list comprehension is an easy way to create a river cell in every row in column 20 of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [((0, 0, 19), 320, 100000.0, 318),\n",
       "  ((0, 1, 19), 320, 100000.0, 318),\n",
       "  ((0, 2, 19), 320, 100000.0, 318),\n",
       "  ((0, 3, 19), 320, 100000.0, 318),\n",
       "  ((0, 4, 19), 320, 100000.0, 318),\n",
       "  ((0, 5, 19), 320, 100000.0, 318),\n",
       "  ((0, 6, 19), 320, 100000.0, 318),\n",
       "  ((0, 7, 19), 320, 100000.0, 318),\n",
       "  ((0, 8, 19), 320, 100000.0, 318),\n",
       "  ((0, 9, 19), 320, 100000.0, 318),\n",
       "  ((0, 10, 19), 320, 100000.0, 318),\n",
       "  ((0, 11, 19), 320, 100000.0, 318),\n",
       "  ((0, 12, 19), 320, 100000.0, 318),\n",
       "  ((0, 13, 19), 320, 100000.0, 318),\n",
       "  ((0, 14, 19), 320, 100000.0, 318),\n",
       "  ((0, 15, 19), 320, 100000.0, 318),\n",
       "  ((0, 16, 19), 320, 100000.0, 318),\n",
       "  ((0, 17, 19), 320, 100000.0, 318),\n",
       "  ((0, 18, 19), 320, 100000.0, 318),\n",
       "  ((0, 19, 19), 320, 100000.0, 318),\n",
       "  ((0, 20, 19), 320, 100000.0, 318)]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riv_spd = {0: [((0, i, 19), 320, 1e5, 318) for i in range(nrow)]}\n",
    "riv_spd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "riv = flopy.mf6.ModflowGwfriv(gwf, stress_period_data=riv_spd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build output control\n",
    "\n",
    "Define the output control package (`OC`) for the model using the `flopy.mf6.ModflowGwfoc()` method to `[('HEAD', 'ALL'), ('BUDGET', 'ALL')]` to save the head and flow for the model. Also the head (`head_filerecord`) and cell-by-cell flow (`budget_filerecord`) files should be set to `f\"{name}.hds\"` and `f\"{name}.cbc\"`, respectively. Use `Shift-Tab` to see the optional variables that can be specified. A description of the data required by the `OC` package (`flopy.mf6.ModflowGwfoc()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/gwf-oc.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "hname = f\"{name}.hds\"\n",
    "cname = f\"{name}.cbc\"\n",
    "oc = flopy.mf6.ModflowGwfoc(\n",
    "    gwf,\n",
    "    budget_filerecord=cname,\n",
    "    head_filerecord=hname,\n",
    "    saverecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we haven't set `SAVE_FLOWS` to `True` in all of the packages we can set `.name_file.save_flows` to `True` for the groundwater flow model (`gwf`) to save flows for all packages that can save flows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gwf.name_file.save_flows = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add head observations \n",
    "\n",
    "Define observations of head at a couple locations in the model. This is helpful to track continuous output at those locations and will write out CSV files that are easy to process for parameter estimation or other purposes. An observation package is in utilities (`OSB`) for the model is created using the `flopy.mf6.ModflowUtlobs()` method. Use `Shift-Tab` to see the optional variables that can be specified although a key format issue may be missing. A description of the data required by the `OBS` package (`flopy.mf6.ModflowUtlobs()`) can be found in the MODFLOW 6 [ReadTheDocs document](https://modflow6.readthedocs.io/en/latest/_mf6io/utl-obs.html). \n",
    "\n",
    "Pro Tip: The `continous` object must be a dictionary with `keys` being filenames in which to write the output, and `values` should be a list of lists with each list representing an observation location including a name, obstype, and cell location. For example `['obswell1','head',(0,4,4)]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = flopy.mf6.ModflowUtlobs(gwf, \n",
    "                              digits=6, \n",
    "                              continuous={(\"head_obs.csv\"): [['obswell1','head',(0,4,4)],\n",
    "                                                              ['obswell2','head',(2,4,4)]]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write the model files and run the model\n",
    "\n",
    "Write the MODFLOW 6 model files using `sim.write_simulation()`. Use `Shift-Tab` to see the optional variables that can be specified for `.write_simulation()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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 model ex01b...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package rcha_0...\n",
      "    writing package wel_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 1 based on size of stress_period_data\n",
      "    writing package riv_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n",
      "    writing package oc...\n",
      "    writing package obs_0...\n"
     ]
    }
   ],
   "source": [
    "sim.write_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the model using `sim.run_simulation()`, which will run the MODFLOW 6 executable installed in the Miniforge class environment (`pyclass`) and the MODFLOW 6 model files created with `.write_simulation()`. Use `Shift-Tab` to see the optional variables that can be specified for `.run_simulation()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: ../../../../../../../miniforge3/envs/pyclass/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.6.2 05/12/2025\n",
      "\n",
      "        MODFLOW 6 compiled May 24 2025 11:40:20 with GCC version 12.4.0\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      "\n",
      " MODFLOW runs in SEQUENTIAL mode\n",
      "\n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2025/09/25 10:54:13\n",
      "\n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      "\n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      "\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2025/09/25 10:54:13\n",
      " Elapsed run time:  0.023 Seconds\n",
      "\n",
      " Normal termination of simulation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.run_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Post-process the results\n",
    "\n",
    "Load the heads and face flows from the hds and cbc files. The head file can be loaded with the `gwf.output.head()` method. The cell-by-cell file can be loaded with the `gwf.output.budget()` method. \n",
    "\n",
    "Name the heads data `hds`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "hobj = gwf.output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "hds = hobj.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cobj = gwf.output.budget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entries in the cell-by-cell file can be determined with the `.list_unique_records()` method on the cell budget file object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECORD           IMETH\n",
      "----------------------\n",
      "FLOW-JA-FACE         1\n",
      "DATA-SPDIS           6\n",
      "WEL                  6\n",
      "RIV                  6\n",
      "RCHA                 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mnfienen/miniforge3/envs/pyclass/lib/python3.12/site-packages/flopy/utils/binaryfile/__init__.py:1327: DeprecationWarning: list_unique_records() is deprecated; use headers[[\"text\", \"imeth\"]].drop_duplicates() instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "cobj.list_unique_records()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the `'DATA-SPDIS'` data type from the cell-by-cell file. Name the specific discharge data `spd`.\n",
    "\n",
    "Cell-by-cell data is returned as a list so access the data by using `spd = gwf.output.budget().get_data(text=\"DATA-SPDIS\")[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "spd = cobj.get_data(text=\"DATA-SPDIS\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the results\n",
    "\n",
    "Plot the results using `flopy.plot.PlotMapView()`. The head results can be plotted using the `.plot_array()` method. The discharge results can be plotted using the `plot_specific_discharge()` method. Boundary conditions can be plotted using the `.plot_bc()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with styles.USGSMap():\n",
    "    mm = flopy.plot.PlotMapView(model=gwf, layer=0, extent=gwf.modelgrid.extent)\n",
    "    cbv = mm.plot_array(hds)\n",
    "    q = mm.plot_vector(spd[\"qx\"], spd[\"qy\"])\n",
    "    mm.plot_bc(\"RIV\", color=\"blue\")\n",
    "    mm.plot_bc(\"WEL\", plotAll=True)\n",
    "    mm.plot_grid(lw=0.5, color=\"black\")\n",
    "\n",
    "    # create data outside of plot limits for legend data\n",
    "    font_prop = mpl.font_manager.FontProperties(size=9, weight=\"bold\")\n",
    "    mm.ax.plot(-100, -100, marker=\"s\", lw=0, ms=4, mfc=\"red\", mec=\"black\", mew=0.5, label=\"Well\")\n",
    "    mm.ax.plot(-100, -100, marker=\"s\", lw=0, ms=4, mfc=\"blue\", mec=\"black\", mew=0.5, label=\"River cell\")\n",
    "    \n",
    "    # plot legend\n",
    "    styles.graph_legend(bbox_to_anchor=(1.05, 1.05))\n",
    "    plt.quiverkey(q, X = 1.15, Y = 0.825, U = .200, label ='Specific\\nDischarge', labelpos=\"E\", fontproperties=font_prop) \n",
    "\n",
    "    # plot colorbar\n",
    "    cb = plt.colorbar(cbv, ax=mm.ax, shrink=0.5)\n",
    "    cb.set_label(label=\"Head, ft\", weight=\"bold\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the output from the observation package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "obs_results  = pd.read_csv(ws + '/head_obs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>OBSWELL1</th>\n",
       "      <th>OBSWELL2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>338.499</td>\n",
       "      <td>331.529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  OBSWELL1  OBSWELL2\n",
       "0   1.0   338.499   331.529"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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