{
 "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": null,
   "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": null,
   "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": null,
   "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": null,
   "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": null,
   "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": null,
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = flopy.plot.PlotMapView(model=gwf)\n",
    "mm.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kh = [50, 0.01, 200]\n",
    "kv = [10, 0.01, 20]\n",
    "icelltype = [1, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "riv_spd = {0: [((0, i, 19), 320, 1e5, 318) for i in range(nrow)]}\n",
    "riv_spd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gwf.name_file.save_flows = True"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hobj = gwf.output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hds = hobj.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}