examples/Notebooks/flopy3_mf6_A_simple-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "## Creating a Simple MODFLOW 6 Model with Flopy\n",
    "\n",
    "The purpose of this notebook is to demonstrate the Flopy capabilities for building a simple MODFLOW 6 model from scratch, running the model, and viewing the results.  This notebook will demonstrate the capabilities using a simple lake example.  A separate notebook is also available in which the same lake example is created for MODFLOW-2005 (flopy3_lake_example.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the Notebook Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:41.779300Z",
     "iopub.status.busy": "2022-03-07T20:05:41.778516Z",
     "iopub.status.idle": "2022-03-07T20:05:42.870728Z",
     "shell.execute_reply": "2022-03-07T20:05:42.871126Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.7 (default, Sep 16 2021, 08:50:36) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.21.2\n",
      "matplotlib version: 3.5.1\n",
      "flopy version: 3.3.5\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join(\"..\", \"..\"))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version: {}\".format(np.__version__))\n",
    "print(\"matplotlib version: {}\".format(mpl.__version__))\n",
    "print(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.875529Z",
     "iopub.status.busy": "2022-03-07T20:05:42.874907Z",
     "iopub.status.idle": "2022-03-07T20:05:42.876752Z",
     "shell.execute_reply": "2022-03-07T20:05:42.877152Z"
    }
   },
   "outputs": [],
   "source": [
    "# For this example, we will set up a model workspace.\n",
    "# Model input files and output files will reside here.\n",
    "workspace = os.path.join(\"data\", \"mf6lake\")\n",
    "if not os.path.isdir(workspace):\n",
    "    os.makedirs(workspace, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Flopy Model Objects\n",
    "\n",
    "We are creating a square model with a specified head equal to `h1` along all boundaries. The head at the cell in the center in the top layer is fixed to `h2`. First, set the name of the model and the parameters of the model: the number of layers `Nlay`, the number of rows and columns `N`, lengths of the sides of the model `L`, aquifer thickness `H`, hydraulic conductivity `k`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.881307Z",
     "iopub.status.busy": "2022-03-07T20:05:42.880720Z",
     "iopub.status.idle": "2022-03-07T20:05:42.882456Z",
     "shell.execute_reply": "2022-03-07T20:05:42.882814Z"
    }
   },
   "outputs": [],
   "source": [
    "name = \"mf6lake\"\n",
    "h1 = 100\n",
    "h2 = 90\n",
    "Nlay = 10\n",
    "N = 101\n",
    "L = 400.0\n",
    "H = 50.0\n",
    "k = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One big difference between MODFLOW 6 and previous MODFLOW versions is that MODFLOW 6 is based on the concept of a simulation.  A simulation consists of the following:\n",
    "\n",
    "* Temporal discretization (TDIS)\n",
    "* One or more models (GWF is the only model supported at present)\n",
    "* Zero or more exchanges (instructions for how models are coupled)\n",
    "* Solutions\n",
    "\n",
    "For this simple lake example, the simulation consists of the temporal discretization (TDIS) package (TDIS), a groundwater flow (GWF) model, and an iterative model solution (IMS), which controls how the GWF model is solved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.940467Z",
     "iopub.status.busy": "2022-03-07T20:05:42.922191Z",
     "iopub.status.idle": "2022-03-07T20:05:42.942652Z",
     "shell.execute_reply": "2022-03-07T20:05:42.943069Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the Flopy simulation object\n",
    "sim = flopy.mf6.MFSimulation(\n",
    "    sim_name=name, exe_name=\"mf6\", version=\"mf6\", sim_ws=workspace\n",
    ")\n",
    "\n",
    "# Create the Flopy temporal discretization object\n",
    "tdis = flopy.mf6.modflow.mftdis.ModflowTdis(\n",
    "    sim, pname=\"tdis\", time_units=\"DAYS\", nper=1, perioddata=[(1.0, 1, 1.0)]\n",
    ")\n",
    "\n",
    "# Create the Flopy groundwater flow (gwf) model object\n",
    "model_nam_file = \"{}.nam\".format(name)\n",
    "gwf = flopy.mf6.ModflowGwf(sim, modelname=name, model_nam_file=model_nam_file)\n",
    "\n",
    "# Create the Flopy iterative model solver (ims) Package object\n",
    "ims = flopy.mf6.modflow.mfims.ModflowIms(sim, pname=\"ims\", complexity=\"SIMPLE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the overall simulation is set up, we can focus on building the groundwater flow model.  The groundwater flow model will be built by adding packages to it that describe the model characteristics.\n",
    "\n",
    "Define the discretization of the model. All layers are given equal thickness. The `bot` array is build from `H` and the `Nlay` values to indicate top and bottom of each layer, and `delrow` and `delcol` are computed from model size `L` and number of cells `N`. Once these are all computed, the Discretization file is built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.951207Z",
     "iopub.status.busy": "2022-03-07T20:05:42.950373Z",
     "iopub.status.idle": "2022-03-07T20:05:42.952371Z",
     "shell.execute_reply": "2022-03-07T20:05:42.952762Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the discretization package\n",
    "bot = np.linspace(-H / Nlay, -H, Nlay)\n",
    "delrow = delcol = L / (N - 1)\n",
    "dis = flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(\n",
    "    gwf,\n",
    "    pname=\"dis\",\n",
    "    nlay=Nlay,\n",
    "    nrow=N,\n",
    "    ncol=N,\n",
    "    delr=delrow,\n",
    "    delc=delcol,\n",
    "    top=0.0,\n",
    "    botm=bot,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.959512Z",
     "iopub.status.busy": "2022-03-07T20:05:42.956968Z",
     "iopub.status.idle": "2022-03-07T20:05:42.961600Z",
     "shell.execute_reply": "2022-03-07T20:05:42.962141Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the initial conditions package\n",
    "start = h1 * np.ones((Nlay, N, N))\n",
    "ic = flopy.mf6.modflow.mfgwfic.ModflowGwfic(gwf, pname=\"ic\", strt=start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:42.968706Z",
     "iopub.status.busy": "2022-03-07T20:05:42.968018Z",
     "iopub.status.idle": "2022-03-07T20:05:42.969917Z",
     "shell.execute_reply": "2022-03-07T20:05:42.970397Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the node property flow package\n",
    "npf = flopy.mf6.modflow.mfgwfnpf.ModflowGwfnpf(\n",
    "    gwf, pname=\"npf\", icelltype=1, k=k, save_flows=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.002379Z",
     "iopub.status.busy": "2022-03-07T20:05:43.001795Z",
     "iopub.status.idle": "2022-03-07T20:05:43.009538Z",
     "shell.execute_reply": "2022-03-07T20:05:43.009939Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the constant head package.\n",
    "# List information is created a bit differently for\n",
    "# MODFLOW 6 than for other MODFLOW versions.  The\n",
    "# cellid (layer, row, column, for a regular grid)\n",
    "# must be entered as a tuple as the first entry.\n",
    "# Remember that these must be zero-based indices!\n",
    "chd_rec = []\n",
    "chd_rec.append(((0, int(N / 4), int(N / 4)), h2))\n",
    "for layer in range(0, Nlay):\n",
    "    for row_col in range(0, N):\n",
    "        chd_rec.append(((layer, row_col, 0), h1))\n",
    "        chd_rec.append(((layer, row_col, N - 1), h1))\n",
    "        if row_col != 0 and row_col != N - 1:\n",
    "            chd_rec.append(((layer, 0, row_col), h1))\n",
    "            chd_rec.append(((layer, N - 1, row_col), h1))\n",
    "chd = flopy.mf6.modflow.mfgwfchd.ModflowGwfchd(\n",
    "    gwf,\n",
    "    pname=\"chd\",\n",
    "    maxbound=len(chd_rec),\n",
    "    stress_period_data=chd_rec,\n",
    "    save_flows=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.016354Z",
     "iopub.status.busy": "2022-03-07T20:05:43.015663Z",
     "iopub.status.idle": "2022-03-07T20:05:43.018324Z",
     "shell.execute_reply": "2022-03-07T20:05:43.018716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 25, 25),  90.), ((0, 0, 0), 100.), ((0, 0, 100), 100.),\n",
       "           ..., ((9, 100, 99), 100.), ((9, 100, 0), 100.),\n",
       "           ((9, 100, 100), 100.)],\n",
       "          dtype=[('cellid', 'O'), ('head', '<f8')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The chd package stored the constant heads in a structured\n",
    "# array, also called a recarray.  We can get a pointer to the\n",
    "# recarray for the first stress period (iper = 0) as follows.\n",
    "iper = 0\n",
    "ra = chd.stress_period_data.get_data(key=iper)\n",
    "ra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.047406Z",
     "iopub.status.busy": "2022-03-07T20:05:43.030642Z",
     "iopub.status.idle": "2022-03-07T20:05:43.162170Z",
     "shell.execute_reply": "2022-03-07T20:05:43.162940Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Layer 1: Constant Head Cells')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can make a quick plot to show where our constant\n",
    "# heads are located by creating an integer array\n",
    "# that starts with ones everywhere, but is assigned\n",
    "# a -1 where chds are located\n",
    "ibd = np.ones((Nlay, N, N), dtype=int)\n",
    "for k, i, j in ra[\"cellid\"]:\n",
    "    ibd[k, i, j] = -1\n",
    "\n",
    "ilay = 0\n",
    "plt.imshow(ibd[ilay, :, :], interpolation=\"none\")\n",
    "plt.title(\"Layer {}: Constant Head Cells\".format(ilay + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.169285Z",
     "iopub.status.busy": "2022-03-07T20:05:43.168554Z",
     "iopub.status.idle": "2022-03-07T20:05:43.195861Z",
     "shell.execute_reply": "2022-03-07T20:05:43.196491Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the output control package\n",
    "headfile = \"{}.hds\".format(name)\n",
    "head_filerecord = [headfile]\n",
    "budgetfile = \"{}.cbb\".format(name)\n",
    "budget_filerecord = [budgetfile]\n",
    "saverecord = [(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")]\n",
    "printrecord = [(\"HEAD\", \"LAST\")]\n",
    "oc = flopy.mf6.modflow.mfgwfoc.ModflowGwfoc(\n",
    "    gwf,\n",
    "    pname=\"oc\",\n",
    "    saverecord=saverecord,\n",
    "    head_filerecord=head_filerecord,\n",
    "    budget_filerecord=budget_filerecord,\n",
    "    printrecord=printrecord,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.207721Z",
     "iopub.status.busy": "2022-03-07T20:05:43.206715Z",
     "iopub.status.idle": "2022-03-07T20:05:43.210328Z",
     "shell.execute_reply": "2022-03-07T20:05:43.210979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on ModflowGwfoc in module flopy.mf6.modflow.mfgwfoc object:\n",
      "\n",
      "class ModflowGwfoc(flopy.mf6.mfpackage.MFPackage)\n",
      " |  ModflowGwfoc(model, loading_package=False, budget_filerecord=None, budgetcsv_filerecord=None, head_filerecord=None, headprintrecord=None, saverecord=None, printrecord=None, filename=None, pname=None, parent_file=None)\n",
      " |  \n",
      " |  ModflowGwfoc defines a oc package within a gwf6 model.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  model : MFModel\n",
      " |      Model that this package is a part of.  Package is automatically\n",
      " |      added to model when it is initialized.\n",
      " |  loading_package : bool\n",
      " |      Do not set this parameter. It is intended for debugging and internal\n",
      " |      processing purposes only.\n",
      " |  budget_filerecord : [budgetfile]\n",
      " |      * budgetfile (string) name of the output file to write budget\n",
      " |        information.\n",
      " |  budgetcsv_filerecord : [budgetcsvfile]\n",
      " |      * budgetcsvfile (string) name of the comma-separated value (CSV) output\n",
      " |        file to write budget summary information. A budget summary record\n",
      " |        will be written to this file for each time step of the simulation.\n",
      " |  head_filerecord : [headfile]\n",
      " |      * headfile (string) name of the output file to write head information.\n",
      " |  headprintrecord : [columns, width, digits, format]\n",
      " |      * columns (integer) number of columns for writing data.\n",
      " |      * width (integer) width for writing each number.\n",
      " |      * digits (integer) number of digits to use for writing a number.\n",
      " |      * format (string) write format can be EXPONENTIAL, FIXED, GENERAL, or\n",
      " |        SCIENTIFIC.\n",
      " |  saverecord : [rtype, ocsetting]\n",
      " |      * rtype (string) type of information to save or print. Can be BUDGET or\n",
      " |        HEAD.\n",
      " |      * ocsetting (keystring) specifies the steps for which the data will be\n",
      " |        saved.\n",
      " |          all : [keyword]\n",
      " |              * all (keyword) keyword to indicate save for all time steps in\n",
      " |                period.\n",
      " |          first : [keyword]\n",
      " |              * first (keyword) keyword to indicate save for first step in\n",
      " |                period. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          last : [keyword]\n",
      " |              * last (keyword) keyword to indicate save for last step in\n",
      " |                period. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          frequency : [integer]\n",
      " |              * frequency (integer) save at the specified time step\n",
      " |                frequency. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          steps : [integer]\n",
      " |              * steps (integer) save for each step specified in STEPS. This\n",
      " |                keyword may be used in conjunction with other keywords to\n",
      " |                print or save results for multiple time steps.\n",
      " |  printrecord : [rtype, ocsetting]\n",
      " |      * rtype (string) type of information to save or print. Can be BUDGET or\n",
      " |        HEAD.\n",
      " |      * ocsetting (keystring) specifies the steps for which the data will be\n",
      " |        saved.\n",
      " |          all : [keyword]\n",
      " |              * all (keyword) keyword to indicate save for all time steps in\n",
      " |                period.\n",
      " |          first : [keyword]\n",
      " |              * first (keyword) keyword to indicate save for first step in\n",
      " |                period. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          last : [keyword]\n",
      " |              * last (keyword) keyword to indicate save for last step in\n",
      " |                period. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          frequency : [integer]\n",
      " |              * frequency (integer) save at the specified time step\n",
      " |                frequency. This keyword may be used in conjunction with other\n",
      " |                keywords to print or save results for multiple time steps.\n",
      " |          steps : [integer]\n",
      " |              * steps (integer) save for each step specified in STEPS. This\n",
      " |                keyword may be used in conjunction with other keywords to\n",
      " |                print or save results for multiple time steps.\n",
      " |  filename : String\n",
      " |      File name for this package.\n",
      " |  pname : String\n",
      " |      Package name for this package.\n",
      " |  parent_file : MFPackage\n",
      " |      Parent package file that references this package. Only needed for\n",
      " |      utility packages (mfutl*). For example, mfutllaktab package must have\n",
      " |      a mfgwflak package parent_file.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      ModflowGwfoc\n",
      " |      flopy.mf6.mfpackage.MFPackage\n",
      " |      flopy.mf6.mfbase.PackageContainer\n",
      " |      flopy.pakbase.PackageInterface\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, model, loading_package=False, budget_filerecord=None, budgetcsv_filerecord=None, head_filerecord=None, headprintrecord=None, saverecord=None, printrecord=None, filename=None, pname=None, parent_file=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  budget_filerecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerator o...\n",
      " |  \n",
      " |  budgetcsv_filerecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerato...\n",
      " |  \n",
      " |  dfn = [['header'], ['block options', 'name budget_filerecord', 'type r...\n",
      " |  \n",
      " |  dfn_file_name = 'gwf-oc.dfn'\n",
      " |  \n",
      " |  head_filerecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerator obj...\n",
      " |  \n",
      " |  headprintrecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerator obj...\n",
      " |  \n",
      " |  package_abbr = 'gwfoc'\n",
      " |  \n",
      " |  printrecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerator object>\n",
      " |  \n",
      " |  saverecord = <flopy.mf6.data.mfdatautil.ListTemplateGenerator object>\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from flopy.mf6.mfpackage.MFPackage:\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setattr__(self, name, value)\n",
      " |      Implement setattr(self, name, value).\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  build_child_package(self, pkg_type, data, parameter_name, filerecord)\n",
      " |      Builds a child package.  This method is only intended for FloPy\n",
      " |      internal use.\n",
      " |  \n",
      " |  build_child_packages_container(self, pkg_type, filerecord)\n",
      " |      Builds a container object for any child packages.  This method is\n",
      " |      only intended for FloPy internal use.\n",
      " |  \n",
      " |  build_mfdata(self, var_name, data=None)\n",
      " |      Returns the appropriate data type object (mfdatalist, mfdataarray,\n",
      " |      or mfdatascalar) given that object the appropriate structure (looked\n",
      " |      up based on var_name) and any data supplied.  This method is for\n",
      " |      internal FloPy library use only.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      var_name : str\n",
      " |          Variable name\n",
      " |      \n",
      " |      data : many supported types\n",
      " |          Data contained in this object\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      data object : MFData subclass\n",
      " |  \n",
      " |  check(self, f=None, verbose=True, level=1, checktype=None)\n",
      " |      Data check, returns True on success.\n",
      " |  \n",
      " |  create_package_dimensions(self)\n",
      " |      Creates a package dimensions object.  For internal FloPy library\n",
      " |      use.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      package dimensions : PackageDimensions\n",
      " |  \n",
      " |  export(self, f, **kwargs)\n",
      " |      Method to export a package to netcdf or shapefile based on the\n",
      " |      extension of the file name (.shp for shapefile, .nc for netcdf)\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      f : str\n",
      " |          Filename\n",
      " |      kwargs : keyword arguments\n",
      " |          modelgrid : flopy.discretization.Grid instance\n",
      " |              User supplied modelgrid which can be used for exporting\n",
      " |              in lieu of the modelgrid associated with the model object\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |          None or Netcdf object\n",
      " |  \n",
      " |  get_file_path(self)\n",
      " |      Returns the package file's path.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      file path : str\n",
      " |  \n",
      " |  inspect_cells(self, cell_list, stress_period=None)\n",
      " |      Inspect model cells.  Returns package data associated with cells.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      cell_list : list of tuples\n",
      " |          List of model cells.  Each model cell is a tuple of integers.\n",
      " |          ex: [(1,1,1), (2,4,3)]\n",
      " |      stress_period : int\n",
      " |          For transient data, only return data from this stress period.  If\n",
      " |          not specified or None, all stress period data will be returned.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      output : array\n",
      " |          Array containing inspection results\n",
      " |  \n",
      " |  is_valid(self)\n",
      " |      Returns whether or not this package is valid.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      is valid : bool\n",
      " |  \n",
      " |  load(self, strict=True)\n",
      " |      Loads the package from file.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      strict : bool\n",
      " |          Enforce strict checking of data.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      success : bool\n",
      " |  \n",
      " |  plot(self, **kwargs)\n",
      " |      Plot 2-D, 3-D, transient 2-D, and stress period list (MfList)\n",
      " |      package input data\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **kwargs : dict\n",
      " |          filename_base : str\n",
      " |              Base file name that will be used to automatically generate\n",
      " |              file names for output image files. Plots will be exported as\n",
      " |              image files if file_name_base is not None. (default is None)\n",
      " |          file_extension : str\n",
      " |              Valid matplotlib.pyplot file extension for savefig(). Only\n",
      " |              used if filename_base is not None. (default is 'png')\n",
      " |          mflay : int\n",
      " |              MODFLOW zero-based layer number to return.  If None, then all\n",
      " |              all layers will be included. (default is None)\n",
      " |          kper : int\n",
      " |              MODFLOW zero-based stress period number to return. (default is\n",
      " |              zero)\n",
      " |          key : str\n",
      " |              MfList dictionary key. (default is None)\n",
      " |      \n",
      " |      Returns\n",
      " |      ----------\n",
      " |      axes : list\n",
      " |          Empty list is returned if filename_base is not None. Otherwise\n",
      " |          a list of matplotlib.pyplot.axis are returned.\n",
      " |  \n",
      " |  remove(self)\n",
      " |      Removes this package from the simulation/model it is currently a\n",
      " |      part of.\n",
      " |  \n",
      " |  set_all_data_external(self, check_data=True, external_data_folder=None)\n",
      " |      Sets the package's list and array data to be stored externally.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |          check_data : bool\n",
      " |              Determine if data error checking is enabled\n",
      " |          external_data_folder\n",
      " |              Folder where external data will be stored\n",
      " |  \n",
      " |  set_all_data_internal(self, check_data=True)\n",
      " |      Sets the package's list and array data to be stored internally.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |          check_data : bool\n",
      " |              Determine if data error checking is enabled\n",
      " |  \n",
      " |  set_model_relative_path(self, model_ws)\n",
      " |      Sets the model path relative to the simulation's path.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      model_ws : str\n",
      " |          Model path relative to the simulation's path.\n",
      " |  \n",
      " |  write(self, ext_file_action=<ExtFileAction.copy_relative_paths: 3>)\n",
      " |      Writes the package to a file.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      ext_file_action : ExtFileAction\n",
      " |          How to handle pathing of external data files.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from flopy.mf6.mfpackage.MFPackage:\n",
      " |  \n",
      " |  __init_subclass__() from builtins.type\n",
      " |      Register package type\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from flopy.mf6.mfpackage.MFPackage:\n",
      " |  \n",
      " |  data_list\n",
      " |      List of data in this package.\n",
      " |  \n",
      " |  output\n",
      " |      Method to get output associated with a specific package\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |          MF6Output object\n",
      " |  \n",
      " |  package_type\n",
      " |      String describing type of package\n",
      " |  \n",
      " |  plottable\n",
      " |      If package is plottable\n",
      " |  \n",
      " |  quoted_filename\n",
      " |      Package's file name with quotes if there is a space.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from flopy.mf6.mfpackage.MFPackage:\n",
      " |  \n",
      " |  filename\n",
      " |      Package's file name.\n",
      " |  \n",
      " |  name\n",
      " |      Name of package\n",
      " |  \n",
      " |  parent\n",
      " |      Parent package\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from flopy.mf6.mfbase.PackageContainer:\n",
      " |  \n",
      " |  get_package(self, name=None)\n",
      " |      Finds a package by package name, package key, package type, or partial\n",
      " |      package name. returns either a single package, a list of packages,\n",
      " |      or None.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : str\n",
      " |          Name of the package, 'RIV', 'LPF', etc.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      pp : Package object\n",
      " |  \n",
      " |  register_package(self, package)\n",
      " |      Base method for registering a package.  Should be overridden.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from flopy.mf6.mfbase.PackageContainer:\n",
      " |  \n",
      " |  get_module_val(module, item, attrb)\n",
      " |      Static method that returns a python class module value.  For\n",
      " |      internal FloPy use only, not intended for end users.\n",
      " |  \n",
      " |  model_factory(model_type)\n",
      " |      Static method that returns the appropriate model type object based\n",
      " |      on the model_type string. For internal FloPy use only, not intended\n",
      " |      for end users.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |          model_type : str\n",
      " |              Type of model that package is a part of\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |          model : MFModel subclass\n",
      " |  \n",
      " |  package_factory(package_type: str, model_type: str)\n",
      " |      Static method that returns the appropriate package type object based\n",
      " |      on the package_type and model_type strings.  For internal FloPy use\n",
      " |      only, not intended for end users.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |          package_type : str\n",
      " |              Type of package to create\n",
      " |          model_type : str\n",
      " |              Type of model that package is a part of\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |          package : MFPackage subclass\n",
      " |  \n",
      " |  package_list()\n",
      " |      Static method that returns the list of available packages.\n",
      " |      For internal FloPy use only, not intended for end users.\n",
      " |      \n",
      " |      Returns a list of MFPackage subclasses\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from flopy.mf6.mfbase.PackageContainer:\n",
      " |  \n",
      " |  package_dict\n",
      " |      Returns a copy of the package name dictionary.\n",
      " |  \n",
      " |  package_names\n",
      " |      Returns a list of package names.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from flopy.mf6.mfbase.PackageContainer:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from flopy.mf6.mfbase.PackageContainer:\n",
      " |  \n",
      " |  models_by_type = {'gwf': <class 'flopy.mf6.modflow.mfgwf.ModflowGwf'>,...\n",
      " |  \n",
      " |  modflow_models = [<class 'flopy.mf6.modflow.mfgwf.ModflowGwf'>, <class...\n",
      " |  \n",
      " |  modflow_packages = [<class 'flopy.mf6.modflow.mfgwfgnc.ModflowGwfgnc'>...\n",
      " |  \n",
      " |  packages_by_abbr = {'gnc': <class 'flopy.mf6.modflow.mfgnc.ModflowGnc'...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from flopy.pakbase.PackageInterface:\n",
      " |  \n",
      " |  has_stress_period_data\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Note that help can always be found for a package\n",
    "# using either forms of the following syntax\n",
    "help(oc)\n",
    "# help(flopy.mf6.modflow.mfgwfoc.ModflowGwfoc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the MODFLOW 6 Input Files and Run the Model\n",
    "\n",
    "Once all the flopy objects are created, it is very easy to create all of the input files and run the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:43.215965Z",
     "iopub.status.busy": "2022-03-07T20:05:43.215088Z",
     "iopub.status.idle": "2022-03-07T20:05:44.077765Z",
     "shell.execute_reply": "2022-03-07T20:05:44.077048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing ims package ims...\n",
      "  writing model mf6lake...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    writing package npf...\n",
      "    writing package chd...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    writing package oc...\n"
     ]
    }
   ],
   "source": [
    "# Write the datasets\n",
    "sim.write_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:44.083035Z",
     "iopub.status.busy": "2022-03-07T20:05:44.082073Z",
     "iopub.status.idle": "2022-03-07T20:05:44.085185Z",
     "shell.execute_reply": "2022-03-07T20:05:44.085683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mf6lake.oc', 'mf6lake.lst', 'mf6lake.nam', 'mf6lake.dis', 'mf6lake.dis.grb', 'mf6lake.cbb', 'mf6lake.ims', 'mf6lake.chd', 'mf6lake.npf', 'mf6lake.ic', 'mf6lake.hds', 'mfsim.nam', 'mf6lake.tdis', 'mfsim.lst']\n"
     ]
    }
   ],
   "source": [
    "# Print a list of the files that were created\n",
    "# in workspace\n",
    "print(os.listdir(workspace))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the Simulation\n",
    "\n",
    "We can also run the simulation from the notebook, but only if the MODFLOW 6 executable is available.  The executable can be made available by putting the executable in a folder that is listed in the system path variable.  Another option is to just put a copy of the executable in the simulation folder, though this should generally be avoided.  A final option is to provide a full path to the executable when the simulation is constructed.  This would be done by specifying exe_name with the full path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:44.090784Z",
     "iopub.status.busy": "2022-03-07T20:05:44.089812Z",
     "iopub.status.idle": "2022-03-07T20:05:45.013757Z",
     "shell.execute_reply": "2022-03-07T20:05:45.014214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.2.2 07/30/2021\n",
      "\n",
      "   MODFLOW 6 compiled Aug 01 2021 12:51:08 with IFORT compiler (ver. 19.10.3)\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",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2022/03/07 14:05:44\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": [
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/03/07 14:05:45\n",
      " Elapsed run time:  0.897 Seconds\n",
      " \n",
      " Normal termination of simulation.\n",
      "\n",
      "Success is:  True\n"
     ]
    }
   ],
   "source": [
    "# Run the simulation\n",
    "success, buff = sim.run_simulation()\n",
    "print(\"\\nSuccess is: \", success)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Process Head Results\n",
    "\n",
    "Post-processing MODFLOW 6 results is still a work in progress.  There aren't any Flopy plotting functions built in yet, like they are for other MODFLOW versions.  So we need to plot the results using general Flopy capabilities.  We can also use some of the Flopy ModelMap capabilities for MODFLOW 6, but in order to do so, we need to manually create a SpatialReference object, that is needed for the plotting.  Examples of both approaches are shown below.\n",
    "\n",
    "First, a link to the heads file is created with `HeadFile`. The link can then be accessed with the `get_data` function, by specifying, in this case, the step number and period number for which we want to retrieve data. A three-dimensional array is returned of size `nlay, nrow, ncol`. Matplotlib contouring functions are used to make contours of the layers or a cross-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:45.020790Z",
     "iopub.status.busy": "2022-03-07T20:05:45.019796Z",
     "iopub.status.idle": "2022-03-07T20:05:45.226376Z",
     "shell.execute_reply": "2022-03-07T20:05:45.226927Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the binary head file and plot the results\n",
    "# We can use the existing Flopy HeadFile class because\n",
    "# the format of the headfile for MODFLOW 6 is the same\n",
    "# as for previous MODFLOW verions\n",
    "fname = os.path.join(workspace, headfile)\n",
    "hds = flopy.utils.binaryfile.HeadFile(fname)\n",
    "h = hds.get_data(kstpkper=(0, 0))\n",
    "x = y = np.linspace(0, L, N)\n",
    "y = y[::-1]\n",
    "c = plt.contour(x, y, h[0], np.arange(90, 100.1, 0.2))\n",
    "plt.clabel(c, fmt=\"%2.1f\")\n",
    "plt.axis(\"scaled\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:45.244787Z",
     "iopub.status.busy": "2022-03-07T20:05:45.235677Z",
     "iopub.status.idle": "2022-03-07T20:05:45.398672Z",
     "shell.execute_reply": "2022-03-07T20:05:45.399071Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = y = np.linspace(0, L, N)\n",
    "y = y[::-1]\n",
    "c = plt.contour(x, y, h[-1], np.arange(90, 100.1, 0.2))\n",
    "plt.clabel(c, fmt=\"%1.1f\")\n",
    "plt.axis(\"scaled\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:45.483359Z",
     "iopub.status.busy": "2022-03-07T20:05:45.460572Z",
     "iopub.status.idle": "2022-03-07T20:05:45.597457Z",
     "shell.execute_reply": "2022-03-07T20:05:45.598139Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.linspace(-H / Nlay / 2, -H + H / Nlay / 2, Nlay)\n",
    "c = plt.contour(x, z, h[:, 50, :], np.arange(90, 100.1, 0.2))\n",
    "plt.axis(\"scaled\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:45.618637Z",
     "iopub.status.busy": "2022-03-07T20:05:45.610742Z",
     "iopub.status.idle": "2022-03-07T20:05:45.982235Z",
     "shell.execute_reply": "2022-03-07T20:05:45.981830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also use the Flopy PlotMapView capabilities for MODFLOW 6\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n",
    "modelmap = flopy.plot.PlotMapView(model=gwf, ax=ax)\n",
    "\n",
    "# Then we can use the plot_grid() method to draw the grid\n",
    "# The return value for this function is a matplotlib LineCollection object,\n",
    "# which could be manipulated (or used) later if necessary.\n",
    "quadmesh = modelmap.plot_ibound(ibound=ibd)\n",
    "linecollection = modelmap.plot_grid()\n",
    "contours = modelmap.contour_array(h[0], levels=np.arange(90, 100.1, 0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:46.002957Z",
     "iopub.status.busy": "2022-03-07T20:05:45.991085Z",
     "iopub.status.idle": "2022-03-07T20:05:46.252146Z",
     "shell.execute_reply": "2022-03-07T20:05:46.252583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also use the Flopy PlotMapView capabilities for MODFLOW 6\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect=\"equal\")\n",
    "\n",
    "# Next we create an instance of the ModelMap class\n",
    "modelmap = flopy.plot.PlotMapView(model=gwf, ax=ax)\n",
    "\n",
    "# Then we can use the plot_grid() method to draw the grid\n",
    "# The return value for this function is a matplotlib LineCollection object,\n",
    "# which could be manipulated (or used) later if necessary.\n",
    "quadmesh = modelmap.plot_ibound(ibound=ibd)\n",
    "linecollection = modelmap.plot_grid()\n",
    "pa = modelmap.plot_array(h[0])\n",
    "cb = plt.colorbar(pa, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Process Flows\n",
    "\n",
    "MODFLOW 6 writes a binary grid file, which contains information about the model grid.  MODFLOW 6 also writes a binary budget file, which contains flow information.  Both of these files can be read using Flopy capabilities.  The MfGrdFile class in Flopy can be used to read the binary grid file.  The CellBudgetFile class in Flopy can be used to read the binary budget file written by MODFLOW 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:46.257865Z",
     "iopub.status.busy": "2022-03-07T20:05:46.257130Z",
     "iopub.status.idle": "2022-03-07T20:05:46.270269Z",
     "shell.execute_reply": "2022-03-07T20:05:46.270838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NCELLS': 102010,\n",
       " 'NLAY': 10,\n",
       " 'NROW': 101,\n",
       " 'NCOL': 101,\n",
       " 'NJA': 689628,\n",
       " 'XORIGIN': 0.0,\n",
       " 'YORIGIN': 0.0,\n",
       " 'ANGROT': 0.0,\n",
       " 'DELR': array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.]),\n",
       " 'DELC': array([4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n",
       "        4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.]),\n",
       " 'TOP': array([0., 0., 0., ..., 0., 0., 0.]),\n",
       " 'BOTM': array([ -5.,  -5.,  -5., ..., -50., -50., -50.]),\n",
       " 'IA': array([     1,      5,     10, ..., 689620, 689625, 689629], dtype=int32),\n",
       " 'JA': array([     1,      2,    102, ...,  91809, 101909, 102009], dtype=int32),\n",
       " 'IDOMAIN': array([1, 1, 1, ..., 1, 1, 1], dtype=int32),\n",
       " 'ICELLTYPE': array([1, 1, 1, ..., 1, 1, 1], dtype=int32)}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the binary grid file\n",
    "fname = os.path.join(workspace, \"{}.dis.grb\".format(name))\n",
    "bgf = flopy.mf6.utils.MfGrdFile(fname)\n",
    "\n",
    "# data read from the binary grid file is stored in a dictionary\n",
    "bgf._datadict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:46.276734Z",
     "iopub.status.busy": "2022-03-07T20:05:46.275891Z",
     "iopub.status.idle": "2022-03-07T20:05:46.287370Z",
     "shell.execute_reply": "2022-03-07T20:05:46.288208Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, b'    FLOW-JA-FACE', 689628, 1, -1, 1, 1., 1., 1., b'', b'', b'', b'')\n",
      "(1, 1, b'             CHD', 101, 101, -10, 6, 1., 1., 1., b'MF6LAKE         ', b'MF6LAKE         ', b'MF6LAKE         ', b'CHD             ')\n"
     ]
    }
   ],
   "source": [
    "# read the cell budget file\n",
    "fname = os.path.join(workspace, \"{}.cbb\".format(name))\n",
    "cbb = flopy.utils.CellBudgetFile(fname, precision=\"double\")\n",
    "cbb.list_records()\n",
    "\n",
    "flowja = cbb.get_data(text=\"FLOW-JA-FACE\")[0][0, 0, :]\n",
    "chdflow = cbb.get_data(text=\"CHD\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-07T20:05:46.297175Z",
     "iopub.status.busy": "2022-03-07T20:05:46.296016Z",
     "iopub.status.idle": "2022-03-07T20:05:46.300363Z",
     "shell.execute_reply": "2022-03-07T20:05:46.301180Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing flows for cell 56105\n",
      "Cell 56105 flow with cell 45904 is -0.0002858449337963975\n",
      "Cell 56105 flow with cell 56004 is -0.025019694309449392\n",
      "Cell 56105 flow with cell 56104 is -0.025019694309449392\n",
      "Cell 56105 flow with cell 56106 is 0.025058524820593675\n",
      "Cell 56105 flow with cell 56206 is 0.02505852482066473\n",
      "Cell 56105 flow with cell 66306 is 0.00033448625827077195\n"
     ]
    }
   ],
   "source": [
    "# By having the ia and ja arrays and the flow-ja-face we can look at\n",
    "# the flows for any cell and process them in the follow manner.\n",
    "k = 5\n",
    "i = 50\n",
    "j = 50\n",
    "celln = k * N * N + i * N + j\n",
    "ia, ja = bgf.ia, bgf.ja\n",
    "print(\"Printing flows for cell {}\".format(celln))\n",
    "for ipos in range(ia[celln] + 1, ia[celln + 1]):\n",
    "    cellm = ja[ipos]\n",
    "    print(\"Cell {} flow with cell {} is {}\".format(celln, cellm, flowja[ipos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}