examples/Notebooks/flopy3_mf6_A_simple-model.ipynb

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   "cell_type": "markdown",
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   "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": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]\n",
      "numpy version: 1.23.1\n",
      "matplotlib version: 3.5.2\n",
      "flopy version: 3.3.6\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "from tempfile import TemporaryDirectory\n",
    "\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": {
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   "outputs": [],
   "source": [
    "# For this example, we will set up a temporary workspace.\n",
    "# Model input files and output files will reside here.\n",
    "temp_dir = TemporaryDirectory()\n",
    "workspace = os.path.join(temp_dir.name, \"mf6lake\")"
   ]
  },
  {
   "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": {
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     "shell.execute_reply": "2022-07-30T20:16:36.876695Z"
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   "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": "2022-07-30T20:16:36.882067Z",
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   "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": "2022-07-30T20:16:36.969025Z",
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     "shell.execute_reply": "2022-07-30T20:16:36.974728Z"
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   },
   "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": "2022-07-30T20:16:36.979502Z",
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     "shell.execute_reply": "2022-07-30T20:16:36.985498Z"
    }
   },
   "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": "2022-07-30T20:16:36.989756Z",
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     "shell.execute_reply": "2022-07-30T20:16:36.994052Z"
    }
   },
   "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": "2022-07-30T20:16:36.998490Z",
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   "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": "2022-07-30T20:16:37.081654Z",
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     "shell.execute_reply": "2022-07-30T20:16:37.090038Z"
    }
   },
   "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": "2022-07-30T20:16:37.094075Z",
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     "shell.execute_reply": "2022-07-30T20:16:37.221347Z"
    }
   },
   "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": "2022-07-30T20:16:37.225821Z",
     "iopub.status.busy": "2022-07-30T20:16:37.225534Z",
     "iopub.status.idle": "2022-07-30T20:16:37.254530Z",
     "shell.execute_reply": "2022-07-30T20:16:37.253800Z"
    }
   },
   "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": "2022-07-30T20:16:37.258192Z",
     "iopub.status.busy": "2022-07-30T20:16:37.257905Z",
     "iopub.status.idle": "2022-07-30T20:16:37.265849Z",
     "shell.execute_reply": "2022-07-30T20:16:37.265068Z"
    }
   },
   "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, **kwargs)\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, **kwargs)\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": "2022-07-30T20:16:37.270242Z",
     "iopub.status.busy": "2022-07-30T20:16:37.269937Z",
     "iopub.status.idle": "2022-07-30T20:16:37.642484Z",
     "shell.execute_reply": "2022-07-30T20:16:37.641628Z"
    }
   },
   "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",
      "    writing package oc...\n"
     ]
    }
   ],
   "source": [
    "# Write the datasets\n",
    "sim.write_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:16:37.646207Z",
     "iopub.status.busy": "2022-07-30T20:16:37.645904Z",
     "iopub.status.idle": "2022-07-30T20:16:37.650121Z",
     "shell.execute_reply": "2022-07-30T20:16:37.649385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mf6lake.oc', 'mf6lake.nam', 'mf6lake.dis', 'mf6lake.ims', 'mf6lake.chd', 'mf6lake.npf', 'mf6lake.ic', 'mfsim.nam', 'mf6lake.tdis']\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": "2022-07-30T20:16:37.653772Z",
     "iopub.status.busy": "2022-07-30T20:16:37.653456Z",
     "iopub.status.idle": "2022-07-30T20:16:38.384590Z",
     "shell.execute_reply": "2022-07-30T20:16:38.383410Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /Users/wes/dev/usgs-executables/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.3.0 03/04/2022\n",
      "\n",
      "   MODFLOW 6 compiled Mar 07 2022 13:50:09 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.5.0\n",
      "                             Build 20211109_000000\n",
      "\n",
      "This software 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): 2022/07/30 16:16:37\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): 2022/07/30 16:16:38\n",
      " Elapsed run time:  0.711 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": "2022-07-30T20:16:38.388650Z",
     "iopub.status.busy": "2022-07-30T20:16:38.388371Z",
     "iopub.status.idle": "2022-07-30T20:16:38.590318Z",
     "shell.execute_reply": "2022-07-30T20:16:38.589458Z"
    }
   },
   "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": "2022-07-30T20:16:38.594208Z",
     "iopub.status.busy": "2022-07-30T20:16:38.593926Z",
     "iopub.status.idle": "2022-07-30T20:16:38.783669Z",
     "shell.execute_reply": "2022-07-30T20:16:38.782853Z"
    }
   },
   "outputs": [
    {
     "data": {
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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": "2022-07-30T20:16:38.788080Z",
     "iopub.status.busy": "2022-07-30T20:16:38.787659Z",
     "iopub.status.idle": "2022-07-30T20:16:38.924646Z",
     "shell.execute_reply": "2022-07-30T20:16:38.923972Z"
    },
    "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": "2022-07-30T20:16:38.928047Z",
     "iopub.status.busy": "2022-07-30T20:16:38.927770Z",
     "iopub.status.idle": "2022-07-30T20:16:39.303156Z",
     "shell.execute_reply": "2022-07-30T20:16:39.302258Z"
    }
   },
   "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": "2022-07-30T20:16:39.307602Z",
     "iopub.status.busy": "2022-07-30T20:16:39.307270Z",
     "iopub.status.idle": "2022-07-30T20:16:39.613666Z",
     "shell.execute_reply": "2022-07-30T20:16:39.612790Z"
    }
   },
   "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": "2022-07-30T20:16:39.617554Z",
     "iopub.status.busy": "2022-07-30T20:16:39.617272Z",
     "iopub.status.idle": "2022-07-30T20:16:39.631484Z",
     "shell.execute_reply": "2022-07-30T20:16:39.630625Z"
    }
   },
   "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": "2022-07-30T20:16:39.635783Z",
     "iopub.status.busy": "2022-07-30T20:16:39.635447Z",
     "iopub.status.idle": "2022-07-30T20:16:39.649227Z",
     "shell.execute_reply": "2022-07-30T20:16:39.648411Z"
    }
   },
   "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": "2022-07-30T20:16:39.653375Z",
     "iopub.status.busy": "2022-07-30T20:16:39.653073Z",
     "iopub.status.idle": "2022-07-30T20:16:39.664474Z",
     "shell.execute_reply": "2022-07-30T20:16:39.660804Z"
    }
   },
   "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": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:16:39.673535Z",
     "iopub.status.busy": "2022-07-30T20:16:39.673112Z",
     "iopub.status.idle": "2022-07-30T20:16:39.679992Z",
     "shell.execute_reply": "2022-07-30T20:16:39.679342Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # ignore PermissionError on Windows\n",
    "    temp_dir.cleanup()\n",
    "except:\n",
    "    pass"
   ]
  }
 ],
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