examples/Notebooks/flopy3_mf6_B_complex-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "## Creating a Complex MODFLOW 6 Model with Flopy\n",
    "\n",
    "The purpose of this notebook is to demonstrate the Flopy capabilities for building a more complex MODFLOW 6 model from scratch.  This notebook will demonstrate the capabilities by replicating the advgw_tidal model that is distributed with MODFLOW 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the Notebook Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:16.500970Z",
     "iopub.status.busy": "2022-12-15T13:11:16.500697Z",
     "iopub.status.idle": "2022-12-15T13:11:17.748561Z",
     "shell.execute_reply": "2022-12-15T13:11:17.745170Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.15 (default, Oct 18 2022, 09:36:49) \n",
      "[GCC 11.2.0]\n",
      "numpy version: 1.21.6\n",
      "matplotlib version: 3.5.3\n",
      "flopy version: 3.3.6\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\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": {
     "iopub.execute_input": "2022-12-15T13:11:17.801781Z",
     "iopub.status.busy": "2022-12-15T13:11:17.800751Z",
     "iopub.status.idle": "2022-12-15T13:11:17.806514Z",
     "shell.execute_reply": "2022-12-15T13:11:17.805528Z"
    }
   },
   "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",
    "model_name = \"advgw_tidal\"\n",
    "workspace = os.path.join(temp_dir.name, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:17.810854Z",
     "iopub.status.busy": "2022-12-15T13:11:17.810266Z",
     "iopub.status.idle": "2022-12-15T13:11:17.815614Z",
     "shell.execute_reply": "2022-12-15T13:11:17.814423Z"
    }
   },
   "outputs": [],
   "source": [
    "data_pth = os.path.join(\n",
    "    \"..\", \"data\", \"mf6\", \"create_tests\", \"test005_advgw_tidal\"\n",
    ")\n",
    "assert os.path.isdir(data_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:17.819959Z",
     "iopub.status.busy": "2022-12-15T13:11:17.819405Z",
     "iopub.status.idle": "2022-12-15T13:11:17.905273Z",
     "shell.execute_reply": "2022-12-15T13:11:17.904238Z"
    }
   },
   "outputs": [],
   "source": [
    "# create simulation\n",
    "sim = flopy.mf6.MFSimulation(\n",
    "    sim_name=model_name, version=\"mf6\", exe_name=\"mf6\", sim_ws=workspace\n",
    ")\n",
    "\n",
    "# create tdis package\n",
    "tdis_rc = [(1.0, 1, 1.0), (10.0, 120, 1.0), (10.0, 120, 1.0), (10.0, 120, 1.0)]\n",
    "tdis = flopy.mf6.ModflowTdis(\n",
    "    sim, pname=\"tdis\", time_units=\"DAYS\", nper=4, perioddata=tdis_rc\n",
    ")\n",
    "\n",
    "# create gwf model\n",
    "gwf = flopy.mf6.ModflowGwf(\n",
    "    sim, modelname=model_name, model_nam_file=\"{}.nam\".format(model_name)\n",
    ")\n",
    "gwf.name_file.save_flows = True\n",
    "\n",
    "# create iterative model solution and register the gwf model with it\n",
    "ims = flopy.mf6.ModflowIms(\n",
    "    sim,\n",
    "    pname=\"ims\",\n",
    "    print_option=\"SUMMARY\",\n",
    "    complexity=\"SIMPLE\",\n",
    "    outer_dvclose=0.0001,\n",
    "    outer_maximum=500,\n",
    "    under_relaxation=\"NONE\",\n",
    "    inner_maximum=100,\n",
    "    inner_dvclose=0.0001,\n",
    "    rcloserecord=0.001,\n",
    "    linear_acceleration=\"CG\",\n",
    "    scaling_method=\"NONE\",\n",
    "    reordering_method=\"NONE\",\n",
    "    relaxation_factor=0.97,\n",
    ")\n",
    "sim.register_ims_package(ims, [gwf.name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:17.909685Z",
     "iopub.status.busy": "2022-12-15T13:11:17.909361Z",
     "iopub.status.idle": "2022-12-15T13:11:17.953376Z",
     "shell.execute_reply": "2022-12-15T13:11:17.952185Z"
    }
   },
   "outputs": [],
   "source": [
    "# discretization package\n",
    "nlay = 3\n",
    "nrow = 15\n",
    "ncol = 10\n",
    "botlay2 = {\"factor\": 1.0, \"data\": [-100 for x in range(150)]}\n",
    "dis = flopy.mf6.ModflowGwfdis(\n",
    "    gwf,\n",
    "    pname=\"dis\",\n",
    "    nlay=nlay,\n",
    "    nrow=nrow,\n",
    "    ncol=ncol,\n",
    "    delr=500.0,\n",
    "    delc=500.0,\n",
    "    top=50.0,\n",
    "    botm=[5.0, -10.0, botlay2],\n",
    "    filename=\"{}.dis\".format(model_name),\n",
    ")\n",
    "\n",
    "# initial conditions\n",
    "ic = flopy.mf6.ModflowGwfic(\n",
    "    gwf, pname=\"ic\", strt=50.0, filename=\"{}.ic\".format(model_name)\n",
    ")\n",
    "\n",
    "# node property flow\n",
    "npf = flopy.mf6.ModflowGwfnpf(\n",
    "    gwf,\n",
    "    pname=\"npf\",\n",
    "    save_flows=True,\n",
    "    icelltype=[1, 0, 0],\n",
    "    k=[5.0, 0.1, 4.0],\n",
    "    k33=[0.5, 0.005, 0.1],\n",
    ")\n",
    "\n",
    "# output control\n",
    "oc = flopy.mf6.ModflowGwfoc(\n",
    "    gwf,\n",
    "    pname=\"oc\",\n",
    "    budget_filerecord=\"{}.cbb\".format(model_name),\n",
    "    head_filerecord=\"{}.hds\".format(model_name),\n",
    "    headprintrecord=[(\"COLUMNS\", 10, \"WIDTH\", 15, \"DIGITS\", 6, \"GENERAL\")],\n",
    "    saverecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n",
    "    printrecord=[(\"HEAD\", \"FIRST\"), (\"HEAD\", \"LAST\"), (\"BUDGET\", \"LAST\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:17.958143Z",
     "iopub.status.busy": "2022-12-15T13:11:17.957683Z",
     "iopub.status.idle": "2022-12-15T13:11:17.968220Z",
     "shell.execute_reply": "2022-12-15T13:11:17.967033Z"
    }
   },
   "outputs": [],
   "source": [
    "# storage package\n",
    "sy = flopy.mf6.ModflowGwfsto.sy.empty(gwf, layered=True)\n",
    "for layer in range(0, 3):\n",
    "    sy[layer][\"data\"] = 0.2\n",
    "\n",
    "ss = flopy.mf6.ModflowGwfsto.ss.empty(\n",
    "    gwf, layered=True, default_value=0.000001\n",
    ")\n",
    "\n",
    "sto = flopy.mf6.ModflowGwfsto(\n",
    "    gwf,\n",
    "    pname=\"sto\",\n",
    "    save_flows=True,\n",
    "    iconvert=1,\n",
    "    ss=ss,\n",
    "    sy=sy,\n",
    "    steady_state={0: True},\n",
    "    transient={1: True},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:17.972472Z",
     "iopub.status.busy": "2022-12-15T13:11:17.972140Z",
     "iopub.status.idle": "2022-12-15T13:11:18.022145Z",
     "shell.execute_reply": "2022-12-15T13:11:18.020877Z"
    }
   },
   "outputs": [],
   "source": [
    "# well package\n",
    "# test empty with aux vars, bound names, and time series\n",
    "period_two = flopy.mf6.ModflowGwfwel.stress_period_data.empty(\n",
    "    gwf,\n",
    "    maxbound=3,\n",
    "    aux_vars=[\"var1\", \"var2\", \"var3\"],\n",
    "    boundnames=True,\n",
    "    timeseries=True,\n",
    ")\n",
    "period_two[0][0] = ((0, 11, 2), -50.0, -1, -2, -3, None)\n",
    "period_two[0][1] = ((2, 4, 7), \"well_1_rate\", 1, 2, 3, \"well_1\")\n",
    "period_two[0][2] = ((2, 3, 2), \"well_2_rate\", 4, 5, 6, \"well_2\")\n",
    "period_three = flopy.mf6.ModflowGwfwel.stress_period_data.empty(\n",
    "    gwf,\n",
    "    maxbound=2,\n",
    "    aux_vars=[\"var1\", \"var2\", \"var3\"],\n",
    "    boundnames=True,\n",
    "    timeseries=True,\n",
    ")\n",
    "period_three[0][0] = ((2, 3, 2), \"well_2_rate\", 1, 2, 3, \"well_2\")\n",
    "period_three[0][1] = ((2, 4, 7), \"well_1_rate\", 4, 5, 6, \"well_1\")\n",
    "period_four = flopy.mf6.ModflowGwfwel.stress_period_data.empty(\n",
    "    gwf,\n",
    "    maxbound=5,\n",
    "    aux_vars=[\"var1\", \"var2\", \"var3\"],\n",
    "    boundnames=True,\n",
    "    timeseries=True,\n",
    ")\n",
    "period_four[0][0] = ((2, 4, 7), \"well_1_rate\", 1, 2, 3, \"well_1\")\n",
    "period_four[0][1] = ((2, 3, 2), \"well_2_rate\", 4, 5, 6, \"well_2\")\n",
    "period_four[0][2] = ((0, 11, 2), -10.0, 7, 8, 9, None)\n",
    "period_four[0][3] = ((0, 2, 4), -20.0, 17, 18, 19, None)\n",
    "period_four[0][4] = ((0, 13, 5), -40.0, 27, 28, 29, None)\n",
    "stress_period_data = {}\n",
    "stress_period_data[1] = period_two[0]\n",
    "stress_period_data[2] = period_three[0]\n",
    "stress_period_data[3] = period_four[0]\n",
    "wel = flopy.mf6.ModflowGwfwel(\n",
    "    gwf,\n",
    "    pname=\"wel\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    auxiliary=[(\"var1\", \"var2\", \"var3\")],\n",
    "    maxbound=5,\n",
    "    stress_period_data=stress_period_data,\n",
    "    boundnames=True,\n",
    "    save_flows=True,\n",
    ")\n",
    "\n",
    "# well ts package\n",
    "ts_data = [\n",
    "    (0.0, 0.0, 0.0, 0.0),\n",
    "    (1.0, -200.0, 0.0, -100.0),\n",
    "    (11.0, -1800.0, -500.0, -200.0),\n",
    "    (21.0, -200.0, -400.0, -300.0),\n",
    "    (31.0, 0.0, -600.0, -400.0),\n",
    "]\n",
    "wel.ts.initialize(\n",
    "    filename=\"well-rates.ts\",\n",
    "    timeseries=ts_data,\n",
    "    time_series_namerecord=[(\"well_1_rate\", \"well_2_rate\", \"well_3_rate\")],\n",
    "    interpolation_methodrecord=[(\"stepwise\", \"stepwise\", \"stepwise\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.026240Z",
     "iopub.status.busy": "2022-12-15T13:11:18.025943Z",
     "iopub.status.idle": "2022-12-15T13:11:18.041415Z",
     "shell.execute_reply": "2022-12-15T13:11:18.040239Z"
    }
   },
   "outputs": [],
   "source": [
    "# Evapotranspiration\n",
    "evt_period = flopy.mf6.ModflowGwfevt.stress_period_data.empty(gwf, 150, nseg=3)\n",
    "for col in range(0, 10):\n",
    "    for row in range(0, 15):\n",
    "        evt_period[0][col * 15 + row] = (\n",
    "            (0, row, col),\n",
    "            50.0,\n",
    "            0.0004,\n",
    "            10.0,\n",
    "            0.2,\n",
    "            0.5,\n",
    "            0.3,\n",
    "            0.1,\n",
    "            None,\n",
    "        )\n",
    "evt = flopy.mf6.ModflowGwfevt(\n",
    "    gwf,\n",
    "    pname=\"evt\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=True,\n",
    "    maxbound=150,\n",
    "    nseg=3,\n",
    "    stress_period_data=evt_period,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.045999Z",
     "iopub.status.busy": "2022-12-15T13:11:18.045292Z",
     "iopub.status.idle": "2022-12-15T13:11:18.095337Z",
     "shell.execute_reply": "2022-12-15T13:11:18.093908Z"
    }
   },
   "outputs": [],
   "source": [
    "# General-Head Boundaries\n",
    "ghb_period = {}\n",
    "ghb_period_array = []\n",
    "for layer, cond in zip(range(1, 3), [15.0, 1500.0]):\n",
    "    for row in range(0, 15):\n",
    "        ghb_period_array.append(((layer, row, 9), \"tides\", cond, \"Estuary-L2\"))\n",
    "ghb_period[0] = ghb_period_array\n",
    "ghb = flopy.mf6.ModflowGwfghb(\n",
    "    gwf,\n",
    "    pname=\"ghb\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=True,\n",
    "    boundnames=True,\n",
    "    maxbound=30,\n",
    "    stress_period_data=ghb_period,\n",
    ")\n",
    "ts_recarray = []\n",
    "fd = open(os.path.join(data_pth, \"tides.txt\"), \"r\")\n",
    "for line in fd:\n",
    "    line_list = line.strip().split(\",\")\n",
    "    ts_recarray.append((float(line_list[0]), float(line_list[1])))\n",
    "ghb.ts.initialize(\n",
    "    filename=\"tides.ts\",\n",
    "    timeseries=ts_recarray,\n",
    "    time_series_namerecord=\"tides\",\n",
    "    interpolation_methodrecord=\"linear\",\n",
    ")\n",
    "obs_recarray = {\n",
    "    \"ghb_obs.csv\": [\n",
    "        (\"ghb-2-6-10\", \"GHB\", (1, 5, 9)),\n",
    "        (\"ghb-3-6-10\", \"GHB\", (2, 5, 9)),\n",
    "    ],\n",
    "    \"ghb_flows.csv\": [\n",
    "        (\"Estuary2\", \"GHB\", \"Estuary-L2\"),\n",
    "        (\"Estuary3\", \"GHB\", \"Estuary-L3\"),\n",
    "    ],\n",
    "}\n",
    "ghb.obs.initialize(\n",
    "    filename=\"{}.ghb.obs\".format(model_name),\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.100104Z",
     "iopub.status.busy": "2022-12-15T13:11:18.099752Z",
     "iopub.status.idle": "2022-12-15T13:11:18.116388Z",
     "shell.execute_reply": "2022-12-15T13:11:18.115484Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_recarray = {\n",
    "    \"head_obs.csv\": [(\"h1_13_8\", \"HEAD\", (2, 12, 7))],\n",
    "    \"intercell_flow_obs1.csv\": [\n",
    "        (\"ICF1_1.0\", \"FLOW-JA-FACE\", (0, 4, 5), (0, 5, 5))\n",
    "    ],\n",
    "    \"head-hydrographs.csv\": [\n",
    "        (\"h3-13-9\", \"HEAD\", (2, 12, 8)),\n",
    "        (\"h3-12-8\", \"HEAD\", (2, 11, 7)),\n",
    "        (\"h1-4-3\", \"HEAD\", (0, 3, 2)),\n",
    "        (\"h1-12-3\", \"HEAD\", (0, 11, 2)),\n",
    "        (\"h1-13-9\", \"HEAD\", (0, 12, 8)),\n",
    "    ],\n",
    "}\n",
    "obs_package = flopy.mf6.ModflowUtlobs(\n",
    "    gwf,\n",
    "    pname=\"head_obs\",\n",
    "    filename=\"{}.obs\".format(model_name),\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.120743Z",
     "iopub.status.busy": "2022-12-15T13:11:18.120412Z",
     "iopub.status.idle": "2022-12-15T13:11:18.265021Z",
     "shell.execute_reply": "2022-12-15T13:11:18.263839Z"
    }
   },
   "outputs": [],
   "source": [
    "# River\n",
    "riv_period = {}\n",
    "riv_period_array = [\n",
    "    ((0, 2, 0), \"river_stage_1\", 1001.0, 35.9, None),\n",
    "    ((0, 3, 1), \"river_stage_1\", 1002.0, 35.8, None),\n",
    "    ((0, 4, 2), \"river_stage_1\", 1003.0, 35.7, None),\n",
    "    ((0, 4, 3), \"river_stage_1\", 1004.0, 35.6, None),\n",
    "    ((0, 5, 4), \"river_stage_1\", 1005.0, 35.5, None),\n",
    "    ((0, 5, 5), \"river_stage_1\", 1006.0, 35.4, \"riv1_c6\"),\n",
    "    ((0, 5, 6), \"river_stage_1\", 1007.0, 35.3, \"riv1_c7\"),\n",
    "    ((0, 4, 7), \"river_stage_1\", 1008.0, 35.2, None),\n",
    "    ((0, 4, 8), \"river_stage_1\", 1009.0, 35.1, None),\n",
    "    ((0, 4, 9), \"river_stage_1\", 1010.0, 35.0, None),\n",
    "    ((0, 9, 0), \"river_stage_2\", 1001.0, 36.9, \"riv2_upper\"),\n",
    "    ((0, 8, 1), \"river_stage_2\", 1002.0, 36.8, \"riv2_upper\"),\n",
    "    ((0, 7, 2), \"river_stage_2\", 1003.0, 36.7, \"riv2_upper\"),\n",
    "    ((0, 6, 3), \"river_stage_2\", 1004.0, 36.6, None),\n",
    "    ((0, 6, 4), \"river_stage_2\", 1005.0, 36.5, None),\n",
    "    ((0, 5, 5), \"river_stage_2\", 1006.0, 36.4, \"riv2_c6\"),\n",
    "    ((0, 5, 6), \"river_stage_2\", 1007.0, 36.3, \"riv2_c7\"),\n",
    "    ((0, 6, 7), \"river_stage_2\", 1008.0, 36.2, None),\n",
    "    ((0, 6, 8), \"river_stage_2\", 1009.0, 36.1),\n",
    "    ((0, 6, 9), \"river_stage_2\", 1010.0, 36.0),\n",
    "]\n",
    "riv_period[0] = riv_period_array\n",
    "riv = flopy.mf6.ModflowGwfriv(\n",
    "    gwf,\n",
    "    pname=\"riv\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=\"{}.cbc\".format(model_name),\n",
    "    boundnames=True,\n",
    "    maxbound=20,\n",
    "    stress_period_data=riv_period,\n",
    ")\n",
    "ts_recarray = [\n",
    "    (0.0, 40.0, 41.0),\n",
    "    (1.0, 41.0, 41.5),\n",
    "    (2.0, 43.0, 42.0),\n",
    "    (3.0, 45.0, 42.8),\n",
    "    (4.0, 44.0, 43.0),\n",
    "    (6.0, 43.0, 43.1),\n",
    "    (9.0, 42.0, 42.4),\n",
    "    (11.0, 41.0, 41.5),\n",
    "    (31.0, 40.0, 41.0),\n",
    "]\n",
    "riv.ts.initialize(\n",
    "    filename=\"river_stages.ts\",\n",
    "    timeseries=ts_recarray,\n",
    "    time_series_namerecord=[(\"river_stage_1\", \"river_stage_2\")],\n",
    "    interpolation_methodrecord=[(\"linear\", \"stepwise\")],\n",
    ")\n",
    "obs_recarray = {\n",
    "    \"riv_obs.csv\": [\n",
    "        (\"rv1-3-1\", \"RIV\", (0, 2, 0)),\n",
    "        (\"rv1-4-2\", \"RIV\", (0, 3, 1)),\n",
    "        (\"rv1-5-3\", \"RIV\", (0, 4, 2)),\n",
    "        (\"rv1-5-4\", \"RIV\", (0, 4, 3)),\n",
    "        (\"rv1-6-5\", \"RIV\", (0, 5, 4)),\n",
    "        (\"rv1-c6\", \"RIV\", \"riv1_c6\"),\n",
    "        (\"rv1-c7\", \"RIV\", \"riv1_c7\"),\n",
    "        (\"rv2-upper\", \"RIV\", \"riv2_upper\"),\n",
    "        (\"rv-2-7-4\", \"RIV\", (0, 6, 3)),\n",
    "        (\"rv2-8-5\", \"RIV\", (0, 6, 4)),\n",
    "        (\n",
    "            \"rv-2-9-6\",\n",
    "            \"RIV\",\n",
    "            (\n",
    "                0,\n",
    "                5,\n",
    "                5,\n",
    "            ),\n",
    "        ),\n",
    "    ],\n",
    "    \"riv_flowsA.csv\": [\n",
    "        (\"riv1-3-1\", \"RIV\", (0, 2, 0)),\n",
    "        (\"riv1-4-2\", \"RIV\", (0, 3, 1)),\n",
    "        (\"riv1-5-3\", \"RIV\", (0, 4, 2)),\n",
    "    ],\n",
    "    \"riv_flowsB.csv\": [\n",
    "        (\"riv2-10-1\", \"RIV\", (0, 9, 0)),\n",
    "        (\"riv-2-9-2\", \"RIV\", (0, 8, 1)),\n",
    "        (\"riv2-8-3\", \"RIV\", (0, 7, 2)),\n",
    "    ],\n",
    "}\n",
    "riv.obs.initialize(\n",
    "    filename=\"{}.riv.obs\".format(model_name),\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.270213Z",
     "iopub.status.busy": "2022-12-15T13:11:18.269878Z",
     "iopub.status.idle": "2022-12-15T13:11:18.308623Z",
     "shell.execute_reply": "2022-12-15T13:11:18.307633Z"
    }
   },
   "outputs": [],
   "source": [
    "# First recharge package\n",
    "rch1_period = {}\n",
    "rch1_period_array = []\n",
    "col_range = {0: 3, 1: 4, 2: 5}\n",
    "for row in range(0, 15):\n",
    "    if row in col_range:\n",
    "        col_max = col_range[row]\n",
    "    else:\n",
    "        col_max = 6\n",
    "    for col in range(0, col_max):\n",
    "        if (\n",
    "            (row == 3 and col == 5)\n",
    "            or (row == 2 and col == 4)\n",
    "            or (row == 1 and col == 3)\n",
    "            or (row == 0 and col == 2)\n",
    "        ):\n",
    "            mult = 0.5\n",
    "        else:\n",
    "            mult = 1.0\n",
    "        if row == 0 and col == 0:\n",
    "            bnd = \"rch-1-1\"\n",
    "        elif row == 0 and col == 1:\n",
    "            bnd = \"rch-1-2\"\n",
    "        elif row == 1 and col == 2:\n",
    "            bnd = \"rch-2-3\"\n",
    "        else:\n",
    "            bnd = None\n",
    "        rch1_period_array.append(((0, row, col), \"rch_1\", mult, bnd))\n",
    "rch1_period[0] = rch1_period_array\n",
    "rch1 = flopy.mf6.ModflowGwfrch(\n",
    "    gwf,\n",
    "    filename=\"{}_1.rch\".format(model_name),\n",
    "    pname=\"rch_1\",\n",
    "    fixed_cell=True,\n",
    "    auxiliary=\"MULTIPLIER\",\n",
    "    auxmultname=\"MULTIPLIER\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=True,\n",
    "    boundnames=True,\n",
    "    maxbound=84,\n",
    "    stress_period_data=rch1_period,\n",
    ")\n",
    "ts_data = [\n",
    "    (0.0, 0.0015),\n",
    "    (1.0, 0.0010),\n",
    "    (11.0, 0.0015),\n",
    "    (21.0, 0.0025),\n",
    "    (31.0, 0.0015),\n",
    "]\n",
    "rch1.ts.initialize(\n",
    "    filename=\"recharge_rates_1.ts\",\n",
    "    timeseries=ts_data,\n",
    "    time_series_namerecord=\"rch_1\",\n",
    "    interpolation_methodrecord=\"stepwise\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.312502Z",
     "iopub.status.busy": "2022-12-15T13:11:18.312115Z",
     "iopub.status.idle": "2022-12-15T13:11:18.353346Z",
     "shell.execute_reply": "2022-12-15T13:11:18.352188Z"
    }
   },
   "outputs": [],
   "source": [
    "# Second recharge package\n",
    "rch2_period = {}\n",
    "rch2_period_array = [\n",
    "    ((0, 0, 2), \"rch_2\", 0.5),\n",
    "    ((0, 0, 3), \"rch_2\", 1.0),\n",
    "    ((0, 0, 4), \"rch_2\", 1.0),\n",
    "    ((0, 0, 5), \"rch_2\", 1.0),\n",
    "    ((0, 0, 6), \"rch_2\", 1.0),\n",
    "    ((0, 0, 7), \"rch_2\", 1.0),\n",
    "    ((0, 0, 8), \"rch_2\", 1.0),\n",
    "    ((0, 0, 9), \"rch_2\", 0.5),\n",
    "    ((0, 1, 3), \"rch_2\", 0.5),\n",
    "    ((0, 1, 4), \"rch_2\", 1.0),\n",
    "    ((0, 1, 5), \"rch_2\", 1.0),\n",
    "    ((0, 1, 6), \"rch_2\", 1.0),\n",
    "    ((0, 1, 7), \"rch_2\", 1.0),\n",
    "    ((0, 1, 8), \"rch_2\", 0.5),\n",
    "    ((0, 2, 4), \"rch_2\", 0.5),\n",
    "    ((0, 2, 5), \"rch_2\", 1.0),\n",
    "    ((0, 2, 6), \"rch_2\", 1.0),\n",
    "    ((0, 2, 7), \"rch_2\", 0.5),\n",
    "    ((0, 3, 5), \"rch_2\", 0.5),\n",
    "    ((0, 3, 6), \"rch_2\", 0.5),\n",
    "]\n",
    "rch2_period[0] = rch2_period_array\n",
    "rch2 = flopy.mf6.ModflowGwfrch(\n",
    "    gwf,\n",
    "    filename=\"{}_2.rch\".format(model_name),\n",
    "    pname=\"rch_2\",\n",
    "    fixed_cell=True,\n",
    "    auxiliary=\"MULTIPLIER\",\n",
    "    auxmultname=\"MULTIPLIER\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=True,\n",
    "    maxbound=20,\n",
    "    stress_period_data=rch2_period,\n",
    ")\n",
    "ts_data = [\n",
    "    (0.0, 0.0016),\n",
    "    (1.0, 0.0018),\n",
    "    (11.0, 0.0019),\n",
    "    (21.0, 0.0016),\n",
    "    (31.0, 0.0018),\n",
    "]\n",
    "rch2.ts.initialize(\n",
    "    filename=\"recharge_rates_2.ts\",\n",
    "    timeseries=ts_data,\n",
    "    time_series_namerecord=\"rch_2\",\n",
    "    interpolation_methodrecord=\"linear\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.358781Z",
     "iopub.status.busy": "2022-12-15T13:11:18.358214Z",
     "iopub.status.idle": "2022-12-15T13:11:18.399098Z",
     "shell.execute_reply": "2022-12-15T13:11:18.398053Z"
    }
   },
   "outputs": [],
   "source": [
    "# Third recharge package\n",
    "rch3_period = {}\n",
    "rch3_period_array = []\n",
    "col_range = {0: 9, 1: 8, 2: 7}\n",
    "for row in range(0, 15):\n",
    "    if row in col_range:\n",
    "        col_min = col_range[row]\n",
    "    else:\n",
    "        col_min = 6\n",
    "    for col in range(col_min, 10):\n",
    "        if (\n",
    "            (row == 0 and col == 9)\n",
    "            or (row == 1 and col == 8)\n",
    "            or (row == 2 and col == 7)\n",
    "            or (row == 3 and col == 6)\n",
    "        ):\n",
    "            mult = 0.5\n",
    "        else:\n",
    "            mult = 1.0\n",
    "        rch3_period_array.append(((0, row, col), \"rch_3\", mult))\n",
    "rch3_period[0] = rch3_period_array\n",
    "rch3 = flopy.mf6.ModflowGwfrch(\n",
    "    gwf,\n",
    "    filename=\"{}_3.rch\".format(model_name),\n",
    "    pname=\"rch_3\",\n",
    "    fixed_cell=True,\n",
    "    auxiliary=\"MULTIPLIER\",\n",
    "    auxmultname=\"MULTIPLIER\",\n",
    "    print_input=True,\n",
    "    print_flows=True,\n",
    "    save_flows=True,\n",
    "    maxbound=54,\n",
    "    stress_period_data=rch3_period,\n",
    ")\n",
    "ts_data = [\n",
    "    (0.0, 0.0017),\n",
    "    (1.0, 0.0020),\n",
    "    (11.0, 0.0017),\n",
    "    (21.0, 0.0018),\n",
    "    (31.0, 0.0020),\n",
    "]\n",
    "rch3.ts.initialize(\n",
    "    filename=\"recharge_rates_3.ts\",\n",
    "    timeseries=ts_data,\n",
    "    time_series_namerecord=\"rch_3\",\n",
    "    interpolation_methodrecord=\"linear\",\n",
    ")"
   ]
  },
  {
   "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": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.404480Z",
     "iopub.status.busy": "2022-12-15T13:11:18.404168Z",
     "iopub.status.idle": "2022-12-15T13:11:18.483768Z",
     "shell.execute_reply": "2022-12-15T13:11:18.482783Z"
    }
   },
   "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 advgw_tidal...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package oc...\n",
      "    writing package sto...\n",
      "    writing package wel...\n",
      "    writing package ts_0...\n",
      "    writing package evt...\n",
      "    writing package ghb...\n",
      "    writing package ts_1...\n",
      "    writing package obs_0...\n",
      "    writing package head_obs...\n",
      "    writing package riv...\n",
      "    writing package ts_2...\n",
      "    writing package obs_1...\n",
      "    writing package rch_1...\n",
      "    writing package ts_3...\n",
      "    writing package rch_2...\n",
      "    writing package ts_4...\n",
      "    writing package rch_3...\n",
      "    writing package ts_5...\n"
     ]
    }
   ],
   "source": [
    "# write simulation to new location\n",
    "sim.write_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.488181Z",
     "iopub.status.busy": "2022-12-15T13:11:18.487881Z",
     "iopub.status.idle": "2022-12-15T13:11:18.496742Z",
     "shell.execute_reply": "2022-12-15T13:11:18.495198Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['advgw_tidal.nam', 'advgw_tidal.ghb', 'advgw_tidal.obs', 'tides.ts', 'advgw_tidal.oc', 'advgw_tidal.riv.obs', 'advgw_tidal_3.rch', 'advgw_tidal.ims', 'advgw_tidal.ghb.obs', 'recharge_rates_3.ts', 'advgw_tidal.npf', 'advgw_tidal.evt', 'recharge_rates_2.ts', 'advgw_tidal.dis', 'advgw_tidal.riv', 'advgw_tidal.sto', 'river_stages.ts', 'advgw_tidal.tdis', 'advgw_tidal.wel', 'advgw_tidal_1.rch', 'advgw_tidal_2.rch', 'mfsim.nam', 'recharge_rates_1.ts', 'well-rates.ts', 'advgw_tidal.ic']\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": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:18.500842Z",
     "iopub.status.busy": "2022-12-15T13:11:18.500395Z",
     "iopub.status.idle": "2022-12-15T13:11:19.708159Z",
     "shell.execute_reply": "2022-12-15T13:11:19.707021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /home/runner/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                        VERSION 6.4.1 Release 12/09/2022\n",
      "\n",
      "   MODFLOW 6 compiled Dec 10 2022 04:48:56 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n",
      "                             Build 20220726_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): 2022/12/15 13:11:18\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      "    Solving:  Stress period:     2    Time step:     1\n",
      "    Solving:  Stress period:     2    Time step:     2\n",
      "    Solving:  Stress period:     2    Time step:     3\n",
      "    Solving:  Stress period:     2    Time step:     4\n",
      "    Solving:  Stress period:     2    Time step:     5\n",
      "    Solving:  Stress period:     2    Time step:     6\n",
      "    Solving:  Stress period:     2    Time step:     7\n",
      "    Solving:  Stress period:     2    Time step:     8\n",
      "    Solving:  Stress period:     2    Time step:     9\n",
      "    Solving:  Stress period:     2    Time step:    10\n",
      "    Solving:  Stress period:     2    Time step:    11\n",
      "    Solving:  Stress period:     2    Time step:    12\n",
      "    Solving:  Stress period:     2    Time step:    13\n",
      "    Solving:  Stress period:     2    Time step:    14\n",
      "    Solving:  Stress period:     2    Time step:    15\n",
      "    Solving:  Stress period:     2    Time step:    16\n",
      "    Solving:  Stress period:     2    Time step:    17\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:    18\n",
      "    Solving:  Stress period:     2    Time step:    19\n",
      "    Solving:  Stress period:     2    Time step:    20\n",
      "    Solving:  Stress period:     2    Time step:    21\n",
      "    Solving:  Stress period:     2    Time step:    22\n",
      "    Solving:  Stress period:     2    Time step:    23\n",
      "    Solving:  Stress period:     2    Time step:    24\n",
      "    Solving:  Stress period:     2    Time step:    25\n",
      "    Solving:  Stress period:     2    Time step:    26\n",
      "    Solving:  Stress period:     2    Time step:    27\n",
      "    Solving:  Stress period:     2    Time step:    28\n",
      "    Solving:  Stress period:     2    Time step:    29\n",
      "    Solving:  Stress period:     2    Time step:    30\n",
      "    Solving:  Stress period:     2    Time step:    31\n",
      "    Solving:  Stress period:     2    Time step:    32\n",
      "    Solving:  Stress period:     2    Time step:    33\n",
      "    Solving:  Stress period:     2    Time step:    34\n",
      "    Solving:  Stress period:     2    Time step:    35\n",
      "    Solving:  Stress period:     2    Time step:    36\n",
      "    Solving:  Stress period:     2    Time step:    37\n",
      "    Solving:  Stress period:     2    Time step:    38\n",
      "    Solving:  Stress period:     2    Time step:    39\n",
      "    Solving:  Stress period:     2    Time step:    40\n",
      "    Solving:  Stress period:     2    Time step:    41\n",
      "    Solving:  Stress period:     2    Time step:    42\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:    43\n",
      "    Solving:  Stress period:     2    Time step:    44\n",
      "    Solving:  Stress period:     2    Time step:    45\n",
      "    Solving:  Stress period:     2    Time step:    46\n",
      "    Solving:  Stress period:     2    Time step:    47\n",
      "    Solving:  Stress period:     2    Time step:    48\n",
      "    Solving:  Stress period:     2    Time step:    49\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:    50\n",
      "    Solving:  Stress period:     2    Time step:    51\n",
      "    Solving:  Stress period:     2    Time step:    52\n",
      "    Solving:  Stress period:     2    Time step:    53\n",
      "    Solving:  Stress period:     2    Time step:    54\n",
      "    Solving:  Stress period:     2    Time step:    55\n",
      "    Solving:  Stress period:     2    Time step:    56\n",
      "    Solving:  Stress period:     2    Time step:    57\n",
      "    Solving:  Stress period:     2    Time step:    58\n",
      "    Solving:  Stress period:     2    Time step:    59\n",
      "    Solving:  Stress period:     2    Time step:    60\n",
      "    Solving:  Stress period:     2    Time step:    61\n",
      "    Solving:  Stress period:     2    Time step:    62\n",
      "    Solving:  Stress period:     2    Time step:    63\n",
      "    Solving:  Stress period:     2    Time step:    64\n",
      "    Solving:  Stress period:     2    Time step:    65\n",
      "    Solving:  Stress period:     2    Time step:    66\n",
      "    Solving:  Stress period:     2    Time step:    67\n",
      "    Solving:  Stress period:     2    Time step:    68\n",
      "    Solving:  Stress period:     2    Time step:    69\n",
      "    Solving:  Stress period:     2    Time step:    70\n",
      "    Solving:  Stress period:     2    Time step:    71\n",
      "    Solving:  Stress period:     2    Time step:    72\n",
      "    Solving:  Stress period:     2    Time step:    73\n",
      "    Solving:  Stress period:     2    Time step:    74\n",
      "    Solving:  Stress period:     2    Time step:    75\n",
      "    Solving:  Stress period:     2    Time step:    76\n",
      "    Solving:  Stress period:     2    Time step:    77\n",
      "    Solving:  Stress period:     2    Time step:    78\n",
      "    Solving:  Stress period:     2    Time step:    79\n",
      "    Solving:  Stress period:     2    Time step:    80\n",
      "    Solving:  Stress period:     2    Time step:    81\n",
      "    Solving:  Stress period:     2    Time step:    82\n",
      "    Solving:  Stress period:     2    Time step:    83\n",
      "    Solving:  Stress period:     2    Time step:    84\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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      "    Solving:  Stress period:     3    Time step:    95\n",
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     ]
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      "    Solving:  Stress period:     4    Time step:     1\n",
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      "    Solving:  Stress period:     4    Time step:     7\n",
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     ]
    },
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      "    Solving:  Stress period:     4    Time step:    41\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "    Solving:  Stress period:     4    Time step:    67\n",
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      "    Solving:  Stress period:     4    Time step:    72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "    Solving:  Stress period:     4    Time step:    73\n",
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      "    Solving:  Stress period:     4    Time step:   103\n",
      "    Solving:  Stress period:     4    Time step:   104\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:   105\n",
      "    Solving:  Stress period:     4    Time step:   106\n",
      "    Solving:  Stress period:     4    Time step:   107\n",
      "    Solving:  Stress period:     4    Time step:   108\n",
      "    Solving:  Stress period:     4    Time step:   109\n",
      "    Solving:  Stress period:     4    Time step:   110\n",
      "    Solving:  Stress period:     4    Time step:   111\n",
      "    Solving:  Stress period:     4    Time step:   112\n",
      "    Solving:  Stress period:     4    Time step:   113\n",
      "    Solving:  Stress period:     4    Time step:   114\n",
      "    Solving:  Stress period:     4    Time step:   115\n",
      "    Solving:  Stress period:     4    Time step:   116\n",
      "    Solving:  Stress period:     4    Time step:   117\n",
      "    Solving:  Stress period:     4    Time step:   118\n",
      "    Solving:  Stress period:     4    Time step:   119\n",
      "    Solving:  Stress period:     4    Time step:   120\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/12/15 13:11:19\n",
      " Elapsed run time:  1.189 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",
    "First, we get the simulated head data using the `.output.head()` method and 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`. FloPy plotting methods are used to make contours of the head in a specific layer (in this case, layer 1). FloPy plotting methods are also used to plot the model grid and the location of GHB cells in the model domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:19.713819Z",
     "iopub.status.busy": "2022-12-15T13:11:19.712719Z",
     "iopub.status.idle": "2022-12-15T13:11:19.784978Z",
     "shell.execute_reply": "2022-12-15T13:11:19.783646Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Retrieve the head data using the .output() method\n",
    "h = gwf.output.head().get_data(kstpkper=(0, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:19.790116Z",
     "iopub.status.busy": "2022-12-15T13:11:19.789764Z",
     "iopub.status.idle": "2022-12-15T13:11:20.085871Z",
     "shell.execute_reply": "2022-12-15T13:11:20.084973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "ghb_quadmesh = modelmap.plot_bc(name=\"ghb\", plotAll=True)\n",
    "riv_quadmesh = modelmap.plot_bc(name=\"riv\", plotAll=True)\n",
    "linecollection = modelmap.plot_grid()\n",
    "contours = modelmap.contour_array(h[0])"
   ]
  },
  {
   "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 methods.  The `MfGrdFile` class in FloPy can be used to read the binary grid file, which contains the cell connectivity (`ia` and `ja`).  The `output.budget()` method in FloPy can be used to read the binary budget file written by MODFLOW 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:20.090170Z",
     "iopub.status.busy": "2022-12-15T13:11:20.089874Z",
     "iopub.status.idle": "2022-12-15T13:11:20.098351Z",
     "shell.execute_reply": "2022-12-15T13:11:20.097322Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = os.path.join(workspace, \"{}.dis.grb\".format(model_name))\n",
    "bgf = flopy.mf6.utils.MfGrdFile(fname)\n",
    "ia, ja = bgf.ia, bgf.ja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:20.102267Z",
     "iopub.status.busy": "2022-12-15T13:11:20.101961Z",
     "iopub.status.idle": "2022-12-15T13:11:21.337093Z",
     "shell.execute_reply": "2022-12-15T13:11:21.335665Z"
    }
   },
   "outputs": [],
   "source": [
    "flowja = gwf.output.budget().get_data(text=\"FLOW-JA-FACE\")[0].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:21.341794Z",
     "iopub.status.busy": "2022-12-15T13:11:21.341454Z",
     "iopub.status.idle": "2022-12-15T13:11:21.351406Z",
     "shell.execute_reply": "2022-12-15T13:11:21.350514Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing flows for cell 412\n",
      "Cell 412 flow with cell 262 is 251.46262091207623\n",
      "Cell 412 flow with cell 402 is 0.7176346498656017\n",
      "Cell 412 flow with cell 411 is 439.8629968543804\n",
      "Cell 412 flow with cell 413 is -693.4212447574185\n",
      "Cell 412 flow with cell 422 is 1.3779378787739915\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. Note\n",
    "# layer, row, column locations are zero-based.\n",
    "k = 2\n",
    "i = 11\n",
    "j = 2\n",
    "cell_nodes = gwf.modelgrid.get_node([(k, i, j)])\n",
    "\n",
    "for celln in cell_nodes:\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(\n",
    "            \"Cell {} flow with cell {} is {}\".format(\n",
    "                celln, cellm, flowja[ipos]\n",
    "            )\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Process Head Observations\n",
    "\n",
    "MODFLOW 6 observations can be read using the `output.obs()` method in FloPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:21.355529Z",
     "iopub.status.busy": "2022-12-15T13:11:21.354852Z",
     "iopub.status.idle": "2022-12-15T13:11:21.631329Z",
     "shell.execute_reply": "2022-12-15T13:11:21.630219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv = gwf.head_obs.output.obs(f=\"head-hydrographs.csv\").get_data()\n",
    "\n",
    "for name in csv.dtype.names[1:]:\n",
    "    plt.plot(csv[\"totim\"], csv[name], label=name)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:11:21.635309Z",
     "iopub.status.busy": "2022-12-15T13:11:21.635010Z",
     "iopub.status.idle": "2022-12-15T13:11:21.650886Z",
     "shell.execute_reply": "2022-12-15T13:11:21.644288Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # ignore PermissionError on Windows\n",
    "    temp_dir.cleanup()\n",
    "except:\n",
    "    pass"
   ]
  }
 ],
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