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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.11 (default, Aug  6 2021, 08:56:27) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.5\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join(\"..\", \"..\"))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version: {}\".format(np.__version__))\n",
    "print(\"matplotlib version: {}\".format(mpl.__version__))\n",
    "print(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this example, we will set up a model workspace.\n",
    "# Model input files and output files will reside here.\n",
    "model_name = \"advgw_tidal\"\n",
    "workspace = os.path.join(\"data\", model_name)\n",
    "if not os.path.isdir(workspace):\n",
    "    os.makedirs(workspace, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": {},
   "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_hclose=0.0001,\n",
    "    outer_maximum=500,\n",
    "    under_relaxation=\"NONE\",\n",
    "    inner_maximum=100,\n",
    "    inner_hclose=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": {},
   "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": {},
   "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": {},
   "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": {},
   "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": {},
   "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",
    "    digits=10,\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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",
    "    digits=10,\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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",
    "    digits=10,\n",
    "    print_input=True,\n",
    "    continuous=obs_recarray,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "# change folder to save simulation\n",
    "# sim.simulation_data.mfpath.set_sim_path(run_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['advgw_tidal.nam', 'advgw_tidal.ic', 'recharge_rates_1.ts', 'advgw_tidal.sto', 'tides.ts', 'advgw_tidal.ims', 'well-rates.ts', 'advgw_tidal.ghb', 'advgw_tidal.obs', 'advgw_tidal.riv.obs', 'advgw_tidal.dis', 'advgw_tidal_1.rch', 'advgw_tidal_3.rch', 'advgw_tidal_2.rch', 'advgw_tidal.oc', 'river_stages.ts', 'advgw_tidal.wel', 'advgw_tidal.ghb.obs', 'advgw_tidal.npf', 'advgw_tidal.tdis', 'recharge_rates_2.ts', 'mfsim.nam', 'advgw_tidal.riv', 'recharge_rates_3.ts', 'advgw_tidal.evt']\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.2.2 07/30/2021\n",
      "                               ***DEVELOP MODE***\n",
      "\n",
      "  MODFLOW 6 compiled Aug 31 2021 16:00:07 with GFORTRAN compiler (ver. 10.3.0)\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      "\n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/10/25 16:36:25\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",
      "    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",
      "    Solving:  Stress period:     2    Time step:    18\n",
      "    Solving:  Stress period:     2    Time step:    19\n",
      "    Solving:  Stress period:     2    Time step:    20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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",
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      "    Solving:  Stress period:     2    Time step:    30\n",
      "    Solving:  Stress period:     2    Time step:    31\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:    32\n",
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      "    Solving:  Stress period:     2    Time step:    57\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     2    Time step:    58\n",
      "    Solving:  Stress period:     2    Time step:    59\n",
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      "    Solving:  Stress period:     2    Time step:   117\n",
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      "    Solving:  Stress period:     2    Time step:   120\n",
      "    Solving:  Stress period:     3    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:     2\n",
      "    Solving:  Stress period:     3    Time step:     3\n",
      "    Solving:  Stress period:     3    Time step:     4\n",
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      "    Solving:  Stress period:     3    Time step:     6\n",
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      "    Solving:  Stress period:     3    Time step:    11\n",
      "    Solving:  Stress period:     3    Time step:    12\n",
      "    Solving:  Stress period:     3    Time step:    13\n",
      "    Solving:  Stress period:     3    Time step:    14\n",
      "    Solving:  Stress period:     3    Time step:    15\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:    16\n",
      "    Solving:  Stress period:     3    Time step:    17\n",
      "    Solving:  Stress period:     3    Time step:    18\n",
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      "    Solving:  Stress period:     3    Time step:    20\n",
      "    Solving:  Stress period:     3    Time step:    21\n",
      "    Solving:  Stress period:     3    Time step:    22\n",
      "    Solving:  Stress period:     3    Time step:    23\n",
      "    Solving:  Stress period:     3    Time step:    24\n",
      "    Solving:  Stress period:     3    Time step:    25\n",
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      "    Solving:  Stress period:     3    Time step:    42\n",
      "    Solving:  Stress period:     3    Time step:    43\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:    44\n",
      "    Solving:  Stress period:     3    Time step:    45\n",
      "    Solving:  Stress period:     3    Time step:    46\n",
      "    Solving:  Stress period:     3    Time step:    47\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "    Solving:  Stress period:     4    Time step:   119\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:   120\n",
      "\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/10/25 16:36:26\n",
      " Elapsed run time:  0.813 Seconds\n",
      "\n",
      "\n",
      "WARNING REPORT:\n",
      "\n",
      "  1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'advgw_tidal.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
      "  2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'advgw_tidal.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\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": 19,
   "metadata": {},
   "outputs": [],
   "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",
    "headfile = \"{}.hds\".format(model_name)\n",
    "fname = os.path.join(workspace, headfile)\n",
    "hds = flopy.utils.binaryfile.HeadFile(fname)\n",
    "h = hds.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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",
    "\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",
    "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 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NCELLS': 450,\n",
       " 'NLAY': 3,\n",
       " 'NROW': 15,\n",
       " 'NCOL': 10,\n",
       " 'NJA': 2700,\n",
       " 'XORIGIN': 0.0,\n",
       " 'YORIGIN': 0.0,\n",
       " 'ANGROT': 0.0,\n",
       " 'DELR': array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500.]),\n",
       " 'DELC': array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500., 500.,\n",
       "        500., 500., 500., 500.]),\n",
       " 'TOP': array([50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,\n",
       "        50., 50., 50., 50., 50., 50., 50.]),\n",
       " 'BOTM': array([   5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,    5.,\n",
       "           5.,    5.,    5.,    5.,    5.,    5.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,  -10.,\n",
       "         -10.,  -10.,  -10., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
       "        -100., -100., -100., -100., -100., -100., -100., -100., -100.]),\n",
       " 'IA': array([   1,    5,   10,   15,   20,   25,   30,   35,   40,   45,   49,\n",
       "          54,   60,   66,   72,   78,   84,   90,   96,  102,  107,  112,\n",
       "         118,  124,  130,  136,  142,  148,  154,  160,  165,  170,  176,\n",
       "         182,  188,  194,  200,  206,  212,  218,  223,  228,  234,  240,\n",
       "         246,  252,  258,  264,  270,  276,  281,  286,  292,  298,  304,\n",
       "         310,  316,  322,  328,  334,  339,  344,  350,  356,  362,  368,\n",
       "         374,  380,  386,  392,  397,  402,  408,  414,  420,  426,  432,\n",
       "         438,  444,  450,  455,  460,  466,  472,  478,  484,  490,  496,\n",
       "         502,  508,  513,  518,  524,  530,  536,  542,  548,  554,  560,\n",
       "         566,  571,  576,  582,  588,  594,  600,  606,  612,  618,  624,\n",
       "         629,  634,  640,  646,  652,  658,  664,  670,  676,  682,  687,\n",
       "         692,  698,  704,  710,  716,  722,  728,  734,  740,  745,  750,\n",
       "         756,  762,  768,  774,  780,  786,  792,  798,  803,  807,  812,\n",
       "         817,  822,  827,  832,  837,  842,  847,  851,  856,  862,  868,\n",
       "         874,  880,  886,  892,  898,  904,  909,  915,  922,  929,  936,\n",
       "         943,  950,  957,  964,  971,  977,  983,  990,  997, 1004, 1011,\n",
       "        1018, 1025, 1032, 1039, 1045, 1051, 1058, 1065, 1072, 1079, 1086,\n",
       "        1093, 1100, 1107, 1113, 1119, 1126, 1133, 1140, 1147, 1154, 1161,\n",
       "        1168, 1175, 1181, 1187, 1194, 1201, 1208, 1215, 1222, 1229, 1236,\n",
       "        1243, 1249, 1255, 1262, 1269, 1276, 1283, 1290, 1297, 1304, 1311,\n",
       "        1317, 1323, 1330, 1337, 1344, 1351, 1358, 1365, 1372, 1379, 1385,\n",
       "        1391, 1398, 1405, 1412, 1419, 1426, 1433, 1440, 1447, 1453, 1459,\n",
       "        1466, 1473, 1480, 1487, 1494, 1501, 1508, 1515, 1521, 1527, 1534,\n",
       "        1541, 1548, 1555, 1562, 1569, 1576, 1583, 1589, 1595, 1602, 1609,\n",
       "        1616, 1623, 1630, 1637, 1644, 1651, 1657, 1663, 1670, 1677, 1684,\n",
       "        1691, 1698, 1705, 1712, 1719, 1725, 1731, 1738, 1745, 1752, 1759,\n",
       "        1766, 1773, 1780, 1787, 1793, 1798, 1804, 1810, 1816, 1822, 1828,\n",
       "        1834, 1840, 1846, 1851, 1855, 1860, 1865, 1870, 1875, 1880, 1885,\n",
       "        1890, 1895, 1899, 1904, 1910, 1916, 1922, 1928, 1934, 1940, 1946,\n",
       "        1952, 1957, 1962, 1968, 1974, 1980, 1986, 1992, 1998, 2004, 2010,\n",
       "        2015, 2020, 2026, 2032, 2038, 2044, 2050, 2056, 2062, 2068, 2073,\n",
       "        2078, 2084, 2090, 2096, 2102, 2108, 2114, 2120, 2126, 2131, 2136,\n",
       "        2142, 2148, 2154, 2160, 2166, 2172, 2178, 2184, 2189, 2194, 2200,\n",
       "        2206, 2212, 2218, 2224, 2230, 2236, 2242, 2247, 2252, 2258, 2264,\n",
       "        2270, 2276, 2282, 2288, 2294, 2300, 2305, 2310, 2316, 2322, 2328,\n",
       "        2334, 2340, 2346, 2352, 2358, 2363, 2368, 2374, 2380, 2386, 2392,\n",
       "        2398, 2404, 2410, 2416, 2421, 2426, 2432, 2438, 2444, 2450, 2456,\n",
       "        2462, 2468, 2474, 2479, 2484, 2490, 2496, 2502, 2508, 2514, 2520,\n",
       "        2526, 2532, 2537, 2542, 2548, 2554, 2560, 2566, 2572, 2578, 2584,\n",
       "        2590, 2595, 2600, 2606, 2612, 2618, 2624, 2630, 2636, 2642, 2648,\n",
       "        2653, 2657, 2662, 2667, 2672, 2677, 2682, 2687, 2692, 2697, 2701],\n",
       "       dtype=int32),\n",
       " 'JA': array([  1,   2,  11, ..., 300, 440, 449], dtype=int32),\n",
       " 'IDOMAIN': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32),\n",
       " 'ICELLTYPE': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 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(model_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": {},
   "outputs": [],
   "source": [
    "# read the cell budget file\n",
    "fname = os.path.join(workspace, \"{}.cbb\".format(model_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, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing flows for cell 412\n",
      "Cell 412 flow with cell 262 is 251.46262091207714\n",
      "Cell 412 flow with cell 402 is 0.7176346498604858\n",
      "Cell 412 flow with cell 411 is 439.86299685437785\n",
      "Cell 412 flow with cell 413 is -693.4212447574212\n",
      "Cell 412 flow with cell 422 is 1.3779378787791075\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 = 2\n",
    "i = 7\n",
    "j = 7\n",
    "celln = k * nrow * ncol + i * nrow + j\n",
    "print(\"Printing flows for cell {}\".format(celln))\n",
    "ia, ja = bgf.ia, bgf.ja\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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d9741c0>"
      ]
     },
     "execution_count": 24,
     "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": [
    "fname = \"head-hydrographs.csv\"\n",
    "fname = os.path.join(workspace, fname)\n",
    "csv = np.genfromtxt(fname, delimiter=\",\", dtype=None, names=True)\n",
    "for name in csv.dtype.names[1:]:\n",
    "    plt.plot(csv[\"time\"], csv[name], label=name)\n",
    "plt.legend()"
   ]
  }
 ],
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