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": [
      "3.8.10 (default, May 19 2021, 11:01:55) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.4\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.exists(workspace):\n",
    "    os.makedirs(workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pth = os.path.join('..', 'data', 'mf6', 'create_tests', \n",
    "                        'test005_advgw_tidal')\n",
    "assert os.path.isdir(data_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create simulation\n",
    "sim = flopy.mf6.MFSimulation(sim_name=model_name, version='mf6', exe_name='mf6', \n",
    "                             sim_ws=workspace)\n",
    "\n",
    "# create tdis package\n",
    "tdis_rc = [(1.0, 1, 1.0), (10.0, 120, 1.0), \n",
    "           (10.0, 120, 1.0), (10.0, 120, 1.0)]\n",
    "tdis = flopy.mf6.ModflowTdis(sim, pname='tdis', time_units='DAYS', \n",
    "                             nper=4, perioddata=tdis_rc)\n",
    "\n",
    "# create gwf model\n",
    "gwf = flopy.mf6.ModflowGwf(sim, modelname=model_name,\n",
    "                           model_nam_file='{}.nam'.format(model_name))\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(sim, pname='ims', print_option='SUMMARY', \n",
    "                           complexity='SIMPLE', outer_hclose=0.0001, \n",
    "                           outer_maximum=500, under_relaxation='NONE', \n",
    "                           inner_maximum=100, inner_hclose=0.0001, \n",
    "                           rcloserecord=0.001, linear_acceleration='CG', \n",
    "                           scaling_method='NONE', reordering_method='NONE', \n",
    "                           relaxation_factor=0.97)\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(gwf, pname='dis', nlay=nlay, nrow=nrow, ncol=ncol, \n",
    "                              delr=500.0, delc=500.0, top=50.0, \n",
    "                              botm=[5.0, -10.0, botlay2], \n",
    "                              filename='{}.dis'.format(model_name))\n",
    "\n",
    "# initial conditions\n",
    "ic = flopy.mf6.ModflowGwfic(gwf, pname='ic', strt=50.0,\n",
    "                            filename='{}.ic'.format(model_name))\n",
    "\n",
    "# node property flow\n",
    "npf = flopy.mf6.ModflowGwfnpf(gwf, pname='npf', 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",
    "# output control\n",
    "oc = flopy.mf6.ModflowGwfoc(gwf, pname='oc', budget_filerecord='{}.cbb'.format(model_name),\n",
    "                            head_filerecord='{}.hds'.format(model_name),\n",
    "                            headprintrecord=[('COLUMNS', 10, 'WIDTH', 15,\n",
    "                                              'DIGITS', 6, 'GENERAL')],\n",
    "                            saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')],\n",
    "                            printrecord=[('HEAD', 'FIRST'), ('HEAD', 'LAST'), \n",
    "                                         ('BUDGET', 'LAST')])"
   ]
  },
  {
   "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(gwf, layered=True, default_value=0.000001)\n",
    "\n",
    "sto = flopy.mf6.ModflowGwfsto(gwf, pname='sto', save_flows=True, iconvert=1, \n",
    "                              ss=ss, sy=sy, steady_state={0:True},\n",
    "                              transient={1:True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_fork/flopy/mf6/data/mfdatalist.py:1688: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n",
      "  if \"check\" in list_item:\n"
     ]
    }
   ],
   "source": [
    "# well package\n",
    "# test empty with aux vars, bound names, and time series\n",
    "period_two = flopy.mf6.ModflowGwfwel.stress_period_data.empty(gwf, maxbound=3, aux_vars=['var1', 'var2', 'var3'],\n",
    "                                                         boundnames=True, timeseries=True)\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(gwf, maxbound=2, aux_vars=['var1', 'var2', 'var3'],\n",
    "                                                           boundnames=True, timeseries=True)\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(gwf, maxbound=5, aux_vars=['var1', 'var2', 'var3'],\n",
    "                                                          boundnames=True, timeseries=True)\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(gwf, pname='wel', print_input=True, print_flows=True,\n",
    "                              auxiliary=[('var1', 'var2', 'var3')], maxbound=5,\n",
    "                              stress_period_data=stress_period_data, boundnames=True, \n",
    "                              save_flows=True)\n",
    "\n",
    "# well ts package\n",
    "ts_data =[(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",
    "wel.ts.initialize(filename='well-rates.ts', timeseries=ts_data,\n",
    "                   time_series_namerecord=[('well_1_rate', 'well_2_rate', 'well_3_rate')],\n",
    "                   interpolation_methodrecord=[('stepwise', 'stepwise', 'stepwise')])"
   ]
  },
  {
   "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] = (((0, row, col), 50.0, 0.0004, 10.0, 0.2, 0.5, 0.3, 0.1, None))\n",
    "evt = flopy.mf6.ModflowGwfevt(gwf, pname='evt', print_input=True, print_flows=True, \n",
    "                              save_flows=True, maxbound=150,\n",
    "                              nseg=3, stress_period_data=evt_period)"
   ]
  },
  {
   "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(gwf, pname='ghb', print_input=True, print_flows=True, \n",
    "                              save_flows=True, boundnames=True,\n",
    "                              maxbound=30, stress_period_data=ghb_period)\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(filename='tides.ts', timeseries=ts_recarray,\n",
    "                  time_series_namerecord='tides',\n",
    "                  interpolation_methodrecord='linear')\n",
    "obs_recarray = {'ghb_obs.csv':[('ghb-2-6-10', 'GHB', (1, 5, 9)), \n",
    "                               ('ghb-3-6-10', 'GHB', (2, 5, 9))],\n",
    "                'ghb_flows.csv':[('Estuary2', 'GHB', 'Estuary-L2'), \n",
    "                                 ('Estuary3', 'GHB', 'Estuary-L3')]}\n",
    "ghb.obs.initialize(filename='{}.ghb.obs'.format(model_name), digits=10, \n",
    "                   print_input=True, continuous=obs_recarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_recarray = {'head_obs.csv':[('h1_13_8', 'HEAD', (2, 12, 7))],\n",
    "                'intercell_flow_obs1.csv':[('ICF1_1.0', 'FLOW-JA-FACE', (0, 4, 5), (0, 5, 5))],\n",
    "                'head-hydrographs.csv':[('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",
    "obs_package = flopy.mf6.ModflowUtlobs(gwf, pname='head_obs', filename='{}.obs'.format(model_name), \n",
    "                                      digits=10, print_input=True,\n",
    "                                      continuous=obs_recarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# River\n",
    "riv_period = {}\n",
    "riv_period_array = [((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",
    "riv_period[0] = riv_period_array\n",
    "riv = flopy.mf6.ModflowGwfriv(gwf, pname='riv', print_input=True, print_flows=True, \n",
    "                              save_flows='{}.cbc'.format(model_name),\n",
    "                              boundnames=True, maxbound=20, \n",
    "                              stress_period_data=riv_period)\n",
    "ts_recarray=[(0.0,40.0,41.0),(1.0,41.0,41.5),\n",
    "             (2.0,43.0,42.0),(3.0,45.0,42.8),\n",
    "             (4.0,44.0,43.0),(6.0,43.0,43.1),\n",
    "             (9.0,42.0,42.4),(11.0,41.0,41.5),\n",
    "             (31.0,40.0,41.0)]\n",
    "riv.ts.initialize(filename='river_stages.ts', timeseries=ts_recarray,\n",
    "                  time_series_namerecord=[('river_stage_1', 'river_stage_2')],\n",
    "                  interpolation_methodrecord=[('linear', 'stepwise')])\n",
    "obs_recarray = {'riv_obs.csv':[('rv1-3-1', 'RIV', (0,2,0)), ('rv1-4-2', 'RIV', (0,3,1)),\n",
    "                               ('rv1-5-3', 'RIV', (0,4,2)), ('rv1-5-4', 'RIV', (0,4,3)),\n",
    "                               ('rv1-6-5', 'RIV', (0,5,4)), ('rv1-c6', 'RIV', 'riv1_c6'),\n",
    "                               ('rv1-c7', 'RIV', 'riv1_c7'), ('rv2-upper', 'RIV', 'riv2_upper'),\n",
    "                               ('rv-2-7-4', 'RIV', (0,6,3)), ('rv2-8-5', 'RIV', (0,6,4)),\n",
    "                               ('rv-2-9-6', 'RIV', (0,5,5,))],\n",
    "                'riv_flowsA.csv':[('riv1-3-1', 'RIV', (0,2,0)), ('riv1-4-2', 'RIV', (0,3,1)),\n",
    "                                  ('riv1-5-3', 'RIV', (0,4,2))],\n",
    "                'riv_flowsB.csv':[('riv2-10-1', 'RIV', (0,9,0)), ('riv-2-9-2', 'RIV', (0,8,1)),\n",
    "                                  ('riv2-8-3', 'RIV', (0,7,2))]}\n",
    "riv.obs.initialize(filename='{}.riv.obs'.format(model_name), digits=10,\n",
    "                   print_input=True, continuous=obs_recarray)"
   ]
  },
  {
   "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 (row == 3 and col == 5) or (row == 2 and col == 4) or (row == 1 and col == 3) or (row == 0 and col == 2):\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(gwf, filename='{}_1.rch'.format(model_name), \n",
    "                               pname='rch_1', fixed_cell=True,\n",
    "                               auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
    "                               print_input=True, print_flows=True, \n",
    "                               save_flows=True, boundnames=True,\n",
    "                               maxbound=84, stress_period_data=rch1_period)\n",
    "ts_data =[(0.0, 0.0015), (1.0, 0.0010),\n",
    "          (11.0, 0.0015),(21.0, 0.0025),\n",
    "          (31.0, 0.0015)]\n",
    "rch1.ts.initialize(filename='recharge_rates_1.ts', timeseries=ts_data,\n",
    "                   time_series_namerecord='rch_1',\n",
    "                   interpolation_methodrecord='stepwise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Second recharge package\n",
    "rch2_period = {}\n",
    "rch2_period_array = [((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",
    "rch2_period[0] = rch2_period_array\n",
    "rch2 = flopy.mf6.ModflowGwfrch(gwf, filename='{}_2.rch'.format(model_name), \n",
    "                               pname='rch_2', fixed_cell=True,\n",
    "                               auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
    "                               print_input=True, print_flows=True, save_flows=True,\n",
    "                               maxbound=20, stress_period_data=rch2_period)\n",
    "ts_data = [(0.0, 0.0016), (1.0, 0.0018),\n",
    "           (11.0, 0.0019),(21.0, 0.0016),\n",
    "           (31.0, 0.0018)]\n",
    "rch2.ts.initialize(filename='recharge_rates_2.ts', timeseries=ts_data,\n",
    "                   time_series_namerecord='rch_2',\n",
    "                   interpolation_methodrecord='linear')"
   ]
  },
  {
   "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 (row == 0 and col == 9) or (row == 1 and col == 8) or (row == 2 and col == 7) or (row == 3 and col == 6):\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(gwf, filename='{}_3.rch'.format(model_name), \n",
    "                               pname='rch_3', fixed_cell=True,\n",
    "                               auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
    "                               print_input=True, print_flows=True, save_flows=True,\n",
    "                               maxbound=54, stress_period_data=rch3_period)\n",
    "ts_data=[(0.0, 0.0017),(1.0, 0.0020),(11.0, 0.0017),(21.0, 0.0018),(31.0, 0.0020)]\n",
    "rch3.ts.initialize(filename='recharge_rates_3.ts', timeseries=ts_data,\n",
    "                   time_series_namerecord='rch_3',\n",
    "                   interpolation_methodrecord='linear')"
   ]
  },
  {
   "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": [
      "['intercell_flow_obs1.csv', 'riv_flowsA.csv', 'advgw_tidal.nam', 'advgw_tidal.ic', 'riv_flowsB.csv', 'advgw_tidal.dis.grb', 'recharge_rates_1.ts', 'advgw_tidal.sto', 'advgw_tidal.lst', 'tides.ts', 'advgw_tidal.cbb', 'advgw_tidal.ims', 'well-rates.ts', 'advgw_tidal.ghb', 'advgw_tidal.obs', 'advgw_tidal.riv.obs', 'ghb_flows.csv', 'advgw_tidal.dis', 'advgw_tidal_1.rch', 'advgw_tidal_3.rch', 'advgw_tidal_2.rch', 'advgw_tidal.oc', 'river_stages.ts', 'advgw_tidal.hds', 'advgw_tidal.wel', 'advgw_tidal.ghb.obs', 'advgw_tidal.npf', 'head_obs.csv', 'advgw_tidal.tdis', 'ghb_obs.csv', 'recharge_rates_2.ts', 'mfsim.nam', 'advgw_tidal.riv', 'head-hydrographs.csv', 'mfsim.lst', 'recharge_rates_3.ts', 'advgw_tidal.evt', 'riv_obs.csv']\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",
      "\n",
      "   MODFLOW 6 compiled Aug 01 2021 12:51:08 with IFORT compiler (ver. 19.10.3)\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/08/06 16:21:06\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     1    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "    Solving:  Stress period:     2    Time step:     1\n",
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    },
    {
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    {
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    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:   106\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     3    Time step:   118\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:    41\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Solving:  Stress period:     4    Time step:    54\n",
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      "    Solving:  Stress period:     4    Time step:   108\n",
      "    Solving:  Stress period:     4    Time step:   109\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/08/06 16:21:07\n",
      " Elapsed run time:  1.388 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": [
       "OrderedDict([('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',\n",
       "              array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500.])),\n",
       "             ('DELC',\n",
       "              array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500., 500.,\n",
       "                     500., 500., 500., 500.])),\n",
       "             ('TOP',\n",
       "              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',\n",
       "              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',\n",
       "              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',\n",
       "              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',\n",
       "              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 251.46262091207623\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.\n",
    "k = 2; i = 7; 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 0x1247b9ac0>"
      ]
     },
     "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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