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": [
      "3.6.5 | packaged by conda-forge | (default, Apr  6 2018, 13:44:09) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]\n",
      "numpy version: 1.14.5\n",
      "matplotlib version: 2.2.2\n",
      "flopy version: 3.2.10\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory structure already exists for simulation path /Users/jdhughes/Documents/Development/flopy_us/examples/Notebooks/data/advgw_tidal\n"
     ]
    }
   ],
   "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",
    "                              fname='{}.dis'.format(model_name))\n",
    "\n",
    "# initial conditions\n",
    "ic = flopy.mf6.ModflowGwfic(gwf, pname='ic', strt=50.0,\n",
    "                            fname='{}.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": [],
   "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",
    "                              ts_filerecord='well-rates.ts')\n",
    "\n",
    "# well ts package\n",
    "ts_recarray =[(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",
    "well_ts_package = flopy.mf6.ModflowUtlts(gwf, pname='well_ts', fname='well-rates.ts', parent_file=wel,\n",
    "                                         timeseries=ts_recarray,\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",
    "                              ts_filerecord='tides.ts', \n",
    "                              obs_filerecord='{}.ghb.obs'.format(model_name),\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_package = flopy.mf6.ModflowUtlts(gwf, pname='tides_ts', fname='tides.ts', \n",
    "                                        parent_file=ghb, 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_package = flopy.mf6.ModflowUtlobs(gwf, pname='ghb_obs', fname='{}.ghb.obs'.format(model_name), \n",
    "                                          parent_file=ghb, digits=10, print_input=True, \n",
    "                                          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', fname='{}.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, ts_filerecord='river_stages.ts',\n",
    "                              maxbound=20, stress_period_data=riv_period, \n",
    "                              obs_filerecord='{}.riv.obs'.format(model_name))\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_package = flopy.mf6.ModflowUtlts(gwf, pname='riv_ts', fname='river_stages.ts', \n",
    "                                        parent_file=riv,\n",
    "                                        timeseries=ts_recarray,\n",
    "                                        time_series_namerecord=[('river_stage_1', \n",
    "                                                                 '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_package = flopy.mf6.ModflowUtlobs(gwf, pname='riv_obs', \n",
    "                                          fname='{}.riv.obs'.format(model_name), \n",
    "                                          parent_file=riv, 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, fname='{}_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",
    "                               ts_filerecord='recharge_rates_1.ts', \n",
    "                               maxbound=84, stress_period_data=rch1_period)\n",
    "ts_recarray=[(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_package = flopy.mf6.ModflowUtlts(gwf, pname='rch_1_ts', \n",
    "                                         fname='recharge_rates_1.ts', \n",
    "                                         parent_file=rch1,\n",
    "                                         timeseries=ts_recarray,\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, fname='{}_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",
    "                               ts_filerecord='recharge_rates_2.ts', maxbound=20, \n",
    "                               stress_period_data=rch2_period)\n",
    "ts_recarray=[(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_package = flopy.mf6.ModflowUtlts(gwf, pname='rch_2_ts', \n",
    "                                         fname='recharge_rates_2.ts', \n",
    "                                         parent_file=rch2,\n",
    "                                         timeseries=ts_recarray,\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, fname='{}_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",
    "                               ts_filerecord='recharge_rates_3.ts', maxbound=54, \n",
    "                               stress_period_data=rch3_period)\n",
    "ts_recarray=[(0.0, 0.0017),(1.0, 0.0020),(11.0, 0.0017),(21.0, 0.0018),(31.0, 0.0020)]\n",
    "rch3_ts_package = flopy.mf6.ModflowUtlts(gwf, pname='rch_3_ts', \n",
    "                                         fname='recharge_rates_3.ts', \n",
    "                                         parent_file=rch3,\n",
    "                                         timeseries=ts_recarray,\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 well_ts...\n",
      "    writing package evt...\n",
      "    writing package ghb...\n",
      "    writing package tides_ts...\n",
      "    writing package ghb_obs...\n",
      "    writing package head_obs...\n",
      "    writing package riv...\n",
      "    writing package riv_ts...\n",
      "    writing package riv_obs...\n",
      "    writing package rch_1...\n",
      "    writing package rch_1_ts...\n",
      "    writing package rch_2...\n",
      "    writing package rch_2_ts...\n",
      "    writing package rch_3...\n",
      "    writing package rch_3_ts...\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 6.0.3 08/09/2018\n",
      "\n",
      "  MODFLOW 6 compiled Sep 24 2018 16:09:01 with GFORTRAN compiler (ver. 6.4.0)\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",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2018/10/19 16:29:49\n",
      "\n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " Solving:  Stress period:     1    Time step:     1\n",
      " Solving:  Stress period:     2    Time step:     1\n",
      " Solving:  Stress period:     2    Time step:     2\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     3    Time step:   102\n",
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      " Solving:  Stress period:     4    Time step:   100\n",
      " Solving:  Stress period:     4    Time step:   101\n",
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      " Solving:  Stress period:     4    Time step:   107\n",
      " Solving:  Stress period:     4    Time step:   108\n",
      " Solving:  Stress period:     4    Time step:   109\n",
      " Solving:  Stress period:     4    Time step:   110\n",
      " Solving:  Stress period:     4    Time step:   111\n",
      " Solving:  Stress period:     4    Time step:   112\n",
      " Solving:  Stress period:     4    Time step:   113\n",
      " Solving:  Stress period:     4    Time step:   114\n",
      " Solving:  Stress period:     4    Time step:   115\n",
      " Solving:  Stress period:     4    Time step:   116\n",
      " Solving:  Stress period:     4    Time step:   117\n",
      " Solving:  Stress period:     4    Time step:   118\n",
      " Solving:  Stress period:     4    Time step:   119\n",
      " Solving:  Stress period:     4    Time step:   120\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2018/10/19 16:29:50\n",
      " Elapsed run time:  0.462 Seconds\n",
      "\n",
      " Normal termination of simulation.\n",
      "\n",
      "Success is:  True\n"
     ]
    }
   ],
   "source": [
    "# Run the simulation\n",
    "success, buff = sim.run_simulation()\n",
    "print('\\nSuccess is: ', success)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-Process Head Results\n",
    "\n",
    "Post-processing MODFLOW 6 results is still a work in progress.  There aren't any Flopy plotting functions built in yet, like they are for other MODFLOW versions.  So we need to plot the results using general Flopy capabilities.  We can also use some of the Flopy ModelMap capabilities for MODFLOW 6, but in order to do so, we need to manually create a SpatialReference object, that is needed for the plotting.  Examples of both approaches are shown below.\n",
    "\n",
    "First, a link to the heads file is created with `HeadFile`. The link can then be accessed with the `get_data` function, by specifying, in this case, the step number and period number for which we want to retrieve data. A three-dimensional array is returned of size `nlay, nrow, ncol`. Matplotlib contouring functions are used to make contours of the layers or a cross-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also use the Flopy model map capabilities for MODFLOW 6\n",
    "# but in order to do so, we need to manually create a\n",
    "# SpatialReference object\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "sr = flopy.utils.reference.SpatialReference(delr=dis.delr[:],\n",
    "                                            delc=dis.delc[:])\n",
    "\n",
    "# Next we create an instance of the ModelMap class\n",
    "modelmap = flopy.plot.ModelMap(sr=sr)\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.utils.mfgrdfile.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": [
    "# Information from the binary grid file is easily retrieved\n",
    "ia = bgf._datadict['IA'] - 1\n",
    "ja = bgf._datadict['JA'] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing flows for cell 413\n",
      "Cell 413 flow with cell 263 is 251.46262091207714\n",
      "Cell 413 flow with cell 403 is 0.7176346498604858\n",
      "Cell 413 flow with cell 412 is 439.86299685437785\n",
      "Cell 413 flow with cell 414 is -693.4212447574212\n",
      "Cell 413 flow with cell 423 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; i = 7; j = 7\n",
    "celln = k * nrow * ncol + i * nrow + j\n",
    "print('Printing flows for cell {}'.format(celln + 1))\n",
    "for ipos in range(ia[celln] + 1, ia[celln + 1]):\n",
    "    cellm = ja[ipos]  # change from one-based to zero-based\n",
    "    print('Cell {} flow with cell {} is {}'.format(celln + 1, cellm + 1, flowja[ipos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1171cadd8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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