flopy3_SWI2_Fogland_UNIX_TR_scenario3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYDROGEOLOGICAL CHARACTERISATION OF FOGLAND POINT, TIVERTON, RHODE ISLAND\n",
    "### MODFLOW, SWI2, FloPy\n",
    "\n",
    "-Spencer Harris  \n",
    "-spharris@sas.upenn.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model simulates transient movement of the freshwater-seawater interface beneath Fogland Point, a peninsula in the Narragansett Bay, Rhode Island. in response to recharge and groundwater withdrawals. One ~200 ft thick aquifer that extends below sea level. The aquifer is unconfined, storage changes are considered, this model contains transient stress periods.\n",
    "\n",
    "The domain is discretized into 100 columns, 100 rows, and 3 layers, with respective cell dimensions of 25$\\ ft$ (`DELR`), 25$\\ ft$ (`DELC`). Layer bottoms are -43, -90, and -200 feet below MSL. A total of 230 years is simulated using six stress periods with lengths of 200, 0.25, 0.75, 0.25, 0.75, and 0.25, with constant time steps of 0.2 years for stress period 1 and 1 day for stress period 2 through 6. \n",
    " \n",
    "The horizontal and vertical hydraulic conductivity of the fractured rock aquifer is $3.95\\times10^{-1}\\ ft/d$ and $3.95\\times10^{-2}\\ ft/d$, respectively. The effective porosity, ssz, is $1.4\\times10^{-3}$ from Carleton, Welty, and Buxton 1999. The model is extended offshore along all sides and the ocean boundary is represented as a general head boundary condition (GHB). A freshwater head of $(Bay\\ depth\\times -0.025)\\ ft$ is specified for the Bay in all general head boundaries. The GHB conductance that controls outflow from the aquifer into the ocean is $5.74\\times10^{2}\\ ft/d$ and corresponds to a leakance of $9.18\\times10^{-4}\\ d^{-1}$ or a resistance of ~$1090$ days.\n",
    "\n",
    "The groundwater is divided into a freshwater zone and a seawater zone, separated by an active ZETA surface between the zones (`NSRF=1`) that approximates the 50-percent seawater salinity contour. Fluid density is represented using the stratified density option (`ISTRAT=1`). The dimensionless density difference ($\\nu$) between freshwater and saltwater is 0.025. The tip and toe tracking parameters are a `TOESLOPE` and `TIPSLOPE` of 0.1, a default `ALPHA` of 0.1, and a default `BETA` of 0.1. Initially, the interface between freshwater and saltwater is 1 m below MSL (0ft) on the peninsula and offshore. The SWI2 `ISOURCE` parameter is set to -2 in cells having GHBs so that water that infiltrates into the aquifer from the GHB cells is saltwater (zone 2), whereas water that flows out of the model at the GHB cells is identical to water at the top of the aquifer. In all other cells, the SWI2 `ISOURCE` parameter is set to 0, indicating boundary conditions have water that is identical to water at the top of the aquifer and can be either freshwater or saltwater, depending on the elevation of the active `ZETA` surface in the cell.  \n",
    "\n",
    "A constant recharge rate of $4.79\\times10^{-3}\\ ft/d$ is used during stress periods 1, 3, and 5. The development of the freshwater lens is simulated for 200 years with recharge on and no pumping, after which pumping wells having variable withdrawal rates are turned on and recharge is turned off for a period of a 92 day peak-season (0.25 yrs): Well 64 $5.35\\ ft^{3}/day$, Well 77 $14.85\\ ft^{3}/day$, Well 78 $1.45\\ ft^{3}/day$, All other wells $7.27\\ ft^{3}/day$ withdrawal over 92 day peak-season (0.25 yrs).  \n",
    "\n",
    "The first time step (200 years) (scenario 1) has a maximum observed hydraulic head of $10.86\\ feet$ above MSL, this is reasonable considering the highest water level/hydraulic head measured by the project was $6.65\\ feet$ above MSL in well 55a on 06-17-17. Scenario 1 yields a 50% ZETA surface at approximately $190\\ feet$ below MSL along row 50. the top of the upper aquifer layer.\n",
    "\n",
    "**Scenario 3 (This notebook)**, runs in a transient state and changes recharge and pumping on a simulated seasonal basis. Scenario 3 has a maximum head of $2.833\\ feet$ above MSL after simulation of three peak-seasons (periods 2, 4, 6) divided by two off-seasons(periods 3, 5). The lowest water level/hydraulic head measured by the project was -0.97 feet above MSL in well 15 on 08-16-17. The significant difference between maximum head between scenario 1 and scenario 3 can be interpreted as the idea that because precipitation is nearly constant year round that ET is having a significant effect on groundwater recharge, which when coupled with increased pumping rates can raise the 50% freshwater-seawater ZETA surface to approximately $-145\\ feet$ above MSL.\n",
    "\n",
    "#### This model and notebook is based off the FloPy notebook of SWI2 Example 4. Upconing Below a Pumping Well in a Two-Aquifer Island System in the [SWI2 documentation] (http://pubs.usgs.gov/tm/6a46/) \n",
    "\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `numpy` and `matplotlib`, set all figures to be inline, import `flopy.modflow` and `flopy.utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 13:51:32) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "numpy version: 1.13.1\n",
      "matplotlib version: 2.0.2\n",
      "flopy version: 3.2.6\n",
      "operating system: Linux\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\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",
    "\n",
    "from flopy.utils.reference import SpatialReference\n",
    "import flopy.utils.binaryfile as bf\n",
    "#from flopy.utils.postprocessing import get_transmissivities, get_water_table, get_gradients, get_saturated_thickness\n",
    "\n",
    "fm = flopy.modflow\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__))\n",
    "print('operating system: {}'.format(platform.system()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define model name of your model and the location of MODFLOW executable. All MODFLOW files and output will be stored in the subdirectory defined by the workspace. Create a model named `ml` and specify that this is a MODFLOW-2005 model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Little extra work to make sure we've tracked down the executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../bin/mf2005.nix\n",
      "/home/sbhgeo/Documents/Fogland/bin/mf2005.nix\n",
      "if this =>  \" True \"  prints true, that means executable is in the correct spot\n"
     ]
    }
   ],
   "source": [
    "if platform.system() == 'Linux':\n",
    "    exe_name = os.path.join(\"..\", \"..\", \"bin\", \"mf2005.nix\") # adds path to mf2005.nix, which will be our executeable, to mfexe\n",
    "    abspath = os.path.abspath(exe_name) # assigns absolute path of mfexe to abspath, double checking\n",
    "    print(exe_name) #printing the variables\n",
    "    print(abspath)\n",
    "    print('if this =>  \\\"', os.path.isfile(exe_name),'\\\"  prints true, that means executable is in the correct spot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set name of MODFLOW exe\n",
    "#  assumes executable is in users path statement\n",
    "\n",
    "if platform.system() == 'Linux':\n",
    "    exe_name = abspath\n",
    "\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name = 'mf2005.exe'\n",
    "\n",
    "workspace = os.path.join('data')\n",
    "#make sure workspace directory exists\n",
    "if not os.path.exists(workspace):\n",
    "    os.makedirs(workspace)\n",
    "    \n",
    "outpath = 'data'\n",
    "if os.path.isdir(outpath):\n",
    "    shutil.rmtree(outpath)\n",
    "os.mkdir(outpath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DIS\n",
    "Model Discretization and Spatial Referencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid bounds are = (403877.0, 172913.0, 406377.0, 175413.0)\n",
      "spatial reference utility = xul:403877; yul:175413; rotation:0; proj4_str:+proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99998983997 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +to_meter=0.3048006096012192 +no_defs ; units:feet; lenuni:1; length_multiplier:1.0\n",
      "DELR = 25.0\n",
      "DELC = 25.0\n"
     ]
    }
   ],
   "source": [
    "nlay = 3\n",
    "nrow, ncol = 100, 100\n",
    "xll, yll = 403877, 172913 # origin of the model (lower left corner)\n",
    "#xul, yul = 406377, 175413 # origin of model (upper left corner)\n",
    "dxdy = 25 # grid spacing (in model units)\n",
    "rot = 0 # rotation (positive counterclockwise)\n",
    "\n",
    "# epsg code specifying coordinate reference system\n",
    "model_epsg = 3654\n",
    "\n",
    "# row and column spacings\n",
    "# (note that delc is column spacings along a row; delr the row spacings along a column)\n",
    "delc = np.ones(nrow, dtype=float) * dxdy\n",
    "delr = np.ones(ncol, dtype=float) * dxdy\n",
    "\n",
    "sr = SpatialReference(delr=delr, delc=delc, xll=xll, yll=yll, rotation=rot, epsg=model_epsg, units = 'feet', lenuni = 1)\n",
    "#sr = SpatialReference(delr=delr, delc=delc, xul=xul, yul=yul, rotation=rot, epsg=model_epsg, units = 'feet', lenuni = 1)\n",
    "\n",
    "print('grid bounds are =',sr.bounds)\n",
    "print('spatial reference utility =', sr)\n",
    "print('DELR =', delr[0])\n",
    "print('DELC =', delc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining temporal discretization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nper = 6\n",
    "perlen = [365.25 * 200., 92, 273, 92, 273, 92]\n",
    "nstp = [1000, 92, 273, 92, 273, 92] # timesteps of 73.05 days; 0.2 yrs\n",
    "#time steps of 1 day for sp2 through sp6\n",
    "save_head = [50] # what is this for?\n",
    "steady = [True, False, False, False, False, False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Elevation CSV, (created in QGIS) for top of layer 1 with sea level as 0 feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "lay1top = np.loadtxt('./importdata/lay1top.csv', delimiter=',', skiprows=1)\n",
    "lay1top = lay1top[:,6]\n",
    "lay1top = lay1top.reshape(100,100)\n",
    "lay1topfud = np.flipud(lay1top) #flip array along up-down\n",
    "#flipping array because CSV was made with grid origin in upper left instead of lowerleft\n",
    "print(lay1topfud.shape, type(lay1topfud))\n",
    "top = lay1topfud\n",
    "botm = np.array([-43., -90., -200.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BAS\n",
    "Basic Package  \n",
    "Define the `IBOUND` array and starting heads for the `BAS` package. The corners of the model are defined to be inactive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 100, 100)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bas data\n",
    "# ibound - active except for the corners\n",
    "ibound = np.ones((nlay, nrow, ncol), dtype= np.int)\n",
    "ibound[:, 0, 0] = 0\n",
    "ibound[:, 0, -1] = 0\n",
    "ibound[:, -1, 0] = 0\n",
    "ibound[:, -1, -1] = 0\n",
    "# initial head data\n",
    "ihead = np.zeros((nlay, nrow, ncol), dtype=np.float) #inital hydraulic heads of 0ft MSL\n",
    "ihead.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LPF  \n",
    "Layer-Property Flow Package  \n",
    "Define the layers to be confined and define the horizontal and vertical hydraulic conductivity of the aquifer for the `LPF` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# lpf data\n",
    "laytyp = np.array([1,1,1]) #lay1, 2, 3;unconfined, unconfined, unconfined\n",
    "\n",
    "sy = 0.0001 # From Carleton 1999, Numerical model pg.27 of wrir 98-4126A\n",
    "ss = 0.0001 #S=Sy for unconfined aquifers\n",
    "# sy is only read in for transient runs, when change in storage is calculated\n",
    "\n",
    "#hk = 1.975 #calculated from pump test at well 17 (1.975 ft/day)\n",
    "#vka = 0.1975 #calculated from pump test at well 17 (0.1975 ft/day)\n",
    "\n",
    "hk = 0.395 #factor of 5 less than calculated from pump test at well 17 (1.975 ft/day)\n",
    "vk = 0.0395 #factor of 5 less than calculated from pump test at well 17 (0.1975 ft/day)\n",
    "\n",
    "#hk = 0.1975 #factor of 10 less than calculated from pump test at well 17\n",
    "#vka = 0.01975 #factor of 10 less calculated from pump test at well 17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Define the boundary condition data for the model\n",
    "## GHB\n",
    "General Head Boundary Package  \n",
    "Pulling in data in for GHB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100) <class 'numpy.ndarray'>\n",
      "(100, 100) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "seawater_GHB = np.loadtxt('./importdata/bay_heads_GHB_ft.csv', delimiter=',', skiprows=1)\n",
    "index = seawater_GHB[:,5]\n",
    "index = index.reshape(100,100)\n",
    "\n",
    "index = np.flipud(index) \n",
    "#new line flipping array because CSV was made with grid origin in upper left instead of lowerleft\n",
    "\n",
    "print(index.shape, type(index))\n",
    "\n",
    "seawater_heads = seawater_GHB[:,4]\n",
    "seawater_heads= seawater_heads.reshape(100,100)\n",
    "print(seawater_heads.shape, type(seawater_heads))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# boundary condition data\n",
    "# ghb data\n",
    "colcell, rowcell = np.meshgrid(np.arange(0, ncol), np.arange(0, nrow))\n",
    "# making an grid of where GHB is active, 1 = active, 0 = inactive\n",
    "\n",
    "nghb = int(np.sum(index))\n",
    "lrchc = np.zeros((nghb, 5))\n",
    "lrchc[:, 0] = 0\n",
    "lrchc[:, 1] = rowcell[index == 1]\n",
    "lrchc[:, 2] = colcell[index == 1]\n",
    "lrchc[:, 3] = seawater_heads[index == 1]\n",
    "lrchc[:, 4] = dxdy * dxdy / (vk/abs(botm[0])) \n",
    "#resistance time of 1089.32.1 days, 0.000918 d**-1 = conductance of 0.574 ft2/day\n",
    "\n",
    "# create ghb dictionary\n",
    "ghb_data = {0:lrchc, 1:lrchc, 2:lrchc, 3:lrchc, 4:lrchc, 5:lrchc}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## RCH\n",
    "Recharge Package  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# recharge data\n",
    "rch = np.zeros((nrow, ncol), dtype=np.float)\n",
    "rch[index == 0] = 0.00479 #estimated as 1/2 of all precip (annual averaged) \n",
    "# need to recalculate recharge.....\n",
    "norch = np.zeros((nrow, ncol), dtype=np.float)\n",
    "# create recharge dictionary\n",
    "rch_data = {0: rch, 1: norch, 2: rch, 3: norch, 4: rch, 5: norch}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## WEL\n",
    "Well Package  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# well data\n",
    "\n",
    "nwells = 16\n",
    "lrcq = np.zeros((nwells, 4))\n",
    "lrcq[0, :] = np.array((0, 60, 32, 0)) #12 shallow well\n",
    "lrcq[1, :] = np.array((0, 58, 37, 0)) #15 shallow well\n",
    "lrcq[2, :] = np.array((1, 58, 42, 0)) #1 medium well\n",
    "lrcq[3, :] = np.array((1, 56, 42, 0)) #21 medium well\n",
    "lrcq[4, :] = np.array((1, 53, 35, 0)) #24 medium well\n",
    "lrcq[5, :] = np.array((1, 51, 37, 0)) #36 medium well\n",
    "lrcq[6, :] = np.array((1, 48, 47, 0)) #33 medium well\n",
    "lrcq[7, :] = np.array((1, 44, 35, 0)) #56 medium well\n",
    "lrcq[8, :] = np.array((1, 43, 37, 0)) #55 medium well\n",
    "lrcq[9, :] = np.array((0, 42, 39, 0)) #55a shallow well\n",
    "lrcq[10, :] = np.array((1, 42, 48, 0)) #65 medium well\n",
    "lrcq[11, :] = np.array((1, 39, 38, 0)) #64 medium well\n",
    "lrcq[12, :] = np.array((2, 37, 42, 0)) #77 deep well\n",
    "lrcq[13, :] = np.array((0, 36, 37, 0)) #78 shallow well\n",
    "lrcq[14, :] = np.array((1, 30, 46, 0)) #5 medium well\n",
    "lrcq[15, :] = np.array((1, 26, 40, 0)) #17 medium well\n",
    "\n",
    "lrcqw = lrcq.copy()\n",
    "lrcqw[:, 3] = -7.27 # 100 gal/day; 13.37 ft3/day for 50 days = 7.27 ft3/day widthdrawl over 92 day peak-season\n",
    "lrcqw[7, 3] = 0 #no pumping well 56\n",
    "lrcqw[6, 3] = 0 #no pumping well 33\n",
    "lrcqw[12, 3] = -14.85 # well 77; 200 gal/day; 26.73 ft3/day for 50 days = 14.85 ft3/day widthdrawl over 92 day peak-season\n",
    "lrcqw[11, 3] = -1.45 # well 77; 20 gal/day; 2.67 ft3/day for 50 days = 1.45 ft3/day widthdrawl over 92 day peak-season\n",
    "\n",
    "lrcq_off = lrcq.copy()\n",
    "base_well_data = {0:lrcq_off, 1:lrcqw, 2:lrcq_off, 3:lrcqw, 4:lrcq_off, 5:lrcqw}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## SWI\n",
    "Seawater Intrusion Package  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# swi2 data\n",
    "nadptmx = 10\n",
    "nadptmn = 1\n",
    "nu = [0, 0.025] #approximates 50% line\n",
    "numult = 0.250\n",
    "toeslope = nu[1] / numult  #0.1\n",
    "tipslope = nu[1] / numult  #0.1\n",
    "z1 = 0.0 * np.ones((nrow, ncol))\n",
    "z1[index == 0] = -1 #not estimated via ghyben-herzberg\n",
    "z = np.array([[z1,z1,z1]])\n",
    "iso = np.zeros((nlay, nrow, ncol), dtype=np.int) #Sources and sinks have \n",
    "# the same fluid density as the active zone at the top of the aquifer\n",
    "iso[0, :, :][index == 0] = 1 # Sources and sinks have the same fluid density \n",
    "#as the zone ISOURCE. If such a zone is not present in the cell,\n",
    "#sources and sinks have the same fluid density as the active zone at the top of the aquifer.\n",
    "\n",
    "iso[0, :, :][index == 1] = -2 # Sources have same fluid density as\n",
    "#the zone with a number equal to the absolute value of ISOURCE. Sinks have the same fluid density as\n",
    "# the active zone at the top of the aquifer.\n",
    "\n",
    "ssz=0.0014  # borrowed from Carleton, Welty, and Buxton 1999\n",
    "# swi2 observations\n",
    "#obsnam = ['layer1_', 'layer2_']\n",
    "#obslrc=[[0, 30, 35],[1,30,35], ]\n",
    "#nobs = len(obsnam)\n",
    "#iswiobs = 1051"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 100, 100)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso.shape\n",
    "z.shape\n",
    "#type(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## OC\n",
    "Output Control Package  \n",
    "Create output control (OC) data using words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# oc data\n",
    "spd = {(0,99): ['print budget', 'save head'],\n",
    "       (0,100): [],\n",
    "       (0,199): ['print budget', 'save head'],\n",
    "       (0,200): [],\n",
    "       (0,299): ['print budget', 'save head'],\n",
    "       (0,300): [],\n",
    "       (0,399): ['print budget', 'save head'],\n",
    "       (0,400): [],\n",
    "       (0,499): ['print budget', 'save head'],\n",
    "       (0,500): [],\n",
    "       (0,599): ['print budget', 'save head'],\n",
    "       (0,600): [],\n",
    "       (0,699): ['print budget', 'save head'],\n",
    "       (0,700): [],\n",
    "       (0,799): ['print budget', 'save head'],\n",
    "       (0,800): [],\n",
    "       (0,899): ['print budget', 'save head'],\n",
    "       (0,900): [],\n",
    "       (0,999): ['print budget', 'save head'],\n",
    "       (1,0): [],\n",
    "       (1,91): ['print budget', 'save head'],\n",
    "       (1,92): [],\n",
    "       (2,0): [],\n",
    "       (2,272): ['print budget', 'save head'],\n",
    "       (2,273): [],\n",
    "       (3,0): [],\n",
    "       (3,91): ['print budget', 'save head'],\n",
    "       (3,92): [],\n",
    "       (4,0): [],\n",
    "       (4,272): ['print budget', 'save head'],\n",
    "       (2,273): [],\n",
    "       (5,0): [],\n",
    "       (5,91): ['print budget', 'save head']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sr = SpatialReference(delr=delr, delc=delc, xll=xll, yll=yll, rotation=rot, epsg=model_epsg, units = 'feet', lenuni = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the model (scenario 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "modelname = 'swifogland_s3'\n",
    "ml = flopy.modflow.Modflow(modelname, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = flopy.modflow.ModflowDis(ml, nlay=nlay, nrow=nrow, ncol=ncol, laycbd=0,\n",
    "                                   delr=delr, delc=delc, top=top, botm=botm,\n",
    "                                   nper=nper, perlen=perlen, nstp=nstp, lenuni = 1, xul = 2500, yul = 2500,\n",
    "                                  steady=steady)\n",
    "\n",
    "bas = flopy.modflow.ModflowBas(ml, ibound=ibound, strt=ihead)\n",
    "lpf = flopy.modflow.ModflowLpf(ml, laytyp=laytyp, hk=hk, vka=vk, sy=sy, ss=ss)\n",
    "wel = flopy.modflow.ModflowWel(ml, stress_period_data=base_well_data)\n",
    "ghb = flopy.modflow.ModflowGhb(ml, stress_period_data=ghb_data)\n",
    "rch = flopy.modflow.ModflowRch(ml, rech=rch_data)\n",
    "swi = flopy.modflow.ModflowSwi2(ml, nsrf=1, istrat=1, toeslope=toeslope, tipslope=tipslope, nu=nu, \n",
    "                                alpha = 0.1, zeta=z, ssz=ssz, isource=iso, nsolver=2, nadptmx=nadptmx, nadptmn=nadptmn,\n",
    "                                iswizt=55) \n",
    "oc = flopy.modflow.ModflowOc(ml, stress_period_data=spd)\n",
    "pcg = flopy.modflow.ModflowPcg(ml, hclose=1.0e-6, rclose=3.0e-3, mxiter=100, iter1=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y7tr+6qp6zx33PD8i/kVEPDEifiMzXxvbE/En7e752jv3fUfEN0bEGyPiBZn5z2K7OuwR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\nb53+/mRm1u1/dtc/5bZrtz7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H16vqDkl+IF3um6T7rvVZSR67/ar5Fi/c9vPnq+rJrbXt\nV7lv3T9essNx3peuAb9Vvt6A7zimtXZVVX0oyXemuzr/7h2Om6T7rvcOT829WRwAAADLZRQNeJKf\n6B9fdJLX3SHdx8E3fT7Jw1trL5/z2ouTvC7JXyT5ZJJvS/JD/fgXVNVXWmtbr7af1T/+ww61N/df\n74BjAAAAWEELb8Cr6qwkP5L5N1/7Bq21C5JcUFVnJLlFuhuivayqzm2tPXrba1+6bfgHk/xaVb03\nyf9I8uyqeklr7auH804OZqe76fVXxo8OHAcAAIBDdig3YTughyS5VubcfG0nrbUrWmvvbq09Lsks\nyXpV3X+PY/8wyceSfGuS2255avNq9Vn/ZNA37t+6pNmpjAEAAGAFjaEB37z52ql+q/31/ePd9zHm\nU/3j1juab97E7VY7jPn2/nHr9713HFNVR9Jdpb8q3dV3AAAAVlgdbOWwOQesunu6dcBf0Vp7yEle\ne06672hf0lq79W6v3eUYj0m3bNjzWmuP38Prz0ryf5NcI8laa+0T/f6z090l/dIkZ2+9E3pVXTfJ\nRpJKcsPW2hf7/Y9I8pIkL2utPWxbnXumu1nbxa21u53Ke+uPc3xtbe3o+vr6qR4CAACAA5jNZtnY\n2Dix01eH92rRV8A3b76269JjVXXHHfafneQp/Y9/tGX/jarqJnNef5103zM/I8mbNpvvJGmtfSDJ\nG5PcPMlPbRv6jHRXy1++2Xz3XpPk00ke1N+hfbPOGUme1f/4G7u9NwAAAFbDodyErarul+R+/Y83\n6h/vXFUX9v/86dbak7aNOTPJA9Ot4f1bJynxxqr6ZJK/TPKRPvfZSb63/+fnt9b+ZMvrb5PkTVX1\n5+k+Mv7JJDdO8j19vg8meeScOo9J8o4k51fVvdItHXZOujXCL0nyC1tf3Fq7vKoela4R/7OqemWS\ny5LcJ90SZa9J8qqTvDcAAABWwGHdBf32SR62bd8t+y1JPpzkSduef3C6q8qv3MPN156Wbg3uO6Vb\n+/vqST6R5KIkL26tvWHb6z+Q7qPh/zJdM3y9JF9K953tFyQ5v7X2+e1FWmsf6K9kPzNdc//96T56\n/rwkz2itfXbOmIuq6m7pmvMfTnd1/f1JntjXObTP+C968flVqzOmLFOrM6YsU6szpixTqzOmLOos\nf5ap1RlTlqnVGVMWdZY/y9TqDJllNjvVW5Z9o0NpwFtr5yU5b59jfiN7/Hh2a+38JOfv49gfSXJK\nX5ruxz58n2Penq5ZBwAAgLkW/R1wAAAAWAkacAAAABiABhwAAAAGcOjrgHO4rAMOAACwWFNZBxwA\nAABWggYcAAAABnBY64AzgEWvfbdqdcaUZWp1xpRlanXGlGVqdcaURZ3lzzK1OmPKMrU6Y8qizvJn\nmVqdIbMc1jrgroADAADAADTgAAAAMAANOAAAAAxAAw4AAAAD0IADAADAAKq1tugM7KKqjq+trR1d\nX19fdBQAAICVNJvNsrGxcaK1duwgx3EFHAAAAAagAQcAAIABHFl0APZu0YvPr1qdMWWZWp0xZZla\nnTFlmVqdMWVRZ/mzTK3OmLJMrc6Ysqiz/FmmVmfILLPZbO7+/XIFHAAAAAagAQcAAIABaMABAABg\nABpwAAAAGIAGHAAAAAZQrbVFZ2AXVXV8bW3t6Pr6+qKjAAAArKTZbJaNjY0TrbVjBzmOK+AAAAAw\nAA04AAAADODIogOwd4tefH7V6owpy9TqjCnL1OqMKcvU6owpizrLn2VqdcaUZWp1xpRFneXPMrU6\nQ2aZzWZz9++XK+AAAAAwAA04AAAADEADDgAAAAPQgAMAAMAArAM+ctYBBwAAWCzrgAMAAMAS0YAD\nAADAAKwDvkQWvfbdqtUZU5ap1RlTlqnVGVOWqdUZUxZ1lj/L1OqMKcvU6owpizrLn2VqdYbMYh1w\nAAAAWCIacAAAABiABhwAAAAGoAEHAACAAWjAAQAAYADVWlt0BnZRVcfX1taOrq+vLzoKAADASprN\nZtnY2DjRWjt2kOO4Ag4AAAAD0IADAADAAI4sOgB7t+jF51etzpiyTK3OmLJMrc6YskytzpiyqLP8\nWaZWZ0xZplZnTFnUWf4sU6szZJbZbDZ3/365Ag4AAAAD0IADAADAADTgAAAAMAANOAAAAAxAAw4A\nAAADqNbaojOwi6o6vra2dnR9fX3RUQAAAFbSbDbLxsbGidbasYMcxxVwAAAAGIAGHAAAAAZwZNEB\n2LtFLz6/anXGlGVqdcaUZWp1xpRlanXGlEWd5c8ytTpjyjK1OmPKos7yZ5lanSGzzGazufv3yxVw\nAAAAGIAGHAAAAAagAQcAAIABaMABAABgANYBHznrgAMAACyWdcABAABgiWjAAQAAYADWAV8ii177\nbtXqjCnL1OqMKcvU6owpy9TqjCmLOsufZWp1xpRlanXGlEWd5c8ytTpDZrEOOAAAACwRDTgAAAAM\nQE0n8zAAABbtSURBVAMOAAAAA9CAAwAAwAA04AAAADCAaq0tOgO7qKrja2trR9fX1xcdBQAAYCXN\nZrNsbGycaK0dO8hxXAEHAACAAWjAAQAAYABHFh2AvVv04vOrVmdMWaZWZ0xZplZnTFmmVmdMWdRZ\n/ixTqzOmLFOrM6Ys6ix/lqnVGTLLbDabu3+/XAEHAACAAWjAAQAAYAAacAAAABiABhwAAAAGoAEH\nAACAAVRrbdEZ2EVVHV9bWzu6vr6+6CgAAAAraTabZWNj40Rr7dhBjuMKOAAAAAxAAw4AAAADOLLo\nAOzdohefX7U6Y8oytTpjyjK1OmPKMrU6Y8qizvJnmVqdMWWZWp0xZVFn+bNMrc6QWWaz2dz9++UK\nOAAAAAxAAw4AAAAD0IADAADAADTgAAAAMAANOAAAAAygWmuLzsAuqur42tra0fX19UVHAQAAWEmz\n2SwbGxsnWmvHDnIcV8ABAABgABpwAAAAGMCRRQdg7xa9+Pyq1RlTlqnVGVOWqdUZU5ap1RlTFnWW\nP8vU6owpy9TqjCmLOsufZWp1hswym83m7t8vV8ABAABgABpwAAAAGIAGHAAAAAagAQcAAIABWAd8\n5KwDDgAAsFjWAQcAAIAlogEHAACAAVgHfIkseu27VaszpixTqzOmLFOrM6YsU6szpizqLH+WqdUZ\nU5ap1RlTFnWWP8vU6gyZxTrgAAAAsEQ04AAAADAADTgAAAAMQAMOAAAAA9CAAwAAwACqtbboDOyi\nqo6vra0dXV9fX3QUAACAlTSbzbKxsXGitXbsIMdxBRwAAAAGoAEHAACAARxZdAD2btGLz69anTFl\nmVqdMWWZWp0xZZlanTFlUWf5s0ytzpiyTK3OmLKos/xZplZnyCyz2Wzu/v1yBRwAAAAGoAEHAACA\nAWjAAQAAYAAacAAAABiABhwAAAAGUK21RWdgF1V1fG1t7ej6+vqiowAAAKyk2WyWjY2NE621Ywc5\njivgAAAAMAANOAAAAAzgyKIDsHeLXnx+1eqMKcvU6owpy9TqjCnL1OqMKYs6y59lanXGlGVqdcaU\nRZ3lzzK1OkNmmc1mc/fvlyvgAAAAMAANOAAAAAxAAw4AAAAD0IADAADAAKwDPnLWAQcAAFgs64AD\nAADAEtGAAwAAwACsA75EFr323arVGVOWqdUZU5ap1RlTlqnVGVMWdZY/y9TqjCnL1OqMKYs6y59l\nanWGzGIdcAAAAFgiGnAAAAAYgAYcAAAABqABBwAAgAFowAEAAGAA1VpbdAZ2UVXH19bWjq6vry86\nCgAAwEqazWbZ2Ng40Vo7dpDjuAIOAAAAA9CAAwAAwACOLDoAe7foxedXrc6YskytzpiyTK3OmLJM\nrc6Ysqiz/FmmVmdMWaZWZ0xZ1Fn+LFOrM2SW2Ww2d/9+uQIOAAAAA9CAAwAAwAA04AAAADAADTgA\nAAAMQAMOAAAAA6jW2qIzsIuqOr62tnZ0fX190VEAAABW0mw2y8bGxonW2rGDHMcVcAAAABiABhwA\nAAAGcGTRAdi7RS8+v2p1xpRlanXGlGVqdcaUZWp1xpRFneXPMrU6Y8oytTpjyqLO8meZWp0hs8xm\ns7n798sVcAAAABiABhwAAAAGoAEHAACAAWjAAQAAYADWAR8564ADAAAslnXAAQAAYIlowAEAAGAA\n1gFfIote+27V6owpy9TqjCnL1OqMKcvU6owpizrLn2VqdcaUZWp1xpRFneXPMrU6Q2axDjgAAAAs\nEQ04AAAADEADDgAAAAPQgAMAAMAANOAAAAAwgGqtLToDu6iq42tra0fX19cXHQUAAGAlzWazbGxs\nnGitHTvIcVwBBwAAgAFowAEAAGAARxYdgL1b9OLzq1ZnTFmmVmdMWaZWZ0xZplZnTFnUWf4sU6sz\npixTqzOmLOosf5ap1Rkyy2w2m7t/v1wBBwAAgAFowAEAAGAAGnAAAAAYgAYcAAAABnAoDXhV3b+q\nnl9Vb6uqy6uqVdVv7/DaC/vnd9v+dNuYu1bVy6vqb6rqM1V1RVV9qKr+oKrutUuua1bVM6rqvf2Y\nT1bV71TVd+wy5iZV9dKq+nhVXVlVl1bVc6vq+ruMuUtVva6qLquqL1fVX1fV46vq6nv59wcAAMD0\nVWvt4Aep+qskt0vyhSQfTXKbJK9orT1kzmvvl+T2OxzqoUlumeRnWmu/umXMeUkeleSd/fG/mORm\nSe6T5DpJntVae+q2OtdI8qdJzk3yriRvTnLTJA9I8o9J7tlae+e2MWcneUeSGyZ5bZL3JLljknsk\neW+Sc1trn9k25r5JfjfJFUleleSyJD+Y5NZJXtNae8AO73VPqur42tra0fX19YMcBgAAgFM0m82y\nsbFxorV27CDHOaxlyJ6QrjF+f5K7JXnLTi9srV2U5KLt+6vqekl+Nl1zfOG2p3+ptXbenDE3TnIi\nyVOq6oWttY0tTz8xXfP9miQPbK19rR/zqr7+S6vquzf3916Yrvn+6dba87fUeU7/Hp+d5NFb9p+Z\n5EVJvprk7q21d/X7n5qu4b9/VT2otfbKnf59AAAAsBoO5SPorbW3tNbe1w52Of2hSa6Z5Pdaa5/e\ndvwrdqj7sXRXrK+W7sp5kqSqKl9vlH92a5PdWnttkrcluW26XxZsjjk7yb2TXJrk17eVenq6q+4P\nraprb9l//yQ3SPLKzeZ7S95f7H/8yd3eNAAAAKvhsK6AH4ZH9Y+/udcBVXXDJOckuTLdR8Q3nZ3u\nI+qXtNY+NGfo65P8qyT3zNev1t+jf3zjtqviaa19vqrenq5Bv1O6j7anH58kfzynxsVJvpTkLlV1\njdbalXt9XztZ9OLzq1ZnTFmmVmdMWaZWZ0xZplZnTFnUWf4sU6szpixTqzOmLOosf5ap1Rkyy2w2\nm7t/v0bRgFfVnZN8d7qGecePr1fVHZL8QLrcN0n3Xeuzkjx221XzW/ePl+xwqPf1j7fa55h792M2\nG/Adx7TWrqqqDyX5znRX59+9w3GTdN/13uGp2+w2DgAAgOUwigY8yU/0jy86yevukO7j4Js+n+Th\nrbWXb3vdWf3jP+xwnM3911vAGAAAAFbQwhvwqjoryY9k/s3XvkFr7YIkF1TVGUluke573i+rqnNb\na4/ebezY7XQ3vf7K+NGB4wAAAHDIFt6AJ3lIkmulu5HZp0/24uT/3+Ts3Uke1y83tl5Vb2qtvaZ/\nyeaV57PmHuDr+z+3Zd9QYwAAAFhBh3IX9APavPnaqX6r/fX949237Nu8IdutMt+3949bv7t9qGOq\n6ki6q/RXJfngDscEAABgRdTBVg6bc8Cqu6e7s/grWmsPOclrz0nyF+luvnbr3V67yzEek27ZsOe1\n1h7f76t0y4ndLMktt98JvaouTn8X9M2bvvXLkL2/H3f21juhV9V1k2wkqSQ3bK19sd//iCQvSfKy\n1trDttW4Z7qbtV3cWrtbTlFVHV9bWzu6vr5+qocAAADgAGazWTY2Nk7s9NXhvVr0FfDNm6/tuvRY\nVd1xh/1nJ3lK/+Mfbe7v1yO/oP/xl6vqalvG3Ddd8/13Sd66ZcwHkrwxyc2T/NS2Us9Icu0kL99s\nvnuvSfLpJA/q79C+WeOMJM/qf/yN3d4bAAAAq+FQvgNeVfdLcr/+xxv1j3euqgv7f/50a+1J28ac\nmeSB6dbw/q2TlHhjVX0yyV8m+Uif++wk39v/8/Nba3+ybcxz0i1Zdv8k76yqP013RfwB6dbnfsT2\n9b6TPCbJO5KcX1X3Svc983PSrRF+SZJf2Pri1trlVfWodI34n1XVK5NcluQ+6ZYoe02SV53kvQEA\nALACDusmbLdP8rBt+27Zb0ny4SRP2vb8g9NdVd7Lzdeelm4N7julW/v76kk+keSiJC9urb1h+4DW\n2pVV9T1Jfj7JjyZ5QpLL+zFPb6393ZwxH+ivZD8zXXP//ek+ev68JM9orX12zpiLqupu6ZrzH05y\nRrqPsj8xyfntED/jv+jF51etzpiyTK3OmLJMrc6YskytzpiyqLP8WaZWZ0xZplZnTFnUWf4sU6sz\nZJbZ7FRvWfaNDqUBb62dl+S8fY75jezx49mttfOTnH8Kub6Urnl/2j7GfCTJw/dZ5+3pmnUAAACY\na9HfAQcAAICVoAEHAACAAWjAAQAAYACHvg44h8s64AAAAIs1lXXAAQAAYCVowAEAAGAAh7UOOANY\n9Np3q1ZnTFmmVmdMWaZWZ0xZplZnTFnUWf4sU6szpixTqzOmLOosf5ap1Rkyy2GtA+4KOAAAAAxA\nAw4AAAAD0IADAADAADTgAAAAMAANOAAAAAygWmuLzsAuqur42tra0fX19UVHAQAAWEmz2SwbGxsn\nWmvHDnIcV8ABAABgABpwAAAAGMCRRQdg784777yT7j+di88Ptcj9WOqMKcvU6owpy9TqjCnL1OqM\nKYs6y59lanXGlGVqdcaURZ3lzzK1Oqcjy04919ra2tz9++UKOAAAAAxAAw4AAAAD0IADAADAADTg\nAAAAMAANOAAAAAygWmuLzsAuquozR44c+eYb3OAGi44CAACwkj71qU/lqquuuqy19i0HOY4GfOSq\n6kNJzkxy6YKj7MVt+sf3LDQF7I95yzIyb1lG5i3LyLxl082TXN5au8VBDqIB59BU1fEkaa0dW3QW\n2CvzlmVk3rKMzFuWkXnLYfMdcAAAABiABhwAAAAGoAEHAACAAWjAAQAAYAAacAAAABiAu6ADAADA\nAFwBBwAAgAFowAEAAGAAGnAAAAAYgAYcAAAABqABBwAAgAFowAEAAGAAGnAAAAAYgAZ8YqrqIVXV\n+u2RO7zmLlX1uqq6rKq+XFV/XVWPr6qr7+H416iqv+mP/9FdXnfNqnpGVb23qq6oqk9W1e9U1Xfs\nMuYmVfXSqvp4VV1ZVZdW1XOr6vq7jDnl98J4jGHeVtWFWzLM226zwzjzdkUd9rytqh87yRx89A41\nnG/ZszHMW+db9ut0/T2hqs6qqmf2r/1CVV3e/31hVlXfNOf1zrcc2JFFB+DwVNVNk7wgyReSXGeH\n19w3ye8muSLJq5JcluQHk/yXJOcmecBJyvzHJP/8JDmukeRP+uO9K8nzkty0P/a/rqp7ttbeuW3M\n2UnekeSGSV6b5D1J7pjkcUm+t6rOba195pDfCyMwlnm7xfOSfG7O/k/PyWXerqjTPG9fm+Sv5ux/\n15wazrfs2Vjm7RbOt5zU6Zq3/S963pjkxknelOT1Sb4pyc2T/EiSf5/kK1te73zL4Wit2SawJal0\nJ48PJPmVJC3JI7e95swkn0xyZZI7bNl/RrqTQ0vyoF1q3D3J15I8un/tR3d43ZP751+d5Gpb9t+3\n3/+3W/f3z72hf+6x2/Y/p99/wWG+F9s4tpHN2wv752++j/zm7Qpup2veJvmxfv+P7SOL861tGeet\n861tr3/up2veXivJJUk+m+ROc+oeSVLb9jnf2g5lW3gA2yH9QXa/SftakrsmOW+HE9Qj+v2/NWf8\nPfvn3rrD8c9McmmSP+l/ntvI9CfKD/fP32LO8xf3z91jy76z+30fmnPium6633h+Mcm1D+O92Maz\njWXe9s9dmH38hdC8Xd3tdM3b7LORcb617Wcby7ztxzjf2vY6V07XvH1iv//Re8zhfGs7tM13wCeg\n/97JLyV5Xmvt4l1ees/+8Y/nPHdxki8luUv/EZvtzk9y/SQ/fpI4Zye5WZJLWmsfmvP867dlSZJ7\n9I9vbK19beuLW2ufT/L2dL+pvNOWpw7yXhiBkc3brb6vqn6uqp5UVferqjN3eJ15u4IGmre377/r\n9/NV9dCquskONZxv2ZORzdutnG/Z0Wmet/82XTP7yqq6eVX9ZFU9uaoeXFXfMuc4zrccGg34kquq\nI0lenuTvkzzlJC+/df94yfYnWmtXpfsN3ZEkt9xW44eSPCzJE1trf3+qNXrv6x9vdbrG7PZeGIcR\nztutXpjuf/i/kuT3k3y0qn5qP7l65u3EDDFve49L912//5TkZUkuraoLquqMvdboOd8yxnm7lfMt\nc53OedvfXO12ST6V5FHp5s8L090v5reTfLiqHrHXGj3nW/ZMA778npbkX6T76NeXT/Las/rHf9jh\n+c3919vcUVX/LMlvJnl9a+0le8iz7xoDjmE8xjZvk+43yw9Md7O2a6b7bfeT+udeUFU/cdBcpziG\n8Tit8zbdX6wem+4vYNdO8m3pbgR0aZL1JC89hBrm7eoZ27xNnG85udM5b785XRP7Lel+YfQf0t1M\n7VuTPDLdlfEXV9XWq9nOtxwaDfgSq6pz0v1W8Ndaa39+msq8KN1Jau6SD7BfY523rbWXttZ+p7X2\n9621K1prH2yt/Vq6j6klybMt/7G6hpi3rbW3ttZe0Fq7pLX2pdbaRmvt1ek+xvjZJD9aVbc7HbWZ\nprHOW+dbdjPAvN3sf66e5MWttWe21j7aWvtM/0v7p6T7zvfPnYbaoAFfVv1Hc16W7iMqT93jsM3f\nmp21w/Ob+z/X1/h36ZY9eFxr7eOno8bAY1iwEc/bHbXW/jDJx9L9Zvy2p5rrAGNYsCHm7W5aax9J\n8rr+x7sesIZ5uyJGPG93G+N8u+IGmrdbry7//pzXb+674wFqDDmGJaMBX17XSfedke9IckVVtc0t\nydP717yo3/fc/uf39o+32naszRPeLZJcleSD/e6j/eNvbT1+XyNJbrxl3+ZHYXas0fv2/nHrd1sO\ndcwO74VxGOu8PZlP9Y/X3rLPvF0dQ8zbkzntc/BUxpi3ozbWeXsqY8zb1XHa521r7UtJPtK/ZF4j\n+9n+8Zpb9jnfcmiOLDoAp+zKJDt9t/Vouu/N/M90/yFvfnznzUkenOR7k/z3bWPumu5OjBe31q7s\n9/15uhPhPD+e7k6Mm8fZHPOBdDfMuFVV3WLOnSK/b0uWTW/pH+9dVVfbeqfIqrpuknP7Wn+xZcx+\n3wvjMNZ5u6OqOivJbfL1pUQ2mberY4h5ezLn9I9b/9LlfMtuxjpvd+R8S4abt29K8vAk35XkndvG\nfFf/uHUOOt9yeBa9Dprt8LfsvE7imel+s3xlkjts2X9Gknf0Yx60xxq7raf85P75V2fLuodJ7tvv\n/9v80/UQ39A/99ht+5/T77/gdL0X2zi2Rc7bJDdKcpM5+6+T7qNoLd0yItufN29XfDvMebv1dVv2\nXW3LOfVTSc7c9rzzrW2p5q3zre1Ut0Oet8eSfDXdnchvsG3Mm/oxT9s2xvnWdjhzedEBbKfhD3WH\nE1T/3P3SfXTlC0lenOSXk7xnywml9lhjtwb8GunWNmxJ/ne6JUb+W5KvJPliknPmjDk7ySf6MRel\nuyvlm/uf35vkW07Xe7GNY1vkvE1y9/74b0v3m/fNZXQ2+jEfSHKzOePM2xXfDnPe9vv/T7plcP5z\nkgv6n1t/7rz3nBrOt7almrfOt7YxzNt+zNP65z+R7uatz0/3cfDWn1fP2PZ651vb4czlRQewnYY/\n1F1OUP3z56a7Mcpnk3y5/x/lE5JcfR81dmzA++evleSZ6X6zeGW63+a9Osltdxlz0yT/tf+f8D8m\n+XCS5ya5/i5jDvxebOPYFjlv+7k3S3Kin6tfSXcjlP+V5BeSXHeXY5q3K7wd5rxNtxbyW5N8PMkV\n6T6a+J4kL0hyy10yON/a9rUtct4639pOdTvMebtlzL9Jtyze5f38/dt+Hl5jh9c739oOvFX/hwwA\nAACcRu6CDgAAAAPQgAMAAMAANOAAAAAwAA04AAAADEADDgAAAAPQgAMAAMAANOAAAAAwAA04AAAA\nDEADDgAAAAPQgAMAAMAANOAAAAAwAA04AAAADEADDgAAAAPQgAMAAMAANOAAAAAwAA04AAAADEAD\nDgAAAAP4f3abJmltP0T0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d3fad3748>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 496
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "modelmap = flopy.plot.ModelMap(sr=sr, model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "linecollection = modelmap.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-220, 10)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OfibH/ruSfGh8eeLMLvkF7KfWES/WY0fGC4Xt9Ttp3H5y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\nsA0AAAAAwKQobAMAAAAAMCn/B+YD05Avx6bmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f309515c9b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 209,
       "width": 731
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the ModelCrossSection class\n",
    "modelxsect = flopy.plot.ModelCrossSection(model=ml, line={'Column': 95})\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",
    "linecollection = modelxsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')\n",
    "ax.set_ylim(-220,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-220, 10)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8JC9bxXqBfWiN8WI9xAtYAN19aXf/4+xncuy/K8mHxpcn\nzuySX8B+ah3xYj12ZLxQ2F6/k8btJ5f5H+crSf4lySFJXrLVCwO2zIFV9dqq+u2q+tWqOmmFe9ed\nPG7/aZl9VyZ5KMnx4890VjPn43NjgGnZiphwVJJnJ/lMd9++yjnAzvUdVfWmMed4U1W9cC9jxQtY\nfP87bh+b6ZNfAMtZLl4smXx+ccBGJu/njhm3n1lh/60Zrug+OsP9ZIDFc2SS8+b6bq+q13f3FTN9\nK8aL7n6sqm5P8r1JvjvJTeMvPb4zw72671zmuLeO26M3tHpgu2xFTFhNnjI/B9i5Xj62/1dVlyc5\no7vvmOkTL2DBVdUBSX5hfDlbYJJfAF9jL/FiyeTzC1dsr99h4/b+FfYv9T99C9YCbL2/TnJKhuL2\noUm+L8lfZri/1Mer6riZsWuNF+ILLLatiAniCCyGh5L8fpIXJTl8bC/L8GCoE5NcMnfrQ/ECFt8f\nJXlBkou7+xMz/fILYN5K8WJh8guFbYB16O6zx/tYfbG7H+ruG7r7zAwPjz04ybu3d4UAwNR1993d\n/bvdfV13f3lsV2b4Zeg1Sb4nyRu3d5XAVqmqX0ny9iQ3J3ndNi8H2MH2Fi8WKb9Q2F6/pW8WDlth\n/1L/l7dgLcDOsfRghpfO9K01XogvsNi2IiaII7DAuvuxJH81vtxIzrHeOcAWq6pfTvJnSW5MclJ3\n3zc3RH4BJFlVvFjWFPMLhe31u2XcrnQvmOeO25XuJQMspv8Zt7M/21kxXoz3vHpOhgc5fC5Juvur\nSf4ryTdW1bcvcwzxBaZtK2KCPAUW39flHOIFLKaqemuSDyS5IUOR6q5lhskvgNXGi72ZVH6hsL1+\nl43bV1TV1/w5VtU3JTkhwz1r/m2rFwZsq5eM28/N9F06bn9smfEvTXJIkqu7e88q5/z43BhgWrYi\nJnw2yR1Jjq6q56xyDjAty+UciXgBC6WqfjPJ+5Jcn6FIdfcKQ+UXsJ9bQ7zYm0nlFwrb69Tdn03y\nyQwPinvz3O6zM3yzcd74rQawQKrq2LkHKSz170pyzvjy/JldFya5J8npVfXimfEHJfmD8eUH595u\n6ZYm76yqw+eO8eYkezI8wBKYnk2PCd3dM3P+ePZL+Ko6NcmPZPhp4hUbPx1gs1TVD8xfRDP2n5Lk\nbePL8+d2ixewIKrqXRke/vapJKd09z17GS6/gP3YWuLFIuUXNRyH9aiqo5JcneSZSS5KclOSH0py\nUoZL6Y/v7nu3b4XAZqiqd2d4CMOVST6f5CtJjkryE0kOSnJxkld196Mzc16ZIdl8JMkFSe5L8tNJ\njhn7f67nAnJV/UmSX0vyhXHM05L8fJIjkrylu88JsCOMn/FXji+PTPKjGa5yuGrsu6e7f31u/KbG\nhKo6MMMVEMcnuTbJJUmeneS0JI8mObm7r9kHpw+swVriRVVdnuGnuldn+OwnyQuTnDz+97u6e6lg\nNXsM8QImrqrOSHJukscz3Fbg/mWG7e7uc2fmyC9gP7TWeLFI+YXC9gZV1bOS/F6GS/GPSHJnko8k\nObu7v7SdawM2R1W9LMmZSb4/wz9ID83wwIPrk5yX4dcaXxdcq+qEJO9M8sMZCuC3Jflwkj/v7sdX\nONYvZvj28/lJnkhyXZL3dPfH9u1ZARsxfuF11l6GfL67d83N2fSYUFWHJHlHktdkSCIfSHJ5krO6\n+8ZVnRywT60lXlTVG5K8KskLknxLkm9I8sUk/5rknO6+aqU3ES9g2lYRK5Lkiu4+cW6e/AL2M2uN\nF4uUXyhsAwAAAAAwKe6xDQAAAADApChsAwAAAAAwKQrbAAAAAABMisI2AAAAAACTorANAAAAAMCk\nKGwDAAAAADApCtsAAAAAAEyKwjYAAAAAAJOisA0AAAAAwKQobAMAAAAAMCkK2wAAAAAATIrCNgAA\nAAAAk6KwDQAAAADApChsAwAAAAAwKQrbAAAAAABMisI2AAAAAACT8n9uzmGJ/6cSbAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f309515c470>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 209,
       "width": 731
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the ModelCrossSection class\n",
    "modelxsect = flopy.plot.ModelCrossSection(model=ml, line={'Row': 50})\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",
    "linecollection = modelxsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')\n",
    "ax.set_ylim(-220,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the scenario 3 MODFLOW input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ml.sr = SpatialReference(delr=delr, delc=delc, xll=xll, yll=yll, rotation=rot, epsg=model_epsg, units = 'feet', lenuni = 1)\n",
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking Spatial Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xul:403877; yul:175413; rotation:0; proj4_str:+proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99998983997 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +to_meter=0.3048006096012192 +no_defs ; units:feet; lenuni:1; length_multiplier:1.0\n",
      "(403877.0, 172913.0, 406377.0, 175413.0)\n"
     ]
    }
   ],
   "source": [
    "print(ml.sr)\n",
    "print(ml.sr.bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import timer and run model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, [])\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "print(ml.run_model(silent=True))\n",
    "elapsed = timeit.default_timer() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "473.09106259500004\n"
     ]
    }
   ],
   "source": [
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 1 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read base model zeta\n",
    "zfile = flopy.utils.CellBudgetFile(os.path.join(ml.model_ws, modelname+'.zta'))\n",
    "kstpkper = zfile.get_kstpkper()\n",
    "zeta = []\n",
    "for kk in kstpkper:\n",
    "    zeta.append(zfile.get_data(kstpkper=kk, text='ZETASRF  1')[0])\n",
    "zeta = np.array(zeta)\n",
    "# read swi obs\n",
    "#zobs = np.genfromtxt(os.path.join(ml.model_ws, modelname+'.zobs.out'), names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 3, 100, 100)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create arrays for the x-coordinates and the output years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1250, 1250, 100)\n",
    "xcell = np.linspace(-1250, 1250, 100) + delr / 2.\n",
    "xedge = np.linspace(-1225, 1225, 101)\n",
    "years = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0.25, 1, 1.25, 2, 2.25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define figure dimensions and colors used for plotting `ZETA` surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# figure dimensions\n",
    "fwid, fhgt = 8.00, 5.50\n",
    "flft, frgt, fbot, ftop = 0.125, 0.95, 0.125, 0.925\n",
    "\n",
    "# line color definition\n",
    "icolor = 15\n",
    "colormap = plt.cm.jet  #winter\n",
    "cc = []\n",
    "cr = np.linspace(0.9, 0.0, icolor)\n",
    "for idx in cr:\n",
    "    cc.append(colormap(idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.9       ,  0.83571429,  0.77142857,  0.70714286,  0.64285714,\n",
       "        0.57857143,  0.51428571,  0.45      ,  0.38571429,  0.32142857,\n",
       "        0.25714286,  0.19285714,  0.12857143,  0.06428571,  0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icolor\n",
    "cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0d3fa55668>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2VJc13Od9k/v4p7RuACwF92l6ZibPtsnk/icAK4TjiwI4BZVF/ok5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\nRlwR7AJ/UXx7FYVEI5wL9xn5ElypvoS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nB4LvAgcLeZfcs515i8/nbgBGDhvgVdAOdcs5l9H3gDeAjQoq6IiIiIiIiISAo1xVso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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d3fa36860>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 567,
       "width": 698
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = os.path.join('./data/swifogland_s3.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()\n",
    "levels = np.arange(-5,10,.5)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 1, aspect='equal')\n",
    "ax.set_title('Fogland Point, Hydraulic heads at the end of 202.25 years'\n",
    "             '\\nRecharge conditions of 200 years of 0.00479 feet/day followed by scenario 3 pumping')\n",
    "modelmap = flopy.plot.ModelMap(model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "quadmesh = modelmap.plot_array(head, masked_values=[999., -9.99989990e+02], alpha=0.6,)\n",
    "modelmap.plot_bc(\"GHB\")\n",
    "linecollection = modelmap.plot_grid(linewidth=0.5)\n",
    "cb = fig.colorbar(quadmesh, ax = ax)\n",
    "cb.set_label('Hydraulic head, feet above MSL')\n",
    "modelmap.plot_bc(\"WEL\", plotAll=True)\n",
    "ax.plot([403877, 406377], [174163, 174163], color='k', linewidth=1.0)\n",
    "ax.text(0.6, .52, 'row 50, ZETA Surface cross section', transform=ax.transAxes, va='top', ha='left', size='11')\n",
    "\n",
    "ax.set_xlabel('feet')\n",
    "ax.set_ylabel('feet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A cross section showing results along the row 50. The position of the interface is shown at 20-year increments for stress period 1, during development of the freshwater lens. After 200 years, the interface approaches the steady-state position. The position of the interface for stress periods 2 through 6 is shown at their respective increments of 0.25 and 0.75 years. Movement of the ZETA surface is the result of changes in groundwater recharge and pumping which cause upconing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0d3fb94b70>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Pb3UtFRW3KYybNgwCIKAgQMH6vU3ffp0CIKg8xgJU3QdY/saEdU6lpOTg7lz56JRo0bs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YluGaDSCWP39LsFAADYb92cxzgVt3XDTCOQ7MLxxx+fNWvWdFV711135VOf+lSOOeaY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\nrhtuuIEwEgDQZjaqJNktAADQalN8hI1TQ+gDAIBkC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NpWIEu28/PzlZPzVbZZVFSkzz5r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\nFFgCMPMzYxXecgvrlTDMgMyJJDRGSz1rOmifVsv7g6FcFTS3PQAA0EoEk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4/e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d3fc2cc18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 649,
       "width": 658
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'legend.fontsize': 8, 'legend.frameon' : False})\n",
    "#fig = plt.figure(figsize=(fwid, fhgt), facecolor='w')\n",
    "fig = plt.figure(figsize=(10, 10), facecolor='w')\n",
    "fig.subplots_adjust(wspace=0.25, hspace=0.25, left=flft, right=frgt, bottom=fbot, top=ftop)\n",
    "# first plot\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "# axes limits\n",
    "#ax.set_xlim(-1500, 1500)\n",
    "ax.set_ylim(-220, 10)\n",
    "for idx in range(15):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2f} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "    # layer 3\n",
    "    ax.plot(xcell, zeta[idx, 2, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "\n",
    "ax.plot([-1500, 1500], [-43, -43], color='k', linewidth=1.0)\n",
    "ax.plot([-1500, 1500], [-90, -90], color='k', linewidth=1.0)\n",
    "\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in feet')\n",
    "ax.set_ylabel('Elevation, in feet')\n",
    "ax.text(0.025, .85, 'Layer 1', transform=ax.transAxes, va='top', ha='left', size='7')\n",
    "ax.text(0.025, .68, 'Layer 2', transform=ax.transAxes, va='top', ha='left', size='7')\n",
    "ax.text(0.025, .5, 'Layer 3', transform=ax.transAxes, va='top', ha='left', size='7')\n",
    "\n",
    "\n",
    "ax.text(0.975, .1, 'Recharge conditions', transform=ax.transAxes, va='center', ha='right', size='8')\n",
    "ax.set_title('Fogland Point, row 50, ZETA Surface, 50% seawater salinity at the end of 202.25 years'\n",
    "             '\\nRecharge conditions of 200 years of 0.00479 feet/day followed by scenario 3 pumping')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0d678454e0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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hrTUt40NzFylo8za/qji/3qkBAHA8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\nvGjXrh0//vhjgXMSQpQdz2TH4c8Bs5sWA/1nRZjdROnnYn3O4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LGZQIvbCP5b+EUsSDFW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\nLl68aO+hiEg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d3b078e48>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 1252
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'legend.fontsize': 6, 'legend.frameon' : False})\n",
    "#fig = plt.figure(figsize=(fwid, fhgt), facecolor='w')\n",
    "fig = plt.figure(figsize=(20, 10), facecolor='w')\n",
    "fig.subplots_adjust(wspace=0.25, hspace=0.25, left=flft, right=frgt, bottom=fbot, top=ftop)\n",
    "fig\n",
    "# first plot\n",
    "ax = fig.add_subplot(2, 2, 1)\n",
    "# axes limits\n",
    "#ax.set_xlim(-1500, 1500)\n",
    "#ax.set_ylim(-50, -10)\n",
    "for idx in range(10):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2f} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "    # layer 3\n",
    "    ax.plot(xcell, zeta[idx, 2, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "    \n",
    "ax.plot([-1500, 1500], [-43, -43], color='k', linewidth=1.0)\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in meters')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .75, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.975, .1, 'Recharge conditions', transform=ax.transAxes, va='center', ha='right', size='8')\n",
    "\n",
    "# second plot\n",
    "ax = fig.add_subplot(2, 2, 2)\n",
    "# axes limits\n",
    "#ax.set_xlim(-1500, 1500)\n",
    "#ax.set_ylim(-50, -10)\n",
    "for idx in range(10,15):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2f} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx-5], label='_None')\n",
    "    # layer 3\n",
    "    ax.plot(xcell, zeta[idx, 2, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\n",
    "    \n",
    "ax.plot([-1500, 1500], [-43, -43], color='k', linewidth=1.0)\n",
    "# legend\n",
    "plt.legend(loc='lower left')\n",
    "# axes labels and text\n",
    "ax.set_xlabel('Horizontal distance, in meters')\n",
    "ax.set_ylabel('Elevation, in meters')\n",
    "ax.text(0.025, .75, 'Layer 1', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='center', ha='left', size='7')\n",
    "ax.text(0.975, .1, 'Freshwater well withdrawal', transform=ax.transAxes, va='center', ha='right', size='8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `ModelCrossSection` plotting class and `plot_fill_between()` method to fill between zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sbhgeo/Documents/Fogland/conda/lib/python3.6/site-packages/flopy/plot/crosssection.py:399: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.\n",
      "Check the NumPy 1.11 release notes for more information.\n",
      "  v[idx] = y2[idx]\n"
     ]
    },
    {
     "data": {
      "image/png": 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cTF/zl49SueNeVeYPDFj+IPBmYA3V72W66lTg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\nJLXm/wB0yM0kkOvBuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d3b268e48>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 204,
       "width": 527
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(fwid, fhgt/2))\n",
    "fig.subplots_adjust(wspace=0.25, hspace=0.25, left=flft, right=frgt, bottom=fbot, top=ftop)\n",
    "\n",
    "colors = ['#40d3f7', '#F76541']\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "modelxsect = flopy.plot.ModelCrossSection(model=ml, line={'Row': 50})\n",
    "#, \n",
    "#                                          extent=(0, 3050, -50, -10))\n",
    "modelxsect.plot_fill_between(zeta[10, :, :, :], colors=colors, ax=ax,\n",
    "                              edgecolors='none')\n",
    "linecollection = modelxsect.plot_grid(ax=ax)\n",
    "ax.set_title('Recharge year {}'.format(years[9]));\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "#ax.set_xlim(0, 3050)\n",
    "#ax.set_ylim(-50, -10)\n",
    "modelxsect.plot_fill_between(zeta[-1, :, :, :], colors=colors, ax=ax)\n",
    "linecollection = modelxsect.plot_grid(ax=ax)\n",
    "ax.set_title('Scenario year {}'.format(years[-1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8332875"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(head)\n",
    "np.max(head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 3, 100, 100)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/sbhgeo/Documents/Fogland/Notebooks/FoglandPumping'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}