flopy3_SWI2_Fogland_UNIX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fogland Point, RI \n",
    "### MODFLOW, SWI2, Flopy\n",
    "### This notebook has been tested in an ubuntu-xenial based linux distro\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 XX-ft thick aquifer that extends below sea level. The aquifer is unconfined, storage changes are not considered (all MODFLOW stress periods are steady-state).\n",
    "\n",
    "The domain is discretized into 100 columns, 100 rows, and 2 layers, with respective cell dimensions of 25 ft (`DELR`), 25 ft (`DELC`), and XX. A total of 230 years is simulated using three stress periods with lengths of 200, 12, and 18 years, with constant time steps of 0.2, 0.1, and 0.1 years, respectively. \n",
    "\n",
    "The horizontal and vertical hydraulic conductivity of the fractured rock aquifer is 10$^{-7}$ ft/d and 10$^{-8}$ ft/d**?**, respectively. The effective porosity is 0.0015. 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\\* -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 15.6 ft$^{2}$/d and corresponds to a leakance of 0.025 d$^{-1}$ (or a resistance of 40 days).**may change?**\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 island and at the top of the upper aquifer 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. ~~`ISOURCE` in layer X, row XX, column XX is set to 2 so that a saltwater well may be simulated in the third stress period of simulation 2.~~ 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 0.00479 feet per day (ft/d) is used in all three stress periods. The development of the freshwater lens is simulated for 200 years\n",
    "\n",
    "-------------------------------------------------current stage-------------------------------------------------\n",
    "\n",
    "\n",
    ", after which a pumping well having a withdrawal rate of XXX ft$^3$/d is started in layer X, row XX, column XX. For the first simulation (simulation 1), the well pumps for XX years, after which the interface almost reaches the top of the upper aquifer layer. In the second simulation (simulation 2), an additional well withdrawing\n",
    "saltwater at a rate of 25 m$^3$/d is simulated below the freshwater well in layer 2 , row 31, column 36, 12 years after the freshwater groundwater withdrawal begins in the well in layer 1. The saltwater well is intended to prevent the interface from\n",
    "upconing into the upper aquifer (model layer).\n",
    "\n",
    "### 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"
   ]
  },
  {
   "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",
    "\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": [
    "Define the number of layers, rows and columns. The heads are computed quasi-steady state (hence a steady MODFLOW run) while the interface will move. There are three stress periods with a length of 200, 12, and 18 years and 1,000, 120, and 180 steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nrow, ncol = 100, 100\n",
    "xll, yll = 403877, 172913 # origin of the model (lower left corner)\n",
    "dxdy = 25 # grid spacing (in model units)\n",
    "rot = 0 # rotation (positive counterclockwise)\n",
    "\n",
    "\n",
    "# http://epsg.io/6568\n",
    "#model_epsg = 6568\n",
    "\n",
    "# alternatively, a proj4 string can be supplied\n",
    "# https://svn.osgeo.org/metacrs/proj/trunk/proj/nad/epsg\n",
    "# figure out a way not to hard code this\n",
    "model_proj4 = '+proj=tmerc +lat_0=41.08333333333334 +lon_0=-71.5 +k=0.99999375 +x_0=99999.99998983997 +y_0=0 +ellps=GRS80 +units=us-ft +no_defs'\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",
    "\n",
    "sr = SpatialReference(delr=delr, delc=delc, xll=xll, yll=yll, rotation=rot, proj4_str= model_proj4, units = 'feet', lenuni = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DELR = 25.0\n",
      "DELC = 25.0\n"
     ]
    }
   ],
   "source": [
    "print('DELR =', delr[0])\n",
    "print('DELC =', delc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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 +units=us-ft +no_defs; units:feet; lenuni:1; length_multiplier:1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X,Y of lower left corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(403877.0, 172913.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.xll, sr.yll"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(403877.0, 172913.0, 406377.0, 175413.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapefile of grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sr.write_shapefile(os.path.join(outpath, 'grid2.shp'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "Define the `DIS` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ncol = 100     already assinged in Spatial Reference Module\n",
    "#nrow = 100     already assinged in Spatial Reference Module\n",
    "nlay = 2\n",
    "\n",
    "nper = 3\n",
    "perlen = [365.25 * 200., 365.25 * 12., 365.25 * 18.]\n",
    "nstp = [1000, 120, 180]\n",
    "save_head = [200, 60, 60] # what is this for?\n",
    "steady = True"
   ]
  },
  {
   "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": 97,
   "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",
    "\n",
    "lay1top = np.flipud(lay1top) \n",
    "#new line flipping array because CSV was made with grid origin in upper left instead of lowerleft\n",
    "\n",
    "print(lay1top.shape, type(lay1top))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dis data\n",
    "#delr, delc = 25.0, 25.0 #defined above\n",
    "top = lay1top\n",
    "botm = np.array([-43., -200.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 100, 100)"
      ]
     },
     "execution_count": 99,
     "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)\n",
    "ihead.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# lpf data\n",
    "#laytyp = np.array([1,0])\n",
    "laytyp = np.array([0,0])\n",
    "#hk = 0.000001 #changed\n",
    "#vka = 0.0000001 #changed\n",
    "hk = 1.975 #calculated from pump test at well 17\n",
    "vka = 0.1975 #calculated from pump test at well 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#laytyp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the boundary condition data for the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pulling in data in for GHB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": 145,
   "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 / 40.0 #resistance time of 40 days\n",
    "# create ghb dictionary\n",
    "ghb_data = {0:lrchc}\n",
    "\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",
    "# create recharge dictionary\n",
    "rch_data = {0: rch}\n",
    "\n",
    "# well data\n",
    "nwells = 16\n",
    "lrcq = np.zeros((nwells, 4))\n",
    "lrcq[0, :] = np.array((0, 60, 32, 0))\n",
    "lrcq[1, :] = np.array([0, 58, 37, 0])\n",
    "lrcq[2, :] = np.array((0, 58, 42, 0))\n",
    "lrcq[3, :] = np.array([0, 56, 42, 0])\n",
    "lrcq[4, :] = np.array((0, 53, 35, 0))\n",
    "lrcq[5, :] = np.array([0, 51, 37, 0])\n",
    "lrcq[6, :] = np.array((0, 48, 47, 0))\n",
    "lrcq[7, :] = np.array([0, 44, 35, 0])\n",
    "lrcq[8, :] = np.array((0, 43, 37, 0))\n",
    "lrcq[9, :] = np.array([0, 42, 39, 0])\n",
    "lrcq[10, :] = np.array((0, 42, 48, 0))\n",
    "lrcq[11, :] = np.array((0, 39, 38, 0))\n",
    "lrcq[12, :] = np.array([0, 37, 42, 0])\n",
    "lrcq[13, :] = np.array([0, 36, 37, 0])\n",
    "lrcq[14, :] = np.array((0, 30, 46, 0))\n",
    "lrcq[15, :] = np.array([0, 26, 40, 0])\n",
    "\n",
    "lrcqw = lrcq.copy()\n",
    "lrcqw[:, 3] = -200   # 20 ft^3/day\n",
    "lrcqsw = lrcq.copy()\n",
    "lrcqsw[0, 3] = -20. # 20 ft^3/day\n",
    "#lrcqsw[1, 3] = -25.\n",
    "# create well dictionary\n",
    "base_well_data = {0:lrcq, 1:lrcqw}\n",
    "swwells_well_data = {0:lrcq, 1:lrcqw, 2:lrcqsw}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#base_well_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# swi2 data\n",
    "nadptmx = 10\n",
    "nadptmn = 1\n",
    "nu = [0, 0.025]\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] = -1.0\n",
    "z = np.array([[z1,z1]])\n",
    "iso = np.zeros((nlay, nrow, ncol), dtype=np.int)\n",
    "iso[0, :, :][index == 0] = 1\n",
    "iso[0, :, :][index == 1] = -2\n",
    "iso[1, 30, 35] = 2\n",
    "ssz=0.2 # 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": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toeslope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 100, 100)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso.shape\n",
    "z.shape\n",
    "#type(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create output control (OC) data using words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# oc data\n",
    "spd = {(0,199): ['print budget', 'save head'],\n",
    "       (0,200): [],\n",
    "       (0,399): ['print budget', 'save head'],\n",
    "       (0,400): [],\n",
    "       (0,599): ['print budget', 'save head'],\n",
    "       (0,600): [],\n",
    "       (0,799): ['print budget', 'save head'],\n",
    "       (0,800): [],\n",
    "       (0,999): ['print budget', 'save head'],\n",
    "       (1,0): [],\n",
    "       (1,59): ['print budget', 'save head'],\n",
    "       (1,60): [],\n",
    "       (1,119): ['print budget', 'save head'],\n",
    "       (1,120): [],\n",
    "       (2,0): [],\n",
    "       (2,59): ['print budget', 'save head'],\n",
    "       (2,60): [],\n",
    "       (2,119): ['print budget', 'save head'],\n",
    "       (2,120): [],\n",
    "       (2,179): ['print budget', 'save head']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model with the freshwater well (Simulation 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModflowSwi2: specification of nobs is deprecated.\n"
     ]
    }
   ],
   "source": [
    "modelname = 'swifogland_s1'\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=lay1top, botm=botm,\n",
    "                                   nper=nper, perlen=perlen, nstp=nstp, lenuni = 1)\n",
    "bas = flopy.modflow.ModflowBas(ml, ibound=ibound, strt=ihead)\n",
    "lpf = flopy.modflow.ModflowLpf(ml, laytyp=laytyp, hk=hk, vka=vka)\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, \n",
    "                                nadptmn=nadptmn, nobs=nobs, iswiobs=iswiobs, obsnam=obsnam, \n",
    "                                obslrc=obslrc, 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": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ml.sr = SpatialReference(delr=delr, delc=delc, xll=xll, yll=yll, rotation=rot, proj4_str= model_proj4, units = 'feet', lenuni = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dNZm54KE7AACmir8QpsAspVLGqQsZry7VMk5dyHh1edlm5AAAYPnxj6YUmaVUyTh1IePV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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89925ca278>"
      ]
     },
     "metadata": {},
     "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": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.plot.map.ModelMap"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(modelmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the simulation 1 MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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 +units=us-ft +no_defs; units:feet; lenuni:1; length_multiplier:1.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.36025569499998\n"
     ]
    }
   ],
   "source": [
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the model to simulate summer conditions (Simulation 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "Define the `DIS` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ncol = 100     already assinged in Spatial Reference Module\n",
    "#nrow = 100     already assinged in Spatial Reference Module\n",
    "nlay = 2\n",
    "\n",
    "nper = 6\n",
    "perlen = [365.25 * 200., 92, 273, 92, 273, 92]\n",
    "nstp = [1000, 92, 273, 92, 273, 92]\n",
    "# sp2 = timesteps of 1 day\n",
    "save_head = [200, 2, 91, 2, 91, 2]\n",
    "steady = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# recharge data\n",
    "rch = np.zeros((nrow, ncol), dtype=np.float)\n",
    "rch[index == 0] = 0.001 #200 yrs\n",
    "rchS = np.zeros((nrow, ncol), dtype=np.float)\n",
    "rchS[index == 0] = 0.0 #summer conditions\n",
    "rchW = np.zeros((nrow, ncol), dtype=np.float)\n",
    "rchW[index == 0] = 0.004 #winter conditions\n",
    "\n",
    "# create recharge dictionary\n",
    "rch_data = {0: rch, 1: rchS}\n",
    "\n",
    "rch_data_15 = {0: rch, 1: rchS, 2: rchW, 3: rchS, 4: rchW, 5: rchS}\n",
    "\n",
    "# well data\n",
    "nwells = 16\n",
    "lrcq = np.zeros((nwells, 4))\n",
    "lrcq[0, :] = np.array((0, 60, 32, 0))\n",
    "lrcq[1, :] = np.array([0, 58, 37, 0])\n",
    "lrcq[2, :] = np.array((0, 58, 42, 0))\n",
    "lrcq[3, :] = np.array([0, 56, 42, 0])\n",
    "lrcq[4, :] = np.array((0, 53, 35, 0))\n",
    "lrcq[5, :] = np.array([0, 51, 37, 0])\n",
    "lrcq[6, :] = np.array((0, 48, 47, 0))\n",
    "lrcq[7, :] = np.array([0, 44, 35, 0])\n",
    "lrcq[8, :] = np.array((0, 43, 37, 0))\n",
    "lrcq[9, :] = np.array([0, 42, 39, 0])\n",
    "lrcq[10, :] = np.array((0, 42, 48, 0))\n",
    "lrcq[10, :] = np.array((0, 39, 38, 0))\n",
    "lrcq[11, :] = np.array([0, 37, 42, 0])\n",
    "lrcq[12, :] = np.array([0, 36, 37, 0])\n",
    "lrcq[13, :] = np.array((0, 30, 46, 0))\n",
    "lrcq[14, :] = np.array([0, 26, 40, 0])\n",
    "\n",
    "lrcqw = lrcq.copy()\n",
    "lrcqw[:, 3] = -200   # 20 ft^3/day\n",
    "lrcqsw = lrcq.copy()\n",
    "lrcqsw[0, 3] = -20. # 20 ft^3/day\n",
    "#lrcqsw[1, 3] = -25.\n",
    "# create well dictionary\n",
    "base_well_data = {0:lrcq, 1:lrcqw}\n",
    "swwells_well_data = {0:lrcq, 1:lrcqw, 2:lrcqsw}\n",
    "\n",
    "base_well_data_15 = {0:lrcq, 1:lrcqw, 2:lrcq, 3:lrcqw, 4:lrcq, 5:lrcqw}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create output control (OC) data using words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# oc data\n",
    "spd = {(0,199): ['print budget', 'save head'],\n",
    "       (0,200): [],\n",
    "       (0,399): ['print budget', 'save head'],\n",
    "       (0,400): [],\n",
    "       (0,599): ['print budget', 'save head'],\n",
    "       (0,600): [],\n",
    "       (0,799): ['print budget', 'save head'],\n",
    "       (0,800): [],\n",
    "       (0,999): ['print budget', 'save head'],\n",
    "       (1,0): [],\n",
    "       (1,45): ['print budget', 'save head'],\n",
    "       (1,91): [],\n",
    "       (2,0): [],\n",
    "       (2,59): ['print budget', 'save head'],\n",
    "       (2,60): [],\n",
    "       (2,119): ['print budget', 'save head'],\n",
    "       (2,120): [],\n",
    "       (2,179): ['print budget', 'save head']}\n",
    "\n",
    "spd15 = {(0,199): ['print budget', 'save head'],\n",
    "       (0,200): [],\n",
    "       (0,399): ['print budget', 'save head'],\n",
    "       (0,400): [],\n",
    "       (0,599): ['print budget', 'save head'],\n",
    "       (0,600): [],\n",
    "       (0,799): ['print budget', 'save head'],\n",
    "       (0,800): [],\n",
    "       (0,999): ['print budget', 'save head'],\n",
    "       (1,0): [],\n",
    "       (1,45): ['print budget', 'save head'],\n",
    "       (1,91): [],\n",
    "       (2,0): [],\n",
    "       (2,135): ['print budget', 'save head']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModflowSwi2: specification of nobs is deprecated.\n"
     ]
    }
   ],
   "source": [
    "modelname15 = 'swifogland_s15'\n",
    "ml15 = flopy.modflow.Modflow(modelname15, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "discret = flopy.modflow.ModflowDis(ml15, nlay=nlay, nrow=nrow, ncol=ncol, laycbd=0,\n",
    "                                   delr=delr, delc=delc, top=lay1top, botm=botm,\n",
    "                                   nper=nper, perlen=perlen, nstp=nstp, lenuni = 1)\n",
    "bas = flopy.modflow.ModflowBas(ml15, ibound=ibound, strt=ihead)\n",
    "lpf = flopy.modflow.ModflowLpf(ml15, laytyp=laytyp, hk=hk, vka=vka)\n",
    "wel = flopy.modflow.ModflowWel(ml15, stress_period_data=base_well_data_15)\n",
    "ghb = flopy.modflow.ModflowGhb(ml15, stress_period_data=ghb_data)\n",
    "rch = flopy.modflow.ModflowRch(ml15, rech=rch_data_15)\n",
    "swi = flopy.modflow.ModflowSwi2(ml15, nsrf=1, istrat=1, toeslope=toeslope, tipslope=tipslope, nu=nu, \n",
    "                                alpha = 0.1, zeta=z, ssz=ssz, isource=iso, nsolver=2, nadptmx=nadptmx, \n",
    "                                nadptmn=nadptmn, nobs=nobs, iswiobs=iswiobs, obsnam=obsnam, \n",
    "                                obslrc=obslrc, iswizt=55)\n",
    "oc = flopy.modflow.ModflowOc(ml15, stress_period_data=spd)\n",
    "pcg = flopy.modflow.ModflowPcg(ml15, hclose=1.0e-6, rclose=3.0e-3, mxiter=100, iter1=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the simulation 1.5 MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import timeit\n",
    "ml15.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, [])\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "print(ml15.run_model(silent=True))\n",
    "elapsed = timeit.default_timer() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143.29751759000004\n"
     ]
    }
   ],
   "source": [
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model with the saltwater well (Simulation 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# modelname2 = 'swiex4_s2'\n",
    "# ml2 = flopy.modflow.Modflow(modelname2, version='mf2005', exe_name=exe_name, model_ws=workspace)\n",
    "\n",
    "# discret = flopy.modflow.ModflowDis(ml2, nlay=nlay, nrow=nrow, ncol=ncol, laycbd=0,\n",
    "#                                    delr=delr, delc=delc, top=botm[0], botm=botm[1:],\n",
    "#                                    nper=nper, perlen=perlen, nstp=nstp)\n",
    "# bas = flopy.modflow.ModflowBas(ml2, ibound=ibound, strt=ihead)\n",
    "# lpf = flopy.modflow.ModflowLpf(ml2, laytyp=laytyp, hk=hk, vka=vka)\n",
    "# wel = flopy.modflow.ModflowWel(ml2, stress_period_data=swwells_well_data)\n",
    "# ghb = flopy.modflow.ModflowGhb(ml2, stress_period_data=ghb_data)\n",
    "# rch = flopy.modflow.ModflowRch(ml2, rech=rch_data)\n",
    "# swi = flopy.modflow.ModflowSwi2(ml2, nsrf=1, istrat=1, \n",
    "#                                 toeslope=toeslope, tipslope=tipslope, nu=nu,\n",
    "#                                 zeta=z, ssz=ssz, isource=iso, nsolver=1,\n",
    "#                                 nadptmx=nadptmx, nadptmn=nadptmn,\n",
    "#                                 nobs=nobs, iswiobs=iswiobs, obsnam=obsnam, obslrc=obslrc, iswizt=55)\n",
    "# oc = flopy.modflow.ModflowOc(ml2, stress_period_data=spd)\n",
    "# pcg = flopy.modflow.ModflowPcg(ml2, hclose=1.0e-6, rclose=3.0e-3, mxiter=100, iter1=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the simulation 2 MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ml2.write_input()\n",
    "# ml2.run_model(silent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 1 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 1.5 (**ml15**) `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read base model zeta\n",
    "zfile15 = flopy.utils.CellBudgetFile(os.path.join(ml15.model_ws, modelname+'.zta'))\n",
    "kstpkper = zfile15.get_kstpkper()\n",
    "zeta15 = []\n",
    "for kk in kstpkper:\n",
    "    zeta15.append(zfile15.get_data(kstpkper=kk, text='ZETASRF  1')[0])\n",
    "zeta15 = np.array(zeta)\n",
    "# read swi obs\n",
    "zobs15 = np.genfromtxt(os.path.join(ml15.model_ws, modelname15+'.zobs.out'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the simulation 2 `ZETA` data and `ZETA` observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # read saltwater well model zeta\n",
    "# zfile2 = flopy.utils.CellBudgetFile(os.path.join(ml2.model_ws, modelname2+'.zta'))\n",
    "# kstpkper = zfile2.get_kstpkper()\n",
    "# zeta2 = []\n",
    "# for kk in kstpkper:\n",
    "#     zeta2.append(zfile2.get_data(kstpkper=kk, text='ZETASRF  1')[0])\n",
    "# zeta2 = np.array(zeta2)\n",
    "# # read swi obs\n",
    "# zobs2 = np.genfromtxt(os.path.join(ml2.model_ws, modelname2+'.zobs.out'), names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create arrays for the x-coordinates and the output years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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 = [40, 80, 120, 160, 200, 6, 12, 18, 24, 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define figure dimensions and colors used for plotting `ZETA` surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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 = 5\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": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-220, 10)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqZ+6Htn+X1X1N70LkhwALAWeCuwNXJZk/6q6t+NaJEmSpFnVjznbRwHnV9XmqroRWAMc\n0oc6JEmSpE51HbbfnOTqJOckeUy7bAGwtmebde2yrSRZlmRlkpV33313x6VKkiRJM2taYTvJZUmu\nHeN2FPAx4AnAQcAG4APb235VnVVVS6pqyW677TadUiVJkqRZN60521V12GS2S3I28JX24Xpgn57V\nC9tlkiRJ0g6ly28j2avn4cuBa9v7FwFLk8xNshjYD/huV3VIkiRJ/dLlt5G8P8lBQAE3AW8EqKpV\nSS4ArgPuAU70m0gkSZK0I+osbFfVaydYdwZwRlfHliRJkgaBP9cuSZIkdcSwLUmSJHXEsC1JkiR1\nxLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXE\nsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSw\nLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHVkWmE7ySuSrEpy\nX5Ilo9adkmRNktVJDu9ZfnCSa9p1H06S6dQgSZIkDarpjmxfCxwDfL13YZIDgKXAU4EjgL9Lsku7\n+mPAG4D92tsR06xBkiRJGkjTCttVdX1VrR5j1VHA+VW1uapuBNYAhyTZC3hUVV1eVQWcCxw9nRok\nSZKkQdXVnO0FwNqex+vaZQva+6OXS5IkSTucXbe1QZLLgPljrDq1qr408yU96NjLgGUACxaYySVJ\nkjRcthm2q+qwKbS7Htin5/HCdtn69v7o5eMd+yzgLIDFixfXFOqQJEmS+qaraSQXAUuTzE2ymOZC\nyO9W1QbgjiSHtt9CcizQ6ei4JEmS1C/T/eq/lydZBzwL+GqSrwFU1SrgAuA64J+BE6vq3na3NwGf\noLlo8gbgkunUIEmSJA2qbU4jmUhVXQhcOM66M4Azxli+EjhwOseVJEmShoG/IClJkiR1xLAtSZIk\ndcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1\nxLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXE\nsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSwLUmSJHXEsC1JkiR1xLAtSZIkdcSw\nLUmSJHVkWmE7ySuSrEpyX5IlPcsXJfl1kqva28d71h2c5Joka5J8OEmmU4MkSZI0qKY7sn0tcAzw\n9THW3VBVB7W3E3qWfwx4A7BfeztimjVIkiRJA2laYbuqrq+q1ZPdPslewKOq6vKqKuBc4Ojp1CBJ\nkiQNqi7nbC9up5D8e5LntcsWAOt6tlnXLhtTkmVJViZZeffdd3dYqiRJkjTzdt3WBkkuA+aPserU\nqvrSOLttAPatqk1JDga+mOSp21tcVZ0FnAWwePHi2t79JUmSpH7aZtiuqsO2t9Gq2gxsbu9fmeQG\nYH9gPbCwZ9OF7TJJkiRph9PJNJIkeybZpb3/BJoLIX9cVRuAO5Ic2n4LybHAeKPjkiRJ0lCb7lf/\nvTzJOuBZwFeTfK1d9Xzg6iRXAZ8DTqiq29p1bwI+AawBbgAumU4NkiRJ0qDa5jSSiVTVhcCFYyz/\nPPD5cfZZCRw4neNKkiRJw8BfkJQkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmSOmLYliRJ\nkjpi2JYkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmS\nOmLYliRJkjpi2JYkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6\nYtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmSOjKtsJ3kr5P8IMnVSS5M8uiedackWZNkdZLDe5Yf\nnOSadt2Hk2Q6NUiSJEmDaroj25cCB1bV04AfAqcAJDkAWAo8FTgC+Lsku7T7fAx4A7BfeztimjVI\nkiRJA2laYbuq/qWq7mkfXg4sbO8fBZxfVZur6kZgDXBIkr2AR1XV5VVVwLnA0dOpQZIkSRpUaTLv\nDDSUfBn4bFWdl+QjwOVVdV677h+AS4CbgOVVdVi7/HnAO6rqyG21v3DhwnrPe97zoGW33HILAPvu\nu+9W209l3Uy3N5vHGvT2hrn2QW9vmGv3XHTX3jDXPujtDXPtnovhaW+Yax/mc7Fx40ZOOeWUK6tq\nyVY7TdE2w3aSy4D5Y6w6taq+1G5zKrAEOKaqaqbCdpJlwDKABQsWHDw6bEuSJEkz6fjjj5/RsL3r\ntjYYCcbjSXIccCTwwnogua8H9unZbGG7bD0PTDXpXT7esc8CzgJYsmRJHXfccdsqV5IkSZqy448/\nfkbbm+63kRwBvB14WVX9qmfVRcDSJHOTLKa5EPK7VbUBuCPJoe23kBwLfGk6NUiSJEmDapsj29vw\nEWAucGn7DX6XV9UJVbUqyQXAdcA9wIlVdW+7z5uAFcDDaKaWXDLNGiRJkqSBNK2wXVVPmmDdGcAZ\nYyxfCRw4neNKkiRJw8BfkJQkSZI6YtiWJEmSOmLYliRJkjpi2JYkSZI6YtiWJEmSOmLYliRJkjpi\n2JYkSZI6YtiWJEmSOmLYliRJkjqSqup3DZOS5E5gdb/r0MCZB/y830Vo4NgvNJp9QmOxX2gsT66q\n3WeqsWn9XPssW11VS/pdhAZLkpX2C41mv9Bo9gmNxX6hsSRZOZPtOY1EkiRJ6ohhW5IkSerIMIXt\ns/pdgAaS/UJjsV9oNPuExmK/0FhmtF8MzQWSkiRJ0rAZppFtSZIkaagMfNhOckSS1UnWJDm53/Vo\ndiW5Kck1Sa4auTo4yWOTXJrkR+1/H9Oz/SltX1md5PD+Va6ZlOScJLcmubZn2Xb3gyQHt/1pTZIP\nJ8lsPxfNnHH6xelJ1revGVcleXHPOvvFDi7JPkn+Ncl1SVYleUu73NeLndgE/WJ2Xi+qamBvwC7A\nDcATgDnA94ED+l2Xt1ntAzcB80Ytez9wcnv/ZOB97f0D2j4yF1jc9p1d+v0cvM1IP3g+8DvAtdPp\nB8B3gUOBAJcAL+r3c/M24/3idOBPx9jWfrET3IC9gN9p7+8O/LD92/t6sRPfJugXs/J6Megj24cA\na6rqx1W1BTgfOKrPNan/jgI+2d7/JHB0z/Lzq2pzVd0IrKHpQxpyVfV14LZRi7erHyTZC3hUVV1e\nzSvmuT37aAiN0y/GY7/YCVTVhqr6Xnv/TuB6YAG+XuzUJugX45nRfjHoYXsBsLbn8TomPjna8RRw\nWZIrkyxrlz2uqja09zcCj2vv2192LtvbDxa090cv147nzUmubqeZjEwXsF/sZJIsAp4BfAdfL9Qa\n1S9gFl4vBj1sS8+tqoOAFwEnJnl+78r2naVfqbOTsx+ox8doph4eBGwAPtDfctQPSR4JfB74k6q6\no3edrxc7rzH6xay8Xgx62F4P7NPzeGG7TDuJqlrf/vdW4EKaaSE/bT/Kof3vre3m9pedy/b2g/Xt\n/dHLtQOpqp9W1b1VdR9wNg9MJbNf7CSS7EYTqP6xqr7QLvb1Yic3Vr+YrdeLQQ/bVwD7JVmcZA6w\nFLiozzVpliR5RJLdR+4DfwBcS9MHXtdu9jrgS+39i4ClSeYmWQzsR3Mhg3ZM29UP2o+Q70hyaHv1\n+LE9+2gHMRKoWi+nec0A+8VOof0b/gNwfVV9sGeVrxc7sfH6xWy9Xuw6Q8+jE1V1T5KTgK/RfDPJ\nOVW1qs9lafY8Driw/VadXYFPV9U/J7kCuCDJ64GbgT8EqKpVSS4ArgPuAU6sqnv7U7pmUpLPAC8A\n5iVZB5wGLGf7+8GbgBXAw2iuIr9kFp+GZtg4/eIFSQ6imSZwE/BGsF/sRJ4DvBa4JslV7bJ34uvF\nzm68fvHK2Xi98BckJUmSpI4M+jQSSZIkaWgZtiVJkqSOGLYlSZKkjhi2JUmSpI4YtiVJkqSOGLYl\nSZKkjhi2JUmSpI4YtiVJkqSO/H+MZ4QpxKqgJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8991fa0e48>"
      ]
     },
     "metadata": {},
     "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': 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": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TngAAAAAAAGaplHJGkj/KaED/u0meeqQB/TpvHj9+4xGOPX5DJhnd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VzL3fqSa7lb67mWu/Uldy6veWD/0am5Q4f3bvtcy926zA2HUw+xDfRiJ/0f2Ek/T9bdAAAAAABA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\neVWt9albr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kNyR5+v7V8ivPTvLOJFeWUh6Z5XPvH5zkEVk+5ubH94drrdeXUp6Z5bD+d0opr0/yqSSPTXK/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\nAAAAAADQiCE9AAAAAAA08v8DyFP7Cfz5XlUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89902caf60>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1133,
       "width": 756
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = os.path.join('./data/swifogland_s1.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()\n",
    "levels = np.arange(10, 800,50)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 1, aspect='equal')\n",
    "ax.set_title('plot_array()')\n",
    "modelmap = flopy.plot.ModelMap(model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "quadmesh = modelmap.plot_array(head, masked_values=[999.], alpha=0.5)\n",
    "modelmap.plot_bc(\"GHB\")\n",
    "linecollection = modelmap.plot_grid()\n",
    "cb = fig.colorbar(quadmesh, ax = ax)\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 2, aspect='equal')\n",
    "ax.set_title('contour_array()')\n",
    "modelmap = flopy.plot.ModelMap(model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "#modelmap.plot_bc(\"WEL\")\n",
    "contour_set = modelmap.contour_array(head, masked_values=[999.], levels=levels)\n",
    "linecollection = modelmap.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rUue7s56VU1kku6azHXG2OpVNTbUv2b54eTaH62Cl3AMpyvr0vNNrgPqUFNsZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f899543e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = os.path.join('./data/swifogland_s15.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()\n",
    "levels = np.arange(10, 800,50)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 1, aspect='equal')\n",
    "ax.set_title('plot_array()')\n",
    "modelmap = flopy.plot.ModelMap(model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "quadmesh = modelmap.plot_array(head, masked_values=[999.], alpha=0.5)\n",
    "#modelmap.plot_bc(\"GHB\")\n",
    "linecollection = modelmap.plot_grid()\n",
    "cb = fig.colorbar(quadmesh, ax = ax)\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 2, aspect='equal')\n",
    "ax.set_title('contour_array()')\n",
    "modelmap = flopy.plot.ModelMap(model=ml)\n",
    "quadmesh = modelmap.plot_ibound()\n",
    "#modelmap.plot_bc(\"WEL\")\n",
    "contour_set = modelmap.contour_array(head, masked_values=[999.], levels=levels)\n",
    "linecollection = modelmap.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#levels\n",
    "#head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recreate **Figure 9** from the [SWI2 documentation](http://pubs.usgs.gov/tm/6a46/).  \n",
    "\n",
    "A cross section showing results along the centerline of the island through the wells is shown in figure 9. The position of the interface is shown at 40-year increments for stress period 1, during development of the freshwater lens (fig. 9A). After 200 years, the interface approaches the steady-state position. The position of the interface for simulation 1 is shown at 6-year increments in figure 9B. In simulation 1, the interface\n",
    "moves into the upper layer after approximately 14 years. The position of the interface for simulation 2 is shown at 6-year increments in figure 9C. During the first 12 years of groundwater withdrawals, the position of the interface is identical for simulations 1 and 2. After 12 years, the saltwater well begins pumping saltwater from the lower aquifer (model layer 2) and the progression of the upconing of the interface is reduced. The position of the interface in the aquifer over time is shown for simulations 1 and 2 in the cells containing the freshwater and saltwater wells (row 31, column 36) in figure 9D. Simulation results are identical during the first 12 years, because groundwater withdrawals are the same in both simulations. Addition of the saltwater well causes an initial decrease in the interface elevation and an eventual increase in the interface elevation, although the rate of upward movement of the interface is less in the simulation with both the freshwater and\n",
    "saltwater wells than the simulation with only the freshwater well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f89891d41d0>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8989423c88>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 635,
       "width": 1252
      }
     },
     "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=(20, 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(5):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2d} 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",
    "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 feet')\n",
    "ax.set_ylabel('Elevation, in feet')\n",
    "ax.text(0.025, .75, 'Layer 1', transform=ax.transAxes, va='top', ha='left', size='7')\n",
    "ax.text(0.025, .45, 'Layer 2', transform=ax.transAxes, va='top', ha='left', size='7')\n",
    "\n",
    "ax.text(0.975, .1, 'Recharge conditions', transform=ax.transAxes, va='center', ha='right', size='8')\n",
    "ax.set_title('Fogland Point, Recharge conditions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f89893b0048>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rY5V/rwcAkcRrYe6pp57Su+++q6uvvjoU8QAA/OR02H3augoARiLXBIznWRgr\n3HT27a3WBLauAkCgeC3MXXbZZSRKAAAACApyTSD0EhXv3jhA8Rqr3q6xP40gAsGcYHbrzGq1mkPe\nKTbsNR6TjthcQ2cDp1QBkcrrT296errGjx+v2267TRaLxXV93LhxQQ0MCAsmk9s/eKevWaULm86Z\nM1msbufM0QwCkab/jB5y1DVtX7VYSewAhA65JhACJqtbTjvWI59tXqQLB0PvGOq2Iq95kQ7/x+l0\nO4qkocymBH5tAhHJ66ev48ePKykpSe+//77rmslkIllCTLAkXay0R5LdryUc0+5mDSFoBoGI49Hs\nweHoobI1ow0MCEAsI9cEQuBCj0ljz4lnAIBhvBbmli1bFoo4gLC0e9nIFtdoCIGIR7MHAGGEXBMA\nAMSyNgtz8+fP11NPPaVHH31UJpOpxeOLFi0KamAAgHPjaxfWdywX6VSzLSsmtfwdDwDBRq4JBA9H\nrQBA5GizMHfmEN709PSQBQMA8J2vXVjnTO2kC+sOu8ZdrMlnuRsAgoNcEwgeX49a8dYMwmhWK80g\nAESvNgtzt956qyTpvvvuC1kwAIDgO2V3auvaDKPDABDjyDWB8OFZhAu3ZhCeRTiaQQCIJrTeA3zl\n2anVo6sVEHY8mj3I1MO4WAAghJ577jk9+eSTqq2tVadOnSRJBQUFWrp0qeLj47Vo0SKNGDHC4CgB\nA3h0aSWfjTxOh6PFdmUAkYnCHOAjz06tnl1aOdMDYYdmDwBi0KFDh/T+++/riiuucF0rKyvT6tWr\ndeDAAVVXVyszM1Pl5eWKj483MFLAAF66tBq9tdWaINk2uI9zbw7Z20cIp09HmQAIXxTmAB95dmr1\n7NLq65keQLD1n9FDjrqmYpzFyq9+ANHv8ccf1/z58zVmzBjXteLiYk2YMEEWi0XdunVTamqqSktL\nNWTIEAMjBcKP0VtbPYtwzYt0ABBtfP509tJLL+niiy/W7bffLrOZD3cAEO4cdqfK1o42OgwAOCeB\nyDWLi4uVkpKia6+91u16VVWVBg8e7Bp37txZVVVVrb5GUVGRioqKJEm1tbXtigNAcNAMAkA08Tnb\ncTqd2rFjh958801t2MDUBQCEmmNBoZyOpjPjOFMEQDQ511wzMzNTNTU1La7n5+dr3rx5ev/99/2K\nIycnRzk5OZLoHAsYvbXVUyw2gygs3CG7vd41jk+IMzAaAIHkc2HuF7/4RTDiAACcI6fDzpkiAKLW\nueaaJSUmQdCRAAAgAElEQVQlrV7fv3+/vv76a9dqucrKSg0YMEClpaVKSUnRoUOHXPdWVlYqJSXF\n/6CBSOelGYRnEe4dHQirQl0ssNvrZbNluMb7X3cYFwyAgPJamHM4HPrd736ngwcPqr6+qUL/b//2\nb0ENDIgYdGlFuKELK4AIEuhcs0+fPvr+++9d465du2rnzp3q1KmTsrKyNGnSJM2cOVPV1dWqqKjQ\noEGD/P4egIjnpRmEJ1/PoHtHB3RKDa4xhTzf7ahwP2svMb7OuGAABJTXwtyYMWOUnJysgQMHymKx\nhCImIKJ469IKhBxdWAFEkFDmmr1791Z2drbS0tJkNpu1ZMkSOrIiJpgsVreGZCaLtUXDskBqrRA3\nXn1dY1+bSXjr0hoLZ87VN0q2rKbxqi8+ksQZwkA08FqYq6ys1ObNm0MRCxCRvHVpDXUiBNCFFUAk\nCXauefDgQbdxXl6e8vLygvZ+QDjyzD2b56aB0NoZdM0Lcf7y1qU1Fs+cAxA9vH5au/7667V//371\n6dMnFPEAUSfYiRDgiS6sACIJuSYQ+diWGny1OuFe/GxsOMvdACKJ18Lcjh07tHz5cnXr1k0Wi0VO\np1Mmk0n79vm2/DiSLF++XJ06ddLo0f5/sG1sbFRc3OmOObNmzdLx48d19OhRvfbaazr//PP9fn0A\nAIBIFq25JvkkgEByyum2CrG+Yq+UZmBAAALGa2Fu06ZNoYgjrH388cd69913VVNTo9mzZ6u4uFg3\n3HCDBg0apOzsbL355puaPXu2HA6HGhoa9Pzzz+vGG2/Urbfeqp/+9KeuQ4UXLFggSVq4cKH27Nmj\nG264wchvC0CEcCwolNPR1MzBZLEaGA0ABFas5Jrkk4goXrq0+qu1ra+sugMQq7wW5q688krt3btX\n27dvlyTdcMMNrvbzsSIxMVGnTp1SUlKS1q9fr4cffli5ubk6efKkhg0bppKSEh08eFBXX321vvnm\nG1VVVSkpKUm5uS3/8aqpqdHOnTv12GOPGfCdICTo0ooAczrsss62tX0DXVgBRLBYyTXPlk+uW7dO\n69atc7v/xRdflCR98sknRoSLWPD0HB+f8P8FJYz28hb9HF+/vQhgWt3iihFhAFHF6XQaHYL3wtwL\nL7ygV199VePGjZMk3X333crJydGjjz4a9ODCxbPPPqtVq1bpk08+0ZYtW5ScnKyOHTtq4cKFWrly\npbZt26af/OQnbsW25OTkFq9TVVWl3NxcOoBFu7hk946YXtrNA36jCyuACBYruebZ8sn//d//1bZt\n2/S3v/3NLZ+844479Pbbb7u9zpl8cvHixbrwwgtD/W2gFe+cXOLWgfR3b16ltQ82NQcrLNwhu73e\n7Tn+dg31fE1fX8+2wb3Dp6/WaF9Amzv4+nqFmyR7XdPYs0urJ5ttq2y2jPYHCL/Z59rcJpon/ft8\nXdUwyO2eHV+ZVbIiurrpAufCa2Fu6dKl+uyzz3TeeedJkn75y19qyJAhUZcseXrppZe0ceNGde/e\nXcOGDdMzzzyjEydO6KKLLpIk3X777Vq1apU6duyoESNGaOrUqZo1a5aOHj2qxYsXt/qaWVlZuuqq\nq5Sbm6tp06ZF5WwwTnfATJvYrCNmQg/tXmFgQIh6dGEFEMmiOdckn4wNYzv8wm28Nu49t3FrBbPC\nwh1+dQ61Ws0+FZpaK2RFMm9dWhH+9pf9s66amOF2LfPHxsQCGM3rpzen0+m2uis+Pj4slvoF0/33\n36/777+/zcfLysq0cOFCzZ8/X5JkNpv12muvud3jObspSbt27QponAhPu5eNdBunZa80KBLECrqw\nAohk0Zprkk/GrsT4OrdCUWurufxZLdce9jr/Vsi9owNuqwITFdjdP5w5F3saGuL8+jsJRBOvhbkH\nHnhAP/7xjzV27FhJ0n/9139pypQpQQ8snKWlpWnNmjVGh4EIZbJYZZ9rcxtbZnEGHQAgNsVqrkk+\nGb2yfvyRJvZqmjAr3NRyRZe3rZf+CvQKuVNqCOjWVU+eRbjmRTpEhwV/lBqarRKNT+RoJ+AMr4W5\nmTNnKiMjQzt27JAkLVu2TP379w96YEC08izCNS/SAQAQa8g1EW0SGxvcCks9b265+qu1Yt3ZeBby\nPAtvrd3PaqQmp7f+bnUbh3rVYqw7ddG3+vcZW13jz7fcYlwwQJhpszB3/PhxXXDBBTp8+LC6du2q\nrl27uh47fPiw/umf/ikU8QEAPNGFFUAUINdEtBrz9QHVr1/vGq+/6VLpJ+6FOV9Xy3kW8oJdeAv2\n1tVQ8yzC+XO+H86NZ4OS0v93jWzbZ7rGiR2+MyIsICy1WZibNGmSNm7cqIEDB8pkamrD7HQ6ZTKZ\n9NVXX4UkQACINY4FhXI6mgpvJovV/Qa6sAKIAuSaiFbmn2TKfH3Tv+OJVRUttmb6eoZaMLe9tibY\nW1cDzZrQsnAZ6v9msc6zEGcu3aHc65vG994+WFdnNf0cXKt4ST8KZYhA2GqzMLdx4+kPel9//XXI\nggGikskkHbE1G1ulCzlTDm1zOuxu7eQ90YUVQDQg10TU8sjzbvtjhszXLHS7xhlqgUWXVuPZ7fVu\nnYL/fVa9CvtmusZmfRxRxV4glLx+mvvZz36mDz/80Os1AK2zJF2stEeSm8YJx7R7hYEBIeLRhRVA\nNCHXRCwKty6k0bZ11RvPM+fOXOPcuXPnuULO6jFR7HCaNbfZdus1J3dJ+kmIogMiS5uFObvdrn/8\n4x/64YcfdOTIEVfb+uPHj6uqqipkAQKRbveykW7jtOyVBkUCAED4INdErHA2mN13T0ga67GD4h0d\nCGmhrrVCXDitZgp24bK1Ahznzp1da4W45ivkPM9B/Lr3/9Oak4tc42gv9gL+aLMw99vf/lbPP/+8\nqqurNXDgQFeydMEFF2j69OkhCxAAAADRh1wTsaKhbKgSxtncL3oW6jyKTsEu1IX7GXKe3ytbf43n\nuVW1xeN17g1JVn3xkcZ3+I/gBwZEgTYLczNmzNCMGTO0ePFiPfroo6GMCQAAAFGOXBOxwmSxyj7X\n5nbN3H+HzLe0/RxvhTpPnoU7zxVxrd0fzWgG4T9vW1ULN50uxp1h/u+tsu/b2nQhOy7IEQLRw+sZ\nc48++qg+//xzlZWVyW5v6i507733Bi2oxYsXa8mSJYqPj9ctt9yi+fPnS5IKCgq0dOlSxcfHa9Gi\nRRoxYkTQYgBCxTNZM1msssyiOQSaOVp4uhPrGaYexsUCAAFmRK4JhFJreV3d+kyfmoN5Wy3X2gq7\ncF4RF2ztaQbhee5crJ05522rqifPFXJrO+5V8U/HNT3/H1uCESYQlbwW5ubMmaOtW7eqrKxMo0aN\n0qZNmzR06NCgJUtbtmxRcXGx9u7dK4vFou+//16SVFZWptWrV+vAgQOqrq5WZmamysvLFR8f3bM9\niH6eyZrnjCogp126yNY0jtvY5q0AEGlCnWsC4aDF9laPra2+MrJxRLTwLMLF2plz3raqeq6Qsya4\nP97wo0OaeGR90wWTVUoKbIxAtPJamHv77be1d+9e9e/fX8uWLdPf//533X333UEL6OWXX1Zubq4s\nFosk6dJLL5UkFRcXa8KECbJYLOrWrZtSU1NVWlqqIUOGBC0WAAgFx4JCOR1Nq0RMFqvb4/1n9JCj\nrqkYZ7F6/dUNABEj1LkmAMD7VlVPnivkWjA1uk8kAzhnXj/ddejQQXFxcTKbzTp+/LguvfRSHTp0\nKGgBlZeXa/v27crLy5PVatVvfvMbXXfddaqqqtLgwYNd93Xu3LnNjl1FRUUqKiqSJNXW1gYtVqBd\nTCafti4g+jkddlln29p83GF3qmzt6NAFBAAhFOpcEwhLJiv54Vn426WVM+da8neFXIvOvo1tn2kI\n4Oy8FubS09N19OhRPfTQQxo4cKA6duzo9yq1zMxM1dTUtLien5+v+vp6HT58WJ9++qn+9Kc/KTs7\nW1999ZVPr5+Tk6OcnBxX/EA4sSRdrLRHkpvGCce0e4WBAQEAYKBg5Jo2m02vvvqqLrnkEknSvHnz\nNGrUKEmcWYzw0PoZw01jf7e2Rht/u7Ry5lzgV8id/Hibxm353jU29z8gpfkdJhCTvBbmXnrpJUnS\n1KlTNXLkSB0/flx9+/p3kGhJSUmbj7388ssaN26cTCaTBg0apLi4OP3www9KSUlxmz2trKxUSkqK\nX3EARti9bKTbOC17pUGRAABgvGDkmpL0+OOP68knn3S7xpnFCBdezxhmBZ3hou3MuYCvkEuukPUX\nTYsNZMoMUKRA7PFamMvKytKECRM0ZswYde3aNegB3XbbbdqyZYtuuukmlZeX69SpU+rUqZOysrI0\nadIkzZw5U9XV1aqoqNCgQYOCHg8AAACCJ5S5JmcWI2J4FuFYQQcfBXqF3Ck1uHX6rf/bbumarf6G\nCUBSnLcbnnjiCe3YsUNpaWm644479Pbbb7u1sg+0yZMn66uvvtI111yjCRMm6I033pDJZFLv3r2V\nnZ2ttLQ0jRw5UkuWLGF2EwAAIMIFK9dcvHix+vbtq8mTJ+vIkSOSpKqqKnXp0sV1j7czi9PT05We\nns6ZxQAizpkVcme+InkbLhDtvK6YGzZsmIYNG6aGhgZ99NFHevXVVzV58mQdP348KAElJiZq5crW\nt/bl5eUpLy8vKO8LAGHjaKHkbPah1NTDuFgAIMjam2ue7cziadOm6emnn5bJZNLTTz+tJ554Qq+/\n/rpPcXFmMcIKW1sDyrMZxJlrZ2sIEe5nzvm6Qs7nratiUQwQLF4Lc5J08uRJvfvuu1qzZo3+/Oc/\n67777gt2XEDMav0wYBKvmOK0u7ebj9toWCgAEArtyTXPdmZxcw899JBGjz7d2ZozixGuvOZ/bG11\n42+X1tYKcN4aQoT7mXPezpBrcb+vW1c3jFS928SxtR1RAmiN18Jcdna2SktLNXLkSE2fPl3Dhg1T\nXJzXHbAA2snrYcCIev1n9JCjrqkYZ/Ey4wkAkSwYueZ3332nH/3oR5Kkd955R9dcc40kcWYxwpbP\n+V+Mr6Dzt0trNAj0CjmvnHaZx2z18UkAzoXXT3tTpkzRqlWrOM8NCBaTKaYTq1jkWFAop6NpxtFk\ncZ9xdNidKls7OtRhAYAhgpFrPvXUU9qzZ49MJpO6du2q3/72t5Lkdmax2WzmzGJELlbQBZzn9lZv\nW1uNFugVcmxdBYzjtTB3ww03qKCgQN9++62KiopUUVGhL7/80rUlAICf4pLdty2SWEU9p8Mu62yb\n0WEAQFgIRq75n//5n20+xpnFiEoxvoIuEDyLcN62tob6zLlgr5Dz3LoKIHS8FuYeeOABDRw4UJ98\n8omk02dz3HnnnRTmgACxWM1Km9hs22JCD+1eYWBAAACEELkmEACeRbijhRTqgizUZ84FeoWcVx7N\nyJwNHK0CBIvXn66//e1vWrNmjVatWiVJSkpKktPpDHpgQKzYvWyk2zgtu/WuxAAARCNyTaAlv5uB\nxVihzt9mEIEQ6BV0vq6Q85XXrasezcgaymxKGBfQEAD8H68/3YmJiTp58qRMJpOk08mTxWIJemAA\nAACIfuSaQEsBbwYW5YW6YDSD8PXMuUCvoPN1hVygt67Wf7JD9bttrrHnmcgAAsdrYW7OnDkaOXKk\nDh06pLvuuksff/yxli9fHoLQACBGeGwVkKmHcbEAQIiRawIG8Fao8xThhbv28PXMOU++rqDzd4Wc\n31tXPdXXcyYyECJef9qHDx+uAQMG6NNPP5XT6dQLL7ygTp06hSI2AArAVgaEP4+tAorb2OatABBt\nyDWBMOCt6OatcOcpBgt5njyLcIWFO866iu50IS/jnF/f1xVyXreucqYcYJg2f9r+/Oc/u41/9KMf\nSZK+/fZbffvttxowYEBwIwMgKQhbGRB2+s/oIUddswYgAT5DBADCEbkmcO4Mn6j1tcgWZVtlAyHQ\nHVt9XSHntesqZ8oBhmnz098TTzzR5pNMJpM++uijoAQEALHGYXeqbC3dBwHEFnJN4NxF3EStZxHO\nl9V2ARCMZhC+njkHAOeqzcLcli1bQhkHgDNMJmYYo4xjQaGcjqatARyeCwDkmkBMMVlDmt8GoxmE\nv2fO+cvXrasAIkebhbn58+frqaeekiStW7dOd955p+uxX/3qV5o3b17wowNikCnuQq25qGndeOI/\ntmisgfHAf06HncNzAcADuSYQQwxeQRcNfN266uuZcvXbP1X9PptrzEQyEDptFuZWr17tSpYKCgrc\nkqXNmzeTLAFB0sWarJcnHnaNjyZ00tgVBgYEAEAQkGsCiGTB3trq7wo5X8+Uq99nYyIZMEibhTmn\n09nqn1sbAwiczcvcD4ZNy15pUCQAAAQPuSbQfoY3g/BXiLe2BuPMuWBvbfV1hRyAyNVmYc5kMrX6\n59bGAAAfeGwdkKmHcbEAgEHINYH2i7hmEJ5CvLU1GGfOefJ3BZ2/K+S8bl0FELbaLMzt3btXF1xw\ngZxOp06ePKkLLrhA0ukZTLvd3tbTAARZxM+QosXWAcVtNCwUADAKuSaAaOLvCjp/V8h53braYmKY\nM+SAcNFmYa6hoaGthwAYKOJnSKH+M3rIUddUjLNY2/xVDABRi1wTgFE8t7aeuebv9tbmPFfQncv9\nQeUxMexYUCino2lMswfAOHwaBIAQc9idKls72ugwAAAAwkOIz5xrrQAX6O2tgWwE0Rp/t646HXaa\nPQBhgsIcEO5MppAmKvDf6RnIpq0CzEACAIBgivijTkJ85lw08Lp1FUDEoDAHhLu4ZPfzyEhUwh4z\nkAAAIJQ46gQAIheFOSDMWaxmpU1sdh5ZQg/tXmFgQAAAAECU8Tx3LtBnzvnL562rNHsAIgaFOSDM\n7V420m2clr3SoEgAAACA6ORZhAv0mXP+8nnrqkezBwDhi8IcEOEi/kwRAACCYPHixVqyZIni4+N1\nyy23aP78+ZKkgoICLV26VPHx8Vq0aJFGjBhhcKQAWghxM4jWhPsKOgDRg8IcEOE4UyQCtNhK0MO4\nWAAgBmzZskXFxcXau3evLBaLvv/+e0lSWVmZVq9erQMHDqi6ulqZmZkqLy9XfLxv3QyBcBfxE7dh\n0Awi3FfQ+YrmZED4ojAHAMHmuZUgbmObtwIA/Pfyyy8rNzdXFotFknTppZdKkoqLizVhwgRZLBZ1\n69ZNqampKi0t1ZAhQ4wMFwg4Jm4jX6DPlKM5GRC+KMwBQJD1n9FDjrpmDTys/OoFgGAqLy/X9u3b\nlZeXJ6vVqt/85je67rrrVFVVpcGDB7vu69y5s6qqqgyMFECkCPXWVs6UA2IHnw6BSGMyGX7mBtx5\n2xrgsDtVtnZ0qMMCgKiWmZmpmpqaFtfz8/NVX1+vw4cP69NPP9Wf/vQnZWdn66uvvvLp9YuKilRU\nVCRJqq2tDUjMANopDM6cC/bWVp9XyAGIGhTmgEgTl+w+G2bAmRtwx9YAAAi9kpKSNh97+eWXNW7c\nOJlMJg0aNEhxcXH64YcflJKSokOHDrnuq6ysVEpKSquvkZOTo5ycHElSenp6YIMHQowz5wIv0Cvo\nfF4hByBqUJgDIozFalbaxGbbIhN6aPeKpscjPvECAMBPt912m7Zs2aKbbrpJ5eXlOnXqlDp16qSs\nrCxNmjRJM2fOVHV1tSoqKjRo0CCjwwWCjjPnAs/fFXSskANwBoU5IMLsXjbSbZyWvdJtTOIFAIh1\nkydP1uTJk3XNNdcoMTFRb7zxhkwmk3r37q3s7GylpaXJbDZryZIldGQFEBDeVtC1Vojza4Wcl2YP\ndGEFIgeFOQAAAESVxMRErVy5stXH8vLylJeXF+KIAEQ7zxV07+hAi0JdQLeqemn2wFErQOSgMAcA\nAAAAiBxh0AzCm2B2bAUQXSjMAUCgtdha0MO4WAAAADxE/JnEYdgMAgDai8IcAPjI25kd/R/rIkdd\nsmtsSeJXLQAACB+cSQwA4YNPi0CkM5nOupQ/4mdEw5C3MzscdqfK1o4OXUAAAACIbl6aPQCIXBTm\ngAhnSbpYaY80W52VcEy7VzR7nBlRAAAARLMIOHPOb16aPQCIXBTmgAi3e9lIt3Fadutd6AAAAIDW\nRPwOC86c83rUCoDwFXaFuT179mjq1Kmy2+0ym8166aWXNGjQIElSQUGBli5dqvj4eC1atEgjRoww\nOFoAAAAAiGzssIh83o5aARC+wq4w99RTT+mZZ57RzTffrD/84Q966qmntHXrVpWVlWn16tU6cOCA\nqqurlZmZqfLycsXHxxsdMgAAAAAgXETD1lbOlANiRtgV5kwmk44fPy5JOnbsmC6//HJJUnFxsSZM\nmCCLxaJu3bopNTVVpaWlGjJkiJHhAogBXrcGtEiceoQoMgAAALQQDVtbOVMOiBlhV5h7/vnnNWLE\nCD355JNqbGzUJ598IkmqqqrS4MGDXfd17txZVVVVrb5GUVGRioqKJEm1tbXBDxoIJ3RpDTivWwM8\nE6e4jcEOCQAAIGjIFwEgdAwpzGVmZqqmpqbF9fz8fH344YdauHChbr/9dq1du1ZTpkxRSUmJT6+f\nk5OjnJwcSVJ6enpAYgYiRlyye5HIY4aQM0QCr/+MHnLUNRXjLNawm/MAAAA4Z1GXL0bD1lYAUcuQ\nT49nK7Tde++9euGFFyRJd955px588EFJUkpKig4dOuS6r7KyUikpKcENFIhAFqtZaRObFYkSemj3\nCgMDigEOu1Nla0cbHQYAAABaEwlbW308U44urED0CLtlHZdffrm2bdumjIwMffTRR+rR4/RZTVlZ\nWZo0aZJmzpyp6upqVVRUuLq1Amiye9lIt3Fa9kqDIgEAAEA0YGtrCPh4phxdWIHoEXaFuVdffVUz\nZsxQfX29rFar66y43r17Kzs7W2lpaTKbzVqyZAkdWYEAINECAADA2UT91tYz19jeCsAAYVeYGzp0\nqHbt2tXqY3l5ecrLywtxREB0i7pEKwDYGgAAABDFWivAhXp7q49bVwFEr7ArzAGA0bxuDWiRSPUI\nekwAAADhIip3XIS6QYSPW1cBRC8Kc0C0M5noQhVonolU3MY2bwUAAIg2UbnjwjM/PloY2ByaFXIA\n2kBhDohylqSLlfZIctM44RhdWv3Uf0YPOeqadb618qsUAAAgqgS6kysr5AC0gU+TQJTztUtrVG5N\nCDCH3amytaONDgMAACAsxET+2FrDCM/HmxfzArxCjjOQgehFYc4Pv//97/Xb3/5WGzZs0B/+8Aft\n3r1bx44d07PPPiuTyWR0eEC7ROXWBC9IdAAARtq5c6fee+89dejQQTNnzjQ6HMBnMZE/etvG2trW\n1wCukPN6BjKAiEVhrp12794tu92u7t27S5I++OADLVy4UCtWrNDevXvVr18/nTx5UnPnzlV+fr4e\ne+wxTZ8+Xa+++qouvPBC3XTTTWpsbNSuXbv03XffqbCwUDfeeKOmTZumI0eOqK6uTqmpqbrlllsM\n/k6B6EeiAwDRZfz48fryyy8lSUePHtWFF16oPXv2SJIKCgq0dOlSxcfHa9GiRRoxYoSRoUqSXn/9\ndfXq1UuS5HQ6ZTKZyCOBSMMZzgDaKSYLc6f+c7kavzl4zvfHXdlViffc73Zt06ZNSkpK0u7du7V7\n9+5Wn9ehQwddcMEFWrNmjW666SZt2LBBl112mS6++GL99a9/1SWXXKLzzjtP+/fvlyT169dPEydO\n1Geffabt27frhx9+aO+3CLSNZhAAgCi3Zs0a15+feOIJJSefPmu1rKxMq1ev1oEDB1RdXa3MzEyV\nl5crPj7e5/cIRD55Rk1NjebPn68333xTf/7znzVw4EDySES0mNjaCgABEpOFubaSIl/86le/kiQd\nPHhQ/fv313fffad58+bp2LFjuueee1z3TZkyRaNGjdKnn36q8vJyvfXWWzrvvPM0ZMgQffDBB0pJ\nSVFdXZ0kKS4uTpJ0+PBhdejQQV988YXfcQItxCW7L6v3cpAtiZVaOSOkh3GxAADOmdPp1Nq1a/XR\nRx9JkoqLizVhwgRZLBZ169ZNqampKi0t1ZAhQ3x+7UDkk2c8/PDDeu6553TkyBHdddddruvkkYhU\nMbG1FQACJCYLc4H0/PPPS5JGjRqlUaNGtXj87bff1syZMxUXF6devXrp17/+teuxfv36SZImTZrk\n9lo333xzsMNGDLNYzUqb2KyjaEKPs3ZpjcbEytcz5d5J7KBTSU0/36a4Q0GLDQAQONu3b9dll12m\nHj1OT6hUVVVp8ODBrsc7d+6sqqqqVp9bVFSkoqIiSVJtbW1Q4xwxYkSrW2rJIwEAiH4U5oJs6tSp\nRocAuPG1S2s08vVMuTlTO+nCusOucRdrchCiAgD4IjMzUzU1NS2u5+fna8yYMZKkVatWaeLEie16\n/ZycHOXk5EiS0tPT2x+oH8gjES3YgeE7mpMBsYPCHACfxGJidcru1Na1GUaHAQBopqSk5KyP19fX\na/369dq1a5frWkpKig4dalr1XFlZqZSUlKDFCOC0aNyBEWw0JwNiB4U5INb52AwiEhMrZhwBIPaU\nlJSoV69e6ty5s+taVlaWJk2apJkzZ6q6uloVFRUaNGiQgVECAIBYR2EOiHU+NoOIRMw4AkDsWb16\ndYttrL1791Z2drbS0tJkNpu1ZMmSdnVkBQAACBQKc+307rvv6r//+7919OhRPfHEE/ryyy+1e/du\nHTt2TM8++6xMJpPRIQLnxNdmEJ6icmsrXVgBIOItX7681et5eXnKy8sLbTBtKC8v17x583Tbbbfp\ntttu08aNG/X555/rq6++0pIlS5SQkGB0iEBQRGX+CADtFJuFuf9dLtUfPPf7zV2l8+93u9ShQwcd\nPnxYDodDl156qV588UUtXLhQK1as0N69e9WvXz+dPHlSc+fOVX5+vh577DFNnz5dr776qi688ELd\ndNNNamxs1K5du/Tdd9+psLBQN954o6ZNm6YjR46orq5OqampuuWWWwL4jQMt+dsMIhy3tvq7dbX/\nYzCtpWEAACAASURBVF3kqGtq8GBJis1flQCAswhAPnnVVVfp/vvv19GjRyVJo0eP1ujRo/Xoo4/q\n1KlTSkhIIJ9EVArH/BEAjBKbnzY9kqL2+PLLL/Xiiy/q448/1rZt21q9p0OHDrrgggu0Zs0a3XTT\nTdqwYYMuu+wyXXzxxfrrX/+qSy65ROedd572798v6XTb+4kTJ+qzzz7T9u3b9cMPP/gdJxBq4TAD\n6u/WVYfdqbK1owMXEAAg+gQgn2zNCy+8oFtvvVXnnXeeJPJJIFZwJjIQu2KzMBcAnTp10ty5c3X0\n6FE9/vjjiouL07x583Ts2DHdc889rvumTJmiUaNG6dNPP1V5ebneeustnXfeeRoyZIg++OADpaSk\nqK6uTpIUFxcnSTp8+LA6dOigL774wpDvDTHOx2YQnoyYASWRAQBEopqaGr399ts6efKk+vfvr3Xr\n1mnXrl1yOp267rrrdNFFF0kinwRiAWciA7HL5HQ6nUYHEUzp6enauXOnYe//yiuv6MILL9SECRMM\niwHwRe+Jv9e/reriGif+Y4vGJs1o9+u1VjQL9Ao6+1xbQBOZtOyVKlt7d8BeD0DkMTp/QHgx+u8D\n+SSiXSjyxXAX6HwWgPHONX9gxVyQTZ061egQAJ90sSbr5YmHXeOjCZ001odmEJ48kyrHgkK/trp6\nJm5nXsMvNHtAGNq6davuu+8+de/eXWazWatXr9bFF198zs8tKSnR3LlzgxxlYGRkZGjr1q0qLCzU\nPffco9raWjU2NmrAgAHas2ePdu3apSlTphgdJmAY8klEO86cQ6w4k99169ZNklRcXKzk5GQvz2r5\nGv7mecuXL9e9997rWmUdDM3jHDp0qHbs2HFOz/vXf/1XPffcc9q+fbuuuOIKde/eXcuXL1d9fb0e\nfPDBVp9js9mUkZGhjIyMAH4H8vq+gUJhDoCbzcuGuo19bQbhjbdCnTcmizXgs4k0e0C4uueeezR3\n7lytXLlSq1at0vTp04P2Xo2NjUFNzs5Fbu7p3w8ffPCB6uvrNWDAAPXr10/9+vUzNC4AAIBAOZPf\neQplLrZ8+XLdfffdXt/PiPzw+eefl3S6sDd06FB17969Xa8TDrntueLTJ4Cz8/PMOW/CYZsCzR4Q\n7s50bJSkzz77TL/85S9VV1enBx98UA888IA+/vhjPfXUU0pISNC0adN02WWXadeuXbr11lt1+PBh\nvffeezp+/Ljuuusu1dXVqW/fvnrppZe0detW/cd//Ickadq0afrmm2+0fPlyDRs2TJ999pm2bt3a\n6vud0djYqJycHFVUVCgpKUmbNm1SSUmJZs+eLUmaO3euMjMzlZGRoYEDB+qPf/yjpk6dqilTpmjj\nxo165pln1LdvX9fZWPfff79mz56toqIi/c///I+2bNmihx56yDXb+uyzz2rDhg2yWCxavny5rrji\nCvXp00d9+vTRgQMH9MYbb+iKK67QuHHjZDKZ1KdPHy1atCiE/6cAAAB8k5GRoUGDBqm6ulqvvvqq\nJk+erL///e+65JJLtHLlSm3cuFEFBQXq2LGjnnzySSUlJbXI8+bMmaN7771XNTU1mjVrlvbs2aP7\n7rtPCxYs0NKlS7V582bZ7Xa98sorqqur0549e/Szn/1MDz74oIYMGaJHHnlEDodDw4cP1+zZs3X/\n/ferY8eOKi8v1/vvvy9J/397dx4VVfn/Afw9bCNiMl8VlIZKFqEQhlExNyRRSBREPFbgwa/ilvrN\n8JRhLiVkuKSZmaWZJUo/XMmFyiU1NQyVIFGzFEQxJTS2QSTZn98fHG4ii8IAA/J+neM5zJ1n7v3c\nDzOXj888z3ORn5+PKVOmYOfOnXB1dcWcOXOgVquxcuVKfPbZZ/jf//6Hy5cvw9jYGP/3f3UP7ggJ\nCak13ldeeQU//PADNm/ejD179sDDwwNOTk44cOAAdu/eDQD4/vvvkZubi7Fjx6Jdu3aQyWTSDIz7\na9uDBw8iKSkJ5eXliIqKQn5+PjZt2oRVq1bB0tIS3377LTQaDX755Re8+OKLmD17NgoLCzF69Ggs\nWLCgaX/x92HHXAMlJydj6dKl8PPzg5+fH7777jv89ttvuHr1Kj777DNs3boVWVlZKCgowKJFi3Qd\nLlHD6ZkC/wn79/H9nXRE1KS+/vprHDx4EP/88w9OnToFAFi0aBFiYmLwxBNPwNPTE4GBgZg/fz72\n7duHLl26oLy8HD/99BOMjIywb98+LFmyBEePHsWIESNw+PBhGBgYYPz48UhJSQEAFBcX4+DBgygt\nLYWrqyt+/vlnJCQk4MyZM7Uez8jICEDF9Atzc3N8+eWXKC8vB1AxlaCygPPy8oKHhwcAYPz48Vi6\ndCk8PT0xZcoULFu2DCdOnEBubi7c3d2rnPerr74qTRs4fvw4gIpF8n/88Uf8/PPPOHnyJJYtW4b1\n69fj77//xqZNm5CYmIgtW7bAx8cHQ4YMQVhYGB7zZXTpMfBgPblhwwbk5OTgypUr+Oijj+o9vYno\ncSGTt9Nq6ZPWgDcva7u+/vprnDx5ElZWVoiIiAAAjBkzBgMGDMDatWvh6+uLcePGYf369YiOjsb+\n/fuxc+dOdO/eHUIInDhxolqdN3DgQMTFxSEjIwNPPvkk8vPzcfv2bZibm2P27NmYP38+rly5gtDQ\nUERFRUGtVuPIkSMwMDCAv78/vvrqKzz11FMYN24cbt68CQAYNGgQPv30UynuJ554Avn5+SgqKkKn\nTp1w6tQpFBQUYODAgfjuu+/w9NNPY/369Thw4AA+//xzDBgwoNYc1BUvUHEjo6CgILi6usLDwwOb\nN2+GpaUl1qxZg2nTpuH8+fM4dOgQpk6disDAQAwfPlzad2VtCwAvvPAC2rdvjyNHjmDDhg0IDw/H\nH3/8gbS0NPTs2RNxcXHIzc3FkCFDYG9vj+PHj0Mmk8Hd3R1vvPFGo//ua9MmO+aOIRV/o+CR25vD\nBO6wqbLNzs4OQUFB0igGHx8f+Pj44PXXX0dxcTGSkpKwevVqLF68GBqNBgqFAunp6di2bRuCg4Ox\nYMECjBkzBgcOHIBMJsPEiRORnJyMy5cvo7i4GP7+/njzzTcxadIknD9/HqamplCpVHBzc2vUXBA9\njLydARzGfffvY8MeOKvFmnMtAteUo1aicqpDUFAQ/vzzTzg5OeHcuXPw9fUFAGRlZSEzMxNCCHTp\n0gXAv3dkdHR0BAAolUpoNBpkZ2dj5syZ0Gg0SEtLw19//QUA6N27t7Svp59+Gvr6+lWmjtZ0PKVS\nCaCiU2HgwIFVjiuTydCxY0cAgL6+vrQfR0dHGBoaSu309PTQoUMHdOjQAWZmZg/NRVpaGlQqFYCK\nhXTfe+89AICtrS3atWsnnaebmxtOnDiBwMBAeHl5VblTOlFjaop6sn379rh8+TL09PSkz9G9e/cQ\nHh6OJUuWIDg4GLNmzcLGjRuhUCjg7u6O8vJyJCYmIiMjA8uXL4ebmxtmzpyJ3NxclJSUwNbWFt7e\n3o134kTNoC2sOce7sLZdNU1l7dOnDwDgjz/+QGJiIjZs2IDCwkKMGzcOCxcuRHh4OEpLS7Fw4UIA\n1eu8ESNGICQkBEIIBAYGYt++fejatSuAio7AqKgo6OnpQSaTVYvn8uXLUr2k0WiQnp5eJab7WVlZ\nYdeuXdKdwuPi4hASEoJdu3Zh+/btOHToEEpLS+vslAMqOv1qi7c2D57z1atX4eNTMeupsp598OcV\nK1bg6NGjKCkpwXPPPQeZTAa5XI4ff/wRs2bNQkxMDLKysvDWW2/h6tWrmDNnDv755x9cvnwZf//9\nd53xNKY22TH3YFHUWNasWYNRo0bBxMRE2nb/G1+pVCI7OxsREREIDAxEREQE1Go1CgsLceXKFeTn\n58PU1BSHDh2Cv78/XF1dMWrUKMjlcly4cAE5OTk1HZaoSZ2N8KryuLHXnNOFPUbGKG4/Unos07uh\nw2iIHm7+/PkICwvDtm3b0KtXL0RHR8PExAQlJSUwNDSETCZDdnY2OnfuLI1cu//vjxACW7duhZ+f\nH4KCghAYGCiNJqvsKOvSpQtu3LiB8vJynD9/XnptTcerZG9vj9OnT8PHx0dax6O8vBx37twBAJSV\nlUltHywEy8vLUVBQgNzcXGRmZlZ5ztDQEEVFRVW2de/eHefOnQMAJCQkwMbGpsbzLCsrw+LFiwEA\narWaHXPUZJqinszOzsZHH32ELVu24OLFi3B0dISxsTE6duyIHTt2wN3dHTExMejatSs6d+6MK1eu\nwMzMDCYmJrhw4QKAivf9uHHjcObMGcTGxiIrK6vR4yQiosZVWY/Z29tj2LBhGDt2LACgpKQEpaWl\n+PLLLxEXF4ePPvoI/v7+1eofc3NzZGRkQKlUYtCgQfDz88PMmTMBAOvWrcPZs2eRmpqKadOmAaio\ntcrKymBgYAB7e3t8/PHHsLCwQFlZGWQyGdavX1/j+mwDBw7Ehx9+iK+//hpnz57FpUuXYGVlBXt7\ne0yYMAFz5syR4v75559rPd+64q1UGWOlB8/ZysoK586dg4ODA86ePSuNmquMOzs7G8ePH0dsbCwO\nHz6MqKgoABUdjp9++imOHTuG6OhoFBUVoV27dli/fj3efvttDBkyBK6urs0686JNdsw1hlu3biE6\nOhr37t1Dr169sGvXLiQmJkIIgb59+0KtVmPVqlUAAIVCIb0uMDAQ8+bNw/Tp03H37l3ExsZCoVDA\nzs4OkZGR6NGjh7TWTuUbSqPRQC6X49KlS81/okQPauI155rDezO6QFHyb0f3U+04VYhaNnt7e2Rm\nZuLWrVt47733MGrUKAgh0KlTJ3zzzTdYtmyZ9EXOjBkzavzGcejQoZgwYQL27t1b4zEMDAwwceJE\nDBw4EAMGDJA64Go6XiVfX198++23cHNzQ4cOHbB//36EhobC09MTAKQOspq8/fbbcHNzQ+/evdGt\nW7cqz/Xv3x9BQUH47bffpMK0W7ducHd3x8CBA2FkZIQtW7bUuN/4+HgsWLAAJSUl0jRaopbqwXpS\nT08PH3zwAW7cuIExY8ZI7aZMmSKNTkhOTsbWrVthYmKCAQMG4PDhw1AqldXqx5ycHBgbG7N+pMdC\nW5jaSgRULOcxbdo0rFu3DkIILFu2DLt378bp06dx9+5dqY+hJhYWFlCpVOjevTsyMzOlWQ3PP/88\n3Nzcqsy+8/b2hp+fH6ZOnYolS5Zg8uTJKCoqgqGhYZVa70GDBg3C66+/DkdHR7i4uEhf/vj6+iI4\nOBhDhw4FUHFn1cqR3/WNt9KQIUMwf/58nDlzBk8//XS110+dOhVjx45FZGQk5HJ5tef/85//oEOH\nDhg6dKg066LyHHbt2gVTU1NYWlpKs068vb0xa9YsODg4SMu2NBeZeMwXYHFxcUFCQoKuwwBQMXIg\nPDwc7u7unJJKrVavSQdRVFgqPZYb5uFsZKAOI3oED0xddZjRA7/vaOExE+lAaWkpDAwMcObMGWza\ntAkbNmzQdUg605LqB9I9Xb8fPv/8cygUCgQEBOgsBqKWpDA8rNVNA61pTTl2LhI93h61fuCIuWak\nr6+P0NBQXYdBpJVWObVVFFa9gYXed7U2JWrL1q5di71796K4uLjWEWlE1PxmzJih6xCISEtcU46I\nasOOOSLSTiuY2tprdg8Uldx3A4t2vPQR1eSNN95o1jtQERERNQSnthLR44T/OyUircjbd4bD//5d\no61iaqsOAwKqTV0tKuqB33f46DAgIiIiImosbeGurUTUdrBjroGSk5OxdOlS+Pn5wc/PDwkJCTh0\n6BCMjY3x5ptvYsuWLcjKykJBQQEWLVqk63CJmky1qa3+UbofQcepq0RE1ArExsYiLi4OKSkpWLFi\nBTp16oTIyEgcO3YMERERug6PiLRQ05pyREQ1aZMdc5t/BtKyH719985A0KCq2+zs7BAUFASNRgMA\n2LRpE5599lkAFbfuTUpKwurVq7F48WJoNBooFAqkp6dj27ZtCA4OxoIFCzBmzBgcOHAAMpkMEydO\nRHJyMi5fvozi4mL4+/vjzTffxKRJk3D+/HmYmppCpVLxphHU8umZVu0Uu7+Trqk8MEKu12w7Tl0l\nIqIm1Rj15ODBgzF48GCEh4dDo9EgMTERzzzzDExN/x2Jfu/ePYSHh2PJkiUIDg7GrFmzsHHjRigU\nCri7u6O8vByJiYnIyMjA8uXL4ebmhpkzZyI3NxclJSWwtbWFt7d3I501UcvUEqe2ck05InpUbfJ/\nqw8WRY3h1q1bWLFiBaKiovDrr79K22UymfSzUqlEdnY2IiIiEBgYiIiICKjVahQWFuLKlSvIz8+H\nqakpDh06BH9/f7i6umLUqFGQy+W4cOECcnJyGj9wokYmb2cAh3H3dYoZ2uHsmrB/GzTFCLoHRsgV\nlXyH37dx6ioRETWdxqont27dCisrK1hbW+OLL75At27dcPbsWaSmpsLGxgbGxsbo2LEjduzYAXd3\nd8TExKBr167o3Lkzrly5AjMzM5iYmODChQsAALVajXHjxuHMmTOIjY1FVlZW4wRK1IK1hKmtHCFH\nRA3VJjvmGsOtW7cQHR2Ne/fuoVevXpg+fTpWrVqF3NxcBAYGQq1WY9WqVQAAhUIhvS4wMBDz5s3D\n9OnTcffuXcTGxkKhUMDOzg6RkZHo0aMHSkpKAAB6enoAAI1GA7lcjkuXLjX/iRLVU7WpreO+qzqC\nTrNcu6muD4yOAzhCjoiIWqedO3ciMjISXl5euH79OpYvXw4ASEtLg42NjdRuypQpGDlyJE6fPo3k\n5GRs3boVJiYmGDBgAA4fPgylUlmtfszJyYGxsTHrR2qTdDGCjiPkiKihZEIIoesgmpKLiwsSEhJ0\nHQYAoKysDOHh4XB3d+eUVGozek06iKLCUumxvJ1B1c67Gjra6lRDR57DOI6QI6LG1ZLqB9I9Xb8f\nPv/8cygUCgQEBOgsBqLWrDA8rNE7zWoaIafr6bNE1LI8av3AYSXNSF9fH6GhoboOg6hZPTiCrtek\ng1WnurZTV2tTl4qOvqo3c+AIOSIiul9SUhJmzJiBwsJCGBgYYN26dXj++ecBAMuWLcNXX30FfX19\nfPLJJxg+fLiOo324GTNm6DoEolatMUbQ1dQRxxFyRNQY+L9ZImpWD+uoexh5OwOOjiMiojrNnTsX\noaGhGDFiBPbv34+5c+fi+PHj+P3337F9+3ZcvHgRf/31Fzw8PJCcnAx9fX1dh0xETejBTriilcvr\nvQ4dO+KIqKmwY46IdKo+o+WIiIgehUwmw507dwAAeXl5ePLJJwEA+/btQ0BAAORyOaysrGBra4v4\n+HgMGDBAl+ESUTPjlFMiaknYMddAsbGxiIuLQ0pKClasWIHTp0/j7NmzyMvLwwcffIDIyEhkZWWh\noKAAixYt0nW4RERERG3Gxx9/jOHDh+Ott95CeXk54uLiAADp6eno37+/1M7S0hLp6ek17uOLL77A\nF198AQDIzMxskjgvXryIQ4cO4dq1awgNDUV8fHyVelImkzXJcYmIiKjl0EnH3K5duxAWFoY//vgD\n8fHxcHFxkZ6rbd2PxMREBAUF4d69exg5ciTWrFnT4GJl8+YkpKVpHrl99+4KBAWpq2wbPHgwBg8e\njPDwcGg0Ghw+fBirV69GZGQkzp07h6SkJKxevRqLFy+GRqOBQqFAeno6tm3bhuDgYCxYsABjxozB\ngQMHIJPJMHHiRCQnJ+Py5csoLi6Gv78/3nzzTUyaNAnnz5+HqakpVCoVbxpBREREBMDDwwO3bt2q\ntn3JkiU4evQoVq9ejbFjx2Lnzp2YMmUKjhw5Uq/9v/rqq3j11VcBoEqtWqkx6smePXsiKSkJsbGx\nMDQ0rFZPqtVq3Lt3D+Hh4ViyZAmCg4Mxa9YsbNy4EQqFAu7u7igvL0diYiIyMjKwfPlyuLm5YebM\nmcjNzUVJSQlsbW3h7e1dr3MnIiKi5qOTjjlHR0fs3r0b06dPr7K9rnU/Zs6ciY0bN6Jfv34YOXIk\nDh48iBEjRjTo+A8WRQ21detWWFlZwdrautY293ceKpVKZGdnIyIiAoGBgYiIiIBarUZhYSGuXLmC\n/Px8mJqa4tChQ/D394erqytGjRoFuVyOCxcuICcnp1HiJiIiImrt6upomzBhAtasWQMAePnllzF1\n6lQAFbXYjRs3pHY3b96EUqls0PEbq54MDAyEQqHAn3/+WePzxsbG6NixI3bs2AF3d3fExMSga9eu\n6Ny5M65cuQIzMzOYmJjgwoULAAC1Wo1x48bhzJkziI2NRVZWVqPESURERE1DTxcHfe6552Bvb19t\ne23rfmRkZODOnTvo378/ZDIZJkyYgL179+og8n/t3LkTkZGRyMzMxPXr1+Hp6YmlS5fi4sWLcHZ2\nhlqtxqpVqwAACoVCel1gYCC+/fZb9OrVCy+//LL0Ta+dnR3++OMPyOVylJSUAAD09Cp+PRqNBnK5\nHJcuXWrmsyQiIiJqfZ588kmcOHECAPDjjz+iR48eAABfX19s374dRUVFuHbtGlJSUqS7terCwYMH\nsWLFCsTExMDMzKxaPVlpypQpWLVqFUaPHg0fHx/cuXMHhYWFUKlU+O2339C+fftq9WNOTg6MjY1Z\nPxIREbVwMiGE0NXBhwwZgg8//FCaHjBr1iz0798f48ePB1BRhIwYMQLdu3fHvHnzpG9GY2Nj8cEH\nH+C772q+k+ODa4Jcv369Gc7m4crKyhAeHg53d3dOSSUiImrBXFxckJCQoOswqIFOnjyJ2bNno7S0\nFO3atcO6devQp08fABVTXTdt2gQDAwN8/PHHjzQDQ9fvh88//xwKhQIBAQE6i4GIiIjq51Hrhyab\nylrXuh+jR49uqsMCePiaILqir6+P0NBQXYdBRERE9FhzdXVFYmJijc8tXLgQCxcubOaItDNjxgxd\nh0BERERNpMk65uq7wC5Q+7ofSqUSN2/erLadiIiIiIiIiIiotdLJGnO1qW3dDwsLC3Ts2BGnT5+G\nEAKRkZFNPuqOiIiIiIiIiIioKemkY27Pnj2wtLTEqVOn4O3tjeHDhwOouGX8K6+8AgcHB3h5eeGz\nzz6Dvr4+AGDdunWYOnUqbG1tYWNj0+A7shIREREREREREbUEOr35Q3PQ9WK9RERE1PqwfqD78f1A\nRERE9fWo9cNj3zHXpUsXdO/evcn2n5mZCTMzsybb/+OO+dMO86cd5k87zJ92mD/tNHX+0tLSkJWV\n1WT7p9aF9WTLxvxph/nTDvOnHeZPO8yfdlpKPfnYd8w1NX6Dqh3mTzvMn3aYP+0wf9ph/rTD/NHj\nhO9n7TB/2mH+tMP8aYf50w7zp52Wkr8WdfMHIiIiIiIiIiKitoIdc0RERERERERERDqgHxYWFqbr\nIFq7Pn366DqEVo350w7zpx3mTzvMn3aYP+0wf/Q44ftZO8yfdpg/7TB/2mH+tMP8aacl5I9rzBER\nEREREREREekAp7ISERERERERERHpADvmiIiIiIiIiIiIdIAdc3XYtWsXevbsCT09vSq30E1LS4Ox\nsTHUajXUajVmzJghPZeYmAgnJyfY2toiODgYlTOFi4qK4O/vD1tbW/Tr1w9paWnNfTrNrrb8AcCy\nZctga2sLe3t7HDp0SNrO/NUsLCwMSqVSes/t379feq6+uSTg4MGDsLe3h62tLZYvX67rcFqs7t27\nw8nJCWq1Gi4uLgCAnJwceHp6okePHvD09ERubq7Uvrb3YlsxefJkmJubw9HRUdrWkHy11c9uTfnj\ntY8eB6wntcN6svHwmtq4WE8+GtaT9cN6Ujuttp4UVKvff/9dXLp0Sbzwwgvil19+kbZfu3ZN9OzZ\ns8bX9O3bV5w6dUqUl5cLLy8vsX//fiGEEJ999pmYPn26EEKIbdu2iVdeeaXpT0DHasvfxYsXhUql\nEoWFheLq1avC2tpalJaWCiGYv9qEhoaKlStXVtvekFy2daWlpcLa2lqkpqaKoqIioVKpxMWLF3Ud\nVov0zDPPiMzMzCrbQkJCxLJly4QQQixbtkzMnTtXCFH3e7GtOHHihEhMTKzy96Eh+Wqrn92a8sdr\nHz0OWE9qh/Vk4+E1tfGwnnx0rCfrh/WkdlprPckRc3V47rnnYG9v/8jtMzIycOfOHfTv3x8ymQwT\nJkzA3r17AQD79u3DxIkTAQAvvfQSjh49+tj3WteWv3379iEgIAByuRxWVlawtbVFfHw889cADcll\nWxcfHw9bW1tYW1vDyMgIAQEB2Ldvn67DajXu/yxOnDixyme0pvdiW+Lm5oZOnTpV2VbffLXlz25N\n+asN80etCetJ7bCebHq8ptYf60ntsJ6sHetJ7bTWepIdcw107do1qNVqvPDCC4iNjQUApKenw9LS\nUmpjaWmJ9PR06bmnnnoKAGBgYABTU1NkZ2c3f+AtwP25AP7NE/NXt7Vr10KlUmHy5MnS8OWG5LKt\nqy1nVJ1MJoOHhwf69OmDL774AgBw+/ZtWFhYAAC6deuG27dvA2Bea1PffPGzWx2vffQ4Yz3ZcKwn\nG4bX1MbBuufRsZ7UHutJ7bX0a59Bk+69FfDw8MCtW7eqbV+yZAlGjx5d42ssLCzw559/onPnzkhM\nTISfnx8uXrzY1KG2SA3JH9WsrlzOnDkT7777LmQyGd59913MmTMHmzZt0kGU1JacPHkSSqUSf//9\nNzw9PfHss89WeV4mk0Emk+koutaH+ao/XvuotWA9qR3Wk42H9SS1NKwnGxfzVX+t4drX5jvmjhw5\nUu/XyOVyyOVyAECfPn1gY2OD5ORkKJVK3Lx5U2p38+ZNKJVKAIBSqcSNGzdgaWmJ0tJS5OXloXPn\nzo1zEjrUkPxV5qJSZZ7aYv7u96i5nDZtGnx8fAA0LJdtXW05o+oq82Jubo4xY8YgPj4eXbt2RUZG\nBiwsLJCRkQFzc3OpLfNaXX3zxc9uVV27dpV+5rWPWjLWk9phPdl4WE82D9Y9j471pPZYT2qnMENk\nawAACkZJREFUNdSTnMraAJmZmSgrKwMAXL16FSkpKbC2toaFhQU6duyI06dPQwiByMhI6Vs+X19f\nbNmyBQAQHR2NoUOHttmebl9fX2zfvh1FRUW4du0aUlJS8PzzzzN/dcjIyJB+3rNnj3SXmYbksq3r\n27cvUlJScO3aNRQXF2P79u3w9fXVdVgtTkFBAfLz86Wff/jhBzg6Olb5LG7ZsqXKZ7Sm92JbV998\n8bNbFa999DhjPakd1pP1x2tq42E9+WhYTzYO1pPaaRXXvia9tUQrt3v3bqFUKoWRkZEwNzcXL774\nohBCiOjoaOHg4CCcnZ1Fr169RExMjPSaX375RfTs2VNYW1uL1157TZSXlwshhLh375546aWXhI2N\njejbt69ITU3VyTk1p9ryJ4QQ4eHhwtraWtjZ2VW5wwnzV7Px48cLR0dH4eTkJEaNGiX++usv6bn6\n5pKE+P7770WPHj2EtbW1CA8P13U4LVJqaqpQqVRCpVIJBwcHKU9ZWVli6NChwtbWVgwbNkxkZ2dL\nr6ntvdhWBAQEiG7dugkDAwOhVCrFl19+2aB8tdXPbk3547WPHgesJ7XDerLx8JrauFhPPhzryfpj\nPamd1lpPyoTgrYiIiIiIiIiIiIiaG6eyEhERERERERER6QA75oiIiIiIiIiIiHSAHXNERERERERE\nREQ6wI45IiIiIiIiIiIiHWDHHBERERERERERkQ6wY47oMdShQ4cqjzdv3oxZs2bVax8xMTFYvnx5\no8Wk0Wiwbt26R2r7YPw1CQsLw4cffggAWLRoEY4cOVJr27179+L3339/tECbUEJCAoKDg5vteGlp\nadi6dWuzHY+IiIgeH6wnq2I9SURNhR1zRFRNaWkpfH19MW/evEbbZ30KqfpavHgxPDw8an2+pRRS\nLi4u+OSTT5rteA0ppEpLS5soGiIiImpLWE82DdaTRI8fdswRtTFpaWkYOnQoVCoVhg0bhj///BMA\nEBQUhBkzZqBfv36YO3dulW9F1Wq19M/Y2BgnTpxATk4O/Pz8oFKp0L9/f5w/fx5AxTePkydPxpAh\nQ2BtbS0VDvPmzUNqairUajVCQkJw9+5dDBs2DL1794aTkxP27dv30NiXLFkCOzs7uLq64vLly9L2\noKAgREdHS8dxcHCASqXCW2+9hbi4OMTExCAkJARqtRqpqanYuHEj+vbtC2dnZ4wdOxb//POPtJ/g\n4GAMHDgQ1tbW0j4B4IMPPoCTkxOcnZ2lAjM1NRVeXl7o06cPBg8ejEuXLtUZ//Hjx+Hj41Nnnh7U\noUMHhISEoGfPnvDw8EB8fLz0mpiYGABAWVkZQkJC0LdvX6hUKmzYsEHKRWxsLNRqNVavXl1ru+PH\nj2Pw4MHw9fWFg4MDCgoK4O3tDWdnZzg6OmLHjh0P/d0QERFR28F6kvUk60miRiSI6LGjp6cnnJ2d\npX9PPfWUeO2114QQQvj4+IjNmzcLIYT46quvxOjRo4UQQkycOFF4e3uL0tJSIYQQERER0msqxcTE\nCFdXV1FcXCxmzZolwsLChBBCHD16VDg7OwshhAgNDRUDBgwQhYWFIjMzU3Tq1EkUFxeLa9euiZ49\ne0r7KikpEXl5eUIIITIzM4WNjY0oLy8XQghhYmJS7ZwSEhKEo6OjKCgoEHl5ecLGxkasXLlSin3X\nrl0iKytL2NnZSfvJzc2t8nylrKws6eeFCxeKTz75RGr30ksvibKyMnHx4kVhY2MjhBBi//79YsCA\nAaKgoEAIIUR2drYQQoihQ4eK5ORkIYQQp0+fFu7u7nX+Xo4dOya8vb3rzNODAIj9+/cLIYTw8/MT\nnp6eori4WCQlJUk537Bhg3j//feFEEIUFhaKPn36iKtXr1Y53sPatW/fXly9elUIIUR0dLSYOnWq\n9DqNRlPneREREdHjh/Uk60nWk0TNw0DXHYNE1PiMjY2RlJQkPd68eTMSEhIAAKdOncLu3bsBAP/9\n738xd+5cqd3LL78MfX39GveZkpKCkJAQHDt2DIaGhjh58iS++eYbAMDQoUORnZ2NO3fuAAC8vb0h\nl8shl8thbm6O27dvV9ufEAILFizATz/9BD09PaSnp+P27dvo1q1bjcePjY3FmDFj0L59ewCAr69v\ntTampqZo164dpkyZAh8fH+nbxAf99ttveOedd6DRaHD37l0MHz5ces7Pzw96enpwcHCQ4j5y5Agm\nTZokHbtTp064e/cu4uLi8PLLL0uvLSoqqvF4takpT5aWllXaGBkZwcvLCwDg5OQEuVwOQ0NDODk5\nIS0tDQDwww8/4Pz589I3snl5eUhJSYGRkVGVfdXV7vnnn4eVlZV0nDlz5uDtt9+Gj48PBg8eXK/z\nIiIiotaP9STrSdaTRM2DHXNEJDExMalx+927d/HKK69g48aNsLCweOh+5HK59LO+vn6N60xERUUh\nMzMTiYmJMDQ0RPfu3VFYWNjw4AEYGBggPj4eR48eRXR0ND799FP8+OOP1doFBQVh7969cHZ2xubN\nm3H8+PEaYxdC1Hqs8vJyKBSKKgVrfT1KngwNDSGTyQAAenp60mv09PSk9kIIrF27tkpBCKDKeT2s\n3f2/ezs7O/z666/Yv38/3nnnHQwbNgyLFi1q8HkSERFR28F6kvUkwHqSqD64xhxRGzNw4EBs374d\nQEUx8yjfXk2ePBmTJk2q0nbw4MGIiooCUPGHuEuXLujYsWOt+3jiiSeQn58vPc7Ly4O5uTkMDQ1x\n7NgxXL9+vc4Y3NzcsHfvXty7dw/5+fn49ttvq7W5e/cu8vLyMHLkSKxevRrnzp2r8dj5+fmwsLBA\nSUmJdA518fT0REREhLR2SE5ODjp27AgrKyvs2rULQEWRUnm8PXv2YP78+Q/db2MZPnw41q9fj5KS\nEgBAcnIyCgoKqp13be0e9Ndff6F9+/YYP348QkJC8OuvvzbPiRAREVGrwHqS9STrSaLGwxFzRG3M\n2rVrMWnSJKxcuRJmZmaIiIios/3169cRHR2N5ORkbNq0CQDw5ZdfSovNqlQqtG/fHlu2bKlzP507\nd8agQYPg6OiIESNG4O2338aoUaPg5OQEFxcXPPvss3W+vnfv3vD394ezszPMzc3Rt2/fam3y8/Mx\nevRoFBYWQgiBjz76CAAQEBCAadOm4ZNPPkF0dDTef/999OvXD2ZmZujXr1+VYqMmXl5eSEpKgouL\nC4yMjDBy5EgsXboUUVFRmDlzJsLDw1FSUoKAgAA4OzsjNTW1zqKysU2dOhVpaWno3bs3hBAwMzPD\n3r17oVKpoK+vD2dnZwQFBWH27Nk1tnvQhQsXEBISAj09PRgaGmL9+vXNdi5ERETU8rGeZD3JepKo\n8chEXWNriYio3saPH4/Vq1fDzMxM16EQERERUSvEepKo7WDHHBERERERERERkQ5wjTkiIiIiIiIi\nIiIdYMccERERERERERGRDrBjjoiIiIiIiIiISAfYMUdERERERERERKQD7JgjIiIiIiIiIiLSAXbM\nERERERERERER6cD/A9uB0N8aZam4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8989520860>"
      ]
     },
     "metadata": {},
     "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(5):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2d} 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",
    "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(5, len(years)):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2d} 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",
    "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": "code",
   "execution_count": 52,
   "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 = [40, 80, 120, 160, 200, 45, 91, 272, 45, 91, 272, 45, 91]\n",
    "days = [45, 91]\n",
    "\n",
    "#perlen = [365.25 * 200., 92, 273, 92, 273, 92]\n",
    "#nstp = [1000, 92, 273, 92, 273, 92]\n",
    "# sp2 = timesteps of 1 day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8, 9, 10, 11, 12]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5, len(years)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2, 100, 100)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta15.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2, 100, 100)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 10 is out of bounds for axis 0 with size 10",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-8ae228c9d1ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myears\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;31m# layer 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     ax.plot(xcell, zeta15[idx, 0, 50, :], drawstyle='steps-mid', \n\u001b[0m\u001b[1;32m     36\u001b[0m             linewidth=0.5, color=cc[idx-5], label='{:2d} days'.format(years[idx]))\n\u001b[1;32m     37\u001b[0m     \u001b[0;31m# layer 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 10 is out of bounds for axis 0 with size 10"
     ]
    },
    {
     "data": {
      "image/png": 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p5QAAAAAAEczv3qxf//rX+vzzz/1ei2RTpkzRggUL9OWXX2rt2rWqXbu2atas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\nIiIiIiIiIrJTbN4RERERERERERHZqf8DMZjHwVbdTJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8992485ef0>"
      ]
     },
     "metadata": {},
     "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(-140, 20)\n",
    "for idx in range(5):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta15[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx], label='{:2d} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta15[idx, 1, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx], label='_None')\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 feet')\n",
    "ax.set_ylabel('Elevation, in feet')\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(-140, 20)\n",
    "for idx in range(5, len(years)):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta15[idx, 0, 50, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2d} days'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta15[idx, 1, 50, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx-5], label='_None')\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 feet')\n",
    "ax.set_ylabel('Elevation, in feet')\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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "\n",
    "# second plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# axes limits\n",
    "#ax.set_xlim(-1500, 1500)\n",
    "#ax.set_ylim(-50, -10)\n",
    "for idx in range(5, len(years)):\n",
    "    # layer 1\n",
    "    ax.plot(xcell, zeta[idx, 0, 30, :], drawstyle='steps-mid', \n",
    "            linewidth=0.5, color=cc[idx-5], label='{:2d} years'.format(years[idx]))\n",
    "    # layer 2\n",
    "    ax.plot(xcell, zeta[idx, 1, 30, :], drawstyle='steps-mid',\n",
    "            linewidth=0.5, color=cc[idx-5], label='_None')\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')\n",
    "\n",
    "# # third plot\n",
    "# ax = fig.add_subplot(2, 2, 3)\n",
    "# # axes limits\n",
    "# ax.set_xlim(-1500, 1500)\n",
    "# ax.set_ylim(-50, -10)\n",
    "# for idx in range(5, len(years)):\n",
    "#     # layer 1\n",
    "#     ax.plot(xcell, zeta2[idx, 0, 30, :], drawstyle='steps-mid', \n",
    "#             linewidth=0.5, color=cc[idx-5], label='{:2d} years'.format(years[idx]))\n",
    "#     # layer 2\n",
    "#     ax.plot(xcell, zeta2[idx, 1, 30, :], drawstyle='steps-mid',\n",
    "#             linewidth=0.5, color=cc[idx-5], label='_None')\n",
    "# ax.plot([-1500, 1500], [-30, -30], 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, .55, '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 and saltwater\\nwell withdrawals', transform=ax.transAxes,\n",
    "#         va='center', ha='right', size='8')\n",
    "\n",
    "# # fourth plot\n",
    "# ax = fig.add_subplot(2, 2, 4)\n",
    "# # axes limits\n",
    "# ax.set_xlim(0, 30)\n",
    "# ax.set_ylim(-50, -10)\n",
    "# t = zobs['TOTIM'][999:] / 365 - 200.\n",
    "# tz2 = zobs['layer1_001'][999:]\n",
    "# tz3 = zobs2['layer1_001'][999:]\n",
    "# for i in range(len(t)):\n",
    "#     if zobs['layer2_001'][i+999] < -30. - 0.1:\n",
    "#         tz2[i] = zobs['layer2_001'][i+999]\n",
    "#     if zobs2['layer2_001'][i+999] < 20. - 0.1:\n",
    "#         tz3[i] = zobs2['layer2_001'][i+999]\n",
    "# ax.plot(t, tz2, linestyle='solid', color='r', linewidth=0.75, label='Freshwater well')\n",
    "# ax.plot(t, tz3, linestyle='dotted', color='r', linewidth=0.75, label='Freshwater and saltwater well')\n",
    "# ax.plot([0, 30], [-30, -30], 'k', linewidth=1.0, label='_None')\n",
    "# # legend\n",
    "# leg = plt.legend(loc='lower right', numpoints=1)\n",
    "# # axes labels and text\n",
    "# ax.set_xlabel('Time, in years')\n",
    "# ax.set_ylabel('Elevation, in meters')\n",
    "# ax.text(0.025, .55, '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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `ModelCrossSection` plotting class and `plot_fill_between()` method to fill between zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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": {
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BnEcy8KBxt346zShzCe9Oadb/fOCzEbEsM2/skK4eGbnjH/kdbEX5g681yNEtgNBLtxYB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\nOVPq2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f899538a2e8>"
      ]
     },
     "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': 30})\n",
    "#, \n",
    "#                                          extent=(0, 3050, -50, -10))\n",
    "modelxsect.plot_fill_between(zeta[4, :, :, :], colors=colors, ax=ax,\n",
    "                              edgecolors='none')\n",
    "linecollection = modelxsect.plot_grid(ax=ax)\n",
    "ax.set_title('Recharge year {}'.format(years[4]));\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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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