examples/Notebooks/flopy3_save_binary_data_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "### Quick demo on how to use FloPy to save array data as a binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy\n",
      "3.6.7 | packaged by conda-forge | (default, Feb 28 2019, 02:16:08) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.14.5\n",
      "matplotlib version: 2.2.2\n",
      "flopy version: 3.2.11\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os \n",
    "import sys\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "    \n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlay,nrow,ncol = 1,20,10\n",
    "model_ws = os.path.join(\"data\",\"binary_data\")\n",
    "\n",
    "if os.path.exists(model_ws):\n",
    "    shutil.rmtree(model_ws)\n",
    "    \n",
    "precision = 'single' # or 'double'\n",
    "dtype = np.float32 # or np.float64\n",
    "\n",
    "mf = flopy.modflow.Modflow(model_ws=model_ws)\n",
    "dis = flopy.modflow.ModflowDis(mf, nlay=nlay, nrow=nrow, ncol=ncol, delr=20, delc=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a linear data array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 10)\n"
     ]
    }
   ],
   "source": [
    "# create the first row of data\n",
    "b = np.linspace(10, 1, num=ncol, dtype=dtype).reshape(1, ncol)\n",
    "\n",
    "# extend data to every row\n",
    "b = np.repeat(b, nrow, axis=0)\n",
    "\n",
    "# print the shape and type of the data\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1219e89b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv = flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(b)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the linear data array to a binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = 'head'\n",
    "\n",
    "# write a binary data file\n",
    "pertim = dtype(1.0)\n",
    "header = flopy.utils.BinaryHeader.create(bintype=text, precision=precision,\n",
    "                                         text=text, nrow=nrow, ncol=ncol,\n",
    "                                         ilay=1, pertim=pertim,\n",
    "                                         totim=pertim, kstp=1, kper=1)\n",
    "pth = os.path.join(model_ws, 'bottom.bin')\n",
    "flopy.utils.Util2d.write_bin(b.shape, pth, b, header_data=header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the binary data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bo = flopy.utils.HeadFile(pth, precision=precision)\n",
    "br = bo.get_data(idx=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data that was read from the binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x121f1cb00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv= flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(br)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the difference in the two values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12200ca90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAD8CAYAAAAPBN1qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHTdJREFUeJzt3XGQXXV99/H3hySlKFQCGAwLnWBNnYJPGyEPMuMjo1IgYSrBR1qTh4FQcSIOTGV0nhFqkTyiHXxapGXQ+CwSAQcDPiAlrVSMCKXtCJJghGCgCYi6IQ8RQiEjEtjd7/PH+d3k7M29d8+9597dey6f18yZ3f2dc777O2zmyzn3nO/3KCIwMzPYb7onYGbWL5wQzcwSJ0Qzs8QJ0cwscUI0M0ucEM3MkkkToqSjJN0rabOkxyR9Io0fImmdpC3p6+w0LknXSNoq6RFJx/X6IMzMuqHIGeIo8KmI+APgROBCSccAlwD3RMR84J70M8BiYH5aVgCruj5rM7MemDQhRsT2iHg4fb8L2AwMAUuAG9NmNwJnpu+XADdF5gHgYElzuz5zM7Mum9nOxpLmAe8EHgQOj4jtkCVNSXPSZkPAL3O7jaSx7XWxVpCdQXLAAQccf+Ds2R1Mv7kZqQJnTHrdxq3SXKsWt0pzrcX9f9u3PxcRb+5q4AFTOCFKOhC4Hbg4Il5S8z9YoxX71AdGxDAwDDBnaCgO/PQni06lkNN2vQzA3Qe94XUbt0pzrVrcKs21Fverf/XZn3c16AAqdJdZ0iyyZHhzRHw7DT9buxROX3ek8RHgqNzuRwLPdGe6Zma9U+Qus4Drgc0R8aXcqrXA8vT9cuDO3Pi56W7zicCLtUtrM7N+VuSS+d3AOcCjkjamsb8ErgS+Jel84BfAn6Z1dwGnA1uBl4E/7+qMzcx6ZNKEGBH/RuPPBQFObrB9ABeWnJeZ2ZRzpYqZWeKEaGaWOCGamSVOiGZmSVuVKr0yI2LPA6ndcsjoOMDrOm6V5lq1uFWaaz6uteYzRDOzpC/OEMekSpVAVSVuleZatbhVmms+rrXmM0Qzs8QJ0cwscUI0M0ucEM3MEidEM7PECdHMLHFCNDNLnBDNzJK+eDDbpXsuL6ta3CrNNR/XWvMZoplZ0hdniC7dc3lZ1eJWaa75uNZakZdMrZa0Q9Km3Nitkjam5enau1YkzZP0m9y6r/Zy8mZm3VTkDPEG4FrgptpARHy49r2kq4AXc9s/GRELujVBM7OpUuQlU/dLmtdoXXpF6Z8B7+/utMzMpl7ZmyrvAZ6NiC25saMl/VjSv0h6T8n4ZmZTpuxNlWXAmtzP24HfjYjnJR0P/IOkYyPipfodJa0AVgAcdvjhJadhZlZex2eIkmYC/x24tTYWEbsj4vn0/QbgSeD3G+0fEcMRsTAiFmrGjE6nYWbWNWUumf8YeDwiRmoDkt4saUb6/q3AfOCpclM0M5saRR67WQP8EHi7pBFJ56dVS5l4uQxwEvCIpJ8AtwEXRMTObk7YzKxXFBHTPQfmHnFEnHnhx7sas1aqtHNmd4txqhS3SnOtWtwqzbUW969XrtwQEQu7GnjAuHTPzCxx6d4Ax63SXKsWt0pzzce11nyGaGaWOCGamSVOiGZmiROimVnihGhmljghmpklTohmZokToplZ0hcPZvute34zXNXiVmmu+bjWms8QzcySvjhDdOmey8uqFrdKc83HtdZ8hmhmljghmpklTohmZokToplZ4oRoZpY4IZqZJUVeMrVa0g5Jm3JjKyVtk7QxLafn1l0qaaukJySd1quJm1l1SFqUcsJWSZc0WH+SpIcljUo6q27dcklb0rI8N368pEdTzGskqew8i5wh3gAsajB+dUQsSMtdaYLHkL2N79i0z1dqryU1s9enlAO+DCwGjgGWpVyR9wvgPOCbdfseAlwOvAs4Abhc0uy0ehWwgux1x/NpnKfaMumD2RFxv6R5BeMtAW6JiN3AzyRtJTuIH7bayaV7Li+rWtwqzTUfd5qcAGyNiKcAJN1Clit+WtsgIp5O6+onehqwrvY6Y0nrgEWS7gN+JyJ+mMZvAs4E/rnMRMtUqlwk6VxgPfCpiHgBGAIeyG0zksb2IWkFWXbnLXPnlpiGmU2V0973xnh+59iEsQ2P7H4MeCU3NBwRw7mfh4Bf5n4eITvjK6LRvkNpGWkwXkqnCXEVcAUQ6etVwEeARtfwDV/8nP6DDQPMGRqKqpVAVSFuleZatbhVmms+blm/2jnKv3/3iAljbzji6Vcmed9z4bzQxr5lYjbV0V3miHg2IsYiYhy4juyUGLIsfVRu0yOBZ8pN0cz6RRC8FmMTlgLK5IVm+46k7zuJ2VRHCVFS/hr3g0DtDvRaYKmk/SUdTfZB54/KTdHM+kUArzE+YSngIWC+pKMl/RbZjde1BX/l3cCpkmanmymnAndHxHZgl6QT093lc4E72z6gOpNeMktaA7wXOEzSCNkdn/dKWkD23+dp4GMAEfGYpG+RfVg6ClwYUex/IWbW/wLYHe3doImIUUkXkSW3GcDqlCs+B6yPiLWS/itwBzAb+ICk/xURx0bETklXkCVVgM/VbrAAHyd7CuYAspsppW6oQLG7zMsaDF/fYvsvAF8oMykz608RwavR/kd16dG8u+rGPpv7/iEmXgLnt1sNrG4wvh54R9uTaaEv+iGaWTUE4rWG9zMGgxOimRU2DrwSg1vx64RoZoUF8JoToplZumSOwa3G7YuE6NI9l5dVLW6V5pqPW9Y44pWY1fZ+khYBf092l/lrEXFl3fr9gZuA44HngQ9HxNOSzgb+Z27TPwSOi4iNqXxvLvCbtO7UiNjR9uRy+iIhmlk1RIhX2zxDzDV3OIXsgeqHJK2NiJ/mNjsfeCEi3iZpKfBFsqR4M3BzivNfgDsjYmNuv7PT3eau6IuE6LfuubysanGrNNd83LKyB7PbvmSetLlD+nll+v424FpJipjwjM8yYE0H0y5scD8dNbOuC8Qr47MmLGRFG+tzy4q63Zo1aGi4TUSMAi8Ch9Zt82H2TYhfTz1ZL+tGP8S+OEM0s2rIbqrskzae60Jzh5bbSHoX8HJEbMqtPzsitkk6CLgdOIfsc8iO+QzRzAobJ/sMMb8UUKS5w55tJM0E3gTszK1fSt3ZYURsS193kTWWPYGSnBDNrLAIsTtmTVgKKNLcYS1Qez3AWcAPap8fStoP+FPgltrGkmZKOix9Pwv4E/Y2memYL5nNrLAAXt33krn1PgWaO5D1R/hG6rK/kyxp1pwEjNRuyiT7A3enZDgD+D5ZK8JSnBDNrLBxxO7x9p9DLNDc4RWys8BG+94HnFg39muyZxa7ygnRzApzpcoUcKWKqymqFrdKc83HLSvCCdHMDNj7HOKg6ouE6EoVV1NULW6V5pqPW1bW7cZniGZmBGJ0gBPipM8hSlotaYekTbmxv5H0uKRHJN0h6eA0Pk/Sb1IpzUZJX+3l5M1sao2H2D02c8JShKRFkp6QtFXSJQ3W7y/p1rT+QUnz0njTnCLpeEmPpn2u6UbpXpEHs28AFtWNrQPeERF/CPwHcGlu3ZMRsSAtF5SdoJn1j1qD2PwymVy3m8XAMcAyScfUbban2w1wNVm3m5pmOWUVsILs7Z7z2TdPtW3So4mI+5lYQkNEfC8VYAM8QJOXw5jZoMkumfNLAXu63UTEq2QVJ0vqtlkC3Ji+vw04udUZX3oV8u9ExA9TRctNwJntHk29bpTufYSJr/87WtKPJf2LpPc020nSilp3jBjzm0rNqqDJJXOvu900yilDKU6rmG0rdVNF0mfI3r98cxraDvxuRDwv6XjgHyQdGxEv1e8bEcPAMMCcoaH232toZlMugNF9L5N72e2mYU4pGLNtHSdEScvJCqpPrhVhR8RuYHf6foOkJ4HfB7rW0dbMplGI0fG27zK30+1mJN/tJuWWRjllhIkf1TWK2baOLpnT+xE+DZwRES/nxt+cPkBF0lvJPuh8qnEUM6uaceDV8RkTlgI67nbTLKdExHZgl6QT02eN5wJ3lj2+Sc8QJa0B3kv2OcEIcDnZXeX9gXXpc88H0t2fk4DPSRoFxoALImJnw8A5Lt1zeVnV4lZprvm4ZQVidLy986iS3W5a5ZSPkz0FcwDZfYz8vYyOTJoQI2JZg+Hrm2x7O1nnWjMbQBEUPSus26+zbjetckp6udQ72p5MC31RqeLSPZeXVS1uleaaj1tWJ2eIVdIXCdHMqmOswMPYVeWEaGaFRYjXxga3ltkJ0cwKC2BsgC+ZB/fIzKz7AsZCE5YiSjR3OEXShtTEYYOk9+f2uS/FrDV+mFP28HyGaGaFBWJ0rL3zqFxzh1PIHqh+SNLaiPhpbrM9zR0kLSVr7vBh4DngAxHxjKR3kD26ky/ROzvdbe4KnyGaWWERMD6+34SlgI6bO0TEjyOiVoHyGPDbkvbvwqE05IRoZm0ZG9eEhd43d6j5EPDjVCJc8/V0uXxZN/oh+pLZzAoL1OimSi+bO2Qrs4YOXwROza0/OyK2STqI7OHtc8jagHWsLxKiS/dcXla1uFWaaz5uaQEx3vaJWMfNHQAkHQncAZwbEU/umUrEtvR1l6Rvkl2al0qIvmQ2s7aMj2vCUkCZ5g4HA98BLo2If69tLGmmpMPS97PIOm9toqS+OEN06Z7Ly6oWt0pzzcctKwLG27zLXLK5w0XA24DLJF2Wxk4Ffg3cnZLhDOD7wHXljq5PEqKZVYU6uWQu09zh88Dnm4Q9vu2JTMIJ0cyK6+wzxMpwQjSz9jghmpmRPQjjhGhmlokxJ0QzMwhQlx5p7EeF7p9LWi1ph6RNubFDJK2TtCV9nZ3GJema1LXiEUnH9WryZjbVlF0y55cie3XY7SatuzSNPyHptKIxO1H0gaIbgEV1Y5cA90TEfOCe9DPAYrI3Y80HVgCryk/TzPpCAGOauEwi1+1mMXAMsEzSMXWb7el2A1xNVqZH2m4pcCxZDvqKpBkFY7at0CVzRNyfz9jJErK38UHWpeI+sleTLgFuSu9TfUDSwZLmptcGNuTSPZeXVS1uleaaj9sNHVwy7+l2AyCp1u0m3/5rCbAyfX8bcG1q1rAEuCU1dPhZenD7hLTdZDHbVqZ07/Bakktfa80Zi3S2QNKKWneMEnMwsymmmLjQ2243zfYtlGfa1YubKkU6WxARw8AwwJyhoahaCVQV4lZprlWLW6W55uOWVrtknqiX3W6ajTc6mdsnz7SrzBnis5LmAqSvO9J4kc4WZlZRGp+4FNBOtxvqut0027cneaZMQsx3p1gO3JkbPzfdbT4ReLHV54dmVh2KjhJix91u0vjSdBf6aLKbtT8qGLNthS6ZJa0hu4FymKQR4HLgSuBbks4HfsHewuy7gNOBrcDLwJ+XnaSZ9Q+1+WB2mW43abtvkd0sGQUujIgxgEYxyx5b0bvMy5qsOrnBtgFcWGZSZtanOnwwu9NuN2ndF4AvFIlZlitVzKw9A1yp4oRoZm0Z5NI9J0QzK27Aa5mdEM2sMAEam+5Z9E5fJESX7rm8rGpxqzTXfNzSBvwM0W/dM7O2dPAcYvNYTbpmNdhuedpmi6TlaewNkr4j6XFJj0m6Mrf9eZJ+lV5iv1HSR4vMpy/OEP3WPZeXVS1uleaaj1tadP2SudY168rUwusSsiYxe0g6hOzZ54XZDNggaS2wG/jbiLg3PZx9j6TFEfHPaddbI+KidibjM0Qza0s3zxDJOtTcmL6/ETizwTanAesiYmdEvACsAxZFxMsRcS9ARLwKPExWwtcxJ0QzKy7InkPML5N3u2mlWdesvEk726QX2n+ArDdrzYdSk+rbJOXrnpvqi0tmM6sGAfvte1bYstuNpO8Db2mw6jNt/Np6ezrbpGYQa4Brav0RgX8E1kTEbkkXkJ19vn+yX+SEaGbtafMyOSL+uNk6Sc/WGkjXdc3KG2FvM2rILovvy/08DGyJiL/L/c7nc+uvI3Xgnowvmc2suM663bTSrGtW3t3AqZJmp7vQp6YxJH2erFXYxfkdaq0JkzOAzUUm4zNEM2vLft29y9ywa5akhcAFEfHRiNgp6Qqyll8An0tjR5Jddj8OPJy9cYBrI+JrwF9IOoOsQ85O4Lwik3FCNLPiajdVuhUuu7Rt1DVrPfDR3M+rgdV124zQ+PNFIuJS4NJ25+OEaGaFicGuVOmLhOjSPZeXVS1uleaaj1tawH5jpV9d0rf6IiGaWXX4DLHHXLrn8rKqxa3SXPNxS3Nzh8YkvT1XOL1R0kuSLpa0UtK23Pjp3ZywmU2frP1XTFhKxSvR3CGN3yfpiVy+mZPG95d0q6Stkh6UNK/IfDpOiBHxREQsiIgFwPFkL5S6I62+urYuvffAzAZB959DrDV3mE9WdndJ/Qa55g7vAk4ALq9LnGfn8k3twe7zgRci4m3A1Uzxg9knA09GxM+7FM/M+tR+oxOXkjpu7tBG3NuAk5UeVGylWwlxKVktYc1Fqah6dYtT4BW1YvAYG+AWvGaDJEDjMWFh+ps7fD1dLl+WS3p79omIUeBF4NDJJlP6pkrqQ3YGex+CXAVcQfYI5xXAVcBH6veLiGGyGkTmDA0N7n18swHS5DnE6WzucHZEbJN0EHA7cA5w0yT7NNWNu8yLgYcj4lmA2lcASdcB/9SF32Fm/SDav5HSy+YOEbEtfd0l6ZtknzHelPY5ChhJ3XDeRFbC11I3LpmXkbtcriuq/iCwqQu/w8z6QXT3LjMlmjtIminpMABJs4A/YW++ycc9C/hBRPT2DFHSG4BTgI/lhv+3pAVkp6dP160zs4rr8nOIZZo7vJEsMc4CZgDfJ2v1BXA98A1JW8nODJcWmUyphBgRL1P3QWVEnNNuHJfuubysanGrNNd83NLSGWK3lGzu8GuyR/4axX2FlFzb0ReVKmZWDbUHswdVXyREl+65vKxqcas013zc0tJjN4OqLxKimVVFoLHBLWZ2QjSz4rr8GWK/cUI0s7YM8iWzXzJlZoUpAo2OT1hKxSvR7UbSQXUdt56T9Hdp3XmSfpVb99FGces5IZpZe8bHJy7ldNztJiJ25brcLAB+Dnw7t+utufVfKzIZJ0QzKy7o6hkiXep2I2k+WWOIfy0zGSdEMysuotEZ4nR3u4GshPjWuvK8D6WuW7dJOqrIZHxTxcza0uAu83R2u6lZStbppuYfgTURsVvSBWRnn++f7Bf1RUJ06Z7Ly6oWt0pzzcctLQJG2+tf2stuNynGHwEzI2JD7nc+n9v+Oqa4Y7aZvV5096ZKx91ucusndNyCfbpunQFsLjKZvjhDdOmey8uqFrdKc83HLS0CutvhvuNuN7kYfwbUv8zuLySdAYySdbs5r8hk+iIhmllFBG1fMrcMV6LbTW7dWxuMXcreLv6FOSGaWRuiG5fJfcsJ0cyKC7p9ydxXnBDNrLgIYrT8u0f7lROimbVngNt/lX7sRtLTkh5NBdTr01ihgm0zq5jaXeb8UkIbzR2+K+k/Jf1T3fjRkh5M+9+aXouMpP3Tz1vT+nlF5tOt5xDflwqoa0+rT1qwbWYVFEG8NjphKalorvgbJlai1HwRuDrt/wJwfho/H3ghIt4GXE3BB7N7dcm8hL1Plt9I9lT5p5tt7EoVV1NULW6V5pqPW1YA0d2bKoVyRUTcI+m9+TFJIivH+x+5/VcCq1LclWn8NuBaSZrsVaTdSIgBfE9SAP8nIoapK9iWtE/BdioAXwHwlrlz61ebWR/aFTvvXvfamsPqhn+79nFZMpzyQBGT5ooWDgX+MyJqp6n5pg97GkJExKikF9P2z7UK2I2E+O6IeCYdyDpJjxfZKf0HGwaYMzQUVXvivwpxqzTXqsWt0lzzccuKiEWTbzVRF5o7NA3dYCwKrGuqdEKMiGfS1x2S7iBr4FikYNvMXge60NyhmeeAgyXNTGeJRwLPpHUjwFHAiKSZwJvISvhaKnVTRdIbJR1U+56s6HoTxQq2zcw6zhXp88B7gbMa7J+Pexbwg8k+P4Tyd5kPB/5N0k+AHwHfiYjvkhVsnyJpC3BK+tnMrF7DXCFpoaQ9bf8l/Svwf4GTJY1IOi2t+jTwSUlbyT4jvD6NXw8cmsY/ScEnXUpdMkfEU8AfNRhvWLBtZpbXRnOH9zTZ/ymyj+nqx18hdc5ph/shmpklTohmZokToplZ4oRoZpb0Rbcbl+65vKxqcas013xca81niGZmSV+cIfolUy4vq1rcKs01H9da8xmimVnihGhmljghmpklTohmZokToplZ4oRoZpY4IZqZJU6IZmZJXzyY7dI9l5dVLW6V5pqPa635DNHMLOmLM0SX7rm8rGpxqzTXfFxrreMzRElHSbpX0mZJj0n6RBpfKWmbpI1pOb170zUz650yZ4ijwKci4uH05r0NktaldVdHxN+Wn56Z2dTpOCFGxHZge/p+l6TNwFC3JmZmNtW6clNF0jzgncCDaegiSY9IWi1pdjd+h5lZr5VOiJIOBG4HLo6Il4BVwO8BC8jOIK9qst8KSeslrY+xsbLTMDMrrVRClDSLLBneHBHfBoiIZyNiLCLGgeto8M7UtN1wRCyMiIWaMaPMNMzMuqLMXWYB1wObI+JLufG5uc0+CGzqfHpmZlOnzF3mdwPnAI9K2pjG/hJYJmkBEMDTwMdKzdDMbIooIqZ7Dsw94og488KPdzVmrVRp58zuFuNUKW6V5lq1uFWaay3uX69cuSEiFnY18IBx6Z6ZWeLSvQGOW6W5Vi1uleaaj2ut+QzRzCxxQjQzS5wQzcwSJ0Qzs8QJ0cwscUI0M0ucEM3MEidEM7OkLx7M9lv3/Ga4qsWt0lzzca01nyGamSV9cYbo0j2Xl1UtbpXmmo9rrfkM0cwscUI0M0ucEM3MEidEM7PECdHMLHFCNDNLepYQJS2S9ISkrZIu6dXvMTPrlp4kREkzgC8Di4FjyN7Ed0wvfpeZWbf06sHsE4CtEfEUgKRbgCXATxtt7NI9l5dVLW6V5pqPa631KiEOAb/M/TwCvCu/gaQVwIr04+6v/tVnB/WF9ocBz033JHrAx1U9b5/uCfS7XiVENRib8ALoiBgGhgEkrR/U98UO6rH5uKpH0vrpnkO/69VNlRHgqNzPRwLP9Oh3mZl1Ra8S4kPAfElHS/otYCmwtke/y8ysK3pyyRwRo5IuAu4GZgCrI+KxFrsM92IefWJQj83HVT2DfGxdoYiYfCszs9cBV6qYmSVOiGZmybQnxEEq8ZP0tKRHJW2sPeIg6RBJ6yRtSV9nT/c8i5C0WtIOSZtyYw2PRZlr0t/wEUnHTd/MW2tyXCslbUt/t42STs+tuzQd1xOSTpueWU9O0lGS7pW0WdJjkj6Rxiv/N5tK05oQB7TE730RsSD3LNslwD0RMR+4J/1cBTcAi+rGmh3LYmB+WlYAq6Zojp24gX2PC+Dq9HdbEBF3AaR/i0uBY9M+X0n/ZvvRKPCpiPgD4ETgwjT/QfibTZnpPkPcU+IXEa8CtRK/QbIEuDF9fyNw5jTOpbCIuB/YWTfc7FiWADdF5gHgYElzp2am7WlyXM0sAW6JiN0R8TNgK9m/2b4TEdsj4uH0/S5gM1nFWOX/ZlNpuhNioxK/oWmaSzcE8D1JG1JpIsDhEbEdsn+0wJxpm115zY5lEP6OF6VLx9W5jzUqeVyS5gHvBB5ksP9mXTfdCXHSEr+KeXdEHEd2OXKhpJOme0JTpOp/x1XA7wELgO3AVWm8cscl6UDgduDiiHip1aYNxvr62KbCdCfEgSrxi4hn0tcdwB1kl1fP1i5F0tcd0zfD0podS6X/jhHxbESMRcQ4cB17L4srdVySZpElw5sj4ttpeCD/Zr0y3QlxYEr8JL1R0kG174FTgU1kx7M8bbYcuHN6ZtgVzY5lLXBuunN5IvBi7TKtCuo+O/sg2d8NsuNaKml/SUeT3YD40VTPrwhJAq4HNkfEl3KrBvJv1jMRMa0LcDrwH8CTwGemez4ljuOtwE/S8ljtWIBDye7ubUlfD5nuuRY8njVkl4+vkZ1NnN/sWMguv76c/oaPAgune/5tHtc30rwfIUsUc3PbfyYd1xPA4umef4vj+m9kl7yPABvTcvog/M2mcnHpnplZMt2XzGZmfcMJ0cwscUI0M0ucEM3MEidEM7PECdHMLHFCNDNL/j8R05GBk0rETAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv = flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(b-br)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}