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_fork/flopy\n",
      "3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 14:38:56) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.17.3\n",
      "matplotlib version: 3.1.1\n",
      "flopy version: 3.3.1\n"
     ]
    }
   ],
   "source": [
    "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": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAAD8CAYAAAAv6IKXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAXh0lEQVR4nO3df5BdZX3H8fdnNwkhCT8SIBASIMEB29gfATOUNhURqCA6RjrFhhkxKtPgDLTQ6hTQGWUqdKgVaGdasUEo0CJIBUqmtUCkICMVkGAIJAEJP8TAkgDhl4Rks/d++8c5q3eT3ew9957dc5/dz2vmzL377D3PeU5u8s1zfnzPVxGBmVmn66p6AGZmzXCwMrMkOFiZWRIcrMwsCQ5WZpYEByszS8KwwUrSIZLulbRe0lpJ5+XtMyStlPR0/jq9YZ2LJG2Q9JSkk0dyB8xsfNBw91lJmgXMiohHJe0FrAI+AXwG2BIRl0m6EJgeERdImg/cBBwDHAz8ADgyImojuB9mNsYNO7OKiJ6IeDR//zawHpgNLAauzz92PVkAI2+/OSK2R8RzwAaywGVm1rIJRT4saS5wFPAQcGBE9EAW0CTNzD82G3iwYbWNedvOfS0DlgFM2XPP9888YFrRse/Wjlo3EdA1odwJXa0v+yOL7nqp/VLL/t+ol3gWsSsfYp9UXqfAhAgIqFFuv91ks/xalNyvsn7r9fL67eoKFFAv+Xihqzt7re0o9+9X98Quel7ueTUiDii141HUdLCSNA24FTg/It7S0P8ABvvFLseaEbEcWA4w77CZ8cxD+zY7lKbcsOIkttTFb5z8w1L7/b///igAby96odR+494jAXhw/o7S+jx23UR6eydw54F7lNYnwCmbttO1XdzTNbXUfj+87R0AHthSbr/HTfklAD9dX96fw1G/uZ3ubX2s+9/e0voEmH/CJGLruzx+/XOl9vvbS+dx5XV///NSOx1lTf0/LmkiWaC6MSJuy5s35eez+s9rbc7bNwKHNKw+B3ipnOGa2XjVzNVAAdcA6yPiioZfrQCW5u+XAnc0tC+RtIekecARwMPlDdnMxqNmDgMXAWcCj0tanbd9CbgMuEXSWcALwOkAEbFW0i3AOqAPOMdXAs2sXcMGq4j4EYOfhwI4cYh1LgUubWNcZmYD+A52M0uCg5WZJcHBysyS4GBlZkkYNjdwNMyZMysu+cpppfb58mvT6QuYMuONUvt987X9AKjts73UfnljTwDemlLe97H3VlGviy2Tyv0/aUZvHQW8Tne5/ea3g7/ZV26/+3Rl/f5ya3l/DtOm1FE92PpGuf9+puwrqNd55+VtpfY79aDJXHLZ11ZFxMJSOx5FnlmZWRIK5QaOlIndNT798R+U2qfTbfrTbbrTSbfpdbpNlm6zbUTSbVLnmZWZJcHBysyS4GBlZklwsDKzJDhYmVkSHKzMLAkOVmaWBAcrM0uC020KcrqN023A6TZV8MzKzJLgdJuCnG7jdBtwuk0VmikYca2kzZKeaGj7rqTV+fJ8/7PZJc2V9G7D7741koM3s/GjmZnVdcA/ATf0N0TEn/a/l3Q58GbD55+JiAVlDdDMDJorGHF/Xol5F3mZrk8CJ5Q7LDOzgdo9wf4BYFNEPN3QNk/STyX9UNIH2uzfzAxo/wT7GcBNDT/3AIdGxGuS3g/8p6T3RcRbO68oaRmwDODgg/dvcxhmNta1PLOSNAH4Y+C7/W0RsT0iXsvfrwKeAY4cbP2IWB4RCyNi4aQJQ5UlNDPLtHMYeBLwZERs7G+QdICk7vz94WSl459tb4hmZs3dunAT8GPgvZI25uXiAZYw8BAQ4DhgjaTHgO8Bn4+ILWUO2MzGJ6fbFOR0G6fbgNNtquB0GzNLgtNtCnK6jdNtwOk2VfDMysyS4GBlZklwsDKzJDhYmVkSHKzMLAkOVmaWBAcrM0uCg5WZJcHpNgU53cbpNuB0myp4ZmVmSXC6TUFOt3G6DTjdpgqeWZlZEhyszCwJDlZmlgQHKzNLgoOVmSXBwcrMktBMwYhrJW2W9ERD28WSXpS0Ol9ObfjdRZI2SHpK0skjNXAzS8MQMWSGpJWSns5fpw/XTzMzq+uAUwZpvzIiFuTL9/MBzCerevO+fJ1v9pfmMrNx6zp2jSEXAvdExBHAPfnPuzXsTaERcb+kuU0OajFwc0RsB56TtAE4hqyU15B21Lq5YcVJTW6iOf3pNk/e9cFS++1+M7uxcK8HDi21X7ZmhV6PXTextC6zdJs6p2wqNzVoRm8dASfW3ym1330mZGkxi2aU2++0rjqQ3chZWp9T6miymH/CpNL6hDzdZu/Jpd/EOfWgyaX2V8QQMWQxcHz+/nrgPuCC3fXTzh3s50r6NPAI8IWIeB2YDTzY8JmNedsuBpSPn3VQG8Mws9Fy8oemxmtbagPaVq3ZvhZoTGZcHhHLh+nqwIjoAYiIHkkzh9t2q8HqKuBrQOSvlwOfAwarAz9opme+M8sB5h02M5xu43Qbp9t0frrNK1v6eODOgwe0TTn4+W2jkSDd0tXAiNgUEbWIqANXkx3qQTaTOqTho3OAl9obopl1iiDYEbUBS4s2SZoFkL9uHm6FloJV/0ZypwH9Z/lXAEsk7SFpHnAE8HAr2zCzzhPADuoDlhatAJbm75cCdwy3wrCHgZJuIjsRtr+kjcBXgeMlLcjH/jxwNkBErJV0C7AO6APOiWg99JpZZwlgexQLUEPEkMuAWySdBbwAnD5cP81cDTxjkOZrdvP5S4FLh+vXzNITEfQWfGDnEDEE4MQi/XTE86zMLA2B2DHodbSR52BlZk2rA9uimiw9Bysza1oAOxyszKzTBWJHVJNB1xHByuk2ON0Gp9tA56fb1BHbory/o0V0RLAyszREiN7xPLNydRun24DTbaDz022ym0LHcbAyszQEYlvdh4Fm1uGyE+zVhA0HKzNrWp1xfs7KzNIQIbb7aqCZdboAen0YaGadro7Y7hPsZtbpfAe772D3Hez4Dnbo/DvYI6oLVi5yamZN67/PqnFphqS/lLRW0hOSbpJUOHp2xMzKd7D7DnbwHeyQyB3sBWdWkmYDfwHMj4h386cJLyGrJ9i0jghWZpaGQPS1dhg4AdhT0g5gCi0Ukmm1fPzfS3pS0hpJt0vaN2+fK+ndhrLy3yo6IDPrXPUQ22sTBixkz1Z/pGFZ1rhORLwIfIPsWes9wJsRcXfRbbdaPn4l8FsR8TvAz4CLGn73TENZ+c8XHZCZda7+h+81LsCrEbGwYRlQ4FTSdLIKzPOAg4Gpkj5VdNvDBquIuB/YslPb3RHRl//4IFl9QDMb87LDwMalCScBz0XEKxGxA7gN+IOiWy7jauDngP9p+HmepJ9K+qGkDwy1kqRl/dPG3r5i1TLMrBpDHAYO5wXgWElTJImsqs36ottuK1hJ+jJZfcAb86Ye4NCIOAr4K+A7kvYebN2IWN4/bZw0oZpqGWZWTAB90TVgGXadiIeA7wGPAo+TxZ3lu11pEC1fDZS0FPgYcGJEVkgsIrYD2/P3qyQ9AxwJPNLqdsysg4Toqxe/GhgRXyUrbtqyloKVpFOAC4APRsTWhvYDgC0RUZN0OFn5+GfbGaCZdY460NtCsCpDq+XjLwL2AFZmh6A8mF/5Ow74G0l9QA34fERsGbTjBk63wek2ON0GEki3QfTVO7QUV5Hy8RFxK3Bru4Mys84U0cEzq9HgdBun24DTbSCFdJsOnlmZmTWquSKzmXW6CLGjNo4PA80sDQHUfBhoZh0voBbV3MTtYGVmTQtEX80zKzPrcBFQ92GgmaWgVvdhoJl1uEDj+wS7021wug1Ot4HOT7chIDyzMrMU1MdzsHK6jdNtwOk2kEC6TUC9oquBrhtoZgWIqA9cmlpL2lfS9/JCM+sl/X7RLXfEzMrMEtH6Oat/BO6MiD+RNImsHFchDlZmVkzBYJU/2vw44DMAEdELFD5+9mGgmTUvyIJV4zJM3UDgcOAV4F/zYjLfllT4xKSDlZkVEjUNWBimbiDZEdzRwFV5MZl3gAuLbtfBysyaF6D6wKUJG4GNeZUbyCrdHF10062Wj58haaWkp/PX6Q2/u0jSBklPSTq56IDMrJNpsMPA3YqIl4FfSHpv3nQisK7ollstH38hcE9EHAHck/+MpPnAEuB9+TrflFTNk7rMrHwB1DRwac6fAzdKWgMsAP626KabKRhxv6S5OzUvJqt4A3A9cB9Zaa7FwM15/cDnJG0AjgF+vLttON0Gp9vgdBtIIN2Gpg/9BoiI1cDCdrbb6jmrAyOiJx9EDzAzb58N/KLhcxvztl00lo+PFnbezKqhGLiMlrLvsxpsTjjo7uRXDJYDzDtsZjjdxuk2Trfp/HSbXx0GVqDVmdUmSbMA8tfNeftG4JCGz80BXmp9eGbWaVq4GliKVoPVCmBp/n4pcEdD+xJJe0iaR1Y+/uH2hmhmnUKt3bpQilbLx18G3CLpLOAF4HSAiFgr6Rayy5J9wDkRURuhsZtZBVTRYWCr5eMhu1disM9fClzazqDMrEPF6M6mGjmR2cyKcbAysxR4ZmVmnc+HgWaWAgGq6JJZRwQrp9vgdBucbgMJpNt4ZmVmqRjXwcrVbZxuA063gTTSbcb1YaCZpWNcz6zMLBFBZfdZ+bHGZtY0AV31gUtT60ndebGI/2p12w5WZlZMfaelOecB69vZrIOVmTWvhacuSJoDfBT4djub9jkrMyuka9ergftLeqTh5+U7leP6B+Cvgb3a2a6DlZk1b/AT7K9GxKDPV5f0MWBzRKySdHw7m3awMrOmicK3LiwCPi7pVGAysLekf4+ITxXddkcEK6fb4HQbnG4DaaTbdNWarxIRERcBFwHkM6svthKooEOClZmlY1zfFOp0G6fbgNNtIJF0mxaDVUTcR1ZjtCUtB6u8FPR3G5oOB74C7Av8GfBK3v6liPh+q9sxs86RPSJmFIsFNmg5WEXEU2RloMlLxL8I3A58FrgyIr5RygjNrHOMgUfEnAg8ExE/l6qpfGFmo6Orr6LtltTPEuCmhp/PlbRG0rWSpg+2QmP5+N6+aqaVZlZQgOoxYBktbQcrSZOAjwP/kTddBbyH7BCxB7h8sPUiYnlELIyIhZMmeDZmloL++6w6sshpEz4CPBoRmwD6XwEkXQ20nGVtZh0morIT7GUcBp5BwyGgpFkNvzsNeKKEbZhZJ4jsamDjMlramllJmgL8EXB2Q/PXJS0gyyJ6fqffmVnikrwaGBFbgf12ajuzaD9Ot8HpNjjdBtJIt0nuPiszG3+SvCm0TE63cboNON0GUkm3GcfBysxSEahWzUkrBysza57PWZlZKnwYaGYdTxGor5rDQFe3MbNi6vWByzAkHSLpXknrJa2VdF4rm/XMysyaF7Qys+oDvhARj0raC1glaWVErCvSiWdWZta8iMIzq4joiYhH8/dvkxU7nV10055ZmVkhg1wNHK5u4K/XleYCRwEPFd1uRwQrp9vgdBucbgMppNsE9O1S5XTIuoGNJE0DbgXOj4i3im66I4KVmSWkiUO/nUmaSBaoboyI21rZbEcEK6fbON0GnG4DKaTbBNR2rR+/O8qedX4NsD4irmh10z7BbmbNC7LDwMZleIuAM4ETJK3Ol1OLbrojZlZmlooofBgYET8ie2BDWxyszKx5QeHDwLI4WJlZ8yKIvmpqcTlYmVkxKT4iRtLzwNtADeiLiIWSZpCVlZ9L9gz2T0bE6+0N08w6QgtXA8tSxtXAD0XEgoabwi4E7omII4B78p/NbCyIIHb0DVhGy0gcBi4Gjs/fXw/cB1ywuxV8Bzu+gx3fwQ6dfwd7AJHoCfYA7pYUwL/k+UAHRkQPZAmMkmYOtqKkZcAygINnHdTmMMxsNLwdW+5aueOm/XdqfnU0tt1usFoUES/lAWmlpCebXTEPbMsB5h02M3wHu+9g9x3snX8He0ScUkpHLWjrnFVEvJS/bgZuB44BNvVXZc5fN7c7SDOzloOVpKn5g7SQNBX4MFmp+BXA0vxjS4E72h2kmVk7h4EHArdnOYpMAL4TEXdK+glwi6SzgBeA09sfppmNdy0Hq4h4FvjdQdpfA05sZ1BmZjvzUxfMLAkOVmaWBAcrM0uCg5WZJUER1ZSCbjRnzqy45Cunldpnf7rNlBlvlNrvm6/tB0Btn3JTWHhjTwDemlLe95Gl24gtk8r9P2lGbx0FvE53uf3WszSON/vK7XefrqzfX24t789h2pQ6qgdb3yj338+UfQX1Ou+8vK3UfqceNJlLLvvaqmYKO3Qqz6zMLAkd8TwrF4xwug043QY6P92mSp5ZmVkSHKzMLAkOVmaWBAcrM0uCg5WZJcHBysyS4GBlZklwsDKzJDjdpiCn2zjdBpxuUwXPrMwsCU63KcjpNk63AafbVKGdghGHSLpX0npJayWdl7dfLOlFSavz5dTyhmtm41U7M6s+4AsR8Whe5WaVpJX5766MiG+0Pzwzs0w7BSN6gP7Ky29LWg/MLmtgZmaNSjnBLmkucBTwUN50rqQ1kq6VNL2MbZjZ+NZ2sJI0DbgVOD8i3gKuAt4DLCCbeV0+xHrLJD0i6ZHevupvnzCzztZWsJI0kSxQ3RgRtwFExKaIqEVEHbiarKT8LiJieUQsjIiFkyaonWGY2TjQztVAAdcA6yPiiob2WQ0fO42spLyZWVvauRq4CDgTeFzS6rztS8AZkhYAATwPnN3WCM3McLpNYU63cboNON2mCk63MbMkON2mIKfbON0GnG5TBc+szCwJDlZmlgQHKzNLgoOVmSXBwcrMkuBgZWZJcLAysyQ4WJlZEpxuU5DTbZxuA063qYJnVmaWBKfbFOR0G6fbgNNtquCZlZklwcHKzJLgYGVmSXCwMrMkOFiZWRIcrMwsCSMWrCSdIukpSRskXThS2zGz8WFEgpWkbuCfgY8A88kq3swfiW2Z2fgwUjeFHgNsiIhnASTdDCwG1g324R21bm5YcVKpA+hPt3nyrg+W2m/3m9mNhXs9cGip/bI1K/R67LqJpXWZpdvUOWVTualBM3rrCDix/k6p/e4zIUuLWTSj3H6nddWB7EbO0vqcUkeTxfwTJpXWJ+TpNntPLv0mzqkHTS61vyqMVLCaDfyi4eeNwO81fkDSMmBZ/uP2z5591Vgthro/8GrVgxgB3q/0vLfqAbRjpILVYPXgB2R8RsRyYDmApEdSTrDcnbG6b96v9Eh6pOoxtGOkTrBvBA5p+HkO8NIIbcvMxoGRClY/AY6QNE/SJGAJsGKEtmVm48CIHAZGRJ+kc4G7gG7g2ohYu5tVlo/EODrEWN0371d6kt63jnj4npnZcHwHu5klwcHKzJJQebAaS2k5kp6X9Lik1f2XiSXNkLRS0tP56/Sqx9kMSddK2izpiYa2IfdF0kX5d/iUpJOrGfXwhtiviyW9mH9vqyWd2vC7VPbrEEn3Slovaa2k8/L25L+zX4mIyhayk+/PAIcDk4DHgPlVjqnN/Xke2H+ntq8DF+bvLwT+rupxNrkvxwFHA08Mty9kKVWPAXsA8/LvtLvqfSiwXxcDXxzksynt1yzg6Pz9XsDP8vEn/531L1XPrH6VlhMRvUB/Ws5Yshi4Pn9/PfCJCsfStIi4H9iyU/NQ+7IYuDkitkfEc8AGsu+24wyxX0NJab96IuLR/P3bwHqyTJLkv7N+VQerwdJyZlc0ljIEcLekVXk6EcCBEdED2V8oYGZlo2vfUPsyFr7HcyWtyQ8T+w+VktwvSXOBo4CHGEPfWdXBati0nMQsioijyZ42cY6k46oe0ChJ/Xu8CngPsADoAS7P25PbL0nTgFuB8yPird19dJC2jt63qoPVmErLiYiX8tfNwO1k0+pNkmYB5K+bqxth24bal6S/x4jYFBG1iKgDV/Prw6Gk9kvSRLJAdWNE3JY3j5nvrOpgNWbSciRNlbRX/3vgw8ATZPuzNP/YUuCOakZYiqH2ZQWwRNIekuYBRwAPVzC+lvT/Y86dRva9QUL7JUnANcD6iLii4Vdj5zur+gw/cCrZlYtngC9XPZ429uNwsqsrjwFr+/cF2A+4B3g6f51R9Vib3J+byA6JdpD9L3zW7vYF+HL+HT4FfKTq8Rfcr38DHgfWkP0jnpXgfv0h2WHcGmB1vpw6Fr6z/sXpNmaWhKoPA83MmuJgZWZJcLAysyQ4WJlZEhyszCwJDlZmlgQHKzNLwv8Dyf4B6LSrJnwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}