examples/Notebooks/flopy3_uzf_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "### UZF example\n",
    "Demonstrates functionality of the flopy UZF module using the example from [Niswonger and others (2006)](http://pubs.usgs.gov/tm/2006/tm6a19/). This is the same as the SFR example problem from Prudic and others (2004;\n",
    "p. 13–19), except the UZF package replaces the ET and RCH packages.\n",
    "\n",
    "#### Problem description:\n",
    "\n",
    "* Grid dimensions: 1 Layer, 15 Rows, 10 Columns  \n",
    "* Stress periods: 12  \n",
    "* Units are in seconds and days\n",
    "* Flow package: LPF  \n",
    "* Stress packages: SFR, GHB, UZF \n",
    "* Solver: SIP  \n",
    "\n",
    "<img src=\"./img/Niswonger2006_fig13.png\" width=\"400\" height=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "pandas version: 1.0.5\n",
      "flopy version: 3.3.3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import platform\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\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('pandas version: {}'.format(pd.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set name of MODFLOW exe\n",
    "#  assumes executable is in users path statement\n",
    "exe_name = 'mf2005'\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name += '.exe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'temp'\n",
    "if not os.path.isdir(path):\n",
    "    os.mkdir(path)\n",
    "gpth = os.path.join('..', 'data', 'mf2005_test', 'UZFtest2.*')\n",
    "for f in glob.glob(gpth):\n",
    "    shutil.copy(f, path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load example dataset, skipping the UZF package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = flopy.modflow.Modflow.load('UZFtest2.nam', version='mf2005', exe_name=exe_name, \n",
    "                               model_ws=path, load_only=['ghb', 'dis', 'bas6', 'oc', 'sip', 'lpf', 'sfr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### remove previous uzf external file references \n",
    "(so they don't conflict with the ones made by flopy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['temp/UZFtest2.uzfot',\n",
       " 'temp/UZFtest2.uzf1',\n",
       " 'temp/UZFtest2.uzf2',\n",
       " 'temp/UZFtest2.uzf3',\n",
       " 'temp/UZFtest2.uzf4',\n",
       " 'temp/UZFtest2.sg1',\n",
       " 'temp/UZFtest2.sg2',\n",
       " 'temp/UZFtest2.sg3',\n",
       " 'temp/UZFtest2.sg4',\n",
       " 'temp/UZFtest2.sg5',\n",
       " 'temp/UZFtest2.sg6',\n",
       " 'temp/UZFtest2.sg7',\n",
       " 'temp/UZFtest2.sg8',\n",
       " 'temp/UZFtest2dv.sg9']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.external_fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm = [True if '.uz' in f else False for f in m.external_fnames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['temp/UZFtest2.sg1',\n",
       " 'temp/UZFtest2.sg2',\n",
       " 'temp/UZFtest2.sg3',\n",
       " 'temp/UZFtest2.sg4',\n",
       " 'temp/UZFtest2.sg5',\n",
       " 'temp/UZFtest2.sg6',\n",
       " 'temp/UZFtest2.sg7',\n",
       " 'temp/UZFtest2.sg8',\n",
       " 'temp/UZFtest2dv.sg9']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.external_fnames = [f for i, f in enumerate(m.external_fnames) if not rm[i]]\n",
    "m.external_fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.external_binflag = [f for i, f in enumerate(m.external_binflag) if not rm[i]]\n",
    "m.external_output = [f for i, f in enumerate(m.external_output) if not rm[i]]\n",
    "m.external_units = [f for i, f in enumerate(m.external_output) if not rm[i]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `izufbnd` array  \n",
    "* in the example, the UZF package **izufbnd** array is the same as the ibound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=m)\n",
    "quadmesh = mapview.plot_ibound()\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the ```irunbnd``` array  \n",
    "* read this in from an external file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "irnbndpth = os.path.join('..', 'data', 'uzf_examples', 'irunbnd.dat')\n",
    "irunbnd = np.loadtxt(irnbndpth)\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=m)\n",
    "irunbndplt = mapview.plot_array(irunbnd)\n",
    "plt.colorbar(irunbndplt, ax=ax, label='SFR segment')\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``vks`` (unsaturated zone vertical hydraulic conductivity) array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vksbndpth = os.path.join('..', 'data', 'uzf_examples', 'vks.dat')\n",
    "vks = np.loadtxt(vksbndpth)\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=m)\n",
    "vksplt = mapview.plot_array(vks)\n",
    "plt.colorbar(vksplt, ax=ax, label='Layer 1 Kv')\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `finf` array  \n",
    "* load infiltration rates from a file into a 3D array\n",
    "* `finf` can be submitted to FloPy as a 3D array, list of 2D arrays, list of numeric values, or single numeric value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 10, 1, 12)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.nrow_ncol_nlay_nper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U2zwI7AGuLZadRfvdwFuZ+UHgAeD+7rskSSrD+5kDuQPYV7zeB9zZ0f5wZr6bmaeA48BNETEMrM7MpzMzgf0Ttmnv6zHglnZ1IkmLUWb0fOm12c6BJPAHEZHAf8jMvcD6zDzT6mieiYgrinWvBL7Vse3pou1i8Xpie3ubV4p9jUXED4DLgbNTHdDFZoP9B26d5eG/f6+du5S1Y7DjRGkhWTsKrF/CNbvLO9dhcBTiIlx3W3nTYysHx4HyY0Yz2bK9MfPKPTK0pvULbMz5iTk+EKX/rix2s/2N/URmvlokiSci4rvTrDtZmstp2qfb5id3HLGH1hAYPzW8YfojlqSa6qvbuWfmq8XXNyLia8BNwOsRMVxUH8PAG8Xqp4GrOjbfCLxatG+cpL1zm9MRsRRYA7w5yXHsBfYCbPrpK3L37U/OqpO9sP/Arbxyfj2Hrikt5I+qnbJjLj2/hCMnB0uLuW1z66Nc2TEbo2Mce6pZWsz2J3Jjzk/Mdy+NBfn9XMxmrPciYigiVrVfAz8HPA8cAEaK1UaArxevDwC7ijOrNtGaLH+2GO66EBE3F/Mbuyds097XXcChYp5EkhafbF1M2Oul12ZTgawHvlbMaS8Fficzfz8i/gh4NCLuBr4PfBogM1+IiEeBY8AY8PnMbH8U+RzwEDAIPF4sAF8GvhIRx2lVHrt60DdJqq063AtrxgSSmSeBD0/Sfg64ZYpt7gPum6T9MHDDJO3vUCQgSVI9eCW6JFVM0kdXokuSNJEViCRVTj0eaWsCkaQKqsN5qA5hSZK6YgUiSRXkJLokqW9ZgUhSxbSuHK9+BWICkaQK8iwsSVKtRMT3gAtAExjreIjge5hAJKmCFvg03p/JzCmfx9TmJLokqStWIJJUQQs4iT7ZE2gnZQKRpIpJ5ucZ5sC6iDjc8X7vJAniPU+gzcynJtuZCUSSFo+z002Kw5RPoJ00gTgHIkkVlPOwzGSaJ9BOygpEktQ26RNop1rZBCJJVbNAV6JP9QTaqTiEJUnqihWIJFVRDZ4HYgKRpAqqw80UHcKSJHXFCkSSKqgOj7StbQK52Gyw/8CtpcV77dylrB2DHSdKC8na0dbXsmPG4DjbNo+WFnPV4DhA6TFjebBle6O0mENrWkMSZcfMRnDdbeX9qq8s/j/Ljjl0rsmnXnintJhDGwZKi1VVtU0gktSvknrMgdQ2gSxrNNl9+5Olxdt/4FZeOb+eQ9eUFvJHlUfZMZeeX8KRk4OlxWxXHmXHbIyOceypZmkx25VH2TGbg43F8f959m2O7jtVWsytI5vmb+cJ1CCBOIkuSepKbSsQSepndZhEtwKRJHXFCkSSqqgGFYgJRJIqZ94eKNVTDmFJkrpiBSJJVVSDISwrEElSV6xAJKlqFuiBUnNlBSJJ6ooViCRVUQ3mQEwgklRJDmFJkvqUFYgkVVENhrCsQCRJXbECkaQqqkEFYgKRpKrxgVKSpH5mBSJJFeQDpSRJfcsKRJKqqAYViAlEkqrISXRJUr+adQKJiEZE/HFEfKN4f1lEPBERLxdfL+1Y996IOB4RL0XEbR3t2yLiaPG9L0REFO0rIuKRov2ZiLi6d12UpPqJ7P3Sa3OpQH4JeLHj/T3Awcy8FjhYvCcitgC7gOuBncAXI6JRbPMgsAe4tlh2Fu13A29l5geBB4D7u+qNJKk0s0ogEbER+NvAf+xovgPYV7zeB9zZ0f5wZr6bmaeA48BNETEMrM7MpzMzgf0Ttmnv6zHglnZ1IkmLTs7T0mOznUT/deAfA6s62tZn5hmAzDwTEVcU7VcC3+pY73TRdrF4PbG9vc0rxb7GIuIHwOXA2akO6N0fLuVX771zqm/33NCGAdauhB0nSgvJ2tHW17JjxuA42zaPlhZz1eA4QOkxY3mwZXtj5pV7ZGhN6zNR2TGzUf7/Zy6DD3/87dJiXjIKS5YOsHVkU2kxhzYMlBarqmasQCLi7wBvZOaRWe5zssohp2mfbpuJx7InIg5HxOGswzluktSVaJ2F1eulx2ZTgXwCuD0ifh4YAFZHxG8Br0fEcFF9DANvFOufBq7q2H4j8GrRvnGS9s5tTkfEUmAN8ObEA8nMvcBegPXrNuTRfadm18se2DqyiXevH+LQNaWF/FHlUXbMpeeXcOTkYGkx25+Oy47ZGB3j2FPN0mK2K4+yYzYHG6X/246tHi/953bFyXco+2/CvKrBZ+QZK5DMvDczN2bm1bQmxw9l5t8DDgAjxWojwNeL1weAXcWZVZtoTZY/Wwx3XYiIm4v5jd0Ttmnv664iRg3++SRp8Xo/FxL+GvBoRNwNfB/4NEBmvhARjwLHgDHg85nZ/sj1OeAhYBB4vFgAvgx8JSKO06o8dr2P45Kk+qvBR+g5JZDM/EPgD4vX54BbpljvPuC+SdoPAzdM0v4ORQKSJNWDtzKRpCrqtwpEklQCHyglSepnViCSVEHzce+qXrMCkSR1xQpEkqrICkSSVCcTH90xHROIJKnTxEd3TMkEIkkVtBAPlJri0R1TMoFI0uKxrn1H82LZM+H77Ud3jM9mZ06iS1IVzc+FhGcz88bJvtH56I6I+ORsdmYFIkmCHz+643vAw8CO4tEdUzKBSFLVLMAjbad5dMeUHMKSpCqqwXUgJhBJ0k/ofHTHdEwgklRB3gtLktS3rEAkqYpqUIGYQCSpimqQQBzCkiR1xQpEkipmtveuWmhWIJKkrliBSFIVzc+9sHqqtgmksWwJW0c2lRZvaMMAg6Ow40RpIVk72vpadswYHGfb5tHSYq4abN34s+yYsTzYsr1RWsyhNa0/CGXHzEb5/5+5AL8rSzYMlP43YV45hCVJ6le1rUCaF8c5uu9UafG2jmzi3euHOHRNaSF/9Amu7JhLzy/hyMnB0mK2Px2XHbMxOsaxp5qlxWxXHmXHbA42Sv+3HVs9XvrP7YqT75T+N2E+OYkuSepbta1AJKmvWYFIkvqVFYgkVU1NLiQ0gUhSFdUggTiEJUnqihWIJFWRFYgkqV9ZgUhSBdVhEt0KRJLUFROIJKkrDmFJUhU5hCVJ6ldWIJJUNV6JLknqWg0SiENYkqSuWIFIUhVZgUiS+pUViCRVTFCPSXQrEElSV6xAJKmKalCBmEAkqWpqch2IQ1iSpK7MmEAiYiAino2I70TECxHxL4r2yyLiiYh4ufh6acc290bE8Yh4KSJu62jfFhFHi+99ISKiaF8REY8U7c9ExNW976ok1UjOw9Jjs6lA3gV2ZOaHgY8AOyPiZuAe4GBmXgscLN4TEVuAXcD1wE7gixHRKPb1ILAHuLZYdhbtdwNvZeYHgQeA+3vQN0nSPJoxgWTLXxRvlxVLAncA+4r2fcCdxes7gIcz893MPAUcB26KiGFgdWY+nZkJ7J+wTXtfjwG3tKsTSVqUalCBzGoSvaggjgAfBP59Zj4TEesz8wxAZp6JiCuK1a8EvtWx+emi7WLxemJ7e5tXin2NRcQPgMuBs1MdU2PZEraObJrN4ffE0IYBBkdhx4nSQrJ2tPW17JgxOM62zaOlxVw1OA5QesxYHmzZ3ph55R4ZWtP6TFR2zGyU//+ZC/C7suqvLuOzX/pAaTFfO7dsXvffN5PomdnMzI8AG2lVEzdMs/pklUNO0z7dNj+544g9EXE4Ig5nHc5xk6Q+NqfTeDPzzyPiD2nNXbweEcNF9TEMvFGsdhq4qmOzjcCrRfvGSdo7tzkdEUuBNcCbk8TfC+wFWL9uQx7dd2ouh/++bB3ZxLvXD3HomtJC/ugTXNkxl55fwpGTg6XFbH86LjtmY3SMY081S4vZrjzKjtkcbJT+bzu2erz0n9trV7/F7tufLC3m/gO3zm+AGnxGns1ZWB+IiLXF60HgVuC7wAFgpFhtBPh68foAsKs4s2oTrcnyZ4vhrgsRcXMxv7F7wjbtfd0FHCrmSSRJFTWbCmQY2FfMgywBHs3Mb0TE08CjEXE38H3g0wCZ+UJEPAocA8aAz2dm+yPX54CHgEHg8WIB+DLwlYg4Tqvy2NWLzklSLc3TpHevzZhAMvNPgI9O0n4OuGWKbe4D7puk/TDwnvmTzHyHIgFJkvpoEl2SpIm8F5YkVZEViCSpLqa6ddVUrEAkqYIWaA6kfeuqv4iIZcB/i4jHM/Nbk61sApEkAa1bVwGT3bpqUg5hSVIVLdC9sCKiERHP0bo4/InMfGaqdU0gklQ185E8WglkXft2UMWy5z2h53DrKoewJGnxOJuZN85mxQm3rnp+snWsQCSpYmKelhnjTn3rqklZgUiS2ia9ddVUK5tAJKmKFuA03qluXTUVE4gkVZD3wpIk9S0rEEmqIisQSVK/sgKRpCqqQQViApGkqkkn0SVJfcwKRJKqyApEktSvrEAkqYKcA5Ek9S0rEEmqohpUILVNII1lS9g6sqm0eEMbBhh6K/nUwfL+V4fWtG7AXHrM5U1uGr5QXszlrX6WHnN5sGV7o7yYxf9n2TGzMc62zaOlxVw1OE6Owo4TpYVk7SicOLeOX733ztJiDm0YmNf9O4QlSepbta1AmhfHObrvVGnxto5sgqFBjj3VLC1m+5OqMY35fmI2BxscOTlYWsxtm0cZWz3OoWtKC8mOE7Di5Dvl/02YL3N4hvlCsgKRJHWlthWIJPW1GlQgJhBJqpjASXRJUh+zApGkKrICkST1KysQSaqgyOqXICYQSaoarwORJPUzKxBJqiBP45Uk9S0rEEmqohpUICYQSaogh7AkSX3LCkSSqsgKRJLUr6xAJKlq0jkQSVIfswKRpCqqQQViApGkivGBUpKkvmYFIklVVIPbuVuBSJK6MmMCiYirIuK/RsSLEfFCRPxS0X5ZRDwRES8XXy/t2ObeiDgeES9FxG0d7dsi4mjxvS9ERBTtKyLikaL9mYi4uvddlaT6iOz90muzqUDGgF/JzP8NuBn4fERsAe4BDmbmtcDB4j3F93YB1wM7gS9GRKPY14PAHuDaYtlZtN8NvJWZHwQeAO7vQd8kqZ5ynpYemzGBZOaZzPx28foC8CJwJXAHsK9YbR9wZ/H6DuDhzHw3M08Bx4GbImIYWJ2ZT2dmAvsnbNPe12PALe3qRJJUTXOaRC+Glj4KPAOsz8wz0EoyEXFFsdqVwLc6NjtdtF0sXk9sb2/zSrGvsYj4AXA5cHaqY2ksW8LWX9gyl8N/X4bWtPLZlu2NGdY0pjGrFrPJTcMXyou5PMjzwc/99/KmWFcNjhMbBtg6sqm0mEMbBuZ1/zE+r7vviVn/D0fEJcB/Bn45M89Pt+okbTlN+3TbTDyGPRFxOCIOV//8BEnqb7OqQCJiGa3k8duZ+V+K5tcjYrioPoaBN4r208BVHZtvBF4t2jdO0t65zemIWAqsAd6ceByZuRfYC7D+ip/KY081Z3P4PdH+1GhMYxpz5pjNwQZHTg6WFnPb5lEaZ9/m6L5TpcWc92qnBp+SZ3MWVgBfBl7MzH/b8a0DwEjxegT4ekf7ruLMqk20JsufLYa7LkTEzcU+d0/Ypr2vu4BDxTyJJC1KdTgLazYVyCeAvw8cjYjnirZ/Avwa8GhE3A18H/g0QGa+EBGPAsdoncH1+cxsf/z5HPAQMAg8XizQSlBfiYjjtCqPXe+zX5KkOYqIq2id4LQBGAf2ZuZvTLX+jAkkM/8bk89RANwyxTb3AfdN0n4YuGGS9ncoEpAkLXrJQl2J3r5s49sRsQo4EhFPZOaxyVb2SnRJEjDtZRuT8l5YklRBC3033gmXbUzKBCJJi8e6iDjc8X5vcXbrT5jtZRsmEEmqovmpQM5m5o3TrTDFZRuTMoFIUsUs1AOlprlsY1JOokuS2tqXbeyIiOeK5eenWtkKRJKqJnNBTuOd4bKN97ACkSR1xQpEkipooU/jnQ0TiCRVUQ0SiENYkqSuWIFIUgXVYQjLCkSS1BUrEEmqmgTGq1+CmEAkqYqqnz8cwpIkdccKRJIqyEl0SVLfsgKRpCpamEfazokViCSpK1YgklRBdZgDMYFIUtUknsYrSepfta1AGg3Ysr1RWryhNa1nrJQdMxvBdbeV99+0cnAcwJh9FDOauQC/K01uGr5QXszlARsG2DqyqbyYGwbmbd+tR9pWvwSxApEkdaW2FUizCceeapYWrzp9DIUAAAo/SURBVP0JruyYzcEGR04OlhZz2+ZRAGP2UczG6Nii+F3h7Xc4uu9UaTHnvdoZn9/d90JtE4gk9TOHsCRJfcsKRJKqxtN4JUn9zApEkiona3EvLBOIJFVQHW5l4hCWJKkrViCSVEU1GMKyApEkdcUKRJKqJiFqcCW6FYgkqStWIJJURTWYAzGBSFIVVT9/OIQlSeqOFYgkVZB345Uk9S0rEEmqohpUICYQSaqapBZPJHQIS5LUFSsQSaqYIJ1ElyT1LysQSaqiGlQgJhBJqqIaJJAZh7Ai4jcj4o2IeL6j7bKIeCIiXi6+XtrxvXsj4nhEvBQRt3W0b4uIo8X3vhARUbSviIhHivZnIuLq3nZRkjQfZjMH8hCwc0LbPcDBzLwWOFi8JyK2ALuA64ttvhgRjWKbB4E9wLXF0t7n3cBbmflB4AHg/m47I0l9oX0ab6+XHpsxgWTmU8CbE5rvAPYVr/cBd3a0P5yZ72bmKeA4cFNEDAOrM/PpzExg/4Rt2vt6DLilXZ1Ikqqr2zmQ9Zl5BiAzz0TEFUX7lcC3OtY7XbRdLF5PbG9v80qxr7GI+AFwOXB2ugNoNGDLth92efhzN7RqgFzW4Lrbyps2Wjk4Ti4b58Mff7u0mJeMtr4as39ixuCS0n9uo5ls2d6YeeUeGVoTsGqArSObyou5YWBe978YT+OdrHLIadqn2+a9O4/YExGHI+Jw1uFex5JUI5PNeU+n248lr0fEcFF9DANvFO2ngas61tsIvFq0b5ykvXOb0xGxFFjDe4fMAMjMvcBegPXrNuTRfae6PPy52zqyiea6VRw5OVhazG2bRxlbPc6ha0oLyY4Tra/G7J+YS88vKf3ntjE6xrGnmqXF3LK9AW+/Q9l/E+bVwlQgDwH/jtY0w4y6rUAOACPF6xHg6x3tu4ozqzbRmix/thjuuhARNxfzG7snbNPe113AoWKeRJIWqWwlkF4vM0WdfM57SjNWIBHxVeCTwLqIOA38c+DXgEcj4m7g+8Cni+AvRMSjwDFgDPh8ZrY/hnyOVnYbBB4vFoAvA1+JiOPFge+a7cFLkuZkXUQc7ni/txjZ6cqMCSQzPzPFt26ZYv37gPsmaT8M3DBJ+zsUCUiSRGsWeH4GYs5m5o292pn3wpIkdcVbmUhSFfk8EElSNyKz58uMMVtz3k8D10XE6WKee0pWIJIkYNo570mZQCSpimpwNYNDWJKkrliBSFLVJDBe/QrEBCJJlTO7K8cXmkNYkqSuWIFIUhVZgUiS+pUViCRVkRWIJKlfWYFIUtV4Gq8kqTsJWf27KTqEJUnqihWIJFWRk+iSpH5lBSJJVeMkuiSpaw5hSZL6lRWIJFWRFYgkqV/VtgJpLFvC1pFNpcUb2jAAXOSm1RfKi7l0gKGLTf7fP3urtJivXbwUwJjzFPPt8w0+9cI7pcUc2jAAS5ul/9yyCrZs+2F5MVcNwKqBBfibMF/q8TyQ2iYQSepbCYxX/0r02iaQ5sVxju47VVq89iebsmNes+kv2H37k6XF3H/gVgBjzlPMP/kflyyKn9vFFHMxq20CkaS+VoMhLCfRJUldsQKRpCqyApEk9SsrEEmqnPReWJKkLiSkD5SSJPUrKxBJqqIaDGFZgUiSumIFIklVVIPTeE0gklQ1mbW4F5ZDWJKkrliBSFIV1WAIywpEktQVKxBJqqCswRyICUSSKqceTyR0CEuS1BUrEEmqmsQr0SVJ/csKRJKqyLvxSpL6lRWIJFVMAlmDORATiCRVTaZDWHMRETsj4qWIOB4R9yz08UjSYjSXv8WVqEAiogH8e+BngdPAH0XEgcw8trBHJkkLYyGGsOb6t7gqFchNwPHMPJmZPwQeBu5Y4GOSpMVmTn+LK1GBAFcCr3S8Pw38jek2aCxbwtaRTfN6UJ2GNgwAlB7ztXPL2H/g1tJivnbuUgBjzlPMoQ2NRfFzu5hizpuFmQOZ09/iqiSQmKTtPfVbROwB9hRv333goX/z/LweVTWsA84u9EGUwH72l8XSz+vmY6cXeOubT+Zj6+Zh1wMRcbjj/d7M3NvxflZ/i9uqkkBOA1d1vN8IvDpxpaKjewEi4nBm3ljO4S0c+9lf7Gd/mfDHuGcyc+d87HcWZvW3uK0qcyB/BFwbEZsiYjmwCziwwMckSYvNnP4WV6ICycyxiPi/gG8CDeA3M/OFBT4sSVpU5vq3uBIJBCAzfw/4vTlssnfmVfqC/ewv9rO/9F0/5/K3OLIGDy2RJFVPVeZAJEk1U8sEUrfbnkTEb0bEGxHxfEfbZRHxRES8XHy9tON79xZ9eykibuto3xYRR4vvfSEiomhfERGPFO3PRMTVZfav4/iuioj/GhEvRsQLEfFLRXtf9TUiBiLi2Yj4TtHPf9GP/ew4xkZE/HFEfKN433f9jIjvFcf3XPvMqn7sZ89lZq0WWhM7J4DNwHLgO8CWhT6uGY55O/Ax4PmOtn8N3FO8vge4v3i9pejTCmBT0ddG8b1ngY/TOlf7ceBTRfsvAF8qXu8CHlmgfg4DHyterwL+R9GfvuprcUyXFK+XAc8AN/dbPzv6+38DvwN8o49/dr8HrJvQ1nf97Pm/20IfQBf/0R8Hvtnx/l7g3oU+rlkc99X8ZAJ5CRguXg8DL03WH1pnQ3y8WOe7He2fAf5D5zrF66W0LuCKCvT567TuqdO3fQVWAt+mdbVu3/WT1nUAB4Ed/DiB9GM/v8d7E0jf9bPXSx2HsCa71P7KBTqW92N9Zp4BKL5eUbRP1b8ri9cT239im8wcA34AXD5vRz4LRYn+UVqfzvuur8WwznPAG8ATmdmX/QR+HfjHQOd9Nfqxnwn8QUQcidYdL6A/+9lTlTmNdw7mdKl9DU3Vv+n6Xal/k4i4BPjPwC9n5vliGHjSVSdpq0VfM7MJfCQi1gJfi4gbplm9lv2MiL8DvJGZRyLik7PZZJK2yvez8InMfDUirgCeiIjvTrNunfvZU3WsQOZ0qX2FvR4RwwDF1zeK9qn6d7p4PbH9J7aJiKXAGuDNeTvyaUTEMlrJ47cz878UzX3ZV4DM/HPgD4Gd9F8/PwHcHhHfo3VX1h0R8Vv0Xz/JzFeLr28AX6N1V9q+62ev1TGB9MttTw4AI8XrEVrzBe32XcVZG5uAa4FnixL6QkTcXJzZsXvCNu193QUcymKwtUzFcX0ZeDEz/23Ht/qqrxHxgaLyICIGgVuB79Jn/czMezNzY2ZeTev37FBm/j36rJ8RMRQRq9qvgZ8DnqfP+jkvFnoSppsF+HlaZ/icAP7pQh/PLI73q8AZ4CKtTyJ30xr/PAi8XHy9rGP9f1r07SWKsziK9htp/WCfAP4dP74QdAD4XeA4rbNANi9QP/8WrbL8T4DniuXn+62vwIeAPy76+Tzwz4r2vurnhD5/kh9PovdVP2md0fmdYnmh/Tel3/o5H4tXokuSulLHISxJUgWYQCRJXTGBSJK6YgKRJHXFBCJJ6ooJRJLUFROIJKkrJhBJUlf+F7xyj/9KC/8SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "finf = np.loadtxt(os.path.join('..', 'data', 'uzf_examples', 'finf.dat'))\n",
    "finf = np.reshape(finf, (m.nper, m.nrow, m.ncol))\n",
    "finf = {i: finf[i] for i in range(finf.shape[0])}\n",
    "\n",
    "# plot using PlotMapView\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=m)\n",
    "quadmesh = mapview.plot_array(finf[0])\n",
    "plt.colorbar(quadmesh)\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(m.dis.perlen.array.cumsum()/864600, \n",
    "         [a.mean() * 86400 * 365 * 12 for a in finf.values()], marker='o')\n",
    "plt.xlabel('Time, in days')\n",
    "plt.ylabel('Average infiltration rate, inches per year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `extwc` (extinction water content) array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "extwc = np.loadtxt(os.path.join('..', 'data', 'uzf_examples', 'extwc.dat'))\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=m)\n",
    "quadmesh = mapview.plot_array(extwc)\n",
    "plt.colorbar(quadmesh)\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the gages (observation points)\n",
    "* supplied as a dictionary keyed by `IFTUNIT`\n",
    "* A positive value [of `IFTUNIT`] is for output of individual cells whereas a negative value is for output that is summed over all model cells. \n",
    "* values are a list of `[IUZROW, IUZCOL, IFTUNIT, IUZOPT]`\n",
    "* `IUZROW` and `IUZCOL` are zero based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "uzgag = {-68: [-68],\n",
    "         65: [2, 5, 65, 1], #Print time, head, uz thickness and cum. vols of infiltration, recharge, storage, change in storage and ground-water discharge to land surface.\n",
    "         66: [5, 2, 66, 2], #Same as option 1 except rates of infiltration, recharge, change in storage, and ground-water discharge also are printed.\n",
    "         67: [9, 4, 67, 3]} #Prints time, ground-water head, thickness of unsaturated zone, followed by a series of depths and water contents in the unsaturated zone. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the UZF package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "uzf = flopy.modflow.ModflowUzf1(m,\n",
    "                                nuztop=1, iuzfopt=1, irunflg=1, ietflg=1,\n",
    "                                ipakcb=0, \n",
    "                                iuzfcb2=61,# binary output of recharge and groundwater discharge\n",
    "                                ntrail2=25, nsets=20,\n",
    "                                surfdep=1.0, uzgag=uzgag,\n",
    "                                iuzfbnd=m.bas6.ibound.array, \n",
    "                                irunbnd=irunbnd, \n",
    "                                vks=vks, # saturated vertical hydraulic conductivity of the uz\n",
    "                                finf=finf, #infiltration rates\n",
    "                                eps=3.5, # Brooks-Corey relation of water content to hydraulic conductivity (epsilon)\n",
    "                                thts = 0.35, # saturated water content of the uz in units of volume of water to total volume\n",
    "                                pet=5.000000E-08, # potential ET\n",
    "                                extdp=15., # ET extinction depth(s)\n",
    "                                extwc=extwc, #extinction water content below which ET cannot be removed from the unsaturated zone\n",
    "                                unitnumber=19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf2005\n",
      "\n",
      "                                  MODFLOW-2005     \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n",
      "                             Version 1.12.00 2/3/2017                        \n",
      "\n",
      " Using NAME file: UZFtest2.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:37:13\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     2    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     4    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     3    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     3    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:    13    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     4    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     4    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     5    Time step:    15    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     6    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     6    Time step:    14    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     6    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:    12    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     7    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     7    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:    10    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     8    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     8    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     6    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     9    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:    15    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    10    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:    12    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    10    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    10    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     6    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    11    Time step:     7    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:    13    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    11    Time step:    14    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    11    Time step:    15    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     1    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     2    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     3    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     4    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     6    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     7    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    12    Time step:     8    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:     9    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:    10    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:    11    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:    12    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:    13    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:    12    Time step:    14    Ground-Water Flow Eqn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:    12    Time step:    15    Ground-Water Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:37:15\n",
      " Elapsed run time:  2.820 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    }
   ],
   "source": [
    "success, buff = m.run_model()\n",
    "if not success:\n",
    "    print('\"{}...did not run\"'.format(m.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "### Look at the budget output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, b'           GW ET', 10, 15, -1, 4, 2628000., 2628000., 2628000., b'', b'', b'', b'')\n",
      "(1, 1, b'    UZF RECHARGE', 10, 15, -1, 4, 2628000., 2628000., 2628000., b'', b'', b'', b'')\n",
      "(1, 1, b' SURFACE LEAKAGE', 10, 15, -1, 4, 2628000., 2628000., 2628000., b'', b'', b'', b'')\n",
      "(1, 1, b'       HORT+DUNN', 10, 15, -1, 4, 2628000., 2628000., 2628000., b'', b'', b'', b'')\n",
      "(1, 1, b'  STORAGE CHANGE', 10, 15, -1, 4, 2628000., 2628000., 2628000., b'', b'', b'', b'')\n",
      "(1, 2, b'           GW ET', 10, 15, -1, 4, 82713.07, 82713.07, 2710713., b'', b'', b'', b'')\n",
      "(1, 2, b'    UZF RECHARGE', 10, 15, -1, 4, 82713.07, 82713.07, 2710713., b'', b'', b'', b'')\n",
      "(1, 2, b' SURFACE LEAKAGE', 10, 15, -1, 4, 82713.07, 82713.07, 2710713., b'', b'', b'', b'')\n",
      "(1, 2, b'       HORT+DUNN', 10, 15, -1, 4, 82713.07, 82713.07, 2710713., b'', b'', b'', b'')\n",
      "(1, 2, b'  STORAGE CHANGE', 10, 15, -1, 4, 82713.07, 82713.07, 2710713., b'', b'', b'', b'')\n",
      "(5, 2, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 3132971.5, b'', b'', b'', b'')\n",
      "(5, 2, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 3132971.5, b'', b'', b'', b'')\n",
      "(5, 2, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 3132971.5, b'', b'', b'', b'')\n",
      "(5, 2, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 3132971.5, b'', b'', b'', b'')\n",
      "(5, 2, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 3132971.5, b'', b'', b'', b'')\n",
      "(10, 2, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 3946233.2, b'', b'', b'', b'')\n",
      "(10, 2, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 3946233.2, b'', b'', b'', b'')\n",
      "(10, 2, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 3946233.2, b'', b'', b'', b'')\n",
      "(10, 2, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 3946233.2, b'', b'', b'', b'')\n",
      "(10, 2, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 3946233.2, b'', b'', b'', b'')\n",
      "(15, 2, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 5256000., b'', b'', b'', b'')\n",
      "(15, 2, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 5256000., b'', b'', b'', b'')\n",
      "(15, 2, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 5256000., b'', b'', b'', b'')\n",
      "(15, 2, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 5256000., b'', b'', b'', b'')\n",
      "(15, 2, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 5256000., b'', b'', b'', b'')\n",
      "(1, 3, b'           GW ET', 10, 15, -1, 4, 82713.07, 82713.07, 5338713., b'', b'', b'', b'')\n",
      "(1, 3, b'    UZF RECHARGE', 10, 15, -1, 4, 82713.07, 82713.07, 5338713., b'', b'', b'', b'')\n",
      "(1, 3, b' SURFACE LEAKAGE', 10, 15, -1, 4, 82713.07, 82713.07, 5338713., b'', b'', b'', b'')\n",
      "(1, 3, b'       HORT+DUNN', 10, 15, -1, 4, 82713.07, 82713.07, 5338713., b'', b'', b'', b'')\n",
      "(1, 3, b'  STORAGE CHANGE', 10, 15, -1, 4, 82713.07, 82713.07, 5338713., b'', b'', b'', b'')\n",
      "(5, 3, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 5760971.5, b'', b'', b'', b'')\n",
      "(5, 3, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 5760971.5, b'', b'', b'', b'')\n",
      "(5, 3, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 5760971.5, b'', b'', b'', b'')\n",
      "(5, 3, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 5760971.5, b'', b'', b'', b'')\n",
      "(5, 3, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 5760971.5, b'', b'', b'', b'')\n",
      "(10, 3, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 6574233.5, b'', b'', b'', b'')\n",
      "(10, 3, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 6574233.5, b'', b'', b'', b'')\n",
      "(10, 3, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 6574233.5, b'', b'', b'', b'')\n",
      "(10, 3, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 6574233.5, b'', b'', b'', b'')\n",
      "(10, 3, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 6574233.5, b'', b'', b'', b'')\n",
      "(15, 3, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 7884000., b'', b'', b'', b'')\n",
      "(15, 3, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 7884000., b'', b'', b'', b'')\n",
      "(15, 3, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 7884000., b'', b'', b'', b'')\n",
      "(15, 3, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 7884000., b'', b'', b'', b'')\n",
      "(15, 3, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 7884000., b'', b'', b'', b'')\n",
      "(5, 4, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 8388972., b'', b'', b'', b'')\n",
      "(5, 4, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 8388972., b'', b'', b'', b'')\n",
      "(5, 4, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 8388972., b'', b'', b'', b'')\n",
      "(5, 4, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 8388972., b'', b'', b'', b'')\n",
      "(5, 4, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 8388972., b'', b'', b'', b'')\n",
      "(10, 4, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 9202233., b'', b'', b'', b'')\n",
      "(10, 4, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 9202233., b'', b'', b'', b'')\n",
      "(10, 4, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 9202233., b'', b'', b'', b'')\n",
      "(10, 4, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 9202233., b'', b'', b'', b'')\n",
      "(10, 4, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 9202233., b'', b'', b'', b'')\n",
      "(15, 4, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 10511999., b'', b'', b'', b'')\n",
      "(15, 4, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 10511999., b'', b'', b'', b'')\n",
      "(15, 4, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 10511999., b'', b'', b'', b'')\n",
      "(15, 4, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 10511999., b'', b'', b'', b'')\n",
      "(15, 4, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 10511999., b'', b'', b'', b'')\n",
      "(5, 5, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 11016970., b'', b'', b'', b'')\n",
      "(5, 5, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 11016970., b'', b'', b'', b'')\n",
      "(5, 5, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 11016970., b'', b'', b'', b'')\n",
      "(5, 5, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 11016970., b'', b'', b'', b'')\n",
      "(5, 5, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 11016970., b'', b'', b'', b'')\n",
      "(10, 5, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 11830231., b'', b'', b'', b'')\n",
      "(10, 5, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 11830231., b'', b'', b'', b'')\n",
      "(10, 5, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 11830231., b'', b'', b'', b'')\n",
      "(10, 5, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 11830231., b'', b'', b'', b'')\n",
      "(10, 5, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 11830231., b'', b'', b'', b'')\n",
      "(15, 5, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 13139997., b'', b'', b'', b'')\n",
      "(15, 5, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 13139997., b'', b'', b'', b'')\n",
      "(15, 5, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 13139997., b'', b'', b'', b'')\n",
      "(15, 5, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 13139997., b'', b'', b'', b'')\n",
      "(15, 5, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 13139997., b'', b'', b'', b'')\n",
      "(5, 6, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 13644968., b'', b'', b'', b'')\n",
      "(5, 6, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 13644968., b'', b'', b'', b'')\n",
      "(5, 6, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 13644968., b'', b'', b'', b'')\n",
      "(5, 6, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 13644968., b'', b'', b'', b'')\n",
      "(5, 6, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 13644968., b'', b'', b'', b'')\n",
      "(10, 6, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 14458229., b'', b'', b'', b'')\n",
      "(10, 6, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 14458229., b'', b'', b'', b'')\n",
      "(10, 6, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 14458229., b'', b'', b'', b'')\n",
      "(10, 6, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 14458229., b'', b'', b'', b'')\n",
      "(10, 6, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 14458229., b'', b'', b'', b'')\n",
      "(15, 6, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 15767995., b'', b'', b'', b'')\n",
      "(15, 6, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 15767995., b'', b'', b'', b'')\n",
      "(15, 6, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 15767995., b'', b'', b'', b'')\n",
      "(15, 6, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 15767995., b'', b'', b'', b'')\n",
      "(15, 6, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 15767995., b'', b'', b'', b'')\n",
      "(5, 7, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 16272966., b'', b'', b'', b'')\n",
      "(5, 7, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 16272966., b'', b'', b'', b'')\n",
      "(5, 7, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 16272966., b'', b'', b'', b'')\n",
      "(5, 7, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 16272966., b'', b'', b'', b'')\n",
      "(5, 7, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 16272966., b'', b'', b'', b'')\n",
      "(10, 7, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 17086228., b'', b'', b'', b'')\n",
      "(10, 7, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 17086228., b'', b'', b'', b'')\n",
      "(10, 7, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 17086228., b'', b'', b'', b'')\n",
      "(10, 7, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 17086228., b'', b'', b'', b'')\n",
      "(10, 7, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 17086228., b'', b'', b'', b'')\n",
      "(15, 7, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 18395994., b'', b'', b'', b'')\n",
      "(15, 7, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 18395994., b'', b'', b'', b'')\n",
      "(15, 7, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 18395994., b'', b'', b'', b'')\n",
      "(15, 7, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 18395994., b'', b'', b'', b'')\n",
      "(15, 7, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 18395994., b'', b'', b'', b'')\n",
      "(5, 8, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 18900966., b'', b'', b'', b'')\n",
      "(5, 8, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 18900966., b'', b'', b'', b'')\n",
      "(5, 8, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 18900966., b'', b'', b'', b'')\n",
      "(5, 8, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 18900966., b'', b'', b'', b'')\n",
      "(5, 8, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 18900966., b'', b'', b'', b'')\n",
      "(10, 8, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 19714228., b'', b'', b'', b'')\n",
      "(10, 8, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 19714228., b'', b'', b'', b'')\n",
      "(10, 8, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 19714228., b'', b'', b'', b'')\n",
      "(10, 8, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 19714228., b'', b'', b'', b'')\n",
      "(10, 8, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 19714228., b'', b'', b'', b'')\n",
      "(15, 8, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 21023994., b'', b'', b'', b'')\n",
      "(15, 8, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 21023994., b'', b'', b'', b'')\n",
      "(15, 8, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 21023994., b'', b'', b'', b'')\n",
      "(15, 8, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 21023994., b'', b'', b'', b'')\n",
      "(15, 8, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 21023994., b'', b'', b'', b'')\n",
      "(5, 9, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 21528966., b'', b'', b'', b'')\n",
      "(5, 9, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 21528966., b'', b'', b'', b'')\n",
      "(5, 9, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 21528966., b'', b'', b'', b'')\n",
      "(5, 9, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 21528966., b'', b'', b'', b'')\n",
      "(5, 9, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 21528966., b'', b'', b'', b'')\n",
      "(10, 9, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 22342228., b'', b'', b'', b'')\n",
      "(10, 9, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 22342228., b'', b'', b'', b'')\n",
      "(10, 9, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 22342228., b'', b'', b'', b'')\n",
      "(10, 9, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 22342228., b'', b'', b'', b'')\n",
      "(10, 9, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 22342228., b'', b'', b'', b'')\n",
      "(15, 9, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 23651994., b'', b'', b'', b'')\n",
      "(15, 9, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 23651994., b'', b'', b'', b'')\n",
      "(15, 9, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 23651994., b'', b'', b'', b'')\n",
      "(15, 9, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 23651994., b'', b'', b'', b'')\n",
      "(15, 9, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 23651994., b'', b'', b'', b'')\n",
      "(5, 10, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 24156966., b'', b'', b'', b'')\n",
      "(5, 10, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 24156966., b'', b'', b'', b'')\n",
      "(5, 10, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 24156966., b'', b'', b'', b'')\n",
      "(5, 10, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 24156966., b'', b'', b'', b'')\n",
      "(5, 10, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 24156966., b'', b'', b'', b'')\n",
      "(10, 10, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 24970228., b'', b'', b'', b'')\n",
      "(10, 10, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 24970228., b'', b'', b'', b'')\n",
      "(10, 10, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 24970228., b'', b'', b'', b'')\n",
      "(10, 10, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 24970228., b'', b'', b'', b'')\n",
      "(10, 10, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 24970228., b'', b'', b'', b'')\n",
      "(15, 10, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 26279994., b'', b'', b'', b'')\n",
      "(15, 10, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 26279994., b'', b'', b'', b'')\n",
      "(15, 10, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 26279994., b'', b'', b'', b'')\n",
      "(15, 10, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 26279994., b'', b'', b'', b'')\n",
      "(15, 10, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 26279994., b'', b'', b'', b'')\n",
      "(5, 11, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 26784966., b'', b'', b'', b'')\n",
      "(5, 11, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 26784966., b'', b'', b'', b'')\n",
      "(5, 11, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 26784966., b'', b'', b'', b'')\n",
      "(5, 11, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 26784966., b'', b'', b'', b'')\n",
      "(5, 11, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 26784966., b'', b'', b'', b'')\n",
      "(10, 11, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 27598228., b'', b'', b'', b'')\n",
      "(10, 11, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 27598228., b'', b'', b'', b'')\n",
      "(10, 11, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 27598228., b'', b'', b'', b'')\n",
      "(10, 11, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 27598228., b'', b'', b'', b'')\n",
      "(10, 11, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 27598228., b'', b'', b'', b'')\n",
      "(15, 11, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 28907994., b'', b'', b'', b'')\n",
      "(15, 11, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 28907994., b'', b'', b'', b'')\n",
      "(15, 11, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 28907994., b'', b'', b'', b'')\n",
      "(15, 11, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 28907994., b'', b'', b'', b'')\n",
      "(15, 11, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 28907994., b'', b'', b'', b'')\n",
      "(5, 12, b'           GW ET', 10, 15, -1, 4, 121100.21, 504971.6, 29412966., b'', b'', b'', b'')\n",
      "(5, 12, b'    UZF RECHARGE', 10, 15, -1, 4, 121100.21, 504971.6, 29412966., b'', b'', b'', b'')\n",
      "(5, 12, b' SURFACE LEAKAGE', 10, 15, -1, 4, 121100.21, 504971.6, 29412966., b'', b'', b'', b'')\n",
      "(5, 12, b'       HORT+DUNN', 10, 15, -1, 4, 121100.21, 504971.6, 29412966., b'', b'', b'', b'')\n",
      "(5, 12, b'  STORAGE CHANGE', 10, 15, -1, 4, 121100.21, 504971.6, 29412966., b'', b'', b'', b'')\n",
      "(10, 12, b'           GW ET', 10, 15, -1, 4, 195033.11, 1318233.4, 30226228., b'', b'', b'', b'')\n",
      "(10, 12, b'    UZF RECHARGE', 10, 15, -1, 4, 195033.11, 1318233.4, 30226228., b'', b'', b'', b'')\n",
      "(10, 12, b' SURFACE LEAKAGE', 10, 15, -1, 4, 195033.11, 1318233.4, 30226228., b'', b'', b'', b'')\n",
      "(10, 12, b'       HORT+DUNN', 10, 15, -1, 4, 195033.11, 1318233.4, 30226228., b'', b'', b'', b'')\n",
      "(10, 12, b'  STORAGE CHANGE', 10, 15, -1, 4, 195033.11, 1318233.4, 30226228., b'', b'', b'', b'')\n",
      "(15, 12, b'           GW ET', 10, 15, -1, 4, 314102.8, 2627999.8, 31535994., b'', b'', b'', b'')\n",
      "(15, 12, b'    UZF RECHARGE', 10, 15, -1, 4, 314102.8, 2627999.8, 31535994., b'', b'', b'', b'')\n",
      "(15, 12, b' SURFACE LEAKAGE', 10, 15, -1, 4, 314102.8, 2627999.8, 31535994., b'', b'', b'', b'')\n",
      "(15, 12, b'       HORT+DUNN', 10, 15, -1, 4, 314102.8, 2627999.8, 31535994., b'', b'', b'', b'')\n",
      "(15, 12, b'  STORAGE CHANGE', 10, 15, -1, 4, 314102.8, 2627999.8, 31535994., b'', b'', b'', b'')\n"
     ]
    }
   ],
   "source": [
    "fpth = os.path.join(path,'UZFtest2.uzfcb2.bin')\n",
    "avail = os.path.isfile(fpth)\n",
    "if avail:\n",
    "    uzfbdobjct = flopy.utils.CellBudgetFile(fpth)\n",
    "    uzfbdobjct.list_records()\n",
    "else:\n",
    "    print('\"{}\" is not available'.format(fpth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if success and avail:\n",
    "    r = uzfbdobjct.get_data(text='UZF RECHARGE')\n",
    "    et = uzfbdobjct.get_data(text='GW ET')\n",
    "    \n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "    mapview = flopy.plot.PlotMapView(model=m)\n",
    "    quadmesh = mapview.plot_array(r[6])\n",
    "    plt.colorbar(quadmesh)\n",
    "    linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if avail:\n",
    "    rtot = [rp.sum() for rp in r]\n",
    "    ettot = [etp.sum() for etp in et]\n",
    "    sltot = [sl.sum() for sl in uzfbdobjct.get_data(text='SURFACE LEAKAGE')]\n",
    "\n",
    "    plt.plot(rtot, label='simulated recharge')\n",
    "    plt.plot(np.abs(ettot), label='simulated actual et')\n",
    "    plt.plot(np.abs(sltot), label='simulated surface leakage')\n",
    "    plt.xlabel('Timestep')\n",
    "    plt.ylabel('Volume, in cubic feet')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the gages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpth = os.path.join(path, 'UZFtest2.uzf68.out')\n",
    "avail = os.path.isfile(fpth)\n",
    "if avail:\n",
    "    dtype = [('TIME', float), ('APPLIED-INFIL', float), ('RUNOFF', float), \n",
    "             ('ACTUAL-INFIL', float), ('SURFACE-LEAK', float), \n",
    "             ('UZ-ET', float), ('GW-ET', float), ('UZSTOR-CHANGE', float), \n",
    "             ('RECHARGE', float)]\n",
    "    # read data from file\n",
    "    df = np.genfromtxt(fpth, skip_header=3, dtype=dtype)\n",
    "    # convert numpy recarray to pandas dataframe\n",
    "    df = pd.DataFrame(data=df)\n",
    "    # set index to the time column\n",
    "    df.set_index(['TIME'], inplace=True)\n",
    "    # plot the data\n",
    "    ax = df.plot(legend=False, figsize=(15, 10))\n",
    "    patches, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(patches, labels, loc=1)\n",
    "    ax.set_ylabel('Volume for whole model, in cubic feet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot water content profile through time at row 10, column 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpth = os.path.join(path, 'UZFtest2.uzf67.out')\n",
    "avail = os.path.isfile(fpth)\n",
    "if avail:\n",
    "    data = []\n",
    "    with open(fpth) as input:\n",
    "        for i in range(3):\n",
    "            next(input)\n",
    "        for line in input:\n",
    "            line = line.strip().split()\n",
    "            if len(line) == 6:\n",
    "                layer = int(line.pop(0))\n",
    "                time = float(line.pop(0))\n",
    "                head = float(line.pop(0))\n",
    "                uzthick = float(line.pop(0))\n",
    "            depth = float(line.pop(0))\n",
    "            watercontent = float(line.pop(0))\n",
    "            data.append([layer, time, head, uzthick, depth, watercontent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "if avail:\n",
    "    df3 = pd.DataFrame(data, columns=['layer', 'time', 'head', 'uzthick', 'depth', 'watercontent'])\n",
    "    df3.head(41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "if avail:\n",
    "    wc = df3.watercontent.values.reshape(len(df3.time.unique()), 40).T\n",
    "    wc = pd.DataFrame(wc, columns=df3.time.unique(), index=df3.depth[0:40])\n",
    "    wc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if avail:\n",
    "    fig, ax = plt.subplots(figsize=(15, 10))\n",
    "    plt.imshow(wc, interpolation='None')\n",
    "    ax.set_aspect(3)\n",
    "    r, c = wc.shape\n",
    "    xcol_locs = np.linspace(0, c-1, 8, dtype=int)\n",
    "    ycol_locs = np.linspace(0, r-1, 5, dtype=int)\n",
    "    ax.set_xticks(xcol_locs)\n",
    "\n",
    "    xlabels = wc.columns\n",
    "    ax.set_xticklabels(xlabels[xcol_locs])\n",
    "    ax.set_ylabel('Depth, in feet')\n",
    "    ax.set_yticks(ycol_locs)\n",
    "    ax.set_yticklabels(wc.index[ycol_locs])\n",
    "    ax.set_xlabel('Time, in seconds')\n",
    "    plt.colorbar(label='Water content')"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}