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": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.16.2\n",
      "matplotlib version: 3.0.3\n",
      "pandas version: 0.24.2\n",
      "flopy version: 3.2.12\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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MwSxJlTGYJakyBrMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVJjJz3HNYkYmJiZyenh5Zv6ZpaLVaNE0zsp69Xu9Ub3va056L63a7C3oPW9M0zM7OHs7MbcM4vmfMklSZDeOewEqdPHmSTqczsn7tdpupqSna7fbIes6vz572tOeZzczMLOg9bMNem2fMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUGYNZkipjMEtSZQxmSaqMwSxJlTGYJakyBrMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyiwZzBHx3oh4JiK+GhEvRcRvlPqWiHg6Io5ExJci4vxSf095fbS8f0XfsXaVejcibumrT5fa0Yi479wvU5JWj+WcMf8D8DOZ+RHgo8B0RGwHfgv4TGZuBb4D3F3G3w18JzN/EvhMGUdEXAncAXwYmAZ+PyLOi4jzgN8DbgWuBO4sYyVpXYrMXP7giB8F/gq4B/ifQJOZb0fE9cADmXlLRBwo238dERuAHvAB4D6AzPzNcqwDwAPl0A9k5i2lvqt/3GImJiZyenp62XN/t5qmodVq0TTNyHr2er1Tve1pT3surtvtLug9bE3TMDs7ezgztw3j+BuWM6ic1R4GfpK5s9tvAN/NzLfLkGPAZWX7MuA1gBLabwHvL/X/03fY/n1eO61+3SLz2AnshNH+pUuqW6vVAmBqamok/Yb9A2BZwZyZ/wh8NCIuBL4M/NSgYeU5FnlvsfqgyykDT+Mzcy+wF+Diiy/OTqdz5omfQ+12m6mpKdrt9sh6zq/Pnva0Z109h509Z3VXRmZ+FzgIbAcuLJcqACaB18v2MeBygPJ+CzjRXz9tn8XqkrQuLeeujA+UM2Ui4gLgXwEvA08Bv1CG7QAeKdv7y2vK+3+Zcxey9wN3lLs2tgBbgWeAZ4Gt5S6P85n7gnD/uVicJK1Gy7mUMQH8YbnO/CPAw5n5aER8DfhiROwGvgJ8voz/PPBHEXGUuTPlOwAy86WIeBj4GvA2cG+5REJE/BJwADgP2JeZL52zFUrSKrNkMGfm88BPD6i/Alw7oP4D4PZFjvVp4NMD6o8Bjy1jvpK05vmbf5JUGYNZkipjMEtSZQxmSaqMwSxJlTGYJakyBrMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTKRmeOew4pMTEzk9PT0yPo1TUOr1aJpmpH17PV6p3rb0572XFy3213Qe9iapmF2dvZwZm4bxvE9Y5akymwY9wRW6uTJk3Q6nZH1a7fbTE1N0W63R9Zzfn32tKc9z2xmZmZB72Eb9to8Y5akyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUGYNZkipjMEtSZQxmSaqMwSxJlTGYJakyBrMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFVmyWCOiMsj4qmIeDkiXoqIT5T6RRHxREQcKc+bSj0i4sGIOBoRz0fE1X3H2lHGH4mIHX31ayLihbLPgxERw1isJK0Gyzljfhv4D5n5U8B24N6IuBK4D3gyM7cCT5bXALcCW8tjJ/A5mAty4H7gOuBa4P75MC9jdvbtN/3ulyZJq1Nk5tntEPEI8LvlcVNmvhERE8DBzJyKiD1l+6EyvgvcNP/IzI+X+h7gYHk8lZkfKvU7+8ctZmJiIqenR5ffTdPQarVommZkPXu93qne9rSnPRfX7XYX9B62pmmYnZ09nJnbhnH8DWczOCKuAH4aeBq4JDPfACjhvLkMuwx4rW+3Y6V2pvqxAfVB/Xcyd2bNpZdeyo033ng2039XRvUXLknLDuaI+DHgT4Ffycy/P8Nl4EFv5Arq7yxm7gX2AmzZsiXb7fYSsz53Op0OAPa0pz3r6zkzM7Og97ANe23LuisjIjYyF8pfyMw/K+U3yyUMyvPxUj8GXN63+yTw+hL1yQF1SVqXlnNXRgCfB17OzP/a99Z+YP7Oih3AI331u8rdGduBt8oljwPAzRGxqXzpdzNwoLz3vYjYXnrd1XcsSVp3lnMp4wbg3wEvRMRzpfafgFng4Yi4G/gWcHt57zHgNuAo8H1gBiAzT0TEp4Bny7hPZuaJsn0P0AEuAB4vD0lal5YM5sz8KwZfBwb42IDxCdy7yLH2AfsG1A8BVy01F0laD/zNP0mqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUGYNZkipjMEtSZQxmSaqMwSxJlTGYJakyBrMkVcZglqTKGMySVJnIzHHPYUUmJydz9+7dI+vX6/UAaJrGnva0Z2U9u93ugt7D1jQNs7OzhzNz2zCO7xmzJFVmw7gnsFIbN26k3W6PrF+n0wGwpz3tWWHPmZmZBb2Hbdhr84xZkipjMEtSZQxmSaqMwSxJlTGYJakyBrMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUmSWDOSL2RcTxiHixr3ZRRDwREUfK86ZSj4h4MCKORsTzEXF13z47yvgjEbGjr35NRLxQ9nkwIuJcL1KSVpPlnDF3gOnTavcBT2bmVuDJ8hrgVmBreewEPgdzQQ7cD1wHXAvcPx/mZczOvv1O7yVJ60pk5tKDIq4AHs3Mq8rrLnBTZr4RERPAwcyciog9Zfuh/nHzj8z8eKnvAQ6Wx1OZ+aFSv7N/3JlMTEzk9PToMrxpGlqtFk3TjKxnr9c71due9rTn4rrd7oLew9Y0DbOzs4czc9swjr/Sa8yXZOYbAOV5c6lfBrzWN+5YqZ2pfmxAfaCI2BkRhyLi0ArnLUnV23COjzfo+nCuoD5QZu4F9gJcfPHF2el0VjDFlWm320xNTdFut0fWc3599rSnPc9sZmZmQe9hG/baVnrG/Ga5hEF5Pl7qx4DL+8ZNAq8vUZ8cUJekdWulwbwfmL+zYgfwSF/9rnJ3xnbgrXKp4wBwc0RsKl/63QwcKO99LyK2l7sx7uo7liStS0teyoiIh5j78u7iiDjG3N0Vs8DDEXE38C3g9jL8MeA24CjwfWAGIDNPRMSngGfLuE9m5omyfQ9zd35cADxeHpK0bi0ZzJl55yJvfWzA2ATuXeQ4+4B9A+qHgKuWmockrRf+5p8kVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUGYNZkipjMEtSZQxmSapMZOa457AiExMTOT09PbJ+TdMA0Ov1Rtqz1Wqd6j0K8+uz57nX7XYX9B6FcX1u13rPpmmYnZ09nJnbhnF8z5glqTIbxj2BlTp58iSdTmdk/drtNsDIe05NTZ3qPQrz67PnuTczM7Og9yiM63O71nsO+3PjGbMkVcZglqTKGMySVBmDWZIqYzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyhjMklQZg1mSKmMwS1JlDGZJqozBLEmVMZglqTIGsyRVxmCWpMoYzJJUGYNZkipjMEtSZQxmSaqMwSxJlTGYJaky1QRzRExHRDcijkbEfeOejySNSxXBHBHnAb8H3ApcCdwZEVeOd1aSNB6RmeOeAxFxPfBAZt5SXu8CyMzfXGyfiYmJnJ6eHtEMoWkaAHq93kh7tlqtU71HYX599jz3ut3ugt6jMK7P7Vrv2TQNs7OzhzNz2zCOv2EYB12By4DX+l4fA647fVBE7AR2lpf/0Ol0XhzB3MbtYuDb457ECLjOtWU9rHNqWAeuJZhjQO0dp/KZuRfYCxARh4b106omrnNtcZ1rR0QcGtaxq7jGzNwZ8uV9ryeB18c0F0kaq1qC+Vlga0RsiYjzgTuA/WOekySNRRWXMjLz7Yj4JeAAcB6wLzNfWmK3vcOfWRVc59riOteOoa2xirsyJEk/VMulDElSYTBLUmVWXTCvxl/djoh9EXE8Il7sq10UEU9ExJHyvKnUIyIeLOt7PiKu7ttnRxl/JCJ29NWviYgXyj4PRsSg2w+HLiIuj4inIuLliHgpIj5R6mtqrRHx3oh4JiK+Wtb5G6W+JSKeLnP+Uvkim4h4T3l9tLx/Rd+xdpV6NyJu6atX8TmPiPMi4isR8Wh5vebWWObyavlcPTd/G9xYP7eZuWoezH0x+A3gg8D5wFeBK8c9r2XM+18AVwMv9tV+G7ivbN8H/FbZvg14nLl7u7cDT5f6RcAr5XlT2d5U3nsGuL7s8zhw65jWOQFcXbZ/HPi/zP2K/Zpaa+n9Y2V7I/B0mf/DwB2l/gfAPWX7F4E/KNt3AF8q21eWz/B7gC3ls31eTZ9z4FeBPwYeLa/X3BrLPF8FLj6tNrbP7Vj+EN7FH971wIG+17uAXeOe1zLnfgULg7kLTJTtCaBbtvcAd54+DrgT2NNX31NqE8DX++oLxo15zY8AP7uW1wr8KPA3zP2m6reBDad/Vpm72+j6sr2hjIvTP7/z42r5nDP3+wRPAj8DPFrmvKbW2Nf/Vd4ZzGP73K62SxmDfnX7sjHN5d26JDPfACjPm0t9sTWeqX5sQH2syj9lf5q5s8k1t9byT/zngOPAE8yd/X03M98eMLdT6ynvvwW8n7Nf/6h9Fvg14J/K6/ez9tY4L4G/iIjDMfc//QBj/NxWcR/zWVjWr26vcout8WzrYxMRPwb8KfArmfn3Z7ictmrXmpn/CHw0Ii4Evgz81KBh5fls1zPohGmk64yInwOOZ+bhiLhpvjxg6Kpd42luyMzXI2Iz8EREfP0MY4f+uV1tZ8xr6Ve334yICYDyfLzUF1vjmeqTA+pjEREbmQvlL2Tmn5XymlwrQGZ+FzjI3LXGCyNi/mSnf26n1lPebwEnOPv1j9INwM9HxKvAF5m7nPFZ1tYaT8nM18vzceZ+0F7LOD+347qms8LrQBuYu6C+hR9+YfDhcc9rmXO/goXXmP8LC79Y+O2y/a9Z+MXCM6V+EfBN5r5U2FS2LyrvPVvGzn+xcNuY1hjAfwc+e1p9Ta0V+ABwYdm+APhfwM8B/4OFX4z9Ytm+l4VfjD1ctj/Mwi/GXmHuS7GqPufATfzwy781t0bgfcCP923/b2B6nJ/bsfxFv8s/xNuY+7b/G8Cvj3s+y5zzQ8AbwEnmfnrezdz1tyeBI+V5/i8wmPs/DfgG8AKwre84/x44Wh4zffVtwItln9+l/EbnGNb5z5n7J9rzwHPlcdtaWyvwz4CvlHW+CPznUv8gc9++Hy0B9p5Sf295fbS8/8G+Y/16WUuXvm/qa/qcszCY19way5q+Wh4vzc9lnJ9bfyVbkiqz2q4xS9KaZzBLUmUMZkmqjMEsSZUxmCWpMgazJFXGYJakyvx/brRKMsYUMxYAAAAASUVORK5CYII=\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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\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): 2019/05/31 19:31:25\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Solving:  Stress period:     9    Time step:     5    Ground-Water Flow Eqn.\n",
      " Solving:  Stress period:     9    Time step:     6    Ground-Water Flow Eqn.\n",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " Solving:  Stress period:    12    Time step:    15    Ground-Water Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2019/05/31 19:31:28\n",
      " Elapsed run time:  2.528 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', np.float), ('APPLIED-INFIL', np.float), ('RUNOFF', np.float), \n",
    "             ('ACTUAL-INFIL', np.float), ('SURFACE-LEAK', np.float), \n",
    "             ('UZ-ET', np.float), ('GW-ET', np.float), ('UZSTOR-CHANGE', np.float), \n",
    "             ('RECHARGE', np.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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}