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)](https://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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:00.761270Z",
     "iopub.status.busy": "2022-07-30T20:19:00.759756Z",
     "iopub.status.idle": "2022-07-30T20:19:02.668139Z",
     "shell.execute_reply": "2022-07-30T20:19:02.660973Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]\n",
      "numpy version: 1.23.1\n",
      "matplotlib version: 3.5.2\n",
      "pandas version: 1.4.3\n",
      "flopy version: 3.3.6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import platform\n",
    "import shutil\n",
    "from tempfile import TemporaryDirectory\n",
    "\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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.723816Z",
     "iopub.status.busy": "2022-07-30T20:19:02.723421Z",
     "iopub.status.idle": "2022-07-30T20:19:02.739079Z",
     "shell.execute_reply": "2022-07-30T20:19:02.734292Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.746428Z",
     "iopub.status.busy": "2022-07-30T20:19:02.746071Z",
     "iopub.status.idle": "2022-07-30T20:19:02.767776Z",
     "shell.execute_reply": "2022-07-30T20:19:02.766347Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_dir = TemporaryDirectory()\n",
    "path = temp_dir.name\n",
    "\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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.787474Z",
     "iopub.status.busy": "2022-07-30T20:19:02.782327Z",
     "iopub.status.idle": "2022-07-30T20:19:02.826213Z",
     "shell.execute_reply": "2022-07-30T20:19:02.824498Z"
    }
   },
   "outputs": [],
   "source": [
    "m = flopy.modflow.Modflow.load(\n",
    "    \"UZFtest2.nam\",\n",
    "    version=\"mf2005\",\n",
    "    exe_name=exe_name,\n",
    "    model_ws=path,\n",
    "    load_only=[\"ghb\", \"dis\", \"bas6\", \"oc\", \"sip\", \"lpf\", \"sfr\"],\n",
    ")"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.843985Z",
     "iopub.status.busy": "2022-07-30T20:19:02.843481Z",
     "iopub.status.idle": "2022-07-30T20:19:02.871987Z",
     "shell.execute_reply": "2022-07-30T20:19:02.868690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.uzfot',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.uzf1',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.uzf2',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.uzf3',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.uzf4',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg1',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg2',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg3',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg4',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg5',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg6',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg7',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg8',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2dv.sg9']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.external_fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.888736Z",
     "iopub.status.busy": "2022-07-30T20:19:02.888106Z",
     "iopub.status.idle": "2022-07-30T20:19:02.896078Z",
     "shell.execute_reply": "2022-07-30T20:19:02.895287Z"
    }
   },
   "outputs": [],
   "source": [
    "rm = [True if \".uz\" in f else False for f in m.external_fnames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.906973Z",
     "iopub.status.busy": "2022-07-30T20:19:02.906653Z",
     "iopub.status.idle": "2022-07-30T20:19:02.912302Z",
     "shell.execute_reply": "2022-07-30T20:19:02.911482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg1',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg2',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg3',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg4',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg5',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg6',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg7',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/UZFtest2.sg8',\n",
       " '/var/folders/4t/2sbpjbgs1n73t8s7rx4gb_ph0000gn/T/tmp815uhghh/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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.915661Z",
     "iopub.status.busy": "2022-07-30T20:19:02.915388Z",
     "iopub.status.idle": "2022-07-30T20:19:02.923441Z",
     "shell.execute_reply": "2022-07-30T20:19:02.921262Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:02.947757Z",
     "iopub.status.busy": "2022-07-30T20:19:02.947297Z",
     "iopub.status.idle": "2022-07-30T20:19:03.171792Z",
     "shell.execute_reply": "2022-07-30T20:19:03.170622Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:03.193755Z",
     "iopub.status.busy": "2022-07-30T20:19:03.192518Z",
     "iopub.status.idle": "2022-07-30T20:19:03.535684Z",
     "shell.execute_reply": "2022-07-30T20:19:03.530594Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:03.545681Z",
     "iopub.status.busy": "2022-07-30T20:19:03.543494Z",
     "iopub.status.idle": "2022-07-30T20:19:03.856209Z",
     "shell.execute_reply": "2022-07-30T20:19:03.852191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wHPAugMzcFRHXAveU/T6YmbvK+nuATwCHA18qiyQdtLr8rLZZE09mPg68YYb2ncA5M7QncMU+znUTcNMM7fcCrx+gv5LUeYlPLpAkqTE+q02SqlPXkwaaZsUjSWqVFY8kVeignk4tSVKTrHgkqUJdntVm4pGkymR2O/E41CZJapUVjyRVyOnUkiQ1xIpHkirU5enUJh5JqpCTCyRJaogVjyRVJgkrHkmSmmLFI0kV6vDcAhOPJFXHJxdIktQcKx5JqlGHx9qseCRJrTLxSFKFMqPxZTYRcURE3BIR346IhyPiTcO4NofaJKlC8/TInI8CX87M346Iw4CXDyOIiUeSRES8EngL8N8DZOZPgZ8OJVYu0CfRrVixPK/7wG+0Fm9y5zIAxo56ypjGNOZBHnNy5zKu/qMP3ZeZpw/j/C876fhc8aH3NH7exy/5X/fZ54g4FVgLPAS8AbgPuDIzn226H1Y8kjrh2d0jbHpkSSuxRsdGWokzBEdHxL19r9dm5tqyfihwGvDezLw7Ij4KXAX8b013YsEmnkUjU6xZfXtr8dZvOBfAmMY0ZqUxNz2yhM3rtrYSb+XE+HADJDCcL5D+YD9V2jZgW2beXV7fQi/xNM5ZbZIkMnMSeCIiXlOazqE37Na4BVvxSFKXzdPt9/cCnywz2h4H3jWMICYeSarRPCSezLwfGMqEiX4OtUmSWmXFI0nV8YfgJElqjBWPJNVoYX63fyAmHkmqjT8EJ0lSc6x4JKlGHR5qs+KRJLXKikeSqtTdezwmHkmqkUNtkiQ1w4pHkmpkxSNJUjNMPJJUm+kfgmt6aUhE/GZEvOxAjzfxSJLm6l8Aj0TEX0fE2yNiTrdtTDySVKHM5pfm+pbvAn4R+CxwKfBYRPzVoMc7uUCSalT55ILMfD4ivkSvp4cDFwP/epBjrXgkSXMSERdExCeAR4HfAv4KGBv0eCseSapR3U+nXgN8Bvg3mfmTuR5sxSNJGkhETABk5qWZ+X9NJ52IWBQRnxr0PAMnnogYiYhvRsQXy+vxiLg7IrZExGci4rDS/rLyekvZfmLfOa4u7d+JiPP72leVti0RcdWgfZKkropsfmnAlRFx+Yv6GTEK/N/Ac4OeZC4Vz5XAw32v/xT4cGb+IvAUcFlpvwx4qrR/uOxHRJwCXAK8DlgF/GVJZiPAXwAXAKcAl5Z9JenglENaXrpzgX8dEf8WICJeBdwJfCMzL9vfgf0GSjwRsQJ4G70bSEREAG8Fbim7rKM3owHgovKasv2csv9FwKcz8yeZuRXYApxRli2Z+Xhm/hT4dNlXklSRzNxFL/n8y4i4Hvgq8DeZOaeRqkEnF3wE+EPgFeX1UcAPM/OF8nobcHxZPx54onTyhYh4uux/PHBX3zn7j3lir/YzZ+vQ81MjrN9w7oDdf+kmdy4DMKYxjVlpzNGxEVZOjLcSb3Rs8ZAjNPukgaZExG+W1bXAnwN3AE9Mt2fm5wY5z6yJJyLeDjyZmfdFxNkH1NuGlLHFywHGxsbY9LUlrcUeHRthdOlUa/EkqUL/om99w15tCTSTeIA3A6sj4kJgMbAU+ChwREQcWqqeFcD2sv924ARgW3mMwiuBnX3t0/qP2Vf7i2TmWnqZlmOPHsvN67YO0P1mrJwY56TxZ1iz+vbWYk7/xWhMYxpz9pibHllCW58JrVRWFX6BtDyx4CWb9R5PZl6dmSsy80R6kwP+PjN/F/gK8NtltwngC2V9Q3lN2f73mZml/ZIy620cOBn4OnAPcHKZJXdYiTGdSSXp4FTn5IJGvJQvkP4vwKcj4jrgm8CNpf1G4K8jYguwi14iITMfjIibgYeAF4ArMnMKICJ+H7gNGAFuyswHX0K/JEkVm1Piycw76U2dIzMfpzcjbe99fgz8zj6O/xPgT2ZovxW4dS59kaROq6hCaZpPLpAkvWQR8euD7uuz2iSpNtM/BLew3Aj8s0F2NPFIkgYSEfua+BX0vq85EBOPJFWooWerNe2/A94JPLNXezDDPf99MfFIUo3qTDx3Ac9l5j/svSEivjPoSUw8kqSBZOYF+9n2lkHP46w2SVKrTDySpFY51CZJFap0ckEjrHgkqUYZzS8NKD/g+cmXcg4TjyRpYOUZm68uD3U+IA61SVJtKnua9AweB/6f8oXSZ6cbM/PPBznYxCNJmqvHynIIP/9l6oGZeCSpRhVXPJn5xwAR8fLMfG6ux3uPR5IqFNn80ljfIt4UEQ8B3y6v3xARfzno8SYeSdJcfQQ4H9gJkJnfAgZ+coFDbZJUo4qH2gAy84mIF03Rnhr0WBOPJGmunoiIXwEyIhYBVwIPD3qwQ22SVKMcwtKc/wG4Ajge2A6cWl4PxIpHkjRXmZm/e6AHW/FIUmWGMaOt4We/3RURn42IC2KvGz2DMPFIUo0qfVZb8c+BtcAa4NGI+FBE/PNBDzbxSJLmJHs2ZualwLuBCeDrEfEPEfGm2Y6PzMrn7O3DccuPy0tWvbO1eKNjixldOsXYUU+1FnNy5zIAYxrTmAPEfHb3CM9O/riVeKNji7nu+mvvy8zTh3H+xStOyBXvfX/j533sqvc30ueIOAp4J/B7wPeBG4EN9CYZfDYzx/d3vJMLJElz9TXgr4GLM3NbX/u9EfHx2Q5esIln6vk9bF63tbV4KyfGOWn8Gdasvr21mOs3nAtgTGMac4CYmx5Z0tpnwsqJ/f5B34jKfwjuNbmP4bLM/NPZDl6wiUeSOq3uxHN0RPwh8Dpg8XRjZr51kIOdXCBJmqtP0ntA6Djwx8B3gXsGPdjEI0m1qf97PEdl5o3A85n5D5n5r4CBqh1wqE2SNHfPl393RMTbgH8Ejhz0YBOPJNWo7ns810XEK4E/AP53YCnwvkEPNvFIUo0qTjyZ+cWy+jTwawAR8b5Bj/cejySpCQN/49WKR5IqVPn3eGYy8MPgrHgkSU0YOFVa8UiSBhIRP2LmBBPA4YOex8QjSRpIZr6iifOYeCSpRgvvHs/ATDySVJvmnzRQFScXSJJaZcUjSTWy4pEkqRlWPJJUow5XPCYeSapM4OQCSZIaY8UjSTWy4pEkqRlWPJJUm45/gdTEI0k16nDimXWoLSIWR8TXI+JbEfFgRPxxaR+PiLsjYktEfCYiDivtLyuvt5TtJ/ad6+rS/p2IOL+vfVVp2xIRVw3hOiVJA4iIkYj4ZkR8cfa9D8wg93h+Arw1M98AnAqsioizgD8FPpyZvwg8BVxW9r8MeKq0f7jsR0ScAlwCvA5YBfxlucAR4C+AC4BTgEvLvpJ08MohLIO5Eni4mYuY2ayJJ3ueKS8XlSWBtwK3lPZ1wMVl/aLymrL9nIiI0v7pzPxJZm4FtgBnlGVLZj6emT8FPl32lSS1KCJWAG8D/mqYcQa6x1OqkvuAX6RXnTwG/DAzXyi7bAOOL+vHA08AZOYLEfE0cFRpv6vvtP3HPLFX+5mz9Wlk0SGsnBgfpPuNGB1bzOTORazfcG5rMSd3LgMwpjGNOUDM0bGR1j4TRscWDz3GPE0u+Ajwh0Ajv7uzLwMlnsycAk6NiCOAzwOvHWan9iUiLgcuBxgbG5uPLkiq1OjSKU4af2b2HRswuXNRK3GG4OiIuLfv9drMXAsQEW8HnszM+yLi7GF2Yk6z2jLzhxHxFeBNwBERcWipelYA28tu24ETgG0RcSjwSmBnX/u0/mP21b53/LXAWoBjjx7Lzeu2zqX7L8nKiXFOGn+GNatvby3m9F+MxjSmMeuK2Uo1N5yK5weZefo+tr0ZWB0RFwKLgaUR8TeZ+c6mOzHIrLZXlUqHiDgc+HV6N56+Avx22W0C+EJZ31BeU7b/fWZmab+kzHobB04Gvg7cA5xcZskdRm8CwoYGrk2SFqZhTCyYJZFl5tWZuSIzT6T3Ofz3w0g6MFjFsxxYV+7zHALcnJlfjIiHgE9HxHXAN4Eby/43An8dEVuAXfQugMx8MCJuBh4CXgCuKEN4RMTvA7cBI8BNmflgY1coSarKrIknMzcBvzxD++P0ZqTt3f5j4Hf2ca4/Af5khvZbgVsH6K8kHRTm88kFmXkncOewzu+z2iRJrfKROZJUow4/MsfEI0kV6vJDQh1qkyS1yopHkmpkxSNJUjOseCSpNnN7mvSCY+KRpMpEWbrKoTZJUquseCSpRh0earPikSS1yopHkirkF0glSWqIFY8k1ajDFY+JR5Jq1OHE41CbJKlVVjySVJt0coEkSY2x4pGkGnW44jHxSFKFHGqTJKkhVjySVCMrHkmSmhGZCzOtHrf8uLxk1Ttbizc6tpjRpVOMHfVUazEndy4DMKYxjVlZzMmdy7j6jz50X2aePozzv/yYE/K1v/X+xs/7zY+/f2h9nguH2iR1wrO7R9j0yJJWYo2OjQw3gL9AWqep5/ewed3W1uKtnBjnpPFnWLP69tZirt9wLoAxjWnMAWJuemRJa58JKyfGW4nTVQs28UhSp3W44nFygSSpVVY8klSZwC+QSpLUGCseSapRhyseE48kVSgW6HcsB+FQmySpVVY8klSbjn+B1IpHktQqKx5JqlCXp1ObeCSpRh1OPA61SZJaZcUjSRXq8lCbFY8kqVVWPJJUow5XPCYeSapNOtQmSVJjrHgkqUZWPJIkNcOKR5Iq0/UfgjPxSFKN/FkESZKaYcUjSRXq8lDbrBVPRJwQEV+JiIci4sGIuLK0HxkRGyPi0fLvstIeEXFDRGyJiE0RcVrfuSbK/o9GxERf+xsjYnM55oaIiGFcrCRp/g0y1PYC8AeZeQpwFnBFRJwCXAXckZknA3eU1wAXACeX5XLgY9BLVMA1wJnAGcA108mq7PPuvuNWvfRLk6QFKoe0VGLWxJOZOzLzG2X9R8DDwPHARcC6sts64OKyfhGwPnvuAo6IiOXA+cDGzNyVmU8BG4FVZdvSzLwrMxNY33cuSVLHzOkeT0ScCPwycDdwbGbuKJsmgWPL+vHAE32HbStt+2vfNkP7fo0sOoSVE+Nz6f5LMjq2mMmdi1i/4dzWYk7u7BWExjSmMWePOTo20tpnwujY4qHHiD1DDzFvBk48EbEE+FvgfZm5u/82TGZmxPBvhUXE5fSG7zhu+Ri/9KZnhh3yZyZ3LmotliTVNDTWtIEST0Qsopd0PpmZnyvN34+I5Zm5owyXPVnatwMn9B2+orRtB87eq/3O0r5ihv3/icxcC6wFGH/1Mblm9e2DdL8R03+9GdOYxqwz5qZHlrB53dZW4rU52tJFg8xqC+BG4OHM/PO+TRuA6ZlpE8AX+trXlNltZwFPlyG524DzImJZmVRwHnBb2bY7Is4qsdb0nUuSDkqRzS+1GKTieTPwe8DmiLi/tP0RcD1wc0RcBnwPeEfZditwIbAFeA54F0Bm7oqIa4F7yn4fzMxdZf09wCeAw4EvlUWS1EGzJp7M/C/0Hh00k3Nm2D+BK/ZxrpuAm2Zovxd4/Wx9kaSDQtLpR+b45AJJqlBNQ2NN81ltkqRWWfFIUo2seCRJaoYVjyRVxh+CkyS1K7PTs9ocapMktcqKR5Iq1OWhNiseSVKrrHgkqUZWPJIkNcOKR5Iq1OV7PCYeSapNAnu6m3kcapMktcqKR5Jq1N2Cx4pHktQuKx5JqpCTCyRJ7fJZbZIkNcOKR5Iq1OWhNiseSRIRcUJEfCUiHoqIByPiymHFsuKRpNok8zGd+gXgDzLzGxHxCuC+iNiYmQ81HcjEI0mV6f0CabuZJzN3ADvK+o8i4mHgeKDxxBO5QGdOrFixPK/7wG+0Fm9y5zIAxo56ypjGNGaFMZ/dPcKzkz9uJd7o2GKuu/7a+zLz9GGcf+nSFXn6mb/f+Hm/cvvVA/U5Ik4Evgq8PjN3N90PKx5JqtGeoZz16Ii4t+/12sxc279DRCwB/hZ43zCSDizgxLNoZIo1q29vLd76DecCGNOYxqw05qZHlrB53dZW4q2cGG8lzhD8YH8VT0Qsopd0PpmZnxtWJxZs4pGkLmv7Hk9EBHAj8HBm/vkwYzmdWpIE8Gbg94C3RsT9ZblwGIGseCSpNvMwnToz/wu9CXVDZ+KRpOqkz2qTJKkpVjySVCGf1SZJUkOseCSpRh2+x2PikaTaJMRwnlxQBYfaJEmtsuKRpBp1eKjNikeS1CorHkmqUXcLHhOPJNWo7YeEtsmhNklSq6x4JKlGVjySJDXDikeSapMM66evq2DFI0lqlRWPJFUmyE7PajPxSFKNOpx4HGqTJLXKikeSanQwVzwRcVNEPBkRD/S1HRkRGyPi0fLvstIeEXFDRGyJiE0RcVrfMRNl/0cjYqKv/Y0Rsbkcc0NERNMXKUmqxyBDbZ8AVu3VdhVwR2aeDNxRXgNcAJxclsuBj0EvUQHXAGcCZwDXTCerss+7+47bO5YkHVymp1M3vVRi1sSTmV8Fdu3VfBGwrqyvAy7ua1+fPXcBR0TEcuB8YGNm7srMp4CNwKqybWlm3pWZCazvO5ckHbQis/GlFgd6j+fYzNxR1ieBY8v68cATffttK237a982Q/usnp8aYf2Gc+fe8wM0ubNXoBnTmMasM+bo2AgrJ8ZbiTc6triVOF31kicXZGZGRCupNCIupzeEx9jYGJu+tqSNsACMjo0wunSqtXiSDnIVVShNO9DE8/2IWJ6ZO8pw2ZOlfTtwQt9+K0rbduDsvdrvLO0rZth/Rpm5FlgLcOzRY7l53dYD7P7crZwY56TxZ1iz+vbWYk7/xWhMYxpz9pibHllCW58JbVVWXXWg3+PZAEzPTJsAvtDXvqbMbjsLeLoMyd0GnBcRy8qkgvOA28q23RFxVpnNtqbvXJJ0kMpexdP0UolZK56I+BS9auXoiNhGb3ba9cDNEXEZ8D3gHWX3W4ELgS3Ac8C7ADJzV0RcC9xT9vtgZk5PWHgPvZlzhwNfKoskHbySqhJF02ZNPJl56T42nTPDvglcsY/z3ATcNEP7vcDrZ+uHJKkbfHKBJNWoou/dNM1ntUmSWmXFI0kVqukLn02z4pEktcqKR5Jq1OGKx8QjSbVJYE93E49DbZKkVlnxSFJ16nrSQNOseCRJrbLikaQadbjiMfFIUo06nHgcapMktcqKR5Jq43RqSZKaY8UjSdVJyO4+ntrEI0k1cnKBJEnNsOKRpNo4uUCSpOZY8UhSjbzHI0lSM6x4JKlGHa54TDySVJ1u/yxC5AK9uOOWH5eXrHpna/FGxxYzunSKsaOeai3m5M5lAMY0pjEHiPns7hGenfxxK/FGxxZz3fXX3peZpw/j/K887Jj8lVf9y8bP++V//I9D6/NcWPHMwbO7R9j0yJLW4o2OjTC6dKq1eBq++XgPAQdNzM5IYI9PLqjO1PN72Lxua2vxVk6MA7Qe86TxZ1iz+vbWYq7fcC6AMYcUc9MjSw6K923XY07H04FZsIlHkjptgd4GGYSJR5Jq1OHE4/d4JEmtsuKRpOqkz2qTJKkpVjySVJuE9IfgJEmtcqhNkqRmWPFIUo2cTi1JUjOseCSpNpmdflabFY8kqVVWPJJUow7f4zHxSFKF0qE2SZKaYcUjSdXp9k9fW/FIklplxSNJtUk6/cgcE48k1ajDDwl1qE2S1CorHkmqTALZ4aE2Kx5JUquseCSpNpne42lDRKyKiO9ExJaIuGq++yNJ8yn3ZOPLbNr6HK4i8UTECPAXwAXAKcClEXHK/PZKkg4ebX4OV5F4gDOALZn5eGb+FPg0cNE890mS5k/uaX7Zv9Y+h2u5x3M88ETf623Amfs7YGTRIaycGB9qp/qNji0GaD3m5M5FrN9wbmsxJ3cuAzDmkGKOjo0cFO/brsecjtcxc/4cPlC1JJ6BRMTlwOXl5U8+/Ik/e2A++9OSo4EfzHcnWuB1dsvBcJ2vGdaJf8RTt92etxw9hFMvjoh7+16vzcy1Q4izX7Uknu3ACX2vV5S2Fyn/gdYCRMS9mXl6O92bP15nt3id3bHXB3ijMnPVsM69HwN9Djehlns89wAnR8R4RBwGXAJsmOc+SdLBpLXP4Soqnsx8ISJ+H7gNGAFuyswH57lbknTQaPNzuIrEA5CZtwK3zuGQ1scl54nX2S1eZ3d07hoP4HP4gER2+MeGJEn1qeUejyTpILHgEs9CfLRORNwUEU9GxAN9bUdGxMaIeLT8u6y0R0TcUK5vU0Sc1nfMRNn/0YiY6Gt/Y0RsLsfcEBHR7hX+rB8nRMRXIuKhiHgwIq4s7Z261ohYHBFfj4hvlev849I+HhF3l759ptygJSJeVl5vKdtP7DvX1aX9OxFxfl97Fe/ziBiJiG9GxBfL685dY+nLd8v76v7p2Wpde99WJTMXzELvhtdjwC8AhwHfAk6Z734N0O+3AKcBD/S1/XvgqrJ+FfCnZf1C4EtAAGcBd5f2I4HHy7/Lyvqysu3rZd8ox14wT9e5HDitrL8CeITeozc6da0l9pKyvgi4u/TpZuCS0v5x4H8s6+8BPl7WLwE+U9ZPKe/hlwHj5b09UtP7HHg/8J+AL5bXnbvG0s/vAkfv1dap921Ny0KreBbko3Uy86vArr2aLwLWlfV1wMV97euz5y7giIhYDpwPbMzMXZn5FLARWFW2Lc3Mu7L3Dl/fd65WZeaOzPxGWf8R8DC9b0N36lpLf58pLxeVJYG3AreU9r2vc/r6bwHOKX/xXgR8OjN/kplbgS303uNVvM8jYgXwNuCvyuugY9c4i069b2uy0BLPTI90OH6e+vJSHZuZO8r6JHBsWd/XNe6vfdsM7fOqDLX8Mr1qoHPXWoag7geepPcB8xjww8x8YYa+/ex6yvangaOY+/W37SPAHwLTD/k6iu5d47QE/i4i7oveE1Kgg+/bWlQznfpglpkZEZ2ZXhgRS4C/Bd6Xmbv7h7O7cq2ZOQWcGhFHAJ8HXju/PWpWRLwdeDIz74uIs+e5O2341czcHhHHABsj4tv9G7vyvq3FQqt4WnukQwu+X0pwyr9PlvZ9XeP+2lfM0D4vImIRvaTzycz8XGnu5LUCZOYPga8Ab6I35DL9x1x/3352PWX7K4GdzP362/RmYHVEfJfeMNhbgY/SrWv8mczcXv59kt4fEmfQ4fftvJvvm0xzWehVaI/Tu0k5fUPydfPdrwH7fiIvnlzwZ7z4xuW/L+tv48U3Lr9e2o8EttK7abmsrB9Ztu194/LCebrGoDd+/ZG92jt1rcCrgCPK+uHAfwbeDnyWF994f09Zv4IX33i/uay/jhffeH+c3k33qt7nwNn8fHJB564RGAVe0bf+/wKruva+rWmZ9w4cwJvkQnqzpR4D/t1892fAPn8K2AE8T2989zJ64993AI8Ct/e9QYPejzE9BmwGTu87z7+id3N2C/CuvvbTgQfKMf+R8sXgebjOX6U3Vr4JuL8sF3btWoFfAr5ZrvMB4AOl/RfKB8wWeh/QLyvti8vrLWX7L/Sd69+Va/kOfTOdanqf8+LE07lrLNf0rbI8ON2Xrr1va1p8coEkqVUL7R6PJGmBM/FIklpl4pEktcrEI0lqlYlHktQqE48kqVUmHklSq0w8kqRW/f8dxBcOLs04HAAAAABJRU5ErkJggg==\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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:03.869745Z",
     "iopub.status.busy": "2022-07-30T20:19:03.868943Z",
     "iopub.status.idle": "2022-07-30T20:19:03.881486Z",
     "shell.execute_reply": "2022-07-30T20:19:03.878997Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:03.892393Z",
     "iopub.status.busy": "2022-07-30T20:19:03.891364Z",
     "iopub.status.idle": "2022-07-30T20:19:04.227037Z",
     "shell.execute_reply": "2022-07-30T20:19:04.226186Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.231588Z",
     "iopub.status.busy": "2022-07-30T20:19:04.230957Z",
     "iopub.status.idle": "2022-07-30T20:19:04.432695Z",
     "shell.execute_reply": "2022-07-30T20:19:04.431432Z"
    }
   },
   "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(\n",
    "    m.dis.perlen.array.cumsum() / 864600,\n",
    "    [a.mean() * 86400 * 365 * 12 for a in finf.values()],\n",
    "    marker=\"o\",\n",
    ")\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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.437567Z",
     "iopub.status.busy": "2022-07-30T20:19:04.437072Z",
     "iopub.status.idle": "2022-07-30T20:19:04.797840Z",
     "shell.execute_reply": "2022-07-30T20:19:04.791391Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.805980Z",
     "iopub.status.busy": "2022-07-30T20:19:04.805645Z",
     "iopub.status.idle": "2022-07-30T20:19:04.818511Z",
     "shell.execute_reply": "2022-07-30T20:19:04.814749Z"
    }
   },
   "outputs": [],
   "source": [
    "uzgag = {\n",
    "    -68: [-68],\n",
    "    65: [\n",
    "        2,\n",
    "        5,\n",
    "        65,\n",
    "        1,\n",
    "    ],  # Print time, head, uz thickness and cum. vols of infiltration, recharge, storage, change in storage and ground-water discharge to land surface.\n",
    "    66: [\n",
    "        5,\n",
    "        2,\n",
    "        66,\n",
    "        2,\n",
    "    ],  # 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],\n",
    "}  # 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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.828042Z",
     "iopub.status.busy": "2022-07-30T20:19:04.827191Z",
     "iopub.status.idle": "2022-07-30T20:19:04.843481Z",
     "shell.execute_reply": "2022-07-30T20:19:04.841575Z"
    }
   },
   "outputs": [],
   "source": [
    "uzf = flopy.modflow.ModflowUzf1(\n",
    "    m,\n",
    "    nuztop=1,\n",
    "    iuzfopt=1,\n",
    "    irunflg=1,\n",
    "    ietflg=1,\n",
    "    ipakcb=0,\n",
    "    iuzfcb2=61,  # binary output of recharge and groundwater discharge\n",
    "    ntrail2=25,\n",
    "    nsets=20,\n",
    "    surfdep=1.0,\n",
    "    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=15.0,  # ET extinction depth(s)\n",
    "    extwc=extwc,  # extinction water content below which ET cannot be removed from the unsaturated zone\n",
    "    unitnumber=19,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.854202Z",
     "iopub.status.busy": "2022-07-30T20:19:04.853804Z",
     "iopub.status.idle": "2022-07-30T20:19:04.877546Z",
     "shell.execute_reply": "2022-07-30T20:19:04.876267Z"
    }
   },
   "outputs": [],
   "source": [
    "m.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:04.881791Z",
     "iopub.status.busy": "2022-07-30T20:19:04.881534Z",
     "iopub.status.idle": "2022-07-30T20:19:09.362451Z",
     "shell.execute_reply": "2022-07-30T20:19:09.360849Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /Users/wes/dev/usgs-executables/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): 2022/07/30 16:19:04\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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",
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/07/30 16:19:09\n",
      " Elapsed run time:  4.450 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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:09.371428Z",
     "iopub.status.busy": "2022-07-30T20:19:09.370804Z",
     "iopub.status.idle": "2022-07-30T20:19:09.523590Z",
     "shell.execute_reply": "2022-07-30T20:19:09.520965Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:09.532358Z",
     "iopub.status.busy": "2022-07-30T20:19:09.530134Z",
     "iopub.status.idle": "2022-07-30T20:19:10.097962Z",
     "shell.execute_reply": "2022-07-30T20:19:10.091632Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.109030Z",
     "iopub.status.busy": "2022-07-30T20:19:10.108598Z",
     "iopub.status.idle": "2022-07-30T20:19:10.381174Z",
     "shell.execute_reply": "2022-07-30T20:19:10.380363Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.391059Z",
     "iopub.status.busy": "2022-07-30T20:19:10.389986Z",
     "iopub.status.idle": "2022-07-30T20:19:10.716093Z",
     "shell.execute_reply": "2022-07-30T20:19:10.715269Z"
    }
   },
   "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 = [\n",
    "        (\"TIME\", float),\n",
    "        (\"APPLIED-INFIL\", float),\n",
    "        (\"RUNOFF\", float),\n",
    "        (\"ACTUAL-INFIL\", float),\n",
    "        (\"SURFACE-LEAK\", float),\n",
    "        (\"UZ-ET\", float),\n",
    "        (\"GW-ET\", float),\n",
    "        (\"UZSTOR-CHANGE\", float),\n",
    "        (\"RECHARGE\", float),\n",
    "    ]\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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.719562Z",
     "iopub.status.busy": "2022-07-30T20:19:10.719256Z",
     "iopub.status.idle": "2022-07-30T20:19:10.734391Z",
     "shell.execute_reply": "2022-07-30T20:19:10.733810Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.737843Z",
     "iopub.status.busy": "2022-07-30T20:19:10.737556Z",
     "iopub.status.idle": "2022-07-30T20:19:10.745557Z",
     "shell.execute_reply": "2022-07-30T20:19:10.744835Z"
    }
   },
   "outputs": [],
   "source": [
    "if avail:\n",
    "    df3 = pd.DataFrame(\n",
    "        data,\n",
    "        columns=[\"layer\", \"time\", \"head\", \"uzthick\", \"depth\", \"watercontent\"],\n",
    "    )\n",
    "    df3.head(41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.749237Z",
     "iopub.status.busy": "2022-07-30T20:19:10.748970Z",
     "iopub.status.idle": "2022-07-30T20:19:10.753946Z",
     "shell.execute_reply": "2022-07-30T20:19:10.753245Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.757527Z",
     "iopub.status.busy": "2022-07-30T20:19:10.757239Z",
     "iopub.status.idle": "2022-07-30T20:19:10.995239Z",
     "shell.execute_reply": "2022-07-30T20:19:10.994401Z"
    }
   },
   "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": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-30T20:19:10.998749Z",
     "iopub.status.busy": "2022-07-30T20:19:10.998464Z",
     "iopub.status.idle": "2022-07-30T20:19:11.005134Z",
     "shell.execute_reply": "2022-07-30T20:19:11.004451Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # ignore PermissionError on Windows\n",
    "    temp_dir.cleanup()\n",
    "except:\n",
    "    pass"
   ]
  }
 ],
 "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}