examples/Notebooks/flopy3_ZoneBudget_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "### ZoneBudget Example\n",
    "\n",
    "This notebook demonstrates how to use the `ZoneBudget` class to extract budget information from the cell by cell budget file using an array of zones.\n",
    "\n",
    "First set the path and import the required packages. The flopy path doesn't have to be set if you install flopy from a binary installer. If you want to run this notebook, you have to set the path to your own flopy path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_fork/flopy\n",
      "3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 14:38:56) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.17.3\n",
      "matplotlib version: 3.1.1\n",
      "pandas version: 0.25.2\n",
      "flopy version: 3.3.1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import platform\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 path to example datafiles\n",
    "loadpth = os.path.join('..', 'data', 'zonbud_examples')\n",
    "cbc_f = os.path.join(loadpth, 'freyberg.gitcbc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read File Containing Zones\n",
    "Using the `read_zbarray` utility, we can import zonebudget-style array files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from flopy.utils import read_zbarray\n",
    "\n",
    "zone_file = os.path.join(loadpth, 'zonef_mlt.zbr')\n",
    "zon = read_zbarray(zone_file)\n",
    "nlay, nrow, ncol = zon.shape\n",
    "\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "\n",
    "for lay in range(nlay):\n",
    "    ax = fig.add_subplot(1, nlay, lay+1)\n",
    "    im = ax.pcolormesh(zon[lay, ::-1, :])\n",
    "    cbar = plt.colorbar(im)\n",
    "    plt.gca().set_aspect('equal')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Budget Information from ZoneBudget Object\n",
    "\n",
    "At the core of the `ZoneBudget` object is a numpy structured array. The class provides some wrapper functions to help us interogate the array and save it to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1097., 0, 1096, 'FROM_STORAGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_CONSTANT_HEAD',  0., 0.0000000e+00, 2.3156659e+02, 8.6217201e+01),\n",
       "       (1097., 0, 1096, 'FROM_WELLS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_DRAINS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_RECHARGE',  0., 5.1455815e+03, 1.4936376e+01, 2.9872751e+01),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_0',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_1',  0., 0.0000000e+00, 3.4751235e+03, 1.3860045e+02),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_2',  0., 3.2693188e+03, 0.0000000e+00, 1.7646553e+03),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_3',  0., 1.9218604e+02, 1.5280482e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_IN',  0., 8.6070859e+03, 5.2496748e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TO_STORAGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_CONSTANT_HEAD',  0., 2.3054836e+02, 2.1570151e+02, 2.9911380e+02),\n",
       "       (1097., 0, 1096, 'TO_WELLS',  0., 4.7627998e+03, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_DRAINS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_RECHARGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_0',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_1',  0., 0.0000000e+00, 3.2693188e+03, 1.9218604e+02),\n",
       "       (1097., 0, 1096, 'TO_ZONE_2',  0., 3.4751235e+03, 0.0000000e+00, 1.5280482e+03),\n",
       "       (1097., 0, 1096, 'TO_ZONE_3',  0., 1.3860045e+02, 1.7646553e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT',  0., 8.6070723e+03, 5.2496758e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT',  0., 1.3671875e-02, 9.7656250e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', nan, 1.5884454e-04, 1.8602341e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('ZONE_0', '<f4'), ('ZONE_1', '<f4'), ('ZONE_2', '<f4'), ('ZONE_3', '<f4')])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a ZoneBudget object and get the budget record array\n",
    "zb = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=(0, 1096))\n",
    "zb.get_budget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FROM_CONSTANT_HEAD', 'FROM_DRAINS', 'FROM_RECHARGE',\n",
       "       'FROM_STORAGE', 'FROM_WELLS', 'FROM_ZONE_0', 'FROM_ZONE_1',\n",
       "       'FROM_ZONE_2', 'FROM_ZONE_3', 'IN-OUT', 'PERCENT_DISCREPANCY',\n",
       "       'TOTAL_IN', 'TOTAL_OUT', 'TO_CONSTANT_HEAD', 'TO_DRAINS',\n",
       "       'TO_RECHARGE', 'TO_STORAGE', 'TO_WELLS', 'TO_ZONE_0', 'TO_ZONE_1',\n",
       "       'TO_ZONE_2', 'TO_ZONE_3'], dtype='<U50')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a list of the unique budget record names\n",
    "zb.get_record_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1097., 0, 1096, 'FROM_RECHARGE', 0., 5145.5815 ,   14.936376,  29.872751),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_1', 0.,    0.     , 3475.1235  , 138.60045 ),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_3', 0.,  192.18604, 1528.0482  ,   0.      )],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('ZONE_0', '<f4'), ('ZONE_1', '<f4'), ('ZONE_2', '<f4'), ('ZONE_3', '<f4')])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at a subset of fluxes\n",
    "names = ['FROM_RECHARGE', 'FROM_ZONE_1', 'FROM_ZONE_3']\n",
    "zb.get_budget(names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1097., 0, 1096, 'FROM_RECHARGE',   14.936376),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_1', 3475.1235  ),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_3', 1528.0482  )],\n",
       "      dtype={'names':['totim','time_step','stress_period','name','ZONE_2'], 'formats':['<f4','<i4','<i4','<U50','<f4'], 'offsets':[0,4,8,12,220], 'itemsize':228})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at fluxes in from zone 2\n",
    "names = ['FROM_RECHARGE', 'FROM_ZONE_1', 'FROM_ZONE_3']\n",
    "zones = ['ZONE_2']\n",
    "zb.get_budget(names=names, zones=zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1097., 0, 1096, 'TOTAL_IN',  0., 8.6070859e+03, 5.249675e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT',  0., 8.6070723e+03, 5.249676e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT',  0., 1.3671875e-02, 9.765625e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', nan, 1.5884454e-04, 1.860234e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('ZONE_0', '<f4'), ('ZONE_1', '<f4'), ('ZONE_2', '<f4'), ('ZONE_3', '<f4')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at all of the mass-balance records\n",
    "names = ['TOTAL_IN', 'TOTAL_OUT', 'IN-OUT', 'PERCENT_DISCREPANCY']\n",
    "zb.get_budget(names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Units\n",
    "The `ZoneBudget` class supports the use of mathematical operators and returns a new copy of the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6,222.7 cubic meters/day\n",
      "176.2 cubic feet/day\n",
      "12.3 inches/year\n"
     ]
    }
   ],
   "source": [
    "cmd = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=(0, 0))\n",
    "cfd = cmd / 35.3147\n",
    "inyr = (cfd / (250 * 250)) * 365 * 12\n",
    "\n",
    "cmdbud = cmd.get_budget()\n",
    "cfdbud = cfd.get_budget()\n",
    "inyrbud = inyr.get_budget()\n",
    "\n",
    "names = ['FROM_RECHARGE']\n",
    "rowidx = np.in1d(cmdbud['name'], names)\n",
    "colidx = 'ZONE_1'\n",
    "\n",
    "print('{:,.1f} cubic meters/day'.format(cmdbud[rowidx][colidx][0]))\n",
    "print('{:,.1f} cubic feet/day'.format(cfdbud[rowidx][colidx][0]))\n",
    "print('{:,.1f} inches/year'.format(inyrbud[rowidx][colidx][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmd is cfd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alias Names\n",
    "A dictionary of {zone: \"alias\"} pairs can be passed to replace the typical \"ZONE_X\" fieldnames of the `ZoneBudget` structured array with more descriptive names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1097., 0, 1096, 'FROM_STORAGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_CONSTANT_HEAD',  0., 0.0000000e+00, 2.3156659e+02, 8.6217201e+01),\n",
       "       (1097., 0, 1096, 'FROM_WELLS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_DRAINS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_RECHARGE',  0., 5.1455815e+03, 1.4936376e+01, 2.9872751e+01),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_0',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_SURF',  0., 0.0000000e+00, 3.4751235e+03, 1.3860045e+02),\n",
       "       (1097., 0, 1096, 'FROM_CONF',  0., 3.2693188e+03, 0.0000000e+00, 1.7646553e+03),\n",
       "       (1097., 0, 1096, 'FROM_UFA',  0., 1.9218604e+02, 1.5280482e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_IN',  0., 8.6070859e+03, 5.2496748e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TO_STORAGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_CONSTANT_HEAD',  0., 2.3054836e+02, 2.1570151e+02, 2.9911380e+02),\n",
       "       (1097., 0, 1096, 'TO_WELLS',  0., 4.7627998e+03, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_DRAINS',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_RECHARGE',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_0',  0., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_SURF',  0., 0.0000000e+00, 3.2693188e+03, 1.9218604e+02),\n",
       "       (1097., 0, 1096, 'TO_CONF',  0., 3.4751235e+03, 0.0000000e+00, 1.5280482e+03),\n",
       "       (1097., 0, 1096, 'TO_UFA',  0., 1.3860045e+02, 1.7646553e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT',  0., 8.6070723e+03, 5.2496758e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT',  0., 1.3671875e-02, 9.7656250e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', nan, 1.5884454e-04, 1.8602341e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('ZONE_0', '<f4'), ('SURF', '<f4'), ('CONF', '<f4'), ('UFA', '<f4')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliases = {1: 'SURF', 2:'CONF', 3: 'UFA'}\n",
    "zb = flopy.utils.ZoneBudget(cbc_f, zon, totim=[1097.], aliases=aliases)\n",
    "zb.get_budget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Return the Budgets as a Pandas DataFrame\n",
    "Set `kstpkper` and `totim` keyword args to `None` (or omit) to return all times.\n",
    "The `get_dataframes()` method will return a DataFrame multi-indexed on `totim` and `name`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ZONE_0</th>\n",
       "      <th>SURF</th>\n",
       "      <th>CONF</th>\n",
       "      <th>UFA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totim</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1092.0</th>\n",
       "      <th>FROM_STORAGE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>393.480286</td>\n",
       "      <td>230.476242</td>\n",
       "      <td>228.273621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.225761</td>\n",
       "      <td>5.042325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_DRAINS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6018.483887</td>\n",
       "      <td>17.470200</td>\n",
       "      <td>34.940399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1097.0</th>\n",
       "      <th>TO_CONF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3475.123535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1528.048218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_UFA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>138.600449</td>\n",
       "      <td>1764.655273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_OUT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8607.072266</td>\n",
       "      <td>5249.675781</td>\n",
       "      <td>2019.348022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN-OUT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.002319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERCENT_DISCREPANCY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            ZONE_0         SURF         CONF          UFA\n",
       "totim  name                                                              \n",
       "1092.0 FROM_STORAGE            0.0   393.480286   230.476242   228.273621\n",
       "       FROM_CONSTANT_HEAD      0.0     0.000000    13.225761     5.042325\n",
       "       FROM_WELLS              0.0     0.000000     0.000000     0.000000\n",
       "       FROM_DRAINS             0.0     0.000000     0.000000     0.000000\n",
       "       FROM_RECHARGE           0.0  6018.483887    17.470200    34.940399\n",
       "...                            ...          ...          ...          ...\n",
       "1097.0 TO_CONF                 0.0  3475.123535     0.000000  1528.048218\n",
       "       TO_UFA                  0.0   138.600449  1764.655273     0.000000\n",
       "       TOTAL_OUT               0.0  8607.072266  5249.675781  2019.348022\n",
       "       IN-OUT                  0.0     0.013672     0.000977     0.002319\n",
       "       PERCENT_DISCREPANCY     NaN     0.000159     0.000019     0.000115\n",
       "\n",
       "[132 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aliases = {1: 'SURF', 2:'CONF', 3: 'UFA'}\n",
    "times = list(range(1092, 1097+1))\n",
    "zb = flopy.utils.ZoneBudget(cbc_f, zon, totim=times, aliases=aliases)\n",
    "zb.get_dataframes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slice the multi-index dataframe to retrieve a subset of the budget.\n",
    "NOTE: We can pass \"names\" directly to the `get_dataframes()` method to return a subset of reocrds. By omitting the `\"FROM_\"` or `\"TO_\"` prefix we get both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SURF</th>\n",
       "      <th>CONF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totim</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1095.0</th>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.531923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>5010.149414</td>\n",
       "      <td>14.543249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>664.912598</td>\n",
       "      <td>493.175781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>794.582886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1096.0</th>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.501888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>6115.663086</td>\n",
       "      <td>17.752289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>690.230591</td>\n",
       "      <td>511.618652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>1373.782715</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1097.0</th>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>231.566589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>5145.581543</td>\n",
       "      <td>14.936376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>230.548355</td>\n",
       "      <td>215.701508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>4762.799805</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  SURF        CONF\n",
       "totim  name                                       \n",
       "1095.0 FROM_CONSTANT_HEAD     0.000000   14.531923\n",
       "       FROM_RECHARGE       5010.149414   14.543249\n",
       "       TO_CONSTANT_HEAD     664.912598  493.175781\n",
       "       TO_WELLS             794.582886    0.000000\n",
       "1096.0 FROM_CONSTANT_HEAD     0.000000    6.501888\n",
       "       FROM_RECHARGE       6115.663086   17.752289\n",
       "       TO_CONSTANT_HEAD     690.230591  511.618652\n",
       "       TO_WELLS            1373.782715    0.000000\n",
       "1097.0 FROM_CONSTANT_HEAD     0.000000  231.566589\n",
       "       FROM_RECHARGE       5145.581543   14.936376\n",
       "       TO_CONSTANT_HEAD     230.548355  215.701508\n",
       "       TO_WELLS            4762.799805    0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateidx1 = 1095.\n",
    "dateidx2 = 1097.\n",
    "names = ['FROM_RECHARGE', 'TO_WELLS', 'CONSTANT_HEAD']\n",
    "zones = ['SURF', 'CONF']\n",
    "df = zb.get_dataframes(names=names)\n",
    "df.loc[(slice(dateidx1, dateidx2), slice(None)), :][zones]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at pumpage (`TO_WELLS`) as a percentage of recharge (`FROM_RECHARGE`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SURF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totim</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1095.0</th>\n",
       "      <td>-15.859466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096.0</th>\n",
       "      <td>-22.463348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097.0</th>\n",
       "      <td>-92.560966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SURF\n",
       "totim            \n",
       "1095.0 -15.859466\n",
       "1096.0 -22.463348\n",
       "1097.0 -92.560966"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateidx1 = 1095.\n",
    "dateidx2 = 1097.\n",
    "zones = ['SURF']\n",
    "\n",
    "# Pull out the individual records of interest\n",
    "rech = df.loc[(slice(dateidx1, dateidx2), ['FROM_RECHARGE']), :][zones]\n",
    "pump = df.loc[(slice(dateidx1, dateidx2), ['TO_WELLS']), :][zones]\n",
    "\n",
    "# Remove the \"record\" field from the index so we can \n",
    "# take the difference of the two DataFrames\n",
    "rech = rech.reset_index()\n",
    "rech = rech.set_index(['totim'])\n",
    "rech = rech[zones]\n",
    "pump = pump.reset_index()\n",
    "pump = pump.set_index(['totim'])\n",
    "pump = pump[zones] * -1\n",
    "\n",
    "# Compute pumping as a percentage of recharge\n",
    "(pump / rech) * 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass `start_datetime` and `timeunit` keyword arguments to return a dataframe with a datetime multi-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SURF</th>\n",
       "      <th>CONF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1972-12-29</th>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.905813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>4203.679199</td>\n",
       "      <td>12.202263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>655.814514</td>\n",
       "      <td>487.094849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>1930.483154</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1972-12-30</th>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.877954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>4047.502441</td>\n",
       "      <td>11.748919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>650.441589</td>\n",
       "      <td>482.853638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>1279.166382</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      SURF        CONF\n",
       "datetime   name                                       \n",
       "1972-12-29 FROM_CONSTANT_HEAD     0.000000   16.905813\n",
       "           FROM_RECHARGE       4203.679199   12.202263\n",
       "           TO_CONSTANT_HEAD     655.814514  487.094849\n",
       "           TO_WELLS            1930.483154    0.000000\n",
       "1972-12-30 FROM_CONSTANT_HEAD     0.000000   18.877954\n",
       "           FROM_RECHARGE       4047.502441   11.748919\n",
       "           TO_CONSTANT_HEAD     650.441589  482.853638\n",
       "           TO_WELLS            1279.166382    0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateidx1 = pd.Timestamp('1972-12-29')\n",
    "dateidx2 = pd.Timestamp('1972-12-30')\n",
    "names = ['FROM_RECHARGE', 'TO_WELLS', 'CONSTANT_HEAD']\n",
    "zones = ['SURF', 'CONF']\n",
    "df = zb.get_dataframes(start_datetime='1970-01-01', timeunit='D', names=names)\n",
    "df.loc[(slice(dateidx1, dateidx2), slice(None)), :][zones]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass `index_key` to indicate which fields to use in the multi-index (default is \"totim\"; valid keys are \"totim\" and \"kstpkper\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ZONE_0</th>\n",
       "      <th>SURF</th>\n",
       "      <th>CONF</th>\n",
       "      <th>UFA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_step</th>\n",
       "      <th>stress_period</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1091</th>\n",
       "      <th>FROM_STORAGE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>393.480286</td>\n",
       "      <td>230.476242</td>\n",
       "      <td>228.273621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.225761</td>\n",
       "      <td>5.042325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_DRAINS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6018.483887</td>\n",
       "      <td>17.470200</td>\n",
       "      <td>34.940399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            ZONE_0         SURF        CONF  \\\n",
       "time_step stress_period name                                                  \n",
       "0         1091          FROM_STORAGE           0.0   393.480286  230.476242   \n",
       "                        FROM_CONSTANT_HEAD     0.0     0.000000   13.225761   \n",
       "                        FROM_WELLS             0.0     0.000000    0.000000   \n",
       "                        FROM_DRAINS            0.0     0.000000    0.000000   \n",
       "                        FROM_RECHARGE          0.0  6018.483887   17.470200   \n",
       "\n",
       "                                                   UFA  \n",
       "time_step stress_period name                            \n",
       "0         1091          FROM_STORAGE        228.273621  \n",
       "                        FROM_CONSTANT_HEAD    5.042325  \n",
       "                        FROM_WELLS            0.000000  \n",
       "                        FROM_DRAINS           0.000000  \n",
       "                        FROM_RECHARGE        34.940399  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = zb.get_dataframes(index_key='kstpkper')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write Budget Output to CSV\n",
    "\n",
    "We can write the resulting recarray to a csv file with the `.to_csv()` method of the `ZoneBudget` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  totim  time_step  stress_period                 name  ZONE_0       ZONE_1       ZONE_2       ZONE_3\n",
      "    1.0          0              0         FROM_STORAGE     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0   FROM_CONSTANT_HEAD     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0           FROM_WELLS     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0          FROM_DRAINS     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0        FROM_RECHARGE     0.0  6222.673300    18.062912    36.125824\n",
      "    1.0          0              0          FROM_ZONE_0     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0          FROM_ZONE_1     0.0     0.000000  4275.257300   491.945070\n",
      "    1.0          0              0          FROM_ZONE_2     0.0  2744.821800     0.000000  2115.654000\n",
      "    1.0          0              0          FROM_ZONE_3     0.0   451.545720  1215.952300     0.000000\n",
      "    1.0          0              0             TOTAL_IN     0.0  9419.041000  5509.272500  2643.725000\n",
      "    1.0          0              0           TO_STORAGE     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0     TO_CONSTANT_HEAD     0.0   821.283200   648.806100   976.233600\n",
      "    1.0          0              0             TO_WELLS     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_DRAINS     0.0  3832.150000     0.000000     0.000000\n",
      "    1.0          0              0          TO_RECHARGE     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_ZONE_0     0.0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_ZONE_1     0.0     0.000000  2744.821800   451.545720\n",
      "    1.0          0              0            TO_ZONE_2     0.0  4275.257300     0.000000  1215.952300\n",
      "    1.0          0              0            TO_ZONE_3     0.0   491.945070  2115.654000     0.000000\n",
      "    1.0          0              0            TOTAL_OUT     0.0  9420.636000  5509.282000  2643.731400\n",
      "    1.0          0              0               IN-OUT     0.0     1.594727     0.009766     0.006348\n",
      "    1.0          0              0  PERCENT_DISCREPANCY     NaN     0.016929     0.000177     0.000240\n",
      " 1097.0          0           1096         FROM_STORAGE     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096   FROM_CONSTANT_HEAD     0.0     0.000000   231.566590    86.217200\n",
      " 1097.0          0           1096           FROM_WELLS     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          FROM_DRAINS     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096        FROM_RECHARGE     0.0  5145.581500    14.936376    29.872751\n",
      " 1097.0          0           1096          FROM_ZONE_0     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          FROM_ZONE_1     0.0     0.000000  3475.123500   138.600450\n",
      " 1097.0          0           1096          FROM_ZONE_2     0.0  3269.318800     0.000000  1764.655300\n",
      " 1097.0          0           1096          FROM_ZONE_3     0.0   192.186040  1528.048200     0.000000\n",
      " 1097.0          0           1096             TOTAL_IN     0.0  8607.086000  5249.675000  2019.345700\n",
      " 1097.0          0           1096           TO_STORAGE     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096     TO_CONSTANT_HEAD     0.0   230.548360   215.701500   299.113800\n",
      " 1097.0          0           1096             TO_WELLS     0.0  4762.800000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_DRAINS     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          TO_RECHARGE     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_ZONE_0     0.0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_ZONE_1     0.0     0.000000  3269.318800   192.186040\n",
      " 1097.0          0           1096            TO_ZONE_2     0.0  3475.123500     0.000000  1528.048200\n",
      " 1097.0          0           1096            TO_ZONE_3     0.0   138.600450  1764.655300     0.000000\n",
      " 1097.0          0           1096            TOTAL_OUT     0.0  8607.072000  5249.676000  2019.348000\n",
      " 1097.0          0           1096               IN-OUT     0.0     0.013672     0.000977     0.002319\n",
      " 1097.0          0           1096  PERCENT_DISCREPANCY     NaN     0.000159     0.000019     0.000115\n"
     ]
    }
   ],
   "source": [
    "zb = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=[(0, 0), (0, 1096)])\n",
    "f_out = os.path.join('data', 'Example_output.csv')\n",
    "zb.to_csv(f_out)\n",
    "\n",
    "# Read the file in to see the contents\n",
    "try:\n",
    "    import pandas as pd\n",
    "    print(pd.read_csv(f_out).to_string(index=False))\n",
    "except:\n",
    "    with open(fname, 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            print('\\t'.join(line.split(',')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Net Budget\n",
    "Using the \"net\" keyword argument, we can request a net budget for each zone/record name or for a subset of zones and record names. Note that we can identify the record names we want without the added `\"_IN\"` or `\"_OUT\"` string suffix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1092., 0, 1091, 'STORAGE',  -386.15085, -425.65155),\n",
       "       (1092., 0, 1091, 'WELLS', -2829.812  ,    0.     ),\n",
       "       (1093., 0, 1092, 'STORAGE',   -12.50354, -151.3594 ),\n",
       "       (1093., 0, 1092, 'WELLS', -1930.4832 ,    0.     ),\n",
       "       (1094., 0, 1093, 'STORAGE',  -198.92935, -270.8744 ),\n",
       "       (1094., 0, 1093, 'WELLS', -1279.1664 ,    0.     ),\n",
       "       (1095., 0, 1094, 'STORAGE',  -718.4885 , -604.8537 ),\n",
       "       (1095., 0, 1094, 'WELLS',  -794.5829 ,    0.     ),\n",
       "       (1096., 0, 1095, 'STORAGE',  -855.1075 , -622.88477),\n",
       "       (1096., 0, 1095, 'WELLS', -1373.7827 ,    0.     ),\n",
       "       (1097., 0, 1096, 'STORAGE',     0.     ,    0.     ),\n",
       "       (1097., 0, 1096, 'WELLS', -4762.8    ,    0.     )],\n",
       "      dtype={'names':['totim','time_step','stress_period','name','SURF','UFA'], 'formats':['<f4','<i4','<i4','<U50','<f4','<f4'], 'offsets':[0,4,8,12,212,220], 'itemsize':224})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zon = np.ones((nlay, nrow, ncol), np.int)\n",
    "zon[1, :, :] = 2\n",
    "zon[2, :, :] = 3\n",
    "\n",
    "aliases = {1: 'SURF', 2:'CONF', 3: 'UFA'}\n",
    "times = list(range(1092, 1097+1))\n",
    "zb = flopy.utils.ZoneBudget(cbc_f, zon, totim=times, aliases=aliases)\n",
    "zb.get_budget(names=['STORAGE', 'WELLS'], zones=['SURF', 'UFA'], net=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SURF</th>\n",
       "      <th>UFA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totim</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1092.0</th>\n",
       "      <th>STORAGE</th>\n",
       "      <td>-386.150848</td>\n",
       "      <td>-425.651550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLS</th>\n",
       "      <td>-2829.812012</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1093.0</th>\n",
       "      <th>STORAGE</th>\n",
       "      <td>-12.503540</td>\n",
       "      <td>-151.359406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLS</th>\n",
       "      <td>-1930.483154</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1094.0</th>\n",
       "      <th>STORAGE</th>\n",
       "      <td>-198.929352</td>\n",
       "      <td>-270.874390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLS</th>\n",
       "      <td>-1279.166382</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SURF         UFA\n",
       "totim  name                            \n",
       "1092.0 STORAGE  -386.150848 -425.651550\n",
       "       WELLS   -2829.812012    0.000000\n",
       "1093.0 STORAGE   -12.503540 -151.359406\n",
       "       WELLS   -1930.483154    0.000000\n",
       "1094.0 STORAGE  -198.929352 -270.874390\n",
       "       WELLS   -1279.166382    0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = zb.get_dataframes(names=['STORAGE', 'WELLS'], zones=['SURF', 'UFA'], net=True)\n",
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Budget Components\n",
    "The following is a function that can be used to better visualize the budget components using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tick_label_formatter_comma_sep(x, pos):\n",
    "    return '{:,.0f}'.format(x)\n",
    "\n",
    "\n",
    "def volumetric_budget_bar_plot(values_in, values_out, labels, **kwargs):\n",
    "    if 'ax' in kwargs:\n",
    "        ax = kwargs.pop('ax')\n",
    "    else:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    x_pos = np.arange(len(values_in))\n",
    "    rects_in = ax.bar(x_pos, values_in, align='center', alpha=0.5)\n",
    "\n",
    "    x_pos = np.arange(len(values_out))\n",
    "    rects_out = ax.bar(x_pos, values_out, align='center', alpha=0.5)\n",
    "\n",
    "    plt.xticks(list(x_pos), labels)\n",
    "    ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=90)\n",
    "    ax.get_yaxis().set_major_formatter(mpl.ticker.FuncFormatter(tick_label_formatter_comma_sep))\n",
    "\n",
    "    ymin, ymax = ax.get_ylim()\n",
    "    if ymax != 0:\n",
    "        if abs(ymin) / ymax < .33:\n",
    "            ymin = -(ymax * .5)\n",
    "        else:\n",
    "            ymin *= 1.35\n",
    "    else:\n",
    "        ymin *= 1.35\n",
    "    plt.ylim([ymin, ymax * 1.25])\n",
    "\n",
    "    for i, rect in enumerate(rects_in):\n",
    "        label = '{:,.0f}'.format(values_in[i])\n",
    "        height = values_in[i]\n",
    "        x = rect.get_x() + rect.get_width() / 2\n",
    "        y = height + (.02 * ymax)\n",
    "        vertical_alignment = 'bottom'\n",
    "        horizontal_alignment = 'center'\n",
    "        ax.text(x, y, label, ha=horizontal_alignment, va=vertical_alignment, rotation=90)\n",
    "\n",
    "    for i, rect in enumerate(rects_out):\n",
    "        label = '{:,.0f}'.format(values_out[i])\n",
    "        height = values_out[i]\n",
    "        x = rect.get_x() + rect.get_width() / 2\n",
    "        y = height + (.02 * ymin)\n",
    "        vertical_alignment = 'top'\n",
    "        horizontal_alignment = 'center'\n",
    "        ax.text(x, y, label, ha=horizontal_alignment, va=vertical_alignment, rotation=90)\n",
    "\n",
    "    # horizontal line indicating zero\n",
    "    ax.plot([rects_in[0].get_x() - rects_in[0].get_width() / 2,\n",
    "             rects_in[-1].get_x() + rects_in[-1].get_width()], [0, 0], \"k\")\n",
    "\n",
    "    return rects_in, rects_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 5))\n",
    "\n",
    "times = [2., 500., 1000., 1095.]\n",
    "\n",
    "for idx, t in enumerate(times):\n",
    "\n",
    "    ax = fig.add_subplot(1, len(times), idx + 1)\n",
    "\n",
    "    zb = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=None, totim=t, aliases=aliases)\n",
    "\n",
    "    recname = 'STORAGE'\n",
    "    values_in = zb.get_dataframes(names='FROM_{}'.format(recname)).T.squeeze()\n",
    "    values_out = zb.get_dataframes(names='TO_{}'.format(recname)).T.squeeze() * -1\n",
    "    labels = values_in.index.tolist()\n",
    "\n",
    "    rects_in, rects_out = volumetric_budget_bar_plot(values_in, values_out, labels, ax=ax)\n",
    "\n",
    "    plt.ylabel('Volumetric rate, in Mgal/d')\n",
    "    plt.title('{} @ totim = {}'.format(recname, t))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "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": 2
}