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_us/flopy\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.16.2\n",
      "matplotlib version: 3.0.3\n",
      "pandas version: 0.24.2\n",
      "flopy version: 3.2.12\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import 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, :, :])\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', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_CONSTANT_HEAD', 0.0000000e+00, 2.3156659e+02, 8.6217201e+01),\n",
       "       (1097., 0, 1096, 'FROM_WELLS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_DRAINS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_RECHARGE', 5.1455815e+03, 1.4936376e+01, 2.9872751e+01),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_0', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_1', 0.0000000e+00, 3.4751235e+03, 1.3860045e+02),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_2', 3.2693188e+03, 0.0000000e+00, 1.7646553e+03),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_3', 1.9218604e+02, 1.5280482e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_IN', 8.6070859e+03, 5.2496748e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TO_STORAGE', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_CONSTANT_HEAD', 2.3054836e+02, 2.1570151e+02, 2.9911380e+02),\n",
       "       (1097., 0, 1096, 'TO_WELLS', 4.7627998e+03, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_DRAINS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_RECHARGE', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_0', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_1', 0.0000000e+00, 3.2693188e+03, 1.9218604e+02),\n",
       "       (1097., 0, 1096, 'TO_ZONE_2', 3.4751235e+03, 0.0000000e+00, 1.5280482e+03),\n",
       "       (1097., 0, 1096, 'TO_ZONE_3', 1.3860045e+02, 1.7646553e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT', 8.6070723e+03, 5.2496758e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT', 1.3671875e-02, 9.7656250e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', 1.5884454e-04, 1.8602341e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('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', 5145.5815 ,   14.936376,  29.872751),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_1',    0.     , 3475.1235  , 138.60045 ),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_3',  192.18604, 1528.0482  ,   0.      )],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('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':[0,4,8,12,216], 'itemsize':224})"
      ]
     },
     "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', 8.6070859e+03, 5.249675e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT', 8.6070723e+03, 5.249676e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT', 1.3671875e-02, 9.765625e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', 1.5884454e-04, 1.860234e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('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', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_CONSTANT_HEAD', 0.0000000e+00, 2.3156659e+02, 8.6217201e+01),\n",
       "       (1097., 0, 1096, 'FROM_WELLS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_DRAINS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_RECHARGE', 5.1455815e+03, 1.4936376e+01, 2.9872751e+01),\n",
       "       (1097., 0, 1096, 'FROM_ZONE_0', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'FROM_SURF', 0.0000000e+00, 3.4751235e+03, 1.3860045e+02),\n",
       "       (1097., 0, 1096, 'FROM_CONF', 3.2693188e+03, 0.0000000e+00, 1.7646553e+03),\n",
       "       (1097., 0, 1096, 'FROM_UFA', 1.9218604e+02, 1.5280482e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_IN', 8.6070859e+03, 5.2496748e+03, 2.0193457e+03),\n",
       "       (1097., 0, 1096, 'TO_STORAGE', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_CONSTANT_HEAD', 2.3054836e+02, 2.1570151e+02, 2.9911380e+02),\n",
       "       (1097., 0, 1096, 'TO_WELLS', 4.7627998e+03, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_DRAINS', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_RECHARGE', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_ZONE_0', 0.0000000e+00, 0.0000000e+00, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TO_SURF', 0.0000000e+00, 3.2693188e+03, 1.9218604e+02),\n",
       "       (1097., 0, 1096, 'TO_CONF', 3.4751235e+03, 0.0000000e+00, 1.5280482e+03),\n",
       "       (1097., 0, 1096, 'TO_UFA', 1.3860045e+02, 1.7646553e+03, 0.0000000e+00),\n",
       "       (1097., 0, 1096, 'TOTAL_OUT', 8.6070723e+03, 5.2496758e+03, 2.0193480e+03),\n",
       "       (1097., 0, 1096, 'IN-OUT', 1.3671875e-02, 9.7656250e-04, 2.3193359e-03),\n",
       "       (1097., 0, 1096, 'PERCENT_DISCREPANCY', 1.5884454e-04, 1.8602341e-05, 1.1485574e-04)],\n",
       "      dtype=[('totim', '<f4'), ('time_step', '<i4'), ('stress_period', '<i4'), ('name', '<U50'), ('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",
       "<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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"22\" valign=\"top\">1092.0</th>\n",
       "      <th>FROM_STORAGE</th>\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>0.000000</td>\n",
       "      <td>13.225761</td>\n",
       "      <td>5.042325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>6018.483887</td>\n",
       "      <td>17.470200</td>\n",
       "      <td>34.940399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4201.251465</td>\n",
       "      <td>394.240234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONF</th>\n",
       "      <td>2163.582275</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2100.073486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_UFA</th>\n",
       "      <td>382.018158</td>\n",
       "      <td>947.430359</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_IN</th>\n",
       "      <td>8957.565430</td>\n",
       "      <td>5409.854004</td>\n",
       "      <td>2762.570068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_STORAGE</th>\n",
       "      <td>777.934448</td>\n",
       "      <td>653.123352</td>\n",
       "      <td>656.431702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>664.500671</td>\n",
       "      <td>493.076324</td>\n",
       "      <td>776.692139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>2829.812012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_DRAINS</th>\n",
       "      <td>89.872528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_RECHARGE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2163.582275</td>\n",
       "      <td>382.018158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONF</th>\n",
       "      <td>4201.251465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>947.430359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_UFA</th>\n",
       "      <td>394.240234</td>\n",
       "      <td>2100.073486</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_OUT</th>\n",
       "      <td>8957.611328</td>\n",
       "      <td>5409.855469</td>\n",
       "      <td>2762.572266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN-OUT</th>\n",
       "      <td>0.045898</td>\n",
       "      <td>0.001465</td>\n",
       "      <td>0.002197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERCENT_DISCREPANCY</th>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1093.0</th>\n",
       "      <th>FROM_STORAGE</th>\n",
       "      <td>613.976074</td>\n",
       "      <td>345.158020</td>\n",
       "      <td>339.268097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.905813</td>\n",
       "      <td>6.389739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>4203.679199</td>\n",
       "      <td>12.202263</td>\n",
       "      <td>24.404526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3201.566406</td>\n",
       "      <td>388.763916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONF</th>\n",
       "      <td>1727.060059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1601.340088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1096.0</th>\n",
       "      <th>TO_RECHARGE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1609.951172</td>\n",
       "      <td>393.884155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONF</th>\n",
       "      <td>4099.374023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>671.023315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_UFA</th>\n",
       "      <td>411.700562</td>\n",
       "      <td>2046.670898</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_OUT</th>\n",
       "      <td>8225.371094</td>\n",
       "      <td>4855.261719</td>\n",
       "      <td>2560.562744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN-OUT</th>\n",
       "      <td>0.240234</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.034912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERCENT_DISCREPANCY</th>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.001363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"22\" valign=\"top\">1097.0</th>\n",
       "      <th>FROM_STORAGE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONSTANT_HEAD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>231.566589</td>\n",
       "      <td>86.217201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\n",
       "      <td>5145.581543</td>\n",
       "      <td>14.936376</td>\n",
       "      <td>29.872751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3475.123535</td>\n",
       "      <td>138.600449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_CONF</th>\n",
       "      <td>3269.318848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1764.655273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_UFA</th>\n",
       "      <td>192.186035</td>\n",
       "      <td>1528.048218</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_IN</th>\n",
       "      <td>8607.085938</td>\n",
       "      <td>5249.674805</td>\n",
       "      <td>2019.345703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_STORAGE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONSTANT_HEAD</th>\n",
       "      <td>230.548355</td>\n",
       "      <td>215.701508</td>\n",
       "      <td>299.113800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_WELLS</th>\n",
       "      <td>4762.799805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_DRAINS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_RECHARGE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_ZONE_0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_SURF</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3269.318848</td>\n",
       "      <td>192.186035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO_CONF</th>\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>138.600449</td>\n",
       "      <td>1764.655273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL_OUT</th>\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>0.013672</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.002319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERCENT_DISCREPANCY</th>\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 × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   SURF         CONF          UFA\n",
       "totim  name                                                      \n",
       "1092.0 FROM_STORAGE          393.480286   230.476242   228.273621\n",
       "       FROM_CONSTANT_HEAD      0.000000    13.225761     5.042325\n",
       "       FROM_WELLS              0.000000     0.000000     0.000000\n",
       "       FROM_DRAINS             0.000000     0.000000     0.000000\n",
       "       FROM_RECHARGE        6018.483887    17.470200    34.940399\n",
       "       FROM_ZONE_0             0.000000     0.000000     0.000000\n",
       "       FROM_SURF               0.000000  4201.251465   394.240234\n",
       "       FROM_CONF            2163.582275     0.000000  2100.073486\n",
       "       FROM_UFA              382.018158   947.430359     0.000000\n",
       "       TOTAL_IN             8957.565430  5409.854004  2762.570068\n",
       "       TO_STORAGE            777.934448   653.123352   656.431702\n",
       "       TO_CONSTANT_HEAD      664.500671   493.076324   776.692139\n",
       "       TO_WELLS             2829.812012     0.000000     0.000000\n",
       "       TO_DRAINS              89.872528     0.000000     0.000000\n",
       "       TO_RECHARGE             0.000000     0.000000     0.000000\n",
       "       TO_ZONE_0               0.000000     0.000000     0.000000\n",
       "       TO_SURF                 0.000000  2163.582275   382.018158\n",
       "       TO_CONF              4201.251465     0.000000   947.430359\n",
       "       TO_UFA                394.240234  2100.073486     0.000000\n",
       "       TOTAL_OUT            8957.611328  5409.855469  2762.572266\n",
       "       IN-OUT                  0.045898     0.001465     0.002197\n",
       "       PERCENT_DISCREPANCY     0.000512     0.000027     0.000080\n",
       "1093.0 FROM_STORAGE          613.976074   345.158020   339.268097\n",
       "       FROM_CONSTANT_HEAD      0.000000    16.905813     6.389739\n",
       "       FROM_WELLS              0.000000     0.000000     0.000000\n",
       "       FROM_DRAINS             0.000000     0.000000     0.000000\n",
       "       FROM_RECHARGE        4203.679199    12.202263    24.404526\n",
       "       FROM_ZONE_0             0.000000     0.000000     0.000000\n",
       "       FROM_SURF               0.000000  3201.566406   388.763916\n",
       "       FROM_CONF            1727.060059     0.000000  1601.340088\n",
       "...                                 ...          ...          ...\n",
       "1096.0 TO_RECHARGE             0.000000     0.000000     0.000000\n",
       "       TO_ZONE_0               0.000000     0.000000     0.000000\n",
       "       TO_SURF                 0.000000  1609.951172   393.884155\n",
       "       TO_CONF              4099.374023     0.000000   671.023315\n",
       "       TO_UFA                411.700562  2046.670898     0.000000\n",
       "       TOTAL_OUT            8225.371094  4855.261719  2560.562744\n",
       "       IN-OUT                  0.240234     0.035156     0.034912\n",
       "       PERCENT_DISCREPANCY     0.002921     0.000724     0.001363\n",
       "1097.0 FROM_STORAGE            0.000000     0.000000     0.000000\n",
       "       FROM_CONSTANT_HEAD      0.000000   231.566589    86.217201\n",
       "       FROM_WELLS              0.000000     0.000000     0.000000\n",
       "       FROM_DRAINS             0.000000     0.000000     0.000000\n",
       "       FROM_RECHARGE        5145.581543    14.936376    29.872751\n",
       "       FROM_ZONE_0             0.000000     0.000000     0.000000\n",
       "       FROM_SURF               0.000000  3475.123535   138.600449\n",
       "       FROM_CONF            3269.318848     0.000000  1764.655273\n",
       "       FROM_UFA              192.186035  1528.048218     0.000000\n",
       "       TOTAL_IN             8607.085938  5249.674805  2019.345703\n",
       "       TO_STORAGE              0.000000     0.000000     0.000000\n",
       "       TO_CONSTANT_HEAD      230.548355   215.701508   299.113800\n",
       "       TO_WELLS             4762.799805     0.000000     0.000000\n",
       "       TO_DRAINS               0.000000     0.000000     0.000000\n",
       "       TO_RECHARGE             0.000000     0.000000     0.000000\n",
       "       TO_ZONE_0               0.000000     0.000000     0.000000\n",
       "       TO_SURF                 0.000000  3269.318848   192.186035\n",
       "       TO_CONF              3475.123535     0.000000  1528.048218\n",
       "       TO_UFA                138.600449  1764.655273     0.000000\n",
       "       TOTAL_OUT            8607.072266  5249.675781  2019.348022\n",
       "       IN-OUT                  0.013672     0.000977     0.002319\n",
       "       PERCENT_DISCREPANCY     0.000159     0.000019     0.000115\n",
       "\n",
       "[132 rows x 3 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",
       "        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>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": [
       "<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>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": [
       "<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>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>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",
       "    </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>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>0.000000</td>\n",
       "      <td>13.225761</td>\n",
       "      <td>5.042325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_WELLS</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FROM_RECHARGE</th>\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": [
       "                                                   SURF        CONF  \\\n",
       "time_step stress_period name                                          \n",
       "0         1091          FROM_STORAGE         393.480286  230.476242   \n",
       "                        FROM_CONSTANT_HEAD     0.000000   13.225761   \n",
       "                        FROM_WELLS             0.000000    0.000000   \n",
       "                        FROM_DRAINS            0.000000    0.000000   \n",
       "                        FROM_RECHARGE       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_1       ZONE_2       ZONE_3\n",
      "    1.0          0              0         FROM_STORAGE     0.000000     0.000000     0.000000\n",
      "    1.0          0              0   FROM_CONSTANT_HEAD     0.000000     0.000000     0.000000\n",
      "    1.0          0              0           FROM_WELLS     0.000000     0.000000     0.000000\n",
      "    1.0          0              0          FROM_DRAINS     0.000000     0.000000     0.000000\n",
      "    1.0          0              0        FROM_RECHARGE  6222.673300    18.062912    36.125824\n",
      "    1.0          0              0          FROM_ZONE_0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0          FROM_ZONE_1     0.000000  4275.257300   491.945070\n",
      "    1.0          0              0          FROM_ZONE_2  2744.821800     0.000000  2115.654000\n",
      "    1.0          0              0          FROM_ZONE_3   451.545720  1215.952300     0.000000\n",
      "    1.0          0              0             TOTAL_IN  9419.041000  5509.272500  2643.725000\n",
      "    1.0          0              0           TO_STORAGE     0.000000     0.000000     0.000000\n",
      "    1.0          0              0     TO_CONSTANT_HEAD   821.283200   648.806100   976.233600\n",
      "    1.0          0              0             TO_WELLS     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_DRAINS  3832.150000     0.000000     0.000000\n",
      "    1.0          0              0          TO_RECHARGE     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_ZONE_0     0.000000     0.000000     0.000000\n",
      "    1.0          0              0            TO_ZONE_1     0.000000  2744.821800   451.545720\n",
      "    1.0          0              0            TO_ZONE_2  4275.257300     0.000000  1215.952300\n",
      "    1.0          0              0            TO_ZONE_3   491.945070  2115.654000     0.000000\n",
      "    1.0          0              0            TOTAL_OUT  9420.636000  5509.282000  2643.731400\n",
      "    1.0          0              0               IN-OUT     1.594727     0.009766     0.006348\n",
      "    1.0          0              0  PERCENT_DISCREPANCY     0.016929     0.000177     0.000240\n",
      " 1097.0          0           1096         FROM_STORAGE     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096   FROM_CONSTANT_HEAD     0.000000   231.566590    86.217200\n",
      " 1097.0          0           1096           FROM_WELLS     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          FROM_DRAINS     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096        FROM_RECHARGE  5145.581500    14.936376    29.872751\n",
      " 1097.0          0           1096          FROM_ZONE_0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          FROM_ZONE_1     0.000000  3475.123500   138.600450\n",
      " 1097.0          0           1096          FROM_ZONE_2  3269.318800     0.000000  1764.655300\n",
      " 1097.0          0           1096          FROM_ZONE_3   192.186040  1528.048200     0.000000\n",
      " 1097.0          0           1096             TOTAL_IN  8607.086000  5249.675000  2019.345700\n",
      " 1097.0          0           1096           TO_STORAGE     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096     TO_CONSTANT_HEAD   230.548360   215.701500   299.113800\n",
      " 1097.0          0           1096             TO_WELLS  4762.800000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_DRAINS     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096          TO_RECHARGE     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_ZONE_0     0.000000     0.000000     0.000000\n",
      " 1097.0          0           1096            TO_ZONE_1     0.000000  3269.318800   192.186040\n",
      " 1097.0          0           1096            TO_ZONE_2  3475.123500     0.000000  1528.048200\n",
      " 1097.0          0           1096            TO_ZONE_3   138.600450  1764.655300     0.000000\n",
      " 1097.0          0           1096            TOTAL_OUT  8607.072000  5249.676000  2019.348000\n",
      " 1097.0          0           1096               IN-OUT     0.013672     0.000977     0.002319\n",
      " 1097.0          0           1096  PERCENT_DISCREPANCY     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
}