examples/Notebooks/flopy3_sfrpackage_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SFR package example\n",
    "Demonstrates functionality of Flopy SFR module using the example documented by [Prudic and others (2004)](http://pubs.er.usgs.gov/publication/ofr20041042):  \n",
    "\n",
    "#### Problem description:\n",
    "\n",
    "* Grid dimensions: 1 Layer, 15 Rows, 10 Columns  \n",
    "* Stress periods: 1 steady  \n",
    "* Flow package: LPF  \n",
    "* Stress packages: SFR, GHB, EVT, RCH  \n",
    "* Solver: SIP  \n",
    "\n",
    "<img src=\"./img/Prudic2004_fig6.png\" width=\"400\" height=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.16.2\n",
      "pandas version: 0.24.2\n",
      "matplotlib version: 3.0.3\n",
      "flopy version: 3.2.12\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import platform\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "import shutil\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",
    "import flopy.utils.binaryfile as bf\n",
    "from flopy.utils.sfroutputfile import SfrFile\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (11, 8.5)\n",
    "\n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('pandas version: {}'.format(pd.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set name of MODFLOW exe\n",
    "#  assumes executable is in users path statement\n",
    "exe_name = 'mf2005'\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name += '.exe'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### copy over the example files to the working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'data'\n",
    "if os.path.isfile(path):\n",
    "    os.remove(path)\n",
    "elif os.path.isdir(path):\n",
    "    shutil.rmtree(path)\n",
    "os.mkdir(path)\n",
    "gpth = os.path.join('..', 'data', 'mf2005_test', 'test1ss.*')\n",
    "for f in glob.glob(gpth):\n",
    "    shutil.copy(f, path)\n",
    "gpth = os.path.join('..', 'data', 'mf2005_test', 'test1tr.*')\n",
    "for f in glob.glob(gpth):\n",
    "    shutil.copy(f, path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load example dataset, skipping the SFR package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = flopy.modflow.Modflow.load('test1ss.nam', version='mf2005', exe_name=exe_name, \n",
    "                               model_ws=path, load_only=['ghb', 'evt', 'rch', 'dis', 'bas6', 'oc', 'sip', 'lpf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(-1, -1): [],\n",
       " (0, 0): ['print budget', 'print head', 'save head', 'save budget']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oc = m.oc\n",
    "oc.stress_period_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read pre-prepared reach and segment data into numpy recarrays using numpy.genfromtxt()\n",
    "Reach data (Item 2 in the SFR input instructions), are input and stored in a numpy record array  \n",
    "http://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html  \n",
    "This allows for reach data to be indexed by their variable names, as described in the SFR input instructions.\n",
    "\n",
    "For more information on Item 2, see the Online Guide to MODFLOW:  \n",
    "<http://water.usgs.gov/nrp/gwsoftware/modflow2000/MFDOC/index.html?sfr.htm>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0.,  0., 0., 1., 1., 4500.), (0.,  1., 1., 1., 2., 7000.),\n",
       "       (0.,  2., 2., 1., 3., 6000.), (0.,  2., 3., 1., 4., 5550.),\n",
       "       (0.,  3., 4., 2., 1., 6500.), (0.,  4., 5., 2., 2., 5000.),\n",
       "       (0.,  5., 5., 2., 3., 5000.), (0.,  6., 5., 2., 4., 5000.),\n",
       "       (0.,  7., 5., 2., 5., 5000.), (0.,  2., 4., 3., 1., 5000.),\n",
       "       (0.,  2., 5., 3., 2., 5000.), (0.,  2., 6., 3., 3., 4500.),\n",
       "       (0.,  3., 7., 3., 4., 6000.), (0.,  4., 7., 3., 5., 5000.),\n",
       "       (0.,  5., 7., 3., 6., 2000.), (0.,  4., 9., 4., 1., 2500.),\n",
       "       (0.,  4., 8., 4., 2., 5000.), (0.,  5., 7., 4., 3., 3500.),\n",
       "       (0.,  5., 7., 5., 1., 4000.), (0.,  6., 6., 5., 2., 5000.),\n",
       "       (0.,  7., 6., 5., 3., 3500.), (0.,  7., 5., 5., 4., 2500.),\n",
       "       (0.,  8., 5., 6., 1., 5000.), (0.,  9., 6., 6., 2., 5000.),\n",
       "       (0., 10., 6., 6., 3., 5000.), (0., 11., 6., 6., 4., 5000.),\n",
       "       (0., 12., 6., 6., 5., 2000.), (0., 13., 8., 7., 1., 5000.),\n",
       "       (0., 12., 7., 7., 2., 5500.), (0., 12., 6., 7., 3., 5000.),\n",
       "       (0., 12., 5., 8., 1., 5000.), (0., 12., 4., 8., 2., 5000.),\n",
       "       (0., 12., 3., 8., 3., 5000.), (0., 12., 2., 8., 4., 5000.),\n",
       "       (0., 12., 1., 8., 5., 5000.), (0., 12., 0., 8., 6., 3000.)],\n",
       "      dtype=[('k', '<f8'), ('i', '<f8'), ('j', '<f8'), ('iseg', '<f8'), ('ireach', '<f8'), ('rchlen', '<f8')])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpth = os.path.join('..', 'data', 'sfr_examples', 'test1ss_reach_data.csv')\n",
    "reach_data = np.genfromtxt(rpth, delimiter=',', names=True)\n",
    "reach_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment Data structure\n",
    "Segment data are input and stored in a dictionary of record arrays, which "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spth = os.path.join('..', 'data', 'sfr_examples', 'test1ss_segment_data.csv')\n",
    "ss_segment_data = np.genfromtxt(spth, delimiter=',', names=True)\n",
    "segment_data = {0: ss_segment_data}\n",
    "segment_data[0][0:1]['width1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define dataset 6e (channel flow data) for segment 1\n",
    "dataset 6e is stored in a nested dictionary keyed by stress period and segment,  \n",
    "with a list of the following lists defined for each segment with icalc == 4  \n",
    "FLOWTAB(1) FLOWTAB(2) ... FLOWTAB(NSTRPTS)  \n",
    "DPTHTAB(1) DPTHTAB(2) ... DPTHTAB(NSTRPTS)  \n",
    "WDTHTAB(1) WDTHTAB(2) ... WDTHTAB(NSTRPTS)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_flow_data = {0: {1: [[0.5, 1.0, 2.0, 4.0, 7.0, 10.0, 20.0, 30.0, 50.0, 75.0, 100.0],\n",
    "                             [0.25, 0.4, 0.55, 0.7, 0.8, 0.9, 1.1, 1.25, 1.4, 1.7, 2.6],\n",
    "                             [3.0, 3.5, 4.2, 5.3, 7.0, 8.5, 12.0, 14.0, 17.0, 20.0, 22.0]]}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define dataset 6d (channel geometry data) for segments 7 and 8\n",
    "dataset 6d is stored in a nested dictionary keyed by stress period and segment,  \n",
    "with a list of the following lists defined for each segment with icalc == 4  \n",
    "FLOWTAB(1) FLOWTAB(2) ... FLOWTAB(NSTRPTS)  \n",
    "DPTHTAB(1) DPTHTAB(2) ... DPTHTAB(NSTRPTS)  \n",
    "WDTHTAB(1) WDTHTAB(2) ... WDTHTAB(NSTRPTS)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_geometry_data = {0: {7: [[0.0, 10.0, 80.0, 100.0, 150.0, 170.0, 240.0, 250.0],\n",
    "                                 [20.0, 13.0, 10.0, 2.0, 0.0, 10.0, 13.0, 20.0]],\n",
    "                             8: [[0.0, 10.0, 80.0, 100.0, 150.0, 170.0, 240.0, 250.0],\n",
    "                                 [25.0, 17.0, 13.0, 4.0, 0.0, 10.0, 16.0, 20.0]]}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define SFR package variables  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nstrm = len(reach_data) # number of reaches\n",
    "nss = len(segment_data[0]) # number of segments\n",
    "nsfrpar = 0 # number of parameters (not supported)\n",
    "nparseg = 0\n",
    "const = 1.486 # constant for manning's equation, units of cfs\n",
    "dleak = 0.0001 # closure tolerance for stream stage computation\n",
    "ipakcb = 53 # flag for writing SFR output to cell-by-cell budget (on unit 53)\n",
    "istcb2 = 81 # flag for writing SFR output to text file\n",
    "dataset_5 = {0: [nss, 0, 0]} # dataset 5 (see online guide)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate SFR package  \n",
    "Input arguments generally follow the variable names defined in the Online Guide to MODFLOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfr = flopy.modflow.ModflowSfr2(m, nstrm=nstrm, nss=nss, const=const, dleak=dleak, ipakcb=ipakcb, istcb2=istcb2, \n",
    "                                reach_data=reach_data,\n",
    "                                segment_data=segment_data,\n",
    "                                channel_geometry_data=channel_geometry_data,\n",
    "                                channel_flow_data=channel_flow_data,\n",
    "                                dataset_5=dataset_5, unit_number=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([(0, 0, 0, 0, 1, 1, 4500., 0., 0., 0., 0., 0., 0., 0., 0., 1, 2)],\n",
       "          dtype=[('node', '<i8'), ('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('iseg', '<i8'), ('ireach', '<i8'), ('rchlen', '<f4'), ('strtop', '<f4'), ('slope', '<f4'), ('strthick', '<f4'), ('strhc1', '<f4'), ('thts', '<f4'), ('thti', '<f4'), ('eps', '<f4'), ('uhc', '<f4'), ('reachID', '<i8'), ('outreach', '<i8')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfr.reach_data[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the SFR segments\n",
    "any column in the reach_data array can be plotted using the ```key``` argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sfr.plot(key='iseg');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the SFR dataset for errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "passed.\n",
      "\n",
      "Checking for continuity in segment and reach numbering...\n",
      "passed.\n",
      "\n",
      "Checking for increasing segment numbers in downstream direction...\n",
      "passed.\n",
      "\n",
      "Checking for circular routing...\n",
      "passed.\n",
      "\n",
      "Checking reach connections for proximity...\n",
      "0 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "\n",
      "\n",
      "0 segments with non-adjacent reaches found.\n",
      "At segments:\n",
      "\n",
      "\n",
      "\n",
      "Checking for model cells with multiple non-zero SFR conductances...\n",
      "3 model cells with multiple non-zero SFR conductances found.\n",
      "This may lead to circular routing between collocated reaches.\n",
      "Nodes with overlapping conductances:\n",
      "k\ti\tj\tiseg\tireach\trchlen\tstrthick\tstrhc1\n",
      "0\t7\t5\t2\t5\t5000.000000\t3.000000\t0.000030\n",
      "0\t5\t7\t3\t6\t2000.000000\t2.000000\t0.000030\n",
      "0\t5\t7\t4\t3\t3500.000000\t3.000000\t0.000030\n",
      "0\t5\t7\t5\t1\t4000.000000\t3.000000\t0.000030\n",
      "0\t7\t5\t5\t4\t2500.000000\t3.000000\t0.000030\n",
      "0\t12\t6\t6\t5\t2000.000000\t3.000000\t0.000030\n",
      "0\t12\t6\t7\t3\t5000.000000\t3.000000\t0.000060\n",
      "\n",
      "Checking for streambed tops of less than -10...\n",
      "strtop not specified for isfropt=0\n",
      "passed.\n",
      "\n",
      "Checking for streambed tops of greater than 15000...\n",
      "strtop not specified for isfropt=0\n",
      "passed.\n",
      "\n",
      "Checking segment_data for downstream rises in streambed elevation...\n",
      "passed.\n",
      "\n",
      "Checking reach_data for downstream rises in streambed elevation...\n",
      "Reach strtop not specified for nstrm=36, reachinput=False and isfropt=0\n",
      "passed.\n",
      "\n",
      "Checking reach_data for inconsistencies between streambed elevations and the model grid...\n",
      "Reach strtop, strthick not specified for nstrm=36, reachinput=False and isfropt=0\n",
      "passed.\n",
      "\n",
      "Checking segment_data for inconsistencies between segment end elevations and the model grid...\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of less than 0.0001...\n",
      "slope not specified for isfropt=0\n",
      "passed.\n",
      "\n",
      "Checking for streambed slopes of greater than 1.0...\n",
      "slope not specified for isfropt=0\n",
      "passed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "chk = sfr.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test1ss.sg1',\n",
       " 'test1ss.sg2',\n",
       " 'test1ss.sg3',\n",
       " 'test1ss.sg4',\n",
       " 'test1ss.sg5',\n",
       " 'test1ss.sg6',\n",
       " 'test1ss.sg7',\n",
       " 'test1ss.sg8',\n",
       " 'test1ss.dvsg9']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.external_fnames = [os.path.split(f)[1] for f in m.external_fnames]\n",
    "m.external_fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf2005\n",
      "\n",
      "                                  MODFLOW-2005     \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n",
      "                             Version 1.12.00 2/3/2017                        \n",
      "\n",
      " Using NAME file: test1ss.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2019/05/31 19:15:54\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Ground-Water Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2019/05/31 19:15:54\n",
      " Elapsed run time:  0.025 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.run_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load SFR formated water balance output into pandas dataframe using the `SfrFile` class \n",
    "* requires the **pandas** library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>layer</th>\n",
       "      <th>row</th>\n",
       "      <th>column</th>\n",
       "      <th>segment</th>\n",
       "      <th>reach</th>\n",
       "      <th>Qin</th>\n",
       "      <th>Qaquifer</th>\n",
       "      <th>Qout</th>\n",
       "      <th>Qovr</th>\n",
       "      <th>Qprecip</th>\n",
       "      <th>Qet</th>\n",
       "      <th>stage</th>\n",
       "      <th>depth</th>\n",
       "      <th>width</th>\n",
       "      <th>Cond</th>\n",
       "      <th>col16</th>\n",
       "      <th>kstpkper</th>\n",
       "      <th>k</th>\n",
       "      <th>i</th>\n",
       "      <th>j</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>0.7923</td>\n",
       "      <td>24.2080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1094.22</td>\n",
       "      <td>1.174</td>\n",
       "      <td>12.98</td>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.4520</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.2080</td>\n",
       "      <td>2.1408</td>\n",
       "      <td>22.0670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1089.21</td>\n",
       "      <td>1.152</td>\n",
       "      <td>12.68</td>\n",
       "      <td>0.8878</td>\n",
       "      <td>0.8038</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0670</td>\n",
       "      <td>2.9909</td>\n",
       "      <td>19.0760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1083.53</td>\n",
       "      <td>1.110</td>\n",
       "      <td>12.13</td>\n",
       "      <td>0.7278</td>\n",
       "      <td>1.3700</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0760</td>\n",
       "      <td>2.5538</td>\n",
       "      <td>16.5220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1078.47</td>\n",
       "      <td>1.064</td>\n",
       "      <td>11.32</td>\n",
       "      <td>0.6285</td>\n",
       "      <td>1.3550</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5222</td>\n",
       "      <td>2.7058</td>\n",
       "      <td>3.8163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1072.40</td>\n",
       "      <td>0.469</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   layer  row  column  segment  reach      Qin  Qaquifer     Qout  Qovr  \\\n",
       "0      1    1       1        1      1  25.0000    0.7923  24.2080   0.0   \n",
       "1      1    2       2        1      2  24.2080    2.1408  22.0670   0.0   \n",
       "2      1    3       3        1      3  22.0670    2.9909  19.0760   0.0   \n",
       "3      1    3       4        1      4  19.0760    2.5538  16.5220   0.0   \n",
       "4      1    4       5        2      1   6.5222    2.7058   3.8163   0.0   \n",
       "\n",
       "   Qprecip  Qet    stage  depth  width    Cond   col16 kstpkper  k  i  j  \n",
       "0      0.0  0.0  1094.22  1.174  12.98  0.5843  0.4520   (0, 0)  0  0  0  \n",
       "1      0.0  0.0  1089.21  1.152  12.68  0.8878  0.8038   (0, 0)  0  1  1  \n",
       "2      0.0  0.0  1083.53  1.110  12.13  0.7278  1.3700   (0, 0)  0  2  2  \n",
       "3      0.0  0.0  1078.47  1.064  11.32  0.6285  1.3550   (0, 0)  0  2  3  \n",
       "4      0.0  0.0  1072.40  0.469  12.00  0.7800  1.1560   (0, 0)  0  3  4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfr_outfile = os.path.join('..', 'data', 'sfr_examples', 'test1ss.flw')\n",
    "sfrout = SfrFile(sfr_outfile)\n",
    "df = sfrout.get_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot streamflow and stream/aquifer interactions for a segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9     1\n",
      "10    2\n",
      "11    3\n",
      "12    4\n",
      "13    5\n",
      "14    6\n",
      "Name: reach, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inds = df.segment == 3\n",
    "print(df.reach[inds].astype(str))\n",
    "#ax = df.ix[inds, ['Qin', 'Qaquifer', 'Qout']].plot(x=df.reach[inds])\n",
    "ax = df.loc[inds, ['reach', 'Qin', 'Qaquifer', 'Qout']].plot(x='reach')\n",
    "ax.set_ylabel('Flow, in cubic feet per second')\n",
    "ax.set_xlabel('SFR reach');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at stage, model top, and streambed top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1075., 1060.)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streambed_top = m.sfr.segment_data[0][m.sfr.segment_data[0].nseg == 3][['elevup', 'elevdn']][0]\n",
    "streambed_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['model_top'] = m.dis.top.array[df.row.values - 1, df.column.values -1]\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot([1, 6], list(streambed_top), label='streambed top')\n",
    "#ax = df.loc[inds, ['stage', 'model_top']].plot(ax=ax, x=df.reach[inds])\n",
    "ax = df.loc[inds, ['reach', 'stage', 'model_top']].plot(ax=ax, x='reach')\n",
    "ax.set_ylabel('Elevation, in feet')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get SFR leakage results from cell budget file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, b'  STREAM LEAKAGE', 10, 15, 1, 0, 0., 0., -1., b'', b'', b'', b'')\n"
     ]
    }
   ],
   "source": [
    "bpth = os.path.join('data', 'test1ss.cbc')\n",
    "cbbobj = bf.CellBudgetFile(bpth)\n",
    "cbbobj.list_records()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfrleak = cbbobj.get_data(text='  STREAM LEAKAGE')[0]\n",
    "sfrleak[sfrleak == 0] = np.nan # remove zero values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot leakage in plan view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = plt.imshow(sfrleak[0], interpolation='none', cmap='coolwarm', vmin = -3, vmax=3)\n",
    "cb = plt.colorbar(im, label='SFR Leakage, in cubic feet per second');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot total streamflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sfrQ = sfrleak[0].copy()\n",
    "sfrQ[sfrQ == 0] = np.nan\n",
    "sfrQ[df.row.values-1, df.column.values-1] = df[['Qin', 'Qout']].mean(axis=1).values\n",
    "im = plt.imshow(sfrQ, interpolation='none')\n",
    "plt.colorbar(im, label='Streamflow, in cubic feet per second');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading transient SFR formatted output\n",
    "the `SfrFile` class handles this the same way\n",
    "\n",
    "files for the transient version of the above example were already copied to the `data` folder in the third cell above\n",
    "first run the transient model to get the output:\n",
    "```\n",
    ">mf2005 test1tr.nam\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flopy.run_model(exe_name, 'test1tr.nam', model_ws=path, silent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>layer</th>\n",
       "      <th>row</th>\n",
       "      <th>column</th>\n",
       "      <th>segment</th>\n",
       "      <th>reach</th>\n",
       "      <th>Qin</th>\n",
       "      <th>Qaquifer</th>\n",
       "      <th>Qout</th>\n",
       "      <th>Qovr</th>\n",
       "      <th>Qprecip</th>\n",
       "      <th>Qet</th>\n",
       "      <th>stage</th>\n",
       "      <th>depth</th>\n",
       "      <th>width</th>\n",
       "      <th>Cond</th>\n",
       "      <th>col16</th>\n",
       "      <th>kstpkper</th>\n",
       "      <th>k</th>\n",
       "      <th>i</th>\n",
       "      <th>j</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>0.77759</td>\n",
       "      <td>24.2220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1094.22</td>\n",
       "      <td>1.1740</td>\n",
       "      <td>12.98</td>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.4436</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.2220</td>\n",
       "      <td>2.21540</td>\n",
       "      <td>22.0070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1089.21</td>\n",
       "      <td>1.1510</td>\n",
       "      <td>12.68</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.8321</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0070</td>\n",
       "      <td>2.98700</td>\n",
       "      <td>19.0200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1083.53</td>\n",
       "      <td>1.1090</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.7270</td>\n",
       "      <td>1.3700</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0200</td>\n",
       "      <td>2.54940</td>\n",
       "      <td>16.4710</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1078.47</td>\n",
       "      <td>1.0630</td>\n",
       "      <td>11.31</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>1.3540</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4706</td>\n",
       "      <td>2.70370</td>\n",
       "      <td>3.7669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1072.40</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>1.1550</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   layer  row  column  segment  reach      Qin  Qaquifer     Qout  Qovr  \\\n",
       "0      1    1       1        1      1  25.0000   0.77759  24.2220   0.0   \n",
       "1      1    2       2        1      2  24.2220   2.21540  22.0070   0.0   \n",
       "2      1    3       3        1      3  22.0070   2.98700  19.0200   0.0   \n",
       "3      1    3       4        1      4  19.0200   2.54940  16.4710   0.0   \n",
       "4      1    4       5        2      1   6.4706   2.70370   3.7669   0.0   \n",
       "\n",
       "   Qprecip  Qet    stage   depth  width    Cond   col16 kstpkper  k  i  j  \n",
       "0      0.0  0.0  1094.22  1.1740  12.98  0.5843  0.4436   (0, 0)  0  0  0  \n",
       "1      0.0  0.0  1089.21  1.1510  12.68  0.8875  0.8321   (0, 0)  0  1  1  \n",
       "2      0.0  0.0  1083.53  1.1090  12.12  0.7270  1.3700   (0, 0)  0  2  2  \n",
       "3      0.0  0.0  1078.47  1.0630  11.31  0.6275  1.3540   (0, 0)  0  2  3  \n",
       "4      0.0  0.0  1072.40  0.4663  12.00  0.7800  1.1550   (0, 0)  0  3  4  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfrout_tr = SfrFile(os.path.join('data', 'test1tr.flw'))\n",
    "dftr = sfrout_tr.get_dataframe()\n",
    "dftr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot a hydrograph\n",
    "plot `Qout` (simulated streamflow) and `Qaquifer` (simulated stream leakage) through time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex=True)\n",
    "dftr8 = dftr.loc[(dftr.segment == 8) & (dftr.reach == 5)]\n",
    "dftr8.Qout.plot(ax=axes[0])\n",
    "axes[0].set_ylabel('Simulated streamflow, cfs')\n",
    "dftr8.Qaquifer.plot(ax=axes[1])\n",
    "axes[1].set_ylabel('Leakage to aquifer, cfs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}