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": [
      "3.8.10 (default, May 19 2021, 11:01:55) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "pandas version: 1.0.5\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.4\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 792x612 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\t5000.0\t3.0\t2.9999999242136255e-05\n",
      "0\t5\t7\t3\t6\t2000.0\t2.0\t2.9999999242136255e-05\n",
      "0\t5\t7\t4\t3\t3500.0\t3.0\t2.9999999242136255e-05\n",
      "0\t5\t7\t5\t1\t4000.0\t3.0\t2.9999999242136255e-05\n",
      "0\t7\t5\t5\t4\t2500.0\t3.0\t2.9999999242136255e-05\n",
      "0\t12\t6\t6\t5\t2000.0\t3.0\t2.9999999242136255e-05\n",
      "0\t12\t6\t7\t3\t5000.0\t3.0\t5.999999848427251e-05\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): 2021/08/06 16:39:49\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): 2021/08/06 16:39:49\n",
      " Elapsed run time:  0.029 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>gradient</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  gradient 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 792x612 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 792x612 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 792x612 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 792x612 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>gradient</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  gradient 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 792x612 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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}