examples/Notebooks/flopy3_PlotCrossSection_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# New plotting classes have been added to flopy\n",
    "\n",
    "these classes seamlessly support vertex model grid cross section plotting using the new `PlotCrossSection` class. This class will replace the previous `ModelCrossSection` class; however backward compatibility is still supported through `ModelCrossSection`.\n",
    "\n",
    "`PlotCrossSection` has a similar interface as `ModelCrossSection`. In most instances the two classes can be used interchangably. The biggest difference is that some of the input parameters have changed.\n",
    "\n",
    "The number of input parameters to the `PlotCrossSection` class has been reduced to make it easier to work with.\n",
    "\n",
    "Transforms using `xll`, `yll`, `xul`, `yul`, `rotation`, and `length_multiplier` have been removed from the list of parameters. The user should apply these transforms before passing their model grid to `PlotCrossSection`.\n",
    "\n",
    "The remaining functionality remains the same!"
   ]
  },
  {
   "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",
      "flopy version: 3.2.12\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import setup_pmv_demo\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "    from flopy.plot import PlotMapView, PlotCrossSection, ModelMap, ModelCrossSection\n",
    "    from flopy.utils import HeadFile, CellBudgetFile, geometry\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "    from flopy.plot import PlotMapView, PlotCrossSection, ModelMap, ModelCrossSection\n",
    "    from flopy.utils import HeadFile, CellBudgetFile, geometry\n",
    "      \n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First let's set up our projects \n",
    "\n",
    "This function runs a couple of modflow models; we'll be working with the output data from these\n",
    "\n",
    "### Freyberg model for the DIS example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "changing model workspace...\n",
      "   data\n",
      "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: freyberg.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2019/05/31 19:08:33\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 19:08:33\n",
      " Elapsed run time:  0.010 Seconds\n",
      "\n",
      "  Normal termination of simulation\n",
      "Output file located: freyberg.hds\n",
      "Output file located: freyberg.cbc\n"
     ]
    }
   ],
   "source": [
    "loadpth = os.path.join('..', 'data', 'freyberg')\n",
    "modelpth = os.path.join('data')\n",
    "dis_ml = flopy.modflow.Modflow.load('freyberg.nam', model_ws=loadpth, \n",
    "                                exe_name=\"mf2005\", version=\"mf2005\")\n",
    "dis_ml.change_model_ws(new_pth=modelpth)\n",
    "dis_ml.write_input()\n",
    "success, buff = dis_ml.run_model()\n",
    "if not success:\n",
    "    print ('Something bad happened.')\n",
    "files = ['freyberg.hds', 'freyberg.cbc']\n",
    "for f in files:\n",
    "    if os.path.isfile(os.path.join(modelpth, f)):\n",
    "        msg = 'Output file located: {}'.format(f)\n",
    "        print (msg)\n",
    "    else:\n",
    "        errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
    "        print (errmsg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DISV example problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwf6...\n",
      "    loading package disv...\n",
      "    loading package ic...\n",
      "WARNING: Block \"options\" is not a valid block name for file type ic.\n",
      "    loading package npf...\n",
      "    loading package chd...\n",
      "    loading package chd...\n",
      "    loading package oc...\n",
      "  loading ims package gwf_1...\n"
     ]
    }
   ],
   "source": [
    "# load up the vertex example problem\n",
    "sim_name = \"mfsim.nam\"\n",
    "sim_path = \"../data/mf6/test003_gwftri_disv\"\n",
    "disv_sim = flopy.mf6.MFSimulation.load(sim_name=sim_name, version=\"mf6\", exe_name=\"mf6\",\n",
    "                                       sim_ws=sim_path)\n",
    "disv_ml = disv_sim.get_model('gwf_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Cross Sections using Structured (DIS) and Vertex (DISV) grids in FloPy\n",
    "\n",
    "In this example the `PlotCrossSection` functionality will be shown, and backward compatibility options with `ModelCrossSection` will also be presented. \n",
    "\n",
    "### Plotting a cross sectional model grid (DIS)\n",
    "\n",
    "the `line` parameter is a dictionary that accepts either `column`: int, `row`: int, or `line`: list(x,y) vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the ModelCrossSection class\n",
    "xsect = flopy.plot.PlotCrossSection(model=dis_ml, line={'Column': 5})\n",
    "\n",
    "# Then we can use the plot_grid() method to draw the grid\n",
    "# The return value for this function is a matplotlib LineCollection object,\n",
    "# which could be manipulated (or used) later if necessary.\n",
    "pc = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward compatibility with `ModelCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy/plot/crosssection.py:764: PendingDeprecationWarning: ModelCrossSection will be replaced by PlotCrossSection(), Calling PlotCrossSection()\n",
      "  warnings.warn(err_msg, PendingDeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the ModelCrossSection class\n",
    "modelxsect = flopy.plot.ModelCrossSection(model=dis_ml, line={'Column': 5})\n",
    "\n",
    "# Then we can use the plot_grid() method to draw the grid\n",
    "# The return value for this function is a matplotlib LineCollection object,\n",
    "# which could be manipulated (or used) later if necessary.\n",
    "pc = modelxsect.plot_ibound()\n",
    "linecollection = modelxsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a cross sectional model grid for DISV (vertex grid)\n",
    "\n",
    "since row and column do not exist in the DISV grid, a line must be provided to plot a crosssection. The line can be straight or it can be an arbitrary line across the grid. \n",
    "\n",
    "Let's view the line on a MapView plot first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a line through the model in model coordiantes as a \n",
    "# series of XY vertices where the cross section will be sliced!\n",
    "line = np.array([(3.5, 0), (3.5, 5), (2, 10)])\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "vmap = PlotMapView(model=disv_ml, layer=0)\n",
    "ax = vmap.plot_ibound()\n",
    "pc = vmap.plot_grid()\n",
    "plt.plot(line[:,0], line[:,1], 'r--', linewidth=2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 2))\n",
    "xsect = PlotCrossSection(model=disv_ml, line={\"line\": line})\n",
    "ax = xsect.plot_ibound()\n",
    "pc = xsect.plot_grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting arrays of Data:\n",
    "\n",
    "Plotting arrays can be accomplished using the `plot_array()` method\n",
    "\n",
    "### Structured grid example (DIS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data from the head object for plotting\n",
    "fname = os.path.join(modelpth, 'freyberg.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x11c5facc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "fig = plt.figure(figsize=(16, 3))\n",
    "\n",
    "plt.title('plot_array()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=dis_ml, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "plt.colorbar(csa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward compatibility with `ModelCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy/plot/crosssection.py:764: PendingDeprecationWarning: ModelCrossSection will be replaced by PlotCrossSection(), Calling PlotCrossSection()\n",
      "  warnings.warn(err_msg, PendingDeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "fig = plt.figure(figsize=(16, 3))\n",
    "\n",
    "plt.title('plot_array()')\n",
    "xsect = flopy.plot.ModelCrossSection(model=dis_ml, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "plt.colorbar(csa);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertex grid example (DISV)\n",
    "\n",
    "The same plotting functionality is available for vertex based model grids. Coming soon is unstructured grid function(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hds_file = os.path.join(sim_path, \"tri_model.hds\")\n",
    "\n",
    "hds = HeadFile(hds_file)\n",
    "hdata = hds.get_alldata()[0]\n",
    "hdata.shape = (disv_ml.modelgrid.nlay, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12.5, 2))\n",
    "\n",
    "xsect = PlotCrossSection(model=disv_ml, line={\"line\": line})\n",
    "\n",
    "ax = xsect.plot_ibound()\n",
    "ax = xsect.plot_array(a=hdata, masked_values=[1e30])\n",
    "plt.colorbar(ax)\n",
    "xsect.plot_grid()\n",
    "plt.title(\"plot_array()\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contouring array values on a cross section\n",
    "\n",
    "By using the `contour_array` method, `PlotCrossSection` is able to draw contour lines using user supplied array data\n",
    "\n",
    "A surface can also be plotted using the `plot_surface` method\n",
    "\n",
    "### Structured grid (DIS) example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(10, 30, .5)\n",
    "\n",
    "fig = plt.figure(figsize=(13, 3))\n",
    "xsect = flopy.plot.PlotCrossSection(model=dis_ml, line={'Column': 5})\n",
    "\n",
    "# contour array and plot ibound\n",
    "ct = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, linewidths=2.5)\n",
    "pc = xsect.plot_ibound(head=head)\n",
    "\n",
    "#plot the surface and model grid\n",
    "wt = xsect.plot_surface(head, masked_values=[999.], color='blue', lw=2.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "plt.title('contour_array() and plot_surface()');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backward compatibility with `ModelCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy/plot/crosssection.py:764: PendingDeprecationWarning: ModelCrossSection will be replaced by PlotCrossSection(), Calling PlotCrossSection()\n",
      "  warnings.warn(err_msg, PendingDeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(10, 30, .5)\n",
    "\n",
    "fig = plt.figure(figsize=(13, 3))\n",
    "xsect = flopy.plot.ModelCrossSection(model=dis_ml, line={'Column': 5})\n",
    "\n",
    "# contour array and plot ibound\n",
    "ct = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, linewidths=2.5)\n",
    "pc = xsect.plot_ibound(head=head)\n",
    "\n",
    "#plot the surface and model grid\n",
    "wt = xsect.plot_surface(head, masked_values=[999.], color='blue', lw=2.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "plt.title('contour_array() and plot_surface()');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertex grid example (DISV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12.5, 2))\n",
    "\n",
    "xsect = PlotCrossSection(model=disv_ml, line={\"line\": line})\n",
    "ax = xsect.plot_ibound()\n",
    "ax = xsect.plot_array(a=hdata, masked_values=[1e30], alpha=0.3)\n",
    "plt.colorbar(ax)\n",
    "\n",
    "# set our own contour levels using the matplotlib keyword argument levels\n",
    "levels = np.arange(1, 10, 0.5)\n",
    "xsect.contour_array(a=hdata, masked_values=[1e30], levels=levels)\n",
    "\n",
    "xsect.plot_grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting discharge Vectors\n",
    "\n",
    "Discharge can be plotted using the `plot_discharge()` method \n",
    "\n",
    "### Structured grid example (DIS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's get the cell budget data\n",
    "fname = os.path.join(modelpth, 'freyberg.cbc')\n",
    "cbb = flopy.utils.CellBudgetFile(fname)\n",
    "frf = cbb.get_data(text='FLOW RIGHT FACE')[0]\n",
    "fff = cbb.get_data(text='FLOW FRONT FACE')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 3))\n",
    "\n",
    "plt.title('plot_array() and plot_discharge()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=dis_ml, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_discharge(frf, fff, head=head, \n",
    "                                   hstep=2, normalize=True, color='green', \n",
    "                                   scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                   zorder=10)\n",
    "patches = xsect.plot_ibound(head=head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward compatibility using `ModelCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy/plot/crosssection.py:764: PendingDeprecationWarning: ModelCrossSection will be replaced by PlotCrossSection(), Calling PlotCrossSection()\n",
      "  warnings.warn(err_msg, PendingDeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13, 3))\n",
    "\n",
    "plt.title('plot_array() and plot_discharge()')\n",
    "modelxsect = flopy.plot.ModelCrossSection(model=dis_ml, line={'Column': 5})\n",
    "csa = modelxsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "linecollection = modelxsect.plot_grid()\n",
    "quiver = modelxsect.plot_discharge(frf, fff, head=head, \n",
    "                                   hstep=2, normalize=True, color='green', \n",
    "                                   scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                   zorder=10)\n",
    "patches = modelxsect.plot_ibound(head=head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discharge vector plotting with DISV\n",
    "\n",
    "The Specific Discharge (SPDIS) recarray is used with plot_specific_discharge to create and plot flow vectors\n",
    "\n",
    "Specific Discharge can be written to the cell budget file by supplying the option SAVE_SPECIFIC_DISCHARGE in the NPF package of MODFLOW-6 \n",
    "\n",
    "### Note:\n",
    "Arbitrary cross section lines cannot be used with this method. A straight cross section must be supplied to calculate discharge vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data from the CellBudgetFile\n",
    "cbc_file = os.path.join(sim_path, \"tri_model.cbc\")\n",
    "cbc = CellBudgetFile(cbc_file, precision='double')\n",
    "spdis = cbc.get_data(text=\"SPDIS\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Selecting the final stress period from Specific Discharge list\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12.5, 2))\n",
    "\n",
    "# define the new cross section line...\n",
    "line = np.array([(0, 3.5), (10, 3.5)])\n",
    "\n",
    "vcs = PlotCrossSection(model=disv_ml, line={\"line\": line})\n",
    "ax = vcs.plot_array(a=hdata, masked_values=[1e30])\n",
    "plt.colorbar(ax)\n",
    "vcs.plot_grid()\n",
    "\n",
    "# plot discharge vectors\n",
    "ax = vcs.plot_specific_discharge(spdis, head=hdata, hstep=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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
}