flopy/examples/Notebooks/flopy3.3_PlotCrossSection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "## Making Cross Sections of Your Model\n",
    "This notebook demonstrates the cross sectional mapping capabilities of FloPy. It demonstrates these capabilities by loading and running existing models and then showing how the `PlotCrossSection` object and its methods can be used to make nice plots of the model grid, boundary conditions, model results, shape files, etc.\n",
    "\n",
    "### Mapping is demonstrated for MODFLOW-2005 and MODFLOW-6 models in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set names of the MODFLOW exes\n",
    "# assumes that the executable is in users path statement\n",
    "v2005 = 'mf2005'\n",
    "exe_name_2005 = 'mf2005'\n",
    "vmf6 = 'mf6'\n",
    "exe_name_mf6 = 'mf6'\n",
    "exe_mp = 'mp6'\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name_2005 += '.exe'\n",
    "    exe_name_mf6 += '.exe'\n",
    "    exe_mp += '.exe'\n",
    "\n",
    "#Set the paths\n",
    "loadpth = os.path.join('..', 'data', 'freyberg')\n",
    "modelpth = os.path.join('data')\n",
    "\n",
    "#make sure modelpth directory exists\n",
    "if not os.path.exists(modelpth):\n",
    "    os.makedirs(modelpth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Run an Existing MODFLOW-2005 Model\n",
    "A model called the \"Freyberg Model\" is located in the loadpth folder.  In the following code block, we load that model, then change into a new workspace (modelpth) where we recreate and run the model.  For this to work properly, the MODFLOW-2005 executable (mf2005) must be in the path.  We verify that it worked correctly by checking for the presence of freyberg.hds and freyberg.cbc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "changing model workspace...\n",
      "   data\n"
     ]
    },
    {
     "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: freyberg.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:25:39\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): 2021/02/18 11:25:39\n",
      " Elapsed run time:  0.080 Seconds\n",
      "\n",
      "  Normal termination of simulation\n",
      "Output file located: freyberg.hds\n",
      "Output file located: freyberg.cbc\n"
     ]
    }
   ],
   "source": [
    "ml = flopy.modflow.Modflow.load('freyberg.nam', model_ws=loadpth, \n",
    "                                exe_name=exe_name_2005, version=v2005)\n",
    "ml.change_model_ws(new_pth=modelpth)\n",
    "ml.write_input()\n",
    "success, buff = 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": [
    "### Creating a Cross-Section of the Model Grid\n",
    "\n",
    "Now that we have a model, we can use the FloPy plotting utilities to make cross-sections. We'll start by making a Map to show the model grid and basic boundary conditions. Then we'll begin making a cross section using the `PlotCrossSection` class and the `plot_grid()` method of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z/hBwCDgO7MvMG5vxTwCfzcybz3bsaW1XmOTxp7nWdoXRalvx6QoRcTNwKjOfGXSTZcbyLOPLHXN3RByJiCNd7emS3q9W067wceDXIuJTwPnARRHxKPBaRGzMzJPNS6RTTf0J4Iq+7TcBrzbjm5YZf4/M3A/sB5idnc02LOW2XaEbtbYrjF47LiOf2WTmnszclJlX0rvw+9XM/AxwENjRlO0AnmjuHwS2R8R5ETFL70Lw05l5EngjIq5v3oW6rW8bSVNiLRox9wEHIuJ24BXgVoDMPBoRB4AXgLeBOzPznWabO4A54AJ613EOrcG8JE3QWMImM78GfK25/7+AG85QtxfYu8z4EeCaccxFUju5glhSCcNGUgnDRlIJw0ZSCcNGUgk/XaFFtZM+/tC18/O92gH++PrCzAysX9+Z59Z2hRa1K0jSMPx0hRbVTvr4Q9fu2tWrHWBZ+9zOnbBlS2eeW9sVWtSuIEnDMGwklTBsJJUwbCSVMGwklTBsJJUwbCSVMGwklbBdoUW1kz7+0LW2K9iuMITOriBWC6xf37vdsmXl2gECSdOts2Fju4K1w9TarjB67bh4zUZSCcNGUgnDRlIJw0ZSCcNGUgnDRlIJw0ZSCVcQt6h20sef5lpXEI9W6x88l9Q5riBuUe2kjz/Nta4gHr12XDyzkVTCsJFUwrCRVMKwkVTCsJFUwrCRVMKwkVTCsJFUwnaFFtVO+vjTXGu7wmi1/sHzaba4CM2nFpzV0i/LAL80Uht0Nmymtl1hfp6dAywTn2v2tfPee8d2/GmutV1h9Npx8ZqNpBKGjaQSho2kEoaNpBKGjaQSho2kEoaNpBKGjaQStiu0qHZhYQEWF5lZWHmh/EKzr5ktW8Z2/GmutV1htFo/XUFS59iu0KLaubk5mJmxBcF2hVbVjotnNpJKGDaSShg2kkoYNpJKGDaSShg2kkoYNpJKGDaSStiu0KLaSR9/mmttVxit1nYFSZ1ju0KLaid9/GmutV1h9Npx8cxGUgnDRlIJw0ZSCcNGUgnDRlIJw0ZSCcNGUgnDRlIJ2xVaVDvp409zre0Ko9XariCpc2xXaFHtpI8/zbW2K4xeOy6e2UgqYdhIKmHYSCph2EgqYdhIKjFy2ETEFRHxxxHxYkQcjYi7mvFLIuLJiHipud3Qt82eiDgWEfMRcVPf+LUR8Vzz2H0REav7sSS1zWrObN4G/klm/g3geuDOiLgKuBs4nJmbgcPN9zSPbQeuBrYCD0TEumZfDwK7gc3N19ZVzEtSC40cNpl5MjO/1dx/A3gRuBzYBjzclD0M3NLc3wY8lplvZubLwDHguojYCFyUmU9lbznzI33bSJoSY2lXiIgrgT8BrgFeycyL+x57PTM3RMT9wDcy89Fm/CHgEHAc2JeZNzbjnwA+m5k3n+2YGzduzK1bVz4BmpmZYf369RNf9j1I7aSPP821tiuMVtuqdoWI+CDwe8BvZeafn610mbE8y/hyx9odEUci4sjwM5U0SatqV4iIc+kFzRcz88vN8GsRsTEzTzYvkU414yeAK/o23wS82oxvWmb8PTJzP7Af4NJLL81BllPv3LmTLVu2tGLZt+0K3XlubVdoUbtC847RQ8CLmfm7fQ8dBHY093cAT/SNb4+I8yJilt6F4Kcz8yTwRkRc3+zztr5tJE2J1ZzZfBz4DeC5iHi2GfvnwD7gQETcDrwC3AqQmUcj4gDwAr13su7MzHea7e4A5oAL6F3HObSKeUlqoZHDJjP/K8tfbwG44Qzb7AX2LjN+hN7FZUlTyhXEkkoYNpJKGDaSShg2kkoYNpJKdPbTFYZpV4C/XKa9Uu0kWxvaskR94rXz873aAf7NFmZmYIB/M9sVRqttVbuCJA2is5+u8NZbbw20nHppWXYXWhvatER9orW7dvVqB/g3m9u5Ewb4N7NdYfTacfHMRlIJw0ZSCcNGUgnDRlIJw0ZSCcNGUgnDRlIJw0ZSCdsVVlE77taGtixRn3it7QqtqR1nu0JnVxBriq1f37vdsmXl2gECSe3Q2bBZq3aFYWrH3drQpiXq01Zru8LotePiNRtJJQwbSSUMG0klDBtJJQwbSSUMG0klDBtJJVxBvIpaVxB3p9YVxKPVuoK4JRYXF5lvltafzVIwSZO2uLgIMPDv7Th1NmzasIJ4mNpBVhu3adXotNW6grhnV/PH5If5HR8Xr9lIKmHYSCph2EgqYdhIKmHYSCph2EgqYdhIKmHYSCphu0JR7SCtDW1Zoj6NtbYr9CytHB7093bfvn1ja1fwzEZSCdsVimptV5hsre0KPbYrSJp6ho2kEoaNpBKGjaQSho2kEoaNpBKGjaQSho2kErYrFNXarmC7wqSfA5hsu0JnVxB3zSCfxLD0yzLuv2qv6TbJT0wYRmfDpmvtCoPULtXde++9K+6zDUvfu1Q7ze0Ko7Qg2K4gaWoZNpJKGDaSShg2kkoYNpJKGDaSShg2kkoYNpJK2K7Qotqlui1btqy4zzYsfe9S7TS3KwzbgjBMre0K0pTrSgvCMDobNrYrzL1rG2vPXtu1doW1bkGwXUHS1DJsJJUwbCSVMGwklTBsJJUwbCSVMGwklTBsJJWwXaFFtb269SwsrLwidGZmodmntYPUDr3P84drV1g4fnyA/Xbnd3GpdpztCp7ZSCphu0KLant1W5ib2znAPueafVo7SO3Q+7xyuHaFLvx+jVo7Lp7ZSCph2EgqYdhIKmHYSCph2Egq0ZqwiYitETEfEcci4u5Jz0fSeLUibCJiHfDvgF8BrgI+HRFXTXZWksapFWEDXAccy8w/zcy/AB4Dtk14TpLGqBXtChHx68DWzPz7zfe/Afx8Zv7mmbaxXaE7rQJtqLVdYbTaafx0hVhm7D0pGBG7gd3Nt2/Ozc09v6azGq9LgT+b9CQG1KW5Qrfm26W5Aqz8uUIDakvYnACu6Pt+E/Dq6UWZuR/YDxARR8aVuBW6NN8uzRW6Nd8uzRV68x3Xvtpyzea/AZsjYjYiPgBsBw5OeE6SxqgVZzaZ+XZE/Cbwh8A64POZeXTC05I0Rq0IG4DM/APgD4bYZP9azWWNdGm+XZordGu+XZorjHG+rXg3StL0a8s1G0lTrpNh04bWhoj4fESciojn+8YuiYgnI+Kl5nZD32N7mvnOR8RNfePXRsRzzWP3RcRyywBWO9crIuKPI+LFiDgaEXe1fL7nR8TTEfGdZr7/ss3zbY6zLiK+HRFf6cBcjzfHeXbp3aaS+WZmp77oXUD+HvAzwAeA7wBXTWAevwB8BHi+b+xfAXc39+8Gfqe5f1Uzz/OA2Wb+65rHngY+Rm+t0SHgV9ZgrhuBjzT3PwT8j2ZObZ1vAB9s7p8LfBO4vq3zbY7zj4H/BHylzb8LzXGOA5eeNrbm8y39DzqmJ+pjwB/2fb8H2DOhuVx5WtjMAxub+xuB+eXmSO9dt481Nd/tG/808O8L5v0E8MtdmC9wIfAt4OfbOl9668IOA7/UFzatnGuz7+XCZs3n28WXUZcDP+j7/kQz1gY/nZknAZrby5rxM8358ub+6eNrJiKuBH6O3tlCa+fbvCx5FjgFPJmZbZ7vvwH+GfDjvrG2zhV6q/P/KCKeaVbll8y3NW99D2Gg1oaWOdOcS3+WiPgg8HvAb2Xmn5/lJfbE55uZ7wA/GxEXA78fEdecpXxi842Im4FTmflMRHxykE3OMKfK34WPZ+arEXEZ8GREfPcstWObbxfPbAZqbZiQ1yJiI0Bze6oZP9OcTzT3Tx8fu4g4l17QfDEzv9z2+S7JzB8BXwO2tnS+Hwd+LSKO0/trBb8UEY+2dK4AZOarze0p4Pfp/dWFNZ9vF8Omza0NB4Edzf0d9K6NLI1vj4jzImIW2Aw83ZyuvhER1zdX8m/r22Zsmn0/BLyYmb/bgfn+VHNGQ0RcANwIfLeN883MPZm5KTOvpPe7+NXM/Ewb5woQET8RER9aug/8XeD5kvmu5cW9tfoCPkXvHZXvAb89oTl8CTgJvEUv5W8HfpLehcKXmttL+up/u5nvPH1X7YGPNv/Y3wPup1loOea5/h16p7j/HXi2+fpUi+f7N4FvN/N9HvgXzXgr59t3rE/ylxeIWzlXeu/ifqf5Orr0/6divq4gllSiiy+jJHWQYSOphGEjqYRhI6mEYSOphGEjqYRhI6mEYSOpxP8HZ+X21yjgslsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's take a look at our grid before making a cross section\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=ml)\n",
    "ibound = mapview.plot_ibound()\n",
    "wel = mapview.plot_bc(\"WEL\")\n",
    "riv = mapview.plot_bc(\"RIV\")\n",
    "linecollection = mapview.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will make a cross-section of the model grid at column 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First step is to set up the plot\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the PlotCrossSection class\n",
    "xsect = flopy.plot.PlotCrossSection(model=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",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting Ibound\n",
    "\n",
    "The `plot_ibound()` method can be used to plot the boundary conditions contained in the ibound arrray, which is part of the MODFLOW Basic Package.  The `plot_ibound()` method returns a matplotlib PatchCollection object (matplotlib.collections.PatchCollection).  If you are familiar with the matplotlib collections, then this may be important to you, but if not, then don't worry about the return objects of these plotting function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with IBOUND Boundary Conditions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Or we could change the colors!\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "patches = xsect.plot_ibound(color_noflow='red', color_ch='orange')\n",
    "linecollection = xsect.plot_grid(color='green')\n",
    "t = ax.set_title('Column 6 Cross-Section with IBOUND Boundary Conditions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Boundary Conditions\n",
    "The `plot_bc()` method can be used to plot boundary conditions on a cross section.  It is setup to use the following dictionary to assign colors, however, these colors can be changed in the method call.\n",
    "\n",
    "    bc_color_dict = {'default': 'black', 'WEL': 'red', 'DRN': 'yellow',\n",
    "                 'RIV': 'green', 'GHB': 'cyan', 'CHD': 'navy'}\n",
    "\n",
    "Just like the `plot_bc()` method for `PlotMapView`, the default boundary condition colors can be changed in the method call.\n",
    "\n",
    "Here, we plot the location of well cells in column 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YgtY0WyMAAADzJZwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATMABw1lV3buq3lNV76+qq6rqvHH5g6rqHVX1kfH+mPlXFwAAYHNaS8/Z55P8k+5+TJLHJjm9qh6f5Nwkl3T3I5NcMj4HAABgHQ4Yznpw1/h0+3jrJGckuWhcflGSZ8ylhgAAAFvAmq45q6qjquqKJDcneUd3X5bkId19Y5KM98fPr5oAAACb25rCWXff092PTfKwJKdV1aPXuoOqOqeqLq+qy/fu3bveegIAAGxqBzVbY3ffluSdSU5PclNVnZAk4/3Nq7zmgu7e0d07tm/ffojVBQAA2JzWMlvjcVX1wPHxfZI8NcmHk7w5yVnjZmcl+b15VRIAAGCz27aGbU5IclFVHZUhzF3c3W+pqkuTXFxVz0tyXZLvn2M9AQAANrUDhrPuvjLJ41ZYfmuSp8yjUgAAAFvNQV1zBgAAwHwIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATcMBwVlUPr6o/rqqrq+qqqnrhuPylVfWpqrpivH33/KsLAACwOW1bwzZ3J3lRd7+vqu6f5L1V9Y5x3a909y/Nr3oAAABbwwHDWXffmOTG8fGdVXV1khPnXTEAAICt5KCuOauqk5M8Lsll46IXVNWVVXVhVR0z47oBAABsGWsOZ1V1dJI3JPmx7r4jySuSfHWSx2boWfvlVV53TlVdXlWX7927dwZVBgAA2HzWFM6qanuGYPaa7n5jknT3Td19T3d/IclvJDltpdd29wXdvaO7d2zfvn1W9QYAANhU1jJbYyV5ZZKru/vly5afsGyzZyb54OyrBwAAsDWsZbbGJyY5M8kHquqKcdmLkzy7qh6bpJN8PMmPzqWGAAAAW8BaZmt8d5JaYdVbZ18dAACAremgZmsEAABgPoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAk4YDirqodX1R9X1dVVdVVVvXBc/qCqekdVfWS8P2b+1QUAANic1tJzdneSF3X31yd5fJLnV9WpSc5Nckl3PzLJJeNzAAAA1uGA4ay7b+zu942P70xydZITk5yR5KJxs4uSPGNelQQAANjsDuqas6o6OcnjklyW5CHdfWMyBLgkx6/ymnOq6vKqunzv3r2HVlsAAIBNas3hrKqOTvKGJD/W3Xes9XXdfUF37+juHdu3b19PHQEAADa9NYWzqtqeIZi9prvfOC6+qapOGNefkOTm+VQRAABg81vLbI2V5JVJru7uly9b9eYkZ42Pz0rye7OvHgAAwNawbQ3bPDHJmUk+UFVXjMtenOT8JBdX1fOSXJfk++dTRQAAgM3vgOGsu9+dpFZZ/ZTZVgcAAGBrOqjZGgEAAJgP4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGACth3pCmwVe/bsya5du2Za3sLCwszKAwAAjiw9Z4fB4uLizIPUwsJCFhcXZ1omAABw5Og5O0wWFxezc+fOmZU3y144AADgyNNzBgAAMAEHDGdVdWFV3VxVH1y27KVV9amqumK8ffd8qwkAALC5raXnbFeS01dY/ivd/djx9tbZVgsAAGBrOWA46+53JfnMYagLAADAlnUo15y9oKquHIc9HrPaRlV1TlVdXlWX79279xB2BwAAsHmtd7bGVyR5WZIe7385yb9YacPuviDJBUlyyimn9Dr3x2GyEX6PbR51BACAI21dPWfdfVN339PdX0jyG0lOm221OBI2wu+xzauOu3fvnmmZAABwsNbVc1ZVJ3T3jePTZyb54P62X7J3797J98psdRvh99jmUcdrrrlmZuUBAMB6HDCcVdVrkzw5yYOr6vokL0ny5Kp6bIZhjR9P8qNr2dn27dvXXdGVzLpXZsmsh83t3r17LvUEAAA2jwOGs+5+9gqLX7menR177LEboldm1kPcZt3TAwAAbD7rnRBkUxOmAACAw+1QptIHAABgRjZ8z9nu3btdHwYAAGx4Gzqc7dy5c+bXnW2kIY2CKQAAbB4bOpwl2TBBata2ejAFAIDNZsOHs61MkAIAgM3DhCAAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATsO1IV4DNb/fu3dm1a9fMylpcXJxJWQAAMCV6zpirnTt3zjRMLS4uZufOnTMrDwAApkLPGXMnTAEceXv27JnZKIal8hYWFmZWHgB6zgBg09u9e/fMg9TCwoJh5gAzpucMALaAWQ8Ln2UvHAADPWcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAE3DAcFZVF1bVzVX1wWXLHlRV76iqj4z3x8y3mgAAAJvbWnrOdiU5fZ9l5ya5pLsfmeSS8TkAAADrdMBw1t3vSvKZfRafkeSi8fFFSZ4x43oBAABsKeu95uwh3X1jkoz3x8+uSgAAAFvP3CcEqapzquryqrr8lltumffuAAAANqT1hrObquqEJBnvb15tw+6+oLt3dPeO4447bp27AwAA2NzWG87enOSs8fFZSX5vNtUBAADYmtYylf5rk1ya5FFVdX1VPS/J+Um+o6o+kuQ7xucAAACs07YDbdDdz15l1VNmXBcAAIAta+4TggAAAHBgwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMwLYjXQEAADaOPXv2ZNeuXTMtb2FhYWblwUam5wwAgDVZXFyceZBaWFjI4uLiTMuEjUrPGQAAa7a4uJidO3fOrLxZ9sLBrC0s7MnOnbtmVt7i4u79rhfOAAAO0lyG9t3rqJmVx+wZzrn1LC4uZvfu3XnSk2Za6n7XCmcAAAdh6QPbLC0sLGTx3vebaZnMztzOueGckzfrnuIkOfvss1ddJ5wBABykuQztu+3OmZXHbHu6du/ebTjnjOmJXJlwBgAwAbvvuiO7/vLSmZW35567s7Btaw6VnHVP1zx6T7YyPZGrE84AAI6wnTt3zrwnZeHvPrthPqzu3r17psc/j56urWzWvVx6IlcnnAEATMCsg8RG+bA6j2AqmM3OPHq5nJ/VCWcAwKY2r2/9mR0f1KdNmDp8/Ag1ALBpzeNHk31QBeZFzxkAsKkJU8BGoecMAABgAoQzAACACTikYY1V9fEkdya5J8nd3b1jFpUCAADYamZxzdm3d/enZ1AOALCBzPq3qfbs2TPzyTu2unn9fhgwHyYEAQAO2lx+NHlhwQf/GfL7YWw1m+HLiEMNZ53k7VXVSf5nd18wgzoBABvAVv3R5I1EkJo2vc+zs1m+jDjUcPbE7r6hqo5P8o6q+nB3v2v5BlV1TpJzkuSkk046xN0BAMDGN6/e52S2X3RspKGsm+HLiEMKZ919w3h/c1W9KclpSd61zzYXJLkgSXbs2NGHsj8AANgs5hEmNkPv0Va27nBWVfdLcq/uvnN8/J1Jfm5mNQMAAA6KILWxHUrP2UOSvKmqlsr57e7+w5nUCgAAYItZdzjr7o8lecwM6wIAALBlmUofAJiMzTAVNsB63etIVwAAIBmulZl1kDKZAbCR6DkDACZDkAK2Mj1nAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAGHFM6q6vSquqaqPlpV586qUgAAAFvNusNZVR2V5NeTPC3JqUmeXVWnzqpiAAAAW8mh9JydluSj3f2x7t6T5HVJzphNtQAAALaW6u71vbDq+5Kc3t0/Mj4/M8k3dfcLVnvNjh07+vLLL1/X/mBedu3alWuuuSa7d++eWZknnXRSkuS6665TpjKVOZEyN0Id51Xm4uJiHvWoR2Xnzp0zKxOA9amq93b3jpXWbTuUcldY9veSXlWdk+Sc8ennq+qDh7BPmJcHJ/n0ka4ErEL7ZCbOPvvsWRepbTJl2idT9YjVVhxKOLs+ycOXPX9Ykhv23ai7L0hyQZJU1eWrpUQ4krRNpkz7ZKq0TaZM+2QjOpRrzv4iySOr6pSqWkjyrCRvnk21AAAAtpZ195x1991V9YIkb0tyVJILu/uqmdUMAABgCzmUYY3p7rcmeetBvOSCQ9kfzJG2yZRpn0yVtsmUaZ9sOOuerREAAIDZOZRrzgAAAJiRwxLOqur0qrqmqj5aVecejn2ytVXVw6vqj6vq6qq6qqpeOC5/UFW9o6o+Mt4fs+w1PzW20Wuq6ruWLf/GqvrAuO6/VNVKPyMBB6Wqjqqqv6yqt4zPtU0moaoeWFWvr6oPj/+GfrP2yRRU1Y+P/6d/sKpeW1X31jbZbOYezqrqqCS/nuRpSU5N8uyqOnXe+2XLuzvJi7r765M8Psnzx3Z3bpJLuvuRSS4Zn2dc96wk/yDJ6Un++9h2k+QVGX6r75Hj7fTDeSBsWi9McvWy59omU/FrSf6wu78uyWMytFPtkyOqqk5M8u+S7OjuR2eYjO5Z0TbZZA5Hz9lpST7a3R/r7j1JXpfkjMOwX7aw7r6xu983Pr4zw4eLEzO0vYvGzS5K8ozx8RlJXtfdn+/ua5N8NMlpVXVCkq/s7kt7uEDzVcteA+tSVQ9L8j1JfnPZYm2TI66qvjLJtyV5ZZJ0957uvi3aJ9OwLcl9qmpbkvtm+H1dbZNN5XCEsxOTfHLZ8+vHZXBYVNXJSR6X5LIkD+nuG5MhwCU5ftxstXZ64vh43+VwKH41yU8m+cKyZdomU/BVSW5J8r/GYbe/WVX3i/bJEdbdn0ryS0muS3Jjktu7++3RNtlkDkc4W2kcrykiOSyq6ugkb0jyY919x/42XWFZ72c5rEtVPT3Jzd393rW+ZIVl2ibzsi3JP0ryiu5+XJLPZhwmtgrtk8NivJbsjCSnJHlokvtV1XP395IVlmmbTN7hCGfXJ3n4sucPy9ANDXNVVdszBLPXdPcbx8U3jUMaMt7fPC5frZ1ePz7edzms1xOTfG9VfTzDMO9/UlW/FW2Tabg+yfXdfdn4/PUZwpr2yZH21CTXdvct3b03yRuTPCHaJpvM4Qhnf5HkkVV1SlUtZLg4882HYb9sYePMS69McnV3v3zZqjcnOWt8fFaS31u2/FlV9RET11cAAAEOSURBVBVVdUqGC4TfMw6RuLOqHj+W+cPLXgMHrbt/qrsf1t0nZ/j38P9293OjbTIB3b07ySer6lHjoqck+VC0T46865I8vqruO7app2S4nlzbZFPZNu8ddPfdVfWCJG/LMLPOhd191bz3y5b3xCRnJvlAVV0xLntxkvOTXFxVz8vwD/33J0l3X1VVF2f4EHJ3kud39z3j6/51kl1J7pPkD8YbzJq2yVT82ySvGb9Q/ViSszN8mat9csR092VV9fok78vQ1v4yyQVJjo62ySZSw0Q1AAAAHEmH5UeoAQAA2D/hDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAm4P8DgzBt2+HtgyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "patches = xsect.plot_bc('WEL', color='pink')\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Boundary Conditions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting an Array\n",
    "\n",
    "`PlotCrossSection` has a `plot_array()` method.  The `plot_array()` method will only accept 3D arrays for structured grids.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random array and plot it\n",
    "a = np.random.random((ml.dis.nlay, ml.dis.nrow, ml.dis.ncol))\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "csa = xsect.plot_array(a)\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Random Data')\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the horizontal hydraulic conductivities\n",
    "a = ml.lpf.hk.array\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "csa = xsect.plot_array(a)\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contouring an Array\n",
    "\n",
    "`PlotCrossSection` also has a `contour_array()` method.  It also accepts a 3D array for structured grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the horizontal hydraulic conductivities\n",
    "a = ml.lpf.hk.array\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "contour_set = xsect.contour_array(a, masked_values=[0], cmap='jet')\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid(color='grey')\n",
    "t = ax.set_title('Column 6 Cross-Section contour_array() horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(contour_set, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Heads\n",
    "\n",
    "We can easily plot results from the simulation by extracting heads using `flopy.utils.HeadFile`. \n",
    "\n",
    "The head can be passed into the `plot_array()` and `contour_array()` using the `head=` keyword argument to fix the top of the colored patch and contour lines at the top of the water table in each cell, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = os.path.join(modelpth, 'freyberg.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('plot_array() used to plotting Heads')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "pc = 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",
    "cb = plt.colorbar(pc, shrink=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# contour array on top of heads\n",
    "levels = np.arange(17, 26, 1)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('contour_array() and plot_array() of head values')\n",
    "\n",
    "# instantiate the PlotCrossSection object\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "\n",
    "# plot the head array and model grid\n",
    "pc = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "# do black contour lines of the head array\n",
    "contour_set = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, colors='k')\n",
    "plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(pc, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a surface on the cross section\n",
    "\n",
    "The `plot_surface()` method allows the user to plot a surface along the cross section. Here is a short example using head data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(10, 30, .5)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "xsect = flopy.plot.PlotCrossSection(model=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()')\n",
    "cb = plt.colorbar(ct, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting discharge vectors\n",
    "\n",
    "`PlotCrossSection` has a `plot_discharge()` method, which takes the 'FLOW RIGHT FACE', 'FLOW FRONT FACE', and 'FLOW LOWER FACE' arrays, which can be written by MODFLOW to the cell by cell flow file.  These array values can be extracted from the cell by cell flow file using the `flopy.utils.CellBudgetFile` object as shown below.  Once they are extracted, they can be passed to the `plot_discharge()` method.  Note that `plot_discharge()` also takes the head array as an argument.  The head array is used by `plot_discharge()` to convert the volumetric flow in dimensions of $L^3/T$ to specific discharge in dimensions of $L/T$ and to plot the specific discharge in the center of each saturated cell. For this problem, there is no 'FLOW LOWER FACE' array since the Freyberg Model is a one layer model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AA8BfjmKfkiRJkiSA6IDWxTXs+PtRbwVqW/31JuCoAbY/BJw4Gk1JkiRJkgaWQPfYTZkcVk2rv0qSJEmSmsdYzpkcjqFSkiRJksaRzE46K6Xcp7IUhkpJkiRJGlc6iNb9a9hv5ah3AoZKSZIkSRpXEoe/SpIkSZJGoIdodAs7GSolSZIkaRzpyS6e6FrT6DZ2MlRKkiRJ0jgS0UFrWy1zKq8e9V7AUClJkiRJ40p1TqXDXyVJkiRJdepJQ6UkSZIkqQ492cXjlbWNbmMnQ6UkSZIkjSMRHbTXNKdybBgqJUmSJGkcSRz+KkmSJEmqW7hQjyRJkiSpPj3ZyWNd6xrdxk6GSkmSJEkaRyI66Gjbr9Ft7GSolCRJkqRxJB3+KkmSJEmqW0JPtjS6i50MlZIkSZI0jnRnF1u71je6jZ0MlZIkSZI0jrREB1PbFjW6jZ0MlZIkSZI0jiTQg8NfJUmSJEl16kkX6pEkSZIk1aE7KzzataHRbexkqJQkSZKkcaQl2tnDOZWSJEmSpHpU51Q6/FWSJEmSVCdDpSRJkiSpLpWs8Ejnpka3sZOhUuXJHfRs+7dS6wHl1SzqSZIkSeNZK+1Mb1vQ6DZ2MlSOJ7mDzm2fLa0WQGXbv5ZaT5IkSdLoc/hrSTJ3sG3rp0qrBfD41k+XVi+AJ7Z+ppR6hjZJkiRJUB3++vBkHf6a2Vl6CJQkSZKkyaQl2pne3jzDX1uG2yEi9ouIX0XE7RFxa0T8Y7H9oxGxJiJuKB6vHv12JUmSJEk9GcM+xsqwoRKoAO/PzGcCxwF/FxGHFu99NjOPLB4XjVqXkiRJkiQAkuEDZS2hcrALiH3e/6eIyIjYZ6g6ww5/zcx1wLri+baIuB1YNGyHkiRJkqTSdWeFhzo3l1Gq9wLi9RExA7guIi7JzNsiYj/g5cADwxXZrTmVEbEEOAq4BjgeeG9EvA1YWTTzyO79HiRJkiRJu6Ml2pjZPm/EdYa4gHgb8FngA8AFw/ZT6wEjYjrwfeB9mbkV+Hfgz4Aji0YGXDY1Is6MiJURsbKrq7vWw0mSJEmSBtFDDPvYHX0vIEbEKcCazLyxls/WdKUyItqpBsrzMvMHAJm5oc/7XwZ+MtBnM3M5sBxgyZJFWcvxJEmSJEmDyCBrW4hnn4hY2ef18iKf7aLvBUSqQ2L/O/CKWtsZNlRGRABfBW7PzM/02b6guFwK8HrglloPKkmSJEmqTyUrbNrxUC27bs7MpUPt0P8CYkQcDhwA3FiNgiwGro+IYzJz/UA1arlSeTxwGnBzRNxQbPsw8OaIOBJIYBXw7hpqSZIkSZJGoDXa2Kt97ojrDHQBMTNvBub22WcVsDQzB10ZqJbVX6+EAQfkegsRSZIkSRpjSfW2IiUY8ALi7t4ucrdWf5UkSZIkNdruL8QzkCEuIPbdZ8lwdQyVkiRJkjSOVHoqbHyypjmVY8JQKUmSJEnjSGtLG3t3jHxOZVkMlZIkSZI0niS13lJkTBgqJUlqUl09XVyw5oel1WpvaS+lliSpsbKkOZVlMVRKkjRJdPV08f3VF5ZWy5AqSY1RyQobnni40W3sZKiUJEl16erp4vwHflJaLUOqJNWmLdqYM2XfRrexk6FSkiQ1ha6eLr51/09Lq5UE560q57banT0VOlr83yZJzSGBnmx0F3/i346SJEkN0tlT4dz7fl5aLYOvNHmkcyolSZLGl86eCivu/UWp9SSpHl093ax74pFGt7GToVKSJGmC6Oyp8NV7Limtllc+pebUFm3sM2VOo9vYyb8pJEmSJGmc8T6VkiRJanqdPRW+eOdlpdXyyqdUjgTvUylJkiRJqk9XTzdrtzunUpIkSZJUh/aWNuZOdU6lJEmSJKkOiXMqJUmSNAnt6K7wrzf9prRaU1r9X1lNXt6nUpIkSZJUl66ebtY4p1KSJEmSVI+2aGWe96mUJEmSJNUrG91AH4ZKSZIkjUs7uit8duVvS6vlHE2NH+FCPZIkSZKk+nT2VFizfUuj29jJUClJkiRJ40h7tDF/6uxGt7GToVKSJEmSxhnnVNZoR6XC12+9lj8+vIl/ecmpjW7nKTKTn957K+ff8QfOfc1pjW5HkiRJI7Cj0s3nfvu70mpNaWstpZY0EOdUDqM3TH762stY89ijvPbphzW6pV30hsn/dfUl3LhxDX82a59GtyRJkiRpkujs6Wb1486pHFD/MNls+ofJ8eCGjav5xFUX8/XXnMbUtvZGtyNJkiRphNpbWlmwx96NbmOnpgiVhsny9YbJC++5pdGtSJIkSSqVtxTZyTBZPsOkJEmSNMElTbVST0NCpWGyfIZJSZKk5rKj0s3nL72qtFou/KNenT3dPPh48+SoYUNlROwHnAvMB3qA5Zn5uYiYDXwHWAKsAt6YmY8MVStbkq/c+LvdDpM/uftW9vm3s3bZ1t1d/fWj/1b99V9P/Ave+qyja645YH+Z/OSeW3Y7TN6zZTNz/++HdtnWVan++vH/W70s/e4jns8nXvTaEfU3EMOkJEmSNLl0tLSyaM9ZjW5jp1quVFaA92fm9RExA7guIi4BlgG/zMyzI+Is4Czgg0MVWvOGDfzXy364200mSWdviuynd3NP1n/9NzO5recOLuu+nLUXrq+rxvZK14Dbe8NlZ8/A/dfLMClJkiRNXuNqTmVmrgPWFc+3RcTtwCLgVOCEYrdzgMsZJlR2T+sZQavly0wuuvdWzr76Um6sTOxhrgcs/xjBn068Jzqr38WnvtgCwAsWHcj3Tn17eY1KkiRJGhWdPd2sbqJphLs1pzIilgBHAdcA84rASWaui4i5g3zmTOBMAD5af6Ojoaunm7WPbWXT9sca3cqw1vas45fdV/Chb9xR1+cffnL7gNu3P1n9dWvnk/W2NmKd3RW6erqZ1j6lYT1IkiRJ40X7OBz+CkBETAe+D7wvM7dG1Ha5NTOXA8sBWj/ckj1TmmeZoo7WNt51xPM57VlH844vXskV3b9hK9sa3dYubti4mv919SX8uDLxhrl2dlf4z1uv4exrLuE7J5/BMQue1uiWJEmSmlZnVzdf+nE5C/90dnXT0e7CP+NWwghm/5WuplAZEe1UA+V5mfmDYvOGiFhQXKVcAGwcrs6i787jbV94Nl+4/jds2fFE/V2XbGpbO89rPYalLUcx/QW38enfX8baJricnJnc/tAGbn+ovnmezapvmHxg25BrO0mSJEl6ioDxNKcyqpckvwrcnpmf6fPWhcDpwNnFrxcMV6ulq4UPHvdy3nPUC/jSH66sOVyedMAz+cqr/nqXbcu//DgAZ75rGgBT20Z+d5T2aOfdRx7P6Ycdwzm3XFtzuFwycza/fsv7du3vK48RwLvfOQOAKa27/y9BEcGbn/lc/vLgI3nH56/isu4reIiHd7tOszBMSpIkSSPX2VPhwSa4CNarliR2PHAacHNE3FBs+zDVMHl+RLwDeAD4y1oPuteUPXYrXLa3tjJzytRdtk2N6mqq/beXYWrb7oXL1pYWZk/dc5dte0YPAU/ZXo+2llae03oER7QcxsIT7+GTV1/C3Vs2jbjuWDFMSpIkSeXpaGljcQlzKoe4feT/AV4LdAL3AGdk5pbB6tSy+uuVwGDXVk/c3cb72t1wOdZ2N1yOttZo5S2HLuWvDjmK79zxh90Kl7e9/cNMbf3T1718RXVxovcsK66klnCltz/DpCRJkjRKyplTOdjtIy8BPpSZlYj438CHGOJOH+UniToYLndPW8uu4fITV1/MPVs2D/mZRdP3Ympb+87Xe0X1ViKLZuxVen/1hMmbNq+lkrvecub+nq0A/G7tFtqixYV8JEmSpJ1GPqdysNtHZubFfXa7GnjDUHWaIlT2Mlzunr7h8tt3XM8nr75k2HA5mipZ4T9u+m1dVybffcm3B33vS9+GffeYzvq/+cRIW5QkSZLGvc7ublZvKzeH9Lt9ZF9vB74z1GebKlT26h8uH9jaXEMn+4fLH911U0P7aWtp5a2HHs2bDnlOQ8JlZ3eFa7pXcnn3b9hyafNMGJYkSZImoo6WVhZPq2nE4T4RsbLP6+XFLR930f/2kX22/3eqQ2TPG+ogTRkqe/WGy55+wyKbRW+4fOezn9foVoDGhMvv/fEGPnjFhTzQ3VzBX5IkSZrIarxP5ebMXDrUDoPcPpKIOB04GTgxc+ijNXWo7NVSzP9rVq0tzdXfQOFytLxyySHcu2Uzn7jyMrbTPEOVJUmSNLTOzgorzr2ytFodHeMiWkwcJdyncrDbR0bESVQX5nlxZm4fro7f/ATWN1y2jlIwn9ExlQ8c8zK6rnk2V/Vcy8r2q3noycdH5ViSJEmSSp1TOdjtI/8NmAJcUs2dXJ2Z7xmsiKFyEmhraR31Y0yJKZzQ+kK+885X8MUbfsOnV/5qt8LlwXvPZVp7xy7b1m+q3ot0/r6t7F3C/T4lSZKkiaCjpZX9aphTedUw7w9x+8iLdqcfQ6VKNaNjKh885uX87ZEv3K1wueKktz7lliH/8vnqPM0PvHXvUelVkiRJGrdKGP5aluaaDKgJozdc3vPO/8knX3Ayc6ZOa3RLkiRJkkaBVyo1quq9cilJkiRpYJ3d3ax+tHlu5Weo1JgwXEqSJEnl6GhtY/H0WY1uYyeHv2pMOSxWkiRJGrmo4TFWvFKphuh/5XJ6x5RGtyRJkqQR6NxR4Rufu7i0Wh1TjCqDyuLRJPym1FC94VKSJElSbTq7K6ze6pxKSZIkSVIdqnMqh79P5VhxTqUkSZIkqW5eqZQkSZKkcSacUylJkiRJqkdXpZvVW7Y2uo2dDJWSJEmSNI60t7ayeObMRrexk3MqJUmSJDWdRx+FiHIeV1zR6N9NuQKIjGEfY8UrlZIkSZI03jinUpIkSZJUj87ubtY4p1KSJEmSVI8O51RKkiRpLOyoPMEFN57L4zu2NboVSWXKGh9jxFApSZI0QfX09PDjm7/BWT86zXApTTRNFCod/ipJklSnrU9soSe7mbXnnEa3MqQnurbz45u/waV3/ICXHfLnvOyQ1zNtyoxGtyWpTs6pnEQ6u+Bb391eWq2O9lJKSZI0bjz4yCqmT5nJ3nvObnQrA9qwbQ3/59IP8ZKDXsOrnvUGw6WkMdHR2srivZpnTqWhcpTMndvCpk3ttLTOL6VeR8d65u1bKaWWJEnjxZ0bbuPrv/8KJz7jJF57+BuaMlx2dXdy8R0/5Fd3/dRwKWnseEuRyWH+/PksW7aslForVqwgu1eXUkuSpF5/3Hgn+83ajz069mx0K4Pq6u7k57dfyC//+HPDZcmaIVxmJhFjd5N2aUJICEOlJElqBlfc/St+d++VnHL46zjpma82XJZgLMPl16/5Ig9uuQ+AJyuPATDl4vN3vt+d3TXVGShcjpUvX/UBjt7/JI5YdAIt0Tpmx5XGs67ubtY8Mo7mVEbE14CTgY2ZeVix7aPAu4BNxW4fzsyLRqtJSZI0erbt2MZ5K7/OhTf/yHBZorEIlw88ch9/3HjLrhs31l+vb7g8pvsYntfy/JE1WIP1W1dx7u8/xrw7zuGVh5xuuJRq0D4O51SuAD4PnNtv+2cz81OldyRJkhrCcPlUv777Urp7KmzseQCA++7avMv7G7atranP8Tgs9gou5+qeq3jpzQ/y0oP/gj07RndY7IZthktpt4yn4a+Z+euIWDL6rUiSpGYwVuFye+d2fnbbhQBs7l4HwE033rnLPvds+uOwdQYKl2X5xu+Xs73z8T9tuLr+Wv3D5bPzIGZEcy+Os4MdXHbn95jSNpWXHfJXYzL30XApjT8jmVP53oh4G7ASeH9mPjLQThFxJnAmwMKF80ZwOEmSNJZGO1w+0bWd8/9w3q4b/1B/vb7h8rn5HF7Q8qKRNThKesPlZbSxtOVo3rz9QGY14fDdDjo4tuU4Tn7t+5g+Za8xP77hUhpcV6WbtQ+PozmVg/h34ONUL7p+HPg08PaBdszM5cBygCVLFjXRRVp1dsHXz3+stFqjcR/Nzi449/xtpdWSpMlk3aPruPLe3wKwvnsDALff0O9K4Oa7h60zULhsZl3dnVzN1azsWcmJ197JyYc155zLChWu7rmK6354HWcc9w8c/2cnNrolAKa07cGJh7yOw+9Ywp6xZ0MCZc2pBJwAABftSURBVF+GS+mpOlpbWTTO5lQ+RWZu6H0eEV8GflJaRxoT8+a2smFTG1HifTTn71vbCnO1mj+3jfWbWqHEHu+7b1UptSRpPFi7dR3nXffNXTdeV3+9vuHy2O5jObbl2JE1OMoqVPjF7RdwxV0X84GX/zPPmHtoo1t6ioWxiL968Zkcsejouj7/5qXv3Dk8d8OvqkOF55xwyM73OytP8oUrPlZTrd4w+Ypn/gXTp8xkzZ3X1NVTX99Z+Tl+ffeFu278YX21+ofLJdlCS7SMuEdJI1dXqIyIBZm5rnj5euCWofbvVakk53/3iXoO+RRdXdA+ClfGJpOy76NJ95pSavVVdo933nnn8DtKkoa0bcc2LuVSftfzO0696Q288pmvYY/25lvQp4UWXnjQyzj18Dcyd8aCRreziyVzDuL4LcdyUDyDAxbXFygBDtzn4J3Pp7f0ALBowXN3bnui73zQQfQPk82uN1zuy768uOUlHJcv8sqlJp3OSjdrHxpHw18j4lvACcA+EbEa+AhwQkQcSXX46yrg3bUcrL19Cq2t+9XdbF8dHevZd99yxzN2dcG3v7u9lFobN/awoLn++yVJUqm2s51vXXcul975c/7nSZ9k7ozmWDuhNVo5giN4YeuLOfr5LxtRreOWvIjOyg623fcQANMO2GeX97c+uYVb1tZ++XfJnIN43bPfyrMXHcOD31k5ot5GaryFyf42sYnv9ZzPb359La846AyOWOCwWE0eHW2tLJrVPH9ua1n99c0DbP5qPQebM2dOqVedursfLKUWwNy5LWza1E5rSUMtFyygtN+rJEnNqIMOXnX4KZx82OuYOXX3593t0b4Hpxz2FwA8fFt1Zs3eh+4aTFc9fC83r61t9Z7WaOWFTz+RU5/9RrZduJZk5CuVvv157wXgngevB2D/43e9qnjXxltrCpV9w+RYrKA6lPEeJvvryQrdPRUykxK+cmncaKbTfSSrv044ZQ61lCRpopraNpWje47m+S3P5zlLX1B3nT07pvGWo5cBcOed1eD49KXP2WWfS++4aNhQ2TdMzptR/cfhbQx//8ixYJgcPbOZw4tbTuD5L/p7WltG739pf3bPV3naXofyzDnHNfw7HMgNW65gZttsDpx+eKNb0VhKxtd9KiVJ0vg0b8ZcTj38FAA23boZgDmHzt1ln5vX3siqh++rqd7Utqm86tDX8NrDX8fq82v7zGgaKEw2C8Pk6Nl32mJeccgyDryhjdZoHdVACfDgtjv4xX1fY/+Zh3LSgW9vunC56vHb+cWGczl05rG8Zv7bDZeTRGd3N2vG05zKZrZxY09pC/84B1KSNNEsnrWYM45dBsBNd1TX1Dv02CN22efLv/vSsKGyb5icObXxocQwWbuOtqn879d/vWFh8g3P+Tv+/Ki/AeChH15NJuz1mhfuss8nLvlrtjyxcdhavWHyqMUvpbWljUdvvGJUeh7MA1tvY/kN/9S04fK2rddw29ZrDJeTREdrK4v3bvzfx73GbahctmxZdcXRkoyHOZAbNnZz3vnDr+JWa635hmhJ0hAMk7VrtjDZq7WltaFXJ1tb2uhdOqct2kigrbVjl31imJlh/cNkoxkupadq/J/MEWj2EFimskP0/HEQoiVJjTG1bSonHfpqXnvY69hrj8be+B6aO0zOnLo373vJx5ouTE4EzRYm+zNcquFKmFMZEfsB5wLzgR5geWZ+LiJmA98BllC928cbM/ORweo0359QDcoQKEkaTc0WJgGevu8hfOrP/6PpwmSveTMXMm/mwka3MaE0e5jsz3CpRuiqdLN2cylzKivA+zPz+oiYAVwXEZcAy4BfZubZEXEWcBbwwcGKNP+fVEmSNKqaMUz2WjLnwEa3oDHSiDB53ZqL+fkfq3fK66lU1+lo/e2/77LPozs211Srf7hckDnicLlu+31csOoLAHR2P0QAHXdftMs+G3bcX1Ot/uFy+og6U6O1t7ayaPbIh7Zn5jpgXfF8W0TcDiwCTgVOKHY7B7gcQ6UkSRrI8w44njce9eamC5OaPPadvh+vPvRdDbkyuaOynYe2r9l14wjXgOwNl4tYzAktL2V2vqTucPlEZRu3PvK7XTeO8OJUb7h8OgdxAiew/8jKqUECiNqGv+4TESv7vF6emcsHrBmxBDgKuAaYVwROMnNdRMwd6DO9DJWSJE1ihy1wKJwa6z3Hf7qphouWZQ2rOa/nXK68aSWv2v8dHDqruYbF3s1d3M1dXL3+Rk6ddQZPn3pYo1vS7qotVG7OzKXD7RQR04HvA+/LzK27e64aKiVJktQwzRS0RsPqx//IzQ/9hiXTn8W09savotzfLU9cyyOVTfzd3I+zoONpjW5HNeosb04lEdFONVCel5k/KDZviIgFxVXKBcCQ9/0xVEqSJEkla6GVo+I5nPKcs5g9tTnv47Yv+/KGfd/L0mkn0BItjW5Hu6GjrZw5lVH9V52vArdn5mf6vHUhcDpwdvHrBUPVMVRKkiRJJWmNNo5d+BqOWfd0ZsXeTRkoF0w9gBfsOIZD81CWTH9po9tRPZJSbikCHA+cBtwcETcU2z5MNUyeHxHvAB4A/nKoIoZKSZIkTUpHLHgpB85+NgCP/fZayGDGMcfsss/37vg092y5YaCP76I3TL5syduYvcd8tqz/1Yj7m7fH03jHIZ8EYNsfbyYS9lqy6zzoax/+BTc8ekVN9RZMPYDXLHg7R816CZtvvJyyUslQnsjtTGWPCT/MuSFK+Poy80qq6/4M5MRa6xgqJUmSNClN65jJtI7qEMKtcT8QzJq+621sprTtOWSN/mGy1P7a9+LIOScA8FD0EMDcvU/YZZ/7t98Bjw5dp2+YHOthrjd0XcNFO77H66eexlFtxxouS9JV6Wbd5mG++DFkqJQkSZJ202iGybI0Mkz2tar7bj77+EdY0vp0w2VJ2kuaU1kWQ6UkSZJUI8Nk/QyXJRv90cs1M1RKkiRJwzBMlsdwOfEYKiVJkqRBtEUbz190qmFyFBgu69fV1c26jeXcp7IMhkpJkiRpEG897CN0tE5tdBuDetG+r+OUhWc2JEz2ZDcPdN4NwPpcC0BL5a5d9tnUs37YOgOFSw2tva2VhXOcU6kJaP3GCueev63UevOb79ZOkiRpEmnmQAkwu6NxV0935JN8bO07d934WP31+obL53Ms+/GskTU4gQWD3wekEQyVKsWyZctYsWJFqTXnL6jWlSRJ0uSxqvtuVnE38zoWwjMOgz+eTHNFqCbhQj2aiAyAklSunkoPd/3kntJqtbSNn7lWkrShZS389Smw9jnw83+FB17Y6JaaRldXhfUbvU+lJEkawvr16zn44IPZZ8o+pdSrdFRo3bu9lFqSNGa2LYAbT4e1SxvdSVNpb2tjwZy9Gt3GToZKSZKa1Pz580sbBbJixQo27XiolFqSNNpmMINju17MpZ87Hyp7NLqdphQOf5UkSZI0ngXBwvYlAHR1PQ5AR8uuAXB7bueR3FxzzVkxm5On/hVLntiXSvceXGqgHERCNk+qNFRKkiRJ2m1TW/bkE4u/DsADqy6HhANnHrPLPld1/oovbj972Fq9YfIlHa+iI6Zw7xPXUhmNpieIrq5u51RKkiRJ0l4xm9f2CZOqTXtbKwv2cU6lJEmSpEmqGibfyEs6Xm2YrJNzKiVJkiRNOobJMjVPqhw2VEbE14CTgY2ZeVixbTbwHWAJsAp4Y2Y+MnptSpIkSRqvDJPlqnR1s379+JpTuQL4PHBun21nAb/MzLMj4qzi9QfLb0+SJEnSeDU9ZvLWPd5jmCxZe3srC/YdR3MqM/PXEbGk3+ZTgROK5+cAl2OolCRJktTH4e3P5XCe2+g2Jp5kQtxSZF5mrgPIzHURMbfEniRJkiRJQ2meTDn6C/VExJnAmQD777//aB9OkiRJkia0rq5uNoyzOZUD2RARC4qrlAuAjYPtmJnLgeUAS5cubaI8LUmSJEnjT3t7KwvmNs+cypY6P3chcHrx/HTggnLakSRJkiQNK3P4xxgZNlRGxLeAq4CDI2J1RLwDOBt4eUTcBby8eC1JkiRJGm1Z42OM1LL665sHeevEknuRJEmSJA2jq6vChnVbGt3GTqO+UI8kSZIkqTzt7W3Mb6I5lYZKSZIkSRpXxnh86zAMlZIkSZI0jlS6utmwdvzfUkSSJEmS1ADtba0smDez0W3sZKiUJEmSpPGmeUa/GiolSZIkadwxVEqSJEmS6tHV2c2GtY80uo2dDJWSJEmSNI60t7cyf563FJEkSZIk1SUhm2f8a0ujG5AkSZIkjV9eqZQkSZKkcaSrs5sNq0c+pzIivgacDGzMzMOKbUcCXwKmAhXgbzPz2qHqGColSZIkaRxpb29l/oJS5lSuAD4PnNtn278AH8vMn0XEq4vXJwxVxOGvkiRJkjTeZA7/GLZE/hp4uP9mYGbxfC9g7XB1vFIpSZIkSePN6K3T8z7gFxHxKaoXIZ8/3AcMlZIkSZI0jnR1Vtiwuv8FxgHtExEr+7xenpnLh/nM3wD/JTO/HxFvBL4KvGyoDxgqJUmSJGkcqc6pnFXLrpszc+lulj8d+Mfi+XeBrwz3AUOlJEnSBJGVbjZefEtptaKttZRakkbB6N2nci3wYuBy4KXAXcN9wFApSZI0AUyZvSe5pZP5e+xdSr31HTvomTm1lFqSRkEJmTIivkV1Zdd9ImI18BHgXcDnIqINeBI4c7g6hkpJkqQJYv78+SxbtqyUWitWrGDt9i2l1JLq0dHRybJlK0qpNX/++lLqNItKZ4UNDz404jqZ+eZB3nru7tQxVEqSpAkpK92s/vkdpdVyKGgTqnTz+FUrh9+vxlq0+r/GzWJK6zRyyg5e/OKyKs4vq1BTaGtvY97CmuZUjgn/5EiSpAlnyuw96dnSxbyps0upt76jE2Z1lFJL5WidNYO2bU8wf0YpN4Bn/eNPUNljz1JqqRxlXnkHOOOMM0qr1Xi13YdyrBgqJUnShFT2UND1T9a0fL+GUunmsd9cX0qp7i3bWLxocanf8epHt5ZSa7zI7Gb9uutKqxV4NX9MGSolSZLGl86Ht7P+4ltLqzfZhtS2zZpOy9YnmT+tnCuLTNur1KtYk03bnjNo6drO/HklXeld/wRRmVJKLQ2vrDmVZTFUSpIkDWPZsmWsWLGi1JrrOzqJJh9SW9nyGFt+eVNptfZfWN6Vxckoeyo8/Mffl1Krsn0b+++3qNQrvRtWP1JKLQ2vOqeynJWey2ColCRJqkHZYWjFihVsaOIhtaUH6T1nGShHoG3aDFp3bGf+nJnlFJwz0+9j3HP4qyRJ0i56Kj08+LNyVmvd8fB2WDinlFqTmaGjuZS9cI3GMxfqkSRJ2sXUOXvS/UgXc6eWFAQXzvF/viVNWJXOChvu39zoNnYyVEqSpKbgVRhJqk1bexvzFtcwp7KcxX2HZaiUJEmSpHEmexz+KkmSJEmqywSaUxkRq4BtQDdQycylZTQlSZIkSRpYV2eFDfdvanQbO5VxpfIlmdk8s0QlSdKAnnxoO/f+9K5SavVUemhpayml1mS24+HtrPvFbaXVYuHsUmpJam7t7W3MW1zDn/ebR78XcPirJEmTQtn3HFzf0UXr3u2l1ZuMSr8P5MLZLnSkhtrR/Thrtt1aWi2oYSGayWyiDH+lesfNiyMigf/IzOUl9CRJkkZBmYFjxYoVbNrxUGn1JitDYHPp3raNbStXllSsAq2T5/pN6f9Iwt7++RhSkhMoVB6fmWsjYi5wSUTckZm/7rtDRJwJnAmw//77j/BwkiRJUvlKv5r/xHZ2VLp59Obfl1Kv8vg22HtmKbVGiyFw7HTtqLDhvo2NbmOnEYXKzFxb/LoxIn4IHAP8ut8+y4HlAEuXLm2eOC1JkiT1UXYoKvXK3d4zDW3aqb2jjXn7zRl+xz+Ofi8wglAZEdOAlszcVjx/BfDPpXUmSZIkjWOGQI2miTL8dR7ww4jorfPNzPx5KV1JkiRJkgYxQe5TmZn3AkeU2IskSZIkaTjJxAiVkiRpcnvyoe3c/7M7S6vFwhrmB0mS2PDYul985vKP7VPDrptHvRkMlZIkqQ7l32NxjvPPJKlGmXlSo3voy1ApSZLqYgiUJAG0NLoBSZIkSdL4ZaiUJEmSJNXNUClJkiRJqpuhUpIkSZJUN0OlJEmSJKluhkpJkiRJUt0MlZIkSZKkuhkqJUmSJEl1M1RKkiRJkupmqJQkSZIk1c1QKUmSJEmqm6FSkiRJklQ3Q6UkSZIkqW6GSkmSJElS3QyVkiRJkqS6GSolSZIkSXUzVEqSJEmS6maolCRJkiTVzVApSZIkSaqboVKSJEmSVDdDpSRJkiSpboZKSZIkSVLdDJWSJEmSpLoZKiVJkiRJdTNUSpIkSZLqZqiUJEmSJNXNUClJkiRJqtuIQmVEnBQRd0bE3RFxVllNSZIkSZLGh7pDZUS0Al8AXgUcCrw5Ig4tqzFJkiRJUvMbyZXKY4C7M/PezOwEvg2cWk5bkiRJkqTxIDKzvg9GvAE4KTPfWbw+DTg2M9872GeWLl2aK1eurOt40kitWLGCO++8k/Xr15dSb//99wfggQcesJ71xn290ahpvZGZP38+Bx98MMuWLSulniRNdhFxXWYubXQfE1HbCD4bA2x7SkKNiDOBM4uXOyLilhEcUxqpfYDNjW5Ck5bnn3bbGWecUWY5z0E1muegGulpjW5gohpJqFwN7Nfn9WJgbf+dMnM5sBwgIlb6rwNqJM9BNZLnnxrNc1CN5jkoTUwjmVP5e+CgiDggIjqANwEXltOWJEmSJGk8qPtKZWZWIuK9wC+AVuBrmXlraZ1JkiRJkpreSIa/kpkXARftxkeWj+R4Ugk8B9VInn9qNM9BNZrnoDQB1b36qyRJkiRJI5lTKUmSJEma5MYkVEbESRFxZ0TcHRFnjcUxNTlExH4R8auIuD0ibo2Ifyy2z46ISyLiruLXvft85kPFuXhnRLyyz/bnRsTNxXv/FhED3TZHeoqIaI2IP0TET4rXnn8aMxExKyK+FxF3FH8XPs9zUGMpIv5L8d/gWyLiWxEx1XNQmlxGPVRGRCvwBeBVwKHAmyPi0NE+riaNCvD+zHwmcBzwd8X5dRbwy8w8CPhl8ZrivTcBzwJOAr5YnKMA/071nqoHFY+TxvI3onHtH4Hb+7z2/NNY+hzw88w8BDiC6rnoOagxERGLgH8AlmbmYVQXb3wTnoPSpDIWVyqPAe7OzHszsxP4NnDqGBxXk0BmrsvM64vn26j+z9QiqufYOcVu5wCvK56fCnw7M3dk5n3A3cAxEbEAmJmZV2V1ovG5fT4jDSoiFgOvAb7SZ7Pnn8ZERMwEXgR8FSAzOzNzC56DGlttwB4R0QbsSfW+5Z6D0iQyFqFyEfBgn9eri21SqSJiCXAUcA0wLzPXQTV4AnOL3QY7HxcVz/tvl4bzr8AHgJ4+2zz/NFYOBDYB/1kMwf5KREzDc1BjJDPXAJ8CHgDWAY9m5sV4DkqTyliEyoHGw7vkrEoVEdOB7wPvy8ytQ+06wLYcYrs0qIg4GdiYmdfV+pEBtnn+aSTagOcA/56ZRwGPUwwzHITnoEpVzJU8FTgAWAhMi4i3DvWRAbZ5Dkrj3FiEytXAfn1eL6Y6LEIqRUS0Uw2U52XmD4rNG4qhNBS/biy2D3Y+ri6e998uDeV44JSIWEV1aP9LI+IbeP5p7KwGVmfmNcXr71ENmZ6DGisvA+7LzE2Z2QX8AHg+noPSpDIWofL3wEERcUBEdFCdnH3hGBxXk0CxMtxXgdsz8zN93roQOL14fjpwQZ/tb4qIKRFxANWFAK4thuZsi4jjippv6/MZaUCZ+aHMXJyZS6j+3XZZZr4Vzz+NkcxcDzwYEQcXm04EbsNzUGPnAeC4iNizOHdOpLq+geegNIm0jfYBMrMSEe8FfkF1RbCvZeato31cTRrHA6cBN0fEDcW2DwNnA+dHxDuo/gfvLwEy89aIOJ/q/3RVgL/LzO7ic38DrAD2AH5WPKR6eP5pLP09cF7xD7f3AmdQ/Udjz0GNusy8JiK+B1xP9Zz6A7AcmI7noDRpRHWBLUmSJEmSdt9YDH+VJEmSJE1QhkpJkiRJUt0MlZIkSZKkuhkqJUmSJEl1M1RKkiRJkupmqJQkSZIk1c1QKUmSJEmqm6FSkiRJklS3/wcvF+WobXCKpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.set_title('plot_array() and plot_discharge()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, ax=ax, 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",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a cross section from Shapefile data\n",
    "\n",
    "A shapefile can be used to define the vertices for a instance of the `PlotCrossSection` class. The function `flopy.plot.plotutil.shapefile_get_vertices()` will return a list of vertices for each polyline in a shapefile.\n",
    "\n",
    "Let's plot the shapefiles and the Freyberg model using `PlotMapView` for visualization purposes and then plot the cross-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup the figure and PlotMapView. Show a very faint map of ibound and \n",
    "# model grid by specifying a transparency alpha value.\n",
    "\n",
    "# set the modelgrid rotation and offset\n",
    "ml.modelgrid.set_coord_info(xoff=-2419.2189559966773, yoff=297.0427372400354, angrot=-14)\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=ml)\n",
    "\n",
    "# Plot a shapefile of \n",
    "shp = os.path.join(loadpth, 'gis', 'bedrock_outcrop_hole_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, #facecolor='none', \n",
    "                                           edgecolor='green', linewidths=2, alpha=0.5)\n",
    "# Plot a shapefile of a cross-section line\n",
    "shp = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, radius=0, lw=3, \n",
    "                                          edgecolor='red', facecolor='None')\n",
    "\n",
    "# Plot a shapefile of well locations\n",
    "shp = os.path.join(loadpth, 'gis', 'wells_locations_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, radius=100, facecolor='red')\n",
    "\n",
    "# Plot the grid and boundary conditions over the top\n",
    "quadmesh = mapview.plot_ibound(alpha = 0.1)\n",
    "linecollection = mapview.plot_grid(alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a cross section based on this arbitrary cross-sectional line. We can load the cross sectional line vertices using `flopy.plot.plotutil.shapefile_get_vertices()`\n",
    "\n",
    "**Note**: in previous examples we passed `line={'column', 5}` to plot a cross section along a column. In this example we pass vertex information into `PlotCrossSection` using `line={'line', line[0]}` where `line[0]` is a list of vertices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the vertices for cross-section lines in a shapefile\n",
    "fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
    "\n",
    "# Set up the figure\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
    "\n",
    "# plot head values along the cross sectional line\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]})\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(lw=0.5)\n",
    "cb = fig.colorbar(csa, ax=ax, shrink=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting geographic coordinates on the x-axis using the `PlotCrossSection` class\n",
    "\n",
    "The default cross section plotting method plots cells with regard to their intersection distance along the cross sectional line defined by the user. While this method is perfectly acceptable and in many cases may be preferred for plotting arbitrary cross sections, a flag has been added to plot based on geographic coordinates.\n",
    "\n",
    "The flag `geographic_coords` defaults to `False` which maintains FloPy's previous method of plotting cross sections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmSVAsTTkmSJElSLUw4JUmSJEm1MOGUJEmSJNXChFOSJEmSVAsTTkmSJElSLUw4JUmSJEm1MOGUJEmSJNXChFOSJEmSVAsTTkmSJElSLUw4JUmSJEm1MOGUJEmSJNXChFOSJEmSVAsTTkmSJElSLTomnBHRHxE3RMStEXFHRHygXH9wRFwVEfeWPw+qP1xJkiRJUlN008O5C3hVZh4DHAucEhEnAGcBV2fmUcDV5XNJkiRJkoAuEs4sPFY+nVcuCZwGXFCuvwB4Yy0RSpIkSZIaqatrOCOiLyJuATYCV2Xm9cCizFwHUP48vL4wJUmSJElN01XCmZl7MvNYYAA4PiKO7vYAEbEyIm6KiJuGh4cnG6ckSZIkqWEmNEttZm4FrgFOATZExBKA8ufGMfZZlZnHZeZxc+Y4Ka4kSZIkzRbdzFJ7WEQsLB/vD7wauBu4DDijLHYG8I26gpQkSZIkNc/cLsosAS6IiD6KBPWSzLw8Iq4DLomIM4GHgDfVGKckSZIkqWE6JpyZ+VPgxW3WbwFOriMoSZIkSVLzeVGlJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqhQmnJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqhQmnJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqhQmnJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqhQmnJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqhQmnJEmSJKkWJpySJEmSpFqYcEqSJEmSamHCKUmSJEmqxdxeByBJkiRJ6t5rT16aW7bs2mv9zbc+8p3MPKUHIY3JhFOSJEmSGmTz5h1cd+Wxe61/xuJrD+1BOOMy4ZQkSZKkBkmSIYZ7HUZXTDglSZIkqUESGMo9vQ6jK04aJEmSJEkNkiS72yydRMTyiPj3iLgrIu6IiHeX6/8+Iu6OiJ9GxKURsXCM/VdHxG0RcUtE3NRNrB0TznGCOjsi1pQHuyUiTu3mgJIkSZKkySt6OIf3WrowBLwnM58PnAD8SUS8ALgKODozXwTcA7xvnDpemZnHZuZx3RywmyG1I0H9OCIWADdHxFXlto9l5oe7OZAkSZIkaeoS2D2Z/TLXAevKxzsi4i5gWWZe2VLsR8DvTj3KQscezsxcl5k/HgkKuAtYVlUAkiRJkqSJGcq9l4mIiBXAi4HrR216J/CtMXZL4MqIuDkiVnZznAldw9kmqHeV43zPj4iDJlKXJEmSJGniEtidsdcCHBoRN7UsbZPCiDgA+CrwZ5m5vWX9X1GMcL1ojEOfmJkvAX6LYjjuf+0Ua9cJZ5ugzgOeCxxL0S37kTH2WzlywsPDzZi6V5IkSZJmrv2YM/eIvRZgc2Ye17KsGr1nRMyjyOsuysyvtaw/A3gd8PuZ2ba/NDPXlj83ApcCx3eKtKvborQLKjM3tGz/DHD5GEGtAlYBLF++fIIdvZIkSZKkVsO5myd3r5nwfhERwOeAuzLzoy3rTwHeC7wiM58YY9/5wJzy2s/5wG8Cf9vpmN3MUjtWUEtaiv0OcHunuiRJkiRJU1NMGjRnr6ULJwJvA1416m4jnwQWAFeV6/4RICKWRsQV5b6LgGsj4lbgBuCbmfntTgfspodzJKjbIuKWct37gdMj4tjyfFcDf9zNGUqSJEmSJi8JhnJC0/EU+2VeC0SbTVe0WTcyhPbU8vH9wDETPWbHhHOiQUmSJEmS6lP0cHZ1dWTPNSNKSZIkSRJQ9HDunkQPZy+YcEqSJElSwwxlX69D6IoJpyRJkiQ1SBLsxoRTkiRJklSxYD+e0be812F0xYRTkiRJkhokgaHuboPScyackiRJktQge3KIbU9t6HUYXTHhlCRJkqQGSZw0SJIkSZJUg+K2KCackiRJkqSKeQ2nJElSDXJokOEdn2izfnvb9e0dWG1QkjTNih7OZqRyzYhSkiRJklTIYCjt4ZQkSZIkVcxJgyRJkiZgz9DDPLH9Ix3KPMEcopJjPbn9o+wZepzHOxxzYg6psC5JGtucmMcB85b0OoyumHBKkiRJUoMM5RBbdm3qdRhdMeGUJEmSpEYJh9RKkiRJkqqXiZMGSZIkSZKql8DQsAmnJEma5XYNDTK49eOj1i7sSSyStK8o7sM58SG1EbEcuBBYDAwDqzLz3Ig4GPgKsAJYDbw5Mx9ts/8pwLlAH/DZzDyn0zFNOCVJ0rTauXuQBx89t9dhSFJjJbBnckNqh4D3ZOaPI2IBcHNEXAW8A7g6M8+JiLOAs4D3tu4YEX3Ap4DXAIPAjRFxWWbeOd4Bm9EPK0mSJEkqZDCUc/ZaOu6WuS4zf1w+3gHcBSwDTgMuKItdALyxze7HA/dl5v2Z+RTw5XK/cdnDKUmSVIEndq/hnkc/NcbW+dMai6R9W3ENZ9shtYdGxE0tz1dl5qp2BSNiBfBi4HpgUWaugyIpjYjD2+yyDHi45fkg8PJOsZpwSpIkSVKD9MU8Dn7GYe02bc7M4zrtHxEHAF8F/iwzt0dEN4dtVyg77WTCKUmSJEkNMpRDbHjykUntGxHzKJLNizLza+XqDRGxpOzdXAJsbLPrILC85fkAsLbT8Uw4JUmSavbY7rXcuuUzk9x7XqWxSGq+yd6HM4quzM8Bd2XmR1s2XQacAZxT/vxGm91vBI6KiOcAa4C3AG/tdEwTTkmSNGE7dq8D4ObN5/c4EkmafZKY7Cy1JwJvA26LiFvKde+nSDQviYgzgYeANwFExFKK25+cmplDEfEu4DsUt0U5PzPv6HRAE05JkiRJapih4a6uu3yazLyW9tdiApzcpvxa4NSW51cAV0zkmCackiRJktQgU7gP57Qz4ZQkSZKkBskMhoZNOCVJkiRJNRi2h1OSJEmSVLW5c+ZyeP8hvQ6jKx0TzohYDlwILAaGgVWZeW5EHAx8BVgBrAbenJmP1heqJEmSJGn38B7WPNGM1KubHs4h4D2Z+eOIWADcHBFXAe8Ars7McyLiLOAs4L31hSpJkqZqy65NXL72q70OQxOwZdcmrvA9k9QiE4b3lWs4M3MdsK58vCMi7gKWAacBJ5XFLgCuwYRTkqRG2bxrE5etuXTS2yVJvbEnJ35blF6Y0DWcEbECeDFwPbCoTEbJzHURcXin/YcZ5utrvj6JMCVJktTJ5l2b/V9LmgWS2Hd6OEdExAHAV4E/y8ztEd1l1BGxElgJsGTJksnEOK5Nuzbz1cHLKq9XkiRJkmak3Md6OCNiHkWyeVFmfq1cvSEilpS9m0uAje32zcxVwCqAZcuXZQUx127jzi1c8tDlvQ5DkiRJkvaSwPBwMxLOjv2wUXRlfg64KzM/2rLpMuCM8vEZwDeqD0+SJEmSNNrwcOy1zETd9HCeCLwNuC0ibinXvR84B7gkIs4EHgLeVE+Izbdh5xa+9OAVvQ5DkiRJ0j4gMxjOfeQazsy8FhgrXT652nAkSZqd1j/5CBc+8O3a95EkNd9+c/pYuv/CXofRlQnNUquZY/2Tj3CB/2RIkiRJs85Tw3sYfHxbr8PoigmnJEmSJDVMztBrNkcz4dTPrXvyET5731WV1mkDkyRJkiqWJpzSrLL2iUdZde+/td223zTHIkmSpH1bAjnc6yi6Y8Ip1WzNE49y3j3f3Wv9/j2IRZKkEWufeJR/vOfqSe/fX2EskiYuc3I9nBFxPvA6YGNmHl2u+wrwvLLIQmBrZh7bZt/VwA5gDzCUmcd1Op4Jp2q15olHOe9neydbU2GiJkmSpFktAyY/pPYLwCeBC39eXebvjTyOiI8A481I9MrM3NztwUw4JUmSJKlpJtnDmZnfj4gV7bZFRABvBl416bhGMeGUJGkCBh/fyj/cec2U69lWUT2SpNlnvzl9DMx/Vh1V/wawITPvHWN7AldGRAL/lJmrOlVowilNweDjW/nEHd/rdRiVquucDqy8Rml67Iufc0lS840xS+2hEXFTy/NV3SSFLU4HLh5n+4mZuTYiDgeuioi7M/P741Vowik12ODjWzn39mb8I1xHrLV8rydJ6sp4v9f9/SzV66k9exjc0fYyy83dTOTTTkTMBf4P4KVjlcnMteXPjRFxKXA8YMKpfcvgY1s597Zx2/WE9eIP48h5+Ed58gYf28rHK24LkiRJTRDV34fz1cDdmTnY9ngR84E5mbmjfPybwN92qnROtTFKkiRJkmqVwHCbpQsRcTFwHfC8iBiMiDPLTW9h1HDaiFgaEVeUTxcB10bErcANwDcz89udjmcPpyRJkiQ1zSR7ODPz9DHWv6PNurXAqeXj+4FjJno8E06pxx7esY2P/eTap617rM06SZIkaURkryPojgmnJEmSJDVJlksDmHBKUotdQ0N87KYfVlrnQZXWJkmSFJMeUjvdTDglSZIkqUH26+tj+YJm3OXchFOSavbw9m187Ib/qLROe00np473QpKk6bZ7aA+DW7f3OoyumHBKkhprcPs2Pn69CaQkafZx0iBJUm0Gt2/j4z+qNtFaWGltkmazwW3b+Ph11f2O8veT1IbXcEqSJEmSKpcQw70OojsmnJIkSZLUMCacUoMMbtvGudde17Hcji7LSZIkSbXyGk5JUpMMbt3GJ35Q7RcqzZiwXZKk5nHSIEmSJElS5cJrOCVJkiRJdZjX18eyZzVjHJEJpySpEQYf3cY//LvXUEuz0cjnf0GvA5FmiN179rD2ke29DqMrJpySJEmS1CSJkwZJkiRJkurhNZySJEmSpHo0JOGc06lARJwfERsj4vaWdWdHxJqIuKVcTq03TEmSJEkSAOUstaOXmaibHs4vAJ8ELhy1/mOZ+eHKI5Ik7TPWPLKNT3+7u4l+Hp1AWUmSZrvJ3oczIs4HXgdszMyjy3VnA/8N2FQWe39mXtFm31OAc4E+4LOZeU6n43VMODPz+xGxosv4JUmSJFVoIl/edeuZldamnph8j+YXmESHYkT0AZ8CXgMMAjdGxGWZeed4B5vKNZzvioi3AzcB78nMR8cIbCWwEmDJkiVTOJwkSZI0863Zso3zvuWIDdVnv74+Bg6a3H04p9CheDxwX2beDxARXwZOA2pJOM8DPkgxGe8HgY8A72xXMDNXAasAli1f1pDJeyVJkqR915ot2zjviuqS4v0rq0nd2D20h7VbKr8PZ6cOxWXAwy3PB4GXd6q046RB7WTmhszck5nDwGcosl1JkiRJ0nQYbrPAoRFxU8uyssvazgOeCxwLrKPoUBwt2qzr2KE4qR7OiFiSmevKp78D3D5eeUmSJElSdcaYNGhzZh430boyc8PP6434DHB5m2KDwPKW5wPA2k51d0w4I+Ji4CSKbHkQ+BvgpIg4liKjXQ38cad6JEmSJEkVyGpvg9Jlh+KNwFER8RxgDfAW4K2d6u5mltrT26z+XKf9JEmSJEn1mGzCOZEOxYhYSnH7k1Mzcygi3gV8h+K2KOdn5h2djjeVWWolSZIkibWbtrHq0v+otM79Kq1tH5NM+rYoE+lQzMy1wKktz68A9ro/53hMOCVJkiSpQQKIbMYNQEw4JUmSNGut3bSNz3y12p45aTpUeQ1nnUw4JUmSJKlB5s3tY+khB/Y6jK6YcEqSJElSg+zevYd1G7f3OoyumHBKkiRJUsOMcR/OGceEU5IkSZIaJYnhZmScJpySJEmS1CCRThokSZIkSapJ7Ol1BN0x4ZQkSZKkJsnm3IdzTq8DkCRJkqTRvvc9iKhu2dfE8N7LTGQPpyRJkiQ1yLy5fSw5zPtwSpIkSZIqFszcHs3RTDglSZIkqUF2797D+vXbeh1GV0w4JUmSJKlpvA+nJEmSJKly3odTkiRJklSXsIdTkiRJklS5TBNOSZIkSVJNGpJwzul1AJIkSZKk7o3cFmX00tW+EedHxMaIuL1l3d9HxN0R8dOIuDQiFo6x7+qIuC0ibomIm7o5ngmnJEmSJDXIvLl9LFl04F5Ll74AnDJq3VXA0Zn5IuAe4H3j7P/KzDw2M4/r5mAOqZUkSZKkBtm9ew/r126d1L6Z+f2IWDFq3ZUtT38E/O6kgxvFHk5JkiRJapIsZqkdvVTkncC3xj4yV0bEzRGxspvK7OGUJEmSpEZJ2NM2wTx01LWVqzJzVby9V3UAAA5RSURBVLe1RsRfAUPARWMUOTEz10bE4cBVEXF3Zn5/vDpNOCVJkiSpYSLbzhK0udtrK/eqL+IM4HXAyZnZNpvNzLXlz40RcSlwPDBuwumQWkmSJElqkqTo4Ry9TFJEnAK8F3hDZj4xRpn5EbFg5DHwm8Dt7cq2MuGUJEmSpIaZ7DWcEXExcB3wvIgYjIgzgU8CCyiGyd4SEf9Yll0aEVeUuy4Cro2IW4EbgG9m5rc7Hc8htZIkSZLUJJkw3OWNN/faNU9vs/pzY5RdC5xaPr4fOGaixzPhlCRJkqQGCahyVtpadUw4I+J8iotHN2bm0eW6g4GvACuA1cCbM/PR+sKUJEnSbLd+9UYuPPuS7srSZdn5UwxK6oG58/pYtGRhr8PoSjfXcH4BOGXUurOAqzPzKODq8rkkSZIkqWZDTw2x4eEtey0zUceEs7yvyiOjVp8GXFA+vgB4Y8VxSZIkSZLGMjy89zIDTfYazkWZuQ4gM9eVN/6UJEmSJNUtmbEJ5mi13xYlIlZGxE0RcVNO4d4wkiRJkiSAhD3Dey8z0GQTzg0RsQSg/LlxrIKZuSozj8vM46IvJnk4SZIkSRJQ9HDm8N7LDDTZhPMy4Izy8RnAN6oJR5IkSZI0vub0cHZzW5SLgZOAQyNiEPgb4Bzgkog4E3gIeFOdQUqSJEmSCpmQDbmGs2PCmZmnj7Hp5IpjkSRJkiR1MG+/PhYtO2jvDT+Z/lg6mewstZIkSZKkHtj91BAbHtzU6zC6YsIpSZIkSU2SzNhrNkcz4ZQkSZKkRklyeE+vg+iKCackSZIkNYk9nJIkSZKkWmSSJpySJEmSpKrN228ui444ZO8N909/LJ2YcEqSJElSg2x4bN13PnrNBw5ts2nztAfTgQmnJEmSJDVIZp7S6xi6NafXAUiSJEmS9k0mnJIkSZKkWphwSpIkSZJqYcIpSZIkSaqFCackSZIkqRYmnJIkSZKkWphwSpIkSZJqYcIpSZIkSaqFCackSZIkqRZzex3ATLT0gEUcfv8BldW3us+8XpIkSdLsY8LZxsDAACeddFJl9d229d7K6pIkSZKkppjWhLMv+lh478JK64y+qLQ+SZIkSVI1pjfh7OurtOcQ4M7tP6u0PkmSJElSNby4UJIkSZJUC6/hlCRJjTGvfwXfv3lgr/X7H7Ce79+8uKs65vb9oOqwJEljMOGUJEmNUcXEflftuq6aYCRJHU3rkNr+/v7pPJwkSZIkqYemNeGcP3/+dB5OkiRJktRDThokSZIkSaqFCackSZIkqRYmnJIkSZKkWjhLrSRJmpWe0f8cbvzx3rdYmayDF6xn7a3tb80yp8+ZcSXNTlNKOCNiNbAD2AMMZeZxVQQlSZI02jP6n1PJjPcLnvVKrv/xTo48sp8TTjihgsg6W//EzdNyHEmaaaro4XxlZm6uoB5JkjRLPaP/OdzQobexqgRxupJMSZJDaiVJ0gwwMDDASSed1OswarPi4BN59Ladk9p3Tt9NFUcjSdNnqglnAldGRAL/lJmrKohJkiRpnzKVXtV7d9xZYSSSNL2mmnCemJlrI+Jw4KqIuDszv99aICJWAisBjjjiiCkeTpIkSZLUFFO6LUpmri1/bgQuBY5vU2ZVZh6XmccddthhUzmcJEmSJKlBJp1wRsT8iFgw8hj4TeD2qgKTJEmSJDXbVIbULgIujYiRer6Umd+uJCpJkrRPmN+/gvtuefrss31zr+1RNJKk6TbphDMz7weOqTAWSZK0j2k3++zWJ3/Um2AkSdOu8bdFecnhx7DzvslNMz6WKm4qLUmSJEmzXeMTTm/eLEmSJEkz05RmqZUkSZIkaSwmnJIkSZKkWphwSpIkSZJqYcIpSZIkSapF4ycNknppxbMOZWBTsv7gJSxe0/7jtLnP73UkSdNj8fzFLLx3YXeF+6LeYCQJE05pStrdX260G7c8MD3BSNI0Omj/5Wx7anBS+y4+6CQeuPXptzTzlmTV6Obv0oibttxfbzCShAmnavbsAw9jYF21PXzX1tBjuOKQQxgY2tWx3PpFi1jcUs5/kCTNVgMDA8x5YtOk9vWWZpI0e5hwqlYT+aa1W/39/ezcsrNzwYnUeeSR/gMkSZIkVcyEU42zrySGKxYeysCWYdYftojF24aetq3dunbWH7KYxZuz0riuneM1p5IkSaqGCafUI3X0/lbh+o2rex2CJEmS9hEmnA01sOBwBh6s9vrBZxy0lMWD8yqt02scJWlmWzx/CQfee9CY2weetbzt9v7+fn6p/1fZcfv4lzj4d2DqXnjYS9h5T3eXkvh6S5ppTDgbaqb2jkmSmsW/JzPfvnIpiaTZyYu1JEmSJEm1sIdTqtlvLHs+O9fuPRTKYU+SJEna15lwToOXLX4hOx+o+DYeJiuN4VAoSZIkzVYmnNPAhEOSJM00v77sBW1H4HTL22hJ6oYJpyRJ0iw01S/E+/v72bm5fcJqMipphAmnJGnWeM7BhzDwxFMT2mf0P84rnnUoAxunHssP+3rzD/mi+Us44J5Dfv7cSzQ0WeMlrD96+OFpjETSTGbCKUmt9qv2XrSaWSZzC5DrN66ech1t6920umOZOngbFEnSdDLhlKQWz126jIHHdldaZ1OGlq049BAG9uyqtM71SxexuL+7OjuV/WGPXsdXHPHL7Nz0i2GD9ghKktQ9E05JTzP6n+uq1JF0rVh4KANbhiuts//IIyuf6Ou6Nc0YWjbTe776+/vZufPpbXM6kvnpmvhtYMHhDDw4sWR2XV/UFI0kSdUw4ZT0NHX9cz3e5BKTrrOG5LAOr3zuUex8otpzX3/4IhbvnNi1iJ3M9J67du/1jwabkcy384rlv8zOjS09p5Noz7c+el/VYUmSVCkTTknTogmJYV1m87lrbLYLSdJs0IwLiyRJkiRJjWMPpySpsU468ih2Ptn9cOWZPmxYkqR9jQmnJKmxHJYqSdLMZsIpSVJDvXzJ0ex8cGITUtnLqyYbOHIpAwOHd1V2v/XDLF7cueyamzZMNSxJ45hSwhkRpwDnAn3AZzPznEqikiRJHdnDq5nqpKOO2us2RlMxch/eOm7fdPOdzZ3tWmqCSSecEdEHfAp4DTAI3BgRl2XmnVUFJ0mSpOap+suQ6x+oLyk88SXVJscAC3YvYvH+uyqr7z+i+nk+n73oEAaeWV2M65cvYvFB1d6u6wdDDsbcF0zlXTweuC8z7weIiC8DpwEmnJIkSWqEJowUuPG+6hPuOnqLq9bf/yPOPPOayup75Ssrq0oTMJWEcxnQ2voHgZdPLRxJkiTp6V7x/KIX0muQZ5cmfBmgzqaScEabdblXoYiVwEqAI444YgqHkyRJ0mxk4iE111QGhA8Cy1ueDwBrRxfKzFWZeVxmHnfYYYdN4XCSJEmSpCaJzL06JbvbMWIucA9wMrAGuBF4a2beMc4+m4AHJ3VATcahwOZeByF1YDvVTGcb1UxnG1UTzIR2+uzMtAdsmk16SG1mDkXEu4DvUNwW5fzxks1yH9/gaRQRN2Xmcb2OQxqP7VQznW1UM51tVE1gO529pjTXcGZeAVxRUSySJEmSpH1I9Tf1kSRJkiQJE8593apeByB1wXaqmc42qpnONqomsJ3OUpOeNEiSJEmSpPHYwylJkiRJqoUJZ8NExP8TEXdExO0RcXFE9EfEwRFxVUTcW/48qKX8+yLivoj4WUS8tmX9SyPitnLbJyIienNG2hdFxMKI+JeIuDsi7oqIX7WdaqaJiL6I+ElEXF4+t41qRoiI5RHx7+Xvzzsi4t3letuoGiciTinb5X0RcVav49H0M+FskIhYBvx34LjMPJridjRvAc4Crs7Mo4Cry+dExAvK7b8CnAJ8OiL6yurOA1YCR5XLKdN4Ktr3nQt8OzN/GTgGuAvbqWaed1O0zRG2Uc0UQ8B7MvP5wAnAn5Tt0DaqRinb4aeA3wJeAJxetlfNIiaczTMX2D8i5gLPBNYCpwEXlNsvAN5YPj4N+HJm7srMB4D7gOMjYglwYGZel8VFvBe27CNNSUQcCPxX4HMAmflUZm7FdqoZJCIGgN8GPtuy2jaqGSEz12Xmj8vHOyi+GFmGbVTNczxwX2ben5lPAV+maK+aRUw4GyQz1wAfBh4C1gHbMvNKYFFmrivLrAMOL3dZBjzcUsVguW5Z+Xj0eqkKvwRsAj5fDlf8bETMx3aqmeXjwF8Cwy3rbKOacSJiBfBi4Hpso2qesdqmZhETzgYpr9U4DXgOsBSYHxF/MN4ubdblOOulKswFXgKcl5kvBh6nHPY1BtupplVEvA7YmJk3d7tLm3W2UdUuIg4Avgr8WWZuH69om3W2Uc0EtkGZcDbMq4EHMnNTZu4Gvgb8GrChHDZD+XNjWX4QWN6y/wDFENzB8vHo9VIVBoHBzLy+fP4vFAmo7VQzxYnAGyJiNcXwrldFxBexjWoGiYh5FMnmRZn5tXK1bVRNM1bb1CxiwtksDwEnRMQzy1nmTqa4ruMy4IyyzBnAN8rHlwFviYhnRMRzKCYLuKEchrMjIk4o63l7yz7SlGTmeuDhiHheuepk4E5sp5ohMvN9mTmQmSsoJlr5bmb+AbZRzRBle/occFdmfrRlk21UTXMjcFREPCci9qP4nXtZj2PSNJvb6wDUvcy8PiL+BfgxxQx2PwFWAQcAl0TEmRRJ6ZvK8ndExCUU/+wPAX+SmXvK6v4v4AvA/sC3ykWqyp8CF5V/XO4H/pDiCy7bqWayc7CNamY4EXgbcFtE3FKuez+2UTVMZg5FxLuA71DcXeH8zLyjx2FpmkUxaZkkSZIkSdVySK0kSZIkqRYmnJIkSZKkWphwSpIkSZJqYcIpSZIkSaqFCackSZIkqRYmnJIkSZKkWphwSpIkSZJqYcIpSZIkSarF/w9B0f9Gz8nNxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the vertices for cross-section lines in a shapefile\n",
    "fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
    "\n",
    "# Set up the figure\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
    "\n",
    "# plot head values along the cross sectional line\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]}, geographic_coords=True)\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(lw=0.5)\n",
    "cb = fig.colorbar(csa, ax=ax, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Cross Sections with MODFLOW-6 models\n",
    "\n",
    "`PlotCrossSection` has support for MODFLOW-6 models and operates in the same fashion for Structured Grids, Vertex Grids, and Unstructured Grids. Here is a short example on how to plot with MODFLOW-6 structured grids using a version of the Freyberg model created for MODFLOW-6|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 dis...\n",
      "    loading package ic...\n",
      "WARNING: Block \"options\" is not a valid block name for file type ic.\n",
      "    loading package oc...\n",
      "    loading package npf...\n",
      "    loading package sto...\n",
      "    loading package chd...\n",
      "    loading package riv...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "  loading ims package gwf_1...\n",
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing ims package gwf_1...\n",
      "  writing model gwf_1...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package oc...\n",
      "    writing package npf...\n",
      "    writing package sto...\n",
      "    writing package chd_0...\n",
      "    writing package riv_0...\n",
      "    writing package wel_0...\n",
      "    writing package rch_0...\n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.2.0 10/22/2020\n",
      "\n",
      "   MODFLOW 6 compiled Oct 29 2020 12:19:52 with IFORT compiler (ver. 19.10.3)\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:25:43\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:25:43\n",
      " Elapsed run time:  0.095 Seconds\n",
      " \n",
      "\n",
      "WARNING REPORT:\n",
      "\n",
      "  1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
      "  2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
      " Normal termination of simulation.\n",
      "Output file located: freyberg.hds\n",
      "Output file located: freyberg.cbc\n"
     ]
    }
   ],
   "source": [
    "# load the Freyberg model into mf6-flopy and run the simulation\n",
    "sim_name = 'mfsim.nam'\n",
    "sim_path = os.path.join(\"..\", \"data\", \"mf6-freyberg\")\n",
    "sim = flopy.mf6.MFSimulation.load(sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
    "                                  sim_ws=sim_path)\n",
    "\n",
    "newpth = os.path.join('data')\n",
    "sim.set_sim_path(newpth)\n",
    "sim.write_simulation()\n",
    "success, buff = sim.run_simulation()\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": [
    "### Plotting boundary conditions and arrays\n",
    "\n",
    "This works the same as modflow-2005, however the simulation object can host a number of modflow-6 models so we need to grab a model before attempting to plot with `PlotCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the modflow-6 model we want to plot\n",
    "ml6 = sim.get_model('gwf_1')\n",
    "\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "# plot boundary conditions\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
    "patches = xsect.plot_bc('WEL_0', color='pink')\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Boundary Conditions')\n",
    "\n",
    "# plot xxxx\n",
    "ax = fig.add_subplot(2, 1, 2)\n",
    "# plot the horizontal hydraulic conductivities\n",
    "a = ml6.npf.k.array\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
    "csa = xsect.plot_array(a)\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting specific discharge with a MODFLOW-6 model\n",
    "\n",
    "MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting. `PlotCrossSection` has the `plot_specific_discharge()` method to use this data. The specific discharge array is stored in the cell budget file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the specific discharge from the cell budget file\n",
    "cbc_file = os.path.join(newpth, \"freyberg.cbc\")\n",
    "cbc = flopy.utils.CellBudgetFile(cbc_file, precision='double')\n",
    "spdis = cbc.get_data(text='SPDIS')[-1]\n",
    "\n",
    "# get the head from the head file\n",
    "head_file = os.path.join(newpth, \"freyberg.hds\")\n",
    "hds = flopy.utils.HeadFile(head_file)\n",
    "head = hds.get_alldata()[0]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.set_title('plot_array() and plot_discharge()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, ax=ax, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[1e30], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_specific_discharge(spdis, head=head, \n",
    "                                       hstep=2, normalize=True, color='green', \n",
    "                                       scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                       zorder=10)\n",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertex cross section plotting with MODFLOW-6 (DISV) \n",
    "\n",
    "FloPy fully supports vertex discretization (DISV) plotting through the `PlotCrossSection` class. The method calls are identical to the ones presented previously for Structured discretization (DIS) and the same matplotlib keyword arguments are supported. Let's run through an example using a vertex model grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing ims package ims...\n",
      "  writing model mp7p2...\n",
      "    writing model name file...\n",
      "    writing package disv...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package wel_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 1 based on size of stress_period_data\n",
      "    writing package rcha...\n",
      "    writing package riv_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n",
      "    writing package oc...\n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.2.0 10/22/2020\n",
      "\n",
      "   MODFLOW 6 compiled Oct 29 2020 12:19:52 with IFORT compiler (ver. 19.10.3)\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:25:45\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:25:45\n",
      " Elapsed run time:  0.131 Seconds\n",
      " \n",
      "\n",
      "WARNING REPORT:\n",
      "\n",
      "  1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'mp7p2.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
      "  2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'mp7p2.ims' WAS DEPRECATED\n",
      "     IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
      " Normal termination of simulation.\n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mp7\n",
      "\n",
      "MODPATH Version 7.2.001   \n",
      "Program compiled Oct 29 2020 12:30:37 with IFORT compiler (ver. 19.10.3)        \n",
      " \n",
      " \n",
      "Run particle tracking simulation ...\n",
      "Processing Time Step     1 Period     1.  Time =  1.00000E+03  Steady-state flow                                                    \n",
      "\n",
      "Particle Summary:\n",
      "         0 particles are pending release.\n",
      "         0 particles remain active.\n",
      "        16 particles terminated at boundary faces.\n",
      "         0 particles terminated at weak sink cells.\n",
      "         0 particles terminated at weak source cells.\n",
      "         0 particles terminated at strong source/sink cells.\n",
      "         0 particles terminated in cells with a specified zone number.\n",
      "         0 particles were stranded in inactive or dry cells.\n",
      "         0 particles were unreleased.\n",
      "         0 particles have an unknown status.\n",
      " \n",
      "Normal termination.                                                        \n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mp7\n",
      "\n",
      "MODPATH Version 7.2.001   \n",
      "Program compiled Oct 29 2020 12:30:37 with IFORT compiler (ver. 19.10.3)        \n",
      " \n",
      " \n",
      "Run particle tracking simulation ...\n",
      "Processing Time Step     1 Period     1.  Time =  1.00000E+03  Steady-state flow                                                    \n",
      "\n",
      "Particle Summary:\n",
      "         0 particles are pending release.\n",
      "         0 particles remain active.\n",
      "       416 particles terminated at boundary faces.\n",
      "         0 particles terminated at weak sink cells.\n",
      "         0 particles terminated at weak source cells.\n",
      "         0 particles terminated at strong source/sink cells.\n",
      "         0 particles terminated in cells with a specified zone number.\n",
      "         0 particles were stranded in inactive or dry cells.\n",
      "         0 particles were unreleased.\n",
      "         0 particles have an unknown status.\n",
      " \n",
      "Normal termination.                                                        \n",
      "Output file located: mp7p2.hds\n",
      "Output file located: mp7p2.cbb\n"
     ]
    }
   ],
   "source": [
    "# build and run vertex model grid demo problem\n",
    "sys.path.append(os.path.join(\"..\", \"common\"))\n",
    "import setup_pmv_demo\n",
    "setup_pmv_demo.run()\n",
    "\n",
    "# check if model ran properly\n",
    "modelpth = os.path.join(\"data\", \"mp7_ex2\", \"mf6\")\n",
    "files = ['mp7p2.hds', 'mp7p2.cbb']\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": "code",
   "execution_count": 24,
   "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",
      "    loading package npf...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "    loading package riv...\n",
      "    loading package oc...\n",
      "  loading ims package mp7p2...\n"
     ]
    }
   ],
   "source": [
    "# load the simulation and get the model\n",
    "vertex_sim_name = \"mfsim.nam\"\n",
    "vertex_sim = flopy.mf6.MFSimulation.load(sim_name=vertex_sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
    "                                         sim_ws=modelpth)\n",
    "vertex_ml6 = vertex_sim.get_model(\"mp7p2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a line based cross section through the model grid\n",
    "\n",
    "Because Vertex model grids have no row or column number, cross sections through the model grid must be defined by a set of vertices and passed to the `PlotCrossSection` class using the parameter `line={\"line\": [(), (), ...]`. Below we show an example of setting up a cross sectional line with a MODFLOW-6 vertex grid model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = np.array([(4700, 0), (4700, 5000), (7250, 10500)])\n",
    "\n",
    "# Let's plot the model grid in map view to look at it\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
    "\n",
    "# use PlotMapView to plot a DISV (vertex) model\n",
    "mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
    "linecollection = mapview.plot_grid()\n",
    "\n",
    "# plot the line over the model grid\n",
    "lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot a cross section of the model grid defined by this line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the PlotCrossSection class\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={'line': line})\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",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Arrays and Contouring with Vertex Model grids\n",
    "\n",
    "`PlotCrossSection` allows the user to plot arrays and contour with DISV based discretization. The `plot_array()` method is called in the same way as using a structured grid. The only difference is that `PlotCrossSection` builds a matplotlib patch collection for Vertex based grids. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the head output for stress period 1 from the modflow6 head file\n",
    "head = flopy.utils.HeadFile(os.path.join(modelpth, 'mp7p2.hds'))\n",
    "hdata = head.get_alldata()[0, :, :, :]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array()\")\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `contour_array()` method operates in the same way as the sturctured example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(329, 337, 1)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"contour_array() with a multi-layer vertex model\")\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "\n",
    "contour_set = xsect.contour_array(hdata, levels=levels, colors='k')\n",
    "plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
    "\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting specific discharge vectors for DISV\n",
    "MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting. `PlotCrossSection` has the `plot_specific_discharge()` method to use this data. The specific discharge array is stored in the cell budget file.\n",
    "\n",
    "**Note**: When plotting specific discharge, an arbitrary cross section cannot be used. The cross sectional line must be orthogonal to the model grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define and plot our orthogonal line\n",
    "line = np.array([(0, 4700), (10000, 4700)])\n",
    "\n",
    "# Let's plot the model grid in map view to look at it\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
    "\n",
    "# use PlotMapView to plot a DISV (vertex) model\n",
    "mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
    "linecollection = mapview.plot_grid()\n",
    "\n",
    "# plot the line over the model grid\n",
    "lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot specific discharge on cross section\n",
    "cbb = flopy.utils.CellBudgetFile(os.path.join(modelpth, 'mp7p2.cbb'), precision='double')\n",
    "spdis = cbb.get_data(text=\"SPDIS\")[-1]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "quiver = xsect.plot_specific_discharge(spdis, head=head, \n",
    "                                       hstep=3, normalize=True, color='green', \n",
    "                                       scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                       zorder=10)\n",
    "\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrates some of the plotting functionality available with flopy.  Although not described here, the plotting functionality tries to be general by passing keyword arguments passed to the `PlotCrossSection` methods down into the `matplot.pyplot` routines that do the actual plotting.  For those looking to customize these plots, it may be necessary to search for the available keywords by understanding the types of objects that are created by the `PlotCrossSection` methods.  The `PlotCrossSection` methods return these matplotlib.collections objects so that they could be fine-tuned later in the script before plotting.\n",
    "\n",
    "Hope this gets you started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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