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": [
      "3.8.11 (default, Aug  6 2021, 08:56:27) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.5\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.isdir(modelpth):\n",
    "    os.makedirs(modelpth, exist_ok=True)"
   ]
  },
  {
   "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": [
      "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): 2021/10/25 16:43:38\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): 2021/10/25 16:43:38\n",
      " Elapsed run time:  0.015 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(\n",
    "    \"freyberg.nam\", model_ws=loadpth, exe_name=exe_name_2005, version=v2005\n",
    ")\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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\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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\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(\n",
    "    \"Column 6 Cross-Section with Horizontal hydraulic conductivity\"\n",
    ")\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(\n",
    "    \"Column 6 Cross-Section contour_array() horizontal hydraulic conductivity\"\n",
    ")\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=[999.0], 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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ompGZM3ruFxFrApcDR2fmkogYA6wH7Aa8Gbg0IrbKzOztPFWTyojoAm4BVi33/1FmTouI9YEfApOAecBHMnNR1XcmSZIkSarbs8+9wB03H1N1vzFrH/1SZk7ub5+IGEuRUF6YmVeUxfOBK8ok8tcRsRzYEHimtzpqGf76MrBnZr4J2BHYOyJ2A44FbszMbYAby3VJkiRJ0hBKkuU1LNVERABnA3My89SKTVcBe5b7vB5YBVjYVz1Vk8os/LFcHVsuSTEb0MyyfCbwvqpRS5IkSZIGLWv4rwa7AwcDe0bE3eXyLuAcYKuIuBe4BJjS19BXqPGeyogYDdwJbA2cmZl3RMQm5WxBZOaCatPMSpIkSZIaIGF53zle7dVk3gpEH5s/Vms9Nc3+mpmvZuaOwObArhGxQ60niIjDImJWRMxatmxZrYdJkiRJknqRNS7NMqBHimTm88BNwN7AUxExDqD82euzSzJzRmZOzszJq6+++uCilSRJkiQ15J7KRqmaVEbERhGxbvl6NeAdwO+Ba4Ap5W5TgKuHKEZJkiRJUoV2SipruadyHDCzvK9yFHBpZl4bEbdTPK/kEOBR4MNDGKckSZIkieYPb62malKZmbOBnXopfxbYayiCkiRJkiT1raOSSkmSJElS+0jg1TbKKk0qJUmSJKnDtFFOaVIpSZIkSZ3GpFKSJEmSVLflRKtDeI1JpSRJkiR1kASWp0mlJEmSJKlOy1sdQAWTSkmSJEnqMOnwV0mSJElSPRJ41aRSkiRJklSfIL2nUpIkSZJUL4e/SpIkSZLqkvhIEUmSJEnSIJhUSpIkSZLqYk+lJEmSJKl+6UQ9kiRJkqRBcKIeSZIkSVJdiudUjmp1GK8xqZQkSZKkDuPwV0mSJElSncKJeiRJkiRJ9ctWB1DBpFKSJEmSOkgCr6b3VEqSJEmS6uTsr5IkSZKkuplUSpIkSZLqkk7UI0mSJEkajOU+UkSSJEmSVC+Hv0oDsHzpt1odgiRJktQ2Ehz+qvbxytLTG1qfCaAkSZI01IJ0+OvI8eKSUxta35+WntbQ+iRJkiR1nhHdU7l0ySltXd8LS77R0PokSZIkqZEy22uinlHVdoiICRHxvxExJyLui4hPl+XTI+LxiLi7XN419OFKkiRJkpKoujRLLT2VrwDHZOZdEbEWcGdE3FBuOy0zG9tVKEmSJEnqVzvN/lq1pzIzF2TmXeXrpcAcYPxQByZJkiRJWlkSvJrVl2r6GpVasf1zEZERsWF/9VRNKntUOgnYCbijLDoyImZHxDkRsd5A6pIkSZIk1adBw1+7R6VuB+wGHBER20ORcAL/ADxarZKak8qIWBO4HDg6M5cA3wVeB+wILAB6neEmIg6LiFkRMWvZsmW1nk6SJEmS1IdGJJVVRqWeBnye4rGY/aopqYyIsRQJ5YWZeUV50qcy89XMXA6cBezaR6AzMnNyZk5effXVazmdJEmSJKkfmdWXgagclRoR7wUez8zf1nJs1Yl6IiKAs4E5mXlqRfm4zFxQrr4fuHdgYUuSJEmSBiqB5VlT/+CGETGrYn1GZs7ouVPlqFSKIbH/AfxjrfHUMvvr7sDBwD0RcXdZdhxwQETsSPGe5gGH13pSSZIkSVL9lte228LMnNzfDj1HpUbEXwNbAr8t+hfZHLgrInbNzCd7q6NqUpmZt0KvA3J/XO1YSZIkSVKjNeY5lL2NSs3Me4CNK/aZB0zOzIV91TOg2V8lSZIkSa2VQGZUXWrQPSp1z4i4u1zeNdB4ahn+KkmSJElqI8sb0FPZz6jUyn0mVavHpFKSJEmSOklSa09kU5hUSpIkSVKHGeATQ4aUSaUkSW3s6sevbHUIkqQ2k9CQiXoaxaRSkqQR5PL517Q6BEnSoAXLHf4qSZKGg0sfvbbVIUjSiOQ9lZIkSb24+JHrWh2CJHUE76mUJElqggvn/bjVIVR1/tyftDoESR3GeyolSZI61HkP/7TVIUgSgPdUSpIkaWic/dANrQ5BUhNkG41/NamUJEkawU4+8HMAfP6iU1ociaSBcPirJEmSJKkumT5SRJIkSR3iO/f/vNUhSOpFG41+NamUJEmSpE7jcyolSZIkSXVJ7KmUJEmSJA2C91RKkiRpRDp99i9aHYI0TLRPUjmq1QFIkiRJA3XGIZ/i3HPPbXUYUstkVl+axZ5KSZIkSeowPqdSkiRJaoDTZt3W6hCkpkua2xNZjUmlJEmSJHWUsKdSkiRJklSnJt8zWY1JpSRJkiR1GHsqJUmSpDb0zdt+2eoQpJrYUylJkiRJql/aUylJkiQNK98+8kgAjjrjjBZHouEuy6VdmFRKkiRJUodx+KskSZIkqW7p8FdJkiRp+DvjZ7e3OgRpyI2qtkNETIiI/42IORFxX0R8uixfPyJuiIgHyp/rDX24kiRJkqQkqi7NUjWpBF4BjsnM7YDdgCMiYnvgWODGzNwGuLFclyRJkiQNpaxxaZKqSWVmLsjMu8rXS4E5wHhgP2BmudtM4H1DFKMkSZIkqUJm9aVZBnRPZURMAnYC7gA2ycwFUCSeEbFx48OTJEmSJK0oyqU91DL8FYCIWBO4HDg6M5cM4LjDImJWRMxatmxZPTFKkiRJ6sMee+zBHnvs0eow1GxtNPy1pp7KiBhLkVBemJlXlMVPRcS4spdyHPB0b8dm5gxgBsBmm23WRk9TkSRJkjrLf/33yrPJPvHskj63aXhqcs5YVS2zvwZwNjAnM0+t2HQNMKV8PQW4uvHhSZIkSZJWklF9aZJaeip3Bw4G7omIu8uy44CvAZdGxCHAo8CHhyRCSZIkSdKK2qirsmpSmZm30vddoHs1NhxJkiRJUjWNyCkjYgJwPrApsByYkZnfjIivA/sCfwIeAj6Rmc/3VU/NE/VIkiRJktpA455T+QpwTGZuB+wGHBER2wM3ADtk5huBPwBf6K8Sk0pJkiRJ6jhRw9K/zFyQmXeVr5cCc4DxmXl9Zr5S7vYrYPP+6jGplCRJkqRO0+BHikTEJGAn4I4em/4Z+J/+jq3pkSKSJEmShr/u513edNNNLY1DDbNhRMyqWJ9RPvJxBRGxJsUjJI/OzCUV5f9BMUT2wv5OYlIpSZIkjVDnnX/rCutPPrW413K1odp6Ihdm5uT+doiIsRQJ5YWZeUVF+RTgPcBemdnv2UwqJUmSJKnTNOA5lBERwNnAnMw8taJ8b2Aq8LbMXFatHpNKSZIkSRqZdgcOBu6JiLvLsuOAbwGrAjcUeSe/ysxP9lWJSaUkSZIkdZoGPKgyM2+l92lifzyQepz9VZIkSZJUN3sqJUmSJKmT1PHIkKFkUilJkiRpSPiIkiFkUilJkiRJqt/gZ39tFJNKSZIkSQ1xwTevX2H9qfmLei3X4EUb9VQ6UY8kSZIkqW72VEqSJElSp7GnUpIkSZI0HNhTKUmSJEmdpo16Kk0qJUmSJKmDRDpRjyRJkiTVbY899njtGZhqPXsqJUmSJLWlqPIoxmrbh7U26qk0qZQkSZKkjhKQ7ZNRO/xVkiRJklQ3eyolSZIkqcM4UY8kSZIkaViwp1KSJEmSOk0b9VSaVEqSJElSJ0lMKiVJkiRJ9WufuV9NKofcyactbXUIkiRJkjRkTCqH2LRp0xpW1/HHH9+wuiRJkiR1MIe/SpIkSZLq1kZJZdVHikTEORHxdETcW1E2PSIej4i7y+VdQxumJEmSJKkd1fKcyvOAvXspPy0zdyyXHzc2LEmSJElSXyKrL81SNanMzFuA55oQiyRJkiSpFlnD0iS19FT25ciImF0Oj12vr50i4rCImBURs5YtWzaI00mSJEmS2k29SeV3gdcBOwILgG/0tWNmzsjMyZk5efXVV6/zdJIkSZIkoLZeyib2VNY1+2tmPtX9OiLOAq5tWERqqq+curjVIVR1wqmLWh2CJEmS1Fai1QFUqCupjIhxmbmgXH0/cG9/+6t9dcJzNDshRkmSJKmp2uiRIlWTyoi4GNgD2DAi5gPTgD0iYkeKtzIPOHzoQpQkSZIkraCTksrMPKCX4rPrOdmoUaM49bQX6jm0aU457Y8Nq6urq6thdUmSJElSO6pr+Gu9Nt1007YfytjI+CRJkiSp0YLmPoeymqYmlZIkSZKkBmijpHIwz6mUJEmSJI1wHd1T2dXV1dB7NL0HUpIkSZIGpqOTyqlTp7Y6hKbq6uriq6cuaWh9kiRJkjpMek+l6jTSkmhJkiRJfWhAUhkRE4DzgU2B5cCMzPxmRKwP/BCYRPEIyY9k5qK+6vGeSkmSJEkamV4BjsnM7YDdgCMiYnvgWODGzNwGuLFc75NJpSRJkiR1mqxhqVZF5oLMvKt8vRSYA4wH9gNmlrvNBN7XXz0Of5UkSZKkDhO17bZhRMyqWJ+RmTN6rS9iErATcAewSWYugCLxjIiN+zuJSaUkSZIkdZra7qlcmJmTq+0UEWsClwNHZ+aSiBpT1pLDXyVJkiSp0zRg+CtARIylSCgvzMwryuKnImJcuX0c8HR/dZhUSpIkSdIIFEWX5NnAnMw8tWLTNcCU8vUU4Or+6nH4qyRJkiR1ksY9p3J34GDgnoi4uyw7DvgacGlEHAI8Cny4v0pMKiVJkiSpoyTk4LPKzLyVvuf82avWehz+KkmSJEmqmz2VkiRJktRhGjT8tSHsqZQkSZIk1c2eSkmSJEnqNG3UU2lSKUmSJEkdJHD4qyRJkiRpmLCnUg3T1dXFCacuanidkiRJkiokDn/V8DR16tRWhyBJkiSNCO00/NWkUpKkNnb392e3OgRJkvplUilJUhubNm1aw+o6/vjjG1aXJKnF7KmUJEmSJNXL4a+SJEmSpPpl+2SVPlJEkiRJklQ3eyolSZIkqZP4SBFJkiRJ0mC00z2VVYe/RsQ5EfF0RNxbUbZ+RNwQEQ+UP9cb2jAlSZIkSe2olnsqzwP27lF2LHBjZm4D3FiuS5IkSZKaIWtYmqRqUpmZtwDP9SjeD5hZvp4JvK+xYUmSJEmS+hI1LM1S7z2Vm2TmAoDMXBARGzcwJkmSJElSf5a3z02VQ/5IkYg4LCJmRcSsZ555ZqhPJ0mSJElqonqTyqciYhxA+fPpvnbMzBmZOTkzJ2+00UZ1nk6SJEmSBNR2P2U73VPZh2uAKeXrKcDVjQlHkiRJktSfWu6nbOY9lbU8UuRi4HZg24iYHxGHAF8D/iEiHgD+oVyXJEmSJDVDZvWlSapO1JOZB/Sxaa8GxyJJkiRJqkX7zNNT9+yvkiRJkqQWCZNKSZIktYPPX3RKq0OQ1OFMKiVJkiSpkyRNvWeyGpNKSZIkSR3mplYH0Hrtk1OaVEqSJKnzHHn2t1h85c9bHYbUMt5TKUmSJKntHPuFb7c6BNUkaaeuSpNKSZIkSeo07ZNTMqrVAUiSJKl1nnjgkV7LX1i8lGXLljU5Gkk1y6y+NIlJpSRJ0gg27d2Hc8U3zl2p/PH753LllVe2ICJJVWWNS5OYVEqSJI1gm2w5nofvnsNlX5uxQvnrd30jixYtalFUGojPnngmnz3xzFaH0WSf7qP8l8C/NzOQlokalmYxqZQkSRrBxq6yCp/+/peZf/88Lpz2bbJiyNzo0aNbGFnfnnxoXq/ly5Ys4YXFS5objFrkij7KtwAub2YgrePwV0mSJLWLsauuwqfOOoHnn3mOU//pWO786a1cdtJZrL/++q0OrVcnf+ifuO7bZ61UvuDBh7lo2gktiGj4uPCCb/Za/sAD9/DDS9qpN/QpYMtelrcC81oXVjM1YPhrRJwTEU9HxL0VZTtGxK8i4u6ImBURu1arx6RSkiRpBNto4jgARo8ZzRHfmcZO79idmy+6lheeX8q+++7b4uh6t9EWm/PI7Pu4/vrrVyh/3c478uzjT7QoquHhzjtv6bV8ww025c5ZNzc5mv5sCFzXy3ItcG8/xw0jjempPA/Yu0fZycDxmbkj8MVyvV8+UkSSJGkE+9iXjuLlZS+y6uqrAbDnwe9lz4PfC8D8C37ZytD6NHaVVTn0jJP43gGf5PKTvsEHPv9ZIoo7yMaMHdvi6DrbksXP8e/HfGSl8ghYuPDJAdf3H0d9vRFh9WIDYPshqnvkyMxbImJSz2Jg7fL1OkDVb2pMKiVJkkawMw6fzie//f9eSyq7vfTHZVxyySUctf9bWxRZ/8auuioHHnggV/3iZr535Gf4uw++n0fuvY8NJ2ze6tA62lprrcPRnz2p1WHU4J5+tj1CcW/lMDd090weDfw0Ik6hGNn6d9UOcPirJEnSCPanl15m/XEbvbY+87jTAOhac3Wef/75FkXVvw02L4fsjh7NJ75+Iju87a388oqrWLZkCR/+wsiY+bPSgkfn9lr+wh+X8MLSgU1ctMaa6zB+/JZ9Lp3hfa0OYOjV/kiRDcv7IruXw2qo/V+Bz2TmBOAzwNnVDjCplCRJGsG6h412u+/WO1sUSe32OeJfVlh/y0c+yCfPOI33fvqIYpzmCHPCpw7m6h/810rlT8x7iPNOO35AdX35KzNXWH/xxReYO/f3LHth6aBibLy/Ak4EFvSybfi3gSCJrL4ACzNzcsUyo1rdwBT+Mr3uZUDViXoc/ipJkjSMDPQ+yDEvLefG/zyXbbfdltmzZ/PqCy9z7ee+Q1dXF6uuuuoQRTk4J39wCnsd8jHesvnrVih/4oGH+Pn5F3Lo6UN1H1//jjrjjJacd5PNJjD3/vu4/Nwz+OAnjnytfJsdduKi71SdY2UF555zEh/44KGss876/O53d/Ld70xjnXU2YPHiZ/mXfzmON+1YdSRkk3ybogPt9cDfA58A9gO8p7YBngDeBtwE7Ak8UO0Ak0pJkqRhZNq0aQPaf7/99mPfffflqquu4u1vfzu33347U6ZM4dVXX2WfffYZoigHZ6NJE3hk9n0s+91DfOjd73it/HU778hlX21NQtlKY8auwhFfPIX/+sqxXPxfp7D/4cdUTFy0yoDqevCBe1hnneJRMlddeQ7HHPMNJm25LU8/9TjfOfOLbZRU/kO5LAIupOi1/DfgoLJsBFg++HsqI+JiYA+KYbLzgWnAocA3I2IM8BJQdcisSaUkSdIItuOOO/LYY4+xaNEi1ltvPQB+/etfA3D88QMbOtkszv66srGrrMq/ffHrfP+k/+RbXzyav9/n/cy9/z42Gjd+QPW88uqrLF++nFGjRvHKn//MpC23BWDjTcazfPmrA47rB1d8l4M/8K8rlf9h7n3cOfuXHLDfof0eP316rW1wPxYsWMBvfvNL7rnnKaZOXfm46dNrrKpjDD6pzMwD+ti0y0DqMamUJEnD1oMz/6/VIXSM7oSyU3TP/nrZZZdx5v5TmDx5MvPnz2edMWNZct3PWh1eU224aZE4jh49hsOP+yo3XXc5v/jJlay/8aYc+G9TB1TXDju8mfPOPZkPffhw/vqNf8PPbvgRf/t372T2b29njTXWrl5BD7Nm39prUrnhepvwf7NvrZpUDrTnvT/Th1tWOWSTvw6cSaUkSRq2GvkHabv22g3Wu9/9bq677rpWhzEglbO/zpkzhzPPPJOrr76a7bbbjpNOOokNNtigrno79Rof/oUTAVi6eBGLFj7NVn+1A7u8ZU/WWqf6FwWPzL1xhfW/+9s3cOONN3LMZz7A2LFjeemll7jwgm+y9dZb8573vGel/atZvPR5PvOlj69UHhEsfO6pAdWlCslQPlJkwEwqJUmSatDV1cUjP/hVq8NouBNOOKGp51t85c8HXccH3roni6/8OV1dXQAcccQRHHHEEYOut1M9Of8RLv7u15k/9wHW3aB4PMzzzz7DhK1ez4H/9nk23mxCn8f29sXLCSecwIsvvsjDDz/Mn//8ZyZOnMj6669fNY7ekvK11libzx3W+DbWiV+GNJxJpSRJUmeZOnVgwwhr0Q49YzvvvHNTz9fI3uN28uTcuWy65crPcVy2dAmZsMbaAx86WquZp3+Zt7/nQ7z5bf/42r2lmcmsW25g5ukn8O8n1/IUiRWtttpqvOENbxh0bGuusTbjN91i0PX01OwvQ9pPmlRKkiSpPSxfvpwf/OAHXHbZZTz66KMATJw4kY9+9KOsssoqPHPJLxp2ru6excFqx16qk6Z8nHcc9DHeffjhK5Q/MfchbvjhBfzrV77RkPM8ddf1K5UtffZJtlg7ePo3N6xQPnEtWPzMgl6Pgd6vR6M/269O/R6Lly7iuecXssX4rRg1anRD6m32lyHqn0mlJEnSCHbkkUcyd+5cDj30ULbYouhReuSRRzjrrLPYaqutOPPMM1sc4crasZdqowkTmPe7+7j6zDPY74i/PCty6zfuxCWnD+xZkf3praf3Jz/5Ca973es48MADGTVqFFB8WXDxxRez7bbbDqh3uNGf7S/v/DkzLzuD9dbdkHXXXp9937E/Z848keX5Kocf9O/s9Ibd6qq3vy9DDjrooNc+h2HNnkpJkqSV/eG8WQ2rq1G9YsPd9ddfzwMPPPDasEmAXXbZhQ984ANsvfXWLYysb+3YSzV2lVU47OSvc/YXvsCPvvENPvjZz762baDPihyo888/n09+8pMcddRRbLbZZgA88cQT7Lzzzpx//vkDqqvRn+21P7uU6Z/9FuM23pwH583h1LOm8Zl/mc7qq63Jdy84qe6ksr8vQ371q1+15ZchDWdSKUmStLLher9dO4sIFi5cyEYbbbRC+cKFC9u2t6dde6nGrroqh558MjO/+J989zNHs+NWW/Hs7x5go/EDe1bkQG2zzTbceOONPPvsszz22GMATJgwoa5ZcBv92Y4aNYpxG28OwNaTtmPVVbrYZsvti42DSIo68cuQhnL2V0mSJLWLY489ll122YX99tuPiRMnAvDoo49y1VVXte1z/dqxl2qDsodw9Jgx/POJX+UXl1/OrKuvYpNt38D+Rzd+kqdeY9hgg7ofp9JtKD7bx598lPGbTuQPc+/jpT+9yO8fuoc1VluTHERS1IlfhgxnJpWSJEkj2CGHHMJee+3F5Zdf/lov16RJk7jtttteSzLbTTv2Uv3zV05cYf2tH/wgf73O2myw+z8O+bkbOblOoz/bD+zzcb78rc+y9lrr8fKfXuKYQ09gxkVfZ9Hi5zj8oM/VHWcnfhnSSEmSy5e3OozXDCqpjIh5wFLgVeCVzJzciKAkSZLUPJMmTeKYY45pdRg1a8deqitOP50PHH30SuUP33cPs2+7mfcdduTKBzVIIyfXafRnu/MOu3HK/zuPp59dwGabTGDVVbo4+bizBx1nJ34Z0nDtM/q1IT2Vb8/MhQ2oR5IkSaqqHXupZt9yc69J5fqbbMrdv7hpSJPKRk6uMxSf7Rqrr8mWq2/TsBi7ddqXIY3XPlmlw18lSRohurq6uPfs37Q6DFXo6upi7vl3NLS+kaAde6mWPvccX/rwh1YoW/7ii4zuWp3nnlowpOdu5OQ6g/lsu7q6eOj521cou/DCCznooIMG+I7+Up/6MYwm6kng+ohI4HuZOaMBMUmSpCEwdWpjJws5/vjjG1rfSNToazKStFsv1RrrrMNhJ399hbIlt/+SdXfefcjP3ejJder9bHtrz/vuu29bPgKm4yWwfPgklbtn5hMRsTFwQ0T8PjNvqdwhIg4DDgNGzvhmSZIkdZyuri6e/9kN9R07ejRdDz24YtnGG7PBFls2IrR+tePERd1MKIfSMEkqM/OJ8ufTEXElsCtwS499ZgAzACZPntw+71ySJEmqMJie496esXrSSSfx7G3XDyakFfQ1HLQdJy7q1q7PFB0W2iizqjupjIg1gFGZubR8/Y/AlxoWmSRJktTBmjW8uR0nLurWjs8UHR5y2NxTuQlwZdnNPga4KDN/0pCoJEmSpA7SyGdFDlQ7TlzUrZ2H5na6zGHwnMrMfBh4UwNjkSRJkjpSI58VWY92m7ioWzsPze1oyfAY/ipJkiSp4IQ0vWvnobkdb5gMf5UkSSNYV1cXc869s6H1SZ3KCWl6185DczvZvAUP/3TK8R/dsIZdFw55MJhUSpKkOvmMRekvnJCmb+06NLeTZeberY6hkkmlJEmSNEhOSKORbGT2w0uSJEkN1D0hTU9OSKORwJ5KSZIkaZCckEYjmUmlJEmSNEhOSKORzKRSkiRJagAnpNFI5QBvSZIkSVLdTColSZIkSXUzqZQkSZIk1c2kUpIkSZJUN5NKSZIkSVLdTColSZIkSXUzqZQkSZIk1c2kUpIkSZJUN5NKSZIkSVLdTColSZIkSXUzqZQkSZIk1c2kUpIkSZJUN5NKSZIkSVLdTColSZIkSXUzqZQkSZIk1c2kUpIkSZJUN5NKSZIkSVLdTColSZIkSXUzqZQkSZIk1c2kUpIkSZJUN5NKSZIkSVLdBpVURsTeEXF/RDwYEcc2KihJkiRJUmeoO6mMiNHAmcA+wPbAARGxfaMCkyRJkiS1v8H0VO4KPJiZD2fmn4BLgP0aE5YkSZIkqRMMJqkcDzxWsT6/LJMkSZIkjRCRmfUdGPFh4J2Z+S/l+sHArpl5VI/9DgMOA5g4ceIujzzyyOAilgbhpJNO4qWXXmp1GJJUk66uLqZOndrqMCRpWIiIOzNzcqvjGI7GDOLY+cCEivXNgSd67pSZM4AZAJMnT64vg5UaxD/OJEmSpMYaTE/lGOAPwF7A48D/AQdm5n39HLMUuL+uE0qNsSGwsNVBaMSy/anVbINqNdugWmnbzFyr1UEMR3X3VGbmKxFxJPBTYDRwTn8JZel+u5zVShExyzaoVrH9qdVsg2o126BaKSJmtTqG4Woww1/JzB8DP25QLJIkSZKkDjOY2V8lSZIkSSNcs5PKGU0+n9STbVCtZPtTq9kG1Wq2QbWS7W+I1D1RjyRJkiRJDn+VJEmSJNWtKUllROwdEfdHxIMRcWwzzqmRISImRMT/RsSciLgvIj5dlq8fETdExAPlz/UqjvlC2Rbvj4h3VpTvEhH3lNu+FRHRivekzhMRoyPiNxFxbblu+1PTRMS6EfGjiPh9+W/h39oG1UwR8Zny/8H3RsTFEdFlG9RQiohzIuLpiLi3oqxhbS4iVo2IH5bld0TEpKa+wQ405EllRIwGzgT2AbYHDoiI7Yf6vBoxXgGOycztgN2AI8r2dSxwY2ZuA9xYrlNu2x94A7A38J2yjQJ8FzgM2KZc9m7mG1FH+zQwp2Ld9qdm+ibwk8z8K+BNFG3RNqimiIjxwKeAyZm5A8Vj5vbHNqihdR4rt49GtrlDgEWZuTVwGnDSkL2TYaIZPZW7Ag9m5sOZ+SfgEmC/JpxXI0BmLsjMu8rXSyn+mBpP0cZmlrvNBN5Xvt4PuCQzX87MucCDwK4RMQ5YOzNvz+JG4/MrjpH6FBGbA+8Gvl9RbPtTU0TE2sDfA2cDZOafMvN5bINqrjHAahExBlgdeALboIZQZt4CPNejuJFtrrKuHwF72XPev2YkleOBxyrW55dlUkOVQxN2Au4ANsnMBVAknsDG5W59tcfx5eue5VI1pwOfB5ZXlNn+1CxbAc8A55ZDsL8fEWtgG1STZObjwCnAo8ACYHFmXo9tUM3XyDb32jGZ+QqwGNhgyCIfBpqRVPaW1TvlrBoqItYELgeOzswl/e3aS1n2Uy71KSLeAzydmXfWekgvZbY/DcYYYGfgu5m5E/AC5ZCvPtgG1VDlfWv7AVsCmwFrRMTH+juklzLboIZSPW3O9jhAzUgq5wMTKtY3pxgWITVERIylSCgvzMwryuKnymENlD+fLsv7ao/zy9c9y6X+7A68NyLmUQzt3zMiLsD2p+aZD8zPzDvK9R9RJJm2QTXLO4C5mflMZv4ZuAL4O2yDar5GtrnXjimHda/DysNtVaEZSeX/AdtExJYRsQrFjbLXNOG8GgHK8e1nA3My89SKTdcAU8rXU4CrK8r3L2f12pLipuxfl8MklkbEbmWdH684RupVZn4hMzfPzEkU/7b9PDM/hu1PTZKZTwKPRcS2ZdFewO+wDap5HgV2i4jVy7azF8X8BrZBNVsj21xlXR+i+P+7PZX9GDPUJ8jMVyLiSOCnFDOCnZOZ9w31eTVi7A4cDNwTEXeXZccBXwMujYhDKP6H92GAzLwvIi6l+KPrFeCIzHy1PO5fKWYTWw34n3KR6mH7UzMdBVxYfnH7MPAJii+NbYMacpl5R0T8CLiLok39BpgBrIltUEMkIi4G9gA2jIj5wDQa+//es4EfRMSDFD2U+zfhbXW0MOmWJEmSJNWrGcNfJUmSJEnDlEmlJEmSJKluJpWSJEmSpLqZVEqSJEmS6mZSKUmSJEmqm0mlJEmSJKluJpWSJEmSpLqZVEqSJEmS6vb/ASs/6uECbGsKAAAAAElFTkSuQmCC\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=[999.0], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "# do black contour lines of the head array\n",
    "contour_set = xsect.contour_array(\n",
    "    head, masked_values=[999.0], head=head, levels=levels, colors=\"k\"\n",
    ")\n",
    "plt.clabel(contour_set, fmt=\"%.1f\", colors=\"k\", fontsize=11)\n",
    "\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, 0.5)\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(\n",
    "    head, masked_values=[999.0], head=head, levels=levels, linewidths=2.5\n",
    ")\n",
    "pc = xsect.plot_ibound(head=head)\n",
    "\n",
    "# plot the surface and model grid\n",
    "wt = xsect.plot_surface(head, masked_values=[999.0], 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_vector()` method, which takes `qx`, `qy`, and `qz` vector arrays (ex. specific discharge or flow across a cell faces). The flow 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 either be can be passed to the `plot_vector()` method or they can be post processed into specific discharge using `postprocessing.get_specific_discharge`.  Note that `get_specific_discharge()` also takes the head array as an argument.  The head array is used by `get_specific_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]\n",
    "qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(\n",
    "    (frf, fff, None), ml, head=head\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.set_title(\"plot_array() and plot_vector()\")\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, ax=ax, line={\"Column\": 5})\n",
    "csa = xsect.plot_array(head, masked_values=[999.0], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_vector(\n",
    "    qx,\n",
    "    qy,\n",
    "    qz,\n",
    "    head=head,\n",
    "    hstep=2,\n",
    "    normalize=True,\n",
    "    color=\"green\",\n",
    "    scale=30,\n",
    "    headwidth=3,\n",
    "    headlength=3,\n",
    "    headaxislength=3,\n",
    "    zorder=10,\n",
    ")\n",
    "\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 864x864 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(\n",
    "    xoff=-2419.2189559966773, yoff=297.0427372400354, angrot=-14\n",
    ")\n",
    "\n",
    "fig = plt.figure(figsize=(12, 12))\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(\n",
    "    shp, edgecolor=\"green\", linewidths=2, alpha=0.5  # facecolor='none',\n",
    ")\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(\n",
    "    shp, radius=0, lw=3, edgecolor=\"red\", facecolor=\"None\"\n",
    ")\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=[999.0], 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 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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\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(\n",
    "    model=ml, line={\"line\": line[0]}, geographic_coords=True\n",
    ")\n",
    "csa = xsect.plot_array(head, masked_values=[999.0], 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 freyberg...\n",
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing ims package freyberg...\n",
      "  writing model freyberg...\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",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.2.2 07/30/2021\n",
      "                               ***DEVELOP MODE***\n",
      "\n",
      "  MODFLOW 6 compiled Aug 31 2021 16:00:07 with GFORTRAN compiler (ver. 10.3.0)\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): 2021/10/25 16:43:43\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): 2021/10/25 16:43:43\n",
      " Elapsed run time:  0.096 Seconds\n",
      "\n",
      " Normal termination of simulation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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(\n",
    "    sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, sim_ws=sim_path\n",
    ")\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(\"freyberg\")\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(\n",
    "    \"Column 6 Cross-Section with Horizontal hydraulic conductivity\"\n",
    ")\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. The `postprocessing.get_specific_discharge()` method will preprocess the data into vectors and `PlotCrossSection` has the `plot_vector()` 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 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",
    "# 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",
    "qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(\n",
    "    spdis, ml6, head=head\n",
    ")\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_vector()\")\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",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_vector(\n",
    "    qx,\n",
    "    qy,\n",
    "    qz,\n",
    "    head=head,\n",
    "    hstep=2,\n",
    "    normalize=True,\n",
    "    color=\"green\",\n",
    "    scale=30,\n",
    "    headwidth=3,\n",
    "    headlength=3,\n",
    "    headaxislength=3,\n",
    "    zorder=10,\n",
    ")\n",
    "\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": [
      "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n",
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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_0...\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 6.2.2 07/30/2021\n",
      "                               ***DEVELOP MODE***\n",
      "\n",
      "  MODFLOW 6 compiled Aug 31 2021 16:00:07 with GFORTRAN compiler (ver. 10.3.0)\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): 2021/10/25 16:43:45\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): 2021/10/25 16:43:45\n",
      " Elapsed run time:  0.209 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 Aug 01 2021 12:57:00 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 Aug 01 2021 12:57:00 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",
    "\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\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(\n",
    "    sim_name=vertex_sim_name,\n",
    "    version=vmf6,\n",
    "    exe_name=exe_name_mf6,\n",
    "    sim_ws=modelpth,\n",
    ")\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.The `postprocessing.get_specific_discharge()` method will preprocess the data into vectors and `PlotCrossSection` has the `plot_vector()` 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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rslYocY0zB0/sprj21/cUiVnh6GH1EieHACUHbu7uR0tVa0DJTjpVMQOYHXg8G7g02AExn7AezjtOp0aV9xQ7zu8+epf52z4t81qxO1+b/wzFXntDzqRqLnn+YVr/8fv0eOQnXPL8w9zz7ks8tnY5K/fuILcgn2P5J/i8gt62P65cxHlP/oZPK5jTLHXDgTMSVoDcwgL+7/1XGf/Y/bxbzu92vDnZw1ra7uzDfPv12Vz77IN8smdbRM5bOmE9aeOBndz+4oPcNff3bDm0u5yjai6fsr3HS7at5oanfsj/vv139uccjsh5SyfKAEXFRfx7zZtc9/h3+MfyFzheUPbnECkFxad/GHPg+CF+u/DP3PnMt1i0ebGSqjDywPupLQfW8dsF3+PX87/Np3s/jnJUEi6L97zEfR9ezwd7X6PI68eHnCKVCdccVjNLNLOVwF5gnrsvDZTfZ2bbges5vYe1q5l9ZGbvmNnYCppt7e5ZAIH7VsHiCGVbm6ga0KIdy2/4Lr/4YD73LX2d/KKqf4K2OGsr/1z7AV/qf3YEIpSaOJp3gm3HSuajbj92mO3HDvP61vWnXk8wo0PDzErbWLl3J6P+9Wt+P+FKrus3IpLhSjX8YvHrrNufRbP0BjRLa0DzjMB9eoOSsvQGHDhe8Ry+zw7t4+rnH+binoP48XmX0K5RZu0FHya7sw9z4PgxGqdm0Cg1jYYpaSQlJJ5Wp7yE9aQVWZu54qlfcXm/s+nCGKBl2GIrL3E86Z2tn/DetjVcM2Ac7Qnv71ZF5y1258W17/LGp0u5YdhUWtIvrOct3cNaWm7BCf7+wXO8uOZNbj7rCjJoH9bzludkD+uZdh7J4n/f/DU9W3bn5pHXM6T9wIjHEu/O7H3btO8THpj/HXq3Hsolg26mW4vwXmdS24y9udv456c/4dVtf2NSx1mc1XIyiQkx/zZXJCJC/LyzhZktL/X8YXd/+PR2vAgYYmaZwAtmNsDdV7v794Hvm9k9wF3Aj4AsoJO7HzCz4cC/zaz/GT2y1VInfpNTEpP471GTubLXEO6c9xQLd35W5TbufncOM7oPpGlaRgQilOpae6Dy3pti91MJbWVyCvK5+bXHeWvbRh4cfzkNU1LDFaLU0JKdm3l/R9V/Z8/00sZVzN+6nu+cPZHbh55HcmJi8INixHPrlvLrxS+fVtYgOZVGKWk0Sk2nUWo626l8LqXjPLd2CSl8CCumMmvIBaQkJld6zNwNy7j3rcdokJJWcktOpUFKOg1SUmmQXJI4b2VrpW0UFhfz2Kq3SON9+HgGVw04n+TEyv/r2Lh/O39Y+gIZyWmkJaWQnpxKelIqGcmppCWnkp6cwi52VdpGbmEejyz7N42Yj627mqm9x5CYUPmgoOy847yx8X1SE1NITUohNSn5tMcpSSkcofKhvwePH+H+d/5GC1qQvGUW53QZXuk2ahVxd7Lzc0hKSKrw51RQVHlP0MZ9n/FfL/+Eoe0HcfPZ19OjRbcqxyElKuqt3rDnI3457yP6tx3JxQNn0bl571qOTMKh9O/ovhM7eGzjfby+/e9M6jCLs1pNISmh8r+VEttyyeV93mUsF5BCSrTDiXkOFHtIg2j3u3tIn0a7+2EzWwBMAUovOvE48DLwI3fPo2TeK+6+wsw+A3oBy89obo+ZtXX3LDNrS0nvbaXqRMJ6Up9mrXnr6rt45JPF3P3uHI7mhz50a19uNj96/xV+O/7KCEYoVbVmf3gXPHl07Qcszfqcxy66kUEtI99DIsGdOT+1Jo4X5PPTRS/z1Nrl/N8FlzGmY4+wtR1Jx/LK/q3KKcgjpyCP3TlVmzuZTz73v/8iz6x5j/8cczkTug2qMKE6lpdLbmE+uYX57D9esw84T3CCXyx8iqc/WcC3x1zF2C4DKzzv3pzDLNy6qkbnO+kYx7jv7b/y9Ko3uOucmYzs2L/CuvtzDvHrd/8ZlvPuZz8/fP1+BrTpzR2jr6df655VOr6ouIgr/nHbqeeGkfLXZJISk0hOTCY5ISnkLbo+2rmKj55fxXndzuGms2bSrknbKsUS7/ILT1DsxZgZCZaIYZglkGAJp67RYMOr12QtY03WMga1H83FA79Eh6bdayN0CRMrZ3jj/hO7eHzT//Ha9r8zscNNnN16GskJSnbqomKK+af9jRf9eaZwEeOZSBpp0Q4rpoVjIqSZtQQKAslqOnAh8HMz6+nuGwPVLgHWl6p/0N2LzKwb0BMob7++OcAs4GeB+xeDxVKnElaABEvgjkFjuLjbAL46/xnmfBb6SrF/+ngRtwwYxZBWHSIYoVTFmYsphcOnh/Zy7uO/4Rfnz+COwedUq3dEwqe8+ak19enBPVzx3ENc3nsoPzrvYlo3aBz2c4TT0bzwbxez7ch+7nrlYUZ36M09511Jr+btytTJicB8zK2H9/D1l3/P2R378v/GXEXPFmX/nkZif9WNB7bzjZd+yehOg7jrnGvo1qzsB1InCsO/qvLq3Rv42gs/ZGy3kdw6ciYdM0NLFs+cn+o4eUX55BVVP8Z3N7/Pe1uWMrnPePoyqNrtxJt/LPk5H21fWOHrZgl4iOtYrNq5mFU7FzOs43lcNHAWbZt0DleYEkHlJawnHczbzVOf/YLXt/+DiR1uZHSbi0hO0CisuiSRkhFVR+wwT/Ev5vq/mcRULmQKDWkY5ehiUdj2WW0LzDazRErWPXra3eea2XNm1puSvPhz4ORqwOcBPzGzQqAIuNPdDwKY2V+Ah9x9OSWJ6tNmdiuwDbgqWCB1LmE9qV3DJnTPbF6lY4rd+dpbz/LONV8nwWJ+val6ISM5hfYNm7AzO7wrdOYVFfKNt55jwfaNPDTxGjLT0sPavoTG3cPaw3qm5zd8xBtb1vKfoyZzy5AxZeaFxopjEdzfdPGODVz6xP8yc8BYvnb2dJqmf/Gfd04VRqFU1dLt67jmqZ9yWb9z+crZM2ie8cWHBrkR3J5l8bZVLN3+CRf3PZ/bR15Gs4wmp16rSTIYzMLNy3h/6wqm9x3PjcMvp1lGZqX1K5qfWlNFXsQr6+YxjwVsXf45lw68lAap9fsNW7De01CT1dI+3P4uH21fyIjOFzB9wE20bqwPuqPlRGEOW459glPys3Qcd8f54nHW8S1B2zmcv5dnNt/P6ztmM7H99ZzT5lJSEpW41gUnE9aTciybF3iGV/0lxjOJKUynCZnRCS4Glfyu1DxhdfdVwNByyq+ooP5zwHMVvHZbqccHgAlViaXOJqzLd2/jNx++U+Xj3t+1hUfXLmdW/5ERiEqq6r6xF3Hf2Is4dOI4a/bvZs2BrJL7/Vms2Z/FoRr2TL2wcRUf7tnOo9Nu5Ox2XcITtITsSF4uRRFeoTs7P4+HPnyHdo0yuahnbPY6HYtAD2tpxe48u/Z9khOT+N65l536QC6SCevJ8z63ZiGHc7O5b9KtpCWVDLeLRA/rmed9ce0C1u/bwgMXfYem6SXJcn5hZJLEk4qKi5izZh7vbVnOry/5AR0q6W2NVMJ6qn0KeO7jZ3l9/et8b8Ld9G9b8VDpeBep7Uwc54PP32L5tgWM6jKRqQOup2XDsiMZJLIO5GXxxzXfDlt7R/P389yW3/DGjkeZ0P46zm17GamJ+lA7lp2ZsJ50wk7wCnOY569yPuOZxiU0p0UtRxebisPTwxoz6mTCWlBUxO3znqS4mkv+f+/dF5nRfQCZWoApZjRNy+DcDt04t8MXi4q4O//euIqZc2dXcmTlEszo0CiTJVmfM6JNp6CLtkh4pSel8Pilt3EgN4eDuTkcyM3m4KnHJfe7so+QU40ExzAmdO3DTQNHMb5Ln5jtXQVo3bAJXTJbcizvBMfyjpMfxn02WzfI5NqBY7l6wBiapTc67bXqfF9DlZKYxPTeo7hu0Pgyw4IjnbAObdebawdPYUyXwaeNlsmLwJDg0ppnNOWKQVOZ3ncCDVMr//8jP8iCSuEwqssoLht0OT1b9or4uWJZpLf/cS9m1c73ad24IxP7Xq0RWrWssuG+NXGs4CD/3vp73tzxL8Z3uJaxbS4nLalBRM4lNRPsGiiwAt7kdd72NzmHsVzEDNpQjz9c8vD0sMaSOpmwPrDibT7et7Pax5cswPQqvxlfbo+2xAgzY1dO1ReKaZSSyqQufZjerT+Tu/ahRXr9Hi4XTalJSYzv0qfSOvcveYNfLnkj5DZbN2jMdf1Hct2AkXRs3KymIdaKn194/WnP8woLOJafW5LA5uey5dBevv3Go1Vqc3jb7tw4eBwTug2ucMXkH11wLd8Zcxk5+SfIyc8jJz+3ZLGn/BPkFJwgO/8Ev1r0/Kk9KkPRMqMJVw8axxX9zyuTIJ80rttQ2jZqwYnCPI4X5JFbkEduYT4nCr54/vbGleXuAVuRxIREJnQ/i2sHT6FPqy7l1mnRoCnT+owlrzA/cCsomTda6vG+o4cq3NqmIl2bdeSqwdMZ32NM0BWSTyr2IlISkykoKqzS9zeYpIRExvc8n14b+jF+woVha7cu87AsL1K+hqmZTOxzFef1vJi0ZH3IHQ2RSlhPyi48zJytf+KtnU/wlf4P0LGhVoqube+xkB18TiFFFFNEEcWB+y8ehaLIiljIAhb5O4xkFBdxGZ2on/PQ420X7zqXsG4+vJ8fL36txu388eOF3DzgbC3AFONCXUW4S5NmTO/Wn+nd+jO2QzdSQnxTKdEX6qJM4zr14qZBo5nYtV+d2tKmPKlJyaQmJdMiMO8zNcj2NCelJCbRr2gAP5w5hX4tO4ZQP5lm6ckVJpZFxcX8clG5003KaEc77po4lYk9hgdN2jpntqFzZptK60zeeHdICWsqqVw1dAJXDphA60aVr1vQp1VX/mv8f1Ra5/Y//oy1rA16XoCh7ftz9eCLOKvj4Cov3ta+SVvm3lbyIURRcRELH1nD4Jt6UFBcSEFRAQVFBcxd+zpzVr8aUnvpyWlM6zuJSwdOp3mDZizbsKZK8cQzs0QSE5Io9uJqzVctT5P05kzsczVje0wnJUmrkUaTRbhH2zCGthjPlE630Daja0TPJeX7iA/4wJaGrT03ZymLWcpihvgwLuYyelB/RqI4hGvRpZhR597Vd23SnB13/IRd2UfYmX2EndmHyco5ys7sw+zKPsqu7MPszD7CnuPHKh0yXOzO1996lgVagCmmrdlf/j6thjGqXedTSWrf5q21GnAdVdmiTM3TG3Bd/7O5YeDZdG5StUXW6pJgqwi3aZjJ9QPHcvWAc3j1kTz6tWwZlvMeDzJ0N8GMC7sP48bBE9j0XDrTeodvq6gCKp/j2b5xS2YOnkTKwk5cMjp8PR7BzptoCZzffSQ9Ng3imosvCMs5ExMSSSGFRmmnf3DQMCX46I/M9CbMGDCNaf0m0aieL65Uka+c99PTnrv7qeTVcQqL8vn2c5eG1FbTjJZM7jeTc7pNJTlRW6DEuyHNxzG10620a6BtjKIpoYI5quGw0j5kJR/SzwdwMZfRl/4R77WPPqNYQ4Kjy8xompZB07QM+reoeMGLwuIi9uQcY1dOSWK7K3DbmX2EXTklj1cf2K0FmGKYu7PmwBc9rA2TU5nYpTfTu/VnSte+tMzQm7d4UF4P65gO3blp0Gimdh9QL3rLK1pFeES77tw0+Hwmdh9Uao5u+OaHVrTtTaPUDK7sfy7XDryAto1Khl1/Rni3oMqn/Pmmg9r25LrBkziv6zASExJ4deHnYT1vRcOB05NSmdb3fK4aNJW2jVvy9qZNYT1veQqKK06e2zRqzRWDL+bCXuNITdJKplVhZiRaIlThTXDzBm2Y0u9aRnWdRFKIIx6kdqQmptOj8VDM7NQeuwYYCRAoO3gii925W0Nuc2CzsUzrdCsdGtafXrdYVtGiSuG0myz2soc+9KsHCavmsNYZSQmJtG+USftGmZxVSb2CosisLig1t/3YYZqlZXBDv7OY3q0/53XoTmpS3F6y9dbJHtamaRlc0+8sbhw4iu5Nw9ODWFeUXkU4NTGZi3sP58bB59OvZWSnLJy5inCXzNbcMHgCF/cZRUZyZJOk0glrghnju4/g2sGTGdAmsj0dZ/awNktvwuUDJzGj/wQap9Xuh2DlrSTcrXkXrhp8Ked2G0ViDC8kVpdUtihTq0btmdLvekZ2GU9igv5/iUVNU1vzjUF/qLTOWzuf5IUtvw3a1oCmY5ja+VY6Nax8bQWpXQlEbqRjY2/CxVzKOC4khfozakJzWONMXZ8LF8/aNWzMhlv/W0N941yPZq34yohxTO8xkLSk+tmzcSz/BI1pzO3nnM9V/c+hWS0tFHYyYR3TqR/XD57AmE79amWKRGFxyWIaGclpXNrvfK4adCHtGtfOVgQne1g7ZbblmsHTmNhrDKlJ0XkTU1hqJeFB7fpz1eBLGdah6vNlpXLF5cxrbdu4M1P7X8/wTueToA8G6ryEID1mfZuOYlqn2+jSqF8tRSRVEYke1gxvwDQuZiJTSaN+zUPXHFaRWhTL25RI+Px52g3RDiHqJnUfROGCPtw0olWtnrdVw0y+wlf48iWDa/W8hcVFTGYy//mlGTRMqd39D9vSlq9PuZbRXYZGff2CouIizulyNlcNmUHvVj2jGks8K72KcIfM7kztfz1DOp4b9Z+/hFEFH/L0zjyLaZ1uo1vjgbUckFTFUEbQ3FuQQAKJgX8Jp+4TSCKJJ3iUbDsWtK1UT2US05jKxTSg/m5TpDmsIiISVq0aNCGRg7V+3jYNm9KqgrmkkZSWlMJoRtd6sgowgxmM6dqj1s9bnttG30R6cu1/D+obd6dzs15M7X8Dg9qPVg92HLIzhpT2bDKMaZ1upUeToVGKSKpiKMMZyvBK6/yb58im4oQ12ZMZzyQuYgaNaRLuEOucCG9PXeuUsIqIiESBktXakZ7cgO9N+oMS1Th2chGdbo0HM73TbfTKrDz5kbonsYJ5romeyFjGMYMraEb87iZQVRoSLCIiIlJHKFGNf60zOvPV/g/SO/Ms/bzj1Jlb35gbozmXS7mS1lS+73d9465tbUREREREYkbvzBHRDkEiLKlUwjrcz+JyrqYDnaIYUWyLsxHBSlhFRERERCR2JZDIAB/EFVxDN2JjHYJYpn1YRUREREREasmX+RptaBftMOoEJ/56WIOu6W5mHc3sbTNbZ2ZrzOwbgfJ7zWynma0M3KaVOuYeM9tkZhvMbHIkvwAREREREYlfSlarpjgwj7WyW10SSg9rIfAdd//QzBoBK8xsXuC1B9z9V6Urm1k/YCbQH2gHvGlmvdy9KJyBi4iIiIiIyJnqVkIaTNAeVnfPcvcPA4+PAeuA9pUcMgN40t3z3H0LsAkYGY5gRUREREREpGLuwW91SdCEtTQz6wIMBZYGiu4ys1Vm9jczaxooaw9sL3XYDipPcEVERERERCQMHAt6C8bM0sxsmZl9HJgW+uNA+U8D+d9KM3vDzNoFyiea2Qoz+yRwP76CdiucVlqRkBNWM2sIPAd8092PAn8CugNDgCzg/pNVyzm8TB5vZreb2XIzW378+PFQwxAREREREZFyOGHrYc0Dxrv7YEryvSlmNgr4pbsPcvchwFzgh4H6+4GL3X0gMAt4tJK2H3D3IYHbK8ECCSlhNbNkSpLVx9z9eQB33+PuRe5eDDzCF8N+dwAdSx3eAdh1Zpvu/rC7j3D3ERkZGaGEISIiIiIiIhUK3rsaSg+rl8gOPE0O3DzQcXlSAwIdk+7+kbufzPnWAGlmlhqOryiUVYIN+Cuwzt1/Xaq8balqlwGrA4/nADPNLNXMugI9gWXhCFZEREREREQqEELvaqhzWM0s0cxWAnuBee6+NFB+n5ltB67nix7W0q4APnL3vAqaLm9aaYVC6WEdA9wIjD9jrPEvAmOUVwEXAN8CcPc1wNPAWuA14KtaIVhERERERCTyQuxhbXFyembgdnuZdkpG0w6hZMTsSDMbECj/vrt3BB4D7ip9jJn1B34O3FFBeBVNK61Q0G1t3H0R5c9LrXC8sbvfB9wXrG0REREREREJnxB7UPe7+4jQ2vPDZrYAmMIXo2oBHgdeBn4EYGYdgBeAm9z9swra2nPysZk9Qsk82EpVaZVgERERERERiWFuwW9BmFlLM8sMPE4HLgTWm1nPUtUuAdYH6mRSkrze4+7vVdJuRdNKKxS0h1VERERERERin1PO9izV0xaYbWaJlHRyPu3uc83sOTPrDRQDnwN3BurfBfQAfmBmPwiUTXL3vWb2F+Ahd19OybTSIYEwt1Lx0OFTlLCKiIiIiIjEiVAXVaq8DV8FDC2n/IoK6v8P8D8VvHZbqcc3VjUWJawiIiIiIiJxwkMY8luXaA6riIiIiIiIxCT1sIqIiIiIiMQJL3eDl7pLCauIiIiIiEg8COOqS7FCCauIiIiIiEicCMeiS7FECasAcCA3h2VZn5cpz0zLYHS7LrUfkITdit3bOJh7nJMfu538Wza0dUdaZjSMWlzxZnf2Ydbv34Wf/A4H7lo0aMTAVp1qNZZjebmszPrsVBjuJVE1SEnlrPa9InbegqJCPtixHsdx/+J6S7QEzuk8IGLnBfhg+2oKi4tOPT95/hEd+5OSmByx867ZvYGc/OOnndNx+rfpTaPU+vH7tevwdvbn7KXYSz7eP3nfrklH2jRuF+3wJMzyi06QkpgW7TAkQvKKc0lNSI92GFItFrjFDyWsAsCqfbu45N+PlCkf3a4L7878RhQiknD7yaJXWLjjszLlT824lYld+0Qhovi0ZMdGvvPGo2XKp/YYwu+n3VKrsWw9vIc7X/pDmfJezdvzwnX/HbHz5hSc4GtzHyxTnp6UyqI7ysYTTj94/fdkBxLH0uZ86Xc0y2gSsfP+btHf2Hyg7Id+v7n0p/Rt3bOcI+LPmxte4fV1c8qUzxz+JS4eeFUUIpJIemHdg5zX5WraNuwW7VAkAh46/L/c2OTrNEtsGe1QpDrirIdVqwSL1Htx9lctRnm8jc+pBo/itRbNc9cXZuV/oq9rP/7sOPopS3fOZdG256IdikTApvw1fHhiEa9kPxntUKQaPMRbXaKEVaSeqOjNpIRXRd/laPznYBVEE+nkLarfgyhd5hV+r+tRshat601ql7vzwvoHcZzlu17jeMHRaIckYfbc0b8BsCBnLoeLDkY5GqkWt+C3OkQJq4hIGMXSBwMVhRLpJKqixCWaIp03xtCPPWrUw1o/fLznbTYf+hgomce6dOfLUY5Iwml93krW5n8IQAH5vJb9dJQjkmqJsy5WJawi9Vwd+5tVZ8VSL1PUIqmFxKXiZDk6X3Us/dwjTT2s8S+/KI85G35/Wtmibc9R7EUVHCF1ibvz/LG/n1Y2//iLHC06HJ2ApNriLF9Vwiol1DkQ//Qzri0V9TLVchhEb5hqhT1ttfBfZPSuc/2GqYc1/i3Y+gSHTuw5rexgbhZr9r0fpYgknNbmf8iG/FWnleX7CV7PeSZKEUm1xOEkVouF/0jatWvnd9xxR7TDqNe2sIXZzC5T3pGO3MqtUYhIwm02s9nCljLl13ItvekdhYji0yd8wnOUXYikN725lmtrNZYssvgzfy5T3oIW3MVdETvvCU7wM35WpjyRRH7ADyJ2XoCf83NyyS1T/m2+TWMaR+y8f+bPZJFVpvxmbqYznSN23lgyj3m8x3tlys/nfC7ggihEJOF0lKP8jt9RQEGZ17rSlVnMikJUEi6O81f+yg52lHkthRS+yTfJICMKkdWee++9d4W7j4h2HDXVsV8f//bjfwla79tDx9aZrzdmtrUZe2dsv2Fe+NCGmI5x4UMbOOeO6m9NkrGzCOaWLW/UOp1zLq35lifv/3k9o+/oW+N2ImXxn9cx8vb+0Q6jQsseXsOw/xhYozZeeKUR7Cxb3mNSF4Z1rlnbHz7yCYNuHVKjNiJt1V9X0u+WYWFrb+3fPqTPzcPLlB/cfBzeKVu/UadM+kwoW/+k9X9fQc8vnRX0vBv/8QE9ZgWvt2n2B3S4eAC8VPa15CbpdLt0JACbZy+j601nB21vyz+X0vnGUUHrff7oEjpecxY8VfY1S0ig0/UlbWx7dAkdbxwdtL3tjy6mww3nBK2341/v0/76c0h8NhnyyiasbS4bQdOMFux87H3aXT8maHu7HnuPttcFr5f1eEm9lNceh4NlE9YWFw6ibasBZD3+Hq2vPTdoe6Ha88QiWs0cG7b29j65kOZXn1+jNjJWfQrryyas6f0603xAzdoGOPD0O2ReHruJ7+Hn36bxJROiHUaFjs6ZT+OpE6t9/Esf/4SCXWWTVSj50Pv4ud1p06j6W9wcfXUemRMmVfv4SDs8/w2anxu78R1Y9AatRlQ/vjVHFrNjY9lkFSCffFa33c9F7W+rdvsAe5e/Qfu+1b8GI+/eaAcQPtHvjwyrmElYpXYcyT3KP5c/W6Z8//HyV4HLOrqH3y78a5nyni27MrXP+LDHJzX3zqaFrN+zATh9CObOw+Vkq8DLa1/jg+0rTv1xO3nMzGFX0bxB88gGW4cd4hCzlz4SGO5Y8j1zYPfRXeXW33pwC395/w+4l/6pOAPaDuacbufVKJYTBbk8+9GjgVE+Du4cZg/2SVq59Y8cP8jfF/+u5DF7aPT+EhynQ2ZnJve7LOTzFnsxz6+cTV7hiVNjno+ym/QPl5Zbv6i4kH8u+S2Oc4w9NFy8FMdpnNaUy4bMqtKCVa+vfYaDOftOXa/ZZNHgg484UVA2WQV4/qO/kpKURg67yVj6AeAkJiRx9fAvk5QQ+n+F7332GjsOb4bAzzGHXWQs/4iDOXvLrf/GuqdZsvVNcsgifVnJ98UdLhtyGw1TI7cvbCRt2b+Gj3cuDFzLfup3YPP+1eXWX71rMUdyD+AUgzvFgWt0VNep9Gw1pFZjl+rZemg1K3a9XmmdhZ8/y1UD/rOWIpJwcnde2vlIpXXe3vs0E1rPJD2pYS1FJfIFJaz1TJP0xmw/vJOVu9aEVP9Q7hHmrp1XpvznF/13uEOTMGndqBX3v/1gyPU/3PFRmbLerXrRLKNZGKOKP01owie7VrLj8LaQ6u/P3subG14rUz613yU1jiUtOZ192XtZsX3x6S9sLb/+8YIc3v701S8KNpXcfW1c1X6vEyyBwqIC3lz/4ukvbCy/vuMs2PhKmfNeNey2Kq+unJKUxvwN/z698NOK6y/ZOr/Mec/veVGVklWAJunN+OfS+0M+78c7S/1MPiu569f2rDqbrAK0bNSBRZ/NIa+w/A8HzrTj8EZ2HD79oshIacSVw74eifAkzIq9mH+v+03Qest3vsb03neSkRy5ofcSGZ8cWcS24+srrZNblM2Cvc8wtd3NtRSV1Eic9bBq0aV66IbhV9bo+AFt+jCkXewOn63v+rTuzdAOQ2rUxrXDro6p7VliUQIJXDmkZnNSz+4yho5NwzO/8fIh19Xo+I5NuzKsY/Ahumea2v8qUhJTq33eRqlNuKDXxVU+7tzuk2mW0bLa501MSGJK/6r//Pq3PYuuzWs2TWL6gBtrdHy0NUxtwrieV9SojQt6XUV6coMwRSSR9OGuN9h2ZG3QegXFeSzdXs7cIolpxV7M3J3B5zsCvLXnKU4U5UQ4IgkL7cMqdd2gdn1rlHDeOOJKJTMx7tph11T72F6tetY44a0vRnY5h05Nu1T7+CsGzwxbLJ2bdeOsTsHnelZkxqDrSLCq/5fQJL0p43tfVO3zTu5/JanJ5Q9drkxyYgrTBlT/A4Mx3SbTvEGrKh9nZlw88KZqn7dvm+F0a9Gv2sfHigt6XUVaNRPO9OQGnN8j9KHnEj15hceZu+FPIdd/b9vz2uKmjvn48DvsyK1gWMwZcoqO8u7eFyIckUhZSljrqer2sqp3tW7o07oXwzoMrdax1w67Rh9IhCjBErhiSPWSzpGdR9OpWZewxnPZ4Or1snbI7MLwGiS7U/tVr5e1YTV7V086t/tkmlUj6UxMSGJqNXpXT+rXdkS1k87pA6qf7MaSBqmNuaBn9f4fGdfzStJTNA+uLnhr8784mrc/5PoHc7NYu1db3NQVxV4Ucu/qSW/ueZy8otCmA0gUhWFbGzNLM7NlZvaxma0xsx8Hyn9qZqvMbKWZvWFm7Uodc4+ZbTKzDWY2uYJ2m5nZPDPbGLhvGiwWJaz1VHV7WW8cfoWSmTri2uFXV/mYXi17Mky9q1VyVufR1eplvaKGw4nL06lZV0Z2Dr6y7Zmq27t6UuP0TCb0qfpc3Mn9riAtOb3a501KTGZ6NXpZz+k2meYNW1f7vNXtZe3TehjdW8bPB37jel1JenLVEs+0pAzG9arZcGKpHQePZ/H2lieqfNzCz7VnZ12x4uB8sk6U3e6uMtmFh1m078XgFSUe5AHj3X0wMASYYmajgF+6+yB3H0LJHiM/BDCzfsBMoD8wBfijmSWW0+7dwHx37wnMDzyvlBLWeuzGEVX7dHxAm94MaT8gQtFIuPVu1YvhHavWy3rtcM1draoES+DKoVXr2Tyr02g6N+sakXguG3wdRug/w5a0ZEQ1ktwzTel3BalJoQ/tTSed8b1rvuDUmG6TaN4g9OQzgQSm9q/5UOy+baqefE4fWLfnrp4pI6Uh43tX7YOx83teTkZKowhFJOH00oY/UlicX+XjNh5YQdaxzRGISMKpyAt5edffqnXsvD2PkV+cF+aIJGxC6V0NoYfVS2QHniYHbu7uR0tVa1CqtRnAk+6e5+5bKFnicGQ5Tc8AZgcezwYuDRaLEtZ6bGDbvgytQgJ643DNXa1rqjKXtWfLHtUeRlzfjeh0dpUS0OoOIw5Fx6ZdGNkl9P0+z+f8GvWuntQ4LZMJVUhAz+GcGvWunlTVXtYhDKFFwzY1Pm9Ve1m70pUeLWu233EsKklAQ1sVNjUpnQt61WzRP6kdnx1cyce736r28Qs/L7t9nsSWDw7MY29eaKvcn+lowQHe31/OJt8SO8KQsAKYWaKZrQT2AvPcfWmg/D4z2w5cT6CHFWgPbC91+I5A2Zlau3sWQOA+6NyeoO9SzKyjmb1tZusC45e/ESivcPxxKOOXJTbcMDy0oVnqXa2berXqyYiOw0Kqq5WBqy/BQl8xeESns+nSvFtE47k8xLms7Zp0pB/hWwBoSr8rSCElaL0GKY0YWe6HrtVzTreJZJIZtF6iJTKWsWE7b5/WQ+lEp5DqjmNc2M4bS9KTGzAhxF7W83pcRoM6vJ1PfVHsRSFtY1OZFTtfIyf/aPCKEhVFxYW8kvXXGrXxRta/KKhGD7zUFgvhRgszW17qdvuZrbh7UWDobwdgpJkNCJR/3907Ao8Bd5U6aZkmwvHVhPKxeiHwHXfvC4wCvhoYo1zu+OMqjF+WGBBqL6t6V+uua4cH72Xt0aI7w0NMbKV8IzqNokuz4IloOFcGrkj7zE4MIPjv9YxB15IQxoE2jdKahJSITu53BalUfyucMyUlJoeUiI7uNpGmBF3bIWRmFlIi2rv1EDoTnu2LYtF5PS4Luq9sSmIaF/S6qpYikpooKMpjSo9buW7QD7m837eZ1ut2Luh6PQNahf5hT0FxHst2aIubWLX1+DpapnagT6Oz6N1oOD0bDaNHwyE0Syl/9EnDpEzapXejbVpXWqd1plVqJ5ITUlh9RAtsxSrz4Ddgv7uPKHV7uKL23P0wsICS3K60x4GTvV87gI6lXusA7CqnuT1m1hYgcL832NcTdMf0QFftyW7bY2a2jpLu3Rlw6n/q2YEv4nuUGr8MbDGzk+OXz9jRXmLFjcOv5KOdqyt8vb96V+u0ni17MKLjcJZvX1FhnWuHa2XgmjIzrhx6Hb+a/z8V1hnecSRdW/SolXjO53zWsAav4MPNtk06cnaXsWxdWPF1UR3ncA7Lkz/kRMHxcl9vkNKQCb0vZt/KT8J63iEMYXHDD9ifvbvc1xMsgWn9ryX/s6otMBJMV7rSs9VANu6t+Ou5aMANsCe7wtfrurTkDCb0nsmLq/5cYZ2xPWbQKC2z9oKSaktNyqB/67LTCtbtW8zqvQvLlHdrOpivnP078gqPk1uYw4mCbE4U5pCYkFwb4Uo1dG84kK/1erBM+WtZs5mzs+zv8dnNp3JFx6/VQmQSS8ysJVDg7ofNLB24EPi5mfV095N7IV0CrA88ngM8bma/BtoBPYFl5TQ9B5gF/CxwH3QVryp9tG5mXYChwFIqHn8c6vhliRED2vZhaPuK51apd7Xuq2zF4B4tuoc8bFgqN7zjSLo1rzghjcTKwBVpSUtGdT2/wtdnDJpJQkL4B79kkMGFlcxlndT3ctJTqrd/Z2USSWT6gIqHQo/uOpGWjdqG/byGcfHAWRW+3qvVIHq1Hhz288aasT1m0Ci1/N7r5MTUKi/OJLHnREFOueVpSQ1JsETSkxvRLL0N7Rr3oFuzwXTOrPv7Ddc37sXllpuWvKl7wjOHtS3wtpmtAj6gZA7rXOBnZrY6UD4J+AaAu68BngbWAq8BX3Uv2ZjZzP5iZiMC7f4MmGhmG4GJgeeVMvfQIjazhsA7wH3u/ryZHXb3zFKvH3L3pmb2B2Cxu/8rUP5X4BV3f+6M9m4Hbgdo0qTJ8G9961shxSGR8Tmf83f+Xqa8Ix25hVuqtOqoxKbHeZxP+bRM+bVcS296RyGi+LSBDTxB2a0getGL66jePqnVtZ/9/IE/lOllbU5zvspXwzocuLTjHOdBHiSf0+c3pZHGN/kmaYS+mnBVFFHE7/k9hzh0WrlhfI2v0YxmETkvwD/4B1vZWqZ8FrPoSmRWhI41i1nM67xepnwUo5hSZhSZ1DXLWc5cyg7zHchArkBbFcWDBYF/ZxrDGCYysfYDqmX33nvvCncfEbxmbOvYt49/5+/BV4D+1ugxdebrDTokGMDMkoHngMfc/flA8R4za+vuWWeMPw5p/HJgnPTDAO3atfOmV46r3ldQSw49u4BYjvHQswvIvGJctY/PBHotXMWne08fHnjRud+gaeuaX8uHn1tA5hUX1LidSDn83Ns0uTx24zvy/Ns0uWx8jdqYfqgdny44fT59h8zenDXuzhr3oB954S0az6hZfJF29MW3aHzJhPC1N2c+jS8u294IH8+ihR+x/cj608qnj/0ujTP7VNzeS/NpfNGFwc87982Q63WbPpNhKzeyYufpScTkwV8ms8Okknovv0nj6SG09/KbNJ4WQr1X3qTNtEs4/9M9zNs0+7TXzu95Pa16XnSqXqjthVqv6bTJTN5eyJOf/N9pr41oP5Uug6+O2HkbT7+Q6Qda8Icld532Wrdmgxk06jbMLOTvc6hisb0Lisby/tvLOZZ34FRZUkIKUy64m8ZpLWoaYti/5nCL+/g+2/PFwL9SGnfuQeMBNf+668T3L4S/CdES6t+syqR8+jlsWlCmPK17Nxr3DsPPOAwxRtS990Y7gvAJy1JHsSOUVYIN+Cuwzt1/Xeqlk+OP4fTxx3OAmWaWamZdqXj8ssSYKX1PH9bWtfkAerUaHqVoJNw6Ne1D/zbnnFY2uc+XNNw7zMyMyb1vPq2sb6tRdKwkWY2kiT1mnTacq0VGB4a2i/wbhvO7XkNa0hdDf9OTGnJel8hvaTK8/WSaZ3wxCyXBErmwR+jbz1RX9+ZD6Nn89L+Xk3reXK9+v1ISU7mw++l7zY7qdElYklWJvhMF5c/DTktqWMuRSKQ4FQ0Jrj9/x+JBKAsuWR1LaEMZDzYGuBEYb2YrA7dpVDD+uLLxyxLburUYdFqCOqWvkpl4M7nPF4lUh8xeZRJYCY++rUbTKbPvqeeTet1cSe3IatWwE8PbTzr1fGLPWSQmhDS4pkYykhtzXpcv5i2e1/Vq0pMbRfy8iQlJTOrxpVPPh7ebRMsGHSJ+XoDJvW459bhr04FlEtj6YFSni2mS1hKAxIRkxner3WHwEjm5heXPYU1PVsIaLyqcwxqGvbpFaiLoFejui9zd3H2Quw8J3F5x9wPuPsHdewbuD5Y65j537+7uvd391ch+CRJOU/p+CVDvarzq2LQ3A9qMAdS7Gklmdip56dPybDo3je7iIxN7fokES6R5RnuGtau9eUjnd72atKSGpCU15LwutbelybB2E2mR0QEjoVZ6V0/q1mwwvVqUTKGY1POWevn7lVyql/XsDtPJTA+6H7zUEScqSFhLj6SQuq24gnVt1MNaB4Vn0aWYEfmP2aVO6dZiIL1bjWB8r5n18s1WfTCp75c4fGIf/QOJq0RGn1Zn07lpfyb1jl7v6kktG3RgePvJdG82uFZ6V09KT27E+V2vxt1rpXf1pMSEJCb2mMXGA8tp2aBj8APCaHLPW8kvPHEqca2Pzu54Ee9seZoJPW6IdigSRhUNCVYPa/yocEiweljrGAOPr/fwSliljKuHfYdmGeVvHi11X8fM3tw88qf6QCLCzIwbh/2IZhnh30qlOqb2uo1GqZFbJbci53W5usI3QZE0rN1EejSv/e2aujYbyA1Df1Svf7+SElO48+wHaJqu/0fiyQXdr2NQ23GcKMjhRGH2qT1XWzboFO3QJEz6tDibtKQGuDtO8an7bs3if2suiW1KWKWM5g1i4w22RE4z/YxrRawkq0DUhmZGq/clMSGJpumto3LuWPq5R4u+B/GnW7PBSlziXM8Ww+nZQtPB4kFdW1QpGPXxi4iIiIiISExSD6uIiIiIiEi8iLMeViWsIiIiIiIi8aAOrgIcjBJWERERERGROBFvy/5pDquIiIiIiIjEJPWwioiIiIiIxAsNCRYREREREZGYFGcJq4YEi4iIiIiISExSD6uIiIiIiEicsDjrYVXCKiIiIiIiEi/iLGHVkGARERERERGJSUpYRURERERE4oGHeAvCzNLMbJmZfWxma8zsx4HyX5rZejNbZWYvmFlmoPx6M1tZ6lZsZkPKafdeM9tZqt60YLEoYRUREREREYkTFsItBHnAeHcfDAwBppjZKGAeMMDdBwGfAvcAuPtj7j7E3YcANwJb3X1lBW0/cLKuu78SLBAlrCIiIiIiIvEiDD2sXiI78DQ5cHN3f8PdCwPlS4AO5Rx+LfBEDb6C0yhhFRERERERiRdhSFgBzCzRzFYCe4F57r70jCq3AK+Wc+g1VJ6w3hUYUvw3M2saLA4lrCIiIiIiIvVLCzNbXup2+5kV3L0oMMS3AzDSzAacfM3Mvg8UAo+VPsbMzgaOu/vqCs77J6A7JcOMs4D7gwWqbW1ERERERETigBHyPqz73X1EKBXd/bCZLQCmAKvNbBZwETDB3c8820wq6V119z2nYjV7BJgb7PzqYRUREREREYkX4VkluGWpFYDTgQuB9WY2BfgecIm7Hz/jmATgKuDJStptW+rpZUBFPbGnqIdVRERERERESmsLzDazREo6OZ9297lmtglIBeaZGcASd78zcMx5wA5331y6ITP7C/CQuy8HfhHY7saBrcAdwQJRwioiIiIiIiKnuPsqYGg55T0qOWYBMKqc8ttKPb6xqrEoYRUREREREYkHHvIc1jpDCauIiIiIiEi8iLOENeiiS4H9cfaa2epSZfea2U4zWxm4TSv12j1mtsnMNpjZ5EgFLiIiIiIiIvEtlFWC/0HJEsZnesDdhwRurwCYWT9KljLuHzjmj4GJuiIiIiIiIhJpYVglOJYETVjd/V3gYIjtzQCedPc8d98CbAJG1iA+ERERERERCZGFcKtLarIP611mtiowZLhpoKw9sL1UnR2BMhEREREREYm0+tbDWoE/Ad2BIUAWcH+gvLyEvdxviZndbmbLzWz58ePHy6siIiIiIiIiVaGEFdx9j7sXuXsx8AhfDPvdAXQsVbUDsKuCNh529xHuPiIjI6M6YYiIiIiIiEgcq1bCamZtSz29DDi5gvAcYKaZpZpZV6AnsKxmIYqIiIiIiEhQgX1Yg93qkqD7sJrZE8A4oIWZ7QB+BIwzsyGUdChvBe4AcPc1ZvY0sBYoBL7q7kURiVxERERERERKcfA6lpEGETRhdfdryyn+ayX17wPuq0lQIiIiIiIiIkETVhEREREREakb6tqQ32Bqsq2NiIiIiIiISMSoh1VERERERCRexFkPqxJWERERERGROGBoSLCIiIiIiIhIrVAPq4iIiIiISDxwNCRYREREREREYpOGBIuIiIiIiEjcMrM0M1tmZh+b2Roz+3Gg/Jdmtt7MVpnZC2aWGSjvYma5ZrYycHuognabmdk8M9sYuG8aLBYlrCIiIiIiIvHCQ7gFlweMd/fBwBBgipmNAuYBA9x9EPApcE+pYz5z9yGB250VtHs3MN/dewLzA88rpYRVREREREQkTpgHvwXjJbIDT5MDN3f3N9y9MFC+BOhQxfBmALMDj2cDlwY7QAmriIiIiIhIvHAPfguBmSWa2UpgLzDP3ZeeUeUW4NVSz7ua2Udm9o6Zja2g2dbunlUSpmcBrYLFoYRVRERERESkfmlhZstL3W4/s4K7F7n7EEp6UUea2YCTr5nZ94FC4LFAURbQyd2HAt8GHjezxuEIVKsEi4iIiIiIxIPQ56jud/cRITXpftjMFgBTgNVmNgu4CJjgXtJd6+55lMx7xd1XmNlnQC9g+RnN7TGztu6eZWZtKem9rZR6WEVEREREROJEOOawmlnLUisApwMXAuvNbArwPeASdz9+Rv3EwONuQE9gczlNzwFmBR7PAl4MFot6WEVERERERKS0tsDsQBKaADzt7nPNbBOQCswzM4AlgRWBzwN+YmaFQBFwp7sfBDCzvwAPufty4GfA02Z2K7ANuCpYIEpYRURERERE4kVoQ4Irb8J9FTC0nPIeFdR/DniugtduK/X4ADChKrEoYRUREREREYkTFu0AwkwJq4iIiIiISLwoDkMXawzRoksiIiIiIiISk5Sw1pJP937Ios9exEPcqFfqjqLiQnYd+SzaYUg1HD2xP9ohVFk22VE571GOUuxFUTl3RT4/vIZ1+5ZE5dwFRXkcOL4rKueurqMnDrDn2NZohyHlOJS7J+Z+v0SkjvIQb3WIEtZasmX/ap756AEeX/4z8ovyoh2OhFFuQTa/mn8bz658gJy8I9EOR6rgT4u/xZMrf8ah3D3RDiVkc5jD35d/v9YTj5Ws5P5Ft7B27+Kof/BWVFzI6xv/zu8Wf4WN+1fU6rndnZVZ8/n5uzewMuutWj13dRUVF7Jwy7P87J3r+PRA7X6/JDRLtr3E/QtvYd3eJVH//ZLw2peznXV7F1NUXBjtUKSesBBvdYnmsNaSbYc2ALDs89fZdWQzt4z+Cc0btI1yVBIOJwpycIp5b/O/+Wj7fCb3u5kxXWeQmKBfr1iXX3iCZdte5sMd8xjb9QrG97yBBimNox1WpYoo4pPd77B690LO6jiFyb1uoWl6m4ift4ACso59xl+Wf5fuzYZycZ+v0Cmzb8TPe6b9OTt5/OOfsvXwaqDkzWBt+fzwWuas+x1bDn0CQF7h8SBHRN/WQ6t5bvX97Dy6EYCc/MPRDUjKVVCcR9axz3jkg/9Hz+bDubjvV+jQpHe0w5IwyC86wSPLv0vDlKYMbXchI9pNpkOT3gS2AxGJjDj74EvvqGvJtkPrTz3ecXgjv5p/B7NG/oA+bc6KYlQSDrkFOaceHy84xgsf/5b3N8/hskF30bu1fr6xrLA4/9T92589wZJtLzG+xw2M7XoFKUlpUY6ufEWUDBt0ilm2/RVW7JzHmM6XcWGPG2mY2jRi5y3ki96Bzw5+xIPv/wdD2o5nWq87aNGgfcTOe5K788GOV3hh7YPkFeWeKt+Xsy3i5z6Uu4eXN/yZD3e9cVp5XlHsJqzZeYeYu/4hlu14uUy5xJ6CohOnHm88sIJfL7qV4e0nM7XXf9AsI/IfSEnkZKa1BiA7/xALtz7Dwq3P0KpBJ4a3n8SwdpNontEuyhFKXIqvfFUJa00VFRfyt8U/okNmDzo160PHpr1pnNbstDpHcvdz9MSB08qO5x/loUX/ybT+t3Bhn+tJMI3OrqtOFJSdU7jn2FYeeu//MaDtGC4Z+BVaNuwQhcgkmJMJ60m5Bdm8vO4hFm55lsm9b2Zkx2kx11N+MmE99by4gHe3PM3SbS8xrvu1nN9tJmlJGWE/b+mE9aSVWW+xavc7nNPpUib2+BKNIpQw5+Qf4elPfsEne94p89r+4zuDDrXLLcgmPblhlc+bV3ictzY/zoLNT1BQXHYqRyz2sBZ7EUu2vcTLG/5MbsGxMq/nFGjaQiwqKGeq0Iqdr7My6y3GdrmSC3vcSEZybI/+kPJlJDciJTGd/FIftO3N2carn/6FVz/9C12bDmJ4+0kMbjM+5kf4SN1hSliltKwjW1id9R6rs947VZaZ3pJOTUuS107N+nA0t/yFXRzn5TV/Zduh9Vx/1j3VekMl0ZdbTsJ60uqs91i3eynn97yKib1vJC25QS1GJsEUFOWXW370xH6e+fiXvPPZU0zrczsD254XM8O3zkxYT8oryuX1T//Goq3Pc2GPmxhAo7Cet4CCcsuLvYhFnz/HBzte4YJu1zOMVmE97yY2MWfh7ziad6Dc14u9iIO5WSRQQNaxzezN3sa+nO3sy9nG3pySx0XFBfzvpDdC/hkWezEf8RFvv1PxeSH2Etbth9fz7Or72X5kXYV1svMOl1teTDE5+UfIyT9CSmIamenh/TlK5Spa26KouIAFm59g6fa5TOxxE2M6X05yYmotRyc1YWY0TW/Nnuyt5b6+5dAqthxaxQtrf0O/lqMZ3n4y/VqOJikxpXYDFYlhQRNWM/sbcBGw190HBMqaAU8BXYCtwNXufijw2j3ArUAR8HV3fz0ikceI0kN9Tzqcu4/DuftYtWthSG18sus97p9/J7eM/gntmnQLd4hSA0dyD/DzN28iLbkh6ckNSU9uQHpyQ9JO3ic1ZF925XPoiryQtz59gg8+f53p/W/jrM5T1aNeC5Z8PpeDOVkkJaaQlJBMIVtpuDWb5IQUkhKTSUxIoai4/CTspL3Z2/jH8v+mU9N+XNT3Tnq0GBrRmDft/5DtR9aTmJBMUkIyiZZEYkISiYHHSQnJ5JJbaRs5+Yd5ce1veZvGTNtezPD2k4P2Em89tJrlO14jOTE18P0puU9OTCUpIZXkxBQOcrDSNvKKcnlt419YREOmbMvn7A4X1ah3uqAoj7kbHmIhz0CQdep+t/jLZHMYKvmTezTvAE3SWgQ976YDH/Liut+zk0+DnndP9ucsZjFs3E5e4fHTbicKj5NXdJyxXa7krA5Tg563JnLyj/LqhodZvO1FPMg4sD3ZW3n2k1+Rk3+Y7MAtJ/8IORzG55Uce17Xq7m039cjGnN9smHfB6zY+TrJiamkJKYFfrfSSh4nppKcmMqB4zsrbSO34Bhz1v2BhVufY1rv2xna7kL9P1KHVJawnlRUXMAne97lkz3vkp7UkMFtL2B4+8l0bTpIP2upGqdezmH9B/B74J+lyu4G5rv7z8zs7sDz75lZP2Am0B9oB7xpZr3c43et9vIS1urYl72DB976CteO+E+GdRwfljal5k4UHCO3IJvcgmxqOvPrWN5BnvzwFyza/CKXDf4a3ZoPDEuMUr6Pdswvu4Lsqteq1da2Q2v54/tfp0+rUUzvewftm/QIQ4Rlrd27mAWbnwhLW0c5ypOr/o+3Nz/B1F7/wcA2FfcS7z62hfe3/Tss580mm2dX/4p3tzzNtN53MLB11Xundx7dyGMrf8Lu7C2hnTOEhYT25WyrNGHdl7ODl9b/gdV7QvugEWB39hZ2swU+rbjO4QiuQF3sxSzf8Sovrf9TyIspZecfCvqzrqgXVqpn97HNLN9Zvb89ZzqUu5vHVv6EdzY/ycV9v0rPFsPD0q6ET2FxAUdO7OVQ7p5Tt4PHs6rURm5hNku2v8SS7S/RNL0Nw9tNZHj7ybRu2CUyQUv8ia98NXjC6u7vmlmXM4pnAOMCj2cDC4DvBcqfdPc8YIuZbQJGAovDFG/MObn6bzjkF51g9tKf8PnBdVwy8I6YmztXH1U23Le6dhzewO/euYthHSZw0YA7aZqhoXeRUBik97Q61u9dwoa9SxnWYSLn0pdwzzYK1uNbHXuyt/KPD79Pp8y+TOt9B71ajChTp7z5czW1N2cb//jw+3RpOpCLe3+Zrs0GBT2m2It5d8tTvPzpw2H/XuzN2UaP5sPKlB8vOMq8jbNZ9PlzFHn4t504EaFhw7vZzWuLv8rWwIrF4ZRTcDjsbdZn5c1/rqkdRz/lT0u/QZ+Wo7ioz5dp17h72M8hoTlwfBcvb/gzh3J3cyh3D8fyDgQd6VAVh3J38+Znj7Jw67PcMuLn9Czn75jImTSHtURrd88CcPcsMzv5jrs9UHoX9x2BsriUX5RH1pHNYW93wcZn2HHoU2aN+lGZBZykdpVeATjcPtwxn0+yFjGh13Vc0HNmzK5KW1eduaBSuDjOih1vsJL5nLN6FxN7hm913kgkTCdtO7yOh5Z+k14tRjCt9x2nbUkTiTfUJ2099Am/W/IVBrQey/Ted1TYQ3A4dy9PrLqPjRHaJ/TMoftFxYW8v+1F3tj4t4guRBTuea65Bdm89ulfWcSz+KHIvCPJUQ9rWOWXWgE43NbvW8KGfUs5q8NUpvS6TXOPo8AwVmbNj+g5ercYydUD/7NWtjCTeODEWxdruLvwyhv3Ve53zMxuB24HaNKkSZjDqB2JlshXz3uAbYfWs/3QBrYd2sD+7MrnoYRq0/6P+dWbt3PL6B/TpXn/sLQpVReJHtbSCoryeG3d31m+7Q2+PPbX2r4gjCLRw1paEUWs3DWfVg07MqbLZWFpM9IxA+w8uokN+5bRvnHPU6M4CiKU3Je2Zs8ikhKSuXrg3WVWMV6ZNZ9nPvkluYWR+X0zjNzCL1bMLSou5F8r7+Xj3Qsicr7SSm/BU1Mf7XqTf6/9HccqWQgqHLSScHhFYgRDaY6zbMcrFBTncc2g/yJFizLVqiZpLTEScIrD3nZaUkMu7fd1zmo/NWYW/pM6Ir7y1WonrHvMrG2gd7UtsDdQvgPoWKpeB2BXeQ24+8PAwwDt2rWrk9/WxIQkerQcTI+Wg0+VHc8/dip53XZoAxv3fljtpOfIif38dsE3uHzI1xjT7RL9sYqCExHsYQVolNqMc7pewuiul9AkvXlEz1XfFFawAnA4tGzQkVE5Qxg74ZthXckx2PYsNZGZ1opx3a7l7I4XkZqUftprkXxDnZSQwsgO0zi/6zW0bNDxtNeKigt5+pNf8MHOV8JyrozkxrRq0ImWDTrRqmHHkvsGHWme0f60lVUTE5K4aehP2XhgOe9ueYa1+94Py/nLE84e1s6ZAxjV8SKWbJ8bkaQ10ZJokJJJo1T9LQqnSP9+jegwhXFdZ9KqYaeInUcqlpiQRJO0Fhw+sTd45Sro32oMVw74bkiLxYmUUQ8XXSrPHGAW8LPA/Yulyh83s19TsuhST2BZTYOsSzJSGtG79Qh6ty6ZJ/b7d77Fxn0fVbu9Ii/k7U+fomXD9qfalNozsvMU+rUdTW5BNicKcjgRWIAptyDnVNnyba9z5ET5WxdVpHPTfoztfjmDO4wjKSE5QtHXb+d2u5zsvEMUFhdQUJRP7patWIeWFBbnU1icT07+EbYcrNr8v06ZfRnf43oGtD2X7JcWhH3bgV4tR5Ce3JDC4gKKigspKi6gyAsoPPm4uLDKQ2ZbN+zC+O7XM6zdxArnxfdvdQ4NUzIpKMqjsDifgqI8Ck7d51FYlM+6Pe+XuxdrRTKSGzOm8+Wc2/mKCvdnTUxI4qI+X6ZL0/6s2v0OGw+soLiaa/TdxE0MmXh7yPXNjF4tzqJXi7PYm72NhZ8/ywc7Xqny8M2hDKVJ196kJmWcuqUlZZCaWPK4cVr4kr9mGW2Y2vs/mNTzZj549Y983GIrG/ZX/b/YEe2n0KP5UBqkNKFBSiYNU5pSvOAjWk6drg9GI2B4+0m0a9zj1O9TftGJksdFgcfFeXyy+90qXfunfr+6XE6jVE0dirbM9NZhS1gbJDfhsv7fZGjbC/X7KNUTfyOCQ9rW5glKFlhqYWY7gB9Rkqg+bWa3AtuAqwDcfY2ZPQ2sBQqBr8bzCsHBFHsx2w9VsnxkBTJSGtOr1TB6txpB79bDad6gbQSik1AkJaaQmd6SzPSWFdbZsPeDkBLWREtiSIcLGNv9Cjo36xu0vtTM2G5XnPb86Ja3aDxswqnn2w9v4IF3bwuprT6tzmZ8j+vp3nxIRN9AnNVhatAtUP7f3PMoDmHoWXvaM2n4V+nf+tygWyJ0bTYo6KJI9748jaMcDXreTDK5oN+XGNlhepme3PI0Sm3K6E4zGN1pBscLjrJmz3us2r2ADfs/qNI85GyqP5y4VcNOXNH/20ztdRtLts9l0dZnQ37zOZGJtOl3SbXPXR2JCUn0ox+jzv46+3N2smT7HJZtfzmk1ZIBujUbzMiO008rO8o6vTmOkFB+v+5+bSL5IQwfb57RjvO7XsNZHaaF9PsltaNpWiu2lnqeltSQpumtaZrWmqbprclMb82CLU8GXc17cJsLuLz/t/QhhNRYvP01D2WV4GsreGlCeYXufh9wX02Cihf7sndwojD4kNKkhGS6tRh4KkFtn9lTe27VIcGGfDdOa86YrjMY3fViGmkRrZgRLBlKsESGtBvPBT2ui9g2NlXl7kGT1V4tzmJCjxtpteQATdqcF7ZzB+td7dC4Fxd0u54uKwtp2mVytc6Rkdz4VNJ+ovA46/a+z4qVT7MpcXPQns8D1HyIbEZyY8Z3u47zu1zNstf+wAdN1wddhTcv2GatEdaiQXsu6vNlpvS6jdW732XxtjlBe+FD3QJHaoe7Bx023CmzHxd0u46BbcaSYIm1FJmEamyXqxjefjKZgQQ1PblhmTpr977Hlgp+9xqmNOWK/t9hcNtxkQ1U6g8NCZZQba9ky5sOmT3p1Wo4vVuPoFuLgVokoQ6raJ5rl2YDGNv9cga1P0/DfmNQRQscJSemcnanixjX/RqaZcTW6IaKVhE2jEFtz2d89xvomNkHgKO8GdZzF1D+96t3i5GM73Y9PZoPw8w4ujI8501LymBouwvpvhLSLjyX9fuWsmr3O6zd+165HwTup2rD8iuTmJBEf/ozevQ3+PzwWhZufYaVWW+VO2Qzn8gvWBWKpIRkhrSbwJB2E9ibvY0l2+bwwY5Xy11AKdSeWKkdRcUFFS7Y07/1uVzQ7Vq6Nh2kHvAY1qXpgKB1MtNaA2U/ABvebjKX9vs6DVLq5gKkEqPCkK+aWRrwLpBKSc74rLv/yMx+CVwM5AOfATe7+2Ezm0jJKNyUwGvfdfe3ymn3XuA/gH2Bov9y90oXs1DCGkHbDn6RsDbNaE3vQILaq9UwGqZmRi8wCRt3P62HNSkhhWEdxnNu9yvo2LRXFCOTYM5clCkjuTHndr2Cc7teHrO/n2fuTZpoSYzoMIULul8X0QVX3P20HtYES2RYuwsZ1/Va2jWOfO9zSmIag9qcz6A251NYlM+nB1awavcCVu9ZyPGCkmHK4ehhLU/nzH50HvIjLurzFd7//Hne3/biqXNC9HtYy9OqYScu6XcXU3v/B6t2v8PibXPYfHDlqddz8rUKcCw5c1spLaQUn5qmtz7teePUFlw14Lv0bz0mShFJXAtPD2seMN7ds80sGVhkZq8C84B73L3QzH4O3AN8D9gPXOzuu8xsAPA6FW9v+oC7/yrUQJSwRlDD1EyuHPpNercaQcuG7fXpaBzKLzpBsReRmd6ScwLDfmM12ZHTnRwS3DS9DeO6X8PITqHNuYymwsAqwimJ6YzufAnnd72mVvZdLPYiHCc1MZ1RnS7hvC5Xl3nzVVuSElPo12o0/VqNpqj4u3x2cCWrdi9g3bZ3cPeI/Z3NTGvJtN53cGGPWazY+Trvbn2GPdlbY6aHtTzJiakMbz+J4e0nsfvYFhZvm8PyHa+qhzXG5AeGA3+xkFLFC5VJ3VV6D9WzO1zEJX2/SnpyoyhGJFI5d3c4tUBEcuDm7v5GqWpLgCsD9UuvMrsGSDOzVHev8Se7SlgjaFLfG6IdgkSYuzPr7B8zsO25Fa7AKrGpYWpTrh/2A4a0G1+nfnbjGMeECd+t1eFjRV7EBCYwfvz/i6k3WIkJSfRqMYJeLUZweFvli9qES0piGqM7zWBUx0v4dP8H2AdVX1gvGto06spl/b/B9D53su3wmmiHI6UkWAKX9/+WFlKKcycXYbp64N30bnlWtMOReBemOaxmlgisAHoAf3D3pWdUuQV4qpxDrwA+qiRZvcvMbgKWA99x90OVxaGVfURqIC05gyHtx9WphEdKdG02kOEdJtWpn12DlMaMY1ytz3VKSUxlLGNjKlk9UwIJtTqKxczo3XIkbWgTvHIMSUlMpUfzYdEOQ0pplNqMc7tcoWQ1znXOHMB3z3tUyapEnod4K9kBZnmpW5m94dy9yN2HAB2AkYGhvgCY2fcp2RXmsdLHmFl/4OfAHRVE+CegOzAEyALuD/Yl1Z13aiIiIiIidVCDlMbRDkHqCcOx0HpY97v7iFAqBhZVWgBMAVab2SzgImBCYOhwybnNOgAvADe5+2cVtLWnVP1HgLnBzq8eVhERERERETnFzFqaWWbgcTpwIbDezKZQssjSJe5+vFT9TOBlShZkeq+SdktvwXAZsDpYLOphFRERERERiRfFYZnD2haYHZjHmgA87e5zzWwTJVvdzAtMxVni7ncCd1Ey1/UHZvaDQBuT3H2vmf0FeMjdlwO/MLMhlAxM3krFQ4dPUcIqIiIiIiISN2qesLr7KmBoOeXl7mfn7v8D/E8Fr91W6vGNVY1FCauIiIiIiEi8CM8iwTFDCauIiIiIiEg8cMK2rU2sUMIqIiIiIiISL5SwioiIiIiISOzxuEtYta2NiIiIiIiIxCT1sIqIiIiIiMSLOOthVcIqIiIiIiISL5SwioiIiIiISMyJw1WCNYdVREREREREYpJ6WEVEREREROKA43hxcbTDCCslrCIiIiIiIvEivkYEK2EVERERERGJH/GVsSphFRERERERiRdxtuiSElYREREREZF44ECxElYRERERERGJSUpYRUREREREJBbFV75as4TVzLYCx4AioNDdR5hZM+ApoAuwFbja3Q/VLEwRERERERGpnMfdHNaEMLRxgbsPcfcRged3A/PdvScwP/BcREREREREIsy9OOitLglHwnqmGcDswOPZwKUROIeIiIiIiIiU5iHe6pCaJqwOvGFmK8zs9kBZa3fPAgjct6rhOURERERERCQU7sFvdUhNF10a4+67zKwVMM/M1od6YCDBvR2gSZMmNQxDRERERESkftuatfn1WT++pkUIVfdHPJgwqVHC6u67Avd7zewFYCSwx8zaunuWmbUF9lZw7MPAwwDt2rWrW2m+iIiIiIhIjHH3KdGOIdyqPSTYzBqYWaOTj4FJwGpgDjArUG0W8GJNgxQREREREZH6pyY9rK2BF8zsZDuPu/trZvYB8LSZ3QpsA66qeZgiIiIiIiJS31Q7YXX3zcDgcsoPABNqEpSIiIiIiIhIJLa1EREREREREakxJawiIiIiIiISk5SwioiIiIiISExSwioiIiIiIiIxSQmriIiIiIiIxCQlrCIiIiIiIhKTlLCKiIiIiIhITFLCKiIiIiIiIjFJCauIiIiIiIjEJCWsIiIiIiIiEpOUsIqIiIiIiEhMUsIqIiIiIiIiMUkJq4iIiIiIiMQkJawiIiIiIiISk5SwioiIiIiISExSwioiIiIiIiIxSQmriIiIiIiIxCQlrCIiIiIiIhKTlLCKiIiIiIhITFLCKiIiIiIiIjFJCauIiIiIiIjEJCWsIiIiIiIiEpOUsIqIiIiIiEhMUsIqIiIiIiIiMUkJq4iIiIiIiMSkiCWsZjbFzDaY2SYzuztS5xEREREREZH4FJGE1cwSgT8AU4F+wLVm1i8S5xIREREREZH4FKke1pHAJnff7O75wJPAjAidS0REREREROJQpBLW9sD2Us93BMpEREREREREQmLuHv5Gza4CJrv7bYHnNwIj3f1rpercDtweeDoAWB32QERC1wLYH+0gpF7TNSjRpOtPok3XoERbb3dvFO0gpKykCLW7A+hY6nkHYFfpCu7+MPAwgJktd/cREYpFJChdgxJtugYlmnT9SbTpGpRoM7Pl0Y5ByhepIcEfAD3NrKuZpQAzgTkROpeIiIiIiIjEoYj0sLp7oZndBbwOJAJ/c/c1kTiXiIiIiIiIxKdIDQnG3V8BXgmx+sORikMkRLoGJdp0DUo06fqTaNM1KNGmazBGRWTRJREREREREZGaitQcVhEREREREZEaiXrCamZTzGyDmW0ys7ujHY/EBzPraGZvm9k6M1tjZt8IlDczs3lmtjFw37TUMfcErsMNZja5VPlwM/sk8Npvzcyi8TVJ3WNmiWb2kZnNDTzX9Se1xswyzexZM1sf+Fs4Wteg1CYz+1bg/+DVZvaEmaXpGpRIMrO/mdleM1tdqixs15yZpZrZU4HypWbWpVa/wHoqqgmrmSUCfwCmAv2Aa82sXzRjkrhRCHzH3fsCo4CvBq6tu4H57t4TmB94TuC1mUB/YArwx8D1CfAnSvYM7hm4TanNL0TqtG8A60o91/Untek3wGvu3gcYTMm1qGtQaoWZtQe+Doxw9wGULMI5E12DEln/oOz1Ec5r7lbgkLv3AB4Afh6xr0ROiXYP60hgk7tvdvd84ElgRpRjkjjg7lnu/mHg8TFK3qi1p+T6mh2oNhu4NPB4BvCku+e5+xZgEzDSzNoCjd19sZdM+P5nqWNEKmRmHYDpwF9KFev6k1phZo2B84C/Arh7vrsfRteg1K4kIN3MkoAMYBe6BiWC3P1d4OAZxeG85kq39SwwQT3+kRfthLU9sL3U8x2BMpGwCQzXGAosBVq7exaUJLVAq0C1iq7F9oHHZ5aLBPMg8J9AcakyXX9SW7oB+4C/B4al/8XMGqBrUGqJu+8EfgVsA7KAI+7+BroGpfaF85o7dYy7FwJHgOYRi1yA6Ces5X0ioWWLJWzMrCHwHPBNdz9aWdVyyryScpEKmdlFwF53XxHqIeWU6fqTmkgChgF/cvehQA6BYXAV0DUoYRWYJzgD6Aq0AxqY2Q2VHVJOma5BiaTqXHO6HqMg2gnrDqBjqecdKBkuIlJjZpZMSbL6mLs/HyjeExjqQeB+b6C8omtxR+DxmeUilRkDXGJmWymZ6jDezP6Frj+pPTuAHe6+NPD8WUoSWF2DUlsuBLa4+z53LwCeB85B16DUvnBec6eOCQx1b0LZIcgSZtFOWD8AeppZVzNLoWTi85woxyRxIDCf4K/AOnf/damX5gCzAo9nAS+WKp8ZWP2tKyUT7JcFho4cM7NRgTZvKnWMSLnc/R537+DuXSj5u/aWu9+Arj+pJe6+G9huZr0DRROAtegalNqzDRhlZhmBa2cCJetJ6BqU2hbOa650W1dS8v+7elgjLCmaJ3f3QjO7C3idktXj/ubua6IZk8SNMcCNwCdmtjJQ9l/Az4CnzexWSv4zvQrA3deY2dOUvKErBL7q7kWB475Myapz6cCrgZtIdej6k9r0NeCxwAfCm4GbKfmgWtegRJy7LzWzZ4EPKbmmPgIeBhqia1AixMyeAMYBLcxsB/Ajwvt/71+BR81sEyU9qzNr4cuq90wfCoiIiIiIiEgsivaQYBEREREREZFyKWEVERERERGRmKSEVURERERERGKSElYRERERERGJSUpYRUREREREJCYpYRUREREREZGYpIRVREREREREYpISVhEREREREYlJ/x9epMRJleZ2vgAAAABJRU5ErkJggg==\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\"))\n",
    "spdis = cbb.get_data(text=\"SPDIS\")[-1]\n",
    "qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(\n",
    "    spdis, vertex_ml6, head=hdata\n",
    ")\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_vector(\n",
    "    qx,\n",
    "    qy,\n",
    "    qz,\n",
    "    head=hdata,\n",
    "    hstep=3,\n",
    "    normalize=True,\n",
    "    color=\"green\",\n",
    "    scale=30,\n",
    "    headwidth=3,\n",
    "    headlength=3,\n",
    "    headaxislength=3,\n",
    "    zorder=10,\n",
    ")\n",
    "\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting using built in styles\n",
    "\n",
    "FloPy's plotting routines can be used with built in styles from the `styles` module. The `styles` module takes advantage of matplotlib's temporary styling routines by reading in pre-built style sheets. Two different types of styles have been built for flopy: `USGSMap()` and `USGSPlot()` styles which can be used to create report quality figures. The styles module also contains a number of methods that can be used for adding axis labels, text, annotations, headings, removing tick lines, and updating the current font.\n",
    "\n",
    "This example will load the Keating groundwater transport model and plot results using `styles`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building mf6gwf model...ex-gwt-keating\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building mf6gwt model...ex-gwt-keating\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running mf6gwf model...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running mf6gwt model...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setup_pmv_demo.run_keating_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the flow and transport models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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 npf...\n",
      "    loading package ic...\n",
      "    loading package chd...\n",
      "    loading package rch...\n",
      "    loading package oc...\n",
      "  loading ims package flow...\n",
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwt6...\n",
      "    loading package dis...\n",
      "    loading package ic...\n",
      "    loading package mst...\n",
      "    loading package adv...\n",
      "    loading package dsp...\n",
      "    loading package fmi...\n",
      "    loading package ssm...\n",
      "    loading package oc...\n",
      "    loading package obs...\n",
      "  loading ims package trans...\n"
     ]
    }
   ],
   "source": [
    "sim_path = os.path.join(\"data\", \"mf6-gwt-keating\", \"mf6gwf\")\n",
    "tr_path = os.path.join(\"data\", \"mf6-gwt-keating\", \"mf6gwt\")\n",
    "sim_name = \"mfsim.nam\"\n",
    "sim = flopy.mf6.MFSimulation.load(\n",
    "    sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, sim_ws=sim_path\n",
    ")\n",
    "gwf6 = sim.get_model(\"flow\")\n",
    "\n",
    "sim = flopy.mf6.MFSimulation.load(\n",
    "    sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, sim_ws=tr_path\n",
    ")\n",
    "gwt6 = sim.get_model(\"trans\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x2400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import styles\n",
    "from flopy.plot import styles\n",
    "\n",
    "# load head file and plot\n",
    "head = gwf6.output.head().get_data()\n",
    "\n",
    "with styles.USGSMap():\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 8), dpi=300, tight_layout=True)\n",
    "\n",
    "    xsect = flopy.plot.PlotCrossSection(model=gwf6, ax=ax, line={\"row\": 0})\n",
    "    pc = xsect.plot_array(head, head=head, cmap=\"jet\")\n",
    "    xsect.plot_bc(ftype=\"RCH\", color=\"red\")\n",
    "    xsect.plot_bc(ftype=\"CHD\")\n",
    "    plt.colorbar(pc, shrink=0.25)\n",
    "\n",
    "    # add a rectangle to show the confining layer\n",
    "    confining_rect = mpl.patches.Rectangle(\n",
    "        (3000, 1000), 3000, 100, color=\"gray\", alpha=0.5\n",
    "    )\n",
    "    ax.add_patch(confining_rect)\n",
    "\n",
    "    # set labels using styles\n",
    "    styles.xlabel(label=\"x-position (m)\")\n",
    "    styles.ylabel(label=\"elevation (m)\")\n",
    "    styles.heading(\n",
    "        letter=\"A.\", heading=\"Simulated hydraulic head\", fontsize=10\n",
    "    )\n",
    "    ax.set_aspect(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting concentration model results using the `USGSMap()` style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the transport output file\n",
    "cobj = gwt6.output.concentration()\n",
    "plot_times = [100, 1000, 3000]\n",
    "obs1 = (48, 0, 118)  # Layer, row, and column for observation 1\n",
    "obs2 = (76, 0, 358)  # Layer, row, and column for observation 2\n",
    "xgrid, _, zgrid = gwf6.modelgrid.xyzcellcenters\n",
    "\n",
    "\n",
    "with styles.USGSPlot():\n",
    "    fig, axes = plt.subplots(3, 1, figsize=(15, 9), tight_layout=True)\n",
    "    for ix, totim in enumerate(plot_times):\n",
    "        heading = \"Time = {}\".format(totim)\n",
    "        conc = cobj.get_data(totim=totim)\n",
    "        ax = axes[ix]\n",
    "        xsect = flopy.plot.PlotCrossSection(model=gwf6, ax=ax, line={\"row\": 0})\n",
    "        pc = xsect.plot_array(conc, head=head, cmap=\"jet\", vmin=0, vmax=1)\n",
    "        xsect.plot_bc(ftype=\"RCH\", color=\"red\")\n",
    "        xsect.plot_bc(ftype=\"CHD\")\n",
    "\n",
    "        # plot confining layer\n",
    "        confining_rect = mpl.patches.Rectangle(\n",
    "            (3000, 1000), 3000, 100, color=\"gray\", alpha=0.5\n",
    "        )\n",
    "        ax.add_patch(confining_rect)\n",
    "\n",
    "        # set axis labels and title using styles\n",
    "        styles.ylabel(ax=ax, label=\"elevation (m)\", fontsize=10)\n",
    "        if ix == 2:\n",
    "            styles.xlabel(ax=ax, label=\"x-position (m)\", fontsize=10)\n",
    "        styles.heading(ax=ax, heading=heading, idx=ix, fontsize=12)\n",
    "\n",
    "        ax.set_aspect(1.0)\n",
    "\n",
    "        # add observation locations based on grid cell centers\n",
    "        for k, i, j in [obs1, obs2]:\n",
    "            x = xgrid[i, j]\n",
    "            z = zgrid[k, i, j]\n",
    "            ax.plot(x, z, mfc=\"yellow\", mec=\"black\", marker=\"o\", ms=\"8\")"
   ]
  },
  {
   "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 `matplotlib.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": []
  }
 ],
 "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}