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": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_fork/flopy\n",
      "3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 14:38:56) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.17.3\n",
      "matplotlib version: 3.1.1\n",
      "flopy version: 3.3.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import platform\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set names of the MODFLOW exes\n",
    "# assumes that the executable is in users path statement\n",
    "v2005 = 'mf2005'\n",
    "exe_name_2005 = 'mf2005'\n",
    "vmf6 = 'mf6'\n",
    "exe_name_mf6 = 'mf6'\n",
    "exe_mp = 'mp6'\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name_2005 += '.exe'\n",
    "    exe_name_mf6 += '.exe'\n",
    "    exe_mp += '.exe'\n",
    "\n",
    "#Set the paths\n",
    "loadpth = os.path.join('..', 'data', 'freyberg')\n",
    "modelpth = os.path.join('data')\n",
    "\n",
    "#make sure modelpth directory exists\n",
    "if not os.path.exists(modelpth):\n",
    "    os.makedirs(modelpth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Run an Existing MODFLOW-2005 Model\n",
    "A model called the \"Freyberg Model\" is located in the loadpth folder.  In the following code block, we load that model, then change into a new workspace (modelpth) where we recreate and run the model.  For this to work properly, the MODFLOW-2005 executable (mf2005) must be in the path.  We verify that it worked correctly by checking for the presence of freyberg.hds and freyberg.cbc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "changing model workspace...\n",
      "   data\n",
      "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): 2020/06/26  9:29:51\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): 2020/06/26  9:29:51\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('freyberg.nam', model_ws=loadpth, \n",
    "                                exe_name=exe_name_2005, version=v2005)\n",
    "ml.change_model_ws(new_pth=modelpth)\n",
    "ml.write_input()\n",
    "success, buff = ml.run_model()\n",
    "if not success:\n",
    "    print ('Something bad happened.')\n",
    "files = ['freyberg.hds', 'freyberg.cbc']\n",
    "for f in files:\n",
    "    if os.path.isfile(os.path.join(modelpth, f)):\n",
    "        msg = 'Output file located: {}'.format(f)\n",
    "        print (msg)\n",
    "    else:\n",
    "        errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
    "        print (errmsg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Cross-Section of the Model Grid\n",
    "\n",
    "Now that we have a model, we can use the FloPy plotting utilities to make cross-sections. We'll start by making a Map to show the model grid and basic boundary conditions. Then we'll begin making a cross section using the `PlotCrossSection` class and the `plot_grid()` method of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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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ynAxD8341yaNnhoz+RoZj+KXxPb92L/vZ02nj/j6d5O2ZuT5s7AX9/QyfrasyXNv57qUK7O7LMvyb0dlLb2EPM6s+KENguaCqrktybpIvZxgSu9T73lsdLsrwhcFrM7TxL2X4jCy07dcyTPLy7rE9PnCP9auuR4ZezDdkaAsXZ/hsuNk6bHA1XFMOAHDgVdWZGa6jW+0srwDr1v66USoAwF7VMOvn/5PkAQe2JgAHxpLDGsdrEd5XVf9Yw409TxuXn1rDDVR3jD+LzXIGALBXVfXCDMNzf7eHm2kDbDpLDmscZxi7VXfvHC9qf1eG60SOT7Kzu39v7asJAACwsS05rHG80enO8emW8ceFagAAAHO0rNkaxyl9dyS5Osk5440ik+TkqvpQVZ1ZVbdds1oCAABscCuarXG8n89fZbiXyTUZpoHuDDfSPLK7f36B15yU5KQkyS3zg1vvuHXPTVZt5/VDh97WQ5S5Wcpcszp2svX6uRWZneMdopSpTGVOp8z1UMc1LbOm/e+7MpU55TLXQx2VuYbniHO8+djXO7nx8ny+uw9faP2Kp9Kvqucn+erstWbj7Epv6e693mDy0Lsf2td9esFbAq3KYS8e7v167SnXLrGlMjdKmWtWxy9/Ode+eG5F5rBTht/KVKYyp1PmeqjjmpZ5m9tM+t93ZSpzymWuhzoqc43K3PXlXHuPuRWZ4y5Lzv8v+UB3b19o/XJmazx87DFLVd0iw804P1ZVR85s9rgMMywBAACwCsu5z9mRSc6qqoMyhLmzu/stVfWqqtqWYVjjJUmesXbVBAAA2NiWM1vjh7LAzSC7+ylrUiMAAIBNaI6XtwEAALBawhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABCwZzqrq5lX1vqr6x6q6qKpOG5ffrqrOqapPjL9vu/bVBQAA2JiW03P2jSQ/2t33T7ItyfFV9cAkpyQ5t7vvleTc8TkAAACrsGQ468HO8emW8aeTPCbJWePys5I8dk1qCAAAsAks65qzqjqoqnYkuTrJOd19QZI7dfcVSTL+vuPaVRMAAGBjW1Y46+4bu3tbkqOTHFNV91vuDqrqpKq6sKou3LVr12rrCQAAsKGtaLbG7r42yXlJjk9yVVUdmSTj76sXec0Z3b29u7dv2bJlH6sLAACwMS1ntsbDq+qw8fEtkjw8yceSvDnJCeNmJyT567WqJAAAwEZ38DK2OTLJWVV1UIYwd3Z3v6Wq3pvk7Kp6epLPJnn8GtYTAABgQ1synHX3h5I8YIHlX0jysLWoFAAAwGazomvOAAAAWBvCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAQIZwAAABMgnAEAAEyAcAYAADABwhkAAMAELBnOquouVfUPVXVxVV1UVc8el59aVZ+rqh3jz0+sfXUBAAA2poOXsc0NSZ7T3R+sqkOTfKCqzhnX/UF3/97aVQ8AAGBzWDKcdfcVSa4YH19XVRcnOWqtKwYAALCZrOias6q6W5IHJLlgXHRyVX2oqs6sqtvOuW4AAACbxrLDWVVtTfLGJL/U3V9J8rIk90iyLUPP2u8v8rqTqurCqrpw165dc6gyAADAxrOscFZVWzIEs9d095uSpLuv6u4bu/ubSV6e5JiFXtvdZ3T39u7evmXLlnnVGwAAYENZzmyNleQVSS7u7tNnlh85s9njknxk/tUDAADYHJYzW+ODkzwlyYerase47LlJnlhV25J0kkuSPGNNaggAALAJLGe2xnclqQVWvXX+1QEAANicVjRbIwAAAGtDOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYgCXDWVXdpar+oaourqqLqurZ4/LbVdU5VfWJ8fdt1766AAAAG9Nyes5uSPKc7r5PkgcmeVZV3TfJKUnO7e57JTl3fA4AAMAqLBnOuvuK7v7g+Pi6JBcnOSrJY5KcNW52VpLHrlUlAQAANroVXXNWVXdL8oAkFyS5U3dfkQwBLskdF3nNSVV1YVVduGvXrn2rLQAAwAa17HBWVVuTvDHJL3X3V5b7uu4+o7u3d/f2LVu2rKaOAAAAG96ywllVbckQzF7T3W8aF19VVUeO649McvXaVBEAAGDjW85sjZXkFUku7u7TZ1a9OckJ4+MTkvz1/KsHAACwORy8jG0enOQpST5cVTvGZc9N8uIkZ1fV05N8Nsnj16aKAAAAG9+S4ay735WkFln9sPlWBwAAYHNa0WyNAAAArA3hDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAIOPtAV2Cx2Xr8zx73yuLmWt/WQrXMrDwAAOLD0nO0H247YNvcgtfWQrdl2xLa5lgkAABw4es72k21HbMt5J543t/Lm2QsHAAAceHrOAAAAJmDJcFZVZ1bV1VX1kZllp1bV56pqx/jzE2tbTQAAgI1tOT1nr0xy/ALL/6C7t40/b51vtQAAADaXJcNZd78zyRf3Q10AAAA2rX255uzkqvrQOOzxtottVFUnVdWFVXXhrl279mF3AAAAG9dqZ2t8WZIXJunx9+8n+fmFNuzuM5KckSSH3v3QXuX+2E/Ww/3Y1qSOcysNAABWZ1U9Z919VXff2N3fTPLyJMfMt1ocCOvhfmxrVscr51okAACs2Kp6zqrqyO6+Ynz6uCQf2dv2u31919cn3yuz2a2H+7GtSR0/fv7cygMAgNVYMpxV1euSHJfkDlV1WZLnJzmuqrZlGNZ4SZJnLGdnt9hyi1VXdCHz7pXZbd7D5nZcuWNN6gkAAGwcS4az7n7iAotfsZqd3fv2914XvTI7rtwx9zLn+b4BAICNZ7UTgmxowhQAALC/7ctU+gAAAMzJuu8523HlDteHAQAA6966DmfnnXje3K87W09DGgVTAADYONZ1OEuyboLUvG32YAoAABvNug9nm5kgBQAAG4cJQQAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACbg4ANdATa+HVfuyHGvPG5uZW07YttcygIAgCnRc8aaOu/E8+YaprYdsS3nnXje3MoDAICp0HPGmhOmAA68ndfvnNsoht3lbT1k69zKA0DPGQBseNuuzNyD1NZDthpmDjBnes4AYBOY97DwefbCATDQcwYAADABwhkAAMAECGcAAAATIJwBAABMgHAGAAAwAUuGs6o6s6qurqqPzCy7XVWdU1WfGH/fdm2rCQAAsLEtp+fslUmO32PZKUnO7e57JTl3fA4AAMAqLRnOuvudSb64x+LHJDlrfHxWksfOuV4AAACbymqvObtTd1+RJOPvO86vSgAAAJvPmk8IUlUnVdWFVXXhNddcs9a7AwAAWJdWG86uqqojk2T8ffViG3b3Gd29vbu3H3744avcHQAAwMa22nD25iQnjI9PSPLX86kOAADA5rScqfRfl+S9Se5dVZdV1dOTvDjJj1XVJ5L82PgcAACAVTp4qQ26+4mLrHrYnOsCAACwaa35hCAAAAAsTTgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACTj4QFcAAID1Y+f1O3PcK4+ba3lbD9k6t/JgPdNzBgDAsmw7Ytvcg9TWQ7Zm2xHb5lomrFd6zgAAWLZtR2zLeSeeN7fy5tkLB/O285vJcZfNr7wd39j7euEMAGCFDO3bfBzzzWfbEduy48odyR3n17O7Lcn5OX/R9cIZAMAKfOuEbY4M7Zs2x3zzmndPcZLU02rRdcIZAMAKGdo3ffPs6dpx5Q7HfM70RC5MOAMAmIAdV+5wsjon8+7pWovek81MT+TihDMAgAPsvBPPm3tPyno6WZ13MF2Lnq7NbN69XHoiFyecAQBMwLyDxHo5WV2LYCqYzc9a9HI5PosTzgCADW2tvvVnfpyoT5swtf+4CTUAsGGtxU2TnagCa0XPGQCwoQlTwHqh5wwAAGAChDMAAIAJ2KdhjVV1SZLrktyY5Ibu3j6PSgEAAGw287jm7Ee6+/NzKAcAWEfcNHn61ur+YcDaMCEIALBim/2myeuB+4ex2WyELyP2NZx1krdXVSf5k+4+Yw51AgDWgc160+T1RJCaNr3P87NRvozY13D24O6+vKrumOScqvpYd79zdoOqOinJSUly17vedR93BwAA699a9T4n8/2iYz0NZd0IX0bsUzjr7svH31dX1V8lOSbJO/fY5owkZyTJ9u3be1/2BwAAG8VahImN0Hu0ma06nFXVrZLcrLuvGx//eJIXzK1mAADAighS69u+9JzdKclfVdXucl7b3X83l1oBAABsMqsOZ939qST3n2NdAAAANi1T6QMAk7ERpsIGWK2bHegKAAAkw7Uy8w5SJjMA1hM9ZwDAZAhSwGam5wwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmQDgDAACYAOEMAABgAoQzAACACRDOAAAAJkA4AwAAmADhDAAAYAKEMwAAgAkQzgAAACZAOAMAAJgA4QwAAGAChDMAAIAJEM4AAAAmYJ/CWVUdX1Ufr6pPVtUp86oUAADAZrPqcFZVByV5aZJHJrlvkidW1X3nVTEAAIDNZF96zo5J8snu/lR3X5/k9UkeM59qAQAAbC4H78Nrj0py6czzy5L88L5VBw6MHUckx504v/J2HjL8VqYylTmdMtdDHdeqzB1HJNvmVxwAa6S6e3UvrHp8kkd09y+Mz5+S5Jju/sU9tjspyUnj0/sl+cjqqwtr5g5JPn+gKwGL0D6ZKm2TKdM+marv7u7DF1qxLz1nlyW5y8zzo5NcvudG3X1GkjOSpKou7O7t+7BPWBPaJlOmfTJV2iZTpn2yHu3LNWfvT3Kvqrp7VR2S5AlJ3jyfagEAAGwuq+456+4bqurkJH+f5KAkZ3b3RXOrGQAAwCayL8Ma091vTfLWFbzkjH3ZH6whbZMp0z6ZKm2TKdM+WXdWPSEIAAAA87Mv15wBAAAwJ/slnFXV8VX18ar6ZFWdsj/2yeZWVXepqn+oqour6qKqeva4/HZVdU5VfWL8fduZ1/za2EY/XlWPmFn+g1X14XHdf6uqOhDviY2lqg6qqv9TVW8Zn2ubTEJVHVZVb6iqj43/hh6rfTIFVfWfxv/TP1JVr6uqm2ubbDRrHs6q6qAkL03yyCT3TfLEqrrvWu+XTe+GJM/p7vskeZ/Dj0UAAAM6SURBVGCSZ43t7pQk53b3vZKcOz7PuO4JSf51kuOT/Pex7SbJyzLcq+9e48/x+/ONsGE9O8nFM8+1TabiD5P8XXf/qyT3z9BOtU8OqKo6Ksl/TLK9u++XYTK6J0TbZIPZHz1nxyT5ZHd/qruvT/L6JI/ZD/tlE+vuK7r7g+Pj6zKcXByVoe2dNW52VpLHjo8fk+T13f2N7v50kk8mOaaqjkxy6+5+bw8XaP75zGtgVarq6CSPSvKnM4u1TQ64qrp1kn+b5BVJ0t3Xd/e10T6ZhoOT3KKqDk5yywz319U22VD2Rzg7KsmlM88vG5fBflFVd0vygCQXJLlTd1+RDAEuyR3HzRZrp0eNj/dcDvviJUl+Nck3Z5Zpm0zB9yS5JsmfjcNu/7SqbhXtkwOsuz+X5PeSfDbJFUm+3N1vj7bJBrM/wtlC43hNEcl+UVVbk7wxyS9191f2tukCy3ovy2FVqurRSa7u7g8s9yULLNM2WSsHJ/mBJC/r7gck+WrGYWKL0D7ZL8ZryR6T5O5J7pzkVlX15L29ZIFl2iaTtz/C2WVJ7jLz/OgM3dCwpqpqS4Zg9pruftO4+KpxSEPG31ePyxdrp5eNj/dcDqv14CQ/VVWXZBjm/aNV9epom0zDZUku6+4LxudvyBDWtE8OtIcn+XR3X9Pdu5K8KcmDom2yweyPcPb+JPeqqrtX1SEZLs58837YL5vYOPPSK5Jc3N2nz6x6c5ITxscnJPnrmeVPqKrvqqq7Z7hA+H3jEInrquqBY5lPnXkNrFh3/1p3H93dd8vw7+H/6u4nR9tkArr7yiSXVtW9x0UPS/LRaJ8ceJ9N8sCquuXYph6W4XpybZMN5eC13kF331BVJyf5+wwz65zZ3Ret9X7Z9B6c5ClJPlxVO8Zlz03y4iRnV9XTM/xD//gk6e6LqursDCchNyR5VnffOL7u3yd5ZZJbJHnb+APzpm0yFb+Y5DXjF6qfSvK0DF/map8cMN19QVW9IckHM7S1/5PkjCRbo22ygdQwUQ0AAAAH0n65CTUAAAB7J5wBAABMgHAGAAAwAcIZAADABAhnAAAAEyCcAQAATIBwBgAAMAHCGQAAwAT8X4qo7X6KljONAAAAAElFTkSuQmCC\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('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contouring an Array\n",
    "\n",
    "`PlotCrossSection` also has a `contour_array()` method.  It also accepts a 3D array for structured grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the horizontal hydraulic conductivities\n",
    "a = ml.lpf.hk.array\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "contour_set = xsect.contour_array(a, masked_values=[0], cmap='jet')\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid(color='grey')\n",
    "t = ax.set_title('Column 6 Cross-Section contour_array() horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(contour_set, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Heads\n",
    "\n",
    "We can easily plot results from the simulation by extracting heads using `flopy.utils.HeadFile`. \n",
    "\n",
    "The head can be passed into the `plot_array()` and `contour_array()` using the `head=` keyword argument to fix the top of the colored patch and contour lines at the top of the water table in each cell, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = os.path.join(modelpth, 'freyberg.hds')\n",
    "hdobj = flopy.utils.HeadFile(fname)\n",
    "head = hdobj.get_data()\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('plot_array() used to plotting Heads')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "pc = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid()\n",
    "cb = plt.colorbar(pc, shrink=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# contour array on top of heads\n",
    "levels = np.arange(17, 26, 1)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('contour_array() and plot_array() of head values')\n",
    "\n",
    "# instantiate the PlotCrossSection object\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "\n",
    "# plot the head array and model grid\n",
    "pc = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "# do black contour lines of the head array\n",
    "contour_set = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, colors='k')\n",
    "plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(pc, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a surface on the cross section\n",
    "\n",
    "The `plot_surface()` method allows the user to plot a surface along the cross section. Here is a short example using head data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(10, 30, .5)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
    "\n",
    "# contour array and plot ibound\n",
    "ct = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, linewidths=2.5)\n",
    "pc = xsect.plot_ibound(head=head)\n",
    "\n",
    "#plot the surface and model grid\n",
    "wt = xsect.plot_surface(head, masked_values=[999.], color='blue', lw=2.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "\n",
    "plt.title('contour_array() and plot_surface()')\n",
    "cb = plt.colorbar(ct, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting discharge vectors\n",
    "\n",
    "`PlotCrossSection` has a `plot_discharge()` method, which takes the 'FLOW RIGHT FACE', 'FLOW FRONT FACE', and 'FLOW LOWER FACE' arrays, which can be written by MODFLOW to the cell by cell flow file.  These array values can be extracted from the cell by cell flow file using the `flopy.utils.CellBudgetFile` object as shown below.  Once they are extracted, they can be passed to the `plot_discharge()` method.  Note that `plot_discharge()` also takes the head array as an argument.  The head array is used by `plot_discharge()` to convert the volumetric flow in dimensions of $L^3/T$ to specific discharge in dimensions of $L/T$ and to plot the specific discharge in the center of each saturated cell. For this problem, there is no 'FLOW LOWER FACE' array since the Freyberg Model is a one layer model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = os.path.join(modelpth, 'freyberg.cbc')\n",
    "cbb = flopy.utils.CellBudgetFile(fname)\n",
    "frf = cbb.get_data(text='FLOW RIGHT FACE')[0]\n",
    "fff = cbb.get_data(text='FLOW FRONT FACE')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.set_title('plot_array() and plot_discharge()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, ax=ax, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_discharge(frf, fff, head=head, \n",
    "                              hstep=2, normalize=True, color='green', \n",
    "                              scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                              zorder=10)\n",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a cross section from Shapefile data\n",
    "\n",
    "A shapefile can be used to define the vertices for a instance of the `PlotCrossSection` class. The function `flopy.plot.plotutil.shapefile_get_vertices()` will return a list of vertices for each polyline in a shapefile.\n",
    "\n",
    "Let's plot the shapefiles and the Freyberg model using `PlotMapView` for visualization purposes and then plot the cross-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HT0gptTn2IVdS9ssCuo0Vy9LVVYrvQNtB2jJsPCU3yI6/X1bscZapj03wHXAlsu1g2FbPl0K8MXdGadWIICwss1vlMtggk57O0TJ7c4w7fx/fOuzaWUC4ZFVYmJ5k0XoW0A2y5hr15gbr+xtdsDW/Sq1YYnKi0lPZPm1gm4NrHj2xebBJ/XAPjqhvjZ3OclxFKZhGuu8AHGurlvgcE1yr+n0HHFflFeYIcQ0y4Mh+Ask3k2Qe3ksj+K6ncgy9bXWO9OUoOHLTK+1egJ7fTdmuUp6+zEJIIzSp799nt7HJzf0NnJ013tq6T9X+KpOFEpPlCuenu5XtmdqZJxpsc3DNoyeifOs49K0h2FomRqmgudIE34Hg36YRCuVjr3uE/bKGulYBWCaiaCuvhIQGWegfawzPMbO0KqO+dTDHhAaZr19TYYXSsqQYCYRJB+GyXaFUu8Tp8gVE0ablHlBvPqBxsMGN5gZuQ4Ot9VUWdl2Mc6c4H6lsnzSwzcE1j55QlEA83xqNo1oHpvkOyLanPsSBKiGpQTbm/bLUOXv1rYm+A22nC7C+n+yNcAQ/geE59oJwYoPMUUoB6UvoOPHSL9L51oE8M4OwXs0IQdmu9FS2LafJdnMNY+Ua/9U//U0256a4vTTN7/7TH1bSr3L1iapsc3DNI4yQb+1k41uBY0uromArPb2li23FV7aBMbeWfxkZ+dZx6FtDsPV8RQnYZqo3QihRG6epyxCAC8HW96FkI2w7UfolDEPxuL4cuvqQCdRK0rFJ5yvZFRamn+GZnXUs12X+4Q7ObI1OYYrl5iZOY523tu5RtV+hVigyWar0NMjOVM9gGhnomG+RyME1jzA2DzapH+xhyiIlvxQak8Q2n0beqiUDCMtI4ynBd0B6Pn67o5bFUZ4xNscRpVUj6FvTpF/S81WFC2pEN813YAQ/AXVcFipEO2ulSL+k7yM7jrqnKO8d1yCL4VuTrpvlXuav3sQzTUzXZe+FF3hh4XsAVdnWD9apN9e4qcH26uYDavbXqJYeP7DNwTWPMAK+tVZRezmlNZ9G36rlaJ31fhpB+j4iuLagJ8fuEt0IHa2O6yfQe1yyvrX/C0EGS3hdaQPxxtzH8BOIi7Slfj/Yer5U/S9dxSY2yDLwrSrHbF+kc9eWkYaB2emw+VzXzrJkVzgzdYkzU4qSarkHNOr3aBxuhmD71sP7VO2vMRmtbGeWuDT3rQe2ObjmEcbq9ip7bpu5uYuIot1TMSqeUfOhvgTXBdsCL1lWBfGjrLGRUVol9BYyomCpYxMaZFJK6LjK4lA3oNJMr8etbxWGgbAtVUESb8wtHVflaJvgGvGVbeTa49a3CkMgbFO5lBHvjQBKRidsE+kmeyNErz3s9z13YxUAy/PYeDbZK7holpmvXeDUjNq/TTXI1FDDzYM+sLWLSo0wc54L0xe4OHuRxcnFbyrY5uCaB9Crb71cOQNEBgaC5WAAYrpJIiVKZqUOHliij2OrloHoA+HEBpnjIYMcUnwHHsV+WXFL/VgawfG07wDdHRBgwL7wOH4Cafeicuyec4BGkBLpuuo+JApck7wRyKYeKe7uUdl8iGdZ+KbJ9sVzyQf37UZRsiYGKtt6c43G/hq3drdp1zc02L5CrVBiqlTh3PQ5lmaWvilgm4NrHoDiWxuHe0jDZiJB39oFWyViF0W7dxS233fA1xWkMFPn5Y8irUqKLtj6ULCgoGf1k3wHpKYW7OE5Zta3ZljqC8MAUyovgb5VQj+NoOgPT8mwhlx6nCCsXkujm6O+N6RexUS9EdDgWzBSczx5bTnkWx8+cwHftlNuJn01E4Dt6eoF5JxHx3CpH6rKdrmvsq0VSkyWJpQa4W0C2xxc8wAi+tYh+2VBt0KJjsLG+g64nqYFDKTrJzefHqW0KvhgJjTI/I4TWir6hpfiO3A0P4H0HCMgHPWzJbpE9/EdV7l+GS6+6yUacz8aaVUf36q/XIStfx8B2HbckDbwgy/A4HcdAceTV292+dYUSgBGp5RKhQnOFLqVbds9ZLv5gEZznZWDLZztDa6tr1EpfU1NkD1isM3BNQ8Abm3fYtdRfGtapHWEB8DW9zFsJa+Kq2wDEAsqtXHul5W01B8AMl8iLKnlX+m+A1muPS5pVXeJbmIA0jAVNxtw3zBozJ3VMWtMICz0cIg6UOVs2FaksvWCH4VgO319FdB863PJWupxUEpFq9xLIxzus7O3xra7ycrBFp3GOlcfPqBqdcE2SiOcnTx7LLDNwTUPxbfWe/nWxBhB36p8BzS4xlW2Gmz9dgfDNJCOEV/ZhtcezU8gUyUMqvFkKSCLbT55qKaOrhZlXGXbPWmma49sHWgZISWR1HySHSX/CuwYExtko5q6ZNS3GqbZzbG/stVgW7m3xkG5jOW6rF2+lMzTPoLdfwtGgfnpC5wqqKGGtntIvbnG9sE6K81NOo113tp6QM36uqIRJiYGOFvLyA6ZObjmwdbBFvWD3VS+NYyR9a2Rpk4MjSA9H+Ga3eowQfoVjKQex08g6V6G+g54qtkUVtkJvgNCiLFLq5JAOE7DKl1PSb+CTr+bZMx9dD+B2BQT7iVa2QobRKfD3OodTNfFMww2L15QdAIM0ghD+NZujkcH4aJV5vTURU5PqdVaALaNnTVu7z/kcHdNga399bCyPTt5lqVZXdlOnU29Zg6ueSi+td1iujQ/9NjR9a0pE0VCKGSwTCWZEiKxQSbRDRO97EwFhjFLqwQKJAJpVaIxN0peJTLlOGYQRg83aGlVmjG3ytHU01npOcIY9a3Ltyl2OhRbLQ7m53DmphB9la26E52jfq1T329jsqgEBbanJpeYLy4iLJMOHT3UsM5Kc4NOQ4Ft9c6reoJsIvWSObjmwer2KvvtDjNTF7qm10HTJAJSR/UTSI3+D0dCg0w6DnhKukRbJvoOPBJpVd8HM9F3wPG6gNt2En0HRpVWHUXfmjSdJd3AfMZP9R2AUZpe2VYzJ64tA4pv3Xz2mYHKVsrIF5Yv1TbnSZVtmOOjsajEEBSNMqcnlzg9uQToylaD7fLeAyp7e6mnysH1KY+Ab93xDrg4ebpr1ux63YMCrjGclz+en0DPsSkfjh4aQUokekNASbLvgCTTTP9I0qohINwFW0AUlKyrJ0fo8R2AzK/juKRVUbAFlLwqMMyJ8x0QGlytbHxrltXM7PVVhJQYvs/Gc4NKAfX7NlXpWrS78rggR7e3QSb1FKEopMi5enI8HqVUtLpge1V8CQ43Uk+Vg+tTHiHfalpUytPdD2f/dJbrITsuSF9xjzGVbRiPoBmhGiZGpCJNMOZ2XPV3UH0P8UYYq7Qq4FsjOcY1yKTrqT9CgJ8g/VIXV3+P4CcwNDQIG5Fj4xpkYY4GKvcE34FRVjNzy7cwXTV0shkDrt2TqnOGO1eY3aGGnhy1RM0HDN+PrWxhtPdZVkqp0VznfKmcekwOrk95BPrWqT59a9x0lvR9BOoNH1fZhkv1t6EZEeYYM50VmJakGnOPMMqqjstQZSaA8ECDrNXu9R1IMuYeg5/A4LFJOfbSCH6rox8fYsydVT3i+cws38L01PsmrnKNHhtXZYaVrQZbXziAkn/FVbbh7zqjgiMrpeR4bQ7be9SmZlOPy8H1KY/V7VV2nTazQ/StoD5UobQqprINQnYchGWG4vy0mf7gvKkxAnggRFdaleA7AKixXSHAtpBGStPmEYAwCERBN8iIl36BB46rANc2kUO8ETJde5TXEdTOumGOCQ0yPf4sLBNJsu9A7f4apYNDLM/jcKrG3qmT8TmOUK0jpdJRBzn2VbYh2Lpuz5dz4vst45dZ/WCdmlXk7HSuFsgjIaSU3KrfYt9pcXliiH9rn3Vgku+A1JWJ9GWq74A6ZrRmRGZpVdB4SqhspecjO6jyZ0iOx92qJfm46Ez/YINMURz6KSneCCrHo/sJxKYY83onNsgcF4EY6jtw4vpKl2999pnk3+UxKKX+yjZskLmu4m61AiWxQZZRwldvrlO1i1yYuZB6XA6uT3FsHWyxfbCDb5hMFCbTDx7SEe42nyQUgmaE6FYS/b4DAj3Tb2WYlx9xV9akpk4AthLlO2CZWvcZU9kGObqekjcNyXGc+tbww25bStYlSJR+CdPAd1SOQyMzCOvXYIgxt8TorhICiiChQTZ18xZCf/Fm4VvHsZoJPH+FpXM0jfjKNrgfxwMzG996rlTm4mz6ai8H16c4Vuor7HfaA3xrXGTWt/r9zYjeyjZ8cwfz8ugqKMF34FE0I0L9ZjDBlOA7IJ3usldKUnM86lYtafcCdK8VI/3q+g64gMQP+NQY3wE4grRqBH1rujG3R+32fVqlEtI0uX/lmUT7wnFbVEZBOKmyDb78fcdFSBO/1UmUfjleh8P2HpNTs5yfOp966Rxcn+JQFoMtZuaWUo+TWZe8DGtGdGkEIdR/DL2lS6LvwJibEUDiUj/OQAVBOBSQZMydeaZ/TNKqKI1gGAY+KkcR5AgD9oUyOE2mptdoX6Sxv++o9MsVLFy9QbXZxDcMti4vxdIIMrjXY0r44nKMW81EwRbDR2hpXvgFFtMgazTXqFkFFqcXsc10CVgOrk9pBHzrQavNpcJJ9WZKqqhG2aolw3H6YIxgvyz971hjbsdVPzOE/hCk+w6MslXLsBASZSQdzTGmQSaDLa8NTYsM9R0YLwgL01BfUhDfIIPeHPVKIC7HkQZFMoJwsbHLzNoGxU4H17bZubykvgT7vRE8D1y/+8SYyhZGl/BlXc2EXhhCJDbIGjtr1IwSSzNLQ6+dg+tTGg8PH/Jwv4EUFmWjojSs0CtXCsBWJlcoPXEMaVVag0wJ8iXST2k+HdNPIC7H/qV+ojG354fSKemnNJ9G9hPIAnCDIBw/QeYjpejmmGDMnYVvDa6bNce5G6uhvlV5uGqevc+Ym7YMz5vaIDumhC82YiilOBqh0d5ksVwayrdCDq5PbSxvL7PvtKlMnsAoFRJlVRgCv+MqqU2GmX4YZzNCV45hM6ILZgPNJ9frTnMd89rRe0mrMoUQyijaNBAFo3dkN2Y6C9eDLFtFj1lapVyyVHUrbKvHK3bAmNvzUbNwQ6aesupbpWT25upQfat6LYWSfxXsGEctCI25g0nBYZNZo65mhlBKru/QdHaZnJkZyrdCDq5PbQR86/TshcSqMTpRFEZcZavjUc55pxlzq2kiF6QJOMmyKn3Oo/gJDM/RSK5sdQWumjgy1XcgvHamrv7RQDjqFdvffPK1t4RsO/GVbd+1s7ijnVi+hTVEKdBPKcX6Dmiw9bW1ogy9EeKbT+Pe/bdxsJ6Zb4UcXJ/KCPjWPafFpcqgvrUHbIWHKNpKzoKMrWyJzqJnkASNY867B2yFCHnUwPYv1ndAKDu+gJ9Mv/bx9a09YGsIPatvhsvvWN8BoZqCRibPg/GAcAC20pdqO5dw+Z1szO17XlcRkhZScuLGqhpPheQNCYd8mQVgG1asAdjHOGoFYOu7rvKYPaaEL4jt5hpVOxvfCjm4PpXx8PAhD5sNPMOgUphKP1hTAUYUNON8B7TnaU9XOgacHpW0KtqMSDLmDr0HpL6HBN+BI/kJZAC4nhyTfAd8X3mcRkE7oUF25K1ahh0X6H+Jk1X5SFeqqtHuyzEGGI1WmxMrt8J/b165lHDt0SglNeKsq9wYY27puci2i29LtZBI8B0Irp3lfbbT3OBsKRvfCjm4PpWh/ATaTE+cGq5vjZFWxdIIbfWXEEa678AIzYiRpFWxzYg+GqHtKOpgqO/ACF39Y+pbB3wHOo66vpluzH3cpld8jvFL/QHfgY6rfo/mEN8BAbO37lJoK5+CxtkzdGqVhByPRyn10wi+q+wfhRV42yb4DgSrnqF+Ah0O2rvUpqY5N5WyY20kcnB9CiPKt6ZFVmmV0DpKo2CpJVuK7wCep0DYttIbZGNuRgRLdKF3MpUxlW0ItrphEoDysWf6R5BWCQDbxCgW1HOTfAd0lYtlpOY46lYtWaRVAsAyMUq2fl6fZMnv5jh18xZSCHzDSKQExrn7b5ijlGCaiIKt3p8DDTINtp5q7Kkv1YTKlgjfOrVIwSwMvT7k4PrUxTC+tSdG0bdGPhxpDTLfcUASTj71V7Y98/LBz9PiiGL9cR0AACAASURBVPrW1AaZ44FUukbpeskNsmP4CSTm2AfCicbcbpCjj3T9FN8Bfe0xSqv6QTjRd8D3mVm9S6dYpFMscvf5K0ry108jvA3SqiRjbpVnALgysUE2Kt8KObg+dbF9uM3D/QaeyMa3wvH1rQGQSUMgbN2MiM6ixxlzu17ywEDctY+pbw1zlFL5Ihh6Hj3Bd4BgFj1TjuPTtwZgKy0Tw7S7ErWkBpluRBmZ1RHHB+GwQeZ6nHvrGpVmE9c0efjMhXhjbt/TNsHHl/BBttVMV8OqqAP07r9JvgO7uxssThQz862Qg+tTF8vbyzQ7bWbteWVplyCrgni+NTaO2oxI8B3wHVfttmqZSJz4yjZy7XHulxXei2n2VI3901m+6ylrRdNQY5tJ0i997dH8BLJLq5Iq2wBs/bbK0W93kqVf0fvO/EWaZTUjOXXtJobvU/B9tp+/glEqDNAIvt5Z128JBgYGYnPMLuEblmMAwmm+A47TpnO4T21mmvPTw/WtQeTg+pSF8m9tMTN1Tr2BEjSsMnxs/HPesc2IqO+A6ykeUf87qUEm1adgvPtlJVSPA74DYY7q05jojSD0tcewVUv0XlSOg+fs+ULwfYRewguRbsw9Dj+BnvB8amsbVHbUPlOH05Psz5/oPjfwHZAS4UuEKVQzNKVB9sh2/02S0Wmw3Wk9oFi0WJheyMy3Qg6uT1WEfKvb4tLUAkaxEDs/DYQb2QVEf1xlG5zzkTQjDCNsRiQ1yBRYqKU5XkJlyyDfmhoZpVUCdI5WN8c434HgNS3YYPipNMK49svqHqf26RJF9TrGVbZ4qLFix1U2jF5CZRvmmH333xM3b/XqW+POGcz+25bKlfgGmZQudHSOCY5avfc9Pkqp3lynVixxcSY7JQA5uD5VsX24zcODXn1r8nIosgtnmu/A29WMiG2QqQmdIE998OAS/Rh+Ask59oJw4nRWx0Wi1Qcpxtzj2KolLscwNx2xNELHRRpqc0cCY270dFaERhh199/5G6vhvxM9XGNWM7ENMtdRO/8iU30Hjivhi4vGwQZni6PxrZCD61MVwX5ZafrWAGyl4WGUA6K/2/3tpxHCHWHHxHlmb0YosBVmQVUz0Wqn33dAz8sjSDe9HqO0qgu2Qk09FazYyjbMUXetKaSbh48bhAPbRGxL86Hx0i+pblz9fIjBeSDhm7++HD628Wz88ECW1YwwBIhojvHSr4DKkq6njNCH5JjJT8DrcHDYoDZ5YSS+FXJwfapidXuV3U6L6dn0N0lU39oDZMHPos2njmpGyLYTX9kG8aiaEcHEU4L0C192ZV+Gm958eoTSqjTfgWC7FCklojOkQTZq02tEfWtag8xvq511heniOyT7DugvjVNXb4QPxRm2HJVSSjXm7jgKXB0X3/WSG2QZ32f1gw2qVnEkfWsQObg+JRHVt17MrG9NIvrVB0pK2W2YBLPoCQ0y3/OGS21gLNKq/hzDJbyWLSU2n1xvxK7+0UF4AGw9H8MQoc9pojG3543W9DqmvrUHbH2JsNEStUFHrdCY2/cp7e0xc39N/bhgU78QM9U0Jkqpv0FmBJtoxlS2oTG3l+19Vm+uUSuUhu6XFRc5uD4lEfKtwqBamE4/WGatMjVo2Wb32DjfASmh4yBtK7myDc85fmmVCCazkipb7fwl247yUEiTVUXve1wDDlFvBFO5VSU1yPxWB8MykUZEjJ+QI2QB4dH0reHuvwnSLwC/1WH6+i1apRKm57F5+SJ+nFnOiBK+kVYzVqSyDaazog0y7f6l7BhjKlsdR+VbIQfXpyYCvjXTflnH0LfGLtEDPa0QqfaF6vAxS6tiPpjxOXpg+bo6TPEd4NF09VWOfa9jH40gPU9726bLqoLm03H8BGLvpS9H9bxeGsH3PIRrcnJ5FdeycGybuy88p/al6qMRHvXuv2GO0ekswPeVykQY6dIvTzoctHaoTZ7nwvTolWuGVz85hBD/tRDiDSHE60KI3xNClIQQs0KIvxJCXNd/z0SO/yUhxA0hxFUhxEcjj79HCPGa/tmviqHvxjxGjdXtVfbabWYm4veLD2KUrVoyNSM0AArbQpQKShYUVJFSNR9kRw0NyFZHTxTJbh4JOUopsysPhoCwEEJVy5bZm6P+QIZuWm0H2dY5SobnqKVQw3McDsJKShXkWFQ5FqyQc1Y5qurbP+yofP301xFGkFZlBGEhVXW7eO0m1f19JppNGpfOq05+ZIXgt9qh+1e2HMcnrQq37ynYGMHv2zLVQIivc+w41OsPmBRFFiZH07cGcWRwFUIsAv8EeElK+SJqh6OfAf4Z8Gkp5RXg0/rfCCFe0D9/J/Ax4F8LIYIS5deBXwCu6D8fO2peeQxGuF9Wu82MfQK/1QmJ/7DxEcSo+8aPAMIh0JpGaKDSA7bBh0+DRDTHng/giNKqLJ31qL41zNEye3MMxmE9ZbGowNZRQOb15TjSl9ToICz0klZY2pwkArbIYGbeV0DW1mDreT05Por9soL7Pv2WamaZvs/2c890gaxg6xzpUjJtR78n3S6NFJ4u+/vsqJRSSMloNUIAto3WBlW7wNLs0vBrx8RxaQELKAshHGACuA/8EvBB/fPfAv4a+G+BjwO/L6VsAytCiBvAe4UQq8CklPLzAEKI3wZ+AvjUMXPLQ0fAt7ZNqFRmEZLE3S2ldowfJw+W2oyILtF9iWGbCCsy5x1nzK1BYvhSdoSufoq+NcwRVF6GEe4Im2jMravGYTmOS1rVQyP4PlKoLVNSfQfCHDOMsmbN0fMxHZfZ1dvhY5tXunxl2HySUnGeBXtQVhUx5pb4+ssnI986BkopoBHqnU0WS+Uj8a1wDHCVUt4TQvxL4DZwCPyllPIvhRCnpJQP9DEPhBDz+imLwBcip7irH3P0//c/nseYYrW+qvjWyjyGFdklNGY6K9izXUZ4xiSDYXhEzQgz2XcgWFYiUECb6jswmp/ASNKqIL8kY+6Oo6tmo/e1HMhxRGlVRhA2LHOADx0w5u64qrqNcspxDbIRd/+du30XU38p1s8u0KkOeriGq5k0WVUgUfN8pIE6bojvwKjuaEnheh0OWjtMHpFvhWOAq+ZSPw5cBBrAHwoh/rO0p8Q8JlMej7vmL6DoA86fH03Q+zTHyvYKe06bmZne16x/Osv3fYRuZkV9B6KVbQC242hGtDpNXrn/WTpeh6IoUqJEsVzjyqnvwDIKkRy7zSfpqyoxuHa6Mffxt2qJPy7BGwHUViR6wi1woko35j6+n0BPimkStT6w9Xwfgy5nm2jMPYKfAPQOD2w+Nzg8kLaa6QdbT0oM00AYQ4y5xyDhi0b9YIOqWTgy3wrHowV+EFiRUm4CCCH+A/A9wLoQ4oyuWs8AG/r4u0BU7HYWRSPc1f/f//hASCl/A/gNgJdeeimdBc8D6PKtu84hS0P0rUI3iURBzXkn+Q74AfBqm7a06jWpGdFyD/i/3/h1nN0VCtJFUERIm33D4ZX7n+Gj7/g55ipnYnJE5Rg2xQYr2+C+cVy1fPfSZ/pH2aoFGArCAjR1oKVVMZVtqGHtOAjLUo+l5ZgZhDXIZNC3hpytlSCrCsA2cCjzvGTpF4RfPqeu3Qwfit3tdYTVTNgMDXPse09qsKXjqi84z0Sm+g5ku3b9YJ2aXTwy3wrHA9fbwPuEEBMoWuAjwJeBJvBzwK/ov/9YH/8nwO8KIf4VsIBqXH1RSukJIfaEEO8DXgZ+Fvi1Y+SVRyTqrTpbzToeGfStfUv9/so2BNugEx3MpTNY2UJyhdJxW/zp6/8Gd3eZy+KQK6VTOJ6BKz3ueXXu7C3zH17933j/xY/z4un39+U42IyInc5yVF5S/78+eMC+cBxbtSS9jj07mcYYcwf8tpR+qu8AjCL/kt1rDrmXaI4QP50VgL4M5GoJ0i/oSvjmr3bBdTNu94FjUEpxNIL0Inu3pfgOBNfO8jvcaW6wUDg63wrH41xfFkL8O+CrgAu8gqoqq8AnhRCfQAHwT+nj3xBCfBJ4Ux//i1LKYE33j4HfBMqoRlbezBpTrGyvjE3fGoKtL6GoZ+ZjKtsAbGXQOBPdOW/X7/Cnb/4GrZ0bXOKQH59+NzPmJI5nYho+Hdp8evdVrrbW+fz1P+D+zg0++MxPU7D00ixLM8IU4BvKiapgqWo3rkEmhAZgP9ss+pj0rWGOUnZz1PcW6zuAniqzjHRvBH0v49K3ioASsPX2PfQPNUAAtgTUgjA4eS3iKfCOy/E5jknfKgwB0tA5WoCIrWylPo90XYRtp76Orteh2WowOXme81NHpx+PpRaQUv4y8Mt9D7dRVWzc8f8C+Bcxj38ZePE4ueQRHwHfOj2TvqnaUfStiZVt0IzouPoDJ5Cm6rL/7fIf0dy+ykXR5Memv5NZaxLfD6pQSVkU+NGp97BwcJMvNG9zb+0L/OH+XT763M8yV1nI1IwAuiAc3E+K70CwlYv0/GRj7iP4CQzPUYFwOBYcGRjomc7qeOHODNKTiRNkaXzrQJ4jSqsUGIrEBpnvuEjHY3J9GxeBLJVoVybYOzHbA2RH8RMYRd8qRHKDLMgRhOovJPgONCJ8a9EqDn+NEiKf0HqCo4dvnRjkL3tiDNKqfrD1InPeQWW7v/eQkx68v/Ycs+YMvpT4UnmNBlc2hODdlcucsWf5q93XuLO3zB+99mu8/9w/4B2zL2GK9Ldt2lK/n0YQ+rhwFj3JmDtrt34U2VICCPdPZwmt1TU0x53ojTAC35o5Ry8ZhKM0giEEvhDML9/G8H1cy+L+O59TX7ARGiGrrOs4FpW9OXbB1hDgCz2uHayqYhpk2821Y/OtkIPrEx31Vp2t/Tp4JhWzll6tPAJplUC/kYNmhJT4FriGhyFMPF9da/n2HgKfy0s1BBK9yuRMYZafmn0/n9l9lautNb5884+4uf4KLz3zMRanr6TkmL3KBEIBuX4wvkGm3b8wjXBrnPiZ/vFLq5BghBNjyb4DyndXAXZ6jqNJq7KCsGGZnLl+k/LhIQC7FxZ7TV48lEOZ/lIJpGrpr+Pw99ko+lZDN0MhXvqFD82dLebLx+NbIQfXJzpWtlfYb7eZKp3o6i9JaD6NyIMdxTpQCIFhWTjCxTfa2KaH7wsEPiC5eWsncgLJMxemKYoCH5t8D2dbq7yyt8bmzlX+39dXWJx9J9+79ONMx43zHkPfGtcgk57mk0W6rEoI8QikVQneCH2+A0j1RSAQGXIcTVqVHYSNnmbW1nPPDJi8SM8bKv0KX8cg/7Q4pr61v0HmuB32nAaXZ88di2+FHFyf6FjZXmHPazE5c1Y1n+L2bQcFGo431KgZRgHh+A+HKSw6CFzpEWCEED4Cl67s2UAiesC2JGf5+JkLvNq+zhvte2xufIlPNq7y3Kn38t0XfpSSNRG59vj0rSIAp6C61frZOGNuidLdCssMZUSpr8+YQFipHui6QfVJ1PrB1g/kX8NylBlBOPIFcDIiwxrYfUAoS0URpYriGmSgGk8j7LYwLn3rzuEGZdtmYep4fCvk4PrEhpSS2/Xb7Gm+NRjpA7r7tgdgGzR1DIEfjBrGTGeNgwezTBuJwJMBuAc/l/p9LwFP0wjQBVuDV199yOmTC1T9ad48uM3O/DZ37vw1Nza/xrvPfoRvW/h+TGFml1YdQd+aKP3SVZnUFS5tEmVVMIq0SoHHKPrWpMo2pDl0rkNzTOFb416f8u4+k2ubgPJw3e73cO2X0SVMkEnPA0+q3SPaTo9X7IAx9xH9BJJiW+tbj+Lf2h85uD6h0Wg12Gxu42BQLQ7qW6NgG/zb0NulJPoOyBFHWWN4MFNY+ICnOgncvFXX1x88TxRsDUD6bQQmk2aR7649y+buLm+27rPlrvPVm/+eq1e+yHed/yhLU89nmpcfh761B2yFQBRRovdQyzq4RJf6+llWAOPQt/aCLUipjWj0vcXmqDXA2XJUq5lo1bp1+SKyX9UxhFIKwVYIKKKlZ6KbI/QYcwcyOiPOK7Y/3kZ9axA5uD6hofStbaYnTg7Xt/q+IvpTZFW4XrcZYQikIQcq2zBSeDAh1JLflX5YmQq8geP6494DtUWzYXQr23l7ghPWZe50tnirtUHj+jJ/e/N3+FrrJN/xrh/m0rd/G4nG3EfwExgaGmSMKKj0N8gC5y/XU9u5+DK2aoQI3zpOaZUGmW6O8Q2yMEeE+lmC70B0NTM/ZDJrVEpJmHoLnyRvhMAKEp1/XGVLMt/aH67v0Gw1juzf2h85uD6hofStLaZnzqYel7TUj9Owqg+HHOo7kMaDbe7fpSQdZswqMhyrkUNyBITB4ukJtHYmPL0pYKl0grPFWb5xeIfX726yK/b47M3fZ7N5l3Mzl8ODF567HILtcf0EBg+LB+FYGiHYLFGkN5/GtVVL77GDIBxLI+hTiSHG3D1869WIp0DfZNY4KKUBY+52B6RJaHAOA1vOBG5r+kZTL9torlMxCyzUzhybb4UcXJ/I6O6XNT59K9Azi57kOyBBjUkGE0iRN/R+e4d68z7PCpeLpVOZwRWGH2cJgxfLF7An6tyrrXG/fZcv3vlTbu8+y3sWP0zJmuD+W9e7Z/Qlp5+5qDhnUjjNrMCVEayFUGIzo2B3bQsTfAdwXSRqtl4aaTlm7Opn9UbQDTKjoCezYqVfAJ4CMykRtsnJtA0JR5DwZZVWCYTywSjY+v76GmSg3peBR6yZIk9DmbWMQ98aRA6uT2A0Wg22UvjWnjiivjVpOkt6egtuYQz4Dtzc/BoTXovTdpWisLm2msy39oaB7sUPOU5QNct8sPYOrrbv8ubhGlv11/nUwV3etfABLs6+oE7h+wjZ9UYI7yuoyKI0whH9BJKiX1qV5DuAVDvrKiVHuu/AcbdqGcixr1pPa5D5jgtSYuwfUtzcplmpYLouG5cu9KoRjiHhS8qxH4QHG2T6Pel4mrqPyBFjGmSN5vrY+FbIwfWJjJXtFfbabaYmTmTiW8ehbw3BFgFFG2wznJcPKtv7m9eY8UwulE/iegIpBYbIzrcOB2F1gCEEL5TPcdae5YvNmzxobfC1W5/iTuMqL539YSYKFfUBKxe7msr+6aygqaOlVUMvPSZpVQC20vdVRWYa6jWPq2w12PqOm3kkGI6vbw3AVgqpqm/T4MS1m5QPW7imydaFs3QKBdXp1zSC7zwiaVWqN4JACgNsU3HMAY0RNBohpBE8PA4OdqjWzo6Fb4UcXJ/IWNleodluM1tdUFM7MfPTMBoPNnIzImh+6Mq27bS5f7jCBQ44b8/jeSZrm22k30FKn7Nnqig5Vm91msS3xsW9B/vq2jrNSavChydf5GrrPm8ePmCz/hZ/2Vzj+y/8BCcqp9TYa8DLBRfrmc5y1eunC7VE3wFGlFaNAMLBHllArDG37+hGo6Y3kmRVKsej+AkMuRdUFXjq+jKFTocCcOf8gjJRCXwH9D5pWCY+nVhHreh9j3O/rCjfmmbMvbO3Rk0UWDymn0A0cnB9AuNW/Ra77gHnaqfSDYZHkFYdtxlxu/ENhLvPjC04USzhej6nT5YQ2Nxf2+fe2kFwNaSUIdhGshiSIyAEi6erEFEfGMLg+fJZFu1ZvtS8we3OFl+89Wd876WPs9jfMOlrPglHqN6TrfZ8SvQdYHgVFeZ5TH1rP40gHD3yalndAZE434HuCzI8xyPs/jvf18zq8R3wPHzdrRf6/HENMqkbjZm3ajmmvjUKtnV3i4nS0ffLioscXJ+wCPjWjmFQqypaIMlgWLqqKhOWiSRF0ziGZsTK9mtU/TYXyrMALN+u68++x9mFcs/AwL0HEbAVBg/rHRZOqQms/sq2G0L/ia9uJ60JPjj5Ip/bfYP7zg5fdT/Dudbz1EozsccDytRFbwCob7CvstVNPC3MV6DsJzZNxrVfVm+OgGEojXI0xz4aIcwx5E8TcjyCOxrAyaspMixfGXMb+nUUxDSfPHrGjINtfJJyHPcW7I3mOgvF8fGtkIPrExeBvjXKtyYth6Trqn9nMBhWTz4aD+b7Pvd2bnJKtlkqnorVt0YHBqJge2+tBUjur3crW6RkcYBGGK4oMIXB99beyd/t3WTj8B5/8sb/zj/8tn9CqRCzx1OMtCp1OivSMAlvqJ9GyNz0yvh6M7jUT2yQ+ep+gom88Pz9NMKou/+aisc8Gdnapd/DNY5Sip3OajtIQxcDacbcx/QT6I9A31qtnRsb3wo5uD5xEehbZ6aS9a0B0S8sqzuL7sVUtgTNCCci6E6JBB5sY/82TrvBlCE4Y89mkmCpFaJSCHz78zOopGIq21BAbnblUClhC4vvrlzm6+ab3Ni/zZ+8+W/4iRf/y64ZdxAZKriw+WQYakoo2Ho7rkEmROiYn6k5BmPRt3ZzFBilAthm75dCvzG33iJ8lOX21L01ik31+ziYmaJ5YrabY0ZKSQGogSgaiIKd7DsgUF9iwfdPao4Z9a0HG1RMe6x8K4QDcXk8KaH8W1vMVNP3y4p+OITQ+7YXevdtF6aB9D2ko6Z11N7yjprc8WOALIEHe7C3StnvcMquYYjAVBBG0beq1axEaBph8XSJxdNlzbEKVm7vgWFyb+2Qu/cPkNJASjGAtxKDsmnzY9PvZokWrZ2b/Plb/xbXdwfuJXh90kL2v47B/l5FW/0J9voC/I4Lro9sO+qP64Vg1nvtEf0ERtC3BgbiAd0hirbSilqmln054HndHIPdVwdyjPCt/ZNZ0bxHpJRC+VckR6NUCHMUWsGBq/bN8tsdnaOX+DoO3S+ruU7NLnFhdnxVK+SV6xMVjVaDzf2HOAhqxWQuEUiVVvX4DhgCIUHoOe+uo1ZQ2eqGiSCxGbHVvEdRupywlOY2zU+gN4LcBkE4SiMsnpkAIViYLyGEiK1sF89U1QcYweWlaUzD50en38UfN15hZfsNPnfz3/PhKz8deX2Or2+N0gjS9xG+3VUoxFS2AY3ga2/UoTEGfWtUwyqlWsGIQP6VYszte164g8LJtD2zjkkphXlqGkFKA+HLnmnAxAZZRmOenYMNThdKY+VbIQfXJyri+NakGEXfqipbS/3d76ilwTZsRgQR8R3Ybq5RxuWEPXkkP4Gs+lYhfIQgpUEm2G44AFxemmLemuGjtXfyp7uvc33jS3zn4oeYmZgf2U8gq7RKvX5W99iYBpn0pVodRGmJlAbZKK5VWfSt4SrGMEj1HWg7SNsCIZi7voIUAiHlQDPrKFu1ZLoX09S/nwTfAVci247Wt3a/FPrzcH2H5mGDWnV8+tYgcnB9giIL3wpH07dG35Sx9oXtDtIXQKSLrv/sH24z6/vMW9NH8BOowBAg7te3dv+/t0EmpQlCNci6XrFlZvYmaZxq8cXVT/HDz/9sJr41zPMYIBzbINN60HBePsl3IHj5jugnEH/g4GomdjrLdcEzFZfr+dRu32e/WsXwPNYuXwx3jFXn5NhbtfQeF7/UH/AdcFz12plmqjfCzsEmE6bFwuQZSlZpeJ4jRA6uT1Dcqt9ir93i/MR8+oEj8GCZmhFCgIg0IyKVbX1vjYLjMSkqlGQVxxdq+cmwIiX4YfrgQJK+dTDHbiVsGnZPZfvcxFke3L/O7Y1XuXr4MrXCNKcvXgibP0kf+HFLq0Igs0yMUkGPFEcq2yjY+r7iUs0hOWY1qCGbvlVdp5tjabvB/O27uKZJa6JM/czprhohWLJr8+60HMctrRKgdMBFW8sR4xtkjcYDamZprPrWIPKG1hMSjVaDzb2HSN9gQlR188mNbz4d0U8gKQaaEcHS0rZ40LmDL5tM2SZCSFbu7LC20eT++iF3HxziSzNsPvVGMt/aG/rDnhWEz9R0jt0G2YxV4LRlYTkN3lj7PEJK1m+sgqOXv2ETr6+xc0Q/gWHHBiCc2iALONthzacjbNWSKUd9L/PXVzA9j2Knw8HiaaiUug2ywG/CHZLjiNKqzJVwBISTGmQ7h5tUHwHfCnnl+sTEyvYK+06bieocpm0nyqowBL7rYYxgwHycZsTW/l1KdJizVRPJEB5n5gsIYQMG99b2iaoCFk5XEUi91JdDL51F39o9LhmE31k+yy3nDe7tXWOn89089+3fpSqpBN8BDEONx6K8DFJjTPrWngaZ53c3LYyrbDWNEACZkbnpNdpqpmdbl2cv9TbIPLO7+WOM9CtYomeXf2V7P2YxQheGgYfLntPgmanx862Qg+sTE6F/69RiKJpWRH9kWen7SE92mxFEiP6YD9U4mhHbB2sUcTlhTYbVqaoYNR96ZrD5hBCs3G1y8fwkvjTR3v2x01lxfGt8pIPwpFXhgj1F02nz1vqXeIf5PvXhTPQd8PBbjt5p1elyoXFL1sxNr9H0rcI00nP0fPyWmuVXOXY52yTz8FFXM/MJk1kBCIfNsRhHrbD51NbuX1FOOdZ3IOMKIOOXWeNgQ/GtU+PnWyEH1ycmAn3r+T59a/90VpfoN9J9B1Rv6tjNiPrhJgvSZd6eTNS39jeffF8B7cL8BPcf7EWQs1vZhu2yDHwrZAPhd5bPcd+5yub+LbYO7nOy1m0M9jefpOchPAthquZSXGUbgO04tmrpPS5+qT+Qo+8jIs2stAbZUfwEgOQNCZMaT30NMvUe9EOjn3Rj7owbT2YE4e3mGjX70fCtkIPrExFdfStUh+hbA6LfKKmJpCTfgXAJZwjw4ytbSG9G7LXqOJ09JgyYNmvcyOjfKoSa0RfCjZVVRU/wsO6wcEr9PMl3IGvTa9Ka4Lw9S9M94OU7f84/eOHnk3PUXzxBwyTRd8D3wfWhaBHM9qc1yEYC4WHNJ1/x4KJgq2OTfAekBMcF21Lgm7JaifKtVqvN3Mpt9bgQbF6O8JYZl/CBN4IoWF1rxYEJMpDShY6r3LY8L9F3ILj2N1PfGkQOrk9AhPrW8kkMMby5Ev1QJvoOdCI8nu8M9x2I+aCv7a5QlC4zZlltNHdEfWucrAoEUlos377P/fVmcHc9jlq9vgPZM7o6ywAAIABJREFUml7vKC+yU7vGte03qR9sMJOkvOhb6if6DnQcJHquP8WY+zh+Aqk3RBeEE30HNHccgmyQRx+N0M+3zi3fwtCVcP3cAk6lu735USmlRGNuR+05Nsx3IOvGk57vsn9Yp1YZr59ANHJwfQJidXuVPafN9NRi6nFZpFUB2ErPUwJsy4qtbAOwVWOH8UvZjeZditJlzjrKflnJ+tYAbO892OHSuQqLp0sMDgyoY6SULJ6epMtzpIWgYpY5aRVZ9R22D9YSwXXYUr8HbIXq9Cf6DmizEun7YFip8/Ij7Zc1ZGvsMEffgEIkx77KVh+MlIqzD4zQo3zrZgzfOg59axdsfSgo1URcZRv6DkhNfwwxOFd8q82ZydOPhG+FHFyfiFitr7LnHA7wrQNxhI5wmsGw9H1lgixAtvuaEYZga/8eJelwwqoewU8gu75VCI+4yjYA29eubrOz2wJqCZVt77WLhoHp+hy6TeLiKPrWNEctfKlkc7oilZFR2IElelY/gVH0rdEqM2HLGTy1XYrU47HS9Zm+voJrWZiex0Z07HVU16pR9a2Rylbo/ENj7o4T0lm+5yUac28315h8hHwr5OD62EfAt3YYzreOYx+jKNgSjokKhOhtkLm+y/buAxZ9n0V7bix+An2ZkLTUj9IIi2fKIODbnptFCBlb2QZgK/VeXQXDwqBN24kH13HoWwfA1vcx6NOwRm8oMObWleRxt2oJc0ypMgfB1sewlbwKz6d26y7NchkDeHDlkhoe0KuZMO+0GJN1YI8xt9RfFrYVW9kGYLvT3HykfCvk4PrYx+r2KvutNjOFkwhPIs30iaJRtmrJ8uEQhp6XN4yeyvbew2vYziEnzQpVZnh9fY0z80UNYH7iqUf1E8gKwkJ4CCFTjbmFYfO+71zgweF9LL9N2z1MuO/sfgJAJn1rOHhhdb+4Yhtk7Q7CNNW/4yrb8KTZml6jrGYECuTf2t3g9dWr/Np//iFs/wP84KvLnLlwmglNI/jtjtJRmwYyxRvhKFu1DMsxAGH1Osb7DviOS3t/h9qpBZZmltKvfYzIwfUxj2B4YKpyOrRikzCwu+Vx/QRiI+bDEVS2yzuvY7PLmeIUpiE5PT/B/fVG9Cq6ey8JwPa4fgLxMQjCcQ0yKQ0kNggoUaYsW3iHLWSnq2ENJU2j+AlwNH1rHI0gXTUrjxCJlW3YfDqitGpYji/fu8bmjav8z7/953zgjRX2SwX+8Hu/jX/5rmv8yOwkZ8qTCNcM5V1J0q/AGey4W7XE3cuARK3Pd6C+u0nBNjg9deqR8a2Qg+tjH6v1VXa9Q87NLvZsCte/uyXozr+dTvSPqxlxb/cGM7LDUvEkiibzdOMJ1DtdDE5nnaoBBlImV7YqR8aqb+3+zKdguRQsH1+0OPQP1esRMaKBAOTUt0EaMIxT36q4RlSjpmBrRiS+QSbRulvbypBjdhDebjW5unabz/3z/5OZZguAycMOn/iPX2Hx4S7/3S9a/PS3/0DoDRx+Y8ZNZwHScdW9DJvOGrO+td7aoFZ8tJQA5OD6WMdOa4et/e2QbxWi9xu623zy8Tsu0vURpofveLH7tgNjaUZs7d/j4PAhFwyfxcIcNwf0repD0J3OCsD2gJU7u4CqaLsDA/1gm8y39uZIZhAO+FYhoGwUQLgcyiZGMTBQiTSeXE/v3tA79RTVsMqsS15GkFb161uTtpwJcjQMaDuxla16ymirmRsP7/OffvaVEFij8dFXrvM/7DfZONhlvlDpvhZxsipfbzXj+4qfbcv4ypbhfGv/65Ntv6xHq28NIgfXxzhWtlfY67SYLJ+I1bd2m0+m3mwPPYs+uG97ALaZQSEFhG8+fJWK32bRnsLEJE3fqi6jryldLp6d0Ntox1S2GmyzyrpGAmEEz5yfBnxKho2JT8s71DkKtRw31cHCFGpbl0A+Fec7oE1ehlWuI0mrUkC4n0YQhlAbAsZJvzTYhjkO+yL1PM58/U0q969x5d5m/PWBpc0GB+0WolSNBbgesJVqz1xRsNQgQYIxt8Lj4b4Do+lbtx+pvjWIHFwf41B+Am1mphZSj+uZ807yHQiWa22nu7uAEVPZBpECwnca16jKNheKZzJLsAalVXGVrQLbh40OQsLC6Yp+YrzvwGhNL8JzlAwbU/p0vPZgnr7EME1VsZv6dYxWthpsZcdRuUWaOsRVVVmlVSPqW41hvgOuF1aP6nftD1SNczdXef7PPsOVv/wb7N197v/gd/LmuXjdrycE107N8lGrNPRe1H1LnWNQkSb4DgQuWkIoPe4Qb4RsfgI2pydPUbbLw/M8RuTg+hhHoG89Vz2TfmCiwXBQ2er6LthNIOo74A76DggjuRnR6jTZ2r/DMzhcLMwfu8qMVrZnz5T1rQgWTtW4v7ZDXGUbgO2oTa8AXB2pwN3znZ6jkqRV0co2AFu1xQg9nG343AjYjmOrlp4cE6wDYxtk0kfIXt+BytY2Vz77//H8X/4Np9+6jul5tItFHNvmZ/726/wn//3P8vN/9SUWt/d6zv/J7/s2JipVZsuVI1NKSTSC0O+5dPPwbF8+9YP1R65vDSIH18c0Ar61jTzWflnREKAbJkpaleQ7IJFIxwsbaFFgWN5+jZLb5pQ1wYRZ4tpKVn1rNhBWYC4RwkmsbMPzGDYL8xNI6SZUtsGRXb4V4PWDO+yKEmenrvQemLE6Qi9Pg5XCgOl1tPnUcdUxQ3wHRtmqReU4vMIVwkDYJsXDFpc/83c89xd/zcIrr+ObJp5pcjihxllbxSJeqcDm+9/DB8on+LF//gn+m09+hg+/tsxeucjvffA7+IMPvYcff+ZdwYnTLzxCFY4QiGLEHzahQYbjquvaFtJIbg42mhucehv4VsjB9bENJcFqHclPIDH657yTfAdcr0slyF7fgdWHb1KVbc4XToZcZjY/gaNJq/or2wBspTT4uy890BWNT1xlGz1nwLfuuE2WO3X27JN89OyHB18fMYK+NWJ6Hdd8UiCrv7hSfAdghKaXzPZFarQ7nPv8V3jxLz7L5b/5PHa703saoF0ucf0D7+f1j3yAe+9+EVkucQGwmg1+6xMz/E/tJiZw8cQZfnLxGWrSzPj6HE3fmlTZSs9HdnTW2huhOzDQpRE832W/tc2VyjmWppfSrz2GyMH1MY3V+iq77TYz09n41rHMeQdgKyUUbbAtRAAUvo/v+mw37nLaM1iyzuD5hvYdyM63HldaFQXbS+drLJwq6PuJN+ZGvzxBjl8/XKVOidNTzzI7cao3zzHpW0OwlRIK+nUMnhfnO4BuNJpGqowOSNe3SsnC19/k+T/7NBc+9wXsgxbV/f3eQ4Tg9kvv4hs/+hGuf/j7aE+UkI6nJXwCfJ+z1RkWn31Pz/1IUOOmdro3AjA2aVUIthIoWGrVpSmrOGPuxuEGFd4evhVycH1sY7W+ykGrzTn7hB47FAPz08BIEzgjzXkbogdoBHBv+yqO22DShGlzCtczWdtsIf3OwMBAb4zQ1R8RhFUBpwcG4oy5DRMhLC6eM3D8DtcPH9IwJvnY4od6r30EP4GhoSuzcFeIpOaT42jA1cMgCQ2yJL51dvk2z3/q0zz/qc8wdX8dgGalguF3X++NZy/xjR/9CG997EPsz5/oPllXgkbwvkjM0Qv3zpK+jJVVBTmOW1oVVutB8zVG+oXvs7O/Qc16e/hWyMH1sYyd1g6bew9pGT7V0uxQg2EYvkwch751uf4aJXnAYqlK0fa5ulzn9MkyhigMVI1RsB2tq388EO6fzvJ8tSODYUiuHtxn15/gRHWJxfJSz3TWuLZq6T02fqk/IKvS5zNsM7ayjRpeB49VNh/y3F/8NS/82ac59daNnvP7QuAbBoczU7z6Qz/ON37kwzyMerEO5Dh4LwM56l9NsKVLUvMp/ALIKq3KAsLBluB95+ynEeqdLeZLbw/fCjm4PpaxWl9l32lRq5zALAam137fcghASYKEqc00Ug2Gjz/nfbdxnUnZZqm4FNoQClyE8FObTw/rbeZmygyfzhovCKvb0Hyr4fJ6Z4V1s8r7Ft6rXrOIhjWoyoRlIiOjsAMx4lYtmZo6UmLYFpON2zz3+d/jxK2v4GOyef47ufbST7I3t6QuubPHhZdf4dv/6rMsvfwKlusOLNFbtSrf+KEf4I0f/iBr734RYSVDwCiUkgikfrYV5hxrzO246rHgvhN9B0YE4aH6Vo/99jaXq2ffFr4VcnB9LGNle4XdTq++tX9+WgYgq/mwNINh4Nhz3o2DTfYPNlg0PM4V5mP1rXHNJykNEGoJF3WriqMRxrVfVhDRPb1utB+w4fmIiRmunP4uhPZVDTSs0nEVtxgZhRXRpW8AtqP6CWSshE/cfYX3/en/yIlX2kzdVM9duPg5Fla+wO1L/5DFLzxg7mtvYHccCo5Dq1wGKTE9D2kY3H7fu7n6sQ+x+v3vxRV6g8MUYO3NcfTVTKJ9oedHFCjJxtzj3i9r53CDsmG9bXwr5OD6WEaob60k+7eGW2bYlpJMQXxlK1AA7Cpp1VGbETcffp0Jv80ZexJbmHj+cIALl+jSY/F0WR+b5DtQBWGycGqCcZu6dOjwleYqDTHB8/PfjWV0PxZCCKTQzl+2qamW3oGB4C6FIfA7rjpuWIygbzXcDi/92a9w4VOHlDe6P5p73WdirYX9kd/lwn+Ew3KVYquF7bp4psnqS+/i6g99gJvf917ataq+Y4EfrGYyzPTrFyHTvaQBoRACaRBujYO2VuyvbIPrha5fwyKjvnW7uf5I98uKixxcH7PYbe+yufdQ6VtLs+kHRz4cUaI/qGxDg+GgGSHAD3jAmAZZGg92f3eZCelwrqCq6ez+rV0ATZJVKbBtsnInAFwvsUE2mp+AwJUe/0/jy6x4Eqc8x7sWfmDwwEh1FDcw0O3y+2Fl68tOfGUbXHsEP4GTd75KedvvAdYgyltQ3IKdJYvCOmwvneXaj3yYtz76IfbOzOvXRKr9tMKpJ09/5yX7DnRzPN7uv/33EhyXZswtXQ/pur3eCAkNsiS+tT92mhvMv0361iBycH3MoqtvjfcTiEbah6PHYFgIfN2MEFL2VraoLmww562ePHi+rf17nJYdFu3ZiL51OOeZVmVGwTbwG1g4VUKIwco2AFvVMMnGt7oS3ppc5prrsVM+zY+/8x9RKlQGD0zRt/b4DggPgY2wTYQktrLFMLq1fEY/gYm9DSprg6O4QRS3YeX7XuTrP/jzNF64MvDznuaT4SGkrfhRGGyQabCVaNVDxir8uNaBPWArREgzCEPEVrYYQu+p5avtiFLC8z32W9s8U3n7+FbIwfWxi4BvnZ5M/wYeVd9qRJoR/ZVtuAOn46rHgnFDLX2pH2zQ6exSMeCENRXhWx+FtCqpstVgKwyW7+yxcKpCsvQLHF/yxf1rrFdcdiZO8WPv/C84UY3fg2wUaZUQIuQyByrbwHfA9dQOpqhmZFxlS/BcoFOd4fBkAXBjL9s8XeLaBz5C/fJSZlrHsCMf/TjfAU97AwcnjKlsgxyPtFVL6nG6GrVNdWzcljNexHsg+LZK8B0I+dba/NvGt0IOro9dBEqBc2PcLyt2zjumQaYqh2BooGvMfW/rGlXPY96qYQiRiW/VV+E40qo4GuHegxaXzk1zfz1Z+uXh8/d717jvC/ZKJ/mxF/4RJxOBdRR96+DW2LE0gtR/C9IbZLpSW3/me2jN/SrtaSg2eq/ZmYSDU7C+9N5Mhilx0qrYCbL/n713D5Jku+s7PycfVdXv93Pec1/SFRJga0GysCRA2IBkHsb2Eg6HweBlg8WgXfDyMF7W5imH114DxkRgR9jgXS8mDCzYIBQLGC+WQIAWW4970ePe7pnp58z0u7q7qjLznP3jnJOVVZWZldVdPdN9Vb+IG3OnJjvzVHbVN3/n+/v+vr868Q4kz5i7X6NaWiIFhFNphIZRHrj5vgNPg2+FAbheqYj5VqkKz8vqRzEiPo/rIMpusxfeZDuPju4zJgWL7hxB6PLqfW2o0q+qfi/SKoTi2mIFIaLUzFYi+djJPapTPselKf7Ci9/C/NiN7PP1Wd+qC2TglEuIkpea2YK5I+bhFQ2X+S/v+g6c6CdY+uApo6v6XNVbsPm2IT76jm8lLA93tw4suJuJ11jyECU/1xsBM/0XzwWRUyDrs7TKXkf4eiKsyvEdqB48ZOYJ6lttDMD1CsXK7grVep2p0gyiESFF9nTLiyxGQGtmu167xxRHLJVuAYqth6coQlA6080qPvVbWtUOwu2ZbSglH6x+ir2ypHY0xru/9puYLS8ia8nik9MKUucY1ZIW7frWvAKZNMUc1QhZu/tnqX/FKO/Y/iHW3xbhIDmcfYGX3v6NPFp+ky7sFBzV0sv03+Ya2zJbC2JWt2rpjqwCWZ+lVe0gnOU7EIUh1fo+t8aWLnReVloMwPUKxeruKkdhjbGx28b0On26pRJPrhhx0qhSrT1m2QlZrEzgolhaKIPSfJlKkVVdWxw12+L+jWrRkQ3CkZJ8sPonvBrWOSzN8M4/9ZXMzt7S/fK2iCeNFpjmFl2GUSs/mRUFRrXo4/L1rRZslaNiyZLtl4/ELLO/V0YBB4vz/Pt//Y/0Go20qvAazwnCFmyVI7TUz3X0OfOMuQ3wXvS8rJY1CsFhsIPvOSxNLDDsD+efs88xANcrFFrfWuP6+FI83TLNhk1Fke71hmY3TEp3Vj+KEev7n6IcNZjxhigJL+ZbhdDOU4Ks4pPDyoNjwDFKgPTiU+9Fr046QirFh44+yUpY56A0zdtvfS1TIwu6Gh1vL03GFskYbFUYoBohUimcRE9/avEphW/Nuo9QQN9qR2jbirkL01sPGa1WCV2XrYUZ/fsLQlQjBM9FiUS7bsrvqpd5WfrixQAuBtcM3wEZhNqE3XNR9dbROGmm1734CRTRt46XnjzfCgNwvTJxWD/kYfUxNSVb9K3t2yEByHrD6PFFId+B8xQj1g9fYUgGLJTHgWx9a/sWXUoNtMsLQwV8B85e9ALYC494EFTZK83yZ+98LbPDi6A6s8y4Mo0GW2m2vML3jN1ievEpXnsB0Xvv1oHNc06ubSKUwg9DThbnccolZKgVHMIzfftZvgN24Rc8/Vf/tZVGEGGk76OdnpvlO6B5kWIPqV70rf6T51thAK5XJlZ3V6k26kwMd9e3QrMYAemyKjDVfqXAc/K3bDmZzMOjBwypBkv+VE/61o0tK62KupheOyA806qqcvArG4TXGjuciBKLY3eYG7sGYcTyC892XaNQClxXz6KiM7ONwTbS/ra66ygbPHsd1dJ+non1zfj/D64tmjUCroNjPSYyik8qiiAyuxiZntnGP9+DhK8ICAuT9Yuyr49tX6P1HYiklqiVfHBkpu9AT34CtV3uTl974nwrDMD1yoSel1Vjciz/CZz25UiVVRmbOF2MkLq7qC2z7VaMCGWDvdMt7hBwzZ85l741TVYFDutbNV65twtq3P50RoEsnW+VSrER7HMiKrxu6oXC20no1Le2Z7Zx8SmSsVRNRbKr70DxUS2ta5xcS4Dr9SWzxtatfm7xCfN7zzPm7oM7Wke0bfWz16iN15VSsdVhandWYT+BR1SEx9L4k+dbYQCuVyZW91Y5atS4NrqQf2CRPm/HQQkFvovjlZp6xawCWRClZhGbB6t4YZ0pr8yIW+mrvlUnOBKIePsXLKEXle47cG1xlPXN4/jnknEQHrMfBcjyNMsTd5pL6yqt6q6iiItPril6+W6mrEo4Qj/AUDgF+db2NU4kwHX/+nKhLDMuPrmO7mQyloCZxtyRjME9d5V91LcmwVa4JW18nZLZmoPjqbWI/DXuPUW+FQbgeiXiqH4U863jXf0EepRW2cwqy3fATDIVnousN1oy27WDTzOkGiz4Y8DZ/AS6H6dBOCuztWC78uCYO7cmWNs8bclsHwSPORFl5kZv4Do+ItI8bq+jWrIiudXP9R2QUlf1HadN+pVSIEvhW5GSiY2t+K8H1xbPJK3Ky2yRSq9RCGiEWg2QVSDrg0Vl+xrtVj9zjbY7KwhNTSHUU2EzCmT7x9tPjW+FAbheiVjZW6HaqDM+NIMj8rdh/dC3tvgORBJp+7wFLZnt7v46o1Kw4M3EU1nP6yfQthL7rjr/JQG2Wm3gsDxfQQhaMtvPHB8RzoxyY+L5WFlRhG/th761xXdAavOUeJJphu+AzR7bq/qjj3bwGnoa7cnkBI3RkXjrfB5pVRLItMGLjNfYzZj7vH4Crcdlb/WT3VnK8tpC6Gw4w3cgQnF0usvdmafDt8IAXK9ErOysUK3VmB69pT9IOUR/v4sRtkAjSp7+QIOZlxWxc7LJNSlZdGcJIo/N7RpL82VM3pYKnmfxEygGwgohIoRoZraH0SlRSVLfdRgar/B4/wEusPTsHfMldDpALF5nD34Ceo0F7qMw1oWOyPYdsG2dvgdOFGe2Ey1866JZY2/SqiLTKOKefic9s1XxGgOzxmzpV8s5+yStaq5R62uzfAcOTx4yqnwWx+efCt8KA3C9ErG6p5UCy0NzsSN+PtF/ccUI0F/Snfo6tWiPsldjplQhkppP3Ng+Is44leLa0iiY6av6FN35VvOj59K3CgEPGo84FIrpuzeYv3sbIoWITLYYJhsGEkU8m1Xak3SJXqwDgZTiUxuNYLfjotWYe3Rtk0aphBuGhfnW1jUWAeHO9526RQ8ilCtb1gh0FMgw7+k8o1o630znZzzNd2Bv/zEj5fJT41thAK6XPizfeuJEjI3PIXDaik8Jot/2eXPxxYiVnY8zIhss+eO4juCVe3ssL1RwYtlUSneWUiwvjgPWqDnv6sVAWB+aDsLrwT6noswzE8+ZzMpIqyrleEx4nDVKsGAbS626TDLtSVpVAISFfWD6Hk6l1JLZjm4+pF4uQ7nMwxvXULWGXqOXPxG237sZratGT1ot+/HPpRbI7H10Ha1fzThvUWkVUAiEhRDsnz5kvlx+anwrDMD10keSb3WtQ35b/3QMtvaD3cgn+vtRjLi//yeMqDq3yksd+lY9pjrZndUE24+9vMPBUQ0YpymrSma2NooVvey520G4Gp6wG9YI/DGuTz4Xn2r5hWf02R0BjhtfqQVs6xq4RBAig87Mtnl/Ckqreta36uOSme3C/TVGqlUi16W6NK+3v2GECF1kmFMgO6c7Wmq0g3CW9Ktupta6xuFLiAzfgWLSql70rUe1Xe5OLz9R/9b2GIDrJY/VXd3yOjV2u+Pfkt1ZSukPcLLPO7MT5pzFiFrjmJ3jdZ4h4E7LvKz0LNOCLQpmpnze+LpphJCZvgOgzl30ehDscipKzIwu43tls51U2f3yCbBVUuKUzOjwlMzWgq0KZfNn86JnfWvncRNrmzhK4YQh1RvLWkbnu8ZjIl36pX/XRkf8BKb/QhuNkPQJzvMdMPKv7vexuL51SLgsji8wkmZ+/oRiAK6XPJr61m7+rSquoOovUobvQBih6oHWZYqMzLb9nG3/9urux6iEdRa8YYbdSozfRaVVuvCUntlasF15cMKdmxMo5dLZMNCMLD+B9cYuJ5R4caKZtQKF9a3CdWI6pCOzNWAraw09iyors7XRo59AGsAlGwj2lxdAoX/XOdIvwkjL54RokVWlAu0ZJHx5kaSUhJvtO2A/jwj0wyBj5Ez8swWuvXeyzVhp6KnyrTAA10sdR/Ujto8eUT+jvjXVhi2M9JfYaTUY7vAdILsYsbr7EqOqzs3SLACv3Ntvv3RGpGeZ7TSC9h1wjO/AEWmZbQy2KXzrSVTjcXhKw5/l+tQL+hq6QnZmfWs7jSAjiYi05WNWZpsskPXmJ9B6bKl6zPD+AQCh73M0OxP/DlvW2FYgk1IizC4l6TvQLv3CFEMv0qKydY1NGsF2ttnr5htzF/cTmPOfLt8KA3C91LG6r1UCYwX0rYWLEdBajGjLbGPfAan0ltJpLUZIKdk8fJVlVedOeaEnP4GiW32rYRUizMxsmwf7zSKeeflB4zGnosTUyBJlr2LeZ5NvzY2C+lbrOyBKfhO8WnwHACKkVLpf3nRG5WWvafpWoKV54PBas0Ova4FMKd04UPL0QyAlswXbbqqn/+p5Yfk+E/3Ut1pvhFhaleWNYFpihe/FD5a0NUQy4uh0hztPmW+FAbhe6ljZWcnkW5NxLmlVe2ZrPtyq3jCAG+k/TWa7cfgqqn7EpOte+Lys5hIzCmTK5T//0QaoMXsVri2OstE45IQKz49bSiCfb21ZZ6/61mTxqd13IJIQhCipNK3QCNqyxiaNkMu3Pmhrez2jvjWzgyzQ2lUlpdbYQqrvwIXMy2rb6md2Z4VhPDSxxXegjUY4PH1M5RLwrTAA10sdq3urHNVrXFs6v58A5EuroI1GiDyTmXktme39Ry8xHTncrCyipBtrtzUA5kV6Vb9zjXQFYQu2a5tV7l4f5dpiBXBQCF7d2GfjOCByxxgbH0fMSoOtqnhVvw/61hhspYKy0DuFDketpjG3MtmkcDq/kpMJN6z9a0t907fGYCsVlH0o6em/7ZktgJ3+qzlpt6v8q1/SqhhslavnOZY8ne1mFMj2jjYZ858+3woDcL20UW1U2T58RBQqxsS4bh7INBi+oD5vWxxLZLb3qp9kgmOu+8tIJXjl/hHbD09RMkhpGEhGOt/aGcVAWL8NDcJCRIDU8svZU6IhxfjYIsvP3OXRvTUcpc+49NwzzWzM7TQPP+uolvxjTYdbVmZrwFYFof4zyXundGftGjesXke15Ibd6tuHRUaBTDX0GhGm0SLTd6C/0ip9TgPC8RrTC2SHR4+ZLpWfWstrMgbgeknD6luHh6dxhJs73bLfPFgWCB/WdzmobbHo1Lg9PI1HhCBiaX6Ija1GR8NAEmz7PS8rS9+6Fx5Tx2FmaBrlOszevYkIIpaevdNafAo7i089+wmc0TowvkYCbCOlcDxHqw+SXrHA0PbOC2BUAAAgAElEQVRjGr6PG0VNH9c+zsvK2up3FMgUCFc1B1Rm+g5Eha7dEwhn0DVJGkGqiIPgMbfGlrkz9XSLWXBOcBVCTAL/Avgc9Ofgm4BPAv8WuA2sAn9FKbVnjv8+4JvR+73vUEp9wLz+p4F/BQwBvw68V8WfzM/OWNlZoRrWmRi/qbeUKf3TWHu4AkQ/UJwHy/hivvr4o4xEDZZL45SEh1TCSKpCri/r/m0Leuub1Raw3TmIeOMLMygVkJ7Z6siSVnVGOgjvRyfU8ZgeNtI1pdNWUfK0+QzpsirQkiDhu3qrmeM70Lu0qjt4aBmdp3cLbZnt6NYj6hVdmNuZnWkOA8ySVSXW2E9pFZj76LmJzDal+NQIEEL3/au0zLZ50kLXbh7XRd968piK47IwPv/U+VY4f+b648BvKKX+khCiBAwDfwf4LaXU+4QQ3wt8L/A9QogXga8H3gAsA78phHheKRUBPw18C/D7aHD9cuD951zblQ6tbz1leUTzrWn905rojwoZDJ93bjzAvb2XGVF1bpSuAc3PfBLgLB+aBFulHHb29ozvgDk2xXegl6LX2mYV0QbCUin2w1MazjjTo9dal5b4YqZ1Z1mwakqW0mVV0J1vTSyoeb28SNG32sxWhBGLK6uISBJ4HtWFeb0tz5BVxaNU+uCOlvZeOqR+bcUnZYp4CHJ9B4QQxf0ECj4Adk+2LoW+1caZwVUIMQ68HfhGAKVUA2gIIb4aeKc57GeB3wG+B/hq4OeVUnVgRQjxGeALhBCrwLhS6vfMeX8O+Bo+i8G12qiyffSImooYH5pNPSb+YEupx2KUTB98lu+A/eB7ZytGNMIGD4/ucUs1uFvSgF9E32rBdmaqxLVF65glWGvLbLXvwBjgolR6w0D7+28H4VNZ51RJHLfMWGlKH1dA36rBSWgvgbJvZLQZvgOIWKLWLbk+r74VYOzhI1wDUifTk0RjI7rIKERzjW00AgJtcF7K90YA+kopxQVR39M7KUNZpRWflFmj8N0WqV/WtYuA8P4l0bfaOE/mehd4BPxLIcTnAh8B3gssKKU2AZRSm0KIeXP8NXRmamPNvBaY/29/vSOEEN+CznC5efPmOZZ+ucO6YI0NzRbXt9ovZpbvgC1G2OaBTN+B9C3Y/f2X8cJTZr0yE97IOfStGmxvtNEIa5tVNh6esPLgEBhNzWxtZPGtO+ERDXzGylNNt//C+lYDcELoU2f4Dsgg0MAlHC3Sz+jOyuNb2yNL3wowmZBh7d3UX4tmUcfwocmRM9JMhDWZuLTa2pTurAuRViVAOFNWJaWeTmzGbaOC9MzWrrGAPE6qiOrpDnenLwffCucDVw/4U8C3K6U+LIT4cTQFkBVpvxWV83rni0r9DPAzAG9+85tfs5zsys4K1aDO5Fj+A6RrMSKhYRVSITwV93ln+w6kFyNWdj/OqKxzY0h3ivVL32oz2xvLw6xtnHDn+ijXFodTM1sLthpc9SqSsRdWaQiXqcpc4uJF9a3Zo7GTNIIDSOHgGIlapu9AD8WavOOSQwn3lpcyQThZfLJ/F76LsPKpNN+Bgg+AflBKzTWKWJsqBLp5wMq/UmgEhbl2l/to9a0LY5eDb4XzgesasKaU+rD5+79Dg+u2EGLJZK1LwMPE8TcSP38d2DCvX095/bM22vnWzOi1GOF75guS7jtApJ2MhJEqqURmu37wGeZUndvlOXPO+Oxd3k16ltm5RgwIjyBElJrZWrDd2a+D0sfqOVs6s92PTmngMjU837q0PutbHc/t6jugAjPy2nHivv5034H87bb1FJBCcLC80PW9xGt0HYTnNdeY0p1l14hAA3Om70CPEr6C+lbHcVoBO813IIwgivQ9VG669AvYPb4cfgLJODO4KqW2hBAPhBAvKKU+CXwp8JL57xuA95k/f8X8yK8C/0YI8Y/RBa3ngD9QSkVCiCMhxFuADwN/HfjJM7+jKx6ab32Yy7fGcd5iRFt3VpzhCFoy252TLeRplQnhsuTNAOf3E0g/rhOEk5ktWLB1WF4YZX3rgGRmW61ERM4QsyPL+mcL+wmcT9+aWiAr6DvQbUqAzVwj1+VwceHM+tb07iwFjvGWyCuQ9WFUS/sa00A4lUbQHSoI8o25948fMnuJ+FY4v1rg24H/0ygFXgX+Bvrr+gtCiG8G7gN/GUAp9QkhxC+gwTcEvs0oBQC+laYU6/18FhezdFdWnfHyrDbGzot+FyMQuuBlixHmC7h++AqjkWTZm0FKn4YEpVyEBYycy/db32reEEIELa2wJ1HA3iu6hz/cqvFY3Gfu2jJLLzwTS52yT5fgW3MvW0xahUDLvkyGm2fMrYJQa1szfAcmH+hNXOS6HC3P903fao8RXkmvMct3AMBM/7VAd+5rFwRhQANryXwmM30HIk6qu4zOLV6K5gEb5wJXpdR/Ad6c8k9fmnH8jwA/kvL6H6G1sp/1sbK7wnG9zuTwNWPFJlqe0i1E/0UXI0xmu36ygsMhc+UlXEcSSYetR6coGaL3wJJrS2O0F5969xPoXd9qM9u9aA85JxkZmWb2zg2ElOzeW2fx2dtQNxmZ7UNv6866OGmV0zw+pUCmq/xKjzmPfQdaM9uJdW3aEnkeh8tdbCcTa+xV35rlO6AiqcFWONqEnZTMNnHOs/gJdD8u6Y3QWSA7PN6hJBwWxuYYLY3mn/MJxqBD65LF6u4qh+Epi+NLTbPmlOKT3jF1NxjuRzHi0fEaswQslyZxHQVIFufKCFzNx3YUnzTYKmXrlf0zdUnTtwLsRcc0cBkfmtH3B4fpW9cQlVLci67sPbTdWa4Ta4SLGKH0Lq3KKD5ZsBXonn4rj2vLbMtHR6ggpFYqcTo8xOnsVEH51/n1rTHYIpprFKRmtsIRKKElao5fAFL6pG+1YLtbf8ToUOXSqARsDMD1EsVx41jzrURMjM4hrAyrfTuU7EU3mVi278D5ihFHtT1O6/uMOJJ5fxLQfKvG+GbDgJVmkezOEi4rD6qZmW0zihW99NtIB+H98IQGHlNDuphl+VYnzuxtd5ZsKeLZoonS6VpqZgvZfGtaaGlV8UzYSTz4kpnt+NYjItelUanw8PYNZBDiKFKlX5DNt2ZduxcQFvaeZPkOBLpAJhU41iQnpUB2Jj+BAvrWWb/MnZkBuA4iI1b2Vjhq1Blt82/NIvrtBz7fd+B8PNja/qeoRA1m3RE84WbqW5MaVgu265un3LkxBirK9R0oyrdm6VsBDqJTGmKE2dHl+FRp+takEY0AZK0Rv56W2Vqw7YefQMexGa5VNrOd3txm9PiYk0qF+vQkjuflFsiKbrf7QSl1+A6IQL/uu4kut84CWWEFRw/61qPTx9yeXr5UfCsMwPVSxcqu1bfe6H6wEE1pVYbvAKBbER1DLzg5RZsMEN44fJVhFbBostai+la9Rri2OIwQUXpma30H9urMTA0BgvypsOkgXIsaVGWA9H0mhuaa+tYC0ioAp+Trnnk6M1tAA0UQoTCmJSLFUctGD34C3bLMyTVdzFKuy+n8bHONCd+BpDG3CsPEOUVXb4R+6luFQhfwSn7z/aUVyEJtxK61rqTKqswJzBq761vLwr10fCsMwPVSxequ1rcuDXeflwW0FSPMBzQBttomzoBuju+APWcq31pdY0QFLJl20rPqW9MyWw2mglfuHTIzPcz61in2ImndWVl8625YJcBntDyJ67jN+3MGaVV7ZmvBVpr7p8IoNbON71sXvjWOAjuKifUtlBBIx9ETX+0ahUi1L1RhqB8/5gHbmjUmaISLsqhM3O+sAlnspBUlHl5p3VlF/QSOtxkrVS5d1goDcL00EfOtKmJi+Hz61hawLdlihEjPbIXQfd6h7vNOnrER1tg/3WZGhCz5/dW3NsEWnrk1zvJ8ybyWViDTYCuEy/LCMO186250REO4jJvOrJ71rXmm16ZLKN4lWN1nWoHMcZBBEIv386+dr28FLcMKzdb98Hr2A1cIgTJNA8Jzm6Os48y2zZg7iMC9AIvKLubhytFOWcJ1jJF4Z2Zrrxe3xnYJzbdWLh3fCgNwvTRh52W1862pcdZiBLQ6almwNcUIMImXySTW9z9DKWow7ZYZckrn8BPIC5t9qxhw0wtkx7o4pjscWjLb/VDbDF5PdGb15CdQOMt0ErKqTvtCGUUauBRI27LpZBXIuo9qmVjfInL17+vw+nKh9xLvRjKNuSNkECKUq6fXZsmqzDnPY1GZvUYnO7M1BVvZ0Ppf6tm+A5pv3eH29NKlUwrAAFwvTazsrnBUgG/tXzGiCbbCEVr4bobo2cx2e2eF8chhoTyNlAIVZ6H9k1Zl6Vs7aQTNIy4vVNjYas1st6oBanaE+eFl80Uvxrf2Q9/a4jsQCt1N6iUpBTqMuZUQXflWJwgY237E6dAQjpQcLndrhc7ezSTBFscx/Kiju57aHLVi3wGNeOf2E2iJnIJbizeCKxHmMy4Emcbch7XHlLl8+lYbA3C9JLG6u8rxaY2lmfnYtSr1w3oRxQhpPshWo2gy2+3T+wypBoveEqF0kFKwuV1jab5EtqwKepFW9apvdRzZ4hVbiwJODgKiXYfoYcAOazjA0vPP6FM66cWns4xq6X6g3lE4pURPf5rvQBRBmLg3KbKq8c2HCKWQjkN9YoyoVMq/dg+7GSGMMbcQqSNnkJH+M4wS1f1034GLkFbFYO1rOquZ2SbuZRhxcLDFuDd0KflWGIDrpYjjxjFbh9vUlWTMn4y36LEMyBShNNH/ZIoRUigenq5xU1RZLo/iuZJP3z8EJdnY7mwYaAXbfL61ee3iIJymbxVCsRcdIuYEldEJ5u7eRIQSIW1Bz3CNolWy1HIfi5q6nNE6MLU7q65Qjj6vyjDmnljbjPnW6uJc/nV73c20gXAqjVAPTGeW6uI7UKyqX1RapdfYCcJpcsTd+iNmyuVLZdaSjAG4XoKwfOvQ6BTuUKXjCR2HMES/2/2Lft5ixMPqfQhPGXddJrwRBAohFMuLFTTAJfnQVrBFuN3FBPoNmT/PDsK7UdUUs2b1g0QIll58FuF7nbKqCCzYEsquvgjAGfSt+ccJR6AcgVP2ESU/03dgeG2T45ER7YZ1rTf1SN4ai4CwbX0WJR9R9vN9B+xrfr4Je1FpVVEQVkgO6zvcGrmcfCsMwPVSRMy3jt5IbxhIAIQtRkCQntnaOCcPtn7wGYZkg3l/DEeI+PsLdkpAp6zKgu2r94+4e2uC9a1aRmZrrnHOeVkA++ExdVyuDy906FuzZFVIiQxDEA6qHqRntvHbLVj0ShnVkhbt+tYs34GpzW0a5TIoxeMb103xKd2Yu+fdTI+UUm7xqdEwUr8IGUS5vgNFrl1Y31rbpWz8BMbKY/nnfEoxANdLEFbfupji39oCto4h+pO96G2ZLbZfXspz9XlvHq0ypAIWjQQry6DaXtaC7bWlERAOy/NlhCDTdwBkYb5VITSfmfK91J1Zw8wOLzeXlml6rcFWSQdRVomtbWdma4FMRlGihTZvkd2lVUDXHYUF25n7awydnuI3GlSNG1amMXcoEb1Iq85JKVmwVY7So2Qc0fTByDLmDkNEXvNF+7W7gPBedevS6lttDMD1KYflW09VpLuL8iIuRritovV2g+FIQhg2YTAtsyW/GPG4us68Clgu6ckDvepbhdAZbqbvAA4rD45BCQ3IOQWyja2qkV61gnAtCkxnVompkYWe9a3C80wGmOE7EChUI0D5Si8/LbM1kTeqpfXaxUB4dPMRAH4YUr2+lOgga92iq0jFU2sdRKbvgL32RYxgF67Rrmb6DoR6jZ6jT5kl/TLXLrLj2jveZtavXFq+FQbg+tRD860NxirTursoL1K2+qk0Qj1AKbfJ0TYPbvMdSN+CHdR2qNf3GXEUc97kufWtaTSCXp8DMsjObGPfgXS+dc/MzBqxnVmhPJe+tcN3oBGA1KL8rMy25d4VnBLQFYSVYnTzISiFE0UcXFtKrLGVRpBBpAHJdfN9BwT9t6jMoJQ6fAci3fIqXCffd0CIHvwELq++1cYAXJ9yaD+BGpOjBfWtXaRVcTHCTDLtkLAku7Ok1JmQ1zqB88HeJ6nIgDlvBFc4LXxr/hoptNUXAtY2jxCInMzWfmEFxIYxraC9Y4pZE5XZDr41d50FpVXCmIc7lVL8c2kFMqznqeu03MeO6xZ0rRra28cNQ4SUNEaHOZ0cz1mjAtdFlH3dBZVlzB3prjL9gM3IbOlNwlfUOlCY9xyvMatAJs1nU/j6gZHxO7J86/zo7KXlW2EArk89VndXOWzUeK7ovKwepVW5BTI7Q8nOKjKZ7eb+qwzJRiG+tTWys8yOIxMgnFsg26rx6r19UOZLlMhs42LW0HxXvjW+P+fQt2YVyKzpdUtPv5tSICu43Z54sIl0HMpBwP7NG/m/87atflaBzD5IlZSZxtz2fPqHe/uc5UbbVj+zQNZQiTWqTGPuq8C3wgBcn2qcBCdsHTzkVIaF+Fbg3H3eFmyVI/R8eUforWICKA6PtpmQPovOHJF0+MzqPqAK7HqLgXA3fWsSbCHii/6bRYSQtGe2D6sRgfKZf+4aIrIGMUX5xPODcFwgCyMc3zX98inSLzTYqki7gzldrj1uhhK6UcT+9aXM44pIq2KwjTR1gJn+mzZyRtsu6jV2bwnun7SqCbYSUSlByUvPbM372T/cZuaS+gkkYwCuTzFW91ap1utMejM4oUQ5dPRPx3FBfd7NYoT+8NfqVXYaW8w4p8yXpggjwdajGkpFIMNc0+t+SKuSYUFYF8daM9taFCJLDeTeENHjgF3WefHtb9WjcdJcv9rfdw9+AvlrbIJMnvRLRRJZD0yDhsgcOQMwvrmVyrd2rrF3aVV2Zmtd1IzHRJILTaMRCnrHnkXfmif9klHEycket+YWLjXfCgNwfaqxsrtCNawxPnodFJnTLRXwpPq81w4/gy/rTPo+I55DJCMW54YQSDa2DlnfOk6co7lFV0oVllZlWQd2RjoICwH70SGnKIZvTDF35wYikrp9V9DCK7cXn/o+LyvnYZYEW5RCmGsLR+Qac49sPsKNIgTkZq79kFYlwRYLwuae5RbI+jSqJY4cEE6CbTXcx/Vgfuxy860wANenGqu7qxwGNZ5bXESU/VRZFSTAwlb7Mz7Uvc6NT/tybBzq5oHFsi2iCIRQCMIuxSfBzn7A8oLd8mebXmeNaul4Pzn61j1ZpSE8xisz+npCxAUTI0fo2KIrGWpf25IHURsf2n7tnkG4u75VOI6WVDlOpjG3CkL83X1CzyPwffbzurMuQFolbDHUdTKNuZUKoRE27Q1zCmS9+AkUWeNudYux8uXnW2EArk8tToITNg+3qakm35pdfGqgaBL9QKukyn54e+HBMkB4++g+QypgKTEvS6+NxJ+dxSelXHb262xsH5Elq9K4V7zolaVvBdgN9EDC60Od+larmEhmjSilp5cKPdyRINt3oKdRLefQt3bSCAqCgPGNhyghqFUq7C4s6O6sNhrhoqf/xmsUotN3IAg1bSBo80ZoLT716iegn5H5a9w/MXzr9OWmBGAArk8tVveMf2uOvrUJtq7+EpW81MwW0PRBZLfnzQ11amSAcCgb7J1sc4cGS/5MIX2rBdv1zUNmJnyuLepxLamyKiVZXhwHnHj7mRXdQPhA1qiLIWZGdGdWnr61CbaO7ihq2yV0+A5IDQr4Xu59LOonAMX0rcIReI2Q2c0tRqtVIs/leGk+db4XoN21uvT092P6b8sa7X00Uj8gxZi7SeRoLwyn+xoL6VslByePuTW9dKmbB2wMwPUphdW3Toxezz2upRiRI6vSfd7GTcsJWwsS7V+YjMxs6/Aeblhj0isx5g6dSd8qRESmrGqzysc+ucPBYQMYS81sm5Fd9KrLkGrUQPoTTA+bmVk96FvbM9v2LboMQn2s6yDDzsw2joLb7V6mso5ubsd8a3VhDjVUjgVuLcbcjQYqlOCESBFlG3NfhLSqnVLKMuZuhDG4Spvhp3VnFXxIHdV2qRh963g5W/t7WWIArk8p+qFvTYKtragKW4nO8B2wXVtpGcr6/mcYUg0WfP3B7Ye+tZ1GQJzyxhdmEELm+g5Ilc3b7gaHNPAYLk/gCh+UPLe+tWWLLvUYRmFGkWR1Z9ls8rx+AvEalWJ8Ywsv0r+3g7ZiVosxt1Ioz2SkSVlVuzF3QUOZflBK0EojoBSO68RDNDONuQven12jb701dSv/vVySGIDrU4hWvnU+/+AzFiOA1AKZUgoagf4CtDlqbRmzliVfF1H6NS/LRlNaFSFE1jgXDbYr94+4c3NCt8m2ZbaPoyPqwmOyMt/XeVl6jYYb9VyzlU6XVRGBrDdwHAeV1jDQcu3iRa+xrYe4Blz3M2RYLbsZL1tWhUT39JutvN3NpPsOXAAIW2lV5sgZDbaqYcZyO2bOVobvwFXiW2EArk8lVvdWqQb5fGsc5y1GtNMIQYRyJQinJbOVKPaPtlmUEcv+9Bn8BM4mrUorkCnlgHBBRqmZ7V5Yo47HHcu3Pt/HeVkZo7E7fAeiCBHqgYV5vgO6JbV40WtiYxsnzlwz5mZlZOEdvgN2moCbL6sSxmfivKNa0o9ru49tBTJpWl6FsA+HdN8BJeDg9DG3ppYG4DqI7FjZXaFaqzExei33uIspRugPd1yMMNnO/vFDRFBnQgwxyiRBZEDOVOqzTlvUTwCK6Vvjf1MR15Yq9lVsZru2VWXrqE7kDFMa9WHSFPFyevqhB2mVKgbCAvR9NNKqTGNutLyKkl9ojZPrW/EjaD9r4mtBfWu8RrObyfQdQKECPf3Xyrsyo8/6VgGaJ/Z10TbLd+CotstQ6DA3OnMl+FYYgOtTidXdVU7qDe5Mz3ZM4GwpPj2JYoTJbLfr60h1xJjn4LmYzqxTlGygOxyUkUXpLLO5pPyqfjJ60re2XKOZ2R6Ex8hSgFseZcgbZffBBovP3dVTQu3Do607q/dRLb3rWzN9B4x/g4oiPd4lJbOFZtFram0jvkRe5noWfWtWd5YKI7NGles7cFHzsvQak14Ynd1Ze3uPGL0CfgLJGIDrE47T4JSto4dURYPxsUUEInO6pe7z7pJJQF+KETvHG5QJmfJHcR3JK/cOWJwrmTHfDuubR6xvndgzxWBbtOjVs741A4R3gkNOhcPw0Djzd26AVDhlvy1rTGR2jnaK0vexaCtrARDustWPwdYoGURCRpdGIyilpVVJcE3jXPupb43BViko+1peZXj6VN8By6P65/cTaK4xX99qwXav8ZCZofKVoQRgAK5PPFb3Vzlq1Bgdmsbz9dY8+YRObodUPdD/6Dox0Z/andWHYsTuyRZlQma9sZhvdeKe/qit+NQE2539hrbmUzIjs7XRKwinH/c4qlLHY3l4yehbn+1oM23XsKqGllYlizqpvgMZfGvnGotLqywId/MdUEFI5fEeoeNSq1RoDA9RHx3u1IeewU+ga1gQTg6ppLNApox7mhTo6QxdfQf6A8JSSc23Tl4dvhUG4PrEY2V3hWqjU9/aYTBsiX7HdAxl+A4g+lOMOKg9ZlqFzHhjCVhrzTKbxScNthbTlxfG2Ng6SMlsmxrW3v0E0jPc3fCEhigxN3RNXyfNqLmtO0tJiWNubFpmG9/LgnxrP/St7WAbKcnUw8e4UhJ6Hg/v3Eqd7/Ukpv8219hWIDttxC5qub4DPfsJ5B93VN+jrARzY1eHb4UBuD7xWN1d5Sio8WwXfWtsMGyLEclsLGnDZhQAuhgh0zNbGxnFiFA2qNb3WBIRM/544a1+s9203XfAZrZNk5fdg4A3vjCr+9JzfAc6+dZmBCriUNYIvBFmRg0f2Y0fVfqLH8uWUn0HTF9/IzDWgDI9s7XRg7RKr7E7wAnhMLW9zdDpKQCN2clUnS2NUPPk0kXleCNchLQKgR6N46ePnInBNghjVYDK9R0odu296iZjpaErxbfCAFyfaJwGp3peluxd35qUVTVpBKX7vDHtmnGHVmeBLC9DeXS0jh8FjLklysLnU6tn07e2Z7ZJ34FX7h+ysX1MnJFmdGfl8a2PGwc08BgpTVJxywY4i2aZiYJJiu+AFttb3wFzHzMLZP2dl2WPm1rfjF86vLbUobNVkSR+7llvBMgw5r4Yi8okEKYVyFQUgZattvkOpNgXFvQT2Dt+yEzpavGtMADXJxqr+7ora6QydW59awy2ypyn5OlsN8t3QBmez+vsRX90vE5JhUx5TUDsh761CbaSZ26Os7xQMu+nM7Mt4juwY/jWiaG5vulbY7CVSns3+J6+dkaBTAl022kp33dAv6WiIKzPP7nWBNeDNhmWcBx9K31PF8fse0uRfumJsCGi5HddYz/1rfq9Gt8B3zP3NU36hb6XQaiPy12e5OD0EbcmlwaZ6yCyw/Ktk/3Ut9qtvs0osrqzgjAe5WKLOzazfXy8TlmFTLtTmXxravSob9VLzC6QbWyfsPLgSB+TktnuhFVqeNwcWiaNb02LXvWtjr1/WQWyIEQFEQjNi+uMrLNA1kvRy0qrphLgup8mw0rwrSKRfbcXyGQQ6TUidE9/hjF3Pywq094L0LwfWcbckf08grRKjpQC2VF9jzKab52oTHRf5yWKAbg+wbB86zPDFzMvKxnt3VkCFXOP7ZntcXWHYSWYdseRsteq/tn1re00QiR1q+u1xZGOzFapiMNKQOgMszBiioHdeMKe9a2dx6WZvCAETsnv5GwTxaeisq6ktGoiQQukTiDI0bcmC2SOEEiB5kZjyoMOY+74N3xOP4HWNWaDcIsxdyh0gdNzEXZXlVIg2zvaYMy/enwrDMD1icVpcMrWwTb1IGKiNJOfUV1EMUKhu2DsNiyRjR3W91iUgklnmg/+4RZKSa4tVgxAZBWf+g/Cm9vV2Fmr3Xfg0+s7HDwCp1Qm3K4hbiiIIt1FllV8OsOolq6hFE7CdyC9QEbcPBAXI7v4Dvi1GnrZKl0AACAASURBVCO7musOfZ/q/EzHGnvRtzpu83fdntnGxtyhnlwbKyVSRs7Y91zk/vSqbxWOwEmuscN3AA4PHjHhXT2+FQbg+sQi1reWp3Ck0NpL7NO8zaziCfR528y2Fp1yGO6w7Bwz41VYnI/Y3E5vGEhqWC9qXlbyuKTvgDdbJxiWjI/PMnvzGot3b+XKqpLm4cX9BLpnme1b/ewCmXYe6+Y7YB8AE+tb8TkPlxdQ7SB6Tn1rqjF3JGPplJJR6siZVhP23j9nmYemgHC770AkI/Ybj7k+sjjIXAeRHda/dWzimu7rT+mftomaCiLds97tpH3o894+vk9Zhkx4FTxXDwO8tlg2q0kWn1rBFsdjeX4YpcJcgM0b1dIa9oD0DHcnrNLAZXFoERz0lFBjrYjJeNrBVgZh3K+ee49ifev5pVXCbLeF6zbHoKRktrExd6CNVSYT4HqQNtqlz/pW25wiSlru18qHttIIRFER6UjxB0DBnVm1sY/vwNz41eNbYQCuTyzu7d7j0PCtWf3TuvAUxYUnaU00UmzY+sWDPaquaaWAP9yib82SVWmwrbJy/9A8DaLUzNZG3qiWZOTpWwF2oxMawmdupKlvjXll0BmPBTFlOOXIgEOdTFkVZPOtHXEGfWvmyBkjW1LGAWtoY5vjkRHcMGTnxrXOB8IZ/QS6vZdYopblOxBJY8otoN7mO9BhzH02P4Gs2KtuXlm+FQbg+kTiNDhl83CLExkwOdypb02Crf674Uct0d82tx3T5w2cmwfbP32kwdWdzPVvTYLttaUREILlhTJC5PkOQFG+NU/fWosCDqNTGt6Q6cwS2cUnNwGayteid0FqZqut7ITxHejOZfZD39oCtkJAGfAcZtY3EVIS+D4715daurOU0Nd2usiW7LX7QSnFYOsIUL4eNSRolVUljLmtRM3xi3DCxfSt+8ePmPKvJt8KA3B9InFv/57mWyvTuE6XWy4VjuMgvDaiv913wGRm+gC3I7ONowsIO/pbHnMSRfStTWlVtqxqfesEhMOrD6p08x1o/mw6L/vx03scUmayskDZrxTWtwohml/2tszWgq3tl1d2IRm+A2fxE+i+Rn2vhesyc3+dYdOddbK82NKdZb0HdH1RdTYMJNbYs4SvYJYpvOb1kplt7DsgjYcD5j5l+Q6Qzrd2XlbrW29OXk2+FQbg+kSiZ31rGtHfQSM0gUilZLYx2HbhwXyvjEQQKAuqZ5NWpdEIUnkGT8PcApnZM6deey885pXGLgfuFG9ffqc+/oz61lQawf5bbHqd4TvQBz+Bjkhs9ZMyrMPrrd1Zst7QD1A3v0B2IRaVGVv9Dt+BhlZH0M13gPzPo41qfY8SMDs6fSX5VhiA6xOJ1d3VmG/NjYJclD5GIHxfFyNSMlswH2PD36KU/qC3fUlKTgUpBI0YrPsjrRICNreOYmlVamYbX88DoTPo9u6s/3qyyh5DLE48y9zosn6ffdK32uKTU/IQpcREWJUsPrVpRM39Pm9Pf1JaJaKIiY3t+N/aTbIFZnyPMTjPMubGjvHxHFDdvRH6Ka0SqLY1tn0mDdgSGW8M1wGRbae5W9260nwrDMD1wiPmW6N0vrUlzlqMyCiQ2S0ljsj0HSh5FSSCrQenXB/tparfGwjnFch+98Mb3L09zfrWKcnMdr3xmPXglFpphncsv7M5L6tHP4HsNabdR3vu1uKTDEL9gOriO1C4Wp94AIxuPcIN9XmPZ6YIh4Za1ti+1c825tbFRUJdhMoy5j6PhC/9sE4Qzi6QRa20DG1rNGvaP354pflWGIDrhce9/Xsc1WuMDk0V4lv7UoywYCuENkH22ooRie6ssiojpI9UOrvtpv/q3TowHYQt2CoVcffWBMvzZbR3rM5sH2xV+aPjDfblGMszzzBcGoVIsXQh87Jy7qMQKKGzMlwnnqybZcyt5V85blXxGps/N5nXmVUArHVBSYHv4rh+U6KWZcwdRNmNF2nX7gMIW7BVoau721w3NbNVgBJwePyYG9PzA3AdRHas7K5wXKsxVVnSUy5TZFXQIw/WazHCftlTpF9lp4ynKhzt1tg4PkHPrsouPvU0qqWQvtVmjdIcqzPbl0/XOC5FeMML3Km8yOOVNVxg8c4tDddtk2tbrt2jn0BRaZVwnVxjbhlGqEaorfYIMqVf+pxNvnWyxVOgHVx7383kGXPrNWprRZmV2Sau3c95WUl9a1Zmi1RUT3coS8Hc6NXUt9oYgOsFx+ruKodhjbuj87nTLeME7wn0eSdpBHdoiLoTUJ6WLI8Os7FVzSw+9SytKqBvtSqB5BJPowYvn26y44zx+UtvZX76jn6gSBm3dLa4fpluIp1lmtfO4SfQERnSqg7fgVBTB3YLn1UgU/oJF7eyTqzlZ67ntQ5s8R2IpDZKMdKqrAKZMp1Z/RjVknwveo0pErUE2O4ePmakUr7SfCsMwPVCoxbWNN+qQiYnlnAcL6V/2oBtGDarzCInm+pzMaLsDlFbD2g4dRwn4vryUEfDQAy2wmFnr8HywrC9Smpm2oufwHqKvvWjp/fYo8TY6HVuT78euxqEo+dl6YvE2VjSiEYP25NGLJye2dr7AxRs1SwGwprVMAbnpojY2Z1l/zQyOiFawHX/RhNc+z3915xUG/iU9BrbM9vYG0FK3T1W9sGJ0jNbin/O9BqL6lsfMnUF/VvbYwCuFxire9pPYGRoMuZb2/unY7AN04j+xLbSgkCfixElt4xwXAIZxq+1Fp+aYLu2VQNgYzvfd6B40cse2zxuJzhipbHHoTfFO69/ceLgVv/WdtevJogZDq9b8ekCpFXxvCyrB83qzqoHqEgPTiQI8Xf2OR4exo0i9pYXm2qEi5BWpVBKqTRCI0Q5Zmx5njF30c8jvelbb0wucHv6dvf3c4ljAK4XGPG8rJGMEckYOZDr6KKT+TM1s0WDbb+LEWV3CBA0VJCzRr0CgeRNr59CZ5k2s+3szlpaLMqTNfWtx1GNtcYOr9Yfss8Qy5OvZ3rESJKU0cN26enHFShH4FTK4LutmW276XUkUXTxHIC+gnATbB1ExUF5LsFDycvjn8enPv8N3Nn8OLvz82YwpTDTf4FSlzpjL7uZAiBsqQFR8hFlP1P6pfRJ9b938cIo7CdQ38MH5kZnmKxM5h572WMArhcY9/bucRTUuDuSYsSRjGQxIi2zjQEiiqvRstbI9B3Q5yxWjPCF1rmGNHKPS27182RV65tHfOJTu7zh+TkUZjS4MbxO0ghSKQ6iU1ZrW7x0eMhueMqpKHFCCVme4POW3564OPH9yV9j84GSndnqeykbgX5YGSDLLD4V9hMoWNQxAKcCh5Nfq+I9OsYJQOHzy+/4NhofKTP0LgfhG6pDAY0wUXxKW2P/LSqTWWZugayuJ+uKRoiETGPuotferW4x7leuPN8KA3C9sKiFNTYONjmJQiaG5/IPzitGJIpPOA5C0SxGZPkOCAoXIx6/ch+FIFR6Aqkrsn4me6ufBNtrSyMsLXo8c3MMIeCV+3soY5oglWI3OGAj2GEz2OPxtkLNlTmQPoE/ytTIIs+OP8PtqTdQKTW1noX1rTmjsZNgq5TSxTGrq0zJbC3YyijCKaI86NG16vQ3q3zeh36Lr/qtn8Ex9/TLP/Sv+eV3fSsfd76YkfeM6S28VSjkGXMXVY/0iVKyYKuUg5D6gSncxDBF6DTmNhxzV7715CFTfuXKUwIwANcLC+snMDI0ief4+Qf3UowQQpu62GJEmu+ALUaUfBBRemZrf9ZUjqPA4XHjgIXyVOqle9G36ueBznCfvzPBZmOPT5yssdY4ZHMtpCYq1MQkojLE5OQytydusjz5DL5XTgcnVXBeViyt6r7Vj++j05bZtvsO1ANUyYu385kFsja+Ne/a4a5EPK7xVb/9z2NgBXCU5Gt+86f52PNvI9iJ8Eb1GGv9kEw35lahQtUDHM+YrGTJqqAvFpWtb9rcb881yofOzNaCraw1cDwX5YTpmS3683hw8pgbk1db32pjAK4XFEX4VuhDMSKtO6sR6g+1IN93wHw5lifusll/zMdqD5grTeKkfPl60rfSxMiHwT6/cvDHbKkhqu4w3vOTXJ94jmcmPofrw3d5dG+9mRRHsimnallDPt8aHxUV1LembPVTfQcCrVm12WGm74BZYlE/gcZDxee9/Ls4sZ9DM1wleeOnPsTHt/483rOi5X2nFsjsNAE7EjzLdwDOLeFLey9AR4bbTiPIMIq9bbMyW1yHo/oePuo1wbfCAFwvLFZ3V6nWa9yZ7Y+fQOFihBB6+2WLEXm+A6a6/uL8W1jdf5nt+iPWGzvcKM+2XbvJt3YPh7s3J7FFqvcf/FfWGGV05kXecfMrWBy7jeM42kFJSq698XVNIxop2fjkK4YGMPdFgfVFsJl71v3Rly9Ste4urYqBzHNxKqXmGhOZbQy29t4aY+y8NdqRLzLH4jByXYSSCOHlAlz8b54b9/R3Sr8Aopizxyl1X2OfpVUisUYhRKYx9/7uBhNuhVtTt7pf+wrEAFwvICzfWg8k496kKT519k8DF1+MSMts4170BkJCxavw4vSbWd36PV463WLZn8FtWU8239p6bWHeiiJUEe8/+GPuSx93/Cbvfv1/R8krNY9NdFEl+dBrn/NCC9huvvxpYu/QMGpmYu0a1hy+tf3+nMU6MNeYu97QxtyhE2ffeb4Dk0NVPn73rXzVf/znuLJ1JxC6Hh9/7q0ML4jCmXBLlpkm/ZJKdwfa3n5ZxHegf6Yu7SCc1Z21X3vEuF95TVAC0NTCDKKPYfnW0sgYXqmi+7ml4fAaAbLW0JlbGKHCJ1yMEIbH8z3wPRZffJbl1z/HF73t6xCVSQ6ky736PlL5SOWilMPa5jHFWln1AQrJ7xx+nFfCgKOhOd794t9sA9b896LXqMX4i88/w+Lrn9XCfNfRaq8w0v6m9UD/Zz1ZVbbLUhy9Sqty76Oepis8D1Ep4ZT8lu6sljUGISoImby3xje+971c3/o0v/ilf4vQbeY3gevzf737b+MuurjjTmF9a+59dJyYE3UqZZyyXqOwcrRA88qy3ojHhuedM3Fx/WdRfWs33wHXYa/xmLHK1W8esDHIXC8gmv6ty/GXLfmEjg2GI12MEJ6jec4cg+F+FyOs7lC4DsJ1KQ8N86ff+uf5/z7zS2yyxdunnuH+g0MUDnuHIdOTFR5snHDdNAxok5W2cxpw/WjtVT5a2+VhaY6veP6vM1FpnWRa2LUq6Y3gOpmyKhVGsTRNOU6u78BZRrXkrjHRRRVncRnG3DOf+BTv+rGfwDus8hd/+yf5pS/5H/ih//7neN29j4AQvHTnC/CXXYb/bIVCPPMZKKU8WZWKpM5wFagkp5zqO1Dw2gXv42uNb4UBuF5IrO6uchTUuDPSyrd2GAwHeosmuhkMiydTjHjD0tv42OYH2aq+wh+cvsQX3X4RlEApPaFgYyvb9No6WnmLDT54dI91b4q33Plqbkw9n3nts1oHtmtYlZSIhE9Alu8AjujLqJa049rBo51GuPnBP+CdP/wTVI5PEEA05FH5slGGnh/l0w/fAUIwsuzhTTrGHvLi5mW1rLPNiEYlPj/5xtzn9xNIxp7Rt75W+FYYgGvfoxbW2Djc5LiAf6sQ6NlElui3rbDtNmyRNB9mH+umlBbnLUZ4jscX3vpKfvtPfpY/PHnEcfTHvHPscxFCIQjzfQeAU3nM5vAa684kzy2+jTctf1H6Oou6VhUEYSGVmWTq62OzfAesAqDkm7HS2QDW66iWvC38697/27zrB/8xjVKZ4dNTGmMj/Po/+gG23vACnlR4k9Jk6RGyFkIQatqmyxr7ZVGZPE5YbwTXyfQdAE0lCN81RbIci8WCILx3ss3ka4hvhQG49j2sf+tIpYC+tW2r39KdBa0mL6EB3EbQkdnGINCTGUk6CD8393mEUZ3//Oov0WjscO/jH+YtI88yatcloNmd1QTbWgS/df/TnKhR5see5+3X/kKLxSI2GyqqoaQHEG7Tt2Z2ZwX63mkdcMJ3oI1GOOuolrT4/H/zy3zJ//bT1MtlUIrq3Ay/+M9+jL3bNzSJ0l58CiIUTd+BeI1tBbKLtKhskX+l0Ai2sNjiO5BRICvysH+t6VttDMC1z7G6u6r51j7oW2OwlRJRKUHJa/MdgCaNoL+QKiezja/dZTv5+sUvZGp4gd/4k3/FidzmPx2+xBeM3mSu1OkZIARIFfFHJ6+wPyUJx6b5ytf/VRzP6zDmJl6jBNGtF70HEO5iHRiDrXShJLRsKSWztW9IYdfo5q8x/h2myKqU4ov+6b/kC//lzwMQui4HN5b5tf/971Gdn+04vKlh1X36ca9+hjG3sp8Br+ga+0MpaQBVYEe6ZEi/rPWjXqOTu0bNt8rXFN8KA3Dte2i+td7Bt3bEGaRVmY5a5ovWWoxIZDodvgPdr704fpu/9Mb/kX+/8c+4X73HSfVTfP7QdZ4dWjKnUDwODlgLdlhvHHCshmjcXeI9L34TQ0Njeo20Sb+kjPWteiCg6Mhsm2vsQVpV4Dho66LK8R1QpglDOA4qlNkFsiy+NYz4sh/9cd74f/+GPr0QrL/p9Xzgh7+HxnQX8LBbfft+UmRVSFPltxmkHeWT441wYdKqhPSrnUaQRn2AI5C2wSOlQLZ3rOdl3Zy82f3aVygG4NrHqId11g83OI4a3edl9UFalQRbPJOhCF1dT3PUaplkWoAHG/HHeNvdr+LDa7/J+t5LBCdr7EZVHASbwQGHUnEiSpyIEUbKM3zxc/8tMyOtZs/tOttIKhw0p5eW2do1WnH5eUe12Mjb6neCrcTx3XikS0eBzHo4RFHzNRNerc67v+9HefY//V782qff/hZ+7Qe+i2hitFiWmeLf2gFk9oHre6mZbbzGokWvPlBK0EojOIB0BE7LGqGdRtivPmTiNca3wgBc+xr39u9RPa0z5k/iSj3XKBPALqDPW/fLm2JESmYbg209QHguys32HdDXlriOz1vvvpuXNmf5k60PUm9UCXA5ESO4lTEWxm7zpskXWBi+wfXZ1+Uv0WZlnqv/w2a2JhtLdpDVG1pFYRsHsu5VzLf2T1olaK4xvkaKfaGqBwhrBuM4lI+P+dr/6X/l+h9/PD7fx//Cn+MD3/vtyF5+1wX0rUBijem+A5jdjBDNNWYWyHqU8BXVtzqe1zImvEP6FSqOj3a4PjM7ANdBZMfK7gpHjVMmKosaFMJOWZVweixG9CytShQjUmgEFYYGrMj3HQA2PvVq3Jz14tIXMlmZ5aXt32e6MsfnTr3A/NhNHEcgIgmqiCync6ufzBot2KpQxl1YKiOzjQtk5xzVkr3GtvvYRiMoa3Busuzhhzu8++/8GNOvrFIvl3HDkI/8ta/jP3/HN6MaoRk6UPT+FP1dt93Htu6s+GElyPVGEEKcS8KXFlkg3F4gOzrZRTgRs6PTrym+FQbg2tdY3V3lKKpzZ3IRUfJbuMZk8QnQW0q3QDGiBx6sazFCCBBOsxhhfi7OxhI0gv03lbj28tQzLE+luFMpWH6hP1NZrTcCvqfvoSCz+KTsA6LkxZl7VvRT35r0HRAlj6n763zNd/xdRh/tErkujVKJD/2tv8FHv+49eupAGOnte5c19lNa1bJG39PHpmS2oH/fysq/uq6xv/rW3dMtRsta39r1nFcsBuDap6iHdTaONjXfOrKQO91SBoEh+kNkFGX7DjyBPu+YD2035m6EWj8qpEltdWHGzn1qXaPqupWFc+hbM4pPtoVY88gqNbO19+csfgL5a9TAtPDyp/m6b/+7DO8fABB5Lr/xA9/Fy1/xJQjb8hxG+v4olZ41ks+3dsQZKKVURy3rOxCGTcCtBwk+tJNGuIh5Wa9FvhX6AK5CCBf4I2BdKfUeIcQ08G+B28Aq8FeUUnvm2O8DvhlNa3+HUuoD5vU/DfwrYAj4deC9Ki4DX424t3+Po1q2vjUJto5SKMdFmFEkWd1ZheVI/SpGJIpPKMXC654xxQhl3KqsK5Zogq29djctY5/0re1bdOEInOQuIYVGUEoW8h3oVd96/Y8/xtf9zz9I6eQUgKBS5lf/4f/C6tu+QO9I7BpBc+HQ6ahlO8eU4XsL8K39oJRawdbgfsnTQxYzjLmVuX6Rh09xfesjrr3G9K02+pG5vhd4GRg3f/9e4LeUUu8TQnyv+fv3CCFeBL4eeAOwDPymEOJ5pVQE/DTwLcDvo8H1y4H392FtTyxivrWtWt4ezQzFyfUdQKKLERiAyPMduKhihGtMkF0SblWyBWxFZAApCJsFkzRg7NFPoKi+1SngO6ACnZnpoo7KLpD1UPS6+x8/yJf/8D+JgfV0Yoxf/vEfZvNNr287p8Jxmr/rLN8BFWj5lx5pnZ412vPpNfZ3NyMcgRPrdbOMuY1BjmjdKbSvsfi8rH08JHMjrz2+Fc4JrkKI68C7gR8BvtO8/NXAO83//yzwO8D3mNd/XilVB1aEEJ8BvkAIsQqMK6V+z5zz54Cv4YqBa6xvHT6bvrXDdyAevyxyfQdED9KqcxcjEpmtBVt5Ujc7XpU6ciYG23P6CXSsMSNbTy0+2a2+IrdAVrRa/7n/9lf5wp/+Ocr1OgBHC7P8u596H7t3W3WaWVv9VPtC23oquhhz90HC17HGFBBOpRFQ+nPmdPMdKMi3nmwy5g+9JvlWOH/m+k+A7wbGEq8tKKU2AZRSm0IIK/i8hs5MbayZ1wLz/+2vX5lo51tzo+CXQwh0wcu3mtDOzBYipAE14bndu7P6DMIoErZ7bkfDQBJsVRDGBjW5vGtREC6ob9Xnaq4xy3eAeI2OBmRS7pNS/Jmf/lk+/1//IoHv40QRO3du8os/9aMcLabomguCtT1GlNy4qJRpzB2EMYApJ+d32WdKSQiBQlMw8RozurMIQg24vovK8R3Q+tbXjsVge5wZXIUQ7wEeKqU+IoR4Z5EfSXlN5byeds1vQdMH3Lx5ebo57h/cz+VbW+KM+tbMAlkYxW2vub4D9L8YEWeZXYy5VWiATEg9giZ+P500Qu9+Ar1Jq7K6s1QUmSxXGVcqNCia7iyhJO963z/lc3/p1zkeHsaNIjbf+Hp++cd/iNrkeMqFO6+dvcZWgEvNbOMON/N7z/EdsNfup466fTeTVyCT5v6pHN+B1zrfCufLXN8GfJUQ4iuBCjAuhPg/gG0hxJLJWpeAh+b4NeBG4uevAxvm9espr3eEUupngJ8BePOb33xpCl4ruysc1U+ZGC3It/ZBWpUEW0BLlxJfwnYaQffLK4Tfn2KEXqMBuKw1WrBVQNnXa0SlZrbCEShDgRSpmPdL39oEMqXdsnxX89yJzNatN3jX+36SZ3/3w9TKZRqlEttveh0f+LHvIxwaSj2vvXYxgMsH4RawLflaXmWLiSkFMiU0sAk/X+oXX7sPuxkLtgqpC2O+26RaUrqzjhuHePK1y7cCZ59EoJT6PqXUdaXUbXSh6reVUn8N+FXgG8xh3wD8ivn/XwW+XghRFkLcAZ4D/sBQCEdCiLcI/Rv+64mfuRKxurvKSa3BlDerCxPGFb8jeixGFNny2i+HY7fnJQ+nUtL2e57bnILQCPUUhEbYnIIgO9fYW9FL9gTCjjHmFr6HUy5pq0XTZqqUNg5XjdCs0UwYsOtJXvcMfgJF1ygcJ24rFWWfcqPB1373D/LC73wIBZwODfGJd72D//CD303geHqNkez4fcdTAs4yqiXzzZg1miKesHOpynqrHk9BsJMazJSBuFiWtkaZPyWgucbeQTimY0q++Ux68RSEveMtJrxhbk7dfE3yrXAxOtf3Ab8ghPhm4D7wlwGUUp8QQvwC8BIQAt9mlAIA30pTivV+rlAxqx7W2TjcoKoaTA7P5U63fFLFCEgpkNUa4IDTzZi7cNGrOJ+YtdVvpxGkCEAIHN9NzWx7LeqcRd+a/KKPPNrhL3779zP/qVcBKAP/9S+/hw9901/VgNZeIEvQCDGQ9VNalUEptdMIIjBbd8ONZvoOUPDzSI+UEukgnOzO2mvsMFEuvWYpAegTuCqlfgetCkAptQN8acZxP4JWFrS//kfA5/RjLU867h/c57BWY2h4An9oGCB1bjthpLk8R4DnolLmtsfRczGi2JPf8f1Yb5lpzG24POGZYkTWudv41swl9iCtQioc32tdY1qBLAh1Mc1z9TY0xxuhyLXTQHjy/jpf923fx+T6Vvzaf3rv3+QPv/5rtbig1DlttcWYOwj1v9lzdu3pL/AgLQjCQqnmnDS7xjTfgVBPwtD3R2SusVcJXzF968PXNN8Kgw6tc4eel9Wqb001GI6k8bg0kqWUzPbsxYjepVUtvgM0wVYFoS44hKYYAekFsi58a3ONvUqr2taYViALQi3/ysps7b0r7CfQeu35lz/NX/z272dkd1//s+vwgR/4Tj7xni8zZi1NTjirQIadHhElHrCJzDbZ09/+vvPX2PtuJrv4JFFKmIdXToHsnBK+9qg29nGRzI5MMVWZyj32KscAXM8ZVt96K0ffKhxHI4DJypL98u00Ao6I+7yfVDECjNTGMa5avquPzTHmlo0Q4RX4+PQsreq2RqfJL7pOamarzyOQRlrVtfNJNsHvxh/8MV/9XX+f8rEeXxNUyvyH930/r779LYXkX3qN6Idl2TdrTJd+YR9iRbL6PlJKWlalj3F8r7WxIcW+UIUhSogCn8diILx3vMX4a1jfamMArueIRtTQfGvUYKoH/1aRyCJ0Zmu/fBIZRkbCIpA2A2nPbOm9A6fnYkQys223L7T98oCsqUxZFZxFWlW8sp6V2VojGhVERtQXdDQMtPbLa771+d/6Xb7y+/8BXhAAUBsb5Zd/4ofY+Nw3tF676H2018nyRohk/ACgHqRntjYuwKLSHpcnq0LasS4Oyq4xTfqVuD/d1rj3Gte32hiA6zni3v49Dms1hisTeG4p/+CcL0ezA5x4WQAAIABJREFU+OTiOA4Soavois4CmfUdsKd5UsWIBNhispjYkzXDmDuuTRdsZe3N1CW/QIYTIZTfXGNKZovjaNmSVLzpP3yAP/e+f6rlbMDR/Cy/+FM/ys4zt1uunTcvq3lcdobbAraObPKjZjeTZsxtJWpOEVOXPlBKSbBVUkDJ1+3awknNbO29lFH3NX628K0wANdzheVbJ4cXc4/rVd/qWId52hoGkr4DYaQ/4EJoRMsoPvW7GKHXqLfZjl1jhjF3vEYbGb4DaXxr9hqLS6ta1mivY7Mx24ARRrzp53+FP/Nzv0Dg+7hRxP6NJX7xp97H0VJzN5I7Lyvj2kVAWAjzILXHthfIkt4D9ucyfAcuajcjhEB4njk2w3cgMBK/JGinrPGzhW+FAbieK2K+dSQfXM9rtpHVnaUQ8TY923egx2LEGaVVqTSC1XqSXXwSltstssYzSKs61pgw5iYMeduP/wue/X/+X4SU1Mtltl94hvf/0HdTn5qIzbBjMTwUUh4Utg5MAeFUGqFhlAeiqO/A2SV8nWvsBOFUGiEI9T3ynNw17h1vMea9Nv1b22MArmeMRtRg42CD4yBgcmgu/+A+61sRxMUI4XuZvgMKIDLth8oYT2eFBY8+SauE0NtZp6zF45kjZ0BX1JVClDxyF3kOaVV7uI0GX/F3/wHXP/QRhFIMn57y6lvfzK+97/sJKuXOLXoU6V+Pr8Gkq4yuT/pWex3b1KBSMtsYyKTUvx+vyxp72ikUXyOei1MuxQ/WtALZwf5Dxv0Kt6dvd732VY8BuJ4x7u3f4/D0lDF/AjcUyKiROd3yIosRkJ3ZIhWyros5qovvQNFixFmtA1Mz2+QaBXpbSUpmG1+7qLQqH4RL1WO++jv/Hjc+8lGqo6OU63Ve/vIv5jf+/t9G+n4T3pMAYXWrQYQiyi6QXYS0KgHCuQWyMIRIxXRHZvGpz9IqfWzbGlMKZCqSHJ4+Zml6ljtTr22+FQbgeuZY3V2lGtYYHVvQW8CU/mkRF0yeXDECmmCrpIzlQFavmNWdJUM98bT7Gnut6uf19LsoRzXX6Pz/7L1pmCVHdSb8RmTmvbf2qt73fV+0IiEhhAAjkFltsIHHjLH92eMx4GUwHzaLPdhgwGMPXpjPeLyNMWN7ENjMsMogmx0jRHdr6W6pq1ut3tfqrqqu9d5cIr4fEZGZN29ELnWz+nnUdc/zQLduZWWczL755olz3vOemI+aNALz/ZjmaB4fW9fuvjqG1//K+7H8yDPwJZXsiTe8Gt9+1y+2+KqAjFMiaHQWlfKJGlqVKpBJUMvbytouCDf5GNggFSLywgbdAVACHoh/e1o2j9pwPgW2U941gPpY3DOIRV2L0te+AawDrnO0k2My37p0Zfp0S9XXr6oRusgW80utUv3cgL5Axn3lo9xO6yJb5WdealVBfiuxLHGsjlYlI0fe8MEcLgIiA/Ur8rF13YGzF/CGt78XQ2eFLpBvWfjBz70Jj7/1J/LJICpwNQhzM8+PJuu6npH6pa67iJ5A3t0MsazoO2QQ5mZ1T/B/iaePbMPrLkjhy/jujk1fQp+zMPKtQAdc52Ru4OLctXOY8hst/NZkdxZzPfHlszIEhguJkbQHwi26A6pgYmfoDijLXdWfO7+1RXfAFwUTYoumAKPugGJPJO7P0uHjeMMvvw89V8fEraEU//qb78CRV/5I/jRD8j4mtujED4SPlmgaMQlzc3nd7egJtPqoB+Gk7gAPAhBmh2unFZ/KoPDFbXz6MvorCyPfCnTAdU52evw0Jhuz6O4azOS3EkTFCKA1sg0FhmWUC5sC3PwwzQe1ioADlgUa+mgokAUCPJTAiykqLTqqJQ+1ikjApFVxv426A6GPBGAisl2z7wn82K9/ANUp2XVVreBLH3kfnrn7ebmj8Dz8ViK1BEjVQSgJqNMdUIBWlbO/DOA5HyPYiaLtVRI+JtII4T2t2ObIVvmZU09gfOYSVg0sw6ZFm7Kv5wawDrjOwcS8rAYGM0a6aPu8NboD4YPHAfhym56IbK9XMUL4aCiQqYeOMXCXpegOFNUTKE6tMndnseieuj42fOdRvOLDfwzi+ggohdfdhf/7Jx/E2Zt3S1X/vFX94tQqY/GJe+CBfCm4sRcRTXRnXQdqVTKyDX30fHBIhkSKMHdeCt+0ew0WY1jcvTDyrUAHXOdkJ8dOYtKtY/2SvCNdUvq8KQUnYktO1aA9TWTLIXJ+YkBcmcWIbOpQWCDzg6gX3UD9IrKoIwKkfPnWMqhVYYHMCkTx0LGx+3NfwX1/9JfglKJRrWJ65XJ8+cPvxdiW9YI9kaU5AJTKbw2BjFKQakVEhWm6A1zeX5LOoiszpRT6GBCg6gj1L01kG/qo6F+VdC2M0emLCyrfCnTAtbDF861ZkWtxahU1RrZhL3pDFCMYXKPugFg7bzFCPigF+K1p1C/OGJjrgVAKVnf1kW3iutulVkWniwpyd//1P+CeP/9U+LMr69fgC//9Q5hYviwsMoJA9MunaCPMRU8gy0e11U+jVYnI25PXI4RTjLoDeV8Ac9jNGCPbmIqaOpabqF9YePlWoAOuhS3Mt9YG4GTpCRSmVrUeFwdbMAbCWPilTxPmLrsYkbbVbxo5wzgI4yAWASE0tUBW1qiWJh8Zw0v+5K9w+2eiYRYXd27F5/7772F20ZCIrtQ2WgJ+qjB3gVZWAG1Rq+Jgq9IchBDx76jTHVDC3EFQCoVPWRoIt4AtY6CEiIjdRP0iBOPTl7ByYOmCybcCHXAtbCdGT2Cq3sBgdakonmhoVUDxPFjeh4PEihG6yBaAiHi8AKja5shW+VlATwDIx28llIA4jvgTiLqzYmkEzn3R1lmxZZupWZg7r56AVW/gJR/9M9z8lYfDz049/1Z8/r99AF5Pd9O1hD39lBqFuTnngOsJnQffTP1qOmeJ1CrhY1TZ1xXIlI9QEoy6yDb0sSC1KkdKiUhgJbbVNCY8/p2caVxD1QOW9AwtmHwr0AHXwnZy7CSmGg2sHViWOt3yuvV569IIrg9OmXAhRZi7jFEtrT62gnBTd5a8ZrieIL4jXRshr56AMz2Dl7/3o1i3/8kw93fk5ffhoQ/9BpiTmMir2eprC2ReAE6F/GOaMDfk8fNNrdKmEeQMLxBqjGyj4tP8Tv8NfUx0Z41OXUF3tXJDz8vSWQdcC5jKt07wOgYGV4JQuzlqjHFYRQTEgcr1K0YAMXCqiCGAppEzimvJGcssRhSiVuUAYUIkODlimGKr7gAQaiOoz2zzJNOu0TH8+K/8FnrPXoQdCIB57E2vw9ff/Tb9fc1BrRIgAdGZJSlqRmFu5aOck2bcJZRMrQqBTA4qVNemK5BxcDHiXAq8pJ23zJQSIQTjsyPoq9ZueInBpHXAtYCduXYmzLdW7Kr4MNE/rb7czJPFCNcH00W2yuaxGAHoItuIssQaYioosXwwtEa2kY9FqVU5fIzlW7W6AwpsG2IKLPEDMD9o0R3oP3cBb3jH+9B3/hLqXV2wggDfffvP4Ac//1PaiLwt6UCTMLf0ERYVfht1B67DbsakOyCnEgs2ilmYez7mZY3PXMLK/oWVbwU64FrIFL91oKd16kB8O8Q5BWFcfOkts+6A6vMuNw+W/nDEhbkJ47IXXRZMTMLcqkU1N7WqPX5rHGw5E5oHxLLAmQe3cRUENix7EItPnMZrf/P30Hv5ClzHASME//qeX8ahN7zKvHhJ1Kp4GgGMg9qWGI+ji2xVAY9J9a8C7JFMH1MArimNINeljmOkfqmuLOFj+xQ+AJhxJwS/dYHoCcStA64FLOK3Zui3KoqRrYoRBt0BX0Q91LbAqa+PbJXNQzECENEqsc20KjCA1V0xkjvuow4gChS9hI/ZACfyrQTjV7+MyZGvoVL34VlAze/Di/7+IrrGp9Co1TDR34fv/Oov4Ox9zw91WLUAMZ/UqpbIlkdNDX4gx6RAsAHSdAfmQOHLMs44qNIdMBXIAhbyf0GpyImn6Q4Ambnw0ZmFx29V1gHXnOYGLs6Nn8NU4M6Z39qiO+D7ItqzrFTdgetVjBA+qsgW0kcGYgeS3mWmVSlKU7ujWnQ+jp79WwwceRRv/8QFrDrngRHg8M01/P0vLILtcux6iuHffuvXcOmmXRIsUoS5c7aytkutikeNBACTHW5E1PCMugMgpBQKX9xMuxldgYwzBiklni7Mrf4NM3wcmxJiLQuJ36qsA6457cy1M5hozKK72l8av5UgKkaErYS6AhmXWzjqlFiMyPdwEIjuMVJxJIC2RrYCbLlgT8iuo7TotQi/tdE4B3d0P975wTOoNoTPlAN7H6/jl/74Cv7HO5dix+B7cWXTBpCKLTrEeHM0psCWyeo6cWxJGUu5TyXzW5U2QkitMukOSJaCSDGYdQfCtUvczUCmA4hMw+giW8TofsS2xWeGl5Wal7Wyf8mCy7cCHXDNbSLfWtfmW+PWVp+3SXfA9SPAZZ5Rd6BYMYLNid9qHDkjx3zwgIveebRGtur+CB/zbGUZZsYfxb0Pj4fAGreNx1101x2cWe2jCwi38E20qrgwtxeInKKiq0ET2SJfK2v8/syF32rszvJ98aJiXPCA1e8k0gjzPf03zcfwRcBYqu7AjDsBygIs7hnE4q7F2X7eYNYB15x2cuwkptw61pagJyAOy344QrBlDLAqMuLSU78IpaUXI8Sx6RFeCLaciwe/EiuY6HQHwGR3VHYuszZ2DUNHD2LxiGs8bnCcYcybBukyA1wItowLJaqKLRSsEsUnBbacy+sm6Y9HIWpVBr81BDJuARVIH6EvPlEhW6hU1LL+FcuiVkU+cqDitPqYSCOMTpxfMPOydNYB1xzmBR7OjZ1DvRFg0FosojRjor9oMSI/5SVLd4C7ngDYeKSjK5DNQU8g00c5GjtLd4B7gv7V4mNsjeVPHcXez3wR6777KB69m+GZ7VXc+43pljUDCzi13sJiZ3muSDjMt6pjTcLcrvCRESLEdIy6AwVepEX5raGPhuKTolapZhBDgWy+pv8CADX5KKl0E5Mj6LOrCzLfCnTANZedvnYaE41ZVGrdcOxKRqK/nFEtylL7vBNgG6g+b5IhzJ0z31oGvzUJtgHnYnQ4bS6QUc/Dlm99Hzf/85ex9uDTcB0HnuPgnm/P4Lc/thInN1aw4URzBPvwqwZhda1FpboiP7VKw29NdmcxDhBLpAXSdQfa1xNo8dEAcMktOgEHKBEKYJrINhS8zkuPKyGlFNcd4BbFqHsZyxZovhXogGsuOzF6ApNeHf39y8W2V9M/rSaYwvOliIVlTPQDmAdqFWsqRugi2xBs1SiSFG2E+Npl8lvDfnnZcdV74RL2fO4h7Pzyv6IyOQ1OKaZ7ejDb1QXHdTG5fgd2X7sFf/yBR3Dvw9dw66OTqHdRfPvlizB80xCWr/+P4uRlVdYVja4S+WgU5nb9kJPL03QHCugJiONyvMw4wum/4e/qCmSuaBgg0kdjgawghS8Pv5VwH4t7F2a+FeiAay47OXYSU16Ub9X1T4d93h7JFhi+DsUIQF8gUy2QHKIqbdRGQL5WVnFgQX4rAdb+8HHc8uAXsOVb/w6qwBkAoxSztRqOv+j5OPKjL8WVnVsBAMu9+3Hg9d/ED152BMSiqKx4AVYN3Qvi28Wq+nPgt+p0B3gQAJ64FpPuQHjv5nn6b+SjvvhEkE79IiSKwudK4Uua4Ld2Ldh8K9AB10zzAg/nxs9h0ncx1K1vHojAlgsqkurVT0S28mBRDQ6ubzECUADKo375lugbCHv6VWrByuFjTmqVPTWDbV/7Fu588PNY8uyplp9PrFiKJ37i1Xjyta/ATG+viB4hHvwKXY6hlT8JrIz9gk9E/rviZPpYJr9V/HtTcR8dO0oRaApkIACX1KpsH8tLKRFCwIlIbRDHitJBhu4sJndcmQLiBfit/c7CzbcCHXDNNMVv7ar25ee3qi99MrJVQObKufKUCOqSoRNmPosRyeg7mUZgni94q4SABcyoO5CHWjV08gxu+ewXseHfvouuyWl0zc42/fzUHbfg8Te9FsdfdDe4bQnADGJRs6lA5vvhfWQqytYUyNrSEzAeF/3baBW1wsJTEBaeGHf1ka3ycR53M0bqF2NCujBDUSv0M8duZqHzW5V1wDXDFL91sKf4vCxlLUDGOWARMTIlEdk26Q4ULXq1Sa2KpxEoiKiWV2I+Ai3yhSZwJUGAjd99FLc++AVseGQ/GCGY7u2F5Yt0idtVw1Ovvh+Pvem1GN20XuOj/lqahLmJaMQgjgXCAR31C5JaJXzMWfRqk9/apDtACAhBs4+aNAJHeRS+yEczCDeBLSHRIM0El1UeLAW8kUsbYcabBGE+FnUPLNh8K9AB10xL5luNVrQYYdvyIdYLDCOQxQgIIOOJfGiTKRAuk1rFWJOP2gIZEDYPEIuCM4La1BT2fuFruPmzX8TguYvh+QIZNV5bvQIH3/gaHH7V/XD7eoz3J+taAEAIptCwqBNGjUlhbumjAAneEtnGzydOlJd5kI/fSikFsRM+JtII3A8AKeIDbumpX2Jx8Wepuxmx46Lxl4qmQKZ85Or+aCJbABibvrDg861AB1xTzQs8nB0/K/OtZc/LShQjNAUy7gcgBJm6A2WMamlyMac2ApdbSmIRLB4+jt2f/yq2fPPfUak3YAcBPMeB5fsgAI7ddzee+LEfxfm7b0tfv4Coi26r3yRfiBiQ0eieRpGt2qJLsJ0PapUGhLVphDC8NhfIQEgpFL7WY00+JtII8r1HCElNI4xNXV7w+VagA66pdubaGUzWZ9Ht9MGmZc/LyihG8EQxwlR8IpIS5Nj5ixF5q/oZDzD1fWz+ziO49Z+/hA0HDoJRCt+yENg2fNvGxKJBHHngpXjq1S/DxJJF4eyv1NVzi7rkj+BAaTiKpFWYOwJbeCJ/q7rNzGuXC8IAxNa86oT/jkZhbjn9l3A7BLp21s6bUhIFMoBWHJCKbaZ+cY7JiRGsGFq0oPOtQAdcU+3E6AlM1utiXlbD09KqgOtUjNBRv2RRRxUlABhpVUABalUGCPeMXMXN//Rl7PzCV2HN1NE9LbqnKGOoMIZLm9fjsTe9Dkfuvw9+xREgVndFXrHhaUfONPuYAzCLUqviPf0aYW4xIpoJFkCK7oA651z0BLKPo5GPmgIZVwMAKRF6E+rcuu6sot+zgiklU4FspnENhPsY6unHku4l6Wvf4NYB1xQ7OXYSk0EDa/uXp0+3hEr0ZwNXacWIEGxloBXr89bqDoQ+zrHoxTlWP3YItz74BWz5xndh+QHqtRp824bFGALbxtH778Vjb3wdLty0MwQ9ov6v6kSjWlQfOtAkzM2JvD9OzoJSEWqV4bgQbAPS7GNTZBtt0TkRAufUyfHoFN7NpPhoWeIaqo6g8MkUgUmYm/lBzsJcOSklBbbjjcvorlWxYWjDgs63Ah1wNVqYbw0aGOpbKXJ7urntQURl4YQAjLZEtsrmh1olixFh7tEkzC19hNz2GgpkyQjFnp3Fzoe+gVse/AKWHXu26VjfsjAz2I8nfvr1ePLHX4mZJQaledWLHnvYiVor3tMfBIK6BHmPNbQqYA7UqgIgTGKRdMvImUD56Av3ONdHtii+mykCwsSyJJjFI9voXjLXE5EtlxwEg+4AMAcKX8Z3t5NvjawDrgZT87Jq1T44VjX8PN4/HYItom1iHoHh+S5GAK3FJ1YXffnEytIdEGsPnL+IW//pS9jz+a+iNjnVcv5Tt+3Fvje+DqfufT54V7Xl58nr0T2ULcLcDTF3jFh2qjB33u12GfzWZBqBeT4AEV2Ll4Oh+KTsOlCrWnQHPCLuo2MhTZg7pNG1SeGLX8v4zGWs6F+84POtQAdcjXZi9AQm3DoGM/RbhRGQiiUoQZrINgRb3wcnBITZ4LSMYkT+hwMQxYhuOobt41/AqquPwuc2Tg/dg+N996NhDwDMw+ofPo6dX/k6dn/ze7B9X0Rn0rxaFU+98kfw+Btfi5FN6wVTIGtybBFqFThoxQ7PaRLmhi8ExMU5iTmaKqAnkJffShgX43sqTnR9LQUyiPZYJn1ULzD94tLH8lJKhHPApqDSR2PxyY/5yJg2slXrCh/T76Pgt3oY6u7kW4EOuBrt1NgpTHkNrF1cjN+qjWwl2DLXB4iQi5MH67uzchcjikkHLnaP4GVnfws93/LR8x0XoMDKl57G3rv+CaefegBb/+FR0AlRnGrUamhAFKkmly/FoR97BZ5+zf1wB/rFSeU15B3Vkn9eVlwboTmyDYW5VSSm0jJojmxDsG1DT8DkY5JapS2QMSY4qzzpo6ZAlvcF0MZuxtidFcgUTBBrEknRHch6ASh+ayffKqwDrhrzmY8z185i0m9gqCdjGGGuYoScL1WxRTEi1uetSyMwNeYjywoUIyh38eKzv4ulfzCD2uPRj6qHGpg50EDtJ/4Zi08RzPT0oFqvgzKG4y+8E4+//pU4ffvN0YPVcEEoBXN9ELtIrq59EBYFJQrYlsjfqjRGIrINC2S+b268KLh2/FoyaXSUApYFUhG+mnQHCCVha+z1kKiM+wiLgFuWiG6lbKFRmNtn4OCZ76ixqcvo6+RbQ+uAq8YEv3WmJd+qtbkWIzS0qqinX+T1OIc+spVWpBixemYfaqe8JmBV1vVdYPp1wOxeC/VL3Xj6dS/HwZ94NcbXrQYgXG3SHfBFUQfcAoNrpH4BRahVOVMcsZdZmjA3CwLB/7UtsLqrj2xj55wvalWq7kDAxEvKotJHTWQb+li2RGV0v9N0B1SBjFAq6IgG6hfnHNdmL2N532JsXLQx3ccFYh1w1VjefGtpxYgY2EaVajvK42kKZDw6QebSnDEM+KfRu39G+3MCwDlCcPBn7sMjt/wS2GB/6zFx3QE/AFNNDjB3kHFCcudbFQi3Q62Kgy0NqOh4tSkIiLFAJnwsZ1RL5KMZhJt0BywGIgtuhCBVmPt6Tf+NfCRy4i8HYbEpE5rIFpRixp8EfA9DXX1Y2r00288FYB1w1djJ0ZOYqbtYPbA0dbrlvBUjLBrxKLW6AzLvyIRvCIjRRxWheHYPGiur6ENDu259TQ2n996GoLs7WxpPcnpVwUREjQndgUAWdXzxsIPAuEUvqieQF4QJlcLcsitMVyDjgQS0qgOQQB/ZQp9vTV07JwgTIoVnlI863QHGRI674oQvWFOapUyJyqZrcezovmgKZOPjFzBgdWPDok6+VVkHXBOm8q3TvouByuL04tP1KkaYurOY+jPNR7H2mb570bjrkwj+DrAmEte8DPC2MlyoPS+fjxr9Vu18rwYHpwI4uRe1mRIrkUZoU09Aa5qtvjaN4LrgjABI6g4k0ghF9ATa4Ldq0whyphcIMeoOhGNzgJJ51K38Vl0aYbxxFT2VSiffGrM8/JgFZSrfanf1oNLTF82ZhwAV7vngDU/8z/XAOcs+acnFCPXg066K6EeXI0lAND42PPCAYdZZiiOLfgyXPtwFd7NcD0BjJzD6fgL6L73gLs0FHnmuJfSx6oDWKtHYFEqkj2LcDKu70scgSnWYrCC1KisSFhGwiMBprQpScaSPNBSl4a700fVkE0aGFVBH46pRIs1HIiULK7a4j1VHjvKh4hy+yC2zugtez+djmdN/lY01LqG3VuvwW2PWiVwTJvRbGxjoWZqa6I8XI9J0BwDMfzEipUDGfR8gwsfHBn4aE9uW4abf/d+ouNOghMG55qH3nxjID6fwwtlP4tvv/9UMH+dGrUqbXMtcmcNzfTDAqDsw39Sq1Mm1dVfcV88H8wzUr/jaBfUEUg+NRZmpBTLPFWDoBYJxYtRGKEbhy7qWWW8SCDr51qR1wDVhJ0dPYtKrY83iVgpWE9gmihFhv7ym+MQC1qyVabKifd4ZwtycUaDCorHcjOGZvlfgeO/L0O2PAMTCuq8fwgPf/1PMVi3s+fK/4vLte3HkgZekrF0OtUqBLecUREZwykejMHdQsOjVJrVKga0AMJm7taiR+gVJrSpDT0BZ1m4mDrY8YKA04aNOmFt+VpZE5ei0mJfVybc2WwdcYxbyW716Lv3WeDECQGuiX23RXb9ZaT6l+FR4VEvqcTKnZ6tihIpsbcw4qwHG8PT9L8aKA4ew+tEnUJudxQv/8M9xfudWXFu3Wu9jznlZRUFYbceN2gicgzc8UehTL5BkZBtbOx/AFQNhYltRBIlYd1aojcDlZF0KGnu56oW5y97NxLQRTJGtBFtWd5sE2E26A3n1BMY7/FatdXKuMYv4rf2o2LX0gzUPh4psiBwASKqOiGxtS2zNk/lQz4+Ao1AxQj83vtVHfTGCKH3TigNSq+B7v/YLmF00CMf3QT0fL/m9j4NMzYA1XOljEOUxgZxb2fb4rXEfaa0S6rHCopEQjcrZemKWlvIxT75Vrd0Ov1W8uKwoH1qxxcvWts05W8ajolcu+cf2QFh8J0VrNq1WwqGKSttW5WzDGoJkJ6jvZC49gdlL6K1UO/nWhHXANWbxfGua5X04wmKEY8WKEXbYw94EtnVXAkSsaKRbu+RiBCEEXm8Pvv0bb0dXo4HeqSlsfPwgXvA/Pw0lRKOKT3y2IfyNA61uXZVvLQDCWeBBICPHsEDmNBfI/ISPEiDSfORKHSzLcvJbCZdNIsrHqhNGu5zxCGxDH2MvVZOfRUAY2SBMZI6Z1Cqg1ZQC2awrQJex1Hs5602CBx6Guvo7+daEdcA1ZqdGT2HSq2NRVstr3k4dND8cYWQrQaIJbFU/ugKJRGQbWsnFCECA/JUdW/CdX/l/QABYQYC7/+5BbPzh481shLBvnomoMRHZtviYt+iVU8A7DsKEkubIVoKtOpYrHxORbfx8wsccVf28IJygVoWRrWM3g62k8XHGokp/LGoM155PalXcx1hkG4KtyCmIF4AhsgVUvrXWybdqrAOu0nzm4/S1M5h08+VbAbRNrYqDLWwbpKsCibt4AAAgAElEQVRqjmwV2HoCJMoqRvDYi2L/W16PEy94XvizBz7wh+i5OhZu0WFbIHFaVSKyDcFW+ViiqAtX2qkGC8HWErsEWq1EtKpkGsH1wT0vH2gW5Ldm0uhkJEtqsXRHU2QbA1sZOZYrUZm+mwnBVkXgXRVtZKvA9trEZfTbNawfWp+59kKzDrhKO3vtLCZnZtBj9cMJLPElSkaNyuarGEGpObKFAFvWcAG1TddFtom1M/OeQQyEKcVDH3w3pqTodc/oOF752/9VFJVklEksqzUfmgBb4aNvjmyT96esynos3xpFtnYijSA5rA1fkPMbEsRkPlRz0lxrF+W3EtlAoY1sFdi6XvRy1US2rfenxN2MannVRrZ26OPU1JVOvtVgHXCVdmL0BCa9Bvr6hA4ll+IkYdQowZYxNn/FCM0EzjjYwpFapxVB8sgqkOXykTeD8OyiITz0od8Mxyev/8FjuPPvPpMaZTYVyNTDV3HMka0qkJWgJ9B8nBms42DbdC+1DQMxsC1BT6DJUsA6CbZwbNCqY45sFdiWQOFrcjFDG0EVbeu0AZfUMdTdj2W5dI8XlnXAVdrJ0ZOY8usY6l/ZHDWq7iwFtuqLHaMImWxeihGURv4ZCmSiGCG7nrJ81FCrTj//Vjz6s28K//ueT3wSy588InzIsdUnlMqXgD6y5V4QdRSp0SkpPhael5Vjq08oES8AU2TrB2ANV/gZBObINn7dhUA4ezcT9vQ7mu4s1lx8UnoTqQWyAhS+PD6OzVxEX0WkBDr51lbrgCsUv/UMJmL8Vi2tSoqAAAI4dZGtAonrV4zQpBHkmpzDGNmKn5sf9H//pbfi/E27xI8Dhpd9+E9RndGraml9jJ1Tm0ZQpHvOjZGt8jEr3xpfe678Vm0aIfQR5sgW+fKtTWvPMaXUtEWXYAul0wCYI1tpZU3/VTY2dQn9spjVsVbrgCtEvnWiPoNqtc/Ibw2BzKIR1UYX2Ya6A5LGktWCmXfLi7zFCCojR1mMSNMdqLviM83azLHx5Y+8B/XeHjBC0DtyFS/9wz+PtrWpPmZE4JJYTxwbtKuaXiCT2gg8c7ddLr+V0OjlSrszdAdCbYQcOeESU0rqGohjg3RXU3UHmKL6Zasj5Oa3Xpu53Mm3plgHXBHxW/PMy2oqRugiWwW2shgRFk1MBTJDvlW3LoDixYhkPjQOtkr0WgFuIrKdWLUCX/sv70QgVah2Pvwt7P0/D5nXnSO/NbVA5vthl1tqgaygnkBRfmtagQzqheD72sg2trj4syCFL9ViKSUjrcqiUdrAZ8bIVq2bx8dZbwoscDHY1dfJtxqs0/4KmW/16liVMS8rbavfIvLCGGi8Fz0uMCxbN0W/fLl93lnFiCaRl4CBOlbIs9VpIxx9yQux4sdfiV0PfR0WY3jJf/tznLtlN0Y3aag3cxR1afEzLszNAW4zENuO+Qi0CHMH+fOt4rh8/FZTuiYu8gJwUErFaJ403YES1dGiY1N8jOkOgAufacVO1x0okm91ap15WSm24CNXlW+d9Mvlt4a6A6bINlYggyqYpBWf8lKrCvBbQ90BU2Qr0wjf+fm34MKWDahXqyB+gFe958Ow6xrR7dx6AsWoVXEfTQUyPusCri+aMNIKZHmjx6L6rTZNp375AXi9Ae56gkqXViArCsI5U0pU8Vc1kW2YRqh74A1XTCpOKZB18q3ZtuDBVeVbK5U+VO2u9IPbLkY0pxGEupYlIthkzjZZfCq5GAFNpKdLI8CxEHTX8M13vx2zPd2Y7epC16WruOtP/8bgY86ijlgw17W09PQn0ghwhOYAbCuV+qWuux09AfNxCRqdJo0AWXxMpX4BbVP4Wlw03UdNGkH4aLdqxSa6szr51mxb8OB6cuwkJusNDHYtTacDoWAeLG8xwrZEZ1Yyso0Vn8JiREZPv/CxKL81o0AG0dN/bfsmPPq2t6J7ZgaVRgN7v/BVbPzm95t9dP1MbQQAxalVmboDJKE7YCiQ1RtgrpfrPpbObwVv1h1IKZCxhodw0kTq2gX5rVnfC46oYJuiOzAzOQbS8Dr51gzrgOvYSVw8fhHBmQbOHx7GucPDLf3TwBzyYHMqRhgKZOrhk/3ypu6sonoCRaUDD73+lXjmpfeg6rronp3FK3/vj9F/ZVTqDjCZE03XRihMrZoDv9VUIAMnuXQH2tETSLsW4aPUmTAUyNSxQnfAENkqP4tS+AqmlEwFsnF3BD0dfmumLeiCFuccP9z3Q1zzZ7G0WgMnBIQD548ciw4iBKu2bwnBNpOiUmoxQha+uPhvWnGatGKTxSclZ5eHRiN8LCgdSAge/q3/jBWHhjFw4RK6Jqfw6t/+fTz4Vx8Ds21wiLEuyWGKAMLik4pssx70dudlxS0qkEHIazkWCEfkI9AkzM2R/z6WNZW1uUAGoGILEXFDgYxD5Xrbp/BFPqaDsCqQjdVH0Fvr6Ldm2YKOXDnnWNa9DMt7+3B0ZD9mvClwm4JbFJzScDT0+SPHcPHpZwDPE7QlTWQbWtE+70LFiBTqVyyqheen6w7krKzrqFWNvl58+SPvBZPrrn7iKbzgLz4lfaRG6le4RVfcVdc306qA4tSqAqOxqdTYbSmQKfnChqKmeXpFrWhx8WfeUS0FUkqUJgpkTboDQk2Lux6YEmM3FMjmQuFLPUbmW/s6+dZMW/Dg+uINL8bupVuxsrsXT116BJP1sTAShEUjsCUQdKCwW8cgw5a7GNFen7e2QGZH467TdAfy5FubfEysfeHmXfje234m/O87/+enserAIb1QcxJsbQu0YkdAZio+FWhllYulHpa21W8V5hajzbWKWnGwzct6KCGl1CLy4lhCJ1hbIIuEucug8MVt1psC8wW/dXlPBrtmgduCBtcgCOBYDh7Y+gBuXr4Ta3oHMHzphxifvdJ6MCWtPf1yyx4HW1b3opxZar/8PBQjVE9/iu4Abwix5vj4FPPaZhB+9GffhFN33goACCwLP/IHf4auaxMtx7UYUT39hsg2FOZ2BQ8z530sk1oFkIQ2glmYG34gCPo57mOZ/FZwgFYcs6JWQpgbUvTa7GNHT6BsW9DgeuDAARBCYFs2Xr755bh95V6s7RvAM5f3Y3TmYnSgem6SoteJLbrofoxEkE0Cw8D1Kka06g6oSLlFG0FbIEspelGKhz70G5gZGkBgWei5OoYf/d2PRX6kXEtYeDJ0kMGi4BC3PZVWJc8539QqnTB35CM3R7ahj/M3/Tf00SBfCAg2RZbuQBE9gb4OvzWXzRlcCSFrCSHfIIQ8TQg5TAj5Nfn5IkLIw4SQY/LPodjvvJcQ8gwhZJgQ8orY57cTQg7Kn32cXIdXIuccjDFQ+UWmhOIlG16C56+6Dev7h/DsyOO4MnVe+KeiGVMxItQdkCChRK81kS1veGBKdyCPnyUWI4gc6Cd8rKQKczPXA/fTtRGmly7GQx98NwLbhhUE2PTdR3HbP/6fFB/TqVUh2KoIPEOYm7kemB+IQXtZViK1qkl3oKvWTKvSCHMzz8+VWy8zpUSI6Lgiji3+rbWRbQS2zPWy/QM6+dYC1k7k6gN4F+d8J4C7ALyDELILwHsA/BvnfCuAf5P/DfmzNwPYDeABAJ8ghKjy6J8D+EUAW+X/HmjDr1zG5BeUxiMUQvDCdS/EC9bciY39i3Hy6pOYqo8DnGPV9s15ThoVI7J0B1QRIkV3oOxihDhQnDMskJmEuVVRzKA7oOzEC+7AYz/5GtiyjfdFH/8bLHvqqOH+FKNWkUSBrIXD6gW5dAfml1qVrjvAfD/0M1V3AHOj8OU5DpnC3Ep3IEjVHejkW4vZnMGVc36Bc35A/n0SwNMAVgN4HYC/k4f9HYAfk39/HYBPc84bnPMTAJ4BcCchZCWAfs7597l4Kj4V+515s0CCAdUUYe5acxfuWHUbVvYM4PiVJ8ByysmlFiPiYKs0OhWQGYS5le5AkVEtmT4a+K0taQQpJp1ZIAsC/ODn3oSRbSKSsXwfr37vR+BMN8sTliUd2AS2lUg8XKuopcA2yFnUKUk6sAlsVX5ZShiaurOY0p6YD4nKDGFuYsenFZuFuUcnL3TyrQWslJwrIWQDgFsB/ADAcs75BUAAMADVwrEawJnYr52Vn62Wf09+Pq/24IMPYnh4GEEQhFFs3J6/+vlY378CVcJwYeLZkosRXABDlu6AFL3OLD4VAI88x4U+ynHWqQWyWRcBB778O/8vpvv7wAnB0JnzeNlHPh7lL4HCegJ5pQOpZYnKfpruQFwWMJfuQE5+a84ok1hE+pimOyB99JkxsgWKU/jyppQIkT7qhLlld9bk+GX00Q6/Na+1Da6EkF4A/wzgP3PO08rFum8DT/lct9YvEkL2EUL2jYyMFHdWnZxzbNq0CYQQHDlyBE899RQOHTqEw4cPw/d9MMZgUQsv2fgSrO4ewsjUaUw0RtNP2m4xQlcgoyIHaYxsFRi0oSeg9VHDb9UWyCqRj9dWrcDXfuMdmOrtxXR3NzZ//XvY8cWHYz62P6ol6aNuq69NI8hcLli6MHfxUS1z47fqhbll0wjn2si2SXcAmBeJyiYfNd1Zo+5l9NVq2Di0MfOcHWuzQ4sQ4kAA6z9wzj8nP75ECFnJOb8gt/yX5ednAayN/foaAOfl52s0n7cY5/wvAfwlADzvec/LUw/SGpN5td27d4NSCsbEbKzh4WEcOXIkfn24afEe1FafxqHz38NdG14Fixo6hormwTKKEYJXK+UApWxh2J2VkC9kni8EQXjGVNg2+a0tPgLiAZQ+Hn/gxdjww8ex/Wvfgus4uOvPPonzu7bh2trVwucioi4lUauUvCOpiJcXl/dQJ18IPwBI/u12WfxWQgk4pWHkyOP/1onuLAQMHByEO/qwJPSx+PTfNKv70/BZHYPdfVjRmzF6vmMA2mMLEAB/A+BpzvkfxX70BQCKYf4zAD4f+/zNhJAqIWQjROHqUZk6mCSE3CXP+dbY78yLxfOthBBYlgXHcbBnzx7s3r0bO3fuxPbt28EYw9iRMXRfpnCfvYx93/8XY3fWXEe1mI9LFCMMBbJQUEMVx9IKZHPQE8jnYyR6/Z13vw315YvROzWFwdExPPDBPwJxXSF6naGNoNbOV1CaGwgbdQcAsSUPMqhfau1SqVVJbYSUybV+IEA3Q3dgTtN/U2x0+iL6nS6sG1zXybfmtHYi13sA/DSAg4SQx+Vn7wPw+wA+Qwj5eQCnAfwkAHDODxNCPgPgKQimwTs45yoEexuATwLoAvCQ/N+8meK3aosREmwty8LOnTsBAIvWLsLVI2N49tppHHt8P3pq/epgoTsQ9qJfnz7vJt0BEsjKrw0Cro1sQ04m5yCmyLvFx/wgHN/Ce91d+NJH34+feuuvwvY8rD10BPf+xf/CN9/xcyKHB9KiOxBqIxAi2midHF/Lkvitoe4AFa3OkHQ1kzA3J+IlpYqR6T6WA8JKd4AzLjrxqBiTkybMzfwgn488HwiPT19Cn9PJtxaxOYMr5/y7MG9MfsTwOx8G8GHN5/sA7JmrL0VM8VutHIIgw8PDIIRg9eBq3L76VkwF38fR8Sdx04p7QAmNRF4YB2EcK3ZukblKIjqREg/VfFGrRDHCavosmUbgQSDyjUT+XD6ELT5mTAlo9bH1HCPbN+Pb7/yPeOkffAIAcNM/fxknb78Jp198tzzWAlQbbmyLHhbJeLaPaVMCmqwgv1UVwwALRF5jk4+qQSQO2nJ30Xp/Coi6IP9uhjhWeCyR68TTCNyXkS1nYmuqfNR878RuJtvH8enL2No32OG3FrAF16Gl47eajHOO7du3AwCet+p5WN+/ElXK8Mzok0LYReoOgJCQyJ6pOwCUW4zQPBzaNIISKyE0XXcg53ZbV/SK22Nveh2eue9uAKI99mV/+An0jVxt9rFFd4DK/C3J8LFAVb9Nfmur7kCUY27Wim1OI1yP6b+Rj4k0gm3JSr+dKsydN986600h8OsYqPVieW+H35rXFhy4mvitSUuCsG3ZuH/z/dg0sBR19wpOj8vCFxFR6qqdW2Oal4buLCl6jSyx5pKLEYQQ0dyQ1B3QCHNHPiLDx4y1CcFXP/AuTCxbAmZZ6Bkdxyvf//sgQWA4nIBA0IHC+2iaXCv75TNFr4uAcE5+K4GIHGm1olfUimkjcM9vBjHd2oVAOF+USSAKjbSWIcw9U498TBHmVvnW9UPrQfMU/DoGYAGCa1q+NW66CHdx92K8YvMrsLF/Ga5OnsKliVMyZ8XDvGNq8Ult3TJ0B8ouRiSjzGwfU6LG6AZBnsy4bn2wH1/6vd8EIwR2EGDt/ifx/L/530Yf4/xWk+6AEL2GuO/xIp7Oxzb0BIw+JqhVZmFumfbIEOYuTOGbAwgbC2SERCmPlAJZJ986N1tQ4Mo5RxAEuVICw8PDAFq3YGsH1uKlG1+MDX2LcWbsCK5NS75tiug1kbOymnr6dZGtBFvm5+xFz1mMyNrqhz7aFoiT0EaAvjtL+JidJzx/824c+KkfB5UR691/+fdYfeCgxsd0fmvIVbWoELuOA1kisg0r/a4f/m76/SkKwikUNQm2sC3QrmprZJsUefG8bAodUGpKSYEtKJU+mkfOcNfHxNQI+iq1Tr61oC0ocGWM4dixY3j66afhum7YMKCzeL41aTuW7MBda+7Auv4hnLhyEDONyQL98imi1xxC1EUJSZsiW+XjPFGrhAiNWRuB+YEQlFZb4DRhbsZx4D+8AWdv2wsAoIzhVe//KGpJecKC1CpiWanC3MwPhPiMl647oNYupieQn1pl1EaQYMsaYmKtMbJNXncBCl+qj7EXRUtkW43AdtadAm80MNDVybcWtQU15iUIAmzZsgW2bePo0WZxEUIItm/f3hTVpkW4d66+E1PuFLh3GJf6TmOttws1p8e8uGGr30SrAkA8XzAA5Pa3iVYlq76gJGphy9Ul1B4It/go88ahjxpaFSRtCZwDFQdf+fB78NY3/xK6rk2i79IVvOJ3P4bPf+x3oki1TX4rkblvES5YkY+WBUKgpVUp+hdKGtUSHWcG4WjkjCVAXW7hFdgCaBo5E1LpSlJHCy0lpUQIASSNa3zmCrpr1U6+dQ62oO7WgQMHYFkWKpVKS8MA57ypFfbUqVNh55bOCCF40boXYeui9VjaVcOBc9+AH7jGtXMXI7h4qGiWMPdMQ/yZIcydVdVv9TFHUYeLvGNLZKvRHeCu6OmfXLIY//Jf3hWeY8s3v49bPvPF0MciegJ5QJgAkXRhmjB3qDvAUnUHiuoJ5AVhQqn0zSzMzWZj2gimyFb5WVAdLVO/dVqM0O7kW4vbgopck/zWeMPAnj17Qg7s4cOHAUR5V3WsimxVREtAcN/6+3CwchD7Lh3F4UuP4OZVL2pZt61iRCJqjLihHJxJhXk3Fs3JiCzk2RalVuUFuBj/NPQREBGP8pF74IG8fs/HM3c/D99/yxtw2z99CVYQ4EV//Bc4d+tuXN68UV1spo9z5bcmI9uw4OT64ES+nLwAycg25LAWUR5rg98aDSq0Ig6r64FTIlzwgyiyVT7Kxof5mP57beYytvQMdPKtc7AFA655KFgKbCmlePnLXw7btlN1BzZu3IiqVcVP3/zTmPzhJ/CDi8cxNnMZQ92JWe5582B5ihESyDgloLUK4NipugPh1NqS2kkjgDM3YYRgSylI3EfO8f1f/A9YcfAIlpw4DQD4kQ98DJ/5xEfhVapAJb1dvii/NQ3gIrAV43tQsVuaGpp0BxRdqWKn+lg2v5WoF6ZUq9LqDkiwheLX2hk+5mRHzHrT8L1ZDHSt7uRb52ALJi0wF35rmu4A5xzf+MY34Ps+jjx2BIuvLsa6rkU4NnJA2y8PFKnq5y1GpOsOABAK8zl0B/IXvfJFR3H91riiFu/rwdc/8E7YLECl0cDQmfO44y/+QURkijVhKpDNE7Uqro1gFub2wX0fyBDmLlz0ysEKie9msnQHuBcAfoYwd04K39j0BaEnMLSuk2+dgy2YO9YOv1VZHGx37dqFdevWoVKpwLIs7Fq6C/ZFH6NHTuGpfd/FucPDUaV/3ooRrcfFwRZhvrHSqhWbAFsWBDmLXgWlAzXnHN+4Dt9699tRdV10zczgpof+Ddu+96hx5EwItn5QsKrfHgg3sRHiHVokTZg7KFb0apNaFQdbocVaTaVVqX9v9btpNjZ9Gb0dfuucbcGkBfLqCTBNHsx0HCEElUoFhBA4joNbV9+Kkfo4Tl87hv7aUqE7wDlIwLFi2yaZwyNGoC1ejMimVhFCRCukOlanO8BlXs+xBdCo7bLuHiTyrWlrCyf1xx1+zf1Y/4P92Pq1b4MAuP8P/j9c2bsd19auiuVsJQk/xg+lttWUW9b7WN68LCCKMsVYlHg+tFW+kNVdMe6a+qm6A2VP/1UgHLYQQ687ACZ9tCh4PK+suQedfGt7tiAi17wpAQA4evSokd8aNx0I7162Gyv7loCjjpH6eXCLghMKTgEQmjqBcz6KEbqtvl53QDQ5gJBUYe7Co1rSQJgQ/Ot7fxVX1q0BOEf3xCRe9b6PgnpeUxoh9LGlp1/fQVZ0VEs7/NZkGgGhNgI1R7byxTjf038jH5vTCFCsDttK1R1Q+db+rp6OfuscbcGAa2DoaY9bEVEXHQhb1MKdq+/Eyp4BnBkfBuNMBG7UAu3Sj84IwbbeEP+N9J7+IqNa8lCrFLgQ1VFkGjkT0x3I6unPC8Jubw+++jvvEswGACsPD+OFf/bJVB9JLUN3IPQx+z6WMS+ryU9EPhpHzjQ8MKWNkKE7ABTYzeQEYSL5yaSarjswPnYOA1ZHT6AdWxB37cCBA2Fn1qFDh4zdWXnBNS11sGXRFqzrX4Uui+DixEmAA6u2bQ6PT47OUGCLQFb6VfHJ1J3VxqgW47ExEE4tkKnteVaBrMColstbN+EHv/BT4Wd3fOqzWP/v+1oPjlGr0nUHOKBmZeXSHchzf+bGbzXrDiCSB0wR5p7P6b9ZBbLx+hX0dPKtbdmCyLkyxrBr1y5QKiLGOH8VALZt2wZKKXw/Xy+6AlddDpcQgjtX3YlzExdwYvw4VnStSe3ph2UJ2mXAhGqVTaP8mKY7Sz2AmdF1SfzWJp4t46COBWLbRuoXLBpGt9lbWfGgP/7m12HzD/ZjowTVH/0vf4BPffp/YGbJotDHNGpVE4eVMZFDrEQUNa0wN8sncF4mtSrszmIMoI7GR6BJmDv0sURRl4yUkuLZjjU6+dZ27YaPXFU6wLIs2LbdRKvasWMHtm/fjqNHj+LIkSP49Kc/jSAIMnUHsopeawfWYv3QWvQ6FVyaPJW/GGFRY2Sr0ghs1gVy6A7MZVRLqo8xfmsa9UtFtXAFbSldd0CubVt46IPvxpQE057Rcbzyt/9r9POC0oHEoubIVqURGm5OH4vrCWSZzkdtGiGMvL1MbYRcPhbkt3byre3ZDR+5mopZhBDYtrj8PXv2hCO2t23b1qI7oCJb9b+sohchBHetvgtnr57HycmzcFkdVdpldtJUjIhFtgSSoaAijxTdAdU8MNdRLanHZWgjKMlC9eIx6Q5AjilR2/zZRUN46EO/gZ94+3tBOMf6HzyGOz71WfzwZ9/UNr812Z3V5CPJ8rHseVnm+x3XHSAAAsZAEYFtGNlC6g4oP0ug8MVtbPoi+p1aJ9/apt3w4JqX36q22tVqNWyFDWSkEE8jqBbZIAjCbi6drehdgU2DG8BWXsKJ0UPYsewO8+J5ixGMy0mmtngQNbQq5SO8QHQdMRa1wuqum+lHtbT6mI/fSmSeN9JDSPgogUz46IsUQ8AASnD6+bfh0Z99E57/t58GALzwz/4WZ2+7Cee3bymVWkWg8bGlO0uyOFxPND9IH40+5Abh/NKBYWRrm6lfgNgpENsS//6WgfoF5Kbwjc9cQq9T6+Rb27QbHlznym9NRrYKVF3Xxac//emwgwtojWzV+W5ZeQtmgkfwxNgxrB/ahS6DatacixEm3QHPE0MTGRNbX0CrO9COnoD5uMQkU6OPfsSM8KSPhOB7P/8WrDpwEGueOAwaMLzyfR/Bpz75cbgDfTl8nBu/1aQ7IF5WpMVHHc92vuZlxSPhZGTLFchSAg7erDsQU9SK7yLy7GbGpy9jc09/J9/apt3Q4FomvzUu8nLHHXdg9+7dTboDcdu2bRsYYxisDuK2wVtwsTGB41eewJ6VL2g5b95RLUCOYoQCMmYBFdkzb9IdoAThrCdiZfaiZ+kJhJahWhX5SIGK6OknHKGPASX48gfehTe87T3omZyCMzaBez7+1/j6b78z28eSpAMjsBVaAqGegC6ylafgARM814yly+S3EpUScEQnHpCIvoFId4AS0T1mOak+1kM9gVWdfGub1gFXzI3falLUUmAbBAHOnj2LO194J9ZUh/DY1VOYGtqN3tpAwslyixFAYquvVdSKjZsJRNqAW6wlso1uUDE9AdUhlGoKhNX1xHycWrsK3/31X8QrfvePMNvVhVX7DmLHFx/G8KteZu7OKqgnUASEw+9FIrJV95JJ8j0oAVMDIzXdWWn51hY/58RvJS2RbeijJ8TNQaSPmsgWUPOyalg32NETaNdu6LtXhp6A7jjd+ZIiL5RSvPSlL8Vg9yD6xnphnWvg6P5HcO7QcHN3VuEOnJzUKoM2QrzSD8cCrTqipVQWyLTzvVQDRt7tdo78tmkqq/Lx2ftfhGMPvBhd9Tq6p6fxko//NQZPnQlJ7i0c1pzFmjL4rS08W8cCqdqgjp2tOwCUO/03BYSb2AgVwZigFcc8csYPMD7dybeWZTd05Do8PIzNmzfD9/2mfGjSiugJAHp+a/I4pTdg2zbWLlqL6uUnMeZexXKyAeePPgvIWQLE51ixZWMY+RijlXmgVhFI3QEVaZoKZK4n1rUtMULc9MLKS0fKWVn/xq//JwwefRYrjp3A1VUEa7/yIXQvt8BhwV+yE2M3vQDm6QoAACAASURBVAbewFpRIHM9GalHPfMmbYQ8PhahVoED1FZi4a2RrSo+cVdOmbCouI8ZugNlTf8FJI9atRCbtBEATF27gvUDvZ18awl2w4JrEATYvHkznnnmmSZQ1RWf2tETSDtOnX9132r0OFWcnRpBQCG2W+rhI2LrGQIZFJGbNoHtfM3Lij+YujQCD2Qzg9QdCE0rzN3eqJak+dUqHn7fr+GuP30/nr25ipu/PYrVz9TBLODZPVfwxMjTuPziX0Z92S7pYzqtSvhYNrXKcB8NBbIWWhVBaxqhMLVqbimlZIFstjGFBpvBQPcKrOxbmb52xzLthgZXSin27t3blA9Ncli3bt1qbBZI2tGjR7Ft27bM45Lg2lPpwdLuxTgxcQVTjWvorw2Jh49A5Ly6qorI2tKdxYEIfDOEmoF8egLCyewtfAgSsj0S6sHXFMg4AbgXCKnDLHHuvCDMOa4t78fhe3rxqr8+h94JuZ4P7Ng/icERF9+0/wpnX/9HcnKtHfJn44paYfEJ4j4S28oWEC8RhAUzAxGtyrbMtCoiIlxi29k+8pwgnPNlNjZ7CX3VaiffWpLdsOAaz7cSQkKg27NnTwi0jDEcOXKk6VhdZAsUL3olQXh1/2r0XjmJidkrAlwhRDQAEj0csYYBzmMg5sUKJpISlYxsoX4nJ7VqLvzWtAIZ94TmAAgRn8stuooeQzpQRitrkwUMXWf3Y+0zjQhYY7bidAM9Y3VULz6N+pLt0TrakTNciF0HcrJAA0Zalbg/80StUkMeNbSq8IWl8twN6CNb5WNQtOjV4bdeT7thwTWN3xoHzh07doAQgp07d2ojWwW2RfKtao24re5bjd5qDVfrV7AGW8WHPBJ1SVq8O0v9N5W96MnIVoEt5+XoCTRfkJnfmgRbQiCKZRLQtLoDefvlJQhXJkew6Py08biBKy7IzLgxEm4CW0jcd+QOIDDoDsj18+wAClOrTPSvEGwBoCIicUAb2RIqJuvm9zEvv3UEm3v6OvnWkuyGBNei/NYdO3Y0NQzEI1sFtkEQ4MCBA8bIVpkJXFf2rUSvXcWpiQsImA+LWBCVkHwPB5W6A9rIVoIt98TAQkIJOOXmAllRalVOfiultJmGpSmQKR85IQC3WiLbpI/ewDKMrO3Fjn1Trf4BGFtWQVAbyld4kveGKh8TkW2TMLcfhGO3TcLcxfQEikWZoY+G4lPoI+ROwVAgy/sirXvT8LwZ9Nc6+daybEGDqwkI48CpOKwHDx7EG9/4xkzdAdM5a3YNy3uX4dmJy5hsjGGwukQtluqj6eFI6g6ohgAimwN0kW0ItjlbWdvltxrTCBIEdJFtuFWX+cTZTXfh3JbPYXLIRt+Y33T+CxtrmBnsgbt0S75IWBNlatMI4CLfSklqgaywnkAbIJxMI7CGGONOrPQCWe5868yljp5AyXZDgmvZ/Na8ugOAAHZVJEued0XvCvRYxzBZH8NQZTGa8q1mJ8WfOR7MeC+6LrIFZFHH88Oeeo6UfGFREM5BjgeNNG1N1C8Aot2UUrBaH0Zv/yl8rfG/cMs3r2LNsVkEFsGze3tx+IWLMfLC/wQQWoDfmn4cIQQcRPBBlY8G3QH4fphm4DTHfSyR3woOIVFZcVJ1B6DkH530It7Y9MVOvrVkuyHBdT7mZQERCOt0B9S0g3/8x39sKqDFI9vlPcvRXalirD4G9Jnzrc2Lz41apYtswwePcXDCU3UH1Npz0RPIuhbE7qMusg2l9RgDPB/T6+7Gsv/7XZza6mH/ixcBFsXsutswfstr4XUtA+HzR60y0arAOJjrCyZHhu5AVr418nHu1CpTgYxJ37gXwEj9QiffOh92w4FrkFdMGsX5raZzxsH2jjvuwM6dO8OtcjyynXVn0UOqOF+/Kr78JRYjskA4BFsQQHZnEfl7OmFuTkQvelj8yVi7DH5r0xa94oiijuSmbnvkBLqvirzr5/7kgxjZtQ2cipHXSjUqc23SPggrsOVggp5mUbELSBHmZtLHTCuR3yqKXlz8+6mhjgbqV4PV4Tdm0L+4k28t025IcD169Cgopdi+fTsopbAsq+Whyhu1AsX5rfGoOZlG+N5XvodgfBqX3GexcssmUBb19CeBcS7UqhxOApApBEISka18+BS1SkU9nOsjW5jzrfq18/NbAYhWXULQf+kylp88DQ5gtrcHV7dvEYd5vux6kr9iolWVKOoSvxYAobi1UXdA3UeZgkmbXFtcHS3/y8wU2YIxXJu6hAG7C+uGOvzWMu2GA9cDBw5g69atsG07tfjULrVKZyYQjke2u/bswtWTDdjdNVBH6oQyffEpb56wDGpVfItOADDiic9jugOhxXzMK0ZSlN8aB+E1+58USwC4vGcHeHcVBADxSLOPhuKThLWcO4X2+K3JNAIhPgAudGvBU3xEfh9L6NaLg+2oN4LuamdeVtl2w4ErYwyO44QCKjpaFRAxClQnV5buQFnKWst7lqPHrqCyrkts2RwJjgGL2k0V2PoiKiO2JbahRt2B/PzWvNQqwiF0ByqO+mVt8UlEZZCVfrREtpGPOXOeGhBeu/9g+Pczt98U81GOnTH5qIS5JTEfoXKU3scy52VFPgKgVHCUlY+aAlnoYwjOBh/noI6WZZ186/zYDQWuOgpWklalgPbw4cMAgCNHjoTHmkSvk+fUWd7jlvUsQ7ddxXjjSvgZkS2mACKwZQzc98UWLkN3IH/Ra+78ViOtyg+EZ0llqmQaIa/IiAY81hx4Mvz72efdFPmYALjUDjIOmV9OKT4VLXrlSCklt/ppBTKuOslUOiYsPsV8LDr9N4vf6s9E/NbeTr61TLvhwTVp6meWZWHHjh1hisDUnXXkyJE56QmYbHFtMbqdCi55E/ACF45VaTmGEAJOKYhSWrKokVZFVMGkwFa2DH6r8BGi6BT6aKBVUSKq1UU0D+SxfRcvY/DsBQCAV6vi4i75b5EjglNgyykFdWzxAtNEtvJgwVIgyFXAA1AKtSoskFECWqsAjqWNbAEBtmJEThHmQfpxal7WuqF1sGiOvHnHctsNBa5F+a2q0GXSHRgeHsaBAwcACNBM687KW/QiIFjcvRgjFR/XZkewpHe1yUnxZ9gvb+jOktqh4ACru0bdAXHOgtSqAvzW1KgxYNELoOGZqV9ybV2+FQDO37QLzHGa186K4Fruo95HRa0iFhXTa1N0BwrrCRRoMkiLbEWBzBdg3PBSdQdy6wko/dahDenX0rHCdkOBaxn81jhw7ty5EwBCupapQBb/3Txr33X7XTj77L9gvH4lBVxTihFx3QEagHCneYidTndAoHL+US1t8lubgIyyKD9KoI1slY9Kd1TZmgNRvvXs7Xub1s5H/zJHuHEfOWMgzAlHXpt0B0ApWMLHrLXzV/UNPkqw5Vz4Ffpo0B0QExGCXN/HsZkRbO7uxabFnXxr2XbDgGuZ87KUKdHrSkVs3U0FMsYY9u/fj61bt4Y+6PxQucw1g2vQ59RwZXbEuHYRfishRGx7oYlsle5AwFq4rDrgLnNeVuyk4jgnRonT6Q7IpgEOhGC2Oha5nrnNnG81+5ifWiV8tKMcuE53wJOTEOKgnVIgKz6qJcUUi8K25Hn1wtzc5yKydeyml0LSx7o/A8+dRv+i5Z186zzYggPXdvmtugJZo9EA5xzHjh1rOjaZRlA+rhtahz6nhuMT58E4a+EWtstv1aYRGl64RU4tkLWpJ6C1oDXK1G7RXV+AFhG6A91Xx1AZvYaZri4w28KFXdvkvSlSMZ87CGt1B1xf/N2i6boDiltVpqgLb93NaNMIvg8Elsg3p+gOdPKt82s3FLjmEb0um99KKcXx48exc+dOVCoVI/ULADZt2gTOOQZqA1jWswTO6HlM1kcx0LUkubg8eTkVYfUAkqoDUnFSdQdEwYRHHVwmK5nfKnxEON+Lc451Tz6FiufBtyycvWk3fCJztowJEJbFH9OLsmxqlfIRtgVaq0SNF7HINgRbdR+tLB8LcHBz8FvFOiT0Uayh1x2YGL+IXqvaybfOk90w4PqZz3ymKeequrNoYjs0Vz2BvMeZqF+MMTz88MO499578cgjjyA4G2CR3YMrk+fQX13c/NDMUU/AZMmtfmqBTM55gheAeYG5QDZHPYFUH2MARwjBugNPotpooArg6u6tQkhFUtQQCB85pEC3Rpi7nVEt5mObfTQLcwdAIP7Hg8BcIGtDTyDteuLfJ1N31nj9Ktb39HTAdZ7shgBXpUSlqv+q0h83NQ57eHgYO3bsyDxnWfxWBbacc6xfvx61Wg2UUqwZWIMDRw/hcuMQasurAIgQcqEEzA9ACwgwt0utUmDLKRejZCwqHs40YW4/iFpo06wdfmss33ru9puiwX6BDVIR6lpa6pcEW+6L/C1to+jV5GLG/Y6DLZdjZ4gCXZMwt8/AwUFz6wm0v5shlKLBZlEPptDfvRSr+w1F1Y61ZTcMuAKAbdvhw66jVXHOsX//fnDOsXPnThBCtLoDwNznZeXxkVKKtYvWorvq4MTkefg8gE1tnD96HOAMJOBYvnUjKGLFCM1DNZdRLenHRREzsahRmJt7Yvw2sah41k3UL0Cbb01bW4FH99UxLD55BgDgOw4u7N0pL1r6aFnSRz2tivsBWF34yImnj2zj9ydX0auYdCBRoi6AsUDGGq4QWaGePrINT1rubmZ0+oLItw528q3zZTcMuOoe4OQW3XVdcM6xfft2Y2RrWVa4BWtHTyBpyXNWrSpW9C7HiYnLuOaNYlH3CnkxEABrqSF24kMRNUbFCIF6aHtUS/Nx+gczmUZgfiDOaYmI3CTMzcWF5y4oNfFbYxSsi7u3w69Vw2sx+xgVyHgQgAQ2iIokU4S5yxrVEr8W4WPzOZNpBB4wEFXMIkgtkJU9/VfwWzv51vm0GwJcH3vssVz8VgBNotfJyFYZYyzMU6YJDBcB4eHh4RYQXtO/Bj2Xj+Ha7JUQXAkAqG4dtUaT7gAAyDwe4+IBZPrIVv1+2dQqwrkQeKk6olPLVCBTYFF1oPrmTf3ySYCLpwTOPC/SE8gbZRL54lE+GrURlL9VR/ia8hLijBUD4azik1QbIxVHHGvSHeBciIc7dj4fc4Dw2MwINnX3YuOijdnX07E52XMeXNvht5qKT/V6HZ/97GebADse2aovdt61TSC8pn8N+is1nJoeQfgV52javmt1BwKGUBowYADzMnUHSqVWJUDYWCDjXHAuGQPcmB9G3YHo/qzd/0T497O3t89vNXZnueLecSYBTBzckkbIq/4FzJ3fauzO8nxwkAhklR+JNEJe9kjDn5X81mWdfOs82oIB1zwsAQW2J0+exFve8hbYtq2NbAEBtr7vI48wtykvu6xnGfqrPWDXrqLuzqDmdAHgWLV9q/FcCmx5EIguIRXNJBW1gHAGVFr0HTlZHrUqBFvKQGoVoGJrI1sA4bacKzAE0DV2DUuOnwIABLaF8zc151vL4Lc2gS1xQKqONrKVB0PNKANNFw8vY15Wk49EAmhFUNRMugPCRwYeZNPoQn5rJ986r3ZDgGtRPYE8xyU1BHRphH379uHWW2/FoUOHwoaBNGHulkGIhGJN/xo8M34J4/URrLDXqR+k+hiPUIyRrQRbrkaRJHr6W3UH5oFaJUE4lfrFGLjrieuhPjglWLH/IALLAg0CXNq1HX5XV2LtjPszB2pVlu4Ad/0QDLmJVgXMgVqVw8cgVrjURbYKbF3RiUcsCu4zo49jM5186/Ww5zy45s23lsFvjYPt7t27wTnH1q1bQSlNFebW5VuVre1fi/7KIVybHcGKnrXCxzYqwnGwhS0BToJpqjB33lbWEkC4SRtBbfUJCcelrHjyKcx0dwOc4/gdt4gokhLJF83XympaO25pINwCtoyBEjv00Sh6LT8rY1QLkM5vbQFbxkAdqVKmiWyVj+NTI9jY09PJt86zPafBVY10iQOZ6cEra15WfG2lO0ApNeoOcM6xb98+bN68Gb7vt+gOrOlfgx67inPTVwTIFJHlywEyYb88pUZhbi7bOoljiy27iVYVP2eJ/NbQRwlkmx89gK7ZWfiWhXM37xa6A1zkR4ltgVtBc862xcf8egLCx2xqVdjTr15cychWAhmru6C2JTRZU3QHiou6ZO9mCOI+6nUH3MYM2OwM+hd3+K3zbc95cAWAY8eONX2Bk91Z8zEvS1ek0qURPM8DYwzPPPNM0/rqhdDj9KC/1gs6PgLfr2PD3r3Isnb4rdo0gie24wBP1R1oZ1SL2cdmEK5dm8CyYydAOAflDJdv3S1yjX4gfCPEOHJGgW27o1pMx8ULRbo0AvdZOAwwjVYlfCxa9Cq+m9GlEcZnz6K7UsHawbWdfOs823MeXLdv345qtdqUC00WnzZv3gzGWNvdVnFL2+orU2BrWVbqyJmJ0xOoVRywQPJZU85ZxrysuIUPoOzpF79n0B3ggj+q5myl+ZgbhBP81tWPHRIUJQCXdm6F19Mt1mrxUV984hAgB4uk9vQD+fQEwrWQDsJx3QFScSQhuDWyFT5K3q2Tx8fy+K2EEIzVL6O3WsXGoU5KYL7tOQ2u8XyrArLdu3cDQBPYHj16FI899lgImlm6A3OlVuksDsKmAtkRfgTHHz+PsfoFbNyzF0y2lmq7s4pSq+bCbzUVnxqeAAVLaJqadQfmpicANPNbz96Wwm81CXO7fugjArMwdzujWoyW3OrrfORcFPCCAJxSIURjKJDNx/Tf8ekRbOzu6QwjvA72nAZXwAxwcbA9dOgQ3vzmN8O2bW1kq8A2L62rXX5r0ken4qB7ZT96hhbLYgQ3dmfxICcolMRvbSo+MQ5qWyCOpY1sQ92BvEUvLb811jyQk9/atEXnHMQioI6jp1XFfMwTXZdFrWrqzuIcnBDQipOuO8CjgmTqugX4ra47if5FS7Gmf03m9XSsPXvOgmve6FFFo7Ztw7ZtbWSrwHbfvn247bbb4HleaoGs6NpZxzlwYINgyZY1IcgRyAc7vkVnCHvRRT+6JrKNFhd/zgO/laTQquAH4ZgUQsX8KmOBLAFclclpLD36rPgRpTh3yx61eNNxqX4yDppDd0DQv0SnGddEtpGPxfQE8lKraA7dAe75AnQpBafMXCDL+TK7OHECfXZXJ996new5Da7t8FuTaQTf98EYw5YtW4yRrQLbPPnW+NpZ4GpTGyAUAfymz0UkGIEtCxhIIFIGaboDStGq3VEtzcfpt/otugOMyWIWUnUH1JiSpnzr44dApT+Xt2+G29eTunbSTBGztvgkC27gSC2Qtasn0OKjgVqlky/knIXqaKnC3Bn5Vs45To4exqkrB7GzbzluWnGT9riOlWvPWXCdmZnJlfMswhSglKJWq6XqDnDOm7QH0s6bF4RtYoOCICBB6nEEACwLpGJLgG2NbIEATIKa0D/N2FaWzG8Ne/oltcqkOxD6KDmZhFKs3dfa8hquXSK1SonJhLQlg+4AAHDXF/SvjJ7+IqNacvkIgBAqRuMYItuwQOZ5QuhH42PAfBy++H1Mzp7H3qHVeOm6+3DLylsy1+5Y+/acBVegXGpVXtFr13Xx4IMPNqUMdLoDRYpeda+OgARwaOuY7YST4k+5RjKyDcHWD6KWUtfTR7ZoT0/AaIktvLE7K/RRcGw5gCUHh+FWKrB8vwlc52NeFoAwkjV1ZwmQ5eFsL3mwVr6w+KiWYi8zXWSrXliXhk+BEyopVwBAsGr7ZtSDWTxx4dtwaAO3LtuA1254DbYu2ZrrO9mx9u05Da5ljWoBskE4Lnp9xx13YNeuXam6Awq48hS9Zr1ZdK0dQNXuTncyY6sfgi3nQuXJsQWtyVAgCwtFBfKtmZbBbw3BlgOoQvTMcw5nagZLjp9Ao1oFr1Zxetd2MQgQEric7BdAWfzWEMg4ByryPqrf0+kOSN5qFkUNiFpZs31M3+pHYMvBlYi41KEkHDj65H48e+VJDFaqWDWwHD9778+gz+qDbT+nH/nnlD2n77Truk0aAHl7+pM2F35rlu5AEAR47LHHQAjRRrbxtWe8GTRqPqp2l3HdwtQqSpqARlcgUwUTRgBqWXrqlzyfvEGpy86J3yrPuebQEfRNToERgos7tsAd7Jc+iuYBRgioFSvqJHwpe16W8FHuPtSxRt0BSVGDVM8yCHO3M6oly8fonASXJs/i9NXDWNM7gB2LNuO+9ffh5FMnwTnHPffck33OjpViz1lw7enpASEEQRCE1ChCSBPYlg2uaVv9JNgePHgQb3zjG0P6V9ziYOv5Hup+HS4YKingWga/taVABoBYOYS581br2+G3HhAULMo5LtyyR+RsATC4ACWipdRA/WqiqOUterXBb02mEYg8H1UUNYMwd94XQBF+6/nh4+G1MM5xeuxpXJ06g00Di3D7qltx95q7YVELrusiCIJOSuA62nMWXAkhuOuuu8ICUxAEOHDgQBPYDg8PY8uWLeGXyrQNLDqqJQ8IK90B27ZTC2RT9Sl4foAa6QJJ21QWpVbleYABIdRsW1E+VCfM7fph2yw3Ub/ESXOtree3RpMHzt4eawHmAHXsEGyNwtyupH/ZltgiG3UHmvOtRhcLUKvAeehjeH06YW5PDH8ktgXOUwpkRUAYHJxQ+IGHo5cPgLEJbBtcjhdveBF2Ld3VdKxppFHH5sees+CqTEWAlmW1gK1qDPA8LzxWl0ZoR09AZzqw1qURgiDAvx+4Ch91eKc9nHcrEMWImTDiCR+GotSqOfBbdboDop8fAFGUpUBPq5Jrz0VPwJ6dxfKnohfOuVsFuOoiPVOBjHviBZClO1BYTyAzEm4F4bQCGQnvY3hwaxqhoJ5A3ZvB0yM/RK9FsGnRWrxi8yuwsm9l5KPczdx2223p5+tYqfacB9ekpYFtMrJVDxiToJBlZfJbVXHMDQLUAXCrC5yK4s754W6IfSEAEKzcOg3u+yLKzFy8PGoVIRKcVFRGiVGYGwTgXiCoQ5k+JvitTzwFSwLOyJYNmB0aCK9F+JjeL8+JVNVyLHGsJrJV18pcH8SxMml0xVWrMnykiKhfUrbQVCBjvh/S2LJ85Bx4dvQgBh0LOxdvwANbHkBftS/hYtRI07HrZzf83c4DtkDURGAqkBXVE8gD1owx1FkdLmeoOjWA2iGkgjOAMxDOcWG4htEzFhatCwBiYdW2GW3xab6pVVpFLQW2vh+qVjHuGnUH2tITSLkW4WOGMLfPIiDj0Ee2ys+56gmYLDYa2xTZKllA7gXyjWXWHQBEvnV8dgR1bwLblq7Fa7a/BjW71upigYJtx8qzGx5ck5YEWwC46667jJFt0eKYAuE82rHDwxY83wf7/9s7t9g4zuuO/87sleQuL8uLSIoUKdmiSMl1bMuiLCUIAjtpUiNoEQQF0uTBQAvkoS3QIg9FggAF+tYkL2nRAmlQ5KFo2qaFm7YI0qapUzdB0diWbdkSKZK6kbpTJJcUbyK5O/P1YWaWs8uZ3eV9JH0/gNBqZnbnz9nlf8+c73znMywikZIaVzFAjILZmsRQRg4B7owVR7au2arCpioHvaox4TKlVV6zRWS9nygENuZWbIxGuzz5Vu9ihDtR31rUG0FMhJgd5Sr/yNbWSFVz+m2Nm61vrTBAZpiIWs+DBzXmViJYpsnEg1E6Uw2c6jzla6xAIWjQ+da95YkzVz9KzdY1WK/Z5vP5wmO3ftXvw7o5E1b09R9n5Mod1vIrgcdOTyRRgt2Ss/AC65FtwWxNk+ytOk58csH+Yw7sO7D5fgIVcf+AnUGdwMbcedPeJoCKEF3L0T60nm+99UJwvjVQ5yZMWMSuPABvZFvSdyBv2l2r3OsTMEC22X4C1ZZWiQhGzPOnWarRtFCmycToB+RW5jj0kefoa+rzTSPofOv+oc3VhyCzjcfjZUu/RKRqc3WPSyfqiUUiLKw+9D9QKcCitTdH0dtVEtmiLGYmBEFx93IdfpHtet+B7fUT2CjRv2uVbxrBDa+dOf2tHw6zkkgQiUZ50NXBcmNDIWKr6tzbNGHfW3S1XvFRvu/A9voJBP0+pSbsp3F1eYXF9CLNqpaWpRbGRscKx3rrsHW+df8IzRUXkc8Af4Z9A/fXSqk/3WdJBaqJbMH+YI+MjBQGq8rdhtkfeoP6ZANxibBiLvgfqOzjkEp/mIISg+beHMpI+Ee2znEdvQ9tg1BQoeu1/e8WSquCZQqSsG/NlVL0vD9ENJ/HjES4fvIjdj9WsKfHCoiKlo+wN7FUi3v+8vIEJWAk4kg8GlhWhasRkHgUpcrkhs3qvgCqrW8VEc6/+zNqE1FefO5ZXj76clETdm+pn2VZhXXeNHtLKMxVRCLAXwKfAm4B74jIvymlhvdXmT/lzNYwDJ566ilWV1cDS7/AzreKCOl4mngkwlruIZayMEpNVFkUaqHK4ZqwO8HcJ7JFWYhlMfQ/jWS6cnYu0i+yddniUi3BGouNUEToee8Dkit2SmT62f5Cr1jLWa3WNdvgxtzVDnptrXVgucEnay0H2BoLz/EbICuTb/XVWOE6Ti/cYnbpHkeaWnip+6WicQR34Uy3udDQ0BDXrl3TM7P2gVCYKzAIXFFKXQMQkX8AfgMIpbmW4jVbd3Cs3ACZiD341N//HNFIjLp4HVHDIJdfJRErnqU1PZF0Sp2qMS4JPq5gthamRFBRwz7cJ7Lt7FtGiZ0nNKqY07/V+tbI6hodFy4Vdt8++aw9W0wsezmXqFGo+wyanWXlzeL8ZIVzb7e+1TVbJWIv5xIxCu0TgxpzW7n85paTKWPCs8v3uXjnfzmYbuT5jo+Qqcn4aoxE7HKzY8eOcfLkycrn1uw4YTHXg8BNz/9vAadLDxKRLwNfBjh06NDeKNsCldII9i2cQS63glIW6ViKmGGwnJsvNtegfKsP0xMJ+0FFE7aAiD0NVqQkslWIsmyztUy7/KvbpLN/NbDvQKVVAoooiTLbL44QXbMneGR7ulhqK0rchwAAE1xJREFUbXaOc0zGnVEU0JhbreVQa3kspTCcAbygxtzKsjZpcNWZsEQi9rEBfQesXN6ObKMRlOQCS78KGsucd35lhvO3f8bT6Wa6pI3BrsEKEnW+dT8Jy1X3+0SpDRuU+i7wXYAXX3xxw/6w4me2g4Om09wlTyqSJBNNMjH5LjejCVKJDOlkM/WJDJCunG91TLhlUyZccvnEjmQVTqRqrmFhTxy4MxYhaIAMn9Iqf4kbTbj7PW99q2fKa8CtfmkNq5XLg6IwKSCo70Dh41VFKdJ261s39B3I5e0KgKhRGMQr4O2N4AoP+AJYWn3Auzff4HB9hvbZDGd7z+5oQyLNzhMWc70FdHv+3wXc2Sctu45rtgBnz0LviefpujbHxOwEV4brWMotMDc/w721NVanO5ivU6RqWmioaSER82lLWJpvDcI14Z5qTLjGNtuoG5GVRLauqVom7YdXClM4K/cd8NS3ntu4XpZ9quoiYVEKIoa9FhUbI9uC2eZN+/wR+/Y9sGP/Dq2XVaQRbI0Jp445qO+A6ZSoGQKmFE0YeJhb5Nytn3IoVc9gx7P0pnqJRir/6er61v0lLOb6DnBURA4Dt4EvAF/cX0l7R2dDJ196/ktYymLy1CTXstcYz44zMTvB2/9ZR0Ld48F8lrszF5FInFSymVSyed1sK+VbXVyDqyoSVrT0rFH4iJRGto7ZzkzEyd5MkOlxl6gJGCArMS4jl6PTk291m2Nvp7QqcHaWp3O/Mq2yvRHsc1dbWrX5+tbgATLFvevXUd4vFREyRzp49/YbdNTUcqrjGT5/7POcf+98VXXUur51fwmFuSql8iLy+8BPsEuxvqeUGtpnWXuOIQYd6Q460h18tOejRWY7MTvBeHa8ENk+mJ8pmK0x3Usi0UAqV0siVhscqbj51qpMWJU/TuyUgJIITT2rKCPuH9m6A2T5vL3iqZtvHRojtrIKwFxXB4sHWu3Dd7C+1TVbFXEGvWIR/8gW22xV3kKhMHZoqZZq6ltds71zbdzpOuaUyCnF0socH/785zTFE7RkmjjSdITcaq5wu18OnW/df0Jz5ZVSPwZ+vN86woSf2d4fvF+IbF2zvTf9EKP+OmO38xCJFiLb+mRLkdkG5ls34Jpwpdte14QNn8hWOXW2FndGa8jeMMh0rUEkRmffMh3vXsAyDAzLKkoJ7PR6Wd5b/bJ9B5zSKjEMrJXg3gjec292qZZAjZ7WgYiAwP2F29zIDnGwroH+zBFe7n2ZmBHj9ddfL1QDeBuxl97+63zr/hMac9VUxhCD9lQ77al2zh46W2K2dUzMTnB5qJal/BIP5rPcmxleN9tEBjPfQfsRA4iVPc/0eJUmrMqYsAhIxDZbI4KJgYrYpnlnrJbsW2v8sm7QTi288KydezSMHVuqpfQ4v4Gior4DlkIs5SxbLmUbc1umaa/cUFHjJutbBSylmJgdJrt4iyP1xQ2v3Z6s7gys0dFRRkZGin4f12wr9TDW7D7aXB9hSs1WKbUhZ+ua7fz8DHMzU0wnZqmrafGNbAH/fGsAU+MJpwt/FSYsEYjE7NRALk/nxcsYZh4VMXgrfZblkSQoi+yNJCdenrfNuUxj7s0u1VKNCYvbutCQwMbcSil70ceYcvLK/mVVsLl+AgCr+RWuTn+IZS1wrLGdT/R+nIHWAY9EW2MsFsMwDJ555pnChAG3CfvIyEhh2xe/+MQMW4QSba6PESLia7bXs9ftNMLsOJeHGlh2I9vsMMqIkk40U5fM2ANkkRrs6baV87KCoqVnlUqRcKkJZ67fJO7kWxfrG1lqb7dN3VxDYXD3SspTnCd09i0V3aLvxFItG/C51fdNI6zlUYYzIaNc34HC9srnnhge5u7COPcWb9BSU0tPk93wuj3VXizRJ1r3zs5yzTaXy5HP53W+dZ/RV/8xxmu2Zw6dQSnF/VP3iyLbsaEalvNLzC9kmZy9hCERZO4w5nyUhtpWErG6gKjPk28th48JHxgZK+yePH7UFcvUjTpEFCoWLc7ZjhU3ouk4smAbbIXG3DtdWiVub9VYFCMZDyz9cl4QZSqIGvY18LmGppVnIjvMyN3/ozYe5WhjCyfaBhg8OEgqntpwvNsnuFwU7u73rnyh2R+0uT5BiAgHUgc4kDoQbLYXk9zLrrKweIPJuRGUESWVyJBKZqivaSXpmm25fGsRG024bfhy4fHkwNF1fVi09qyiiBXnbKHIbO9dTjJzK06mx+67UBrZFtjGUi2Bx5rrJlx2gGzVZPLKOCq6PoGh89jTYAgr+WVuz1/l1twY9UaEnlQjh1t6ON11mpa6Fv/zbqJPsK5vDQfaXJ9ggsz2+ux1xrMpxrPjjA3VspxfZmFhjvuzI1gSJZXMYE51UxdvRFFh6ZkSE5a8SdvY1cLuyf6jnuPszl4BYgtmOzWRoLVnBWXEfCNb12zd2/adWKoFKte3es32zuhViIqdQlAKLBg5/xb3F26yuJolHU8ycKKXw/XdtNa1cqTtSAWJ1fcJ1vWt4UCbq6aA12xf6raXxJkanCoq/XLNdmrlDrm6d5m+YZvthsjWYUO+9cZN4k4XrKWmRhYPOJFatZGwa8KGXYXgW/rlmG12wqC5a42OgbWyfQc2v1RLdY3QlWGQs9aYWrjFvaUbiJWjNZmio7GNw4299Bl9DL0xRLIrydDUEMeOHStEnKVRp1++1Q9d3xoe9DugCUREaKtro62uLcBs53wj2zrHbBuSLaDqaeldw823tg1fKbz+5EBfYfBnajyJoLZe/lWaRrBMLARlWNwZi+EX2XoHyDa3VEsFgzNN5h/OcG/5FrMP71MfT9BdV0dbTYYTbSc41nKM2lgtlmUx3z3P0aNHuXr1qm9ZlWu21eRbQde3hgltrpqq8TPb6cHpdbOdHWfsYg3L+WUWF+aYyo6wOt3GfJ2irqaZhmQLB4bXB7PuB+Vby2lw+9tWzCcqlERR0bhtxEEDZErR3rsMsWjZvgNQubRqafUBt+evMpm9zsPZaVLJOP1NBzjS2MvxtuN0N3QX9et1lwtKJBKBZVXucTdu3OCVV14plGMFmazOt4YHba6aLSMitNa10lrXyunu07bZnio221/+Rz1J7rK48ICpmWHqr18hH4tgmBYTRw/aKzY4SyIE5ls9TE0kqzRht9+C2zw8YIDMzDH88ykyh5YdwxY6+45sSCMETWXNmWtMLoxz+8FVlldnaalJ0TSd42jrYY4dOMbRzNENS127lEaZfmVVlmWRy+U4d+4cly9fLphmaWRr9wjW+dYwoc1Vs2NUMtuJ7Dj/8pXPkrl8h8ydGd41L2HduEw6kcFc6qZ2LUlNor5Cb4RqTThR3oQds0W5zcPt1R/t5uHX8KYREOh46rBdbiVRFldmyS7fI/vwHtmluzTEk3TUpGjNPM0zLSdQSUVnQyexWPkvgEpLsLtma1kWp06d4vjx4wXD9Zud9fTTT6OU0vnWkKDfBc2u4We2M6dnCmb76dkJLl1IsJJbZnLlLlfv3cMUIZVsIZXIUF/TTE087VkuZZODXlWZ8BVae5acef2g3NoHdz0rS7G6usSFc79g8sY4+UweJRZdfZ00JJL0tHbRl3ma59qfY6B1ALGEd+beKbSUDJS4idIq14TdCLc0svWarWEYnD17tuJranYfba6aPUNEaKltoaW2hcGuQdtsT8146mwXuHQhwcP8MkuLc0zNjXrMtomGeANKtVQxe6za7l/2bbnyHKcUrOaWeLAyw7zzg8pj3M/ReaiNmliKxkQD7WvttMfbaTfaSa+kGcgMECFCzrRXVtipRtblTLg0jbCysqLzrSFCm6tm3yhnthPZCSZmJxi+EGfFXGZxcY5btxpItp9j7l4z6aQb2W5MI2zItwbhLPW9klviwWqWhZUsD1anUVaeuliCVCxOS7qRhqQdPZ/sO8nBxoM0JBoAClGjZVmcO3cOgDfeeIPu7m6OHz9edpbUZpdgr9aEn3/++fK/s2bP0OaqCQ1+Zps9lS2aQTb8YSMr1kMWF68zNTeGKUJdothspyvkWy1lMb8yzYOF29ydHiYanaA2FicdS3A41UhDMkVnupPOdCcH0wdJx9KMjIww0DZQZHLeFSWUUpimSXe3vaBGUFmV+/xK+daC1k3Wt1ZKR2j2Dm2umtAiIjTXNtNc28yprlO+ZnvpYoKH+XWztZRgzB7GbDJI17QWBsiUUiyszjKzdIeZpTtERRG9P0//08uka3vpSHXYZlp/kKZkU5GZ5XKVb/W9c/qPHz+OiPiWVblYlkV/f3/Fa7DZ+lZtruFBm6vmkaGc2borNVy6EGfywRpLS5Pcn7+CqYSaRCPLuXkUFk2JGo6kMxyozRCL5PnYr7TSlmora16jo6P09fVV1Fd6C+9XVmVZFpcuXeK9994rmKZfZAub7ydQ1fLmmj1Dm6vmkSXIbMdnx7mevb6es82vciCWprmmkd6mp3iq+SmakhnGxt6ntS5W1pBcg6tmxlO5W33v4NPAwAAiwsDAAEqpwKbXbt1qtf0ETp48WVGjZu/Q5qp5bPCa7cmDJ1FKMXtqlptzN0nH0nTUdXD+vN3wem1tGdPMkc+vt+fzMzHTtJu/bGdUv5TR0VEMwyjUowaVVdlLr7+PiNDf31+x74BOCYQLba6axxYRIVOTIVOTKWw7c8Y2I9M0GRwc5P333yefzxf2e43WMIyqI9ftllb5zc66cOFCIRot13dAm2s40eaqeeJwjTMWi3HmzJmC2bqRotdsR0dHOXr0aGHOfhA7XVrlHtPf3080Gg3sOwB2dN3f36/zrSFDm6vmiaec2V6+bDf2XltbKzq2NI2wW6VVlfoOmKbJyMiI7icQQrS5ajQleM32tddeKxvZurfmSqldKa2qNEAG0NfXp1MCIUSbq0ZTgUppBNM0sSyLtbW1QvrAb4BsK/0EdH3ro4s2V41mk/iZ7eDgYGBku9nBMV3f+nigzVWj2SbVDpC52yqVd212vSxd3xpOtLlqNDtMObONRCIbDLY0jaD7CTweaHPVaHYZr9mePXu24gCZ25dVm+ujjTZXjWaPqZRGADh8+HChP2vQAJnOt4Ybba4azT5TaranT58uG9l6y790vjW8aHPVaEJGNQNklmWhlNIpgRAjhVUtHzFEZAqY2ObLtADTOyBntwizvjBrA61vu2h91dGjlGr12/HImutOICLnlFIv7reOIMKsL8zaQOvbLlrf9qncPUKj0Wg0m0abq0aj0ewCT7q5fne/BVQgzPrCrA20vu2i9W2TJzrnqtFoNLvFkx65ajQaza7w2JqriHxLREZE5EMR+aGINHr2fU1ErojIqIh82rP9pIhccPb9uThTX0QkISI/cLa/JSK9O6DvN0VkSEQsEXmxZN++66ug/TOOtisi8tXdPFfJeb8nIvdF5KJnW0ZEfioil51/mzz7NnUdt6mtW0T+W0QuOe/rH4RMX1JE3haRDxx9fxImfZ7XjojI+yLyozDq2xRuZ53H7Qf4VSDqPP4G8A3n8XHgAyABHAauAhFn39vAGUCAfwd+zdn+u8B3nMdfAH6wA/oGgGPAm8CLnu2h0FdGd8TRdASIO1qP79F7+nHgBeCiZ9s3ga86j7+6nfd5m9o6gBecx2lgzNEQFn0CpJzHMeAt4KWw6PPo/Arwd8CPwvT+bul32Y+T7vkvCZ8Dvu88/hrwNc++nzhvRAcw4tn+W8BfeY9xHkexi5dlh7S9SbG5hkqfj94zwE+C9O7Be9lLsbmOAh3O4w5gdKvXcYd1/ivwqTDqA2qB94DTYdIHdAFvAC+zbq6h0bfZn8c2LVDCb2N/gwEcBG569t1yth10HpduL3qOUioPPACad0nro6pvvziglLoL4Pzb5mzfynXcEZy0zPPY0WFo9Dm33OeB+8BPlVKh0gd8G/gjwPJsC5O+TfFI9xYQkf8C2n12fV0p9a/OMV8H8sD33af5HK/KbC/3nG3r83vaXunbInt5ru2wleu4/ZOKpIDXgT9USs2XSfftuT6llAk854w//FBEnilz+J7qE5HPAveVUu+KyCeqeUqAjtB8Ph9pc1VKfbLcfhF5Dfgs8Ipy7hGwv8m6PYd1AXec7V0+273PuSUiUaAByG5XXwB7pm+LBOnbLyZFpEMpdVdEOrCjMtjaddwWIhLDNtbvK6X+OWz6XJRScyLyJvCZEOn7KPDrIvIqkATqReRvQ6Rv8+xHLmIvfrA/OMNAa8n2ExQnwq+xngh/BzvJ7ybCX3W2/x7FA0b/uIM636Q45xoqfT56o46mw6wPaJ3Yw/e1l+Kc67coHvD45lav4zZ1CfA3wLdLtodFXyvQ6DyuAX6BHXiEQl+J1k+wnnMNnb6qf4/9OOme/GJwBTsnc975+Y5n39exRxdH8YwkAi8CF519f8H6JIsk8E/Oa74NHNkBfZ/D/pZdBSYpHiTad30VtL+KPRp+FTvFsVfv6d8Dd4Gcc+1+Bzu3/AZw2fk3s9XruE1tH8O+/fzQ85l7NUT6ngXed/RdBP7Y2R4KfSVaP8G6uYZOX7U/eoaWRqPR7AJPSrWARqPR7CnaXDUajWYX0Oaq0Wg0u4A2V41Go9kFtLlqNBrNLqDNVaPRaHYBba4ajUazC2hz1Wg0ml3g/wEs41C2DNc2PwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup the figure and PlotMapView. Show a very faint map of ibound and \n",
    "# model grid by specifying a transparency alpha value.\n",
    "\n",
    "# set the modelgrid rotation and offset\n",
    "ml.modelgrid.set_coord_info(xoff=-2419.2189559966773, yoff=297.0427372400354, angrot=-14)\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "mapview = flopy.plot.PlotMapView(model=ml)\n",
    "\n",
    "# Plot a shapefile of \n",
    "shp = os.path.join(loadpth, 'gis', 'bedrock_outcrop_hole_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, #facecolor='none', \n",
    "                                           edgecolor='green', linewidths=2, alpha=0.5)\n",
    "# Plot a shapefile of a cross-section line\n",
    "shp = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, radius=0, lw=3, \n",
    "                                          edgecolor='red', facecolor='None')\n",
    "\n",
    "# Plot a shapefile of well locations\n",
    "shp = os.path.join(loadpth, 'gis', 'wells_locations_rotate14')\n",
    "patch_collection = mapview.plot_shapefile(shp, radius=100, facecolor='red')\n",
    "\n",
    "# Plot the grid and boundary conditions over the top\n",
    "quadmesh = mapview.plot_ibound(alpha = 0.1)\n",
    "linecollection = mapview.plot_grid(alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a cross section based on this arbitrary cross-sectional line. We can load the cross sectional line vertices using `flopy.plot.plotutil.shapefile_get_vertices()`\n",
    "\n",
    "**Note**: in previous examples we passed `line={'column', 5}` to plot a cross section along a column. In this example we pass vertex information into `PlotCrossSection` using `line={'line', line[0]}` where `line[0]` is a list of vertices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the vertices for cross-section lines in a shapefile\n",
    "fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
    "\n",
    "# Set up the figure\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
    "\n",
    "# plot head values along the cross sectional line\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]})\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid(lw=0.5)\n",
    "cb = fig.colorbar(csa, ax=ax, shrink=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting geographic coordinates on the x-axis using the `PlotCrossSection` class\n",
    "\n",
    "The default cross section plotting method plots cells with regard to their intersection distance along the cross sectional line defined by the user. While this method is perfectly acceptable and in many cases may be preferred for plotting arbitrary cross sections, a flag has been added to plot based on geographic coordinates.\n",
    "\n",
    "The flag `geographic_coords` defaults to `False` which maintains FloPy's previous method of plotting cross sections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the vertices for cross-section lines in a shapefile\n",
    "fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
    "line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
    "\n",
    "# Set up the figure\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
    "\n",
    "# plot head values along the cross sectional line\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]}, geographic_coords=True)\n",
    "csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "linecollection = xsect.plot_grid(lw=0.5)\n",
    "cb = fig.colorbar(csa, ax=ax, shrink=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Cross Sections with MODFLOW-6 models\n",
    "\n",
    "`PlotCrossSection` has support for MODFLOW-6 models and operates in the same fashion for Structured Grids, Vertex Grids, and Unstructured Grids. Here is a short example on how to plot with MODFLOW-6 structured grids using a version of the Freyberg model created for MODFLOW-6|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwf6...\n",
      "    loading package dis...\n",
      "    loading package ic...\n",
      "WARNING: Block \"options\" is not a valid block name for file type ic.\n",
      "    loading package oc...\n",
      "    loading package npf...\n",
      "    loading package sto...\n",
      "    loading package chd...\n",
      "    loading package riv...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "  loading ims package gwf_1...\n",
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing ims package gwf_1...\n",
      "  writing model gwf_1...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package ic...\n",
      "    writing package oc...\n",
      "    writing package npf...\n",
      "    writing package sto...\n",
      "    writing package chd_0...\n",
      "    writing package riv_0...\n",
      "    writing package wel_0...\n",
      "    writing package rch_0...\n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.1.1 06/12/2020\n",
      "\n",
      "   MODFLOW 6 compiled Jun 12 2020 12:02:16 with IFORT compiler (ver. 19.0.5)\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",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2020/06/26  9:29:55\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): 2020/06/26  9:29:55\n",
      " Elapsed run time:  0.093 Seconds\n",
      " \n",
      "\n",
      "WARNING REPORT:\n",
      "\n",
      "  1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
      "  2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
      "     DEPRECATED IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
      " Normal termination of simulation.\n",
      "Output file located: freyberg.hds\n",
      "Output file located: freyberg.cbc\n"
     ]
    }
   ],
   "source": [
    "# load the Freyberg model into mf6-flopy and run the simulation\n",
    "sim_name = 'mfsim.nam'\n",
    "sim_path = os.path.join(\"..\", \"data\", \"mf6-freyberg\")\n",
    "sim = flopy.mf6.MFSimulation.load(sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
    "                                  sim_ws=sim_path)\n",
    "\n",
    "newpth = os.path.join('data')\n",
    "sim.set_sim_path(newpth)\n",
    "sim.write_simulation()\n",
    "success, buff = sim.run_simulation()\n",
    "if not success:\n",
    "    print ('Something bad happened.')\n",
    "files = ['freyberg.hds', 'freyberg.cbc']\n",
    "for f in files:\n",
    "    if os.path.isfile(os.path.join(modelpth, f)):\n",
    "        msg = 'Output file located: {}'.format(f)\n",
    "        print (msg)\n",
    "    else:\n",
    "        errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
    "        print (errmsg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting boundary conditions and arrays\n",
    "\n",
    "This works the same as modflow-2005, however the simulation object can host a number of modflow-6 models so we need to grab a model before attempting to plot with `PlotCrossSection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the modflow-6 model we want to plot\n",
    "ml6 = sim.get_model('gwf_1')\n",
    "\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "# plot boundary conditions\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
    "patches = xsect.plot_bc('WEL_0', color='pink')\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Boundary Conditions')\n",
    "\n",
    "# plot xxxx\n",
    "ax = fig.add_subplot(2, 1, 2)\n",
    "# plot the horizontal hydraulic conductivities\n",
    "a = ml6.npf.k.array\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
    "csa = xsect.plot_array(a)\n",
    "patches = xsect.plot_ibound()\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting specific discharge with a MODFLOW-6 model\n",
    "\n",
    "MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting. `PlotCrossSection` has the `plot_specific_discharge()` method to use this data. The specific discharge array is stored in the cell budget file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the specific discharge from the cell budget file\n",
    "cbc_file = os.path.join(newpth, \"freyberg.cbc\")\n",
    "cbc = flopy.utils.CellBudgetFile(cbc_file, precision='double')\n",
    "spdis = cbc.get_data(text='SPDIS')[-1]\n",
    "\n",
    "# get the head from the head file\n",
    "head_file = os.path.join(newpth, \"freyberg.hds\")\n",
    "hds = flopy.utils.HeadFile(head_file)\n",
    "head = hds.get_alldata()[0]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.set_title('plot_array() and plot_discharge()')\n",
    "xsect = flopy.plot.PlotCrossSection(model=ml6, ax=ax, line={'Column': 5})\n",
    "csa = xsect.plot_array(head, masked_values=[1e30], head=head, alpha=0.5)\n",
    "linecollection = xsect.plot_grid()\n",
    "quiver = xsect.plot_specific_discharge(spdis, head=head, \n",
    "                                       hstep=2, normalize=True, color='green', \n",
    "                                       scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                       zorder=10)\n",
    "\n",
    "patches = xsect.plot_ibound(head=head)\n",
    "cb = plt.colorbar(csa, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertex cross section plotting with MODFLOW-6 (DISV) \n",
    "\n",
    "FloPy fully supports vertex discretization (DISV) plotting through the `PlotCrossSection` class. The method calls are identical to the ones presented previously for Structured discretization (DIS) and the same matplotlib keyword arguments are supported. Let's run through an example using a vertex model grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy version: 1.17.3\n",
      "matplotlib version: 3.1.1\n",
      "flopy version: 3.3.1\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",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package wel_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 1 based on size of stress_period_data\n",
      "    writing package rcha...\n",
      "    writing package riv_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n",
      "    writing package oc...\n",
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.1.1 06/12/2020\n",
      "\n",
      "   MODFLOW 6 compiled Jun 12 2020 12:02:16 with IFORT compiler (ver. 19.0.5)\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",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2020/06/26  9:29:56\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): 2020/06/26  9:29:57\n",
      " Elapsed run time:  0.139 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 Jun 12 2020 12:05:21 with IFORT compiler (ver. 19.0.5)         \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 Jun 12 2020 12:05:21 with IFORT compiler (ver. 19.0.5)         \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",
    "import setup_pmv_demo\n",
    "setup_pmv_demo.run()\n",
    "\n",
    "# check if model ran properly\n",
    "modelpth = os.path.join(\"data\", \"mp7_ex2\", \"mf6\")\n",
    "files = ['mp7p2.hds', 'mp7p2.cbb']\n",
    "for f in files:\n",
    "    if os.path.isfile(os.path.join(modelpth, f)):\n",
    "        msg = 'Output file located: {}'.format(f)\n",
    "        print (msg)\n",
    "    else:\n",
    "        errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
    "        print (errmsg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwf6...\n",
      "    loading package disv...\n",
      "    loading package ic...\n",
      "    loading package npf...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "    loading package riv...\n",
      "    loading package oc...\n",
      "  loading ims package mp7p2...\n"
     ]
    }
   ],
   "source": [
    "# load the simulation and get the model\n",
    "vertex_sim_name = \"mfsim.nam\"\n",
    "vertex_sim = flopy.mf6.MFSimulation.load(sim_name=vertex_sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
    "                                         sim_ws=modelpth)\n",
    "vertex_ml6 = vertex_sim.get_model(\"mp7p2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a line based cross section through the model grid\n",
    "\n",
    "Because Vertex model grids have no row or column number, cross sections through the model grid must be defined by a set of vertices and passed to the `PlotCrossSection` class using the parameter `line={\"line\": [(), (), ...]`. Below we show an example of setting up a cross sectional line with a MODFLOW-6 vertex grid model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = np.array([(4700, 0), (4700, 5000), (7250, 10500)])\n",
    "\n",
    "# Let's plot the model grid in map view to look at it\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
    "\n",
    "# use PlotMapView to plot a DISV (vertex) model\n",
    "mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
    "linecollection = mapview.plot_grid()\n",
    "\n",
    "# plot the line over the model grid\n",
    "lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot a cross section of the model grid defined by this line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Next we create an instance of the PlotCrossSection class\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={'line': line})\n",
    "\n",
    "# Then we can use the plot_grid() method to draw the grid\n",
    "# The return value for this function is a matplotlib LineCollection object,\n",
    "# which could be manipulated (or used) later if necessary.\n",
    "linecollection = xsect.plot_grid()\n",
    "t = ax.set_title('Column 6 Cross-Section - Model Grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Arrays and Contouring with Vertex Model grids\n",
    "\n",
    "`PlotCrossSection` allows the user to plot arrays and contour with DISV based discretization. The `plot_array()` method is called in the same way as using a structured grid. The only difference is that `PlotCrossSection` builds a matplotlib patch collection for Vertex based grids. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the head output for stress period 1 from the modflow6 head file\n",
    "head = flopy.utils.HeadFile(os.path.join(modelpth, 'mp7p2.hds'))\n",
    "hdata = head.get_alldata()[0, :, :, :]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"plot_array()\")\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `contour_array()` method operates in the same way as the sturctured example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.arange(329, 337, 1)\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title(\"contour_array() with a multi-layer vertex model\")\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "\n",
    "contour_set = xsect.contour_array(hdata, levels=levels, colors='k')\n",
    "plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
    "\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting specific discharge vectors for DISV\n",
    "MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting. `PlotCrossSection` has the `plot_specific_discharge()` method to use this data. The specific discharge array is stored in the cell budget file.\n",
    "\n",
    "**Note**: When plotting specific discharge, an arbitrary cross section cannot be used. The cross sectional line must be orthogonal to the model grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define and plot our orthogonal line\n",
    "line = np.array([(0, 4700), (10000, 4700)])\n",
    "\n",
    "# Let's plot the model grid in map view to look at it\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
    "\n",
    "# use PlotMapView to plot a DISV (vertex) model\n",
    "mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
    "linecollection = mapview.plot_grid()\n",
    "\n",
    "# plot the line over the model grid\n",
    "lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bxi7CEfwezmJP9cMWTxfl8rf3lvHclhe5YsRCpvebTHQNazaIf6q7AeJwbPzyTTbtfYvRPaYwO3MJHVppLnFTFFVD/8vRwq94fNevWLn370zvuoRxneYSG9W4Iymk4aJr+D3gzLGetaxnLVluBPO4iD6kV1u3ebFA7cMaNsI+YZ3ULZ1NV/4nN772OO/u/6LWujWt/vjXLeu4dvBYxqT1DEKE0hD5xUXsOn6YUufIPn2clbvPDIMnyow+Ke3ontwGj6t5LvJTn33IpgN7+efsJYxK69EYYUsdfP/V5Wz6ag+pCS1om9TS+9iCtgktaJvYktTEFrWOgjhRmM8tbz7No5+8x12TL2ZYp4AMc2lUb+7eymuff0yruESS4xNpFZdAqwqPyfGJnKbmz4HD8fjWd3lhx/uM5zwu9swhzo9hs58e2struzbTIi6elnGJJMXG0yIugRaxCWfKarsuwJtffMjqPVsYxWimFF5GcnySz+sePH2MVV98QGJsPIkx8WWP5UdMPImxceSRV2sbH+bs4JtP3kEmmQw5eTVpfizMlFdcwMa9W4mPiSU+Jo746LiyR+/ruOi4Gm8MlNt9bB+3vvQbetCDDgeuo3/Hvj6vWx3nHDsOf05MVAyx0bEc4xhH8455X5eVlQ8JrsmRvGP8YfVfeOqj57h61GLO6TU2qMPDI1ltPfbOlbJ+92ts2PMGY3vNYHbmlbRtoYVdIsnxooM88flvWJm9lOldrmB8pwuJi1bi2lRU1yFV2WbbxGY2MdBlMo+L6M8gv86LRKWuiNPFPjstm5SwT1gBBrVL463Lvs1fPnyHW9c8z6k6boHhcHzrjSdZu/j7Ed1D0xRtO3qQ0hr+kCh1jh3HDrHDj+1sdp84yqTH/8gdE+bw3RETiTJ9ncPF58cPsf3IAd8VffjwQDazH/sjV2aO4dZzZpGa2CIA0TWOD7/aw2NbGr5/3+miAl7hFS54ZDO3TLiYST0za01gth7cw30bXmzwdUtKPaxlLfMe/ogbR8/jkszzalylHWDX0f388u1HGnxdgC1s4bJHbmXhkOlcPWIureJr/rofPHWEH738+4Bcdw97uOmZnzCpz1iuHb2IznVcUbik1MPNT992duE/6xfLvhM5/M9rvyW9fR+uGX0FWV0G16+hCPXgO//Nh9nvYBZFlEVhZhjlz6OIMqPI43sxsVJXyrufv8T63a8yvvdMZg26gjZJjbeCtVTvUH42S7ffjiv/z5UCzvu3g8NRyqniWrdwPONk0WGe+uL3vJL9MFO7XM6EtIuIj9borFB7mRfYxQ48lOChlFI8FR49ddoG7RPbwidsoa/L4AIuZCjDm13iahZHXEy3UIcRUE0iYQWIsij+PetcLugzmG+98STP7fq4TudvOrCXB7es5YYh5wQpQqmPynNTG6KktJRb336eVV/u4KGZl9M+SYuWhIOahvvWh8Px8JZ1vLDzI26bMIfFg0Y1iZsTJwvzA9renuMH+fcV9zGh+wBuPfcS+qR2qrZeoPe3PV6Qy11vP8ryj1fx/QmXMKFH9YlTfoDnXpYtzPQSK7at5tqR87lo0GRiqulhLvTRY1kfq3atY/UXG5g3cBpXjriYlET/tl3y1XtaHzsO7eJHL/yCYV2GMIqxDCEr4NdoijylJZSUBu7z7SktYfXOFaz9fCXn9p3LjIGLaZ2YGrD2pW6KSwvZc/qTgLZ5qvgo/9p9D69l/5MpXRdzbqeLSYhpOjdBI81OtrPB1ge2TfuM3/ErursezOUiRjGmxqHjkcZF4JDgJveV69oqhafnXcvyudfQqY77Nf54zQotwBRmtlZY/TdQVu7exqiHf82qL7V1QTgIZMJa7mhBHj947QkuePxePj64z/cJIXaqKLAJa7k1X37KvEfu5M63n+BEQdUhtrnFwdmi5vNjOdz8/B/5f8/9np1Hqi6NXxCkbadOFJzmt2uWcfljt/HW55uqDPMsDNJWQJ5SD89sWcmSR77Lsk3P+PXxBSNhLffBvo+4n/v5zRu/JudkZA37qg9Xy5SRhigpLebNz57hJ88v4akP/sKpguNBuY7ULpi9Y6dLjvPc7j/zs40LWLn37+SX6G/EUKi8qFIgfWl7+JPdzY/4AatZVafe2ibLQamL8nk0JU0rWi8zY0FGFj8dN7NO5x0tyOO2NT73ppVG9FVucPZAzMk9ycwn7+MX777cLPf0DBee0lKOV5NIBcqmr/Yw49G7+dGbz3CiIDhJYSCcKgze3qYeV8rDH65ixsO388hHb5+193Sw91R998utLHzs59y5atlZ+8bmFwd3Rc69Jw5wy8t/5OZnf8mxCvuoBithLZdXnM9fNyznmsf/g+zjtSeKxY2wD+Q7X6zhB898j605W4N+rXBWGqSEtVyxp5C3djzLm589E/RrSTUaYd52fskpvsrbzeli3ZQIhZoWVQqkBBJIoU2zGB7sccWcLP7K59GUNJkhwZUdzDvFj+uRfD708TquzRzHaC3OExb+NusKfjf5Yj458hVbDuew9XDOmedHG5joOBx3rnuFt/buZOnsK+naKiVAUYu/jhfm1zhHOVBKneOTwzm8/9WXTO7ZL6jXqq+ThcFL2gFio6KZ0H0Agzp0O2uefrAT1rjoGGZnjOHSzIkkxMSdKc8LUs/u19eNZU7/CVw25HzaVBiiG4whwRUlxMQzZ8AUFgyZRcdWtc9tDGYPK0AcccwePJu5g+aR2qJ5D1d1QewxiY9J4Ly+85jW/1KSE9sE7TpSs2AmGIYxrN0UZnW/lk5JPYN2HaldMBPWLq4rF3MZIxjVLJJVgCiLIyEmslY8b7IJ6/dXPVOvhKZsAaYneFcLMIWNlIRExnfpxfguX2/g7pzjq9xTPPHZB/znqmcb1P6nRw7wh/ff4n/PvUBf80aWEp/I2m/cwtH8XI7kn+ZIfi5H8nPPvD6an8u2I1+x96R/C2ZU1Do+kYUDRnLl4DH0a1v9HM5wcU3WJKb2yuRUUQGnivI5Vfj148nCPE4U5pFdaQ9pf7RLasVlmedyWeYEOrRoXeX983pm0jqhBblFBWVHcdnj6aIC8ooLyS0q4EBu3XsU2iUls3DwJC4ZdB6pSVWnZvRJ7cK8/ueQX1JIXnEhBcVlj/klheQXF5FfXFCvJD41MZlLBk/jokGTSUlsVeX95PgWjO6WSWFJMYUlRRR6is56XlRSTFE9EsnUpBQuypzBBYOm0Sre37nxRo82XSkpLaHYU0Lu6XwsriyRLS4trveNnDaJrZmXOYdOG7oxZvS4erURaYJxUywhJolJ/S5kar8FtIyv+rMljSdYSUZW20nM6n4tnVv0CUr74r9gDAnu4DpyEZcylnOazdzVcg4ojbCP2WfCamYJwNtAvLf+k865n5lZKvA40BPYDSx0zh3znnMrcC3gAb7tnFsZyKBf+uITHt22qd7nbzywl4e2rOP6IeMDGJUEkpmR1jKZpAq9NnUxoG1H5vQexJzegxiT1kOJaohER0XRK6UdvVLa1VjnV2tX8tv1r/rd5si0HiwZPJZ5GVkkxsQGIsygm95nSK3v7z5+iKn/uMPv9oZ27MmVQycxo++wWre3mdJ7KFN6D63xfeccg++5qcYtwSob0L47VwydyvnpI4mLrvlzP75HJuN7ZNba1qx7f8RBDvp13T6pXVmcNYPp6WNrve6QtAx+e8F/1drWDX/6JVvxbwht9zZdWDh0DlPTJ9R63eqkJXfggYW/PvP63b9sY/Q3Bp157Sn18NgHT7Ns03K/2uvUqiOXDJ3PtIyJxMXE8d6G5j0MuKIbJtyOx5VQ6kpxrhTn3NfPcZR4ivnpiqv8aisxtiVT+l3M5H4X0SKu6k0RaXxt4jvyncH3YpSvAG2AYRZV9syi2HjwFd7c/5hf7Q1OPZfZ3a+la8uMoMYt/juXiWS4fkQTTZT3/2WP5f9F8Wf+wAk74bOtVNeW+SxgAhOJabr9cg1W6iKrN9mfr2QhMMU5d9rMYoE1ZvYScDHwunPuLjO7BbgF+KGZDQQWAYOAzsBrZpbhnAvIRMLTRYXc9PoTDW7ntjXPsyB9KG2b0NYYzdHWw/4tKBITFcW5XfucSVJ7p7QNcmQSKEf8WAitZVw8l/QfwVWDxzKwfedGiKpx+bOKcGxUNLPTh9N++1D+Y+GwgFy30FPsM1mNMmNq72F03TWUby8M3D6gRfiebzqu+2D6fpnFv182JWDXLcZ3D+vQtP4MzBnGNxfODdoq1NFR0X4tFtSnbS8uyZrPhF5jia5lK6HmLC4mnrJ76tUr9vj+XmsRl8y0/pcwMWM+ibH6uyCcxEUn0Ld17f/m7Trxoc92Mtucw6we19K9Zf9AhSYBkk4/0ql9Sk88CUDNCWsrl8wFXMhkphNH/To7IoXHlXCiuOHbCYYTnwmrK1uGsfwvyljv4YD5wCRv+VJgFfBDb/ljzrlC4Asz2wmMBtYGIuDTxYXccc4cck6fYJ/3yMkte9x/+gTFfi6wU74A033TLwtEWBIknxypeVJ4akISM3sNYE7vQUzv2Y/W8dpLrSk6WssqwkM7duWqweO4MCOLFnGRu8l7basId2iRzKLMCSzKPIf2LZJZtv1IwK5b2xzXVnGJXDxwAouHTKJLcjueuicnYEkj1JywxkXHMKvfeBYNOZ/ebbvw0r17AnrdmhLWKDPO7TWKxVlzGNCxD2/+eWfQt0yqbZ7r0M6ZXJp1IcO6DAnox98cVV5NuqLkhDZM638p5/a9gIRY/Q5pqmr7GRnQZiyzu19Hz1YDGzEiCbSa5rkmuSRmM4/pzCKBhEaOKjxFWSyJzXEOq5lFA5uAvsC9zrn1ZtbROZcD4JzLMbMO3updgHUVTs/2lgVEpxbJXDFgZLXvlbpSjuTnse/0cfZXSmb3nT5xJsk95O3RefDjtVw7eCyjOmkBpnDknGNLpR7WfqkdzvSiju3cgxj1ODR5lbe9SYqN4+J+w1gyeBxDO3YNUVSN63Q1qwgP69SLJUPPY0bfrFqH/TZEbjXbs/Ro3YErhk5hfv9xJMUF75d/5cSxTWIrLsmcwsWZU6qdFxsoJZy9em98TByz+0/k0iEz6dK6Y9CuW53KKwkbxvheo7l06IVkdOjbqLFEsupW9k1JbMf0AZcxoc9sbw+tNGXVzXPtlzKK2d2vo3dy9ftFS9NSeR5qvIvnfGYzi7m0wN+1BZqHsjmskXWj06+/grzDebPMLAV4xsxqm5xU3Weoyu1NM7seuB6gdevALGgQZVG0T2pJ+6SWZHWo+Q/dIk8JObkn2Xf6BLFKeMLWwbzTHCvIZ1K3vszuPYg5vQfSt03tK3NK01PewzqoXWeuGjKWi/sNp1V887pLWt7DGhsVw9yM4SwZOpHBHbsH/boVe1jHdhvAkqFTmdBjUNB7Fktd6ZmEtVebzlyeNYMZGeOIb4Q5yeXXTUlI5qLB07lw0LRqF3BqDMWesoQ1JiqaqRmTWDDkArqmRNZd8XBQcRXh1KQOzBi4mHG9ZxAb3byHDUYSq5DMpLcezuzu1/ocRixNS3kPa4yLYQrTmcuFtEa7P9SkWSas5Zxzx81sFTATOGBmad7e1TQ4s3pGNtCtwmldgSo7yzvn7gfuB+jcuXNw972oJC46hh7JqfRIbt5bAYS7FrFx7Pv3X9AmISnUoUgQTenZn99Ou5Rhnbo326GPcdGxTGYy//vN6bRNarzkqdjjYTjD+fHiuaS3bbxEqbCkmN705ttzL2Rs98xG/bq3pjVXnDeLGf3OJb6ei7oFSmx0DAuGzOPCwXNo28y3pgkm5xztWqYxc+DljOk5jZg6LqAlTYBB7+ShzOl+HRkpI0IdjQRBLLFMdFOZz8W0peaFHAVKXAnHig6FOoyA8meV4PZAsTdZTQSmAb8EngOuBu7yPpbvPfIc8IiZ/ZayRZfSgfeCELtEuJYRPGdRvvbTc+eGOoSQm9dvBCdW9mrUZBVgSKeezGMe6W3TGvW6ibHxXMVVjOvR+NMxFrGIKYPSG/261blu7FVB780WSIhJ5PY5f9eiVRFsdPuZnNvp4mZ707M5+D630BKt3O2PaGJpGdO4v9eDzZ8e1jRgqXceaxSw3Dm3wszWAsvN7FrgS+BSAOfcVjNbDnwClAA3BWqFYO5g4l8AACAASURBVBERkYYIp43jlaw2jiglqhEvIUYrO0c6Jat10+yGBDvnPgKqTARwzh0BptZwzp3AnQ2OTkRERERERPxS4ko42tyGBIuIiIiIiEj4i7JYWsZG1pBgjUcSERERERGJEKXOfB6+mFmCmb1nZh+a2VYz+7m3/A4z+8jMNpvZK2bW2Vve08zyveWbzey+GtpNNbNXzWyH97GNr1iUsIqIiIiIiEQAh+9k1Z+EFSgEpjjnhgJZwEwzGwv8n3NuiHMuC1gB/LTCObucc1ne48Ya2r0FeN05lw687n1dKw0JFhERERERiQAeV8KRosMNbsc554DT3pex3sM5505WqNYCqOv2pPOBSd7nS4FVwA9rO0EJq4iIiIiISASIshiSYzsGpC3vLjGbgL7Avc659d7yO4GrgBPA5Aqn9DKzD4CTwI+dc6urabajcy4HwDmXY2YdfMWhIcEiIiIiIiIRohTzeQDtzGxjheP6yu045zzeob9dgdFmluktv8051w1YBtzsrZ4DdHfODQO+DzxiZsmB+HjUwyoiIiIiIhIJnOH8m6N62Dk30q8mnTtuZquAmcCWCm89ArwA/Mw5V0jZvFecc5vMbBeQAWys1NwBM0vz9q6mAQd9XV8Jq4iIiIiISAQocSUcKjzS4HbMrD1Q7E1WE4FpwC/NLN05t8NbbR6wrUL9o845j5n1BtKBz6tp+jngauAu7+OzvmJRwioiIiIiIhIBoi2G1rE+p4X6Iw1Y6p3HGgUsd86tMLOnzKwfUArsAcpXAz4P+IWZlQAe4Ebn3FEAM3sQuM85t5GyRHW5mV0LfAlc6isQJawiIiIiIiIRwFG2tU2D23HuI2BYNeULaqj/FPBUDe9dV+H5EWBqXWJRwioiIiIiIhIRziyqFDGUsIqIiIiIiESAktISDhY0fA5rOFHCKiIiIiIiEgGio2JoExeQOaxhQwmriIiIiIhIJHD4u61Nk6GEVUREREREJAI4zWEVERERERGRcFTiSjiQfzTUYQSUElYREREREZEIEGMxtI1vH+owAkoJq4iIiIiISARwQKkLdRSBpYRVREREREQkQjjNYRUREREREZFwU1zqISf/WKjDCCglrCIiIiIiIhEgxmJoF9821GEElBJWERERERGRCKF9WEVERERERCTsONA+rCIiIiIiIhJ+iks97M/THFYREREREREJM7FRMXRIiKw5rFG+KphZNzN708w+NbOtZvYdb/ntZrbPzDZ7j9kVzrnVzHaa2XYzmxHMD0BERERERETKhgQ7Zz6PpsSfHtYS4AfOuffNrBWwycxe9b73O+fcrytWNrOBwCJgENAZeM3MMpxznkAGLiIiIiIiImdrdvuwOudygBzv81Nm9inQpZZT5gOPOecKgS/MbCcwGlgbgHhFRERERESkGsWlHvZF2BxWn0OCKzKznsAwYL236GYz+8jM/mpmbbxlXYC9FU7LpvYEV0RERERERBooxqLpGN/W5+GLmSWY2Xtm9qF3WujPveV3ePO/zWb2ipl19pZPN7NNZvax93FKDe3WOK20Jn4nrGbWEngK+K5z7iTwZ6APkEVZD+xvyqtWc7qrpr3rzWyjmW3My8vzNwwRERERERGpgfPj8EMhMMU5N5SyfG+mmY0F/s85N8Q5lwWsAH7qrX8YuMA5Nxi4Gni4lrZ/55zL8h4v+grEr4TVzGIpS1aXOeeeBnDOHXDOeZxzpcADlA37hbIe1W4VTu8K7K/cpnPufufcSOfcyKSkJH/CEBERERERkRr5XnDJn0WXXJnT3pex3sN5Oy7LtcCb/zrnPnDOled8W4EEM4sPxEfkcw6rmRnwEPCpc+63FcrTvPNbAS4CtnifPwc8Yma/pWzRpXTgvUAEKyIiIiIiItUrKi1hX97xgLRlZtHAJqAvcK9zbr23/E7gKuAEMLmaUxcAH3jXNKrOzWZ2FbCRssV9a510688qwecAS4CPzWyzt+xHwGIzy6Isq94N3ADgnNtqZsuBTyhbYfgmrRAsIiIiIiISXLEWQ6eEVH+qtjOzjRVe3++cu79iBW8Ol2VmKcAzZpbpnNvinLsNuM3MbgVuBn5Wfo6ZDQJ+CZxfw3X/DNxBWQ55B2XTSr9ZW6D+rBK8hurnpdY43tg5dydwp6+2RUREREREJHD8nKN62Dk30q/2nDtuZquAmXw9qhbgEeAFvAmrmXUFngGucs7tqqGtA+XPzewByubB1qpOqwSLiIiIiIhI+ArEHFYza+/tWcXMEoFpwDYzS69QbR6wzVsnhbLk9Vbn3Du1tJtW4WXFaaU18mdIsIiIiIiIiIS5olIP2bkBmcOaBiz1zmONApY751aY2VNm1g8oBfYAN3rr30zZXNefmNlPvGXnO+cOmtmDwH3OuY3Ar6qbVlobJawiIiIiIiIRIDYqmrTENg1uxzn3ETCsmvIFNdT/b+C/a3jvugrPl9Q1FiWsIiIiIiIiEcG/Ib9NiRJWERERERGRSODwe9WlpkIJq4iIiIiISAQoKvWwN/dEqMMIKCWsIiIiIiIiESAuKpouSSmhDiOglLCKiIiIiIhECM1hlYh0JD+X93L2VClPSUhiXOeejR+QBNymr77kaH4e5RMbyqc3DOvYjfZJLUMWV6T56vRxth3ejyv/DHsf2rVoxeAO3Rs1llOF+WzO2XUmDOfKomoRF8+oLhlBu26xp4QN2dtwOJz7+vst2qIY3yMzaNcF2LB3CyWlnjOvy68/stsg4qJjg3bdrV9tJ7co76xrOhyDOvWjVXzz+Pnaf3wvh3MPUurKJlCVP3Zu3Y1OyZ1DHZ4EWJGngLjohFCHIUFSWJpPfFRiqMOQeigq9ZB9WkOCJQJ9dGg/8/71QJXycZ178vai74QgIgm0X6x5kdXZu6qUPz7/Wqb36h+CiCLTuuwd/OCVh6uUz+qbxT2zv9mosew+foAbn7+3SnlG2y48c/mPg3bd3OICvrXi7irliTHxrLmhajyB9JOV93DamzhW9Nw3/khqUuugXfePa/7K50eq3vT7/YV3MKBjejVnRJ7Xtr/Iyk+fq1K+aMQ3uGDwpSGISILpmU/v5ryeC0lr2TvUoUgQ3Hf8f1jS+tukRrcPdShSR7EROCQ4KtQBiEioRdhScmGqvNetOXMh/F4L5bWbC7Pqh6Dpez/yZJ/8jPX7VrDmy6dCHYoEwc6irbxfsIYXTz8W6lCkPhw4P46mRAmrSDNR0x+TElg1fZZD8bvBaogm2MlbSD8HIfo2r/Fz3dT+KmiAUH2/SeNyzvHMtrtxODbuf5m84pOhDkkC7KmTfwVgVe4KjnuOhjgaqTsD58fRhGhIsIhIAIXTjYGaQgl2ElVT4hJKwc4bw+jLHjLqYW0ePjzwJp8f+xAom8e6ft8LTO65OMRRSaBsK9zMJ0XvA1BMES+fXs6i1jeGOCqpi6LSEvZqDquIRBL9Kdk4wqmXKWSRNELiUnOyHJqPOpy+7sGmHtbIV+Qp5Lnt95xVtubLp5jYYyFRFh2iqCRQnHM8fepvZ5W9nvcss1suIjk6suZERrK4qBi6ag6rRCJ1DkQ+fY0bS029TI0cBqEbplpjT1sjJC6h+z7XT5h6WCPfqt2PcqzgwFllR/Nz2Hro3RBFJIH0SdH7bC/66KyyIlfAytwnQhSR1Jvz42hCLBx+kXTu3NndcMMNoQ6jWfuCL1jK0irl3ejGtVwbgogk0JaylC/4okr5YhbTj34hiCgyfczHPEXVhUj60Y/FNO6wuRxy+At/qVLejnbczM1Bu24BBdzFXVXKo4nmJ/wkaNcF+CW/JJ/8KuXf5/skkxy06/6Fv5BDTpXya7iGHvQI2nXDyau8yju8U6V8IhOZzOQQRCSBdJKT/JE/Ukxxlfd60YuruToEUUmgOBwP8RDZZFd5L444vst3SSIpBJE1nttvv32Tc25kqONoqNSM3m76H/7bZ73ls65oMh9v2AwJPvfG8P6DefV928M6xtX3bWf8DfXfmiRpnwdWVC1v1TGR8Rc2fMuTd/+yjXE3DGhwO8Gy9i+fMvr6QaEOo0bv3b+V4f82uEFtPPNiK9hXtbzv+T0Z3qNhbb//wMcMuTarQW0E20cPbWbgN4cHrL1P/vo+/a8ZUaX86Od58FbV+q26p9B/atX65bb9bRPp3xjl87o7/r6Bvlf7rrdz6Qa6XpAJz1d9L7Z1Ir0vHA3A50vfo9dVY3y298U/1tNjyVif9fY8vI5ul42Cx6u+Z1FRdL+irI0vH15HtyXjfLa39+G1dL1yvM962f98ly5XjCf6yVgorJqwdrpoJG2S2rFv2bt0vuIcn+3tX/YOaZf7rpfzSFm9uJcfgaNVE9Z204aQ1iGTnEfeoePiCT7b89eBR9fQYdG5AWvv4GOrabtwYoPaSProM9hWNWFNHNiDtpkNaxvgyPK3SLk4fBPf40+/SfK8qaEOo0Ynn3ud5FnT633+8x/+guL9VZNVKLvpnTehD51a1X+Lm5MvvUrK1PPrfX6wHX/9FdpOCN/4jqx5hQ4j6x/f1hNryd5RNVkFKKKILWmHmdvlunq3D3Bw4yt0GVD/78Hguz3UAQREkcdD9inNYZUm7ET+Sf6x8ckq5Yfzql8FLufkAf6w+qEq5entezGr/5SAxycN99bO1Ww7sB04ewjmvuPVZKvAC5+8zIa9m84MDyk/Z9HwS2nbom1wg23CjnGMpesf8A53LPucOeCrk/urrb/76Bc8+O69OFfxq+LITBvK+N7nNSiWguJ8nvzgYe8on7L16o9zAPs4odr6J/KO8re1fyx7zgFavbsOh6NrSg9mDLzI7+uWulKe3ryUwpKCM2OeT/IVie+vr7a+p7SEf6z7Aw7HKQ7Qcu16HI7khDZclHV1nRasWvnJExzNPXTm+/U0ObTY8AEFxVWTVYCnP3iIuJgEcvmKpPUbAEd0VAwLR/w7MVH+/yp8Z9fLZB//HLxfx1z2k7TxA47mHqy2/iufLmfd7tfIJYfE98o+L87BRVnX0TI+ePvCBtMXh7fy4b7V3u9ld+Zn4PPDW6qtv2X/Wk7kH8FRCs5R6v0eHdtrFukdwvtGl5TZfWwLm/avrLXO6j1PcmnmfzVSRBJIzjme3/dArXXePLicqR0XkRjTspGikvqKi4qma4um+fulJkpYm5nWicnsPb6Pzfu3+lX/WP4JVnzyapXyX879caBDkwDp2KoDv3nzbr/rv5/9QZWyfh0ySE1KDWRYEac1rfl4/2ayj3/pV/3Dpw/y2vaXq5TPGjivwbEkxCZy6PRBNu1de/Ybu6uvn1ecy5ufvfR1wc6yh29NqtvPdZRFUeIp5rVtz579xo7q6zscq3a8WOW6lw6/rs6rK8fFJPD69n+dXfhZzfXX7X69ynUnps+tU7IK0DoxlX+s/43f1/1wX4Wvya6yh4Fpo5pssgrQvlVX1ux6jsKS6m8OVJZ9fAfZx8/+pkiKa8Ulw78djPAkwEpdKf/69Pc+623c9zJz+t1IUmzwht5LcHx8Yg1f5m2rtU6+5zSrDj7BrM7XNFJU0hBhMOMzoLToUjN05YhLGnR+Zqf+ZHUO3+GzzV3/jv0Y1rVhvRaLhy8Mq+1ZwlEUUVyS1bA5qWN6nkO3NoGZ33hx1uUNOr9bm14M7+Z7iG5lswZdSlx0fL2v2yq+NZMzLqjzeRP6zCA1qX29rxsdFcPMQXX/+g1KG0Wvtg2bJjEnc0mDzg+1lvGtmZS+oEFtTM64lMTYFgGKSILp/f2v8OWJT3zWKy4tZP3eauYWSVgrdaWs2PegX3XfOPA4BZ7cIEckARFh+7AqYW2GhnQe0KCEc8nIS5TMhLnFwy+r97kZHdIbnPA2F6N7jqd7m571Pn/B0EUBi6VHam9Gdfc917Mm84dcTpTV/VdC68Q2TOk3t97XnTHoEuJjqx+6XJvY6DhmZ9b/hsE5vWfQtkWHOp9nZlww+Kp6X3dApxH0bjew3ueHi8kZl5JQz4QzMbYFE/v6P/RcQqewJI8V2//sd/13vnyaUucJYkQSaB8ef4vs/BqGxVSS6znJ2wefCXJE0lDlc1h9HU2JEtZmqr69rOpdbRr6d8xgeNdh9Tp38fDLdEPCT1EWxYKs+iWdo3uMo3tqz4DGc9HQ+vWydk3pyYgGJLuzBtavl7VlPXtXy03oM4PUeiSd0VExzKpH72q5gWkj6510zsmsf7IbTlrEJzM5vX6/RyalX0JinObBNQVvfP5PThYe9rv+0fwcPjmoLW6ailLn8bt3tdxrBx6h0OPfdAAJjbioaLq1aO3z8MXMEszsPTP70My2mtnPveV3mNlHZrbZzF4xs84VzrnVzHaa2XYzm1FDu6lm9qqZ7fA+tvEVixLWZqq+vaxLRixQMtNELB6xsM7nZLRPZ7h6V+tkVI9x9eplXdDA4cTV6Z7ai9E9fK9sW1l9e1fLJSemMLV/3efizhi4gITYxHpfNyY6ljn16GUd33sGbVt2rPd169vL2r/jcPq0j5wbfpMyLiExtm6JZ0JMEpMyGjacWBrH0bwc3vzi0Tqft3qP9uxsKjYdfZ2cgqrb3dXmdMlx1hx61ndFCa3ADAkuBKY454YCWcBMMxsL/J9zbohzLouyPUZ+CmBmA4FFwCBgJvAnM4uupt1bgNedc+nA697XtVLC2owtGVm3u+OZnfqR1SUzSNFIoPXrkMGIbnXrZV08QnNX6yrKorhkWN16Nkd1H0eP1F5BieeioZdj+P81bE97RtYjya1s5sAFxMf4P7Q3kUSm9Gv4glPn9D6fti38Tz6jiGLWoIYPxR7Qqe7J55zBTXvuamVJcS2Z0q9uN8Ympl9MUlyrIEUkgfT89j9RUlpU5/N2HNlEzqnPgxCRBJLHlfDC/r/W69xXDyyjqLQwwBFJuHFlTntfxnoP55w7WaFaC87sM8F84DHnXKFz7gvKljgcXU3T84Gl3udLgQt9xaKEtRkbnDaAYXVIQJeM0NzVpqYuc1nT2/et9zDi5m5k9zF1SkDrO4zYH93a9GR0T//3+5zIxAb1rpZLTkhhah0S0PGMb1Dvarm69rJmkUW7lp0afN269rL2ohd92zdsv+NwVJaA+rcqbHxMIpMzGrbonzSOXUc38+FXb9T7/NV7qm6fJ+Flw5FXOVjo3yr3lZ0sPsK7h6vZ5FvCQpHHQ/aJEz4Pf5hZtJltBg4Crzrn1nvL7zSzvcAVeHtYgS7A3gqnZ3vLKuvonMsB8D76nNvj868UM+tmZm+a2afe8cvf8ZbXOP7Yn/HLEh6uHOHf0Cz1rjZNGR3SGdltuF91tTJw/UWZ/ysGj+w+hp5tewc1nov9nMvauXU3BhK4BYBmDlxAHHE+67WIa8Xoam+61s/43tNJIcVnvWiL5lzODdh1+3ccRne6+1V3EpMCdt1wkhjbgql+9rKe1/ciWjTh7Xyai1Ln8Wsbm9ps2vcyuUUnfVeUkPCUlvBizkMNauOVnH9SXI8eeAm+uOgYurZM8XkA7cxsY4Xj+sptOec83qG/XYHRZpbpLb/NOdcNWAbc7K1e3R+RAdlgx5/b6iXAD5xzA4CxwE3eMcrVjj+uw/hlCQP+9rKqd7XpWjzCdy9r33Z9GOFnYivVG9l9LD1TfSeigVwZuCZdUrqTie+f6/lDFhMVwIE2rRJa+5WIzhi4gHjqvxVOZTHRsX4louN6T6cNPtd28JuZ+ZWI9uuYRQ8Cs31RODqv70U+95WNi05gcsaljRSRNESxp5CZfa/l8iE/5eKB32d2xvVM7nUFmR38v9lTXFrIe9na4iZc7c77lPbxXenfahT9Wo0gvdVw+rbMIjWu+tEnLWNS6JzYm7SEXnRM6EGH+O7ERsWx5YQW2ApX5scBHHbOjaxw3F9Te86548AqynK7ih4Bynu/soFuFd7rCuyvprkDZpYG4H086Ovj8bljurertrzb9pSZfUpZ9+58OPObeqn3g/ghFcYvA1+YWfn45Uo72ku4WDLiEj7Yt6XG9wepd7VJS2/fl5HdRrBx76Ya6yweoZWBG8rMuGTY5fz69f+usc6IbqPp1a5vo8QzkYlsZSuuhpubaa27MabnuexeXfP3RX2MZzwbY9+noDiv2vdbxLVkar8LOLT544BeN4ss1rbcwOHTX1X7fpRFMXvQYop21W2BEV960Yv0DoPZcbDmj2du5pVw4HSN7zd1CbFJTO23iGc/+kuNdc7tO59WCb57wSX04mOSGNSx6rSCTw+tZcvB1VXKe7cZyv8b80cKS/LIL8mloPg0BSW5REfFNka4Ug99Wg7mWxl3Vyl/OWcpz+2r+nM8pu0sFnT7VmOEJoHgCEi/ppm1B4qdc8fNLBGYBvzSzNKdc+V7Ic0DtnmfPwc8Yma/BToD6cB71TT9HHA1cJf30ecqXnW6tW5mPYFhwHpqHn/s7/hlCROZaf0Z1qXmuVXqXW36alsxuG+7Pn4PG5bajeg2mt5ta05Ig7EycE3a056xvSbW+P78IYuIigr84JckkphWy1zW8wdcTGJc/fbvrE000czJrHko9Lhe02nfKi3g1zWMCwZfXeP7GR2GkNFxaMCvG27O7TufVvHV917HRsfXeXEmCT8FxbnVlifEtCTKokmMbUVqYic6J/eld+pQeqQ0/f2GmxvnSqstNy1506QUeUrIPnnC5+GHNOBNM/sI2EDZHNYVwF1mtsVbfj7wHQDn3FZgOfAJ8DJwk3NlGzOb2YNmNtLb7l3AdDPbAUz3vq6VOedfCm5mLYG3gDudc0+b2XHnXEqF948559qY2b3AWufcP73lDwEvOueeqtTe9cD1AK1btx7xve99z684JDj2sIe/8bcq5d3oxjf5Zp1WHZXw9AiP8BmfVSlfzGL60S8EEUWm7WznUapuBZFBBpdTv31S6+swh7mXe6v0sralLTdxU0CHA1eURx53czdFnD2/KYEEvst3ScD/1YTrwoOHe7iHYxw7q9wwvsW3SCU1KNcF+Dt/Zze7q5RfzdX0IjgrQoebtaxlJSurlI9lLDOrjCKTpmYjG1lB1WG+gxnMArRVUSRY5f2vsnM4h+lMb/yAGtntt9++yTk30nfN8Jbap487/67/8Vnv8YWLmszH63NIMICZxQJPAcucc097iw+YWZpzLqfS+GO/xi97x0nfD9C5c2fX5pJJ9fsIGsmxJ1cRzjEee3IVKQsm1fv8FCBj9Ud8dvDs4YFzJ3yHNh0b/r18/KlVpCyY3OB2guX4U2/S+uLwje/E02/S+qIpDWpjzrHOfLbq7Pn0XVP6MWrSjQ3uQT/xzBskz29YfMF28tk3SJ43NXDtPfc6yRdUbW+km8Ka1R+w98S2s8rnnPufJKf0r7m9518nee4039dd8Zrf9XrPWcTwzTvYtO/sJGLG0H8npev5ZfVeeI3kOX6098JrJM/2o96Lr9Fp9jwmfnaAV3cuPeu9ielX0CF97pl6/rbnb702s2cwY28Jj338v2e9N7LLLHoOXRi06ybPmcacI+24d93NZ73XO3UoQ8Zeh5n5/Xn2Vzi2N9lzLu++uZFThUfOlMVExTFz8i0kJ7RraIgB/5gDLeLj23Xg64F/FST36EtyZsM/7ibx+fPj34RQ8fffrNrEfbYHdq6qUp7QpzfJ/QLwNQ5AjEF1++2hjkBq4M8qwQY8BHzqnPtthbfKxx/D2eOPnwMWmVm8mfWi5vHLEmZmDjh7WFuvtplkdBgRomgk0Lq36c+gTuPPKpvR/xsa7h1gZsaMftecVTagw1i61ZKsBtP0vlefNZyrXVJXhnUO/h8ME3tdRkLM10N/E2Nacl7P4G9pMqLLDNomfT0LJcqimdbX/+1n6qtP2yzS25797+X56dc0q5+vuOh4pvU5e6/Zsd3nBSRZldArKK5+HnZCTMtGjkSCxVHTkODm8+9YpDDn+2hK/BkPdg6wBJhiZpu9x2xqGH9c2/hlCW+92w05K0GdOUDJTKSZ0f/rRKprSkaVBFYCY0CHcXRPGXDm9fkZ19RSO7g6tOzOiC7nn3k9Pf1qoqP8GlzTIEmxyZzX8+t5i+f1WkhibKugXzc6Kobz+37jzOsRnc+nfYuuQb8uwIyMb5553qvN4CoJbHMwtvsFtE5oD0B0VCxTejfuMHgJnvyS6uewJsYqYY0UNc5hDcBe3dJ4iks8ZB8/6fNoSnx+Bzrn1jjnzDk3xDmX5T1edM4dcc5Ndc6lex+PVjjnTudcH+dcP+fcS8H9ECSQZg74BqDe1UjVrU0/MjudA6h3NZjM7Ezy0r/9GHq0Ce3iI9PTv0GURdM2qQvDOzfePKSJvRaSENOShJiWnNez8bY0Gd55Ou2SumJENUrvarneqUPJaFc2heL89G82y5+v2Aq9rGO6ziEl0ed+8NJEFNSQsFYcSSFNW2kN69qoh7VpiY2Opmtyss+jKQn+bXZpUnq3G0y/DiOZkrGoWf6x1RycP+AbHC84xCBv4irB0b/DGHq0GcT5/ULXu1qufYuujOgygz6pQxuld7VcYmwrJvZaiHOuUXpXy0VHxTC979XsOLKR9i26+T4hgGakX0tRScGZxLU5GtNtLm99sZypfa8MdSgSQDUNCVYPa+SocUiwelibFAPMRdbf8EpYpYqFw39AalL1m0dL09ctpR/XjL5DNySCzMxYMvxnpCYFfiuV+piVcR2t4oO3Sm5Nzuu5sMY/goJpeOfp9G3b+Ns19UodzJXDftasf75iouO4cczvaJOo3yORZHKfyxmSNomC4lwKSk6f2XO1fYvuoQ5NAqR/uzEkxLTAOYej9Mxj79TI35or4jSxOaq+KGGVKtq2CI8/sCV4UvU1bhThkqwCIRuaGarel+ioGNokdgzJtcPp6x4q+hxEnt6pQ5W4RLj0diNIb6fpYE1dkcfDviY2R9UXJawiIiIiIiIRIM47hzWSKGEVERERERGJBA4NCRYREREREZEwpYRVREREREREwo3msIqIiIiIiEhYiouOpmtrzWEVtSet1QAAIABJREFUERERERGRcKQhwSIiIiIiIhJ2HJgSVhEREREREQk3xR4P+45pDquIiIiIiIiEmVjNYRUREREREZGwFWFDgqNCHYCIiIiIiIhIddTDKiIiIiIiEgGKSzzsP9rwOaxmlgC8DcRTljM+6Zz7mZn9H3ABUATsAq5xzh03syuA/6zQxBBguHNuc6V2bwf+DTjkLfqRc+7F2mJRwioiIiIiIhIB4qKj6RKYOayFwBTn3GkziwXWmNlLwKvArc65EjP7JXAr8EPn3DJgGYCZDQaerZysVvA759yv/Q1EQ4JFRERERETkDFfmtPdlrPdwzrlXnHMl3vJ1QNdqTl8MPBqoWNTDKiIiIiIiEgGKSjzsPxKYbW3MLBrYBPQF7nXOra9U5ZvA49Wcehkwv5ambzazq4CNwA+cc8dqi0MJq4iIiIiISASIi4mmS4pfQ4LbmdnGCq/vd87dX7GCc84DZJlZCvCMmWU657YAmNltQAneYcDlzGwMkFderxp/Bu6gbC3jO4DfUJb41kgJq4iIiIiISIQw/6odds6N9Keid1GlVcBMYIuZXQ3MBaY65ypvorOIWoYDO+cOnInT7AFgha/raw6riIiIiIhIJHB+Hj6YWXtvzypmlghMA7aZ2Uzgh8A851xepXOigEuBx2ppN63Cy4uAmnpiz1APq4iIiIiISAQo8njYF5g5rGnw/9u78/go63Pv458rkwkJREjYAklAQDZZlE0qVRTBBRW1arVaqz5Vq7Za2z6ntto+52VPe/o8trX1nC6nHmptPee0brRW3LVYxAUQUAooqxBDJCQsYUkIyczkev6YgQZImEBmMpPx+3695jX3/Zt77vtK8pvJXPPbeDQ2jjULeNLdnzOzDUSXunnVzAAWufvtseecBVS4+8bmJzKzh4GH3H0p8GMzG0c0bS4DbosXiBJWERERERGRDJATCFBa2P5lbdx9BTC+hfKhR3nOfOD0FspvabZ9/bHGoi7BIiIiIiIikpbUwioiIiIiIpIp2jBGtTOJm7Ca2SNEZ4GqdvcxsbLvAV8CtsUO+467vxB77F7gZiAC3OXuLychbhEREREREWkmFI6wZXti1mFNF21pYf098Evgvw4rf9DdH2heYGajiE5lPBooBv5qZsNja/iIiIiIiIhIkgQDAUp6tn8MazqJO4bV3RcAO9t4vsuAx929wd03ARuAye2IT0RERERERNrAAPP4t86kPZMu3WlmK8zsETMrjJWVAJubHVMRKxMREREREZFkS8A6rOnkeCdd+jXwA6I/7g+AnwI3EU3qD9fir8TMbgVuBejRo8dxhiEiIiIiIiIAjZ/QMaxHcPeqA9tm9hvgudhuBTCg2aGlwJZWzjEbmA1QXFzcyfJ8ERERERGR9JKT/Qkcw9oSM+vfbPdyYFVsey5wjZl1MbPBwDDgnfaFKCIiIiIiInG1pTtwJ2sqbMuyNo8B04DeZlYB3AdMM7NxRH/cMuA2AHd/38yeBD4AwsAdmiFYRERERESkg3SyhDSeuAmru1/bQvFvj3L8D4EfticoEREREREROTahcITK7btTHUZCHe+kSyIiIiIiIpJGghk4hlUJq4iIiIiISKbIsC7B7VmHVURERERERCRp1MIqIiIiIiKSAUKhCJXVWodVRERERERE0kwwO0BxL41hFRERERERkTRjsVsmUcIqIiIiIiKSKTJs0iUlrCIiIiIiIhkgFAqztbr967CaWS6wAOhCNGec4+73mdlPgEuARuBD4IvuvsvMBgGrgbWxUyxy99tbOG9P4AlgEFAGXO3uNUeLRQmriIiIiIhIBghmZ9O/V49EnKoBmO7utWYWBN40sxeBV4F73T1sZj8C7gW+HXvOh+4+Ls557wHmufv9ZnZPbP/bR3uClrURERERERHJEObxb/F4VG1sNxi7ubu/4u7hWPkioPQYw7sMeDS2/SjwmXhPUMIqIiIiIiKSERy8Dbc2MLOAmS0HqoFX3X3xYYfcBLzYbH+wmb1nZq+b2dRWTlvk7pUAsfu+8eJQl2AREREREZEMEApF2jqGtbeZLW22P9vdZzc/wN0jwDgzKwCeNrMx7r4KwMy+C4SBP8QOrwQGuvsOM5sI/MXMRrt7uxeFVcIqIiIiIiKSAYLZAfr3btMY1u3uPqktB8YmVZoPzARWmdmNwCxghnu0udbdG4iOe8Xdl5nZh8BwYOlhp6sys/7uXmlm/Ym23h6VugSLiIiIiIhkiESMYTWzPrGWVcwsDzgXWGNmM4lOknSpu+877PhAbHsIMAzY2MKp5wI3xrZvBJ6JF4taWEVERERERDJGQhZi7Q88GktCs4An3f05M9tAdKmbV80M/rF8zVnA980sDESA2919J4CZPQw85O5LgfuBJ83sZqAcuCpeIEpYRUREREREMkA4FGHr1vavw+ruK4DxLZQPbeX4PwF/auWxW5pt7wBmHEssSlhFREREREQyQDAYoH+fhKzDmjaUsIqIiIiIiGQCp83L1nQWSlhFREREREQyRWblq0pYO8q66nep3ruZM4ZcSmyAsmSISFOYqr0fUdzjpFSHIsdoz/7tdM/tneowjkkttXRPwXX3sId8j5AVnQAwLXy06332hfZycp/TO/zaoUgDexp2EOzwKx+/Pft3UB/aS9EJg1Idihympr6KHrm90+r1JSKdUygUoSoBY1jTiZa16SCbtq/iqfce5I9L76cx0pDqcCSB6kO1PDDvFuYsf5C6hsx6g8h0v174DR5ffj819VWpDqXN5jKX3y39LlV7yzr0ustZzk/fvIkPqhfiKe5qFGkK8/L63/GLhV9h/fZlHXptd2d55Tx+tOALLK98rUOvfbwiTWHe2DSH+1//POt2dOzvS9pmUfmz/PSNm1hdvSjlry9JrG11m1ldvZBIUzjVocgnRDAYoH/fHnFvnYlaWDtIec1aAN756GW27N7ITVO+T69u/VMclSTC/lAdThNvbfwL722exwWjvsgZgy8jkKWXV7prDO/nnfLnebfiVaYOvpLpw75At5xUtF+2XYQIK7e+zqqtb3DagJlcMPwmCvP6Jf26IUJU7v2Qh5fezUk9x3PJyK8wsODkpF/3cNvrPuaPf/8BZbtWAdEPgx3lo10fMHf1L9hUsxKAhvC+OM9IvbKaVfxp1U/5eM96AOoad6U4ImlJqKmByr0f8psl32RYr4lccvJXKO0xItVhSQI0Rvbzm6V3k59TyPjic5lUfAGlPUaot50kV4Z98aVP1B2kvGbNwe2KXet5YN5t3Dj5nxnZ77QURiWJUB+qO7i9L7SXp//+c97eOJfLT7mTEUX6+6azcFPjwfu/ffgYi8qfZfrQLzB18JXkZOemOLqWRYgA4DTxzuYXWPbxq5xx4uWcO/R68rsUJu26Yf7ROvDhzvf4t7e/xLj+07lo+G307laStOse4O4sqXiBpz/4Nxoi9QfLt9WVJ/3aNfVVPL/2P3l3yyuHlDdE0jdhrW2o4bk1D/FOxfNHlEv6CUX2H9xev2MZP3vzZiaWXMCFw79Ez67J/0JKkqcgtwiA2sYa3ih7ijfKnqJvt4FMLDmfCcXn06trcYojlIzjaAyrHCrSFOaRhfdRWjCUgT1HMqBwBN1zex5yzO767ezZv+OQsn2Ne3jozW9x0eibOHfkdWSZemd3VvtDtUeUVe0t46G3vsmY/mdw6div0Ce/NAWRSTwHEtYD6kO1PL/6Id7YNIcLRnyRyQMuSruW8gMJ68H9phALNj3J4vJnmXbStZw95Bpys7sm/LrNE9YDlle+xoqtr/PpgZ/hvKH/ixOSlDDXNe7myZU/ZmXV60c8tn3fx3G72tWHaskL5h/zdRvC+3ht4x+Zv/ExQk1HDuVIxxbWJo+wqPxZnl/7n9SH9h7xeF1IwxbSUaiFoULLPn6Z5ZWvMXXQZzl36PV0DaZ37w9pWdfgCeQE8mhs9kVbdV05L657mBfXPczgwlOYWHI+p/abnvY9fKRzCIXCVFVmVm+a9Pok1glV7t7Eqsq3WFX51sGygrw+DCyMJq8De45kT/32Fp/rOM+//1vKa9Zw3Wn3HtcHKkm9+hYS1gNWVb7F6q2LOXvYVZw34npyg906MDKJJxRpbLF8z/7tPPX3n/D6h09w0chbGdv/rLTpvnV4wnpAQ6Sel9c9wptlf+bcoTcwhhMSet0QoRbLmzzCmx/9iSUVL3DOkOuYQN+EXncDG5j7xi/Y07CjxcebPMLO+kqyCFG5dyPVteVsq9vMtrpyquui25GmEP/3/Ffa/Dds8ibe4z3+9nrr14X0S1g371rDnFU/ZfPu1a0eU9vQ8oeYJpqoa9xNXeNucgK5FOQl9u8oR9fa3BaRphDzNz7G4s3Pcd7QGzjjxCsIBrp0cHTSHmZGYV4RVbVlLT6+qWYFm2pW8PQH/86oPlOYWHIBo/pMITuQ07GBSsYIBrPp18nGqMYTN2E1s0eAWUC1u4+JlfUEngAGAWXA1e5eE3vsXuBmIALc5e4vJyXyNNG8q+8Bu+q3sat+Gyu2vNGmc6zc8hY/nXc7N035PsU9hiQ6RGmH3fU7+NFfbyA3mE9eMJ+8YDfygvnkHrjPzmdb7dHH0EU8zGvrHmPJRy9z8ehbOO3EC9Wi3gEWffQcO+sqyQ7kkJ0VJEwZ+WW1BLNyyA4ECWTlEGlqOQk7oLq2nN8v/T8MLBzFrJNvZ2jv8UmNecP2d9m8ew2BrCDZWUEClk0gK5tAbDs7K0g99Uc9R13jLp754Of8je5ctLmJiSUXxG0lLqtZxdKKlwgGusR+P9H7YKAL2VldCAZy2MnOo56jIVLPS+sf5k3ymVneyKdKZ7WrdToUaeC5tQ/xBk9BnHnqfrHwy9SyC47ylrunYQc92jAj9IYd7/LM6l/yMeviXreq9iMWshDWb6YhvO+Q2/7wPhoi+5g66LOcVnph3Ou2R13jHl5cO5uF5c/gcfqBVdWWMWflA9Q17qI2dqtr3E0du/BXo889a/DVfGbUXUmN+ZNk7bYlLPv4ZYKBLuQEcmOvrdzodqALwUAXduz7+KjnqA/tZe7qX/FG2Z+4aMStjC8+V/9HOpGjJawHRJpCrKxawMqqBeRl53Nq/3OYWHIBgwtP0d9ajlHm9Qluy6eJ3wO/BP6rWdk9wDx3v9/M7ontf9vMRgHXAKOBYuCvZjbc3VtuEsgALSWsx2NbbQUPvvYVrp30LSYMmJ6Qc0r77Q/tpT5US32olvaO/NrbsJPH3/0xb258hstP/SpDeo1NSIzSsvcq5h05g+yKl47rXOU1H/Afb9/FyL6nc/HJt1HSY2gCIjzSB9ULmb/xsYScaw97eHzF/+NvGx/jwuFfYmy/1luJt+7dxNvlf0nIdWupZc6qB1iw6UkuGnEbY4uOvXX64z3r+cPy77O1dlPbrtmGiYS21ZUfNWHdVlfBs2t+xaqqtn3RCLC1dhNb2QTrWj9mVxJnoG7yJpZWvMiza37d5smUahtr4v6tW2uFleOzde9Gln58fO89h6up38ofln+f1zc+ziUn38Gw3hMTcl5JnHBTiN37q6mprzp427mv8pjOUR+uZdHmZ1m0+VkK8/oxsfg8JpZcQFH+oOQELRklHIpQtSWzhn/ETVjdfYGZDTqs+DJgWmz7UWA+8O1Y+ePu3gBsMrMNwGRgYWLCTT8HZv9NhMbIfh5d/H0+2rmaS8felnZj5z6Jjtbd93hV7FrLL16/kwmlM5g15nYKu6rrXTKE47SeHo811YtYW72YCaXncSYnJ3w91HgtvsejqraM37/7XQYWnMxFI25jeO9JRxzT0vi59qquK+f3736XQYVjuWTElxnc85S4z2nyJhZseoLn181O+O+iuq6cob0mHFG+L7SHV9c/ypsf/YmIJ37Zif1J6ja8la28tPAOymIzFidSXUgJayK1NP65vSr2rOPXi7/GyD6nM2vklynurnXAU2XHvi08v/Y/qanfSk19FXsbdsTt6XAsauq38tcP/5s3yuZw06QfMayF9zGR5oLZAfoXZdZ46OPNiIrcvRLA3SvN7MAn7hJgUbPjKmJlGakx0kDl7o0JP+/89U9RUbOOG0+/74gJnKRjNZ8BONHerZjHyso3mTH885wz7Jq0nZW2szp8QqVEcZxlFa+wnHl8etUWzhuWuNl5k5EwHVC+azUPLf46w3tP4qIRtx2yJE0yPlAfUFazkl8s+gpjiqZy8YjbWm0h2FVfzWMrfsj6JK0TenjX/UhTmLfLn+GV9Y8kdSKiRI9zrQ/V8tK63/Imc/Ca5HT5qlMLa0I1NpsBONHWbFvE2m2LOa30QmYOv0Vjj1PAMJZXzkvqNUb0nszVY7/VIUuYSYbIrB7BCZ90qaV+Xy3+yszsVuBWgB49OufA4IAFuOOsBymvWcPmmrWU16xle+3Rx6G01Ybtf+eBv97KTVP+hUG9RifknHLsktHC2lwo0sBLq3/H0vJX+PLUn2n5ggRKRgtrcxEiLN8yj775Azhj0OUJOWeyYwb4eM8G1m57h5Luww724gglKblv7v2qN8nOCnL12HuOmMV4eeU8nlr5E+rDyXm9GUZ9+B8z5kaawvzP8u/x963zk3K95povwdNe7235K3/54BfsPcpEUImgmYQTKxk9GJpznHcqXiDU1MDnTvkOOZqUqUP1yO2DkYXTlPBz52bn85lRd3FayYVpM/GfdBJKWAGoMrP+sdbV/kB1rLwCGNDsuFJgS0sncPfZwGyA4uLiTvlrDWRlM7TPqQztc+rBsn2New8mr+U1a1lf/e5xJz2792/n5/O/xhXjvsoZQy7Vm1UK7E9iCyvACV168unBlzJl8KX0yOuV1Gt90oRbmQE4Efp0G8DpdeOYOuPrCZ3JMd7yLO1RkNuXaUOu5VMDZtElO++Qx5L5gTo7K4fJpRdx9uDP0afbgEMeizSFeXLlj1ny8QsJuVbXYHf6dhtIn24D6Zs/IHrfbQC9upYcMrNqICubG8b/gPU7lrJg01N8sO3thFy/JYlsYT2xYAynD5jFos3PJSVpDVg23XIKOKGL3osSKdmvr0mlM5k2+Br65g9M2nWkdYGsbHrk9mbX/ur4Bx+D0X3P4LNj7m7TZHEizYUaI1Rtyaw1t483YZ0L3AjcH7t/pln5H83sZ0QnXRoGvNPeIDuTrjknMKJoEiOKouPEfvn6N1i/7b3jPl/Ew/xt3RP0yS85eE7pOJNPnMmo/lOoD9WyP1TH/tgETPWhuoNlS8tfZvf+lpcuas2JhaOYetIVnFo6jeysYJKi/2Q7c8gV1DbUEG4KEYo0Ur+pDCvtQ7ipkXBTI3WNu9m089jG/w0sOJnpQ69jTP8zqX12fsKXHRjeZxJ5wXzCTSEiTWEiTSEiHiJ8YLspfMxdZovyBzH9pOuYUHxeq+PiR/f9NPk5BYQiDYSbGglFGggdvG8gHGlkddXbLa7F2pquwe6cceIVnHnila2uzxrIymbWyC8zqHA0K7a+zvody2g6zjn6buAGxp13a5uPNzOG9z6N4b1Po7q2nDc+msOSiheOufvmeMbTY/AIumR3PXjLze5Kl0B0u3tu4pK/nl37ceGIL3H+sC+y5MX/4O+9y1i7/dj/xU4qmcnQXuPpltODbjkF5OcU0jT/PfpceLG+GE2CiSXnU9x96MHXU2Nkf3Q7EttuamDl1gXHVPcPvr4GXcEJXTR0KNUK8ooSlrB2C/bg8tFfZ3z/c/V6lOMSDAboV9Q5e6+2pi3L2jxGdIKl3mZWAdxHNFF90sxuBsqBqwDc/X0zexL4AAgDd2TyDMHxNHkTm2uOMn1kK7rmdGd43wmM6DuJEUUT6dWtfxKik7bIDuRQkNeHgrw+rR6ztnpJmxLWgGUzrvQcpp50JSf2PDnu8dI+U4dcecj+nk2v0X3CjIP7m3et5cEFt7TpXCP7forpQ6/jpF7jkvoB4rTSC+MugfLN586iqQ1dz0oo4fyJdzC66My4SyIM7nlK3EmRvvf8RexhT9zrFlDAOaP+F5NLLz6iJbclJ3QpZMrAy5gy8DL2hfbwftVbrNg6n7XblxzTOORajr87cd/8gVw5+n9z4fBbWLT5Od4sm9PmD5/ncR79Rl163Nc+HoGsbEYxitM/dRfb6z5m0ea5vLP5+TbNlgwwpOepTB5w8SFle1itD8dJ0pbX1z0vnUdjG7qP9+pazNmDP8dppRe16fUlHaMwty9lzfZzs/MpzCuiMLeIwrwiCvKKmL/p8bizeZ/a7xyuGP0NfQkh7eTgnbLzaqvaMkvwta08NKOlQnf/IfDD9gSVKbbVVrA/HL9LaXZWkCG9xx5MUEsKhmnNrU4kXpfv7rm9OGPwZUwZfAknaBKttBEvGcqyAOOKp3PO0M8nbRmbY+XucZPV4b1PY8bQ6+m7aAc9+p2VsGvHa10t7T6cc4Zcx6DlYQoHXXBc1+ga7H4wad8f3sfq6rdZtvxJNgQ2xm353EH7u8h2DXZn+pDPc/agq3nnpV+xpHBN3Fl4G+It1ppkvbuVMGvkl5k5/BZWbV3AwvK5cVvh27oEjnQMd4/bbXhgwSjOGfJ5xvabSpYFOigyaaupg65iYskFFMQS1Lxg/hHHfFD9Fptaee3l5xRy5eh/4tT+05IcqUjnpHVTkmjzUZa8KS0YxvC+ExlRNIkhvcdqkoROrLVxroN6jmHqSVdwSslZ6vabhlqb4CgY6MKnBs5i2kmfo2fX9Ord0NoswoZxSv+zmX7SFxhQMBKAPfw1odcO0fLva0TvyUwfch1De03AzNizPDHXzc3uyvjiczlpOeSeeyZrti1mxdbX+aD6rRa/CNzOsXXLP5pAVjajGc2UKV/jo10f8EbZUyyvfK3FLpuNJH/CqrbIzgoyrngG44pnUF1bzqLyuSypeLHFCZTa2hIrHSPSFGp1wp7RRWdyzpBrGVx4ilrA09igwjFxjynILQKO/AJsYvEFfGbUXXTLyawunJI6ocYIVRXtH8NqZrnAAqAL0ZxxjrvfZ2Y/AS4BGoEPgS+6+y4zO49oL9yc2GN3u/trLZz3e8CXgG2xou+4+1Ens1DCmkTlO/+RsBZ2LWJELEEd3ncC+V0KUhiZJIq7H9LCmp2Vw4TS6Zx50pUMKByewsgknsMnZeoa7M6Zg6/kzMFXpO3r8/C1SQOWzaTSmZxz0ueTOuGKux/SwpplASYUn8u0wddS3D35rc85gVxO6Xc2p/Q7m3CkkXU7lrFi63xWVb3BvlC0m3IiWlhbcmLBKE4cdx+zRn6Ftz/6M2+XP3PwmpD6FtaW9M0fyKWj7uTCEV9ixdbXWVg+l407lx98vK5RswCnk8OXldJESpmpMK/okP3uXXpz1Zi7GV10RooikkwVDAbo1z8hX4A0ANPdvdbMgsCbZvYi8Cpwr7uHzexHwL3At4HtwCXuvsXMxgAv0/rypg+6+wNtDUQJaxLldyngs+O/zoi+k+iTX6JvRzNQY2Q/TR6hIK8Pn451+03XZEcOdaBLcGFeP6ad9DkmD2zbmMtUCsdmEc4J5DHlxEs5e/DnOmTdxSaP4DhdAnmcPvBSzhp09REfvjpKdiCHUX2nMKrvFCJNd/PhzuWs2Dqf1eWv4+5Je58tyO3DRSNu49yhN7Ls45dZUPYUVbVladPC2pJgoAsTS85nYsn5bN27iYXlc1la8aJaWNNMY6w78D8mUmp9ojLpvJqvofqp0llcevId5AVPSGFEktESMIbV3R0OThARjN3c3V9pdtgi4LOx45vPMvs+kGtmXdy93d/sKmFNovNP/kKqQ5Akc3du/NS/MLb/ma3OwCrpKb9LIddN+GfGFU/vVH+7aUxjxoy7O7T7WMQjzGAG06d/M60+YAWyshneexLDe09iV/nRJ7VJlJxALlMGXsbpAy5l3fYl2JJjn1gvFfqdMJjLR3+Ni0feTvmu91MdjjSTZVlcMfobmkgpwx2YhOnqsfcwos9pqQ5HMl2C5lwyswCwDBgK/MrdFx92yE3AEy089UrgvaMkq3ea2Q3AUuCf3P2ofZg7z6c0kTSUG+zKuJJpqQ5DjsPgnmMZ3HNsqsM4Jt1yujONaR0+1ikn0IWpTE2rZPVwWWR1aC8WM2NEn8ltmjk5neQEujC014RUhyHNnNClJ2cOujL+gdKpnVgwhrvP+m9ys7umOhTJcKHGMFUVO9tyaG8zW9psf7a7z25+QGy1l3FmVgA8bWZj3H0VgJl9l+iqMH9o/hwzGw38CDi/lev+GvgB0bT6B8BPiSa+rVLCKiIiIiKSRN1yuqc6BPmEiI5hbdPwtO3uPqktB8YmVZoPzARWmdmNwCxgRqzrMABmVgo8Ddzg7h+2cq6qZsf/Bngu3vW1doqIiIiIiEimcI9/i8PM+sRaVjGzPOBcYI2ZzSQ6ydKl7r6v2fEFwPNEJ2R66yjnbb4Ew+XAqnixqIVVREREREQkUyRmDGt/4NHYONYs4El3f87MNhBd6ubV2FCcRe5+O3An0bGu/2xm/xw7x/nuXm1mDwMPuftS4MdmNi4WZRlwW7xAlLCKiIiIiIhkgHBjmKrN7V/qzd1XAONbKG9xPTt3/1fgX1t57JZm29cfayxKWEVERERERDJAdjCbouLMWmJRCauIiIiIiEhGaNsY1c5ECauIiIiIiEimUMIqIiIiIiIi6SZRY1jTiRJWERERERGRDBAdw1qY6jASSgmriIiIiIhIxlCXYBEREREREUk7mnRJRERERERE0lC4MUzVR9tTHUZCKWEVERERERHJANnBbIpK2zCGdVnyY0kUJawiIiIiIiIZwpvUJVhERERERETSjsawioiIiIiISBoKNYap+mhbqsNIKCWsIiIiIiIiGSAYzKaotGf8A1cmP5ZEUcIqIiIiIiKSKdQlWERERERERNKP40pYRUREREREJN2EGsJUbapOdRgJ1a6E1czKgL1ABAi7+yQz6wk8AQwCyoCr3b2mfWGKiIiIiIjI0QTniaj0AAAH1UlEQVRzsika0Cv+geuSH0uiZCXgHOe4+zh3nxTbvweY5+7DgHmxfREREREREUkyd49760wSkbAe7jLg0dj2o8BnknANEREREREROURsHdZ4t06kvQmrA6+Y2TIzuzVWVuTulQCx+77tvIaIiIiIiIjE42RcwtreSZfOcPctZtYXeNXM1rT1ibEE91aAHj16tDMMERERERGRT7aq2sqXfzb/X3q34dDtSQ8mQdqVsLr7lth9tZk9DUwGqsysv7tXmll/oMVpqtx9NjAboLi4uHOl+SIiIiIiImnG3WemOoZEO+4uwWbWzcxOOLANnA+sAuYCN8YOuxF4pr1BioiIiIiIyCdPe1pYi4CnzezAef7o7i+Z2RLgSTO7GSgHrmp/mCIiIiIiIvJJc9wJq7tvBE5toXwHMKM9QYmIiIiIiIgkY1kbERERERERkXZTwioiIiIiIiJpSQmriIiIiIiIpCUlrCIiIiIiIpKWlLCKiIiIiIhIWlLCKiIiIiIiImlJCauIiIiIiIikJSWsIiIiIiIikpaUsIqIiIiIiEhaUsIqIiIiIiIiaUkJq4iIiIiIiKQlJawiIiIiIiKSlpSwioiIiIiISFpSwioiIiIiIiJpSQmriIiIiIiIpCUlrCIiIiIiIpKWlLCKiIiIiIhIWlLCKiIiIiIiImlJCauIiIiIiIikJSWsIiIiIiIikpaUsIqIiIiIiEhaUsIqIiIiIiIiaUkJq4iIiIiIiKQlJawiIiIiIiKSlpSwioiIiIiISFpKWsJqZjPNbK2ZbTCze5J1HREREREREclMSUlYzSwA/Aq4EBgFXGtmo5JxLREREREREclMyWphnQxscPeN7t4IPA5clqRriYiIiIiISAZKVsJaAmxutl8RKxMRERERERFpE3P3xJ/U7CrgAne/JbZ/PTDZ3b/a7JhbgVtju2OAVQkPRKTtegPbUx2EfKKpDkoqqf5JqqkOSqqNcPcTUh2EHCk7SeetAAY02y8FtjQ/wN1nA7MBzGypu09KUiwicakOSqqpDkoqqf5JqqkOSqqZ2dJUxyAtS1aX4CXAMDMbbGY5wDXA3CRdS0RERERERDJQUlpY3T1sZncCLwMB4BF3fz8Z1xIREREREZHMlKwuwbj7C8ALbTx8drLiEGkj1UFJNdVBSSXVP0k11UFJNdXBNJWUSZdERERERERE2itZY1hFRERERERE2iXlCauZzTSztWa2wczuSXU8khnMbICZ/c3MVpvZ+2b2tVh5TzN71czWx+4Lmz3n3lg9XGtmFzQrn2hmK2OP/dzMLBU/k3Q+ZhYws/fM7LnYvuqfdBgzKzCzOWa2JvZeOEV1UDqSmX0j9j94lZk9Zma5qoOSTGb2iJlVm9mqZmUJq3Nm1sXMnoiVLzazQR35831SpTRhNbMA8CvgQmAUcK2ZjUplTJIxwsA/ufvJwOnAHbG6dQ8wz92HAfNi+8QeuwYYDcwE/iNWPwF+TXTN4GGx28yO/EGkU/sasLrZvuqfdKR/B15y95HAqUTrouqgdAgzKwHuAia5+xiik3Beg+qgJNfvObJ+JLLO3QzUuPtQ4EHgR0n7SeSgVLewTgY2uPtGd28EHgcuS3FMkgHcvdLd341t7yX6Qa2EaP16NHbYo8BnYtuXAY+7e4O7bwI2AJPNrD/Q3d0XenTA9381e45Iq8ysFLgYeLhZseqfdAgz6w6cBfwWwN0b3X0XqoPSsbKBPDPLBroCW1AdlCRy9wXAzsOKE1nnmp9rDjBDLf7Jl+qEtQTY3Gy/IlYmkjCx7hrjgcVAkbtXQjSpBfrGDmutLpbEtg8vF4nn34BvAU3NylT/pKMMAbYBv4t1S3/YzLqhOigdxN0/Bh4AyoFKYLe7v4LqoHS8RNa5g89x9zCwG+iVtMgFSH3C2tI3Epq2WBLGzPKBPwFfd/c9Rzu0hTI/SrlIq8xsFlDt7sva+pQWylT/pD2ygQnAr919PFBHrBtcK1QHJaFi4wQvAwYDxUA3M/vC0Z7SQpnqoCTT8dQ51ccUSHXCWgEMaLZfSrS7iEi7mVmQaLL6B3f/c6y4KtbVg9h9day8tbpYEds+vFzkaM4ALjWzMqJDHaab2f+g+icdpwKocPfFsf05RBNY1UHpKOcCm9x9m7uHgD8Dn0Z1UDpeIuvcwefEurr34MguyJJgqU5YlwDDzGywmeUQHfg8N8UxSQaIjSf4LbDa3X/W7KG5wI2x7RuBZ5qVXxOb/W0w0QH278S6juw1s9Nj57yh2XNEWuTu97p7qbsPIvq+9pq7fwHVP+kg7r4V2GxmI2JFM4APUB2UjlMOnG5mXWN1ZwbR+SRUB6WjJbLONT/XZ4n+f1cLa5Jlp/Li7h42szuBl4nOHveIu7+fypgkY5wBXA+sNLPlsbLvAPcDT5rZzUT/mV4F4O7vm9mTRD/QhYE73D0Se96Xic46lwe8GLuJHA/VP+lIXwX+EPtCeCPwRaJfVKsOStK5+2IzmwO8S7ROvQfMBvJRHZQkMbPHgGlAbzOrAO4jsf97fwv8t5ltINqyek0H/FifeKYvBURERERERCQdpbpLsIiIiIiIiEiLlLCKiIiIiIhIWlLCKiIiIiIiImlJCauIiIiIiIikJSWsIiIiIiIikpaUsIqIiIiIiEhaUsIqIiIiIiIiaUkJq4iIiIiIiKSl/w9Em6pstnCwPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot specific discharge on cross section\n",
    "cbb = flopy.utils.CellBudgetFile(os.path.join(modelpth, 'mp7p2.cbb'), precision='double')\n",
    "spdis = cbb.get_data(text=\"SPDIS\")[-1]\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
    "patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
    "line_collection = xsect.plot_grid()\n",
    "quiver = xsect.plot_specific_discharge(spdis, head=head, \n",
    "                                       hstep=3, normalize=True, color='green', \n",
    "                                       scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
    "                                       zorder=10)\n",
    "\n",
    "cb = plt.colorbar(patch_collection, shrink=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrates some of the plotting functionality available with flopy.  Although not described here, the plotting functionality tries to be general by passing keyword arguments passed to the `PlotCrossSection` methods down into the `matplot.pyplot` routines that do the actual plotting.  For those looking to customize these plots, it may be necessary to search for the available keywords by understanding the types of objects that are created by the `PlotCrossSection` methods.  The `PlotCrossSection` methods return these matplotlib.collections objects so that they could be fine-tuned later in the script before plotting.\n",
    "\n",
    "Hope this gets you started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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