Isamar_Sergio_workshop espanol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Depositional mechanics with python\n",
    "\n",
    "Hoy aprenderemos como usar python para simular un modelo 2D de deposicion/erosion en un rio\n",
    "\n",
    "Las librerias que usaremos son `numpy, maplotlib, seaborn, pandas`.\n",
    "\n",
    "Las [slides para la presentacion estan aqui](https://docs.google.com/presentation/d/12Kx96Auxgq_vJuZCQK21aLqQSgJL3AkN/edit?usp=sharing&ouid=118190230180154301607&rtpof=true&sd=true)\n",
    "\n",
    "Las herramientas que nesecitamos:\n",
    "1. Un heatmap para mostrar las diferencias de elevacion en la superficie.\n",
    "2. Un campo vectorial para representar el flujo de sedimento. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "***\n",
    "\n",
    "### Creating a heatmap with seaborn\n",
    "Un heatmap es una grafico en 2D que usa color para representar una tercera dimension. Un ejemplo se ve asi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QDnabnPZOkjOAjwBvrKrvLrqe3aqqzap6Dlt3D52f5NkLLmnXklwCHKmqzy+6llXXQ9AOdpuc9kaSU9gK2Wuq6vpF1zOkqvpv4Cb66K9fCLw0yT1steRekOSDiy1pNfUQtIPdJqf2kgR4P3Coqt616HqGkOSxSR49/voRwAuBLy+0qAFU1Vur6kBVncPW79WnqurlCy5rJa180FbVBnD0NrlDwIfnvU1umSS5FvgM8PQkh5NcvuiaBnIh8Aq2Zkd3jrcXL7qoXToL+HSSL7L1D/+NVeVSKP2It+BKUmMrP6OVpGVn0EpSYwatJDVm0EpSYwatJDVm0EpSYwatJDX2f6PVoZ5w6yxFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heat=[\n",
    "    [0,1,2,3,4],\n",
    "    [5,6,7,8,9],\n",
    "    [10,11,12,13,14]]\n",
    "\n",
    "sns.heatmap(heat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1 (Class Discussion):\n",
    "\n",
    "Porque estoy definiendo la variable \"heat\"?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using online documentation to edit our heatplot\n",
    "Podemos jugar con el estilo de la grafica usando la documentacion de seaborn y ejemplos en linea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(\n",
    "    data=heat,\n",
    "    annot=True,  #muestra los numeros\n",
    "    cmap=\"viridis\",  #usa el color map viridis\n",
    "    linewidths=1,  #ancho de linea entre bloques\n",
    "    square=True,  # hace cuadradas las celdas \n",
    "     )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1:\n",
    "Has un heatmap de tamanio 3 por 3 con un \"hot center\" y uno de 4 por 4 con el color map \"cubehelix\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do it here!\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "***\n",
    "\n",
    "### Formating data for input on heatmap\n",
    "Los graficos en 2D usan una o 3 matrices como entrada. Hemos visto que pasa si le damos solo una matriz. Si en vez de esto le damos 3, requerira:\n",
    "\n",
    "- Los $X$ values, \n",
    "- Los $Y$ values y,\n",
    "- Los $heat(X,Y)$ values.\n",
    "\n",
    "Antes de que hagamos eso, comencemos haciendo matrices mas grandes para $X$ e $Y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.  0.5 1.  1.5 2.  2.5]\n",
      " [0.  0.5 1.  1.5 2.  2.5]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0]\n",
      " [5 5 5 5 5 5]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Primero definimos el rango de los valores de X e Y\n",
    "xrange=np.arange(0,3,0.5) # Que hace np.arange?\n",
    "yrange=np.arange(0,10,5)\n",
    "\n",
    "# Luego los volvemos matrices\n",
    "X,Y=np.meshgrid(xrange,yrange)\n",
    "\n",
    "print(X)\n",
    "sns.heatmap(X)\n",
    "plt.show()\n",
    "\n",
    "print(Y)\n",
    "sns.heatmap(Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora que tenemos las coordenadas haremos una funcion para el heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.  0.5 1.  1.5 2.  2.5]\n",
      " [5.  5.5 6.  6.5 7.  7.5]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recuerda que las funciones se definen con def\n",
    "def James(xmatrix,ymatrix):\n",
    "    return xmatrix+ymatrix #Suma las matrices\n",
    "\n",
    "# Hacemos el \"heat\" usando las coordenadas X e Y\n",
    "Z=James(X,Y)\n",
    "\n",
    "print(Z)\n",
    "sns.heatmap(Z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eso esta bueno y todo, pero esos ejes no son $X$ e $Y$. Para resolver esto vamos a usar un dataframe para hacer un pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x  y    z\n",
       "0   0.0  0  0.0\n",
       "1   0.5  0  0.5\n",
       "2   1.0  0  1.0\n",
       "3   1.5  0  1.5\n",
       "4   2.0  0  2.0\n",
       "5   2.5  0  2.5\n",
       "6   0.0  5  5.0\n",
       "7   0.5  5  5.5\n",
       "8   1.0  5  6.0\n",
       "9   1.5  5  6.5\n",
       "10  2.0  5  7.0\n",
       "11  2.5  5  7.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Hacemos un diccionario  X, Y, Z values y darselo a un pandas df\n",
    "Zdf=pd.DataFrame( {\"x\": X.flatten() ,\"y\":Y.flatten(),\"z\": Z.flatten()} );\n",
    "display(Zdf)\n",
    "\n",
    "# has una pivot table... porque pues toca\n",
    "heat=Zdf.pivot_table(columns=\"x\",index=\"y\",values=\"z\")\n",
    "\n",
    "#muestra la grafica\n",
    "sns.heatmap(heat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2\n",
    "Has la funcion \"heat\" definida por \n",
    "$$h=elevation(x,y)=100+2x-y$$ \n",
    "en el rango $20<x<30$ y $200<y<300$. (usa el tamanio de grilla que te paresca apropiado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aqui hay unos pasos para seguir si no sabes por donde comenzar\n",
    "#1 Haz una grilla/mesh para el rando indicado\n",
    "\n",
    "\n",
    "\n",
    "#2 Define tu funcion y guarda en una variable tus valores de heat\n",
    "\n",
    "\n",
    "\n",
    "#3 Haz el df y la pivot table\n",
    "\n",
    "\n",
    "\n",
    "#4 Muestra tus resultados! \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Vector fields with quiver\n",
    "Bueno, que pasa si quieres poner una flecha en cada punto? a.k.a. un campo vectorial. En ese caso nesecitas la ubicacion $(x,y)$ de la flecha con direcion $(u,v)$.\n",
    "\n",
    "Comencemos haciendo las componentes en $X$ e $Y$\n",
    "Lets start by making the $X$ and $Y$ components as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xrange=np.arange(0,10,0.5)\n",
    "yrange=np.arange(0,5,0.5)\n",
    "\n",
    "X,Y=np.meshgrid(xrange,yrange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ahora definamos las componentes $U$ y $V$ del campo vectorial\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define functions of the vector at every (x,y) location\n",
    "def Ucomp(xmatrix,ymatrix):\n",
    "    return xmatrix\n",
    "\n",
    "def Vcomp(xmatrix,ymatrix):\n",
    "    return ymatrix*0\n",
    "\n",
    "#asign them\n",
    "U=Ucomp(X,Y)\n",
    "V=Vcomp(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "finalmente, has un campo vectorial con la funcion `quiver`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis=plt.quiver(X,Y,U,V)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3\n",
    "Haz un campo vectorial de flechas apuntando hacia arriba. (puedes escoger el rango que quieras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create vector field here\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Exner equation: erosion and deposition of sediment\n",
    "\n",
    "De acuerdo con Wikipedia: \n",
    "\n",
    "&emsp; \"La ecuacion de Exner describe la conservacion de masa entre el sedimento de la cama de un canal y el sedimento que esta siendo transportado\"\n",
    "\n",
    "$$ \\frac{\\partial \\eta}{\\partial t}=-\\frac{1}{\\varepsilon_0}\\nabla\\cdot q_s$$\n",
    "\n",
    "donde $\\eta(x,y)=\\eta$ es la elevacion y $q_s(x,y)=(U_q,V_q)$ es el campo vectorial de flujo de sedimento."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a tratar de entender la erosion y deposicion en una superficie plana donde el flujo de sedimento esta cambiando. Usaremos nuestro rango de valores de antes y haremos un nuevo flujo de sedimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oTJkyVLVqVSpbtqwoFRme5+nWrVs0YcIEKlmyJJUuXVonBaR79+5Rjx49qEiRIuTq6qo1TUBAANWpU4dsbW2z/G3ChAlaOzx5RnYvWOtzSPnghed5FC5cGEuWLJHwqrhu7Ny5E6ampqhZs2ae+ON5HpcvX0bXrl1hZGQEIoKpqanovTLUrF69GtbW1iAi4dBlm9YrV66gQ4cOMDY2Fuyy2wFPpVJh48aNsLW1BRHh5s2bWdK8evUKcrlc69dls2fPhrGxcbb7AQ8YMABEBIVCge7du+e418LLly9haWkp5Nnc3By//fabzp8Nq1QqtGzZUrCXyWSoW7eu6P0dvkW92ZKpqSnGjBmj10co2lCpVFAoFBg6dGie+v2WTp06QerHZzmhUqmwc+dOuLu7C0Ia+sDzPDZu3AgfHx+Nsi/247Vdu3bB2dlZw8e6deuyTZ+amordu3ejWrVqQvrff/9da9q7d+/CwcEBvr6+WvNvb2+v1/5CwL/ky8QXL16AiFC7dm2p15ktKSkpGDhwoPAw9Pl6LzPJyclYunQp5HK54FufzXlevXoFFxcXwdf06dN1souNjUWVKlUEO19fX61f6Pn7+2sUylq1amn117ZtWxARDh06lOVv6k2vTExMcOnSpSx/j4qKgqOjo3AOe3v7HLeIPX78ODiOAxFBLpeL/rIuJiYGJUqUEM73yy+/6L1HdGJiItzc3ASfpqam2Lt3r14+v0XtX59tMXNi9+7dICJUq1YtT/1eu3YN5cuX17g3+qiaqPn06ROcnJwEv6NHjxbt48uXLyhatKjgI7ctfo8cOQJPT08hfenSpbV2Evz8/GBqagoiwqxZs7L8PTg4GEQELy8vvcrevyJQL1iwQKisUVFRUq81C9HR0ahRo4ZGr0sXmSldiIiIQP369cFxHDiOQ/PmzSU9KKVSiaVLl8LY2Bhubm4oUKAA2rdvr9OnrNHR0cJ2qHZ2dnBwcNC6TwjP89i/f79GwdS2ec+NGzeEv2vrIYSGhgp/z27XO3WQICJUqVIl1x7pnDlzQEQoU6YMZDIZJk2aJGoznpcvX8LCwgLFixcXhAH02eL222v4+eef80wnUY26wVQoFJJ0A3Pi8+fPwshM3x35viU5OVkQwaC/t+DVl5s3b6Jw4cIwMTGBnZ1dth2N7OB5Hjt37oS1tTXs7Ozg6emJ2rVr57odwOXLl4WRpVwu17rv9saNGzU6Ytq2TD116pTwdzHKM9/yrwjUNWvWFC5Wn13ItPHq1SthaqJ+/fp54vPBgweCFt+JEydQr149SQ3AmzdvhIakX79+iI2NxezZs3Xa2/nmzZvw8PCAmZkZduzYgZUrV+bYI1XvjObh4YHy5ctnaVR4nkf16tWF5zBz5kytfgoUKAAigo2NDQYNGpRlfw+e59GgQQNMnz4dtra2KFKkSI7TQTzPY8SIEUhJScGkSZMgk8lQrlw5URtbHTlyBKdOncJff/0FBwcH2NjYYN++fTrba8tTjRo10LNnTxgYGKBBgwailXlyQt24EhGcnZ312uMmM2oJOrXvunXr5olfNVu2bAHHcahduzZsbGz0krlLT0/HjBkzIJfL4ePjgxcvXmDMmDGieuihoaFo3bo1iAitW7dGaGgoFixYkOvOlxs3boRCoUCNGjUwd+5cTJgwIUua1NRU/PXXX3B1dQURwcrKSquMm7qTSZQh8CGVfB+oIyMjYWNjA4VCATs7O3Tt2lXyxX5LWloaKleuDDc3N6xZs0aS5t63bN++HcbGxihdurSwlaLYLVFVKhWWLVsGExMTuLm5aUwP5NYrV+/XK5fLUaFCBSEI5rQ145kzZ6BQKNC5c2e8fftWqzjAgwcPhCkic3PzbOdQmzdvjiZNmkAmk2W729y7d++QmJiIV69ewdPTE/b29jluvZr5mm/duoXixYvDwMAAc+fO1bl3pR6BhIaGonnz5iAidOvWTXIwefDgAWJjY3Hy5EkYGxujRo0aeutvqhk+fDhKliwJIsLr168lbwb1LWFhYTh69CgsLS3h6OiYZ9qJQEa55zgO3bt3h1Kp1KshfP/+PapXrw6O4zB27FhhxCKmJ71v3z7Y2dnB2toau3btEspQTvVHqVRixIgRIMqQxUtJSUFISEi2defw4cPClqvt27fXmqZbt24wMzODoaEhfHx8JMuW5ftAHRoaiq9fvwrbnErdplQbEydOhEwmw9WrV6FSqfTqFaWlpeG3334DEaFjx46Sd4p7+/atMILo27cvYmJidLYNCQlB/fr1QURCLzQ37t27BzMzM9StW1dIn11h9vf3BxHh5MmT2e5r/OzZMyQmJsLFxQVt27bN9fyhoaGoVKkSTExM8Ndff+WaHsiYJx42bJgwz/r69Wud7NSoF6jMzMzg6uoqSopJG5cvX4aFhQXKly8veXe2zNy8eRMxMTGwsrLSWRxYjG8iwunTp3Hy5Mk88blr1y5wHIeuXbvqJQ6snqawsLBAwYIFJT2XiIgIdOjQAUSEpk2b6rwlcGxsLJo0aQKO47BgwYJcO0RxcXFwdXVFvXr1wPN8trsCnj9/HsuWLYNMJkN6errkabJ8H6jV5KW4LZChG8hxXLYruWIICQlBzZo1IZPJsGjRIklz0SqVCsuXL4eJiQlcXV1x+vRpUfanT5+Go6Mj7OzsdJZIevv2LRwcHFCuXDmdNPnWr18PItKp8VizZg2ISKetURMSEtCsWTPIZDJRi7kXL16Eu7s7TExMsGLFCtFbUL579w4//fQTiAgjR46UtCG8mrt37wr7Hes7B65m+PDhsLKyytNplTFjxsDa2jrPeul79uyBTCZDp06dJG8jC2Ssp3Tu3BlEhHbt2kkSGD58+DAcHR1haWmJLVu26FwPAwIC4OXlBTMzM507CyNHjoShoaFOnYRDhw6BiESr0WTmPxmoIyMjUbBgQfz00096FS4go4dSoEAB2NnZSV4sePfuHXx9fUFE+PXXX0X1olNTUzF27FjhTQ1dg0RYWBiKFCmCQoUK6azc3KdPH5QsWVLnfBUuXBgNGjTQKX16errw+t64ceN0DrqxsbH49ddfhTlXsUos6enpmDNnDhQKBby8vCRpYKp5+vQpnJ2d4enpqbNCdU68fv1a77eFMsPzPDw9PdGjR4888bd//37I5XK0b99er3p05coVuLu7w8zMDJs3bxbd0fn69asgndagQQNRAfHKlSuws7ODu7u7zvtFP3z4EDKZDNOmTdMpvVo4Q6z+Z2b+c4Ga53m0adMGFhYWesnc8zyPdevWwcDAABUrVpQk1aRSqbBixQqYmpqiYMGCooeiAQEBqFKlCmQyGWbMmKHzsDM+Ph4VK1aEvb29qGmD0qVLi6rku3btAhHh4sWLOqXneR5z584FEaFz586ihonHjx+Hs7MzLCwsJFX2Bw8eoFSpUjAwMMC8efMkD+Hfvn0LDw8PuLi4SFKE+ZaGDRuiTJkyer9WCPz/1NXRo0f19nXo0CHI5XK0adNGcu88LS0NkyZNAsdxqFKliqRvDPz8/ODi4gIzMzOsW7dO1H3avHkzDAwM8NNPP+m82K9UKlGlShUUK1ZM5xFYSEgIiEiv1zj/c4FaPXz/888/JftITk4WenG9evWSpNwREBCAWrVqgShD5FPsQtTevXthaWkJV1dXUa9wpaamomHDhjA1NRWlwBwXFweO40RNTSiVSpQuXRrVqlUTVYF27NgBhUKBWrVqibovkZGR6NixI4gIzZs3F/0Ob1JSEoYPHw4iQo0aNST3ioOCglC8eHHY2dnhwYMHknyoOXr0KIhIsoxXZqZMmQIzMzO9lGYA4K+//oJCoUDLli1F6xaqefPmDSpXrgyZTIYpU6aIDvYxMTHo3bu38H2FmE6XUqnE6NGjhXfrxUx5rV27FkSEc+fO6WyjUqlgYGCARYsW6WzzLXkSqIlITkT+ROSXW9ofGahfvHgBExMTdO/eXbKPoKAgVK5cWbJAp0qlwsqVK2FqaooCBQqIlrpPTEwUVJxbtmwpai5PpVLhl19+gVwuh5+fn6jzXr58GUSk9X3SnFAHGrG9uPPnz8PS0hJeXl6i5/b27t0LW1tb2NraSnr74Ny5cyhYsCDMzc0liRADGVNL5cqVg6WlJa5duybaXo1SqYSHhwc6deok2YcaLy8vdOzYUS8fx44dg4GBAZo1ayZpYYzneWzatAlmZmZwd3eX1ACdOXMGrq6uMDU1xcqVK0WtTcTGxqJZs2YgIsydO1fUsw0JCYGVlZWkN888PT31UoDPq0A9ioh25+dAnZycjHLlyqFw4cI6LZxp49KlS3BwcICzs7Ok+aaAgADUrl1b6ImL7UU/efIEpUqVgqGhIVasWCE6gIwbNw5EhC1btoiyA4CFCxfC0NBQdA+K53lUq1YNpUuXFj2d8PjxYxQsWBAuLi7w9/cXZRscHCxUyM6dO4tenIqKihI+3mjVqpWkT8WjoqLw008/wcTEJMcvMHNj/vz5UCgUOq8laOPly5d6D79PnDgBQ0NDNG7cWNLCa2RkpPBla5cuXUSX/7i4OPTv318Y8YhVEv/w4QNKly4NU1NTSQK5Xbt2hZWVlaSvLX19fdGmTRvRdmr0DtRE5EpEF4iobn4O1FOmTIFcLhc13M/M4cOHIZfLUb16dUkq4OfPn4eZmRkKFCggujcLZPTy1CrmYoMWkDGdQESYPXu2aFsAaN++PapWrSrJ9uLFi5KDxKdPn1C6dGlYWFiInvPleR6bN2+GhYUFnJ2dJQmj7t69G9bW1pJVzOPj41GvXj0YGhpq3TtFFyIiImBkZIQZM2ZIsgcyvvA0MjKSLK788OFDGBkZoUGDBpKCdGBgIAoUKABLS0vs2rVLtH1wcDA8PDxgbGyMJUuWiG70AwMDYW9vD1dXV0kLxteuXQMRYc2aNaJtgYwg/0NVyInoIBFVJKLa2QVqIupPRPeJ6L67u7ukjD58+FCv157Cw8P1UiGPjIzElClTJM/JRUdHY/jw4ZI/gY+JicHYsWMlv58dGxuLVatWSV6UCgoK0uuNiIMHD0p+hzQmJgazZs2SvMD34cMHjB49WrL9p0+f9LJPTk7G3LlzJZcdADh79qyot4G+JSYmRudFXW0olUosXLhQ8vw2z/OYMGGC5AV8nucxfvx4yd9R8DyP2bNnSx6VpKenY+fOnZLLwOvXryU19GpyCtS5qpBzHNeciJoCGMxxXG0iGgOgeU42TIWcwWAwxKGvCnkNImrJcdxHItpLRHU5jtuVh/ljMBgMRg7kGqgBTATgCqAQEXUmoosAfvnuOWMwGAwGEeUzhRcGg8FgZCVnfZpvAHCZiC5/l5wwGAwGQyusR81gMBj5HBaoGQwGI5/DAjWDwWDkc/JVoL5z5w7l9l53XvDw4UN68ODBd/H94sULmj17Nj19+lQvP+np6XTnzh1atGgRLV68mHie19k2NDSUtm3bRvPmzSOVSpVtuqSkJFq9ejUlJSVl+duLFy8oOjo6y++PHTuW7TPatm0b+fn5kVKpzDWPx44do99//50uXrxIycnJuab/lsjISBozZgzt3r2bQkNDRdtnx5kzZ+jQoUM6XYNUgoOD6dmzZ9/Nf2YuX778Xf0DoNOnT9PZs2fz1Ofz589p2bJltG7dOkkxQaVSkb+/Py1fvpxmzZql0/NMSkqiffv20fbt27NNc+vWLQoMDMzy+9u3b4vOoyiy+xJGn0PqJ+R9+vTB/fv3JdnqQmpqKqZNmwYXFxe9NpDPDM/z8Pf3x+TJkwVppXHjxknyc/nyZcycORP169cXVI8rVKiQ69dq6enpuH79OiZPniwoRLu4uGSrSJGamopVq1bB2dlZq9I5z/OoV6+e1i/Mhg8fjt9++03rJjnh4eFwdnaGs7Mzxo8fn+NXWjzPCzujGRoawtfXF1OnTsXFixd1/rrxypUrghamt7c3hg0bhr/++kuvr/tSUlJQunRpuLq6Yvbs2ZL2/8iN1NRUeHh4SNo2Vwz37t3LM43Qb0lPT8euXbtQtmxZFClSRO/d+kJDQ7Fr1y707NlT0OSsUqWKzoIKPM/j7t27WLhwIZo1awYrKysQEYoXL57jM0xPT8epU6fwyy+/wMzMDIULF9Zafniex+rVq1G0aFGtX/7mhaAy/Ru2OVWpVHByctIaOPKCR48ewcfHB0SkVchSCmFhYRpisPT3puZSP0E9fvy4EHiICCVKlNBJ9mn9+vUwMzMT7ExNTbVuvZmeno6tW7fCw8NDCObaKoJaJ07bJuvqnfI6deqkNaCePXtW437UqFEj2/0/0tPT0bJlS430EydOFPUZ+qFDhyCTyQR7b29vvRv7+/fvC8rThoaG+OWXXyTvH5MdxYsXR4kSJURrbepKdHQ0PD090aJFizz1m5CQgBUrVghliP6W/NIHpVKJ2bNnw8DAQPBZpEgR0Y2kejdGtQ9XV9dsG0Oe57Fw4UI4ODgI6Y2MjLTWm8TERHTv3h1EhNGjR2v15+joqHdc+VcE6tu3b4OIILU3nh1paWmYNWuWUAhkMlme9WTS09MxYcIE4UEXKlRIkrwQANy4cQMVK1YUfLm7uyMoKChXO/WWqubm5iAicByndbvR+Ph4NG3aVCMobtq0KUu6pKQkFCpUKNv9kaOjo4XAWL9+fa0bAI0ZM0Y4h4eHR457LyQlJeHnn3/WqKBit4VV7x9MRDAxMcG8efP02nMDyNjgS+3T3t4efn5+ebKxvxp1A1W5cmXJe7tkB8/zwg52v/zyS5763rBhA5ydnYV7kxdbs4aFheGXX37RuN9id8179uwZ6tatK/iws7PDixcvcrQJCAiAnZ2dYLNu3bosad69e4dy5coJae7cuZMlTUREBIgI1tbWej3Lf0Wgnjx5snAzpOxclx1Pnz4VWkP6e3/nvODt27eCFl/z5s1hYmKis8xPZr58+SIUUm9vb5w5cwaOjo46qbI8f/5cyEPPnj1RokQJLF++PNv0p0+fFnqK2W1JOmvWLOFeZbcDYKVKlYQ0lSpVytLzSU1NRfny5eHs7AyZTIaaNWvm+Eyjo6NRtmxZrFy5EjVq1AARoUWLFqI29f/999/RokULDB48GBzHoWTJknoJ2qqnQIyNjUGUIZ+mb/DPjHo7WiJCw4YN89T3H3/8Ifj+7bff8swvkKG2op6Ws7S01KuuqlQqbNiwATY2NrC0tMSKFStgbm4uavQSExODESNGQC6Xw93dHQcPHoSbm1uuOp7Hjh2DnZ0dHBwcUKBAAXTt2jVLQ/zx40dhFK7udGhrrK9evSqkESO68S3/ikCdudXKK/04IGNXs6pVq8LBwQFmZmZ67RkM/L88l6mpKdzc3HDhwgVERERg9+7dovykpKRg/vz5MDMzg7W1NVasWCFo0j179ixX2+nTp8PAwACFChXC2bNnASDHXp+fnx8MDQ1Rr1491K5dW+twNSgoCCYmJsJz2LNnj1Zfav1Gda9c2xD11atXmD9/Ps6fPw8HBwc4OTnh0qVL2V5TcHAwgoODwfM8tm/fDicnJxgZGWHatGk6zX/yPC9U8Hv37gmNSZcuXSTvpnb//n2MGTMGa9asgVwuR82aNfNsznrLli0a02V5IZ0FZGj3qedniQhTpkzJE78AsHHjRsjlcjRs2BCzZ8/GypUrJft6+vSp0Ch37NhRCPiXL1/WyV6lUmHLli1wdHSEkZERpk6disTERAAZe7pnR3JysqBuX79+fQQHB2POnDnZ9oTVorWGhoYYO3as1jTr1q0T7nfRokUlT33m+0AdGBgo9F5KlSqVJ8MpIKPy9ujRAwYGBrh27ZpopYhvCQkJEaYPfvnlF9GboqvzdPz4cRQtWhQcx6F///46zUOruXnzJry8vCCTyTBq1CidFluOHDkCAwMDNG7cGElJSdkW5KtXrwp6hiVKlMi2wTx16hSmTp0Kd3d31KlTJ9vGQd3wfP78GdWrV4dMJsP8+fN1egaZe0qenp44evSoqKkHpVKJtWvXwtraGhYWFli+fLkkcVZ1Bb548SJsbW3h4eGBx48fi/bzLTdv3kSzZs1gZ2eHPn366O1PTUpKCs6fPw8iQs2aNbF48WK9ffI8j2nTpgkjt7S0NISFhUkKSImJiZgwYQIUCgU8PT1x6tQp0T7u3LmDKlWqgIjQpk0bnbdVffXqFXx8fKBQKDTKYXbl6suXL7C1tUXjxo2xYcOGbJWPhg4diiJFioCI0L9/f9y4cUP0NQH/gkAdFxcHlUolCAdIVWf5lsWLF4OIsHHjRr19HTx4EHZ2drC1tcX+/fsl+Xj16hWaNGkiLLKJ0dqLi4vD0KFDwXEcypYtm+vQTs3+/fuhUCjQokULnRbpFi1aBAMDA8TGxmY7x5eUlIS0tDRh0TG7nndm0tLSMGLECGH6SddG7smTJ4J6e5MmTUTPXYaFhaFnz54gIpQrV07yxv5AxnxlqVKlYGZmhr/++kuyHyDjear34FYoFKKlyHKiR48ecHNzQ1pamiQx2cykpaWhT58+Qu9cn3n6kydPwtPTEwqFAhMmTBB6wLoSFhYm5KVkyZLCSDI3eJ7H1q1bYWZmhkKFCuHWrVu52qhUKjRo0AD29vYICQlBenp6ttf++vVrHD9+HEQkunxmJt8HajV5JW4LZBQKmUyGoUOH6uUnJiYGPXr0ABGhcePGkubkYmNjMWbMGCgUChQsWBB//vmnqAJ/4sQJuLm5wcjICHPmzNFZJHTXrl2QyWRo27atznOgLVq0QI0aNXRKy/M8GjduDBcXF51VRfbt2wdzc3MULlxYZxUbnuexe/duuLi4wNDQEJMnTxZdya9evYrSpUsL881S37aIiYkRRlVz5szRe4ExOjoaFhYWeTaXHB8fDzMzM0yaNClPfDVp0gQymUzrQpuufPnyBR06dBA6KE+fPhVln56ejuXLl8PKygoWFhZYvHixzuU5NjYWXbt2FaZYdO0gLFu2DESkc4P87NkzEJHOjYc2/nOB+uXLl7C0tET9+vUlDXfVXLp0Ce7u7jA1NcXatWslidxu27YNTk5OMDQ0xKRJk0StCoeHhwuafr6+vqLUI7Zu3QqO49C5c2edA7tKpYK1tbWo14zevHkDQ0PDbF9b0sbLly/h5eUFY2NjbN68WWe7uLg4ocFzd3fHoUOHRD2TtLQ0LF68GObm5rC1tcXGjRslTYUplUrhzZauXbvq/Q7xhAkTYGxsjNDQUL38AP8vx6aP0giQ0XutVKkSTExMJM+fK5VKrFq1CpaWlrCxsZF0vy9evAhvb28QEXr06CFqveHu3bsoUqQITExMRAkYP3nyBEZGRujXr5/O50pMTNR7fe0/FaijoqJQrFgxFC1aFF+/fpXkIzk5GaNGjQLHcahSpYpOb2B8S+Z5tJYtW4rS8uN5Hjt27ICdnR0sLS2xfv16UQV8/fr1ICJ0795d1Dzi48ePQUQ4efKkzjZAxhsXcrlcVE8pPj5e6On06dNHVLB7/vy58CpWw4YNRQelT58+CT28atWqSZYf27ZtGwwNDVG5cmW93n4ICwuDiYkJxo8fL9mHmnr16knWvVTz5s0bFC5cGHZ2djpNE2jj4cOHqFy5srCeI3YRNjAwUHhGFSpUEDXvq1KpsGjRIigUCpQpU0aUDmdycjLKlCmDYsWK6fyxjRonJye93qX+zwTq9PR0NGjQABYWFrm+Q5kd/v7+8Pb2hkKhwMyZM0X3yENCQoQv7kqUKCH6Y4APHz6gYcOGIMpQxhb7tdPKlSuF4b3YxZ6VK1dCJpOJ/rIvMTERhQoVQq1atUT1cNVfexkYGMDHx0fU63g8z2P//v1wdXWFgYEBxo8fL/od1tOnT6No0aKQyWQYNmyYpC8ab9y4AUdHRxQoUEDndQNtDBs2DBYWFpL1NoGMt3Y4jsPq1asl+7h9+zbs7e3h6ekpqYMSHx+PkSNHQiaToVixYjh//rwo++TkZMyaNQsmJiaws7PD+vXrRZXj0NBQNGrUCESEwYMHix7tjBw5EgqFQtKzrFatml4vQvxnAvXs2bPBcZzoDybUHD9+HAYGBihZsmS2K7w5cfnyZVhYWMDS0hJLliwR/W7spUuXYGpqCmdnZxw8eFD0VMv+/ftBRBg0aJCkIX3Hjh1RoUIF0XbA/3+xKGWR7fbt23Bzc4OVlZXoxa/4+HhMmDABBgYGOX6Jlh3JycmYMWMGjIyMJKuYBwYGwsfHB8bGxpLKDZARZA0MDDBv3jxJ9gCwYMECGBgYSB5JPnv2DCYmJqhYsaKkaZjg4GC4ubnB0NAQU6dOFb1NQ3h4OAoXLgyZTIYhQ4aIvo7g4GA4OTnBxsYGhw8fFmULZEyVEBFmz54t2hYAunTpgipVqkiyBf5FgfrFixd6zdNFRUXppUIeHR2NKVOmSJ5zjI2NxbBhwyRfQ3x8PMaMGSO5V5WQkKCXCnlERESO76DmBM/zOHLkiOQ1gYiICMyePVty3l+9eoUxY8ZItn/37h3GjBkj+fXNhIQEzJ07V681kQsXLug1352UlKT1a1JdUalUWLx4seSv63iex+TJkyXPj/M8j0mTJkn6cExtP3fuXMlv0KhUKuzZs0fye9Dv37/X6+2dnAJ1rirkUmAq5AwGgyEOfVXIGQwGg/EDYYGawWAw8jksUDMYDEY+hwVqBoPByOewQM1gMBj5HBaoGQwGI5/DAjWDwWDkc1igZjAYjHxOvgrU3+Pjmx91rrz0r/466XvlI7s02n6fkz8xedT3/nyv5/dPlMH/pXL+vc+jT9n/1k9epRVbL/KCfBWo/fz8KD09/bueIzU1lRYuXEhPnjzJU7/Jycl06dIlmjZtGrVs2ZJevXolyU98fDzdunWLNmzYQEOHDqXatWvT/PnzdbJNTEykq1ev0qJFi6hdu3Y0adIk4nlea1qe5+n06dM0bNgwSklJyfL3U6dOUUxMTJbfr1ixgqKjo7X63LhxIw0cOJBOnDhBycnJOeb1zJkz5OvrSwMGDKA1a9bQjRs3KD4+PveL/Jvo6Gjq1KkTdezYkebNm0enT5+msLAwne2z4+LFi9S3b186e/YsKZVKvf1pIyIigvbs2fNdfGcGAG3duvW7+n/48CENGzaMLl68mCf+Pn78SIcOHaJJkyZRw4YNaf369cRxnM4+UlJSyN/fn7Zv306jR4+mBg0a0OTJk7OtB2rCwsJo165d1KNHj2zvmVKppIULF1JoaGiWvx0+fFjnPEoiu2/L9Tmk7vXRvn37HHX19IHneRw4cACenp6oX79+nvh89+4dpk6dCl9fXxgaGoIoQ3JeV923zKSlpQlKJJmPGTNm5Lh/Bc/zmDNnDsqVKycI1xIROnfurHXPgtjYWKxYsQLFixcHEWnNa3x8PDw8PLRKhK1fvx5FihTRKkelUqnQrl07EBFMTU3RsmVLbNy4ESEhIVrz7ufnByMjI43r/emnn3TeKyUqKkrYSlN9uLi46KQ4kxMjR44EUYaSdf/+/XH+/Hm99vDQhqenJ7Zu3ZqnPr9l6dKlaN++fZ77DQsLw9KlS1GmTBlhm1p9SE5ORu/evTUUwelvYQZdUalUGDZsmEYdICIMGzZM6/4taWlpOH/+PMaNG6chYNuvXz+t9e358+eoXLkyWrRoofX8derUwf3793W/aC3Qv2FTptTUVFhYWGDUqFFSrzNb7t27h59//ll4GFL32P2WtLQ0dOrUSfArk8kkSzRFRUVh4sSJGoVs2bJlOtlevXpVaCiICB06dMgSWD5+/ChspalON2TIEK3+Ro0aBSLSukl7eHg45HI5TE1NtQr6JiQkoEKFCsI5DA0NMWvWrGw3ujl//rygaq1uYMRsDh8TE4Pq1asL9gULFtRLbgvIeK6ZywsRoV27dnoLBGRGXW7yUsg5M35+foKuZl6RmpqKAQMGQKFQCPelRIkSovdt/haVSoXly5dr3G+xuwgmJiZi4cKFkMlkgo9JkyZl28nheR4bN27USN+0adMs9UapVGLBggVChyI7cWw3Nze0a9dOVJ6/5V8RqM+dOwciQrFixaRep1bUysmZH0ZeEBISIkh0qf1v2bJFtJ+oqChMnToVlpaWMDQ0hK+vLziOw6ZNm3K1/fTpE3r27AmO4+Dk5ASiDLFPbYouqampGD16tHAfPDw8tO6Sdu/ePaHwBgUFaT1vvXr1BD8jR47Mcr5Pnz7BxcVFSNO7d+8cJZCuXbsGCwsLeHt7w9zcHMbGxhg1apTOm83HxcXB19cXRkZGwkihefPmkndhAzKer/oaOI7DnDlzJO+qpg211BMRYcWKFXnmF8hQ+FY3yMuXL89T31euXIGBgYHQCEsVXQAygqWfnx/Kli0LogwFbyLCggULdPaRlJSEZcuWwcnJCRzHCQ1sboH+1q1bwnnpb3GCb+vDy5cvUa1aNSFN8eLFtfbOk5KShHKij7LOvyJQqyXcifSXEcpMfHy8oLRCRHoPT9LS0rB06VJh3+nly5djxowZWLRokSg/3wbowYMHIygoCOfPn8e+fftytI2JicHEiRNhbGwMa2trLF68GKGhoWjRokW2e2AfPHgQJiYmQvA5d+6c1mvLPAzMbiP/devWCWmKFi2qVU7rzp07cHR0xMqVK2FqaooCBQrg+PHj2V7T3bt3sXz5ckRERGDcuHEwNTWFmZkZJkyYgMjIyBzvB5DRk+/WrRvS09OxefNmuLu7g4jQqVMnyeXp2rVrMDQ0xG+//QaiDDk0sftdZ8ft27c1epBiy092hIWFwcPDQ/B76NChPPHL8zxWrlwJAwMDlCtXDjY2Nno1AtevXxeCapUqVXDx4kWcPXtW5/uQnJyMP/74Ay4uLoLk3IsXL/DgwQOsWrUqW7uYmBgMHjwYHMehdOnSOH36NDw8PLSO4l6+fClM5eXU6D19+lRIo880UL4P1DzPw9PTM88LbVJSEurWrQsTExPMmjULrVq10svfxYsX4eXlBSJCr169hLnU7OZgtREdHY1p06bBysoKBgYGGDRokEbPNaf56NTUVKxYsQL29vaCTqF6c3WlUqlVZZznecyaNQtEGZJgkZGR2Qr+LliwABzHCc8hO4WPsLAwyGQyODo6omzZstmqm9++fRsAEBAQgDp16oAoQ5Ypuw3hM/dWQkNDMWLECBgZGcHCwgJTp07NVZg0c483JSUFK1asgJOTE2QyGfr06SMpyKo3oD99+jScnZ1hZWWldcpHLKmpqcJwulKlSnj+/LnkvbAzs27dOtSoUUN4hlKFDDITGxuLjh07gojQt29fJCUl4Y8//pC09/fTp0/RokULEGUoiWfWvdRFaCMlJQWrVq1CwYIFhWm+Z8+eCX/PaarjwIEDcHFxgbGxMebNm4e0tDTwPJ9tOff394epqSl8fX1haWmZbfk7cuSIcL8NDAzw6dOnXK9DG/k+UL948QKdOnWCmZkZatWqJUpUMjtSU1PRtGlTGBoa4uzZs1CpVJLUO4AM9Q11Qa1QoYKkOdDo6GhMnz5dI0Drusk4z/M4ePCgMDTs0qUL3r9/n6tdUlKSoEs4btw4IZBltzAWFBSEcePGwcrKCrVr185Ra27JkiW4c+cO5HI5xowZk2teVCoV1q5dC3Nzczg5OemswPH582cMHjwYBgYGsLa2xqxZs3RWPAcyetrz5s2DjY0NDA0NMXToUFENa2YiIiLQqlUrEBG6desmSborM3369MGqVauyXdSVSt26deHj44OmTZvqLZj7+PFjFCtWDKampti+fbtkPx8+fECPHj3AcRxcXV2xefNmUQu0qampWLt2LVxdXYU1A11FLj58+IBmzZqBKENjUxfJt7CwMLi7u8Pb2xtxcXE56oguWrQIDRo0EKZcxMqPqcn3gVrdk1BLcenbs0hPT0e7du2gUChw7NgxyX5SUlIwb948mJqawtbWFuvWrRM9T/ltgB44cKAoFYgbN27gp59+AhGhVq1aOveQQkJCULVqVRgYGGDbtm06n8/Hxwft27dHcnIyYmNjs02n7rnMmDEDHMfh4sWLOvn/+PGjUKg7deqk9c2S7Oz69u0LuVwOOzs7LFiwQNQillq9x8zMDKamppgwYYIkySqe57FhwwaYmprCw8MD165dE+1DTWpqKpRKJcqWLYsKFSrkSY9aPQzfunUrYmNj9fK5ZcsWGBsbo2TJkhq9VjGEh4dj+PDhMDQ0hK2tLRYvXixqUTYtLQ0bNmwQprJat24Nf39/nWzT09OxaNEimJqawtHREbt379ZpFJCSkoIaNWrAzs5Op6AeFRWFoKAgEJFeClN6BWoiMiaiu0T0mIieE9GM3Gx+pAq5UqlEt27dIJPJsH//fsl+Tp06hWLFioHjOAwYMECnedLMxMTEYMaMGbC2toaBgQEGDBggauj95s0btG3bFkSEUqVK4fjx4zoPNf39/eHm5gZ7e3tRgSQ4OBhEpNNCppr09HRUq1YNbm5uuU5NqOF5Hps2bYKlpSUcHBxEPad3796hZ8+ekMlkcHJywrJly0Rp84WFhWHkyJEwMjKCpaWl6B66mtevX6NixYqQyWSYMmWK1gVcXVEvpO/YsUOyDzX9+/eHvb29aL3CzCQmJgoCzV26dJEkzRUXF4fp06fD3NwcpqammDx5sqgRSFpaGjZt2oRChQqBiNCiRQs8ePBAZ/s7d+6gXLlyUL9yp6u8Hc/z6NOnDxQKhahXhVUqFUxNTSXrLQL6B2qOiMz//tmAiO4QUbWcbH5UoOZ5Hv369QMRSR6mvX//XhjeVqtWTfTiY0xMDGbOnCk5QIeHh+O3336DQqGAk5MT1q9fL2qIeOTIEZiamqJ06dI6TY9kZtu2bSAi0XNs7969g5mZGbp27SrK7tOnT2jatKkwlBUzTH/16hW6dOkCjuNQoEABrF69Otu58uzO3b9/f8jlctjb22Pp0qWig1tqaiomTpwIjuNQpUoV0cK8mWnatClcXV2RmJgo2cfXr19hYmKCyZMnS/bx+vVrlClTBoaGhlizZo3oeeiUlBT88ccfcHBwgEKhwODBg0W9bpmeno6tW7eicOHCICI0a9ZM1Dx7bGwshg4dCo7j4OXlJXrEo34bZ+3ataLsAKBcuXLo0aOHaDs1eTb1QUSmRPSQiKrmlO5HBWp9bjIAnDlzBsbGxnB0dMTWrVtFDxuvX78Oa2trKBQK9O/fX/Ti1fXr12FhYQEzMzNMnz5ddE/m2LFjQuHOadoiO7p06YLSpUuLtgOATZs2gYhw9uxZUXY8z2P79u2wtraGnZ0dPnz4IMr+6dOnwsq8u7s7vnz5Isr+7du36NatGziOQ8GCBSWJk16+fBlubm4wMzOTLA78/PlzyOVyvd6k+OOPPyCXy/H582dJ9m/fvoWFhQU8PT0lvR0VEREh9IC7dOkiek0oOjoaxYoVAxGhcePGwmK0mPMXKFAARkZGmD17tk6Lk5l5+vQpZDIZBg8eLMpOTceOHVGtWjVJtkAeBGoikhPRIyJKIKIF2aTpT0T3iei+u7u7pIx++PBBstQ9kNGa7t27Vy/7iRMn6jyE/5a4uDgMGzZMdLBRk5iYiNGjR4vqgWRG/cqS1Pd94+Pj8eLFC0m2PM/jr7/+kjwn+uXLF8yZM0eyivjDhw8xbtw4yfZPnz7F2LFjJdtHR0dj7ty5es0JX7x4Ua8plPT0dL0+9uF5HkuWLNF5mkAbU6ZM0XkOWRuTJ0/W6xrmz58veWTD8zz2798v+RkEBQXpvOaijZwCtSgVco7jrInoCBENBfAsu3RMhZzBYDDEkWcq5ABiiOgyETXWP1sMBoPB0IVcAzXHcQ5/96SJ4zgTIqpPRNK2hmMwGAyGaBQ6pHEhou0cx8kpI7DvB+D3fbPFYDAYDDW5BmoAT4io/D+QFwaDwWBoIV8JBzAYDAYjKyxQMxgMRj6HBWoGg8HI57BAzWAwGPkcFqgZDAYjn8MCNYPBYORz8lWgViqV/9i5kpOTv5vvlJQU+vr1a574SkpKooCAgFzl7r+F53n69OkTqVSqHNOlpaVRamqqVntttmlpadn6ioyM1Dl/nz9/pri4OJ3Tf0tcXByFhISIvi+5ER0dTWK2VZBCenr6dz9H5nP9E8TGxua5z7S0NAoMDNTrXqWkpND79+91ji0AKDw8PMe/a6sv3zt25atAvX37dkpKSvpu/nmep+PHj1Pz5s0pIiJCb3/p6en0/Plz2r9/P02dOpXatWtHJUqUoLp164r2lZycTLNmzaI+ffpQo0aNqHTp0mRjY0NOTk707Nkzksm0PyqlUklnzpyh1atX06hRo6hly5bk5eVFVlZWdO7cOZLL5VlsUlJS6NixY9SjRw8aOHAgGRgYZEmzfPlyrUF5wYIFdPLkSa15uXfvHhUuXJjatm1LCxYsoMuXL1NCQoLWtAkJCVSxYkWysrIib29vaty4MfXt25dmzJhBHz58yOlWERGRqakpzZ07l0xMTKho0aJUr1496tOnD02fPp38/KR/jxUcHExly5alLl260Lp16+jVq1d5HlTT09Np6NChWit8XhIXF0dz5879Lr4TEhLo+PHjNGTIEKpQoQIFBQVJ9hUbG0vbtm2jadOmUc+ePcnX15fc3NzIxcWF3r17RxzH5eojPT2dFi9eTAMHDqTmzZtT+fLlycHBgWxsbOjGjRukUGT9ZESlUtHLly/pzz//pDFjxlC9evXI3d2dnj59qvUcd+/epU6dOmmtF2vWrMm1U6QX2e3WpM8hdZvThg0b4ujRo5JscyI5ORkbNmxAiRIl8lSZ+e7du4LyhPqoU6eOpI3oeZ7HggULNHx5enri6dOnudpu2LBBw87c3BynTp3SSJOYmIhDhw6hS5cuMDc3FzTrtG2H6u/vDyMjI607yakVRLp37651p8Njx44JKtVEBJlMhtatW2tVY/n69augpag+WrZsKWoHupUrVwqq6UQEMzMzne5ZTty6dQumpqaCTycnJ3Tp0kWyhJc2WrZsierVq+stlZUdCQkJqFmzJiZOnJhnPiMjI7Fw4ULUrVtXeMYKhUL01rbfwvM8JkyYoFEOChUqlKMUnDY2b96s4cPJyQm3bt3SmjY2NjZL2TMzM8OVK1eypH337p0gxffHH39o9de4cWPs2bNHVH6/hfK7FBeQsUWooaEh+vbtK/U6s5CSkoIZM2bAwcFBeBiVKlWSvA1oZmJjYzF37lzY2dkJvlu1aiV68/mkpCRs2bIFFStWBP0tOa8O+LmpykRFRWHu3LlwcnIS8uDs7IyHDx9mSXv58mWULFlSSGdhYYGXL19mSZeYmIhSpUrB2Ng42/PWrVtXqAgHDx7M8vfDhw9DLpcL55o8eXK28ktpaWkYOHCgRoUpVaoUVq9erfN+3KdOnYKlpaVgb2Njg7Fjx0raW1rNmTNnNBocfbZQ1YY6qLi5uWl9XvqgFnUmolwV7cXA8zxmzpyp8azEqAFp4969e+jatSsUCoXgs2rVqjo3YOnp6Th8+LBwveqjQoUKGqLR3/L27Vt06NBBSG9paYkbN25opAkPD8ewYcOEcuDh4ZGtOIW3tzdKlCihV2z5VwTqQ4cOCYEmL7Tj1Fy/fl1Qe5bL5XrtlQtkKLjMmjULNjY2kMvl6NOnDzw9PdG9e3dRSiwBAQEYM2YMbG1tBT3E/fv3Y+jQoRgyZEiOe+IGBgZixIgRMDMzg0KhQPfu3TF+/HiUKlVKq1gBz/PYunUrLCwshIJ55MgRrb6HDBkCIoKtrW225z969KhGpejYsWOWHvP+/fshl8tRpUoVyGQyuLm5Yfv27VqfLc/zWLlyJapXr47Tp0+jWbNm4DgOlpaWGD58uE77Cz979gyFChXCvn37MGTIEJiZmUEmk6F9+/a4du2apCC7b98+cBwHZ2dnEBFq164tWRjgW8LCwoRG2dTUVGuDJ4WUlBQ0adJEeDbZKWyLJS0tDfPnz4exsbEQuKQqySiVShw5cgS+vr4gIhQoUADz5s1Dz5490b59e500FcPCwjBnzhy4ubmBiFC+fHls2bIFv//+Ozp27JitUk5QUBD69esHuVwOBwcHNGjQANbW1rh7965GuvT0dEyZMkWjA5CTapS6bu3cuVPczcjEvyJQ9+rVS7gh3940qbx79w7FixeHlZUVHB0dMXbsWMm+1CK1agWXvn37ClJXGzdu1KlxUalUOHHiBJo2bQqO42Bubo7BgwdrDNVzCkqPHj1Ct27dIJfLYWFhgdGjRwu9hnv37mnd8D0iIgJt2rQBEaFDhw7o378/pkyZotX/8ePHhWdQsGDBbPOhVCoFqSQTE5NsxQb27NmDCxcu4OnTp0Lw8PHxwblz57Smzyyg+vbtW4wcORJWVlYgIjRp0gQnT57M8T6HhYUJMmLR0dFYvHixoDhSoUIFbN++XZRcFwCsXbsWe/fuxZEjR1CoUCHI5XIMHTpUsrhEZmrUqCHc765du0oWnFCTlpaG1q1bawzl82L0ePfuXUF/cNCgQTh58iS6du0quvGLj4/HypUrUaRIEeGZ7Ny5U1BiuX37dq716M6dO+jevTsMDQ1hYGCArl274ubNm0JeXr16pTVfYWFhGDFiBIyMjGBlZYXZs2cjPj4eR48ezXZEo+5syOVyeHt7Z3svY2JihHterFgxUR22zOT7QK1UKjWmJ/QVuAUy5hnt7e3h4eGB58+fY/78+aJUq9V8/foVU6dOFVTEBwwYILpCff36FYsWLRKCW6lSpbBq1Sqd5LJ4nse5c+fQsGFDEBFcXFywYMECnQLFiRMn4OTkBEtLS+zcuRM8z+P58+daC1xiYiK6dOkiTKMULVo0R9/Lli1Dz5494ejoiMqVK2fbg8lc8c6fP4/y5csLUku69E7j4+Oxdu1aeHl5CRXhjz/+0FlqTKlU4vDhw6hVq5YwXTN9+nRRc8Pq6aykpCTMmDEDxsbGcHBwwKZNm/Qa/S1cuBBFixYFEWH+/PmS/ahJSkrCs2fPYGtrCwMDA/z00096+YuLi8Pw4cMhk8ng5eWF69evA8iY9hOrTzlu3DhYW1uD4zi0atUKly9f1jnQJycnY9u2bahUqZLQA585c6ZOawbR0dGYPHmyoD4/ceJEnVSkDh48CLlcjjZt2mDRokU5rp2p123Ux7Zt23S6rm/J94H65cuXmD17NqysrNCxY0dMnz5d0oWqOXToEIyNjVGpUiXhYYpt/SMjIzF58mRYWFjA0NAQgwYNEj3nef/+ffTu3RvGxsaQy+Vo164dLl68qFNe0tPT8eeff8LHxwdEBC8vL2zZskWnCpKQkCDM+9auXVvnfKekpMDKygq9e/fOVag2JiYGnz9/xs2bN2FkZIQOHTroPKrYuXMn3N3dIZPJ0KdPH500/niex/nz59GqVSvIZDKYm5tjyJAhWufZs8Pf3x+9evWCoaEhDA0N0aNHD1HK1mo+fPggKMRXrlwZd+7cEe0DyBg93b9/H0OGDIGhoaHGiEIqq1atAhHhwoULmDt3rmQ/fn5+cHNzg6GhIWbOnClafxDQnH82NTXFkCFDRMlkffz4ERMmTIC9vb0wPXjgwAGdpLISEhIwd+5cWFtbw9DQEMOGDdN5Mfjw4cNQKBRo1aoVUlNTERsbm2OdPX36NEaPHg2O47Bo0SJJepPAvyBQq8kLFfKlS5eC4zi0bNlSUg86IiICEyZMgLm5OQwNDTFkyJAcFyW+JTk5Gdu3b0eVKlWEHtzvv/+us7J3fHw8li9fDg8PDxARfH19cfz4cZ17brdv30axYsVgaGiIJUuWiOrxqeee79+/L6pi/vnnnyAi/P777zrbJCcnY8GCBbCysoKJiQmmTJmi89syHz58wNixY2FjYwMiQoMGDXDs2DGdh/lhYWGYOXOmMPdcs2ZNHDx4UPSQ9dy5c8ICbZ8+fRAWFibKXk18fDwKFy6MSpUqSR42AxmjImdnZ3To0AGA+M4JAISEhAhvOPj6+uLVq1ei7NXzzz///LMwhTZ//nydtVDVI8jWrVtDJpPB1NQUAwYM0HltQK2C7uTkBLlcjl9//VVUB+uvv/6CQqFAixYtdK4D6oajaNGiGDhwoM7n+pb/TKDeuHEjiAhDhw6VNDd36dIlmJmZwcjICMOGDROt5nznzh2h9a9Zsyb27NkjKuDdvXsXNjY24DgO7dq1E63CfO7cOcjlcpQtW1bSolfv3r1RpEgRSRX8999/BxGJFiaNjIzE8OHDYWBgAEdHR1Eq4omJidiwYQPKlCkDIkLhwoVFiYumpqZi586dwpBaiop5amoqFi9eDAsLC1hZWUlevLty5QqICJs3b5ZkD2QowctkMskCxYGBgbC2toa1tbXO6y6ZiYmJ0Zh//vPPP0UJxcbHx6NUqVLCFNfy5ctFrQXExMQIHZzOnTuLfhbv3r2DgYEBmjVrJnotAwDatm2L6tWri7ZT868J1KGhoZLeQVaTkJCAHTt2SLaPi4vD+PHjJauAx8fHY8iQIXj06JEk+6SkJIwcORJv376VZJ+amoolS5ZIKmRARm9EbA9KjUqlwl9//SX5FbZ3795hzpw5kmx5nsfly5clv0LH8zxu3LiBMWPGSM5/cHAwZs+erdcrfBcvXtRr8U+lUmX73rAu8DyPxYsX6/W++O+//44rV65Ivg9TpkzB6dOnJc/9L1iwQHL9AzIWEMW+Yqvmy5cvOr9Sqo2cArUoFXJdYSrkDAaDIY48UyFnMBgMxj8PC9QMBoORz2GBmsFgMPI5LFAzGAxGPocFagaDwcjnsEDNYDAY+RwWqBkMBiOfwwI1g8Fg5HNYoGYwGIx8DgvUDAaDkc9hgZrBYDDyOSxQMxgMRj4nq4b6DyQhIYHMzc2/+3mioqIoNTWVXFxc8sxnSkoKRUREUHh4OIWHh1PJkiXJ09NTZ/vk5GR69+4dpaSkZDmMjY2pWbNmJJNpb1cBUFJSEsXGxlJsbCzFxMRQbGwspaenU9OmTUkul2u1S01NpcePH1O5cuXIyMhI42+JiYlkZGRECoVmEQkODqYCBQpo9Xfr1i0yMDCgQoUKkZ2dHXEcl+313r17lwIDA8nU1JRMTEzI1NRU+NnR0ZGsrKxyul0UExNDZ86cIVNTUzIzMxMOc3NzMjc3J3t7+xzts+PFixeUkpJCxYsX/25lMSkpiQCQmZnZd/GfmejoaLKxsflu/pVKJb19+5Y4jqOSJUtK8sHzPCUkJFB8fLzGkZCQQGZmZlS3bt0cyxIRUVpaGr1//56Sk5MpJSWFkpOThZ9lMhm1bNky23pAlFEXgoODKTg4mDiOo+rVq2tN9/XrV1IoFFnK53ePXdltq6fPIXWb0wULFuSqvC2VsLAwrF+/Hg0bNkSNGjUkbwWamePHj6No0aIaAphEhGnTponeplGpVOKXX37R8EN/q1rktPF5cnIyGjdunMXO29sbz58/F9LxPI/AwEDs27cPI0aMQLVq1WBkZIQNGzZk8alSqdCjRw+tW1WuX78e7dq109B5VPPp0ydBW8/MzAze3t5o1qwZfvvttyz7RCcmJmLQoEFZ8l28eHGd94Q+ceKEhoSb+li1apVO9tqIj48XZM8KFiyIunXrYuDAgVi2bJnk7We/hed5NGvWDBcuXMgTf9lx4cIFTJo0Kc/8RUVF4fz581i6dCl69eqFChUqwMjICL6+vnptT/z161dhT/DMR4MGDXTeclWlUmVRsyciVKxYUeu+1CEhIWjTpg3Kli0r7CFPRKhWrVqWc/I8j5s3b6J79+5o0aKF1ro9bdo0xMTESLsBf0P/lv2of/rpJ+zatUuSrTbCw8OxevVq1KlTBzKZDEQER0dHndVWciI1NRUHDhxAgQIFhIdsZWWF48ePi/KjVCpx8eJFDBo0CI6OjoIvAwMDLFiwIMf9iZOTk3HgwAE0atRIo3D26dMni4bh4sWLBdVr9bFy5UqtfqdNm4ZixYpp/Vt6ejrKlCkDjuPQsWNHjcYAyAh0zZs31zhP//79s72Oo0ePws7OTuMe9uzZE6dOndJp0/ng4GA0aNBAsOc4Dg0aNMCWLVskC9CmpqaiW7duWYKGFDmq7FizZg2ICAMGDNBZ/1EMmzdvhkKhEF0ecyI0NFTQnlQfjRs3zlYvUxcCAwMxf/58QTCAiCCXyzF//nydOjs8z+Pu3bsYM2YM3N3dNcrBxIkTs31mERER6NSpk8a19OrVS6MDFxcXh3Xr1gmdD0tLy2zVnjp37qyXeDbwLwnUYWFh4DgOnTp1knqdWYiPj0e/fv2EB6FQKHDlyhW9fL558wbjxo0Tgqpaiql06dI668EplUpcunQJgwYNEsRknZycMGTIEBQpUgQlS5bMVstPvcn9gAEDYG1tLZy7VKlSMDU11Sppn5qaiuXLl8Pc3Fy4F4sXL9bqf9++fSAi1KlTJ9v8X7hwQaNCdOnSRUNwQKlUYvjw4UKDQ0Tw9PTEsmXLtAalzMF29OjRgkqHnZ0d+vXrh/Pnz+fYYKlUKixatAgGBgZo3LgxqlWrBiKCoaEhWrdujX379okOJiqVCmPGjNGoyHXq1MHZs2f1EgdQEx8fLyisu7q64tSpU3r7BDLyPX78eBARjIyMJMnRaePr16+YPHmyRhlq06aNpJHp169fsX79evj6+gplpFWrVnBxcYGHh0euKkE8z+PBgwcYP348PD09hQa+V69eKFOmDNzd3bOt53fu3EGPHj1gZGQEhUIBS0tLyGQyLF26VOO5fvz4Ed7e3hrPPyfR2oYNG8LQ0BABAQGi74eaf0Wg3rp1q3DDxcj3ZAfP89i2bRssLCwgl8tBRPjjjz8k+UpJScHu3btRp04dEBHMzc3Rv39/3Lt3D69fv0anTp1yVXZQB+fBgwdrBOfBgwfj8uXLQiCaO3eu1qASEBCA6dOnC1JHTk5OGDVqFPz9/cHzPMaNG5dFgonneRw8eFCw6d69O2QyWbZKKvfv34eJiYmQNidatWolFODu3btrnbJatWoVunfvjqtXr6JNmzbgOA4WFhYYMWIE3r9/r5FWpVJhyZIlCA4OBs/zuHXrFoYPHy6MWNQN2dWrV7Ptad27d0+Qsnr//j3mzp0ryHSZmZmhW7du8PPzE9UzXrJkCWrVqoWdO3cKFbdixYo4cOCAXmosADBq1CjhHspkMsnlU01iYiLatWsn+GzUqJFe/oAMeavp06fD0tJS0BCtV68efvnlF1H6jklJSdi/fz9atWolNN6+vr5Yv369oKc4dOjQbEdBPM/j0aNHmDRpkqDcbmlpie7du8PPz09oMObPn5/FR1JSErZu3SpMr7i4uGD69On48uUL2rZti9OnT2c5X1pamsZUSqtWrXJsoNW+1XqVUtArUBORGxFdIqKXRPSciIbnZiMlUGcuYPrO3UVERAgq0e3atcORI0fQvXt30T2hFy9eYOTIkcLQvEqVKti4caNGUE5JScnWr1KpxOXLlzFkyBAhODs6OmLw4MG4dOlSrhU9OjoaGzZsQM2aNUFEMDY2RufOnXHy5MksleTbPNy+fRs1atQQ5rnVysjZ9aSDg4NRsGBB4RlMnDgxx7y9efMGBgYGKFq0KIyMjODn56c1XeZppoCAAIwYMQIWFhaQyWRo27Ytrl69muNzUalUuHLlCgYNGiTMRxcsWBAjR47E7du3dXqmT58+xaRJk1C4cGEQEWxtbdG/f3+dpa/8/f2FvBw9elTosRcvXhybN2+WPCXy/v17YTqqSpUqkiWg1Jw5cwZNmjQRnqE+gT8uLg6zZ8+GtbU1DAwMMHDgQGHYr6tUllKpxLlz59CrVy9hHadMmTKYP3++1nWXb58lz/N4+vQppkyZguLFiwudpG7duuHo0aO53q+AgACMHTsWtra2Qj3Yv3+/RkdQW8MQHh6OWrVqQSaTYcyYMbC3t89VuFhdtogI169fzzFtdugbqF2IqMLfP1sQ0Rsi8srJRmygTklJ0RhSjRgxQtKFAhkLTE5OTrC0tMSOHTvA8zzi4uKQlJSkk31SUhK2b98uBEdLS0tROohKpRJXrlzBkCFDBJVrR0dHDBo0SKfgnJaWhuPHj6Njx44wMjISeh6bNm3SabHi/fv36Ny5sxBIjh49qlMwe/78ORYtWiRMGaxZsyZXm7lz5yI2NhaNGzeGQqHAvn37crUBgNjYWCxbtkwYtlasWBG7du3KNeClp6fj3Llz6Nu3r6BAXqhQIYwfPx4PHz7M9Tp5nsft27cxfPhw4dkUKFAAI0aMwJ07d3RuyHmex6VLl4RFR1dXVyxbtkzSNEObNm2wbds2GBgYoFu3bnpNqyiVSlSuXBlFihSBs7OzpMXP+Ph4zJs3D7a2tpDL5ejbty8+fPigsz3P87h//z5GjhwJFxcXEBHc3Nwwfvx4nQWXX7x4gWnTpgnz1mZmZujcuTMOHz6caz1WqVQ4ceIEmjZtCo7jYG5ujsGDB2td/NbGw4cP4e7uDhsbG2Ga6+LFi7naqcujk5MTmjRpIknzMU+nPojoKBE1yCmN2ED99u1bYVHpt99+w5YtW0RfJADs3bsXRITatWuLkohXc+PGDWHet3r16ti2bZuouc0HDx5kCc5iBEv9/f2FXmPRokUxc+bMLFMEOXH9+nUYGhrCzs4OK1euFD2FNGzYMLi4uOD69es4d+5crunVQSUlJQVt27aFTCYTpX6uVCpx6NAhoVEsUKCAzm/9pKam4sSJE+jevTssLCyEhknXBUSlUokLFy5oBP0iRYogIiJC5/wDGdNFHTp0AMdxsLW1FRXUAAhvw2zZsgVEhEOHDomyz8yBAwdARLh48SIuX74s2j4sLAwODg6Qy+Xo3bu36PnWhIQEIbja2Nigf//+uHLlis5BKzk5GWXLlgURwcTEBB06dMDBgwd1roNJSUkoVqwYiAilSpXCqlWrRC3UfvnyBaampvD29sa7d+90tlMqlRg6dCgaNWqEunXrQqVS/dhATUSFiCiIiCy1/K0/Ed0novvu7u6iMwlkzIfp2vPVRmJiItavXy9ZwTguLg6jRo3Cs2fPJNnHx8dj8ODBuHDhgqj5OzVJSUkYOnQobt26JalnlZaWhrlz50p+TSg9PV3rq0y62uoTZO7du4dZs2ZJsk1OTsaRI0ckr7qnpqbi2LFjGD16tOQe7evXrzFjxgy9esRnzpyRXHYBCHP7+rBw4UKdF8W1MWXKFPz111+SX3+dMmUK9u3bJ3kRdN68ebh48aLk57B3717RrxqqzxUSEqLXm0E5BWqdVcg5jjMnoitENAfA4ZzSMhVyBoPBEIfeKuQcxxkQ0SEi+jO3IM1gMBiMvCXXQM1lfLu5mYheAlj6/bPEYDAYjMzo0qOuQUTdiagux3GP/j6afud8MRgMBuNvct2UCcB1Isp5RxQGg8FgfDfYNqcMBoORz2GBmsFgMPI5LFAzGAxGPocFagaDwcjnsEDNYDAY+RwWqBkMBiOfwwI1g8Fg5HNYoGYwGIx8DgvUDAaDkc9hgZrBYDDyObl+Qv5PEhoaSs7Ozt/Nv1KppFevXpG/vz/VrVuXChYsqJe/o0eP0vnz5ylj3yoijuOI4zgyMDCgIUOGUKFChXT2FR8fT23btqXw8HAyNzcXDjMzM6pVqxb17t2bZLKs7WpycjINHjyYoqOjydDQUOMoV64c9e/fn+RyORFl7D0eGxtLwcHB9OXLF/ry5QulpqZS7969ydDQUMNvYGAgubm5ZTnnlStXqESJElqfk5+fH926dYuMjIyyHI0aNdK439evX6etW7dSYmIiJSUlCf+mpaXR9u3bqXTp0jner9jYWFq4cCF9/vyZkpOTKTk5mZKSkig5OZkaNGhAM2bM0PneZ8bf35+OHTtG5ubmZGFhQRYWFsLPnp6e5OHhIclvZlJTU+nx48dUpUoVvX3lBAB69uwZlSlTJk/8paSk0NevX+nr168UFRWl8XPjxo3Jx8dHkt/w8HDq1asXKZVKMjY2JiMjIzI2NiZjY2Nq2LAhtW/fXqhjOeWtc+fOFBgYKNQb9VGlShUaNGiQUA+IMsr3rl27SKVSZTlq1apFzZo105rPa9euUcOGDcnCwkLjb8HBwVSgQAFJ168T2W1Urc8hRTMRACZOnJitHLsUnj9/jrVr16J///6oXLkyjI2NQURYvny53r55nsfNmzcFdRH1Ua5cOTx8+FCUn4CAAGzevBm1a9fW8GVra4t169ZlqxCjFvzs3r27hp2RkRFmzpwpbN6uVCoxcOBAmJqaaqTz9vbWquLx8uVLNG3aVOs537x5AxsbG9SpUwfr1q3TUERRKpVYuHChIF6qPho3bqx1Q/zz588LKu7qo0iRIlizZg1evnyZ6+bv4eHh6NOnj4Y9EWH8+PG4ceOGZJHknTt3akjDqfP1+fNnSf60MXToUNSqVQunTp3KE1XzbwkKCkLz5s1zVM4Wy4MHDwQFIvVhbGwsCApLIT4+HidOnECtWrU0/NrZ2WHr1q063Zvg4GDs3r0bzZo10/Bhbm6OxYsXay0HKSkpqFu3rkZ6U1NTrFq1SiirMTExOHbsGIYPHy6IJG/atElrHqZOnYoHDx5Ivg9AHktx6XJIDdSlS5fWSatPVz59+iRIA6mPtWvXSvaXlJQEPz8/DBw4EK6urhp+DQwMMGvWrFyDA8/zeP/+PbZs2YIePXrA3d1d8OHl5QWZTAaO4zBgwACtslShoaHYuXMnfvnlF0Ew19DQUAiOdevWzaLS8unTJ4wfP14jvy1bttSqZPHkyRM4Ojpi4MCB2V7DkSNHBD9yuRwNGzbEtm3bhALu7+8vKHarjxIlSmDy5Ml49OiRRuVLTU3FggULhEakcOHCguCri4sLunbtik2bNiEgICDbSnvjxg1BwsnW1lZQUjc3N0ezZs2wZMkSPHr0SJR6yps3b1CxYkUh/xzHoXbt2ti4cSOioqJ09pMdMTExgmxbuXLlsGfPHkmqQN+iVCqxcuVKmJubw9nZWbLSyrf4+/tj2LBhgmyZuvFSC//qSnp6Om7evImZM2fC19dXKLeZG4C+ffvmKMkWGhqKvXv3YuDAgShRooRgV7hwYUFntGvXrvjy5YuGXUxMDHbv3o2OHTtmaYhr1KihoTF5+PBhoWOnPmbPnp1tniZNmoSyZct+N4WXfBOoP3z4ACJCkyZNpF6nBv7+/hotLMdxklr+T58+Yd26dWjevLkQAAoWLIgBAwbg+PHjWLx4MapUqZKjfNeHDx+wdevWLIG5VKlSGDRoEPbv34/Q0FAEBwejatWquHfvnmCbnJyM8+fPY9y4cShXrpyG7fDhw3HixAnEx8ejSJEi2L59uxDMeJ7HhQsX0LZtW8jlchgbGwu9gsmTJ2sNWvfv3xcUm7dv357jfRk3bpyQFzMzM9y9e1fj78nJyRgxYgSMjY1x8OBBdO7cGWZmZiAiFCtWDBMnTtQQpA0KCkL79u2xf/9+fP36FYcPH8bQoUM1Ar6Hhwd69eqFHTt2aKibAxkBYNmyZWjZsiVSUlJw+fJlTJ06FTVq1IBCoQARwd7eHh07dsS6devw9u3bXHtrqampGDVqlFBJ1aruhoaGaN26NQ4cOKCXcvju3bs1AoGPj0+W6xLDs2fP8NNPP+kUWHQhMjISK1asgI+Pj6CDOGDAABARWrdurZM+Jc/zePnyJVauXIlWrVoJauSmpqZo3LgxFi9ejMePHyMgIAClS5fWquAdHh6OAwcOYPDgwfDy8hKuz93dHT179sS2bdvw8eNHJCYmwsfHR0Mv8suXL1i7di0aNmwoNAre3t6YPHkyrl69ChMTEyxevDjLqPXy5cuC+DIRYciQITmWl0mTJoGIMH36dN1v8Df8KwL1ihUrhGG7VL00AHj16hU6duwoPMgtW7agTJky2Llzp072KpUKt2/fxpQpU4QCynEcqlatilmzZsHf31/jgT1//jzLQ1YH5p49e8LDwyNLYN63bx9CQ0OznDsxMRFKpRLPnz/HsmXL0KRJE6FxsLGxQYcOHbBp06Yswr0pKSnCFERsbCxWrlwpjCQKFy6MxYsXIzIyEmvXrsWePXu0XveNGzeESkREuSpYp6eno06dOkJv3svLS6tS+/nz54Wee1JSEo4cOYKuXbsKU0ZFihTB+PHjce/ePfA8r1WMVN2DGjBggCBeqg74/fv3x969e4X7qa23GxcXh5MnT2L06NHCM1WXj969e2Pnzp0IDg7O9lpPnDghTBG9f/8ec+bMEQKGpaUlevfujXPnzuksYqyG53nUq1dPyM/SpUtF2WcmMjISXbt2hUwmA1GGOKxYoV4go0d+8uRJdOjQAYaGhpDJZGjcuDH279+PlJQUBAcHY+HChTkGrZCQEOzatQs9e/ZEwYIFQUSQyWSoWrUqpkyZgsuXL2fp6UdHRwuj0cjISKGhLl26tHB/XF1d0b17d2zevFmr6HNycjLS0tLw6tUrzJs3D1WrVhXqb/Xq1bPoQUZERODFixdZ8v7LL7+AiFC+fHkUK1YM7du3z/XZqgO1QqHA48ePc73P2vhXBOqRI0eiYMGCqFmzJs6ePSvpQk+cOAGZTAYnJyesXLlSKAz379/Xyf7evXtwdHQUhs1t27bF1q1btQZVbTx9+hSFChUSClbJkiUxcOBA7N27FyEhIbnaP3v2TJhSkcvlqFmzJmbOnIk7d+7oFAQePHgAc3NzcByHZs2a4eTJkxo955yG1pGRkRg/fjwMDQ3h4OCg09xgaGgoOnTogAcPHqBEiRIwMjLSWYU7OTkZR48eRffu3YUGwtPTUydh0U+fPmHHjh3o3bu3RkPo5eWlUyMfERGBAwcOYODAgRqBv1SpUjqrVqvXB8aOHSs8MxcXF52ec2Zev34NS0tL9OjRA0QkST1czaVLl4R545ymrrIjJiZGuJaiRYti7ty5oublU1NThSko9XTXkCFDcOTIEZ163+np6ahYsWKWqa+NGzfqNAJKT08XAruhoSGaNm2KDRs26PxMoqOjYWtrCysrK6xcuRJKpRKrVq3SadQ0adIkODo6om7dujh58qRO5/uWf0Wg5nkeqampUCqVkhdXEhISsGTJEsk98tjYWAwbNgxnz56VNLcXFxeHAQMGYM+ePaIrLJDR4xw8eDCOHDkiSUk8LS0NU6dO1bpAqAsqlQrv3r3DypUrdbZR3+uEhATs3r1b0nlTUlLg5+eHadOmSbJ///49Nm/ejFGjRkmyDwoKwrZt2zBy5EhJ9iqVCpcvX8bkyZMlld3z58+D53n89ddfei8sXrlyBXPnzsWrV68k2c+dOxfXrl2TnI9JkyZhy5YtWUZ9YuzXrVuHV69eScrD7NmzsW/fPp0b3G/ZsWOHpLr7+vVrREdHix5VZSanQK2zCrkYmAo5g/HjSEtLy/K6JSP/o7cKOYPB+PfAgvT/HixQMxgMRj6HBWoGg8HI57BAzWAwGPkcFqgZDAYjn8MCNYPBYORzWKBmMBiMfA4L1AwGg5HPYYGawWAw8jksUDMYDEY+hwVqBoPByOewQM1gMBj5HBaoGQwGI5/DAjWDwWDkc1igZjAYjHyO4kdn4N/MtWvXKDw8nFxcXMjFxYWcnZ3JxMREkq+kpCSaOXMmOTk5UaFChahQoULk6elJ1tbWOdqlpqbS6tWrydHRkVxdXYXD2NhYUj7yCp7nKTY2liIjI+nr16/k6elJTk5OWdKlpqZSUFAQffz4kQIDA+njx4/UrVs3KlWqlE7nAUCRkZEUGBhIgYGBFBQURJaWlvTrr7/myXUAoMTERIqNjSWFQqH1Gv4rpKamkoGBAclkedu/i4yMpHXr1pGrqyu5u7uTu7u76DKcnp5OM2fOJBsbG6H+FCpUiGxsbIjjuCzpv3z5Qo8fPyZHR0fh+NF1JkeyUxTQ55CqQv78+XO9FS4yc+zYMcydOxeTJ0/G8OHD0bdvX3Tp0gX9+vXTWV4rJ548eQJDQ0MNgVIrKyu0bt0aHz9+1NlPcnIyHj16hLZt22r4IiI4Oztjy5Yt2Spox8bGom/fvlrtVq9eDaVSifT0dHTu3BnFixeHt7c3fHx8ULlyZVSvXh2zZs3KopzM8zz8/Py0PoujR49i0qRJ2L59O27fvq0hsXTy5En4+PjA3t5e0O4jIlSqVCmLnNGNGzdQvnz5LPkuUqQI7t27h8TExBzvWWxsLIYOHSpoSmY+Vq9eLUkvEAAeP36M+vXrw9PTE7a2tpDL5YI24rf6elJJTU3F0qVLcezYMZ0kqqTA8zxu376NEydO5JnPq1evwsjICCYmJrC3t4eHhwe8vLzQuHFjPH36VHI+P378qKEdqT4KFy6MgwcP5hoT0tLS8Pz5c/Tq1SuLDzs7O0FWKzPp6emoXLmyRlpLS0uUK1cOJ0+eRHBwMPz9/XHq1Cls3boV8+bNw/DhwzFx4kStClIPHz4UpXKvDdJHiouIthBROBE9yy0t9AzUgwcPxvPnzyVeZlYePXqURfK9UaNGkoJ0dHQ0Lly4gIULF6JTp04oWrRolkLh6uqKTZs2ZatNmJKSgsePH2P37t2YPHkyWrdujeLFi2sENfVhYmKCUaNGCaKrapmsQ4cOYerUqWjVqpWGPqP6kMlk6NGjhyBOGxsbiyNHjqBDhw5ZCnBm1XI1Hz9+RKNGjTBp0iSt16BSqTB27FgNX05OTmjatCnCwsIQFBSE7t27a/yd4ziUKVMG/fr1w5YtW/DixQuoVCokJiZi2bJlcHZ2znIdHMehSJEiaNWqFSZPnozdu3fjyZMnWRqV69evo0GDBlns1Y1V/fr1MXLkSGzevBl37tzRSaYtKioKw4cPF4I0/a3B5+vri8mTJ+PUqVOSpZ7UPH/+HDY2NpDJZKhYsSJGjx6N48ePS5Jgy0xYWBiWLFkCLy8vFCxYEJGRkXr5A/5f6iyzvqX6GfXr10/nRjEmJgbXr1/HmjVrMGjQINSoUUPDn/owNTXF2LFjER4ermGfnp6OV69e4dChQ5g5cyY6deqE0qVLC+rimQ8DAwMMHDhQ6DBFRkbi1KlTmDFjBpo3bw4nJyeN9HK5HAMGDBDq29KlSwXtRvXRoEGDbO/nzp07MXr0aD3usv6B2peIKnzvQM3zPNzd3bFgwQI9LhWIj4/Hrl270KxZMygUCo0Ht2TJEp1ava9fv+LcuXOYP38+OnTogMKFC2tU2IoVK6J///5Yt24d6tevDzs7OyxZskToNaakpODJkyfYs2cPpkyZgjZt2mQJyDY2NqhZsyYGDBiAP/74A+fPn8fhw4dhZmaGESNG4MSJE1i3bh0GDRqE6tWrw9zcXKPlr1mzJoYMGYL169fj0KFD4DgOnTt3xvPnz3H//n3Mnj0bP//8s3APXF1dYWNjAyJC9+7ds1QClUqFlStXwszMDAqFIldR0yVLlmgU4l69emn0Du/fv49atWqBiDB06FB0795do3GztrZGo0aNMG3aNBw9ehTz5s1DgQIFMG7cOFy5cgVr1qzB4MGD4evrC1tbW40KVapUKXTo0AHTp0/HwYMH8erVK1y+fBkNGjSAo6MjHjx4gB07dmDs2LFo0qSJINiqDi6FCxcWGoC9e/fi2bNnggJ2Zp4/f46GDRuCiNCsWTM0adJECCwymQw+Pj4YOnQo9u3bhy9fvuhQOjW5desWTE1NNZ7rtm3bRPsBMurPtGnThOfNcRwuXLggyVdQUBC2b9+OXr16ZREP/vnnn4VR0p07d7Tap6Wl4dmzZ9izZw8mTZqE5s2ba/jhOA5FixZFmzZtMG3aNBw8eBCrV6+GiYkJRo8ejeDgYLx9+xZ//fUX5syZg65du6JcuXIwMjISfJiZmaFy5cro1asXFi9ejFOnTuH8+fMwNDRE//79ceDAASxZsgSdOnXSqL9mZmbw9fXF6NGjhQ5H27ZtBY3JtLQ0HDhwIEsPf+zYsTmKQ9+4cQNEhFWrVkm650AeiNsSUaHvHahfv34Nd3d39OnTR/KFXr16VRgKly9fHgsXLsS8efNQvHhxPHjwIFf7x48fa/RSjYyMULlyZQwcOBAbN27EgwcPsvToNm3aJPSuXr58iRIlSmj0xKytrVGjRg30798ff/zxB86dO4fg4GCtw7njx4/D09NToyUvXLgw2rRpg+nTp+PIkSN4//59FtsnT57g6dOnePnyJRwcHECUoUTdqFEjLF26VJhSqlOnDs6dO6f12tPT07Fq1SoQEbp06aLT/d61axcUCgUKFCgAhUIBa2trfP36Vfg7z/M4evQo/Pz8hN9FRETg+PHjmDx5MurVq6fRAJUsWRLnz5/Pch6e5xESEoJz585h+fLl6Nu3L3766SdYWFhoNKA+Pj64dOkSoqKisviIjo7GtWvXsHbtWqEBUDdc6oa8TJkySEpKynLu48ePCxVQqVTi8ePHWLVqFTp37oyCBQtqPCtt586JU6dOCcFVLpdLnkIAgMDAQKEzMG7cONH2KSkpGqrsxYsXx4ABA7B3715hFLps2TKsX79eq4irSqVCpUqVNKYDbW1tUbt2bQwbNgwbN27MdlRz8+ZNBAcHo3r16hrTWSYmJqhYsSJ69OiBBQsWwM/PDx8+fNDa4Xr+/Dl8fHyEe6BQKFCxYkUMHDgQW7ZswdOnTzXyfeLECdy8eVPj+t3c3EBEqFChAjp16gQTExPs2bMn13sXEhICQ0NDtG/fXrK49j8SqImoPxHdJ6L77u7ukjKqL3FxcZg2bRpevnwp/O7hw4eIj4/Xyf7r168YPHgwNm/ejEePHmntZeVEdHQ0+vXrh+XLl+Ps2bP48uWLqDn3+Ph4DBgwAGvWrMGNGzdED69TU1MxZswYnD59WmvA+fZ338LzPC5cuJBtT0kbZ86cwbhx4xAYGIgNGzaIyi+QEfiePHmC9evXY+LEiaJseZ5HYGAgTp48iYULF2L48OGi7b98+YLTp09j8eLFGDp0qCh7tY8PHz5gx44dGDNmjGh7APjzzz9RoEABbNu2Te81mvPnz6NKlSpZOhS6MnXqVOzatSvbEUJuI9Jx48Zh4cKFOH36tOjyDwATJkzA3LlzcfToUQQEBIie9502bRpWrFiB27dvZ1kX0YV169bh3r17ADI6Tv7+/jrZ8TyPN2/e6PX8cgrUOqmQcxxXiIj8AJTONTExFfL/GsnJyZLfdmFkcPbsWWrYsGGe+AoPDydHR8c88cX452Aq5IzvCgvS+pNXQZqIWJD+H4QFagaDwcjn5BqoOY7bQ0S3iKgEx3GfOY7Lmy8JGAwGg6ETuX6ZCKDLP5ERBoPBYGiHTX0wGAxGPocFagaDwcjnsEDNYDAY+RwWqBkMBiOfwwI1g8Fg5HNYoGYwGIx8DgvUDAaDkc9hgZrBYDDyOSxQMxgMRj6HBWoGg8HI57BAzWAwGPkcFqgZDAYjn8MCtR68ffuWgoODSRfxhdxQKpV04sQJCg0NFWXH8zxdvnyZoqOjs02D/1fh+SGEhoZSbGxsrukSExPp2rVrFBwcLOk8AOjdu3d08+ZNSfY5kZqaSlFRUXnu999EYmLidylHCQkJdPnyZYqLi5Psg+d5OnXqFH358kWn9DExMfT+/fsfWi/EkOvuef9mNmzYQDdu3CB3d3fy8PAgDw8Pcnd3J3d39zzZ7D4tLY28vb3J1taWKlSooHF4eHgQx3E6+5LJZHTkyBFq3rw5ubm5UZUqVYSjYsWKZGFhka3dX3/9RXXq1KGiRYtSpUqVhKNChQqCXefOnSkgIIBKlixJJUqUEP4tVqyY6HsBgBISEig2NlbjKF68OCUlJdHjx481DltbW3r06JGGj/T0dHr27BndvXuX7t27R3fv3qXnz59T0aJF6cmTJzrlIzg4mO7du6dxREdH09GjR0VdT2Z4nqfAwEB6+vSpxvH582d68OAB2draSvb9b+fNmzdUv359KlasGHl5eZG3t7dwuLq6iirvmTE3N6dFixbRqVOnqESJElS5cmWqXLkyVapUiXx8fHQqnxzH0cWLF6lp06ZUoEABjfpTqVIlsrKy0khvampKP//8M3369Il8fHzIx8eHypcvTz4+PlSqVClau3YtKZVKcnZ2JicnJ3J2diZnZ2eytbUlmeyf79/qJMUlFqlSXFevXqWaNWvqfSOio6Pp2bNndOvWLRo/frzG3xQKBf366680a9YscnBwyNZHSkoKhYSECEdwcLDWnyMjIzXsbGxsqHfv3jR8+HCysLCg8PBwioiIoIiICOFnbb+LjIwkpVKZJR/VqlWj+vXrU+HChSkiIoJCQ0MpLCxM49+vX79msatYsSI1aNCAXF1d6f3793T37l26fv26Rhp3d3caM2YMDRw4kAwMDCg+Pp5u3rxJly9fJgcHBxo5cmSWynfhwgUaMGAABQQEaPzex8eH5s+fTytWrKCTJ09q/M3CwoKKFi1Krq6u5OrqSm5ubpSWlkY3b96kK1euUGpqqkZ6mUxG9vb25OjoqHE4ODgIP5uZmdH58+fp4MGD9P79+yzXb21trWHr5OSk9WdHR0eytrYWrvPFixc0f/582rt3L6Wnpwv+TExMqHz58kJDn7nRd3d3J2tr6yx5yA6lUkmLFi2iFStWkEqlIqKMxs/U1JSOHz9OZcuW1dlXZt6/f0/Lly+nyMhIioyMpKSkJJo9ezbVrl1blB8AFBUVRR8+fKCPHz9q/Hv16lVKTEwU0ioUCurcuTNNmjSJSpUqpeEjJiaGwsLCNI7w8HCt/09KStLIA8dx1KhRI+rUqROVKFGCIiMjs9Sf8PBwjbqUlpaW5VrKlStHtWrVIk9PTwoJCaEvX75QcHAwffnyhYKCgiglJUXjnM2bN6cBAwbQ06dPaeLEiRq+jIyMaPTo0TRlypQsjceDBw8oLCyMmjZtKupef3PN2Upx5atAPXLkSOrbty95e3tLOq+/vz+1aNFCGP5wHEcKhYLS09PJwMCA+vTpQxMnTiQPDw+t9q9evaJ27dpRSEhIlqkEjuPI0dGRXFxcqECBAuTi4kIuLi60bds2+vz5M1WpUoXat29PO3fupMjISIqIiNAaeC0tLYWAk/lfBwcHevjwIe3atYtkMhnxPJ/F1tjYWGjdM/8bFRVFq1evJgsLC5LL5ZSQkKBxbldXVypUqJAQqH18fGjs2LHUoUMHMjAwENLduHGDatasSfXr1yc/Pz8yMjLSep+Sk5NpyZIlNG/ePI0K5unpSa9fv6bAwEBas2YNbdmyhWJjY6lu3brk6upKnz9/ps+fP9OnT58oOTlZw6dcLieVSkV16tSh1q1bC5Uwc2UMDw/XOjxWKBRkYGBAKSkpJJPJaMmSJRQbGysEAvURFhamdYrIwMBACNp3794lhUJBoaGhtG7dOlq7di2Fh4eTl5cXlS5dmoKCgigwMJBCQkKyPFd3d3e6e/euziOUGzduUJ8+fejNmzdERGRra0uvX78me3t7ney/JTk5mVxdXSkqKoqKFi1Khw4dEhX0AVClSpXo7du3FB8fL/ze0NCQPDw8yNPTk96/f0/v3r0jGxsbGjhwIA0ZMoQKFiwopP3555/pw4cPFB4ertHQEWXUITs7O3JychIaS/XPT548ob1795KRkREZGxtTSkpKlgacKKMn/G2jrf45NDSUFi9eTBzHZZnSkMlk5OzsTAULFqQCBQpQwYIF6e3bt3Tu3Dmyt7envn37Uv/+/alDhw704MGDLLa9e/em6dOnk6urq9Z7p1KpyMvLi+bNm0dt27bV+Z5/c3/+HYFaXyIiImjevHlUpkwZKlOmDJUqVYqqVq1KNWvWzDFAqwkPD6cpU6ZoBGL1z05OTqRQaM4URUVF0aRJk6h///5UoUIFioiIoIkTJ2YJwJl/zi74ERGtXLmS/Pz8qGLFilSgQAGhEKsDsqWlpdbh5fbt2wkANW/enKZNm0ZFihShokWLUtGiRcnT05NMTEzowoULtGDBAho7dizVr19fqx8AtGrVKurduzeZm5vner8/f/5MEyZMoD///JMmTpxIFhYWGr2QhIQE+vPPP+njx480b948jfPExMTQp0+fNIJ3QEAAFStWjKZPn57tMDolJSVLb0r9c2RkJJUuXZpq1qxJVapU0WqflpYm9M7UwVv9c1xcHK1du1YjfWpqKu3bt49OnDhBe/fuFfKVmpoq9MrUwTs0NJRWr16d633LTHJyMk2fPp0WL15Mffv2pXXr1kmeQiAimjx5Mj1//py2bdsmqpevZty4cWRpaUmFChUiT09PKlSoELm4uJBMJiMA1Lp1a2rcuDH16NGDzMzMstiPGjWKFApFlkDs5ORE9vb2WeqQmqVLl1KJEiXo4sWLQqfo22Ds4OCg9Zxqdu/eTQcOHKDixYuTu7s7FSxYUAjM2urv5MmTqWTJktShQwcyNjYmIqKFCxeStbU1VahQgRo3bky+vr40Z84cjdFCdgQHB5Ozs7PkGYH/TKD+lrS0NAoJCck1QP8XSElJEQpjXnPz5k1KSkqi+vXrfxf/+QF1PdEniObEvXv3KCEhgerUqaOXn7CwMHJwcPgu86jqRekfMUf7TxMdHU2vX7+matWq/WPn/M8GagaDwfi3kFOg/t9vGhkMBuNfDgvUDAaDkc9hgZrBYDDyOSxQMxgMRj6HBWoGg8HI57BAzWAwGPkcFqgZDAYjn8MCNYPBYORzWKBmMBiMfA4L1AwGg5HPYYGawWAw8jksUDMYDEY+hwVqBoPByOewQM1gMBj5HBaoGQwGI5/DAjWDwWDkc3QK1BzHNeY47jXHce84jpvwvTOVV0RHR39XOfjY2FhBnDQv0Kbnl1d2Un3rSmJiolaNOyKi+Pj4LPp52ZGcnJxFT1EMAL7LtaalpVFCQkKe+/03IeY5ioHneYqJidHbj5jnnp6enuPz/N71RSy5BmqO4+REtJqImhCRFxF14TjO63tnLC949uwZubu706BBg+jkyZMaisN5QVhYGLm5uVHv3r3p8OHDelfkkSNHUo0aNWjevHn07NkznRuZ33//napWrUqzZ8+mR48eabX7448/yMfHh37//Xe6e/euVvFcsY3a58+fae3atdS0aVPy9fXV0KQLDAykVatWUaNGjahx48bZauUREYWEhNDGjRupZcuWVKlSJZLL5aLykZaWRhcuXKARI0ZQ0aJF6eLFi6Lss+Pr16+0a9cu6tSpExUrVkyrWLG+AKAHDx7Q9OnTaeDAgXp3LPz8/Cg0NDSPcqdJamoqFSlShLp06UJ//vknRUVF5YlfmUxGXbt2pbp169KyZcvo3bt3kvzMmDGDKleuTDNnziR/f/8c76VCoaAGDRpQ48aNafXq1RQYGKjx971791KpUqVo3LhxdO3ate/y7MWQqxQXx3E/EdF0AI3+/v9EIiIA87KzkSrFNXHiRGratCn9/PPPom2JiF6/fk2//PKLxu8ePnwoBCVTU1Nq0KABNW/enNq0aUN2dnYaaQMCAqhz586izvns2TOhATAwMCATExOytrYmGxsbDYVvXQgPD6egoCDh/4aGhmRtbU3W1tZkaWmZrV1sbCy9fftW+L+BgYFgZ2VlRUQZPd6XL18KaZydnalZs2bUokULatq0KRkYGFBCQgLVqlWLateuTS1btiRfX98sGoGPHj2iw4cP0/Hjx+nRo0fC752cnMjd3Z0aNGhAx48fp6dPnwp/c3Fx0VCqJsroOUdHR1NMTIyGkrm9vT0VKlQo13ulUqkoJiaGYmJiKC4uTmNkU6FCBUm6fnK5nP788086dOgQ+fn50Y0bN4SyY2lpScWLF8/Vx927d3PVVVQqlXTq1Ck6fvw4nThxgoKDg4mIqHjx4vTs2TPR5SYzlSpVogcPHlDlypWpRYsW1LZtW/L29tbZvk6dOjl2OF6/fi0olMvlcqpRowa1aNGC2rVrR56entSgQQNJveOwsDD69OmT8H9jY2OhDOsitExEFBcXJyi6E2mvB5n58uWLhpp82bJlKSUlheRyORkYGNCTJ0+Ev9na2lLTpk2pRYsW1KJFiyxK81+/fqUjR45Q+/btJYkKE+UsxZV9N+f/KUhEnzL9/zMRVdVykv5E1J+IyN3dXUI2Mwqqg4ODJFsiIhMTE41CyfM8PXz4UPh/kSJFyNvbm0qXLk02NjZZ7I2NjUUVaiLSCH6urq5kYmJCbm5u5OjoKDpYyGQyIVDLZDJydHQkNzc3cnV1JVNT02ztAgMDhUAtk8nIwcFBsFMX8rCwMCGvCoWCvLy8yNvbm7y9vYXAoFAohN+VKFFCa8ApVKgQeXt704cPHygoKEjoVVlbW1OZMmXI29ub3r9/T0FBQRQbG0tEGYX82/uanp5OwcHB9OnTJ/ry5YvQ2FlaWur0DABQVFSUoGD+9etX4W/FixfPUe09O2QyGRUoUIBKly5NHz58oA8fPtDnz5+JKGvZ0gf1fX7//j19+PCBIiIiKD09nWxtbfUWzzUxMSFHR0cqXbo0eXt7i66LpUqV0mg4vyUwMFAI1Op75e3tTS4uLkREVLJkSeHvYuB5XgjUahVzV1dXcnV11VmUOSgoSAjU6nrg6upKbm5uWoN9UlKSEKitra3J29ub4uLiyNLSkpKTk4VALZPJqFSpUsK1asuP2l5KudMJtbJwdgcRdSCiTZn+352IVuZkU7FiReQHTpw4gUaNGmHVqlX4+PFjnvu/f/8+atWqhcWLF+P169d6++vTpw/69euHY8eOITExUWe73377DX369MHhw4cRHx+vNc2UKVPQrVs37N27F9HR0XrnFQDS09Nx9epVjB07FvXq1UNycrLwt7S0NFy6dAmjRo1Co0aNkJaWlq0fpVKJmzdvYuLEiahVqxbi4uJE5+XLly/YuHEjWrZsiW3btkm6nm/heR4PHz7EzJkzUbNmTXz69ClP/H5LTEwM9u/fj+nTp4Pneb18PX36FCqVKo9ypsnHjx9Rs2ZNzJo1C48ePdI7r2p4nkebNm0wdOhQnD17FikpKZL8DB8+HL169cKhQ4dyLUOpqalo1KgRRo8ejUuXLmUpn8uWLUP79u2xfft2RERESMqPWIjoPrKJqflq6iOvAaB3D+Wf9C/Vny523/te5HYOMefXN6/f61r/iXuYn/me95WI9Padl2XsRzxrfVXI7xFRMY7jPDmOMySizkR0LC8z+L343jc6r/1L9aeL3T9R6HI6h5jz65vX73Wt/+UgTfR972te+M7LMpbfnnWuc9QAlBzH/UZEZ4hITkRbADz/7jljMBgMBhHptphIAE4S0cnvnBcGg8FgaIF9mchgMBj5HBaoGQwGI5/DAjWDwWDkc1igZjAYjHxOru9RS3LKcRFEFJhrQu3YE1FkHmbn3wa7fnb97Pr/m3gA0Ppp9ncJ1PrAcdz97F76/i/Arp9dP7v+/+71Zweb+mAwGIx8DgvUDAaDkc/Jj4F6w4/OwA+GXf9/G3b9jCzkuzlqBoPBYGiSH3vUDAaDwcgEC9QMBoORz8k3gfrfKqCbF3Ac58Zx3CWO415yHPec47jhPzpPPwKO4+Qcx/lzHOf3o/PyI+A4zprjuIMcx736uyz89KPz9E/CcdzIv8v/M47j9nAcp5u0y3+AfBGo/80CunmEkohGAyhFRNWIaMh/7PrVDCeil7mm+t/lDyI6DaAkEZWj/9C94DiuIBENI6JKAEpTxpbK4gRM/4fJF4GaiKoQ0TsA7wGkEdFeImr1g/P0jwEgBMDDv3+Op4wKWjBnq/8tOI5zJaJmRLTpR+flR8BxnCUR+RLRZiIiAGkAYn5opv55FERkwnGcgohMiSj4B+cn35BfArU2Ad3/VKBSw3FcISIqT0R3fnBW/mmWE9E4IuJ/cD5+FIWJKIKItv49/bOJ4zizH52pfwoAX4hoMREFEVEIEcUCOPtjc5V/yC+BWpvuzX/uvUGO48yJ6BARjQAQ96Pz80/BcVxzIgoH8OBH5+UHoiCiCkS0FkB5Ikokov/MWg3HcTaUMYr2JKICRGTGcdwvPzZX+Yf8Eqg/E5Fbpv+70n9s2MNxnAFlBOk/ARz+0fn5h6lBRC05jvtIGdNedTmO2/Vjs/SP85mIPgNQj6QOUkbg/q9Qn4g+AIgAkE5Eh4mo+g/OU74hvwTqf62Abl7AZShpbiailwCW/uj8/NMAmAjAFUAhynj2FwH8p3pTAEKJ6BPHcSX+/lU9InrxA7P0TxNERNU4jjP9uz7Uo//QYmpu6KSZ+L1hArpUg4i6E9FTjuMe/f27SX9rVTL+Owwloj//7qy8J6LePzg//xgA7nAcd5CIHlLGW1D+xD4nF2CfkDMYDEY+J79MfTAYDAYjG1igZjAYjHwOC9QMBoORz2GBmsFgMPI5LFAzGAxGPocFagaDwcjnsEDNYDAY+Zz/A5Ey4sGfK9Y1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define functions of the vector at every (x,y) location\n",
    "def Uflux(xmatrix,ymatrix):\n",
    "    return np.sin(xmatrix)*2\n",
    "    \n",
    "def Vflux(xmatrix,ymatrix):\n",
    "    return ymatrix/3\n",
    "\n",
    "#assing them\n",
    "U=Uflux(X,Y)\n",
    "V=Vflux(X,Y)\n",
    "\n",
    "plt.quiver(X,Y,U,V,scale=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui esta la funcion que usaremos para applicar la Exner equation al flujo, para encontrar los lugares donde haya erosion o deposicion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exner function\n",
    "def ExnerCalculator(X,Y,Uq,Vq,eps=1,dt=0.1):\n",
    "    \"\"\"\n",
    "    ExnerCalculator takes in the X and Y locations and the vector field defined by U and V and returns the\n",
    "    deposition/erosion happening at the current time.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X: matrix \n",
    "        x locations of the flux\n",
    "    Y: matrix \n",
    "        y locations of the flux\n",
    "    Uq: matrix \n",
    "        first components of the flux\n",
    "    Vq: matrix \n",
    "        second components of the flux\n",
    "    eps: float\n",
    "        1-porosity of the bed\n",
    "    dt: float\n",
    "        time delta for the method\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    Xs: matrix \n",
    "        x locations with n-1 locations corresponding to dh\n",
    "    Ys: matrix \n",
    "        y locations with n-1 locations corresponding to dh\n",
    "    dh: matrix\n",
    "        The difference in elevation in dt time units based on the flux\n",
    "    \"\"\"\n",
    "    m,n=X.shape\n",
    "    dx=X[0][1]-X[0][0]\n",
    "    dy=Y[1][0]-Y[0][0]\n",
    "\n",
    "    #gradient\n",
    "    dUq=[ [Uq[j][i+1]-Uq[j][i] for i in range(n-1) ] for j in range(m-1) ]\n",
    "    dVq=[ [Vq[j+1][i]-Vq[j][i] for i in range(n-1) ] for j in range(m-1) ]\n",
    "    \n",
    "    #exner\n",
    "    dh=(-dt/eps)*(dUq/dx+dVq/dy)\n",
    "\n",
    "    #new Xs and Ys\n",
    "    Xs=np.array([ [X[j][i] for i in range(n-1) ] for j in range(m-1) ])\n",
    "    Ys=np.array([ [Y[j][i] for i in range(n-1) ] for j in range(m-1) ])\n",
    "\n",
    "    return Xs,Ys,dh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ahora le pasamos nuestro campo vectorial a esta funcion y graficamos un heatmap de erosion/deposition (los valores de $\\Delta \\eta$ ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use the function on the flux\n",
    "newx,newy,deltah=ExnerCalculator(X,Y,U,V)\n",
    "\n",
    "#plot the heatmap\n",
    "erosion=pd.DataFrame( {\"x\": newx.flatten() ,\"y\":newy.flatten(),\"erosion\": deltah.flatten()} )\n",
    "erosion_table=erosion.pivot_table(columns=\"x\",index=\"y\",values=\"erosion\")\n",
    "sns.heatmap(erosion_table,cmap=\"RdYlBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos el quiver una vez mas para darle sentido a esto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.quiver(X,Y,U,V)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Homework\n",
    "1. Plot the vector field $q=(U,V)$ in the domain from $-1<x<1$ and $-1<y<1$ given by\n",
    "     $$U=-X$$\n",
    "     $$V=-Y$$\n",
    "\n",
    "2. Use the `ExnerCalculator` function to display an erosional/deposition heatmap for the change in elevation $\\Delta \\eta$\n",
    "\n",
    "3. Given the data of the three variables $weight, height$ and $age$ below, find the correlation matrix between them and make a heatmap of the correlation matrix. (check the correlation example in __[here](https://python-graph-gallery.com/90-heatmaps-with-various-input-format)__  )\n",
    "\n",
    "4. Create a repository and put it up on your github."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight=[162.22, 156.12, 169.31, 179.76, 173.12, 165.66, 166.61, 170.91, 156.67, 170.94]\n",
    "height=[7.8954, 7.7507, 7.7312, 7.9815, 7.8738, 7.6802, 7.7422, 7.8297, 7.8992, 7.9033]\n",
    "age=[32, 33, 22, 45, 33, 49, 36, 20, 41, 25]"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "135e24ee289c60ba69d5004678bf472a320b24bde767457479ce4e07bf7a1633"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}