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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fonones en Grafeno\n",
+    "\n",
+    "En el presente notebook se presenta una implementación de la teoría de fonones mediante matriz dinámica en el grafeno. Se calculan las bandas de dispersión considerando primeros vecinos de una celda primitiva con dos átomos. La base de la teoría aquí presentada se encuentra en el libro de Kaxiras, E. (2019) Quantum Theory of Materials, capítulo 7."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Análisis analítico"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Análisis numérico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importamos las librerias necesarias\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Definimos la matriz dinamica\n",
+    "def D(Kx, Ky, Kz, params):\n",
+    "    '''\n",
+    "    Esta función calcula la matriz dinamica de un cristal hexagonal\n",
+    "    para un vector de onda K = (Kx, Ky, Kz). La notación de las componentes\n",
+    "    de la matriz dinamica se puede comprender en la forma:\n",
+    "    D_0x,0x = D_0,0, D_1x,1x = D_3,3, D_0y,1z = D_1,5, etc.\n",
+    "    '''\n",
+    "    #Definimos las constantes del problema\n",
+    "    cx, cy, cz, m, a1, a2 = params\n",
+    "\n",
+    "    K = np.array([Kx, Ky, Kz])\n",
+    "    dinamic = np.zeros((6,6))\n",
+    "    dinamic[0,0] = 3*cx/m\n",
+    "    dinamic[1,1] = 3*cy/m\n",
+    "    dinamic[2,2] = 3*cz/m\n",
+    "    dinamic[3,3] = 3*cx/m\n",
+    "    dinamic[4,4] = 3*cy/m\n",
+    "    dinamic[5,5] = 3*cz/m\n",
+    "    dinamic[0,3] = -cx/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+    "    dinamic[1,4] = -cy/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+    "    dinamic[2,5] = -cz/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+    "    dinamic[3,0] = -cx/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+    "    dinamic[4,1] = -cy/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+    "    dinamic[5,2] = -cz/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+    "    return dinamic\n",
+    "\n",
+    "#Definimos una función para calcular los valores propios de la matriz dinamica\n",
+    "def eigenvalues(Kx, Ky, Kz, params):\n",
+    "    '''\n",
+    "    Esta función calcula los valores propios de la matriz dinamica para un\n",
+    "    vector de onda K = (Kx, Ky, Kz)\n",
+    "    '''\n",
+    "    dinamic = D(Kx, Ky, Kz, params)\n",
+    "    squared = np.linalg.eigvals(dinamic)\n",
+    "    return np.sqrt(squared)\n",
+    "\n",
+    "#Definimos la función que calcula la disperción en el camino Gamma-M-K-Gamma\n",
+    "def dispersion(params, b1, b2):\n",
+    "    '''\n",
+    "    Esta función calcula la disperción en el camino Gamma-M-K-Gamma\n",
+    "    '''\n",
+    "    #Definimos los puntos del camino\n",
+    "    Gamma = np.array([0,0,0])\n",
+    "    M = b2/2\n",
+    "    K = np.linalg.norm(b1+b2)/2*np.array([1,1/np.sqrt(3),0])\n",
+    "\n",
+    "    #Definimos el número de puntos en cada segmento\n",
+    "    n = 100\n",
+    "\n",
+    "    #Definimos los vectores de onda en cada segmento\n",
+    "    GM = np.array([np.linspace(Gamma[i], M[i], n) for i in range(3)])\n",
+    "    MK = np.array([np.linspace(M[i], K[i], n) for i in range(3)])\n",
+    "    KG = np.array([np.linspace(K[i], Gamma[i], n) for i in range(3)])\n",
+    "\n",
+    "    #Calculamos los valores propios en cada segmento\n",
+    "    GM_eigenvalues = np.array([eigenvalues(GM[0,i], GM[1,i], GM[2,i], params) for i in range(n)]).T\n",
+    "    MK_eigenvalues = np.array([eigenvalues(MK[0,i], MK[1,i], MK[2,i], params) for i in range(n)]).T\n",
+    "    KG_eigenvalues = np.array([eigenvalues(KG[0,i], KG[1,i], KG[2,i], params) for i in range(n)]).T\n",
+    "\n",
+    "    #Cambiamos los nan por ceros\n",
+    "    GM_eigenvalues = np.nan_to_num(GM_eigenvalues)\n",
+    "    MK_eigenvalues = np.nan_to_num(MK_eigenvalues)\n",
+    "    KG_eigenvalues = np.nan_to_num(KG_eigenvalues)\n",
+    "    \n",
+    "    return np.array([GM_eigenvalues, MK_eigenvalues, KG_eigenvalues])\n",
+    "\n",
+    "#Definimos una función para graficar la disperción en el camino Gamma-M-K-Gamma\n",
+    "def plot_dispersion(dispersion):\n",
+    "    '''\n",
+    "    Esta función grafica la disperción en el camino Gamma-M-K-Gamma\n",
+    "    '''\n",
+    "    \n",
+    "    #Separamos los caminos\n",
+    "    GM = dispersion[0]\n",
+    "    MK = dispersion[1]\n",
+    "    KG = dispersion[2]\n",
+    "\n",
+    "    #Inicializamos la figura\n",
+    "    fig = plt.figure(figsize=(10,10))\n",
+    "\n",
+    "    #Graficamos la disperción para cada uno de los 6 modos\n",
+    "    colors = ['r', 'g', 'b', 'c', 'm', 'y']\n",
+    "    for i in range(6):\n",
+    "        plt.plot(np.linspace(0,1,100), GM[i], str(colors[i])+'.', markersize=2)\n",
+    "        plt.plot(np.linspace(1,2,100), MK[i], str(colors[i])+'.', markersize=2)\n",
+    "        plt.plot(np.linspace(2,3,100), KG[i], str(colors[i])+'.', markersize=2)\n",
+    "\n",
+    "    #Lineas verticales para separar los caminos\n",
+    "    plt.axvline(x=1, color='k', linestyle='--')\n",
+    "    plt.axvline(x=2, color='k', linestyle='--')\n",
+    "        \n",
+    "    #Agregamos las etiquetas\n",
+    "    plt.xticks([0,1,2,3], ['$\\Gamma$', 'M', 'K', '$\\Gamma$'])\n",
+    "    plt.ylabel('Frecuencia (Hz)')\n",
+    "    plt.title('Dispersión en el camino $\\Gamma$-M-K-$\\Gamma$')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 6, 100)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "disp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:20: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[0,3] = -cx/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:21: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[1,4] = -cy/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:22: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[2,5] = -cz/m*(1+np.exp(-1j*np.dot(K,a1))+np.exp(-1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:23: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[3,0] = -cx/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:24: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[4,1] = -cy/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:25: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  dinamic[5,2] = -cz/m*(1+np.exp(1j*np.dot(K,a1))+np.exp(1j*np.dot(K,a2)))\n",
+      "C:\\Users\\nicom\\AppData\\Local\\Temp\\ipykernel_13392\\234641334.py:36: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  return np.sqrt(squared)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Definimos las constantes del cristal hexagonal y los vectores de la red y de la red recíproca\n",
+    "a = 1 #constante de red, Angstrom\n",
+    "cx = 1 #constantes de resorte, N/m\n",
+    "cy = 1\n",
+    "cz = 1\n",
+    "m = 1 #masa, kg\n",
+    "\n",
+    "a1 = np.array(a/2*np.array([np.sqrt(3),-1,0]))\n",
+    "a2 = np.array(a/2*np.array([np.sqrt(3),1,0]))\n",
+    "\n",
+    "b1 = 2*np.pi/(a*np.sqrt(3))*np.array([1,-np.sqrt(3),0])\n",
+    "b2 = 2*np.pi/(a*np.sqrt(3))*np.array([1,np.sqrt(3),0])\n",
+    "\n",
+    "params = [cx, cy, cz, m, a1, a2]\n",
+    "\n",
+    "#Calculamos la disperción\n",
+    "disp = dispersion(params, b1, b2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Graficamos la disperción\n",
+    "plot_dispersion(disp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x24afbff0eb0>]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DTxvet3IR3ldyUFYubqZ7QghxFYoRH4kLAnKi6HyZxqDCxK4zYkYwaT+e95mRrE5mRNktlmLEPIIgYM3SFqxZ2oI/2Fxc3pnP5vHaUNE9OXRyHAdPjuH0ZLrYD2VoEt9/6QQAYElLAzavXIT3rerElpWL6J4QQhxDMeIj8ZiAXEE0dkYMvnVKBoS+W2AvMwIUT+jZvGjaGSkUREg3DSIzklW8luy14YzGZLxiFo8oijg1MY9DJ8dw8ERRnLx+agJnZzL4jzdO4z/eKLoniZiAi3rbi+5JqQstsyeEECtQjPiI3MfDc2fEXjWN9NhWxIgy/+JFZsMoM6J8zXjycxdBELCiswkrOpvwe5f2Aii6J6+fmpDFyYETYzgzlS6VG09g18/fBQB0t6ewZVVxaWfLqkXY2NvByh1CiC4UIz7iVht348yI/eoWyZUxLUaUAVIvOrDGzWVGuETjD43JOLasWowtq8ruydD4XDEIe2Ks5J5M4vRkGk+/NoKnXxsBAKQSMVza14H3rVqErasW430rO7GkNRXkUyGE1BEUIz5i2MY9b02M6FfTSNux/k3UzGA6JRXVLJ5U05jLjFCMBIMgCOhb1Iy+Rc346KaiezKXyeOVwaJzcrA0sXhsNotfvTuGX707hn/COwCANUtb8L6Vi7B1ddE9OX9Zq+WuwYSQaEAx4iNG3UTdckbKTchs7GPcuP26Eq8DpGYzI8yL1A9NDXFcvnYJLl+7BEDRPXlndEZ2Tva/O4ZjZ6ZxfHQGx0dn5MGA7Y0J/M5Fy/HtT1xKUULIAoNixEdiBksgZgOscrmrjsNSbgfvwBnRmXujJudxgNRsZiRhR3kRXxAEAecta8V5y1rxh1v7AQATs1k5c3LgRLH3yeR8DrsPDuK/XbsW53e1BbzXhBA/oRjxEaOeGWYDrNK3xrzO0oXdQXlm9lGN1wFSOTNiMBSQyzThoqM5iQ9f2IUPX9gFoNju/8q//RnOTKUxn7U2NZoQEn4oRnwk5pIYMVzucRJgNXBd1Hg5l0a53axBOTTFSLhJxGNoLA1DSufMi5FCQcS9e3+DTF7E1lXFpmxtjUmvdpMQ4hEUIz7iljMit4N32Fa+9j6aOyF4LQaMXjM5M8JW8KFHKv3VCytrcWhgDHc985Z8OSYA65e3Y2up38mWVYvQt6iJZd+E1DkUIz5i5IyYdRnMlvbaKbWNWcyMeNl9FbDiAjEzEnYaSrmfjAVnZHI+BwBoTSWwqCWJgXNzeGN4Em8MT+IHLxc7xko9T/7L+1fi6lI7fEJIfUEx4iNG3/ILJh0NoxyFLBBsdmCttY9qvO7zEZeqe5gZiTxJG86IJFzWdbfiiVuvwunJeRw4UazYOXDiXEXPk1+9O4ZffeMjnuw7IcQZFCM+IlfT6Cyv5Ew6Gma3Yy8zUrt6RY3XzkhSdkZ0wros7Y0MKRvOiHRbyVXpbm/EjZf04MZLegAUe57se+sMbnnkICbnsjW3deDEGI6PzuCy/k6sXdrC8mJCfIRixEckR0PvRF+Qy1RNlvYaTO210w7eujPirRgw21PFzhweUl8kE8X/w4wNZ0Sv1XxTQ1zuFpvOFSCKomZ+JJMr4OaHfonpdHHZp7M5WZpU3In3rVqEy/o70dzAj0tCvIJHl4/IwVODAXdGIsK490bxt6NqGovOiFeZDaN28OUOrMyMhB07mRFJuKRqzL1RCpVsXkRDovq4mMvkZSGSSsQwPpvFz46ewc+OngFQPC429LRhS2kY4PtWMhhLiJtQjPiI0bf8gkkRIfX30m8Hb7/CxCgwqsb70t6S8NLLjLC0NzIkJTFiwRmR8iW1hvAphUomX9C8bTqfBwAIAvDaX16PN4Yni91iT4zh0IkxnJqYx6+HJvHroUn8Py8Vg7HL2lJF56QkUC5Z0SGXJxNCrEEx4iNGM2VkZ8RQjNR2RsxmT7QwqvhR43VpLzMjCwe5tNdGZiRZowNvg+JvmVwB0JjPl86WsycNiRg29XdiU38nPnvVGgDAqfE5uWPswZPjeH1oAu9NpfHT10/jp6+fLu2DgIt62rFZdk86saKT7gkhZqAY8RFp+UV/Nk3xt1lnRLcqx4FASBic/NXkTA73s4vpCcXMjIQeSYxYcUbSqgCrFrGYgGRcQDYvIp3La94mY+Cw9HY2obezCb93aXEY4Hw2j9eGJoripCRQRqfTeGVwAq8MTmDXz98FUHRPNvcXcyeb+ztxSV8HsyeEaMCjwkekE6ZuszKLzohRvxI71QB2MyOe9RkxyIzkmBmJDJKgyJrscQMYB1iV287m87p5FOn6VMLcMktjMo73r16M968uhmNFUcTg2Jw8qfjQwDiOnJrEe1NpPHPkNJ45UnRP4jEBFy5vw+aVnbisfxErdwgpQTHiI7IzotsfpPjbyNHwsh289cxIcae9ciaMMiNsBx8dJEFhpR28kaOh3PZMxowYsSdqBUFA/+Jm9C9uxu9ftgJA2T05dHIMB0+M49DAGE5PpvH6qUm8fmoSj7x8EkBxWvGm/k5cpvhZ0qqxlkRIhKEY8RGjabty8NSwmqZ29qTsVtiZ2lvbdVFTbtTmjTNhlBnJMjMSGZI2qmmyZp0RA6GTNrkdK6jdEwAYnpjDoZPjODwwjoMnxvDa0AQm53N4/tgonj82Kt+uf3ETNvWVxcnG3g40NTAcS6ILxYiPmBcRLrWDt/G5arnPiMcdUJkZWTjYmU0jOyMGb3YjMeLUGTFLT0cTei5pkpuyZfMFvDkyhUMD4zh0cgyvDIzj7fdmMHBuDgPn5vD/vjoMoHgcXNDdhsv6O3BpXycu7evABd1tNYO7hIQJihEfMS8izDY905lk68CtsJ0Z8ahigJmRhYMdZ0TdgVUPKQuiu0xTKu110xkxQzIew8UrOnDxig586vJVAIDJ+SxeHZjAK4PjOHRyHK8MjuO9qbQ8c+effzkAoCicNvYWq3e+ePVadLU3+rrvhLgJxYiPyM6Iw2m75Xbw2n931g4+bH1GSpkVLtOEnpSD2TRmAqyAfqWOsrQ3aNobk/jQuqX40LqlAIrh2JHJebwyMI7DAxN4dXAcrw1OYCqdw8GT4zh4chwA8N9/76IA95oQZ1CM+IhhGNNkmWzZLdBxRvL2q2mMXJeqx/J4mcSo1NhrMUT8w0kHVrOZEX1npLRMkwxejKgRBKG4vNPRhP90cXF5p1AQcfzsDO7b+zZ+dGAQY7MZ3ftPp3P41r8fRSoRwyV9HdjY24E1S1t4zJC6gmLER6TztWvOiG6/kug4I6bbwdfBN1rijGTp/zrthTMiZ0a0+4yY6VdST8RiAs5b1opLVnTgRwcGZWdHi5/+egQ/ePlExXXNDXFs6GnHxl7ppwPrultNlzYT4jYUIz5idqaM+cyIQfbEzqA86eRvstdD3uNlEqNSY7aDjw4NpROhpQ6seeMOrEB5CUjPGfGimsYPmkrt5+ey2iILAKbmi9OKV3Q2YXlHI46cmsRsJo8DJ4odZSWScQHnd7Xhop52XNTbXvzd046O5qS3T4IQUIz4ilHnVLMndrOTbO24FXKAVce9cfOxzO1PqdTYIDNCyzn8SM6Inam9RlUwRmLEatOzekFaVpqvIUbmS8/tg2sX4zt/dBnyBRHHR6fx66FJvH5qQu57MjGXlUOyuw+W79/b0YgNPe3yz4U9bVi9hMs8xF0oRnzEqIeHdL3x1N7azkjBQY4jYbHPiOdTe0vPNWuQGaEzEn5slfaaXF4xajVvdrmn3pAG89UUI6W/SS5KPFZ0QM7vasPHNhcbtImiiKHxORw5NYkjw5Py78GxOZyamMepiXn8f6UJxkBR3F3Q3Yb1y9tw4fI2XLi8HRcsb8Wy1hRn8RBbUIz4iLEzYu7EbiRG5IF7dgblGczPqXosj2fTMDOycLATYDUztVe5bb1shZQlCa8Y0X/NpCWcWhOFBUFA36Jm9C1qxraNy+XrJ+ayODo8iaMjU7Jr8ubpKcxnC3htaAKvDU1UbGdRcxLrl7fhgu42rOtuwwVdrbiguw2LWhqcPE2yAKAY8REj10FaGjEyGRKG2RMH7eANTv5qCg7CsmYwuyRFZyT8lN0L87NpzGY95D4jBs6I103P3KaxtL/zOsFcoCzAGm1UCnU0JfHBtUvwwbVL5OvyBREnz83KIuXoyCTeOj2Nd8/OYGw2i5ffOYeX3zlXsZ2lrSms62rF+V2tWNfdivOXteL8bjoppAzFiI+U+4M47cBa/O20k6v2tmvPz1HjeTVNSXiJYvH5qsuVmRmJDkblt1qYDbCa7cAaVmekVjWNepnGKfGYgDVLW7BmaQtuKHWSBYC5TB5vvzeNoyNTOHZ6Cm+dnsJbp6cxND6H0ek0RqfTeOmdsxXbamtM4LxlRZFy3rJWrF3WgrVLW7BySXPo8jvEGRQjPmK45GBajJhzRuycoO32GfF6mQYo5kZSscoPKA7Kiw7lDqz63/LVWC3tNaqmSYVsuU+aV1OrmsbMMo1b+yJ1k1Uyk87h2Jlp/Eb+mcKxM9MYODeLqfkcDg8UZ/UoiQlA36JmWfSsWdqC1UtbsGZJC3o7G7ksG0EoRnykVn8QURQhXW08KK/422m/Eu1tW6ym8Tozotiu1uuWY2YkMpQDrOaXaawGWPX6jMjLNB6fsN2mMWE+wBrUc2tJJeSBf0rms3mcODuLt98ripS335vGO+/N4PjoDKbTOZw8N4uT52ax7633Ku6XiBUnJK9c3IxVS5qxakkLVpYu9y9uQnMDT2thhP9rPlKrP4jyOuMAq0GLdAfzWqwOyvO6z4hS5Gg5QcyMRAcnAVbHpb0mB+7VG42K0l5RFDXzF1K41a1lGrdoTMaxfnmxIkeJKIp4byqNd0aLwuTd0u/jozM4cW4WmVxBvqzF0taGUhi3CX2LigJlRWcT+hY1obeTYqVe4f+Kj8RqnOiVJ1rjAKu55R471bYx25kRr0p7y9vV6jXCdvDRwUlpr9nMiP4yTTiraSS3oyAWHaWGRPVxUF6mCcdzEwQBXe2N6GpvxOWK4CxQzI2NTM7j3bMzOHl2Fu+encXJc8UpxyfPzWJiLovR6QxGpzNVSz8Si5qT6O0sCpOejkb0dDSht7P4e3l7I7raU54vaZFqLImRnTt34vHHH8fRo0fR1NSEK6+8Et/61rewfv36mvfbt28ftm/fjtdffx29vb244447cMsttzja8TBSq5uocsnFyNEwDMKKfjoj3s6miccECEIxwKrVa0RyZpIePT7xD09n0xgMygttNY1CYMzn8pqvQ1oSIxEIhMZigiwkrjyv+u8Ts1kMjM1icGwWg2NzpZ/iv4fG5zA1n8PYbBZjs1m8fmpS93EWNSfR3d6I7vZGLGtLoUv6KV1e2prC0tYGtKYSrAZyCUtiZN++fbjtttvw/ve/H7lcDt/4xjewbds2HDlyBC0tLZr3OX78OG688UZ84QtfwCOPPIIXX3wRt956K5YtW4aPf/zjrjyJsCBP7XXqjJgOwtrZx9rhWDV+OBOJmIBsXqyZGfHKmSH+kZRyHSadkUJBlPMlxqW90WwH3xCPyWJ9PptHe2N16/Z5ubQ3/GLEiI7mJDqaq0O0EpPzWZwan8NQqZnb8PgchifmMTxR/D0yMY90riALlqMjUzUfL5WIYWlrCktaG7C4pQFLWor/XtTcgEXNSSxqKf+7ozmJjqYkq4R0sCRGfvKTn1Rcfvjhh9HV1YUDBw7g6quv1rzP/fffj5UrV+Luu+8GAGzYsAH79+/HXXfdteDESK2GYgVLmRGDSbYOTtC2nREPxUi8JEa0lo6YGYkOknuRzRd08w9KlE6Z+UF50RIjgiCgKRnHbCaP+Yz2c5N6kDQ1hOu5eUF7YxLty5O4cHm75t9FUcTkXA4jk/MYmZzH6cl5vDeVxpnJeZyZSuPMVLFEeXQqjZlMHulcAUPjRdfFLI3JGDqbGtDelEBbYxJtjYrfqQRaUgm0ln6aU3G0NCTQ1BBHc+mnMVn8aSr9jsoStaPMyMREsfve4sWLdW/z0ksvYdu2bRXXXX/99XjwwQeRzWaRTFYr+XQ6jXQ6LV+enNS308KEdMLUqoKpcEYM3ltStY1e9a2TRmRG3V3V+NHnoyjOCjWzNlE5IBcykhgRxeL/q9HSm9LlMAqeyk3PIjabBig6HrOZvG7js7lMqZomhM/NbwRBKLkryapgrZrZTA6jUxmMzqRxbjqDszNpnJ3J4Nx0BudmMxifzWJsNoOxmQzGZrOYnM+WHKwCRrLzGHHptJaMC2iIx9CQiCGViKMhEUMiLiAZK/5OxGNIxgTEYgLigoB46d8xARBKz7n4G/jCb62taHDnJ7bFiCiK2L59Oz70oQ/h4osv1r3dyMgIuru7K67r7u5GLpfD6Ogoenp6qu6zc+dO3HnnnXZ3rW6J1cqMKE6qRt8IDZ0RkzNutDCakqtGctS9dCakZSmt5ytX8zAzEnqUrkQ2XzAMpVoRI0YB1rA2PQMUXVh1ynvnfeozstBobkhg5ZIEVi5pNnX7QkHEVDqHybksxkviZGo+i8n54nVT8znMpHOYyeQwNZ/DdDqH2Uwes5ni77lMvig6s/kKhy+bF5HN5zGTyQPIOnpOH71shaP7O8G2GPnSl76EV199FS+88ILhbdUnV7H0zV3vpLtjxw5s375dvjw5OYn+/n67u1o3JExkRox6jADKzIj2350MyitX/JhteiY5I959iNcSSFkHZcykvlA6IZlcAc0G40ykMGqi9E2vFvJsGp2DRq6mCVlpL2A8n0aa2is1SCPBEIsJ6Ggq5kb69RcTTFEoiEjnCpjL5pHO5ZHJFZDOFeTfuXwBuYKITL6AXF5EvlBAvlAsbpD+XRBFoPSRKkKEKAKb+rSzNn5gS4x8+ctfxpNPPonnnnsOfX19NW+7fPlyjIyMVFx35swZJBIJLFmibQelUimkUik7u1bX1CrttdLJNC7UFgxOli6sOiM5B2FZs9RqUe91B1jiH4l4DDGhWKZqpqLGipshZ0Z03ANJ2KRCUv6qJFVjcm++IMqvU2MIXR+iTSwmoKkhHimBaendKYoivvSlL+Hxxx/Hz372M6xZs8bwPldccQX27NlTcd0zzzyDrVu3auZFokytE70lMWIgGPIWXBa9bVsNsHrrjOgPGGSANVqUh+UZixGzE3sBRTWNQWlvOJ2R4j5rtYRXdpzlMg2pZywdebfddhseeeQRPProo2hra8PIyAhGRkYwN1dOEu/YsQOf/vSn5cu33HILTpw4ge3bt+ONN97AQw89hAcffBC33367e88iJMjBU40Aq5UW7rVKhAGns2nslfYyM0LcIGmh10jagoAwPZsmhO5BrZbwyqUbihFSz1g68u677z5MTEzg2muvRU9Pj/zz2GOPybcZHh7GyZMn5ctr1qzB008/jb179+Kyyy7DX//1X+Oee+5ZcGW9QG1HwwtnxM4J2kjoVD2Wx7NplNvWLO1lZiRSpCzMpzHbfRUwH2ANY8WJZNVrTe6VBEpDPMalTFLXWMqMiCaGp+3atavqumuuuQYHDx608lCRpNaJ3ooYqbVsASjKbX2opvHFGTHRRp8ftNHAijNipWtqyqDPSKiraaT5NBqlvXPykLzwPS+ysOA71EdqleRayXlIJoBWO/iK6b92+owYdHdVk/ehz0itrrDSfrIdfDSwlhkx130VqN1nJF8Q5fdWKMVIzWUalvWScBC+Iy/ElJ2R6r/ZcUZEsdplUYoIW2KkRpdYLXIOloTMkqyRGfGj6RrxD0vOSN78cLtaIkf5WGHMjKRqlPbW68ReQtSE78gLMbWcESvLDcrbqEVDzqEYKS+JmO0z4n01DTMjCwejgXZKrFTASLfJF0TkVNuuaJ4WQjFSq5pmPmQTe8nChe9QH5H7g2iYDlZauCtFhroyx8r031rbNt+BlZkR4h6SGMhaqKaxEmAFqoWOVP4qCOEsEW+s0WeEyzQkLFCM+Eg5j6GfGTHqJAlUfmDWckbsmAVGE4HV+NF0zEwVEjMj0cCWM2IhwKq8n4SyrDeM4+CbTCzTUIyQeodixEfKnVOr/2bFYVDOnFGLBivTfzX30aBSR42VNvZ2kb75ai1vSY2v6IxEA9kZcTnAKnV3BarFiCR8wtjwDCgvwWh1l6UzQsJCOI++kFIrjyE7I2Zm08T0xYiV6b+199GiM+KhM2GmHTwzI9FAcrj0SnCVZHLmA6zK26m3LfXnaAhhjxFAsUxTo7SXreBJvcN3qI+YmU1jpiolFhMgaRa1W6BcNrFjOcfsVtMEnRnhMk0kMGpOpsSqoyEPy9NxRsJYSQMoS3v1m55FaYYJiSbhPPpCSq2TqhVnBFC0lld9/jjNcFjPjPjRZ8REZoTLNJFAWpIzs0xjdZ6MVAJbtUwT4lbwQLmh2VxGazaNNCSPYoTUN+E8+kKK7IxoNCuzWhWiVybstLqlvCRirrQ358MySULKjKj2SRRFTu2NGJacEYtdU/XCsWmLyz31Rs1lmgxLe0k44DvUR2RnRCP7ULAwKK9iWzqZEbuBUtuZER+WaWpVDjEzEg1SFgKsGQsBVuW2o+aM1K6mYYCVhINwHn0hJSaYcEZMigi9/InTQGm8hnujRbnpmPfLNHrPFeDU3qhgZzaN9QBrpYMQ5rk0QFloaFbT5ChGSDgI59EXUvROqkC5JNfsSVXPwbAy40Z7u9ZKe+vFGeEyTTQoL6WYmNpbagdvpukZoL8ElA69GCkNytMs7WWfERIOwnn0hRQzVSGmA6w6DobTWS3SaofpDqyiNRFlh3JmRPVcFVZ+GDtnkmqSNjIjZpdXjJdpwnnCLmdGql+zObaDJyGB71AfidWoCilYDJ7q9d6Q8qx2T861hvBp4W87+MoPWzoj0aPsjFR/y1cjNz2z6oyoA6xhb3pWElGa1TRSaS+dEVLnhPPoCynSSVXrJC87I6YDrNrLKZIzYnY7amoN4dNCcifMOjpO9km9P0ohFMY23qSa8mwaE8s0Nqtp0qqgp3TCDv0yTS4PUeWUcpmGhIVwHn0hpVZDsbyFQXlAeTlFvUxjZeCeFrW6u2rhRwdUveUttoKPHlZm01jNesgBVvXU3pA3PZP6p4hi9evGZRoSFvgO9REpV6GetAsA+bw1R0PXGck7C5Qq72emosaPDqgJuRGWvjNCooHeUooW0m3MBlilTIheZiSszohyCUZd3svSXhIWwnn0hZTyoDwtZ6T422lmxHEHVqUYMVHRUA+ZEToj0cFaaa+92TRRq6ZJxgV5DpW6vJdihISFcB59IUU6aRZEVK3tym3VrbaDV29Hbp5m77+2MjNS+4QgiqIvgsAoM2L2mzGpf6x0YLUbYNXrMxLWahpBEMoVNVXOCDMjJBzwU9xH4jXyGJIrbb0dvE4HVpv/s4JQ/pZllBlR/tlLZ0QSG8yMRB9paq+V2TRmsx4NOq5L2JdpgLLYmNN1RsL73MjCgO9QH6lVqWJ14Jzssqi3k3fmjADlPIpRNY3SOfHDGWFmJPro9QLRwqqIkAbKRa0dPAA0JrQbn82ztJeEhPAefSFEecKuWl5xyRmxWpVTa9tGzkhFO3Zfqmm0MyMJLtNEhgZLs2ksBlgNBuWFWozIyzRlMSKKotwIjcs0pN4J79EXQtx0RvRO0E7bwVdu27wYCTIzQmckOkjCIu3lbBpVrkISJ1FYplF2Yc3my1OtG0OahyELh/AefSFEKRCqllcsTu0tD8qrvN6NWTFSma7RMo1fYiTBzMiCoSFu3Rlx2oE1Ess0GvNp5hVB3caG8D43sjDgO9RHajkjVqf2lofHaTsjTmbFmHVGlM/BSz2QMMiMUIxEh6SVPiNWMyM6fUbCXtoLaC/TSP8WhPC2uicLB75DfURZqaJ2RqTLZpuH6eU6rA7c06LcKbb2CcGvduxxg8wIS3ujg17FixaWq2kS2ktAshiJh3cpQ1OMZEp5kUSc4xJI3cNPcZ/Rq1SRA6xWp/bqiBonOQqrzojXzkRCJzPitNssqT/KAVYTs2ksBljl2TS6fUbC+3FYXqYpCy1pmaapIbwiiywcwnv0hRR5poxOgNWsiNATDG4IBNOZkbw/AVK9zIjV14zUP2adkXyhHM5c6B1YgXJAVWuZpjHEz4ssHPgu9Rm9mTJSgNXsbBrZGdHr5OrIGdHeRzU5Fx7L3P5ot75nO/joYXY2jTLgalmMVAVYwz21FwAaG6o7sLL7KgkT4T36Qorc3bRKRFhzGfSWaVypprFY2ut1n4+4QViXmZHooJxNox6ZoESZ+zAbztRrqBb2qb2AwhlRLEFJ3VhTFCMkBIT36Asp+ksOVp2RUvbEA7egXjMj1aW9dEaihtKdqJUbUQqKpMnQt26ANRuBZRqt0l65+2p4nxdZOPBd6jMxncm9Vkt7pc/f6k6u7jkjZvuM+JUZ0WsUx8xIdFC6HLV6jSh7jJitFIm0M1KjtJfLNCQMhPfoCyl63/ILFkVEXK8qx9V28OZKe4POjDjpqULqC6U7USvEmrUROpVKd/Vn04T3pK1ZTUMxQkIExYjPGPUHsd4OXm9QngvOiEF5pV/LNEaZES/n4hB/icfKvXhMOSMWxIg8KK9qNk0Ulmm0nJHi8+KQPBIGwnv0hRTpvKl2NAoWHY2Ynhix2FZeC6uzaZgZIW6il+1QIndftRBe1iobrigRDnEQulZpb4qZERIC+C71GelbvDrrIbkQZgOseo3A3HAL9MqG1eR8ymwwM7KwMDOfRhIqyYT5//uyyCmfsJXCJMwnbZb2krAT3qMvpEjnTfUSiFVnxKhFupN28Gb7jJSdEW/fRsyMLCzM9BrJWhySp9xuNi/KGS2lMAm3M1LKjGiU9nKZhoSB8B59IUXXGbEoIspipPL6ggsnaKuZEa+dCb3qnnI7eL6No4SZLqzlIXnmT7TKahlJ6EjbiQne98vxEsn9mMtoVdOE93mRhQPfpT4TM1peMSkiEgbOiC9Nz3zKbBg9Vy7TRItkwniZxurEXvVtJTGSjkAlDVAWI8qcjeT6NIb8uZGFAcWIz0gnTvXU3rxFZySm44zISycOlmnM9hnxSwwYZUYYYI0W5YF2xtU0KRsBVqDc6CwKlTSAdtMzySVhZoSEgXAfgSFEtwrGYvBUzy1wo8JFb9tq6qXPiNkOnCQcmJncm7ERYBUEobwEpFqmCb8Y0S/tbeTUXhICwn0EhhCjKhizX/R0cxQ+LtPI1TQeiwHd3izMjESSpJnMiI0AK1DdhTUK3VeBcki1opomx6m9JDzwXeoz0vJJVRt30dqJVXc7rkzttdYO3rdqGt2mZ3RGokSDTtt2JXYdDfW209nwT+wFymXJ87m8PGCQyzQkTIT7CAwheo6GZWckrr10IWVInLWDt1ja67EWkDIjBbEya+OXM0P8xUyfETvVNMXbV/Yaseuw1BuS4BDFcg5mPscOrCQ8hPsIDCFxgwCrWZdBtx28C86I9LlcL86I8rko94nOSDQx5YzYFBHqbctzaUJ+wlZWzMjhXM6mISGCYsRnDJ0Rs9U00vTfquWeysext4/mnBHfqmkU21fuU5aZkUgiBZJrNj2TnRFr7z11ZkQu7Q25M5KMl2f6SFkR9hkhYYLvUp8xdkactoN33iLdcmbEpwBrcZ8q54oAdEaihrT04qUzko5YNY0gCFUVNXN0RkiICPcRGEKMMyPWOrCqRY3VGTdWtq3GL2ckqTjh5CsyI2wHH0XU5bda2A6wSj1MsqplmpCLEaC6ooazaUiYCP8RGDLiussrVsWIdiMwqzNutDDvjPjTdEy5eWXviVyeg/KiiLT0kq3hjNhtVqaeeyMFWcPujADVvUa4TEPCBN+lPiMtaeRV3/pyFlur6wVYcy6ESuV9NGh65pczIgiC5vN147mS+sOUMyIv01itpqlcAopKB1agXN47l82jUBDl50ZnhISB8B+BIaPsjFReb9XRMOrk6iSPZ9oZ8TFAWl7eYmYk6iRNiJGsjQ6sQHSbngHlipr5bL6ilT5Le0kYCP8RGDKM2ribnU2jX9rrgjMiaG9bjZ+D6qQTFDMj0ceP0l5peUbKjkTBGSnPpylUtIWnM0LCQPiPwJBhNODO7IlVyykoXnZjUJ52HkWNX7NplI+hlRnhoLxokbTQ9Myqo5GKazsjVpd76pHy5N68XN6bjAs8PkgooBjxGV1nRLTmjJQrXiqvd2VQXtxcNY3V0K0TamVGzA4XJOHAz3bw5aZn4X8PNSkCrGwFT8JG+I/AkKHrjOStLXnoOSNu5Cj0yo/V+JnZqJkZ4TJNpFDnOrSQHI2k3UF56mqakDc9A5TVNAWW9ZLQEf4jMGSU8xjazojp0l6dIKwrzojZqb0WK4CcIJ10lLN4/MysEP8oL9Pov/+cOiNplTMShcyIsppGntgbAceHLAz4TvUZ6cSt7jMinVjNNivTK791Q4yYd0b86/OhtU/MjEQTtWDQwvXZNBEQI8o+I/OlZRpW0pCwEP4jMGTonegLFr/ly+W3VVN73VumMdtnxI/SXi23Js/MSCSxEmC13oFVCnmqZtNEQYwkFMs0OWZGSLgI/xEYMhIx7XCo7IyYDbCWbleocliKH65utINXCx01fmY2tDIjLO2NJl4GWKWlDLUzEoVlmnJpb76cGUlQjJBwEP4jMGTENJwRpTCxHmBVt4O3th0tTGdGfCztTWhlRtgOPpI0mJnaa3eZRppNU3IOyk3Pwn/SrijtLfUZiUKVEFkY8J3qM1rOiFJQmHU0EnFtwZBzYV6MtOyizrWo8bOapnY7eIqRKCG5FJ4s06hclyg1PVMOypMm9jIzQsKC5SPwueeew0033YTe3l4IgoAf//jHNW+/d+9eCIJQ9XP06FG7+xxqYhqD8pRLLabbwet0Sc27UOFSj86IlhPEzEg0kXIdpjqwOh2UZ9NhqUekZZq5TJ6lvSR0JKzeYWZmBps2bcJnP/tZfPzjHzd9vzfffBPt7e3y5WXLlll96EhQ6xs+YGVQXnV7dMCdRmTmMyP+LZNoNYtjZiSaJE0s08gD7uz2GYlg07OU5IwolmlY2kvCgmUxcsMNN+CGG26w/EBdXV3o7Oy0fL+ooTXgLm9DjMR13As3S3uNnBF5no4vmRH9dvDMjEQLTwOsqrLhaDY9K4sRLtOQsODbEbh582b09PTguuuuw7PPPuvXw9YdWhNxK8SIxXbwemLEnQ6stUt7/c2M6A/KY2YkWpgp7bUdYNVxRqKQGWlMVA/K4zINCQuWnRGr9PT04IEHHsCWLVuQTqfxgx/8ANdddx327t2Lq6++WvM+6XQa6XRavjw5Oen1bvpGXCPAKp1gBcFC0zOdaho3en9Yz4x4/0FeKzNitSU4qW+M2sHn8gW5asxun5HqpmfhP2lXOiPS8lP4nxdZGHguRtavX4/169fLl6+44goMDAzgrrvu0hUjO3fuxJ133un1rgVCrZOqlUm7WqLG7rb0tl2f1TSKzIiP7eiJfxgt0yizJLb7jMizaaLjjDQ1lBu6sZqGhI1AjsDLL78cx44d0/37jh07MDExIf8MDAz4uHfeIosIxYneTuhUa7kHUIgRB6FOaUnEKMDqpxjQzIz4GKAl/mE0m0YpUmz3GSmdrCPVDr7k7hSraRhgJeHCc2dEi0OHDqGnp0f376lUCqlUysc98g+tShU75bh+ZEbMBliDyIwUCqJs1dMZiRbK8ltRFCGoXD7J1YgJ5WZ4drat/B0JMSJ1YM2xtJeED8tiZHp6Gr/5zW/ky8ePH8fhw4exePFirFy5Ejt27MDQ0BC+//3vAwDuvvturF69Ghs3bkQmk8EjjzyC3bt3Y/fu3e49ixCh1cbdjjNiOHDPwTKNXkM1NW40WDOLenlL+bytnpBIfaPMAGXzIhoSKjFScjPsZIWUQ/hy+YL8Ho/CMo0yM5Lm1F4SMiyLkf379+PDH/6wfHn79u0AgJtvvhm7du3C8PAwTp48Kf89k8ng9ttvx9DQEJqamrBx40Y89dRTuPHGG13Y/fChnRmxflJXuhfSt0c7beW1kISM8dRe//p8qDMjSmeJyzTRQulSZPKFKqHgpAJGWqbJ5AqOsif1SEqeTVPALKf2kpBhWYxce+21EGsEG3ft2lVx+Y477sAdd9xheceiitYSiPSZaOWkqrxtviAiERdstZWvte16qqZRZ0aUZcdcpokWSscjkysAqhVbJ0sr0gk7nSs4yp7UI8olman5LABW05DwEP4jMGRoiRF50q6FpRWl2JCWLPIuOSP1mBmJqzIjbj1XUn/EY4L8HtTqNZKx2X0VAFLx8sl5Op2THy8KS31KF2RstihGOLWXhIXwH4EhQ+tEX3DBGQEqcxSOZtPErS3TOMmnmN4n1fKWnRb6JDwol1PUSAIlaWeZRnEfSYxEwRUBio6SdCyMz2YAlMt9Cal3onEUhgjt2TQlZ8TCSVUpAGQxkndJjGj09NDC18xIvHKfpMxIIiZUVVuQ8FNrPo3duTRApRiZmi+KkSjMpZFolCceF48PBlhJWOA71WdqTe111Rlx1PSs1Gekjqb2ys6IKjNCVySaNCT0J/c6CbDGY4L8XpJyFVFxRoDqUl4u05CwEJ2jMCRolc1KX/6sOCPKk3B56aLcf8FJgFUSMvWYGcmpMiNsBR9NGuTAsrtiRHk/yRmJQiWNhFqMcJmGhIXoHIUhIaZxorfTSVQQBEg3L6hO0E7dgrjJzIif7oR6eSvLVvCRplZL+IzNIXnqbUuZkSg0PJNQLznRGSFhITpHYUioFWC1GgRN6LgFTk/Q0olfPfdGTdkZ8a+0VxJAfroyxH+SJgKstp2RuNoZic4JWy0+opSHIdGG71SfqVXaazUIqt6WW+JA2ZitVk8ZZkaIV6jbtitxUtoLlE/QcmYkQs6IcllGEKLl+pBow3eqz2jlMaQAq9XQqVqMlFvBO9tHpdtQyxzJ55kZId5Qc5nGaWak9J6Zno/eMo2yeqYxEWelGQkN0TkKQ4IcYFU4DtK3fauhU3VreWlZxWkDp8pwrH55bxDOCDMjC4Nak3vTjgOsRfdgKopiRLFMw7JeEib4bvUZee5LvtoZseowSCdi6f5uDMlTbheoXVETRJ8RtTPCzEg0ScnLNPmqv0kCxWmAdSpiTc+AymoaTuwlYSI6R2FIkPIcyqm9dkVEXJWjcOsErVU2rEXe5vKSHcqZkVLTM2ZGIk2tAKs8tdemo5FKVGZGohTyVD4XDskjYSI6R2FIkLKllX1G7DkM6qUL96ppym+LvIZNDgCiKLr2eGbQy4xEYaYIqUZuB6/x/pPcEtsBVnWfkQi9h5RuCIfkkTARnaMwJCRUA9+U/7bqjKi7ubqV4VDePa9TTVM5qM6H0l51WNfH8Czxn6SJAKvdrIccYE1Hr+lZU5KZERJO+G71GelLmPIkb3d5RT2vxW72RI0gCJozdJRUDKoLIDPiZ3iW+E+DHGD1rgNruZomOg5CI5dpSEihGPEZablBufxhd7lDnRnJuVhhoq7UUVPpjPixTFOZGZEEWNIHIUT8p3YHVpcCrFFsB59ggJWEk+gchSEhrjEoTw6CWhUjqm25meGQnRGdzEiFM+JLaW9lZoSlvdFGmk3jZYA147CTaz3SyGUaElL4bvUZrbkvTp0R9dReN07QMdkZ0e4zonRG/Kim8arbLKlPJIGguUzj0mwaiUj1GVE1PSMkLETnKAwJ0om7oClGrP13VJ+grQ/c08M4M+LOhGCzJJkZWVBIpb1pTWekVE1jO8BaeZKOkjOirKBp5MReEiKicxSGBK0shixGLJ5X9SpM3BAHcrbFoJrGL2eCmZGFRS1nRG565jDAKl+OUGlvRTUNnRESIqJzFIYE5Tf5QkGd9bDnjMjt4F2qplFuI6eXGZGFj+OHMrk/lSXRzIxEGzOzaewur6jvF6V+HMyMkLDCd6vPaHU3LS852NtWQbUdp+3gldvWW6YpCx+fnRFmRhYEZkp77Q5JrMqMRMgZYWkvCSvROQpDQoUzIlYOuHPqjLg5K0bd10ON35mNZFy1JOXjXBziPw2qihclaYcBVrUzEqXMCGfTkLASnaMwJCRcdEbUSxd2l3u0kMuGDfqM+NUBVRI9WVVmhMs00cTMbBq3MiORqqbh1F4SUvhu9RnlEkq+Kuth7b8jpg6w2gzCahE3KO11s8GaGfQyI2wHH01qzabJOuwPEm1nRFHaS2eEhIjoHIUhQXnyVIsIq1kPdTWN3eUeLcp5FO2/B+WMqJek3HiupP4oz6bJV/3N7cxItMQIl2lIOInOURgSYhpipGAz/yCHTFWD8lyppokbOCPSMolPmQ29zAhLe6NJOcCqMbXX8aC8ypN0tGbTUIyQcEIxEgB63UStOiNSrqPaLXCxz4hhZsTfahrJopf6jTAzEk1SNWfTuJsZiZYzEtP8NyH1Dt+tAeCWoyG5Enk51On+bJp6qabRC+syMxJNTAVY3aqmiVRpb9kNYWkvCRPROQpDhFypkq8MsFrtnFoelFe87OYJ2qjPiO+ZEd128HwLRxFTs2ncqqaJkIOQjMfkY5fLNCRMROcoDBEJl5yRcoC1ULEdV9rBC+acETcarJkhqbO0xcxINJH+X9WzaURRdD/AGiFnBACaSyKkibNpSIhIBL0DC5GYSkQUbC55eNoOPl490E+J3dCtXZROjSiK8jdmZkaiiZ4zogy02h+UF90+IwDwpd8+H8fOTGPt0pagd4UQ01CMBEDZ0Shetpu/qGoH7+qgvPrMjEiPzcxItCn3GakUI8rLdkWEOtgZpWoaAPjiNecFvQuEWCZaXwlCQkw+0aucEavVNOreGx4MysvrlPZK1/udGSk+tqhoB8+3cBSRnRHVMo3yst3lFXVpb5SqaQgJKzwKAyAhOxrFy3azHgmVM+Jmi/T6c0Yq2+jTGYk2erNppMuJmGDbAYxyaS8hYYVHYQDE5CoYZ45GTCUYcjYdFi3UpbRq/O4zUiFG8gVmRiJOUtH0TJlbchpeBSrFRyIm8D1ESB1AMRIAiXjlEkjeZtZDtx28C6FSWehodMBUXu/XB3mczsiCQikYsoqlwrTDIXnq+9IVIaQ+4JEYAOWJuMXLTp0RdYt0NzMjUoWOGr/FgCAIFRU1zIxEG2UeRNn4zOnEXqAy+EoxQkh9wCMxANQTcfM2l1fUXVLtbqf2PtZHZgSofL5sBx9tlMswynJeeWKvAxGaiAmQDpGolfUSElZ4JAaAeiKu3Tbu6vkxbk6yVS8BqZGraXxsOiaLkXzBVReI1B9xRZajwhnJOxuSBxRdNknM0BkhpD7gkRgAus6I7XbwajHi4j7qZUZ87sBasU+KzAidkejSoDGfxo0AK1AWIVHrvkpIWOGRGABxVR7D7olVDsLm3Z/XYtxnxH9nQsqHKDMjTk9KpH7RKu91IzMClBudRa3hGSFhhZ/kAaB2Hewv06jawbsoENRlw2rcXBIyS0LxujEzEn20Jvc6HZInIS3zcJmGkPqAR2IASMsrBVWfEbvLNAXVwD032sEbZUaCyGwkFMtbLO2NPimN+TSyM+LWMg3FCCF1AY/EAFA7GnablXnZDl4djlUjOyM+Blilx8qxtHdBIE3u1VqmSToUEZKYYTUNIfUBj8QAiKtcB7vNytSD8vIuNiJTlw2rCcYZKQskOiPRR2s+TcaF0l4ASCUpRgipJ3gkBoBajDh3RgqV23FxNo1RaW8gfUbyItvBLwAkMZLWcEacigiW9hJSX/BIDAA9Z8Tqt/yqdvA2sye1tl1PzkicmZEFhVaANetSgFW6P6tpCKkPKEYCQC0iJGfDavDUy3bwcdX8HDXlJSEfq2mYGVlQNMSrA6xptwOsfP8QUhfwSAwA9dReyXyw64yU28G7t3QhLRnVlzNSyozkRbksms5IdJH7jGg1PUs4+39naS8h9QWPxACozozYc0bU23GzK6k6HKtGeiw3yojNkoxVOyPMjEQXLWekHGB1trzSIDc940cgIfUAj8QAqM6MFK+3+i1fV4y40KLdKDPiZhmxWSozIwXfH5/4i2bTM5c6sDLASkh9wSMxAPREhNU5L9XZExedEUXrdS3cLCM2i9z+npmRBYFcTeNBgLWtMQEAaC39JoQEC4/EANAr7bU6Abcqe2JzO1rUYzWN1Gckx8zIgkDuM6IY1uhWae9nr1qNxmQcn9jS52g7hBB3oBgJAHU4VC7JteqMxLVFjRuTdOu5z4iy6RkzI9Gl1jJN0qHgXrWkBV+/4UJH2yCEuAc97gCQRITkZNgd+hZXOAWAcpKu8//W+qymKT5WtlCQQ79uuECkPtGaTZN2qQMrIaS+4BEdALEqZ6R4vdNBeW66BWrBpKY8m8b/PiPZXEFRDs23cFTRmk2TlQOsbFZGSJTgJ3kASG5CedpuqbTX6aA8D0p7czpNz4LMjCgDjVymiS6afUZcCrASQuoLHtEBoO6cKpf2WlxyUGdG8h60gzec2htAZkQpRhhgjS5yZkRjNg3FCCHRgkd0AOi1g7caYJWraeTsiYvt4KU8Sh1mRuazefk6ZkaiS60OrA38fyckUlCMBIDSGRFF0XZmRC1qXM2M1GM1TVwSI0pnhG/hqKLuwDpwbhYjk/PFv9EZISRSsLQ3AJQ9PJQne+vVNN4t00iCKZevp2UaKTNSdka4ShNdJMFxanwO/8ePXsXug4PIFUQIArCiszngvSOEuAnFSAAoq2AkAQHYFyNeBFjVIVs1+QCXaaTMSCImQHChpwqpTyRn5FfvjuFX744BAH5r3VJ89SPrsH55W5C7RghxGcte53PPPYebbroJvb29EAQBP/7xjw3vs2/fPmzZsgWNjY1Yu3Yt7r//fjv7GhmUeQwnzkh5KaVQ+u1FNU3tzEjcx2WShCozwrxItOlsbpD/fc0Fy7D7v12JH3zug9iyanGAe0UI8QLLzsjMzAw2bdqEz372s/j4xz9uePvjx4/jxhtvxBe+8AU88sgjePHFF3Hrrbdi2bJlpu4fRaTWHAWHYkRdleOmW2G2msZXZ0SVGWFeJNr89oVd+Nv/fAk29LRjU39n0LtDCPEQy2LkhhtuwA033GD69vfffz9WrlyJu+++GwCwYcMG7N+/H3fdddcCFiM6zojjQXn2+pVo76NBn5EABuUlVZkR9hiJNg2JGP7LB1YGvRuEEB/w/KvlSy+9hG3btlVcd/3112P//v3IZrOa90mn05icnKz4iRJuOSPqpRS7/Uq0kFyHvEGANcjMiNP5JIQQQuoDz8XIyMgIuru7K67r7u5GLpfD6Oio5n127tyJjo4O+ae/v9/r3fQVLWckJsByGDOuCpnmXCy3Nc6MBDcoL52lM0IIIVHCl0V39UlWLJ089U6+O3bswMTEhPwzMDDg+T76ifSFPq+oprGTf1AKhop+Jb5M7S3tt4/uBDMjhBASTTwv7V2+fDlGRkYqrjtz5gwSiQSWLFmieZ9UKoVUKuX1rgWGNFwunxfl7IWd86p0MhZFIKtYTnFlaq8kRnRKe4OopmFmhBBCoonnZ5IrrrgCe/bsqbjumWeewdatW5FMJr1++LpEci7yoigvsdhyRhQOiHJ+R9yVzEhpH42anvnY56OqzwgzI4QQEgksnwGnp6dx+PBhHD58GECxdPfw4cM4efIkgOISy6c//Wn59rfccgtOnDiB7du344033sBDDz2EBx98ELfffrs7zyCESAHWfEGUHQY7X/KVokM5v8PNZRq9zEggHVjlZZpSnxE6I4QQEgksL9Ps378fH/7wh+XL27dvBwDcfPPN2LVrF4aHh2VhAgBr1qzB008/ja997Wv47ne/i97eXtxzzz0LtqwXKC9t5AsiCnL2wqEzohQjbvQZiddhZiRWmRnxc4mIEEKId1gWI9dee60cQNVi165dVdddc801OHjwoNWHiizazoj1k7pSdCjntbhbTaPTZyQAZ0TKjMzn6IwQQkiU4FfLAFA6I076dSjvo3RG3DhHl+fnQFN8BtlnRNodZkYIISQaUIwEgBxgVYgROw5DrMIZcXd4nDJQq7VUE0ifEZX4oDNCCCHRgGIkAJRls1LprN2TekJVYRJz6QStDMdqhVjLzoh/byH1a8TSXkIIiQYUIwGgrFQpOMxeSPfLKJwRN1BuR9sZCaCaRiV8kjZCv4QQQuoPfpoHgHSiLygCrI7FSN7dZRPldtTOSKEglnMbAbSDl6AzQggh0YBiJACkpRRlaa/d3iBqZ8Q1MSLoOyNKceLWspCpfWJmhBBCIgnFSAAkFGLENWfE5WWaWEyApEfU5b1KcUJnhBBCiFMoRgIgpmgH716ANV+xbTcoLydVXq+cVxNkZsROozhCCCH1Bz/NA0DZ3TQvD8qrL2dEue0qZyQfkDPCZRpCCIkkFCMBEFP2GRGdNQ+Tsh1ygNXFRmAJRXM2JUpx4qczwtJeQgiJJhQjAaDMjDidfiuJj7Q0r8XFZRq9YXnKRm1uNFgzi1qw0RkhhJBoQDESAHEtMWI7M1L8L3S7tLe4be1heUH0GCnuDzMjhBASRfhpHgBKx8GpGJHuVm4H795/qbyfeW1nxG9ngpkRQgiJJhQjASCd5Auii86Iy+3glftUEOvDGWFmhBBCognFSACUHYeCc2fEl2oatTPi/mOZQf14bAdPCCHRgJ/mASCFTAsiXO8z4k1mpLK0N7DMiEp80BkhhJBoQDESAMr+Hbk6bQev3JY6MyJd9j/AyswIIYREEYqRAJCzGAWUZ9PY7A/i1aA8QL/PSEHujeLv24eZEUIIiSYUIwGQ8MAZ8bPPSHClvcyMEEJIFOGneQBIodOC6DwMmlA5I+ryVyfI/VDEeintZWaEEEKiCMVIACjdi6zLs2m8yIzkmRkhhBDiIRQjAaDMh6QdluRWiREPpvbWagfvJ8yMEEJINKEYCQClYHDarEzaVtqDAKuybb0SaVCem0tCZqhyRpgZIYSQSMBP8wBQCganzcrKAdZinxE3BYK0rZyqz0jZGfH37SMIQsVrx2UaQgiJBhQjAVAhRvJFERGzubwiCQYpwGp3O1rEdUp7cwEFWIv7JGj+mxBCSHihGAkArWUauyd2SXx40g6+tCndzIiLwscsyueX9HmZiBBCiDdQjARALCZAOo87rYIpt4OXtuPm1N7itgp10mdE/Zh+LxMRQgjxBn6aB4QkIrIOy2Tjqqm9bmY69appJHHid4AVqGx0xswIIYREA4qRgJCWV9IOnRHp3JzxwhmJ61XT1IszQjFCCCFRgGIkINSdUx07I3n/nBGnXWPd2CeAmRFCCIkKFCMBIfUVyeSK1TR2w6DSybncot3NzIi07crS3vpxRvj2JYSQKMBP84CQnRFpecXh1F69y04w6sDq99RegJkRQgiJIhQjARFXL9M4nNqrd9kJ0raqqmkCmk2jfkxmRgghJBpQjASEWwPu/BAj+s4IMyOEEEKcQzESEHHBGzHipkBIGHRgDcKZUJYTMzNCCCHRgJ/mASFlRNxqeibhbjv42tU0wSzTMDNCCCFRg2IkIGRnxGFpr1p8uOuM1F+fEeXzC6LpGiGEEPehGAkIedpuzlmAVS0+7FblaCE7I3mdDqwBB1jpjBBCSDSgGAkI1wKsKvHh5vA6uZpG1HNGgijtZWaEEEKiBj/NA0I6kTpuB68SH95U01Q2PcsHOJuGmRFCCIkeFCMBoW7bXo+lvcyMEEII8QOKkYBwy9HwsrRXciHUmZEg+4yw6RkhhEQPipGAcEtEVAVYXcxR6DsjwZX2KjMjQbSjJ4QQ4j78NA8I9Yncbn8Qtfhwc2pvPXZgVT5fOiOEEBINKEYCosoZsT0oT33ZRWektE95dTVNPrhqGraDJ4SQ6EExEhBeOSNuuhXSPuV1MiNuujBmYWaEEEKiB8VIQFSLCHv/FVXt4D2oplEv09RLnxFmRgghJBrw0zwg1CsMds+ravHhbjWNFGBV9RkRg6+mEQQ6I4QQEhUoRgLCK2fE1T4jcZ0Aaz7IPiOx0m8KEUIIiQoUIwFRHTy1tx111sTddvDFndJrehakM0JXhBBCogPFSEConRC7+QsvB+XpZUbyAfYZkdwa5kUIISQ68BM9INRZD7uOhpeD8iTXpaDnjARQWisJJLaCJ4SQ6EAxEhBuZT3U4sPNpRN9ZyS4apo4MyOEEBI5KEYCoirr4Vo7eBczI1LTszrKjCSYGSGEkMhBMRIQrjkjPkzt1XdGmBkhhBDiHH6iB0RVZqQOxYhenxG56ZmL+RSzMDNCCCHRg2IkIKqcEdvt4NWZETen9ha3pVtNE4AgkDIjXKYhhJDoQDESEFWOhu1Beep28LZ3SWPbxd/qapp8ySgJMjPCACshhEQHipGAqBIjdeiMxI2cEWZGCCGEuAA/0QPCraxHdfM027uksW2jahr/3z7MjBBCSPSgGAkIt8RIdVt5N52R+qumYWaEEEKiB8VIQKiXZeyLEfXAPfdLe6uckXzwfUaYGSGEkOhgS4zce++9WLNmDRobG7FlyxY8//zzurfdu3cvBEGo+jl69KjtnY4CbpX2qk/K6u06QXZG8pWlvUE6I4tbGip+E0IICT8Jq3d47LHH8NWvfhX33nsvrrrqKvzTP/0TbrjhBhw5cgQrV67Uvd+bb76J9vZ2+fKyZcvs7XFEUIsIu9/01eLDXWekqFVVxkigs2muOn8p7vnfNmPLqkW+PzYhhBBvsOyMfOc738HnPvc5fP7zn8eGDRtw9913o7+/H/fdd1/N+3V1dWH58uXyTzwet73TUaCqJNdmNY2X7eClFaBcQe2MFDQf2w/iMQEf3dSLFZ1Nvj82IYQQb7AkRjKZDA4cOIBt27ZVXL9t2zb8/Oc/r3nfzZs3o6enB9dddx2effbZmrdNp9OYnJys+Ika1SW5LnVgdbErquSM6FXT2BVQhBBCiBJLYmR0dBT5fB7d3d0V13d3d2NkZETzPj09PXjggQewe/duPP7441i/fj2uu+46PPfcc7qPs3PnTnR0dMg//f39VnYzFKhFg92sR1UQ1sWlE6NqGvb6IIQQ4gaWMyMAIKhOgKIoVl0nsX79eqxfv16+fMUVV2BgYAB33XUXrr76as377NixA9u3b5cvT05ORk6QKB0NJ8sdavHhRTWNKBa7sMZU1TVBtIMnhBASPSx9tV26dCni8XiVC3LmzJkqt6QWl19+OY4dO6b791Qqhfb29oqfqKEUI04qYKocFheXTpRiQ+mOlJ0RihFCCCHOsSRGGhoasGXLFuzZs6fi+j179uDKK680vZ1Dhw6hp6fHykNHDtecEU+racrbKohFASKKYnlqL8UIIYQQF7C8TLN9+3Z86lOfwtatW3HFFVfggQcewMmTJ3HLLbcAKC6xDA0N4fvf/z4A4O6778bq1auxceNGZDIZPPLII9i9ezd2797t7jMJGcoTuZPQqafVNEK1M6KMj9AZIYQQ4gaWxcgnP/lJnD17Fn/1V3+F4eFhXHzxxXj66aexatUqAMDw8DBOnjwp3z6TyeD222/H0NAQmpqasHHjRjz11FO48cYb3XsWIaRCjDjIXlQs9wjVeR4nKMVGvtR1VVnmS2eEEEKIG9gKsN5666249dZbNf+2a9euist33HEH7rjjDjsPE2mUbogTZ0QQBMSEomPhdnWLUmxIIkRZ5stqGkIIIW7As0lAKLuXOnUYJFHgtjYQBEHeN0mEKIOsdEYIIYS4AcVIQCjzGE5P6pII8cKpUPcakZZrlH8jhBBCnEAxEhDKPIZbzogX4kA9uVfpjFCLEEIIcQOKkYCIuShGpLt7IUakPIskRpQ9RtwMyxJCCFm4UIwEhKvOSNw7Z0Sq9JGXaUT2GCGEEOIuFCMBEXOpzwhQFgZe9P1QL9NImRH2GCGEEOIWFCMB4aYzIokZL6bolgOshYrfdEYIIYS4BcVIQMRdrKaRnREPBtdJ4diqzEicbx1CCCHuwDNKQMRdzYwU7+90uUcLdWkv59IQQghxG4qRgHBTjEgixMvS3oJGNQ0hhBDiBhQjAeHWoDzltrwQIzGVM/LeVNqzxyKEELIwsTWbhjjHVWfEQzEiOSDZfAEPvXAcf/uTowCA5e2Nrj8WIYSQhQnFSEC42Q7ey9Jeads7Hn8Ng2NzAIBrLliGb3/iUtcfixBCyMKEYiQg3B2U570zMjg2h8ZkDN+4cQP+98tXsfsqIYQQ16AYCQg3S3tjHoqRRS0NAIBL+zrwf33yMpy3rNX1xyCEELKwoRgJCKVwcLq84qUz8n/+wSU4dHIM129cjiR7ixBCCPEAipGAUAoHp51Ty5kR98XCis4mrOhscn27hBBCiAS/6gZEhTPisHOqtK0Yy20JIYSEEIqRgHDXGSn+N7IRGSGEkDBCMRIQbmZGJGPFi0F5hBBCiNdQjASEsprG6fIKnRFCCCFhhmIkIJRhU9eqaTyY2ksIIYR4DcVIQCgLX1xrB89lGkIIISGEYiQglM5IPbeDJ4QQQryGYiQgKpwRh46Gl03PCCGEEK+hGAmISmfE2X+Dl+3gCSGEEK+hGAkIpW5w2mWdzgghhJAwQzESEIIglIOnDp2ROMUIIYSQEEMxEiBSVsSpM0IxQgghJMxQjASI284Iq2kIIYSEEYqRAHGrP0hnUwMAoKMp6XifCCGEEL9JBL0DCxnZ0XDYOfXmK1dhWVsKv3tJjxu7RQghhPgKxUiASGLE6YC7zuYG/PEHV7qxS4QQQojvcJkmQGIuBVgJIYSQMMPTYIAkXAqwEkIIIWGGZ8EAKQdYA94RQgghJEAoRgJEFiNcpyGEELKA4VkwQNwq7SWEEELCDMVIgLBZGSGEEEIxEiiSIxKjGCGEELKAoRgJEDojhBBCCMVIoMhNzyhGCCGELGAoRgKEzgghhBBCMRIo0mC79kYOuCOEELJw4WyaAPnvv3cRrj9+DlectyToXSGEEEICg2IkQM7vasX5Xa1B7wYhhBASKFymIYQQQkigUIwQQgghJFAoRgghhBASKBQjhBBCCAkUihFCCCGEBArFCCGEEEIChWKEEEIIIYFCMUIIIYSQQKEYIYQQQkigUIwQQgghJFAoRgghhBASKBQjhBBCCAkUihFCCCGEBEoopvaKoggAmJycDHhPCCGEEGIW6bwtncf1CIUYmZqaAgD09/cHvCeEEEIIscrU1BQ6Ojp0/y6IRnKlDigUCjh16hTa2togCIJr252cnER/fz8GBgbQ3t7u2nZJNXyt/YGvsz/wdfYHvs7+4OXrLIoipqam0Nvbi1hMPxkSCmckFouhr6/Ps+23t7fzje4TfK39ga+zP/B19ge+zv7g1etcyxGRYICVEEIIIYFCMUIIIYSQQFnQYiSVSuGb3/wmUqlU0LsSefha+wNfZ3/g6+wPfJ39oR5e51AEWAkhhBASXRa0M0IIIYSQ4KEYIYQQQkigUIwQQgghJFAoRgghhBASKJEXI/feey/WrFmDxsZGbNmyBc8//3zN2+/btw9btmxBY2Mj1q5di/vvv9+nPQ03Vl7nxx9/HL/zO7+DZcuWob29HVdccQV++tOf+ri34cbqe1rixRdfRCKRwGWXXebtDkYEq69zOp3GN77xDaxatQqpVArnnXceHnroIZ/2NrxYfZ1/+MMfYtOmTWhubkZPTw8++9nP4uzZsz7tbTh57rnncNNNN6G3txeCIODHP/6x4X18PxeKEeZf/uVfxGQyKX7ve98Tjxw5In7lK18RW1paxBMnTmje/p133hGbm5vFr3zlK+KRI0fE733ve2IymRR/9KMf+bzn4cLq6/yVr3xF/Na3viX+8pe/FN966y1xx44dYjKZFA8ePOjznocPq6+1xPj4uLh27Vpx27Zt4qZNm/zZ2RBj53X+6Ec/Kn7wgx8U9+zZIx4/flz8xS9+Ib744os+7nX4sPo6P//882IsFhP/4R/+QXznnXfE559/Xty4caP4sY99zOc9DxdPP/20+I1vfEPcvXu3CEB84oknat4+iHNhpMXIBz7wAfGWW26puO7CCy8Uv/71r2ve/o477hAvvPDCiuu++MUvipdffrln+xgFrL7OWlx00UXinXfe6fauRQ67r/UnP/lJ8c///M/Fb37zmxQjJrD6Ov/7v/+72NHRIZ49e9aP3YsMVl/nb3/72+LatWsrrrvnnnvEvr4+z/YxapgRI0GcCyO7TJPJZHDgwAFs27at4vpt27bh5z//ueZ9XnrpparbX3/99di/fz+y2axn+xpm7LzOagqFAqamprB48WIvdjEy2H2tH374Ybz99tv45je/6fUuRgI7r/OTTz6JrVu34u/+7u+wYsUKXHDBBbj99tsxNzfnxy6HEjuv85VXXonBwUE8/fTTEEURp0+fxo9+9CP87u/+rh+7vGAI4lwYikF5dhgdHUU+n0d3d3fF9d3d3RgZGdG8z8jIiObtc7kcRkdH0dPT49n+hhU7r7Oav//7v8fMzAz+6I/+yItdjAx2Xutjx47h61//Op5//nkkEpE93F3Fzuv8zjvv4IUXXkBjYyOeeOIJjI6O4tZbb8W5c+eYG9HBzut85ZVX4oc//CE++clPYn5+HrlcDh/96Efxj//4j37s8oIhiHNhZJ0RCUEQKi6Lolh1ndHtta4nlVh9nSX++Z//GX/5l3+Jxx57DF1dXV7tXqQw+1rn83n88R//Me68805ccMEFfu1eZLDyni4UChAEAT/84Q/xgQ98ADfeeCO+853vYNeuXXRHDLDyOh85cgR/+qd/ir/4i7/AgQMH8JOf/ATHjx/HLbfc4seuLij8PhdG9qvS0qVLEY/HqxT2mTNnqhSfxPLlyzVvn0gksGTJEs/2NczYeZ0lHnvsMXzuc5/Dv/7rv+IjH/mIl7sZCay+1lNTU9i/fz8OHTqEL33pSwCKJ01RFJFIJPDMM8/gt3/7t33Z9zBh5z3d09ODFStWVIxK37BhA0RRxODgINatW+fpPocRO6/zzp07cdVVV+HP/uzPAACXXnopWlpa8Fu/9Vv4m7/5G7rXLhHEuTCyzkhDQwO2bNmCPXv2VFy/Z88eXHnllZr3ueKKK6pu/8wzz2Dr1q1IJpOe7WuYsfM6A0VH5DOf+QweffRRrveaxOpr3d7ejtdeew2HDx+Wf2655RasX78ehw8fxgc/+EG/dj1U2HlPX3XVVTh16hSmp6fl69566y3EYjH09fV5ur9hxc7rPDs7i1is8rQVj8cBlL+5E+cEci70LBpbB0hlYw8++KB45MgR8atf/arY0tIivvvuu6IoiuLXv/518VOf+pR8e6mc6Wtf+5p45MgR8cEHH2Rprwmsvs6PPvqomEgkxO9+97vi8PCw/DM+Ph7UUwgNVl9rNaymMYfV13lqakrs6+sTP/GJT4ivv/66uG/fPnHdunXi5z//+aCeQiiw+jo//PDDYiKREO+9917x7bffFl944QVx69at4gc+8IGgnkIomJqaEg8dOiQeOnRIBCB+5zvfEQ8dOiSXUNfDuTDSYkQURfG73/2uuGrVKrGhoUF83/veJ+7bt0/+28033yxec801Fbffu3evuHnzZrGhoUFcvXq1eN999/m8x+HEyut8zTXXiACqfm6++Wb/dzyEWH1PK6EYMY/V1/mNN94QP/KRj4hNTU1iX1+fuH37dnF2dtbnvQ4fVl/ne+65R7zooovEpqYmsaenR/yTP/kTcXBw0Oe9DhfPPvtszc/cejgXCqJIb4sQQgghwRHZzAghhBBCwgHFCCGEEEIChWKEEEIIIYFCMUIIIYSQQKEYIYQQQkigUIwQQgghJFAoRgghhBASKBQjhBBCCAkUihFCCCGEBArFCCGEEEIChWKEEEIIIYFCMUIIIYSQQPn/AfrWGho552NrAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.linspace(0,1,100), disp[0][2])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}