PCA and t-SNE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from sklearn import decomposition\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.loadtxt(\"./DATA/digits2k_pixels.data.gz\", ndmin=2)/255.0\n",
    "data.shape = (data.shape[0], int(np.sqrt(data.shape[1])), int(np.sqrt(data.shape[1])))\n",
    "labels = np.loadtxt(\"./DATA/digits2k_pixels.labels.gz\", dtype='int')\n",
    "# display:\n",
    "i = 122\n",
    "print(labels[i])\n",
    "plt.imshow(data[i,:,:], cmap=plt.get_cmap(\"gray\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. PCA\n",
    "\n",
    "MNIST dataset consists of 60k images, each of them is 28x28 pixels. So that gives us the dataset which has a shape of (60000, 784). It's a lot of data and proceeding on such a high dimensionality dataset can bevery time consuming. MNIST data points are embedded in 784-dimensional space, however they live in a very small subspace. We can observe it by this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3b7c0789d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random1 = np.random.uniform(low=0, high=1, size=(28,28))\n",
    "random2 = np.random.uniform(low=0, high=1, size=(28,28))\n",
    "random3 = np.random.uniform(low=0, high=1, size=(28,28))\n",
    "\n",
    "fig, ax = plt.subplots(1,3)\n",
    "ax[0].imshow(random1, cmap=plt.get_cmap(\"gray\"))\n",
    "ax[1].imshow(random2, cmap=plt.get_cmap(\"gray\"))\n",
    "ax[2].imshow(random3, cmap=plt.get_cmap(\"gray\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These images look nothing like mnist numbers - even though they both are embedded in 784 - dimensional space. That's why it is said that MNIST has a lowe dimensional strcuture. The most popular method of reducing dimension is PCA. PCA description bla bla bla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Data preprocessing\n",
    "\n",
    "Firstly, let's modify our dataset to 2-dimensional array so it has shape of (60000, 784) as it was mentioned earlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reshaped = data.reshape(2000, 28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for PCA to be efficient every variable of data has to be standard scaled (mean = 0, devaiation = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 784)\n"
     ]
    }
   ],
   "source": [
    "standardized_data = StandardScaler().fit_transform(data_reshaped)\n",
    "sample_data = standardized_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Possible dimension reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHaMGlPD5S0/NdBwRkYSitFd8lWCk0jfcfTzBVcMvpSRVL7HvUDP//ec3ALjvA6frjmUikrWiFIMWd98L5JlZnrsvBI47SJ3At55dR11jK++eNJhL3qGLy0Qke0X5V/WAmZURDFL3czOrAVpTE6vnW7O9jsdf3kJ+nnHvFVMIrtsTEclOUY4MrgIOA58nGKBuA/CBVITq6dydrzy9mpjDR84dyyTdrEZEslyUI4M5wK/cvRr4cYry9ArPrtnFoo37GNC3kM+/R53GIpL9ohwZVADzzewFM/uMmVWmKlRPFos5//Vs0Gl85yWT6Ne3MMOJREQ61+Vi4O73u/vpwGeAEcBzZvaXlCXrof6wcgfrdh1kRL9irp85JtNxRES6pDuXwtYAO4G9gE6RidMWc779l+Co4PaLJ9GnQAPRiUjPEOWis0+bWRWwABgM3KpxiY71u+Xb2LD7EKMHlnDNjFGZjiMi0mVROpDHAp9z9+WJVprZAHffn5xYPU9rW4zvLHgTgDsvOZVCjT8kIj1IlLGJ7u5kkwXAtJOL03P9YeUONu89zPjBpXxw6ohMxxERiSSZ/77m7FVV7s5Dz20E4J8unKBRSUWkx0nmp1avvon9iby4fg9rdtQxpLwPH3znyEzHERGJTP/CJkH7UcEtF4zTrSxFpEdSM9FJWrWtlhfX76G0KJ8bZ47NdBwRkW7ptAPZzAaeaL277wsnL0lKoh7msb9tAuC6c8bQr0RXG4tIz9SVs4mWEfQHGDAG2B9O9we2AOPhmKKQMw4cbub3K7YD8NHzdFQgIj1Xp81E7j7e3ScA84EPuPtgdx8EXAE8meqA2ezXy6ppao1x0alDGDuoNNNxRES6LUqfwdnu/kz7jLv/Ebgo+ZF6hljM+dmizQDcdK6OCkSkZ4tyBfIeM/s34GcEzUY3EYxPlJNeXL+HTXsPM7J/CRdP1hBNItKzRTkyuB4YAjwVPoaEy3LSzxcHRwU3zBxDfl5OnkglIr1IlOEo9gF3mlmZu9enMFPW21vfxIK1NeTnmQakE5FeIcqopeeb2RpgTTh/lpl9P2XJstjvlm+nNebMOnUIQ8uLMx1HROSkRWkm+m/gfYT9BO6+ArgwFaGy3a+XVQPw4ek6KhCR3iHSFcjuvrXDorYkZukRVm+vZc2OOvqVFHLJO9RxLCK9Q5Szibaa2fmAm1kR8FlgbWpiZa/fLNsGwFVTR+hOZiLSa0Q5MvgUwf2PRwLVwNRw/oTM7DIzW2dm680s4T0RzOwfzWyNma02s19EyJRWLW0xfrc8KAYfnqYmIhHpPaKcTbQHuDHKi5tZPvAAcClBAVliZvPcfU3cNpOAe4AL3H2/mWVt28uijXvZe6iZU4aUcuaofpmOIyKSNF0uBmY2BLgVGBe/n7t//AS7nQOsd/eN4WvMBa4iPCMpdCvwQPstM929pquZ0u2Pq3YC8P5/GI6Zri0Qkd4jSp/B74AXgL/Q9Y7jkUB8p3M1MLPDNqcCmNlLQD5wn7v/KUKutGiLOc+uDorB+04fluE0IiLJZe5du0GZmS1396mRXtzsGuB97v7JcP4jwDnufkfcNk8DLcA/AqMICs4Z7n6gw2vNAeYAVFZWTp87d26UKEfU19dTVlYWeb91+9r4j5cbGVJifP3CkpQcGXQ3WzooW/coW3TZmgt6frbZs2cvc/cZCVe6e5cewNeA93d1+3Cf84D5cfP3APd02OZB4Oa4+QUEg+Id93WnT5/u3bVw4cJu7XffvFU+9otP+7//YU2337sz3c2WDsrWPcoWXbbmcu/52YClfpzP1ShnE90JPG1mDWZWZ2YHzayuk32WAJPMbHx4Oup1wLwO2/wWmA1gZoMJmo02RsiVcu7O/LC/4LIz1EQkIr1PlLOJyqO+uLu3mtntBPdCyAcedffVZvYVggo1L1z33nCoizbgX9w9q0ZDXVFdy/baRoZVFDN1VP9MxxERSbqu3PZysru/bmbTEq1391dOtL8H90B4psOye+OmHbgrfGSlv6zZBcB7T68kTyOUikgv1JUjg7sIOm6/lWCdAxcnNVEWqnojONtV9y0Qkd6q02Lg7nPC59mpj5N9auoaWbWtjuLCPM6dMCjTcUREUiLKdQaY2RnAFODIuM3u/pNkh8omVW/sBuD8UwZTXKixiESkd4pyBfKXgVkExeAZ4HLgRaBXF4Pn1gXFYNZpQzKcREQkdaKcWno1cAmw091vAc4C+qQkVZZobYvx/JthMThV/QUi0ntFKQYN7h4DWs2sAqgBJqQmVnZ4ZcsBDja2MmFIKWMG9c10HBGRlInSZ7DUzPoDPwSWAfXAyylJlSUWrgvOIpp9mo4KRKR3i3LR2W3h5INm9iegwt1fS02s7PDCm+ovEJHc0JWLzhJebNa+rrOLznqq2oYWVm+vozDfmDF2YKbjiIikVFeODBJdbNau1150tuStfbjD1NH9KSnSKaUi0rt15aKznLzYbPFbwfBIutBMRHJBlOsMioHbgHcRHBG8ADzo7o0pypZRizbuA1QMRCQ3RDmb6CfAQeB74fz1wE+Ba5IdKtOC/oJaCvONaWMGZDqOiEjKRSkGp7n7WXHzC81sRbIDZYOlm/YRc5g2Sv0FIpIbolx09qqZnds+Y2YzgZeSHynzFm1Uf4GI5JYoRwYzgY+a2ZZwfgyw1sxWEtyW4Mykp8uQZZv3A3DOeJ1SKiK5IUoxuCxlKbJIS1uMVduDu3mepbuaiUiOiFIMJrn7X+IXmNnH3P3HSc6UUet2HqS5NcaEwaX061uY6TgiImkRpc/gXjP7gZmVmlmlmf0e+ECqgmXK8q0HADhrtI4KRCR3RCkGFwEbgOUE9zH4hbtfnZJUGbSivRiM6pfhJCIi6ROlGAwg6ETeADQBY82s190dfkV1UAzO1JGBiOSQKMVgEfBHd78MOBsYQS87tbS+qZU3a+opyDOmDK/IdBwRkbSJ0oH8HnffAuDuDcBnzezC1MTKjFXbanGHySPKdb9jEckpUY4MtprZTWZ2L4CZjQF61bhEa3cEp5SeMUL9BSKSW6IUg+8D5xGMSQTBOEUPJD1RBrUXg8nDyjOcREQkvSJdgezu08zsVQB3329mRSnKlRFrdxwE4B3qLxCRHBPlyKDFzPIJhq/GzIYAsZSkyoDWthhv7AqKwWQVAxHJMVGKwXeBp4ChZvbvBNca/J+UpMqATXsP0dQaY2T/EvqV6MpjEcktXW4mcvefm9ky4BLAgA+6+9r29WY2wN33pyBjWqw50kSk/gIRyT1R+gxw99eB14+zegEw7aQTZcjrYeex+gtEJBdFaSbqTI++GnmtioGI5LBkFgNP4mulXfuZRDqtVERyUTKLQY+1/1AzO+saKSnMZ+yg0kzHERFJOzUTAevCU0pPHVZOfl6P/TJERLotUjEws3eZ2S3h9BAzGx+3+pLj7HOZma0zs/VmdvcJXvtqM3MzmxElUzKsr6kHYOKQsnS/tYhIVuhyMTCzLwNfBO4JFxUCP2tf7+77EuyTTzBkxeXAFOB6M5uSYLty4LPA4ijhk2XD7rAYDFUxEJHcFOXI4EPAlcAhAHffDnTW23oOsN7dN7p7MzAXuCrBdl8Fvk6GBr47cmSgYiAiOSrKdQbN7u5m1j4cRVd6WkcCW+PmqwlukHOEmb0TGO3uT5vZF473QmY2B5gDUFlZSVVVVYToR9XX179t39VbDwOw963VVNWsTbBXeiTKli2UrXuULbpszQW9PJu7d+kBfAF4CNgI3Ar8Hbijk32uAR6Jm/8I8L24+TygChgXzlcBMzrLMn36dO+uhQsXHjNf39jiY7/4tE/833/wlta2br9uMnTMlk2UrXuULbpszeXe87MBS/04n6tRhqP4ppldCtQBpwH3uvufO9mtGhgdNz8K2B43Xw6cAVSFd9AcBswzsyvdfWlXs52MjbsPATBuUCkF+TrTVkRyU5eLgZl9HvhVFwpAvCXApPCso23AdcAN7SvdvRYYHPceVcAX0lUIANbvDk4rVX+BiOSyKP8KVwDzzewFM/uMmVV2toO7twK3A/OBtcAT7r7azL5iZld2L3JybagJjgxUDEQkl0VpJrofuN/MzgSuBZ4zs2p3f08n+z0DPNNh2b3H2XZWV/MkS/uZRKfoGgMRyWHdaSSvAXYCe4GhyY2Tfm/tCY4MJgzRMBQikruiXHT26bBNfwFBO/+t7n5mqoKlg7uzeV9QDDQmkYjksijXGYwFPufuy1MVJt1qDjbR2BJjQN9C3d1MRHJap8XAzCrcvY7gCmHMbGD8ek8wDEVPsWmPjgpERKBrRwa/AK4AlhHcsyB+WE8HJqQgV1ps3hdceTxuUN8MJxERyaxOi4G7XxE+j+9s255m897gyGCMjgxEJMdF6UBe0JVlPcmmvToyEBGBrvUZFAN9gcFmNoCjzUQVwIgUZku5LWExUJ+BiOS6rvQZ/BPwOYIP/mUcLQZ1BPcq6LG2hH0GYwbqyEBEcltX+gy+A3zHzO5w9++lIVNaHGpqpbahhaKCPAaXFWU6johIRkUZjuJ7ZnYGwR3LiuOW/yQVwVJtR21wH53h/YoJR0wVEclZUUYt/TIwi6AYPENwK8sXgR5aDBqAoBiIiOS6KGMTXU1w0/ud7n4LcBbQJyWp0qD9yGBEv5IMJxERybwoxaDB3WNAq5lVEAxY12MvONtxICgGw3RkICISaWyipWbWH/ghwVlF9cDLKUmVBjvrwmai/joyEBGJ0oF8Wzj5oJn9Cahw99dSEyv1th9obybSkYGISFcuOpt2onXu/kpyI6VHeweymolERLp2ZPCtE6xz4OIkZUkrdSCLiBzVlYvOZqcjSDrVN7VysLGV4sI8+vfVfQxERKJcZ/DRRMt74kVnew42ATCkvI8uOBMRIdrZRGfHTRcTXHPwCj3worO9h5oBGFjaYy+TEBFJqihnE90RP29m/YCfJj1RGuwLi8GgUo1JJCIC0S466+gwMClZQdJp36GgmWigioGICBCtz+D3BGcPQVBEpgBPpCJUqu3VkYGIyDGi9Bl8M266Fdjs7tVJzpMW++rDYqChq0VEgGh9Bs8BhOMSFYTTA919X4qypYw6kEVEjhWlmWgO8FWgAYgR3PHM6YGD1amZSETkWFGaif4FON3d96QqTLqoA1lE5FhRzibaQHAGUY/X3megYiAiEohyZHAP8DczWww0tS90988mPVUKufvRZiJ1IIuIANGKwUPAX4GVBH0GPVJTGzS1xiguzKNvUZQvX0Sk94ryadjq7nelLEmaHGwOLpUYpDOJRESOiNJnsNDM5pjZcDMb2P5IWbIUaS8G6i8QETkqSjG4gbDfgOC2l8uApZ3tZGaXmdk6M1tvZncnWH+Xma0xs9fMbIGZjY2QKbL6lqAYaOhqEZGjolx0Nj7qi5tZPvAAcClQDSwxs3nuviZus1eBGe5+2Mw+DXwduDbqe3VVQ2vwXFGsYiAi0i7V9zM4B1jv7hvD15gLXAUcKQbuvjBu+0XATV3N1B0NrcGRQXmxOo9FRNqZu3e+FWBm34ubPXI/A3e/+gT7XA1c5u6fDOc/Asx099uPs/3/A3a6+9cSrJsDzAGorKycPnfu3C7l7ui3r9fz203GZeMKuG5ydnUi19fXU1ZWlukYCSlb9yhbdNmaC3p+ttmzZy9z9xmJ1qX6fgaJbiOWsPqY2U3ADOCi47z/w8DDADNmzPBZs2Z18taJ/eaNZ4EWpkyawKxZ2TUCd1VVFd39ulJN2bpH2aLL1lzQu7OdTFtJV+5nUA2MjpsfBWzvuJGZvQf4EnCRuzd1XJ9Mh9VMJCLyNqm+n8ESYJKZjQe2AdcRnJUU/7rvJLig7TJ3r+lqnu46WgzUgSwi0q7TYmBmE4FK3n4/g3yCD/jjcvdWM7sdmB9u/6i7rzazrwBL3X0e8A2gDPhVeHP6Le5+ZXe+mK5oaAmedWQgInJUVz4Rvw38b3d/LX6hmc0I133gRDu7+zPAMx2W3Rs3/Z4up00CnU0kIvJ2XbnobFzHQgDg7kuBcUlPlGKHdZ2BiMjbdKUYFJ9gXUmygqSLjgxERN6uK8VgiZnd2nGhmX2CYEiKHuVwizqQRUQ66sq/x58DnjKzGzn64T8DKAI+lKpgqeDuR6RNB8kAAAlDSURBVIaj0JGBiMhRnX4iuvsu4Hwzmw2cES7+g7v/NaXJUqChpQ0H+hTkUZgfZYw+EZHeLcoVyAuBhZ1umMUamtsA6FuUn+EkIiLZJaf+PW5oCYpBSaGKgYhIvJwqBo1hMSjWkYGIyDFyqhgcbtaRgYhIIjlVDNRnICKSWG4Vg/ZmIh0ZiIgcI7eKgZqJREQSyq1i0KJmIhGRRHKyGJSoGIiIHCO3ikGz+gxERBLJyWKgPgMRkWPlVjFQn4GISEI5WQzUTCQicqycKgaN6kAWEUkop4qBhqMQEUksp4qBhqMQEUkst4qB+gxERBLKqWLQqPsZiIgklFPF4PCRZiLd/1hEJF5OFYPrzxnDFRMKqezXJ9NRRESySk79i3zTuWMZ1fgWQ8uLMx1FRCSr5NSRgYiIJKZiICIiKgYiIqJiICIiqBiIiAgqBiIigoqBiIgA5u6ZzhCZme0GNndz98HAniTGSSZl6x5l655szZatuaDnZxvr7kMSreiRxeBkmNlSd5+R6RyJKFv3KFv3ZGu2bM0FvTubmolERETFQEREcrMYPJzpACegbN2jbN2TrdmyNRf04mw512cgIiJvl4tHBiIi0oGKgYiI5FYxMLPLzGydma03s7sz8P6PmlmNma2KWzbQzP5sZm+GzwPC5WZm3w2zvmZm01KYa7SZLTSztWa22szuzKJsxWb2spmtCLPdHy4fb2aLw2y/NLOicHmfcH59uH5cqrLFZcw3s1fN7OlsymZmm8xspZktN7Ol4bKM/0zD9+tvZr82s9fD37vzsiGbmZ0Wfr/aH3Vm9rksyfb58G9glZk9Hv5tJO93zd1z4gHkAxuACUARsAKYkuYMFwLTgFVxy74O3B1O3w38Zzj9fuCPgAHnAotTmGs4MC2cLgfeAKZkSTYDysLpQmBx+J5PANeFyx8EPh1O3wY8GE5fB/wyDT/Xu4BfAE+H81mRDdgEDO6wLOM/0/D9fgx8MpwuAvpnS7a4jPnATmBsprMBI4G3gJK437Gbk/m7lvJvaLY8gPOA+XHz9wD3ZCDHOI4tBuuA4eH0cGBdOP0QcH2i7dKQ8XfApdmWDegLvALMJLjSsqDjzxaYD5wXTheE21kKM40CFgAXA0+HHwrZkm0Tby8GGf+ZAhXhB5tlW7YOed4LvJQN2QiKwVZgYPi78zTwvmT+ruVSM1H7N7Nddbgs0yrdfQdA+Dw0XJ6RvOHh5DsJ/gPPimxhM8xyoAb4M8ER3gF3b03w/keyhetrgUGpygZ8G/hXIBbOD8qibA48a2bLzGxOuCwbfqYTgN3Aj8LmtUfMrDRLssW7Dng8nM5oNnffBnwT2ALsIPjdWUYSf9dyqRhYgmXZfF5t2vOaWRnwG+Bz7l53ok0TLEtZNndvc/epBP+FnwO84wTvn7ZsZnYFUOPuy+IXn+D90/0zvcDdpwGXA58xswtPsG06sxUQNJf+wN3fCRwiaHo5nkz8LRQBVwK/6mzTBMuSni3so7gKGA+MAEoJfq7He+/IuXKpGFQDo+PmRwHbM5Ql3i4zGw4QPteEy9Oa18wKCQrBz939yWzK1s7dDwBVBG2z/c2sIMH7H8kWru8H7EtRpAuAK81sEzCXoKno21mSDXffHj7XAE8RFNJs+JlWA9Xuvjic/zVBcciGbO0uB15x913hfKazvQd4y913u3sL8CRwPkn8XculYrAEmBT2vhcRHALOy3AmCDJ8LJz+GEF7ffvyj4ZnK5wL1LYfpiabmRnwP8Bad/+vLMs2xMz6h9MlBH8Ua4GFwNXHydae+Wrgrx42nCabu9/j7qPcfRzB79Nf3f3GbMhmZqVmVt4+TdD+vYos+Jm6+05gq5mdFi66BFiTDdniXM/RJqL2DJnMtgU418z6hn+v7d+z5P2upboTJpseBD3/bxC0OX8pA+//OEF7XwtB5f4EQTveAuDN8HlguK0BD4RZVwIzUpjrXQSHkK8By8PH+7Mk25nAq2G2VcC94fIJwMvAeoJD+T7h8uJwfn24fkKafrazOHo2UcazhRlWhI/V7b/v2fAzDd9vKrA0/Ln+FhiQRdn6AnuBfnHLMp4NuB94Pfw7+CnQJ5m/axqOQkREcqqZSEREjkPFQEREVAxERETFQEREUDEQ6dHMbJaZnZ/pHNLzqRiI9GyzCC4+EjkpKgbS45nZuHAY5B+GQ/w+G16glmjbiWb2FwuGxH7FzE4JLxj6Rjg08EozuzbcdpaZPWdmT5jZG2b2f83sRguG1F5pZqeE2z1mZg+a2QvhdleEy4vN7Efhtq+a2exw+c1m9qSZ/Skcevjrcfnea2Z/D7P9KhwipH046vvD5SvNbHI4jtSngM9bMNzyu83smvDrWGFmz6fy+y69TCov3tBDj3Q8CEaCbQWmhvNPADcdZ9vFwIfC6WKCC4w+TDAAXj5QSXC153CC/7oPhNN9gG3A/eG+dwLfDqcfA/5E8M/VJIILCouBfwZ+FG4zOXzdYoKhhzcSDBFQDGwmGDpgMPA8UBru80WOXmS3CbgjnL4NeCScvg/4QtzXtxIYGU73z/TPRo+e89CRgfQWb7n78nB6GUGBOEY4PMNId38KwN0b3f0wwRXYj3swIN4u4Dng7HC3Je6+w92bCK4yfTZcvrLDezzh7jF3f5Pgg35y+Lo/Dd/rdYIP/VPD7Re4e627NxIMKzCWYMylKcBLFozS+rFwebv2MaMSfn2hl4DHzOxWguIm0iUFnW8i0iM0xU23AYmaiRKN5Hii5R1fNxY3H+PYv5+Ol/J7hNdtC1/LgD+7+/Wd7NO+/du4+6fMbCbwv4DlZjbV3feeIIcIoD4DySEeDMtdbWYfhCO3BuxL0DRzrQX3TRhCcEe6lyO+/DVmlhf2I0wguMnJ88CN4XudCowJlx/PIuACM5sY7tM33O9EDhLcnY5wn1PcfbG730twQ5PRx91TJI6KgeSajwCfNbPXgL8BwwiGd36NYFC3vwL/6sHImlGsI2he+iPwqbD55/tAvpmtBH4J3Bw2NyXk7rsJ+hMeD/MtImhuOpHfAx9q70AGvhF2MK8iKEYrIn4dkqM0UJ3ISTKzxwhGLP11prOIdJeODEREREcG0juZ2QMEdyKL9x13/1Em8ohkOxUDERFRM5GIiKgYiIgIKgYiIoKKgYiIoGIgIiKoGIiICPD/AQblFV7XG5GPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initializing the pca\n",
    "pca = decomposition.PCA()\n",
    "pca.n_components = 784\n",
    "\n",
    "lol = pca.fit(sample_data)\n",
    "percentage_var_explained = pca.explained_variance_ / np.sum(pca.explained_variance_);\n",
    "cum_var_explained = np.cumsum(percentage_var_explained)\n",
    "plt.figure(1, figsize=(6, 4))\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(cum_var_explained, linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.grid()\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel('Cumulative_explained_variance')\n",
    "plt.show()\n",
    "\n",
    "# If we take 200-dimensions, approx. 90% of variance is expalined.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. PCA components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '16-Component PCA')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(12, 8))\n",
    "\n",
    "k = 0\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        sns.heatmap(pca.components_[k, :].reshape(28, 28), ax = axes[i][j],\n",
    "                    cmap = sns.diverging_palette(240, 10, n=20))\n",
    "        axes[i][j].set_title(\n",
    "            \"{0:.2f}% Explained Variance\".format(pca.explained_variance_ratio_[k]*100),\n",
    "            fontsize=12)\n",
    "        axes[i][j].set_aspect('equal')\n",
    "        axes[i][j].axis('off')\n",
    "        k += 1\n",
    "\n",
    "\n",
    "plt.suptitle('16-Component PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. PCA 2-dim visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1st_principal  2nd_principal  label\n",
      "0       3.885769       0.420347    5.0\n",
      "1       6.513312      -9.226496    0.0\n",
      "2      -9.116109      -2.220315    4.0\n",
      "3       2.773206       7.376189    1.0\n",
      "4      -3.844514       4.831797    9.0\n"
     ]
    }
   ],
   "source": [
    "covar_matrix = np.matmul(sample_data.T , sample_data)\n",
    "# finding the top two eigen-values and corresponding eigen-vectors \n",
    "# for projecting onto a 2-Dim space.\n",
    "\n",
    "from scipy.linalg import eigh \n",
    "\n",
    "# eigh function will return the eigen values in asending order\n",
    "# this code generates only the top 2 (782 and 783)(index) eigenvalues.\n",
    "values, vectors = eigh(covar_matrix, eigvals=(782,783))\n",
    "vectors = vectors.T\n",
    "# here the vectors[1] represent the eigen vector corresponding 1st principal eigen vector\n",
    "# here the vectors[0] represent the eigen vector corresponding 2nd principal eigen vector\n",
    "\n",
    "new_coordinates = np.matmul(vectors, sample_data.T)\n",
    "\n",
    "# appending label to the 2d projected data(vertical stack)\n",
    "new_coordinates = np.vstack((new_coordinates, labels)).T\n",
    "dataframe = pd.DataFrame(data=new_coordinates, columns=(\"1st_principal\", \"2nd_principal\", \"label\"))\n",
    "print(dataframe.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataframe' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-88e7f593a672>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Generate data x, y for scatter and an array of images.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1st_principal\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"2nd_principal\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dataframe' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "import numpy as np; np.random.seed(42)\n",
    "\n",
    "# Generate data x, y for scatter and an array of images.\n",
    "x = dataframe[\"1st_principal\"]\n",
    "y = dataframe[\"2nd_principal\"]\n",
    "arr = np.empty((len(x),28,28))\n",
    "for i in range(len(x)):\n",
    "    arr[i,:,:] = sample_data[i].reshape(28, 28)\n",
    "\n",
    "# create figure and plot scatter\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111)\n",
    "line = ax.scatter(x,y, c = dataframe[\"label\"])\n",
    "\n",
    "# create the annotations box\n",
    "im = OffsetImage(arr[0,:,:], zoom=5, cmap = plt.get_cmap(\"gray\"))\n",
    "xybox=(50., 50.)\n",
    "ab = AnnotationBbox(im, (0,0), xybox=xybox, xycoords='data',\n",
    "        boxcoords=\"offset points\",  pad=0.3,  arrowprops=dict(arrowstyle=\"->\"))\n",
    "# add it to the axes and make it invisible\n",
    "ax.add_artist(ab)\n",
    "ab.set_visible(False)\n",
    "\n",
    "def hover(event):\n",
    "    # if the mouse is over the scatter points\n",
    "    if line.contains(event)[0]:\n",
    "        # find out the index within the array from the event\n",
    "        ind, = line.contains(event)[1][\"ind\"]\n",
    "        # get the figure size\n",
    "        w,h = fig.get_size_inches()*fig.dpi\n",
    "        ws = (event.x > w/2.)*-1 + (event.x <= w/2.) \n",
    "        hs = (event.y > h/2.)*-1 + (event.y <= h/2.)\n",
    "        # if event occurs in the top or right quadrant of the figure,\n",
    "        # change the annotation box position relative to mouse.\n",
    "        ab.xybox = (xybox[0]*ws, xybox[1]*hs)\n",
    "        # make annotation box visible\n",
    "        ab.set_visible(True)\n",
    "        # place it at the position of the hovered scatter point\n",
    "        ab.xy =(x[ind], y[ind])\n",
    "        # set the image corresponding to that point\n",
    "        im.set_data(arr[ind,:,:])\n",
    "    else:\n",
    "        #if the mouse is not over a scatter point\n",
    "        ab.set_visible(False)\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "# add callback for mouse moves\n",
    "fig.canvas.mpl_connect('motion_notify_event', hover)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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y9C9s7KBLPfHWRkQ78hCYe54HO6rwWfl00Nd3yMRJJld0kouZMGCDuQqemWNIG6MGEoSt6CwGhOjus2zCNWjexpxcRvz19KAyewvXJiI/jj1rYZU+29FOolMz5WYSc2u6dtKCJxCBIhwqQSbCJdKoGgYIRCGQKQIF+NNB02F2Jc/HoPGRJRnZYAZnl6dnYrafftQXV2N3t5exGmMGGrTwqUbhKi1Q+MxwDiUh3i7idtixVc1o8ncGJhrwJAAp1YHF9MqqYgcecmcj8WpHe+7Z1LLUcw9tBNvzL4Eg34Xe4U5WIRqcq8Tr2x3BVD6wSwj9qRp5bVcYViesAiXlzkOM1gGYsWVZB1B315C4CQiQIRLJbhEuFQCRcMIgTAEmPHpihUrRuDC6A3zxAruRcgoAIv1LM5OxYuTxgVc5t/+bQ3aG6yK2MaPsaLFtQuSnzQBOK42Pn7ipZZ0+SogmW0Fi1ZtKp6M2vzxEUmeye7ByvWDAQuJj/N0eKjKoE7LJUkYb2vGfUdfxdXmtXjPdAGW5y8JqbhkOrGYKxg5/9ICS18gV3r6BhMCJwkBIlwqgSXCpRIoGvYvReBE9yw8ES/D1vT444/DZrOFTBfJ9Z2RrmvNjRjX3IDU1FQMHUiBxpoJgduJSnAYzLAltELUOqDxxAM6N9ya0PlXTJ0Ps1Kj6kiu8tEc58NASbTb8L0vhm0rIrUPYunHyb0evLJ9eK2MqDEt1zqTDiKz7woS6nsDUcOeXt7/ZuovCSZHJzris3jj7HBX+xfrHj4O0iUARbOAmz87EdtOcxAChMCICHXwPwkJHkUEiHDR4TgVCIyGMKnpWcgq+k7mpbR+9vN333035NER+xoGRY74TRIQb89CUl85BlLr4TB0eucKyoqFv9f64smoU4o2RSNVwenFKGL5cNuKSERSK0p4aK8dl7W5OXE8rOlArbYZ/RobjmSPRV1BHnoNHhQ62jFp4BA2ps9Ag7EwJpuKaJWMkiTAJp6HAfdiuKUc6IQOJOlWwqhZDyElF7i3/mQeEZqbEDhjEaAIl8qtJ8KlEigadtwIjJYwRetZuHTpUkyePDni+vyEafv27ejs7AT7MyNpWVlZmDVrFqqqqvif5a5o62f/tqurqwvcGi3lN8LwVAIEUQ9J4woYugevgxEYFgHzX2/MvBBWY5Jyek8F6TK4XIh3O9DHBfEj31sQvT0USy3NgecGUqVZ+d5olSBwYb1GAhaa3Xhsl53H6dbq9+KIxsKJV3AhQCUOYJnwEU+tXjH1OcV+ikq+X5F0XoxsMUsKmzjXx1bZU1iMTYJRswUZE9ZDuEXZZPa4DzfdSAgQAuzvgSDRAwGiiAARLjocJxuB0RKmaPYLRUVFuPnmmxVfQ4kwBd9QWVmJZcuWyZIurtV65x0cyC7gIvh+Q0KggXSppQVLlywB+x75hfD/rDwHTYak2AxPfbr20NJF7wrDCddL86+EqNEe/7ZJEsZY23HZzm1YxWwrsvM5pfOn+BhpKu4y44J920YshxcDZBeiLrcUA4YkjHWIuK7JibnSRvQVrYXdaIbVEY/WtlJYzOOHWwKx/5IkLBU+wWTUI5obvdLLKVUyDnnOR7frbgByuHiQMceKhKuvOn7M6E5CgBBQ5hFEuNSdDiJc6nCiUcePwGgJUzT7heTkZNx7772KC4xE+Pw3se+BUqTsT6+8guVJObLEZLylFd/saEBSYiKvOkxLS4N18gw8MSDF3LQ6EsK+1tU80jVqwgXAr88KrqZkFZBJ9iFMam1EWWcrJJFFiGQuCdC5UpDePRXnJglwTHsRA6YdLEzHI1ref+oK6LQUob5+wTDpkoCiBDtuvnQyrjgmYEdSmXyPR4UIHYtwTbHV4lHnA7AZNDDaRRS22ZFjdsDi+C2cUrmvNCF8zRLixqTA9OOpx3+I6U5CgBAgwjXaM0CEa7QI0v3REBgtYRotYYt2v3/9SpGym//8OlaOq1TVQJrPJQjYNusC7DEkw+MnDyoNT+X5jYQB3QAcWgcS3Yl4a/piDBpS1NtCjOAfLA0oIbu/h5Mrxo9YBWJw5C6YcAWTsp6EZJ5OZPdn9QpYbK3FgnG/gUbjGbF0lubbv38uLOYJgc8YOb7n7nvw9+2NuG+oT9aFXymlqJE8uE16FudiQ1A0Dsi2OJCx81lIYB5g8pcmJQ75D5wT7ajS54QAIXAcCFBKUSVoRLhUAkXDjhuBaIQnWkpwtCnJaITP/2JKkbJZH2/CsXgF889wEbxvMkZSGvPGYlfOGARHjkrMzXJZw4jYsugWI1x16XVoSmyCNeNGOJIuHBXhCqkQ9D/dVzXIUoplfZ04b9cW/km4xUWAEPFQloiJOMytTjuRzQ1WL8YnmINNPIVotWZj965Fgfdje311/Bx8sP9zvFw1Fg1ZeQE9GJuX6cGyHBI641mForeykc3D1jQDWzEd1ViNRSHPYunMor2XI7HtWoUIFxA3NgWm26cc9xmmGwkBQkAZASJcKk8HES6VQNGw40ZgtIQpmmjdr71SqiRkQvnm5mHxt9KLKBG/SZ/vRJdfYyVz8wgRvAqkgqNGwZGlko7mED9Tv35LgohsUyOyCvfi94k/xxGhLHbCFU1MH7RuJjlfWLeD68fWlU+TT/35xwdVO7K0HyNHM7ENd+ApuBwGbP9iGR/J/q5ZfPaFGNzehvXaWoiCFGLoytKZVS1HcFNLChpzCvFWkR6tRg3ybSLO0/8T2xLHoQazfU24NQh+1i97X0Th9mfkNVwaIOMbZUg426RiZ2gIIUAIxIoAES6ViBHhUgkUDTtuBNQSpvAHhBMovd7beNnpdPI+gzNnzgxUF0Z6Rn5+PlpbW0PMQ8OfFUnDdcWOA6jpG1S0MAi3T2BzKxIqs5f4yUWNGFFhmrAlx+owNMgMRAOMBuXlG5CV3cRnvlN4CT1CZmz7EaPbPKuJHNNnhR0u5ebZEVbA0n+3Ss/iLGs9du9cxLGrrKzAeZYSvN3+OcyaPtmKTAacSUxBpaeI20oMaGxIEg3onLcfL+h/BJGZmIZd7Fl3uJ7GrevL5asUz8pBxvXlEGJoph0buDSaEDizESDCpXL/iXCpBIqGjQqBWH24YiVp0YTxBQUFEUlXpCrFt9u7cVfdMVkRPDPrTLENwa3VBioXi83NWB3mNB/ctHpsVzs+V4gaMW3UNeZGZNXvDuCdbWpAWdkWCII3zPYQfoNDKI0cdRrVbnlvNrkkuDxu9Bi8RDeWi0WfirEf36lfA7dzOuYvnIuzzjoL7f9djdedazAkOBWn00osQclia8O2Ep/Nq0KDdoK8hYUkotxTj7Wbfhzkw2WCTjAjKa0GxvtfIrIVy+bRWEIgRgSIcKkEjAiXSqBo2ClFINY0ZDSdWGFhIffbYtYNFoslNh8uScKt+45gpaXP5+wUBEWYkSiLUmVZe9CZkq4osk+2DSr6aLHIUt5AL67a8XngIZOnfIKUFEsg1bgZ8/EC7pSN9pyoTWLrmKbVw9LVhWOpybGnLwEYxSHkJqah2e4lV4WGOMw42octcQ70GOMDBDVE1yZjj8F+9Oq5i+DQGRRfz+SyYPcWb+py+BK8LX2mfEs9LKwyc+9bQPWfqAG2etRo5BmOABEulQeACJdKoGjYKUUgGoEK11tFE8ZHs47wv5xSJK5y0iS8Z+nDqy1daLI7kaDV4IjNwQnYiCuKg7tGEiP6aKW4nfj25pWBaWed8xbi44Nb5gh4FndjO+Z4LRf8/RFj0GhF20yWuLs3AajZUYO15dNjJ1zBNojB/RtlCCpLo15cV+MtJggjXH6z1YbsAu+SZXpByhqiBppWvwqo7ULAyNZbNwF1H/A2Q97FMHypAXa080Kfn9kIEOFSuf9EuFQCRcNOKQKxEqhYCZrcy8SSxuS6LuuQPCZRiI9G9EDSsK6BIy9GOip1wIK17wU0Z+ERLnbXRizAi7jzxKcVfRzjyuxUVK39AN3d3Xh53hXwaHWx7X8M5I8RpoV1X6LM3AINBHiYn5fvitROyD+GCfyfTWrDtbufAnqPAWljgJm3AFXL1JMtNtnuN4F3bgOkkRYX1AA7tu2n0WcWAkS4VO43ES6VQNGwU4rAyy+/HLGyMDjCxYjSypUrUVNTI7vGSIL44Bui6cCuXrIEDbljsLylE19ah+SjW/4JlQiHJCHd7oDVaJDXhAG4MS8DU3Zvw0dd/dibNw6DCfHI0zUH7BY0kLiO6yBKZTVNo9ooScLVugQ8P78Ujz7yCE+9RmyWHf4wpchWpEVJEuI8HvywrhMtmi5sLcwNeILZ9HHKbYx8z8rTuFE972zodDGSwvA1vXIx0FQdFGYLHkANsEd1rujm0xoBIlwqt5cIl0qgaNgpQ4D9kmcRq5aWlqgEyh+Vqq2tVRxbUVGh2LYn+KZIUTIW+FlbNRsNmbnDOq5IURwlt3RRwtkNdhwqToCVxbiCjFH96TIWscmJ16HD4YYY+FwEI1oz8QXuwJO4Cy/GXqmoRI78/luShJK2flxyoAbQOOGWHDyjpibK5J9aK7mhdYtw6uNiOy/hndh8a+KTyKQRgyfnTa37P8O1V/5cfURLTqtlOQDYe5XXnZxHDbBj21UafYYgQIRL5UYT4VIJFA07ZQjw3oUrVig+j1Ucst6JrNl0tKjUjBkzsHjxYsXG1MEPiZTGjIV0hCw8mFAB0HkkuJk9Acsd+klFFEIRPB+r37tD+xd84jgXB7TF8hGuYLInF3Hy/Szb2s0bSQeMWVsawQTs7DqYUxjSN5Il+Swp6aGaMRkCZ4ANCU4HuuPSohIl1QcqWnpSkjChqx/LhR6UXnNV9IpEJa2WfPdL3zJZhOsc4GZqgK1632jgGYMAES6VW02ESyVQNOyUIRBNj8UqDm+55Ra+nmhjo7nYB79UpLnemTpf2Y8qGiEIfojasUoRMgAzUhIw5av38cqECxR7Efrd2ZmZaEG3BS3pJgwYjJzoJduHMO3ofs75QppxtzSi2KJsaZFt7YVdr1dO8bH35ManTG4eJOY/BScneciDuz/owYRpubj0lkmRSVckrZbSWpn/F6t4nHzdKXgbegQh8PVCgAiXyv0iwqUSKBp2yhCIRTAfy9hoLxApsvba7EsxFG+MNkX0z9USrggzpXpc+NamjyKap15cVw2TqRF5eQdgMAzANZiFttYytHfl8nY5SsarjFSZU1h0isW+Qi+vuH0HrGUefKmZGXB8l12qUmQthmhedDB9IyQRRZ0efH9tP1/2hTdWouycXOXbI2q1/Ld5aSNVKareBRp4BiNAhEvl5hPhUgkUDTtlCMQStYplbLQXYHqwxx9/HDbbsAWD/56oES428GSQifBFSxL8zvbBbvah/RqbUBHkTB+QQ0kadHYW4v3ub2Nd+XTF6Bh/pNy78L6RXVikex+fJCxCBxipUYhksb6Iogfxbjccej1EP4E7CRgxp/lvHtqD4i/H8KXnTkzFNfdOg22XBQNbW+HudUCXFo+kOfkwTsmG8FQF0N+myBPrbeOws7cAfUMiUhM0mHr+ApRfdx+E0Yryox1A+pwQ+JoiQIRL5cYR4VIJFA07ZQjEYnoabeySJUt4Dz9meNrb24u0tLSQlkDhL7Vr1y68++67I9oARdRwcc+mU5NCE0QRC+u/RKlFuTdkuDN98DtKooBfOJ5GszFfkVRFIo5ajxuihsn32SuPjIKF4OlrOs18tsypaRiITzqxZ4iROoiYji9wlvUoPnMuQ2+iFhkOCT9BPBbW9EATbJSmAYyTspBhuwtC88hqRBaU+7ClHAcHsiCxztk+4snOT8msubjiP34GQa2n14l9U5qNEPi3RuCMIlzf//738cYbbyAubrgyaNWqVZgzhxkjRr6IcEVDiD4/1QjE4ocVaWx5eTknW3V1dSEEiv1MqXJRaT4RItZML0FjYiXXJ0mMbPj0SuMsrVy31JidP6xdOgGpw3Dywv48gZuEVntNQhUuOd+uwFBJwI/xMvo0abFv63ESS5aKZO76/cYkns6UvWKtUmSRPrRiKYWDxWoAACAASURBVP4POzALNTgHrI6TkUWmX9NIwEKzG4/tsvPCgMDFGlmf04OEnd8f4bdV15eNj1vLvHsYdjGiddmP70bF/IWx40Z3EAKnOQJnHOFi/3L//e9/H/O2EuGKGTK64RQg4PF48PHHH/MqRJfLBda4evLkybjsssug1YY2MFZyh5ckSTZaxZYfyZtr5Hyp0Ob8GblGM7ZhHtYKl8ICE7JgxjnWGiTuZJEbAQdNrLJvHPrjE+HRaOBg1gjRUmgxNJXWud24Zf1qQGuXb/zs25dwZ/rg7WKS9tul5RgQkqKvLXyf+VpZf8MokS2Z+4xOB2xx8YpRtVTbQEDIv2NsGfqMCuvzRc1mYSvuwFPYinmKbY60ooSH9tpxWZs7ZEVxY5JhynpqhKP8349MRqstRf50CwLyS8tx/X89fgpOPz2CEPh6IUCES+V+EeFSCRQNO2UIxBLhirSoE6Xvam9/D3tr74MgY3XKUk/798+FxTwhsBRGSw6YCrCh9Gx4NFrl1B1YSkwKRGYiAuwjZqWtViw8uC4ij5s85WOkpHTKjtkkzccLwl2xOdT7UndauOES4o/vHIS19AlYYkDyRe18rX0AfDhzJpqNeYqWFyyy9Tj+g2MXyQCWRbom93rwyvZQTZ4mJQ75v5jp65n4csCd/oUtqRgcGKnf879wUkYmbn3+L8f3/nQXIXAaI3DGEa733nuP/6s9Ly+PexT99Kc/VeU9RITrNP4WfE1fLZoua+nSpTzaFe06ERWMzHj0d9sexvu2cnQiG1mwhDi+Mx5htWZj965FfDn+3n8h6UW5hYYTEP+YKBExQZS8Gi5zk3yUiwnbsw+jpHwbBJZXC7u8BKVMfXRLksC8tW7CS1iB69ABRoRk0oKRInURXPcrWxsx/9DukCTe3+efHzHlmSD140XcxNOGdwgvoUfIUDwK2XYRH68fDI1wjU2B6fYpI+75+6/uR+vBeq8ZbfhFEa5oXzf6/AxG4IwiXF9++SWY31BGRgYXB1933XW4++67+f9G/IX70EN4+OGHQ37MUi90EQL/LgicqMjUaOdhZOvWfUfwkaUbIhNRc92W97uShH58D3/GXGyCy2HA9i+W8Z8ft0EqZ2sSWBTJA1+LGsVKwW4s3bkxtNFzyHdYQnnFRmRlN3kdsQT/sgX8WHoFVm2q6q1mFYC34ll8iZm8WbaSvxbTafFXkEs3RiBcOVbfuwStaNW8SjRoiiNWUc721OBe6wo8FP8z1CakyWrDwiNcLJZozd+K3smfwCG18ycaDAUYP+4O5OZejfpN6/HxH5+CxIxRwy7ScKk+MjTwDETgjCJc4fv7xz/+Ea+99hq2bdsWdespwhUVopABSnqhqqoqVRHF2J52+o2Ww4+5wbOL9UJklYSDg4O8h5/SlZycjLvvuQfvdPTwvobNdicKDXG4qSALS3PSofERlYiRMknCnNo6lOq0SP/2t5Fy+eWBCjRGtNjcTxxpR6PNKb8Mn5ZoJrbihr7XsXfXpXxcRPuIaNvJolNoRQcUKgh99yc6bPjetiDHcwmItw3BYWTGpv7ok4TsIB8uuz0JbW2leCn/RmUDVx/p8wrPmdpLwgxswzTU4CXcAZGZf4ZfQTiwkBsTr/uLCtgc0QxQR7wLAMeMbvwl4Ub55/me79dnsT8+VGWAh7n3h10aSULBkAi7VkCeTcSFWIlpqa9BI4z8B2Z29iJUVT6Nj555Age3b/EWWfiIIlUpRju49PmZjsAZTbheeOEFvPrqq0S4TvC34ERpi07wsr420ynhF+sLFBQVYfs5F2ClpS/Q15D9umVS7sXZqXhx0jhOumSfxwiCJKGwuRlztmzlaXhoNEi8+CJs/9mDeP5gBw64nXD5f39HSfGxCNBVrZ8i95CXmI3WIJXN56+2k8UlyIcr8LkE6NwuuJlPVJT1sgjc2vLpyuN8UbaJOISL8CnmYBP+C48qN8r2VQsyTRW7mIh9NYaLCvo8GTBrTYppyFxrF5Z8tcmbHmV6fAgoy+7EGxUV2C74qqxl3omRuXJHO5avN+LBKUlYZ9JBZJE8H1nk3JGvyGvX4XXeFzET27jY3mdsEQSxgEmVv0OO6UrUb16PnatWwmoxIyXbhKkXL0b5ueeRJUSsX1Qaf8YgcEYRrjfffBOLFi0C+5f/jh07eKPen/zkJ7j//vujbjhFuKJCFBhworRF6p/49R/J0jPWjz5Czxtv4IDbg62VFcrWACpel53XlMVL8MSABI/MeBaDeaZiDK7N9ep6giNqPe3tMLS1YeLBQxh79GhAN+QRBPzyrl+gpqwqakRmxCMlEd602Cb+0WgjXFGrACUJF9TvQKmv5+Ew6WJsJZJZhHckIyEvz7sCHq0vbSmDYbrUhefwI+94CbiT66QyFXcneHz4oM2e8/Ci5k7ZCBSLUt22rw2p5j2waz1IFo2o8BRinJiNexa0YYuhPOI7sec+b/s5xm18HKvyDHirSI82owdJYgcajYWyKUlGaG/DszgXG0e8T2rqdMyY/qaKU0hDCAFCIBiBM4pwLViwgJfPu91u+Bv73nfffapSXES41H9xRqsJUv+k02MkI1st99yD/lWrGfPB6gsWoisrSxUxkEPA75/12sSpqLEyIbQcwZAwMyURH0wvHTHFkeuvh23nrhBRNEtcPn3Trfhg5oLYKveCZg9Oi41Kw+VliF58FPRb8S4nvr/144g+XNFOz4qp82FOyZB9BosclWA//hMP+giXgP+UHlPUVIWPD3+2RxLwdNcj2JlZHhSBCvXJYn5mPY52pMfn8vf6JD9OMU0YTDBLUI+HpAeR1rQQOfU38nrP/b1v4ZfnXYimnDGK7YmC3y94vfFuLebt0QBpRcDMHwJVy3j0ky5CgBCIjMAZRbhGcxiIcKlH70RUval/2td/ZN8HH6D1F78EPN5Y1HtXXQl7QkJML8Y8txISEkIc4is3fYVej4yeyDdzmtaN+gVeXVjwdXDBeXCbzcO/swGsvuhCvHLR0si6pkgrDkvxsQjSZxUzuAlqzO7zcv0Hg5/NehnW78A8bAz0SPRrsyzm8QoEdOTiI5FCfwRorsQiQAI6LUX43H0R3s9fJKupihQxYk9mr+S0ZKG5/Sm8PSYOrUYN8m0iljW5cGmbm6eBJUlEp6MVh61foTJtLn46rxB70rSRI6GShIvwCW6SXobgNqBk7fNwiDa8d+wZPP+9n2MgUcFPC4BsRE6SkGp1Y8auPuqfGNM3lAYTAmApeyq9U3MQiHCpQck7hiJc6rFiI8MjSqsvvCDmCBervmU2J8FX2eqP0KdR8mkSkSq2Yf9Fl0OURKxsXIl/1v8TLQMt+MWf+5Bp6sfgfBGeTAnuAQMO9E/H7ybeftyNqVmE58ahV7FAuxaM/LS2leKNzOvQkF3gXbK/RFBFum+YCfoMRv3tgnx/lWlFEUWeY7hM9z7mYCO0guQrmvQSo/r6BapIl6x1hU/8frbrS9wmPoPNnvPxgW4pevVe4qKDC04hziu1EjQBUf14HObaqC4Zywz/+wi2FJRseNqrl1O4PKIbEiRoBR0Wn58EiyF62yAe4WKROElA2arlcHhsePfYM/jbkh+i1VQkG51SjMhJEibtH0Cu2TG8QlYksPQFYPJ1sR18Gk0InGEIEOFSueFEuFQCBfC07TvvvDOiz57396oAtf5Q6p/49R4ZHlE6MnYstp8zC1IMaRpWwbh48eKQ9Pj09+5DS/L1gEzVHPOpumxHFxYjDXuyP8eOofUYbx2HBLcRU4o3w5Td7k3HBRweBDzofhxH9ONiS3X6zEBn4AvcIT0ZID+bsQAvCgoVfeHbGcEugTmvs/RhV1Kq1zzVR9781YPh4m85A1Z+D2NXcrZZgM8ZfzwG4hOQ5BjCpNZGFJubsbpiRihh5PN4JeiJ6EccXNxlX4QWDZjo070Nk7DgtTG3iPjeCRhf82vFwxz4t7EgoT93G26vLMMB3ZioKV4eqZJ+FIhwuUUnVhz7PWpLpmDlwmsg+XELerJSRE7wSFi4uYuP7DDFoznPAJtBA8mRiM7mKpj3xiE1OxdTLwkV0LPoXEfHB2hufh02ewuMhgIUFn4XOTlXQojVkf/r/XWn1Z/BCBDhUrn5RLhUAuUTYL/11lsx9eZTP/vpNzI8wsV+ZW+dOwfNhUzQrL7Zc2VlJS8E0fiI2ndXfg8bxXlwJLC04bA/Fqv2L2924NqtQ2y3YE2tg8PQxeLdMJkaUVa2BYKMJcBGiZGkO6P+gg/eIa3owY/wHOYKG0Mq3iI5n/ttBgLGmhEiPkwXdk7DPqwrn6ZK/B1uwBpYqwLhUjpt0aoYGeG7Hc/w21/AnVHTjKxZdvKuW5BvnhsxwsX0V62Tn8eAaQc2C+fiBUF+bv+6A5Eq6UEkdFWg8Mv74fQ48O6xp5Fa3I93zv02vjLMBDO38PqnsdpPSbZKkWEX1wPM29OJrSVZsOUOZ4N99mLobUzG0TWFnET5G1mzqffuuwsW82dcP+ZntwI0yDZdgqpJzxDpOv3+WqM3kkGACJfKY0GESyVQvmHkw6Uer3ANlz/gcnTsWBwuKYY9Lw/publgUaympibuwyV3hUcPP2z4EP9v068wZJwFW9IF0GjyYeozYMZhB6qOOnlAx27oQH/q/kB0R6mhM/uF/AzuRjWYBUEQCYzinJ4oDeB53MQjW8HXHYhc0RfiYh6hkXOutZsHpzpUitvZGlwuPYaGUmEwDMJuT0Rbaxk6W/Ih6Vk7HvZu0fcuapWlJKEU9TyqdRClisL0Yia8F3+N/uazMWHv7cjQamUJF4tusf215m5BW9XLgIa1CRfwHO6OaLQaiFQxrZkEJJlnQrf9cjRO+h3SxvdDFARsFeZhBb6BTpj4i7MuAazZNatQDLaFYKTKuikNBp0bcbMHZFtFsuPQtS8NzZtzIWi0vJF1eokVtbX3Q75eFigo+A7KSh8i0hX92NGIrzkCRLhUbiARLpVA0bCYEQivUgxEeDQaJF98EQqefDLgbRSLPo5ps+5ffz/WHFvDdVpX77kLOQPjwCIL/qsn4yu49f0BkqHU0Hkz5itGagLrlf0XnTfSE24vwCJch1Aa0SU9mn0DSyBefGgXNhSVRtSWhYu/R3QLksAjMwe3T4YzdyIkf6sfn+eVHAFT4yMmSB5oIMIj6BXPBFvbIwf/Bv3e7+DsBH3AjNZ/A9NrHdK0o07bggGNDRVTP0JCSntgvxjp2oJ5+CtuwgB8AvggQ9YRflqiBvaD4xBf0sAJk5+0VWP2iCjXDGkb7hSeAks/s6u3MQlDRxOQO68TGr0YsXNRb4M30pVfWoGSq46gz/pVwPFLDgyT6TKKdMX8Nwfd8HVDgAiXyh0jwqUSKBp2XAgM+3D9Ha7WVujz85H+7etDnN3ZxLFWgAYL4qd8sgxGZ3LI+rqyt0HUDrvEK0W4oqYA2ayyFg1e+wQu2g66ohI4pfnYz7kpK3BFThpmfbEWz2WOUx3hUpSDicCxdfnoaawATAthS2iDqLVDFNyAwCwoQrdVTYQrqFdQRGuJ3/S8isIv/h90PHA4/CBGttbq9+KIxuw1JxUAJULMiBMzU10jXg6LYEKW0BowZA0xL5UAj0sTIEyR9oFFx26x/xHTe7/gUauUcQM8KhYc4FQ67CwaxvB0dU9A5XcOwunsiPi9EKBFZeXjvHUQXYTA6YoAES6VO0uESyVQNOykIhBbhMvbesff1sfY5cSUfUOBdCJbaHiEK9vUIKvhipoCjPDWcvYCjCA8i3vAIiuBtja+CkCNR4CHsQ+FS+Px4Pz9O3FVdjIqysrw283VWBtFwzVH2gjW1SYS4Ro0G3Ho/SrEp93Co4AsaefgKdeDI7hkNA2XmkPgT/ctsO9B9oHr0Fu0Fi5jJ/S2LKQ3XYAO8zhs0NdDCkrHKhFi/jxJgKG3GC6jBR5Dr+ISGBny69QjEmlRRH5HE77z3p+QXtyHMQtbZdOIcg9iOIsupgmLQ1yCFh5PaGPskfcISE2dRoaqag4OjfnaIkCES+XWEeFSCRQNO6kIqK0A9TeUDm/rA1FCebMT124dlNVwMaFPefmGEQ2dHxZ+E7FtDX9pBRPS8a5GPKL7WUiDaFYM8KxPExZOuOIdgD1e2dSUtbn5YetryM87gLQ0Dwa6gCd196E+uWS4yMBH3qbba3Cn87foSkpCQZwAt9uquD/OAR3q/1aB7qIrkOTMQH98N2pNm5Ah6VA4VBhSyei3jAixteCkR8HJPiiPGVJBKT0FnSsRom5oOJLGGoBLAqyWCdhVPzsEVyVCzF9K1CBv7y3oKVoLe9phXgQRfjGyJbo10OhETp6iEemkgT7c/vrjKL76CBJzbNGyvCGPU4JCaQPi43Mw79wtJ/X7Q5MTAv9KBIhwqUSfCJdKoGjYCUFAqehg0qRJePvtt6NWgL7d3o276o7JtvURRBEX7avF1AYt4uzZ6MvYCXfcQNC6RzZ0/mzwKryXv0hBc8Uqz+QJEiMXzIT0XGkjCk1NMCR1Qhs3hC1CBE2YyATi3n5/4Reb7zvWv2FR8nshBI5FzDZ5zscazcVoEwrhlnRglu1pQ/2YU2fGeVVpKMn7M6wKWiJGRAY7jFh+JAmHC0OjMQt7Z2JcVxmsgi0ktcjoTHtxHL7In8IF525E7tGolVxIgRXZMA+n+3zQyZIjScD+/XNhMU/wweDdl4kTqqHTe9PAHCJuR6ZBknk68nffzi0j/MJ6OcLVVZuGzMpeTrgiaumCIlxn3dgIrcF+Qs62/CQU4TqJ4NLU/yYIEOFSuRFEuFQCRcNGjUC05t/XXnst9u3bh+rqavT29iI1NRW5ublob29HX18fd5v/W+l01Ln9jYnDliRJ3r6GX21Cqi4Xl154BTbvW4mWlpbhCE1Y9WEkE9BxllYeLWOu8eHRqvGWVlxcWwODPQvJfRUQICF/9kt4csw1ESNmRocER5zA29z4tVDs3nLrQfwy+UFoma4q7GLtcf4gsErKYAG4twl3RYsL9+uqEZfLrBpGdpdkhKt58zQc7r8U+3I34WDWl4EI0RNH7sUusRVDwrDWzf/o4BRf5EIAEaXYj19LDw5LuyQBWncCRP2gvAeYBFit2di9axFnVXKRR96M3JUEU/31SGmfw1OhAeuI3C+57Qe7N8S2YXU+xl7UyvVYm4X53OpDlPFqYynP7/e+iOIddcia1I3EXGZ2OjJqNuoDz+k6abhOBI40x783AkS4VO4PES6VQNGwUSOgNm3IHqREzqJV0fn7GvqtJKqqqrD9tde45cRgQgK0bjcGkpJCXMjZr9qDpkLsyx+PAUMCkuxeE9ASX4Po8M+qWo6hvKUHCUN5iLczywEBe8fGoWZiPFqydBFb0mRCwMNlhfifHfvQo9cHnnVN3itISzHLprYiCcBZpd2S6gF8M/+/4MlrYBk7v+0U3y/rsRK0fnEv/yEjLI0Zu7Gq9C/8OX89+BtsFg7BrOkLECM/FkdLU9CtyeRWCmPRiDW4BJKc0SyrEm39BJckvg+DYYC77be3lqFs/C7A0K94ZhwOI7Z/sQzRUolC7TLUW0y8kjFJMmJyXin64t8G0rYiLplZgAjQ2NJh7KqAI/kYXAYznE4Rgl7CC4l3ocanpQt2x2cVjj+RnoJGkjBkMSAh265aw6X+S8BXRn5c6gGjkV9jBIhwqdw8IlwqgaJho0YgFmG8EjmLWEUX1tfQ3xaIGbAO7doJ23QPBheIsOXpMehK4T5VFou6HoQsMMaIV11eKfrjE5E2KHLfr0lHnVgxJxF1hXFe0hLBzJR9PIM31i4Z0bVAqUqPgR6tkrKw042fvrMTFbrH+fu5svUYchSh9/D56Ds2C3vHGjgZ7E3UIm3IgwzbZpiyduDB/T9Au6UT6/W1XMAeGu1j7zLsHp+GHvQgnZNLP3lha+ORvrqa0GJHCZgxdRWMKR2yeivGCh19eajedRHOmvoxUlIsilYMPBK2c5EXWx6dlFBRsgaZue3ee/ypR//p9P2ZjWTp2G3CPKzGpbDAFJry9EW0WISMka7EHBblGxldjPXQC4IecXEZMBgKUVjwHXKcjxVAGv+1RIAIl8ptI8KlEigaNmoEYrF+UCJnkRove3VVX6LUF5lKTk7GvffeiwPnLYDlilbYpzLhd2hbH9kehH53dl+WSfC4saZ8Fg7mFfEIEvtN7xeI5w/2oDkhQ7ZvXzhgzCXsmo4GFDUeApxx8LhFOLUsCiQhUpVeVAH44ADu+edWnF/9Z/56NWffA2vKeE6M3p6TiPrCuMC6GWlh67jclIannAnoeWs/1mqZRYMF+00FWFeh7Gx/AT7DMYzj5CXD04OxB3tQYm6R9VMtzG7C2LKN2KqZi1VYhM7gXoviFuTvvRlm83g45j8KfbxypZ8/EubHMmJE7HhOqARou8cgveVCdE/6O7fMOP6L9FrHjx3d+XVGgAiXyt0jwqUSKBo2agRiiXA98cQTGBgIFrx7Hx+IwmTlj6jcC4+2+CNcex68GObzGgBfS8LgFxnRg9An1NZIOmjc8Zh46DA6jE789sbbIcr05vNHXSLmpHhlITDB0oqL6qq9BMVH5nSuJB6myczbh5KKDRD85qRBi1QnAH8Zub39mHq0A6svug/9Kb2oz8/E6orJsmSQka5nysdg4doODO2z4DA6cN9ME5pTUxS9tbh7vPQgBqzZ2Ft9LdwKGi32buliMtbPycc2Q2nAeNRPUufYD+DJDfnQQoOjMx9VrjwMaL0u5aL6vLwDSE7ugiDIm5OGH1C11YQaZwLiBvPhTGr1VVUe31Envdbx4UZ3ff0RIMKlcg+JcKkEioaNGoFIGi6W0DJPMOPyeZdj8fjFeO7Z59Dd3S37TNYOpiklCweyi9CWnRNovMw0V/76v+B2QNtWXYhB4QhvWC33S3lYwO0lQjpXMtK7JiO1ZycSupfjwR8/hO60EkV7iIieAiyi5JEQ73HAoY/jj0+y2zDjaD3XiDGHg+S+MhhsWciZ9yzS8mtDqhRZ2iyyhkvEhXu2oIQJ/F0OJDjcGEhjqT9gxdnzYVZoDcSIYnZ/F771xWFUOYsxUR+Hby1IRKdBBiQfaGliD+6sXw6tx4C8/IPQJ1q4ZqutrRQW83Bqlr2TLX0W/nJWHiRmEhZ2aUUJD+21Y1GrE2bTavRO+Qdv6TNyb7zVjJkZTSF2HqM+iCGMO+gPQelJHuTkSw/PWco/nZEt6p94QneGJvsaIUCES+VmEeFSCRQNGzUCckJ4RrTY/7UktKDaVM0bVF845kKM3T1WkXAxspDU34/FKz+WbYbNznRFRQVveM2ky59/zqr7OhXXH562gqTD7uTDOJi1g6fiOgtZjz8vWVJggBG1WyOaVfsqJSdYWnilo96hQ8KxoxDFPiTN1MA04QgMhn7eaFuvd/AIkb9KkcXKWKoQIm/LjPGdrFqy2uvkHlaB+ee5i+H0kTy5dcc7nbhpy0qeY2WeWSumzUFrhlGRWDKfsF/Yf4OsrGburTX8SAE8NVu3gAvFx4nZeP3sWdiTplWIlkmY3OvBS1/0Y2fiC4ifsRO6eDdfYmBOSUBnZxG6ugtRVrZVtun4qA8km0CpubcEuB0aGA3jIYqt8Ghsio9juq3Kiv8lvdYJ2RCa5OuIABEulbtGhEslUDTshCDg9+H6dOOn6OzuxKBuEA0pDWhKbApUymkFLb7R9g24bC7FZxqHhnDV+x/w35e8GXbxRAwmJyOrpAQzZ84Eq05kZOvTl/fBlXYPjJkNsr+0GUfxR7j8FXp7C8ZiID4JyfYhpA/2oD5vojKhitTkmv9CVzAM9X22pPVjXJrwLgz6Pjj79ejcl+6NFiVnQoxLQFZOG3ILDiPO2Ift9jn4SHsxuvSZSB4aQEVnK0os8hoqNv0r514Ol1bBQ0uSoPe4cfPmj7wYS4BXHzddNirFCN6Mnt3oyzCE6rGwiRs2sNRse91FKGy9EBPFHCw+LwmWCNGybLuIZywPwpN/gAvrg/mi2xUHy6H5OGzJw+SpnyqK6mM6kMGuD/6gm/9ncub/vlZBvVuKkDPmGNzjJNkIKTu0sTjJS5KIjo4P0Nz8Omz2FhgNBSgs/C6RtZg2kwb/uyFAhEvljhDhUgkUDTuhCHxv5fewy7KLR7fCL0aULrNcBuOAUTGilNXZiQvXrA0QrkPFxRhKS0XWhAkBwnWw2ow1f6lFcuE25M1aDiFC2spsnoBVFTNGeG4FFqBUfegnXP7wTPiKoxCuEtRzXRQnHL6sGms4fWxVPiYfs6CgN1THtqU4H70JBgyOq4BoTIwYWYsa4XL5I1wBzoXVDIOsAh7Z4watbP2ShBSHAwOGuBF6rEATaQm8/c7Y6v/HJ/vBLCOPcMmbvEqYONCJhxJuk5W+sUceqJ8LsX0qJsz9M+LjowjZ5ciU3MlRimYpnOwAl2Y2XSzAKZNtjUW3xcjW3n13wWL+jFt0+MNrZB9xQv9qocn+BQgQ4VIJOhEulUDRsBOKwAVvXgCLzaI45yTnJFS0VoDptUYQMlHErC+2Y+zRoxFTiprG8ehoZFWAIjclTS70mn6OSIXVL8ABUxHvWxizIZOKljeRgJPrx+htkJwHaWc85h5u9QegeCSvtryU+4hJLHKlUdZbsZuiWWhwk9idG0OWx17HnF2KhvxStBo1yLeJKOkX8U6hHh4ZPZa/b+K52AidPR0TNzzF51uZq8VDZxkhyt4j4W7Dezjb9ldFOwiXKx6Nm3+AknOXQ9DbFcdJogaDA5lw9ZuQnnOACfDkJXWRolnRTnaE6Fi2aRHOqnoWgr+JY9hcw83b30BvxiF0Le0eNXGLtlz6nBA41QgQ4VKJOBEulUDRsBOKQLQI19Tsqbhi4IqRrX4kCYXNzZizZSuOjhmD7efMgiRDPNi5znRUQujO9K1bhHHiKqQVrw8YdAaLvVdMjSwwV/yNz2ZX6LWohwsu6COmI0tZhAsPhpIe1o7HbETT2uFwTgAAIABJREFU/xXiwrqjPIq3de4cNBcWhlRm8hRoThE3bO03JPAUqN+wlWXJYrHQCF5AghSPbzvmBX4UOVolooRXL/4qJMJ1QNOOX01JQEN23giX/gmWNvyX6WFA6lI8U4z4WVorYMqvl/fy8mVr99efC4tlAo/CsUrGsrItspWeaisWYzrkIlA56Qnk5lyN+s3rsfOzleizdCA1OwdTL1mMsjnz0XrffehftZpr7iz3OOEaf2JSkzGtkwYTAicZASJcKgEmwqUSKBp2QhH4sOFDPLjpQXikke1omIbr0XmPYvG4xdi7d2+g1Q9r7TNjxgwe2er9+z/wYW4OOlPkbQzYYhM06UhsPSuwbpa+7E+tg8PgE9D7/BmysxvxRPmtsGkSY3zHyDmqBHEQQwIToctHophNwu14Biw6FH6xhtNtLxVhzqEWHBk7dgSxjNSSyG+PweaUS5OyVkJnu77Ej8Wn4bQnBlUZAgyLsfmHkRRvh96WhfSmC3Bd2cURqxd5lE68jTeYTm6bwwnmB/oadGj6cDBH3sF/1qy1iI9vUSzwDKTzOKEduS3sc49HB6fTCIPB6+NltyeA6b+Sk73VrQx2f5qWCeD1BlF2rkBWW07LFelESOD6rbaN03Fw+xawaJb3wQIvYhhXOA7lK9dC8HjPePtjTohpyhNSk+sYv340/N8GASJcKreCCJdKoGjYCUVAlETcv/5+rDm2Buy/GRnizVAEb5Xi4+c9zv870hXNSNUYn4jkprORXLgdacWfQ5/YBddgBixHp6G1JxsasQclk7cj3XQEt2j+AjuUK/QC6/DlIzUQYcQQBsF0VCPXychUwVAb9IMSGrMLAr+I+X/42MQsbMWdeJKLzoMvf8PpxGeNyO8dwMrFizGQnIjsnCPci4q10NnguQB/Sfi+bNNt1sR74X6vAWxI26L4BJg0bVik+wBzsBFa5i7PH82qDAshQoOD2WOxWrh0WBgvfYo17mtxQM90XfJNt0twAI+3v4u8XbdxoZNGEPBG/EbZHo3+9ywqasG48WtHdabC6xX8fx7oS4VgdyAhBYizmZDSdD48kgt9U/4paz/h3RPfUmIkXRqk4as/FQyTraA3YlNNPmZGQY+3xZHlXleECBdgNI5DnD6DxPSjOhV0878CASJcKlEnwqUSKBp2whFgRGtl40r8s/6faB1sRX5iPr5Z/k3uwxWNbEmihN/997MYcHbLRy0AFBUVYkzWBgiJW0NtDCQNnIfHwbTpKHpvlLiY/kf4CwYFZkIqf+ncbmRbh2BLAbK1rbhQ+hSfCZehAfL+XEzbdH79l9yJnaX2dowt430a2cVSf+f3bMS1ectlm1UzwtW/rhClKyzYMmc21k2fg4biLPTpU5EFMy7GJ/gMi3AIpfLRM97EuwtLv9oY5LMA5CTvRcnUnbIBN9Yg+znhbt57kBtOBLX1GS8dRqNQDFGGWLL3/MWxfVhaP5ap7OFmPmYC8EHcjpAejaGoSqgs34jMbG+6NEI3JMX9iFiLIAJxO67EuO6lnMSzv+NEyYO2KS9gwLSDnwVZe/yYT7gAZ28Gav/J+mnKXJKEtEF7QIc3NNOD3hs8sga83rsZcfe2L2ILJDF9zBtCN/yLECDCpRJ4IlwqgTpNh/ltGqqrq9Hb2wuWtvPbKjBPrH/Xa/8X7fjoH+tgTdkvn3ICMHe6EdqEl7kz+YiL/d7rBNwm7y98b7/CMmUPqr4e3F23FQWzXuUEjZmRPo87ZRs6s7BRTl8fluz8XFnALUgoL98QYujpbRUowKidjsr+67Hl+Zfw2I0/RoMp3xuACeTIfGacESKASUP9uGH9u5D08dwQNa7HgoqFXyIxxya7pk3SfLwo3AlRtkG1B8XuFjRoiyD6qhcFZugqAQvNbjy2yx4o4HOKEidcDdoOfK7fx6sd/c2//TqzOQNf4BoFsnkizhvD0cgrJsO0cRDRn7sN7RWvQdLZR51eZBWKbdsmoH2XzrdsCenFVmRO6kFcsovbfPTtSsGM97shSMw/TULPD9whLaZCFyFXsatFZeXjyM29+kRAQ3MQAicFASJcKmElwqUSqNNwmJwRKf+9HmQceqpJF4tcHajuwN71zejvsiM504Cq8wpROjMHQlDF29u/rUFbQy/6U+tDNVm+31laRxqmTvvI5781cvM4ufEAgu93ZWQ3dwlXbR/AFRMfQXxmA2+I/Gf8SDkFKUrI6xnC1XtWKfxS93tzeYXe/jQhc223mCsxe/ZdOKvqLPzkjy/j3UkzZdOG/I0iVEimDlqRYBuANSEZqf09OHvfdlxzztswJMp7m0VqkM3SoyXiYXx3bwHeKtKjLUHLqxeXNblwaZs7QLZYRWmnow12jwMFxiK8btiMDydNHWG1wdKxM7ANd+KpEenUE/U1C66YDJ/z0IKfwmPoVX5UeFXiiCrF4ejT3n8a0HpwPxeLjb2oBWnj+/meB9t8GL8SkP5nXYB02WaIGFwgAeOTkZBeDKezCzbb0aC8ZvDSYvP5OlH40TyEQCwIEOFSiRYRLpVAnYbDIrXaCW6NE+uri5KEdzp6sLylE812JwoNcbipIAtLc9K5vkfpYmSLGZU27DQHxM7+wM6EqSZcesukAOl69eebMNjn5Novh8EMW0Ibbzys8RhgHMqD3p6NziUrsC5ugaxRJ5tXFDWBvnwsjfYc7kY1ZnM1GXNzZ0SDhWjKmx24dusQJlz5M7xovImPYXqnSLmw1AE7vr+pGg6DrxIvuENMFJ2Qn/D+j9GE1sTU2HNu4d5grJejxITyO/BT/f9y7Vb4Fa1BdobYh08+Aw5bd2JiylTZlC9LEW+3fISjg7UYm1iBD6dX4NOKqbKEMdhOgu8FBGzFvJGNrn3GqjGdQe4JVhLwBAu/N1L/Rrbf8b0TkdF0AXqK1sFt7ITWlgljfxHsKU0Q0/tgTBmDwoLvwJR9OdYufwm7Vq1EenEfxixsla+P8ABpf9UhYbvvzGg0SL74IhQ8+SQEjQYbN82F09mh+IqxiOnJWDWmk0KDTxACRLhUAkmESyVQp+GwWJpJq319j8eDm9d8gVVag9faUcOIi8SJ1uLsVLw4aZwi6WJpQmZU6q8s8z5TRMoYr+g9KbsPycljuDP35tfy0NZg5TqX8ItJ8FfMScL+Ip2iUSdbk82WDKNxIOBA7/+lvxqXwgITxhgTkdl8CLPX5EALLVov+xTLk6+TTbuFxCREEZMaDuKBv36E/eXT0ZPhgEdrR+LgEEStCwPJySrgFPDX2ZdiMN4Qfaw/0uXNSXrL+hQE7rdJz2KesGHEnJEaZDPiWTnYh0c+PYStlncxc1oJtMW1cBm7eCVjWtNCJLWdg5ZB9vl7gbn/+s270J6WrbgWbieBB/ke+cluuH5s2FiVRQWjQ+E7MkhtXghHchNcxs5AtWVy+2xOoK15W9Fexdo1yaSaJQG5e25Bavu5sg+LG5sC0+1TuEj+w6d/i4PbNyNtQi8K57dDo1doqi0B8R1JML2YBsf8eAye54HLOASjoZCf5abmv8Jq3TnqCBcZq6o8HzTshCNAhEslpES4VAJ1Gg6LVuWXnJyMe++9V/Wbs19CLz/5HP5z6rkQtdoR97GfPFMxBtfmZsjOydKE7Q3WoM/8hqVfBUTvfjGxQbsAX/7jOgiSXz8zfNvusXq8PytJtkWNP7IyV9ok2xg5ULXXOQYV5U+iquosvPbMGgwcEPDXxcCxxMyo5qgaj4hvrm9EZbMAg70LhS0bkWOuYXEzHKuowLapU2QNXUNAkYB3p56H9tQ0VREuRiBN1m7Y9PGwKjnQSxLGuxrxiO5+/qjgdjrrBq7B8uTrFYXx39u8Daa9K0ekzbwabw2cx/JQ+ykjksOs6I83/AKDCcqFCH7T10jpXL5f0nM4FxuUNVdhrXpEWyo0Bpba84njvbb5SDJPR+6uW7mA3jz1TxjIqfFCHnZ/YscMFOz+sSyZ16TEIf+Bc1C3cR0+/uOTGHNBU0gaUenLEhdnQlrqdJgtn4IRIz/2zDA1JbkK1v593hx32BWLk317+3uorb0f0ijnUf2Fp4GEgA8BIlwqjwIRLpVAnYbDTnSEq++DD3CN2YZ944plXdAZKZiRmoQPppfIoulPE/o/TBmj3JKHlXp1Hr0B5i/O4b8xNWDKIG+7lOUXJqM1M14xslKM/fiJ+SXU1y/gjwrXUfkNUbPScpHnnAlrlw2dhr14fs5UDMUrtBtiE/na4JR0dOG69YCGCdB5uE6CybITk2qXQ2fKxs47fjLC0FUOkIOZ+VhbOUPW2DWUnEnItXZjyc6NeG32pRHXmOiw4QnbfyA5xZvq9P/iFyUtfu94CDuN5RxFv0BfI0kobqjFVavfREZxr2LazOuOn4+eQ6mBpf1tyQ/RmjMmQoTrAG9r9JDwKA4qVFz6o2uP9bMKQ2+nAP4/RqLYMj1xkDQu/medPROa/gQ4co7IZ3tFDfQ7Lkeu5RK4CnejvepP8v0RRQF5e3+IlPa5YdsiIW5sKo9w/f1X98MmblNOI4bcKcBoHIuhoaOK/Tx12gR4xKGwu7TINl2CqknPKDrZB99QU/MN9Fm/GnWk7DT8q45e6SQjQIRLJcBEuFQCdRoOi0XDpaaa8cj11+Py636IrjT5CBaDUC8AmXqdrK4rPMI15oL/gTHzsIJUSkBKytn4cO9siPtSkeTIQH98N2pzNqPmrJvhjFOOrCS5+vHdLavlHTWD91kSkNxXynViA6kH8M7Z89GRkqFYychuNVl7sOSrDUjtK4PBnjM8m+hB5f6/oshogTY9DQfdHmwvL4M7KBIY7JnFK/qG+iE4HejI9nlgKejfGClZWL8DpeaWqO18imyt+G/Df8j+4veIWnzc8AA2pk1CV7IHqX3tmL63GhUHd/PoXPHVRxSrHLl3mNmIQ++NC7xzbfFkrLzgWkgamWinKOHnTXswPfkN3JL+c/QI6YrfMNboeuX6fl5h6NdV6bgp60KwNCGj26w3YevkP46MWgXNyviwo0+PJDEP7rRmZXsIlgLsLca48CpHSUTmtyqQcLYJL9x2A/IWKFd9Br8Mi1JpkAm3ZFZsWBC+tTpdGkpLHuTViUptg8IBO5FasNPwrzt6pZOIABEuleAS4VIJ1Gk4TG2VotpxBxech9tu+DFqx5fIR2V4vo7pi7xZHKa+CtZ1hWu4Jl55P/TGXkVB9flxBzHv3I0jvLwOp96BBkecIjGKdznx/a0fR5cE+bTlgqiDpHHjQE4h1pZPj5ji4+Sn7ktUtliR3j2Vnxou7I/vgCv+CIA+JA4OovjQIR6kqZ7lbU0k7xzPRPsScs2t0EgizNn5cLMeigxHZtkhMsWThDTrPly38xD/xRytnc+NQ6/iIuNHCr/4Bdg6J+DYul9AhAedzj8gedDFHaHYVfndg4hLdCt+E5g7fu3fhqOXbJlrLvshdo0ZE2iGLWcnEa3R9eReD17ZbhvxXFYV6f37S0Rf3lZ0cF3WyIKAEA7t39MoejCNMwHF6/7gm59F1ER4umsx9qXbeeEGi3ClzXo/Ih5sUwXBG6Vqa1oHbbQm3EELjSWV6L+NIlyn4V/SX5NXIsKlcqOIcKkE6jQdpiZypTYSxiJc7+uT8D833A5Rq87DK1jXFV6lyCJc8ZmN+IPwU19lYKgh51z9frw573q+M/6qyCabAy6XiG4WclGqiJQkXMAjQs3qdtWXwfqsYsZI1/jwGbjpaDeu3fEFMi2s4lGmnZCvarCg2fv8lsJC7M8dg3Xl0xSc4z1YvG4FjzQdmFCFwxOmoiMzCzldnbhq4yoUNW1B9/jZfB5RELCqciYas/JD+i4yYsZa/jyQ8ksY40NTV8Gv4BpKw+EPH+fp2R7NRhi7a6Dxpe94hMtkk63E87vjH3p/XMBJ/9ysq5CbXI7P8vR4e0xcoBn2tcecWNTuCZQ7fJynw0NVBtnm2FpRwkN77bisLZToMbLlEp1wS04MuvvQMfsp6LK7jstEdSSTAwS3AaXrXvASZvasI+uh0dZh3Btv8J/VbliDxs6fID5Vvlk258QuDSqnPoGcnCvx0f9NgyGjP4be6LHbQZCGS93XmUadeASIcKnElAiXSqDO4GFqtV5Mw9X8ywdw90+fxp7x6V6ZTbAyW65yDsCMlMSArivgw7WhBR79WtSdVYsXhTtkKwO1kPB0xVh82tmHlZa+YalweM8XBVK0dGdoD8MRKT1/M+iOZm7eua5CnhCFT59ot+GmjVt5hMtu6EB/qrw5K4uAlNWakdbfiF/96B40p2XJk0RRRH5HE77z3p84mZnC2sX0DgQe669NPDZ2LA4XT8RAYiIOFk1CTVEhd7dPCmpqPWXKJ0hJsSimtjyORBx6/0kefxzQ96BJ8xzGdiTwgr70EmsEDZeAjlUF6DlghNHpwlnaYuSVXw9BJp3ICIzXYZ6J2SVODB+YbMC6HF3AWJX9nAWrLuhw47Hdw8aq/pcOtqBgP4sWfYv168siXCWf/9FHuER4ehqQfmUWUq+8kkfU9u69Cx0dHysVhHL9+3jXdzFh8cN8jhXPfRMp5TUxEC4gFjsILzEUsXffXbCYP+MpVnKsj3XXafzxIkCESyVyRLhUAnUGD1NbzciqFFvuuQcf9S3AV6Um1EyMR1+iFoPxAkStcg4nN06PnedOGhlokERcuOkz1LqYHbxMv0IA441xOGpzytRlRd4wJh7/7rZPAy7oVkMCr9Bz6OO8N/rIgD8yNBBvhFlJvxX8KF+E64bPzVzD1ZPxFdx6rxnmyBcEdK5klNWLePjGmehMUe5snDTQh9tf/y33e8op70ZCogPaLgGJGzQw1GgCUSj/M9Yu/R4scc4Rz802NaCsbLNy8E8U0Lb9B+g9NgsdyUfw3qSnMaE1EWVHk5DZr0Xxwm6kju/hwnUvRN4KQNYqaezvmwMZPeP8n0GbMUFWf8QjRpIEfRABd0gS/vz/2bvu+CjK/P3MbC/ZbHrvCZDQQgvSpYmAvZwFG+rd6d15d3bP3k49OcvdWe9nL+gpFhRBBUS61AACAZIAIb0nm+1l5vd539nd7GZnNhsQT459/UM+2SnvfN/ZmWe/3+f7PIlyfJurRLeWQ6y5DSP3bsLCrlxk6wbR8JGSKdmXDA/vRqezGdWmcqr7FS77NmCfRKrj1Uepnrch48lZVDcrXCaJKnTwgL4xDeOu/J5u39z8JQ7tfwEO9xEwMpIu9fYkhC1vChmuMaM/pPvX1b0XkceiX4er/n3Y7XVQE+mJjAU0yxYpF+w0ftRFL/04IxAFXBEGLgq4IgzUabxZpBku+iub4/DRvavR1tkrDPrmjBjUJcrFu9UAjInRYmFmoqhQ6qhN+9DslOYNERI+8e8Lz9zps3heUERA15EkYpvjkwb3KcAHb0/MoBUeN5w+MBbuXuB5DD/WiHN/UFDtrrakH8DLnJJ7sB4lElrG4aPpLA6lJIhfB81wHcO9loeDJQi8SQz1LgbGN+RBoOuLCy+GTRUqmUEiNf6Mj6FQOCSzXLa2AhxdcyfWFL6PyqQd/rkTLleWPhPjzBqU6jug1pjAQg2ZwwyGN0PuBYCa7Sz0Zz0NViNNhCcZqmpZMypk9TCzNug5NQa7M1HRmUA5YwRYxSevR1PVTmRrhqDAMApxyhTIvNYAPu4WWfc6yyEciH1HumOQB2QOAzwqU2RaXhyLtL03BnQpku5Eor8lcPLCcqXIuToUGKl8ErHnnIt9FX8Oyjj5kq+cm4HLIoPK4BbNehEOV3Hx39DWvjqasTqNn72nyqVHAVeEKxUFXBEG6jTeLFIOly9EfcnvP+YosbRMJ66LBWBkjAZ7emwBRZBeQn2jw4UdJqsoECFZD+Lb5xoQ2iIZCA7FDUdRkZ4rbZvTJ2tFAJeLENb7c1rmeejNDbh8fSNiHPGwxh4AwzokCPwkw2VAXPsIHC1RYvEIg2imjuE8uHT/eziv+EswYqRwomT+jgzabb3dgKtmzkR7IilRht64ZeM/hioMgZvwuL7ZdCVWDXpbSNd4h488TwqCJN94TYITI7QeEOkIep4AAJh+6F7I4wtEsyoEbK1R7sNRtpVy3Oi+3v+lupPgah9CQVdKfgyGTjBj17fLoWnTYLRhpqjCvZvn8LJ+L9aNSUaXOh6JaKUG3xP4jTTjRvS39C2laBr2uoTYqbdhNUCvK33PzX4dLpI1It2JmtJEmm3aX3E3eF7cIomEiu0EUh/UgL9uCBrH7BHV1yKcN9OBsUgZCth5IuXgKwEKwSZdiinJ81Df8J8T1uc6jR9d0Uv/mSIQBVwRBjoKuCIM1Gm8WaRdir4Q9SW/k1f2JxN0OJApdA6S95qvS3GEF2yFSj4SpS3gqrR4vNvQTsngfQd50edoVThqIxY/EQxCVveSxyMuEXoPSzobnXJ5hADNhZj216CybsbsigwYleMkujaBGK98hMaoxOarMigXjSjl06wbRwyEeIxybMOfFc+CZYn5o8h1coCnjkH63xQUHxEwdCQnB9vGCx2QfceIfnhcLk4GK8+h08Ngo1mBFlku6nrqvTpnwtHGaN24Mt4J0UqxB+DWlmKw64+iAKmSbcQ6RQU1c+47yJ/UTR3QOAdDYxyG+DQd9dQcr2Cgd1Md+qBBYMq9I1T4LkUBjmp0CZZMZJ3HO/fhsQONiCUK8wAaRrwMc/KOAC0vwSycdetAOlEVtgTE1s6AoXE8WEZOOVGES0a6E7Nf/S32VfzJm20Su1u90+IAxREGSc8o0HqHG648r/hq6JX6S4akRHmo8nG43WH8HSX2Hzvmowhu/Ogm0Qic3AhEAVeE8Y0CrggDdZpvFkk3Y2CIAsnv5IWpS1CjeWI8vlG7UGt3IUutxHUZCbSMGC6DNUQGOBvrcTgpLaj0R16o+a2NuDgvA38386KALGjJeB6xNjPG1BxEUUsdtc0JK2IadDE80rpM0Lh6grv/pO4JWjfioLJux+OvbURTfgksMXZh6wBPRZU9ETHdxRQkGa21mOD+BhuvuREfJWfhQFMrkph6zFEtxwRsEPU/DDx9lxtYtVKNs8p55KrLoCuch436VhyTkSxS8Hkpj2vIJkkRTro5wXveHVvbM7G5ehiqDYdRq6ul1/CnZDtylBwC/MR7p8MD8o5s5Gx7wFsCJHwvwr8i8IjBl8odaGG7JXltrK0HuppDYBVFUOjmU3L9WQY5NCInG0iHIyGSh9Xy4nlUmcphVCVDJzfA6uqGdv+3UCY5oX72MkkV96DbICDb2PSEE5w0Lc9Pig/HCQv32Bkoqf40f4RFL/8kRiAKuCIMbhRwRRio6GYnJQKlG/eiKQxHi+g2JZk6kGjuRrs+NqTrLjMrC+/GZaLSmBTMxfKhBq/6e7bJDV2ikpppx9itcCjVaFdG4FNIIALH47ytZhQ11+FQIYd1ebno0mj7LS+yHIfztpgxrMYBh6YtxGBbZU8WNK54D0oq3kVq63aqr0WMjffmpEExXMLvr+9K8KCcuSd61Li74QZM6Rnt36KabcZ+WR26WAuccHtBDo8hQ9YhMamW1vJo8tCXbBLJoBFi/MGDE9Hckot6bT22Jm/Fw+lWGMUoYt4zy+1xKFj3XAjJPUGVjg/VG2FlpHltjMsJfRUpxTFQaM+GTFWMyXoZ4mWM0NkYMI5Xw0vsZia8sTZHPb5rfJ9+rHS6MetQHdKfehKVae+EUXH3xo8DCJ8u7g05GJ5B6+0uuPK8KceQE/bKPoTXz5L62g1cNuKkfIGjB41GgHxTeV87SzQcYSMQBVzRG2QgERhopkvq2BzPU+2suw7VweIRMREmOwaYMvtKgbMriCdh79C6XIgxGrFea8S+jHwQhXa5x023ISKhOrsVDC9Dq8HgFxilHW9e1ozo/ALPywND6py4eLOF7pOpYDBSK8P9pRqsSZbD45O+EH+DI7PNjeu+M8GhboFN2wBO5gDrUUFjTYfKlkizW8ltgu0PuUYyM3XZb9Bw/grYjVWiWSCfyfZKnI02JFHO0kUV9Sja8iNGpC+kNkeBg0T3ycxabMpMofEhgHNow2FMxHqkpx2CWm2GXOaGTOYSPR8Jh8mUhD27z6Zlxe1J23FRfmWYDBfj7fK7zz8Nn4xDrn4Ytsa2oYWVILATfEIzXAfpvowsHSrD5TTuo7SyEOPzudN0aFVLa74RlfoVay393uK+7Fd71kqYlbVw9ihg36nHBFcZDDNmYRd7Fzw6ad4WXIDxPRlIwwABW2RYyzh0XeMWtQ8KFDbtTyFebPLHI4zabxCiG0QjcJwRiAKuCAMXBVwRBiq6GQbK5QoHtn6772iwdlYE8RUsbHb2CpbyPBLb2lBQXY2tXsX2voc5lJQpCIqKcJkCgRtN8NCSF8loWcDwLsT3sJh0kMXQGic1ZxG8Gj0YWrwJiqwtWKvLx8uK6+Hxds6JXUKMxYHr12+DQ90mfEyzSTz1b8xMqYJe3glFq4vKO5CXtSJjPNSjF6L6zNvhUYdyegjYegG3hgjBEnHSM+vNeGIfD1lAFohynEaqsTqFqJb1dmP2BbDjx38CZRhBVIdDg61bLqHssnZVO8yFq6U5XCFdfoJGVJujgWaPSvOvwgblAZ8dYnDYeB7qhiNQmDq88dJDbfwN/fdYrQxpCoG67+tSvGG8Dj8aZYLIa59BsqM+lXqfKr1vk97y4ndwaloB1g1OQQRhBfa+oJvLQN+Qipi/daLtTw7pbBXZxQ7EfhgMuHg5C/uzQ9Al/1FUF2toyfNoafkKBw4+AI+nP1AYmIYkc1MhRl+CrKyro5IPETw7opuc3AhEAVeE8Y0CrggDFd0MA+1WlArZJ00d+GPFMWntLF+Gqe8BvHIOPsFSItdQtmUrcmpqsHniBNRlZgaoqws7Lxs2EXXxieJm2l4drwSFArV2J3Qwo63xLSgsG4VsE8+gqG00Spon047DHlU7Ms94DQXGNm+mhcfD+CuqMEicTE/kHNpbcd6+zQGZI5FynrcrSqFMAAAgAElEQVRBTbmLxT7n0/i0OAu1sRYkMo1Ctx02UPI8GRsxBa/gFnEhWBFV9nAcJz+Aba7D2NKV0Biag7oS/eAkIMNF/maT2fB19vLeLkW6oVebg2dpV2Bgl5/vOFZ3D76sfRly7Ryo0xk0yfvyy3jITZ1QNxwOyGIqoNDOAqscQgFQpgIolLkQwzvAWdvxVko3Xp0+RdSvkZR0H/zhGOa1q8EotP5ypOC76CPQe0ntXtepkHvWy8kiC9h1rXi2ynf5BI/TkuKbCmrpQ0rD6c/8HS2tX6Gujy5WcvJ87Nvvk4wIQ8IHi4yMK2DuqUCPeR84zhEwRQI+2QEZXEcfY9EInIwIRAFXhFGNAq4IA3WKbfZTlf4CL/u1115DndeORiwcmZmZuPHGG0M+Itpcpq++QufixXDVN+B3N9+JvSleQ2YRUBVOeoFoZ129+WvCGUBmXR0mbNrs56EfycnFF9NnY+ug4bR0ltreiqa4RJh10kbWgWba16TH49UNV6DF2iS62mKdeeEAEJFzmPnjFhR2tvgBlyA8GkpYp5kr/lZsYybQTBTJ2Pi67cbhB/wBz1HQRQBeJQaJC8EGZHR8FxCO40QybcSG6KLyDZiS4ABf8rGobIKPw9Xakk+zczZZDxrGtOCywb/CsNZO1Ld8CJeiBXJbAuJqZ8LgNZQODKIvw/V92zZKhCeLRsus6mp4ZFYwLgeUna2QmzroeuboSqj2lk4eS617Dlsa0ciOBANO4Lu1bKOH9xCV+ptuxfZho6nnJuHAESBOjnFmbTeeKO8BrO1UOV8Wl0Pj1pO2GY3Ud1GilB04cW/XYeba36F17haYU3ZKm17TCQHJawuQPf4WGObPp8KnYiMSojwpGxIfxmFD/ynIUey/E7zIz5RoefEUezj/D043CrgiXNQo4IowUCe4WV/QochIR9yVVwY9lH8qkPRTlf76XvJTTz0Fu93bbScSD5Zlcf/994P83zd86vM9K1dRmQPywr7kyRfRboyXjmiYDFd6Zyuu/24pCqqqaWbLV0gishGP3nAL1peWCR2L3hevv9TUj36WwOviobBsRUz7i/TfBNRN2sdjzk4OSd2A+RYXZJlekrl39mIlPp+pdNHh/Zhx9BCg6NXHkpJkCAfcWN6Dm/AvTOLX4w/M/6GTkY5dX85SfxwnYkP04KZyTHeVoHHEK17ZBCHrI4h0MmhrzcKBA1OFeijPQ9N4FCVDijEh+TzY9rbTv/lKfGSbvsR2chTC4dphqkM9lxr0OVWdt3wFzlXpZ+5PSDofmVRdXjgWAWtkKg32btTWHgzguwmLQNZ79diJ+GLqbDQlJCHdweKSZgXmNLoh83VHEgshlw2MUodjZX+V5MeJ3ZQykxIFm14GackkXY5Nxe+Al9slRFQZaDQ5UCriwyrD90eUl8l0GDL4MX+5MGpMfYIP4OjuJzUCUcAVYXijgCvCQJ3AZmKgg2ZxvB1pGc8+S18oS5YsQUVFhb+zi77uGAbFxcW45JJLgoBMuOn8VKW/vud44okn4HRKd5eR7S+66CKMGDHCvyvxV2y45y+Ap7ds8oc7Hsb+vCIJbSppvxOW8+Cet1/B7K0bQi5/5bhJeOpaYprdC256UZ+4grxoDHkPYtr/DY1lI/70OYfxBwnwEnjPUm3+PhL7t56z0WRPg6GnC6P2b6Vm07bcUfCovdwpAGXjl0ClsoWcOnzmikMhV4Vbe57FE8oHUKdJF1ft5zgUdvVg8TYBhhLA0l+Ga3CXHe9tdQnq7n7ZhO9g1jTB4tCgsWEQWlvz/F2MvpJfbsxQjE+cL3RZ9hm+fqUgNXjrMZS7MsRDzvPgnAfgtq9GtqYQZUnzRfW7CGiz73wLntofQs/ppcfJM8soB07Kw5HMqWrqn0X5cWKTI6BTYY5H3qanqTYXGZHt7+NciZf9+iPKy+Wx0GkL/KDNbKmCx2OS/NpHJSJO4AEd3fWEIxAFXBGGMAq4IgzUCWwmBjr8h5PJaNt5TU4OPvvssyCw5duGrNGFF14YBGTCTWcgVjwDuaz+Mlwk05Fss2HBlCn+zN3RK66AbdduX7qEni4cOCJltPieDnTExHv1o1gvmZ1HUds+vPLoIrDuUKsfAuL25RWJcrX83Y6RXCzPQe6oxLnfPYrfL+NArO98I1ybPyFZW5o1qPoi17+9ws3AGh8Pe3quHyBJZbj+gH+jk0mQnCHJRF295RscIibapAlAxFuSAFJ9279xdpMbV7bNRYorAd+kKfDoMB04EQ0rmQjnyzcBQo6vYpuwm6lCN8whJb8ZaQuQqMoQzWaRjJSbc8HFO2k5sNq0Cw18NpV3CDd45xuYZJgf9rjERNq27mkRwMVAnjkO6pELwMjVovPymTnXjHscdmO1KF8tFDySzRjoW8b6eWkD2T/weOnpV2LI4EeoJll/GS5hv17QFt68KioREclXO7rNyYtAFHBFGNso4IowUCewmRjo8B+OYaApLcXqWTNRW0t0kcRHVlYWbrjhhohmEanZdEQHC9ioPw4X2VRjteK8r5ZTwjDJ3FWdOR3ulpagUwWW/0jJj3YQ0g5BnoqF6ttfglN7Bmz6GeDkSWDdrdCYv0M2V4W3Nw9HYHnSlym8dNGraNPoBnpJotuz7g689NTvUVQf3NFvHedB1zUeQQK/z+A5BsfWlqCzUguGNUCOGLjdB2i5y56eD7dB8BVMSjkiyuEKS77neaSaOnDBrvUUhK4sHhviAUkI4iMrdmGy++9IGexGnJwoxbNYb1bge80f4dCOE8RHSU6K56nlzfQWN/662waZRJbK7rHii9oXRGN0btbvoJXHSMZbIMi/REEDqyiEQneOBAgSpDaKDEqoPBbIeSVkjEiAfWfibehZdpu/PC1IqTLQjP015OmjBKX5fsrHptRN0hwuKfJ8QOdl2P37uQOTks7G8GH/CsvJGuhN/HNyuPzm2BGaaQ/0WqLbn5oRiAKuCNctCrgiDNQJbFY5dVoI6Ag8nDwlBV+cdy56enokzxITE4Pbb789olmcrAwXKVV++umn0nPwyjTMXP0d4M3cEaJ83wwXOQDl3YybhGVzz8f+JD3gaqagitjhCHpU3sEDWZYsFJgKEMvFIjclF8PkcqR88y3cDQ1QpBMu3BW42JCPvR5nv2Kk/QaQgBF3Ez669zbY5XrUJBpgUyqgcbphdMZDeakZmrxKSpwm73VCKCfdjD11o9DwA5EvEOQj7D1vw6MBnPEp4InpNeGvEVEFlkVxyQYkpDQKvQFee5uw5Ps+chgkOpXJmdiXnkeFYA3WHiz8/D8oHb4OrlIOvNc3nCjFk2332BTYpPs9XIY5VOU/wwNcUuvCrCorYPVImFiTHBdPjaE3ty4NCVv4DBcREG3C+sZlyOh0oDH3BjCsuErqFL0McXKB8ycoZghrLwaayGednmbstn2NnHYTlEcOQmFzISa5DBqJMmLgxDl4YE7dgs6s1XAYjoFnvbpawYoLEgr4vdpiwV2OpFzNe/luEdx+PIPkNXlQrXeg4yorLPlEcZ9csw/pRWJSFb5c2e89fpwbELC1d98fo2baxxm//+XdooArwtWNAq4IA3UCm/Wb4RpVitUzf7oM18nicBEyPgFz9fX1fjRE9KTSvOKZDrsO2nUyZH1NOs1YmrkjYKgvh8sfSi8o+4PsI+xu3S0YGQcOHihrKUOGNUMgUHszMWK8trtf24G381hKbA4ZUiR80TXlIXM24YGX/garSuctaxJQJfjdsIoCJJQkI67geyh07XBZEtBVfSZMx8oo2CLDAzc6dV+B13t9XQRkRj8TOFDVmDRuGLi8XbAbjgCshwJQn76W0KXo9QRkgBJzFybtWCdqo0iOS7TIJphWSmffwKKk+Gmkp10YHF6Ox56HNiHeKfDrpMjuW1u/Qo1lf9C+oxPPQmFMqSSH66jDA/fON5HSsh37ShaiNWlUSBl0qkEOIyvGAhP/snl4N3brXgVfuBeqGBfMDjmSvnEhG/dIGmX7jiQpBUF11xRQ2XLhUNWBV4Ty63zHkNmNKFj7LM0UcrwbprQtaE1fDru6GU6zAnK1G2pjGHFUciDCCTsseC0SYGwbx8N+jh58mhJqTSbM5sqwXC3K7dIVwm6vg1qdicyMBT+bDle4zsqfM8t2Ao/i6K4nKQJRwBVhYKOAK8JAncBmPzeHK5IuRfKaO7StGXvX1lFz4JgENYZNy8SgcSlgRA3yhAB4PB688cYbqK+vC7GHobgmwN5EkZyKwjXfof6220TLgL6y41dHl+P+DffDwwfrEWWZszC2dWyIcroPHATy2t64ewNeGKtCQ4I8gjRD+MVUOLvwpzcWiYKJQLsZ4f3pzchQwQIhi1WbuAMamU28tMXzUDUcxsXGK6CVxSCwPOUj36/CHLQiGUloxbUZiRhtS8TSpZ+L0nh8WmS6S6rC2sgo2WFo2Xp/71pPzaAz3/7VEYyxu6AnwEekFBcoWEqj5u1InJt7A2KYRHHISgyfOQ+c5W/DU7eVnqetaAZaJl+L9noLPC4OGXIGo7UyyfJfX+I9ie3hoYvgST9IM1B+DEvuueZiHDwwA2bWDj2nQYknEwVcStD6hS8jsige8jR2bXoUyjgTqUqGYnbSYNsSh9TNt0KlMsLiNqHaVB4EROOKTMieXh9O1YQel+0EUu9T9p7D+8Mj9txz++F2DZyr9VOWAKOdkifwEvgf3zUKuCJc4CjgijBQJ7DZz92lSKYaTmKCgK1vXtuHw7tafOLq9OrIiya/NBlzbhwaFnRxHg+2f3s7TIplYAgZqO8gYpHvypHgHoPcxYvRK4nxAVwBZUCfThHpPrtz7Z1YfWx1EOg6s+FMxDviJYAPEMhr++Tp7Xgyl0ddogTgijTLxXNIaqvBtZ+8Jnlen92McNk8nKwDTrkdPaoO7E/ZgAxXjPS8ScnSZsZ851jsyMvBkiwl6gw2JLD1mM1/gwnMesqvIqgiOWUOSkqex11r70b39m6kW9J7M30E+ARokTX3MUrua/+TwHVi2LZUDKtxUmhoyN4KY6GQpdPZE5FQOwMxIvpZZCaEj/X1kX+B5XhonC7kdPRgUNkDYDXSzswUMBHyfEM57Dteg2rUSKzL/j0sXYJwp5Q3YuCt5OF5ODkbzK4O1MYvhXbiNnEwRLweD0xEayvRCRM4XblcEma4hvnXUCC6i1slUTuldiNsq5zApRZJwFX+fSkUFXZ45EyITpgAvvZh2DWHIFd7xVTFnhkBGS7/x14eZ+4Hi/FTZpF+6hJgf52V0U7JE3hJnOK7RgFXhAsYBVwRBuoEN+sPdPQHkgK1rU5wKji4pQmr394fBLZ8xySga+a1JRg8PlX8NISLtGQhtivWoNsgAW68YpFFDVcj9YEHJMUfKVzhOBzYuBbl336FlqZj6FY5UJ1jRYlNAY9zNFxyaYdktVqNe+65h86TXNP89gaYNdK+euRIiUoFtDIWR20Omo8KGbwHs9Z9jFEVe6XDzPTazZCN3Bob3ii915vhAuYdmwcNIXBJDZcTWwrHY092NjhKAROI7AQIlXl24nbTJ6j0pCB5zMV0jg9ufBAc58YsmRFnGGzQKe1wW9RQbVTR8i2x9QnsoJSy/yFcsyF1DvyBfxaxmeV+Hhr12KGdeKEK8b4M15qG9zDiWAsyusz0qjRT7oIsPp+W18INnvPAvuttHDnzMlQe683qnGWQQxMmk0oAW4eHx+qGl8F7zCg8/yh0KSRrKLJkfZTwyRaks3CaqwSFnHAf9yflQLJOyfcrcOQWOdSDhLIiuTSv2xO6jyXh7sr7cEH957jQcAayRHTC6iwHUat6HYnz2iQ0ugQDT+PbMmi3BTcGEB5n0drvqeZYX54UrUbzgKM1A3kZD6F48nT6nepP2++nBG8kHtEM14k+ef939z/tAJfL5cKtt96KxYsX01VdsGABnnvuOcjDvLDoQ4UKC0ZC1PzfvVlOtysj2aCmw9KaPqkFsbj4zjHiYdnzEfDZTVhfZoBTJd1NRssmD6gRc9Zs2q0oprhNXhjL/vE0Krduoi8P4S0ngI+ULgsOj52IivRcvym1YLp8BEUtdZTPxMpZNGY3Qt2ohs6tw5LSuejSaCVLivkaFTadUQxinO3zcvS66gR1Sf7qyyVI6VJGmOECUvJioLqkCU9vfRpdzi6EzczxPKrikrB6+ARROxoi1fDbXVVgdr8Os8aDmkI39ie14ppEJ0ZoPMJ1eylhgeVb21hilCx0UIYXUeXwW/5fmMysC11fEQ9En+l0jXkfjBY7JlY30P18elc+MdDOrO/g0rRBYUtEXEC2jAAIhuvAcmc8XA4PfdYQza2psalIUCVKAjay3U6rB7WWj+Cw1qFkwSEodaFyIL6L8Hk9+i+KB1K4WJzrGkv/FFbKISDrxDE8Gqcq4ZjshizWDU+3HKoNcuR1l+DHu59B7afrcb4zTVQnjMzZ47agadiLsGRXeO9n74y8oElVDsS/ofAbXPvuec2oUpoNJoPEjIClH7ctgodpp0ba7fvi0FkdS+NVVDYR82+5Aw133BG2VL9j52XoNpX7BWWDF3zg5cmfGsCdbs/d/+XrPe0A10MPPYSlS5dixYoVdF3nzp1LRSgffPDBsOscBVz/y18D8Wt76+4NsHRLC5jqjCpc99Qk8Z1fnw3UbsP2kYZ+M1yEGOzrViT8lL6jYv0arHjpuV6wFbgBD3x64W9wOCVT0nSZdJ35yPTk/4vHzYRJow8DuJTYdEYJPQsBXZ81d+Kt+naUd9YHdUkW1GkwdXeixHEYKLRnB2lKDZ2agTOvHEzV1JcfWY6vNnyF5MPJ4oCN57F0aBkaE9NE7XkIRyq96RgWLP0/yg8jySfPmFaMHt0OmUh2hzi9kPKtZhuDzus9sJdyeFgWzv6HQyEO4mHcH7q+POnEK0DOtvv9P8I43gMHZ6N6WjUd2zF4K3m+CB11qrE3onXeFpiTA+xuRLJlrEGJz2st8HAcrMy3cBg4sEot4hkjSjxZIXwroRuRxwaLBwplNUxNX6Dw/CPQJdskSoqAyZSEPbvPDromLa/ClY7J9G/dqZvQJGXnwzEwvs16s06Cnpci70yw2gTq1+g68j3gOoaitWvQ8tIuOI6ZJJsFyPOUEupTN6M9dwncehMYuRxKjxHqdzuh3Up+UPRZyAAOl+8Cwn03yI+XaRPOhPbVN4IEhf0X7z3enrgn4XQ2Sz7gBloC/KlLlKffk/d/94pPO8BF+Cwko0UUycn4+OOPcccdd6CmpiYKuE7h+7y/soHYpfVnEXRCGa5nhgA9jWhKVmH/YL1gFt13eA1/fWUTdWkp8j78IGSzDx64Ew2HvJmAPp/uLxyB5TMuEVWjF0yXd3gzXb3nf+eMObCqpEt5qUoFdk0aGjKPq5dfHdQlOflHDpN+TEJLbECXIt2LdCkWUS/AQIJ5ar4BF98lZFIomOM46hqwb/8+oeM/oEtRZurEW7N+BYtW2t9Rb+7Gze8t8h8v//wa7EkZg1XM2WhDEhLR6je1JtlARbcKrImHJ4GHXB2L38ifRgdiJe/6OL4dL4BIWIQOuT0OBeue839AwE+gYrynfifs2/+Pgi5rGY+ua4ihs0iG3Jsti2maAFWOAZ8d6EKbcjNcWq81FI2JUHnL8SRhpnu4vymAgK31Zg/V58pVstCyDrQnr4Z51CeinMEgr0ffzHkgmWS4nCRTK0hcNI70WRcJUg7+UmrjUMQ8cQAMz0Lt1fPiGR7mtK1UQsKlaYfCokf+5FvBvZkC3iSdaQuMKCmnuqo/Rf4n/6Q/KvprHgnMAof7bpD7KYFnMH5PVZCgsP/cXk5Y2+2unzTDRY7vJ+H3MeNOSTm33/LyKfwYjk69nwicVoCrs7MT8fHxqKysRGFhIQ0N+fegQYPQ1dWF2Fjph280w/XL/S4N9CEd+LIPZxFUua3l+Dlc3gwXeYHtHaJHa5JK0KIiVB5vfU69i0HcG3L/L3lGpcLg8p0hZcVXbroGls4O0QV4/4JfoyElWzzLRE2X26kQaKCowGelU9BsiBe3vQEw1qDDl2OKQs637PCyoC7Jx95xo7AeaDLqUZNgQLc+AZAZIVeNBKscEtJZJ5YR/LL6S7z69asY2VAEJTRUqV3R2QqX3ohPpp0vOU+i15XeXEszXPQFBwZrb5iHHfIywZzZJxcBHoGm1oGq5FRElRkkZAb7DAJWi3AQD0lmuAqRs+0+yS8FLQmam+E8uAyNl2+CK0+CIE6zZYXI2voXuHOAqtY2bOZ+pBgndE7AGY58JNt1OOaJRxPLolQGpCmE1RWyRh40jnyZej0SMNSLYft4PXoPTvDUZMcgJNhlSFClCaDRb120Bm5NG+S0/Dkd+rphsCy7I6hM2jBCOBdtu6WyV4Lbpqp5OLJ3/xFsOHFW7xyoB6SjGdnP/0pYS7+Ju3jzSGBcwn03yHZqD4cZe49IrhPhhMX859ao4fUv9/H+PzWz0wpwEYXy7OxstLa2IjFRaNUm/05OTqbq5ZmZmf7Fffjhh/HII48ELXaUw/XLvPcjkZPoW6qLRINr+LDhx9+l6OVwgffQwlJzsgpVGj1cBgayDga6dSw024mGVPCbNX3R0+g7V/orvvKA6K/0l6+6C2a9QXJhtA4rrvnh26DPw9neELbZP4uzcXFqqPFzYJck+ffL/3IhXuCGU9DyQ9kDsGmSRYCc0O2XPGw9tPHd0KgzkJl5FdVFumbFtULWjOeR36DD4Bo9dLIUcEmFqEzJkrTnYTgP5q35FCWVu+n59xeNxIoZF4ETecH7Ta2xPigOPyWHS2wBfB2I1WcST0JpsV6iW5W2+lFo5HosU+5AC2uSEBUVOjcNDQ2Ye8tzaPjmKAosTrB9sqcEMHWn/oCG9NXgtO3QOxLR0DgIFa0JXr00odrp61KcZC+AklWL8q1810VAkc1UA1nbl3AnngP34Ca0FyyFS9ssOlfiKJC690YYmyRK7n0BrpJDxqPTBvyACffdiCjDNaoUOe+/FxUqHXDkozscTwROK8Dly3BVVVWhoKCAxov8u6ioKJrhOp675xeyT7+CqaWlIK3kgSNSlXme4wUdrnX1QdpM/elwUcX0JQuBii/9RPPuoxo0/EAkAsRIRsLsNKNGhcx1/7rvsOLFZ0Wj/f75v0ZjSpaEwTWHhJ4WXFJOVOl7zylle0NyG+ckx+HVobkhL3HfyX38q/8c+A8uf243cmsFQ2ci2tmSNEq4tiAAwCH9jH8jJrPcW+YSeE08ZNhruAn/6MiFg4kF626DxryaKuhPb5hG5SLIdmL2PKQ8WHR4H85b9ZFfbV/I9GWJ8r2kslVCl+Jt2IbxNPsoWCcJJtzBXYokUxRQWgvoUgw0shYjw5O41ZR5PQn7CtYKSBVoS0PRzr9SwLNYtR5WRpo3yLicyGhpwSyzBT3pv4ZRLpdUmyfdi3YOSFeQePM4LGtBhawOPawdMZwaxZ5M5HuS4OHckLPKsFY/ZN1/rPsMXZ17kLcgHraU3b1ZLZE7k3QtyjoyULDjMW/2jaWgWkoZ3xHL4ZHS11FvrkeGPgOXDbkM8/LmhQWB5LQ/BYeL/MCJlgB/IQ/z//FpnFaAi6wl4XA9//zzuPjii+nSEg7JbbfdhmPHjoVd6mhJ8Zf7TYjEEoi0kgeOv//97zCbvekZkUsbiEWQZGQI6Nq7BNj2GtB1DHxsFg7+owketwerx07E0mmz0ZSQhNT2Vpy/diVmbt8EZXIybXun72KvFMTGjxeju7lR+BsYVBSNQPnQMphi4iB3OdFtiBcFXMQRMLvuY5xVLQ8RRe1re6O3WzHVYcI/fnWBJNjqe52+zGJTwmjsL74aEMkuGbJ/QFrZm2DYXnEJMTmGQI/Iy8oPQ+uVixCb59DaSgzfswmuuGRqB0RAyNvTLgjL95LiY5G5bLFNwfcNZ+NYah4MNgZjqx3BOlxetXzWmoD0hpmI9epwiauyh0pHUDHR4a8JAKXv4AFD7XSkVFxFy29fKLajhSU2NmIoRshwpR3TYfKuj6E850Wo5ArJ28/J8ZCTKnYYz0QCpNycA0qZNKePAKVay0Fsa/wUMUN6kDO9GQhYT6kJOM1yWD4/HyXGSYhRCB6ZUoCrXtmC3xQ+SoEhAfAEfM7MnolF0xaFz7wFdvBSTTOSuhPEaSPtUhTrDP7lPu2iMzuVI3DaAS7Sjbhs2TIsX76crtu8efNwwQUXRLsUT+G7uN8MV0ArOblMQtZetGgRbDZpe5JITLCPh6h/+PIr8JexU7G+tIza1IBlQVTQSdZmyq5teHLnOuR7RVD7SkGQV9EXs36FyvwSgYRPBJAIqPMRdSh9hhUkEci9nRQL85H7oK3QhNj+9F1u8oIKVKSP5HbwcefWNA+DyZArml3Knv4kNImHg5Je4Up5pARbVrUeoxo6JeUmOM4FlvHqjnmv/bNRU6V5af3xsTrzkPHNlWhrP4YtSRNFgaOglM9jnFbh50z1pG2WNncOkI4gwKxxzKswxW3pTQAGWgLyrF/bq5ptwVrFfsq/CgVnPDI7eIyXT0MsZwGv0IGVKSRBjJsHBVyiJtVeYNKpaoOn2+Hlb4VqhRGw1ePqwIo6gStXeEGNpM5X0Hx5QNmZj7Qtd9ByJe2SDQP8ergurK56DjGD7Ygf1gXW6EQjwyAjcwHOHv5gWKK574fJrpXLYWptgSEpGaWz52HIpGl9dLj654RFct9Ht4lG4HgjcNoBLqLD9ec//zlIh4tkvKI6XMd7C/339xsohyscf8t3NUQqZMSIEZIXdzxEfXKwd1esxt0KIzhZqDYXAV4XHt2Hgq4WKFkG1ordkHW3+5MdhKe0fPpForpU5FWZq1HB6uGQpVbiuowEXJgSh2tWXI3dLbupsXV+dz6MTiNkRISKFuyEUhPJJhQXF9PO3YEKx5I4vHnrd7A5xIU9C869EwpNV1AcCVm9EoMk5B446Gy1WLBth6hVETmQLwsSeD8zZkkAACAASURBVNBwvDQpDhfd39cl2HgGzY60W7qw0aUTQKxX0ZOAn+r4PTgWvw9nW69AllWDHDmDjjGPwm6QUGX3kuF9xHpFjg6bPTdBX9wubmkT0K34nWIvjrKtgh2S3zCaR4pDg3n8GTQugRl3cW9HHgRwkftIcsg4pD8yGdVL1oM0cBBfz74jUF+MTLzkqsqwOl/+/f1xnRAWaNH15Dm0OmpxuOTvMOb39FoSEZsgBkhNmYthQ/8Z7e777z9qozM4wQicdoDreOMVLSkeb+RO/n4DBT/98bc0Gg3uvPPOsOBjoCDPF4VzdhzC9m6LZFehyuXEdZtX0IwXGYKJ82H63g3LUxLpLiQaWuO+uBPNyjJw8kQ/T6qwpQ4FpjwqgupUOrFw7kIMGzZswGDLd03h5DOKZj4FWfxhQV7AO/6Af6OTSZC8MXR2F27c8AMcCtKZ2bsf+Q4S1XyrzRqS/fLx0o4mpdGilN/UOqBL0RdTmmiRUI0nEgXNVWtRoUqHVZsKt0wGu8KDLnUz9qVthK7YjXfnv0vn3p+Fi186ggXUBRbsYv8EZLvFAVcAQCNAq5pt9vKtbIDDhnizG7N1cyATKdsG+SkKCIbqc5GYxMukvR9NjkaUPPsrWs1tfXU3XLVmP8+KHJP8R1ThN7d+4V8romSvT7FJ0xBpIrA3YycG4sRA3S7dy5KWRCRf271/NFr2qhCblILSs+Zh8IQp6FmxAp2LF8NV3wBFRjrirrwSPhusk//UiZ4hGoGBRyAKuCKMWRRwRRio/9JmA2klf+aZZ9DTI901Fgl/q98ypghRn4SmdONeNDnD6BPxPGYc2IFBLXVCJKmJ8xEoTR14+eq7YNZJdyQG6mf5VOKXtXR6O9N8/is8VNbtiGl/keYzSpNL8c7cd05o1cJZII0t2grzyNeDOD9UjgGDKCgKGTyPrDY3rv3OBIe6BTZtI1i1EykZSRg3bhxWrlwpuXbkXc9NqMdaxQyvqXULZuEbTMAGED4bGZwHYJyx2MbNpnpdLbJYpNk4XFrrwpxGNwjB3tF9GGt72mCLKfXmAVm/4XZD8kE88fDvKRDcsno2LOxRCb5Vr9xDk+sI9tV/iqwr9oMxSLtVBGp7UakEAHWWQ9jcuhQz0hYgUZUhWT4k7LBv0uRYkq1Eo4al1zT3qBMXNrshFynlCZmrZRh63Twk16bCtrfN2wXb66jR7mjA6oZ3g34cxBV2I2d6kziHiyjRW1OQWH2+pN9k4HoLoI6j19g68Z9hLYkszRpULSVlawFAZio1GLZ9Hy3H+3hbpDzvM3qP8rJO6Csd3fkkRSAKuCIMbBRwRRioU2Cz/jJckfC3joeoT0JDM1wmq3SUqHZWBy7c5ZUwIJkujxv6yt1YTDrxkrMo76vvIEmbQP2sT5o68MeKY0RgXQTUeBDT/m/obFvw+OTHMT93vtCJubautxNzWib67cT0Hpl0cvpNvj0CaZmmTXgecwwM2ke/5tVqEoQ0N/JT8QrzB1EJB4bjcf5WC4bX9HbqBfpWhls78voeOfprGPRt4pxzDuhp1uI/+DfWp6qD/BmJJun0Fjee2G2HibVgTYcMLGQ0yyMAvwZwMgdYjwrjxo5D8dpP0dr5NTqvcYsbk3MsdOUXoa6CR41lP41FkZf/JD45AaBlb72XKvF3OBshGD3vp1E+N+t30MpjRO8bArbuHanGmmR5yDVNarTg6R89tIhM7G76ArmRhbMwhB8jaMP1Gf5yoncO3l8AGHeOHK60/ULWUqJ7s7/HAAVbbjt2tn2DavvBfkuVhIC///1ebTiSrQz0rPSfT0SNvr+5RD+PRuDnikAUcEUY6SjgijBQp8BmkWhwheNvkUvsN8PVh6jvCwsBQr+vCN8Rq3PYcPUP3/RGkuehbjhCdamkOFx99bMIsNthsooJEVAwpHBW4VfqLXh6ytNY+XoFDu9qCTLpJiAnvzQZc24cCiYcDygAdBHQtvvzH2Fq6oHa3o6M+vVIGr0ABpUK5rQt6MwShDRZWxJeYP+EDYYUP0DolWNwYn75YdgDAI7Gmo6ctEKoL20ObwfEALEjD2BozDZRex+CAb+oOA9LSq4BJ3JNxJ/xob02jKrvwQ8mFg51Gywx1eBZb0aSliJ5JCUfQW5qFbRqK3iWA6ckANonMioow3cdiUHNqvQgCZCioXroJu4Qzw55OU/6xgko76hCpekzuA1xcHo7MY2MASO4PBRyaSHl1OWpMjwyXAOPyDWxHI+rN23G5e1x0MljqfVQIJCbnXUt4uXiBuyCIXc9vmsMllTJ0RVjyIgEdGd9HySMGtNEeHC9JcxwkhngeNh3vol16jp0adUCGV/KkogDaIbri9yg70SgZ6X/A696fF8ZmFPgsRSd4mkQgSjginCRo4ArwkCdApv5LGXCqcz3Rx4/Xg4XKfUN3bAXnW7R3BN9oaeaOqg6vH/wghyA9lglvvR3KQpFssCOxED9rP5Kl0aZG/unjEbl1hNQ0xdZ6758usNnLsJwQ0yINAFJqHydKsObmTI0JbiR3KlG6UErcrp2w6luE47sJ4wDClccPh78Js3+jGsZF9J1Sb6fxmwj3mLfwIIEG0YSA2uSgPFVUiGAoCf0j0gq89OsSZcHz21qxw/qahyTtfTOg/6Lx5Ah65CYVOsHWELzIgO4tHB5FHBZEtC6V4XOQ+QKgwnr09OugKvsy6BsXyCXLG33TfDUl8O84zV8OfccOGPU3vMLLtyCUGkyZriGBYGu68s02GOUiTsH8DzyWltx2zerUGAYFQy6rBW4IPcWqCAtCUE0unwekYFAbULSecjSksYHArCEzJkvWD61elEVep6Bvnk0klaMh2P7a6iP0+LYDDVSxrRBFesS5beRQx9bk47OqmAnEJXTjZkVoZZsRD2+rwzMKfBYik7xNIhAFHBFuMhRwBVhoE6RzfrzUezvMgZK1A883hJvuU+kikM5RNMP7OzlcHl3ZD1uFDEOjJg1j+pwvd3QgVq7E1lqBebYFUjZ2A5Lux0xCWoMm5aJ2+WmMBkuHtk9DlxzpJwK/sKpBMkiqeyhRtKpBbG4+E7isxd+kHhUbPge61d9izY3wMnk0DmcUPdkY7RmKNIVMr/9jMDdARpcHJbJqrBs+Av4/dFFMHW0oSf2oCgnioCnHn0bvk1aR8t8tOvSlE+J/4nxiZgzZQ4W1S7CnpY9uPTYBEzXG8Dl7KT+fs4eBRp+VKGzyoCXr747LA8uyc7hn+ursE5RESDNIGS1srP3QKPpkewytO+4HkePjIe961WAt4QEjJQFNXIdelJ/8Gf7fLY5JDvk4hjU/PAabJoWbD1jvASAAqa6SlDEpVGCO8kinT1bhg6ZeLmRTIJc0/LvBc25QL/HOushpKcPgrxbvMM0iIzv55QdxA8tS5HSbcVYTw4cxTPhyauGKXstnOoWyG0JSKqfRwFp87A3JLJ5gPFtOTTbiZG4G7ZRQjdmgJWmvypN5ixkCzOCASzPI85ix4TqhuA4kwyXRHa5v3s4+nk0Aic7AlHAFWGEo4ArwkCdYpv5leSPg780EKJ+YFh8hPblrd0+W0VvBoNkIxowu2J7CM2H8MquX3h9CNeKcIZbakyBzXw0q9M0JRGvp3KiHC7CkyKm1pSYH5BFUtkTEdNdHJQ90RmVuO6pyWFXlcThy+f/hj11TXDHECX9QCNqBuS4xdYS5Klk0LIMLByHcr4J9ap6OBQ9YDkldNYsOFUtcCsEWYCQQU2WDTCqWTyV8YYfDJGsz8ikkXh33ruY+eFM3LP9EgyWlfh1n3o5S5XY3L4Fiy+4GvVJMZLZIJLhOr98fYD4qEhWSywaPAN5ZwH2rrobDtMH4D2CUG3gEIjv6aLyBgTcdLh5/NhwCJ3xu9BOrMfEdKt4IMETg/N7igC5Gj1ZO3HTiFzJJgRf1u71raGacwSu6canwrqtWZTDJXaZxKuxqeID6CvXo82YhNbruhFbECzlQM3LXWrwSoluRg5QHGHg3K4FLrXQ+7XvoE26bSyO7UnxZraCb4qBcrj8SvJ178Fqq4NcrqWn9His0Kgz/VZTJFsXHdEInKwIRAFXhJGNAq4IA3UKbRZE9g5INw2Uv3Q8l0xA12fNnXirvp1mquI5F9L27kBhc20I3iD3HhHnbdwiC+FaCUhLBKGwwPrL07De4wgGdTyQ11YvCurIoWK6B0NtT/Ffkkonxw2LpgTxuHxzf7O+DXV2JxKcNmRtWoUcp11Sc0nfNcifQTPF7odT3e4FZoHKD6y4Grt3NlpehcscE/BM+jtYE7vNP8dkbTJWX7oarzx9P+a1z6Y4RcgifQef3U5s7XRU70vC2lQjPpyWB04W+mIlHK6H99rR2bHGb6+TlHwYgwdvEqx9+hnEE7Hy43thYavgMW8IkrQgu+boSlCWNF9UOZ3EtNzqQWtPB9qSt8KulS7zsR4livcpMT5rEGqnvojVxjS8wtwi6SP5yI9OzG0U74xVZseANShh39vmlYQQLHjIEFeF5+DpOAzb+qfRc6YWpku6JAFTGJ1TeHpksFuU0KbYJDKGAiirXZKJ5lidt9NWSIMNtEuRgK29+/6I1pZvwYv/BKEaZEnJZ0X1vvq7yaOfn1AEooArwvBFAVeEgTqFNgsnZxDYGfdzXFJ/vLIR2VPw3TsVQcT2/uaVXBAL9uo8PLP1KFplQKzFg+Kmg8hvr5LQggLkLgMMjSq4HbvAcyYwMgNGzp6PGdecR1W7JbNzhCvUJp6do/wjpw1MtwYyTRac+jrJ8wtvepEr44EULhbzXaNRoTmCO3KfETbiAdaZh0L33bj/SAMS2CQ0jnzFy5MiKvy9mluqhmIYn6rEY9f/MUjpn6j1k4Knr0txWYC9zoiRX8NgaBWfb+A0eQaqtgwc+qAN7lHPwVL5NThXZR/QxWBiypXI0GSElFcbXTy2W1wwmI6gIbMRNq137n1DQdTjXQbEtQ/HWbIu1J/zFFzqbryAW7ENZ4RokJV5duKfq4eISJoKByZgi2v5ALb93VDkTAWrTQCjjAEj8yr5iywFZ+uE5dt70HaXB44s0qUZupEXs4kn6XjA0qSB0uAKK6LKdgIp9ynRYNSjJjEWzsQEGLOzqYp8rw5X/+rxTU1LsX//nRJgq3fuDGQoKVmE1NTz+/tqRT+PRuC4IhAFXBGGLQq4IgzUKbRZOMFOchmR8pcGcsnhuGPkOHv37sW2bdsot8poNFLtKSJK+tnfd6LpsGkgp4LOqMJ1T03CW3dvgKVbkFloT/oBnCyMObKbg76yPAQoDDpjEs750134tKVLUm5Cin9GMZTLCW31bnQVDIJcYRC37fElkUQAF8MzmOYqQSGXijZ5J64uuk/AWzwLe8Ol8JhG4mu3HJ7MPWHtdoxvM1Bvl1Mvy/fmzEdHbDySrQ5c16jH2U0eCkyq2Ca/vU7Z+CVQqaQtoPwLwrFo+LEIu0YOwbBvLoKlywHOeQBux24BuLIGyFUjwSqHIEvJIlfF0vKqleNx1MGhzsVTkbD0ho04XDAIPcZDksBTyEImo8jdBt3c12E3HqbdnpsxGaswp1eDjP8W07uakLftXsn7ho1xo3vxLaS25t2GgW7uM2CUuvAZro1/R9PjdnDeCrLYCbzuQSEfCST4NCQM7ZLsTCRpWZLhSnpGQbNaMXPOQsazz9Ieg+XVy7D3g1cwbF0dkrsBeUY68hf+DrHnnEN/FPQd27dfim5T33tabMYMYmNHY+yYjwb0PYtuHI1ApBGIAq4IIxUFXBEG6hTaLBCIiE3bB1h+qkvqL4tFrHVI6mP5keX4z4H/oN5cjwx9Bi4bchlaXzXC6gVNEc2HdDsWGinhPRBYdsaXh+FJCd2QupqDIacgL7K5v7sVd2ozwpLxgzTEfEfxdlmS45oLR1DDacnBkZKRQKr38cuE7rwk2p1HPvFluKhEmS0LtprfQp3+H7xTcw5cE9+C3VgtCVYUh70vcQD1Rj32ZCdjevpVQaKi5Bw+e53hpSvCZriETA4DZ0cRPKU3YV7+fLx77zvoqN0cALRKKdCS9BL0poOSW3fCropDTGIeWg0H0CAn9j7B3ZqBPDuNikNZ+nI0j/kyrNSEoWmieLhZwNOyEtaNS7yip4A8swzq0deLAhcB4HKwl78FzlSB5hva4MojKUaRw5NMnMIIj7uHEvupIZO3bO8jwccVmpA9vUE0Q0Yqf8Z3ZNBuk0GemwPXvXeBeCXW1VVCabagpK4b6Z1mqi9GD8syMMw+C5nPPRsy9/4cAQJnzzAKlBT/DSkp50athCJ60EQ3GkgEooArwmhFAVeEgTqFNvu5M1zh9L/IS/5A+gHUG+rR4yAvKeE/6nDHsLj60P1Qt8VHHF2SbZq5cChSW7dj78dbsFtzJlV2t6ubJTsBSemP6H0pTMRSp89gGMSm5eOv866DjTgiS4wQDTHhLe0/riVnCDiN16uw7zF4QOaKQaYjgyrMm1k7Yjg1ij2ZKOBSaCw88Pg5XALgyoarcwLU6R/j8pqJmDxuKzzqYO/GwNOQMlXqfQLgI2CmPCcFZcPvgqaPqKjPXqcufTVSi1eJcrjI+W22GLS1jMfV17wAGSvDl//4Gw5t2RjoZEQRE6soglw7D7zrYG+5lma9BDCmcFkxZdM9sMx5HilqFZ3dYVkL9svqYGZtULoZuLqVULtG+jlgJAN7wZ9HYMvKq2BT7hT4b31si1L3/Jbyk6h5dGCtlgU0QxPR8erNcLc0+0OkmXIXZPH5ksR+3mmGbeOj4KxmWEc50XWNB15rzuDV9AAlw/5Oj1O+6XHiUQWnWYH2fXG0W9SHpnNm1cOYJ7g+BEp4aMoZxL0hp5IbuwZlo0mnAmnOEBaOfC+AlG4LRtU0+6+KZ1lk/O0pxJ57btBcIs9wCbuR0mKUzxXxoya64QAiEAVcEQYrCrgiDNQptNnPzeHqTyW9XdWOtelrRSM4qG0cZlQtEDSfQoAKFYMKeGPxSHUdRVnMPvSsWoV6gxYHMgvhlHkIaQf29FS4dN60REAvfqBvYygWYvDF7CtxKH+I960kPo9UUzsuKF8f1OMfeFyXIR729DzJDjxSLtM4UzB3SCxkDWY/UPAB0A36cjyV2dulyLkM4FxGyDTHwPIMnjXEgjE2Bnk3+q8lsEzl/SOJnHz6X6Ax5IiCDNLnWTniefApewPETYXXcltrFg4cmAaf0XnF+jVY/tKzIKKeoYMBI0sG7yHaXoGfEzBWiER3Mc5o2wjVmBtCNMvIsQTV9+Wodbqg0M0HK2Mw45ohqNz8Dg5t3QBjfhfyR7Jg9BYobImIq50BfdN4CraO6ZoxYu5k2La1wN1phzxODf0ZadCMTELNgith27Xbn+HSzfkbWE2c5LeY8re+uVvAPQxPZR3spV6TbXJLCcks6GriMf6GLTSmr9x0DSydIiBeOApIpithaCeUeheV8MAWFQpXWuh61sfF0CykaET7qM2TU2tLSxG/4Mogj0Xu6kE4ov6A9CRG/HSK8rkiDlV0wwFEIAq4IgxWFHBFGKhTaLOfu0uxPw9Hm8yG5dnLRSPI8CwuqfkjEpvzeonzXvscQw9RrufgUMVD7ehARuNGDD4zD10ff4zyzMTgLi/v695tiIczLgm8QgXG5YCysxVyU4coX51MaH/RSCyffjFIFkFqELmJOVvWIb+rlpYNxY5LXpyunEFw6mL93XC+tykplxl6ilEwMhkZaEHsEXko4Iop98tCkAwTzynBsEQwU3glj9F4cFWCU7yrMKBMFXgNsswyaMfeIMHWJ5H14IfMTyDP2Aa12gy7XY/GhkFobclDVloBFv7mKmr8/cEDd6L+UIVkDKW/GgyS7bmYkjwasvgCSe5Um6OBqr4r9Gdj0PhpyB7Siq9ffo5mfsJ1QHrAIemyYmhHJYdMgQr43n0PSOMAGeEzXL0dir4DEdBlG8vBMpWDJ56HrIOBboMcBZf8DcZzBfI5iUvDQWIFJPFjwQf6A8B/qjd7tbkwA106rwBs39nzPPqqzTMqFXg3EYLr9VjkZQx67o6DOYOA3UhBV5TPdQo9yk+ZqUYBV4RLFQVcEQbqJG12okKlUtPy63Ctq+/1EZyaEbGP4EAu90QyXOQ8yZpkvJTzPvbSudqg6m5EasVXSGneRgVT6QvNa+DrbmpGVU019mQlgQ/Xny9xAQS+CKAsmYKnz8aciSZjkvRLE0BxrRMXb7aAc1TAZV0RBl8wKLl4ARpMFrS1dIBxKaG1ZyDVmIvh0zKRqWDQ9tEBEDfDvoOWFNPehqtuK2bt5HEofyK+nDIbDUlJcKMNWvMq/FqzFiM1bro3vXTy7iXXU8Eg8yU5zZz45MfI3w+OiMPU816DfX87OI6nBTgvi8xfhvOVGEmJb1tqHPbnpqBbrUWyrA3nqn7ElTkj8cW9i2G1hSHYSyh4kGtkZGm4IO1yKMPw26zuHnxZ+xLi0guw8Jnn8eFDd6HhUAUN0byMX0OviBMFaxw4qHOMSL55ZEg8Obcb1TNnwd3cTGOkoByuhWBYwo4KHjznoXY87rqt4msbcP8RgruPwE4yfyteeEbCZsrrvdnniFRnq64NB3NSYSeO4xIjUG2eQEapnwO8nIX66UvQkVIBm60WMpmW/j8cAFOpUjB50qaBfMWj20YjEDYCUcAV4Q0SBVwRBuokbBYJ2bw/K56TMK0BHzIch4u8FLcnbUetnrwEQgeBCKXJpXhn7jv+D8MJr1adOR3rDHLqU9e/pkGfFysAe0Y+3DHe0hLD4J0z5sCqktaGUjs43PF5lwBkeB6O7pcB3i75Yk4fNARXPLpI9POWl3bBfqwbrMjrk8Sp23oY/KpF+Ov1t4TIOxDylMq6DU+ufx6ZhW5/1kW7joVqO4vDqQw4FkgyAc2xDN46Zwo6xi2EGXoY7VZMP9SI8+oZxLljoECwNIJgEq3CdylygajNsBToEng2jt+ChYdeQfWaTKG0G4JWxIGFfzNGj1kZNyFezvaT4Xof+rh4/PaVd/yluglJ5yNLN1ialO+Vf0i/d3zItEItqhiox/4a8vRRwbY9PA93Qzns2/8vRF8MCgXk8fFQpKfDePnl9POuDz+Eq74Biox0GK+4AqvXr0JNTXWvnpZPN4LGUTzz5dS4qb2RvlXaBitQbT4MnqXn0JSWItBjMTy3K5rhGvAD7iTskJubi+eff57qEIYb1113He3qJtsezyDv9/LycpSWlh7P7hHvEwVcEYYqCrgiDNRJ2OynMJsm0wpUm7bZ66FRZ/ysCtNiwJFmUohKuTMWTpce+1I3ojKREKCDWSsyRobHJz+Oc/LPiSjCxFx7maMTDoW0npIosGNZpE44E4fau4NehJ+VTkGzIV7y5ThMpsTNW+3+LGFjxbNwWLol56rV6DDXwfpfynFXXgnD/Pk0K9LwxBZwJmnpCsIj+rzjCzx17c3gZKGZGIbz4J63X8ZZWzeGnN/DAC+ew2LDMDlMib+DUzuO5kVotGkbHY+Mpm14d6MKMfpg8viKNDkeGq4CJwKoWN6D33L/QtaaKnRWiWglhBOmohmudBQkXI5RWnkYDtdXOGrZB04lw+1vfoqPHv4LFA0syhLniYqp+i6e3GOqnFjRDJe4CTsDeeY4KPLOpLpcnLUdriPfw11HxGb7sKkCrHTC2V3pZs7AMbsFBxtqYFPIoXG6YFYr4ZaHrp9v3haVGzuGdGHyngSalew7fGrzaV1m2qUoUyjBOxyCbRTV7jLAplTQc+W0mZCt0mHQ2u/9hwmnzxXlcEX0mDnpG0UB10kP8S/zBFHA9d9bl3ClODIrYntzww2EgyM9gtWmvcxeoQfwZ+1ICiyNNtQ1g3GooLamUXV3oYzF43D8Hqwa9DYFXb4uxZnZM7Fo2qKwL9bAqyeZiyWv/gNdWtUAMlwMiN5Wc0wi6urqgoJ5KDkTa4aMpp2OfYeM5/HwQSfmETVzloEiWYs99d9jd/VqPxm77z46pxxTD1SGlEJJKar1lT1wHDOJanWRdSRK5zeM12JfXhEtoYYMjsPQw5V44ZmHQz8CcCiTwf03z0RH/EKQAmLfwXo8uPfLb3G+agJ4VoZv0uT4OEuB/bEyuMl7XyQjQzJdRTiIv9Q9iopl+SGZHJvKAY1DWAuyqsQPs3xoGUwxcTD0dGJspQMjGhJRpvYgVaXwWhP1qr57eDc6nc0gBtJHLPuQ8auZGJU8Co5PmpFA7YKkO0fJJcZfOliUw1U5dRrcLV6TbpGvjy9rJJk9YhikPfUUDOedg3VvPI6kZz4AK2ISystksP56ISoq98PU1gKN0w2nUgGLkCsMGeS70BLnwIozmjGtPBE5zVrBjDyg3Ku3C12KGsihUmoBtxseq5V2ngap03u7GtMZBS774FN/qfOX8kz47z1Zf/lnjgKuX/4anZQZRgHXSQlrRAftj2weExOD22+/Peyxfmm/ZsN1SBKS9pYhn6Ex4yDSdelUh2te3jwQ8c9D25qxNwLfR5Jt2Hjzr7G1s0mSwyVTjgDnaaN6UazMgOGz5iJpdD6WLl0KAgwDB3nhriweiyNJ6RQwUIsVoq/Fw6/QHghdCJm61nwQm1uWiqwLA4X2LKR12zF0/5v0FUoHy0KRlQlWNxiKoovFpQm8PKILfn8t2o3SMhmJne34+N4/iN4T7Xrg8scegVtVKFr+Y7yA7dW1DXho3iSsSVFQYdH+uHBxfDte6vwNPIviUZPQm11Jtlnwwa25SFjVguwmPb6cdSkq80uE41EtBJ6CiSF1Dkzcuxc9mZtw7dFZSFCkQsYIGcog02nLQazEV3jwn5/hyP3fQckRGQnpoRmeiPgrhgTZM/m2Fs9wCZ+SVXGqWHggh8bhlGwGSH3qSTxuXI8pj61AUX0ohPXJbzQb9b35MR9ADCwtBlwCx/DYMKIdhzMsdCL5DToMrtFDb5PDrHHjYHYPzt3WgzMOCvegD276tNXE1orEcO7vb0PxlOn+M/mzArbosQAAIABJREFU3vXvw26vg5r4KmYsiOpwRfRkPvkb+QDX6NGj6Y/qXbt2we12Y+LEiXjxxRdBPieDlBQJraSzsxMrV65Efn4+XnrpJUyeLPjAulwuPPbYY3j//fepqPSkSZPwyiuvID093f/9ipYUT/56RnyGKOCKOFQ/+YY/RYbrl8bXCKcBRoQi22PrUTV1FeZzV0JekQRzux1uNweH1R1iVJ1fmow5Nw4NeaESQvTnf7kNR48dFjoC/VkQQReKyAuQ+5q88/NGJqEnrgIVFRW93YN9VpK8OCuTMrE/rRB2tQE5dg6X1Lowp1EgqPcdJEuxpfUr1Jj39rb+x7jgMseg68iFNLuVnvEJWEMPZO0MdOtYaLYTXhQL9bhfQ57WyyMi3C3a4Wg6AMeaf+LG+1/EkbRY8WwTBUyH8K9nHhGZE9AYD1z9yIvg5OEB228++xBPXXczuDCdmb4T+DJcj1c/IKij+z9gYCvOwfUXNMLDeZDVfSnKh50j2u1JujwLa75Gl2wx7t54FqbGnSfhu8hhQ9cyXPnqIhC+m1Q2kExBlqBB6u1jRMEW+TyUwxUQMpkMDTfdha4PPsCQjqNS+qawF+dg4QWNeOlfTsSbQ++DcCCIbE2frRThkTuGg7HIBMPIdshinej0sNhglmOnVSYAfe+YvJfD75dxkPWpcG4qTJfmLTIMxLiDvVzIxaIl7p/8gRY9YMQR8AEuwq0iz6bp06fD6XRS8EWAEwFXPsD1wQcfYMmSJZg7dy5ee+013HvvvTh8+DDldt11113YsWMH3nvvPSQkJNDPtm7dinXr1kUBV8Sr8TNuGAVcP2Ow+5zqp+Bw9ac2fbwdScfbPdmfyr1Z2YVm/RHkdYzwC1dKrYDP93HQuGSYvvoqSIOIEJYJn6V81Qps0sRjx5Ax6NbHw2hjUXbEiclOBUZMzaCCqJ9//rkk2PKf22twPU+fjRi3eDkocJ6dvAWHih5BbG4LTUMEdP4HYhLyrqVcNmZ/PFJf7qGcHcojyj8TitR8KNKMVDvKWbsF5c9+gv+cdT2Wjo8Bz4aW0liOwz1vvYTZ20I5XOQ0jXHAtQ8+AreyULQkKWS4DtEMlGTZss9iEA7XTdy/MP/tTVQd3T9kMiy9PBuLs+toybgz+QG4VUWSXDi5oxJxLY/h9T33IE2eJUmgb+YaMXbR5bCWt6Dj44NeufXQO0Q3Pg3G8wskAVc43lXM7Fn4c9HF+NP/3YFEu7StVHsMcPMf5HjsHRcy0zlYiUREAu8H0bs6M9FFzLglyp72GAZWpRsqM4+CGfVIzrLSCyFLSyTNyJrtscnwTrvSD7oee8eNovrQrsTVJTlheYv6+AT89uW3e29njkP9bbehZ+WqICkJX7dvYLflf+8JePqeWaqkSDJd48ePh81mo5ktkuFqaWnB8uW9sjrFxcW47777sGDBApAqyMaNGzFypNCpa7fbodPpcPToUUpJiZLmf2H3WBRw/fcW5KfoUpTKcJEes82YgjXyi9Apy0OmWomFGYm4MCVOlLwcGIUTmVe4DBfJ5pjUrTDYE/H/7F0HeBNntj2jYlsucu8G23Rjmk3vHQKpJEACpCdvySbZTQLpdZPsppG26bvpIQHSaSGFFnoIhGowHdu4d8uWLFvSzPvujEdWmZFGLglkNe/bLzzrn3/+uTOaObr33HNUkjLe7tcioZsew4s/lXxxhEydgqdvvQvrKupEXUo+V0BZqZmx4fhPZho+/OADnDsn3SHJH60lkyBqZV0ap4PK7F3TqDJhJyr7vQdGitgjcUtxrArGDRPQc+UOodTo0l1GAGH5Hd+gmo3A16PCcCwlQNCCbUFyVJrrn1+LV5bcAZVMuYpKis8tGIN9/W+TJN0Th4tI9/+5cr7HsiUt37FL8d79ryL6AzUv+O4o0XHDiIMob6rkz7Yq6d8eM2sqazWii+/C8mPPIoILl/3SNWmb0e3JSTjxaynM351BBJHQE3ejtssmWHSVdvHTsPIRCM6Mky0p8peWZVuAursR9IjnNuG+tS8iozpfMsNFp3oiGXjiejUWaZrQJa5F7d5BBLXuTCjObkqR1TkjcvyXk4uQrbNiQXQz1BJ0NBsHLKsOwG8mocT6zutWyWyaxwwXZfy0WgSFhiE8Nh6Dps1EYk09Sh562MFL0iHkajWSnnvWTbn+j3sS/u8dWQRcVAK86667sG3bNtTVCc049fX1fJYrPDycB1w6nQ5vv/22PUgXXXQRnxG7+eabERcXB71e7/QDpqmpCRs3buTLk37AdZ7dW37A9cdekLZmksRVS3G4CGy9gXuwByN4MrhAUXcGIioPZOT2ZN48cbg42FAXWIXwphg+u6VkI1+9kRvulnxxrB8+Fs/f8FfYJM6FCO//zuiK/GUf8Q8w2Y1jEFbXC0HNceg+KB5pxgoEl5Fwg+f1nRn2TzSHn1KsTMFxDBoruyHx89GIie7Kd8lxFgNi/28ar4zOqBi7GTdhwJzUAOztHoi6EDXCjTYMOd2ErDNVGLPtQXmAkELVKwafzxBkJSiTRYKulNkiXtrQ3FO4cqcBb1zRH+diZZoOOA4a2NCdy8MlwTm8DlfIbyrULvscluJiXiIhcv48vvPy+h9uwMGKgw4ZLmnuGKHaGJRDc+4BPH/2bmSY0iVLilQeDugahv1aLU7vL0Nz/VoMmJoLJJOHpLu9T1LOXxE9x1n4lEDWsR1bsP/H71BXWICgRjPSaoxIi4pF1IIF9o7Rq97aAf2OTVi8bwXUohmiw00idn02DmMxP0oaLAlm1UmoOeUOIO3k+JFluCvOjNQAls9suW6U6cprVuG1ckEE9elPbJJ8MW/lS/u8VEqnEiOrwoBDJ/jr7rZJSEko+S76x3RcBETAtXbtWlRWVuLdd99FbGwsz+XKysriOVtUMpTKcPXt25cvHc6fP5/PcFFJsU+fFqcMlyX6AVfHXbMOmckPuDokjF4n6Sw+hVRH0g6Mwzu4Eyzj3ppOf3ktoyuuSpDn+bSHWyao3OfgzL4yoZwmGslxHGIrD6AiujuglpAXaIkgvaiagsrRGFwMVt0EjQXIOrAVqXn5buTmO+99Eke79ZQkfRPIGFBTgTm159w6Ex0vVqAtHL3049CvRRT25Y+fwuzj46H2lIFTAacmLYJNJWfrIn07MI0R6Ln1lRYrHerUY8GoVbz3X+TVvbD0kY9R5cEc2hpQiMnrn+Xh88Yho7Bq/FSURscioaoCl21dj9z4X3DdZhsijC2fjyPh1CToG4HsMzZkFtj4GB5OC8SqYSGSZUviDi0JicC8oamy5Trx7NaeWYtHtz8KG2eDOXgU6qP/QjoQbidPf/l3RhcEmXbh+JbfZOPLchxOhwQgt9QEa2MuQpM+R+rEEo8G1jGBF9llIeg7tvbfL+Dkrzul/QkLK6GfOgVUTlt1sAT3frEf9+1eitElOXxGz568YoDdvRn8+woV/h7fJAuW+Owoq0KAIRXaM4NRmMsi3ygItjqS4/+RaEKEBxWTWivwzzI9ooKiMPVYIC5bns+DZMdNIOgnoDQixOuzhgaI0hLJJC0hsWni49HTQUpC0aT+QR0WARFwLVu2DBqNBh9//DGf4SIO1+rVq50A14oVK/D1119j+vTp+PDDD3neFnG4IiMj+aaqY8eO8UR5KiFWVVXx2a25c+fya/UDrg67ZB0zkR9wdUwcPc3ijU/SXj6Fa0fSY7aHcMyW4kTGFddHL9wh+hCsGdxTdsmO3ZM8oTwuBUeS0lEfFIwwswlDqkvw7k0L3EqT9ML8tqwG7x0+iTyTBXoTh6EnTBh2rABdirYhtuI3vD/7KWzr3xW1IWpEtGRu+uULnWIEturDc9EUJJSpWtRG+ZdHSmEhRu7c5QS6Zj/7pueOvtpqvFdXgB/z8yU5XHTvz5o1CwMGDLDHYvLnk3HXkesxqCmdP5b4/RD/S/GoDlCj+bJXYDDs9ynDpavtgdQ9j7jHXQXkhZ7Ar4dW8SCsdWttAqDuyJ97fIqLtu/B+jF3YvugYWBbFND5DBY4aBv34JVXXkPvIiGneaTvTSiPJcHDlq5BO6gFvh4Z4ly2BAc1GHsp1lMGVFwf+SDet+U+bCzYCBvHwRB9O5qCh9iPJ5VVJUCe/+YBqArrneLLc9AsHPaabEIXoWE5ul+yC6Hxja2tek4IhEFQbQ+kH38SovApr/z+lmAJ5LrZAUh9I19OC7v4Evxt+T78mFOCsQX7cWnJF4irt6IiHPgpS4UdmQwP5L2BJf44VPvlGISWZ8O6cxJ2Vq5GfrwJW7Iq+bUryXBtw0he/NfTsyJ0ymT8xJlQW1ai6KHF2wOdKnIf66Axpmgi/6AOj4AIuHr37o0bbrgBR48eRUpKChYtWoSFCxc6AS7iclVXV2PDhg1IT0/nuxjHjRvHr4mI9i+88AIP2EpLS3ni/LRp0/Df//7XD7g6/Kp1wIR+wNX+IIpA48OiShSam934Ut46pjqaTzFoRw5Km62yJ5YQoMWB0Zmyn7/22mv8F1xKMoFv9QdwSVwEz5ESX8wUg4VH8gQ+FXVlUWmjpZQ19sCvePSDN/DPm+/EtkHDwVJtxYGb1KdQsM4hgntD+HHJFyzNNWz3r0jLz7evm89wpfeQ6YxjkXn2JN7duAp7Zl3h1qVI9z2RT2fPns3Pl5OTgz179uBs6VmozcFINaUjm0mEnkpyLVSvepZDXhOLQguHpMu2IyTwY0lejmRgWRUScm5FeOkot48JaFaai7Cp5DOJXRmog6chP7GZ1zBrChmB+qiFvI6W+0Q2TD/+FR54fTXKYgbjaMZ1LRknFvquvyKix8/QhlTBYoxG9akJ2MWMxd7uQagLUSHYbMLUYCuenzYearVQTiVw1HiwAg27imGtbYImIhChI5PsJVAaQ6Br3dl1+PzY5ygylkCln4jG0CkwIgQhMELXsAG2uk1ICW2VAfn2hd+gKWxAWqAKwSoGJoe4UiwILJpr/4O+Cw4gIET+PtaYI5GR/z5iF/bnuVrffPouqjmbLHGfByBnSuzK7GR3tPpgMZb+ko9c/AsqXYEbgPYEltziz6oQn3MLVtbl4/PU7fb7eHCwVbYsSRyuFdVBuHLw83bxX0/cs//cfqMHw2znFQVarJh8tPX7Yv/Uz+Fq/0PfP4NTBPxK8wpvCD/gUhgomWFOQKPlxez6y75g/nw0HjgoLZbZCXyKS347gd8MJkmPNyUZLhFweRQFdSlNflVShb8fOycp90hk7Uu2bcTasZNlFNQ5XPZrA5IbfgGrMcpkNDjEVFZi8sZN9itBHK7nrlvokSA+o+AUum/eZAdUREYlbsTQoUPRr18/fi5quXaSjXAg0ofVZUgKlRoiS6Ad/SQG6IQyndh55kjTETClkP0ILhuMLof/KstdE/0EXW8zWkp9qAbfjD3DC8YK3YAyOlvg+Ozl20vfwuayfjDo0/hYJo34L8JS9vM8KHs3JcegLi8WeRu6gFGFQxU4EOrA3gjtzWFTr6UobijG4nPXoV9VN379YrmN9tdlRiN6foZsydEx80X/FkGUilGBhG77fT8Lpjp5xX0a32xYwWe4QuIbpbOIHGW4uqNXzd1oOvwj6jdswMbeKR47+UR/Qqly2tT/vIySwI/B8J0BrZsnsOQOeIU1GXPmYXHaS/aPKft4fXSz271C1/ZwowbFIRdjyYQXFYn/8obZJ4/JCu+2HpRBjCYQw/Yf83cptu8R799bQQT8gEtBkGiIH3ApDJTMsK9Lq/H33AJI9bWJfKkBc2d5VL2mF0D3nzfz5Ti5LJkvq1SyJk8crhdffBENDQ3wZHvjCNyI+D96w26c1Uj7G1J2KqjJjMbAIGkFdY5DWEMJrjy4BTqbvK+hzmTCZavX8BUcMiGmLsUH+w3Hz2m93AjilFV7/IM3YO6RgWcuugeFtY1IidDhupFpuGxgElQtDOaDBw9i5bcr7abOjnGmzsDQut68Wr7r1hhowCfZjyI72IbRoVZEqVlU21TYUa9Bv9Kx6B2f15pNOj0eA6tHIkqj4Q2k3UAVx6GySS7DBYjdbrSft25Ayl7uH5mBD+/ZhMYmFfRdf0HisA8luymdSd9C+VIdchF+7rkcKQEMFhdfL8llI0gSOacnzmrL+aygK4hdl7fOzu1yPVeycrojbwmai6Wtbwie8b7cTcd4DlfXiUWSFo7EnYrdNhnaL7YJP3M4Dh47+TgOvD8hZbiyBiFt2TKnpX2z7xwe2fEA1GFHwHA2jDnKYdo+FrEGDg032KDuI4BVb37plHWL2/0M5na7T+Do8aBbUJIfxMRjbEQDYnTVqGVVyEcqhva8HTO7XaIIbNGCPZVNne5dlQoX/fVuJNcZUbPMvUtTNOD25bniH+uPgFwE/IBL4b3hB1wKAyUzTEk26fUXH/eY4QrMGoRnH/6XR3kDJZwacYlKsm6e5hNJ896MncXSJHU1XlFs9GgCrWJtYKXKYC2LJtmAq/Z+i6imKBnrGw4hhkqM2r4JleEMiqcOwMJ7P4Vh3fdY+tUarB4zGaXRMUioquQJ5JP37uS5RC8PvgabUrL5bB+fiVIxuCgzHq/Py+b//d9/v4Pi6lKZrBoQYtUjuMrd+NUUXIelA/4BziUjwp8Ox6BnZTb6lo1BWFMU6gOrERxeggUNYyUBDIli7q5Yh/yGI+5gTLSCGVnGf+Ytw5WtDsCHORY0nKtHvYVF2fB/QRV1WtpHmQWM5TqcWiWoWlMQ1MHTURkThLGhGmQ0pkt2a9rAYkNILgpZaoxo7YITy7RrQtbgYCVldIEJhiG4uGYc4i3RKNNW4bvIbVAFpqDXwfGCxaPLRoCrVM0i3srAalyLruO3ICLdYNc6E1yiGOiORSHizXona06PauwchwEF5Uhu4XCFX3qp05GpvHjnZ3uAMwfw16oghDMRdq9Fc9EelE3QonkcB22MCSpHCXjHWfisWw9EnXkKl1vXQx2xCyptDVhLJCw1I2A1DIRapcZLcwbiiqzkNj14JBsDnJCW0KXYc9goXHLX/Xa7nzYdzL+TPwIKI+AHXEoDRa3jMro+Cqf4nx6mhC+1pfoMih98SFYTZ++L/8aDQdEes2SeMlJSF0DklX1UVIVz5mZ0CQrAjcnRinS4RFmIbwaOkTV2dsxwEUB7LTrNowm0ztwIc2CQNN8K4GUDUo++huyKbMmXPJWZOHM0Qo0pPIA5mrgTN15xJS5Jn+km8EhSGKXxQ3AsdToMwXEwqDgcijiN08lroAqoBWeJxNW9r8ZjExfgpadfgJE1y97DQWwASPNJ2By5UJWob9LhO2sN9jUK0hueNrIverDoZoyuH8SXFe2ZLhVgiWPx7e6XJMneTlYwJGzooRuQ9LmezDFjRrHAe6Lv9enx98AWVCu7tOYGDY5+1tpAQWbTlpiLMF2vRYxVupv0lKoUW7RHQUR+141e9rlJuTiqPepwvsIZC6r6HPZG5CIkcgzOHKywgy7BbZPDCa0Na3QWZFjVGGRWIcJ8HJEpPyGhdx4CQ5t40dHIzVyLcr9zzO1WO+EhTp6PNCq+zogshy5FWjfJRxz4aR3qKsoQHhOP4TGXQFsqpKQYklMhRMhxqKg7ip9rvuPnjOxpQNeJJWAIdLmhRRUSj96KlU2j8W5VnWxJPzs1El//1Z3Lp/RBKkpfHFi/DnXlZdAGBfHg1trUBH1cHAZNnYk+o8f7wZbSgPrHtTsCfsClMIT+DJfCQMkMU5LhWp3V3aPq823X3d4uzlX7zkDivcGyPK9pVVU9NvXOkjZ2duBwUVfjb7pwWRNoKinOyj2IVX0HSWtmtcgGBBp3YsvaLQipEVrf7Ya+HIMAczQKIgbit+461IaoEG5kkVFViI//cjnPnxKU6JejubgYh7tfi5KAdPscgnkOazfPFjhFKkxLm4IeW7qgjK2VzXDFsuHgKkgVn5PkQtHMB13UwqWuBwGOgTEDsFBzLXqciYetpgmayCBeZT6ofzS+e32JIGdAP35afgAR2HLsdhMgnwp1PI+ru3AYvmZFXYrApDIrnj3Y5KTTlT/0nzBHkI6VO0AgPGEs0+HUajHDRfOFoqbLFKcMF8XrtKoMR9WFaFA1ohk2WOnngQzGbAxtRGNAJRYXXyeZ0aMMWczcPnzzQc7WItRXNSJWA4Q0WxHOMChlWHyDZmyAlb9yahWDbzc/A21NlddbncaTA4Ho+RhsY5HexCItknS45vM6XLS5ykekhmZiWMxMWcuhX8nKyXiUh4WpU4oQmd4gnD/F1d6lOBjducdwSV4JSuubhLVyHHoZT6K/IQd6az0MmjCci8/C58/f7gdEXq+mf8CFEgE/4FJ4pfyAS2GgZIYp5Ut56jzK2nW0XV2F7TsD6b2Jl3Xo8GHcW1CFIyERrcbOEgKqlOEqOHfOzQRa6Gjk0KeuBj9dOgG3HyvwWjZ1FII9VXIKVpZDXHV/rMvqIam+Pl4bhGVj+9i7Jb2ZZ2/u8RlOxv7GnzTxiZ4reRC7TcelszUcMKipF87VxiO8625ZLhSJV66o0eFXozQC4fk7cYP4tn+5zTFrYagoR1hMHI52qcXawD32zBBFsz76DifpBRGc9TCYsfQXC7QuKMiQsBMl/d6T1LKSEu5k1InYnm1BSoCK53ARUNykzUGeqkLguYktmx4SetpgLYY2pqBPY5p0tpJjodazSHxoPA+rq5cfQ+MRMhsXpieATNXGrbDiKcaM6f3ice+aF2E+KN14IpaLXWNLoq/Jzz/npqguxYO6vOvfEKjS2RW7aQX1Cb+gpkXhnjHpcXq/FTWn9PxhInsYkNS/CYF6KwKb4xHdMAMpfecieFA8Zr+zE/sKannwfFH5enQ3neG/B8K5CT8keo8Y7S/5dcaDyz/nHxIBP+BSGHY/4FIYKJlh7eVL0bRKsmSedLOUngG1+Z/YU4acLYWorzIjLDoI/canoNfQeA8dZ4K2lqfSpFiCpFiIml0NQcEINZuQWXQWg0+FIDOjP6be0hcrK2o9zuV4Ltetuw5pmyagPLoXVg8LlfYX5Di83jfVLuTqzVqoLCwPq/r9W0gOgcH1tisRezbODVDQZ6lsDHT1qag0B6PrpOegi5bmQtFMtsA03H+6khcBdd0I2P1zzD/tbf9Kr5ej5EKxsRhG3XDkBV8lKS6qYlvKiSXOMgoEHIoHvI2GOAKZLG83IHKnas+GIX8DcYlE9MSgKq4P1gz+kf8LlUATjInYps0VbIYUbiTAOLqgC0Kb5Bsg2MYaqJhNiLz+QdR8dVLSM5EimT88FmMu743679bKluV5KlgLEHQULz0+IBJXLt/ulkniO/1OCAKltKWG9MXw2EucwJY9ZnaFeyHx6BqzIJbDtdffZlewp/lW7i/C4i8PorvhOKZWbAIVnF03Iq3PuP0eZIydqDCq/mH+CJy/EfADLoXXxg+4FAbKw7D28KVoWqVZsvasVFCAP4IzB8qdyMokBN9tUBym35rpVVlc7vhO3ouU7hGzIADIo5CkFYigPvmGvug9PEHxaZCS+dFXLPhibAoKYzSy+kpDw0PtQq5KzLM/HfyEfQ3xunisaH4NOceOIJcpRL3KjFA2EH1sySgJLsax2jDENaSh56UPQKuT50IFBMRjpXUkLwIqJYWwZPwSp3KVk35VQxGSQ5NxdZ+rMTNduqxFCyZgvtdglPTu44U9a214/9dGt/iynBV1YWtQE78atigOZmMASo5Ho+ZkuENMGVgSYrAi6wBYhsRHOTCcCpeemwmtlWyAFF82Xkw2YQeH5gKDTAMEC1v1GTTufAnh818HWy8vwx6QqueV5KUEQQloERDc3Qv4rReDqfs5xNbBLl56ckg8NlzdKiMinsE7t13vpGU1KXEBYgKT7YBLcVZQ7HzMKwMZYosCxkTAJ1FV3Y9vI6GpVDp0ZL/Tqw/mPbVEeWD9I/0ROE8j4AdcCi+MH3ApDFQnDuuILJm35XkqtRHo8hUMuR6PQNcHz61GacMZsGozVLYg6EyJCDTH2V+6Cd3DcdV9g70t1f45gZJXHvsGb49MR0OwtIwADXYUcvU1w0Wlvo+nfywIfP5SgtLicyjRVOK7yK34Wb8XPSqzMfHUAqRNWgJd9BkwElwoeuuHafpj8Jiv8H3+97wIKGWkkkJaxT5Jg0rcvOlUuYIzcT9vDRqxZhbfbyFA5rxxrA3mfR/CWvgr/wHPc4rSozS7H4w2K/SxAtG616ix+D7/B6f1dz/QC2j2bubteEQCXD3ZRFR/eVwyc2VfT9EehF78EhhNqOw9odIH2JXkXcvyZ3UNWNnfjO2ZBLycEaGnMq6rltWlXW5HsCbMvgaB93ZKEmTyvLeWzk4n+xwXMVECXa/937WwNQiGxFJbaFQ0Fr79seLvg3+gPwLnawT8gEvhlfEDLoWB6uRh7c2SeVueJyDCgxYXMGS3Cyr8FI3mIuiCkpGSci3i4y/lO7ikNm/ZpZCIQNz43GhvS3X6/NjuElxbUIjCaOkMl6uQq68cLtdSH5UxRUNmAZ0wmHLiBgwKbUTysI8l9axIE4q60+KjL0PUvD5eM4WOHoSuwfBUfvRUeqZ6V0aNDR//anKzzLEV/Qbz3ndboBaR19ROGRlPF8Sbr6bUvlRSvPmmm3lulukQyVmQVEFr15+1eD/Me9+DJmUIggZdC4b022Q2McMl9XFb4+jK4RIyXEn2+/rUuLu9dnbmftpD6HzMLxMyWBICxh5FSv0ZLp+eA/7B53cE/IBL4fXxAy6FgbrAh/kChqQMsQXGkwqxcdPQL/M1N9DVmuE6zZtOq2yB0JmS2pXh4vEOy+GJzw/j3TibtNmyi+K9VOmUl5QAi7NRh7C+F6mJkzK8oHrumk2SfInzulpZmJNYidj4E0KWy6U7LenQX3muUNSc3gjOivN4t4igTk6nqrZ7Mz65eKnbHJ5KzwzL4fJfjZhRbGm1zLFxKKsrRZftT7aCLa0WSc/8y4lz5GmxIj/PF+mQXOETAAAgAElEQVSYsLAw3lSXrkXBX54Ax6RDFRxt17WyFu5F0JBboUnK4s3N6RkkuangMZ5tzRQ6aVlxHFKDMzAs9mJ7yddTZyddM3NxIALfDEFSbYNTudBVwd6jt6MLh8veNCHKVMTGY9A0v7zDBf7Y/UOWb7FYcM8994CMsWlbsGABXnnlFd4k23XzZaynk/EDLoWX2g+4FAbqAh/mS4artHQVjh69D5yEMhgDNfr2XYKEhMvtEeksDpd4AJuNxYIdx7HVahasg8inUaJbUhxvbw7gJQfMCIsKhCWjAt+plqHYJF/qo/09vsS7TMJfitJQqVsHq64SmsYYRJ6biLDSETwYJVJ1g1YF5pJ0sM3HcZB0kirKoI+NR+SAwSiqa0BdXR0KrYU4EXIC82unYExDVguUddapuvL+29wyZXKlZ7AcRD9KJ+jC2tD32FIklO8RQuNgWqw0g+l0bRXq9VGG65ZbbuEPKeUjqkkZhqDsm3i3ANlNRRZCMW4Zw9ay4jJYioqhSUpC/tQMvB9/AsWNJbJlXNfj0Dy523/Grq9XwFBZjhFRlyA5uCcP/uoTdsl2dlI2M3pNDwT+mOc8pYQhtCuwoxtE0G3lUNc1AMP/cjMu7n4xj91dZSr4e9wvYHqBP3Vbl2/37dyVJ+t60VEn+8QTT2DVqlX4/vvv+SlnzJiBK6+8Eo8//rjbIXwZ6wdcHXCF/ICrA4J4AUzhC4dr7945qDPsb82KOJ0fg/DwbAwZ/IX9rx6zIBygN/RG34z+7SPmc967JTvqMrh2BzpysUqf3QPWIO8D2GhrxupzbwOcIKZKL1hzUjdY9ZF2grrw2gXSbXGYbOnvRiwnnarYqzMkM2WtpedKnDQawDWXYcDxBozLiYaKbyUUdLno5R5XeQCZRz/kJQn4Ta1G8W334y11OsbEvo5+Ufv5bJ3wuXQGk8Br1bJcp6YCDaeCgWmUNhlnGJ40P2DAAOH8WdZNg0439n6oo7rJlqaZADUiZ/VwMsmWm0tIV6oUl0jFe0RKsZ20uLqHDUJUUAJKBrwDI3V28tnMlgvJMQgpH4z4zdNh3uZCdpcxhKbjHN3xM1Z99RasNQ1o0FlxPLUBZ5NIsV7Ist6svhg/vvWqpPBtR3UztqVJo6O+T//r84hNFD8cKQP9W871oqPiRD94KKM1e/Zsfsovv/wS9957L/Lz3Y3MfRnrB1wdcIX8gKsDgngBTOFLl+K27aPQ3CxYyUhtgYHxGDOarHOEzRvPJyYiHnf8/Tb+rSUIlArZCW1yEiLnC2KU9GJxzV7Q5xHz5vFClgfXfy8ogkuUWtoid9HWS1b+1gE0F9RL7k5lN1dPRIs+CuakdMkOS3qXj7dkogfr3LlJj+TA1HC+O09qc8vCcUDPysHILBuNGGsiYs0mJOZ+h/iyPWB4cSsBlJzsNRiL+szBkPi9uLnfZ1Cr3L11XDOYpv3lbuR3Wp9dm8uhiUC09qEHPYEJR3AjCtNaiosRNPQBxUR5x/MXs2WczSaIm8bo0Righa7ZgtTqBgx58BFEXHaZokvrqdx3aZfbUJO9FA3xJKXh2HXLILRsMBL334qGtXeBSPNibEOnTEbYpMmoXbHc7d7+zou35P8dHgxzgcz3rQO4Xm0tvSoJ5O+ZuVGynvNxjCgTYqMObpeNRH3bY/XkOl9NTQ2ioqJw8uRJ9OjRg/+Y/t2rVy/e8zQ8PNy+iy9jvcXVX1L0FqGWz/2AS2GgZIaJGYeOMJ12PERnzOtWaiMdrnHJbjpcvma4SGm+vl4ahNA5Eadn0T33eFTbT3rxRRQtvhenDtaiMHEMzEFRCGysgsX0Pep0za1KRi6lFko/tFXuoi0xlgIg4nWjF1urIrnwV2NqH7C6EBlJCyCeDcelliFud5djd57rh17J4qOewthcxsm0+PSoi7CwOApWjsFDQ19Bt4izvEK/++acwZQDmGTTs0tzAqe0ZbzqvFar5bNa6WE6HNrwgyw4puN5Aq30uRxRPm/ePJgOHMT+rnEok7DvSWK0uHr5N4oU3F21uBzjMHpIJoxZ30iKxVJJMSHnVoTkmGA5uQXapCREXHM1GjZtRv2GDQArZBcdM28PTanEwapD0gbpYDBvcyoCGiWsgloW1RQQhlUZt0iaryt5enm9X9qgEUfH/b0zN0rO9Xwcc9VbOwQhXMlvG9BeqyfHac+dO4euXbuioqICMTEx/Ef077i4ONBnKSkp9uG+jPUWVz/g8hYhP+BSGCH5YZ0l59BZ8yo9YW8croyM5/lyUGFLB6PBoEFeXhoqyslOx/0tTmnr2XFxHv0kw+fMwY7DISiPoawOw5OpbU1HYTH9KFnaTA3LxJAeM6Eya1HbYMHZJpa3inHcPMldtDXGBFqp+67hcCXIu1D8wUJZn0LjCeyqWOW0hoYeA8BpA2RDH8wFYn7TGLfPPXXnOXZSUpZl9BEO0/exLRpUDI5NSMMDj6x1Ah6OD/0Xxz2GyCB5uQLSFKtLepiXh7h/1zWItAjq6uIml+GizzWGGgQVnbZ37knxkDyBVhJmlWs8ODluPPKbTTjUNc5NBoKOTbGY8bd7PYqJiqW1w0/9RxbkZM4qgza2WsbuSTCo7ln+vD0DKcVTswdLrcaHV4Ti+17uch3imJk7ExBbGyip10U5yNLABHydNEvSfF3Jd9qt89ZhJyokD4wdiKUz3Zs0vM39e2ZuvK3lfP58+DMbUGZosXqSWGiCPhC/PDylQ05BzFqdOnUK3bsL9l/07549e8pmuJSM9bY4P+DyFiE/4FIYIflhnSVY2lnzKj1hj12KsdP4X+qVFetbDFgERgL9qK+s6IJjx8Y5gS564RKnR//ss2g8IG3NQtmAspTRONJtrpOKepNhOThbiduyR8ZejpSQXjzY4f0WW8jIJRYOe03OmlFy2l/tiTGBrvue3oyr61WIUKlgYjmcrNmA/AbivTlvUhkuipioyG8KCkW6SY055yyYXmIVfBBdQAcdj9cJ21UMa20TjtlOYWX4RmwJ24O/r7Jh+HESKeV345sKiMoVMX26XYiTpnR86HvLcFUjEv8qtPANBEvyFiGjMd3JoseTeTXdCEHFZ6E1VNsDQRA81dgMQ0gQzLoghCd3xcjYS6EtVznrdMkQ5cWJKMO1sb4CtcEkxCrd2ZjUO0NWTNSxtDZ9R0wLyHGfp++1JxEQ4qzY73hVNeZI9Dzwpl0fjNblfm8z0KQMhTZ9AtiwGJwMr7Jru7mafncrCsGYQyIHz/n+IZfM9bGTcCK0VyuG87EMNemLSahorJD9+scFx2HjnI1KHw/2cb9n5sbnxZ1HO/zecaIfuK+++iquuuoqPgrki7to0SIUFBS4RcWXsZ5C6gdcCm84f0lRYaAkhnWWJU9nzevLmdq72Io+g9lciKCgFKQkLwD9PTf3AckORo5jcPz4KFSUd+MP5cjpOT1hIqzl5bJL2Ju1GAZ9Gp/ZEjdz7X8AzjkzQDYsji38jhNS1mq/yeaU6ZLT/mpLjB2BT3lhPSqtNtQ0cii2cJADh64cLgJb6zOG4GxsUqs/ZYvn5PDmw/gb+zp0qgSkDbwVCQmX8ehJ9BoUWjQFr0GSuTjO7kfSmnehluoedCFxOz70hyfswS0yHC6i0FdYgACGQ41NheryXpibuxgaaO2hXq3di3JVnUwGiIOqsQEh+cdbL43j+lqAEnH2hvW/HL0iBsNSY8ZZXSVvjl1vNSIiIgJDhw5Fv379nLhglEla+uGbaNLKK9N7EhN1LK15Ajk9Ls9DaLx0UwBdD9cMF2XenO9tBkFD/q9F9kLQIKMSLF21HWEH8FzyB87encTl2x+D1LLgFo6+0MZA1+J0cDf8EDfVCWASRPSlDOUtw+XN51PuS/t7Zm58eXadb2N/70wgdSOuXbsW69at40Mxc+ZMXHHFFZJdir6M9QOuDriz/ICr7UH0pvztqIDuy1E6a15f1iA31hO/i96r9XUJyM25FPHJsU4vTeksQMtRGAY7Rv4LTQGthE76RArEuNqwOK6TMl3VNg7bG1qzXHIZLl9jLJYSyWTZEfjQm7HYwuKXmhzYHMqf9MK06qPQFBkHLkhnB5In4rtgc59scBLisSrOhtvwOkZju13zrFvzo7JegxxnQ+NvH8LWoiDvdM1chDgdH/oMWCwc8BGy4w6BYVieyyU6MtEctHbxb/Tv5qoe6LfvAaig5jNdywK3wcTId2rCaoWq2cyXUhlLMwJqyqExVLuVzCgTd2BwExICBiK0NlQ4sP2WYJCRkcF3WokEfGqq+Pj6uahqJjAkkeHyQjB3Ah4eQI4lMwBDRx2CSqKpQBS5TRt7k72L1PXe9iR7YYMNLyV9gs3hLVId4glzQLfiEPTOD4XeHIj6QAZHuxhxKjoYltqRsBqo1N76Y8SXMlRncbh+78xNRzy//og5fm+uG2lr3X333U46XJTxIh2u226j5iXgnXfe4f/raawvsfJnuBRGyw+4FAZKYlhbsiRKjtZZ8yo5trcx3joYLaYInFm3xM0qyBvP5cCkp1HdHOaU4bI15cJi+sGJw+Vqw+K63kaWw08GoRzkicPla4w98Y5sHIdNjRZUVq8FYznFZzKaRCkIfiEk1SCgiW+zxqFMHyXTtciiJ47jCTwqrB9qJBffiZCcLMnLQtlG3pNw2wuSnzsKcbo+9Ak1Dk/Yi0ndVyI60IhmDojRcJJEehsHHCjsg4F5cxBvicYGTQ7qYZb0SRTPUziB1vPmuV3FZ5xAF8WpKkEPbWRPp5KlHXSBwWR9NrInDrdLRBzdugk/vPUKX0Z23bxJKLiV1hxATmijBg1BHA4EXIRTwel4tNdrSE0rELJ4LiK33bnHED2vr10nrXbVahQ/9BAYIswD8CR7QZmuXN1Z3Jv2kuxXjefcCzdASzhVsBoyYS6ex4MuXzNcndWl+Htnbrw9m87nz+3dnL/ko6jGhOTIYFw3IhWXDUzifWYv9M0PuBReQT/gUhgoiWHt4QF5Ompnzdv2M23d03OGi0FjZTcUbH7Q3SpIQo/JsZOr4er7sOnjXHAOv+LppWoxfgfWcrJlARxcbVgcz8kxw+XNlNvXGJe1yEFIPRqpWHRcxeKdFC2u0JfBeOJXlDLSPKNPRkyHKVAneykiuSq8gb+0fM5AZ+iJrr88LDuebayB8ccH3D6nV39T3zQM+vo7u4K660M/LOYwygI/4suTd8WZkRogZLtcN8p+5TWr8Hq5DmqVGomGRAypGCIJkuwdeq6TSHC7aEh9em9wQaEy4K2li9M2xC6CSmkwu0goIZOWjkAlIqFypTUi24+ixoO9WsTUBEJrsyLUaoJ5CIvGKTrYogFtYzQiC0eh67i/IiQr0Q62eCC7bC8GfvgiRhXn8DIcYdOfh0oXKXvNKjU1uK7nI5Kfiw2O7uFTwVw8B1ZDFtoiJeBJW87R59OXZ8TvnbnxZW3+sb9vBPyAS2G8/YBLYaAkhrW1083bETtrXm/HVfK5pw5GjlWh5NebYCgYASnulKv5MLXUR86fx+twCfIOOTizv5zX7bRnRxgO0UnF4CyHeUXw7lFZ6M1mg+FFPp03ynkcAQNjtE5S7sJxtK8xPvOPnQgwy5s4N+vU6PbEKP4QnnTJvh00VnGGi+bSWKLQffPLMpeGg7X6NBq3ume4bAzw9qUaBM2c5mZfJMqDfLtyM1QNVL6qxpApLyE0QL5MWGsFnioJRUpYCs4ZzmFo+VAkm5J5oMQ3LlA90J6ZkUBtnDu3i/Yx9BoAlTpQ9tazd3E6NBLYbXDWr4OhohxhMXHAoER8H7QPRcZiJIcm4+o+V2Nm+kw72KQDSJXWCGzdtZJ1ajwQhSlpH97o20HzKzwxGUOuuRZ9Ro/nu0DFLA9rs2FC4QFcfHYnBvWfh8CIVElhV68ZLpbDmKOOXafAj9kqbO/LwGZOQ9O523FRZjxen5d9XmRG/uyZGyXPRP8YvkNYoQ/F/3i0/ICrfTdAZ5lO+zqvUquW9p0tJRRY5Bz5O8rKBLkGUionsjyRiesLs1D8C2VnVG4ZLiXHVaITJsWl4uf20uEmdXxfYrz18W1Ia6b8mzuYoAxXXiCDcU+O5Q/jrkvGITbuLBITT2B/8AC8r10IlnG3tWnlcG3j56G46jX9kfTjIuduPvFkiNJj/hWGHz7gRWMduxR392bw7ytUUKk0cDTodhTApZclgSUCAWmTnocu+owkNYqepGebVHj59FD0DR+B0/gANtaGLsYu6GbohhBrCIwaI/QWPQJYeQkM4nOFnjpkvxQsw6EsowdCuAjPGa4WnTIpqQwl5TKqCJLwavVny1B1NheFoc34iUBMJjDmCHD7WhvUEiJJ9Kf9qfHOml98qVqFnsNG4ZK77sfsd3a5aSxNhQaPQAeNxL1CIPO7iK14K+ELHqTy4K6ldEjg7+5VzuBP7Dql6/nmZVF4POvzP00ZSskzwT/mwoiAH3ApvE5+wKUwUOfxsLaYTbfndOh4B3YuRWHRZ9AGV8FijEbt6QkwFAwTOCYquHG42nM8131ZK4szK0/BcqACWgsLi1YF7aBYdLuiB1SaVmJxRx7z3ic34c5GjeRL1AoOb+qsWPLEJIkMF4c+fbYiJvYcD1A5hsHruAd7MQLU8s+bN3P0Lw5D8QvuxCvkyigALlaFzMwlCNjUG45kfUeAGXl1L7zw7KXovSUPsXUcKsKBn7JU2JHJ8Mdy1VmSs3hKS9+NoMEfyIp91uTOxQdnR2NQsxYx6mbUBJTiSPx2nIz5zW4GfknlJdAYZDoIKcNlakBIwXG+DMiqgPx4E8701GFIpXR5krKY4y197Ur8UmKwXgnho57CoDc2oX59qyhpy+8DHMgIQmyDCin5JkkNrKKIUHnNrxbz6Ru32dw0lgiSPwkdxkEDgtViBlD8LwF0Q7oVi7gDKOI2g9HWgLVEYtyJCvztB4Mk+KOM5XcLuuGBR79TdFt3hgJ8Z8yp6GT8g877CPgBl8JL5AdcCgN1Hg/zJlTqajbdEafii1VQW47nqj2liQhE6MgkBPWPwU8fHMWZAy2lR3FyBijTq/Cj3oqUSB2uG5nWoZmA2W9ux2XnmvmXKEE6ynTRi5MyEFthxequgfjq9tH8ahy9JWPjzqB37518JlDcCF7twhisx3SUNSUihivH9MB1GMlth1rVmjG01A7DRVct5bOHvA7XLyWw1pihiQxC6IhEO5HcF50lKRPzIcFqJGg5lA58Bw12/0AqEQqZy9DywUg4eBv2mTgUWWj1grkg6UkVxR5D4fDduDrjanSp74KVK1dKEtoJZMUU54NtqkORrgHHUutxJkmQ/BhWPowvT1IHpIA0BZCSxsZikqWfPfslleHyJnkwvyAFl68oAGwS5WC1GqqQELAGg+QturNHEmqDgzx2RH6VcIWkijhF6B4EYhakBU3pVM8MjcVNe85AtHx5acdz6FNR6cBibF2WyMnL/kYwJPa0dQa3qjPm9HYe/s8vnAj4AZfCa+UHXAoDdR4P89WKp6NORUkJsC3H8lQ2tCaGYl1ujcDzctkIAK0LtuBYgI3nt3Qk14W4Ovd+cRATOTVmIQAJUKEULL5FMzYzNrw4dyCuyErmV8SyLC82mJubi/4DvodeXyFdqmMBY7kOp1alIrKHAdGZNQgIY2E1paD2zAQMnXgj+oxI8hpCb6DDUWfpowe2w1jXytVK0TLIClZDxVCOjUV9wi+o6bIZVl0lNI0xiDw3EWGlIwQ9MBfJDVqYYzbT8bwdGR2OemyE1e7bch82FmzkhVWpqEaZrK7GrhhVPxyaRg3C2CBk2FLQnY1vLTXKKNB7A5vPfcqg2zmLdAwZBqrgYLAmk72L1HHgxr6pXjW/4v/yDBZ/edAOmviYcCzP57pf3x2h+mRZk25t1zA8FW6DYGpsw9L1DyPa6EFwNT4OPbds8Xo/dEb3YGfM6fVE/AMumAj8TwCuvLw8pKenIyQkxH5hJk6ciDVr1ii+UH7ApThU5+1Ab1INrmbTHXEiduLyT+s8+ua15VieJBgoT7TPaHWz8eGBDjgUq1ksDxMAhZJuLrlMmm5grL0TTQBRHP62fF/Ly7GFe0OZLhlgR+AjJycHJaXXQaNpkA1Dc4MGRz/r6fB5IBhNFJJ6j8Pch+dDpXHnerlO5rWs5uCV55rhGhOqRpSaxDm9t6Y7Sm44rsFR60w87z179vBWIiRiqknVYEPTBhQZi3hC+9zec3mg9eXxL1FsLEZSSBJPcp+ROgO1K07Ilk+j5vVxuia0Bk9gU8UBS1+0QWuV9ylU6fVgjUbJDJjHDBeAxF59cM2TS5zuC/ol8OCeTzG6JAf6ac967FakEmnCg8Ow+mAx3tzzBe74/F30LHLs022NMiUbg7OykPrppx4N4GmPztDH6ow52/Js8O9zfkbgfwpwkX8SPdjasvkBV1uidn7t0xkZrtaOwmWwFBVDm0wdhfNbOgrR2prfoj0kRIRBcEQGJt50N3oPa22d9zVansyNicBdbWOdxE0d569nOLwTbm5ZTasit5RR9Y1JMRi/qRRNR6oUWcy0pSPLo4wGZbjKdDi1Os0tRI7EbPq3p00JcZxa/wlcbllxAke2Ftmnm6bXQKdAB0hKVFacRE7NX+m6HM/NDoBlyqe+gM1xR4A7Vlsl+VniPLqsLGji45w4Xnw6kmFQnBiDg9Ghsr6NCaomXP3ZD2AYNQ+alv6Sjy6/bcGtOz6FmmM96nHR8R1LpAQcwzbvlyXwE+ct+dnn0bDZmY/mKK2S/PLLPKG/MxTgO2NOX58L/vHnbwT8gEvhtfEDLoWBOo+HdTSHi8BW0aJF7i8hlQphU6fAcOVl+OHtV/nOOPeNgTb0IvQaPh7Tb810y0goCWPxM7vBGuQlCuQyLa4ZLjoWKXLvfGgyFh7Jw7qKOsFrsMXtkWDMxFILnjlodufNeDBRVnIO4hjPMhoMCjZ3Qc2p1gy149zehDwdx3rTWRI5d6f3lzspuivNcEnZJonHl1Pz9yXz5ktMXc/brURJOv2MCq+s0CHhTK3HqZOWvMD/iKAuxpply2EpLgbJlURcczUv8bX2wzdREagV2hha2gkpFxhfZ8SAgjJU3TsPE2553H4MR9V5T4rzrn6ZVBqtNJW7SVSIXYoH+wbh4uv/gZKHH5HloyU99yzCL73Un+Fq683k36/NEfifAlxJSUmw2WwYNmwYXnjhBfTp00dx4PyAS3GoztuBHd2l6E0Vfu+E4SivLJONB6NOQlDENW3uVPSY4QJQbZXOcIkcrtwAgSAtKnLPn9UHf88tgJSKlprl8I8cM2aUuHNnpEjavt4EUteGF6zhVLyMxtmf9OBspdLTerGq8WUtct2Jjhwuqfk8GYPzMfbQkeqp3Ef7hqpDMalhGGZWjUGiLQ4hMeEIG5lkbwZQen5yYLPXjUtg8+DfyQQGovf+fXxWyAnEWa3YecdCHC/KhylAA41N+GFhU6mga7YgtcqApNoGHoSdTGHw9aLBdt2v0+MdPUPdPRXpfmDUKich12M7tuCLz1+FytCEBp0VrNqAkSfqEVcHvut0fZYaDROy8NSnVo8G8LpBg5C2fJldG0wk4zuem5Iyu1Tc/RwupXfjHz/ujTfewEcffYTDhw9jxowZfCOL3GYwGHi7H/Je1Ol0uPPOO/HYY4/5fBIXPOAijyMCUXJbYGAgjEYjjh49iqysLP7fTz/9NL744gv+b2FhYZK7/uMf/8CTTz7p9Jlfsszn++u820HObDo+/lKetOupROj6wvHme7hpQHeYHY3vXKPBhCIo4i9t0uKiqTxzuFicCKjCMXoTtYifEtCi7YTWhjXBFrupsvhyeY814jeDSXLFpH00oNaG939tdLumUjIEroMcX/ZFDQJHyVV00/XaVBcHo+7kJF5Gw1z7rptBt+MxPJkx+3ITSnUnivtTl2JSgLPCGIFCimutjUNekwA40gJVCFYxMLHC34psHLoNipPNZEoS2jkGPSuzkVk6BhchFV1I9JRp7fokTpwuMwZSfC1fzpfGeryPAQRlDUL68uXOz0KWxbcP3I28/NOSWa2s/DKnEmVVGHD7nVo+oza562Tc+U4xGg8eJCHIlnkZaFKGQps+AargaHC2BsB8DM0ntkKTlID9XeKQV5hnzxbz2lyMIJmxJauSj42aUfNaar1vXOLRAF60cvKVb6gkrp0xp5Lj/mnGUDUg5ytgz7tA7Tkgogsw9P+AfrOJCNqhp/nNN9/w/qMbNmxAYWGhR8B1ww03oKysDCtWrEB5eTmmTJmCf/7zn7j++ut9WtMFD7jI3XvVqlWyJ3327FmkpTlzPwg4JSYm8uj2oosuUhQwf4ZLUZgu6EHeSoQi90M8yZPjxnt8sO/KSENNoEays4vmoAxXoP4aSbV5JYF07FKkf/P6RXxHG1BoOoFfKtcgoUc2AsIuRkN1EypYG362NeKoxsa/rCiz5Uhmz951BKXN8t1fsWYW328RJAocN28ZrrZwlGj+d59aj6ZiOiuVpEG34xqSemVg3tNLlITN4xjX7kTXwd0jApCdGmaXnTha2YgjRSZ+GAGyRK0gBiE+L+haNJrLEDcpCBGXXuyWJaL93DJcHIMpJ25At+oB6KJVIztYw3dHum0dVM71lqkVS3COx8/dthnfv/GSPDgvKEdyrdAEQTD0RArw+HWC9hgBoyuPjMWVqzfxHC7JjV6uLZZERZFhONQlVpIjRqKw2wdUIT/FzAO5JeOXoGD+As8ZrqxBSFu2TFgby9l5ZR3l3dcZc7b7xr4QJiCw9dVNQO4aEtdrJTVQejjjUmD2hx0OuigslFw5cOCALOAymUyIjIzEjh07MGTIED6SS5Ys4bNdWxR0wzqG/oIHXG29jwhwffjhh37A1RJAu3TBlkLUV5kRFh2EfuNT0GtofJv4RW29Ln/kfr6+eLxluCoGZmIv08TLH1j1UWiOjAOnDQApiQfUVEBnHQpNYN82Z7goVnFgP30AACAASURBVHTdTn2xhdeeCtGEw2itw2nDfuQbj/KhdOQ3eXsReDSqlstwKXjpt5WjdGx3CTZ8dAQMp4KUQbd4r5DiVWX2lXj2vhvbbePiKcNFx3PlYYklyGR1q2yE6z3MsTaYD3wMXd9wuIJ2Gusan54VgzHx1AKooIY37pg3sCveI7w+2a5iWGubQFptISMS+WUaedJ9E9jaEjQeXAXruV+Flx0BvBYuorhmxyxl1zWViKpSSWtvcRwijGaMOl3MH4PESN+8RIXt/cQMBQPW2AV//yqU71IkeQhR/V+UP3WEl566ICnTZYzVYPCi/7NbFPn6Pf4jnzn+YztE4NAXwLe3AZxExYocJ2a9AwyY2+Eh8wa49u/fj+zsbFA1TaMRfjSsX78ec+fOBTXi+bJ1CuCaNWuWovZpSun9Htvu3buh1+vRq1cvNDY28iXFpUuX8vo/4eHhipbwZ85wdbY4p6IAnweDvAEokfshLtXbgz3x2Wew9dQRHCoshTWspTtW9Ceh7itzLMLrMzDlhkz0Hp7Q5ggse/RelJw8Jr2/D/wmj0bVHPCPnEbMKHbIgCm0CfLIUeKAILY7Hsl6002AVbgvc0AEdgKLVuO6FoNuoQwl5EYYnA7uhvUJU3D91FqcMv8EuZKlkgDLcbgE8OrsDEAE+W9Ka/Dq/gLUMCySG1nMOWfB9BKrU3MBZR1t1WfQuPMlSGWLXDOAlx/+O+Ib0vjMnrfuSG/lXFmtNslgcGCNp9F06BNokxLt/p0E2l3XOHtjEkKaZNTyAQQ2WzEpNx8sA4j2SaToL26sJQzNZTMwqXw9ph+uQVwdh7LgKOhNHJKMzqKmSnS+Fr79sX1uXzPVSu4L/5jfIQLvTwXO7WnJbLkejwG6DANu+anDF+INcG3bto3neDU0tMrWkJzLyJEjYbXKVwSkFtopgMuV+yQXoSeeeKLDgyc14fLly/Hoo4+itLQUwcHBGD58OJ577jn069dP8fH/zIDLl5eM4oBdgAO9lQhF7od4akoe7IcOH8bKb7+VZnJxQI/IYVjw9xltziLSC/W1GxbA2iytAk5rVcpv8mZU/UpzMEy7SyVV3D1dbm+im6xFD/OZRyQFWB1FY/PyalFvPgpb80FobPUwaMJwWJ+JEyHdEZS8Alr9EcGzkkRCWzrwxDITcYeUbEp/fEjGimx5OGBiudWto5NtrIHxpwfhCtrtAIRjse7sOnx+7HMM/GE2dM0Ct9RThouoEaYgDXo9MdJ+/7jaylyuDcJ11SSaquTsBa/NqDm9EZwV57SDaxZuxs54xNWSOry0ATdluNJqivHV6Fb7pFZEBLC2YDBqkiVh7R6JdPB3XucQbXQWYPWc4QKCkrvj9hdfdcpuOhvAF6E+Ohg/ZTFY37MRSfoUSdNuhRHyD+usCLzUB6gvkZ89LBFYLPPDsh1r8ga4KMM1ePBgNDc32zNcxPuaM2fO+ZHhase5n7e7/pkBl69llPP2IrVzYV4zXA7cD0fQ5doqHzl/Ht9CT5mB999/H+fOkT+g9NalSxfccsstbV45geV1rz0G1ir3oGKQ1LsP5j2ljN/ki1G10kV7ynARTcfWmIrG/L96FWCV0zjS6PcjKOlLMIw7H0gkUl/S7RKly+XLtCf2lCFna1FreX1cslN53WM20KWj057h2vYCXEG71KKE72Mdn73z1B3JS1A02tBnQQafIZUibL+NYGRCLWkmLhcQOWugAxUH7Lt0KwrBmEPRULU0ZKSG9EV3fRZf1jZZ61BQswWPTdjBe0G6bRyp9VM10h0FPvWJDb1cRE09eTVSOXlj3GR0HzEWr8/Ldispt4U/6PdCVPxV6diBXjNcw4FbfuzYY/rA4dq5cycPvGh78cUXsXr1amzdutWn9XRKhktqBSUlJaisrHTyDxswYIBPi/0jB/+ZAZc3orCcYOMfeT0649jeSoRS5SBv63jppZdQX18vO4y6ZBcvXuxtGtnP6eVclLsbFtMPsqn4mXcuQsbYiW0+Rnt39MTh4jgVzMVzYDVk2eUpvv7rKMlDyql461LfglpXIGkL5GpK3d5zEff3he/Gc7j2fQhr0R7oJEC765pcM85yZPwSC4e9JpudVyYlSfAtQhEr6TooHwmpMuXEzyei0lzZuhMHjN8fg9SyYIyOuRwpIb35bJfwnGRBZPYdYQfwXPIHvJckbWLWMUAVDJO1XvJ6jc5hceda1smYmvbenxqPsvAQsLzRuHM5+Ye4qVCrVXhpTqttlLhQX/mD/i7DjvqGtGGe35nDReVA+h91G5KvKykXUNdiQECA2+KpG5HwC1XLxC5Foiadd12KdCJELjtx4oS9c0e0x/Ak59CGy9Wpu/yZAdefNcPlayOAkhKhNzVz15vQY4aLA0IDorH4oTvbXFIksNxQ2wSL8TsnfpOwDgYBwb1x5/svSHbHdeoXxmFyxyyDjbXx7ZGCGoAKVkMmzMXzhFpWiwDrLw9PkVyanMZRSI9noNLKl1TjguOwcc7GDj3dQTtyvHZ0rvu5nj9Ra/F+mPe+Sx5Kkhwu14U5lzUF+VnKdLnKTRRaBCAj/iCSAqS+ZrhoxsBUPeL+OtBpWRd/czEK6gucl8oBV+ePwfWma3i5B9fNBhteSvoE2yMPIDIo0m5N9PLel1HRWCF5PUgm4oE1WmQfs1ALoXCjEIhTqbC7R18cCQ9DmLXBoZzck/9c1JJzBeve/DMHxg7E0plL7Wvx62h16NfEt8l+5y5FKemn8ePH4+eff+Y5W2PHjsXDDz/MnwPpcC1cuNBJh+vxx1uFfJWeaKdnuOgEiOFPfC3yMyRfw/vvvx/jxo3DggULlK7zDx/3ZwZcFxKHS8qDbujQoTwfj36diJtSLo77y451U9N2LBH6eiPSD45vicNl1xtymIED9IbeuPiaiW0mzYtgmeZnm4/B2nQQHGsAo9JDEzgQSRnDMPv+ob4uu8PHix1uT/78HkxsBVhLJCw1I2A10ItduG5yL01xMXLZB13q21AHExhwL1HRq9jRlFoK3PDlQx+7c71luPpXN+G/P+bCcvZnWIv28jIcOd2ysWfIjbiUDUCKSo2QuGBZAVPHsmZ5ngGsTZ6EJXZOSpVcp0KDR6CDxqNxT2tUqD8s9mp3Dpck4KLSSt5iZDSmQyWRRWPBIld3Fp8N24RPZnxiP4g3EDQoZiBeZ+c6KdrTd3D6oUCU1ssYbEuAdbrnxq4YC4MHfqMrGPd7IXb4V9+3Ce06XO8BtQVARFdg6K2dosPl28I6ZnSnAy7SryDBMErTkY8hGbUS23/gwIE4ffp0x5zF7zDLnxlwtRWc/A5h5w9hb0fP/Rxhx8MQa4h1IuvStcnIyMDs2bPtoOt8AZEEEP/9r3dRJ3KsCFW0vDsDzTEIq8tAYvcIXHWfwA3wtEll7KJTwnBkW5EU1vCobu7tWHKfS3kt3pQcg1nxkdJaUS4TtTeDICVtMaD3aXxb+BJsEu3knjhc7bnvPXG4VDYbHvz4bUzdswPQaFCckI7lCUMxLnkUxjFaHpqQfCoxzug3gjcBU6X3shRYoNvtSegwDhr+mIL8gjN4E9bC8ev5VcNizlPj3TKuco0PS08+gxirvD9tpaYWhTczcOTQ+VrmE28hX8CQmFX9KV++q00KjPu9ENv6ZPDvpyQCnQ644uPjkZ+fj6CgID7DRcQzkmKgv3vitihZ/O855s8MuCiOSojCv2e87VkNjoXoAZdcn4zBFYMlf03T9SE5EpEXeD6VST98YBuqmwrRGFwCVm2GyhYEnSkRgeY4Hjgq4ch5AgfB+kAY65qcQBdVeEjdfNotGSivWIvCwk/RaC6CLigZKSnXQlTW9+Waeuti/E9mmlfQ1RkcmbYQo+m8lQIZqRiJsfiutJoX5KSSF8OyvHL62AO/4vH3X4eK41ATHIFrpz+KyZyHTJMXLTOlwFAOzBLomsZocXd0BGw1TSiwWbESzTzsmoUAJECFUrD836q7huKr20e7nbJcVspbhqskogZDH7jMqeTY1uv1zb5CLP7ioLTYKoD5w7viWIkBhbWN0EcfQlnQxy0wUvoulwLjvoI6sbO0PVIkvnwH/WMv7Ah0OuAiJXgqHVIL5e233w5qsSTwReWfjRs7llfRmZfizw64OjN27Znb8dfwhOIJiGqKkm5FB+DY8Xc+NQJ0BPhzBQetJcQDfAkxJCIWuojBYLme0Mfo0G9cMnoOicWR3LtQUf5Ty4tH4ANRniM2bhr6Zb7G2xkp3Tx25gF4LaMrrkqI8jqdNwFWrxM4DCAwQqKe9buKYaysQ4m6HN9H70ADE47MsjHQGHXQy4j4tue6EN/v3KJFWFnVgNXjpqI0OgYJVZW4bOt6TN67kwdb1EFX3mcmzH0uRh+oQapVkjIKpMkmwZtyjIOSH0RKwOzqg8VY/OVB+OofKJeVmlg3FIuLr4caarfLRvIcqtAAqAPVYJtZaCIDEdriAUlEehGsFBuL7fyumekzJflgNDkBrkVfHJS9PUSBCrrLg1PfgkqmkUKcYFrqNF6Z3pF/pjQD21bQ6Mu97R/754tApwMu8i6ksgp1Y5nNZr6dkjJbixYt4rNcF8rmB1x/zJVy/GU9s2AmdDad7EIcO/7a8zLt6DNtTyZFXIvj+RDYkiLJE6G/57BRuOSu+3mSfGnpKhw9eh84CTtqBmr07bsE8XGX8YDFUYVcfCkyKmeNJY+8JbK20YdgzeCeHR2+1pLysc+dRE1npM5A7YoTaDxSKbat8cemF26xhcVeY6titZjxm35rpr1c1h5Q7rGjlV8Fg8Ch/wd10mBBtN0Lh8qbgKnSoHoDs0pAGdk9uW5OjQ+i7Qrx2Tk1nq24FwNrU52ugex6FZRQ5fal7NNvBbWKQuGtkUIfoMe2a7a5gTul8WlrWVTR4v2D/rQR6HTA9WeJnB9w/TFXUuSOkJRlt6abkR/XH/VBwQgzm5BZfBY9ywvtrzLHDFdHgJyOOmOlJSFPx3MEB55sbhytfPbunYM6w35ZuYhwfRbSjz/lBliI6CPFK/LWmRfezGF1RGKH2kF5yiTcobkBM3KGSL7oeY0qkw1iJx8PgVyU4tsDyj1pthHg06QMQ9Dgm2WzNa7X2luGy9u96It2lDdQJncsq82Ga5a9haMNP4LR1vCND9aaEeDqB2FRcixmNapgqyIxUy8bA0TO6YWQbN9+cHvjVzke1ZtUiKdGCiXx8Ub8d+1+9BYS/+f/GxHodMBFOhfPPvssb6VTVFSE5ORkXHvttXjooYeg1WovmCj7Adcfc6nowXag4hAM0bejKZiMQ1v82ziCYBzSK4oxNXcvzx1y5HB1BMjpyDNWUhKSOx7t+/5929BkFGwkmgzLwdlkhE4drHy2bR+F5uYy2dMIYGKRvn6JdGZCglfkKcNF7ftdKq246ed6njvmmElqTxw9ZRJeyruX75CTKtNRFrDaxmF7g7Mvm6MXYntAuTdXgoBxD0Ibme6V08bHRoEfpRhDqcaJzHHJeP1UEX44KlggCYVjZ2NyqayVr9fFW7ntu8gYhFY1eZ2W9z9kGBybk47LBiUr9r/0xK9yPWhHi+G6zu/NPaEzpEi8BtY/4LyPQKcDrjvuuIMnyhPASk1N5Qn0ZKtDPkRvvvnmeR8gcYF+wPXHXCp64S7e9x3qom4FyMDUZSPj20nH9+Py6DCnLkUa1h6Q88ecrfRRCRhs+OionRRvrv0PwBlllyha+XjLcAWbeqPL9gflAZkLr8gTh4thOVz+qxH985s7tDvSUybBW4dcI8vhJ4Oz15ljg0JbQLnYMRtw+z+QnGeUlBSlbj/djJcQECjY88htSrsUHcHWj+8dwZkD5by/tH1jgOMaG1YHNwtIy2FTqxhJQVDHMY6m1J7I3zzgya9BT+NJ9DfkQG8V7ZX64WRIT6zRhCNCwndY6vwJdP2TMUPTL1pSIV5qH0+Az308i6Ck5dCQ3RP1XzKtwqu+2j1JrcVbhstTBu18erb41/L7RqDTAVdsbCwOHz6MhIRWc97i4mK+m4yUWy+UzQ+4/pgrRS+DAT9vQCUXI9SEXDeOQ18tgw2jBzjpcLV1tUpfPm2dvy37uZa+lGa4iMN15Oh9gASHC1Aj6eRChJ0dJrskV16RY5eirUWQkjJbJCTep7AZV+0y2t/3jpmktpyzSIb/Ze16RDXpUaatwneRW/GznjStBFkDTx1ySjJcNIcvoNyxvDnqsBW3r7VBzTHQpAyFNn0CVMHRYE1VaMrbiqp+VyElUC/J3aLVcyogMCVMVodLKmaeMnIk67Au2ILcAGfE41XbzKELmM7Pkw/l8H+tR9aptehuOsNnl10V32cnzkIf1v1HkRzgOgwb/qZqdAKEnr5/JGb2t+X78MORMqdMHr8QTkoZhUVA+CH07HEIZlQqIuYrvVf9HC6lkfKPc4xApwMukoIg8UciNItbXV0dBg0ahLNnz14wV8MPuP64SzVwRw7KmuVd2RMCtDgwOrPdC7TZrPjPi9ci6adDiK3jUBEO/JStxq5+akxKneLW0dTuAyqcwJXcrZTDxdps2PfDzagL3EnsZuF/pMDJMQhvGoWQE9cjpIz0mdyBLIlWmuJZ9LnH2RJI1OF6YedZVAcyCDfaMOR0E/pRZsvhfJRIXcidPoGg6uXHeG4ZlcgEnSgBDDjaxUysG9bSISexfgUcLl/BteNLluQf7lrJYWLYrdAmZglq54yKt7WhdR4PKEUvSxLUUmR5H0qIjjHyxDmjoxarWSwPa3YLa4I+EHLq/b4Ah4VP/Ac9jq2FSkKYgToyg3otwCxbsjLyPIBysLgKDchOjQQpxCvp/KN7lzotl/6Sj6IaE5Ijg7FgeFdsOFqGH486AzEqo16UGa84g6bw68gPU7JWpYbpvhzXP/bCjkCnA6733nsPK1euxJNPPomuXbvyJcWnnnoKl19+OS8VIW56vf68jqQfcP1xl+f36I6jNv+9C+dBt/0Qj0sEgUgBn+zuzeCNWVo8PfZfTgKObY2Ir5ZDri9ayS5F/oXPOHUpmvaXo+rLXNTH/YKaLpth1VVC0xiDyHMTEVY+ArvjczG0hAQL3LMSZMvyVe8tWHzTE5Kn2R7Cube40bqrvzwu+eIW7WI2h++BBhp80vQiIvMCfO5SbMsL07WMNLF2CO4tvgEqiVI3rfNkUAF6mlNbhDgYEFil/zPVnID62GcISE5E5Pz5dqNz/kXOcgKg2JXH60mlROhw3cg0XDYwCZ88tAPGOndAJcaznuHwTrgzaV3FsbjRfBLXl++BpagY2uQkp2N6K405kr/fWPR3mIsou+W+8WXU5O6YN/g2NByu5DNgBJTFjJnrHgRMC5rrcW0Ah/hwHQ8IfQF/rvMpIbp7u+98/dwRsCuVtvD1GP7xnROBpqYm3HnnndiwYQNfaSNuOTng3HzzzZIHJGuf2267zcna57HHHvN5cZ0OuBztVlxBi/j/03/Pd19FP+Dy+d7qsB06Sv/J04Kozb/wgfuhcuTGtOxgY4C3LlGjfmKWk+9aW06wLbwhqVKSqMNlaz6IgCATopITMWjqTPQZTSrhQsan/K0DaC6QNs6mjMgJXT7KNVUYXT+oBRSonDJJH/Rciw1zN0ieZlsJ56zVhtNfbUPzwToE2ALRrG5CwMBwdJ89FiqNAPw8r1uwi3kg/VUQF+eFsS+g6VAVGn4pgbXGDE1kEEKGJ6CwmUXOtmLUV5kRRjpc45Kduifb8nJ3JUp7E/3MDTqLtZHbMLN8FuKtYdA0lCLwxA+wntstZIl4vQgVwqZOQfLLL/NFOqmSmZipmVjEoeyMtGekVIaL+I0P7f0UY0pyeEFW0ZfQ8ZiTv5oi62tI18KR/P3ObdfDWFMte9uHRkbjL299hPue3oxJjQxSoYJesLR224cMvU37P8bPNhPWz/4bvrp9DDyBP5pAo9KgX3Q/XN3nanjS62rL99K/z/9WBEiu6vnnn8cNN9yAbt26Yffu3bx/4ueff45p06a5BYPGkWPOihUr7ObVZHp93plXU0ZLyUaE+vN58wOuP+7qdITCubfVU5u/cf8BWRL0iRTgjYVJ7TZBbgtQaQtIo/MtfmY3WIN8RqRSU4PrezyKCYYhuLhmHOItUSjTVvNcqS363zAwfqCTB55jDNuyJgJbR55ejXCzIF4rfKcEo5m6oGpkPnYZD7q8rbsuoAFnb7S166XrS2ZHPG/XfbyR9im+1/Z4BLbGVIzYPhyL962A2ont3jKzWs2bWm9OyW4RJbVBoz8IbeQuqLS1YC0RsNWOwhPdrkDdljJnwnzLFFIcronn9uHefStAWS63reWYd6q/wMEKUm/37kO5/LH7UHzymOg87jylQ3es2E1IA8hWaDyn5r9XYsnV0dDbxnDYc8NluOGBZzHlS8/gj+bjZXsZFQ+2XUVLvX3H/Z+f/xHwtczfkWd05ZVX8p68VIFz3EwmE8iicMeOHRgyhDrlgSVLlvDZri1btvi0hE7PcPm0mvN4sB9w/bEXR+QOfVRUhXPmZnQJCsCNydGKPfy8rd5bm39VGPDfp4bKAhBv8xOwKCtbg4O/vgNOXQ6LMRq1pybAUECk9VYOkhzZ3Bdyt7gWJZmie9Nekly6Jw9CcQdf13Ryxc8I3C+8MF03etA2ZXHoec0Ejxku2q+9mlU0R1va+l2zYt4yXGfV+fiu8hOENgYg0gD0Ka1Acm2De76HYaAbNAiLx96BfQXVCGzprqM6KSXBBN9zFcKsWXiCXYwzByrsoEv0RTyhtWFNsMWpS/GNnW+he8VZWYBEx8z513w8uv1RRT6Uuds24/u3XgGV3103R/03x25Cym19cGwT0mP72ZsKeEPvwj08051mOpnCYNujM1BmKsOhikOS4M/1eEruT2/fSf/n51cE2lLm76gzIFH2Hj164NVXX+W73R03csfJzs6GxWKBRkN+EcD69esxd+5c1NTU+LSETgFcJAFB2lu0kaK83Pbyyy/7tNg/cvD/s/cd8FFVafvPTHovhCQkoUhHEELoXQRdwC7Cioi67v4/rOtaQGUVe1kU1/Kt4rfuiqCgoKIIWADpRUGK0kGQkASSAAnpdeb/e28yw5Rbzr1z77Scuz9/sMy557znOWfmPvc97/u8nHD5En3jxyYPV9WePUL8lhsZAHAky4Sw/5ujKYaLyNa+/X8VSuxYrI3ND9Gm4PWyvL44sed6VEefgSWkFmGIwtWTxghvWo7H8VoQYI2FcuzbSA/C/ie+QrwlSfBsuV6EUZm5BD1fvgFydqvRrJLDbNqK2xC3YQ+u2tWI1hcgJEh8l2PGlp6kH2CGWFq/6wPh8gv9Jcva0DpvP7sSuRUHmo7UBN04IO1CJfqeLGwmXRczHEPiUnE4KhafhR/GlnbvAGYRfQWrGS+NeBFdz/bHvo35KDpTiVO1tdgd3oiDYY1OZIvkIJatewlhJeckYQhNS0On9T/Ya5UqZSkS0Vrx5hwc/Wkr6EjbdkTpGjvoqta+4NvnkFIjfhRKxtHLzP0PRGBi14n44shn6BNVh+GxDUgKsaCk0YzNFaHYVRXSnBfZNB3ClIuLavlV8N97tBzz6zEb2svTpk0TdEKp3KDr7+6mTZuE48aKigr7cDt27BCkrUhnVM1lCOG655578O677wp2/OlPf5K054MPPlBjq0/bcsLlU/gNH1yuVAvFcG28sw/unrmIWTnc0WC5EjtWiwmHDw9FcVFH+wOT9lqPHj3cdMXUgmDL9qv4lR7wVEGxKUaLrnJzFepNDSgMd5ZbSAxPxMyBM3F1x6tVz1XpOODozG8RZY6RnEa1pRJd5owTpBpsWYrN5jbd40FZGMdBPUmQcJzj6YrTeDj3NvQ6d4mgSWDDl47n8iqPYHvRcre5UixV79wiZJZWIrL//0NoxsUMRyFJAxZsjtuNf2T+1y5/Ye/ECoEILpywUPgnpTI0j379Gmr27pX2cPXNRodFi5xKJykFfxN2h7ZswJ7Vq1BWXIT41qlusYM222zZhHcsegGdin+XyG6klxlg9rRQ9EnpjXFRuUi1FjQJt5ooG7BJ7uGX6hAsOBfuRLq4uKjaXwT/bq/lmN/TGRHZIr7y888/CwH0CQkJbl2Sh6tfv36oq6uze7ioLSX9+YWHy1MQ/PF+Trj8cVX0s4keJPkPP4zy1WtAAb3k6RKoiRmoGtYbOfM+RkhIkztZ6rIdG+blfYTqmnxERWYiK+s2nMpbiLKyPaIldshRUFbWGr/sHefULe03R+V8rTMl8vL6h8+hV257pNYn4TQu4HfTWVSYaxBriUKPxgxcYknF1ri9eCXzv8Lb3QvDXxA8earKxTDoOR2c9TXiLYmiBbMdPVw0V5sOlz0YPjECF5JLsXPfSlw4W4iE1mnIvso5SYAVIz3JNdlZuacQW7/chFRLpKAXlnd2L0qKjogXqrZakVhZg5G1WYjM+RNMZvEM0bkZC0CZmK6XK8mQy84rX7kCBY8/ATS6e8ssZqD4kSkYedeTqok1K862dkp4/+saMzb3MuPyxCjcEEe1Et3tbbQCi86H4+eqpu8gvUBwcVG1K+Hf7bUc83syIyJbJMy+fft2wbNFcVpily2GiwTciXjRRTWhly9fjo0bN6oywRAPl6MFX3zxBbp164aePS/qJO3btw9Hjx4VHiiBcnHCFSgrpd1OIl1lK1eiZNFi1BcUICyDUuinOKXty5Et27FhU2RKU4EV8iuZzKGwWKRLntTWRuGnH53jBmgcx9qQ2meFpnT7TU9ifN6ViKyLaQqPbhaLJAs7WFpjVH0PvJ6xEOsTdgpHNR+OWyCbMff2lBynkiwsxwHdfopliuFynavTUZYtfkhEBoMVI9k6iCYgKrsvLlm8iLU7od1N72zCr6UbEJq0HVO2WRBTKyLS29xjRF0Drkn5I0KSO4qST/JCUiama3wdC8m4uIcXCTIQlro6WC5cEI4AbUKlJHXyUzcT3r4xzCv6craXmdLvqHeuOgAAIABJREFUv4PJ4i658uYNZlhNJsxsY0VGKMlauJ/rk6fr9zoz3iqKFFDUEsOl5gVC1eLzxrogoOTh0ptgE9navHkzfvjhB7Rq1Up2DpSNSPIRixcvtmcpPv/88/6XpdipUyehtE9a2sVCpWfOnMHw4cNx7NgxXRbKG51wwuUNlAN3DLljQ7lZSXm46B4SC37kkUc8BoWOwZ5Y8gSiDkaJ9mWymjCyvjvqIqqFhzx5UR7ovKA5Y8794SdWLkbpx5JI3AdX/hfbnlqI9tZOwsP/olAocNL0G4Y8Pw2hIvVVpYK128dcis7xOUhOyEBkWjyiB7fBGtQLopiuGlaOtQSVEiQotqnLhvWqcHcMFJ9Y8AXSawtFM14p7impph5jO/0V5gj34wvboJThOK3L351sUCIZjl5aEDFtrgZAK1geaUV9CFCcCHzftylWjUgOSd6Hl9yKDhHD7XpfetRdFCPNTz1zOfptLbbHzDnZAeCFzDrEmqVjYkobgGdPx2jKUlQ6fnV9gVC1+LyxLgiwvLSR512Pi9QTOnTogIiICPsxIfVLdZ7nzZsnxGyNGDECs2bNEoYjHa7p06c76XDNnj1btSmGe7joTJSU5R0vetsiodPycnGNINWz8MINnHB5AeQAGMKWLflB/lnk1dQhKzIcf8pMQdtT01Fevlv07VyecDnEcLk01MvDRd2SAHFeXp64KVYgzZKAUXWdcGTn48goD8X5mHb4NGMQ1mdlw+qSVShWLoblOOChfg/hqY1PYeLJwbiqbDgSzIm4YCnF9/Gb8Xn77Xh+5POiSQmCHMGRg062D2l9PbJiutrlJehD8ituQD2eRrXwd9cCztSG4ooiH56Otqd/EydElDHYHNukZjs6PtA7lx3G2OIfRGOWKIbrstwidOl9H0KTKG7PPYGAMg8PRh3HjA6kzUWeKXEpBPux67YCNJTWAtYqVG5YgIbcH932IcUh2o7unH+LIchW1Jy8R/BYKimzSx2bp6VdK+qtcxxLaY88lFaPtmH1QuyW60UerlP1YVhnGaBJh0up8PbcSX1wQ99MNUvubqOMaK0RJNYjY/3wZl9mKXoLDsMJV58+fYQi1eTRsl3kxiPVVjpaDJSLE65AWSnj7JTTAxto+hn3Wl4WfciSRWZzBBob64UHoclkhbW5xM7Zs1k4dGhkMz24aLteMVy2HufOnSv7ghNtDcfkgjao3DTHQWXfjC1teuGVAbc5ky4rMCQ0AjcnJNqFRTclfo0fwr+Elc6MXC7bURi9aMlpPkllnbkKbpJna2Br8aD+BljxIqqxGhc9JeSRe/Xm3lh7sFCowzfy5M+KmlgJ116reiM5Hlll/vpvtC0pbqqoJJKlGJY1UCaGy4r3Wx/HrnbfoMZ6FhmxGW4kQyyxgMiQo8aV49EcrYotON2NzNTHo/JY05u8XLFrx2xb12Pz1qlXoVfPt2RJl5IXdGCsFZMTqxAiQrgohuv76gy8ds0m5nVxXI9f8i6gnlibyKVUb5JlQO5BY0FJuU2wq/cbTrg++ugjQRrib3/7m6BzQceIb731liAcRqmYgXJxwhUoK8Vmp8ViEQg/pfeWlpYiMTERAwYMkJVjkFO8p/y/u61vYxjEgihNiI/vixObByCqzRqExZwTdLhKfrsc+RfiUBvZnLrf/KDRK0vREYn//Oc/OHXqlDg4ViDVEo8rt+1FQ95PTm0aTWbMzbkF69rmNP27FbiuKgxd6qkGo8NlsuK35L1Y3WV+U81Gh8t2FPb6zteZVc0d73cV3LyizVSkRGSKykuQd2gfGnEvquxdEKztW0XjVEk1Gi1UbNuCx3d8hGGCArtFmAcdr5GWlE313abWz7ab3FtNW3kbyvYew5U7YwBrGKLq6tH+XBky7DpcJkQOoCzFHDtJIduJGG1EA55BNUwyHqfKXWdQsuSIG1EXlsjSiJpdH7itJckv3HO/c+IHnTqSh6v65D3CJOTIh9yxuQkhuPTSV5Gefr0kZEpHRuSjvC25Br2jGkWzFBeXxODnaeRFVr7ECJDcXXL1JpVHA7zhQWOxg7fxbwQMJ1w0faql+N577yE3NxekKE9noVRLMZAuTrgCabXkbSWy9dlnn+HgwYNNekLNlxLRka/paEUX62E8Dee4m6aHWNPDaMuCTJxxKc1CR0a1kUWojj4NS0gNIsNiMO6G0brocDmiQAXkly1b5jRf+7ytwPDCKGSs/9DtKIoIwMHkDnh05P1C8x51IZhQFSbUyXO9yLu1rvMiHE3ZKXoUdsc3dzCrmjv27RrDdW3bexEdGie5yFQU+SZc1MyhhmEhJjQ0XtRTJ6J1ed4eXH1iK9KqzqMyKRVDHv4fpgQJlm+C7fhs3tsNSHY2xeF2EyJ6jkXtkNtQc7Yap2HBMtRhDchP13SJeZyEWK3HlsNqJk0z9+B88kQ1nj+O6k1z7GNJebisVjNqCiahoayvva0U+di5cxIulEkdm5uQkJCD/v2WSMKjdGT0w6kf0GipR050I4bFNiA5xILzjWZsadbhCjGHYTcj4ZIjQK4G6uHhsqnri/nQ9OifZc/xNv6PgFcIl//DoGwhJ1zKGAVKC1nyISPHkL1lH87USQf1ppgr8Vbjn5r9FBezFG3HLUd+KsKaDw7IwhSTGIE7XxmmO5SSJBMmtCutwaBvvxJKDYtdZyPjMW3cbIFiTakIR0ZDiGgBY7o3PLMRG/stgJiek5KHwyZH4UbkbIKb2zcLhPGKjNuQEpEhkeEn7uEKDTGhns6lJC45DwdrdptjhmDRb/tRENeAuCor0kscawk48q2meLFHhpPCfKko+mIPa5JZuLAGMEclSpOb6hJUfvfYRcJlNgn1QDdRsDz9r1m9vqGsJ2oKptirHciRg02bh6KurlByzIiINAwftlV278odGV277FrkludK3h9mSUXY6SecCnpLxUbJESDXAeSOUVm/iINeWoPCMulMZE89aKx28Hb+jYBXCNeJEyewZ88etxgStYUffQklJ1y+RF/fsWWP12TkGOQ9XED/+Gj8O/Mg8vI/Rk1NHiIjs5CVORW2gGKKu/nPjE2orZQmbVKlffRAQOoYNe7ll1GzR1wgk47actM74ZkrH0RmUjTGHq2HpUpEBb3ZQDnCqOThkKuNR2Rm/bgrcTwMaNUqGzltrhPVj5KK4WqbFIWT56qYSY0Nb9bYHLEMQVs0m5CRKbaAzfUMr/o1StXDmmQtEHuVpKyEk4eruUB27Nix2PvAFfj0yFIcLzmFkrJY1JUMRkNZH6fSUnLkw1MPl9IeXv7bcvx9s7uHmO4jglhbMBn1ZTluyRBipEuJAFGfrkkVngS2cw+X0uryz4U9Z3U8UzEAkzfffBOPPvooLrnkEsTEXFSZJgKza9cuA0Y0pktOuIzB1Re9KgWQS8kxyMVwkXzlWz3aYWJ6suyUDm0/jbUfHhSTGqKKMhhzx6XoNijdq7DICVOimRTYgsg/n7NTOBbNCjOhQ4QZ0WYTqixW/F5rQV69FWKE0TmzLQ/ViMae40DZtjDEVIcJauWXXzcVlw67XIijkrou6mfBTaVdCBgHsN7UKJqlOKZHGmZ89osQw6XGw8EamyOHoWBZs+6ZEC9G/zckxB4vdvO8bao8XCRrgfAOkkH3FMNlOb8OdUc3iGrJsZJIm2dvwbbfcayoAn1TfsTUHgsRIpoYoRzDpbSpiZA/uu4R/LZ9G7qdjEVsdSgqohpwqF0FjsR2QM3pqczkUMnDRUfMvbMSMW1we1zXJ8NJU07MTiUvJ+s+UcKAfx7cCBhOuNq0aYNPPvkEo0aNCmgkOeEK6OVzMl6rh0suS3FC6wS817MDzCaToJJ+ZEch9m3Is2fx9RqVha4DmrTovnt/P47vKbIXIBaex2agY3Yq/vCXnkKwtNylVEJH7UpJ6TdBJIj88PbTKF16BG1Chdw7IXCd3tmIRJxusCJxUld0G9zGbkJjYyNWb/0fhNRRMoFFSPkXjrOsQOmJOJxckylM3rUen9gcnEnNxTqE5uhWsFSfQ8zANtiS0wsLf8xFfkmV4JGzPVCpvwcW7xKyFOnh2XTgS9mj4lIINoyfXf9vVFnOwlKfiPqSIU4eIcfjNzkxVSJcxa1CUBcXiTblIYht1wnJDoK6ah/WTWP9gsh+f3EqDWTLUrRWn0DbN++U3UdyCvWEiSMps5FUEyyY3ns+clJ/gcnUtJY2cV+WLEWlfUn78Os3/9FUq5GSG5rJKa3Vb9GXYE3nVIQlb4c5rFRYj4aSIbgs6XJ8cc/FDHjbGGoxdbTN9ftFWaI15wZh98FLYLE0HbyLSY6o2V9KWPDPgxMBwwkXCZ4WFBQgJMS9hEUgQcoJVyCtlrytWmO4qFebDtf8/HM4VVOHtpHhuDOzFW5MS7KTLSVCRf0IhGxj/kVCNjJTIGQsZGvGhhlYm7tWqIEnp9OkZsVYVfYrdxXi/BIqW+N+0YMoeXJXxOQ0EUt6aM9Z9iZyEv6FELO7XAQ5pXLXZaDkWJMAKHm3xt/7EHqMGC1quhpiKNaBEsmw3eN49NloaSoKLRXzZIvN8URM1d3jZEFY/F6EJW1HVPQF9GjdHrd0vwUTLpkgHKNeJJ4WhGYNQNgll0MgnVXnUP/7BqTcdyOSbvAsKUmKsBDpGpj+M0a33YyUqPOIi2mLPt3ush+bq9lzrm2lBG6FvQRgU+9zOJ5Z0Vz8nf7VjNDqy7Dr7gVux8usXjxXG5zW3tp8dC7UczTDNd6N7nU8gmXdX55gxO8NbAQMJ1wkf09HiSQNEcgXJ1yBvHrOtmvNUmRB4PCPZ7D2wwNO3ivbfXocGWoNPBezXc4TJ0X8it7Zg7pcacHi8PbxSL2H4oKaUuULT9yBjgknRMUsiXBVFkXh2FcdmswzmZDRtTtueWaOjIfQqrn8Esv6URs5jB2z+lg9XDQvJTFV+/Hd9hM4jnlojPoVVAeHqgBcXjYA15SMRDtLGySlpSJ2cDrOf/gyKr773n1KJhPirroKmf98XfZ4VgkLpSM5YbkA5LRPwuf3DFXqjulzMYFb2430YlGUWItvhroE7VvNeHnki6KCuVoIEOva27/TOmPABBRvFLAIGE64+vbti/379yMlJQXp6c6xKTyGK2D3TcAbrkWHi2XSthgnqbaeBsXLiUfSIzC7dR8snLBQ0VQiW0qeODHSVfDSj7CU1Un2b44PR8asQcLn9NCe0uEBJEU6V5pwvLmuIhQHPu5i/6fYpFZonzPDoyNXxckrNJDD2FG3ytG7oSYOTm54xwc+ka3H8+/CsPLs5qqc5iaPptmEkMQ6lM5/ECAPnOvlEnenBQ+WoHPqV8/sO1eBW1e7KyMasHRMvss/s+95Fhxo7fcUU6F598tVs8zWQk8MWGzkbfRB4IEHHhAkq6gSDsXtTpo0CXPmzEF4eLjbAFTah8TaV6xYgaioKNx///146qmnVBtiOOH68EPS9hG/7rjjDtUG++oG7uHyFfLO49Kx0qEtG7Dn+1W4UFyIhNZpyL5qAroPG+XRG71es5v/2GZUXpAmJJ7KPiiVRwlHEnZM26AYBKzVE6fGw0UP7bu6vYKOiTIersIoHFt+0cOV1KYTamqvM8xDyLLOShhb6uNRc/zvTmVwPD3utNnlSPZGXxiARwpuRwjcwzEoXksQNz1FZXxcOYgJUdnZ6KCyALdjLz7zcB09ZDu/dZqU4OFKqsU3Q9xlKaj259pJa1mWVrENy9rbVPmpM729fIoG8ga6IUA6jO3atRNO4IqLizF58mSMGTMGTz75pNsYxFUKCwuFePSioiKMHTsWL7zwgv8Vr9YNHR93xAmXjxdAUNC2YMWbc5qDaptjgkgh3GRCl4FDcc2DM31Ourzh4dpTvFe0ZiO9gVuq2+Olwe8p1oXTamfV7iKcX3q4KajG9TIDyZO6IbpvqvAJPbTD6lbjrl4fM8dwtWp3AyouNBMwkSE89RCy7GJZL6IViLR0wt/7/sstu401Dk7OBscH/mu/P4Ie1ZfALFL1UUzc1LFfLQW4He9nEQ4NMVtx+9gSHKv5HvkV+ciMzdRU59A2rmwMl8mKzUIMV6UTfLayUQvGL2BZWsU2goeraI+ojoeYh0sPDS9Fo1pQA0ctu/r8AoRlZiDp1lt1EySWgpII1y233IKsrCy4OomqqqqQlJSELVu2oH///kIXVCmHvF0bNmxQtTqGeLhWrVqFCRMmCIYsX75c0qDrrrtOlbG+bMwJly/Rbxpb7gdZKeDaW9Zr9Ryx2kdHTk9s/LsQ3+N6UXxRbcEk9E66QjGuRqsnTqyGn2CHGYjqmYLkKd3tgf/00H506W78pdcHTpltclmKZ08PQ1UZ1ZwUvzz1ELLgrGecHMt4jm0cyd7Coy8hpYFd3NTeD0PMmJJdYlmKjveYTRa077EMJdilW/KG0wsVbRL6j3TETMDvqZVY37fYjQjZykZd0/EapSkxfa70/bKp8uup4cVkWAtopJeXWA1Ur7zyCl588UVUVFSgVatW+Pbbb+2kytbP7t27kZOTg/r6eoSGNpXGWr16teARKykpUTOcMTpcvXr1shemJv0tsYsIzPHjx1UZ68vGnHD5Ev2mseWCam0B11Oee9WnhmqNjWI1mrKocubdjgYKqobFKWPLlkWVHh+F7bPGynap1cNFndIcq/cWo2L7aTSU1CA0KRKxg9sgqk9rpyxL20P7u/2n0T91Jy5vzmw7V52MqvO9kZZrRvnZYkGHK/vKpmPhL17b5Vb+yHEich4uVrkMx7fo2oLTWDdyDL4adRUKomLsWadbD76EH07pmwnKssaOZE/Ow0V5cw3nf0P1xovle+z96xDDRX05ak8dK65EQ6MFpNjfqXUs+nQ/jmV5c9Foy+RzmBwLCXLWZstHVGQmsrJuQ2rrq3F46ybsWb0KZcVFwt7ofeV4/LdhBdae+sFO7mzDJUQkYOaAmULQPGVwenrRHrr1q/uxr3SL5PcrISocnVPjmDW8PLWppdyvVxykFrzoePHjjz8W4rTIy+V4bdq0CePHjxdIme2iGrxDhgxBQ4O0iLUo7zFa+LSystJJ8FQLGP5wDydcvl8FpaDa2ORWmP6udMwg6ww8jROzZ/9pkH1gsfGmdzbh19INCE3cjs7nKtE9NwaxVWEoC0nGvvheiL10ID6/V748kNGeONs81GaKabGL8K7cU4gD329HREUICsPOYUXSRmyI/1kggGPajYFNxd7xLZokPp67635syh4IS7Miu+C5AEC6atdG7cfSw5+KliliWSctbRxlCUaW9sMjBdNEY7gEI2t+Qvl3HxAzuugNkinALUVIx7cfj9pfzqFiWwEaSmsRmhiB2CEZbgTacT5yx650zNdHJnmDyNa+/X9FcdH3kmWwXGtEku0rj6/EnJ/moLSu1AlaIniOa6wFd8d7Ghobccuid3Cg4juYwkpgqU9CQ8lgWCqyMa5nG7w9JUcxRtJTG1ri/XJadkKmr4dxiUqYLl26VKj5vGbNGqem5OHq168f6urq7B4uakNB9n7h4bJZS6KHsbGxQhaAWOS/EgD+9DknXL5fDcHDJRFUq5eHKxDixIT4miV7cOWZ79Gp6rhQA5GIQtMhowlx3XIw/ZmnZePZjPbEad0tau2yHXFW7SsWBFgp3smCJn2yLXF78Ermf2E2m2Gr0+j4Fr16wDC8csc9sIhoBLJWDlCap6Na++FQC+oyo2GOCUOXuEj8KTPFrt/m2I+NGC05+Cmu/3Uo+pdeKhQLb5KapcsKS+VvqN37IUxRkcK/WKqrEZ6ZiSQHQVXXPsX02ygg/7XzM9GtOMs5Lk/kiNixP6XgcrlA9jNnvsKBAzNghXuGpa3Qe3q6u46YN4961b4oKO0D/rkyAp5o2Sn3rtxi8eLFePzxx3Hy5EmnxrYYrq1btwrEi67XXntNCJfauJEEndkvQ2K4HIen48W1a9eCBFAD+eKEy/er540YLm+M4SmS9DB44tX5SNn1BUgowPVyjWezHwG6eDAiL0vB0Z+LNAmwejoHuftdPYSxyeFISs1H4bHNKDtb5JSZWr33rGQQfyMaMTdjAdYn7LR7XBzfou9/9Bnsv6QLyc27YwiqjRmDr/tdlKxQO2fbkeo3+wtRc1kiLKlRTTFI5E2zWhFiMgmeNFuFArH+XY9vLSUFqN67HA2nfqKz3aa+ZLxatj6lyIpcJiR50RyTIBztU/JwZadmwxbI7upZ+0vSeaSGlEsUQDchISEH/fstcYNDaUw5r5ratZNqz3psrdd4LakfRQ9X32x0WLRIF0joeHDJkiW48cYbkZiYKEhXUdD80KFD8X//939uY1Dd57Nnz4JImS1LkTRG1daDNpxwvf322/j000/x2GOPoW3btsLbpu3q3bu3LuB5oxNOuLyBsvwYUkG1emYpejtOTK5GG3mupCQwFs+eidPk7RO7mgVEKZ5NTZC771fY3QIlj+OgiHGSQqzk6ToYdQKPdpgLm8flyKiRKG93BpUjLZje8T2UmFtJTjs9PAx7hvXUDIst068uLRL1vZIgpv6qxpPmSYyLFFmRjxMDylMi8GR0PfJKq5GVGIVpQzoI2Zmrfl+JJzc/qRjDJVaw/Nk21UgIdX9RsAEdEZGG4cO2uuHuiVdNaRGVaiXS/Z4UX1can38OhwoKxmjLOWJMoU433HCDUM+5trYWqampmDhxIp599llER0cLMVsjRozArFmzhNtIh2v69OlOOlyzZ89WvWyGEy5HguVoHT0k6cgxUC5OuPxjpezxVQ5BtbaAa7nCx6zWeytOTPgBt1il6/v1SMGQg5+g8LcjTqbTHDsPHILf9u+Cpbxaclq2eDY1Mg6sGLm2M+Kt3/YA/ObLFeh8aIWkJ+/mro/CXCtde/JsaAlu7/IkyOPy4bj5+PH9gajsUCJ4mp4xv4hj6AqrSLA19eiph8umZVUzMAXWxPAmb5TLpWYcRQ+ATIyLFFlRyoQshgU3ocKtfuCbt2TjsU0zFUtMiXnWHkytQftwWy1Gd0RiGzsg7b+t4SoLcPu3d2Bv8V7huNj9LpOwxlrkIVjLAHnzSFPrdzGQ7/NFlqK38TKccHl7QkaNxwmXUcj6V7/eiBOzzVhS68hqxaTTXyC9tkgUHKsJKIuqR3xVqENcj0NTBw+XGqFSLSthxFu/4wPwxrzPkV5bKH78ZDJh3CV/QYI1WdR0m4frsUveEGK4+kc3Yv/+R+2SGlswAvPwACwmd2FRNZ4nKdxsau01o9KBSOlasqyeNOUYl1R0kdAF0uLhoki4fWjEvahymqJNe+q67DZYdWIVPj0knVggNm6/6AbcmlyHEDGebDEhcWEoon8yuyUC7Ln/Cjy5dbaoV40MbBfXDvdk3yPUnITVhOV7C7Bw2+9u3jkqzu14sRa69ocjTS3f0UC6Rw8tO3+er9cIFxWwzs/PF1Iu27Rp48+YiNrmDcLlK9G3gFsMAw32ZgyXlJp314ojuKp4rUSMC4VMW1EW3YC46lCYiX25ek0cikCrKcWjBVapt34qS3NF+UDc3TAV8dXRTJlvYkT0T7kfIrbR+YHvaGfXtIHoGztaVIiVYrj+mfkRQi5LELIUd/38R1wo220XjbXAhP/FQ9iBwULaAXm6hBArWHFZYS6uX/85kjyoZOBNDxclTBR1SsKoFZtF5RG0xHA1wIoXUY3VcE59t6mrL50+RJHUiHnWCN/bW9Whd1SjgHcT/6G4NhMidwNJ/wkRakg6s7wQtHn5JTwfv0HwqonJUVBSAUlDXNF2DGryb8F3B4oFLzL5w+R0s+RU9R2V5I080tTy3eP3BB4ChhOu06dPY+rUqVi/fj0iIiKE1MqRI0cKmhcZGRkBg5jRhKsluFMDYbG9ESdmw0GqXt3Egi/QRsqr03wz1ZWjUiftC6NhIn1IgSY0CUV2GzTcrrpvtIdL7K3fuQYg5Q02PzwVMt9suDg+AAkL8nCJKiyRJ69Ld1zV/S5U7z9rJ11NOYoW7E/5HZYbUzCh4wThQbxp81DU1TmXhiHStQ3DsQZ/wFlTOhIrgPiTR1DUKh1lcUmILy9B3wM7cF1yLK5TWcnAiBiu/Mceh4lkIFyuRhPw7rWhGH/3y+KFnK0WqMlSbIQVG9CAp1EtcoAHpMVHoH/7JHy7v1CW1Eh5hWi35kRbcFVSONpFRSMyMgsRy0oQ8kWemKavsK9JFqDdoo8Er9q7e95Fbnmu6E8K7bjqgsmou5Dt9rmYMrxS3UhbrUT5OqZAqDkUvVr18khtPxB+I7mN2hEwnHBR8BllKFJRSApMowh/CqAnIkaqroFyGU24PAmIDRQMA8VOo+PExIiFIzZKXh17XbnBhehYEINuJ2MRWx2KiqgGnO5qxoKHvrVLQhgdwyX21q8l880xk/L0qTIUWBvxBeqQW3EAY4t/kM3G7D7sciYh1p07Jzl5uJz3owkhps54+rfbcOSSHrAKCudmQeOKyEHXEwfxTtdM9Bw5mnkb65Gl6DgY7csvpgxDt19KBZJNJJSoF9maO2AgQjtdjszGdLROTxfV0XKMtSuoLEBGTIZADuw6XA5Ctu9XlOP9cxdEKzgRfe6QEoPc81VotLjHUzmSGjVxT8pHpmnosmG9AIlS+aXG6vaoOnmP21qJ1T5k9XDJzcU2kM3LpqcuGPOG4w39HgHDCVdCQoJQ9DEyskkvhq7q6mrhWLG01FnAzp/RMppweRIQ68+4cdukEZCKHZH16ggHYlZs6sNWV87oLEWxB59S5lt4+3ik3tPHDoyYjeSlIjKx0VqPrUXLBb0xmjmRDHrEUzKO2vqZBae/xP4DM0BlaVwvi9WMtfnT8GHm1bCa3eOtTJZGTD20Ha/dd5+qLW3X4dp+EkdCGlGbGY2QZh2uOyV0uKQGoL4GLhiBAftLcNVuC1pfAIoTTIjJ+X/oZu4reDntdRcZvYlSYynFNbVNisLJc1Wi3i9HUqMmxk/xN9BBFkDpeI8KjDsWmXZ0Bnf2AAAgAElEQVScJx1h9m2baM+4pFivR5buVSSPYnORwo9FbV/VRuKNgwIBwwkXCYVRhe0uXS7q2Rw5cgRTpkzBzz//HDAgGk241LzdBQxo3FBZBKSyo7pVHsXYojWSMVzFCbVYOfQMc1051lI8LMvlmpEYFRqFU+WnnDLHlDLfzPHhyJg1yD6cnBdOiCOyVuNk5QFcVrYf8Q3laJWejjE33iCUAlKTmfrlrlM4fOgh9E3dC5OpKUuOHDRUg3J3UR8sCbkT+a0zmjxbbozMgrbnTmPH5KtZYDKkDZGgWdunwxx10p7wqMWbyGKcUubejt/Po6i8TrIr2zEcNZDyrFFwu2M5HjVefq0eLpvBjjFdb/6xLx78dLfs8Si5FOko85ODn+D4hePCnGoaayQD+JXU9lnWgLcJPgQMJ1wvvfQS/vOf/+Dee+9Fu3btBBVXks+/66670LPnRY0bfy9kbTThUvN2F3zbsOXOSEzR+rZBbRGy/iMc/WlrU6aWw5XWuQt2jwHW5q3TpWgwi/6QbXgpbwV97piqP/f3R9C9+pKL3haX5XX1cMnFmdky5e5DlVBOZVzPNM2lVejoaHfueQxI/xmjm+s6nq1OxrpTw7HjTD9gRBKqomMlN2NCTRUOjx+qerPqJZtB9v9Sug4RGUsFwkiXWm+iGuPl1NYnzduKXbmlih4uNeOpiWOVO96Ti+FytceecdknoykBYPtJ5JdUITMp2l4rkciWWPybmDyFY/9yavtqcOFtgwcBwwmXVPFqRwgDoZC10YRLzdtd8Gw/PhMpBOTiyCiBSykdnwVZJS+Ga804pYdcVlyW8NY/oWIkJh8Z7Z5pRkaJqJcrZVKeM1nxXLtQj4sFKwVHhw2MRXlinLiHy2pBL1Mj1owewAKtvY2aIzWljpvsr0ZkxmKExu8XIrg+OvYSUhoSJW919SYqjcH6udKR49xJfXBD30zW7uztWGUBHHFtJMV9on7Cu4kZPROHIan8T/j+4MUsRSlDxGK6XNuyxG653kMeLq26YKpB4zcEDAKGEy4WJCiA3t+lIowmXGre7lgw5W04AkoIqH1o3rbqNkF4UuyyHaF8OH4BvjhTgvf3nkK+tRFtqi2YdKoefzjdIFTQieqZguQp3YWi0rbL6ExK2zjywdEWxGbvw9nWV4nGcJmtFrzVoz1ubiOtTC+Gi5qgcaX1umi/BaHxexGWtB1vFP4RPWraSXoTQ1MiYY4OYy5KrWSD7XO1ZJ21XzXt5IpM/+HSdIy9NB0f/5iL3bklwtGx1OV4/CnWRik7UeweHsOlZiVbTlu/IFzx8fGCdL4/X0YTLpo769udP+PEbQscBFizs2hG5FEY+PFA1DbWSk6wdXQaOvX4N1YVN2W3CfpHQjadFWPKTXinbRvEZKc6kS3qzOhMSpvBcgQzPGGPcFRX1uoe1Eb3b1JuoliuZu9Jv+hafD1oCMwiavFyK66nWKaY/VciFH9HFEgCV4IJN3t+mj/1MJjecQy1BZ71OlplWU/HTEk1+1wMQ6UAfbrHJsvCsxQD5/fPF5b6BeGKi4tDeXm5R/MnLxnVOtq5c6cgObF7925kZzvrsHz55ZeYMWOGIMCak5OD999/H927d2ca1xuEi8kQ3ogjoBMCSkdsjm/+5Kl5YtMTsiOnp0/BgfAJECvYJafcrpRJmfjHbvj619NMquFyBsp5ZdK6/RsV+E0girXRQ1AdewUsoa1hbihGdMUPGBhThY8mLFSNvNTD2mS1Yth+K8btAjpWxyK2XUckT70V8VdfLZkIIGY/hfc/iyiMQpizVhnxLymvjkxRatUTZLxBz6NV25CsREqtJ9d1SkqkuV18OyRFJMFRasM1IYARJt7MBwiQasJll10mFKeWUk4gh9Ddd9/tVEvxqaeeUm2tXxAuPTxcJD3x+eefo3///hg0aJAb4aLMyL59+wqFtMeOHQsK5qe/U5Xw0NBQReA44VKEiDcIMARYH1g0LXro7CneIzvD+M7v43hdhGQgtVxtQqlMyojLUvBXhQwy11ItSqTLFhydV1KF6PBQWK1WnGv1JBAi7WXXGgAtLgxrxYNfWjDosNWupyV4A0NCEHflWGS+/ros6XIL7h7UDmMRhqofz6ChpAahSZForKhD47kaSShcExeM3rp6Hq3abGV9YfD0+FOL7bxqiLYdRb8DR3YUYt+GPJSfq0Fcq0j0GpWFrgPS3Dzj2kZwv4ucMFTEmlQTpAjXHXfcIchbkeICaYkSh3jhhRdw++23qzIjaAiX46yJHLl6uIiN0r+tWLFCaFpfXy8IsX7xxRcYPVpZzJATLlX7ijcOAATUvPkrHatEhESg/pL5KKxzLgPjCANLzUDXrMmosBBBYFMsBkdMNdyebPD9KlwoLkSCSGkeanNg8wZ8uWgJGsrOozw0Foc6nsHv7XNFFSE8CYAWe1gP32fBfSssCBHzQIWEIOOVl5Fw7bWiO4g1q1QpEcGTYHpWGxwnoOQl6tO6Dxaq9CCqeWFQe/zpaLta7xyPx9X240dk67v39+P4nqKmk/zmi072O2an4g9/6ak76SKSRWTq9ddfx+TJk0UJV1VVFZKSkrBlyxbBoUPXq6++KnCJDRK1S6UQaDGE6/rrrxfchsRKbdewYcMEkB988EHFHcIJlyJELbqBloeQK2AsZEFPkNW8+SsFDme3zkZJ2lP4uUxaDFPOw0XzErNHbr6uGWZOZZls5W9MJtB31yaSSv2teHMOjvy4FVYrqchfLMN4Mr0KG/oWM+ubsayFczZd02Hr8wsa0CW/SSne7WouYdNh8SK3j9Ssl1GJCGpscJyAEmHX4kFU88LAslZybVy1xNpEt0G3Vt1w+PxhFFQUIDM2017Sp3zFShQ8/gTQKHK4rkCoPbUzkO8//OMZrP3wgBPZciRdY+64FN0Gpes2xYaGBgwcOBBvvPEGLBYLbrjhBlHCRY4aCkEiJ43tNGz16tUCdygpKVFlj18QLqUYLppoo9jmbZ4q1WikH1X74oh4uMaMGQMqM/Too4/a21199dUYMmQInnzySTfQnnnmGTz77LNO/05HD/ziCLgioPUh5NgPC1lQI/LJukqsb/4sxyq10UPx14O5qmO4bLbKPUCl5uMYZ8ZSeJz6+eadfwoJKm7rCGBTb1LwrxCERfUKgLY9rJ/a8hQaLA2Y93YDkiukVyg07WIJG8dWagiGUYkIamxwtF3Jw6VFQkGP7x3r98SxnZLH6/55BajZu9dNQ0/oQ4ZQi9lCLwWFhV8jL+8jVNfkIyoyE1lZtyEt7VqYxAR6tUzIT+75fM5OnDkufayf3ikBE2f0083al19+GceOHRN0QqnWsxTh2rRpk8AdKioufml37NghcAcibWouvyBcixYtwq233ippNwHx1VdfSX5+4sQJdOjQQZZwkYerd+/eeP755+3thg8fjkmTJnEPl5odw9u6IaD1IeTYEQtZ6DFC+ejbqOVResi8OupVIVdr+v7fnbMUm6S3MKF1At7r2UE2y0/uiEhsXq4ersVPzUDBkYPiEFCh667dhUByqTZEwQqjY7FyYAXCIkrRJ/0SXQsR20jHcwvq5T1cDiVsHCej5ghNKRHBVZqDdd+oscGxTxbCfk3Ha1jNsLdjfWFQ3bHMDUpzWTgvAqHnpYmDFKF2HZLI1r79f0Vx0ffNha6ESD+hgFPr1KvQq+dbQUW65j+2GZUXpKsXxCRG4M5XhumylES0yAlD3qvk5GRZwkVtqGJOXV2d3cO1Zs0agTv4hYeLVORZrv/+978szVS3kYrh2rNnD77++muhP/KaUVHtzz77DFdccYXiGPxIURGiFttA60PIMUA0b9+/0VCbL0sWpjxHpMZ3F0uJFovVimWFJZiffw6naurQNjIcd2a2wo1pSYqSCkpB0K4zd43hmnf37agsOS8JUGxyKyFAXq5NRUgM5re7HTntk/D5PepV5eVWx/agHvJrveoYLiIW2c99j7IamRi5+AhsnzXWboKeJZ1snSqtkZSmFQthdyzz47tdrjyykrfutcXhaHuyStrDJUGoXUc+c+YrHDgwA1YRn7EJIbj00leRnn69ssEB0sKbHq758+cLWYexsU2VJYgPkFICka+VK1cKiXe2yxbDtXXrVoF40fXaa69h+fLl2Lhxoyp0DfFwPfTQQ3YjaBIfffSRQGrat2+P3Nxc/PDDD7jtttvw73//W5WxSo1rapqycqKiovDjjz8KHq3w8HCh0O3hw4eFc9ilS5cKzJbcieRZO3DgAM9SVAJW4nPbw/WD/LPIq6lDVmQ4/qSyIK/Gof3qNi0PIdcA0ZrS9wBrpSxZmP7uh341b72NYfVwOdbBc1TDFzxcRw9JPujsHi6JNuThOhORji+zboJWpXQ5TGyk44eTa/DAsnoMZMxStB2drfz1jGT3LIrpeqyX1pcLGluKsI9rPx4rfjnjseyHHvNj6UMpHm3C0Vjcuazc4xiunTsn4ULZbgl9DxMSEnLQv98SFpMDoo03Y7iIRJ0/f/HlbNu2bfjLX/4iqBZQMh3xBseLshFJNmLx4sX2LEU6LfO7LMWbb74Zd955J6655qK7mBgkebdIxkHPyzGOy9bvunXrcPnllwv/d9myZZg5cyby8vIE8kVnt1yHS9sKENny5PhI26j+eZeWh5Drj0tt2WJYG0+LT7D5OMzXHi6j0Zc7mqWYqg6tYlBd1+BU585REoLlWJbmIB3DZcLa1DHoNHiEvV6jHskQjrg1Njbg0y/fwtF1GxB1vg5xtY3odKEGnVMy0UpCh4sltk0sY1NpvbQIkepxfO5ol6/isJSwkftcycOVndIHL61uhfLVaygTpOkFgDaw2awo++E47qbNQ1FXVyhpSkREGoYP2+rJVPzqXl9kKdoAcI3hopitESNGYNasWUIT0uEinU/KTCSHzv3334/Zs2erxs8QD5ejFaSxRdoW5GWyXRQAT2mW/q4u7zgPfqTovLc+P3PeowBp1TvVj2/Q8hBydZ831h5EfdW3om+zFCw//t6H4MsYLm/A7+nD1ynxgB5yzQ86sSxFKgwuJMFYm0oQW2HC+dbdMPCuB3F9dpZQKNtTe1wx05oYweL5u/qydFVFvbUe8emNiZbvjjf2otwYSjFcLwx/AVd3mICylStRsmgx6gsKEJaRgaRbp8gK27qO2dI8XDR/e5jFxvyLOlwjMw3V4fLmfjKccNGx3l//+lfBXWe7yLNEqZi//vqrN+fq0ViccDnDd83PRzySAPBoMfzsZi0PIdcAUXr411euhKX+6EXS5SJpYESWop9BKZAcN2HPwe1xXZ8MgQQpXXJFv234sbShcfQmA/IeOBNG3T0U1phdbtlog1/+AYVl0iWVEqJCsfupq5jwseHHQhqkgtiV1kiNV1CLd1hpDxj9uVayqtaulhbDpRafQGxvOOGioDLKEMzMzBRiuE6ePCmU1qGsw5EjRwYMZpxwOS9V9pZ9OOOhyGXALD6DoUoPIdcuxAJEiXRZ6g6hoXYvzOZypHXMQvaVE9B92ChJ5XEG0wxpYg/I3lage2FkQwzW0KneZEA6i9KK9mMLkNixXDh5aq5Cac9Ge2bjH7Ert0xSwV9LgL/SsZgWIVKyXM3Lh5AI8Pz3KKtmTwTQsIyG3MKSQOLpwC0tS9FTvALhfsMJF4FAR4qUHUg1DjMyMkD6V3SkGEgXJ1zcw6V1v4q98U9MSUbp+tNeE/lztd0TwmSU5IBWfI26T0syhJwtUlmUSZ0voN3oAgmV+xBURT+BB5enoVFEbl9L7BbZqBT4rUWIlPpl9QrSd+L+RT9j1T7pGCVPEwG0xKgZtZe09mvX4cr/GDU1eYiMzEJW5tSg1OHSilEg3ecVwhVIgEjZygmXMzI8hottV0u+8ZtM+HNILBLPN3itjIXNYk8Jk1GimmyIeq+VIR4ukQzJztf/jpi06mbvluv8TIiP74v5hx/Ht/sLBQ9SkxoTxWCbMK5nmqrYLVvvSh4uLUKk1DcrZl/sysPDS/bKLqZWMil42qwWzNgwA2tz1wp/J9T0ErL13g7kIwUbAoYTLpKF+Oc//ykUhqS/O14kDxEoFydczivFsxTZdq7sG7/JhOf7XoKIk1VeDRD1lDAZVTaGDVHvtWL11rBaJBXDdeltRxEeI32sRtloQ4ds8Si2zdVGT2K45ObL6hW8/NV1+P1clSx0ahMBHDszan6sa83bcQTEEDCccJFK/KlTpzBx4kTExMQ42cBSw9Bflo0TLveV8ETk0l/W1Wg7WN/4jbbDsX9PCZMehZE9OdJUwkqvoyQ18UhKNtHnUlmUXa47gWgZD5cRektGeYBY93uXv69CfaN0qTTybh19YbyqRADHNVDy4GmNUWNZZ96GIyCFgOGEKzExURA7JXmIQL444Qrk1fOd7axv/N600FPC5Clh8/RIUw4rtURCKaNObTKEmG2OFQXKzlbDbDqKxrq9qK8uRXxqKrqOSUZp4weAlxXFjQj8ZvUKdpm1CvUiMWk2/MJCTDj64gTNXwujYtQ0G8Rv5AhQKIDV4IrMl112GUh8NCUlJaAB54QroJfPZ8azvvF700BPCZPSkeTxAa3x5ulzyCutRlZiFKYN6eAk66B0f/Kkbojum6oJEjVHSXp7sKTI1nfv78fxPUWSsXowWYOmZh4rpkpHipekxGDdo02C1VouOQ8X9RcTGoPosGhkxmbqWi9Ti638npaDgOGEa968eUI5nccff1yoXeh4kUZXoFyccAXKSvmXnaxv/N602lPCI+ehOhgXgnvKStHYLCgqFtztKeGTw0rNUZI31oa1XEkwZaOxeAUpaP6RJXslpS7mTu6Dm3KyFL8WUsfH9O+zt8xGo7VRtg8eSK8IMW+gIwKGEy5HhXlHu4nAkOJ8oFyccAXKSvmXnaxv/N60Wo8jPbHCyIfSI3DXT8fRQAruLpdjxpmnR5pyWKk5SvKG99GbBXm9uYc8HatJFmIXvt1/Bo4ni6RtO65nOv731hzF+C254+Mr2l0hyJn9cOoHe5ainM0hphCQQryU2Kun87Xdr3SErdc4vB9lBKjkINVTdqybuHr1agwZMkT0ZipwTXWi6R66pk6dKiQEhoaGKg/W3MJwwsVsiZ835ITLzxfIQPM8DfBmeeM30HzRrh3nVFNYhvK68zhesRcVCRXI/oM2sVVWAuNLD5ej3IE34utcKwq4LkZMYgTufGWYt5ffL8bz9HuhdHz8/LDnQb/bnx76FAWVBaioq0BVg3hmJHm6jA6k98eXL7/YCA5G2KtAfL8KF4oLkdA6DdlXafs9UpobES6KMaeqNyzX008/LQi2f/PNN0Jzqrd40003qaqpaCjhWrNmDTZv3ow+ffrgxhtvdJrTvffei3feeYdlnn7RhhMuv1gGrxuhhzfI60Y3D6j046W1tp/UfFgJjKdHmnJ4Kj2EHb0YrATRk/XzlodLaa09mYO/3qvm+JjmoMb7acScvXGEbYTd3upT798jJbvVEq62bdsKHq2bb75Z6JpCpR599FGheg7rZRjh+uCDD/DII49g9OjR+Omnn9CtWzcsW7YMcXFxgm2UtciLV7MuE2/nKwSMJAdGzonlx+vQlg345p1/CnIFrpeWgtmsBMZIEqsmS9EbD0DWGC5P9gLLWgdjHU61BEqJoKkVe1V7PMj6/fBkLwTyvfK1Rs0Yf+9D6DFitG5TJMJFHityprRp0wZ//vOf8be//Q1iYVAlJSVITk7G0aNH0blzZ8EG+nvXrl2FSjoJCQlMdhlGuC699FIQ6Ro0aBBqamqE4tVkIJ2REtki4uUqhMpksY8acQ+Xj4D38bBGHn8ZOTWWH689369CwZGD4maYTMjo2h1TnnuV2Uw1BEYsBix2cBtE9WkNE0ORajmjWOUOvHHEQ/NUylL0dL4sa63ng4p5QxjcUC2BUuP9VDJdy95h9QArjR2sn0vXGiU9BfW/R0o47dq1C+S1IiK1Y8cOTJ48WYjRov9cL9ISbdeuHYqLi+2KC/T31NRUQWc0K0s5wYP6NIxwEeO7cOGCk9133303du7cKZAuKmTNPVxKW4J/7msEjAzwNnJuLD9eF4oKUVlyXtKM2ORWmP7uh8xmankIMXduUENP44hYzLLrcG3MN6SiAMtaqyHOLHPyhzZqCZQa76fS/NS8XNj64h4ueVSlao3a7lL7e6S0hq6fU4jTggULsH37drdbbR6uY8eOoVOnTsLn9PcuXbr4h4erY8eOoNI9HTp0cDKeYre2bduGw4cPo6pKvrSDWsCMbM89XEai6799B6qHi+XHKz4lFQUitf2E1dD4RukNAuO/u8U3lrGstRri7JtZuI+qdGSnhUCxej+VMNBCnrSQNCU7gulz4cVB598jNfiQhNX8+fNFCRf1Q94wCrCnqjl0ffbZZ3j44YcFYXfWyzAP1//8z/8gIyMDzzzzjJst9913H959911YRGJHWA33djtOuLyNuH+MF6gxXCw/XtlXTtA1hss/VqzlWcGy1oHm4WL1lupFoNTuGi3Hg6xzUmtLsLT39tH4kiVLMG7cOCG8iWo9UzA8cZMZM2aIQjp79mysWLECq1atEj6fMGECqHQh/TvrZRjhqqurQ0NDA6Kjo0VtIVZIZ6KBcnHCFSgr5Wynp9lbRgZ4G4koy49X92GjsOLNOTj601YIBSfoP5NJCCLtMnAornlwJrwdbO1YBqf8XA3iWkWi16gsdB2Q5nFsl5F4+7JvlrUOtBguf/cGafFw0R7hHmDpb4pUrVGjfo9GjhyJX375ReApmZmZQtA8ZR3aguYpBIou8nzRRTpcFFTvqMNFHi+uw2XArx8nXAaAanCXemVvGRngbRQErD9edkK6ehXKiosQ3zoV5PkiMuYLsmV0gLlReHu7X6fjtpIqXHH6O6ScPUIlsv2COHuKh1ZC4+m4rPf7OyFknYe/tfOn3yMjsDHMw2WEsb7skxMuX6KvbexgfPNXg0Sg/Xh5Q0JBDX7+2lb0aMpqRbeqYxhRfxgpqBKKYvuKOOuBm5YjOz3GZe2DHw+yIsXbOSLACRfjfuCEixEoP2rWUrO3/GgJVJmit0ioUtC1KuP8oLFtPm+sOYLfz4knHDmWUPIDkzWb4G8eLrG9NHVwe2F+H/+Yi/ySKmQmRWPa4PZOhdo1A8BvDEoEOOFiXFZOuBiB8qNmwZq95UcQ62qKnmVwgs0DYZvPN/ucaw+6LgAVC89pn4TP7xmq69p4uzN/OrILtr3k7bXk411EgBMuxt3ACRcjUH7ULBizt/wIXt1N0dPD5U8PbD2Aovk8vGSPU6FnqX7T4yOwfdZYt48DyePnTyQn2PaSHvuR96ENAU64GHHjhIsRKD9q1tJjuPxoKZhM0TOGy9+OpJgAkGk08V+bUfVbBfrWhiDeYkaZ2YLdEY04GNYIkFur+ZLycPkTgWHFwl8y+oJtL7Hiz9vpjwAnXIyYcsLFCJQfNWPN1PMjk1u0KXqWwfH3oGs1C024zHzkB7SrbuJWJphgoWxEAEfCGvF1dL2ddEnFcHEvjRrEndsq7aW0+DA8dUsdPj30KfIr8pEZm4k/dv8jJlwyAWaTWfvA/M6gQ4ATLsYl5YSLESg/axZomXp+Bp/XzdGrDE4weSXI8/f9Bwcg9ugm4rUquh4HwxuFtbr6snS8PSUHZpd6lN7Gg753ZStXomTRItTnFyAsMwNJt96K+Kuv9rrciKebWB47C9K7fI7qMDrupdWwCoSYiNaYdmPw6qhXOenydAGC6H5OuBgXkxMuRqC81IwLZHoJ6AAdxh88OnrFTMnFttEjviDEgsVxdejQKho/PHK5G9miJVTy0kjFfWlZfiJb+Q8/jPLVa0jp064LBrMZcVeORebrrwcU6ZLbS+EJexCZsQRWWNygCjGF4IXhL+CajtdogZHfE4QIcMLFuKiccDEC5YVmeh49ecFcPoQPEPB1zJKe4ytlb5abrPh3Ui3mTuqDG/pmiqLtTQ/Xha+/RsHjTwCNTV43pyskBBmvvIyEa6/1wa7QNqTcWqZ1+zcq8Jvg2XK9yNPVp3UfLJywUNvA/K6gQ4ATLsYl5YSLESgvNNMzuNoL5qoegnvvVEMmeoMvg6719LApebhOh1pwYUiy6FGiDRg97VFand+nTEH1nr1Nni03FmJCVHY2OixepNSNbp/r4WmU2ktvHZ2G4upiSVtTo1OxdtJa3ebCO9IXgeXLlwu1EI8ePYqEhATh77aSPo4jlZWVCf9OtRSjoqJw//3346mnnlJtDCdcjJBxwsUIlBea6Skf4AVzVQ3BvXeq4PLbxnp6lOReMOggq9UVbXDLzd1FjxJtAOnpcVMC/ejIUWgoKpJsFpqWhi4b1jt9rgcpcpzr8r0FWLjtd5wqqUJdgxVlNfUC/yMKSIkHFOM2rmeaLElVmid9Pm3VNOwt3ivp4cpOzcaC8QtYuuJtvIzAt99+K9RPXLhwIUaNGgUiVYWFhejevbubJXfccYfw2SeffIKioiKMHTsWL7zwAm6//XZVVnPCxQgXJ1yMQHmhmdIRS0xiBO58ZZgXLNF/iGD33umPmH/2qGfMlF4k3FseP0UPV99sdFh00cOlJxkU60tqh+ihyr/i+Ao8uflJNFrdj095DJf676a9bu22AjSU1iI0MQKxQzIQ1ae17sXrBwwYIBAuMY+Wo+VVVVVISkrCli1b0L9/f+GjV199VfB2bdiwQdUkOeFihIsTLkagvNAsmD1cwTw3sa2hp2fDC1uPeQg9PVw0qF7Zm8wT8KCh2hguPY875fpynZIeqvyUmThjwwyszV0bUFmK/vi9oz1+fvEhVO8/C6ccBDMQ1TMFyVO660a6KisrERcXhzlz5uD9999HaWmp4OV68803kZ6e7rRVdu/ejZycHNTX1yM0NFT4bPXq1Zg8eTJKSkpUfVM44WKEixMuRqC80CyYvUDB7L1z3Rp6eja8sO1UDaEniVA1sB80VpulqEmxJ9EAACAASURBVCc5letLDBo9sjOJdK06sUrQ4SqoLEBGTIZf63D56/euancRzi897Ey2bItmBpIndUN031RddnheXh7atm2L3r17g+K4WrVqJXi66NiQyJTjtWnTJowfPx4VFRX2f96xYweGDBmChoYGVfZwwsUIFydcjEB5oZleRyxeMFX1EC3JwxXMpMRfH2qqNyTDDaLekkHtcHnebpR+8gnqCwoQlkE6XFNEdbj0PH5V6stxOnp4uBjgcWvia++Sv37vit7Zg7rccklIw9vHI/WePlogd7uHPFp0TEjeLTpWpOu3335Dly5dUF5ejpiYGPs95OHq168f6urq7B6uNWvWYNKkSdzDpctqiHTCCZdRyLr3y5KlF0hHLGqQC2bvnSsOot4IK9CjPkQoYZNiCkH7dvHoNSoLXQek6XacoGY9PGnrrZgpT2z09F49iOVN72zBrtxSUVPUkiI1Hi49YrjU4qcHXmrHZPreNTdSi7entjjeX/DSj7CU1Ul2aY4PR8asQboN2b59ezz99NO46667nAgXBc/Hxsbax7HFcG3dulUgXnS99tprgmds48aNquzhHi5GuDjhYgTKw2bB7L1igaYlzd/NG2EFrq0KQ9f6EHsJG8KMqqN0zE7FH/7SM+BIF8uaB3IbT70lRECIcO3JuyAKg1pSxBLDpWeWotq18xQvteOJtVfyAupxzKrFTm96uMi+F198EUuXLsXKlSuRnJwsHCkWFBS4HSlSW8pGPHv2LBYvXmzPUnz++ed5lqKWhWa5hxMuFpQ8b9OSPDxSaAWr907pTbtHXQgmVIXB7FiN2fbmbQbG3HEpug1yDmj1fMfxHjxBwNP4KyIgDy+hsjjiVmS3TcAX9wyTlbxwvFMqS9FkAhKjwhAWYkJ0eFPgc3VdI7KSojBtSAdc1yeDeQxf4uXJ2LZ7PV0zPWwQ68ObMVw0fmNjI2bOnIkPP/xQMGf06NF4++23haB5itkaMWIEZs2aJXxGXq/p06c76XCRZpfai3u4GBHjhIsRKA+btaQYJg+hCvjbXd/2by0PR0Yj0S3yQbhf6Z0SMHFGk0ufX/6BgKfeEqUjwJx2ifjiXnUSL1JHuddc1gYPfrob3+4vBLXRW5OLZUU8xYtlDKU2/uBlE7PRm1mKShgZ9TknXIzIcsLFCJSHzQIpS8/Xwa8eQu3z2129EdMvRCLOKk62yNhA1lfzOdgGGeCpt8QIAiL1vbRYrZjx2S9oFHGnqT261Aqnp3hpHdfxPn+II5Oah12Ha/tpNJTUIDQpErGD2xiiw6UHlmr74ISLETFOuBiB8rBZoHi4/PlHy8Ml8Ortjt6IXger0bpOyr8F+MLDxUm1/HaQ85aYTcCtA9vh4Oky5JVWIyvR/fhObwIi972MiwhFaXW96IS8FSzuL96llpDQ4dUfMsbBOOFiBcpkglWsNhjj/bwZGwKBEsPlLz+cbKgGRit/W3tOqpX3jRRGFDOVGh+B4vI62eM7vb9HLEHzUrPyRrA431PKeyqYW3DCxbi63MPFCJSHzQIlS0/vN3MPYdP9dl94dhobLJj/+k5UHa+ACVZ7LJdclqKRpUD0JgO6L5KfdCjmLemRHofFO04pHt/pTUCUYsKkIPOWh4vG594lP9m4PjCDEy5G0DnhYgRKh2b+lKUnRTxeXnUQheW1krP1xtuyDlCLdqH3Q5DFTvuY+wrRrdaMPrUhSLCYURZigbVTLF54YBBCQs1OXRkdZBvspJplXbS2UYOdFgKi9XspNR9vxXBpxZPfFxwIcMLFuI6ccDECFUTNZONBIkNxoapeyHRyvbz5tmwE3L7w7GgZ0+g0ciMCutWslxipmDq4vdDFx9tPSsZFqRnDqLZGYufJ9zIxOgxlNQ0+y1I0Cm/eb2AgwAkX4zpxwsUIVBA1kyMBFKNCxEpMPyjQ35aVjmVIy6h3ZoKu+kVqPCK2LWa0UKIWm/Ta/lKkgvp3JPm+FPGUm6uR2HnyvXz15t4wm0xYuP0k8kuqkJkUjWmD23tNh0uv/cH7CUwEOOFiXDdOuBiBCqJmSsSD3pbLg/BtWck7QUus94NeaUyxI1qjS4GweN1IMHP53gIs3Pa7rh4ntcHf/kbyWbC7oW+mpl+Llvq91AQWv8mvEOCEi3E5OOFiBCqImimRgLS4cDwx4dKge1tWeqA5LrHYg15LwL0Wj4jRHi6lWLY3/9jXMCFNNWtgI8A57ZPw+T1D/eIbqITd21NyNCu7t9TvpV8sLDfCIwQ44WKEjxMuRqCCqJkWEhAM01fjXXGNV9P6oNXiETE6hovWUi6gmzxbjyzdq5iJp2VPKJEKsT79LVFDSzA8C1ZGfi+1vCyw2Mzb+B8CjgWqybra2lr06NEDv/zyi6ix9fX1eOihh7Bo0SLh86lTp+Kf//wnQkObSkWxXJxwsaBERyhch4sRqeBppoUEBMrs5R4sNIcHFu9yKoEiNy/HB71WzLQQNaOzFJXW0ogHv21d/r7sV1TWNSqZYP880BM1mCcKQOseUxpDyx5U6pN/HjgI9O7dG7fccou9fqKr5U8//TS++uorfPPNN8JHVG/xpptugpqaipxwMe4HTrgYgQqiZsH6A8wyL1pGITZp+0n8kleK+kbx6sKuD3pPSIgWj4gvS4EoeaHUepwc10Ws/IzcV8vfYriM/Blg2b9mkrlXeRlF5FSa0aKbWywW7Nu3Dzt27EBpaSkSExMxYMAA9OrVC2azsyyMnkD99NNPGDp0KHJzc5GRkSHaddu2bQWP1s033yx8vnTpUjz66KM4efIksymccDFCxQkXI1BB1kwLCfB3CNQ+WNS015uE+DOWnpBLsXmxHOUSjQiELEWj182I76Xe62k0BsHWP5Gtzz77DAcPHnSq6kLPXjrqI6JjFOmaPn06CgoK8PXXX4vCWlJSguTkZBw9ehSdO3cW2tDfu3btKhDDhIQEpuXghIsJJn6kyAgTbxYACKh9sKjxKKjtOwDgkjRRiSDdNqgdnrue3szZvC1KgfIx4SF4/oZegj0f/5jLZQ102DyOR+u7T5WKyrzYhrF5LHmclw7Ai3RBsVPLli0TLaFHpOvGG28EHfvpfVVVVaFNmzZYsGABrr/+etHuT506hXbt2qG4uBgpKSlCG/p7amoq6LOsrCwms4KGcJ0+fRrEUnfu3An6++7du5GdnW0HYf369Rg9ejRiYmLs/3bnnXfif//3f9mA4jFcTDjxRsYgYLVYULZyJUoWLUJ9fgHCMjOQdOutiL/6aphUutq1eKFYPQpqvGHGIOW9XpWOAIlnje+VDtaMPC3rwjJbThDEURJ7kZDC03Z0vnT6ELf4Rr0lUljWNBjb/Oc//xHIi9RFR3p//vOfdZ/6/Pnz8cQTTwhjSwXA2zxcx44dQ6dOnQQb6O9dunRpmR6uwsJCfP755+jfvz8GDRokSrhuuOEGARwtFz9S1IIav0ctAqIPx0Ht0G/BXFSsWUNpcwAVUSflVbMZcVeORebrr6siXUZ6odR4w9Riw9Le2+SCxpv91T589GOuqHlqYquMWBdfrwfLmnmrjeveiAoLQe75Klmvls022zrS/5fLTJ0yoC0Oni7TVZPNW/j4epy5c+eivLxc0oy4uDg88sgjups5fPhw0H+vvPKKbN9E+N544w1MnDhRaEfHnw8//LAQ98V6BY2Hy3HCRI7EPFyccLFuC97OFwhIPRyvyNuFh3/+BGarxd2skBBkvPIyEq69ltlko71QrN4wZoMZG/qKXOhFlIxYF6U+WwpBUOPNcnqWgN5rTBjXM03wVE6atxW7cktFS3rZ7rPF2XHPF+MXt7mZLzxchw8fFuLDDh06JMRjyV2UjbhixQqsWrVKaDZhwgQQp2jxWYpShGvs2LHCmSsF3o0aNQpz5sxBZiab2jH3cKn78vDW6hGQeji+tvFt9Dh/EqI5OiYTorKz0WFxkzYMy2U0MfG2l8k2ZyVyMXdSH2hVN6cxpOb10qoDKCqvk4SeNVvRiHVRigsjo1sCQVCKtxNbPDoS7tsuyan0j9Kxr1g/arycLN/fYG3jixiumTNn4scff8SGDRvcYL377ruFf5s3b57wJ+lw/e1vf3PS4SKPV9DpcNFEGxulNWkiIiIEnSz7G4aIh+vMmTM4e/aswGbpTxIwI1ZLMV9imQ/PPPMMnn32WadFsNJRDr84AgYhIPVwXPjtc0ipKZMcNTQtDV02rFdllVFeKCNIA+vE9PI0iY0nN6+4iFCUVteLmqlWH0vvdeEEoYkoXzF3PX4/V8W6lQQSKqbcz0JgXQdRuweYjQyyhr7MUvQWlAFxpEhuOxIck7pOnDiBDh06yBIu13srKiqEVM79+/eje/fuinhzD5ciRLyBhwhIPRwVPVx9s9GhWf3YQxM8vt1oL5OcgUrkgtXTJDaG3LzIE0KvYmLvY3p4NzzxGLZ0gmAjyit/PaNqb0utmxZPGQ3syd5TZXiAN/aVDpe3YAsIwqUWDLEjRdc+KisrER8fzwmXWnB5e8MQkHo4jj61C4/s+gQhOsVwGTYBAEZ6mZTsNnJspb6pkHmZAYXMPfUYtnSCoHb+SnFXUushd/bBPVxK39yW83lQEa6amhph5aKiooRzWdLsCA8PF44M161bJ3jB6L/z588LR4p79uwRgutDQkIUV5x7uBQh4g08REDq4WCyWvDEzo8w/PQ+mMiN4mGWoodmyt5upJdJyW4jvWtK80qLj8AT43voXshciTDER4aiS2ospg3pgOv6ZLhpfrV0gsDi4SNCFB5qRn2jBZS5eGPfTDxzbU+Ehoorm4sd+/ZIj8Oin3JFMx718HIq7X3+eWAgEFSEyzGOywY/Ea3LL78cr7/+uiDLT2SLPFv0b//4xz8EMTOWixMuFpR4G08QkPVmXNoaL8adRuknn6C+oABhGaTDNUWTDpcnNirdq+QJEouLUeqT9XNPvUFy4/hqXqyEwTGTzlVotSUTBCWi7LrmSh4uqT1i5N5j3f+8nf8jEFSEy0i4OeEyEl3etw0BvYOmvY2skV4mlrkYhZ+v5qWGMKjxpLQUgsBCWMX2lRosg+W7y/L94m08Q4ATLkb8OOFiBIo3a9EIBOuD3FfzUkMY1MYKGUVO/ekLoHQkK2WrWiz9ac7cFv9FgBMuxrXhhIsRKN6sxSMQrA9yX8xLLWHQMxvOk+xIf/kSyBHlULMJtQ0iYsLNxuuJpb/gwe3wLQKccDHizwkXI1C8GUeAI6AbAmoU0vX0yvjKo6cbcA4dSRHlBVtPYPepC6Kq8XpiacSceJ+BiQAnXIzrxgkXI1C8GUeAI6ArAo6E4VhROS5UN4j2ryXuSMpQX8Ws6QqcQmctYY7exJOPpYwAJ1zKGAktOOFiBIo34whwBAxDwFueJ19lZRoGnEjH3sLSm3PiY/k3ApxwMa4PJ1yMQPFmHAGOgKEIeCOWTCk7Umt8k7/FhXkDS0M3A+9cMwL5+fm47777sGnTJsGhcsUVV+Bf//oXWrdu7dYnlRck7c5FzRU9pk6dKshMqamjKDhurLxAINOCccLFBBNvpICAvz1w+IJxBMQQMMLDxT1KfK8pIWC1WlBY+DXy8j5CdU0+oiIzkZV1G9LSroXJJC5Eq9Sn1OdUMpCujz76CESDiETFxMRg8eLFbrc8/fTTQnnBb775Rvhs/PjxuOmmmzB79mxVw3PCxQgXJ1yMQPFmkgjwBw7fHIGCgBHxTUb0GSh4cjuVESCytW//X1Fc9D2soOxRKphkgglmtE69Cr16vqUr6aJKNI8//jhuvfVWwbiPP/4YL7/8Mvbt2+dmbNu2bQWP1s033yx8tnTpUjz66KM4efKk8sQcWnDCxQgXJ1yMQGloZrVYcGjLBuz5fhUuFBcioXUasq+agO7DRsFk1vetRoN5ut3CHzi6Qck7MhgBI14OjPCaGQwD796LCJw58xUOHJgBKxrdRjUhBJde+irS06/XzaL58+cLXiv6kzxct912G3r27ClUoHG8SkpKkJycjKNHj6Jz587CR/T3rl27orS0FAkJCcw2ccLFCBUnXIxAqWxGZGvFm3Nw9KetoL8Ll8kknKl3GTgU1zw4M2hIF3/gqNwcvLlPEdA7vsmouDCjQeJhAEYj3NT/zp2TcKFsd7Nny3VMExISctC/3xLdjCHSdOedd2Lbtm1Cn4MHD8Z3332HuLg4pzFOnTollAAsLi5GSkqK8Bn9PTU1FfRZVlYWs02ccDFCxQkXI1Aqmx3ctA7fvPPPi2TL4X7ybo2/9yH0GDFaZa/+2TxQHzj+iWZwWhXMD3d/fuGQwv2ay9rgwU9349v9haA2TYdcEIqEj+uZhren5LgVDA/OnWn8rDZtHoq6ukLJgSIi0jB82FZdDLFYLOjYsSMmT56MZ555RuiT/ty8eTO2bnUew+bhOnbsGDp16iS0pb936dKFe7h0WQ2RTjjhMgbZxU/NQMGRg+Kdm0zI6NodU5571ZjBvdyrPz9wvAwFH04EASOO8fwJ6C925eGRJXslhUbnTu6Dm3LYvQV6zU0O98sy4vFrQRkaLUS1nC89dc/0mksg9+NND9fZs2eFbERHD5WYJ8uGJ8VwvfHGG5g4caLwT5999hkefvhh5ObmqoKce7gY4eKEixEolc3m3X07KkvOS94Vm9wK09/9UGWv/tmcx3D557r4i1XBvj+IcD28ZK8k3K/7iHCpLZ9kmwBXo9f3m+PtGC7yUFEQPGUg0kUeLgqcJ+LlelE24ooVK7Bq1SrhowkTJoCyHHmWor57wN4bJ1zGACt4uI4eAqzub5AUyxVMHq5g92AYs0NaTq/B7gGl+f2cWyq5oP3aJ+Hze4Z6tOBajmTVFAh3NU6rHplHkwzSm72dpXjgwAFBW2vnzp2gI8a+ffti7ty5wp933323gPK8efOEP0mH629/+5uTDhd5vLgOl0GbkRMuY4BtSTFchKDegciuq6LlgWPMyvJe1SIQjDF+jvtx96lSiJzM2WHylLxofaFRwl1qHbmHS+0OV25v1+HK/xg1NXmIjMxCVuZUQ3S4lK3RvwU/UmTElBMuRqBUNnPKUiQvF/0XpFmKKqFR3VzrA0f1QPwGOwJ6Etxg83B5u/C21iNZrR4uHsPFfwjUIsAJFyNinHAxAqWhmV2Ha/UqlBUXIb51KrKvDD4dLg3QqLpF6wNH1SC8sRPZemDxLt0y2IJt/dTERulBXrQSVjk7zSagd1YCfs0v41mK/LvvMQKccDFCyAkXI1C8mc8Q0PrA8ZnBAT6w3gQp2DyULJ4jPSUWlI4GpY4slXB/8499seLX01i4/STyS6qQmRSNaYPb47o+GVwSIsC/w942nxMuRsQ54WIEijfzGQJaHzg+MzjAB9aT4NqOJhdsPYFjZyvR2GhFaIgZnVvHYNqQDgH5cFfaj+Q96tsuSTfy4sl6GB1bGeBbnZuvEwKccDECyQkXI1C8mc8Q8OSB402j9Yx78qbdrmMpEQrWIHAlD0ugimt6ez/q7XH05d7iYwcnApxwMa4rJ1yMQPFmPkMgEB44wUQu9CIUgbBuWja1t+cVTHtLC978Hv9HgBMuxjXihIsRKN7MZwgEwgPH2w9hIxdDr7noRdyMnKuWvn2xH/nRoJaV4vd4CwFOuBiR5oSLESjezKcI+PsDJ5jIhV6EQq+jSZ9uPInB/X0/+iNm3KbgRYATLsa15YSLESjejCMgg0CwkQs9CEUwkVC++TkCHAFpBDjhYtwdnHAxAsWbcQRkEODkwh0cvY4m+cbjCHAE2BH47bffcP/992P79u2Ijo7Ggw8+iJkzZ4p2UFZWJpT7oXqKUVFRwn1PPfUU+2DNLTnhYoSMEy5GoHgzjoAMApxcuIOj19Ek33gcgUBHwGK1YllhCT7IP4u8mjpkRYbjT5kpuDEtCWYTqbbpczU2NqJ3795CAWoqWn38+HFceeWVeOWVV3Drrbe6DXLHHXegsLAQn3zyCYqKijB27Fi88MILuP3221UZxAkXI1yccDECxZtxBGQQ4ORCHBw9jib5xuMIBDICRLam7/8dq4ovwALACkAQxgUwoXUC3uvZQTfSRYWriXBVVVUhPDxcgO3ZZ5/FunXrsH79eicYqU1SUhK2bNmC/v37C5+9+uqrgrdrw4YNqiDnhIsRLk64GIHizTgCCghwcsG3CEeAI+CKwOdnzuOvB3PRKAJNCIC3erTDxPRkXYDbt28fsrOzUVlZiYiICKHPp59+Gm+//TbOnz/vNMbu3buRk5OD+vp6hIaGCp+tXr0akydPRklJiSp7OOFihIsTLkageDOOAEeAI8AR0IxAsAgDqwXgmp+P4OeyKsGz5XqRp6t/fAy+7tdFbbei7Yk89ejRAxMnTsRzzz2HY8eOYdy4cTh9+jQaGhqc7tm0aRPGjx+PiooK+7/v2LEDQ4YMcWurZBwnXEoINX/OCRcjULwZR4AjwBHgCGhCoCUfuWdv2Yczdc5kxxHE9PAw7BnWUxOuYjft378fDz30EHbt2oWsrCxcd911eO+994RYLceLPFz9+vVDXV2d3cO1Zs0aTJo0iXu4dFsNl4444TIKWd6vKwIt9Q030HcCX7dAX0Hf29+Sk0q86eESW+nHHnsMJ06cwJIlS5w+tsVwbd26VSBedL322mtYvnw5Nm7cqGrTcA8XI1yccDECxZt5hEBLfsP1CDgf38zXzccLoDB8oJDhliyb4s0YLtouv/zyCzp16oSwsDAhAH769OlYu3atEEzvelE24tmzZ7F48WJ7luLzzz/PsxSN+tpzwmUUsrxfRwRa8htuIO8Evm7+u3qBRIZZhYEDhUCq2RXezFIku5588km88847qK2tRZ8+fYTMw2HDhgkmU8zWiBEjMGvWLOH/kw4XETJHHa7Zs2ermZ7Qlnu4GCHjhIsRKN5MEwK2H9C/L/sVlXVieTpNKdI57ZPw+T1DNY3BbzIOAX/2TATjw1nNSgYSGWbZR0unD8EDi3fh2/2FoLW1yyeYTRjXMw1vT8mB2ayfZpUarD1ta9Phmp9/Dqdq6tA2Mhx3ZrbSXYfLUzu13s8JFyNynHAxAsWbqUbA8Q280SKWo3Oxy/T4CGyfNVb1GPwGYxFg9UwYa4V774Hk3TEKGxYS4y8vMSzkkHB6ZOleiP1WhJhNmDupD27om2kUnLxfDxDghIsRPE64GIHizVQjIPcj69gZ93CphtZrN/jrQ53lAR7sD2d/JcNim5OFIE+atxW7cksl5RO4F9xrX3vVA3HCxQgZJ1yMQPFmqhGQe1g7dsbfXlVD67Ub/JXY+CsR9NrCAAg0DJSEgQOJQHpznQNhLE64GFeJEy5GoHgz1Qgo/YBSh0S2Aj0+QzUwfnaDXCwUmeqPcTVKeytQjqg9iUPzVzKsdXsHGoHUOs9gvI8TLsZV5YSLESjeTDUCSh6umIgQvHjDZbiuT0bABsOqBsXPbmA56iGTl+8twMLtJ5FfUoXMpGhMG9xel3XTSjiC4eHMgr1ckLin9/vZVkSwEUh/w9dIezjhYkSXEy5GoHgz1QjwH1DVkHn9Bl+ukSeEwZd267VIesxB6ZhOL1u90Y8n+8Eb9vExpBHghItxd3DCxQgUb6YaAf4Dqhoyr9/gS0+RJ4QjGPaWL7H3+kZjHDCYCCTjlIOiGSdcjMvICRcjULyZJgT4D6gm2Lx2ky9joTwlHIG+t3yJvdc2GB+oRSDACRfjMnPCxQgUb8YRCEIEFOPswkMQGxmKrMQoTBvSQZe4LRuMLZ1weEo4g3A78ikFKAKccDEuHCdcjEDxZhyBIERAjVYaBXDrmVHa0gmHJ0eqQbgV+ZQCGAFOuBgXjxMuRqB4M45AECIgFgslN009NdNaOuEIhji0IPxK8ClpQIATLkbQjCBcVosVR3YUYt+GPJSfq0Fcq0j0GpWFrgPSYArQWliMcPJmHIGAQ8A1Fqq8psErdS854YBQM9AoyY2A24jc4IBFgBMuxqXTm3AR2fru/f04vqcIVstFI0xmoGN2Kv7wl56cdDGuDW/GEfAFAt6MrZIjHDR3gYxs+x15pdWGxJHZ8NWqB+aL9eFjcgT8DQFOuBhXRG/CdfjHM1j74QEnsmUzhUjXmDsuRbdB6YzW8WYcAY6AtxHwh9gqb3q/vDnW/2/vXICkqK4+flgWVxbDw89vQUDAEA3EIAJGJVA8lwQipfiASkqMkKSIRikkEbQIKAQKAUWpghBJsBRN8MFDENBKJPH1hUcFBXkJJBFQEAQCqIAsuOxX51ozWYadndu9PTO3u39dIdnsnu57z++c6fn3uae7cx1LxoNALggguCwpBy24Fk1bJ/s/+Czt6E1aN5BbRnWynB1mEIBArgm40FuVyznkcqxcx5LxIJALAgguS8pBC66n7/8/Of7pqbSj12tYJEOmdLGcXfbNWErIPmNGyB8BP/ntQsUnl1W2XI6Vv0xgZAhkjwCCy5Jt0IIrTBUuF75YLMOEWQ4IeBEnXmxzMPUqh6hJfue7mTuXfWS5HCtfucC4EMgmgcgIrhUrVsjUqVNl06ZNUqdOHenWrZvMmDFDmjdvnuS3ZMkSGTVqlOzdu1c6duwoc+fOlTZt2ljxDVpwhamHi6UEqxRxzshW7NjaqYNexIkX23zCC3N+57LqlMux8pkPjA2BbBGIjOCaP3++NGjQQLp37y4qjoYPHy7btm2TVatWGXY7duyQDh06yAsvvCClpaUyefJk8/OWLVuksLAwI9+gBVeY7lLkRJsxPZwzsBU7tnYJB72IEy+2+QQY5vzOJeNcjpXPfGBsCGSLQGQEVyqgjRs3GoFVVlZmBNW4ceNk/fr1snz5cmN6+vRpKSkpkcWLF0vPnj0z8g1acOmAyedwvbX3v8/h6tbMuedwsZSQMT2cM7D9crS1SzjoRZx4sc0nwDDnt1fBXBPOuRyrJvNkXwi4SiCygmvmzJkyZ84c2bx5s2F/4403Srt27WTSpEnJWHTp0kUGDRokCW5o/QAAHLxJREFUI0aMyBifbAiujIM6YhCWL05HcDkxDduY2dolnPIiTrzY5gpaVcun/zl+Snb950SVU6glIh1bNpJFd303V1P0PE4u+8hyOZZnEOwAAccJhEJwaTWqvLw8LcqioiKzjJjYtJKlVasFCxZInz59zK979+4t/fr1k/vuuy9pd/3110vnzp1l7Nix5xx7/PjxMmHChLN+X1FR4Xg4szM9r1WQ7MyCo3ohYCt2bO0SY3sRaF5svfjm1zZdhUZPHWfSfLSDfEWP33mzHwQgEA0CoRBcAwYMkKVLl6YlvnPnTmnVqpX5uzbNa4/W9OnTZfDgwcl9tMJ15ZVXysSJE5O/69q1qwwcOJAKV4ZcZikhfB92W7Fja5cg4EV8e7HNBeHq5mMu1/S/Ksx/zI9Bv4Q6Fz4yBgQg4C6BUAguW3y6fKiVrClTpsjQoUPP2k17uDZs2CDLli0zv9eqWePGjWXhwoXSq1evjEPEeUlR4cRlKcHLHXsZkyaPBrZix9Yu4YoX8e3F1isqP3HKJC5bXVRPLqx3nuw9ckKaNSqW269rKTe0b2qEFxsEIACBmhKIjODSuw1VOGkFa9iwYedw2b59u3kUhC4zqih7+OGHRe9s3Lp1a17uUqxp4Ng/eALZFAjBz7b6I9r6YmtXeTQv4tuLrS0jP3PWY3tdPrWdD3YQgAAEbAhERnBpRWvevHlSXFx8lt8qqFq0aGF+99JLL8no0aNlz549Rnw9+eSTeXsOl01wsMktAa/VntzOzvto54idhnWl7cX1ZevHn8reT08mX3Lcv93FsnzTPnl2ze5QVHf8xilThcv15njvGcAeEICASwQiI7iyDTXuS4rZ5uvC8aP8hey3KuRCXFLn4DdOfoWaiwyYEwQgED4CCC7LmCG4LEGF2CzKS05REht+4xQl0RnijxlTh0BsCSC4LEOP4LIEFWIzv5WTMLgcJd9q4ks2esrCEH/mCAEI5J8AgssyBgguS1AhNotSFSg1DH6rQi6GM8pxcpE3c4IABIIhgOCy5IjgsgQVYrMoLznVpCrkWkijHCfXWDMfCEAgOAIILkuWCC5LUCE3i+qSU9SqQlGNU8g/PkwfAhCohgCCyzI9EFyWoDBzkgBVISfDwqQgAIEYEUBwWQYbwWUJCjNnCVAVcjY0TAwCEIgBAQSXZZARXJagMIOAgwT8vArIQTeYEgQgEGICCC7L4CG4LEFhBgHHCLCc6lhAmA4EYkoAwWUZeASXJSjMIOAYgajdMOAYXqYDAQhYEkBw2YKqVUsqKiosrTGDAARcIRClR2K4wpR5QAAC3gkguCyZUeGyBIUZBBwjEKWHvjqGlulAAAIeCCC4LGEhuCxBYQYBxwhQ4XIsIEwHAjElgOCyDDyC6ytQFWcq5Iv3Dsqx1R/Ll0fLpLBhkVzQuanUbf+/UqugliVNzCCQOwL0cOWONSNBAALpCSC4LLMDwfWV2Dr83Db5YsshkTOVwBWI1L3iIrnwR20QXZb5hFnuCHCXYu5YMxIEIIDgqnEOILhETqw/IIcXbD9bbCXIFohcOPCbUtyhpMasOQAEgibAQ1+DJsrxIAABrwSocFkSQ3CJHJi9QU59+HlaYue1rC8ld7W3JIqZKwR4KKgrkWAeEIBAlAkguCyji+AS+XjyWjnz2am0xArqnydNx1xrSRQzFwiw3OZCFJgDBCAQBwIILssoI7iocFmmSqjMaCgPVbiYLAQgEGICCC7L4CG46OGyTJVQmfHIhFCFi8lCAAIhJoDgsgwegou7FC1TJVRmPBQ0VOGK/WTpN4x9CoQaAILLMnwIrq9AJZ/DtWaffHnkpBQ2Ol8uuO5insNlmUeumVHhci0izCcdAfoNyY2wE0BwWUYQwWUJyocZV60+oAW0Cz1cAYHkMFknQK5mHTEDZJkAgssSMILLEpRHM65aPQIL2Bz+AQPlcFkjQDU2a2g5cI4IILgsQSO4LEF5NOOq1SOwLJjzUNAsQOWQgROg3zBwpBwwxwQQXJbAEVyWoDyaxfmqlaVUj8mCeawJxPlcEevAR8h5BJdlMBFclqA8msX1qpWlPI+JgnnsCVANj30KhB4AgssyhAguS1AezeJ61cqXh8dEwTz2BLhIiX0KhB4AgssyhAguS1AezeIqPOIqND2mB+YQOIsA/YYkRJgJILgso4fgsgTl0SyuV61xXUr1mB6YQwACEIgMAQSXZSgRXJagfJjF8aqVCpePRGEXCEAAAiEmgOCyDB6CyxIUZlYE4rqUagUngxF3dwZBkWNAAAK5JoDgsiSO4LIEhZkVgbgtpQYlkuLGzSqZMIIABEJBAMFlGSYElyUozKwJxGUpNUiRFIXKYFDi0zrRMIQABJwggOCyDAOCyxIUZhBIIRCkSAp771uQ4pNEgwAEwkUAwWUZLwSXJSjMIJBCIEiRFPa7O4MUnyQaBCAQLgIILst4IbgsQWEGgRQCQYqkIMVbPgIV9vnngxljQiAqBBBclpFEcFmCwgwCWaxwhbVClOjbGrXwPTldXpE2R5rUL5I1Y0rJIQhAIIIEEFyWQUVwWYLCDAIpBIIUSWHsgao85/Iz6cVWLRHp2LKRLLrru+QQBCAQQQIILsugIrgsQWEGgRQCtiLJ9u69sN3dWZ3grIyqdkEtmT6wvQzo0IwcggAEIkgAwWUZVASXJSjMIFAFgUwiyVaUhRFudX1bCX9UbPW9orHM/FFHKSjQWhcbBCAQNQIILsuIIrgsQWEGAR8Eglx29DF8oLukVuoOHz9Vbd9Wndq15JFb28sN7ZsitgKNBAeDgFsEEFyW8UBwWYLCDAI+CETl7r2qKnXV4aBvy0eysAsEQkoAwWUZOASXJSjMIOCDQJCPjvAxfGC72PZrJQakbysw9BwIAs4TQHBZhgjBZQkKMwj4IBCVCpdNv5bi0cqW9mrRt+UjWdgFAiElgOCyDByCyxIUZhDwQSAqPVyZKnXar/U/9c6TZo2K5fbrWtK35SNX2AUCYSWA4LKMHILLEhRmEPBBICp3KUalUucjhOwCAQhkIIDgskwRBJclKMwg4JNApkdH+DxsTneLSqUup9AYDAIxIYDgsgw0gssSFGYQiDGBqFTqYhxCXIdA1ggguCzRIrgsQWEGAUsCtk+WtzycM2ZRqNQ5A5OJQCBCBBBclsFEcFmCwgwCFgSoBFlAwgQCEIgUgcgIrhUrVsjUqVNl06ZNUqdOHenWrZvMmDFDmjdvbgL2xhtvSM+ePaVevXrJAA4ZMkRmzZplFVAElxUmjCBgRYBeJytMGEEAAhEiEBnBNX/+fGnQoIF0795dVBwNHz5ctm3bJqtWrUoKrgEDBsjRo0d9hQ/B5QsbO0GgSgLczUdiQAACcSMQGcGVGriNGzdKhw4dpKysTAoLC02FC8EVt/TGX1cJZHpeVZP6RbJmTKmr02deEIAABDwTiKzgmjlzpsyZM0c2b96crHCVlpZKSUmJFBQUmErYtGnTpFmzZlbQqHBZYcIIAlYEqHBZYcIIAhCIEIFQCK7Tp09LeXl5WuxFRUVmGTGxrV+/3vRrLViwQPr06WN+vX//fjl06JC0bdvW/O/IkSPNkuO6deuMAEvdxo8fLxMmTDjr1xUVFREKPa5AIH8E6OHKH3tGhgAE8kMgFIJLlwKXLl2altDOnTulVatW5u/aNK+VrOnTp8vgwYPT7nPs2DHT87VlyxZp06ZNRvpUuDIiwgAC1gS4S9EaFYYQgEBECIRCcNmy1uXD3r17y5QpU2To0KHV7nb8+HGpX78+gssWLnYQCJgAz6sKGCiHgwAEnCYQGcGllapevXrJxIkTZdiwYedAf/31100VTP8dPnzYLClu2LBBdPmxdu3aGYNEhSsjIgwgAAEIQAACEEhDIDKCSyta8+bNk+Li4rNc3bp1q7Ro0UIee+wxefzxx43Y0spWjx49zHO79G82G4LLhhI2EIAABCAAAQhURSAygivb4UVwZZswx4cABCAAAQhElwCCyzK2CC5LUJhBAAIQgAAEIHAOAQSXZVIguCxBYQYBCEAAAhCAAILLbw4guPySYz8IQAACEIAABKhwWeYAgssSFGYQgEAsCSQf87F6l+w5+oU0b1hXbu/cSm5o31QKCv77YOpYwsFpCIgIgssyDRBclqAwgwAEYkeAB9nGLuQ47IMAgssSGoLLEhRmEIBA7AjwqqbYhRyHfRBAcFlCQ3BZgsIMAhCIHQFeRh67kOOwDwIILktoCC5LUJhBAAKxI3Dt5JXyyWdlaf1uUr9I1owpjR0XHIZAZQIILst8QHBZgsIMAhCIHQEqXLELOQ77IIDgsoSG4LIEhRkEIBA7AvRwxS7kOOyDAILLEhqCyxIUZhCAQOwIcJdi7EKOwz4IILgsoSG4LEFhBgEIxJJA8jlca3bL3iMnpFmjYrn9upY8hyuW2YDTVRFAcFnmBYLLEhRmEIAABCAAAQicQwDBZZkUCC5LUJhBAAIQgAAEIIDg8psDCC6/5NgPAhCAAAQgAAEqXJY5gOCyBIUZBCAAAQhAAAJUuPzmAILLLzn2gwAEIAABCECACpdlDiC4LEFhBgEIQAACEIAAFS6/OYDg8kuO/SAAAQhAAAIQoMJlmQMILktQmEEAAhCAAAQgQIXLbw4guPySYz8IQAACEIAABKhwWeYAgssSFGYQgAAEIAABCFDh8psDCC6/5NgPAhCAAAQgAAEqXJY5gOCyBIUZBCAAAQhAAAJUuPzmAILLLzn2gwAEIAABCECACpdlDiC4LEFhBgEIQAACEIAAFS6/OYDg8kuO/SAAAQhAAAIQoMJlmQMILktQmEEAAhCAAAQgQIXLbw6o4GKDAAQgAAEIQCAzgYqKisxGMbOgwhW3gNeqJVH6IESx8hg1n6Lmj54youYT/rj/RRC1GLlPPPgZIriCZ+r0EaP2oY2aP3yZO/3xSU4uanmHP+7nXdRi5D7x4GeI4AqeqdNHjNqHNmr+ILic/vgguMIRnshVIKN4XghJKgU6TQRXoDjdP9j48eNF/0Vli5o/Gpeo+RQ1f4iR+2cPcs79GMVxhgiuOEYdnyEAAQhAAAIQyCkBBFdOcTMYBCAAAQhAAAJxJIDgimPU8RkCEIAABCAAgZwSQHDlFDeDQQACEIAABCAQRwIIrjhGHZ8hAAEIQAACEMgpAQRXTnHnZ7AVK1bI1KlTZdOmTVKnTh3p1q2bzJgxQ5o3b56c0JIlS2TUqFGyd+9e6dixo8ydO1fatGmTnwlnGHXfvn3y85//XNatWyf68/r16+Wqq65K7vXGG29Iz549pV69esnfDRkyRGbNmuWkPzqpTD6pTZhilAp6165dcumll54VE43RsmXLnI1J6sROnz4tI0eOlPnz55s/3XbbbfL4449LYWFhaHyoPFH9TKgv5513XvLXr732mnTu3Dk0/uhn+umnnzbntn79+pnPSGL77LPP5M4775Tly5dL3bp15Z577pFx48Y57Vt1/vTo0UNWr15tzuGJbceOHdK0aVOnfWJy/yWA4IpBNuhJtUGDBtK9e3fzfJrhw4fLtm3bZNWqVcZ7/dB26NBBXnjhBSktLZXJkyebn7ds2eLkl8knn3wiixYtkquvvlquvfbaKgXXgAED5OjRo6GJbiafwhajdILryJEj0rBhw9DEpfJEH3roIVm6dKm8+uqr5tf6BX/zzTfLgw8+GEp/VHBpLPTiK6zb4sWLpaCgQFauXCl79uw5S3Ddcccdop+r559/Xg4cOGDObZMmTZIf//jHzrpbnT8quPS8du+99zo7fyZWPQEEVwwzZOPGjUZglZWVGUGlV31aJdIrQd30Sr6kpET0w69VCJc3FZBVVbjCJrgqM67KpzDHSH1LVLjCLLguueQSU9G69dZbTbgWLFgg9913n+zevdvlj0jauUVBcCWc0+dubdiwISm4Tpw4IY0aNZK///3v5sJMt0ceecSc4958803n45Xqj04YweV82DJOEMGVEVH0DGbOnClz5syRzZs3G+duvPFGadeunbn6S2xdunSRQYMGyYgRI5wGkE5w6dWsika9+tXK3rRp06RZs2ZO+5KYXFU+hTlGlQWXLn+Ul5fLNddcY2Li6rJ1aqKoULzwwgvln//8p3zjG98wf9afL7/8clNJ1Qpy2DYVXFqx03y7+OKL5ac//ampnuhnJmxbqkDRizBtjdCLx8SSry6X6jlNY+n6lk5w6Tn7zJkz0rJlS7O87XK1znXG+Zgfgisf1AMcU08o+gWWbisqKjIn1MSmJyKtWunVeZ8+fcyve/fubZZH9Go9sV1//fWml2Ps2LEBzjbzobz6U5U42b9/vxw6dEjatm1r/ldPTLqEqj1f+fgyCcInl2KUGkUb/44fPy5bt241lVX9eeLEifLiiy+a333ta1/LnBh5tvjoo4+kRYsWcvDgQbnooovMbPRnFfX6t8r9kHmeqvXw7777rmjVToXkP/7xDyNG9LOi/8K2pQqUt99+25zTjh07lnRFfdRz2pdffum8e1UJLu3f+ta3viXFxcXyt7/9zcRL+9duuukm5/1hgl8RQHCFPBN06UyvUtNtO3fulFatWpk/a2OpVn6mT58ugwcPTu6i1ZMrr7zSfAkmtq5du8rAgQNzXuHy4o9J4CqWFFNZ6ElXKxDak5aPikoQPrkUo1S+Xv3T/SsqKkxVRb8w+vbt6/ynMFHh+te//iWtW7c289WfL7vsstBWuFKhz549W5555hlZs2aN8/FInWBVFa5OnTrJqVOnkhUu7fPSc1pYK1ypPo8ePVo+/PBD06PGFg4CCK5wxKnGs9RStFZJpkyZIkOHDj3reNofpP0PiTvGtGLRuHFjWbhwofTq1avGY2fzADaCSysq9evXz5vg8up/uh6usMYonf8quJ566qlQCC71QatB2mB+yy23GJf08/HLX/7SfOlFYXviiSeMAI6C4Er0cOmNQSq8dHv00Ufl5Zdflrfeesv5cFVV4Uqd9AMPPGB6IxFczoczOUEEV3hi5XumWtlR4aQVrGHDhp1znO3bt5t+B11mVFH28MMPm9vFdbnH1VveT548afzQ273Xrl1rKnR6e7suGb7++uumqqf/Dh8+bJZIVKzocmrt2rV9c8z2jtX5FMYYVealMVLRqz1PX3zxhcnFZ599Vt5///3Q9D/p3YjadP3KK68Y137wgx+Yu8bCepeiLulqdVGXdN955x1zM8Ddd99tHg8Tlk2XB/Wf9p/qzUDqk54D9Fyg/U3aUvDcc88l71LUvHO57ymdPyogVTxq47y2ieijbzRev//9703Vji0cBBBc4YhTjWapFa158+aZtf/Kmwoq7UvR7aWXXhItUeut1Sq+nnzyybwsv9k6WrkvLbGPCi09IT322GPmbjIVW/olr7/T55AlfLUdI9d21fkUxhhV5qdfetoPqP11mof6OA+ttn7729/ONWbf42nlV5vKKz+HSyterl6UZHJUn8enIkW/5PWGEm2a1z7OfPQ5Zpprur9rJWjChAln/VlvklFBos/h0uf1VX4Ol+viOJ0/ejHcv39/c4Gim15Mai7+5Cc/8YuO/fJAAMGVB+gMCQEIQAACEIBAvAgguOIVb7yFAAQgAAEIQCAPBBBceYDOkBCAAAQgAAEIxIsAgite8cZbCEAAAhCAAATyQADBlQfoDAkBCEAAAhCAQLwIILjiFW+8hQAEIAABCEAgDwQQXHmAzpAQgAAEIAABCMSLAIIrXvHGWwhAAAIQgAAE8kAAwZUH6AwJAQjUnMCf/vQn+e1vf2uewF2TTV/Noy8F3rt3byBPvbd53VRN5su+EIBAOAkguMIZN2YNgZwSmDVrlnnPnr4AvV+/frJkyZKM49u8Dy7jQUJogOAKYdCYMgRyQADBlQPIDAGBsBNYvHixeeXLypUrzeuf8i249DU7derUcRIrgsvJsDApCOSdAIIr7yFgAhAID4HUqlVFRYU88MAD5l2d+oLdJk2amHdZ6vv5Bg0aJGfOnJHzzz/fOHjs2LG0jmr1TN9LqC+EnjNnjtSrV88c9xe/+IXZR8ddt26deeefvldO3yGn72HUffTF5Lrp++Xuuusu815QfWG7vhP0j3/8o1xyySXm7/v27ZNf/epX5uXm+gLtTp06yV//+lfZtWuXXHrppXLkyBFp2LChDBkyxIhL/f+vvfaafP3rX5fZs2dL165dzXF0KVNf8L57925p1KiR3HHHHfKb3/xGEu/CRHCFJ5+ZKQRySQDBlUvajAWBkBNIFVx/+ctfzEuP165dK02bNhXthzp58qRcfvnlRiSpGLKphqng+tnPfib333+/PPTQQ/LOO+/I97//ffPiYX3Jsh5r0qRJMnfuXBk8eLCcOnVKXnzxxXME1wUXXCAvv/yymcvNN98sJSUlZim0vLxcrrnmGrnqqqtk+vTpRtC9/fbb0qtXryoFl75se+HChWb5VMccM2aMfPDBB0aQvfrqq9K6dWu57LLL5L333jPzVJF52223megiuEKe5EwfAlkigODKElgOC4EoEkgVUVot0krW/PnzpUePHmct83kVXCNGjJBDhw4lj6HVKl06VMGjx1LhlqhmKdtEVaxyhUurYnfeeWeyEjVlyhTTd7ZmzRrp06ePHDx4MFlxS8SnqgrXgQMH5JVXXkmGsG3btvLrX//aiL3U7d5775Xjx4/LH/7wBwRXFJMenyAQEAEEV0AgOQwE4kCgKhGly23PPPOMvP/++1JaWiqPPvqoWaLzKrimTp1qjpHYVCy9+eabpqKkx3r33XdN9SqxVSW4dIlxwIABxkQFmoohFVRaDXvwwQdl27Zt54SpKsFVt25d+d3vfpe07du3r/Ts2dNU4P785z/LhAkTZMeOHUYQlpWVmUqYLmXqRoUrDp8EfISAdwIILu/M2AMCsSVQnYj69NNPTQ/V559/LsuWLTN9TSqSbJcUUytc2r+lS4eJClfq8qQXwaVLnioG/Va49LERuqyo1Tzt21KR+cMf/lCKioqSoi7hJ4Irth8PHIdAtQQQXCQIBCCQkYA2wes/7aPauHGjqRhpY7n2MGmV5+qrrzbHuPvuu82yoFZ7tPn9iSeeMM3utWvXrnaMRA+XipqxY8fK+vXr5Xvf+54Ra1pZqkroeRFc2rz/ne98xzTSawUu0cOlx66qwvX888/LokWLTH/WU089JaNHjzY9XIWFhaaPa+nSpdK/f39ZvXq13HDDDdKlS5eksERwZUwnDCAQSwIIrliGHach4I2ACh5dRqu8de/eXcaNG2fu/Pv3v/9teq86d+5sluJatGghhw8flptuuskINL2b8ejRo2kHTb1Lsbi42Czf3XPPPWafmgouPcbHH38sI0eONHcpJkSi3oWY7i5Fnb8+BkOXR/UBq9q8r5uKSK3e6V2X2rfWsmVL+eijjxBc3lIKawjEjgCCK3Yhx2EIuEcgtVqVzxnqYyG0iqX9YGwQgAAEgiKA4AqKJMeBAAR8E0Bw+UbHjhCAQEgIILhCEiimCYGwE7jiiivMw0JTN60oaQ9Y5YeY5tNXKlz5pM/YEIguAQRXdGOLZxCAAAQgAAEIOEIAweVIIJgGBCAAAQhAAALRJYDgim5s8QwCEIAABCAAAUcIILgcCQTTgAAEIAABCEAgugQQXNGNLZ5BAAIQgAAEIOAIAQSXI4FgGhCAAAQgAAEIRJcAgiu6scUzCEAAAhCAAAQcIYDgciQQTAMCEIAABCAAgegSQHBFN7Z4BgEIQAACEICAIwQQXI4EgmlAAAIQgAAEIBBdAgiu6MYWzyAAAQhAAAIQcIQAgsuRQDANCEAAAhCAAASiSwDBFd3Y4hkEIAABCEAAAo4Q+H9VvZRIpP1LxQAAAABJRU5ErkJggg==\" width=\"671.1111288894847\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(dataframe, hue=\"label\", height=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. t-SNE approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}