From de6f7f0c4134256a243e46fd2505558f50ae39d5 Mon Sep 17 00:00:00 2001
From: Dmitri Soshnikov <dmitri@soshnikov.com>
Date: Tue, 28 Sep 2021 16:20:46 +0300
Subject: [PATCH] Add OwnFramework
---
.../04-OwnFramework/OwnFramework.ipynb | 2760 ++++-------------
3-NeuralNetworks/04-OwnFramework/README.md | 52 +
README.md | 2 +-
3 files changed, 670 insertions(+), 2144 deletions(-)
create mode 100644 3-NeuralNetworks/04-OwnFramework/README.md
diff --git a/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
index 48a8ac1..968a6bc 100644
--- a/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
+++ b/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
@@ -2,37 +2,32 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"# Введение в нейронные сети\n",
"\n",
"## Эпизод 2: Многослойный персептрон\n",
"\n",
"Дмитрий Сошников | dmitri@soshnikov.com"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
- "slide_type": "notes"
+ "slide_type": "slide"
}
- },
- "source": [
- "Данная презентация представляет собой введение в современные нейронные сети на основе Microsoft Cognitive Toolkit (CNTK). Идея однодневного мастер-класса основана на Neural Network Workshop в Microsoft Research Cambridge. Материал и фрагменты кода частично взяты из презентаций [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/) и [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/) из Microsoft Research Cambridge. [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop) подготовлен [Дмитрием Сошниковым](http://blog.soshnikov.com), Microsoft Russia."
- ]
+ }
},
{
"cell_type": "markdown",
+ "source": [
+ "Данная презентация представляет собой введение в современные нейронные сети на основе Microsoft Cognitive Toolkit (CNTK). Идея однодневного мастер-класса основана на Neural Network Workshop в Microsoft Research Cambridge. Материал и фрагменты кода частично взяты из презентаций [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/) и [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/) из Microsoft Research Cambridge. [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop) подготовлен [Дмитрием Сошниковым](http://blog.soshnikov.com), Microsoft Russia."
+ ],
"metadata": {
"slideshow": {
"slide_type": "notes"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Обучение с учителем\n",
"\n",
@@ -43,15 +38,15 @@
" * Известные значения целевой функции $\\mathbf{Y}$ ($y_i$ соответствует вектору свойств $x_i$)\n",
" * $\\mathbf{Y} \\in \\mathbb{R}^{n \\times 1}$ (задачи регрессии)\n",
" * $\\mathbf{Y} \\in C^{n \\times 1}$, где $y_i \\in C$ (задачи классификации на $|C|$ классов)\n"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "notes"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Задача\n",
"\n",
@@ -61,140 +56,148 @@
"\n",
"**Необходимо построить:**\n",
" * Функцию $f : \\mathbf{X} \\rightarrow \\mathbf{Y}$ который _точно предсказывает_ значение целевой функции на новом наборе входных данных $\\mathbf{X}_{new}$\n"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "notes"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 1,
+ "source": [
+ "import matplotlib.pyplot as plt \r\n",
+ "from matplotlib import gridspec\r\n",
+ "from sklearn.datasets import make_classification\r\n",
+ "import numpy as np"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt \n",
- "from matplotlib import gridspec\n",
- "from sklearn.datasets import make_classification\n",
- "import numpy as np"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 2,
+ "source": [
+ "# pick the seed for reproducability - change it to explore the effects of random variations\r\n",
+ "np.random.seed(0)\r\n",
+ "import random"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "# pick the seed for reproducability - change it to explore the effects of random variations\n",
- "np.random.seed(0)\n",
- "import random"
- ]
+ }
},
{
"cell_type": "markdown",
+ "source": [
+ "## Пример\n",
+ "Рассмотрим пример двухмерной задачи классификации на 2 класса. Примером такой задачи может быть классификация опухоли на 2 типа - доброкачественная и злокачественная, в зависимости от её размера и возраста.\n"
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
- "source": [
- "## Пример\n",
- "Рассмотрим пример двухмерной задачи классификации на 2 класса. Примером такой задачи может быть классификация опухоли на 2 типа - доброкачественная и злокачественная, в зависимости от её размера и возраста.\n"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 3,
+ "source": [
+ "n = 100\r\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\r\n",
+ " n_redundant=0, n_informative=2, flip_y=0.2)\r\n",
+ "X = X.astype(np.float32)\r\n",
+ "Y = Y.astype(np.int32)\r\n",
+ "\r\n",
+ "# Разбиваем на обучающую и тестовые выборки\r\n",
+ "train_x, test_x = np.split(X, [n*8//10])\r\n",
+ "train_labels, test_labels = np.split(Y, [n*8//10])"
+ ],
+ "outputs": [],
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
- },
- "outputs": [],
- "source": [
- "n = 100\n",
- "X, Y = make_classification(n_samples = n, n_features=2,\n",
- " n_redundant=0, n_informative=2, flip_y=0.2)\n",
- "X = X.astype(np.float32)\n",
- "Y = Y.astype(np.int32)\n",
- "\n",
- "# Разбиваем на обучающую и тестовые выборки\n",
- "train_x, test_x = np.split(X, [n*8//10])\n",
- "train_labels, test_labels = np.split(Y, [n*8//10])"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 4,
+ "source": [
+ "def plot_dataset(suptitle, features, labels):\r\n",
+ " # prepare the plot\r\n",
+ " fig, ax = plt.subplots(1, 1)\r\n",
+ " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\r\n",
+ " fig.suptitle(suptitle, fontsize = 16)\r\n",
+ " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\r\n",
+ " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\r\n",
+ "\r\n",
+ " colors = ['r' if l else 'b' for l in labels]\r\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\r\n",
+ " fig.show()"
+ ],
+ "outputs": [],
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "def plot_dataset(suptitle, features, labels):\n",
- " # prepare the plot\n",
- " fig, ax = plt.subplots(1, 1)\n",
- " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\n",
- " fig.suptitle(suptitle, fontsize = 16)\n",
- " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
- " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
- "\n",
- " colors = ['r' if l else 'b' for l in labels]\n",
- " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
- " fig.show()"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "scrolled": false,
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "plot_dataset('Scatterplot of the training data', train_x, train_labels)"
+ ],
"outputs": [
{
- "name": "stderr",
"output_type": "stream",
+ "name": "stderr",
"text": [
"C:\\winapp\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
" # This is added back by InteractiveShellApp.init_path()\n"
]
},
{
+ "output_type": "display_data",
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEnCAYAAACpNTSTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd3hb1fnHv6/kve14JHbiOAtIAkmALAghgx2gYRYoFChQoKUtlA7oD2gptJRO2jJaoIPSQVkFQoAQCBlAAsQhg+yB7cTxTLynLOn8/vjqIlnWtLVsn8/z6EksXd177tW973vOO0UpBY1Go9FoTNEegEaj0WhiA60QNBqNRgNAKwSNRqPRONAKQaPRaDQAtELQaDQajQOtEDQajUYDQCuEsCEiF4nIOhGpE5FOEakQkVdF5NwwHe8OEbnEyzjuDMcxA0VErhcRJSIlQX6vRETuF5HxIR7PSBFZJiINjnHd4WW7GY7j53j4TInIz0I5Lg/H8Pibhmjf5SLyTD++V+I49+tDP6rgEZGFjvEs7Md37xeRxWEY1qBFK4QwICLfAfAKgH0AbgRwPgBDeITrBrwDgCfhcRGAqCqEAVAC4CcAQqoQAPwYwALwtzkFwH+9bDfDcfw+CiFCePtNQ8HFAB7sx/eqwWv2RmiHExV+gvA9j4OSuGgPYIjyfQCvKqVudHnvPQBPi8igV8IikqiU6o72OAbAZABblVKvRHsgoSLY30Qptbk/x3Ec46P+fFczCFBK6VeIXwDaAPwpwG3HAfgngBoA3QA+B/AHl89nAXgJQCWATgB7ADwEINllm3IAyu31jOPl/n65y/dyAfwJwGHHsXcDuNltfNc7vnc6gBcBNAHY4vjsGce4TgWwEUCXYyzf9rKPEpf34sFVUzkAi+PfnwGId3y+0MPYFYCFPq6lAPiu4xpZwNnsYwAyHJ+XeNlniYd9Xe9rW8f/fwbgOwDKALQCWAtgqod9XQIK0Q7H9XsRQLGf+8Ljb+r47H7H38cDeBu8315zfHY2gDcd594BYDuA7wEwe9j/Mx7Ody6AfwNoAVAF4I8Akly2M67h9S7vGffBiQDedxx3H4BbPZzXmQA2O+6V/QBucny/3Nf1cHw3D8B/HGNrAvAsuALudV8Ecg28/Lb3B/rMDdWXXiGEh08AXCcin4MP6l5PG4nIOMe2HeDydR+AMeANbVAMYAv40LQCmAqaPMYDuNKxzcXgA7AVFBYAUO/4Nw+8wb/k+LvbcewMAB8CSHZ8pwzAOQD+5JhtPuo23H8DeA7AZei9sswA8DyAX4IP+JUA/igirUqpZzxeHfIPAF8GH7QPQDPEvY7z+gqATwHcBuBxUOhudHxvp499/hzAjxzfeR3AFNAsMl1EFsBp7ngSgA3ANx3fq/awrzdAgX8vgMtB4eC+7TWgsLgdQAKAXwN4TUSOU0pZAUBEbgWV7t8BPAAgHbzea0VkmlKq1cu5+PpNDV4D8Ffw2tsd740HsArAo6DQnen4fh6Au70cy5V/gr/zJeC1uh9AI3h/+iIDFNa/B8/za+C9tEcptRoARGQKeF0/Ae+TBAD3Ach0Gb8v/gdgOoD/A5+VKxzn6U4g1+AUABvA5+pJx3vGbxzIMzc0ibZGGoovAMcA2AbnzOMI+JCd7bbds+DsrjDA/QoojK8BH6ARLp+VA/iXh+88A6DSw/v3gQ/LJLf3n3aMN87x9/WOc3jEy74VgCvd3n8HQAUAcdtHiePv4+EyI3P53r2O96c5/l7o+PvMAK5NjuN8nnF7/xrHPr7k8t4HANYEsE9j3BM9fKZAoRTv8t5ljvdPdfydBqAZwN/cvlsCrmDu8HN8b7/p/Y7j3B7g/XIPKNRNbvt+xsO5/tRtH8sB7HUbu6cVggKwyOW9RMd99JTLe/8BlVqKy3ujHL9buZ9zOcvLvfYWfKwc/VwDBeBn/X3mhuJr0NuzYxHFFcGJoOPy5+Bs42IAb4vIvS6bng1guVKqytu+RCRDRH4pIgfA2X0POIsTAJMGMMxzAXwMoExE4owXaIIYAc6uXfFmb7cBeNntvf+Cs6wiL9853fHvv9zeN/5e4GfsnpgLCiH3ff4XgLWf+/THO0qpHpe/P3P8W+z49xRw5vxvt2tcCZrnTsfA6PObiMgoEXlSRCpApdMDrnSyAOQHsE93Z/FncJ6PLzqUYyUAfOFr2Of23bkA3lRKdbhsVw1gfQD7PwXe77VeDPQahPGZi3m0yShMKKVsANY5XhCRQgArAPxERB5XSjWCgrfS+14A0NRwJrhk3QKgHcBs0CySNIAh5gOYCN7snhjh9rcnswoANLoJRQCodfxbBM/nZ0TtuO+zxu3zYPC4T6WUVUSO9nOf/mhw+9tw6hq/iyF83vXy/cYBHr/XuToCFpYBKARXEbtBG/hF4Aw5kPvF0zklBvA9T+fS7XbMUQDqPGxXC/+RZKPg+14DELJrEK5nLubRCiFCKKWqROQvAP4AzjI+AZfU3mbREJEkAEtB08ofXN4/IQRDOgo+nLd7+XyP29/Ky3bZIhLv9qAWOP497OU7htAZCeCAy/sjXcYWLK773GG86ZiRj+jnPgeKcczrXcfkgjf/QaC4/yYTQHv5V5VSX6yUROTCAR4nVFTD8wy9wMN7nr7r614zGNA1CPMzF/Nok1EYEJExXj46zvGvMRNeCeACERnlZftEAGb0ncVf72HbbtBBHOj7KxzjOaiUKvXwClRYmQFc6vbelQAOwrtCWOuynStXO/5d5zJ2eBm/Ox85tnff5xXgxGdtn2/4J5jje2I9KPQnernG7krX0/GDOXaK498v7hcRiYfzukabjwAsERFjnHDc+/MC+O4GeL/XXAnmGljQ9/oG88wNOfQKITxsF5HVoI23DLQjLwFwK4AXlFIHHdv9BExaWy8iD4FROkUAzlVKXaOUahaRjwB8T0SqwRXFDfC8qtgJYL6IXAAqnCNKqXLH+zki8g0ApQC6lFKfAXgEFJbvi8gj4IogFVQS85VSSwM811YAvxKRXNBmfBW43L5eObxy7iildojIcwDud8zg14M24vsAPKeU2ubYdC9o/79BRBpAAbnHk7JSSjWIyO8A/EhE2sEIncmg7fgD9C+Ryohouk1E/gEKiW1KKUsgX1ZKtYjIDwA8LiJ5oAO0Gfz9FoCO7f/4Ob6n39Qbu0Bn/s9FxOYY73cDGWuE+BnoeH9bRH4DCt/7QLOPzygjpdQ7IvIBgCdd7rUrwAAFV4K5BjsBnC8iK0CTV5VjJR/oMzf0iLZXeyi+QMG/DLwxu0Ab5GYAPwSQ4LbtBDAC6QiceQiPuHxeAgqSVtDE8xioRNxjr4+DMwbcNWY91bH/RvTNQ8gGFUMZOFuqc+zjDpdtrof3SJtn0DcPoQLAd9y2M/ZR4vKekYdQAT60FXDJQ3DZ7hbHNbG6n7OH8XjKQ3gcjjwEl+0CijJybPsTcKVjcz0HeIhQgYcIHMf7SwCsBuPnO0HF/zcAU/wc29tver/j7zgP35nhOL8Ox2/zABjr7379y+E5ymii2/7uB6B8nSO8R7Ktcb/OYLTQFjjv9VvAidPmAH6LPPBeboUzD2Gp+30RxDWYB2ATeN+65iGUIIBnbii+jLBAjSZoHLVwzlRKjY72WDSDExFJAxXkG6p3Zr8mCmiTkUajiRgi8ihoIqwCI4FuB1eqf/D1PU1k0ApBo9FEkiQws7oANOt9Aq4yt/n8liYiaJORRqPRaADosFONRqPRONAKQaPRaDQAtELQaDQajQOtEDQajUYDQCsEjUaj0TjQCkGj0Wg0ALRC0Gg0Go0DrRA0Go1GA0ArBI1Go9E40ApBo9FoNAC0QtBoNBqNA60QNBqNRgNAKwSNRqPROBjU5a9zc3NVSUlJtIeh0Wg0g4pNmzYdUUrlub8/qBVCSUkJSktLoz0MjUajGVSISIWn97XJSKPRaDQAtELQaDQajYNBbTLSaDQhoLsbaGsDEhKA9PRoj2bgKAXU1QEdHUBqKpCXB4hEe1SDAq0QNJrhSk0NsHIl8P77gM1GQTppEnD++cC0aYNPiCoFbNkCvPoqcPAgYDbzvMaNA5YuBaZPj/YIY55B3VN55syZSjuVYxuLBaio4L+ZmUBR0eCTM0OSAweAX/0KsNuB/HwgPp4CtaEBaG4GLroIuPjiwfVjvfkm8N//Ajk5vNlEeE5NTTyva68Fzjor2qOMCURkk1Jqpvv7eoWgCQtWK/D223xGOzv5bNrtQHExcNllwAknRHuEw5iODuD3vwdSUig4DUSAESP43iuvABMmDJ5Z9eefA88/D4wZQ+VmIAJkZ9N09O9/A8cey5tQ45GYcSqLyBgRWS0iu0Rkh4jcHu0xafqH1Qo8+SSfz/R0Pn9jxvDf5mbg178GPvww2qMcxnz6KX0GrsrAlbg4ICsLWL6cM+zBwKpVQFJSb2XgSkICP1uzJqLDGmzEjEIAYAXwPaXUZABzAdwmIlOiPCZNP/joI77GjeMzaiBCOVNYCPz971zFa6LA+vX+ncc5OcD+/UBLS2TGNBCUAj75hM5jX+TlcTuNV2LGZKSUqgZQ7fh/q4jsAlAEYGdUB6YJCqVoJvIV2JGURPPRRx8BS5ZEdnwa0IbnbSZtIAKYTHT+RIKWFuDjj4F167h6yc0FzjgDOPFEIDHR//etVjqRfWE2R+58BimxtEL4AhEpAXAigI89fHaziJSKSGl9fX2kh6bxQ3MzUF3tfwKamQls2kQ5sH8/fZydnZEZ47Bn5Eigvd33NlYrFUJaWvjHs3cvcNddwH/+Q/9GYiLDRp98EvjJTwB/z7kIMGoU0Nrqe7vWVi5PNV6JmRWCgYikAXgZwB1KqT7rVaXUUwCeAhhlFOHhafxgt/P59Bec0tXF1fsddziDQeLigEWLgAsvjIwcGrYsWABs2MCL7u2Hqq0FTj0VSE4O71hqa4Hf/IY/eEGB8/2kJDqDa2qA3/4W+OlPfa8UzjkH+NvfgIwM79s0NgJXXhm6sQ9BYkohiEg8qAz+rZT6X7THowme9HQ+y11dvf0HrrS20jIwYgQwY4Zzpd/Tw7D4zZtpLfjgA8qD5GTKptNP1xO8kHDMMcBxx3FmPmZMX6XQ0sL3zj03/GN57z3OIrwJ8pEjgbIyYOtWYPZsJtFVVHAFk53Nz0X42cqVQFVV35tEKeDwYaCkhCYojVdiJg9BRATAPwA0KKXuCOQ7Og8hNnnlFWDZMmDs2L6fKQWsXs2J4Xnn0cnsSkcHw1VTU4G5c/mv1Uqrgd0O3HgjMG9eZM5jSNPWBjz2GLBrF8NPU1OpkZub+fcdd1BxhBOrFfjmN+kvSEjwvl1DA51SU6cC77xDpWDEMU+YAHz5yxxrYyPw6KMMQU1I4Iqiq4vndcwxwG23eY+sGmYMhjyEeQC+CuAzEdnieO//lFJvRnFMmn6weLFzdl9Q0HsC2tDAydrUqX2fTcPRbDbTn5CQwP+bzcDo0Xy2n3qKeVSTJkX2nIYcaWnAD37AVcKaNc6l2CWXADNnUkGEG0NY+1IGAAX7O+/Q2VRY6DQdKcWZxUMPUYHNmAHcey+3++gjJqRlZwOnnELFMZiS7KJEzCgEpdQHAPQvNgTIzATuvht44gmu9uPi+LJYgEOHmK08fXrf57O+ntaKrCxOVJuaaBEwSEqinHrjDT7/mgFiNgOTJ/MVDRIT6bi22XxHCB04wBuipKT3TWMk0iUlAX/+M30NqalcDYR7dTNEiRmFoBla5OUBP/4xV+9btjCopaAAOHKEZmNPz//Bg72jIe32vtvk5tKc3No6NOqwDWvi42n7//RTRgl5wm4Hdu9maru3GX5qKm+sTZvoaNL0G60QNGFDhCv1CROc75WWcvXvic5OriSU4islpe82JhNf7e1aIQwJzjqLEU/d3Z6jiGpqeDOMH+97P6mpnHlohTAgYjIPQTN0Of54ZxSSO4mJ9DN2dzPoxJP/z27ny5Oy0AxCxo8HbriBySvV1TQfAU77YkcHbxp/yWlmM28ezYDQCkETUZKSGApeVdU3aXTsWD7/XV3eLQRHj9Ih7SvcXDPIOP10JqCdfDJvjIoKRh+ccw7w859zZuBP2Le10cegGRDaZKSJOAsWcCL43HP8NzmZVoGODgacTJrkuSxNdzd9BxdcEPkxD2tqamjnb2ykx//EE0OfEDJuHHDzzVwtWCxcERiOpvnz2bNh9GjP37XZ+NLxyANGKwRNxBFh4tns2fQpHDjA96ZOpZx59FGgvJwOZCMPoa6O/95wA3OqNBGgs5NVCD/5hI6b+HiGib70EkM8b7op9OGpRkiaK0uW8Eapr+87U7BauaJYsqR3prOmX8RMYlp/0IlpQ5O2NsqgFSsoAxISmKm8aJEuZR8xrFbgd79j4lpxcW/7nVJAZSXt/z/4gf88glBQVcVEuqoqp9IwEtSWLGEzH3/F7TRfMBgS0zQaAMyZWryYL1/ldjRhZNs2YPt2mnLcfwARmm/27KEpae7c8I+nsBD42c+YSLdtGx1NhYVMonNPd/dFdTWwcSNnGmlp9FvopLUv0ApBE3Gqq2kCEuHk09fzrJ/TKPH22842lJ4QYc+Et96KjEIAaLY67rj+2Qy7uoBnnmEGs8nE6AaLheMfP55lLUaMCPmQBxtaIWgiRnk5Kxzv3ctn0mDuXJajCWaipwkz5eX+G85kZjKbMNaXcXY7S2lv3sxQNnfz1+HD7C99333DvsyuDjvVRIQDBxhBePgwn8niYr6KitgX5ec/Z6kKTYxgNvtvn2m3Dw67vWHaclcGgLOXQm0tC3ANc7RC0IQdmw34058YkOLeSc1sZgXmhgbgxRejN8aA6exk+KWnzLqhxIknshyEL44cAaZNi+3VAcB+yykpvseZn88oBk/1UoYR2mSkCTu7d1N2+MobKixkBYMvfzlGKxTv30+B8emnzg5Ap5wCnH02NdpQwyhZa7X2DQMFqOU7Onj+sU5Fhf9MxpQU3qSdnZGp9Bqj6BWCJuzs3etZprhiWB4OHQr/eIJm3TrgwQeBHTsYXTNmDM0MH3/MDNutW6M9wtAzfjywdCmFqXu7zY4O+hjOPRc49tioDC8o4uL8z/yNAlqDwQQWRvQKQRNy2tpYZ6y2lgmnVVWBfzfmVuwVFWzN6FqHH6DgKCyksHzsMeAXv2Am3VBBhLH9ubnAq6/yOhgml4wMZgguXBj75iIAmDWLNdN9zfwbGpgi763N3zBBKwRNyLDbGcX3yiu0NMTH07JQW0tncna292fSZmPJ+4YG9lAYO7Z3JFLUWLXK2X3LE6mpLLC0fj3wpS9FdmzhRoR1hubN44qgvZ2mlXHjBtdM+rTTgOXLvVdUtdsZ0XDjjZEfW4yhFYImZLz2GvC//zF6yLWvwahRFPKrV7NkhWvfdqUoazZvptz95z/5fOblARddRDN91CahSlHQu3bp8URuLs1KQ00hGJjNvWuYDzby84HrrgP+8hfmGrjmV7S3s1bTmWeya9MwRysETUior3f2UXb3F8THM9dg7Vqa4Wc6EuaVYjLsjh2cuM2f73Qot7YyMunoUeDCCyN7Ll9gtXLp4s8BEh+vY2ZjnQULmOjyv/8xd8Js5swjMxP42tcGj/krzGiFoAkJGzbwefImO4uLaXnYuJHPZXIyhf3WrZzAzZ7dO7ooPZ3m3JdfZmTj2LGROY9exMUxUamzs/eyxp32dv9JXJroM306b6aqKs44kpIYIDCYzF9hRisETUg4cMB/kuf48Xz2LrmERTOXL6e/75hjPPsL4uP5Wr0auP76sAzbNyKsyW/YwbzR1ARcfnlQu1aK7UVra3nuxcWhryit8YAIsyE1HtEKQRMSTCb/ia3GdiefTLP86697VwYGubkM/Q9aIbS3s2TqypW0ZyUm0rm4YEFwkve001jX5+hRz7VuampYdvmkkwLe5b59LKtz+LDTSqEUe91fd51/l4VGEy5iIY5DMwSYNo3hpr7o7GSQSl6eM+zbn9nWZHJ2VQyYujrg/vuBf/yDBcwKCxkquWoVcO+9dBQHSlYW8MMf0nxUXs4wqPZ2KojychZ4+/73fZuUXNi9mxGqbW00g7m+yspYwqO2Nsjz1WhChFYImpAwaxbNO52dnj9XilVOzzmH2xlh/K2tvvfb3Byk/6Cnh3X8jZaK6enUKomJTCrLzweeeoo2rkAZMwZ46CHglls46IQE7vs736HiCdB/YLPx0FlZDMF1VYYiXBlYLOwkp9FEA20y0oSEtDTg619njlZ2du/Ivp4e+vGOPZbRfQZLlgBPP+29qoBSVBjnnBPEQLZvpxnHW52MpCTO5t94gwI9UJKTGQN7yilBDKY3u3ZxgeGrhEdBAR3tnpqDaTThRq8QNCFj1iw20EpLY2Sf8aqroyK4887eiaCzZtHRXFnZ1/+gFL97wglsrRkw69b5r0WTl8dUaveSDGGmrMx/QIvJREVaWRmZMWk0rugVgiakHH88y/4cPMiioHFxTGz1JKMTE6kknnoK+OwzCsOEBJpN7HZgzhw6k/2lAfSisdF/+QFD6nZ0RLSQWaDdanU4vCZaxJRCEJG/AbgAQJ1S6vhoj0fTP0ScjlJ/pKdTKRw+zGii5mb6aSdMYFjmI4/Q9j5pEhPXCgv9CMzsbFatTE/3vo3dTukcoCM4VJSU+HeQK8VtRo2KyJA0ml7ElEIA8AyAxwA8G+VxaCKI0aJ39Gj+/f77wG9+40wkFQHefZfVp886C7jySh+ml9NPZx0MX4Xm6uuBGTMi3h1ryhSeT1ub90PX1XGVpUNPNdEgpnwISql1ABqiPQ5N9Ni8mY7m3Fwma2Vm0ulsVJ1esYLFN71iSNO6Os+fd3XRVLRkSVjG74u4ONZPO3IEaGnp/ZlS1FMAcNVVER+aRgMgxhRCIIjIzSJSKiKl9cYTpBkSKAW88ALzvzy5AcxmmqHefLOvQP2C+Hjgu9+lOai8nNNxpeiYqKxkkP/Xvw5MnBjOU/HKtGlMWxBhRenycr4qKujrvucenUiriR6iAvV0RQgRKQGwPBAfwsyZM1VpaWnYx6SJDGVlwAMPcGXgy09QUcGM3oULfeysvZ0NbN5+m1PvpCQWU1q4MCYkrs3GxkE1Nc7SFSUl2qGsiQwiskkpNdP9/VjzIWiGMc3NzgAgX8TFOc0rXklNZRvIxYsDS4mOMGYzS1VMnhztkWg0TgadyUgzdElICCw002oNMkAoxpSBRhOrxNQKQUSeA7AQQK6IVAL4iVLqr9Edlaa/2GwsZWE202LjTy6PG0cXgMVC5WAgyo4RR/cio/kQFIDutjE4Yeox0PMZjSa0xJRCUErp+IohQEsLE4bffpumfKWYR3DeecCM47ogFeWsZ5GT0yuxIDmZYaWvv+60p484uhcnbXoaKR20EXV2ADNSgeLH84Cbv85yqRqNJiTElELQDH7q64GHH2bNnvx8RgwpBdRXduOD25chxfwuJpX0wGQSJhqUlABXXAEcdxwAdqE8eJDhp1MT9mHep79Ed2I6jqaVoK0NSMkFZpwGiKUJ+OUvgbvvprbRaDQDRq+5NSHDbmdxu/Z2hocadv44WzeW7P09Tmtejt31I3BQFTOpoLiYZaQffpi1hUBT0be/DdzwNYVZ2/+Oo91pqOvOQnc3i+MtWMAS2sjKYnbX3/8eeE0IjUbjE71C0ISMffsYEupezXNc2Srk1e9EU1YJknsEe/dSYYgIzUaJiWyg/LvfAampiI8HFhUfgP24anTlF0OBPog+2clZWTzggQNRyyvQaIYSeoWgCRkbN/Z2BgOAyW7FpH1voS1tJCCChAQ6mnsllqWm0pPsmlNSXQ2TKKSkClJTvZSqMLzU1dWhPhWNZliiVwiakNHezighV1Lb6xDf0wFrXBJS29gKrNOWAavVLW40NZWNABYs4N/BhIoOJKzUamWjgg8/dFbWmzeP9qlYbr7e1UUz23vvscJrRgaT7k4+2WFT02iCRysETcjIy6OcciWttQp59dsRZ+0GIFAA8i0KqbuKgJOmOoWXe6/MMWP4r6+kMsN3YGwbLPX1wB/+wJIWyck0XZWVUTkYHdFycvq373BSXQ389rcsipSZSXva0aPAX/8KvPwy8L3v9f+aaIY12mSkCZq2NvYG3rmTMslgzhzKdENOZ7RUYmbpn5HU1QxLQjq6kzLRbma1usSGasamdnRw4/Z2JiIYFBfzb9cDuHPkCLcpLg7+JNrbgV//mvsoKWGrsqwsFsYrKWGLt9/+tq+Gizbt7SwF29HBcWZnU5llZfFvux341a+42tFogkQrBE3AtLayb/0dd1Dm/Pa37JD2yCOcZBcVAbNn08+r7AonbXoaNnMijuQeh3hLO+x2Wmhy80yQjAzmImzbxjftdppqDESAG26ghqmr6x1JpBTfs9m4TX9MRh9/zH14qjNtt9OEtXs3sGZN8PsOJxs3cjWQn+/585wcKo0PP4zsuDRDAm0yGiJYrZzMJib2teOHgpYWRofW1DCXzOhiZrcDe/YAP/sZ8KMfUT53dwOH3y9DQk0FmkeMRXt6KuLrq4GedowcnepsUpaWxpn4nj3Al7/ct4nwmDHAvfcCzz7LSnCG4FeKCWnXXutsohAsK1b07Zlgt9NktG8fT6Knh3kOe/YAS5f6boYcLqqrqWFtNiqBt99mcocv8vKAlSujUuJbM7jRCmGQU1MDrFoFrF1LpSBC083ZZ4dWfr30EitHu1tnTCZOshsagCeeAH7xC+D224FDWeXoOAwc6RI0NaWgPuU0nNC1ES2HmmA9KkhKMaG70wZ7Wxc2ZMxGzZFLsXgfo0d7TfhHj6amOXyYygOgRioq6r8z2W7n6sC1pZvdziinykoqqsxMvmfYx7ZtY93qSFWjq6ujIty+vfd5bt4MzJ3r7AinFBWXCLW0CH0KtbUcv0kbATSBoxXCIGbXLobuK0UTeHw8J5KbNgEbNgA33dTbCtNfWlpogfDV1jEnh3X99+yhzBxb2IPqbIGtmhPalJR01GIREjsa0FVRh+4mKxKy05BZaMPhCadj62bB3nfKsGB6E86/KB7miS6NmN1bqg0UEefFMpY6Bw9SGWRnOwWw3c7tCgpoL3v0UdrJwt1688gR4KGHGJ/LhA3nZ1u3Ah98AJxyCm+f9KYAACAASURBVFcx+/ZxO4CRRpMmcSWRmKiL+vUHpbhK3LqVfpqCAkZuZWdHe2QRQSuEQUpTEwNkMjJ6tw82mym4u7qAv/yFMjSQ3sa+qKjgc+Kv2b3ZTMvO5MlAlRqFA/sV0ke6fk9Q1TUCdTIC8VmA1aJwslQgo+corvn8HqS2VKN1m+DQWoWSCXEsXX3xxZ675QwEEc6yP/6Yqw2lOPC0tN5CtKPD6ehOT6ft/tNPQ6NlffHCC/QDeOrbMGkSVytvvEGTV1oaHcpKUUGUljJy69ZbtUIIliNHmCB54ABv5rg45sf8+99ccl9+uf+HYJCj15ODlA0b+Px76yWflMTJ7apVAz+W3R6YbDGZaL0AgLcPTYElKRPJ9vZe+zl6lJPXuDgg3daEurYUHL/9OcT1dKIlqxjWUcXY1jQWliyHvfyRR3iioeaMM/iwWywUvp2dvbPqrFYKWVe7W3p67+S5cNDQwGN4W46NHcvVSkcHx2uM2TAVpaYynNZiCe84hxqGk+zwYV7jMWP4G4wdy1nVW28B//rXkC+TErRCEJFUEYnhjJ3hwbp1/n2L+fnA+vUUxAMhL4/78Pcs9PTw2VEK+PDjOOyZc+0XiWkAZZhh1k62tiJLmvBey0y0ZRShOykTEIHZzG3qm+L5MO7aBaxePbAT8ERJCXDNNTQTuYa2KkUF0doKzJjRd/kVbkFbVUXh7s32b0RAmUxUHsaPa7c7FcW8eVQqhilJ459VqzhbGTmy7+zHbOb9smYNTYtDGL8KQURMIvIVEXlDROoA7AZQLSI7ROTXIqJLTUaB9va+ZSLciYujmdyYtfeXUaPo7D161Ps2RoTT9On4Iry0dvTJ+GT2txBvaUNmUwXSWw8jv+cwCrrKYYINb2RdjYNSAmtcX5NQdzf4YI4cydmZ1Tqwk/DEmWcCd91Fs1B7OzN+m5tpL54/v6+tra2tfzkPweBP69bW0odRVMQZQVsbx9zaSvPXwoU8n54eYP/+8I51qNDTA7zzjucQZAOTiQ/U2rWRG1cUCMQgthrAuwB+BGC7UsoOACKSA2ARgIdF5BWl1L/CN0yNO7m5nCD6Ugrd3ZQd/hSHP0SAr3yFoaXNzQzAcT9OVRVDTg1/a3Y2J6hVRbNQO3I68mu2QZWVY2+3oDl3IspTpyKjfAtykuo8HvOL0NmUFGqio0fp4As1U6fyNWIEl1PFxZ59FoaWO+200I/BlcJCHstbhFBPj/P9E0/kNbFaKazcS22Ew9Q2FGlp4bVKTPS9XWYmHc5DmEBMRmcqpR5USm0zlAEAKKUalFIvK6UuBfB8+Iao8cQZZ9Cx7IvaWk6CQ+FbHDeOk2mlGE1UVeUMkT96FPja15xliAD64Iy+xzZzAqqLZuLg7MuwJudS7EuZDpvEoc2SiDmZe3sdx/BXuKckDNju5Y/LL+cD78nMYrfzpE8/nQI7nIwYQUFfW+v58+RkCi8j+slspiBzVwZKeXcwaXpjMgV2f9ntsV3fKgT4XSEopXpE5DgARQA+Vkq1GZ+JyLlKqRVKqQEaJTTBctJJ9BHU1nqeODc1UU64CumBcswxrJqwYwdD861WWlVOOqlvPbV585gbVV/vFO4JCcD48YyUjIsDRmfF44T4XegGHahKcbI2dqzLJN1i4caOsL/mZgYH7djhzE879dQQlBwqKGAS2h//SOGfnMyHv7OTdrfFi4Grr45M5M4VVwAPPshchLy83sdMTaVCOOUU78KpvZ2RR7okeGBkZfH3b2lh2J43mpvZ0m8II8qPzVJEvgPgNgC7AMwAcLtS6jXHZ58qpU4K+yi9MHPmTFUa7qiPGKaujmHxdXW8j43JY1MToxG/+10K4GhRXc08ifp6jicxkbLq008pZ8+aUYevfnYXmtOL0NUTh85OKrk5c1yi+yoqgHPPBa64AmvXMlfLbndOftsc05PLLmOLzgHL654eapvSUl7MwkIKX1/25XBQXc244QMHnAX+RCi8Ro1iwlpJSV+zUk8PHZ8330w/iCYw1q0Dnn6aD4ynm6iri8EHv/71kMhJEJFNSqmZfd4PQCF8BuAUpVSbiJQAeAnAP5VSfxCRzUqpE8Mx4EAY7goBoMzasoVBEg0NFLwLFwIzZ/L/0cZiYdj8++9zApadTTN8Vxf9eDmrX8ZJh15Fz8hiTJwcj1GjHBNfpbj8SU0F7rsPH+/JwmOPMYrJ3dRryMDrr6cpbcigFHDoEFcsNhtnsccey/effZYOzqQkmrqUolPcaqV2PP98nYcQDFYrU+1LS+mwN24ypbgyaGgAbrkl/DkoEWIgCmGnUmqKy99poFLYCWCxUmpGqAcbKFohDG6UAnq67ZA3lyPuzWUQwzlqlEydOBG45RbYcvLw/e9TUXhTcl1dzmQ9f77BIYGRUfvee7TBmUwM8YqEn2Oo0tPDiLYVK3hDiaPvd1ERa21NmxbtEYYMbwohkCijGhGZoZTaAgCOlcIFAP4G4IQQj1MzjBABEpJMwCVfAs5ezKVOXR1nvVOmfFG2Yd9uCntfGddJSXyGd+ygT2PII0LzRjRtgkON+HjgS1+iibKsjMvbzEwmqQ2T1VYgCuFaAL2CwJVSVgDXisiTYRmVZviRluY1pLOxMbBdiPjOlfCJUb7i3Xd7e6zPOovKSReJGz4kJNA0NwwJJMqo0sdnuui6JuwEWs5bqX6ai2w2liV47z165o0U8H37WF30lFNYKTAcdcW9jaeqiiaMrKzY7NqmGZIM7UpNmiGBURLbZvMeaWm4wib1J29+xQquDMaN670SyM9n2OdHH1EwX3VVP3YeBDYbldIbb9ADb9iwjz8euOSS3h3lNJowoBWCJqQYUZtr1zJKLz2dfs4ZM/pftDQri5P0DRu8V46orgZOOMF3iW6PdHcDy5czfMmTWUiENuRVq4ALLghfspfNBjz5JJVPQYHzRO12hp4++CBw551UDhpNmAhYIYiIALgawHil1AMiUgxgpFLqk1ANRkTOBfAHAGYAf1FKPRyqfWvCT0MDi5MeOsRo0eRkRuz9+c8U6nfe6b8UkBFpuXYtLTZmM4M7zjyTdejKy5kSYCgXi4XKID+fpTOCZvduZyEmb8TFMSxx+3ZqpnDwwQfUeO5x8CYTT66tDXj8cWYGftFyTqMJLcGsEJ4AYAewGMADAFoBvAxgVigG4qig+jiAswBUAtgoIsuUUjtDsX9NeOnuZpKc0bPelZwcKotf/YoTXW95PTYbS8+/9x7N9UZ4/fLlwOuv02py4onMXzDKYsTHs1PkOef4TjL1SkdHYNuJ0IwTDux2mony871Hs6SlOfsx6IQzTZgIRiHMUUqdJCKbAUAp1SgiAyyb1ovZAPYrpT4HABH5L4ClYL6DJsbZsoWl5L217czJYfLY++8zss8Tr75KYe9uyk9P50rghReA227jKuTIESqL3NwB5h0E2v0snLWBjh7lCflbPqWlARs3aoWgCRvBxNL1OGbxCgBEJA9cMYSKIgCHXP6udLzXCxG5WURKRaS03pgmaqLOu+/2rYLqjtEj3lMuZGsr8OabzDXwZMpPSOD3X3yRFhyjrfKAk9COPZbLDF99Dmw2DmrKFO/bDASjGbY/zOaB1zLXaHwQzArhjwBeAZAvIj8HcBmAe0M4Fk9PRB/RoZR6CsBTADOVQ3h8zQCoq3OatltaaOs/dIiyNC0NmDCBQryjg+Yldwfzli20nPjqUJiWxtJGn38ewrptyclMRHr1Vc+1gQynxqJFdISEg8xMHtfI1PZGWxsd3MFgs9FHEhcX2hTu7m76VKqqqFAnTuSPHMoErs5O3kzx8b17XWvCRkAKweFQXgdgE4AzQOF9kVJqVwjHUgnA9W4fDaAqhPvXhJGkJE5eq6oo3E0mZ2Mvi4Xh/Hv20BzkKZz/yJHAKguL0FEdUr70JTolPvyQjoicHB6osZGvk04CrrwyxAd1ISWFSXnr1nkX+EY/hkDNRU1N9MyvXOn0k0ybRmfL5Mn9F65K8Tr95z/cr9HiTimO/ZZbGLE1EGpqWELiww+5X7ud5rTzzwdmzdKKIYwEpBCUUkpEXlVKnQx2TAsHGwFMEpFxAA4DuBLAV8J0LE2ImT8f+Pvf2aQrPb33RDcxka/aWu85VikpnMwGwkAnuobJ6gu5EhcHfP3rwNy5tGnt3s2NJkwArr2WMbPhbq5+3nms693Q0PciufZjCETYVlUBv/wlZ9cFBc4eqEai3SWXAEuX9k+wrlvHKqyFhbThGShFX8gvfgHcd1//q8MeOMDoA5uN+4iLcxaYe/RRNtq45hqtFMJEMHf5RyIySym1MRwDUUpZReRbAN4Gw07/ppTaEY5jaULPqacygsjoNOiOzeZUDLt3s0mZK8cfTwtBbS33kZnZt9ObxcLVxYQJwY+vsxP45BPK++pqjnH2bFZHHT8ezuJw06d70BgRID+/dz+GpCQO0ujHsGhRYIKwpwf4/e/5HdfiT0b4ak4O8PLLnHEHW/Spo4NhYEVFfW1+IvTwV1cDL70EfOtbwe0boGnrD3+gGc/VPGeU/c7I4Ipn0iQqb03ICUYhLAJwi4hUAGgHzUZKKRWyEoBKqTcBvBmq/Wkih8nECd3Bg3QQG+YipShHLBbK2tRUhty7KoSKCuD557m6aG3lakGE8mzyZCoGpRjFdMEFgQcGGTQ2Mny/spJVKYqLKS9LSzmWK65w66UQbkVgt3PGqxSFnKFBi4uBhx8Gdu7k4Do7KXznzg18xr19Ox063sK94uKoFF5/nTG8wZzrp59S4fjKMCwo4HaeVjr+2LKFqxpvYzeZ+AMuX86mGXqVEHKCUQjnhW0UgxBDQLW3U4AVFQ3v+mcdHZwgjh9PwV7pUgGroIA+x9xcmrZdi9Xt30/rRnw8Z+vr11MmpKSw4OTRo1QkTU2UX0uXBjcuu52Whvr63pUf4uKY1dzTA/z3v/z/ieHu7NHdTQ30xhvO/qfp6bTrL1zIk46Lo62/v6WW16/v277OnawsauGGBmfdpkAoK/NvrzOZKKjr6oJXCB9/7L+JR2YmZx1Hj/KG0oSUgBWCUqoinAMZLCjFydsrr3B1bLRjzc+nsDrllOExcWlqYmLt1q3O3i2trVSMJ59MId7TQ5+jq+mnu9tpDTB6kqSmOt+bP5+yav9+Xsfqak6if/hDXttg68vt20c55q10thHAsmwZXQU+fzuLhbPfFSsYeRQXx05Eixd777Rl0NnJBIpdu3qXpujo4PJowwae5EBzHdra+tra3BHhjdvVFdy+jSVfIPTnIejs9P8DG2PX4bdhIZjSFT/29L5S6oHQDSf2efNNzihzc/lMG/d9Swvwpz8xQOLii4e2UvjwQzqQbTZnxGRZGf2BjY0U3HFxnn0JXV3OKtc7d3J7V2GdkEAT8YQJlL89PbSunHxy/4qNfvSR/+9lZdFsX1/f20/ai9ZWCvQDB6hBioo4Eygt5QW5+GJGK3n74V98kdrJXXGkpHDpUlnJi/qd7wR/kq7k5zMu11eIrN3OV7At9Y47jpmDvjByKor6pBD5Z9Qojt1XQosReZCQwG3tdjrN/SXBaAIiGJNRu8v/kwBcAPZZHjaUlzNbtri4r5DJyOCz/dprdJAec0xUhhh2tm1jDbbCwt6mZMMUvnIl5cz06X2/W1XFa7R/P5XB9u18nj1hMnH/SUlUGocOUR4FS1OT/6J6xqTTaxULpXjSFRW97U4mE4WY1UpH7ciRtG2709rK6JyiIu8Ko7CQEUB1dT60UgDMm8dwU6MPsyfq67kcClaInnACf9zWVu8rmepqLvP60791/nxg9WrfY6+s5MP3f//nVD52O/0sS5dy9aXpN8GYjH7r+reI/AbAspCPKIZZvZoTE28zzrg4OjzffXdoKgSluDoaMcKzkB05ktFGH39MBTF6NK9JWxv9LZWVfG/ZMr6/ezejijo7Key9+WCMZ74/5OSw+qq/87LbfZjeDx6k9vJmd4qL45LxlVcYJ+9+Ivv2cWbrK3TVsD3u3j0whTBxIi/m3r0U2hUVzsJPubn8kbq76Z0PloQE4BvfoIe+p6d3spjRwyEvj6ul/jBuHBXV1q29l98GR47ws4kT+TJMYzYbS3ps20ZFoVuI9puBuEFTAAyr/n2bNvF+90VeHs3M/kytra3AZ58xsOLQocBNs9Hk4EGnTd8bEyfSOTtqFP1+Bw/yebXbKaeOPZbPemEhzUKJiUxY8ya0DeuGv+vujblzKf98Xd/GRlpyvB6jtJTOEF92wIwMzu4PH/Z8gOpqYM0a+h/WruWFcbeDm0zUjgPBZKLQ7uhgNM7evZxJWyz0X7z3Hm1y3iJ5/DF1KvCjH1EZVFTwPA4e5HnPns3P+mu+EWFi2/Tp3Hd1NW2xDQ38e9cuKo0pU3r7Scxmrr6UolNqMDxMMUowPoTP4CwlYQaQB+DBcAwqVunp8R9JZDI5e8R7kh9tbQzT/uADCjqj8cu4cey/Essri6YmZxCJL7KyqBSuuILX4aWXmHjqnlM1apRzVXXgAGWUuyWivp4muP4qhAkTeE3LyjzndFksPK+bbvJxXo2NgWXDifS1O1VU0Ddw4ACXK93d3F9FBZXIokVOAapUaLqjbdhAIXnGGTxOayvHP2kSNfHu3fxBzj+/f/s/5hjg/vupCOrreaySEu9lbIMhORm4/Xb6B9ato6JJSmLU1b//7bt0R24ux3TgQAhrmwwvgvEhuK4xrQBqHb2Vhw1jxviPpmtp4TPnSXG0tzMJs7KS2xgWBNckzzvvpKk2FjHyAfxhtTrNLxYLe8t4CqNPSKCw37KFsvTgwd75CU1NVJZf/nL/x2wyMUfqd7+jUsjOptKx2SjLrFYmI/uM8szOpiD3h1K97U6NjcCvf83vJyRQOLvS0sIIo4suohY1LshA6O6mI6u4mErAk/kkIYF2u8WLg0/qMDASRbyZ0QaCCDW5awbiZ59R8fgyu4nwtWuXVgj9JBiT0TeVUhWO12FHZvEvwzayGOSccyikfAnFhgYmOXli+XKah4qLe9/XIlQyI0awmUwgsicaGHWIfBUGNezxhlxrauLKylskpGE2ttk4sauu5qSwooLP/913B1/PzZ3MTJqWv/EN/r+2lrJ4/nzggQfYfMcnM2c6l33eaGmh7d81uubDD7li6Oriy2bjhTC85WlpvDgrV3LWvnRp/wW0wa5d/hv+JCTwR9w5iCrLB1rXRFeEHRDBrBDOAnCX23vneXhvyDJjBm3gBw5QSLmaGJSiT62khPLDnc5Omm99tXhMS3P6zWbPDvnwB0xSEkvJLFvG8/RkYqmt5cTOMFH7C10Xof0+LY3bzZ9PZTllCrOU/RW86+zkNROhPPameBIT6U/oV8WD4mIu23bupN3J/cStVg7ittt6Lw1XrqSmX7uW0S8pKU4HrzEjMIroJSd7n0kEQ2trYNspFfi2scCIEc4ier5sllZrP/qoagz8KgQR+QaAbwIYLyLbXD5KB7A+XAOLReLjad586ikGNMTFUQB1d3MCM3kyZ6GeInCqqnzPlA2Sk+lgjUWFAAAXXsjZ++bNlHFGdGF3N3MwcnJ4DYxnNieH1pD2dt+dH7u6WFD07LMDG0dTE83gq1c7J+9JScBZZ3El5y9ZNyhEgJtvZo2g/ftpAsrI4IGPHOHJX3pp7x/NbneWbu7p4cmPGEF7VXMzLwjgrAQYH09tOlBhFkzlv1CWww43o0dzluGrJEZ3N6+jp5hnTUAEskL4D4C3APwCwN0u77cqpRrCMqoYJi0N+O53afopLaXtPyuL0YZjx3qfvPib2BgYTuZYJSEB+Pa32fnszTdp9xfhc3jhhfRjugaZmM30Xf7jHzQPeboGXV2cWHsK4ffEkSP0tzQ00ERuhAF3ddF8vmUL8IMf9C8U3ivp6cBdd1ETvvWW/0xlEWqo+vreq4aEBHrIDS95Tw+Vh9kcGoVgxO/66q1gNPyZPHlgx4okIsBXvsIfPiGh749rsdA5d+21IZ4NDC/8KgSlVDOAZgBXiUg2gElgYhpEBEqpdeEdYuwhQiuCv46HruTlUSnYbL7NIJ2dvXOfYpH4eMrAhQtp7bDbPVcnNTj9dMrR7dspwI3tjKrGDQ3ArbcGFq2oFPD004zWcvdnJiVxEnnoEH21N944kLP0QEICtVYgmksEWLAA+NvffNvM2tspmAOdMfigqQnYuDEDSeYFKHr/PaRPHYvcPOm9W6UoOOfPD1/Dn3BxzDGcjT35JGdiSUm8Zp2dVHDXXBOAQ0jji2DCTm8CcDvYuGYLgLkANgBYHJ6hDS0yM2lR+PRT73kzFgvv61mzIju2/mIUn/RHfDwrMixbxsoHhs9PKfpgb7gh8FpulZXMW/AV3FJYyBpvl10W5YoGCxeyZEV1tefPLRbODgoLqRX76T232ZgT98Yb/DvV/GUs6KxF4dufYXdOLqafls7ckdZWLq+mTGGM82Bk2jSGjG3ZwhmGzUan1axZvhNkNAERjFP5dgCzAHyklFokIscB+Gl4hjU0ufhiRs8dOdK3UGN3N4XdNdcMzfs6IYEC+oILGP5psXCC6ikh1Rd79/JfX98xm6lsDhwIvuR/SBk1imFSN9zAHz0nx5moYvgQ5syhr2HWrH7nILz4IpXB2LGGlSgJu0bdgZbDH6No65vYseIgZswAkscWMOFi7lz/zqxYJjEx8JWaJiiCUQhdSqkuEYGIJCqldovIsWEb2RCkoIDhj088wbpIJhOFl9XKWfS118b+itdqZbDNhg2UaXl5LJ/jzT/gTlLSwEzX3d2BlRlXimONOrNns+rhPffQzBEf70zkGjuWtq+MjH636KyrY9OfkpLepki7OR6Hik/DoTHzUFveif2nAl+9OXloV13UDJhgFEKliGQBeBXAOyLSCN3zOGiKithZ7MABhp5bLLQYTJ8e+76w6moW/KyrYzRUfDzD3t99lyv5W2/1HUkUCvLzA69rFIrE2ZAwfz696s8+y7pGcXHUak1NLON61VX9Xh2sX08Z79UvJYIRY1KwdiNw8dX0W5eW0nqUk0N95avmnmZ4IaofdT9EZAGATAArlFI+0pTCy8yZM1VpaWm0Dj+saGoCfvITzrrdy0goRUfusccC3/++/9yBgdDVxdDfnBzvUZOtrRzDww/HWNMio6tSXZ0zMiGYBjUeeOQRmuD86ZMDB2jBqqvrGy594olsKR3rExJN6BCRTUqpPhlTAT8uQq4RkR8rpdaCjuUZoRykJnZZs4aC1lNNIRH6Q3fu5KonlHR3c1K9cydXKImJwOWX09/iKSG1q4uRnldeGWPKAOCFGj2ajo0TTxywMgC4SvO3YurpYd7M4cO0Uo0ezZXWmDH8e+tWdpWLCRObJqoEYzJ6AoAdjCp6AEArgJdBR7NmCGOzMTrIV1VmEZqLVq3qXY+ov1gszHNYuZJC3iiBPWECaxtdeSVbEADOLOe2Ns5+b701ys7kCDJjBqvw+uomuX8/r+eECX1NQ4Yy37GDL53TNbwJRiHMUUqdJCKbAUAp1SgigzhUQRMoHR0M9fZXcTQ93XP152CxWIA//pERWYWFTkWkFG3gDz3EcPTf/IbObaPd5vHHM1hnoF0oPdLTwzDHTZu4bCkqYmu4KDdkOekk4F//8p4Jbrdz2JMn++43kZFB5asVwvAmGIXQIyJmOEpgi0geuGLQDHHi4iiMAykjEwo79Jo1NHG4Ry6JOJvzPPEE7ef9reAcFGVl1FCNjTx4XBwVw6uvMjX7qqt8V+EMIykpwDe/yWvR2cnrY1wzI5Q5Odn/qi0jg1nnmuFNMHfxHwG8AiBfRH4O4DIA94ZlVJqYIjmZFREOHfJtmmhsZAbzQLDZaCoaOdK78klNZVh/aSmzoMNKdTW900YatCt2O6fVSjFmOEpMm8ZwZqNts1FQMCHB2ebZXxSR14ZubW3AJ5/wZbHQAbFgQd9SHZohQSDF7f6plPoqgFwAPwRwBgABcJFSalj1VB7OnHceTTTZ2Z6jiDo6+P4ppwzsOHV1zNPyVxYkLY3lMMKuEF5/ndLVUwyrycRlzHvvsapeFKtsTppEpVBTQ8UcF+dsiVBZSUXhywd09CgXO7347DPg8ce51DCaZh8+zFpOqal82e0873POoUNjMBXM0/QhkDiMk0VkLIAbANQCeA4seFcrIiFo76QZDJxwArOMy8tZf8iIVrbbKcTr6tj9cKANv2y2wCaeJlMEyt63trJBtC8/gZFduD42Cv+OHEl/waRJTtl89tmc6Hsrmmix8LNeyrWsjHaotDSGIhmdhSwWpot/+CG1TE4OtckTT9C509wc9nPUhI9AFMKfAawAcByATQBKHS/j/wNGRC4XkR0iYhcRD90ENNFGhOGe3/oWJ4aHDnHmeeiQs07T3r2cLA9EJhjVHfyFQLa10WoRVhob+a+/xIrU1Jg2wE+ZAixZQmXu2gJBKeaXHDrEQqKuvX3w2mvUKK5VRevrWYwrPZ3e/ro6Lg2zs7lSqqpi/GqgmYOamCOQaqd/BPBHEfmTUuobYRrHdgCXAHgyTPvXhAARlo+ZPZuyoLKSVUWrq50JT93djHpZsoS1m4JNUktJYWLvunWeeyADnM3abCyZEVYMb7o/jE5oMYoI+1sXFbHAYEUFla7dzmt83XVMi/iCxkZ69d1/gD17qCQMZ4PJxJvAqCBYVMQMuH37mKWoGXQE4kMQRbwqA2Ob/g7C8EWIdlINCkQo//75TyoA9z4QVisnmBYLZ57BsmQJsHGj5yKANhtnukuWRCDis6CAws5fd5/2dpagiGFEqGjnzaMM7+riKRUWejDRtbTwTdc4VaM1nWv52Pj4vl3XEhJoPvOnELq6uNr48EMu9/LyWB02kDZ5mrARSJTRahF5GcBrSqkv1sWOHITTAFwHYDWAZ8IyQk1M8vbb3p2/cXEMyFm5Eli0KHhfa24u8KMfAY89RuFv1IPr7qac+tKXqBA2bGAdpSNHuLI4ctdV9wAAHlpJREFU/XQ6tUNW5t9s5oGefdZ7VE1LCyXrjMGRtG8yBdDHIz6+78rIqM3ueg1sNmd3IoOkJPoUfFFRQf9EUxOd1QkJNEeVltL0dPvtg69XwxAhEIVwLuhQfk5ExgFoAhvkmAGsBPCIUmqLv52IyLsARnr46B6l1GuBDlhEbgZwMwAUB9OhRhMyurrYunKkp1/TgdnM1wcf0PcQLIWFwM9+Rr/E1q08ZmEhG5RZrSwQWFNDuZGSQgfzCy9wZfKd79BuHhIWLWIFv40bqdmMRAvDm261sj2bp76pg5WRI6mVW1udWX6GknBNRrFa3RwPoNb21YTi6FHgV79yzhoMDF9FZSVbld5zT19lowk7gfgQusCyFU+ISDwYftqplGoK5kBKqZAUdlZKPQXgKYDF7UKxT01wNDZSFvgzm6enA59/3v/jmEzMfzjuOOd73d0sstfU1FueGP7Plhbg/vuBr36VFp9x4/xnWPskLo6ZX+++y3DLI0ecBvgZM4ClS3136xmMmEwMKXv6aa5+TCYmo2Rl0YmcnEwTUkpK34vb3e3bubNmDbW7t2ZARUVcFm7f7ubY0ESCoNIrlVI9ALy0f9IMFwI18RqtgkPJtm1cGbjniAGcsG/bxn9rali7B6BcueaaAYTExsUB557LZhVVVdSG2dkRr69dU0N9ZDbT7BPWUuOnnUZtvmoV058zM+kX+OADCnSjSY3rD1xTQ0e0N/+BzUbF6ishAuBM4t13tUKIAtHJt3dDRC4G8CiAPABviMgWpdQ54TiWxcLKmc3NvKePOWbgsfPDDUM++PO1traG/pl+7z3PHeVqauhTSE7myqC9nbJJhPlVDz0E3HdtBTI3r6H5p6eHs9Szz+ZMP5AoISPbK8J8/jnNYXv2OLOQzWb6YC+6KEyKwWRi9vXkycDy5QyrNSrhtbdTIxtVBdvbqany82n/9zYL6OzkCsJf8lpKCotWaSJOTCgEpdQrYFmMMB6Dk53//Y+rXsBpCp07lzPIcDd3GSqYzcxc/uc/vXdK6+yk/Jw9O7THPnqUQt8Vq5WlhVJTnXLdbuf7iYlAUaFC5kcrUPuNF5A5JY5mDpOJjszHH2cW1+23h6kq3sDYs4cm96Sk3u1Ge3o4id63D/jhD8PUy8BkcsYZNzXxgmZk0M6/ahXLWVit9Ddcey239fUQJSTwQbTbfdcmD1VRLE3Q9EshiMhIpVRNqAcTTl5/nbVeiop6r1htNt7XNTV8sNyFjcYzCxfS2WtUJDUmfd3ddAQfPsyon48+ojwJVbP79PS+SqG2lgLSkEVGXpQxUR1V/SnmVP0H+8zFGJsbj0RjMZCdTbt4WRnw5JPA974XU/V5LBbqq8zMvqui+Hi6LsrLeW9fcYWfnXV0UGtu3+6sIz5nTmBmL5He202YwNfXvx6cXTAhgUvGXbt8xww3NLAUhibi9LeFyJshHUWYqakBXnmFD5B7MIjZzFXwgQNMhtIERnw8o3mWLqWT+eBBypuXX+a1HDeOMui554A772Q/hf5nqjiZP5+TVVeOHu0tk9rbqaTi4gAoheN2v4LO1BGwSnyfsPkvmtZs385wyBhi+3Y6yT2ZyAwKC2lG6+rysaNNm1gv/G9/40537WJG4fe+x0qC/f1hfPbu9MI55/DG8FZ3pK3N6Z/QRJz+KoTYmUYFwIcfcoXqq0LxyJEMIvFW70XTl4QE4JJLGCV4wQV874wzqCSOPZaWhOJiTgaffTY0Ctfod+CqFNwjIa1WYOJE/p3eVo30lsPoTvSxRBHhzbFx48AHGEK2bfMfzZqQwPOtrPSywfbtLN2dmckZUV4ef5ixYxlG+9xztD1FimOPZXejQ4dosjOUkc1Gh31jI/Dtb4duSakJiv4qhKdDOoows2OH//srJYWzMV2bK3gSEujQnTSJwt/d6pKQQFPd88/TpOSNo0c5mf3kE5pCPE1cU1O54uju5qqku5vWDIuFTuy2NiYNGxaOeEs7YDLDrjgo19I8vUhMpKkihrDZAmsDanST64NSFPjZ2Z5t8vHxXB29+CKdPpHivPOAu+/mMvLgQSqHqiquCn7609C03NP0i/46lf8X0lFoBjX79zPIxFc4flISTXc7dvRtb9nQAPznP1QGIs78pzFjmE9wzDG9tx83jklra9dyctvTQ4VQWMicBdckV2t8MmC3o61VoahIvM+4/SVURYGSEkZ5+sJu58tjrkV5ubORsjcSE3nxtm5ldEWkmDKFr7Y2Zx0N7cCLOsPChzB1qv+Zf0cHbbUxJhMGBYFOrEVoJXClsZEhoVu3UgEUF1N+jR3LFdvDD1OJuDNiBM1Vjz3G/Kmnn2awgHtya3NaEeqkAGloxeTJXgamFO0uoQ6JGiCzZvkv811b23tF1IuGhr7lJjwRF0dtHQ3S0mjC0sogJhgWPoR585xhiN6oqeFKVtfVCp5Au0fa7X3D/V96if6AoqLe5hEjsCU7mwFA3oSiycTVx/z5wG23cbJZXu58VRwUHJ59EeZOqEdqopcboKqKy5Bx4wI7kQiRkQFceimtKhZL388bGnidLr7Yyw4C/WFsNt3YRgOg/yajQeVDGDmSD40RdupqNrDZuKqeODEC3beGKBMmUDDZbN4VquEPmDTJ+V5zM30P7uVwXElPp2DfscN//bg5c2iO2rWLM+e4OI5tzOjZkGWXMtQsKYlLCZOJToejR7ksue22mAo5NTjvPA715ZepFBMTeZ2tVp7G975HU5lHxo93NpfwphwM+5xrfRDNsKVfCkEp9USoBxJuLryQZsqXX6awMASUCCtkXn21XrX2l+xsCuNPPvFeoqa2lkmvrsL/8OHAIhfj45mgFUhB0fh49hjujTD0aepUJlSVllKqjhoF3HQTbTMxWpxOhFUzTjuNPpbDh7nKmjKFATs+r116Or+4bp33DOu6Oq6MPNUC0Qw7YiJTORKIMCRy/nzOIJub+WDp0hWh4aqraNooL+eM1TAN9fSwgU5WFmWv6yQ80PB3r1E0wSDC5cmkSc5ZcSAhPDFCWhp72wfN5Zcz8a68nArQMA1Zrfxh0tKAW2+NydWRJvIMG4VgkJAATJ8e7VEMPTIy2MPgrbcY+WP4a0wmYPFi4Pzz+zo+CwqcUTK+ZLPFEuJ2mSLDRwCmpgJ33QWsWMEfpqvLef6nn84EkhEjoj1KTYwQSMe0QObP9mDLYWsCQylnFnVlJRXanDm0cniNqY8SaWmckF54odMsl5fnvbxNbi7NQDt3em+i09lJa45W4gMgJYUhWeedx25mGzdSy7a3O1tgBuqA1gxpxF/nSxHpAlAF35FFZqVUxMtAzpw5U5WWlkb6sBGjq4vhlKWlFIppaZx5t7Rw5X/bbcAJJ0R7lAOjupo5BQAVhOvEvaOD0V/f+Ab9PJoB0NTEjOXPP+fNlJREpdDRwaiL7343Aj1Je1NbywAvo3qIe7tUTfgQkU1KqZl93g9AIWxWSvksYhzINuFgKCsEpYA//Qn4+GP6+9wtHG1tDDv88Y8Hvz+wspKhpYcO8W+j/0x6OqvQ6rI2A8RiAX7+c0pfTyFddXVUED/9aUQqvh4+zETEHTucpkKluFq86ir/7RI0A8ebQghknRjI3EzP30LMoUOM2vGkDACuFtrbgWXLWGRuMDN6NPDAA5y8HjjgDACaOlV3UQwJW7awcJ+3mUN+Pj9fvz7sVUYrK6mbACYfGve23U7T4YMPAvfeG/HFisaB3zALRwtNj4jI1/xto+kf69dTGPryfebl8Vl3r/45GBFhzsDZZ9PUPWOGVgYh4513/Kfg5+UxIiAUJWm9oBRNoCZT35pXJhMnAT09LISoiQ4Djbv7aUhGoelDdbX/vAijKoEuyKfxiRFe6ouUFM4sfNXJGCDl5VyI+PIVFBTQlFStG/VGhUCijLZ5+wiAXtiFiZQU36U2DDyVg9BoepGYyJvJ15LLSDMPY7RReTn/9bXqNSJiDx70Hnk2LFGKduT16+mNT0lh7a2pU0P6mwWypwIA5wBodHtfAKwP2Ug0vZg9m2UdfNHaSvOvtrdqfHLqqWyE4y2NHKBj2aimFyaCsUYNOBFxKNHZCfzlL0xVj4+n6aCnh41e8vKAO+7wXf8lCAL59ZcDSFNKVbi9ygGsCckoNH04/njmC3mrJGq38xm+4IJBlXCriQbz5/Nfbz0PenoY43zmmWEdxsiR/vMBjSRyPclxYLczBG/TJnrhi4pYWqGggEECnZ0sCXz0aEgOF4hT+UallMeq7Eqpr4RkFJo+xMdT8dtsXCkapl2laOotK2MpjnnzojtOzSAgP5/9j2trOYswpt9KUZAcPMguZkabuTBx7LHMVu/TxtSFpiZGncVY4dnosX8/sHlz75AsV3JzGW743nshOZyeW8YwY8YA999PwV9fT8Vw8CADRr71LeDaa/XqQBMgc+cC99zDWk6HDjlvplGjgB/+kKFdYcZsBr72NTZTamvr+3lLC5XFddcNn8oiflmzhjkivi7IyJEs2hiCgIBAEtM+VUqdNNBtwsFQTkxzp7ubD0x8PBWCfmA0/aa5mbPK5GQvnXXCy9atwF//yvvZqNZqs9EScvPNuhJ3L+67j2Yhf1FiBw8Cv/1twL/nQBLTJvuINALoXNZ9xsJMYqKXNomaoU17O7B9O1vLJSezhvhAU3kzM6PaGnD6dMqunTsZeWQy0UQ0ebJuUNWHhATPyylXDMdLCKKNAtlDIPraNtCBaDQaF+x24PXXgeXLaQow6nmIADNn0q4Sa9UNgyA+nopBFy30w5w5wH//61uBNzVRo4bgfvCrEJRSFa5/i8gDAMwAtgDYopTaN+BRaDQaJ0pRCLz1FhvbuOYP2O3Ap5/SqXTXXbqr01Bnzhz2mW1v91w22G5nKOJXvxoSO3LQLkml1I8B/BFAK4BLRWTA7TRF5NcisltEtonIKyKSNdB9ajSDlkOHgJUrGVbonkxmMjHaoKwM+MBj8J9mKJGZCXzzm/TEu0aIAfTAl5UBZ50FnHxySA4XsEIQkd+LUAUppWqVUiuUUg8rpb4egnG8A+B4pdQ0AHsB/CgE+9RoBidr1tAe7Mugnp/PFYTO4Br6zJjBin/uEWLx8cAtt7AkcIiiTILxQrQBWCYiVyql2kXkbAA/UUoNOBJeKbXS5c+PAFw20H1qNIOWPXv8R4ukplIotLWxXZ1maDNhAntWNDYyPCshgclpIY47D1ghKKXuFZGvAFgjIt0A2gHcHdLRkBsAPB+G/Wo0gwORwOo8KKXjj4cb2dlhDRUOxmR0BoCvg4ogD8B3lFLvB/H9d0Vku4fXUpdt7gFgBfBvH/u5WURKRaS0vr4+0MNrNIOHE07gTNAXLS2cIQ7iSCNN7BGMyegeAPcppT4QkRMAPC8idyqlAsqZVkr5LJQiItcBuADAGcpHtpxS6ikATwFMTAt49BrNYOH004EVKxhu6qlCqVJ0Mt5449BfIbS3A9u2scSGkYdRWBjtUQ1ZgjEZLXb5/2cich6AlwGcOtBBiMi5AO4CsEAp1THQ/Wk0g5pRo4BLLwWef57/T0lxftbTwx6U06axiulQxW5nDsbrr/OczWamMwNcQd14I5ClgxFDjd/SFT6/LJKslPJSQjGo/ewHkAjAKNn3kVLqVn/fG06lKzTDDKWAdeuA//2P5iHDr2A2A4sXU2EkJkZ7lOHjhReoDNzzMJRib+i8POD//k+bzPrJQEpXeCUUysCxn/CWWdRoBhsiwIIFLGe7dy+VQmIicMwxnhOUhhKHD7N/Q0lJ39BbEZaALitjeO4FF0RjhEOW8LVH0mg0AycuDpgyJdqjiCzvv+8/D2PkSPpZzjlHN98OIbp4skajiS327fOfW5GcDHR0cOWkCRl6haDR+KGzk2ZrpZggrPPAwkygeRjGtpqQoRWCRuOFtjb6NVevZo96Q07NnQtcdNHAq1BrvDBtGvDqq74rfLa1MUErimW8hyLaZKTReKC1FfjFL1hjLjeXwS5jxtCfuXEj8OCDQHV1tEc5RDHCaS0Wz58rxUJvS5boBgohRisEjcYDL7wA1NSwlW1CgvN9s5lKwWYD/vznwC0bmiDIzQWuvhqorOzbHKanhzWcpk4F5s+PzviGMNpkpNG40dwMrF/vOyE2Lw+oqGD04/jxkRvbsGHxYobXvvACL7ThKzCbgTPPZB6Gq6bWhAStEDQaN8rKnDlg3hDha8+eYaQQOjuBzz/nLD0zk3kC4XLqitBZM2sWcOAAtXR8PDBxok5GCyNaIWiihs3G6r2xFihiC7AhrMnk3cw9pLBYgGXLgHfeoTIAqDELCoArrgBOPDF8xzabmYyniQhaIWgiSkMDG3298w4dt0lJTMhduJBle2KBESNYSsdfdWmbLXbGHDZ6eoBHH2WBuaKi3maalhbgkUeAm25iQT7NoEcrBE3EKCsDfvMbWh7y8yl4LRZg1Srg3XeB224DTjop2qOkI3nMGPYu91Z6vrublSSmTYvs2CLO++8DW7bQLuauHTMyqCCefZYF58JYp18TGXSUkSYitLYCv/sdzcDFxVwZAJQno0dTOTz+OMvYRBsR4Ctfodm6vb3v5xYLA2CuvNJ5HkMSu501hQoKvC+VkpK43YYNkR2bJixohaCJCJ98wghCbxWLU1Jok1+1KrLj8sbkyexY2NYGlJcDtbUMfa+o4L/XXUcz15CmsZE2Pn9O3MxMriI0gx5tMtJEhNWrgZwc39sUFNBCcfXVsZFvNH06TeRbtgA7dtCnMH48A1/S06M9uggQaItOkcA98ZqYRisETURobe3d58UTcXEsEWGxsHZZLJCUxOjHuXOjPZIokJlJR4nhMPFGSwu1pGbQo01GmoiQlQV0dfnexmKhT2Eo930ZVMTHA2edxZRtb9hs1OI6ymhIoBWCJiIsXsyoHV/U1QGLFtGXoIkRzjiDpSSqq/vW6bBa6WA56yzd53iIoB89TUSYNYurhCNHPH/e2kpT9KJFkR2Xxg8ZGcDddzMWt6KCr8OH/7+9+4/1qq7jOP58CcgPUZl4mYok2hil5mCaM3UNJia5pmFlOio3Xc6ZkySbGk1NcqOxtVazZZss28jGJhnTsotkqS1KMFQU8UeTgRKCv/ACegXe/XE+V75cv/fe871wv+ec7309tju+53vP93te4uX7vp/POefzzv7cvDlb9vWyy8p3d6H1ywH1VC6aeypXy2uvwcKF2Uhh7NjsPEFnZ1Ykhg2DOXMGX3OwyojIFpV79tls7q+tLbtD2c0hKqmnnsouCNZUHR3Z8tHLl++7onHatGzF476uQjKzg6OnguCrjKypRo/OpoU8NWRWPj6HYGZmgAuCmZklLghmZga4IJiZWeKCYGZmQEkKgqT5kp6RtEZSuyTf9mhm1mSlKAjAwog4LSKmAA8CtxYdyMxssClFQYiI7TWbhwHVvVvOzKyiSnNjmqQ7gW8B7wK+bcnMrMmaNkKQ9IiktXW+LgaIiHkRMQFYDFzXy/tcLWmVpFVbt25tVnwzs5ZXurWMJJ0APBQRp/a1r9cyMjNrXE9rGZXiHIKkSTWbFwEvFJXFzGywKss5hAWSJgN7gQ3ANQXnMTMbdEpRECLiK0VnMDMb7EoxZWRmZsVzQTAzM8AFwczMEhcEMzMDXBDMzCxxQTAzM8AFwczMEhcEMzMDXBDMzCwpxZ3K1hwffghvvQUSHHUUDPX/fTOr4Y+EQaCjA1asgPZ2eP/97LnRo2HmTJg+HUaMKDafmZWDC0KLe/ddWLAANm+GY46Btrbs+Z074b77YPVqmDsXRo0qNqeZFc/nEFrcokWwbRtMnLj/SGDUKDjxRHjlFViypLB4ZlYiLggtbPNmePppOO64+t+XYPx4ePxx2L69/j5mNni4ILSw9euzP6We9xk6FPbuhZdeak4mMysvF4QW9sEHvReDLlJ2BZKZDW4uCC1s7FjI0zI7AsaMGfg8ZlZuLggt7JRTYPhw6OzseZ8dO7JiMGlSz/uY2eDggtDCRo6EWbNg40bYvfvj3+/shC1b4NJLYciQ5uczs3LxfQgt7oILYNcuWLYMDjkEjjgimyLquqroiivgrLOKzWhm5eCC0OKkbJRwzjnwxBPw4ovZczNmwNlnZ0tYmJmBC8KgMW4cXHJJ0SnMrMx8DsHMzAAXBDMzS1wQzMwMcEEwM7NEkedW1pKStBXYMMCHORrYNsDHGAjO3VxVzQ3Vze7c/XdCRLR1f7LSBaEZJK2KiDOKztEo526uquaG6mZ37oPPU0ZmZga4IJiZWeKC0LdfFx2gn5y7uaqaG6qb3bkPMp9DMDMzwCMEMzNLXBBykDRf0jOS1khql9RDl+JykbRQ0gsp+x8kVaINjqSvSXpO0l5Jpbwao5akmZLWS3pZ0s1F58lL0iJJb0haW3SWvCRNkPSopHXpZ2RO0ZnykjRC0r8lPZ2y/6joTN15yigHSUdExPb0+Hrg5Ii4puBYfZL0BeCvEbFb0k8AIuKmgmP1SdKngb3A3cCNEbGq4Eg9kjQEeBE4H9gEPAlcHhHPFxosB0mfBzqA30bEqUXnyUPSscCxEfGUpMOB1cCXK/L3LeCwiOiQNAx4ApgTESsLjvYRjxBy6CoGyWFAJapoRLRHRFdrnJXA8UXmySsi1kXE+qJz5HQm8HJE/DciOoHfAxcXnCmXiHgMeKvoHI2IiM0R8VR6/B6wDhhfbKp8ItORNoelr1J9lrgg5CTpTkkbgdnArUXn6YcrgT8XHaIFjQc21mxvoiIfUFUnaSIwFfhXsUnykzRE0hrgDWB5RJQquwtCIukRSWvrfF0MEBHzImICsBi4rti0+/SVO+0zD9hNlr0U8uSuCNV5rlS/9bUiSaOB+4HvdhvBl1pE7ImIKWSj9TMllWqqzg1ykoiYkXPX3wEPAbcNYJzc+sot6QrgS8B5UaITRg38fZfdJmBCzfbxwOsFZRkU0vz7/cDiiFhadJ7+iIh3JP0NmAmU5qS+Rwg5SJpUs3kR8EJRWRohaSZwE3BRROwsOk+LehKYJOlESYcClwHLCs7UstKJ2XuAdRHx06LzNEJSW9eVfpJGAjMo2WeJrzLKQdL9wGSyK182ANdExGvFpuqbpJeB4cCb6amVFbk6ahbwC6ANeAdYExEXFJuqZ5IuBH4GDAEWRcSdBUfKRdJ9wDSy1Te3ALdFxD2FhuqDpHOBx4Fnyf49AvwgIv5UXKp8JJ0G3Ev2c3IIsCQi7ig21f5cEMzMDPCUkZmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmAtQdJESbvSwmFdz32sT4GkkamvRaekow/wmCMl/T0tgY2k69M6/Q2tGSVpjKRrDyRLjmN8rPeBpEMlPSbJS9gY4IJgreWVtHBYV5+Cu4AvAicDl0s6OSJ2pX0OxnpDVwJLI2JP2r4WuDAiZjf4PmPSaxuiTN5/w78hWzfnI2m57hXA1xs9trUmFwSrjNQp6/z0+MeSft7L7s3oUzAb+GPK8yvgJGCZpBskfSN1x1oj6e6aUcQDklanjllXp/dZAHwy7bswjXZqf5O/UdLt6fHENAr5JfAUMKGnY9XqpffBA+m/w8wFwSrlNmCepNlk6+Df0Mu+A9qnIC1kd1JEvAqQ1oh6HZgOPEz2W/c5aTSyh30fuldGxOnAGcD1ksYCN5NGNxHx/RyHn0zW5WwqMKqXY+WxFvhsA/tbC/PcoVVGRDyWVrucC0zrmqqRNJ9sBcxaA92n4GiyhffqOQ84HXgyi8tIsoYokBWBWenxBGAS8L8Gj72hpu1ib8fqU0TsSedTDk8dyGwQc0GwypD0GeBYYFvXh5ekY6j/c9xwnwJJ3wG+nTYvBGbVbkdE7et3ASN6eivg3oi4pdv7TyNb8vhzEbEzrYdf7z12s//ovfs+O/o6VoOGA+8fwOutRXjKyCpBWXP1xWTnAXZI6loOeyqwps5LGu5TEBF3pWmbKRHxevftbvu+DQyRVO8DfQXwVUnjUvajJJ0AHAm8nYrBp4Cz0v7vAYfXvH4LME7SWEnDyRoc9aSnY+WSpqy2RsSHeV9jrcsFwUpP0ihgKfC9iFgHzAduT9+eQp2CEBG7yVqd/oWsEfuSiHjuIEdrB86tc+zngR8C7ZKeAZaTjWweBoam5+YDK9P+bwL/SC1EF6YP5zvIegU/SC9NVHo51n5S74N/ApMlbZJ0VfrWdKD0vQSsOdwPwSpN0j1k0zqfAB6MiFw9aiW9CpwREdsO4NhTgbkR8c3+vkfRJC0FbomI9UVnseJ5hGCVFhFXRcResqtrjqy9Ma2erhvTgGHs67jV32P/B3i03mWeVZCm0h5wMbAuHiGYmRngEYKZmSUuCGZmBrggmJlZ4oJgZmaAC4KZmSUuCGZmBrggmJlZ4oJgZmYA/B84qOy0PfvCMwAAAABJRU5ErkJggg==\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
- },
- "output_type": "display_data"
+ }
}
],
- "source": [
- "plot_dataset('Scatterplot of the training data', train_x, train_labels)"
- ]
+ "metadata": {
+ "scrolled": false,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "source": [
+ "print(train_x[:5])\r\n",
+ "print(train_labels[:5])"
+ ],
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"[[ 1.3382818 -0.98613256]\n",
" [ 0.5128146 0.43299454]\n",
@@ -205,18 +208,10 @@
]
}
],
- "source": [
- "print(train_x[:5])\n",
- "print(train_labels[:5])"
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"## Подход\n",
"\n",
@@ -227,15 +222,15 @@
" * Проверяем качество модели на тестовой выборке\n",
"\n",
"Результат: $f_{\\theta}$, которая делает предсказания на новых данных: $\\hat{Y} = f_{\\theta}(X_{new})$"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Функции потерь\n",
"\n",
@@ -247,70 +242,70 @@
"Абсолютная ошибка: $\\mathcal{L}_{abs}(\\theta) = \\sum_{i=1}^n |y_i - f_{\\theta}(x_i)|$\n",
"\n",
"Среднеквадратичная ошибка: $\\mathcal{L}_{sq}(\\theta) = \\sum_{i=1}^n (y_i - f_{\\theta}(x_i))^2$\n"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 7,
+ "source": [
+ "# helper function for plotting various loss functions\r\n",
+ "def plot_loss_functions(suptitle, functions, ylabels, xlabel):\r\n",
+ " fig, ax = plt.subplots(1,len(functions), figsize=(9, 3))\r\n",
+ " plt.subplots_adjust(bottom=0.2, wspace=0.4)\r\n",
+ " fig.suptitle(suptitle)\r\n",
+ " for i, fun in enumerate(functions):\r\n",
+ " ax[i].set_xlabel(xlabel)\r\n",
+ " if len(ylabels) > i:\r\n",
+ " ax[i].set_ylabel(ylabels[i])\r\n",
+ " ax[i].plot(x, fun)\r\n",
+ " plt.show()"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "# helper function for plotting various loss functions\n",
- "def plot_loss_functions(suptitle, functions, ylabels, xlabel):\n",
- " fig, ax = plt.subplots(1,len(functions), figsize=(9, 3))\n",
- " plt.subplots_adjust(bottom=0.2, wspace=0.4)\n",
- " fig.suptitle(suptitle)\n",
- " for i, fun in enumerate(functions):\n",
- " ax[i].set_xlabel(xlabel)\n",
- " if len(ylabels) > i:\n",
- " ax[i].set_ylabel(ylabels[i])\n",
- " ax[i].plot(x, fun)\n",
- " plt.show()"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "x = np.linspace(-2, 2, 101)\r\n",
+ "plot_loss_functions(\r\n",
+ " suptitle = 'Common loss functions for regression',\r\n",
+ " functions = [np.abs(x), np.power(x, 2)],\r\n",
+ " ylabels = ['$\\mathcal{L}_{abs}}$ (absolute loss)',\r\n",
+ " '$\\mathcal{L}_{sq}$ (squared loss)'],\r\n",
+ " xlabel = '$y - f(x_i)$')"
+ ],
"outputs": [
{
+ "output_type": "display_data",
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAADZCAYAAADsdQBUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3hUZdrH8e+dSknoPYGE3iEUKVbEhhUQQbD31dUVlLWvvXfBsrvWtQIiiNhFBUGlSAmhQyiB0EJPKOn3+8cM+2ZjIJlkZs6U+3Nd52Imc8pvJuHMc57zFFFVjDHGGGMCXYTTAYwxxhhjKsIKLcYYY4wJClZoMcYYY0xQsEKLMcYYY4KCFVqMMcYYExSs0GKMMcaYoGCFFmOCiIioiLTx07Eai8hsEckRkRf9ccwSxz4oIq38eDwRkfdEZJ+ILPDXcQONvz93YzxlhRYTMkTkMhFZ6D7xbheRb0XkZKdzBbGbgN1ALVUd66uDiMgsEbmh5M9UNU5VN/jqmGU4GTgLSFTVPn48bkBx4HM3xiNWaDEhQUTuBF4BngIaAy2AN4DBTuYKcknASg2PESiTgE2qesjTDUUkyhvrePuYxoQkVbXFlqBegNrAQWD4cdaJxVWo2eZeXgFi3a8NADKBu4EsYDswBDgPWAvsBe4vsa9HgMnAR0AOsAxoB9zn3n4LcHaJ9ZsB0937SQduLLWvT4EP3PtaAfQ+zvtQoE2J9/0BsAvIAP4BRLhfawP8AhzAVVsyyf1zAV525zwApAFdyjjOf4ACIN/92Z7p/tkTJdYZAGSWeL4J+Lt7nweASUC1Eq8PBlKBbGA9MAh4EigCct3Hec3D93kN8CvwArAP2AicW+KY1wAb3J/tRuDyMt7r9e7jF7kzPOr++Y3u39de9++vWanfw63AOmBjGftMdq9zPbAZmO3+eT/gd2A/sBQYUGKblsBsd9YfgdeBj6qwvzLf+7H+Nrz5udtii68WxwPYYktVF/eXXyEQdZx1HgPmAY2Ahu4T/ePu1wa4t38IiHZ/We0CPgHigc7uL7VW7vUfcT8/B4hyn9g3Ag+U2H5jiWP/gqvWpxqQ4t73GaX2dR4QCTwNzDvO+yj5pfIB8IU7YzKuAtb17tcmuPNEuI97svvn5wCLgDq4CjAdgabHONZ/+N9CSunnA/hzoWUBrkJaPWAVcLP7tT7uL8mz3JkSgA7u12YBN1TyfV6Dq3B1o/vzuwVXoVSAmrgKSO3d6zYFOh/jvV4D/Fri+UBcX+g9cRV4X8VdUCiRb4b7fVYvY3/J7nU+cOeo7n7Pe9y/6wj3Z7EHaOjeZi6uQkAMrttV2fy50FKh/R3vvR/rb8Nbn7vT5wNbQntxPIAttlR1AS4HdpSzznrgvBLPz8F1OwBcX75HgEj383j3ybtvifUXAUPcjx8BZpR47UJcV+ilt68DNMd1BR9fYv2ngf+U2NePJV7rBBw5zvtQXFfKkUAe0KnEa38BZrkffwC8iauNRsntB7q/fPrhvmo+zrH+g+eFlitKPH8O+Jf78b+Bl49xnFkco9BSgfd5DZBe4rUa7m2b4Pri3g8Mo4yCRanjXcP/FlreAZ4r8TzO/SWdXCLfwOPsL9m9TqsSP7sH+LDUet8DV+O6nVkI1Cjx2kf8udBS0f0d870f62/DW5+7r/6f22KLqlqbFhMS9gANyrnP3wxXFfdRGe6f/XcfqlrkfnzE/e/OEq8fwfXFxTFe213G9nHuY+xV1ZxSx04o8XxHiceHgWoVaLPQANcVeen3dHS/d+OqbVggIitE5DoAVf0ZeA3XrYedIvKmiNQq51ieKP1ejn5mzXEVHD1V3vv8n2Oq6mH3wzh1tU+5FLgZ2C4iX4tIhwoe93/+XlT1IK6/s5LH3VKB/ZRcJwkYLiL7jy64alSa8v9/J4ePsa1H+yvnvZf5t1FKpT/3MvZljNdYocWEgrm4brEMOc4623Cd5I9q4f6Zr20D6olIfKljb63ifnfjuvIv/Z62AqjqDlW9UVWb4bpCfuNoV2lVHa+qvXDd9moH3FXBYx7CdUV9VBMP8m4BWh/jNT3Odsd9n+VR1e9V9SxcBYPVwFsV2Y5Sfy8iUhOoX+q4x8td1jpbcNWM1Cmx1FTVZ3C1o6onIiU/3+ZV2N8x3/vx/jZKqNLnboyvWKHFBD1VPYCrPcrrIjJERGqISLSInCsiz7lXmwD8Q0QaikgD9/of+SHbFlztZ54WkWoi0g1XY8qPq7jfIlwNeJ8UkXgRSQLuxP2eRGS4iCS6V9+H68uuSEROEJG+IhKNqxBytAFqRaQC54lIPRFpAozxIPI7wLUicoaIRIhIQokr/51AmWODlPc+j8c9zsxF7gJHHq5beBV9r5+486aISCyuXmnzVXVTBbcvy0fAhSJyjohEuv8eBohIoqpmAAuBR0QkRkT647rtWKn9He+9H+tvo+SOq/K5G+NLVmgxIUFVX8J1Uv0HroauW4DbgGnuVZ7A9aWQhqu3z2L3z/xhFK42CduAz4GHVXWGF/b7N1wFjw24enJ8Arzrfu0EYL6IHMTV82W0qm4EauG64t6Hq7p/D67GnxXxIa4eKpuAH3D1DqoQVV0AXIur59IBXI2Tj17FjwMucQ/sNt7D93k8EcBYXJ/7XuA04K8VzPsT8CAwBVctSGtgZEW2Pc4+t+DqQXU///83ehf/fx6+HOiP63fyBK7PN6+S+zveez/W30Zplf3cjfEZUa1IDacxxhh/EpFJwGpVfdjpLMYECqtpMcaYAOC+ddfafftsEK5alGnlbWdMOLFRFY0xJjA0AabiavCbCdyiqkucjWRMYLHbQ8YYY4wJCnZ7yBhjjDFBwQotxhhjjAkKVmgxxhhjTFCwQosxxhhjgoIVWowxxhgTFKzQYowxxpigYIUWY4wxxgSFoB9crkGDBpqcnOx0DGOC1qJFi3arakOnc4QKOycZUzXHOycFfaElOTmZhQsXOh3DmKAlIhlOZwgldk4ypmqOd06y20PGGGOMCQp+K7SISHMRmSkiq0RkhYiMLmMdEZHxIpIuImki0tNf+Ywx5lhEJFJElojIV05nMSac+bOmpRAYq6odgX7ArSLSqdQ65wJt3ctNwD/9mM8YY45lNLDK6RDGhDu/FVpUdbuqLnY/zsF1Akgotdpg4AN1mQfUEZGmVTlucbGydMv+quzCmKC0ZPM+pyOEBBFJBM4H3vbG/gqKilm+9YA3dmVM0Ch0/91XdZJmR9q0iEgy0AOYX+qlBGBLieeZ/Llg45FXf07nkn/9bicJE1a+W76DoW/8zvSl25yOEgpeAe4Gir2xs+e/X8PF//yd7NwCb+zOmKDw2/o9XPDqr8xas6tK+/F7oUVE4oApwBhVzS79chmb/KlYJiI3ichCEVm4a9fxP4Cr+idRr2YMoycu4Uh+UaVzGxMsdhzI5d6paXRNqM2gzk2cjhPUROQCIEtVF5WzXoXPSed2aUJ+YTHfLdvhzajGBLRpS7ZSq1oU/VvXr9J+/FpoEZFoXAWWj1V1ahmrZALNSzxPBP50qaiqb6pqb1Xt3bDh8YeXqFszhheHp7B+1yGe+HplFdIbE/iKi5Wxk1PJKyhm3MgUYqKsg2AVnQRcJCKbgInAQBH5qPRKnpyTUprXIbl+DT5fstUngY0JNIfyCvlu+Q7O79aUatGRVdqXP3sPCfAOsEpVXzrGatOBq9y9iPoBB1R1e1WPfXLbBtx4Sks+nr+ZH1furOrujAlYb/+6gd/S9/DwhZ1o1TDO6ThBT1XvU9VEVU0GRgI/q+oVVdmniDCkRwLzNu5h2/4jXslpTCCbsXInRwqKGJJSpdYegH9rWk4CrsR1pZLqXs4TkZtF5Gb3Ot8AG4B04C3gr946+N/PaU+nprW4e0oaWdm53tqtMQFj+dYDPP/9Gs7p3JhLT2he/gbGMUNSElDF2hyZsPD5kq0k1KnOCcn1qrwvf/Ye+lVVRVW7qWqKe/lGVf+lqv9yr6OqequqtlbVrqrqtWElY6MiGT8qhcP5hYydvJTi4qq1YDYmkBzJL2L0xCXUqxnDMxd3w1WxabxJVWep6gXe2Fdyg5r0aFGHzxfbLSIT2nbl5PFr+m4GpzQjIqLq56WwuuHdplE8/zi/E3PW7ea93zc5HccYr3ni65Vs2H2Il0akULdmjNNxTAVc3COBNTtzWLHNejaa0PVF6laKipWhPap+awjCrNACcHnfFpzZsRHPfruaVdtLd14yJvjMWLmTj+dv5qZTWnFSmwZOxzEVdEG3ZkRHClOttsWEsKmLt9ItsTZtG8d7ZX9hV2gREZ4d1o3aNaK5fcIScgusG7QJXlnZudwzJY3OzWpx59ntnI5jPFC3ZgxndGjMF6lbKSzyyhAwxgSUVduzWbk9m2E9E722z7ArtADUj4vlheHdWZd1kKe/sZG5TXBydW9eyuH8QsaNTCE2qmpdCY3/Xdwzgd0H85mzbrfTUYzxus+XbCUqQriwezOv7TMsCy0Ap7VryHUnteT9uRn8vNq6QZvg897vm5izbjf/OL8TbRp5p+rV+NeA9o2oWyOazxZnOh3FGK8qKlamLdnK6R0aUc+L7ezCttACcPeg9nRoEs/dn6WxKyfP6TjGVNiq7dk8++1qzurUmMv7tnA6jqmkmKgILurejBkrd3LgiA3rb0LHnHW7yMrJY1hP7zTAPSqsCy3VoiMZP6oHObmF3P3Z0ipP5GSMP+QWFHH7hCXUrhHNMxd3te7NQe6SXs3JLyzmqzQbs8WEjs8WZVK3RjQDOzT26n7DutAC0K5xPPef15GZa3bxvnWDNkHg6W9WsS7rIC+N6E79uFin45gq6pJQi/aN4/lskd0iMqHhwOECfli5k8EpCV6fSiTsCy3gmlTx9PYNeerb1azdmeN0HGOO6efVO3l/bgbXn9ySU9oef44bExxEhEt6JbJk837Ssw46HceYKvsybRv5hcVc0st7vYaOskILrpPGc5d0p1a1KOsGbQLWrpw87pqc5mqHNai903GMFw3u0YzICGGKNcg1IeCzRZl0aBJP52a1vL5vK7S4NYyP5flLurN6Rw7Pfrfa6TjG/A9V5e+Tl3Iwr5Dxo3pY9+YQ0yi+GgPaNWTq4kwbs8UEtXU7c0jdsp9hPRN90t7OCi0lnN6hEdecmMx7v21i1posp+MY81/v/76JX9bu4v7zOtLOSyNLmsAyvHdzdmbn2ZgtJqhNXpRJVIQw1Mu9ho6yQksp957bgXaN4/j75DT2HLRu0MZ5a3bk8NS3qxnYoRFX9U9yOo7xkYEdGlG/ZgyfLtzidBRjKqWgqJipizM5o2MjGviok4AVWkqpFh3JuJE9yD5SwD1T0qwbtHFUboFr9uZa1aJ57hKbvTmUxURFMLRHAj+u2mkXTCYozVydxe6D+Yzo3dxnx7BCSxk6Nq3FPed24MdVWXw8f7PTcUwYe+67NazekcPzw7v57MrFBI4RJzSnoEj5fIlNomiCz6cLt9AoPpbT2vmuZ6PHhRYRqSkiId8K8NoTkzm1XUOe+Hol6VnWDdr43y9rd/Hubxu55sRkTm/fyOk4xg/aNY4npXkdPl24xWp5TVDZmZ3LzDW7uLhnIlGRvqsPKXfPIhIhIpeJyNcikgWsBraLyAoReV5E2vosnYMiIoQXhnejRkwUt09IJa/QukEb/9lzMI+xny6lfeN47j23g9NxjB+NPKE5a3ceZMmW/U5HMabCPluUSVGxcukJvrs1BBWraZkJtAbuA5qoanNVbQScAswDnhGRK3yY0TGN4qvx3LBurNyezQvfr3E6jgkTqsrdn6WRnVvAuFEpVIsO+YpNU8IF3ZtRIyaSSQusQa4JDsXFyqcLt9CvVT1aNqjp02NVpNBypqo+rqppqvrfAQRUda+qTlHVYcAk30V01pmdGnNFvxa8NWcjv1pXROMHH83fzE+rs7jv3A50aOL9wZlMYIuLjeLCbs34Mm0bB/MKnY5jTLnmbdxDxp7DjDzB95O3lltoUdUCABEZLiLx7scPishUEelZcp1Q9cB5nWjTKI47P01l36F8p+OYEJaelcMTX63ktHYNuebEZKfjhIRgbId3aZ/mHM4v4sulNomiCXwTF2yhVrUoBnVp4vNjedJa5kFVzRGRk4GzgfeBf/omVmCpHhPJuJEp7Ducb92gjc/kFRZx+4RU4mKjeH64dW+urFBoh9ejeR3aN45nwgLrvWgC295D+Xy3fAdDeyT45Va2J4WWoy1Rzwf+qapfADHejxSYOjerzd3ndOCHlTuZ+Ifdazbe98L3a1i5PZvnLulGo/hqTscJZkHfDk9EGNWnOWmZB1i+9YDTcYw5pqmLM8kvKuayvv4Z+NKTQstWEfk3MAL4RkRiPdw+6F1/cktObtOAx75cyfpdNhur8Z5f1+3mrTkbubJfEmd0bOx0nGAXEu3whvZMJDYqgk+stsUEKFXlkwWb6ZVUl/ZN/DO9iCeFjhHA98AgVd0P1APu8kmqABURIbw4ojux0RGMnriE/EKb2MxU3d5D+dz5aSptGsXxwPkdnY4T9EKlHV7t6tFc0K0ZXyzZag1yTUCat2EvG3YdYlQf3zfAParChRZVPayqU1V1nfv5dlX9wXfRAlPjWtV4dlg3lm/N5qUZa52OY4KcqnLPlDT2Hy5g/Mge1r3Zu4K+Hd5lfVtwKL+I6anWINcEngkLNlOrWhQXdGvqt2NWuNBS6qrlHyWvWiq4/bsikiUiy4/x+gAROSAiqe7loYru29/O6dyEUX1a8O/Z6/l9vXWDNpU3YcEWZqzcyd2D2tOpmXVv9rIqt8MTkWoiskBElrob8j7q9ZTH0bNFHTo0iefj+RnWAcAElN0H8/h2+XYu7pno14utyvYeOgfPr1r+AwwqZ505qpriXh7zYN9+9+AFHWlZvyZ3TlrK/sPWDdp4bv2ugzz+1UpOaduA605q6XScUOSNdnh5wEBV7Q6kAINEpJ+Xcx6TiHB5vyRWbMsm1UbINQHk04VbKChSrujnv1tD4MfeQ6o6G9jrwfECWo2YKMaP6sGeQ3ncN3WZXQUZj+QXFjN64hKqRUfwwvDuRERY92YfKN0Ory4etsNTl6Ot7qPdi1//sw/tkUDNmEg+mmcNck1gKCpWPpm/mX6t6tGmkX8a4B4VaL2H+rurYb8Vkc7HWklEbhKRhSKycNeuXV6OUHFdEmoz9uz2fLt8B5MXZjqWwwSfF2esYfnWbJ4Z1o3Gtax7s4+cD8xQ1XUi8g/gDcDj+7kiEikiqUCWe3/zy1jHZ+ekuNgohvRI4Ku0bVarawLC7HW7yNx3hCv6+aebc0mB1HtoMZDkroZ9FZh2rBVV9U1V7a2qvRs29N0U2BVx0ymt6N+qPo98uYKNuw85msUEh9/Td/Pm7A2M6tOcczr7fgTJMFbVW9oAqGqRqqYAiUAfEelSxjo+PSdd3jeJvMJiPltkF0fGeR/NzaBBXCxnd/L/+cuj3kPAeuAcEbkNaOTN3kOqmn20GlZVvwGiRaSBt/bvKxERwkuXdic6MoIxE5dQUGTdoM2x7T+cz52fLqVlg5o8eEEnp+OEOq8OiOm+WJtF+W3zvK5Ts1r0TqrLh/MyKC62W9HGOVv2HubnNVmM6tOcmCj/D9XmSe+h0cDHQCP38pGI/M1bQUSkibjHLReRPu5se7y1f19qWrs6z1zclaWZB3jlR+sGbcqmqtw3dRl7DuUxfmQPasREOR0p1FX5lraINBSROu7H1YEzcU0L4HdX9k8iY89hflnn3C1xYz6al0GECJf19W8D3KM8+Q98PdBXVR9S1YeAfsCNFd1YRCYAc4H2IpIpIteLyM0icrN7lUuA5SKyFBgPjNQgat16btemjOidyBuz1jN/Q1CUtYyfTV6YybfLdzD27PZ0SajtdJxw4I1b2k2BmSKSBvyBq03LV96NWTHndmlKg7hYPpyb4cThjSG3oIhJC7dwdqfGNK1d3ZEMnlzqCf9f3Yr7cYW7PKjqqHJefw14zYM8AefhCzuzYONe7piUyrejT6V2jWinI5kAsXH3IR75cgUntq7PTae0cjpOWFDVwyJy9Jb2ObiGVPDolraqpgE9fBLQQzFREVzWpzmvzkwnY88hkurXdDqSCTPTl25j/+ECruzv/wa4R3lS0/IeMF9EHhGRR3BNPPaOT1IFqZqxUYwb2YOsnDzun2bdoI1LQZGre3N0ZAQvjrDuzf7i61vaTrisbxKRIlbbYvxOVXn/9020bRRH/1b1HcvhSUPcl4DrcI21sg+4VlVf8VWwYNW9eR3uOKsdX6dtZ+rirU7HMQHg5RlrScs8wDMXd3WsSjVMVemWdiBqUrsag7o0YdLCLRyy+YiMHy3M2MeKbdlcc1Iy7uanjvCoUZqqLlLV8ao6TlWX+CpUsLv5tNb0aVmPh75YTsYe6wYdzuZt2MM/f1nPiN6JnNvVf/NzGKCKt7QD1bUnJZOTW8jnS+yiyPjPf37fRK1qUQztkeBojnILLSKSIyLZZSw5IpLtj5DBJjJCePnSFCIihDGTUq0bdJg6cLiAOyelkly/Jg9feMyxEo3vhOQt7Z4t6tI1oTb/+X2T3YI2frH9wBG+W76DkX1aON7rsdxCi6rGq2qtMpZ4VbUZ3o4hoU51nhralSWb9/PqT+ucjmP8TFW5f9oysnLyeOXSFGrGWvdmfwvVW9oiwjUnJpOedZA562zCVuN7H851Tdh5pQMj4Jbm/5FhwsiF3ZsxrGcir81M549NITPtkqmAKYu38nXadu44qx3dm9dxOk7YCtVb2hd0d3V/fve3jU5HMSHuSH4RnyzYzNmdmtC8Xg2n41ihxdceHdyZxLo1GDMxlezcAqfjGD/I2HOIh79YTp+W9bj5tNZOxwk74XBLOzYqkiv7JTFrzS7Ssw6Wv4ExlTR1SSb7Dxdw3cmBMRO9FVp8LC42ildGprAjO5eHpi13Oo7xMVf35tT/tmuKtO7Nfhcut7Qv79eCmKgI3rPaFuMjxcXKu79upGtCbU5Irut0HMCzYfxFRK4QkYfcz1u4h9s35ejZoi6jz2jLtNRtTLMW/yHt1Z/WkbplP09d3JWEOta92fhOg7hYhqQ0Y8riTPYdstmfjff9sm4X63cd4rqTne3mXJInNS1vAP2BoyPb5gCvez1RiPrrgNb0TqrLg9OWs2XvYafjGB/4Y9NeXpuZzsU9E7igWzOn45gwcP3JrcgtKObj+TbYnPG+t+dsoHGtWM7vGjjnM08KLX1V9VYgF0BV91GFGVPDTVRkBC9fmgLAmEmpFFo36JCSnVvAmImpJNatwWODuzgdx4SJ9k3iOa1dQ/7zewa5BUXlb2BMBa3YdoDf0vdw7UktHZnN+Vg8SVIgIpGAgmv2U8C+eT3QvF4NnhjahUUZ+3h95nqn4xgvenDacnZk5/LKyBTirHuz8aMbT2nF7oN5TE/d5nQUE0LenrORmjGRjOrjzGzOx+LJ2XU88DnQSESexDUr84M+SRXCBqckMHN1FuN/XsfJbRvQKykwGjeZypu2ZCtfpG7jzrPa0bOF/T6dJiJ3Hu919/gtIeOkNvXp2LQWb83ZwCW9Em1uK1Nl2w8c4cul27iqfzK1qwfWxL+ezD30MXA38DSwHRiiqp/6Klgoe2xIF5rWrsaYSUvIsW7QQW3L3sP8Y9pyeifV5a8DrHtzgIh3L72BW4AE93Iz0MnBXD4hItx0akvWZR1k5posp+OYEPDOnI0orikjAo0nvYeeVdXVqvq6qr6mqqtE5FlfhgtVtapF88qlKWzdd4SHp69wOo6ppMKiYsZMSkWAly9NISoycO77hjNVfVRVHwUaAD1VdayqjgV6AYnOpvONC7o1I6FOdf79ywano5ggd+BwARMWbObCbk0DYjC50jw5y55Vxs/O9VaQcNM7uR63DWzL1MVbmb7U7kUHo9dnrmdRxj6eGNolIP9zG1oAJfsC5wPJzkTxrejICK4/uSULNu1lUcY+p+OYIPbR/AwO5Rdx06mBWXNckQkTbxGRZUAHEUlzL8tEZCOwzPcRQ9ftA9vQo0UdHvh8GZn7rBt0MFmUsY/xP69jaI8EBqc4O+upOaYPgQXuCRMfBuYDHzicyWdG9mlOnRrR/OsXa+RvKie3oIj3ftvIae0a0qlZYI7DWJGalk+AC4EvgAvcjy8Aeqnq5T7MFvKiIiMYd2kPVOHOSUspKrYZW4NBTm4BYyYtoWntajw62GZvDlSq+iRwLa7JEvfjmjDxKWdT+U6NmCiu6p/MjJU7Wbczx+k4JghNXpTJ7oP5/OW0Vk5HOaaKzPJ8QFU3AauBa4Cr3cttR0fHNZXXon4NHr2oMws27eWfs9KdjmMq4OEvVrB13xHGjUyhVrXAallv/p+4hvDsBNRW1XHAnlAfxfvaE5OpHh3JP2dZbYvxTGFRMf/+ZT09WtShf6v6Tsc5Jk/atBwEDrmXIlztWZJ9kCnsuEZQbcorP7qGgDeB64vUrUxdspW/DWxLr6R6Tscxxxd2o3jXrRnDZX1b8MXSbTbytvHIl2nbyNx3hFsHtAmYIfvL4kmX5xdLLE8CA3B1IzRVJCI8ObQrjWtVY/TEJRzKK3Q6kilD5j5X9+aeLerwt4FtnI5jyheWo3jfeEorIgT+PdtqW0zFFBcrb8xcT4cm8Qzs0MjpOMdVlT6aNYDAvfEVZGpXj+alEd3Zsvcwj1g36IBTVKzcOWkpqvDKpT2se3NwCMtRvJvUrsYlvRL5dGEmO7NznY5jgsAPK3ewLusgtwxoHfCDE3oyTsuyEr2HVgBrgHG+ixZ++raqz18HtGHyoky+TtvudBxTwj9npbNg014eG9yZFvWte3OQKD2K969AyDbELemW09pQVKy8OdvGbTHHp6q8+nM6LRvUDIqJXj0Zxv+CEo8LgZ2qavcxvGz0mW2Zs24X901No0eLOjSrU93pSGEvdct+Xv5xHRd0a8rQHnZHNBi4G+HOBhYBZwCCaxTvVY4G85MW9WswOKUZH8/P4JYBrWkQF+t0JBOgfl6dxYpt2Tx/STciA7yWBTxr05JRYtnqaYFFRN4VkSwRWX6M10VExotIurs2p6cn+w8V0ZERjBvZg8Ji5c5PU60btMMO5RUyeuISmtSqxpNDu/oks9oAACAASURBVAZ0AzXz/1RVgWmlR/H2dD8i0lxEZorIKhFZISKjfRDXJ249vQ15hcW8NcdqW0zZVJXxP6eTWLc6Q4Lkgqwig8vliEh2iSWn5L8eHOs/wKDjvH4u0Na93AT804N9h5TkBjV55KLOzNuw16p3HfbI9BVs2XuYl0Z0D7iJw0y55onICVXcRyEwVlU7Av2AW0UkKOYvat0wjou6N+PDuRnsOZjndBwTgGat3cXSLfu59fQ2RAdJO72KjNMSr6q1SizxJf+t6IFUdTaw9zirDAY+UJd5QB0RaVrR/Yea4b0SOa9rE178YQ1pmdYN2glfp21n8qJM/jqgDX0DeNwCc0ynA3NFZH2JkbzTPNmBqm5X1cXuxznAKoKo1+TfBrblSEERb83Z6HQUE2BUlVd+XEdi3eoM6xk8U3J5VLQSke4icpt76eblLAnAlhLPMznGyUFEbhKRhSKycNeuXV6OERhEhKeGdqVhfCxjJqZyON+aD/nTtv1HuG9qGt0TazP6zLZOxzGVcy7QGhjI/4/kfWFldyYiyUAPXNMBlH4tIM9JbRq5als+mLvJalvM/5i1xlXLctvpbYiJCo5aFvCs99Bo4GOgkXv5WET+5sUsZTUWKLNBh6q+qaq9VbV3w4YNvRghsNSpEcOLI7qzcc8hHv9qpdNxwkZRsXLHpFQKi5VxI3sETbWp+V+qmgFkA42BpBKLx0QkDpgCjFHVP90WD+Rz0t8GtiW3oIh/261m46aqvPzjWlctS6/gqWUBz2parsc1WNNDqvoQrvu7N3oxSybQvMTzRCDspz8+sXUD/nJqayYs2MJ3y60btD+8OXsD8zfu5ZGLOpPcoKbTcUwlicgNuHoQfQ886v73kUrsJxpXgeVjVZ3qzYz+0KZRHENSEvhg7iaybNwWA8xYuZO0zAPcPrBt0F2UeZJWcA3ff1QRZdeOVNZ04Cp3L6J+wAFVtW9p4M6z2tE1oTb3Tl3GjgN20vGltMz9vPjDGs7r2oThQXYFYv5kNHACkKGqp+O6tePRvRt31+l3gFWq+pL3I/rH6DPbUlCkvGFzEoW94mLlpRlradmgJhf3DJrmWf/lSaHlPWC+e5r3R4F5uP4zV4iITADmAu1FJFNErheRm0XkZvcq3wAbgHTgLeCvHmQLaTFREbwyMoW8gmLGTk6l2LpB+8Th/EJGT0ylYXwsT1n35lCQq6q5ACISq6qrgfYe7uMk4EpgoIikupfzvB3U15Lq12RE70Q+mb+ZrfuPOB3HOOjrZdtZvSOHMWe2DcqRvSs8uJyqviQis4CT3T+6RlVTPdh+VDmvK3BrRfcXblo3jOOhCztx39RlvPPrRm481WZQ8LbHv1rJpj2H+PiGvtSpEfJT1ISDTBGpA0wDZojIPjy85ayqv+LdGmXH3DawLVMWbWXcj2t57pLuTscxDigsKublGWtp3zieC4Ng9NuyeNIQdziwTlXHA7WBh0Skh8+SmT8ZeUJzzuncmOe+X83yrQecjhNSvlu+gwkLtvCXU1tzYusGTscxXqCqQ1V1v6o+AjyIq2Z4sLOpnJNQpzpX9Evis0WZpGcddDqOccDkRZls2H2Iv5/TPuDnGDoWT+qGHlTVHBE5GTgLeB/4l29imbKICM9c3I16NWMYPXEJR/KLyt/IlGvHgVzunZpG14Ta3HlWO6fjGC8RkYeOLsBpQApwn8OxHHXr6a2pHh3JC9+vcTqK8bMj+UW88uNaeraow5kdA3sm5+PxpNBy9BvyfOBfqvoFYTDNe6CpWzOGF4ensH7XIZ78xrpBV1VxsTJ2cip5BcW8MjIlqMYrMOU6VGIpwjVuS7KTgZxWPy6WG09txXcrdrBk8z6n4xg/en/uJnZm53HPoA5B3V7PkzP0VhH5NzAC+EZEYj3c3njJyW0bcOMpLflo3mZmrNzpdJyg9vavG/gtfQ8PXdiJ1g3jnI5jvEhVXyyxPAkMIIhGs/WVG05pRYO4GJ75djWupoQm1O07lM/rM9M5vX3DoB/d25NCxwhc4xwMUtX9QD3gLp+kMuX6+znt6dS0Fnd/ttTGXqik5VsP8Pz3azinc2NGntC8/A1MsKsBhH0L9rjYKEaf0Zb5G/fy8+osp+MYP3h9ZjqH8gq599yOTkepMk9meT6sqlNVdZ37+XZV/cF30czxxEZFMn5UCkcKihg7eal1g/bQkfwiRk9cQr2aMTxzcbegri41ZTs615B7WQGsAcY5nSsQjOzTglYNavLMt6spLCp2Oo7xoS17D/PB3AyG92pO+ybxTsepMk96D1UTkTtFZKqITBGRO0Skmi/DmeNr0yief5zfiTnrdvPe75ucjhNUnvh6Jet3HeKlESnUrWlNs0LU0bmGLgTOBpqp6mvORgoM0ZER3D2oA+uyDjJp4ZbyNzBB69nvVhMRAXeESCcDT24PfQB0Bl4FXgM6Ah/6IpSpuMv7tuDMjo149tvVrNz2pylRTBlmrNzJx/M3c9OprTipjXVvDlWqmlFi2aqqNutoCed0bkyflvV46Ye1ZOcWOB3H+MCijL18lbadm05tTZPaoVHHUOHB5YD2qlpyRKKZIrLU24GMZ0SEZ4d1Y9C4OYyeuIQv/3Yy1aIjnY4VsLKyc7lnShqdmtZi7NmhceVhyiYidx7v9WAelt8bRIQHz+/Eha/9yhsz13PvuR2cjmS8qLhYeeyrVTSKj+Xm00KnKZcnNS1L3HMCASAifYHfvB/JeKp+XCwvDO/OuqyDPP3NKqfjBCxX9+alHM4vZPyoFGKjrHAX4noDt+DqMZQA3Ax0AuLdS9jrmlibi3sm8O6vG9m857DTcYwXTV+6jaVb9nPXOe2pEeNJ/URgK7fQcrQxG9AX+F1ENonIJlzzCJ3q43ymgk5r15DrTmrJ+3Mz+Hm1dYMuy3u/b2LOut08cH4n2jSy76ww0ADoqapjVXUs0AtIVNVHVfVRh7MFjHsGdSAqUnjiaxv3KVQcyivk6W9X0TWhNsN6htbErxWpaTnamG0Q0BLXyJKnuR+f77toxlN3D2pPhybx3DU5jV05eU7HCSgrt2Xz7LerObNjY67o28LpOMY/WgD5JZ7nE+aDy5Wlca1q3Hp6G35YuZM56zyaBNsEqDdmpbMzO49HLuoctMP1H0u5hZaSjdmAbKAxkFRiMQGiWnQk40f14GBeIXd9ttQGjnLLLXB1b65dI5pnh9nszWHkQ2CBe2b6R4AFuDoUmFKuP7klLerV4NEvV1JgXaCDWsaeQ7w1eyNDeyTQK6mu03G8zpMuzzcAs3ENMPeo+99HfBPLVFa7xvHcf15HZq3ZxQdzM5yOExCe/mYV67IO8sLw7tSPi3U6jvET9yi41wL7gL3A1ar6lLOpAlO16EgeuqAT6VkHee+3jU7HMZWkqjwyfQXRkRKyDas9aYg7GjgByFDV04EegNUlBqCr+idxevuGPPnNKtbsyHE6jqN+Xr2T9+dmcN1JLTmtXUOn4xg/KjEz/ThsZvpyndmpMWd0aMQrP65j+4EjTscxlTBj5U5mrtnFHWe1o3Gt0OjiXJonhZZcVc0FEJFYVV0NtPdNLFMVIsLzw7tTq1oUoycuIbcgPGeD3pWTx12T0+jQJJ67B9mfahiymek99PCFnSkqVp742nohBpsj+UU8+uVK2jWO4+oTk52O4zOeFFoyRaQOMA2YISJfANt8E8tUVYO4WJ4f3p3VO3J4PgynoVdV7vpsKQfzChk/qoeNXROebGZ6D7WoX4NbT2/D12nbmbXG5iUKJuN+WsfW/Ud4fHAXoiNDdy5jT+YeGqqq+1X1EeBB4B1giK+Cmao7vX0jrjkxmXd+3cgva8PrTt77v29i1ppdPHB+R9o1tu7NYcpmpq+Ev5zWilYNa/LgF8s5kh+etbTBZvWObN6es4ERvRODfhbn8lRknJY/dbVQ1V9Udbqq5h9rHRMY7j23A+0bx/P3yUvZczA8ukGv2ZHDU9+uZmCHRlzZzzq4hTGbmb4SYqMieWpoV7bsPcK4n9Y5HceUo7hYuX/qMmpVj+a+EJjFuTwVueqYKSJ/E5H/GdxCRGJEZKCIvA9c7Zt4pqqqRUcyblQKB44UcM+UtJDvBp1bUMTtE5ZQq1oUz11iszeHo6MXUcebmd4utI6vX6v6DO+VyFtzNrBi2wGn45jj+HBeBos37+cf53cMi8lfK1JoGYTr3vAEEdkmIitFZAOwDhgFvKyq//FhRlNFHZrU4t5BHfhxVRYfzd/sdByfeva71azZmcPzw7vTwLo3hyu70PKCB87vSN0aMdwzJY1CG7slIGXuO8yz363mlLYNGNojwek4flGRweVyVfUNVT0J12ByZ+AaGjtJVW9U1VSfpzRVds2JyZzariFPfr2S9KzQ7AY9a00W7/22iWtOTOb09o2cjmOcYxdaXlCnRgyPDe7M8q3ZvDXHxm4JNKrKA58vB+CpoeEzaKZHjdJUtcBdxbrfV4GMb0RECC8M70aNmChun5BKXmFoNbDbfTCPv09Oo33j+JAdVMlUjF1oec+5XZpwTufGvPzj2pC92AlWkxdl8svaXdx1Tnua16vhdBy/sZb0YaRRfDWeG9aNlduzeSGEukGrKvd8lkZ2bgHjRqVY92bzX3ahVTUiwhNDulIzJpKxk+02UaDYtv8Ij3+5kj4t63F1/2Sn4/iVFVrCzJmdGnNFvxa8NWcjv67b7XQcr/ho/mZ+Wp3FvYM60KFJLafjGBNSGsbH8tjgLizdsp9/z97gdJywp6rcMyWNIlVeuKR7yE2IWJ5KFVpEZKD736YiUuHLWhEZJCJrRCRdRO4t4/VrRGSXiKS6lxsqk88c3wPndaJNozjGTk5l36H88jcIYOlZOTzx1UpOa9eQa09KdjqOCUEi8q6IZInIcqezOOWCbk05v2tTXvlxLcu3Wm8iJ30wN4M563Zz33kdaVE/fG4LHVXZmpZBIpKIa0jslyuygbtw8zpwLtAJGCUincpYdZKqpriXtyuZzxxH9ZhIxo1MYd+hAu6dGrzdoPMKi/jbhFRqxkbx/HDr3myOTUSqMuLWf3A17g1brttEXahbI4Yxk1LDdmoQp6Vn5fDUN6sY0L4hV/RtUf4GIaiyhZY6wD3A3UBFRyzrA6Sr6gb3oHQTgcGVPL6pos7NanP3oPZ8v2InE//Y4nScSnn+uzWs2p7N85d0o1F8aE4OZrzmSxH5QkTeFJG/i8iAim6oqrNxzRId1urWjOGF4d1JzzrI09/Y3ET+lldYxJhJqdSIieS5YeF7kVbZQstjwCxVXcP/z+9RngSg5LdjpvtnpQ0TkTQR+UxEmpe1IxG5SUQWisjCXbvCa3h6b7rupJac3KYBj325kvW7DjodxyNz1u3i7V83cmW/JM7o2NjpOCbw/aKqg4G/A82BJt7cebick05t15DrTmrJ+3Mz+HHlTqfjhJXnv1vD8q3ZPDusG41CdAbniqhsoeV54Cz3IE3fVHCbsoqFpe9LfAkkq2o34Edcs7L+eSPVN1W1t6r2btiwYUUzm1IiIoQXR3SnWnQEYyamkl8YHD0D9h7KZ+ynS2nTKI77zwv9YauNV8SJSE/gCFBPVSd6c+fhdE6659z2dGpai7s+W8qOA7lOxwkLM9dk8favG7mqfxJnd/ZqeTvoVLbQskpVb1bVq4GRFdwmE9cVzlGJlJolWlX3qOrR201vAb0qmc9UUONa1XhmWDeWbT3ASzPWOh2nXEdbzu8/XMC4kSlUj7HuzaZCpgP9ga+A7xzOEtRioyJ59bIe5BYUc/uEJdYN2se2HzjC2E+X0r5xvF2kUYlCi4i8hauW5Xb3feGKjn/wB9BWRFqKSAyuws70UvtuWuLpRYDdOPWDczo3YVSfFvx79np+Xx/Y3aAnLNjCjJU7uXtQezo3q+10HBM8zgK+AHKBvg5nCXqtG8bx1MVdWLBpb1Bc7ASrgiJXwTCvoIjXL+9pY1BRiUKLqt6I6wTwB9AdqFCrfFUtBG7DNevqKuBTVV0hIo+JyEXu1W4XkRUishS4HbjG03ymch68oCMtG9TkzklL2X84MLtBp2cd5LGvVnBymwZcd1JLp+OY4FKy84BH9zREZAIwF2gvIpkicr0P8gWdoT0SGdWnBW/MWs9Pq6x9iy88//0a/ti0j6cu7kqbRnFOxwkIlb09dA/wENANSKvoRqr6jaq2U9XWqvqk+2cPqep09+P7VLWzqnZX1dNVdXUl8xkP1YiJYvzIHuw5lMf9ny8LuG7Q+YXFjJm0hOrRkbw4IvwGVDJV9hjwhbvzgEf3M1R1lKo2VdVoVU1U1Xd8EzH4PHxhJzo3q8WYSals3H3I6Tgh5au0bbw5ewNX9kticEp4TIZYEZUttNQF5gFPAO29F8c4qUtCbcae3Z5vlu1g8qJMp+P8jxdnuFrOPzOsG43DuOW8qRxVzQS2uh//aWBLUznVoiP51xW9iIoQ/vLhQg7lFTodKSSs2ZHD3Z+l0SupLg9eUNZwZuGrsoWWvUAkkIWNXxBSbjqlFSe2rs8j01ewKUCunH5P382bszdwWd8WnBPmLedNxYjIXSLyu4i0KfHjTBG52bFQIap5vRqMH9WD9KyD3DEpleLiwKqlDTZ7D+Vzwwd/UDM2ijcu70lMlM22U1KlPg1VfQzXaLjjARvTOYQc7QYdHRnB6IlLKHC4Z8D+w/nc+elSWjaoyT/Ot5bzpsLaAHdQoqOAquYAFzqWKISd0rYhD5zfiR9W7uTlH61hbmXlFxZzy0eL2Jmdx5tX9rJa5TJUpQhXC7hBVSs0jL8JHk1rV+eZi7uyNPMA435c51gOVeW+qcvYcyiPcZf2oEZMlGNZTND5CVeHgYKjPxCRBsBJjiUKcdedlMzIE5rz6s/pTF0cWLeXg4Gq8o9py5i/cS/PDetGjxZ1nY4UkCpUaDlGVetW4C++iWWcdm7Xpozoncjrs9KZv2GPIxkmL8zk2+U7GHt2e7omWvdmU3Gq+imuWuB0EflDRJ4ETgTWOJssdIkIjw3uwomt63PPlLSAHz4h0Lw+M51PF2Zy+8A2DOlhDW+PpaI1LVbVGoYevrAzSfVqcOenSzlwpKD8Dbxo4+5DPPLlCk5sXZ+bTmnl12Ob0KCqrwItgIdxtcH7O5DjaKgQFxMVwT+v6EVy/Zr85cNFrN6R7XSkoDBlUSYv/LCWi3skcMdZ7ZyOE9AqWmixqtYwVDM2inEje7AzO5cH/NgNuqComNETlxAdGWHdm02VqOoR91AL96rqqcDjTmcKdbWrR/PetSdQMyaKq95ZwJa9h52OFNB+Xr2Tu6ekcVKb+jw9rGvYToRYURUqtFhVa/jq3rwOd5zVjq/StjN18Va/HPOVH9eSlnmAZy7uStPa1f1yTBMeVPUXpzOEg8S6NXj/uj7kFhRx5TvzycqxOYrKsmDjXv768WI6Na3Fv6/sTWyUjXhbngo3xLWq1vB182mt6duyHg9PX8HmPb69apq3YQ9vzFrPiN6JnNu1afkbGGMCUvsm8bx37Qlk5eRxxdvz2XsoMEfadsqSzfu49r0FJNSpznvXnkBcrHU0qAiPeg9ZVWt4iowQXro0hQiB0ZN8N0HagcMF3DkpleT6NXn4ws4+OYYxxn96JdXj7at7k7HnMJdbweW/lm7Zz9XvLqB+XCwf39CPBnGxTkcKGlUatcaqWsNHQp3qPHVxV5Zs3s/4n9O9vn9V5f5py8jKyeOVS1OoaVcdxoSEE1s34M2rerN+10Eue2seew7mOR3JUYs37+OKt+dTq3o0n9zYlya1bSwWT9hQe6bCLujWjGE9E3nt53Us3OTdgZCnLN7K12nbueOsdnRvXser+zbGOOu0dg159+oT2Lj7EJe+OY/tB444HckRc9fv4ap3FlAvLoZJf+lPYt0aTkcKOlZoMR555KJOJNatwZhJqWTneqcbdMaeQzz8xXL6tKzHzae19so+jTGB5eS2DXj/uj7sOJDLJf+cy4ZdB52O5Fc/rNjB1e8toGntaky6qT8JdayTQWVYocV4JL5aNK+MTGH7gVwemra8yvtzdW9OJTJCePnSFCKte7MxIatfq/pMuLEfRwqKGPbP31mUsc/pSH7x4bwMbv5oER2b1uLTv/S3W0JVYIUW47GeLeoy+oy2TEvdxrQlVesG/epP60jdsp8nh3a1Kw9jwkDXxNpMueVEaleP5rK35vFV2janI/lMUbHy1DereHDacga0b8QnN/Slbs0Yp2MFNSu0mEr564DW9E6qy4PTlld68Kg/Nu3ltZnpDOuZyIXdm3k5oTEmULVsUJMpt5xIl4Ta3PbJEl78YU3IzQ594EgB17//B2/O3sCV/ZJ488pe1sHAC6zQYiolKjKCly9NAeCOSaked4M+cKSAMRNTSaxbg0cHW/dmY8JN/bhYPrmxLyN6J/Lqz+lc//4f7AuRLtErt2Uz5PXf+HXdbp4Y0oXHh3QhKtK+br3BPkVTac3r1eDxIV1YmLGPN2at92jbh75Yzo7sXMaNTLFBlYwJU7FRkTw7rBuPD+nCb+l7OH/8HK/3TPQnVeWT+ZsZ+sZvHM4v5JMb+3FFvySnY4UUK7SYKhnSI4EhKc0Y99O6Cjeq+3xJJl+kbmPMGW1t+nVjwpyIcGW/JKbcciJRkRGM+Pdcnv9+NfmFvhnE0ld2H8zjxg8Wcv/ny+jTsh5f334KfVrWczpWyLFCi6myx4Z0oUmtaoyZtISccrpBb9l7mAenraB3Ul3+enobPyU0xgS6rom1+Wb0KVzSK5HXZ67nwld/JXXLfqdjlUtVmbo4kzNf+oXZ63bz0AWdeP/aPjbKrY9YocVUWS13N+it+47wyPSVx1yvsKiYMZNSEbDuzcaYP4mLjeK5S7rzztW9OXCkgKFv/Mb9ny8L2LYua3fmcPnb87nz06W0alCTb24/metObmkz0/uQNSYwXnFCcj1uO70N439OZ0D7hmX2Bnp95noWZexj3MgUmtezkSCNMWU7o2Nj+rSsx0sz1vLB3Ay+TtvOXwe05uoTk6kW7fxMyDuzcxn/0zom/rGFuNgoHh/cmcv6JtmFmB9YocV4ze1ntGVO+m7u/3wZPZPq/s+4K4sy9jH+53UMSWnG4JQEB1MaY4JBfLVoHr6wMyNPaMHT367i6W9X8+5vG7nxlFZc1rcFNWL8//W1bf8R3pqzgQkLNlNYpFzetwVjzmxHPRt7xW9ENbj7xvfu3VsXLlzodAzjlrHnEOeNm0PnhNpMuLEfkRFCTm4B542fgyp8M/oUalWLdjqmKUFEFqlqb6dzhAo7J/nGvA17eOXHtczbsJfa1aMZ0TuRy/smkdygpk+Pq6oszNjHB3Mz+HbZdhQYnNKM0We0Jam+b48dro53TvJrUVVEBgHjgEjgbVV9ptTrscAHQC9gD3Cpqm7yZ0ZTNUn1a/LY4C6MnbyUf/2ynltPb8PD01ewdd8RJt/c3wosJuiUd94y/tGvVX0m3tSfRRl7effXTbz72ybemrORXkl1uah7M87q1JhmXhpVW1VZsS2bH1bu5IvUrWTsOUx8tSiu6p/MtScl2+1tB/mt0CIikcDrwFlAJvCHiExX1ZItN68H9qlqGxEZCTwLXOqvjMY7Lu6ZwMw1Wbw8Yy05uYVMXbyV0We0pVeSdf8zwaWC5y3jR72S6tErqR47s3OZungrny/J5OHpK3h4+graNY6jf6v69GhRly4JtUiqX5PoCgzqdiS/iLU7c1i29QCLMvYxd/0edmTnIgL9W9XnttPbcH63po7ckjL/y5+/gT5AuqpuABCRicBgoOR//sHAI+7HnwGviYhosN/DCjMiwpNDurI4Yx//+mU9PVvU4W8DrXuzCUoVOW8ZBzSuVY1bBrTmlgGtWb/rIDNW7uS39N18ujCT9+dmABAZISTUqU7D+Fjq1oihekwkURFCflExh/MK2Xsonx3ZuezMzvvvfhvExdC3VX1ObduAMzo2tq7LAcafhZYEYEuJ55lA32Oto6qFInIAqA/sLrmSiNwE3ATQokULX+U1VVC7RjSvXtaDF75fy7PDutkQ1iZYVeS8Zeckh7VuGEfr0+K4+bTWFBYVk77rIMu3ZrNp9yEy9h5md04emfsOk1dYTGFxMdGREVSPjqR+XCxtG8eTXL8GrRvG0SWhNol1qyNivYAClT8LLWX9FZSuQanIOqjqm8Cb4Gr0VvVoxhd6JdVjwk39nI5hTFXYOSnIREVG0KFJLTo0qeV0FOMD/rz8zQSal3ieCJSek/y/64hIFFAbCN6JKIwxwa4i5y1jjJ/4s9DyB9BWRFqKSAwwEpheap3pwNXux5cAP1t7FmOMgypy3jLG+Infbg+526jcBnyPq+vgu6q6QkQeAxaq6nTgHeBDEUnHVcMy0l/5jDGmtGOdtxyOZUzY8mv/LVX9Bvim1M8eKvE4Fxjuz0zGGHM8ZZ23jDHOsC4dxhhjjAkKQT+Mv4jsAjLKWa0BpbpNB5lgzh/M2SE88iepakN/hAkHdk4KeMGcHYI7f0WzH/OcFPSFlooQkYXBPLdKMOcP5uxg+Y1vBPvvJZjzB3N2CO783shut4eMMcYYExSs0GKMMcaYoBAuhZY3nQ5QRcGcP5izg+U3vhHsv5dgzh/M2SG481c5e1i0aTHGGGNM8AuXmhZjjDHGBDkrtBhjjDEmKIRNoUVEnheR1SKSJiKfi0gdpzN5QkSGi8gKESkWkaDo7iYig0RkjYiki8i9TufxhIi8KyJZIrLc6SyeEpHmIjJTRFa5/2ZGO53J/Jmdk/zPzknO8OY5KWwKLcAMoIuqdgPWAvc5nMdTy4GLgdlOB6kIEYkEXgfOBToBo0Skk7OpPPIfYJDTISqpEBirqh2BfsCtQfbZhws7J/mRnZMc5bVzUtgUWlT1B1UtdD+dh2uK+aChqqtUdY3TOTzQB0hX1Q2qmg9MBAY7nKnCVHU2rkk7g46qblfVxe7HOcAqIMHZVKY0Oyf5TpGZpQAABAtJREFUnZ2THOLNc1LYFFpKuQ741ukQIS4B2FLieSb2xel3IpIM9ADmO5vElMPOSb5n56QAUNVzkl9nefY1EfkRaFLGSw+o6hfudR7AVVX1sT+zVURF8gcRKeNn1r/ej0QkDpgCjFHVbKfzhCM7JwUUOyc5zBvnpJAqtKjqmcd7XUSuBi4AztAAHKCmvPxBJhNoXuJ5IrDNoSxhR0SicZ0cPlbVqU7nCVd2Tgoodk5ykLfOSWFze0hEBgH3ABep6mGn84SBP4C2ItJSRGKAkcB0hzOFBRER4B1glaq+5HQeUzY7J/mdnZMc4s1zUtgUWoDXgHhghoikisi/nA7kCREZKiKZQH/gaxH53ulMx+NuYHgb8D2uRlefquoKZ1NVnIhMAOYC7UUkU0SudzqTB04CrgQGuv/WU0XkPKdDmT+xc5If2TnJUV47J9kw/sYYY4wJCuFU02KMMcaYIGaFFmOMMcYEBSu0GGOMMSYoWKHFGGOMMUHBCi3GGGOMCQpWaDHGGGNMULBCizHGGGOCghVajF+IyIsislJE3hKRX9zTxFdkuxgRmS0iITXlhDHGWXZOCk5WaDE+JyKtgJNUtROQCkxV1aKKbOueQv4n4FIfRjTGhBE7JwUvK7SYMolIVxH5rcTzniLycyX20x74BUgSkSXADcAXJV6fKSJnuR8/ISLjy9jNNOByT49tjAkddk4yYMP4m2MQkQhcM6AmqGqRiMwExqrq4krs6wlgE/ABsFlVm5R47VTgMeAt4DJck8cVldo+Etihqg0r+36MMcHNzkkGrKbFHIOqFgMrgM4iMgzXf+z/OTmIyI8isryMZXCp3XUFlgINgP2ljjMbEOBOYOTRk4OIPF5inSIgX/6vvftniSOKwjD+HIT4B1LZaJ3SBGKfJiBol09gFWws06ULJE36tBYp0ppUFja2Qoo0guYDWClYiBKxeFPsCIuSweg2d/f5dbN39nAHlpczdy6zVU9HfJmSGmEmCcCNROqzz+DfOTeBtduDSVbuWWeJQdhMAzPDA1X1AlgETpOcd58tcPe3OQ38+Z/JSxo7ZtKEc6VFffaBT8D3JMcPKdDdiVwnuUxyBkxV1Uw3tgh8A94AF1W12n1tmcHmuJsa88BJkuuHX4qkMWAmTTibFvU5Aq6Az4+o8Rw4GDreBV5V1RywzeCZ9CHwEfjQnfOSoYAAXgM7j5iDpPFgJk04N+Lqn6rqC/AzydcR1lwG3iVZ7zlnC9jonmFTVdvA+yS/RzUPSe0xk+RKi+6oqmdVdQTMjjIcAJL8Avb6XuSU5O1QODwBfhgO0uQyk3TDlRZJktQEV1okSVITbFokSVITbFokSVITbFokSVITbFokSVITbFokSVITbFokSVIT/gLBqJo0q8gj7wAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 648x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
- },
- "output_type": "display_data"
+ }
}
],
- "source": [
- "x = np.linspace(-2, 2, 101)\n",
- "plot_loss_functions(\n",
- " suptitle = 'Common loss functions for regression',\n",
- " functions = [np.abs(x), np.power(x, 2)],\n",
- " ylabels = ['$\\mathcal{L}_{abs}}$ (absolute loss)',\n",
- " '$\\mathcal{L}_{sq}$ (squared loss)'],\n",
- " xlabel = '$y - f(x_i)$')"
- ]
- },
- {
- "cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Функции ошибки для классификации\n",
"\n",
@@ -327,65 +322,65 @@
"**логистическая функция ошибки**\n",
"\n",
"$\\mathcal{L}_{log}(\\theta) = \\sum_{i=1}^n \\frac{1}{\\log(2)} \\log(1 + e^{-y_i f_{\\theta}(x)})$"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
- "outputs": [],
"source": [
- "# define and vectorize zero-one loss\n",
- "def zero_one(d):\n",
- " if d < 0:\n",
- " return 1\n",
- " return 0\n",
- "\n",
- "def logistic_loss(fx):\n",
- " # assumes y == 1\n",
- " y = 1\n",
- " return 1 / np.log(2) * np.log(1 + np.exp(-y * fx))\n",
- "\n",
+ "# define and vectorize zero-one loss\r\n",
+ "def zero_one(d):\r\n",
+ " if d < 0:\r\n",
+ " return 1\r\n",
+ " return 0\r\n",
+ "\r\n",
+ "def logistic_loss(fx):\r\n",
+ " # assumes y == 1\r\n",
+ " y = 1\r\n",
+ " return 1 / np.log(2) * np.log(1 + np.exp(-y * fx))\r\n",
+ "\r\n",
"zero_one_v = np.vectorize(zero_one)"
- ]
+ ],
+ "outputs": [],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "plot_loss_functions(suptitle = 'Common loss functions for classification',\r\n",
+ " functions = [zero_one_v(x), logistic_loss(x)],\r\n",
+ " ylabels = ['$\\mathcal{L}_{0-1}}$ (0-1 loss)',\r\n",
+ " '$\\mathcal{L}_{log}$ (logistic loss)'],\r\n",
+ " xlabel = '$y f(x_i)$')\r\n"
+ ],
"outputs": [
{
+ "output_type": "display_data",
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 648x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
- },
- "output_type": "display_data"
+ }
}
],
- "source": [
- "plot_loss_functions(suptitle = 'Common loss functions for classification',\n",
- " functions = [zero_one_v(x), logistic_loss(x)],\n",
- " ylabels = ['$\\mathcal{L}_{0-1}}$ (0-1 loss)',\n",
- " '$\\mathcal{L}_{log}$ (logistic loss)'],\n",
- " xlabel = '$y f(x_i)$')\n"
- ]
- },
- {
- "cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Строим нейросеть\n",
"Рассмотрим решение нашей задачи при помощи простейшей однослойной нейросети такого вида:\n",
@@ -396,14 +391,31 @@
"f_\\theta(x) = W\\times x + b\n",
"$$\n",
"где параметры $$\\theta = <W,b>$$"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "source": [
+ "class Linear:\r\n",
+ " def __init__(self,nin,nout):\r\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\r\n",
+ " self.b = np.zeros((1,nout))\r\n",
+ " \r\n",
+ " def forward(self, x):\r\n",
+ " return np.dot(x, self.W.T) + self.b\r\n",
+ " \r\n",
+ "net = Linear(2,2)\r\n",
+ "net.forward(train_x[0:5])"
+ ],
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 1.77202116, -0.25384488],\n",
@@ -413,31 +425,14 @@
" [-1.23519653, 0.3394973 ]])"
]
},
- "execution_count": 11,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 11
}
],
- "source": [
- "class Linear:\n",
- " def __init__(self,nin,nout):\n",
- " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
- " self.b = np.zeros((1,nout))\n",
- " \n",
- " def forward(self, x):\n",
- " return np.dot(x, self.W.T) + self.b\n",
- " \n",
- "net = Linear(2,2)\n",
- "net.forward(train_x[0:5])"
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"## Переходим к вероятностям\n",
"Расширяем нейросетевую модель с помощью функции **softmax**: $\\sigma(\\mathbf{z}_c) = \\frac{e^{z_c}}{\\sum_{j \\in J} e^{z_j}}$ для $c \\in 1 .. |C|$\n",
@@ -445,14 +440,30 @@
"<img src=\"https://raw.githubusercontent.com/shwars/NeuroWorkshop/master/images/NeuroArch-softmax.PNG\" width=\"50%\">\n",
"\n",
"Можем рассматривать $\\sigma(\\mathbf{z})$ как распределение вероятности на классах $C$: $q = \\sigma(\\mathbf{z}_c) = \\hat{p}(c | x)$\n"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "source": [
+ "class Softmax:\r\n",
+ " def forward(self,z):\r\n",
+ " zmax = z.max(axis=1,keepdims=True)\r\n",
+ " expz = np.exp(z-zmax)\r\n",
+ " Z = expz.sum(axis=1,keepdims=True)\r\n",
+ " return expz / Z\r\n",
+ "\r\n",
+ "softmax = Softmax()\r\n",
+ "softmax.forward(net.forward(train_x[0:10]))"
+ ],
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.88348621, 0.11651379],\n",
@@ -467,43 +478,27 @@
" [0.72746882, 0.27253118]])"
]
},
- "execution_count": 12,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 12
}
],
- "source": [
- "class Softmax:\n",
- " def forward(self,z):\n",
- " zmax = z.max(axis=1,keepdims=True)\n",
- " expz = np.exp(z-zmax)\n",
- " Z = expz.sum(axis=1,keepdims=True)\n",
- " return expz / Z\n",
- "\n",
- "softmax = Softmax()\n",
- "softmax.forward(net.forward(train_x[0:10]))"
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"## Ещё один взгляд на архитектуру сети\n",
"\n",
"\n"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Cross-Entropy Loss\n",
"\n",
@@ -519,97 +514,97 @@
" = & ~\\color{red}{-\\sum_{c \\in C} p(c) \\log p(c)} + \\color{blue}{\\sum_{c \\in C} p(c) \\log \\frac{p(c)}{q(c)}} \\\\\n",
" = & ~-\\sum_{c \\in C} p(c) \\log q(c)\n",
"\\end{align}$\n"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 13,
+ "source": [
+ "def plot_cross_ent():\r\n",
+ " p = np.linspace(0.01, 0.99, 101) # estimated probability p(y|x)\r\n",
+ " cross_ent_v = np.vectorize(cross_ent)\r\n",
+ " f3, ax = plt.subplots(1,1, figsize=(8, 3))\r\n",
+ " l1, = plt.plot(p, cross_ent_v(p, 1), 'r--')\r\n",
+ " l2, = plt.plot(p, cross_ent_v(p, 0), 'r-')\r\n",
+ " plt.legend([l1, l2], ['$y = 1$', '$y = 0$'], loc = 'upper center', ncol = 2)\r\n",
+ " plt.xlabel('$\\hat{p}(y|x)$', size=18)\r\n",
+ " plt.ylabel('$\\mathcal{L}_{CE}$', size=18)\r\n",
+ " plt.show()"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "def plot_cross_ent():\n",
- " p = np.linspace(0.01, 0.99, 101) # estimated probability p(y|x)\n",
- " cross_ent_v = np.vectorize(cross_ent)\n",
- " f3, ax = plt.subplots(1,1, figsize=(8, 3))\n",
- " l1, = plt.plot(p, cross_ent_v(p, 1), 'r--')\n",
- " l2, = plt.plot(p, cross_ent_v(p, 0), 'r-')\n",
- " plt.legend([l1, l2], ['$y = 1$', '$y = 0$'], loc = 'upper center', ncol = 2)\n",
- " plt.xlabel('$\\hat{p}(y|x)$', size=18)\n",
- " plt.ylabel('$\\mathcal{L}_{CE}$', size=18)\n",
- " plt.show()"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "scrolled": true,
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "def cross_ent(prediction, ground_truth):\r\n",
+ " t = 1 if ground_truth > 0.5 else 0\r\n",
+ " return -t * np.log(prediction) - (1 - t) * np.log(1 - prediction)\r\n",
+ "plot_cross_ent()"
+ ],
"outputs": [
{
+ "output_type": "display_data",
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
- },
- "output_type": "display_data"
+ }
}
],
- "source": [
- "def cross_ent(prediction, ground_truth):\n",
- " t = 1 if ground_truth > 0.5 else 0\n",
- " return -t * np.log(prediction) - (1 - t) * np.log(1 - prediction)\n",
- "plot_cross_ent()"
- ]
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "source": [
+ "class CrossEntropyLoss:\r\n",
+ " def forward(self,p,y):\r\n",
+ " self.p = p\r\n",
+ " self.y = y\r\n",
+ " p_of_y = p[np.arange(len(y)), y]\r\n",
+ " log_prob = np.log(p_of_y)\r\n",
+ " return -log_prob.mean()\r\n",
+ "\r\n",
+ "cross_ent_loss = CrossEntropyLoss()\r\n",
+ "p = softmax.forward(net.forward(train_x[0:10]))\r\n",
+ "cross_ent_loss.forward(p,train_labels[0:10])"
+ ],
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"1.429664938969559"
]
},
- "execution_count": 15,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 15
}
],
- "source": [
- "class CrossEntropyLoss:\n",
- " def forward(self,p,y):\n",
- " self.p = p\n",
- " self.y = y\n",
- " p_of_y = p[np.arange(len(y)), y]\n",
- " log_prob = np.log(p_of_y)\n",
- " return -log_prob.mean()\n",
- "\n",
- "cross_ent_loss = CrossEntropyLoss()\n",
- "p = softmax.forward(net.forward(train_x[0:10]))\n",
- "cross_ent_loss.forward(p,train_labels[0:10])"
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"## Задача минимизации\n",
"Описав нейронную сеть как модель $f_\\theta$ и функцию ошибки $\\mathcal{L}(Y,f_\\theta(X))$, можем рассмотреть $\\mathcal{L}$ как функцию $\\theta$ на всем множестве обучающей выборки $\\mathcal{L}(\\theta) = \\mathcal{L}(Y,f_\\theta(X))$\n",
@@ -620,15 +615,15 @@
"$$\n",
"\n",
"Минимизацию можно осуществлять разными методами, например, стохастическим градиентным спуском (stochastic gradient descent, SGD)"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Реализация нейронных сетей\n",
"\n",
@@ -638,48 +633,48 @@
" - Tensorflow\n",
" - Chainer\n",
" - [Microsoft Cognitive Toolkit](http://cntk.ai)"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Вычислительный граф\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/shwars/NeuroWorkshop/master/images/ComputeGraph.PNG\" width=\"600px\"/>\n"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
+ "source": [
+ "z = net.forward(train_x[0:10])\r\n",
+ "p = softmax.forward(z)\r\n",
+ "loss = cross_ent_loss.forward(p,train_labels[0:10])\r\n",
+ "print(loss)"
+ ],
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"1.429664938969559\n"
]
}
],
- "source": [
- "z = net.forward(train_x[0:10])\n",
- "p = softmax.forward(z)\n",
- "loss = cross_ent_loss.forward(p,train_labels[0:10])\n",
- "print(loss)"
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
"source": [
"## Обучение сети\n",
"\n",
@@ -691,15 +686,15 @@
" b^{i+1}&=b^i-\\eta\\frac{\\partial\\L}{\\partial b}\n",
" \\end{align}\n",
" $$"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Обратное распространение ошибки\n",
"\n",
@@ -711,15 +706,15 @@
"\\zz{\\L}{b} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{b}\n",
"\\end{align}\n",
"$$"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Обратное распространение ошибки\n",
"\n",
@@ -729,11 +724,15 @@
" * Вычисляем ошибку на каждом узле начиная с конца\n",
" * Обратное распространение ошибки\n",
" * Все вычисления фреймворк берёт на себя"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"### Реализация обратного распространения\n",
"\n",
@@ -754,184 +753,215 @@
"\\end{align}$$\n",
"\n",
"**ВАЖНО:** Вычисления производятся не для одного элемента обучающей выборки, а сразу для целой последовательности, называемой **minibatch**. Необходимые значения градиентов $\\Delta W$ и $\\Delta b$ вычисляются по всей выборке, а вектора имеют соответствующую размерность: $x\\in\\mathbb{R}^{\\mathrm{minibatch}\\, \\times\\, \\mathrm{nclass}}$"
- ]
+ ],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {},
- "outputs": [],
"source": [
- "class Linear:\n",
- " def __init__(self,nin,nout):\n",
- " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
- " self.b = np.zeros((1,nout))\n",
- " self.dW = np.zeros_like(self.W)\n",
- " self.db = np.zeros_like(self.b)\n",
- " \n",
- " def forward(self, x):\n",
- " self.x=x\n",
- " return np.dot(x, self.W.T) + self.b\n",
- " \n",
- " def backward(self, dz):\n",
- " dx = np.dot(dz, self.W)\n",
- " dW = np.dot(dz.T, self.x)\n",
- " db = dz.sum(axis=0)\n",
- " self.dW = dW\n",
- " self.db = db\n",
- " return dx\n",
- " \n",
- " def update(self,lr):\n",
- " self.W -= lr*self.dW\n",
+ "class Linear:\r\n",
+ " def __init__(self,nin,nout):\r\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\r\n",
+ " self.b = np.zeros((1,nout))\r\n",
+ " self.dW = np.zeros_like(self.W)\r\n",
+ " self.db = np.zeros_like(self.b)\r\n",
+ " \r\n",
+ " def forward(self, x):\r\n",
+ " self.x=x\r\n",
+ " return np.dot(x, self.W.T) + self.b\r\n",
+ " \r\n",
+ " def backward(self, dz):\r\n",
+ " dx = np.dot(dz, self.W)\r\n",
+ " dW = np.dot(dz.T, self.x)\r\n",
+ " db = dz.sum(axis=0)\r\n",
+ " self.dW = dW\r\n",
+ " self.db = db\r\n",
+ " return dx\r\n",
+ " \r\n",
+ " def update(self,lr):\r\n",
+ " self.W -= lr*self.dW\r\n",
" self.b -= lr*self.db"
- ]
+ ],
+ "outputs": [],
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"Аналогичный образом функции обратного распространения `backward` добавляются к другим составляющим вычислительного графа:"
- ]
+ ],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {},
- "outputs": [],
"source": [
- "class Softmax:\n",
- " def forward(self,z):\n",
- " self.z = z\n",
- " zmax = z.max(axis=1,keepdims=True)\n",
- " expz = np.exp(z-zmax)\n",
- " Z = expz.sum(axis=1,keepdims=True)\n",
- " return expz / Z\n",
- " def backward(self,dp):\n",
- " p = self.forward(self.z)\n",
- " pdp = p * dp\n",
- " return pdp - p * pdp.sum(axis=1, keepdims=True)\n",
- " \n",
- "class CrossEntropyLoss:\n",
- " def forward(self,p,y):\n",
- " self.p = p\n",
- " self.y = y\n",
- " p_of_y = p[np.arange(len(y)), y]\n",
- " log_prob = np.log(p_of_y)\n",
- " return -log_prob.mean()\n",
- " def backward(self,loss):\n",
- " dlog_softmax = np.zeros_like(self.p)\n",
- " dlog_softmax[np.arange(len(self.y)), self.y] -= 1.0/len(self.y)\n",
+ "class Softmax:\r\n",
+ " def forward(self,z):\r\n",
+ " self.z = z\r\n",
+ " zmax = z.max(axis=1,keepdims=True)\r\n",
+ " expz = np.exp(z-zmax)\r\n",
+ " Z = expz.sum(axis=1,keepdims=True)\r\n",
+ " return expz / Z\r\n",
+ " def backward(self,dp):\r\n",
+ " p = self.forward(self.z)\r\n",
+ " pdp = p * dp\r\n",
+ " return pdp - p * pdp.sum(axis=1, keepdims=True)\r\n",
+ " \r\n",
+ "class CrossEntropyLoss:\r\n",
+ " def forward(self,p,y):\r\n",
+ " self.p = p\r\n",
+ " self.y = y\r\n",
+ " p_of_y = p[np.arange(len(y)), y]\r\n",
+ " log_prob = np.log(p_of_y)\r\n",
+ " return -log_prob.mean()\r\n",
+ " def backward(self,loss):\r\n",
+ " dlog_softmax = np.zeros_like(self.p)\r\n",
+ " dlog_softmax[np.arange(len(self.y)), self.y] -= 1.0/len(self.y)\r\n",
" return dlog_softmax / self.p"
- ]
+ ],
+ "outputs": [],
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"Теперь напишем цикл обучения модели на нашем датасете. Будем рассматривать один проход по модели - т.н. **эпоху**"
- ]
+ ],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {},
+ "source": [
+ "lin = Linear(2,2)\r\n",
+ "softmax = Softmax()\r\n",
+ "cross_ent_loss = CrossEntropyLoss()\r\n",
+ "\r\n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\r\n",
+ "acc = (pred==train_labels).mean()\r\n",
+ "print(\"Initial accuracy: \",acc)\r\n",
+ "\r\n",
+ "batch_size=4\r\n",
+ "for i in range(0,len(train_x),batch_size):\r\n",
+ " xb = train_x[i:i+batch_size]\r\n",
+ " yb = train_labels[i:i+batch_size]\r\n",
+ " \r\n",
+ " # forward pass\r\n",
+ " z = lin.forward(xb)\r\n",
+ " p = softmax.forward(z)\r\n",
+ " loss = cross_ent_loss.forward(p,yb)\r\n",
+ " \r\n",
+ " # backward pass\r\n",
+ " dp = cross_ent_loss.backward(loss)\r\n",
+ " dz = softmax.backward(dp)\r\n",
+ " dx = lin.backward(dz)\r\n",
+ " lin.update(0.1)\r\n",
+ " \r\n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\r\n",
+ "acc = (pred==train_labels).mean()\r\n",
+ "print(\"Final accuracy: \",acc)\r\n",
+ " "
+ ],
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"Initial accuracy: 0.725\n",
"Final accuracy: 0.825\n"
]
}
],
- "source": [
- "lin = Linear(2,2)\n",
- "softmax = Softmax()\n",
- "cross_ent_loss = CrossEntropyLoss()\n",
- "\n",
- "pred = np.argmax(lin.forward(train_x),axis=1)\n",
- "acc = (pred==train_labels).mean()\n",
- "print(\"Initial accuracy: \",acc)\n",
- "\n",
- "batch_size=4\n",
- "for i in range(0,len(train_x),batch_size):\n",
- " xb = train_x[i:i+batch_size]\n",
- " yb = train_labels[i:i+batch_size]\n",
- " \n",
- " # forward pass\n",
- " z = lin.forward(xb)\n",
- " p = softmax.forward(z)\n",
- " loss = cross_ent_loss.forward(p,yb)\n",
- " \n",
- " # backward pass\n",
- " dp = cross_ent_loss.backward(loss)\n",
- " dz = softmax.backward(dp)\n",
- " dx = lin.backward(dz)\n",
- " lin.update(0.1)\n",
- " \n",
- "pred = np.argmax(lin.forward(train_x),axis=1)\n",
- "acc = (pred==train_labels).mean()\n",
- "print(\"Final accuracy: \",acc)\n",
- " "
- ]
+ "metadata": {}
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"Для удобства опишем класс, который позволяет объединять узлы вычислительного графа в единую сеть, и применять функции `forward` и `backward` сразу ко всей сети последовательно:"
- ]
+ ],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 20,
+ "source": [
+ "class Net:\r\n",
+ " def __init__(self):\r\n",
+ " self.layers = []\r\n",
+ " \r\n",
+ " def add(self,l):\r\n",
+ " self.layers.append(l)\r\n",
+ " \r\n",
+ " def forward(self,x):\r\n",
+ " for l in self.layers:\r\n",
+ " x = l.forward(x)\r\n",
+ " return x\r\n",
+ " \r\n",
+ " def backward(self,z):\r\n",
+ " for l in self.layers[::-1]:\r\n",
+ " z = l.backward(z)\r\n",
+ " return z\r\n",
+ " \r\n",
+ " def update(self,lr):\r\n",
+ " for l in self.layers:\r\n",
+ " if 'update' in l.__dir__():\r\n",
+ " l.update(lr)"
+ ],
+ "outputs": [],
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "class Net:\n",
- " def __init__(self):\n",
- " self.layers = []\n",
- " \n",
- " def add(self,l):\n",
- " self.layers.append(l)\n",
- " \n",
- " def forward(self,x):\n",
- " for l in self.layers:\n",
- " x = l.forward(x)\n",
- " return x\n",
- " \n",
- " def backward(self,z):\n",
- " for l in self.layers[::-1]:\n",
- " z = l.backward(z)\n",
- " return z\n",
- " \n",
- " def update(self,lr):\n",
- " for l in self.layers:\n",
- " if 'update' in l.__dir__():\n",
- " l.update(lr)"
- ]
+ }
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"Ещё раз пробуем создать и обучить нашу нейросеть:"
- ]
+ ],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {},
+ "source": [
+ "net = Net()\r\n",
+ "net.add(Linear(2,2))\r\n",
+ "net.add(Softmax())\r\n",
+ "loss = CrossEntropyLoss()\r\n",
+ "\r\n",
+ "def get_loss_acc(x,y,loss=CrossEntropyLoss()):\r\n",
+ " p = net.forward(x)\r\n",
+ " l = loss.forward(p,y)\r\n",
+ " pred = np.argmax(p,axis=1)\r\n",
+ " acc = (pred==y).mean()\r\n",
+ " return l,acc\r\n",
+ "\r\n",
+ "print(\"Initial loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\r\n",
+ "\r\n",
+ "def train_epoch(net, train_x, train_labels, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\r\n",
+ " for i in range(0,len(train_x),batch_size):\r\n",
+ " xb = train_x[i:i+batch_size]\r\n",
+ " yb = train_labels[i:i+batch_size]\r\n",
+ "\r\n",
+ " p = net.forward(xb)\r\n",
+ " l = loss.forward(p,yb)\r\n",
+ " dp = loss.backward(l)\r\n",
+ " dx = net.backward(dp)\r\n",
+ " net.update(lr)\r\n",
+ " \r\n",
+ "train_epoch(net,train_x,train_labels)\r\n",
+ " \r\n",
+ "print(\"Final loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\r\n",
+ "print(\"Test loss={}, accuracy={}: \".format(*get_loss_acc(test_x,test_labels)))"
+ ],
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"Initial loss=0.6212072429381601, accuracy=0.6875: \n",
"Final loss=0.44369925927417986, accuracy=0.8: \n",
@@ -939,939 +969,144 @@
]
}
],
- "source": [
- "net = Net()\n",
- "net.add(Linear(2,2))\n",
- "net.add(Softmax())\n",
- "loss = CrossEntropyLoss()\n",
- "\n",
- "def get_loss_acc(x,y,loss=CrossEntropyLoss()):\n",
- " p = net.forward(x)\n",
- " l = loss.forward(p,y)\n",
- " pred = np.argmax(p,axis=1)\n",
- " acc = (pred==y).mean()\n",
- " return l,acc\n",
- "\n",
- "print(\"Initial loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
- "\n",
- "def train_epoch(net, train_x, train_labels, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
- " for i in range(0,len(train_x),batch_size):\n",
- " xb = train_x[i:i+batch_size]\n",
- " yb = train_labels[i:i+batch_size]\n",
- "\n",
- " p = net.forward(xb)\n",
- " l = loss.forward(p,yb)\n",
- " dp = loss.backward(l)\n",
- " dx = net.backward(dp)\n",
- " net.update(lr)\n",
- " \n",
- "train_epoch(net,train_x,train_labels)\n",
- " \n",
- "print(\"Final loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
- "print(\"Test loss={}, accuracy={}: \".format(*get_loss_acc(test_x,test_labels)))"
- ]
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 22,
+ "source": [
+ "def train_and_plot(n_epoch, net, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\r\n",
+ " fig, ax = plt.subplots(2, 1)\r\n",
+ " ax[0].set_xlim(0, n_epoch + 1)\r\n",
+ " ax[0].set_ylim(0,1)\r\n",
+ "\r\n",
+ " train_acc = np.empty((n_epoch, 3))\r\n",
+ " train_acc[:] = np.NAN\r\n",
+ " valid_acc = np.empty((n_epoch, 3))\r\n",
+ " valid_acc[:] = np.NAN\r\n",
+ "\r\n",
+ " for epoch in range(1, n_epoch + 1):\r\n",
+ "\r\n",
+ " train_epoch(net,train_x,train_labels,loss,batch_size,lr)\r\n",
+ " tloss, taccuracy = get_loss_acc(train_x,train_labels,loss)\r\n",
+ " train_acc[epoch-1, :] = [epoch, tloss, taccuracy]\r\n",
+ " vloss, vaccuracy = get_loss_acc(test_x,test_labels,loss)\r\n",
+ " valid_acc[epoch-1, :] = [epoch, vloss, vaccuracy]\r\n",
+ " \r\n",
+ " ax[0].set_ylim(0, max(max(train_acc[:, 2]), max(valid_acc[:, 2])) * 1.1)\r\n",
+ "\r\n",
+ " plot_training_progress(train_acc[:, 0], (train_acc[:, 2],\r\n",
+ " valid_acc[:, 2]), fig, ax[0])\r\n",
+ " plot_decision_boundary(net, fig, ax[1])\r\n",
+ " fig.canvas.draw()\r\n",
+ " fig.canvas.flush_events()\r\n",
+ "\r\n",
+ " return train_acc, valid_acc"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "def train_and_plot(n_epoch, net, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
- " fig, ax = plt.subplots(2, 1)\n",
- " ax[0].set_xlim(0, n_epoch + 1)\n",
- " ax[0].set_ylim(0,1)\n",
- "\n",
- " train_acc = np.empty((n_epoch, 3))\n",
- " train_acc[:] = np.NAN\n",
- " valid_acc = np.empty((n_epoch, 3))\n",
- " valid_acc[:] = np.NAN\n",
- "\n",
- " for epoch in range(1, n_epoch + 1):\n",
- "\n",
- " train_epoch(net,train_x,train_labels,loss,batch_size,lr)\n",
- " tloss, taccuracy = get_loss_acc(train_x,train_labels,loss)\n",
- " train_acc[epoch-1, :] = [epoch, tloss, taccuracy]\n",
- " vloss, vaccuracy = get_loss_acc(test_x,test_labels,loss)\n",
- " valid_acc[epoch-1, :] = [epoch, vloss, vaccuracy]\n",
- " \n",
- " ax[0].set_ylim(0, max(max(train_acc[:, 2]), max(valid_acc[:, 2])) * 1.1)\n",
- "\n",
- " plot_training_progress(train_acc[:, 0], (train_acc[:, 2],\n",
- " valid_acc[:, 2]), fig, ax[0])\n",
- " plot_decision_boundary(net, fig, ax[1])\n",
- " fig.canvas.draw()\n",
- " fig.canvas.flush_events()\n",
- "\n",
- " return train_acc, valid_acc"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 23,
+ "source": [
+ "import matplotlib.cm as cm\r\n",
+ "\r\n",
+ "def plot_decision_boundary(net, fig, ax):\r\n",
+ " draw_colorbar = True\r\n",
+ " # remove previous plot\r\n",
+ " while ax.collections:\r\n",
+ " ax.collections.pop()\r\n",
+ " draw_colorbar = False\r\n",
+ "\r\n",
+ " # generate countour grid\r\n",
+ " x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 1\r\n",
+ " y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 1\r\n",
+ " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\r\n",
+ " np.arange(y_min, y_max, 0.1))\r\n",
+ " grid_points = np.c_[xx.ravel().astype('float32'), yy.ravel().astype('float32')]\r\n",
+ " n_classes = max(train_labels)+1\r\n",
+ " while train_x.shape[1] > grid_points.shape[1]:\r\n",
+ " # pad dimensions (plot only the first two)\r\n",
+ " grid_points = np.c_[grid_points,\r\n",
+ " np.empty(len(xx.ravel())).astype('float32')]\r\n",
+ " grid_points[:, -1].fill(train_x[:, grid_points.shape[1]-1].mean())\r\n",
+ "\r\n",
+ " # evaluate predictions\r\n",
+ " prediction = np.array(net.forward(grid_points))\r\n",
+ " # for two classes: prediction difference\r\n",
+ " if (n_classes == 2):\r\n",
+ " Z = np.array([0.5+(p[0]-p[1])/2.0 for p in prediction]).reshape(xx.shape)\r\n",
+ " else:\r\n",
+ " Z = np.array([p.argsort()[-1]/float(n_classes-1) for p in prediction]).reshape(xx.shape)\r\n",
+ " \r\n",
+ " # draw contour\r\n",
+ " levels = np.linspace(0, 1, 40)\r\n",
+ " cs = ax.contourf(xx, yy, Z, alpha=0.4, levels = levels)\r\n",
+ " if draw_colorbar:\r\n",
+ " fig.colorbar(cs, ax=ax, ticks = [0, 0.5, 1])\r\n",
+ " c_map = [cm.jet(x) for x in np.linspace(0.0, 1.0, n_classes) ]\r\n",
+ " colors = [c_map[l] for l in train_labels]\r\n",
+ " ax.scatter(train_x[:, 0], train_x[:, 1], marker='o', c=colors, s=60, alpha = 0.5)"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "import matplotlib.cm as cm\n",
- "\n",
- "def plot_decision_boundary(net, fig, ax):\n",
- " draw_colorbar = True\n",
- " # remove previous plot\n",
- " while ax.collections:\n",
- " ax.collections.pop()\n",
- " draw_colorbar = False\n",
- "\n",
- " # generate countour grid\n",
- " x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 1\n",
- " y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 1\n",
- " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
- " np.arange(y_min, y_max, 0.1))\n",
- " grid_points = np.c_[xx.ravel().astype('float32'), yy.ravel().astype('float32')]\n",
- " n_classes = max(train_labels)+1\n",
- " while train_x.shape[1] > grid_points.shape[1]:\n",
- " # pad dimensions (plot only the first two)\n",
- " grid_points = np.c_[grid_points,\n",
- " np.empty(len(xx.ravel())).astype('float32')]\n",
- " grid_points[:, -1].fill(train_x[:, grid_points.shape[1]-1].mean())\n",
- "\n",
- " # evaluate predictions\n",
- " prediction = np.array(net.forward(grid_points))\n",
- " # for two classes: prediction difference\n",
- " if (n_classes == 2):\n",
- " Z = np.array([0.5+(p[0]-p[1])/2.0 for p in prediction]).reshape(xx.shape)\n",
- " else:\n",
- " Z = np.array([p.argsort()[-1]/float(n_classes-1) for p in prediction]).reshape(xx.shape)\n",
- " \n",
- " # draw contour\n",
- " levels = np.linspace(0, 1, 40)\n",
- " cs = ax.contourf(xx, yy, Z, alpha=0.4, levels = levels)\n",
- " if draw_colorbar:\n",
- " fig.colorbar(cs, ax=ax, ticks = [0, 0.5, 1])\n",
- " c_map = [cm.jet(x) for x in np.linspace(0.0, 1.0, n_classes) ]\n",
- " colors = [c_map[l] for l in train_labels]\n",
- " ax.scatter(train_x[:, 0], train_x[:, 1], marker='o', c=colors, s=60, alpha = 0.5)"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 24,
+ "source": [
+ "def plot_training_progress(x, y_data, fig, ax):\r\n",
+ " styles = ['k--', 'g-']\r\n",
+ " # remove previous plot\r\n",
+ " while ax.lines:\r\n",
+ " ax.lines.pop()\r\n",
+ " # draw updated lines\r\n",
+ " for i in range(len(y_data)):\r\n",
+ " ax.plot(x, y_data[i], styles[i])\r\n",
+ " ax.legend(ax.lines, ['training accuracy', 'validation accuracy'],\r\n",
+ " loc='upper center', ncol = 2)"
+ ],
+ "outputs": [],
"metadata": {
"slideshow": {
"slide_type": "skip"
}
- },
- "outputs": [],
- "source": [
- "def plot_training_progress(x, y_data, fig, ax):\n",
- " styles = ['k--', 'g-']\n",
- " # remove previous plot\n",
- " while ax.lines:\n",
- " ax.lines.pop()\n",
- " # draw updated lines\n",
- " for i in range(len(y_data)):\n",
- " ax.plot(x, y_data[i], styles[i])\n",
- " ax.legend(ax.lines, ['training accuracy', 'validation accuracy'],\n",
- " loc='upper center', ncol = 2)"
- ]
+ }
},
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "%matplotlib nbagg \r\n",
+ "\r\n",
+ "net = Net()\r\n",
+ "net.add(Linear(2,2))\r\n",
+ "net.add(Softmax())\r\n",
+ "\r\n",
+ "res = train_and_plot(30,net,lr=0.005)"
+ ],
"outputs": [
{
+ "output_type": "display_data",
"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, fig.canvas.height);\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",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\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"
- ],
+ "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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, fig.canvas.height);\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function(fig, msg){\n fig.send_message('closing', msg);\n // fig.ws.close()\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 event.shiftKey = false;\n // Send a \"J\" for go to next cell\n event.which = 74;\n event.keyCode = 74;\n manager.command_mode();\n manager.handle_keydown(event);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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"
+ "metadata": {}
},
{
+ "output_type": "display_data",
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"640\">"
@@ -1880,855 +1115,78 @@
"<IPython.core.display.HTML object>"
]
},
- "metadata": {},
- "output_type": "display_data"
+ "metadata": {}
}
],
- "source": [
- "%matplotlib nbagg \n",
- "\n",
- "net = Net()\n",
- "net.add(Linear(2,2))\n",
- "net.add(Softmax())\n",
- "\n",
- "res = train_and_plot(30,net,lr=0.005)"
- ]
- },
- {
- "cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Многослойная модель\n",
"\n",
" * С нашей архитектурой вычислительного графа легко описывать многослойные персептроны!\n",
" * Нельзя забывать про передаточную функцию (мы будем использовать `tanh`)\n",
" * В более глубоких сетях очень важна первоначальная активация весов"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
},
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {},
- "outputs": [],
"source": [
- "class Tanh:\n",
- " def forward(self,x):\n",
- " y = np.tanh(x)\n",
- " self.y = y\n",
- " return y\n",
- " def backward(self,dy):\n",
+ "class Tanh:\r\n",
+ " def forward(self,x):\r\n",
+ " y = np.tanh(x)\r\n",
+ " self.y = y\r\n",
+ " return y\r\n",
+ " def backward(self,dy):\r\n",
" return (1.0-self.y**2)*dy"
- ]
+ ],
+ "outputs": [],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {},
- "outputs": [],
"source": [
- "net = Net()\n",
- "net.add(Linear(2,10))\n",
- "net.add(Tanh())\n",
- "net.add(Linear(10,2))\n",
- "net.add(Softmax())\n",
+ "net = Net()\r\n",
+ "net.add(Linear(2,10))\r\n",
+ "net.add(Tanh())\r\n",
+ "net.add(Linear(10,2))\r\n",
+ "net.add(Softmax())\r\n",
"loss = CrossEntropyLoss()"
- ]
+ ],
+ "outputs": [],
+ "metadata": {}
},
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {
- "scrolled": false,
- "slideshow": {
- "slide_type": "slide"
- }
- },
+ "source": [
+ "res = train_and_plot(30,net,lr=0.01)"
+ ],
"outputs": [
{
+ "output_type": "display_data",
"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, fig.canvas.height);\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",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\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"
- ],
+ "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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, fig.canvas.height);\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function(fig, msg){\n fig.send_message('closing', msg);\n // fig.ws.close()\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 event.shiftKey = false;\n // Send a \"J\" for go to next cell\n event.which = 74;\n event.keyCode = 74;\n manager.command_mode();\n manager.handle_keydown(event);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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"
+ "metadata": {}
},
{
+ "output_type": "display_data",
"data": {
"text/html": [
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABGAAAANICAYAAACIVpl8AAAgAElEQVR4XuzdCbxVU//H8V+jZqHSHClSSYoSGoRMUQpJSI+hlCk8IfNYySP0JOQxJkOkwTyrkFIyRCqkkeY0T7f/67v779O55+577znnnn3O2ed+1uvVq1v37LXXfq+999nrt9dQZPfu3buNhAACCCCAAAIIIIAAAggggAACCCDgm0ARAjC+2ZIxAggggAACCCCAAAIIIIAAAggg4AgQgOFEQAABBBBAAAEEEEAAAQQQQAABBHwWIADjMzDZI4AAAggggAACCCCAAAIIIIAAAgRgOAcQQAABBBBAAAEEEEAAAQQQQAABnwUIwPgMTPYIIIAAAggggAACCCCAAAIIIIAAARjOAQQQQAABBBBAAAEEEEAAAQQQQMBnAQIwPgOTPQIIIIAAAggggAACCCCAAAIIIEAAhnMAAQQQQAABBBBAAAEEEEAAAQQQ8FmAAIzPwGSPAAIIIIAAAggggAACCCCAAAIIEIDhHEAAAQQQQAABBBBAAAEEEEAAAQR8FiAA4zMw2SOAAAIIIIAAAggggAACCCCAAAIEYDgHEEAAAQQQQAABBBBAAAEEEEAAAZ8FCMD4DEz2CCCAAAIIIIAAAggggAACCCCAAAEYzgEEEEAAAQQQQAABBBBAAAEEEEDAZwECMD4Dkz0CCCCAAAIIIIAAAggggAACCCBAAIZzAAEEEEAAAQQQQAABBBBAAAEEEPBZgACMz8BkjwACCCCAAAIIIIAAAggggAACCBCA4RxAAAEEEEAAAQQQQAABBBBAAAEEfBYgAOMzMNkjgAACCCCAAAIIIIAAAggggAACBGA4BxBAAAEEEEAAAQQQQAABBBBAAAGfBQjA+AxM9ggggAACCCCAAAIIIIAAAggggAABGM4BBBBAAAEEEEAAAQQQQAABBBBAwGcBAjA+A5M9AggggAACCCCAAAIIIIAAAgggQACGcwABBBBAAAEEEEAAAQQQQAABBBDwWYAAjM/AZI8AAggggAACCCCAAAIIIIAAAggQgOEcQAABBBBAAAEEEEAAAQQQQAABBHwWIADjMzDZI4AAAggggAACCCCAAAIIIIAAAgRgOAcQQAABBBBAAAEEEEAAAQQQQAABnwUIwPgMTPYIIIAAAggggAACCCCAAAIIIIAAARjOAQQQQAABBBBAAAEEEEAAAQQQQMBnAQIwPgOTPQIIIIAAAggggAACCCCAAAIIIEAAhnMAAQQQQAABBBBAAAEEEEAAAQQQ8FmAAIzPwGSPAAIIIIAAAggggAACCCCAAAIIEIDhHEAAAQQQQAABBBBAAAEEEEAAAQR8FiAA4zMw2SOAAAIIIIAAAggggAACCCCAAAIEYDgHEEAAAQQQQAABBBBAAAEEEEAAAZ8FCMD4DEz2CCCAAAIIIIAAAggggAACCCCAAAEYzgEEEEAAAQQQQAABBBBAAAEEEEDAZwECMD4Dkz0CCCCAAAIIIIAAAggggAACCCBAAIZzAAEEEEAAAQQQQAABBBBAAAEEEPBZgACMz8BkjwACCCCAAAIIIIAAAggggAACCBCA4RxAAAEEEEAAAQQQQAABBBBAAAEEfBYgAOMzMNkjgAACCCCAAAIIIIAAAggggAACBGA4BxBAAAEEEEAAAQQQQAABBBBAAAGfBQjA+AxM9ggggAACCCCAAAIIIIAAAggggAABGM4BBBBAAAEEEEAAAQQQQAABBBBAwGcBAjA+A5M9AggggAACCCCAAAIIIIAAAgggQACGcwABBBBAAAEEEEAAAQQQQAABBBDwWYAAjM/AZI8AAggggAACCCCAAAIIIIAAAggQgOEcQAABBBBAAAEEEEAAAQQQQAABBHwWIADjMzDZI4AAAggggAACCCCAAAIIIIAAAgRgOAcQQAABBBBAAAEEEEAAAQQQQAABnwUIwPgMTPYIIIAAAggggAACCCCAAAIIIIAAARjOAQQQQAABBBBAAAEEEEAAAQQQQMBnAQIwPgOTPQIIIIAAAggggAACCCCAAAIIIEAAhnMAAQQQQAABBBBAAAEEEEAAAQQQ8FmAAIzPwGSPAAIIIIAAAggggAACCCCAAAIIEIDhHEAAAQQQQAABBBBAAAEEEEAAAQR8FiAA4zMw2SOAAAIIIIAAAggggAACCCCAAAIEYDgHEEAAAQQQQAABBBBAAAEEEEAAAZ8FCMD4DEz2CCCAAAIIIIAAAggggAACCCCAAAEYzgEEEEAAAQQQQAABBBBAAAEEEEDAZwECMD4Dkz0CCCCAAAIIIIAAAggggAACCCBAAIZzAAEEEEAAAQQQQAABBBBAAAEEEPBZgACMz8BkjwACCCCAAAIIIIAAAggggAACCBCA4RxAAAEEEEAAAQQQQAABBBBAAAEEfBYgAOMzMNkjgAACCCCAAAIIIIAAAggggAACBGA4BxBAAAEEEEAAAQQQQAABBBBAAAGfBQjA+AxM9ggggAACCCCAAAIIIIAAAggggAABGM4BBBBAAAEEEEAAAQQQQAABBBBAwGcBAjA+A5M9AggggAACCCCAAAIIIIAAAgggQACGcwABBBBAAAEEEEAAAQQQQAABBBDwWYAAjM/AZI8AAggggAACCCCAAAIIIIAAAggQgOEcQAABBBBAAAEEEEAAAQQQQAABBHwWIADjMzDZI4AAAggggAACCCCAAAIIIIAAAgRgOAcQQAABBBBAAAEEEEAAAQQQQAABnwUIwPgMTPYIIIAAAggggAACCCCAAAIIIIAAARjOAQQQQAABBBBAAAEEEEAAAQQQQMBnAQIwPgOTPQIIIIAAAggggAACCCCAAAIIIEAAhnMAAQQQQAABBBBAAAEEEEAAAQQQ8FmAAIzPwGSPAAIIIIAAAggggAACCCCAAAIIEIDhHEAAAQQQQAABBBBAAAEEEEAAAQR8FiAA4zMw2SOAAAIIIIAAAggggAACCCCAAAIEYDgHEEAAAQQQQAABBBBAAAEEEEAAAZ8FCMD4DEz2CCCAAAIIIIAAAggggAACCCCAAAEYzgEEEEAAAQQQQAABBBBAAAEEEEDAZwECMD4Dkz0CCCCAAAIIIIAAAggggAACCCBAAIZzAAEEEEAAAQQQQAABBBBAAAEEEPBZgACMz8BkjwACCCCAAAIIIIAAAggggAACCBCA4RxAAAEEEEAAAQQQQAABBBBAAAEEfBYgAOMzMNkjgAACCCCAAAIIIIAAAggggAACBGA4BxBAAAEEEEAAAQQQQAABBBBAAAGfBQjA+AxM9ggggAACCCCAAAIIIIAAAggggAABGM4BBBBAAAEEEEAAAQQQQAABBBBAwGcBAjA+A5M9AggggAACCCCAAAIIIIAAAgggQACGcwABBBBAAAEEEEAAAQQQQAABBBDwWYAAjM/AZI8AAggggAACCCCAAAIIIIAAAggQgOEcQAABBBBAAAEEEEAAAQQQQAABBHwWIADjMzDZI4AAAggggAACCCCAAAIIIIAAAgRgOAeiFihSpIjz2ZIlS0a9DR9EAAEEEEAAAQQQQAABBBAouMD27dudTHbv3l3wzMghJQIEYFLCHsydEoAJZr1RagQQQAABBBBAAAEEEAi+AAGY4NchAZjg12HSjmCfffZx9rVt27ak7ZMdIYAAAggggAACCCCAAAIImNEeC/5ZQAAm+HWYtCPggk8aNTtCAAEEEEAAAQQQQAABBLIJ0B4L/glBACb4dZi0I+CCTxo1O0IAAQQQQAABBBBAAAEECMBk2DlAACbDKtTPwyEA46cueSOAAAIIIIAAAggggAACuQvQHgv+2UEAJvh1mLQj4IJPGjU7QgABBBBAAAEEEEAAAQSyCdAeC/4JQQAm+HWYtCPggk8aNTtCAAEEEEAAAQQQQAABBAjAZNg5QAAmwyrUz8MhAOOnLnkjgAACCCCAAAIIIIAAArkL0B4L/tlBACb4dZi0I+CCTxo1O0IAAQQQQAABBBBAAAEEsgnQHgv+CUEAJvh1mLQj4IJPGjU7QgABBBBAAAEEEEAAAQQIwGTYOUAAJsMq1M/DIQDjp66/ee/cudP0Z/fu3f7uiNwRQAABBBBAAAEE0lqgSJEiVrx4cecPKVgCtMeCVV9epSUAE/w6TNoRcMEnjTohO1KwZd26dbZ+/XrbsmVLQvIkEwQQQAABBBBAAIHMEChdurTtu+++VrFiRVNQhpT+ArTH0r+O8ishAZj8hPh9SIALPjgng4IvK1assDVr1gSn0JQUAQQQQAABBBBAIOkC+++/v1WpUoUgTNLlY98h7bHYzdJtCwIw6VYjaVweLvg0rpyIoq1du9b++usv539LlizpvN3QW46iRYsG5yAoKQIIIIAAAggggEDCBbKyspze0eolvX37dif/qlWr2n777ZfwfZFhYgVojyXWMxW5EYBJhXpA98kFH5yKW7hwofPFquDLQQcdZMWKFQtO4SkpAggggAACCCCAgO8Cu3btsj/++MN27NjhvKjTMyMpvQVoj6V3/URTOgIw0SjxGUeACz4YJ4Im250/f75T2MqVK1ulSpWCUXBKiQACCCCAAAIIIJBUgVWrVtnKlSudfdavX5+JeZOqH/vOaI/FbpZuWxCASbcaSePycMGnceWEFW3r1q3O2wyl2rVrW9myZYNRcEqJAAIIIIAAAgggkFSBTZs22aJFi5x9HnzwwVaqVKmk7p+dxSZAeyw2r3T8NAGYdKyVNC0TF3yaVkxEsTT0SEOQlNSVVF1KSQgggAACCCCAAAIIRArw3Bisc4L2WLDqy6u0BGCCX4dJOwIu+KRRF2hHfJEWiI+NEUAAAQQQQACBQiPAc2Owqpr2WLDqiwBM8OsrpUfABZ9S/qh3zhdp1FR8EAEEEEAAAQQQKNQCPDcGq/ppjwWrvgjABL++UnoEXPAp5Y9653yRRk3FBxFAAAEEEEAAgUItwHNjsKqf9liw6osATPDrK6VHwAWfUv6od84XadRUfBABBBBAAAEEECjUAjw3Bqv6aY8Fq74IwAS/vlJ6BFzwKeWPeud8kUZNFYgPakJlrUqgtHv37oSWuUiRIk5+WjVLEzaTEEAAgXQWeP75561Xr17Wtm1b+/zzz7MVtV27dvbFF1/Yc889Z5deemlMh6G8TjzxRKtTp05oEvuYMijAh/28xxegWGxaiAR4bgxWZdMeC1Z9EYAJfn2l9Ai44FPKH/XO+SKNmirPD+pBXw/GeqjXn1QlPx/OCcCkqlbZLwIIxCMQtADM+PHjbfbs2da0aVPr3Lmz5yH7eY+Px5htCp8Az43BqnPaY8GqLwIwwa+vlB4BF3xK+aPeeWH8IlXPkK07t9q2Xduidsrvgx07dLQvp3xpN992s91y+y35fdy33y9busw6n7HnwX3699MTup8WR7Zw8hv/7nirXqN6QvOOJ7N9iu1jpYqXMjcwFE8ebIMAApkrkFcA5pJLLrHp06fboEGD7JxzzokJwa8eMOqJ88ILL1jPnj1NZfdKS5cutZNOOsn51dy5c2MqNx9GIBEChfG5MRFuqcqD9liq5BO3X5ahTpxlxufEBR+MKi5sX6Qbt2+0pf8stQ3bNyS0gnqf29tmfT3LrrjhCrvyxisTmjeZ5S5QvmR5q1GhhpUrWQ4mBBBAIJtAXgGYglClMgBTkHKzLQKJEChsz42JMEtlHrTHUqmfmH0TgEmMY6HIhQs+GNVcWL5It+zYYks3LLV1W9f5UjEEYHxhjTrTiqUqWo3yNax0idJRb8MHEUAgswUIwGR2/XJ0qREoLM+NqdFN/F5pjyXeNNk5EoBJtniA98cFH4zKy/Qv0m07t9myjcts9ebVvlTIpNcm2b033Jtr3tVqVrOJ30x0fj/zq5nW57w+zs8zls6w72d8b6OfHG0/zPzB1q1eZ5ddf1mo98yCuQts8geTbdoX02z5kuW2euVqK1W6lB1U7yA7uePJ1vWSrrZPqX1y7HfZ4mXW6dhOoX2Ef+Dp/zxtox4ZZc1aNbOn3njKJn842cY8PcZ+nfOr7dq5yw457BC74PIL7NTOp3oezzE1jnH+f8K0CVa91t4hSK6Be6w6rueHP28/zvrRtm7ZarUPrm2de3S28y49L9fhQtu3bXcs3h//vi1btMzKlC1jRzQ/wi69+lLn79z2HVnQA8ocYNXLVbd9iu+x2bhxo73//vs2YcIEZ24Fdd/fvHmzVa1a1Vq3bm39+/e3Zs2a5XluLFu2zB599FH74IMPnAmId+3aZTVr1nS2u+CCC6xTpz3e4Wnnzp324osv2muvvWbfffedrV+/3ipXrmyHHHKIM7eDhj8ccMABoU2imV8nt8+4jUx3QtB33nnHHnvsMWe/q1evtmeffTY0yaiGXMji008/tcWLF9uKFSusQoUK1qRJE7v44oudchUrVixXj2iPS/lrmESJEiVsyZIlVqVKFc885VKtWjXTfejdd9+1008/3ZfrlExTJ6D6r127tjMp+Ndff23HHntsroVp1KiR/fzzz/bggw/arbfe6nxO19uUKVOc81Z/6xrWeb3ffvtZ8+bN7fLLL7cuXbp45lmQOWB0bdxzzz02adIk5zrROXzGGWfYHXfcYfPnz891Et547jluj5q8asmdVD2aOWB+/PFH+89//mOfffaZ/fXXX1a2bFnnGtf1rWFORYsWzbGryOFPGgr1xBNPOPWhe4+sBw4caKecckrMJ1NB6tDdme4RTz31lI0bN87mzJnj3Nt1H2/QoIF17drVevTo4RxnZPrkk09s1KhR9tVXXzn1WL58eed87NChg2Nx2GGHhTaJZmLm3D4TXi/6ntD3jM5j1a3q4KKLLgoNK/v9999N8/289957pp/1HVOyZEmrV6+enX322XbddddZxYoV83SO5rg2bNjg3F83bdpkb731Vq7zCmlHxx13nHN9DhgwwIYMGZLnvjP9uTHmEzzNN6A9luYVFEXxCMBEgcRH9ghwwQfjTMjUL9Idu3bY8o3LbeWmlbbbcq4GpLlDqpStYsWK5N7YjKYG33rjLbvt37fZ2rVrbceOHc4DYPhDYPWa1e3jKR87WU2dPNU6n75nfpZRL4yyPv/q4zQu9q24r23auMn6D+jvzCGjdGjtQ23N6jXOz3pYLl+hvK1ftz5UpKbNmtpb777lPEyGp0V/LrJmDfcEFFZtWpXtd0MeGGJDHxxqx7U+ztq0a2OD7xvs5F2ufDn7Z/0/oc/eP+R+63P1nkBReKpUtpLzz1k/z7LadWqHfvXKS6/YNX2usVq1a9mAgQPs+n7XW1ZWllPm8Hx79+1tDwx9IEe+ekg896xzbeaMmc7v1GgvXaa0s61+/t9L/7NLLrgk27537d5lKzatcObyiUxFrIhTt1XLVbWnRj5l11xzTegjqhsFEbZt2zP/T/HixZ0AhYIPXkkPyXpw1gOsku5relBWmd0UudrU8uXLnYfob7/91vmIjBXkULDB/WzkyiuJCsBcffXV9u9//9tpMOkBXvv83//+5zQ01GAJP1907GXKlLF//tlb92eeeabTMNDvIlMsx6XjPPTQQ23BggX28MMP24033ujp++STT9pVV13lBLTUgMkr+OOZAf8ZCAGtGKSGqM7P4cOHe5ZZAdKjjjrKOXfVKHVXWvvpp5/siCOOCG2j60/XYfg12Lt3b9O5FJniDcD8+uuvzmTqajgr6TpRuXQfUCBVDesrrrjCcxWk//73vzHfcxQcUBBJ1+vWrVutVKlStu+++2Y7HLcs+QVgdG9R2fTdoqR8VG7d95ROPvlk5xqPDFaEB2B0/eu+oetRn3PvEbqXvf76607AI5ZUkDrUfjTXTceOHe23335zdqty6V62bt3eHq0KNoVPgK/v4yuvvDLbXDq6D+uZR79TipxrJ1EBGAWvdF9TEEb7VJ127949VJajjz7aZs7c832n80p1FP79oEC9rhfdFyNTrMelAKXqUn4KJnol+R5++OHOr/RzeFDK6/OZ+twYyzkdpM/SHgtSbXmXlQBM8OswaUfABZ806gLtKNO+SHdl7bK/N/1tf238y7J2Z+WwKVmspFUvX90OKH1AQidvdR/c7rrrLrv77rs96yT8LWe5cuWct6lDhw513sZt377d1MBVLwYlTQqp3gCnnnqq1ahRw2kQq67UC0MNbDVs+/btayNGjMi2r7wezlUuvdFVw1yNF/1bDSL9+++//3Z+fuONN5yHf71l3n///bPlnV8PDDVS9JCvh97bb7/dDjzwQOcB+c4773QaXdpeb2b1ljs8XXbZZU4QRPcMfU5vafWzGmEqk97KuQ/a4Utgq5G/avMqJ9C2fdf2HOZFixS1ae9Nszkz59iFF15oDRs2dN6aazs5PfLII6bGko5XDS7VQ3j65ptvnF4yeuA9/vjjnbeCrVq1cgIqegOvAIv7RtbdTvWoz+p3clX9duvWzWksKOij/YwdO9b0AB7ecyYRARgdh8qq80Jv6dVQVMNLjSe9BVVj4Nxzz3XeFOt81f/pWBSYUb3ffPPNzhvihx56yDnHwlM8xyWvW265xXHXG2uvdMwxxzhWOl/uu+++At3L2Dh9BZ555hknKKBeJHrb7xVo0zmnYN0JJ5zg9HRx07x58+y2225zAqEtW7Z07iu6XtasWeP0MtO5o/NcgYHzzjsvG0I8ARhdQ+rtoXtVrVq1nMarghbapxr5ul9p32owey1D/corr9iXX37pNLhjvedEMwlvXvf4adOmOX4Kvuj7RffTunXrOgEA3WPV40/Xspbm1r/Dk7tv3bf0+WHDhjn3Yt3Xdd/V7ydPnuzcNxYtWuQZpM3tDCxIHerlhgJzf/75pxOQ0H37rLPOcu7buqfp3vLyyy873jo/3HTttdeGvnfUq0OBeH2X6uWAyq/eJ6pHnVtuSlQARt/vLVq0cL5fFNjQPlVvqgslXQs6NxQU0feOvu9ULzrvdR9WcEb1p96MkSnW49L3mHqd6ZpTr0fVX2TK7drLrT4z7bkxfe+ciSkZ7bHEOKYyFwIwqdQP2L654INRYbF+kYa/iYz2CPv162d9+uTsUaGHwY8/3tM7JNqkNzNqLEamiZMm2s233Gw7s3Z69nhRr4jiRYvbnJ/mmBrmiU6xBmDatGnjvOGKZwUfBV/0UKc3watWrbLSpffOexJNAEbHfv/992d78NT/6aFbD4MrV650VuLQw3d4yi8Ao8/qbZu6e0cmdX9Xg0YBIAVk3OQ+lCooou20fXjSQ6mCFdpWKTwA435OD7crNq9wgm6q/8ikeq9WrppVLls5R92rIaJGmoJRCp6FJz20ug+vX3zxheOdX3J7dKjnjt5qq+zRpEQEYLQfNVJfeumlaHaZ4zM6Rp3H6nkg5/AUz3EpmKMGkxq0ahiGN46Ut+pU54WOXee02ziJq/BpsFGX17rY/DXz06AkBS9C/f3r27hu4wqe0f/noACqhosoCKkhgQoshydd/7r3aLjSyJEjPb8vciuM7lUKDujcVYAkPMUTgBk9erTTI05Bbw3ja9y4cbY8FUzQeatj8QrA5IeW1z2noAEYBYo0NEUBJAWudR8KT7JVgFbXnHo6qJeam9x9698yUKA2POkFge4NuifrXqHvsESlvOpQQVwFcxUYUi8p9yVFXvv+5ZdfnEC/zisNx1TQIpqUqABM/fr17YcffnCCRLEmBfc1rErf7XoJcfDBB4eyiPe4jjzySKc8gwcPdgI84UkvTXSf1kuYyJ6ZuZU91ufGWA34fGIFaI8l1jMVuRGASYV6QPfJBR+Miov1izSegMG9997rvJGPTOrGrPHcsSQ9SOghzE16wFq9ZbWNen6UDew3MN+sIoeL5LtBlB+INQCjLuBec4dEuTtzH6j0plVjt90UTQBGD4V6uPMaL6+Ghx6+9UZMPSHCUzQBGHUR92pIq/4V9NEbar2pdpPeZmp4it5qq9eN15tx9+Fc23gFYNy8FHz5e+PfTg+oaHs/aVy8uv5rXoMPP/wwVK7wLtl666ueMNEkN2jzr3/9y3lzHm1KVABG14bOjXiSrg31ENKbfdVF9ep75/mJ97jU4+bNN9903vg+/fTT2Yp1/fXXO40jzRUTayA2nuPze5sjRh5hP634ye/dJCX/xlUa249X7Ql6JirpOtP1psCurunw5PYOVMBADf3w+ZHy2796SKi3nu5r6s0Vfg+JJwCjOZo034y+n7yC/SqPjkGBzngCMLndc5RvQQIw6s1RqVIlJ+ig71WvpbXVM0aBLvVCUo8z9R5yk7tv/V7fI17f9boPTp061el5qUBOolJedageGxp+ldtzhFcZ3KCNhvJovp5on1sSFYDRnGGaxyXepGeDiRMnOj171HvTTfEelzssTgE39cIMT3oW0bmiXppyVo+n/FKsz4355cfv/RWgPeavbzJyJwCTDOUM2QcXfDAqMtYv0mgfZMKP3o8AjB4ytaKRVjbSPCDvjXvP7rxmb8+K3PTTJQCjBx0FHfJKejDSQ766I6s3geoqMmk4ixq5boomAKPJY93x55H5uQ94Xj1Z8gvAqBGkt3deye1BoTe0H330UegjbsAnr8aOgi5uUCevAIybaeT8P6v+XmWvP/e6fTPlG1v8x2LbtGGT0yU8POltqeYpcJPbcNPYfDUOojnv1dPDHYaloENuE4N6+SQiAKN9qwGaV1nVANNDvSYH/v77751AnDsfTni5ZsyYEeq9U5DjUlBLvR00D4Ia1u7Dvd6iK8Cj82XMmDHO8IGgJwIwedegrgndq9TQ09v28J57GraoHnAaWqKGZ2RS7zwNY1LwQkNOFGxw5/EI/6x67ykIEXkdt23b1ulxGJ5ya2xr2JF64mj4joZAeiUN39FQpNwCMDrX1ejVvU4BAA0DzO+eo/0UJACjni+6vyqpx1HkHDLucaiXnO4BanSHvwBx9637lurKK2nScd07vHpQ5nf9xlOH4d9n+s7Kb8J0twxu3WrY0eOPP55f0UK/T1QAxqvHX2QhdD7qnFYvSwXENJwqMmkI604FXmoAACAASURBVE033ZSjfLEel84H3W/1DKFhThqm5iZdc2+//bYzdFhDaqNJsT43RpMnn/FPgPaYf7bJypkATLKkM2A/XPDBqMRYv0jTYQjSsy8/a0v/WWqbduyZGFVpykdTbMTgPfOhaGLdEkVLeDZE3aEsia6dWHvAqNtvbhOOqpGst17hPUU0/EXjyt0u5W4DRIECTSTopmgCMF6NEXd7d56YyMkJ9fv8AjB5vQ3O7U30aaed5sxrk9cDpQIEblfuaAIw7rFoBayJH020Xt16OUEXN5UtX9YZc6/j0epP69auyzHsxp2/RMMPoj1n1KjUMAslzWuiYQDRpkQEYNSNXGP8c0uaJ0PzCqhHj5vkqvPKPRfVgFVDUY0DnSdKBTkuBTz1Flr1Fj6sTee25sZRjxs1PuLpqh+tbbI+xxCkvKV1HSvorB5WasSff/75zgYKxum6UaDz1Vdfdc6L8KTzr3379s5qPG5SIE89+NzVfPQZJd3/woeoxNMDRueiyppXEFX3LN27vO556pWo6yx8cmsFIJWvrnMdr47Va6hfQQIwslMgU9dz+ATFkbXiBtkj59qJZt/uZ/Ka68zrLIi3Dt0hoMpTweJoe0ZpiK56MeY1AbhXORMVgNGQSt33cksKqmiVKjdpuJsCk+4wV3cy5sihsfEel/aj73TNmRQ+/48ChQo46plD1pq3JpoU63NjNHnyGf8EaI/5Z5usnAnAJEs6A/bDBR+MSgzSF+mm7ZucHi//bNu7aku4csVSFa1G+RpWusTeOVGSVQuxBmDy6omjoRpa1UNviLXaht5I6iEpvGeD2xU8csx20AIw6h2hXhJ5BWDUYHHvJ7EEYBTk0kOwJlxs1bqV9byupzVq2shZzttN06dMt34X9HNWdfpz4Z+h/w9iACa/4RBaQnbQoEFOI0ZvVjXJsxswcg/cffsfvqJIQQIwyveBBx5whjpozgjNHaHkBt7yWhUnWdcu+0megDvhtoZYqIefkob7aNiPV88Y/V7BaE1sq4CFzlsFY8InCFfj0V21K/L+4FcARvPY6PqJvObC7zkqp+a7UqM2vLeP21PF63qNJgiS2z3eDcDIMTz4E1m7qQjAxFuHQQ3A5PU95fYKVNBbEwCrF6h6eIYvDe72DI0MdBUkAKPAoIJuClyqB64CdZoTRsu9x/KiQedTkJ4bk3d3S9890R5L37qJtmQEYKKV4nMsQx2QcyAIX6Rbd2x1Ai9rt671VC1fsrzVqFDDypUslzL1RAZgNCfGp59+6jSW9bDslTTJn96yBT0A4z5oamiChlN5pfAGRywBGHW1VqNfvSw0pCCreJYt+WeJbdy+MbQbd+hatZrVbMr3U5wAXqkSpZzeGmoMxTIESY0vvZnX0IhYhyCpAamGpCZZ1ASMkUlvRDUJpVJujcz8AjAKRmlSRzVmNZQgMmn/eijXUIHwAExBjkv7UA8XlU35aDiGHgbVmFZPm4LMWZOyi50dxy2g80qBCb3pV2BP57R6wuja9+p1p2vJXcZXk8pqLqLIpPmK3OV6ExGAKcgQpMh7jtd8Ghr+o2FAiQ7AJGoIklc9uObx9IApSB1q5SN3OfJYhiC5y57HOlTHncQ4r4mgtSKT7lt5fffm9T3lBiFzWzpd1pqTTPNiRQZg4j0ut/401FY9yTT0SeXQnDC6J8c6Z00Qnhvjvkll4IYEYIJfqQRggl+HSTsCLvikURdoR+n8RaqlhZdtWOYsNeyVypQo4zSYK+xTIao5OgoElc/GalSocaE3nlrpxyuFL0OdVw8Y96HIa7UQ5RsekAh6AMadhFc9MdSQCn8L6BpGOwlvpLnmFtFKHmq0qfGmJHf1oFJAb/OOzXZP/3vs7dffNgVgJn6zZ+6JSmUq2cblG+2Ihkc4/45lEl5NiKx9xToJr5aMVhd7zRnhzuMQfjx6GNdDuVK8ARg1etUYUtd8rSYWmdxVkPT/4QEY/Tve43L34U5sql44+m5Qw0JDtDRUi1R4BBR00ySvutY154uGG2lYkr6H1DPAPcddEQXvtHSwkgKD7nNFuFhe94d4esC452peQeHcJuH1uudE1q67CpJXAEb3Dd3TvSYqdvPJrQdMNJPwhvtHzuMSTe+beAIwBa1DzV2ioTKxTMLr9vaLdRJeTRKviZfVM0U+kUlzbGmOIQ1RizcA06FDB+c+r3nRFITx2oeOWcPIIgMw8R6Xuw8FWrT6pO7nesGjYab6XlAdRTu8S3ml83Nj4bmbRn+ktMeit0rXTxKASdeaScNyccGnYaV4FCkdv0h37tppyzcutxWbVnguKb1PsX2cHi/7ldov5YGXyAamVj7QQ45XijYAc8wxxzgNUw2DGTBgQI6s3IkQ9YugB2AUTNBDsgIjWjVIDZDwpICBlnLWEppKsfSAeeedd6xjx47OcAU1+MLnGdH+Jn8z2U5pe4rt2L4jWwBG+9Gy5b3P6W0zp890AjjRLkPtDh/TQ626fEe7DLUbwLvqqqvsiSeeyGagsiooo15RXgZuIzO/HjBukCd8/g13R+qdol5cKrNSZAAm3uNy83frQo1p2agedZw6XlLhEtAKa5qbQ+ebGvT6owCseqlFzoulBq96wOga8JqjQo1UTcyq3oB5XRuxTMLr9lDRfFvq6dCwYcNsFaQeA1qGWgGhyGsur3uOMlF+Wo5dwyq9rld3ZbDwIVqRZ0dew0zdnhP6Dvnqq69CQ7PcPNzrOK9lqBPdA6agdegGHdSTUcuCR7MMtVb60XCdWJehVpBHQQ/1FNHcX5ETmuvliuZlye+7N6/vKTfI43WvV75aJtpdgTAyABPvcbn1ryCdgjsKIB1//PHO/V490PSdEEtKx+fGWMpf2D5Leyz4NU4AJvh1mLQj4IJPGnWBdpROX6S7snY5SwhrKeFdu3flOC5NrFu9fHU7oMwBVrRI0QIdd6I31hszzdei4SNqrFepUiXHLqINwGi+DM2bobH8alyfffbZzoO0Huq0H431V9d9TeQY9ACMkNy3qgqQaOUQdc/X/UPHe+211zrLnmoVB68GVl71qG00NEGTz2oeHa2GoQCAGj+af0Lzj+iNsFbiCe8B4+Y557s5dvk5l9vOHTvtuOOPs6EPDbVWrVo5D+V6kFW59Bbz3XffDRVDeevBVgE01ZEamnrAVV3qoVe9T1SnGhoVvkysVp/o06ePE5zQ6it6Ay4PLeutIJwCIqrvvBqZ+QVg3NVPZKDJGNUAVo8jrf50ww03OD19dJ7JKzIAE+9xuTBy1lACd5JgzYmht9q5rdSS6OuT/NJHQEEIDePQuadAhv6twMOwYcM8C6l5K9RQVKNYvV3cya3V00zXsIapuKuvJWIIkoK+2oca4LqmFBhWgFTXve7tChJrfxoWGHnNxXLP8bpe1StIq9GoN8L06dNDq7+Fw+QVgNHqO/LScEKtbqN7nq47BYt035Gz7kNePfT86gGjshekDnXf0/mietbwME1eq2PT/VErByk4ryE1WupewS036WWIjl/nme6hGo6k4IPuRZoXTMPe9PyjXqtu0hBQnWcK3ChAou9hBX50v1de6hWje7nqOd4eMG5Zda/Vct5y131f87LopYte4Kj+dY55TXYcz3GFnz/qFaqeWm7ShNLqlRNLSqfnxljKXVg/S3ss+DVPACb4dZi0I+CCTxp1gXbk9UXqLvG8bde2AuUdy8YKvqzcvNJ2Zu3MsZlWNapWvppVKVPFc4hKLPvx67NqWB955JFO414PfOpWr4cqBQDUUFeKNgCjB049SOpNq5Ie1DRxnh74le677z5nfLgaA5kQgNFx6c2tlj5W0ptnHa8ecmWoFXM0LEBJXaWrVasWdTXqATd8KVk1+PXQrkaWGkB6wHbnY5j20zRbvmF5tuDfZ+99ZndcfYdt27rnWtBDf4mSJWzDPxtCZdA24envv/62i869yH76fs+y1jofKuxbwf5Zv3cp2kdHPmrdLtq72ot6oHQ5rYvN+GaPgXoClC1X1tmmdJnS9vyrz1u3s/d8fvpP061WnVqhXb42+jW7/qrrrWbtmjZjzp7tvdLiPxfb6e1Ot9Wr9iwVrnu0jmXjho3O/h4Z8YgNfXCoLVm0xN589007rvVxBT6u8AwefvBh+8+gPSt/nNv9XBv+9PCo65EPZpZA22Pa2ry580IH9d4X71nTZk09D3L2zNnW5YwutmXzFuf3uh6U9G9NqK1r44JOe+Y0yu3aaHVCKxv33rhs+Xc5vYt9PfVri7wW9aEF8xZY1zO62oq/V4T2qQDM5k2brVLlSnbrXbfajVff6HnNPff0czbwxoGhfenaV1l1z9E1esudt9jVl1/tua2uzdbNWjvBVu1P+9qn1D5OXu61reu4ReM9q9VE3nv0f6+8+Ir9+9p/O0EYpX0r7uuU2122u3W71o5ZmbJlsnlc1/s6e33M63b+hefbY0895lkX7mduvPVGu2ng3uWR8zs7C1KHynv+r/Ode+qihYucXek7sVz5cs4Kdm6KvGfpeG/od4O98cob2epi65atzve0ktex3nf7ffbEY3t7IcpP93sFblR3X3z6hed5E14vkedhuI/2fe4Z54bu9fp+KF+hvHOv17PXhZdc6MyXpbrwco73uNwyfDXlK+fcVtL5+M2P38T8XLVj2w5bu3ytrdmyxubummubd+dcQju/c4Lf7xnufMmRl/hOQXvMd2Lfd0AAxnfizNkBF3ww6tIrALNo/SJn+E+qk3q5HFj2QDuw3IFWvGjxVBcn3/2rB4HGVSuQoAdoPbCFv+WMNgCjHentl7o7a4UQ9RRQbwq9ldVbPC1xmttymUFbBclF1Rta9RZR93+9xdawA/UkUffzevXqhVY9UfAkfEWRfCvFzCZNmuTkrUkc3TkQ1MVfExx///33pokN3XpSAFA9sNQTK2t3lpP9X0v/sjFPj7FpX0yz5UuWOw+rlatWtsOPPNxOOesUa9OhTY5iqNfMxFcn2ocTPrQFvyxwgj77HbCf1TqolrU7vZ2d3uV023e/fbNtp0bSc48/Zx9N+shWLF/hPJQ3P665XXb9ZXbIYYfYMTWOcT4/YdoEq16remjbSa9NsntvuNezF09kwXQsTz38lH39+de2fu16pwxHHn2k9ejdw5oc3cTObnm2c4xPjn3S2Xdkiue43Dzk0P3k7s4/n3zjSWveKvpluqOpZz4THAGd508M2dPIrV23tr055c08C79g7gIb9Z9RNvPrmbZp4ybbv9L+dszxx9gl/S6xuofWzffaaNaqmT31xlPZ9tH73N426+tZducjd9pZ3c7Ksf/VK1fbqEdG2ZSPptja1WudfbY6sZVd0f8KW/zHYutzXp9cr7nJH0620U+Ntrk/zHXuOVVrVLW2p7a1nv162vyf5+e5rX4/atgo+37G97Z+zfpQIGXG0j3B1WWLl1mnYzs5P7v/F1l4XWujnxxt3371ra1ZtcYJVNVrUM/OPO9M63h+xxxDvbT93dffbe+Mfcf5zN2P7hlmE5ncz1xxwxV25Y1XxnTCxVuH7k4UxHrjhTfs03c+tYW/LbTt27ZbpSqVrE69OnbSmSfZqZ1PzbbCnbvdV599ZW+9/Jb9NOsnW7dmnRMMP7D6gdaqXSvnWHX+RSZ9ftxL4+yP+X84QerDGh3m3CNbn9Lacjtvwusl8h4dmb+CQM8Nf84+mvCRLV+63HnhULdBXet8YWc7o+sZobrIyzme43LLcXLjk537v+pQ+4g57TCz9Wa/rPzF7vn+Hvttw28xZ8EGZo2rNLYfr/rRdwraY74T+74DAjC+E2fODrjgg1GXkQGYnUV32q+rf01p4TX/ht4MqNdLyWIlU1oWdp56AXcCWnWlV3AmGUkTQOvtsiaA3m27k7HLjN/HK8+8Yo/c9YgThHpz6ptpM39TxsNzgAgggMD/C8ybM896dOjhvEhQoEjBwZgTAZiYybw2IACTEMZCkQkBmEJRzYk5SAIwiXH0O5fwAEztOrXtj41/2NadW/3eba757196f2eel1LFS6WsDOw4fQTUJfvUU091Vo3Q3AiaKyWZSdeCVuJSV2tS/AKqx27tu9kf8/6wa267xi7p63+36/hLy5YIIIBAZgoMvmWwvfnSm3Zc++PssZe8h5rle+QEYPIliuYDBGCiUeIzEiAAw3kQtQABmKipUvrB8ABMmcplbOX2laHyaJnnOvvWSVr51NulRLESSdsfO0oPgb///ttuuukmZ2JIrRqkSQ6VNLmiJiHUhLmaF0YrYGiCxFSkHbt2mHrFkGIXUPBl2H+G2Z233enU7Zx5c0JDymLPjS0QQAABBOIRmDplqp19xtnOHDiT3p1kJ550YjzZOHOiaaiq87JOI2l5bMvVcdv2bbZ82XJbsnSJsxrj0iVLnd5HmhBbLxobVfH/mYb2WFyneVptRAAmraojvQvDBZ/e9eOWzg3AaL6LrWW32u4Se4dbHF7pcCtbsmwwDoRSBlZAy89qdQs3aaJcPSDq3FTSBLHq+XLZZZcF9hgLY8G1IouWTNdkyu4E0pqL58YbbyyMHBwzAgggkBIBdyUsvexQ6tixozM3WryJVZCyy+n7TasJ/v77784fDZV2f1bQJTJplatVq1bFyx/zdrTHYiZLuw0IwKRdlaRvgbjg07duwkvmfpHqTcauCrtCbzKqlK1itffNOTldMI6KUgZJQMuiajlnLYepZUBXrFjhTFyp1Y7atm3rvCnSClOkYAm4k05rxZKDDz7YWY1Ky4qTEEAAAQSSJ6AVtfRH36ldunSxBx98MNTTNJ5SFLYAjCbRV1BFy5G3bt06B9lrr73mvGyIJSloo8UGkpFojyVD2d99EIDx1zejcueCD0Z16ov025+/tdIlSoe6kmooUKPKjaxY0WLBOAhKiQACCCCAAAJpKaBhiPoTntygQGSBFXyPNeWWl9d+o8lbQ0QiU7x5ZeJx6rnxzz//dIhq1qxppUqVcoYJ61jDk8zc5c+jcXc/o16v+hOZlFfkeZRfviqTyhaZtEy7u1S78ly5cmW2nituDxb9/ddffzmbH3jggaGfw/PTypctWuxZGj6apJcSWoGxYcOG0Xy8wJ+hPVZgwpRnQAAm5VUQnAJwwQejrpauWWqTv59s9Q+oHwrA1Nu/nlUsVTEYB0ApEUAAAQQQQCClAmrMqjej+0fDSMP/HdlwbtCggZUrVy5HmWfNmuX0gIwlqWEcPozV3Va9FlavXh1LVqZn1yOOOCLHNhpKuWDBgpjy0ofVyC5TpkyO7WbOnBlzMEE9WGrUqJEjr99++83Wrl0bU9kUNGncuHGObdTLQ0GHaJJ6rN5zzz2m/etn1Wl40jmhYEOs6f7777fbbrstx2adO3e2CRMmxJRds2bNTNaR6aWXXrJLLol9MviNGzc6y4aHJw0nqly5crb/q1SpktWtWzfHH/UGVdAqHpeYDjzsw7TH4pVLn+0IwCShLhSFHTx4sE2cONE0N4IudN1A+vbta7r5xJsmT55sI0eOtK+++sqJ4Cq6rJtAmzZt7Jprrkl4F38u+HhrKrnb9X+7vzUr3cwOr3y4E4CpWL6iKQBDQgABBBBAAAEEJKAASmQPB/2/Gv7qDbFz586YoAjAEIDJ7YRJ5wDMjz/+mCNwpWvj0UcfNc21o6CLgizJGl4UzUVHeywapfT+DAEYn+tnzpw51r59e2cOBCWtGKGxh243OY2ff+yx2JeNu/322+2BBx4IlV7RePdthf5TwZgRI0ZY7969E3aEXPAJo/Qto49//9j6jO9jdx15lxOAKVKxiB1R4wjTECQSAggggAACCBQeAQVRInuuuL1YFHzx6jHxzz//2Lx582JGIgBDACbdAzDqWeUGVNzeLGeddZapd0uQEu2xINWWd1kJwPhYh/qSU1dFdf3Tl9zo0aOdXikKwAwbNszuuOMO5w3Es88+a7169Yq6JB999JF16NDB+fz5559vgwYNcm4oyktjEK+77jpT7xh1h1MA6NBDD40677w+yAWfEEbfMtmyY4sdMfIIs50WCsBUrVHVah5QM8c+N2zYEPPbLY259epevHXr1tDqNpE7UiBQ503JkiU937T5huFjxupKrXHLur6VdHyy8Rpj7mMxyBoBBBBAIB+B8Pu1++IrGjS97faasyLWYSHal4aGlC5dOsduN23a5ARHYkkqk9ebeOWjSUAjhwzl1YtFARj1xo7sBaM81CvAK+mz+s7Tn8ghFxpOo2ONTOpNE+sQJK2ct//+++fISz3KNWQklqTvZ/UOj0x6FndXEYolv+rVqzvHH5kWLlwY8xCkihUr2n777ZcjL7201fkRS9JzltdwJuXjvgTOLT+dJ/rc8uXL7eOPP3Ymp9XEvqrT8KR6jGf1Qk0UrEBHZHr88cftu+++i+UwrU6dOnb33Xfn2EajAUaNGhX6f73wDh8ypF4skUONYtpxGn2Y9lgaVUacRSEAEydcNJsNHz7cWSFCvVM0lrJ27ewr0GgFCfVS0c1cN26vSaW89nPppZfaCy+8YPXq1XPyjfwS1Jewvmz0JfXQQw/Zv//972iKm+9nuODzJUrpB2775DZ7cOqDVqtsLXvgqAesYeWGdujBh3rOjD937tyYH2L00OcVzNMDzOLFi/M9dvehzQ3I6I1DtOd8vpn7/AEFXBRIdd8keu3OPa7w49S17/VA6nNxyR4BBBAoNAJqPCq44tUoVoPSa9nY/HAaNWrkGTT59ttv89s0x+/1jKc/kSmeeT4UyFHZIpPmRdH8KLEmzY0S6aaXeb/++mso0OJ+p6mB7zUxa6z75PPpJ6D2gvscp0AFzy3pV0fhJaI9lt71E03pCMBEoxTnZ1q2bGnTp093osXPPPNMjlwWLVrkjC/Ul52Wa3V7teS3u9NOO835fNeuXe2NN97w/Hjz5s1NE58pSnzXXXfll2VUv+eCj4opJR/6acVPdtRTR9nOrJ22T9F9bMSxI6xF9Ra23777OW9EIt9wpSIAEwnTpEkTp2dMeNLblWXLljn/H/7Ql+jeJdpPeLfs8J8V2PQql66nWJOCTLrGI5PeNKnR4AZtEn18sZaTzyOAAALpKpDX/Vq9NRR80Zvtww8/PMchxDIBafjGmRyA0feN+92jiW5pbKfrmZ+ccqkNoiClekbr3Khfvz49epNDH/deaI/FTZc2GxKA8akqFE1WjwHd2MaOHWvnnnuu5570Jf/zzz/bgAEDbMiQIVGV5qqrrrInn3zSuUlqW68eMPpS1c1UE/96dfuLakcRH+KCj0fN/22ydmdZ6+da21eLvwrt7LmTnrOWB7R0/q1eGDoXVX9uIEZvytT9NpakB1yvYILevLlL+kWbn8qhh2Wvrs9eqxLorZv+KDAS/kf/py7ZXhMJ6qFcgZXIP+rNktcyijpGr26qGhMf6/KLVapUyTGTvow0Gbd6qrkp8vjCjzW344vWms8hgAACQRLQvVHPULp353e/do9Lz0GHHXZYjsPU8rrRrgATvvEhhxziGZjQsO5Yk1ZT0XdBZFKPA823EkvS97heEkQmregT3tPH/U7x+t7kOyUW8cz9rNonCmDqHHSfBzUcqmrVqpl70BlyZLTHgl+RBGB8qsPwNeS9lnJzd9utWzd7/fXXrWPHjjZp0qSoSqPxkurhopunttc4TXcOmB9++MEZ9qQ5YDT5r8ZyejVOo9pRxIe44ONR83+bJ7990q5656rQjg6ueLDNvnK2rf5rtWl+Fq+k4ETkEpL5lVTnkdeYeL2dzG18t/bh/tFn3J/1lsVrTLzKlVuZcyufOyY9smzx5KV9eI1t1/+rXCq/yq59uteV1/G5Zc0rr2jnI3D3peOL7Jmj/agnTazj67WdV17KJ9aVL5SXGj9evXhind9AeSkfr+UcOc68r9BE1mduwwziqU+dt173jXjqU9eC17DFeM/bZBynAgjx3Gu9jlP3jGjvG+Fni9dxqkyxBpSVZ271Getxav/Ky+tadwMv+X0nRf5eLxsin3fc4wy/Z0eTb25Binjuj7qned0fE/097K5qFOuxRuPBZzJfQD2hNH1BUIaGZ36N5H6EtMeCX/sEYHyqQ/U86dSpk5O73ubktnxZ//79naXOFFCJZWyxJvS98sorQ5Ofhq+CpOj1v/71L7vzzjs9x0Tndshe46fDP6uHIj3ku5OP+kRHtjEILN+w3A4fcbit37a3N8X7Pd63U+ud6jTKP/30U2fyNfWW8ppAN4ZdJeyjeoDVH69uz5pgTxP2xZrUmyay14rOVwUko01u405j9QsyI76OzZ0IUeZeDWNNlh1r40eT9Xm9+dRb1FgnElSjp2nTpjlo1GtOY/9jTXrzrAnvItPs2bNjDujoTXHkfFnKV+eFzo9Ykh4kNfF5ZEr0Kh8KisfaMNZ92mtiSPVOU6+yWJLu3ZrLITLprbhXj7L88tbk8fpOiUwzZyZulQ/1StDwkFhSbvNfaGJUzacRa9Lk+JH3ITVidZyxJg31jJywUnnIX/UQS5K96iAyJXKeD30/xDOsUr1rtZJIZJo/f362Xn3RHK/etqunSWSKZl4x934dPueW7h1ewb5oysJnECisAnpG0fe3nnsYDh2Ms4AATDDqKa9SEoDxqQ7HjBljPXr0cHJXQ8vrLY9+d9tttzk9WDS5aawNHzWuL7roImfW8vCkhtrFF19s9913n2km+WgTAZhopdLnc+ePPd/G/jw2VKDujbvbmK5jQv8+4YQT7Msvv3TOg7Zt2zozxHs1lNPliBS80PmsIToKLOhv94/+ndvqB1OnTs1xrquBcfTRR2cLdCggogaEGr7uH/ffahAn482PGnhff/11tuNS1/G8jk/1o2v95ptvzlFVGrqogGwsSeeDzCKTGp6a5DvWpJXcjjnmmBybtW7dOubGZ/fu3W3gwIE58rr33nud4ZyxJHX9130yMslfAexYk7uSXeR2mu8r1iF9cr7xxhtzFEHfCQrgx5J0Lr/33ns5u+/34AAAIABJREFUNvn888/tmmuuiSUr57Ny1pKykUlBu1gDTXL2KoMmh3///fdjKpt6ek6YMCHHNpoT7aabboopL31YzppwMjzp+LyCk/llrmP0OqfUI/Wzzz7Lb/Nsv1dAWT1jI5N6yXpdG/llLufI1VE0NKdFixb5bZrj93Lu2bNnjv/v3bu3aRWSWJICt17z2MlLbl73a/e+rWBXMu7XsRwPn0UgSAIKYqptklv7JEjHUtjKSgAm+DVOAManOvQzAKNGqh72NA+MHqAUwNFSgnrj/8033zjzySiYo7d7Xg3TeA+ZCz5eOX+2e2feO9bxlY6hzCuWqmhz+821A8vteTupCaDVMHSTfp42bZo/hUlSrnrTrTfn4X8UsFGjzGuo3dNPP+0s8eguRei13GOSih7VbryOzz1WNdb79u2bI5/rr7/eHnvssajydz+k5T29ellMmTLF2rRpE1Ne+rAaTO3atcuxnQIgq1atiik/rQ6nFeQiU58+feypp56KKS/1ZvJaAeWjjz6KetLz8B0qcHPsscfmKIOCmrEujaqG7NChQ3Pk5a5yF8uB6vz26gGiBvvZZ58dS1bOZ9WjxysIoQZvrEMwbr/9dudlQGS64IIL7LXXXoupbOoV4jUHhwJG559/fkx56cNew4MVgImnQXL//fc7L1QiU+fOnT2DRnkVVt/nXr1wXnrpJbvkkktiPk71qoqcv0sTgcfTK/Lhhx/2DBy6iwPEUjjdj3Ufirx3KzikIZ/pfr+O5Vj5LAIIIJAoAdpjiZJMXT4EYHyy93MI0uDBg+3WW2913lDqQTmyC7W66WtyX/19yy232KBBgxJylFzwCWFMSCYbt2+0Rk80skXrF4XyG3XWKLu82eWhf6snwauvvhr6txo78TRSElJgMvFNQMOdYh3OpMJ4Nb7U+FTjJ9akoSFeXf9jDUpov2rke/XGU4Ms1sa/GnZeEyorn1jnGlLZOM6NsZ4aCa1PdY/3GhoVb30qr8gu9+qhpuBErMmdHDxyO11PsfYayu04dZ3HMwQ4XY9Tzy7xBLtirRs+jwACCGSSAO2x4NcmARif6lDzubhd8hM9Ca/GX2teD70l1ttir6S35Y888oizKoCWHE5E4oJPhGJi8rjxgxvtkWmPhDJrXbu1fX7p51a0SFHn/7TEud6Kuw/+mlNDb8h52E2MP7kggAACCCCAAAIIIJBsAdpjyRZP/P4IwCTe1MkxfBlqjXHu2rWr5540TEhdqqNdhlrddd0JQt955x0744wzPPMdMWKEE5zRRRrPm16vTLngfTpZYsx21vJZdsyoY0zLTyuVKFrCvu/zvR1e+fBQTppfQV3F3ZRbt/EYd83HEUAAAQQQQAABBBBAIEUCtMdSBJ/A3RKASSBmZFaaK0Bzslx++eXO5KeRSZOLqmeCujxrEsEOHTrkWxrNEaH5G5RGjhxpmhfBK2myzoceesg0B4N6yyQiccEnQrFgeezK2mUtn2lpM5fvXaXjzjZ32j0n3hPKWCvZaGJZrb6lpKEmOtdimZC5YKVkawQQQAABBBBAAAEEEEi0AO2xRIsmPz8CMD6aa4iQZvLXHAQahqRGcXjS7/QZTRS5cOHCqGf012R6WpJVS1crwBM594KWWNUcMGp0d+zY0TQRYyISF3wiFAuWx2PTHrPrP7g+lMmhBxzq9H4pVbxU6P8ef/xxu+6660L/1iStw4YNK9iO2RoBBBBAAAEEEEAAAQRSKkB7LKX8Cdk5AZiEMHpnosnytGqDVjFp0qSJaQUD/a1J+bRqiZaTVO8XLeHaq1evbJm4QRYt9/j8889n+52WndXkukpnnnmm09NFE/Jq2d1Zs2aZGtxarUNJq32cfPLJCTlKLviEMMadyeL1i63hEw1NE/C66bOen1m7g/auPqM5X+rXr29a9UJJkzkuWLAgx1KrcReCDRFAAAEEEEAAAQQQQCAlArTHUsKe0J0SgEkoZ87MNL9L+/btQ8OAKlSoYJs3bw6t5qHlpNVjITLlFYBRI7tHjx7ZlvDUagL6f3c1FDW8tTy1hiIlKnHBJ0oy9nwUqOv8Wmeb+OvE0Ma9mvayZzs9my2zt956y7p06RL6P809pDmISAgggAACCCCAAAIIIBBsAdpjwa4/lZ4ATBLqUHOwaOloDQVavHixMySpWbNm1q9fP+vcubNnCfIKwLgbTJgwwekdM2PGDGfJaS25WqNGDTvhhBOcvFu0aJHQo+OCTyhnTJmN+2WcdX1970TOlcpUsrn95toBZQ7Ilk/r1q1t6tSpof/Tz8cff3xM++LDCCCAAAIIIIAAAgggkH4CtMfSr05iLREBmFjFCvHnueBTU/nrt653hh4t27AsVICXznnJLmpyUbYCKRAXHnTTz9OmTXMCcyQEEEAAAQQQQAABBBAItgDtsWDXn0pPACb4dZi0I+CCTxp1th1d/e7VNmLGiND/nVL3FPvgog9yBFbef/9969u3b2j+l1dffdW6deuWmkKzVwQQQAABBBBAAAEEEEioAO2xhHKmJDMCMClhD+ZOueCTX2/Tlkyz4/53nO223c7OtdrRT1f9ZIfsf4hnYTQPkIamjRkzxhSAKV68ePILzR4RQAABBBBAAAEEEEAg4QK0xxJOmvQMCcAknTy4O+SCT27d7di1w5o/3dx+XPFjaMcPtH/ABrYemNyCsDcEEEAAAQQQQAABBBBIuQDtsZRXQYELQACmwISFJwMu+OTW9ZCpQ+yWT/YsN67UqHIjm9V7lpUsVjK5BWFvCCCAAAIIIIAAAgggkHIB2mMpr4ICF4AATIEJC08GXPDJq+vf1/5ujZ9obFt2bgnt9Mt/fWnH1ToueYVgTwgggAACCCCAAAIIIJA2ArTH0qYq4i4IAZi46Qrfhlzwyanz3bt322kvn2Yf/vZhaId9mvexkR1H5iiA5nzRMufVqlVLTuHYCwIIIIAAAggggAACCKREgPZYStgTulMCMAnlzOzMuOCTU79jfhxjPcb1CO2sarmq9ku/X6xiqYo5CqAJd8877zzr3r279e/f35o2bZqcQrIXBBBAAAEEEEAAAQQQSKoA7bGkcvuyMwIwvrBmZqZc8P7X65ota+zwEYfbik0rQjt7/dzX7bxG53nuvG3btjZ58uTQ77777juCMP5XE3tAAAEEEEAAAQQQQCDpArTHkk6e8B0SgEk4aeZmyAXvf91eMfEKe+a7Z0I7OrP+mTap+yQrUqRIjp3PnDnTjj766ND/6+fp06d7ftb/krMHBBBAAAEEEEAAAQQQ8FOA9pifusnJmwBMcpwzYi9c8P5W45Q/p1ib59uEdlKmRBn7ue/PVqdiHc8dX3TRRfbyyy+HfqefL7zwQn8LSe4IIIAAAggggAACCCCQEgHaYylhT+hOCcAklDOzM+OC969+t+3cZk2fampzV80N7eQ/Hf5jN7S6wXOnS5cutYMOOsh27tzp/L5GjRr2xx9/WIkSJfwrJDkjgAACCCCAAAIIIIBAygRoj6WMPmE7JgCTMMrMz4gL3r86vveLe+2uz+8K7aBZtWb2zeXfWPGixT13euutt9rgwYNDvxsyZIgNGDDAvwKSMwIIIIAAAggggAACCKRUgPZYSvkTsnMCMAlhLByZcMH7U8+/rvrVmjzZxLbv2u7soGiRojb98unWvHpzzx1u2rTJatWqZWvXrnV+X6ZMGVuyZIntt99+/hSQXBFAAAEEEEAAAQQQQCDlArTHUl4FBS4AAZgCExaeDLjgE1/Xu3fvtvYvtrfPF34eyvz6ltfbsNOG5bqzJ554wvr16xf6/dVXX23Dhw9PfOHIEQEEEEAAAQQQQAABBNJGgPZY2lRF3AUhABM3XeHbkAs+8XX+3HfP2b8m/iuUca0Kteznfj9buZLlPHeWlZVlDRo0sPnz5zu/1+pI8+bNs3r16iW+cOSIAAIIIIAAAggggAACaSNAeyxtqiLughCAiZuu8G3IBZ/YOl+5aaU1GNHA1mxZE8p4wgUT7OzDzs51R5MmTbKzz977+06dOtn48eMTWzByQwABBBBAAAEEEEAAgbQToD2WdlUSc4EIwMRMVng34IJPbN1f/NbFNvqH0aFMuxzexd48/808d3LiiSfa55/vHa70xRdfWJs2e5euTmwJyQ0BBBBAAAEEEEAAAQTSRYD2WLrURPzlIAATv12h2zKTL/iZy2Zav3f72Z/r/0xKvWrul783/R3aV/mS5e2Xfr9YjQo1ct3/d999Z82aNQv9Xj9/++23zjAkEgIIIIAAAggggAACCGS2QCa3xzK75vYeHQGYwlLTCTjOTL3gv//re2v3Qjtbt3VdApTiy+K/p//X+rXYO7GuVy5a9eipp55yJtxdtmyZjR492nr06BHfDtkKAQQQQAABBBBAAAEEAiWQqe2xQFVCAQtLAKaAgIVp80y84Oetnmetn2ttKzatSFlVHlvzWJvaa6oVK1osqjJs377dxo0bZ126dLGSJUtGtQ0fQgABBBBAAAEEEEAAgWALZGJ7LNg1EnvpCcDEblZot8i0C37R+kV2wrMn2OJ/FofqtHKZyta4SuOk1XHd/eragyc9aFXKVknaPtkRAggggAACCCCAAAIIBE8g09pjwauBgpeYAEzBDQtNDpl0wf+98W+n58v8NXuWc1ZS8GVyr8nWoFKDQlOnHCgCCCCAAAIIIIAAAggEQyCT2mPBEE98KQnAJN40Y3PMlAteyz63e76d/bjix1Bd7bvPvvZZz8/sqGpHZWz9cWAIIIAAAggggAACCCAQXIFMaY8FtwYKXnICMAU3LDQ5ZMIFv2HbBjvlpVPsm6XfhOqtTIky9uFFH9rxtY9Pu7rMysqy//73v3bhhRdapUqV0q58FAgBBBBAAAEEEEAAAQSSI5AJ7bHkSKXvXgjApG/dpF3Jgn7Bb9251c4cc6Z9+senIduSxUra293ftlMOOSXtvFWgt99+28466ywrVaqUXXLJJXbzzTdb3bp107KsFAoBBBBAAAEEEEAAAQT8Ewh6e8w/meDkTAAmOHWV8pIG+YLfsWuHdX29q02aNynkWKxIMRt73lg75/BzUm6bWwHat29vn332WejX+rldu3ZpW14KhgACCCCAAAIIIIAAAv4IBLk95o9I8HIlABO8OktZiYN6we/K2mUXv3WxvfLTK9nsXuz8ol185MUp88xvx7Nnz7ajjto7J03Tpk1t1qxZVqRIkfw25fcIIIAAAggggAACCCCQYQJBbY9lWDUU6HAIwBSIr3BtHMQLfvfu3dbn7T729Kyns1XWiDNGWN9j+qZ1Bfbs2dNefPHFUBn188UXp2/AKK0xKRwCCCCAAAIIIIAAAgEXCGJ7LODkCS8+AZiEk2ZuhkG74BV8GfDRAHv464ezVcqgkwbZLSfcktYVtXz5cqtTp47t2LHDKWe1atVs4cKFVrJkybQuN4VDAAEEEEAAAQQQQAABfwSC1h7zRyHYuRKACXb9JbX0Qbvg7598v93x2R3ZjG45/hYbdPKgpLrFs7Pbb7/dHnjggdCm+nngwIHxZMU2CCCAAAIIIIAAAgggkAECQWuPZQB5wg+BAEzCSTM3wyBd8I9/87hd9/512Sqj79F97b9n/Dft51DZvHmz1a5d21avXu2Uv3Tp0rZ48WI74IADMvfk4sgQQAABBBBAAAEEEEAgT4EgtceoSm8BAjCcGVELBOWCf37289ZrQq9sx3Vxk4vt+c7PW9EiRaM+3lR98KmnnrI+ffqEdq+fR44cmarisF8EEEAAAQQQQAABBBBIA4GgtMfSgCpti0AAJm2rJv0KFoQL/o2f37Bub3SzrN1ZIcDODTo7y00XL1o8/VAjSpSVlWWNGjWyuXPnhn6jnw877LC0LzsFRAABBBBAAAEEEEAAAf8EgtAe8+/oMyNnAjCZUY9JOYp0v+DfX/C+nf3K2bYja8/EtUon1z3Z3u7+tu1TfJ+kGBV0J++++66deeaZoWz089tvv13QbNkeAQQQQAABBBBAAAEEAi6Q7u2xgPMmpfgEYJLCnBk7SecLfsqfU+zU0afalp1bQtjH1TrOPrzoQytbsmxgKuCUU06xjz/+OFTeTz75xNq3bx+Y8lNQBBBAAAEEEEAAAQQQ8Ecgndtj/hxx5uVKACbz6tS3I0rXC37mspl24gsn2obtG0LH3rRqU/us52dWsVRF3zwSnfGyZcusbt26tm3bNifrI4880r777ru0nzQ40Q7khwACCCCAAAIIIIAAAjkF0rU9Rl1FL0AAJnqrQv/JdLzgf175s7V5ro2t3rJnxSClww44zCb3mmxVylYJXJ39/fff9sQTTziT7g4dOtR69uwZuGOgwAgggAACCCCAAAIIIJB4gXRsjyX+KDM7RwIwmV2/CT26dLvgf1/7u53w7Am2fOPy0HHW2beOTek1xWrtWyuhx57szLZu3WpFixa1kiVLJnvX7A8BBBBAAAEEEEAAAQTSUCDd2mNpSJT2RSIAk/ZVlD4FTKcLfuk/S631c63tj3V/hICqlqvqBF/q7V8vfdAoCQIIIIAAAggggAACCCCQAIF0ao8l4HAKZRYEYApltcd30Olywa/avMoZdvTLql9CB7Jfqf3si0u/sCMOPCK+g2MrBBBAAAEEEEAAAQQQQCCNBdKlPZbGRGlfNAIwSaiilStX2uDBg23ixIm2ZMkSK1u2rDVr1sz69u1rnTt3LlAJNm3a5MwXMm7cOJs3b55t3LjRDjzwQGvYsKGdfPLJduONNxYo//CN0+GCX791vbV/sb3NWj4rVLRyJcvZJ5d8Yi1qtEjYsZIRAggggAACCCCAAAIIIJBOAunQHksnjyCWhQCMz7U2Z84cZxnhFStWOHsqX768bd682Xbt2uX8+9prr7XHHnssrlJohRwFcBYtWuRsX6JECStTpoytX7/e+XexYsVs586dceXttVGqL/jNOzY7S01PXTQ1VLxSxUvZez3es3YHtUvYcSYzo927d9sZZ5zhBMsuv/xy23fffZO5e/aFAAIIIIAAAggggAACARFIdXssIExpXUwCMD5Wj5YTVk+U33//3Ro3bmyjR492lhZWAGbYsGF2xx13mBrgzz77rPXq1SumksyfP9+OPfZYW7NmjR1//PE2aNAg529N3KpeMbNmzXJ6xWg/iUqpvOC37dxmnV7tZB/89kHocIoXLW7ju423Mw89M1GHmPR83n//fTv99NOd/ZYrV84effRRu+yyy5JeDnaIAAIIIIAAAggggAAC6S2QyvZYessEp3QEYHysq+HDhzs9XNQr5ZdffrHatWtn29vVV19tI0aMsOrVq9vChQudHizRpjZt2tiUKVOsXbt29sEHHyRltZxUXfA7s3baBW9cYG/+8maIp4gVsVe6vmLdGneLliwtP9ehQwf76KOPQmX7+OOP7aSTTkrLslIoBBBAAAEEEEAAAQQQSJ1AqtpjqTvizNszARgf67Rly5Y2ffp0p0fDM888k2NPGjp00EEHOb1gFERRYzyaNHnyZGvbtq0VKVLE5s6da4ceemg0mxX4M6m44LN2Z9llEy+z52c/n638o84aZZc3u7zAx5TKDH766Sc74oi9kwY3adLEZs+e7dQrCQEEEEAAAQQQQAABBBAIF0hFe4waSKwAAZjEeoZy02S4FSpUcIIrY8eOtXPPPddzT40aNbKff/7ZBgwYYEOGDImqNL1797ann37aTjjhBKcXTLJSsi942V33/nU2fPrwbIf4SIdHrH+r/sk6bN/2o8Cchp+5KZ6haL4VjowRQAABBBBAAAEEEEAgrQSS3R5Lq4PPkMIQgPGpImfMmGEtWuxZlUfDjxo0aOC5p27dutnrr79uHTt2tEmTJkVVGvWaUO+JW2+91fr06WP33nuvaS4RrbZUqVIlJzBzww03mHrgJDIl+4K/49M77P4p92c7hLva3mV3t7s7kYeVkrz+/vtvq1OnjmmeICWtXPXnn3+aa5ySQrFTBBBAAAEEEEAAAQQQSFuBZLfH0hYiwAUjAONT5WnJ6U6dOjm5a1Ui9YbxSv3793cmXm3evLl9++23UZWmVKlSTsP9mmuusZdeesnWrVvnzAGjuWb0s5Im49XqSppnJlEpmRf80C+H2oCPB2Qr+vUtr7dHTn0k8EN01DvqiiuusFdffTV0fPfcc4/deeediaoq8kEAAQQQQAABBBBAAIEME0hmeyzD6NLmcAjA+FQVY8aMsR49eji579ixw4oXL+65p9tuu80efPBBZx6XX3/9Nd/SbN261UqXLu18TnOF6OeRI0faBRdc4ARhfvvtN+vXr58zp4yCMJovRqsjRZPy632xfft2Zx9ur41o8oznMxPmTrDOr3XOtullR11mmvcl6POjfPrpp86cQJp02U1yX7x4sVWuXDkeLrZBAAEEEEAAAQQQQACBQiBAACb4lUwAxqc69CsAoyWsy5YtGyq1erlopaXwtGXLFqtfv74tXbrUzjzzTHv77bejOsp0CcBs3bnVzh97vk2at2dI1vmNzrcxXcZYsaLFojqOdPzQhg0bnHl+nnzyyRzF01AyBeFICCCAAAIIIIAAAggggEBuAgRggn9uEIDxqQ79HIJUrFgxy8rKMg1F0vAm9UqJTGrQq3eNhiX9888/pm0KmpJ5we/YtcN6ju9p67aus/EXjLeSxXIeY0GPJ1nbazLhVq1a2TfffJNtl6q3u+++2wnMJKJ+knU87AcBBBBAAAEEEEAAAQSSL5DM9ljyj65w7JEAjE/1rPlcjjnmGCf3RE/CW7NmTad3S8OGDW3OnDmeRzBu3Djr2rWr8ztN+FqlSpUCH2myL/hdWbtsR9YOK1W8VIHLnuoMxo8fb+ecc06oGJqg+bnnnnPqkIQAAggggAACCCCAAAII5CeQ7PZYfuXh97ELEICJ3SyqLcKXoX7jjTdCwZDIjRs3buwEUWJZhvq0005z5njJKwDz5ptvhpa+XrFiRULmF+GCj6rqc/3QhRdeaAqMadUqrVKV27xABdsLWyOAAAIIIIAAAggggEAmCtAeC36tEoDxsQ6PPfZYZ9jJ5ZdfbqNGjcqxpyVLlljt2rVNQ1QUUOnQoUNUpXn44Yft3//+d55DkB544AG7/fbbrXz58s7KSJqQt6CJCz5/QVnv3LnTWQ48Mq1atcr0J7clyfPPnU8ggAACCCCAAAIIIIBAYRWgPRb8micA42MdDh8+3JkgV5PmahhSrVq1su1Nv9Nnqlev7qyKU6JEiahKoxVz6tWrZ1qVaNiwYXb99ddn204T9WoS3mXLljm9YMaOHRtVvvl9iAs+b6F33nnHrrzyStPwIvV0CfqKTfmdD/weAQQQQAABBBBAAAEEkidAeyx51n7tiQCMX7JmznLNGib0+++/W5MmTeyll15y/tYqRVq9aODAgU7vl2effdZ69eqVrSQHHXSQ/fnnn9azZ097/vnnc5RSQ5aGDh0aWoa6e/fuoWWo+/btax9++KHz7+nTp9uRRx6ZkKPkgvdmXLt2rfXv399eeOGF0AdeeeUVZ2lwEgIIIIAAAggggAACCCCQCAHaY4lQTG0eBGB89tf8Lu3btzfNw6JUoUIFUw8VDVNRuuaaa+zxxx/PUYr8AjDa/rzzzjNN7qqki1ErHikY4P5bgZtEBgG44HOeLJMmTbLevXvb8uXLs/1SQ8sWLFgQda8mn09DskcAAQQQQAABBBBAAIGAC9AeC3gFmhkBmCTUoYIvgwcPNjXWNXxIQ5KaNWtm/fr1s86dO3uWIL8AjLuRetX873//sx9//NE08W+1atXspJNOsptuuskOP/zwhB4dF/xezjVr1th1111no0ePzmHcunVrp1eThomREEAAAQQQQAABBBBAAIFECNAeS4RiavMgAJNa/0DtnQt+T3VNmDDB+vTpY3/99Ve2+lMPpCFDhpiGgCVi0uNAnRwUFgEEEEAAAQQQQAABBHwVoD3mK29SMicAkxTmzNhJYb/gV69e7UyqPGbMmBwV2q5dO6cnUt26dTOjsjkKBBBAAAEEEEAAAQQQSCuBwt4eS6vKiLMwBGDihCuMmxXmC16rGl111VWhuXzc+tdwsoceesjpEUOvl8J4VXDMCCCAAAIIIIAAAggkR6Awt8eSI+z/XgjA+G+cMXsorBf8jh07nJWktJR4eNLkys8884wdfPDBGVPHHAgCCCCAAAIIIIAAAgikp0BhbY+lZ23EVyoCMPG5FcqtCvMFP23aNDv++OMtKyvLypUrZw8//LBdeeWVVqRIkUJ5LnDQCCCAAAIIIIAAAgggkFyBwtweS660f3sjAOOfbcblXNgv+AEDBtjs2bNt1KhRVqdOnYyrXw4IAQQQQAABBBBAAAEE0legsLfH0rdmoi8ZAZjorQr9JzP9gt+9e7e99tpr1rRpU2vQoEGO+tZQpOLFi9PrpdBfCQAggAACCCCAAAIIIJB8gUxvjyVfNPl7JACTfPPA7jGTL/h//vnHLr30Unvrrbfs2GOPtalTp1qxYsUCW1cUHAEEEEAAAQQQQAABBDJLIJPbY5lVU7kfDQGYwlLTCTjOTL7gL7jgAqf3i5s0x8uNN96YADWyQAABBBBAAAEEEEAAAQQKLpDJ7bGC6wQjBwIwwaintChlpl7w8+bNs8MOOyybcdeuXW3s2LEMN0qLM49CIIAAAggggAACCCCAQKa2xwpTzRKAKUy1XcBjzdQLvm/fvjZy5MiQzt1332133nknwZcCni9sjgACCCCAAAIIIIAAAokTyNT2WOKE0j8nAjDpX0dpU8JMvODXrFljNWvWtC1btjjO+++/vy1evNjKlCmTNu4UBAEEEEAAAQQQQAABBBDIxPZYYatVAjCFrcYLcLyZeMEPGjTIBg4cGFLRzw888EABlNgUAQQQQAABBBBAAAEEEEi8QCa2xxKvlN45EoBJ7/pJq9Jl2gW/fft2O/jgg23ZsmWOc4kSJWzhwoVWvXr1tHKnMAjaLaAMAAAgAElEQVQggAACCCCAAAIIIIBAprXHCmONEoApjLUe5zFn2gU/evRou/jii0Ma+vnFF1+MU4fNEEAAAQQQQAABBBBAAAH/BDKtPeafVPrmTAAmfesm7UqWSRf87t277eijj7ZZs2aFnPXzUUcdlXbuFAgBBBBAAAEEEEAAAQQQyKT2WGGtTQIwhbXm4zjuTLrgv/jiC2vXrl1I4cQTT7RPP/00DhU2QQABBBBAAAEEEEAAAQT8F8ik9pj/Wum5BwIw6VkvaVmqTLrgO3fubBMmTAg5T5w40c4666y0dKdQCCCAAAIIIIAAAggggEAmtccKa20SgCmsNR/HcWfKBa8lp5s3b26//PKLo1C/fn2bO3euFS1aNA4VNkEAAQQQQAABBBBAAAEE/BfIlPaY/1LpuwcCMOlbN2lXsky64LOysuy9996zYcOGWdeuXe2qq65KO28KhAACCCCAAAIIIIAAAgi4ApnUHiustUoAprDWfBzHnakXvCbkLVKkSBwibIIAAggggAACCCCAAAIIJEcgU9tjydFLj70QgEmPeghEKbjgA1FNFBIBBBBAAAEEEEAAAQQyUID2WPArlQBM8OswaUfABZ80anaEAAIIIIAAAggggAACCGQToD0W/BOCAEzw6zBpRxD0C37Hjh1WokSJpHmxIwQQQAABBBBAAAEEEEAgUQJBb48lyiHI+RCACXLtJbnsQb7gNc9Lq1atrE6dOnbDDTdYy5Ytk6zH7hBAAAEEEEAAAQQQQACB+AWC3B6L/6gza0sCMJlVn74eTZAv+ClTplibNm1CPldffbUNHz7cVy8yRwABBBBAAAEEEEAAAQQSJRDk9liiDIKeDwGYoNdgEssf5Av+nHPOsfHjx4e09HOnTp2SqMeuEEAAAQQQQAABBBBAAIH4BYLcHov/qDNrSwIwmVWfvh5NUC/43377zerXr28ahqRUr149mzt3rhUrVsxXLzJHAAEEEEAAAQQQQAABBBIlENT2WKKOPxPyIQCTCbWYpGMI6gV/7bXXZhtupKFHGoJEQgABBBBAAAEEEEAAAQSCIhDU9lhQfJNRTgIwyVDOkH0E8YJft26d1axZ0zZt2uTUQsWKFW3x4sVWrly5DKkVDgMBBBBAAAEEEEAAAQQKg0AQ22OFoV5iOUYCMLFoFfLPBvGCHzp0qA0YMCBUc/p5yJAhhbwmOXwEEEAAAQQQQAABBBAImkAQ22NBM/a7vARg/BbOoPyDdsHv2LHD6tata0uWLHFqoXjx4vbHH384PWJICCCAAAIIIIAAAggggECQBILWHguSbbLKSgAmWdIZsJ+gXfCvvvqqde/ePSR/4YUX2ssvv5wBNcEhIIAAAggggAACCCCAQGETCFp7rLDVTzTHSwAmGiU+4wgE6YLXikctW7a0GTNmhGpPPx999NHUJgIIIIAAAggggAACCCAQOIEgtccCh5ukAhOASRJ0JuwmSBf8l19+aSeccEKIvXXr1jZ58uRMqAaOAQEEEEAAAQQQQAABBAqhQJDaY4WweqI6ZAIwUTHxIQkE6YLv2rWrjRs3LlRx+vmcc86hIhFAAAEEEEAAAQQQQACBQAoEqT0WSOAkFJoATBKQM2UXQbrgH3/8cRs2bJgtXLjQmYh33rx5VqxYsUypCo4DAQQQQAABBBBAAAEECplAkNpjhaxqoj5cAjBRU/HBoF3wO3futPHjx1vRokWtS5cuVCACCCCAAAIIIIAAAgggEFiBoLXHAgvtY8EJwPiIm2lZc8FnWo1yPAgggAACCCCAAAIIIBAUAdpjQamp3MtJACb4dZi0I+CCTxo1O0IAAQQQQAABBBBAAAEEsgnQHgv+CUEAJvh1mLQj4IJPGjU7QgABBBBAAAEEEEAAAQQIwGTYOUAAJsMq1M/DSfcAzA8//GBVqlSxqlWr+slA3ggggAACCCCAAAIIIIBA0gXSvT2WdJAA7pAATBIqbeXKlTZ48GCbOHGiLVmyxMqWLWvNmjWzvn37WufOnRNWgmuvvdaGDx/u5Ne2bVv7/PPPE5a3MkrnC3737t127LHH2uzZs6179+7Wv39/O/LIIxN6/GSGAAIIIIAAAggggAACCKRKIJ3bY6kyCdp+CcD4XGNz5syx9u3b24oVK5w9lS9f3jZv3my7du1y/q2gyWOPPVbgUkyfPt1atWplWVlZhTIA89VXX9nxxx8fctTPU6dOLbArGSCAAAIIIIAAAggggAAC6SBAACYdaqFgZSAAUzC/PLfetm2bNWzY0H7//Xdr3LixjR492umVoQDMsGHD7I477jD13Hj22WetV69ecZdEyy03b97cfv75Zyf/mTNnFroeMOedd5698cYbIcM333yTpafjPqPYEAEEEEAAAQQQQAABBNJNgABMutVI7OUhABO7WdRbaDiQeriUKVPGfvnlF6tdu3a2ba+++mobMWKEVa9e3RYuXGglSpSIOu/wDw4aNMgGDhxoN910k2m40wsvvFCoAjB//PGH1atXL9T75+CDD7b58+dbsWLF4vJkIwQQQAABBBBAAAEEEEAg3QQIwKRbjcReHgIwsZtFvUXLli1NQ4Muu+wye+aZZ3Jst2jRIjvooIOcXjAffPCBdejQIeq83Q8uWLDAmjRpYpUrV3Z6wPTr16/QBWBuuOEGp0eRmx599FG77rrrYrZkAwQQQAABBBBAAAEEEEAgXQUIwKRrzURfLgIw0VvF9MmNGzdahQoVnODK2LFj7dxzz/XcvlGjRk7gZMCAATZkyJCY9qEPn3zyyfbJJ5/Y+PHjrVOnTnbppZcWqgDMP//8YzVr1rQNGzY4djLXRMeaa4eEAAIIIIAAAggggAACCGSKAAGY4NckARif6nDGjBnWokULJ3cNP2rQoIHnnrp16/Z/7J0JeFTlvf+/WSb7zh4ghFWBCMimuGstVWsVi0tbvSqtba2itre1ij793/b2PlS99rrgclu9iNeldbm4UGupbVFrVTZBEJQtJCQBEgLZt5nJ5P/8Xjohk8xkzpk558ycM9/3eXiA5D3v8nnfc+a83/ktePnll3HppZdizZo1ukazatUqFTvmsssuwxtvvKGuTTQBRixfxALGX3784x/jwQcf1MWRlUmABEiABEiABEiABEiABEgg3glQgIn3FQo/Pgow4RlFVENSTotFipSmpiZlmRGsSLpkcZmRILqbNm3S3JfEepk6dSo6OzuVBY0/vkw0Aoz/hg41CLfbjbS0NEhw4XgoEnxYYr9UVlaq4UjMl3379mHcuHHxMDyOgQRIgARIgARIgARIgARIgAQMI0ABxjCUMWuIAoxJ6F988UVce+21qnWPx4PU1NSgPd17771Yvnw5pkyZgl27dmkejbQtfTzwwAO48847e69LJAFGXLuuvvrq3rnLv1966SXNDFmRBEiABEiABEiABEiABEiABOxCgAKMXVYq9DgpwJi0hmYKMBKw96KLLsIpp5yCTz75JEDciUaACYci3m74M844Ax999FHvsD/++GNI4GMWEiABEiABEiABEiABEiABEnAagXg7jzmNrxXzoQBjEmWzXJDa29shgXvF7eaDDz6AiBB9S6IIMCK2LFiwoHfq8u8PP/zQpNVksyRAAiRAAiRAAiRAAiRAAiQQWwIUYGLL34jeKcAYQTFIGxLPZd68eeo3Rgbh/fnPf45f/OIXuO666/Dkk08O6Pnmm2/GCy+8gLPOOgtvv/22+n1mZqaKjxJtiacbXuYv8/SXwTJNRTtvXk8CJEACJEACJEACJEACJEACsSYQT+exWLOwa/8UYExaub5pqF999VUsXrw4aE9lZWXYsWOH5jTUfgsXPcN+7bXXsGjRIj2XBK0bTzf80aNH8dvf/hYrVqxQgYH37t0bMs5O1BNnAyRAAiRAAiRAAiRAAiRAAiQQYwLxdB6LMQrbdk8BxsSlO/3007F+/XrcdNNNeOqppwb0VF1drbIX9fT0QOK6LFy4MOxoKMAEIpLMTOXl5SHTfIcFygokQAIkQAIkQAIkQAIkQAIkYAMCFGBssEhhhkgBxsQ1FOuM22+/HdnZ2coNaezYsQG9ye+kTnFxMSoqKuByuaIeTaLEgIkaFBsgARIgARIgARIgARIgARIgARsRoABjo8UKMVQKMCauYVdXF6ZNm6YsNGbMmIHnnntO/d3R0YFHHnkE99xzj7J+WblyJZYsWRIwktLSUhVo94YbbsCqVas0j5ICjGZUrEgCJEACJEACJEACJEACJEACtiFAAcY2SxVyoBRgTF5Die9ywQUXoK6uTvWUl5cHyWTk9XrV/2+77TY8+uijA0ZBAcbkhWHzJEACJEACJEACJEACJEACJGAjAhRgbLRYIYZKAcaCNRTx5b777sOaNWtQVVWlXJJmz56NW2+9NWRwXAowAxdGsjpt2bIF3//+9zFkyBALVo5dkAAJkAAJkAAJkAAJkAAJkEB8EKAAEx/rEM0oKMBEQy/Bro31DX/mmWfiww8/VGm1xTXr4Ycfhn9MCbYUnC4JkAAJkAAJkAAJkAAJkECCEYj1eSzBcJsyXQowpmB1ZqOxvOElm5RklfIX+fdHH33kTNCcFQmQAAmQAAmQAAmQAAmQAAn0IxDL8xgXwxgCFGCM4ZgQrcTyhv/GN76Bl156qZez/Pvqq69OCO6cJAmQAAmQAAmQAAmQAAmQAAnE8jxG+sYQoABjDMeEaCVWN/yBAwcwYcIEdHd3K84lJSXYt28fUlNTE4I7J0kCJEACJEACJEACJEACJEACsTqPkbxxBCjAGMfS8S3F6oa/88478eCDD/bylX//+Mc/djxvTpAESIAESIAESIAESIAESIAE/ARidR7jChhHgAKMcSwd31IsbviWlhaMGTMGzc3Nim9OTg6qq6uRn5/veN6cIAmQAAmQAAmQAAmQAAmQAAlQgHHOHqAA45y1NH0msRBgHn30Udxxxx29c5N/S/YjFhIgARIgARIgARIgARIgARJIJAKxOI8lEl8r5koBxgrKDunD6hteYr5MnjwZ+/fvVwSTk5OxZ88eFQ+GhQRIgARIgARIgARIgARIgAQSiYDV57FEYmvVXCnAWEXaAf1YfcOvXr0aixcv7iUn/3711VcdQJJTIAESIAESIAESIAESIAESIAF9BKw+j+kbHWtrIUABRgsl1lEErL7hzz77bHzwwQe99OXfZ555JleDBEiABEiABEiABEiABEiABBKOgNXnsYQDbMGEKcBYANkpXVh5w2/cuBHz58/vRSf//vjjj5GUlOQUnJwHCZAACZAACZAACZAACZAACWgmYOV5TPOgWFEXAQowunAldmUrb/jf/va3uO222+B2uxX03/3ud/jGN76R2AvA2ZMACZAACZAACZAACZAACSQsASvPYwkL2eSJU4AxGbCTmrf6hj98+DCeeOIJvPXWW8r6xeVyOQkn50ICJEACJEACJEACJEACJEACmglYfR7TPDBW1EyAAoxmVKwYqxu+p6eHrkfcfiRAAiRAAiRAAiRAAiRAAglNIFbnsYSGbvDkKcAYDNTJzfGGd/Lqcm4kQAIkQAIkQAIkQAIkQALxTIDnsXheHW1jowCjjRNrxSALEqGTAAmQAAmQAAmQAAmQAAmQAAkcJ0ABxv47gQKM/dfQshnwhrcMNTsiARIgARIgARIgARIgARIggQACPI/Zf0NQgLH/Glo2A97wlqFmRyRAAiRAAiRAAiRAAiRAAiRAAcZhe4ACjMMW1MzpUIAxky7bJgESIAESIAESIAESIAESIIHQBHges//uoABj/zW0bAa84S1DzY5IgARIgARIgARIgARIgARIIIAAz2P23xAUYOy/hpbNgDe8ZajZEQmQAAmQAAmQAAmQAAmQAAlQgHHYHqAA47AFNXM6FGDMpMu2SYAESIAESIAESIAESIAESCA0AZ7H7L87KMDYfw0tmwFveMtQsyMSIAESIAESIAESIAESIAESCCDA85j9NwQFGPuvoWUz4A1vGWp2RAIkQAIkQAIkQAIkQAIkQAIUYBy2ByjAOGxBzZwOBRgz6bJtEiABEiABEiABEiABEiABEghNgOcx++8OCjD2X0PLZsAb3jLU7IgESIAESIAESIAESIAESIAEAgjwPGb/DUEBxv5raNkMeMNbhpodkQAJkAAJkAAJkAAJkAAJkAAFGIftAQowDltQM6dDAcZMumybBEiABEiABEiABEiABEiABEIT4HnM/ruDAoz919CyGfCGtww1OyIBEiABEiABEiABEiABEiCBAAI8j9l/Q1CAsf8aWjYD3vCWoWZHJEACJEACJEACJEACJEACJEABxmF7gAKMwxbUzOlQgDGTLtsmARIgARIgARIgARIgARIggdAEeB6z/+6gAGP/NbRsBrzhLUPNjkiABEiABEiABEiABEiABEgggADPY/bfEBRg7L+Gls2AN7xlqNkRCZAACZAACZAACZAACZAACVCAcdgeoADjsAU1czoUYMyky7ZJgARIgARIgARIgARIgARIIDQBnsfsvzsowNh/DS2bAW94y1CzIxIgARIgARIgARIgARIgARIIIMDzmP03BAUY+6+hZTPgDW8ZanZEAiRAAiRAAiRAAiRAAiRAAhRgHLYHKMA4bEHNnA4FGDPpsm0SIAESIAESIAESIAESIAESCE2A5zH77w4KMPZfQ8tmwBveMtTsiARIgARIgARIgARIgARIgAQCCPA8Zv8NQQHG/mto2Qx4w1uGmh2RAAmQAAmQAAmQAAmQAAmQAAUYh+0BCjAOW1Azp0MBxky6bJsESIAESIAESIAESIAESIAEQhPgecz+u4MCjP3X0LIZ8Ia3DDU7IgESIAESIAESIAESIAESIIEAAjyP2X9DUICxYA2PHDmC++67D2+++Saqq6uRnZ2N2bNn45ZbbsGiRYt0j6ClpUW1tXbtWmzatAmVlZXo7u7GyJEjccYZZ+AHP/gBzj77bN3thruAN3w4Qvw9CZAACZAACZAACZAACZAACZhDgOcxc7ha2SoFGJNp79ixAxdccAHq6upUT7m5uWhvb1eCiZTbb78djzzyiK5RTJ48GXv37u29JjMzE0lJSapdf/nXf/1X/PrXv9bVbrjKvOHDEeLvSYAESIAESIAESIAESIAESMAcAjyPmcPVylYpwJhIu6urC9OmTUN5eTnKysrw/PPPY+bMmUooeeihh/Czn/0MPT09WLlyJZYsWaJ5JKWlpSgsLMR3v/tdXHzxxRg/frxqZ8+ePVi2bBlWr16t2nriiSeUNYxRhTe8USTZDgmQAAmQAAmQAAmQAAmQAAnoI8DzmD5e8VibAoyJq7JixQpl4ZKVlYXPP/8cJSUlAb0tXboUjz/+OIqLi1FRUQGXy6VpNO+//z7OOeecoHV9Ph++9KUv4d1331XCjIg/RhXe8EaRZDskQAIkQAIkQAIkQAIkQAIkoI8Az2P6eMVjbQowJq7Kaaedhg0bNuA73/kOnn766QE9HThwAGLNItYrEs9l4cKFhoxm1apVvRY1x44dU9YyRhTe8EZQZBskQAIkQAIkQAIkQAIkQAIkoJ8Az2P6mcXbFRRgTFqR1tZW5OXlKXHllVdewZVXXhm0p+nTp2Pnzp346U9/ivvvv9+Q0fzhD3/A1772NdWWxJ4ZNmyYIe3yhjcEIxshARIgARIgARIgARIgARIgAd0EeB7TjSzuLqAAY9KSbNy4EfPnz1eti/vRySefHLSna665Bi+//DIuvfRSrFmzxpDR3HnnnXjwwQcxYsQIHDp0SAXoNaLwhjeCItsgARIgARIgARIgARIgARIgAf0EeB7TzyzerqAAY9KKSJroyy+/XLXe1NSkrGGClR/96Ed4+OGHMWfOHJVSOtpSVVWlAv+KBc5dd92l0l9rLf4bOlR9t9uNtLQ0SHBhFhIgARIgARIgARIgARIgARIgAesIUICxjrVZPVGAMYnsiy++iGuvvVa17vF4kJqaGrSne++9F8uXL8eUKVOwa9euqEYjAomkvP7HP/6BcePG4dNPP0V+fr7mNinAaEbFiiRAAiRAAiRAAiRAAiRAAiRgKQEKMJbiNqUzCjCmYAWsFmAk1swNN9yA5557DhkZGVi3bh1OP/10Q2fHG95QnGyMBEiABEiABEiABEiABEiABDQT4HlMM6q4rUgBxqSlsdoFyZ/SWixtVq9e3RuE18jp8YY3kibbIgESIAESIAESIAESIAESIAHtBHge084qXmtSgDFpZSSey7x581TrZgfh9QfdTUlJwQsvvAAJ7GtG4Q1vBlW2SQIkQAIkQAIkQAIkQAIkQALhCfA8Fp5RvNegAGPSCvVNQ/3qq69i8eLFQXsqKyvDjh07Ik5Dfc899+BXv/qVynS0cuVK3HjjjSbNCOANbxpaNkwCJEACJEACJEACJEACJEACgxLgecz+G4QCjIlrKDFY1q9fj5tuuglPPfXUgJ6qq6tRUlICid+ydu1aLFy4UNdofv7zn+MXv/iFuubJJ5/EzTffrOt6vZV5w+slxvokQAIkQAIkQAIkQAIkQAIkYAwBnseM4RjLVijAmEh/xYoVuP3225Gdna3ckMaOHRvQm/xO6hQXF6OiogIul0vzaCS99LJly1T9hx56CD/84Q81XxtpRd7wkZLjdSRAAiRAAiRAAiRAAiRAAiQQHQGex6LjFw9XU4AxcRW6urowbdo0lJeXY8aMGSpDkfzd0dGBRx55BOI+JNYv4jq0ZMmSgJGUlpaisrJSZTZatWpVwO/kWr/gIu5Hd999t4mzONE0b3hLMLMTEiABEiABEiABEiABEiABEhhAgOcx+28KCjAmr6HEd7ngggtQV1enesrLy0N7ezu8Xq/6/2233YZHH310wCgGE2CSk5OVcCNxX4YPHz7oDCQj0hlnnGHILHnDG4KRjZAACZAACZAACZAACZAACZCAbgI8j+lGFncXUICxYElEfBGXoTVr1qCqqkq5JM2ePRu33norFi1aFHQEgwkwIrxoLevWrcN5552ntfqg9XjDG4KRjZAACZAACZAACZAACZAACZCAbgI8j+lGFncXUICJuyWJ3wHxho/fteHISIAESIAESIAESIAESIAEnE2A5zH7ry8FGPuvoWUz4A1vGWp2RAIkQAIkQAIkQAIkQAIkQAIBBHges/+GoABj/zW0bAa84S1DzY5IgARIgARIgARIgARIgARIgAKMw/YABRiHLaiZ06EAYyZdtk0CJEACJEACJEACJEACJEACoQnwPGb/3UEBxv5raNkMeMNbhpodkQAJkAAJkAAJkAAJkAAJkEAAAZ7H7L8hKMDYfw0tmwFveMtQsyMSIAESIAESIAESIAESIAESoADjsD1AAcZhC2rmdCjAmEmXbZMACZAACZAACZAACZAACZBAaAI8j9l/d1CAsf8aWjYD3vCWoWZHJEACJEACJEACJEACJEACJBBAgOcx+28ICjD2X0PLZsAb3jLU7IgESIAESIAESIAESIAESIAEKMA4bA9QgHHYgpo5HQowZtJl2yRAAiRAAiRAAiRAAiRAAiQQmgDPY/bfHRRg7L+Gls2AN7xlqNkRCZAACZAACZAACZAACZAACQQQ4HnM/huCAoz919CyGfCGtww1OyIBEiABEiABEiABEiABEiABCjAO2wMUYBy2oGZOhwKMmXTZNgmQAAmQAAmQAAmQAAmQAAmEJsDzmP13BwUY+6+hZTPgDW8ZanZEAiRAAiRAAiRAAiRAAiRAAgEEeB6z/4agAGP/NbRsBrzhLUPNjkiABEiABEiABEiABEiABEiAAozD9gAFGIctqJnToQBjJl22TQIkQAIkQAIkQAIkQAIkQAKhCfA8Zv/dQQHG/mto2Qx4w1uGmh2RAAmQAAmQAAmQAAmQAAmQQAABnsfsvyEowNh/DS2bAW94y1CzIxIgARIgARIgARIgARIgARKgAOOwPUABxmELauZ0KMCYSZdtkwAJkAAJkAAJkAAJkAAJkEBoAjyP2X93UICx/xpaNgPe8JahZkckQAIkQAIkQAIkQAIkQAIkEECA5zH7bwgKMPZfQ8tmwBveMtTsiARIgARIgARIgARIgARIgAQowDhsD1CAcdiCmjkdCjBm0mXbJEACJEACJEACJEACJEACJBCaAM9j9t8dFGDsv4aWzYA3vGWo2REJkAAJkAAJkAAJkAAJkAAJBBDgecz+G4ICjP3X0LIZ8Ia3DDU7IgESIAESIAESIAESIAESIAEKMA7bAxRgHLagZk6HAoyZdNk2CZAACZAACZAACZAACZAACYQmwPOY/XcHBRj7r6FlM+ANbxlqdkQCJEACJEACJEACJEACJEACAQR4HrP/hqAAY/81tGwGvOEtQ82OSIAESIAESIAESIAESIAESIACjMP2AAUYhy2omdOhAGMmXbZNAiRAAiRAAiRAAiRAAiRAAqEJ8Dxm/91BAcb+a2jZDHjDW4aaHZEACZAACZAACZAACZAACZBAAAGex+y/ISjA2H8NLZsBb3jLULMjEiABEiABEiABEiABEiABEqAA47A9QAHGYQtq5nQowJhJl22TAAmQAAmQAAmQAAmQAAmQQGgCPI/Zf3dQgLH/Glo2A97wlqFmRyRAAiRAAiRAAiRAAiRAAiQQQIDnMftvCAow9l9Dy2bAG94y1OyIBEiABEiABEiABEiABEiABCjAOGwPUIBx2IKaOR0KMGbSZdskQAIkQAIkQAIkQAIkQAIkEJoAz2P23x0UYOy/hpbNgDe8ZajZEQmQAAmQAAmQAAmQAAmQAAkEEOB5zP4bggKM/dfQshnwhrcMNTsiARIgARIgARIgARIgARIgAQowDtsDFGActqBmTocCjJl02TYJkAAJkAAJkAAJkAAJkAAJhCbA85j9dwcFGPuvoWUz4A1vGWp2RAIkQAIkQAIkQAIkQAIkQAIBBHges/+GoABj/zW0bAa84S1DzY5IgARIgARIgF676eIAACAASURBVARIgARIgARIgAKMw/YABRiHLaiZ06EAYyZdtk0CJEACJEACJEACJEACJEACoQnwPGb/3UEBxv5raNkMeMNbhpodkQAJkAAJkAAJkAAJkAAJkEAAAZ7H7L8hKMBYsIZHjhzBfffdhzfffBPV1dXIzs7G7Nmzccstt2DRokURj8Dj8WDFihV44YUXsGfPHtXOlClTcO2112Lp0qVwuVwRtx3sQt7whuJkYyRAAiRAAiRAAiRAAiRAAiSgmQDPY5pRxW1FCjAmL82OHTtwwQUXoK6uTvWUm5uL9vZ2dHd3q//ffvvteOSRR3SPorW1FRdeeCHWr1+vrs3IyFB/d3Z2qr8XLFiAd955R4k9RhXe8EaRZDskQAIkQAIkQAIkQAIkQAIkoI8Az2P6eMVjbQowJq5KV1cXpk2bhvLycpSVleH555/HzJkzlQDz0EMP4Wc/+xl6enqwcuVKLFmyRNdIxMrlxRdfREFBgbreb0nz+uuv49vf/jYaGxtx/fXX49lnn9XV7mCVecMbhpINkQAJkAAJkAAJkAAJkAAJkIAuAjyP6cIVl5UpwJi4LOIeJBYuWVlZ+Pzzz1FSUhLQm7gJPf744yguLkZFRYVml6Ft27Zh1qxZSrx59dVXsXjx4oB25WdXXXUVkpKSIHVF/DGi8IY3giLbIAESIAESIAESIAESIAESIAH9BHge088s3q6gAGPiipx22mnYsGEDvvOd7+Dpp58e0NOBAwdQWlqqhJS1a9di4cKFmkZz11134YEHHsCkSZOwe/duJbT0LdKexILZu3cvli1bhuXLl2tqN1wl3vDhCPH3JEACJEACJEACJEACJEACJGAOAZ7HzOFqZasUYEyiLTFa8vLylLjyyiuv4Morrwza0/Tp07Fz50789Kc/xf33369pNPPmzcOmTZtw66234rHHHgt6jfzuiSeewPz583vjxGhqfJBKvOGjJcjrSYAESIAESIAESIAESIAESCAyAjyPRcYtnq6iAGPSamzcuFGJH1LE/ejkk08O2tM111yDl19+GZdeeinWrFkTdjQi6Egg37a2Njz55JO4+eabg14jv5MsS1K3ubk5bLtaKvCG10KJdUiABEiABEiABEiABEiABEjAeAI8jxnP1OoWKcCYRFxSTl9++eWq9aamJmUNE6z86Ec/wsMPP4w5c+Yoq5ZwRcSU/Px8Ve2NN97AZZddFvQS+Z0/MG9LSwtycnLCNR3297zhwyJiBRIgARIgARIgARIgARIgARIwhQDPY6ZgtbRRCjAm4ZYMRZKpSIrH40FqamrQnu69914Vo0VituzatSvsaA4ePIjRo0erepJmWlJRByvyO39MGblm1KhRYdv239ChKrrdbvWrtLS0sG2xAgmQAAmQAAmQAAmQAAmQAAmQgHEE/Ocx8YpgsScBCjAmrRsFGJPAslnbEKBgZ5uliouBcr/ExTLYZhDcL7ZZqrgYKPdLXCyDbQbB/WKbpYqLgVq9XyjAxMWyRzUICjBR4Qt9MV2QTALLZm1DgCaStlmquBgo90tcLINtBsH9YpuliouBcr/ExTLYZhDcL7ZZqrgYKPdLXCyDrQZBAcak5ZJ4LpKtSIrRQXglnoxkWWIQXpMWj80aQoAfSIZgTJhGuF8SZqkNmSj3iyEYE6YR7peEWWpDJsr9YgjGhGmE+yVhltqwiVKAMQxlYEN901C/+uqrWLx4cdCeysrKsGPHDl1pqCW7kmRZWrp0KVasWBG0Xfnd448/zjTUJq0vmw1PgB9I4RmxxgkC3C/cDXoIcL/oocW63C/cA3oIcL/oocW63C/cA3oJUIDRS0xH/dNPPx3r16/HTTfdhKeeemrAldXV1SgpKYEEUVq7dm1v0NxwXdx99924//77MXnyZOzevTtodQnqu2fPHixbtkwF+TWi8AFjBMXEaYP7JXHW2oiZcr8YQTFx2uB+SZy1NmKm3C9GUEycNrhfEmetjZgp94sRFBOrDQowJq63WKfcfvvtyM7OVm5IY8eODehNfid1iouLUVFRAZfLpWk027Ztw6xZs5Rws3r1alxxxRUB18nPxOImKSkJUlesbIwofMAYQTFx2uB+SZy1NmKm3C9GUEycNrhfEmetjZgp94sRFBOnDe6XxFlrI2bK/WIExcRqgwKMievd1dWFadOmoby8HDNmzMBzzz2n/u7o6MAjjzyCe+65R4koK1euxJIlSwJGUlpaisrKStxwww1YtWrVgFFKimvJtFRYWIhnnnkGl112maojwX+lrYaGBlx//fV49tlnDZshHzCGoUyIhrhfEmKZDZsk94thKBOiIe6XhFhmwybJ/WIYyoRoiPslIZbZsElyvxiGMmEaogBj8lJLfJcLLrgAdXV1qicJoNve3g6v16v+f9ttt+HRRx8dMIpwAozEmLnwwguVi5OUzMxMJeZ0dnaq/y9YsADvvPOOsr4xqvABYxTJxGiH+yUx1tmoWXK/GEUyMdrhfkmMdTZqltwvRpFMjHa4XxJjnY2aJfeLUSQTpx0KMBastYgv9913H9asWYOqqiolisyePRu33norFi1aFHQE4QQYucjj8SgXphdeeKE3FozEfrnuuutUgF6tLk1aEfABo5UU6wkB7hfuAz0EuF/00GJd7hfuAT0EuF/00GJd7hfuAT0EuF/00GJdIUABhvuABEiABEiABEiABEiABEiABEiABEiABEwmQAHGZMBsngRIgARIgARIgARIgARIgARIgARIgAQowHAPkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDJBCjAmAyYzZMACZAACZAACZAACZAACZAACZAACZAABRjuARIgARIgARIgARIgARIgARIgARIgARIwmQAFGJMBs3kSIAESIAESIAESIAESIAESIAESIAESoADDPUACJEACJEACJEACJEACJEACJEACJEACJhOgAGMyYDZPAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhRguAdIgARIgARIgARIgARIgARIgARIgARIwGQCFGBMBuyE5o8cOYL77rsPb775Jqqrq5GdnY3Zs2fjlltuwaJFi5wwRc7BAAKrVq3CkiVLwrYk+2no0KFh67GCvQm0tLRg3bp12LRpk/qzefNm1NXVqUnJz88777ywE3zttdfw5JNP4pNPPkFbWxvGjh2Lr33ta1i2bBn3UFh69qoQzX4pLS1FZWXloBO+9dZb8dhjj9kLCkcbkoC8i6xevRp//etf8emnn+LQoUNwuVwYN24cLrzwQtxxxx2YMGFCyOt7enrwzDPPYOXKldixYwfcbjfGjx+PK6+8Enfeead6z2FxDoFo9ktSUlJYEP/5n/+Jn/zkJ2HrsYI9CMj7ipx5Nm7ciL1796p3l/b2dgwZMgSzZs3Ctddei29961tITk4OOSG+v9hjrWM1SgowsSJvk37lxeSCCy7oPTjl5uaqh1B3d7eawe23345HHnnEJrPhMM0k4Bdg5ANp2LBhIbvauXMnioqKzBwK244DAq+//jquuOKKoCPRIsAsXboUjz/+uLo+JSUFWVlZkEO6lBEjRigRZ+rUqXEwUw7BCALR7Be/AJOXl4fMzMygw/n2t7+N5cuXGzFUthFjAlVVVUpoERHFX2TtOzo64PF41I9kH8hn0tVXXz1gtF6vF4sXL1YHLCki3KSlpSmRV8qUKVPw/vvvq+cMi/0JRLtf/AJMYWGh2ifByr/927/hBz/4gf1hcQaKwM0334zf/OY3vTT8gqz/GSG/OPfcc9UzRJ49/QvfX7iRwhGgABOOUAL/vqurC9OmTUN5eTnKysrw/PPPY+bMmUqAeeihh/Czn/1MvQDJN0haLB8SGGVCTN0vwMiLcUVFRULMmZMMTUAO1N/73vcwZ84czJ07Vz1L5BsjKeEEmKeeekpdKy++v/zlL/GjH/1ICTDyTfd1112Hzz77DJMmTVLfXId6Ieba2ItANPvFL8CIRcONN95or4lztLoJyOeLWLdccsklar3PP/989c20CCsffvghbrvtNmzbtg2pqanK8m7GjBkBfdx7771KjEtPT8eKFStUGyLCvPfee+r5ItYS55xzjvo/i/0JRLtf/AJMuM8t+5PiDPwE5H22oaEBZ599Nk466STIl89SxBLmf/7nf9T5R76IFmFf/t+38P2F+0gLAQowWiglaB15MRELFzn4fP755ygpKQkg4Vd4i4uL1YFbXmBYEpcABZjEXftgM5eXE7Fc8ZfW1tbel5jBXmTFFUBEvMOHD0OeMfIc6lvE1UTEHBGCxUJGXCFZ7E8g0v0iM6cAY//11zODpqYm9c4hXwgFK7W1tTjllFMg7q7y5ZB8SeQvcoCS/SLWMg8++CB+/OMfBzQhLgennXaa+nLprbfeUiIPi70JRLNfZOYUYOy9/maM3i/iZmRkQPaX/4sgvr+YQduZbVKAcea6GjIreQnZsGEDvvOd7+Dpp58e0OaBAwfUi4y8qKxduxYLFy40pF82Yk8CFGDsuW5WjVqrAPP222+rQ4+89IrYInFf+pebbrpJfeu0YMEC9Y03i/MIaN0vFGCct/ZGzEisWp599lll/SKWc/4iMaVEtBW3AYkbI18w9S8SQ0Ziy3zzm9/Eiy++aMRw2EacEwi1XyjAxPnCxWh44np0+eWXq97lyyK/uyLfX2K0IDbslgKMDRfNiiHLy6+8oIi48sorr6jAdMHK9OnTITE9fvrTn+L++++3YmjsI04JUICJ04WJk2FpPVBLAEz5ZlqeLeJqFKzIM0liO4hII3FhGDAzThbZwGFo3S8UYAyE7qCmJCDqr3/9a2UtJ66K/nLVVVfh1VdfxVe/+lX84Q9/CDpjCagq7zTDhw+HWNOwOJ9AqP1CAcb5ax/JDO+++2515snJyUFzc3OvlRTfXyKhmZjXUIBJzHUPO2sxw50/f76qJ+5HJ598ctBrrrnmGrz88su49NJLsWbNmrDtsoJzCfgFGPlGUXxmd+3apSY7evRolfFG/PLFLJwlMQloPVDLweiPf/wj5Nny+9//PigseSbJwUqKZFiSODMsziKgdb/IrP0uSCLaHTt2DPX19cjPz1fWD/LlgbihiKk4S+IQmDdvnno2iFD70ksv9U7c/6XRXXfdpbI7Biv+b7Hld7KXJL4Mi7MJhNovMmu/C5Jkv5H4QOJyIokEJBuoxAySz6q+7rbOJpW4sxO3Z7H8f+6559Szw+fzQYSYX/3qV71Q+P6SuPtD78wpwOglliD1+5rXyYdNsCjfgkKCYz788MPqACQvOyyJS6BvGmp5YSkoKIAcovxZKeQFRT60mKoxMfeI1gO1vNRu2bJFPVv+67/+KygseSbJ/pIiwq8IwCzOIqB1v/QVYOTfIrSIP758K+kvIvyKtUP/OGbOIsbZ+An0zaglYspFF13UC0cOzhJcU7I3Soy7YEVcluSwLWX79u0qCQGLcwkMtl9k1n3TUIu1pfxfnk/+ItlwpA3/Z5JzSSXezBobGyHZr/oXCfB96623Kmtd+be/8P0l8fZIpDOmABMpOYdfJ37Pkudeihyg+z5g+k7dH4hK0jb6LR4cjobTC0Hgz3/+M/7xj3/g61//urKAkYOQ7J0PPvgAy5Ytw/r169WVL7zwQm82HMJMHAJaD9TyLNmzZw/k2fIf//EfQQHJvvIHvZNnlcRqYHEWAa37RWZ9xx134Mwzz1RpQf2++BLfQ+IEyR6SjH5i+fDJJ58wa5aztsmA2UjcKMm6JpYrEqNBDsZ9izw35PkhmUokllSwIs8feQ5JkRhTEmuKxZkEwu0XmfUNN9ygLKlOP/30Xmuo/fv349FHH1V/xBLiy1/+MuQdiMVZBETI9z8L5Iufzs5OJcD98Ic/VG6KI0eODJgw31+ctf5mzoYCjJl0bdw2BRgbL14cDl0OQHI4EhFmzJgxKrhqcnJyHI6UQzKLgNYDtZYXGEk368+6RgHGrBWLbbta90u4Ufa15vzNb36j0puzOJOAfFst6aPFakXSVIsrtVi8UIBx5npHOyst+yVcHyLAiAAshckowtGy9+8lJqa8u4r13GOPPabcXEXgPeuss3onxvcXe6+xlaOnAGMlbRv1RRckGy2WTYYqWSUku4QUxu2wyaIZOEytB2qa8BoI3cZNad0vWqZ49tlnK0s8xirTQsuedWS/iBXCxx9/jOLiYvz9739XIkz/Qhcke66v0aPWul/C9SvWL+PHj1exQZYuXYoVK1aEu4S/dwABEWHECkZiHO7evbs3mxrfXxywuBZNgQKMRaDt1o0ckCUomRQG4bXb6sXneNva2lTEeCkSuFmyUbAkDgGtB2o5JL/11lsMwps4WyPoTLXuFy2Y/Jkp+mfE0XIt68Q/AQmOefHFF+P999/HsGHD8N5772Hq1KlBBy7xXCQrEoPwxv+6mjVCPftFyxj8mbUuueQS9dnF4nwC4ookMX/Euluyqi1evFhNmu8vzl97o2ZIAcYokg5rp28a6r4Pl/7T9L/MMA21wzaACdPpK8AMltrchK7ZZBwQ0HqglmeJpIGVZ4u4EgQr8kySl16moY6DhTVpCFr3i5butaQG1dIO68QfgY6ODnXo+dvf/qaCZcrf/gC6wUYrsTzk82cwaygJrCl7hmmo42+9ox2R3v2ipT8tqc21tMM69iIg1i8HDx5U6ajlvUUK31/stYaxHC0FmFjSj/O+JeCYxOyQQHUSsK5/kXR8klVC/CLp+xrnixkHw5MX4y996UtqJOKbL4ESWRKHgNYD9Z/+9Cf1bbaIK2LWLTGD+pfvfve7ePrpp1VwTAmSyeI8Alr3i5aZS1wQcUmRFKGSDYnFGQTk22cJtCvvH5Kp8Z133sH8+fMHndx///d/4wc/+IGqf/jwYWRmZg6oL65Mf/nLX1Rwb4kxxeIMApHsl3Azl/dfcUGS2CCSFUdig7A4n4B8Pok7owT0fvzxx3HLLbeoSfP9xflrb9QMKcAYRdKB7Ygvq6RplLR74oY0duzYgFnK76SO+FtXVFT0BsV0IApOKQwBeQnpm6qxf3X5kDrvvPPUYVm+NZCDNYPwJta20nqgdrvdGDdunDocyTNGfK37lqqqKuVeIBZVfV98Eoum82erdb+Ee/aIS4A/Tbkcvr///e87H14CzFA+UyTjnghq8o4iIoxkwgpX6urqUFpaCrGEkDT3ku6+b9m8ebNyv5Z9JXtH3EpY7E8g0v0S7vkin0ES+8V/+P7KV75if1gJPoPu7m71fjrYO+0vf/lL/L//9/8Uqb6p6vn+kuCbR8f0KcDogJVoVeXbAvGZLy8vx4wZM/Dcc8+pv+XFRQ5F99xzj3pJWblyJZYsWZJoeDjfPgREgLvmmmuUtZR8eygvuFIkW42kppa94rdUeP7553tTnBOiswlIKlh/EcHEvy8kc0Dfw5JkE/BnNZL6YnEn2WrkBWj58uUqy4R8U71t2zb8y7/8i/p70qRJKpaDPx21s0kmxuwi2S+33Xab2idXXnmlsqrLyspSsETAe+aZZ/Dv//7vKnUo01A7Zw/JAUmsU8SVSJ4LIpScf/75micoKe7luZKenq5EXEkznJqaqmLIXHfddRCRV6ymJJYMi/0JRLNfxL1o8uTJWLRoEWbOnKn2jBR555G9IyKeBOKVBANigcVifwKytiLuilXLwoULlaW/FDnvfPHFF2rdn3jiCfV/qfd///d/AZPm+4v994AVM6AAYwVlG/chB5wLLrgA8q2RFDHblQBmcrCWIi+/koaPJbEJyAeWmOH6S0ZGhgq429zcDPlGQIocsB944AEVOZ4lMQgM9g1SXwLr1q1TFlJ9i5hzy0uOFDkcycFa9pOUESNGQK4JFWgzMeg6b5aR7Jcbb7wRzz77rIIh14uYJ0VSzPrLqaeeijfeeGOAFafzCCbGjEQoOffcc9Vk5UAswTAHKyLG9S3y/iJBMyXboxQRceXzSURiKZJKVsSXkSNHJgZQh88ymv0in0t+IS4lJUU9X8SapqWlpZeaiC8Sl8z/7HE4TsdPL9T7rFhlipjvL+LS+tJLLykLvP6F7y+O3yZRT5ACTNQInd+AiC/33Xcf1qxZo74ZkoeNpFqTB4x8K8BCAmIVJTE5xMpl69atOHLkCJqamtShWVKByreTN998s3qxZUkcApEcqPvSee2115QI88knn6jDkbhBXnbZZbj77rtVthMWZxGIZL9I2uHVq1fjo48+UnEYxIpGvpEeMmSI+pySb7DFWqKvhZWzqCXebN59911dFi/yTXX/Ij8TCymx4P3ss8/UoVq+RBBLKgnAG+xQlXiknTHjaPbLn//8Z2VhJc8ZiXt49OhRJfTKlwDiqiYWU/KZpPXZ5Qyizp6FfGkoVroSt3DDhg04dOiQ+lwRsVdi0kmcqWuvvRbh3M34/uLsfRLt7CjAREuQ15MACZAACZAACZAACZAACZAACZAACZBAGAIUYLhFSIAESIAESIAESIAESIAESIAESIAESMBkAhRgTAbM5kmABEiABEiABEiABEiABEiABEiABEiAAgz3AAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmYTIACjMmA2TwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJUIDhHiABEiABEiABEiABEiABEiABEiABEiABkwlQgDEZMJsnARIgARIgARIgARIgARIgARIgARIgAQow3AMkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYDIBCjAmA2bzJEACJEACJEACJEACJEACJEACJEACJEABhnuABEiABEiABEiABEiABEiABEiABEiABEwmQAHGZMBsngRIgARIgARIgARIgARIgARIgARIgAQowHAPkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDJBCjAmAyYzZMACZAACZAACZAACZAACZAACZAACZAABRjuARIgARIgARIgARIgARIgARIgARIgARIwmQAFGJMBs3kSIAESIAESIAESIAESIAEScAqBlpYWrFu3Dps2bVJ/Nm/ejLq6OjU9+fl5550X1VQ9Hg9WrFiBF154AXv27FFtTZkyBddeey2WLl0Kl8sVVfu8mARiSYACTCzps28SIAESIAESIAESIAESIAESsBGB119/HVdccUXQEUcrwLS2tuLCCy/E+vXrVfsZGRnq787OTvX3ggUL8M477yA7O9tGxDhUEjhBgAIMdwMJkAAJkAAJkAAJkAAJkAAJkIAmAiLAfO9738OcOXMwd+5cTJs2Dd/61rfUtdEKMGLl8uKLL6KgoAArV67EokWLVLvS57e//W00Njbi+uuvx7PPPqtprKxEAvFGgAJMvK0Ix0MCJEACJEACJEACJEACJEACcUqgu7sbKSkpvaMTq5Xc3NyoBZht27Zh1qxZ6OnpwauvvorFixcHEJCfXXXVVUhKSoLULSsri1NCHBYJhCZAAYa7gwRIgARIgARIgARIgARIgARIICICRgkwd911Fx544AFMmjQJu3fvVkJL3yLCjMSC2bt3L5YtW4bly5dHNF5eRAKxJEABJpb02TcJkAAJkAAJkAAJkAAJkAAJ2JiAUQLMvHnzVFDfW2+9FY899lhQIvK7J554AvPnz++NE2NjdBx6AhKgAJOAi84pkwAJkAAJkAAJkAAJkAAJkIARBIwQYMS6RdyY2tra8OSTT+Lmm28OOjT53S233KLqNjc3GzF8tkEClhKgAGMpbnZGAiRAAiRAAiRAAiRAAiRAAs4hYIQAI2JKfn6+gvLGG2/gsssuCwpIfucPzCvpsHNycpwDkjNJCAIUYBJimY2ZpN8PMy0tzZgG2QoJkAAJkAAJkAAJkAAJxICA2+1WvYrlhR2KWHz4xxzJeOXacO/wXV1dkTQNIwSYgwcPYvTo0ap/STMtqaiDFfndwoUL1a/kmlGjRkU0Zl5EArEiQAEmVuRt2O8JASZV9+h70PfDLTCglu7GIrrAmv77zjMJyRGNdLCLeuBTvzan7eOMkmDc+vhfavoHUYsUjK+nZ0BANr1tRTumaK6Xa/WyUNdoXBP//tNSX2tdPfUG6zfc3Af7fSS/C3WNnp+HWuuQbfh6kJQ88P7pkZ/3CyTof+kPtR/C8Qp3fd/7Qktbwe6jSK/Tck8qJkFYabmWdU4QEI7quU2W3BYkoJuA1+tR19hFgElPT1cCTFqa/vdwt9ur5koBRvc24QUkYDgBCjCGI3Vug/LgB3zYfeR3EU+ytmNr77UFGXMjbifSC2vat/deOjTz1EibCXtdZdvO3jojM2eGra+3Qnnr5+qSMVnGpt/b3bJbtVuaPU3vkELW/7x5j/rdpJyTDWnzs6Z9OCl3SlRtbW3cr66fnjcponY+OVahrptRMEHX9ZvrK1X9WYXjNV+38cgBVXfOkHGartlQW4W5QzXWPVyF+cPHhm13w6FqzB8xeL2NNTWYP2pMyLY2VlWr380vDl5n44FqzBsd/HebKmswb+zxb8X6l837azC3ZODvNpfXYG7pwJ9/srcGc8YP/PmWPTWYMzHIz3fVYPakwJ9v/bwap04ZONZtO6sw6+SBP/90exVmTQ1Sf3sVZk4PzWz71gOYURaa+/YtlZgxM/z6bd9UgVNmlYRd5/4VPttQjrLZ2vaS7sYBfLZ+L8rmar8XIukjEa757MPjz+2y+fqeR4nAhnMkgcEIXP69hUhOTkKkVh9W0/W/h7c1PKu76+zCGwAkmzZXIyxg6IKke1l5gU0JUICx6cLFYthGCDD+cSeaEEMRxpkijF4BRva/iDB6BBi5RkQYrQKM1Ncqwmw4XHVcFAkjwogAo+oNIsKIAKPqhBFhIhVgpO1gIozdBRiZVygRhgJMLD7p7NmniDAUYOy5dhx17AhQgDGOvRECjFgi5eXlKXcmBuE1bm3YUvwRoAATf2sStyMyUoChEGPcMtvFGsZISxixgpFihCWM1VYwkYgwkVjBSD9aLGFEhAknwChRxyArmFACjBKaIrCC9xQSLAAAIABJREFUGUyAUQz6WcGIBYyU/lYweixg5PpgVjB6LWCknW2DWMFQgDHuOen0lmgF4/QV5vzMIEABxjiqRggwMhpJLb1x40YsXboUK1asCDpA+d3jjz/ONNTGLR9bspgABRiLgdu5OzMEmEQSYsx0S/KLMMLTSLcko12SKMIc3/GRuiLF2grGyW5IFGAGfjrRBclen9gUYey1Xhxt7AlQgDFuDYwSYO6++27cf//9mDx5MnbvPu5e2b9MmTIFe/bswbJly7B8+XLjJsGWSMAiAhRgLALthG7MFGD68vG7Jzk1RowVQowZIoyskVGxYYwSYoywhDEiHkw8uiKJG5IUK61gzHZDikUcmC1BYsAIV1rARP+pxhgw0TPs3wJdkYxnyhadS4ACjHFra5QAs23bNsyaNUsFRl69ejWuuOKKgEHKzxYvXqwC20vdsjJjYyEaR4QtkUBoAhRguDs0E7BKgPEPiEKM5qUZUNEMt6R4tYZJJBEmElckrQKMbKJwrkhmuyGJC5KUYMF4JRCv+l2QYLxmxoHRI8DI+IK5IYUKwqvq0wUp8gcdrxxAgFYw3BQkoJ0ABRjtrILVrK+v7/1xW1sbSktL1f9ff/11nHnmmb2/y8/Ph8vl6v2/1KusrMQNN9yAVatWDWj62muvxYsvvojCwkI888wzuOyyy1SdN998E0uWLEFDQwOuv/56PPus/mDE0c2YV5OAMQQowBjDMSFasVqA6S/EyP+ttoqxImuS3yLG6EC9ZrglUYQJfqtbnRVJqyuS0VYwWoPxhgvEq8QeA7MhxbsAI/ONJBOS02PACBdawRj/+kARxnimbNGZBOwpwHjRcHip7gUpHPkYgFRDsyCJFYqWsm7dOpx33nmaBRixprnwwguxfv16dU1mZqayiOns7FT/X7BgAd555x1kZ2dr6Z51SCDuCFCAibslid8BxUqAoRAT3Z6Id2sYp7gjRSPC6MmKlAhWMHrTUYsAI6V/OmpJRa1+HiQQrx1SUVOAie7Zl8hXU4RJ5NXn3LUSsKcA40PDsWe0TrG3XmHREsPTUJslwMigPR6PCsL7wgsv9MaCkdgv1113nQrQ29eiRjcMXkACMSZAASbGC2Cn7mMtwFCIiXy3xLs1DEUYfamp9Ygw8WoFM5gFjOz0UG5IRsSBMSITUrAYMDJuvZmQ4t0FSeZUNntc5A+fMFfSAsY0tGA8GPPYsmVnEKAA44x15CxIwG4EKMDYbcViON54EWASQYgxK1Cv0dYwRrokxZsIE016ar1Bec3OiiQijJWxYMIF491YdTzWi143JLPjwOjJhJQIAoysETMhxfBDN8quaQUTJUBe7ngCFGAcv8ScIAnEJQEKMHG5LPE5qHgTYCjERL5PnC7EGBWYNxIRJh5dkcywgjEiJbVeAUZ2vIgwZgXipQAz8JlCASby52w8XEkRJh5WgWOIVwIUYOJ1ZTguEnA2AQowzl5fQ2cXrwIMhZjIltlot6R4s4ZJFBFGT0Beq61gwgXjjTcBRu6kYCKMnkxIVrogyXi3b6nEjJljwz4Etm+qwCmzSsLW61/BCgFG+iybO1732HiBNgIUYbRxYq3EI0ABJvHWnDMmgXggQAEmHlbBJmOIdwEmkYQYIzMmxas1jBEuSdGKMFsb96ttZZUlTDy5IpmdkjoaN6RQFjCyVmYE4o2lACNzmlEWWmCxuwAj82McGPNfAhgPxnzG7MF+BCjA2G/NOGIScAIBCjBOWEWL5mAXAYZCTGQbwkghxihrGCeIMGbHgzEjIO+Gw1VqEw0mwhiVklqvFYxRcWAkEK/WTEhmCjDCOd4D8ZoZhJcCTGTPa71X0QpGLzHWTwQC9hRgvKg59APdyzN61JOGp6HWPQheQAIkoAhQgOFG0EzAbgKM04UYMwL1GumW5BdhZB1Ks6dp3mfBKholxJyUOyWicURrCROJCKM3NbUZrkjRWsFoCcarV4CRBTQiDoyeTEgiwEiZPWl0wP7RE4j30+1VmDV1TND9RwFmL12QInoy6buIIow+XqztfAL2FGB8OHxUfxrqkUOMT0Pt/B3CGZKAOQQowJjD1ZGt2lWAoRCjfzs62RrGahEmkqC8el2R7GoFM5gb0sYDxzMl6UlHvXl/zQAXJGljc3kN5pYGiify82BWMGYF4hUBRkowEYYCDAUY/U/pyK6gCBMZN17lTAIUYJy5rpwVCcQ7AQow8b5CcTQ+uwsw8STEDM081fCVNdoiJh6tYYyyhBH4kQgxkVrCxKMIozUgrxFWMEYH4x3MAkbWNlgcmFgLMDKuUFYwFGD2quchA/Ea/rEQtEHGg7GGM3uJfwIUYOJ/jThCEnAiAQowTlxVk+YkAkwPurG1+ofITj/dpF6sbba2Y6vqsCBjrqUd17RvV/2ZKcQYFag33qxhKMKE3qpiCaPFFUlrWmorYsGIFYzZbkhiAaOEmX5WMKEsYKTunImBFjPB4sDocUGyqwAj4zY7E5LqYz2tYKz8EKIIYyVt9hWvBCjAxOvKcFwk4GwCFGCcvb6Gzs5vAVNZ/7841rm5t20niDEUYsJvFaOEGKNiw0QrxESTIclqSxit8WDMckUy0womlm5I0QbiFQFGyqlTAmO7WJmK2uwsSBRgwj8b7ViDrkh2XDWO2WgCFGCMJsr2SIAEtBCgAKOFEusoAn0FGD8SpwoxMj8rrWLsYhFjhltSNAF67SrC6AnKa3Y8mHCuSEZYwZgRjDcWgXj7B+GV50QwKxgKMPo/NGkBo59ZtFdQhImWIK+3OwEKMHZfQY6fBOxJgAKMPdctJqMOJsAEE2LkZ3a3ivFbxDhFiInX+DBGpKumCDPwcWCGK5LZVjB63ZBCpaM2KxCvmamorYgBI7tk+6YKnDKrRPfnB12QdCOzzQUUYWyzVByoCQQowJgAlU2SAAmEJUABJiwiVvATGEyA6UvJSVYxFGIG3/9GuyVFag3jF2FktJNyTtZ901rtjmR2UF69rkhGWcHMHzE2JHuxggkVjNcKN6RoMyHpSUVtpQWMEla2VGLGzNDs/YsSzwKMjJFWMLofXYZcwHgwhmBkIzYkYE8BxotdNUt10z5p9GMAUtHV1aX7Wl5AAiRgLAEKMMbydHRrWgUYPwQKMdFvB7Nck4y0iDHKLSnW1jB2EWGMjgejJyBvNFYw8eyGNGd8v4C7e2oGBOGVuznaQLyRZkGSvmeUhRZYKMBE/6xN9BYowiT6DkjM+dtTgPFBYjHqLeOGXg8gmQKMXnCsTwImEKAAYwJUpzapV4Dpy8EpYkwsLGL8IozwNDprkl+IMSJjUrwIMdG4JMW7CBNJPBitWZHiwQomEjekeWMDxRO5T/S4IVmZCSmUACNjtsINiRYwTv10jn5edEWKniFbsB8BCjD2WzOOmAScQIACjBNW0aI5RCPAOM0qhkJM6E1nhFtStNYwsXJJiiQ7kl53JD0ijBmuSINZwWw4dDwrUChXpEitYDYeqMa80YGZhqQfI+LAGJEJqX8WJCWo7KzCrJMHjjlSK5hYWsDIfBgHxqIP2hh2QxEmhvDZdUwIUICJCXZ2SgIJT4ACTMJvAe0AjBBg+vbmBKsYCjHB908iW8PYVYTR4oqkNSNSNLFgzLSCMSIOTP9MSMGyIFGA0f650r8m48BEzs6IK6NxRerp7obP60ZScgqSUl1ISkoyYkhsgwRMI0ABxjS0bJgESGAQAhRguD00EzBagPF3TCFG8xIEVDTLNSne4sMYZQ1jZXBeq0QYPfFgjHRFitYKZrBgvGYLMHITzS0NdFsK5YY0Z2K/2DC7asBU1JE9r7ReFY0A42luQFv55+isrUGPuws9vm4lBKRm5yKrZDIySyYiOdWldSgJWU+vFYy3tRltFbvQXrUPvq7OXmZJKSnIGDEG2eNPRtrQkRRjEnI3xf+kKcDE/xpxhCTgRAIUYJy4qibNySwBpu9w7S7GOMkixgwhZkxWWcS7MxohJhqXpEjjwsSTCKPVFUmLFYwsoFjChBNhjLaCsdoNaUuQQLzxmglJaxBeWTsnxoHpOHQArXu2o6v+MLrbWuFpPoYerwfo6QGSU5CSkQlXwVCkZGYpISZ3ygz1b5bgBLSIMO6GejR/sQVdtTXwdrTB01ivBBgleiUlI8nlgiuvCKm5+XDlFSJ38inIHDuRQgw3XVwRoAATV8vBwZBAwhCgAJMwSx39RK0QYJxiFUMhJvh+izY+jF+EkdYjSVkdaYBeq0WYGQUTNN2weuLBrD9UifryTmQdzEZLgxtetw/JKUlIy0jGsDFZmDijAAXDMqBFhAnniqQlFoxeKxgRYKSEigXTPxivEYF4+1vASP9aMyHFYwwYuwgwMs6yuePD3gM9PT1o2bkZzV9shaehHu7GevS43UjNLUByWjqQnIyebi+621rQ3dUBV24B0oqGI61oGIYs+DJcBUPC9pGoFQZzReo4WImGze/D3XAE7mNHlPCSmpOPlKwciOVLj88Hn7sTnqZjQFIS0vKLkDZkJHImTUN+2XwkJScnKlbOO84I2FGA6UE3tlb/UDfJWWMeRhJSmAVJNzleQALGE6AAYzxTS1qsrq7G6tWr8de//hWffvopDh06BJfLhXHjxuHCCy/EHXfcgQkTtB3itA7YSgEmmBAjP8tOP13rcOOmnl+MKciYa8mYzHZNijZjkhHxYYywhrHKJSlSSxijRJiujm7s2nQM+3c2YV/tUWS0ZqCj1Qtft099G53iSkJ+UTqyC1wYOioLk08txMG8RmjJihSpFcxgwXg3Vv0zkG/xwAC2g1nBaM2GFCwOjLggSdGSjlqPACNt9g/EG8sgvHYQYGSMWtyQRHxp2rYerbu3oaNmP3xdXXAVDYMrv2iAm5HU7RYrjYZ6eFoakDFyLNKHjcKwsy+hCDPIp1IwEUasjY6t/xs6a6uVwCJilit/CJJdaQNaEiHG29KIrqO1SnTJHDsBuROnI3/mAlrCWPI2wE7CEbCjAAP4sPvI78JNbcDvpwz7JtNQ66bGC0jAHAIUYMzhamqrVVVVSmiRl0p/ycvLQ0dHBzwej/pRZmYmVq1ahauvvtqwscRCgOk7eLon6VtKuwgxkbol2ckaJlYijFi6/P31ahw91ImjhzrQdNSNdnRh9PB8pKQmKQ8NT2c3mhvccKUlo3B4BgpHZAATujFufi7mDS8NueliZQWj1QJGBh7MCibWgXgHE2BkzDOnDxSetm89gMGyIClhZUslZswcG/YhEY0LkjReNntc2D6iraBFgGndtxONWz5E+4E9xz/zxkzQFN9FRIPOQweQPnIsMkeNxbDzL0NKBt2Rgq1Zf1ckibFz5L230HGoUokvWWMmICUzO+xyixWSiGQiyGSVTELBzAXImTgt7HWsQAJmE6AAYzZhtk8CJBCMAAUYG+6LiooKZd1yySWX4MYbb8T555+PIUOGwOv14sMPP8Rtt92Gbdu2ITU1FZs3b8aMGTMMmWWsBRj/JPoKMfIzu1nFWO2elChCTLy7JFktwrQ2uvG3l6tQV9WOmn2tyMl3oWhEOhpd7erb5+LMgt7ngtfjQ1N9F44d7kRqWhLGTslDT2k3rl50yqDfVEcTCyacFUywYLyh3JDMSkcdKg5MNJmQRICRMmvqQKFl2/aqoAKMEljCiDBmCzAyBitSUat+1u9VjEK5Ifm8XtT+6SW0Ve6Gr6MNWeOmICklVfPnnIgHIiJkl56EgpmnI2+6NdaJmgcYQUXl9uPpQo/Ho9yAklzpSE7VziRUl32tYI5tfh/Nn29B58EKZI2dpEl88bcrGZJELEvOzEL2uMkYufBqNU4WEoglAQowsaTPvkkgcQlQgLHh2jc1NUFEmJkzZwYdfW1tLU455RQcOXIES5YswcqVKw2ZZbwIMH0nY2erGCcIMUYF6o3WLSkaaxgr48LoFWE+OVahtrsedyTJjOTp6sZffn8AB/e1onpPK0aWZqFILFv+WWram9S/+oow8n8RYqr3tKC7uwel0/Lgmg4sunB6yOeHWVYwRrkhiQWMlLklgdmMQrkhReOCJP2cOiVQVInHODCRWsAoYWRDuSUWMKqv9XtDCjBtFbtxbMM6tO3doQK7SpYjvaW9ulwJACLCjLxIxIDoxQq9YzCivohJkvlJshD1eL0BTaYPL1ZZiDJGlUQcd8VvBTN15ijUrn1FiV7JaRkqw5He4m1rVpYwOROno2j++cgaO1FvE6xPAoYSoABjKE42RgIkoJEABRiNoOxWTSxjnn32WWX9IjFijCjxKMD452VnqxgKMSd2p1FCTDxbw1ghwmTsy8fmvxzGvu1NGD42C0NGnhBf/LRFhOkvwMjvurt9qNzZDFdGClJLejDnG8Mxe2gJGus6IfFkfL4e5a6UW5iO3MK0qDIiiRVMJMF4zXBD0pOKWjhpsYKJVwFGxn/KrBLdHwvxIMCI6+2Rd9egdc9n8DY3IGv8yRHFE5H0yUoMmDQdRad/yXZiQFd9LZp3bELXUcn81ALJSuTr6jiRhSjVpeLhyB8RqHImlSF70vSIWIkIM66gDQ1b/4G28i+UqJOSPvCZEm5Dydq17f9Cxd3JnTQdw875arhL+HsSMJUABRhT8bJxEiCBEAQowDh0a/zkJz/Br3/9a0ybNg07duwwZJbxLMD0naBdrWIoxAwUYqKND6NXiLHaGmZ63iRN96YeS5hNRyqw6XfH0Lk3WQXbHT89P2QfoUSYjjYv9n/WiDFTctGR58aotHx4unzo9vr+mdk3ScWQGTY6CxNOKURNdiNOHxX8QB9pRqRQVjBGuCFZGYi3fxBeWQwG4g2/7UNZwEgcktq/rFbWL2lDRiCtcFj4xoLUUGJA+U51fe7UUzH0zK9E1E4sLhJrl8bNf0fX0Tq4j9WhxyuZnwqV0NKbhahLshAdhbj+iAgjQYezSqegcPZZEVn7bH3qt8j1HIKvs0PFcYm0yHjdjUeRM2EqRiy8EqlZOZE2xetIIGoCFGCiRsgGSIAEIiBAASYCaHa4ZN68edi0aZMKwvvSSy8ZMmS7CDB2F2MoxBxfQbtZw0SSqlqPNYxWEebQ/jb87rkdqNnejimThiJ/aPqgAoz8sr8ljMfdjU/fr1PWLtl5afBkeuHqdAEn4n4jLSNFBe0tGJaOo0ntKD0tDxefNkWluN6/oxFHatrh7jxuMXOwsxllJSMwfloBRpRkB3wLb7YVTKhAvDLvuaWBrkl6rGDsagEj87azG5Jk36l//49o2b0d2ROmRmSJ4b8hJBhvD3qQe9JMjLjw64Z8TprdSEf1fuV+1Xn4ADxNDUgbOvJ4FqIg8V5EZPK2NsFdfxg9PT5l5SPWK4XzzhtgCdPd1alSR0vA3OTUNCRnZAYENd7yxKPIcR9WvNOHFUc8TclG1XZgj2I+/JxLVRYlFhKIFQEKMLEiz35JILEJUIBx4Pq//vrruOKKK9TM3n77bVx00UWGzNKOAox/4na0inGSEBNN6upYCTGRWMPEgwjz0VuHsO2DeuzZdwyjT8nEqKzCQe///vFgujq8qPyiGQ21nWg80oWM7BTklrpQMDodYwoKkZQEZQkjQX4b6rrgdXejcEQmWlI6MXFkEZKSk9De4kHzsS50e3vQ4+tBo6cTadkpmDS6SLkuTSgrwITphUhNS1ZjCyXCiBVMqGC8Wt2QrIwD0z8GjMwtmBuSXS1gZD6xdkMSAaL+H2vRuneHch8Klv5Y6wdeZ10Nejxu1c7Ii7+h9bKY1ZN4L+J+1VFTodI7S/wbLRmcenzd6hqfu0tZweSXzUPe1FOV2CI/lxgy7mNHAuYlaaMziscpkUssjbY+9l/wHalA4eiRSB8yImIG3V0daNu/C7lTTsHQsy5CxvBAETTihnkhCURAwI4CTA+68cGBu3XP9qyS+5CEFHR1dem+lheQAAkYS4ACjLE8Y95aZWUl5s6di/r6elx++eUQMUZrOS6whC5utxtpaamorP9frU3GZT07izEFGeZn6zAja5I/WK8RQkwkbkmRBun1izCykSflnKx5P+sVYoy0hPnr76vw2UdHIVYsqWN8aswjM05kPAo2Cb8rklyzf0cTWhs9aDoq4ooPI8ZmoXRaPloyOlGcFdiOfMMuFi97tzagsbUDqWkpKMrLVO5JOflpcKUnq2/aRbCpqW1BXlo6CoYeT3U9fEw2zvzqWGRkpw4qwMh4tYowkg1p3tiBBzqt6aiNtoCRsesRYFT9EJmQwmVBkmudlAlJ5hPMDanj0AHUf/AntO7erlIZJ6cN/rk12E3bebhKpUYWMUDcYeK9HNv4Lpp3foLOg5XIKpmMlEzt6bNlnu0H9iLZ5ULmuMmKXXvFLnjb2+BpPApv0zH4vB7A5wNSUpSli6tgKFJzC5CWX4TO+oM48vluFI4cqtyZIi19LWAkBkx60fBIm+J1JBA1AXsKMD58VveK7rmXDb8KSUimAKObHC8gAeMJUIAxnmnMWmxsbMQ555yD7du3qzTVGzduRFFRkebxJIoA4wdiZyFG5mC2GGO0EGN0xqRohBgrYsPESoT50/9W4vMNx9Q2L56YjUP/zHgUVoRpa0JXRRKajnSi6agb2fmpaGv0KLFE0lKLu1FNW1OACCMCzOGKNhypaUNtZRu88GHY8GycPG+IEmD6luqmJnQ0eeFqSVHBfMdMzlMizHlfH4ftjbWqarCAvEZZwRidCUnGa1c3JLu4IAUTYSSGSN26NWjdvU1ZaLhyBxcXB/sAbK/cg5SsbOSePAvDzr1U82dlLCqK5Ujt2y+hrWLX8SxEI8fqHkZ3eytaK3cjNTtPxV6RtNUSyDgpNQ1pBUOQLIF1k5KVCONtb4FY3IgljKtwmBJomo+1IH9YYVQBi90NRyB/ciZMw4gvL44og5XuifMCEghBgAIMtwYJkEAsCFCAiQV1E/psbW3Fl7/8ZXz88ccoLi7G3//+dyXCGFns7IIUjoPdxBgr3ZPiUYiJxi3JSmuYWIgw77x4ADs+Pqrcf8ZMPh7gUosIs7/uGGp3dcBd34OMrFRk5qTi6KEOFI3MRMlJeSqWjAgwStj5pyVM/cF21B5oU65K4mrU3uVGVpELEycUYdT4gcE1a1qaUZydq+LDHD3cqdodMzEP515Rgk9qD4UUYJQ4UxyY9nawYLz9rWCCuSFJIF4pjAMT7ukY+HtxQZJSNnucvgsjrN3fCkbcaVQ65P1fwOfxRCwGSMwTcb3JnjgVhaeehdyTZkQ4Qmsua9n1KRq2/ANt+z6POPaNz+dD09YP0dPtgUTTTh8+WlmzpGTlBs2OJKwl6HHXkUPo8XrQ2tAC+LwYOXeBEoH0FhFsxeomNa9ApaIW0Uss5FhIIFYEKMDEijz7JYHEJkABxgHr397ejosvvhjvv/8+hg0bhvfeew9Tp041fGZOFmD8sCjEhN42ThVizLaG8YswQvak3Clh78to3ZHeX12D7R/Wo6nejQkz8noPOCLCDGYFU7OvFeXlDeho7Mao0TnwenyqjSGjMlA6NR85BcctWvwizHBX3nHXo/pOJfaIQNPW5Ea724NRo3IweVYRUlKPx3jxFxFgpIzOyUNdVRsajnRiYlkh5n9lNOrz29TvorWCiTc3pHhMRS2c7WIFE8wNqfnzLWgUMWL/rojdkMT9SILOZpeehBEXX4OU9Myw92YsK9T97Q207N4GX0c7ssZNjmgoIqSIG5JYEaXmFaJg5umaYshI7BixvJGsSp2tHcifMBnZYyfqHoO3vRUdVfuQPWkaiuaeh+wI56G7Y15AAiEIUIDh1iABEogFAQowsaBuYJ8dHR249NJL8be//Q2FhYXq71mzZhnYw4mmEkGA6QvOTmIMLWI+V0tnlVuS2QF6/SKMzClcqur+2ZH2ftqIj98+jD1bGlE6PQ9Zuam92zqUCOP19mD3Jw1orOtEF7zIzE9BT0uyynokrkeTTy1EcsqJb6pFhElvSsPB8lYVrLdgeAZSXckqZoyIKmlFSTjppKHKeqZ/ERFGBBj5Nrx8RyPyitIxeWYRLriqFJsOHoxagJH+gokwsYoDQwEm+o+j/iKMxBE5vPZlJcAkp7qQUVyqy5JC3HnaK3Yjo7gEedPmomjeudEP0uQWDr/9exV4OMmVFlHgWrFmkevdR2vhbW1GxuhSJT5pTQMtFkPN2z9GR2snMrLSVSBfLQGA/Vik//YD+5Ccno7skskY8ZWrg2ZuMhkjmyeBAAIUYLghSIAEYkGAAkwsqBvUp0Qyl0C7a9euRV5eHt555x3Mnz/foNYHNpNoAoyfQF8hRn6WnX66aYyjbZhCTGRCTLRuSVoD9JrlktRXhPF0dePNp/aj/LMmpKQkYfSkQFegYCJM8zG3ynwkLkciuLR1d8F9FMqqZWRJNkaWZgdszerWRhze2QFfA+Bx+1Aw7IQ7QkNdJ7pd3cgflY6ZM0YG3dJ+EeZYbYfqc9LMIpx/ZSnKu4/HrtFqBaPXDcnoODBaYsDIfPQE4g0VhFfaCReIV4LwSpkxM3x8ELtYwMh8glnBNGx+Hy17PlNCiiuvEOkjxmgSYcSaQ6xAUrJykDmmFMPO+xrSCoZG++g1/fqDa55TLlMpGdkRBcF1N9arjEciwEhA3ozhxcgqmYTUnHzNY2+vLkfjni+QmZOlrssaNwUpGeEth3qzMHndSnzJP2U+cqfEt8uXZiisaGsCFGBsvXwcPAnYlgAFGJsuncfjwde//nX84Q9/QHZ2thJhzjzzTFNnk6gCTF+odrGKSWQhxoj4MHrckvRaw5jtkjSjYAI2/7UOn/79CCq/aMH46Xkqnou/BIsHI+mkD+w6nnpaXI7amr1o7ejCqOJcTJxRiPTMlIBnS1uzB9u31qKlzoOioiwVM8ZfJP20CD+pQ5Nw6qnBs6X4XZFGZuZgz9YGjJ6QixlnjcDs80YakpLaaDekLXuOx4uZM/FEhqUtu47/TIsIE0qAketnTQ2MbaMEm0EyISmBpSy0wGJVJiQjr6CrAAAgAElEQVQZR6ziwEjfPq8XR//+R3QcrER79T6kZOYgfeiokIKAiA6SullST4vVhwTwLZxzDrJLw7sFmvrBqrHxXguY1DRkjNCXutkfe0Vcj7o72uHzdSNj2CgloGi1gFHM3V2o3fghMnMzkexKVwF6JSW1K78ISSknngH+KUm/3tYmuOsPqx9ljpmA7PEnKe6M/aJx4VnNVAL2FGC6sbbi33Rz+UrpL5iGWjc1XkAC5hCgAGMOV1Nb7e7uxje/+U288soryMzMxFtvvYXzzz/f1D6lcQowgYgpxgTyMCtGTKSpqyMVYuLRGkZvXJhS3xj8+fkDqN7bArFuGTc1N0Ak6S/CHKvtRNXuFhw73IHsPBc62rqRWgDkDHNhRtlAK5bGI504sKsFhw+2YtSYXCQnn3BPamnoUoeroaOzkF6ahNHZwb9h91vBHNjVhIxsF8oWDMPZl5UoAUZKfysYyYakfh4kGO+80QNFDLohDf6RIBYwUk6ZVRLRZ4cE47VKgJEBBrOCEbeYYx//BZ2HDqCzthrethakZmar9MkqPXVSEnq6u+Fta1YZfcSnLq1wmBJq8meeruLH9C2ShrmjuhyepgYlNiQlJymhIa1ouHJXSkoOFCIjAhfhRXXr3lSZn7xtrbpFI5lL676dKvZLUkqKYiJziiSN9+GtnyInJ1WxceUPURmNJEBvam6hEnNU+z6f4icxY8T6xZVXpKx2ssdNQcGpZ8SUY4T4eZlDCdhTgPFhc+1ruldkzogrmIZaNzVeQALmEKAAYw5XU1uVYLvnnnvcZ11EkYKCwdNwHj58/NunaAsFmOAEKcQEcolXIUZvfJhIhJhIrWHMCM5bt7cL9e+n4OC+VrQ0eDBiXBbyh6T1xnLpK8I01ndh/2eNqKvqUNYu+cPSkVuQhvTxQHJqEoozA58xRw93oHpPi4oZkzYsGXlpJ9wQmo92IcWVpFJMp/7TUCOYCOO3gkFtkgrWO3XeUBUHRoqIMFrdkFT9A9XoL8JQgAn/xLe7G5LMUCxhmj/bAEkpLdYdIghI5p6ebq+KYSRiYHJGphJeJPuOpK0WF5iMUSeEJ6kvWZXENUniy0i6ZhEpgCQkpaYgNSsXKdm5KmaKWHCkZAa65IUnHX2Nlj3b0bj5A7SW71Tj0BN/ReYkQXS76g/LjJT7VdqQ4aodveXwji+Ql5uKjJFjVCyaLrGqaWuBu/EofF0dSnBJSkpGUqpLWcaIe1hqTp7KepQ9YSotX/QCZ31TCVCAMRUvGycBEghBgAKMDbfGu+++q8viRcyAjSgUYMJTtIMYY5V7kpFCTGXbzl740VrERCrEmOWWpMclSW9w3sNfdKJpvQt1Ve04eqhTMs+iYLiIKy4lkhxua4a3qwe+uhTU7G1FV0e3+v3QUZJ6Oheu9BTUtP8z9XQfEUZclap2N0PivYgAI4fcXFeGCqwrMV1yC9MwoiQbxRNyUdPaNKgVjO8QkJaRgumnDcO5VxxPbWyEFYwIMFL6pqTWmo76k73/dDka38flaE9NgAuStC1uSJG6IMn1n26v0uWCJNdoiQOjJQaMamtTha0tYPo+kcUapr1ytwrMK4JAj6gvvh7lJiOWMBmjxiJ7/FSkDy/uFQFkv7Z8vgUtX2xV1jMi3ojLjAT29bvUiHWHWHRI1qC0wqHKyqPg1DMjzkQU/lMkeA2ZX+2fXlJCSrK4IfURkMK1KXMTNp11B9Xc04aORObo8UgrGBLu0gG/r925G7lZUEJU8df+RVkWKfGquhw9Xu+J+klJx61exp+MDInPI+vAQgJxRoACTJwtCIdDAglCgAJMgiy0EdM8LsB4UVn/vBHNOboNOwTuTUQhxkq3pHiwhpHgvA3VbrRvykDTUTckPsuxw13oaPUqsURKW08XsgpTkdLsQnOjW8VvmXn2UKS6Trhb9BdhxMpFYsaIqFM4PB3t8Ki2Ut0p6GjxomhkBkqnFSAr16V+HkqEqW5uQu2udpSOKcApZwzHgotPuBLpsYKxIhhvqDgwWgUY4TDr5EBXKbMEGOlLiwgTrQAj/cTaDan/B4k/7oi4wECsMVxpKj5MSvqJQNFyjdRTqaz37URHzX50t7cpCxkJyJucmR0g0nS3NcPdUA/5OzW/CBkjx8YkkKwEHm7esRkd1fuRKQF0swIDbIf6UBULmNbyL9B5cL8Knps2ZARyJpVFJIr4LWDEfWnUxd/o7VK5HXm60ONxK/EqyZU+aJYjcfdyHzuCHrmm+/g6pWbnwZU3uEWvo18cODnLCVCAsRw5OyQBEhD72h6jzCOI0/EE/ALMrpqlAXPNiOOsQPGwKPFuFUMhpkzzNvG7JZltDWOkS5KIMPKYH9kyCvu2NaFmXyt8vh74unvUITMpGTiKFrgyk5ByOBMVO5tQOk3SVx8XT/ylrwjj9fiwZ0uDsoARSxpxV2p2d8LbAKRnpqJoRAbGlxX0HmJFgJHS3xWpvcWD7Z/WYfa8UTht4WiMn37i8DWYFUz/ODDSth3dkAYTYGROM6cPjG0jFjBKYIlxIF4ZQzzEgdF88/ar2LxzsxIz2g/sUa5GEiA22ZU2aHPdne0qRkxyeqaqL+mrJZOQVcXT0ogj695Ex8ED8DTWI2vMBOVOFK7IuBu3fqhi4YjAlFV6kgqeq7fUVR1FTne9EqhyJ5Vh+PmX6W1CuYeJFU971T74ukR8EVcxH5CSooSb9KLjrlFioSPxZFhIwEwCFGDMpMu2SYAEQhGgAMO9oZmA3wXp8NFneq9p6tw44HoKMqGRUowB4sk1yW8RY7ZbklnWMFqD8/ZNUy1uRpLFyNPlU8Fz0zKSkVuUhvXV+/HeE3Vo3NmN9lYvCoamIyX1eHyWrLxUFA3PwNGktt54MDV7W1BX3Y6WBreyeGlv8aKpuRMjR+Vi9KRclc66bwlmBVOzrwU9vh6kliTh+7fORWpaoJuCXiuYYHFgZAyRuiHN6eOCJO2IFUzfTEjqZ3RD0vwZEm1FCcQrpWzu+Kia8jQeRe3fXkfHgb3KvShr7CTNh32fx61SX6fm5ikRZuRF1xwP+GtR6TxchaMf/UUFHfY0HFHBdFXQ4SDikcr81NyArqO1SrARmzeJz5J/6hlIFuVVZ6ktr0FmVy1yxk9FwewzlQijtXR3daDhkw/QVVsDb3sLPA318Igw28dDOjktTQX2dRUMQWpWNvKnz7NU4NI6F9ZzDgEKMM5ZS86EBOxEgAKMnVYrxmMNJsD0H1J/QYZiTPBFi3cXJSusYuwuxJgdpNcfGyacNUwkIkzfXenp6sauTxpRvr0JW7YdQu2uTrgbetSX0iK8iAAjgXlFqBGrGG+BFxNHDVFuTPt3NKnMSeLNJH9S84HMQhdmzhwZkBlJ+utvBdPR5kXFzkaUnJSPjGnJKD0tH/NHBKZXjqUVDOPAhP/AsdoCRkYULBtS+JEG1hAhoPmzjeioqUD2hGlIdgVae4VrT4L0th3Yo7IIFc4+GzmTtQsR4drW8vuOQwfQsPFd5cLjPlqrgt+Ka5EECpZMTT09PmVdIlmIJP6NiBoiEnWL8NHciKwx41V9PUWsX3yNh5GXn6ayGY1YeNUAt65Q7Xlbm1H/0Z9VEGARkHrcbqTmFiihRQlHIgb5vPC2SjDfevR43WrMEq8nb+ps5J08S89QWZcENBOgAKMZFSuSAAkYSIACjIEwnd6UFgGGgoz+XRDPVjGJJMREGx9Gq1uSHmuYSAL0Ts8L7RLR1xJGdmpbkwfvv16DIzUdKp5LU30Xjhxrh9ftAzqSIa5GrrTk4+KKKxkZOSnwpnUjd7gLJ00Yit2fNKhrxZWocESGCt6bNi5JuSUVZw2M5eAXYYYmZ6Pyi2blujR6Yi4u/OZ4fN5ZN0CAkTEaYQXT1wJG2pRgvHNLTgTYVT8rr8Hc0sCfiQjT1wrGTnFgtMSAkXk7MQ7MYE9hiQ1zWILZ7t+FpNRUZI46HvhZb2mr2I2UrGzkTJiK4V9ebHl2HxEqWnZuUZYwIgjJ/32dfbIQuVwq/bPK/JRXiOyJ01TAYXG5EqEja9yUQWO09OdRu/cAsjxHkTlmvBJFCmefpQmZWL4cef8tdNbWoPNghRJWJAaNBDoOVsRdUoQiEWpENMocPQEFQVKGa+qclUggDAE7CjA+dOO18uW61/aKCfcgGSno6urSfS0vIAESMJYABRhjeTq6tUgEGAoy2rdEPFvFUIgJv45648NEIsQYYQ3jF2EmpYzFX39fpVJPV+1pQWZOKoaMzEBOgQu7yuvReNCNVLcLYqni6exW36SLMCNuS8jsUe5JQ4dmo6vNq2LKuDt8KnaMpJ+u9bQoYP1FGHE32ltzFL66JGTnifiSg9O+Mhpjp+Rhw6FqdU00VjBag/HqyYYUiRvS1s+rAY8H04ekKUsEMSdKSkvHF4facOqpgal/zQjEq4SVLZWmB+KVfuxmBdO6bycaNr0H+Tt7nKSUzgp/cwepIdl/OuuqVTDboWdfgozhxRG1E+1FkuFIxCQRVkSA8ReJn5I+YgxyJk5F2tBRSiCSeg2b/472qr2Az4fMsRNDCiF9x1W7two9DTUoHDdWBSAedu7X4MrVZkFz9KN30FqxCx1V+5A+dJRymdJSJEhvR9VeJKVlIGv0eAw756tIKxqm5VLWIQHNBOwowEiGtw8PrdE8R3/FM0Z9TaWhpwCjGx0vIAHDCVCAMRypcxs0QoDpS4fxY0LvlXi1irGrEBNJ6upILGL0uiX5RRjZCZNyTh704WGkS5JYtqz8n09xpLwL7fuTMXxsJoYUZwR8i7+/9hha6jxIaXXB4/ahq70b3V4f2lqOp5pNK0zCkAkZmFU2Eo11Xej29aDuQBvcnT7kFaWhM9uNMQWFotug29ujYsU0Huk8HgA4z4epk4bh/7P3psFxpPeZ51OZWVVZ94VCFe6bAEnwAAEe3S2qT7WOlmVZvi2NLXtsz4w9u/tl99NE7IeN2E+7ERszs7Nhj2PGjpElSy1L3ZLVOvpWH2zeBAECBEDcZwGo+8w6MmvjfYtFFEAcVYUbeN+ODhJA5puZz/tWkfXj83/+F150rwreJRBmLYAh19ovF0w5ZUhpvw+JkQGMf3YLbrNIO7wQCxH5QLzkj6LhbCf0J05DW1NPu9AchCBeovGZ8/Vl/eF12AAMARDB+58h5VuiJUTlDpKvEh3po92I7JdfhKntTLlT7dh5JNCWgAvahYj8T158a0ao7wYiw/dzbaPTqVyGjMX+pO124eFKipQj+hCcmoKtoR5aVw0cV16BWLnaKbbRA5Byp6X330R04iEEvYm2oy5lELdSbGqEnmc+dQH2nudLOZ0dyxTYUgEGYLaUiB3AFGAK7IICDMDsgqhHdcqdBjBP/cWQBfquu3WOK4zZjYyY7YCYUoJ6d8sNU2xJUj4Xhmyo9UqSHvWG8NkvPLh7exFmt4D25vX/VXohHqKuF01YpOVKBKTIchah5SQ0Ig/LKQHP/aUL5231uP2uB4tTMRrw61+UKHAJJyWY1LkwXlHPw+bSweLQws/H0fiMGV+80PbUni8FwtyamcXajkjruWBuT83R66wN493JMqRzVRaEPnkfqYVZyJEwvBNTMCop0vD4yTPGMio4GuohkJBRswXmy1ch1jRsCmHW64REJiTdkHaiExKd6/bktgAMmWOv21HTa5YRxuu7/i5C/bcgR8PQNzy9/0r58zvyqB9iVT3s3VdhPt1Tyqn7diwp8YmQDlBDvTRDhoThEnBDSpV4rY7msWQVGXIsAuKuiUtZOFqaoLE6YL/4IkRXcfCFPGCw7zpCJGtnZpyWQG1UdrSZGNLSHEgXJ0PjCbhf/T3wom7ftGMXPhoK9H8y9ORB/sM//E805+ywuELI38OZA+Zo7EP2FMdbAQZgjvf6l/T0uw1gtgIyxz3Q96CWKO22K+a4gJjdcMN8MvYIgbkMavgqKDKg0XIw2dTo/dhHW1KH/Sno2xT6L+Uu0bbu+wGBMGS4xZVMFymeoee3nrPC0K1CbZcB3Y4GhH1JjD8IYnIwDBLuOxMK0fyYWpMVnKCCu96A5jM2uBoMuL30uOSocnXw7malSJeqnv4X9I0gzHodkbbKgiE5MGQUZsGszYEhP1/bDenerQHUjd1ByjOP5Ow0dRZEORHOhjpaekTaHJMPud6pWSDsh1Gngdrpoo4Cy5XP45FkwvmTTz9bX//Muq2oDxKAIfdymFwwvuvvIdR/E3I0RHNQtjMIgNFV1cPW83mYT3VvZ6o9PzcxN4HoSD+SgWUKo0hJFXHPkNIkcByFMaE4B5VGh4aL52FqP0ezZIodZC7Pr16nHaMI1NFVl5e1IyclxCaGYGhqh/X8MzC3s0DeYteAHZdToBC45DXpfCYHX7/+775My2oZgGG7hSnAFNhLBRiA2Uu1D/m19hrAMCCz8YY5iK6YwwJipmKDT4Qt1RGzl2VJxZYkkYcpzIYhDpWZkSge9YWxOJPAtN8POZ2FXW0BJ3BQ5CzmxmKIBtOo7zCislaHhUSQalIKhJkYDFOgU99uRuVXeKh1PC66Guk8xDUT9Eq0HOn+0gJ4jQrPNjfRLkqF46ZnBpfWABjy81JdMOScQifMRi6YrQAMmWdtGO9WAEaORuB/68eYfzAEU2gJaqcb6koXloK5PA63w7zqmReXAnCoFSTnpiEYzdDWN2Gp6iy6XnzuqRf8dgEMmbCYMN7tOGDINQ4TgMmVIF1HyuehHZDWK9Mp5o/qJyVIDa2wXzoYJUjF3PfaY4gLhgAOyTMNJZXKlctxHJY8Magq6tH5xefLysmRlubh/eSXiDzqo22+Bb2xnNuj55B8G15vhKn9PJxXv1z2POzE46HAZsBlrQIMwByPPcGekilw0BRgAOagrcgBvp/9BjCF0rD8mBU1DhqMYSBm/RdxKWVJ5bphfB4JH/10kZb/+JdS8C8mkUzIiGclqDgVdBARCaSRlGQKSSrr9HDV6+CsEeGRck6XrSCMVTEhsCjBMx1D2JeCo0pE0ykLQpoELNUavPxMC6qajavaUN9cnKFz91Ss/ldwAmAoPCnBBUOPX+OEKdcFs1EYbzHdkLpbakDKOQI/+zES44/gffAAjrZ2qB0VTzbAojf8NIBZDsFdYaaBqYnxEfBmK0JiBc58+88hrAk2JQCGjPXKkEgJEgUsnasdRIW7r5QgXnLeYcqBIfdbTkvq2PgQ/Lc/RHR0AIb6NvrBvpyRDvogLc/D2Hoazue/Cm2Fu5xpDtQ5ZD+TzKKBG2MUTHVeai77/ojDxnvtHRp2TLJ2aLvpMgeBQ6SQj+TsuF7+rTJnYacdZQXWQpe8w2WrZ2YAZiuF2M+ZAkyB3VCAAZjdUPWIznmQAMxaidcCmeNYrnQQS5R2E8bsVGnSTjhidisfpthOSSQbxjeTxsy7eizOxLEwmYCg4eBwaWGr1NAW0mQsJPyYuishNEO6FskwOzQ0MJd0P6pu1m8KYeKRDEZGvZAiMjSyBolwGiFfCqJRgN0lwmTXIG1IQ+8Q0OZ2oq3LRv/POwwIhFkLYMg9lQNh1gMwFMxUr5TxlOuCKaUM6YxBBf9bbyA+2IeIYID7xOpMkc0ADLlfOR5DYnQIUXM1VCe70fX1rz31p8d2XTDFOGDIRbfjgiEOGDL2MgemXACjpFPw/OL7IG2kidNDV51zbZUyCKiITw5TBxPJNql8+bfKdtKUct29OPbBtZHcWm4DvpDz49OjIHk70fEh2imKE4Syb5+02ia5NKbWTri+8Ntlz8NOPDoKlOJy2eypDyOAIW2ov/vo/y55Mb/Z9r+yNtQlq8ZOYArsjgIMwOyOrkdy1oMMYNYKftyBzHFyxRxGEFNKt6Ri3DC+xSTe/v4C+ocWEZjP4MSJClidmnU/FN6540FgPg1eVgMKeeVkYa3UwlWng6tev245UsibwtxoFPFoBoFggpYxmQxaGsxLQnUrG/TQGQTaDSkoJaC3C+hocaLxtAU9r7hpyCEZ5UCY7balLsyCKTeMd6MypNaZQUTv3kRyZgoRdyPA8XDbV0qONgIwRAvigiFDmhpDFiqETVU4/2/+PVTC6jKtwwBgKAy5Ob4vAIbCghLDeIO912gQL2mNbGg+CY7m9BQ/aDjtzCiMLadp/st2uikVf9XdP3Kn4Au508TCNLzXfoXoowEYmzvAaXJh3OWMxPwU7SJGcmgqX/iNcqZg5xwBBcp1uWz26IcRwJAQ3g/nfl7yir5Q8xXWhrpk1dgJTIHdUYABmN3R9UjOepgATOECHOdypYPmijnKjpjt5MM0GrZuh7uRG4b8a/xP/vssJodimB2Pw9wsQ2/hUKF1rPs+NPYgjMnJEJQMUFFhQHA5RVtFE2DT3GmGwZwDAPlcGF3MgJmRCC1dIuG75OcZTYbkykJazMLmElHfboKtUqQlOeS4RxN+mHkR9R1mNHdacfnLVRQGbVSKRK5XSh7Mem2pSRkSGTvlgtmyDKl/FLW33kVi5CE4nR7a2np4SMlRAYAh97OVCyYTjUAaH0G0ohEdv/kN6FtPrlq37QIYMlkxLpjtOGDINfYDwNDr3hgtGcCkwwEsvfsG4jNjNDCZdEMibZuLGaQ1c2zqEW3drKtphPtLv7+t8ppirrkXx+wkfCH3S1pQL77/BmLjD6GxOqGxO8t6DJq1MzYIsbI614r64gtlzcNOOnwK7AZwWasCAzCHb1+wO2YKHAUFGIA5Cqu4R89wWAHMWnmOqzvmIMGY3QQxZL3zrpgKXVfZr45yS5P2GsTMTcTxzusLeHgnDHe9CIdbiyUp8OS514KYqeEoFqbiSERlaCsU2h1J8pLuSAJcdXrUnVjJxJjy+bHwUIIS5pGMy7A4NVA/LmdKpxQsemIwu9Q4ddoJs2PFRUAcMn0DSzAoWuqCOfucE6eu5LJRdjIP5qlSpMkpXNDqoEgS7eai0mjQG47iYttKt5tiXDDFlCH1fvAJuOsfwjQ3Dn1HJzidriwAQ8tZhgcQ4QxoeukV2K5+4SkAQ75RTg4MCWT+4L0RaHUGxOMZpNMKBIGDKPKoqzWisd5EvyaDABgytpMDQ84/DGVI5D4jw30I9d+gpTLkRSDWNIPXbu7SyMSjkOYmaG6MrqYJ9isvl93dp+w3pl04cafhS/4Wlz96C5FHD2iHJUNTR1llWuRc0oqaZO1UPPvFktpg74JUbMpdVmAvoEvhIzAAs8sLyqZnCjAF1lWAARi2MYpW4KgAGAZkgINUorSbMOYwgZhyy5Jm3rHi/rUgFqYTONltXhV+mwcxhRDGv5TE9HAU3oUkdb2k+BQSIZmWJNldWpy4YIVak/tQPvsoivGJIOIBGZUuw5Pv0w+wgRRkGVDbsqg+q0eNcXWLWgJheu8vwmk0Ugjz2p81Q63ly4Ywm5UiyaEwpIEBjF6/AVUyiWq9AcgqAC9gPhKFUl2N05+7CnVdPf0QSCBMYUekYsJ4SRkSGd1NNfTX+EAfRn/8Y5gWp2E4103zRIgDhoxiypDyJUjk+MTEI3CiHsGKVnT98Z889Z5cqgtGkkheTxCjYyFMjC5DI4pIJmXIShacSgWNhoPVooVeL6C5yYz2NhtMRvW2cmDITe+HC4Y4YCj4KbEMiYCvcP9NREb6kZifpG2peYMZGlsF/TWfXUQcGJlIEKmAF7IUg8bmhOiqhfX8szA0dxT95+dBPXC34At9jZAcmFsfIjY2QIGVYFjdEWwrTSicnB6lXZiMTR2ofOUbZUGcra7Dfr5/Cuw1cFn7pAzA7N/asyszBY6zAgzAHOfVL/HZjyqAKZThuLljjosr5iCAmGKDekvplnR3bhjv/0MM3kENjFYBVQ26p17Va90wBIw8vBOkHZLIsDg0CEkJhBczcFcZUd1ioK2pM2kFw3eCCCwlkVJloLPwMAo5h4CiZOFbkGiIL8mN4arkHHgQrauuH4+k0Xd/GRcuunHpS9VoObPy83KcME9BmLExnBweRWpiEnIkgszSEoILHpjUKzkqnN6AgF4Pd20dBKsNhqtX0afkQNBaCNNTn4MrZGzlgon33cPom2/AvDwHPQEwpI4LeMoFQ0qQqDZr21E/7oZEfpaYGqNlLEFHC7q+/WdPrWEpACYQkPDBR3Mgv/p8EmZnQ7BadBS2cJwKSjYLKSEjmZJhMWvgsIuwWLS4+mwVfHPLZTtgyE3vB4Ch1y2jDImcRz7gR0cfIDxwmwYipwLLyISDIDV5JHOEtN7JyqTcTgW11QGNtQK83gBb9+dp+dFhH7sJX6i+sgzP268jPj2GTDQEfcMJcGsyjjbTMOnzIOVfgqGxHdau52gILxuHX4H9hi6FCjIAc/j3E3sCpsBhVIABmMO4avt0z8cBwKyV9jgBmYPiitkLRwxZ53LLk7ZbmrSTIGZiKIbvf/chxntTaDyrgduycc5CoRtmfjKOhYk4Assp2iWJOF4WPFGotSrYa9U4c7YKy3MS5sai8HuSsLu1SCBFXx4EwkSDadre2l4louWMBTqjgIV4ro11IYQhH3DHH4SQ1KVx5XIdXvmjhlX/gr0ZhNmqNTVxvTz4x++C8yzC7vUhG0+As1rhF0VkNRpUmczIZjJQgkH4Z0k+TBb2+gaoq2tgvPp5PNBbynLB5B0wieFBPPrhD6GaGIHr8jO5D+zrABjyva1yYEg7as5gRNB5Al3f/FbZAMbnl/DeBzNYXIxjfj4Gg1FNAUsiGoO7agV+UWdBPANyfCicgrNChyq3AS6LgkqHtmwIc5i6IRWKTEqLSFcj0hlJjkUgJ2K06w4JOiLrSkqO1CYrLaMheTGlhvbu0x/ZG152t8FL4YWJg8V/5yMkZsehpNPQ1zZvqR/Znyn/IpJeDz1eV92AiqtfKQneHDTNj/P9HCTgsnYdGIA5zjuTPTtTYP8UYABm/7Q/dFc+jgCmcJEYjNnbLXtYQIxbd64kYfIZMcWAmK3KkoZ7I3j/zSWQX6u6MuB4FeyaXNbKeiMPYUxZK3nga4YAACAASURBVB71hhHypyDFZdicGsTCufNFdxY1p7QIDwtYnkuAZL3YKlfyXbyBGKSIgspKA+2e1HhqpaxgPQjjX5TgnUvA0MHh2395Hmb76o4z5UCYHlMFwj9+A6mpaXgHBpG12VDZ3AxOm5t7PhRGjWnlvkgZiWdmBla/H5zZAk1TE2ZOnYPS0LilC6YwjLewDCk1P4vAz9/E0s2bqDjRDsFmfwJgKIgqohsSKUMiDovYYB/Ehhb4nO1QnezB+ZMr7bTJXMQBQ8ZmOTAtTW786p1pzM1HMb8QQ02NETZrTo+lhSBcbsu6eyIaS2NqOkKPJbkwtQ7gc1dbStrThQfvhwumXAfM2ock0EVamEY6FICSTkKl4qBSa6CxV0JbWX0kyl/2Er7k9Q0P9SI8eBeJhSnI8SgNMFZbHE9l7jwp9wr6oKQStEW4tsIN5+dfA68zlL0n2Yl7r8BBhi6FajAAs/d7g12RKcAUABiAYbugaAWOO4BZK9RxADIHpURpt2DMTrSwzjtiSgExpQb1bgRihu6FKYAZuR/F2Wcs8KX9T7bpViAmFpARGhcQ9ufcLKSrkVbk4a7XwXhCxnSvBGmJAyeoYLKqIWcUxCMZSDEZvCULvZXH2fNuCI9DefMXXgthErEMxvtDOHXZAfvLPF7qbHvqPWe99tSkKxIZ6zlhOm7chzT4EKnhEQg11Vg2GFBdAFzWgzAEyiApwTY/D95qoxBm6spV9Jxe6UBFsmCKLUMiHxb9//w9zN28BasA6NpW8kDWdkNarwxpcTnnGLJnE0h7F6HvOAvna7+DwXnpKQCThzDrARjys/7eacRjavQ98GJiIoy6OiMtK8qPzQAMOSYeT2N8MoLqKj0Mahl/9ielQcX9BjDk+jsFYYr+A/EQHrgf8CUvE+lkFOq/CdKBKhX00pIvQW/IuWFUHC1XkuMRWhZGAA3J4iHwxXHlFQZfDsleOyzQ5bADGAUK/m7oP5a8K/6i438BBw7JZK78mA2mAFNg/xRgAGb/tD90V2YAZuMlO24wxqC9si/7l4GYEap7vm31+GAU7/5oCYN3wjjVY4ZGmwvPXU76ch/ut3DDRHwyolMCdb94PUnwnIoCmBNdFvTeXUJkmbhiAFHQIinJFLYQGGOwqCE2yVBrObjE1eG75LqFEIbAndH7QbT32GF/kYe9XotuR8NT+6dYCHPrQR+0//ILOManwVkt0NTXYz4cofMVQhgKXIBVThjyPbfAQxoagra1DfMt7ZC7L5btgon13UXkkw+xfPcuKs9foJ2QyCi6HfVSECbfDNT2ChhOnUPFl7+B+/0zJQOYu7cm8Wg0ibGxEF2j2pqVTlbkfgiAIWMjFwzdM94E/AEJVj1wpduBZ59rLus1fljLkMp62ENy0n6Cl0KJUv5lRMcHc6HHiTgNN1YyaRLGAxXHgxf1EMxWqI0W6JvaafZLKZkxh2Q5jtRtHkboUrgAh9UB8/bM2yXvo1frXiVFlQzAlKwcO4EpsPMKMACz85oe2RkZgCl+aY86kNnvvJjjDGIK3TCWeCve/Ps5PLwbQUWVFpXVq8t7igExU0teLI6lEZhWQW/gIRp4CnJIBkw8moGsUmCw8rBa9DQrxmzXoLpZTz/oLyQef7BfA2HIv2JPeQNIJxToUzrMj8fQctaKL3yrAV5nzvmxFsIUG8ob/eDXmHjnXfATU6i8dAkqQaDzbQRhCkuR8lDG4fPSNtVixylMfP5lXGxeCVQtxQWjJOLw/uA7WOy9D4uag9jWQTNDiu6GNDgEo5KAob0T1s9/AfqWDgpgyCilDOmtnz7EslfB8EgArS1W6HQ5TQrHVi6YTEbB0EgQDfVG2PVZ/N7vni7+DW/NkYe5DKnshz6gJx4U+FIojywlEJ8agbQ0j2w6Sd0vpNRLMJhoxo7WeTTKvQ7oltj2bRVCl85nnnY0bvsCezgBAzB7KDa7FFOAKfBEAQZg2GYoWgEGYIqWatWBRxnG7HeJ0kEFMXsR1JsHMR/9IIGFPgP8iyl0dJmeyqkoBsIMjy/BPyfD7bTA3aCjAObR/TCW5yVkUgpEF2By8miqqYBGzIXN5kchhCEdlkK+JA3uJaVH4YSEZFRGVuLgcIvUBVN3woxEbRz2Bi16nKs7yWyVB6Mkkwj+/Xdo+ZE/nYZSX4sa40rWC4EwxbhgXByH5PAQxDNnMXvyLJS2EyW7YPJhvLHeOxj71dtQTY/BYtRDbGqFSq3etBsSDRldmEVwahqoaUb9hfOwf+FrT4J8S3XBvPXWBK5/NguNRoOW5vWzXrYCMGQ9Z2ajtFW1Savgr/9NF3g+56gqdTAAU6piO398HryQmTsvledm2vm7YjMeVgWOEnQpXAMGYA7rjmT3zRQ43AowAHO4129P754BmJ2R+6gCmaPoitluRsxegJj3bg7ixlsS5u5r0NhugMW+0oK5cMduBGIINBnuDUOoSKG5W8TZL+pRLTdi8HYQvZ/4MDUURfsFCyLqKJ3Oqc2Fza4do3Ne+KbSEGUNEjEZUiwDWc4iGknRHBmzWQt3owF2lwhLhRYmmwa6y1m8cLJ11VSbQZjzMRnhn/wMibu9EE91YCGrPAVgyGSbQRjqgslmYZ+bBW93wHD5CkZOnt0SwJB584G8hWG8BKZEPv01Jj/5FNbAIrKpJASHE36VCHelY9WzLS4F4eAzSPuWkE0mKazxqow498ffBieutBAvBcCQ6//gByO4e2cB1dVmVDr1665PMQAmFEpi3hOH3aTC5y46cOlKU1lvevtRhkQyYChs6Cnvnst60AN60kF0vRxQqdhtbaLAUYUuDMCwDBj2wmcK7LcCDMDs9wocouszALPzi8VgzM5ruhuumP0AMcUG9WYyWfzgb+Zw774HoWUFZ867IOpWu1TyKuchDPma5MOQD+/TI3EKTNrOmvDSb7kRcXvo4Y1CC9742ymM9odp1kttS64LiUcK0F8LQYx3QcLCRAzLvjhScQU6jRo6gwAVr0LYm4TWqYK9Tgu7xojAUpKCGVe9Hs5aPYxXgFe6TmwJYUgoLz8yhvrP7kHqH4DuwnnMJ+L0vLUumPUgzNpSJId3mTpV9D0XMdp1mc5zsa7myX2sV4q0tiNS3gVDdLz/5s9Q7Z1Gxu9F2ruMrJRARKWBzWahIaOQMwguLMKk10KwV0BdUQltdR1mq86i69xqCFVKGRIpHXr99RHcubOAxgYbbTu90dgKwkSjaUxOh9F5yoEmlwrPfu54dkPa+XelvZuRwZe90/ooXumwZ7qUuibMAVOqYux4pgBTYCcUYABmJ1Q8JnMwALO7C30UYcx+umKOE4iZGo3j3R970fdgEYlIFjUneFTZXRtuWAJiiPMlOqVHLJJBa6eJApjnv1ZJS5gehMbouQ/ei2P5tojJ4SiaTxlhMOfcNYUQJrCcxOyjKMK+FNKpLHizAl6jgoHXIbichKBWwVYpwtShom2uXVoLQt4UBTZ2t4iqZiNsL6jwwqnVWQLrOWHufPA+NO9/BOvELHQ9F6DiOMxFHoftlliK5BkYgN1ggP5CN6y/83u4PTW3JYAhz76eCyYv9L1PbqI5uoTU3AzkaATL03Ow6TQUdJFsGN5ohC8toKalAfoTndCfOIn+kRzwOt+xuvV0sS4YRcni+98fxuhoEIqcwYm2TdqQb9KOmtxDJJLC9GwUp0/a0VLF4cqz5ZeusDKk3f3zYu3sDLzsrd5H6WrHwemy0XoxAHOUdjJ7FqbA4VGAAZjDs1b7fqcMwOztEhw1ILNfMOa4gJiHvRF8+is/ZsclTM+FYLSqUF9bAZNVWJULQ7oZ+TwpTM8HwQtATbsa507X46VvuJ5qKX19cgTXfxBFYkZEYDGFhpNGGEy5gFcCYdKSgtkHSWSCPFJJBTanBrzAIZxMgLS51kANq1OLqkY9KmpyJTb5DknmtAFTDyOorNejptWIytcEXHRtngmTHBrByPd/AH5wGJWXL1MHCxnFQphCF4ynrw9ZrRb1z78Iy9d+kwIYMsp1wZBz7z2aQ3dLDTLhEBLDAxjrHQTSSdTazbTdLm+xYpK34fwzPavWpG9wpmwAQ677wx8+wqNHAcRjEk6drNzwjWqrbkhenwSfP4H2Nhs66gRoNBzOnK8v641vv8qQjmMJEoMvZW3RY33ScXO6HCUAo2QV/KfB/6/k/fs/n/orcCrWhrpk4dgJTIFdUIABmF0Q9ahOyQDM/q3sUYIx+xncu9MwZj9Lk2r1nU9tyPGhOD56y4dwKA3vQgpziyHwvAoWgxkqFamCySIRl6E38qhwa2GtUEPbGMG5L4hot55cd4N/cH8Id38WQ2peB58niYpqEQ6XlpYlLUzF6Qf/qFeGy20Ar1ZBisuIRzJQARCsWZgq1Th90gX6jccjD2F0UR3mJ2I40WXDs1+txqIjuGl3pPT8AkI/fANLN25CrqlCdd1KO2sCYYotRSJdV6T+PgTtDjS+/AWYXnyJ3tl2XTAEwJBBIEx+3Buew4XWla97H87SH3WdWHG8rAdgyDHruWD6HndJOnd65fyPP55DX58Xj0Z8qKvTo8pt3RTCrNeOmrh0RsdCMJk0aD9hxWtfbMCDO1NlAxhyA3vtgiE5MMcJwDDwsn9/Jh/WKx9nt8t6a3ZYHTBvTb1X8hZ8reFl1oa6ZNXYCUyB3VGAAZjd0fVIzsoAzMFY1qMKYwzaK3sm8FEGMdFwBkO9UQz3RRENZTC95EMmnQUUwCLaoDfx1MVS16pH+3kTqhtEDEVyAaZktBo7nlqHmdEY/vlHwwh6MsgsixSyGCwCfB4JkUAGyUwKGj2HrJTLniH5LwazQEuMuKo0hS+uNa2qCYQhH/qjwyrYXFp0dNthfDF36Y1aVHc76hH6zj8h0T8Av3cZ7q4LT+51IxcMOSAfyptvQ+1KppCenUWgsRGpV15Fz4XuJ/MUA2E2yoIhk+RdMPkJ1wIY8n0CYdYCGPL9csuQPJ4Y3n13GsPDAagFBa0tpZchxeJpjE+E0XHCimevVOFEqxX9tycZgNmzd6XSLsTgS2l6HeejGXTZePUZgDnOrwz27EyB/VOAAZj90/7QXTkHYDKYW/h39N55zd59YD50Yu3RDR8VGLNfrpidBjFk2fOumApdV8m7oNSuSZsF9ZJw3qmROOanJSQTCuZiC9CIKrS4m9DaaYTRnCslKhwPw4/ol+tBGN9iEj97ZwjeqQx0SQumRqJYmIwjGsxAZ+SRVcsUwlhNemh1AnXK2Cu1FL7kW1VXQQMhHgSXSQPIYjGdgi+iQyZuQet5K179ZiMeYZHew3oQpqeiAYl79xF990NIff0INNaixul+8ghFlSIFQ3DMzoGzmKHrPIPRZ54DsQddrMk5StaWIpEwXjJ66nMuljvjj79uzH1d2BGJfF2sC6YQwJDztlOGREDWW29NYmQkgLnZEM6fc1Pn03pjvTIkcv7kdAScSkXbWH/ja01Qq3kKYMg4LGVIx8EBw8BLyW+rx/YEBl62XnoGYLbWiB3BFGAK7LwCDMDsvKZHdsa8Aybg/3v6jPHk9aeelUGZ/V3+owBkjkpWzEEBMcueJIbuRbE0n6I5LUvSMtRaFVpr6tF+zgh3vbgqj4SU5zxcfgjwHFrtnat+Rnb3zekRzDxI4c6/xBAc4xANp+Fwa8FxHEw2NRRbEjozh0rxcbvqrAJ1cAnJmQfQhZZgggCVItMXSpbjEcpkMBuwwNFzHuf/sAsnLuRaN99anl4XwlwwuhD879+BNDSCrCTBV1eFGstKa+y1EGY+HKHz5V0w6fl5BCan4LxwAaaXXoLuzFncmp59AmDIsYfRBfPoURDXry+g954HgqDCmU4XOG5jCJMvQyLwZX4hjlA4SeHLuc4KdHc5n7yRHSYXzFEHMAy+7O+fr4fh6gy6lLZKDMCUphc7minAFNgZBRiA2Rkdj8UsawHM2odmQOZgbQMGY8pbj512xWwnJybviHHrzhX1MHlHDPlQLU004+G9CJbmkwgFMgj7M8iklRw8RRR6MwezQ4XGqhq0tXGoys4j/nAMciTX2pl0SiKde6pPnIPxfAfExppVMOY73+vDw48TQFhES6cZHE+MJLkP/PkuSVUpwDR6C3w8AiEehhxeAp9OQsOtuG8UtYj5qAYqpxWtPU048ZdfhbqmalMIc3rSj9gn15AcHEIwnYTc1IAaaw7ckLFuHkw2C2csjszsHIJVVVDqanHuT/81DfIlAIaM3XLBkDIkMtZmwey0C+azzxYwMhJE3/1F2Gx61FQbIIpPO53y7ahTKRkLi3HEYmk0NpjRWG/CS8/XgOc5BmCKesXtzUEMvOyNzof5Kgy8lLd6DMCUpxs7iynAFNieAgzAbE+/Y3X2VgBmrRgMyByc7XHYYcx+lCgdVhBDSo9e/1E/JgczSPqs8HpSNAPGYs91tSElQQTEhPwZyIklNGEC1VwcFn0GbmMCWSmBbEYGOA6cRkDMwoNzmOByt8DU0wnDuXYKWt7/sQfvvTuFeFhBTYcGFdoVAEJ2fmhhCM6xXljiMWhCy8iqOGQMVoQ0WigcD4MgQiVnICTCSM57wWtlqFtcaL3SCfOXXoK2LdcCea0ThranzmbR0TcJ6X4/kiOjCEXCUJx2uGsbodKsdEYiobwERinBIJYnp2GRFWhamqGpqcHo5cuAwYBL1bnSo712wRQbxkuCeMk4f3J1m+r1wnhlOYtr1+Zx4/oMUkkOkWgKBoMaDrsIvV5NHTGkbfXcjB/hSAYcL0AnCqirNVJY88LVGmi1uRyf/NiJMqTOCythybv5rngUHTAMvuzmjjn8czPwsr01ZABme/qxs5kCTIHyFGAApjzdjuVZpQKY9URaC2VYydLeb6WjBGP2Irj3MIEY8gH87R/7MDoYw+hAHLFMFHY3h7pqFy1LKRycbxHcvRvILPsgxJZh0Kagq6hAbacTvJZHVslCSUhILQUQS8fAOS2oaDoB47kO2F99Fp/+0odbH/hoZyRrR66kKA9hhLAX5sFPoPF7oISXEbc6oTZV0qyV/IimJfpbAmKWZ+KoMCVhUi1C2+iAq+M0LF//MjT1j+HImnKkPIQ5ObyAxJ17kJe98E1PA6kkrE4XOK0WXikByDKsUpr+yjvs8BoMqG87AdNrXwFvNuPWzOwTALMWwux0FsxeuGDIMxDA8pM3BjE9HYPZrIfPJyEYTEJWsk+0p04lJYOWZjsMBoG6Xy51V0IQVpwvayHMdnJgGIAp/X0+D17ImZ2XcjCSDaZAXoE8eOl8po2Jsg0FGIDZhnjsVKYAU6BsBRiAKVu643fiTgCYtaoxILO/++gww5j9dMVYxZ5tL9xulCZ9/KsA7t8IY7gvDp2BQ1O7DmHZ9+RezepK+ns+uAzD4HUIwSXwYR8iMMMrizC5tLA4OXSecT8pJ8oqCtK+IJKzS4hkYhDaa1F38VnMO87h+tteDN8Po6PLjJAqTOd28mZY770NjX8e6rAXyYo6hB+XtOgF3SrdCIRJSwoyIRUcVTrUN/KITw4BLgfcJ8/A/qd/AE4U6TnrOmEAnA1nId3vQ3p2HvML8+CCIdh4NXXJBFIpZA16uOvqwZstmHA5IXd04FJDU27OmVzpUaELhny9WSlSPoyXHEcCebfTEakUF8xaBwy5PnHBFLajLhT3zs0J6EQTbS0diaSQySgUwhAXDAEtIX8En3uuAW2tFhgNOdfQRuOw5MAcFQcMc71s++31yE7AHC87u7SHEcDIWQX/V//flSzE/3bmL8CrOCSTyZLPZScwBZgCO6sAAzA7q+eRnm03AMxawRiQ2d8tVAhkxD1sC73dp97r4N6ddMWUC2LWdkwKeNP44X/z4OH9GHW7tJzUrQphDaS8VGZOSqB6cACCbwFCxI9URQ0UrZ6WJKUkBYZKGXUnBdRWulYtCylLig1PIhwLQH26HlXPvYp37tsxcj8MvUlAdWMOrsSm78Mx1gf70hySjhooOhP9fjSTc7yshTAeTxScoEJ1lQltF2xQQiFIQ2MIt9eh9Wtfh67rzJP72AjCkO5IGb8fyf5BjA4OoF6jQ1ZWwGk1mISCTFsLunuuQMVxuDWXy2O5VPXYXbMOhMkDGFK+dGd6Hhfrch2PyCBdkcrtiFSMC4Z0QyKjsCV1KWVI+fvs753G2c466ojx+hKIxzMUwhD4Imp5eOaWwXMqnD1Xt+XLr1wAo2QyyKYkDN6dwumeVnDC03k0W168yAOOAnxhrpciF/sYHsbAy+4s+mEEMAqy+MnEhyUL8ptNL4CDigGYkpVjJzAFdl4BBmB2XtMjO+NeAJi14jEgs3/b6TDCmL12xRwkEHP7PRlDn9owOhjHmR4j1CTvZZ2RHv4ExqlHMHq9yBgsgKCGSiF/pVPBH1CgtZuhr1Ohtj33YdmqWemIQzokRQfGwGnUSJywY6jmKjz3TZifSKCj20waJ8F8/wNgdhCCFEfW3ba67GgNhCFZNH5PEiQ+xtGowYnmnEMn8WAIvMkEb2sVlN//Es7bV0owNoMw+ce96ZnBpcoVsHBz4bHTxZX7HoEweQBDv35ciiRHo0g+HMTIrduoU2uQTacxH40jK4pou9AN8eQp9PpyXZUKIcx2XTA7GcZbuOR5CLPRu0j/vamiAQyZo5gyJCUlIT75CLGxh8hEQ/TSS3MBVFZZoLbYoW/qgL6+FZxas6NvbocdwDDXy45uhyMzGQMvu7uUDMDsrr5sdqYAU2B9BRiAYTujaAX2A8AwIFP08uzqgYcdxux2Vsx+g5h0SsF//H/6MdqvgHQePnkq10XoqZFJw/Tpv4CfGgCXTkLRiBCyKlquQ/JZUikgrfAQnWY0Xa5FwpahpUiFECYTiSHa/wjm7lOYPVmH6wOV8D7UQCNyaK2WYHnwEcSFMYQNJqQNFuj51WVH5J6IG4Y4M1J+DrzAwerUwHpKBV6tQqVoo7k0qalZ6C+cwexLXUCdG+dtubIhMraCMATAkLEWwlx6DGDoHAUQ5nbvfQgPBlAXDkOJRrE0NQ0kk7BqtLQddzCjoLKujkIhdV09xhzV6L5wnl6DlCGRkYcwd0dzX3c35Vwz9x49/rrl8ddFdEQq1QWzURnSTgEY8hxbuWBkKYHwg1tITI1ClmJI+b2QY2FQaBdKwGQzQjCaobZVgNcZoG9og/lUNzii8Q6MwwxgGHzZgQ1wBKdgOS+7v6gMwOy+xuwKTAGmwNMKMADDdkXRChwEALP2ZplDpujl27EDDxuM2UtXzG6AGCmeBR/vhCQpNMNWFDnYnQI02hWHy3B/HL98w4/7t2Kobk/AYM6F3RrVBWVEigzDzbehG70PPuRFVtAgq6RB2iJlVSpwKo4kuCKZ4sAbdTC5jLBUm2A82YigKkrnIyCGlOVE+0YgWM0wd3Vg1PUcPr0xjZmBNFozHrQkB6H1zUKqbqOdlGJ510sBiCFhwYuLMdqRyeUyoqrJgIrqXNaLJxGk92F8MANtWxPMrz6P4Y7cc2wHwqzngiFznosmEH3vfcxPTILzemGWFfAWM/xQocJkBhQZSiyG4OISLDYbBGclltUi5O7LuPD85+l97UQWTLkumPW6IeVf7ATAkEFKkdYbxAFDf77NMqRMJATvx79AankByeUFZKIR8HoD1GYbVIIa/qUwbFYt0iE/5GQCgtECrbMKYmU17M99EYLeuO33p8MIYBh42fayH8kJmOtl75aVAZi905pdiSnAFFhRgAEYthuKVuAgApjCm2dtr4teyh078DDDmL1yxZQT2Esgx8JsGv1347j3YBqktbScAfRqN0iMBikvOnFah9NdBlS41Lj9aQTv/ksAow8ldF0xUNdKKLW8AmHkDPQD1yEO34F6eRaqTBqK3gxFqwN4AZksATEAn+WRCsWhVaWgFVUw1dmgNhthPN2MkBCnxxAIk1z0QZpegLnnNNx//DV8/HEGt/tmoFwbQK3vEaxKAJn6VvCPOy/lIYxa0SIRlSHFZfoMglWBxS2go81FYUx+UAjzYBT2+maYXv4czK9cxR1vDhbsJIS588mn0Hz0CSr8Acg+P0IGA1wNjbSLEhnzoTCqzebc75e8UAX8sMQi4I0m+M02tP3Gb0LXeZbqXQhh1nPBdD92wJC51mbB7GYY7065YDZywGTiUXjf+wmkxTlIc5MQLDZoHJXgRf2T9cyXINGW4Ik4Uv5FCml0tU0QXbWoeOE3wItPO6WKfaMi8IWMzp4Vl1Sx5+7XcQy+7JfyB/u6zPWyt+vDAMze6s2uxhRgCuQUYACG7YSiFTjoAGbtgzAgU/TS7siBhwnGHGRXzOJCGu//IoRlTxoBnwzydSQkQ5JjuTdtFVBRYUVllRoWK4/qei0MRh43Pw5jejyJc5dWuwlCyUWYhvpg8oUhjt6HKp1EVqODos+F4xYOAmISsSw0agFmbRpaVQIauwVqmxnms6001NaX9EGJJ8ENeGG5fAbuP/wSNLXV+OSXXgz/6JfQDw1ACAQQNDRRyMLxhKxkEU+lIKcBg04LnZGHVsfDVaeDUiHRZ3Jq7avuxXP7LuC0oeGLX4T5Sy/Sn+0khOlS1Ai/+VN4HjyAKhyBs7MTnMFAoUsNcb48BjDk1yqjEXIwCO/UNEzJBDI+H1LEOWSxwtl9EcaLlzEkWgGjaVUpUr4MicxBSpHWQpgLrSvhvgTCbMcFs5tlSCRM98G1ASCTRkdnLS0b4un+yWL53TeRmJ1AfGYMorsWGnsux6dw5AFM/nsExKS8HqS8i9A1tEFf14KK51970nmr1Dekw+Z+YfCl1BU+Hscz+LL368wAzN5rzq7IFGAKMADD9kAJChw2ALP20Vi5UgmLvc1DDwKMSSQUPOhLYmoiTct3iIOEuDqsNh6dZ7WoqRXoB7696qBUTHnS5FgSv3gjiPmZFOamU5BlwFkp0JIjQU2yWgB/bBkBLxD0EphhRl2TFpl0Fqm0gsW5NLqurAYw6vkJYOgaxKV58LEoeAXIqjPLVgAAIABJREFUZDkkVTpS6UNLilTkPxUxw6iQTGWg1gJGqwpmLQ9pKYSsyQJYbRAaGuh9yNoAVEOT0F5owok/+iMY2hvpPCM/uIa5H32E6MQEgrYGcLKWXoMMjgOFLikuCaODR2NtBe2elB8eKUB/mwcxsTv9iLgsyL7YjbOvfePJcTsFYTqu9yJxrxfp6WkEamtR/bjrEwEwZBAIQ57JMzoGazwGRZIQC4UAKQktslBiUWQENbJWOyouXaaumDmtEWe+/GUIdgdKzYLZLRfMdsqQ0gEvYqODiE+NIitnsDgXgKvKSvUhOS4k0yU+O474+DA0FW5one513znWApj8QdLiLNKhAIytpymAEStXgFQpb0GHBcAw8FLKqh6vYwl86Xym7Xg99AF4WgZgDsAisFtgChxDBZgD5hguermPfNgBDAMy5a789s7baxiztJjBnVsShgaTiEUVeJczNFyWfJgm7ZkNRg4OB49Kl4BzF0QKY8j39xvEEODy5vcDmBqT4JlPo65RC6dLAE8dJE8Pf3wRXg+wOAvYrGaEgzIFNj3PFXRAymahv/sB1POTQCKGRFyCEAlRKJLkjDR7l0CdfPkPcasQmKPWqKDRKRS28IkkkE4jrbMi7syV52i4FEyBaXA9dXD/+bNoP/sCvcFI3wiWf/ohAjcGsGyzI5nVQsmQLBoTbTVtMPGwVWrhl4P0+ArS/qhg5CGMPS0i0T8EfddpLD7XAZxpxVnrSiekciFMPpRXDgQw8Dd/B8fEDHi7DcumnBuoutD5IsuoIJ2ifH7E/D5oMySQmIMkCMjyAnSyDCUeQ1qtgd5uB2+xIqgzIeuowKnf/l1oausohNmOC2a/wngJeAne+RRJrwdyNIyUfxlyPIpoMAaDSYSKF6A2W5GJhmmuC9lIlq5nwQnqJ6spS3Gkg35kMykaxBsOJmCtMNP8F95opvAzqyiIjj6AWFkL06kLcDzzSslvNoel/IjBl5KXdtMTiCtLSSagpFN0P1JXljaXI3XYBnO+7N+KHUYAI2cV/B+9/1CyaP/7+W+DV3GsDXXJyrETmAI7rwADMDuv6ZGd8agBmMKFYuVKu79tC0EMuZqovbLjF+29K+HdX0Xh98nwzGcQDCowGlU0uJZkzJIslXBIAc8DLrdA/69vVOMbv2uG3rASarsXMKbQESNmL+A7/9WL8REJ89Mpmu9itvBF6TPtWcT4IKCkdSCBvacv6FDTkPsgwgeXoeu/Bn5mHD7FBi4Wghj3QyWnkOBNVBMSvks5TBYg4biKnAXHqXLwhcKfNPTpMNJaK2I6J6IaOwwpP2zJBagsNiTqXajrMkJrVaOyohXRgVFIs4vgBB76tgYsJ330XuyailXPs/TY8bIehMlOLsCkaKHvbEflX/4RehMeeu5mEIZ0RiKj29Hw5Do3F2fQU1Hw9eP21LGPP8X4L98GPzaByos9UGk0mA/n2ksTCEPAwGJfP8yxODJ+H5KygqzBAKPJRMFBRErCqFEjs7iIpN6ArMMJu8NOvw4bzKjqaIflS1/Fg1zVWNEdkbbjgtkqjHejIF5yf4XtqKWFGfivvUtLhJJLCxSgCBY7BS5ebxwVThOy6RQN203MjENJJ2nYLnHA6OqaoUgJpP3LINkwSipJnTOE+EmJNHRGEZxGBKcVobY6oLZWIOVbpHDH0NQB95f/gIb3ljIOg/uFwZdSVnTjY2npWmAZsYkhmjdEXqeFQ2OroPtIrG4ER8KyDsFg8GV/F+kwAhgFWfzz2MclC/c7LVfBQcUATMnKsROYAjuvAAMwO6/pkZ3xKAOYtYvGypV2dxvvBoy58VkCv34vhtGRFPx+GU4nD3eVQB0vhYNAGO+yDM8CccZk0XFKi/oGNf7ojy2rIAw5Z69ATN8tFX79SxVGes1oOynC5ijtwwNxzNy87odaAxiNelx5PucwEB/eBj8xhOSsB4uaOvCREGzyMoxKGEmNGTLpWU0NMDnYk4gr1ClEiAxxwxAII+o5iEqYumRUWhMyohHcsofCmgynARxOiDoOxtostE499AkNkh4v5LgE2/Pd4EXthhCGXHMtiMmmM/DcuA20VKPx5Vdh+WKu09Bd/yT9dTsQJt+euvWn70EaGIQ/EYfSUI8a4+PMl3CEApjU1DSCk5NQ+f3Q6fXgrFZEkymYHofzEgBDhl5K0A+BksGEysvPQIlG4H0wgGyFE9WnTsH2jd/DfU94310wxQbxEuDi+/VbIBCG5LNoKqtBPtSqHn+YXZwPwuW20GdPLs3T3Je0bxEq0s7cZEU2nQSvN0JJJmkragJeiDuB1J8lEyloaChzFpxWB16npzBGdNVAWpiGvqkD9ksvwtjWWdKbz0EHMAy+lLScGx4sLc0jPHAbqaAPmWgI6aCPdtQiXcoISSbuK8Fso2BP0BlgaD4J04mzUJHaxwM6GHzZ/4VhAGb/14DdAVPgOCrAAMxxXPUyn/k4ARgGZMrcJGWcthMw5uFAEj97M4KHg0nqAuk4rYFev/lfvAlomJrMYGE+g1OdWrSf1OAPvmWh5Uhrx26CGHIf//C3fty4OY9YBGg/k4UoVJekJJnj2odRkNybuCShsweor6+C7sY7kAZHIKU4LCt2iNosXMlpiKkwsioOKXXObaBkZaSToDk5ZBANCMAh0EWtVkGnzkCUApA5DrIgQJNIQNFokdA7EVRMMBgFGPSAyRSEKpMBH5WhSCkIBhG2Fy+tgjBk/o3cMA61DYmhcWTTaYRPuMH/7ovobLv0RIsdgTCzExC/8zocE7MQKiuxqMt1PHoCYXx+2MYnkVlehpROI2u1wiTmHEUEvBRCGIOiQI6EkTRbIbefQrXDDjkUxPLAACrPnoPxynMYeezIyZcikTBeMvKBvKQj0towXvLzwkBeUop0vqN21Z643z+D8ydXf4+4YMoJ4yUOmGwqgcrZz5CYm6LtpHUNrRAMTwc15yGMND+FxMI05HiMln8Q1wtt0aUo1MVCuiBxov7JB+BYRILeoIGSkmgnpGwmTWGNYDBDyaShb2yDred5WDovFr33D3r5EYMvRS/lpgfGpkYQ7P2MuqWIA4aUtKktdhoErSJ2RkWh+4rAGerYMlmhdVZDX9tM99RBdcOw3Jed2R/bmYUBmO2ox85lCjAFylWAAZhylTuG5x1nAFO43Mwds3ubv5y8GOJo+dv/N4DBB0n4ljM4c04LrVj8v3pOTaax6MngXJeIr/yGEV09m7fD3WkYMzWewj//UwB3b8TR0KKF3uZ9InApIGZ2Oon+uwnodCpkBQktp4GKux9BmFlEJKVByuiARquCIbEEg+SHJh1FUm2CwgnIpLJIpUgiL3G+5MJyySDfJ2VK2mwCFjmILMntUIHCl5TJBrnuFBISEPSnYa9Qw1mlQZ0rg6W7fch4I1BDAG82wE4gjD4HMTYqSVqM+aCMzsGQEqA/cwLGnk7YXr2K3uAETptbtwVhSCkSGaQcSYnF8eA//WcIg8NwNLdAcDgwFwk/ATALj8bAz85CGwxRQBOT5aecL3kIEw2HoY3HoXa7EaptQrUzV2aVHB9FIC6h+kI3HH/4r3BvcmnbLphiAAy59kYQZisXzP03fg57cBixsUHo61voh9j1Rh7AkM5HkmcGciJOYV06GkI2KYHTGaB1VlEoUzgIgDEYV75HPjBnwiHqhCFlTIbWTpoBYz3/bNFvMAfZ/cLgS9HLuOmBxGXlv/0RJM80zRzSVlTRkjcKXtYMAqJJORvJLsrKaQpg9PWtsF966cA5YZj7ZWf2x3ZnYQBmuwqy85kCTIFyFGAAphzVjuk5DMCsv/AMyOzOC6JYGEPAy09+FMbtmxI6TmpgsxeXnZK/a/KX9r7eJD2vq1uHb/+Fpah2uDsFYj58N4L3fhHB+GgKFy7paP5KfsTSueyTYkAMASi3rsVotyGLVUAknEHd2C9gicxDUomAtZI6Wng5BUtsFpp0DEImhThngJTmHofxqqARSQZMriyJRCxkkhnokIBJDoITOCiiHrLZgbjJiLTFAS1vQDiQQSqpoMKtwaluEy2FWvjoOtKzPqgFLThRhOn8CWirnOA06lUQRkmlkV7yIeXxIZqVwLfXoeZMDxxff+XJhywCYcjIg5hynDB5CNNtrob/b/8bFm/dhlzpQHV9E52bQJhqgwnJvn5E5uZJYBAM7lxXn+ga58sTABMKQRWNwlBTA92Zc/DEJFRbzNQV4+3vh9J2Eu3f+G2IJzpWBfKW6oLZiTDezQAMKaPq/Zv/AktigToJ9I0nNnxRFzpgSOlQKuinrxcSukvyXnidEYLR9BTAWQtgyAVIgGom5Kfnad31qP76n5TsgOnsya3fQRoMvuzMaqQCXng//jni85OQoxHo6lqKCtqlZYGeaQoH9Q1tMJ/sguV0z87c1A7NwtwvOyTkNqdhAGabArLTmQJMgbIUYACmLNmO50kMwGy97gzGbK1RqUdsVaL0vf8Rwq3rCepi6erWFgVP1t4DOXd6Ko3uSzr84bcsqGtY6eay1f1uF8T88qdhfPBOBMtLGZw5v777plgQc+d6DFU1aggaFRIxBZl3fwVbZArQ8EiZ7bTVNKfSQJ2Owhj1QE0gjJJCUiUilVVDrRXAk3bXNJRXpsm8cjwDMZuAIRsFpxWQsVZCNtuRqaiBpCTosSQPJrQowFGpQW2zDjWNIs1GCXzWj/DULHizDjpyfZ6HYDFSCBNKR4B0BmJMBU6nhcblgLrSAX+zCcLLF9Bh7Vgl/VoIQ35IQEwpmTAUwmSzaP4hKc0agj+bQVX7SXodAmBU0RisU7PIeDyQ9HoYLbm8EwJgyCgsPyK/J6AlEYvD6K6CvusCFkJRelyV2QTp4QBCGh3qn38B1i99ddOOSKQMiYy1pUjlliGRudaWIm3WjjoxM4GHr38fhtAkxJpG6jDYaOQBDM2AmZtEamEGNNU6m6XuKFUW4EUd1A7XKtfBegCGXIN2UgosQ1tVj+qv/TFs3Ve3esnRnx9U9wuDL0UtX1EH+W//GuGHd2kmkb6+je6rYkc2q9CQaEKTDQ0n4P7S74EjZPiADAZgDsZCMABzMNaB3QVT4LgpwADMcVvxbTwvAzClicdgTGl6FXP0WleM15vB3//XIO7eklBVzaO6pnhwUng9AhCIg6alVYOrL+rx2teezr4o5v7KgTH/8qMQPno/ioAvg9PnNv6AkYcw5D42csTcuxlDQ7MWl68acf3jKILvfALT8igsCMEj1EFBrmuISsXDgBjs8jL4tAQhI4FTKSDWFZlXIwsV7UytSkvgM0kISjoXdGnQQXbXI1NRTT/Y5IckxxHyKdDwIipcGpy+aKJOHkVRELzWh0QmDpVZB0dLO9KBMLIZOXcfPI+IQYbKrEd161kYu05C11qPgfA4/Xm7abUTY6cgjPDBNVQNTCA9OQV/awNqzHZ6vfmZGdoZSfQHkbRZKVgwPg7eXeuCIcBBDAWR1IpQnC64z56lcywEwtQFk5ycQCAhQenoxPk//0v6s8K21MW4YNYCGDJHYSnSejkw5JhSy5ACNz7AxNtvIxtchLv74qYQMw9giOMlMngPkmf2sfPFABWnoq4DkgGjNtvBFXxg3gjAkNKlTNAPbWU17YLkePYLxbzUDjSA6by00jK9qIdhBz2lgCwl4Hn7dcQmR2iekFhZU7JKZI+S/Bhj8ynYLnwOxpZTJc+xWycwALNbypY272EEMKQN9X+4/Y+lPSiA/7PnW6wNdcmqsROYArujAAMwu6PrkZyVAZjtLSsDMtvTb+3ZBMaMPeLwox9o0Xvbhq4eETpd8dkva+cbHkpBo1Hh2at6fPNPcs6HckcpIObtt8L44FcRLCxkcO7C1v/CuxGIIaVUd67H0XZSi1d/w4rJsSRuvfEIxpFbcMSmERGdSJLQ3SygIE3dMGYNoPJ7oZLiEJCGVpWCSpGhoo2pqbUFGUUFTskgpdYDFS6oGxsIwXlKmlA0hkggi5o6E05eMEJ8nCMjzS8jOe9FstUC7ek6VKhcUB47SjhRi7Tegj7kAoErhRqoNRwMJgH1JwwYTec6HxWCmJ2AMLfu30HDe7cg9fYj4DAj67CjxmBBZtkL34MBiIEQhCo3Yuk0vf56ECYaDEGbiENwuRF2VyNrMlPwQgAMGRWhALKKDF9NE87/27+i3yMAhozCQN7CMF7ys7wLZrstqdcL492oDMn3658jfP8G/PNLqDp3btNtX9gJKXjnY9qOmrSb5o0W6niRYxH6gVmw2MDrVlpKrwtgSFth/xKFe4LRTD8ku7/6zaKcCgfRAcPcL+W+Yz59XmT4PgK91xCbGKatpXltLkOq1EEADAl7NjafROVLXy/LIVnqNYs5ngGYYlTa/WMOI4Ahbai//+jTksX5g7bnWBvqklVjJzAFdkcBBmB2R9cjOSsDMDu3rAzG7IyW/X1JfPd7j3D/noCeSxkIakDgS/+XUnI3Y6Mpmnly6YoO//rfblyCUcqdFwNirn8Swzs/D+PBfYkGABcbILwWxETCMh72J3D+oh6vfcOGW9eiuPtZBO6xj+BMz1HoIdlqKDwh5hWFi9NfPbNAJqLAhAgMiEKVfeySkWWo0kmoMmkksxoEdZXg6l1w21eHq+b1IBkwvqU0rNUZNJ8R4LK56I+UdAah2w9haG9A6rka6F44jXrdScyOJTDUG8H8ZALxaAaLEQIsAJtooyHAeiOPhnYj0OKHxcWjw9z+RPpiIcx520o+yK3laXQ/7kh00zMNzRu/QOXYPGSvD76GGkAtwJ1UkBweQWx2DvrqGqgEHtFkchWAITdhVKsRW/AAWg1M1TUQO89gIRShAIYMAmEcAR/Ac/DVtSDz8lfQ05jbl9t1wRQTxkscMGQUW4bkff+nCPfdQmDRj6qzZ7bc4nkIE+q7AWl+moboEuiiIg6qOClVE2kZE/ngmx/rAZhMLAxFkih8IW4oy9nLqHz5t2gr4c3GQex+xODLltumpAOWPvwpIiP9UKQEDdItd6RDfkjL8zC1dlIAozavHy5d7vzlnsdCeMtVbmfPYwBmZ/VkszEFmALFKcAATHE6saMAMACzO9uAwZjydR0cSOL170dw+3YSly6LyHILTyYrFcSMjqRox58rz+rxJ3++s39J3wzEhAIy/v5vfei7m4DVxqOusbScgjyImRgBBM5Oy5j+9K+d+Od/9OP2tRiMS6NoSA1D8ExDMVqgWCqeOFiyyGJ6KoZElFQfqaEhjpisAlVWhiqVgjoWgDHlxaKqGmmjDebGSpjEXJcmDbfy4Zp8nQcwVXVaVJ1KQmdUwayupMdGHozR7BfHi90wvvx5fPd7AwguylBCFng9KUhxGYJahbgShyKTrkskf0WAw62F2a4GVxPFmS/ocdqxMYTZKJh3Iwhz+9rHUL9/DfapXNCxr6YSbk6D5OAQYrOzUMxmmMyPM2AKIUxCgi4apZknCaMJjvZ2CM5KzAdzzhfqgvGHwI2NwFFXB8OFixht71oFYMhxO+WC2YkyJN/Hv0S49zr8sx5UnT+/5QsyD2BoPsfMBM37IUG6pPMRdcPo9BAIgClwwBC9CgcBNeR/weKgJUvZdAqmjvNwPv9VWo602Tio7hdWerTl1in6AM+vXkd0dIBCvXLKj/IXIqVMsclhmNrPwnn1K9BW5IK1D8JgLpj9XwUGYPZ/DdgdMAWOowIMwBzHVS/zmRmAKVO4Ek5jMKYEsQCMj6XxT98N4/pnEm0/bTbnSpCSmflVExUDYx70JWG2cHju83r8/je3V4K00VNsBGJ+8sMgiBNmZiqNrourOyEVo0gqlcXN60toPZXF5ReyeO2r3Xjje37c+DiKkC+JLtV9CN558MtzUAwWKDbnEwgzP5tCLCIjq5JBXP4cp6FwQYz5YIotIKaIWOBrEbE3ob0qDqsuCUmO0dsqhDAEooQCGbhqtDjdbUSC9z+5dW40Bk4ngj93Hr2ZTnhmJAw+9FEHTm21HQ63hpYekUHaVCfjCmS/EcHlFAQ1h6YOA1AVQ89vGtFZUT6EIS4YMvJOmLs//Sn43gHYZ5aAjAy/3QybN4DMkheJRALGfBekZC6E15DNIu7zU+0MlS6o3S54rTZUPwY1BMLQLkjhMHwPHqCy5yLML7+KASGXKbSRCyZfhkSOIYG8OxXGW2wZUvD2xwjdvQbvw0G4uy+t2+K3cB/mAUxkqBcp7yLtgET6ZimkE5IigzeYoLE5qRMmP/IOGAJa5EQMSioFwWyl7hetuw6J6VGYTnblAIyzatNtzwBMMe8Kh/uYhbe+h+jEQ+qsIq2nyx0ECEbHH8LY1gnnc1+E6K4rd6odP4+5YHZc0pInZACmZMnYCUwBpsAOKMAAzA6IeFymYABmb1eawZit9ZYkBf/lP4dwv1eCWq1C24mn3SOFMGYjEJNIKLh3W8K5CyK+8GUjnv2cfuuLb+OItSBmYiyJH/9TEL13ErBYeTS3aYrOKlCULIYHk8ikszjTpcOXvzUPWwUw1nsS7/08hP47cZzp5GAbuwk+6KUQhgAE6oYxmOH1A5GQjGQyC50hDTEdg5iOQJNKIpEV4U8bMW44C5NVQE/1InhuxclQCGIC3lyuDGlFfYaG8OYECqS8kB7OQ22owV3VBYSMdZgaTaCqXoTgiNDgVqvGuUpNAmHIsHAOzI3HEQlm0HzKCE1jHN1fM+CktXgIc8c7RefKO2EKIQzJzbn3k5+gdmwB6ZlZBObmgIQEfUaGEo5AsphhFHXIptNIhELQqjio9DoktCKstbXQNDRiIRyh81ebzU9cMA7vEs1D8VXW4Oxf/TVUghp3xufWBTDkXBLIu1kWTDlhvKUE8aa8Hiy/8yaiw/2IcUa42zZv7ZwHMNFHDyiAIR1niANGjoahkNI1jqMlSCSEl/yejEQkAQ0nQ3nslBH0/z977x0kx3me+z7TMz0574TNOUdkghSzGCVSpEgq0rIsS5ZtBfro3CrXLbt8b9X9w6Vz6rpkiZKPfSTr2iZNWSKpQIikwCRSJAEib47YnCbnHG+932CB3cWGmcUC2AX6q0Ltaqb766/f7hmxf/u8z6NhcdWKinqkY2HE5qehae6C5b4nwOtypsjrjZ0GYIT2oyv4MlxnV9sbL+YUMBL+ChUwEWbkuxMVMHTqAoTZ/nunkBkFAFNItYRthQoIFdiuCggAZrsqeRPMIwCY63eRBRizfu1ffzWMd38fwdBQAgcPyRmIWWtsBGKmJhIIhrLo2iPH179pgFqzdTPfQu6SJRBD3Rlv/qYZg30xDPREUWSRoKZucwhD6U3nR+IIhzJo75Jj/y0q3PdwTm0x7e7GC//CYbzfwHxlaqpEkA2fgdjrBBcOgAv5gGQSiawEoXAW8WgGckkKnJhDhFewaOp0DOiON0GpV6CmKIxao/+y0yMIk04BPjuPIguPijoFyqsv+cQQ5Fg8cQZ9qRa4VV1YiOjYNpSWRMN9AbasB2EMPEGYKALeJBo6NLDcFkfNPvmGxryr25E2gjC0htPv/R41o/NIB4KwTYxDPTyOtD+AVDqNrEIOqUyGOCcGZDxUKg344mK4tBqU6S+kJ/kDDMDQWJydg95hg6yxGYu1zdj76KPsdQIwNApVweRjxnulbUh0jZxHX0Z4tA+e2QUU792XVxJSdG4SFEedCvog1hmQdCxAJJHmWo/EHLKJBGtPopFIpKHQqMEplOAkPPN5IaULeb/k4oLFUNU0ofgTn4dILNnwY7QTAYzQflTIN9/m2zr/8CqCo71IhYJQVa9MQ9t870tbJDxOJHxOloRkve8JprjaaUNoRbp+V0QAMNev9sKRhQrczBUQAMzNfPULPHcBwBRYsKu4+XIgI5YevopH2vlT22wp/Nu/+pkPjMkkRnXN5lHUy2FMOlmC7nNx1NZLcfudSjz66a1FUF9JpQjEJOLAr37GY2G6FMODcZbIVFzKw2gSQyxeCZVSqSxcjhRsCymIRFk0t8rR1CbHp57Srdj25y/34NT7IkyNidC11wqFAhB7HJAsTkPsc0IUj5J5CzyOBCLRLFJZCcRaFRJSDVwyA2x2HrRJqSqMrtowlHxqzdN0uUNs/SWlGtZ+tNxIOOENwtE3j3cznVgUlcFcXoyK2pVpT+tBGDoYqWEIwpzvC0GplqBpjxb1n4mzmOvV6Uht2ktmnflAmIumvPZZlvjUGQJifQOYefNt6Dx+JG12JJIpSKU8RDyPGM8z411SxQSTKWT1elgrq2BL5upCtsOu/gFkLRaUtrZC/+Rn0O0M4EBlzoB3PRXMViKp8zXjzbcNKTw+BO+xt2E/exrGmqoN24CWFDCpSAiRyWEk3A4GWjheytQrUlMxUiE/MvHYxfuFWpC0Jj14g5mpYwi80MgkEwifH4CyqhG6vbdB135w04+SAGA2LdGu34Dahrxn3kdoYhCqyoaVfkJ5nh2BxfDkMLsnNdSCdOcn89zz2m8mQJhrX3M6ogBgrk/dhaMKFbjZKyAAmJv9Dijg/AUAU0CxrtGmgjImV+gXng+gtyeOgYEE6up5FBdv/Bf0pcsTiixgsF/MvE/a2ovxhT/Wobxic4BztS7vgvcMXv+NBFPnObjtFjjtKRYIbTCKwUtFzMc0Ec/C60lBIhHBWiKBtYRHW4ccDzyiZa8tHw5bEj/7Ny96++0IB4GOLutFOCKKhiBxLjAI47XHYHdk4PZzSGgskFiKmCIovOCDCQsozc6hpYkZxFx26pFwGj53CkVmHmprDNXNHNR8LgGJRnBwCiM+E0ZSdZjM6tGwVwKT5tL7S9ttBmG4gBZz4xG0HtDhvqdK4DMtbimeeq1WJFrDSfssDpiq2HJO9p2D4ifPQzuziLTbjUQiASkvZaqOZDoNqYSHSCZDlOehVmuYv40vK4IuHgNfUgKnWofWLz4NvqQUZybnVwAYmn8rKpir3YaUSSXheuvXmD1xGorQPKQGCyR6IziJBJxCzX4uDQIwNCxWLcJFTyo4AAAgAElEQVTjg0zBQrBFVloJubkE8pJK9v6SOS/1pjltAVhLL08Xi9lmmSeMqqYF1oc+A4lqc4WCAGCu1jfQzpmXwBwZ8VL7ECmiFBfuqUJWmAoHEZ2fgKquFUUH77miNKVCjrvVbYV2pK1Wbuv77UYAk85m8Ncf/azgk/6fh78AsYhD/IKnWcETCDsIFRAqsG0VEADMtpXyxp9IADA7+xrfzDAmEMjguX8PYGI8gdHRJMrLJSgrl1wGJJauIP1lNBjIYnQ0wZQm9c1u3HlPEgcPpyCXXV9FEbUVvf9RN/q6xfC4RAh6i+H3pUGqF8IrlBZEQIb+kUKma78SLe2ydVtG+s5FWcz16FAcHr8flbVZWC0lK7YnH5nh/ijczhQI2tAQiUQwaVMwR6bQmfwAnEYBkTH3YM0erumvy6EMAt4UdEYJ9EYJWjqViHI5/xaCMMlAGL7ecRzP3IZFcSnEBj2K6smwFRcTkpZ/qjZqR8pmsrD3SWGtkKPzVgM+/mQx+v3jBUGYzVqRCMLsgRrBI69hbmQY2qFxZPx+JFJJdr5SXoaERIIsJ4I8k0E8HIEokwEv4ZGQy5Cub0BpWwem2rqQLS7BwYoLypdVEOZqRFKv1YaUbxx1Np1GdG4CwYGz8J18D0G7E3wyDJFUColSDU6uBK8zQGo0g1Oo2L1hm/OgSJHbL+F1MeNmiUYP7Z5bwHGXA1DHvBeWkpXm1rRf3DYHRWU91PWtKLrtgby+ZAUAk1eZdv1Gvu7j8A+eZoBPUVZTUPsQeRJFZs6zljdS0BQ/+NlNzaV3QsEECHNtr8JuBDAZZPGfw8cLLtTTzbeCg0gAMAVXTthBqMD2V0AAMNtf0xt2RgHA7J5LezO2KDmdKfziv0KYnUlibCzJgIXZLIa1WAKFQsTEG8kk4PWksbiYRiSSgcUiZoqZ2z6mwF13KxCIn754ka83iKEH/r7RcxjoEcPlFCGbrGImt3JFrjWpc6+C/VxvkEqm71wMbleKwZfR4Rgo8joYDkOjByoriqA3ihmkIpuOUDCNnjMReF0pZsgrk4tgMktwm6YfhvAc4JhEUl+EpFKDRFSJSCgNgkWGIgk0Wgka2xVQqnNtJf6EE+lwHOmRINJ6C973tGImU4bKJhWMptyayZx3KaY6XwjjnE5BllKjeZ8On/1mTq2yGYQpqBVpbAjSI2/C6gkhMTEFv1aFIoUaabcHgWAAqlQGmWgU8XgCMrGYqZNIHSODCJxBj5DJjIZv/xXkDY04PT2fF4Chc8g3knq1CmY72pDIx8V/+n3mtZH0uZFwLiI4OwNelGHtQfQgS0aoEpWGpRrRP95ggt/mhFojB68rYuoXSj9iEdRyBeSlVawdaflYDmBIGZNw2xF32aAorWaKGfM9j7LEm3yGAGDyqdLu3yYVCsD53m8Rtc2wNjdFWXVeCqlMKsWULwQFlZX10HfdCnVd664piABhrt2lEgDMtau1cCShAkIFLlVAADC7/G5wOp347ne/i1deeQVzc3NQqVTYt28fvvGNb+Dxxx/f1rMTAMy2lvOaTXYzwRhSwrzy6xBmZ5PwuDOwLabg89Hfiy4N5q1SnAMzWg2Hu+9VYO++S3G5S1v6Y6fYr9cbxNAalsx6VZuocwjajI8l0HMmipmpBAL+NPtHMCoUyLCWJrLeIMuSRCoCekZWKnKeNwR3CMhEwhnEohlmZiyViZDwR1HtOgl13AVFyIGwRImYUguNTgulimPQpb5VAbmCQyTOYdrGs5Ymz7wDvE4GqbkKi4pazC5yaO5UQa29pI4oFMKMTjkQ8mSwd38pnv5ONVNiEIChUagfzFIrEu1LyUj7DBXwvfASZnv7oJ9zgK+pgl2Rg0WlCg0WF+bBudzQJlIIxXKx1GqZDEEy6pXJoY3HETBbkCkrQ8fX/xJnF51sG1LBUBsSjXy8YNaLpN6qGe9GaUi1Uh+LnyYVCj3gcjzPPFo84Sz0fJwBmXQ0hnTQm4uazoIlG4kVSsS1pShuagCvNUJZ04y4fRYEc1g7UizCIqZpLrFSza4TARhTkZypZWheuuEUpTmvmaI7HgZPVDDPIQCYPAt1A2wWdy7CffxNxBwLDNqReTOvN0G8LOJ86TRJyZUMeJHwOCCSSKAor4Wmvg26zsN5p8rtlJItQRhaT/utDTtlWTfcOgQAc8NdUuGEhArsigoIAGZXXKa1FzkwMIB7770XDoeDbaDRaBCJRJBOp9n/fuaZZ/D9739/285QADDbVsrrMtHN1KK0sJDCuTNxloxEEdMU0UwqD7KxIKhgtUqwd58MrW0yyGRrpyYtXaSdAmJWR1evvokIshw9EsDQQAwuewp2WwrRSAYa7ZJ/TBbxWM68l5QrnFgEThqBtSyL6ioreJ6DXClGS4ecqVpmJhOYGI0hHEoj4Q5APXoK0ogH8pALokwIEqMK1oYK6K0KuMMyjC/IMLsgQsifQiwtgUihRFaZhbjEiEDQCKctieZOJcprKFXokpdMIRDGMR/DnN2HynYpnvk/D1x8qMoXwnTqa1nZ1mpFEk3Po+LdbjhOnEK62ISSmtxDz3zIjzJVrnVmPhhAqUoDpNJYDARQqtMzX5yFQBDWZAqJyQn4qqrR8KnHoWjvyFsFs6SAoWOsjqTeV59rY6JBEGYjFQy1IdHY01J+cZ/12pB6Xn8PZlfOvyUdCTHVilijy8GSxQseLxYNUuyB1sV8WtLhANtWojUiJjOg+Utfh7K6ke1DChj/ueMITwyBvDeSXidSgdw8lHAU9oehVEkZkJEaTKxdSVpkQdHh+yFWqgr6TtxpAIYWT1HUQhJSQZcx740JvnhO/R5JrwuUakTmz6TIEis1EIk5IJNFJhFj8IXMnQnSkIeRprEd2raDuw6+LC+MoIbJ+zbZ0oYCgNlS2YSdhAoIFbjCCggA5goLeL12JxOt1tZWTExMoL29Hc8//zy6uroYgPne976Hv/u7v2OeBT/96U/xla98ZVuWKQCYbSnjjpjkZlHFUJvR/FwK0WiWQQdqqzEYxEwBQw+NhYydBmKWq2EIvvzq5z4WST08EEMmDVhKJLBYJStgB50vKVzstiRs8ykGn4rKA2jpzOL+B9thKZGuiPEOBdIYG46x9qSYL4JUTzdETgfU0jii7mnE3BEMehoxH9YhnJQhkFEjzimgUIrAyyWQmvXwJxJwzGeRiklQZOWhM/CoaZRDX3SpfSpfCLMwFUUskkZRawLf+D9WpuVcaSvSuf94DtahGSTnFuFpqECpOhcxTQCGBkEYAjDsd7WWQRcapRotFvy514tm5+CVSJBubELXn/05zswssNdXq2DyjaQ+N5JTzixBmM0ADG27nhfM8jQkAiPO117EYk8f1JIUgyjUWrR8EISxFq/0bKH/T0n6vYjPT0FeXgMv9Njzla+t+Cylgn5QolJkagSpaJi1JVHCFIEqtzMMa1UxU76oalsgpRjqAj+HtEYCMDTaD9QU8hG+qtsKAOaqlhfJoB/BkW7EFqbZfcXUWRTRRl92Io61yVG6lkStg9RQBHV9B5TlO+f+uJLqCGqYK6nexvsKAObq1VaYWaiAUIH1KyAAmF16dzz77LNM4aJUKjE0NITKykvmmHRK3/rWt/CjH/0IpaWlmJqaAk/xqVc4BABzhQXcgbvfTKqY7Sr/TgMxSukteP03AfSejaK/NwaNlkNdo+yy6OrV5x+PZzAyGGfGvq2dcnTdaUPnwSz08gMbliruCcBzbhzOcxN47WQKi24FFt1GpDMc9Jo0rBUK6KuM4HUq1maSTmfwh3fscM5T5LAYpmIptHoJqhsVKLJc8oOhg27kCaPjTRg8E4SlVIaOwzpUPRxEvbp5xVrXgjCro6mXVDC0IylhqBUp7fOj/5/+BdzQOIyWEvDlpZgP+1GqzLXFrFbBEIChQRCGAAz73R+ANZ5Acn4O3qpqtPzRH4MvLStYBbNRJPV2tSH5T3/IWo9c/X0wd+5Z03tlLQCzVGzybkm47IhoKtD6uacht15S6SxtQz4c8cUZplbIJhNMmTB53oO2uw4WrHhZ64YUVDDb9Y22u+ZJx6KITI8yg11SZZGfEH3PcFIp5JZyqGqamUfRVsDeTq+EoIbZ/iskAJjtr6kwo1ABoQKbV0AAMJvXaEduccstt+DkyZP46le/ip/85CeXrXFmZgbV1dVMBXP06FE88EB+6RIbnawAYHbkrbBti7pZVDHbVbCdAGKoLWlmUoTXXq4GpR2RD0tjy8pEpGQig2Aw14ZFHh5iiQgKJcf8W+i1gd4YU6y0tMvx6J8ugLpBNoMw9L3y6ose9H7kQ0+vF0pVFo2NRZCq5eCklyfgzE7EMHbeB/ciJQlJoVBxMBTxqG9VQmvIbU8qmI0gjCikx/RoGG0HdLj30xbESnPqkuUQZqutSE1TIQRefwuOs90wHzzIHuZWAxg61qYqmEwGxolJ+ExmJA8dwr5HH9sQwNCclIh09nxO6bKeGe92tiFlkknYf/0cIueH4PdHIbJUwGpZqXShtSy1Ia1WwdB79NAbGulFWKyHuKoNe774hbw+Vv0nJ9h27ftyBspXMnaqCoad36Fcq5swrm4F6HuIfF9EHMf+3SxDADHbd6V3I4ChGOrvfPCLgovwvds/K8RQF1w1YQehAlenAgKAuTp1vaqzhkIhaLVaBldefPFFPPXUU2ser62tDYODg/jrv/5r/I//8T+ueE0CgLniEu6KCQQQk/9lWoIwtMf1Mut9/mc9OPmhGHNTHPbfUpxTvmSzIENi+2ISHlcureiCNRQz2yUvHLVGDGsJz+BLf3cMXfsV+PhDGlTvG2MF2AjCjA5G8erLPgx0R6BSi2Gu9bN5lXzJmsUjU9++M2EszIcg4YF0XAqVVgyjmUf7fjU4LtcOtl4rkivmwuxwGgaVDo1dGjz15+XsPIcCY3mpYGjuJSUMpSKtVsE0DDoReONduIaHkOlqQYnigvJlCyoYakMSm0ywNTVj72c+x84r30SktQAM7U+tSMvbkOi1JS+Y3sELvi/Nl3xfNmpDCp8fhPfDtxEe7oWytgmuYHpNAEPH2EgFE7PNMT+YsKIEnX/658zbJZ9BEGYjAEPKmYRrkZn+ZlMpcBIenFIFmakYIi6XsrU0rocKhpJ5wpPDiNnnculP6RRLfKLzV1bUY2I+A5FYsiMgDLV/xWyzrJYE3jiJhJkoy4srIJYp8rlcwjY7uAICiLnyi7MbAUwmm8VzQx8VfPJfajkMTiTEUBdcOGEHoQJXoQICgLkKRb3aU546dQqHDh1ih6H2o+bmlTL8peN/7nOfwy9+8Qs88sgjOHLkyBUvSwAwV1zCXTeBAGPyu2TXSw1DCUc//icfekj9ovGhoiqLdLIUY8Nx5ttC3jf0MxFfngOVU8Go1RxUao55xGQyWeYX07FXgT/9yyI44z3sxNeDMC8/78GZj0JwLKbQsU/BAEogaWf7rAdhRvsjsC8k4XRGIFMCqZgU1lIp6tuUTA2zNNaCMAvTMUzP+VDdLsEDDzViz8cuJeashjBbUcGIjnXD8tEIktOz8DZXXRmAmV+AWK+HrbEJez//RXZaWwEwtN9SItJyAEOvb+YFsxGA8X7wJvxnjyPpcUDZ0AaHPbAlAEPwITQ2gKi2As1PfAbKqvySWtZTwTDvmMkhRKbHkI5GkEklwZyzOe4i4FBVN7EWkyXT3mupgok7FxAc7UPcPo9UNISU3wOCRbRGaq9i8dyUzqNQwhlWgiuuR8ftbfl9gWzzVpQ0FZ4aRnRuCul4BJlEgpkkk0qEwSJZLiqcaik1Wm7Idp1tLumOnk4AMVu/PAKA2XrthD2FCggV2HoFBACz9dpdtz0pcvqxxx5jx/f7/UwNs9b4zne+g3/8x3/E/v37cfr06U3XmwMs649EIgGpVAKv5//bdC5hgxurAgKIye96XmsQc+yDCN54LYTe7jj2HZAhGHVgdJBDOKSGz5smIQxUFBWt4pjqhQxfyLOSkqHCwQxTxpCCRUof/awId92vxpNf0KOmTgZ7tJud9GoI43Ik8cK/utB7JgKLlUdJufRicTaCMPFoBoM9Efi9KbjdUYgyPPQmCUoq5GhsV16cY3krEoEhgi8uWwJ1rSooq4J4+o/3gpdeajcgAENjdSvS6lhq2qZFXYdkPIMzjinsMdeCl3Hs4fPs716D9cMhxMcm4GvLGXeSCobakGgU4gVjP30GpopKLDa3IHX4VhwsK2cAhsb1NONdSkMqt51DoOckMrEoFNX1sNty57hWGxK9vp4KhhSYwYGzUNU2w2tsQ9cn78vvQ0ImustUMARafGc/QHR2HKlwKJeeFPQjm81cnI/AASUvSY1miBUqdkxd5y1MEXO1IQydZ2isD/6+U0j63SyJh+CTRK3NGReLONYGk44EmSksxWnzRjM8fkDScAs67tmfd12udEOqpffM+4guTiMdCiDhc7FEKuaHIhJT7xhrH5Oo1AwWkWGt3FIC48F7wLEvAWHs1goIRr1bu3ICgNla3YS9hAoIFbiyCggA5srqd132fuGFF/D000+zYyeTSUjYk9Xl42//9m/x93//92hsbMTIyMimaxUAzKYluuk3EEDM5rfAtYQwv/1NEL9/MwyHPYWGZhn6e2JwuUPwe0XQGxRQaTgmOV5rZJGLpfa40yCPbvr9wGEl/vjrRdh/KAdE1oIw770RwPtvBzAyEGNmvzRIqEAtQRTxnZG71lXBUKrS6EAEbm8YblsWyIhhLZNh/8e0UGkutZc4gk4kPDq47AkGkepalCitUqDxkQDkKhGqVa0rTmkjFQw9QDvmYnjj2HmI5tVM7UNjMepDmaYIVc0axGJ9UJ88Ac3wLORtzbBzqS2pYLKU+HTyFMydXdDcfTcGrCUMwNDYThXMajPeQtqQyhZOI9h7mqUTKarqctfZ5t8SgAkNnoOiugE+AjCP5O8ztqSCaWmzwvPhUURts4jNTyETj0Ki0YE3mCGWK1h8NRHDdCQMUnVQDDanUEFRVgNFeQ2Mh+9jbTVXsxUpMHQOgYHTiM5PsVYeqd7EYo5JSbJ80H1GprAEaJJBL+TWcviiUkiabrsmEIaup+vYUcTs82yt2VQSvFafU+XILwHOdDyGpM+Vi2zmOCjKqiEzl8J02wMMbglj91dAUMTkfw0FAJN/rYQthQoIFdi+CggAZvtqec1muloAZrMTEFqQNqvQzfO+AGI2vtbXyhvm5Z8H8Iffh5nfSzKRhcuZhtuVhq4oDoUiCzG3tjpu+eopwtppTyEczqC8ksc3/rsZDz5yaT+CMMtVMM//byfeetXP9tEbxUxRQ5CEOI+YGIosAlMxUF5azFqdVo9oOI2xwShc3iDss4BSKWVeMHqjhLUykSqH1DIZWQQVZSYYzDzKquT4+OMmyJVijAZH1wQwdJzVKhjZXAn6T/jgdcYxseiGyCdHIk4qgCyC6RjMWjUMFjk06iw0A79BbdoDuVYOd4lhBYChuUkFs1kktTkQhGdxEemGRrT/+V/grD8XVb2kgiEFDI0zk/M4UHnh94l5ZsRLY7vNeEmZ0dVYDBEvZSoIUsFkut+D3jnKFBLkAbMEYOhnISoYmjs41A1VfQuKbn8QE14FOvasTOPb6FPS99EY0gN/gFEcYIk2ErUGMks5uA0S+5inyeIMg0fKygYoa5pgvOVeBhKuBoShdXlOvcvUOeRHoyivzUspQnCD4pJl1jL44gpIWu+6qu1IdC0IvkTnJxGZnWDqHEoEotao9Qa1JMWdi0j6PTmgVVoF0+0PXQaWbp7/V7vxzlRQxWx+TQUAs3mNhC2ECggV2P4KCABm+2t61We8Wi1Imy1cADCbVWhnv08PtrFYFvTALZdzkErpoXltdUS+ZyKAmPxAzNUy6P3ViwG89w6Z26YYgKGfWh0HjYZDMhNii8sHwiSTWZwfiaOsgsdjn9HhL/6becWJEYRRivbhnd8F8Mv/9GB6Io5YJMMAC8GXpUFBJOQrI1HGQQKBykojSivYjbZivkw6C4ctiRMfumEyqVBcLmMKGII5NCelOSUVXhRXcrjtcDMqanM+MzQIwNDYSAVDaoRfvj6AidMxpJ0qeBxx6r4A9AlYdXpwYhEckQBiwQxEAZ61NFli3WiKzsCUsSN+oI45FRdkxuvzwzA5Db60BLaaGuz9oz9m6zw1M3dFKpglHxiaazMzXjrvVgOHyEg/4guzWJx3o9isZZ9zsUoDRV0TzveNw+SbRHT6PNTNnRBdUFBupoKh4y9PREr6PAyGqJs6Yb73UYzOJtn55gth/D0fYfyNt5BxTMJQVgJZcXle30fUmkRqGYojVlY3wbDvdqgb2tmxtxPCEKCwH30R4alR1hKlrGrcEA6t/iYgCBNdmIKqqhG6jkOYDRnYJlcjIYlUOv7+UwhPj7IWKJmlLM9aZpFw25DwOhnQ0jbvgb7r1ny//oXtdkkFBBCz/oUSAMwuuYmFZQoVuMEqIACYXXhByc/l4MGDbOWCCe8uvIDXcMnUbjE6msSZs3FMTyVXPCwrVSJ0dsqwb68MBsP6fynNZ7lLIEYsPZzP5jfVNtvZkhQKZnB+NMHUKvF4Fj1nY+jriWFinPyZAL8vi9IyyUXekS+EITg3PhaHTi/G7Xer8X99txjSZT4rU65uvP4yB+esFe+/HYTbkYJUKoK5mAfPi8gGgwGOeDznLZNIZCGRJxlcMRdLUd2wMhqbbgACgcc/tKOinkNXVyWbi14jGEIwhiKq3fwYypW5h+vlYz0VzJIC5vS7bvQd96G33wFZRglLuRz6IikcSR/MMuPFqagNySTRweeIIzDpQLXrXVRmHNCVahBvLUGJMvfQvDqSmuKo2evBAMrUuUQ6+/AIdLE4FB0dmD5wAAcOHWavnzl9Fs3BINLhEKYWHaixmlkSzRivwr7D5GPC4cw6KphzYxfiqetyCpn1zHjpONHzwxh7+/ewyjJI+b1I+TzwewJMnqTVqpg5LF9kYTDMMXIeai4BmaUEUnMxm7tQL5jIxAg4hRKq+lZYP/l59sDfd3oqLwBDXiW2I/+JyOQI/O4grB3teQGDpQtHPiaRqVGWPKSqa4H1gc9cVMHQNu0Hcj4+VzJITeI+/hZCY/3MsJbARqGDWoEo+53Mg60Pfw6DJyfZFNsJYUj9YnvjRQZfqAVOUVFXWC2zWcQWpti9SrCo+KHPCiqYQi/0LtleADGXXygBwOySm1dYplCBG6wCAoDZhRd0eQz1Sy+9hCeffHLNs2hvb8fAwIAQQ70Lr/GVLpn+Y/rEiThOnorB603D4cj9owdj8uugP3pr1BxKSiXQaTnU1vG4524FrNa1/YTyXY8AYtau1JVAGLqWczMpdJ+NYWwkgRDBlygZ6IIZ7U5OJjA1kWQ+LgajBCazmLx2Lw6CMJupYIKBNPx+6iUC7vuEBk9+3oDOfbmY2kQig5df8KGnfw7To1qEAhlQrHQqnYXJcim9aPmZU5uPyx1l81mKVcyot7JWtkIJQ61I/eciqN0bxWefbkd14+WxuBOhobwBDB2fvGBEU2X44DUHxgdCCCQiaO+wQsLnTHttMS/7uQRhCMDQsMoNiEfT8L/zFqrSdliSC+Aq1Cjf284e7GmsC2ECfli8AaQcTnjLy1C1Zy9GOzognppCrd2J2clJiAIBmMQSuANBlupjMujhFPHIqtVoOHwrhuV6QK5Y0Yq0ViQ1ARgaKyKpM2nUeiYQGRmAY/Q8EPBCrZKBNxjBSeVweyMwauVIBwOgCGUyYPW7vEAsArVeA3XHgYsP7BtBGDLjZbUq1rGUovDEENT1bdAfugua5q6Llz8fCBMeH4LnxDsMbiirG+HxJmEpyUGtfEfS70XMNgN1QwdrnZGXXGp/2g5jXtf7ryM42ouE2wFVXWtBUGPpHFKREKIz56Gqb4Px0N0MxPQfyym4aGwHiInMTeRqOT4IRWk1az8qdJB3TXhmDOraVhj23wF1bUuhUwjb77IKCDAmd8F2I4BJZzN45t2XCr7jfnD3UxCLOMTj8YL3FXYQKiBUYHsrIACY7a3nNZvt8OHDOHHiBL72ta/hxz/+8WXHnZubQ2VlJfur1tGjR/HAA/kbJK53EkIL0jW7vFd0IGoneeWVMPr64pieTsLuSEMiEcFqFUOpILWCCOlUFh5PBm53Giq1CBXlPMrKJPj0p1Wor19pLlnoYgQIs3bFtgJhIpEMjvwqyNRL5O1iW0wh6E9Dwosg5kRIpjIYH0siEEgjnQKMRWKWeFRkEkN8IShoMxUMfUfQvBRLTUoYiqI+dJsKn3hMy1qB3n0zhJPHwjh3zokiSxZScRFrQSIPGGspz+6ttQb5rNjsEXAiKQM19S0K5vWyNGYn4yBT3rLWMP7s23ug1l4O/wjA0MhXBTPgG0X/f6kx2htAwJNEfYcanrQfJlnRxeMShFmtgiEAQ2PW6ULRR6dQInHBAjs4E4eiimqIjUYsxIIr0pBKFRpkfH44p6ehywDShnosajVoe+JJhN58C7Pj4zBHY0i7XAhEotAbi6gfDN5QGIQamIeKTAFLVRXEegNmmvdi/4G9bB3kBbMcwNBra0VS07Xr+dkvYA3aEJscY2qXIK+GtbqcJQTRsNkD7Ce1IlHyUdLjRHx+BpFgCFKkoaishaK2aQWE2cgLJptOQROxMRWPsroB1kefhpjSgJaNzSCM442XGXxJBX1Q1rawYzvmvQVBGGpFCo8NQGouhbZlD4o+9uCKNVwJhCFwYv/dLxhkkmgNkJGp0RYGXZ/I5DCbQ9PUBfNdn7w4y3aBGOf7ryE41s+MdVU1uVpuZVCrlViphNRoZca8mWiYRYGLxBIG8hQllVBW1uflgbOV4wv7XJ8K3OwgZjcCmEw2i3/vP1nwDfPl9kPMlF8AMAWXTthBqMC2V0AAMNte0msz4bPPPotnnnkGKpWKtSFVVFSsODC9R9uUlpZiamoK/AbGhvmuWFsDcY8AACAASURBVAAw+Vbq+m1HLUcvvRRCb28c/f0J9nBcUSlBkTEXt7t6kCeMzZbC3FwKZeUS1NXy+MIXNKiuXlvZUMiZCSDm8moVAmFIlfKLFwKYmU5iZCjOlEsWixjWEgkUiksxzB99GEF/XxyhYBZKJaDWiCGTi2CxSHKmuJSWtoEKJhzKwONOQa3lWBISzV1ZLUVLhxypZBZjI3HQWshbprzBD1G6GL2nI7DbksxLSKtfv30tmgzDbQe0GjmsZVK07smlrBDo6T0VRmWNDAfv0KDlQRuKFZdUFMsrV4gK5t2eIYy9psDgaT/K65TQm6RwxLx5Axg67sjoIionB1Cp8kCr88IYSzCw4VPKUHShDcUV9IPzBaCV8PAplchaTKhsacNYbTWk732A4lAY7sEhZOVyFkntEEtQqs+1sCz4AijRapDx++CamoEoHIKxthZOhRbp2+7G/lsOsO1WQ5jlAIbeJxVMqOc0xn93FJgdh06vhby6Hg5Pzvun2HRJCUEQhgDM0kgFfHCd+JBFKEu5DJS1jZBX5lpXNlLBZJIJOHr7WJx5cWcXdPtuW6F+WZqfAAyNtfxgMokYFn/zHELnByA1mCGltqgLYwnCZFIpFvWczaRYdjqZyYplistMZSnxh6ASxVKXPv4nl33gtgph4i47nO/9FsGRbtaWcyXpQDH7HEskIp+a4oc+d/kaLyhitqKGIcCzcOQ/mE+NRKnZMigi0EKGw3HXYs5DxlyKTCqRizcTccz7htcZWZoSpU+p69rAa3PQUhg3TgVuRhgjAJgb5/4VzkSowG6qgABgdtPVWrZWItitra2YmJhAZ2cnnnvuOfYzGo3i+9//Pv7mb/6GqV9++tOf4itf+cq2nKUAYLaljFd1krffieAP70XR3ROHSsWhuYln8cCbDZ8vjcHBBIMwDQ1SfO2r2iv2haFjChDm8soThNnMlDcWy+DnzwcwNhrH8GAC1mIxKqvWvpaT40kc/zDCWs3IqFbMZaFSi6FQimCxSnDBu3ZNCBMKpbE4l4JYAubpkkplkM2KoNOJUVzGIxLOIBRMIxjIoLxKClNZEMVlwOx5A+ZnEgj40rCUbHyPef1hxEJSppYhAEP+LvaFBBZmEug6pMLjXzQhbR7fFgDz78/1YOxUHNFFOZr3a1k9CMDQWFLBbNSGRNvN+rwIDKbRavHAKJuASReHmVq+fH54Q0HopQrWw+fhORSXV0Ks02Gy2ISuw7cj8OvfYH5gEHq7A3xFBRxyBco0OfCx4A+gVHvhd18Apbrc74szc9C77JBYi+HSGNH+pS9DYizaVAWzp9IE14vPITI2BL/LB1NzE6EK1ubl8kUAiXQFdFkNYZI+N9wnjwGkbpDz4A0m8EVmFrHscOYgzpIShmAIi1f2upjyJSg1oe7OOxiAWU9xsR6ESYX8sL/+CwSHe5jSgmKnaSzFOLvOTyIbCUKlloK8XmhQG5hIxEGiMzBoQ342NCiamtZEAKbksT9hkdSrxxKEodfz9YaJ2WaRa0Hqg6q25TKFz2bfp8vfp6ShdCzM5il99Etr7rpVNQwBscVXX0BocggyowW87pLSK981UrR2dG4CiYCPJSKJpVJm4itR63LAK5NBOh5lEdwSlSYXw601QL/vDijLr9xrJ991CttduwosBzHsc3Nrw7U7+DU+kgBgrnHBhcMJFRAqkPvvmiz9V48wdmUFyN/l3nvvhcPhYOvXarWIRCJIpeivhsC3v/1t/OAHP9i2cxMAzLaV8qpMRO0qzz7rR3dPjPmDdLRLLybH5HNAeoDvH0igs0OKj9+nxIMP5NQKhQ7ymRkcSjBlTSyaRSI1CupQKK9sQ1OzlJm23swjHxXM20dD+PD9CPp64igtlaCian1FEtX57Oko5qaTTPEUTwCcKAu1hmOeMHp9Ti2zXAVDX/teTxr2xRQudKowNQulEyk1HMrLeVTVStHfHcPifBLU1maySJjaRW0Io7TUhLGhGFz2FFNDFJlzEdJrjRipYGw8DEU8yqtlKLJIMDYQRVW9HI2tCnzua2bMRIbWBTA0Zz4qGPKd+dmzMzhzxgZrkR7FlZc8ZQpRwSxG/EjNilmrXl2nDiWHvKif8SFlc2DW7UCZUg+RTIppXoTWW28HX12FU/YFtI1MIfLRCTjP9aCooQESi4VBl00BjDcAKwfEz4/BbzAjU9+EvV/+kw0jqakm515/G5aB4wj3nkGMk1J8FFQKMjvOXYVwEjBWVYI3FLEH6eWtSEvXKTo5Bv/MDNRGHXhTMVI+N8mTIFZr4Q/Gcwa+Sh7pcBBipQq80cwevjUdBzCVNKNrk9jptSAMpQM5jr7EIqwVFbXMs4Q8SGILMwxUZOJxRLw+IJUEL11SV4nASaVMicJJZewnGeNSm0zc7YC6rgUlj35pw/aYQtQwcecCnH94jUEiVU0TU35sdcQc8yw2m/xySj7xhQ2nKRTE0LyLr/0MoYkhyIqsTKVSyCCoQsoXarlKBTzIpFOQFZWwdqnVbWW0LUG7ZMADXmuEzFoO4/47WFuSMG7cCtzoqhgBwNy4965wZkIFdnIFBACzk69OHmsj+PLd734XR44cwezsLGtJ2rdvH775zW/i8ccfz2OG/DcRAEz+tboeWx4/HsVvXw3jzOk49u6VMU+PQsfQMLVbAF17ZPirZ/RMFZHvcLnSOHM2ht7eBILBDEhVQ74kbHAO6HQZ6A0V6OiSsfUZjFeWvJTvunbidhupYChJ6J+f9aKvO8bgBkGrjXwd/P4M+ntimJyglKssg2/UVkRRywRhKqskkIgJwISBrIa9F/CnQYlKS+lFDK7oODgdKZjMEtTUyVirU/fpKBbmEpArOXoeZt5B2qI4LBYda1WyLSSZFwz51BpMkjX9YOLpMMI+OTKZDDQ6CWQyDiUVUlTXy/H40yaYrDymw4NXDGAC3iR++ZN5fHRsEU1NZmiNl6BVoQAmYEuCD0tR065H1RdkOGCqZrfRSfssDpiqcr/bZtnPQ5YKnJyehPylX8M0vQBvLIZMVSVKNVoGYGgQhFn6nVQw1IZE46IKxhuAmR6CvW74SqrQ9tU/g8RgXFcFE5ubwcj3/idEi7Pgk3EGJ+KRKCQURUVZ3tTmBQ6QyiBXqyErrYC8qhb2C8qWpXakVCgIV18vYLSiuLYK1LNG7UnpSIiBGB8pacQSmCvLIFGqIS+rhqqxHTJLKTtG37lpdHatbH9d/XlbDWEItth++wJCo32QWcuYVw0pMAgCEOgh6MPJlYgnRcywWKmSgSKhM/EoazciYkhKDEpAInhDKhAyty194k839T/JVw3DINFbv0JwrBdyS3nBYGN5DSKz4+z6aBo6Ybnv03l9HeULYkghtPDKf7AEJIlaD9mydq7NDkR1i0yPIhkKsLrTGqn1ixRG1C7FkSxujbGkmGGx5mXVMN36AGTmks0OJ7x/A1TgRoQxAoC5AW5M4RSECuzCCggAZhdetOu1ZAHAXK/Kb35cevD+p3/ys7hperDu6pJtvtMaW1ASTm9fAocOyfHpx9UMlGw26NjHjsXw3ntR1gazsJhm5r4Eb6QX1C6kiiEVhd7oQ2m5laky7v24EgcOrjTv3OxYN8r7G6lgzp2O4rUjIZw9FUN7pwwa7cYgjSSM507Hcga9gQyDIdTCRJ4wEh6Qy0XsWqSzSVY+npchHsswmJJMAWXlPDRaMTPypZaj4hKeJSCRn1B/dxRzM0mYrRI2B6lmwpE4iktUKC7lWay5w5aEx5Visdhk2qtSc8yDZgkaRRNheJ1ANExtUWLccpcGZZUyPPykkSlilsZGECYfBYzbHseR/1jEsfcX0dZhhlq3NQBD6xmbdkHk5VHXqUfF5+QQS0TYX3QBvKyCMARgYv0DGP3lr2E4PwVZcxPs2SwDMDTyVcGUKBWI9vfCby5BunM/9n06B9BXe8G0pgPwvvU6QqePI7a4AIlMxpQfZI6bTKQgEWUZsMimUszolwAKJR9JtHooG1vhSclQbLnU9hMZG0AoKwdq29H+iYcQGR9mMdaZZJy1/czbQqjb28bingl6rB6FQhiCBrZXnkN4chjpeBzZeAzUlpSOhCFWa3JqkwsynnAwxg6nUl+4T7IZlsBEgIgAAMUuy8troG3dB+sDT+X99bCZGobWaH/zZWbCS9BHWbW1FgxSqIQnBqGsaoR+z63QtuX8ffId+YAYxzu/ZglIqXAIqurGfKcGtVnFnItIBbys3SgdjzAYRi1GFC3O2tnWGdSSRMoZUt2QQsh896c2hV95L0zYcMdX4EZqURIAzI6/3YQFChW4ISsgAJgb8rJenZMSAMzVqet2zLqwkMJPf+rH8Y9iqK+TMrPWrQyCKWfPJWA2c7jzTiWe/qJmw2lo+zfejOCjj2IYGkrA78vAZBKjpEQCrfbSQzhtR0oNgjNOpxdFZgMaG3ncfqcCd961dXn/Vs5xp+yzngrm337sYy1FBDs691BLyeYqpPn5FM6PJmBbSMFi4RjsWlhIs+dY8gIquhBNnUEY0agK0UgG0Wg2l4ylEjPlDEtC0nAMyLR2KphKZqA3ivmZJCzFBGA4tp3dEYYoK4elmEfnAQWcthQW55Ig5Q6pa2huGtSSRNuTjUc8mQQv5lFaKcW9nzTgoScMsJSsTNu6UgCzpIDpO+mHujKBSusyc9dVPjC0vo3SkEYmXBcVME8904DTrtkNAYz/5y9h9thxGGJJyNpasBjMeajkq4JZ9OYUMUUeF5DNwG0pQ9c3n4FIIlkBYGLnRzH6y19Ca59BfHoSyVCIiBp4sZiZpqap/0zEQcxTFLnognKEQIoInELJoIW8shoBlRXF1ly7SmxuiqlOAsYacLd+El1t5Zd9RPq6Z9DZvr7SJR8IQ5MuqWEqMQ/vmQ/g7z4GTqZEJhaGRGcEx6+dwLYaxBDYoNQfAiXy0mqUP/mn0DTnEqQKGRuBGIqg9p39gLX3qGqamQlwoYNamVLhINufABEpd7YyNgIxBF+8bJ2DUFU25GUYTGAuND6AhM/N/HWopYv8dKSGIsitFXmZ+SZ8LiRcdhbRbb7zEwzGCOPmq8BuhzG7EcBQDPW33vplwTfbD+97QoihLrhqwg5CBa5OBQQAc3XqekPOKgCYnXtZR0cT+M8XggyEHDwgW5GSU+iqR8cSbJeP3abA17+e+0v5euPDD6N4++0IazsixURbm5R5iWw0AqE5DA1KIJUZ0NYhxYMPqbD/wM2nhFkLwJBS6Af/rxvd52Isxai0bO02gNX1JeBy+mQMLmcKNAeZ75J5Lqmh9AYxWttlDIgksy7MTurhdqUYGKF2oyyy8LrTLAGJ2o6aWuXM04XUML1no5ifTbD/vXRdI4kQ3DYZU8XU1MuZOS8pbpy2XDsSqWtoPSxAJddFgkA4CjmvwP6PafDf/5/yNVuV1gIwdE8lE1nMxodRqem47LYaDY6y16pVrVjygBnpCSKtjKCxbmV0cCFtSH39Dtb+deBgGR79Wh1OOWfWBTB0/PpfHmUqGI+YQ0l9Ti2xEAgWrIKxZFJIzM3AX16L5EOP4UB7M5uLVDCdWhm8R16Gra8fIpcD8nQCSZcDGXAQI8OULpRWk8zxL0j53L2TTCSBWBSiTAoiCc8MfhNqE1BWyyBMfGEWWWrjaemEvf5uts/VhjDZSACa3l8jMHAG6ViUPfBzq6KsV1/s1RAm4XEw1Qyl8lR84VtQ17cW+lXHtl+vLSkdj8H+u58jPDXCkpgU5bV5wdClRTD1y9QIM7TVtuxF0a33bWl9q3daDWOolchGkdnTo0zBoijNKbU2GgmvE9HFGSS9Tkh0Ray9i5KayIBZXdcKTrI2CFs+J6msCP7IiyvY+RkP3LXZYYX3b/AK7EYYsxsBDIuh7t1CDHWnEEN9g38EhdPbRRUQAMwuuljXe6kCgLneV2D94/f3x/FfPw/i1Kk4Dt8iL8i7ZfWs4+NJ9hB/y2E5vv2tXHTuWoPajP7lX/zo64+zh/fOTlneBrvh6Bz6+01McdHSKsVffEMP7SatNju3+ltb2VoAhuKe/+WHXgZTqmskDJDkOxbmU5gYT8DpSCObyUKp4pgipaRUgoO3KMBLRZhbtGFiVMeMdYtMEtYq5PPmWo9I0UJeMM1tOdVNKpXFmRMROGwpEllAb8itJZEJwe9WMLBC++w5qLxowEvAJAdzMmx/lsokBsanvKisNGLPLWo89WXzmqe0BGC8riQGz4UwMRxFNJLzM/EmXJAqRNjTXoOWPWoYLZceEAnCEICh8eZLdvQc92F80oN9h0pWGAPnC2CSiQzGzvqgqAHuf6Aee+6yMABDY802pPkp1L38O8T6B+FRKVBanUuG2QjA0PtresHMLUBnX4Bq7wHM7LsdB/bmoBMBGO70MVjnziM+NY4gJJDNTbJWIYIqGV4GXnqpJslk6iKAof2pNQmJGETZNFM8yMqqEJWogdJq6JN+lnSjbtsLy6OfR29fzttmNYQhFQyNzZQwbJtNfGF6T00ieeRZSNxTSIf8zNCV2qjyGQRisskY+HSUKXcIABTd9gDMdz+Sz+7rbrOWGsbXfQzBkR4W88yrdZAVV+QFYZi/ysx5BpWU5bUw3fHwtvukLAcx5VIbAsPncm1B5jJIDaYNa0FgiABWJpFgiVLJkJ+ZK1OSkqIs/2QjitimWHGK6i5++PPrKpiu6MIIO+/KCuwWGCMAmF15ewmLFiqw6ysgAJhdfwmv3QkIAOba1brQI10PBcybb0Xw5hth5hlzYH9hqptkeg7hMNDdU8R8Zh58WIXb7yhc4l9onXbS9msBGGr7+d8/IgATRU2tFEWm/FvJyAtmaiKJ+bkkXM40QqEMAzEUX33r7UoG5Xr77ViYUSMQyECtESESyjIfF2pRosSk1o6c38nSOD8cx8x0grVDlZRRpLWIAZhUXMV8X0rKeLR0KqDTrw+K6Jx6e13Yf8iK+x8zsCjqtcbJ4X7MnC7D4mwcAV8KbnsSsWiGJTORoXBSEkJFqQkavQTF5XLsuU2L8hoFlgOY2fEI3nzRjpMf2dDQaILedAlKrAVgaB1mWa4VZzHqYz85lxx+dwKqBuDLX98LrTE3x7oqmIVp1L30OmIDQ/AoZMiaTShTaxmAoVGQF8zsAvROG5R79mHmwJ2ATo8D1WXMh6Tvhz+E0TkPkVQG38wsRLY5iOJR1laUIjdl8ve5EMNMAIbGkgqGfk/GE+CSMdbWRNBGWlKOiNoCBLwwlhZD07EfRfc/xvZbD8LQe/m0I9F2G0GYmG0O9tdfhOODt1nLlFQC5lHDSTdXwjETWLcbIrkaCrWctc+oG7tgvf+JKzLLXbonl4MY8tVxfXAU0flJkJku+dPIzKUQy9f+rspmM0gFA4g75hh8UZTVQt91mJnaXq1BICabjCM99AfopRFEF6chN5eCN5jXhUXBsT4kPE7QeskpXazSMkNjAinrtYGttf5UKMCOp2nogOXex8Fr1wf2V+v8hXl3fgV2MowRAMzOv3+EFQoVuBErIACYG/GqXqVzEgDMVSrsNkx7rT1gqMXk+z/wobubomqBlpbNJeurT5MgzOCQGTKZCHv2yvGX39RBLN7c72QbynXdp1jPhJd8VJ79Bw+6z8ZQXCJh/woZBGHmZpOYmcpBGFK3kAlvSxsZtfoxNyOCxylnfi1qjZglZZFpLsGXhhbZZa1BQX8a/b1R2OZT0Og4qNViBmBEWTVs80lYS3g0tclhsqwfk31+OIZI2o89e0vx5W9ZwUsvb1EbOBfGK789j6jTAMdigiloKLZapREzBQ15yTp8XsR9Sra/uUQKS6kMt9xrgLRp8aIChhQ4v/zxPE6enkfYl0HnnmJI+NzxVgMYem21D8yMx4PQeQ7WSiXkzRl0PKLHHkNOEbAawNBrS4lIPf/wPZgn5yCSyeA06hmAoVGoCiblcsI9MYniA4dg/OKX0G33MwAT6T2H868cgWhiFMbqGsTnZxEdH2PHEEvEzN+FoMsSgKHX14QwoRDE1JqUzUJSZGbQIxyMAlVNaHrkMaiaL7V5bQZhGGRZxxeGPGHY++soYfzdH8H10XuITAwhEk1DlIhCCkpzoohpJUT8Su8j8hLKJmLMgDebTEKi0UGsUiMsMQKuOZjbO2A4dA80TZ2FfFw23HYJxLR2lsL90VuILc4w41rydJEoVOD1phwwIilYOsVep5jmbDbN3iNQo2vbD3VTV16qmStdeN/bZ5Aa+RDZoBvKtJeBFGoposhoiiFfGhQ17e87ydqPqEeQUo/IXFlZUVdw3DYlWoVnxlhstfnOTzIFjTCECmxUgZ0GYwQAI9yvQgWEClyPCggA5npUfZceUwAwO/fCbV8KUga9ffFNU5B6euP45cshnDwZQ3u7FHp9/kqNpSoSgPH4SjA6nMChW+R46rMaFrl8M4z1DHjpOv7kf/kYgKH2IUpB2soghcuJY1HWBmQsEjMvmXDMg5kJDTzuFCS8iMEdgjAEUUwW8doPidkses/FYLclEfBnmO8LxGGIRWoszObMeRtaFGyOtYbTnsTk+TgqW8O457563HH/5Z5CBF/efd2H/kEHEiE1rKVSFk29GtRQG5ISJqaMsc/HodaKUd2oRNltIdx3R9fFw4/1BfH+ay50d9uhFKtR06JaF8IsBzDxKCl1HLAatKhoUOPup8oxq3CuADB0kLXakM49/zxKR6aQnJ2Dp66aPZRvRQUTGx6CHxzSdY3o+vO/ZNfkzMQ86vpOIHLuDDz23EOzMuhDyuNGKpMBl4iDUyqZ/8dmEIZ8PlKRCMQKBVPBiJVKSM3FCJlqgbs+jb2dtSsu43ZDmHQ6i7n5ECbfOIrk8ElIol4k1cWAcxLidBxqaRoSJCEhE2EJnzMSJviSSjDQy8kVDNCQEkVRmQMGtlMnINIXQ1x/AF1PPLqVj8umIIb8TipkTkSmRhkEIg8ViqqmpCk2RGAmvQReeJ2Bme1qOw6x9qNrOQgCuY+/ifm+EWTDPiDih0otZWocuj/oPMjbJuG2M88XAi+UekT+NuJNPHjWOg+KDo/OjUPT2AXLPZ/aFgXStayXcKzrX4HrDWQEAHP97wFhBUIFbsYKCADmZrzqWzxnAcBssXDXaLfjx6N49dUwTp+OY+8+GdSqjc1w11rW8HCCtaR07ZHhr57Rr+slQ8lHr74awuRECocO5ZfUs/p4BGDAleHE8RgDL498So077rw52pDWAzBUoxPHo3jjtRDOnYlhz14Z83IpdJD5bm9PHB2dMtTU81CpxHAHp9Bz0orpyThTGu3Zr2AKk81GwJfGUH+MGffGYlnojHFIpZsrYCieeno8jup6GYxVXnz5a11Qqlceb24qjiP/5cb54ShcvgDaOyyQK9deEwEYLZ/7CzvBkvODESiUHHR1MRx+VIbDHZfUDyd/78E7703CPy5DKpmFpUzG2pFcSR9MsqKLp0wAxiDWw+dIwDkfRVKRQFtLMQ4+YEVdhw5nXNMXAQzttF4b0unTJyA9+nsYRyfBV1fCLuULV8FEo9DPz0LR3oH5hjbsu/9+tk4CMJKjr6BobgJIZ+BftEEW8jO1TcwfgCgWhVjMQSRXIJXKeeasVsIstSJRalAmEkaGlCbEDTgOirommB/5DKZUtdjTcnkC0pVAGFoLqWEIosiUGgwOexEKJcCNfAjFYg/4RBBeeTkk2RSUmQBkCT+koiSUfAZymQi8hJLUOKbg4GQKppCh1hqp0QSROKcOC431Q2YphV9dD3HtXrTv29yEdrN7fvX7S2oYavWpNiURnhhGOhzMtfDQFybHMVgks5ZCVdPCfGmottdjZFIpROcmWMz3XO8wEAsD6QT0RapcLaWynJIn5Gfx0+raFgZntjIoRYlgDhn3spSnNWLKtzKvsM/NWYHrAWMEAHNz3mvCWQsVuN4VEADM9b4Cu+j4AoDZ2RcrEsng2Wf96O6JsZaNjnbpChPSzVbv9abRP5BAZ4cUH79PiQcfWNurg+Z55ZUQjr4RgW0xjX37tqbSIAAjEpfj9KkYqqokeOhhFe5/cP1jbrb+3fL+eu1HS+snQ9x//qEXfd0xZpJb31C4KmhsJIFkKovOPXL8xbcNLMHIEzuDd37bgg/fDWFhPok9BxR5t0ZQutH4aBw+bwqBQBw8L0UinkVZlZRFVi95wNCDNpnwEnwh75faBhnKKqW47Uk7Gir2XXaJfvW8C90nQ1iYSaCsJYIi7crkouU7LAcw9Dq1KY32hlgrUlFbBH/ytX0Xz4fW8avX++Hp1cA+F4PbnlNQiPRxWPUG9rkgb5k5tw8hdxp6hQpFJXKYSuRQHUjh4dvbcvDDlWul2awNiY7X+6N/gmVqAZlAEO6KUpRd8MPIxwuGxXv398Mgk0He3ILpj30ckMlxoLKMHb/n2WdRtDDF2m8CDidEbieUJaUMpsS8PiJSEPM8RDL5hhCGgEEmHEZGwgOUeqPWQFnXhMrv/N+Q6AzouWDAuxrEXAmEIdXLSy/2YW4+ggwk8PoSKAv3ozI2BGXSi4ihlpK3kUimEYsmwUX9UIuikErSsJgU0GjluXYanQFitW7FPUt1C430QlFWDcOhu6FtP4D+kxOsZlcVxGSzaG4ygJKOyCeG1kdeNPRvJw3yeSEYQ745M4OzuVYkXo4SE4fI3DhiCzNQ17WtaFEqZP1kTixRqaGqbWEeMKTYEoZQge2owLWCMQKA2Y6rJcwhVECoQKEVEABMoRW7ibcXAMzOv/hvvxPBH96LorsnDpWKQ3MTn5evit+fxsBAAmXlEjQ0SPG1r2phMKz/V9EjR8I4ejSMxS0CGKZ+ob/AE4A5GUNVtQQPfUKF+zeAPju/+vmtcCP1y9IMrx0J4qMPo+g5F4NGw7E/spPvDoE1eoaSykQwWySwWMSXJU/ZFlOYnEiy9qWP3anEAw+rGXxRyQ5jZDCGV1724+yJCPN8Ie+XfAeZ7g4OuRGNAM5FnmwvoDdKRSjb+QAAIABJREFU0NgqYwoMetAmzxj6SZ4w1JZkKeHxqc8aEFUOwaTYu+JQLnsSv/ipA31nIjAX81BY/FDz1nWXsxrA0IbOxThrRypuT+CPvtoFc8klGDgUGAM3U46+j3zwuRLMWHdq3gtZRnnR2FemECOjj6HKYoapVI72W4uwqHWgU3+pdWS5CmajNKQz77wF/oMTKJpeAKdSwmkuWgFh1jPjpYtrDoWQdNjhLylHzV13QXPn3TgzOX8JwPyvfwY3MgBdNo1MJITIwgKyRjNUSjnSXg/igSDE1KZDSgxeilSGaBMpSC5EUV8w5ZVwQCYUYg/cGYoaVqhguecBlH3l2xfrvp0QhgDJiZN2DI960ddrB1GwhvoiWOITUM+fgdwzgVhRPbLiHGSk7WPxNELhJNLJNNRKMerrjTCb1lbGMQ+SyRFmcmu89T6oaprYPEsQ5mqDGDb/gfxTg/L9rF3N7Zhpb8iL5NB7UMVskBrNzAem0LHk/6KubYVh3+1MBSMMoQJXowKrYQz73N3asC2H2o0AJp3N4FtHf1Xw+f/wwU9DLOIQj8cL3lfYQaiAUIHtrYAAYLa3njf0bAKA2fmXl0xIX3ophN7eOPr7E8xUtaJCgqIibs2/TlJLic2WwtxcisGX+joen/+8BtXV65uqUhUoAenV34ZBkdW33FJ4C9KS+oXWe+KjGDPx/eSjN34LUj7wheo7OhzHD/7Bg/OjCTjsaUj5LCRS+usytWSAQRcyz6VkI5NZjLIKHgo5MD+Xwux0Cg3NUtTVS/H0l3WshWkJwBAc+dcfudHXHWVR481tm6fOLL/r3QEbHHMGDHRHYTRJoDeIoTNIGHSh9CRqCaLobFLEtO9VoGOfEnIFh/lI32UA5r3f+XDs9wGcH4qi86AakayrYACTTmXRfzoIXXUc99xbh9s/sTJ+lyBMnaoJttkYRrsDmD0fgS3iudiGRAa9mfIw7r21CUZrrhZnPVPrAhh6fz0zXmrv6f7Zz1A+50R8ZAR+EWBpagYnk62biEQeIvbRMWhjUcjq6mHTGZC89wEcrK1ma1mCMP7Xj2Dm3fcgWpyHSsoj5XQgoTVArVKAjpsmg+JgCOJMCiBfEo5DGhzAiSBhrTpZpOIJyqMmVxVmYEupN3GVAWg/hL1/9hcrvty2AmFogtVR1cMjXpw6bcP4ZICB4KpKDdyOAMSpKBr9xyD3TCLDK5BUrwRv6XQGXn8CqUQKapUYJRY5qmtyrWP+GIcZrxThBIcIpSFl01AXF6P5oftQW2uEVHoJHF9tEEPrWSu+eqf/PwWBLue7RzB77BgQ9kFkroa1YuPo6uXnRPuTsoa+jFgE9UOfLSg9aafXR1jfzq7AdqpjdiOAyWSz+PfuUwVfpC/vOciSDAUAU3DphB2ECmx7BQQAs+0lvXEnFADM7ri2pJR45ZUw+vrimJ5Owu5IMxBjtYqhVIgg4kTMF8PjzcDjTkOlFqG8XILyMh5PPKFGXd3G8IWqMDAQx4svhXDyRA6eGI35ewgsV79QUs/5sZwJ72c/r9lSu83uuCr04Jj7Dya57PCGSyYD3reOhjAzlcC5s3EE/RmmfpHyIihUIgZdkkmwFiBqUaLrR+01SiXHIAj56ZRX8vjc0zpmwLsEX5YOeuy9EN55I8hanBqaZSgy5a+CCSVsmBk1sPWQuqWti5QJIiQTGcjkHNQaDk3tCtTUyy62vxF8obFcAUMPcP/6PRuGeiIsYrqmUQ5/wlkwgKF5Z8aj8IQCLGXp6f9WuaLtjgBMvbr5Yr0J+HU7z6NWVsdMfnkZhx7/FHu/TVt/cbuNIMxyAEM7nLTPXkxDOjk3ieZTA4iPT8DR3w9RJAq9xQqJxQx7FijV6RkwWbQ7YI5GkfJ4EExnkK6oQEVzC3SffBRnXT4crMi1HhGAodEeDyHw1lE4PvwASokEaZ8HEqMJkXQWaoq+zmYRdnuAWBRSTsRalchkNZ1KQ8yJ2IMygzKJJCDhoTBbITYWgdcb4TPXQXTrA9jTvNL/5UohTFtLOX5z5P9n772DG7vO++8v2kVvBMDee1mSW7krWV2yiiVZkmVJtmzHcZzEiWP790udeZM3f2TemYwzySRxXOLYji3LthJLcpMs25LWsrQrabXL7YW9N5BE78BFe+c5WJAgCJIAl9old+/RcFa499xzz33OBcj7wfN8v2MYGXUjGIqhvk7HMqWoLVrdMC+eRBk/CZl/HmFTE7OjzmwEYZwuyuSTgpMkIdYa4BMXwRmUIsCLEOJF4D1uVgKjNJthqa+CSilDXbUWzY16GPTL2VBXE8Sw9doBWTHBmTE433sDwalh+FxBiIoqmBZMcYVx3c8nutciCzOI+j1Q1zRB17oH+s6enfIxLMzzOozAlQAZAcBchzeEcElCBHZABAQAswMWabtMUQAw22UlNp4HS/0/HsGJ3jBI22VxMfUT5ZOg6gSpBMyCuKxcCr1OjPoGGe68Q4kScrnJo5G7zle/5saZMxGSkkBHR/46JensFzrNhfOpUikSDf7cn+oL0qzJY5rbpku+8OV0bwiHXw2wzBdaNwIoLmcckUiSuSLRD5UhUWUJrWMolKBnbiiVVJIkwb4DCrS2K/DYE1roLztTZQOYgD+OH/63C2MjEUyN83mXIhG86OtfQNivR3u3Evtv0uCO+1J2y+u1af95qOLdzPqaGunRyDjgO/+2gIunA8z1iEqQNgIwdGyuMiTnIo+RcSf29ZTjY39WtULENxvA0BgXPaNo0TavmPJZ93jeAIYOXEuMl/admJ1A+8AUIn39mJu3wuwPIe50wRcKISkSQctxTLDVy3GwVFdDrNdjRq1F9+NPQKxQ4ORkCrpkQph95SVwPPcsrCdPQjw7DTmJ7kokCHOp0hyCMNT8gTDAh6GIR5EgSnfZjpqVIiXizLUnpjMDZVXQhDxQNrVC13MrxhUp4JMvhKG+G+nCLCyEYJ2Pg7Jgqqq00GlXfkbI/AuQXHgNWt8EZGodeG15ChRltEAwCn8oDoekFGK1HnZPAj5eyvRuuKiP/KIgUesgMpaBT4ih1XAwFcnZuQ7sLUZT/UrXLQHELAeXfkd4zh+Hb+gcQlOjqfdmSSXs9vBStmQ2jEnwYURsVsRCAebwpKyog/nmezetIbPRZ4ewX4hAoREoFMYIAKbQCK/ub7PZ8OUvfxkvvfQSZmZmoFarsXfvXnz+85/Ho48+WvAJJiYmUFe3cWnnCy+8gI9+9KMFjy8cIERgO0RAADDbYRV2yBwEALNDFipjmvTQPDQUxanTEUxORJmWSLqp1CJ0dcmxd498Xb2Xta76d78L4tVXgzh7NsKEeAmkrNcyM1+on4+ceujYfXI88CE1Dt18fTog5QtfRod5/OxFLwb6eAZa2jo4ltVCa0iZQlQq5vMmEAknmb10IsGSGhiQ8XnjKCmVomuPAl/8yyKUlqaymAi+UCP9l8w2PxfFiz9yY3KCx/QEj9Jy0myRsnKh7EYPam5nHLMz9DBMTkUlaG5X4OEnjevqC5EezMWzQbzbOw5xdKW4rlwuwsRIGPOzPGoaFCiypOa7EYTJBWA8zigmR0Ko6I7is3+2G1rDcgbXWgCGzpUJYfIBMHRMLjFe2r4iC2Z+Gj3FVYh7vDh39Agkw6OogBTJSATzXi9KtTqIOA5Wnke8rg5dt9yGM6FUTf6BilQWCkGY7CyYVusU/O+9A8fRI0giCXksBllxCQKRFGhZgjDB1FhquQx0kwTZ6yRk4QAknJxlzvglSuaOU7p3P8yPPg2pVofzfdPsuK2CMIcPT+Gdo1PweHns21u2ugQymYR2+hji0wPQ+Kch0RoR1ZStgDB8DDg5pwUvUSEADZQKCbhEEJq4C5I4D3VpKThTMZTlNSzLxuEMs/MRgKmsUGNflwXtraszOt5Pod70+yddmkSvt2tGDGVjuc++i8BYP3NGImttZqdtNMPliqaykkghORqBVh4F2VxLVGooS6uhKK2E6dA9zJlKaEIEtmsENgIyAoC5spW7dOkS7rrrLiwuLrKBtFotgsEg4vSHCYAvfelL+MpXvlLQSTIBjNlshoSE73K073znO3jooYcKGlvoLERgu0RAADDbZSV2wDwEALMDFmmdKZJOB2m+0MM7ZSFwHD3rbN61goR7v/END/r6eHi9CXR3y0EP1rlaNnwJh8kmmYepSIzWdjn+5PP6DQHOTox+vvCFIMcz3/bg3JkQrHMxJqCrzAFDyF7a4Ygz/RZy8aHyHSpJ4mTAxEQMLW0cbr5FhYce1a4JX9JxnJ3m8dKLHljnopibjjK4Q5ouBpOElazRcxc5DS0uxEBaK5oiP2rrCL4ocd8jenBcbuBGttVHf+vF+HAEPl8ck9N2RPxaVvZGTSoTQauXMPjissdRXi1Dxx41KwnaDIBx2nhYJyMo7eTxuf+7F0r1yj/W8smCIQBDrZAyJOq/z5SyPM4GMLRtr0SNhNuNC9OTaImJWfbJMIkfqpTYVVUDWVUVTi3a0FOegi69Uylh6jUhTCiIupPvIjTYD8/pU6zUSKzTQ6LRIhCKLAEYGsN/GcKw8iSCMAskgAuoS0rBlZYj6rDDrzQg2dSJPZ/45NJba6sgTCQSx09+MozhYTcS8SgMBg4lxSuzUdhJ4zHoJ46A81khWhhDQiSGxFCMuEKPBMQY9Bgx65XBxaugV8RRVxRGgmyVKbNHooZIpYeouAolZcuQhUR8J6Z8kHNi1FZrccuhMvZvriaAmJTwsX/4AnyD50DCulGXA1GPYymDiuIW8IUhUukgUhkg4pSovekA9J0HIb4s8rwTP5uFOd+YEcgGMv/vs19imbc7RReF/g7fLhowFLP29naMjY1h165d+OEPf4ju7m4GYP7t3/4Nf//3f88+X7773e/iM5/5TN43XCaAGR8fR21tShNNaEIErqcICADmelrN9/laBADzPgd4Bw5PJUi//KWf2VeHQ0m0t3OstCmzrcp88SbQ389DqxGhtZ3DI49q0N5x/X2Lmi98oVhNT0bxPz/04NSJEKprZCybpdA2PkaZMynr6c99wYiI9MyqzJfsMV3OGN55M4BRgiVeso9OZdkQpKPsGhL7NVmkzHFJrF7AwYPN2HtIvWapGNlPv/yCC9YZHjOTPAK+BDhdAGWlZsg4UcrNiU/CvsBj6FIYfCQBtVaCyho5mjuUCEscBevAzI6HEQzEYWoJ4wt/s39VVk4+AIbikk8WTDoDhvrnEuPdZ6gAPzKK/rffRmWARG+jsHrcLKujTF/EBHmniozYdettkJWUoHc6BV0yIUwmgKF9mZkw3UoZPK+8BP+xtxGYmoI0mWAZLWFZ6v2TzoKh//f7Q1CCXJMCTNsjolAjqTdBJyX3MQmUDc2Yb+gBLGXY07ys/7IVEMbjieCVV8Zx6ZIT1dUaBAOhpVsvG8SI4lFop98D551FzLEAjvewrAuHvAZDoTLMhXWQJ0Molnmh1ckhUarYj8xggqKiFovzXjZ2Seky4CFLaxL+1es4Bl8ee7AOUrKAWqNdDRBDp97OWTGJKI/Q9BgC4/3gPU6WWpdMxFPOWmIppCo1VLUtmFwQMwhDbVfPsltYoZ9XQn8hAtc6AgRj/u6ZL0Ii3TnOQNsJwHz1q19lGS4qlQr9/f2orq5esaRf+MIX8PWvfx3l5eUgqCKTbawvSAMIAOZavzOE81+NCAgA5mpE+To5hwBgrpOF3OLLOHo0hDffCmJ4KCX4a9CLUV6ecl6KJVJ6FklRBRyOBMgi2etJoLRUgvpGGe75oBoHegpz4tni6b8vwxUCX2gCL//Mh3eOBDE6wmN/jyIv6/DsiQeDCZw9FcbufQocvGsOB26Obwhg0mMQfLl4LoxL50KsNCzdKEGqsppD914F1JUDMKn3rxkvsql+8QcOVtI0MRphNtRU2hSR2KCSla06buB8AKMDYbidMVTWcjCaZChvC8KoXlmulHlgdgkSZQGRC1JlnQJl+4N48sm9q85zJQCGBktbUpMdNbW1ypBOHnsbtefGkPD5MD81CbHDCW0y9dDvi1AJhxwSnR5OtQpJnRY1Tc3Q3HMPTnu8+WfBAOhWSOF+6WdYOPoWRA47pKEgxCo1eLmSPSwrOSmSsTiS4RD4eAIKvZ6VjUiNRXDb3cwZqaSrG5rdB6Dddwhn+1MQqBAIw+LQtlK0N60JQ/sqS8149dUJXLzoQF2dDmp16g/vhXkP+3dVNkwiAbl7AkrnKCQhN3iXHYM+M+yJIvijHIrEbnAqJcylekhVGnAmC6T6ohUZfAtz7tTYl0GMz89jcsqP1mYDbrmpDA21G+sVXTg+CoR8aG4xIxmPQSSVMdgjVW98bCEfJNsZxNA31jGvi2XDEJShGFCZEWc0M5CX2cjSOt0EGFPIHSD03S4ReOSP791xGTBkQ/3FV35RcAi/+uAjW2pDffDgQZw4cQKf/exnQeVA2W1qaoplr9Bnyquvvop77703rzkLACavMAmddngEBACzwxfwak5fADBXM9o761yUCfPqqwFWimS1xhiIiSe8kEmTgNjAyk8oo6K4RMrgi04vwQMfUt3wmS+0ylRO9LV/deL8uTDUahHq6vMXNM6+Sy5diECpEqF1tx2f+8K+Td1E5HBEpUdUhsTJRUvZLguhszAocgMYOua579gxNhzG2FAE9c1ymC/runijCzkBjN8bR/+5AKbGIuwPtNJKOZSGMLqZXkjuqWcDGMcCj9nJMHbt16L9w0FYqiSoVbevOJgADLVMNyR6nS3GW2gZEo2RzoIJnjqL4d+8CvHsPPSeIEScHE6tEqVllRDJpJjzemihYQlGEHc44I3FYG5ugay8HGN79yJpNOadBUPn3VOkg++t32H6Fz+DyOMB53On7KhFYiRlHBRKJcRKJYJUhi8SQ6NWMD0YqcEIt9qMZF0Ldn/ksSWAkQvC0HkoGyZbE4a2r+WQxI65MI1gIIbxkTD6+pyorNRAp1u+p9MQhvquAjHJJKRBB+Lz4xicSWLOLYFCFIGSS0Kh06Gxu45lYqzV0hCG9heX6DA06oHRIEdbsxH331215nEEGoKTwwiM9iHmdWNhxsmycIrLjSxGnKkE6vo2KCtqWTYTlemQIC2DNDIOUpUWUm2OEqsN3oHbGcQU8uGRCWLoOAHGFBI9oe+1jMBOBDBUgvTs6ZMFh+339u7fMhtqv98PnU7HfnevJ4bb0dGBvr4+/M3f/A3+6Z/+Ka85CwAmrzAJnXZ4BAQAs8MX8GpOXwAwVzPaO+9cfn+CCfKeODkEl0sEf6AMsVjqOkgqQKcTM1ef3Xvk6OqWM4HZ660VmvlC109W4N/9LxdOvBdCY3Nhlt7Z8ZuZjmLR4cTe/RX44l9btiy868EXOknfuSB+/XM3zp0MorpOzrJf0m0tAEN/uPWdDWJhjofLFoOUE6GoPIaOrmLojWuXYKUhDFlfD10IwGiWoW2PFo9/thTD/uFVAIbmcSVZMOkMGBqHsmCyy5A6rEH4Xn8T/PAoXC4nzC1tEOt1mAukSmMq1KkH81mfFxUaHdOCWRibgGhxEaaWFnDV1Rg9eBA9zSm77FxaMLQ9sxRpf3XKtShqt6H/m/8Jg2MBcY8HCb8X4WAIcnJakisgVikRTIiR1BlRUlfDMmU0ew5giCtmIGFvY2ocalsJYU6fmcSRNxfg9yagUklRVrYamqyZDUOxmvXDOh9ggrqJWAxiiRh6rQwH9q/OpMp1k6dBjMcbRQIStDQZ8OC9NQzGZDaCVr5Lp+AfuXRZ/8SOqMuOBFm7pbVPpBygNsBSXYZ4OAixjINYpWFAiyANZYVQSVcmpBFJCishvF5ADMVMgDFb9rErDHQVIiAAmM0Fube3Fz09Kft5Kj9qbU39/spuTz31FJ5//nkmlvvyyy/ndbJMAHPgwAEMDw8zXRmLxQLKuvmDP/gDPPjgg3mNJXQSIrBdIyAAmO26MttwXgKA2YaLso2mFIy8x2ZDzyUTU3uZ7XUolGTZDAqFCGVlUtTVS69I+HcbXe6KqWwGvKQHoLKsH37PjWNvh9DRybHsoM22iel52BfF2L2vjAEYymLZirYegCGQ8uNnnDjbG4DDFkPnHuWKNV4LwNC8nPYoRvpDcCzGWClSUWkctfVGNLar1pw2ARhl0ozR/iBV06Bxlxp3PmxGY4caQ76hLQcwNJG1IMzJgfOQ/uxVmGYcSPgDcNaUgtSty1UGNv9Zv2cFgKFtBGGoWSenYFiwgWtqhNViQfS++9BTkcrSWE+Ql/afGp9FGsKcGp2BaHEejT47IhPjSPIRLNhcKNaqAIkUYoUc8xERkrVN2H3HLRArU7E9M5gqD8wXwlDfQhySjrw1g7fenIDDGcGBfWU5dYPWyoYZG/NgfjGIaDTBfoqMcqjkQJGRW6H1stG9PTvlxNRsCNUVSjz10TbUVC2L8RLYcR3/LYJTowhbpxDzeSBRkAOQhf0LKrdJxNl2Z/8lJCMBQCKDXCmHWCJhECYtYk4lOnQcZzBBotJAv+sAVLUrrc43mmt6vwBj8o2U0E+IwJVHQAAwm4shWU4/8sgj7GCPx8OyYXK1P//zP8e///u/Y9++fTh5Mr+snWwbaho7Go0iFFrWEnviiSeY6C9HbhJCEyKwAyMgAJgduGjXasoCgLlWkd/+503DFwm30u54+8/8ymd4JfCFzm63x/DMt9w4fiyE5lYub0vwRJIARhxOZ5yVeNFrh9ODWFSDnpvU+H/+v5Itg13rAZiFuSh+/IydZb+UVXAoKV8ptLcegKHrn52MXP7hEQpHUV6twv5bdFCqVoMosuSetNrgs6qZQHB9mwp7PmBAzx0p4LEegKH9W12G5Dv8FsZePwz9tA2KXW2s7Gc24FkBYOi82VkwtG3O64N4fgGGQBCK7i5MHDqEA7uXNWwIwqwnyEtjLEGYsRRM2WPRIzo3i/7RGSai2lZbAYlWB1lZBc6OWrGvYTnjhfoThMkEMLRtvUwY2p8vhLFaA3jj8BROnpxDUZEcjQ2mNd9s2dkwwyNuLC4GEQ7HmHhuUZECLc1GuB2+pTEyRXfXGpic3y71O2HUiNDdrkdlmRKdu6tZuZbz2GEEJwYRnBxhWS3ykgqIFaoV7xkmTDs1iqjPjZjbCd7nZVALmiIY6+ogLy5HMh5HPOAD77IhGY0ycWB5aSV0bXugbd+36ffg9QRiaH0EvZgr/10jjLD1EbjRAEwsGt0QWuTjCPXcc8/hE5/4BFsQgiPSNRzR/u7v/g7/+I//iObmZgwODua1gPPz8/iHf/gHPPnkk9i7dy/0+lQWKVleUxnTD37wA/b6j/7oj/Ctb30rrzGFTkIEtlsEBACz3VZkG89HADDbeHGu0dRuZPBCIb9S+EJjBAMJfOMrTpw5FUZxiQQVles7BUQiSSZmTPbQZCseCiboWRuxRAhBv5wJ+FbVyPDQRwzo3KNAa8fmRH0zb6n1AMy7b/rw2195MNIfxp4eNSRZWTcbARjKoJmb4jE1FsbEWBCcHDBb1KhtVjCHJIlYhHgiiaAvDvtCFMFYABXlRSirlqP7kB4HbtcvPeQSgKGWrQND266kDImOzxbj7VKVw/mdH8J66jSSMilKWzvYuTMBDHu9ThbMnMsN49g4uNpaqA4exEBra16CvDRuZhYMez02i/21y4Dl9EgKyuyrW952Znj2fYEwdJ5sXRha11deHsfwsAtT0x6UlytRWZ4CZblaZjaMz5vE3HwAPl8UxRYl+6mpWf6GddGaEtylth6IiccTuNTvQnOTAXfcWo6gw5W6PzgH3OeOITg+yLJdFBV1EJFIVUYjsBKcGErBF48TYirpUqgR93sQCUchUhsgNleipCHl/EHXSyAmPD8NsZSDsroB+q6D0LZ0r3nN+ezIBDHUf9f+unwO29Z9BBizrZfnhpqcAGBWL/e1BjAb3YB/8Rd/wWyuKQOR9GXWKn/aaBxhvxCBaxkBAcBcy+jvsHMLAGaHLdj7ON00eKFT3MhZL3T9CvmVZ/386Pse9B4PMRvoPfvka35r7nLGMThAlt8JkOZOMJASNxZLScgW8Lg4cArAbJaivVOB4lIZKqtleOQJA1TqzWvurAdgDv/Sgzdf92LRGkXnntWlQxsBmPRt6rBFceKIDzJVBKWlBnZdJAZMcEkiAeQKMUwlHER6N7RGMe64vQXNnZpVd3mhZUg0QIt2uVwk246a9p92TqwqQ2qZ8MP72psInb0AT30FyszL+iT5ZsHQ2PP9AzBCDEV7G8buvpvq9XIK8lLfk5OzS1ow9Hq7Q5ipSS+OHpnF5KSXlRKRfi5ltJQWr+0qRCBmejoAuyPKSszKyzVoqNdDqVytq5IGMWtBmFAohuFRD9pbjbjr9kpUVWqYcO7Z//w6EtZRaNUSqGqaV8EXim1oZhy8YwFRtwMSpRoSdap8iayZ2TaFChGJBqLiOvZ+LS5LfUtL+jGhyWGIZHIoq+phuf0hyC35ades9ZFNcIfmMnD4KJJ8CIjHUNVYxqCQoqwGitKqnNfwPv4K2LKhBRizZaEUBtpEBG40ACMWiZAPYNkolO9nCdJG5yYBYNKDCYfD+Jd/+Rf85V/+5UaHCPuFCGy7CAgAZtstyfadkABgtu/aXK2Z3ejgheK8FVkv2etF7kUv/dSLkyfCaOvgYDCsLr+x2+MYGojA7UrA501AoRRBoxGzf2MJP4J+NYMy5DRVViFjdt+xaALN7QpU1XB48pMGaLSb05dZD8D86mduvP1bL1z2ONq7lTlvxXwhzLkTfohVfmiURaysin6okZaNRidB90Etdh/SgbeMoUrdmfNchQAYGiDbDYm2ZUOYXACm7sggAu8cR8zhgquhnM2lTHlZ+yWjDIm2r5sFs7AIw+QUlLt3Q/fQgzjPydfNgqHxMgV56XV2KVJ2Jkx2Fgwdk1mOlEsPhsUhh0U1bSd3JGr5OCRdvGjH2dM2TE15EQ7HIRLHodXKUHEZWGReqjXkAAAgAElEQVQuIs9TSV0EA/12RGOk/5LEgX0lsFjW1gSi49cCMbPWACLhOBob9Hjs4TooFFKW1eJ45zACQ+cRUBRDpNCsyqKh0iP/0EVEXTaQoK5UtzJzhyya48EAOFMx1HWtcLj4pcsgEJPgIwiMD7KyJipFMt2cn/1q9g1NOjWhqWEExvrBux2I+T1IRMJwLbiZuxVZRBfXV0Gq1rJ5qOpaICE78h3aBBizQxduB09bADCbWzzScyGBXGpbLcKbz4zo3DSHz3/+8/j617+ezyFCHyEC2yoCAoDZVsuxvScjAJjtvT7v5+wE8LIMXijOW5H1krlesVgS3/yqCxfOhREKJbCrU75CtJTsvQnSOO0xBIJJmM0SJmxMLZrwsywRx6ISGq0YZZUy7OpWspKI6cko5mejrAypsUWOJz5pAMcVlgmzkQPS4Vc8eOs1L0gLpnNv7gflfACM1xNjGTBiLoLqGiMUKjH4SIJ0UJkdttEig1YnRUObCppWK7rqunLe8lejDIlOfOY734Xp/CSSPA95Uz3mgp4VAIb6ZIrx0utcWjBU6mJ79z2Y9+6F7oEHcOGymGFPeSW7vkIEeak/lSJR2w4QhubR3VqB/j4nzpxZhMMRhtMRZtkwyWQMnFwMi0kD0moJBKKs5EiplMBgkGNhIQS/L8RgTXtb8YYfb9llSTTmwJALleVqdHea8YGbUlkotsM/h2/wPHM7Ujd2sOyVtGtSOpMmsjjHhHmpj6zIwiBMZiMNmahzEVKtgUEWZk9NIGg2VeJETS8JIBbwQl3XhpL7n2SQpJAWC/jgeOc18M5FRN0pd6ZkMsEyb8h5if4/EQnB5wpApNJBpDairKkGRYfugdxcUsiptmVfAcZsy2W57ia1EwFMPJHAl176RcFr8R8ffgQSsXhLMmAybahffPFFPP744znns2vXLqbdUogNdT4XlgYwf/Znf4avfe1r+Rwi9BEisK0iIACYbbUc23syAoDZ3uvzfsxOAC+pqL4fWS/Z63XsnSDePBzAhXMRqFQiJsgrFotAOSBnT4WZ5ovHTRkuEnDcsrtROOqHy6Fkfc0WCVo6FCgyLT8wTk/wmLdG0bVbibvu16Ln5tWWwOvdOxsBmPeO+JkGzODFELoPqHICnvUADIGiqbEIZicisE7zkChi0OtVsJRyTGiXGs8nmNaNziBl2xMaFzpukuHeu1L6GlR+RdefboVkwVAGDLXsMiTa1qFrXBozOwvmzH99B6aLk0jG4pA31q0AMHRQvlow9DBve+cYzHv2QHv/fVB2dqJ3emYpC4bGyhTkpddbVYpEY11JJgwdT9kwuTJhaF+mLszcrB+XLjlgWwzC74/C5YrA7vCTkzMMeiXkcgkT21WrZSgvV4PKh8gNaXY2AINexEqXSopTZT7rtTSIcXl4JCFFa7MB995dzXRkKHvE+vNnERi5BJnBDC4DVKQhDE1I5Z1OAY9EHDJ9Uc7TkTsS9SX3I23rSp0XAjHJGI+kdQSWzm7od9/EnJHybTG/F7a3fgneZkVodgIplyUzZHojgy/pRhAm5vOyTB3PvA0ifTHEOgvaHn8CipKVgsv5nns79hNgzHZcletjTjsRwCSSSfygNz9HocxV+tSB/diqEiQa99ChQzh+/Dj+8A//EN/+9rdX3RAzMzOorq5mXwa9+uqruPfezWUCZg8cCARYCRK5Iv3zP/8z/uqv/ur6uBmFq7ihIiAAmBtqua/sYgUAc2Xx2ylHC9BleaXS4IW2bHXWS/b9QA4/L//Mj/Nnw+i7GGGQpaZWytyNKPtlbpbAhJiVHVGjP2r8wQB8HiVzzDVbpKiq5Vi5UWajfn0XIlCrRcwi+g/+1JTTEnit+3MjAON0xPCjb9lx/nSQzaG8arUt5FoAhuY2OhCGYzGKmYkIKzkh3ZrWQzxKTCt1M4L+OGzzPJy2GNOEScgDsBiLUFolByCCVCaCwSRDS7cGicpZNBpToriZLZcQL+3fTBnSmWeehfjEeeijEihaGxmAoZZZhkSvN8qCIeec0JlzcNfVouWpj0He0swADLX1smBo/1qlSLQvX1Fe6rsVEIbGyackyeUKY2TYjelpHyKROObnPSwLRS4X4wM3VaOhwQCNRgabLYTXXptc0o9Rk6u2JAXZNgIxHk8E/f12lFjk2L+vAvd/sIqdI+p1Y/HXz8PXfxbKyjpItauBzsK4FYn5USQ9dqjMZqazkqtRGRJloBDI0bbvzanbFJwagdcbhaSyBdLuD2LX3poNfw3EI2HYf/cSQtYphGbHmdMSV1S8oZsSASHqH0ioIDaWQdpxG0Qq/XUh2psZNAHGbHgLCR0KiIAAYAoIVlbXr371q/jSl74EtVrNypCqqqpW9KB91Ke8vBxkLS2TrW8wkD6Y/i6gz+u12l//9V8z7RfqQ9k1bW1tm78I4UghAtcoAgKAuUaB34mnFQDMTly1/OcsgJeVsboaWS/Zq0MA4pVf+NB/KYKx0Sizmfb6Ekx0NxBIoKxMCkqJISckjy+EZEIEjVYBvUHCwEd1rSznHy4OWwxjIxHs7VHhsacMaGgiaJFf2wjA0Cg/e86Jk8f8rNype/9KK1/avxaAmRoNwzrDwzYfhd8bR1mVDFV1Cpjq/NDIVpZRUJaLfZ6HdSqCuakIgpEI1CoF9EUylhlD7kvkmmQq5RCSuVHdLsEH7+iCUr2cMZAI8+gbOYlKcQUQT0As5yA16jAodazIgKE5byTG6/3tUYz/5jVopx1Q7umESCrdVBZMSSiC6MwcXPW1iHzoARzo2s0WZiuyYGicfJyRqF++EIb+OC7XFWFyzMPKhkhnSCIRY8HuhrlEgQ/e0cheZ7Zsh6TMP7SpVIjAyoWLKejU3ZEqvaJGGTOnTy9iYsKLWCyBqioNfN4g25cLwhDEpDKnhcUgysvU7KeqRMQEfLu6q1hWy+JrP4Wv7zSU1Y2QalaLARPIIJ0Y/+wMoC2CSCyFWrP6/RIPBZEIB1IZMG27V2SmpOcfnp9BMspD09IFu+Xg0nWtB2I8596D52Ivc2ii8iauyJLfGxUAlS2RbbaivAaahnbMK1qWz3kduCdlB0KAMXnfGkLHNSIgAJjN3xok5tve3o6xsTF0dXUxe2j6lzJTvvKVr+Bv//Zv2RdF3/3ud/GZz3xmxYlqa2sxOTmJT3/603jmmWdW7Lv99ttx33334aGHHmLjpy2uyfGIwMv3vvc91n+tzJvNX5FwpBCBqxcBAcBcvVjv+DMJAGZzS0i/gMbGYjh/PgKPJ8Gsg6VSQKkSo7FBhq4uDkplYbocm5tJ7qME8LIyLtcCvGTOgB4ij70dwqneMFzOGI6+GWS20/SFUEr3RQT6IkmhjkCj0UChEKOyRsYyR9ZqNOaZ3hCqqmU4dKuauSLl2/IBMCMDYfzyRRfO9gZRXEqZOCsfWHMBmIAvjr6zAdgWoggFEuAUIpSWc2jrViOucK4AMIkEMDEcgmOBZ6Am4I/B541CqeJgNMlQ15oqwXI7YgiH4gzKcJYAmmorcM/jxVAl/PCeGUCgbwyLXisMYgMotUgklTBwIq8swXyDGm177oCI0msuAxj6d60ypOj8Ii595xmILwzDWF0LWWlxXlkwaR0YGnvW64FpahYSvR6Kzl0Yvu0W9JQvf4uYCWFyacHQGOksGPr/XK5ItH09PRjav5Y9Ne3b25gqZYnycYyNePDWW+MI+KJQSeVM4JYACsVexokRiUWgUEpwy6EaNLZQJsvKbKi1QEz6Xjx/ISXuS41ADH12nj1rYyCGSpHc7gjTiEEyBpVKypyyii16kHgvlTS53BHQO6SiXA1LsQp33l4JU5ECF85MsjGTIT/Mk0fhGzjHIIUsS1yXXafXzQAMb19gIrvBYGxpTpkgJh70M7FdzmiGpm1PTvAZWZhFPBKCpnEXyh75FBvn4omxpfGyQQw5KC386n+YgC+RVmVlfb5v06V+Efs8oh4nAzDFH3wcMp0R16OVdXZgMmEM7dvVU3jsCg62cMCOj4AAYK5sCSkD5a677sLi4iIbSKfTIRgMIhZLfW5+8YtfxH/8x3+sOsl6ACa9jw4i+KLX6xnUoXHT7emnn2YghuNWZ9xe2RUJRwsRuDoREADM1YnzdXEWAcAUtoyUpXDmTASnToXhcMZhs8URCCRBgqv0BTGVmFiKJdBpJWhv53DggBwlJautVgs7a369BeiyOk5Xs9won1Xi+SSO/C6AZ77txmA/D0uxmImWktQJCe/q9EmUVxbDWCTJq6SIMmBI0Hb/IRU+8yemfKbA+uQDYAjwvPgDJ4b7wxi4GEJFNYfyyuVsHAIw1FSy5bKi8eEwZsYpAyYKuZxsfGUwl3BoalfAE7Wz/pQFQ5kvo/1BVqbktEUZiKJMF9KHsVqDKKEsh3olymsU7IGdwI7NysO66EFrnQY1gUtorQpBEg6An7fDY1uAQrLsFCNRKMCVmuDWi1FsqYHhzkNQtqQe3nK5IdH2LkM9O5frf36O6XePQefloehqZw/hmWK81Hc9LZi5eSskI2Ow7D8A7T134YIppTfSU3ZZgDcPLRjqX0gpEvU/PTKLTGck2rYehGkuseCt16fhtIeYiO7UlJvNs8SiSekUJYBwOJYS1xXFoTXI0Fhrwi23VaCsYqVV+EYQhsZNg5h0NszQkAtnztiYLozTGQaVMVFGjN8fSd0najlUKhmDLXodx7Rkbv1AObTalX+cnz85ivg7P0XCOgZjsRGKsupV7wPKIgmODSJis7LsE3IaohbwhZf6EoghyEGwjsqDNE27cr6fSMiXNH60Ld0oeeDJFX1ygRgCL84TbyAwfAnKmkZIVatt1jd64xLEYcdXN0DfdRCG3TevPO/xkaXXu67DrBi6OAHGbHSXCPvTERAAzJXfCwRfvvzlL+Pll1/G9PQ0K0nau3cvSCD30UcfzXmC9QDMCy+8gMOHD6O3txdWqxVOp5OBGCpluummm1g2zZ133nnlExdGECJwDSMgAJhrGPyddmoBwOS/Ym53HD/+sR/TM1HMzcWxsBBj4mek4UHZL/SNfjCYZLbBxiIxKy2xmCX40INqdHflXx6S/4yATOhCx0m4Q4Ucfl33vdZZL2sFd2yEx//+yI333g6htV3O3ICoXCMhWYBatVyqkX283xfH4nyMlS2RlTNBC687Qc61uPk2Nf7q7/N3SckHwLAHVH8cLzzrxPREBJQRo9VLUFbOQasXMzCRmQUT5RPM8WhuhmcP7xU1HAxFUrR2qlgpETUPb2MAZpogzVQE9oUogy5Gs3Qp22B+wQ+5VAFzKYeO/doVIGpqeByq06dQqQ7DELMz5yh5sRFcSRGcogCMcjOS0RhiLg/4BQf8QS/0lTWQV5dDf/tBaA90bliGFLo4iMEXfwrd0CykpiJwtVV5AxjSfon0DcAtFaOsezcMv/97EHMcemdnlwAMxeFaliLR+d85NYFz79ghi0kxM+mDXCGFyaxAMBZh8S4zpcp4GPzyRxmgsVq90Bs45l50863lqK5ZWepTKIQh8Vz30ACGz01hbJaH159ELClBQiIDpzfAy0sgk0txYG8FmpoMTHB3LQ0B17E3MP7Gb5G0z0JU2YzS8pUiu5TVQhbU5D4kUSghUa10LyIQk4zHAL8T6pJSlkkjt6zUK1qKx8glBnFII8Z824dyvs0zQUyJ5xxzaIp5XVDVt26o+7LW5wYJ9xL4IWvqsoeeXuXixCDFDQBiBBhzXf/a3pKLEwDMloRRGESIgBCBAiMgAJgCA3YjdxcATH6r73LF8f1nfZiajGJggGeiqQRYzGbxqkwFAjBWawyLi3HmYNPUJMMD96uxf39u4cf8ZrCyl5DtsnbUtlvWS/ZMJ8YIwHhw4t0Qdu9VQKUWIxSzsm4y6UoAQw/A9sU4Fqxk5xtHOJREJJxgsI8a2VvTA3NVjYyVIO3ep0RTq3zDh7x8AQydw+eJ46UXXJiZjGBumjRsYqxEqsgiBS9yQSE1gY8kMTkSZrovkUiCadeQs1Fjm5IJ6aYbARguYcGFk344FqKsJKPIslLjxhMIIOCUobiCQ0ObGkZzKltBRGUf548gMDUJud0BrtSC0t0VKK5OOUDZIg4Ucealc1HsFhdmkBifh9ZghrK5Dsb7b8PwZUHhtcqQyEL67DPPQjQyBd34AqQWM+wWLYh4rSvGy/MwzSySqiwclaWI3tSDfbffzeZDAIbaelkwtP9ARWr9yRGJva5adr3JtxSJjsvMhKEsGLatITVWJBzD67+cwPnz87Bbw2hsKILJspw9NG/3sn5pCJMOaMDPY2rCh6Q4DkuJAk9/fBcrCcpuG4GYqNuJS6++AcxPwqKVIOpyIMHzcIVlCCY4xCUKcDo9FFol4mo9FHUtEMmV6Nq1Ugwy87y8fR62138O/+AFBMQaiLRFSNtPp/sFx4dApTxUZkRlSFT2l9lifg/CviA7VlTehNLK5Xsp3Y+J4s6MQdPciaKb74WqumHV9WduIBATPf5zJgBsLLPkhDrrDpCxk8qoKPtG29KFkvuezKl1s+LcAozJN7RCv+ssAjsRwJAN9f/5aeE21F/5yNbZUF9nt4FwOUIErnoEBABz1UO+c08oAJiN1y4cTuCZZ3wYHOLR38+jrlaGigrJhg+5/kACly7x0GrFrBzpiY9q0Ny8+dpWIdtl47XarlkvmTMn7Zcffs+NY++E0NrGsXIjAjDZ8IV0OEYGIyCxXYJ6AX+ClRsplCJILltZe9wxUFk2jUFivMUlUuzqVuKDD2qX3GXWilohEIagT++7AfSdD8HvjcE2T/bZcfgjXsglOkhkImYpTXot9G/3ATVKK1OW25mNAIx3zoCp0RCDNcXlHKSXs2PS/QiczEwHYLaoUduiRFmVgnlSqy++DW5hGlgchxslSOqMMJfJ0bpHy96L2QAmPd6CZxHKEQckWjWDMMWfehSXZJ5VOjDUn8qQqCVCYZz77jMotoUQGRqFSCaD26BESXnNkp5MugyJ+sYWbXDNzsBQZALX2ADl/n24VF+JnpIM7ZctyIKhue2vzoAyY7MrtGBo/1qlSLSPIMzZ3gWcPbGI8WE3pCpAa+RQVrRauJZATDaECQajmBj1QKYEKqrV+Oxnduf8HFwLwpCYrPvdNxh0sY9PAkE/9GYjxEoVWCpXIo54wI9ElGdW0TJzMbNqXrR0MTCyFoShe8b26k+YFTVvm2eZJnZnqrwoDWKiHhdCUyNMB0aqM65wQqIMmZjHybbLi8vglSxn0GSCHHJAEkmkUNe3opSyUDLso3O9zyhjZe4n/43A2AD8vIxdA7Xiso2tt7PHiwX9CE4OQ9e2B5a7HgFnzE/I90bJiqF4CWVKG/+OvBF67EQAw2yo39uEDfWhrbWhvhHuD+EahQi8XxEQAMz7FdnrcFwBwGy8qEeOhPCbVwM4eyaCWgZf8td0oQyFs+d4lJVJ0N0tx+f/VJ+Xtkd6VgJ02Xh9qMdOAC/pKyFXpP/6motZUycTSbS0yVcBGNJf6b8YYdkmdluMfVev0YpZtkwaalAf62yUwRedXsLKfEjAtLVDwX4efly/LoRZC8DQ/HzeOPhwImUlrBSx8em8tG+kP4yLZ4OwLcTgCs0zDRiCKCS8S45GUT6J9j2prJTsRtd7/L0FeG0KJvRqsuQWGZ6Z8aOoSIXqRiUq65SQ+FzQnHsLnHUMQYUcPqkREa8UlnI5GndpmD4JtVwQZjHsgjGuQvD8IJQtddDf1oOJnooVAIaBC+fEEoCh16emh1B7dAD81AyiCzY4Z2ZYnaHBXMxEfh0hP0QRHtpIAmKtBk6NEkmjHk333AvFvj3onZ8tGMDQeQvJgmHzLADCxGMJzJ4KYXTQxcrEqut1sKYzXvKEMA5bCHZbCHqzBPtvtuC2Q7mFUbMhDGmwuI/9DpHZSQZgpIYiyEwW2L0pYcfS4uWyp7jfi6jDBso4kVdUM9tm090PY3A6BVVygZiwdRqOI79GeGoUBCtIb8Xh4pduweJiLQLDF8FTxg1ZTRvNDKAw+OJ1QaJUMQCjqmthZUrUFuZSujjUjByPyPwMVPUtMOz5AHS79m/44URgaO7F/0ZgfICJA3OmEizOupbnVACISbshkTtT8d2PQWbIX/MpfUIBxmy4ZEKH6yACAoC5DhZRuAQhAjswAgKA2YGLdq2mLACY9SNPD4lf+7oHZ06HmdNRZ2fhWi6LtjhGRngc7FHg4x/Xoqlp4ywYocQov3fEdi83Wusqfnc4APq5dD6C9r1ukOh/OgOGHtpGBnlWdrS4EGNitkWm1RlXVJLk9yVQVi5F5x4lFEoxhgciCAYS2NWtwP5Datx9/0qti8z5ZAMY+2IUF8+EmOAuQRT6T3S5TEOjlaBjtxLt3UrQ/6cbiU/PBS+gRLsXh1924e3XPbAvxtC+e3VpCh3jccVw7qwdrnkZjCYpc9fJ1dIApqZJhYpaBZTDp6GY6IPMaUWkvAGBeAS+BQl0RhnKapWgftTWAjC0Tz3tZ/ow6s4WlH7uYzgfnlvTDSk9p1PzY2ix8gif70PUZod1bgamqAhUpkRwyhHjUVxeCbFaDa6hHgPlJhzo3Lt0SSesMwVDmDSAoUGoFCmzDIm2bbYUiY79zW+H0NfrhHc2ipoGHTSXBW0LgTDxeAKDfU5UVmuht4jR1mXE7tbc2kVpCJN0LqB08iRCU2OI+zxQ1DZBolqGdAsLHhazNIRJB5DKlcIzE5CXVUJRUQPLfR9B3/AywMgGMYHRfriPv4nw3CRiXjeDLLIiy1I2TDLggSq0CBqXyp5In4cspSUqDSvpYSK5OWys5wfHANc8YKpARWc7LPc8Bok8v5JS6y+ehX/kEsQyORRlyxlRhYKYqNvBRIRJHLjkgY9tSsz3RgQxS9f87tCKjxrBUSm/37E7sZcAYHbiqglzFiKw8yMgAJidv4ZX7QoEALN+qKnk6PkXfDh+PIzWFg4mU+4HxvVGoUyFE70R1NRIccstSnzsqdwPxQJ0Key230lZL9lX5nTE8d3/cuHs6TCUWjcampbLSryeOC6dC2FhPgaZVIQi82r4QvcU7SctInIm6uhOfWNP2wcvRZgrV+duBT75hyZYinNnbKUBDIGXt17zYXY6Aq/7st6MJw7SJCX+QhouxiIpSsqlUGskaGpV4LYP6pjlOrXZ4AWYlXtw9HUP3vqNG1NjEXT3qHOWplDZ0aWLDrgXZDlLlNLXsDDLQ22KobnNjLISQHvyNXDzk0hKZYgVlSIYCyLklEHGiVBWo0RDR8pZZq0yJJYFE1UicGEQmj3tTAtmpEG9bhkSgx32Sew21jEx2tjcAi4cO4raGIdkOMLsrscjATS1dkLR3gaxRo0T89PoKV5+yN4MgKHzrgdhCMBQyy5FYttql+8jKkWilqkH88YrE3j76DSkMQkaWw0r1mgjCENjpUuS5mb8zCa6ocmAhk4lpFLxmhCGYnfuW99GcmYYmogHyvoWlm2Sq+UCMVQ6FJ4eg7K2GbruHhgO3s4OvXB2iv2bDWGC44Nwn3iLZdmQ6C5pvkg0OkgUKrhdQST9LiTdi5BEQ6n7W6Nj5U6U+ZIJX2jeBIt4lw3xgA+Kilq4IgpIuu+CSKVD5+7Vbku5rsnxzmvw9Z1GeGEGmqZOiChNLaPlC2LIRlusUEFd34aS+5/csAQ230/TGykrJh0ToVQp37tjZ/YTAMzOXDdh1kIEdnoEBACz01fwKs5fADDrB/t/f+zD0SMhzMzEmKX0Wi4cGy3ZxCQ5icSxb58CX/iCATpd6o9wAbpsFLnV+3cyeMm8mp8+78WZU2GcO29HQ6MZJWWpMhrKYpme5OFyxll2S7aOCtWKO2xxJOJJWEqlaGlTwGRZhiyUvXKmN4iWdgVuu1uDu+7LDfwIwHitnfj1z9zMuWh2imdiukVmKYpMUuZOlCTR1kgC9oWU5otWJ0Z1nRyVNRw+/JQReoN0CcCM9Ifw6584cfaEHw2tSuaAlN0IrIwMhDBvDaKyRrOUYZPZL+iPw+eJQVcSQ+eeEhiiNqgvvQv57Aj40lokOXLrCcLnTDC3pNIqBZq7l69xrSwYs9yEwPkBSE0G6G89gOk7mzYuQ7oMYNLz67WlHvr3mWrYvydIk4bAh/ny6/nU6zSEIQDDXl+hFgyNsZ4gL+3PpxTpFz8awlCfE8Eoj/aW1Roi+UIYn5fH9KQX7Z1m3P9wPaasi+w6c2XC8IvzsP/mp1jo7QUMFpQ21q37ps8FYSgLJhmLQdXQipLHPgUxt5yJmAvE8E4b/P1nEZ4ZRzwUSAn9RnmmMQOxBJ5FJ5KREESJGJQqOStFkhmMEHMKsDq+eBwxv5c5IzEtGlMxE+413fYAKyW6cHJi6Ro2AjHh+RnY33oF/qELUJRWrlk6tB6IiYdDTEdG09AOw75bmRDvVrcbEcRQDAUYs9V30rUfTwAw134NhBkIEbgRIyAAmBtx1Td5zQKAWT9w3/iGG++9F2Z/k+dTOrTWaD5fAmfORnDLLQo8+bFRVFWlbGwE2+j8b9ydWm601hWSqO6Pvu9B3+AsJkcNLJPFXCzF+dMhLFpjLLvDYFyZcUWZLU5HjD1HWkqkKKuQob6RYw49mY3Ee6l8rrNbiT/8ogly+cpv3anv+ZGzeOV5MRamzLBO8yiv5lBcKmPgJVcjPSPrDM9EgRtbFaipl+OjnyqCW9zHuhu53fjBNxbQdzYInk+iuWPZWSc9nm2ex0hfCHNzQVRWa1YBTeb6tBBl5VSa4ih27ysFtzgFdd9xcPPjiFQ2sQdoamRXreQULAOGdGCWzpHlhkTbKQOGmnrSywR1dTfvgeXpD6+ypM7WgaFj0lkw6fEJwqQBDG0jCJMGMOz1NsqCoflkivK+8L0BjA26wCMKnUmO8hy6L/lAGL1SibFhNzq6zLj7/loUl6QyWs73pQBUJohxv/1b+M4eR3h6HOq2bizYA6xPqWW18G/mfZcJYuLBADfrvY8AACAASURBVIKjA1C3dMJw6A5oWlcCiDSEoeMzM2IIvtBxoZlxpvuSjEUhknIsA0dmKMJ4/xTgWoBZL2FlSbSfWYxJJCxjhvpI1FqWdaJu3rWq7CgfEMPcuH7zPPwjfSyThjJtsrNgst9vmTDGUqpjJVUEoNR1LSj50MfzLn/K/9N1ZU8BxqTiIZQpbfYOurbHCQDm2sZfOLsQgRs1AgKAuVFXfhPXLQCY9YP2r//mwsneMBM/JfejzTZfYAanTspwoCeKpz/ViqYrcEPa7Bx28nHXS9ZL9ho4HGRvfgm2uXKMDEXgccURDKYcj8oqpeBkYlb+QvpDtC0cSkCuEMNkljBY09RC396vBiYkott3Pow9B5R48DE9c0bKbORU9P1vOXFpwArbjBZN7QqWzZJPIwgzM8WjpUOJlg4FHv9kEeZCF1kZ0omjPhx51c0gTFu3CuoMvRga22WPYugSZZQFUFKiBpcFhuj6qA+5I5nqIygqEcNs90M10AtufgKRqhYGm5i46WwAer0S5bVK1LYs64msV4aknvIxFyPtwW4U/95jOQEMe4i/7IZE/78ZAEPHrZUFk21JTX17p2fQU76so9I7NbOqDIn65ZMFQ/3WKkX6yfcHMDrgglrLISZLid+uBWFyOSNRf3JHCodiCDgS6Ogy4YMfqoM5w8Y6E8IkolEsPv89BIb7IZbLoahMZQrNL6SsrguBMMGRflZKRPDF8qEnct6qa5UlrXdfU4nSxVffQtJpRXmximW9iGQcpGotVLXNrPSI7pn12kYgxj98Ea6TR0DlUQR/2Jjk+rRBW5hxAl47Ej4Hiju7oG3phnH/bRsdtmX7b1QQkw6gkB2zZbfSVR1IADBXNdzCyYQICBG4HAEBwAi3Qt4READM+qH6yn+4ceJEGAq5CPX1hQGYaDxVfkAtFCnH6VMRHLpJgac/oUN9Q2Fj5b2g11nH6xW8ZC7TlO0UfvdKCysDOn8mjOGBMBPXJdcjSmxJJlMJLgQB1RoxgxblFTJUExDMynxJj0uA4tTxEBqaOdz3oA4fuGM5Q4T69B4L4PVf+XCy14a2DgsTxC2kTY2n7LF3H1Dho58yIWkaZADG743juW8tgsqRSHC3tUvFgFG6xWNJnDvhZ45JMURgMS+XDkX5BByLUQZtDGYZOvdp4E04YHJFoOp/D/K5MUQqGgGJFOFQHDZbEGUVWtS1qmG0rBS2XqsMSTXmhkSlhO4De2F+6sFVAIbmucoNaYMyJDrmWmTB0HmzBXnZtjVckWjf3PEghi45mTtWebUWVkcKhBQKYUbGHHDZw+g5UIEPPVoPnW6lOHkawuwqU8P20v/Cf/EMFNX1kOqW7ZfzhTA0P8qGSboWoecSUDd1oOzJz657u24GxFw4M7k0Zlf3so5PIe+LtUAM2VE7jx1mACal5aKEorwGYtnaguwEgiKLVkTddgRlZohN5ZDsugOdPU2FTGnL+gowRhDx3bKb6X0eaCcCmHgigf/7/EsFR+bfn/wwJGIxIpFIwccKBwgRECKwtREQAMzWxvO6Hk0AMOsv77e/7cG7x0Kg320d7eu7F2UCFxpVJFn+RpsyHYYGeNx0swKf/gM9ysoKe+C9rm/CNS7uRoAvzvApqOWHWEbH7HQU3/8vB949GoDPm2CCzwy+iMFsnglkFJdKUVIqXZU5kiuEZ09SmQ/HnJAydWBIqPd733Ti/OkgXF43unaXFXx7UXnTmRNB1DfJceg2DXZ9MFV6QhCGMlxef8mJUQJJ3jjqWxTQ6peFhCdHwpgej2BxMYjySipDAsvs8ThjUKjEMJpkaGxXQWeUwsXbYQxLoTl3BNzcKGJ6CxIaPZw2nmnjqMwx7D1YtkonJxeAWQjYkTgziqLWNujv6IHx3lsZgKHWoWtcisHVKkOiE/aUpT4jKAOGvd5EFgwdt5EgL/WhUqTYFHD+pA1z0340dxghkYgZhCm0FGli1INgJILyGjU+98f7VsWfzkcQJum2o3T4GHznTzL9Fol6JQhMQxjqv1E2zPzQGOB3o2zPHpR97I83LONZqyxpo5v9/QIxiVgMzndeQ2h2HKHpMSTCQUi1esiMFhYXyoihz4FEOISoy4aoxwmRRAplZR3kxRUw3f4hDFxMae1Q27U3lU10tduNDmIo3kJmzNW+6wo7304EMKTt9sN3TxZ2oQA+efN+iEUiAcAUHDnhACECWx8BAcBsfUyv2xEFALP+0r5+OIjXXwvg/AUePQcUzBI4s60HXTL79V3iIZMC3Xvk+PwXDOyBWmi5I3AjgJfMK09DGNr22itevPZLLybGeAY3CJZQtoJcIYLBIMlZbrTWfXT6RBC19RzueUCH2+9ZfvAdG4ngp//rBu0vq/XBaAYU0vKCb8fJ0Qgrl+rYrcLvf97MtGAIwFA7fzKAt193M9BCdtrkmGQplaHIIkM0ksCFU37MzQYhFnEpzdMYoNFLoDNIUdukZP2oEYAhClV+8RI46xgk4QB8uio47VGYSjnoq6JoaihdNfdcAIaft8MxMY7inh5YPv4w5BUl7DiCMNkAhrYXWoZEx2y1GC+NWagtNR2TKwuGtr95fAyL58IsC8ZSqkKROVWaVgiECYdjGBlwoanVCEOFFDWNWuxpXsOG+r3zwDu/gsY6AkVdbotnOn8+2TC8bR5xrxs+bQVEdz+F3bvyy1LZTDYMzen9ADHJRByeM8eYoG4s6EPUaWN22clkggElypQhayapRsvss6UaPRQllTAeumuF7svFE2NL9/y1AjE0AQHGpJZBADIF//p4Xw8QAMz7Gl5hcCECQgTWiIAAYIRbI+8ICABm/VBR5so3v+nBqdNhmM0S1FTLkC90SY8cDidwqjeCrt1y3HufCrffkduCNe9Fuw46UgbF4CCP4aEogoEEeB6Ii8eh1SbR0hZHc8uBTTtO7bTwZAKYo2/48fqvvRgZ5LG3R8ngy2YaifWeOh5E+y4F7v+wDj03L2ukvP4rL9583Y/JcZ5pxITiC5sCMKQjQ1k0u/er8ODjRugaR5YADM15dCCEN3/tgd8bYyVHZEFN4rwEXByLMXg8YUTCgN7AwVwig0IpQW2zkkGYzEYQhnRglEOnIZ0exbyoDHKjGoYiGYo7eFg0xatCRACGWhFnZv9SZkHg3CD8WgkqDt4Ey6ceXbq/sgEM9b9WZUh07q3IgqFxckGYU8MzOPs7G8LzcXhcEdS3GJayYOiYjTJhWKbWlA/RaAINLUY8/NFGDE7MsxjngjDxgB8Xv/k1YGIQprpayEyrnZfo2EQsisXRKSASgl6VyjQkm2+xUg2p0QSxVAZyQiJ3IhLiXWi+k/Xp7sgNfrJviM1mw9A47weIIYelwFg/guNDiIf8SPARBl/IZY/0ZyRyJZTVDVA3tIErWn1/Z17fdoAxAohZXhEBxmzmN9bWHiMAmK2NpzCaEAEhAvlFQAAw+cVJ6AVAADAb3wbP/Y8Pbx2ZxPS0GLt3x8CR6UxGedF6I9ADy8hwFIFAEnv2yvEnf6qH3rC+oOPGM9q5PZgb1Okwzp3l4fHEYbfFEYkkEeYdJO0BjaaYld6QyGz3HgU6d8tzOvjs3AisnnkmgJka5/HCcy6cPhFCTR3HnI420+bnorDORrFnvxJPf6ZoyeKaxvrFix4cfcMHjyuB9i4FAtH5TQEYurd73w2grVOJ+x4xoLQr9a18OguG/p90XYb7QrhwKgj7Io9QIMHcmUgLZnw4DKfHj2hAgeJyGRraVdBoV18vARhVTA/uyG8gmp2EShaFpLYWDZ168EoPO6eBW/1gn5kFE56YA7/oQLitFI2PfQTqrtalsOYLYOiA3cZlC+XNuCHRGGlL6nzFeOmYzWbB0LGZgrz0+te/HcJArxPhxQRkMjGq6nWshGgjPRhab2lUCocthLpGA1p3mXDo1lTm1Nn+VAlVNoShYxwv/RiBgQtwzy0AVQ0otSzrwBCgiToWEXO7mE100OMHkimHOIVSzuymSSdFotMj6rRDVd8C3e6DMNx8F+tz/kKq9O1qgpjN6sPQPLM1YqIRHq7JKYR9ASAehVwug8aggrKsKmWJXUDbDiCGpivAmJWLJgCZAm7iLeoqAJgtCqQwjBABIQIFRUAAMAWF68buLACYtdc/GHmP7RwdleCF5+Xo67fQF7Do2MWtadWbORo9fMxMx9hPZ5cc+w4o8NhHVmog3Eh339RkFD990Q+7I475uRhstjgg8UKlItfXJJAsAgEaasUlUpSWpX4+8qQORabrF1plAhi6Z579thNnT4bg9cZXuRflc7/QGOdPh2EplmBvjwof//2iFYf95H/ceOdNPwKBBFo7UgCGPfBuogzp5LEAGlvluPdhAw7eqsFs8MIKAJM+Mc1pYS7KRHZJ74W+6U8mknjr3THEPBbMjIfh9cSg0UpgKpExjRvKlIlFk5hZcCLiVsEcnkZdZADG0ByKiqUo3t8IsZyDI+JYE8AYZSZEpqzg52xQdzTCVaWG7CO3oNXYtiImW1WGlG1HTSdJuyHR/5+wziwBGHpNECatA8NeZ7khsW15OiJR30wtGHqdKwuG1uJ/f3wRCYcIkyMeSKVilFdrIFdI14QwsVgCA4N2BLxRdLRbUFGtxd0P1IDjlt+Xa0GYIMGXt3+LYP8FBIoqAIUKJSYt+PlZ8ItWJCJhJIIBJHgeIqmMleJEIjEGYmTiJMQyOSAWsf2atm6U//4XIbcsl50VCmEoLpstS2LHXhbrvRIQ8+5bI5iZC2HeFoYpy45bo5ahqV6P+jodFPLCP/cEEJPPp+S16SPAmKsTdwHAXJ04C2cRIiBEYGUEBAAj3BF5R0AAMMuhSgOX9BYJd2hp51tvBnH0SAiXLvDsG/zGZg5arWjNMhkqtSDgYFuMo7WNY7bTn/iUFkrlxtajeS/eDuo4OsLjpz+hspcoJqdiMBolKCp2wGBIQiatWLoS0jyhrJj5+RgC/iSaWjhUVcvw1Cd1MJs3lw2y3cOUCWBorqd7g3jtZS/OnAqhpV0OY1Fh121bjGF8OII9B1R48DEdOrpWWlC//BMPjrzhh8sRQ8dle+rNZMGwDJh3AmjvVuL+Rw3Y06NeE8CstQYjzksYf68GwxcDCPjjsM3zcNtjrMSFBIiZVpIyiKJSEcrMJSjzDaEyOYnkzDTi/hC4kiIEjWIYdSs1bJLxBBatE1A4YkiEeaha6qCsKUfx0w+iPzqHFm3zugCGrYNzYkMdGOq3z7Qshnqlbki5IEw2gKE+JydnV1hSM9gyPpsTwDAwU7v8HmPHD81gsNcJsVeCmQkfAj6eWVMXmRXw8WFUmHUgOZJIOAanPQyvO8KyZTidCEaLAp98uhMq9Wont1wQhsCJ7cVnERzuY3o+XrUFsM1CzQcQdTuRjEUhVqggUakYgEm3QCDCxIHkCR4xlz3lHFTTAPP9j8Nw050rPnvTEIaOvZrZMHS+QkCM0xXGydM2LNqC8PujGB9zIhSOI5EEtBoFJFIR9DoOJqMCKqUUNdVa7Os2rwBd+X6ebRcQQ/MVsmJWr1o2jKEeu3rq811eod86ERAAjHB7CBEQInAtIiAAmGsR9R16zhsdwKwHXTKXlB42D78eRO/xMEZGoix7Q6USo6xMwmAClc/QAwvpYszPx+Gwx6FQihh4qa2V4cmnNDds6RHBlOd+4MPwMI+5uRjqm9wwGJMsvFLJygfDzJhb52KYGIsy2NXQxOETn9YzG+brrWUDGNIM+t5/OjA6HGHOSG27FNDq8vsm3OOOY+BSGLV1HBpb5Pj050yrsrXePOzD7171YWggwjJkCHJsBsB4PXEMXAwx8PLwE0Y0tSkYgKGWWYa00XpNBvpgSO7C4MUg+s744XPHVhxCD/6aBgdu6elAUbEM7mMX4Dh6BrzNhYjVAZ/HAY2xGGIF1QaKkYzFEHP7mEV3qEgGU2UDgy/mx+6GRK3CRc/opgEMTWwry5BovK3KglkPwmQDGNZ3aAaci8PgBSdCwSgDLR5nmAk/+4IRaJVyBjm0uhSYUWtlqKjSQl4KyDgx9jbmfu/mgjDe3nfgP9uL0Eg/kvE4Al4/4HODk3OQGowQiXPf3wF/CPB5IJOmYLfMaIKmcx903T3QdvesurWuJBuGwZQ8hX3TJy5EH2Z2LoC3j1lhs4eYA1g0loRez0Gv5eCy+0CfiKTdRBAqGIhCrZahrFSFshIV7rylHCrVauC10XuLwY9tItgrgJj1V0vIjsnnbs6vz04EMGRD/RfPFW5D/a9PCzbU+d0VQi8hAu9/BAQA8/7H+Lo5w40IYPKFLtmLTBDmZG8Ebx8JwetLYGE+hnlrHHw0BROokaUuaZgQmCGtl8ZGGR58WH3DZr5QTH70Ay9OnQwzLZymNjd0+uS64CUz7hTjsdEodnXJcdsdKtx93/VXwpUNYOj6Z6Z45lQ0NswzF6GaBg6WYmlOu1/qT1lZi/MxTE3wqKySoa5Bjo9+wrBC+yUdVxrvue85ceZkCGUVMpSWyzZVhjQyECbDFpZF85kvFC+BnrXKkNb60CQAU6rsZruZWK4vzsqUEnGAU4hYWdJUZBCVql1LQ4SmF+A52Y/A6DRs9hnEHX6ok2p2/P/P3nuASXKVd7//jtOTc9zd2Zmd2Z0Ns2F2NgiErIwARUAggk0QtjEYG/uzDRfrw5+518YEXwPXgD8wFxmwRBBJAhRAQhIobc47OzMbJuecOnd/z3tme6d7pkNVd1V1Vfdbz6NntV3nvOec/zld3f3bN1DyVmthPmzlxZgq9KHxwI3I37kFZipDRj9IYwAYuqdGOWqyKzcMSfSJU5Ka7kv1gqG2saoiUWnq7dXVuNI1g0sXprG44IXX48fwxLw4a+sqi+BwWNHQXIymlhKUlC3nJTnRORgTwND91RAm6Pdh+je/xGLnWcwffQVBjxs+kxUoLkN+fqSH1vImBxH0euGboxw/QXjsBUBxBYpE/i0Lcje1oOquB2AtLl1zrJLxhiEjSoQlkZ1oHjGjY0v47YuDGBpeFACmuioPZaU5Ivwr4nk3NCP+6vEEEDTbMD/vQf2GQqyvy8ftN69HTljIV6z3U7zXGcYko5r2fdg7JjXNjQhgqAz1I7+XX4b6vTdwGerUTgv3ZgWUU4ABjHJaZrylbAAwyQKXWJtPSWPPn3Pj+DE3xsZ8yxV8fMvVXWx2E/JyzWjdZUdbmwMVldI8FzL1oJE+3/7PWRw75kZpxQzWrQ9Ihi8hTboueISL/q49Dnz4YyUZl5Q3GoChtZMHzJM/nxMwZqDPK+SgpLx0pmy25epIFOo2MeYToW50/uobyEvBjnveXowNDcvVZKJd3/+vKRw7TIlx/di11yG8C+R4wXjcAZw8uiQS8N5wWyFuuLXo2jCpAJhY87280BEBYELtfPOLmDvVjZ7OkygNloNCjygvjK2sCAU7t+BKyTw2F0bme6G+UrxgKASJLjnlqKl9MmFI1C9eLhgKQ6JrdTJe8dqGSE8UOaFIBGDoam9cJzxfxkeWhPcFJU++NDwBq92MWw40whblh79cCEO5Xoa+/f9h8fRxeMeGYLI74DNbgZw85BXmLdPrYFDAGf/S0tXQpFxYi4phK6/CjK1UhCQVTPQgt3ELitpfj+J918c841p7w9BEouWHcTp9+OXTvRgYXMD4uBMNGwuFd0u8a3SISlMHMT3rhT9owaaGIjQ1FuGWG2J7DMr5DGEQI0et9Lalc3D2pQ7AbLkWdsehSvH3hAFMes8sj84KZKsCDGCydeeTWHemAhiloUs0aemLEYUbzc4G4HYFYbNBeLqsW2+F3Z5c+eAktlDXXX799CKeeqYHXRcsaNvnQ65D/g8ISkZ77rQHe/c7cOe9hdizV151EF0LdHVysSAMVTL69a/mMDHuw+S4X3jDLFxNVBxaV1GxWXi6UKLiqhob7ri7SHjLxLvOnXbiVz+bExCldr0N6+vtkgEMnfuu8y4RLrFjdx7+8MMVEXlq5IYhkQcMXSEvmGjzJgBDV7gXTHi7rvkuNORvX9O1Y64bzQUrFY9CDaQAGGqbKA8MtUm1GpKwkUQyXuqnlBcMAZho14nuQbQ3xbjXuQxvYoUi0b1wT5iA14vxHz2MucMvgaofmfPyEXAuwj03L8CL7aqHEpFES24+zLl5MNtssFfXwVZZI/jMyOgcMNqHksJc5DVvQ9Vb/0i0iXWlwxuG5hIOYk6fncTho6O4eHlW5HQpKowNRlevY2RwGpPTHjidfrTvrcFbbq9HWalyzz89gRhaO+eKWT4B3rlpUabcOdQrklSLhFhmEyyOPIzO2mGuaoApJ//acWEgE/nOYQBjhG89PEdWIPMUYACTeXuq2ooyBcBoAVxU24QMNUw/0L/wxbM4d9oKm61MJNRN5hJVfU66UVpmwb6DuXjfgyXJmNF1n1gAhiZN6+/v8eLUcafwiqHKQKQtXVabScC+zS0O4clSt94WMzF0uADU/wffmcbFTjcunHdhw0Y7iqomxY/ceNWQyEvicpcb83N+kXyX8r/c+paVssKhMbT0gqEx4wEYur8awqQCYMie3Dww1EdOGBK1X10RSS0vGBqLPGGiQRgCMHQpAWFazPOYefHXWDp/Gjn1mxB0O+GdHBflp8nFbWmJQtpMIiTJ7HAIrxfK+UIhR+GXf2Ee42fOABtbsPW++5HX1JLwvZ2MNwwZTTUsid4vLx+awMIS4PUF0LhxxVMs4aSvNqAKVEePDaO6IgebGwvxwDt2SO0quR2DGMlSqdrQNTqI+c6TcE+MCEjpnZ1EwONeLs1utoiy7PaSCljyCuCoXofCrXvQdWE5bC10MYwBGMCoekzZOCvACsRQgAEMHw3JChgZwDB0kbzNaWnYN3IUD3/TgRNHy7CpyY7yiuTDsfr7vJibDWBPuwMf/9vytKxHzUHjAZjwcRfm/cIThhL1EizJyTGLsKS8fPnJiQmi/PB70yJvzMULbhQWW1BcNYPK8to1EId+SE5N+DAy6IXPH0TLjlxs2ebA3e8shcWy1tsrHQCGdJLqBUMAhq7wakhUipouJfLAkJ3VJakTARjqI7ckNfVRyguGbKkKYTqOo7L3DNyDfcjbvvtqKfIAAs7lcKNgIICpWScoo3n1umpxvqNdBCSXzp/CfG45TK3Xoe3euyW9NVP1hqFB5Cbp7e2bx2OPnUPfwBKqKnKwaVNyzy4KXyIQU5ATxA0Hy2GzmrFzT72kdctppDcQQ3PPBq8YOtMLF89i9vRheGfG4ZmeQNDjgbWoRHi9iPhSvx9+5yJ8C7Mw5+TCXlYJe2klStpvQN6GpmvbzMl8GcDIec9zW1aAFVBOAQYwymmZ8ZaMBGAYuBjnOM66jmB0xITHHmnCqy87saPVjqLi5AEMVUQaG/Vhd5sDf/3J8qg/+o2jztqZEoChKz9npfS5FuuhqkmPPzaD/l4vhga8GBmbQZ6jGGUVluUS0AAo5xHBF7oI9tSss6NlhwNvvKdkTYWl0JyTCUOKF4JEdmPlgQmNqUYYklZ5YGgN0cKQ6PVkkvFSPzm5YKh9LC8YuqeIJ8wPfwzT2SMo8i4gr2UlofLqcz46Tol3gZqK2N4ilMzXXlmDqeptMO04iD3b1kt+u2jpDfPyq0M4foLKTjtRWkgOPsvvqeqatV5j8RawtOTDpSuz2NZSiptuWIe5sclrzdUAMWQ8BGNa966UWZcssgoNMxnEzHeexuyZw3AO9YiwPFtZFWzFZTCHlWUPSUoeY96ZCXimxmEtKoWjZgPK9t+IvPrmNapnK4xhDxgV3oBskhVgBRIqwAAmoUTcIKSA3gEMQxfjnVWCL3TNz+7Dd741g9decWLrdjtKSpIHMEODPkxM+LF7jwP/4/8qkxRmYzTlpHrBKL0u8qY5+uoSzp5yYWRyCBNjJrgXS0Sokyj9awNKy60oq1j+b/e+fLS25casyBSanxwvmGzLA0MaHR4ewIHqDde2czWAoRurw5DEa30DEcl46TUlvGDITiIIEysUifomSso798oLuPLUU8D0OGra98c9xokgzOKFs7BX1aL44B+gJ79R2NICwtA4csKSnnu+H6dOT4AS8TY0LAOlseHlkBE5EIY80M6en8LmpmL8wRvqsKV5OQzzzNHlRNF0qQ1iaAyGMUo/fQHnYA8mX3sOzoHLItyIvFko1CjR5Xctwdl/CZaCYuSta0D5DW9GTnl13G7ZAmQYwCQ6PXyfFWAF1FCAAYwaqmaoTb0BGAYuxj5oIfjiyLlO5An5xlenceyIC+s3WFFdEz8xbLyVX7nkgcsdRFt7Lj7212XGFinG7NMFYELTobww3RfcePlwJ5yzG+BxBUSZaUeuWeSW2bk3D+s32iXDLzkAhuYQXo461gbH84LRcx4YWs/hkf6EeWConVJhSGQrmheMeP3yIPY1rE2uG14VafUeJPKCofbxIMz8sdcw9/LzmDjfATRtQ01F/FxOsSAMhSotnj8FR/0mlLz+ZhS2HRRTPXWmX/ypBYgJQRgaL15Y0q+f7cPpMxPwegOory+MkFQOiBGVcM5PiWpIb3h9LbZvjXwGMogBWvctgzgjXbSvY8/9TCTc9U5PIG/jFknwJbRGgjBLvd1w1KxHwZZdqLj+DsnLz+RS10YEMP5AAH/z3Sck71+o4f/7vntgMZvhdrtl9+UOrAAroKwCDGCU1TOjraUbwKwGLiS2xa5tGEZGb7CGiyP4QuAldNGXy///f8/g5HEX5ucC2Lk78b/qRZsu/evv0cMuNGyy4/ob8nDv2yN/yGi4RNWHSjeEoQWOOk+KdRbntIs/Q2ETchevFwBD845WDSnZRLxk79hEb8JEvNROTh4Yai81DInaJluSWsz/KoAJ+v3LOVg8HpFngpLfnhyYipoLhvolgjAEYOiKVhnJPdCL6V//AovnTmGhoAI1DYlzmESDMN7pSbgH+5G/fRfKbrsLjnUrITJaQhhaZyJvmOdfGMCJ0+OgEKLG+aoE0QAAIABJREFUqx4w4e+jEISh1+J5xFzzgGkuxo3X12HzVQ+Y1e9JBjHLihgFxrjHhzH+u19h4eI55FTWigS7ci+yQXlh8hu3ovqN98NaID/RM42ZSd4xRgQwgWAQj754VO724z037oPZZGIAI1s57sAKKK8AAxjlNc1Yi+kAMOzlknnHaTV8Ca3w6CEnnv7VAo4fdWHXnhwUFMhPFku5X3queLHvQC4eeG8RNjYmV03JCKrrAcCQTgRhShz7UpJM73lg1EzES8IdHu1PCGCondphSAIE1Ud6u/gmJ3Hmhd9hw9wkgl5vxD4PBywING7Bnhuuj1riOV5pagFpYkAY8lyZ+OmjcHaeg9+5hLmSOtSUJ/6xuBrCLF28AEtePvK27EDlfe9ZAwhTgTA0/907pOeTCYcw9P+rvWEOHR7BsRNjGBxaxNaW0phhe4lAzNy8B3398yIHzK03rce6uoK4781sBTECJBy6eE0bvYOYqcPPY+78cbhG+lHQ3AoTJduVeVFOmMVL55Fb34yS3deheOcBmRaiNzcykGEAo8gRYCOsACsgUwEGMDIFy+bmWgAYBi6ZfcLCw45Wr9TpDOB///s0zpx0URVNtGyVHsJCtvz+IM6ccqOk1IK2dgce/HBJ0h4ZRtiFdCXjXa2NEgCGbMrxguE8MMseMALK1K5AADl5YKjv/g2RsCU8DMk7PobFQ6/AMzyEkYFhlLmXEFhaBHnCUNkhk80Ga0kpJi0O1NXVInfrduS37YfJGhk+mCyEWTxzHLOvvAjnhbNYKFsH5ObLgjCVDhOWLnciv6UVxa+7CQU72qK+rZOBMGQo2QS94SAmHMKMji3hN8/24ULXNKqr81BW6oj7GIoFYnr65kXi8c2binHf3Y2wWKT9UGcQsyK33mBMwOfD8C+/h6WeLlHVyFEtD/yFHyTn4BXAZEYBhfa9+V2qfNQZCcgwgFHlCLBRVoAVSKAAAxg+IpIVUAPAcFiRZPkN3zAefAkt7jdPLeDVl5cESKmptaJ+o1USRCG3+64LHiwtBUX40pvuLEDbvlzDa5ZoAXrwggmFISnhBVORG/1HcjQd1MwD01ywdc2QyYYhrQ5BIsNHxvvQXr4SDrPaA4baJMoDI+wMDq4BMALK1IVBmb4BsZbwMCT6e6xkvHRvZ8CD+eefhXd0GN6RERF2NGdzoKKuFiaLFQgGEHC74ZucQNDlxLw1B1WbtyBnYyOKb70D5pwVeJAoFInGi5YPJuByYuInj8DVewneqcllCGN3SIMwg6PAwGWUrl8n8r9UvfW9MDtiPw/SCWFo/QRiKAzzV0/1oLN7GgvzXjQ1FUt69oXnh/F4/OjsnkHTpmLs31uFPbvkh6lkM4ihvdCjV4xvcR6jzzwmyk/nVK+HrTB+TqR4nxueqTF456aR39CCuvs+kJQnTaLPpfD7es8fwwBGzm5yW1aAFVBKAQYwSimpoR2Xy4WnnnoKTz/9NA4dOoRLly6JmM7KykocOHAADz74IO6++27FZ6QUgGEvF8W3RvcGpcAXWoTbHcAPH5lDd6cHF867UV5hwcYGW8wSxtSHKvNc6vbC6Qxix84cUX767rcWSPrxonvhEkwwk7xg5HjAkCxqAhiyvxrCJAtgyJZWeWAElOkfiAAw4jWJ1ZCo7fFXj6D+zDF4+nrhGx+HrbYO1ooKjMw7UVsSGQZE0CCwuICJi5cBtwuVra1wNGxCyZvuhimsLG6yXjCu3suYeeEZuHsuwzc/i4XCSiCvEDUV0Usz03z887Nw913BQtAG1NRj29vfAUe9tKSr6QQxBGE6u6bx6mvD6OqeEV4wFRXSIfLo0DRGxlwoLMpF86Zi3HNnIwrybUk/4rQAMQJ4HL4s5qiXqkkhwfQEYryzUxh77ueY7zyF3PWNsOYnDseLtfHemUm4p8ZQsGkbau96r6xEvkkfprCOevOOYQCjxK6yDVaAFZCrAAMYuYrpoP3tt9+OZ5999tpM7HY7CI7Mz89fe+1d73oXvve978G6yh08leknC2AYuKSiemb0jZX3JdrqFuYD+PEP5tDb4xFeLW5XUICY6loL8vPNlP8TFAUxNxfA6LAP09MBFBWZsWWrHS3bcnDP2wrjApvMUHRlFXrxglHCA4ZWJdULRi0AQ3PQOhEvjZkoD4xoo2A5arK32gvGPzeHc9/6FkyD/SheWkRO82ZY8ldyiAxPz62BMGRHVGg534EivweOLVuR17oHRTfcfO2QSvWCoQ6rk/IudXVg7tUX4BkagHd8BAseACVlqG6oX/bGofH9PvimJ+GdHEfA7YKtsgY5tesxWr0Vpg3N2LNVeshGOiAMrYGS9Pp8AQwOBTAwMI++/gXU1uSjvDx+KBL1JQ/A/oEFjI/Oo642Fw31+Xjn/TsUedRpDWJo0gxjIrcu5AEz330GjpoNKXrAjMM7N6WZB0yiQ5huIMMAJtEO8X1WgBVQQwEGMGqoqrLNm266CUNDQ/iTP/kT3HXXXdi2bZsYcWBgAP/0T/+Eb3zjG+Lvn/jEJ/D5z39esdlIBTAMXBSTPCMMyYEvoQWTV8tTv1jAxW4PZqf9GCHQMhUQP/Qo/wSCQZgtJlRUWkSoUmGhBXv25uDm2/NjJq/MCDGjLCJTAAwtTY4XjNQ8MOvzWmNufbxy1NE8YMhQS+GWCHsnZ65gR1HztdeOT/VgV8mmiDbRPGCoQbwwpNUhSNQ+GoCh11fngRGvJRGGtPDKy1h87WWMnzqNytadsBRG/kt7LABD441MzsI02Itiu03kgyl/x3thKVipQpYKhHH392D2pd/Cv7ggIMt0Xz/g9aIg/6qHSDAAk80OW3klrGWVsOYXoPgNtyBnfQNOn79actogEMbp9GFgwA/KCUNQpajQLiBMXt7acEwCL7OzbkxMuhAMBNGwsQgNDUUoK/SJaid07dq9QZFHXwjE7NyTuCJVKgOGPGLIBoOYZSUDPi+Gn/geFns6hfcLVUFK9nIO9xKxE5WQyANGb5fWQIYBjN5OAM+HFcgOBRjAGHCfX375ZVx33XWwWCxRZ//+978f3/3ud5GXl4eJiQnk5kp3Y44nRywAw3lcDHiINJqy1NCjWNOZGPfh5DEXzp11Y2kxAI8nKLxfyLHLnmNCcbEFu/Y4RN6XouLo7weNlprWYTIFwsgBMCS4Wl4w0TxgaLxkw5C0zAND85QThkTtKRkvVTiaeuS7cF7owPSiE8H19agrXhvqEBfCjEygaKgHjpZtKHzdDShoPxjxvkgUikSNo+WDodeDPi9cVy5i6cJZeCfGMTo4BgT8qCgpEIl/KceLvbwSuVtbkdvQHBECpSWEobmmkqCX+h965TJOnJqGzZqD8XEnqLJRjt2CoiI7LFYT8Wd4PX7MzHpAmKW0NEeEKzU1FuN119VcS7x75kSv0F8pCEO2GMQsH2mtQ5QmXn4a8xdOwzMxgvzm7TCZpCVXDn8DkqcYlbHOXdeAotb9KN37hrR+biUaXIv8MUYEMP5AAH/37ScSybfm/hcfvAcWs5nLUMtWjjuwAsorwABGeU3TbvGFF17AzTcvu38fP34cbW3SE1vGm3wIwAwOf3hNM4v9urSvmyegPwWS8X6JtgrKDdPb48XSYhA+b1DAl8JCM+obbKLiR7ZfDGBin4DLCx2I5QUjxwNG/OiavZTQA0Y8d1d5wSgJYMi+UuWoyVYoDMl1oQNzz/0GzjOnkLO5BWO+oHwAMzUHU38PSosLkduyHeUP/BFMYf9QIMULhuYUC8LQPfKC802MwTs1ga6ufmzeUCXyWAjvl/LKmLmfjAZhjh2+gp7eRQwPO1FUmI+paRcWl7yi2pvJZILVQgA6B8VFdhSX5KBlcyk2N0dP3MsgRr1PCK1AjHOoF5Ov/Fok4nXUbIStSH4iXkrA65keR37TdlTdci/sJfKTNKunZGLLagAZIwKYQDCI7//2aGLBVrV49y37hGcc5YzkixVgBdKrAAOY9Oqvyuhnz57Fzp07he3Dhw9j//79ioyzDGB8GBz+CBi4KCJpxhtRCsBkvFApLlAPyXiVKEdNHjB0pTsPjNIeMLSmRIl4xfN6tB/7KsKqI43040BVZAhJKnlgaIxY1ZBmn/oVFo8chn9yAjnbd2BkZj4qgCEbcb1g+gZQNDWKvJ17UPqW+2APC4UScKV7uXx2e1NkCezwtwABGLpW54OJ9jY52TGAti3ScrwkA2FoTMoLs2ebtDHC55iqJ4zQ62gPRsdcMMEhPGG83oD4EWWzm1FdlYfNzSWorspNmHScIEzQ6wYWZ7GlqZxQlgBX1uIyWMKqVsl9FLFHzIpiasKYYCCA0V8/hsUrnfAvziNv4+ZrOZCk7FnA6xFlrAlUFja3ovIm5Qs1SJmHkm2UADIMYJTcEbbFCrACUhVgACNVKQO1+9rXvoaPfexjsNlsGB8fR3Fx9IoRcpcU8oAZmXxYbldun4UKMHzRdtPT7QWTjnLUaoYg0e5pWQkpWQBD/VaXoxavyaiGJNZ65BUsHT+GoN+PnMZNArLQJTsMaXQS5osdqGzfj5I77oRj0+Y1bwSpoUhSIAwBGLq0gDA0TjpADCXoDV2t29eLNFjkBSPlIo8h7+QoFrvPw9l/CSMDkyJ0q6qqSHgnma1WONZvEmEt9opqyXbDx9YqUS+NqeccMSFN1AIxC5c7MHP8JSz1dsNktoiKSKFE1PHOQsDrhbP/Ikw2G3LXN6H89bcjt1bdXD5SzqbSbZIBMgxglN4FtscKsAJSFGAAI0UlA7VZWFgQSXkpIe8DDzyAH/zgB4rNngGMYlJmhSEGMNpucyZ5wUj1gCGFE0EYCkGiS4kwpFghSGRfrUS8ZDvcC2a1BwzdTwXAUH8KQ2p+9XdYOnlCJLm2b2wQmhGEiQZgQvdWl6Wm1wngjL/2Kirb9qL4trcgt2U5SXz4JcULhtrHC0UKt5cNEIbWGwIxVLJaykUlh2cOvwjP5Bh8czOgEBTynqBrcd4l/iyuroCNEhcXlcJeWoHSAzfCVppcaAqDmMhdCQcxdKd1n7Ry6LH2lmDa7MlXMN99VsA0SgZkr6yFtSB66Bl5zfjmZ+AeHxbeTgRsinceQGHLbinHx/BtpAAZBjCG32ZeACtgSAUYwBhy22JP+p3vfCcee+wx4fVy6tQpbNy44s6eaKnLgCX25fF4YLdbwR4wiZTk+6QAAxjtz4EevGCUKEetJIChXVAqDwwBGLqkVEKiduHVkJTMAxOeA4bGkQNgqH20MKTNR1/D4rGjoqQzecDQlYwXTMDjgfPsKcyv24SWB94FR+NKhajwd4SSXjBkN9sgjDhfcUCMe3QQUy89A/f4CNwjg8LrxVpSDltJ2bUExeNDkyIkKbgwg8LiPORU1Ykf9GXX3w5HjfyQq9D+agliaMyQV4zeqiaFn3elvGIIqsyeehULl87DPT4E7+yU8IaxlVTA4sgFKDkvgRfngrhHoWaU68VeUYOi7e0o3LonKS8n7T/NlB8xGpB56Ft/JionGiUvCn1P5xwwyp8NtsgKaK0AAxiNFP/c5z6HT33qU0mNduONN4IS6ya6Pv3pT4sy1OSa/OMf/xhve9vbEnWJuM8ARpZc3DiBAgxgtD8iDGCia64UgBE/9tKciJfmIDUPDLUNL0ctYE3fQFQAY33pRdRc6oZ3dASO1p3XfqTF8oIJwZnVXjC+6Sl4+nowt74J2/7o/bBXxy6Zm80QhvZi944UIMfVsKRoEMYzNY7J3/4CrqE+8SM9p3odbKWVEQmRw98po4OTwMIMgtMjKGtsgKO2HhU33wV7eVVKDzEGMWvlUwLEkCfM4uUOLHSdhm9xXoAW+i9AOX4CAcBsER4vBF6sRSWwFZagcPte5K1fBqt8LStAQObvv/lhWKzGqQzEAIZPLyuQGQowgNFoH9UGMP/6r/+Kv/u7vxOr+frXv46PfOQjiq+MQ5AUlzRjDaZafjpjhdFgYdkGYRKFIJHkegEwNJc9pSthCEfG+9BeHumlmEwiXgFWBgdxoDbyB73UPDDU//jvX8GGU8eWqyA1NsFSslxlRW4YkqvrAsx5ecjdtgO9bTeivSl2uIzSoUg0XzlJeal9Kol5xX4mkZxXjHumX+irBIQhOyEQE/B5MfarH8A10APXyAByNzbDWrC2nHi0R9Ho5QEEx/pQ3twEx7qNqH7LAzDb7Ck/tdIFYmjimewVQ94wruE+LF65APfYUOQ+mUxw1G5AfuM24dkkNWdQypttMAP3PniL4TxgqAz1J78pvwz15/+Uy1Ab7HjydDNYAQYwGm0u/YuF3+9PajT64LSElfJcbeSrX/0q/uIv/kK8/MUvfhF/+7d/m9Q4iToxgEmkEN8PKcDeL+k7C9kIYEjtmtzYeQ30AGBojmpVQiLbcgAMtV8ThnS5D5t+/zxcHecR9HmQ07xFHOJEYUjUJuQFE3A64bpwDo7trSh6w424kF+N9sbY1Y6or9JeMGQzmyAMrTc8N8zipQ5Mv/Kc8I5wrGuArYQqHkm/Ri/2IjgxANP6Fmy9516RnFepi0FMdCWV8IoJeNyg/4I+rwgxM+c4FIFnSu29Xu0YEcBQCNIPn5VfhvqB27gMtV7PIc8r+xRgAGPwPf/GN74hvF0I8HzmM5/BP/zDP6i2IgYwqkmbcYYZwKRvS9OdjFepctRK5oFJFsDQLkqthNSAjZgZd8PtCogqNd2uYVzX1Iy8Qtu1w5AMgKHOh1eVo04lEa+ANTHCkCxnTmFdV4eAMPbGRljLln+8S/GCCQb8cF/shslihmPLVpS/649gzs3D8YuDcSGMHC8YmovU0tTUVmplJGprZE+YEISh7wF1Q0ew0HlGhKTkNW+X7flANpYudWDBa4V5/RaY2+/A7j3KVszRqnR16I1nhMpJNFelk/am71PIOCMzgDHOXvFMWYFMUoABjIF38+GHH8aHPvQhAV8++clPgsKc1LwYwKipbmbZ5hCk9O5nOr1glChHPbh0BloBGNqprvkuNOSv/Zf+jrnuuAAmEAhiuGcJz75yCRN9XpSZSuDzBcTmT3jnUFdUjrpN+WjeXYzq+jwcn+yLCEESMGRVGNLqEKRYAIZeD0/GG80DRtiXWI6a2h7tvIRNh16Cp7cXnv6+ZQhTWpYQwFCC19LpcQRdLjhatiG//QAK9l8ndEgEYKiNFC8Y0a5zUBKAobZyk/KmE8KIsRUISfJMjOLcdx5GcKALZQ0NSedwoRwynrEhFLTswmTtPpiKK7Frt7TKS3KefAxiYqulhFeMnL3I1rYMYLJ153ndrEB6FWAAk179kx79kUcewfve9z4EAgF8/OMfx5e//OWkbUntyABGqlLcjhRgL5j0nQOje8EQgKFLKoTRMg9MKAnv+KALrz49htlJD2YmPLgyMA2re6WS3LzXhcqSApRVO1BcYUdxeQ5yD/pww5bl0J7QlQyAob6peMFE84Ahm1SOui3XjplfPA7v4AC8A/2wlFfAWlmFUbdvTUlqykHhn57C5JUeFOU54GjaLLxfim55I0xm87U1JoIwanjBZCOEmT9zFNOvPo+Js2eADVtRU1uW1EOISokvdJ5C7oYmlB68Cb3+6mt2GMQkJWnSnRjEJC2dpI4MYCTJxI1YAVZAYQUYwCgsqBbmfvrTn+KBBx6Az+cT4UeUdFeLiwGMFipnzhgMYNK7l+n2gtGyHLXWAGb0khdDL+RitN+J0QGnSOIYLPFgY20FrHaT2HifJ4DLg1PAzHIS06oNufCWevC2t29HbWNBTAAj4MpoP/ZVrCTnXR2ClCqAof6xwpDo3p4cK+aeeQre0VH4Rofhn5vHvMmC8toamCxWBIMBBN1u+KYmxTrmcvJQ3dSE3C3bUHjDTTBZrRGHPxGAocZyvGCovZRQJGonNx8M9aFwpD1b5VcoOnXViyXZxLxi7BQ8YWaPvoyZwy/CMzqI/JadGB2ZFftQXVUs+2G02HUW9soalOy7ASX7/0D0P3Oi95odBjGyJU25A8OYlCVcY4ABjPKaskVWgBVIrAADmMQa6a7Fpk2bcOXKleUv9VVVcWO8v/KVrwhYo8TFAEYJFbPHBgOY9O51Or1gtM4DoyWAGe5z4vuPnIerPxdTY27UNeahpMKOMc8MKnMiPQ6GnTOotJdgdtwtQpVMZX6U1efggffuREVdrjgg6aiEJMaNkgeGXicvmP0b1sE/P4elo0fgvnwJgcVFjF3pRYkpgKDPD5hNItGntawMlpIyWEtL0Vtai7Zbbor5eaQ0hJEDYGhdcvLBGBXCEHyZPfx7UChS/pYd4nwlC2EWL56HvawSxe3Xo/TgzREPsxCIURPC0IA7Fc49E+uJbJQcMaH5M4hR7rOVAYxyWrIlVoAVkK4AAxjpWummZUNDA3p7V/4lKt7EKE/MBz7wAUXmzgBGERmzxggDmPRvtZG9YOTkgdEKwHhcfvzsWwM4fX4EnjE7GrcXIq9w2dtjxDUdFcBUO0rF/cU5L/ouzMNcGcDObbV4ywcaYbWbJQMYsnGgaiUPh9QQJAFb5OSBuQpgQqc34HLC1dkJd2cH+nsHUFe47L1jstthr1snKh7Z6tbh+JUh7GuIXfFIKoAh2+1NCSondQ6KOagJYZJNyhvSjbxhtPaEmTv+KqYPvQD3UB8Ktq1UBQtBGJqbVG+Yhc7TcFSvR8mBG1G89/VrHmZaecMwiIn9OcIgJvXPWAYwqWvIFlgBVkC+Agxg5GuWtT0YwGTt1ie9cIYwSUunSMdsATAkViIIk2wlpPAqSB3HZvHyU+M4dngU21qrUFS6UuEoEYChOc5NetBxfgJt+2tx3ZtrsWlniSQAQ32VDkMiDxi61pSj7l2GG+QFs/o6dnkA7XXVgNkMk8UScfvY5eV+sSAMARi6lChLTXYoIS9dciCMHC8Y78Qozj/3O2B6HHWluUAgCFNODmwlZcjdvA05dfUJKwylCmFofXJCkha7zmH6lWex2HkWec3bYMnNi9gjqd4wfpcTSxfPIX/zDpS+7lYUbNkZ83nEIEaRR3XKRhjGJCehEQGMPxDAp77+hOwF/8tH74HFbIbb7ZbdlzuwAqyAsgowgFFWz4y2xgAmo7dXlcUxgFFFVllGk4EwM1M+jAz74HItV/TJyTGjstqKisrI3B7xJpJqGJLSiXiTATC0vlAlJKo29/i3B9B5Yg7DU3PY01YbsXwpAIZsnDg2ivXVJdi8pxS3vWf5R3w6EvHS5BOFIa3e32NXrkKW+ugeKgRhtPKCCUEYqQCG2ifKB0P747rSjaWO0/BMjME/N4vJ/kHA70dxoUPkv7HkF4rKUNaiEuRt2YH8lh0iHCvWpSWEIW+l0ccfweLFDpjtdjjWreQRCs1PCoRxDfUh4HYiv2kbqu/5Q1gcy+Fy8S41w5Jo3FDFJPp/Dk2KvRMMYhKd1Mj7RgQwgWAQP3rmqLyFAnjnHftgNpkYwMhWjjuwAsorwABGeU0z1iIDmIzdWtUWxuWoVZNWsmGpAIZKKl++6MHp4070XvHA7QrA5wsCQcBqNSHHYcK6DXbs2puL5pYc8Vq8K1UAQ7aVDEOKB2BorESlqId7nfj1D4dw/ugcHHVeNK6rili+FAAjxumZQHDCis1tpbjlgXqRC8YoAIbmTxBmXwoAhmyk0wuGxo/mCUOVf2ZfeR7Oix3wTozDOzUO+HywFJdgdskr9rok3w7/7AyCQcBWWg5bZTVy6jag9OY3x4UUWkKYmVefx9ypQ3D2X0bB1l0CGkW7YoEY0mGRwo/WNaBo1wGUvu4Wyc8atb1hwkGMVhAmtPhQnpjWvWuhlmSBNGzIIEaa2AxgpOnErVgBVkBZBRjAKKtnRltjAJPR26va4tgLRjVpJRmWkox3aMCLp56Yw/SUDxNjPowO+7C0uOz9ErocuSZU19iEJ0xxiQW331mIxqaVsstRf+Q5TyKVakhKAxia4/q81qi6JQIwx383hVeeHkdf9yIqdgZQlVuxxs5qCENJeEM5YEKNB+dnMHsuiPqthThwRy22HyyXFIaUaggSjX+gLrKyTzwPGGofNQwpDoChPkbwgqF5hkMYKqc988LTcF7ugutyN4iw2CqqYC0rvwYwRsevVhQqK4BvbgbeiTEE3C7kNjQjp3YDyu+4F+Y4niJaQRjP5BjGn/4plrrPwWzPgWNjU8xQqdUQRngA9V9e9n5p3oHK2++DvaJG0nMmvJHa3jA0VsgjhkFM4u1hGBNbIwYwic8Pt2AFWAHlFWAAo7ymGWuRAUzGbq2qC2MvGFXllWQ8nhfMpW43nvz5HAb6POjv8cBiMaGq1oqKKivsV0sqe71BTI77MTrshdcTRO16GzY22HHrmwvRujt2eEKqXjBKAhgSKpkwpFAI0qvPjOPQs5MYH3KhdJsfFTnlCQEMNVgNYYaXZjHfEURVfR7ab61B201VkgGMgCgSEvGKdrWrYEuURLzULh1hSDSuUl4wZIvywaQSijR36PdYOHsczu4OmHPz4KjftCbHDY1DEKamokjsO8EKz/AAvJPjyG1qESCm7PZ7YDKbo74nlShRTYal5ISZfvW3Ig/M0qUOETLlWN8Qc14hCEMQqtg7Df/CPPKatiK/ebvI/0IhcsleDGKSVU6dfgxi1urKAEads8ZWWQFWIL4CDGD4hEhWgAGMZKm44SoF2AsmvUciFoAh6PLTH8zgcrcHoyNe4dFSUWWJ+aOLfnROT/lxqcuD0jILNrfk4K63F6N5S3RPmEwCMC89OY4jv53A5KgbW3YXYcw1vQbCSAlDIgAzdNaJTRvLsPeWarTfWi0JwNAJStULZrUHTDIAhvokCkOiNlrngqExk4EwvrlZTPz8USx1dwhI4WjcHBtWXPWCiYAwQ/3wzkwhf+tOEYqUu7Ep5ptdKwgT9Psw9cJTcPZdgrPnImACbGVVsJWWrQlJopAj78wkpq70Cc+fqtZW5K7fhPKb3hIzfEnO00zLsCSaV7o8YmhsDk+SczJ9FJHsAAAgAElEQVT00ZYBjD72gWfBCmSbAgxgsm3HU1gvA5gUxMvyruwFk94DEC0Mibxavv31SVzscgvvl22tDhQWRVa2iTXrpaUAzp92obLKKuDLBz9ShvyCtX2VADA0h4rctoQCJqqCRAZS8YA5/NwkXvv1OIb7nNjWXpw0gCGIdfTVEbS0VGDf7TXY9YZK3QIY0kytMCSynW4vGJpD81wf5o68BOelTuRt3SWS18a7QqFI4RBm6cIZ2CprUNDahvLb74nbXykIQ4PE84YJ+rwgTxjnlW54piinzQSCfi+shSUiaTA5tgS8XvjnZwGLBbaySsx4c2Cqrsfud7wTZlvsxMIJ34xRGmjpDcMgRvoOZbtXDAMY6WeFW7ICrIByCjCAUU7LjLfEACbjt1jVBbIXjKryJjS+2gvm7Eknnnx8FsePOIUnS1mF9ApHNNjcrF9AGErKe9ubC3Hw+vyoc1ACwkgFMDSBmtzdMbVIBcB0HJ/FS78aF1WQNu8uxLxlLikPmKV5H86cHFsuRf2WOmxqLRYARgCJ8pUEn4dH+7GvIjLhZ6oeMDSG1Dww1PZo72BUAEP3tPCCoXFOdA+ivSl61aXwjZZblpr6njzbAzz3M5RODwHBABwNzQnfR9RgNYTxjA3DOz2J/JZWVNz9gChVHe/SDMIEAnD2dGOx6yw8k+Pwz8/ANz8L8nqhi0qJWwuKYSkqhr2sEvlbWnFx1iE8gHa1bpCkhZxGWnjD0HzSUTEppEMoWS/93SgeMTTXbAUxRgQwVIb6oa88LuetJ9r+88fv5TLUslXjDqyAOgowgFFH14y0ygAmI7dVs0WxF4xmUkcdKBzAkBfGow9P4/iRJUxO+LGrzZFUroeOMy5Qcl6CMA9+pBxm89p8EVoBGFp0Ii+YVACMa8mPn3yjD92n52F3mGFf504KwAxeXMDk0iLa2upw1x9vgi1n2XNI7UpIYgyF8sCQrXgARtyXUJKa2qXTC8Z15SI6f/RDFIxcRm7jFlgKl/O7SLnCIUzA58XS+dNwbNqC4oN/gKK91yU0oRWECU2EkvMudZ8X1Z0CHrd4mZL02korkLd5O2zlVRHPgDMnl6GgmiBm127lIU+48AxiEh7DNQ2yDcQYEcBQGerHnpRfhvodb+Ey1PLfEdyDFVBHAQYw6uiakVYZwGTktmq6KIYwmsodMVh4GNLwoBff/84UThxxom6DDTW1yYUbTE34cKnLjb0H83DfO0ui5oIxOoAhEUOJeF9+ahxUDan/4iLKdwRQnR9ZCSlRDhifN4Cu4zNw1AO33d4kcsCErmgAhu6Fe8FI9YChfkcGB1VLxCsAy5VBMfVkS1JT3+MXBxMCGGon1QtGtJWRkHfx7Alc/NnPgIHLqD74etkQMjwpL+WQsZaWo2jf61F6w+2S3uhaQxhJkwprpCaEoWG0CEsS4xztEavSOj8MjWlUjxgx90MXr52G1n2Nco+PIdozgDHENvEkWYGMU4ABTMZtqXoLYgCjnrbZZJlDkdK32yEvmCOvLuKpx+dw/owL7dflwWpNrtIJedIcO+REY9NyRaSbby9cs7hMAjATw2786r8H0X1qDk6TE607ayJ+tMcDMMFAEL0X5hHwB+HYFMT7/rgNJZUryYtXAxgScnUYEgEYulZXQhKvVUd6EygBYMiu2mFINIbSXjBkU0pC3oXjhzD70m8x0XEBaNqOmnLpHjChgx6CMFTC2pxfgKK261B2652S3+QMYXqvaaWVRwyDGMnHUzTMZBDDAEbeWeDWrAAroIwCDGCU0TErrDCAyYptVn2R7AWjusQxBwgBmN89t4DfPDmHK5c8aD+Yl9KEzpxwoqraiptuL8Sb7ln7A5YADF0ljn1JjaNkKep4IUg0ua75LjTkb18zz5AHDN0gL5gLx2dx4vgYKkqKUL85H2bLMsAiAENXZc5KDhAqQ03QxdNng9vpx6bWIli3+HD/PbsixpECYKiDVC+YWACGbGiRB4bGSRSGRG3S6QWzcOoYZn//LFyXu7FQtzlpAEPrKJoZgrWoBEXtr0fpTXfIOut6hzC0mEzzhqE1MYiRdUwzEsQwgJF3Brg1K8AKKKMAAxhldMwKKwxgsmKbNVkkQxhNZI46CEGYI7/fgWefnEfvFQ/2HkgNwJw95UR5hRU33lqAO99aHHXMVLxgCMDQJTURb7wkvGQnlTww1N/vD+L5n47gyOkBzF2yU+VelFXnoKzKDqvNLCBMCMB4PQFMj7nROzCDEkc+6rcWomFbERyv86CtfJPmAIYGVDoPDNnUYxgSzSuRF8xS5znMvPAMljrOYrG6EbDZk4MwY9NATyfKmzej+MAbUHzdjbLf4AxhVkKSSDytvGEYxMg+qhkFYhjAyN9/7sEKsAKpK8AAJnUNs8YCA5is2WpNFsqhSJrIvGYQAjCnD+/E00/MouuCG/tflyc790W40ZNHl7Bugw23vqkIt75pbQgStU0FwFB/qV4wiZLwKgFgBITxBfCDn5zF4AUPzDMFmBx1w+MOoLDYhnksothWCIIvi7Ne2B0WBEu82LS+Ak07i9F+WzUsFhOOTfRiT+lKXgUtPGCSBTDUL1o5ano91WS8ZCNdXjD+hTlM/ORREIihBLzTtqKkAIxvZgqTFy6get9BlN3yFjjqk8uXQRBmz7b1ijwY4pWoTmUAtT1haG5a5YYRY13ND8MgJrlTYfTwJAYwye0792IFWIHUFGAAk5p+WdWbAUxWbbcmi2UIo4nMEYMQgBm4tAdP/HgWxw8voWWHA8Uly5V45F5LiwGcPuHE7r25uOPuIrTH8KbJNABDOlH+m+eOXYCnuwyj/UtYmPVhYcaLCecCim0FsFhNKCyxIb/YBlf5Em553WbUNeVfg11qAxgBWxRIxEt2ks0DIwBNgmpIagEYsislIe/0c09h8dQRuIcGkbd9F8amFmRDGOelTphzHJgpXoc9H/6oKOOc7GUkCENrVKNKUjiEEWOoXC2JQUyyp3Wln1FBDAOY1PeeLbACrIB8BRjAyNcsa3swgMnarVdt4RyKpJq0cQ2PLRzDo99swtlTLgQCQWzZ5khqIlcuuuFyBrGzLRd//OflyM2L/sNTSwBDC4kXhpRqCFK4UGdnL6GlcAtmJjy4eHoOU2NuXJ4aQ62jUni+UGhS8+5iXMQQdpVEhhytBjACmIz3ob1847UhVifhpRtSc8DoCcDQXPY1rIt5xsgDhq5EyXgFVOkeRHtTbFvhgxCAoSteKJJ7sA/TzzyBxfOnYauswbQlX/SRmpDXNzcL15Uu5LW0Yrx2G0xNrdizNXkvFiVDkWgdannCCGihYqnq0D5q6Q0j1pTGikk0vpGrJon5G6xykhEBjN8fwD/8289lf2b/3//jPlgsZrjdy2Xo+WIFWIH0KcAAJn3aG25kBjCG2zJDTJghjPbbRF4wx19uxfO/mcf50y7s2ZeLHIe8f7X3+YLCg6Z5Sw6uv6kAb7o7dgUZrQAMKZkoDEkNABO+gydnrmBHUXPEph6f6kkKwJARKZWQRLvhAUmVkKitknlgyJ5ew5Bobom8YMiTaeqJx+DsuQR3z0XYNzRgKiAtF4x/cQGuy12wVdUgZ0MDKu59N85dmRR7zxBGuedaCMKQRS28YRjEKLN3RoAxRgQwgWAQP/nlEdmb9Pa79sNsMjGAka0cd2AFlFeAAYzymmasRQYwGbu1aV8YhyJpuwUEYGZngB89vEkAGJ8/iO07HSI3iZSLvGY6z7vh9ZD3iwPvel8Z6tbb4nZNBcJIzQGTDQBGwJaR/ohS1GoDGLKvRRgSjZMOLxjf/Bymn/wZyBvG3d8DW2U1ps15qKkuj3qmg34/fNMTcA8NwFZRBcf6jSi59U7k1C57vpw+358SgCEb7AmzVvp0ecPQTNJRMYnGNbpHjFiDjr1iGMBI+cTnNqwAK6C0AgxglFY0g+0xgMngzdXB0hjCaLsJBGFOvdaKl19cxLnTTpGzpGWbAzZ7fAhDni8XO92g/C+tux1o25+HN94Z2/sltCqjAxhaR3PB1ohNCoUghb+otgeMUQEMzTteGBLdVyMZL9lN5AVDbbxTk5h59pfwjA7DPdSP+alZoKAYFfUbYLbZARMQ9Pngm52Gb2oCMJthr64T/xX/we1wbGiIOBvZCmFIBLXywoQEZhCzEqao7adGaqPpEcQwgEltT7k3K8AKJKcAA5jkdMvKXgxgsnLbNVs0hyJpJrUYiABMnv0gnvnlPM6ccKLzvAtOZxCV1VZU11jhyI0MSXK7Axgb8Yn/bARrduSgZbsD99xfLMlzxsgAhvTqmOs2JIChuR+46pkROmHxQpCozf51a3OYxPOAoT5ahiEJqKJwLhiy6V+Yx9wrLwjPFlEh6UovCgJuyrh8VToTLAWFsJVXwlJcCltZBYquuxH2yuo1b14CMHSlEopE/Y3kCUPz1SIvjBjnRK/QV6uQJDFmmism0RzYI0bZz0kGMMrqydZYAVZAmgIMYKTpxK0AMIDhY6C2Agxh1FZ4xT4BmPyc60Q1n9//dhFHX1vE5IQfo8NezM8FUFhkhj1nGcJQqNH8nF8k2a2ptaKiyoodu3Nx+1sKJcEXspEqgCEbFbltCQVSIwcMDZoqgBE/FsMS8UpJwkt91ErES7YP1K0FLUf6BmICGOqjdjlqGkPpMCSyKcULJnS4yMtl6cI5uC51YnhoAtUllJg3CJgtMFssyNnYhLyWHSJpr8kU22NMSQijVHlqWqOaiXm1hDDpBjHpCkvKFBAj1pHm8CQGMAk/UrkBK8AKqKAAAxgVRM1UkwxgMnVn9bUuhjDa7UcIwtCI/b0enDrmxKVut4AtM1N+eH3L//JvtZhQXGpBUbEFDZvs2LU3F41N9rg/PlevIhUAQ7ak5oGRAmDI3vq81qhCd813oSF/+5p7UgEMdZQShkQAhq49pY3XxlpdBYluqAFgyK7cRLzUJ5U8MNRfyZLUZE+OF4xo3zkYtyLS6k0P+rw4deQc4PVi+8YqmHIcsBYWwezIlfwmVSIUiQZTsjw12WMII3kLYzZMd8UkmlgmeMSIdaQJxDCASf19wBZYAVZAvgIMYORrlrU9GMBk7dZrvnCGMNpIHg5gQiMSfKHy1OQJQyWmyUOGwpEqq6wi50tJmTWpyekFwNDklaqEFC0HjFQAI4DERO8aAEOvJ1OKWsAaFSshSQEw1GZffewS0VIBjNCgMXGpabUBTOign+wYQNuW5EpLK+kFQ/NhT5jYj59sDUsKKRKCMa17jZkjJh0gxogAhspQ/6/P/0z25/BnPvlWLkMtWzXuwAqoowADGHV0zUirDGAyclt1uyhOyqv+1kQDMGqNSgCGrhLHvqSGUMoDRmkAQ/ZaCrdErEmKB0w0AEOvrfaCIQ8YATYqIn9UqVkJicZTKw+MWEtDfLgiNRkv2dICwhCAoYshjPy3rlY5YUIzSweEobH1kB+GQYz882lEAENVCH/6+GHZi33bvQdgNnMZatnCcQdWQAUFGMCoIGqmmmQAk6k7q991MYRRf2+0hjCZBGBod4xQCYnmKTURr4BAMfLA0D2twpBoLLW8YMj23ubEHjbh775UvGDIjpKhSGTPSJ4wAlCc7BNyql0hSYx1NUGvGG/3BvUfomEjMIhRXm41w5MYwCi/X2yRFWAFEivAACaxRtziqgIMYPgoaK0AhyKpr3imARhSTEoeGCVywBgBwAigMji4BsCI1/sHZCXilQJgqE2qYUhkQ29eMDQnPUEYJQGMAERnlj2tdu9ILtRKypNKSwgTDmK0hjBi7KsVk9KZqDe0J5kQmiSetSrkiWEAI+Wdy21YAVZAaQUYwCitaAbbYwCTwZur46UxhFF3cxjAROorJwkvA5i1Z1NKOWoBadIchkRzSMYLhvqlOxSJ5qB0Ut5MhzC0PgYxl8Ub1sg5YpQGMQxg1P1+wdZZAVYgugIMYPhkSFaAAYxkqbihwgowhFFY0DBzDGAYwKw+XRSCRFesPDDi3oboITyJAAz1lZKMl9pJ9YKhPDB0tTdJDyuSWxEppJGevGBoTuwJI+3ZmK7cMDQ7PYUlCYBxmEFM6NQwgJH2/uFWrAAroKwCDGCU1TOjrTGAyejt1f3iGMKot0VaQZhUKiFJTcJLKmVrCBKtXYlKSGQnlTww1N8IYUg0T629YGhMPeeDEfPLwHAkAULSmBtGzyCG5patXjEMYNT7XsGWWQFWILYCDGD4dEhWgAGMZKm4oUoKcFJedYRlALOiq5FDkPQAYGgOSnnBkAcMXVKT8Yq2GnnB0Fh6CUWiuajhCaNmPhgBJDRMzBv+5EynN0w4iNFDfhiaT8gjJhtBDAMYdb5TsFVWgBWIrwADGD4hkhVgACNZKm6oogIMYZQXVysAQzPXwgsmGzxgBGwZ6ceBqsgqL7E8YKj96kpI9JrSiXiVBDBkS2oYErVNpiQ19ZPrBUN99BKKRHNRIx8M2SVPGIYwyj9vQxb1lKg3W0GMEQGM3x/AZ/75p7IP5v966G2wWMxwu92y+3IHVoAVUFYBBjDK6pnR1hjAZPT2GmZxHIqk/FYxgFnRNJYHDLXomOtGc8HWiA2QWoZawISpHuwq2RTR/9hEL/aUNl577cj4crne9vKN1147PLpcoWZfxcpr9HepAIbaJlMJifpFywNDrycqRy3mWx87JwvlgRFtJCTjFXo0Js7vonUuGJpXsl4w1FfvoUhijhpAGBqHvGG0KFG9+umpF28Ymhd7xCj/2ZaocpIRAUwgEMTPfnZItlhvfetBmM0mBjCyleMOrIDyCjCAUV7TjLXIACZjt9ZwC2MIo/yWaQVhjOoBQ4pLBTDU9uTMFewoao7YKCkARsCS8b4IACNgy2i/pgBGzKNvICkAQ32VCkMiW2p6wZD9dCbkpfH3bE299DN5wQhb21K3FX5otcgHExovW0OSBIC6WraaQYzyn21kMRaIYQCjjt5slRVgBeIrwACGT4hkBRjASJaKG2qgAEMYZUXONgBD6q3Pa10johIeMAxg0gtgSH8j5IKheSrlBUO21AxFIvtqhyMJEJHmvDA0h3SUq74GoRjEKPvBFsVaOIx56OsfMpRXCH0PZw8Y1Y8ID8AKqK4AAxjVJc6cARjAZM5eZspKGMIot5PZBGBItcsLHVEBDN2Tk4g3WghSOgEMja1VJSQaK145arqvdRgSjSk3F4zo0zmYllwwDGHWPsPSBWEEALpaKSmdEEbM4yqI0UtYEs0pk5L1ivUcuoi//+qDsFiNkxeFAYxy33nYEiuQTgUYwKRTfYONzQDGYBuWJdPlpLzKbDQDmBUd5QIY6tlSuCViI9IVgiQXwFD7ZBLxUr94eWDofjrDkGh8LbxgBGzrGEg5FwzZ0XMokgBFGpSnDr2JGMIsK8EgRpnPt1hW7n3/jewBo67EbJ0VYAWiKMAAho+FZAUYwEiWihtqqAB7wSgjNgEYuvJzrlPGYAwrWuWAoeFrcnfHXItSHjA0gNREvFrkgIkFYARoGRyUVQlJ9NFRHhgBVSQk46V2WnvB0Jh6SMhL81ArH0wIwmgRiiTgQ5rCkUIPDb15w9C82CNG2Y8nBjDK6snWWAFWQJoCDGCk6cStADCA4WOgVwUYwiizM1p4wRCAoavEsU/2pAeXzqAit01Sv2woRU1VkOiSUoo6XQCGxo0XhkT3qSJSompI1E5uMl4BbJoSV08KHSgKQ6IrXWWpaWwlvGBCEEbphLwhnbSqjMQQJvJRp8dEvTRDI4cmGRHAUBnq/+cffyzpczC80af/8X4uQy1bNe7ACqijAAMYdXTNSKsMYDJyWzNmUQxhUt9KLQAMzTJZLxgGMJFlqElLNUtRC2iTwAOG2sTKAyPgypXBtAAYGtuIXjBGgDBahiIxhFn7XNdjWJJRQYwRAQwl4f35Y6/J/sC/7x3XGSrcSvYCuQMrYCAFGMAYaLPSPVUGMOneAR4/kQKcDyaRQvHvM4BZ1kdODhjxw2P2kiY5YGisfRWRECadAIbmo2UeGBovk71gaH1KV0USQEfh0tRinhrmg9EDhBFz0EmC3tBTnEFMap931JsBTOoasgVWgBWQrwADGPmaZW0PBjBZu/WGWjhDmOS3iwGMfgEMzezwaL9kAEPtD1RviDgMsXLAUKN4iXjp/v5166MeLCkARoCj+vihQGqEIdG4WnrB0Hh6SshL81E7HwyNkS05YfQMYWhuesoPQ/MxQmgSA5jkvy9wT1aAFUheAQYwyWuXdT0ZwGTdlhtywUqGIs3P+XHurBtTk364XUHhvpubZ8KGehs2t9hhtZoMqVGsSWdaIl6jJuEVQGS8D+3lq7xdJAIYAWuGB6ICGAFmatfClFgARswlhUS81F/pMCSyqXYyXhoj2Vww1DfVhLxkwyj5YBjCpP9jQK/5YfQOYhjApP/s8gxYgWxUgAFMNu56kmtmAJOkcNxNcwVSgTDBYBD9vT6cPO7CxS4P5uf9mJ8LwOcLwmQ2wWYzobTMjOJiC3budmB3Ww6Kii2ar1GtAbXwgkk2BwytWWoeGKMk4RWAYqIXe0obI7ZUDQAjYIrClZDIphQvGCkeMGRL6WS8ZNNoXjA0Z6OEIom5nunXzAuGxkt3daTQG1VvIUlCm6M9Ynp684bRK4hhAKPWNwm2ywqwAvEUYADD50OyAgxgJEvFDXWgQDIQhiDLM08u4NwZNybH/RgZ9oG8YPLyzcLbJRgE3O4AvF6gotKCmlorSkoteNOdBdi2I0cHq059Cgxg1MsBQ7uTaaWolQIwAkTJqIZE7dkLRvr7Xc1QJIYwwK7dkeF+0ndGnZYMYqTpygBGmk7cihVgBZRVgAGMsnpmtDUGMBm9vRm5ODn5YAi+/PRH8+i64EbHOTe83iCqqq2orrEgN9d8TR/ykJmZCQg4Mz0VEPc3Ndlx2x35aNuXa3gdGcAwgFl9iCkEia5k88AIuKJgNSQBsi4OSgYw1D7bvWBIA4IwaiTkDQEY+lOrfDA0FnvCxP+40XNYEs1cDzliGMAY/isLL4AVMKQCDGAMuW3pmTQDmPTozqMmr4BULxiCKr96fEGEHZ055UJBgRlbttphscTP8bKwEBCwpqjIgpbtdtzztkJsaTG2JwwDGOUADJ3cHUXNEQdYaQ8YMq5EJSSyk0oeGAFoNsROtCsVwJAdPYUh0XzSlQtGgI3z/YrlgmEIk/xniZSeegxJEqDqalgS/b8eQ5PCYUzr3si8V1J0T6WNEQGM3x/APz30I9nL/p///E5YLGa43W7ZfbkDK8AKKKsAAxhl9cxoawxgMnp7M3ZxUiDMxW4PfvKDOZw64RJJdrdus8NkkpZg1+kM4OxpNyqrrCIM6U//vFTkiTHqpUUiXsoBQ1eJY59smdKZA4Ym2zHXjeaCrRHzjlaGmhqcnLmSFQCG1qpEHhiyIzUMidrK8YIhDxi62pviV2NafSBPdA4mBWDE/ncMpJSMl2wQgKFLyYS8wp4KpanFfDUuT01j6sUTRsxFZ6Wqw8+znsOSQvMMecVoBWKMCGACgSAe/+Ersj87733g9aKQAAMY2dJxB1ZAcQUYwCguaeYaZACTuXub6StLFIr02PfncOiVJQz0edG2z5HQ82W1XtNTflzo8GDfAQfufmshWnc5DC2pnr1gGMCs/RfiaB4wdACjVUKi19ORiFfAFYOGIdHck/GCURLCKAVgaE5qhiKFIIyWoUgMYeR93DCIWdGLAYy8s8OtWQFWQBkFGMAoo2NWWGEAkxXbnJGLjOcFQyWmv/2NaRF+VF5uwYaNNtkaUAjTyeNuVFZbBYT5ww+UyLahpw7ZAmBI8/V5rWuk75rvQkP+9qhbkm4PGJrUvopV5alH+nGgam0SUCMCGAFqVEzGS/a19oKhMfVUlpohjDZPWyN4w5ASeg1LormpnSeGAYw27wUehRVgBSIVYADDJ0KyAgxgJEvFDXWoQCwI8+JvF/HsM4s4f8aN9v0O2HOSCx8aGvRheMiHvfsdeP+HSkQCX6Ne2QBgaG8uL3QYCsDQnA+P9qsKYGiMA3Xrox5dSsYbLxEvdUo1D4wcAENt5YQhUftkk/FS33R7wdAclPKE0aIqEs03mz1haP16hjBifgbID6MmiGEAY9RvKTxvVsDYCjCAMfb+aTp7BjCays2DqaBAtFCkH/9gDr9/YRFzswHs2Jl8Al2PO4ijh52iEtLb3lmUki0Vli7LZLYDGBIrlhdMNA8Y8QNh9hJaCrdE6Kx0Dhi1AQzZTzYRL/WVkgeG2u2rj5+HhTxgRLuGxPlaCMDQJacktWivcS4YGjMVLxjqr0ZCXgF1MigfjIAKJ/vEmdjVqo/S0HqHMOEgRs/eMGqAGAYwsr4acGNWgBVQSAEGMAoJmQ1mGMBkwy5n/hpXQ5hHvjOLV36/BK8nKCofJXtRGNKrLzmxc48D976tEHv3G7cktVaJeNVMwkv72Lt4HjW5u2NuaSwPGOqQbgAjYMh4H9rLV4UbqegBozaAIftS8sCIdhLDkKgte8Ek+9TSJh8MzS4dnjB6ATACcOg4OW/o9BjFG0ZJEMMAJvlnB/dkBViB5BVgAJO8dlnXkwFM1m15Ri54dSjSo9+dxcu/W4LHHUDLtuQ9YKgywWsvO7GrbRnAkCeMkS+1vWCoEhIDmOUTcmyiF3tKG9ccFzUBDA12oHZtqJGaHjB6AjA0F/aCWQYwdKnlBUO2qTKS1gBGQI+TfbrxgrkGORjEKPqxmGqOGCMCGCpD/c+f+L5sHR/6wru5DLVs1bgDK6COAgxg1NE1I60ygMnIbc3KRYV7wfz0R3P43fOLmJryY9fu5KsXuVwBHD/iEp4vb3+gSJSkNvLFACZ6Il61QpDEj+BVEEYqgKG+0SohxUrCS+2TqYQk+sXJA0P3jRCGRPPkXDArTye1IUw6SlNfAx4MYZL+GDJCtaTQ4pIFMUYEMKIM9SMvyd7Xe9/7Bi5DLVs17sAKqKMAAxh1dM1Iq753zvMAACAASURBVAxgMnJbs3ZRIQjz2itL+PWTCzh5zIU97Q7k5ZmT0qSv1ysgzp42B/74I6UoKbUkZUcvnRjAaAdgaM+jecGkCmAEmBkewIHqtbkw4gEY6pdMIl4pAEasVUI5atFO5TAkGsOoXjAC2G2Nniw5mWeIFqWpaV5ae8LoLR/MNTBkAE+Ya3O9mqhX7/lhaL5yQQwDmGSeFtyHFWAFUlWAAUyqCmZRfwYwWbTZWbDUUCiS33sA3/jaNE6fcCG/wITGTfLzwNC/SB074hIlrK97fR7uf1eR4RXUM4AhcSty2xJqbJQcMHoCMDQXo4YhCaDSmDhxb+jgGNULhuZvtIS8Ys5Xw50YwiyfQCPkhVkNYejvmQRiGMAk/BjlBqwAK6CCAgxgVBA1U00ygMnUnc3edYW8YH7583m89LslXOr2oG2v/FLUoyM+9FzxYt+BXLz9XUVo3iwf4uhtF/QKYEinwaUzhgUwNP/jUz3YVbIpYsv14gGjBIAhG4nKUVObRNWQBJjKMC8YWtPJjgFFKiKRLaW9YIRNlaoihSCM1gBGwA6dVUa6BjYM5AkjdDRI2eqQvok8YhjA6O2bB8+HFcgOBRjAZMc+K7JKBjCKyMhGdKRAyAtmcrwdj35nFufPuuHzBUUJaavVJGmmc7N+nD/rQcMmm0ji+6E/KxFx1ka/1AYwpI/aiXj16AGjBIAR8KJiVXWkkX4cqFobaiQ3BCkRgBH3U8wDI8CKDsKQaB7JeMGIfp2D2Nss3dsm/HlAAIYuvZWlpjllaiiSniGMmJtBQYwRvGFI31gghgGM0b+p8PxZAWMqwADGmPuWllkzgEmL7DyoygqEvGBeenEJL724iHNn3AgGIUpSx8sHQ2WnJ8b9uNTtRd16KzY12/GuPyxGTa1V5RlrZ15tCMMAZmUvpXrAUI/DCpWiJltyKyGlA8AI4NQgDXZoUZI6VQBD/ZXwgiE7HIok73moV08YI0MYmrtRQQwDGHnvH27NCrACyijAAEYZHXVh5d/+7d/wN3/zN2IuGzduRE9Pj6LzYgCjqJxsTCcKhLxgcuwH8ezTizh21InuTg+mJ/0ikS4BldIyM0ymZa8WrzeIsVEfRkf8cLuDaGi0idwvVHq6scn4oUfh28IAZvuaU5pqFSQymEoIklIARoCUwcGYAEbAmbroSV6V8oARYKU+MVhRMwxJwJTuQdnJeFOFMEp6wdBclA5FUjMMSYCjNOWDEaBDp+FIRoQwYs4GC0uiOYc8Yv7+y39kqNLM9D2cqyDp5MsjT4MVSEEBBjApiKenrr29vdixYwcWFxcZwOhpY3guhlAg5AVDXi2HXnXild85MT/vx8iwD+NjfuERY7FA/On3Aw6HCdW1VlRVW1BaZsW9bytAbZ3NEGuVM0kGMNkJYASc6R9IGsBQ/0TlqKmNWmFIZFvtZLypAhjqr1cvGJqb2qFIIQiTjnwwIQizq3VtyJ6c56NabY0WjhTSwUhlq0NzNlppZgIwfn8An/2r78k+fkaDTbIXyB1YAQMpwADGQJsVb6p33nknnnzySRw8eBCHDh1iD5gM2VdehnYKhCAMjTg/58eZU26cPunCzLQfi4tB+P1BkBOMzWZCfoEZ6zfYsGevA5tb7JLzxWi3GmVGYgCTGoARP7JnrmBHUXPEhqjhAUMDHNYoDwx5wNC1f13sMshKAxgaT60wJAFT2Asm4owSgKFLTU+YdHrBMIRR5jMimhUjgRgjAhjhAfOdF2VvoNHCrWQvkDuwAgZSgAGMgTYr1lS///3v4z3veQ/uv/9+4QXzmc98hgFMBuwrL0FbBcIBTGhkgi5XLnsxNemHyxmAxWIS3i8UclRVnTm5XmIpzQAmvQCG9uXIeB/ay1cl3I2SA0ZLACPmpVAiXgFWVAhDIrtaecHQWKkk5E01GS+Nr3QuGLLJEEbbz6CoMONEL3bt1qenTjx1jBKWxAAm/WecZ8AKZKMCDGAMvuvT09PYtm0blpaW0NHRgf/8z/9kAGPwPeXpp0+BaBAmfbNJ/8gMYBjAxDqFUgAM9Y1XjpruqxGGRHaTScYroE1T4pw0qzVJpSIS2eJQpGVvm3SEI+k5H0zonBk1JInmr3dvGAYw6f+ewTNgBbJRAQYwBt/1Bx98EA8//DC+/OUv4+Mf/zj+8R//kQGMwfeUp58+BUIJeR0516VvEjoamQAMXfkq6ZHpVZDEj+sUQpCov9oeMDRGtEpIYmwd5YERsObyoG7DkGh+qXjBUP9UPWHIC4YupRPyCpvbYoebKfHIonCkdAAYAQl0nJQ3EyCMnkEMAxgl3r1sgxVgBeQqwABGrmI6av/CCy/g5ptvRltbG44cOQKLxcIARkf7w1MxpgLsBRO5b2p6wTCAWdE6WhlqtQGMsB+jEpIUAENtlMgDQ3aUDkMim+wFo8wzWKuEvDRbhjCx98zInjDhEIb+Xy9lqxnAKPOMYCusACsgTwEGMPL00k1rl8uF3bt34+LFi3jttdewf/9+MTf2gNHNFvFEDKoAe8EwgCEFuua70JCvbQgSjbuntDFiA9T0gEkFwIi+CuSBITtywpAErGmQFiYkF8CQ7VSS8VL/TPSCoXUxhNHHB5rRIUw4iNEDhGEAo49zzbNgBbJNAQYwBt3xhx56CJ/97Gfx0Y9+FF/72teurSIVAEPl7eJdHo8HdrsVI5MPG1Q1njYrIE0B9oJZ0Yk9YNaemY65bjQXbI24cXb2EloKt6xpLDUESYCIiV5JAIbaHo6SiFduFSS1AQzZV7IaktBIZhgS9ZGbjFf04VwwEWdZi4S8NGA6Q5EEHOBwJGkfkgq00kN+GCMCGL8vgM9+7Nuyd+Dvv/ogLFYz3G637L7cgRVgBZRVgAGMsnrGtPa5z30On/rUp5Ia7cYbbwSFG4Wus2fPYu/evSgvL8eFCxdQXFx87R4DmKQk5k6sQIQC7AXDACaWBwwpYzQAI4DN8AAOVK+tppJsCJKANwk8YOQAGGqrhzAkmofRvWAEyDjfr2guGLKphReMXiDMrlZ9Vx7KBE8YAbyO9lz7sEmHR4wRAYwoQ/3t38r+1nbvg7fAbDYxgJGtHHdgBZRXgAGM8ppGtagUgAkEArj++utF2NGjjz6Kd7/73RHjpQJgEkmx7CETYA+YRELx/YxQgL1glreRPWDWHme9AhgBWkb6caBq7Y/HZACMgCwJEvFSm1TzwJANNcOQyH42esHQupVMyKsVhCEvGLo4H0z8j9JMgTDhIEZrCMMAJiO+rvEiWAHDKcAARqMtCwaD8Pv9SY1mMplEgl26/uu//gsf/OAHccMNN+DJJ59cY4/Ckv7lX/4F9fX1OHfunLhP4MRmsyU1dngnBjApS8gGDKQAAxj1AQyNkEwi3sGlM6jIbUt4mnoXz6Mmd3fMdpcXOrA+rzXqfSN6wCQLYKhfMpWQqF8iLxgKQaJLqXLUZEtOGBK1z8ZcMLRutbxgyLYWVZFoHIYwCR9zIBCza7e+PXYSr2K5hdZhSQxgpO4Mt2MFWAElFWAAo6SaGtgKebjIGepLX/oS/uqv/kpOl6htGcCkLCEbMJgCDGHU9YBJN4Ch8WNBGLkAhmxFywOjZQ6YZACMgChJVkKSAmCojdQ8MNRWT2FINB+j54IRsGSrsiWksykUifTTeziSABcnesWnayaAGC3DkhjAGOxLGU+XFcgQBRjAGGwjGcAYbMN4uoZWgAEMA5hoBzhaCJIcAENtj0/1YFfJpgjzqSbhNTKAoblLDUMSbWUk4xV6XxyUFYZEfTgXTPTHt5YJeWkG6fKCEWDDAEl5Q7uUSRBGaH81P4yaYUkMYAz9FY0nzwoYVgEGMIbduugT5xwwGbahvJy0KsDJeI0NYOjwqBGGxABm5W1JIUh0aZkHRksAQ2MZ2QuG5s+hSKl9jDCESU2/VHqr7Q3DACaV3eG+rAArkKwCDGCSVU6n/RjA6HRjeFqGVYC9YNSFMGrmgMkEAENrODLeh/byjRHvoWhlqKmB3CS8wn6cECRxP04iXnE/TdWQaOx9DeskPVvIA0bAlEZp7UNG0+UFQ+Of7BhA25bUw4cIwNDFoUiSjkrURgRhjBCKRJPPNE8YsSaVqiUxgEn+PcE9WQFWIHkFGMAkr50uezKA0eW28KQMrAADGAYwq4+vlh4wSgIYsiW3FLWWAIbGypQwJFrLic5B7G2WB3zCzxoBGLqUgjBqABgBdralDokSfURQZaR0hiIJCMAQJtE2qX5f6bAkIwIYvy+Az/7pN2Rr/fff/DAsVjOXoZatHHdgBZRXgAGM8pqm1SIDmLTKz4NnqALZDmH0VopaahUkOo5GD0FSCsCQnXilqAWcqY3+Q1orDxgtAAyNwV4wyj2oOR+McloqbSkTPWEECFPQG8aIACYQCOLxb/5a9nG590/fCLPZxABGtnLcgRVQXgEGMMprmrEWuQpSxm4tLyyBAgxgjiE/5zpVzkkyIUg0EakQJlMBjAAqo/3YV7EqNGlkOdzkQNXasrSxAIyAPClWQiIbifLAiDYb4nuEkAcMXVKqIQlgo+NkvDS/TPeCoTVqWRWJxkunJ4yR8sEIWJFB1ZFWfwAp4Q3DAEaVj3U2ygqwAgkUYADDR0SyAgxgJEvFDTNMgWxPxqs3DxgGMMtvMK3ywCTygBEAR6E8MAKqXBnUFYARIKV7MOlkvNQ/1VAkJcKQaB5GTsgr5q+TUCSaC+eE0ccHfSoghgGMPvaQZ8EKZJsCDGCybcdTWC8DmBTE466GVyCbvWAYwEQeXz3kgNESwAjAolEi3mQADPWRmoyX2mpZklrAG53lgqE5qZEPRqtcMDT/dHrB0PhGygcj5pvBnjBifUmGJTGAMfxXM14AK2BIBRjAGHLb0jNpBjDp0Z1H1YcCDGA4BCl0EhnArH1PSvWAoZ6JwpCSgTBaABiaV7IlqalvJnvB0Po4FEkfn1WxZpHpECYcxOzcUy9pMxjASJKJG7ECrIDCCjCAUVjQTDbHACaTd5fXlkiBbA5DYg+YyNMhB8BQz5MzV7CjqDnCyPGpHuwq2bTm2B2b6MWe0saI16OVoaYGWoUg0VhKeMCQnaO9g7oAMAKmaFSSmsbKFi8YWqtWnjB68IKh9RolFEkAigz3hAk9OKWGJTGASfTNh++zAqyAGgowgFFD1Qy1yQAmQzeWlyVZgWz1gmEAkxkARkCb4YGYpajpfrKVkASkSWMeGBqfvWAkP8pUyQVDo2eTF4wAGif7hOgMYaSfPa1aSglLYgCT+m6Mj4/jc5/7HJ544gkMDAwgPz8fe/fuxUc/+lHcd999SQ/g9Xrx7//+73jkkUfQ3d0t7GzZsgXvfe978bGPfQw2my1p29yRFUi3Agxg0r0DBhqfAYyBNounqooCDGCUlzXTqyCRYnrxgIkHYARASaESktIAhuypnYyXxjCSF4w4Sx0DUCIhLyXjpUvpXDAMYZR/RqphMVs8YQQku5ofJlpYkhEBjN8XwD9/4N9lH4uH/usvYLGaFS1Dfe7cOdxyyy0YGxsT8yksLMTS0hL8fr/4+1/+5V/iK1/5iuy5Liws4LbbbsOhQ4dEX4fDIf50uVziz9e97nX4zW9+I2APX6yAERVgAGPEXUvTnBnApEl4HlY3CmRrGBJ7wEQeQaOGINEqki1FTX31HoZEc2QvGOmPS7UgDHnBCLizbb30ySTZUg9VkcSP/JN9hvKCEXPOknAksdYYSXqNCGACgSB+/h9PyX7H3PeRN8NsNikGYNxuN7Zv347Lly+jtbUV//3f/43du3cLAPOlL30Jn/70pxEMBvHtb38bH/zgB2XNl7xcHn30UZSUlIj+IU+an//853jwwQcxMzOD973vffjOd74jyy43ZgX0ogADGL3shAHmwQDGAJvEU1RdgWz0gmEAwwBGKoChdvvXxf/hLScPjIAq9eskva+PXR6UDWDIcLZ6wdDa1ShLTXa1CkUSa9BBaWqGMJLeomlvtNobhgFM8ltC4UHk4ZKXl4eOjg7U10cmPqYwoa997Wuoq6tDT0+P5JCh06dPY8+ePQLe/PjHP8bb3/72iEnSa+94xztgMplAbQn+8MUKGE0BBjBG27E0zpcBTBrF56F1o0A2AhgSXy0IwyFIkUdbiSS8ZPHwSD8OVG1Y875R0wNGQBoF88CQPTXDkMh+siWpBbhpkgaGwjeBkvHSlWpFJLKh51Akrb1gSA9Oypvcx2Q2ecIIUBbmDfM/v/geRb1CktsB6b3oe7hePGAOHjyIw4cP40Mf+hC+9a1vrVlEX18fGhoaBEh55pln8MY3vlHSQj/5yU/iC1/4Apqbm9HV1SVAS/hF9igXzMWLF/GpT30Kn/3sZyXZ5UasgJ4UYACjp93Q+VwYwOh8g3h6mijAYUjKyswAJjGAEWBjvA/t5RsjGseqgpQKgKG+WiTipXHUKEdNdtUOQ6IxTnQPJgVgRN/OwZQADNlQKhcM2VLTC4bsaxWKxBAm+WdztkGYEIh56AvvhsWibF6U5HchcU+9ABjK0VJUVCTgymOPPYb7778/6uR37NiB8+fP4xOf+AQ+//nPJ14gfS7s34+jR4/iz//8z/HVr341ah+69/Wvfx0HDhy4lidGknFuxAroRAEGMDrZCCNMgwGMEXaJ56iFAtnoBcMeMCsnS+scMLEAjAAto/3YVxEJZpIFMGKcBIl4BaCpix1iJMUDhmzoJQyJ5sJeMMZPyCtAEocipfTxl40Q5t4HXs8eMEmcmiNHjgj4QReFH23dujWqlQceeAA/+tGPcNddd+EXv/hFwpEI6FAi38XFRfzHf/wH/uzP/ixqH7pHVZao7dzcXEK73IAV0JsCDGD0tiM6ng8DGB1vDk9NUwUYwCgnN3vARGoZLQRJSQAj4EyMUtSJAIy43z+QEMBQO6XywJAtuWFI1Ie9YOS9R9kLRp5eiVobMSkvrSnbIAwDmEQnOfp9Kjl97733ipuzs7PCGyba9dd//df48pe/jPb2duHVkugimFJcXCyaPf7447jnnnuidqF7ocS88/PzKCgoSGSa77MCulKAAYyutkPfkwnFYdrtVn1PlGfHCqiuQACAWfVR9DRAEEGYEBmLrcT8yC6Ssiut3/K8Y+9VEIGY92Otmf6VbnVcOmkRiPF6tPaxbMR9PYpOMecYZ7/i7WWifY41v/CzoFSbkE0p9uSOv7o9/T3ansY743LntdqWEv2TmXesNQUDQZjMKrzHye6qPA5KPDui2UhVU6XmRfNQcm+UmpcUO0aeu5T1hbfx+bzir6E1y+2vdXv6h1CPxwOr1SZ76NBa7XZ73L5U3SjRRRWKqFIRXV6vF1Zr9N8FDz30kMjRQjlbOjs7E5nF0NAQ1q1bzq1FZaapFHW0i+6FcspQn9ra2oS2uQEroCcFGMDoaTd0PpcVABP/4R1rGfShQVeih7/OZUj79FhHZbaAdUxdR9YwdQ3JAuvIOiqjQOpW+CymriG/p5XRUAsdQ+fdKACGQm5Cc05GZeqb6Ds4A5hklOU+rIA8BRjAyNOLW6egwHIIEyDl4Z7CMBnflXVUZotZx9R1ZA1T15AssI6sozIKpG6Fz2LqGvJ7WhkNWUfldFTaEocgKa0o28s2BRjAZNuOp3G9/MVOGfFZR9ZRGQVSt8JnMXUN+UeGMhqyjsroyO9p1lEZBZSxwudRGR2VtkL5XKhaEV1KJ+GlfDJUZYmT8Cq9a2xPTwowgNHTbmT4XPiDVJkNZh1ZR2UUSN0Kn8XUNWRwoIyGrKMyOvJ7mnVURgFlrPB5VEZHpa38n/buBdjG6g/j+C/llvyjhhoh1egiSacbSjiDLrqQihC51MjlqJFC021qxHQRUmPUcSSVcFSKjIo0NVGaSalQLjEup4ZK7qf6z7OmfQ7OZd/ezbvX/q6ZRpz3ffe7Pmvts/f7vOtd6+BlqGfNmmWdO3cu9SUaN25sK1eujGsZaq2upFWWBg0aZBMmTCj1uPrZxIkTWYY66IbleEdMgADmiFHzQnyQBtMHcMQxGIHkj0JfTN6Q4CAYQxyDceQ9jWMwAsEchf4YjGMqjtKsWTNbunSp9evXzyZPnlziJTZt2mT169d3ExwvWLCgaNLcaOcyfPhwGzNmjDVs2NBWr15d6uaa1HfNmjU2YsQIN8kvBYF0EyCASbcWS+Pz5YM0mMbDEcdgBJI/Cn0xeUOCg2AMcQzGkfc0jsEIBHMU+mMwjqk4ikan5OTkWLVq1dxjSPXq1TvkZfQzbVOnTh1bv369VawY28pNK1assKZNm7rgJj8/3zp16nTIcfVvGnGjhUG0rUbZUBBINwECmHRrsTQ+Xz5Ig2k8HHEMRiD5o9AXkzckOAjGEMdgHHlP4xiMQDBHoT8G45iKo2hBjUaNGtnatWutSZMmNm3aNPfnnj17bNy4cTZy5EgXouTm5lrv3r0POYUGDRrYhg0brFevXpaXl1fi9LTEtZa6rlmzpk2ZMsVuvPFGt40m/9WxduzYYT179rSpU6emomocE4GUCxDApJyYF4gI8EEaTF/AEcdgBJI/Cn0xeUOCg2AMcQzGkfc0jsEIBHMU+mMwjqk6iuZ3yc7OtoKCAvcSmkB39+7dVlhY6P4+ePBgGz9+fImXjxbAaI6Ztm3bukecVKpWrerCnL1797q/N2/e3BYuXOhG31AQSEcBAph0bLU0PWc+SINpOBxxDEYg+aPQF5M3JDgIxhDHYBx5T+MYjEAwR6E/BuOYyqMofBk9erTNnTvXNm7c6EKRrKwsGzhwoHXs2LHUl44WwGinAwcOuEeYpk+fXjQXjOZ+6dGjh5ugN9ZHmlJZd46NQKICBDCJyrEfAggggAACCCCAAAIIIIAAAgggEKMAAUyMUGyGAAIIIIAAAggggAACCCCAAAIIJCpAAJOoHPshgAACCCCAAAIIIIAAAggggAACMQoQwMQIxWYIIIAAAggggAACCCCAAAIIIIBAogIEMInKsR8CCCCAAAIIIIAAAggggAACCCAQowABTIxQbIYAAggggAACCCCAAAIIIIAAAggkKkAAk6gc+yGAAAIIIIAAAggggAACCCCAAAIxChDAxAjFZggggAACCCCAAAIIIIAAAggggECiAgQwicqxHwIIIIAAAggggAACCCCAAAIIIBCjAAFMjFBsdmQFnnvuORs6dKh70dNPP93Wr19/ZE8gjV7t999/t2nTptmXX35p33zzjW3bts22b99uVatWtYYNG9o111xjgwcPtlNOOSWNanXkT3Xv3r02f/58++CDD2zp0qX2888/2759+6xWrVp22WWXWZ8+feyGG2448ieWhq/4xRdf2LJly+yrr75y/61atcr++ecf69Wrl+Xl5aVhjVJzyr/++quNHj3a3n33Xdu0aZNVq1bNsrKybMCAAdaxY8fUvKhHR925c6ctWrSoqJ8tX77cCgoKXA31761bt/aotqmpivpdfn6+ffTRR+7zY8uWLVaxYkX3udu2bVsbMmSInXnmmal5cY+Oqr6n97E+h3/66SfXD3fv3m0nn3yyNW3a1Lp3727dunWzChUqeFTr1Ffl77//dp+/X3/9tXuxRx991B577LHUvzCvgAACCKRQgAAmhbgcOjGBDRs22Pnnn2+7du0igImBUBe7zZs3L9ryuOOOsxNOOMEUzERKjRo1bM6cOVyQlOPZrl07+/DDD4u2qFSpklWuXNl0kRcpXbt2dWGXjCllC6i//fHHHyU2IIApJlm5cqVlZ2cXBQbVq1d3F2y64FDJycmxcePG0c3KEXj77betU6dOpW5BABO962zcuNEFLf/++2/Rxv/73/9sz549duDAAfdvCvIVmt52223RD5jBW/Tv398mTZpUJKAwVSXyPUb/36pVKxfSyJgSm8Azzzxjw4YNK9qYACY2N7ZCAIFwCxDAhLt9MvLsOnToYPPmzbPLL7/cjURgBEz53WDNmjX2yiuvWMuWLe3iiy+22rVru7tsGr2xcOFCN5Jo9erV7k6ctq1Zs2ZG9qtoldbd8s2bN9tdd91l119/vZ133nluF90hfvLJJ4u+XD/wwAM2ZsyYaIfL6J+feuqpVr9+fdcfL7nkEnv11VdtyZIljID5r1fovdmoUSNbu3atNW7c2F577TW78MILXQAzduxYe/jhh91FcW5urvXu3Tuj+1J5lVcAc/fddxf1M5lqlIEKAUz0bqORpRrdct1119mdd95pbdq0cZ8ThYWF9vnnn7uRkytWrHCBs0Z4NGnSJPpBM3QLhVQ7duxwn8PnnHOOKVBV0UgYfT7rPa1wVSMp9XdKdAH1T92M03cahYIa3UsAE92NLRBAIPwCBDDhb6OMOsM33njDfYG+5ZZb3Afv448/TgCTZA9Q6HL22We7o+hC+I477kjyiH7u/tlnn1mzZs3s2GOPLbWCGr0hv+OPP95+++03d2eYUrqALjQOdtT7efbs2QQw/3FNmDDBjXBRX/rhhx9cWHVwGTRokE2cONHq1KnjHr/UIyGUkgKH97O//vqr6MKXACZ6j9EoNfUvhX+lFV3wXnDBBaZH5RQEKhCkJCbw0EMP2ahRo6xKlSpudKBGWFLKF9Dj0wsWLLD33nvPBg4caBodTQBDr0EAAR8ECGB8aEVP6qC7Rxp1oLvAuiiZPHkyAUxAbXvSSSe5u3Oab+LBBx8M6KiZdZjFixe7O8Qqeh79oosuyiyAJGpLAHMonkb3aY6cvn372ssvv1xC9pdffrEGDRq4UTC6AGnfvn0S+pmzKwFM8G2tkTFTp051o180RwwlMQE9enTTTTe5nbdu3cqcbFEYp0+fbj169LCbb77Zhff6fUgAk1jfYy8EEAifAAFM+NokY89IQ3OnTJlizz//vJv4TxOtMQIm+e7w448/Fj1O89Zbb9mtt96a/EEz8AjfffeduxusoovnSy+9NAMVEqsyAUyxm0ICzQGhcGXmzJlutF9pRSMAv//+e+ORt9j7HAFM7FaxYjuoMwAADfFJREFUbnn//ffbs88+6x6Z07xFlMQEhg8f7h5d1fxsf/75px1zzDGJHSgD9tIiAueee6577Eg34+rWrUsAkwHtThURyCQBAphMau0Q1zUyukCjCrSKgB5fIIBJvME0NF/Dxz/55BPT0Od169a5LzD6MqMh0JT4BfRIiB4N0eMgGpJ/4oknxn+QDN2DAKa44fX7Tat6qOj9qAuN0kqXLl1MganmI5o7d26G9pz4qk0AE59XLFsraNZKZpqEd8aMGbHswjb/CWg0r0azaeJ2jT7VSnAKYp566imMyhGIjLrSapj33Xef25IRMHQZBBDwSYAAxqfWTNO6aPlfPYOupRu1ok9kZAEBTPwNqqVr33nnnRI7ajJUfXk+66yz4j8oe5gu7PR4nCbk1YXxm2++iUocAgQwxVgHP4qguSDKWhFFFx4aDaj3ri6AKdEFCGCiG8WzxcGrTM2fP980JwelfAGtPljaRPeayFjzmGhVH1bRK9tQy6Fr+XMt3a3fe5G5xAhgeOchgIBPAgQwPrVmmtYlMjndgAED3MSTkUIAE3+DaqJEfVHev3+/m/NFRavQaDnbFi1axH9A9nACuvurx0U06kXzIGhlLkrsAgQwxVavv/66de/e3f2Dlvot62Is8ntRE2ivWrUqduwM3pIAJrjG13wb+uzQhOOau0RhDCW6gB4vikx6r4BVN5j0uNG9997rHifUCnGU0gVkpcd8tTqcVuHSXFmRQgBDr0EAAZ8ECGB8as0jVBcNpR0xYkRCr9aqVSvT40aRonk1srKy3NKXmqvk4Mc6fA5ggjQsqyH05U+rB4wcOdINg9bku3pdn8qRcNTyoVqGWl+iZ82a5SYF9K2k2pEAhgDmSLxnCGCCUdYojquuusq+/fZbt0y1HpvTRO6U+AQ0z5OCLN0AeeGFF9z3GwVZV155ZXwHypCt9b1Sn0X9+/e3l1566ZBaE8BkSCegmghkiAABTIY0dJDVDOpiTc9DX3HFFe6xI90Vvv322w85TQKY0lvt8BArWttu3LjRLem9c+dO9+UvshJDtP3S4edB9cWy6qrh4sOGDXM/fvHFF+2ee+5JB5a4zzHVjgQwxU3CI0hxd8+YdyCAiZmqzA1l2K5dO/e5rGXQP/30UxfCUJITUAijUTCnnXaarV692i1BTykWWLFihXvcMnIzrkaNGgQwdBAEEPBWgADG26ZNXcV0V0eTvCZSNIog8kxvXl6e6ZGZli1b2rx580ocbtSoUW6yuvr16xetvlC5cmU3CWq6l6AMY3WITGrn24SeqXTUHcvBgwc74qefftq0GoivJZWOMiOAKe45mtcgMs8Vk/AG+44igEnOU5PGXnvttbZkyRKrVauWm8Rdc19RkhfQ4zUKFfbt2+dGUnbu3Dn5g3p0hNatW7v+NmnSJOvWrVuJmukmkkbyapSMRvWqaEUpCgIIIJCOAgQw6dhqnpxzZIRLPNUZO3asu4tEiU8gMp+EvkxraVtK+QL6EqjRLgomtBT6I488AlkSAgQwxXgHL0Nd3oVY48aNXfDMMtSxdzwCmNitDt9SS/4qoP/444/dJLL6UxOhUoIT0OiXzZs3u+Wo9b6mFAtEHjGKx0Tz3B0+Uiae/dkWAQQQOFoCBDBHS57XLVpmOh4KAph4tIq31eNdWrlHd96XLVuW2EEyZK8pU6ZY3759Xfji47w5R6MZCWAOVW/WrJktXbrU+vXrZ5MnTy7RJFptSyP/1AcXLFhg7du3PxrNlnavSQCTWJNpVIYeTVVf06pcCxcuLFoqPbEjstfhAuqbmkdHE29rsQEtOkAhgKEPIIBAZgoQwGRmu6dFrX2eAybIBigsLCx3WUs9b647mbrDOXToULcMJqV0genTp1vPnj1N8xMNGTLELQNMSV6AAOZQwwkTJlhOTo5Vq1bN9BhSvXr1DtlAP9M2moNj/fr1Xjx2mXwvin4EApjoRodvoUBAE4trwnb1R4UwmpuNEruAHsmuUKGCm6i9rPLEE08UjaTU5MYa4UaJXYBJeGO3YksEEAi/AAFM+NsoY8+QACa2pu/YsaN7Tl8XuVrCsVKlSm7H7du325w5c0yPH23bts3d2dTjRxoGTSkpkJ+fb126dDEFWnr8SJPuUhIT0IWw5jyIlD59+tjcuXOta9euLliIlCpVqmTkc/wacdCoUSO33GqTJk1s2rRp7k+FpJqsU3McaPRLbm6umyeLUraAlkmOlF27dpku1FQ04fjBQYJWoPFh/rAg+4KCA42OnDlzplWtWtXef/99a9OmTZAvkRHHUkiqEEujWjRaTaPXVPQe1uqOGvGizxP9XdvNnj07I1yCrCQBTJCaHAsBBI62AAHM0W4BXr9MAQKY2DpHZPI6ba0JjnWhoS/WWoY6UnQnXV+yW7RoEdtBM3ArrfSxbt06V/PatWuXezdTF8kKayilC0QmfY7m06tXL9Nk3JlYNL9Ldna2FRQUuOorINUkqAoAVTQB9Pjx4zORJq46lzfq4OADLVq0yPS7klIsoMl2taqeiia4jzafxtatW+ErRUABzBlnnFH0k0iwfHgQ3aFDB5sxY4YbaUSJT4AAJj4vtkYAgXALEMCEu30y+uwIYGJr/uXLl7vh41pBQAGCLuh0EaflHDUiRhMr6oK4evXqsR0wQ7eKZxJAzRMjU0rpAgQwsfUMvVe1BLhGB2m5eF2YZWVl2cCBA00j2yjRBQhgohuVtcXixYvjGvGiERyUkgL79+93I640cbHmWNuyZYtpZJZCrbp167r5dLp3725XX301fAkKEMAkCMduCCAQSgECmFA2CyeFAAIIIIAAAggggAACCCCAAAI+CRDA+NSa1AUBBBBAAAEEEEAAAQQQQAABBEIpQAATymbhpBBAAAEEEEAAAQQQQAABBBBAwCcBAhifWpO6IIAAAggggAACCCCAAAIIIIBAKAUIYELZLJwUAggggAACCCCAAAIIIIAAAgj4JEAA41NrUhcEEEAAAQQQQAABBBBAAAEEEAilAAFMKJuFk0IAAQQQQAABBBBAAAEEEEAAAZ8ECGB8ak3qggACCCCAAAIIIIAAAggggAACoRQggAlls3BSCCCAAAIIIIAAAggggAACCCDgkwABjE+tSV0QQAABBBBAAAEEEEAAAQQQQCCUAgQwoWwWTgoBBBBAAAEEEEAAAQQQQAABBHwSIIDxqTWpCwIIIIAAAggggAACCCCAAAIIhFKAACaUzcJJIYAAAggggAACCCCAAAIIIICATwIEMD61JnVBAAEEEEAAAQQQQAABBBBAAIFQChDAhLJZOCkEEEAAAQQQQAABBBBAAAEEEPBJgADGp9akLggggAACCCCAAAIIIIAAAgggEEoBAphQNgsnhQACCCCAAAIIIIAAAggggAACPgkQwPjUmtQFAQQQQAABBBBAAAEEEEAAAQRCKUAAE8pm4aQQQAABBBBAAAEEEEAAAQQQQMAnAQIYn1qTuiCAAAIIIIAAAggggAACCCCAQCgFCGBC2SycFAIIIIAAAggggAACCCCAAAII+CRAAONTa1IXBBBAAAEEEEAAAQQQQAABBBAIpQABTCibhZNCAAEEEEAAAQQQQAABBBBAAAGfBAhgfGpN6oIAAggggAACCCCAAAIIIIAAAqEUIIAJZbNwUggggAACCCCAAAIIIIAAAggg4JMAAYxPrUldEEAAAQQQQAABBBBAAAEEEEAglAIEMKFsFk4KAQQQQAABBBBAAAEEEEAAAQR8EiCA8ak1qQsCCCCAAAIIIIAAAggggAACCIRSgAAmlM3CSSGAAAIIIIAAAggggAACCCCAgE8CBDA+tSZ1QQABBBBAAAEEEEAAAQQQQACBUAoQwISyWTgpBBBAAAEEEEAAAQQQQAABBBDwSYAAxqfWpC4IIIAAAggggAACCCCAAAIIIBBKAQKYUDYLJ4UAAggggAACCCCAAAIIIIAAAj4JEMD41JrUBQEEEEAAAQQQQAABBBBAAAEEQilAABPKZuGkEEAAAQQQQAABBBBAAAEEEEDAJwECGJ9ak7oggAACCCCAAAIIIIAAAggggEAoBQhgQtksnBQCCCCAAAIIIIAAAggggAACCPgkQADjU2tSFwQQQAABBBBAAAEEEEAAAQQQCKUAAUwom4WTQgABBBBAAAEEEEAAAQQQQAABnwQIYHxqTeqCAAIIIIAAAggggAACCCCAAAKhFCCACWWzcFIIIIAAAggggAACCCCAAAIIIOCTAAGMT61JXRBAAAEEEEAAAQQQQAABBBBAIJQCBDChbBZOCgEEEEAAAQQQQAABBBBAAAEEfBIggPGpNakLAggggAACCCCAAAIIIIAAAgiEUoAAJpTNwkkhgAACCCCAAAIIIIAAAggggIBPAgQwPrUmdUEAAQQQQAABBBBAAAEEEEAAgVAKEMCEslk4KQQQQAABBBBAAAEEEEAAAQQQ8EmAAMan1qQuCCCAAAIIIIAAAggggAACCCAQSgECmFA2CyeFAAIIIIAAAggggAACCCCAAAI+CRDA+NSa1AUBBBBAAAEEEEAAAQQQQAABBEIpQAATymbhpBBAAAEEEEAAAQQQQAABBBBAwCcBAhifWpO6IIAAAggggAACCCCAAAIIIIBAKAUIYELZLJwUAggggAACCCCAAAIIIIAAAgj4JEAA41NrUhcEEEAAAQQQQAABBBBAAAEEEAilAAFMKJuFk0IAAQQQQAABBBBAAAEEEEAAAZ8ECGB8ak3qggACCCCAAAIIIIAAAggggAACoRT4P8vngUQBVUzvAAAAAElFTkSuQmCC\" width=\"640\">"
@@ -2737,21 +1195,18 @@
"<IPython.core.display.HTML object>"
]
},
- "metadata": {},
- "output_type": "display_data"
+ "metadata": {}
}
],
- "source": [
- "res = train_and_plot(30,net,lr=0.01)"
- ]
- },
- {
- "cell_type": "markdown",
"metadata": {
+ "scrolled": false,
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Важное замечание\n",
"\n",
@@ -2762,15 +1217,15 @@
"Сложная многослойная модель\n",
"* низкий training loss - почти идеально приближает обучающую выборку (но может переобучиться)\n",
"* validation loss >> training loss и может возрастать - плохо обобщает данные"
- ]
- },
- {
- "cell_type": "markdown",
+ ],
"metadata": {
"slideshow": {
"slide_type": "slide"
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
"source": [
"## Выводы\n",
"\n",
@@ -2778,7 +1233,26 @@
"* Более сложные модели (high capacity) могут переобучиться (надо следить за validation error)\n",
"* Для более сложных моделей необходимо иметь больше данных\n",
"* \"bias-variance trade-off\" - необходимо достичь компромисса между недообучением и переобучением (обучением на распознавание нерелевантного шума во входных данных)"
- ]
+ ],
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Credits\r\n",
+ "\r\n",
+ "This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners), and has been prepared by [Dmitry Soshnikov](http://soshnikov.com). It is inspired by Neural Network Workshop at Microsoft Research Cambridge. Some code and illustrative materials are taken from presentations by [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/) and [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/), and from [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop) repository."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {}
}
],
"metadata": {
@@ -2806,4 +1280,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
-}
+}
\ No newline at end of file
diff --git a/3-NeuralNetworks/04-OwnFramework/README.md b/3-NeuralNetworks/04-OwnFramework/README.md
new file mode 100644
index 0000000..ecd5f42
--- /dev/null
+++ b/3-NeuralNetworks/04-OwnFramework/README.md
@@ -0,0 +1,52 @@
+# Introduction to Neural Networks. Multi-Layered Perceptron
+
+In the previous section, we have learnt about simplest neural network model - one-layered perceptron. It was a liner two-class classification model.
+
+In this section we will extend this model into more flexible framework, allowing us to:
+
+* perform **multi-class classification** in addition to two-class
+* solve **regression problems** in addition to classification
+* separate classes that are not linearly separable
+
+We will also develop our own modular framework in Python that will allows us to construct different neural network architectures.
+
+## Formalization of Machine Learning
+
+Let's start with formalizing the Machine Learning problem. Suppose we have a training dataset **X** with labels **Y**, and we need to build a model *f* that will make most accurate predictions. The quality of predictions is measured by **Loss function** ℒ. The following loss functions are often used:
+
+* For regression problem, when we need to predict a number, we can use **absolute error** ∑<sub>i</sub>|f(x<sup>(i)</sup>)-y<sup>(i)</sub>|, or **squared error** ∑<sub>i</sub>(f(x<sup>(i)</sup>)-y<sup>(i)</sub>)<sup>2</sup>
+* For classification, we use **0-1 loss** (which is essentially the same as **accuracy** of the model), or **logistic loss**.
+
+For one-level perceptron, function *f* was defined as a linear function *f(x)=wx+b* (here *w* is the weight matrix, *x* is the vector if input features, and *b* is bias vector). For different neural network architectures, this function can take more complex form.
+
+> In the case of classification, it is often desirable to get probabilities of corresponding classes as network output. To convert arbitrary numbers to probabilities (eg. to normalize the output), we often use **softmax** function σ, for the function *f* becomes *f=σ(wx+b)*
+
+In the definition of *f* above, *w* and *b* are called **parameters** θ=*w,b*. Given the dataset <**X**,**Y**>, we can compute an overall error on the whole dataset as a function of parameters θ.
+
+**The goal of neural network training is to minimize the error by varying parameters θ**
+
+## Gradient Descent Optimization
+
+There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case call **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
+
+* Initialize parameters by some random values w<sup>(0)</sup>, b<sup>(0)</sup>
+* Repeat the following step many times:
+ - w<sup>(i+1)</sup> = w<sup>(i)</sup>-η∂ℒ/∂w
+ - b<sup>(i+1)</sup> = b<sup>(i)</sup>-η∂ℒ/∂b
+
+During training, the optimization steps are supposed to be calculated considering the whole dataset (remember that loss is calculated as a sum through all training samples). However, in real life we take small portions of the dataset called **minibatches**, and calculate gradients based on a subset of data. Because subset is taken randomly each time, such method is called **stochastic gradient descent** (SGD).
+
+## Multi-Layered Perceptrons and Back Propagation
+
+One-layer network, as we have seen above, is capable of classifying linearly separable classes. To build reacher model, we can combine several layers of the network. Mathematically it would just mean that the function *f* would have more complex form, such as *f(x) = σ(w<sub>1</sub>α(w<sub>2</sub>x+b<sub>2</sub>)+b<sub>1</sub>)*, where α is a **non-linear activation function**, and θ=<*w<sub>1</sub>,b<sub>1</sub>,w<sub>2</sub>,b<sub>2</sub>*> are parameters.
+
+The gradient descent algorithm would remain the same, but it would be more difficult to calculate gradients. Given the chain differentiation rule, we can calculate derivatives as
+
+* ∂ℒ/∂w<sub>1</sub> = (∂ℒ/∂σ)(∂σ/∂w<sub>1</sub>)
+* ∂ℒ/∂w<sub>2</sub> = (∂ℒ/∂σ)(∂σ/∂α)(∂α/∂z)
+
+
+
+## [Proceed to Notebook](OwnFramework.ipynb)
+
+To see how we can use perceptron to solve some toy as well as real-life problems, and to continue learning - go to [OwnFramework](OwnFramework.ipynb) notebook.
diff --git a/README.md b/README.md
index 6a5713d..d468d21 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking [Ge
<tr><td>3</td><td>Perceptron</td>
<td><a href="3-NeuralNetworks/03-Perceptron/README.md">Text</a>
<td colspan="2"><a href="3-NeuralNetworks/03-Perceptron/Perceptron.ipynb">Notebook</a></td><td></td></tr>
-<tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td>Text</td><td colspan="2"><a href="3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td></td></tr>
+<tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td><a href="3-NeuralNetworks/04-OwnFramework/README.md">Text</a></td><td colspan="2"><a href="3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td></td></tr>
<tr><td>5</td>
<td>Intro to Frameworks (PyTorch/Tensorflow)</td>
<td><a href="3-NeuralNetworks/05-Frameworks/README.md">Text</a></td>
--
GitLab