{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In supervised learning, regression is the modelling and prediction of continuous variables. One of the basic techniques of regression is Linear Regression.\n", "\n", "Linear regression is usually among the first few topics in learning predictive modeling. Here, the dependent variable (or target variable) is continuous while the independent variables (or predictors) can be continuous or discrete. The nature of regression line is linear, meaning it can be graphically represented as a line on the graph.\n", "\n", "Linear Regression establishes a relationship between dependent variable (Y) and one or more independent variables (X) using a best fit straight line.\n", "\n", "It is represented by an equation Y=a*X + b, where b is intercept, a is slope of the line. This equation can be used to predict the value of target variable based on given predictor variable(s).\n", "\n", "In Machine Learning using python, We can use couple of libraries for performing Linear Regression. One of them is statsmodels. This library has basic modules to perform Ordinary Least Square technique of Linear Regression. Alternatively, we could use another library known as sklearn (Scikit-Learn). This provides python implementations of various Machine Learning algorithms. \n", "\n", "In this demonstration notebook we would see an example of both a statsmodels based algorithm as well as a sklearn based model.\n", "\n", "### Importing the libraries\n", "\n", "Import the required libraries and initialize the code. Here we will be using:\n", "\n", "Pandas, Numpy for data manipulation and arrays
\n", "Matplotlib, Seaborn, Pyplot and BQplot for visualizations
\n", "Sklearn and Statsmodels for Machine Learning algorithms and statistics.
\n", "

\n", "And some additional helpers...." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "# %load ../standard_import.txt\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import axes3d\n", "import seaborn as sns\n", "\n", "from sklearn.preprocessing import scale\n", "import sklearn.linear_model as skl_lm\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", "%matplotlib inline\n", "plt.style.use('seaborn-white')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Datasets\n", "\n", "The example datasets on this tutorial come from the book:\n", " An Introduction to Statistical Learning with Applications in R\n", "\n", "We will be using the Auto.csv and Advertising.csv datasets. \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 200 entries, 0 to 199\n", "Data columns (total 4 columns):\n", "TV 200 non-null float64\n", "Radio 200 non-null float64\n", "Newspaper 200 non-null float64\n", "Sales 200 non-null float64\n", "dtypes: float64(4)\n", "memory usage: 6.3 KB\n" ] }, { "data": { "text/html": [ "
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TVRadioNewspaperSales
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" ], "text/plain": [ " TV Radio Newspaper Sales\n", "0 230.1 37.8 69.2 22.1\n", "1 44.5 39.3 45.1 10.4\n", "2 17.2 45.9 69.3 9.3\n", "3 151.5 41.3 58.5 18.5\n", "4 180.8 10.8 58.4 12.9" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "advertising = pd.read_csv('https://raw.githubusercontent.com/colaberry/DSin100days/master/data/Advertising.csv', usecols=[1,2,3,4])\n", "advertising.info()\n", "advertising.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 392 entries, 0 to 396\n", "Data columns (total 9 columns):\n", "mpg 392 non-null float64\n", "cylinders 392 non-null int64\n", "displacement 392 non-null float64\n", "horsepower 392 non-null float64\n", "weight 392 non-null int64\n", "acceleration 392 non-null float64\n", "year 392 non-null int64\n", "origin 392 non-null int64\n", "name 392 non-null object\n", "dtypes: float64(4), int64(4), object(1)\n", "memory usage: 30.6+ KB\n" ] }, { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130.0350412.0701chevrolet chevelle malibu
115.08350.0165.0369311.5701buick skylark 320
218.08318.0150.0343611.0701plymouth satellite
316.08304.0150.0343312.0701amc rebel sst
417.08302.0140.0344910.5701ford torino
\n", "
" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration year \\\n", "0 18.0 8 307.0 130.0 3504 12.0 70 \n", "1 15.0 8 350.0 165.0 3693 11.5 70 \n", "2 18.0 8 318.0 150.0 3436 11.0 70 \n", "3 16.0 8 304.0 150.0 3433 12.0 70 \n", "4 17.0 8 302.0 140.0 3449 10.5 70 \n", "\n", " origin name \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auto = pd.read_csv('https://raw.githubusercontent.com/colaberry/DSin100days/master/data/Auto.csv', na_values='?').dropna()\n", "auto.info()\n", "auto.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Least squares fit\n", "\n", "#### Visualizations using Seaborn" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(advertising.TV, advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})\n", "plt.xlim(-10,310)\n", "plt.ylim(ymin=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualizations using plotly" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "# Create a trace\n", "trace = go.Scatter(\n", " x = advertising.TV,\n", " y = advertising.Sales,\n", " mode = 'markers'\n", ")\n", "\n", "data = [trace]\n", "\n", "# Plot and embed in ipython notebook!\n", "py.iplot(data, filename='basic-scatter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizations with BQPlot\n", "\n", "Let us do an interactive visualization of the scatterplot to see how TV Advertising affects the Sales, with some interactions.\n", "Select some outliers visually and print them." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "44aad585a2324f8e8562580f9d5575b2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale(), tick_format='0.2f')]…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from __future__ import print_function\n", "from bqplot import *\n", "import numpy as np\n", "import pandas as pd\n", "from ipywidgets import Layout\n", "\n", "x_sc = LinearScale()\n", "y_sc = LinearScale()\n", "\n", "x_data = np.arange(20)\n", "y_data = np.random.randn(20)\n", "\n", "scatter_chart = Scatter(x=advertising.TV, y=advertising.Sales, scales= {'x': x_sc, 'y': y_sc}, colors=['dodgerblue'],\n", " interactions={'click': 'select'},\n", " selected_style={'opacity': 1.0, 'fill': 'DarkOrange', 'stroke': 'Red'},\n", " unselected_style={'opacity': 0.5})\n", "\n", "ax_x = Axis(scale=x_sc)\n", "ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n", "\n", "Figure(marks=[scatter_chart], axes=[ax_x, ax_y])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#If you need to find out the visually selected points and print them as array, you could use .selected attribute.\n", "scatter_chart.selected" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regression coefficients - RSS\n", "Note that the text in the ISLR book describes the coefficients based on uncentered data, whereas the plot shows the model based on centered data. The latter is visually more appealing for explaining the concept of a minimum RSS. I think that, in order not to confuse the reader, the values on the axis of the B0 coefficients have been changed to correspond with the text. The axes on the plots below are unaltered." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14.0225\n", "[ 0.04753664]\n" ] } ], "source": [ "# Regression coefficients (Ordinary Least Squares)\n", "regr = skl_lm.LinearRegression()\n", "\n", "X = scale(advertising.TV, with_mean=True, with_std=False).reshape(-1,1)\n", "y = advertising.Sales\n", "\n", "regr.fit(X,y)\n", "print(regr.intercept_)\n", "print(regr.coef_)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.1025305831313514" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create grid coordinates for plotting\n", "B0 = np.linspace(regr.intercept_-2, regr.intercept_+2, 50)\n", "B1 = np.linspace(regr.coef_-0.02, regr.coef_+0.02, 50)\n", "xx, yy = np.meshgrid(B0, B1, indexing='xy')\n", "Z = np.zeros((B0.size,B1.size))\n", "\n", "# Calculate Z-values (RSS) based on grid of coefficients\n", "for (i,j),v in np.ndenumerate(Z):\n", " Z[i,j] =((y - (xx[i,j]+X.ravel()*yy[i,j]))**2).sum()/1000\n", "\n", "# Minimized RSS\n", "min_RSS = r'$\\beta_0$, $\\beta_1$ for minimized RSS'\n", "min_rss = np.sum((regr.intercept_+regr.coef_*X - y.values.reshape(-1,1))**2)/1000\n", "min_rss" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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DJCXBnDngcX1vXcnJyTz44INYrVYWLlzI8OHDG/343NxcKioqWLp06bWGfVmJ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WR5mHwkEsGyrFJZZLFEcqXyGFeKIIJDrqPxuWqhcnKoKKJm9qUQUP0OKYVeX7euLfs9KD/v4Ny0\n6evrK+UyJhIJDMPA7XZXkMbystSWgtjCEwJ779iPbdrs2r1j3donF/GTe8c5EEpx1vGb2Na3NpmH\n0rYdcnjTTWtKDu10msxPfkrmllvI/e73UCj1dD3tabiOPgpt1y5cu45A2/UU1A1Dh/zeCiHI3nEH\nic99AaW9Dc+LXkT72y8tPWYeOEDuj38kduVH0I8+GtG2su+PQxJvqCSJX/kyYol31JuBnjY3Fz53\nG1/5zV6++4f9vPHUxmR7rfGU3TsYu3eSx27bxwmva+VzttBCNXK5HBMTE9i2zfbt20ubwXIUHRub\ntTHeNNTH9Ey4EB/hbHrjiRT3Pfg47V4VVVWWpRpWr3kpBDFrmDy6P0Q2myM0O4umqgwNDeFyaRVk\nSsA8dlW+ia+/oIa8rwSJRNPqq4LVJao2ctEqYvlLbSnRVIWFGieEqO266jzorLjatKZY7vj4QYck\n+r2NczKXisX2MRaJi643twd9JRRJy7YZmYgRT83d9FZVUXFdiaINbRlURcFGVhgfCSGwbBtN1eht\nohmjbdu4XC78fn/JrMq2bTKZDNlslmg0ytTUFKqqYts29913H6qq4vUe2hosy+Lyyy9nZGQEIQRX\nXHEFu3bNVQXddtttfOELX0DTNM455xxe/epXH9J85WgRxCcJHv/tfoQiOOIF29d6KQ2RN22++tt9\neFwqb1jDDXjyc59fU3Jo3H8/qe98j8yPf4xMJgFwHXUU3pe/DO/LX4a2bVvT57RCISJvuxRz9CA9\nX7sR1675pYlKTw/+884j94c/4j7pBJRVuDtWTRJjV1xJ11X/ueLz1sIFz93GD+85yPf+sJ/zTtxK\nR5O//JuJbSdsRve52PPbEZ5z0THr/sZQCy2sFqSUJaWur6+vlF1XD83KFYRiL2IPB8amcWnOZjIW\ni7F/f5StWzby3Oc885BNSZay3lze5JGRIKHZCPF4nO7ubvx+f2GM+cpNdR+YbdvIGkSpHEVTmVoK\nkJSQSiXJ5nLouo5HdyOU+W6jakERq57fsorlhPWPUQjmzb2YHkpVCMwGAzv5iPOPpzj+46NhnrI1\ngNeztO+JpZKwYh9je3u7sy7bJpvNkslkiMfj2LbN/v37K5QuTdOW9Z3QbIJo25LxUIbOQK7E/2qV\nLle/f0UiqCpKRXmpIpwrUQKBzuYRxFomNfViTSYnJ7nrrrsYHx/nmmuu4fbbb+f444/nta997ZIJ\n4+233w7A97//ff70pz9x7bXXct111wFOWfzVV1/NTTfdhNfr5fzzz2f37t309vY24YhbJjVPCiRD\nKWYeCzH0jAF83es73uIXD0wyE89y1vGb6G1ffZUIIHf33cQ/9V+oQ0P0fPMbq0oOzYMHmX3jvxB8\n6ctJf+c7KB0dtL/zHQz8/g76b/0Z7W99y4qQQwDh8+F50YvQNm8m8dnPkfrmt7AL5BQKH5BtbViT\nk1gTE7hPPHFF1lFzbR4PPTd8Ce2pTyH1jW+S+fnPV23ucvjcGhectI20YfHDuw+uyRoWC82tsfU5\nm0iG0oT2hNd6OS200HQsZ6OazWbZt28fiUSCHTt20Nvbu+A4xTLTZmHThj5cLhXTNJFSkkyl2Lhx\nA5YU3P/wXvL5RYYC1sFicxCzuTz3//0gowfHyWSzDA4N0tbWVnk+yhXEAmksh5SgKfUJrao68mEt\nnmVZFqFQkHjcifxIp9NMzUwTCs0QjUbJpNNYZiHmwFnAvDEkoIrGW1m15vu78LWzGI7tkN+5+e0y\nYmNaNo8eCJPOrp7JGzjXq8/no6enh8GC62x5zMPo6CgjIyNMTk4SjUbJ5XKLvqHQzNgMx600TCpj\nlSJRnBsSlc9zIi6qCGJxPeXkUHFIo7QlHX437iaaMS4mB7FYlrp9+3Y++clPYpomH/zgB3nlK19J\nNBollUo1fH0tvPCFL+QjH/kIABMTExVRO3v37mXLli10dnai6zrHH388d99995LnqIeWgvgkwP4/\njQGw/cS1jYpYCLYt+fadI6iK4Pznbl2bNUQiRN7yNpCS7i98DjWwOgY5diZD8gtfJHHddZDNoR9/\nPO3veDvuF5yMaILF9WKg+Hy0Xfx62i5+Pca995L+nx/Bj29G6erCs/tU0HVQVTI/vxXXETtRN21a\nlXUVIbxeAtd9keAZLyPyfy7DddRRaKu8BoCzn7WZr/9uHz/44wFec9JWPOvYEXj7iZt5/Dcj7Pvj\nKH1H9Kz1clpoYc1g2zbBYJBwOMzAwADd3d2LJpjNJoiaquLVNUb2TaEogv6+flwuDdOyiCdS/PWB\nx3j6U7bT5l/eDd3FKIiJVJZ7HthHaDZMTyCAv62j5vmwpXQ26LasSdCgvrskzBmLSClRVFEiUOlM\nhvDsLH6/n0BPD7ZlUZQC82YeI2eQyWaJxeIIAbpbdyIq7ALbLFuLaVnOe1TnmOutziG3oq4xjr3I\nOAxHyRI1VdIiSTxic/ei++GanYOoKEpT+xiboSDmTYvHRyMkUlkQToCJolDzPRSiMppEVRQs2wZR\nfr7nrnnLtgl0NE9gkFIuWTktlp9u2LCBE088kZe+9KXLnl/TNN773vfyy1/+ks9+9rOl3yeTyZJq\nDOD3+0mW3dQ/VLQUxCcBRu4aBdY/Qfz9Y0H2B1OcfvQQA00sDVgspJRELnsP1vg47e96J+4TTliV\nOdM338LMyaeQuPbTKF1ddH/us/T++H/w7D511chhNfRjj6XryivwXXA+2q4jEF5vaS3Z//0lnt27\nEdrq319y7dpF50euRMZiRN7yNmQDx9aVgt+jcc6zNxNJGfz03vFVn38p2HTcBjRdZeQPo0+IwOQW\nWlgJpNNp9u7dSzabZefOnUs2f2kmQczlcuzbt492n4utWzeXjFdMyyr9O5PN8be/72VyenZZcxSP\nrd7f/PhUmNv/8ACxWILBgUGnRK7B6Siuy2kDqxF0b8uSAlQOtVr5kc7mfXZ2lnA4TG9vH93d3Sjl\nCqAQuFw6nZ3tBAIBBgcHGOjvw617MC2LnJFjamqacDhMMpkkXzBrq/d2CuaXJ5bDsuU885u5tdd9\nWQVkYZx615Rl2Tw2GiaRytV8vBZWsiWg2MfY2dnJ4OAg27dvZ9u2bXR2dmLbNqFQiL179zI6Okow\nGCSZTGJZjpLbjBLTTM7k7/tnSWfzSArXl5ROv+cC51xRC+QQKq654vulqAJVge5FupcuBiXzviUc\nd7YQzdUsF9OPfexj/OIXv+CDH/wg6XQagLa2tgpVMpVKVRDGQ0WLIB7myCZyTD40Q/+uXvw969du\nV0rJN3+3D4CLnrc2fZKpb3yT7M9vRT/pRNovfduKz5d/+O+E/vHVRP7tzVihEG1vfQsDd/wW36vO\nXjf9YkIIXEfMOclaU1Mo/f24nnn0mq3Jd/5r8L7yFRj33EPiU/+1Jmt49Ylb0TWF7/5h/7rORXS5\nNTYdt4HYRILoeHytl9NCC03FQp+Ttm0zOTnJ6OgofX19bNmyBZdr6X3Diy3ZbAQpJcFgkH379tHV\n1cXOncNs3jjobIqd+smK3kPLtHh07yj3P7yHTHbxxKK43loqopE3ueeBvfzhLw/j9rgZGBzA5XIh\nZb0yzMJaLAshnLzCujea6qiP5chkssxMTyOBoaEhPJ4ylafq9ZYlS2Wlqqbh8/to87fhdrvp6+vD\n6/FgmiaRaJTJqSlmZkKkk0mnXLKMECqKaMg56j62zP48tQZRBoekPjYaYXaJOYmHisUSumIfY/Hv\nZHh4mN7eXhRFqchjDIfDWJa15DzGIhJpg0f2z2IYc4RTEYWy0jrj2YXv2Oq/w+JRCSFKkSNCCtq8\nLlxNvIG9mPLSahRJ3KG6mP7oRz/iS1/6EgBer5PpWFzL8PAwBw4cIBqNYhgG99xzD8ce2zxDulaJ\n6WGOg3+ZQNqSrc9Z/VK8peC+AxEeHIvx/Kf0sb1/9Z1L8w89TOzKj6B0dxP43GdXVLmT2Syxq64m\n9bWvg23jefGL6PzQB9G2r28DIQB1cJDAZz+9pmsQQtD1sWsw7rufxOc+j/t5z8P9D89b1TX0tLl5\n2TEb+Z97DnL7w9O86KihVZ1/Kdj2nE3s/+NBDvx5rBV30cKTBslkkvHxcXw+Hzt37kQ7hA2joiiH\npMBns1nGx8dRFIXh4eGSs+SmDX24dVdpo2tajrMpOM6OqqoQiSa4575H2DDYy5aNA7hcizuOcoJo\n2zb7D07ywEOPYyseBgYG0F2V5Y62tIB633vCUflEfbJlWzZKmetnubuplBCNRUklk/T29OBZhFGH\nxMk5tO357qWqpuLVfHgL6oxt2xg5A8PIkUynMU2z5PTp9brRVB1Rh7g5xz7fDbVhZmMtiDnXTU1V\n5q0ZHDI0Mh7FMCyGGji0r0SUxFJR7GMszwPN5XKlnsXR0dFSHmOxNNXtdjdc90w4xcHpRMXfkoLT\np1qvTLncJEkImEsdmTvfgtI9FgC62pZnwFMPy3k/MhnnRsChKogvfvGLef/738+FF16IaZp84AMf\n4Je//CXpdJrzzjuP973vfVxyySVIKTnnnHMYaGLsWIsgHubY/2en/3DbOieI375zPwAX/cPqkySZ\nyRB+81sgl6PrS9ejDq3cZt8KBpn9p38mf//f0IaH6bziw3hOPXXF5lsr2Ok09uws2uaVKWtWOjoI\nfPHzBM96FeG3XUr/r3+1av2iRVz4vG38+C8H+dbvR3jhMwbX/Au9HrY8ayMIOHD3GMe86ulrvZwW\nWlhRWJbF1NQUiUSCDRs2VJg6LBfLLTGVUhIKhQiFQjVzDTVVZaC3C9O20TTF6YnTHPMamFNILdvm\n4MQM08Ewge4Ohvp76Gj3L+i8mk5nCUXi7Nk3yvT0DG3tbQwO9CKU+SqqbUlUrb4xi2XbNWMuKuZU\nBFhFUur8P5/PEwyFHKOUoSE0Va2baVcNJ9ReqZyzxssURcHj9eDxegiognzexjByGIZBLBbHMPK4\nNA3d7UbXddw1oh+cuUShVLR2L1wjqGUliKZlo2pKhWFNOcaDCQzTZPNAZ83S3GaiWWSzaMDi9/ux\nbZuhoaGKPsZoNFq3j9G2JaNTcULRdMWYiiLISxu5wHUMcl6cihODUfl7VQh0XcWjqk12Wl2+gnio\nBNHn8/GZz3ym7uO7d+9m9+7dhzRHPbQI4mEMaUvG/zZJW6+P7i3rVzmYiGS487EgR23u4ugtq7vJ\nB0h8/guYe/bgv/j1eF/0whWbxxwbI/SaC7BGRvCeey7d11yFOMSMnPUIaVmE3/BG8n9/hN7vfQfX\nU5+6IvPoxx5Lx2XvJn71NcSvvpruT3x8Reaph00BH6ccOcBtD0/z0FiMZ2zuWtX5Fwtvp4f+Xb1M\nPxLCSOfRfes3mqOFFpaC6k1gPB5nYmKC9vZ2jjjiiEOOiyhiOQSxnmpYjcH+AMFoBiEU8qZVKqcD\nME0LIZRSWLuRN5kJRZmaCePSNDo7/Ph9Hty6C1XVsG2bvGmSSmd5ZM8YB6fjxGIx8vk8ff19uN1u\nbNNA1et8BkibRp1H6gKbZMuSIJx+MNOUJJIJYtEYXV1dBXdU53lL2bsLUZ5xt/DzTVOiqgK3x4Pf\n58MsuFrmzTy5XI50Kk00GkUgEIogk06j625UTUXajgi1ULzFwmt2zHhKZio1EI7lSGVnGd7UPc9t\nsxn94lJKMtkcgubnFhZLmGvlMRbjNUKhELlcDqG4CMZNTKni1vUS0VIUgS0lti3LVMH5sKV0+g6r\nWzmkc0OiMisReru8pGOJJRO6RrBte9kK4qHmIK4lWj2IhzHCo1FyCYMNR69fdQMoGX2cefzqq5z5\nvftIfPE6lMFBOt77npWbZ88eQme9CmtkhLa3voXuT//XYUkOAYSq4jntNOyZGYLnnIvx13tXbK62\nf/0XtCOfSvq73yN3z19WbJ56KF6zN/91bNXnXgo2HDWAtCVTjwTXeikttNB0mKbJwYMHmZqaYtOm\nTWzcuLFp5BCW1oNY7DUcGRmhu7ubbdu2NQwrV1WFjYO9hRI7WSh3nLt3r6lVQeyZYlMAACAASURB\nVOyF7/K8aRIKxzgwNs2+0Un+/vh+Ht07yr4DE0wHw6SzOaanp9FUjaHBIdxup98vn88BtY/FsuyG\n4Q+ysL5GUIXAMExmgjOkUikGBwdob2+rIHeWVdvUZt5YilP2uaT9i5g7R8WyRaE4ZKa9vZ2e3h6G\nBgfx+X0oQpDJZgkGg0xPTTEbDpPJpMmb5uIyLspQq0TStKy6pNqWNulMnr/vCxFLzu8xPdQ923Qw\nwsHxmUMaoxYaKZKaptHW1lbqY+zq3UAoKcgYFqlUiqnpaaZnZojHYySTqcINEOoy/2JZcC0l1pKy\n4loVBaG5t9Pb9BLdWhmICyGdTuNyuRr+7a93tAjiYYyJB6YB2PCM5tUkNxuWLbnl3nF8bpXTnr66\n65RSEvv3y8Ew6LryCpS2lel9NB54gNDZ52BNTtLx7x+g8/3vW9eEvRlou+Riuj99LTKeIHTea8j+\n7vcrMo9wuei6+ioAYu//wKq7mj57Rw+DXR5++eAUqdzqO6ouFsXPgIkHptZ4JS200DxIKYlGo+zZ\nsweXy8XOnTtpW4HP8cX2IBYzFpPJJMPDw4tyS1UUhb6eTtwuHZfmkFpZRuAcEyxR8XP1kOWmLLZt\nMxuaxTQtent76Q50z+u/k5ZRdz1CqX+cEjEvGL4aqXSKyYlJ3G43gwODdU2Byo+hkQMpwFLFINOW\naIXSxnqTuzQNTXOVnFJ7envR3XqBMM7Md0ptVFpL7YeFEA5JrDpp5XmSpmXz+GiYg1PxRZXdLgaW\nZXNgbIpQOEY+bzadLC0E07LZNx7lwGQc3e2mo72D3h6HmPf2BBCKUiLm4XAE0zRJJpMY+XzFeVQQ\nhRNbOafjVFpZpqwIhe4OL6qqLKsktBGWW2LaLAfTtUKLIB7GeCIQxD/tDTETz3L6UUN49dWteM7c\nfAu53/0O96mn4HnpGSsyR+5PfyL0j+dhRyJ0fewa2t/8bysyz3qE7x/PJXDD9UjTZPZ1/0Tm1ltX\nZB73s5+N7zXnkX/4Ycf4ZxWhKIJXHLuRjGHx6wfXL/kaPLIfRRVMFj4TWmjhcEA8HicYDLJlyxYG\nBwebuiksx0IlpktVDctR3Lxv3TyAwAmVtyyJWgifl1KiaeXHJeepo04On0o2m2VychKhCFwuDb0O\nOTNyWer5d1qmXdcwxImMqH0cTnxFiNnZCINDA3R1djYsCbUs2dA4RghKDtFWoT8QUd/MpBpSLjbF\n0JlM0zT8fj+9PT0MDAwxMDDfKXU2NEsykcAwjEqn1AbHKQrmNWqZElzr+dPhFA+PhEhl8oesgI1P\nBckZJpYtCc5Gm35DutF44XiGB/cGCRfcWsufqmkKqqrR3tZOTyDA4MAAHR3tKIpCPm8SiUSYnJok\nNDtLPJEgm8vWNPsRVJaWOj2tNn3dTlXWSiiIyykxbRHEFtYlpC2ZfGia9n4/7QOr7wq6WNzyV6e8\n9BXHrW55qZ1KEbviCnC76froR1ZE0cv++jZCF1yIzGTo/sLn8b/2wqbPsd7hPeMMer7xdYSmEf6X\nN5H+75tWZJ6Of/8AoquL+Cc+iTXT/LKaRnjZMRsRAm5Zx5mILo9G3xG9BPeGMdL11YMWWngiobOz\nk+Hh4RXfiDUiiMtRDWuN3d/bjaZpuApksDwa0K7qv6r+WdqScHiWUChEIBAgEAigCKVuiaOUNtj5\nmuuRUtaNvCiW2laXh2YKxFRVVYY2bEBbZJRII2JVJITLhhB1IycavgyHOquqhs/no6uri/7+fgb6\n+/H5fVi2TSwadYhMKEQ8HsfIL/yZ6hiqFEpf6ywrmzN5ZP8s05Hs/J67RSKfNzk4MddKMDMbXdY4\n9VCPLOUMk8dGw+wbi2Kac2sv/tmoimMqUw6nFFjgcrno7u5ioL+fgf4B/H4/ioBoNMbU5BTBYJBY\nPE4mm8W27QoVs5ij6PW48Hv1ZYXaL4SWgtjCYYXZ/RFySYOhdaweRlIGdzwyw86BNo7ccOhOc0tB\n8oYvY0/P0P5vb0Lbtq3p42d+8lNmL74EgJ6v3ojvzFc2fY4nCjwnP5/e738P0dZG5B3vJPn1rzd9\nDjUQoOM9lyFTKRLXrm4Mx2CXlxOGe3ngYJSRmeSqzr0UbHhGoQ/x4VYfYguHB8ozwVYStQjioaiG\n5SiPo9iyqb9kamKac5tSpy9xTjW0pUQrlKPmcjkmJycxTYuNGzfOmWIUqvNEnfOTN+rn8Vm2VbOU\nsDpb0ZaScCTMbChEf28PXV3dhdBzFtVj2EhFtK3qNdXPGKw3tmXZpX7ERb+uGA0iK/skFVXF6/XS\n2dlJX38/g4ODtBfKmROJFIaRZ2ZmhlgsRiaTrXlDwbKKmX8NynilJBTN8dDI7LIyEw+MT1eQy0Qy\nTSbbvJuC1eTLtGwOTsd5cG+IeFUvpUCAKJjJ1DhmRVWQxecVoKoKfr/POc99fQwODdLR0YEQglQq\nRTA4zeTUNNFolHQ6g7QtbNtmIOCvWF+zCeJSx2sRxBbWLSYfclSUDUetX4J4+8PTWLbkpcdsXNWe\nPDseJ3n9l1ACAdre9K9NH9948CHCb7sU4fHQ+51v4zltZSyIn0jQjz+Ovv/33yj9/cT+/YNkf/vb\nps/hv+B81O3bSX33e5hjq2sa87JjNwDwvw9Mruq8S0Hxs2DioVaZaQuHB1bre6PapOZQVcNylJPP\nwf4eXC5XqRexnPxWl1balk0kEiEYDNLV3UVPbw96WUZiMR7ANM2KDXgRlmUhZO2+aWnLhr2Gti3J\n5w2mJiexTIuhoSHcHk/lGIus76y1Nk1RqFUgKllcD5ymiEI4nqhwQV0ISllvIDiktNokqPRcRcHt\n8dDd1UlXVxe67qKzsxOhKBWGLA6RSWOaBcYrZGFpja8X07QZGY/y95HZmiY2tZDJ5picnq34nZTN\nVRGLBMyyJVOzSR7YE2R6NlXzfVFU5/lyAUJcfioURRQUcuc1ihC43W462tvp6+1lcHAD3d2O2p7N\nZpiZmWF6eppMMkwkEiGbzTbtWMvXuNQbUYdDiWkr5uIwxczjIQAGn9q3xiupjzsfc5SMU45cXRKb\n/sH/RSaTtL/3PSjt7U0dW+ZyRN7+djAMAl/9Cu4TT2zq+E9kuI48kp5vfI3gK84k8q53M/DrX6J0\nNS8aQrhctF/6NqLvfBepb3yTzn//QNPGXgjPO6IPlyq48/Eg/3raEas271LQv6sXBAQfn134yS20\n0EIJRZOaomo4OzvLwMAA3d3dh0xSq1W5jQM97B+bQlMVTNN0FCfpqGGqomLZFoZhEAqFcLt1hoaG\nSj2JZsGFVFaNq2lOhEY18vksml67BcW2bRBVTrCiYO4Wi5FMxuns7Mbv96Np6rySSCnnZ9fVm0dV\nBOUFr/X6DKVkyWWjlu04wZqLCL4XQsxjtqZpO8exkIFMgci43W5od44/n89jGIaTExiLoSoKHo8b\nTdNxu13oLr2hK6xAkMoYPD4aps2nM9TbRmebu+7z9x+cqknMw5EEpmVVqNDLRd60mI3lGJudKfWI\n1oJjzCPrtboikc45lbJUSizE3A2A6nJUKPTAStBdLnSXC1VRkEh6OnQ62rRSHqOUkrGxsXl5jMvF\nE6HEdGxsjJe85CUMDw8DzppTqRRnnXUWl156Kbfeeis33HADpmkipeTMM8/kDW94AwATExNceeWV\njI+PI6VkeHiYD33oQy2CeLgitCeM7nfRMdhcAtQs5PIW94zMsq3Pz4bu1Yt7kLZN8uvfALcb3wr0\nBMY/+SnMRx7F/7qL8Jx6atPHf6JDP/po2t/xdhKf/BTRD36YwOfqB8AuB75XvoL4R/+T1He/R/u7\n3omySlEiPrfGM7d2c8++MLOJHD3t9b/E1wq6z0XXxg5Ce8NIu7FBRAsttDAHx0Qjz969e1FVtWGu\n4XLGLlcnN28aYO/oFIriODM6ikpRfYJYLEYinqA70E1HR3sFAZOF0lPTtBCIknJjWXaBNVbObeYN\nXLqNrFFMZtsSVavKqLcdcqiqKv39g+i6q+TUWUsJtG271rTzUE4FFFG7HLEIy5IlElwLAua93ipE\nZSykPtZ8vEDQi0R9/mvqrKMsJ7CtrQ0pHTU3n8+TzeZIJOJICV6vG01z4hBcLud8ykJ2oaJC8a1P\nph2i6NE1+gI+eju9Fe6o0XiSYChaeH3lMdlSEgxFGRroaXj8jZBIG8xGM4wcjACCzs767rSSwjXX\n4OaJoihIW5ZuZoBDiIulvdVluKqqFCRku/B6gWnbaIpgqK8T3aXS0dGBYRiMjY3R1dVVkcfodrtL\nhNHr9S4pBse2bTRtaXRpLRTE/v5+fvzjH5d+np6e5vTTT+d5z3seH/vYx/jhD39Id3c3qVSKiy66\niO3bt3PaaafxoQ99iLPOOouXv/zlAHzpS1/iwx/+cKvE9HCEkTaITsTp3RFYt5vA+w5EyOVtTtrZ\nu6rz5m7/Ddb+/fjOOhM1EGju2H/+M8nrrkfdtpWOD17e1LEPJ7S/7a24jj2GzA9/SOZnP2/q2MLj\nwX/hBcholMz//KipYy+Ek3Y6av1de0KrOu9S0DscwEjniU8l1nopLbRwyFiNElMpJYlEglQqRSAQ\nWHavYT1UExchBFs29ZUy4izbibXIG3nGx8YxcjmGhobw+/1ObqGo3MaVMuPEnBJnS4nmqr0hNvP1\nS/JEYV1SSpLJJJlsFl3XGRgYwOVyFUoyZU1y6LyuUO65AGxboqnFAPX6zyumGzZSEVVFzCOPjpFJ\n4zWIOgQQnOOoRXTLFa+FIIRjxuLz+QgEuhkYGKCvrw9dd2Oa+YLxzRSzs7MgJYZh1OxVzBomB6fi\n3P/YDHsORpiNZTBNi30HJh1jnjq1wRNTS/9eSmUMxmcSPLAnyKP7ZwlF01h2scdv/vNVVZRSKZQa\namw5SlmVhecoytz5rx5bUQSmZVeY+0icUuRApxe97NouloOW5zEODw/T29uLoihEo1FGRkbYv38/\nU1NTxGIxx5W2wVqXm4Po9/uX9JpmIxgMIqXEsqzCjQnnb93v93PNNdewc+dOAEKhEJnMXL/rhRde\nyIUXXthSEA9HhPZFQELfzuXfLVpp3FUogT3piNUtgU1+9asA+C9+fVPHtVMpIu94JwhB96evRXmC\n156vJISm0f3pa5k5/SVE3/s+9Gc/C7WvedeB/3UXkfjCF0l+9Wv4zn/NqvUpPfeIXj73v49y1+Mh\nXn7sxlWZc6no29nDnt/uJ7hnls5VNoZqoYUnGrLZLGNjYwgh8Hg8BJp8UxHm9zcCDG/dyIGDU2hC\nRUUQTyYIz4bp6uqiq6ujorRPUwX5slZCW9poqooiKrMb6/WB5Y0cqstDLUsK07JACGZnw+TzeXxe\nL263e+4zVZQEnbqw65CrakgpUUTt0sJ567Kd59Y6pHrio2U3LnlVFahRhVuxPrXKbEUVAqtQerwY\nlCtjQgg0TUXTfHNKk5Rkslmy2RyxWIxwOIxL13EXlEhdd5eMc2wpiSayzn+xODPBIF63C7/HhUtT\nyoiiLJSq5ojGknR11isplmRyeVIZ579YKlfhRjr/fFQel6Malt3oUAQ0eC9L50w6NwfKyXC1v4+j\nMs659xbfR6FCf6Byr1XLwVRRFHy+ufMspSSXy5HJZEilUoRCzn60XGEsv86XU2KayWTYuHF19wEz\nMzOceeaZ5HI5IpEIRx11FJ///Od5znOew2mnncYLX/hCjjzySE444QRe8YpXsHXrVgDe9a53cdll\nl/G5z32Ok046iZNPPpkzzjijRRAPRwT3OD1GvcPN/zJrFu7aE8KrqxyztXvV5szv2UvuN79Ff86z\n0Z/xjKaOHf/IR7EOjNL2ljfjfvazmzr24QjXzp10vv/9xD78H0Tf+z4CN36laUROHRrC+9IzyNzy\nE4w//hH3SSc1ZdyFsK3Pz2Cnhz/vDWFadumO+HpC37Bz0yi4J8zOk7ev8WpaaOHQsZjSwaWiutfQ\n7XYzObkyBlSKomBZ85nJ9s1DPLr3INFoBE1VGRwaRNM0zKpySdOyqRZrJFBdh2nZdh2CJMEyQPVQ\nDaenK4LP305PTw/R6JzZiao6sQWaqjTsRZNSotVxsSyHLR3CZS5BkatWqBqpgOAQjHrEcjHTWrZE\nUxTMGg6l9VTUyvUt/ASf10tcjTI4OIBl2RiGQS6XI5FIkM+HcblcJbLodutICVPTQYy8STZnEoln\nSi6euqaQz+ewLQtLyfC3R0Y5YscW7ELZad60yZsWRt4mZxTuMizE5gsEzC5TEmspnQ3/Jste4yiH\nZQ8pouJmhqoohZxPBUs617pl2QhF0O734HVXlrouhswVb/h4PB66u7tL5b+ZTIZMJkMsFsM0TTwe\nD16vl3y+diRMI6yFi2mxxNS2ba655hoeffRRTiz4YFxxxRW8+c1v5ve//z2///3vefWrX80nP/lJ\nXvziF3PyySdzxx138Kc//Ym77rqLT3ziE/z0pz9tlZgejgjtDQPrV0GciKQ5EEpx/PYAurZ6l2Cq\nEK/QdvHFTR03e/vtpL71bbQjn0rH/3lXU8c+nOG/+PXoz30u2V/8L+n/+9/NHfsS5z1OfvXrTR23\nEYQQnHREL4msyUPjsVWbdyno3dHtGNXsbRnVtNBCLWSzWfbu3Us6nS45lKqq2nQSWkQtgiulpLPd\nw8TkBO1tbfT09uItuYTKijJCKSWaWnmv37IsNFWpYfZSe+efy2UpZ5NSSsLhMOFwmO7uAN3dXaVN\nd7XSV4ssVaOoIi74PGRjmlXWX1cr9mKhe3Ll/W7OzwU1DxadO2jadsnZdEHjmios4lQ5SpwtUXD6\nST0ez1zkw+CQE/lQ5pT690cfJxKNkcvlSsegFFTprGGSypgk03ki8Qx7R4PsG59lMpRkejZFOJYh\nkTLmyCELx5M45kPCMYiRsm5kR6Moj+L7pqpKoV92DuWxJI45kF04L3PKKzg3EwYC8wnYcjIQi+W/\nHR0dDAwMsG3bNrZv305XVxe27ZD06elpRkdHCQaDJJNJTLO2A3ARmUxmzUpMFUXhPe95D7Ozs3z1\nq1/lN7/5DT/72c8YGBjgnHPO4dprr+Xyyy/npptuIhqNctVVV+F2uzn55JN573vfyy233MKdd97Z\nIoiHI0L7IgWDmtqlBGuNu/c5BPbEVew/lIZB+v/9EGVwAM9LTm/euKZJ9PIPgabR/elPI9zrz5xk\nvUIoCt3XfgrR1kb8Ix/FTjYvQ1B/1rNwPf3pZH/xC6xwuGnjLoTiNX33OiVgLq9jVDO7L7JiG94W\nWngiQkrJzMwMIyMj9PT0sHXr1lKvYa0cxGaheux8Ps+BAweIRCK84HnPRtEc4xLHxdTZsjlEYG4T\nbFnWPAYmFGXe37hlWTUJgJQ2SEclyeVyTExOYEuboaEhxwGyWFFaiM6oOBOSun1vc+PLBXsRVUXB\nthceqxx2od+tiMVwvFJ0RRmJUBSxCHlvDqa18PFUYyn9is46HZKlKKL0ninKXORDb28PPYEe8qaF\noigYOYNYLEY0GiUWj5PN5DAty3lviyYwiiAUijReZ6MeUCEK14IoEbdaWNBpVoqSmi2L9aMFlCuL\nRcIrcBRLpVCKKgR4PS7afPN7gZeTWVjzGFS11Meo6zobNmyo6GPcv38/IyMjdfsYm6Eg5vN5Lrvs\nMi644ALOPfdcfv3rX1c8fvPNN3P22WdzzjnncPPNN1c8pmka73nPe7j++utJJpN86lOfYqwQ/yWl\nZM+ePRx55JG0t7dz22238aMfzXk2jI6O0tPTs35KTG3b5j/+4z949NFH0XWdj370o6X6WIDbbruN\nL3zhC2iaxjnnnMOrX/1qAM4++2zaCmGlmzZt4uqrr16T9a8X2JZNfCrhGNSsYrbgUvDgmFOm8swt\nzYs4WAi5O+9ExuP4Xv2PCFdt963lIH3TTVj79+N/3UXoz3h608Z9skDbtIm2f3sTiU98kuRXbqTj\nHW9vyrhCCLxnn0X+o/9J9pe/xH/eeU0ZdyEctdm5pterggjQtamT6FicTCyLr2v1HIRbaGEl0IwS\n02KvoaZpNR1KV5IgFtcvpSQajTI1NUVPTw99fX0IIRgZnS71vmmKgqqqjkJYcCuFORMas6yJrhiL\nUQ1FUbHt+eqHkUmTNlIkk44ZT/nm1rJsUBSEKJZ2Vr62EVkool4vopQQT8RIJpK4XDput47b7XZc\nJhfYwxTVLMuu7zJaC8XA+kOBBBRVzJkCLQCl0K+4uMFl6VyV9yyWkyaAyakgLpfmnCsvBUMSG9M0\nsS2LRDyNlA7ZyuUUNFUnHI0x0N+LptU2LarVU6ooAoHjVmvakgVvg4tGPrPOgRWPQyJRCqx0rry0\n0mhIUR311S4ppApDvbXVueUYyiwE5zpz1NzyPkbDMEin06TT6VIfYz6f589//jO67lzHh4Kbb76Z\nrq4uPvGJTxCNRjnrrLM47bTTSo9//OMf5yc/+Qk+n4/TTz993ufgySefzDHHHMNdd93FW9/6Vt70\npjeVymWf//zn85a3vAVVVbnhhhu45ppr+MxnPoPH46G/v5/rr79+/RDEX/3qVxiGwQ9+8APuu+8+\nrrnmGq677jrAOeFXX301N910E16vl/PPP5/du3fT3t6OlJJvfetba7z69YPETArbtOnauH4NKB4e\nj+FxqWzvWz2FM/PzWwHwvvSMpo0pDYPEtZ8Bt5v2S9/WtHGfbGh7wyWkvnIjyRu+TNvr/xmls7Mp\n43pfcjrxj/4n2Z/dumoEMdDmZqjLy8PjsWWVuqwGugrmNNHxeIsgtvCkRnmv4eDgIF1dXTX/ZmsZ\nyTQLiuLkHY6OjmIYBtu2bcNbFs9z7FE7ufPPD9LR7se2JVI6vYR2VXRFtQlNkVBUwzTNeaQ6n88z\nG5rF29ZRkatYRFEBVBS1NhmULJh56JTCigojk7xpMhuaRVGhu7ubfN4kl8uRSiWxLIleIIu6ruPS\ntJo3AypcNRfLv6gsL21UDtnoeKS9cElm+ZyLRS33eYf8SRCgCoV4Ikk8mawgunPGN6pzk9T2kk5n\nsCzLceNNxrFtyZ59ChuHBtDdbvRCtAYUcwYLCp6Y6+m0C1KtEIJCCEfD9TcsL1Ury3md70nn3woC\nC4miVI7hRCUWHXMd9bDDX5t8rcT3bq2+RlGee1mY1zRNJicnefzxx5mZmeHKK6/kpz/9Kc961rO4\n+OKL8Xjm9/k2wkte8hJOP/300vjVf5dPecpTSCQSaJpzk+CHP/zhvDG+9rWvlf599tln15xnx44d\n3HDDDfN+v25KTP/yl7/w/Oc/H4BjjjmGBx98sPTY3r172bJlC52dnei6zvHHH8/dd9/NI488QiaT\n4eKLL+Z1r3sd991331otf90gWlAuOjesz/zDdM5kZCbJUzd0rJqJh7Qssrf+AqW3F/1Zz2rauKnv\nfR9rbAz/Ra9FHRpq2rhPNihtbbS95c3IWIzkDV9u2rja9u1oRx5J9o47mlq+uhCevqmTWDrPeCSz\n8JPXAJ2Fm0ex8fgar6SFFtYOmUym1Gu4c+fOhqH3SqFccyXKsotOim63m+Hh4QpyCNDT3UlXRxvg\nGIuomubk+ilKhQpk2XZFELqTT2jWFOGK371SQiKRYHpqGn+bn0BXe+18OOFoOlLKum4uiyFZ5c9J\npdNMTU7h9Xno73PIis/vozvQTV//AP39fXjcHvKGQTgcZnJqilg0hm3b5A2jYh2KkItWD4uQ0imF\ndMLWlwalzB/HtuSCERqweBIqi5kadZ/gEOuD41Ol5ytivkWO47qqIhSBS1PxerwFF9xO0pkchmk6\n0RqTkwRDIRKJBGY+T7Ha1pYO+a4+rQuRL6HUV/Sd8uGqfV9Ziaks9NeWnyvn2ObcX1VNYUNf/d6+\nZpWYLnXMYh/jli1buPLKK0kmk3z84x/nta99rXPNLsPoxu/309bWRjKZ5NJLL+Ud73hHxeNHHHEE\n55xzDi972cs45ZRT6OhorjC0bghiMpkslYqCU/9bbAJNJpO0t88RHr/fTzKZxOPxcMkll3DjjTdy\nxRVX8O53v3vBxtHDHbFxJ99svSqIf5+IY0t4+sbmqESLgXH33dizs3hOPx2xhHDURpCZDInPfhbh\n9dL+1rc0ZcwnM/z//E8ofX0kv/yVpvYMel96BhgG2ara/ZVE8dp+aCy6wDPXBsXPhmiLILZwGGCp\nm8Fir+H+/ftLvYauBdoOiq6QzSSIlmVx8OBBotEobrebwcHBuqVxxz9zF7PhGCALZXYSy5y/aS03\npRFCIG0qSGMRpmVjmRbB4AypZIqBwQHa29uxLAshK/dQQjgkKm/aTs5dHfZSLPdsBCkdohmanSUa\njdI/0E+gu7aTue7S8Pl9dHU7mYH9ff243U7pbzgSKWUGJhMJ8nlzWSWjjsnM0pXhCtVQOOWPjaoa\nF6syFlFtplONmVAYI59HFEoxbTn3rhSvVUURCMUhWKrilOsKIVBVFV13gZQMDQ6yYcMQ7X4/tm0T\njccYG59gZiZILBYjk8nOV85lY/1QqfNoUWGuJsqyEMNRPN5qFbqYBQrOefS666uHzvJWpsR0KWMa\nhoFlWfT397N7927e+ta3VnCYpWBycpLXve51nHnmmbziFa8o/f6RRx7hN7/5Db/+9a+57bbbCIfD\n/Pznzc2VXjcEsa2tjVQqVfrZtm00Tav5WCqVor29ne3bt/PKV74SIUTJcSgYDK762tcTipu+znVK\nEIub5qdvWj2CmPlZsbz0JU0bM/W972NPTeO/+PVNzfB7skLxeml/21uRqRTJL80vdVguvGc473m2\ncA2sBp62qUgQ12cfYudG54uqpSC28GTDUlTDajSzDzGRSPD444+jaRobN25ccPPp93nZtKEfw8g7\n/YaqCgLypoVLm+sUKu87FMIhc5Zlz9uyp5IpZmamcbvdDAwOVhBkMz9X+VAkNkW3Ttuy6+YpwsIu\nnblcjrGJCRAwNDSEW9frKmumXdknqGoqXq8PRQgGBgYY6O/H5/NhWhazh4wrEwAAIABJREFUs2HG\nxyeYDTmE0ahSGOvBKalczk2Gqp9xSFo9IriYCIzSc4VouPRcziAYcm6i1jKUKSrdti2dnlQJNnbp\n98XHpmfCGHkTEHi8Xjo7O+nt7Z3vlDo1xfTMDNFYjEwmU4hVqX88tW4gqIpSiqao5dgrhKPm1szU\nrCihhQ11eg8rx1v5EtNGKIbOH6pJTSgU4uKLL+ayyy7j3HPPrXisvb0dj8dT6tcNBALE4839Tl83\nBPG4447jjjvuAOC+++5j165dpceGh4c5cOAA0WgUwzC45557OPbYY7npppu45pprAJieniaZTNL3\nJN+sx8bjIKBzaH2WmD5cKIFdLYIopSR7662Ijg7cz31u08ZM3ngjeNy0velfmzJmC+C/8AKU3l5S\n3/42MtOc8kztqU9F3baN7G23NW3MhfCUoQ5URaxboxpvhwd3u95SEFt40sC2baanp5ekGlajGQTR\nsizGx8eZmJhg06ZNDA0NoWnaosY97qgjiMVTSGlXbLJt20ZV1ZLD6VyGvbMZd8pS1dJzQ6EQ0WiU\nnt5eOjs655WgOqVwFqqqUF3tKGnsUFlPRZRSEo3FmJmZoburm4HePkfZUuc7rZaj0T5fUVW8Xi+B\n7m76+vvpHxigvd2PZdvEolEmJicJFUonc7lcTWKrKA5BVMTileFiDt+835ecNucveinlr2IBQ9XJ\nqWDpnC00bLE3U9RgkqZlMRueq3IRhfGqnVKHhobo6upCVRSy2QxGPk8kEiESiZBKp8mbZtX1WLmo\n8qiKmiqvY2OKLe155LLclEdVFTr9HtobqIfO/EsPtW+E4rEthXSm02ng0Ani9ddfTzwe54tf/CIX\nXXQRF110ETfffDM/+MEP2LhxI+eddx4XXHAB559/PolEom6P4XKxbkxqXvSiF3HnnXfymte8Bikl\nV111FbfccgvpdJrzzjuP973vfVxyySVIKTnnnHMYGBjg3HPP5f3vfz/nn38+Qgiuuuqqkur4ZEUi\nmMLX7UVzr8/zsHcmSafPRX/H0pp1lwvrwAGs8XG8L385Qp9vibwc5B96CGv/AbyvOhs1EGjKmC2A\n8Hjw/eO5JK+7ntydf8DzwtMWftFCYwqB54WnkfrKjRh/+xvuE05owkobw+NS2dbrZ99Mct0a1XQM\ntBE+EF2362uhhcVioes3k8kwPj6Opmns3LlzycSwfJ5DIYipVIqxsTHa2trYuXNnqddvsaWrLpeL\npz1lG3v2HaSzswNNVTEtqxTbYFoSl0vDtCyHLJaZttiWTTaTYTYcxuv1MjQ0hFBEaYxyKEIgTQNL\nzN9DCBxiUSwLrIXqU+QY0YRACIY2DKGpmhN4zsLZg8Wsw0Z5g4oCtuUQeN3tBJtbtnTy63IGhpEj\nHouRN81CwLyOW9eRZf11lj0XyL4QVCGwGlxylmmjaUrJjKeZpcmxeIJ4WT/9QuMqioJZFo9SjWAo\nTE+gE1VVUeocvxACd+GcqarCxOQU7W1t2FKSy+VKqpXb7cbrcaNpLjRNQyjCcW6tMqSphgRcmjLv\nuhEVyqFzMddzLq0Yr8nfacshnEWCeKg5iJdffjmXX3553cfPP/98zj///EOaoxHWDYtQFIUrr7yy\n4nfDw8Olf+/evZvdu3dXPK7rOp/61KdWZX1PBEgpSYfTBLbVrulfa5iWzUQkw5EbOlZtU2rcey8A\n+vHHNW3M3G23A+A5bfcCz2xhqfCctpvkddeTvf32phBEAP2440hxI8a9964KQQTY1ONj70yScNKg\np339ZWP6Aj6Ce8Lkkgaedbi+Flo4VNi2TTAYJBwON3QoXSyKRjXLWcf09DSxWIyNGzfO60VaCvF8\n2q5tPPzYAfJ5s6DkFSIBnOY+8qaFEE78giIUJBJpS2ajEbK5DIFAoMIEp9oMRFGcqIxcLotHc4Oo\n6l8srFVTRN3MwaKKaJo2qVSKSCRCZ2cn7e3tc+dfgqY5fY0LoehcWfQyqX4LrKqYicKpQFEUPF4P\nHq9zM1raEsPIkTMM4gVDFgBFieF2u3HpOrqmYS7wXix0BTiZlbLUc+eoYEvIP6xzYi3LYnxypmKe\nRtejlHPOtkodQ0DLtpgJhRkaWFzlXfF6cblczo0Wv99x77QsDMMgbxjE4wmktNH1wjnVdXSX7sSQ\n1CD6AjlPMpWAKuZKm4WAnk4fXs/CN3eabVKznPGaVWK61lg3BLGFQ0cuaWDlbfyB9WldPxXLYtmS\nzT2HdldlKTDudZxtXcce07Qxs7ffDoqC++QXNG3MFhzoz3oWor2d7O23N+1OoF547/P33n/IYy0W\nmwPOF8PBcHpdEsTiZ0Q6nGkRxBYOOxRVQ5fLdUiqYTmWU2KaTqcZGxvD6/Wyc+fOmhVOSyGeQgie\nc8yR/OHuvzE02FdQAG1HaSsLHs/nLSgQiJngDJqmMjQ4NG8jLgS4VBUpHWXQtqxSbIZt5lBcvnnz\nS4pEof5ns2lahGZnyRt5+gcGcFdV7wiBQw5rMb4q2BI0RWDWIBeaoswjdLacU1Qr5lQEbo8HdyFq\nIJfNEE8kQQgSySSGYaBpGl6PB5dLR3fr86MNmF9CWQ9WQblbyk0FRRGOklbje29yOlRhwrjQqdNU\nFcu2UIolsXXestlwlN5AN7ga0wFnPlnMnCj7vcClaYUYEh/dioKRz2PkDHK5HNF0Gsu08Hp0VJej\nRLp0vXBDQmBa9ryFzTncOzcbFKGwoXdxsWjNNqlZznjpdBpFUQ45B3Gt0SKIhxHS4cJdi8D6vGsx\nFnZk902rSGCNv94Lmob+jGc0ZTw7FsP4y19xHXMMamB9KrVPZAiXC/fz/4Hsz36OuW8E1/COQx5T\n3bwZpaenpCavBoo3QcbCaY7Zuv6uE1/hbzAVThPY2rXGq2mhheWj/CZSs1XDciyFIBbXEYlEGBoa\norNBtutSSxC3bh7gwUf8JJJpujr81Nr5SwlGLks6naGnp8cpdRPg0rTChtwxK3FKCpWCUlM5hpHL\n4NU8yLL+NadPzXmdpqk1TWmy2Syh2Vna/D56enpr9p0Vj1ktU9caxRhatkRBzPMbteu8wrQWLk11\nnD6VUjSAtCVG3lHBUukk4YiBS9OcrMBC6LmuqQ3HnL9uG0VZFA921lSHdKdSGcKRSlfsRmZBIErl\nosW+QrVAGGs9d3omyKaNjaO6FCGwCiY3tf6mFCEQhZ5BVXH6Q4tqdTHmIVsoSc3nTXS3C5emA7Jw\nngo9tIqzdlGI7rBs2DjoR9MWR9LWQ4lpJpPB5/M94ds3WgTxMEKqQMB83avT37dUHJx1nGhXS0GU\nhkH+oYdwHXkkwtscUpq743dgWXh2n9qU8VqYD8+pp5L92c/J3X57UwiiEAL92GPJ/upXWDMzqP39\nTVhlY2wqKoizqQWeuTbwF9ZXvKnUQgtPdGQyGcbGxtB1vWmqYTkWSxCz2SxjY2O4XC6Gh4cXXMdy\nlMnnnXA0P/r57/B5PbjdGqZpY1k2mqqQzeUIhWaxbRuPW6+ID7OlRFZZRVoFk5v5pY0CK59B0ee+\nr0UZ05FVpLJoRJNKJgn09OD3eWuSIkclcx6wLNspPVyAPUkKMRL2XMzGQgRQSuqqZoqY3ysplLng\n806lA8uyC0pYjnQqTTQaRdc0NJfLIYu6XpYZWXsdxWMVVcddD3aRgFF+00MyNjFVuda6MzpQlTmC\naEsbBRXbtuadDyEEtmURjSXp7s7g99XfJ8m56uB5EAKEKrDqlAwrqoJbcVcoaoZhkM1mEVISnAmi\nuTSn19HtxuN2I4XTF+rWVfq6Fi96rIcS03Q6PS/P9ImIdeNi2sKhI10I5vavewVxddaXf/hhMIxS\niWEzkL290H946ilNG7OFSnhOOQWYO9fNQLHE2LjvvqaN2Qibe5xrvHjNrzcUFcTiZ0YLLTxRUe5Q\n2tvby5YtW5pODmHhXkEpJcFgkJGREXp6eha9jqKathQVsaujjeGtG5gJhbEthwlJKUkkk0xNTuHz\n+QgEuueF3jskslYWcO25DSNLvZxAR/VxNs75fJ6p6WkMw2BoaAif1yGHao3et3nHuciMQMuWqEvY\nqNtS1pwfart6Vs+lqQq6rtPW3k5Pbw9Dg4N0dnahqirpdJrpmRmmp6eJRqPkcrmaJLes5dJR1hrk\nRNZTkoOhWXKGsej1izJjmHKiKWHe9SCEgELp6MTkTMNr0C4r2S0/CkUVTpRGgz7LChW5oDTquk5H\nRwcSweDgAJ2dnbhcGqlUmumZKYLBGYKhEJ0+MKucUhthvZSYPtH7D6GlIB5WKJWYdq/POxdjhfVt\nXKX1Gff/DQDXMc0jiLk7fofS04Pr6KObNmYLlVA3DKEd+VRyd/0RaRhNcZ/VjzsWgPx99+N98YsP\nebyF0Nvmxu1SOLheCWJ3scS0RRBbeGIjl8uRy+VWRDUsR6NewVwux9jYGIqiMDw8jL6Ez6xisPlS\nS+NOOO7p7P/Rr0mmM7T5PEwHg+TzeYY2DKGqGrlcDtO05il0tY6gSBzNGiqizOcQLm9xsRWvty2L\nRDJFNBqls6uL9ra2imOodsXUVGXeHLbllPBZVq3yx0oUtEuEWFwvoGXbNZXGxcROmHZlL6OqKQjh\nwqU715iUknw+j2EYZDIZDCPP9PR0wSnVja67kLhKZKpoXqMoIOV8Mlg0syn/dSaTZSY4O0/5q5U1\nWFyToihIMWfuUt4yaJX1mBZveIjCeJlsllA4Sl/P/JaIcvWz/Dot5RuyQJm0mDNUUsreO6E416YQ\nCj6PB8vW8fvaCsY3JgomwjYYHR1FCFEqW/V6vei6XvPvZSVKTJdjUtMiiC2sK+RSzl0mva05cQ7N\nRiRloGsKHd6V+xIvhzU2BoC249DLFAHsRAJrchL3KS9ANPEOVQvzoR99NObfH8EcPYhr5/DCL1gA\n2vbtAJgHxw55rMVAUQQ9bW6iqfyqzLdUuAufEUbKWOCZLbSwvuHz+diyZcuKz1OrFFRKSTgcZmZm\nhv7+fgKBwLI2p8uJQtB1jeOeuYvf3XUf7T4Nn7+N3p4eNJfmbNqFs/FXVYFtzo1d7BGr7kerN3su\nl8HjcgNKxTptyyIYDiNtycDAQG1SLOdIoRCiBgFdaPaqZ0lHcFQKxGQxsGVlD+BCpanlqOhlrBHD\nUIzNKKqKHR0d5AzHnCWZjCMlDll0O6WTmqpi285iVK3S3bT8/XcIsM3B8amC8qeU9RTWX7uqqhXX\naCnDr8QKQVVUTMucu04V4ZwkYHo6RGd7G7peuUeruKSLyqycK2NVFOq62oLALjyoqHP/Liyn7PWy\n9BwpAUvwlB2DdLZ5S2Q8k8mQyWSIRCJYloXX68Xn8+H1enG73SXS21IQm4MWQTyMkM84Dlf6KhGw\npSKeMejwulatcdeanAQcRaoZMPfvB+bIRgsrhxKhGxlpCkFUBwaAuWtiNdDhdTESTC78xDWAq/AZ\nUfzMaKGFFhqjmiAahsH4+DhSSnbs2HFIjoXFsatLABvBtm26291YVh6peOnr7XVyES0bVVUKqo7j\nFrpQzxo46pJDHOfv9GU+i3D5nA29lI4RTSiE3+cj0BdAKPXXnS+Q1UZGLbYtHZVqEXEQtr2QQUvV\n2qV0XFCXmUVoF0pbF1QdhaiIf1AUQc7Ik8vlMHIGyUTCIYwFsujWdXSXq0Bq5DxFdCYUJpvLOWso\ne/fqKdkCgbTtyp9rvOuWZaGqmtOTWH2s0iGlO7ZtqjJ/mlP8iupt+Tau0Zkpqo/V5LBYdutcVGVX\nqJRYlqTDr9PZ5ijX5WS8aPhUThhjsRimaeLxeDBNk1wuh8vlagpRXG4O4qFmIK4HtAjiYYR8xlEr\nXN71+bbGM3l621fPQMeamgIhmmZKYo7sB0Dbtq0p47VQH8VzbBVI+aFC6DpKb69zTawSOrwucnmb\nbN7C41r8xm81MEcQ16fC2UIL6w1FdUJKSSQSYXp6mt7eXnp7ew/5pudSFcRifIbP5+NVLz/t/7P3\npmGSXPWZ7++ciMi1lqy9q7qrtyohhDYkS4AwixZsGI+xGSPBHfBgmw82HjyYscF3xo/QtbHx9RjB\nNRgEBs8D3PvA2AP2gLyBbbYZD+Bl0CDEJnWru9W1b7lvsZxzP5yIrMyqrLWzq0utfHkadWdmnDix\nVNZ54/3/35c//5v/SblcIZ5oIqlCNAxpbNvC89cJgSm9bBOMvsVh1Os1krb53e35PisrKwwPD5NI\nJNCAvY2CJAhVxB0yD6P8wp3OghRivWJxl2guF91NeWnLvAAhQhfVPWyrNY34hygvMAiUIYxunXKp\nhFKaWDxGMhnHsWPYto1GU6vXKZby62Mp3VSG3J5oiw0mOI17Sm90pgmzB5vGbka5UmkpNY1C6mVo\nQmMU2dYbZbtyX8F6BEszLEviez52E+G1pEBpKFc9bpoe3nJMWM9ijFxogyCgWq0yPz/f+PlMJBKN\nktREIrGnBzDrx9YtMb0sCIKAD33oQ/zSL/3S5dxNFyEiNcA5hApioDTFms/U6MHNLZhfQA4NITrU\nlxKcOwd0FcSDgNWkIHZszCNH8M+e7XiPwlboD38Oi1Xv0BFEy5ZYjuwSxC6e9jioihQpJZ7nceHC\nBXzf59SpUyQSnXnguVsnU601S0tLrK2tMTEx0VBTnj19gifOzXDN6WMoZYiIY1sNIuH5wSZi0e68\nGXVJtg12r1WLrGZLaK05OjGBbFpsB4FCC9E2pkFKgecFO+Y9RqV87RTMpk+hdJj7uIdSUTNHvWWe\n4s6QaK12TeQbmYEtrwls28K2U6TThjz4foDr1vE9j2KhaD4nLeYWVkjEE1i21bhODTOjNvOPejib\nr+lWfYpRiWmkirU7nqXlVXpSSVKphCn/DEAHuq0p7HbXQYTqYLtyYBGW/rZsKgTFcp1jY73E43tb\nt1mWRU/YA3vsmFFAI4VxbW2NWq1GLBZr6WNsl0u6Ec/kEtPL2kgVBAEf/OAHL+cuumhCQ0FMHD4F\nsVTz0JoD6z8EUMvLHY00iEpMra6CeNlhnzwBrJ/zTkCOjqCrVXT5YKInons9f0hJmJNwcLslpl1c\nBbjcJFGHZZW5XI5kMsnU1FTHyCHsTkGs1+s8+eSTVKtVpqenW7IVX/BD12PZkoWltfUF/AZGaG9w\n9PTDIPedoLWmWCzw1FPnSaeSWFK2kEMIw+nbuZEKQhMS0S77fRMCpbY1NbUtCx3mLSitdzVm4zjA\nOGjufpOWeelwkHb73Gw4s7ultW1bpFIp+vv7GRs7wvDwCMViGd93KVfKZLNZCoUi1WqVWt1tq7BG\nDrgbfwaiOZnjNq8JxHr/oWi8u2FA0Erx1Ow8vh+0qpW6zQnY5iK0VanD+Wg0fhhzYoYRVGo+Gs30\n5P6zg6NzIaUknU4zPDzM5OQkU1NTjI6OYts2+Xye8+fPc/78eRYXF8Nsxva/p/ebg9gtMQU+8IEP\nbPme73cXHwcJr+ohbYl1yNQKWF8kHxRBVOUyulxGjnWQIJ47D5aFPXmsY2N20R6ypwc5Otoo6+0E\noocFamkZ2ZQNdrkQ3euFw0oQk3ZXQeyiix3g+z5zc3ONvqKxsJ+5k9iOIO7GCCcWs7nj1uv526/+\nE/09KeKJeLiwXf+c5wdIIVvLJNuqiKrRixgEAWurq/iBz5GxI8TiFuVqe0IQBAFaWK0RCGK97FEp\njZBix/5BM8f2KmJzL57W7Km3UIro2JpUxF1sajeRHI3JNGx3vVqzC3efaynleiZjpVojXywDFv39\n/Wit8H0f1/Vwy2VqQoOwsB1TumpbdmhK1NpP2Oxcq1kn0lLKxvnXSof5l9G2AilBSEngB9TrLk9d\nnOfE8aMt99FGbGUW1DCbaYNGT2dEOENTnuVsmTtv3b/h1FZkGcyxR8rh4OAgWmvq9TrVapVSqcTy\n8nJbp1Sl1K6UxmZcLQriJRPED33oQ9xzzz1t2fJubIu76Bx8N8COHz71EKBSNw8L0gekbqpcDgA5\nsP8nURsRzM9jjY52JHbhQFGpwPw8jI/D0+hLyzp6FO+xxzo2nhwcBCBYW8M+ffnLhHsShiCWaofz\nQZmdsBvROF100cVmFAoF5ubmyGQy9Pf3kwt/r3QaW5WYep7H7OwsQRDsaIRz7fRxvnf2Kc7PLHDt\nNScQROQsKgwUSEuimnsRQ0ObTYt8oalWK6yurjVUGCElbr2G1u3XdUqDba2TnXbj7qZ10N8imsKS\nTaWKIbHw91BqKoUg0NpkHO5BEdpYqml2rbck9duZ8bSDQAKGCM7MtvbICyFxnBiOYyIdJJpa3cX1\nfCrlinkIYMlGP55t2YgtyJwUssW51twfRsHTGIMapSU03R+lSoW5+UWOHT0SHnsr+ZIb+h4bRyTB\n7Ko9edSYc2T6Ow21nlkuc+pYP5m+/SvzuqFW76wTCyFIJBIkEgkGBgZanFIrlQpra2uN8SLH1Mgp\ndSd0exBDXHPNNbz61a/mpS996ab36vU6Dz/88KXuoos9YIf81yuG6AtT7uKHq7M77KCaqjXs8UnS\nFYXvw9veBp/7HDz1FBw/Dj/5k/Dgg0+L4xC2vb7a6ASiRcE+3ez2vLsDutX3i4Pq3eqii8uN/cRE\nbIcgCJifn6dSqTA5OUk6naZYLO5JGdoL2s0/n88zNzfH4OAgo6Oju/p5fcnzb+KP/9uXmJtbZvLo\nqDGjsawGsfJ9f7OKuKHoUivN8toK9Vq9YUSzPk9Q7tYPlUzOnsRpm6lIk5tl67EqrSmXy0jLIubE\nwn7GVjKp25U3Rq+3a47btO+mee5QyhpBEAbAb/is8X3RzQkRDUghCbZQQNshMhKamVvE26bqTitN\nICARTxCLmTkJIXDrdVzPo1Kp4gc+tmXjODa27RiDHB2qm6HRDkTELHIjDfsrEVghiV4/fsFaLo9l\nWYwfGdl0miMDm6YXGjmHhjxuPg7LWo/3iEx3Ftaq2JbkOSe2N6bZCZfiL7CVU+rc3BxKKRYWFhpO\nqc3GN+3KT7supiHuvffeLWt3bdvuGtR00cWVwtveBu973/q/z59f//fv//4VmVIXXXTRxWFGqVRi\ndnaW3t5epqamGs6HO5msXAqaFcRmcnrixIk9KRGDmT5uuO4U3/rOWXrSSSwrHLexsheb4iSacxE9\n12N5ZRnHcZg4Ot7o94sgENTrVQQK3cbCQmtwrO3yDjf363mex/LKiiGuKsDzfWKxGKlkAst2iDkx\nbFtuqRIa5XJ78xmrDSk1Stv217OZzGw+ELP1xvLLrcxh2kGEpZZr2RyFYqkxbjuKI8NQeYU2+ZbC\nXDPbcbBDIz6tNEHgU/c8qrUqpZBwuq6LUhrHaVYYo15O04uqQpfV5p1Hpb7Lq2sADA8PNAiYRrc8\nMBFSgN7e0dRq9MGulx0v5XySSY87bhrHucT2qP04jm4Hx3GwbZv+/n56enoaTqmVSoXl5WVc120h\njMlkEillRxREz/P49V//dWZnZ3Fdl1/8xV/knnvuabz/6KOP8ru/+7torRkZGeHd7373JUXttMMl\nE8Sf/umf3vI9y7K6BLGLLq4EKhX47Gfbv/e5z8Hv/M7Tqty0iy666OJyIlIJisUiExMT9Pb2try/\nW6fR/SBSEJvJ6fT09L5y3J5/y3M4e36W2cVVHOkjAMu28MOWHz8Iwh615q00hUKBQr5AZiBDOt1j\nCmbCkPumiYKGwK0iY5sVkijOQmwzb63CiANfUS6XyGZz9Gf6SaVSaG1Ih1t3cd06lUoF1/NNZmA8\nTixs79goJu5Yatrm5Ujlamu8En1mhwcCGuPuaYWka72sd3eQEsrlOnPzSzt/OOSzxlVThGrtho9I\nQcyOtRDGfCFvTJbqNUplD0tahlTatslhDEt3hZTGWjQaq0nV1lqzspalWquRTJprIMX6AxMZ9pZG\nWws2E0VLhiXH4bl2fcX8ao0gUIwPp5kc69v1edsK+3Ec3QnNJjWRU2pP6GGglGpxSj1z5gx/9Ed/\nRCqV4syZM6ytrTEYtrbsFQ8//DCZTIZ3v/vd5HI5XvWqVzUIotaad7zjHbz//e/nxIkTfPrTn2Z2\ndpbTp0935qBD7EgQq9Uqf/AHf8AjjzxCIpHg2muv5WUvexm33XYbABcuXODLX/4yo6Oj3HXXXSST\nyY5OsIsuutgH5ufh4sX27128aN6fuvQA+i666KKLpzvK5TKzs7OkUimmp6fb5qVdboKYzWap1+sc\nPXp0EzndC+Jxh+fdch1f+vtvki8VOeF6pJJ2U2+cQEoLFRLGwPdZXF1BCMGR8SMthhzt1DCNxnXr\nJJ0EWqyfJ7spHqNd6WUzPN9nbW0Nt+4yNjZGLOYYkxvMeU4kEySSCWMQoxXVSg3XrVPIm2zA1bVV\n4iFhjDkxkwGo2/c4NhvlbIRSxhnVb3NdhTDlpTsqUiIkqJYxZtlScWwD3w+4cHGuhYi229oQ5/V5\nRCWj7dBc3SqkudbxRALbttBa4/sBKjBh8uVyGSkl8XgMy7KwLLuh8jWfS9uyCVRALl9gdc1jMDNA\nKhUnCNo7lW40IxIheY/6UgvlKrmSou4GSAm3XNsZM8HLEWG1nSoZOaVG5aQTExMkk0ne9a538Q//\n8A/81//6X5mYmOCTn/xkI69xt3jFK17By1/+csAcV/N30rlz58hkMnz84x/niSee4KUvfWnHySHs\ngiC+853v5LOf/Sw33ngj9XqdT33qU3ziE5/gzjvv5O1vfzuvec1rKJWMNJ5KpfjN3/xNXvnKV3Z8\nol100cUeMD5ueg7bxURMTpr3u+iiiy6uAux3UaiUYmlpiVwux8TExLaLOCHEZSGItVqNYrGI4zhM\nT0/v2TGxHa6/9hQ/OHORb393lYWlNSaPjuFYEl8ptDYqoiUFxVKZtbU1enrSDA0OblroB4EyBCpU\nEVtUO6+KFTNKir0hOzEIAqS1nsPYjHq9zvLKCj3pFIPj4+u+BG3cXYLQRCUijABzs3Ok02k816WQ\nz+P5PvFYnFjclKVK2WrUIjf01W1EoFTbfERLCnbN9bTJCtyVLWp+9bYEAAAgAElEQVQEARdnFqi7\nbtv3mtFc3hxlRbZ3Um0l9RsfagghcBwbGYsRTyhDGIOAwPep1+oEqoLWGscxpb2WY5vy04aLK9Rq\nHk88eZ6xkWFGhwd2yK2MlEizdd31mZtfxiMGMoaUiqNjaXpSnTH+208kxU7YiyppWRa33XYb8/Pz\nPPjggzz3uc/l4sWLDcVxL4hIZ6lU4i1veQtvfetbG+9ls1keeeQRHnjgAY4fP86b3vQmbrjhBu64\n444972c77PhN9OUvf5nXv/713H///YD5Mvv4xz/OBz7wAb75zW+STCb5xCc+gVKKj33sY/zar/0a\nR44c4fbbb+/oRLvooos9IJUyhjTNPYgRfvInnx7lpQdkJtNFF10881CtVpmZmSEej++KmHW6B1Fr\nzcrKCisrKySTSVKpVEfIYYSXvPAmvvv9JygUy+TyRTL9vSb3UILv+axl85QqZUZGRojH48bd01ov\nRW2eZ9TEKFiPLvBcF9vxEdJpo5oJRCMUYn2cfKFAsVBgaGiIVCq1Y+yFDJXBFncUAfF4vFGt1lyS\nuraWJQgCHCdGLB4jEY+DYyN2cO8LlMaS0FxNu5dSUTAGgUFgyj9N3ML2n89lC+Tyxd2N3cSddUjI\njBlOsOFzTaRRN127DYQz6i0UQhCPxVG2TSKRaGzrui5118Url7EtC2lJbMdpDKO1ZmFphWwuz8BA\nhsFMX0PhEk3OplH0R7VSJZsvspbN48QSOMkeQJG0Bdce76dTuFwK4l5IZ7VqTJyin+dTp/bvmD4/\nP8+b3/xmXve617UIb5lMhhMnTjAVVoG9+MUv5rHHHjt4glgul7nmmmsa/04kErzpTW8in8/z8Y9/\nnDe/+c1cf/31ALz3ve8lm83y0Y9+tEsQrwCkJVDe5SmBuVQ4tvkBq3sHE30iwl8eutK5UHTR20Mw\nO3dZvoQuCx580Pz3c58zZaWTk+supk8DqFwO0du5vEJdNveCOKAy+Fp4rzvW4bxXAi9AWofU9riL\nLi4TtNYsLy+zurrK+Pg4/f39u/o+72SJqeu6zMzMADA1NUU2m+3IuM0YHshw7dRRZpcKLCytkUzE\nSaeTDQv/uBPj2NGjm0jcRgRKYdsWvm/6xyLnSSklvlvFjrfPNg4CbcxhNPiBz8rKKmjNkfFx467J\n9rEXkRumJozMUOukpuVzTSWpYAiU67rU63UKhTyu6xELFcZ4PG767tpcb6VCB05t3En3+izACsdU\nYa1r+wgIg0qlxuz84hYjbe6JjMpGm+9BPzCOtM2KYfPeZGhQpLVuyWhsJpECYUqNxfp7AD3pNF7c\nBwS+7+H7xsDI8z0EUCqVcWIxPD+gWltiYXGZVNJERiQTMZTSBFrhuR6VSo2665m5a3ASveb8ADdN\nD2N38Pfj5Vib7XXMSqUCcMkmNSsrK7zxjW/kgQce2ET8JicnKZfLXLhwgRMnTvDP//zP3HvvvZe0\nv3bYkSCePHmSr33ta7z2ta9tef3Hf/zH+djHPsa1117b8vo999zDRz/60c7OsotdwU44eDUfHQbS\nHiYcdGi4HBgAy0ItLXdsTPvkSfzv/wC1uoo1fGl2zAcC2zZupb/zO0+7HEQdBPhPPYVz/XM6NmYQ\n3gvWWGf6HXZCdK/3d6h8ptPwqj5O8vDHnXTRxU7Y7QKuVqsxMzODbdtMT0/jOO3JzVb72C6IezfQ\nWpPL5VhYWGBkZIShoaFGbtvlKF+98dmnqLlPUq7WOT+zwOhgL5VKmUwmQzqdxrbtdfdKjJJmyGDr\ng1wVGCdUS0psS6KVItBhCWrMA7HVedRUKsbAo6e3l/6+vpZzp5Re72HbwMikEPjha4FS2xKuZggp\nSaVMZp2UkiAwoe+uWyefy+H7PrFYPHR4DZquZ6SS7i82ZYPnT0is5SZ11fN9LszM7nr8qGy0Xbno\nRgW2Qfy2uZ+aSflWCq7n+Vi21Yj2sm2bRDKO73mUy1Usy8Z1XTzXQ2PcPqvVGvF4zJjd6IggK2QY\nNaa0JpXuAWEhtGbySC9D/fEtUxD2g8tRYnopCuKl4MMf/jCFQoGHHnqIhx56CID77ruParXKa1/7\nWt71rnfxq7/6q2itueWWW7jzzjsvaX/tsOPq4N/8m3/DAw88wAMPPMDP/MzPNCTNa665ht/6rd/a\nRBABVldXOz7RLnZGLFzseTWfWGr3v/gOAgdNEIWUyJFhguVduIPtEvbJkwD4584/PQhihFTqaWdI\nE8zNges2znknoJaXQErkPl3F9ooGQUwerp/FCF7VI5nZfyhxF108XdBczjk2NsbAwMCeSZ4QorFI\nb2disxN832dubo56vc6pU6da8gUjItNp2LbFi19wE3/5t9+gWMpRKZV47k3PbpRcen6AZZlgcylk\nqFyZbDyBIRBCGNpkWQLP9wlCVS86e/VqiViyf1MZp9KK1eUsrrs5T7H1c5sJitWmJ7CRrr4LmHJZ\nY4wipSSZTJAMFUalFPW6S6Vawfd95ufnWxTGZDy2Z3IYZQq2vmYMa6IgeBU6tD51cY4gCMz912Y/\nm/oKQxLXjhwHQdAofTb5lir8u2iQwGaTVq3X9UYTK9LOnMcUBzfPQ4SSqgodaNPplDEVShki7Ps+\nnufh1usEOsCxY9i2RTyeCLMvJZaUSDuN1ppkXHLLs0YplQodVfw6rSDu54FQpxTE+++/v9Ha1w53\n3HEHn/nMZy5pHzthR4L4mte8hmw2y0MPPcSnP/1pMpkMz372sxt/qtUqQRBgWRbVarXh2tPFwcMJ\nF6Je1Tt0BDHhWMQdSf6ACCKANTqK9/jjHfvSsMNacv/cOeK333bJ43WxNfxz54D1c94JBIuLyOFh\nxD4Wd/tBdK/3HUKCqJXGq/mN74wuurhaUa/XmZ2dRQjB1NRUIyphP9iPsgRQLBaZnZ0lk8lw7Nix\nTYrE5VIQhRCkEg6DmSTVWg3LibG8mmd8bKihGmpl3DyVWN9/oxexSf1q9ZLRjX8rpRDaA7GeweZ6\nLivLKziOw5GxI0hr66WmcWgUNPPjdqRRaY1lW0bj28U10Lp9mWdEGA2pUwwNDrYojFkVEIvFjFFL\nPLFlSWrrmFu/H5EyyxLMzi1TrlTD/MOtr3dzSWhzmP2mzwmBRBA06Ze2bW962BCN1+ij1VvnNUZk\nV4dBj7Zt4XtRGapZS/lBECqtxqDItmKkk0k0KiTgPp7nUq8XUNrkL6b7hxG+RyLucPM1I8RidscJ\nXadzEKP57YcgXg2JDruqL/qFX/gF7rvvPr70pS/x6KOP8vjjj/Mnf/InVCqV0BXJadTRLy4u8upX\nv5rV1VWGhoYu9/y7aEKDINb8KzyT9uhLOgemIAJYR47gPfptdC6HGBi45PEiNSto5wzaRUfhnzsP\n0DEFUWtNsLCI8+zNFQ+XC4WqhxSQjh++Mk6/br4jYt0S0y6uArRbwGmtWVtbY2lpqaWc81Kw1z7E\n5mzFY8eObelm2GkDHDDqkuu6rK2t8cqXv4Q//9tvkC+WWVzJkkrG6UknARO+bjutZaWBCjaZippq\nQ9n4ezNqtTLxlAMISqUSuVyOTCZDT0+PUR83GMBsnqsOsxn15uzFls8pdvCbAYwCqZRGRAYtbU5t\ndC9sVBjRmmqtTuC7LSWp2/Uw7uaeWF7JspbNtzxkaBcR0TJHTC9hsM3JC7Rqui/Ftkp0tG/Ltrb8\nnMm/tA3ZC2MxjJJsrk+kiG6cqR8YNRpMbEYqlVx3P9WgcahWKsQ0yCDF2pq5P/dS5r0TOp2DuJ+S\n1Wq1SiKR2FeVwWHDrlcHg4OD3HvvvS2NkBcvXuQHP/gB3//+93n88ccbhPEzn/kMf/qnf0pfXx/X\nXHMN09PT/MZv/MblmH8XTYj6ibwDJGF7QX/SYSFfO7D9WWGUQzC/YHoSL3W8SEHsEsTLjoiEd0pB\nVNks1OuNe+IgUKh69CWdbZ8uXym44XdEV0Hs4mqE67rMzs6ilOL06dPE4/GdN9oF9kIQK5UKMzMz\n22YrRui0ghjtWwjB+Pg4PT09vPgFN/OXf/s10JoLM4tcOzXZcE01JY9NTpmatkTN91VbfwOtNIFb\nIZuv4vkeR8aOtCz8/SBAyvaxFw2IsLR0OyYJoEMjme2HahyHlKIRC7EThACNCMligv7+flzPx3U9\n6ht6GCPCGI/Hts18BGPqMju3uO4F2zC0UZtVtKaxpCUJfLXJhXTzxE0ppCVlG/W1qUdRGcMa3/fb\nno/1ftighVNHpZaWLQn8zQKEJSUIvZ6DKY1CKqUErYn3DiEsm0RMcvetx0B7VKtVKpUKSilqtRrJ\nZJJkMkkikdg3yTsMimSlUrnk8tLDgkt6fDw5Ocnk5CQve9nLGq/VajUef/xxfvCDHzT+fP7zn+8S\nxANAtNhzK4eTIPYlHc4slvB81XA1vZyIyIA/O4vznOs6MN4RSMTxnzhzyWN1sT28M+YcWx1SEIO5\nOTPekSMdGW83yFe8Q1leCuBVugSxi6sPzSYwQ0NDjIyMdHTBuBuCqLVmaWmJtbU1JiYm6O/f2ca/\nUwpis0PrxMQEuVyucfzHxke4ZmqSJ84+hesrzl6YY+rEBLZtozU4toXXpCL6QRCWNrbOy5aSjZJc\nrV5jdnaWwZEJhoaPmH7GlomJdjGHG+YOYouyx42wt3FflsL0IEZQYXzHTnl9EPWCrn8uUJqYYzdU\nRjOe6WGMCKNSPkJYCCGou+4mhbFWq3Ph4ty6MQzrJbRCCJw20SJhNacxkNnF7auVyeDb7t6M1EMp\nJUJtHFSYPMqoV3HLHUVjSeP0CmgtGuWolhSh020Yx2GBHU8jLAdLam6aGqa3x5Dv3t5etNbYtk08\nHqdarbK8vIzrusYNNYx+2Qth7LRJzX4UyWq12iWIWyGRSHDTTTdx0003dXroLnZAst98gVVzB6fS\n7QVHMkkgy3y+yvGh9GXfnx0aKHmPPUbyR162w6d3hpCS2K0/hPu1rxGsrDy9jGqeRtD1Ou43/gF7\nagpr8NKVXwDv0W8D4Fx36Q8KdoNK3SdbdrnmSO+B7G+vqIRKfvSd0UUXT2cIIfA8j7m5OTzP4+TJ\nk5elB2gnpa9erzMzM4NlWXtySd1vb2MzougMIURj3/l8vmXcH779RmbnlvGLZVzP5+LcMsePjmFZ\nEs/3w569xqywpNxEXgK1rtJorcnnc5RKZYaGBunvSWwmh9F2vsKyrS2dSAXg+aFiswOpDwKF41ht\nexXbncvduqC2I5GBMsqZaqhj6yWpkbJWKBSou2FJahAQc2Kho6fFxZn5lnGb56xD4xqjKra+Z9nb\nl5Y2Q29j4KObHIXaOqFi1D4VGd1scX9blkWtVg/HNC62Inw90LoRxWFZYRmrZSGkxIqlEEIzOpDi\n1NHMhrmZz/X09DTKr4MgoFarUalUWFlZoV6vE4/HSaVSDZVxK9J2GEpMuwpiF4cS6UHzC7GSrVzh\nmbTHsUHzQzOzVjkQghi75bkAuN98pGNjJu6+C/drX6P+la+SuvfVHRu3i3XU//Gf0JUK8bvu7NiY\n7iPmHnCe+9yOjbkdZtbMz2B0zx82VFZDK+7Bp38jfRdd1Ot1zp49y8DAAJOTkx23uo+wldLX3O84\nOjrK4ODgnpTLS81YzOVyzM/Pb+q13EiW4jGHH37ejfzd//hn6q5PqVJhZn6JyYkxpBSGILA+Dz8I\nNvXJidDyxPc9VlZWEQLGx4+E0Qd1kk4CRPulpVKK1lS+aFEvCLRGWhYSQ9S0NkRECLmprFWH/7+x\nvTDKTmy/b70tEbekaFEeW7YNzBxV0KromagMjR0+CMhkMusuqbUqZ8+epVKtYtsOjmMTizlIabVG\nfej1/ksAIY2a2y5+oh2aSZ5ltfYWthyr1qFKax52R+R2K7OaDXsJx1pnm5Grrx8Y8m3GCV0/pUAr\nRSzZh5QWMQtue/bmeKl2hM6yLNLpNOm0WSMqpahWq1SrJi6lVqsZt9mQLCaTyUb59uVwMX0ml5h2\nU5KvIqTCxWh5rXqFZ9Iek0NmfhdXD4bAWiMjWMeO4T3ySMcMABIhaal9+csdGa+LzaiH5zZx910d\nG9N95BFEMnlgJjURQZw8rAQxfIiU7hLELq4CxGIxTp48ydjY2GUjh9CeyHmex4ULF8jlcpw+fXpf\nZjj7VRCDIODixYssLy9z8uRJhoeHW/bdTvE8fWKCk8fGwzJNQblSZW5xBQA/UKHRSPPkNk2WYqnM\n0tISyVSS0dExrCaXUrdW2pJ0aKVp7i5RoRuq7wcQ9r9pIXAc20Qj2HaYvKHDqIdmJS4saWyZ2i76\nDLd4faezr5Qhic3btxtLSmPSks+XSCSTDGQyJOJxlFKUy2Wy2SyFQpFqtYrn+ea6i9ZRXT/KZzR5\nkDKMHWkH25INR9TADzacA6NMWtLCsm0i79Ko97EZjmNvrR5GhBLWr5NeN+dpmO5YFoZMghOPI5wE\nUsJN14yQSm52D94NAZNSkk6nGR4eZnJykqmpKYaHh5FSks1mefLJJ7lw4QJLS0vU6/Vtx9orugpi\nF1cNGgriYSWITQriQSF2y3Op/vlfEDz1FPaJE5c8nn3ttVjj49S+8lV0EBxYZMIzCbUvfwWRTBJ/\n/vM7Mp4ql/F/8Dix229D2AfzlRc9BDk2dDh/UUQPkVKHlMB20cVeIKXcMmev0/tpXkTn83nm5+cZ\nGBhgdHR03+rFfhTEcrnMzMwMvb29TE1NtV3IbkU8X/LCm5lfXqVUqhAoTaFYYsGyODI6GDKldaUo\nCFTDsEYrRRD4FAoFxkZHcdpEhgRBQEy5INsbA0WGNUHoMqrU5pJSw5lCpSr8HauUolQsGeMZYbIB\nXaWwLNl4bacS0kit3KjO7WZboMXFU6PbKo5aa56amadUNr8DhJQNQxuNRiuF5/l4vkelXCZQAZZl\nE4/FTAluqFZGlyFS5iKsu4gKYwwTHhOYpkBhCZQfYIiaCudgSrC3uz99r737vRAiVJAjI5z1XkZD\nmk1MihBGaY3UXivehyUEowMJTm8oLW0+V3v9mZFSkkqlSKVSDA0NobVulKS6rsvKygr5fL7Rw5hM\nJhtmTHvFfkxqrqYexK6CeBUhOXC4CWJUbndQCiKAc8stwHqJ4aVCCEH87rvQuRzuI/+7I2N2sQ5/\nZgb/8ceJvfCFiA4t+LxHHwWliB1QeSk8DRTE8DuiqyB20cXuERG5IAiYmZlhcXGR48ePMzY2dkml\nbXtRELXWLC4ucvHiRcbHx5mYmNhS5diqJDYRi3HnC24h5liG7ChYzeZZXF4jUBpnw4JaKY3nuswv\nLAAwPDyME4vh2O0fkNZqZaA94Y1KRyPy0k5tNHEX6+cz8ANWV1Zx6y5jY2PYjmMURqvJGVWbDL6d\niJ5Sxu2zGXu5djos1bTb9FpqrZmZXaBQLG16r5EjGRLGdDpNf6afgUyGZCKBxhCdcqVE4HutCuOG\nfSitTRQJAhWYY1ZKobQi8EPH2DDiw8x3e6Jj29aWCmqkYmpolMKKJkIdbSelhQj7D5OpXizLJmYL\nnvecrY3hOlESKoQgmUwyNDREIpFgbGyMsbExHMehUChw/vx5zp8/z+LiIoVCAc/bvYnjfnoaK5VK\nozz26Y4uQbyKYNmSRH+cSvZwEsSehMNAOnawCuKtIUH8ZufIXFRmWu+WmXYc9S9F5aV3dmzMiMjH\nwocFB4GLaxWkgImBw00Qk5muSU0XVwc62Xu03T7q9TpnzpxBSsn09HRH1ILdKoj1ep0nn3ySarXK\n9PQ0fX19O853K+J5YnKM6VOT2DFDBqUQrGbzLK/mQgdT8zmtNflCnpWVZfr6+nDsdZdOPwiM++UG\naKVR3mazPKWNCun7PrYlto2H8AOjVtVqNRYXF4nHY4yMDGOFpDSKZJBSEnMsNObvQkTmL3rLcxoo\n1VSeKnblcLrhCE3Pn2ycJADmFpbI5gvtt9jiWJsJY19vH6lUGiGNmU+lXCabzW0uSQUsy96ylNfE\nlhhF0bHtbUmzFOsGPJveC1VCjemTjAyK1ntcIwdVaXROpZGWjXRSIDS3XjdKIr61UdPl6Bm0LItk\nMsng4CBHjx5lamqKI0eOEIvFKBaLXLhwgXPnzrGwsEA+n9+WMO43B/FqURC7JaZXGXqGUuRmCybv\n5hDmrx0fSvHYTJ6aG5CIXf7yzNgNN0AsRv1rX+vYmPEXvQhiMSqfe5jeX/0VxGXseXmmofLwwwAk\n7r67Y2NG19659daOjbkdtNacWy5xJJMkdgBxLvtBaaVMsj+B5XRLpLvoYjdQSlGpVKjX60xOTtLb\n2zmH4p0URK012WyWxcXFPZng7DTui553E3OLKxT8ChqzWF9YXkNKydjIAPW6y9rqKn7gMzwyQiwW\np1QqNcbUGixboPzN+6jXqySdGGCIjA77CIUwhMJk5m1zDFqTzeUbDqnxbSpKtDI9dM2un8pIlS1z\nhXVFKFAaS4IlYZdmoQ1EofGE2YNaa+YXl7ccRyC2JHNAwy3WtkzPpem7S4XHphslqeVyGaUUjuNg\nWRYxx8Gyrc33QuTeI2j0J7aFBiW21A7RSrU8KDDHu+5E27i/hEAHxlgokTaxLqcn+pkc3f4BxkGY\nygghSCQSJBIJBgYG0Frjui7VapVyuczKykpDhYxKUp0wqmS/JjVdBbGLQ4m+8T78ekD5AFW6veC6\niX4Cpfn+fPunbJ2GSCaJv/jF+N/7XscC7mVvL6mf/AmCc+eo/c3fdGTMLsD93/8b9+vfIP6SF2Mf\nP96RMVU+T/3v/yfODTdgH53oyJg7YTZbJV/xuG5i5/yzK4HACygulek/ejgjOLro4rChVqtx9uxZ\nlFJkMpmOkkPYXkH0fZ+LFy+ytrbGqVOn9mSCsxNBdBybe150G8KSRK1uUkjmFle4cHGOxcUFbMcJ\ng+9jWFKEhGF9TN83fYDt4NUModFKGXVJivBYzWJ+q+dngR+wtLxMve5ydOLItuTQDsPhg9BNM4IM\n1UXLsrAsC2nJRsltFC/hBwq2JEhbo/mZsB8ELCyukCsUURsiQSJsd7kEAj/sdQtUsNl4RwpicYd0\nOkUm008mNL0BTWkbhVGH+RbbiaOWZSFpH1BpWSJ8+N18D+mWKA6ltDG3CZlxPJFCWjH60g43T+8c\nA3Ylgu2FEMTjcTKZDBMTE5w+fZpjx46RTCapVCrMzMzw5JNPMj8/T6VSaWvosx2uJgWxSxCvMmSO\nmic2udmDIWB7xfXHzKL5OzP5A9tn8sdeAUD1rz/fsTF73vxvQUoKD77XBMJ2cckovPtBAHrf8u86\nNmbti18CzyPxL17RsTF3wndnzb0d3euHDYWFElppMhPbP93toounEy5HiWkUPH/u3DmGh4cZGOhM\nLutGRERu40K0VCpx5swZHMfh9OnTezbi2U1v45HRQW676VqEDAmKBeVyiSfOXkBjMzCQaVTJ+IHC\ntqzNYzYiEFrheR5a1Y3SGLqmNhMME5HQuk21WmVxcZFEIsHI8DAauYk0NY4PWoxilGJDiMY6mgmj\n67rkcnn6envxfR3GY6hdGwUFwToJm5ldIJsrGHJtyU39je2ua/P8ERt01B24iGNbODGHnp40mbCH\n0bik6tAl1RDGulcn8L0tyXtESNtBSkEQqE3kSEqr6e9g2xIvNLcRUuIke5ASnn/DGPYW/akth3oZ\nFMS9loQKIYjFYmQyGcbHxzl16hSTk5OkUik8z6NYLPLkk08yNzdHNpulXq9v+zPVVRC7OLSIVIH8\nISeI353NHdg+Ez/6oyAl1b/6646N6VxzDclXvQr/e9+j+hd/2bFxn6mo/+M/Uv/KV4m/6EXE77ij\nY+NW/9pc8+SP/YuOjbkTvjNj7u3DShCjh0f9R7sEsYsutoLrupw7d45iscjU1BQDAwOXnFe4FaK+\nrmjhqZRiYWGBmZkZjh07xvj4+L7iO3ZrfnPbzdcyPjaMUpql5RVQmqGhIdYKZVbXWtcS7ahgoDR2\nU9RFZIyilKJeLWFJozy1LbOMyKXW5HI5ctksQ8NDpr8yJA9qYxJECBPJ0Tympo13TMu8crkc2WyW\n4eEh0ukeE80gZBitYbUojO169yKVUinFhYtzLT2HUb9e6+dlW84XObU2Xx8hBJ5SyC0OQjZlUjbK\nPKUIexhTDcKYSiXxXJ9ypYLn+5RLZarVWqgwNgmGbUpQpRSN95vJm5QS3zfGOBGzVU1us6mefrSW\nPHd6iIHe3Slo+3EJ3Q6dMr2JxWL09/eTSqUYHh7m+PHj9PT0UK/XmZub4+zZs8zOzpLNZqnVai3X\nsBMKoud5vP3tb+d1r3sd9957L1/84hfbfu4d73gHDz744CXtazt0exCvMjQUxLniFZ5Je4xnkmRS\nDt+ZPTgF0RocJPaCF+B+7WsE8/NY4+MdGbfvV95K9XOfo/je/4fkv/yxbuTFJaDwe6F6+Pa3dWxM\nVa1S/9KXsaencZ71rI6NuxO+M5PHkoJrjxxOAhY9PMp0CWIXXWxCRCIWFhYYHh5uyRbcyhW0E4jI\nQq1WY2ZmBsdxmJ6e3rdFfzTmbgnt7TdO8f0fPEEqmSSVThH4Aba0WFhapVZ3GR8bbkREOG3IaqCC\n0MjEqFJKaSxLIhB4bgUr1tN2v0oZMrm0vIplWYwdObKJDJtyVInfRGakEPiB2qQYGpfSzaH3vh+w\nuraKFIKxsTFiMbuhBJp9hOWqDVLarofRnFPf9zn/1CyVahsjHhWqWKFRTugVswm2JTeZ44jwf+3Y\ncORYat7emvgLKbGlpKfHJggCCoUidphxGPUwxuMxpGWRiDkgLIQw2xnWKtEblMWIw0fTMoru+sOS\nWDKFsBIcH0szNTnYdl7tsB/Fbzvsx1RmN+M5joPjOA1TKN/3qVQqVKtV8vk8vu9Tr9f5+te/TiKR\nIB5vH/GyWzz88MNkMhne/e53k8vleNWrXsU999zT8pk//uM/5vHHH+f222+/pH1th66CeJWhPywb\nO6wKohCC649lWMjVWC11NtR0O0QKUvULX+jYmPapU6Rec4Y+9vsAACAASURBVB/+E09Q/eznOjbu\nMw31v/+fuF//OvG77yJ+2w91btyvfAVdqx1oeannKx5fKDI11nMgJkz7QW6uqyB2cfWhE0pE1O+3\nsrLCyZMnGRkZ2aSiXA4FMcLa2hrnzp1jcHCQ48ePXxI5hN0piFFkR7lc5Cdf8VKjfmijovhKoYFC\nqczF2QVjAiMEnh+wseoz5BaNRb9jGzOTQCnceh1Ue7fISqXC3Nw8vekUw0NDWy7wfWXyGBvHxtbl\npEFITiNUQyfUZCLB8PCwicfYcBlVGB8RubK262G0LUmtVufMuacol6ub8hTXz4UycRBRa+eGa2DJ\nzeQwUl3ROnQhbToPTYrfTjDkzRxHpFLG4/GGScvgQAbHcVCBolAskc9lKZVKVCoVfD8g8A05bL53\nbNtqKMBR5mIQ9ltKaeHEe0knbW579taRFu3P0+U3qbkUbKVw2rZNX18fY2NjnDx5kpMnT9Lf38/c\n3BwrKys88MADvPGNb+QjH/nInmI1IrziFa/gl3/5l4F1Z9ZmfPOb3+Rb3/oWr33ta/d3YLtElyBe\nZYinYyQziUPbgwhw/dEr0If48pcDUP2Lv+rouL1v/WVwHArveQ+6tvlpYhfbQytF/nf/EwB9HVQP\nAap/aa71QZaXnlks4vqK67cIBj4MyM8WEFLQN9b+iX4XXTwTUSwWOXPmDLFYjKmpKZLJzRmhl4sg\n+r7fUC5Pnz69a5fSnbCT4lmtVjl79iwAU1NTXHftaa6/9hR+EBj1C8NN/EBRrta4MLMAYTC7ZVmG\nRAkRms+EpiWWRRCEBjBNqFdLLeWXSmnW1rLk83lGhkfo6e3Z8Zj9QCGFCCMytie+nhcQBD7Ly8uG\nHKaSTSWmaywuLbG4tMTCwgJzc3MsLS6aeWmNbNPzKIWgWq9z7sIMnucjpESEiqofmD68iDBqbc69\nCA1eZFM0hGxDDqPXGzwwjOtovGfJlnO31TWV0mqMvfEThvApbNsiFouFPYwZ+vozxGIxVBBQLJfI\n5rIUiyVqtVpYUirDBwPr/FSzfjyJnn4c2+KFN45j79G1+7ATxN0qnLZtc+zYMe6//35yuRzvec97\neP3rX4/rug0ivRek02l6enoolUq85S1v4a1vfWvjvaWlJT74wQ/ywAMP7HncvaJbYnoVYuB4hrlH\nF3ArLrFU7EpPZxNuPmEa/f/pyVVe8uzRA9mnNTFO7AXPRy0toSoVZIdcpuxjx0j/zBso/9F/pvB7\n76b/gXd0ZNxnCkof/SO8Rx4h+cofJ3bTTR0bN1hbo/pXf4118iTOjTd2bNyd8E9PrgJw84nDSRC1\n0qxdyNE/0duNuOiiC9b7/YrFIseOHaOnZ+sHJ5eDIBaLRWZnZxFCMDk5ecnlac3YSkHUWrO6usry\n8jLj4+NkMuvfVy+87XoWltZYXs3h2BZuqChpDZVqldW1CsePShLJFLYl8UJyG6YdoKVoEMtmKKXQ\nbgURS+N5HqurqziOw9jYWCNvz7JlS9nnVscUBAFaQxAogsDH982fIAgIggA/CAh8n3K5jBCCVCpF\nrbqeD22qKaMcRQvLtltUmnZlqqurWWYXlhrnU4Tmn6VyGaUCent7DWHU2sRHoIiyJnR4Ai0pTc9f\nm4SPjVEYSiksIWGDmYwUcovoCmFcVJtjKdjYR2gIpG1ZKK3CclijFieTSYLwwYDve3ieTzUsobVt\ny8SIKEMwdagsJ0LX0luePUKmd++Zup0kdFHvaKcVxL2UrNZCkWB0dJSbbrppU1noXjA/P8+b3/xm\nXve61/HKV76y8frnP/95stksP//zP8/y8jK1Wo3Tp0/zUz/1U/ve11boEsSrECPTg8w9usDK2TUm\nbtyb5H8QuGkyQypm8fUnVg50v4N/+GHk4GDHcwv7/s9fo/Z3X6T0kY+S+NEfIf6CF3R0/KsV3uOP\nU/hPv4ccHqb/Xb/d0bErn/wU1Ov0/NzPHkiAdoSvP7GCEPCCqZ0tvq8E8vNF3IrHiecdu9JT6aKL\njmI/P+eRrX0qlWJ6enpTKVe7fXSKICqlWFxcpFAocOzYMRYWFjre39iOIPq+z8zMDEEQMDU1RSzW\n+hDZcRx+9M7b+bO/+u+UK1Ucy8ZXpmTSlhauF3BxbhFp2YwM9bcQJnNcGtu28f3Nykm9XkPVPXKF\nCv39faTT6ZbrFvgB0pKbohkCPzCkxfdQvo8fBLiu1/Z8GXdORbVaJd3TQ39fH5aUCMvCkhaOLdFC\nNkpJt0JUpuq6HnPzS+TyhZb9+X5AsVjEcWx6ezON428+HkNmDSEUUhhFThopTgqJRjV6GyPS10IS\nAd1E+ja+3wwpzOfN3w2JbFwbjIusH6pZfljCGqmKQsimklFBPJEgFlNYlkUQ+LieHzrSBmRzBRzH\nIZVKEsgE0+N9nDyyd0O2dgT2UhCRwyupSFYqJl7uUl1MV1ZWeOMb38gDDzzAHRtM+97whjfwhje8\nAYA/+7M/48knn7ws5BC6BPGqxMjUEADLh5QgOrbk9tNDfPX7S1xcLTM5dDCWwNbw5Vm0y1SKgff9\nPiv/6qfI/vtfZfRvv4Dc5il0F6A9j+xb3gr1OpkPfRBraKhzY/s+5U/8v4h0mtRr7uvYuDuhVPN4\n9GKO6yb6yaQPn3IPsHLWKJzDU7s3Euiii6sNUXzF6uoqExMT9PfvboHbKZOaWq3GxYsXicfjDWK6\nW8fRvWDjmJFaOTAwwOjo6JaL34H+Xu564XP5/Jf+EYRoqGm+CozxjBAsLK1SKlc4cWxs0/a+72NZ\ncoMJjCKbzeH7PuOTp4nF2iulvudTrxsy6Lnmv1GUlJQiNH4RxGIOQlrYto1thSqgtCiXS5TLZSYn\nJxuxINF2QKMsdDcol6vMzC1QrdVaaFm9VqdULtOTThNPtD8OKUyOoNaE5j2qRWEMWCdkMsxybJ2X\n0WEty2ooiFHJ6qZ9bVC2NRohZCO30qitrW6pOlQyLcvMsWVUbbY37FUSc2LEHIey1iQSSSOA2mls\nPPqsPLOzQSNoPh6P74pUHfbyUti7glgNVepLdTH98Ic/TKFQ4KGHHuKhhx4C4L777qNarV72vsNm\ndAniVYiRabP4Wz6zeoVnsjXuuGaYr35/ia8/sXJgBPFyIn7bD9Hzb3+R0gc+SP6dv83A7/3ulZ7S\noUbx/X+A9+1vk3rNfY3+0E6h9vkvEMzPk/7Zn0H2HZwRyz8+uUqgNHdcczjVQ4DlM2sAjEx3jpB3\n0cXTCfV6nZmZGSzLYnp6Gsdxdr3tpZaYNpd2HjlyhEwm09Kb1uny1UjxVEqxtLRELpfbsYw2wukT\nR3nerdfx9X9+DMdxQuMVQEOpVCWRVARBwA/O1Jk8NkZqA1Fq5jGe57K6ukosFmdkZBhbRGWrGs/z\ncF0X1/Vw3XpjUR5tb9k2TjxOPOYgLRsnLAc1atH6foIgYHXVfL+NjY21KRmVgN7kbroVVlazzC8s\nobQmZtu4vomIKJdLeJ5Pf3//tll/Smu072NbFoHSDdMZ0VQCGpnZaBU0THNE+BlLmj7LQAWN8tPm\n440gLYEOozG00Eii0lhFEJaCSstCtcs8jFRfrbCijENh1NGYY9RiKUEIiZTrPaCJdD+JdB8vu32S\ndMKiWq1SqVRYWFjA932SyeSOhPFKl4Pudsz9KIiXShDvv/9+7r///h0/d7mUwwhdgngVonesh3hP\nrLEYPIyIFtFfP7PCa15w4grPpjPo+5V/T+2LX6LyyU+SfPmPkrjn7is9pUMJ91vfovi+92MdPUr/\nb/5Gx8cvfexjAKR/7mc7PvZ2iEqmDzdBXAUBw6cuT+B3F11cKey0kNNas7a2xtLSEqOjo/sygrkU\nEud5HrOzswRBwOnTpzf1Gl4uBTEIAs6dO4dt23uOzbjt5mdTKFT4/tkLSGF63BKJBLZjh2HzFYSA\nXL7A6PAAxybGcBwzvgp73XL5AoVCnkwmQyqZwvM9VleWkXYC1w+dO0NIKUkkkjiOTTKRQNoOUsrQ\nkbPduTEqW61WY21tjXQ6TV9fX9vrGigV5iZuD9d1mZlbpFgqN17zVYBSimKxgBAWmUz/tvdOVOJp\nbUNYDMG11kmf1GilqHseiVjMlOiKpoxMpVEtzYuhIU4QZVMaRVCJpvc0WEISBH5jvs33WVSOa87P\nuqLpODau54efNeOpwPRuYtnY8TS3PmuE/h5zD/f29tLbazK4fd/fFWE8jBmI7cbcj4LYzuDq6Ygu\nQbwKIYRgeGqQ2W8tUC+5xHsOX7nbWH+S06M9fPPcGjUvIHEVGGaIeJzB97+PpR/7l2Tf/nbGvvh3\nyIHuQrwZulo1paVBwMB739Nxhc/7zndxv/EPxF/6Epzp6Y6OvR201nzjzAr9KYfrJvbej3EQ0Eqz\ncnaNgWP9OMndqyZddPF0x07kbLfYL0EsFArMzc1tW9p5ORTEcrlMvV5nfHx8386oL33hzazm8iyv\n5ACMe6mUxGNxdNo4eHqez+LyGnMLS4yNDDI00E88HqNSqRD4xsDFrbsUC8XGMUqrTLJviHgsRSwW\nIxaLb1Dkwn4yITapZhFMtl+JfL7I4NAgycTWRimm5NWQxHZGOEopVlazLC6vtlwHKQS1Wp1KuUI8\nGSedTLKzCGmcS4062KqIbkREEJUymYWJeJx4whAMHfYRBjrAti3Twyiat5UtBNts0/Q+Ublpa19k\nVOa6ySgnnIvv+y3bRK6ugYJ4qp9TR82fdrBte1eEMRaLNebSCWJ3OQjnXlXJSqWC4zib+nqfrujG\nXFyliErIDnWZ6fQwdV/xv84dHqXzUp/gOs+5jr63/SpqcYm1f/eWbvRFE3QQkP0Pv45/5gzpN/4c\n8Rf9cMf3UfzwHwLQ88Y3dnzs7fD4QpHlQp3nTw2HOVSHD7mZPF7N7/YfdvGMQj6f5+zZsySTyUsi\nh7CuUu7294RSitnZWebn55mcnGRsbGzLRWwnFcQgCLh48SLZbBbHcRgaGtr34llKyY+89DZicRtp\nm7iFyGjFsixsy6h9PT09ZAYGKFVcnppd5MJTM6ysrJLP51leWqZQKKDRJJNJMgMDjAwPMzLQY5TF\nVKpNuaZGCB16gG4+L0EQsLKyQrlSZWLiyLbkEJqUsqA1IxGgVCpz5skLzC8ut5J0DeVyhVKpRE9P\nmmTCkMPtDG5kSGpVk0onxdZLbaVM2XMunyedTpFMpRqqoZRW449W2pSRahrRGRvLRs01jtRDbZxr\ntNiwP/O+tKyW+80QIdH0x8CyLEQ4z2Syj6FMDz907e7d5yPC2JwZ2NfXh+d5BEHA2bNnmZ2dJZvN\nUqvV9v0zsFe1bzfjwd5MdCqVyiWXlx4mdAniVYoj140AMP+dpSs8k61x13NMc/sXHp27wjMBf3YW\n//x51MLCJY/V84tvIn73XdS//BVWf+bnUOXyzhtd5dCuS/aX/h3Vz3wG57k30/fr/7Hj+/C++z2q\n/+2/4TznOcTvvqvj42+HLzw6D6zf04cR0XfBkeccTLRMF10cJDYu5KIA+MXFRY4fP74tOdsLdqv0\nVatVzpw5g9aa6enpHZ0NO+WQWqlUOHv2LFJKjh8/vu9jjhSUIAjoTae454dvxZbGCVQro0AZ9cd8\nPggCqpUquWyWuflFFldy1D1FMpWit7cHx3Hw/QDP8/BcF8/3qVeroLYOEhe0J2P1ep3FxUWcWIzR\nkZHQkGXr5axl0cIxg0BhSUG1WuPJ8xc5e/4i1Vq9ZRulFIViAdfzyGQy2I7T6GtUoerVbm5SWiZu\nogmBCmj33FAKKFfKlMslBjKZLZUnIdazBy1LYtl2qGKKBmFUSrfIh5ZlozdkUcqwD1OGZadmbDOu\n0gopNpJGY2YTKI0TSxJPpnjB9WObCPZeEBHGwcFBYrFYgzC6rsvCwsK+CeOVykBsRrVavaoIYrfE\n9CrFkeeMIqRg7rEF4OYrPZ22uP5YPyeG03zle0vkKy79VzCz0fvOd6l8+jMQ+Az+0UcvKQpDWBZD\nH/0Ia//2zdS+8DesvPZfM/z/feIZW26qqlXWfv4XqH/py8SedztDH/8YssM1+lpr8r/5TtCavv/4\nHzoeZbIdPF/x19+aoz/l8MPPGjmw/e4Vs982QdATNxxeEttFF51AuVxmZmaG3t5epqenO6osRARx\nq0gMrTUrKyusrKxsyhjcadxLURCb9xs5s7quu68xlVKbcuWOHzvCTddP8cijTzScQLVW+L5PpVLF\n8zzqdVMx09fXRyIRRzpxilUfJ55i7MgwIHDdOrVanVzOOJom4qv0DY2RiKdwYk7DfEYI2TBbsSyT\nk6i1plgqUSxsLikNlGqUdbae180lpeVKleWVNYqlcltS7nkexWKRZCJBMplazxZU6xmGWpvAiWaH\nVEtK/MBfd6JpxgaHGaUCCqUSWsPAwGC4yfbqsgasyBG1KdLBkJn1BwxCCHzfQ28YMroXlDYqsBTr\nDzscx8Z1N5SWCqPfCmERT/VyKhPQm+5MTmd0b21XkprP5xslqalUimQyuaXpTadNavYz3tWmIHYJ\n4lWKeDrG8OkBlh5fxav7OPHDd6mFEPzErUf5g795nC88On/FzGq01iTuvovEj7yMwn/6PQq/9dv0\n/18PXNKYIpFg8CN/SPZX3kb1T/+U5XvvY/hTn8Qae2YtzlWhwOrP/hzuP/wj8bvuZPCjH+k4OQSo\nPvww9b//e+J33038rjs7Pv52+B+PL5Etu/wfd5wgZh/OogytNfOPLZIaTNI/0Xulp9NFF5cFzW6d\nR48ebSw6O4ntFETXdZmdnUVr3TZjcDtcSomp53nMzMxs2u9+xoyIYTtF5nnPvY7llRwX5xbxtSab\nzWJZEs/zcV2XZCpNf18/jm2FpjCm729lLc9arkhfT4rBgb4GaVZBQL1ep17OUy5XUUqRSCRIxOPE\n4gls2w6VVY0KFKvZNZRSjI2NtXUQNWRmg9NnlDOoFMVimeXVNcoVYyZiSdl6PTVUa1UqlSr9fb3Y\nGxxulTblqUHT9Y/IoWPbLYH2GxE9VAgChed7lIsFnHiCdDoVxldsf00Ij01pjW1Z+E3qoAijSEA2\njl8IQb1ex2oqJVVKY9lW4wSt38dhRmPT9XYciyDQaA3p/gzXnRoioXMdzy3ciP0SxsMQm9EliF08\nbTB+wxjLZ9ZY/P4yx24ev9LTaYt/cfMED/3dEzz8zRnue/7+y2H2Aq0UpQ//IcH8PPEXvwhreITY\nrbcAEH/hHbj//L868mUjbJuB338vsq+X8sc+zvJPvZrh//Ip7OPHO3EYhx7B6iqrr/tpvMceI/nK\nH2fg/e9DXIbmbVUokP+Nd0IiTua333kg91Az/vybswD8xK2HN3w+N1Ogmq8x/ZKTB35+uujiIOD7\nPmfPnm1kC+7FrXMv2KoUNJ/PMzc3x9DQECMjIwfmkBplGw4ODm7a714IYjMxjLZthx95ye382V/9\nd3KFIolkArfuYluS3qGhRn9nVILp+8q0wREStHKFXKFMPO6Q6euhvzdNMlxQJ5JpsGJUq3VqtRqF\nYgmtFfGQKFarZZLJNP39WzuINn5vC93I+CsUy+RyBXL5QiMovhkqVB79IKBUKqNUwEAmg21bm9RI\nCIPqmwxeBObaeYGPRLRVMRvbKtUgO319vYbIb8wg3ICGQhmWPAZK4wfr5DtC0PiMhet7FAom0L6n\np6ehMEohCZSPuc10w6U0Zlt4TefGcWw8PzCRFj39HBvLcMPUMGfO5LaZ6d6w2zXWbglj9PPTSdOb\nbolpF1ctjt54hEc/+z3mvr14aAniYE+cF187wle+t8T35wpct4UzVichpESk05Q/+Smca6+l/J8/\nhvY97JMnqX72cwx84P0dW0QLKen/rXci+/sp/v77WP5XP8Xwf/kUzrOe1ZHxDyv82TlW//Xr8M+e\nJfX615H5v38HsUVJ1qWi8OB7UEtL9L79bdgnDlaFXsxX+caZFW441s/p0Z2zxa4U5r5temsnbnxm\nKdhdPHNg2zbj4+Ok0+nL+hBkYyloEATMz89TqVQ4ceLEvheIe1X7lFIsLi5SKBSYnJxs2+O4277G\ndiWlWyEWs3nF3c/jz/7iqwRegGVJ0mlTUup5XqMnzpIml09YAh2WeJreP4t63WNxOcvicpZUMk46\nlaCvt0ZmcJRUKtU4h57nUSjkKRaLRhGrVckqQxrj8Y2upwau61Kp1igWyxRKJrNwK0Slmq7nUy4V\nsWyb3t5MI4Ow/bnSWLYhZ1E5bBCSiYZDqzC9fs1jaA2FYpFAB2Qy/diR+cu2Z5uG2mdZVotyGQRB\nuE/dIJGWZVGtVSmWSvSk0sTDElzLEghEqHBKhIx6SA2J/v/ZO/PwyMoy7f/es9aaquzdnU463ekN\naJClbWgQF5RlFMYNBHEEB8YZAeGbD3Vw/BARcdzGZUYFx3FHFBwFERQcEdxQ2UGbraHX7Eknqb3q\nrO/3x6mqTnpN0kkn3dTvurhy0ZWq961TS859nue5b8dzg9deUdBUBcfxUFSBYYZpbqxnXXm2fiar\ndNN9rL0JxlQqhWVZVUOq/bWk7o9ai2lNIB7WLDiyGaEI+jcMzvVW9snfnrCY3zw3xM+e6DkoAhEg\ndvFF2I8+irpwIU23/xDnpU34Izuo+5cPzXgbqBCCug99EFFXR+aGT7DjbefS+N3vYJxw/IyuM19w\nXnyRkXe9G6+3l9hl76Pu/31k1k7Y7A0byH/7O2jLlhG/7H2zssa++PmTfUg5v6uHAH3l74BFRy+Y\n453UqDE7CCGIx+MzniW4K+OFQKFQoKenh2g0SldX117nEif7uI6zd8OW8ViWRXd3N4ZhsHz58r2u\nOxnRWTGjGT/Ttj90VbCis4lUJkMoFApcO30fgajO53m+H4gN158wkudLH6GA9IOWSdt2KJZKDI+k\nUbYPEKtrIBQKYRg6xUIeKSUtzc3ouo7neThO4PhZKBaREjRVrUZhWLaN5/nlk/uds4H7wrZtMpks\nsWiUcDiEXzZ88T0ffMlO75dKqU/iiiCb0RvX+qkID5Sy4Yvig6g4moLruGSyWRRFJV6XwFAVfJ+9\nitAKigBfgqqICeIwIJgDDeYzg+ebyWaxLIu6ujp0TS+bzEgElWNRzkIMAhYDYx8hoWy4IwHPC6qk\nitBJJhs45RULqkJ8PgjEXakIRtd1MU2ThoaGac0wzsT+ahXEGocMRsSguauBoY075m0eIsCJXU20\nJkLc95d+3vf6FQfNrCZ+1ZWMXf1B9OOORV/eBcu7Jtzu9fejNDbOWFtk/J/+ESVRR+pD1zB87nkk\n/vXDRC/5e8QstUIdbKTvU/if/yH90Y8h83nqPnwNsfdfMWvi0C8WGbvy/4Dvk/jkjYgDsK+fDrbr\nc+dj3UQMldevmb/Cy/d8ev8yQLQxQt2C+VvlrFHjUEBRFDzPY2hoiNHRURYuXEgiceAXNicr5sbG\nxhgcHKS1tZX6+vp9fr+ONzHZ9fcqFcOp5MdVjHBSqRTHHXMU9Q3NPPTIBlzPw/U9NE2rOpu6rofr\n+ShqID4q7ZCBqFOQIvhu2tkuGQizQi5NNlcgl8ui6waxWJTctl5UZefsX0X8BRmMDo7t4LgOmqqi\n6zqRUAhPqlXxFCwMvuvjuT5++b9cLo9jO0SMCMWCRcG3oBI4v7fcQspV5GpwPXsUo4oiEIrAkz6W\nVSISDaGHNZyMBaaGFAJFV/Z97MuzhZ7nsScDG0kguAFS6TS+79NQX49SNvXxK26zmghaY3d7fFkV\n2Z7no6kqvi/RNY26ZBPrj1lISNfwPG9asQ/7YrZmBvdUYSwUCtUqo+d5s2Z6UygUqD+MzAgPjzPT\nGnul45VtDL04QveTfSw/tXOut7NHVEVw/klL+M9fvsBPHunmktd27f9OM4C+ciWx91yM/fgThE9/\nA7DzSyb3jW9SuPNO9NWrqf/8v8/YmtELLkBduJCxK64k/fEbyN92G4mPf5zQqa+asTXmAvupp0hd\nex3Ok08iIhHqb/oKkTe/eVbXTH/setyNG4leegmhV586q2vtifue7mM4a3HhyZ1E56EJVIXB53dg\nZW26zlpSmz+sUWMGGBwcRNd1urq60HcxMpku+5tB9DyP3t5eLMti6dKlQeVuP1SqgruejE+lpbSC\n4zj09QWRVEuXLkXTNNasjpHNFfjrc1uwXQfXdVHVINA9qF6BgsD3gxZJX/pIPxCPqqpWJUvFoVQI\nyOWy2I5HPNE4IbPS9TxURQnC56VEEYExjK5pEA6XA94dHMclPZajVLRQpEBFBX9iHqD0fUqlEoqi\nYIZCCD+4WdEEihoY4yhlESsgyH0EgsKbKJvA7DR5qVTopAyUpe9LpC+xiha2ZWMaJp4FJctGUQQF\nSigEglfRBIquouoqmqGg6CqaoaKowZ6DttC9v0bS88hmspghE9MMARPFqm6oOM6us5fBrKIQsmoC\npGoqvhe8BtFYIycf205TMmhb9jyP4eFhDMOYcFHhQFxDZzrYfm+CTtM06urqqKurA/YtGCORCIZh\nTNv0plgs0tbWNiPPZz4wf89qaswIS165mMd+8Be2PdIzbwUiwJtPWMx3freJHz28jQtP7iRkzM68\n2q5Ezn07hZ/cgTc4iDouJytywfnE/uFSUtd+lOxNNxO//LIZWzP0mtfQ8psHyHzmsxR+eBsjF7yT\n0Bv/hsR1H0Vrb5+xdQ4G3vAwmU99msLtPwIgfM7Z1H30WrRZ/pIs/OxuCrf+AP2oo0j8v4/M6lp7\nwvMlt/5xK5oquGD93LjvTpatj3QDwXdBjRqHMzMZNr8nUqkUuVyOeDxOe3v7jJ7g7mvvhUKB7u5u\n4vE4ixcvntKJ+fjHrZzcj79tMuRyOfr6+mhoaKCxsXHC/davXUM2V2Rrz2Awg4gsu3V6Qbsp5Tw9\nAtFUwfO8auVQlsPnM9lMkLsYryNkqhNqXkIEFTnKvy/KmfCu7eFaLq7t4Vkevu3hA4YwcF0P23MB\nH1/46KaOoqt4nktdQx2RaBhVVRCqqArB8eJqfNVSngR3pAAAIABJREFUVVU8d2KEhaqoSOnv1ioq\npSSbzWJEdZriDQgU8GXQ/upJPMdDehJhefiej19y8UsuDlSPmaqrqIaKZqropoZmqhPjMyRYVolc\noUAkFMY0QyhKYGFaeQqKIvYgDkFTterzquT9ea6PoiqY0TpOPGYxCxt3isOKM++SJUvQNG3CBYbK\nYwTrTf59ORvB9pN5vMkKxn1V3/dGbQaxxiFF49J6Ys1Rtj/eGwyU6wdHeE2VqKnx9ld28O3fbebu\nJ3s478SDd9IdOvMMnKf/QuEnd2C+6hSUxsaqwDHWraN0/6+RxSJiBuMZ1KYm6j/3WaLv/jvSH/0Y\npV/cS+mBB4hfdhmxKy6flSiImUTaNrlvf4fsF7+EzGbRjlhN8oYbME9eP+tru9u3k/qXa8qVyq8e\n9NZSgN89P8S2HXnOOa6Nlrr9X8mfK6SUbH24By2kseiY+dsGW6PGfMbzPPr6+iiVSsTj8VkxwtlT\nBVFKyfDwMKOjoyxatKh6UjsVKgJxOlVDKSVDQ0NkMhkWL16815Pf1596PHf/7x8Z2pHCdhwURa22\nXfpSoiiVaubECArfr7SjOmQyWXRdJ5FMIFCwSwWMUBTJznMW13XxHR+74OCWXDzbY/whUxSBEtYw\nDBXD1EEVaIZSrQBmMlkcx8ZQdBzfolDy0XUDXdcxTH2CgAWqUR1SUha8E4+bL3ev7rnlip5uaNRF\ng5Z+RQikKlANtSxAg6pzxYlU+hLP8fEcF8/2cW0XafuULA+ylecGmqmhhTW0kIbtlvBcl1gshq7p\nO48nQQuvvxd7VFVV8KVfnW+sVER1TUMPhVl7VAdLFgYt05ZlVWdsW8ddQN9ViE1HMM50i6nv+9Oq\n5u9NMKZSKWzbnmB6M77CuCeKxeIezaIOVWoC8TBHCEHnSe1suPt5+v46SPvxi+Z6S3vlvBM7+MEf\nt3LrH7fy1rXtaOrByZRTYjFKDzxA7r++Tuwf34v9l7+gH3kUslREZjKE3/bWGRWH4zGOOYamn95B\n8c6fkv7kJ8l+8UsUbv8RdR+9lvA5Z8/LlsDSb35D+mMfx33pJUQyQeKTNxL9u3cdlFlK6TiMXn4F\nMpsl+cUvBLOjBxkpJbf8YQtCwLtO6Tzo60+F0W0pMv1Zlp3SgXaQqvI1ahxO5PN5enp6iMfjdHV1\nMTQ0NK04iv2xawXRcRy6u7sRQhxQK2vFyXSqbXOVTEdN06otpXtDVVXe+Pr13HXfHxhJZ5C+h5Ri\nnDgNAu91XcVxdh47KSW2bZHL5YlGo0Qi4WqovZTgWEUULYxTdHHKojBw4yw7exoK4ZCOYiiYYR00\npSrXgjZUUY7YCCp6qgL19fVVMe44Do7jUCoVEVlQNQ1dNzB0DUVTg+D7cj7gngS8QCDKM6kQCKpc\nPk8sGq22x6rlaIrg93cRoJ4fzBgCmqmWq4SgKQqu5+M7Ho7l4loebsnFLrrYRYdSqRS4xyajSA2k\nIqsiODCa8ZAIVFVFVeSE2U1ZNt0JMiMr51gS1TBYs6qDlR0NQBCf0t/fT0tLSzW3cm9MRzDOh9zC\nPVERjI7jEIlESCaTu1UYK/OLuwrGmaggOo7DRz7yEXp7e7Ftm8suu4zXv/711dvvuecevvvd76Kq\nKitXruT666+f0UrseGoC8WXA0vWBQNzy5+3zWiA2xEzOOb6NHz/Szf3PDHDWMQdvr4mPXos/MkLo\nzDOIf+Bq7MefwNu+HW3VKrSu2RUhQggib3sroTPPIPufXyb39f9m7LLLyf7754lceAGRc89FbWqa\n1T3sDz+dpnDnTyn88DacDRtAUYhefBHxD34QteHgDWVnPvs5nCefIvy2txE579yDtu54ntg6yrO9\naV5zRAudzfPb9GXLn4P20qUnvTyyN2u8vJnpE86hoSHGxsYmVO+mm1e4P8ZHUmQyGXp7e6edqVhB\nyqDds6+vr1r5nIzQzGQyDAwM0NjYSENDw6TWNwyNc848mbvu+wOjqQyBXtnp9AkC1yvHXxC0W+Zy\nOTzPo6GhHlDwvZ1zfFbewc7bSDeNakQAgaIpaCGVSMRAC6mI8omxrgWPPZ5gjlClVCqRzeYIhUJE\nIuFqZVNRFEzTxDTNag6i57pYloVVKuJ5PoYRVBd1IzhmFcFZFT4ERjsgyeUDw5tEIoiwgPEtqqJa\nsdsVryregwLl+PeBoquYuopZ/jPjWA6pkRQh3USTCnbexc67CAWMsIYeNdDDOrqmVIViBVVTCMYZ\nJaoIRLYQBG6rqs7qrg6OWd6MlJKRkRHGxsb2WTXeF/sTjL7v73VmcLrMRkVSVdVJtaT6vs/DDz+M\nruuTmg3eFz/72c9IJpN87nOfI5VK8Za3vKUqEEulEl/60pe4++67CYfDXH311Tz44IMTBORMUhOI\nLwMWrG4mlDDZ+uceXvVPQZ/5fOXCkzu587Eebvn9Fs5YszDoqT9IRN51IZlPfZrG798yJ6YnSjQa\nOJtecD6ZL3yR4s9/QeYTnyTzqc8Qeu1rCZ/9JkJnnoEyjTaj6eAXCli/foDiPT+neP+voGSBqhI6\n60zqPvAB9COPOCj7qFC85+fkbroZtbOT5Kc+OWfV1e/+fgsA737V0jlZfyps+eN2FE2hY+38vTBU\no8Z8o9Jap6oqy5cvn1A9m0ocxVSoCM++vj6y2ewBZSrCzpPyxYsXUygUyOfzDA0NoSgK0Wi0XLGL\nTHhuvu8zNDRELpejvb2d8BQ7Z0Kmwdmnr+euXz7EWCqHEHLCHJ/0JUJVKFk2+VwWwzSJxWKAQFGg\nlLFwCi523qnW2rSQSjguMOviKOURGU0NKmwQZPw5ZeE5XrhLIJvLUSoVicXiGMbONkxFKPhlt9KA\noEKohUKBEQuUIzVcLMcmV8ijCAVd19B1PRCV5fV83yefzyF9SCaTwd+lcr6gNy5aIvi5pxnTnZXW\nyq/uSUgG1ckciaY6DMMABK7l4RQc7IKNlXex8i5qWUSH60xUUwuqhr4MAhfLa3i+j1LJYRQKR61a\nytojF+D7Pv39/di2TWdn54waMI0nn8+TzWZpbm6eEG5/oKY3Myk4fd/fY9V8V8HoOA6Dg4O88MIL\nDA0NceONN/LAAw9w8sknc/755085+uass87izDPPBHZe4KlgGAa33XZb9XNZifaYLWoC8WWAoios\nW9/Bs/e9SM/TA3TM4yriovoIZxy9kHuf7uOXf+3nb15x8PZqvvKVRN/9d7ibt2CsOeqgrbsr2tKl\nNHz5P/Fv+HhQtbvtdkr330/p/vvBMAi9+tWE3vg3GMccjbZs2YzN4EnHwd2yBeeZZyjd97+Ufh3M\nXgJoXV1Ezn8HkfPORW1pmZH1pkLx579g9Ir3I6JRGr52E0psbip3j20e4ZFNI6xd1sCaxftuu5lr\nRraMMbotxZJ1izEOUnRMjRqHMlJKUqkUAwMDtLS07LF6NltGOLZtY1kWpmnuM9twMlSqNVJKDMPA\nMAySySRSSizLIp/Pk0ql6O/vxzAMIpEIpmkyMjKCaZosXbp02utHI2HOOeNk7v7fhxhL5wLDlXEi\nMVfIY5dKhMPBmp7r4WQdipkSFYcW1VQwYgZm1ECUL2grqgflecTgsYKA+Er7pucH7ZoVN9FMNov0\nfRrqG9lVmPmy7KApghNw36fsPhpEPbhe4LKqqiqhUPD31fU8nPJrlMvlytUlFcuyMU2TRH28PFMZ\nCJ3xuYVB9XDv7xkpdwppIXb/vXwhj1WySCYSaGWXVV/KaltquD6EZ3t4JY9CpoRTcHHyLpqpYsQM\nwnVm1XxHAErZQdYXgjWrOll75MJqS7NpmixZsmTW2hbT6TSDg4MsXLiwGkUxvsI4fk52qqY3M91B\nMJn1dV1n8eLFfOITn+Ckk07is5/9LFJKnn32WWzbnvJFlsoMYy6X46qrruKf//mfq7cpikJTuZvs\nlltuoVAocMopp0zp8adCTSC+TFh5WhfP3vciG3+9aV4LRID3vm4592/o578eeJHTjmzFPIjGOpG3\nvvWgrbU/lPp6Ypf8PbFL/h5n02ZK99xD4e57KN1/P346TerqD4CqonV2oq1aib5iRfBz5UrUtrZg\nqn1PSIk3MID7wkacjRtxN5Z/bt4C466Mq0uXEjnnbMLnnIN2xOo5q9gVf3Evo5dfgTBNGr//PYyj\nj56Tffi+5Cu/2gjA+09fOSd7mAovPLAJgFWnLZvjndSoMf9xXZe+vj5s295njMRMt5iOzzZUFIXF\nixcfUEvp+Fa+PYnbUChEKBSisbERKSXFYpHR0VFGR0erOXJjY2NEo1FCodC09hKPRnjzma/i7v/9\nI2OZXGVxstkMrusSj9chXMgO5HCKbnCzKogkQoTiJkJTdssVtK0ShilA0aFsquJ4Ex06PV8ifY90\nOoNuGETjdYDcTdSLSki8lLiOh+X5uF4wq+f5Dm45qsKXVGM1dm5HB3RK+RKOW0RTBBSKDKQtDEPD\nNAwMTUVTFTRVQVcF+r66tgTluUofTQsiQipI6ZPN5JBI6pP1ZTdY9phpaIQ1PEPBTJh4JZdCpoSd\nd/Eci8JYiVCdSSRpogg1eE5CcOSKJaxbE1SYe3p6ptRSPFUqhkuZTIaOjo4Jn689taTuKhr3Jxhn\no8V0KgLVtm08z6OtrY3jjjuOc845Z9pr9/f3c8UVV3DhhRfu9ji+7/O5z32OLVu28OUvf3lWz8tq\nAvFlQsvKRpKL69j6cDdWzsKMHXznx8myqD7MeScu4Qd/3MpPHu3mwpM753pLc47etQz9/1xF/P9c\nhfPSJuwnnkBbuXKnuNu0iRL3TvvxRSyGvmYN+qqVaCtWEDr1VLQjj5hzk5zivfcyetnlCMOg8fvf\nw1y3bs72cv8zAzzfl+GMoxewetGBB2PPJp7r8+JvtxCKm3SsPXxymWrU2BfT/b7K5XL09PSQSCT2\nGyMxkwLRdV16e3txHIf29nZ6e3un/Rym61KaSqWq2Yq6rlMsFsnn8/T391eNOiotqftycNyVaCTM\nm896FXf/8o8Mj46STmXQDJ2IHiU/WMB3fAQCPaxh1pkYkXI7oyLK818KQaZfECMhEDhWETOsgFBx\nXDcQfuPWtEol8vk88Xi83IYZIAAfgeVKSo5HyXJwfInrSVzf381dVRFKNYB+579V7W8olSx838cM\nhavVPA/IWy5j+Ty+56EqatDuqWkoioqhKRiqgqEJDE0hpAf/JhDV6qJXzpH0PB/Xc8lmMhiGQTQW\nC/Iky46wu779VKVyW7naamrEmmPQDIWxIlbWppgq4WZttDqDUCLMESs7WH9MB2NjYwwPD7No0aJy\nu+/MU2mddl2Xzs7OfRoeQfAZG/8ZnIxgnI0W06l8FovlbqsDNanZsWMHl1xyCddddx3r1+/uCn/d\ndddhGAY33XTTrFV5K9QE4ssEIQSrTuvi4e89yabfb+PIv5nfFZCLT13K3U/08J3fbeLs49qoC89M\nL/yBIEslSr//A+HT3zCn+9CXd6Ev7yL6jvOCfUmJPzSE80KlGvgi/uDgPh9DaWxAW7kyEIQrV6Eu\nWjjnYnBXivfey+j7yuLw1lvmVBzars/Xfv0imip43+tXzNk+Jkv3472U0hZrzl41b6NtatSYayoz\nd6lUira2tmrL276YKYFYcUetq6ujvb29ehI8HcaHl0/2e9yyLHp7ezFNk87OzmpLaSwWqwoF13XJ\n5/Pk83lGR0eRUk4QjPubUQuHTF578hp+8rMHMNUQVsrBcUr4vo8R1YnUh9FMdUK1UPpB66hAVOcM\nBQJVBK2Rtl0kFI4B5Rk6KUH65PJ5XMeloaEBVVFxfUnOcinYLkXLxfMnVt6EEGiKIGJo6JrAUBVE\n2UFUVUBXlHL24s4pQt/zyOWyKGGTaCxWNnkRCAmO7+P7Ek8KPM/Dsh2KJZuSY2PbPhYKQlHRVA1F\nCaI3FAGmphAyVGKGhqEr+G5glpPOZohFY5imsfN57mJ0oyjB+9H1AltSKXbOPCqKwPd8ovVhwgkT\nv+iSHSvhpCwWhhpYnayjr7ePYqlIZ2fnBEE9k1RaV0OhEB0dHfsUNW5ZIO/6Hp6MYJyrFtMKhUIB\nOHCB+LWvfY1MJsNNN93ETTfdBMB5551HsVhkzZo1/PjHP2bt2rVcfPHFAFx00UWcfvrpB7Tm3qgJ\nxJcRK167lEe+/xQvPLB53gvERMTgolOX8dVfbeRbv9nEP//N6rneEmMfuobiHXeQ/Mynif7du+Z6\nO1WEEKitraitrTAH5jqzwXwShwC3/3kbfWNFzj+pg0X18z8I94UHNgOw6vUHPwakRo1DAcuy6O7u\nRtf13Yxo9sV4l8npMN4ddbwoHd8aOpXHGm/yMdn7pNNphoaGaGlpIZFI7PW+mqaRSCRIJBJIKXEc\nh3w+Ty6XY2hoCFVVq2Y30Wh0wtyi7/sMDAyQHctx+pEn8bvH/kK/M4IRNQknDBRDKVffgoD68RmE\nvi8RStllU4Kilqt05fbLYj6DEYrhCwXp+2TSGYSioBhRBjMWhbIgFIqoxl1EQxqGKqqCTFUEuz5r\nIUS5oucHTp/jXg/btsnnc4TD0epcYjXHEIJWU0WgCIHvC2IhHSUZgXKbqmM75EsliqVK9VLBlQqO\nq1CwPMYUO3iu0kWVDgub6jENbUKFUa1UTEXQ8ip98OROs51g7bI4rLwnFNBVFV9TqY8ZtCebWaKG\neOnRzegRnVXrl8+aOCwUCvT29tLQ0LDf1lXXdenu7iYcDk/IXNwT4wVjxXXVK8+NzpXpTUUgHmgO\n4rXXXsu1116719uff/75A3r8qVATiC8joo0RFh+7kO4n+hjrSVO/eH63yb3jxA7ueryH/3lkO2cf\n38by1v1f3Z1N4ldchvWb35C65sMA80okHk4U77tvXonDoXSJb/12E8mIziWvmf+Cq5gpsf2xXho6\nkzQuPXgRJDVqzDWTEUnjZ/5aW1upr6+fUuVBUZRpm9TYtk1PTw+KouyWbTjVyuR0Wko9z2NgYADL\nsliyZMmUHBCFEFXDm/r6+r0a3lRaUXcM78Ad9VFyGj4+b1h/As+P9bCxuxdVDfIKlXL2g9xlTrDy\nfHRdw3HdajZiBSnBtvL4UmM4XcATGo5UoGgBgbNpXVglpKtEQxpaWSgGERvs9fWTMtiLIhQ86VeN\nY/KFAlapRCKRQFXVoN1UCDxv99fLlxJVC9pEA3EIINANg6Rh0FSv4roujmNjOw6lkoXl+di+RsFy\ncH0IR8JsHysRMXVihkpdWEMIqoY8wTECMa7dVIiyQ2tZbFeOb/noArDu2OWs6Ghk+5bt1EcSiLxK\n31NDJNviNK1omFGH+1QqxdDQ0KRbVxUlaMfNZrMoikLLJMzwpJQMDAxQLBarLdKwe4WxIvZm0/Rm\nplpM5xPzN++gxqywsmxY8fyvXprjnewfU1e5+o2r8XzJZ+5+dsKX41ygr15N0//cjtLQQOqaD5P9\nyldnxc3u5Uz+9tsZ/afL5sXMIQR/JP79F89RtD0uf8NKEoeAG+iLv9mC7/qsOm3ZvGsbrlFjLnFd\nl+3btzM6OsqyZcumZcgx3RbTdDrNpk2bqKurY8mSJbu1Z1b2MZm/KZWT3/H32x+lUomtW7eiKAqd\nnZ0HbI9fMbxpbGyko6ODFStW0NLSElRmN/cw9NdRUtsyFO0i9cvjtJ+wkDNOX8cxRy4rB7lLHHf8\n8wgqfpVMQM+X2LaLpuzeIl9yXLYOjPHXzb3kPAVHaJi6SkPMoKMxzLLmKK2JEImIjlau6glFVHMT\n94cv/eocYjqdqbauamVnU78sOvZ6f89DVZWJcRVSoqkKjuuCEOiGSTQao7GhgeZkgrjhs7BOZ0FM\nxZAW0rNJZYsMZEq8NJRnIGNRcrzqsYKJs4iV6idy57H0keW2VpXXrT+SJQvibN++nQVtC1i1fgXt\nr1yEGdVJ9WbZ/kgfpYw1qeOzLyoV8h07drBkyZJJzzUqisKiRYswTZN0Os3IyMg+f9/zPLZv347r\nurt9nipiU9M0VFVF1/XqBQjf96vZhftiOhXESrbm4UKtgvgyY+lJ7YQSJi/cv4m1F74C3Zzfb4GT\nVzRz+poF/GrDALc+tIWLTp1bR0Z99WqafvwjRt71bjKf+jT2009T/7nPoiTnd+TBfMcvFklf9zEK\nP/ghIpmk8TvfwnzlK+d6W/zi6T5+9/wQx3XWc/Zx89/sRfqSZ37+AqqusOJ1NffSGjUqZLNZent7\nSSaTtLe3T7v9bDqVvv7+fvL5PJ2dnfu0va889t4iJsZXRKbSUjo2NsaOHTtobW0lkZi9zqFMJsNY\ndxota9IYD2M06JitGlk7w+jGEaLRKGtWtiOlZMPzW3ZGOyCQCFRF4O5ybF3XQ1WDal3WchnN2aSz\nBQQQj0VImh6N9SE0RWVvh8SXEgWQvo+yS+toFQk4DtKxwXaC6mgmQ8TQ0VUVMmlcb9xxFwQlPFUB\nRQVVRWgaGDroBtKsJB8KhAiMbybcv4zjOmRy2fK8oYkQQaSGbdsUSzbpQpGCK7FtndEMREMmjXGT\nqLHzPaIogagO5iVFeaZToqkqhq5x6rpVGIrL0NDQBAfRcCJE+7o2Rl4aZaw7Q/fj/bSubqJu4fTM\najzPo6+vD9/3J2VGsyuqqtLW1la9iGMYxh7ngm3bpru7m2g0Oql21PE/K5/dyZjeTLWCGIlEDquL\nsvNbHdSYcVRd5cgzV/DEjzbw0m+3cMQZ899w44NvOoInt43x9QdfYv2KZlYsmNtWU33VKprv+Rmj\nV7yf0i/uZeipp6n/6pfnvNp1qOI8+xyjl1+B++KL6EceSf3XbkbvmntxM5Aq8oVfPE/EVPnoW44O\n2qHmOduf6CUzkGPVG7oI1+3Zpr9GjcOVPZ2c+b7P4OAgmUyGxYsXH7BT41QEYrFYpKenh3A4TFdX\n136zBfeVsTjdltLxweezNWtm2zbd27sp9FqYdhgtpLHgqGaiTTvb7RzHoVAokM/nWdQUZaQxxkvb\nBzAME1VR0Y2gndTQNTzfr1bHJJAtuYzkbPJFi5JlkYiYtNZHiRhB1Ug6BbRInD10fO58kHJFTRUC\n6XlQKiGLJSgWoVhEuk51ls9xHWzLJmwa6K4eNGjKnQ+1Nyq3KQIcBGrIRJomhCO4oRAiHGa8is3n\nc9i2TaKcb6gEBUBURSUcChMOhWlIgu+5jOVKjOUsRjM5UtkcYVOntS5MXdQMsiMVZacRjwjy+XRN\n4XUnHYFTylLci2hTFEHzykYijWEGNgwz8Owwdt6maXnD/l/4cVTap8PhMAsWLJi2UFJVlUWLFtHT\n08Pg4CCmaU5431YiOZqbm6mvn/oIxZ4E465mN5X/plpBnGrm4XynJhBfhhx51kqe+skzbLjnBVaf\nvnzeX/FIRAw+8rdHcfWtT3DDnX/lW+89CV2b2+5odcECmn50O9n//DLZL3yRHW8/j/jV/5f4VVci\nDiDg+OWElJL8d79H+oZPgGURvfQSEh/5V8Re8scOJr4vufGuDeQtl4/87VEsqj80vvg33PMCAGve\ntGqOd1KjxtxTKpWq4d8HGj5fYV8iroKUktHRUYaGhli4cCHJSXaY7MkAZ9eq4WT/XheLRXp7e4nF\nYixatGjWLPEzmQx9PX3IIQXTDRFKhlh4dAt6aOLppa7rEwxvOjo6eOSJZ/nT489g2xYSCJshHEfD\nDJkoQlJyJYPpIkXbw/M8dFxWLEgSDU9s45O+LBvXREHs/horisDL55HZHF4+h27ZOOOPs6YhwhHQ\ndQqeiycE8foGhKYFLtBCwUOgqkpgBOP5gUMMgOcFgtPzwXWCKqRlge2AbSOLFn4qU96IQEQiyEiE\nvCIQhk4ikSzPx1Uqf7vvHTSakjGa62MULY/hTJFMwWHbjizhVIbmuBkIKd1A01QUoRAOGbz6lSvI\npUeIRCL7FW3RxgjtaxfS95chRrel8VyfllWNk3q/VURbU1PTlGd694RpmrS0tDAwMMDAwADt7e0I\nIaY81zgZdv1ceJ7Hjh07qkK6IiD3991RKBQOq/lDqAnElyXRxghL13ew6Q/b6N8wyKKjF8z1lvbL\nySubecsJi/np4z184zebuOwNc1/5FKpK3f/9Z8yT1zN2xZVk//3zWA/9kYYv/wfqwoVzvb15jT82\nxtiH/oXSvfeh1NeT/NrNhM+YHavm6fCTR7fz2OZRTlnZzDnHz//WUoBUT5qeJ/tZcGQLTcumdvW3\nRo3DiYqz4fDwMAsWLCCZTM7YhdBKBXFvlbxKtqHruixbtmxKM0m7GuBMN9twdHSUkZERFixYQF1d\n3aTXnwqViJD0WAZ1xER6EG+N0npk037NToQQmKbJqeuPY2FrMw/+6UlKJRvLsrBKJTKZLHlXkLWD\nypipShqjgkQ8Scgwq/EXE583OFYB3QiBUp5HKxYhk8FJp8FxK4vjhENosSjSDEE4DLqO70vyuSwI\nhbpYNGg5FDsdRDVFxfO94P9VBUWowWujqhPcUBVRafcM3ifCslAdGy+Xh1wON5OhNDiErqkYiQQk\nXdSGhrLHwu6vcfDaV4554M3Q3hTD9nwGMxZFy2Mw75HwHEKKhZSSlqYEa1e3khoZpLm5mYaGyf09\nMKIG7ScspPepAdK9WQBaVzft8z6zIdoA4vE4+XyebDbL6OgonueRyWSmbK40FSrOu47jVKut4z+D\nsPOCza6CsdJiejhRE4gvU9acvZpNf9jGhp+/cEgIRIArz1zFI5tHuOUPm3nVqmaObp8fc3/miSfS\n8qtfMvbBD1G675cMnX4myS98fl4JnvmE9fDDjL3/Kry+Poz1J9Hw5f+cV4J6+448X/nVRhIRnY/8\n7VHzvsJeYcMvNgJw9Nm16mGNlydCCBzHobe3F8/zpizQJrtGpYq463dDLpejp6eHZDJJR0fHlL87\nxlcnp5Pv5roufX19eJ436y2lvb29KCgYqTCO7ZJsi9M8yYrTeJYvW0wyEefeBx+mWDIolGxG7CIl\n38VQPaKqS0gDTeh4rotTFpeO67FrIdf3JY5G12AcAAAgAElEQVRVRM2PIrIFZKEYtH2qKqI+iYjX\nIaIRqDhaSoGiCooli3QmTSQcntAm6Lsu0nHAdXHLpiW+H7iTekowf6joOkLT8IWCIiq5hD6yXKEU\npolvGOiJBIVCkVw6RUyCaZXwcnmUoSHsoUFEIoFoaECEdq4fVA+DSIuq62v5eZqaSntDhLG8zUjW\nJuOAHgtzwupFrFicoJDPoWkaw8PD5PP5analYRj7fI1UQ6XtuAX0PBGIRM1U9+iGXTGjyeVysyba\nmpubyefzbN68mfr6+mnNNU4W13Xp6elB07QJeY27Vhh3FYyVin8ul6vOdh4u1ATiy5TW1U00dTWw\n9eEeskM54i0zd+VntoiaGte99Wgu+/Yj3HDnX/ne+9YTNubHW1ipr6fhG/9dbZkc/ftLiF58EfEP\nfgB1klfvDnf8XI7cV28i+5WvAhD/4AfmXUuu6/l8/M6/Yjk+1731aBrjh4YjmV2w2fjrTUQbI3Se\n1D7X26lRY07wfZ/NmzeTTCZpaWmZtYs7FaEwPottcHCQVCp1QHOOiqJUHRan2lJayZxLJBI0NzfP\n2nPPZrP09/fTUN9AcZuNVbBItsVp2U+laV80NSY475zX8KP7/swLAz0gFeKGQki41NXVoWk6juNg\n2zalYgnPT2MaBqqmYZomarliKVMp/KEhcD0URUVtakVJNkBdvGoYg/TxJXjl1y+TyWEVctSpOmom\niz80DLYFrjshmxHARyCEnCBMK7VMVVORIQPXCIFpIiPR6riEEIJ0Oo3ruSQaGzF0Hen76FLijY0h\nRseQY2nkWBqRqENpbUUx9HKeYRA34JfjQCB4n4iycExGDBIRk75UiVhzM0XFoGRZ1RgV13XJ5/MU\nCgVGR0fxfX9CdmXF4XM8qq7Sdmwr3Y/1M7I5hRkziTXvrI55nkdvby8AnZ2dM9K6vScqUSoQxEfM\nljismN7EYrH9fm/sSTAODw9z11137dFQ51Bmfpxd1zjoCCFYc/YqfvMff+IvP3ueU/5h7VxvaVIc\nu6SeC9d3cudj3bw4kOWYjvmT8yaEIPaeizHXrWP08ivIf/d7FH5yB7H3/gOxf3wvyiy1+sx3ZLFI\n7rvfJfeVm/DHxlAXLZq3pj43//pFnulJc8bRC3j9UYdGZR3g2ftexCm5HHfemhnNsqpR41Ciki84\nWyeSFcbPClqWRU9PD6qqsnz58gNaWwhBLperWvNPBiklO3bsYGxsbMbb/HZdZ2hoiGw2S3t7O9lt\neYppi3hLlOZVjQf8+P0Zl4YlK+l0VEa7N2PgEo3XB3N/ikBRTAwjcPn0PBfHcfFcl1QqBcUixugY\nhu+jaBpKUwNufQN+KIRuhCfmQUCQT1jIkxscQuZzRBFl0QUeoOoa0gwhdB10PZhRFIAQwTyjF7iE\n4nngeSiehyyV8PIlyJWqywhNQUSi5FUFGY2RrEugqTpSSKQQSCFQGhsRjY3IfA45OIRMp/GzGZQF\nC3GSSUTFHK38Q1VUpPTwpEARoKkahqFx8SlH8Uz3MKNFl363iaXleBBN06qznxCIoYpZ0PDwMIqi\nVMViJBKpvu80U2PRMS1sf6yfweeGCdW1oZnalBxED4TK/HBTUxPRaJR8Po9lWTNeqTxQ05stW7bw\n/ve/n7a2Nm666aYZ3dtcUxOIL2OWn9rJY7c+zfO/fJHjz11DOHlolMff9/oVvHntYjoao3O9lT2i\nH3kELff9gvz3byX75a+Q/eKXyH37O8Qvv4zo378H5TDrU98b0rbJ//A2sv/xH/iDQ4h4nPgHP0Ds\nvf+AMksnMQfCb58b5NaHttLeGOGas4+a6+1MGtdy+ctdz2FEdI48a+Vcb6dGjTlF1/VZz6etzApW\nAuKbm5tpbJx6e+V4pJQ0NjYyNjbGyMgIuq4Ti8WIRqOEw+Fq5cKyLBRFQdeDqlpfXx/AhKDwmabS\ntquqKkuXLqUwUiLVm8WMGbQe2XTAImHLcJ5nejOoQvKaNQuwl9XxzEt92LaDlBLX8wJxJgKtp2k6\niqKB6RMp5PHHxnA8Fysaxa6rQzEMTNdBLUp8z8MMR0Fo4PvIVBp3eJhiJoOqqoRDJkRjgcNoJIwR\njYCq7tEwBgKXUb0cz6GqKp7nlSvKElVKpGVDqYjM54N5w/4BNF3DDGfxCkX01mb88kUEOe4xicZg\naQzSKbThIZz+fkQmg2hrAz34fVVV8T2vHAmiIIRCS1OCdUe3M7JjmHVdTfQWdHrGijy2ZYwTuxpQ\ndnltDMPAMAySySRSSmzbrs76DQ4OoqpqVSxGIhGaltczvHGU4RdHqVsapbe3d9piarJUqtSVWJZc\nLkd/fz+pVIrW1tYZWyeTyTAwMDDtCyuPPPIIH/jABzjppJP4/Oc/X2sxnS183+f666/nhRdewDAM\nbrzxRpYsWVK9/YEHHuCrX/0qmqbx9re/nXe84x3V20ZGRnjb297Gt771Lbq6uuZi+4ckqq7yircd\nxUNff5S/3PUcJ1583FxvaVLomjJvxWEFEQoR+4dLibzzAvLf+jbZm79G5t8+Re6/v0H8qiuJvutC\nxGEUqDoe6boU7riT7Be+iNfdjQiHib3/CuLv+yeUWfyjciBsGcpxw50bMHWFT51/LNHQvPlq3C/P\n/+oliqkSx557FGZsdmaOatSosRMhxIT4iAOxt684lEopicVixGIxpJQUi8VqlceyLMLhMNFolHQ6\njed5NDc3Mzw8TDKZpKnpwEXa3sjlcvT19dHY2EhDQwO+6zP0/A6EIli4pvmAOxYG0iWe6c3guzbt\n4SKLW1qor+9izZrV/Pahp9jeO4gQohpQrwmJRKAJH3vLNmShgDBNwsu7MM0wIHFdF8exKZWK5LI5\nVDVFxHYw8iWk62HZDkZTI2ZTEyIaRdV0PL8s9KQEWW4llLJqUlNBiEpqhoLn+ShC2SkmhUCETAiZ\n2OEwuXCI2KI29GIR0mmU1Ail1Ciivh6lpQVV1/HGVTeFALWxAS8eR/T0InM5/C1bEJ1L0EJhfM8v\nX5wQ6LrG8Ud1sqgpUnXLjcfjNEmJ40sG0yWe78tyZNveO5cqZkGmadLQ0ICUklKpRKFQqF780DQN\ny7fIbEwzYuksXd1JNDo751+VzM6RkRHa29urn6toNIqmaWSzWZqamg64pbVi5DQ6OjohF3Iq/Pzn\nP+f666/nne98J//6r/86a222c8m8OQu6//77sW2b22+/naeeeopPf/rT3HzzzUBw9epTn/oUP/7x\njwmHw7zzne/ktNNOo6mpCcdxuO666w475X6wWP2GLp788QY2/Px5jnnzEYdMFfFQQYlGiV/5fqIX\nvZvcf32d3De+Sfqj15H72n8Ru+x9RN76FpRJWqDPd/x8nuI995C76Wu4L70EhkH00kuJX3kFanPz\nXG9vr4zkLK6+9QnylssN5x7D8tZDZ47AsVye/PEGtJDGMX97xFxvp0aNOWcyMRQHQrFYxLZtdF1n\n+fLlBxQfsTeXUiFEtYLT3NyM53kUCgXGxsYYGBioVq7q6+tn1KF1PFJKhoeHSafTLF68uOrQOLol\nhef4NC2vx4ge2AWpguXy5LYUuVyWVUnJymVLqqIgZBic+bp1vLilm0eeeI5UJg8E2XSqlBRf2oRq\nWRCLo7S14asqmhoYxGiajqbpRKMCN5/H3d6NWyhQ9H3U5gUoixcjIxE83UATCp7voesqjrtTrFWE\nYbUCpwgEAs/zyu+vwMnS27V9Fcjn8xPyDdWGerzWFmQmjRgaRo6M4WUyyPaOwE1VBuLQ92XQvqqq\nKEs68IeHkUPDiO3bYelShKYjEbQ2Jzjl+OWUCllGRkYmmMQIITiuI8EfNrpsHs6zIBGiYZIXDoUQ\nhMtGPY2NjUgpgxbMXC9jQ2m8fpfhxp2mN+Mr2weKlJKBgQGKxSJLliyZYLAkhCCRSDAyMkIul6u2\nyx7oOp2dnVOuuksp+eY3v8nNN9/MNddcw3ve855p72W+M2+GVR5//HFOPfVUAI499lg2bNhQvW3T\npk10dHSQSCQwDIMTTjiBRx99FIDPfOYzXHDBBbS0tMzJvg91NFPj+PPW4FoeT/5kw/7vUGNaKIkE\ndf/yIVr/9BCxf3wv3o4dpK/9KP0nrGX0yquwHvpj1fXsUEJKif3kk4z9yzUMHHcCqas/iLtlC5F3\nXUjrH35P8obr57U4LDke//LDJ+lPFXnv67o44+j546Y6GZ79xUYKYyWOPmc14UTt4k6NGrNFZdZv\n69atGIZBY2PjAYvDyhzj/gSeqqrE43FUVUVRFJLJJM3Nzbiuy5YtW9i0aRMDAwNks1k8z5v2nio4\njsO2bdsolUosXbq0Kg492yPVm0UPaSTbp3+SXuHJraMMDY+wNKly9OquPVZiVyxt59xzXsfRRyxD\nK7d+Wlu3otkWan0DakcHlKuYrucH84rlw+ml0oht2xCugxuLwrJOQm2tmKHAETWbzTIyGoiObDaP\nHB+fUQ5KV1QVnyBrUTJOyIsgt3B8C6fv+0GF1/VIJpOYZcfQoA1VIJJJlOXLEa0t4Hr4W7fgjoyU\nxaGPpilU31ISQgsXoLa0IC0X2d1DJGRw8nErOPNVR5Ee21GtYO86l6epCsd0BK/Ps32Zab8+vu8z\nMjJCuD5Ex/J2kqEGkrGgC2hoaIgXX3yRbdu2sWPHDorF4rQvzHieR3d3N47j7CYOK1RaQPP5/LSf\nz67rTFUcuq7LjTfeyDe+8Q2++MUvHtbiEOZRBTGXy03oAVZVFdd10TSNXC43wR0oGo2Sy+W44447\naGho4NRTT+XrX//6XGz7sGD16ct56o5nefa+F3nFW44k2njoz8gNpUs8vnWU/lSRi161FG2eGHeo\njY0kPnYdscsvo/A/P6bww9so3nEnxTvuRO1cQvSCC4icdy7qgvltkOKNjlG84w7yP/wh7vNBOLu6\naBGRf/pHIue/A23x4jne4f7xfckn7vwrz/SkOeuYhVzymkOrPd0uODx1xzMYUZ1XvKVWPaxRY7YY\nH53R1dVFf3//tE+Gdw2+nyyZTIatW7cSj8dZsWLFBAdVy7LI5/OMjY3R19eHaZrVWINwODyldSot\npQ0NDbvNVab7skhfkuyoq0YuTJctA2M8v62f9qY61h3Vuc89mobOq096BSuXtfPAT+9jRy6HH48j\nFi0M7ieDCIjKSyKRKPkcTk8PnvQpJuowGhsJhyNU3F40HWLRKFJo2JaN7ToUSyUUBJqho6kauqHv\nPM5QdTVVBAih4Esf35MoqoplW+SyOQzTIBaLohC0oAJlN9Ly5hSB0tyEDIehpwe/rx9X1dEScVzX\nQwgFVQCqgutJRHMLhuOyTJRYu7IJpa2++j7Yl+NmQ9SgNRFiMF1iNGdPuopYoWK+VHH2zGg5Bp/b\ngcxDy5KgIFOpbBcKBfr7+3Ecp1r5jkajmKa53/feZE1vDMNA1/WqEJ1q1dxxHLq7uwmHwyxYsGDK\n9y8UClxzzTVs2LCB73znOxx//PFTuv+hyLwRiLFYbMKVgeBqirbH2/L5PPF4nFtuuQUhBH/60594\n7rnnuOaaa7j55ptpnscVi/mIqquccP7R/PYrf+aJH/2VUy87ca63dMBIJP2pIqM5mw/c+gSffedx\nmPr86RFXm5uJX34Zscveh/3II+R/cBule+4h8+nPkPncv2O+5tWEXvtajHWvRD/iCMQsu/LtD+n7\nuBs3Yj/yKKXf/57S/b8G2wZdJ/SmNxG98ALMU0+dV5EV+0JKyVd/tZFfPzPIsUvq+cib1xwyeYcV\nNtzzPKWMxdoLj8GMHZ7zrDVqTJWZ/hxns1l6e3upr6+vnpBX8/CmyHSD74eGhqrzhi0tLRMql0II\nQqEQoVCIxsZGfN+vzi9WQr8rJ+z7ysEb31La1ta2xzmz7GAeIaBu4fTb8KWUjIyM8NSL/SQSCU48\nom3Sx6I1GePN9QaPLW5iY30rtl8OLddUfNevmr5g27jbtyM9j0JzM/GmJkzT2BlPUckrdCwUxSEU\nMjFFqDxz6GNZNpZl7XSUNXR0Td8ZCaEoeLL8+isKpVKRUrFIPBbFDIXxPBePPYjDccdAq4vjd3Yi\nNm9CDvTix1aCoiIQgAiEpyJYtqSF4167htBvH6T4+GMMSY/WhQsn1Wa5tCnCYLpE91hxSgKxcpGg\npaWFZHkEJtoYVHcLo0XqlwRrVyrblQKO67pVh9RUKoXneRMcUnd971UcRJuammiYRBxYKBQim81i\n2/aU3EwrjqgNDQ00NDRM+TtieHiYq666inw+z+23305nZ+eU7n+oMm8E4vHHH8+DDz7IG9/4Rp56\n6ilWrtzpxtfV1cW2bdtIpVJEIhEee+wxLr30Us4666zq77z73e/m+uuvr4nDabLidct46o5neO5/\nX+KoN62ioePQnotrTYT5u1OWYmgKX7z3OZ7aPsaJXdPPaZothBCYJ56IeeKJ+DdcT+Gnd1G47Tas\nBx7EeuDB4HeiUYwTjsdYtw5j7VqM449DmaUh8QqyVMJ++mnsRx/DeuRR7MceQ6bT1du1lSuJnP8O\nIue+HbVp/h3X/fHt323m1j9upaMxwqfPPxZDmx8V5smSHy3w5E+eIZQwOfqc1XO9nRo1Djt832dw\ncLA6gze+w2mqAnHXquFkT1ArgfSaptHS0kI6na62e+4NRVGqYrClpaWag5fP5xkZGQGo3l4x/3Bd\nt5prt3Tp0j1GdThFBytnE2uKoE7z+9LzPPr6+ihZDkqknsZYiER48m1+/tataJ7HKWe8hmPbO3j0\nyefYvL0fx3HLrp7gehJvYIBSoYTb3ExdSwu6rgett0KgiMCB1nWDVlzf93FdF0XV0HQTRQQunn4o\neH0d18GxHYrFItlsBl030DQd3dDQVZ1cIYfnOtTVJVBVpbqOQKAIEVQz2dk3qqhBZdHzJYRCiJYW\n/MFh/OFh9IWLyo6t0NIQ5+QTVtJUHw/am+uTFJ99jsXRKJFJzuA1xgxURTCWtyf1+xWTmB07dkyY\nO4VgHMmI6JQy1l7vr2kadXV11JUjvRzHqWYw7tixA6AqFn3fZ8eOHVNyEDVNk2w2i+M4kxaIFbG7\nYMGC6r6mwqZNm7jyyitpbm7m29/+9qSE7OHCvBGIp59+Og899BAXXHABUkr+7d/+jbvvvptCocD5\n55/Phz/8YS699FKklLz97W+fUavbGqBqCusvOYH7bvwNf/rm47zx+tMOuYpK0XYZzdlETY2C7eL5\nkoih8bvnhzlp+fwXMUoiQezii4hdfBHu9u3YDz+C9eij2I88ivW732P97vfBL6oq+hFHoC5Zgta2\nCHXRItS2NtS24KcyCbt1KSX+WAqvrxevtxevtw+vrw+vtxe3uxvnmWeDCmEZtXMJ5hmnByJ13SvR\nuroOufdHhdv+tJWvP/ASC5IhvnzxWpIHaLQwFzz6/adxSy7rLzkBI3Lo7b9GjfmMZVl0d3dXjWh2\nFUxTEYjTqRrCTgv+pqYm6uvrq3EWUzXkG5+DNz7WoPL4lXGeeDzOggUL9urGWBEG0zWyK5VK1ZZF\no64ZNTdGS93UOh+8nkDEqks7SYTDvOHVa8nlCzz+9EZe2tKD43gIz6UwOIQSjZDoWAwoSOmjamrV\nlXRP7cG+52J7bmCCo5kougEyiE3RdR2KRfQHH8RPpfHiMdKnnIJnmggEoZCJVxb/StB/GsQkyp1B\nFooQIAITnQqqIvDqG1CGdyBLFp7nEtIFJxzdxREr2oN9+T59fX140SjNTY0YucnP4AkhqAvrjOVt\nPF+i7qMteFfzlj3NAephDbvg4Ln+pC4S6LpOMpmsRmo4jkMul2NkZATbtqvOpL7vE4lE9pshWpkZ\ndBxnv2sDjI2NMTw8vJvYnSyPPfYYV199NevWrePzn//8AbkVH4rMG4GoKAo33HDDhH8bH1lx2mmn\ncdppp+31/rfccsus7e3lQsfaNhYfu5Cep/rpfryPjrVtc72lSWO7Pv921zPc/8wAF67v5Lm+NIoi\naI6HOOuYhaxfcWhVlrWODrSODiLnnfv/2XvzMDnu8lz7rqru6n16evZ9075Y1mbJkvBu4zUG2wHi\nz0m+j8DJCWBjQ0JC2GLAgGMHHGwcAiGGk2OCbQzYOOBdtiVvsvbVo12ame7Ze1+ru6q+P1rVmpFm\nn2mPZtT3dc0l8ExP/bq6pvv31Pu+zwOA6vejbNuGsnUbyntbUfbuJb1vGFMhqwXJU5L9kBoKXUcL\nBtETiWEObsK8eBHyRWuwrLkI+aLVSLPkhsyz2zv41xcOUuay8KP/9yIq3TPvDb/3SD8HNx6lpKmY\nhVfPrLnJD5KxbiIKzC4mm0UYDAbp6uqioqJi2HY0QRDGJBANYTgecWhULmOx2CCr/1QqhclkmpSd\n/sBYA4/HQ29vL8FgkKKiIhRF4ciRI1it1lx10Wq15tadjmcAkB3jz1oMBoP09PTkcu2O9mRFzniq\nhwB6MIhQ5MpmFp7C6bBz2frlLF86lzfe3sn+7XuwyWasFWXogoimZ7MTjddLkrItwqo69OunaRqa\nkgAlgVm2IIpmBFFCevElONiKSQdzl46qqSRuuBGzyUQ6nSYZDqPpOrJZxmoxY7ZYkATjtdKzYnGA\nMM2KNRHJZELRM5jiQRa2rGTVsvlYLVlxpigKHR0dWK1WqubORT3ZBsnkuM6Z1XzawEcSh752jAqy\nKIo0NjYOe42Jp0ShNkaBOBBBEDCZTCQSCSRJYu7cubkZxlAoRGdnJ2azeVAG45nrMATkaEZMRlt2\nNBodVuyOxvPPP88//dM/8YlPfIKvfOUrszLGYjTOGYH4QZLvEN2ZiiAIrPurVTx9zx9457Ht1C6v\nnnAryQeNbBL5zNXz6AknWVLv5q5rF5BKq+g6WOXTf9jbjvUjigJzK10UjfPDaTqRSkqwffjD2D78\nYSA7E6j195+u/nm9ZLzeXBVQCwRH/H2mlhakutrTlceaWkyn/rdYXj5jZgnHw4t7fNz/3H6K7WYe\n+cvV1JXMPDMmXdd5+z+3gw7rP7V60hlks5mNGzdO9xIKzCBy7Y+nnDtHqtRls+iG30dMtKU0lUrh\n9XqRZZnm5ubcplTTNFRVnbIKRiaTwefzoes6zc3NucqMsWGPxWLZqtWAGbJUIttRIslj/2zQNG1Q\ndIHRFpg+Jc7G3dqv6yCc/Rhd11FSCRbNqeKixkr2//5FjgsiqiAgSwIZTQM9OwuoqtmICtlsQlXV\ns+YDB6JmFJAyqIqO3NWOpCTQJTOaZMIcSyA5s6MeFmv2eWmqiprJkEgpxOIJEARkszlrsGIyZ2ck\ndR1JAj2jkFEzuB02lpXZWNhSi+Oipbljx2IxvF5vroKsd/egQs6xdaxk1FPGOsNUD41qeVFREeXl\n5XnrDMpkMnR0dGA2m2loaEAURcxmM1ardVAGYywWw+/34/P5kGV5kNmSwUh/e0bFNZPJ0NTUNG5h\np+s6v/jFL/jRj37El770JT75yU/O2G6pyXJeCsTe3l7q6+unexnnJCWNxSy6dh4Hnj/EgecPzaj5\nphqPnXtvW8b3fr+fcDzNR1effo2Nto7H3zpOQ6mDH/zxfR7+y9WUzFBzD0EUkcrLsxESy5dP93LO\neZ7d3sE/P7cfh8XED/9yNc0VY5t5ONc4/nYbXQd6aFpbR+2yc9vpdjoJBoP4/f7pXkaBGUI8Hqe9\nvR2Xy8WcOXNGja8QRXHYCrVRqRqvS6lRZSsvLz8r29DYEE9FFSMej+P1enG73WcJgjNNR4wZslgs\nRpe3h2R/GmuviWLcOByOEddjVL8sFgvNzc2Dzqn5lMhJD1PFGw7BbkOPxQZVZFVVxev1omlaVlSn\n05Q0VrC2pISjtc0cPeGlpy+IKIKuAqeiKpR0GpMkIQkDKopCNspC13REQQId0ulsi7BW5EL1+UBN\nZy1NbbXo6QSiIGZFqyBgkkTMJjOybD4155hGSaWIhqOkUklkk4QkSVgsFpoaa7hwUQvVmRSZZA9i\naSkweA5woFmQ2tuTPQenfm6sxFIZZJOYO+cDGcqMZiS0UzmR4y0cGO3FbrebsrKyYavyRgZjWVlZ\nzmwpHo/T29tLMplEkiQikQg2m23IqvxQInQ8ZDIZ/vmf/5nf//73/OAHP+D6668f1+NnG+elQDxx\n4sR5YVE7UVbfvowjm46z/Yk9zLusGes45wSmk+piG/948xJ+t7WdI92RXOi5EXPx0J+vQhAE/uO1\nI2xq7RkkIgvMTn751nEeeekQxXYz//oXq1hQPf5B9XOBjKLy7i92IJpELv7/Cu9fI3HkyJHpXkKB\naWI8oszINjQ242M1sRhuBnGiLaVGla2hoWHIyuVY8xJHwnAP9fv9YzYGGThDJset9Kr9mEQTwWCQ\nzs7OYeM0zpyfPHPdNjn7eRxLjS+zUSguRg+F0f1+hNLSnPAYFPlgMiGUlyH39rFs6RKWL7mMUCRG\n6+GTnOjoIhiMZquG+uncRE1Vs6JQEhER0AR9UGVR13V6L16HK53BEo+iukvQrvkwuqIgiSKilM1E\n1LSzK1tmk4BFtlNS7MLjdlJdUUxJsR0tk0GNBglu3Yas6zgWL8pdC8lkclBrpK5pqMeOg8mEOA4j\nxoSiEldUylyD93C6ruP3+/H7/eOaz0tFFUyylGs1HQuGCDXai8fKQLOl8vJyVFXF7/cTDofx+/3E\nYjFsNluuJVUQhFFF6EjE43G+/OUvs2fPHn7xi1+watWqcT1+NnJeCsS2trZcxmKBs7G5raz8+AW8\n+/MdvPf4Li797MyKvagutvHJy1p4eW8XDaUOdrcFuLDBQ084yfO7fFyysIKX9nby2avnj/7LCsxY\nVE3n4RdbefLdNsqLLDz8l6tpLp+ZlUOA3b/bT6QnxrKPLMJdMzNF7geBruscPny48P5eYETS6TQd\nHR3ous6cOXPGNad05gyi0U5qCMOxbk6TySRerxebzXZWle3M4xnHmQhGS6lRZRtvQDiAbJcxmc04\nrU6Kql2D4jS6u7tRFCVX2VEUZUTh4Rda0qYAACAASURBVDllrNUbSTGnYuyO3FJTM9rJNtR9+4gs\nX053d/eQwsO0ahXpl14mvXUb8pVX4nY5WbtyMWtXLiYWT9Dd46cvEKY/ECIUjhGOxEAUyKhabkxQ\nFLMupOl0mlA4jNnlRPzYx1DFUw6lp0M1yGQGXwuiIGI2S5SVuamrLKO2upzKcs+g1zeTSBB/5RVS\nsRj+xsacCZHZbKa6unrQa6QePASJBNKC+QjjuE59wazPQOWAm/xnitCxXgvpZIZMSsVZPvbRDL/f\nP6Qj6kSQJAlZlikqKqKiogKHw5HLYOzo6CCTyWCxWJAkCUVRho1zGYq+vj4+//nPE4lEePLJJ2lu\nbp7UWmcL5+UnaDqd5tixY4OiNAoMZumNCzj4ylHef/Ew869ooWrRzDJ5sckmblxRiyjAE++cpMpt\npb7Uwfu+ELvbA9yyup4rFs8O45UCZxNLZfjG03t461AvzeUOvn/HKmo8M8+QxiDoDbPz1/uwe2ys\n/MQF072ccxqfz0ckEimMERQYlnA4nAuDn8jc1cAZxIlmGwaDQXp7e8fU3mcIi9HMOYbCaCk1NtYT\nrUIa5jTJsEJR9eAKD5xuIzTOi9frPStOw8AmSxTbzfRFUsRTGeyWsW1FxeoqhIpyAgcPEjOZaFi1\nasiKq1hcjLR0KeqePSivvIJ5w3rEimy4u8Nuo6Wplpam0yZ8ipLB29lDd6+f/mCUcDhGJBYnGo8T\nj8Wy+ZFmGUEAAUAAIVuExOW0UeRyUlzkwGm3I8smit1OykrcWOShxZcWCKC99TaWWBzbsgtwXngh\n7e3t2O12RFHE6/Wi63rWrEVRsOzaiWSzIS1ePKbzBNnq6NGeGCZJpPbUZ5/RgilJEk1NTeNqwYz2\nZo2F7CWjf47qup4zWpqoScxQKKec1S0WSy5SQ9d1wuEwNTU1ALkZRk3TcteekcE4FMeOHeOuu+6i\npKSEp556itJxtvDOZs5LgQhw8ODBgkAcAcksccln1/L7f3yJzT/ewq0/uGHGGNYYSKfuCLptZt47\n2k99qYOGUgfLGou5cnFhfmu20h1K8Le/3MGR7ihr55TynY9fiNM6cwyJzkTXdTb/eAtqWmPD/1qN\nZQbGcnyQtLa2AoNdsAucP4wkgIzqiXEDYagw+LFgtJgaX+OpGqqqSmdnJ4qiDDJuGe14RhzFWDHa\nCPv7+6murs7NFU4Ua5EFySQS64vDgsGbaMPUpri4mLJTubjG/GIkEqGrqyvnUGls2JvLHew8GeRw\nT4wL68fWephOp+ksK0M+coSqDi/y/PkwjJmQafEiBItMZvsO0q+/gbRgAdKihUNW4GTZRHNjDc2N\nWZFhCJz+QAC3uwQQyHal6tlYC4uM3W7BZrWMS2TpioK6fz/qocNZs5oli4nW19PT3j7oNTIiIWI+\nH6mNrxGJxVAvWo0tEMiZBo02j/p+ZwQlozG/yoVsEnMCfqJmNGFfFAFGrSAaM6HAhExiRiJ5ysFV\nluVce3goFBr0d2TEuQycn+3p6UEUxdy5M25YbNu2jb/9279l1apV/OAHP5h0lXO2cV4KxKqqKjo7\nOwkGg2MazD1fqV5cwaJr5/L+i0fY9fQ+Vv3Zsule0rgxSSJ3X7eA7z67n91tAeyyCcepu5XjzaUq\ncO5zwBviS/+9g/6owi2r6/nbGxbm5k9nKq0vH8G3t5uGi2ppXt8w3cs5p0kmkxw/fnzQRrVAAThd\n4ZJlmblz505q4yqK2Q13f39/tro0xna2RCKRq6yNt4JjNptJJpNomjbq4wxH1kwmM+GW0jMRRAFH\nuZ1wZ5R4IIHdYxtxrlGWZWRZzjpwDnCo7O/vzzq1WizoaY2jXWlq3BbKi0bOVzREqKemBs9NN5HZ\n/CbpTZsxb9iAWDV0N5A0Zw6C00lmy3uo77+PeuwYUksL0ry5CMOIgYFVtoXz50+JwNETCdSDh1CP\nHoV0GsHpRFq5gj5JItLXd9bsqSAISD092Ldtx+5wIF1+GZnaWmKxWG7+80yHz4HXRFcoycm+OE6r\niTkVDiKRCJ2dneOeAzSI9cdJRRVcFQ5MI1R7DWMiu91OZWXllO6vVFUlmUzmolc6OztJpVI0NTWd\nNU4gCMJZ19/A/M9vf/vbvP/++0SjUS688EK++93vFsThEJyXArGlpYX9+/fT2trKxRdfPN3LOadZ\n+5cradvmY8dTe2lcU0dZS8l0L2ncuO0y3/zTZXT445Q45JxzaUEczi5e3tvJfc/uQ8lo3HPdAj5x\nceOMf40jPVHeeWwHst3MJf97zYx/Pvnm8OHDaJrGwoULC+eqAHDaFdKYVxvKNGU8aJqGxWKhoqIi\nJ3gEQRjUTnmmqBjoTFlVVTVmM5yBWK1WkskkyWRyxM2sIUJdLhd1dXVT+nfgrnER7owSOBnCUiTn\nojBGE6FDOVTGYjEWEOLtI35eeC/A+rkeStyunOA2GFgJHSRCL15LZst7pF9/HWnpEqQlS4Z8rmJl\nJeYbrkc9dBj18KGsUGxtRayuQmxoQKyszOUqJhIJOjo6cjeYJmUKFImidfrQvD60np5sRIfVgrRo\nGcydg7erCz2dPkvg6Ok06p49qIePgCRhWr8eqaEeE9lroLS0dND8p+HwaRi2ZASZHR0xJFFgeYOb\ngL+fQCAwKFNzXM9D0+k7nHWELmkaXlwarcylpaWT/hsbCiNyxWKx0NbWlstsHMtNljPzPxcvXkxr\naysrV64knU5z1VVX8elPf5rPfe5zU7rmmc55KRDr6+s5cuQIhw8f5qKLLjovAzDHisUpc9mdF/PH\nb27k9Yff4ZYHr0Myz7zzZTVLOUdTKFQPZxPJtMq/Pt/KM9s7sMkSD9y+gksWVEz3siaNruu88ci7\npBNpLv/8OpzlE2uHO1/QdZ3W1lZEUWT+/Pn09fVN95IKTAMD39eNdjdFUWhpaRlTO+dwnGlE43a7\nc+1sRnViKHdPWZbp7OzM5bJNdB7LZrMRDAaJx+NDCsSpEKGjrqHYiq3YSrAzREgPUFLtmZAIFUUx\nF6ch2T3saw9w0K+ySIzS29ubm2+02+2Ew+EhK6FSY2O2OvjW26j79qN1eDGtWjmky6dgMmFavAhp\n7pysyc3x42i+TjRfZ/b77iJiNhv+TIbKljm4iorGPk+qqhBPoMdj6MEgWl8/Wl8vJE4H2gulpUgt\nzYhNTSiZDB3t7TidzkEzobqmoZ08ibp3H3o8jlDkwrRuHaLHM+T5Gzj/aeRX+vpCvHe8EyWtsqKh\niEB3Ak3TaGxsnPB15z8ZJBVL465xYXEN/fcTCoXo7u6eklbm4ZBlmfLyctqHOHdjJZPJ8MADD/DM\nM8/wwAMPcMMNNwDZyudE5ntnO+elQBRFkXnz5rFv3z5OnjxJS0vLdC/pnKZ+ZQ0LPzyX1peOsOOp\nfVx0x4XTvaRJM9QbS1t/jIbSwiZ8JnGsJ8o3nt7Nke4ocyudfOfjy2ksmx2v4YEXDuPd00XD6lrm\nX1l4jxqNnp4eAoEALS0tI4acFzg/iMViuZmrurq6cWeiDWQkI5qB1YmSkpJB1R2fz0c6ncZsNlNS\nMrnuG8PKPxaLndU+bcw1pk9VpKbKFORMdF3HXC4SOBig1FpKxYWTbyOcU+Egoaic6IvRFjdxUXM1\nop4hFArR2dmJrutYrVYCgcCgSAMAsbQU87XXktm1E+34CdKvbkSsqUZasiSXKzgQQZaR5s1FmjcX\nLRRC6+xE6+omeOQISiJBlceDqa8fBUASESxWsFiyc4uCgG441+o6ZDLoiTgkU2c/KasFsa4OsaoK\nsbYmV6E0Wj0HGhPpmUxWGB48iB6OgCQiLV6UrYiOsXghSRIR1cSxiAlPSRmLq2xo0T4ymaxh0MmT\nJwfNf4615TgRTOI/HsRkkSibe7ZQHTgHOFxEy1RhVHdLS0sn9LeUSCT48pe/zK5du/j5z3/ORRdd\nlPtevv5eZjrnpUAEWLRoEfv27eP9998vCMQxsO6TK+nY1cnOp/dRt7ya6iUzv0IzkFf3d/H1X+/m\nLz7Uwqcvn4N5hhnynG+oms4T75zgJxuPoGQ0blldz93XLcA6A6vbQxFoC/LuY9uxOGUu/ezaQrV7\nDBw4cACAhQsXTvNKCkwnuq7T09OD3++ntrZ20hWN8WYbGmYYiUS2elNdXZ0Tdn19fYiiiNPpzG3W\nx9rBZFSNotEoiUQi1y5otJQ6nU5qamomJYRHIpfXmE7QvKyRWHeSvsN+KhZM3vVxaV0RoHOiL85b\nR/wsKJVIhkO5mTlDcPf09OTiNAzBY7FYMK9dizZnDpndu3OVQaGsFGnOHMTa2iGNaUS3G9Vux2e1\nIjU1UuV0Ikaj2ZzFcAg9nkBPJiEcQlfPzrxEkrJzjEVuBIcdwW5HcLkQS8sQXIPjlIxZzYGtnlog\ngHbiJOrx46AoIAiILS2YlixGGId5kqrpHOyMcKw321Z6QY0dJdQzaA5bURTi8XjOMMhkMg0SjENd\ng+lkhs69PaBD1eLyszrHNE3LmS0NNQc4lRi5mhOtUPb393P33XcTCoV44oknCgZmY+S8FYgej4ea\nmhq8Xi9+v3/Sd/dmO7Jd5qovbuD3X3mZV7//Jn/6wxuxDtNuMBMpdVqodNv4P5uP8dahHv7p1guY\nV1XImjsX6fDH+fbv9rK7LYjHIXPfx5Zw6cLZc8Mik8rwyr+8SUZRufKLG3CUFobnRyMej3P06FGK\ni4upra0d/QEFZjVGtuFkzFkMUThel9LhMgeNdtRUKkUsFiMQCODz+bBYLDnBaBhwDIfb7SYajRIK\nhXJVtXy2lBoY5iMWiyWbEadDe9RHsCOMxSXjrpl8W+HSOjdWs8R7h3x0+JKsml+Du7gYccB8J2Sr\npYY7ZSAQQNf102Ln0ksx+f2oBw+i+TrJ9PWDJCLW1CLW1WbnDU9VuYyZuUHzhsMYW+kDMi+FcQrw\nnJBKJmlwOpGOHkVpb0cPhbM/YLFkK4ZzhzfOGY5ATGFPe4hIMoPDYmJ+qUQs2H3W9TBw/m6gYdDA\na9Bw+LTZbOiqjm9XFxlFpXxuyVnRFoaRj8lkGvMc4EQw5k/9fv+EZyhPnDjBnXfeSXFxMU8++WTB\nvGwcnLcCEWDp0qX4fD7279/PJZdcMt3LOeepWlTB6tuXsfWXu3njkXf48D9eNmsqG8sbPTz+2fU8\n8uJBntnewSd/+i6fvnwuf76haca7YM4WdF3nd9s6eOSlgyQUlSsXV/L3Ny2meJbFPrzz2Hb8J4Ms\nvn4+zesKrqVjobW1FU3TWDKMUUWB8wdBEKiqqppwqDxMLNsQTjttut3uIaMEBEHAarUOMhuJx+PE\nYrHcnKLdbs8JxjMFrs1mQ5ZlQqEQqVQKVVXz2lIKp6s35eXlFBcX555T9bJK2rf66GntwyRLOMom\ndyMrk8lgTvazpNxEV7qctkCaYLKfpXVFlAx4j5ckiaKiopwAMuY/B8VpLFiAff58rL296O3taKe+\nAAS3m5jdhl/TqJw/H9cYzGjGKwoB9FQKpaubrvcPYIknqFZVUFVUAFHMtqA2NSJWV4+5ldQglVY5\n2BWlrT8OQH2JjSprmnCof1QhNdAwCBjUEt3T00MyliR+UkHMiJS3lFLcMPjGQyqVor29fcJxGWPF\niBqJx+M0NTVN6GbPjh07+OIXv8iKFSt46KGHCk6l4+S8FogNDQ04nU4OHz7MmjVrJjXAfr6w/LYl\nePd2c2JLB/v/eIilNy6Y7iVNGQ6LiS/fvIRLF1Xw3Wf38++vHublvZ188YaFrGouhKdOJ+97Q/zg\n+Vb2tgdxWU1887YL+PAF1bNODBx7u40DLxympKmYdX+1arqXMyNQVZUDBw5gNpsL2bYFgOwmeKIC\ncbwtpcZj+vr6CAQCZ8U9jITRbmr8/JnZbZIk4XA4cDqduRB1p9OJ1+vF7XazaNGivFZvenp6cpmR\nZ4oO2W6m5sJKvDu78O3toXppxagZecNhzJe53W4aGspZrOkc8IZp9yd4+3A/NcVW5le7cA4RsTBc\nnIY/FiMpy1iXLMGRUbFFwkh+P6Gjx1CSyey8YXcPiiQhOJ0Dvhwgy9m2VFkGsxlBEEEATp1rPZOB\ndBqUNHomDSkFPRY7/RWNokQiBAIBnE4HDrsDsagIsbICobIyW8mcgKhXMhrHe2Mc642hajpOq4kl\nNS6USD/xmDIhITXQ8EaJp+nY2UkGMJVJJG1xDh8+nKsuQnbWu6qqakJxGWNF0zS8Xi+6rtPY2Dgh\nI8kXX3yRb3zjG9x66618/etfz2sL7GzlvD5joiiyZMkStmzZwsGDB1m2bObl/H3QiJLIlfes5+l7\n/sA7j22nalH5jIy+GIn188r55WfX86OXD/E/O7187hfbuHJxJXddu4Dq4vG3OBSYOP5oih+/epj/\n2elF1+HKxZV84fqFo2ZmzUQi3VHe+NE7mCwSV//dJZjk2TFPmW+OHz9OPB5n6dKlU5L3VuD8ZKAw\nhLHHIKXTaXw+H8CkMwfNZjPFxcUUFxcPmR1oMplIp9O43W7MZjOJRCK3cZ9K0uk0Xq8XURRHnC+z\nFVupXlZB554eOvd0U7m4jKLqsbeb6rpOMBikt7d3UFukWRK4sKGY+hI7+31hfMEkvmCSmmIrcyqc\nuO1Dn+Oh4jSMCm0XOorFglRdTanJhJROI0ai6MEgeiSCHgqN/0QNhSgSRydoMlG2bh2O+jqEkhKE\nSRQgEorKsd4Ybf1xVE3HYpZYVOOgusiMz+tFluVJt3rGAwk69/agpjWqFlRSPje7rzNuWgQCAZLJ\nJJIkEYvFAHKB81NJOp2mvb0dq9Wam98dD7qu81//9V88/PDDfPGLX+TTn/70rLuR/EFxXgtEgAUL\nFrBt2zb279/P0qVL83Y3bjbhKLVzxd3ref7br/HKg5u57Qc3YLbNro2Z2y7z1Y8s5baL6vn+H1vZ\neKCbtw718ucfauYvNjRjLWze80pG1fj1ljZ+9vpRYqkMcyqcs7qSq2Y0Xv3+myixNJfddTGe+vzd\nnZ1t7N+/H4AlS5ZM80oKzFQm2lIajUaz4e0ez6Rz885koNgpKSnB5/ORSCRwuVxEIhF6enqIRqM0\nNTXhcrmm7OZILpDe46G0tHTU5+QotVO7ogrf7m66DvSRjCiUzy1BEEd+XM70JpGgsbFxyA6uEqfM\nh+aV0hVKcbg7mhOKHodMQ6mNmmIb0gjHMSquoigSDocpKSnBYrEQj8fxx2IIpSU4Guqz84uShJhI\noMfioCjoipL9N5PJupbqGhiziCYzgtkMZlP2X1nOzg86HHSHQsQTCerr6yfV+qtqOt3hJO39CXoj\nWadUq1lifpWDpjI7SipJ28mTY36dhkPXdQInQvQfC4AAlQtLcdeebis1mUwkk0k0TcsZOhqB87mW\n3lNmN+MxXRqKZDJJe3v7hJ+Tqqo8+OCD/Pa3v+XBBx/kpptumvBaChQEIlarlXnz5tHa2kpbWxtN\nTU3TvaQZQcPqWpZ9dBF7nnmfzf/+Hlfcs35W3qVZWOPmp59awwt7Onn05UP85+tH+Z+dXv76yrlc\nt6xmxA+nAuNH13XeaO3hx68c5mRfjCKbib+7YREfXV03q2dBt/5yF90H+5h7aRMLrio4rI2V3t5e\nuru7aWhoyGvLU4GZxXg+iyZiRGO0X4bDYerq6vI625RMJvF6vdhsNubMmZO7id3V1UV3dzc+nw+z\n2ZzbqBub9fHe7DacNg331/FUJm3FVupXV+Pb00OwPUwqnKJqSfmwN44VRcF7qvLV3Nw84loFQaC6\n2Ep1sZXecIqjvTH6IikCMYV9HWGq3NnvlbssZ30eD6xQDmz9NSq0g/IrEwlkWc6ew7JSbDbbuM6h\nYdwiSRJNTU0TEkqqptMbSdEZTNIVSqJq2Wp2sd1MY5mdWo8NURBy4myy5kRKPE33gV4SoRQmi0T1\n0gpsxae7c4wcUV3XBz0nI9JlUEuv34/X68VqteauQ5vNNua/KeNmi+FcO14SiQRf+cpX2L59O489\n9hhr1qwZ9+8oMJjzXiBC1qymtbWVPXv2FATiOFjz58vpOtDL4deP46l3s+JPl073kvKCIAhcf2EN\nly6s4P9sOsYT757k27/bx3+/dYI//1AzVy+pKsRiTBJV09l8sIf/++Zx9neEkESBW1bX87+vnDvr\nTGjO5ODGo+z+7QGKql1c8pk1s/JGS77YvXs3kH0PL1BgPJzpUjpWFEXB5/MhiiLNzc15nW0KBoP0\n9PQMys0zqKioQFEUUqkUFRUVyLJMNBqlr6+PVCp1VhTESM9RVVV8Ph+qqk64TVZ2yDRcVEN3ax+R\n7hgnt3gpn1cyqBoFp4VAWVkZHo9nXOe+vMhCeZGFeCpDuz9BRyCB99SXJAqUuSyUu2TKXRZsZnGQ\nycmZ1bwz8yt1Xc+1ow4XpzHcWpPJZC5zczzGLbquE01l6Iso9EZS9EeVnCi0yRI1xVbqSmy4rObc\nz/f29hIMBieVO6hrOoG2EP7jQTRNx1XhoGJBKdKAziij1dNms1FVVTXkcxqupTcej9Pd3T3mcxgI\nBOjt7Z3wzRa/38/dd9+N3+/nySefLMRYTBEFgQiUlJRQX19Pe3t77q5MgdGRzBIf/vKl/O5LL/De\n/92Fu9pFy4bG6V5W3nBYTHz2mvnctqaen248wvO7fXzzt3v5t1cO8fG1jXx0VR2uWdZqm28SSoY/\n7PTxxLsn6PAnALh8UQWfuXr+rAm8Hwnf3i42PboF2SFz3dcuR7bPbjE8lYRCIY4fP05paWkh2qLA\nuJhoS6lRuTHCuvN1M2dg++VwQkAURaqqqmhvbycej+N2u7HZbJSXl6OqKvF4nGg0elYUxJlzYwNz\nFOvq6ib1nESTSPXSChylUXoP9dPd2k+4M0r5/FIsLjmXBTjZqqvdYmJBtYsF1S4CMYWuUJLOYJLu\nUPZLVVXikRAeh8yc+kqiCrgkDfMIXSjCKHEamqYNOoeGiB5PNS+VVgknMgQTaQIxhUAsTXpAxqLT\naqKyyEKV24rnjBujmqbh8/nIZDKTujER64vTe9iPEk9jkiUq55fgqhxsqmSYBpWUlIzrOj/TdGng\nOQwGg6iqmqtwG+ewt7eXcDg8bJvxaJw8eZI777wTt9vNU089RXl5+bh/R4GhEfTJeEHPMDo6Orjq\nqqt49dVXqaurG/S9zs5OnnvuORoaGrjuuuumaYUzk/4TAZ79hxfRNJ0/ue8aKhecHzkzvkCCp949\nye93dBBXVOyyxJ+srOUTFzdS4ynYKY9EXyTFr7e08btt7YQTaWSTyHXLqrl9XRPNFWNzAJzpBDtC\nPPMPL5JOpLnh3quoXVa4MTUeNm3aRGtrK1deeSVz58496/sjvd8XmN2oqkomkznrvxsVw/GiaVpu\n5q+2tnZCeWxjJZVK4fV6sVgsVFdXj9rmqCgKZrN5xE280UoZjUaJx+O5dlRd1wmFQlRXV095jmI6\nmaH3UD/R3ji6pqHIKezVFhqaG/JmJhVLZejoDdF6ohNVsmG22hl4WmyyhMtqwmExYZcl7BYJuyxh\nNUsjikc4fQ5jsRjxeBxJkhBFkXQ6nRO8mq6jZDSSaZWEohFXMkRTKvFUhkgyg5IZfO3ZZQmPQ6bU\nKVPmlLEP4dIKg41bqqqqJuSVkQgm6T8WIB5IIgDuWhelczxI5sGtsJMNpR+JgS69sVgMTdMQRZHy\n8nJcLte4Re+uXbu45557WL58OQ899FBeDJvOZwoVxFNUVVVRVVVFW1sbfX19hTDNcVDa5OHqL13C\nC995nRe/8zq3PHjdWXekZiM1Hhv3XL+QT10+h2e2d/DUlpM8+W4bv97SxhWLK/n4xY0sqy8utAwO\n4FBnmKe2tPHiHh9pVcdtN/Opy+Zw65p6Sp3nT8xMIpzk+W+/RiqqcPnn1xXE4TiJRqMcOnQIt9ud\nM04oUGAkJlo1NELijXm5yZhwjEYoFKK7u/uszMGRGIsRyplREAPbKCHbyprJZHA4HMiyPCWfWWar\niZpllQQ6gxzacgQpYsKq2ggSxtNUPOUuzbquk4qFEeL9XLW8CafTSTyVIRBPE4qnCSezIq0nnAJS\nZz1eEARkk4gsCZgkEUkUMIkCkiggCoAggC6g4yRtstHd00s6nUEXRLZ1HEYXTIgmGYtFxmyWOfMU\n2mUJj9uKy2qi2G6m2G7Gah79HEy0mmcQDyTwHw8SDyQBcJTaKJtbgsU5+LoxZlADgcCk2ldHwnDp\ndTqdtLe35yqO0WiUnp6ecc3Rvvzyy3zta1/jlltu4Rvf+EYhxiIPFM7oKQRBYMWKFTz//PPs3LmT\na665ZrqXNKNoWF3L+k+v5q2fbuX5b7/GR/75WiyzfHbMwGUz8xcfaubPLm7klf1d/OrtE7y6v5tX\n93dTV2Lj2mU1XLusmobS8/PuVncowUt7u3hhj4+j3VEA6kvt3L6uiRsurDnvHGHVtMpL33uDcFeU\nFX+6tGBKMwF2796NpmksX7684DxdYFQmkm0IpwXbROblxoOmabl5uXxtzg0URaGnpweLxUJTU1NO\nMBpxGkAue9HhcExKEIdCIXojPSy4bC5CQsJ/PEigPUzQG6GoyomnoQh5CvYJRktuMpkcNG9ot5iw\nW0zUek5XfJWMRiyVIa6oJBSVmKKSSqsoGY1URiOuqKja2dVng0xGJRDwI1ssFLlKEUUBuwCoaXRV\nQUvHEDIR3E4bpW4H5Z4i3A7rhEzWjOtvvNU8TdOJ9sQItodIhrM3AeweK6UtnkEmNKd/Pnv+UqnU\nhEPpx4qiKLS1tQ2a1ywtLUXXdRKJRO467OjoGNLwRtd1Hn/8cf71X/+Ve+65h7/+678u3ITPEwWB\nOIC6ujrKy8s5fvw4gUAAj8cz3UuaUSy9cQGhzgj7nmvllQc2c93Xr0A6j8xbzCaR6y+s4bpl1Ww/\n7ue5nV7eeL+H/3z9KP/5+lGW1Lm5blk1Vy6pmvXVsnAizRvvd/PCnk52nPCj62CSBC5bVMFNK2rZ\nMK8c8Tx0gNV1ndcfeYeuA73MaOJnKgAAIABJREFU+VAjF91x4XQvacYRj8dpbW3F6XQyb9686V5O\ngXMQY8N4phHNWDeSH7Rg6+joyAm2fFYojfbBMyuURUVFFBUVDXL2DIVCdHZ25pw9nU7nmF0pdV2n\nu7ubaDR6+vx5wFXlJOyLEGgLEfJFCPki2D1W3LVFOMvto0ZjDEU6naajowOz2UxTU9OoN4xkk4hs\nkvGMcr9W1fTcl6br6EA8FqOrs5PyhlpKSzxIpyqMZ5LJZHKi29/tJSAI2O12nE4ndrt91GqXYUYT\nDofHdf0pMYWQL0q4M4KazrazOsvteBrcQwpDY60dHR2YTKZJZymORjwep6Ojg/Ly8rP218Kpc2TM\npg7KsOzq4tixY/zmN7/BZrOxefNm7r//fj7ykY/kba0FCgJxEEYV8aWXXmLHjh1cddVV072kGce6\nT64k3BWhbauXTY++y+V3rZvQm/5MRhAEVreUsrqllFgqw6bWHl7c4+O9o/3s7wjx0POtLG/08KH5\nFaxuKWFupWvGiyVd1znRG2P7CT9vH+rlvWP9ZNTsePPyRg/XLavmisWVuM9zE5b3/u8ujrxxgooF\nZVz++fPvb2Mq2L17N6qqcuGFFxaqhwWGZaItpQNnAKdLsE01RixHJBKhvr5+2BnKM509NU3LVXUM\nV0rDYMTpdA7Z3ppOp/F6vUiSdFZLrigKFNcV4a51EeuLE2wPEw8kiQeSmGQJZ6WDoion1qKx3UCN\nx+N4vd5JZwEOxUDxp+s6fr+fkN/P3OaGUQ12TCYTbrcbt9s9oug2KmMD38c0TcPr9aKqKk1NTaOK\nyYyiEumOEumK5qqFklnE0+jGXeNCtg9fDUylUrS3t4/bfXUiGNXQgXEjI3Gm4U1JSQl//OMfaW1t\npaSkhAceeIC33nqLb37zm3mdCT6fKQjEM2hsbKSsrIyjR4+yfPlySktnZzB3vhAlkav/9kP8z9df\n4dDGY1icMuv+atV52wLgsJi4/sIarr+whv5Iipf3dfLq/m76IykefukgAEU2E8sbS1jZ5GFVcwlz\nKs59wajrOif6Yuw47mfHiQA7TvgJxJTc9+dXu7hqcRXXXFBNjafw5g2w+3cH2PWb/bhrXFz31csx\nDWNIUGB4YrEYBw4cwOFwsHDhwuleToFzlPvuu48jR46wfv16NmzYQEtLy6ifQYZhixEr4Xa789pS\napjejCTYpgJDsBmxHOMRvKIoDnL2HFgZ6+/vH+T86XA4cuK6uLiYsrKyYc+fIAg4yx04yx3Zqpc3\nQrgzSrA9TLA9jNlmwlXhwFFux1p0djSCrusEAgH6+vrGLDgmiqZpdHZ2Trj9cqg4DUN09/b2Dook\nsVgsdHd3Y7PZRnSUVeJpYn1xor1xEsHsbKFAdr6wqNqFo9w+6h4iFovh9XqHjFCZSqZittHv93PP\nPffQ19fHz372M+bOnUt7ezt79+6d8tlDTdO49957OXjwILIsc99999HYeNqdf+PGjTz66KOYTCZu\nu+02Pv7xjwPwk5/8hI0bN5JOp7n99tv52Mc+NqXrmg4KO5QzEASB1atX88ILL7Bt2zauvfba6V7S\njMNsM3P9N67k2X98ib2/b8XqsrDy4xdM97KmnVKXhT9b18SfrWuiN5xk67H+nLja1NrDptYe4LRg\nXFDtornCSUu5k7oS+7QFxauaji8Q53hvjOM9UQ51Rdh50o8/eloQlrssXLusmpVNJaxuLqG2pODi\nOpDWV47y7i924Ci1c+O3rsbmzl/L2mxmx44dqKrKqlWr8lrZKTCzue222/jNb37Db3/7Wx566CGq\nqqpYt24dGzZsYM2aNWfNc6mqmpthm6jd/lgxQuLNZnPeTW9isRg+n2/KKmxnVsZSqVQuwsDn86Hr\nOkVFReMSbLJDpnx+KWVzS4j7E4S7osR64/hPhvCfDGGSJRylNuylNuweG4JJGHLeMB8Y7auyLI+p\nfXUsDGylHBhJYmReCoKA1WolFArloiC0jEYimCTmTxDri5NOnJ6RtLktOCsduCqdYzb+mWzu4FjR\ndZ3Ozs7cazWR2caTJ09y11134XQ6eeqpp6ioqACgvr6e+vr6qV4yr7zyCoqi8OSTT7Jr1y7uv/9+\nfvzjHwPZ6+F73/seTz/9NDabjdtvv50rr7ySo0ePsnPnTn71q1+RSCR47LHHpnxd00FBIA5BfX09\nlZWVnDx5Mnc3scD4sBZZuPGbV/Lsl19i6y93I5pElt+6ZLqXdc5QXmTlhuW13LA8m9/mCyTYedKf\nq8gNFIyQnd9rLHXkBGNjuYNSpwWPQ6bYbsZlNU+46qjrOtFkhkBcIRBT8EcV2vpiHOuNcrw3ysne\nGKkz7LnLXBY+fEE1K5s8rGwuob7Eft5WiUfj0MZjvPGjd7C4ZG6890pc5eenWdFkCYfDtLa24na7\nmT9//nQvp8A5zJIlS1iyJPt509bWxubNm9m8eTNf//rXURSFCy64IFddzGQyHD16lLVr19Lc3JzX\ntmWjpTTfpjdG1cbv91NbW5sX+39DyMiyTDKZxGw2U1paSiqVorOzk3Q6nassOp3OUcWBIAo4yuw4\nyuxoqkbcnyTWGyPaFyfUGSXUGUVVVaKpCHaPjbo5tQhafufl8tW+OhBJklBVlUQiQX19PRaLhVB/\niL62fo72nkCNa4gZCcspJ1rJLOEst+MoteMot4/LDdZoNY5Go3kX16qq4vV6EQRhwuJ69+7d3HPP\nPVxwwQX88Ic//EBiLLZv384ll1wCwPLly9m3b1/ue0ePHqWhoQG32w3AqlWr2Lp1KwcOHGD+/Pl8\n7nOfIxqN8vd///d5X+cHQUEgDoEgCKxZs4bnnnuOrVu3cuONN073kmYkzjIHN337av7nay+z5f/s\nBCiIxGGo8dio8dRy4ynB2B1KcLQnyvGeKMd6skLteG+Moz3RIR8viQJuuxmPQ8Zjl3FYTQz3caYD\n8ZRKMK4QjCkE4kpuXvBMLCaRxnIHLeVOWiqcNJU7mVPhpMYzNrOC851DG4/x2sNvY3HI3PStq/E0\n5K+VZ7azfft2dF1n1apVhdnDAmOmoaGBO+64gzvuuANFUdi+fTtvvvkmL7/8Mv/2b/+G3W7n8ssv\nx2w2s379ekpKSqZ8DQNNW/LdUqqqKj6fD1VVaW5uzrsjpeE2eaa4Hph519vbiyRJg9pRR/obFiUR\nZ7kdZ7mdCl0nFVHo9/rpOOLFghVzUqb7QB8AJlnC4pKxFlmwuGRkh4zZZprU55NRzctHFuBANFXD\n19ZJsDdIqbuc8LEYyZCfjKICIi69iIycQbdr6BYNxZLAWerA7BIxOyQk89jfB43ZRk3T8j5ba+Q2\n2u12KisrJ/RavPLKK3zta1/j5ptv5t577/3AYiyi0eigCrgkSWQyGUwmE9FodND14HA4iEajBAIB\nfD4f//7v/05HRwef+cxneOGFF2b8HqkgEIehurqauro6Ojo68Pl81NTUTPeSZiTuahc33XcNz321\nIBLHQ6XbRqXbxvp55bn/pmk63eEkx3uitPfH8ccUArEUwXiaQCxb/esOJXNREqNht0h47DILqoty\nwrLYIVNsl6kvtdNc7qDGYx/Spa3A6Bx6bYA4/PbVlLVM/cbzfCEQCHD48GFKSkqYM6cQC1JgYsiy\nzLp161i7di3t7e0A3HTTTbz//vs8+OCDhMNhFi1alKsuXnDBBZPemBptiiaTKe8tpYlEAq/Xi8vl\noqKiIq8b1EgkQmdn57AGO0bmXXFxca4dNRqN4vf78fl8gyIMrFbriGuNZ2LEpAhLrliIw+4gGU6R\nCCVJhlKkIgqx/gSx/kTu5wVRQLabke1mzDYTZlv2X5NFwmQxIQ7jrm4I+VgsNmWtxmpGI5PMkEll\nSCeMrzSpmEKvrxdN1SguLiYSiQFZwes8NXtpLbJgdVsQT42XnOnsmU6nc6ZBI2VYGoLNZrNRVVWV\n1+tisrmNAI8//jgPPfQQn//85/mbv/mbD1RoOZ1OYrFY7v9rmpZ7Dzjze7FYDJfLRXFxMS0tLciy\nTEtLCxaLBb/fP+M9TAoCcQRWr15NR0cHW7du5eabb57xdwOmC3e1iz/5TkEkThZRFKgutlFdPPLd\nZyWjkVCGz3ECsJolLGMI6S0wMQZWDm/81lUFcThJtm3bBmTfkwvvwwUmSyaTYe3atTz44IM5EZDJ\nZNi7d2+uHfWxxx7D4XCwZs0a1q9fz/r166murh7XcQwRVVpaOuHN8ljQdZ1gMEhvby9VVVUUFRXl\n5TjGsXp7ewmFQmOuhhrtqFarlbKyspzQiUajuWrnwOqiUfUcLt/QVmwdFNuQUVRS4RSpmIISTaPE\nFJRYmtSAOfmBiJKAyWJCMouIJhFJlkDQ6fP3IUoildWVJPsVUoICRjyKAOjZ54+uo2unnHJVHU3V\n0DI6WkZDTauo6VP/plQ07ezuHDWTIRgOYi+yUVFXgdVpQXaas9VP6/Db8jOdPc80DQIGnUeTyTQl\ngm2sGNf7RK9BVVX5/ve/z69//etpi7FYuXIlr732GjfccAO7du0aNM4wZ84cTp48STAYxG63s23b\nNj71qU9hsVj4r//6Lz75yU/S09NDIpHIq/HPB4Wg6/rQvWWzkI6ODq666ipeffVV6urqxvSYl156\niRMnTnDNNdfQ3Nyc5xXObkKdEZ776svE+uOs+NhSLrrjwsJmr8CsY98fDvLWT7ciO2Ru+vZVlM+Z\n2XcRp5vu7m6effZZKioq+MhHPjLm94yJvN8XKGAQCAR4++232bRpE2+++SZ9fX20tLTkxOLKlSuH\nrTAZs17hcJja2tq8GoEMFFG1tbV5NdjJZDI5M5ra2topa/sz2lGj0SjxeByTyYTVaiUej2O1Wqmp\nqZlQW3kmlUGJp0knMmQSGTKKSiaVIZNSURUVNa2i65BJpwkEAlhtNlxOJ0xyXyIIIJklTBYJSZYw\nWU2YLSZMVhNpXaHH30NlTcWUzqEOjNOIxWLE4/Fce2RpaSmlpaV5bc33+/309/dTV1c3oRbqZDLJ\nV7/6VbZu3cojjzzCunXr8rDK0TFcTA8dOoSu63z3u9/lwIEDxONxPvGJT+RcTHVd57b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8z5xAucT7yAa2Arug4PJmiAP1Y2MgNlTvQ6PRm/ZfH7D6lcTM4HrrY46fFoZwL7+0nLCjNz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\nhBBCCCGEEAKA/w8+N+XuJP90DAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15,6))\n", "fig.suptitle('RSS - Regression coefficients', fontsize=20)\n", "\n", "ax1 = fig.add_subplot(121)\n", "ax2 = fig.add_subplot(122, projection='3d')\n", "\n", "# Left plot\n", "CS = ax1.contour(xx, yy, Z, cmap=plt.cm.Set1, levels=[2.15, 2.2, 2.3, 2.5, 3])\n", "ax1.scatter(regr.intercept_, regr.coef_[0], c='r', label=min_RSS)\n", "ax1.clabel(CS, inline=True, fontsize=10, fmt='%1.1f')\n", "\n", "# Right plot\n", "ax2.plot_surface(xx, yy, Z, rstride=3, cstride=3, alpha=0.3)\n", "ax2.contour(xx, yy, Z, zdir='z', offset=Z.min(), cmap=plt.cm.Set1,\n", " alpha=0.4, levels=[2.15, 2.2, 2.3, 2.5, 3])\n", "ax2.scatter3D(regr.intercept_, regr.coef_[0], min_rss, c='r', label=min_RSS)\n", "ax2.set_zlabel('RSS')\n", "ax2.set_zlim(Z.min(),Z.max())\n", "ax2.set_ylim(0.02,0.07)\n", "\n", "# settings common to both plots\n", "for ax in fig.axes:\n", " ax.set_xlabel(r'$\\beta_0$', fontsize=17)\n", " ax.set_ylabel(r'$\\beta_1$', fontsize=17)\n", " ax.set_yticks([0.03,0.04,0.05,0.06])\n", " ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confidence interval - Statsmodels " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 7.0326 0.458 15.360 0.000 6.130 7.935
TV 0.0475 0.003 17.668 0.000 0.042 0.053
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('Sales ~ TV', advertising).fit()\n", "est.summary().tables[1]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.1025305831313506" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# RSS with regression coefficients\n", "((advertising.Sales - (est.params[0] + est.params[1]*advertising.TV))**2).sum()/1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scikit-learn" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.03259354913\n", "[ 0.04753664]\n" ] } ], "source": [ "regr = skl_lm.LinearRegression()\n", "\n", "X = advertising.TV.values.reshape(-1,1)\n", "y = advertising.Sales\n", "\n", "regr.fit(X,y)\n", "print(regr.intercept_)\n", "print(regr.coef_)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.61187505085007099" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sales_pred = regr.predict(X)\n", "r2_score(y, Sales_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interactive Outlier Analysis using bqplot\n", "\n", "Next up, we will plot the regression line for TV Vs Sales, Then move some outliers around to see how it impacts the regression line. This would give us an idea if we need to remove some outliers to get a more consistent model with the general direction of the data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipywidgets version 7.2.1\n", "bqplot version 0.10.5\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "21fe4444711c4a78ba450d5ce6148156", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Label(value='y = 7.03 + 0.05x'), Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vert…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import bqplot.marks as bqm\n", "import bqplot.scales as bqs\n", "import bqplot.axes as bqa\n", "import bqplot as bq\n", "from IPython.display import display\n", "import ipywidgets as widgets\n", "\n", "print('ipywidgets version', widgets.__version__)\n", "print('bqplot version', bq.__version__)\n", "def update_line(change):\n", " # create line fit to data and display equation\n", " lin.x = [np.min(scat.x), np.max(scat.x)]\n", " poly = np.polyfit(scat.x, scat.y, 1)\n", " lin.y = np.polyval(poly, lin.x)\n", " label.value = 'y = {:.2f} + {:.2f}x'.format(poly[1], poly[0]) \n", " \n", "# create initial data set\n", "size = 10\n", "np.random.seed(0)\n", "x_data = advertising.TV \n", "y_data = advertising.Sales \n", "\n", "# set up plot elements\n", "sc_x = bqs.LinearScale()\n", "sc_y = bqs.LinearScale()\n", "ax_x = bqa.Axis(scale=sc_x)\n", "ax_y = bqa.Axis(scale=sc_y, tick_format='0.2f', orientation='vertical')\n", "\n", "# place data on scatter plot that allows point dragging\n", "scat = bqm.Scatter(x=x_data, \n", " y=y_data, \n", " scales={'x': sc_x, 'y': sc_y}, \n", " enable_move=True)\n", "# set up callback\n", "scat.observe(update_line, names=['x', 'y'])\n", "\n", "# linear fit line\n", "lin = bqm.Lines(scales={'x': sc_x, 'y': sc_y})\n", "# equation label\n", "label = widgets.Label()\n", "# containers\n", "fig = bq.Figure(marks=[scat, lin], axes=[ax_x, ax_y])\n", "box = widgets.VBox([label, fig])\n", "\n", "# initialize plot and equation and display\n", "update_line(None)\n", "display(box)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statsmodels\n", "\n", "Let us start by including two separate variables (Radio and NewsPaper) in two different models." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 9.3116 0.563 16.542 0.000 8.202 10.422
Radio 0.2025 0.020 9.921 0.000 0.162 0.243
" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('Sales ~ Radio', advertising).fit()\n", "est.summary().tables[1]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 12.3514 0.621 19.876 0.000 11.126 13.577
Newspaper 0.0547 0.017 3.300 0.001 0.022 0.087
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('Sales ~ Newspaper', advertising).fit()\n", "est.summary().tables[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statsmodels\n", "\n", "Now let us including the two variables (Radio and NewsPaper) together in a single model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sales R-squared: 0.897
Model: OLS Adj. R-squared: 0.896
Method: Least Squares F-statistic: 570.3
Date: Wed, 20 Jun 2018 Prob (F-statistic): 1.58e-96
Time: 19:03:45 Log-Likelihood: -386.18
No. Observations: 200 AIC: 780.4
Df Residuals: 196 BIC: 793.6
Df Model: 3
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 2.9389 0.312 9.422 0.000 2.324 3.554
TV 0.0458 0.001 32.809 0.000 0.043 0.049
Radio 0.1885 0.009 21.893 0.000 0.172 0.206
Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 60.414 Durbin-Watson: 2.084
Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241
Skew: -1.327 Prob(JB): 1.44e-33
Kurtosis: 6.332 Cond. No. 454.
" ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sales R-squared: 0.897\n", "Model: OLS Adj. R-squared: 0.896\n", "Method: Least Squares F-statistic: 570.3\n", "Date: Wed, 20 Jun 2018 Prob (F-statistic): 1.58e-96\n", "Time: 19:03:45 Log-Likelihood: -386.18\n", "No. Observations: 200 AIC: 780.4\n", "Df Residuals: 196 BIC: 793.6\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 2.9389 0.312 9.422 0.000 2.324 3.554\n", "TV 0.0458 0.001 32.809 0.000 0.043 0.049\n", "Radio 0.1885 0.009 21.893 0.000 0.172 0.206\n", "Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011\n", "==============================================================================\n", "Omnibus: 60.414 Durbin-Watson: 2.084\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241\n", "Skew: -1.327 Prob(JB): 1.44e-33\n", "Kurtosis: 6.332 Cond. No. 454.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('Sales ~ TV + Radio + Newspaper', advertising).fit()\n", "est.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation Matrix" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TVRadioNewspaperSales
TV1.0000000.0548090.0566480.782224
Radio0.0548091.0000000.3541040.576223
Newspaper0.0566480.3541041.0000000.228299
Sales0.7822240.5762230.2282991.000000
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" ], "text/plain": [ " TV Radio Newspaper Sales\n", "TV 1.000000 0.054809 0.056648 0.782224\n", "Radio 0.054809 1.000000 0.354104 0.576223\n", "Newspaper 0.056648 0.354104 1.000000 0.228299\n", "Sales 0.782224 0.576223 0.228299 1.000000" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "advertising.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple Linear Regression\n", "\n", "Let us do a multiple regression using sklearn.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.18799423 0.04575482]\n", "2.92109991241\n", "[ 13.22390813]\n" ] } ], "source": [ "regr = skl_lm.LinearRegression()\n", "\n", "X = advertising[['Radio', 'TV']].as_matrix()\n", "y = advertising.Sales\n", "\n", "regr.fit(X,y)\n", "print(regr.coef_)\n", "print(regr.intercept_)\n", "\n", "regr_model = regr\n", "\n", "def predict(tv, radio):\n", " data = pd.DataFrame({'TV': [tv], 'Radio': [radio]})\n", " return regr_model.predict(data)\n", "\n", "#prediction = predict(advertising[['TV','Radio']])\n", "prediction = predict(180.8,10.8)\n", "print(prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3-D visualization using Matplotlib\n", "\n", "Let us perform a 3-d visualization of the hyperplane using matplotlib. Here are the steps:\n", "\n", "1. First let us draw thw axes and mark the min and max range of predictors.\n", "2. Then shade the area that would be forming the hyperplane\n", "3. Elevate and tilt the hyperplane according to the intercept and regression coefficients.\n", "\n", "This involves bit of python coding as below." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RadioTV
count200.000000200.000000
mean23.264000147.042500
std14.84680985.854236
min0.0000000.700000
25%9.97500074.375000
50%22.900000149.750000
75%36.525000218.825000
max49.600000296.400000
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" ], "text/plain": [ " Radio TV\n", "count 200.000000 200.000000\n", "mean 23.264000 147.042500\n", "std 14.846809 85.854236\n", "min 0.000000 0.700000\n", "25% 9.975000 74.375000\n", "50% 22.900000 149.750000\n", "75% 36.525000 218.825000\n", "max 49.600000 296.400000" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What are the min/max values of Radio & TV?\n", "# Use these values to set up the grid for plotting.\n", "advertising[['Radio', 'TV']].describe()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a coordinate grid\n", "Radio = np.arange(0,50)\n", "TV = np.arange(0,300)\n", "\n", "B1, B2 = np.meshgrid(Radio, TV, indexing='xy')\n", "Z = np.zeros((TV.size, Radio.size))\n", "\n", "# Here is the place where we tilt and elevate the hyperplane\n", "for (i,j),v in np.ndenumerate(Z):\n", " Z[i,j] =(regr.intercept_ + B1[i,j]*regr.coef_[0] + B2[i,j]*regr.coef_[1])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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55BPceuutuOyyy+IuR1/P6NGjYwr9MWPGYPny5Tj33HOxdetW47i95pprsGTJ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\ntsMjvlJIpCfxu0I0LNYRA6MpFMZoNCPiuLGuptXzdoaQj5R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2dBQjSaOeLegWNpYYqdte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oa5TMdOVi3uEK6veR1X5dfG4qm2jZ+TDtsOu7/08a65zmKlh8m/qBVo6W2YetgLypPEkcEPClgr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create plot\n", "fig = plt.figure(figsize=(10,6))\n", "fig.suptitle('Regression: Sales ~ Radio + TV Advertising', fontsize=20)\n", "\n", "ax = axes3d.Axes3D(fig)\n", "\n", "ax.plot_surface(B1, B2, Z, rstride=10, cstride=5, alpha=0.4)\n", "ax.scatter3D(advertising.Radio, advertising.TV, advertising.Sales, c='r')\n", "\n", "ax.set_xlabel('Radio')\n", "ax.set_xlim(0,50)\n", "ax.set_ylabel('TV')\n", "ax.set_ylim(ymin=0)\n", "ax.set_zlabel('Sales');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interaction Variables" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 6.7502 0.248 27.233 0.000 6.261 7.239
TV 0.0191 0.002 12.699 0.000 0.016 0.022
Radio 0.0289 0.009 3.241 0.001 0.011 0.046
TV:Radio 0.0011 5.24e-05 20.727 0.000 0.001 0.001
" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('Sales ~ TV + Radio + TV*Radio', advertising).fit()\n", "est.summary().tables[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Non-linear relationships" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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x2tra6OvrIzk5mblz57Jz506USiXJycn4fD6MRqPcmGWs5RqlUolKpcJsNh/3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# With Seaborn's regplot() you can easily plot higher order polynomials.\n", "plt.scatter(auto.horsepower, auto.mpg, facecolors='None', edgecolors='k', alpha=.5) \n", "sns.regplot(auto.horsepower, auto.mpg, ci=None, label='Linear', scatter=False, color='orange')\n", "sns.regplot(auto.horsepower, auto.mpg, ci=None, label='Degree 2', order=2, scatter=False, color='lightblue')\n", "sns.regplot(auto.horsepower, auto.mpg, ci=None, label='Degree 5', order=5, scatter=False, color='g')\n", "plt.legend()\n", "plt.ylim(5,55)\n", "plt.xlim(40,240);" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.157844733354\n", "39.9358610212\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Scientific libraries\n", "from numpy import arange,array,ones\n", "from scipy import stats\n", "\n", "\n", "xi = arange(0,9)\n", "A = array([ xi, ones(9)])\n", "\n", "# (Almost) linear sequence\n", "y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24]\n", "\n", "# Generated linear fit\n", "slope, intercept, r_value, p_value, std_err = stats.linregress(auto.horsepower,auto.mpg)\n", "line = slope*auto.horsepower+intercept\n", "print(slope)\n", "print(intercept)\n", "\n", "# Creating the dataset, and generating the plot\n", "trace1 = go.Scatter(\n", " x = auto.horsepower,\n", " y = auto.mpg,\n", " mode='markers',\n", " marker=go.Marker(color='rgb(255, 127, 14)'),\n", " name='Data'\n", " )\n", "\n", "trace2 = go.Scatter(\n", " x=auto.horsepower,\n", " y=line,\n", " mode='lines',\n", " marker=go.Marker(color='rgb(31, 119, 180)'),\n", " name='Fit'\n", " )\n", "\n", "#annotation = go.Annotation(\n", "# x=3.5,\n", "# y=23.5,\n", "# text='$R^2 = 0.9551,\\\\Y = 0.716X + 19.18$',\n", "# showarrow=False,\n", "# font=go.Font(size=16)\n", "# )\n", "layout = go.Layout(\n", " title='Linear Fit in Python',\n", " plot_bgcolor='rgb(229, 229, 229)',\n", " xaxis=go.XAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),\n", " yaxis=go.YAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)')\n", "# annotations=[annotation]\n", " )\n", "\n", "data = [trace1, trace2]\n", "fig = go.Figure(data=data, layout=layout)\n", "\n", "py.iplot(fig, filename='Linear-Fit-in-python')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Generated linear fit\n", "slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(advertising.TV,advertising.Sales)\n", "line2 = slope2*advertising.TV+intercept2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using a Polynomial relationship of 2nd Degree" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginnamehorsepower2
018.08307.0130.0350412.0701chevrolet chevelle malibu16900.0
115.08350.0165.0369311.5701buick skylark 32027225.0
218.08318.0150.0343611.0701plymouth satellite22500.0
\n", "
" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration year \\\n", "0 18.0 8 307.0 130.0 3504 12.0 70 \n", "1 15.0 8 350.0 165.0 3693 11.5 70 \n", "2 18.0 8 318.0 150.0 3436 11.0 70 \n", "\n", " origin name horsepower2 \n", "0 1 chevrolet chevelle malibu 16900.0 \n", "1 1 buick skylark 320 27225.0 \n", "2 1 plymouth satellite 22500.0 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auto['horsepower2'] = auto.horsepower**2\n", "auto.head(3)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 56.9001 1.800 31.604 0.000 53.360 60.440
horsepower -0.4662 0.031 -14.978 0.000 -0.527 -0.405
horsepower2 0.0012 0.000 10.080 0.000 0.001 0.001
" ], "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = smf.ols('mpg ~ horsepower + horsepower2', auto).fit()\n", "est.summary().tables[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the residuals" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "regr = skl_lm.LinearRegression()\n", "\n", "# Linear fit\n", "X = auto.horsepower.values.reshape(-1,1)\n", "y = auto.mpg\n", "regr.fit(X, y)\n", "\n", "auto['pred1'] = regr.predict(X)\n", "auto['resid1'] = auto.mpg - auto.pred1\n", "\n", "# Quadratic fit\n", "X2 = auto[['horsepower', 'horsepower2']].as_matrix()\n", "regr.fit(X2, y)\n", "\n", "auto['pred2'] = regr.predict(X2)\n", "auto['resid2'] = auto.mpg - auto.pred2" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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vb9Y9lyhhdZVuIVVBetq0abcc27dvn/r06ZPmBSEd1atnmp7PmWPWTterZwIJ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\n/iNJFSqoQt26KpQOmzvAGuW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(12,5))\n", "\n", "# Left plot\n", "sns.regplot(auto.pred1, auto.resid1, lowess=True, \n", " ax=ax1, line_kws={'color':'r', 'lw':1},\n", " scatter_kws={'facecolors':'None', 'edgecolors':'k', 'alpha':0.5})\n", "ax1.hlines(0,xmin=ax1.xaxis.get_data_interval()[0],\n", " xmax=ax1.xaxis.get_data_interval()[1], linestyles='dotted')\n", "ax1.set_title('Residual Plot for Linear Fit')\n", "\n", "# Right plot\n", "sns.regplot(auto.pred2, auto.resid2, lowess=True,\n", " line_kws={'color':'r', 'lw':1}, ax=ax2,\n", " scatter_kws={'facecolors':'None', 'edgecolors':'k', 'alpha':0.5})\n", "ax2.hlines(0,xmin=ax2.xaxis.get_data_interval()[0],\n", " xmax=ax2.xaxis.get_data_interval()[1], linestyles='dotted')\n", "ax2.set_title('Residual Plot for Quadratic Fit')\n", "\n", "for ax in fig.axes:\n", " ax.set_xlabel('Fitted values')\n", " ax.set_ylabel('Residuals')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Front End App to test Regression" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import dash\n", "import dash_core_components as dcc\n", "import dash_html_components as html\n", "from dash.dependencies import Input, Output\n", "from IPython import display\n", "\n", "# Here is the App display embedded in the Jupyter notebook.\n", "#This can be run only once in offline mode.\n", "def show_app(app, # type: dash.Dash\n", " port=9999,\n", " width=1000,\n", " height=350,\n", " offline=True,\n", " style=True,\n", " **dash_flask_kwargs):\n", " \"\"\"\n", " Run the application inside a Jupyter notebook and show an iframe with it\n", " :param app:\n", " :param port:\n", " :param width:\n", " :param height:\n", " :param offline:\n", " :return:\n", " \"\"\"\n", " url = 'http://localhost:%d' % port\n", " iframe = ''.format(url=url,\n", " width=width,\n", " height=height)\n", " display.display_html(iframe, raw=True)\n", " if offline:\n", " app.css.config.serve_locally = False\n", " app.scripts.config.serve_locally = False\n", " if style:\n", " external_css = [\"https://fonts.googleapis.com/css?family=Raleway:400,300,600\",\n", " \"https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\",\n", " \"http://getbootstrap.com/dist/css/bootstrap.min.css\", \n", " \"https://codepen.io/chriddyp/pen/bWLwgP.css\"]\n", "\n", " for css in external_css:\n", " app.css.append_css({\"external_url\": css})\n", "\n", " external_js = [\"https://code.jquery.com/jquery-3.2.1.min.js\",\n", " \"https://cdn.rawgit.com/plotly/dash-app-stylesheets/a3401de132a6d0b652ba11548736b1d1e80aa10d/dash-goldman-sachs-report-js.js\",\n", " \"http://getbootstrap.com/dist/js/bootstrap.min.js\"]\n", "\n", " for js in external_js:\n", " app.scripts.append_script({\"external_url\": js})\n", "\n", " return app.run_server(debug=True, # needs to be false in Jupyter\n", " port=port,\n", " **dash_flask_kwargs)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Here is the Dash App Layout and interaction behaviour defined.\n", "app = dash.Dash()\n", "#stylesheets = {'stylesheet.css': 'https://codepen.io/chriddyp/pen/bWLwgP.css'}\n", "app.layout = html.Div(children=[\n", " html.Link(\n", " rel='stylesheet',\n", " href='/static/bWLwgP.css'\n", " ),\n", " html.H1(children='Predicting Sales using Regression'),\n", "\n", " html.Div(children=[html.Label('TV Advertising Spend in $million '),\n", " dcc.Input(id='tv-id', value='230.1', type='text')]),\n", "\n", " html.Div(children=[html.Label('Radio Advertising Spend in $million '),\n", " dcc.Input(id='radio-id', value='37.8', type='text')]),\n", "\n", " html.Div(id='predicted-div'),\n", " dcc.Graph(\n", " id='advert-graph',\n", " figure={\n", " 'data': [\n", " go.Scatter(\n", " x=advertising['TV'],\n", " y=advertising['Sales'],\n", " #text='' + df2['TV'],\n", " mode='markers',\n", " opacity=0.7,\n", " marker={\n", " 'size': 15,\n", " 'line': {'width': 0.5, 'color': 'white'}\n", " }\n", " )\n", "\n", " ],\n", " 'layout': go.Layout(\n", " xaxis={'type': 'log', 'title': 'TV Ad Spend'},\n", " yaxis={'title': 'Sales Turnover'},\n", " margin={'l': 40, 'b': 40, 't': 10, 'r': 10},\n", " legend={'x': 0, 'y': 1},\n", " hovermode='closest'\n", " )\n", " } \n", " )\n", "])\n", "@app.callback(\n", " Output(component_id='predicted-div', component_property='children'),\n", " [Input(component_id='tv-id', component_property='value'), \n", " Input(component_id='radio-id', component_property='value')]\n", ")\n", "def update_output_div(tv_value,radio_value):\n", " prediction = predict(tv_value, radio_value)\n", " return 'You\\'ve entered TV Spend as \"{}\" and Radio spend as \"{}\". And the predicted sales is \"{}\"'.format(tv_value, radio_value, prediction[0]) \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ " * Debugger is active!\n", " * Debugger PIN: 166-122-810\n", " * Running on http://127.0.0.1:9999/ (Press CTRL+C to quit)\n", "127.0.0.1 - - [20/Jun/2018 19:04:03] \"GET / HTTP/1.1\" 200 -\n", "127.0.0.1 - - [20/Jun/2018 19:04:03] \"GET /_dash-dependencies HTTP/1.1\" 200 -\n", "127.0.0.1 - - [20/Jun/2018 19:04:03] \"GET /_dash-layout HTTP/1.1\" 200 -\n", "127.0.0.1 - - [20/Jun/2018 19:04:03] \"POST /_dash-update-component HTTP/1.1\" 200 -\n" ] } ], "source": [ "show_app(app)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }