{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#A-Brief-matplotlib-API-Primer\" data-toc-modified-id=\"A-Brief-matplotlib-API-Primer-1\"><span class=\"toc-item-num\">1 </span>A Brief matplotlib API Primer</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Adjusting-the-spacing-around-subplots\" data-toc-modified-id=\"Adjusting-the-spacing-around-subplots-1.0.1\"><span class=\"toc-item-num\">1.0.1 </span>Adjusting the spacing around subplots</a></span></li></ul></li><li><span><a href=\"#Colors,-Markers,-and-Line-Styles\" data-toc-modified-id=\"Colors,-Markers,-and-Line-Styles-1.1\"><span class=\"toc-item-num\">1.1 </span>Colors, Markers, and Line Styles</a></span></li><li><span><a href=\"#Ticks,-Labels,-Legend\" data-toc-modified-id=\"Ticks,-Labels,-Legend-1.2\"><span class=\"toc-item-num\">1.2 </span>Ticks, Labels, Legend</a></span><ul class=\"toc-item\"><li><span><a href=\"#Setting-the-title,-axis-labels,-ticks,-and-ticklabels\" data-toc-modified-id=\"Setting-the-title,-axis-labels,-ticks,-and-ticklabels-1.2.1\"><span class=\"toc-item-num\">1.2.1 </span>Setting the title, axis labels, ticks, and ticklabels</a></span></li><li><span><a href=\"#Adding-Legends\" data-toc-modified-id=\"Adding-Legends-1.2.2\"><span class=\"toc-item-num\">1.2.2 </span>Adding Legends</a></span></li></ul></li><li><span><a href=\"#Annotations-and-drawing-on-a-Subplot\" data-toc-modified-id=\"Annotations-and-drawing-on-a-Subplot-1.3\"><span class=\"toc-item-num\">1.3 </span>Annotations and drawing on a Subplot</a></span></li></ul></li><li><span><a href=\"#Plotting-with-pandas-and-seaborn\" data-toc-modified-id=\"Plotting-with-pandas-and-seaborn-2\"><span class=\"toc-item-num\">2 </span>Plotting with pandas and seaborn</a></span><ul class=\"toc-item\"><li><span><a href=\"#Line-Plots\" data-toc-modified-id=\"Line-Plots-2.1\"><span class=\"toc-item-num\">2.1 </span>Line Plots</a></span></li><li><span><a href=\"#Bar-Plots\" data-toc-modified-id=\"Bar-Plots-2.2\"><span class=\"toc-item-num\">2.2 </span>Bar Plots</a></span></li><li><span><a href=\"#Histograms-and-Density-Plots\" data-toc-modified-id=\"Histograms-and-Density-Plots-2.3\"><span class=\"toc-item-num\">2.3 </span>Histograms and Density Plots</a></span></li><li><span><a href=\"#Scatter-or-Points-Plots\" data-toc-modified-id=\"Scatter-or-Points-Plots-2.4\"><span class=\"toc-item-num\">2.4 </span>Scatter or Points Plots</a></span></li><li><span><a href=\"#Facet-Grids-and-Categorical-Data\" data-toc-modified-id=\"Facet-Grids-and-Categorical-Data-2.5\"><span class=\"toc-item-num\">2.5 </span>Facet Grids and Categorical Data</a></span></li></ul></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Code and text from **Python for Data Analysis** By **Wes McKinney** \n", " - Chapter 9 => **Plotting and Visualization**\n", "Github - [pydata-book](https://github.com/wesm/pydata-book)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:42.056332Z", "start_time": "2019-12-24T22:46:41.674831Z" } }, "outputs": [], "source": [ "# %matplotlib notebook\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:42.404793Z", "start_time": "2019-12-24T22:46:42.057329Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "\n", "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n", "pd.options.display.max_rows = 20\n", "np.random.seed(12345)\n", "plt.rc('figure', figsize=(10, 6))\n", "np.set_printoptions(precision=4, suppress=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A Brief matplotlib API Primer" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:42.420671Z", "start_time": "2019-12-24T22:46:42.409745Z" } }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = np.arange(10)\n", "data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:42.699157Z", "start_time": "2019-12-24T22:46:42.422665Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96d8ebd08>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plots in matplotlib reside within a **Figure** object. You can create a new figure with **plt.figure**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can’t make a plot with a blank figure. You have to create one or more subplots using **add_subplot**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:43.038294Z", "start_time": "2019-12-24T22:46:42.701154Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96d983d08>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax1 = fig.add_subplot(2, 2, 1)\n", "ax2 = fig.add_subplot(2, 2, 2)\n", "ax3 = fig.add_subplot(2, 2, 3)\n", "plt.plot(np.random.randn(50).cumsum(), 'k--')\n", "\n", "# 'k--' is a style option instructing matplotlib to plot a black dashed line" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-12-24T18:48:20.848884Z", "start_time": "2019-12-24T18:48:20.809027Z" } }, "source": [ "The objects returned by **fig.add_subplot** here are **AxesSubplot objects**, on which you can directly plot on the other empty subplots by calling each one’s instance method" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:43.059196Z", "start_time": "2019-12-24T22:46:43.039291Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x1f96d9b8fc8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ = ax1.hist(np.random.randn(100), bins=20, color='k', alpha=0.3)\n", "ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.randn(30))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:43.451391Z", "start_time": "2019-12-24T22:46:43.061192Z" } }, "outputs": [ { "data": { "image/png": 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LA1rUy+LOgR1iNiIV7zlY8Y5f0pcuNYqI/GgsxfNRx5fYdwMw293vMrMbgu0/hhBbSqnMXX/9O7eIW6KSiDlY8Yxf0pdGvEREAu4+F/iq1O5+wLjg+Tigf0KDSlFh3/VX1lwrzcGSsCnxEhEpXxN3XwcQPDYOOZ6UEPZdf5qDJclKiZeISIxordkfhT3ipDlYkqw0x0tEpHzrzayZu68zs2bAhrIauvtoYDRATk5OtS4SPaJX273meEHiR5w0B0uSkRKvNJZMxULjGUsyvU9JS1OBocBdweOL4YaTGnYnPPFcy1BrJUoqUuIlIhIws2eB7kAjM1sL3EpxwjXJzIYBnwHnhRdhaonniJPWSpRUpcRLRCTg7ueXcej0hAYiQPkjWuXdNanES5KZEi8REUk6FY1ohX3XpMj+0l2NIiKSdCqqAxb2XZMi+0uJl4iIJJ2KRrRUp0tSlRIvERFJOhWNaKlOl6SqmM3xMrNPgG1AIbDL3XNKHTfgfqAP8C1wibsvilX/IiKSPipTB0x1uiQVxXpyfQ93/7KMY2cBRwU/XYBHgkcREZG9JKIOmEgYEnlXYz9gvLs78LaZ1dtdDTqBMYiISIrQiJako1gmXg68YmYOPBYsnVFSC+DzEttrg317JV5mNhwYDtC6desYhiciIvIjVb6XMMQy8TrJ3QvMrDEwy8xWuvvcEsctwmv2WctMa52JiEi8qfK9hCVmdzW6e0HwuAF4Acgt1WQt0KrEdkugIFb9i4iIVFZFdcJE4iUmiZeZ1TGzg3Y/B3oCy0o1mwpcbMW6Al9rfpeIiIRBle8lLLG61NgEeKG4YgQ1gYnu/m8zuwLA3R8FplNcSmI1xeUkLo1R3yIiIlXSvF4W+RGSLFW+l3iLSeLl7muAYyPsf7TEcweujEV/IiISveo8ubwydcJE4kGLZIuIVEJFRaJTTXWfXK46YRIWJV4iIpVXXpHolFLe5PLKJh+pPmKmOmESBiVeIiLVULSTy6v7iJnI/lLilWKmTZsWdggi1VVFRaJTqgB0tJPLYzFiJlIdxayOl4hImjvJ3Y+jeN3ZK83slNIN3H20u+e4e052dnbiI6yCEb3akpWZsde+qkwuVzkGkf2jxEtEpBIqUSQ6pfTv3II7B3agRb0sDGhRL4s7B3ao9GhVWSNjKscgUj5dahQRqUBQGLqGu28rUSR6ZMhhRS2ayeUqxyCyf5R4iYhULGKR6HBDCpfKMYjsHyVeIiIVKKtIdCIkc8kGlWMQqTolXiIiSUolG0TSjybXi4gkqfJKNohIalLiJSKSpFSyQST9KPESEUlSKtkgkn40xysOVF1e4qEq36u+ffvGMRJJFJVsEEk/UY94mVkrM5tjZivMbLmZXR2hTXcz+9rMlgQ/t0Tbr4hIuou2yKmIJJ9YjHjtAq5190VmdhCw0MxmufsHpdq96e7nxKA/EZFqQyUbRNJL1CNe7r7O3RcFz7cBKwD9lRAREREpJaZzvMysDdAZWBDhcDczew8oAK5z9+VlnGM4MBygdevWsQxPRCStJHNxVRGJLGZ3NZpZXeB54Bp331rq8CLgUHc/FngQmFLWedx9tLvnuHtOdnZ2rMITEUkru4ur5m/ZgfNjcdUpi/PDDk1EyhGTxMvMMilOup5x98mlj7v7VnffHjyfDmSaWaNY9C0iUh2puKpIaor6UqMVrxo7Bljh7v8oo01TYL27u5nlUpzwbYq2bxGR6ioRxVV1KVMk9mIxx+sk4CJgqZktCfbdBLQGcPdHgUHAr81sF7ADGOzuHoO+RUQSwsx6A/cDGcAT7n5XmPE0r5dFfoQkK1bFVbVOpEh8RJ14ufs8wCpo8xDwULR9laeqRUurUmBSBVElHuL5vUqm34d0KOZqZhnA/wJnAmuBd81saoSyOQkT7+Kq5V3KVOIlsv9UuV5EpGK5wGp3XwNgZs8B/YDQEq/dyU+8LgVqnUiR+FDiJSJSsRbA5yW21wJdSjdKdDmceBZXjfelTJHqSotki4hULNJ0in3mqaZTOZwRvdqSlZmx1z6tEykSPY14iYhUbC3QqsR2S4qLQaeteF/KFKmulHiJiFTsXeAoMzsMyAcGAxeEG1L8aZ1IkdhT4iUiUgF332VmVwEzKS4n8WRZy56JiJRHiZeISCUEq25MDzsOEUltSrxERNKUKs+LJB8lXiKiIsEhildypMrzIslJ5SREREKyOznK37ID58fkaMri/KjPrUW0RZKTEi8RkZDEMzlS5XmR5KTES0QkJPFMjsqqMK/K8yLhUuIlIhKSeCZHqjwvkpyUeImIhCSeyVH/zi24c2AHWtTLwoAW9bK4c2AHTawXCZnuahQRCUm8l+VR5XmR5BOTxMvMegP3U1zR+Ql3v6vU8drAeOB4YBPwC3f/JBZ9i4ikMiVHItVL1JcazSwD+F/gLKAdcL6ZtSvVbBiw2d2PBEYBf4+2XxEREZFUE4s5XrnAandf4+7fA88B/Uq16QeMC57/H3C6mVkM+hYRERFJGbG41NgC+LzE9lqgS1ltgsVmvwYaAl+WPpmZDQeGB5vbzSxR1f4aRYonRaXTe4H0ej/p9F4g9u/n0BieK1QLFy780sw+rcJLkvm7kcyxgeKLRjLHBqkXX4V/w2KReEUaufL9aFO80300MDraoKrKzPLcPSfR/cZDOr0XSK/3k07vBdLv/cSSu2dXpX0yf5bJHBsovmgkc2yQnvHF4lLjWqBVie2WQEFZbcysJnAI8FUM+hYRERFJGbFIvN4FjjKzw8ysFjAYmFqqzVRgaPB8EPCau0cc8RIRERFJV1FfagzmbF0FzKS4nMST7r7czEYCee4+FRgDTDCz1RSPdA2Ott84SPjlzThKp/cC6fV+0um9QPq9nzAl82eZzLGB4otGMscGaRifaeBJREREJDG0ZJCIiIhIgijxEhEREUkQJV4lmNndZrbSzN43sxfMrF7YMe0vMzvPzJabWZGZJe2tuOUxs95mtsrMVpvZDWHHEw0ze9LMNpjZsrBjiQUza2Vmc8xsRfA9uzrsmFJVsn/PzewTM1tqZkvMLC8J4tnnd8nMGpjZLDP7KHisn0Sx3WZm+cHnt8TM+oQRWxBLxN/bJPr8yoov9M/QzA4ws3fM7L0gttuD/YeZ2YLgs/tncJNhuZR47W0W0N7dOwIfAjeGHE80lgEDgblhB7I/KrkUVSoZC/QOO4gY2gVc6+4/BboCV6b4f59QpND3vIe7d0qSekpj2fd36QZgtrsfBcwOtsMwlsi/56OCz6+Tu09PcEwllfV7myyfX3l/V8L+DL8DTnP3Y4FOQG8z60rxEoijgs9uM8VLJJZLiVcJ7v6Ku+8KNt+muCZZSnL3Fe6eqKr/8VCZpahShrvPJY1q17n7OndfFDzfBqygeIUKqZq0+p4nQhm/SyWXpRsH9E9oUIFk/z0v5/c2WT6/pP274sW2B5uZwY8Dp1G8FCJU8rNT4lW2y4AZYQdRjUVaiiopfgFlb2bWBugMLAg3kpSUCt9zB14xs4XBkm7JqIm7r4Pif7yBxiHHU9pVwRSWJ8O6jFdaqd/bpPv8IvxdCf0zNLMMM1sCbKD4CtnHwJYSAzaV+v2tdomXmb1qZssi/PQr0eZPFA95PhNepBWrzHtJYZVeZkrCY2Z1geeBa9x9a9jxpKBU+J6f5O7HUXw59EozOyXsgFLMI8ARFF+eWgfcG244yf97GyG+pPgM3b3Q3TtRfDUsF/hppGYVnScWazWmFHc/o7zjZjYUOAc4Pdmr61f0XlJcZZaikhCZWSbFfxyfcffJYceTopL+e+7uBcHjBjN7geJ/cJJt7uh6M2vm7uvMrBnFIxJJwd3X735uZo8DL4UYTlm/t0nz+UWKL9k+Q3ffYmavUzwPrZ6Z1QxGvSr1+1vtRrzKY2a9gT8C57r7t2HHU81VZikqCYmZGcUrUqxw93+EHU8KS+rvuZnVMbODdj8HelJ8406yKbks3VDgxRBj2UuQyOw2gBA/v3J+b5Pi8ysrvmT4DM0s24JKB2aWBZxB8Ry0ORQvhQiV/OxUub4EK17SqDawKdj1trtfEWJI+83MBgAPAtnAFmCJu/cKN6qqCW4Zvo8fl6L6a8gh7TczexboDjQC1gO3uvuYUIOKgpn9D/AmsBQoCnbfFPIdWykpmb/nZnY48EKwWROYGHZ8kX6XgCnAJKA18BlwnrsnfJJ7GbF1p/gSmQOfAL/aPZ8qhPgi/t5SPI8qGT6/suI7n5A/QzPrSPHk+QyKB60mufvI4HfkOaABsBi40N2/K/dcSrxEREREEkOXGkVEREQSRImXiIiISIIo8RIRERFJECVeIiIiIgmixEtEREQkQZR4iYiIiCSIEi8RERGRBFHiJSIiIpIgSrxEREREEkSJl4iIiEiCKPESERERSRAlXiIiIiIJosRLREREJEFqhh1AeRo1auRt2rQJOwwRSZCFCxd+6e7ZYcchIhIvSZ14tWnThry8vLDDEJEEMbNPw45BRCSedKlRREREJEGUeImIiIgkSJUSLzN70sw2mNmyEvsamNksM/soeKxfxmuHBm0+MrOh0QYuIiIikmqqOuI1Fuhdat8NwGx3PwqYHWzvxcwaALcCXYBc4NayEjSRWCssLGT8+PFs37497FBERKSaq1Li5e5zga9K7e4HjAuejwP6R3hpL2CWu3/l7puBWeybwInERWFhIf/617/41a9+FXYoIiJSzcVijlcTd18HEDw2jtCmBfB5ie21wT6RuKtVqxYnnHACEydO5Lnnngs7HBERqcYSNbneIuzziA3NhptZnpnlbdy4Mc5hSbrbsGED99xzD1dccQXdunXjiiuu4LPPPgs7LBERqaZikXitN7NmAMHjhght1gKtSmy3BAoinczdR7t7jrvnZGerjqJEZ+LEiYwYMYKNGzfy9NNPU1hYyNChQyksLAw7NBERqYZikXhNBXbfpTgUeDFCm5lATzOrH0yq7xnsE4mrsWPHcsIJJ3DMMcdw+OGH8+CDD1JQUMAXX3wRdmgiIlINVbWcxLPAW0BbM1trZsOAu4Azzewj4MxgGzPLMbMnANz9K+AO4N3gZ2SwTyRulixZwnvvvccll1yyZ9/QoUN57733aNFCUwxFRCTxqnpX4/nu3szdM929pbuPcfdN7n66ux8VPH4VtM1z91+WeO2T7n5k8PNUrN+IJKetW7fSvn17Zs2alfC+x44dS61atRg8ePCefWbGAQccwLfffsvtt9/Od999l/C4RESk+lLleomrJUuWsHz5cq6//nrcI95PETcbN25kwIABNGjQYJ9jb731Frfddhv33HNPQmMSEZHqTYmXxNXKlSuB4gTspZdeSmjfzzzzDM8880zEY6effjqDBg3iL3/5C//9738TGpeIiFRfSrwkro4//nhuvvlm7rjjDnJzcxPW79dffw1ARkZGmW1GjRpFRkYGV199daLCEhGRak6Jl8TV8ccfz8iRI/nzn/9MkyZNEtLnxo0badq0KU888US57Vq2bMmtt97KtGnTmDZtWkJiExGR6k2Jl8TV4sWL2bp1KwBz587l17/+ddznek2cOJGdO3fSrVu3Cttec801DB8+nJ/85CdxjUlERASUeEkc7dy5k5ycHO69916geL7Xo48+GrM7HN2dt99+e8/24MGDadq0KX/84x/JycnhmGOOqfAcmZmZPPbYY7Rt2zYmMYmIiJRHiZfEzerVqykqKuLoo48GimtotWrVittvvz0mo1633nor3bp14/XXXwfgxBNPpH///lxyySXcd999VTrXunXrGDx4MB9++GHUcYmIiJSlZtgBSPpatWoVwJ7RpNq1a3PDDTdw5ZVX8tprr3H66afv97nvvPNO7rjjDoYNG8Ypp5wCwO9+97v9Pp+ZMWPGDNydf/7zn/t9HhERkfJoxEviZncpiZLzp4YNG0aLFi24/fbb9/u8999/PzfddBMXXHABjz32GDVqRP81btq0KQMHDuT1119PeL0xERGpPpR4SdysXLmSVq1aUbdu3T37ateuzc0338yQIUP265zvv/8+11xzDQMHDmTcuHHllouoqtzcXDZs2MBnn30Ws3OKiIiUpEuNEje///3v+fnPf77P/l/96ld7nm/dupWDD/pjzBEAACAASURBVD64wnPt3LmTAw44gI4dOzJlyhTOOussataM7dd3d52xBQsWcOihh8b03CIiIqARL4mj4447jr59+5Z5fO7cubRp04a5c+eW2cbdefrppznssMP4z3/+A0C/fv2oVatWzOPt2LEjJ5xwAmYW83OLiIiAEi+Jky1btvD888/z5Zdfltmmffv2NG7cmP79+++ZD7abu7N48WK6d+/ORRddRKtWrahTp05cY87MzOSdd97hvPPOi2s/IiJSfSnxkrhYtGgRgwYN4r333iuzTYMGDZgxYwaZmZmcddZZLF++fM+xXr16cdxxx7Fs2TJGjx7N22+/TadOnRIROkVFRRQVFSWkLxERqV6UeElc7B7Bqqgw6WGHHcbLL7/Mhg0b6NatG7t27QJgyJAhPPzww3z44YdcfvnlMblzsTJmz55N/fr1Wbp0aUL6ExGR6iXq2clm1hYoWfjocOAWd7+vRJvuwIvAf4Ndk919ZLR9S/JatWoVderUoUWLFhW2zcnJ4dVXX2X58uXs2rWLmjVrMnTo0AREua82bdqwdetWFixYwLHHHhtKDCIikr6iTrzcfRXQCcDMMoB84IUITd9093Oi7U9Sw8qVKzn66KMrPVG9W7dulVpbMd4OP/xwGjZsyDvvvMPw4cPDDkdERNJMrK/fnA587O6fxvi8kmJWrVq1Z6mgVGJm5Obm8s4774QdioiIpKFY1/EaDDxbxrFuZvYeUABc5+7LIzUys+HAcIDWrVvHODxJlNmzZ6dsBfjc3FxmzpzJ9u3b9yr+KiIiEq2YJV5mVgs4F7gxwuFFwKHuvt3M+gBTgKMincfdRwOjAXJyclLzX+4k8cADD9CyZUsGDhyY8L6POOKIhPcZK3369KGwsJDvv/8+7FBERCTNWKxGJcysH3Clu/esRNtPgBx3L7vIE8WJV15eXkziq24++eQTjj76aBo0aMB5553H/fffn7C+8/LymDdvHr/85S81YiRVYmYL3T0n7DhEROIllnO8zqeMy4xm1tSCWdZmlhv0uymGfUspf/zjH6lRowYXXnghDzzwAJ9+mrhpdy+//DJ/+MMfYrqOYqJt376dDz74IOwwREQkzcQk8TKzA4Ezgckl9l1hZlcEm4OAZcEcrweAwZ6qE4BSwH/+8x8mTZrE9ddfz4UXXgjA66+/nrD+V65cSZs2bcjKykpYn7E2fPhwevXqFXYYIiKSZmKSeLn7t+7e0N2/LrHvUXd/NHj+kLsf4+7HuntXd58fi35lX0VFRfz+97+nefPmjBgxgvbt29OwYcOEJl6rVq2qsHBqssvNzWXt2rUUFBSEHYqIiKQRVa5PM2bG1VdfzYMPPkidOnWoUaMGp556apUTr88//5xvvvmmyv0XFRWlbCmJknJzcwF49913Q45ERETSiRKvNGNmDBkyZK87Gbt3705WVhZff/11Oa/c25QpUzj11FNZs2ZNlfpft24dO3bsSPkRr86dO1OzZk0WLFgQdigiIpJGYnZXYzzorsaquffee3F3rr322r0qxrt7pSvI7zZv3jzOPfdcatasybRp0+jSpUulX7tjxw6KioqoU6dOlfpMNscffzz169fn1VdfDTuUakN3NYpIutOIV5r4/vvvufPOO5k3b94+Sdbu7com2e+++y7t2rXjrbfeom7duvTo0YMpU6ZUOpasrKyUT7oA7rnnHv7+97+HHYaIiKQRJV5pYvr06WzatKnM9QX/3//7f3Tu3LnC5MvdGTBgAJdffjlt27bl7bffpmPHjgwcOJCFCxdWGMejjz7Kbbfdtj9vIen06NGD448/PuwwREQkjSjxShPjxo2jadOm9OwZuX5tnTp1eO+99/jkk0/KPc/ixYvJz8+nb9++ADRu3JjXXnuNxx9/nOOOO67COJ599llmzJhR5fiT0Q8//MC//vUvTbAXEZGYUeKVBjZu3MhLL73EkCFDqFkz8ipQ3bt3B2DOnDnlnmvatGmYGX369Nmz78ADD2TYsGGYGcuXL2f8+PH7vM7dufHGG5k7d26ZyV+qycjI4De/+Q0PPvhg2KGIiEiaUOKVBr7++mvOPvtshg4dWmabdu3akZ2dXWFZiWnTptG1a1caN24c8fjf//53LrnkEp577rk9+4qKivjtb3/LXXfdxRVXXMHtt9++X+8j2dSoUYMzzjiDV199NWUX/BYRkeSixCtk7s6CBQvYvn37fp/jyCOPZMqUKXTo0KHMNmZG9+7def3118tMIr744gsWLlzIueeeW+Z5HnvsMU4++WQuuugipk+fDkBBQQGTJk3iuuuu4+GHH6ZGjfT5Wp155pmsW7eO5cuXhx2KiIikgcjXpSQhtm3bxqpVq+jatStPPPEEw4YNq/I5CgoK+O677zjssMMqbHvBBRdwxBFH8MMPP1CrVq19jjdt2pQPP/yQgw8+uMxzZGVlMW3aNHr06EG/fv2YPXs2p5xyCu+//z5NmjSpctmKZHfmmWcCMGvWLNq3bx9yNCIikupUxytEvXr1om7duqxYsYJ69eoxf37VV1IaMWIEDzzwABs2bOCQQw6JQ5SRbdy4ka5du9KzZ08eeeSRhPUbhp/+9Kccf/zxPP3002GHkvZUx0tE0l36XBNKMfPnz+eVV16ha9euDBs2jLfeeosPPvigSufYtWsXEyZMoE+fPpVOunbu3MmKFSv22f/tt98yZMgQKpvoZmdns2DBAi6++OIqxZyK3nzzTSZMmBB2GCIikgaUeIXk9ttvp1GjRvzmN7/hoosuombNmowZM6ZK53jllVdYv359uZPqS7vssss488wz95nn9eqrrzJx4kS2bNlS6XM1atSIbt26Vbp9qmrUqFHaXUIVEZFwKPEKwe7Rruuvv546derQuHFj+vXrx7Rp06p099y4ceNo2LDhXqUfKtKjRw/y8/P585//zK5du/bsnzp1KgcffDCnnHJKld5LdeDu/PKXv+See+4JOxQREUlxMUu8zOwTM1tqZkvMbJ/rVVbsATNbbWbvm1nF1TjT1AMPPLBntKvkvvfee6/SIys7duxg+vTpXHDBBREnypfl4osvZtiwYfztb3+je/fufP755xQVFfHSSy/Ru3fvKp2rujAzVq1atVcJDRERkf0R6xGvHu7eqYzJsWcBRwU/w4H0npFdjieffJLp06fvtZ5h8+bNycrKqtSI19atW8nKymLNmjXceOONVeq7du3aPPHEE0ycOJH333+fm266iby8PNavX19uGYnqrmfPnixatIgvv/wy7FBERCSFJfJSYz9gvBd7G6hnZs0S2H9SKCws5MADD+SEE07Y59i8efNo164dn3322T7HioqKmD59Oj169OB//ud/cHeys7Np1mz/PsLzzz+fRYsWcd9997F582batWvHWWedtV/nqg52z4ubPXt22KGIiEgKi2Xi5cArZrbQzCKt1NwC+LzE9tpg317MbLiZ5ZlZ3saNG2MYXvgWLFjA0UcfzbJlyyIeb9myJatWreKpp57as8/dmTJlCh07duTss89m9erVDB06dK/5WfvryCOPpGHDhvTq1Yvly5fToEGDqM+ZrnJycjjkkEOYNWtW2KGIiEgKi2XidZK7H0fxJcUrzaz0LO1Ik5f2ua7m7qPdPcfdc7Kzs2MYXvgefvhh1q9fz6GHHhrxeJs2bTjjjDN46qmnKCoqAmDy5MkMGDCAwsJCxo8fz5o1a7j22mvJzMxMZOjVXs2aNbnkkkto3bp12KGIiEgKi1ni5e4FweMG4AUgt1STtUCrEtstgYJY9Z/svvrqKyZNmsSQIUM46KCDymw3bNgwPv30U66//noA+vXrx4QJE1i6dCkXXXSREq4Q3Xfffdxyyy1hhyEiIiksJomXmdUxs4N2Pwd6AqWvp00FLg7ubuwKfO3u62LRfyoYP348O3fu5Iorrii3Xf/+/cnOzuaJJ56gsLCQmjVrcuGFF1KzplZ3SgaFhYVs2rQp7DBERCRFxepf8ybAC0EphJrARHf/t5ldAeDujwLTgT7AauBb4NIY9Z303J3HHnuMLl26cOyxx5bbtnbt2uTl5XHggQeSkZGRoAilsrp27Urz5s158cUXww5FRERSUEwSL3dfA+yTUQQJ1+7nDlwZi/5SjbszcuTIchefLknziJLX8ccfz8SJE/nhhx902VdERKpMlesToEaNGpx33nn06tUr7FAkSn379mXbtm173XkqIiJSWUq84mzjxo3ccccdpFtpjOqqT58+nHzyydx0001s3rw57HBERCTFKPGKs7Fjx3LLLbco8UoTZsYDDzzA5s2befrppyO2KSwsZOTIkSxYsCDB0YmISLKzqizKnGg5OTmel7fPso8po6ioiLZt29K0aVPefPPNsMORGFqyZAnHHnvsPmtruju//vWv99xM8fbbb4cUYWoys4VlLDkmIpIWNOIVR3PmzGH16tUVlpCQ1NOpUyfMjC+++GKv9TXHjRvHY489Rvv27VmwYAHvv/9+iFGKiEiyUeIVR48++igNGzbkZz/7WdihSBzk5eVx+OGHM3ny5D37Bg8ezMMPP8ycOXPo2bMn33//fYgRiohIslFVzjhxdzIzM7nkkks44IADwg5H4qBTp04ceeSRXHvttdSpU4cuXbpQv359fv3rXwMwc+bMkCMUEZFkozlecaZ6T+nt9ddfp0ePHgBcfPHFjBs3bq/jX3zxBRs2bKBjx45hhJdyNMdLRNKdLjXGwcSJE1m5ciWAkq401717dy677DI6dOjAPffcs8/xM844g9/85jchRCYiIslIiVeMrVixgksvvZSRI0eGHYokyBNPPMGSJUvIzs7e59ill17Kf/7zH5YvXx5CZCIikmyUeMVQYWEhw4YNo27duowaNSrscCRBzIwaNSL/Kg0dOpRatWrx+OOPJzgqERFJRkq8YujBBx/krbfe4v7776dJkyZhhyNJoFGjRgwcOJDx48ezY8eOsMMREZGQKfGKkY8//pibbrqJs88+myFDhoQdjiSR4cOHs2XLFubPnx92KCIiEjKVk4iRZs2acfXVV3PllVfuU81cqrfu3bvz6aef0qpVK9auXcubb77JvHnzePPNN6lVqxbz58+nVq1aYYcpIiIJEPWIl5m1MrM5ZrbCzJab2dUR2nQ3s6/NbEnwc0u0/SaDt956i5NPPplly5Zx4IEHcuedd9KyZcuww5IkY2a0atWKvLw8WrVqxQUXXMD48eM56KCD2LlzJ59//nnYIYqISILEYsRrF3Ctuy8ys4OAhWY2y90/KNXuTXc/Jwb9heq///0vS5Ys4bnnnmPSpEk0bdqU/Px82rdvH3ZokuQ6duzIqFGjOPnkkzn22GPJyMjA3cucmC8iIukn6sTL3dcB64Ln28xsBdACKJ14paTCwkKmTp3KgAEDAOjRoweffvopWVlZ3HLLLYwYMYK6deuGHKWkglq1anHNNdfstc/M2Lp1KwUFBRx99NEhRSYiIokS0zleZtYG6AwsiHC4m5m9BxQA17l7ShQ2mjlzJldeeSUnnngiTZo04dFHH6V+/fp06NCBAw88MOzwJA3sXtNx4cKFmh8oIpLmYnaNw8zqAs8D17j71lKHFwGHuvuxwIPAlHLOM9zM8swsb+PGjbEKb79NmDCB7777jvr16wPQu3dvunTpoqRLYmb48OEsXryYV155JexQREQkzmKSeJlZJsVJ1zPuPrn0cXff6u7bg+fTgUwzaxTpXO4+2t1z3D0nUiXwRNq6dStTpkzhF7/4he46k7i58MILadmyJX/7298iHv/2228THJGIiMRLLO5qNGAMsMLd/1FGm6ZBO8wsN+h3U7R9x9sLL7zAzp07ueiii8IORdJYrVq1uPbaa5k7d+5etb6+/fZbfve731GnTh3VABMRSROxGPE6CbgIOK1EuYg+ZnaFmV0RtBkELAvmeD0ADHZ3j0HfcfX0009z+OGH07Vr17BDkTR3+eWX07BhQ/79738DMG/ePDp16sSDDz7IH/7wB7p16xZyhCIiEguxuKtxHlDujGB3fwh4KNq+Em3MmDF8+umnmvAscVenTh0++OAD6tWrxzXXXMMDDzzAoYceyuzZsznttNPCDk9ERGJEBYTK0bp1a04++eSww5BqonHjxmRmZvLhhx9y5ZVXsnTpUk477TSmTZvGSSedxM6dO8MOUUREoqTEqwy//e1vmT17dthhSDVjZkydOpUHH3xwT324Xbt2MX/+fBYvXhxydCIiEi0lXhG8//77PPTQQ6xcuTLsUKQaqllz7xkAXbp0AeCdd94JIxwREYkhJV4RTJgwgZo1a/KLX/wi7FBEaN68OS1btmTBgkh1iUVEJJUo8SqlsLCQiRMnctZZZ9GoUcRSYyIJl5ubqxEvEZE0ENMlg9LBnDlzKCgo4L777gs7FJE9evfuDcAPP/xAZmZmyNGIiMj+0ohXKTt37iQ3N5dzzjkn7FBE9rj88st5/vnnlXSJiKQ4JV6lnHPOOSxYsICsrKywQxHZx3fffRd2CCIiEgVdaixh+/btZGZmUrt27bBDEdlHnz59cHdmzJgRdigiIrKfNOJVwuOPP079+vXZtCnpl5GUaqh58+a88847pMBqWyIiUgYlXiW89tprtGzZkoYNG4Ydisg+unTpwldffcXHH38cdigiIrKflHgFdu3axRtvvKF18SRp7S6kqnpeIiKpS4lXYNGiRWzbto0ePXqEHYpIRMcccwx16tRR4iUiksI0uT7w2muvAdC9e/dwAxEpQ0ZGBrfddhtHH3102KGIiMh+UuIV6NOnD3Xr1qVJkyZhhyJSpuuuuy7sEEREJAoxudRoZr3NbJWZrTazGyIcr21m/wyOLzCzNrHotywffvhhlV/TsWNHrrrqqjhEIxI7RUVFLF++nA0bNoQdioiI7IeoEy8zywD+FzgLaAecb2btSjUbBmx29yOBUcDfo+23LM899xzHHHMMTz/9NAsXLqRfv35s37693Nd8/PHHvPzyy+zcuTNeYYnERH5+Pu3bt2fSpElhhyIiIvshFiNeucBqd1/j7t8DzwH9SrXpB4wLnv8fcLqZWQz63suYMWO44IILOPHEEzn33HP59NNPefnll+nbty/ffvttma975plnKmwjkgxatmxJs2bNNMFeRCRFxSLxagF8XmJ7bbAvYht33wV8DUQslmVmw80sz8zyNm7cWOkg7r//fn75y1/Ss2dPZsyYwcEHH8zAgQMZP348b7zxBgMGDChzRGvOnDl07tyZBg0aVLo/kTCYGbm5ubzzzjthhyIiIvshFolXpJGr0qW1K9OmeKf7aHfPcfec7OzsSgWwdOlSfv/73zNgwABefPFFDjzwwD3HLrjgAsaMGcMrr7zCoEGD+P777/d67Y4dO5g/f77KSEjKOPHEE/nwww8ZMGCA1m4UEUkxsbircS3QqsR2S6CgjDZrzawmcAjwVQz6BqBDhw7MnDmTHj16ULPmvm/p0ksv5bvvvmPGjBkUFhbudWz+/Pl8//33KpwqKeOqq65i06ZNrFmzZs+6ov/617845ZRTdFeuiEiSs2jXfQsSqQ+B04F84F3gAndfXqLNlUAHd7/CzAYDA9395xWdOycnx/Py8qKKryR3x8zYuXMntWvXxsy4/fbbueOOO9i8eTMHHXRQzPoSSZStW7fSuHFjOnTowLvvvht2OFExs4XunhN2HCIi8RL1pcZgztZVwExgBTDJ3Zeb2UgzOzdoNgZoaGargT8A+5ScSAQzY/PmzXTt2pV77rkHgJtvvpkPPvhASZekrIMPPpi//e1v5OXl8dFHH4UdjoiIlCMmdbzcfbq7/8Tdj3D3vwb7bnH3qcHzne5+nrsf6e657r4mFv3uj0MOOYSjjz6a66+/nnHjxlGjRg1+8pOfhBWOSEwMGjQIgOeffz7kSEREpDzVbq3GGjVqMG7cOM444wwuueQSWrZsqWKUkvJat25Nbm6uEi8RkSRX7RIvgNq1azN58mQOOeQQ8vPzqVu3btghiURt0KBBFBYWqh6diEgSi3pyfTzFenJ9ad988w1ffPEFRxxxRNz6EEmUoqIiatRI7f+X0uR6EUl3qf1XOkp16tRR0iVpY3fSpdpeIiLJq1onXiLp5vnnn6dhw4asXbs27FBERCQCJV4iaaR9+/Z88803TJ48OexQREQkAiVeImmkbdu2tG/fXnc3iogkKSVeImnmZz/7GW+++SZffPFF2KGIiEgpSrxE0sygQYNwd6ZMmRJ2KCIiUooSL5E0c8wxx3D33XfTvXv3sEMREZFSaoYdgIjElplx3XXXhR2GiIhEoBEvkTRUVFTEjBkzmDNnTtihiIhICRrxEklDZsaf/vQnvv76a1auXElmZmbYIYmICBrxEklLZsYdd9zBmjVreOqpp8IOR0REAlGt1WhmdwN9ge+Bj4FL3X1LhHafANuAQmBXZddii/dajSLpzN058cQTWbt2LR999BEHHHBA2CFVSGs1iki6i3bEaxbQ3t07Ah8CN5bTtoe7d9IfVZHEMDP++te/snbtWh577LGwwxEREaJMvNz9FXffFWy+DbSMPiQRiZXTTjuNwYMHc9BBB4UdioiIENvJ9ZcB/yzjmAOvmJkDj7n76LJOYmbDgeEArVu3jmF4ItXTs88+G3YIIiISqHDEy8xeNbNlEX76lWjzJ2AX8EwZpznJ3Y8DzgKuNLNTyurP3Ue7e46752RnZ1fx7YhIJLt27WLs2LFs2bLPFEwREUmgCke83P2M8o6b2VDgHOB0L2OmvrsXBI8bzOwFIBeYW/VwRWR/fPDBB1x66aWsWbOGkSNHJqTP0aNHU6dOHYYMGZKQ/kREUkFUc7zMrDfwR+Bcd/+2jDZ1zOyg3c+BnsCyaPoVkarp2LEj5513HqNGjWLTpk1x7++HH37glltu4fnnn497XyIiqSTauxofAg4CZpnZEjN7FMDMmpvZ9KBNE2Cemb0HvAO87O7/jrJfEamim2++me3btzN+/Pi49zVz5kzWr1/P0KFD496XiEgqiaqOV7ypjpdIbHXp0oVvvvmGpUuXYmZx6+e8887jjTfeID8/v0pV81XHS0TSnSrXi1Qjw4YNIyMjI66XG7/66iumTp3KBRdcoKWKRERKUeIlUo0MGzaMJUuW0KhRo7j1sXbtWtq1a6fLjCIiEWiRbJFqJCMjA4Bt27aRmZkZl2WEOnbsyOLFi2N+XhGRdKARL5Fq5qOPPqJZs2ZMmjSpUu2XLVvGxRdfzOrVqytsu2nTJrZt2xZtiCIiaUuJl0g1c+SRR9KsWTOefPLJCtuOHTuW3NxcJkyYwIUXXkhhYWG57e+++25atWrFN998E6twRUTSihIvkWrGzLjssst44403yh3F+vjjj7n88svp2rUr9913HwsWLODFF18ss31hYSETJkzglFNOoU6dOvEIXUQk5SnxEqmGhg4dSo0aNXjqqaf2ObZ+/XoAjjjiCObOncusWbP43e9+x+uvv86AAQPKPOesWbMoKCjgkksuiVfYIiIpT4mXSDXUvHlzzjrrLMaOHbvn8uGWLVu47rrraN269Z6RrW7dupGRkYGZceqpp2JmrFmzhl27du11vs2bN3PXXXfRoEEDzj777IS/HxGRVKHES6Sa+utf/8q///1v3J1HHnmEo446in/84x9ceOGFdO3aNeJrVq9eTfv27bn33nv32r98+XLmz5/PX/7yF2rXrp2I8EVEUpIq14tUc6effjqvvfYap556KqNGjaJz585ltnV3Bg0axEsvvcSrr77KZ599tmcR7IKCApo3bx5VLKpcLyLpTomXSDU3adIkatasyYABAyq1jND69es55phj2LRpE7Vr12bNmjVRJ1y7KfESkXSnS40i1dzPf/5zBg4cWOm1G5s0acKECRPo27cv7777bsySLhGR6kAjXiKSNDTiJSLpTiNeIiIiIgmixEtEREQkQZR4iYiIiCSIEi8RERGRBFHiJSIiIpIgSX1Xo5ltBD6tZPNGwJdxDCeeFHs4FHs4yov9UHfPTmQwIiKJlNSJV1WYWV6q3oau2MOh2MORyrGLiERLlxpFREREEkSJl4iIiEiCpFPiNTrsAKKg2MOh2MORyrGLiEQlbeZ4iYiIiCS7dBrxEhEREUlqaZF4mVlvM1tlZqvN7Iaw4ymPmT1pZhvMbFmJfQ3MbJaZfRQ81g8zxrKYWSszm2NmK8xsuZldHexP+vjN7AAze8fM3gtivz3Yf5iZLQhi/6eZ1Qo71rKYWYaZLTazl4LtlIjdzD4xs6VmtsTM8oJ9Sf+dERGJh5RPvMwsA/hf4CygHXC+mbULN6pyjQV6l9p3AzDb3Y8CZgfbyWgXcK27/xToClwZfNapEP93wGnufizQCehtZl2BvwOjgtg3A8NCjLEiVwMrSmynUuw93L1TiTISqfCdERGJuZRPvIBcYLW7r3H374HngH4hx1Qmd58LfFVqdz9gXPB8HNA/oUFVkruvc/dFwfNtFCcBLUiB+L3Y9mAzM/hx4DTg/4L9SRk7gJm1BM4Gngi2jRSJvQxJ/50REYmHdEi8WgCfl9heG+xLJU3cfR0UJzdA45DjqZCZtQE6AwtIkfiDS3VLgA3ALOBjYIu77wqaJPN35z7geqAo2G5I6sTuwCtmttDMhgf7UuI7IyISazXDDiAGLMI+3aoZR2ZWF3geuMbdtxYPviQ/dy8EOplZPeAF4KeRmiU2qoqZ2TnABndfaGbdd++O0DTpYg+c5O4FZtYYmGVmK8MOSEQkLOkw4rUWaFViuyVQEFIs+2u9mTUDCB43hBxPmcwsk+Kk6xl3nxzsTpn4Adx9C/A6xfPU6pnZ7v8BSdbvzknAuWb2CcWX0k+jeAQsFWLH3QuCxw0UJ7y5pNh3RkQkVtIh8XoXOCq4w6sWMBiYGnJMVTUVGBo8Hwq8GGIsZQrmFY0BVrj7P0ocSvr4zSw7GOnCzLKAMyieSkqrWwAAAQJJREFUozYHGBQ0S8rY3f1Gd2/p7m0o/n6/5u5DSIHYzayOmR20+znQE1hGCnxnRETiIS0KqJpZH4pHADKAJ939ryGHVCYzexboDjQC1gO3AlOASUBr4DPgPHcvPQE/dGb2P8CbwFJ+nGt0E8XzvJI6fjPrSPEk7gyK/4djkruPNLPD+f/t2bERAjEMRcHvGSqAtiiBMmiBVkgphXqIRHCOyRD4ZjdzpkDBG2v7RTomeSa5VNXrd5N+Nk+N16o6rzD7nPExn4ck96q6jTFO+fOdAfiGXYQXAMAK9nBqBABYgvACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJm8kxAY1vcUYvQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x432 with 3 Axes>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a figure with a grid of subplots is a very common task, so matplotlib includes a convenience method, **plt.subplots**, that creates a new figure and returns a NumPy array containing the created subplot objects" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:44.026975Z", "start_time": "2019-12-24T22:46:43.452388Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DA02A48>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DC6D748>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DB280C8>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DAB5188>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DB89608>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x000001F96DBC1808>]],\n", " dtype=object)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, 3)\n", "axes\n", "\n", "\n", "# axes array can be easily indexed like a two-dimensional\n", "# array; for example, axes[0, 1]." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:44.535268Z", "start_time": "2019-12-24T22:46:44.028624Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# ou can also indicate that subplots should have the\n", "# same x- or y-axis using sharex and sharey, respectively\n", "\n", "fig, axes = plt.subplots(2, 3, sharey=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**pyplot.subplots options**\n", "\n", "|Argument| Description|\n", "|--|--|\n", "|nrows| Number of rows of subplots|\n", "|ncols| Number of columns of subplots|\n", "|sharex| All subplots should use the same x-axis ticks (adjusting the xlim will affect all subplots)|\n", "|sharey| All subplots should use the same y-axis ticks (adjusting the ylim will affect all subplots)|\n", "|subplot_kw| Dict of keywords passed to add_subplot call used to create each subplot|\n", "|**fig_kw| Additional keywords to subplots are used when creating the figure, such as plt.subplots(2, 2, figsize=(8, 6))|\n" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-12-24T19:37:06.860771Z", "start_time": "2019-12-24T19:37:06.856678Z" } }, "source": [ "### Adjusting the spacing around subplots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default matplotlib leaves a certain amount of padding around the outside of the subplots and spacing between subplots. This spacing is all specified relative to the height and width of the plot, so that if you resize the plot either programmatically or manually using the GUI window, the plot will dynamically adjust itself. You can change the spacing using the **subplots_adjust** method on Figure objects, also available as a top-level function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)***\n", "\n", "**wspace** and **hspace** controls the percent of the figure width and figure height, respec‐\n", "tively, to use as spacing between subplots." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:45.315181Z", "start_time": "2019-12-24T22:46:44.536222Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# shrinking spaces to zero\n", "\n", "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n", "for i in range(2):\n", " for j in range(2):\n", " axes[i, j].hist(np.random.randn(500), bins=50, color='k', alpha=0.5)\n", "\n", "plt.subplots_adjust(wspace=0, hspace=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Colors, Markers, and Line Styles\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:45.320127Z", "start_time": "2019-12-24T22:46:45.316139Z" } }, "outputs": [], "source": [ "# plot function accepts an array of x and y and optionally a string abbreviation\n", "# indicating color and line style\n", "# ax.plot(x, y, 'g--')\n", "\n", "# In practice \n", "# ax.plot(x, y, linestyle='--', color='g')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:45.460043Z", "start_time": "2019-12-24T22:46:45.321125Z" } }, "outputs": [], "source": [ "# ?matplotlib.pyplot.plot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:45.769320Z", "start_time": "2019-12-24T22:46:45.462072Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96e4472c8>]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# using MARKERS to highlight the actual data points\n", "\n", "from numpy.random import randn\n", "# plt.plot(randn(30).cumsum(), 'ko--')\n", "plt.plot(randn(30).cumsum(), color='k', linestyle='dashed', marker='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For line plots, you will notice that subsequent points are linearly interpolated by default. This can be altered with the **drawstyle** option" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:45.773313Z", "start_time": "2019-12-24T22:46:45.770282Z" } }, "outputs": [], "source": [ "data = np.random.randn(30).cumsum()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.081310Z", "start_time": "2019-12-24T22:46:45.774271Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1f96e4a47c8>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data, 'k--', label='Default')\n", "plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')\n", "plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ticks, Labels, Legend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pyplot interface, designed for interactive use, consists of methods like **xlim, xticks, xticklabels => plot range, tick locations, tick labels**, respectively. \n", "They can be used in two ways:\n", " * Called with no arguments returns the current parameter value (e.g., plt.xlim() returns the current x-axis plotting range)\n", " * Called with parameters sets the parameter value (e.g., plt.xlim([0, 10]), sets the x-axis range to 0 to 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the title, axis labels, ticks, and ticklabels\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.263888Z", "start_time": "2019-12-24T22:46:46.082307Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96e222c88>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.plot(np.random.randn(1000).cumsum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the x-axis ticks, it’s easiest to use **set_xticks** and **set_xticklabels**. The former instructs matplotlib where to place the ticks along the data range; by default these locations will also be the labels. But we can set any other values as the labels using set_xticklabels" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.287758Z", "start_time": "2019-12-24T22:46:46.264821Z" } }, "outputs": [], "source": [ "ticks = ax.set_xticks([0, 250, 500, 750, 1000])\n", "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n", " rotation=30, fontsize='small')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.427935Z", "start_time": "2019-12-24T22:46:46.288755Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'My first matplotlib Plotting and Visualization.ipynb')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.set_title('My first matplotlib Plotting and Visualization.ipynb')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.573696Z", "start_time": "2019-12-24T22:46:46.428896Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 3.200000000000003, 'Stages')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.set_xlabel('Stages')\n", "\n", "# similar steps for y-axis" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.835105Z", "start_time": "2019-12-24T22:46:46.577684Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The axes class has a **set** method that allows batch setting of plot properties. From the prior example, we could also have written" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:46.869085Z", "start_time": "2019-12-24T22:46:46.836141Z" } }, "outputs": [ { "data": { "text/plain": [ "[Text(0.5, 17.200000000000003, 'Stages'),\n", " Text(0.5, 1, 'My first matplotlib plot')]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "props = {\n", " 'title': 'My first matplotlib plot',\n", " 'xlabel': 'Stages'\n", "}\n", "ax.set(**props)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.091058Z", "start_time": "2019-12-24T22:46:46.872039Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Legends\n", "\n", "Legends are another critical element for identifying plot elements" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.272662Z", "start_time": "2019-12-24T22:46:47.093009Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(); ax=fig.add_subplot(1, 1, 1)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.319431Z", "start_time": "2019-12-24T22:46:47.273660Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96de3e088>]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.plot(randn(1000).cumsum(), 'k', label='one')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.468194Z", "start_time": "2019-12-24T22:46:47.320428Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96dffffc8>]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.plot(randn(1000).cumsum(), 'k--', label='two')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.586465Z", "start_time": "2019-12-24T22:46:47.469190Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1f96dfb1cc8>]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.plot(randn(1000).cumsum(), 'k.', label='three')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.743829Z", "start_time": "2019-12-24T22:46:47.588461Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1f96de5c888>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:47.999561Z", "start_time": "2019-12-24T22:46:47.744827Z" } }, "outputs": [ { "data": { "image/png": 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9TqeTlddxOBxISUkBUBaEfu+99wIoK+6sHovBYIDD4YDH48HUqVMxadIkjBkzBj/88IPmOtu2bYuQkBCkp6ezdRw+fBjPPPMMAKCgoACEEKSnp8Pj8bAah3zKkMPhcKqPqr7mxsbGokWLFgCAzp074/Dhw+xe849//ANAWeWQwsJCPPbYY6yfw+EAAHz77bfYv38/+/z8+fO4cOECK+V2rfAg9waMyWTCvHnzoNfrIYoiDAYD9Ho9BEFgxsi1cuLECQBQBJQfOHDA71jWrVuHMWPGsM9ef/11LFmyBKGhoRg5cqRXn4EDB2L37t3MuAKAsLAw9v/+/fuRnp7O1N0dDgfy8vIQGRkJURTZNnNBUg6Hw6k6KioaXVGMRiP7n8YNUxo1agSgLGErIiICu3fvZi8a1+vxeGCz2djnv//+e6WNK4AbWA2elJQUzJ07F3379sWcOXOwceNGvPXWWxV+wkhMTIRer2fvv/76a9hsNsTGxmLbtm1444038NRTT/nsT+fnH3vsMZYNOH36dADAvn378O6772LVqlUsSDE0NBSDBg3y+p4mTZqw//fs2QO3261YvnHjRjz//PNwu90QRRFZWVnce8XhcDhVCH2AfvPNN1kptMoQHh6OCxcuVKhPkyZN0Lp1a3z66acAymZs9uzZAwCIj49XZLnv3r27UuOj8CnCBozNZkNeXh5yc3NZvcGkpKRrKuxsMpmQnJwMi8XCagFSI61bt27o1q2b33HQos4GgwFr1qxhxaYp27Ztw4ABA/C3v/0Nhw8fxsqVKzXHKH+SURtXAPDTTz8ppkJ37dqFadOm8WB3Tq2FJ2Rw6gMmkylox29kZCR69uyJjh07IjQ0FDfeeGNA/ZYsWYK0tDRMnToVpaWleOKJJ3DXXXdh9uzZGD16NDp16gSXy4W4uDgsXLiw8gP1FZxVEy8e5F590KBDdWC7IAiKIMTCwkKSmZkZUFCiViDjxo0byezZs4nH4/HZTx7gLkkSyczMJC1atCAAyE033UR69erFAuSnT59OAJANGzb4HBfdlg4dOvgN4gdAdDodD3bn1Crk5xxPyODURioa5F5f4EHunICgQYdEFdhOCFGIiso9S+W5drVqB44fPx6LFi3C2LFjffbTklE4cOAAcnNzMXHiRDz//POs7YQJE3DvvffCbDajtLQUer3eazrzn//8J9asWYOHHnoIhw4dgtPphCRJEATBa5vpXL3D4eDB7pwaR+3NHTZsGE/I4HDqKNzAaqDIs/YEQUBMTAyKiopACGFGjjzzo6SkhGUG+kPtBi4uLkbTpk3L7aM2zEpKSvDpp59qapFMnz5dM2OR8u677+Ldd98FACQkJLDvBcqyHRctWuQ1fejxeBAZGel3nBxOVSM/5xwOB3bu3MkyfXU6HU/I4HDqENzAaqCYTCaMHTsW7733HtxuN3bv3g1BECBJkiLwm8ZCEUKQk5NTbnyWOl7EbreXa2Bpcd999+HMmTMQRWUehs1mQ0FBQbn9CSEoLi5Gt27dvMZ74sQJFBQUMHkKABBFEXa7vcLj5HCCCX3wKSkpgcfjwdatWwEAgiBUSkaFw+FUPzyLsIFis9nw/vvvK4wMOm9MDQ2TyYT+/fuz5aWlpcjLy/P7neqaf8XFxeV6hnzVCtTpdF4GVl5enpdhpCUn8eWXX6JZs2bYtWsX+yw7Oxu9e/dGQUEBBEHw6iMfZ3Z2Nh588EFkZ2f7HTuHE0xMJhOysrK8PieEwO12V6oeKIcTTBqawX8t28sNrAaK1WpVGCpA2VOyIAgKQyMqKkrR5osvvvBpdGiJyQXiwdLqFygDBw7U9Ki1bt0aAFgxaZvNhtGjR6O0tJQVsJZDCMG4ceNgs9nw8ssvY9SoUVizZg1GjRrFjSxOtfL11197Xcy5ZhunNhESEgK73d5gjCzqeAgJCalQPz5F2EAxm80wGo2sDuE//vEPfPLJJ3C73Rg3bhxiYmJgMpmQmJiI3Nxcpnh77NgxjBo1CgAUdQoB4OjRo9DpdCCEMEPNZrNpyiWox6IOcvdFYmIiPvzwQxbgnpGRodnu1ltvBQD88ssvALwNSurBohcIGtyfl5cHi8Wi+K78/HyFmCmHU1XYbDasWrXK6/O+fftiypQpPMCdUyto0aIFjh07pqgrW98JCQlhavGBwg2sBoo6sJwaIB6PR5GtZDKZsGHDBiQmJjJjBQBycnJQVFSkyNBzuVzMC+bxeBSGWnkMGzYMAMqN8TKZTLBareXqAoWGhuKWW25hHiyz2QydTseC40VRhE6nYx4t6iHYv3+/11PZkCFDyh0/hxMMrFar1/EnSRKGDBnCjStOrUGv17NZAo5vuIHVwFAHocsv2r68SCaTCRMmTGCeK6DMmqd1A+W1DOWeIafTiUmTJuHdd99lwqNqo0idlh5IeZ5ABeuio6Nx7Ngx1icpKYkJoQLA8OHDER0djcjISNjtdkRGRiI9PV3xHXFxcdx7xak2IiMjIUmS17lUkYcVDodTS/AlkFUTLy40WrX4Ey0sLCwkqampJDU1VVPMsLCwkMTFxRFRFIkgCESn0ykEO0VRJKIoEoPBQIxGI5EkiRgMBgKADBs2zOd6tURGg8XSpUvJf/7zn4C2n45FLkwqSRIXduRUG/T4FEWR6HQ6kpCQwM4NURRJfHw8Px45nFoG/AiN8iD3BoSvYHLqRVq0aBEWL17s1Y8u37RpEzweD8tokkO9WOPGjcOGDRvwyCOPsDZ5eXkoKSnRDGKvyiKgTz75JB5//HH2vrx6WDQujU4fzp8/n3sMONUGPT/pORYVFQWDwcB0sL799ltFli2Hw6ndcAOrAeHLmCkvi89qtcLhcChiQwgh0Ov1CrkDj8eDmTNnoqioCF988QUzsJg1r5EJFewioHLOnj3LCkfL1zdp0iTN9dCxTJ06Fd999x1iYmIwbdo0fkPjVAvq8zMxMRHr1q1D3759mZFV0SxbDodTc/AYrAYENSDy8vJw4sQJpmlVXhZfZGSkl6QDAHTo0AFNmjTBpk2bmPHldruRn5+vKQHhKxMqmEVA5cybNw+TJ0/GlStXKpxeW1RUhHHjxgVcJojDqSxaFQ1sNhtuvfVW6PV6uFwuLtXA4dQhgmJgCYLwIYABAE4SQjpe/awpgP8AaAXgMIDHCSFngrE+TuXIyclBaWkp+3/jxo1eF3Y5drudPUHL2b17t+K9KIowGo0YMmQI84pRDAYDM67UgfZVBdXzKi4uxs033+y3rc1mw/Tp07Fq1SrmbdPKquRwKkJFjnV1W3kCiCRJGDlyZLlZthwOp/YQLA/WvwHMBSCX+Z4IYB0h5G1BECZeff9ykNbHCQCtC/aIESOYcQX8pc6+YMECnxduGpvkdDqZHIMaURTRt29fjB49Gl9++SUefPBBhZ7PbbfdxsbUp08fpr81b968KsvSowaW3W7Hp59+ilOnTmHq1Kle7Ww2G8xms8IgJIRAp9NBEATuNeBcE+oMWX9eUK228ql7oCwrlhtXHE7dISgGFiHkO0EQWqk+fhSA+er/iwFYwQ2sakN9wc7KysLYsWMVRkSgyKcufv75Z69AeEEQYDQaMWXKFMyZMwfLli2DTqc8tPbv349evXrhkUceUcg7jBkzBkCZERRsj5bcwLLZbNi2bZumgZWXl+e1X3Q6HV588UXs3r2baxBxrgmt2EZfx5FWW/nUvSRJOHr0KGw2Gz8WOZy6gq/0woq+UDYVuE/2/qxq+Rkf/VIAbAewPTo6ugqSKBsmavmD+Ph4IgiCQloBANHr9QGlfns8HmI2m0lMTIyXPANdz6xZs0iTJk1IRESE5rroWGh7AEQQBKLX631KJ1SG3bt3EwDks88+I6+++ioRRZGUlJQo2hQWFjI5CfkYMzIy/Eo6WCwWEh8fTywWS9DGy6lf0GNLEARiMBj8HttqiQZ6XFH5FCp9EuxzhMPhVA7UZpkGQkg2IaQbIaRb8+bNa3o4dRKbzeaV7abOSGrevLkiC1AQBCQkJGDjxo0BPRH/9ttvsFqtiqw8QRBwxx13wO12IywsDC+88ALOnz+PCxcu+KxRRQhBz549IYoie7lcrmuqQ1gerVu3xgcffIBOnTrhjTfegMfjUajRA2WeA7nkRIsWLTB//nxERET4zKxU1yp8+WXumOVoIwgCkzVRZ7TKoUWeJUliVRCotyo6OpqdIyUlJX4LrnM4nFqEL8uroi94e7B+BnDT1f9vAvBzed/BhUYrTnnioZmZmcRisSiEQQVBIKmpqYrvWbt2Lbl8+bLmOtxuN+vbunVr9r9Op2PCnKGhoeS1114j/fv39/JeUcFEuRApFSaVtzUajVXydH7s2DG2jq+++kpz/9HtEEWRhIaGEovForlfCwsLFWKktA/3KnDUqIVry/MW+xLdVXtZA/U6czicqgc15MEqADDs6v/DAKyswnU1WNSxG3l5eUhLS8OgQYOQl5cHs9kMu92u8NLodDpFSZr9+/fjgQcewPPPP6+5DlEUccMNNwAAwsLC0KRJEwBlXjKaWeh0OmE0GjF58mRIkqTo//nnn2PTpk1ISUlBmzZtmJgiFVQEyp70hw8fHvT4kp07d2Ljxo3sfXFxsWI5jS9Taw3Z7XZNfS6tWnGEEK5NxPGCxgBS3G633+MkMjKSeXXliRUmkwn9+/dn7WhiCofDqd0ES6ZhGcoC2psJgnAMwGsA3gbwiSAIyQCOAngsGOviKKFTgSUlJfB4PMjOzlbIKeTm5uKFF15QGAUvvviiwpA5d+4cAODQoUM+1/Pjjz9i+/btePDBB9G0aVMAwA033ICQkBCUlpayG4LJZML48eMxffp0NGnSBLfeeiv7jg8//NBnkL0oigHVIawoCQkJOHOmTB3ktdde08wGNJlMGDJkCDZs2AAAim0BwG6KJpMJZrMZer3eK+NQfTPlNGxsNhvGjRvHzjsqYRIZGYlp06bBbDajqKgI+fn5rJj4mDFj4HK5IEkSsrKyFOdoVFSU4vtPnDhRfRvD4XCuiWBlET7pY1GfYHw/xzcmkwljx47F9OnTAcDLu+J0OrF7927mnRFFEREREV5tgLIyN19++SW6du2quKDPnDkTf/75J95++22sXbsW119/Pbp164alS5di7NixCtkHoMyomT59Os6fP4/rr78ewF+eNl888sgjVZId1bRpU/z2228QBAEZGRkICwvzakNvhm63G6IospubVuo8AIV6PX2/a9euoI+dUzfQ0rqSVz+gIrtDhgxh4rVyuZM1a9awWC2grCKC3W5XrCMxMVGhX1dQUIBBgwYhIyODZxVyOLWUGg9y51QeteCnHEEQ0LlzZxiNRkiSBKPR6OXFoR6sM2fOYMCAAUhISFA8Ia9evRrr168HAPTt2xdt2rRhyzp27IjFixdj0aJFrE5abGwsW06NErPZDFHUPtz0ej0yMjIqttEB0rJlSwDA0KFDsXnzZmzbts2rjboGHL250Zuk2+2Gw+GA1WqF1Wr10gEjhCAnJ4eX1GmAUCP8lVdeUdQJlFc/IIQgLCwMu3btYtP5WscQRRAEr3PUZDIhOTmZvfd4PFixYgXuu+8+ftxxOLUUbmDVA+gUgxYejwezZs1CVlaWz3p/PXr0QIcOHbBo0SIAwJYtW7B27Vq2/M8//8SNN97I3jdq1Ij9f+rUKa9sO0mSsHz5cqxfv17h7ZEbWJIkISEhAQkJCYobR7Bp27YtDAYDli1bhlGjRmHWrFlebXzVaJTfJD0eD7Zu3YrIyEjWVr49PC6mYeKrjqfao7ly5Up8+OGH0Ol0kCTJSydOji9vbmJiolc/XpuQw6nF+Ip+r4kXzyKsGBcuXCAFBQWEEEIyMjI0dadwNWuQZiT54p577vHq9/PPPxNCCLnppptIcnKyov3AgQNJhw4d/GYxypFnSNEsxkD7Vob58+cTAOSXX34h3bp1I/Hx8ZrtaMalfAzqLDBBEFiGYWZmJklISFDsL3VmJqf+o3UMFxYWEr1e73U+iaJI2rVrR2JjY4nFYiEWi4XExsYqdOHKyxC0WCyK9lWVecvhcAIDfrIIa9yokr+4gVUxRowYQQCQxYsXk9TUVC/5APmFPSEhwetCvGDBAvLCCy+QvXv3KtovWLCAGI1GMpjRP94AACAASURBVGbMGHL69GkiiiKZNGmSom+jRo3IvffeSwjRNk7UaN2IfKWlB5NDhw6R3r17k+LiYvLUU08RvV5PVq9eHVBfOma5lIRW+nwgQpKc+otadDYuLs7r/NOS9khISCCpqaleDx7lUVhYSBISEpihxuFwag5uYNVTHnjgAXbT9+e90nrafeGFF5iu1YQJExR9Tp8+TQYOHEhatmxJzp07R5o1a0Z2796tWPfgwYPJwoULKzRetSFWHR4sOSdPniStW7cm7du3D7gPVdKmHglJkhQ3NbnWWHlGZkMjEMO7rqM+hrU8ydQY0jo/9Xo90ev1RBCEgLxRXNmdw6ldcAOrntKvXz+fhhUtuaEW/XzooYcIIYS9V08NtmrVirhcLrJ161aSn59PLl68yKYKq4LqvgnPmDGDxMfHE5fLFXAfi8Wi8ECob4QWi4Xo9XomUspveNVvPNcUai9smzZtvB5w6PHh61ylr0DL6fjyqHI4nOrHn4HFg9zrCFrlcMp+WyV33XUX3n77bYwdOxbz5s3zEv385ptv8PHHH7P3hYWFAIDRo0dj586dKCoqgiRJ6N69OzZv3owJEybgjjvuqKKtKsuOmjRpUrWlmo8fPx6rV6/22i9AmQjpr7/+qtARs9lsSE9PV3wmDyy22WwYPXo0SktL4fF4WLZhQ0crA7M+ok6QGDx4sGL5hAkTsGvXLi8pEy1cLpff/SSXfqDodDpNbTcOh1PzcAOrDuArFfzVV19FSEiIom18fDyys7Nx+PBhxMTEMENCkiTExcUBAJ58sky2rFWrVrjuuusAALNmzUKXLl3QuHFjAMDFixfx/vvvN6gU8E8//RS33norxo4dyz7Ly8tTqOADUGQaWq1WhfGllWLfEFFnYNZnIdZhw4Zh5MiRWLduHd555x1YLBbEx8fDYrHgnXfe8WofFxeH1NRUtGjRwmuZv2NHLXVSVdUPOBxOcOAGVh3AVyq4uoQGACxYsADdunXD3r17vTSb1CneKSkpOHjwIA4dOuTl0Tl8+DAA4ODBg8HfoBrE6XSiXbt2yMrK8lq2b98+AN5ConJuuOEGzJ49m93U1Dc9X1pfDQ273c72hSiKXsKZgUC9ttnZ2Qrvrc1mQ1paGtLS0mr0AYA++CxatAiLFy9mn6ekpGD16tVISUkB4C2vsGXLFiQmJuKVV15RfN8///lPv8aSyWTCvHnzoNfrIYoi9Ho9GweHw6mF+Jo7rIkXj8HSRiueZfPmzaRVq1akbdu2pEWLFiwmY+nSpeT1118nAIjZbGb9IIv1aNu2LQHgN0jdbrcTAMRkMlXjllYPERERZPTo0V6fd+rUiQAgzzzzDPtMXWhXEASi0+lYoHthYaHX/o2Nja23MUeBUtkYLH9FuOW/R03KFMhlPERR9BsLlZqaymKnJEkiqampJDMzk2RkZCgyEANBnnjBM1g5nJoFPAarbkMLElOhUAC47777cPjwYfz88884fvw4QkNDUVhYiCeffBJ33XUXgLKYjqVLl+LZZ59VfF98fDz27NmDQYMG+Vxn06ZNkZ+fjxUrVlTdhtUQLVq0wO+//6747Pjx49i7dy8AsNqFQNm+t1qtiI+PZ+VMXC4XxowZw0qkEFUs3NatW9G7d+8G71mQT51VdBpLrq4PgBXhzs/PV8QzORyOGhN41ZoG1YqVBMq8WCEhIZAkCZIkITc3F6+88grmzJmDKVOmMG9XINB9WVpaCkIInE4nK5XF4XBqD0GpRcipWtS1zqZNm6a4ydCbj9VqhclkwoABA7BlyxZ0794dgiDA6XTi3//+N9544w1MnDgRTqdTocbuC3XAbn3hlltu8TKwPvnkEwDATTfdhLNnzyqWmUwmTJkyBevXr2dTrm63m/0mRqMRV65cUfShyu4NMT4mOzsbY8aMgdvtVkyNVWRf0OBxh8MBj8cDQRCg0+kwZMgQrFu3ThEX98EHHyAxMbHa9zWdBqXjy8nJwZ49e+ByuVjtSjom+pBktVpx9OhRLFq0SDHlX9mxr1q1CjabrUEebxxObYV7sGo5NM5j8uTJiIuLw4wZM9CkSRNFG1EUFYHXkiQhNjYWDocDf/zxB/PIJCUlQa/XB2Rc1Weuv/56hZcKKAv8/+qrr5CWlqZ5k1LHv9CajvTG2a9fv+oafq0mOzsbaWlpLKvS6XTCYrEokjMCge7XgQMHslgutaeQ4nK5qt2LZbPZsHXrVgiCwDyb27ZtY5mTWiVsaMZsYmKiZmmmipCYmKiImySE1NtMTQ6nzuJr7rAmXjwGyxt1uRZ16RZoiF9S4uLiSGxsLHE4HOT48eMV0n6qz8ydO5ekpaVdU1+1ajclLy/PS0CyocXFaMWj0de16DUVFhYSnU6nOPbj4+M1KxZUZ5kirbg8uTYVLalUnqZVZfXfaNkcHofF4dQc8BODxacIazk0S00e6wGAPTUDZdNV6uKyANCnTx9MmTIF586dw0033VR9g67ljB49WvH++PHj+OSTT/DYY4/hlltugcvlgsfjgcFgULSz2WwYN24cnE4nNm3ahJiYGObtWrhwIWsnCAKSk5Mb3HSNlqQF4O1hDRS1BAYANG/eHJIkKc4DvV6PxMTEax53RbFarYopekII9Ho9PB4PJElCUlJSuVOWJpOp0sdHTEwMdDodnE6n38xXDodTM/ApwmqEEIJVq1YppBPKw2QyYfz48V4XUGpc+aNXr14ghOBf//oXZsyYUeHxNhS2bt2KF198EcePH8dPP/2E66+/HgUFBV7t1HIZubm56NatG86dO8d+H0EQEBISUq03/NrCiRMnvD4TRRF9+/a9pkB3Gt9G963H48GSJUvgcrmg0+mQkZGBt956KygxTBUdF5VIAACj0Yi5c+di5MiRARlXwYLKsJCriRd8ipDDqV1wA6saKSgowMCBA/H+++8H3Mdms2HOnDl+2/h6gqdeq0WLFmHRokUVG2w9Ji8vD+Hh4cwg2L9/PwDgzjvvROvWrXHp0iUUFRV59VOrdjdt2hQ7duzARx99xIzmzp07IysrC1artUFlEb788stYuXKl4jMaqzZlypRrMjhoHNYDDzyg0BcjhMDj8SAiIgKTJk0CgGrVxaKZpampqUhNTcWGDRsAADk5OcjOzq5wvNm1oj4eucAth1O74FOE1QgV7zxw4EDAfajXxJfHyt901I033sj+b9q0acUGW4/R6/W4ePEizp49i6ioKGzevBktW7ZEeHg4gDKF+//9739e/eSZYGazGT169EBubi527tyJP//8E48//jjGjRuHPn36wOl0emWS1QfUGa1AWWC7lkyAKIoYO3asQhj3WtY1ZMgQdh5Q5LIIZrOZLcvNzcWGDRuqfJ/Lp/houSRqZNPSQNUxBvnxWJ+OMw6nPsANrGrkt99+AwDFjaI86FNqSUmJppGl0+l8TkdFREQgPT0d8+fPx4ABA65t0PWQiIgIAMCvv/6Kxo0b46uvvsKrr77Klt922234+OOPMX36dPztb39T9JXfWI8fPw5CCH7++WccPnwYqampmqr79eXGRzNa1cZjfn6+ZnuPx4OZM2eyeLaKGJvydUmS5BXbJQgC7Ha7VzxUdexztZGpjhWTJKnavEnBiOXicDhVAzewqhHqFTl06FBA7emFPCsrC7t27UJOTo7iZiJJEubOnevzAisIAlq1agWgTKKBUwatv9i/f3+sWLECBoMBffv2ZctjY2Px7bffYtu2bV4GlpyRI0fi1KlTEAQB9957L3777TecO3cOBoOBGSH1adrGV8mmsLAwr7aCIEAURbjdbi+dtoquy1fgPN23er2ePbQEc59reevUhl9SUhK6dOkCo9EIh8MBQRDw8MMPB2X9HA6nbsMNrGrkzz//VPz1h9pbsHr1apw/fx6XLl2CIAiIiooKKJj2yJEj6NixI6KiooKyDfWBTp06oXv37hg8eDB69eqFy5cvK5a/+eabeO2117yyCNXQ+npjx47F5MmTMWLECKxfv77eTttQbyo9JiMjI9GnTx9mWABggqDU8KBZlxU1fMxms6bnivLII4+wfWu1WpkOVrACzH1569SGn8ViQUhICHsIys3NxapVq7B69ep6Nz3M4XAqBjewqpHTp08DAIqLi3Hw4EHceuutPtOrrVYrU7EuKSlBTk4Oli5ditmzZ2Ps2LEBr3Po0KF4/PHHgzL++kLjxo2xdetWn8uprEB5nD59Go899hj+8Y9/oKSkBBEREV4q8HIIIVizZo0iaFvLS1JbUcf8yI9RoMyjOnLkSIWRExMTc80ioJ07d8a2bdtACFGcJ3q9HhkZGQDK9l9FjatA9rmvqV6z2QydTscMP3K1VI3dbkd0dDRcLle9nB7mcDjXgC+BrJp41Xeh0fDwcIU44qxZs3y2tVgsirZUcPH//b//V40jrt9s2LCBPPzww+Rf//qX17JHH32UjBw50m//6667jvTr148AIIsXLyZvvvkmAeCzyHFBQQEBQKZPn04IqXxB5JqCimRSoUvIhEDVYqLXso3+Cj3LxTnVgp96vZ6kpqYq1iEX9CwsLCQJCQlMnFNetFtrDEaj0UvIVy1+ClnB6br6e3I4nGsHXGi05nE4HLhw4QIiIyPZ1NLatWvx/PPPa7a32+0KMVGaoXT99ddXz4AbAM8//zyKiopw8eJFr2V2ux3nzp3DqlWr8O9//xufffaZwotSWlqKc+fO4euvvwZQJolx/vx5AGCejytXrmDo0KH47LPPYDKZcO7cOQDAypUrMWHChDoZEJ+dnY309HQmqkmPT+CvzD458m0sKSkJqD6jvNAz1dHSknpQB7iXlpZi4cKF+OCDDzBv3jzExMQo4qUIIYr2tGi3XDAW+MsrJq87+fzzzyMmJgZWq9Vr2nL48OGsf01ND1+LJ4/D4VQtXAermqDTg3fccQf7TH6xV0NjUNRwAyt40AB2LQmLyMhIFBcX47nnnsPy5cu9dLEIIbBYLOx9eHg4IiMjER4eDr1ez4yx48eP4/7774fNZmP1D++55x4AdU/HyGazIT09HW63m4lbqpXW6cMDhVYiAMr2WU5Ojl+NKJvNhqNHj0Kn00GSJL86WmrBT4rL5UJ6ejry8vKYcVdaWqp5vtGi3fL19+nTBxaLRWFIUQNYS2SUZvHW1HQvlapYuHAhFi5ciPvuu69BabBxOLUVbmBVE6dOnQKgNLCcTifWrFmDuXPnerU3mUwYMWIEe09v2NzACh7lGVh2ux1dunQBAOzdu1ex3GAwICUlBQUFBTAajWjfvj2efPJJnD9/HrNmzVJ4u6guUseOHZGeno5HH30U06ZNQ1FREYYNG4aRI0fWiYBordI1cmgBbDVyQ6W0tFRTMwso84717t0bFosFLpcLjzzyiN/9YjKZMGfOHLRr184rltHtdmP//v0QRRGiKGo+rNBCzVu3bmUGiS/dOVEUERkZqSkyajKZmGH2yiuvVJvQKIUakhStQtMcDqcG8DV3WBOv+hyDReNvZsyYoVkMVwt1TMfGjRuJ2+2u5pHXX6ZOnUoAkLFjx3otmzhxItHr9aRfv37k7rvv9lpeXFxMduzYQS5fvuy17M033/Qq/ltYWEguXLjgM76oLsTrqGOe5NuXkJCguQ2ZmZmKfYGrMU3qOKmEhASvdoIg+IyRov1CQ0O9+tGXKIpEFEWi0+lIQkKCokh0y5YtFf1ocW6tQs70e/z9TpmZmSwe7VoKW18rWjFhDbHQOIdTU8BPDBb3YFUTVMXd15MtUT0xyzWw3nzzTaxbtw5xcXGKkiGcynHDDTcA0K7rGBsbi8GDByMtLQ2vv/661/KNGzeia9eu+Pnnn9lnv//+Ox577DF89913iu/U6XT46aefEB4ejuTkZFy5ckVRvJvGJtVGbDYbpk2bxo7bpKQkpq1GIYQgKioq4Gk8QgjzsNDprRUrVnj9DoQQpKen+zxnyqty4PF44PF4mAfNaDSy8+fIkSOKfqWlpSw+LCkpiXnEyNWyPHItLy1qarpXHivG4XBqGb4sr5p41VcPlsfjIffcc4/X03VISAh7f+bMGdZeKxspMTGRZxAGmZMnT5Lt27eTkpISzeUul4ucPXuWTJo0iYwaNUqxbNGiRQQAOXr0KPvs6NGjBABp3769l8eG/v7yrDuovA7qLLmaRn4cGgwGYjQaiSRJmtuQmprq93to9p7cE1RYWEji4+M19wd9CYLg0xskH59Op/O5b3E1089isZD4+HiFJ0trG9QZvPLfKCEhwStTUT6e6v79UlNTK7TPOBxOcIEfD1aNG1XyV20zsLQumB6Ph6SlpZHly5f77ZeamsouxLt27fJ7EwFADhw4wPqrpxvGjx9PAJA33nijSreXo+SRRx4hAEi3bt1I27Zt2ecej4f07t2bREZGEofDwT4/f/48AeAlx0Gnom655RaSnp7u8xjQ6/W1KsU/MzNT0xihU2bycQcqv0DPC4vFojndqGUY+ftueo6mpqb6NbBEUSTx8fE+1yufIoyPj/c57VjRba5qCgsLiV6v9zLo/U2tcjic4MENrGvAl6ZNWloau5D56iePiTAajeS9995jF3n5hfDhhx9m///www+a65bfND799NNq2XYOIZcvX1b8Vk2aNCEej4cQQsjmzZsJADJ79mxFH4/HQ0RRJBEREQrjqlWrVgQAeeihh8jSpUs1b9hUm4n2iY+Pr/EbuC9PDtWPkj9EBPp9er2eiKLo1xii+8BXXJcWWvFYgiAQvV6v2K+hoaFe8V6xsbEKHSt6npZnZPnz2lUn1HCNi4sjoigSQRDKNUw5HE5w8Gdg8YAeH2hpFJWUlGDBggUAytLytVDHRDidTmzbtg0A0Lp1a/a5KIro2bMntmzZAuAvGQegLDsqKysLffr0QUJCAvu8bdu2wdtAjl9CQ0Px/fffs/fnz59nxbppHM6wYcMUfQRBwHXXXYc+ffogNDQUkiQhJCQEXbt2BQC0atUKcXFxrC1QdhwkJCTgpZdeYjFBhBCsXbu22rPR1Njtds2YP7fbjZiYGCxYsAALFiwIWD199OjRKC0tVcRFaaHT6bBw4UJ8/vnnAWdWUpX5UaNGsVgrSZLw+OOPK+KpHA4HoqKiEBISAkmSEBoaiqysLEUZHBof1717dyQkJPistlBbMJlMWLBgAdq3bw+Px8O2szri+uQxeup4PQ6nocOFRn2grrtmNptZQHP37t2xbds2nDp1Cs2bN/f7PYIgsBTxgwcPss/1ej3MZjOaNWsGQFmf0GazsRpu8vTyNm3aBG37OOXTs2dPfPHFFzAYDIiPj0dhYSGio6Mxbtw4PPPMM2jSpIlXnzvvvBNt2rTxKimTn5+Pt956CyEhIRg/fjzatm0Lu93ONJOmTZumEJalN8maFB+lZWHkEgBA2TFd0XGVJ/EAaJfaqQgmkwkmkwldunTBmDFj4Ha78Z///EexXo/Hgy5duiAxMZH9PgAwbdo0REZGKoypPXv2IDk5GatXr1aUBALKzl+qf1VfKU/XS134WhAEuFwuRe1GDqchww0sP1APBb3gT506FQDwzDPPYNu2bdi9ezceeOABRZ/ExETk5OQwUUNJkhSGFWX48OEAgGXLlsFgMCiELNUFZSmhoaHB3UBOuTz88MNwuVzo3r07q08YFhaG6OhozfaFhYUYMGAADh06hE8++QQ2mw07d+4EAHz77bc4ePAghg4d6nXzocKycu+nJEk1Kj5qMpnQv39/rFixQvG5L70rf5jNZhiNRpSUlDAjUk3Xrl2Zh7gy2O12lvkHlHkJ6f+CIMButzNjTG0kyI2o0tJS2O12ZixHRkZi165dAGqnWnpiYiI+/PBDlJaWVtoA9FXsWo68FiXdb4SQOlOVgMOpcnzNHdbEq7bEYMljRWj8ldPpJI0aNSLR0dHkxIkTBAB55513NAPhU1NTWfyGJEmkS5cumtlINCtLFEXSuXNn1l8eg0X75OTk1MSu4GiwdOlSMm/ePJ/LY2JiyKOPPuoV02MwGJiu0rhx47z60eNOEAQiSVKFYpCqgsLCQhIbG6s4dmm8UrC+T/4KVmA2rSNIY5Gefvppn1pW8oQSdcxVbQlkrwjBymQMRNdLHaMXiF4Yh1PfAA9y/4vyLkDqIHVawHbv3r0E+KvYclRUFBkwYIBmILw6QH7IkCEkPDycpKamMsNKfTEPCwtjY5g9eza56667WCHhFi1akP/+979Vvm84/jl16hQZNWoUeeihh0iXLl0029CCz8nJyX5v3nKDQn5MWiwWEhsbW+MZhVoB3waDodJjUcsK3H777Sy7L1hQsVBa0NloNDLBUfV6tKQotNrWhATDtRKMsQZSuFory5RnMHIaGv4MrAY1RUhFDUtLSyGKIrp27Yrk5GSkpKSgpKQETz31FG688UbFNIEkSQgLC8OaNWsAgJVOueGGG3Dw4EHNYr004JbGL8yfPx/NmjXDggULMG3aNBQUFHhNk1y+fBkXL15Ebm4uKwC9b98+6PV6HDhwACEhIdW0lzi+WL58OSwWC1q2bOkltkn55ZdfAJQdH/I4PhqfIic/P9+rILEgCArxzJqabpFP/wiCgAceeMBnTcCKoJ7GWrx4cdC3jRZkJqSsXiL9n04PylGfq7S/PO5Ift3Q6/W1evorkKm9QFDvF18isuoYPbfbzaZROZyGToMysOQ1u9xuN7Zu3YqtW7eipKQETqcTn3/+OZo1awZJkkAIgSRJmDVrFtLT09l30FqCzZo1w6lTp7wC4Sn0gmS1WnH48GEWEG02m31mJakzE91uN5o2bcqNq1oCjbs6cuQIOnXq5Ldt8+bN2U0qLy8Pubm5XgbWkCFDvOLt5IHugiDUWBHoyMhIRVzNkCFDgmJU0Gy9qiyKTOPZaPwiIQSiKPrcl/ShSP5ejvy64XQ6meJ7bUQr+/lax6reL1rLtWL06jo2mw15eXk4ceIEoqKiamW8Hadu0KAMLF9MnjwZFy5cAPCXXIIgCBg/fjwGDBjADKymTZuysh/NmjXD77//7vMpjz5JOhwOEEIUGYCiKPpNU6cIgoDOnTsHbTs5lYMWhwagmUEI/GUkx8TEAPjLoKDGlSAIMBqNiI6ORkpKCmw2m6YxoNPpkJSUxAKVp02bVmUGiRZUosHj8UAURS/PT2Uo78YdjO9PSkqCxWJh+7Nv375B8cDVdrSyn6uSqKgoxXtRFOt0diX1Vsq9cjk5Odi4cWO9P3Y4wafKdbAEQXhIEISfBUH4RRCEiVW9Pn88++yzCtkDCjWu5DpVhBDMmDGDTQ0CUEgyNGvWDKdPn4bJZMKkSZMU0wnTpk1DXl4em2IhhOCXX35hac/lpatTQkJCfHqvVq5ciZ9++img7+EEh0AMrNtuuw0AcPfdd7PP5HXqQkJCcNddd+G6664DUGYM9OjRQ/Ed3bp1w5w5cxAdHY2ioiL06dMHr7zySrXqYtGsP0mSrilrsKZJTExkWldGo9GvcUXP2ezsbE0dp8TERBiNRmYc12YDgnpNaf3SazUKbDYb0tLSkJaW5veYo/sGKDOuBg4ceE3rqy1QD6Cc0tJSjBgxgut7cSqOr+CsYLwASAAOArgVgAHAHgDtfbWv6iB3qsCNq8G1UGXANG/e3CsQ+cEHH2T/33vvvYSQsgDQ+++/nwAgKSkpTM1ann1oMBi81Kpp4KlWfTf1euWvs2fPktatW5Nly5YRQsoUw3E1y4lTvTz99NPks88+I5cuXdJcnp+fT7p160aOHz9OCPmrDl+7du1YVuCyZcvIBx98wJarf/+EhARFjT0aSOwrm6sqUJd7qosEEuytDub3lQVXl4LcKwtNEqDHYyDlilJTU/0mE9QVtM7HQPcDp2GCmsoiBGACsFr2fhKASb7aV7WBdfHiRXayxMTE+DVq6EueUdipUyd2QVa3U9cDE0WRla6gN0d6s1LXZHvrrbdYP61sM1pqJyYmhhBCyPHjx9kyTu1FnZEKgGRkZCjaqDOxqDwDPQ7kZWWqK0MrkAyy+oI801N+7jbkYsmZmZle16HyygKpj+O6KHFBycjI0LwO8yLaHC1q0sAaCuAD2ftnAcz11b46ZBoCMaqaNm3q09jyp+OjPhnpE538RhkaGkosFovXDUx+g1V/V8+ePZm3jBBCvv/+e25g1QEyMzO9fktRFMnatWvJoUOHCCFKD4pOpyMZGRkK74G8RmF1PEVTrSq5jlt9vqmovTX0VVc9MMFA68EgEC+WlrxNsMZTXd5D+flI6zpyDxbHH/4MrKqOwdJKlyOKBoKQIgjCdkEQtp86daqKhwM8+eST5bYpLi5WvDcYDLj55ptBCMHWrVsDXhetuwaA1V9zOp3Iz8/3yvShaGUYbt68GQDgcDgAAIcOHQLgOw6IUzugaexq3nrrLdx+++0ghLCYmalTp+K7775DRESEIgGCxvBR1MdLMMnOzkavXr2wdetWtk6dTlfn4q8qAg2IVxPMoP7qJhg1AdWxqi6XC1ar1WftQZPJhHnz5kGv10MUxaDF7dFkoYrEIFZm++X1KAVBwKOPPorY2FgkJCRgw4YNPNCdUzF8WV7BeKGWTRESou1VKO8lCIJCnT3QPjQeC6onwYp6sOSvH3/8kaSlpREApGXLllW+vziVo7CwkE0V0/geKrZ55coVzfby+Cv1MVdVT9FaXgugLB6svlPRmKPajNojei2eOPW0qSAIzPNORZL1ej2LH1WLLFNvUzA8T+qqGOV5xSo7vZ2RkcHOVXmMbH2fKudcO6jBKUIdgEMAWuOvIPcOvtpXh4H18ccfK25aTZo0CchwSk1N1ZxK0HrRG6nFYmHTe3JDjRBvtzdtQy8O6pgurVfz5s2rfH9xgoP89549ezYBQE6ePOm3Lb2h0WOqKkvnaMXd1HVjoyLUh6B+QiofCyUPWKeJOHSfqFX4/Rk+wYjjUxu+gWxLICV+fKEu/RMXF3fN38VpOPgzsKpUB4sQ4hIE6Zfk5QAAIABJREFUYQyA1SjLKPyQEPLfqlxneTzxxBOK91Tnx58ulSiK6NKlC5KSknDixAkcPnwYu3fv9tm+d+/eeOutt2AymSBJEjZv3szc5jTF25cWENXVKi0txbp16/Ddd9/5XM/Zs2dBCPEpXMqpPch/759//hkAcPHiRYX0h7qtzWZjv69Op0NGRkaVTVFoFZsGyqa5a7NyebCoam2u6sJsNiuKW7vd7oB/P3Xh65EjRzKRTZvNho0bNyraC4LgJeBKpWiOHj1aacFTeh2kBCJvQyVRHA4HBEFAZGRkwOvLyclRvC8pKalWTTFO/aPKhUYJIV8B+Kqq13OtXLx4sdw20dHRSE9Ph9vthiRJmD9/PoCymJUdO3Z4tR81ahS7mLRo0QIAMHLkSAwbNszrIkMvSFRsUi5a+uqrr/o0nm6//XYcOHAAp0+f1rxJc2ofH3zwAZYsWcKOua+++gqjR4/22V5e8qUiN8qKQI+/yMhI3HPPPdi0aZMi5kuSJH5jqUPQWKgxY8bA7XZXKBZKrgIP/FW5YNCgQSgoKPAycARB8DLC5AaaTqdjDwhyQ4cec+UJ59IHSAohhJ0Dvr7DZDJh7NixeO+99+B2uzFu3DjExMSUe97YbDbs3LlT8VlycjJiYmKQl5cHACgqKqrSCgQulwvvvvsu0tPTmU4ep47jy7VVE6+qniJ0OBx+Y6bkMSgPPPAA6dOnj2ZbSZJIYWEh+fXXXzWX//DDD2ydixcvJgDISy+9pKmtQ+MlaMwNdalv3ryZEEJ8jpfGYb344osV2gcnT54k69evJy6Xq/I7tJJ4PB6yYMECcv78+ZoeSrWg9Tv6o6rlEtQaUFrnQ0POpqvLXMuUp/p4s1gs5YZFyGP01NNzCQkJLA6VHr/+YsTU8VvqmEA6RejvvFD3C1RaQR13RrdLq+h5VZ0XGzdubDBxj/UJ1FQMVkVfVW1g/fHHH+Tuu+8mAMjNN9/M4luAMhHRb7/9lr3fsmULGTFihE9jLDMzk7hcLs3lM2fOZOscPXo0u+CoLwZa1ehpW71eT1asWOHzwlZUVETatWtH+vfvX6F90LFjRwKAfPXVV0Hbr9fKN998w4zFhkBFDSyLxUJiY2MrHXvlK9hYSwOKvuLj4+t0LFJFqW9ConLR44oY53LDLJDEHvqwSfvKRZTbt2/vFaflK0ZMbTSlpqYq2omiyIwa+XcIgkASEhLYb6cVSxgXF1fu9vsy2nydI1Wh87V+/fqArguc2oU/A6tB1SKMiorCypUr0aNHDzz55JMYPHgw4uPjcfHiRbRt2xb3338/a9uoUSOcOHFC83vofLwkSejatSvCw8MVqfPffvstnnnmGTRr1gx79+4FAM1YBHW8BEUURZSWliIiIsJr3T179sTmzZvRokULtGnTBkeOHAl4+w8dOoR9+/YBAI4dOxZwv6qCpsKrZTEaEsRHDF12djZGjRrF3vfr1++apiXk0zYGgwFZWVmw2+0wm804e/asZlxLeaVl6hvqfVSZEjO1AZvNhtGjR7N4OofDUaHp5dzcXDidTuh0Ouh0OkUcFABFQXIim7ajkiPTp09HQUEB9u/fz/rI5T60YsQAKGK2gLLj0OFwQBRFjB8/Hna7HTabzasQ+YoVK1BQUACj0YisrCyvWMLvvvsO9913n1+ZBTp29RSgPKZLfq5UxZS9VrjKnDlzsG7dOixfvhyiWOWV7ThBpkEZWACwfv16/P7770hNTcVtt92Gffv2oVWrVrDb7YobXVhYmFchUwBo164devfuzd5v374dNpsNvXr1YppFX375Jbp3745Dhw7h9OnTmsGgwF/xEjS+Cyi7eNETWV77rlGjRrh06RIWLFiA5s2bIyIiAi1atEBhYWHA237w4EH2/8SJE7FkyRKsWLFC05CrDkpKSgCUxR707t0bM2fOVNTwawicO3dOc//n5+drvs/JyYHT6YTD4UDbtm3Rr18/2O12REZGMsNJftGXx9VcuXKFFS4XBMEroB0oi7maPXt2nTYwKop8H11rQHZtQl3vtCJxdLSGKlCW4JCQkIDi4mJ8//33AMqMnrFjx2LmzJlwuVwQRdErkPyLL77wMtyHDx/O9qlWjFhRURFEUQQhBAaDAYmJiejSpQvy8/PRuXNnzJkzhxnAw4YNUxh5QFkAvNPpxNdff43bb78dP/30k2J5IL+rVqIDNbzy8vKwf/9+bN68GYSQKqnPKTewiouL0bRpUzz//PMAyvZpXa/z2CDx5dqqiVd1yDSsXLmSACA7duxgn61bt47s2rWLEPLXNM6JEydIYWGhQi7Bl/YLIWUu5ldffVXhRpbXPvQ1/VBYWKiYqgT+Sn8uKSkhc+bMIeHh4SQyMpIAIMeOHWN9p06dSgCQy5cvB7TtS5Ys8XJ10/qGNcG8efMIAHLLLbcQoEyxvr5y9uxZAkBRFgkA2bt3r2Z7dcr4008/7XeqBlDKg8hjWQKVFwEaZjp6fSsNVBktLLUUA62Lqf4uX1OQWmEPaqkPdXyY1njVenDqkmPqY1peUsrXcX2tvyu9RlP9r6qS8sjOzmbj3bp1K7ly5Qp7/9577wV9fZzggBpUcq91UHf3vHnz2Gf3338/OnfurGgXFhYGk8mEjRs3IjU1FampqUhOTobL5dJUYDeZTHjttdcU30Gn4V588UVMmjRJ8+lJnYoMlHkXrr/+ehiNRowZMwbnz5/HzTffDAAIDw9n7WiWz9GjRwPadqqU36hRI/bZpUuXAupbFZw/fx4A8PvvvwPwVo+uT9Cn/IiICHz77bd44IEHAMBnZYCUlBRYLBbEx8fDYrEgkCoHHo8HDocDY8aMYcrXADSVyn1NN9R35XYtqJfizTffrPPTg8Bf20OrA6SkpJTbh6qfd+nSBQaDAYIgwGAwICoqiimbE0LYtL7dbofH42GeI3otVFcviIuLU0zN0enYRYsWYfHixQCU6ulutxu7du1SeBU9Hg8kSYIkScy7ZbVakZCQgHbt2qFz585o27ZtQDIO1wL16hFC2LU62MfIkSNHFNI/R44cYRU7gLpdWaCm+Oijj/DDDz/U6Bga3BRh06ZNAQA33ngj+6ykpAT5+flo164d+ywsLAyA0m1ss9mwePFin7oo6pvWH3/8AQCYMGGCz/GYzWavGJzOnTtjwIABis++/vprrF+/XlEep02bNgCAH374AW3btvW90Vc5deoURFFEmzZtsGfPHgDAlStXyu1XVahjr/7880+/7bdv3468vDzMmjWrzml/0Sng6667Dn369IHdbsfatWuxY8cOJCcna/ZJSUlR3BzXrFlT7noEQWA3JXrjS0xMxKJFixRab4IgeE2zCIKgmMppSNQXHSxKRbZHLa/Qv39/REVFMc0+es2TJAlHjx6FzWZjsUla10L5MWWz2ZjMgclkQl5eHkpKSkAIgdPpRF5eHk6cOKGI6crNzcXs2bMV3z927FhYrVbcfPPNKCoqwq5du/Dll196PZzKkcd6EVmsmHrbadwVgCqVYVAzdOhQ5Ofn46abbmL3Csq5c+cU18PTp09X+XjqG2PGjMGzzz6LHj161NwgfLm2auJVHVOEhBCyatUq4nQ62fvLly8TQRDIlClTys3iKC/bCDK3NM1C1CqJIichIUHRr7zK9ZTTp0+zPr5UweWkpKSQ5s2bkx49erB+06dPD2hdVUFycrJiu5s0aeK3fXR0NAFAjh49Wk0j1Ob48eMkLy+vQn3oNs6dO5cQQojdbicASOvWrQP+DvVxon75KmFisVgU0zaCILBpaDq1IggCMRgMdX56jFNxfJXGkWcHytXd5ZIL6muhr4oAvgqZ+8qilmcFBiIXIX+JokgyMjLYNKYvaQV11qO/8A+DwRDUc2Tv3r1+t2HGjBlk2bJl7P3gwYMrvc6GgsPhID/88AMBQN5+++0qXx/4FKGSAQMGQK/Xs/ehoaFo1aoVfvrpJ+Tm5uLhhx/22ddkMvmc7lNTVFSE0NBQhISE+G2XkZEBo9EIQRBgNBqRkJCgeAr0hTy4VP0EpMWpU6fQvHlzRSD55cuXy+1XVajHfP78eaSnp+PAgQOa7UNDQwEAv/zyS5WPzR8DBgxAYmIiTp48eU19gTJPar9+/XD58mUmLugPm82GqKgodpxoQQiBx+PB8OHDMXLkSDz44IOYOHEiUlNTFdMn9OSnCvHU81rXvIIc/wRa9Jh6oyjkqneJTvuZTCZER0d7hUdoXQvNZrPi2kpxuVx47733FIkVdIpRDp2ajIyMZN4ku93u11MlR5IkLFiwAO+88w5SUlIwd+5c6HQ6eDwejBs3TrEv5NOQpaWlXokOFJPJBKvVilGjRqF///7Iy8urVCFtAOjUqZPf5efPn2cerJtvvhnLly9niQYejwfJycnYsmVLpcZQX0lJSWFeK3miWI3gy/KqiVd1ebC0eOihh0iXLl0q/T0XL14k27dvJwBIWFgYueWWWwLqR5/WqBZXoJ4lGlj/3Xffldv2tttuI48++igpKSkhs2bNIgDIyy+/HNB6gs3OnTvJHXfcwZ7Q/va3v7H/b7/9ds0+9957LwFQ4+KXdJzyRAl/lJaWEuD/s3feUVGcaxh/ZgFBUClSBBQVUVCxoKhBE8WemBjFEksUjSXxxmjUa00s2JLYEluMGo1XY6492GMXDYJdY+8IgvQOwsKyc//Y+33O7M72Xer8zvEcd3Z2d9gy83xveV6wixYt4m2fPXs2bwWvDuWVdv/+/QVX/oCiEYMMlxa6X/lfq1atxHlrlRB9C/dnzpyp8j1SjuIom5BqatyZMGEC279/f5XImJWVFS1cF/o+WlhYsDNnztTJ8JSYmQr5bRE0zSbUNYJF9jXlQHBNv8maNWuyU6ZMYefMmcN7//r27cuuXr2adXFxYQHF3FoRVbjv5YULF0rj9cQIljb8/f1x//59FBYW4tatWwbXJtnZ2aFRo0YAFNEhR0dHnR5HVoONGzcG8LZWTBt9+/YFoBgroYmYmBg8f/4cwcHBsLa2xuTJk2Fvb18mNViRkZFo06YNnjx5Qrdx8+TqIlikLq6sI1gEXb3EyGejbMdw6dIlnQpzuSvtkpIStG/fXm3LdnFxMS5evCj4vKTuisudO3dgaWlJC4jNXeB+5swZHD161KyvIcL/zhQWFtI6KHUoz1YNCAhQGUFDGgFWr16NKVOm0EYK5WhOUFAQfvnlF4SHh2PDhg2wsrKCRCKBjY0Npk6diu7du2PIkCGCxyGXy/Hnn39CKpXSaFJ6ejotaudGW//9738jPDwcW7ZsgY2NDSQSicpYHuJXyDCMil0F+Zv69u2L1q1b4+uvv1bb6KDcjKQc5TIltWrVojVYrq6uGDZsGACFj+OUKVNow0tZ1s+WVzIzM3m3yai6skIUWP/nvffeQ1FRES5evIg2bdrg008/Nfi5uHOkdBVKBHUXY22vlZ2drXYfuVyOL774AtWrV0e/fv3o9urVq5fJj1TIHJXrLQZAsGuOvDdlaZLKFS6vXr3S6THkuJXFtvJtVk1amKRwuCJo5syZvLSONiQSCTZu3KjScEEK20urg87S0hIjRoygTRYi5oEIC0Dxvdq6davGtNbAgQN5t4UaL8giMD09XW06TRmSpuvRowcmTZpEjTP37NkjmJJmWRbPnj2DXC7neQcGBQVh5syZVGCxLIs1a9YgOjqazh8EQOcPcv9W8jrc1yPp04MHD+LgwYO4evUqli9fjqysLMHfgHLq05jFiPLi54cffgAAfPPNN/j1119hb29PU4Rubm7YsWMHGjdujNevX2t9rqrOb7/9xrtNOu3LiirXRaiOHj16ICEhgZpfhoeHG/xc3B/y4sWL9XqsOQTWlStXcPr0aaxZswYNGzak28tKYAmh3OkRERGBwYMH87aR1QmxdSgLuCukmJgYvR6j/JmOGTMGhw8fprffvHnDs9AgqHOZjoiIwI4dO7B161atNSrKHYkrV64EoDCOJMN6S4PatWsjOzsbDx48wK+//or4+HgcPHiwVF67KhEUFIQ+ffrQ97a4uBg7duzgdURzv0/ku3HgwAEMHDhQo7WDpg5CZaKjozFlyhRIpVKcOXPmbepEIoFEIlHpbOUuMgIDA7F69Wre9527P1fc/fjjj1RscJ3rIyIiIJPJwLIsZDIZz05C6DezcuVK9O/fX9BwlPzekpKSBE2odYXY0/z444/o06cPfH19MWvWLHr/1q1bkZ2djezsbLi6usLS0hJ+fn6CC9OsrCy9F/GViezsbEilUty/fx9du3ZFVFQUGjVqhODgYHTr1o1nGVIWiALr/9ja2sLW1pb6Ehnrmvvdd9/B2toanTt31utxhgosdSnCly9fomPHjgAUUTou1atXL5Mid649Q9++fXH37l0q/Ozt7ZGdnY2HDx/SfbZt2wY/Pz/aqiy0kistuJGXv/76iwoVTZDjVj4RNmvWjHc7JydHUGAB6l2mg4KCEBoaiilTpqj11CIiirBs2TL079+/VFvSAUXq96+//gIADB8+vFResyqjTgQIjQYCFF5LuoxIUif4hYiIiFAZMyORSGBtbY3evXvj0KFDtOHCz8+P97v38PAQLKAno3RIMXxYWBhPeHFTgUJicMeOHfQ51B2z0N9EtpH3bvv27XpHfb/55hvqaeXo6Chor2Nvb4/MzEwkJyfDz8+PvhdCEcjU1NQqK7AKCgp418l58+bhzz//xLBhw7Bly5YyPDIO6oqzyuJfWRa5syzLrlmzhv3yyy/Z4OBg9vbt22VyDA8ePGAXLFigk+0CwcbGhp0xY4bgfR999BEt+IuJieHd16hRIxYAGxERofU1fvrpJ7Wu4/rCdbwvKSmh2+Pi4tiXL1+ybm5u7Lhx41iWZdns7GyVItDq1auzcrncJMeiDyUlJfQYPvjgAxYAm5aWpvVxq1evZgGwycnJKvdx/65Hjx4ZfGxCUwf69+9vNtdpTVy5coVt0qQJm5WVxdu+bds2waLesvgsqwJcB3JuUbZy4feECRPM5mQfFRWlUtBOBokLFc5z9xUqJOe6wG/atIk6wOP/RfTq7Bi4Bfnq7E6U7SmE4DrVSyQSvZtCuK938OBBwX2GDBlC95k+fTrLsiw7Z84cwWPWpbmpsnL79m3B9+Snn34q1eOAWOSuG3v37sXDhw9x/vx5uLq6Ys+ePaV+DE2bNkVYWBhcXFx0foyDg4PaCNbNmzfp/5VrfshswpEjR2p8fplMhqlTp2ptLdaFb7/9FosWLaK3ueas9erVQ/369eHl5UXd6ZXTgdOnT0dBQYHWon5zwB3+TawudDEAfPToERwdHQU/0/3796N3794AFPPaDEV56sCFCxcQHh5OXa+NbSvXh1u3buHJkyfIzc3lbSez1rhmudztIqYlKCgI58+fx9KlS3lu6tyaPgsLC9y8eZNXVG7K4u2goCBMmzaNt61169a84dCk/u/zzz/HuHHjaIkFMSJVfr5ffvkFv/zyC60FI/VaPXv2FHSuDwoKQnBwMCIiIjBr1iwcOXJE8Fh79uypNSLFHTQtl8tV5jDqg7oGKG7kkdR9qctoGGIVU1ngDhPn0r59+1I+EvWIAouDh4cHTT+FhYVh1KhRpV5EmJ6ezruQ6wJJqynz1Vdf8dJpyhc2wqtXrzT6bpGaAeDtgGZD+e677wAA7u7uasc/1KtXT1BgHTlyBO3atVPZXhoUFxfD09OT3iau/7qMsHj8+DF8fX0Fi3pDQkJoQ0V0dLTGWjptcC8+QUFBNBWkrttLGyUlJQgODsbGjRv1ehz5vty7d4/3XSaCa8OGDbz9xTEg5oMrLsjnz+2ek8lkuHr1qkpRuSlxcHDgffd/+ukn3rFwvbRCQ0OpqGBZhau7uu8tVyhaW1urTW+S38HcuXOxfPlyXjqRYGVlBW9vb61/S3p6Ol0USiQSvb67yjVfTZo0EdyPK7Dq168PgC+w6tevTztxS3PhVN7gppO5KI+9K0tEgcXB3d0djx8/RrNmzdCiRQtIpdJSXyF899131OZBV9RFsLjzFgHNRpIPHjzAypUrBWsCuAKLOx/LGBo2bKi2dsDT0xOvX7+GXC6nM/sAoEGDBlTklLbA4tZede7cmZ4cdYlgpaSkwN3dXfA+Pz8/Xn3UhQsXjDzSt3Bb9Q2JTFhYWODevXu4c+eOXo8jn80HH3xAa64AhcBSnnU4ZcoUNGjQQK/nF9Edrrjo3LkzNm/eTO87cuSIigEtt6jcVHA7GgHwis2VCQoKwpgxY+i5iruvsnGqrjMkherAAMX3u3///tT+4ddff9W6ECE2N6RIX58IlrIYU1cjR7Y3aNAA48aNA8AXWEeOHEGvXr0AAKtWrRIUjFUBoSajp0+fUjuf8oAosDiQgcpxcXHUAba0LQHS0tL0Sg8C6iNY2ujTpw/9/9WrVzFjxgw8efJERURxBZYxHlTcKJmzs7Pa/RwcHJCbm6tyQqpXr16ZCSxSCAwohr+S49dlBZuZmamzH9q5c+cAKFZnM2fONPj7t2fPHtSqVUvF3kEf4uLikJ6ejhMnTuj1OG6Uk3sSzMvLQ40aNXidq1X14lBaREREoLCwEHK5HDKZDF9++SXtIBR6780RTVROE7Isq1GYhIaGwsbGhve9VReN1WWyBjetR5BIJNiwYQPCw8PRvn17FZd6TX/L6tWrYWFhIegOrwmu9czQoUPV7kfEVPPmzakw5QosDw8PWFlZ0Q51bTNcKysvX75U2VbeFmuiwOJAogw2NjZldiFPSUmBq6urXo8xVGAdPXqU1r8kJSVRUzblEwxXYKkzAdUF7vOo65YDFKlMlmV5bcmff/45atWqRUVwaX4uJSUl1KsGUKwwicDSJYKlSWCR99PV1RU9e/bE+fPnASgiWStWrMDx48f1Pl6WZTF06FB89dVXmDZtGj1+fSMTRNzpakexceNGXLx4kVdTRR6bn58PhmFw8eJFeHt7U9+iHTt26NSJKWIYtWvX5i1sSkpKsHz5csTFxamMtDGn0ayDg4POqTWhyJQx0Vih1/Lw8ECLFi0ACPvMaXs+MuZHn2Mh54pz585h165davcjpRzcTAZXYJHIf6tWrQCUrS9gWRIbG4vhw4cjKiqKbitrWwZlRIHFYfjw4fDz80OXLl3KVGDpG8ESShHK5XIwDIMFCxZgz5492L17t8rjGIaBnZ0d7O3tkZSURFeVxGG9uLgYDMNg7ty59DHq8t66wK3H4aYMlKlZsyaAtxfnEydOYNOmTXROmYuLS6l+LmlpacjKysLKlSvx7NkzWFtbo0aNGrCystIqsKRSKQoKCrRGsDIzM9G0aVMaPSSrYkNShqR+DXg7a1K5/i4pKQleXl64f/++2ufhCiUh41dlpk2bhkOHDmH+/PkIDAwEAOzcuRNz586Fh4cH1q5di5MnT0IikWDt2rVISEiAr6+vQSJSHc+fP+fZgFR1hMTFkSNH8Ouvv4JlWTRt2hSdO3fGhAkTeIXwpoak1ki9lDYRoxyZ0lcEKb+28oU3ISGBRsJ0TTVyn69atWqCzvGaIJ+Ftv27du2KHTt28BZ1XIFF0qcky6Kr4XFlori4GAkJCWjUqFHZzxvUgCiwOFhYWODVq1fw8vKCm5sbzpw5g/Hjx5fqMaSmppokgpWTkwOWZWFvb49PPvlE7WgKQBGR+fXXX2mdERE25IdLLvIeHh64c+cOWJalXUC6DmEFdBdYRAyQ41A+IXl6epaqwCIh+Pr169NVJcMw8PLy0hrdISaj6gTW48ePMXToUBQXF8PBwQF5eXnIzc3FsWPHABiWsuGOPSEhczJS6aOPPkKXLl1w+PBhvHr1Cj/99JPa5+F2Abq6umpshMjPz0dBQQHy8vKwatUqRERE0O7IpUuX0ujl0qVL6WM8PDwQEBCA8+fP89LVQhBzUk1kZmbCx8cHn3zyicb9qhLKg5wB0JFLMpkMjx49wuXLl81+HPqKGFM+PigoCBcvXkTnzp2pOGFZlhqSkn20pRq5z6cuTVhSUqL2d0J+A9xJH0IwDIORI0fS4fYA6KJ7xIgRdBvJOFTFCFZqairkcjnc3d3VNm+VB0SBxUEqlSI/Px+enp6QSCTo3r274GR4c7Jw4ULej0gXHBwcUFBQwDPPI4JLF8PS+Ph4Xl0MEQ3K4uG9997DvXv3EBMTg8uXL+Py5ctITEzU+Ti5AktTKLe8CSxy3G5ubrzt/v7+iI6O5rmxK6NNYDVp0gQLFy7E/v37afrz5s2bVFilpaXh0aNHWL9+PRYuXKjT8XI/ExJ5Iif1Y8eO4eLFi/TkreygTzhw4IBKvZ2mNDTpVt28eTM2b96MiRMnYtGiRSrmtsrF/s2bNwegMG2VyWRqn3/8+PFo3rw5Bg8erDaleOjQIQDAtWvX1D5PVYOk17hz/LiwLIuioiJs2rTJoE5TfY9FVxFj6scHBQXh/fff5zX6KM8m1Ad1acImTZrwJlAUFxfj2rVriIuLo+OHDBEEjo6OuH//PrZt20a31a5dG5aWlnp3nVcGyHnNxcUFNWrUAAC9Tb1LA1FgcbCxsUFMTAy+/vprAIoRM2FhYRpdf03NZ599hu7du+v1GHLxjImJwZw5c3D79m2aMtS2WgJUZ+DFxMTg+vXrtE6G0KZNGxQUFPAEhVD34pYtW3DgwAGV7dwTAakfEKK8CSwSwVIWWC1atEB8fDz69eun1l1em8ACFCflgQMH0rlZu3fvpu/BjRs38Mcff2DSpEkICwvTKVXHTVsuWbIEALBmzRrePiSN1q1bN5XHP3r0CIMGDcLs2bMBAKtXrwag2UFf+b6OHTuiffv2vK61/Px8laHCxO4C4Hv6ZGVloXfv3jS6EhISAkDhGzZjxgzB1Cx5/PTp09UeZ1UkKCgI7du3V/mdkyHIwFuhZa4Bxvqg3C1oKrgdgJaWlli/fr3BYk8oZSmXy/HixQscOHCALkaWLVuG9u3bo0OHDvSxhkage9WHAAAgAElEQVRcmjVrxluYMgyj0QOxMsMVWBKJBPfv36dR/3KFOgfSsvhX1k7uyqxbt44FoJerujEUFBSwN2/eVHHA1saOHTtYAOw333zDAmA7duzIXrhwgQXAnj17Vuvjr127xi5atIi6pEPAHRcAu3//fpVtFy5cUHk+cl9OTg5v+6xZs1grKyv20qVLGt27iUOvp6cnW61aNZV9Fy5cyAJg165dq+M7ZBzLly9nAbDZ2dm87WfPnqV/68OHDwUfe/z4cRYAGx0drfb58/Pz2VOnTrHHjh2jz9evXz929uzZLAD2ypUr7OTJk1kA7JkzZ7Qe79dff837jFxcXNhPP/2UlcvldFubNm3Ytm3bss+ePVN5/JEjR+h+9evXZyMiIrS+9h9//EEf884777Asy7K3bt1i+/TpQ7erg/xt169fp9uuXbvGAmAnTpxIt3H/JqFj+eOPP9iePXuycrmcLSwsZIuLi7W+V1WFqKgotlq1ajyX9E2bNrETJkxgra2tzeLibuhxmstVnjw/19XdlM+VlZVF39/NmzezLMuyo0aNUjlnmpLGjRtXSUf3//73vywA9v79+2V9KKKTu6GQlQa3+82cvHjxAm3atOF5B+kCSQMSvyKpVEqPWZfVUmBgIObNm4fY2FisWrVK7X5CXk7KqyduhOXGjRu8+8iQ1I4dO2r05CLHnJCQgA8//FBlX9LirBwNMRfp6emwsrKixfcEbnpB3XeEFIqTMLYQb968Qa9evXjdMIcOHcLOnTsRGxuL6tWr45tvvgHA9+PSdLxcHBwckJKSwrNPKCoqQlpaGs9VH1CkBkkqA1AswNzd3VGjRg2Nv4PGjRvT/xOX+9WrV+P48eMYP368YESTQOYSciOcgYGBcHFxgVQqBaBIITo4ONCIl9BnP3z4cJw6dQq5ubmwsbFBjx491L5mVYObKmzfvj3Wrl2Lzz//HL/88gvOnz9vcG2UqTHWu00bxqYplZ+LmLiuW7eOfu8BRdczwzC8OipzQCJaXbt2NevrlDe4EazyTPnqaSxnlLbAIkXFyhdybZA04N27dwEoamXIhV2THYIyXl5etHBSCCFjvEOHDtHB2CzL8vyiXrx4wRMhSUlJKmk2Ibh1YxMnTlS5v0mTJmjatKnBnwsxHdT15JeXl4eaNWuqCD2JRIILFy6gS5cuKmNhCPn5+QA0fw7Ozs5o0KCBSodmfHw8dXK+c+cO+vbtq1MDRFpaGnx8fGgNFTHfy8/Ppw0R9+7dQ+PGjVXE2O7du2mqrX///jh8+DAaN26s9u8jtGvXDosWLcL8+fNp4TzxWerYsSMGDBig9rGenp4ICAhQqcurX78+tmzZgkOHDiE7OxtFRUVo1KgRkpKS1JrUhoeH09cypWlrZeHkyZMoKirC3bt30aJFC9qsUtbCiiA0nLk8Eh0djR07dmDbtm2QyWSQy+WCxe1Xrlwx63GQ2lkbGxuzvk55IzU1FQzDlPtB12IESwNEYCUmJuLUqVNmr/sxVGARQUJ8o9LS0nS6sAuhznEcUK1BAoDffvuNdrHs3LkTw4YNo/cpG5ZmZGRoNBglODo64vvvv8fIkSMFa4QAxWdjqMB655130KxZM533z8vLU/s+ks9K3bHo+jm0bdsWN27cgEQigb+/v0rxubu7Ow4fPqxTAwQRWFz++ecf3Lx5E1lZWXS+29OnT1UE1pMnT9CtWzekpqZi27ZtePDggcZoIyEzM5M2hJD6E+LCr6neDlB0Q928eZN2HQKKrsfr168DUJxMi4qKsGzZMly/fh1ubm6CzSdDhw7Fl19+qfVYqyrmjg6ZAmO7DUsDYnq6adMmOsNRSFwBirmc5oTYsOh7zajopKamonbt2hq70csDosDSABFYH374IXr37o0PP/zQrK9nbASLkJWVRVN3mlJTQigLLNLeb29vzxMJ3Hb79PR0PHz4kDfypWHDhoICS1dH89mzZ2PHjh1qL+6GmqumpKTg9u3bePnyJS1A1wZxIBeCfEeMiWABiuhgbGwsddwmK1MSsdJnpabspUbEGhEwpFsR4HcGyuVyPHv2DK1atYKzszMcHBzo6KTFixerDO3lMmHCBPz888/IysqiIjo0NBTJyckICAjQ+dgJQt2ppOli06ZNgvMRY2Nj6feL/I3c7tiqTu3atemIl/IcHTJlGs8cEKGqTlSVJkRgJSUl4bfffivjoyk9UlNTy316EBAFlkaUV966uHYbg7ERLAB0DhMxm9Q3guXh4YF///vfeOedd7B9+3bs3bsXCxYsoKtdEg4nNUEAsG/fPpWIkLe3t4rAyszMNFlI11CBxa1hcnJyolESTeTn56sVWOSzUiewSKpWWzqS+xnGx8fT79rQoUMRGRkJiUSCGTNm8LruhGBZFklJSXB3d8fo0aMBgCdwPvnkEzg7O2Ps2LGwtrbmRd4SExPx5s0bXj0V4cGDBxrtKDIyMlCvXj3Y29vzRLGunm7Dhg3DhAkT6O3c3FwMGTIER44cAaDoeCPv4b59+2gUjktWVhb8/PwAKBYWhw8fLvcr3NIiOjoaU6ZMQUlJCSQSiVlmDlYVlDsI1SH0m9eUITAErsjg1k5WdkSBVQmwsrJC+/btqVcPt4jRHBBRo++PkCvIyDHGxsZqPQEIwTAMVq5ciejoaDoTLCwsjE4o57Z2k+jW3r17ec8RGRmpIrDkcrleM/m0YajA4rqcA6BT6TWhSwRLU4rQ1tZW0IOIy/jx4zFmzBj6ekRgJSYmolOnTgAUBa3Pnz/XuHLOzMxEUVER6tSpg9q1a4NhGF40aN++fXBwcMCWLVtw5MgRzJs3j973+vVrWFtbw9vbW+V5GzRogNjYWJXX3rVrFxiGQWxsrFHiOTU1lTdUOicnB7Vq1aInUa5VR+3atQUNWLkp6CdPnqBx48YIDw83+JgqEyTqQrybDhw4YFbPq8oMSWOOHz+emtq+++67OHDgABX4AN+XqX79+nj8+DEeP35s0mM5c+aMSZ+voiAKrEqATCbD1atXkZ+fj7Fjx+L9998322t9//338PPzg52dnd5RJ+7Fm4jB5ORk1KhRQ6f6GUPZunUrAEUtD7dbsVOnTvD29kZqaiqN7BBneVNFsGrVqmWwwJJIJHR0kC4pJE01WNbW1rC0tNSYItTl86xfvz62bt2Kvn37omHDhrR77tSpU9Qt39HREcXFxTQtIAQRU+7u7pg8eTIaNGiAnJwcSCQSDBgwABYWFnQMVM+ePfHFF1/Qx7Zr1w5v3rwR7L5zcHCATCbjdSICwH//+18Aiu+AriNDhHB1deV1oBKB5eXlhdDQUJ4QtrKywpMnT3hiTyqVIiUlBZ6ennjy5AlSU1Nx4MABDB06lDfyp6rCHe8il8tx5swZsxuLVna2b99Ov/99+/bFgAEDeI0q5PcQHByMly9fokmTJiavlfL29uYtiKrKd10UWJUACwsL9OrVCytXrsSWLVvMVkCbn5+PpUuXgmVZau5oKCSFlJycrLdQ0xdHR0cq4Lp06cK7j/zonz9/DuCtsaUpI1hv3rzR6P4tRFxcHDw8PDBkyBA0atRIJaIlhKYUIcMwGsWergIrPj4e69atg52dHTVYrVOnDrKzs/HLL78A0J6OBN5aHbi7u8PLyws+Pj7Izc1FcnIywsPDUb16ddqtl5OTg/v37/PeQ4lEIphWU/faM2fOpP83ZiZYzZo16XOzLIthw4bhnXfegbu7O7Zv34527drRfYkQu3btGjWBLSgoQGhoKDp06IDGjRvD2dmZ/hYePXpk8HFVFkjUpUePHlRklddC94oANyIIAC9fvqT3DR06FL/99hsVWPrWweoLd8F16tQps75WWSGTyTB9+nTExMSgpKQEGRkZosCq6DAMg5MnT2LgwIFmfZ2CggJ8+umnAKAynkRXfv/9d4SFhaFjx44AFIXO5hZYlpaWVDBxJ78Db+t+yNgSktIxVQSLnLw0uYsLkZCQQAug69evTzsvNaEpRQgAfn5+gu3YiYmJ2Llzp04i8NGjR5g8eTJvzMvPP/8M4O2AbV1sQ8j8yI0bN6JTp06oVq0a4uLi8PjxY7Asy0th7NmzB/7+/lQEr1ixQq3Ar1OnDvz8/FSmGhBbj1atWtGUsSHUqFED6enpGDlyJJYvX44tW7Zg0KBBgvuuXbsWAwcOxPz58+Ht7Y3nz5/DwcEB27dv50WZicAyZkB5ZSIoKAhhYWF06HJ5LnQv75CIIFlgcqO+u3btwmeffUbPUea2UOAKLKHaxPLM77//jpYtW2qciQoAf//9N1atWoXJkycjPT0dLMuKAqsy8f7775ttiKyzszM2bdqEFy9e4PTp0wY9x4gRI7BgwQIaacjPzy8V/y7yeh4eHoiKisKTJ08AAD4+PnB2dqbmmf/5z39gZWWltWVfV4h9w8GDBwG8nUigjezsbCoKvby8dIpgaRNYH374IW7cuKEyxubXX38FoNswVhJV4ha7u7u7IzAwkNay+fr64rPPPhMsnk1KSsLixYtx48YN2NraYvfu3YiKikJ6ejqSk5PRpk0bREdH0/cLeCvY/Pz8EBkZiaNHj/LMTrmQ9IeyT9rcuXMBKEbZcMeB6EuLFi3g4uKCnTt3Yvbs2RpTt76+vmjVqhVOnjyJN2/ewMfHRzBt6uPjA0tLS1FgcagINggVgaCgIJw4cYKec4SGlZMOYqGmEVNy/PhxhIaGYsaMGTh8+HCFShOGhobi7t27mDZtGi2FEIKcQ2vUqFFhTEYBUWDpTF5enmBhrSkoLi6GXC5Hw4YNjXaf5katSPrEnJAiend3dwQFBdGTCcMw6NixI71gX79+HV26dKHGmcbStGlTBAUFYf78+Xjz5g2srKzURjy4kNoeQCGwXr9+rfGHzbKsxhosQCEOAMXsRFJEDEAnzy8C8RjjzqF0dnamERpA4W7+22+/CabiRo8ejfnz52PDhg1o27Yt3U5Ez4gRI9CqVStafwXwXf6PHj2KFy9eoGHDhjofM6BIATMMQwv0DWX06NF0FduvXz+4ublprDUjtYake3Xq1KmwsbHhTRawsrKCt7c3Ff0iCsq7DUJFgXjNjR07VjBKNXr0aAwcONDosg9tdOrUCdu3b0fTpk3BsqxO80rLA8oBAO55SxniQenu7i4KrMqItbW1SoGvqdiyZQusrKxMMhXd3GlBZaytrQHwvZUIHTt2xJMnT5CWlobMzEy9BIcujB8/HtnZ2UhJSUFJSQn+/PNPrY9RFlgsy2qMMOXn54NlWY3FqUTwvHr1CpaWljRNRVaSYWFhWo+radOmuHXrFs9fzNnZGY0aNUJsbCxNM6qL1JE6I7lcDl9fXxQWFmL//v0YPHgwAODPP//E9u3beY/hpvuWLVuG+Ph4tQLr6dOneO+993Dx4kXe9vT0dAwZMsSo+isCidSlpqbCx8eHWo4IQQTWgwcPAAD379+HVCpViTQePnyYDqsWETEl5EJfUFAg2CzQrFkz7N+/3+DhzvpCUpLmCgSYmnv37gFQZDcAxSQSdfWSpJSjWrVqosCqjNjY2NDOLlOTmpoKuVxukvokrsBSl+4xJeRLLnQSCQwMBKAY82JKiwYCuZjqUkdFyM7OpsdKomk3b95Uuz+xS9D0Y+YKLJZlaZo3KysLlpaWmD9/vk7H1rp1a964GHt7e3h7e0MmkyE+Ph5Pnz5FtWrVaOcSF+4K+sSJE8jNzcXAgQOpECHHw4V8PlwaNGggeGwlJSWIjIxUmWaQnp5uVPcg4ciRI9RbLSoqSutihtT8NWnSBIDiZM0t4Cf4+vrStKZMJtO7KUJERB1kFNPu3bvLRUdmRRNY5Nzq7++PpKQkWFpaYsuWLfT+v/76i2YXSBPRmzdvRIFVGbG2tjaLwCosLMSCBQtgaWmpt2eVENznKI0UwPbt2zFz5kxBt25ysf7pp5+Qnp5u8rlRRGBxvWVWrFghuG9YWBhmzZpF5/EBitQewzBYvnw53S8iIgIuLi44fvw4AN2Gijo5OaF69eoqDQpZWVlwcHDQ2yrj3XffBaDo6CPdmC9evICdnR1kMplgFyH3vY2Pj6ejZLg1Xc+ePcOzZ89QvXp13L17l5cuJKibRSk0EqigoACZmZkmMU8kUTliNurl5aVxf0tLSzx9+hRXrlzBf//7X3To0EFQ5J89exYMw6Br166wsrLSaJYqIqIPZAErl8tRWFhY5gXmFU1gkRIAOzs7uLm5ITg4GOfOnQMAXL16FX369KFdymRfrsAyxcLO3IgCS0e6detmVJeUOkhaRJtDt66Y0/dKiHr16mHZsmWCrf3kYn306FGwLGu2CBY3rDxz5kzBFNrChQupkCIXYjc3N4wePZonjLZt24a0tDTs2rULM2fOpKtUTQKLYRi4u7vzjDKBtwJLX06fPk1PkiRS8+zZM41jeZS7+7iRzFWrVgFQmG/u27cPhYWF2LJli0oh+fTp09XWAHI7GL/77jvcuHEDs2bNgouLCzWhNQYi4D755BMsW7ZMp4uVj48PHBwcMGzYMDg5OQmmcfft2wcA1I5g+/bt5WLEiUjFp06dOvT/LMti27ZtZRrFqmgCizQBkFKAunXr0kHzRESRhS4RWPn5+Xj69CmcnZ1NEpAwN5bad1EPwzCDAYQBaAqgPcuy1zn3zQEwFkAJgMksy5405rXKmq+++soszyuVSuHu7q61TbUiQuqzCOYSWMq1RdHR0dSuAlAVH9xIh5eXFzIyMiCTyWBpaUlD0Tt37uQ9Rlv9mL29Pf7++296OykpCbt27aIpLH2wsbGhKb+6devCw8MDu3btwvjx4yGRSAQ9t7hu7dWqVeOlyqZNm4Z//vkH58+fp6JKKpWiffv2vOcgY20yMjLg4ODAM7C1s7ND/fr1sW3bNjx8+BCLFy9GYWEh6tevb5IZneTvvXr1KkaOHAlra2ssWbIEjx8/xvz58zV2YsXExODGjRvo1auXyn3KczoPHz6M//znP/jss8+MPmaRqo2dnR1d0LIsC5lMhoiIiDJrHiDn14omsMhi0MXFBampqWBZlp7PSPkHEVjEIPqDDz4o7cM1CGMjWPcADADAq3xlGKYZgKEAmgN4H8AGhmHEoWACBAQE4PXr17zuscoE90JvaoFFIhYZGRmYM2cO8vLy6BgYQl5eHvUYI3AvuiQyRU5KynVKyvupQ/lCTtJmxnawWVhYYNKkSYiIiEBcXBzc3NyQmJiIkJAQTJ48me7H7YQUanRo0KABEhIScP/+fQCKonBSZEqYOXMmHj58iNq1a6sMU5ZIJFixYgU6deqExo0b0xopTR2Y+kCO+dWrV/D09ISDgwPmzZuHnTt3UisIdZD6tH79+qncJxTR3bNnj0mOWaRqk5CQAGdnZ9jY2JQLXzFLS0s4ODhUGIHFTRECinNsUVERcnNzqb+hVCrFwoULVTqKK8rcRaMEFsuyD1mWFRqu1A/AbpZlpSzLxgB4BqC9wH4VhhkzZhic892wYQNWrlxp4iOqGCQnJ9MLpLIZqbFwO8bmzJkDOzs7+Pr68roCL1y4gP379/MeJySwSEg6KytLRSwBwkX8utxvig42YnR77NgxTJw4EZ07d8bBgwexbt06ZGZmAgCveFtoZmbDhg0hl8tpp2VkZKTga5EUx/nz51XuGzx4MJYtW8ZrKnj9+jVdiRpDy5YtERkZifHjx6vcp83Xh9SSEZNVLqQBgZvOOXnyJDVxFRExlISEBHh7e5crXzF1czp1IS0tDfv27Su130Z+fj4YhqGZDu65mGsgHRYWxhNYHTt2NLv5t6kwVw2WJwDu2S7+/9tUYBjmc4ZhrjMMc708+3dIJBKDLyQTJ07EjBkzBO/bt28f+vbtq9HzR19ev35NOzTKGicnJyxevBi5ublo2bKlSZ+bK7BINMvJyYl3giH/5w795RZQCwms3r17o3v37nSIdf/+/bXWtpGi8kmTJvG2f/311/r9UQL4+PjAxsYGL1++xLfffstzKyf1WNxIEqlb4EK8ugDwDHOVU2///PMPAGHbDUBR2EtSrkVFRXjy5InJrEE6deqkUhfm4+OjUtumDDnWcePGqdxHhC+xQFm3bh0GDhyIW7dumeKQRaowCQkJgo0iZYmhAuv69etwcXHBJ598gq+++qpU6hTfvHnDS7OSc/HTp09x4MABXk0lt7mGeAZWBLQKLIZhzjAMc0/gn2o8nvMwgW2CnxjLsptZlg1kWTawPLddWlhYQCqVGpVeEEqn3Lt3D0ePHlWpVzIGd3f3ctdhYY55XEI+SconGBLhUZ5sTxASWHXq1MGZM2cwePBgPHz4UKfPnHy2rVq1oq7OQpEwQ2AYBs7OzkhNTaXjd1q3bo01a9bQVCT3uyXU7dq2bVvapBEYGEgFITeq6OvrS2sfyPvG5dixY7xGD0tLS5O7VCubD4aEhNAUoDpI/ZaQk3xISAg+//xzAAoriK+++gq///47Pv30U17dmoiIviQkJEAikaB79+6YN29eubFqMERgKVv6CEWDTU1+fj7vHE48+BYuXEgbjUhdJfe3SmpFKwJaBRbLsj1YlvUX+HdIw8PiAXCdB+sC0G9oXDmDjNsgFyZ1xMTEYMSIEby0BikmForQZWZmwsHBQbALT0Qz3CJsQu3atREbG0u9qDIyMsAwDE/scD2juAIrLCwM2dnZvH39/Px06lYhUZ3atWvj2LFjiIuL0ygK9MXFxQVpaWlo3749pk+fjqNHj2Ly5Mn0+8gdhqyuDoTULbRu3Rr+/v4A+PYODRo0oAX0JAJ6584dGlXingxHjBhhllWussAaNWoUAIUnjiYSEhJw5swZle01atSgHbqdOnUCoIhmdevWDSdPVui+G5EyJDMzEzk5OcjLy0NRURFKSkrKxfBsQwXW3bt3ebdJJNuckAgWoVmzZhgwYAAuX74MQJFpIIsjLqau5TUn5koRHgYwlGEYa4ZhGgJoDOCqmV6rVJg3bx4mTJiAjz/+WON+7733Hv744w9ERkbSC9OVK1fAsqxg2sUc/lBVDe58Q/JekpVPZmYm7O3tYWFhoWJCCbxtbU5NTcXChQsB6GdcSiARJDInsF69eiY1wiMRrIyMDACKbrhVq1bRMTpc4a/OOLVfv36IjY1Fz549cfToUfz73/9GaGgovT8nJ4cKnNTUVKSnp6Nt27a0S5NbZ/b7778LClxjUbagaN68OZo0aSKY9uTi4eGhNkpKFi+kBZwIRXVp+YiICGzdulWv4xapWhDvvW7duqFatWqQSCRgGKbMMweGCixiF0Qojehufn6+SnlBly5d6P/d3d15tZPK5sIVAaPOkAzDhDAMEw8gCMAxhmFOAgDLsvcB7AXwAMAJABNZli0x9mDLkjZt2mDYsGEICQkR9CEifPvttwAUbaTEJDI5OVmtM3ViYqJJjBqrKklJSbh06RK9rdwFmJGRQUVXSkqKysnH0tISTk5OSElJoSmmL774Qu/jICcCU6UFlXF2dkZaWhoVcF9++SWmT5+Of/75B8XFxXj58iWcnJzQq1cvrFu3Tu3zkPozDw8PrFy5Er1798adO3cQEhKCjIwMGsG6fv06fvnlF8hkMlq/VBojP4jAO378OO0G7du3L86ePavRcV8T5OJBIlzkPRQaKF1UVISuXbsK1nOJVAx69OiBxYsXm/U1SEYjJCQEq1evhoWFBeRyOaZMmSKYJoyOjsb3339v9hRi7dq1kZubq2JNow3l+mKyGDEnyilCgD9dwsnJiVcvO2jQILx48QIjR440+7GZCmO7CMNZlq3Lsqw1y7JuLMv25ty3lGXZRizL+rIsqzm+X0F48uQJjh49qraVH+DX+iQlJUEul6NOnTqoXr06zz6A4OrqyovAiOiHm5sbbxXEfS9XrVqFjIwMGlJ2dHQUjBYS/xVLS0t07dqV56GlKz///DO2bt0qWAdkCuzs7PD8+XMVgbhr1y7cuHEDMTExcHd3x8mTJ/X2bGvRogWCgoLw+PFjPHz4kJ7USJE/WVAQgcUtkjc15LV69OhBU5rffPMNatSoYbBX3LfffouxY8di9OjRAN4KLKEI1suXLw16DZGyRy6Xo3nz5jh79qzO46kM5fnz57CwsECDBg2Qnp5Oh7wLpQmjo6NLrU6L+PWRSLeuKP8WkpOTTXZMml5TOYLFLXWQSCSoV68e5syZA0BRF9awYUPBTER5RXRy1wOum7UQT548wZEjR3jbuKtk0g31+++/g2EYnDt3Drt37xZbxk1IaGgobfWfPn06Tp8+rTUFSwRWbm6uwVEae3t7jBkzxmxO+qRwXpn9+/fTiBW3tkxfhgwZQv8fEhIChmGoZxYRPQ4ODpg8eTImTpxo8OtoIycnBzY2NryUp5OTEwYMGIBDhw7x6r6mT58uaCehjLOzM7Zs2UJP5lZWVrC0tBSMYHGj06Lje8Xi9OnTdPg3AFrLYw5I7aylpSWCg4NRrVo1tV5YERERpVanZaibu7LAKo0IVl5enorAsrKyQmhoKAYMGEC3kQUdtzmpoiAKLD3gCqxx48Zh165dvPujoqIwZ84cnDx5EuPGjUPNmjV5oVeS1yaFu2VdEFkZYRiGd4KTyWQ0VasOIrBycnJKJQ1mCCEhITx7Bi6kA5VEZgyBe6JzdHREzZo1IZfLAbwVHdbW1lizZg0vSmtq1Incli1bIjc3l67Mi4uLsWrVKnTr1s2g1zl06BA+/vhjmv4kcJtTCgoKUFRUhJ49e6o4+4uUP65du8a7rSlVbixcv7ygoCCNXljaBJgpMUZgvffee/S2OQXWtWvXYGtri3v37gmWVGzfvh0HDhygt1u3bo3Lly9jwYIFZjsmcyEKLD0gJ/7s7Gxs3boVw4cP591PugQ7dOiAZcuW4fXr17wTNjmZEyPIe/fuoVGjRjqtwkV0Z9iwYbyiaG1WAl5eXoiNjUV2dna5FVgAqI/YrFmzeNsvXlQMUhCyrdAV7mPt7e1572N1dCMAACAASURBVIOmmkNl4uLiMHXqVKSnp+PgwYNao0CXLl3iRX3ViVwy15KYyBp7AejTpw82bdqkkg7m/l7z8vLw8OFDnDlzBiNHjhQjWuUcZbFszkJt5W5jQPHdX758Of71r38hOjoa0dHR+Ne//oXly5ejd+/eGD9+vNnNSA0VWPn5+WjTpg1YlsWAAQPMKrB+/PFHFBQUoLi4WOfzbYcOHUxqZVRaVJxkZjnA0dERtra2dA5S7969efenpaXB2toas2fPxsaNG7F//37exZ3UbuXm5sLS0pKaXxL/DxHTwDAMbckHtHedNGrUCPn5+cjPzy/XAmvatGlo1qwZQkJCsH37dnpBIZ2ExrQvc9OLxgisbdu2YfXq1dTB/ty5c+jatava/d99910AikijhYUFcnJyBIc2E4H11Vdfobi42OjZnefOnUNWVhZiYmIQHx8PGxsbODs78/7W/Px8Xvt6Xl6e4LGJlA+4gsrGxsbsAosMco+OjkZwcDCvsJx0oXL96aytrREQEICIiAgEBwebRWgZE8EiiywHBwfBeaemgvsbK8/nW1MgRrD0oGnTpjxHcOUajtTUVLi4uNA5bkuWLEFhYSF++OEHDB06FH5+fgAUFyFu3VWDBg3Mf/BVDO6FkPggqcPHx4f+vzz/4N3c3DBq1CjUqlWL111D4Lq16wu3dsxQgXX06FGEhYXxtulabEvSO+pShERgRUZG4sqVK9iwYQMA1YHiujJx4kRcv66YTc8tpFWOYJE6NKB0Cn9FDIcbwWratClv3IquREVFqdTRKnP48GFERkbSCFZERISKiXRxcbHKNqlUigkTJuDbb781W7G7IQKruLgYMpmMJ7A0NXIZy+3bt+n/y/P51hSIESw94Z5wlc3Y0tLS4OzsTNMYt2/fRmZmpkpKp2PHjggICMDZs2fVjtARMQ6uYNBWg9W8eXP6f3UNDOWNevXq4epVvrWcqew+atWqRS8epO1bFw4ePKiyTdcRUC9evMA777yDnJwcwfEjXD8c4O3oI0ML+x89esS7TQqiuX9ry5Yt6SxDQCGwuGJcpHzBjVj5+fnh1q1bvMiMLpDIt7p0sEwmo0PFSY1icHAwrKyseBEs0qShLLLI80qlUkRERJg8imVrawsbGxu9RqWR3yh5n+zt7ZGfn4/i4mKtxtr6kpiYiISEBHq7sgssMYKlByzLYtq0afR29+7deaaUv/zyC3bv3o1NmzZRH5a7d+/i+fPn9IdWVFSEXbt2ITExEXv27OH5foiYB22GmF5eXrh58ya6d+9uVgsCU8K98BOMKXLnwo1gtWnTRmeBRU7U3Po3TbUc3AgweQ11ESwLCwve0G7SPGJM5yTw1taD1E8qD5Z+9eoVvcgo1/iIlB9KSkp4ESvScWbqz4wr4ohQCAoKQkREBCZMmID+/ftjwoQJuHDhAtavX4/27dujdevWgt9pcxW7+/r66uUZR35LpNGFpD6fPXuGBw8eCE5IMBTiWUjOVZVdYIkRLD3gRkX69OmDP//8E+fPn6dpELLy9vX1hUwmw4IFC7B+/XrMmDED8+fPx969exEeHo7hw4dj06ZNgmMAREzHzZs3db4ABwQEmPREYm6EBJapXKSJwKpWrRr8/f1x8eJFsCyr1YLi2bNn6NmzJ6/bUVNajXtBJAJLXQ0WoBrFAoTnewrx/PlzWFlZ0dTqggULkJGRgZUrV2LWrFlYv349WJZVEViAIoX/9OlTMUVYjomNjYVUKsXSpUtRs2ZN+jkLzdTUhby8PMHJANzvLDfaHRQUxItGRUdHY8qUKZBKpTTSxYXUHpqD7t27Y/369SgsLNTp/KccwSICq1mzZnQfUzV47Nq1C66urujQoQOOHDlSIQvX9UGMYOnJ2bNnERcXh3nz5gHg+wf99NNPNK9uaWkJT09PGuFiGAaPHj1CTEwMAIjjcUqBgIAArfVXFRUhgSW0zRDs7e0xYcIE/Pzzz3B3d4dUKtUpihUbG4sGDRrwhBgRJQ8ePKDjRQjcNAa5WGnyIhMSWNnZ2Tqd/H18fHg+OmFhYVi7di2qVasGT09PyGQyZGVlCdae2NvbQyKRiBGscgxJ+Xbp0gWTJk2iiw19DTcJ6sQ0EVhdunTB7t271T5++fLlKCgoEBRXEokEkZGRZqvDCggI0MtvSyhFqIzQHF1DePDgATp37kwbcpQd5CsbosDSk27duqFevXpUYH344YcAFCvpadOm8aIg3EJkMoeQnAiE5hKKiOiKkJgSql0yBHt7ewQGBmLcuHFwc3MDoL3Am2VZZGRkUCfpvLw8tGvXji4oOnfuDD8/P94AbO7JNTc3F8XFxSgoKFArsIRqzEpKSgSjTvrQtWtXhIWFwcPDA+Hh4SozJEtKSuDs7CxGsMoxRLz7+voCeLuANVRgkUYlZYjA2rNnD7XbIRBbhoCAAMF6RAJxfSd1WKaGnBs++OADncxWye+QpO2UzT8BxcQRU1BYWIjq1atj0KBBABQeV5UZUWAZCJlXR1a8pGuDe3K+ePEiNUcjq2/iNGyqaINI1YT7/enVqxfmz59vsnoGblqBfG+1iYupU6dCJpPRC5udnR3atm2Le/fugWVZ+vuYOnUqfQy3AD4nJ4dGydSlCG1tbbF7926VkVPKg2r1pW3btnj//fdRWFhIO4G53cJSqRR16tQRI1jllPT0dPz444+oXbs2FfgkQqKPwOLWBK5cuVJwn8TERFhYWKiIcGLVsHHjRl6XnCYsLCzMUodFOm4B4D//+Y/a/R4/foznz5/TSDJ570xVyykESVv27duXem9VZkSBZSCDBg3C8OHDaVs6CaGSLymgCAXn5+ejevXq9Ad/9epVSCQSccCziFEQ4SORSHDy5EksXLjQLK+jSwSroKAAa9asAcBPffv7+yMzMxPx8fG00YA7ykQ5gkXqZTQJxSFDhtCoMeHJkyca/4aSkrdz5oVmDRYXF8Pe3h5z586lt0NCQuj9y5cvh5ubmxjBKqdMmjQJ8fHxvNQ0Od/qU4OlvK9Q6jkxMRFubm4qjTNCVg0EiUSCatWqoX///rC2tgbDMJBIJJg6dapZvLC4kWx1TSY5OTnw8/NDp06daOE+uSZ17NgRmzZtUnmMVCo1+tikUildwBljjFxREAWWEdjb29MIFlkFcFc2Bw8exJ9//om1a9eidevW6NSpE7777jtER0dXqIGVIuUPCwsLbN68GTdu3DDZc86ePVtlWDURcspjobhwzTi5hfbt27cHoEinyOVyODo6Ii4ujl6IiMByc3NDdnY2tSzRljZwc3PD+PHjaRpGubZLGW5dlZBlR1xcHJo2bUrNFbkXkhYtWuDDDz9EnTp1cOPGDeTm5grW1YiUHSRFXK1aNbrNxsYGtra2ghGsjIwMjB07VqWukAgsYtUwfPhwFZPipKQkwVpAYtXAhWEYfPrpp1iyZAkiIiIQHh6OtWvX0nP/unXrzFKDxY1AqxOYpJQlOTmZCiyymGIYBp9//jlkMhmvzszQdCuXwsLCSl/YzkW8yhuBvb09LbIlKwVu6ubJkyd48eIFhg0bBjs7O/z9999mGwYsUvUgQ61Nxffff6+yzdXVFYGBgThw4AAyMzOxYcMGzJ07lzqvA2+HmAP8CFbbtm3h7u5O61k6deqEo0ePIi4ujrrnA4oT+6lTp+jjiHWCOhiGwebNm+nrkQuEuk5H7oVBKCpBjH7XrVuHunXrUiEIADNnzgSgaPuXyWQ0uvbPP//Q0UUiZQsRFEePHuVtd3JyEhQFK1aswG+//YbmzZvzbHdIFqJVq1a4dOkSFRcymYyKInUCi1g17NixAw8ePEBhYSHGjh2r0imenp5Oa7BIIbo5oljx8fEIDQ1V64dFaiEdHR2RmJgIZ2dnnkAFFIu4nj170tsZGRlGZV5YluVFsKoCYgTLCBwcHCCTyVBQUICQkBDcv3+ft0Imq3myShHFlUhFg2EYLFu2DABw5coVmkYjKb1jx47h1atXdH9u/ZREIsHHH39MR/mQEVP37t0D8DaCRbr7nJycEB0drdW3jEutWrVoJEIikajMBwWAoUOH0v8L1XwQoUj+z23P7969OwDQv5swduxYAMDXX3+NkydP6ny8IqYnOTkZnTt3RkBAAG97rVq1BI2DScq4sLCQt50skpUjqGRqAKBeYAEKkRUaGopr167hxo0bmDJlikqEqrQGP3t6esLLy0ttBOvZs2cAFO9FYmKiWuHk5OSE06dPAzA+giWTySCXy0WBJaIbxC8kPT0dc+fOVVktEIH18ccfl/qxiYiYCpLqIwIJUKTdhg0bho8++oiXPlTuZAwNDUWNGjWwYsUKjBkzBtbW1rhw4QIyMzPpeJrVq1fj4MGDSExMxDvvvKPXsRGBRdJ2QqlMYrro4eGhMt6K4O/vD0CRUuHOYSMiUDmt4ejoCKlUirVr1/J8v0RKn+TkZJre4qJOYBEBr1xTRGrsunbtSs/tgEJEA4ruv+TkZLUCC1DUYhUVFaGkpETQKiEoKAhnz57F4sWLzT742dHRUa0oIgIrJycHUVFRPM8rZYztyCSQ91tMEYroxEcffYRr166hevXqWLFiBerUqYPOnTvT+9u2bYtWrVrxvLJERCoaQoaLWVlZNHLz9OlTBAQE4OrVqyq1hR07dkROTg6N3gYFBeHChQu8+Zve3t5axxmpo2bNmsjJyVHr00NSgq6urggODkZUVJTgftevX4dMJkONGjWQmZmJ999/H23atFFbcK/sHC5SepSUlMDHxwcLFy5EaGioVoF18+ZNODk50e8cqcl79eoV+vTpg8TERNy6dQspKSmQSCTw9vbGgQMHaPSSkJ6ejpKSEo0Ci0SoioqK1EaolE1JzYWTk5PakTdEYAGK+uH+/furfR4SKDDWC4tEDMUIlohOeHp6IjAwkK5yuaseQFGPdfv2bbpSFxGpqCgPkla2RvDw8FDbuMFNjXfp0gW3bt0y2GFbGXIR5Y4w4UIKoKdPnw47Ozu1ESyZTIbBgwcDULS5//XXX1i6dKmg/4+fnx8SExPx22+/meRvENEdlmUxd+5cvHz5EqNGjYJUKkVWVpagwKpZsyYuX76Mtm3b4tNPP6Xbid3Gtm3b8Ndff+H27dsoLi5GcnIyXFxcIJFI0K1bN7x48YL6HZaUlNDHaRJYpRmh0oY6q4o3b94gPj4ejRs3ptu40WllPDw8YGFhgX379qn9/egCEVhVKYIlCiwjOXDgALZu3Qrg7RdaRKSycfz4cV5R/cOHD3n362qc27lzZ7AsiyNHjpjkuGrWrInc3Fy10SRSV+Pq6orq1aurvUCcO3cOf/31FwBFrdnTp0+RkpIiuH+LFi0QHx+PJUuW0G2aXL1FTENubi4WLVqEH374gW4jn6+6CBaB20kqZLcRHR2NV69e8WqRGjZsSF3N8/LydBJYgEJkzZkzR6O4io6Oxvfff2+WLkICSdfHx8fztm/btg2AIsMCKJo8hNzbCVZWVigpKcGpU6fojF1DIClCMYIlojPLli3DihUrAKhGsEREKgt169bFhAkT6G0isEjnka4Ci4wuMpW9RK1atfD48WMsX75c8H5yMXVzc9MosGQyGe92eHg43NzccOLECbqNFMO3a9dOpcV/2LBhuHbtmsF/h4h2QkJCqO8ggfv5KqPOZykuLg6jRo3iOZYPHjwYJ06cULEpIU0bubm5OgssbURHR6N79+6YN2+e2cblAAqBCKhGm4nZ71dffYUxY8YgMjJS5+c0pg5LTBGK6I2Hhwf94ogCS6Qyw3WIJt5XpD5DV4FVp04d3oVN+YKpL6Qj7MKFCwAU6UiuFQO5KBKBVVxcjJKSEjx69AgMw+Cff/7h7Udo1KgRAL4Z6rVr17B8+XL07t2bbiMF0ICiGYDrTi9iWs6ePauyrV27dgCEBRb3syD1Q0VFRUhMTESDBg3od7ZHjx40EqZcd0UEVnZ2Nu2WNVZgaSuENxVEYMXExEAul2PRokXYuHEjiouLMXPmTHTq1Albt27VacTWN998A8Dwvz0mJoam4MUUoYjOeHp6wsnJCYWFhaIvjkilhjul4M6dO7C1taUdRrr64zAMAx8fHwCKDkMySspQlJ3ZWZbliSKSHqlbty4dAVJQUEDTJF26dMHo0aPx5Zdf8p6HzLT7888/6baAgADMmDGDV4/22Wef0VQL8PaCD6hGxUSMg3x+VlZWPJELCM/KI/V3jRo1ov5TCQkJYFkWXl5e1ER06NChePXqFc6fP4+BAwfynoMIrP79++Pbb7+Fra2tYNOHPpBCeIlEAoZheOa8pqRWrVqoXbs2nj17hq1bt2LBggX417/+BQDo27evXs+1dOlS2NjYGLyAmDx5Mo16ixEsEZ2pUaMGMjIyIJfLeX46IiKVDYlEApZl6Qp0zJgxtIBdn5UtES/cYeiGwn0OobE+r169go2NDZydnXkCi0QjsrOzsX37drr/J598AolEgqZNm+Kjjz6iHkBcGIahXVl169bljUh58OABiouLsWvXLlhZWRk9J1HkLUTYREVF4bPPPuPdJxTBImnchg0bQi6XIzMzkxrIenl5UX80d3d31K1bF8HBwSoebERgka47FxcXo/0Mg4KCsHr1alhYWEAulwv6ZZmKwMBAREZG8ry8HBwcVFKhumBnZ8dbvOiKXC7HpUuX6G1RYInoDFm5x8TElPGRiIiUDuQC4+PjQzuRhCII6iCpGWXnaENYt24dIiIikJ+fj/379wPgF+DHxcWhbt26YBiGJ7CuXr2q8lytW7dGUVERXSx16NABeXl5KCoqUtn35s2bWLhwIZycnFTuv3jxIvbt2wfAdLVmVRniep6amoqwsDAEBgbyJgbcuHFDsN7qhx9+QIcOHfDJJ58AUIht0gzh6emJESNG4N69exo76JQHj+tjgqsJYvkgl8shlUrNlibs3r07Hj58yBtA3bt3bxXbBl2wtbU1SGDt2LGD1zUspghFdGby5Ml4/vy5RqM2EZHKBBFY9vb22LZtG44cOULd2HXho48+AgBeas1QatasiS5dusDW1pam7ohTPKC4qJLxVURgxcbGUnd5Lo6OjpgxYwb++OMPAIoI3Z07dwQj0/7+/pg/fz4YhuG1uwPA4cOHaZ2ZWJNlHHl5eXBxcUFoaCiAt5FSblpNyJ0fUIy8uXz5Mvr06QMAOHbsmEpRfPPmzTVGpJQFlqmoXbs2NceVy+VmSxOSphIA1EaFzFrUFzs7O4O+z8qTDgwRdxUVUWAZCTGmExGpajg4OMDe3p4KJl3p3r07EhMT6YXPVNjb26Nu3bq4f/8+3SYksIgv3e7du3kGi1ZWVujYsSMdt+Ph4YEWLVpoTf1zU4yAIrpFIiqGrPhF3pKYmIiMjAzs2bMHgCJFB/BnXmrD09MTrVq1QmRkJFJSUmBpaalzQ5Im+wJjuHXrFv0/wzC826aEWxv5ww8/YObMmQbPMDU0RXj16lUMHjwYJ0+ehK+vb5W6XooCS0RExCCMufgY24mlDn9/fxrBkkqliI+PpwKLpCQvXboEKysr9OvXjyewlFOWSUlJWL9+PWJjYzW+pqOjI21wCQgIwJ07d6iY4/ovieiPsiEtabTQR2ABQJMmTfDixQskJyfD1dVV51SfcmTJ2KYMQGHTwDWpZVkW27ZtM0sdFldgtWzZEsuWLTO4BspQgZWSkgJPT0/06tULjx49qlLd9qLAEhERMYjyeKJs3rw5Hj58iJKSEnohIQKLO8aqTZs2sLGxwYgRI2hrvnLq4vXr15g0aZJO0QViF9GlSxfk5ORgzZo1AIDvv/+eZxshoh/KApUIrJo1a8LV1ZVXvK0Jb29vvHz5EomJiYIF8ergRi+joqIwatQonR+rjoiICPp9IchkMrPUYXFrI3W1UlGHra2t3inCoqIi5OXlmS0FWt4RBZaIiIhecGuwyhvNmjWDVCqlHWLAW0frWrVqYdmyZQDedjJaWFjgxx9/BKA6LoRcFNLT07W+LvEJmjdvHq+zMS8vj1dgLKIf6gQWwzBITk6mtgPa8Pb2RlFRESIjIw2OnpL0pLFwbRoARZmJurmFxsIdX+Xn52fUc9nZ2eHatWs8J31tkAikvhHHyoIosERERAyiPAosIm5IRyEAXs0HGWfl7+9Pt7Vs2RIJCQkYN24c77nI6n/v3r3UU0kdw4cPB8uycHJyUjGrJJYQIvqjnCI09EIdGBgIQGHd0LNnT4OeQ59OWU2QeYVLlizBpk2bsGTJErPOLdy7dy+ioqKMthEijRtz5sxRGZWlDrI4qaoCS3g6q4iIiIgaNmzYgKlTp5ZLgaXsSj1r1ixeh+/o0aNRXFysIqaE0iekjurUqVMYPHgw9uzZgzdv3miNgJDXs7a2hlQqRUJCgkF/i8jbCFbDhg0RExOjdqC4NgICAuDr64unT59ixIgRBj2HKTsKg4KCSm0QNHFQNxauwer27duxdOlSKtrevHkDa2trFRFHRuuIKUIRERERHRg1ahQyMjIMvtiZE+44H0C1hd/Kygpffvml3h5cJ06cgL29PWbOnKl1XyKwpFIpLCwsRIFlBFlZWbCyssKjR48EhzTrCsMwiIyMRFZWlt6pvvHjx8PKyspog9GKDneSw7Jly2g3LsuysLOzU5mGAIgRLFFgiYiIVBqUowzGpnVWr17Nu33nzh3e7aVLl6Jly5Y8N3duxKxOnTp0XI+I/mRmZsLBwQHVqlUz+rN0dnY2KAq1efNmQbPZqoby+0/84oiI2rx5s8pjSARLFFgiIiIilYAhQ4bQ6Joug2w18fXXX1MPJgsLC9qhCCg6v+bOnYu7d+/yoivcIveWLVsiMjJS7CQ0kOTkZL26/kTMh3Lkj3TpEnd8IcQUoYiIiEglYvfu3SgqKsKDBw9UXNYNgRTGDxo0CEVFRUhISMDff/+N999/n+7D7TSUSCT4/vvvER4ejn79+uH58+eCMw1FtJOYmGg2z7TywubNm9GhQweEhISYbSahKVAXQUxMTFT7mPT0dFhaWprNEb+8IwosERGRSgfDMLwxIcYQHh4OABg4cCAAxeDfadOm4ezZs9i7dy8aNGig4ms0e/Zs9O/fHyNHjoSfnx9Gjx5Nhw+L6E5iYiLPLLOysXnzZnzxxRe4evUqDh48iC5dupRbkUUWGs7OzujWrRtevXqF4uJiXgSrqKgIOTk5kMlkABQRLCcnpypbvyYKLBEREREN9O3bF5MmTUJwcDAWL16M0NBQWvDbrl07xMTEqJ2HZ2tri++++w6JiYm8GYki2mFZFklJSZVaYB04cIB3u7i42GyDnzVx//59rX5tpNN23Lhx+Oyzz1BcXIw7d+4gLi6O7hMXFwd7e3tMnz4dwFuBVVURBZaIiIiIBj744AOsXbsWLi4u+Oabb/DmzRtcvXoVAHQyuiQpruzsbEyZMsVkbfOVnYyMDBQVFVVqgUWiogQrKyuzGI5qw9/fHwEBARr3cXFxwevXr7FkyRI6WD0wMBCRkZF0n40bNwIA1qxZg61btyI9PV0UWCIiIiIi2pFIJMjMzKTFuydOnECnTp0EO6gIZKRQVlYW1qxZg/379+PQoUOlcrwVGZJ6MnbES3nm888/x6ZNm9C0aVM0a9YM69evLzV/LMLr16/Rq1cveHp6IioqCleuXFG7r7u7OywsLHiu8GfOnKH1WQ8ePKDbx40bh3PnzpXLkVqlhSiwRERERPRAuTPxn3/+waNHj9TuTwxZs7Oz0bBhQwDAunXrAGjuwKrqEP8wZW+zykaLFi3w8uVLPH78GFOmTCn1Gixvb2+cOnUKKSkpmDVrFkaOHIm0tDSNj7G2tubZj3Tr1g0A8Pz5cwBAjx49MGjQIACmGzFUETFKYDEMs4JhmEcMw9xhGCacYRgHzn1zGIZ5xjDMY4Zheht/qCIiIiJlz7lz57B+/Xq0a9cOvXv3Rr169RAbG6t2f7KCnzBhAh39kpaWhosXL8LT0xP79u0rleOuaJALuLFWG+WdiIgIFBUVoaSkBEVFRaVegyWVSgEo6r+ePXuGp0+f0mHlmuB+Lu+88w4AhcDy9/fH6dOnsXfvXpw6dQqLFy82z4FXAIyNYJ0G4M+ybEsATwDMAQCGYZoBGAqgOYD3AWxgGMa4QUgiIiIi5YAmTZpg4sSJSEpKgpubGx3jog4ycgd4O/olOzsb169fBwBcunTJvAdcgTh06BDWrl0LQBHBYhimUtdgAW+HP1tYWJht6LOuJCUlAQBSUlJ02p80e5C0ZklJCY1YMQyDnj17ol69emY40oqBUbMuWJY9xbl5GcCg//+/H4DdLMtKAcQwDPMMQHsA5bP/VERERERPXr16hbS0NDRs2FBjWkeoRT07O5taO5THkUNlRf/+/QEAkydPRkJCAlxdXfUea1TRIMOfIyIiEBwcXOo1WKtWrYKnpye8vb3RoUMHsCyLlJQU5Obm4sWLF2jWrBmsrKwEHxsVFYULFy6gfv36dBt3pE5Vx5S/7DEA9vz//55QCC5C/P+3qcAwzOcAPgf4DsgiIiIi5ZmYmBi4urpi3759ePnyJaRSKaytrbU+jmEYZGZm0mjW5cuXcezYMXz44YfmPuQKQ+PGjZGYmAhfX9+yPpRSoTSHPyszbdo0AEBhYSGdOJCcnIyIiAh8/PHH+Pvvv/Huu+8KPrZx48Zo3LgxCgoK6DaSchQBGG0jHBiGOQNAyEr3W5ZlD/1/n28BBAIYwLIsyzDMzwCiWZbd+f/7twI4zrLsAYHnoQQGBrIkbC4iIiJSWXBxcaGFw56enoIDoNu1a4etW7fSFviqiHK07+OPPxY7Ls3IzZs3YWlpCR8fH+zcuRP5+fmIivpfe/ceJ3PZPnD8c8/s0VqnddhFkbMVkVPrkGUlKelXSAklkRwiPUlHPRQdRCpCEs9DemolIodkJdYpK0JFjutsnZZld2fm/v0x+/2a2ePs7qxdXO/Xyyvzne9prUDOugAAIABJREFUdnaay31f93Wt57vvvqNHjx7MmzePEydO5JiorrXGYnFmHG3evJkmTZpci9svEpRSv2mtM33BOeZgaa3ba61vz+SPEVz1AR4Aeuqr0Vo84DrxWhmQ5TJCiJvSsWPHzJVWWSVtb968mSVLllzL2yrybvQE98I2ZMgQ7rjjDg4fPszgwYM5ceIEU6dOZdasWZw/fx6tNdWqVcvxPEopVq9ezYkTJ26q4Con+V1F2BEYCTyotU5yeWoR0EMp5a+Uug2oCWzKz7WEEKIoSkpKomrVqkyaNCnLfXx8fMzVhNnVBfrnn3/o1asXf//9t9fv83rkOvUkIDY2lnHjxnmtlMOhQ4d44oknqF27NqmpqUyYMIHixYvTq1cvtm7dCsDFixdxOBw5nisyMjLLfoU3q/zmYH0C+AMr04Z2N2itn9Va71RK/Q/YBdiAQVprezbnEUKI61KxYsW4dOkSmzdvzna/oKAgIPvaV59//jngXMW1fPly793kdSIgIIArV66Yj1u3bl2Id1O0xMbGEhUVRXJyMhaLhU8//ZT+/fvn+Xw2m42jR4+6JajbbDaUUhw6dIijR4+ilEJrTVJSEsWLF/fGy7ip5HcVYY1snnsbeDs/5xdCiOvBww8/zH//+1+SkpIoVqxYpvsYX1APPfRQjn0J9+3b5/V7LMo+//xzzpw5g5+fHyEhIfzrX/+iV69eZoPhm0FsbGy2KwljYmLMRHSHw8HgwYOpX79+npPjjx07hsPhMMsotG7dmj///BN/f39CQ0N59dVX8fPz48033+TixYsSYOWBVHIXQoh8euCBB0hKSiIuLi7LfV544QXq1KnDoEGDOHnyJJ06dXJ73nXF3N69e81Vhjeyc+fO0bVrV5555hlGjhzJxYsX6dOnD88//zxlypTJtMTFjcgYnXr99deJiorKdAowJCQE10Vpdrs9X0VJExMTqVevnhlgxcTEmKOrfn5+jB071sy/SkxMzPN1bmYSYAkhRD41btwYgN9++y3LfWrUqMHu3bsJDQ2lXLlybgntV65cMZfLG4wcmBvZb7/9RnT01cXlDofDnEq9mXhSzT0hIcEt4LRarfkqSlqnTh0efPBBcxrWYrFkqMnWoEEDXnnlFUqUKJHn69zMJMASQoh8qlixIq+//jrh4eG5Om7nzp1s2bIFf39/s0BjvXr1ANizZ4/X77OoSUhIAGD48OHmtpsxwPKkmntISIgZAFksFoYPH+42PbhgwQJq1apFSkpKjtdLTEwkMjKSli1bEhwcnOV+DRo04O2336ZChQq5f1FCAiwhhPCGf//737Rv3z5Xx4SHh5ujXy1btsTPz493330XcFZ7v9EZtcFeeuklOnbsCGRe+f5GZ1RzHzNmDKtWrcqQVxUbG8uwYcOw2WyAs+7Uxx9/7DaV2KtXL/bs2eNR/t7+/ftZu3YtSUlJ2e5nt9s5deoUZ8+eZeLEiZQrV47Dhw/n4RXenCTAEkIIL7h48SKHDh3K8/EhISEkJyfTqVMnrFarWYforbfeyrbX4fXMGMEKCQnhvvvuA672w7vZREREMGrUqCwT3FNSUswcLK11hqlEI1j6559/cryWUbw1pxHXP//8k/Lly9OgQQNeeOEFTp8+zfbt2z19STc9CbCEEMILhgwZQsuWLfN07N69e/H19eXbb79FKUXJkiU5f/48f//9N6NHj6ZHjx5evtui4fTp05QoUQJfX1+eeeYZnn/+eYYNG1bYt1XkREZGYrVazcdKKZRShISEmNuMLig5jXzOmjWLN954gzvvvNOcjs5KaKiziUt8fLy57WaYuvYWCbCEEMILypcvT3x8PBMmTMj1scbUz6+//gpgBliXLl0CcKsNdSOIi4sjLi6OhIQEM/csMDCQSZMm3XDFKhctWoQnLeByKiJqBFVWqxWLxYLD4WDYsGHm/nfeeScOh4PHH3882+t88skn3HbbbW6LC7ISEhJCv3793LadOXMmx+OyYrPZ8nX89UYCLCGE8AJjhOHFF1/M9bG1a9fG398fPz8/4GqAZZRq8KSJ9PXkzjvv5M477+To0aM59rm73nXp0oWmTZtmu09OZRpiYmKw2WxmDSzjjzFNePLkSSZOnMiBAwdyvJ+YmBi2bt1K1apVPbp/10KkAGfPnvXouMysXr2aypUrZ7va9kYiAZYQQnjBiBEj6N27Nxs3bsz1sfHx8SQnJ3Pw4EHgaoBlJIEHBAR49V6LitWrV+e5UGZRceDAAaZOneq27dixY4wdOxatNY0bN85x8UNOZRpcVxn6+vq6rTisXbs2Tz31FCNGjKBt27a89dZb2V4rODg423ZN6bVo0QJfX1/AWVA3p2nF7Bw6dIjLly9nO8oWFxfHmjVr8nyNoiS/rXKEEELgnE6ZPXt2no6dOXMmgLlCq2TJkmzZssUMsNauXUvFihWJjo6+7gOS9Dp06FDYt5AnGzdu5P3332fhwoXY7XYef/xxSpYsCThX9K1atYqmTZvi4+NDampqtucyAqjk5OQMuVVwdZWhUekdMP/+6KOPmr83Bw8eZOnSpbz55ptZXuu1116jVatW5qrNnLRr147ffvuNtWvX0r9//wy1snLD+H3OLhF/9OjRnD9/Pl9FVIsKCbCEEMJLjh07xq+//so999yTq1GCkydPAs5q71euXKFYsWIcPXqU6dOnu5170qRJN0SAZbVasdud7WnvuOOOQr6bvPnmm2/c8pjOnTtnBljHjh0DMIMY1wT1zERERDBp0iSee+457HY7Q4cOzdAGJyIiIsNjIEPpBOPamdFaM27cOF5++WWPAyyHw0HVqlWpU6dOvoIruBpg2e12rly5kunI7Llz526YUh0yRSiEEF6ydetWunfvzt9//52r444dO0Z4eDiTJ08mMDCQUaNGAfD777+77XcjJIDb7XYzuAIICwsrxLvJm3//+98ZFjO45iYZ7WcMrq83K3FxcdjtdrTWJCcnM2fOnGz3N5LijVIX4CxAml0wl5ycnOtq+TExMZQoUYJ58+YxYMCAfAXERoAFcOrUqUz32bJlC2vWrPHoZ1bUSYAlhBBeYoxa5baP4K5duyhbtixr164FnIGUkffiKj8JxkVF+r5219toxZIlS8wpuEaNGuHj40NycjINGzY09xkyZAjTp0+nbdu2AGzYsCHX13GtB5Z+heH06dNp06YNr776qpm3B3DXXXeZK1IzY6xKzU2AZbTJefLJJ1FKZTtClpN7773XHHkzRm3TM+p5nT59mvfee++6XnUoAZYQQnhJXgKsy5cvs3fvXnbv3m1uCwsLy5C3U7JkSbfRirw6ffo0Xbt2dRtNuJaMOk1jxoy5LquCT548GYBBgwYxePBgbDZbhpyie++9l/fff5/ffvuNl156yazWn53evXu7TcH9+OOPxMbGEhMT47bCcPr06QwaNIjU1FS35s8AKSkpGUbPXOUlwDKm8W655RbKlCnD2bNnM1zXUz169GDq1Kncc889OU43RkdHM3LkSOrXr3/d1t6SAEsIIbwkLwGW1Wrlxx9/ZOjQodnuV6dOHa/8a/7IkSNER0fz3Xff5ftceWEEWHXr1qVy5cqFcg/5cebMGe677z4++eQTs1FyeHg4q1evNvfZuXMne/bs4cKFC9x+++106tQpx/NGRETQr18/c0QvJSWFOXPmMGzYMC5fvmyuMIyOjsbhcGR6jkqVKlG3bl3mzZuX6fPG6FBuAqyaNWvSpk0b5s+fT+nSpbHZbGagllvx8fHUq1ePFStWZDnVaNybUcrh6NGjzJ07N0/XK2wSYAkhhJfkJcDy8/OjQ4cO3Hnnndnud9ttt3H06NF8j/rUrVsXHx8fj2omFYRevXoBV6eerjcJCQmUKVMGgBo1apir+oxiog6Hw+29PHToECtXrvSoCXPv3r3NqWGtNbNmzXILpnx8fHjkkUeyHP1ZunQpP/zwAz179uTy5cs899xzzJo1y3y+Tp06pKSk0K1bN49fr7+/PzExMbRo0YLSpUsDeSs2arPZqFq1ao5lJNavX+/2Xx8fH/76669cXUtrzeXLl3N9j94mAZYQQnhJsWLF+PXXX+ndu7fHx+zYsYNFixZRvHhxt+0bNmwgOjqamJgY/v3vfxMSEkJ8fDy33nprvu7x119/xWaz8ccff+TrPHm1Y8cOAJo0aVIo18+vhIQEs4yCUoqff/4ZX19fjh49CjhzzIyg6I033jBHZC5evJjjuSMiIujbt685imWz2di1a5f5vNaa+vXr07dv30yPT01NNXObJk6cyNSpU+nbt6/bdLOvr2+eVwPWr1+fAQMGmAVxcyM+Ph673U7VqlWpW7cuo0ePznQ/o/Bs06ZNiYiIIDIykvnz57Np0yaPr/XRRx9RrFixLPO8rhUJsIQQwkuUUrRs2dLs4eaJ//73v3Tr1o1WrVrxzTffMGnSJMCZ8/Lwww/Tpk0bXn/9dW677TbzmJzqKrlKvxrr66+/BuDEiRPmtu3bt2c57eRNCQkJaK15//33zdGQ64nNZuPChQtudaqUUkRFRfHNN99gs9nMhQhffPEFb731lhk4ezqtZiTOWywW/Pz83N4/u91OTEwMvXv3zhDkpF8sYDR0BsxE+O3bt/Pcc8/luSl58+bNmTp1KhUqVMj1scaIaZUqVUhMTMxwD8ePH6dWrVpUrFiRV199lQkTJhATE8MjjzwCkKvq78Yq3vSrcK81CbCEEMKLfvrpJ+bPn+/x/ocPH6Zy5cpYLBa6du1K586diY6ONmsqvfHGG4SFhXHXXXeZx2Q2/TF8+HAWLlzI9u3bzZVes2fPpkqVKuaXe4cOHczaWkY+zhdffMEdd9zBwoUL8/aCc8FI5A8PDy/waxUEHx8fjh8/zlNPPeW2vX///hw5cgRfX1+WLFkCYAaQxlSoJ9PGsbGxDBs2DLvdjsViMYNtg6+vL5GRkURERBATE8NDDz0EOFctdunSBavVagZaDocDf39/HA4HNWrUAGDdunVMnTo1X8F037598zQCZvxOVqpUiXLlyrFjxw46depklmuYOHGimcy+d+9eevbsyYULF3jyySd55ZVXaNSokcfXMsqc7N27N9f36VVa6yLzp3HjxloIIa5n3bp109WrV/d4//bt2+uIiIgM2+fNm6fPnTunAQ24/f3YsWNu++7fv998DtAVKlTQDodDP/jggxrQmzZt0lpr8/mwsDC9ePFi/c8//5jbpk2blr8X7oHp06drQO/fv7/Ar3UtpaSkuP38Ab1mzRqttdabNm3St99+u968eXOO53nnnXe01WrVgLZarfqdd97R48eP13Xr1tWAnjdvntv+y5cv14D+7LPPdGBgoLZYLNpisejSpUvroUOH6gEDBuj169eb+/fu3VuXL19eOxyOPL3OQ4cOma8vt6ZNm6YBHR8frzt06GCe5/XXX9d2u123a9cuw8/w0qVLebpPh8Oh+/btq5cvX56n43MD2KKziGlkBEsIIbwoPDyc/fv3c+XKFbftR44coWvXrhnqCJ07d86t6ntiYiLvv/8+jz/+ONHR0TRo0ABwrr774osvgKujT4YVK1YAmCMVZcqUISkpiUWLFgHO0QPX+kjHjh2jc+fOVK9e3dx24cKFfL1uT+zatYtixYrlO4+ssERHR/POO+9k2O7r6+s2BWixWKhbty7gzCXasWOHRzlnRssci8VitswZOXIk7777LnD1/TUY79muXbtISUkxm0CfPXuWyZMnM2PGDFq3bs27776L1ppffvmFiIiIPNcec23MndtRsObNm/P+++9TpkwZt/OMGTOGOnXq8PPPP2dYVVqsWDHAWVokNwtHnn/+eeLi4gq9DZMEWEII4UXVq1fH4XDwn//8B6WUmfw8ffp0oqOjeemll9z2d22xAs5cFWOfpk2bMm7cOMDZUsbIY0kfYB04cAAfHx+mTZsGOJN8XWtmHT16lCNHjmR739eioOPOnTupU6cOFsv1+dWzePFiPvvss0yfK1asGJGRkTRv3pzz58+7BRGeMlrmWK1WHA4Hw4YNY9GiRfj7+wMZi7RaLBYqVqxImzZt8PPzyxA4ORwO7HY7K1asYOfOnRw4cMCjkhFZCQgI4MiRI/z++++5DtLuuOMOXnzxRQIDA82VlwZjajA+Pj7TY2vXrs1rr73m8bVOnTqV51IS3nR9/pYLIUQRZeTcGKNNRlmFpUuXAmRYcr5w4UL+/e9/m49dW8c88cQTnDlzhmHDhnHu3Dm++eYbIGMOVsOGDXn22Wf5+eefzee3b99uPn/s2DEsFgv9+vVj4sSJGe7Z19e3wAOsy5cvs27dOpo3b16g1ylIiYmJ2ZaXWL16NRs2bMiwIvTxxx9n0KBBHl0jISHBHIlKSUmhS5cu7Nu3j0WLFpmjmYaHH36YI0eO8PDDD7Nq1aoMK/yMICg4OJj4+HiqV6+e71GdihUr0qBBg1wHWEePHjULsvbr148jR46Yv68AK1eu5Pvvv2f27Nn8888/xMXFmc8FBQXlKmC6ePFihvegMEizZyGE8KLg4GDgak2sBg0akJqayt13343NZqN9+/Zu+9erV8/tcdmyZZk9ezZ9+vRh+/bt9OrViw0bNnDy5EkzSEs/gtW9e3e6d+9Oz549AejSpYv5XJUqVahduza33HILM2bM4Icffshwz9WqVSvQAOvgwYO0aNGCpKQkOnfuXGDXKWiJiYnm+5sbqampTJkyBa01U6ZMyXZfY5owJSUFi8WC3W5n//793HHHHZQtW9Zt36SkJHMazdC2bVuWL18OQNWqVc2pw44dO3ol6Xv37t2sWrWKPn365OpnMWbMGKKjo83SCWFhYWYQNWzYsAyfC1fXa4AlI1hCCOFFERERHDhwAKvVaq7q8vX1ZcKECcTFxfHCCy+YbWqSk5OZMmWKW60jcBacdF1i3qBBA9q0aWPmoaQPsIzHc+fOpXv37m7PLV68mMcff9xsJJzZF1WZMmUKNMC65557zKnSmjVrFth1ClpeAyzjNU+dOjXHfSMiIli1ahVjxoyhWbNmAEyYMIHIyEizxAY4SzZUr16defPmERsbS2RkJJ999pkZXAHs37+fY8eOubVhyq9NmzYxZMiQXLdacg16VqxYgcVi4YEHHgBg4MCB2R4bFBSU4Xfe02sVJgmwhBDCiwIDA/nuu+9YsmQJdrudTz/9lMuXL5OUlITWmnLlypk1rU6fPs2gQYPMJs+uGjRowN69e1m2bBmBgYHmly3AokWLSE5ONh9XqlSJ4cOHA/DCCy9QvXp1Dhw4QFxcHDVr1kRrzdSpU7FarW71rwwFHWC59pJzrSF1vbl48WKeKtC/8MILPPDAA2a195wYBTaNESejTY5r/ldCQgLHjx/nzJkzxMTEZFkbzeFwkJqaSuPGjfnoo49yfe/pGb0J0y/iyIlrcJo+yK5SpUq2xxYrVixXI1gdO3bknnvuydX9FQQJsIQQwosuXbpkBjvgDKI+++wzgoKCmDBhAnC1qrfRl891FaGr6tWrc++99wJw++23m9s/++wzc0QoKSmJc+fOUb58ecC5Wmvv3r1UqVKFhg0b8vTTT9OkSRMSExPRWrsVrvz111/ZuXMnpUqVMgtkFjTXhP7rybx581i9enWe+uKVLVuWxYsXe9T0GZz1sKKiosxg2FhVaCS7w9VCsRUqVCAyMtJssZOev78/06dPZ+vWrR6168lJXgMs11GlqlWruj23devWbI997rnnGDBggMfXGjNmDMOGDcvV/RUECbCEEMKL0ldOHz9+vJnYfv/995vbtdbmlF9WAZYrHx8ft+mf48ePA5iBVsWKFc3nvvzyS5RSfPnll/j4+JCQkEBiYiI+Pj7myNe6deto3Lgx77zzDvv37y+wEaz0IyvX4wrC48eP07NnTzp16uQW5OTGli1b+OqrrzzaNyYmxm2EUmtN9erV3RY3GO9/hQoVzMKjzZo1c0s+b9asGatXrzZHDV3LcuSVEWDlttffxYsXzREspRRjxoyhUqVKADmO7PXo0YMePXp4fK1r0ZXAE9ffb7oQQhRhmeV+/P777xQvXpy6deuauVBKqVwFWOD+BZk+wDK+rADzy/lf//oXpUqV4uDBg+zZs4cSJUqYX4x33XUXAQEB7Ny5kxMnTnDhwoVcteDxVG5zdYoi4z3dvHmzudAgt+bNm8czzzzj0b6RkZFugZKznuXVljfgPoIFV0s8BAQEYLVaCQwMZNKkSQQFBZk1uKpVq5ane3eV1xGsV1991W1k97XXXmPv3r28/fbb9O/fP9tjT58+7TbNnB2bzYafn59ZO6wwSYAlhBBeZLFYCAoK4oUXXuDs2bMkJSURHh5uTo25juDkNsByLcR4/PhxUlNT+fDDDwH3EaxWrVoBzi8m49zffvstwcHBXLp0icDAQKZPn86ff/7JkSNHzOXz8fHx7Nq1i//+979eGwUwWqFcz4oXL27WI3MdRcyNkiVLcunSJTMgzk5ERESG1ZbFixfn2LFjZrBVoUIFunTp4lbWwzVBftWqVURERLidwxsFXps1a8Y///xDixYtcnVc586dzeluQ0BAAK+88kqOo4JjxoyhadOmHl3n7Nmz2O12s8l2YZIASwghvOzSpUt8+OGHlCpVisDAQC5cuGAmR+/fv5/u3buzadMmunTpwl9//eXWyDk7fn5++Pj4oJTi+PHjHD16lO+//55y5cpxyy23mPvVq1ePoUOHMmvWLLcvmmeffZakpCQCAgIYOHAgK1eudAuAZsyYwYsvvkivXr1YsGCBV34WruevVauWV855rR0+fJimTZsyZMgQnn322Tydw3j/XaeJs+NakNZqtdK9e3c2bNjA1q1bqV+/PmXKlGHhwoUZku4jIiIYNWqUGVy5Fjz1RoPtwMBAqlWrZo5k5WTbtm0EBwfz/vvv5zloL1euHOfPn3ebNs2KUWC3TJkyebqWN0mAJYQQXtahQwdeeeUVwBnU7N+/nyFDhgCQkpLCN998w969ewkKCqJWrVpuxSGzo5SiZMmSPPzwwwwcONCsfD1nzpwM5QM++ugjnnzySbcm0SNGjODSpUvmvkbuizEdderUKfMLytMpmZwYU5lxcXHs2LHDK+e81r777jvat2/PG2+8YbbAyS0jENq3b5/Hx/j7+6OUQmvNmDFj2Lp1K927d+ePP/7gf//7X47Hx8bG8uabbwLO4p55bZHj6uzZs4wfP97j93Lw4MFcvHiRl156Kc/5d8YCDk9GQ41cwqKwWlUCLCGE8LLly5fz9ttvA7Bx40ZCQ0PNWj/GlN25c+dYvnw5kydPztW5g4ODKVasGKGhoWaAlb6Hm6u7776bL7/8Ej8/P06fPk1SUhJBQUGUL1+eQ4cO0aFDB7PFTvny5c0VjkZglB9Xrlxh4cKFgLPdiaeBZFFz6tQpLBZLvkZFjJ+rJz0fjVWEycnJaK3Nqu79+vUzA7T33nsvyxWNsbGxDBw4kDZt2jBjxgwAZs2aRWxsbJ7v33DhwgVGjRrF5s2bPdrfyBvLT/6XMQrnSYAlI1hCCHGTCAkJIS4uzuwj5xpgRUdHm7k9nipRogT79u1j7ty5Zn9B1wT39Hr06MH27dsZNmwYoaGhXLp0iaCgIE6ePElMTAxDhgwxCz5WqFDB/BJL35Q6L9q0acO3334LOKeWrlfHjx+nbNmy+VoBOWjQIBo3boyvr6+ZR5WVmJgYt5IKRrFaQ+3atQHnzzc9IzibNm2a26IFu91OTExMnu/fkNskd2NEKbP6a54yRrCMKvDZueWWWxgyZEiRaCguAZYQQhSgkJAQjh49ak4Z+vv7ExgYyIEDB9i/f7/HCe6GEiVK8M8//zBq1Cji4+MpVqxYlufQWvP111/z4Ycf8u6776KUIjExkWLFijF06FDAWa4hNDSUkJAQNm/ebK76y+8IlsPhYNOmTfk6R1Fx8ODBDLWbcsvHx4cePXqQmpqaoWlzeka7HHDmXw0YMIDvv/8ecFY9/+GHH5g7d26mI5dGcJY+iLNYLF6ZNstNmYbU1FSzAnt+mi/Xrl2bGTNmEB4eTnx8PCEhIYSFhdGrVy/+/vtvt30bNmzI5MmTzdWVhUprXWT+NG7cWAshxI2kX79+GtCjR482tzVq1EgDGtCtWrXK1fk6deqkQ0NDta+vrz569KjeuHFjlvuePHnSvI7WWicmJmp/f389dOhQrbV2e65SpUraYrGY26pXr57bl+omPj7ePNf48ePzda7CtGbNGg3o7t275/tcW7du1R999JFOTEzMcd+1a9fqHj166P/85z/mtgMHDmibzZbtcevXr9eBgYFaKWX+/I0/VqtVT5s2zdzvnXfe0evXr8/Va7DZbFoppV977bUc901ISNBlypTRzZs31zExMbm6Tlbi4uLcXlP9+vXdnr906ZK+fPmyV67lCWCLziKmkREsIYQoQMbokuvogWvl6twWfwwODsZut5OampqhhU56ronqZ86c4ddffyU5OTnThsvVqlUzV3k1adKEQ4cOZSiampPU1FT+/PNPALPNy/Llyxk5cmSuzlNUrF69mjZt2hAQEMDLL7+c7/M1atSIHj160Llz52yTxB0OBxs2bKBt27Y88cQT5vYqVapgtVqzvYZRqmHAgAEZyh/Y7Xaee+45Ro4cSZs2bXjttdeIiorKVW6W1WqlbNmyHuVDlSlThoSEBDZs2JDpdGZu7Ny5k99//93MsTKEhYXRuHFj83f3jTfeKBIJ7iBThEIIUaCefPJJwH25vFH/CvC4RIOhRIkSZn7O2LFjs/1ydJ0++f3335k5cybg7HMIztWHU6ZMATCX9Y8YMYIBAwaQmprKoUOHcryfUaNG8csvvwAwceJE6taty5YtW8zk5ty+vqKkU6dOALRu3ZpGjRp55ZwHDx4kJiaG/fv3Z7nPnj17+Ne//sWAAQMyTIF5IiIigqlTp7J69eoMAbjdbueDDz4gNTXBjGk5AAAgAElEQVQVh8NBcnJyrnOzduzYkevFGfn15JNP0rBhQx588EEAfvrpJ2JiYlixYgVbt241f1fz2pC7IEiAJYQQBcjIaSpbtqy5bffu3QB88803btWtPVGiRAkzwXjChAn8+OOPWe7rOoLVrl07M+HcCPZ69eplrm6sU6cO4FxWbzTjrVatGitWrMjy/OfOnWP8+PFERUWxa9cuc6Rq7ty5ZkJykciFyaPnn38egG7dunntnMbPI7ukb6MvZL169fK1OCAiIoKnn37aLTlfKeVWj0opRWRkJOBMkB83blyOI1oVKlTwaEXob7/9Rrdu3czRzPwwcuCMnK66deuaBXUBsx3VDRNgKaXGKKW2K6W2KaVWKKUqpm1XSqnJSqm9ac/f6Z3bFUKI64tRwd11KrB58+bExMTw8MMPZygU6cn5kpOTzaR541/06Wmt+fHHH6lRo0aG5zKrh2Qsaz9z5gz16tUzt6dvxHv+/Hni4uIA2LVrF+CcBm3fvr25z7p16zh16hR+fn5F5ssuL8aPH8/Zs2fp16+f187pyYo4own49OnT3QrI5lZsbKzZ9Nh4z3UmKxh37NjBwIEDadu2La+//nqO04YLFixgzJgxvPfee2adrsz89ddffPvttx4VCM2J6yKDRo0aERISYq6S7NOnD/feey+DBw/mq6++KjK/cz75PP59rfXrAEqpocAbwLPAfUDNtD/Ngalp/xVCiJtKw4YNM13RldecFGMkbO/evZQrV47GjRtnut+CBQuIi4ujVatWDBkyBJvNRmBgYKa9EgHCw8N59dVXCQkJoWzZsiQmJhIaGmrm2thsNmrWrEmpUqXYuXMnAwcONFe2nT9/3q0kQFxcHKGhoZQtW9YrxS0Ly5UrVyhZsqRXX0NAQAAlSpTIdgTLCLCM4DyvjBWF2VVQt9vtDB48GJvNZv6epqSkEBMTk6HVjuHnn39m3rx55lS3ayNnVzNnzqRYsWLZ1mnzlBFgtWrVirVr15rbz58/T1BQEGvWrOHTTz8FuDECLK21a8W0IJxZ/QBdgDlpGfYblFKllFJhWuv8F1YRQoibmDG997///Y/ixYujlOLPP/+kQoUKbq1QVq9eDcDHH39Mw4YNczxvzZo1GTt2rPm4ePHilCtXzhxpOXfuHAcOHDCf//zzz83pmvRNopVSLF68mDvuuCNvL7KIuPXWW+nWrZv5xe0tDRo0yLbVzH333cf27dtzvQAiPaPcw5UrV7IcZbJarWYDcnC+d35+fua0YWaqVKnC2bNn6dq1K+vWrcsyoNm2bRs9e/bMd6AIV3P50vdALFGiBHPmzKFPnz7mtr59++b7et6Q7xwspdTbSqnDQE+cI1gAlYDDLrvFp20TQgiRD0aAVbJkSV555RUuXLhA3bp1M+QJbdy4kbZt23oUXIFz6ujMmTNmYUjjWsYIVqlSpdwSpo3gKjQ0NMO5jMT+63n0SmvN2bNncz2F64m1a9fy3nvvZfn81q1bqVOnjsf9/rKS3YpCi8XCQw89xJQpU/D398dqteLn58eAAQMybRTtymgXtGnTpix/PlprLly44La4Iz+aN2/Ojz/+yKhRozI8Z7R8MkRFRXnlmvmVY4CllPpJKfVHJn+6AGitX9Va3wLMBQYbh2VyqkzDZ6VUf6XUFqXUlhuh67oQQhQk4wtr2rRpjBo1ymxFs2rVKrf9Dhw4YCaue6pSpUpuleXLly9vjmD5+Pjw7rvvZjjGtSDp4MGDiY6ONouYFoV2JXl16dIlbDabVxok5+T//u//aNmyJQCHDh0iMjLSLQ8uP4wVhZMnT85Qib5Zs2b079+fVatWMWbMGD7++GOPKqAbAdahQ4f466+/WLt2LXfffTevvfYaX331FR07diQpKYkuXbqYK1bzKyQkhI4dO2ZaVLdr167A1YUaixYt8so18yvHKUKtdfuc9kkzD1gCvIlzxMo1M68ycDSL808HpgM0adIk+/4BQghxkzNysIx/kBorEv38/LDZbPj4+BAeHs7p06dztYJPKUXFihU5evTq/6rLlSvHtm3bADh8+DDbtm3jqaeeYtasWRmOf+ONN1iwYAF79uxh2bJlbNmyxUzovh4Z+UW5rbTviWnTpvHtt9+ycuVKADNIhqttj7zVbNuQvn6U1Wo1pwGN0Sqj/6HFYuHTTz+lf//+mZ6ratWqPProo0RFRdG/f3/WrVvH2rVrWbt2LX379mX58uX88MMP5qrVgnb33XezcuVK6tWrR8WKFfPVlseb8ruKsKbLwweBP9P+vgjonbaa8C7gvORfCSFE/oWEhODr62vWRzIaPqekpJhL1Y2gK7Ppu+ykD7BKlSplJlzv3LmT4cOH07NnT4AMBS9r1KjBH3/8wfLlywFo3LhxjivgEhMTWbZsmVf6Hnqb0Zy5IBKmT5w4wU8//ZQhd01r7fbz96bIyEj8/f2xWCz4+PjwySefmIGVsdrw8uXLOBwObDYbgwcPznIlodVqZf78+fTu3RtwrhoF59RwsWLFAOcii2upffv2hIWF4XA4eOutt67ptbOS3xys8WnThduBDsDzaduXAvuAvcAM4Ll8XkcIIQTOL7cuXbowb9487HY78fHx5ihLXFwcNpvN3De3AVZoaKhbsFOiRAkuXryIw+Ew++dVqFCBpKQkQkND3VYkfv311+bfXROq+/fvzwsvvJDhWna7nVatWnHfffexZs2aXN3ntVCqVClGjRrF7bff7vVzGyOLxvRr9+7due222/jzzz+59dZb+b//+z9Gjx7t1Wsa+Vhjx47ll19+oX///sTGxjJw4EDatGmToW9kTs2h//77bzNH7IcffgAwS0KAc6VhSEiIOUp3rRSlvL/8riJ8JIvtGhiUn3MLIYTIXJcuXfj222/ZvXs38fHxREVFsWTJErZt20aHDh3M/fLSSNq1EbExenPx4kVze3BwMP/5z384cuQIHTt2ZNmyZQAsWbLEPO7MmTOEhISgtWbGjBnUqFGDDz/80O1av/zyC9u3bwc8axx8rYWGhvLOO+8UyLldi41WqlTJDE6NgGbIkCG0bdvW69eNiIhwG7WKiorKdIWhUgp/f/9sVxKmr23VrVs39u3bx9KlS4GrBXZ9fX29+AquL1LJXQghrjPGar4NGzYQHx9P1apVqVGjBnv27DG/2CD3fQ4fe+wxt+kVY4XYhQsX3AKsP/74A3BOy1SvXp3vvvvObSn+zp07gavTbJlV8nYttFkUA6wrV66QkJCQ636MnjAS58+ePYvNZmP16tUcP37czJO6FosDjBpZ6YMrq9Xq0UpC19HRZs2amYVH9+3b57ZfQazCvF5IgCWEENeZGjVqUKpUKX788UeuXLnCLbfcQvXq1dm3b58ZYC1dujTXBR7bt2/vVrXc+HJMTEx0C7ASEhKoVq0aI0aMYO/evTz00EMcPnyYVatWMXPmTGrVqgXgFuyl57pq3Cj5UJQsXbqUsmXLmsGiN5UrV45mzZoREBDAqVOnaNeuHWFhYeboz7VoVhwZGZlp4+j0Kw2zEhISQp06dZg/fz4bN26kdu3alC1blkaNGjFo0NUJLAmwhBBCXDcsFgtNmzY1E4krV65MtWrV2Ldvnxm4hIWF5fq8Z8+eZfv27eaojTFFOG/ePIYOHcr+/fvx9fUlISEhQxAQHBxMu3bt6Nu3rzm6kV3pnccee4xff/0VKFojWOPHj6dnz55cunQJgKCgIK9fIzw8nI0bN9KyZUuz7yDAF198AVybEayIiAj69u2bIWcpNTWVadOm5dgux2KxsHv3bh599FFzW0hICKdPn+aDDz6gY8eOFC9ePNd5gDcSCbCEEOI65Dp9YwRYSUlJZtBiLPfPjTlz5nDHHXeYKweN0YexY8eSkJBgtitJSEhwa17tKi4uzkyYzqzRtSEkJISWLVuyfPlynnjiiVzfa0EZNWoU8+bNMwMsY1VcQTl06JDb41q1ahX4NQ2NGjVymyJ07VdotMvJjbJly3L48GFGjhxJnz59mDhxYpatmW4GEmAJIcR1yHXFVuXKlc3pwClTptCwYcM8VdA2vgznzp0LOIuLGoYPH868efMAZ+CU1TTW8OHDefHFFwE4cuQIAJs3b86w38KFC/n+++/p0KGD2QalKCnIEawrV67QsGFDPv/8c5YuXUpAQADffPMNQLaJ5d6WkJBgTgkqpWjatKlbVffc3osRSO/YsYMePXp4tUn29Si/zZ6FEEIUgtKlS7Nu3To+//xzwsLCzBGrlJQUs1VNbhnBxNChQ3nuuefMJHWA77//HovFwuOPP57pFKEhJCSEBQsWsHjxYjp37szixYszrYc1adIkHA4HPj4+hISEcNddd+Xpnr2tUaNGVK5cuUBHsPz9/dm+fTuHDh1iy5YtNG/enK5du7Jx40Zq1qyZ8wm8xKiNlZKSgp+fH5MmTQKcI5l58cQTTzBq1KhrUv3+eiABlhBCXKdatGhhNr91nRIcPHhwVodky3W05sSJE0RHR7s9HxYWZpZsyKpKvDGK8eCDD3Lx4kXOnTvH7bffzubNm92mixITEwkLC2PIkCG0bNmyyARYTZs2JTg4mLvvvpu3337bbRTPW5RSFC9enMTERD7//HOz4Khrr8drwaiNFRMTQ2RkJBEREcTGxjJ79mxSUlKYPXt2jqsJXVWuXJkqVarc1NOCriTAEkKIG4BrwJPZ6jBPGF+Mt912GykpKUybNs3t+bCwMHbt2gVc7UeXnuvI1ujRozl//jx//vknS5cupXv37uZziYmJ1KpVi8DAwCKV5N6mTRt69uxJVFQU//rXvwrsOsHBwSQmJhIeHl5g1/CEa20suFq+wW63m3lYngZYWmsOHjzI+vXrC+p2ryuSgyWEEDcAq9VKYGAgvXr1yvM5ateuDUC7du3MXnyAOboUHh5u1sDKqsK5a4D1wQcfMGPGDAC31WbgDLCCg4MpVqxYkSrTYExvderUidmzZxfYdYKDgzl69CjTpk3LUDuqMEVGRuLn55enPCylFHfffTejRo0quBu8jkiAJYQQN4ikpKR8BQUVK1bEbrcTFhZGo0aNAGei+4YNG8wk6N9++42goKAsE9ObNWtGlSpVzMedO3cG4OOPP3bbzwiwitII1rJly+jUqZP5OKdeivkRGRlJQEAAzz77LFu3bi2w6+SWMW34zDPP0KdPn1wfv2bNGvr27VsAd3b9kQBLCCFuIPntxWaxWNz6GRo5SHXr1qVy5cqsWrWKu+++O8tpyNatW7Nnzx7q1asHOHOxHA5HhrywXbt2MXLkSIKCgti4cWO2NZeuFaPQpyGrPDNv+Oyzz8zG2a4BaVExe/ZsZsyYkWM9rGth+vTp3HvvvUyfPr1Q7yO3JMASQgjhxvUL36iTZLFYOHfuHH/99Rdt2rTJ9nhfX1+zaGZYWBgnT55k4cKFbqsSb731VsqXL8+4ceO4fPkyLVq0YOPGjQXwajyzbt26DKNsBRlgAWbNMiMYLSoyy8MqLNOnT2fAgAGsWLGCAQMGXFdBlgRYQggh3BgB1ssvv8zKlSsBOHjwIA888ADgWUBw8uRJ/Pz8KF++POvWreP//u//+PvvvwE4d+4cb7/9Nn/88QcNGzZk3bp1AEyYMKEgXo5HTpw4AUC1atXMbXmpJZYbRlmEa1VY1FORkZH4+PiglMJisXDo0KFCG8VKv5I1/eOiTAIsIYQQbm699VbAGWy0bt2aiRMnkpiYaK4Oq1OnTo7naNKkCQ888AD16tWjfPnywNXK7iNHjuS1115jx44dgLPcxKOPPkpcXFxBvByPGHWvli1bxvr165k4cWKBlGhwNXPmTD7//PMCvUZeaa3RWpOamsr06dMLbarwkUceyfZxUSZlGoQQQripXbs2999/v1n0Mn1Cu9EyJzuhoaHmaIOxsjAhIQHAnOZxbSxcpkwZt5WLeWVMaeYmF81ut5s9AYOCgqhZs6bHpQnyo6gmg8+ZM8eszQXgcDhyXbLBW/r37w84R64eeeQR8/H1QAIsIYQQbnx8fPjhhx/Mx64B1RdffJHrkR2j+KgRYH3yyScMHjzYbarxgQceMNv95NUHH3xA+fLl6dOnD6VKlXJrpJydESNGmFOhBdEa53oSGxvLF1984daj0GKxeFyyITY21q1wqTfuJyEhgdGjR3t0Pm9fP1+MYcCi8Kdx48ZaCCFE0XLhwgUNaEDv3r0718enpqZqpZR+8803zW02my1f9/Tjjz/qjh076nPnzpnbAF2uXDnzXlNTU7M9x4ULF/Ts2bN1hQoVNKBbtWqV4zE3unfeeUdbrVbzZwhopZR+6aWXcjx2/fr1OjAwUFutVh0YGKjXr1+fr3vJ7fm8fX1PAFt0FjGN5GAJIYTIVnBwsPn3vKys8/Hx4eeff+bpp59m165dTJ48mcTERLd9UlJSOHLkCCkpKR6d88svv2TZsmX89NNP5vGu/wXYs2dPtue4fPkyffr04cSJEzRs2JD58+cXeN5VUWcUGnWdYtVaM2HChBxzsLy9+jAmJobk5GTsdjtXrlxh9OjR2d5DUVr9CJLkLoQQwgNhYWEAlCpVKk/HR0ZGcsstt7Bu3Tqef/75DAHWokWLqFy5Mn/99ZdH57t06RINGjQwk57nzZsHwPnz5819Nm3alO05zp49a+aBbdu2jcmTJ3v8em5URqHRpk2bum232+05NoE2gjOLxYJSKsuG4J4KCQnB4XAAziBv5cqVREVFMXLkyEzrYuWnCn1BkABLCCFEjuLi4li7dm2eC5keO3aMhg0bmm1UypQp4/a80aImfd7U5s2badGiBaVLl+b77783tx88eNDMDdNa89RTT5nPdevWjeDgYDZv3pzl/Xz11VfUqVPH/AIHeO+99/L02m40ERERTJo0KcNo3syZMxk4cGCWo0jGcVarFYfDwbBhw/K18jAhIcFtIYTWmsuXL/Pee+9lWhcrv1XovU0CLCGEEDmqUKECrVq1yvPxZ8+e5ffffychIQFfX98MtZ+MkTHXUacTJ07Qpk0bYmNjOXfuHK+//jrg/KI9cOAAixYtYuDAgW6V56dMmcKMGTNo06aN2bw6M48//nieX8vNICIigl9++YVmzZqZ21JTU5k2bVq2JRsSEhJwOBxuKw/zKjIyEn9/f7cgK73M6mIVlSr0EmAJIYQocOHh4eaoQunSpTOMhDVo0IDq1aszc+ZMc9vMmTPd+hRu2rSJJUuW8NhjjzFlyhSUUhw4cABfX19zOqhr166ULFmSxYsXM378+Bzvy5jGCg8PdwsmhDPIevrpp922aa2zDZy8OU1njEiNHTuWhx56KNN9GjZs6Pa4KOVhSYAlhBDimqhUqRKQeR6Xr68vHTt2NCuqgzMvqmbNmpw8eZItW7YQEBBA586d+frrr2natClt27YlMTGRkydP4uvrS2BgoEfV13VaCQKr1cr27ds5duwYf/zxR6G26imqEhISMgTDPj4+WQZORlA0ZswYVq1ale9SCREREYwaNYr77rvPbbtSCqUUH3/8sdsoVWRkpFufzPzmgeWHBFhCCCGuiYoVKwLO/KfMjBgxwm3E4cSJE4SFhVGuXDkuXbpE69atzeBo27ZtaK25ePEi8+fPZ+XKlXTv3t2cLpw4cSJt27Y1z3Xq1CmzkKlRtX3cuHFUrFiR0NDQfDfJvlGlD1iUUjz11FNugVNsbCzjxo0jNjY2z3WoXM+RmfRV/o1SCFeuXMmQfG/8jtjtdoYOHVpo04Q393pUIYQQ10znzp2pW7cu4eHhmT6fvmL88ePHzSmgK1eumM2RAXr06EHVqlWxWCzs3LmT0qVLM2vWLDNQOn36NGvXrkVrjVLKbNfTqlUrPv30UwBKlizp9dd4o4mIiODTTz9l8ODB2Gw2LBYLx48fJzY2loiICGJjY4mKiiIlJQWr1YpSCpvNhp+fn8cjWK7nyOw4o/hpZrTWzJo1i969exMREZGhCn1hVaAHGcESQghxjdx66620a9eOgICATJ8/ePAgU6ZM4fTp08yZM4e///6bFi1aAO6Nlx977DHAWf29Xbt2bNu2jYYNG7qNQpUtWxa73e5WtgHg119/ZcGCBfTp08ejptXC2a7mk08+wWKxYLfbWbhwobn4wDXnKTU1NU/5TznlTcXExGC32922ub7XNpuNmJiYTAOxwizXICNYQgghioRdu3YxaNAgGjVqZCbEDxo0CLjabicgIMCcYnzzzTcJCQkhODg4QzK2EZAdP36cUqVKUb58eU6ePAk4A73Ro0dfi5d0w0hISHALclJTU5kzZw69e/fGz88v0xEsTwObkJAQLBYLWmvzuNjYWHPqr1GjRvj5+ZGcnOxWF8tg5ISlH71q1qwZkyZNKrSWORJgCSGEKBJcm0JHRERQrFgxsxZTaGgotWrV4p133mHq1KmsWrWK0qVLk5iYyKVLlzL0MTSmG/ft20edOnXM4AqgXbt22O12t9wikT0jFyv9SJKR1O4aDCUkJHicgxUbG8uwYcOw2+1YLBYmTZpkXs+oyu/v78/kyZOJjo7mp59+cqtdppTivvvuY86cOcycOdMMvPz9/Qs1uAIJsIQQQhQRrgHW8ePHzelBcK4yNKq8d+zYkfj4eJYsWULXrl1577336NKli9u5atSoQevWrbn//vvp3bu3uX3BggVMmjSJ+fPnc/To0WxrLAl3nTt3ZtGiRTgcDqxWK40aNTKfmz17dpY5VNkxpgcdDgdKKRISEoiJicmQR2U0fF67dq05kmWxWPDx8WHJkiVu+2eWiF8YJMASQghRJKQPsEJDQzPdLygoiNq1a3PmzBlSU1OpW7cutWrVctunQoUKLF26lODgYObMmcPw4cOZOHEis2fPZtWqVdSvX1+CKw+5JqEbbXC01gwbNoz69etnmUOVfjVhZisMjbpZRnBmTCv6+vqaI1jGFKAxWhYTE0NISAhxcXEsW7aMAwcOmPeqlCIgIMAtqC4sEmAJIYQoEkqWLInVauX48eN07dqVu+66K9v9jaBq0qRJREVFERgY6PZ88eLFGTFiBBMmTODLL78EMNvt1KxZ0/sv4AblGkAZyeWuBUeNICk5ORmlFDt37uTNN9/Ebrfj7+/PqlWrADJdKZh+itHQqVMnFi1aZK4CNRjHxMbGMmTIkAzNwZs2bVroU4MGCbCEEEIUCUopdu/eTYUKFShRokSO+xsjXqtWreL8+fMZAiyARx99lAkTJtCuXTu3tipVqlTx3o3f4FxHmaxWK1prbDYbVqvVHHG69957Wbx4MXa7nblz55rHJicnmyNaRpBm1K5yDYKMKcYvvvgCpRQpKSlu9azSl1pIP40IYLFYuPPOOwvop5B7Mj4qhBCiyKhZsyZBQUFuq8Sys2LFCh5++GGzzlV6TZs2ZevWrURHR/Pkk0+a2yXA8pxrdfaPP/7YnFpVSpklG77//nvsdnuG980IwlwLlmqtmTlzplkANP0UY3JysnkepVSmKxIjIyPx9fU1HxujXNOnTy/0HoQGCbCEEEIUGf/73/9o0aIFwcHBHDt2LMf977nnHqKjo7PNpzJa4PTo0QN/f3+AHKcfhTujZU1CQgI2mw2tNampqbz//vukpqZmCKyUUvj4+PDJJ58AziCqbt265vNGmQfIWC3e4Ovry4ABAzJNmo+IiCAmJoZnn32Whx56CIvFYjaZdh01K0wyRSiEEKLImDt3Lps2bcJqtXrUV9ATS5cuBeDy5ctUr16dunXrSpHRPHKdLgTcSiYYfHx86Nevn5loHhUVxZUrV7IclYyIiKBv37589tlnbtvvv/9+pk6dmuW9GEHX6NGj3e7DdeqyMMkIlhBCiCKjdevWgLMxtFEDK7969eoFQN26dfnhhx+YMmWKV857M3KdLuzcuXOm+zgcDm699VZzlMl1ys+glHIr8+D6d8OPP/6Y7VSfsbrxp59+MpPhjVEzSXIXQgghXAwdOhSbzcYDDzzgtXN269aNK1eumNODIn+M4OXQoUP4+vpmSDa3Wq2EhIQwbtw4s0p7ZiNdQ4YMIS4ujt69e5OQkJBhv5SUlAzJ8K5ca2hZLBbat2/P6NGji0RwBTKCJYQQogjx8/Pj5Zdf5vbbb/fqeSW48h5j5GjGjBkopXjooYfw9/c3C38OHz6cYcOG8frrrzNs2DBeeOEFfH19UUqZuXJGmYfPPvuMqKgoQkJCMoxYGo2csxrFMqYrrVYrPj4+VKtWrcBfe25IgCWEEEIIj7mu+rPb7TRr1ozVq1czduxYfvnlF0qVKuVWkuHChQusWbOGt99+mxdffDHD+ZKTk0lISKBv375uNa/gaiPnzBjTlc888wxKKWbMmFFkVhCCTBEKIYQQIhfSFxYNCQkxC4AajL6FWmumTZvG8ePHeemll7IMloyk9NmzZ7u1wsmpabSR52Wz2dwqyReFaUKvjGAppV5USmmlVNm0x0opNVkptVcptV0pVXQqfwkhhBAizyIiIhgyZAjgLAI6bNgwt1GjiIgIOnXqZD7WWrNw4ULatm2b6VTgiy++6FbVfezYsUybNo2xY8d61NfQdaowp4DsWsr3CJZS6hbgHuCQy+b7gJppf5oDU9P+K4QQQojrWGxsLB9++KGZkG7UnXINhDLrI5mcnExcXBy//PIL7733Hn/99RflypXj77//ZuDAgfTu3TvDSJgnXHsUuvY5LGzK02q5WZ5AqW+BMcD3QBOt9Wml1DQgRmv9Vdo+fwGRWutsq8Y1adJEb9myJV/3I4QQQoiCM27cOF577TUzwPL19WXNmjVugU1sbCx33303NpvN7Vh/f39Wr14NOEeeXHsJGs8VlQDJE0qp37TWTTJ7Ll9ThEqpB4EjWuvf0z1VCTjs8jg+bVtm5+ivlNqilNpy6tSp/NyOEEIIIQpYZGSk26rBzOpORURE8Omnn2ao0G4krWfWS9DIn7pR5DhFqJT6Ccg41gevAq8AHTI7LJNtmQ6VaUIEROUAAAakSURBVK2nA9PBOYKV0/0IIYQQovB4OiWXkJDg9jh9X0FfX1+3EayilD/lDTkGWFrr9pltV0rVB24Dfk9bVlkZ2KqUaoZzxOoWl90rA0fzfbdCCCGEKHSe5Eq5ttWxWq307dvXzLMCZ7mHOXPmcPz4cUJDQ92euxHkOwfLPJFSB7iag3U/MBjohDO5fbLWullO55AcLCGEEOL6EBsbm+Molif7XM+yy8EqqDpYS3EGV3uBJOCpArqOEEIIIa4xo5p7SkoKfn5+WZZTyMuqwBuF1yq5a62raq1Pp/1da60Haa2ra63ra61lWEoIIYS4QbhWc7/RktO9RVrlCCGEECJXimpxz6JEWuUIIYQQIleKanHPokQCLCGEEELk2s2cX+UJmSIUQgghhPAyCbCEEEIIIbxMAiwhhBBCCC+TAEsIIYQQwsskwBJCCCGE8DIJsIQQQgghvEwCLCGEEEIIL5MASwghhBDCyyTAEkIIIYTwMgmwhBBCCCG8TAIsIYQQQggvU1rrwr4Hk1LqFHDwGlyqLHD6GlxHeE7ek6JJ3peiR96Toknel6LnWrwnVbTW5TJ7okgFWNeKUmqL1rpJYd+HuErek6JJ3peiR96Toknel6KnsN8TmSIUQgghhPAyCbCEEEIIIbzsZg2wphf2DYgM5D0pmuR9KXrkPSma5H0pegr1Pbkpc7CEEEIIIQrSzTqCJYQQQghRYG6qAEsp1VEp9ZdSaq9S6uXCvp+bhVLqFqXUaqXUbqXUTqXU82nbyyilViql9qT9t3TadqWUmpz2Pm1XSt1ZuK/gxqaUsiql4pRSP6Q9vk0ptTHtfflaKeWXtt0/7fHetOerFuZ938iUUqWUUt8qpf5M+9xEyOelcCmlhqf9/+sPpdRXSqkA+axce0qpL5RSJ5VSf7hsy/VnQynVJ23/PUqpPgVxrzdNgKWUsgKfAvcB4cBjSqnwwr2rm4YNGKG1rgvcBQxK+9m/DKzSWtcEVqU9Bud7VDPtT39g6rW/5ZvK88Bul8fvAhPT3pezwNNp258GzmqtawAT0/YTBeMjYJnWug5wB873Rz4vhUQpVQkYCjTRWt8OWIEeyGelMHwJdEy3LVefDaVUGeBNoDnQDHjTCMq86aYJsHD+EPdqrfdprVOA+UCXQr6nm4LW+pjWemva3xNxfllUwvnzn52222zgobS/dwHmaKcNQCmlVNg1vu2bglKqMnA/8HnaYwW0A75N2yX9+2K8X98CUWn7Cy9SSpUA7gZmAmitU7TW55DPS2HzAQKVUj5AMeAY8lm55rTWvwBn0m3O7WfjXmCl1vqM1vossJKMQVu+3UwBViXgsMvj+LRt4hpKGypvBGwEKmitj4EzCAPKp+0m79W1Mwl4CXCkPQ4BzmmtbWmPXX/25vuS9vz5tP2Fd1UDTgGz0qZuP1dKBSGfl0KjtT4CfAAcwhlYnQd+Qz4rRUVuPxvX5DNzMwVYmf3rQZZQXkNKqeJANDBMa30hu10z2SbvlZcppR4ATmqtf3PdnMmu2oPnhPf4AHcCU7XWjYBLXJ3yyIy8LwUsbfqoC3AbUBEIwjn9lJ58VoqWrN6Ha/L+3EwBVjxwi8vjysDRQrqXm45SyhdncDVXa70gbfMJYyoj7b8n07bLe3VttAQeVEodwDll3g7niFaptGkQcP/Zm+9L2vMlyThUL/IvHojXWm9Me/wtzoBLPi+Fpz2wX2t9SmudCiwAWiCflaIit5+Na/KZuZkCrM1AzbRVH344ExQXFfI93RTScg9mAru11h+6PLUIMFZv9AG+d9neO20FyF3AeWP4V3iP1nqU1rqy1roqzs/Dz1rrnsBqoGvabunfF+P96pq2v/yr3Mu01seBw0qp2mmbooBdyOelMB0C7lJKFUv7/5nxnshnpWjI7WdjOdBBKVU6bXSyQ9o2r7qpCo0qpTrh/Be6FfhCa/12Id/STUEp1QpYC+zgaq7PKzjzsP4H3Irzf2DdtNZn0v4H9gnOpMMk4Cmt9ZZrfuM3EaVUJPCi1voBpVQ1nCNaZYA44AmtdbJSKgD4D84cujNAD631vsK65xuZUqohzoUHfsA+4Cmc/yCWz0shUUq9BTyKc1V0HNAPZ96OfFauIaXUV0AkUBY4gXM14EJy+dlQSvXF+T0E8LbWepbX7/VmCrCEEEIIIa6Fm2mKUAghhBDimpAASwghhBDCyyTAEkIIIYTwMgmwhBBCCCG8TAIsIYQQQggvkwBLCCGEEMLLJMASQgghhPAyCbCEEEIIIbzs/wFbGJt4voGOxAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Annotations and drawing on a Subplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the standard plot types, you may wish to draw your own plot annotations, which could consist of text, arrows, or other shapes. You can add annotations and text using the **text**, **arrow**, and **annotate** functions." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:48.003551Z", "start_time": "2019-12-24T22:46:48.000559Z" } }, "outputs": [], "source": [ "# text draws text at given coordinates (x, y) on the plot \n", "# with optional custom styling\n", "\n", "# ax.text(x, y, 'hello world',\n", "# family='monospace', fontsize=10)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:48.405248Z", "start_time": "2019-12-24T22:46:48.004547Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Important dates in the 2008-2009 financial crisis')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Annotations can draw both text and arrows arranged appropriately\n", "\n", "from datetime import datetime\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "data = pd.read_csv(r'examples/spx.csv', index_col=0, parse_dates=True)\n", "spx = data['SPX']\n", "\n", "spx.plot(ax=ax, style='k-')\n", "\n", "crisis_data = [\n", " (datetime(2007, 10, 11), 'Peak of bull market'),\n", " (datetime(2008, 3, 12), 'Bear Stearns Fails'),\n", " (datetime(2008, 9, 15), 'Lehman Bankruptcy')\n", "]\n", "\n", "for date, label in crisis_data:\n", " ax.annotate(label, xy=(date, spx.asof(date) + 75),\n", " xytext=(date, spx.asof(date) + 255),\n", " arrowprops=dict(facecolor='black', headwidth=4, width=2,\n", " headlength=4),\n", " horizontalalignment='left', verticalalignment='top')\n", " \n", "# Zoom in on 2007-2010\n", "ax.set_xlim(['1/1/2007', '1/1/2011'])\n", "ax.set_ylim([600, 1800])\n", "ax.set_title('Important dates in the 2008-2009 financial crisis')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:48.409236Z", "start_time": "2019-12-24T22:46:48.406244Z" } }, "outputs": [], "source": [ "# drawing shapes\n", "# matplotlib has objects that represent many common shapes, referred to as patches\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:48.708152Z", "start_time": "2019-12-24T22:46:48.410235Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.patches.Polygon at 0x1f96e280588>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)\n", "circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)\n", "\n", "pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],\n", " color='g', alpha=0.5)\n", "\n", "ax.add_patch(rect)\n", "ax.add_patch(circ)\n", "ax.add_patch(pgon)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting with pandas and seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In pandas we may have multiple columns of data, along with row and column labels. Pandas itself has built-in methods that simplify creating visualizations from DataFrame and Series objects. Another library is **seaborn**, a statistical graphics library created by Michael Waskom. Seaborn simplifies creating many common visualization\n", "types.\n", "\n", "\n", "Importing seaborn modifies the default matplotlib color schemes\n", "and plot styles to improve readability and aesthetics. Even if you do\n", "not use the seaborn API, you may prefer to import seaborn as a\n", "simple way to improve the visual aesthetics of general matplotlib\n", "plots.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series and DataFrame each have a plot attribute for making some basic plot types. By default, ***plot()*** makes line plots" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:48.714174Z", "start_time": "2019-12-24T22:46:48.710151Z" } }, "outputs": [], "source": [ "s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.014108Z", "start_time": "2019-12-24T22:46:48.715133Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96e28a588>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "s.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Series object’s ***index*** is passed to matplotlib for plotting on the ***x-axis***, though you can disable this by passing **use_index=False**. The x-axis ticks and limits can be adjusted with the xticks and xlim options, and y-axis respectively with yticks and ylim. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of pandas’s plotting methods accept an optional ***ax*** parameter, which can be a\n", "matplotlib subplot object." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.203602Z", "start_time": "2019-12-24T22:46:49.015106Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96e328608>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(np.random.randn(10, 4).cumsum(0), columns=['A', 'B', 'C', 'D'],\n", " index=np.arange(0, 100, 10))\n", "\n", "df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Series.plot method arguments**\n", "\n", "|Argument| Description|\n", "|--|--|\n", "|label| Label for plot legend|\n", "|ax| matplotlib subplot object to plot on; if nothing passed, uses active matplotlib subplot|\n", "|style| Style string, like 'ko--', to be passed to matplotlib|\n", "|alpha| The plot fill opacity (from 0 to 1)|\n", "|kind| Can be 'area', 'bar', 'barh', 'density', 'hist', 'kde', 'line', 'pie'|\n", "|logy| Use logarithmic scaling on the y-axis|\n", "|use_index| Use the object index for tick labels|\n", "|rot| Rotation of tick labels (0 through 360)|\n", "|xticks| Values to use for x-axis ticks|\n", "|yticks| Values to use for y-axis ticks\n", "|xlim| x-axis limits (e.g., [0, 10])|\n", "|ylim| y-axis limits|\n", "|grid| Display axis grid (on by default)|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***DataFrame-specific plot arguments***\n", "\n", "|Argument| Description|\n", "|--|--|\n", "|subplots| Plot each DataFrame column in a separate subplot|\n", "|sharex| If subplots=True, share the same x-axis, linking ticks and limits|\n", "sharey| If subplots=True, share the same y-axis|\n", "|figsize| Size of figure to create as tuple|\n", "|title| Plot title as string|\n", "|legend| Add a subplot legend (True by default)|\n", "|sort_columns| Plot columns in alphabetical order; by default uses existing column order|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Plots" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.207631Z", "start_time": "2019-12-24T22:46:49.204600Z" } }, "outputs": [], "source": [ "# plot.bar() and plot.barh() make vertical and horizontal bar plots" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.620962Z", "start_time": "2019-12-24T22:46:49.208588Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96e55f348>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, 1)\n", "data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n", "data.plot.bar(ax=axes[0], color='k', alpha=0.7)\n", "data.plot.barh(ax=axes[1], color='k', alpha=0.7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a DataFrame, bar plots group the values in each row together in a group in bars,\n", "side by side, for each value. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.635937Z", "start_time": "2019-12-24T22:46:49.621958Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Genus</th>\n", " <th>A</th>\n", " <th>B</th>\n", " <th>C</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>one</th>\n", " <td>0.301686</td>\n", " <td>0.156333</td>\n", " <td>0.371943</td>\n", " <td>0.270731</td>\n", " </tr>\n", " <tr>\n", " <th>two</th>\n", " <td>0.750589</td>\n", " <td>0.525587</td>\n", " <td>0.689429</td>\n", " <td>0.358974</td>\n", " </tr>\n", " <tr>\n", " <th>three</th>\n", " <td>0.381504</td>\n", " <td>0.667707</td>\n", " <td>0.473772</td>\n", " <td>0.632528</td>\n", " </tr>\n", " <tr>\n", " <th>four</th>\n", " <td>0.942408</td>\n", " <td>0.180186</td>\n", " <td>0.708284</td>\n", " <td>0.641783</td>\n", " </tr>\n", " <tr>\n", " <th>five</th>\n", " <td>0.840278</td>\n", " <td>0.909589</td>\n", " <td>0.010041</td>\n", " <td>0.653207</td>\n", " </tr>\n", " <tr>\n", " <th>six</th>\n", " <td>0.062854</td>\n", " <td>0.589813</td>\n", " <td>0.811318</td>\n", " <td>0.060217</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Genus A B C D\n", "one 0.301686 0.156333 0.371943 0.270731\n", "two 0.750589 0.525587 0.689429 0.358974\n", "three 0.381504 0.667707 0.473772 0.632528\n", "four 0.942408 0.180186 0.708284 0.641783\n", "five 0.840278 0.909589 0.010041 0.653207\n", "six 0.062854 0.589813 0.811318 0.060217" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.rand(6, 4), index=[\n", " 'one', 'two', 'three', 'four', 'five', 'six'],\n", " columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:49.951367Z", "start_time": "2019-12-24T22:46:49.636881Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96f7a8448>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot.bar()\n", "# Note that the name “Genus” on the DataFrame’s columns is used to title the legend." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create stacked bar plots from a DataFrame by passing **stacked=True**, resulting in the value in each row being stacked together" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.146808Z", "start_time": "2019-12-24T22:46:49.952328Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96f6d0288>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot.barh(stacked=True, alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.159773Z", "start_time": "2019-12-24T22:46:50.147805Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_bill</th>\n", " <th>tip</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " total_bill tip smoker day time size\n", "0 16.99 1.01 No Sun Dinner 2\n", "1 10.34 1.66 No Sun Dinner 3\n", "2 21.01 3.50 No Sun Dinner 3\n", "3 23.68 3.31 No Sun Dinner 2\n", "4 24.59 3.61 No Sun Dinner 4" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips = pd.read_csv(r'examples/tips.csv')\n", "tips.head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.358614Z", "start_time": "2019-12-24T22:46:50.160771Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>size</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " </tr>\n", " <tr>\n", " <th>day</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Fri</th>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Sat</th>\n", " <td>2</td>\n", " <td>53</td>\n", " <td>18</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Sun</th>\n", " <td>0</td>\n", " <td>39</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>3</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Thur</th>\n", " <td>1</td>\n", " <td>48</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "size 1 2 3 4 5 6\n", "day \n", "Fri 1 16 1 1 0 0\n", "Sat 2 53 18 13 1 0\n", "Sun 0 39 15 18 3 1\n", "Thur 1 48 4 5 1 3" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# https://chrisalbon.com/python/data_wrangling/pandas_crosstabs/\n", "\n", "party_counts = pd.crosstab(tips['day'], tips['size'])\n", "party_counts" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.477442Z", "start_time": "2019-12-24T22:46:50.359573Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>size</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " </tr>\n", " <tr>\n", " <th>day</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Fri</th>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Sat</th>\n", " <td>53</td>\n", " <td>18</td>\n", " <td>13</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Sun</th>\n", " <td>39</td>\n", " <td>15</td>\n", " <td>18</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>Thur</th>\n", " <td>48</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "size 2 3 4 5\n", "day \n", "Fri 16 1 1 0\n", "Sat 53 18 13 1\n", "Sun 39 15 18 3\n", "Thur 48 4 5 1" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "party_counts = party_counts.loc[:, 2:5]\n", "party_counts" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.605157Z", "start_time": "2019-12-24T22:46:50.479387Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "day\n", "Fri 18\n", "Sat 85\n", "Sun 75\n", "Thur 58\n", "dtype: int64\n" ] } ], "source": [ "print(party_counts.sum(1))\n", "party_pcts = party_counts.div(party_counts.sum(1), axis=0)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:50.778057Z", "start_time": "2019-12-24T22:46:50.606156Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>size</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " </tr>\n", " <tr>\n", " <th>day</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Fri</th>\n", " <td>0.888889</td>\n", " <td>0.055556</td>\n", " <td>0.055556</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Sat</th>\n", " <td>0.623529</td>\n", " <td>0.211765</td>\n", " <td>0.152941</td>\n", " <td>0.011765</td>\n", " </tr>\n", " <tr>\n", " <th>Sun</th>\n", " <td>0.520000</td>\n", " <td>0.200000</td>\n", " <td>0.240000</td>\n", " <td>0.040000</td>\n", " </tr>\n", " <tr>\n", " <th>Thur</th>\n", " <td>0.827586</td>\n", " <td>0.068966</td>\n", " <td>0.086207</td>\n", " <td>0.017241</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "size 2 3 4 5\n", "day \n", "Fri 0.888889 0.055556 0.055556 0.000000\n", "Sat 0.623529 0.211765 0.152941 0.011765\n", "Sun 0.520000 0.200000 0.240000 0.040000\n", "Thur 0.827586 0.068966 0.086207 0.017241" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "party_pcts" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:51.079724Z", "start_time": "2019-12-24T22:46:50.779056Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f96dfe9b88>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "party_pcts.plot.bar()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:51.445802Z", "start_time": "2019-12-24T22:46:51.080710Z" } }, "outputs": [], "source": [ "# same plot using seaborn\n", "\n", "import seaborn as sns\n", "\n", "tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:51.458767Z", "start_time": "2019-12-24T22:46:51.446761Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_bill</th>\n", " <th>tip</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " <th>tip_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>0.063204</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>0.191244</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>0.199886</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>0.162494</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " <td>0.172069</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " total_bill tip smoker day time size tip_pct\n", "0 16.99 1.01 No Sun Dinner 2 0.063204\n", "1 10.34 1.66 No Sun Dinner 3 0.191244\n", "2 21.01 3.50 No Sun Dinner 3 0.199886\n", "3 23.68 3.31 No Sun Dinner 2 0.162494\n", "4 24.59 3.61 No Sun Dinner 4 0.172069" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips.head()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:51.768466Z", "start_time": "2019-12-24T22:46:51.459729Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f9713602c8>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='tip_pct', y='day', data=tips, orient='h')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "seaborn.barplot has a ***hue*** option that enables us to split by an additional categorical value" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.004873Z", "start_time": "2019-12-24T22:46:51.769464Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f97151c3c8>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histograms and Density Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A histogram is a kind of bar plot that gives a discretized display of value frequency.\n", "The data points are split into discrete, evenly spaced bins, and the number of data\n", "points in each bin is plotted." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.218304Z", "start_time": "2019-12-24T22:46:52.005831Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f97143c808>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tips['tip_pct'].plot.hist(bins=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A related plot type is a density plot, which is formed by computing an estimate of a continuous probability distribution that might have generated the observed data. The usual procedure is to approximate this distribution as a mixture of “kernels”—that is, simpler distributions like the normal distribution. Thus, density plots are also known as kernel density estimate (KDE) plots. Using plot.kde makes a density plot using the conventional mixture-of-normals estimate " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.363919Z", "start_time": "2019-12-24T22:46:52.219274Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f9716bb648>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tips['tip_pct'].plot.density()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn makes histograms and density plots even easier through its **distplot**\n", "method, which can plot both a histogram and a continuous density estimate simulta‐\n", "neously" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.378835Z", "start_time": "2019-12-24T22:46:52.364873Z" } }, "outputs": [], "source": [ "# example - bimodal distribution consisting of draws from\n", "# two different standard normal distributions" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.530516Z", "start_time": "2019-12-24T22:46:52.379831Z" } }, "outputs": [], "source": [ "comp1 = np.random.normal(0, 1, size=200)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:52.681026Z", "start_time": "2019-12-24T22:46:52.531512Z" } }, "outputs": [], "source": [ "comp2 = np.random.normal(10, 2, size=200)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:53.180808Z", "start_time": "2019-12-24T22:46:52.683021Z" } }, "outputs": [], "source": [ "values = pd.Series(np.concatenate([comp1, comp2]))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:53.682769Z", "start_time": "2019-12-24T22:46:53.186782Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1f97159f4c8>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(values, bins=100, color='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter or Points Plots\n", "\n", "Point plots or scatter plots can be a useful way of examining the relationship between two one-dimensional data series. " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:53.690787Z", "start_time": "2019-12-24T22:46:53.683809Z" } }, "outputs": [], "source": [ "macro = pd.read_csv(r'examples/macrodata.csv')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:53.825892Z", "start_time": "2019-12-24T22:46:53.691744Z" } }, "outputs": [], "source": [ "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:53.967351Z", "start_time": "2019-12-24T22:46:53.827629Z" } }, "outputs": [], "source": [ "trans_data = np.log(data).diff().dropna()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:54.141421Z", "start_time": "2019-12-24T22:46:53.968348Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cpi</th>\n", " <th>m1</th>\n", " <th>tbilrate</th>\n", " <th>unemp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>198</th>\n", " <td>-0.007904</td>\n", " <td>0.045361</td>\n", " <td>-0.396881</td>\n", " <td>0.105361</td>\n", " </tr>\n", " <tr>\n", " <th>199</th>\n", " <td>-0.021979</td>\n", " <td>0.066753</td>\n", " <td>-2.277267</td>\n", " <td>0.139762</td>\n", " </tr>\n", " <tr>\n", " <th>200</th>\n", " <td>0.002340</td>\n", " <td>0.010286</td>\n", " <td>0.606136</td>\n", " <td>0.160343</td>\n", " </tr>\n", " <tr>\n", " <th>201</th>\n", " <td>0.008419</td>\n", " <td>0.037461</td>\n", " <td>-0.200671</td>\n", " <td>0.127339</td>\n", " </tr>\n", " <tr>\n", " <th>202</th>\n", " <td>0.008894</td>\n", " <td>0.012202</td>\n", " <td>-0.405465</td>\n", " <td>0.042560</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cpi m1 tbilrate unemp\n", "198 -0.007904 0.045361 -0.396881 0.105361\n", "199 -0.021979 0.066753 -2.277267 0.139762\n", "200 0.002340 0.010286 0.606136 0.160343\n", "201 0.008419 0.037461 -0.200671 0.127339\n", "202 0.008894 0.012202 -0.405465 0.042560" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trans_data[-5:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***We can then use seaborn’s regplot method, which makes a scatter plot and fits a linear regression line***" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:54.552572Z", "start_time": "2019-12-24T22:46:54.142418Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Changes in log m1 versus log unemp')" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.regplot('m1', 'unemp', data=trans_data)\n", "plt.title(f\"Changes in log {'m1'} versus log {'unemp'}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In exploratory data analysis it’s helpful to be able to look at all the scatter plots among a group of variables; this is known as a ***pairs plot*** or ***scatter plot matrix***. Making such a plot from scratch is a bit of work, so seaborn has a convenient **pairplot** function, which supports placing histograms or density estimates of each variable along the diagonal" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:57.522249Z", "start_time": "2019-12-24T22:46:54.553570Z" } }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x1f97178ef08>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 20 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(trans_data, diag_kind='kde', plot_kws={'alpha': 0.7})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**plot_kws** - enables us to pass down configuration options to the individual plotting calls on the off-diagonal elements." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Facet Grids and Categorical Data\n", "\n", "What about datasets where we have additional grouping dimensions? One way to visualize data with many categorical variables is to use a facet grid. Seaborn has a useful built-in function **factorplot** that simplifies making many kinds of faceted plots \n", "\n", "* renamed to **catplot**" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:58.051871Z", "start_time": "2019-12-24T22:46:57.523246Z" } }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1f97203d888>" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vapafnOSKYdc0Hts941BVNwI3JrkSeKCqtgAkmQPsPgX1SZIkSTu7JwKvAt5TVWuBU4dcz7gM+gK4a4ATgPub9T2btl/ooihJ6nfMGz407rFz129kDnDP+o3j3s+Vc8f98ZIkAVwIHJxkBfAt4GlVdXiSM4EXAXOAw4G/BHYDXg5sAl5QVRuSHAxcBMwDHgR+r6q+OdU/xKA3R+9RVVtDA83yY7spSZIkSZpRzgFur6qjgDeM2nY48FLgOOD/Ag9W1dHAvwOvaPpcDLymqo4BXg+8Z0qqHmXQMw4PJHlGVX0NIMkxwEPdlSVJkiTNCp+vqo3AxiTfAz7ZtN8MHJlkL3pX+VyeZOuYodwyMGhw+GN6xa5t1n8aOK2bkiRJkqRZY1Pf8iN964/Q+139McB9zdmKoRooOFTVsiRPBQ4BAnyzqv6708okSZKkmWEjMK475qrq+0nuTPLiqro8vdMORzYPMZpSAwWHJHvQuxP8F4ECvpjkvVX1gy6LkyRJknZ2VfVfSb6U5BbgG+PYxcuAv03yZmBX4DJgegYH4EP0ktK7m/WXAB8GXtxFUZIkSdJMUlUvHaPtUuDSvvUDx9pWVXcCi7qtsN2gweGQqnp63/rnk0x5ypEkSZI0HIM+jvXrSX5+60qS44EvdVOSJEmShuGR3R7Hlt0fzyO7PW7YpWgaGvSMw/HAK5Lc06wfAHwjyc1AVdWR2xqYZBHw1/RebPH+qrpw1PbnAO8EjgROr6or+rZtofcoKoB7qurkAeuVJEnSDnpgwYnDLkHT2KDBYbvXVCXZu6q+O0b7HHpvuXsesBpYlmRpVd3a1+0e4Ex6L7MY7aHp8OgpSZKm0tZve/3WV9J0MujjWO/e3vYkXwOeMcam44BVVXVH0+8y4BTgh8Ghqu5qtj0yWMmSJM1sfusraToa9B6HNtlG+77AvX3rq5u2Qe2RZHmSryR50ZgfnJzV9Fm+bt26Hdi1JM0+zpmSpPGarOBQ22gfK1Bsq+9YDqiqhcBLgXcmOfhRO6u6uKoWVtXCefPm7cCuJWn2cc6UpOkpyZYkK5KsTHJjktcleUyzbWGSdw27xkHvcRiv1cD+fev7AWsHHVxVa5u/70hyHXA0cPtkFihJkiT1O+YNH9qRL7pb3fC2V2zr6px+P7y3N8lPAh8BngCcV1XLgeWTWdNoSXapqs3b69P1pUrLgAVJDkqyG3A6sHSgHSZ7J9m9Wd4HeBZ990ZIkiRJM1FVfQc4Czg7Pc9N8imAJOcnuSTJdUnuSPLapv3AJN9I8nfNWYtrkuzZbDs4yWeS3JDki0me2rRfmuQdST4PvLWtroGDQ5JnJHltktckGX0j9K9s44feDJwNXE3v9dofq6qVSS5IcnKz32OTrKb3Fur3JVnZDH8asLx50dzngQtHPY1JkiRJmpGahws9BvjJMTY/FTiJ3oOIzkuya9O+ALioqg4D7gN+s2m/GHhNVR1D70mm7+nb188BJ1TVn7TVNNClSknOpfeL/Seapr9PcnlVvaX5wTZsa2xVXQVcNart3L7lZfQuYRo97svAEYPUJ0mSJM1A27qq55+rahOwKcl3gJ9q2u+sqhXN8g3AgUn2An4BuDz54e5279vX5VW1ZZBiBr3H4SXA0VX1A4AkFwJfA94y4HhJkiRJA0ryFGAL8B16V+L029S3vIUf/U4/un1Pemct7tvOu9EeGLSmQS9VugvYo299d7xJWZIkSZp0SeYB7wX+pqomdKN2VX0fuDPJi5t9J8nTx7OvQc84bAJWJrmW3uNUnwf829bHQlXVa8fz4ZKkmck3H0vSDtszyQpgV2Az8GHgHZO075cBf5vkzc3+LwNu3NGdDBocrmz+bHXdjn6QJGn28M3HknZmAz4+dVJV1ZztbLuO5vfvqjp/1LbD+1YP72t/e9/yncCiMfZ75o7UOFBwqKoP7shOJUmSJM0s2w0OST5WVf8zyc2M8cbnqjqys8okSZIkTRttZxz+qPn7G8Ab+toDLOmkIkmSJEnTznaDQ1V9u1n82aq6u3/b1jfOSZIkSZr52i5V+kPgVcBTktzUt2ku8KUuC5OkyeDTfSRJmhxtlyp9BPg08BfAOX3tG7f3tmhJmi58uo8kSZOj7VKl7wHfo/fmaEmSJEkdSHJ/Ve3V0b7PBBZW1dkT2c+g73GQJEmSZoV7LjhiQm9rHu2Ac2+e8vdCdOExwy5AkiRJ0qMluTTJqX3r9zd/PzfJdUmuSPLNJP+YJM22Y5N8OcmNSa5PMrcZ/uQkn0nyrSTjejqqZxwkSZKknc/RwGHAWnoPLXpWkuuBjwKnVdWyJI8HHmr6H9WM2QTcluTdVXXvjnygwUGSJEna+VxfVasBkqwADqR3b/K3q2oZQFV9v9kO8C/N/cskuRX4GWCHgoOXKkmSJEnT02aa39ebS5F269u2qW95C70TAgG2dX/GWP13iMFBkiRJmp7uAo5plk8Bdm3p/0169zIcC5BkbpJJu8LIS5UkSZKk4XtsktV96+8A/g74f829C/8CPLC9HVTVw0lOA96dZE969zecMFkFGhwkSZKkPsN4fGpVbetKoJ/vW35j0/c64Lq+sWf3LS8bNQbg0ubP1j6/Op4avVRJkiRJUiuDgyRJkqRWBgdJkiRJrQwOkiRJkloZHCRJkiS1MjhIkiRJamVwkCRJktTK4CBJkiSplcFBkiRJUiuDgyRJkqRWBgdJkiRJrQwOkiRJkloZHCRJkiS1MjhIkiRJamVwkCRJktTK4CBJkiSplcFBkiRJUiuDgyRJkqRWBgdJkiRJrQwOkiRJklp1HhySLEpyW5JVSc4ZY/tzknwtyeYkp47adkaSbzV/zui6VkmSJElj6zQ4JJkDXAQ8HzgUeEmSQ0d1uwc4E/jIqLFPAs4DjgeOA85LsneX9UqSJEkaW9dnHI4DVlXVHVX1MHAZcEp/h6q6q6puAh4ZNfYk4Nqq2lBV3wWuBRZ1XK8kSZKkMXQdHPYF7u1bX920TdrYJGclWZ5k+bp168ZdqCTNBs6ZkqTx6jo4ZIy2msyxVXVxVS2sqoXz5s3boeIkabZxzpQkjVfXwWE1sH/f+n7A2ikYK0mSJGkSdR0clgELkhyUZDfgdGDpgGOvBk5MsndzU/SJTZskSZKkKdZpcKiqzcDZ9H7h/wbwsapameSCJCcDJDk2yWrgxcD7kqxsxm4A/oxe+FgGXNC0SZIkSZpiu3T9AVV1FXDVqLZz+5aX0bsMaayxlwCXdFqgJEmSpFa+OVqSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1Krz4JBkUZLbkqxKcs4Y23dP8tFm+1eTHNi0H5jkoSQrmj/v7bpWSZIkSWPbpcudJ5kDXAQ8D1gNLEuytKpu7ev2SuC7VfWzSU4H3gqc1my7vaqO6rJGSZIkSe26PuNwHLCqqu6oqoeBy4BTRvU5Bfhgs3wF8CtJ0nFdkiRJknZA18FhX+DevvXVTduYfapqM/A94CeabQcl+XqSf03y7LE+IMlZSZYnWb5u3brJrV6SZhjnTEnSeHUdHMY6c1AD9vk2cEBVHQ28DvhIksc/qmPVxVW1sKoWzps3b8IFS9JM5pwpSRqvroPDamD/vvX9gLXb6pNkF+AJwIaq2lRV/wVQVTcAtwM/13G9kiRJksbQdXBYBixIclCS3YDTgaWj+iwFzmiWTwU+V1WVZF5zczVJngIsAO7ouF5JkiRJY+j0qUpVtTnJ2cDVwBzgkqpameQCYHlVLQU+AHw4ySpgA71wAfAc4IIkm4EtwB9U1YYu65UkSZI0tk6DA0BVXQVcNart3L7lHwAvHmPcx4GPd12fJEmSpHa+OVqSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1MrgIEmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWhkcJEmSJLUyOEiSJElqZXCQJEmS1Krz4JBkUZLbkqxKcs4Y23dP8tFm+1eTHNi37Y1N+21JTuq6VkmSJElj6zQ4JJkDXAQ8HzgUeEmSQ0d1eyXw3ar6WeCvgLc2Yw8FTgcOAxYB72n2J0mSJGmKdX3G4ThgVVXdUVUPA5cBp4zqcwrwwWb5CuBXkqRpv6yqNlXVncCqZn+SJEmSpliqqrudJ6cCi6rqd5v1lwPHV9XZfX1uafqsbtZvB44Hzge+UlX/0LR/APh0VV0x6jPOAs5qVg8BbuvsB5q4fYD1wy5iJ+bxmxiP3/hN92O3vqoWDdLROXNW8fhNjMdv/Kb7sRt4ztSP26Xj/WeMttFJZVt9BhlLVV0MXLzjpU29JMurauGw69hZefwmxuM3fjPp2Dlnzh4ev4nx+I2fx27m6vpSpdXA/n3r+wFrt9UnyS7AE4ANA46VJEmSNAW6Dg7LgAVJDkqyG72bnZeO6rMUOKNZPhX4XPWun1oKnN48dekgYAFwfcf1SpIkSRpDp5cqVdXmJGcDVwNzgEuqamWSC4DlVbUU+ADw4SSr6J1pOL0ZuzLJx4Bbgc3Aq6tqS5f1ToGd4vKAaczjNzEev/Hz2A2Hx31iPH4T4/EbP4/dDNXpzdGSJEmSZgbfHC1JkiSplcFBkiRJUiuDwyRK8qYkK5PclGRFkuOHXdPOZkeOYZIzkzx5KuubTpL8RHOMViQZSbKmWb4vya3Drm9nlWRL33FdkeTAMfo8OckVjx6tHeGcOXHOmYNzzuyGc+bs0vV7HGaNJM8EfhV4RlVtSrIPsNuQy9qpjOMYngncwix9TG9V/RdwFECS84H7q+rtzaT9qfHuN8kuVbV5MmrcST1UVUdta2NzfNbSewqcxsk5c+KcM3eMc2ZnnDNnEc84TJ6fpvcmwk0AVbW+qtYmuauZzEmyMMl1zfL5SS5Jcl2SO5K8dnilTxvbOobnJlmW5JYkF6fnVGAh8I/NNxx7DrXy6WdOkr9rvom8Zuvxaf5/W9gs75Pkrmb5zCSXJ/kkcM3wyp6eRh+fJAc2b73X+DlnTpxz5uRxzpxEzpkzl8Fh8lwD7J/kP5K8J8kvDTDmqcBJwHHAeUl27bTC6W9bx/BvqurYqjoc2BP41aq6AlgOvKyqjqqqh4ZV9DS1ALioqg4D7gN+c4AxzwTOqKpf7rSy6W/PvlPuV/a1e3wml3PmxDlnTh7nzPFzzpxFvFRpklTV/UmOAZ4N/A/go0nOaRn2z803RZuSfAf4KXpvzJ6VtnMMNyZZDDwWeBKwEvjk8CrdKdxZVSua5RuAAwcYc21VbeiupJ3Gtk67e3wmkXPmxDlnTirnzPFzzpxFDA6TqHlB3XXAdUlupvdG7M386MzOHqOGbOpb3oL/PcY6hr8PHAksrKp7m+tSRx9HPdro/7e2Xpawvf8fH+i6qJ2cx2eSOWdOnHPmpHHOnHwenxnIS5UmSZJDkizoazoKuBu4CzimaRvk1OestY1jeFuzvD7JXvz4zVUbgblTVd8McRc/+v/RG9U0NM6ZE+ecOSXuwjlT+qFZ/23NJNoLeHeSJ9L7hmIVcBbwNOADSf438NUh1rcz2NYxvA+4md4EvjA24VQAAAHmSURBVKyv/6XAe5M8BDzTa3YH8nbgY0leDnxu2MVoVnPOnDjnzO45Z0p9UlXDrkGSJEnSNOelSpIkSZJaGRwkSZIktTI4SJIkSWplcJAkSZLUyuAgSZIkqZXBQRqHJOcnef2w65CknYFzpjQzGBwkSZIktTI4SANK8qYktyX5LHBI0/Z7SZYluTHJx5M8NsncJHcm2bXp8/gkd21dl6TZwDlTmnkMDtIAkhwDnA4cDfwGcGyz6RNVdWxVPR34BvDKqtoIXAe8sOlzOvDxqvrvqa1akobDOVOamQwO0mCeDVxZVQ9W1feBpU374Um+mORm4GXAYU37+4HfbpZ/G/j7Ka1WkobLOVOagQwO0uBqjLZLgbOr6gjg/wB7AFTVl4ADk/wSMKeqbpmyKiVpenDOlGYYg4M0mC8Av55kzyRzgV9r2ucC326uxX3ZqDEfAv4JvzmTNPs4Z0ozUKrG+kJA0mhJ3gS8ArgbWA3cCjwALG7abgbmVtWZTf/5wJ3AT1fVfcOoWZKGxTlTmnkMDlJHkpwKnFJVLx92LZI03TlnStPfLsMuQJqJkrwbeD7wgmHXIknTnXOmtHPwjIMkSZKkVt4cLUmSJKmVwUGSJElSK4ODJEmSpFYGB0mSJEmtDA6SJEmSWv1/ouRHuYgzDPMAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 789.625x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(x='day', y='tip_pct', hue='time', col='smoker',\n", " kind='bar', data=tips[tips.tip_pct < 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of grouping by 'time' by different bar colors within a facet, we can also expand the facet grid by adding one row per time value" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:58.709121Z", "start_time": "2019-12-24T22:46:58.052833Z" } }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1f971f88988>" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(x='day', y='tip_pct', row='time', col='smoker', kind='bar',\n", " data=tips[tips.tip_pct < 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***catplot*** supports other plot types that may be useful depending on what you are trying to display. For example, box plots (which show the median, quartiles, and outliers) can be an effective visualization type" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2019-12-24T22:46:59.738449Z", "start_time": "2019-12-24T22:46:58.710079Z" } }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1f9728666c8>" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(x='tip_pct', y='day', kind='box', data=tips[tips.tip_pct < 0.5])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "281.4px" }, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, 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