Unit9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Python计算生态概览\n",
    "> 从数据处理到人工智能；实例15：霍兰德人格分析雷达图；从Web解析到网络空间；从人机交互到艺术设计；实例16：玫瑰花绘制；\n",
    "\n",
    "## 9.1 从数据处理到人工智能\n",
    "数据表示（采用合适方式用程序表达数据）-数据清洗（数据归一化、数据转换、异常值处理）-数据统计（数据的概要理解、数量、分布、中位数等）-数据可视化（直观展示数据内涵的方式）-数据挖掘（从数据分析获得知识，生产数据外的价值）-人工智能（数据/语言/图像/视觉等方面深度分析与决策）；       \n",
    "\n",
    "### 9.1.1 Python库数据分析\n",
    "1. Numpy：表达N维数组的最基础库；Python接口，C语言实现，计算速度优异；数据分析和科学计算的基础库，支撑Pandas等；提供直接的矩阵运算、广播函数、线性代数等；    \n",
    "2. Pandas：Python数据分析高层次应用库；提供简单易用的数据结构和数据分析工具；理解数据类型与索引的关系，操作索引即操作数据；基于Numpy开发；Series=索引+一维数据；DataFrame=行列索引+二维数据；      \n",
    "3. SciPy：数学、科学和工程计算功能库；提供了一批数学算法及工程数据运算功能；类似Matlab，可用于如傅里叶变换、信号处理等应用；科学计算，基于Numpy开发；     \n",
    "\n",
    "### 9.1.2 数据可视化\n",
    "1. Matplotlib：高质量的二维数据可视化功能库；提供了超过100种数据可视化展示效果；通过matplotlib.pyplot子库调用各可视化效果；基于Numpy开发；    \n",
    "2. Seaborn：统计类数据可视化功能库；提供了一批高层次的统计类数据可视化展示效果；主要展示数据间分布、分类和线性关系等内容；基于Matplotlib开发，支持Numpy和Pandas；     \n",
    "3. Mayavi：三维科学数据可视化功能库；提供了一批简单易用的3D科学计算数据可视化展示效果；支持Numpy、TVTK、Traits、Envisage等第三方库；\n",
    "\n",
    "### 9.1.3 文本处理\n",
    "1. PyPDF2：处理PDF文件的工具箱；支持获取信息、分隔/整合文件、加密解密等；不需要额外依赖；    \n",
    "2. NLTK：自然语言文本处理第三方库；简单易用的自然语言处理功能；支持语言文本分类、标记、语法句法、语义分析等；优秀语言处理库；    \n",
    "3. Python-docx：创建或更新Microsoft Word文件的第三方库；提供创建或更新.doc、.docx等文件的计算功能；增加并配置段落、图片、表格、文字等；   \n",
    "\n",
    "### 9.1.4 机器学习\n",
    "1. Scikit-learn：机器学习方法工具集；提供一批统一化的机器学习方法功能接口；提供聚类、分类、回归、强化学习等计算功能；优秀机器学习库；   \n",
    "2. TensorFlow：AlphaGo背后的机器学习计算框架；谷歌公司推动的开源机器学习框架；将数据流图作为基础，图节点代表运算，边代表张量；应用机器学习方法的一种方式，支撑谷歌人工智能应用；     \n",
    "3. MXNet：基于神经网络的深度学习计算框架；提供可扩展的神经网络及深度学习计算功能；可用于自动驾驶、机器翻译、语音识别等众多领域；    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2 实例15：霍兰德人格分析雷达图\n",
    "雷达图是多特性直观展示的重要方式；     \n",
    "霍兰德认为：人格兴趣与职业之间应有一种内在的对应关系；   \n",
    "人格分类：研究型、艺术型、社会型、企业型、传统型、现实型；   \n",
    "职业：工程师、实验员、艺术家、推销员、记事员、社会工作者；     \n",
    "需求：雷达图方式验证霍兰德人格分析；   \n",
    "输入：各职业人群结合兴趣的调研数据；   \n",
    "输出：雷达图；    \n",
    "通用雷达图绘制：matplotlib库；专业的多维数据表示：numpy库；    \n",
    "\n",
    "### 9.2.1 实例展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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yNzcnd3d3kslkdbaLj48nFotFp06dYoQvEdGWLVtIT0+PBg4cSCdOnFCxEnn8+DHt37+f+vfvT4sXL37t+CZNmkQcDoekUqlS+dWrVwmAymy3mrKyMjI3NycHBweaM2cOGRoakkKhIDc3N7KysqLy8nLS1NSkTZs2MW3auvAlaj5rh6ysLGKxWJSQkEAKhUJl/9mzZ8nExIQeP35MkydPppKSEmbfF198QcuXL2c+y+VyOnjwIEkkEhKJRHT06FEaNGgQhYSEUGpqKkkkEpowYQLt2bOHiIj27dtHurq6dO3atSY5l4agXnBrBRQKBcaMGYPjx49j/PjxuHHjBrMg9DJisRirV6/Gl19+yZTJZDJ4eHjg7t27Kv2OHz8e3t7e0NLSUllEq2kMUqlUJbCHiYkJdu/ejdDQUEyePJkp/+233xAREQEOhwMul1uj3pmImH6nTJmCbdu21Xr869evIzo6Gh999BHatWsHAFiyZAnmzp2LL7/8EsuWLau17fz589GtWzcMHToUmzdvZsrnzJmDiIgIjBw5EiNGjKi1/aFDhxAWFlbr/mr+/PNPeHh4qJxrtXNMbV5us2fPRkFBAX744QfGmJ/FYmHfvn2wtbVFamoq5HI5c95vC0Y63DdeXKsJS0tLzJgxA5MnT2bcuV9GX18fW7duRceOHVWcIcRiMWQyGfNZQ0MDEydOBFDl7RkdHY3PPvsM/v7+yMvLg5GREYYMGYIJEyYAAKZOnQo/Pz+4uLg0+Xm9ES33H/DvY+fOnbRq1SoiInJycqIJEyaoPL5raWnR2rVrmc/l5eWUnZ1N6enpLTLGl2e+NSGVSmnHjh0N7re0tJQ6dOhAZmZmlJubq9Sfu7s7sdnsWtUPpaWl5OrqSidPniQiUpr5VhMaGkq2trYqqpZq2+r6mBfFxsYSgBpnyJs3byYAlJycrLIvKSmJWCwW+fj4kEKhYJwsXmbv3r0EgFGJEL0dM99/AvVVibUEdV1/dWCdZmTmzJlK7rfV5k0vb2KxGEuWLGE+GxgYwNbWFkuXLm3x8cpkMnzzzTd48eIFoqKisH//fpw9exazZs1SsmyIj4/HkSNHau1HLBZj+PDhePbsGfbt26cU04HNZuPw4cPQ0tLC0KFDcfnyZZX2hoaGiI2NxbBhw2o9RrWVQrXrcfVWXV4fU7ylS5eCxWIhPDxcZV/1zNfW1lZln6urK7Zu3Yp9+/bVuop+4cIFWFhYMKZpapTZtGkTsrKy6l2/tLQUoaGh9WrD4/HeZGgthlr4tiDjxo1DQUGB0qalpYVVq1YplWVnZ9cYr6C5uXTpEj799FMkJibixIkTOHr0KEJDQxEUFIRVq1Yx9b766iuEh4fj/v37Kn0UFhYiKCgIly9fxldffYXQ0FCVOq6uroiOjoZEIkFwcDDWr1+vEoWqplgJL/OmpkNff/01zp07hylTptT4OPr8+XPo6enVags8e/bsWoO75Obm4vTp0/D393+jMf6TuXXrFvbt26dSfvLkSXh7e8PPzw/dunXDgwcPAABRUVHIycnBgQMHauxv+PDhcHJygoeHh9LWsWNH9OnTpzlPpdGodb4tiEgkUgmiQ0Tg8/kq5XK5HM+fP4e3tzc0NDRw/PhxxMXFgcvlMhHH6ku1jtbGxgYff/yx0j6JRAKpVAoAOHz4MGxsbBAYGIgDBw4wTiHbtm2Djo4OiouLsXnzZnz22We4efMmJkyYgNjYWGamcerUKXz88cfIzc3F+vXrsXDhwlrHNHjwYJw8eRJjx47F4sWLER0djdWrV2Pw4MEqdalqYVjlnLKzs2sVwq/Wr0Yul+OLL77A6tWr4e7uji1btqjUKSgowKVLl+Do6Fjr+F9GKpUqHW/hwoUQiUSYMmWKypjrGts/GX9/f+Tn5yv9mT19+hRnzpwBUGUHb2Njg6tXr6J79+5o164dhgwZAkNDQ1RUVGDfvn24cuUKRo4ciT59+qgIVC0tLezcuRMDBw5UKr9y5QrWrFnT/CfYCNTCt4UQiUT4/fffcf36daVyfX197N+/X2UhTi6XQyAQoKioCHp6erh48SIOHjwIDodTb+82AExwdolEAl9f3xqFr0QigUgkwunTpzFlyhRoaGjAwMAAt27dwt27d6GnpwehUIjDhw9jzZo1sLOzw/bt23H79m3GW+0///kPNm7cCD09PRw7doyJWlUXQ4YMwZ07d/DBBx/g3r17OHnyZI3CVy6XKy24VJdZWlriypUrSuV///03Jk2apFJfoVDg8OHDWLVqFR4/fowePXrg7NmzSg4Qx44dw6xZs1BcXAypVIrIyMjXnsPL1xAANm/ejKioKHh6eqoIArFYrPTaJhEUA8VpgKgM4BkCJk6AzuvDhr4OTU1N7N69G3Z2dmjfvj3YbDYuX74MIkK/fv1w5swZfPPNNwCAgIAAPH36FIWFhTA3N8eMGTMwZ84cGBsb49ChQxg0aBDGjBmDWbNmMd6BhoaGWLRokYq6TiqVtr2FtmqaX+WshojI1NSUFixY0NrDUKFfv37k5eVFRFULXfn5+URE9Mcff5ClpaWSyRqHw6GgoKAaFzQqKipo5syZjVoolMlkdOjQISXD+Zfx8vKioKAgpbIBAwaQra2tSt3Lly8TADp48KBSuVQqpSlTphCXy6Xly5fXeCypVEo2Njb07rvv0ubNm2s0iaqJKVOmEIvFIiKilJQU6t+/f41mTZmZmQRAKUJXc9OgBTd+EVHq70TpN4iyYqteU3+vKn9DkpOTqby8nNauXUvu7u50+vRp+vLLL2nhwoVEVLVIVv3dc3d3Z16PHj1Kw4cPJy8vL/Ly8iJPT0+aPXs2jR49muLi4t54XM1NXddf7V6sps2Tnp4ODQ0NpcWrsrIyKBQKlVjHdSGTyZCWltbkM6GcnByUlZUxMY+JqM3E6q3TvfVVsu4AMgnA1flfmUQAsLmAXY83Gkdubi5GjRqF5cuXo1OnTkhNTcXVq1fRvn17fPTRR0p13d3d8eDBA3h7eyM2NhYKhQIsFgv37t2Dt7c3nj17BgsLC+joVI0zJSUF3bp1U4rL/DIPHjxAUlISOnXq9Ebn0Bjquv5qtYOaNk9NCQgNDRsWTwCosoxojkdQa2trWFtbM5/biuCtkRdxte/LvgdoGQAvj58IEJcDGnWIChvP1x7WysoKBw8exLx587B9+3YEBwfj4MGD6N+/v0pdFosFDw8PVFRUYMyYMXB0dMTQoUPx0Ucf4eHDh5gxYwaWLl0KPz8/AFXWDaampoxd/M2bN6FQKJgFTzMzs9cu4LYGauGrRs2/iboEpUJW+8y3HgK2Nm7duoVJkyZBV1cXGhoaGDFiBBQKBVJSUpCYmAg2mw2FQoGKigqcP3+esaI5d+4cTpw4AT8/P1y6dAkuLi5IT0/HixcvGMFbE9HR0TA0NFSyNmmLD/hq4atGjZoqTJyA7P/3quRoA1IhIOUDFvVUW9RCz5498ejRI+YzEWH27NkICAhA7969ERwczCyc7dq1C8OHD4dcLsfo0aNhZmYGPz8/bN68GR9//DHu378PHx8fpf4VCgUTRlShUODixYvYuXMnkpKS0LlzZ3h4eDAWPW0JtZ2vGjVqqtAxAWy7V810hcVVr7bdm8TaAaiyPDh79ix69uyJiooKrF27Frdv30bXrl2xZcsWSCQShISE4LfffkNiYiI6dOjAqAwOHjyIUaNGISUlBYMHD1YSpo6Ojrh37x7KysowdepUuLq6olevXpgxYwbc3NwwYcKEtunm3TJrfmrUqGkN2oJ7sUKhoDlz5pClpSW9//779Mcffyjtz8zMpGHDhlHfvn2Z+kOGDCFra2v6/fffyd3dncn5Vr0NHjyYiKryw4WHh5O/vz/Z29vT+vXrlYI2xcXFUUhICLVv377ODCfNhdraQY2afykNsnZoRvLz86GrqwtdXd1a65SVlTELqTdv3oSFhUW9LBT27NmDrl27wsfHp1Yb+NjYWHh7ezdu8G9AXddfLXzVqPkH01aE77+Vuq6/WuerRo2aNodIJMIff/yh4hFaF0uXLkVOTk6Ni2tyuVwph2JbQG3toEaNmmZn3bp12LNnD5N4tdodm8vlQqFQgM/nY/78+QgPD8fAgQORm5sLKysrfPTRR5DJZBg5ciSTu08qlaK4uJiJPFfNiRMnMGjQIISGhoLL5SIvLw9isRj29vaQyWRYvHhxnZHyWhq12kGNmn8wDVU7lInLkFGegQpJBfS5+nAwcIChVsMdWl7H4sWLwePxsHLlyhr3HzlyBCkpKVi5ciX+/PNP7Nmzh4lolpWVBV9fX2RkZIDP58PBwQGFhYXo2rUrbty4ASMjIwDAvn37kJWVVesxWgK1h5saNWpeS5m4DA8LHkKbow1DLUOI5CI8LHgIN3O3ZhHANZGRkYFvvvkGaWlpKCoqglQqRf/+/XHy5EncunULQJWbePXMWUtLSyk4ElAVXa+yshK3bt1CcXExfvzxRwDA2LFjmUBQbQG1zleNGjUAgIzyDGhztKHN1gaLxYI2WxvaHG1klGe02BiMjY0xcuRIcLlcdO7cGYMHD0bfvn1RVlaGlJQUpKSk4MmTJyoqh5dZtGgRMjMzUVlZCYFAgMLCQsyZM0cl0l1ro575qlHzLyKxKLHWffGF8dDn6CvFpiAiVEgroKlRexjTLqZdmmx8BgYG8PT0xPXr1zF+/HgUFxdj165d2Lt3L3g8HkQiEbS1tVFRUYHvv/++xmA6HA4HH3/8MY4dO4asrCzMnTsXW7ZsaVOzXkAtfNWo+VdRl6CUK+SQKCTQZv8vCI1QJgRXg9ukAvZ1zJ07F/b29uByuVi4cCHOnTuHGTNm4MmTJ4iIiFCK4fzybFYgELTJADq1oVY7NBEikajejzX1MXl59uxZjeVPnz5tyLDUtDJv0712MHCAUCqEUCYEEUEoE0IoFcLBoOXy0P3444/466+/MG/ePBgYGODSpUuoqKiAs7Mzhg8fjtjYWHh5eaFXr14AqmI5EBFEIhFEIhFjTfE2oJ751sGoUaMQFRXF/JuOGzcOS5YsQbdu3VTqzps3DxKJRCUjRXU/169fR48ePXD79m3MmDEDHTt2RHBwMD744AOcP39eKXX5+vXr0alTJ7Rv316lr19//RXm5ub1SovenBARysvL8eLFC+Tk5DAxbWUymdIml8tVklxyOByYmZnB2toaNjY2sLa2bhNJD0NCQhAVFQUTExP06dMH165dq7Hehg0bYGhoiKFDh2Lx4sX44YcfmNROb/O9NtQyhJu5GzLKM1AmLoM+Vx+dzDu12GIbALz//vvo1KkT0tPTAVSF67S0tERycjLS09NrnPm+8847OHDgAPr16wcAKna+MTEx4HA4Ste9LdC2RtPGCA0NRWFhIROUg81m1/hYs2XLFtjY2EAgEGDv3r2YNm2a0v5jx47h/fffx6+//oqgoCCsXr0as2bNQkBAAHg8ntKX4tmzZ8jMzMSiRYvA5/PxwQcfoLi4GPb29jh48CA++eQTTJgwAe+//36tyR2bAolEgsTERCQkJCA7O5sRsC9evIBAIACLxYKBgQETy9bGxgYGBgbQ0dFhvuhsNhsaGhpMGiCpVMqsVKempuLKlSvIyclBbm4uhEIhAMDExITp09raGvb29vD09ESHDh2aJU5ubGwsFixYAB6Ph3v37mHcuHHQ0NBAYmIiBg4cCIlEgkmTJmHSpElMGy6XCy0tLVhbW8POzg5//vknE+KwsfcaAIqLi+Hg4ICCggLweLwWu9cvUy2Am5KdO3diw4YNSr+d8vJyAGCyYBMRBAIBZs6cic8++wxPnjxhnhrqylmoo6ODFStWYNSoUcyf5dSpU8Hj8ZiceTo6OoiJiWnSc2oK1MK3Bk6dOoULFy6AxWLhyZMnWL58udIXQCKRgMvlQiqVYuHChbC0tMSyZctARPj8888xefJkfPXVV7C0tERMTAx++uknPHjwABEREUhOTsa0adNqfTyKiopicodFRUWhV69eWLhwISIiInD37l306NEDH3zwAU6dOoWJEyc2yflKJBIkJCQgNjYWsbGxuH//PmQyGbp06QI3Nzc4OjrC19eXEYh1+ee/CUSEkpISJUGfmJiIqKgopKenwwLpdoEAACAASURBVNzcHN7e3vD29oaXlxecnJzeWCB7e3szM6n+/fvj9OnT4PF4eO+993D+/HkAwOXLl9GrVy9oaWmBiJCRkQFNTU3G7jQmJgabNm2CUChs9L0GqtLNi0QiXLt2DQMGDACAJr/XrUFkZGS98+FVU50XLysrC/3794eOjg5kMhlycnLQvXt3SKVS9O3bF7Nnz8aUKVNw+vRpdOzYEQCYxK1isRhisRi9e/du8nNqEpo1pM9bSllZGeXn59PixYtp48aNtGXLFurTpw9ZWFjQu+++S76+vnTlyhXq3bs3bdmyhfr160chISEUEhJC/fr1o3379pGbmxudOnWKKisrKS4ujvr160cFBQXk7+9Pubm59NFHH1FKSgqFhIQoHXvSpEnM+9OnT1PPnj0pNTVVqU5xcTHNmzevUeemUCgoMTGRdu3aRdOmTSMfHx/q3r07TZo0ibZt20Y3b96kysrKRvXd3OTl5dG5c+dozZo1NHz4cHJ3d6f33nuP/vOf/9Dhw4cpLy+vUf1u27aN+vbtS++99x4FBwdTcHAw9e3blwICAuj+/ftKdXfs2EHW1tbk5OREv/zyi9K+N7nXRETh4eH0ySefKN3bN7nXRG0jqllzI5fLW3sItVLX9VcL31q4ePEi2dra0tatW0kikRAR0YQJE+jx48dERMTn8yk7O7vGGy+Xy0koFJJUKqXKykoaN24c/fTTT0RE1LNnT6be33//TZ6enkptX/1B/vzzz9S1a1eaPXs2EypPKBTSjBkz6n0uEomELl26RHPnziUPDw8aOXIk7dixg/766y/i8/n17qctkp+fT+fPn6c1a9ZQQEAA+fn50bp16yghIaHeCTBfprS0lCIiImj48OH0999/K+0TCATUtWtX+vzzz2nVqlXUt29fpfv/pvfaxcWFCgoKyM3NjSlr6L1+lbYqfEUiERFRrUlT/ynUdf3V1g41cPToUWzatAkODg7Q1tbGmTNnVOro6Ohg0aJFCAkJwZAhQ9CxY0cMGTIEQ4YMQUhICGbMmAE2m42SkhL06NED48ePBwCEhYXh2LFjAKr0Xq+6Pmpra6OyshIA8PjxYwwcOBD3799HQUEB46mTnp7+2uDQpaWlOHLkCMLCwuDt7Y1jx45h4MCBuHXrFo4dO4bIyEj07NmTSUL4tmJubo7g4GAsWbIEV65cwcmTJ2FjY4OVK1fC09MTc+fOxaVLl+qVyeDs2bN4//33MXLkSJw4cQI9eignjVy0aBHCwsJgbm4Oe3t79O7dG8uWLWP2v8m9fvjwIQoLCzFq1ChGFwzU716/Dbi7uzPZLMrKymBvb4/c3Fy88847yM/PV6obHh6Oq1evAqhaPJNKpUhPT4evry8AgM/nt+zgm4sW/BN4ayguLiaZTEaBgYFK5S/PfF9m48aNFB4eTkKhkL799luV/T///DP5+/tTYGAgubm5kYuLCwUGBlJAQADt2LFDqe7p06fpwIEDRES0cOFC5v2aNWto165dRES0cuVKevDggcpx0tPTafPmzRQYGEg9e/akVatWUVxcXKNmgP8ERCIRnT9/niIjI8nNzY2ZlZaVlSnVk8vltGDBAho3bhyVl5cr7Xvw4AHJZDL6/PPP6f333ye5XE6bN2+mH374gaRSKQ0aNIgiIiKosLCQiBp/rzds2EAbN24kIqKlS5fS3r17iaj2e11f2srM19/fnwlmfuTIERo3bhwREX399dd069Ytpl5qairp6urSixcviIhowYIFtGnTJnrx4gUFBASQXC4nJycnSktLY9oMGzaMHB0dyd3dXWlzcnIif3//FjxLVdRqh0YSGBhIeXl51LNnTwoMDKT27duTn58fvffee7Ru3TqSyWS0Zs0aWrx4MSkUCpLJZBQZGUnLli2rtc+TJ0/S9u3ba92vUCho9OjRlJeXR9nZ2YzucejQocTn8+nRo0c0bdo0pr5YLKbo6GgaMGAABQYG0q5duygrK6tJr8M/AYVCQXFxcbRq1Sry9PSkKVOm0F9//UUKhYKioqIoNDSUpFKpUhuJREIuLi70xRdf0KhRo5hH5WrhW11nyZIllJCQoHLMhtzrwMBAiouLIyKiS5cu0ciRI1XudWNoqPCVlZQQ//59Kr92jfj375OspOSNji+TyUgqlVJAQABlZWWRTCajAQMGUI8ePWjkyJE0cuRIGjFiBPXv359iYmJowIABZG5uTu7u7jR58mTKyMgga2trSktLo4CAAPr5559pxIgRSscYO3YsnTt3TuXYly9fVplAtTTqTBaNxMfHB7dv3651/9GjRzFnzhy4uLiAx+NBW1sbhoaGSE1Nxfz58zFq1CicPn0aGzduZFwbi4uLIRaLmVTjcrkcI0aMwOzZs5l+s7KycO3atRrtO3ft2oWwsDDk5eVh7969OH/+PEJCQjB16lRmtVdN3RARrl69ir179+Lx48cIDAxEfHw8+Hy+UpZbDQ0N9OzZE19++aVS+6+++grGxsaYPn26Unlz3evq5JKNoSFRzeSlpRA8eAgNHR2weDyQSASFQAAddzdo/n+ksIYSGxuLWbNmISkpCR07dsSwYcOwfPlybNu2DZ6enhgxYgROnDgBsViMgoICbN68GdeuXWMsRBQKBe7duwcdHR1MmzYNe/bsgUwmU3Irnj59Om7duqXiPiyVSuHi4oLo6OhGjb0pUGeyaGEqKyvB5XLB5XKbtF8iwh9//IFt27ZBKpVixowZeP/999ucz/rbRHFxMaKiovDf//4XPXv2xJw5c/DOO++09rCajIYIX8GDByCJFBov2eMqhEKwuBzo1BBDoSH07dsXR44cwejRo5Geno4ff/wRffv2hZ2dHTIzMyGVSsHlcrF48WJER0czrsVBQUHo3r0740ABVJn+5eXlvdF4Wgp1SMkW5tUQd28Kn8/HwYMHsX//fnh7e2PDhg3o0qXlfO3/yZiYmOCTTz7B7Nmzcf78ecydOxcsFgtz5sxBcHBwnQb+byPChNoD6wjjE6Chp6cSWEdRWQmWZu2iQrtr/b6Lubm5GD9+PB4+fIjZs2fD0NAQBQUF8PPzA4vFwo0bN/DVV18hNzcXM2bMQM+ePZGTk4POnTszzhgAYGdnx7xPSUlBt27dagywAwAPHjxAUlJSvXLBtTRq4duGkUgk2L17N/bu3YuwsDD8/vvvMDU1be1h/SPR0NDA4MGDMXjwYCQnJ2Pr1q1YvXo1Vq1ahf79+7f28JqMugQlyWW1znzrK2Bf5cGDBzh8+DCSkpKwY8cOrFmzBg8fPsT27duZme/NmzeZuCiffPIJ7ty5A7FYjNOnT2POnDmIi4tjPAhfhcfjwdTUFHfv3gVQlXhToVDA398fAJjU822Rf9bf+j8EhUKBQ4cOwcfHB2VlZfjzzz+xePFiteBtIVxdXbFr1y78+OOPOHjwIAYMGIA7d+609rCaHS0HBygEAiiEVYF1FEIhFAIBtBwaH1hHT08PvXv3RufOnbFmzRpYWVkBqNLTdu/eHfn5+fD29kbPnj0BAMHBwbC1tUXPnj3x3nvvAQA8PT1x48YNZquL6OhoXLhwQamsrWpW1cK3DUFE+O2339CrVy/ExsbiwoULWLp0aZOrMdTUD0dHR0RFReHrr7/GqlWrMHr0aKSmprb2sJoNTSMj6Li7gcXlQF5a8v+63sYvtgGAk5MThg4dqlK+e/du3L17FxYWFoiNjWVmroMHD4aNjQ28vb3h5OQEAMzMt3p7GYVCARsbG+b9xYsXERgYiKSkJACAh4dHvWy8WwO12qGNcOvWLXz++eewt7dHdHQ0HN5gtqGmaXF3d8eZM2dw7do1REREwNXVFStWrGB+9P8kNI2MoPMGwvZViAiPHz9GSUkJo0tWKBSMzlcgEMDPzw9CoRDBwcH48ssvUVlZiYiICMydOxfDhg2Dp6cnE39DJpMpRYBzdHTEvXv3UFZWhrlz58LV1RW9evVCUFAQSktLMW/evLbrpNLMZm5qXkNSUhINHTqU+vfvT/Hx8a09HDWvQaFQ0IkTJ6hr1660aNEiKi4ubu0h1UlbcLLo0aMHTZw4kXH2CQ8Pp8uXL9daf9SoUYzd7rNnzyggIICIqmyGfXx8aNiwYURElJubS+Hh4eTv70/29va0fv16xgWfiCguLo5CQkKoffv2jINHS6O2822DiMVirF69GleuXMG6desgl8vh4+PTZhcH1PyP7Oxs5Obm4uHDh9iyZQuWL1+OkSNHtvawaqSh2YvbOpWVlUpquD179qBr167w8fGBpmbNqY5iY2Ph7e3dUkNUoq7rr9b5tgKxsbHw9/eHsbExrl69Cn9/f7zzzjtITk5u7aGpeQ0ymQypqano2rUrJk+ejMuXL+PkyZMYN24cCgsLW3t4bZazZ89i1apVSmVHjx7FmDFj6my3e/dufP3118znV9c/PvroI/Tu3btWwQtARfBWVFQwZmwAEBAQgN9//71e5+Hs7Nxken+1zrcFkUgkWL16Na5evYqoqCi4uLgw+ywtLZGeno6SkhIYGxu34ihfj0QiAZ/Ph1gshkQigVQqVXqtK3UOh8MBh8MBl8tlXrlcLng8HnR1dev8EbUF0tLSYG9vz3hgmZiY4Mcff8SpU6cQGBiIFStWYMSIEa08yrZHhw4dMHv2bKxYsYIp43A4dQZ2unv3LrZv3w4ej4fQ0FA4Ozur1Fm3bh327NnD3I/qlPJcLhcKhQJ8Ph/z58/HggULmDa//vorWCwWrK2tkZCQgOfPnys5cbyKSCQCh8OBpqYmtLS0wOFwIJVKoampifnz52PChAkqQZjqg1rt0ELcu3cPH3/8MUaPHo158+bVKGTKy8uRePcGenYyA0tcDvAMARMnQMekxcdLRKisrERpaSn4fD6zKRQKcDgc6OrqgsfjqQjS16VreVVQV78KhUKV/nV1daGvrw8jI6M2kWZIKBTi9u3b6NOnT43OF0VFRZgzZw4UCgV27NjRJkwDG6p2EPGlKM0XQCyQQUuHDSMLHfB039yDkohQWlqqNLE4deoUTp06xQSlf5m//voL8+fPx9GjR1FRUYHx48dj9+7d8PHxqfM4ixcvBo/HU4kg9zI9evTA7Nmz8eGHHyIsLAx//PGH0iKeXC7Hxo0bGYE8b948XL16FRoaGoybNFAVfP/UqVO4ePFijWmgALWHW6sikUiwZs0aXLlyBf/973/rdF01YMtgJctCfo4YlrYOgFQIZN8FbLs3uwAWiUQoKSlBaWkpSkpKIBaLoaenByMjIxgaGsLGxqZJZqb10WlXz6z5fD6Ki4vx9OlTZjzGxsYwMjKCkZFRi+fkSkxMROfOnWv1ejM1NcWhQ4dw6tQp9OvXDytXrsTw4cNbdIxvgogvRV56GdhcTfB02ZBJFMhLL4NlB8NGC+DTp09j7dq1jAu8r68v1qxZo/Q9IiLGvVihUGDbtm04cuQIfvnlF1haWgIAjh8/jrFjxyIgIACfffYZzMzMGjWeM2fOMCZuN2/exN27d7Fu3TpkZGRgzZo1AFSTnm7evJl57+HhgVOnTiEhIQGffvopLl++DHt7+0aNRS18m5G4uDjMmDEDo0aNwuXLl18vuIrTYOfogvj4hzC1sAKbq8OUN7XwVSgUKC4uRl5eHgoLC8Fms2FiYgIjIyN06NChVWea1aqIl2dJ1TPxkpISJr0Qm82GpaUlLCwsoK+v3yw53qopKiqCTCaDhYXFa+sOGzYM/v7+mD17No4dO4Zt27a1iVnw6yjNF4DN1QRHq+p7Wv1ami+AVYfGJdHs168fk/anf//+GDp0KJYuXYoTJ05AIBCgsrISf/75J/T09LB3715MmjQJQ4YMQa9evTBs2DDmNyOTyTB06FCwWCx06dIFv/zyC+OYUV+EQiHmz5/POHrMmzcPGzduRFFRkVI9IgKbzUZ2djaCg4Ohr6/PjKO4uBh9+/aFQCCAlZUVwsLCUFlZie3btzNedfVFLXybiQMHDmD37t04cOBA/R/7RGXgaJvAxlgHufFXYNfVF+DqAcLiJhmTWCxGbm4u8vLywOfzYWJiAktLS7i4uLS5zK6vwmKxoK+vD319fWamIRQKkZ+fj0ePHqGyspI5HwsLiyaNyUBESExMhJeXV73bmJqa4qeffsKJEycQFBSEAwcO1Bp/oCXJzyivdV/eszJo8dgqsR3EIhk0NGr/Y7NwqD3qmp6eHvT09DBnzhx4enrC0NAQ69evx/r161XUDjKZDFFRUfD09KzzHD755JNGRXr7/vvv4evri4KCAgDA+fPnYWJiggMHDmDfvn04c+YMiAju7u44ePAgbG1tkZCQAKBqshIXF4e5c+fC19cXX3zxxRsHzmrbv7i3EJlMhoULFyI7OxsXL15sWKYIniEgFcLCzhHJdzJglnEXPFMHwKjxRuJyuRx5eXnIzMyEWCyGjY0NXF1dofdKAJW3EW1tbTg4OMDBwYGZyefm5iI5ORkmJiZo164djI2NG32epQIJ0gv5eJKRDQ25LmQaDf+xjRgxAt26dUNYWBgWL17c6iZpdQlKhYIglyqYGS8ASMVyGHE06mxXFxKJBIsWLcKhQ4cwatQohIaG4vjx4/Dw8FCpy2az8dNPP2HatGnQ0dFhMl9XzzrlcjmEQiEGDhzIqAgawtSpU/Hhhx8ymUZMTP73NBkREVFrnwKBAAEBAcjMzIS+vj5iYmIQExMDkUiEd999F99//32DxwKohW+TUlJSgrCwMPj5+WHjxo0N/9GbOAHZd6GhwYWdmT7SSxRwlSUC2iaATAywa86C+ypEhOLiYmRmZqKkpASWlpZwdXV9o7iwbR0NDQ2YmZnBzMwMRISCggKkp6fj4cOHTIr3hmRdLhVIEPe8BFoaLFQU5uKdLm6Ie14CT3tjGOk0TAh36tQJMTExCAsLQ0JCApYtW9Ymo6UZWeggL70MAMDmakAmUUAmkcPUtvHu7TExMRCLxVi7di1yc3Px/fff1+ku/7JZ2ZEjR7Bnzx5cunQJFRUV0NfXb/Q4gKogPPVRp8lkMrDZbJSVleH777/H9u3bUV5ejmPHjqFv375MvStXrmDv3r2NHk/b+wa8paSkpCAoKAjTp0/HkiVLGjfb0jGpWlzT0oWhNgcEFoqM3QEpH3h2AxCW1tlcLpfj+fPnuHbtGjIyMmBra4u+ffuic+fO/2jB+yosFgsWFhbw9vaGn58fdHR0cP/+fdy+fRuFhYX1CrSSXsgHj6OJJxkZsLGzhb6OFnS4bKQXNi5/mKGhIU6fPg0+n48xY8YwudvaEjxdDiw7GEKTowERXwZNjsYbLbYBVbEavv32W0atFRAQUK+g/3fu3MGSJUuwY8cOAEBQUBBOnjzZ6HG8jn379sHDwwMeHh5wd3dHSUkJBAIBUlJScPHiRfj5+THBgKq36dOnv9HTo3rm2wScO3cOn3/+OQ4ePIhu3bq9WWc6JlUbzwgOekm4m6+JPsYGYMnEQPo1wModMLYHXrrpYrEYz549w4sXL2BtbQ0fH582YZrVFmCz2WjXrh3atWuH0tJSpKWlISkpCY6OjrCxsalxBkpEyCwWIL+kAjl5pejcyREAoM3RRIlA0uixaGpqYsOGDYiKikJgYCB+/vnnWk2UWgueLqfRi2v14caNG5g6dSr09fVRWVmJiooKZkFu+PDhWLlyJWJiYjBnzhz89ttvcHFxgVwux9atWzFw4ECYmJggICAAALBz505s2LBByYKmvLxKp10d/5eIIBAIMHPmTHz22WcAqvS3r/4B16Z22L17t9L7ppz5qoXvG0BE2LhxI86dO4cLFy402vylRnRMoctlwVyPg4xiMdqb8gBNDpATB4iKAYsuEErlSE1NRUlJCdq3b48+ffq0eSeF1sTIyAje3t4QCoVIT0/H48eP0a5dO3To0IG5bhUiKVJzK5FZIoS44AWMza2Y2Y1QKoeB9pvbvE6cOBHOzs4IDQ3Fjh07GrxK/jYiEokgFovh5+fHZDGuiYqKCgwZMgRmZmaYOHEioyowMDBASEgIRo8ejfj4eFhaWiIyMhKRkZENHku1bfnLn18X+UwsFmP69OlKqo+Kioo3mmypnSwaiUQiQUREBPT09LB169amT+UjkwBPLkDK1seNtHL4OemDo1k1S5NUluB5YQVyFcZw6uwJKyurt37xrDWQyWRMmnZb+/aQ8YzxvEQItoYGxJWlSH72AjxzW/R2NINUThBIZI3S+dZGdnY2xowZg8mTJyMiIqJJ+nyVtzG2A5/Pr1U/HxcX91priLaEOrZDEyMSiTBixAh4e3vj22+/bZ4camwuoGUEDkngZMbDozwhZHLCozwh/nyhAZ6uIXytZbDmSaAWu42DzWajfQdH2Lt64d6zIjy8dwdccRm0ORpAaRa6OjuCo6GJUoEUXLZGkwpeALC1tUVMTAzOnj2LjRs3Nlm/bzt1LYy+TYL3dahnvg2Ez+djxIgRGDVqFKZNm9a8BytMA/KToNAywMVH5dBgAR3MeGhvolVld6mQA6IywNgBsOhcJbDV1AsiQkGFGCm55RBI5NDT4gByKQQFmRBXFIOjawhDO2dUimUIfMeiTjvXN0UqlWLixIno2rUrli5d2qR9t5WZr0AgQHx8/Gvdg/l8PlJTU5UmNLt27YKZmRlGjRrFlEkkEri4uDTIgqU1ULsXNxEVFRUIDQ3F5MmTMXHixOY/oI4xKkQyPHhRCRNdTYhlBEezlxbSNDSrFudKM6ssIWw9q2yF1dRJmVCK1NwKFFaKocNlw1D7//+0NLjQtbCHpLIECqkE5dmPQUbtmlXwAlUBZg4dOoQpU6Zg6dKlWL169T9OjZSTk4OwsDCsXbsWgYGB8PX1hZ6eHrPgKZFIsHjxYvj6+mLv3r1KDgz379+Hjo4OSkv/Z+0jFouxePFiRvgOHz4cDx8+VDFHq6yshI2NDa5du9YCZ9kw1DPfelK9EBAZGfnaMHhNgUwmw6PkRIjT/4ZTx44w1GYj9nkl7Iy4sDSoYYYrFQJyCWDtDhjaKVlDqKlCJJXjaUElnhcLwNHUgA5Xde5R8SINHB19aBmaQ1hagKfPszDoXVfY2to2u0BUKBT4+OOPGS+wpjheQ2e+wsoKlOa8gIhfCZ6uHoysbaCt92b2tdWkp6dj6tSpOH/+vIp32MqVK2FnZ8foviMjI3H79m0AVVmPORwO46Lt5uam4tgwbtw4hIeHY+DAgUrlV65cwZo1axATE9Mk59BQ1DPfN4TP52Po0KGYNWsWRo8e3ezHy8/PR1JSEhwcHNC5mytYMhEANjpb6eB2RgXM9TiqszGONqDJBbLvVbkjW3Suso5QA6lcgewSIR7nVYAAGPA4NQo2mYgPmYgPPWtHsFgsaBmaw+kdQxQW5iMzMxNubm7N+piroaGBXbt2YcaMGVi+fDlWr17dbMeqCWFlBXJSH4GrzYO2vgFkEjFyUh/B2tnljQRwdZhRa2trxMTE1MvBJD09HTt27ED79u2xY8cOmJubY+zYsXj27BkWLlyoUt/Q0BCLFi1SUdtIpVKl0K1tCbXwfQ1CoRDDhw/H1KlTm13wyuVyJCYmQigU/i+rRaECyE8C2FrQ5mrA2oCL9CIxnMxrsOOtVkOUPAeEJYCNF8D79zhXvAoRIb+8Sq8rlMqhr1XDn9ZLdSty0qFn3YERzAQCl8uBR2cPFBUV4c6dO3B0dES7du2abRbMYrHw3XffYcqUKVi7di2WLFnSLMepidKcF+Bq88DRqvpuVb+W5ryAdqfGC7BTp05h/fr1yMjIwMGDBzF48ODXtunbty927doFAIiPj4e2tjbi4uIAVIVyfJWX7XHfFtTCtw7EYjFGjRqFsWPH4oMPPmjWY5WXlyMuLg729vbo1q3b/37cOsYA63+aoY7mPFx7Ug47Yy602LXMILSNAKmgyinDxgMwsP3XqSHKBFI8yqvS6+q+rNetBUlFMTQ5XHC0/zfDIwLY/z9LMzU1hZ+fHx4+fIiCggK4ubk1j5ULqmbA+/fvx4QJE7Bx40alQOBvSt7TJ7Xuy3mSCi1dXdXAOnw+NOqwH7d0rNtjbcyYMRgzZgzCw8MZdUNERASuXLnCuBrn5uZi+fLlAIBLly7h8OHDjO11Xl4eOBwORCIRgCo3/pdJSUlBt27dag1c9ODBAyQlJaFTp051jrOlUQvfWlAoFJg4cSIGDx6MqVOnNttxiAgZGRnIyMiAp6enqhuwlj7A+t9t0tRgwcVCG8m5QnjY1fEIzNEBNGVA1l3ApBiwcP1XqCGEEjnSCiqRWSyAFlsTxvUwDSOFAvz85zB06KJcToDmS/9vbDYbXl5eyMrKws2bN+Hu7t5sWUc0NTURFRWFMWPGwMjIqMm+g3UJSoVcDrlMysx4AUAqFkHTmvNaAdtQeDwedu3ahaCgIADAw4cPoaWlhfLycuTn52Pp0qWM8D1x4gSMjIzQr18/iEQiLFiwAEePHsW7774LBwcH8Hg8mJqaMunnb968CYVCwTivmJmZtcnciGrhWwtffPEF2rdv3ygPmvoilUoRFxcHLS0t+Pr61hzWUZNTNZOV8AF21Y/C2pCD9GIRyoQyGGrXcQs12FVBeUozqtQQtl5VwvwfiFSuQGaxAE8KqmImGGrXrNetCUHRC2gZmkOToyyoFUTQrKEPOzs7GBsb4969e7CyskLHjh2bRQ3B4XDw008/ISgoCM7Ozs3uCWdkbYOc1CrvMzZXCzKJGBKhCNbODk16nFdnrkDVIhoAFBYWIjU1VWlBrqCgAEKhECkpKZBIJJBIJHj8+HGtC1nR0dEwNDRUul5t0a5ALXxr4NixY7hz5w5Onz7dbMfg8/m4e/cuOnbsCFtb27or61sDufGM8GWxWOhqrYOEHAF6d3hNEHEWC+AZARIB8PQaYOMJGFj/Y9QQCgUhv1yElLwKiF6j160JuVQCcVkBjB1rfmRl17I4pKurC19fXyQkJODevXvwlzHYwgAAIABJREFU8PBoFtdubW1tREdHIyQkBL/88gscHJpWECodS08f1s4uKM15AWFFOXi6erB2dmgyawciwrVr17BkyZIac54lJCSgS5cu8PPzw4IFC5jrWW3tkJ6eDoVCAWNjY3z++edMO4VCARsbG+b9xYsXsXPnTiQlJaFz587w8PB4rftwa6AWvq9w//59fPXVV7h48WKzxUkoKirCw4cP4eHhUb/HVm0j4BU/NkNtNnS5msgpk8LGqB7OFVwdQCEDsv4GTDsC5u8Amm/37S8VSJCSW4FivgR6Wq/X69YEP+8ZdC3swapByCqIoFHHV0BDQwNubm5IT0/HX3/9hR49ejCJHJsSW1tb7N69G2PHjsXFixeb1eJCW0//jRbXakMul+PevXu4fv06Lly4gK1btyIvL0+pzvjx47Fq1SqEhobizp07zJPgl19+CRsbG4SHh0MkEmH+/PlK7RwdHXHv3j2UlZVh7ty5cHV1Ra9evRAUFITS0lLMmzcP7do1PiZ2c6F2L36J/Px8hIeH46effoKhYfM4K2RmZiIxMRE9e/asv76Qq19lyfDKo5OrlTYe5QshV9TzkapaDVH8FHj+JyBue2EN64NAIsPDrFL8lVYEoUQOYx0uE/eiIUgFFVDIJODq15yiiQg1qh1epUOHDnB2dsZff/3FRNVqanx8fDBz5kyEh4dDoVA0yzGaE5lMBk9PT1y/fh1OTk4ICAhAZGQkE8axY8eO4HA4CAkJgaamJiN4582bh02bNqFz584AqnTF3377LdNvXl4eJk+ejD59+sDNzQ2urq44cuQIuFwurl27hoMHD+L48eNwdnbGixcvWuXca0PtZPH/SCQSBAcH47PPPsOAAQOavH8iQnJyMiorK+Hl5dXwtD0ZtwBJBaN6qCatQASZguBi2cAFBYkAIMX/W0NY17sZEUEkEqGyspJJcikUCplMxPX5OmloaDB52nR0dKCrqws9PT3o6ur+H3tvHmRJXeZ7f3LPs9a+9b7Tm7I0i90yyDui9w4DAr7KqDDqCOKEok44jNcJdcRArhoTr0GAjuA1RkfUuYLggsoIiozSLE1v9Fq91b6fOlVnPyfX3/vH6Sq6uvaqrJaG+kRUdFedzCczT1V+zy+fdcrRLLbr0zGUpyWRAyRi5twDiEIIUq0HiS1Zi2pOvJLMlVxW1oTZ0Dizx+5sNsuePXvYtGnT6ODHoPmnf/onYrHYaGbAdLxWyovnSjqdxjTNKZ8ovvOd77B161auuOKKSZ9W9+zZw7Zt2xbqNCdlqvd/UXwp34h33HEHW7Zs4R/+4R8WxP6BAweQZZmtW7fOLTgz1Ab9B8v+2zPwfcEfT2W4YlWs3BBmNngOWBmo3VD+OssNIYSgWCwyPDw8OtnYdV1CodDoaPdIJEIoFBodGz+TBHrP80YDJ4VCgXw+Pyrmtm0TCoWoqqoanVSsqhr9mRLNfVks1ydmqsjz9FmXUgmcQobYkrWTbpMtOaypi7CufuY+T9u2eemll1izZs30vvw54Hke73rXu7jtttt497vfPe32R48eZePGja+7cuXzASEEzc3NixVuU/Gtb30Lz/P49Kc/HbhtIQT79u3DNE02bdo095sgVAETfEzKssSmhhBHegtsWzHLcS+KVnZDJE9CYQiWXIQjGyQSCfr7+0mlUoTDYSorK6mvr+eCCy4IJLdVURRCoRChUGice2dE8FOpFIlEgj2HmunPuoQiUWpraojHKuYtJML3KAx2Urlq6l6sZ+b5zhRd13nLW97Crl278H0/cF+joij86Ec/4pprrmHt2rXTDuU0TZNkMklNTc2iAJ9DhBAkk8kphxq84Ve+zz77LF/60pd46qmnAg+W+L7P3r17icVi8y9x9Fw4+TRokQkzFV5szbKh3qR6DiNfbNenJzHM8NAgJbOOyqUbaGhooLKy8s82ayxvuZwcyNKTKqHLAsXOYWWHcYsZFD2MWVmHHq2aMFA2Hbn+dmRVI1yzZMrtUgWbLUvirKiZfYDLdV127drF0qVLFyRD4fjx46MBuDMHQZ6N4zh0dXWNFigscu4wTZNly5ZNumB5Q4tvJpPhqquu4te//nXgj4ie57Fnzx6qq6tnNLNqRnS8BKUMaOM/TbMlj/1dea5cO03q2cj5+YL+rEPnsIXl+iytMFgSkwiJHNRM7IY4F1iuR0eyQOtgHgmJqDneFeKW8pRSCezcMHqkArOyDjU0w+u2S6Q7m6lac+G026cKNhcuq2RJ1dwS9D3P4+WXX6a+vp41a9bMycZUPPbYY/zsZz/jhz/8YeC2F1l43tDi+7GPfYwdO3bwoQ99KFC7Qghefvllamtrg73phtqh7xUITZwlcagnT9xUWVE9+Qrecn1aB0v0ZhzqYxrLq3TiZwqcEFBKQbimHIzTz02/VM8X9KVLHO/PYrkeMVOb1q8rhMDOpSilBvDsIqHqJsyKuilXw+mOZkLVjejRykm3GWG4YHPpyirq43Ofh+d5Hrt376a+vp7Vq1fP2c5kvO997+P9738/N9xwQ+C2F1lY3rDi+/TTT3P//ffzy1/+MlBf2EhwzTTN4LspFVPQ9qdxQbcRHM/nuVNZrlwbR1PGXlOm5HIqYZEpuayuMVlaqaNMVYxg58o+5qWXQGxhIvdwesx93qa5L0Om6BA1tVn7WQF816aQ7MXKJDEr6ghVNyKrYx/37HyawmA3lSs3z8hmqmBz6apq6mLzc0d5nseLL77I6tWrR4sBgmJwcJC3v/3tPPPMM6MtFxc5P3hDiu+Iu+E3v/lN4DfDsWPHKJVKvPnNbw4+wOF7cOKpSf2+AG3JEgXbZ3NTGICc5dHcV8T2fNbVhaiLqjM/r5FsiLqNULueKSsO5kDOcjnen6U/XSKkKxjq/O0L36OUSlAc6kWPVROuXYqsqAghGG45QHzZBlRjZm6EVMHmstXV1EbnHwtwHIfnn3+erVu3Bi6Sjz/+OI899hg/+tGPArW7yMLyhiyyuOuuu/jHf/zHwIW3ra2NdDq9MMILp1tG1oA7efBkZbVBIucwlHd4pSvPvs48K2sMdqyJUx+beb8D4NVsiMTxsr/ZLgRwEWW/7vH+LDtPDpLM2VSG9UCEF0CSFULVjVStvRBF00m1HqAw2E1xqA89Ep+x8JaNMe+UthE0TePyyy/n4MGDgRdivPvd78b3fX7+858HaneRheUNJ75PPfUU/f39gbeI7O3tpbu7m23bti1sSk+scUrxFQLipsILrVnqYhpXro1RF51HepgkldtaltLQ+t+QG5izKc8XdA7lee7EIK2JHFFDJWosTFBPkmRC1U1UrbkQ33PJ9bWgmrNLxZOAICcIhUIhtm3bxp49eygUgvkgG+GBBx7gS1/6EslkMlC7iywcbyjxTafTfPazn+XBBx8MVCAzmQzHjh3j8ssvX7B+EKOYFZzd52GEZN7hT6cyRAyFuqiKpkjBXacRBcWA9uchcazsApkhQggGsyWePzXIoZ4MuioTD+mBrSqnQpIVhO8TaViFlR0i3XEUz7Fmvn/A5xiLxbjwwgvZvXs3njfz93A6amtrufvuu/nkJz8ZmM1FFpY3lPjedddd3HXXXTQ1zbycdjocx2Hv3r1ccsklC9Zcewx6tNyj4QxXveP57O/Kc3ygxKUromyoD7F1SYQjvQX8IF36ilbOtEgcg85dM3JDZEsO+zpS7GobxvehMqTPKaA2V1yriFvMEK5ZQsXyCzCrGkm3H6GQ7Jm2FFqSpAVp/lZdXc2KFSs4cOBAoK0Ob7rpJqDsA17ktc8bRnx///vfMzAwwC233BKYzZHqtfXr149vgr5QyApEXvX7DuVddp7KUhNRecuqKBGjvPIO6wr1MY32oZmv8maEJJcFuDhcnpSRS0y4WcnxaO7NsPPkIEN5m6qwjj7Z5I0FJNfXQqTh1dFARqyKqjVvLuf7th/Bd+1J9y27HRZmdb5y5crRRvpB8sADD/DlL395zKTfRV6bvCHE1/d9vvCFL3DfffcF+hh58uRJwuHwgtTwT0msEeEUOT5Q5EhfgctWRVleZYy7tvV1IdqSFra7AF2wjFh5JdzxfDkgd9oN4Xo+HcmyX7ctmSduakQWyK87HVZ2GElW0SNjPxglWSHWtIZQzRJSbYexc+Obe4+wUFPjJUniwgsvpKOjY8Lm4nOlpqaGT37yk/zrv/5rYDYXWRjeEOL7yCOPcPnllwea5J5IJBgYGBhtdXcusaQQ+9qG8HzBjjUxIvrEfmZVkVhXZ3Ksv7gwJ6Lo5ZzjxFFE5y4SQ8O8cCrJ4Z4MpqZQEdL/bP0EhPDJ97cTbVg16TZGrIqKlVsoDPaQ62ub0AUgTeJfDwJFUdi2bRv79+/HsoJ7Qvnwhz/Mb3/7W3p7ewOzuUjwvO7zfG3b5oorruC3v/0t9fX1gdncuXMn27dvn7JxxkKQyWTYu+dl3hxNUV1TW3YDTIEQgudOZblwWYS4uTDBwIwlaOsfIm/Z+A1vRo7WLchxZkMh2YPwXCL1K6bdVghBIdGJU8wRX7YB+XRZdbbkcNWGOkxtYYOofX19dHR0cNlllwX2YfX444/z9NNP8+1vfzsQe4sEz+t+5fvd736XG2+8MTDhhfIo6w0bNpxz4e3v72fv3r1su/RyqptWTJlyNoIkSWxdEuZwbyHwOVYlV9CcdNjbU6AkNCpMjZrkHsx0S7lX8J8J33UoDfcTrp2ZO0iSJCL1KzAr60i1HcKzS6d/vnA+3zNpbGxE0zS6u7sDs3nTTTdx6NAhTpw4EZjNRYLldS2+uVyOhx56aNzYkfnQ01OOkp9rP29LSwsnT55kx44dxGIxiDaBO7NH1aqwiqFK9GeDmWPl+oKOjMeuriJD2SK1ukdEA6HoeHqcUOoY0cR+5Bl8OCwE+YF2wnXLkGZZkWdW1BFrWku64yhOIQtIC+bzPZutW7dy4sQJisVgXESSJHHPPffwhS98IRB7iwTP61p877vvPm6//fayWAWAZVkcO3ZsdNLqueLYsWMkk0m2b9/+6qQHMz6rIZibGsM09xXxZzpyaAKEEAwUPHZ1l2hP5qnUXCr0s05DkvGMKjRriHj/i6iloTkfby64pTyuVcSI185pfy0co2LFJrI9Jynm0ufMZ61pGlu2bOGVV14J7Anl6quvHp2uschrj9et+A4ODvLTn/6UO+64IzCbBw4cYOPGjVOOugmSkdFD+XyeSy+9dGxvXT0Ksjbjx/uQJrOkUqclObfVaNoS7O1zONKXJ4RDtQHKFLrkaVEEMrH+XefMDSGEINvbQrRx9bxEU9FNKlZuYaC7k8HE3Cv6Zkt9fT2hUIjOzs7AbN57771jJv0u8trhdSu+X/3qV7nrrrsCa5De29uLLMuBFmhMhRCCw4cPY1kWF1988XgxkWWI1IEzczFdW2vSNWxTcmYuhEVHcGTQYV9PAd8uUWfCJMkV469hxA0x3Exk8BUkL+Cc47Ows0kU3UQLzXKixwTIqsbytRdw7Ngx+vr6Aji7mbF582ZOnToVWPbDxRdfTE1NDb///e8DsbdIcLwuxbejo4PnnnuOD3zgA4HY8zyPY8eOsWXLlkDszYTm5mY8z+PCC6do+h1rhFkImiJLbGgI0TyD1DPHE7SlPHb1FEnnyn7d8FwK+CQZz6xGLw0S73sR1VqY5H/h++QHOok2BDM1QohXRwIdP36cRGLiYpKg0TSNCy64gKNHjwZm85577uGLX/xi4AHXRebH61J8v/GNb/C5z30usBE4LS0tLF269JxlN7S2tpLL5abvjmbGp001O5umuEbB9kgV3Alf94WgP++xq6dE53CeKtUlfrZfdxKEEHi+wPYEJUdQPOMrL0VxPIj2Po+eaQ3cDVFIdmNW1iGrwbiEfASqIqHrOpdffjmHDx8mnU4HYns6mpqayOfzgR1v7dq1bNmyhaeffjoQe4sEw+suz3ekV++ePXsCaXJTKpV48cUX+Yu/+IuFb5pDOZuira1tyjHYo/j+6f6+oVmJcLro8kJ7gfqqMFlLEDckVlcoSBKcSNrkLYsKFSZLbxVC4HjlVDPLLf/f9cVoapYilb0i0ph9wBPlakPdGkRColSxBtmMo4WjqKEYijY3F5HnWKTbj5RHAwX0get6PooisWNtOXCXy+V4+eWXueKKKwiHw4EcYyrS6TSHDh1ix44dgQT9Dh06xOc+9zl+9atfBXB2iwTB62568fe//30++MEPBiaUR48e5YILLjgnwptMJjl58iTbt2+f2fFkGaL15f4K+swFQUgK3QUJV7ZZUamTsgS/OFGiRndpCAlqJ9BA1xPkHUHBFjieQFckTE0iashoSjn4NjORUIAmZDuDKxKk1TjFYp7iUD++a6OGYhixKrRIxWixw3Tk+9uJ1K8MTHgBfAH6GXlm0WiUiy66iF27drFjx44FD7pWVFQQiUTo7e0NpO/01q1bsW2b48ePs2HDhgDOcJH58rpyO/i+z/e//31uu+22QOylUimKxSKNjY2B2JuKUqnEgQMHuOyyy2bXHS3aMKNiizNpTXtcUGfQl3boynq0DNl4joPlCMJn6J3nC9JFn66US3/ORwioCcssr1RojCtUhmRCmoQqz751pa/HUVSVunwzNWGoXLmZqrUXYVbW4RSypFoPke5oxsokEVO4KJxCFt910GMTz7WbKwKBctY1VVVVsWHDBvbt23dO/KebNm3i2LFjgbWe/NSnPsUDDzwQiK1F5s/rSnx/85vf8Na3vpWKiopA7DU3N7N58+YFz/X0fZ89e/awdetWQqFZTso147Me75OxBHFDRtVVTiWKVKgeVQbkT7uBLVfQn/XoyXj4AhrjCksrymKrq8H1CBaKgavHCQ8dIZI8gOzb6JEKoo2rqFp7IeG6Zdj5NEMn95Mf6MB3y0UiXmKYwgsHyf72JdK/f46QWhX470gIJpxxt2TJEqLRKMePHw/0eBNhGAZLliyho6MjEHvXXnstO3fuPGe+60Wm5nUlvg8++CAf//jHA7E10pKvsnL6Kbfz5ciRI9TX11NXN4eeCFqk3F1sFs3N44ZE0QVdlZDx8YTA8kBF0J32SBZ8YobEsgqFqrCMupBlXqeLMvRCP/G+F1Hs8ogdSZLQQlFiTWuoXnsRsmaQaj9M+tB+8ruOImwPXxfIQsU53ImXCK4zGJTFV57kujdt2kQymWRgYOFzgNesWUNbWxu+P3b1f9ttt7F9+3a+8pWvTLjf8PAw1157LZdeeikf+9jHAJBlmVtvvXVx1PxrhNeN+HZ2dpLP59m0aVMg9s6Vb6y3t5dCocC6devmZmDE7zsL18PqCoWMLchaPtWmTF/WoyfrEVZ8aiMyS+IKYV0+px3JPD0OCOK9L6BnO8c0i5dkmVBVQzmglihilZJYhSGs9CBmQxOSaWCd7Ar0fAQCbRLxlWWZbdu2cfjwYUqlhS2h1jSNpqamMYUXjz/+OJ7n8cILL9DS0jJh/4aHH36YW265hd27d5PNZtm9ezcAH/zgB/nBD36wmHb2GuB1I77//u//zkc+8pFAbGUyGTzPo7q6OhB7k2FZFs3NzVx00UXzE7ro7PJ9K02JDVUKKj7JkkAI2Fojsb5GwVD/PC0gAYRi4ukxokOHCA8dQvLGNjqXLAspVcTwTNzOJKRKuMUckqniZYOdieZPsfKFsksg6HLgyVizZg2tra2jq99nn32Wm2++GYB3vvOdPPfcc+P2qamp4dChQ6RSKTo7O1m+fDlQHje0Zs2aUTFe5M/H60J8Pc/j8ccf5z3veU8g9s7VqjewcmUjBtLs/L6pvENUcrmkXuKty2SE7782VkOSjGtUoed7ife+gJLqQ+rrRt6/F++FXdgDOeyhPMQ1VM/ELRUpdLcjhwLOPhBM626pr6/HNM1Ay4EnQtd16uvrR7ue5fP50cZO1dXV9Pf3j9vnyiuvpL29nfvvv59NmzaNWUh89KMf5Tvf+c6CnvMi0/O6EN+nnnqKq666avbBqgnI5XJYlkVNTU0AZzY53d3dKIoSTLmyHik3Np+B39f3BYd6CjQPWqysUqgwZQxVxtQkstZrQHx9H3IF/EQRDh8j9qf/i7T/ZfK9acgPUFc9gC71o+dthOujEUbTYpRCeUrp4KrQfMSM2klu3ryZlpaWwLqRTcbatWs5deoUQgii0ejo8XK53Dh/MMCXv/xlHnzwQf7lX/6FjRs38r3vfW/0tauvvnrUHbHIn4/Xhfg+8sgjgY2Cb29vD3TixUSM5Ftu3bo1GIOSNCO/b8H22NmSxVAlmuIqUe1VcakKyaSK/ry6ns0Zx4VMFtq64GAzHDuJ6Bug6EdIuMsR2QQNpRcIKTn86ijhZQpRbxCpmMO3baJveRNVF78FK5Mk030CMYEYzRYxg5UvlH2yW7du5cCBA/M+5lQYhkFFRQWDg4Ns27Zt1NXwyiuvsGrVqnHbDw8Pc/DgQTzP46WXXhrj1pJlmRtuuIEnn3xyQc95kak578XX8zz27NnDZZddFoitgYGBBc/rPXbsGOvWrQs2UT/aMKXfdyjv8FJbji1NYZoqTQT+mJJhRZaoCMkMFc9RE3TLguQwnGiFQ83lf1NphKZRUuMMubVkCjGQZEwlD76Lbg/g63k0PU5oRZzqNT5aPIxSV4WsqMSXXYBqRki1Hx5NS5srQogpfb5nUltbi6IoC579sGrVKtrb27nxxht5+OGH+cxnPsMjjzzCli1bxvXt/ed//mfuuOMOKioqGBoa4v3vf/+Y16+//np++ctfLuj5LjI1532F24svvsgVV1wRSB+H3t5eGhsbA+sJMRGZTIZUKhXcqneEKfy+3SmLU4MlrlgVJawr9Oc9pAkKF+KGRFfaxz5dwRYovg8lC7I5SKagVCovLzUNQiZCgGWr5LMGnqugKB6aVnajKF4RW6rA1SXCxSSOiOJLOqaWI5ct4BdLyCETSZII1yxB0UOk2g4RX74R1ZibK0oAmjLzv4MtW7bw0ksvUVtbu2B/P5WVlRQKBQzD4Nlnn+Xpp5/ms5/9LI2NjVx44YVjth3pRzEZl1xyCQcOHMB1XVT1vJeB85LzfuX7xBNP8K53vSsQW+3t7axcGUxXrIkYaRO5devW4NO49Aioxji/b8eQRduQxfbVccKne0EOFX2MCX7zkiRRG1FI5gNa/XoeZPPQ1QOHj0HzSejuKwtxOASRcHmla6kMZSJkcmEQoGkeZ+qXJ4WQJJdSLI6ed9H8PLKwceQwwvXxU7kxhzViVcSXbSDT2Yxbys/p1CVm1aueUChEY2Mjra2tczrejM5JklixYgUdHR1UVVVx8803z/kpTZIk3vrWt7Jz586Az3KRmXLei+/TTz/N29/+9nnbyWQyKIqyoE1TBgYG0HWdqqpgS2GBM/y+rwZ+2pIlutM2V6yKoZ2xkk0VPSabpRnSJLLFYXa37OfFY89zoP0VhvOzKGBwHEilobWj7L89fgoSQ6AoZcENh0BVEAJKlspwJkImHwYhjRPdtJvhWP44L+QTHPD6sJK9uL6BYfWg+gWKahOSruB0jw+0qWaE+PILyHQdxynmxr0+LXOY37Zu3To6OjpwnLEuj+kKIkb4+Mc/zhNPPDHlNkuXLqWrqyuQzJRF18Ofl/NafE+ePMnSpUsDEcyOjo4FX/UeP36cjRs3Ltgxyn0eyjd+d8qiN+1w+cromMBRyRU4noc6yW9+OD9MItlMpmgTMaO4rkNz55HJBbisojA4VBbag8fgZDtkcmDoEAlDyGREVUfcCyOiK4SEpnrI8lgxSbsZWgutuMJFM6vJGDW0ZAZIe2kkT1BQm3CVGJJp4g0MIybof6AaYeLLN5LtPjE6FHM2zPbZRFVVVq1aRUtLy+jPZlIQAfCnP/2Jvr4+rr/++imPoWkaVVVVDA4OzvLsxvOXf/mXPPPMM6+NFMM3IOe1sycol4MQgkQiEVh13EQkEgkikQiRSGTBjoERA1kmkXM4NWixfXVsXH+CgiMQU6SkdQ52EDJMtIJPqaWbkOfgK3CitJ+1695U3sj3kUo2Uq4AwxlwbIQQCF0DbeRPyh0zZUMIcByVUsnA9X0kuVQWXJ/y11m05zvw8JCRKZmCiGXgKbWc8FRUYyWqmwNNICkywnHw0zmU6vE9PVQjRGzpetIdR6lctWXG/X4l5ja5eMWKFfzxj39kzZo1aJo2YUHE+vXrx+zjOA4f/ehHufbaa/nFL37BDTfcMOUxli5dSk9Pz9zK0c/ANE1Wr17NsWPHFnZRsMiEnPfi+6Mf/WjedlKpFPF4fEHbRp44cWLhB29qYQquxJHuLFesqRzjahghZfloU2hKoZQn5MuU2luwhUZKKpVdCX09nMjlkDwPrWAjhI8ngaeprw5zm2BxKQQI18RzQ/i+BIqNNJHankXCGUCTVGQjjF+0kTI+pmJSlG28sI7uplH9HK4SQ0gSXiI1ofgCaKEo0cbVpDuaqVy1dUatJyWkOYmvoiisXLmStrY21q9fP64gYu/eveP2+cEPfsDmzZv57Gc/ywMPPEBHRwef/OQnJz3GSPWaEGLesYPrr7+eJ554YlF8/wyct26HoaEhLMsKpEiht7d3QWezJZNJDMMIbIryZLiex8HONBc1ypjaxL/aoYKHOcVHrqypDLSdpOQ5aLpCXI4SKblUlQRNbcPELRnDjGJGKoiEK4hrEeJyeNxXTAoT9asw7eWYTj1RyaBCVamQwsSl6LRfESWMKqlE65ehFR1SWp6sl4WSTSrVT7rgohY6ywJk6LgT+H3PRI9WYsRryPXNPCA2yyEho6xcuZKuri5c151RQcS+ffu44447aGxs5NZbb+UPf/jDlPZlWaaqqoqhoflPhv7rv/7rxQbrfybOW/F98sknufbaa+dtRwjBwMAA9fX1AZzVxLS0tMy9cc4MEULwyiuvsHTtRiq0iVeWri8o2B7GBAt8IQT99iA5K4eWzGHmiuh9Awjbw5YljGhBfS1hAAAgAElEQVQEZBnf0GCK/FchwHcN7FI1dikGwkFWSkjS7DIoKuUK/EgYKzuE7HrgCtJKkRqzFlmSyBct8skuioPHcbwibjqNX5i6yixUswTfcyilZpaPO9c1paIoLFmyhJ6enhkVRKxbt27UT7x79+4ZxR6ampro6emZ4xm+Sn19PUKIQHzIi8yO81Z8n3nmGd75znfO204mkyESiSxYrmOpVKJYLC54a8qenh4kSWLZ6o0gT3wteYcJZ6e5jkVH/3G69r1Mw6lhGnwVhCBrgusXqG/aQkSoePrk79F40XXnJLojRLQYy5ZdiMjksHwbE41KpQJLF0iSjKrqoIQxvUGKmQTZ4W6GD+3HSiXxHXtCm5IkEVuyjsJgN549dSMiaQ7ZDmeyYsWKGRdE3HbbbfzhD3/gqquu4t/+7d+46667prVfV1fH4OBgIMGya665ZtrV9iLBc976fA8ePBiID7Wvr29BXQ4LnUUBZYE/fvw4V155Jajqq/m+ZzVZz9o+8oi/1fWgUMBKDNCVbCGfy2GUQERMJL2epYNpPElBCBnNBbQohQlihUKA8AwcJ4LwZCTZRlbmnyesL12BN5RiiVJfnjwECF+Q9vOEZI2YH8LFJOxlMExwjTB+3zDZcCuyLKNF4xhVdajhyJhxRLKiEm1cTbbnBBUrt0zuM52n+IZCIQzDQAgxbUFELBbj0UcfnZV9WZaJx+OkUql5py5eeuml/PGPf+S9733vvOwsMjvOy5VvsVhEkiQMY24DF89kcHBw3lHjyRBC0N3dPRpwWSgOHjzI5s2by+OHJKmccuaMfwQfztsYhdxoD4Xs8SOcSJ+g4DrgqRA2kGQZz9Ao1laALKNaLm66h9KGyyhK9VjFemyrEs/VAl3pnokcCiMbJu7wWJ+mhETMMRmQ01hSOaXO8TVCXhJhaJCxUA0T2TBxCjmy7SdIHT9Ivqcdp5Ab7fmgRytR9NDU7gcxuyKLiVi1ahVtbW3zLoiYjJHV73zZtm3bhIHARRaW81J8Dxw4EMiq1/M8HMcJRMQnIplMUlVVtaDlmyOPng0NDa/+MNYAflmchOfjZnIUT7aT2XMU82QLfipFn1HglJlBsSVEwUPWlDGrQM/QKNZVkFtaQ76ikkLKRYktQZJshGNSzK3AKlSNFV1JQjZDqJXVaHWN6I1L0JcsQ2tYglZbjxKvQJpBPwtj+SqszrYJX1OQCTsqfWoKDx8HE8XJoUo2+B4iZyFJEopulFe9ukEplSTdcpTh4wcp9HfjlgqE61dQTHbje+4Y+//7c5/mo+/5n/zfh783pfj29/dz8cUXT3kddXV1DA0NBTaD7Wxqa2tJJpPzttPQ0EB/f/9ivu855rwU3z179rBt27Z52xkeHl7Qhuk9PT2BTJ6djJFy5S1btoz9uRLCy1uUWrvI7TlE4eBxMv0pHMPAixm0Gxn63RShPDhZG1lVpkxZct0IItcHoWo8rwrXNcsiLGRkQ0VvXEJowybCG7eiNy1DDoVB+PhWCS+fR9gWSBJKrAJj+WrCm9+MuWY9ak0dnJX2pVZV45eK+MXJm6MbvgYODKoZBCOr3yF8fMTw2HJiSZJQzRBaOIqsqhQH+0idOkq29RiybJLtPjm67bO//RW+7/F/fvpf9Pd203Lq1KTncNddd03bRlKSpMBWpxMRDocpFosTZlDMlpUrV9Le3h7AWS0yU85Ln++ePXv4+7//+3nbGRwcpLa2NoAzGo8QgmQyGXwDnTPo7OyktraWSCSC8H38bBYnkcDt7kZ0tSNQkcNhZNOgZMtYhTQn/F6E4xIuSRQzxXEr3onwvRC+q+INDKPV1eH0dSJFYijxesxqCyc5iNN6EuHMvJOYHAqjVlQR3rgVv5DH7uvGt2z0pmUUjx+Zdv+op5GRS2QUnQovRNjNoOiViME0rJrYjSTJMmoofPqaPLx8His/iG85hOqWsO/F5/jLa8sFDpdcdgXP79zJpgvGN9V/5plniEQiM3IjNDU10dHRMfbJJEAqKytJp9Pz9vtu27aNPXv2TJiNscjCcF6ufA8ePMib3vSmedtJJpML1jR9eHiYysrKBetwJYSg5dQpVtfXUzrVQn7nTvK7XsZuawdNR6lfhmLKowUFrVaGTq8bxfHRLYlitjSt8ApfxrVieLaJ8GUoJZFNA3PNepRIJe5gF8XjR3GTiVkJL4BfLGD3dVM4cgAnmcBYsYbwpq04qSGE6067f9n/azAoZShKDq6nYchZ/JKFKE6c7XAmsqKgRaKEqptwrRz5zhZEKkFc8rCyKWKRGAMTTIiwbZt77rmHr33tazO6zurqalKpVCCr04moqakJxPUwIr6LnDvOO/EdGVhomua87Pi+v6D+3p6engXJohBC4OVydL60i+pEAnffPuyWFpAVlHgcJRZDUhSEUYUkXDzh0+YmOVpIUO3LyJagmCkgK5MPyBRCwrPD2MVqXFdFUYcRkoxatwwhBL4DVl8/kp8K5Jq8bIZS60mQZbTKarTameVcy8hEbI1+NUUBHcNLgW8jMjOf5yYrOsighMM4iko2lSLbfoJs50m8bJpCJo1/Rjn21772NT7+8Y/POHVQkiSqq6sDEciJqK2tDSzotii+55bzTnyDCrZls9kFrThLJpOBuTTKgpvHbm8n//wL5F94gdb2NpY0NqFUVKLE40hnB/XUMCXZp9nuoys/TMgxkR2XUqaArChIExRKCAGea2IXq3HsEEglZNlBCavENzfhW3lKrR34dh6zXkGWp1+hzhRj2QqsjjYKzYeQwxHM1evH+YMnQhcqiiMxoGWxXRldziEGMzM+riRJKKqJ55ZYt34jR5qPoIQinOjqJR6N0HXkEG3795LobKeUz/G73/2Ob33rW1x99dXs37+f22+/fdpjNDQ0kEgEN+LoTEKhUCATlJuamujr61sMup1Dzjufb1DBtkwmQzweD+CMxmPbNoqizDvLwc/ncYeGsbs68fN58H3kcIR8KIxW4RGaYtWeweWUM4hv+5gihlQqUcoVkSYRXt/Tce0ovicjyRayUr4JlWgcY8UqSq0nkYoFNANEOkPogs0UsoPl3rzzRI5EkRQVL1NeSVsdrag1dYQ3bKZ4snlaN0TE00lLJfpUheV+mmwqi+x5SDPs1SErBk4pzfbtV/JPd91JMplkf0sXf/c/Ps03v/cf/OOdHyfd10Oqp5sfPHAfFfUNRCqreOdfXct3v/vdae3X1NRM2s1svkiShKZp2LY978koI72CFzovfZEy55347t+/n7/7u7+bt51MJrNgwbbBwcE5+5L9QgF3eBinqwsvkwUEshlCib36QdGdTrMsPHF3NCEEfU6Slnw7NXIUVWTpKPq42QyGKo0TXt9X8ewInqufLpB41V+qROMYy1dSPHF0rE/Xc3ES/eiNS7B7uuZ0nWdiLFtJqW1sZoGbTCBsi9D6TeXjTyPAMdcgLReoQEd3U4hcCSpm1kFOkiRkRcPUNL7+rw+wZ8/L3Pi+D7F541I2b7ygfI6n32/PcxnsbGews50ffOt+0ol+whWVaPrkH4SapiGEWLCpEbFYLJC/50suuYR9+/Ytiu854rwT346OjkAGXGYyGdasWRPAGY0nmUzOKqHeL5VOC243fjqF8H2kUBhlgpW5LwQpx2bjBK+5wqOt1MVAIUEVYTSjCi+dJJH00RUxJvgnhIznhPGcEOAgK2PTpuRQuCy8J5snDKY5iX7Cm96EMziAsKcPcE2GWlOHn88hrPGPzl42g9XVTmjdRgrHj4xbZf+fJ35Dz2CSC9et5ca/2EHE0enSbdaKJP5gioIq+Nq9d+P5PqZp8s+f/3K5EGUCZNXAc4rEYjHeetX/g6RMvJ2iqCiRKACu49DfchJZlgnFK6iobyQUi6Go4/etrq5maGhoQXqIxOPxQMR31apVdHZ2BnRWi0zHeefzDaoirVQqzTtoNxnDw8PTpv74loXT309+zx7yzz1H6eBB/GIRORZHqahEnuQRcti2qdb1ccGyom9xJH+CwXyCaiIokozlyWRSBYq+in76Ny2EhOeEsAvVuI4OUhHpLN+tpKqYq9dRbDkxZRaD1dWBsWweqyRZRm9owuqdfPXsZTPYiX7MlWvH/Pzl5mP4QvClv/tbEqkUfUNDaEJBcxR69SzyQB9/+P3T3PT//g3/+2vfoKq6mj27X5r0OJKkIHyv3JdYMKPhmaqmEYrGMMIRrEKenmNHad2/h75TJ8qBujOKK4LKSpiIEfGdL01NTfT29gZwRovMhPNu5et53rzTtxzHQdO04OeoUc6i8H1/wsdL37bxUimc7m7coSHwPCQzhBybue95oFSkMTR2KOSwm+F4rhXNFVTJr67KUokMqqKiny7F9V0Dx44ifMp+XWni4Iqxcg1Wd+eEq9Ez8bJptPpGlGgML5ed8TWMoDctxRnoK896mwI3mUCJRNFq63EGyyXBR9s7uWJzuQft1jWrON7RRWN1NSFfJy+75EsdXHfN/yyPLQLSqRQVlZN/II64HnzPQUjaVI3bJkTTDTS93MshnxoikxhA1TVitfXEqmuJx2J0dHTMzugMicViZLOzf//PZlF8zy3nlfg6jhOIzyyfzy/YRIl8Pk80Gh39Xtg2bjqN29ODkxgE30PSjXKQaZbiL4Qg7ThsjJebhvvCp8dJ0JHrJCZM9NPdzDzHJd0/CAgstQLDK+B4lfiudjqYNnmQTK2qBs/DS89sbpvV1U5o9ToKzYdmdS2SbqDGKih0z2w/q6ud8MatuOkUwrGxbJuq09kqUTNEW+rVnNywGyLrp1EGWjBXbeHokUPkclk2bdoymXmgnHbmeRZC1lDm2MxXkiR0MwRm+YM43d/HcG8Pqq4z3NeDVSigh0KBfvBrmhZICXNQbSoXmRnnlfj29/cH0pykWCwSCs1tpPh0ZDIZoqEQbjKJ09OLmxhAOC6SYSBHZy+4Z1L0PEylXBjhCJdTxU6GCoNUSRHk02LhuS7pgUGEL5BkncFiDYoNPmKcX3cckoTetIzCsekrzEYQVgk3mxmzKp0JxvKVWF2zWAn6PlZ3B8bS5ZTaTmHq+uigytLpMUajl4GEKiLkepvJV9by7W/dx+f/ZerhlQCSrCLsPEKGWUyNnxRZljHCr1bUCSFofWUv4ViMivpGwhUVaEYwri9JkvB9f15PhUH1ilhkZpxX4hvUxImF8PcK18XLZkkfOEDYccl3dSHrBlI4Mq/WhGcybNtU6Tp5r8ixQiuuVaJGfjVX2XM90gNJXMfH98NYRY1UyWWZkiYvps9p1mobcIeS4M0uf9fu7SK8cSvOcHJaFwJQztwQ4OVm56f00in0xiVIhsnqpkaOdXaxbtlSOvoHaDoru0SoKlqqyLf//ct88CMfpaFh+g9tSZJAkvE9f9Zuh+mQFYV4RSVC0/E9l4HWUyBJhGJxKuobCMUrUCcJBs4E0zQplUrzGiYry/Jinu855LwKuL3WxFd4Hu7wMKWjR8n96TkKL79MplQiVFWFWlmFHA4H+niZdmx8qciBTDOy5VIhv3qj+Z5HemCQUkGiVIhjFWRcycFVQJEVZKYXRa2uHntgDj4/38fu78VoWjajzY2lK7C65tbExe7pRm9cwrYL1rPz4GF+9PTv2XWkmaV1tTz6hz+ObifJEvuPdaH3DvKTJ7/PP911J//97O+ntS/LKp7vzCjgNlsMXcNyHBRVw4xGMSMRnFKRnhPNtO3fQ+/JYxTSqTGBupkyIr7zZSRneJGF57xa+QZVslssFudsR/g+fiaDMzCA09Nb7tilnG5gEw5juS7mAuRyesKnrdRHzMlSLYVRz2iU7rkeyZ4hihkN0JAVB1mFgi8j+y62HEbzilhM7mpR4pXloNkciybcZAJ94xYkw5wyUKfVNeBm0uX3bQ542TTG8pWEQiE+/7cf4FBrK3+9/Qoqo1FWNoxN43rTpvVcvmMVPevrqYltpkqdvoOdJCv4rstCjFLVFQXLGftUoeo6qq4jhKCQTpNNDqKoGvHaOqI1tZjhyIwGfpqmOW2XtZnQ0NBAX18fK1asmLetRabmvBLf3t5eLr300nnbKZVKs/L5nt0xzLdsUJSy4J5lxxMCNeBmOpbvcKLQiu251KrRMW4M2xIk2rNYeRnVkMeU/BY9CRUfWzIJkWcqudNq6rD75xdsKXW2Yy5fSfHksYk3UFS0uoZZB+fOxh1OolZWE/F9rti8adLthA5uRqM+n6Zba8cMm4TkqR/LJVnF960FWflqqkpuktVpOVBnAiZC+KQT/aT6elB1g3h9I9GqKvTQ5E9SQZUZL1myhN7e3kXxPQecd+IbxMp3JqWYQgj8XA5nIIHb041fLIEsI4fDKObEwr0Q/rKsV6A5dwrPdqlVapClctMYz4NCRmaoN4NnO+ghFRh7/IIHIXwcdKKKx1SeBzkUwi/kJ99gBvj5HMLzyqvozPimO8aSZdh9PfMuSXZTw+iNS3CHpmkoo0j4loResIlFXLqsdlab61ClyX2r0mmfr7Ig4qtgu9O7FCRJxhhpfel7DHV3MNTdiW6aVDY2EY5Xop3lNjMMg1wuN+9zXEw3O3ecV+I7n7Lds5loBSGEKPdTSCTK1WalIiCVBbeiYlqbjhBoAa16hRAMOElO5TuI+ipIUfKSj++DVVAopBXymSTCzaOb40tbPQGuD6ok8FFBUpHx8Cd4oJbDkXkL7whWVwehdRdQyKbLnXpGjmGGkMORSSdUzAa/WCg3bJ/JtsLHKqpUWSnyqkGP3cUyfeVodsjZSJKEAJTgtRdNVXFmIL5nIssK5umKOs91GWhtAUnCjMWorDsdqNN1FEUJJN0sqC5pi0zPnzXgNpM/lra2ttH/O44zumIdGbU9X0Y7hnV0jHYMs06W2xsq8YqJO4ZNguv7qHPMDx1jR3i0lLo4lW2j0jcxJB1XSPgliXTCIJ9WKOWT+E4edZKeApaQUcSr768thdGYOJCihCN4+fmvmgCEY+OmhtHqxjYPL6eWBTcpQdg20gyyA4QOZBRkJ0+lJ8jbaYbc6dKpJCSCf4qRJQkxD7uKqo4G6lyrRO+p47S+soee48ewcjncGfZUFkKM6S/s+/7oU9tIwO3sp7hDhw7NuCfxbO/rMwnqvj4fmJVSfO5zn+PJJ5+c9PVPfOITPPjgg9x///3cfffd415/z3veQ0NDA9dddx11dXXcfffd/Md//Ad9fX1cc801uGc1T/n6178+ZrCf67qjtfnf/va3+eMf/8hc8fN57M4uCi++RP7FFyg1N4MQpwW3YsaCO8amEPNOKyv5FkfyJxnM94+WCTu2THZIx8nLCAR2cQjHmlx4AYq+hOyfKb4mKhPfPLIZOr3KDwa7vwettn70PVQqKhGOgx+QwAP4pSLyJO6fMdspIBUERVchYg0SIcyA1U3Om7wiTEgSnr8wc9eCQtX0cmlzKEwplybZ0Uqyo5WB1lMUs5kxPYjP5uDBg1x99dXU19dz1VVX8cgjj7B69Wq2b9/OF7/4Rb761a/S3Nw8Zp97772X559/fkJ7872vz+SJJ57gxz/+8SzfjfOTGSvM448/zuHDh9m9ezcXXnjhhLPJKioqCIfDGIYxYbrKT3/6U66//nqeeOIJrrnmGu655x7uvPNO3va2t2Ga5pjqtba2Njo7O/lf/+t/cffdd/OTn/yEvr4+brrpJr71rW/x1a9+lfe9731ceeWVEyaWe6kUVns7XiaDEo9jrFyJZBi4Q0Mo7R3kHJeJOobNBw+BMkvxTXs5uqx+sn4Bz5bJDLuELJOoXoVteNi2Qj7jUnAS4JYoFAsgwAhHJ7SX92USrkaHrRL2QZEEIclDEh41Sj9xT8VGJ0slNmW/oWQY+Nbcsg8mxPexe7vRlyzH6mzDWLKc4snm6febzSGsErJh4mWnzhVWcgKSJmLYQNGGWLv0RczaFIeNFzkaXUJGUYkqFaw1NlCr1pEoeOztFxzJ5lhZ4XBxo8nS+NzzbxcaSZLQDJOQpKDaLtnkIKmBPjTdIF5XT7S6Bj0UJtXfR9eRg+RSQ0Qrq7n3X77Ar57+PV//+teBcrfAW2+9lXvvvZfq6moOHz7M9ddfP2as0JkLqv3797N7925WrVo15/t6hHXr1nHyZHmW3qc//WluueUWrr/++gXtt/1aYEYr34ceeohvfvOb/PjHP+b+++/nhhtu4Oc//zkAiUSC9773vQDouo4svzohwXVdNm/ejGVZ/O53v+MjH/kIr7zyCrfffjtHjx7lox/96KTHfPjhh/nEJz4x+v3nP/95LrroIp588kkuuugiVFXl7W9/Ozt37hy3r5dKUXjlAMJ2kMNhnN4+hh79KdmnnqJ46BBORbw89WGG03RnSnnlO/Pt016Ow8UWLOGQL3mc7BpmsJhCUnwKOYW+9gi5YQ87lwA8PMfGLuRwSgU8d/wjZt6XabMNHCEh+z4IQZdn4vs2NfQjIWGjIuNTQz865ei4pCiIgCfsusNJZDOEvnQ57nBy1mOGpkN4HkzTr1fO+EjJOMKXUclj2iXyLbX0JCs47vdhZfZT44DjW+wt7OJ4to+X+ixsoNqQKfmCp1oLdGeCPfeFQJYlEKCHQoSiMWRVYaini/ZDr3D4v3/H3id/STGXJVZTh2OV2PXrX3LFheOHEoz4jjVN44477uDRRx/lxhtv5KmnnuLRRx/lHe94Bz/72c/40Ic+hGEY876vJ+LWW28d1ZfXM1OK786dO7nqqqvYu3cvv/71rzly5AgPPfQQv/nNb3jsscd485vfTFtbG83NzaOfXGeyZ88e1qxZg2EYbN++nU996lOsX7+er33ta6xdu5avfOUrOI6DNcGq69SpU2zaNDaNyHVdlDNuuLe85S3s27dv3L5WeztyOIyfSVPctw+nqxMkCS9fQK6oDFRwz8QXzMrt0GX1E5INsl6evkSaStUgpOj0pRzckoKq+xQyOWRFxfddXKuIaoSQFQ27OD5AlnA1DFmgSAJJAl0SaPjgp3HQsaQoKh4eGi4aMcoZCZKswAI8Zlv9vegNS7D7g4+eK+EIev3UmS/ysA6Si6zZmK4Huo+ilMj0GESlCCBTsk4QUiKEpRAvZ5oJqzLCdyk4LjFdIWZI7OubfwrXCEVrvD81CIQQpAuvjk8aCdSFIlES7W1YuSzZZILMQB9aKMxzL77Ejx/6N975zneO8dG2tLTw5JNPct1117FixQre8Y53EA6HefHFF6msrKSuro4dO3awdetWGhsbA7mvz2ay+/r1xpRuB0mSuOeeezh16hR79+7FMAw8z6Ouro7vfe97nDhxgrVr13LzzTfzk5/8ZNz+jz/+OH/7t387+v3Xv/51br/9dmpra3Ech4aGBh566CFefvnlaRt63HvvvfT29rJjxw6+8Y1voCgKHR0dHD58mGeffXbMtvrhw/iRCHgecjYLloUXjiCl01jxOAXP5aUFiOi6vo8rBEPWzCqEjtp9RKQwKT+DbFXi6zLC17A0lwgasiEo5DxE1CyHaYSLJyvIuoFnW4jGseJTyMhEFJAkqHSg4EJcFYQLg5ixWnS/gOxbWGoFCIHu5ylGNiObJuGNwU9Zlk77pKMXXz4m8yEY49KrtifBTnQih2Vk4aNmkshqDFWywY5RKYVIIVNhFchYORTfZ7g0TK2fR5HAsfK09xfwBaQsWKYE1/Ng/6m2wDvqjQj6/lNto9/7roNvWSRPnEQNh1AtFymVIX/8JFdefTWV4TBOXRMPP/wwTU1NPPvss6xbt462tjYefPBBOjs7+a//+i/S6TQf+9jHRle51113HT/84Q9Hjz2f+3oiQqFQIAUjr3WmFN8dO3YA5VHZTU1N1NXV8Ytf/ILm5mYcx+Hhhx9G13Vuuukm7rzzTq6++urRfX3f55e//OWon2h4eJjLLruM97///QB84AMf4Kc//Snvec97yGQy4wJ0oVCIXC432iHs85//PN///vd59NFHR3vlPvnkk7ztbW8bc1yAQlVV2eVwugDCz2UpNR/Dty1CNTXsGkpyxQJMsUjbNj3FIptmkJYGEC2ksXFJux4DmTymMHFUmwpVpsYpkkuphFEwciW8kI6hhXEzaayBBIpmIHljH1zCtoGDhC4JYoDiQ5+lEBcl4ukkJQQRd4iSCKPgYCFTwCO07gJK7a0IJ7iyUjkcwVi6glLrCUIbNlM4ejBQAdZq60GScBLjJwyPoBZUvLyKioWrCxS3RJ4QwijQL/KEJQXDqEM1ohS9PDG9GlnEaNIdqiIhdE0ja3sskyUuWhuM/7G5s5uNy5cGYutMLMehK5FkeXUlnusgKwqRympiNbWcrK/Cd1zMaPkaMuk0v3j8MS75H9fy/z38I371q19xyy23cN999/H8889z44038olPfILPfOYzvO9972N4eJje3l6uueYajhw5wn/+53/ymc98Bpj/fT0Rra2tLF++PPD36LXGjAJukiSNflLfcMMNfPOb3xzz+tatW/nd737HV77yaucoWZbZvXv3aCXZsmXLWLZsGVdddRW6rpNIJLAsiwcffBDXdUf9xiNce+21PPbYY3zoQx969WRVdUzk9Oc//zn33nvvuPM1Vq6k8MqB8rmbJigq+soVqHV1OP39SIWZT7edDbIk4c8ilWiZ0cDhYguO8CBcwErruL5HvVGJoguMsI+qRXFKA/iujCwr6GYIz7GQVBUhxJgVVJ3q0GYbIIOGQJUk4hoU/Xp87wQKMrLso3gOKg5Jyulgvm0j6Xqg4mssW4nV2YZwXZxkAr2hqVxgERCSbkybHudX2UhJE991sXUJ2QUhdCqWDZITRSqESTaylKKXJ+cVeHPsMgollYJjUSkga3tkLcH21QvTAS8IhPBxLItCvoCbyxJZs5p4bR1GJIpyOtC1cuvFHP3TswDo4Qi6IiP7Hl++7z5ae/qwbZvm5mbq6urwfX/UtfeNb3wDgHvuuYeKigo+9alP8eEPf3iM6yNtXM8AACAASURBVC+I+/psHnnkEW666aYg36bXJNOK71Q5e57n4bousixPOJ4lEongui6e52EYBjfffDM333wzUBbOrq4u7rzzzgltX3fddfzN3/wNf/VX/z977x1k113e/78+p95e9m7vq9Vq1WVJrngISSAk9AwEknFImEnAMARC2qTAkCHkm4RAgCGAE8AwGTCE4mBafgxDHAwZY5DlItuSbXVt1fa9/Z76+f1xtau25e7uWWPJ9zXj8czq3s89q9W+z3Oe8n5eAVTTDpOTk7z2ta/lT/7kT+jo6KCpqWnJ1SlqKkVk395qt8P8HGoiQWT/ftRUCrOvD+2++/Dm56qeuhtwkrrqc4XAX0N0l1Rj7Apv4YniSYpagaaUSdJqQnfDKLpPQ1sF3xPMTzZRcfK4joWuaqTb+3CtCpXCfDUCviDAUcWn17CYcnWKUiEifLaZFXShM2ZtIWWdJ4WLJMQMLYvdDgttW0G1gmnpDH6ljF+u3uScyfNV17OZqcAKb0oohDOz8kZgP6GgevMoczq+lyQX02hqPYWZmeeA1sVEtJVpRRIVBtv0PdzW0Yvl+nz/2TwzFUlTRHBbX/h51+0gpY9TsfA9D0XTiKYzhJtacaMJWvsHrnp9urWNHS/+ZUaOPUl+ZopYqoGsFuE//vNr3HvvvfzFX/wFr3/96wEuE1+Ar3zlK3znO99ZTO0tWFf+7//+L47j8Ou//uvr/r1eaqXS8ePHGR0dDWRD+fMdIVfJ/r/yla9c1uWop6eHF73oRXz2s59ddjeW67q85S1voaOjg49+9KOLr5udncWyrMVxYc/zeP3rX8+73/3uxfeOjIzwk5/8hDvuuAOA3/zN3+QTn/gEPT09fOxjH+Pd7373sp+7Eg/86EfcvnMnlWeeBdtCxBOB5OAsz+NYLsv+9OoGLpcy42Y5MX9icQvFVeeWFeazJjKsEL5gzCKlpFLMUcnNo+pXrxW6Ek/CuKMRKY8Scsu44mKPsBpPoKUaApk+Q1GIbN9d9QS+xJpSTabQ05mrFmWul8jOvZSOPbHq67RKhXDcZ6Z5G7aSQml0KIoSPZGtRJSqoX7WkrSnwvSmq4XYp4dH2d7Zvil52WdHxtaVdvB9H8eq4Hsemq4Ta2gk1pAhFI2hqCozMzOMjY2xZ8+ems6bmZkhmUySy+V43/vex7/+67+i6zqve93r6Ovr41d/9Vf5sz/7Mw4cOMCnPvWpRaG86667+Ld/+zccx+Guu+6iUChs+Pf6Uv793/+dO+64Y9M2iz+fWFV8n0+85S1v4c///M83fFd84IEHeMlLXoJ0HOzTZ7CHziHMEMoGbSZ9KTk8M8PNa8wnz7t5nsk+S1osnwcrljVm82Fi2Fza1mwV85Rys6iqvqr7lS+hYBexivPoUrnYFicUItt3VfOyG8Ro60R6bnU90BWEB7ZjjY1sOMIWhkGoZwvlE6v3DsdKc1id7czFusHV8RpLKCGDXrMfIQRFxycaDrG90URVBFJKnhkZY8cm5GUd1+Xc5DRb22tbCOD7Pk6lgvQ9VN0gnqkKrhmJolzRZjc5OcnU1BS7dq28rWM1PvShD9HX18erXvUqzp8/z9atWzd0Xp3luab8fIMy/VjIHSuGQWj7INFbbkExDLz5OeQGTF/WOz5a9RlYOcqKhFyUqER64rKuMDMaJ5rK4HnOqteuCGjQFZIGzIoorrzwmdJHui5C31gLntANtFR62SKYNXxuYws3L6AlUri57KqvU6SLqXhkI9UoTEqJ5bk0G60IIbA9CarG1oy5aKTjeB76Kv3D68V2PXRt5bN938MqFakUC/iuS7Kllc5de+m94QBNPX2E44mrhBdqM4uqhQXzqlgsVhfeTeaaMtZZsLvbKAvepwuPSmoySeTmm3BGR6mcqPYrqytUY1dCXMj7rqXfV0VZbJ1a/lxQDYg3WORnzQtrgap/ZoRjIFSKc1MoqCjK8r/gvtDIaBZlGWPGjZLwSxjCx5mdrtpKnh+t+bqvxOzswRodWrarwa+U8UtFtIbG1R3JVkDLNFE5fWLV18WcPPlEK75S/Tk7WJgiSkRE8XxJyVfY2xrBvMRFp2LZhDapD9xxXXR1icWqvleNcGV1I3KqrZ1YKoMRCa/4s7yUoBYEjI+PLzm9Wid4XpCR71Lep0JVMbq7id12K1qmoRoFr6M4ZCoK1hqnxRShrBb4AqALDwxBotFCysvnIoxQmFhDM9L3VtyE4KMhhCCpWLQaLjklSkWqVY/chvU7xqmxOEJV8FaJSK2xYYzWdlin+5sSCoHnrdqZoWOhoJCPXyzqWJpFymtECEHOgS2ZEHHz8uso2w5hY3MKbI7nYVyIfH3Po1IsUCkU8F2fdFsnXTv30LvvAI2dPYRisZqFF4IV3yBsW+uszjUnvkFsV11p5YoSiRDet4/w/v3ge3i57JomksKqRnmN4quyetoBquJr+SqaLklmquLjuRffp5shYplWQOIvt4dNCHzFQMUlIjw6DJuyFqHoqXj5LFp6fQJsdvZQGa7BtczzcKYmMFrXl1M1WjtrWHUkiSgWZSWJa1bbxBwcdEPDdMJkLZ+WZIjW6NVRaNGyiCxh0RkE5UoFaduUiwV836eho5uu3Xvp3befTGcXoVispq0VS54d0FLYzdzsXedyrjnxDSrtsJLrvxACvbmZ6K23YvT04Oey+DVuCYhqGgV3bQsoa418Q8LFkhecwnRJImMjFHmZAGu6QSzTAogl/R8APCWMSvUaTeHTrll4eojpsZlqVLpGtEwTXiG/4vqgS3GmJtASycUJuFpRQiGEYawaXYcpY8sIthlDXuh1rSgV0mojFUdi6jp9qas7RKSUVGybUIDth57nViPcYpHC3Bxtvb307N5H774DZDo6CUXXL7iXEkTkew3V3q8LXpDiW+v4ojAMQtu2VQtypoE3P7+qAU1c1ygsI3rLoaLUVKbThY8jLz6Kqpok0WCjKD6eIy75uk4s04yiaksKsCcMVHGxOKcLSZtaQRGCiak8avMaBFhRMVrasMZHan8PVNfAr7H4Znb3VXPKK10OLrrqU3Ej2PFq3t7FQdMMQn4UG0F/0kRbwgGp4jiEjNXb9lbDc13KF1IKAkFjVw/du/ehpzO09vRhRqKBt7F5nneZe9h6yOVyJGuczqyzca4p8Y1EIoHsqQqHw5TWMOWmJpNEbrqJ0M6d+OUy3grrWqKaTnGNka8QAgVl1QENIUDDx5EXf2yqJolnbBTN51KdVTWdWEMTiqbhXiHAntAvE1+obm5oVSsYuRnmo034Rm1RlNHWUW0rW2OqxcvnQFCznafe2IxfLq3aphahRFHNIBC4ZvV7qCgVUqKRAiqdSQ19maaQXLFMIry+6NFzHcqFPJViAaEoNHX30bP3Brr33EC6rQOhaZimGbjoQrUlLYhz6/ne55ZrSnyh+mi00cej9azZFqqK0dVZLcg1NVYLcksMnyxMua31GnVVw1/G7PxSwopDyb/8sVhVIdFgo+kS95JLUlSNWLoZXTdxLylQeehLWl8KAU1KhcjUMIWOQRyxcsFHmCG0eAJnenLV614Ka/gcZsfqixqVSBS9sRlrdHjF1xlUQAtR8UOgKHiGgYuLoul4MkZLzCQdUrHKS/89zxeLJNeQ73Qdh0qhQKVURFE1Wvr66dm7n+7d+0i3tl228DKbzW7a4MBqXgm1Uhff55ZrTnxTqRTz81cvZ1wLQgh0XcdZRzeDEokQ3rOH8IEDgFyyIBfTdPJrjH41odXkCxFWHCr+1Y+XigrxBhvdlLi2uOTrKtF0I3oofFGAhYJUdBS59DWm3Typ/CSFju1YcnkBNjs3thpI2hZuPls1yVkGoRuEevspnz6x4uJNgU9IdcgpGVTXxYlGQAgspYIpG4mHQrRGVBQN7Ip31c/Mdt3qv4tV+nBdx6acz1MpFlF1neYt/fTuqQpusrkVIxReMgqdnZ2loWFtk4+1ksvlAjEeHxsbq4vvc8g1J74dHR2MjKwtv7gUiUSCXG7lLQjLIYRAb2oiesstGL29+Pkc/iU55LRhMGevbTOELjRkDZGvIkBBYvtX/+gUBWJpG8P0LhNgoShEkxmMcBTXtqp76xRzsei2FLHCNM1+nlLXICV5tdiriSRIH6+w/DqeWrDHR9GbW5c0Rhe6QXjrINbQGeQqf59hylhKEk/oaJ6HG4ng4eIqKhE1RU9MR7lgEOV7/lX2xXP5AunY0lGva9uUC3msUgnNMGndOkDv3v1079pLsqnlqk3CSzEzMxPY8tcrCSpXOzIyQkdH8JN9dZbmmhPfPXv2cOTIkQ2fsxHxXUAYBqGBgWpBLhJeLMilDIO5ZfwwlkMTWs2mPDHVpugvPQhQFWAHI3y1AEeSDZixOK5r4woDZaVd8kB4dpxWv4DTPUhemJSkxrAf44Sf4nyql7nhjd8E8X3siTHM9s7Lv49QuDqOPHx2VYFXcdA0QVFUH+sFEtc0KYoKptLM1lQY49J1xNLHcy7+XUspmc0XaYhffHR3bGtRcPVQmNb+bfTsvYGunXtINDbXJLgXv0Uf27YD6cNdilwuF0hK48iRIzV7Q9TZONec+B48eJBHHnlkw+cEIb4LqIkEkYMHLxTkShjlMhXv6kfbldAUtWY7yoWWM3+ZlwsFYikHM+zhWJcIsBCE42nC0SS2A0oNkbY5O06bNU2lewfPKC04UiGeSmLlCwxZoSWj4rXizkyjRGKL3hpaqoFQ31Yqp0/UEFlLoqJMXskghYLiOvimiaNASWhsSzQS1S//Zy4lOPbF7z1frhA2DfyFolmpiBmJ0ta/jZ59++ncsYtEYxO6uT7xnJubW/Sg3gzK5XIgwv7MM8+wffv2AK6oTi1cU+PFAAcOHOCDH/zghs+Jx+OBiS9cLMhpjRkqJ0+SfPJJsuEIqRoLOLqoreAG1cJYRHEo+gZxdekIWwiIphwQYJVUVL26WkgIQSiexBIe/tQkUpWrVsq13CwUINragVuKoqUieOdOYSCYkSEiYuNWlNbIOcyuPqTrgoDS8WM1dVCYVHC1KLaoDhiotovVmGZW2vTG2mgJXd2zq6pglX0iCYlr24yMjtDWmMGMxmjqaSYci6MFOGI8NTW1pPVpENi2ja7rG+52yOVyRCKRDber1amday7yTafTZLNZ/A0Y4ADouo7v+1ettd4oSjhMePduWvftY3ZmulqQq+Faay24LRBTLIq+seJyCCEgmnQIRV1cW1l8bVWAM0RiSTyntp1iluXRNHWaUCzCjKtRjjagSJ9KEPdvVUVLpFCjMXyrXPVtqEF4FTxM1aMg0pd8zSev6WghlcFYZslBCt+3KMzOY5XKuEIhHE+y85bb6di2g3hDJlDhBZiYmFjSuzYIZmZmAinkPfbYY+zfvz+AK6pTK9ec+AJs27aNEydWN1ZZjYaGBmZnZwO4ossRQtC2fTsz6TR6by9+Pn9ZQW4pNKX2nC9UC2/hC9HvytcCkYRLJG5fJsAARjxFPBHFc91VbxARxcU1QiQ0SWb6LHooTK59G366CUdf3yOvEo1h9mwhMrgL6dgUjz2BlkyvajK0eE2UKSlpPHHhBuD7eIrCXEiwO9WKecHERkqJa1ewS3k8u0I4liDV1kPXrhuYs132HDiIFuBU26UUCgVM01yX73QtzMzMBBJVP/rooxw8eDCAK6pTK9fkM8ZC3ndwcHBD5zQ2NjIzM7MpUYmqqkSTSezmZmItLVSefRZ3ZhY1kUAsUdlXUNZ8K4wrFpNujIhir7iyXggIxz1QbMpZHdWopiA8JUzYsPATSYr5HArV9U9L0ahUOJfqhqlJdM8lnJ0ENBqTJqW2rWiaRtjKYlrFqnuZbVenAaVfHZ9VNRTTRAmFUaNxlHAYv1TCmZnEOnd68XPc+Vn05lacVTYe61gITad8iQeyatvMRuK0JlVa9BSOVUF6DkLVCMdTRJMZ9FAUVdOoFB1mZqoti5uZjx0bG9tUl7DZ2dlA8rSPPPIIf/mXfxnAFdWplWtWfL///e8v6YS/FjKZDKdOBbNZYSna29sZGxtjx44dRA4exBkbwzp+HF9K1NjlfZlqDZ6+V6KIavoh55mktJVbsYSAcNRDAMWsjmZIfC2MwiyGaSJEkmIui8/SAhxNxOkxykw7ZYpSI6K4bFFyRIsesnCeotDI6kmyWph4Mk7EVFE1tVr98z2k5+FbFn6ljH1+dHHF0JXY58eJ7NiNOzNVzf8uSdU4J6u0XRYlF10FLS7pEjFwXCLxBiKpBoxQBOUKK0cpJceeeobbfunGFf/eNsr58+e59dZbN+Vsx3EQQgSSpz127Bg7d+4M4Krq1Mo1Kb4HDhxYcnHmWjEMY3EP3WYUGlpaWjh+/Djbt29HKApGZydaJoN16hTOyChKNIq4kF9U1pkBiioOk24URzroYuXUgRAQjnkIAYV5HVXT0ZVqHkI3DGKpFMVstrob7JLoXAoBzW1Ez50gprqgXn1uDJeYM0PFVpjP68x7AsO3iGKjruWeIn3ssRGMju7LIuJLCVPBUuM4wqxOPHouJU8Q1T3CHQ30d99ENJJa0nR8gfnSJLFwMpDJsOXI5XKYphmIyflSBNU7nM/nCYfD9WLbc8w1mfNtaGhgfn5+w0W3hbM2I+8L1Y0ZmUyGycmL47dKOExo1y7CN1bzawsFOQVRc67zUoSAlFphzg3XvJk9FPWIp21sNwSSRfNzTdOJJVMgBLZlUS4WKeZylDUDb/o8oobiZEj4tGoWXYZFyNCZU+LMyxCOrP17c+dnkULg+h5WMY9dLi1aZCq4aIpLzoviu3a1Z9eMYcQbyfQ20b/lRuLxzIrCazkVZguTtDS0b6qT19mzZ+nt7d2084Pqonj88ce54YYbAriiOmvhmhRfgK1bt3Ly5MkNn9Pa2hqIU9py9Pb2cvbs2cu+JoRAb2wkeustGFu24OfziIrFGvTpMkzFwxQu+VWKb5e9J+ITa3CwZfyysV1V0whFotiVMp7rIkwTGY5RGTmHt4bOEF1IGlWbHqNCwlAoaDHmiKw4rryA77kUTjyN1tiCUFSQEqtUwrUtdK9AXjSghBOYySa0VCtOrInOCCjNaZrMphXPllJydvw43a39IAWes/Eb+FK4rrtp9QSofh/T09OBRL6PPPJIvdj2C+CaFd8Xv/jF/OhHP9rwOZlMhtnZ2U2LgBKJBI7jLGlhKXSdUH8/0dtuRY/E0LLFNbuDLX6OalH29SXHjpfDDPtEMj7Sk/j+ReV3bYtoPIGm6/jxFMxPg5RUyiU8z1vTf/gecSq0iyJp1aEsDKY9k6Ir8Nzq1o0r/7PLZXyrgnQslGgEKau9yJq0CaVaEA19GLEUQjPJo9MaURGiRFPTFgxlZY/gidkRIqEY8UgS6YNrb474jo6O0t4e/AbkBbLZLPF4/LI17+vlRz/6ES9+8YsDuKo6a+GaFd9Xv/rVfOc739nwOYqikEqlNi31ANDT08OZM2eW/XM1Hid6442I/l6wbGRxda/hKxECGrQys14Ebw0htBE1Scbm8H11UYA910XVNMxUGqFp6I6NYZgoioJhmOv6L2SapEMafVHojKvokTgFM0lFi6DoJqpx8T+hqKi6iTc3h5mIkQlXaA/nyGhzuGZq0Xy84CukQhopxUWaCk2JlR3ScsV55guzdDT1Ln6tUl67udJqSCk5d+4cPT0bXxa6HEF1UZTLZUZGRhgYGAjgquqshWs2w75lyxYmJycDWXuysJhzs4xPOjo6+PGPf8zWrVuXLb5oqo7f3IDX1Io4O4o4Pw0hE9awT0wXPkm1wqwbplEr1ZRC9hWDkOmQ0LLkcwkcV2C7UVwLZLqBiDWHHongeR6KohAJwD0rAmQAyxfMeRpZS6JLl6hw0IQE6eN7EkN1SNmjeLEYMm+DHiXljeMJg3liaIZBa0hQLs3R0LGFkLp8v7HlVBiePMVA1+4L26JB0xUqeQdWzlSsmYmJCZLJ5KZ5OUgpmZycZNu2bRs+6/777+elL31pAFdVZ61cs5EvwK/92q/xwx/+cMPnNDY2MjU1tWmpB0VR6Ovr4/Tppav3cCEPrOj4hoYc7MPbu60azuYLLGvisARhxcVUXOa82gpwvlIdTdUNl0i0SKkURdcSSE1BVipYBRXHri58NMORmq+jFkxF0qo79EddEiGNnBphzjfwjQhSeoTceZxCBakroCj4sVYcYRJyp3AUg46IQCDxsWluXn7Nues5nBo9Rk/rAIZ2MS2hagqVkhPoz11KyYkTJzY1kszn84GNAn/nO9/hta99bQBXVWetXNPi+9rXvjaQ1IOqqiQSCebm5gK4qqXp7u5mfHx8RQ/h6pTbhRxkOol3YAd+TwcUS1Cu3fw9rtgIJDlv9R1pUtEvFLV8HEcnFs+hmTpKUw+GXQFZwfEMoonkpk2BaQIaNYcBs0hHqIJugBY2CFNASBe1UoTWHqZlmNOVMFO2RyokMBUoyCIZM0UotvRTi+97nBw5RntjD7Hw5c5fQhH4rh9o0W1qaopYLEYkEuyN6lKCSjn4vs/PfvYzbrvttgCuqs5auabF9+abb+bw4cPVws4G6erqYmho5f1gG0FRFHp7e1eMfnVVvyi+AJqG7GnHO7gLGY1ALl9TQW6h/cxFIeuZq0bAvh5BkS6up6PrLqRMDNsjFE4TSzVhhhuDE14pUaSL5pcxvDwhP0eULDElT8SAdDRGR6aFhvat2C27sRr7mQt1MUYCXxVo2Ch6lElbJe9KXK9MY7oXsUQU6PkeJ0eO0phqJbWMOEsfHHvj/36q35rk2Wef3dSoV0rJ+Pg4ra2tGz7rkUceYf/+/YEU7eqsnWtafBVF4aabbuLQoUMbPqupqYn5+fl1bbeolZ6eHsbHx5dd3qkrV4jvAtEw/p4B/MF+sBxkcfX9c0JAg1rGk6sLsK/FUHwXTXVwpIljRGgInUfTPFxHQ1HWJ05Ceqi+he4XCMk8EZkjJnKENRctFIZ4C268m3JsgEJsO6VIH5VQC66ewNTDJBPtdIVcXAGVkovQBXHdQ420YCpwzq6QJkK4+WoDcM9zOTH8FJlkC43JlhX/nqxyMOZKo6OjJBKJTR3cmJiYIJPJ1FMO1wHXtPhCcKkHIURgWzKWQ1EUtm/fzrFjx5b8c0M1lhbf6puRrRm8G3chmzPVKNhe+UYhBKTVMhKxYg5YaiYKklC4Qk40E63MIJCY4TKq5qGqcuXoWfqo0kb3i5henrDMESVHRK1ghHSUSAYv1kElvoVCfDvFaD+VUAe2lsZVo/iKvuSAiatGqUS2YKg6W4wSmqJQTPTjqDF0IckJh2Y1hRK9vAhoOxbHh5+kOd1OZgXhhQt53/zGb7iu63LixAl27Nix4bNWIsjBjR/84Ae8/OUvD+SsOmvnmhffl73sZfzP//xPIGd1d3czNDS0qVNPLS0t2La9ZGvbZTnf5TAN5LZevH3bq+YOuZULcgspCF14TLnRJdvQhO8SsaaIuSM0m6cIe3O4no6q+DQ1TZFIZPFcHemBIp3FlEF4MWVQxNRBDSfx4+3Y8V5K8QGKsW2UQ11YRiOOGsdXzKrXwxpw1Sj5cB/Dod04Rh9zIYMxMcLTPI2vzSBNDS4xOS+W85wYeYrO5i00JFZvY1B1hUpx40W3kydP0tPTs2mjxFBtC3NdN5CtFefOnaOhoSGQ3W911sc1L77RaJSWlpYVc6m1Ypom0Wh0UwtvQgh2797N0aNHr/qFNxSjZkN1Ugm8/Tvwe1cvyAkBcdUmrlpMuVEs/2KOT3WLhEqjCDzmzR4a/GFatWdoio+QjM4QVrIkzVkaY+cxsdAFaKEoxFtx4j0XUgaD1ZSB2YKjJfCUMFIE18XYYkgsH3J+mWlniLwi8YWgVxqcCc1R8PJIKZmaH+fcxEm2duwkHqltp9nCTjd3A0W3UqnExMTEpo4SA4H2Dn/3u9/lNa95TSBn1Vkf17z4Arz+9a/na1/7WiBn9fb2rjgQEQTxeJzGxsarHNV0VV9bBHZpQS4WqUbBKxTkwopLo1Zi3guR86pG7EZ5Cl8NUTYaiNkTmLKCLnzi3jx6OI5IduKltkBrH3pPO+VYL0W1DVtL4aqRZVMGQRLTYEvYJ69MIx0N0y6zZ65I79kpYjNFRs4/xemxpylW8gx278U0wms6X0pw11l0k1Ly+OOPs3v37mXtOIPA8zzGx8cDs6e89957ed3rXhfIWXXWx3Uhvm9605v4+te/HojRTiaToVQqUSwWA7iy5RkcHGR0dPSyVUa6skbxXSAaxt89gL99C9gOslBiuSStJnyatSK+FEy5UXAreEInpzUTikRxE92UUwO44UasWBeO2YCnR5GqgWYIEhkLTb98Pf1zQUyDtFmmlyzbpscJCxURDmO7LhOHf0RahOht3YaqrL1yL1h/0e3s2bMkEolNG9BZYHh4mPb29kA6E55++mlisRidnZ2rv7jOpnFdiG8sFuOmm24KxOtBCMHAwEAgmzJWQlEUbrjhBo4cObJ401CEsn4vAEVBtmSqUXBLBvLFZQtyQkBKs0ipZeZEipyjYmgavh7BVw0U6eGqS/epqirEG2zMiIdjKTUNcgSB5Vv40qJhvkK70ojhSqb8EvOiTHvrNhL5tY9kL7DeoluxWOTcuXObXmTzfZ+zZ8+yZcuWQM67++67eetb3xrIWXXWz3UhvgBve9vb+NznPhfIWS0tLeRyuWVbwoIimUzS3Ny8KPSqCKDfcrEgN7hqQc5QfPRIEuHZWJ6k5Gooro3iWdjh5YtVQqnuhosmbVxbsFqNcEWkrKZKHBtRqSBKRUQ+h5LPoxTyKIUc5fwYip3jNnMXKQuyqo1UFEoxjXg8RWuqD1lcbcvx8qyn6Ob7Po8//jh79+7d9D7ZkZERWlpaAllFZFkWP/zhD3nVq14VwJXV2QjXrLfDldx4442cPXuW6enpDXucCiHYunUrJ06cYO/evQFd4dIMDAzwzcljhAAAIABJREFU0EMPkU6nCafCwblgpRJ4+3cixqeqXhGqAuGrvQZmRYaGpCBROY/tOEyrCYj0YKr6infmBWN2RZUU5g0UKbnqid/3wPMRvgeeh/B8pJDVfR2Cxf+j6UhdR8ZMMAykoSNVDUuRlJQKrYn9ZIwmZrOT+POTmJpExMOoxRIpP0XEVSG6tjzv5d9LddLNdXx0ozYhffrpp2lqagpkeeVKSCk5ffp0YFNo3/rWt3jNa16zaTvl6tSOkJvZV/Uc8+///u9ks1n+6q/+asNnSSn5yU9+wi233LJpBikLVCoVHnroIfYc3MMTc08QNwNu/ymVUU4NI2bmIRoBy0ZMzWKXHMpmjERrdFG8fAlF36DoG5jCJarY6MK/vKbm++D51YjV83ArkM8aCF+iahIhfECApiINA2kYYOhI06gWCTUNVBWpqtU8xhIbhvNeDk1TaVIyWIUSnu/SnO4gKXW840cRoTCeYTA09BRdyXbM7fsQ8fW3YFVKDu0DKcKx1VvFxsbGGB4e5uabb940y8gFRkZGyGaz7Nq1K5DzfvVXf5XPf/7z9PX1BXJenfVzXYlvsVjkRS96EY888kggE0Cjo6NMT0+zb9++AK5uZWZnZ3nsyceQPZJkqLY2qTXh+4ipOZSnjiPOjiLjcWbVBCk/j2bb+D3t1cj4QrQqHQ/LE5RdFc+DkOphaj6aBkLXkKEQ0tQhbIJp4gmd4rSCY4MWrYor66j+255N1p7B8FRCrkk62khjsoVI6OLUmMzn8MaGkcU8OelQSsfp2rKxTQxWyaGhM0aqaWVPhnw+zyOPPMLtt9++6dGj7/v85Cc/4dZbbw0kAHj88cf5wAc+wLe+9a0Arq7ORrlu0g5Q7fl9+ctfzn333ccb3/jGDZ/X3t7OmTNnyOVygTS2r0RDQwNdnV0cPnuYxGAi+IjqQkHOP59CqVhUZkpElCyq5iN9DzEyjuzpQIZMZMyAkIlhGhimgScUrIJLtujiWD563MSIGxhRAy2iIYRAAaKNkvKET2X+gkjXcFnS9/FdF9e2ydnzFL0CnfFuuhp6SUTTS/49iHgCbbAaCTZIydTQEcpWkbC5fmvRxaLbCnMZlmXxyCOPcODAgefksf306dO0tbUF9uT1iU98gve85z2BnFVn41xXkS9UG9Hf8pa38MADDwRy3vz8PMeOHeO2227b9EdMz/e496F7adQbSXRvjtirh57AjUTIPztJsieJ0FRQBaJQxrtt9ehRSolTdHAKDnbRxi1VW7RUQ0UNqQhNwS0I3AKo4apz2ML7pO+DlPi+j3TdxQKX1AQFpURzvIX+5DYMZW1TYsVyntHpswx07l73z0hKiev49OzOLHmG67o89NBDDA4ObtpqoEuxLIuf/vSn/NIv/VIgBb3JyUle/epX8/Of/3zT/x3XqY3rKvKFqnlNJpMJbC9VKpUiHA5z/vx52traArjC5VEVleauZuxzNoXzBWKtwRu0+LEI5aF5wl0NiPgFy8mKhZ+s7bOEEBgxAyNmEKUaaUop8WwPr+Lhuz6q5oPwseZ81BDVQpwQCCEQqoqqKNUFl0JQ9AqgwZ7YDTQY6+uVjYbj6KpBtjBLKr6+M6qTbnLJopvv+xw+fJje3t7nRHihWtAbHBwMrJPiM5/5DG9/+9vrwvs84rppNbuUv/iLv+Dv//7vAztvx44dPPvss4FYV66Gruok+hJY8xal6dXdy9aK05DBz5UwDFlt86pYULaQneu3KBRCoJkaZtIknAkTbYnSsC1O094ERjiCqkcwIhH0cBjNNFF1HU/4ZP05kuEEe1N71y28C3Q09zI2fW5DgzZSyqsm3RYm2DKZDF1dXRu6xlqZn5+nWCwGdrPPZrN84xvf4I477gjkvDrBcF2K70JbzkMPPRTIeaFQiI6OjqvGgTcDQzGQiiQ9kKY0WQpUgKWUzM95hH5pDxg6IldA6hr+zn5IBB9la2FBvFdDNQRu+WJ2q+gVqFBia3KArbFtqy69rAVDM0knGpmcG133GQKwShcn3RaENxQKsXXr8psygkRKydGjR9m9e/0plCv56Ec/yh/90R8RDq+/Ha9O8FyX4gvwD//wD7zvfe8LzKGsv7+f8fHxy8aBN4MFT19FVWgYbKA0VaI0GYwAV+YqaKaG1ppG7tyKd/Ne5M6tmyK8C6iGINalYiQUrKLLnDtHLBRlT3ofjUZToI/BLelOZnKTOK69vmu9ZNLN930effRRIpEIO3bseM4e18+cOUMymSSZDKbjZWJigv/+7//mD/7gDwI5r05wXLfiu2vXLnp6evj+978fyHlLjQNvBpp60VZSURUygxnKc2WKExvzmpC+JD+aJ9713FsIKpqA5jIi7dCt9rI1MogZQLR71ecoCu2NPYxOnV3X+1W9utPN8zwOHz5MIpFgcHDwORPeQqHA0NBQoOPKf//3f89f//Vf14cqnodcd90OlzI0NMQb3/hGHnroocAcp5599lmEEIFsjl3y/NlnOV88T0S/2G8qfcncyTm0sEa8M74uMciP5hGKINa2eVHuUni+R87O0RRpYjA9iJtXmDqXQzdUVC34e7+UkuPDT9LZ3Ec0tPYbTalQZqJ4hvbOtsC8FGpBSslPf/pTduzYEdjU3KlTp/i93/s9HnzwwXqh7XnIdRv5QtUc/fbbb+crX/lKYGcODAxw/vx5stlsYGdeylKG6kIRpAfSIGHu5By+t7bI27M9KrMVoi3r74NdDyWnRMktsSuzi31N+4joERINIdq3pvA9H8cKvoAphKCruZ/hidNrTjlV7DInh56ms6P7ORVeqPb0ptPpQMeV3//+9/PBD36wLrzPU65r8QV473vfy7/8y79g2+vLA17JZqcfrlqieQEhBInuBKFUiJlnZvDW4D+bG85VI2blufkl9KXPfGWemB7j1rZb6Yh3oFyywSIcN2jflkY1FKxy8DvzIqEoYTPCXH6q5vfkinOcGj1GV+uWdberrZd8Ps/IyAiDg4OBnfn4448zMzPDy172ssDOrBMs1734NjY28lu/9Vt85jOfCezMRCJBe3s7R48eDezMBVbbZhFpipDoSjDz7AxW1lr1PDtv4zs+Zir4HOtSlN0yBbvAYGaQ/S37L0ufXIoR0mjfmiKSMAJZ43MlHU29jM8M4/kr36SklIzPDDE2PcS2rj3E4/FAdrrViuu6PProo+zbty9Qd7T3vve9/OM//mNg59UJnutefAH+9E//lLvvvpt8fv22g1fS399PpVIJfOGmpqw+92ImTDKDGQrjBXJDOeQylpFSSnLDORI9mzCufAW+9Jm35gmrYW5pu4XuePdl0e5SqJpCc2+SVEuEStHFX2EX3VrRVJ3GZCsTs8v/fGzH4sTwk3i+x7buPeiaUe142ISbwVJIKTly5Ai9vb2kUqnAzn3ggQeIx+OBDBnV2TxeEOIbjUZ5xzvewT/90z8FdqYQghtuuIGTJ08G2n6mChVRgyuCaqg0DDagaAozT8/gVq7exFCeLqNHdfTw5la6K26FvJ1nW2obB1oOEDNqL+opiqChPUpzTwyr7OK5waVymtJtzBdmsJyr99vNF2Y4MfIUrZkuOpv6Fm8UQgh8X25op1utnDlzBlVVA9vLBtVI+m/+5m/4f//v/wV2Zp3N4QUhvgB33nknDz74IIcPHw7sTF3XOXjwII8++iiOE8yj6mrR4qUIIYi1x0j0JJg7OUd+LL8YBfueT+F8gXjH5rWW+dIna2UxVIOb226mJ9mzvjU+QpBojNA+kMRzfZx17lO7EkUodDT2Mjp5cSef49qcHnua6fkJtnXtIRFNX/U+KSXuJhQDL2V2dpbR0VH27NkT6Lkf+chHeMUrXsHAwECg59YJnheM+Kqqyuc+9zne+c53Ylmr50prJR6Ps23bNh599NFAHlVrjXwvxYgZNO5sBAnTx6ax8zaFsQLR5ijKJrRzAViuRd7O05fs42DLQRLGxo2AInGTjsE0qhZcIS4Za8CTHrniPFPz4xwffpKGeDNbO3eia0sb+Gxkp1stVCoVjhw5wsGDBwPN8z711FN85zvf4W/+5m8CO7PO5vGCEV+Abdu28Tu/8zt88IMfDPTc9vZ24vF4IAU4RShrFl+otqPFO+Kkt6bJDmUpThY3pcgmpSRrZVGEwk2tN9Gf6q8pT10rC4W4cNygHFDuNZNo4eToUcpWie09N6zazaDq69vpVguu6/Lwww+ze/duIpGVvYPXeu7b3/52PvOZz9QHKq4RXlDiC/Ce97yHBx98kIcffjjQc3fs2IFt25w8eXJD56iKuqHimBbSUDSFaEuUuRNz5IZy+AHlUW3PJmtl6Un0cFPbTSTNTTB9pyp+LX1JUs3hqgCvsxBXtoqcHDnKbG6SdKyRiBmtKS2yWHQLsAAI1ZHlhx9+mL6+PpqaVjAOXgcf/vCH+Y3f+I1NX3tVJziuO0vJ1VBVlc9+9rP87u/+Lv/3f/8XmFH1QgHu0KFDmKa5bgesteR8l8LKWtUcamcC2SEpT5eZfnqacEPVbWw9aQgpJXk7T0gLcWPLjaTDV+dJg0ZRBJmOGJqpMj1cwDBrn4ir2CXGZ4axnQrtjb3EI0lcz+XZoSOk442o6sr/7C8tuulmMGkBKSWPPfYYzc3Nga9sf+qpp/je977Hj3/840DPrbO5XNfjxSvx8Y9/nMnJyUA7IGDjptu+9Pnx8I/X1DGwgJSS6aPTpLem0UIXBUb6ktJkieJUETNuEm2NXvbnK+F4DgW3QHe8m/5kP7r63D/SlnIW50/nUFWBtsyCSykl+VKWibkRfN+ntaHzqk0YU/PjVOwyXc2rT69Vihd2usXXZuy+HAspqaB2sS3gOA4veclL+MxnPhN48a7O5vKCSzss8Md//Mc89NBDHDp0KNBzNU3j5ptv5tixY8zOzq75/YpQUISyrlxnabKEmTSvElahCKKtUZp2N2EkDOZPzzN7fJbKfGXFz8lZOTzpcbD5INsbtv9ChBcgkjDpHEwjVIF9RUud57lMz5/nmXOPM5M9T0djL4Pde0nGGq5K3zQmWymUslTs8qqfKURwRbcTJ05QqVTYuXNnIOddyoc//GFe+cpX1oX3GuQFG/lC9ZfijjvuCDT9sECpVOLQoUPs2bOHTGZt46o/Hf0pilDW1Lbluz7TT0/TuLMRRV39nmoXbUpTJeycjZk0iTRG0KNVcXV8h6JdpDPeSX+qH0MNJvrbKK7jMXk2TylvUfHzzOUmqdhl0vFGMskWDH31AmO+lOX87DADnbtX/SwzrNPav7G89vHjx8lmsxw8eDAwc6cFnnzySd7xjnfwwAMP1Its1yAvaPEF+OQnP8nTTz/NXXfdFfjZ5XKZQ4cOsXPnzjUVWB4+/zCO76Artf9CZc9l0SM6kVW2716J9CWV+Qrl6TKe5WFHbYyUwcHugzRHm583piy2bTM1NcX58fNMjmSJmDFaW1uIhtfu8nZq9GkaUy0ko8ub2FS3WlzY6bYOTwwpJc888wylUon9+/cHLrz5fJ6XvvSlfOELX2D37pVvJHWen7zgxVdKye///u9z++238453vCPw8y3L4mc/+xnbt2+npaWlpvc8Pvk4BbuAqdXWKuaUHbKns2R2Lr38sRZc3yVXzhFxI4RKIQrzBSKRCI2NjaTTaZLJZKA9qath2zZzc3PMzc0xPT2NlJKmpiZaWlpIJpPkpitMDxcww2pNkf6lWE6FU6PH2N5zw4oFTqvs0rWjYc1FNyklx44dw3Ec9u3bF/gNzPd93vCGN/DGN76xvhroGuYFL75QbXp/2ctexj/8wz/wkpe8JPDzbdvm5z//Of39/bS3t6/6+qPTR5mpzBDWVl/7IqVk9tlZ4h1xjHUWhwp2ASEEOxp20BypRrtSSgqFArOzs8zNzZHNZhFCkEwmicViRKNRotEokUhkQ6LsOA7FYpFisUihUCCfz1MoFNB1nVQqRTqdJpPJYJpX34iK8xYTZ3OomkDT13YNo1Nn0VSdloaOZV+znqKblJInn3wSgD179mzKk8Pf/u3f4jhO4MXiOs8tdfG9wNjYGK94xSv41re+RV9fX+DnO47DoUOHaGtro6+vb8VfyuOzxxkrjhHVV/ffrcxVKM+WSfevvf1rwei8JdrCtvS2VcXedV1yuRyFQmFRMIvF4mLRTtM0dF3HMIyrBFlKieu6OI6DbduLdpyapi0KeTQaJR6PE4/Xnkqwyi7nT2fxXR+jxg6Ohe/9mXOPs61rL7q2dHrHKjk0tMdItdSWynFdl8cee4xoNLppq4e+/vWvc88993Dfffc9p08idYKnLr6XcOjQId797ndz//33E4sFv/HB8zyOHDmCqqrs2bNn2TzgmewZzmTPEDdW9mWQfrW1rGGwAXWZFqzlKDpFfOmzvWE7bdG2DQvFcuJ6KSuJ80ZwbY+JczkqBYdQpPY8+WxuknwpS0/r0j4IruOhX5i4W41yuczhw4fp7u4O1CjnUh577DHuvPNO7r//fhKJjY9z1/nFUhffK7jnnnv45je/yb333ht4kQSqInXy5Emmpqa48cYbMYyrH2lH8iM8M/vMqhNkhfEC0pdrMs+5cq3Pcn671xq+5zM9UiA3UyEU0Wq6mUgpeXboCbpbthIJXf2UUWvRbX5+nscee4w9e/bQ2Ni4oe9jOSYmJvj1X/917r333udsk3KdzeUF2+e7HG9+85sZGBjg7/7u7zblfCEEAwMD9PX18dOf/pRCoXDVaxY2GK+E53iUpktr2slWckqUnBI7MzsX1/pcLyiqQlNXnEx7FKvk4nurxxRCCLpatjAyeWrJfuda7CXHxsY4cuQIN99886YJr2VZ/PZv/zYf/ehH68J7HVEX3yX4x3/8Rx555BG+8Y1vbNpntLW1sX//fg4fPszo6Ohlf6Yqqzub5YfzxDtqWw20YP0Y1aPc0n4LnfHODY8xPx8RiiDdGqW1L4FjeTV58kZDcQzdZL4ws+SfS1/iLmFx6fs+R48eZWhoiBe96EVEo5uzH09KyTvf+U7e8IY38NKXvnRTPqPOL4br7zcwAFRV5ctf/jL//M//zM9+9rNN+5xkMsntt9/O+Pg4jz/+OK5bnahSxcrmOk7RwbVcQunVB0MW1voMpAfY37y/piLetU40HaJjMIWU8qqJuKXoaOpjbPrcknlqIapdD5dSKBR48MEHMU2TW265ZVMHHD7ykY+gKArvete7Nu0z6vxiqOd8V2BoaIjXve51fP7zn+fAgQOb9jlSSoaGhjhz5gz79++HEBw+f5iEeXVRRUrJzNMzJHuSixNpS+FLn5yVI2Em2JnZuWrx7nrEsT0mzuSwSw7mKoW48zPDSCRtme7Lvn5l0W14eJhTp05xww03BLr6Zyk+/elPc//99/O1r32tPsF2HVIX31U4ffo0r3/96/nSl7606fPz+Xyexx57jHRLmglzgmTo6oJbeaaMlbNI9S3/i19xK1ieRX+qn+5497q2S1wveJ7P9HCB/EyFUHT5Qpzv+zx97jEGunZjXDLcslB0ax9M8ORTTyKEYM+ePZsuhnfffTf33Xcf3/zmN5fsca5z7VMX3xo4fvw4b3rTm/jqV7/K9u3bN/WzPM/jyNEjHD1/lP6t/eiXRGzSl0wdnSKzPYO6xFCBL33ydp6YHmNn485AtktcD0hfMj9RYma8gBnSUdSlBXg+P8NcYZq+tosr3KWUTJyfZN4ZY3DHtsDtIJfinnvu4Ytf/CLf/va3CYdXH7Spc21SF98aOXr0KL/7u7/7nAiw7dn874n/RY5JjJhBrCOGoirkR/MIRSzZ4WB5FmW3zJbkFnoSPYFul7heyM9WmDybQ1/GG1hKyYmRp+ho7CUajmPZZYYmToGjs//m3SQzwfd+X8lXvvIV7r77br773e9uWhGvzvODuviugQUB3uwUhOu7/N/o/xHVolUf3skisdYYxfNFGnc1XtbhIKUkZ+eIaBF2Ne7atO0S1wuVosP5U1mAJT0bylaRs+PHScUyzBWm6WruxyBCui1KunVzxfALX/gCX/3qV7nvvvvqwvsCoN7tsAZ27drF17/+dX7v936PRx99dNM+RxUqQgqEEERbomQGM+RH8iC4bEW87dnMW/ObvtbneiIU1ekYTKOZ6lVLOqWUVOwyllOhWCmwvecG4pEkqq5SLmzOTrcF/u3f/o3/+q//4tvf/nZdeF8g1MV3jWzbto1vfvOb/OEf/uGmtaEJIVAVdXHQwrVctIhGsjdJ9lyW2ZOzzOZmkVJyY8uNDKQH1mQ/+UJHN1Xa+pNEkmZ1V5uU5IrzPDt0hFxxjsHuvVhOeXHwQtUEdskNfKfbAh//+Mf5wQ9+wDe/+c16jvcFRF1818GWLVv49re/zbve9a5NG8TQFA1f+lVhGMqR7E5ixAwS2xJYCYvsUJbIdISIuH6m1J5LVE2hpSeBGnU5+sxTTM2N0ds2SE/rAGEzSlOqjfMzw0Btk27rwXVd3vOe93D48GG+8Y1v1LsaXmDUxXeddHd3c//99/PFL36Rv/3bv12yQX8j6IqOlNUFmEbMQAtr5KwcvvR50dYX8caXvpGWphZ+9rOf8cQTTyw5plxnaaSUTE1N8fNDP+f83BC7b9hOR8MAunJR/JpSbWSLs1h2pfoez8exrp50Wy+zs7O86lWvoqWlhXvuuafex/sCpF5w2yCe5/G+972P48eP88UvfjEQN7SsleX+c/czWZhEjkv6t/cjVEF7rJ2t6a2Y6uV9qBMTE5w6dQpN0+jv7yeTWb+p+vWM53mMjo5y5swZEokE/f39i+5g5bzNxJksIBYLcbniHFPz4/R37MQqu6RbI4EU3Y4dO8ab3/xmPvCBD/Da1752w+fVuTapi29AfPnLX+YTn/gEX/va1zbkB5y1sjwx9QRTxSkmRydxdAc1pvLyvpfTn+xfUVTn5+c5deoUxWKR3t5e2tvb0bR6y1m5XGZoaIixsTHa2tro7e1dcmefXXGZOJvDqbiY4WokenLkKC0NHYT1BHpIrcleciW+973v8f73v5977rkn8E3Gda4t6uIbIA8//DBvfetb+cQnPsEv//Ivr+uMJ6aewPZtRmdHefrk0+zYvoOmSBMxI8bepr01nbEgNuPj48Tjcbq6umhqanpBRcOO4zA+Ps7IyAhSSjo7O+ns7FzVR9hzfKaGcxTnbcyIhuVUODP2DIPd+/AcuaGdbh/+8If5n//5H7761a+uealqneuPuvgGzNjYGG9605t485vfvK6dcA+OPkjSTHLoyCFaWlvobelFIslaWW7vuH1NZ0kpmZubY3h4mNnZWZqbm+no6CCZTF6XQux5HtPT04yMjJDP52lra6Orq4tIZG1FSd+XzI0XmTtfIhTRGJ0+i2mESJhNdG5Pr2ljBlRvhm9961tpaGjgYx/7WD2/Wweoi++mUKlUuPPOO4lGo3z84x9f01r6J6aeYHJmkrnJOXbu2glUnckMxag58l0K3/eZmJhgdHSUfD5PKpWipaWFpqama1oMSqUSExMTTE5OUi6XyWQydHZ2kkqlNnSDkVKSnykzNVxAUSUnxp6kJ7OTrsFGIonad7oNDQ1xxx138Pu///vceeed676eOtcfdfHdJKSUfPrTn+Y//uM/+PSnP80tt9xS0/vmynP85wP/yd6de0lFU1S8CmWnzN6mvYENUSxExBMTE0xNTaGqKo2NjTQ0NJBKpZ7XYlwul5mbm2N2dpbp6WlM06SlpYXm5uZNWf1UyllMnMkxm58kP1+go7UHI6xjRjRSzRFCyzjLSSm5++67ueuuu/jUpz7F7bev7amlzvVPXXw3mZMnT/K2t72Nm2++mb/7u79bNQo+ffo0M8UZzFaTvJ0nbsTpSfRs6vRapVJhenqaubk55ufn8TyPRCJBOp0mlUoRi8Wec0GWUmLbNrlcbnGFfKlUIhQKkU6nSafTNDY2PidLJO2Ky7mjMxx59Cg9nd1072jCtX1c26OlL3mVAA8NDfH2t7+dgYEB/umf/qk+sVZnSeri+xzg+z6f/OQn+dKXvsRdd93FzTffvOTrbNvmwQcf5MUvfvEvtEvB9/1F0Zufn6dQKOB5HqqqXrZpOBQKYRjGmpdiXrls07ZtyuXy4jbkUqmElBLDMIjH44tiG4lEfmG56tETcwwfn2ByepJbXnIDQoBjeai6QmtfcvH7+vznP8+nP/1pPv7xj6+76FrnhUFdfJ9DTpw4wdve9jZuu+02PvCBD1w10fTEE0+QTqfp6ur6BV3hyriue9nKeMuysG17UUQ9r/YhBF3XF0Vb13XC4fCiqEcikU1ZXroRzh2dwYxoPPnYMTp7Wsk0ZqpeEEWXnl0ZhoeHufPOO+nv7+dDH/rQpqRA6lxf1MX3OcbzPD75yU/y5S9/mbvuuoubbroJgFwuxxNPPMHtt99+XXYiXOucP5PFc3x8XJ566ikOHDiAa/somuD/+9F/8alPfYqPfexj/Mqv/Mov+lLrXCPUxfcXxEIUfOv/397dxzR19XEA//JSlEFkTlhFcUyJRDYdyou2qLy1jMBkYwhMmA6mEDIBg0S2OHDgQAaisj3gQHSyYWSKILi6FxWj8g4OnFNYJGHgHgQLWh+gIJW2v+cPY5MO3BTRVj2f5Ia0t9xz7vklv9x7zj3n8njYvHkzmpubYWNjg6lTp2q6aswYhgdHIG7vg76BHq51/xc6pIuhoSGk/ScJc6xmIz09nV3tMg+FTX/SkLlz5+L06dPYt28fli1bBi8vL9jb22u6Wsx9TDbigDvbBP/rGQIpdJCycwtu3urBF2lfwNnZWdPVY55C2tWx9pzR09NDREQE6urqYGJigiVLlqCgoOCh+k6ZJ0cmH0LWvu1YHxeKkLWrUVVdxRIvM24s+WoBIyMjxMfH4/Tp07h06RL4fD6OHz8O1iOkHYaHh7Fz5044Oztj9uzZqK+vx8qVK1nfPPNIWPLVItOmTUNGRgZKSkorh6kFAAAKqElEQVRQWloKNzc3VFdXa7pazy25XI79+/eDx+NBqVSirq4OYWFhbLEiZkKwATct1tLSgvj4eCiVSiQkJKiejGAeL7lcjqNHj2L79u3w8PDAxx9/zAZCmQnHku9ToL6+Hjt27IBYLEZkZCT8/Py0egrw00oikWDv3r0oLCzEm2++iZiYGMycOVPT1WKeUSz5PkX++usv7N69G7/88gtWrVqFdevW4eWXX9Z0tZ56ly5dQnZ2Nn799VeEhYXhgw8+YFOCmceOJd+n0ODgIA4ePIj8/HzMmjUL4eHhEAgEWjcrTJtJpVIcPnwY+fn5MDU1xUcffQQPDw/WhswTw5LvU66pqQl79+5FbW0tAgICEBgYiLlz52q6WlpJoVCgrq4OBw4cQH19PQICAhAaGooZM2ZoumrMc4gl30eUnZ2NoKCgh3ozwb1Fav5JR0cHXn311VHf//nnn5gzZ86o7wcHB3HkyBGUlpais7MTQqEQPj4+4PP5T2TlL20llUpx8uRJ/PDDD2hqagKPx0NgYCDc3d3HfZXb1dUFU1NTGBj8+7q+jyPWzLOB3WP9g7i4OAwPD6OwsBBZWVlQKpUIDw9XmwRx5swZTJ48GdXV1bCysoKrqytcXV3Vrj79/f3B5XKxYsUKmJmZISkpCd999x2uX78OoVAIuVyuVm56ejqamprGrJNIJEJhYeGo742MjBAaGopjx46hqqoKy5cvR0FBAezs7BASEoKSkhIMDAxMUMtot87OTuTk5MDLywvu7u64cOEC1q9fj99++w15eXkQCoWqxHvnzh1V+ysUCqSmpsLe3h5CoRBCoRBcLnfU8ePj41FcXDxm2RMZ65ycHFRUVDxKUzDajJhRpFIpiUQiWrduHaWlpdEnn3xC69evp6SkJAoMDCSRSEQ9PT3k6upKZmZm5OLiQvn5+ZSRkUFSqZSIiN566y21Y65YsYKIiAQCARERRUZGUnt7+6jftbe3U2RkpOpzQkIC8fl88vX1pYGBASIiCg4Opv7+/gc6F4VCQQ0NDZSQkECLFy8mT09P2r17N7W0tJBcLh9fA2mZoaEhqqmpocTERFqyZAl5eHhQVlYWdXR0/Ov/nj17lpYuXUrTp08nT09PysjIoDNnzqj234uPtbU1CQQCEggE5OTkRIsXL1Z9trKyUjvmeGIdEhJCCxcuJB6PR/7+/nTnzh0aGRmhlStXkkKhGHfbMNqLPS0+BrFYjF27dmHDhg2j9tna2uL777+HoaEhTp48iaCgIBQUFODy5cuoqKjA5s2b4ePjo/p9eXk5CgsLcfHiRYSFheGPP/5AeHj4qOUk7zlw4AAiIyMBADU1NaisrER1dTVyc3ORl5eH2NhYrF69GmVlZVizZs2/nouuri4cHR3h6OiI5ORkXL16FSKRCFu3bkVrayuMjY1hZ2cHOzs72NvbY968eVrdTTE0NISLFy+isbERjY2NuHz5MvT09GBrawuBQICNGzfCxOTBF553cXFBWVkZNm3ahG+//RY7duwAALz33ntIT09X/U5fXx9FRUVobW0Fj8dDUVERbGxssGDBAtjZ2QF4tFgDQFZWFpYtW4YPP/wQ5eXl8PLygkAgUK3xzDxbWPIdA4fDgYuLC3JycnDt2jW1fbq6uoiLi4OxsbHq1lVHR0fVFXH79m21ARw+nw9TU1NcvXoVaWlpaG1tRUpKCj777DPIZLJRZbe1tcHGxgYAcOLECXh7e0NHRweenp5obm4GAPB4PCQnJz9Q8v07S0tLREVFISoqCsDd181fuHABjY2NSElJwZUrV2BkZIRFixbB3t4etra2sLCwwLRp057odNqRkRGIxWJ0dHSgqakJjY2NaG5uBofDga2tLezt7REdHY358+c/UN/r/dTV1WHjxo3o6OjA2rVr8dprd9+bNzg4qPaM788//4zu7m6UlJTg+PHj6O/vx/Tp0wFA1W3wKLG+h4gglUpV58Tj8VBZWcmS7zOIJd8xzJo1C4mJifDy8kJSUpLavuzsbKxZswYnTpyAl5cXmpqa4Ovri/nz58Pc3By9vb2YOXOmWt9eeno6wsLCYGpqipGREXC5XOzZswfnz59HV1fXfeshFovh4OAAAJgzZ45q8MXQ0BC3b9+ekHN98cUX4ebmprYObV9fnyohf/nll+jq6oJEIoFSqYSBgQHMzc1HbTNmzICJiQk4HA709fVVm66uLhQKherNFff+3rhxA11dXeju7lZtXV1d6O3tBRFBT08PXC4Xr7zyChYtWoSYmBi8/vrrj5Rox8Lj8SASibBp0ybs378faWlpAIAXXngBHA5HFcfBwUGkpKQgKCgIPj4+CAwMhLW19ajjjTfWABAdHQ2JRAIfHx+4u7sDmNhYM9qFJd9/kJycjBs3bqCqqgr9/f3w9vZGcnIyAMDT0xOenp6wtrbG559/Dmtra0gkEhw6dAg1NTWq29dbt27B0dERQUFBAIDg4GAUFxfD398f/f39o5K7oaEhpFIpjI2NMWXKFEilUgBAQ0MDzp07h7i4OLS3tz/Wt12YmJioBg7/TiaT4fr162oJs7a2Ft3d3ejr64NcLlfbFAqFWjLW19cHh8OBqampKmk7OTmpkriZmZlGuz38/PxgaGiItrY2HDt2DKmpqRCJRNi+fTuMjIyQnZ2Nr7/+GufPn0doaCiAu4k5NTUVs2fPHnesgbvdDlVVVZg0aZLqLuNxx5rRHJZ872PLli348ccf8dJLL6GnpwcjIyNoaWmBXC7H1KlTUVpairNnzyIiIgI5OTlwcHAAEcHDwwM//fQTvvrqKygUClhYWMDCwgLOzs4wMDBAb28vZDIZcnNzIZfLERAQoFaut7c3SkpKEBISgqVLlyIvLw8xMTE4d+4cDA0NAQBFRUV49913NdEsmDRpEiwtLWFpaamR8ieaTCbD77//jvr6egiFQpSXlyMvLw98Ph+1tbV45513YGdnp+rHJyJ8+umncHV1BZfLxerVq9Wmeo831vdERERg+fLl2LBhA/T09FBWVoZt27Y9mcZgnizNjvdpr8TERDp16hQRER05coRycnKIiKinp4cCAgLo5s2b5ObmRv39/TQ8PExbt24lPz8/amtro7Vr11JNTQ3FxsaOOm5paSllZWXdt1ylUkkBAQEkFotJqVRSVFQU8fl88vHxIalUSleuXKHw8PDHc9LPoczMTHr77bfp6NGjNDg4SA0NDeTr60sVFRUUFxdH5eXllJ6eTp2dnfTNN9+Qk5MTbdu2jQYGBig5OZkcHByouLh4zGM/TKxDQkKosrKSiIiio6Pp8OHDVFlZSfHx8Y/lvBnNY5Ms7uPgwYNYsGAB3njjDRw6dAi9vb1YuHAhEhMTERsbi76+PgDA+++/j4iICHA4HGRmZkImk8HKygpDQ0MoLi6GTCbDzp07VVdHEokEMpkM5ubmAO4+W+rn54fo6GhV2Z2dnaioqEBwcPCoeuXm5iI4OBhTpkx5Aq3wfMnOzkZlZSX27dsHiUQCFxcXAMCePXtQWlqKefPmYdWqVaqBNuBuv3xmZiYSExNx6tSpCY31rl27EB0dzRZRekax5DsB5HI5W+P1GSCTye77WBjDTDSWfBmGYTSATS9mGIbRAJZ8GYZhNIAlX4ZhGA1gyZdhGEYD/g9PqENj0RDGegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HollandRadarDraw.py\n",
    "import numpy as np   #引入第三方库\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "#数据\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "radar_labels = np.array(['研究型(I)','艺术型(A)','社会型(S)','企业型(E)','常规型(C)','现实型(R)'])\n",
    "data = np.array([[0.40, 0.32, 0.35, 0.30, 0.30, 0.88],\n",
    "                [0.85, 0.35, 0.30, 0.40, 0.40, 0.30],\n",
    "                [0.43, 0.89, 0.30, 0.28, 0.22, 0.30],\n",
    "                [0.30, 0.25, 0.48, 0.85, 0.45, 0.40],\n",
    "                [0.20, 0.38, 0.87, 0.45, 0.32, 0.28],\n",
    "                [0.34, 0.31, 0.38, 0.40, 0.92, 0.28]])    #数据值\n",
    "data_labels = ('艺术家','实验员','工程师','推销员','社会工作者','记事员')\n",
    "\n",
    "#雷达图绘制\n",
    "angles= np.linspace(0, 2 * np.pi, 6, endpoint=False)\n",
    "data = np.concatenate((data, [data[0]]))\n",
    "angles = np.concatenate((angles, [angles[0]]))\n",
    "fig = plt.figure(facecolor=\"white\")\n",
    "plt.subplot(111, polar=True)\n",
    "plt.plot(angles, data, 'o-', linewidth = 1, alpha = 0.2)\n",
    "plt.fill(angles, data, alpha = 0.25)\n",
    "plt.thetagrids(angles*180/np.pi, radar_labels)  \n",
    "#plt.thetagrids(angles*180/np.pi, radar_labels, frac = 1.2)\n",
    "\n",
    "#输出\n",
    "plt.figtext(0.52, 0.95, '霍兰德人格分析', ha = 'center', size = 20)\n",
    "legend = plt.legend(data_labels, loc = (0.94, 0.80), labelspacing = 0.1)\n",
    "plt.setp(legend.get_texts(), fontsize = 'large')\n",
    "plt.grid(True)\n",
    "plt.savefig('holland_radar.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3 Wed解析到网络空间\n",
    "> 网络爬虫；Web信息提取；Web网站开发；网络应用开发；    \n",
    "\n",
    "### 9.3.1 网络爬虫\n",
    "1. Requests：最友好的网络爬虫功能库；提供了简单易用的类HTTP协议网络爬虫功能；支持连接池、SSL、Cookies、HTTP(S)代理等；     \n",
    "``\n",
    "import requests\n",
    "r = requests.get('http://api.github.com/user', auth = ('user', 'pass'))\n",
    "r.status_code\n",
    "r.headers['content-type']\n",
    "r.encoding\n",
    "r.text\n",
    "``     \n",
    "\n",
    "2. Scrapy：优秀的网络爬虫框架；提供了构建网络爬虫系统的框架功能，功能半成品；支持批量和定时网页爬取、提供数据处理流程等；    \n",
    "3. pyspider：强大的Web页面爬取系统；提供了完整的网页爬取系统构建功能；支持数据库后端、信息队列、优先级、分布式架构等；    \n",
    "\n",
    "### 9.3.2 Web信息提取\n",
    "1. Beautiful Soup：HTML和XML的解析库；提供解析HTML和XML等Web信息的功能；又名beautifulsoup4或bs4，可以加载多种解析引擎；常与网络爬虫库搭配使用，如Scrapy、requests等；    \n",
    "2. Re：正则表达式解析和处理功能库；提供了定义和解析正则表达式的一批通用功能；可用于各类场景，包括定点的Web信息提取；Python标准库；    \n",
    "3. python-Goose：提取文章类型Web页面的功能库；提供对Web页面中文章信息/视频等元素数据的提取功能；针对特定类型Web页面，应用覆盖较广；    \n",
    "\n",
    "### 9.3.3 Web网站开发\n",
    "1. Django：Web应用框架；提供构建Web系统的基本应用框架；MTV模式：模型（model）、模板（Template）、视图（Views）；   \n",
    "2. Pyramid：规模适中的Web应用框架；提供简单方便构建Web系统的应用框架；不大不小，规模适中，合适快速构建并适度扩展类应用；   \n",
    "3. Flask：Web应用开发微框架；提供最简单构建Web系统的应用框架；特点是简单、规模小、快速；    \n",
    "    Django>Pyramid>Flask\n",
    "    \n",
    "### 9.3.4 网络应用开发\n",
    "1. WeRoBot：微信公众号开发框架；提供解析微信服务器消息及反馈消息的功能；建立微信机器人的重要技术手段；    \n",
    "2. aip：百度AI开放平台接口；提供访问百度AI服务的Python功能接口；语音、人脸、OCR、NLP、知识图谱、图像搜索等领域；   \n",
    "3. MyQR：二维码生成第三方库；提供生成二维码的系列功能；基本二维码、艺术二维码和动态二维码；    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "('Connection aborted.', ConnectionResetError(10054, '远程主机强迫关闭了一个现有的连接。', None, 10054, None))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    599\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    383\u001b[0m                     \u001b[1;31m# otherwise it looks like a programming error was the cause.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m                     \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    385\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    379\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m                     \u001b[0mhttplib_response\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    381\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1320\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1322\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    256\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 257\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionResetError\u001b[0m: [WinError 10054] 远程主机强迫关闭了一个现有的连接。",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProtocolError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    637\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 638\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    639\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    367\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mread\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_method_retryable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    369\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mread\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    684\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    686\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    599\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    383\u001b[0m                     \u001b[1;31m# otherwise it looks like a programming error was the cause.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m                     \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    385\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    379\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m                     \u001b[0mhttplib_response\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    381\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1320\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1322\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    256\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 257\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProtocolError\u001b[0m: ('Connection aborted.', ConnectionResetError(10054, '远程主机强迫关闭了一个现有的连接。', None, 10054, None))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-bad17dc783ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'http://api.github.com/user'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'user'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pass'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'content-type'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    531\u001b[0m         }\n\u001b[0;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 533\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mProtocolError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mMaxRetryError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(10054, '远程主机强迫关闭了一个现有的连接。', None, 10054, None))"
     ]
    }
   ],
   "source": [
    "#9.3.1\n",
    "import requests\n",
    "r = requests.get('http://api.github.com/user', auth = ('user', 'pass'))\n",
    "r.status_code\n",
    "r.headers['content-type']\n",
    "r.encoding\n",
    "r.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.4 人机交互到艺术设计\n",
    "> 图形用户界面；游戏开发；虚拟现实；图形艺术；\n",
    "\n",
    "### 9.4.1 图形用户界面GUI\n",
    "1. PyQt5：Qt开发框架的Python接口；提供创建Qt5程序的PythonAPI接口；Qt是跨平台桌面应用开发系统，完备GUI；   \n",
    "2. wxPython：跨平台GUI开发框架；提供专用于Python的跨平台GUI开发框架；理解数据类型与索引的关系，操作索引即操作数据；   \n",
    "3. PyGObject：使用GTK+开发GUI的功能库；提供整合GTK+、WebKitGTK+等库的功能；GTK+是跨平台的一种用户图形界面GUI框架；Anaconda采用的该库构建GUI；    \n",
    "\n",
    "### 9.4.2 游戏开发\n",
    "1. PyGame：简单的游戏开发功能库；提供基于SDL的简单游戏开发功能及实现引擎；理解游戏对外部输入的响应机制及角色构建和交互机制；   \n",
    "2. Panda3D：开源、跨平台的3D渲染和游戏开发库；3D游戏引擎，提供Python和C++两种接口；支持很多先进特性：法线贴图、光泽贴图、卡通渲染等；由迪士尼和卡尼吉梅隆大学共同开发；    \n",
    "3. cocos2d：构建2D游戏和图形界面交互式应用的框架；提供基于OpenGL的游戏开发图形渲染功能；支持GPU加速，采用树形结构分层管理游戏对象类型；   \n",
    "\n",
    "### 9.4.3 虚拟现实\n",
    "1. VR Zero：在树莓派上开发VR应用的Python库；提供大量与VR开发相关的功能；针对树莓派的VR开发库，支持设备小型化，配置简单化；    \n",
    "2. pyovr：Oculus Rift的Python开发接口；针对Oculus VR设备的Python开发库；基于成熟的VR设备，提供全套文档，工业级应用设备；   \n",
    "3. Vizard：基于Python的通用VR开发引擎；专业的企业级虚拟现实开发引擎；提供详细的官方文档；支持多种主流的VR硬件设备，具有一定通用性；  \n",
    "\n",
    "### 9.4.4 图形艺术\n",
    "1. Quads：迭代的艺术；对图片进行四分迭代，形成像素风；可以生成动图或静图图像；简单易用，具有很高展示度；   \n",
    "2. ascii_art：ASCII艺术库；将普通图片转为ASCII艺术分格；输出可以是纯文本或彩色文本；可采用图片格式输出；     \n",
    "3. turtle：海龟绘图体系；Random Art；   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.5 实例16：玫瑰花绘制\n",
    "> 需求：用python绘制一朵玫瑰花；输入：想象力；输出：玫瑰花；\n",
    "> 绘制机理：turtle基本图形绘制；\n",
    "> 绘制思想：因人而异；  \n",
    "\n",
    "### 9.5.1 实例展示\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#9.5.1\n",
    "import turtle\n",
    "\n",
    "# 设置初始位置\n",
    "turtle.penup()\n",
    "turtle.left(90)\n",
    "turtle.fd(200)\n",
    "turtle.pendown()\n",
    "turtle.right(90)\n",
    "\n",
    "# 花蕊\n",
    "turtle.fillcolor(\"red\")\n",
    "turtle.begin_fill()\n",
    "turtle.circle(10,180)\n",
    "turtle.circle(25,110)\n",
    "turtle.left(50)\n",
    "turtle.circle(60,45)\n",
    "turtle.circle(20,170)\n",
    "turtle.right(24)\n",
    "turtle.fd(30)\n",
    "turtle.left(10)\n",
    "turtle.circle(30,110)\n",
    "turtle.fd(20)\n",
    "turtle.left(40)\n",
    "turtle.circle(90,70)\n",
    "turtle.circle(30,150)\n",
    "turtle.right(30)\n",
    "turtle.fd(15)\n",
    "turtle.circle(80,90)\n",
    "turtle.left(15)\n",
    "turtle.fd(45)\n",
    "turtle.right(165)\n",
    "turtle.fd(20)\n",
    "turtle.left(155)\n",
    "turtle.circle(150,80)\n",
    "turtle.left(50)\n",
    "turtle.circle(150,90)\n",
    "turtle.end_fill()\n",
    "\n",
    "# 花瓣1\n",
    "turtle.left(150)\n",
    "turtle.circle(-90,70)\n",
    "turtle.left(20)\n",
    "turtle.circle(75,105)\n",
    "turtle.setheading(60)\n",
    "turtle.circle(80,98)\n",
    "turtle.circle(-90,40)\n",
    "\n",
    "# 花瓣2\n",
    "turtle.left(180)\n",
    "turtle.circle(90,40)\n",
    "turtle.circle(-80,98)\n",
    "turtle.setheading(-83)\n",
    "\n",
    "# 叶子1\n",
    "turtle.fd(30)\n",
    "turtle.left(90)\n",
    "turtle.fd(25)\n",
    "turtle.left(45)\n",
    "turtle.fillcolor(\"green\")\n",
    "turtle.begin_fill()\n",
    "turtle.circle(-80,90)\n",
    "turtle.right(90)\n",
    "turtle.circle(-80,90)\n",
    "turtle.end_fill()\n",
    "\n",
    "turtle.right(135)\n",
    "turtle.fd(60)\n",
    "turtle.left(180)\n",
    "turtle.fd(85)\n",
    "turtle.left(90)\n",
    "turtle.fd(80)\n",
    "\n",
    "# 叶子2\n",
    "turtle.right(90)\n",
    "turtle.right(45)\n",
    "turtle.fillcolor(\"green\")\n",
    "turtle.begin_fill()\n",
    "turtle.circle(80,90)\n",
    "turtle.left(90)\n",
    "turtle.circle(80,90)\n",
    "turtle.end_fill()\n",
    "\n",
    "turtle.left(135)\n",
    "turtle.fd(60)\n",
    "turtle.left(180)\n",
    "turtle.fd(60)\n",
    "turtle.right(90)\n",
    "turtle.circle(200,60)"
   ]
  }
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