diff --git a/.gitignore b/.gitignore index c46f820..fc1788c 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,4 @@ python_basic/.ipynb_checkpoints +python_pandas_basic_notebook/COVID-19/.ipynb_checkpoints/ +python_study_basic_notebook/.ipynb_checkpoints/ +python_study_basic_notebook/files/ diff --git "a/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" new file mode 100644 index 0000000..6634c1a --- /dev/null +++ "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" @@ -0,0 +1,1268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - 面向对象编程 01\n", + "\n", + "* Python Senior, Lesson 10, v1.0.0, 2016.11 by David.Yi\n", + "* v1.1, 2020.5.4, 6.13 edit by David Yi\n", + "\n", + "\n", + "## 本次内容要点\n", + "\n", + "* 类与实例\n", + "* 类的属性\n", + "* 类的方法\n", + "* 创建和使用子类\n", + "* 构造器方法和解构器方法\n", + "* 调用父类的 super() 方法" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 面向对象编程\n", + "\n", + "#### 类与实例\n", + "\n", + "类，是一个定义；实例是真正的对象的实现，创建一个实例的过程称作实例化。\n", + "\n", + "所有的类都继承自一个叫做 object 的类。继承的定义之后再说。\n", + "\n", + "类的一些操作方式和函数很像，在没有面向对象编程方式的时候，就是面向过程（函数）的开发方式。\n", + "\n", + "类可以很复杂，也可以很简单，取决于应用的需要。面向对象的编程方式，是现代流行的开发方式，博大精深，需要慢慢理解体会。一开始有些不太清楚，也没有关系。\n", + "\n", + "#### 类的属性\n", + "\n", + "类可以理解为一种称之为命名空间（ namespace），在这个类之下，数据都是属于这个类的实例的，我们称为属性，用实例名字+点+属性名字。这样的类比较简单，在 c 语言中称为结构体，在 pascal 中为记录类型，python 的数据结构比较简单。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "5\n" + ] + } + ], + "source": [ + "# 申明一个 class MyData\n", + "class MyData(object):\n", + " pass\n", + "\n", + "# 实例化 MyData, 实例的名字叫做 obj_math\n", + "obj_math = MyData()\n", + "obj_math.x = 4\n", + "print(obj_math.x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### 类的方法\n", + "\n", + "光是把类作为命名空间太简单了，可以给类添加功能，称为方法。\n", + "\n", + "方法定义在类中，使用在实例中。可以理解为实例是真正做事情的代码，所以在实例中调用方法完成某个功能是合理的。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "Hello!\n" + ] + } + ], + "source": [ + "class MyData(object):\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + "# 实例化\n", + "obj_math = MyData()\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n" + ] + } + ], + "source": [ + "# 类的方法的直接调用，其实还是实例化了\n", + "\n", + "MyData().SayHello()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "9\n", + "4\n" + ] + } + ], + "source": [ + "# 在上面基础上，复杂一点的例子\n", + "\n", + "class MyData(object):\n", + " \n", + " # 初始化方法，双下划线前后\n", + " # 实例化的时候，需要传递 self 之后的参数\n", + " def __init__(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + " def Add(self):\n", + " print(self.x + self.y)\n", + " \n", + "# 实例化\n", + "obj_math = MyData(3,4)\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()\n", + "\n", + "obj_math.x = 5\n", + "\n", + "obj_math.Add()\n", + "\n", + "o1 = MyData(1,3)\n", + "o1.Add()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "7\n", + "11\n" + ] + } + ], + "source": [ + "# 再复杂一点，创建多个实例\n", + "\n", + "class MyData(object):\n", + " \n", + " # 初始化方法，双下划线前后\n", + " # 实例化的时候，需要传递 self 之后的参数\n", + " def __init__(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + " def Add(self):\n", + " print(self.x + self.y)\n", + " \n", + "# 实例化\n", + "obj_math = MyData(3,4)\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()\n", + "obj_math.Add()\n", + "\n", + "# 再创建一个实例\n", + "obj_math2 = MyData(5,6)\n", + "obj_math2.Add()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0]\n", + "[1, 2]\n", + "[4, 4]\n", + "[9, 6]\n", + "[16, 8]\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 我们建立一个有趣的简单的模仿游戏的类来说明面向对象编程的概念\n", + "# v1.0.0\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "n1 = NPC('AA1')\n", + "n1.show_properties()\n", + "n1.fight_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "这个 NPC 的类，在初始化这里定义了 NPC 拥有的三个属性，name、weapon、blood，其中 name 需要创建实例的时候设置。\n", + "\n", + "NPC 类有两个方法，一个是 show_properties()，显示 NPC 的属性；另外一个是 fight_common()，我们称之为普通攻击。\n", + "\n", + "#### 创建和使用子类\n", + "\n", + "通过 `class B(A)`，即表示从 A 开始创建一个子类，我们称之为继承。\n", + "\n", + "面向对象的编程方式的有点已经可以体现，我们可以通过继承来构造一个对象体系，比如这里现在 NPC 这个类中，定义了游戏中人物的最基本的一些属性和方法，可以理解不光 NPC 是战士，还是巫师，对会有这些属性和方法；然后我们把各自独特的属性和方法定义在从 NPC 中继承的各个子类中。我们先来定义一个战士 Soldier 类。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类，继承自 NPC class\n", + "class Soldier(NPC):\n", + " # 暂时什么也不干\n", + " pass\n", + " \n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "\n", + "# 调用方法，因为 Soldier 中此刻并没有任何实际的方法等，所以实际上自动调用了父类的方法\n", + "n1.show_properties()\n", + "n1.fight_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "在子类中，可以覆盖父类的方法。\n", + "\n", + "比如 `__init__()` ，我们需要在 Soldier 类的初始化中加入一个只有 soldier 才有的级别 level 属性。\n", + "所以，我们先调用父类的`__init__()` 方法，再写 Soldier 类需要的代码。\n", + "\n", + "而 `show_properties()`方法，我们先用简单的办法，完全覆盖，也就是使用这个新的`show_properties()`方法，来显示 NPC 标准的三个属性以及 Soldier 的一个独立属性。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('soldier_level:', self.soldier_level)\n", + "\n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "n1.show_properties()\n", + "n1.fight_common() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "再来看看 `show_properties()` 这个方法，总感觉这样有点笨重，因为不管是否是面向对象的编程方式，都不应该有太多重复的代码，现在 NPC 类中也有 `show_properties()` 方法，用来显示标准的三个属性，而 Soldier 类中同样名字的方法显示四个属性，如果 NPC 类中增加了属性，那么两边类中的这个方法都要修改。\n", + "\n", + "我们来看看有没有更好的办法，可以用 super(需要知道父类的类，self) 的方法来访问父类的方法，这样代码就简洁优雅了。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + "\n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "n1.show_properties()\n", + "n1.fight_common() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### __init__() 构造器方法和 __del__() 解构器方法\n", + "\n", + "可以理解构造就是创建，解构就是销毁。每一个对象的实例都是有声明周期的，从构造到解构。\n", + "\n", + "类别调用的时候，对象被创建的时候，python 先检查是否实现了 __init__() 方法，如果没有，则什么也不干。如果有 __init__()方法，则执行。\n", + "\n", + "python 作为高级语言，具有垃圾对象回收机制，当 python 发现对于该实例对象的引用都被清除掉后，会执行 __del__() 方法。通常不需要自己去实现这个 __del__() 方法，python 会做好这一切。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 先整理一下上面的代码\n", + "# v1.0.1\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n", + "\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 创建两个 soldier\n", + "\n", + "n1 = Soldier('AA1')\n", + "n1.show_properties()\n", + "n1.fight_common() \n", + "\n", + "print()\n", + "\n", + "n2 = Soldier('AA2')\n", + "n2.show_properties()\n", + "n2.fight_common() " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 连续创建多个 soldier 的实例\n", + "# 并存储在 list 中\n", + "\n", + "s = []\n", + "\n", + "for i in range(3):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " n.fight_common() \n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[<__main__.Soldier object at 0x1063f2a58>, <__main__.Soldier object at 0x1063d7e48>, <__main__.Soldier object at 0x1063d7240>]\n" + ] + } + ], + "source": [ + "# 看一下存储了对象的列表\n", + "\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "AA1\n", + "AA2\n", + "3\n" + ] + } + ], + "source": [ + "# 可以和一般访问列表一样访问列表中的对象\n", + "\n", + "for i in s:\n", + " print(i.name)\n", + " \n", + "print(len(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "AA2\n", + "2\n" + ] + } + ], + "source": [ + "# 可以删除一个实例\n", + "s.pop(1)\n", + "\n", + "# 显示列表中的实例\n", + "for i in s:\n", + " print(i.name)\n", + " \n", + "print(len(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "Fight Common\n", + "Wizard Magic!\n" + ] + } + ], + "source": [ + "# 再增加一个巫师 Wizard 的类\n", + "\n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def wizard_fight_magic(self):\n", + " print('Wizard Magic!')\n", + " \n", + "# 创建一个 wizard\n", + "\n", + "c1 = Wizard('CC1')\n", + "c1.show_properties()\n", + "c1.fight_common() \n", + "c1.wizard_fight_magic()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "name: CC2\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "AA0\n", + "--\n", + "AA1\n", + "--\n", + "AA2\n", + "--\n", + "CC0\n", + "--\n", + "CC1\n", + "--\n", + "CC2\n", + "--\n" + ] + } + ], + "source": [ + "# 创建复杂的 NPC，3个 wizards，3个 soldiers\n", + "\n", + "# 创建多个 soldier 的实例\n", + "s = []\n", + "\n", + "for i in range(3):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(3):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "for i in s:\n", + " print(i.name)\n", + " print('--')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'fight_common', 'show_properties']\n" + ] + } + ], + "source": [ + "# 显示类的方法\n", + "\n", + "print(dir(Soldier))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'fight_common', 'show_properties', 'wizard_fight_magic']\n" + ] + } + ], + "source": [ + "# 显示类的方法\n", + "\n", + "print(dir(Wizard))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 当前版本的代码\n", + "# v1.0.2\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "NPC created!\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "\n", + "NPC created!\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 在 NPC 的 __init__() 加入显示创建的是什么角色\n", + "# v1.0.3\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " # 先简单的显示\n", + " print('')\n", + " print('NPC created!')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 但是在 NPC 这个父类中没有显示出具体的子类名称\n", + "# 所以我们用下面的方法来显示子类的名称\n", + "# type(self).__name__ 来访问类的名称\n", + "# v1.0.4\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " print('')\n", + " print(type(self).__name__, 'NPC created!')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "面向对象程序设计（英语：Object-oriented programming，缩写：OOP）是种具有对象概念的程序编程范型，同时也是一种程序开发的方法。它可能包含数据、属性、代码与方法。对象则指的是类的实例。它将对象作为程序的基本单元，将程序和数据封装其中，以提高软件的重用性、灵活性和扩展性，对象里的程序可以访问及经常修改对象相关连的数据。在面向对象程序编程里，计算机程序会被设计成彼此相关的对象。\n", + "\n", + "面向对象程序设计可以看作一种在程序中包含各种独立而又互相调用的对象的思想，这与传统的思想刚好相反：传统的程序设计主张将程序看作一系列函数的集合，或者直接就是一系列对电脑下达的指令。面向对象程序设计中的每一个对象都应该能够接受数据、处理数据并将数据传达给其它对象，因此它们都可以被看作一个小型的“机器”，即对象。目前已经被证实的是，面向对象程序设计推广了程序的灵活性和可维护性，并且在大型项目设计中广为应用。此外，支持者声称面向对象程序设计要比以往的做法更加便于学习，因为它能够让人们更简单地设计并维护程序，使得程序更加便于分析、设计、理解。反对者在某些领域对此予以否认。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 2000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 继续优化，根据 NPC 类型来设定 blood 和 weapon\n", + "# 将代码尽量集中，是有好处的\n", + "# v1.0.5\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 2000\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" new file mode 100644 index 0000000..0139f1d --- /dev/null +++ "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" @@ -0,0 +1,1324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - 面向对象编程 02\n", + "\n", + "* Python Begginer, Level 2, Lesson 12, v1.0.0, 2016.12 by David.Yi\n", + "* v1.1, 2020.5.4,6.13 edit by David Yi\n", + "\n", + "## 本次内容要点\n", + "\n", + "\n", + "* 更加优雅的程序设计思路\n", + "* 类方法\n", + "* 静态方法\n", + "* 类属性\n", + "* 类属性安全的访问方法\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: short gun\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: short gun\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n" + ] + } + ], + "source": [ + "# 修改战斗的输出，战斗不能只喊喊口号，需要对对方有输出，\n", + "# 在 NPC 的 fight_common() 中我们设计返回值表示输出\n", + "# 再在主程序中增加一个 调用 soldier 的 fight_common()，来检查输出\n", + "# v1.0.6\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'short gun'\n", + " self.blood = 2000\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "print(s[1].fight_common())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "wizard_level 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 在 soldier 中增加专门的战斗方式\n", + "# soldier 和 wizard 的战斗中都增加输出\n", + "# v1.0.7\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = ' sword'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码 \n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "# 测试一下各类进攻方式\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将子类中的 show_properties 去除，显示不同的 level 功能放到 NPC 类中\n", + "# 修改子类中的 solider_level 和 wizard_level 统一为 level，也放到 NPC 类中\n", + "# v1.0.8\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " self.level = 1 \n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " self.level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "程序是否功能越来越强大，但是也很简洁，并且很容易扩充功能？这就是面向对象编程思想的好处和强大能力！\n", + "\n", + "思考一下\n", + "\n", + "* 给 NPC 增加一个 MagicPoint MP 值，应该怎么操作？\n", + "* 考虑程序的发展，如果把武器 weapon 和伤害值不固定写死在类中比较好，应该怎么操作？\n", + "\n", + "我们还可以继续来增强功能\n", + "\n", + "* 引入类方法，来简化不同子类继承后进行的操作\n", + "* 战斗输出放在子对象中，如果我们要修改输出值的话，有点不方便，我们引入类方法\n", + "\n", + "类方法和静态方法\n", + "* 类方法是为了访问类属性更加方便\n", + "* 类方法和静态方法可以通过类和实例来访问,效果是相同的\n", + "* 静态方法跟普通函数没有什么区别" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Fight Common\n", + "{'blood': -100}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 引入类方法，修改 NPC类的 fight_common() 来完成统一的并且能区分角色的战斗输出\n", + "# v1.0.9\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " self.level = 1 \n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " self.level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # 使用类方法，传入的参数是 class，因此通过 class的 name 来获得具体名称\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, 'Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_common())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Fight Common\n", + "{'blood': -100}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将 NPC 对不同角色的初始化值从对象定义代码中剥离，便于调整数值\n", + "# v1.0.10\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'Soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'Wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # 使用类方法，传入的参数是 class，因此通过 class的 name 来获得具体名称\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, 'Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_common())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "('blood', -100)\n", + "Fight Sword Fight!\n", + "('blood', -200)\n", + "Wizard Fight Common\n", + "('blood', -100)\n", + "Wizard Staff Magic!\n", + "('blood', -300)\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将战斗输出的值修改为从外部数据读入\n", + "# 战斗的方法的名称修改\n", + "# v1.0.11\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, fight_output_dict.get('fight_common').get('name'))\n", + " output = fight_output_dict.get('fight_common').get('blood')\n", + " return ('blood',output)\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " print('Fight', fight_output_dict.get('sword_fight').get('name'))\n", + " output = fight_output_dict.get('sword_fight').get('blood')\n", + " return ('blood',output)\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def staff_magic(self):\n", + " print('Wizard', fight_output_dict.get('staff_magic').get('name'))\n", + " output = fight_output_dict.get('staff_magic').get('blood')\n", + " return ('blood',output)\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n", + "\n", + "npc count: 4\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 加入类属性，npc_count, 计算创建了多少 npc \n", + "# v1.0.13\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " npc_count = 0\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # npc 计数器 +1 \n", + " NPC.npc_count += 1 \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())\n", + "# 查看 npc 数量\n", + "print('\\nnpc count: ', NPC.npc_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "npc count: 5\n", + "4\n" + ] + } + ], + "source": [ + "# 类变量容易引起问题，每个实例会和类拥有同名的类变量\n", + "print(s[0].name)\n", + "s[0].npc_count += 1\n", + "print('npc count:',s[0].npc_count)\n", + "print(NPC.npc_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n", + "\n", + "npc count: 4\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将类属性修改为私有，__npc_count, 并通过方法来获得创建了多少 npc \n", + "# v1.0.14\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " __npc_count = 0\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # npc 计数器 +1 \n", + " NPC.__npc_count += 1 \n", + " \n", + " # 显示 npc 数量方法\n", + " @staticmethod\n", + " def get_npc_count():\n", + " return NPC.__npc_count\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())\n", + "# 查看 npc 数量\n", + "print('\\nnpc count: ', NPC.get_npc_count())" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 战斗输出内容从外部数据读入了，代码就显得很重复，我们继续简化\n", + "# 通过 静态方法 fight_output() \n", + "# v1.0.12\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/COVID-19.py" "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/COVID-19.py" new file mode 100644 index 0000000..a9f8629 --- /dev/null +++ "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/COVID-19.py" @@ -0,0 +1,84 @@ +import requests +import pandas as pd + +url = 'https://covid-19.adapay.tech/api/v1/' +token = '497115d0c2ff9586bf0fe03088cfdbe2' +region = 'US' + +headers = { + 'token': token +} + +payload = { + 'region': region, + + 'start_date': '2020-03-26', + 'end_date': '2020-04-04' +} + +# interface: region + +r = requests.get(url+'infection/region', params=payload, headers=headers) + +data = r.json() + +print('---') +# json raw data +print(data) + +print('---') +# filter data +for key, value in data['data']['region'][region].items(): + print(key, value) + +print('---') +# load to dataframe +df = pd.DataFrame.from_dict(data['data']['region'][region]) +print(df) + +print('---') +# rotate dataframe +df = df.T +print(df) + +# interface: region detail + +payload = { + 'region': region, + 'start_date': '2020-03-26', + 'end_date': '2020-03-27' +} + +r = requests.get(url+'infection/region/detail', params=payload, headers=headers) + +data = r.json() + +print('---') +# json raw data +print(data) + +print('---') +# filter data +for key, value in data['data']['area'].items(): + print(key, value) + +print('---') +# load to dataframe +df = pd.DataFrame.from_dict(data['data']) +df = pd.DataFrame(df, index=['a', 'b']) +print(df) + +print('---') +df = pd.DataFrame.from_dict(data['data']['area']['New York']) +print(df) + +print('---') +# rotate dataframe +df = df.T +print(df) + +print('---') +df1 = df['a'] +print(df1) + + diff --git "a/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/pandas-COVID-19.ipynb" "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/pandas-COVID-19.ipynb" new file mode 100644 index 0000000..3539de9 --- /dev/null +++ "b/Python \344\270\223\351\242\230\350\257\276\347\250\213/pandas/pandas-COVID-19.ipynb" @@ -0,0 +1,1392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "本培训教程，通过 jupyter-notebook 来指导怎么通过 Python Pandas 来进行数据分析和图表呈现。\n", + "包括下面这些内容：\n", + "\n", + "* 数据的各类获得方式\n", + "* 数据接口的调用\n", + "* 数据的整理和清洗\n", + "* 数据的存储\n", + "* 计算各类指标\n", + "* 基本制图\n", + "* 用图表来进行数据分析" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们用通过分析新冠疫情数据为例，来看看怎么从接口获取数据、数据整理和制作图表等。\n", + "\n", + "其中会涉及到各类知识点，我们会尽量详细解释清楚。\n", + "\n", + "我们先举一个完整的例子，从调用API接口开始，用 Pandas来处理基本数据，整个过程通过 Python 的 jupyter notebook 环境中实现。\n", + "\n", + "1. 设置 API的地址，调用token\n", + "2. 设置 headers、payload等需要调用的参数\n", + "3. 通过 requests 的 get 方法来访问数据\n", + "4. 通过 Pandas 来简单处理数据\n", + "5. 显示数据" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2020-03-24\n", + "confirmed 69176\n", + "confirmed_add 5249\n", + "deaths 6820\n", + "deaths_add 743\n", + "recovered 8326\n", + "recovered_add 894\n", + "---\n" + ] + } + ], + "source": [ + "# demo for infection/region\n", + "# input region, start_date, get data\n", + "# 接口：感染/国家地区\n", + "\n", + "import requests\n", + "import pandas as pd\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# region or country\n", + "region='Italy'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date':'2020-03-24'\n", + "}\n", + "\n", + "# call requets to load \n", + "r = requests.get(url+'infection/region', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "# use pandas to get the data\n", + "df = pd.DataFrame.from_dict(data['data']['region'][region])\n", + "print(df)\n", + "print('---')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 \\\n", + "confirmed_add 5249 5210 6203 5909 5974 \n", + "deaths_add 743 683 712 919 889 \n", + "recovered_add 894 1036 999 589 1434 \n", + "confirmed 69176 74386 80589 86498 92472 \n", + "deaths 6820 7503 8215 9134 10023 \n", + "recovered 8326 9362 10361 10950 12384 \n", + "\n", + " 2020-03-29 2020-03-30 2020-03-31 \n", + "confirmed_add 5217 4050 4053 \n", + "deaths_add 756 812 837 \n", + "recovered_add 646 1590 1109 \n", + "confirmed 97689 101739 105792 \n", + "deaths 10779 11591 12428 \n", + "recovered 13030 14620 15729 \n", + "---\n" + ] + } + ], + "source": [ + "# demo for infection/region\n", + "# input region, start_date, end_date, get data\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date': '2020-03-24',\n", + " 'end_date': '2020-03-31'\n", + "}\n", + "\n", + "# call requets to load \n", + "r = requests.get(url+'infection/region', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "# use pandas to get the data\n", + "df = pd.DataFrame.from_dict(data['data']['region'][region])\n", + "print(df)\n", + "print('---')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " confirmed_add deaths_add recovered_add confirmed deaths \\\n", + "2020-03-24 5249 743 894 69176 6820 \n", + "2020-03-25 5210 683 1036 74386 7503 \n", + "2020-03-26 6203 712 999 80589 8215 \n", + "2020-03-27 5909 919 589 86498 9134 \n", + "2020-03-28 5974 889 1434 92472 10023 \n", + "2020-03-29 5217 756 646 97689 10779 \n", + "2020-03-30 4050 812 1590 101739 11591 \n", + "2020-03-31 4053 837 1109 105792 12428 \n", + "\n", + " recovered \n", + "2020-03-24 8326 \n", + "2020-03-25 9362 \n", + "2020-03-26 10361 \n", + "2020-03-27 10950 \n", + "2020-03-28 12384 \n", + "2020-03-29 13030 \n", + "2020-03-30 14620 \n", + "2020-03-31 15729 \n", + "---\n" + ] + } + ], + "source": [ + "# demo for infection/region\n", + "# input region, start_date, end_date, get data\n", + "# exchange the row and column by Pandas, the row index is date\n", + "# 交换数据的行和列\n", + "\n", + "df = df.T\n", + "print(df)\n", + "print('---')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " confirmed_add deaths_add recovered_add confirmed deaths \\\n", + "2020-03-24 5249 743 894 69176 6820 \n", + "2020-03-25 5210 683 1036 74386 7503 \n", + "2020-03-26 6203 712 999 80589 8215 \n", + "2020-03-27 5909 919 589 86498 9134 \n", + "2020-03-28 5974 889 1434 92472 10023 \n", + "2020-03-29 5217 756 646 97689 10779 \n", + "2020-03-30 4050 812 1590 101739 11591 \n", + "2020-03-31 4053 837 1109 105792 12428 \n", + "\n", + " recovered mortality rate \n", + "2020-03-24 8326 0.098589 \n", + "2020-03-25 9362 0.100866 \n", + "2020-03-26 10361 0.101937 \n", + "2020-03-27 10950 0.105598 \n", + "2020-03-28 12384 0.108390 \n", + "2020-03-29 13030 0.110340 \n", + "2020-03-30 14620 0.113929 \n", + "2020-03-31 15729 0.117476 \n", + "---\n" + ] + } + ], + "source": [ + "# demo for infection/region\n", + "# input region, start_date, end_date, get data\n", + "# exchange the row and column by Pandas, the row index is date\n", + "# add calucate column, mortailty rate\n", + "\n", + "df['mortality rate'] = df.apply(lambda x: x['deaths'] / x['confirmed'], axis=1)\n", + "print(df)\n", + "print('---')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# draw the line chart, for column confirmed and deaths\n", + "\n", + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# region or country\n", + "region='Italy'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date':'2020-03-24',\n", + " 'end_date':'2020-03-31'\n", + "}\n", + "\n", + "# call requets to load \n", + "r = requests.get(url+'infection/region', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "# use pandas to get the data\n", + "df = pd.DataFrame.from_dict(data['data']['region'][region])\n", + "\n", + "df = df.T\n", + "\n", + "plt.figure()\n", + "df[['confirmed','deaths']].plot(kind='line')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# draw the line chart, for column confirmed and deaths\n", + "\n", + "plt.figure();\n", + "df[['confirmed_add','deaths_add','recovered_add']].plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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area
Alabama{'2020-03-24': {'confirmed_add': 46, 'deaths_a...
Alaska{'2020-03-24': {'confirmed_add': 4, 'deaths_ad...
American Samoa{'2020-03-24': {'confirmed_add': 0, 'deaths_ad...
Arizona{'2020-03-24': {'confirmed_add': 91, 'deaths_a...
Arkansas{'2020-03-24': {'confirmed_add': 27, 'deaths_a...
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" + ], + "text/plain": [ + " area\n", + "Alabama {'2020-03-24': {'confirmed_add': 46, 'deaths_a...\n", + "Alaska {'2020-03-24': {'confirmed_add': 4, 'deaths_ad...\n", + "American Samoa {'2020-03-24': {'confirmed_add': 0, 'deaths_ad...\n", + "Arizona {'2020-03-24': {'confirmed_add': 91, 'deaths_a...\n", + "Arkansas {'2020-03-24': {'confirmed_add': 27, 'deaths_a..." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# demo for infection/region/detail \n", + "\n", + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# region or country\n", + "region='US'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date':'2020-03-24',\n", + " 'end_date':'2020-03-31'\n", + "}\n", + "r = requests.get(url+'infection/region/detail', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "df = pd.DataFrame.from_dict(data['data'])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AlabamaAlaskaAmerican SamoaArizonaArkansasCaliforniaColoradoConnecticutDelawareDistrict of Columbia...TexasUtahVermontVirgin IslandsVirginiaWashingtonWest VirginiaWisconsinWuhan EvacueeWyoming
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2020-03-25{'confirmed_add': 139, 'deaths_add': 1, 'recov...{'confirmed_add': 7, 'deaths_add': 1, 'recover...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 75, 'deaths_add': 1, 'recove...{'confirmed_add': 61, 'deaths_add': 0, 'recove...{'confirmed_add': 460, 'deaths_add': 15, 'reco...{'confirmed_add': 298, 'deaths_add': 8, 'recov...{'confirmed_add': 257, 'deaths_add': 7, 'recov...{'confirmed_add': 15, 'deaths_add': 0, 'recove...{'confirmed_add': 46, 'deaths_add': 0, 'recove......{'confirmed_add': 274, 'deaths_add': 3, 'recov...{'confirmed_add': 42, 'deaths_add': 0, 'recove...{'confirmed_add': 30, 'deaths_add': 1, 'recove...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 103, 'deaths_add': 0, 'recov...{'confirmed_add': 263, 'deaths_add': 17, 'reco...{'confirmed_add': 17, 'deaths_add': 0, 'recove...{'confirmed_add': 140, 'deaths_add': 2, 'recov...NaN{'confirmed_add': 15, 'deaths_add': 0, 'recove...
2020-03-26{'confirmed_add': 136, 'deaths_add': 0, 'recov...{'confirmed_add': 15, 'deaths_add': 0, 'recove...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 107, 'deaths_add': 2, 'recov...{'confirmed_add': 55, 'deaths_add': 0, 'recove...{'confirmed_add': 901, 'deaths_add': 16, 'reco...{'confirmed_add': 409, 'deaths_add': 3, 'recov...{'confirmed_add': 137, 'deaths_add': 2, 'recov...{'confirmed_add': 11, 'deaths_add': 1, 'recove...{'confirmed_add': 44, 'deaths_add': 1, 'recove......{'confirmed_add': 334, 'deaths_add': 6, 'recov...{'confirmed_add': 56, 'deaths_add': 0, 'recove...{'confirmed_add': 33, 'deaths_add': 1, 'recove...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 70, 'deaths_add': 1, 'recove...{'confirmed_add': 616, 'deaths_add': 17, 'reco...{'confirmed_add': 13, 'deaths_add': 0, 'recove...{'confirmed_add': 107, 'deaths_add': 3, 'recov...NaN{'confirmed_add': 9, 'deaths_add': 0, 'recover...
2020-03-27{'confirmed_add': 70, 'deaths_add': 3, 'recove...{'confirmed_add': 2, 'deaths_add': 0, 'recover...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 157, 'deaths_add': 5, 'recov...{'confirmed_add': 46, 'deaths_add': 1, 'recove...{'confirmed_add': 758, 'deaths_add': 13, 'reco...{'confirmed_add': 3, 'deaths_add': 8, 'recover...{'confirmed_add': 279, 'deaths_add': 6, 'recov...{'confirmed_add': 33, 'deaths_add': 1, 'recove...{'confirmed_add': 40, 'deaths_add': 0, 'recove......{'confirmed_add': 374, 'deaths_add': 5, 'recov...{'confirmed_add': 76, 'deaths_add': 0, 'recove...{'confirmed_add': 26, 'deaths_add': 1, 'recove...{'confirmed_add': 2, 'deaths_add': 0, 'recover...{'confirmed_add': 141, 'deaths_add': 0, 'recov...{'confirmed_add': 270, 'deaths_add': 7, 'recov...{'confirmed_add': 24, 'deaths_add': 0, 'recove...{'confirmed_add': 198, 'deaths_add': 4, 'recov...NaN{'confirmed_add': 17, 'deaths_add': 0, 'recove...
2020-03-28{'confirmed_add': 107, 'deaths_add': 0, 'recov...{'confirmed_add': 27, 'deaths_add': 1, 'recove...{'confirmed_add': 0, 'deaths_add': 0, 'recover...{'confirmed_add': 108, 'deaths_add': 2, 'recov...{'confirmed_add': 28, 'deaths_add': 2, 'recove...{'confirmed_add': 438, 'deaths_add': 16, 'reco...{'confirmed_add': 307, 'deaths_add': 4, 'recov...{'confirmed_add': 233, 'deaths_add': 6, 'recov...{'confirmed_add': 51, 'deaths_add': 3, 'recove...{'confirmed_add': 33, 'deaths_add': 1, 'recove......{'confirmed_add': 518, 'deaths_add': 4, 'recov...{'confirmed_add': 130, 'deaths_add': 0, 'recov...{'confirmed_add': 27, 'deaths_add': 2, 'recove...{'confirmed_add': 3, 'deaths_add': 0, 'recover...{'confirmed_add': 133, 'deaths_add': 3, 'recov...{'confirmed_add': 553, 'deaths_add': 31, 'reco...{'confirmed_add': 20, 'deaths_add': 0, 'recove...{'confirmed_add': 129, 'deaths_add': 3, 'recov...NaN{'confirmed_add': 12, 'deaths_add': 0, 'recove...
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5 rows × 58 columns

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+ "2020-03-27 {'confirmed_add': 0, 'deaths_add': 0, 'recover... \n", + "2020-03-28 {'confirmed_add': 0, 'deaths_add': 0, 'recover... \n", + "\n", + " Arizona \\\n", + "2020-03-24 {'confirmed_add': 91, 'deaths_add': 3, 'recove... \n", + "2020-03-25 {'confirmed_add': 75, 'deaths_add': 1, 'recove... \n", + "2020-03-26 {'confirmed_add': 107, 'deaths_add': 2, 'recov... \n", + "2020-03-27 {'confirmed_add': 157, 'deaths_add': 5, 'recov... \n", + "2020-03-28 {'confirmed_add': 108, 'deaths_add': 2, 'recov... \n", + "\n", + " Arkansas \\\n", + "2020-03-24 {'confirmed_add': 27, 'deaths_add': 2, 'recove... \n", + "2020-03-25 {'confirmed_add': 61, 'deaths_add': 0, 'recove... \n", + "2020-03-26 {'confirmed_add': 55, 'deaths_add': 0, 'recove... \n", + "2020-03-27 {'confirmed_add': 46, 'deaths_add': 1, 'recove... \n", + "2020-03-28 {'confirmed_add': 28, 'deaths_add': 2, 'recove... \n", + "\n", + " California \\\n", + "2020-03-24 {'confirmed_add': 430, 'deaths_add': 11, 'reco... \n", + "2020-03-25 {'confirmed_add': 460, 'deaths_add': 15, 'reco... \n", + "2020-03-26 {'confirmed_add': 901, 'deaths_add': 16, 'reco... \n", + "2020-03-27 {'confirmed_add': 758, 'deaths_add': 13, 'reco... \n", + "2020-03-28 {'confirmed_add': 438, 'deaths_add': 16, 'reco... \n", + "\n", + " Colorado \\\n", + "2020-03-24 {'confirmed_add': 19, 'deaths_add': 1, 'recove... \n", + "2020-03-25 {'confirmed_add': 298, 'deaths_add': 8, 'recov... \n", + "2020-03-26 {'confirmed_add': 409, 'deaths_add': 3, 'recov... \n", + "2020-03-27 {'confirmed_add': 3, 'deaths_add': 8, 'recover... \n", + "2020-03-28 {'confirmed_add': 307, 'deaths_add': 4, 'recov... \n", + "\n", + " Connecticut \\\n", + "2020-03-24 {'confirmed_add': 203, 'deaths_add': 2, 'recov... \n", + "2020-03-25 {'confirmed_add': 257, 'deaths_add': 7, 'recov... \n", + "2020-03-26 {'confirmed_add': 137, 'deaths_add': 2, 'recov... \n", + "2020-03-27 {'confirmed_add': 279, 'deaths_add': 6, 'recov... \n", + "2020-03-28 {'confirmed_add': 233, 'deaths_add': 6, 'recov... \n", + "\n", + " Delaware \\\n", + "2020-03-24 {'confirmed_add': 36, 'deaths_add': 0, 'recove... \n", + "2020-03-25 {'confirmed_add': 15, 'deaths_add': 0, 'recove... \n", + "2020-03-26 {'confirmed_add': 11, 'deaths_add': 1, 'recove... \n", + "2020-03-27 {'confirmed_add': 33, 'deaths_add': 1, 'recove... \n", + "2020-03-28 {'confirmed_add': 51, 'deaths_add': 3, 'recove... \n", + "\n", + " District of Columbia ... \\\n", + "2020-03-24 {'confirmed_add': 21, 'deaths_add': 0, 'recove... ... \n", + "2020-03-25 {'confirmed_add': 46, 'deaths_add': 0, 'recove... ... \n", + "2020-03-26 {'confirmed_add': 44, 'deaths_add': 1, 'recove... ... \n", + "2020-03-27 {'confirmed_add': 40, 'deaths_add': 0, 'recove... ... \n", + "2020-03-28 {'confirmed_add': 33, 'deaths_add': 1, 'recove... ... \n", + "\n", + " Texas \\\n", + "2020-03-24 {'confirmed_add': 197, 'deaths_add': 3, 'recov... \n", + "2020-03-25 {'confirmed_add': 274, 'deaths_add': 3, 'recov... \n", + "2020-03-26 {'confirmed_add': 334, 'deaths_add': 6, 'recov... \n", + "2020-03-27 {'confirmed_add': 374, 'deaths_add': 5, 'recov... \n", + "2020-03-28 {'confirmed_add': 518, 'deaths_add': 4, 'recov... \n", + "\n", + " Utah \\\n", + "2020-03-24 {'confirmed_add': 41, 'deaths_add': 0, 'recove... \n", + "2020-03-25 {'confirmed_add': 42, 'deaths_add': 0, 'recove... \n", + "2020-03-26 {'confirmed_add': 56, 'deaths_add': 0, 'recove... \n", + "2020-03-27 {'confirmed_add': 76, 'deaths_add': 0, 'recove... \n", + "2020-03-28 {'confirmed_add': 130, 'deaths_add': 0, 'recov... \n", + "\n", + " Vermont \\\n", + "2020-03-24 {'confirmed_add': 20, 'deaths_add': 2, 'recove... \n", + "2020-03-25 {'confirmed_add': 30, 'deaths_add': 1, 'recove... \n", + "2020-03-26 {'confirmed_add': 33, 'deaths_add': 1, 'recove... \n", + "2020-03-27 {'confirmed_add': 26, 'deaths_add': 1, 'recove... \n", + "2020-03-28 {'confirmed_add': 27, 'deaths_add': 2, 'recove... \n", + "\n", + " Virgin Islands \\\n", + "2020-03-24 {'confirmed_add': 10, 'deaths_add': 0, 'recove... \n", + "2020-03-25 {'confirmed_add': 0, 'deaths_add': 0, 'recover... \n", + "2020-03-26 {'confirmed_add': 0, 'deaths_add': 0, 'recover... \n", + "2020-03-27 {'confirmed_add': 2, 'deaths_add': 0, 'recover... \n", + "2020-03-28 {'confirmed_add': 3, 'deaths_add': 0, 'recover... \n", + "\n", + " Virginia \\\n", + "2020-03-24 {'confirmed_add': 39, 'deaths_add': 3, 'recove... \n", + "2020-03-25 {'confirmed_add': 103, 'deaths_add': 0, 'recov... \n", + "2020-03-26 {'confirmed_add': 70, 'deaths_add': 1, 'recove... \n", + "2020-03-27 {'confirmed_add': 141, 'deaths_add': 0, 'recov... \n", + "2020-03-28 {'confirmed_add': 133, 'deaths_add': 3, 'recov... \n", + "\n", + " Washington \\\n", + "2020-03-24 {'confirmed_add': 107, 'deaths_add': 7, 'recov... \n", + "2020-03-25 {'confirmed_add': 263, 'deaths_add': 17, 'reco... \n", + "2020-03-26 {'confirmed_add': 616, 'deaths_add': 17, 'reco... \n", + "2020-03-27 {'confirmed_add': 270, 'deaths_add': 7, 'recov... \n", + "2020-03-28 {'confirmed_add': 553, 'deaths_add': 31, 'reco... \n", + "\n", + " West Virginia \\\n", + "2020-03-24 {'confirmed_add': 6, 'deaths_add': 0, 'recover... \n", + "2020-03-25 {'confirmed_add': 17, 'deaths_add': 0, 'recove... \n", + "2020-03-26 {'confirmed_add': 13, 'deaths_add': 0, 'recove... \n", + "2020-03-27 {'confirmed_add': 24, 'deaths_add': 0, 'recove... \n", + "2020-03-28 {'confirmed_add': 20, 'deaths_add': 0, 'recove... \n", + "\n", + " Wisconsin \\\n", + "2020-03-24 {'confirmed_add': 56, 'deaths_add': 0, 'recove... \n", + "2020-03-25 {'confirmed_add': 140, 'deaths_add': 2, 'recov... \n", + "2020-03-26 {'confirmed_add': 107, 'deaths_add': 3, 'recov... \n", + "2020-03-27 {'confirmed_add': 198, 'deaths_add': 4, 'recov... \n", + "2020-03-28 {'confirmed_add': 129, 'deaths_add': 3, 'recov... \n", + "\n", + " Wuhan Evacuee \\\n", + "2020-03-24 {'confirmed_add': 0, 'deaths_add': 0, 'recover... \n", + "2020-03-25 NaN \n", + "2020-03-26 NaN \n", + "2020-03-27 NaN \n", + "2020-03-28 NaN \n", 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189 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " confirmed_add deaths_add recovered_add\n", + "Afghanistan 664 21 32\n", + "Albania 465 23 232\n", + "Algeria 1982 313 601\n", + "Andorra 645 29 128\n", + "Angola 18 2 4\n", + "... ... ... ...\n", + "Vietnam 255 0 146\n", + "Western Sahara 2 0 0\n", + "Yemen 0 0 0\n", + "Zambia 43 2 30\n", + "Zimbabwe 16 3 0\n", + "\n", + "[189 rows x 3 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# demo for infection/global\n", + "\n", + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "r = requests.get(url+'infection/global', headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "df = pd.DataFrame.from_dict(data['data']['global']['region'])\n", + "df = df.T\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(189, 3)\n", + "Index(['confirmed_add', 'deaths_add', 'recovered_add'], dtype='object')\n", + "\n", + "Index: 189 entries, Afghanistan to Zimbabwe\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 confirmed_add 189 non-null int64\n", + " 1 deaths_add 189 non-null int64\n", + " 2 recovered_add 189 non-null int64\n", + "dtypes: int64(3)\n", + "memory usage: 5.9+ KB\n", + "None\n" + ] + } + ], + "source": [ + "print(df.shape)\n", + "print(df.columns)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame.from_dict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\" \n", + "@author:Bingo.he \n", + "@file: get_target_value.py \n", + "@time: 2017/12/22 \n", + "\"\"\"\n", + "def get_target_value(key, dic, tmp_list):\n", + " \"\"\"\n", + " :param key: 目标key值\n", + " :param dic: JSON数据\n", + " :param tmp_list: 用于存储获取的数据\n", + " :return: list\n", + " \"\"\"\n", + " if not isinstance(dic, dict) or not isinstance(tmp_list, list): # 对传入数据进行格式校验\n", + " return 'argv[1] not an dict or argv[-1] not an list '\n", + "\n", + " if key in dic.keys():\n", + " tmp_list.append(dic[key]) # 传入数据存在则存入tmp_list\n", + "\n", + " for value in dic.values(): # 传入数据不符合则对其value值进行遍历\n", + " if isinstance(value, dict):\n", + " get_target_value(key, value, tmp_list) # 传入数据的value值是字典，则直接调用自身\n", + " elif isinstance(value, (list, tuple)):\n", + " _get_value(key, value, tmp_list) # 传入数据的value值是列表或者元组，则调用_get_value\n", + "\n", + "\n", + " return tmp_list\n", + "\n", + "\n", + "def _get_value(key, val, tmp_list):\n", + " for val_ in val:\n", + " if isinstance(val_, dict): \n", + " get_target_value(key, val_, tmp_list) # 传入数据的value值是字典，则调用get_target_value\n", + " elif isinstance(val_, (list, tuple)):\n", + " _get_value(key, val_, tmp_list) # 传入数据的value值是列表或者元组，则调用自身\n", + " \n", + "list0=[]\n", + "print(get_target_value('New York',data,list0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "\n", + "r = requests.post(url+'authentication/register', data = {'email':'wingfish@gmail.com'})\n", + "\n", + "print(r)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 01 - \347\256\200\344\273\213.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 01 - \347\256\200\344\273\213.ipynb" new file mode 100644 index 0000000..4626e46 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 01 - \347\256\200\344\273\213.ipynb" @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 1\n", + "Python Basic, Lesson 1, \n", + "* v1.0.1, 2016.12 by David Yi \n", + "* v1.0.2, 2017.2 edit by Eamon Zhang\n", + "* v1.0.3, 2018.2 edit by David Yi\n", + "* v1.0.4, 2018.12 edit by David Yi\n", + "* v1.1, 2020.2,3,4 edit by David Yi\n", + "\n", + "### 本课内容要点\n", + "\n", + "* Python 简介\n", + " * Python 是什么\n", + " * 创始人\n", + " * 版本\n", + " * Python 能做什么\n", + " * Python 的优点和缺点\n", + " * Python 的设计哲学\n", + " * 提高编程能力的体会\n", + "* 怎么运行 Python\n", + " * 使用标准的 Python 和 IDLE\n", + " * anaconda 介绍\n", + " * Jupyter Notebook介绍\n", + " * Pycharm 介绍\n", + " * Visual Studio Code 介绍" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Python 简介\n", + "\n", + "### Python 是什么\n", + "下面的定义来自维基百科，https://zh.wikipedia.org/wiki/Python\n", + "\n", + "Python（英国发音：/ˈpaɪθən/ 美国发音：/ˈpaɪθɑːn/），是一种广泛使用的高级编程语言，属于通用型编程语言，由吉多·范罗苏姆 创造，第一版发布于 1991 年。可以视之为一种改良 (加入一些其他编程语言的优点，如面向对象) 的 LISP。作为一种解释型语言，Python 的设计哲学强调代码的可读性和简洁的语法（尤其是使用空格缩进划分代码块，而非使用大括号或者关键词）。相比于 C++ 或 Java，Python 让开发者能够用更少的代码表达想法。不管是小型还是大型程序，该语言都试图让程序的结构清晰明了。\n", + "\n", + "与 Scheme、Ruby、Perl、Tcl 等动态类型编程语言一样，Python 拥有动态类型系统和垃圾回收功能，能够自动管理内存使用，并且支持多种编程范式，包括面向对象、命令式、函数式和过程式编程。其本身拥有一个巨大而广泛的标准库。\n", + "\n", + "Python 虚拟机本身几乎可以在所有的操作系统中运行。Python 的正式解释器CPython是用 C语言编写的、是一个由社区驱动的自由软件，目前由 Python软件基金会管理。\n", + "\n", + "上面这些 Python 语言的介绍，如果看了有点晕，不知所云，也没关系，不影响对 Python 的学习。\n", + "\n", + "### 创始人\n", + "\n", + "![](imgs/guido-headshot-2019.jpg)\n", + "\n", + "Python 的创始人为吉多·范罗苏姆（Guido van Rossum），是一个非常可爱的荷兰人，不像其他很多目前常用的语言的创始者都退隐江湖那样，Python 的创始者至今还是把握着Python 发展的方向。在 2018年，Rossum 宣布不再参与 Python 的发展，而是改成一个小组。在 2019 年，他又宣布退出 Python 语言的具体工作。在复杂项目的技术方向和进度方面，Rossum 是我学习的榜样之一，可以带领这么大的社区持续的迭代更新，平衡民主与集中，甚至不以经济目的为主要目标，非常厉害的管理能力。\n", + "\n", + "### 版本\n", + "\n", + "Python 2.0 于 2000年10月16日发布，增加了实现完整的垃圾回收，并且支持 Unicode。同时，整个开发过程更加透明，社区对开发进度的影响逐渐扩大。目前 2.x系列的最新版本是 2.7.18，2.x系列将在 2020年4月被停止支持。还有不少以前的项目使用 Python 2.x系列开发并且维护着，不过这几年很多新项目已经都不支持 Python 2.x 了，所以建议大家不要再学习 Python 2.x 了。对于现在学习 Python 的朋友来说，已经不太需要了解 Python 2 和 Python 3 的差异了，肯定 Python 3 要好得多，知道这个就够了。\n", + "\n", + "Python 3.0 于 2008年12月3日发布，此版不完全兼容之前的Python源代码。Python 3.7.x 系列发布于 2018年10月。3.7.x 系列相比之前的 3.6.x 系列，新功能增加的频率快了不少。目前 Python 的最新稳定版本是 3.8.x 系列，3.8.1 发布于 2019年12月。一般教学我们会用 3.7.x 或者 3.8.x 系列，在绝大多数场景下都没有什么问题，一般不会碰到 3.8.x 系列支持的新特性带来的问题。\n", + "\n", + "### Python 能做什么？\n", + "\n", + "Python 能做的事情很多，否则也不会在这几年这么热门。Python 是一门计算机语言，和其他编程语言各有优势，按照国外网友的一些总结，Python 在以下方面有一定优势，如果你正好是想开发相关的程序，可以更加关注一下。\n", + "\n", + "1. Web 开发：不管是 web 网站，还是api 接口，或者微服务，以及现在流行的 Serverless，Python 都很强大，老牌的 Python 框架 Django 和 Flask不论，近几年又涌现出相当多的 web 框架，可以非常快速的开发 web 程序。现在已经是一个线上的时代，Python 这方面与时俱进。\n", + "\n", + "2. 计算机视觉： 可以用一个通用的框架叫做 Opencv 来做很多高级的图形和视觉处理。我的团队在 2016 年的时候使用 Python+Opencv 做当时刚兴起的人脸识别、图像识别等。\n", + "\n", + "3. 网络爬虫： 这也是 Python 非常强悍的功能，并且几乎在所有语言中没有什么对手，这个要归功于在网络处理领域里面，Python 有两个神一般的框架 Requests 和 Scrapy，如果要进行网络爬虫的学习研究，Python 是最好的语言。\n", + "\n", + "4. 机器学习： 这个就不用多说了，也是目前 Python 火起来的重要原因之一，目前绝大多数机器学习的项目还是用 Python 开发，老牌的有 scikit-learn、theano、tensorflow 这些框架，这两年 PyTorch 非常火红，有后来追上的趋势。\n", + "\n", + "5. 数据分析： 除了机器学习以外，Python 大热的另外一个原因就是数据分析，同样有一个神级的框架，叫做 Pandas，通过极致优化的性能和易用性，凭借一己之力将 R 语言打的落花流水。\n", + "\n", + "等等，还有很多方面，这里就不一一列举了。\n", + "\n", + "### Python 的优点和缺点\n", + "\n", + "优点 \n", + "\n", + "1. 软件质量 \n", + "注重可读性，被称作“可执行的伪代码”。比一般脚本语言高很多可维护性和可重用性。极简主义的设计思想，尽管实现一个任务可能有很多种方法，往往只有一种方法是显而易见的，明了的解决办法胜于“魔术”般的方法。\n", + "\n", + "2. 提高开发者的效率 \n", + "代码大小只有C++、Java 的1/3-1/5，意味着可以输入、调试和维护更少的代码。 Python 代码是可立即执行代码，无须传统编译语言或静态语言的编译与链接步骤，意味着修改代码直接看到效果，具有快速调整能力。\n", + "\n", + "3. 可移植性 \n", + "大多数 Python 程序不做任何改变即可在各类平台上运行，Windos 或者 macOS 都可以。\n", + "\n", + "4. 标准库/第三方库的支持 \n", + "有众多成熟的功能模块，包括网站开发、数值计算、服务器应用、数据库应用、游戏开发、Web、人工智能等各个方面，特别是前几年 Python 在人工智能、机器学习、数据统计方面大放异彩，这也归功于一些非常优秀的第三方库。现在 Python 已经面临和 Java、JavaScript 差不多的问题了，优秀的库太多，不知道选哪一个合适。\n", + "\n", + "5. 组件集成 \n", + "轻松与其他应用程序进行通信。可以调用C/C++的库，也可以被C/C++调用，也可以和 Java 集成。这几年随着像 Go 这样一些新的编译型语言发展， 很多语言都可以给 Python 写库函数。\n", + "\n", + "6. 简单易学，非常适合编程初学者\n", + "支持所有现代编程语言的特性，但是也适当做了简化，在学习曲线、程序效果、程序性能上做到了较好的平衡。\n", + "\n", + "缺点\n", + "1. 执行速度不够快（与 C/C++,Java 等相比） \n", + "Python 标准实现方式是将源代码的语句转换为字节码，再将字节码解释出来。但没有将代码编译为底层的二进制代码，所以速度比较慢。\n", + "\n", + "2. 本地图形化应用能力还不够\n", + "Python 有好几种开发本地图形化应用程序的方法，但是和当年的 Visual Basic 和 Delphi 等神器相比，差距还很大。Python 开发 web 页面和服务端程序，进行数据处理和分析，人工智能建模、训练和实战，或者做一些小工具都是没问题的，但是高效率的开发漂亮的图形化应用还有一段路要走。\n", + "\n", + "3. 在很多领域，优秀的第三方库太多，选择困难。前面说了，这个算是甜蜜的烦恼吧\n", + "\n", + "2020.3 补充：过去Python 有个缺点就是是性能不够强，相比较 Java、Go 语言等速度不够快，最近两年，Python 语言发展非常快，在 Python 3.5 版本之后，通过异步处理等方式，性能大大提升，用在服务端处理高并发也足够胜任，我们会在之后高级教程中介绍这些内容。对于一般应用，Python 的性能是足够的。\n", + "\n", + "2020.4 补充：影响一个项目在实际运营时候速度和性能的因素非常多，这是一个非常有趣的话题，对于一般学习 Python 的朋友来说，不需要研究的太深，我们在之后的高级教程中会分析一下 Python 应用程序链路上的性能监测和提升。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python 的设计哲学\n", + "\n", + "Python 的设计哲学是“优雅”、“明确”、“简单”。Python 开发者的哲学是“用一种方法，最好是只有一种方法来做一件事”，也因此它和拥有明显个人风格的其他语言很不一样。在设计Python语言时，如果面临多种选择，Python 开发者一般会拒绝花俏的语法，而选择明确的、没有或者很少有歧义的语法。\n", + "\n", + "在 Python 解释器内运行 import this，可以看到如下的内容，称之为 Python 之禅。（之后等 Python 环境安装好之后可以看看）\n", + "\n", + " The Zen of Python, by Tim Peters\n", + "\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n", + " \n", + "Python 之禅的中文翻译如下：\n", + "\n", + " 优美优于丑陋，\n", + " 明了优于隐晦；\n", + " 简单优于复杂，\n", + " 复杂优于凌乱，\n", + " 扁平优于嵌套，\n", + " 稀疏优于稠密，\n", + " 可读性很重要！\n", + " 即使实用比纯粹更优，\n", + " 特例亦不可违背原则。\n", + " 错误绝不能悄悄忽略，\n", + " 除非它明确需要如此。\n", + " 面对不确定性，拒绝妄加猜测。\n", + " 任何问题应有一种，且最好只有一种，显而易见的解决方法。\n", + " 尽管这方法一开始并非如此直观，除非你是荷兰人。\n", + " 做优于不做，\n", + " 然而不假思索还不如不做。\n", + " 很难解释的，必然是坏方法。\n", + " 很好解释的，可能是好方法。\n", + " 命名空间是个绝妙的主意，我们应好好利用它。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 提高编程能力的体会\n", + "\n", + "在看这个教程的朋友，肯定是想学习 Python，看看它到底好学么？能做什么？或许也有一些创意想去实现。我们不能承诺学好 Python 之后月薪年薪多少的话，如果你想做一个程序员，或者想用程序来改变一些事情，分享一些心得体会。\n", + "\n", + "* 世界观和人生观\n", + "\n", + "我相信电脑技术是改变了世界的现在和未来，如同当年发明电、发明汽车一样，电脑从 1945 年发明到现在，也只有几十年。电脑技术还会有巨大的跃进，我们有幸可以目睹和参与这个过程。作为程序员，使用编程技术来让生活更美好、工作效率更高。我们需要不断学习，有分享精神，并不能把编程技术只是作为一个谋生工具，只是赚钱。用技术来改变世界，让世界变得更加美好。没有几个是天才，或许我们的技术能力也不是那么强，但是，信念要存在，要有理想，一砖一瓦，一起构筑高楼大厦。\n", + "\n", + "* 天赋和勤奋\n", + "\n", + "刚才说到，天才是不多的，我们需要更多的还是勤奋。每个人都有不同的天赋，有的人擅长逻辑思维，有的人语言文字能力很强。在学习编程领域里面也是，其实光是编程，从前端开发到后端设计和架构师，可以细分十几种岗位，各种各样的编程语言更是五花八门。学习的过程是艰苦的，就算再容易入门的语言，比如 Python，也是入门容易精通难，更不说那些入门也不容易的语言了。好在计算机科学家和全世界的程序员不断地在努力，降低编程语言的学习成本，提升编程语言的功能，作为学习编程，我们已经比二十年、十年前幸福多了。所以，加上你的勤奋，开始梦想。\n", + "\n", + "* 阅读\n", + "\n", + "多看书，多看文章，多看代码。现在是一个快知识的时代，很多人不太愿意静下心来看书，那么阅读还包括看一些视频教程。介质的形态并不是最重要的，与时俱进。\n", + "\n", + "* 实践，实践，再实践\n", + "\n", + "学习编程就像学习外语一样，要多实践，光是看代码是不够的。一开始可以跟着教程，将代码原原本本的输入，看看运行结果。在这个过程中，因为可能发生输入错误之类，所以可以掌握很多细节，这是非常重要的。再复杂的程序也是从一行行的基础代码开始累积的。而基本功的扎实与否在你以后的编程学习乃至工作中就会很有用处。学习到一定程度之后，可以自己写点小程序来解决一些简单问题，用程序来创造一些东西。\n", + "\n", + "* 提问题，会搜索\n", + "\n", + "编程学习中有时候会需要一些或许傻傻的问题，或许一些别人没碰到过的问题，我们可以依靠搜索引擎来寻找答案。首选 Google 来搜索，准确度高很多。程序员还有专门的搜索引擎，比如 Stack Overflow，准确度非常高，个人经验几乎所有编程问题都可以在 Stack Overflow找到答案。老外这方面比较好，愿意回答问题、分享使用经验。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 如何运行 Python\n", + "\n", + "简单的给大家介绍一下 Python 程序怎么运行，便于之后的学习\n", + "\n", + "* 准备工作\n", + "* 使用标准的 Python 和 IDLE\n", + "* anaconda 介绍\n", + "* Jupyter Notebook\n", + "* Jetbrain PyCharm\n", + "* Microsoft VS Code\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 怎么运行 Python\n", + "\n", + "* 下载安装 Python 最新版本。除非你原来安装过Python，并且由于一些原因一定要用Python 的官方版本，从学习和一般开发角度都不推荐直接安装官方Python了。使用下面安装 anaconda 的方法比较好。\n", + "\n", + "* 或者下载安装 anaconda 最新版本。anaconda 是一个强大的、开源的 Python 整体解决方案，包括最新版本的 Python、jupyter等各类工具、精选的 Python 第三方库等。目前 anaconda 也包含了 Microsoft 开源的 VSCode开发工具。\n", + "\n", + "### 使用标准的 Python 和 IDLE\n", + "\n", + "Python 官网 (www.python.org) 还是可以看一下的。目前 Python 进入到了 3.8+ 系列，不建议再使用 2.x 系列了，有不少项目习惯用 Python 2.7.x，Python 官方已经明确除了安全补丁以外，不会继续支持 2.7.x 系列，所以如果是初学者，就直接选择 Python 3，目前所有的开发工具、第三方函数包等都对 Python 3.x 版本很友好。\n", + "\n", + "截止 2020 年 2 月，python 最新版本是 3.8.1，而 3.9 版本也在测试中了。\n", + "\n", + "从 Python 官网的 download 链接开始，以 Python 3.5.1 为例，简单介绍一下如何安装。\n", + "\n", + "![](imgs/python_install.jpeg)\n", + "\n", + "Windows 操作系统只需要下载32位版本即可，对于 Python 的学习不会有任何影响，macOS 电脑下载对应的最新版本就可以了。\n", + "\n", + "以 Windows 操作系统举例，下载后，执行安装程序，如下：\n", + "\n", + "![](imgs/python_install_1.jpeg)\n", + "\n", + "一般情况下不用做任何修改，几分钟就安装好了，如下:\n", + "\n", + "![](imgs/python_install_2.jpeg)\n", + "\n", + "安装好后，在开始菜单找到 Python 3.8 程序组，看到其中的 IDLE(Python 3.8 32bit)，运行后就会出现下面的 Python 命令行界面：\n", + "\n", + "![](imgs/python_install_5.png)\n", + "\n", + "可以在这里进行输入 Python 程序，正确的话，会执行。\n", + "\n", + "![](imgs/python_install_3.jpeg)\n", + "\n", + "不过在这个类似 shell 的环境里面编辑复杂的 python 程序会比较麻烦，所以，可以在 shell 的 File 菜单里面选择第一项 New File，这时候，一个编辑器窗口就出现了。\n", + "\n", + "在编辑器里可以编写比较长的程序，选择菜单 Run 里面的 Run Module，就可以运行程序，结果会显示在一个 shell 窗口中，就像下面这样，注意在运行程序前是需要保存程序文件的。\n", + "\n", + "![](imgs/python_install_4.jpeg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### anaconda 介绍\n", + "\n", + "![](imgs/anaconda.png)\n", + "\n", + "对于初学者，Python 的安装也许并不容易，尤其面临 Python 版本的问题，会浪费很多时间，甚至让人放弃的感觉，anaconda 是一种简便的安装方法，可以完美的兼容 Python 2.7 和 Python 3.x，并集成了许多 packages（第三方包），免去配置环境变量的烦恼，\n", + "\n", + "anaconda的优势如下：\n", + "\n", + "* 集成很多第三方库，省去一一下载的麻烦； \n", + "* anaconda 的 conda 命令除了可以安装第三方库以外，还可以将 Python 环境作为安装内容的一部分，因此一台电脑上配置多个 Python 开发环境非常容易；\n", + "* PyCharm、VSCode 等都直接支持 conda 配置；\n", + "* 自带很多强大工具，比如数据分析、机器学习等；\n", + "\n", + "anaconda的下载地址：https://www.continuum.io/downloads \n", + "\n", + "选择操作系统和版本就可以。国内下载 anaconda 速度比较慢，可以通过镜像地址下载：https://mirrors.tuna.tsinghua.edu.cn/anaconda/\n", + "\n", + "anaconda 最新版本是 2020.02 版本。\n", + "\n", + "有了 anaconda 之后，安装多个 Python版本、升级 Python 等都会比较容易。\n", + "\n", + "---\n", + "\n", + "### Jupyter 介绍\n", + "\n", + "![](imgs/jupyter.png)\n", + "\n", + "Jupyter Notebook(此前被称为 IPython notebook)是一个交互式笔记本。Jupyter Notebook 是一款开源的网络应用，我们可以将其用于创建和共享代码与文档。\n", + "\n", + "Jupyter 提供了一个环境，无需离开这个环境，就可以在其中编写代码、运行代码、查看输出、可视化数据并查看结果。因此，这是一款可执行端到端的数据科学工作流程的便捷工具，其中包括数据清理、统计建模、构建和训练机器学习模型、可视化数据等等。当然，对于学习编程来说 Jupyter 同样是一个非常好的工具。\n", + "\n", + "当还处于原型开发阶段时，Jupyter Notebook 的作用更是引人注目。因为代码是按独立单元(cell)的形式编写的，而且这些单元是独立执行的。这让用户可以测试一个项目中的特定代码块，而无需从项目开始处执行代码。对于通过 Python 来学习和探索的项目来说，特别适合。对于其他 Python 比较复杂的项目开发可能 IDE 开发工具更加适合。\n", + "\n", + "如果安装了 anaconda，只要执行`jupyter notebook` 就可以启动 Jupyter 的服务端。或者启动 anaconda-navigator 这个图形化导航工具，来执行 Jupyter。\n", + "\n", + "如果没有安装 anaconda，可以执行`pip install jupyter` ，来安装 Jupyter，其实它也是 Python 的一个第三方扩展包。\n", + "\n", + "这里所有的教程都推荐使用 Jupyter Notebook 方式来访问和学习。 \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyCharm 介绍\n", + "\n", + "Python 的 IDE 开发环境大概至少有几十个，一般的学习、调试用 Python 自带的 shell、IDLE 即可，用 Jupyter notebook 也可以满足很多需求。\n", + "\n", + "真正的项目开发还是需要专业的编辑器，推荐 PyCharm，分为商业版本和免费的教育版本，免费版本用在一般的项目开发绰绰有余。\n", + "\n", + "![](imgs/pycharm_202001.png)\n", + "\n", + "PyCharm 目前最新版本是 2020.1，下载链接：https://www.jetbrains.com/pycharm/download/ \n", + "\n", + "### VS Code 介绍\n", + "\n", + "Visual Studio Code（简称VS Code）是一个由微软开发，同时支持Windows、Linux 和 macOS 等操作系统且开放源代码的代码编辑器，它支持测试，并内置了 Git 版本控制功能，同时也具有开发环境功能，例如代码补全（类似于 IntelliSense）、代码片段和代码重构等。该编辑器支持用户个性化配置，例如改变主题颜色、键盘快捷方式等各种属性和参数，同时还在编辑器中内置了扩展程序管理的功能。\n", + "\n", + "在2019年的Stack Overflow 组织的开发者调研中，VS Code被认为是最受开发者欢迎的开发环境，据调查87317名受访者中有50.7%的受访者声称正在使用 VS Code。\n", + "\n", + "微软是一个非常强大的软件公司，隔一段时间就会有一些惊艳之作。VS Code 也是其中之一。" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 02 - \345\217\230\351\207\217, print, input.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 02 - \345\217\230\351\207\217, print, input.ipynb" new file mode 100644 index 0000000..34ce05e --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 02 - \345\217\230\351\207\217, print, input.ipynb" @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 2\n", + "Python Basic, Lesson 2, \n", + "* v1.0, 2016\n", + "* v1.1, 2020.2,3,4 edit by David Yi\n", + "\n", + "### 本章内容要点\n", + "\n", + "* 学习之前\n", + "* 基本变量概念\n", + "* print() 和 input() 用法\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 学习之前\n", + "\n", + "从现在开始，我们进入到 Python 正式的学习课程。本课程使用 Jupyter Notebook 格式撰写，推荐你也安装 jupyter notebook 或者通过安装 anaconda 来使用。\n", + "\n", + "\n", + "我们通过简单的描述和举例来进行 Python 编程开发的讲解，需要你自己运行 notebook 里面的代码，并且最好建议自己输入一遍代码去运行，看看是否得到了预料中的结果，不要怕发生错误，发生错误后，首先检查一下代码输入是否正确，有时候也可以在 jupyter 菜单上找到 Kernel-Restart，重启 jupyter 来解决问题。\n", + "\n", + "我们并不想像标准的编程书籍和教材一样，去系统的讲解很多基本概念，一方面可能有些朋友已经学习过一些电脑编程语言，很多概念都是相通的，另一方面很多基本概念的学习其实是一件比较枯燥的事情，会让我们失去一些学习的兴趣。因此我们尽量用实战来代替，通过例子和自己操作来掌握 Python 的语法，掌握编程的一些理念。或许会碰到搞不懂的一些问题，就先记住怎么操作即可，通过输入代码联系、作业、思考、自己动手编程等方式来学习。\n", + "\n", + "## 基本变量概念\n", + "\n", + "变量用来在程序中存储内容。变量是编程开发中最基本的概念。\n", + "\n", + "Python 中的变量赋值不需要类型声明。\n", + "\n", + "每个变量在内存中创建，都包括变量的标识，名称和数据这些信息，\n", + "每个变量在使用前都必须赋值，变量赋值以后该变量才会被创建，\n", + "等号= 用来给变量赋值，\n", + "等号= 左边是一个变量名,等号右边是存储在变量中的值。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "counter = 100 # 整型变量\n", + "miles = 1000.0 # 浮点型（小数）\n", + "name = \"John\" # 字符串\n", + "name2 = 'Tom'\n", + "\n", + "# 显示指定变量名的内容\n", + "print(name2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "flag = False # 布尔值\n", + "\n", + "#显示变量的类型\n", + "print(type(flag)) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 多个变量赋值, Python 的写法比较简洁\n", + "\n", + "a = b = c = 1\n", + "b = 2 \n", + "print(a,b,c)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# 字符串变量赋值\n", + "\n", + "s = s1 = 'Hello'\n", + "print(s,s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# 多个变量赋值\n", + "\n", + "a, b, c = 1, 2, 3\n", + "print(a,b,c)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# 交换变量\n", + "\n", + "a, b = 1, 2\n", + "a, b = b, a\n", + "print(a,b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 变量是动态的\n", + "Python是动态语言，即变量本身的类型不固定。\n", + "\n", + "与之对应的静态语言（如Java），在定义变量时，必须先指定变量类型，赋值时如不匹配会报错。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# 定义一个整数变量\n", + "a = 1\n", + "print(a)\n", + "print(type(a))\n", + "\n", + "# 变成字符串变量\n", + "a = 's'\n", + "print(a)\n", + "print(type(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 理解 Python 变量在内存中的表示\n", + "当我们输入 a = 'ABC' 时，Python解释器做了2件事情：\n", + "\n", + "1. 在内存中创建了一个'ABC'的字符串，\n", + "\n", + "2. 在内存中创建了一个名为a的变量，并把它指向'ABC'。\n", + "\n", + "也可以把一个变量a赋值给另一个变量b，这个操作实际上是把变量b指向变量a所指向的数据" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# 改变变量的内容\n", + "\n", + "a = 'ABC'\n", + "b = a\n", + "a = 'XYZ'\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# 用 id() 可以得到变量的内存地址\n", + "\n", + "print(id(a))\n", + "print(id(b))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# 指向同一个变量内容的变量内存地址其实是一样的\n", + "# 注意id(100)的地址和a 的地址是一样的\n", + "\n", + "a = 100\n", + "b = 101\n", + "print(id(a))\n", + "print(id(b))\n", + "print(id(100))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# 变量内容发生变化后，内存地址会发生变化\n", + "\n", + "b = b + 1\n", + "print(id(a))\n", + "print(id(b))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4560441072\n", + "4560441168\n" + ] + } + ], + "source": [ + "# 指向同一个变量内容的变量内存地址这里又不一样了\n", + "\n", + "a = 257\n", + "b = 257\n", + "print(id(a))\n", + "print(id(b))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 有一些很优雅的设计，来提升性能，对于0-256这些常用的数字，Python 内部是有缓存的。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## print() 和 input() 用法\n", + "\n", + "print() 用来显示内容，input() 用来输入内容，在类似命令行的操作中，这两条语句非常有用。程序要做的事情主要就是三种，输入、计算、输出。所以输入输出命令很重要，让我们迅速的了解电脑程序处理的基本原理，并且了解 Python 语言的基本规则。\n", + "\n", + "在实际应用开发中，在电脑屏幕上输出内容已经比较少了，现在我们看到的大多是网页、移动App和小程序等。在学习过程中，我们主要用 print 来输出内容。\n", + "\n", + "print() 也支持格式化输出，可以将内容按照指定的格式整理输出。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "100\n" + ] + } + ], + "source": [ + "# 输出数字和变量内容\n", + "\n", + "print(100)\n", + "\n", + "a = 100\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name:david\n", + "hello david\n" + ] + } + ], + "source": [ + "# 先通过输入内容到变量，然后再输出\n", + "\n", + "name = input('name:')\n", + "print( 'hello', name) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "下面的例子引入条件判断语句，if 语句。Python 中 if 语句很容易理解，注意条件后面的冒号不要忘记，还有缩进格式。\n", + "\n", + "Python 官方目前有写得不错的中文文档，可以学习。 https://docs.python.org/zh-cn/3/tutorial/controlflow.html#if-statements" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "small\n" + ] + } + ], + "source": [ + "# 根据变量a 的值来判断\n", + "\n", + "a = 10\n", + "if a > 10:\n", + " print('big')\n", + " g=0\n", + "else:\n", + " print('small')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "11\n", + "12\n" + ] + } + ], + "source": [ + "for i in range(13):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "下面的例子引入循环语句 for，后面还会详细解释，这里先有个印象即可。" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h\n", + "e\n", + "l\n", + "l\n", + "o\n" + ] + } + ], + "source": [ + "for i in 'hello':\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The length of hello is 5\n" + ] + } + ], + "source": [ + "# 支持格式化的 print\n", + "\n", + "s = 'hello'\n", + "x = len(s) \n", + "print('The length of %s is %d' % (s,x)) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello from English\n", + "hello from English\n" + ] + } + ], + "source": [ + "# 更加好，更加 pythonic 的写法：format函数-增强的格式化字符串函数\n", + "# 关于format的更多细节，参见 https://docs.python.org/zh-cn/3/library/string.html#format-string-syntax\n", + "# 通过关键字传参\n", + "print('{greet} from {language}'.format(greet='hello', language='English'))\n", + "\n", + "# 通过位置传参\n", + "print('{0} from {1}'.format('hello', 'English'))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The length of hello is 5\n" + ] + } + ], + "source": [ + "# 支持格式化的 print 的另外一种写法\n", + "\n", + "s = 'hello'\n", + "x = len(s) \n", + "print('The length of {word} is {length}'.format(word=s,length=x)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "print() 格式化输出说明\n", + "* %字符：标记转换说明符的开始\n", + "\n", + "* 转换标志：-表示左对齐；+表示在转换值之前要加上正负号；“”（空白字符）表示正数之前保留空格；0表示转换值若位数不够则用0填充。\n", + "\n", + "* 最小字段宽度：转换后的字符串至少应该具有该值指定的宽度。如果是*，则宽度会从值元组中读出。\n", + "\n", + "* 点(.)后跟精度值：如果转换的是实数，精度值就表示出现在小数点后的位数。如果转换的是字符串，那么该数字就表示最大字段宽度。如果是*，那么精度将从元组中读出。\n", + "\n", + "* 字符串格式化转换类型。\n", + "\n", + "转换类型 含义\n", + "* d,i：带符号的十进制整数\n", + "* o：不带符号的八进制\n", + "* u：不带符号的十进制\n", + "* x：不带符号的十六进制（小写）\n", + "* X：不带符号的十六进制（大写）\n", + "* e：科学计数法表示的浮点数（小写）\n", + "* E：科学计数法表示的浮点数（大写）\n", + "* f,F：十进制浮点数\n", + "* g：如果指数大于-4或者小于精度值则和e相同，其他情况和f相同\n", + "* G：如果指数大于-4或者小于精度值则和E相同，其他情况和F相同\n", + "* C：单字符（接受整数或者单字符字符串）\n", + "* r：字符串（使用repr转换任意python对象)\n", + "* s：字符串（使用str转换任意python对象）" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3.142\n", + "pi = 3.142\n", + "000003.142\n", + "3.142 \n", + "+3.141593\n" + ] + } + ], + "source": [ + "# 格式化 print 举例\n", + "\n", + "pi = 3.141592653 \n", + "\n", + "print('%10.3f' % pi) #字段宽10，精度3 \n", + "\n", + "print(\"pi = %.*f\" % (3,pi)) #用*从后面的元组中读取字段宽度或精度 \n", + " \n", + "print('%010.3f' % pi) #用0填充空白 \n", + " \n", + "print('%-10.3f' % pi) #左对齐 \n", + " \n", + "print('%+f' % pi) #显示正则表达式" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Python 从3.6版本开始引入 f-string，称为格式化字符串常量，主要目的是使格式化字符串的操作更加简便。f-string在形式上是以 f 或 F 修饰符引领的字符串（f'xxx' 或 F'xxx'），以大括号 {} 标明被替换的字段；f-string在本质上并不是字符串常量，而是一个在运行时运算求值的表达式。\n", + "\n", + "f-string 在功能方面不逊于传统的 %-formatting语句和 str.format()函数，同时性能又优于二者，且使用起来也更加简洁明了，因此对于 Python3.6及以后的版本，推荐使用f-string进行字符串格式化。" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a is 3.142'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 3.141592653 \n", + "f'a is {a:10.3f}'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 03 - \345\276\252\347\216\257 for, \345\255\227\347\254\246\344\270\262.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 03 - \345\276\252\347\216\257 for, \345\255\227\347\254\246\344\270\262.ipynb" new file mode 100644 index 0000000..821725a --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 03 - \345\276\252\347\216\257 for, \345\255\227\347\254\246\344\270\262.ipynb" @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 3\n", + "Python Basic, Lesson 3, \n", + "* v1.0, 2016\n", + "* v1.1, 2020.2,3,4, 6.13 edit by David Yi\n", + "\n", + "### 本章内容要点\n", + "\n", + "* for 循环语句和 range() 函数\n", + "* 常用数据类型 \n", + " * 字符串\n", + " * 字符串处理\n", + "* 思考" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### for 循环语句和 range() 函数\n", + "\n", + "Python的循环语句主要是 for...in 循环，依次把 in 后面的 list列表或 tuple元组等中的每个元素迭代出来，进行循环；\n", + "\n", + "Python 也有 while 循环，一般情况下不用。\n", + "\n", + "`for x in ...` 循环就是把每个元素代入变量x，然后执行后面缩进块的语句。\n", + "\n", + "如果是简单的按照次数的循环，一般用 range() 函数来产生一个可以生成迭代数字的序列。\n", + "\n", + "我们看一下实际例子。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n", + "d\n", + "e\n", + "f\n" + ] + } + ], + "source": [ + "# 按照字符串进行迭代循环\n", + "\n", + "s = 'abcdef'\n", + "for i in s:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\n", + "c\n" + ] + } + ], + "source": [ + "# 按照列表进行循环，列表内容为字符\n", + "\n", + "s = ['a', 'b', 'c']\n", + "for i in s:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n" + ] + } + ], + "source": [ + "# 按照列列表进行循环，列表内容为数字\n", + "\n", + "for i in range(3): \n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 循环语句 while \n", + "while 循环是在 Python中的循环结构之一。 while 执行循环中的内容，直到表达式变为假。 \n", + "\n", + "表达式是指一个逻辑表达式，返回一个 True 或 False 值。\n", + "一定要小心设置表达式，如果永远是 True 的话，循环就一直执行下去了，称之为死循环。\n", + "\n", + "大部分场景里，循环语句不建议使用 while，但有时候使用 while 语句会让程序很简洁。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The count is: 0\n", + "The count is: 1\n", + "The count is: 2\n", + "The count is: 3\n", + "The count is: 4\n", + "The count is: 5\n", + "The count is: 6\n", + "The count is: 7\n", + "The count is: 8\n", + "Good bye!\n" + ] + } + ], + "source": [ + "# 用 while 进行循环\n", + "\n", + "count = 0\n", + "while (count < 9):\n", + " print('The count is:', count)\n", + " count += 1\n", + " \n", + "print(\"Good bye!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### range() 函数\n", + "\n", + "range() 函数产生一个等差序列，range(x,y,z)，表示从 x 到 y（不含 y），z 为步长，可以为负。\n", + "\n", + "修改下面例子中的 range() 中的起始、结束和步长，来看看各种效果。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "4\n", + "7\n" + ] + } + ], + "source": [ + "# for 循环，使用range()\n", + "\n", + "for i in range(1,10,3):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "9\n", + "8\n", + "7\n", + "6\n", + "5\n", + "4\n", + "3\n" + ] + } + ], + "source": [ + "# for 循环，使用range()\n", + "\n", + "for i in range(10,2,-1):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "109876543" + ] + } + ], + "source": [ + "# print 时候输出内容，可以不换行\n", + "\n", + "for i in range(10,2,-1):\n", + " print(i,end='')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10,9,8,7,6,5,4,3," + ] + } + ], + "source": [ + "# print 时候不换行,优雅的分割\n", + "\n", + "for i in range(10,2,-1):\n", + " print(i,end=',')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Mary \n", + "1 had\n", + "2 a\n", + "3 little \n", + "4 lamb\n" + ] + } + ], + "source": [ + "# 同时获得列表中的序号和内容，可以这样写\n", + "# len() 是获得列表的长度，可以理解为元素个数\n", + "\n", + "s = ['Mary ', 'had', 'a', 'little ', 'lamb']\n", + "for i in range(len(s)):\n", + " print(i, s[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Mary \n", + "1 had\n", + "2 a\n", + "3 little \n", + "4 lamb\n" + ] + } + ], + "source": [ + "# 更加好的写法, 使用 enumerate()\n", + "\n", + "s = ['Mary ', 'had', 'a', 'little ', 'lamb']\n", + "for i, item in enumerate(s):\n", + " print(i, item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 字符串\n", + "\n", + "字符串即有序的字符的集合，用来存储或表现基于文本的信息。在程序开发中字符串被广泛使用。\n", + "Python 中使用单双引号都可以。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam\n" + ] + } + ], + "source": [ + "# 单引号\n", + "\n", + "a = 'spam' \n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam\n", + "spam's log\n" + ] + } + ], + "source": [ + "# 双引号\n", + "\n", + "a = \"spam\" \n", + "print(a)\n", + "b = \"spam's log\"\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "multile lines\n", + "this is an example\n" + ] + } + ], + "source": [ + "# 多行字符串\n", + "a = '''multile lines\n", + "this is an example'''\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "b\tc\n" + ] + } + ], + "source": [ + "s = 'a\\nb\\tc' # 转义字符串\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\n", + "ew\text.txt\n", + "C:\\new\\text.txt\n" + ] + } + ], + "source": [ + "# 输出内容因为转义而发生变化\n", + "a = 'C:\\new\\text.txt'\n", + "print(a)\n", + "\n", + "# 输出内容不带转义\n", + "b = r'C:\\new\\text.txt'\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ABC.txt123\n", + "ABC.txtABC.txt\n", + "A\n", + "t\n", + "txt.CBA\n", + "AB\n" + ] + } + ], + "source": [ + "# 常用的字符串表达式\n", + "\n", + "s = 'ABC.txt'\n", + "print(s + '123') # 字符串拼接\n", + "print(s * 2) # 重复\n", + "print(s[0]) # 索引\n", + "print(s[-1])\n", + "print(s[::-1]) # 反转\n", + "print(s[0:2]) # 切片\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "abc.txt\n", + "ABC.TXT\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "print(len(s)) # 长度\n", + "print(s.lower()) # 小写转换\n", + "print(s.upper()) # 大写转换\n", + "print(s.endswith('.txt')) # 后缀测试\n", + "print('AB' in s) # 成员关系测试" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### 字符串处理\n", + "\n", + "字符串是程序中经常碰到的数据类型，字符串的很多处理和 list 有点像，但也有些区别" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "False\n" + ] + } + ], + "source": [ + "# 判断是否是字母\n", + "\n", + "s1 = 'abcde'\n", + "s2 = '12'\n", + "s3 = '12s'\n", + "print(s1.isalpha())\n", + "print(s2.isalpha())\n", + "print(s3.isalpha())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# 判断是否是字母、是否是数字\n", + "\n", + "s1 = 'abcde'\n", + "s2 = '12'\n", + "s3 = '12s'\n", + "print(s1.isalpha())\n", + "print(s2.isdigit())\n", + "print(s3.isalpha())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# 判断是否是小写\n", + "\n", + "s1 = 'abc'\n", + "print(s1.islower())\n", + "print(''.islower())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# 判断是否是大写 \n", + "\n", + "s1 = 'ABC'\n", + "print(s1.isupper())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# 是否字母数字混合\n", + "\n", + "s1 ='123abc'\n", + "print(s1.isalnum())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "find it\n", + "8\n" + ] + } + ], + "source": [ + "# 字符串查找\n", + "\n", + "s1 = 'What is your name'\n", + "if s1.find('your') != -1:\n", + " print('find it')\n", + " print(s1.find('your'))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "find it\n" + ] + } + ], + "source": [ + "# 字符串查找的另外一种方式\n", + "\n", + "s1 = 'What is your name'\n", + "if 'your' in s1 != -1:\n", + " print('find it')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is my name\n" + ] + } + ], + "source": [ + "# 字符串替换\n", + "\n", + "s1 = 'What is your name'\n", + "s2 = s1.replace('your', 'my')\n", + "print(s2)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fedcba\n" + ] + } + ], + "source": [ + "# 字符串切片\n", + "\n", + "s1 = 'abcdef'\n", + "s2 = s1[::-1]\n", + "print(s2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'e', 'f']\n", + "['', 'a', 'b', 'c', '', 'd', 'e', 'f']\n" + ] + } + ], + "source": [ + "# 字符串和列表的转换\n", + "\n", + "s1 = ' a b c d e f'\n", + "s2 = s1.split()\n", + "s3 = s1.split(' ')\n", + "print(s2)\n", + "print(s3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "abcdef\n", + "ABCDEF\n" + ] + } + ], + "source": [ + "# 字符串转换为小写、大写\n", + "\n", + "s1 = 'aBCdef'\n", + "print(s1.lower())\n", + "print(s1.upper())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a b c d e f 12\n", + "a b c d e f 14\n", + " a b c d e f\n" + ] + } + ], + "source": [ + "# 字符串去除空格\n", + "\n", + "s1 = ' a b c d e f '\n", + "print(s1.strip(' '),len(s1.strip(' ')))\n", + "print(s1.lstrip(' '),len(s1.lstrip(' ')))\n", + "print(s1.rstrip(' '))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "012\n", + "2302\n", + "12 \n", + "2302\n", + " 12\n", + "2302\n" + ] + } + ], + "source": [ + "# 字符串对齐\n", + "\n", + "s1 = '12'\n", + "s2 = '2302'\n", + "print(s1.zfill(3))\n", + "print(s2.zfill(3))\n", + "\n", + "print(s1.ljust(4))\n", + "print(s2.ljust(4))\n", + "\n", + "print(s1.rjust(4))\n", + "print(s2.rjust(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0976\n" + ] + } + ], + "source": [ + "a='12345679012456'\n", + "print(a[8:4:-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 练习\n", + "\n", + "题目：有四个数字：1、2、3、4，能组成多少个互不相同且无重复数字的三位数？ \n", + "\n", + "程序分析：可填在百位、十位、个位的数字都是1、2、3、4。组成所有的排列后再去掉不满足条件的排列。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 2 3\n", + "1 2 4\n", + "1 3 2\n", + "1 3 4\n", + "1 4 2\n", + "1 4 3\n", + "2 1 3\n", + "2 1 4\n", + "2 3 1\n", + "2 3 4\n", + "2 4 1\n", + "2 4 3\n", + "3 1 2\n", + "3 1 4\n", + "3 2 1\n", + "3 2 4\n", + "3 4 1\n", + "3 4 2\n", + "4 1 2\n", + "4 1 3\n", + "4 2 1\n", + "4 2 3\n", + "4 3 1\n", + "4 3 2\n" + ] + } + ], + "source": [ + "for i in range(1,5):\n", + " for j in range(1,5):\n", + " for k in range(1,5):\n", + " if( i != k ) and (i != j) and (j != k):\n", + " print(i,j,k)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 04 - \345\210\227\350\241\250 list.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 04 - \345\210\227\350\241\250 list.ipynb" new file mode 100644 index 0000000..b290bea --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 04 - \345\210\227\350\241\250 list.ipynb" @@ -0,0 +1,619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 4\n", + "\n", + "v1.0.0, 2016.12 by David Yi \n", + "v1.0.1, 2017.02 modified by Yimeng Zhang \n", + "v1.1, 2020.4 edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* list 列表用法\n", + "* pickle 序列化\n", + "* 思考：判断两个字符串的相似性等" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "## list 列表用法\n", + "\n", + "列表是 Python 内置的一种数据类型。list 是一种可变、有序的集合，可以随时添加和删除其中的元素。\n", + "列表是 Python 程序中最常用的数据类型之一，掌握列表的用法会大大提升 Python 的开发能力。\n", + "\n", + "列表的基本操作：\n", + "* 创建列表\n", + "* 访问列表中的元素\n", + "* 增加元素\n", + "* 删除元素\n", + "* 排序\n", + "* 多维\n", + "* 保存和载入\n", + "\n", + "list 中除了保存一般的数字、字符以外，可以存储 Python 中各种复杂的数据类型，包括 list 本身、对象等。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['pig', 'cat', 'dog']\n", + "cat\n", + "dog\n" + ] + } + ], + "source": [ + "# 创建列表\n", + "\n", + "a = ['pig', 'cat', 'dog']\n", + "\n", + "print(a)\n", + "\n", + "# 用序号访问列表中的元素，支持双向\n", + "# 列表支持一种比较复杂的切片式访问，后面会有专门提及\n", + "print(a[1])\n", + "print(a[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['bird']\n", + "['bird', 'snake']\n" + ] + } + ], + "source": [ + "# 列表初始化\n", + "a = []\n", + "\n", + "# 列表末尾追加元素\n", + "a.append('bird')\n", + "print(a)\n", + "a.append('snake')\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sheep', 'bird', 'snake']\n" + ] + } + ], + "source": [ + "# 列表指定位置插入元素\n", + "a.insert(0,'sheep')\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sheep', 'snake']\n" + ] + } + ], + "source": [ + "# 列表删除指定序号的元素\n", + "\n", + "a.pop(1)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['pig', 'cat']\n" + ] + } + ], + "source": [ + "# 列表删除指定内容的元素\n", + "\n", + "a = ['pig', 'cat', 'dog']\n", + "a.remove('dog')\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['cat', 'dog', 'pig', 'snake']\n", + "['snake', 'pig', 'dog', 'cat']\n" + ] + } + ], + "source": [ + "# 列表排序\n", + "\n", + "a = ['pig', 'cat', 'dog', 'snake']\n", + "a.sort()\n", + "print(a)\n", + "\n", + "# 倒序\n", + "a.sort(reverse = True)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 3, 2, 1]\n" + ] + } + ], + "source": [ + "# 列表数字排序\n", + "\n", + "a = [1,2,3,4]\n", + "a.sort(reverse = True)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 3, 2, 1, ['cat1', 'cat2', 'cat3']]\n" + ] + } + ], + "source": [ + "# 列表追加另一个列表\n", + "# 使用 append 一个列表的话，这个列表会以一个元素的方式追加到原来列表\n", + "\n", + "b = ['cat1', 'cat2', 'cat3']\n", + "a.append(b)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['pig', 'cat', 'dog', 'snake', 'cat1', 'cat2', 'cat3']\n" + ] + } + ], + "source": [ + "# 列表追加另一个列表的元素\n", + "# 使用 extend 一个列表，会以元素的形式追加到原来的列表\n", + "\n", + "a = ['pig', 'cat', 'dog', 'snake']\n", + "b = ['cat1', 'cat2', 'cat3']\n", + "a.extend(b)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "# 统计某个元素在列表中出现次数\n", + "\n", + "a = ['a','b','b','c']\n", + "print(a.count('b'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### pickle 序列化\n", + "\n", + "Python 中可以使用 pickle 模块将对象转化为文件保存在磁盘上，在需要的时候再读取并还原。具体用法如下：\n", + "\n", + "```pickle.dump(obj, file[, protocol])```\n", + "\n", + "pickle 的格式和 Python 版本等有关，不同版本之间不兼容。比较通用的序列化方式是用 json 格式，可以跨平台、跨语言。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# 保存列表内容\n", + "\n", + "# 使用 pickle 模块，import 可以导入另外一个模块，执行其中的功能\n", + "import pickle\n", + "\n", + "# files 在这里是路径，需要预先建立好，也可以用这样的 c:\\xxx.txt 文件名写法\n", + "f = open('files/list_dump.txt', 'wb')\n", + "a = ['pig', 'cat', 'dog', 'snake', 'snake']\n", + "pickle.dump(a, f)\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['pig', 'cat', 'dog', 'snake', 'snake']\n" + ] + } + ], + "source": [ + "# 读出列表内容\n", + "\n", + "f = open('files/list_dump.txt', 'rb')\n", + "a1 = pickle.load(f)\n", + "f.close()\n", + "print(a1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['pig', 'cat', 'dog'], [1, 2, 3]]\n", + "cat\n", + "3\n" + ] + } + ], + "source": [ + "# 两维列表，列表中的每个元素都是一个列表\n", + "\n", + "a = ['pig', 'cat', 'dog']\n", + "b = [1,2,3]\n", + "c = []\n", + "\n", + "c.append(a)\n", + "c.append(b)\n", + "\n", + "print(c)\n", + "\n", + "# 第0个元素的第1个元素\n", + "print(c[0][1]) # cat\n", + "\n", + "# 第1个元素的第2个元素\n", + "print(c[1][2]) # 3" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['pig', 'cat', 'dog']\n" + ] + } + ], + "source": [ + "# 列表生成式 初步\n", + "# 过滤列表中的重复元素\n", + "\n", + "b = [x for x in a if a.count(x) == 1]\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n" + ] + } + ], + "source": [ + "# 显示一个对象的方法\n", + "# python 中一切皆对象\n", + "# 面向对象的编程是现代编程技术中的基本概念\n", + "\n", + "print(dir(list))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['append',\n", + " 'clear',\n", + " 'copy',\n", + " 'count',\n", + " 'extend',\n", + " 'index',\n", + " 'insert',\n", + " 'pop',\n", + " 'remove',\n", + " 'reverse',\n", + " 'sort']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 用列表生成式来生成一个对象的方法\n", + "# 过滤掉继承的方法，也就是带下划线的\n", + "\n", + "[x for x in dir(list) if x[0] != '_']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. 判断两个字符串的相似性" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "0.8571428571428571\n", + "0.11764705882352941\n", + "0.5833333333333334\n" + ] + } + ], + "source": [ + "# 利用 Python 内置函数判断两个字符串的相似性\n", + "\n", + "from difflib import SequenceMatcher\n", + "\n", + "def similarity(a, b):\n", + " return SequenceMatcher(None, a, b).ratio()\n", + "\n", + "print(similarity('这是一个有趣的问题', '这是一个有趣的问题'))\n", + "print(similarity('这是一个有趣的问题', '这基本上是一个有趣的问题'))\n", + "print(similarity('这是一个有趣的问题', '高高的山上有只羊'))\n", + "\n", + "# 其实并不是那么聪明\n", + "print(similarity('快捷支付是哪四要素', '快捷支付接口这里的四要素是什么'))\n", + "\n", + "# python 内置的 difflib 虽然不是那么智能，不过功能还是很强大的，可以用它很容易的制作出类似代码比较的工具" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "2. 给一个用户名列表（英文），输入开头几个字母，程序会搜索用户名列表中是否存在和开头字母类似的，并提示用户。执行效果如下：\n", + "\n", + "```\n", + "please input name : C\n", + "Do you want to find ['Candy', 'Chris']?1\n", + "Chris\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "please input name : Ch\n", + "Do you want to find ['Chris']?0\n", + "Chris\n" + ] + } + ], + "source": [ + "# 补充用户名字，利用切片的例子\n", + "name = ['Adam','Alex','Amy','Bob','Boom','Candy','Chris','David','Jason','Jasonstatham','Bill'];\n", + "i_name = input('please input name : ')\n", + "\n", + "wname = [];\n", + "for n in name:\n", + " if n[0:len(i_name)] == i_name:\n", + " wname.append(n);\n", + "\n", + "if len(wname) != 0:\n", + " number = input('Do you want to find %s?'%(wname))\n", + " print(wname[int(number)])\n", + "else:\n", + " print('%s not find'%(i_name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. 判断用户输入的是数字还是字符串\n", + "\n", + "有很多种方法，一般用的比较多也建议使用是的捕捉错误的办法，通过将输入的默认的字符串进行转换为数字，如果不能转换，说明用户输入的不是数字。\n", + "这里介绍一种比较优雅的方法来判断是否输入的是数字，通过使用 `isdigit` 函数，顾名思义，就是判断是否是数字的函数。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter number12\n", + "Number \n" + ] + } + ], + "source": [ + "number = input(\"Enter number\")\n", + "if( number.isdigit()):\n", + " print(\"Number \")\n", + "else:\n", + " print(\"String \")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isascii', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']\n" + ] + } + ], + "source": [ + "# 看看 str 字符串有多少方法\n", + "print(dir(str))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> python判断字符串，str函数isdigit、isdecimal、isnumeric的区别 https://www.cnblogs.com/guigujun/p/6133057.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 05 - \345\255\227\345\205\270 dict, \345\205\203\347\273\204 tuple.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 05 - \345\255\227\345\205\270 dict, \345\205\203\347\273\204 tuple.ipynb" new file mode 100644 index 0000000..3a65e85 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 05 - \345\255\227\345\205\270 dict, \345\205\203\347\273\204 tuple.ipynb" @@ -0,0 +1,768 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 5\n", + "\n", + "v1.0.0, 2016.12 by David Yi \n", + "v1.0.1, 2017.02 modified by Yimeng Zhang \n", + "v1.1, 2020.4 edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* 字典dict 用法\n", + "* 元组tuple 用法\n", + "* 思考：中文分词" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "## 字典dict 用法\n", + "\n", + "字典是另一种可变容器模型，可存储任意类型对象。\n", + "\n", + "字典的每个键值(key=>value)对用冒号(:)分割，每个对之间用逗号(,)分割，整个字典包括在花括号({})中，格式如 `d = {key1 : value1, key2 : value2 }`\n", + "\n", + "字典中的 key 值不可以重复（第一次定义后不能重复定义）。\n", + "\n", + "字典的几个特点：\n", + "1. 查找和插入的速度极快，不会随着key的增加而变慢； \n", + "2. 需要占用大量的内存，内存浪费多。 \n", + "\n", + "而 list 相反：\n", + "1. 查找和插入的时间随着元素的增加而增加； \n", + "2. 占用空间小，浪费内存很少。 \n", + "\n", + "字典的基本操作：\n", + "* 创建字典\n", + "* 访问字典中的 key-value\n", + "* 修改字典中的 key-value\n", + "* 获得字典中指定 key 的 value\n", + "* 删除字典中的 key（value 也就消失了）" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Tom': 95, 'Mary': 90, 'Tracy': 92}\n", + "95\n" + ] + } + ], + "source": [ + "# 定义字典\n", + "# 访问字典中的 key-value\n", + "\n", + "d = {'Tom': 95, 'Mary': 90, 'Tracy': 92}\n", + "print(d)\n", + "print(d['Tom'])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Tom': 95, 'Mary': 90, 'Tracy': 92, 'Hugo': 85}\n" + ] + } + ], + "source": [ + "# 字典增加元素，直接定义值即可\n", + "\n", + "d['Hugo'] = 85\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Tom': 97, 'Mary': 90, 'Tracy': 92, 'Hugo': 85}\n", + "True\n", + "80\n" + ] + } + ], + "source": [ + "# 修改字典元素的值\n", + "\n", + "d['Tom'] = 97\n", + "print(d)\n", + "\n", + "# 字典是否存在某个 key\n", + "print('Tom' in d)\n", + "\n", + "# 如果要获得不存在的 key 的 value，可以设置默认值\n", + "print(d.get('Tommy',80))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Tommy'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 去获得不存在的 key 的 value，会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Tommy'\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[1;31mKeyError\u001b[0m: 'Tommy'" + ] + } + ], + "source": [ + "# 去获得不存在的 key 的 value，会报错\n", + "print(d['Tommy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Tom': 95, 'Mary': 90, 'Tracy': 92}\n", + "{'Mary': 90, 'Tracy': 92}\n" + ] + } + ], + "source": [ + "# 字典删除 key\n", + "\n", + "d = {'Tom': 95, 'Mary': 90, 'Tracy': 92}\n", + "print(d)\n", + "\n", + "# 删除 key\n", + "d.pop('Tom')\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'id_0': 0, 'id_1': 3, 'id_2': 6, 'id_3': 9, 'id_4': 12, 'id_5': 15, 'id_6': 18, 'id_7': 21, 'id_8': 24, 'id_9': 27, 'id_10': 30, 'id_11': 33, 'id_12': 36, 'id_13': 39, 'id_14': 42, 'id_15': 45, 'id_16': 48, 'id_17': 51, 'id_18': 54, 'id_19': 57, 'id_20': 60, 'id_21': 63, 'id_22': 66, 'id_23': 69, 'id_24': 72, 'id_25': 75, 'id_26': 78, 'id_27': 81, 'id_28': 84, 'id_29': 87}\n", + "30\n" + ] + } + ], + "source": [ + "# 获得字典的长度\n", + "\n", + "# 定义一个空的字典\n", + "d = {}\n", + "\n", + "# 造一些字典内容\n", + "# str 函数将整数转换为字符串\n", + "for i in range(30):\n", + " d['id_'+str(i)] = i*3\n", + "print(d)\n", + "\n", + "print(len(d))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "## 元组Tuple 用法\n", + "\n", + "Tuple 也是一种有序列表，在存储数据方面和列表很相似，为了区分，我们叫它元组。\n", + "Tuple 一旦内容存储后，就不能修改；这样的好处是数据很安全。\n", + "\n", + "应用范围：在我们需要使用列表功能的时候，但是又不需要改变这个列表的内容，用元组 Tuple 功能会很安全，不用担心程序中不小心修改了其内容。Python 在向函数传递多个参数的时候，就是采用元组，保证参数在被调用的过程中的安全。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Tom', 'Jerry', 'Mary')\n" + ] + } + ], + "source": [ + "# 创建元组，用小括号\n", + "\n", + "t = ('Tom', 'Jerry', 'Mary')\n", + "print(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jerry\n" + ] + } + ], + "source": [ + "# 访问元组的元素\n", + "\n", + "print(t[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'append'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Someone'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" + ] + } + ], + "source": [ + "# 元组创建后是不能修改的\n", + "\n", + "# 会报错\n", + "t.append('Someone')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# 像列表一样去定义值也会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# 'tuple' object does not support item assignment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mt\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'aaa'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "source": [ + "# 元组创建后是不能修改的\n", + "\n", + "# 像列表一样去定义值也会报错\n", + "# 'tuple' object does not support item assignment\n", + "t[1] = 'aaa'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']\n" + ] + } + ], + "source": [ + "# 看看 tuple 有什么方法，你会发现很少\n", + "\n", + "print(dir(tuple))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(['A', 'B', 'C'], 100, 200)\n" + ] + } + ], + "source": [ + "# 创建复杂一点的元组\n", + "\n", + "t1 = ['A', 'B', 'C']\n", + "t2 =(t1, 100, 200)\n", + "print(t2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'B', 'C']\n", + "(['A', 'B', 'C'], 100, 200)\n", + "['A', 'B', 'C', 'D']\n", + "(['A', 'B', 'C', 'D'], 100, 200)\n" + ] + } + ], + "source": [ + "# 变通的实现\"可变\"元组内容\n", + "\n", + "t1 = ['A', 'B', 'C']\n", + "t2 =(t1, 100, 200)\n", + "print(t1)\n", + "print(t2)\n", + "\n", + "# tuple的每个元素，指向永远不变，但指向的元素本身是可变的\n", + "t1.append('D')\n", + "\n", + "print(t1)\n", + "print(t2) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "(1,)\n" + ] + } + ], + "source": [ + "# 创建只有1个元素的元组\n", + "\n", + "# 下面这样是不行的\n", + "t = (1)\n", + "# l成了一个整数，因为这里的括号有歧义，被认作数学计算里的小括号\n", + "print(type(t)) \n", + "\n", + "# 1个元素的元组必须加逗号来消除歧义\n", + "t = (1,)\n", + "print(type(t))\n", + "print(t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 思考\n", + "\n", + "* 中文分词，是一个很有趣的话题，也是机器学习中关于语言处理的最基本的概念。今天我们看看 Python 怎么处理中文分词;\n", + "* 需要先安装 jieba 这个 Python 的库，使用 `pip install jieba`;\n", + "* 在 notebook 中执行命令可以在命令前面价格 ！来进行;\n", + "* 导入 jieba 之后，第一次运行会有一个初始化过程，稍稍产生一些影响。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install jieba" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Building prefix dict from the default dictionary ...\n", + "Loading model from cache C:\\Users\\yijun\\AppData\\Local\\Temp\\jieba.cache\n", + "Loading model cost 0.555 seconds.\n", + "Prefix dict has been built successfully.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Mode: 今天/ 天上/ 上海/ 的/ 天气/ 怎么/ 怎么样\n" + ] + } + ], + "source": [ + "import jieba\n", + "\n", + "# 全模式\n", + "# 把句子中所有的可以称此的词语都扫描出来，速度非常快，但是不能解决歧义\n", + "\n", + "seg_list = jieba.cut(\"今天上海的天气怎么样\", cut_all = True)\n", + "print(\"Full Mode: \" + \"/ \".join(seg_list)) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default Mode: 明天/ 纽约/ 下雨/ 么\n" + ] + } + ], + "source": [ + "# 精确模式\n", + "# 试图将句子最精确的切开，适合文本分析\n", + "\n", + "seg_list = jieba.cut(\"明天纽约下雨么\", cut_all = False)\n", + "print(\"Default Mode: \" + \"/ \".join(seg_list)) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "现在, 天气, 怎么样\n" + ] + } + ], + "source": [ + "# 默认是精确模式\n", + "\n", + "seg_list = jieba.cut(\"现在天气怎么样\") \n", + "print(\", \".join(seg_list))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "小明, 硕士, 毕业, 于, 中国科学院, 计算所, ，, 后, 在, 日本京都大学, 深造\n" + ] + } + ], + "source": [ + "# 默认是精确模式\n", + "\n", + "seg_list = jieba.cut(\"小明硕士毕业于中国科学院计算所，后在日本京都大学深造\") \n", + "print(\", \".join(seg_list))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, ，, 后, 在, 日本, 京都, 大学, 日本京都大学, 深造\n" + ] + } + ], + "source": [ + "# 搜索引擎模式\n", + "# 在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词 \n", + "\n", + "seg_list = jieba.cut_for_search(\"小明硕士毕业于中国科学院计算所，后在日本京都大学深造\") \n", + "print(\", \".join(seg_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "黑夜, 总会, 过去\n", + "黑夜, 夜总会, 总会, 过去\n" + ] + } + ], + "source": [ + "# 看看网络上的段子，分词带来的烦恼\n", + "\n", + "seg_list = jieba.cut_for_search(\"黑夜总会过去\") \n", + "print(\", \".join(seg_list))\n", + "seg_list = jieba.cut(\"黑夜总会过去\", cut_all = True)\n", + "print(\", \".join(seg_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016,年,第一季度,支付,事业部,交易量,报表\n" + ] + } + ], + "source": [ + "# 默认是精确模式\n", + "seg_list = jieba.cut(\"2016年第一季度支付事业部交易量报表\") \n", + "print(','.join(seg_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016\n", + "年\n", + "第一季度\n", + "支付\n", + "事业部\n", + "交易量\n", + "报表\n" + ] + } + ], + "source": [ + "# 默认是精确模式\n", + "seg_list = jieba.cut(\"2016年第一季度支付事业部交易量报表\") \n", + "for i in seg_list:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "我 r\n", + "爱 v\n", + "北京 ns\n", + "天安门 ns\n" + ] + } + ], + "source": [ + "import jieba.posseg as pseg\n", + "words = pseg.cut(\"我爱北京天安门\")\n", + "\n", + "for word, flag in words:\n", + " print('%s %s' % (word, flag))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 词性\n", + "\n", + "北大词性标注集\n", + "\n", + "* Ag     形语素     形容词性语素。形容词代码为a，语素代码ｇ前面置以A。\n", + "* a       形容词      取英语形容词adjective的第1个字母。\n", + "* ad 副形词 直接作状语的形容词。形容词代码a和副词代码d并在一起。\n", + "* an 名形词 具有名词功能的形容词。形容词代码a和名词代码n并在一起。\n", + "* b       区别词      取汉字“别”的声母。\n", + "* c       连词        取英语连词conjunction的第1个字母。\n", + "* Dg     副语素     副词性语素。副词代码为d，语素代码ｇ前面置以D。\n", + "* d       副词     取adverb的第2个字母，因其第1个字母已用于形容词。\n", + "* e       叹词     取英语叹词exclamation的第1个字母。\n", + "* f        方位词      取汉字“方”的声母。\n", + "* g  语素    绝大多数语素都能作为合成词的“词根”，取汉字“根”的声母。\n", + "* h       前接成分   取英语head的第1个字母。\n", + "* i        成语        取英语成语idiom的第1个字母。\n", + "* j        简称略语  取汉字“简”的声母。\n", + "* k       后接成分\n", + "* l        习用语     习用语尚未成为成语，有点“临时性”，取“临”的声母。\n", + "* m       数词     取英语numeral的第3个字母，n，u已有他用。\n", + "* Ng     名语素     名词性语素。名词代码为n，语素代码ｇ前面置以N。\n", + "* n   名词        取英语名词noun的第1个字母。\n", + "* nr  人名        名词代码n和“人(ren)”的声母并在一起。\n", + "* ns      地名     名词代码n和处所词代码s并在一起。\n", + "* nt      机构团体    “团”的声母为t，名词代码n和t并在一起。\n", + "* nz     其他专名    “专”的声母的第1个字母为z，名词代码n和z并在一起。 \n", + "* o       拟声词     取英语拟声词onomatopoeia的第1个字母。\n", + "* p       介词     取英语介词prepositional的第1个字母。\n", + "* q       量词        取英语quantity的第1个字母。\n", + "* r       代词        取英语代词pronoun的第2个字母,因p已用于介词。\n", + "* s       处所词     取英语space的第1个字母。\n", + "* Tg     时语素      时间词性语素。时间词代码为t,在语素的代码g前面置以T。\n", + "* t     时间词      取英语time的第1个字母。\n", + "* u       助词        取英语助词auxiliary 的第2个字母,因a已用于形容词。\n", + "* Vg     动语素      动词性语素。动词代码为v。在语素的代码g前面置以V。\n", + "* v       动词        取英语动词verb的第一个字母。\n", + "* vd     副动词      直接作状语的动词。动词和副词的代码并在一起。\n", + "* vn     名动词      指具有名词功能的动词。动词和名词的代码并在一起。\n", + "* w      标点符号   \n", + "* x       非语素字    非语素字只是一个符号，字母x通常用于代表未知数、符号。\n", + "* y       语气词      取汉字“语”的声母。\n", + "* z      状态词      取汉字“状”的声母的前一个字母。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 延展思考：\n", + "\n", + "* 能够根据分词和词性的情况，做一个简单的机器人聊天软件？\n", + "\n", + "彩蛋：" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " OUILOVEYO OVEYOUILO \n", + " VEYOUILOVEYOUILOV UILOVEYOUILOVEYOU \n", + " OVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILO \n", + " OVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVE \n", + " OVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYO \n", + " VEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOU \n", + " EYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUI \n", + " YOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUIL \n", + " OUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILO \n", + " UILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " LOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " VEYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " EYOUILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOVE \n", + " UILOVEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " LOVEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " VEYOUILOVEYOUILOVEYOUILOVEYOUILOV \n", + " OUILOVEYOUILOVEYOUILOVEYOUILO \n", + " LOVEYOUILOVEYOUILOVEYOUIL \n", + " EYOUILOVEYOUILOVEYOUI \n", + " ILOVEYOUILOVEYO \n", + " EYOUILOVE \n", + " ILO \n", + " O \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "print('\\n'.join([''.join([('ILOVEYOU'[(x-y)%8]if((x*0.05)**2+(y*0.1)**2-1)**3-(x*0.05)**2*(y*0.1)**3<=0 else' ')for x in range(-30,30)])for y in range(15,-15,-1)]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 06 - \351\232\217\346\234\272\346\225\260.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 06 - \351\232\217\346\234\272\346\225\260.ipynb" new file mode 100644 index 0000000..8d157dc --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 06 - \351\232\217\346\234\272\346\225\260.ipynb" @@ -0,0 +1,501 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 6\n", + "\n", + "v1.1, 2020.4 5, edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* 随机数\n", + "* 思考：猜数游戏等" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 随机数\n", + "\n", + "随机数这一概念在不同领域有着不同的含义，在密码学、通信领域有着非常重要的用途。\n", + "\n", + "Python 的随机数模块是 random，random 模块主要有以下函数，结合例子来看看。\n", + "\n", + "* random.choice() 从序列中获取一个随机元素\n", + "* random.sample() 创建指定范围内指定个数的随机数\n", + "* random.random() 用于生成一个0到1的随机浮点数\n", + "* random.uniform() 用于生成一个指定范围内的随机浮点数\n", + "* random.randint() 生成一个指定范围内的整数\n", + "* random.shuffle() 将一个列表中的元素打乱" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42\n", + "b\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "# random.choice(sequence)。参数sequence表示一个有序类型。\n", + "# random.choice 从序列中获取一个随机元素。\n", + "\n", + "print(random.choice(range(1,100)))\n", + "\n", + "# 从一个列表中产生随机元素\n", + "list1 = ['a', 'b', 'c']\n", + "print(random.choice(list1))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[68, 73, 84, 54, 28, 94, 93, 71, 86, 16]\n", + "[3, 5, 9, 7, 1]\n" + ] + } + ], + "source": [ + "# random.sample()\n", + "\n", + "# 创建指定范围内指定个数的整数随机数\n", + "print(random.sample(range(1,100), 10))\n", + "\n", + "print(random.sample(range(1,10), 5))\n", + "\n", + "# 如果要产生的随机数数量大于范围边界，会怎么样？\n", + "# print(random.sample(range(1,10), 15))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27\n", + "4\n", + "1\n" + ] + } + ], + "source": [ + "# random.randint(a, b)，用于生成一个指定范围内的整数。\n", + "# 其中参数a是下限，参数b是上限，生成的随机数n: a <= n <= b\n", + "print(random.randint(1,100))\n", + "\n", + "# random.randrange([start], stop[, step])，\n", + "# 从指定范围内，按指定基数递增的集合中 获取一个随机数。\n", + "print(random.randrange(1,10))\n", + "\n", + "# 可以多运行几次，看看结果总是哪几个数字\n", + "print(random.randrange(1,10,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6293773053237454\n" + ] + } + ], + "source": [ + "# random.random()用于生成一个0到1的随机浮点数: 0 <= n < 1.0\n", + "\n", + "print(random.random())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "34.2581965175054\n", + "11.869058993400074\n" + ] + } + ], + "source": [ + "# random.uniform(a, b)，\n", + "# 用于生成一个指定范围内的随机浮点数，两个参数其中一个是上限，一个是下限。\n", + "# 如果a < b，则生成的随机数n: a <= n <= b。如果 a > b， 则 b <= n <= a。\n", + "\n", + "print(random.uniform(1,100))\n", + "print(random.uniform(50,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 87, 12, 1, 23]\n" + ] + } + ], + "source": [ + "# random.shuffle(x[, random])，\n", + "# 用于将一个列表中的元素打乱\n", + "a = [12, 23, 1, 5, 87]\n", + "random.shuffle(a)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8, 1, 4, 0, 9]\n", + "[4, 9, 1, 0, 2, 7, 6]\n" + ] + } + ], + "source": [ + "# random.sample(sequence, k)，\n", + "# 从指定序列中随机获取指定长度的片断。sample函数不会修改原有序列。\n", + "\n", + "print(random.sample(range(10),5))\n", + "print(random.sample(range(10),7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考\n", + "\n", + "1. 一个猜数程序" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now you can guess...\n", + "please input number:565\n", + "too small\n", + "please input number:760\n", + "too small\n", + "please input number:900\n", + "too big\n", + "please input number:850\n", + "too big\n", + "please input number:800\n", + "too big\n", + "please input number:777\n", + "too big\n", + "please input number:766\n", + "too big\n", + "please input number:765\n", + "too big\n", + "please input number:762\n", + "too big\n", + "please input number:761\n", + "bingo!\n" + ] + } + ], + "source": [ + "# 猜数，人猜\n", + "# 简单版本\n", + "\n", + "import random\n", + "\n", + "a = random.randint(1,1000)\n", + "\n", + "print('Now you can guess...')\n", + "\n", + "guess_mark = True\n", + "\n", + "while guess_mark:\n", + " user_number =int(input('please input number:'))\n", + " if user_number > a:\n", + " print('too big')\n", + " if user_number < a:\n", + " print('too small')\n", + " if user_number == a:\n", + " print('bingo!')\n", + " guess_mark = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 猜数，人猜\n", + "# 记录猜数的过程\n", + "\n", + "import random\n", + "\n", + "# 记录人猜了多少数字\n", + "user_number_list = []\n", + "\n", + "# 记录人猜了几次\n", + "user_guess_count = 0\n", + "\n", + "a = random.randint(1,100)\n", + "\n", + "print('Now you can guess...')\n", + "guess_mark = True\n", + "\n", + "# 主循环\n", + "while guess_mark:\n", + " user_number =int(input('please input number:'))\n", + " \n", + " user_number_list.append(user_number)\n", + " user_guess_count += 1\n", + " \n", + " if user_number > a:\n", + " print('too big')\n", + " if user_number < a:\n", + " print('too small')\n", + " if user_number == a:\n", + " print('bingo!')\n", + " print('your guess number list:', user_number_list)\n", + " print('you try times:', user_guess_count)\n", + " guess_mark = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 猜数，人猜\n", + "# 增加判断次数，如果猜了大于4次显示不同提示语\n", + "\n", + "import random\n", + "\n", + "# 记录人猜了多少数字\n", + "user_number_list = []\n", + "\n", + "# 记录人猜了几次\n", + "user_guess_count = 0\n", + "\n", + "a = random.randint(1,100)\n", + "\n", + "print('Now you can guess...')\n", + "\n", + "guess_mark = True\n", + "\n", + "# 主循环\n", + "while guess_mark:\n", + " \n", + " if 0 <= user_guess_count <= 4:\n", + " user_number =int(input('please input number:'))\n", + " if 4 < user_guess_count <= 100:\n", + " user_number =int(input('try harder, please input number:'))\n", + " \n", + " user_number_list.append(user_number)\n", + " user_guess_count += 1\n", + " \n", + " if user_number > a:\n", + " print('too big')\n", + " if user_number < a:\n", + " print('too small')\n", + " if user_number == a:\n", + " print('bingo!')\n", + " print('your guess number list:', user_number_list)\n", + " print('you try times:', user_guess_count)\n", + " guess_mark = False\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. 更加复杂的生成随机内容。\n", + "\n", + "可以参考我们开发的python 函数包中的 random 部分，https://fishbase.readthedocs.io/en/latest/fish_random.html\n", + "\n", + "fish_random.gen_random_address(zone)\t通过省份行政区划代码，返回该省份的随机地址\n", + "\n", + "fish_random.get_random_areanote(zone)\t省份行政区划代码，返回下辖的随机地区名称\n", + "\n", + "fish_random.gen_random_bank_card([…])\t通过指定的银行名称，随机生成该银行的卡号\n", + "\n", + "fish_random.gen_random_company_name()\t随机生成一个公司名称\n", + "\n", + "fish_random.gen_random_float(minimum, maximum)\t指定一个浮点数范围，随机生成并返回区间内的一个浮点数，区间为闭区间 受限于 random.random 精度限制，支持最大 15 位精度\n", + "\n", + "fish_random.gen_random_id_card([zone, …])\t根据指定的省份编号、性别或年龄，随机生成一个身份证号\n", + "\n", + "fish_random.gen_random_mobile()\t随机生成一个手机号\n", + "\n", + "fish_random.gen_random_name([family_name, …])\t指定姓氏、性别、长度，返回随机人名，也可不指定生成随机人名\n", + "\n", + "fish_random.gen_random_str(min_length, …)\t指定一个前后缀、字符串长度以及字符串包含字符类型，返回随机生成带有前后缀及指定长度的字符串\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4367456382851478\n", + "6227586313118152\n", + "6216673914496348917\n" + ] + } + ], + "source": [ + "from fishbase.fish_random import *\n", + "\n", + "# 这些银行卡卡号只是符合规范，可以通过最基本的银行卡号规范检查，但是实际上是不存在的\n", + "\n", + "# 随机生成一张银行卡卡号\n", + "print(gen_random_bank_card())\n", + "\n", + "# 随机生成一张中国银行的借记卡卡号\n", + "print(gen_random_bank_card('中国银行', 'CC'))\n", + "\n", + "# 随机生成一张中国银行的贷记卡卡号\n", + "print(gen_random_bank_card('中国银行', 'DC'))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['310104197810177752']\n", + "['310119197001192703']\n", + "['310104199011217980']\n", + "['53092419900907401X', '532424199005059597', '530927199012121779', '530324199004146034', '532101199001157199', '533500199006285057', '533222199007069497', '530827199001145171', '532124199004143539', '530921199001069532', '530702199008158036', '532425199002159118', '532325199008035233', '533422199006189391', '532724199008132776', '530121199012223810', '532326199003308390', '532628199011097238', '533525199006195892', '532524199004067179']\n" + ] + } + ], + "source": [ + "from fishbase.fish_random import *\n", + "\n", + "# 生成假的身份证号码，符合标准身份证的分段设置和校验位\n", + "\n", + "# 指定身份证的地域\n", + "print(gen_random_id_card('310000'))\n", + "\n", + "# 增加指定年龄\n", + "print(gen_random_id_card('310000', age=70))\n", + "\n", + "# 增加年龄和性别\n", + "print(gen_random_id_card('310000', age=30, gender='00'))\n", + "\n", + "# 生成一组\n", + "print(gen_random_id_card(age=30, gender='01', result_type='LIST'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. 猜数程序修改为机器猜，根据每次人返回的结果来调整策略" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 猜数，机器猜\n", + "\n", + "min = 0\n", + "max = 1000\n", + "\n", + "guess_ok_mark = False\n", + "\n", + "while not guess_ok_mark:\n", + "\n", + " cur_guess = int((min + max) / 2)\n", + " print('I guess:', cur_guess)\n", + " human_answer = input('Please tell me big or small:')\n", + " if human_answer == 'big':\n", + " max = cur_guess\n", + " if human_answer == 'small':\n", + " min = cur_guess\n", + " if human_answer == 'ok':\n", + " print('HAHAHA')\n", + " guess_ok_mark = True" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 07 - \346\226\207\344\273\266\347\233\256\345\275\225\346\223\215\344\275\234.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 07 - \346\226\207\344\273\266\347\233\256\345\275\225\346\223\215\344\275\234.ipynb" new file mode 100644 index 0000000..86a1852 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 07 - \346\226\207\344\273\266\347\233\256\345\275\225\346\223\215\344\275\234.ipynb" @@ -0,0 +1,1636 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 7\n", + "\n", + "Python Basic, Lesson 5, v1.0.1, 2016.12 by David.Yi \n", + "Python Basic, Lesson 5, v1.0.2, 2017.03 modified by Yimeng.Zhang \n", + "v1.1, 2020.4 2020.5 edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* 文件和目录操作之一：文件和目录操作\n", + "* 文件和目录操作之二：读写文本文件\n", + "* 思考：搜索电脑上指定路径指定类型的文件" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 文件和目录操作之一\n", + "\n", + "Python 的 os 库有很多和文件、路径和执行系统命令相关的函数。\n", + "\n", + "### os 库常用函数\n", + "\n", + "* os.sep 可以取代操作系统特定的路径分割符\n", + "* os.name 字符串指示你正在使用的平台。比如对于Windows，它是'nt'，而对于Linux/Unix用户，它是'posix' \n", + "* os.getcwd() 函数得到当前工作目录，即当前Python脚本工作的目录路径\n", + "* os.chdir(dirname) 改变工作目录到dirname\n", + "* os.getenv() 用来读取环境变量\n", + "* os.putenv() 用来设置环境变量 \n", + "* os.listdir() 返回指定目录下的所有文件名和目录名 \n", + "* os.remove() 删除一个文件\n", + "* os.system() 运行shell命令\n", + "* os.linesep 字符串给出当前平台使用的行终止符。例如，Windows使用'/r/n'，Mac使用'\\n'。\n", + "* os.mkdir() 建立路径\n", + "* os.rmdir() 删除路径\n", + "\n", + ">不同操作系统在路径和文件处理上有一定差异，这里的举例在 Windows 和 macOS 下都测试过\n", + "\n", + "### 关于文件系统的延展阅读\n", + "* 文件系统介绍 https://zh.wikipedia.org/wiki/%E6%96%87%E4%BB%B6%E7%B3%BB%E7%BB%9F\n", + "* windows 文件系统 FAT、FAT32、NTFS 介绍 https://support.microsoft.com/zh-cn/kb/100108\n", + "* linux 文件系统介绍 http://cn.linux.vbird.org/linux_basic/0230filesystem.php\n", + "\n", + "目前 python 社区逐渐推荐使用pathlib 函数包来进行文件目录相关的访问。和 python 其他一些函数包一样，文件目录操作由于其基本性和发展，造成库比较多，功能有重叠，对于理解python 如何处理文件目录，我们还是沿用了原来的函数包，而 pathlib 是从 python 3.4 版本开始引入并且不断得到完善。之后会有专题来介绍 pathlib。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\\n", + "nt\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# 操作系统路径分隔符\n", + "print(os.sep)\n", + "\n", + "# 操作系统平台名称\n", + "print(os.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 获取当前路径\n", + "os.getcwd()\n", + "\n", + "# 记录一下这是 zhang yimeng 当时执行后的结果：'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic'\n", + "# 这是我现在在 windows 电脑上执行的结果：'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 切换路径\n", + "\n", + "# os.chdir('/Users/david.yi')\n", + "# 切换路径大家要参考上面获取的当前路径，根据自己的电脑做适当调整，替换下面 yijeng.zhang 为自己电脑上的用户名\n", + "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class')\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'draft',\n", + " 'files',\n", + " 'imgs',\n", + " 'imgs_old',\n", + " 'Python Basic Exercise A.ipynb',\n", + " 'Python Basic Exercise B.ipynb',\n", + " 'Python Basic Lesson 01 - 简介.ipynb',\n", + " 'Python Basic Lesson 02 - 变量, print, input.ipynb',\n", + " 'Python Basic Lesson 03 - 循环 for, 字符串.ipynb',\n", + " 'Python Basic Lesson 04 - 列表 list .ipynb',\n", + " 'Python Basic Lesson 05 - 字典 dict, 元组 tuple .ipynb',\n", + " 'Python Basic Lesson 06 - 随机数.ipynb',\n", + " 'Python Basic Lesson 07 - 文件目录操作.ipynb',\n", + " 'Python Basic Lesson 08 - 函数.ipynb',\n", + " 'python_basic_outline.md']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回指定的文件夹包含的文件或文件夹的名字的列表。这个列表以字母顺序。 \n", + "# 不包括 '.' 和'..' 即使它在文件夹中。\n", + "\n", + "import os\n", + "\n", + "# os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", + "os.listdir()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# 注意返回的数据类型是什么，是一个列表\n", + "\n", + "print(type(os.listdir()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16\n" + ] + } + ], + "source": [ + "# 计算目录下有多少文件，因为返回结果是 list，因此各类计算都比较方便\n", + "\n", + "a = os.listdir()\n", + "print(len(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python Basic Exercise A.ipynb\n", + "Python Basic Exercise B.ipynb\n", + "Python Basic Lesson 01 - 简介.ipynb\n", + "Python Basic Lesson 02 - 变量, print, input.ipynb\n", + "Python Basic Lesson 03 - 循环 for, 字符串.ipynb\n", + "Python Basic Lesson 04 - 列表 list .ipynb\n", + "Python Basic Lesson 05 - 字典 dict, 元组 tuple .ipynb\n", + "Python Basic Lesson 06 - 随机数.ipynb\n", + "Python Basic Lesson 07 - 文件目录操作.ipynb\n", + "Python Basic Lesson 08 - 函数.ipynb\n" + ] + } + ], + "source": [ + "# 可以指定路径参数，来列出该目录下所有文件\n", + "\n", + "# list_a = os.listdir('/Users/david.yi')\n", + "list_a = os.listdir('C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook')\n", + "\n", + "# 可以判断各类情况，比如第一个是 P 字母\n", + "for i in list_a:\n", + " if i[0] == 'P':\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\r\\n'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 操作系统换行符\n", + "# 在一些文本文件处理中有用\n", + "\n", + "os.linesep" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ok\n" + ] + } + ], + "source": [ + "# 建立路径\n", + "\n", + "# 切换到当前路径\n", + "os.getcwd()\n", + "os.mkdir('test')\n", + "print('ok')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### os.path 常用函数\n", + "\n", + "* os.path.isdir() 检查给出的路径是否是一个目录\n", + "* os.path.isfile() 检查给出的路径是否一个文件\n", + "* os.path.exists() 检查给出的路径或者文件是否存在\n", + "* os.path.getsize() 获得路径或者文件的大小\n", + "* os.path.getatime() 返回所指向的文件或者目录的最后存取时间\n", + "* os.path.getmtime() 返回所指向的文件或者目录的最后修改时间 \n", + "* os.path.split() 返回一个路径的目录名和文件名\n", + "* os.path.abspath() 返回规范化的绝对路径\n", + "* os.path.isabs() 如果输入是绝对路径，返回True\n", + "* os.path.split() 将路径分割成目录和文件名的二元素元组\n", + "* os.path.splitdrive() 返回（drivername，fpath）元组 \n", + "* os.path.dirname() 返回路径的目录，其实就是 os.path.split(path)的第一个元素\n", + "* os.path.basename() 返回路径最后的文件名，其实就是 os.path.split(path)的第二个元素\n", + "* os.path.splitext() 分离文件名与扩展名，返回(fname,fextension)元组 \n", + "* os.path.join() 将多个路径组合后返回，第一个绝对路径之前的参数将被忽略\n", + "* os.path.commonprefix(list) 返回list中，所有路径共有的最长的路径" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\dev_python\\python_study\\python_study_basic_notebook\n", + "True\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "# 检查给出的路径是否是一个存在的目录，存在\n", + "\n", + "# 确保执行这些测试代码的时候先设定路径到当前 notebook 的路径，或者设定的某个路径\n", + "# 进行路径、文件操作时候，还是要谨慎小心一些\n", + "\n", + "os.chdir('C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook')\n", + "s_dir = os.getcwd()\n", + "print(s_dir)\n", + "print(os.path.isdir(s_dir))\n", + "print(os.path.isdir('C:\\\\Users'))\n", + "print(os.path.isdir('C:\\\\Users222'))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 检查给出的路径是否是一个存在的目录\n", + "# 下面再当前路径下加了个字母，当然是不存在的\n", + "\n", + "os.path.isdir(s_dir + 's')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 文件不是路径，即便文件存在，也返回 False\n", + "\n", + "os.path.isdir(s_dir + 'test.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test.txt\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 检查给出的路径是否一个文件，存在\n", + "import os\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'files/test.txt')\n", + "print(s_file)\n", + "os.path.isfile(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 检查给出的路径是否一个文件，不存在\n", + "\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'test222.txt')\n", + "os.path.isfile(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 路径不是文件，所以返回 False\n", + "\n", + "s_dir = os.getcwd()\n", + "os.path.isfile(s_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n" + ] + } + ], + "source": [ + "# 对路径和文件都通用的检查方式\n", + "\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'test.txt')\n", + "\n", + "print(os.path.exists(s_dir))\n", + "print(os.path.exists(s_file))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 获得路径或者文件的大小\n", + "\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'test.txt')\n", + "\n", + "os.path.getsize(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4096" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 获得路径或者文件的大小\n", + "\n", + "os.path.getsize(s_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1586952838.4941905" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回所指向的文件或者目录的最后存取时间\n", + "# 返回的时间格式可能和大家想象的不太一样\n", + "\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'files/test.txt')\n", + "\n", + "os.path.getatime(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-04-25 15:21:44\n" + ] + } + ], + "source": [ + "# 返回所指向的文件或者目录的最后存取时间\n", + "import os\n", + "import time\n", + "\n", + "# 将日期格式化\n", + "dt = time.localtime(os.path.getatime(s_dir))\n", + "# print(dt)\n", + "print(time.strftime('%Y-%m-%d %H:%M:%S', dt))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1586952838.4941905" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回所指向的文件或者目录的最后修改时间\n", + "\n", + "s_file = os.path.join(s_dir, 'files/test.txt')\n", + "\n", + "os.path.getmtime(s_file) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Wed Apr 15 20:13:58 2020'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回所指向的文件或者目录的最后修改时间\n", + "# 使用 time.ctime() 方法来格式化日期\n", + "\n", + "import time, os\n", + "\n", + "s_file = os.path.join(s_dir, 'files/test.txt')\n", + "\n", + "time.ctime(os.path.getmtime(s_file))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/tt1211.txt'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回规范化的绝对路径\n", + "# 会自动补齐完整路径,不管文件是否存在\n", + "\n", + "os.path.abspath('tt1211.txt') " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# 如果输入是绝对路径，返回True\n", + "\n", + "print(os.path.isabs('test.txt'))\n", + "print(os.path.isabs('/Users/yijun/test.txt'))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\dev_python\\python_study\\python_study_basic_notebook\\test.txt\n" + ] + }, + { + "data": { + "text/plain": [ + "('C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook', 'test.txt')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回一个路径的目录名和文件名\n", + "\n", + "# os.chdir('/Users/david.yi/Documents/dev/python_study/python_basic')\n", + "# os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", + "s_dir = os.getcwd()\n", + "s_file = os.path.join(s_dir, 'test.txt')\n", + "print(s_file)\n", + "\n", + "# 分拆路径和文件名\n", + "os.path.split(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/yijun'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回路径的目录，其实就是 os.path.split(path)的第一个元素\n", + "\n", + "os.path.dirname('/Users/yijun/test.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'test.txt'" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 返回路径最后的文件名，其实就是 os.path.split(path)的第二个元素\n", + "\n", + "os.path.basename(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\test', '.txt')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 分离文件名与扩展名，返回(fname,fextension)元组 \n", + "\n", + "os.path.splitext(s_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\files\\\\test.txt'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 将多个路径组合后返回，第一个绝对路径之前的参数将被忽略\n", + "\n", + "# os.path.join('/Users/yijun', 'test.txt')\n", + "os.path.join('C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\files', 'test.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + 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"/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson07-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson13-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/面向对象的编程之四-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson08-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/面向对象的编程之三-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson06-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson12-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson09-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/面向对象的编程之二-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/Python Basic Lesson 15 - 图片处理 Pillow-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson11-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson05-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/面向对象的编程-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson10-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/Python Basic Lesson 12 - 列表生成式-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson04-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/Python Basic Lesson 13 - 完整的 for 语句结构和 pprint-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/python_basic_2/.ipynb_checkpoints/python_basic_L2_lesson14-checkpoint.ipynb\n", + 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Exercise A-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/draft/单元测试 unittest.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Exercise B.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.DS_Store\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/窗口界面编程 tk.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/pycharm.png\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/pycharm_202001.png\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install_5.png\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/.DS_Store\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/guido-headshot-2019.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/jupyter.png\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install_1.jpeg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/anaconda.png\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install_2.jpeg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install_3.jpeg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install_4.jpeg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs/python_install.jpeg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs_old\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/imgs_old/python_use.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Exercise A.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 10 - 函数参数和匿名函数.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/test_thumbnail.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/__pycache__\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/__pycache__/parsetab.cpython-37.pyc\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 14 - 访问网络初步和requests包.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 06 - 随机数.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 02 - 变量, print, input.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 04 - 列表 list.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 07 - 文件目录操作.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 01 - 简介.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 13 - 完整的 for 语句结构和 pprint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 11 - 集合库 collections.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 12 - 列表生成式.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/ply\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/ply/parsetab.py\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/ply/parser.out\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/ply/calc.py\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test2.txt\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_thumbnail.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_rotate.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/list_dump.txt\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_enhance.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_edge.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test.txt\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_drawline.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_crop.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test_blur.jpg\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/baidu_logo.gif\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 03 - 循环 for, 字符串-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 04 - 列表 list -checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 02 - 变量, print, input-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 01 - 简介-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 03 - for 循环 range 字符串-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 07 - 文件目录操作-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 02 Exercise-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 02 - 变量 print input-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 03 Exercise-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 14 - 访问网络初步和requests包-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 04 - list -checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 06 - 随机数-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/python_basic_lesson_04-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 05 - 字典 dict, 元组 tuple -checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/python_basic_lesson_03a-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 05 - dict 字典 tuple 元组-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/Python Basic Lesson 08 - 函数简介-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/.ipynb_checkpoints/python_basic_lesson_05-checkpoint.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 08 - 函数简介.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/异常处理.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 05 - 字典 dict, 元组 tuple.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 09 - 日期函数.ipynb\n", + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/Python Basic Lesson 16 - 函数式编程.ipynb\n" + ] + } + ], + "source": [ + "# 遍历一个目录下的所有文件\n", + "\n", + "import os \n", + "\n", + "def list_dir(root_dir): \n", + " for lists in os.listdir(root_dir): \n", + " path = os.path.join(root_dir, lists) \n", + " print(path)\n", + " if os.path.isdir(path): \n", + " list_dir(path) \n", + "\n", + "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", + "list_dir(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft 4096\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\Python Basic Lesson 05a.ipynb 4386\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\python_basic_lesson_04.ipynb 21033\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\python_basic_lesson_04_exercise.ipynb 6449\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\python_basic_lesson_05.ipynb 46070\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\python_basic_lesson_05_exercise.ipynb 3328\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\python_basic_lesson_06.ipynb 35160\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\函数的不同参数.ipynb 9128\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\列表切片.ipynb 4719\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\匿名函数.ipynb 2241\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\draft\\日期处理.ipynb 9834\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\files 0\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\files\\list_dump.txt 52\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\files\\test.txt 17\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\files\\test2.txt 51\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs 4096\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\anaconda.png 834508\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\guido-headshot-2019.jpg 566262\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\jupyter.png 105782\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\pycharm.png 192052\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\pycharm_202001.png 370526\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install.jpeg 21799\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install_1.jpeg 28779\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install_2.jpeg 22304\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install_3.jpeg 31846\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install_4.jpeg 15478\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs\\python_install_5.png 33686\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs_old 0\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\imgs_old\\python_use.jpg 102903\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Exercise A.ipynb 5354\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Exercise B.ipynb 7618\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 01 - 简介.ipynb 24389\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 02 - 变量, print, input.ipynb 15027\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 03 - 循环 for, 字符串.ipynb 11220\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 04 - 列表 list .ipynb 12606\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 05 - 字典 dict, 元组 tuple .ipynb 21842\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 06 - 随机数.ipynb 11817\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 07 - 文件目录操作.ipynb 41351\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 08 - 函数.ipynb 11458\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\python_basic_outline.md 1605\n" + ] + } + ], + "source": [ + "# 遍历一个目录下的所有文件\n", + "# 显示文件的字节数，用 getsize() \n", + "\n", + "import os \n", + "\n", + "def list_dir(root_dir): \n", + " for lists in os.listdir(root_dir): \n", + " path = os.path.join(root_dir, lists) \n", + " if lists[0:1] != '.': \n", + " filesize = os.path.getsize(path)\n", + " print(path, ' ', filesize)\n", + " if os.path.isdir(path): \n", + " list_dir(path) \n", + "\n", + "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", + "#list_dir('/Users/david.yi/Documents/dev/dig/doc')\n", + "list_dir(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Exercise A.ipynb 8620\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Exercise B.ipynb 13968\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 01 - 简介.ipynb 24745\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 02 - 变量, print, input.ipynb 15893\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 03 - 循环 for, 字符串.ipynb 11789\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 04 - 列表 list.ipynb 15143\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 05 - 字典 dict, 元组 tuple.ipynb 24665\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 06 - 随机数.ipynb 13111\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 07 - 文件目录操作.ipynb 55173\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 08 - 函数简介.ipynb 15008\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 09 - 日期函数.ipynb 11459\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 10 - 函数参数和匿名函数.ipynb 14624\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 11 - 集合库 collections.ipynb 34546\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 12 - 列表生成式.ipynb 7597\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 13 - 完整的 for 语句结构和 pprint.ipynb 11247\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 14 - 访问网络初步和requests包.ipynb 44865\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 15 - 图片处理 Pillow.ipynb 2806102\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\Python Basic Lesson 16 - 函数式编程.ipynb 16314\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\窗口界面编程 tk.ipynb 2968\n" + ] + } + ], + "source": [ + "# 遍历一个目录下的所有文件\n", + "# 过滤 . 开头的文件，一般是系统文件\n", + "# 显示文件的字节数\n", + "# 显示指定后缀 ipynb 的文件，引入 endswith 用法\n", + "\n", + "import os \n", + "\n", + "def list_dir(root_dir): \n", + " for lists in os.listdir(root_dir): \n", + " path = os.path.join(root_dir, lists) \n", + " if lists[0:1] != '.' and lists.endswith('.ipynb'):\n", + " filesize = os.path.getsize(path)\n", + " print(path, ' ', filesize)\n", + " if os.path.isdir(path): \n", + " list_dir(path) \n", + "\n", + "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", + "\n", + "# list_dir('/Users/david.yi/Documents/dev/dig/n_query')\n", + "list_dir(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\guido-headshot-2019.jpg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install.jpeg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install_1.jpeg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install_2.jpeg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install_3.jpeg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install_4.jpeg', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\anaconda.png', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\jupyter.png', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\pycharm.png', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\pycharm_202001.png', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs\\\\python_install_5.png', 'C:\\\\dev_python\\\\python_study\\\\python_study_basic_notebook\\\\imgs_old\\\\python_use.jpg']\n" + ] + } + ], + "source": [ + "# 写一个可以搜索硬盘上指定路径指定类型的文件\n", + "# os.walk() 返回一个三元tuple(root, dirnames, filenames)\n", + "# 第一个为起始路径，String\n", + "# 第二个为起始路径下的文件夹, List\n", + "# 第三个是起始路径下的文件. List\n", + "\n", + "# fnmatch python自带的文件名模式匹配包\n", + "# https://docs.python.org/zh-tw/3/library/fnmatch.html\n", + "import fnmatch\n", + "import os\n", + "\n", + "images = ['*.jpg', '*.jpeg', '*.png', '*.tif', '*.tiff']\n", + "matches = []\n", + "\n", + "# for root, dirnames, filenames in os.walk('/Users/david.yi/Documents/dev/'):\n", + "for root, dirnames, filenames in os.walk(os.getcwd()):\n", + " for extensions in images:\n", + " for filename in fnmatch.filter(filenames, extensions):\n", + " matches.append(os.path.join(root, filename))\n", + "\n", + "print(matches)\n", + "\n", + "# import os\n", + "# for root, dirnames, filenames in os.walk('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic'):\n", + "# print(filenames)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 文件和目录操作之二\n", + "\n", + "读写文件是最常见的IO操作。Python内置了读写文件的函数。\n", + "\n", + "读写文件前，我们先必须了解一下，在磁盘上读写文件的功能都是由操作系统提供的，现代操作系统不允许普通的程序直接操作磁盘，所以，读写文件就是请求操作系统打开一个文件对象，然后，通过操作系统提供的接口从这个文件对象中读取数据，或者把数据写入这个文件对象。\n", + "\n", + "### 读文件\n", + "\n", + "函数 `open()` 返回 文件对象，通常的用法需要两个参数：`open(filename, mode)`。分别是文件名和打开模式\n", + "\n", + "在做下面的例子前，我们要创建一个 `test.txt` 文件，并且保证其中的内容是如下样式，包含三行内容：\n", + "\n", + "> hello\n", + "\n", + "> hi\n", + "\n", + "> byebye\n", + "\n", + "文件保存在可以访问的目录，我们用到的文件都保存在 notebook 下面的 files 目录；\n", + "\n", + "> 使用 jupyter 可以直接新建 Text File，来完成建立和编辑文本文件" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\dev_python\\python_study\\python_study_basic_notebook\n", + "C:\\dev_python\\python_study\\python_study_basic_notebook\\files/test.txt\n", + "hello\n", + "hi\n", + "byebye\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# 获得当前路径\n", + "s_dir = os.getcwd()\n", + "\n", + "print(s_dir)\n", + "\n", + "# 拼接完整文件名\n", + "filename = os.path.join(s_dir, 'files/test.txt')\n", + "\n", + "print(filename)\n", + "\n", + "# 和读写文件打交道是一件需要小心的事情，这里用了 try-finally 的方式来避免出错\n", + "try:\n", + " # 打开文件\n", + " f = open(filename, 'r')\n", + " print(f.read())\n", + "finally:\n", + " if f:\n", + " f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "hi\n", + "byebye\n" + ] + } + ], + "source": [ + "# 简化调用方式\n", + "# 省却了 try...finally，会有 with 来自动控制保证可以关闭文件等\n", + "\n", + "with open(filename, 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['hello\\n', 'hi\\n', 'byebye']\n" + ] + } + ], + "source": [ + "# 读入文件所有的内容\n", + "# 这样操作对于一般的文件没啥问题，太大的文件不能这样读，内存会不够\n", + "\n", + "with open(filename, 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + "print(type(lines))\n", + "print(lines)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "\n", + "hi\n", + "\n", + "byebye\n" + ] + } + ], + "source": [ + "# 把读入的文件显示出来\n", + "\n", + "for i in lines:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "\n", + "hi\n", + "\n", + "byebye\n" + ] + } + ], + "source": [ + "# 更简单的按行读取文件内容方法\n", + "\n", + "with open(filename, 'r') as f:\n", + " for eachline in f:\n", + " print(eachline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 写文件\n", + "\n", + "写文件和读文件是一样的，唯一区别是调用 `open()` 函数时，传入标识符 `'w'` 或者 `'wb'` 表示写文本文件或写二进制文件。\n", + "\n", + "r 以读方式打开\n", + "w 以写方式打开\n", + "a 以追加模式打开（必要时候创建新文件）" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook/files/test2.txt\n", + "Hello, World!\n", + "Hello, Shanghai!\n", + "Hello, CHINA!\n", + "\n" + ] + } + ], + "source": [ + "# 写文件\n", + "import os\n", + "\n", + "# 获得当前路径\n", + "s_dir = os.getcwd()\n", + "\n", + "# 拼接完整文件名\n", + "filename= os.path.join(s_dir, 'files/test2.txt')\n", + "print(filename)\n", + "\n", + "# 换行符\n", + "br = os.linesep\n", + "\n", + "# 写文件\n", + "with open(filename, 'w') as f:\n", + " f.write('Hello, World!' + br)\n", + " f.write('Hello, Shanghai!' + br)\n", + " f.write('Hello, CHINA!' + br)\n", + " f.close()\n", + " \n", + "with open(filename, 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 操作系统和文件系统差异处理\n", + "\n", + "如果要写一个 windows 和 macOS 都能用的文件处理软件，很多时候要考虑操作系统带来的差异\n", + "\n", + "* `linesep` 文件中分隔行的字符串；\n", + "* `path.sep` 分割文件路径名的字符串；\n", + "* `curdir` 当前工作目录的字符串；\n", + "* `pardir` 当前工作目录的父目录字符串；" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 使用 glob 包查找文件\n", + "\n", + "glob 是 python 自己带的一个文件操作相关模块，很简洁，用它可以查找符合自己目的的文件，就类似于 Windows 下的文件搜索，而且也支持通配符: `*,?,[]` 这三个通配符，\\* 代表0个或多个字符，? 代表一个字符，[] 匹配指定范围内的字符，如[0-9]匹配数字。\n", + "\n", + "glob 的主要方法也叫 glob，该方法返回所有匹配的文件路径列表，该方法需要一个参数用来指定匹配的路径字符串。\n", + "\n", + "> 2020.5.10 之前的教程我们推荐试用 glob 函数包来进行查找文件，随着 python 的演进，现在推荐试用 pathlib 函数包来进行文件操作，来获得更加好的兼容性和性能，在pathlib 函数包中同样有 glob 函数，下面简单的演示了试用 pathlib 中的 glob 来遍历搜索文件。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('Python Basic Lesson 15 - 图片处理 Pillow.ipynb'),\n", + " PosixPath('Python Basic Lesson 03 - 循环 for, 字符串.ipynb'),\n", + " PosixPath('Python Basic Exercise B.ipynb'),\n", + " PosixPath('窗口界面编程 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PosixPath('.ipynb_checkpoints/python_basic_lesson_04-checkpoint.ipynb'),\n", + " PosixPath('.ipynb_checkpoints/Python Basic Lesson 05 - 字典 dict, 元组 tuple -checkpoint.ipynb'),\n", + " PosixPath('.ipynb_checkpoints/python_basic_lesson_03a-checkpoint.ipynb'),\n", + " PosixPath('.ipynb_checkpoints/Python Basic Lesson 05 - dict 字典 tuple 元组-checkpoint.ipynb'),\n", + " PosixPath('.ipynb_checkpoints/Python Basic Lesson 08 - 函数简介-checkpoint.ipynb'),\n", + " PosixPath('.ipynb_checkpoints/python_basic_lesson_05-checkpoint.ipynb')]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pathlib\n", + "\n", + "list(pathlib.Path('.').glob('**/*.ipynb'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们在 fishbase 的 fish_file 包内，也实现了一个搜索文件的功能，也使用了 python 自带的 pathlib 函数包。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# v1.0.14 edit by Hu Jun, edit by Jia Chunying, #38\n", + "# v1.0.17 edit by Hu Jun, #212\n", + "# v1.3 edit by David Yi, #272\n", + "def find_files(path, exts=None):\n", + " \"\"\"\n", + " 查找路径下的文件，返回指定类型的文件列表\n", + "\n", + " :param:\n", + " * path: (string) 查找路径\n", + " * exts: (list) 文件类型列表，默认为空\n", + "\n", + " :return:\n", + " * files_list: (list) 文件列表\n", + "\n", + " 举例如下::\n", + "\n", + " print('--- find_files demo ---')\n", + " path1 = '/root/fishbase_issue'\n", + " all_files = find_files(path1)\n", + " print(all_files)\n", + " exts_files = find_files(path1, exts=['.png', '.py'])\n", + " print(exts_files)\n", + " print('---')\n", + "\n", + " 执行结果::\n", + "\n", + " --- find_files demo ---\n", + " ['/root/fishbase_issue/test.png', '/root/fishbase_issue/head.jpg','/root/fishbase_issue/py/man.png'\n", + " ['/root/fishbase_issue/test.png', '/root/fishbase_issue/py/man.png']\n", + " ---\n", + "\n", + " \"\"\"\n", + " files_list = []\n", + " for root, dirs, files in os.walk(path):\n", + " for name in files:\n", + " files_list.append(os.path.join(root, name))\n", + "\n", + " if exts is not None:\n", + " return [file for file in files_list if pathlib.Path(file).suffix in exts]\n", + "\n", + " return files_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "## 思考\n", + "\n", + "* 想一下，如果要搜索指定目录下的 office 文件，包括 word、excel 和 powerpoint 等，然后将搜索到的文件名放在一个字典中，字典的 key 是 doc、xls、ppt 这些后缀，而 value 则是搜索到的文件的绝对路径名的列表；这个题目为我们之后的桌面文档整理程序做准备。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 08 - \345\207\275\346\225\260.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 08 - \345\207\275\346\225\260.ipynb" new file mode 100644 index 0000000..1325795 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 08 - \345\207\275\346\225\260.ipynb" @@ -0,0 +1,673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 8\n", + "\n", + "* v1.1, 2020.4 2020.5 edit by David Yi\n", + "\n", + " \n", + "### 本课内容要点\n", + "\n", + "* 函数介绍和用法\n", + "* 思考一下：剪刀石头布" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 函数用法\n", + "\n", + "函数是组织好的、可重复使用的、用来实现单一或相关联功能的代码段。函数能提高应用的模块性，和代码的重复利用率。\n", + "\n", + "python 提供了许多内建函数，比如 print(), max()；\n", + "\n", + "python 提供的大量内建函数，可以满足绝大多数基本的功能，有时候 python 语言是瑞士军刀，就是这个意思。\n", + "\n", + "函数可以大大简化重复代码，降低工作量，减少错误。\n", + "\n", + "函数的基本语法\n", + "\n", + "```\n", + "def 函数名称( 参数 ): \n", + "\t函数语句 \n", + "\treturn [值]\n", + "```\n", + "\n", + "虽然 Python 内置了大量函数，但是大多数提供的是基本功能，有时候还是不能满足真正程序的需要；\n", + "函数可以做的很复杂，发展为包(Package)。Python 目前有七万多个各类功能的包，几乎都可以可以免费使用在自己的程序中，这是最近几年 Python 飞速发展的原因之一。\n", + "学会使用函数来简化程序之后，可以为以后学习面向对象的编程方法打好基础，也可以真正的开始使用 Python 来解决各类问题。\n", + "\n", + "> 2017年编写这个文档的时候，python 有大约7万多个包，现在，2020年4月，我看了一下 pypi，python 包的大本营，目前有 23万个包了。增长非常迅速。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50.24\n", + "113.03999999999999\n", + "98.82239400000002\n" + ] + } + ], + "source": [ + "# 计算圆的面积\n", + "# 不用函数的话，每次需要写一些重复的代码\n", + "\n", + "r1 = 4\n", + "r2 = 6\n", + "r3 = 5.61\n", + "s1 = 3.14 * r1 * r1\n", + "s2 = 3.14 * r2 * r2\n", + "s3 = 3.14 * r3 * r3\n", + "\n", + "print(s1)\n", + "print(s2)\n", + "print(s3)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50.24\n", + "113.03999999999999\n", + "98.82239400000002\n" + ] + } + ], + "source": [ + "# 定义一个函数，用来计算圆面积\n", + "# 输入 半径，返回 圆面积\n", + "\n", + "def func1(r):\n", + " s = 3.14 * r * r\n", + " return s\n", + "\n", + "print(func1(4))\n", + "print(func1(6))\n", + "print(func1(5.61))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "9\n" + ] + } + ], + "source": [ + "# 先来看看 python 内置的获取最大值的函数\n", + "\n", + "print(max(1,5))\n", + "\n", + "print(max(1,4,6,3,9))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "# 请注意，函数体内部的语句在执行时，一旦执行到return时，函数就执行完毕，并将结果返回。\n", + "# 因此，函数内部通过条件判断和循环可以实现非常复杂的逻辑。\n", + "# 从优雅的写法来说，不是很推荐一个函数内有多个返回点的方式，但是有时候这样写的确很方便\n", + "\n", + "def my_max(a,b):\n", + " if a > b:\n", + " return a\n", + " else:\n", + " return b\n", + " \n", + "print(my_max(3,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果上面的函数要修改成只有一个 return 应该怎么修改呢？" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "def my_max(a,b):\n", + " if a > b:\n", + " c = a\n", + " else:\n", + " c = b\n", + " \n", + " return c\n", + "\n", + "print(my_max(3,6))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "100\n" + ] + } + ], + "source": [ + "# *number 这样的写法，可以理解为传入任意数目个参数，包括0个参数，就是在参数名称前面加上一个 * \n", + "# 为了简化程序逻辑，返回的结果现在是假的，后面我们会再修改\n", + "\n", + "def my_max(*number):\n", + " print(type(number))\n", + " return 100\n", + "\n", + "print(my_max(1,5,30,14,25))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n" + ] + } + ], + "source": [ + "# 用列表来排序，来完成这个取得任意值的最大值的函数\n", + "\n", + "def my_max(*number):\n", + " a_list = list(number)\n", + " a_list.sort()\n", + " a = a_list[-1]\n", + " return a\n", + "\n", + "print(my_max(1,5,30,14,25))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(30, 1)\n" + ] + } + ], + "source": [ + "# Python的函数返回多值其实就是返回一个tuple，但语法上可以省略括号，写起来更方便\n", + "\n", + "# 同时返回最大值和最小值\n", + "\n", + "def my_max_min(*number):\n", + " a_list = list(number)\n", + " a_list.sort()\n", + " my_max = a_list[-1]\n", + " my_min = a_list[0]\n", + " return my_max, my_min\n", + "\n", + "print(my_max_min(1,5,30,14,25)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* 写一个函数，输入一个数字，判断其是否是偶数，是偶数的话，返回 True，反之返回 False\n", + "* 写一个函数，输入一个英文单词，返回其中的元音字母的个数" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input a number:4\n", + "True\n" + ] + } + ], + "source": [ + "# 判断奇偶数\n", + "\n", + "a = int(input('Input a number:'))\n", + "\n", + "def oushu():\n", + " if int(a/2) == a/2:\n", + " return True\n", + " #print('这是个偶数')\n", + " else:\n", + " return False\n", + " #print('这是个奇数')\n", + " \n", + "result = oushu() \n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "plese input a word:reason\n", + "3\n" + ] + } + ], + "source": [ + "# 判断元音\n", + "\n", + "def reason(s):\n", + " b = ['a','e','i','o','u']\n", + " c = 0\n", + " for i in list(s):\n", + " if i in b:\n", + " # c=c+1 的简约写法\n", + " c += 1\n", + " return c\n", + "\n", + "a = input('plese input a word:')\n", + "print(reason(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 5, 10]\n" + ] + } + ], + "source": [ + "# 计算一个正整数的因数\n", + "\n", + "def yinshu(number):\n", + " b = []\n", + " for i in range(1,number+1):\n", + " if number % i == 0:\n", + " b.append(i)\n", + " return b\n", + "\n", + "print(yinshu(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n", + "-3\n" + ] + } + ], + "source": [ + "# 找到最大数 #1\n", + "\n", + "def find_max(list1):\n", + " \n", + " # max = float('-inf') 表示负无穷大\n", + " max = float('-inf')\n", + " for x in list1:\n", + " if x > max:\n", + " max = x\n", + " return max\n", + "\n", + "print(find_max([-20,1,6,7,20,5]))\n", + "print(find_max([-20,-3,-6,-7,-5]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "# 找到最大数 #2\n", + "# 使用递归，递归是编程中非常有效的一种解决问题的方式，将内在逻辑分解为一个固定的逻辑，而调用参数来自于上一次的执行结果\n", + "# 使用递归要注意初始化和防止递归陷入死循环\n", + "# 排序、斐波那契数列这些都是递归的经典实现\n", + "\n", + "def find_max(list1):\n", + " if len(list1) == 1:\n", + " return list1[0]\n", + " v1 = list1[0]\n", + " v2 = find_max(list1[1:])\n", + " if v1 > v2:\n", + " return v1\n", + " else:\n", + " return v2\n", + "\n", + "print(find_max([1,6,7,20,5]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考一下\n", + "\n", + "1. 写一个简单的剪刀石头布的游戏；" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:剪刀 1:石头 2:布\n", + "你出了:2\n", + "电脑出了 布\n", + "draw\n" + ] + } + ], + "source": [ + "# 简单的剪刀石头布\n", + "\n", + "import random\n", + "\n", + "FIRST = 0\n", + "SECOND = 1 \n", + "BOTH = 2 \n", + "\n", + "t1 = ('剪刀', '石头', '布')\n", + "t2 = ('human win', 'computer win', 'draw')\n", + "\n", + "def which_win(i1, i2):\n", + " if i1 == 0 and i2 == 1:\n", + " return SECOND\n", + " if i1 == 0 and i2 == 2:\n", + " return FIRST\n", + " if i1 == 1 and i2 == 0:\n", + " return FIRST\n", + " if i1 == 1 and i2 == 2:\n", + " return SECOND\n", + " if i1 == 2 and i2 == 0:\n", + " return SECOND\n", + " if i1 == 2 and i2 == 1:\n", + " return FIRST\n", + " if i1 == i2:\n", + " return BOTH\n", + "\n", + "print('0:剪刀 1:石头 2:布')\n", + "human = int(input('你出了:'))\n", + "\n", + "c_index = random.randint(0,2)\n", + "computer = t1[c_index]\n", + "\n", + "print(\"电脑出了\",computer)\n", + "\n", + "print(t2[which_win(human, c_index)])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 scissors , 1 stone , 2 cloth , please input:1\n", + "human: 1 stone\n", + "computer: 0 scissors\n", + "human win\n" + ] + } + ], + "source": [ + "# 优化的剪刀石头布程序\n", + "\n", + "import random\n", + "\n", + "FIRST = 0\n", + "SECOND = 1 \n", + "BOTH = 2 \n", + "\n", + "t1 = ('scissors', 'stone', 'cloth')\n", + "t2 = ('human win', 'computer win', 'draw')\n", + "\n", + "table = {'01':1, '02':0, '10':0, '12':1, '20':1, '21':0, '00':2, '11':2, '22':2}\n", + "\n", + "def which_win(i1, i2):\n", + " s = i1 + i2\n", + " return table.get(s)\n", + "\n", + "# 显示规则\n", + "for i, s in enumerate(t1):\n", + " print(i, s, ', ', end='')\n", + " \n", + "human = input('please input:')\n", + "h_index = int(human)\n", + "human_choice = t1[h_index]\n", + "\n", + "c_index = random.randint(0,2)\n", + "computer_choice = t1[c_index]\n", + "\n", + "print('human:',h_index,human_choice)\n", + "print('computer:',c_index, computer_choice)\n", + "print(t2[which_win(str(h_index), str(c_index))])\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 scissors , 1 stone , 2 cloth , please input:2\n", + "1 stone\n", + "human win\n", + "human:computer 1 : 0\n", + "\n", + "0 scissors , 1 stone , 2 cloth , please input:1\n", + "0 scissors\n", + "human win\n", + "human:computer 2 : 0\n", + "\n", + "0 scissors , 1 stone , 2 cloth , please input:2\n", + "2 cloth\n", + "draw\n", + "human:computer 2 : 0\n", + "\n", + "0 scissors , 1 stone , 2 cloth , please input:1\n", + "2 cloth\n", + "computer win\n", + "human:computer 2 : 1\n", + "\n", + "0 scissors , 1 stone , 2 cloth , please input:1\n", + "1 stone\n", + "draw\n", + "human:computer 2 : 1\n", + "\n", + "0 scissors , 1 stone , 2 cloth , please input:1\n", + "0 scissors\n", + "human win\n", + "human:computer 3 : 1\n", + "\n", + "final human win\n" + ] + } + ], + "source": [ + "# 五局三胜的剪刀石头布程序\n", + "\n", + "import random\n", + "\n", + "FIRST = 0\n", + "SECOND = 1 \n", + "BOTH = 2 \n", + "\n", + "t1 = ('scissors', 'stone', 'cloth')\n", + "t2 = ('human win', 'computer win', 'draw')\n", + "\n", + "table = {'01':1, '02':2, '10':0, '12':1, '20':1, '21':0, '00':2, '11':2, '22':2}\n", + "\n", + "def which_win(i1, i2):\n", + " s = i1 + i2\n", + " return table.get(s)\n", + "\n", + "mark = True\n", + "computer_win = 0\n", + "human_win = 0\n", + "\n", + "while mark:\n", + "\n", + " # 显示规则\n", + " for i, s in enumerate(t1):\n", + " print(i, s, ', ', end='')\n", + " \n", + " human = input('please input:')\n", + "\n", + " c_index = random.randint(0,2)\n", + " computer = t1[c_index]\n", + "\n", + " print(c_index, computer)\n", + "\n", + " who_win = which_win(human, str(c_index))\n", + " \n", + " if who_win == 0:\n", + " human_win += 1\n", + " if who_win == 1:\n", + " computer_win += 1\n", + " \n", + " print(t2[which_win(human, str(c_index))])\n", + " print('human:computer ', human_win, ':', computer_win)\n", + " print()\n", + " \n", + " if (human_win == 3) or (computer_win == 3):\n", + " mark = False\n", + "\n", + "if human_win == 3:\n", + " print('final human win')\n", + "else:\n", + " print('final computer win')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "思考一下：上面的这个五局三胜的用户交互界面实在有点丑陋，所以我们看看能不能做得更加好一些，信息显示更加完善，提升用户体验。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 09 - \346\227\245\346\234\237\345\207\275\346\225\260.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 09 - \346\227\245\346\234\237\345\207\275\346\225\260.ipynb" new file mode 100644 index 0000000..640c50a --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 09 - \346\227\245\346\234\237\345\207\275\346\225\260.ipynb" @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 9\n", + "\n", + "* Python Basic, Lesson 4, v1.0.1, 2016.12 by David.Yi \n", + "* Python Basic, Lesson 4, v1.0.2, 2017.03 modified by Yimeng.Zhang\n", + "* v1.1,2020.4 5, edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* 日期函数库 datetime 用法介绍，datetime、time 等库的介绍，获得日期，字符串和日期转换，日期格式介绍，日期加减计算\n", + "* 思考一下：距离某个日期还有几天\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 日期处理\n", + "\n", + "datetime 是 Python处理日期和时间的标准库，用于获取日期和进行日期计算等;\n", + "\n", + "Python 的日期相关的标准库比较多（略有杂乱），有 datetime, time, calendar等，这是 python 长期发展过程中造成的问题。也有很好的第三方库来解决 python 日期库比较多并且功能略有重叠的问题。\n", + "\n", + "datetime 库包括 date日期，time时间， datetime日期和时间，tzinfo时区，timedelt 时间跨度计算等主要对象。\n", + "\n", + "获取当前日期和时间：`now = datetime.now()`\n", + "\n", + "日期戳和日期的区别，日期戳更加精确，日期只是年月日,根据需要使用，大多数情况下只需要日期即可；\n", + "\n", + "Time 对于时间的处理更加精确，用时间戳的表达方式；\n", + "\n", + "时间戳定义为格林威治时间1970年01月01日00时00分00秒起至现在的总秒数，时间戳是惟一的。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-29 13:06:54.013485\n", + "2020-05-29\n", + "1590728814.0138612\n" + ] + } + ], + "source": [ + "# 显示今天日期\n", + "# 可以比较一下三种结果的差异\n", + "\n", + "from datetime import datetime, date\n", + "import time\n", + "\n", + "print(datetime.now())\n", + "print(date.today())\n", + "print(time.time())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# 各种日期时间类型的数据类型\n", + "\n", + "print(type(datetime.now()))\n", + "print(type(date.today()))\n", + "print(type(time.time()))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n", + "1587821102.8680825\n" + ] + } + ], + "source": [ + "# 连续运行显示时间戳，看看时间戳差了多少毫秒\n", + "# 因为电脑运行速度太快，没有意外的话，可能看到的时间是一样的\n", + "\n", + "for i in range(10):\n", + " print(time.time())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.518088\n", + "1589289883.5190854\n", + "1589289883.5190854\n", + "1589289883.5190854\n" + ] + } + ], + "source": [ + "# 如果循环中只是需要记数，而不需要使用变量，for 循环可以写的更加简洁一点点\n", + "\n", + "for _ in range(10):\n", + " print(time.time())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 0.012572288513183594\n" + ] + } + ], + "source": [ + "# 用 time() 来计时，算10万次平方，看看哪台电脑速度快\n", + "# 算是一个简单的性能检测程序\n", + "\n", + "import time\n", + "\n", + "a = time.time()\n", + "for i in range(100000):\n", + " j = i * i \n", + "\n", + "b = time.time()\n", + " \n", + "print('time:', b-a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### 日期库-字符串和日期的转换\n", + "\n", + "字符串转化为日期：datetime.strptime()\n", + "\n", + "日期转换为字符串：datetime.strftime()\n", + "\n", + "日期字符串格式，举例\n", + "\n", + "```python\n", + "cday1 = datetime.now().strftime('%a, %b %d %H:%M')\n", + "cday2 = datetime.now().strftime('%A, %b %d %H:%M, %j')\n", + "cday3 = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", + "```\n", + "\n", + "#### 日期库 – 日期格式说明\n", + "\n", + "* %a 英文星期的简写 (shorthand of week in English)\n", + "* %A 英文星期的完整拼写 (longhand of week in English)\n", + "* %b 英文月份的简写 (shorthand of month in English)\n", + "* %B 英文月份的完整拼写 (longhand of month in English)\n", + "* %c 本地当前的日期与时间 (current local date and time)\n", + "* %d 日期数, 1-31之间 (date, between 1-31))\n", + "* %H 小时数, 00-23之间 (hour, between 00-23))\n", + "* %I 小时数, 01-12之间 (hour, between 01-12)\n", + "* %m 月份, 01-12之间 (month, between 01-12)\n", + "* %M 分钟数, 01-59之间 (minute, 01-59)\n", + "* %j 本年从第1天开始计数到当天的天数 (total days from 1st day of this year till now)\n", + "* %w 星期数, 0-6之间(0是周日) (day of the week, between 0-6, 0=Sunday)\n", + "* %W 当天属于本年的第几周,周一作为一周的第一天进行计算 (week of the year, starting with Monday )\n", + "* %x 本地的当天日期 (local date)\n", + "* %X 本地的当前时间 (local time)\n", + "* %y 年份,0-99之间 (year, between 0-99)\n", + "* %Y 年份的完整拼写 (longhand of year)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017-01-02 18:19:59\n", + "\n" + ] + } + ], + "source": [ + "# 字符串转化为日期\n", + "\n", + "day1 = datetime.strptime('2017-1-2 18:19:59', '%Y-%m-%d %H:%M:%S')\n", + "print(day1)\n", + "print(type(day1))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020, 17, 04 29 22:36\n", + "\n" + ] + } + ], + "source": [ + "# 日期转换为字符串\n", + "\n", + "day1 = datetime.now().strftime('%Y, %W, %m %d %H:%M')\n", + "print(day1)\n", + "print(type(day1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat, Apr 25 21:27\n", + "Saturday, Apr 25 21:27, 116\n", + "2020-04-25 21:27:45\n" + ] + } + ], + "source": [ + "# 日期转换为字符串，各种格式\n", + "\n", + "cday1 = datetime.now().strftime('%a, %b %d %H:%M')\n", + "cday2 = datetime.now().strftime('%A, %b %d %H:%M, %j')\n", + "cday3 = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", + "print(cday1)\n", + "print(cday2)\n", + "print(cday3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time.struct_time(tm_year=2020, tm_mon=5, tm_mday=12, tm_hour=21, tm_min=45, tm_sec=25, tm_wday=1, tm_yday=133, tm_isdst=0)\n", + "2020-05-12 21:45:25\n", + "\n" + ] + } + ], + "source": [ + "# 本地时间转换为字符串\n", + "\n", + "t1 = time.localtime()\n", + "print(t1)\n", + "\n", + "t2 = time.strftime('%Y-%m-%d %H:%M:%S',t1)\n", + "print(t2)\n", + "print(type(t2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### 日期计算\n", + "\n", + "对日期和时间进行加减实际上就是把 datetime 往后或往前计算，得到新的 datetime，需要导入 datetime 的 timedelta 类。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-04-25 21:28:12.071665\n", + "2020-04-25 11:28:12.071665\n" + ] + } + ], + "source": [ + "# 日期计算\n", + "# timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0)\n", + "\n", + "from datetime import timedelta\n", + "\n", + "now = datetime.now()\n", + "\n", + "# 往后减去十个小时\n", + "now1 = now + timedelta(hours=-10)\n", + "print(now)\n", + "print(now1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-04-25 21:28:16.185182\n", + "2020-04-26 07:48:06.185182\n" + ] + } + ], + "source": [ + "# 日期的各个变量的计算\n", + "\n", + "now = datetime.now()\n", + "now1 = now + timedelta(hours=10, minutes=20, seconds=-10)\n", + "print(now)\n", + "print(now1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考一下\n", + "\n", + "* 计算倒计时，现在距离2021元旦距离现在还有多少天" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5852 days, 10:41:57.714259\n", + "5852\n" + ] + } + ], + "source": [ + "from datetime import datetime\n", + "\n", + "#日期格式转换\n", + "newyear = datetime.strptime('2036-06-07', '%Y-%m-%d')\n", + "#获取现在的时间\n", + "now = datetime.now()\n", + "#时间差\n", + "timedelta = newyear-now\n", + "print(timedelta)\n", + "print(timedelta.days)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "思考一下：能否做成一个函数，用户输入日期，比如 2021-1-1，然后告知用户距离今天还有多少天。对于用户输入的日期要做判断，格式上是否合法等。" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 10 - \345\207\275\346\225\260\345\217\202\346\225\260\345\222\214\345\214\277\345\220\215\345\207\275\346\225\260.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 10 - \345\207\275\346\225\260\345\217\202\346\225\260\345\222\214\345\214\277\345\220\215\345\207\275\346\225\260.ipynb" new file mode 100644 index 0000000..a00a30e --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 10 - \345\207\275\346\225\260\345\217\202\346\225\260\345\222\214\345\214\277\345\220\215\345\207\275\346\225\260.ipynb" @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 10\n", + "\n", + "* Python Basic, Lesson 4, v1.0.1, 2016.12 by David.Yi \n", + "* Python Basic, Lesson 4, v1.0.2, 2017.03 modified by Yimeng.Zhang\n", + "* v1.1, 2020.4 5，edit by David Yi\n", + "\n", + " \n", + "### 本次内容要点\n", + "\n", + "* 函数不同参数形式\n", + "* 匿名函数\n", + "* 思考一下：函数可变参数练习" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 函数不同参数形式\n", + "\n", + "### 位置参数和默认参数\n", + "\n", + "位置参数：必须按照顺序准确传递，如果数量和顺序不对，就会可能造成程序错误；调用函数时候，如果写了参数名称，那么位置就不重要了； \n", + "\n", + "默认参数：在参数申明的时候跟一个用于默认值的赋值语句，如果调用函数的时候没有给出值，那么这个赋值语句就执行；\n", + "\n", + "注意：所有必须的参数要在默认参数之前；\n", + "\n", + "默认参数的好处：\n", + "* 减少程序复杂度\n", + "* 降低程序错误可能性\n", + "* 更好的兼容性\n", + "\n", + "### 可变长度的参数-元组和字典\n", + "\n", + "可变长度的参数，分为不提供关键字和提供关键字两种模式，分别为元组 tuple 和字典 dict；\n", + "\n", + "可变长度的参数，如果是提供关键字，就是字典 dict，需要提供 key – value；\n", + "\n", + "将字典作为参数传递的时候，可以直接传一个字典变量，也可以在参数列表中写明 key 和 value。\n", + "\n", + "\n", + "函数参数小结\n", + "\n", + "* 位置参数\n", + "* 默认参数\n", + "* 元组参数，一个星号\n", + "* 字典参数，两个星号，需要传递 key 和 value" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "110.0\n", + "120.0\n", + "130.0\n" + ] + } + ], + "source": [ + "# 函数默认参数\n", + "\n", + "def cal_0(money, rate=0.1):\n", + " return money + money * rate \n", + "\n", + "print(cal_0(100))\n", + "print(cal_0(100,0.2))\n", + "print(cal_0(rate=0.3,money=100))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "61000\n", + "62000\n", + "52000\n" + ] + } + ], + "source": [ + "# 函数默认参数\n", + "\n", + "def cal_1(money, bonus=1000, month=12，a=1, b=2):\n", + " i = money * month + bonus \n", + " return i\n", + "\n", + "print(cal_1(5000))\n", + "print(cal_1(5000, 2000))\n", + "print(cal_1(5000, 2000, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 画一个三角形 ，n=高度\n", + "'''\n", + " *\n", + " ***\n", + " *****\n", + " *******\n", + " *********\n", + " ***********\n", + " *************\n", + "\n", + "''' " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " *\n", + " ***\n", + " *****\n" + ] + } + ], + "source": [ + "# 函数默认参数\n", + "\n", + "def draw_triangle(n=5):\n", + "\n", + " for i in range(n+1):\n", + " print(' '*(n-i),'*'*(2*i-1))\n", + " \n", + "draw_triangle(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " *\n", + " ***\n", + " *****\n", + " *******\n", + " *********\n", + " ***********\n", + " *************\n" + ] + } + ], + "source": [ + "draw_triangle(7)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5\n" + ] + } + ], + "source": [ + "# 函数可变长度的参数 元组\n", + "\n", + "# 计算平均数\n", + "def cal_2(kind, *numbers):\n", + " if kind == 'avg':\n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " return n / len(numbers)\n", + "\n", + "t = cal_2('avg', 1,2,3,4)\n", + "print(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5\n", + "10\n", + "10\n" + ] + } + ], + "source": [ + "# 函数可变长度的参数 元组\n", + "# 输入计算的类别，输入要计算的数字\n", + "\n", + "def cal_3(kind, *numbers):\n", + " \n", + " if kind == 'avg':\n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " return n / len(numbers)\n", + " if kind == 'sum':\n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " return n\n", + " \n", + "print(cal_3('avg', 1,2,3,4))\n", + "print(cal_3('sum', 1,2,3,4))\n", + "\n", + "# 如果已经有一个list或者tuple，要要作为可变参数传给函数怎么办?\n", + "# 在list或tuple前面加一个*号，把list或tuple的元素变成可变参数传进去：\n", + "print(cal_3('sum',*[1,2,3,4]))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5\n", + "10\n" + ] + } + ], + "source": [ + "# 函数可变长度的参数 元组\n", + "# 简化程序逻辑\n", + "\n", + "def cal_4(kind, *numbers):\n", + " \n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " \n", + " if kind == 'avg':\n", + " return n / len(numbers)\n", + " if kind == 'sum':\n", + " return n\n", + " \n", + "print(cal_4(kind='avg', 1,2,3,4))\n", + "print(cal_4('sum', 1,2,3,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5\n", + "10\n" + ] + } + ], + "source": [ + "# 传递元组和字典参数优雅的写法\n", + "\n", + "def cal_5(*numbers, **kw):\n", + " \n", + " # 判断是否有 kind 这个 key\n", + " if 'kind' in kw:\n", + " kind_value = kw.get('kind')\n", + " \n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " \n", + " if kind_value == 'avg':\n", + " return n / len(numbers)\n", + " if kind_value == 'sum':\n", + " return n\n", + " \n", + "print(cal_5(1,2,3,4,kind='avg',max='ignore'))\n", + "print(cal_5(1,2,3,4,kind='sum'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 匿名函数\n", + "\n", + "Python 允许用 lambda 关键字创造匿名函数。\n", + "lambda 匿名函数不需要常规函数的 def 和 return 关键字，因为匿名函数代码较短，因此适用于一些简单处理运算的场景。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 等价的函数一般写法和匿名函数写法\n", + "\n", + "def add(x, y):\n", + " return x + y\n", + "\n", + "a = lambda x, y: x + y\n", + "a(1,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lambda 同样可以有默认参数\n", + "\n", + "a = lambda x, y=2 : x + y \n", + "a(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a(3, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44\n" + ] + } + ], + "source": [ + "a = lambda x : x * x +40\n", + "\n", + "print(a(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "匿名函数的用法特点\n", + "\n", + "* 只用一次的场景，不用取名称，因为给函数取名是比较麻烦的一件事；\n", + "* 函数逻辑简单，使用匿名函数前确认 Python 没有自带类似功能的函数，实际开发中，不要去重复发明轮子；\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考一下\n", + "\n", + "函数可变参数练习，参数 kind 中增加 max 这个key，如果设置为 ignore，则输入的数字中的最大数忽略" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0\n", + "2.5\n", + "10\n" + ] + } + ], + "source": [ + "# kind 中 增加 max key， \n", + "# max = ingnore， 则忽略最大值\n", + "def cal_6(*numbers, **kind):\n", + " \n", + " if 'kind' in kind:\n", + " kind_value = kind.get('kind')\n", + " \n", + " if 'max' in kind:\n", + " if kind.get('max') == 'ignore':\n", + " numbers = list(numbers)\n", + " numbers.remove(max(numbers))\n", + " \n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " \n", + " if kind_value == 'avg':\n", + " return n / len(numbers)\n", + " if kind_value == 'sum':\n", + " return n\n", + " \n", + "print(cal_6(1,2,3,4, kind='avg', max='ignore',min='ignore'))\n", + "print(cal_6(1,2,3,4, kind='avg'))\n", + "print(cal_6(1,2,3,4, kind='sum'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.75\n", + "2.5\n", + "10\n" + ] + } + ], + "source": [ + "# kind 中 增加 min key，\n", + "# min key = double, 则最小值计算两次\n", + "\n", + "def cal_7(*numbers, **kw):\n", + " \n", + " numbers = list(numbers)\n", + " \n", + " if 'kind' in kw:\n", + " kind_value = kw.get('kind')\n", + " \n", + " if 'max' in kw:\n", + " if kw.get('max') == 'ignore':\n", + " numbers.remove(max(numbers))\n", + "\n", + " if 'min' in kw:\n", + " if kw.get('min') == 'double':\n", + " numbers.append(min(numbers))\n", + " \n", + " n = 0\n", + " for i in numbers:\n", + " n = n + i\n", + " \n", + " if kind_value == 'avg':\n", + " return n / len(numbers)\n", + " if kind_value == 'sum':\n", + " return n\n", + " \n", + "print(cal_7(1,2,3,4, kind='avg', max='ignore', min='double'))\n", + "print(cal_7(1,2,3,4, kind='avg'))\n", + "print(cal_7(1,2,3,4, kind='sum'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python_basic_notebook/python_basic_lesson_06.ipynb "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 11 - \351\233\206\345\220\210\345\272\223 collections.ipynb" similarity index 53% rename from python_basic_notebook/python_basic_lesson_06.ipynb rename to "Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 11 - \351\233\206\345\220\210\345\272\223 collections.ipynb" index 65ee0b2..37d9678 100644 --- a/python_basic_notebook/python_basic_lesson_06.ipynb +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 11 - \351\233\206\345\220\210\345\272\223 collections.ipynb" @@ -4,17 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Lesson 6\n", + "# Lesson 11\n", "\n", - "Python Basic, Lesson 6, v1.0.1, 2017.1 by David.Yi \n", - "Python Basic, Lesson 6, v1.0.2, 2017.5 modified by Yimeng.Zhang \n", + "* Python Basic, Lesson 6, v1.0.1, 2017.1 by David.Yi \n", + "* Python Basic, Lesson 6, v1.0.2, 2017.5 modified by Yimeng.Zhang \n", + "* v1.1, 2020.4. edit by David Yi\n", "\n", - "\n", - "### 上次内容要点\n", - "\n", - "* 文件和目录操作之一：文件和目录操作\n", - "* 文件和目录操作之二：读写文本文件\n", - "* 搜索硬盘上指定路径指定类型的文件\n", " \n", "### 本次内容要点\n", "\n", @@ -24,9 +19,7 @@ " * defaultdict: 带有默认值的字典\n", " * OrderedDict: 有序字典\n", " * Counter: 计数器\n", - "* 错误处理介绍\n", - "* PEP 8 规定以及 python 编程基本规范\n", - "* 单元测试基础\n" + "* 思考一下：回文数\n" ] }, { @@ -52,9 +45,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "\n", - "#### namedtuple 有名字的元组\n", + "## namedtuple 有名字的元组\n", "\n", "namedtuple 主要用来产生可以使用名称来访问元素的数据对象，通常用来增强代码的可读性，在访问一些 tuple 类型的数据时尤其好用。\n", "\n", @@ -65,16 +56,15 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1 2\n" + "1 2\n", + "\n" ] } ], @@ -83,17 +73,16 @@ "\n", "from collections import namedtuple\n", "\n", - "Point = namedtuple('Point', ['x', 'y'])\n", + "point = namedtuple('Point', ['x', 'y'])\n", "p = Point(1, 2)\n", - "print(p.x, p.y) " + "print(p.x, p.y) \n", + "print(type(p))" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -111,9 +100,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -138,9 +125,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -157,9 +142,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -176,9 +159,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -200,9 +181,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -232,9 +211,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -254,9 +231,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "\n", - "#### deque\n", + "## deque\n", "\n", "使用 list 存储数据时，按索引访问元素很快，但是插入和删除元素就很慢了，因为 list 是线性存储，数据量大的时候，插入和删除效率很低。\n", "\n", @@ -268,9 +243,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -294,9 +267,8 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -304,8 +276,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.008998870849609375\n", - "0.0\n" + "0.03583407402038574\n", + "0.0000638961791992\n" ] } ], @@ -320,8 +292,7 @@ "\n", "# list\n", "a = time.time()\n", - "q0.insert(0,888)\n", - "# q0.append(999)\n", + "q0.insert(0,88888)\n", "b = time.time()\n", "print(b-a)\n", "\n", @@ -330,18 +301,15 @@ "\n", "# deque\n", "a = time.time()\n", - "q1.appendleft(888)\n", - "# q1.append(999)\n", + "q1.appendleft(88888)\n", "b = time.time()\n", - "print(b-a)" + "print('%2.16f' % (b-a))" ] }, { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -362,9 +330,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -389,9 +355,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -412,9 +376,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -425,6 +387,8 @@ } ], "source": [ + "# 删除左边的元素\n", + "\n", "l = deque(['a','b','c'])\n", "l.popleft()\n", "print(l)" @@ -434,21 +398,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "\n", - "#### defaultdict\n", + "## defaultdict\n", "\n", - "我们都知道，在使用Python原生的数据结构dict的时候，如果用 d[key] 这样的方式访问， 当指定的key不存在时，是会抛出KeyError异常的，也就是发生错误。（当然可以用 get 方法来避免这样的错误）\n", + "我们都知道，在使用 Python 原生数据结构字典的时候，如果用 d[key] 这样的方式访问，当指定的key不存在时，是会抛出KeyError异常的，也就是发生错误。（可以用 get 方法来避免这样的错误）\n", "\n", - "如果使用defaultdict，只要你传入一个默认的方法，那么请求一个不存在的key时， 便会调用这个方法，使用其结果来作为这个key的默认值。" + "如果使用 defaultdict，只要你传入一个默认的方法，那么请求一个不存在的 key 时，便会调用这个方法，使用其结果来作为这个key的默认值。" ] }, { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -468,9 +428,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "KeyError", @@ -493,10 +451,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -511,17 +467,15 @@ "\n", "from collections import defaultdict\n", "\n", - "i = defaultdict(lambda: 100)\n", - "i['name']='David'\n", - "print(i['name'])" + "d = defaultdict(lambda: 100)\n", + "d['name']='David'\n", + "print(d['name'])" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -535,16 +489,14 @@ "source": [ "# default 返回默认值，不会报错\n", "\n", - "print(i['score'])\n", - "print(i['best_score'])" + "print(d['score'])\n", + "print(d['best_score'])" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -558,36 +510,35 @@ "source": [ "from collections import defaultdict\n", "\n", - "i = defaultdict(lambda: '100')\n", - "i['name']='David'\n", - "print(i['name'])\n", - "print(i['score'])" + "d = defaultdict(lambda: '100')\n", + "d['name']='David'\n", + "print(d['name'])\n", + "print(d['score'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "##### OrderedDict\n", + "## OrderedDict\n", "\n", "使用 dict 字典时，Key 是无序的。在对 dict 做迭代访问时，我们无法确定 Key 的顺序。\n", "\n", - "如果要保持 Key 的顺序，可以用 OrderedDict，这是一个 Key 值有序的字典数据类型。" + "如果要保持 Key 的顺序，可以用 OrderedDict，这是一个 Key 值有序的字典数据类型。\n", + "\n", + "> 2020.4.30 从 Python 3.7 开始，默认的 dict 是有顺序的，而不是之前版本是乱序的，也就是默认的 dict 就是记住插入的顺序的。所以本节内容意义已经不大了。反过来证明有序字典是一个多么重要的功能点，已经从包中的功能被移植到了主功能中。" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'c': 3, 'b': 2, 'a': 1}\n" + "{'a': 1, 'b': 2, 'c': 3}\n" ] } ], @@ -601,17 +552,15 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'c': 3, 'a': 1, 'b': 2}\n", - "{'c': 3, 'a': 1, 'd': 4, 'b': 2}\n" + "{'a': 1, 'b': 2, 'c': 3}\n", + "{'a': 1, 'b': 2, 'c': 3, 'd': 4}\n" ] } ], @@ -629,9 +578,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -657,9 +604,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -672,7 +617,7 @@ ], "source": [ "# 使用 OrderedDict, 追加一对 key value\n", - "# OrderedDict的Key会按照插入的顺序排列，不是Key本身排序\n", + "# OrderedDict 的 key 会按照插入的顺序排列，不是 key 本身排序\n", "\n", "from collections import OrderedDict\n", "\n", @@ -689,10 +634,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -737,10 +680,8 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -764,10 +705,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -782,13 +721,14 @@ "OrderedDict([('b', 2), ('c', 3), ('new_key', 4)])" ] }, - "execution_count": 4, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 简化的先进先出Dict\n", + "\n", "from collections import OrderedDict\n", "d = OrderedDict()\n", "d['a'] = 1\n", @@ -814,25 +754,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "\n", - "##### Counter\n", + "## Counter\n", "\n", "Counter是一个简单的计数器，例如，统计字符出现的个数：" ] }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[(' ', 6), ('s', 4), ('a', 3), ('c', 3), ('t', 2)]\n" + "[(' ', 6), ('s', 4), ('a', 3), ('c', 3), ('u', 2)]\n" ] } ], @@ -851,436 +787,170 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Please input:aaaabbbccdefghi\n", - "[('a', 4), ('b', 3), ('c', 2), ('i', 1), ('d', 1)]\n" + "[('自', 2), ('远', 2), ('去', 2), ('他', 1), ('会', 1)]\n" ] } ], "source": [ - "# Counter 举例\n", - "\n", - "from collections import Counter\n", - "\n", - "s = input('Please input:')\n", + "# 是否可以统计中文呢？\n", "\n", - "s = s.lower()\n", + "s = '他会自己长大远去我们也各自远去'\n", "\n", "c = Counter(s)\n", - "# 获取出现频率最高的5个字符\n", + "\n", "print(c.most_common(5))" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3\n", - "5\n", - "0\n" + "Please input:whatisyourname\n", + "[('a', 2), ('w', 1), ('h', 1), ('t', 1), ('i', 1)]\n" ] } ], "source": [ - "# 计数值的访问与缺失的键\n", - "\n", - "print(c['a'])\n", - "print(c['b'])\n", - "print(c['z'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", + "# Counter 举例，用户输入内容\n", "\n", - "### 错误处理\n", + "from collections import Counter\n", "\n", - "异常即是一个事件，该事件会在程序执行过程中发生，影响了程序的正常执行。一般情况下，在Python无法正常处理程序时就会发生一个异常。异常是Python对象，表示一个错误。当Python脚本发生异常时我们需要捕获处理它，否则程序会终止执行。\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a:a\n" - ] - }, - { - "ename": "ValueError", - "evalue": "invalid literal for int() with base 10: 'a'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 看似健壮的程序，输入字母会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a:'\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 4\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"you cannot input 0\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'" - ] - } - ], - "source": [ - "# 看似健壮的程序，输入字母会报错\n", - "\n", - "a = int(input('a:'))\n", - "if a ==0:\n", - " print(\"you cannot input 0\")\n", - "else: \n", - " r = 10 / a\n", - " print('result:', r)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a:0\n", - "except: division by zero\n" - ] - } - ], - "source": [ - "# 防止输入0作为除数\n", - "\n", - "try:\n", - " a = int(input('a:'))\n", - " r = 10 / a\n", - " print('result:', r)\n", - "except ZeroDivisionError as e:\n", - " print('except:',e)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a:0\n" - ] - }, - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a:'\u001b[0m\u001b[1;33m)\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[1;36m10\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" - ] - } - ], - "source": [ - "a = int(input('a:'))\n", - "r = 10 / a" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a:0\n", - "except: division by zero\n" - ] - } - ], - "source": [ - "# 如何捕捉多个类型的错误？\n", + "s = input('Please input:')\n", "\n", + "s = s.lower()\n", "\n", - "try:\n", - " a = int(input('a:'))\n", - " r = 10 / a\n", - " print('result:', r)\n", - "except ZeroDivisionError as e:\n", - " print('except:',e)\n", - "except ValueError as e:\n", - " print('except:',e)\n" + "c = Counter(s)\n", + "# 获取出现频率最高的5个字符\n", + "print(c.most_common(5))" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "a:0\n", - "except: division by zero\n", - "finish...\n" + "[' ', 's', 'a', 'c', 'u', 't', 'i', 'o', 'n', 'e', 'r', 'd', 'b', 'l', '.']\n", + "[6, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]\n" ] } ], "source": [ - "# try...except...finally\n", + "# 读出 Counter 对象中的key 和value\n", "\n", - "a = int(input('a:'))\n", + "s = 'A Counter is a dict subclass. '.lower()\n", + "c = Counter(s)\n", "\n", - "try:\n", - " r = 10 / a\n", - " print('result:', r)\n", - "except ZeroDivisionError as e:\n", - " print('except:', e)\n", - "finally:\n", - " print('finish...')" + "# 用列表生成式获得 counter 中的 key\n", + "list1 = [k for k,v in c.most_common()]\n", + "\n", + "# 用列表生成式获得 counter 中的 value\n", + "list2 = [v for k,v in c.most_common()]\n", + "print(list1)\n", + "print(list2)" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 38, "metadata": { - "collapsed": false + "scrolled": true }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "except: name 'c' is not defined\n" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "# 防止错误举例\n", - "\n", - "a = 100\n", - "try:\n", - " b = a + c \n", - " print(b)\n", - "except NameError as e:\n", - " print('except:',e)" + "# 我们在 notebook 中画个图\n", + "\n", + "import matplotlib.pyplot as plt\n", + " \n", + "plt.bar(range(len(list2)), list2,color='rgb',tick_label=list1)\n", + "plt.show()\n" ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "except: name 'c' is not defined\n", - "100\n" - ] - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "# 防止错误举例，略完善的做法\n", - "\n", - "a = 100\n", + "## 思考一下\n", "\n", - "try:\n", - " b = a + c \n", - "except NameError as e:\n", - " print('except:', e)\n", - " b = a + 0\n", - "print(b)" + "1. 回文数：判断一个整数是否是回文数。回文数是指正序（从左向右）和倒序（从右向左）读都是一样的整数，比如 121 就是一个回文数，201 就不是。" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "a:a\n", - "except: invalid literal for int() with base 10: 'a'\n", - "covert ok\n", - "result: 10.0\n", - "finish...\n" - ] + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# 对 int 进行判断\n", - "\n", - "a = input('a:')\n", + "# 回文数\n", + "# 参考 leetcode 用了类来表示\n", "\n", - "# 对输入值转换还有更完善的方法\n", - "try:\n", - " a = int(a)\n", - "except ValueError as e:\n", - " print('except:', e) \n", - " a = 1\n", - "finally:\n", - " print('covert ok')\n", + "class Solution:\n", + " def isPalindrome(self, x: int) -> bool:\n", + " x = str(x)\n", + " return x == x[::-1]\n", "\n", - " \n", - "try:\n", - " r = 10 / a\n", - " print('result:', r)\n", - "except ZeroDivisionError as e:\n", - " print('except:', e)\n", - "finally:\n", - " print('finish...')" + "Solution().isPalindrome(x='121')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 单元测试\n", - "用Python搭建自动化测试框架，我们需要组织用例以及测试执行，推荐Python的标准库——unittest。 \n", - "unittest是xUnit系列框架中的一员，如果你了解xUnit的其他成员，那你用unittest来应该是很轻松的，它们的工作方式都差不多。\n", - "\n", - "官方说明文档： https://docs.python.org/3/library/unittest.html \n", - "The unittest module is actually a testing framework that was originally inspired by JUnit. It currently supports test automation, the sharing of setup and shutdown code, aggregating tests into collections and the independence of tests from the reporting framework.\n", - "\n", - "Unittest框架支持以下4个概念:\n", - "* Test Fixture –对一个测试用例环境的搭建和销毁。 A fixture is what is used to setup a test so it can be run and also tears down when the test is finished. For example, you might need to create a temporary database before the test can be run and destroy it once the test finishes. \n", - "* Test Case – 一个TestCase的实例就是一个测试用例，通过运行这个测试单元，可以对某一个问题进行验证。 The test case is your actual test. It will typically check (or assert) that a specific response comes from a specific set of inputs. The unittest frameworks provides a base class called TestCase that you can use to create new test cases. \n", - "* Test Suite – 多个测试用例集合在一起，就是TestSuite。 The test suite is a collection of test cases, test suites or both. \n", - "* Test Runner – 是来执行测试用例的。 A runner is what controls or orchestrates the running of the tests or suites. It will also provide the outcome to the user (i.e. did they pass or fail). A runner can use a graphical user interface or be a simple text interface.\n", - "\n", - "我们使用PYTHON的单元测试的主要方法是:\n", - "* assert-基础断言，允许用户自己扩展断言；\n", - "* assertEqual(a, b) -检查a和b是否相等；\n", - "* assertNotEqual(a, b) -检查a和b是否不相等；\n", - "* assertIn(a, b) -检查a是否在b中；\n", - "* assertNotIn(a, b) -检查a是否不在b中；\n", - "* assertFalse(a) -检查a的值是否是FALSE；\n", - "* assertTrue(a) -检查a的值是否是TRUE；\n", - "* assertIsInstance(a, b) -检查a的类型是否是“b”；\n", - "* assertRaises(ERROR, A, ARGS)-检查当A使用参数ARGS被调用的时候是否会引发ERROR；\n", - "\n" + "类似 -> bool 这样的标示是类型标注，从 python 3.5 开始引入这个功能，表明改函数返回的类型是什么。写了可以提高代码的可读性。" ] }, { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "# 一个简单例子， 将该脚本保存为 mymath.py\n", - "# 建立一个自己的脚本，定义了 add substract multiply divide 四个数学功能，我们用单元测试来检查它们是否与预期的功能一致！\n", + "在 leetcode 上有这道题目 https://leetcode-cn.com/problems/palindrome-number/\n", "\n", - "def add(a, b):\n", - " return a + b\n", - " \n", - "def subtract(a, b):\n", - " return a - b\n", - " \n", - "def multiply(a, b):\n", - " return a * b\n", - " \n", - "def divide(numerator, denominator):\n", - " return float(numerator) / denominator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# 将以下脚本保存为 test_mymath.py，并运行\n", - "\n", - "\"\"\"\n", - "构建unittest基本使用方法\n", - "1.import unittest 导入unittest库\n", - "2.定义一个继承自unittest.TestCase的测试用例类\n", - "3.定义setUp和tearDown，在每个测试用例前后做一些辅助工作\n", - "4.定义测试用例，函数名字以test开头\n", - "5.一个测试用例应该只测试一个方面，测试目的和测试内容应很明确。主要是调用assertEqual、assertRaises等断言方法判断程序执行结果和预期值是否相符。\n", - "6.调用unittest.main()启动测试\n", - "7.如果测试未通过，会输出相应的错误提示。如果测试全部通过则不显示任何东西，这时可以添加-v参数显示详细信息。\n", - "\"\"\"\n", - "\n", - "import mymath\n", - "import unittest\n", - " \n", - "class TestAdd(unittest.TestCase):\n", - " \"\"\"\n", - " Test the add function from the mymath library\n", - " \"\"\"\n", - " \n", - " def test_add_integers(self):\n", - " \"\"\"\n", - " Test that the addition of two integers returns the correct total\n", - " \"\"\"\n", - " result = mymath.add(1, 2)\n", - " self.assertNotEqual(result, 555)\n", - " self.assertEqual(result, 3)\n", - " \n", - " def test_add_floats(self):\n", - " \"\"\"\n", - " Test that the addition of two floats returns the correct result\n", - " \"\"\"\n", - " result = mymath.add(10.5, 2)\n", - " self.assertEqual(result, 12.5)\n", - " \n", - " def test_add_strings(self):\n", - " \"\"\"\n", - " Test the addition of two strings returns the two string as one\n", - " concatenated string\n", - " \"\"\"\n", - " result = mymath.add('abc', 'def')\n", - " self.assertEqual(result, 'abcdef')\n", - " \n", - " \n", - "if __name__ == '__main__':\n", - " unittest.main()" + "如上的方式是最简单的，能够通过，但是我们看一下反馈结果：\n", + "\n", + "执行用时 : 84 ms, 在所有 Python3 提交中击败了 66.08% 的用户\n", + "内存消耗 : 13.5 MB, 在所有 Python3 提交中击败了 5.88% 的用户\n", + "\n", + "执行速度还不错，但是内存消耗有点大。\n", + "\n" ] } ], @@ -1300,9 +970,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 12 - \345\210\227\350\241\250\347\224\237\346\210\220\345\274\217.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 12 - \345\210\227\350\241\250\347\224\237\346\210\220\345\274\217.ipynb" new file mode 100644 index 0000000..98b89a6 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 12 - \345\210\227\350\241\250\347\224\237\346\210\220\345\274\217.ipynb" @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 12\n", + "\n", + "* v1.0.0，2016.10 by David.Yi\n", + "* v1.1, 2020.4.30 5，edit by David Yi\n", + "\n", + "## 本次内容要点\n", + "* 列表生成器用法\n", + "* 思考一下：整理文件的应用需要哪些功能" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 列表生成式\n", + "\n", + "\n", + "列表生成式是 Python 内置的非常简单却强大的可以用来创建列表list的方法。\n", + "\n", + "大家都知道，要生成一个这样的 list：[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "\n", + "可以用 `list(range(1, 11))` \n", + "\n", + "那么如果要生成这样的 list：[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]，应该怎么办呢？" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n" + ] + } + ], + "source": [ + "print(list(range(1,11)))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n" + ] + } + ], + "source": [ + "# 用循环来生成\n", + "\n", + "list1 = []\n", + "for x in range(1, 11):\n", + " list1.append(x * x)\n", + " \n", + "print(list1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n" + ] + } + ], + "source": [ + "# 用列表生成式\n", + "# 是不是更加简洁和优雅？\n", + "\n", + "list1 = [ x * x for x in range(1, 11)]\n", + "print(list1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### 列表生成式用法\n", + "\n", + "写列表生成式时，把要生成的元素 `x * x` 放到前面，后面跟 `for` 循环，就可以把 list 创建出来，十分有用。\n", + "\n", + "在列表生成式后面还可以加上判断，过滤出结果为偶数的结果\n", + "\n", + "[x * x for x in range(1, 11) if x % 2 == 0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 16, 36, 64, 100]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 在列表生成式后面加上判断，过滤出结果为偶数的结果\n", + "\n", + "[x * x for x in range(1, 11) if x % 2 == 0 ]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(3, 2), (4, 1), (5, 0)]\n" + ] + } + ], + "source": [ + "# 可以在列表生成式中使用双重循环\n", + "# 输出一对元组，每个数在10以内，且加在一起等于5\n", + "\n", + "list1 = [(x, y) for x in range(10) for y in range(10) if x + y == 5 if x > y]\n", + "\n", + "print(list1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Pictures', 'Public', 'PAServer']\n" + ] + } + ], + "source": [ + "# 改进之前寻找目录下指定字母开头的文件的判断方式\n", + "# 修改为使用列表生成式\n", + "\n", + "import os\n", + "\n", + "# 可以判断各类情况，比如第一个是大写的 P 字母, 用列表生成式的方式，代码精简了很多\n", + "list1 = [x for x in os.listdir('/Users/yijun') if x[0] == 'P']\n", + "print(list1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 字典也可以用列表生成式生成\n", + "\n", + "可以理解列表生成式的改变被泛化了，可以用类似的方法来生成字典。\n", + "\n", + "zip() 函数用来把多个可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。所以像个拉链一样，将不同的可迭代对象装配起来。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# zip 对象没有办法直接显示其内容，因为它也是一个可迭代的对象\n", + "\n", + "list1 = zip(range(5),'hello')\n", + "print(list1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 h\n", + "1 e\n", + "2 l\n", + "3 l\n", + "4 o\n" + ] + } + ], + "source": [ + "# 我们用循环来读取 zip 输出的内容\n", + "\n", + "for k,v in zip(range(5),'hello'):\n", + " print(k,v)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 'h', 1: 'e', 2: 'l', 3: 'l', 4: 'o', 5: ' ', 6: 'w', 7: 'o', 8: 'r', 9: 'l', 10: 'd'}\n" + ] + } + ], + "source": [ + "# 用列表生成式来生成字典\n", + "\n", + "s = 'hello world'\n", + "dict1 = {k:v for (k,v) in zip(range(11),s)}\n", + "print(dict1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 'h', 1: 'e', 2: 'l', 3: 'l', 4: 'o', 5: ' ', 6: 'w', 7: 'o', 8: 'r', 9: 'l', 10: 'd'}\n" + ] + } + ], + "source": [ + "# 也可以直接使用 dict() 类型转换，因为这里逻辑比较简单\n", + "\n", + "dict1 = dict(zip(range(11),s))\n", + "print(dict1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[range(0, 5), range(5, 10), range(10, 15)]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n" + ] + } + ], + "source": [ + "# 将矩阵降维\n", + "\n", + "def list_flatten(matrix):\n", + " return [x for row in matrix for x in row]\n", + "\n", + "matrix = [range(0,5),range(5,10),range(10,15)]\n", + "\n", + "print(matrix)\n", + "\n", + "print(list_flatten(matrix))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考一下\n", + "\n", + "我习惯将所有的文件存放在文档目录，然后一周到两周整理一次，但是我发现这个是一个很辛苦的事情，也比较无聊。我们尝试写一个根据一些基本规则来整理文件的程序。(我们终于要开始做这个项目了，市面上相信肯定有专门的软件，做一个不太复杂、也不太简单、实用的工具，是学习编程的乐趣之一)\n", + "\n", + "1. 整理文件的规则之一是根据文件名中的关键字，存放到指定的文件夹中\n", + "2. 文件名关键字-文件夹的关系可以自定义\n", + "3. 可以在文件夹下面建立一层按照周或者月的日期范围文件夹，相关文件根据文件创建日期可以放到相应文件夹\n", + "\n", + "大家看看还需要什么功能？\n", + "\n", + "作为一个小小的产品，也可以遵循产品开发的流程，我们先明确需求，先了解好用户需要的功能，再做抽象和归纳。\n", + "\n", + "这个思考一下的场景会比较复杂，我们循序渐进。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 13 - \345\256\214\346\225\264\347\232\204 for \350\257\255\345\217\245\347\273\223\346\236\204\345\222\214 pprint.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 13 - \345\256\214\346\225\264\347\232\204 for \350\257\255\345\217\245\347\273\223\346\236\204\345\222\214 pprint.ipynb" new file mode 100644 index 0000000..4851788 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 13 - \345\256\214\346\225\264\347\232\204 for \350\257\255\345\217\245\347\273\223\346\236\204\345\222\214 pprint.ipynb" @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 13\n", + "\n", + "* v1.0.0 2016.11 by David.Yi\n", + "* v1.1 2020.5 edit by David Yi\n", + "\n", + "\n", + "## 本次内容要点\n", + "* for break continue else：完整的 for 语句结构 \n", + "* pprint 高级打印模块使用\n", + "* 思考一下：\n", + " * 简单的四则运算器实现\n", + " * 更加通用的四则运算器实现" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 完整的 for 语句结构\n", + "\n", + "* for continue\n", + "* for break\n", + "* for else\n", + "\n", + "\n", + "* continue 语句跳出本次循环，而 break 跳出整个循环;\n", + "* continue 语句用来告诉 Python 跳过当前循环的剩余语句，然后继续进行下一轮循环;\n", + "* continue 语句可以用在 while 和 for 循环中;\n", + "\n", + "break 语句用来终止循环语句。\n", + "\n", + "else 语句会在循环正常执行完（即 for 不是通过 break 跳出而中断的）的情况下执行。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "letter: P\n", + "letter: y\n", + "letter: t\n", + "letter: o\n", + "letter: n\n" + ] + } + ], + "source": [ + "# for continue 举例\n", + "\n", + "for l in 'Python':\n", + " if l == 'h':\n", + " continue\n", + " print('letter:', l)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# for continue 举例\n", + "# 去除输入的内容中的元音字母\n", + "\n", + "list1 = input('Input:')\n", + "\n", + "list2 = []\n", + "\n", + "for s in list1:\n", + " if s in ['a','e','i','o','u']:\n", + " continue\n", + " list2.append(s)\n", + "\n", + "# 将列表转换为字符串\n", + "s2 = ''.join(list2)\n", + "print('Output:', s2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for break else 举例\n", + "# 判断质数\n", + "\n", + "import time\n", + "\n", + "a = time.time()\n", + "\n", + "for num in range(3,20):\n", + " for i in range(2,num): \n", + " if num%i == 0: \n", + " j=num/i \n", + " print(num,i,'*',j)\n", + " break \n", + " else: \n", + " print(num, '是质数')\n", + " \n", + "b = time.time()\n", + "print(b-a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## pprint() 介绍\n", + "\n", + "pprint module提供了可以按照某个格式正确的显示 Python 已知类型数据的一种方法，这种格式很易读。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook', '/Users/yijun/anaconda3/envs/py37/lib/python37.zip', '/Users/yijun/anaconda3/envs/py37/lib/python3.7', '/Users/yijun/anaconda3/envs/py37/lib/python3.7/lib-dynload', '', '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages', '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/extensions', '/Users/yijun/.ipython']\n" + ] + } + ], + "source": [ + "import sys\n", + "print(sys.path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python37.zip',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/lib-dynload',\n", + " '',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/extensions',\n", + " '/Users/yijun/.ipython']\n" + ] + } + ], + "source": [ + "import sys\n", + "import pprint\n", + "pprint.pprint(sys.path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['/Users/yijun/Documents/dev_python/python_study/python_study_basic_notebook',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python37.zip',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/lib-dynload',\n", + " '',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages',\n", + " '/Users/yijun/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/extensions',\n", + " '/Users/yijun/.ipython']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# jupyter notebook 中直接显示一些变量的结果就和采用了 pprint 的方法\n", + " \n", + "import sys\n", + "sys.path" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# 实际上对 list 类型比较优雅的处理\n", + "\n", + "import sys\n", + "print(type(sys.path))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 思考一下\n", + "\n", + "简单的四则运算器，想一下怎么实现。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " please type in the math operation you would like to complete:\n", + " + for addition\n", + " - for subtraction\n", + " * for multiplication\n", + " / for division\n", + " -\n", + "Please enter the first number: 5\n", + "Please enter the second number: 2\n", + "5.0 - 2.0 = 3.0 \n" + ] + } + ], + "source": [ + "# 简单四则计算器代码，下面代码参考 https://www.jianshu.com/p/c22bfd91df49\n", + "\n", + "def calculate():\n", + " operation = input('''\n", + " please type in the math operation you would like to complete:\n", + " + for addition\n", + " - for subtraction\n", + " * for multiplication\n", + " / for division\n", + " ''')\n", + " number_1 = float(input('Please enter the first number: '))\n", + " number_2 = float(input('Please enter the second number: '))\n", + " if operation == '+': \n", + " print('{} + {} = {} '.format(number_1, number_2,number_1 + number_2)) \n", + " \n", + " elif operation == '-':\n", + " print('{} - {} = {} '.format(number_1,number_2,number_1 - number_2))\n", + " elif operation == '*': \n", + " print('{} * {} = {} '.format(number_1, number_2,number_1 * number_2))\n", + " elif operation == '/' and number_2 != 0: \n", + " print('{} / {} = {} '.format(number_1, number_2,number_1 / number_2))\n", + " else:\n", + " print('You have not typed a valid operator, please run the program again.')\n", + "\n", + "\n", + "calculate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果你对使用 python 来构造一种语言，比如适用一些场景的 DSL 感兴趣，如果你觉得 lex 和 yacc 这些编译原理中的概念感兴趣的话，可以参考python ply包作者的网站：https://www.dabeaz.com/software.html\n", + "\n", + "Python 下有很多优秀的包，都可以来实现语言定义和解析，但是 ply 是其中最经典的包，从2001年诞生至今，一致在维护更新，并且不断发展。\n", + "\n", + "下面这个是 ply 自己带的例子，用来实现一个比较完整的计算器。因为是通过语法分析的方式实现，会比较费解，但是功能强大，并且容易扩充。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": " is a built-in module", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlex\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mlexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;31m# Parsing rules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/ply/lex.py\u001b[0m in \u001b[0;36mlex\u001b[0;34m(module, object, debug, optimize, lextab, reflags, nowarn, outputdir, debuglog, errorlog)\u001b[0m\n\u001b[1;32m 906\u001b[0m \u001b[0mlinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moptimize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 908\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mlinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 909\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mSyntaxError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can't build lexer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/ply/lex.py\u001b[0m in \u001b[0;36mvalidate_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_tokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_literals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_rules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 580\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/ply/lex.py\u001b[0m in \u001b[0;36mvalidate_rules\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 822\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 823\u001b[0m \u001b[0;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/ply/lex.py\u001b[0m in \u001b[0;36mvalidate_module\u001b[0;34m(self, module)\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m \u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetsourcelines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 834\u001b[0m \u001b[0;32mexcept\u001b[0m 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"\u001b[0;32m~/anaconda3/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mgetsourcefile\u001b[0;34m(object)\u001b[0m\n\u001b[1;32m 682\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mno\u001b[0m \u001b[0mway\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mbe\u001b[0m \u001b[0midentified\u001b[0m \u001b[0mto\u001b[0m \u001b[0mget\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 683\u001b[0m \"\"\"\n\u001b[0;32m--> 684\u001b[0;31m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 685\u001b[0m \u001b[0mall_bytecode_suffixes\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__module__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: is a built-in module" + ] + } + ], + "source": [ + "# -----------------------------------------------------------------------------\n", + "# calc.py\n", + "#\n", + "# A simple calculator with variables -- all in one file.\n", + "# -----------------------------------------------------------------------------\n", + "\n", + "tokens = (\n", + " 'NAME', 'NUMBER',\n", + " 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'EQUALS',\n", + " 'LPAREN', 'RPAREN',\n", + ")\n", + "\n", + "# Tokens\n", + "\n", + "t_PLUS = r'\\+'\n", + "t_MINUS = r'-'\n", + "t_TIMES = r'\\*'\n", + "t_DIVIDE = r'/'\n", + "t_EQUALS = r'='\n", + "t_LPAREN = r'\\('\n", + "t_RPAREN = r'\\)'\n", + "t_NAME = r'[a-zA-Z_][a-zA-Z0-9_]*'\n", + "\n", + "\n", + "def t_NUMBER(t):\n", + " r'\\d+'\n", + " try:\n", + " t.value = int(t.value)\n", + " except ValueError:\n", + " print(\"Integer value too large %d\", t.value)\n", + " t.value = 0\n", + " return t\n", + "\n", + "\n", + "# Ignored characters\n", + "t_ignore = \" \\t\"\n", + "\n", + "\n", + "def t_newline(t):\n", + " r'\\n+'\n", + " t.lexer.lineno += t.value.count(\"\\n\")\n", + "\n", + "\n", + "def t_error(t):\n", + " print(\"Illegal character '%s'\" % t.value[0])\n", + " t.lexer.skip(1)\n", + "\n", + "\n", + "# Build the lexer\n", + "import ply.lex as lex\n", + "\n", + "lexer = lex.lex()\n", + "\n", + "# Parsing rules\n", + "\n", + "precedence = (\n", + " ('left', 'PLUS', 'MINUS'),\n", + " ('left', 'TIMES', 'DIVIDE'),\n", + " ('right', 'UMINUS'),\n", + ")\n", + "\n", + "# dictionary of names\n", + "names = {}\n", + "\n", + "\n", + "def p_statement_assign(t):\n", + " 'statement : NAME EQUALS expression'\n", + " names[t[1]] = t[3]\n", + "\n", + "\n", + "def p_statement_expr(t):\n", + " 'statement : expression'\n", + " print(t[1])\n", + "\n", + "\n", + "def p_expression_binop(t):\n", + " '''expression : expression PLUS expression\n", + " | expression MINUS expression\n", + " | expression TIMES expression\n", + " | expression DIVIDE expression'''\n", + " if t[2] == '+':\n", + " t[0] = t[1] + t[3]\n", + " elif t[2] == '-':\n", + " t[0] = t[1] - t[3]\n", + " elif t[2] == '*':\n", + " t[0] = t[1] * t[3]\n", + " elif t[2] == '/':\n", + " t[0] = t[1] / t[3]\n", + "\n", + "\n", + "def p_expression_uminus(t):\n", + " 'expression : MINUS expression %prec UMINUS'\n", + " t[0] = -t[2]\n", + "\n", + "\n", + "def p_expression_group(t):\n", + " 'expression : LPAREN expression RPAREN'\n", + " t[0] = t[2]\n", + "\n", + "\n", + "def p_expression_number(t):\n", + " 'expression : NUMBER'\n", + " t[0] = t[1]\n", + "\n", + "\n", + "def p_expression_name(t):\n", + " 'expression : NAME'\n", + " try:\n", + " t[0] = names[t[1]]\n", + " except LookupError:\n", + " print(\"Undefined name '%s'\" % t[1])\n", + " t[0] = 0\n", + "\n", + "\n", + "def p_error(t):\n", + " print(\"Syntax error at '%s'\" % t.value)\n", + "\n", + "\n", + "import ply.yacc as yacc\n", + "\n", + "parser = yacc.yacc()\n", + "\n", + "while True:\n", + " try:\n", + " s = input('calc > ') # Use raw_input on Python 2\n", + " except EOFError:\n", + " break\n", + " parser.parse(s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 不能直接在 notebook 中执行上面代码，%run 命令执行 python 脚本\n", + "# 执行上面的计算器代码，支持加减乘除和括号，支持变量，并且具备非常好的扩展性\n", + "\n", + "%run \"calc.py\"\n", + "\n", + "# 1+1*2\n", + "# (1+1)*2\n", + "# (((2+3)*41)-8)+78" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calc > 1+2*3-(5+6)+7685-54\n", + "7627\n", + "calc > h=4\n", + "calc > h+8\n", + "12\n" + ] + } + ], + "source": [ + "%run \"files/calc.py\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 14 - \350\256\277\351\227\256\347\275\221\347\273\234.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 14 - \350\256\277\351\227\256\347\275\221\347\273\234.ipynb" new file mode 100644 index 0000000..678c367 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 14 - \350\256\277\351\227\256\347\275\221\347\273\234.ipynb" @@ -0,0 +1,354 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Lesson 14 访问网络初步和 requests 包\n", + "\n", + "* v1.0.0 2016.11 by David.Yi\n", + "* v1.1 2020.5 2020.6 edit by David Yi\n", + "\n", + "\n", + "## 本次内容要点\n", + "* requests 包介绍\n", + "* 访问网页\n", + "* 调用接口\n", + "* 思考一下：写个同步数据的软件需要注意哪些方面" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## requests 包\n", + "\n", + "requests 包是 python 目前最好用的网站内容访问包，设计上比较人性化，可以大大简化代码的复杂度。\n", + "\n", + "网络访问、接口调用、网络检查、互联网爬虫等，都离不开 requests 包。\n", + "\n", + "一般来说，python 自带的函数包还是做的非常不错的，但是也有例外，其中之一就是网络访问方面。与其说是 python 原生的网络访问函数包做的不够人性化，不如说是 requests 函数包做的太人性化了，设计的非常好，配合 python 自带的优雅属性，在 python 的迅速发展过程中起到了正向的作用。requests 之后，也有很多函数包打着人性化的旗子，甚至有一些滥用了。\n", + "\n", + "使用 requests 包之前需要安装 requests。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'\\n\\n\\n \\n \\n \\n \\n \\xe6\\xb1\\x87\\xe4\\xbb\\x98\\xe5\\xa4\\xa9\\xe4\\xb8\\x8b | \\xe7\\xa7\\x91\\xe6\\x8a\\x80\\xe8\\xae\\xa9\\xe6\\x94\\xaf\\xe4\\xbb\\x98\\xe6\\x9b\\xb4\\xe6\\x87\\x82\\xe4\\xbd\\xa0\\n \\n \\n \\n \\n \\r\\n\\n\\n\\n\\n\\n\\r\\n\\r\\n
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\\n \\n 2020-03-03\\n \\xe6\\x88\\xaa\\xe8\\x87\\xb32020\\xe5\\xb9\\xb42\\xe6\\x9c\\x8829\\xe6\\x97\\xa5\\xe6\\xad\\xa2\\xe6\\x9c\\x88\\xe4\\xbb\\xbd\\xe4\\xb9\\x8b\\xe8\\x82\\xa1\\xe4\\xbb\\xbd\\xe5\\x8f\\x91\\xe8\\xa1\\x8c\\xe4\\xba\\xba\\xe7\\x9a\\x84\\xe8\\xaf\\x81\\xe5\\x88\\xb8\\xe5\\x8f\\x98\\xe5\\x8a\\xa8\\xe6\\x9c\\x88\\xe6\\x8a\\xa5\\xe8\\xa1\\xa8\\n \\n

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\\n \\n 2020-02-10\\n \\xe8\\x87\\xaa\\xe6\\x84\\xbf\\xe6\\x80\\xa7\\xe5\\x85\\xac\\xe5\\x91\\x8a-\\xe6\\x8d\\x90\\xe6\\xac\\xbe\\xe7\\x94\\xa8\\xe4\\xba\\x8e\\xe6\\x8a\\x97\\xe5\\x87\\xbb\\xe6\\x96\\xb0\\xe5\\x9e\\x8b\\xe5\\x86\\xa0\\xe7\\x8a\\xb6\\xe7\\x97\\x85\\xe6\\xaf\\x92\\xe7\\x96\\xab\\xe6\\x83\\x85\\n \\n

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\\n \\n 2020-02-10\\n \\xe4\\xbc\\x97\\xe5\\xbf\\x97\\xe6\\x88\\x90\\xe5\\x9f\\x8e \\xe6\\xb1\\x87\\xe4\\xbb\\x98\\xe5\\xa4\\xa9\\xe4\\xb8\\x8b\\xe5\\xae\\xa3\\xe5\\xb8\\x83\\xe6\\x8d\\x90\\xe8\\xb5\\xa0520\\xe4\\xb8\\x87\\xe5\\x85\\x83\\xe9\\xa9\\xb0\\xe6\\x8f\\xb4\\xe7\\x96\\xab\\xe6\\x83\\x85\\xe9\\x98\\xb2\\xe6\\x8e\\xa7\\n \\n

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\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n'\n" + ] + } + ], + "source": [ + "# 获得一个网站的信息\n", + "\n", + "import requests\n", + "\n", + "r = requests.get('http://www.huifu.com')\n", + "print(r.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Server': 'Tengine', 'Content-Type': 'text/html; charset=UTF-8', 'Content-Length': '10979', 'Connection': 'keep-alive', 'Date': 'Tue, 05 May 2020 07:11:03 GMT', 'Set-Cookie': 'acw_tc=65e321a715886626634018176ee2d179c2065e236f01385331cd67a3b7;path=/;HttpOnly;Max-Age=2678401', 'ALIWAF-CACHE': 'HIT', 'Ali-Swift-Global-Savetime': '1588662663', 'Via': 'cache14.l2cn1809[65,200-0,M], cache4.l2cn1809[66,0], vcache2.cn2539[74,200-0,M], vcache19.cn2539[78,0]', 'X-Cache': 'MISS TCP_MISS dirn:-2:-2', 'X-Swift-SaveTime': 'Tue, 05 May 2020 07:11:03 GMT', 'X-Swift-CacheTime': '0', 'Timing-Allow-Origin': '*', 'EagleId': '65e321a715886626634018176e'}\n" + ] + } + ], + "source": [ + "print(r.headers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 下载文件\n", + "\n", + "使用requets 可以很方便的获得网站中的图片、文件等。下面只是简单的举例，下载 baidu 的 logo 文件。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download ok\n" + ] + } + ], + "source": [ + "# 下载文件 使用 requests\n", + "# baidu 的 logo 文件： http://home.baidu.com/resource/r/home/img/logo-yy.gif \n", + "\n", + "import requests\n", + "\n", + "url = 'http://home.baidu.com/resource/r/home/img/logo-yy.gif'\n", + "\n", + "r = requests.get(url)\n", + "with open(\"files/baidu_logo.gif\", \"wb\") as code:\n", + " code.write(r.content)\n", + " print('download ok')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 读取接口\n", + "\n", + "我们来做一个读取新冠疫情数据的demo。\n", + "\n", + "平时我们说的接口，可以简单的理解为满足一定的认证方式后，通过输入参数的值，获得需要的内容。认证方式、入参、出参这些都是事先约定的。包括接口文档、参数列表、自动联调等这些，在目前 python 的接口开发中使用一些新技术都是可以自动生成的。python 本身开发接口非常容易，有机会另外专门讲述。相对来说，读取和调用接口的操作更为常见。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'code': '90000', 'message': 'success', 'data': {'region': {'US': {'2020-06-04': {'confirmed_add': 21140, 'deaths_add': 1036, 'recovered_add': 5744, 'confirmed': 1872611, 'deaths': 108211, 'recovered': 485002}}}}}\n", + "\n" + ] + } + ], + "source": [ + "# demo for infection/region\n", + "# input region, start_date, then get data\n", + "# 接口：感染/国家地区\n", + "\n", + "import requests\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# region or country\n", + "region='US'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date':'2020-06-04'\n", + "}\n", + "\n", + "# call requets to load \n", + "r = requests.get(url+'infection/region', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "print(data)\n", + "\n", + "print(type(data))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "905284\n" + ] + } + ], + "source": [ + "# 获得指定key 的内容，实际上是字典，因此可以一层层嵌套访问\n", + "\n", + "print(data['data']['region']['US']['2020-04-24']['confirmed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'code': '90000', 'message': 'success', 'data': {'region': {'US': {'2020-04-24': {'confirmed_add': 36163, 'deaths_add': 1995, 'recovered_add': 18876, 'confirmed': 905284, 'deaths': 51949, 'recovered': 99079}, '2020-04-25': {'confirmed_add': 32821, 'deaths_add': 1806, 'recovered_add': 1293, 'confirmed': 938105, 'deaths': 53755, 'recovered': 100372}, '2020-04-26': {'confirmed_add': 27629, 'deaths_add': 1126, 'recovered_add': 6616, 'confirmed': 965734, 'deaths': 54881, 'recovered': 106988}, '2020-04-27': {'confirmed_add': 22414, 'deaths_add': 1378, 'recovered_add': 4436, 'confirmed': 988148, 'deaths': 56259, 'recovered': 111424}, '2020-04-28': {'confirmed_add': 24385, 'deaths_add': 2096, 'recovered_add': 4512, 'confirmed': 1012533, 'deaths': 58355, 'recovered': 115936}, '2020-04-29': {'confirmed_add': 27327, 'deaths_add': 2612, 'recovered_add': 4784, 'confirmed': 1039860, 'deaths': 60967, 'recovered': 120720}, '2020-04-30': {'confirmed_add': 29515, 'deaths_add': 2029, 'recovered_add': 33227, 'confirmed': 1069375, 'deaths': 62996, 'recovered': 153947}, '2020-05-01': {'confirmed_add': 34037, 'deaths_add': 1947, 'recovered_add': 10068, 'confirmed': 1103412, 'deaths': 64943, 'recovered': 164015}, '2020-05-02': {'confirmed_add': 29078, 'deaths_add': 1426, 'recovered_add': 11367, 'confirmed': 1132490, 'deaths': 66369, 'recovered': 175382}, '2020-05-03': {'confirmed_add': 25501, 'deaths_add': 1313, 'recovered_add': 4770, 'confirmed': 1157991, 'deaths': 67682, 'recovered': 180152}}}}}\n" + ] + } + ], + "source": [ + "# 我们模拟一个实际的使用方式，获得10天的数据\n", + "\n", + "# demo for infection/region\n", + "# input region, start_date, end_date, then get data\n", + "# 接口：感染/国家地区\n", + "\n", + "import requests\n", + "\n", + "# API url\n", + "url = 'https://covid-19.adapay.tech/api/v1/'\n", + "# token, can call register function get the API token\n", + "token = '497115d0c2ff9586bf0fe03088cfdbe2'\n", + "\n", + "# region or country\n", + "region='US'\n", + "\n", + "# headers, need the API token\n", + "headers = {\n", + " 'token': token\n", + "}\n", + "\n", + "# the params\n", + "payload = {\n", + " 'region': region,\n", + " 'start_date':'2020-04-24',\n", + " 'end_date':'2020-05-03'\n", + "}\n", + "\n", + "# call requets to load \n", + "r = requests.get(url+'infection/region', params=payload, headers=headers)\n", + "\n", + "data = r.json()\n", + "\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'2020-04-24': {'confirmed_add': 36163, 'deaths_add': 1995, 'recovered_add': 18876, 'confirmed': 905284, 'deaths': 51949, 'recovered': 99079}, '2020-04-25': {'confirmed_add': 32821, 'deaths_add': 1806, 'recovered_add': 1293, 'confirmed': 938105, 'deaths': 53755, 'recovered': 100372}, '2020-04-26': {'confirmed_add': 27629, 'deaths_add': 1126, 'recovered_add': 6616, 'confirmed': 965734, 'deaths': 54881, 'recovered': 106988}, '2020-04-27': {'confirmed_add': 22414, 'deaths_add': 1378, 'recovered_add': 4436, 'confirmed': 988148, 'deaths': 56259, 'recovered': 111424}, '2020-04-28': {'confirmed_add': 24385, 'deaths_add': 2096, 'recovered_add': 4512, 'confirmed': 1012533, 'deaths': 58355, 'recovered': 115936}, '2020-04-29': {'confirmed_add': 27327, 'deaths_add': 2612, 'recovered_add': 4784, 'confirmed': 1039860, 'deaths': 60967, 'recovered': 120720}, '2020-04-30': {'confirmed_add': 29515, 'deaths_add': 2029, 'recovered_add': 33227, 'confirmed': 1069375, 'deaths': 62996, 'recovered': 153947}, '2020-05-01': {'confirmed_add': 34037, 'deaths_add': 1947, 'recovered_add': 10068, 'confirmed': 1103412, 'deaths': 64943, 'recovered': 164015}, '2020-05-02': {'confirmed_add': 29078, 'deaths_add': 1426, 'recovered_add': 11367, 'confirmed': 1132490, 'deaths': 66369, 'recovered': 175382}, '2020-05-03': {'confirmed_add': 25501, 'deaths_add': 1313, 'recovered_add': 4770, 'confirmed': 1157991, 'deaths': 67682, 'recovered': 180152}}\n", + "---\n", + "2020-04-24 {'confirmed_add': 36163, 'deaths_add': 1995, 'recovered_add': 18876, 'confirmed': 905284, 'deaths': 51949, 'recovered': 99079}\n", + "2020-04-25 {'confirmed_add': 32821, 'deaths_add': 1806, 'recovered_add': 1293, 'confirmed': 938105, 'deaths': 53755, 'recovered': 100372}\n", + "2020-04-26 {'confirmed_add': 27629, 'deaths_add': 1126, 'recovered_add': 6616, 'confirmed': 965734, 'deaths': 54881, 'recovered': 106988}\n", + "2020-04-27 {'confirmed_add': 22414, 'deaths_add': 1378, 'recovered_add': 4436, 'confirmed': 988148, 'deaths': 56259, 'recovered': 111424}\n", + "2020-04-28 {'confirmed_add': 24385, 'deaths_add': 2096, 'recovered_add': 4512, 'confirmed': 1012533, 'deaths': 58355, 'recovered': 115936}\n", + "2020-04-29 {'confirmed_add': 27327, 'deaths_add': 2612, 'recovered_add': 4784, 'confirmed': 1039860, 'deaths': 60967, 'recovered': 120720}\n", + "2020-04-30 {'confirmed_add': 29515, 'deaths_add': 2029, 'recovered_add': 33227, 'confirmed': 1069375, 'deaths': 62996, 'recovered': 153947}\n", + "2020-05-01 {'confirmed_add': 34037, 'deaths_add': 1947, 'recovered_add': 10068, 'confirmed': 1103412, 'deaths': 64943, 'recovered': 164015}\n", + "2020-05-02 {'confirmed_add': 29078, 'deaths_add': 1426, 'recovered_add': 11367, 'confirmed': 1132490, 'deaths': 66369, 'recovered': 175382}\n", + "2020-05-03 {'confirmed_add': 25501, 'deaths_add': 1313, 'recovered_add': 4770, 'confirmed': 1157991, 'deaths': 67682, 'recovered': 180152}\n", + "---\n", + "[905284, 938105, 965734, 988148, 1012533, 1039860, 1069375, 1103412, 1132490, 1157991]\n", + "---\n", + "['04-24', '04-25', '04-26', '04-27', '04-28', '04-29', '04-30', '05-01', '05-02', '05-03']\n" + ] + } + ], + "source": [ + "# 截取需要的字典内容\n", + "dict1 = data['data']['region']['US']\n", + "print(dict1)\n", + "\n", + "print('---')\n", + "\n", + "# 根据字典进行遍历\n", + "list1 = []\n", + "list2 = []\n", + "for key, value in dict1.items():\n", + " print(key,value)\n", + " list1.append(value['confirmed'])\n", + " list2.append(key[5:10])\n", + " \n", + "print('---')\n", + "print(list1)\n", + "\n", + "print('---')\n", + "print(list2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Qtf0QV3RrzJhL21NNXwqULChARAQIjBb49vdxPP9NNNUqhjDu+u7061Df77KkCFOAiAib9x5h1NSVLP3tAAM71mfsZR116XXJlgJEpBRzzvHR4t/4x5frKVfWePGqLgzu0hAzXYpEspftV0fNbLyZ7TazNUFtNc1sjpnFeD9reO19zSzBzFZ4t78FzTPAzDaYWayZjQ5qb25mP3vL+tjMQr328t7jWG96s6B5HvbaN5hZ//x5KURKl+0HjzF8/C/8dUYUPZrXZPb9fbisayOFh+RYTq498D4wIFPbaGCucy4CmOs9zvC9c66Ld3sCwMzKAq8BA4H2wNVm1t7r/wzwgresA8BIr30kcMA51wp4weuHN98woINX1+ve8kUkB5xzfLI0nv4vLmTpbwcYe1lHJtzYgwbVK/pdmhQz2QaIc24hsD9T82Bggnd/AnBZNovpCcQ65+Kcc8nAZGCwBf7VOQ+YlsWygtcxDTjf6z8YmOycO+6c2wTEessXkWzsPXycWz9cyqipK2lbvypf3Xs2153ZVFsdkiu5PQZSzzm3A8A5t8PM6gZN621mK4HtwAPOuSigEbA1qE880AuoBRx0zqUGtTfy7v93HudcqpkleP0bAYszLasRWTCzW4BbAMLDw3P5VEVKhq/X7OTRT1eTmJTKI4PaMvKPLSiry65LHuT3QfRlQFPn3GEzGwR8BkQAWf2WulO0k8t5Tmx0bhwwDiAyMjLLPiIlXcKxFB6fGcWny7fRsVE1Jv2pC601UqDkg9wGyC4za+BtfTQAdgM45w5ldHDOfWlmr5tZbQJbCU2C5m9MYAtlLxBmZiHeVkhGO0HzxJtZCFCdwK60ky1LRDJZGL2HB6etYs/h49x7fgR3ndeKcrrsuuST3P4mzQRGePdHADMAzKy+d5wCM+vpLX8f8CsQ4Z1xFUrgIPhM55wD5gFDMy8r0zqGAt95/WcCw7yztJoT2ML5JZfPQ6REOnI8lcc+W83w8b9QpUIIn95xFvdf2FrhIfkq2y0QM5sE9AVqm1k8MAZ4GphiZiOBLcCVXvehwO1mlgocA4Z5H/qpZnYXMBsoC4z3jo0APARMNrOxwHLgXa/9XeBDM4slsOUxDMA5F2VmU4C1QCpwp3MuLQ+vgUiJ8uvm/TwwdSVb9h/lpj8254H+bahQTicqSv6zwOd7yRcZGemWLFnidxkiBSYpJY0X5kQz7vs4GteoyLNDO9OrRS2/y5JizsyWOucis5qmb6KLlABrtiXw5ykriN51mKt7hvPoRe2oUl5/3lKw9BsmUowlpaTx0twY3l4YR83Kobx3Yw/ObVM3+xlF8oECRKSY+iFmL49+tprf9h3lyu6NefSidoRVCvW7LClFFCAixcy+w8d5atY6pi/fRvPalZl4cy/OaqnBnqTwKUBEignnHNOXbWPsrLUkJqVy93mtuPPcVjrDSnyjABEpBjbvPcKjn63mx9h9dAsP459DOtGmvr5NLv5SgIgUYSlp6YxbGMfLc2MILVuGJy/ryLU9wymja1hJEaAAESmilm05wCPTV7N+ZyIDO9bn8Us7UK9aBb/LEvkvBYhIEZOYlMKzszfwweLfqFe1gsYmlyJLASJShMyO2smYGVHsSkxiRO9mjOrXmqoVyvldlkiWFCAiRcDOhCTGzFzD7KhdtK1flTeu60bX8Bp+lyVySgoQER+lpzv+8/NvPPP1BlLS0nloQFtuOru5rporxYICRMQnG3Ym8vD0VSzbcpA/tqrNU5d3pGmtyn6XJZJjChCRQpaUksYr38Xw1oI4qlUsx/N/6szlXRtpXHIpdhQgIoXop9i9PPLpajbvO8oV3QLXr6pZWdevkuJJASJSCA4cSeapL9cxbWk8TWtV4j839eIPrXT9KineFCAiBcg5x2crtvHkF+s4dCyFO/q25J7zI3T9KikRFCAiBWTLvqM8+tlqvo/ZS5cmYTx9xRm0rV/N77JE8o0CRCSfpaSl8+4Pm3jx22hCypThicEduLZXU8rq+lVSwihARPLRyq0HGT19Net2HKJf+3r8fXAHGlSv6HdZIgVCASKSDw4fT+XZ2RuYsGgzdauW583rujOgo65fJSWbAkQkj75du4u/zljDzkNJXH9mUx7o34Zqun6VlAIKEJFc2rr/KE/NWsfXUTtpU68qr17Tje5Ndf0qKT0UICKn6VBSCq/Ni+W9HzZTtozxl/5tuPnsFoSG6PpVUrooQERyKDUtncm/buWFOdHsO5LMFd0a85f+bahfXYM8SemkABHJgfkbdvPUrHXE7D5Mz+Y1ef+i9pzRuLrfZYn4SgEicgrRuxJ5atY6FkTvoWmtSrx5XXf6d6inCx+KoAARydLew8d5YU40k37ZQuXyITx2UTuG926m4xwiQRQgIkGSUtJ4/6fNvPZdLEdT0rj+zKbce0FrXTFXJAsKEBECFz38cvVOnv56HVv3H+O8tnV5ZFA7WtWt4ndpIkVWjrbHzWy8me02szVBbTXNbI6ZxXg/a3jtZmYvm1msma0ys25B84zw+seY2Yig9u5mttqb52XzdjDnZh0ip2vF1oNc+eYi7py4jMqhIXw4sifjb+ih8BDJRk536L4PDMjUNhqY65yLAOZ6jwEGAhHe7RbgDQiEATAG6AX0BMZkBILX55ag+QbkZh0ip2P7wWPcN3k5l732I5v3HeGfQ85g1j1nc3ZEHb9LEykWcrQLyzm30MyaZWoeDPT17k8A5gMPee0fOOccsNjMwsysgdd3jnNuP4CZzQEGmNl8oJpzbpHX/gFwGfDV6a7DObcj509dSqsjx1N5c8FGxi2MwwF3ntuS2/u2okp57dEVOR15+Yupl/GB7ZzbYWZ1vfZGwNagfvFe26na47Noz806TggQM7uFwBYK4eHhuXiKUpKkpTs+WRrPv7/ZwJ7E41zauSEPDmhD4xqV/C5NpFgqiH+5sjpB3uWiPTfrOLHBuXHAOIDIyMjslikl2E+xe3ly1jrW7ThEt/Aw3rq+O93Cdd0qkbzIS4Dsytht5O2i2u21xwNNgvo1BrZ77X0ztc/32htn0T836xA5Qdyew/zjy/V8u24XjcIq8srVXbm4UwN9EVAkH+TlW1EzgYwzqUYAM4Lah3tnSp0JJHi7oWYD/cyshnfwvB8w25uWaGZnemdfDc+0rNNZhwgAB48m8/fPo+j3wkIWx+3jwQFtmDvqHC7p3FDhIZJPcrQFYmaTCGw91DazeAJnUz0NTDGzkcAW4Eqv+5fAICAWOArcCOCc229mTwK/ev2eyDigDtxO4EyvigQOnn/ltZ/WOkSSU9P5aPFvvDQ3hsSkFK7qEc6fL2xNnarl/S5NpMSxwIlMJV9kZKRbsmSJ32VIAXHO8e263fzjy3Vs2nuEsyNq8+hF7Whbv5rfpYkUa2a21DkXmdU0nbcoxV7U9gTGfrGORXH7aFmnMu/d0IO+bepoV5VIAVOASLG1+1ASz36zgalL4wmrWI4nBnfg6p7hlCurCx6KFAYFiBQ7iUkpvPvDJsYtjCMlLZ2b/ticu86LoHpFjUMuUpgUIFJsHEtO44NFm3lzwUYOHE1hQIf6PDyoLU1rVfa7NJFSSQEiRd7x1DQm/7KVV+fFsifxOH1a1+GBfq3p1DjM79JESjUFiBRZqWnpTF+2jZfmxrDt4DF6Nq/Ja9d0o2fzmn6XJiIoQKQISk93fLF6By/OiSZu7xE6Na7OP4ecwdkRtXVmlUgRogCRIsM5x5y1u3h+TjTrdybSpl5V3rq+O/3aawxykaJIASK+c87xfcxenvtmAyvjE2heuzIvDevCJZ0aUqaMgkOkqFKAiK9+3byff8/ewC+b9tMorCL/uqITQ7o1IkTf5RAp8hQg4otV8Qd57ptoFkTvoU7V8vz90g4M69mE8iFl/S5NRHJIASKFasPORJ6fs4HZUbsIq1SOhwe2ZXjvZlQMVXCIFDcKECkUm/ce4YVvo5m5cjuVQ0O474IIRv6xOVUr6NvjIsWVAkQK1LaDx3hlbgxTl8ZTrqxxa5+W3NqnBTUqh/pdmojkkQJECsTuxCRen7eRiT9vAeD6M5tyx7ktqVu1gs+ViUh+UYBIvjp4NJk3F8Qx4afNJKelc2X3xtx9fgSNwir6XZqI5DMFiOSLjCvkvvv9Jg4np3Jp54bcd0FrmtfWhQ5FSioFiORJ5ivk9u9Qjz9f2IY29av6XZqIFDAFiORK5ivkntO6DqN0hVyRUkUBIqdFV8gVkQwKEMmx1fEJPPTJKtbuOETnxtV5+ooz+GMrXSFXpLRSgEi2jian8vw30Yz/cRO1q5Tn9Wu7MbBjfQWHSCmnAJFTWhi9h0c+XU38gWNc0yuchwa01djjIgIoQOQk9h9JZuwXa5m+fBst6lRmyq29dZxDRE6gAJETOOeYuXI7f/98LYeOpXDPea2449xWVCinix2KyIkUIPJf8QeO8uina1gQvYcuTcJ4+oozaFu/mt9liUgRpQAR0tId7/+0mee+2QDA45e05/rezSir0QBF5BQUIKXcuh2HGP3JKlbGJ3BumzqMvfwMXbdKRHJEAVJKJaWk8cp3Mby1II7qFcvx8tVduaRTA52aKyI5pgAphRbH7eOR6auJ23uEod0b8+igdhqfQ0ROW5m8zGxm95rZGjOLMrP7vLbHzWybma3wboOC+j9sZrFmtsHM+ge1D/DaYs1sdFB7czP72cxizOxjMwv12st7j2O96c3y8jxKi4RjKTw8fRXDxi0mNd3x0chePHtlZ4WHiORKrrdAzKwjcDPQE0gGvjazWd7kF5xzz2bq3x4YBnQAGgLfmllrb/JrwIVAPPCrmc10zq0FnvGWNdnM3gRGAm94Pw8451qZ2TCv31W5fS6lwddrdvDXGVHsO3ycW/u04L4LWmscchHJk7zswmoHLHbOHQUwswXA5afoPxiY7Jw7Dmwys1gC4QMQ65yL85YzGRhsZuuA84BrvD4TgMcJBMhg7z7ANOBVMzPnnMvD8ymRdiYk8bcZa/hm7S46NKzGezf0oGOj6n6XJSIlQF52Ya0B+phZLTOrBAwCmnjT7jKzVWY23sxqeG2NgK1B88d7bSdrrwUcdM6lZmo/YVne9ASv/wnM7BYzW2JmS/bs2ZOHp1r8pKc7Plr8Gxc+v4CFMXt4eGBbZtz5B4WHiOSbXAeIc24dgV1Hc4CvgZVAKoEthJZAF2AH8Jw3S1an97hctJ9qWZlrHOeci3TORdapU+fkT6aEid19mKvGLeKxz9bQqUl1Zt/Xh1vPaUlI2Twd8hIROUGezsJyzr0LvAtgZv8A4p1zuzKmm9nbwBfew3h+30IBaAxs9+5n1b4XCDOzEG8rI7h/xrLizSwEqA7sz8tzKQmSU9N5c8FGXv0uloqhZfn30E4M7d5Yp+aKSIHIU4CYWV3n3G4zCweGAL3NrIFzbofX5XICu7oAZgITzex5AgfRI4BfCGxNRJhZc2AbgQPt1zjnnJnNA4YCk4ERwIygZY0AFnnTvyvtxz+W/naAh6evInrXYS7p3JC/XdyeOlXL+12WiJRgef0eyCdmVgtIAe50zh0wsw/NrAuBXUqbgVsBnHNRZjYFWEtgV9edzrk0ADO7C5gNlAXGO+eivOU/BEw2s7HAcrytHe/nh96B+P0EQqdUOnw8lWdnb2DCos00qFaB8TdEcl7ben6XJSKlgJWWf9wjIyPdkiVL/C4jX323fhePfbqGHYeSGNG7GQ/0b0OV8vpuqIjkHzNb6pyLzGqaPm2KoT2Jx3nii7V8vnI7retV4ZNrz6JbeI3sZxQRyUcKkGLEOcfUpfE8NWsdx5LTGHVha249pyWhITq7SkQKnwKkmIg/cJSHPlnFj7H76NmsJv8Ycgat6lbxuywRKcUUIMXAd+t3cf/HK0lLdzx1eUeu7hFOGY3VISI+U4AUYalp6Tw3J5o35m+kQ8NqvH5tN5rWqux3WSIigAKkyNp1KIm7Jy3nl037uaZXOH+7uL3GJReRIkUBUgT9GLuXeycv52hyGi9e1YXLujbKfiYRkUKmAClC0tIdr34Xy4tzo2lVpwqTb+lGq7pV/S5LRCRLCpAiYt/h49z38Qq+j9nLkK6NGHt5RyqF6u0RkaJLn1BFwK+b93P3xOXsP5rM04O2hKcAAApxSURBVEPO4KoeTXQBRBEp8hQgPnLO8fb3cTzz9Qaa1KjIp3ecRYeGGq9DRIoHBYhPEo6mMGrqCr5dt5uBHevzzNBOVKtQzu+yRERyTAHig5VbD3LnxGXsOpTE45e0Z8RZzbTLSkSKHQVIIXLO8eHi3xj7xTrqVC3PlFt701UXQRSRYkoBUkgSk1IYPX01s1bt4Ly2dXn+T50JqxTqd1kiIrmmACkE63Yc4o7/LGPL/qM8NKAtt/ZpoWtZiUixpwApYFN+3cpfZ6yhesVyTLypF71a1PK7JBGRfKEAKSDHktP464w1TFsazx9a1eLFq7pqjHIRKVEUIAUgdvdh7vzPMqJ3J3Lv+RHcc34EZbXLSkRKGAVIPpuxYhuPTF9N+XJlmXBjT/q0ruN3SSIiBUIBkk+SUtIYO2stHy3eQmTTGrx6TTfqV6/gd1kiIgVGAZIPtuw7yh0Tl7Jm2yFu7dOCB/q3oVxZjVMuIiWbAiSPZkft5IGpKzHg7eGRXNi+nt8liYgUCgVILqWkpfPMV+t554dNdGpcndeu6UaTmpX8LktEpNAoQHJh+8Fj3DVxGcu2HGRE76Y8clE7yodouFkRKV0UIKdp/obd3P/xCpJT03nl6q5c0rmh3yWJiPhCAZJDaemOF7+N5tV5sbSpV5XXr+1GizpV/C5LRMQ3CpAc2J2YxL2TVrAobh9XRTbh8Us7UDFUu6xEpHRTgGRjzbYEbnz/VxKTUnj2ys4M7d7Y75JERIoEBUg2GoZVpG39qjx2UXva1K/qdzkiIkVGnr7tZmb3mtkaM4sys/u8tppmNsfMYryfNbx2M7OXzSzWzFaZWbeg5Yzw+seY2Yig9u5mttqb52Xzhu072ToKQs3KoXw4spfCQ0Qkk1wHiJl1BG4GegKdgYvNLAIYDcx1zkUAc73HAAOBCO92C/CGt5yawBigl7esMUGB8IbXN2O+AV77ydYhIiKFJC9bIO2Axc65o865VGABcDkwGJjg9ZkAXObdHwx84AIWA2Fm1gDoD8xxzu13zh0A5gADvGnVnHOLnHMO+CDTsrJah4iIFJK8BMgaoI+Z1TKzSsAgoAlQzzm3A8D7Wdfr3wjYGjR/vNd2qvb4LNo5xTpOYGa3mNkSM1uyZ8+eXD9RERH5X7kOEOfcOuAZAlsMXwMrgdRTzJLVgBguF+2nU+M451ykcy6yTh1dVl1EJD/l6SC6c+5d51w351wfYD8QA+zydj/h/dztdY8nsIWSoTGwPZv2xlm0c4p1iIhIIcnrWVh1vZ/hwBBgEjATyDiTagQww7s/ExjunY11JpDg7X6aDfQzsxrewfN+wGxvWqKZnemdfTU807KyWoeIiBSSvH4P5BMzqwWkAHc65w6Y2dPAFDMbCWwBrvT6fkngOEkscBS4EcA5t9/MngR+9fo94Zzb792/HXgfqAh85d0ATrYOEREpJBY4wanki4yMdEuWLPG7DBGRYsXMljrnIrOcVloCxMz2AL/lYRG1gb35VE5xrgFUR2aqo2jVAKojs7zU0dQ5l+VZSKUmQPLKzJacLIVLUw2qQ3UU9RpUR+HVoYG7RUQkVxQgIiKSKwqQnBvndwEUjRpAdWSmOn5XFGoA1ZFZgdShYyAiIpIr2gIREZFcUYCIiEiulMoAMbMBZrbBG6hqdKZpr5jZ4ZPMV8nMZpnZem8Qraez6DPUzJyZZXvKXEHUYWY3mNkeM1vh3W4q7Bq86X8ys7XetIk+vRYvBL0O0WZ20Kc6ws1snpktt8BgaoN8qqOpmc31aphvZtmOz5zbOrzpX5vZSq+ON82srNd+WgPCZVWDmb1vZpuC3t8uJ5m3uZn97K3rYzML9dr7mNkyM0s1s6HZvQ4FWMefvb+TVd5709SnOm6zwOB9K8zsBzNrn5PXBOdcqboBZYGNQAsglMBVhNt70yKBD4HDJ5m3EnCudz8U+B4YGDS9KrAQWAxE+lEHcAPwqp+vBYHBv5YDNbzHdf16T4L63Q2M9+n1GAfc7t1vD2z2qY6pwAjv/nnAhwVVh9enmvfTgE+AYd7jfwGjvfujgWdOtwYClzgamoPf8SlB630z6H1oBnQiMM5QTpZTUHWcC1Ty7t8OfOxTHdWC+lwKfJ3dspxzpXILpCcQ65yLc84lA5OBwd5/R/8GHjzZjC4weNY8734ysIwTrxj8JIE/jiSf68ipgqrhZuA1FxggDOdcdldLLozX4moCF/v0ow4HVPPuV+f3q0oXdh3tCYzgCTCPwMBsBVKHt/5D3t0QAh92GWfsnM6AcFnWkE3dQGAYbQJBOS3zupxzm51zq4D0nCyrAOuY55w76rUvJvu/44Kq41BQ18rkcOiM0hggJxvA6i5gpvMGqsqOmYUBl+D9QZpZV6CJc+4LP+vwXOFtEk8zsyYnmbUga2gNtDazH81ssZkNOPncBVpHRntToDnwnU91PA5cZ2bxBC4qerdPdawErvDuXw5UtcDFUAusDjObTWC4hUR+/+DK0YBw2dQA8JT3e/6CmZXPYt5awEEXGDE187ynqzDqGMnvF4wt9DrM7E4z20jgn+B7sqkDKJ0BktVAVeUJXNH3lRwtwCyEwH+zLzvn4sysDPACMMrPOrzmz4FmzrlOwLf8/p9eYdYQQmA3Vl8C//m/432YFXYdGYYB05xzadktpoDquBp43znXmMAVqT/0fmcKu44HgHPMbDlwDrCN0x8E7rTqcM71Bxp4852Xk3lyUIMDHgbaAj2AmsBDpzFvbhRoHWZ2HYHdgv/2qw7n3GvOuZbevI9lUwdQOgMkqwGsNgOtgFgz2wxU8g5QlQ06KPVE0DzjgBjn3Ive46pAR2C+N/+ZwEw79YH0gqgD59w+59xx7+HbQPfCrsFb7gznXIpzbhOwgUCgFHYdGYaR/e6rgqxjJIF9zzjnFgEVCFzcrlDrcM5td84Ncc51BR712hIKuA6cc0kExvDJ2NVyOgPCZTngnHNuhws4DrxHYNcOZjbbq+EdAhcPDPPC9L/znmJdp1JgdZjZBQTej0uD/nYLvY4gkzn1bsXf5eRASUm6EfjvOI7ALo2Mg1AdMvU51YHBsQQOCJY5RZ/5ZH8QvUDqABoE3b8cWOxDDQOACd792gQ2uWv58Z4AbQh86JlfvxsEdkvc4N1vR+CP9qT1FGAdtTPagKcIjL1TIK8HUCXjd9FbzsfAXd7jf3PiQfR/nW4NQcs24EXg6ZPMP5UTDxrfkWn6++Ts4HOB1AF0JXBQPCK7Ggq4joigPpcAS3JUT046lbQbgd0I0d4b92gW00/2R9GYwCbfOmCFd7spi37zySZACqoO4J9AlPeLNQ9o60MNBjwPrAVWZ/zC+vGeEDj+kOUfUyG+Hu2BH733ZAXQz6c6hhIYdjoaeAcoX4B11CMwSNwq7/fxFSDEm1aLwHGZGO9nzdOtgcDxrNXAGuAjoMpJ5m0B/EJgILupGc+ZwK6eeOAIsA+Iys1rkQ91fAvsCnqvZvpUx0ve+7SCwOdGh+zqcM7pUiYiIpI7pfEYiIiI5AMFiIiI5IoCREREckUBIiIiuaIAERGRXFGAiIhIrihAREQkV/4f4YCctAftipMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制一个折线图\n", + "\n", + "import matplotlib.pyplot as plt\n", + " \n", + "plt.plot(list2,list1)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 15 - \345\233\276\347\211\207\345\244\204\347\220\206 Pillow.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 15 - \345\233\276\347\211\207\345\244\204\347\220\206 Pillow.ipynb" new file mode 100644 index 0000000..50c2ff3 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 15 - \345\233\276\347\211\207\345\244\204\347\220\206 Pillow.ipynb" @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 15\n", + "\n", + "* Python Senior, Lesson 9, v1.0.0, 2016.11 by David.Yi\n", + "* v1.1, 2020.5.4，6.6 edit by David Yi\n", + "\n", + "\n", + "## 本次内容要点\n", + "* 图片处理模块 PIL " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PIL (Pillow) 模块介绍\n", + "\n", + "PIL：Python Imaging Library，已经是Python平台事实上的图像处理标准库了。PIL功能非常强大，但API却非常简单易用。\n", + "\n", + "由于 PIL 仅支持到 Python 2.7，加上年久失修，于是一群志愿者在PIL的基础上创建了兼容的版本，名字叫Pillow，支持最新Python 3.x，又加入了许多新特性，因此，我们可以直接安装使用 Pillow。\n", + "\n", + "##### 安装 Pillow\n", + "\n", + "Pillow（PIL）的最新版本是 7.1.2(2020.5, 我发现 Pillow 的版本增加很快，2016年的时候还是 3.4.2），如果安装了 anaconda，已经默认安装了 Pillow 包。\n", + "\n", + "需要准备一张用来测试的图片，最好是 jpg 格式，然后命名为 test.jpg，保存在 python 同样目录或者可以比较方面访问到的目录，比如 files/test.jpg 这样。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JPEG (1924, 1080) RGB\n" + ] + } + ], + "source": [ + "# 读取照片\n", + "\n", + "# 导入 PIL 的 Image 模块\n", + "from PIL import Image\n", + "# 打开一个图片文件\n", + "img = Image.open('files/test.jpg')\n", + "print(img.format, img.size, img.mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 在 IPython 状态下显示图片 \n", + "# 使用 IDLE 或者其他python 运行环境的可以用电脑自带的程序检查图片\n", + "\n", + "from IPython.display import Image as img\n", + "img(filename='files/test.jpg') " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "create thumbnail ok\n" + ] + } + ], + "source": [ + "# 生成缩略图\n", + "\n", + "from PIL import Image\n", + "\n", + "# 设定缩略图大小\n", + "size = (100, 100)\n", + "\n", + "try:\n", + " # 打开图片\n", + " img = Image.open('files/test.jpg')\n", + " # 生成缩略图\n", + " img.thumbnail(size)\n", + " # 保存缩略图图片\n", + " img.save('files/test_thumbnail.jpg', 'JPEG')\n", + " print('create thumbnail ok')\n", + "except IOError:\n", + " print('cannot create thumbnail for')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAA4AGQDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDxRYlmBZCEccn0puTGdjrj0/8ArGrF3Zz2KokiEbgGViOuR61LpenzaxeRWUSlnc4B/u+pPsKBx1ZXmkaQxk8NjrjH0P1p3ktOhlQAAHaW9TjvW9qdvHY3NxaTxl44js3fxrgDnPer+gaNaz6eyrN5yykkFQcgngD3NK/YtRu9dil4O8b6v4L1QXFlITG5Hn2zk7JVHr79cEcj3r6Ht7rwt8YfCxicYmjAZkyBNaSf3lPp79DXzlquhtbztEqZUKHBAPQ9wf6VS0fWdS8M6tFf6dcvBPEflkXoR3BHcHuDwaaYmrHuV3d6ppU1n4K8cSiTTJ5iLfWs7RLHsYBX9HBK8n8c9a8c8U+H5fDGuXGly3cd3bq2Y54WDKw/Do2MZFe0w+LrT4t+BrzQFe0stfkjG2K4XKOQQcxk9M9PUZ7159ovwj8ZXN7FZ3tgLazuWZJZpmDiEKfvAA5B4+X1+lNEnASGSaYK8qBVXhh91V9hXv8A8GvH8urufDFzCdtrbg2ko5PloApD+/cH3x6V5P4s8Dar4R15tNltZLqCZiLSdYyRKD0xj+L1FfRfgDwNpvgzRIkgiD388am6uWHzO2M4Geig9B+fNAaWOuooopCGkZNFOoouB8sQarp2qsYdSgW0dhgb1zGewHPT/PNdDoPhux0K5lu7Pe7TJtDcHaOvy+1V7rw5CJZJoCZHaPZ5coyD9D+lYEP9p6AW+zzGMbubeb7jD/Z/Xoe1LmtpJFcid3BlDxRds2ozCaIoWkbbldvPQHn1AFX9D2pYxPBJE0qYJyAwz1wRTtQubDxGI01FDZXLL8vmE7D9G6D8a5+40rVPD10ZYxI8ZXqv9R3FKyT7mqm+Wz0O8lZNaeC28tA91lWiLHChcgKOAc4UfnxXPax4ZLPcSQEMFJO3uB6H16VBpviWC5dFnkEUyOGR1Bzkfy//AF109ncNEJ0GXSRVzuJbkZHr1xihR6R2/FDb7nmLW1zZXC+WWjfIYAHBHuDXuPwx1D4iX62921w19pIby2+1sBkZwcMfmOPUZ9KxfCngC28VX8mualcpbaHbnEiltjyOOSpJ4UcjJ/D3r2Xwb4j0jxHoxl0WEw2dtIbdI2CrgKB0UE4Hpn0qou6uZSXK2joSAcZAODkZ7UtFFBAUVm6trllo8Ra4fdJjKxJyzfh2Hua4ifxhqtwZbppo7GyCkbGAGB/e3HnP+fegdjsV8TaYxcLK7bWKkhD1FFeVxX+m6gGmVpwA23dtYb8fxf8A6/SikOyPJNN8T6vpIWJZfOhX/llLk4+h6iuusfFuk6pEIL5RbuRgrLyufY//AKq6bxb8J4NF0tb6LU7SWCLHnx3cKxZHcqyDOfavLrrwxrc0E2oW+myyWgICNHHjK9iF64xQm+oml0O2uvDsFy0UtiiTAL8sTtlWXBzgj6+/SsOKHUtHh2FlMRPzWs4ynfoc8fUflXLadrOqaTKDaXcsLI2DF2z7iu2074h2t3ELbXrBMMcNMi7s/XuP1qZLsaxlpaRiTaZpmryCSEnT71uRHLwr+wPQ/Q4NLGmo6M+GVctK2+EK21FwMEHnuOnvXW3OhaXq6pPpFxHcxHJ2O27jqRnqPxrFnGoaGArq01sD8sNyOgOeEf8AD3HtUqTW5fLr7py3iGd5mQhpFiLEPCWO0OO+Oh+tHh7xVq/hLU0vtKuDC2AHU8pKP7rDuP19K3b+xsdajdIN1pdk7vIlHO4cceo+lcjd6feaaxWeMhc8uBlT7VpGOiZlOpuj3k/Hm3uvD6Gx0xv7bb5WhkYCJD/eB6sPbr6+pw9P8f8AxE1b7N5UkKRrPueXyFxIB1U+35fWvH7OAXE4Aysf8XoK9Bt2utK0uWTR5JI7VVHmSyHbuJxnyx1z7n07VT0JSudx4h8SW0OqPd3TRy6jOFjS2hOM+gPp9TzXKa19tuRFLfXatdI6utjGP3arnnd6fWs/UtQ0y207ywmyN2DNJLzPIw54HUVj51DW2dLWOSKAkkxq2XI9XbtWTk3saJKO5q3vi0w3JjF8Itox5dsmUX2z3oqPTNF0ySxQlmDgkMvA2n0oo5fMXOux6BqFxN47vdPvb7zYdNhBna2343ZJ8vI+i59s1pareTWemPdhQEjQJHnvggf1ooqjM4G40abxPdOZrZLe1ic+ffSLg/QY5PsK5rW/DVtYTsmnXbPuPEUwG765FFFDGjJaDWdDljuSlzZu33ZFBCt+PQ10tj8Qp/s4s9XtIryA9XVRux9On5YoooaKi2W3g0XWUDabOHPLGKXOV4H4jpVcyXVl5kVzGLu0YBfLkILY9j3+hooocbRbQ1LmmovqMgs9JhtWuPsr7ZVKpG52ouOCzfzxWRPrdzP5VrZl7qSJdiSuPlUAfwr3+tFFT8T1C/IrIfaaUwtl1K9cykS7Jxn5k989h06etdqstlplis6tHDCg+gI/qaKKohnGalMdRvpLnTrK7aBurINoY9yBRRRQU9ND/9k=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_thumbnail.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crop image ok\n" + ] + } + ], + "source": [ + "# 裁剪图片\n", + "\n", + "from PIL import Image\n", + "\n", + "# 设定裁剪的大小\n", + "box = (300, 300, 500, 500)\n", + "\n", + "try:\n", + " img = Image.open('files/test.jpg')\n", + " # 生成裁剪图片\n", + " region = img.crop(box)\n", + " region.save('files/test_crop.jpg', 'JPEG')\n", + " print('crop image ok')\n", + "except:\n", + " print('something wrong')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_crop.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rotate image ok\n" + ] + } + ], + "source": [ + "from PIL import Image\n", + "\n", + "img = Image.open('files/test.jpg')\n", + "img2 = img.rotate(180)\n", + "img2.save('files/test_rotate.jpg', 'JPEG')\n", + "print('rotate image ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_rotate.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "也可以用如下方法，进行图片的旋转等处理\n", + "\n", + "```python\n", + "out = im.transpose(Image.FLIP_LEFT_RIGHT)\n", + "out = im.transpose(Image.FLIP_TOP_BOTTOM)\n", + "out = im.transpose(Image.ROTATE_90)\n", + "out = im.transpose(Image.ROTATE_180)\n", + "out = im.transpose(Image.ROTATE_270)\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enhance crop image ok\n" + ] + } + ], + "source": [ + "# 增强图片效果\n", + "\n", + "from PIL import Image\n", + "from PIL import ImageEnhance\n", + "\n", + "img = Image.open('files/test.jpg')\n", + "enh = ImageEnhance.Color(img)\n", + "img2 = enh.enhance(0.1)\n", + "img2.save('files/test_enhance.jpg', 'JPEG')\n", + "print('enhance crop image ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/jpeg": 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r8dpcf2pCbl1kjTCMfLwTg5Jf1btyBxWdYs90InluX8hI3u/LfDKecKBnkAB+x4HagQhQxxwMY1eNgUciXbnHbBBwR1HPT6U0Kyz7ZptswUCEj7vl5JXJHJJycsOh4xVmSSBLJt5ljEm4xgoX+dRngqDnrg5A4NRFbea0Tz7qGJdokSR3w0eRkfKeSp7jv25pgFwzraXU7p++gCSAbtpkcMAoJH3s54PJz+VVnne7nkQ3UWAuVjuUUkMPVjjj3XkdwKVpHuYLKWOBhFb5lDzYyxYbeFGSFGDk8EHnAxTZIRqDTZTeEi3lwR5qgZyGGMOAOQT1HQ54pAZ2oXv2rRbDy4GG2WRg68hcruZWyeh+8DWMJivIrcug8PhyXY0LREt+8KkZBIHygEYb1zkelcsZc98j2oKRr6ZdjzzGXwWbj68Vwepvv1K6Yc7pW/nW1cXLWskU0ZbIzn5uvNc7KSxkc9yP1NAup6D8LVbydTP990XP0UtisbxVobaj4jv71yVhj8scA4ACKCSewFbvw1UnSLvD4JlkI+uwCuY8RXk3/CTahF9oaIS3DRsZPlUKAFOW/u4HIpDOQv333ZC/dUBU/wB0dKj3NFIHQspXoRwQR3zWjeQwHUDILqOVdwCrHkrtA4+b2Fa0ulvFZTQrbxXBc8OuGIA5BBHTNMCO18VXMtlLZ3a+b5mwCToQA6scjvwuKqxwXU11fSacrCBC0pOAdqA8c/jUFtayoLmJ42XCZ5XoRz+HGa6zSdZe3gk+0w2UlrMgjmBbyWYdQAwHXjvSA5qGKDV7NopbmKC/jP7rzOFmB/hJ6A56E8dqs+Do3TW5chgyW8gweoJwKp6tYpFLJJal/KJLeVJ99QfpwRV7wnOX1G4Ln52g2g/Vh1oAbZWzLqeno4wXWQ/kMVvJpaMScLxVadTF4l05GHzoswPvya6CEAyuMdqYkchrlqsRjAKgkH8cViqgMe7PQ9K6PxRhGtV28EN/SuewB1NAxucHBFTqR5YqA9cGn7sKKQCFuaY8h2AUjEk0koK496YCbq2dNRm8Na86JuJFsh/3TIWP/oIrBBro9BuXtNF1OZCPlntSR/wKQc0hmhfxXKaTHY2100ckjR2zW7uo+XG4HnlQSOccE9asWc3ibw7qF1qVva31vpN1KEnIT91KDwScgjqTg471JEza3r+gJceU13JO0spC7XEY5VT2IOCRjtXrOv6sLWyS2kjRoJlMXlSD5TlcAHPY+o6e1AHhOsNEtnPNbo4jvpwSJDkqUZsfofyrpPDkQtvBMBx891dSSH3C4UfyNZV1eaYsuo20QX+znmfyup2hRhWByDy3PNdF5ZtdF0mzIwYrOMsP9pvmP86AZXIy1PFGO9AqiRadTadQAYpCKWigDM1vI0i6P/TP+org1GWfPevQdViafTbmNBlmjOB7jmvPCSpoA938B63bWcJnmD/vYo1UBewUAnP/AAGuU+IXjya81BrWzk2GNhkj+EDt/jXJ6LdawkEn2Sd1hjUkI+SCcZwK512dpi7sxLHJJ6kmpsO57t4YvtO8a2iaV4itYrwNFvt5m4kTA5UN147c0yb4GWn9qRva6pKLAtl43QM4HoG6H8a8+8G6xHazCKadYHQ5ikLYHPBBNer6X4zu4iECpcwgdEbLAD0NLYZ3+m6NDpUbpCWYMc5PXjoK80+LmuS6VqenQCHdE8bMSehweRXf2HijTr638xbhIyPvLI2CKz9cXwx4ntPsWpSW8yg5U78Mh9VbtSA8os/Fen3uk3Wl3s2yzuIyrRyH/VP1V19CDg+9YOjeKbeK3eK/lcSYADjJBx61teJfhBqFuGudAuBqVn12ZHmr7ccNXmd1aXVhcmG6hlikHBSRCp/I1VhXPcfB+um+1G2+z3rlJCR+8J4weRg9zXrqudua+cPhvZyTiabLACeONfTJUkn6jivotz5Vszn+FSc/QVLGc94g8KWWskzwsILvuR0b6j+tcJf+FNX04kvbNLGP44uRUFt4hF9r8gstRxcK7ZUuQcg88d/wr0HQfFkd7mC5dBKp2lkbKk07tBY8vZHQ4ZCp9CMUgNey6rZ6TeW7R30cKbhgSHAYE9xXjkyCKeVAdwViufXBxn8apO4hOKaTS5pr4xmmIQmvONYvLu4vpPtLtlGKhOy4OOBXUah4gSCR4rbbIyjr2B/rXSf8IJp/jLQ2uNIzbazbgGQSNlLgEZBP91u2RxSbA850m4WC7TAzuBU/jXu2xfE3gmDUVGb2zUo57sq9Qfw5rwubSr/SL37NfWssE8bAFHU9j2PQ/hXvHgC3ubHT9StrmJ1C/MARxypzUsZxbNjkVJBumkSNFZpHOFQdSfQVha3rkemQBdm6eRchew9ya44apc3F3FczTSs8bAgB9uMemOlUI9SIZSVZWBHBB6g0hrX0bzPF2hs7L/xNLdQyydDPGem7/aHr3rHO5WKsMEcEGhMA4qMrUvFRyNtjZvlGAeTwPxpgcn4zuJYoLaBJGCSbi4DdcYxn2rkIL2e0Y+W/B6g9DWvLdRy6zJLqu6dVJGIWBHsAfSqD6a8tvNd2/wA1tG2Dn7yg9Mj+tADLeBruQFyy5BIz6Voz2o0sR7UVvNXPmZ5welUIbLUJ4t8EDssShjjrtPQ+uOKuWGoLdN9m1F2EZACv/dI6de1IY+O5BjHmuqx5xjpzVu8vLlY43xFcRcLkruI9MmoL/TDax72VXgb5tw6VDY3iQrJE4V7dlwU78f1pAS6peXsS20kUzvHOmfn+baQcYXPT6Cr2m3d5GkbSlmG3JB5P4YpdLvVb/R7WNTb5JxcfNj6Ht9avw4MUs8cexQMZPIyfegZkT2cJEaPPtG4sc5GR6YqtHpZeUtGGcE5HyY/DmtaWKRLy2GVcTAnJXIPpyelWJb9LeMLLxJyCB6ikBz8unB/NDcGPGT7k45xVKNJrTJC74yeE+n8XHSt2HUIoGuXHJkIK59emKl+zrf2+8IquwORtwGx1FO4jEaOC/h3q+1x/H0H0YDp9enrVM27QNiTgj/PFXrlXs3Eke1NnHlBPvZ6hqeY8gNFtkgYZ8rcPNi9QAecD0pgVrd3iYNH8p7f/AF6sXl406xxyIijGcDmqk6iJfOjfdGehHT6H0NVBchjhxwf0oET7GjO5OncelJGrCRXRuMj8KcGK85ynY9xSPtHzKcH26GgRq+aPOjjUJl+shySF6VUeJydqjcrd8cg+9RxTkYVuOnHr3q6joRuk+brhB7UDMi4t8HKjBPb196qkEHBroZ0Ywh9iGQrnHpg8is2aESZYhh1PuKBFCjNK6MhwaSgBaKSloAKKKKAHKQDVyK5MEkcqHDociqNLmgB7sZZizfxEn86tW1mJpo0/vZH6VUU4OasLcbR8vBFAFdl2uR6GkApwGW5q3HEhHPHWgZTzil3UMQ3SkVS3SgBwY08Ox4JpPLI600gg4oESZqzBKFqmFNSJxQBZnkaWXdlgPTdRCF8zmod/+zTkcqdwoGa92yS2QeZ2ypwD1/CsnZu+5z9Kle4mni8hUyOvHtUa2s4ORx/wICgZPBpdzcKXSP5B3NVXjKkg9q0reW+QbBMhHu9W4tNilGZbqFC3UAFsfpQBz+cUoyTxXTxeGrSc4S+cn2j4qiugahHcPHBayzkEj/VMR+lAjLt7qe0l8y2nlhk9Y3Kn8cVeZ9Q8QX1vCI3uLpgEiWNMtIT9Bzn1q5F4H8Q3BITTJufXC/zNek+FfiTe6Vr/ANi8WW9nawQQbBLBahX3DAGWB6Yz0HWgD2PwzYTaV4Y0ywuDma3to43PX5goBrXrzm7+M3hiCGRrcX12V7RQ469OSRWXF8ahcXMaw+GNQNsGAllLrlQe4AGDj0zQB63RTFYMoYdDzT6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+FPNIOAcVMtxtxtHToO2fWtnWvDV9pUkq3tq8R6R+hA7g9DWFbR/6XGkg4zyPwoA0be6JaKIMrHPyoem71aoriWYXZkkdvMJzkcYx6emKpzfu5gY+MHIxV53+2xB8fvOjfX1/GgDe0vWHuQEvk3on/LwF+YY/vDuP1rS/4TqVtWMsGz7NHCtuglj3blBLFjyCCSex6VzETmKI2ULr50wIPPRe/wCJ6VnyK0D7TxQI9VtPElhfAfabLaT/ABwsG/Q4P6mumtfsv2GO3t5P3l08aQqVIYgOGYkEdgMk14Vb3csJyjV614e1ZfsmkJcNgm7EX1UxsGoGiO4ki1HWdRhWT91PeO1u5b5VkGFH/AWwVPvg1Z0O6eIm3lGSuRsfuvRlP8q5yC5hKmBCpRCyAhuoDEDH5Vri4a6iW/T/AI+YyqXHux+6/wDwIDB/2h70IGNurY6PqeYyxiYboju6xnjB9x0P51f1WJbyyj1SD/WQgCUjqV/hf6jofbFW0W31vTzAxVJU+aMtn92x6g/7J/8Ar1Q0+d9K1GTT7xMZHMb8ghuMZ6FSOhoEauk3aXcGyRQyOMMp6Zxgg+xHH/6qo2FvPo2qag8JLwxQo8QP/LQtJsVT6HBKn86riBtH1TYrbraQZjJ/iTsfqOhrpnnV9InZucBHx/tKwYH8QPz+tDGjE1Tw7qS3krw2LMpPSKVXI9R1B/Ssb7FeaexCwXtoT/dR4/5cVv8Ai+ee11J7mB3w4yoU4zkjHP49aS8u9c0NoYrm7QmQkAJMHwQM4IIB/HpTEUrGGH/hHru+vnuLgzXJjP758hEAHcnkk55qoNGs7vmxuxuPSK5+VvwYcH8QK17e7udT+1R3g/czKD5oXAjcDGW9iOCexArBnjWCRopJEDqTxvH+NIDS0XS7+yv7x5I1hENsFKSniTzGyNrDI/g69O1NUklbLUg0coy0Up5KknPX+JT7U7QjPcXktslzF5ctq+4SfNnaQRjByCMmra2N6IDEDb3kQziPzOQexXIBB+hpgOtluX1AXyWybIMw3E0kyxxSEDGVY5ydvB47VFBa3Gk+HrtFVXlaERCW3cSKqnClmxyMKSckYrb0W0tzoyvcorR22mIqxtyFYq5kbHTcWGCfarOntKlni6tvOggPliZDmSMqozk9eDnvU3KsYNpbJbafLc3277FINgtx1mJ+6FHr3B7daq2VnFZwxzalc5jcAK8QLbnA+4fRvrwevtVzUobNr3zI7xltYFUyjYdsatyHVcYAJ4O3jkEcVFdSw3Fk2n2cbRpKyFrmbjaVYMCqjnPGOcdaYh1zqK3Fu1qlskdi42yx9ZJF9GYcKPYfnVi5hXUZ/Onuovs8i4Ek2ByP4G7Z9PX61Xh0u5meWPCCSMgMN47jII9VI6Gr9vpNwtvcwzBRHJGSDu+64GVb8CKBFSG30e2yqXavzkiFGbJ9c45/OrN0w1myurWGGUIIYwss2FBcM2e5IyrFQTxnrWbZQXOoWsdxbxt5TAHzGYKh45AJ6/gDVkx3GnbLj900e4IzxuHUE8BW4HXp0x2pgXo5Hu9Bk3gpMRmRCMFXACOCOx3ID/wMVhy6RZaxpt3HcJLJeozGKNUVt0YA27Vxlucg4OR6Yrq0lgNncXJX51tzKnzfeVB8yE+oGAD6Yz92sS60u6WV1NpLtByAhDlT/wABOQRSQzz6fwrqejuL/RZJYpMDdEewPUMD2HvkfStDT/GKxXX2DW4WsbtDtZyv7sn37r+o966iW/vrSKXZe3CyIjHZIRngcffUmofEXhrRdUEMVzczLMsKbbl0D7gVyCcAHvQI1LTVWRBkrJEw68MCP5EVtRXcF3EEcLIg6I7cr/ut1H0PHvXkn9i+IvChD6Y66np5YZjjO4cn+71Un2H51uaL4pstTk8uN2trscG3l4bI64/vfz9qAPRDD+7dRvuYlGCA2yeIfh1H5j0rNOnw+WkmmzL+4AXpkqMYwy9cYHJHHfiorXVcbBL1X7rjII+h6j+XtWkJbe6kEknyy9riLhx/vAdfqPyFIrcxZre4SzmndFwswmAi+4ATtYLyeMHNQrcxwWzC5mQeRF5bRScedGDxtOfvAcY65Fbt0iQoJbqC3uYW/wCXgxblP+8y4I/EH61XYxXPKCGQem/J/AnB/PJpiMnabaaON5JTCH81ZI/vEHgnH8wOhGehqu9lHqMJfDoikmQCMg7c4BC8EA5BK9v0ro4NM+0siF7eERtu8sIzP0weGAAB74Bqy+mJbxvJLMPs8Y8wxRxBN2OQGbJJGQOBjNK4WOF8Uxf2ZosdnslXLf8ALRNueckgdccVxalc5Xoa6/xrI8ksfnPuuFY+YD0yQD8o7Afdrjk4FBSM+7lHzqOc9fY5rNfiNz6sB+hqzOcsapuTjHvTJ6np3w9t2HhzzF/5aSSLz7kjNeaXErXuuagrq8nmzSAEc4JbrXpHh/X9N0DwdplvfuYmu1Zo2wSuQxzuI6da5J9N1DRNQGoJsu7eQlllTDKQT3HakUY//CO6vagTPZPJD1+Tnj6dRV+DUGto3G/GAVBPbAxtr0nw9rdvcwC3uihYr8pPrjAGO4rj/FenWbXclzbDypB1IxtY+4H8xQIxrdJlje6uiwjW2YRxnOSWyOB3wvJP0qpbbb/SJrb5jIsqup69AR+uanh1O7uoo7GUo0PC/vEBZR04YYP51La6X5F4YoZGHmlV2cqQCeoPQgd6AI/FNqLOW2MW4AxhG3nncFGT+OfzzVfw2wS9kc7RtVeT7tTfFN/9q1u6jR2aKOd9mWzgDjj8qZoRXz5Q3RgoI9s80AdLqzqvi60cHObdmI7AnNbEM6iUsO4rktRuhJq1o6n7toq/zqwdQeOPIOSB09RTEiLxVPvntwOwb+YrCV88Vb1GY3LBzu44596z84oGSk8k0B8jFRls5p8Q3NQBIqZqbEDY3oWx+FKV2xk1HSGizCIZJVijtl3yMEX5u5OB2966rxLOYrG50s6bb21sJVW2lXCvJHGSG3euW5+tYvheNZPEti7jKwsbhvoilv5gV02uaSNQ0/RZHuhBLGZLb947SRyAsXWRWOTtPGR2zjtQBY8OXcOuPpetyQql/ZBrKYxJtEgCAxOwAIzt3KTjtWn4j1CG98zTYypa6V0gIYcSbSyEY4zkAdAcGsnRhP4evrpLoItvdSpCsmThZVUMr9iFOSufeqJtU1TW7a4utSisRCZGbzd/yyKVVNpHLksRwOmOeKQHD2azX11BbNuJllWMD/eIGK9P1Zg+qXIX7qtsH0HFZ1za+Z8SbBpbGK1lV3uJ/KbdHLs3MJAR3OMH3qdnMkrSHqxLfmc1SExDQBRS0xBS0UUALSYpaQnFACFazpdIs5Z/Na3Qydc4/mOlaX1pMZb8KAIkhVRXGa1olxDdySW8LNCx3AoucZ6giu5xTGSgDy/cycMtW7TULm2ljkgnlQoQRhyBx7V2eoaNbaguXG2QdJE6/j61jr4RIlG67ymecJg/zpWA3U8dWLEyNavHIRkpwRn2+tZo8czlpC8CkE/KM9BTD4WtGYgTTAds4P8ASsu68MXkLZgKzjtg4P5GlYdzq9G8etay+Zve2lHeM8H6jofxq/4x8eWviTwwbNhYvdiRfmeE+YVzyUPQMO/PSuFtfDl7IS1x+4Qdc4LH6AVkPgSYXpRYLns/wtslNjYjvLdSyn6LtUf+gmvZbpDJYzIvVkYD8RXkvwpUmz0sYx8srfm716+MdKl7jPjSd3W9mWRsSCRs49cnNdd4b8TxwOEu52hfaV8w8hvTPoazPE2g3cXinUY4LZzGbmQqQOMFs9au6V4ajixLdsJGx9zsD/WrsI7CXXWv2PkOzoODKWPJ9vWq4OajVQqhVGAOgpwoSsFx+ay/EMskWjTPGdp+UEj0Jwa081Wv7cXdjNbn/lopH49v1piPN4pdjnvmvU/h34imsE8/5XAHlSL3wOQfyFeUzwyW85R0ZXU4INT2uqT2bb4CyHvjoaloaPpc+LtGu5FW9sXx2kZA4H5c108Vzb3dput3R4mUgFTkYxXy7YeLdRtLxZ7j97G6/cdOCp7jP869O8DeKY59ThSFWSC5IRkLZ+Y9/wA6mwzgPFtoJLNbhesLbT/un/A1yFqhzJgZ4rtvFN5BFBc2jOvmvIcJ3wHqDwXoVprt7FbRzMt4rh2ilwFlQEEhT6461Yj2H4f6ctqJm7wxRwk+p2jP8qzvGltaxar59u6Zk4mQdnHf8e/vXfWGlxadbPHEcl2Lse+TXic93qF3rVzJMES2WRhg8uxz+g/nUrcZaz6VleIra4u9IkjtwxfcCUHVgOorUBpRVknnNr4d1G7b5YGjT+/L8o/xrrtK0KLTrKWCQrMZv9bkcEY6YrYooAy5NIjWOJbaeW2EcRj/AHeCSuc4Oc9DWJeeFoFH+j7v9Xjk8lx3/Guuxzio5E5BoA4W1N/YaimmOFnikwPLPIwe49hU194aaNjLa7l7mI84+h710Vxpgl1O2vlfa0YKsPUGrbqW+8MjrSsM8/srj7DdujcFcg1sxXqGzVt6+WABIn9RWhq+gwaiPNj+SftJ6+zf41i/2LdwReWNolJ4y3y47nNIDeOrww/J5KSBvl5xjn0z0rCuC0l5fRlMmNCwGex6Y78Vl3yX1lNC9y+5M5jZGyvHXGK6KS086E3kKIWMS8j72c4JJ/pQM5JlleTEW5vpzW9ZXKfaUTzlVguChJxuPpUmnrHuldggkBJH1I6n2rDhRvtxBGOtAjpp4nmjL5aMngkdT6GuZubOe0lefa67TkO55P4110K/8S0M3sOa5S/tZmvY4mjmXewX533LyccGhAWpin9oGOKNUaSISNk/K2V3EEfyrMa0S7jMtn1UZaE/eHuPUVswQLcXtzctsMjM8cAfkAKMZx37CsyK7Z5wtwio4PyzRrtZT68YpgZ8crxHH6VISSc9B6CrtwkV1M0M+yC9U43jhJfr2B/Q+1UTFJBOYpkZXHBBoEXy0M9tvfesigLgdDjvUcchjYbzkdiKIcZKN1/zzSPG0fTkHt2NAi+s+4fezwcd+voKXcrRTIq8JjcZOcknBrNV9nI6eholumaPYOBkn8/WgZJIUlO1tuenHQ/Sqc0BjYgdq09HRvtAu2GYrch2+vart1FbzaebtRtlklI2BeNvXd7UAc1RVm4t9vzKMcnj2qrQIdRSZozQAtFJmigB1LTaXBoAcDg1L53y7cVFg0UAO4qWHGagpwYjoaALNy33AvB71X+Y9acCzfxU4RuegoGSwQCTrTpIvKcoeop8KSr/AAMfpTJ7a6mmMhR+f9k0AAWgptGT3pFguk7E08LNnLRk0BcjVJGPHFSrbSnvU8aXR+5Bj61dh03VJ/upj6KTQBSSymb+M1p2mi+cR5k7gf72Ktw+F9UkGXkdR/vAf1q7D4NJP+kXQA95KANjTtHt9MhDQzLLv5ySGI9jitEXDLwNn5VFaWuk6dAkcuoW6BR/eH+NWTr/AIXtfv3vmEf88x/gKAsXtKuyk+9uo5z7d6h8W6ekjR3sW3B4J2g/Q1APGuijIt7G4nA5yRgfqRWPqPxHguoJbaLTOikAlxge/ANILHOXUI0xgHSETAlkMcWQc/3juGRVr+2priC2mnht0uYSGUhCUkwc87SSD6giuebxDeyApOIZk5++gBH0I5ptpqs9vPvtz5ZbrsY0wPYbf4ya1b2MU97pFrcBj1gcqce4OcV7NbTedbRTbCvmKG2nqMjOK+bfD2iXni29s41LbnlHmHpiIfeZuTnjj6mvpZFCIFUYAGBQBJRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfImkeP7y3gFnqKJqNmeDFc8kD/Zbk/nmtVdB0DxG6T6Dd/Z7ocmxuWwf+At3/WvMxmporl4iGUtx0pWGberaHdaVdmO8glSTnAKdfoehrEllkWU7dyduP612Wl+PrlYFs9Xgi1Oz6bLjlwP9luv51dm8M6L4mUy+Hb1Y7kjJsbk7X/4C3f8AWgLHnauytvBwRzmtcXCajDtcbbgDn0b3HvUeoaBf6bdGC6tnhkHZ1x+Xr+FRhEtYi7jn+Ed8+tMRb0+xIczzOqQpzk9K0bnWmUJOm5BCpW2j77jwXPviuce9mcgO+7b0B6flUTzNK252yaANO3v5LcjY9dRoXiOOK9iS73GCT91P7xk8n6g4Ye4rhlYjpViHfIdiBiT6UAevabeJZ6nNZzSL8rGMkdDjow+oOfxq74ktPOuVv+okVI2PdWUYH4EfrmvNL7UZYrmCRmxKYUEnPVhx/LFd/wCHfEMWpWUdvdn5ZR5bH0PY/UHBpiZq6OseqWYsrv5wHwsg4aJiOCD/AD7Gl0u4MMk9pMd5hkeInbjcgOAcetZ+n3a2moyW0rbXVijfUdCKvapA8FymowdHOJR6P3/Aj9aALV/ZnUrE2Um03NuMxHtNH2I9wOCKyBqVwu1Lq2trsxjaklwGDqB23Dr+Iz710dpLDd26nLDHzxyJ95fXHuPTvyO9ULrSftc0lzcXFvbosrJmJC3m7f4sHAXOemTQBkXl/fX1vLa+csMTKf3NsmxT7MeSw/HHtW5f36wi3ubW2txbzorr+5Xg45HTsciqwi0W1Pzia7f0kl+X/vlcD9aI7yO6n+yR2OLRhnZEOYj/AHlXuD3HXPIoAjl1m6kRWjSFZEYMp2D8jgDgjg+1Lb2DX8I1Bnhs4pGb5CxkYEHBHAA6+pqKPS55bt7e2dJAvLS7vkjH+0e3061buPs2l2v2a3VrsOd0wdgm5jxuXqFxjGD17880gHQT2tu0Nit3M8LyZLhVQE53bD1G0kZGe+QeDXQhZrDS5v7P/wBLtisnA4kiZgSSc+5yQf0rndPaKw0iK/2LLd3UZwcZWEHgoPcdCaSyvLmz0iW+Vt/7944UJZW2KADhgcj5s4ByBQ0NM34LeCbT7e0YIGnu2iYDH3BHtYcdgoH6VzEUt21hDcSQ2k8br/rPKxk9PvK3X6ipz56ztp1vDFZm9hZhci4aaRiw3FFJACFhnnHNNhA8v/Qv9GuUUCS3T7sijjKqchh6g8j6c0IGUr5JLm9trua2hWJ4PsybGLncpLYJIBHBOPYUghi3AmGJvYoCDVnUbi6EGlokKQI8zXPmxHqyLjaFIOPv5PJGKBdxycXNqrf9NIf3bf8AfPIP6UCLs1tJcx2y2j7YHjKxxdFVl6qB6EHIHbmqbabf22ny2CQxLbSyiR3Ef7w4cNtyDgjI6kZA4qa8ENxp1jp1tBcXEstz9oAeHACKjbvmJx/EOhqErf6cOXu7Yf77Bf5kUwNGFXk0W5jb5XB+XPH3jt/9mqHxBdNBO16rvsMXm/J1PGePzosLi5v2MEt65O5HUeUg8zawbaWAB5I61Z1AQambmKBWyjMY43GPlYZKEe2SR6j6Gl1AguZdUswkd2qMG7OVlTOMlT6H2qJkN1p4WRFjkiyISeBJH2Az3Xpj0warx313CqKUtLgRjajXMbNIoHbcCN348+5p66lM2o2l5ez5jgkKgBAkcSsCpIUZ5GQSTk4pgZ+8D5d4GehDDg+vXseara14Pg8S28Oqxi3trq4Tf8kuMsCRnkAc4zjJxXQXeoXtrdyQtsGDgDZ1FQRSyajdxWjmIRylmV8HMbhScADrux09frSA4z7b4h8LSi31e2e7tAOJo8M4X1yM5H15966bS9Zt9QgE1hcpKgxkdwfcdQavuum2quss80+5WQoibAVPXB5II6jnrWLq/wAPUZhf+HbxrS/Uf6ot5e7HpnjPqp5B9aBnY6fqwU7Wfy3PXuG+o6H68Gn3Wl2l0DJb7LWU/jCxP6oT+H0NeZweJ73S5/sfiK0aEglBcxg7SRwdy9vw/KussdYzHFPbziaF+MhsjH1/pQBfaS/0yUR3COozwG5U+6sP6HPtV2bX4H0mcSvh8bRn72cjjHf6j8RT7bUYriExFUaM/ehlXKn6Dkj8Mj2qKPTIY7l57EkTOoQRSMNyruy3lt0JI465ApMaPO/E+oJc6zfxoVZIrglGHPBUZAPpurAc/uiw6gVqeIIJIdTuZZkZJZ5ncoey5wvrzgc1kzfJA5/2abGtjIlPzmq7fdNTtySarStiN/oaCUdB4st5V8J+GHCHYsLgn0JCMM/UGuh0ZIrbwFp908ip5SuZiem0uNufzrrtOhgj8BXE1zCsi2loHAIB5WPOOQRg4xXnupeINNv/AAdexWjJbt5UaC1PBGZFJCjuAB2pFE93YxzHzISsbtyoHKNn0rnNQNxa/JLvHoO34Uzw1rEFl59vfTSiB1zEByiyZ6kdgR3FXtb1qKC+S1j8q5s/IiY4IOHZAWwfYnoelAGAk80d8gXI8wjB+vFX7jZpkNjcPG8vntK4jdzsKKxUDAIPUEnmr0EiXMEX2Ge3i8pdnmmHdMiE7jj1bPQ/hmszWnmnuCiWMy2sEaCDchJWMdSSOOSST7mgRW1hP3lpJsVBJbIwQdhkgfyqXSQq+ZKexUfnmpNUSO5tbG4g5LRHzeigMGxtUewx9c5qOI+XYzEdQR/KmAt1KH1AMO0QH5UGUlapCQmXcfSniSgSJxypqu4qRGo2lutAyCpomUGgwkUzy2zxQBddgYwBUY6UxQQOaeKQzqPBttDJNqE9zP8AZ7ZLcRNMGCmIO3LAkEcBT29q2rVLW2uG0y71C3vtE1yGSKG92BDBMDlSwH3SGxn2OayNEeCPQvsLxs0+qNN5TrkmMxoNnHcFiVI96oQxTnw9eXcEMVxFtxPCOXtMMAWZW5AI4DDPoaAKV2+qrdmwvndpYiYnRySVK8EZPpitVbS31M3V1NMI5Y5XIYZOdsZYBl/vMw6jrUtlcaff2pubxJpL5rNoy4bmRw2Aw9WC4HqcVR1Swt9I1O6tWupeMNE0ildwKgruBAOSCR060AdNop8r7VE7wzta27SrMjHcvnbRsOeOcE8H8qB6VS8Ms7eGb25k5kuLmOEHaB8kaZ7e7c1cFNCY6iiimIdQKSlFAC0UnWloAKQdSaWkB4oAWkNLSGgBhUnrTCNpyO9P78UuARQA1TkZpDgfWobq4itIDJNJsQd/6CuZfxLK15G8fEMcgJG3mQA9Dnt7UAdPckLbTN0wjH8hXnSjMnNey2mkWXjnw/K+jlLHVGiOYHY+U/qVPJX3HQV5xJ4Q1y01pNLudOmS7lYJEpGQxPAww4I96Vx2PavhpbolvaBekdkpH1IBP8zXcapqMenQeY55PT8KxPB+iXGkxuLhNu2KOIe+AAfw4qt8RoZjpltPGWCLIVkx6Hp/KoGedalKLrUricDiR2b8zUIUYHFLj5qdWhIlApDRnFACk00mgmql9cm2tWddu/Hyg927UAVNRFifmu44mIHG9eao6/4QurSyi1W2hhudLcc3Fq5YKx7MMAr/ACq5o/g268U2ctxFrFu+qHcyWT/xKDg/N0B9BjpzXReBh4h0y8ktJdLuXiiYRXUEifK0bcYweDjsalsdiHwte2PiOFNJ163SaykBWKTbh7dwuAVYcgEDp0rqtA+Fw0XVvPTVTNYFleNNgD8HOCfcdxXNeLdItfBnieN7cNFpt8vmoP8Ank4PzKPb09KW28Zv50UNvqT7uSuHz+GPekMw/GXw98QQ+IdVvLa0a4sxKZVaM5by25GF6kDocdK2vhn4ZeSe1vjvSXz9wP8A0zXr+ZzXrmgai+q6VbXjgCRlIOO/v+NacMUUOQkaqO2FAxnrRcBj31rFKsLzxrK3RSQCa4Lxd4bNtNJqlmmYJDmZR/Ax7/Q/pXE+KPFRtvE9zZ3kEu6OYhy3Hfgj2x3rtdC8TiK3EVy/2vTpFxnO5owex9RS2A5QU4Ve1qwj0/UmjhdXtnG+FhyCh6fl0qgDWhI6iiigBaa2KfTGoATFNK08UYoAiKDtUckKyRPHjhgQR9Rg49KsEUu0EYNAHB6nodzZxSpA++xPzFCOY2/z3FO0u9WOzFvclmgDbQ6cGNgevuDmu1eMEYcZ96w73w/G8M6wMqecQ3tuHf8ALtUsZmoYtP1by2mRpMlVRF35B6bj0wKjNubnU5I4o8YkOewH/wBb0rGurN9Pn8yK6U7W2t1V1+oPP411qwWd3pUzB8TfK6yY4OB6+9AyS6jg+zfZoJEmlUDegyB06fnXH/ZbtZn8zfCkXz/OxI4OQPfPat6RkuLuP5ngulAw+eD9Kq31vq8l3H5szSxKckxkYA+gA5/CgCpcmFTtedoIpGNxBKF3AZ+8px71LbR2moxTpGG+TA+0Sf8ALRj146Cqu+a2EsbWqvCDuMcwyFz79jVmHUVe28uW2iWFxjEYxtz6CgCrLc29xclUsomk6NIzFgcccU6Qo2LW7RiF+46ctH9D/EPY/hQ1otlbS3Kzo3KGLbzk55BB7YplxPJew7ycSg7l7dOtAiG4tpLYRs5Vo2/1U0fKt/8AX9QeaaJCvyP3/I1bhmuGjjnt4RNHMdtxbn7hYd/Ykcgiln09ZYnmsNzRqMy278yRe/H3l9x+OKYjOmUY3J/9emQQGST5uB3PXAoLtnYO3errrE3li3mX5owZB0we4yetADo5nhjMCn90W+Yjo1XZ7qBz5hVhGg+4hxk+9ZDSttKDheow3Ga14rCFraOGfd9plG73X0H+NAFBr1riVFWNMsQuO3PH4VWvbJ7e7eGQbCD+FXLG1SC6ld5FxDnaeoLCrV7IxsTHPDm5mcSKdvIX2+tAHOSRtG21hg00VuTWLT6eZNn7yEc/7vv7isjySGxQBFRTmQr1pUAyM9DQAAHGam25GRSomCUfjPf3p6gqSjcUCK53KcUBzVl49wqvtIoAUOacHHcD8qjAp2KBkodO6D8qkR4P4ox/30RVcCpFQE8tQBeiltR1Dj6SkVehurYdLm7X6Sg/zFZaQKf4qc9uqxlh2oA6GG8gPH9oXP8AwONGrTtVW5JSKeKWQAnZJaAZx7g1yFhCrTgksGHIGOorvNJgdNLkYFICTkSlhz7e1AGFca9JZzmBYEVh/dQCpJNYvltfPln8sH7o28mmT20Zv5ZmLTGMDCHGT+Xauevrqa5nLScY6D0oAty+INRkJAuXx/vf4VZslurvMlzM4iAySWPT/Gse22CYF/wrpZYBe6X5du+0ryU9TQBi6jerNLst0VIl44Xk/Wk0+xkupQW6e/8AM+1VHiaOQo4wRWrpV75b7X6YwR6igBdR1BIo/sdqfkH3m9TWfEdsEz+2PzrQ1LTdsvmW/wA6vyAnJ/StPQ/A3iHxDbOmn6e+Nw3SS/u0H4nr+FAHIY5qzbQySzRxxRvJK5CqiLkknoAK9h0r4A3TMG1XWIo17paxbj/30cfyr0nwx8OfD/hV/Ps7dpbvGPtM53OPp2X8BTAofDnwSPDWlQXV8GbVposSZPESk52Ae3GT6139FFIAooooAKKKKACkpaSgBaKSloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopKAFopKMigBaKZvX1WkMqDq6/mKAJKKhNzAOsyD/AIEKYb22HW5iH/Ax/jQBZoqn/aVn/wA/UP8A32KKAPinyYJYHKff6j1Hsf6GqYjGNhGJB+tPlePzMpuRs8ipAon+VuWHHFAFYjAGOvcVatZArIyz7CD3yMH1BFaCeGryS0FyuzaT3fkY9aqEC1Oy7tFc9juKn8xwaBnVaf49uEjFlrEMWqWQOB5v3wPVW6/nVq68Kab4hh+2eHrsmSTOLK8bbJx1CseGxXDXctvL5YtoZUx97ewP5YrenZ7jw5C8D7f7PGcDgkMQCwPqDj86QGHf6PeadcNBcwSxSL1SRSDVLbjrXZ6f43eS2FlrlqmqWnQea2JY/wDdbr+f51ePhHStehM/hy9WSTqbK4YLMv07N/nmgLHAodrZAB+vNTrJMwwGYD0HH8qvXulyaZcNBdI0MqdUdDn+VVftVuh5Dt9OKYhUgkJz/OtzS7h7VdinOTmsRdUCfctU/wCBkmlbVr1xtV9g9IxigR1fiPU5IdWhuozjzoEYj/aHyn+ldf4b8TQanZi3uzxIPLk9j2b8OteQhbiVtzbmPq7f41r6S0tsz/N97t70AetWFx9k1GSxkfbIjYH+92P0IrVnRZ/MsyMR3Kloj2EgGGT8VwR7CvNPEWsPDd2F0pxJLAu713Lxn9a7DQ9cg1uxjikfbJJja4bBjlB+Vgfr+hpgGlvY22o3EWqQREsqqjzLlI2BOQfTIIw3tWmtzYaFDI9tNDdXjkmKKOXeFycgsw6KPzNOu7BNVhhuE2pdNkegkIOGA9GBHK/lxWDNB9iysibMHoBjJP06k0AaWk3btZyacZFEzyvPFIcDzXY7mU/7WeR7cdqqyLIkrCTO/POetaljYxaVam81CNWunX5YnAIhB9R3Y/pVcSy3enahIUEhghaSMNneAP8Aa/ix1wRnjGaQCWETxaRc3KztGk0p2IUDoQo2lip4yW4yMHAqC1njbSbbTriRIZoWbDvwk2WLEg9FbnkH8DVzVFSG1tYbX5rRYl8t+zKBwfqTkn3qnYZ/tnTcd7tB+YYf1oAtfZptR0+2W2PmTW+bd3iYMY2VvlbIPpgii4utLjujc399Db3EB/fxRv8AOJQOSmOue2PXB71m31jbm7mIjVHyRvjyhGfcYq3PBC1jbXNtCkaMCksaLgCQcNx79fxoAg+06o13DcX6W4kjiKLbeVtUK2C27knccDkdO3FW1ghvMtabhKBlrZ2+Ye6now9x+IotpFvYls5TiZBi3c/xAf8ALMn19Py9KhghP2yNGDZVs8ZBGO+RyDQBoBWTQYy8ggP22NEdztxu3Bhk+oqAm90+8hN1NcRW2W84phuCPlPIOVB6j0qTUoLu+1CS3ku4Xis5f3VuyhGLFFO9m6MQGIGcfnVWK5urAmAdBybeYErj27r9VOKYCTyWttqsBsZ4XWSLzWSFwyxsGA4x90MDnHYg1c8QGWzli1O2/wBaVAkQcCRSTwffcuQeoL8VFANPubkBU+ySkhmjkxsb/dfgH6HB9qk1u6Sa1RoZFaN5ore2I/5aCMtJK49V3EKD0NJjLTWkN15t3dT7Ygw2PCBulRkDK7E8AkHHA5IquNS061MkdrZI5TG6Sb5zz0yTkDP0FQaRdja+n3LL5ZQ+Uf8AYBJ2/VSSR/sn/ZqWG4l0Se7tJQ4husbpEQPtIXaGK/xKV69cEZ6UAQyCS508zShAEmSKOTnJ3cKhAySewP51ag0uHTQLjVDiVSCsMb4KsOQWYdx6L+NUru6tjpVtpli7vFGQ8szKULMBhQMgE4659cYqQJJrNuLlS32qMlG67ZWAyWX39QOQfagRNZXEVzrHmSQKZFDPAB/y1kByMr03AZIA4JqvatcXF3qly8kqSrbAnoRuL4AZSCG4z1FJo9nHNqoNzHmK1BuJEfPUcKD9W5/CoZtXkiu7xpN09rMmxwNokUKSwZWx83U8N1HegB15YW+oWlpHdPEtreKDI8dvGhEoOMMQOVDDkelcpc+F9Q0q6lk0CYx3KEmWyPKS46lQf1XqOoOK7Cc2tsr6XeG5hjVvOjuJIcqNwwy4Uk4PBz061JJe2cQy8NxdSeSBHMF2LIMfK+SQRjuQOccUAchZ+KTDdCy1i2bTr3AIEn3GB6EN2B9/zrq7bVyigyNujJB9ePX3+vWsTWNDiuT5V/8A6S7qD5zsW80Y4cN1Htj8Qa56HSNd0idItLke8s2P/HvJy8YPcAdf+A9e4FAD9Xa+NyY9Rj2zJnGepQkspJyc8HrWVdjFqe/StjV7lb6/eZU2jaq7MEYIGDwenPbpWPfZFucUi+hjMTzVSYcY9R/OrkrfLnuarqvmXUCf3mjH5kUyUe16oJofhzqkUILM0JTA6n92M4+leEyQIIYgpy7qTntkHGBXoPjHxLqeg+Kjb2tzm2+zRGS2k5jJIJ6djg9RzWO7aT4lAFt/oN/u3+WUGGbHOMdense+KSGc3ZaY80kfnZSJhuyMFiPYf41Zey0y5klhsZrlLgZMSXAXa4AyVyMEN6ZGDV2bT1sWC6g9wi8YMKcHB4AbOP60y6NhLDC9paot4j7t0bv8yg9GBJycdxg/WgDAimeKQPG7K46EcVrwa150IguS4TcGyjEKSP7yg1Db2NtkvIefNUBOowTzkewpZdPt7JxJLPmNlJXCE8+npwaYFi+ee5gZWkEkIG6EADEZJycY9R+NVfu6bMPVv5AVVsriX7ZGqnHmOqkY45OOlaN/Gken7lGCzOD+DACgDJB5p2ajHWnZoAsw88VaWM9KqW/Oa0MZO78jQBJGgYcjn0qRraMYwME1AsuCM1ajuON/ykICcGgDOcDewHY4pBTRS+ZGhAc8dwOuO9IZ1cVhdRXulpAzQMNP3wybsfvSDI4B7HHH5VZ1G1vNbtLWS4t7G1SIutxcRKBcS5OWZwBknbgD1zk8mukjt9O1jwzcXOmajbyeVKZGEnyiPO1VVlJ+TjgnGKs2el6kPt1vEIbi4jiHKFS0ZK4+YZ+YEDkg+9AHDQWf2fVFJ06WC0V1W3cO3zEHmTePukAhh2Hema/oeqalqN1qUdxLe+ZksZiBIdoA4BJ3cc8HpXYz6Jq/yi33b2Cboy6ZGcjIweT23Y5HFFrpPiF57ZVsUeWItL5TzKvzjKk/Q5yV9aAMbT4hbeFNHi7yRyXB+rOcfoBTx0q9fRPJFEyQqot4UjeNGyYsDHzLgFfyx6GqAb1polkluEuLtLVZ4VmcgKJJAuSegyeK25vCOuwPtfTpTn+4Qf61wV+rRzSiTdhiTnsQa77SvGOtXtjElpdRXMyRAiEviUhCAcAj5jgZwDkii47Gdd6be6eR9stZoN3A8xMA/Q1WrUu/HVxr1m9hKiMGwxO3lSDwfY9qyzTEAooooAD7UUhpaACiiigBOlGOKWg0AYniOFpdJkYf8s2D/wBD/OuJjOJCK9GvoftFnNEOrxkfpXm8mUfnj1oA9J+HutHT5EZnx5MqnPopPOfYjNfQcTQXiI/yPt5U9cfQ/SvkjRdWl0q+W4jOUPyyp2ZD1Fex+F/FYs7lGWQvZvweei9vyqGij15jkVxHjjVo106axG1i5UEeneuxjnSeFZI2DIwyCO4rxjxHcGfXr1hIzRmUkDsO1JAZtLTc0taEiGkNOrL1nVl023DBN0j8KD047mgDSiR5544IkLSyttRB1YnsK4bXZtQi1ea3vY5beSNivlSAqVHbj39an0jWpYdXguSvnSAlQX52lvlyo7EZ4r2o+HdP+InhWL+0U8vUbfMS3Sj5wR03eo9QalsdjyPwbfTWWrxvE+GiYSrz1IPI/Gvb4PHdojA3No4k+6Wjwf0614V4j8Ma34MvNt3ERHn91dRZ2N+PY+xqPQtTvr7V4YGk37lfO/0Clv6Umhn0J4o8O6d458OLC0u0/wCstrheSjH+YPQivnnV/DOr+E9Wjj1K3YIsgKzKMxyD1Df0PNe2Nqr6H8PrO6Sby8sFLnHGSeOfWsyx8dNdyC2u4be9iPVWwPxGeKEB0Hw9kl/sMwyIy+U3GRjg81z/AIj8Y3em+I57IXvlNG+FTjp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GOOR61yllfPpWtQ3a8YOJR2OOM/j0rs9VjiZI72M7rS5AV8dv7r/UdDQxo5m6vrPTJopZYZUUtlZVZipx2zzj3BGagvGk1Cz3aa6uYCJoXQj5gDkow9f51cls0knNjebfKc4kz7Dgj39K53+xby0vGfR52mkjPzRcrIAPVTww9xQSy/FNI1pdSwndaTQ+fGh6xyK2GQ/QnOKy9Cmlv7e7tpCzuB5yk9zXSaMHvorlXtXt5ZQy3EbjaMkcupPHOOfpWbZ6Bc6XZXy+eiyyqVjkB4VT0Yn0xn8aBlMQuk6DBWQMMZXAzUcmnPYi/uYpommkOw4YMsRzls/hng1ZtruFruHTlu2umJ+aQr8nH06/hUqyxGWdBCoMzDfJt+ZgpOOOmffGe1IZiTW011awP51upYnyg+VHXluB6nvioLls3l3bJsCW65BKAtgEbsHt61q3FwzSm1vEVJGP7mbOYiPRem3PfNQbII55riSFnuJE8pkPCkEYZifUj9aYinLG0+Elha6hIyrof3sf1z1xWZc6SwDSWp86MdRtw6/7ynn+lbvl+XaeVC+Y3yV3YJ/usp9x2NQSW/kSktNmWIfMEJ3Rj2PsfwoA5ZkIODxTSMV0l1BHLIY7yH5+1xCB35G5RWZdaXNBH5ibZYT0kTkfj6H60AZtLSlaSgYhpV6UhoBwKAFJphOaC2aZQAuaUgjrSocZGFIPrQ3HC9KBD4QDdRj/AGh+lbyXjAYNYEH+tz6VeVzigC+Zd5rUs9re+etYcTc/WtO3YKuV5OR0NAzZR4I+XHHIwRkGuZ16GK2MRjjwSzByM47YAzWyG82Iqu7zUPBHXkVEbTzoGS8j3RH0fkHtQByqkNyKdRfQC01CaFSzeWcZP0piuD1oAmikkglWSJ2SRTlXQ4IPsRXY2/jq5vtO+wawYpTgBbieESDjjDrjI/3lOR6GuLFLmkB61D/Zeu6FDo92qWxwPJKOPLYDvGw4PPUE5HeueurE6XcTabq9k8kUYLJcwkGQR56gnhh7HBrk7HVLqwWSONla3k/1sMnKN9R2PuMH3rrNB8QwFo7aS+hS0Y7Wt9RBbylPB2SYORg/dbH9aBiXf2ezgje2uvtVm2TC8Z2Mr4w0bqTlcjn0PahtY+z6faJ5DrKFwJfOwpYHP3TkdMVpyeH9I1qQS+HZomlVj5sXWJyD91QeVPcAnBHQ1nm0u9O1aRLjSN0EGJvJAMq7R1Zc8kA/Ur0NAGr9uFxax3OkwXi6wkiN5dsmUbHUlf4WHtwfXHFMj169u5QuqOl7cRsUMNy6wyBT6Zwcg9uQKztN8SWsOqebGkVrh8rFI5TGT/CwBAz3B4+lbmuWsXii6jd7q3nmhC5eJ0clc5Cs3A3Dpnv6UARTqyStm2lgGeFkYHH0YcMPcVZ0KzttevRZwajbxXLZ2xyKwLY67TjBPtmuT1a11fw05NheXEmkzgHt8ob+B1BIVvccHqDSxJe2c0VzHbSgxESAp2xznI9KdxWPUW8AyLE3/Ezh81f4CuBntznv61yEiNFKyOMOhKke44NK+qXGoQyT2mqq0ikAw3HySFD0ZeSGwTyByKjyTyxye59aEAoNFJ3ozTELVe8tku7WWB+jjH0qxQRQB5lc2U0N29sUYyqcYHOfp9a6DRvD8glSW7fbtIIRG5/E9q6doU8zfsXd0345x9aeigHNAEgGOKWikoAWkFLRQAD3pelIKWgBwNSqarj0qVTxQBI/3cVx/ibRJZZjeW0bPu++idc+oFdc33KZ2oA87j0PU1g85YdpHIBOG/Ks2eWd5y9wWaTuX616kY9xIriPFsEcepIVGC0YJ9zk80gN34e3N9dXkln5xa0hCyFCemWC/L9a+iJUFjpUnlkgRxkgnkivnL4abxrtwqtgG3yR64dcflmvofXpfI8OXkh42wt/KpZRyel+NZIZNt7++gJwJB1H+IruNPubS5tBJaOjRHpt6D/CvlnS/FFzpsjRt++tmYnYeoyexrufC3ixpZGlsZmt51+9C7ZDD3HQ0rAd5431NopxbK3O0ED6964FYYxI8oH7x/vP3OK6HxFqK6wlrd7NkqgxunuOfyrDPFVETFWpBUYp4qhDqBSZozQAoG6SNfWRP/QhXP69Dc3PiPVILK6RJE2tNEW2uY9oyVJODj25ro4BuniX1kX+YrH8X6LHcatJqcAd54Jk85CBt2hcgg9T0561LGjB0+01ZwYhM7J5gXY6A5QE5B45zXX6bpsOnQwzXDKHi3bAg4yeenOPapNPcWVm91MUAQAsTjBPXHPXr2rEt9bsb6YSS3rxys7hVGNoA4yR6EAdOaQw8Ug6pZW1xbKqeXuXZuO9gTyQPSuOj1iS18yCWNHQkZEqbjgAjjPbmvRVmgstMhmjeKeaLIEYwMknhgT+ua5XW7+4W3hursadqO4lSJYxvjYfw/KRkD360wI4NQnu/Al5575S2uEjgkPUo2SUz3xXNkjbw+c10GnXj6/oWpadMEEkH+mQiNAowMKy4HoORXPJF8wVjx2P9KBMRSQcN/8ArqYLIBvhZvcCqrXKtLgqoTpV/wC1QQQ/uJN7EfM+P8aYhiLHdr5b8Oen1qhPby20mx/w9DV21RLqYkzJCP4nkbH/AOs1Lqd7DLGttB+8RP8Alq/U/SgDJBp4pFQnpzSck4oAu20DOwVQzEnAA6knoB9a+kPC/wAJPD9v4XtYNa0qG51Bx5k8j53Kx52ggjgDisX4S+ANJuvDWl+Ir62dr/z3mjJc7SoYhcr04xke9ezAYoA5G2+Gvg+0uo7iLQLQSR/d3AsOO+CSCfqK6tESKMIgCqowAOABT6XNACUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUALRRRQAUUUUALRRRQAUUUUAFFFFABRRRQAUUUUAJRRRQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSUhIAyeAKw9S8RQ2uY7fDyevYf40AalzeQWke+Vwo7DufpXK6n4jlucx2/yR/qayLq8nu5C8rlifWq2QOtADmZmO5jmoZJkjBLHAHUmqGqa1baZCXmkUHsB1NcFqniG+1qUwwbo4T2Hf60AdBrvjGK0VorP95KO/YV5jqOq3epTs9xIxJ7Zrs9M0DJDyruNWh4RgXUBIkG8ScAYyAfpQB5qFYnAr0r4Wx65pWrC/txtsnG2YScLIPQD1HY9q2NP8A6bZ3Ru7v5wOREfur9fWrupeIobaIx2m1Qgxv6AD2oA6jX/ABNM42FxuPSNaxbPTLvVFN3fSLbWaH5pZDhQPb1PsK4i08YWlnraz3do17EAeDJtBbsTxyPWupsZda+I1x+6/c2EZxgLiNR6D/OaALV14kVM6X4YgbL/ACvc4zJJ9PQewrW8P/DYOou9adi7fMY88/ie1bfhjQNN8KxSxzR/6YDnzTzuB6FfT3p+seJAqkb9kfoOpoAmtb0aLZnT08sxwnbCw7J2BHqPWuY1nxOSxWN98h71jX2rXGoOUg4jPem2tgq/O/Le9AFfy7m/l3zlsHtWra2aRgbRU0UQ9KsFgiNtRiQuaAFRFH4VXNwWEitHsA6HPP8A9aoDczbSZUxGx9cn/wDVUlvbhR5h3fPzzQBIqnACbgnfPU5qZUA4FOGAOKM0AApMEUUvWgBuTUNzAlzCUccGpqMUAcfc6bNBOyCPeOob1FFdfsHpRQA46ZcG5hmxb6ekQYEySBzID1UgYGO/XrVC71Cxs3IkmsZiP+eNwc/ltP8AOsIaXcSnLxZJ7yNn+easRaHMeMqP9xSf5UBoTPr1teR/ZpbZ1tNwaSONdzS45ALHAAz1xk1aPiW3jObXQrZcdHkYZ/IA/wA6ZF4alb+CU/hgfrU/9hW9vj7RJDH/ANdZgKAuZ13r19eXCzPHZK6Dap+zByB6AsT/ACqP+0tXmG1b27A9IcRj/wAdArWH9hQfevrc4/55IXP6ZobVtIhX93Dez+wQIP1IosFzANncXchSVJZT3812b+ZqK48G218P3qRRv6xr/QcVtDxGizbv7F/dLzxc5c/gRj8KgPiPVJz5kbraRtysUca5UHoCxBOfWgRyGo/DPUYUMmn7rgD+AIQayrbxD4h8MyGzuBL5XQ212hZSPoe30r0Q/wBq3g3SXV3ID/emIH5AioLnwvFqUYW725HQ7mYj8zRYdzD0qHwP4inFxdyPpkuCZYSwWJuP4WOcfSul8W67pt1otomlzrNb20TcjOBgBVHIGeBXOS/DRknEltM80IOWUId34VV8SxPYaPLC0Zh4CqCCOPbNFguch4TsTq3jjS7UjIku0LfQHc36CvTvjFdC5mtba1nlW8sYmuisb7cI3BY9+AvauX+Ddgbrxw90V+W0gkkz7n5V/ma0viZPaR65NqNt5014832QMj/LF5aYK7R1JyeDS6jRyNzBeXnhhHuSxlVh5WCSZIyoJzzyM81seHrOzs7e30rXbFZPtb+YjmYHyxjAO0Y25xjOeawNcutSEWnyPC1tF5WYwibMk8MQBjGa0tStrnW1sNQsAxu4kVHQfebHzBlA64zg4oGdt4R1ay1Lxja6Vp9sotoC07vkk/IuFBJ9yKi+KV6DdXwB+6Etx+WT/M1e+E/h+5tNZ1XU76OKOZkjTZG3CliWb6EgA49643x/e/aZ5W3f625d/wAOcUITOIz8teqeA2TRfhT4m1lztMpaNWHJ4QKMfi9eVt90V6nqVs9r8CtM09GWOXUJfO+dsblBaQj8gKARyKarb3Gli3s/OV5JljaUvhgdpw20cEY6Vz15pU9rq7W9o0srIFbzNu0gkZ55OPrmrvhrTVmuxPJIgEfzfOeP/r16NJ4Sn8SWMlzBfW0dwFUeXt2+YqqADkegouCRxcBvPIlluvJkMa5YxswZsf3sfe+vX3qi2mT63JLqFjOPNQAyxSSYMajjIY9V+vTue9NvrO80HVzZajA0UynPLcEeoPce9EF3LpOu2t/aBchg2zsezKR6EcH60DNO3uYrgJZ6sXikQMkFwUIaKT+E7geVB6jn2qbzNb0jW7bVLhEkdAqrdlfMSTH3Tuxye3ODVa7Szt55Li0/eaXctuWM/wDLPPJT2Zeg9sHpWrpjtbStNZTSy2rj958m8Mp6q6E5B9cbgeooA6DTZ7XVtatru5t3g1G5cxXEkcY8toiuVYLyAQwAOKltvhg8ulXulTSQwQzSC6tJkfcwcZADKeSpU4PpWJ9k0i4Hm22oxWbhvmtpJGVf+AkgEf7p/OmT6YI7qO+i1pGji+XCedIF9gQDt/A0ANuvhPeW3kw3Gp2Uc0oYqDvYZAyMsBgZA4zXEaxoN5ot0kNyYmEih0kicMrA+hHoeCOxr0y01mxEpzraDzYvJlEyOryjsDKV6fkR61Fc+FrC9ureaQOthATNJ++DqUx8yqwwM5GckjjrRcR5KdyHB6iup07xxex2qWGrwxavpw4EN1y8f+4/VSO3UVqeIfDmkyacZdItLtJQVkSZ5fNjmQnkdBtx2J64PtXBzRvbzPFICrKcEGgDtF8O6ZriyTeFb7N2wydOvSEmHrsbo/t3rmRZXel6iYLy2lglQ4aORSCPwNUYTIZN0ecp82R1GO9dZaeN7ia1Wz8RWg1exIwrynbPGPVJRzx6HIpgcxDbymU7Fb5WIz9KsS3scc8TNGs7IpVvmwPbkeldG3hGz1mGS48Laj9s43Np9xhLmP1wOjj3FcbcW09pO8FxC8cqHDI4KkfUGgRqOf7SjDK4WVeFj/hx6D39z1qmHZGKMGDjgg9aghkZG+X8q1d4doGlRGmLAJvzyO+cdh60CNjS55bHRL3YrG4vYmt40A5wR8zfQL+tXtH8VXmmEpHJtjb78Uih45P95Txn3GD71yc99cC+83zMSJ8q7OAo9BWlHqlvfDZex4k7Spw349j+PPvQB6MPEUWp2UVslpb24WVZGcTMw4UjCqR8uc889K6e6t2n8MQ7/wDWCCUj6x4kX9Nw/GvI9JhS1vo5Jbpfsn/Pb+Fcc/MOo9vU12d744tYLCVDv3PA1vZxFcNh+Hlb0yOAP8eBjROJDLbgRo3nWil4sdWhzllHuhO4exI7Vo3Z/tbSluYVzdWwJKjqydWA+n3h+I71hWmpxT+VPazKs0RDRH0I/mPUdxWrZXa2V3HcwpstLjOFB5hcH5o/+AnkeoIoEWoWXX9IFvkfaVO6F+2/HT/dccfXFQWpTWtMk0+dtkynMZf/AJZyDhWP/oJ9qW5gGl6mLm3GLK4JOB0Vjyyj/wBCHtkdqbq6Nb3UesW/KSELcAdNxHDfRx/48PemByVzEQZYbhGWRSVdD1Ujgj8DW74U1WNopNFvjmNuImPv/LP6GrHiC2S7t49Vh+YEKlx9Dwsn/sp98VzEtsdyPG22VOVPof8ACkPc6q/0yVbcpIjGW3+UPjO6M9M+46VQurbVb6ztrbTSyhRmWaQgBc8nk+nQD0rS0TxB9viFrcv5N5GMK56H2NTa3Dm3jjkRWibiSE52BvUYI4PpSHuYNhHb2zTrNqlxqTKhSds5hjBz8oycZPoO2c0rQadceHRbQK8lqxPlB5Tzk8KD2APTNUtYihtvDnk2cLQx+cfNG7P3gOc9cHGKpaLcImjP9o3fZ4pdk2OojcgbvwYg0xXI9Pk0mzkY2sbJfocCK7faMg9zj9O9WLm3MQjm+VXck7A4b8cjtmo9RsIrkTW92jG9tMBriJcmSM8q5X+IY645qvp9nqtjKYTbPd2ki5HltkH3XPcdcUAaMkkzWqvbBQSMNhfm4Pb8/wAqpvE0cha6RJNwCkls+WR0zgitKCB7W4TeJVjYq6ttwQQen6YNZVvHN/a2qx3DOtsIzJkrnAHKkCkBXurlZiLWSNra4jOVQrxIMYOGHXI5FVvssn9vC5KMbWYYlc8ADbg5/HpW3M4W0jUlJMnJkli5x2C55B96XzxJkvdMYfJ2CF0zhh90j09/WmFjn5XLWpFtBLwCXJAZtvZRjnFMcmC6dY/NFysQLhSNuMD7ykc46GrTxhZSUPQ8EfzpRY+ZqTajJJtheI+bnk7yMEAd89RQBlXVtbTybXH2WZhlX/5Zyf8AxNZd1ZTWrbZEIB6HqD9D3rpkiENo6ugmI5HzcYI64IPQ9RVfypY4wXRBDJ/C4+TPbjqufUUAcuRUbVuT6dFNIVtz5U3/ADxkP/oJ71lT28kTlJEZWHUGgCtRSspFJQAA4p2cmm0vSgCSI4OasK9VA3NSB6ALqSEGrkc2KzFepkkxQM3YbrIxnnH41Y+05zuPDcZH9RWGkvP9asJO44bkdc0ATXukC+vkmM3l+ao3HGfnHHT3FZN/py2ihkdmxw2f5jHatcXRk4zgqQR9aejj96jhXjcYIPI96BHMqxFSKwNJIqrPIidAxAz9aTymzxQA+gk9qkiglmmjgj+aRyAMVe1HRLnTiS22SL/npH0/EdRQMq2GoXWmXaXNnO0Uy9x3HoR0I9jXq/hTxzbaxcQ217Pb2F/vUiR0ykhA2kK2flYjrnr715Bmj60gPTPih4atIDHrVgFHmSbblI+VJIyH44Geh9a4KylaFd0cyxsQQCR0z6detXIPEl6bD7BeXNxNajAUiQ74sf3c8Eex/MU62t2SC4ksZluI2jIcj7yqeuVPI/Ue9Ay/LcXL2qQJdIoVdn7kn5l9GyBkZPfNStfX00Xl3M/mkhVzgBsAYAJHWsyBXDeaz5RuRswQ3+HvXQ2MZt9PvbwQzHULXZLGmdrLFg7pFUgh8EgFT0HNAE1haC3j3OMyt+g9KuGsvT/Edpq0iLcPFZ3bcHI2wyn1Uj7hPoePQiteSN4m2yBlPUZ7j1B6Ee4pkjKKKKYCg0UgpaAENApaB7UALS0lFABS0lFADqKbTqAENSLTBTxQA5j8tNBoY8UgNADhXEeLju1ZV9IlH6mu2rgPEMhk124/2SF/ICgDtPhZp6PqkszdR5cY/Ftx/wDQa9l8Z4/4RLUF6b4iv58V498NZTHqkyjp+7b8nx/WvXvGrbfDM4/vFR+tQ9yj5i1XQ7nTXLhd8PaQf19KqWklyswNt5olHePOf0r1NolYEEKQeoNRwWkFsCIIUiB67BjNUSQaONQSwVb+ZXY4IG3kf7x7mtHvTVBAwacaAAUtJS0wCiikNAEkLFZo2HUMD+tRz6gs/iK/094VwpUGQJ8ygqM/zp0JxcR56bh/Ok1HSZj4subm1fc0o/exjk4AGCM+3apY0Y3iWaddHGkLABNE2C+3ls55H4Vxui6LNf6msWxfLH33ckKoHPbqfavUb57S90GSWcP9qtCBuUZOzPcdwPzqqI7awhuI3jUSCFpZXQc+WOSQPU5H4UDOB1u5dr2ONT/osTBcDuAeTirZ026g1R7eKxhnsZ8SCR+VEZ5DBu3H+FWpNatEnQQ2UQmbG2R/vBTngg5q3oNw9zDdXCTxW+mWz7pAXL4PXhcZIOT7ZoAraLpY0m/v79ZEe0SylGVbPL/Kqn3rlPnhPzcqe9dr4m1O0TSYLOBDB9pY3MoxglP4dwHTPXHpiuHlvSzbAP3VAmTS2i3Cbov9YOorNKFTg9q1reW3t4hOZkLDpGG5/GqVxO1zOZGABPpTEVxUgGaVE3nA613PgH4a6n4r1KGa4geHSI5MzTtwJADyiepPTPQUAchbx4+b06mnRWs+palHb2MDyTysFSOMZJY8flX1nN4B8KT3cdzJodkXjXaAIwFx7qODj3FaOleG9F0UudM0y2tC/wB4xRhSfx60AQ+E9EPh3wrpukF9z2sCo57FurY9sk1uGiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAFooooAKKKKAClpKWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKYzKilmIAHUmgB1VLzUILGPdM/PZR1NY+p+JEizHafM397/CuXnuJbmQvI7MT60AaWp6/cXrFEOyL0H9axySTkmg4FZmq63a6XCWnkAPZO5oAvySJGpZioA7muP1zxjHbkwWP7yXpv7Cuf1PxFf65KYoNyW/oO9S6XoGcPKMnrQBQjtb3V7jzrl2Oeea6jTtFSFB8nNallpqrhUTmt61sUjQySdu1AFKy00tjAwPWrc8qacONufQ/eNMudVEC7E4PonWsqGV7m4aaZN24gKT1/CgB93dyTxGS4O2L+FPWvMfEeoiS7MUQbCcH0zXpWr2N5qVuUsNnp85A+pHFULD4b2wt2N/Numf/lojZ2n2B6/jQB5TBb3N5MixozuxwAOSTX0H8PbnU9A8LJp99HENhJi/vKp5w2OCazdN0HSvDURkiT94essnLn2HoPpVa81We9fyrYbUoA3NZ8Sjedj+ZKe/auc23F/LvmdsHtU1tYYO9+X9TWpFbgdqAK8FqsYCqtW1iAXLHAqTasa7j2quJ1eXDbWA6DkfoaAFe5SPGPxq15n7s5Tgjr61m3bNIUUR4LH5eg6dSakmuSmCrxDJ7tu/LFACySrD1RWkJ4HWrsoP7vaOTVOC3EjJM7qc87BxVxi/mMpVsEAAigBOVO00tVoQq+a/zKAerknn8asRuHUMOQaAFNFBpKACk6UtIfagBeKKbRQBjnxJdk/uLGzh+u5z/Smtq2tzji5ZB/0yhVf55NbENhCtobh5IYYl6k8AfU1WbUtEi+9qKyH0hQv/ACFMRjvb6ndH99PdS5/vzED8gQKRNDcnLLED+ZrRfxFp0fEGn3c/vIVjH8yf0qs/ii6P/HtY2cHoX3SH+goAlg0N243u3+6KvR+HsDLQvj1ZsD+lYjazrd1wL2VQe1vEqD88E1A2nXt0d07TyH1mlY/oTj9KLgdE8GlWP/Hze2MR93DH8qrzapoCgAXTzDPWK2Zh+eKy4tCZf+eSf7i//qq9DoO4/wDLVz7L/wDrpAS3niHTLO0iFoYr29lYqsT7kCgDJLDGcY/WqB8R6qwwn2O2/wCuUG4/mxP8q0m8LBpo53hZfL6GRwOox1PNWlsLG3GGubZSO0alz+gNAHNteatecSX15MD2DlR+S4qC48PHU4vLuIHx/fLnP6k10q6lpIk2RvcT+pCbEH4n/CrL3cCRCSNdP+Y7VTzmmkZj0AUYyTQOxkeBdEi8IXV9I371boIA6tkxhSTjB5Oc9ia5Xxrp0Ok+JmuVnL2OqzNMDESJIZBjOPfJ49RkV33/ABNkbM2o21nkf6qOEFh9cA4/M1FcW0GoQmC8mhvQeoeaSM59skjP5Uhpnn93Pp97c6Zo15N9pEPmkvJ8khcDKx56ck4IODmsHzv7R1ZtOhWYW7oPJDqEe3kPpgDvxz1FdvqngXS5dRkvWuZYZnXIim2ZLAYBBPDj6H3rm00/xTfX39n2FoyyR4bfwCwDDHzE8jOOAaBnoHw+tZ9A+HF3c3Py3Mks8h3ckkfIBn6rXkviqYSXyRj/AJZjFe4a3E+keAbazmfMwWONyO7nlv1zXz/q0/2jUZH7ZoRLKBVmIROS3A+p4Fer/FlYrXTvDegb9hhti3tkBUGfY4Nef+FrMah4v0m1IyJLqPd/ug7j+grpPi7qgufH89qU8wW8MUagdQxG44/76oGc3pqiGUIoO8feHU5rs9HN5LIEt5lQgEgSPtHPauBs3aOYud6nIGMnI9u1el+EtbsodQVXhSGJl25POCe+TSYzmfG0t7fiFNUO2e0yFcnPyntnuK5a1llKRyeT5m3O3nGSB/SvpC4stP1i08i6hjkzGcZbIz6g+p9jxXifxB0S60rU4rW1hd4SokVlXJHbBx3/AJ00BSvZmX4eWFsibJUvpBNkc8gFffn+QqPURFomsT6YJrjyTFH+9R8MGKAk9gRnPB7d6Zps66WkUesWr3Wn3q7njztYMpxlT2YfqDU/j+e2u/Fapa/NHDbRJu24LEjdz7gNj8KBFjT7+0t5YjBdajcSrjLgKq7j2Gc5/Gu107VY9UmYvPMhhUBkNirc9ySCDXHaTOdJsllt023dxnY8f/LFRkF8ep6D061u2N0+kaWLpfKmkkJ2yI29ZD/FuB5Vh1x0IzSGJrMbyfaC+luQsrslzFAsR2gKDlRnpnPXpVS30qZrN7jRtUaC4RtkscpKoc/wsrDIP1yPpXPap4ru5n2CSU7BtB3EYGMY/IAfSsqO8aedXXImB3eZuJYkUwNbWp/ESvFb32+zljG2KMARKygnoRgHBPHbnisNtO1O8vo1uIpWmmIALkEtj/61b+uR3mr6VHcOi+ZYx/MS43yKcc7fQe3TNYujS3I12wj/ANbI0qxAEnox2kZHsaYmWLbR57NIbp2DrOzx+SgJeSMcMw+nbvxV2GFZlhsHubSG5tQRbyy/6q5jOeCTwGHv/MVqX+mxadqs2jeemp21nM6wiOYRT27E5KndgEeuDjNY625TUJbV4YhYRgSMly4cqD6MmcEn0/Ggkoajaz6dJFdLJCpdjsNtIMAjupBJFbUHjVNRt1svFNimqW68LcjCXMQ9nH3vo351h6raI5a6sLdksUVRvLAk5OM9uM8CsoHJwKCkdw/hGC+hN34UvU1GFRmSFlC3UX1Q8Nj1WuOnS4trsrNvWaNud+cgj1zWvqyWujzWA02W5W6FrHLLNv2kSMCWC45AHGOa0Y/Fttq0SW/iqxW/AGFvYcR3MY/3uj/RqAMWJ4ZpjOqI0hHMcmMBvUZ61FfGPz1EYQOV/eBOgb2rdn8Gfa7d77QL5NUs15ZFXbcRD/aj6/iuRXPebDaEqsbNIOPnGAD9OtAjXsJXs7WRnf55UMaDd1zwfwArPurpZ72WRehIA+gGBVCS4klYs7sSf849hTQ1AGva30tu25HYV2XhbXEvr8aVeSbY70YjkLf6qZQTG/48qfUGvPIi7nCBj7Vp2am1uIbiedIhG4YJuyxx6AUAj2Pw/qEOohrC72NEcCSN85BBwwz1yD0I5FSWbra3t3pcqPPbedJAFkPzeXnhWPfjBB69DXk0GuS2+rPe27soaYvg+hPP581315rqSRWOqn5W3fZ537ZA3Rsfwyp/CgRueQNGnihkZbnTL0SBS/BAGAyOO2c43DjPPFYWpaTLpkmX3SWTH91cdiOyt/dYd89eorfuHTWNDWW2PmyQ5ljQcllI+dB74G4e496k8M3LOHh8zzgQwjQ8qwK/KDngjd60wOHmgckOh2yLyrj+vtW5pXiQMosNUQ9MBzyCB/Mfy9qy448QRhWbfjBB9e/0wc1FcIfJLgfvIjv46kDgj8QaRR0+p6StuArbWt7kbMdeGHykfiQR3rHt9KttHsZbOQLcyzqVn+b5Rz+YPoO3euqiuTqXhqOa3VXngGQD3IBI5+mf0rDffdwR3R3fvByevzDg5oEVrGziCiQSfvbeMokjsN/lkcBvXa3AP901QaaQxogwuAQSuRuz64OK6LRoI/tkhdFLhPl4yAc8/jjpSyWsUkM17Hpn76NivkyPtQnsc9MGkMxdNhufOWQqzW2Ru3tx7Yz3+lTpHqEkt4LjyjbgboJo8DjP3GA7inXciyy2r3UayxxFWJicgK4J4wDjA9Mc1i6ct1ZTaqLiF5IkmDvsycoT8xGPRSGoAgntbjzT5qOz+u0nrT7a0dG3gqd0TcDqCO3PfIq5Oklv5lp9pdEPzQzDkAHocd1IxmsV9RvLWUW2pqw+bKXMX6MOxHtQJk6tb6jLbsNymYMQ/rtBLKf9oY4qmt5HfXlxZJGE2L+59SwPIz3rQtreNbyN7d1ktbiZZFKdIpehH+6wyKxGsZbTxV9mjDbxcfL9DzTAftIJKnB9aWRXl1q6R3/cSweZnqAQM8fQ1osLS7urg25cbJGBGPl4PPPHH51PLbSSmVGghFqId0ci4BDADKsM5OeoOKQHP3k0S28aMYp27vt/1YHAxnv61FcKnMd0UZRgCN8iRT6BsHj6mtYWvk6d8phmZSXmPlArjgBeemD2qkbeaa61B12iOVVljJOBnd09j1FMDCn03IL2z+Yg6j+IfUVmvGQfSutnSJY1nkWXzycHHykEcHmqM0MdwCZo2U9fMH3sepHf6igDnCCKKv3Fi8Q3ja8Z6OnIqkUxQA2jJFFFADw1Sh6r0oYigC6snNTLNiqAepVagC+JPmyf0qyk+VA7issPUize9AEEwIupAP7xNT28E1zKI4E3E9fQfU08COQguMmtG1nWDAiG31QdDQBpafp0OnDl1edhzJ/Qe1aXmZHzd+PrWU8/m4Yce1XLaZC2H5oA57XbeG2kjkjTZ5hOQOn4VlAg9K1PEV8l5N5dvzBCcAnqW749qzFi3LuRsemVwKBgDT43eKQSI7Ky9HRsEfiKiLFThuKduGOKQGzb62jOi6jA0q9PNt2EcgH5EH8Rn3r0q9udPn06z1fR5FMklxEYtmCwlB2uXB5AZMgjoa8b61p6Rrd7o0ztbTOI5BiWPcQJB+HQ+hHIoGa9/oqySarLZQjCTMoj7L84YbfwyBWj4duL/Sokgkmiu7Agt9mdC20kcbTwUJ6HHQ9RV7wnc6Ot3dXULajcytbriASKJUbPz8dHG3kMOeMVtXWrWWo24v4VW4hSUw/ajD5Tb8ZG4D0Jx6+lAFKI2+oDdYiVJNodrOcbZlB7gfxr7r+IqHPODVPU5JLuyhitS8K9INz5aOUH7obg7s8DuRipJ9RvtK+yweKYQjzqTFewkM3DbWWRRwxB6kc/WncTRZ4zTqUxqYlnikSa3f7s0TbkPtnsfY4NRElT6imIeKWmgg9KnW2maGSVYz5cYBY46A96AIqKTsD1BpaACiiloAKdTaUUAKKeKYKeKAEYjpSClkHIpgoAf2rz3WR5muXIHOZSK9Brza4uC+oSTHq0hP60AepfDSwD6g7n+JkjA+nzH+leneOOfDb/APXRf514b4I8RyaZrUZ3sYywbH04P6V7V4xu47jwuXQ5BlUfrUPco83HXiloFIwDEZ7VZI7NLnNNHSlx3oAWg0lOoATNFFJ3oAdDzPGPVh/OsW91Y2vxMvo5JyqKwaL5sDeFGBntnmtmBh9qhUtgswwPWua8X6Te6h4iv7mC3t441mVRIXw0p2jJ/DvUsaO2uPF0dzZ3Vq8EUczxsF2KCCT0yOPzqKy1C11iW2luSsQkga3ZOAwU4z164xxXIapYTroNvfiRIL62+SWPzA+6MHAYMOM+3pWTa6hNBLGzv87dHP8AKkM2vE/hWfQb6O42faIHk+SYdNuO/pXPW26zu0udIuWSde2chh3DDoQe4Nel+Htbg1GxfTNTXfC/HPOPf8K43VfDz6HrUzJH/o0YAjwOTnnJ9aYFHXI11LTpNbs49sm7Zew5z5L9iP8AZPb06VyOK7nw/mXXrmxYqY7y0lRsDAJALKcexFcQRhip7HFAmN2mlBwcGpUQk1pXunQDRob+OZDI0vltF/EpxnkfyNMkTSdMudTvbezsoTPczsFSNOpJ/oO57CvrLwX4fl8M+FbPS55/PmjBZ2HQMSSQvsM4FeM/AXw9dXPiKbXmUiztImhVj/FK4GQPovX6ivogUDCiiigAooooAKKKKACiiigAooooAjllSGJpHO1FBZiewHWqGl6h9tsIryT5FuTuhU9dh+7+JHP41hfE3U5NK8BalLD/AK2VRCmPVjj+Vc3D45svCH9mReKmP9pXVsJG+zplLRMfLGF69Op9fagD1WiuUHxD8LNoraqmsW7W6ru278SZ/u7Dg59sVB4S+IujeLpZILV2huV5EMvDMvqP/rUAdjRRRQAUUUUAFFFFAC0UUUAFFFFABS0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJio5pUhjLyOqqO5rmNV8TnmKy47GTv+FAG1qOr22nRkyNl+yjrXIahrdxftgnbH2UdKznkklYvI7MT61EzhOtIZLnvTJJkiUs5UAdzWVqevWmlwF5pFzjhB1Nefan4h1DXJjFDuSHPAFMDpdd8axwbrex/ey9N/YVycdpe6vP51y7tk96v6X4f6PIMk111npyoAFSgRmaboqQqOK6Wy01pPujCjqT0q5bacsY3TcD071X1DXre1PkxFeOMjoKALrvbafFknJ/U/Ssy6u725izGEiTsCcGsq48QWEBLyMzSdcyuFGPoCan0fXItbaaGDyiEGd4G7r0xnAoAs2tqkUIkuCskpPyohzV8WzzlDcyeTHngDiiCK209XlmLPL22Lz/AIVn3V9PMD8nkxnq78sR6UAbFtLbIzx24VkXuVwKrahrUdsCiHfL2x0FZTi6Nrttk2xnqe596dZabg75Pmb3oAh8q51CTfOWAPatO3sljACircduFwKeZEjkCHqRQAkcQ71DNdASiEI/qdjYNML3ZG3qSezAKB9RzSwWj+fu+VQ33sNuz+YoAt7kaMBSo9d/WqlxFbg7cKjt0IbBP9aJhMkmyGBuBw/GPwxTTbPKHaZ2BIwEKjp9etADI44UYiJFw3G8tnn9TnvUsKI0xC/OF5yWJIPfrxT0jhWMRCPpycqRj8fWnLNGISqJ65HtQBLDuM8mdgTaMYOfxpJrpI4wrO3zDjYKqJMiBlgO+V+MpkgD6mrlrZKgDEfNQBVEclzhAjJCOx6mtFLbEeF7VOkSryajmu0jU4PTvQBXxg4NBGKxn8SWX9pLamZfMc4B7E+ma11bcM0AFJSniigBKKXFFAGdpN/HdW8ltdBXjddsq+qnvXO39lLpt61rKzMo+aKT/npGeh+vY+9aeq2wsLyO+tRi2nPQfwv3U+x6itB4Itf0tYg6rcploHPZu6n2NMRSstNtbkR+WC5fpz3rVGjxW43Si3gA7yOB/PJrndMu3s7owyKyENgg9VYV0tzIws767tFRLq5j2mTaMxygYU5OcBuh9DSAlhtrZxhJJZ/+veFmH54Ap8nkWqbntlj97y5SP9ASa4ttSv7qP/Sb29cjhkkmYYI4IKjHIPFVQiA7giZ9duT+ZosM7Jtes4vu31iOcYs7Z52/76OBUEniWA8Kup3GenmSJAv5LzXLgs/fpV7SLWO7uJ3uN5trZVMgj4aR2OEjU9iT39KLAaEWo3N9f20FjplmrvMqs7h52VSeSWPAwPauq1O4TzLiNtotLbarRp8olkI3bTj+EDkjvmsq71CHR4UgcLuHS3hYpEp9ABgtjuWPNZzyLqGmSRabtjZmLyWxblmPUoxPU+h/CkFyKfxLdNMIrZ9qc7UUY4HoopbPUH1RgkkiJfxMWtLkryr4wVbvtI4I/GsnT76TStTjvkR22K8UsY4bYcbsZ6MCAQPbFWtbmee4s7uGFm3Mx+1xsNk0WPl3d94b19xTEVLq8uZTIJwySx7g0Xow6j/69aS6G76day2t2k17PbicQ7NoZSu4hWyeR70y7RdVtDfxD/TIFxOg6yxj+IerL+o+lQadOlzBFprzvBIu77DcRuVK7vvR7h0B7fl6UAXLW8e88NPPHua40+RL6DK5OF+8MH/ZJ/Ko5vEF1Kd80isg5BP889qTQ8aTqkVtJHtix5TIf7pGCOayL6FtNae3cb/sUwyP7yKwYfmuKANfVdXn1jTIrF5AWiJYE53HIwN2ece+K8n1bR7/AE6ctcQFUckq/VW+jDg/zr2HxJqUc0uqR3gLfukm0uWOIsxYjIG4dAehB4IqCCOK68L60t5GskSWzsA/OHx8pHoQT1oGeP8Ah/W38P8AiSz1RI95t5NxQ9wQVI/I8V13j7w7cahcjxbpm66sb/bKzgZaIlQNrAdhj8Ktal4I0W/iDWE0tpcAAFJPnQkdeRgisC3ufFHgefdbTSxQE84+eGT6g8fyNIdzJS7H2Q/aC3BAj7n8D6VftL5A25duz03c10dv4j8JeJR5Wu6XDpl83/L1DFuiJ9WUYK/hUes/DeZdIOpaJMl5GMnFs/mow9VPUH2OfrQMLTxNPax7ILqWEd8NjNU9Q1iTxHrNjaT/AOkSsQsbv/Dkgbj0z+NczDY3s1pLNtwYjhstgj8DS6JcyWWvwu0aGZWAZJeB19T0zRYVzoNYvV1KSayhtENlAZPJJGZBhSobd15646flXPTRPe3sUuMkxorH3Ax/KrtjqkWn3bR39rKncAqQ20njr6ii/nglu2ubc7IpWJKjK98gY+lAy6siJciSQywgFVimi58oD2z+PWuwtby1Ey6lPbPerCBuktrfaSvQOyg4z7150l2oLmCaVJSMFOob6V6P4UtdU0vThdajpifYZmAGXCS7ccfKRyuP8aQEF1baNe3Uj3+iwyaTK2Y9T03IaNT/AHwOhHcEA1m+IfCWjaHdxy26XyWpVWF1E6yKCexU4PuMHkV0l74b02ZbowQrCLgrmTfjbxkdM9c9+DWdp+meItJItI7q0nsllVBHc/MoznlQcHA6nBx7UAcperpTy6VbaXq5ub+4uCk0ptmQRxthQGySGGeSMHvzVGCNtOuN9zpj201pN5guIWLAMh4O05BXcOcGvSZl1XR5ItV1Cx067tE+VpbKMLLbgkg7fUY5PsetS3ml7tOuNQ0uS2vNPvCWm8qBehB3ZA5YkZ4yCD707iseeeONMF6p8WWIZ7LUZS046m3nONyN7EnIPcGuXgcLCFRtmRtlAHUdQfwr3LRvDElz8PrrTpdOlt4pr1pjFLGQXhJBXALZBAAxk8Yritc8JT2l7I+haWyWliglluLmZMMAmWyCcnk44HahMGjmbFFska3kPmwXsDxEZ5GSCGAPocEVjJYmLUJLWZwsqdM8AsOcH6jpW/deFb3TPFFravsjFxh1Zn+VV7jcfTt61r+L9D0iDxRPdtc77TyYisUP3pXACsM9FGBkn8qYGHq1ulxZWlyzqsgHkjK8EDJGT29BWdHpq3VrHcQbSFYRzpvAKsTgNz2PrXomq6bYa1YCz0VUgmgdCYbuM4ZCmQR3GPWuTg8KTRatGbx4beFWG8QsSeOcDPc0gaH3uiReHtZe3ttUvI763VW82KHAyVDAKQwOORyR+FA8T6broEPinT983QalaAJOP95ej/oa1fGOoWR10XVuLsFbWKMlMKkrKCOueSBgHrXns0xmmeQqoLHOB0FAHR3vg2c2732h3MWr2C8l7fPmxj/bjPI/UVzgVgcBOR/s1NZahdaddLc2VzLbzp92SMlWH5V3Giz2Pju6ax1JIbXWmRjBexsI1uHHRHXpuPqOtMDh1imPQsP+BYqeK0ZznLE+wzUt+93pGoTWNzYpb3MDbJEkyxBH14+lRLqGoTLhJmUekeFH6UCNG3sCh3MiqB3mOBVu/wBXtf7Hewjm+0SPKj5Cnau30Pc44rA+y3M7Zcs592LH+tWY9KkXDSDaPWRgg/WgDpfCev3OnuUhmYbGDqM8V06a7b6dr1xG22OCQrNDjtHIN238GzivP4bmysuJJ9+D9y3XP/jx4pmoaydRuInWBYUii8tRu3MVByMn2oEera7ZoZY9QgK+Vcth9nQS4zn2Djn/AHs+tZBJUgsO/X0ql4e1drjTf7PuXYRzxGPnqGHKMPcMBipLTWLe5byrn91cr8rg8AsOox9RQxo1fD+pf2VqPkuf9Fl4Ht3x+Hb2+ldLdQW1q6ssMs0crfuUiwqZOSSzdQPYVyD2iSxld2Q33SOx7EYrX0TVDGP7K1LlDkJIOn4f54oAtTPL9owk7pFErFbaNNgLAccg5JHJ5qpZ6rcX+mPFc7GlJ8skJgyDGcZB4JHTA5I9a0ruGWCSMEKV/wCWcoyQT1G70P6GqKWsUdo8Mce3ewY7zk7gcjaR0x2PX2pAURZ3EfDTb7Jj+7844cenPQ/zqIxTvlId7IrD548/hkjt9a27mBZzbyK4iMuF57MeSP6jFZ8qQsC9vLuUEo2GPUcH6f0oAhurUzWSRvt+0wgkY/iUHDD8CRWYyPd2gtj9nWOIbmEsW5SM9iCCGJP/AOqtqysbpb6IPMk1srFhMOHBCnjHRsjg/rS3Gm20kl1HaXKAYMc4k+YxZ5yMAZH+c0AcjaPolvrCLa3N4A7BWjRNyNk4/Trmt6a3sW1kRrbSpfJFuSXbxIByQD6j3pkdp/Zstta6TZNGGZRPfyYLso5bb12ZxTLLX7i41m5tZgoiQExyAcjBw3I7e3pTA56VdWuNQigSxlt7VGHyInykepYcEmr18jR3fmYVoyFHGCvCgEDFNuk1OK7naydvPiJMtsTuWRTyJF77SOoqCx1G2vLc2YhS0mY8gdCRzj2oC+pLc2MDYvlk+zMCAHjXIlGOjL0Jx+NUpIhPJIlu+2LhtkjbRnvxz+FaSg+RLbyozFPmCjrx1x+HI9arSMJry5s7dGEtrho8Lkkd/fPQ0gIp4kja2O8TQuwRnK5BIORn3x39qyoGmumuS4VpLaXIycZBOMVqNp9rHIk0UzwG4JBt8ZViOp/2ef8AIpLqCGaK9jjtXgkjZDJnnzFB+VgR696YGRKqxTxxpGyNLywHQA9tvQmqdxZRyDcdkMh9HBU/lyPxrQhiiWVWdG3KSQwc8EjjI7iqdujJBIZIVLxTFZN47Hsfx/nQBkT2kkDbXTHvVYriumljihGzfkHnyz8yjv8AUVmy2Cycw/KT/AT1+h70AZVFSyQsh2sMEVERigApwcim0UATB6cr4qvmnBqALKyVOk5Ugn86ohqkR6AN1Jd8QYHkcVYS4IUkHB2nH5VjQS/KQKtJKDj1oAzG/wBVHnpjn655p1t9ow4hG6PuDUk8SxSHO7ym6exNOhnMURRXTZ9cf/XoArSRyyfM4UY4/KrXh+3gu9ZitrlC0codeDgg7SQR9CKrSzgcKc+/uepo02c22qWswONkqn9aANfWtAudIkD/AOstn+7KP5N6GsmvUpdlzAYpQkkLjbsPfkVzmp+FYpi0unnyz18l24P0Pb8aBnJpI0bh0dldTkENgg+oI6V2Gi+O5beyutPv4IZI7kqWuPJ3NuAwCyggNx1Iw31rkJ4JbaZop42SReqOMGoSfSkB6bYatqEWn/YbaZJ4pA5tUSTKSMcdCR1HXBIYGuz0XR5Lma1uL6xi+wzgtcW98uXgl2FWZW6bXAX6n3FeIabqj2KSQuiz2k3+thboSOjKf4WHYj6GuttHjMcd9pt3NNFDsLEvteIk4KunIIx/EOMUDNMeDNU0rSbnxB4cvJJLZGfzrG4gIcop5BU5Dgeo5I6HNZEetxak0aR20sMkqkSRiX5Cf4SrEE4J9eR6mupOoa+5tfEOnFXht4jFLZecWCjkb9vGQRg49q5nU7+K2miu7PyYxJkm2PMasx+YBTyOecZ47UAdFYR20ctis4uJLrakskcabw2OWU9Du45HPFNtJb+41C5uxqs0kDwOMyNmIs3VRkDGB6cjjFZzX13JoUU1tMlu0sh86YH94Iwccnqo68qDnjNNi1K1fQdYkXUXu5Y2RwksQZSDwSucYGTycZoA66LQ7u7iEMGqJcjCq8UoVWC44ZD149+tc9NE1vPJC/30JU/UVz9pIlvA0lrdTfaWO5VhYDL/AMJyeePStw6lFqTRyLM73JiU3G9Cp8wDDZ7dfShCYtFFJVCFFOptLmgBwp4qMGng0AI5+YD2pBQ4/eZ9qQUADYxXmupwG11C4iP8LnH07V6Ua5HxbYlZY7xBw42t9R0/MUAZem3yWd1FMUZwp+YDrj2r1waubrw1HCHLK7q659Mf4CvG9P0+7vJdsEZK93PQV6Fplm9larE9y85HTPQewFKw7mkKWmg0tMQClpKQGgB9FIKWgCOedLeFpZDhV6nr/KsS48SRsVWwj8/LBTI/yxrn1Jqx4g1P+ztPcoHMkgKqR0XPcmvO47p432k/u2OTQB1OtjV7M2us/brR/LlIgNs+dpB5yMdPrW/FfyX3hyJbGOK4huiGuXupgPLcEZjA7AnODXLW9xHDiMyfJcgbQeRnPOPr3q5bO+mTyG2DJDMu2dI+VIPRgOxFSUdDNbWFlp32KS1iMPmljHK5BAI9epAzWDruifaoIpdGtP3EfysIn34PYk56mt238jWbULLatPfxAIssLA7k/usp6/zqnpunXSXE8UWtadbQhTmOWUo+eqgoQOR0yO1AEulG5s1jjaDfNhS6Hg5A5I9Mda6HxIj31ijI4SYxDlzjGOpP0FZWnahqM9xJHd2SlJW3eaYlAUnuCOo9BitK5uUKXNu214Ug2tnjcS2Tkn/OKQHGeFRaR+K1W2nmuHSCYySPgKfkP3R1/Gqfgzw1Z+KPFUOmX149nFPv2yIgYlwMheeBnmr/AIYtFg8YBlkhZJIpwoi6D5TxXefBzwVM1+nia6IW1jZ1toyvMjY2l/ZRkgepqkJmhqHwE0/y7QaXqU8TKcXD3GH3L3KgYAPoOldjZfC7wfaW8ER0aG4aEf62fLMx9W5wfyxXaZpaBFa0s7awt1t7S3ighXpHEgVR+AqxRRQAUUUUAFFFFABRRRQBXvb220+1e5u5khgjGWkc4AFcrZ/FDwhdy3Ef9rxQeScbp/kDD1XPUV538edYu49R0zTFdltfKacjsz5wM+uK8VZixyaAPqtfix4JYuP7egG045jfn6fLzWVqfxs8LWULNZG6v2Xj91EVXPuzYr5nyackjKCobg9aAPUPFXxfn8TaHJYrpQtZVnSWGUTB8BTnlSBk/TivPtR1nUNU1MahfTy3N+SMyS4PToAOgx6VSVGYbo+XHanuskh3uqqepIPP5dqYE9w6XGJZph5jcnCd/wAKXT5b221G3fTpJRd+aogMWQ28nC4+pqmRiuy+FWn3F78RNK8hRi3Zp5SVyAiqc/iSQB70AfUll9oFlbi62m48pfN29N+Pmx+NWaKKQBRRRQAUUUUALRRRQAUUUUALRRRQAUUUUAFFFFABRRRQAUUUUAJRRRQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVBcXMNpEZJ5FRB60ATVlalrltp4K7t8v90dvrWHqniZ58xWnyR/3+5rn2ZnbJOSaAL19qtxfybpH+Tsg6CqOKTOKy9W16z0mEvNIu7so6mgDQmmjhQs5UAdSa4nX/GccZNvYfvJem/sK5/VPEGoa7MY4tyW/wDdFWNL0Do8gyaAM+Gyu9Vn865dmz611Wm6KkKj5K07LTUjAAFb9tpoUBpeB6d/x9KAKVnpxfAQcdz2rWJtdMiLuV3ep/oKz7/XIbAGGIKzjsOgrmb/AFEsDc38zJGfuj+Jv90f1oA09Q1ue8cxW+5Y+56cepPYVyGp+IbeyJjtmW4ue8h5Vf8AdB6n3rMv9XvdYnFjp8LCJjgRx8k+7Hv/ACrofCXgyMzSSalG7XMTcxyL8i9wc/xUAYOleF9U8S3Bubh2jib/AJayLkn6DrXquiaHBommraoqjHLEfxH1Perebewi3fKoA6/4Vg3usT3jGG2DBTxnuaANLUdYgtgVXa8np2FY0Mdze3Qnc4AOcGprPS+d8vzPW1DbhRgDFAE0SKYsYpoi2N7VKhUcCpGXIoAriRzKyBMRgdahPkxzAqiByP42OasjKgiqh+zKzl4WDN6gn8qAE3WwYsY/LcdwmRTUlz8qOxxk7yMcntirMAijjdxHtA65/wD11EL8NyIFEQP33wB+FAEu94wA8ygt0GzJp0G9iRMVIB4KcfzqGRoZT5pmXgYyjdKj82CPAid55OwzkZNAFm4ukjBT5HP9wtjrUGy4uF2CNYYj1A5NTWtk7sZJQpc8/StJYlUZPagCtBahABheKsOyRD5vyqKe9WMEJt471yuseJ7ezjY+cv8AvnnP0HegDZ1DVorWMtK6qOw7mvOPEHjR5y0Ns2F6cf1rn9U1u71i6dUd9hJ+uPes4+XbHnE036CgZKGkeQXVzIV7j1P09K9L8JeKE1RPs0p2zoOMn7wHf6+teaQWc94fMlO2P++f6etb+j21wLhE0yNg45Mp7Y7k9hQB62CCM0tZ2n3yTRiMyI0qABtnTPt7VoryuaBCUUlFAGRpt5Df2clrdLmOQbXHcejD3FUbdptH1KS1nboR83ZlP3XHsazIhc6RqTW03+tix9GU9CPrXSTW6a3p8Zi/4+oQTCf7wP3kP9PegRDr9j9qh/tW3H71ABcoP4lHR/qO/tSaNqKuDDNtdGG1kP8AEpo0TUipET9RkYPcdCDVPVtPbSrtJrXP2WU5hI/hPdD9O3tTAn13TjATdIWbABlf/npGeFk+o+634GsYZ711mm3a6hbBMoJBkx7+gYjBU/7LDg1zuo2X2C4CqrC3kz5W/qpH3oz/ALSn8xg0kMq5x0rc8LlTI27gLqsJk+hhYJ/49+tYUjqkbO3RQSfwrqbbSTHYS2EbiGd1ilvrnqYm4ZI1H94dSabBGD4jmlju7mQDdIiZUep5P861pdESCxt7my1NZJniEi+YqoknGcKwPB+uar6lp91d3D3EMiXmeG2YWTI9V6E/Q/hWdbXlxYq9soR4ST5lrcJlcnr8p5U+4pCLvm2+sxhpXW3vMYEx+7J7OB1/3hyPeqKXNzo93JbPsXdgy2sxzHJn+JcevZl/EVZE+kyphvN09hwA43xfgw5A+oFX9Ekhihv9SkkS5mglNvBIGDCOIKDlfQlm60AVbFmudZtk00zWbFXklNwn+qVcZ29nz2/Wp5tJ0S3Bhe6u+SSfmTuc/d28DPQdqmTWPtzGG/LeWTlJA3zRN2ZSe/8APvWVeW9xZ3fl3J3l8tHMPuyr6j39R2+lMDTntUW7hj1LUZng8kPbTIirJJg4ZWbnJHBGByDUM9pp13dSTpqlys0gUE3KB1OBgdAD04p0Svq2jSWKn/S4D59sT3YDlfxGV/KseB0eAOhYqwyM8n6fgeKANOe3ubeytdJlhlnuoI2eCS3QuskG7AGeoKk4wR0xT3mit/Cd9bSNsubiaKLypFKuQXBPBwcYFT61cSwf2HdQuwd4Jo+D1wY2qL+2PtMX2fUIEuoT2deR7j0P0xRYDOs4JL/VLWxifY9xIcuVztUAszY74HT3rSu7VbSzN/bTNd2Am8qXzo1HVtuRjhl3cHjikt7aPQvEmnXjzN/Z9zFNFC8v3opGQHax7jA4b8DVD7LNdXL2cE8z2xm8zyUlzEWJznA9+frSAydc8B2eqWh1DRcW0in9/bu2FXP8Snsueo7delcdZX3iHwhePJazXFqynDANlSRwQw5H517FezQ6FYy2EWyTUZ4irA8rErDBZvfHRazPsml6nZxW02+OZFCCRyW3YGPmJ559j+BoHcw7Hx9oGvK9v4l01LWeQbWvbVOG/wB9e/61S8U+BJpYota8PSJqltjDG2XcQAOCQCTn170/W/h5t3yW5CYwTjoAehPbB7MOOxANcxF/wkPhC88+1luLZgeWiJ2n/eHQ/iKQxItM1jxLF9lW1uJ7yFlEL7T908FCT0A6jPAwasQ+EruO3Vb++tLULIybCxdww6jaoJ/Wuw0P4m6fe3Ucmv2Pk3YGwX1pwSD1DL0I/Oo/E/hW81kXOs6Ldw6nYzSeYyWXDRjaF5Xk5AHPr6UDMezks9DxLp0lzdTJ8ssxhCj3Vcg7SPXOaTXvE108trPPDcN+7Urvc4Ujggn3xn6GuVgg1uyeRYZHgizyS+xT+B7/AIVpwar4hMflxahDMvAMXmKwP/ASME0WFc6rTNbvRpttqb/6NamdkU7chsDBJU5GB047V0CatBqK/KbRzOoDE/e3DGCMEED3HavOLbxHeWN3Ha6hbPBbIxZ4oQEdSR95QeM9+nNacYsbrYumahFdyb9/3/JuAD1G0jDH6UWC56Bba7M03+kvjzhtPlsrR7hwOcEHjtnNcxrMEHhoW2r6Lc3aRLdq09tvKxyoDyCPUnjB6DrWDHfyxy3MP2p4JEbiG4gKMwz1+XI4PrVTV7+81ASW731s0XBb99jzMdOCo6fSkM9V1fTX8XSWuq2OqXkUMkIaGNHZQCScHAxg9j1OR6VSvZtZ0KcWEr6Rq9zjdHDdOFuSvUdMBj6Dqa5HUvFFzdeIo7Sw1Ca20yLYMQsVGwAZOBgn2FdaNJ0jxJYwRI6SXTA/vYn+bcO5zzyOcflQBzV7Pp/iDXk1S6utQt7gcNay2+UXHBVWyAMY6dfbNVJTpxfzjNvlLDbnoMD0PXPal8dyJp0cCJlfLm2tl9z9MfeOSfXntWJZ3mlyRO4u4hIf4JgRz6ggj8jTA0FuLeK4xLOkZlbJmPLZHfGent+FZep2Gpvk2uqW9xE3AMbiM49wcY/Orx1fUPskiC1tr2zVd8oijDoFHBZlIyv1FYvkWc8he2vzZB+drbmRfow5x9QfrQBv+D/DjaUbzWdXmhhhtrWXygJVc72QqGGCR34964a1jt1uIzfea1s2QxhI3D3APHHp3ropZ7PTtOMM5TVTJLkyDzEjjAHQHIJJ69qydSht4fKuLJJbbzVDfZ5SWBH95WI+ZT6Hke9MQ28sLNLGOSzmmnczFN5QqrDGR16MO45qz4NhabxroceOt9Fx9HB/pWXLqEs8AgfaI1O4BBjDevFdJ8N1ab4iaEDyftO78lY0AT/FqQSfErVcfw+Wn5RrVSy0WUadbXMYbLIHHHrS/EmTzfiFrr+lyw/JQP6V6HfRaZong7S0vryK1uLiwUQF1Jy3lg54BwASKQHldy+pRyGOS5mx9cfyxUUVjLcncC8p9gW/WtDUDdaNMkM7i9t3RW8wsSCSOSrdRz609LVbuGVLYzQyoAXiOQwB6HjqPemFimdK8rmZ0j/66SBf0609f7Otxl7xCR2iG4/mayLq3e3l2v1PrUGaBHRx+IltDmytVLjpJM24j6AcVSbUJbi4knlfMsjF2IGMknJ6Vlg4pwagDrNN8R3FowV33R+9dTb6jYapFscsCeRhsFT6j0NeYJKRVuC7aNgVfBFAz17T9en0z/RtQTzrQ/Ks3Y59euPx4rfihtLyASae8RxzsOD+vP6flXkmm+Jp7fCSHenoa6OwvreVhPp8/wBln6lB9w/UdvwoFY663ivDe+W8fK8sJGwgAOVIb1BJHfIOKqW2m3NlNNE0HmyTsPJjQ5yO7E9OO/NEPiRk8uLVocHOFlTofcMP5HBrYhSK4s5FjneaKUE7S3zxk/Tn269O9AFGWNLSKN4kW7uQ2Io45dqKx/iyTlmHtTbbULWXUbpBa4nUeWXGMSDPAHORz259qY7I2ImRopFwFGMYx35NVI7BFnu5ndgtx8vlocMCeTg9uRkGkBWH7uYTKMMOTjjPsa542U1n4jjeP5o2bfx/cYc5+nSuxmhMsKTryW4k4/jHXj36/niq/kNaxErkSEnkj7qnoAexzz3oBmJd2d5DD56BvtenMRGU/wCW1sT93/eTOMelNvtOspxI9xGrSqFcFH8t8HsfUjNa1l5y3sSxhWMjY2P0JPBz+FLqmkTGbeoRkOEG1uR2Gc+vrQFjEV7bfGuyaMR8K5fefbdwMj15qWbTIxqc06XaC+mgzJFjnYeN6joeB0q3JZQ2sEjp5V3cxtt8vzAI1bHQt3I7gU3+0YW8SPbTQRfaLRCYpnyCARyvHGOfwoGYlxcM3yxl8dN78sf8PoKZAGnglikncRoAcZ9+v4HFaMscEksizQSxzKeUjI5x1wCOvtVP7bbXtr5EB8wbTFKmAJFXsR0zjuKAuZkljJ5wjAzk4BPA98+mKbcqYQFiZSpPzEL95h65zmrlutx5wRz++RTbzp2II+SQfXoaytG33C3Nq7cj5xnsRnNMQ5URw7vGrGNSwHbjt7gVXQJcxiURs0UnDIFxhu3TpWrBp8pmBcMsZyC4GQfbjrVcWg+zRtpzu8UjN0yGBHUMOox6UAZcscckjRHfME4yfvZ9Ae/41QuNNdMtHuYDqCMMPqK2ru2jhhuRD5vm4HD4IwDyVI/z71AkIfG1dpdA6tuIIPegDnGjIqPGK6S5tonOybaZP+eka/zHQ/hWZc6bJCN42vH/AH06f/WoAz6KcyEUzpQAtODU2igCzE+DirCyVQDEVMkmaALM75iIqgSandsrVY0AFAJByO1FFAHotndZgjferB1DAj37H3Bq2LkE/KfauT0y7CWkW18ELgj15q296McfKc9R3oAu65cWr2krXEPmEbSp75IwMGuMBBJ7exq/qOpbt8a/MTgHuBisjcxfcW5NAFsVPbXU9nOs9tM8Mq9HQ4P/AOr2qmsnrUgOaQz0Hw745giIju40tpSQfNTIiY9PmUfdz7ZHsKl8T6RaySfb7RMhxuZB8wPfcuM5HuK87rW0fxFqGiyqbeZjErbvKYnbn1HdT7igZck1CC/thbXszr5ZykoUHHXI+hq1u0+C30y0jdUBU3NxK6ck5+Vc+hA6VduItB8VNLPYbrO/lG42nbf3CnoVJ5459q5rU9ImsJxE25SoAPJZSf6fSgDf1OO4k8xInXBZpC5UZAGMgEfWtXwy/wBp0O9Xz5WkifDw5GATgrJjqQcFT74rnItQVvLt7rcgdQu8fxZGGOenp+VWLG3e3iXVUmVFSQrO4l25UMF+UD72eTigDpKKnuUgF4baznec/eijlb55IyMhkJ4kGD0HzD0NVwwbJU5wcH2PofQ0yRcUGkzQTTAdmnr0qIVItACOf3hFFMc/vDSg0APHNNkiSVSjorA9iuRSin9aAIEhRSFUKAOmBirKjFNxzTxQA4GnUylFAC80goooAdS03PFGaAK9/Yw6hamCYZB5HsfWuC1/STp8qRrH+752yeo9D7ivRqwfFcLy6TlBnZIGPsKAOEF9Ji0jIXFtu2nucnPNdPpN5ugDP0IPv0POa5ZIGmuo4lHzscAVo6TFMuqxW29lLNjB9aTGjpLSBoojNa3LR3Qk+VP4ZF64J7N6etX7a60281S3GpoBcsud8i42v02nPB+tZExkt9abSm2krja44xkZx7jPHNaVhfxrNcwTbHiMShklXcV5xgE9gakZ0Gp6hFbxiNBiToq8jtjgg1h3onW3+zJYvcSSMZGzKF5A6Bc5bA/CopjFbHzlRFkXjybm48oj0KkZyKwJdHvL7UI5IA0G8k+bJMWRcDOd/OaYGl8P4muPiBZQ3IfDl0KHg4IwR+VfVttbw2lvHbwRrHDGoVEUYCgcACvnTwZZJH4n0O6a8S4mEzRykKQwI6Zz1B7GvpIfdpiYUUUUCCiiigAooooAKKKKACiiigDh/iP4HtvGOj7g3lX9qrNBL68ZKn2NfLKqqySJL1Q4x64OCK+25FDxlT0YEfnXxz4u0S58P+KL/T7hCpWVmjJ/iQklSPwpgZBjJ5TkEnA71F0qcTldi/dKnORTPORX3bFZ+vPSkBEzFeVLCt/w1pK6vcSQM+3K5yOtYLymTJO3nmtTw/fT2OoeZAjMdpBA6kUAaGs+GZtJY7X8yP129PrXunwU0CHTfBaak0a/atQdnL99gJCr9OCfxrzm21yy1Gzdb6HzYwvzDoysOfxBHpXuXgwWaeE7CGxcNbRJsUg56HP580AdDRRRQAUUUUAFFFFAC0UUUAFFFFAC0UUUAFFFFABRRRQAUUUUAFFFFABRSUtABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAlFVLy/t7CLfPIF9B3P0FchqniSe8zHb/u4f1P1oA3tU8RW9ipjhIkm9ugrj7vULi/kLzyE+3YVWJJOSaTNACjikklSKMs5UAdSaztU1q00uEvPIu/svc1wt3qmreJ7r7Pao6QnsOOPc0AbOueNFiL22nr5kvTf2FZen+Fr/WGN3qMzJu5Afkn8Owrf0PwnbaaEll2zXHXJ6A+1dIq9gtAHG2mhpaSFMcqa6Gx09pDhE4HUnoK1v7KSSQSznbjsOp/wqC/1q2sI/JgVWcdAOg+tAFnFtpsPmSOoI/jPX8BWFf65PeEw2gZQe46/jWTf3rS5u7+fy4u2e/sorktR8SXN632LTI2SNztwnLyH3P8ASgDW1TWrXTztV/tF1nqGyqn+v8qx7Cx1DxRqmyWbapG4u/cZx8o710XhvwCXdLrVPmfr5W7gf7x7/Su9FlYWOycxxK8Ywsm3BA9BQBS0Pw1ZaPAFijwSPmc/eb6n+lWNQ1aG0XZHtLDoB0FZ19rMlyTBajjuajtNNLESTfM/WgCvsutSl3yFgvpWraaekQAUVbhtxwAKnDRp1PSgBFiCLkikEySqUUZQ8Z9cVVMzNLK++U4GMds9gBU7vtWNmHVf1oAfB5SB1TcNvXJz1qyrBqz4ykVo7BP3j+pyTVi3kAwh++Bk0AWJF7ioZEdh8j7T9M1YBzTGUg0AQRWpW2ZJJGbcck1BM6RRlcNIDjACcfiTVi6ldYtkce4sMZ9KjttOUkO4y/uSaAIo7a4uAd5VUb+AKMYrRt7KOMcBRVhUSNOegqCe9WNTtOAOpNAE7yJEP6Vj6nrMVtGWmkUAdEHWuf13xfbWauiSLn+91/IV5nqviC71WUopYAn8T9aAOk17xu0xaK3+nt+PrXISSXF9IZrmRgp7n+gqJY44Pml+eTsnb8asxWk13+8nby4R+v0FAyNWeU+RaxsAfTkn61et9PityGn/AH03aMcgfX1q/YWLz/urGHZF/wAtJX/qa2IhZ6Qu6HbNcY5mft/uj+tAENvo7FRcak/kxnlYR94j6dhT7vVoreLyLcLHGOkaf+zHvWXc6pLcykIWJPc9agig3t8/zOe1Ai7Zapdw3sdyj4Cnp2I7ivTtL1CK/tUljbIPUdwfSvNYdOkkYB9qrXUaQfsBCp9w/eoA7CiokkV0DZ60UgM2W2t9a1GxjV18yS2mXzB/CQVZc/jkY9KyrOebS717edWTa2CD/CwrSstaN1dLFIGwenOefb0PvVnWrL+0raa5Rd15Zgebgf62I8q+PUDr9DTYFPWLUcatbcZI88D+Fuz/AEPf3q5ZzwapYSWlz91+D6xsOjCqOiairA28u10KlSh6Mh6ioJ4X0TUgqlmtpBuic/xJ6H/aHemIqRNPpGpSQTjDocN6EdmH16iujvIodU05md8BsGVhztI+7KPp0PqPpVfUrL+2NPWaBc3cCkp/01TqV+o6j3qhoWpGOQJuyO2e47gikBiXsUlvHcQXCbJYiBKO3BBJB7gjkHuK7q/3RRa0E5f7a0mR3Vo1Kn/CsrXNKW5gDwIzOsbeUO8sQ+9GfVk6j1HFT6JqkV9pyiZ182GJILs+qD/VS/T+E+lAzF0W1uL6W7EF1FAbdY2Jk3c7s4xj6cmrl1dPDPHa63ZJPuXdFKjclRwSrDB47j9KpXcNzo2sO8B8qUAjkZDIf4WHcf8A6xT2vNPupklvLG5SVQQDbyb0564UkEZ/GgQ3+zra61rTLW1unNtdSsJVkXEiqqFiNw4OcY6ZrVvW0lZjZfYlt41AxJD8rqT6n+L3zmsx73SrXy7i1j1D7VFIrQExADeTtAOWzg5wfrVrXYZVvTO1ncQKQN29MgH03DI4poCtc6dPbxmaMi5tuvmxDkD/AGl5I+oyKdHqcI02a3vkaW22lgVb5lYDgqex/wA9KhtL6a1bzIJPybI/SpbKXSzeapeXVrbmWSSMRJIu5MbPmwOgYt1oAfDpWsWEUTie0+3KA3lKSZB3PGMFsfw55NENjp1zcGKyvrgyzuzgyouxnPPIABXJ9OlEqNKRd6eXbyxlrcsTJFjuvdl9uo96rXUiGL+14CqruBuMNwrk8SD0BPX0PPekBPJ9r1TS9NSN7NntmcmLeySAldrJyMEgj1HSmaXbG71X7KyMssTYlicYYHrgj34/ChmiPiQPE6FbsrdKgIOGbhxj/eGfxqWeZB4r1VzbpMTcrGpK4ZdiKvysMFec9KLgWdY1Fj4iH2cr5WnAwoexkP8ArG/kv51PaXcdzMZLUpZakQdsuzdGxxxuXofr1HvWJeWLadqTW0Qmmh8kXBJUu0O5j8rEdc9QTzjr61t2unWllapqGo3MUcBwVO7IY9toHLH6UwOcZJbeaWK5SX7WHxMCdzSSN0Of4s54ra/sVYZYrW41BY7+Vdwt44TIIwMfeYHjGRkjpT4511jxNZahBDFbixUiJLrO+c/wlscKBk4zkjNU2g1GbV5ZB9ogvo2OTG+1l3eh6FSPqDSAv6VfypPLpt9+7lhleIMfmEbjg/7ykdR3HvVPUbVYJjG8P7piV8p/m8pxyUz3GDuU9wfarNt4YukUyzu4yS8kspySx5JLHAzUuqi3vY4ks7pLq6WIpJ5LblDL80e5hwDncnXkPigZzcPgLTdcIkSRrUySzklVBASJVB47ksfyrjLmy1zwbqpa1uZUZMfvrckAjryP5jkV6x4akWSN0Bw8dxPCUPBAmUMuQenzIV+tZt3FFJ4ghtb+Ro7Sdm3Px97blVyQQuW4zQFzk08Z6P4jiFt4v0vdJ0F/ZLslHuy9G/zxVS++HZmspL/wxfJrFsuW/ckCaMdgydc+pH5Vp694Kt11SS3t3dQYg6u8RXBzgqxAwT3BHX2rm00bX9Eu3ubIXKSQrv8ANt8g7R346j16+9IdyzpGqfaLmLRvEtszEfJDNKu14iRgKx6lTWJqPhq8tbqUW/7+FHK5B5Ug4wfcV0//AAmNhr0SW3i3TPtLLwuo2uI7hfqOjVcuvCs2r6gur+F9Ri1WJwouIlcRzccZZTjqMZ96AsUJxLdeE7Rbx/MuW8yNZh95kR8KW9ehH0HNcNd5t7kx55XrXpVmx0yJV1GNTFp91LbMY/mwrDeMgDnBYjjrXOfYYL3ULtRaI9vcbikh3K8WejAcZx6Hg0AYcV7mQTK21goB+orf0rX2gnEgdBIq4BzgnPoexrk5NNvYG2vbuMe2f5U5YpY2Ak/d8gfPnv3+lFgPUtfWw8XeGpJpHWPUrSHeZHQklRyTuB+bpg8cV5+NO0ZIoQ97eLJKqsriFSnPXIyCMGrOia7NpV5iXayA4KOu4e4x6Eda68+G9Pu7eK9t9Oe40q4zHgy4a0kJ52nqVHUZzwcUAcnoepWuh3lykx88srwsNxCMp4H8881U1fRILLTobyzvjcRNgFHTay56dCQRXQav4Q0/Q4oIJruV55wJcFFChASB0ycn61TuTAmmyWsVubktH5cSlec/3h3yOtAGTAt3aWttNYSPIJwzFFQNtZT8wIIIzjmtQySa4r28++9jjAH2nASSHJ4DKR1GO3Bqn4dt7ic3elTpMsVwuMBcFZB0ODjgjrW5p3g7X7LV7BxvRZFfddRv+7KA/wAR9h2NMVjk9Y0YaXDpk8c/nxX1qJwdm3adxVl6noR1rpvhJF5vxJ0v/YEj/lGf8aj8XazZRwWeiaWjSWNgrwtLJ1mYnJIB6ANnFaXwaRf+E+jkI/1dpIR9TtH9aQzlvGbmfxjrL92vJv8A0MivT/ifY6Wtnov9o3Usa2ttHEI4hksWAzyc7R8nXB+leWX5+3+KpCOfPvD/AOPSf/Xr1P4nwQT+KZbS5uwsc+nx+XEF3MkiOxD4JAxjIOOeelAHOs1leXOi6fYwCS1lDjypC0gZlA2PuwDtH3T0xzxSeG7i3vPFNxDrCC11aWIwMhBAkkDDAPYHaOPWs+3vmtvEj2EcdvaBYZI7SVEPyM+0qx5JOQMYHqcCrVvo95q93Fe3RSK80+9iikkOcTAuoXaT1PII9s0DMr4h6cun6zbwpwTBvI+rEf0rjulenfGSNF8bKinpZx59ss5rzhk6n2NCJGeVLs8zY3l9N+0459+lNwa9j8Y+J7nwt4c8L6XZ2tm8c+nRyTieEOGAAG3B7HnJ61y8cHhnxCSLXbompng2ty5+zSN6K/VCfRqYzhQSKeGrodV0WbRpxDquny2xb7r79ySD1VsYP51nPYxS825JOM7CuG/Lv+FAiqkxBq5DduhDIWB9qoSQyRk5FNElAzs9N8UTQjy58TRHggjPH48V09hqMMgD6dc+WevlOTt/Duv6j2rypJiKuW95JEwZHZSO4oA9pi16GZI7bVbbBOME9/cMOG/Dmrn2HzV8y2m86E9w3zD/ABrzHT/FBEfkXqLLEeuRn8xXSadqEkf77Srrg/8ALJ34/Buo+h/OgR0MjKsZjG1BkLKOQ2DwGUHuDg47jNUWilTX7kMWMEi7y5zjgDp+NXrbxBbXsqw6hD5NyhBDuO45Bx3H04rXZJZ5A7yRTWpHyuFy0bjv3yD3oAwo4xbxLfzO1vBH84bZl2I5+Vev54qaMWE2tGZJGS4khBmt34BUjIPfp6daevmNA6tdO85yZZHfnjPH09B6dqzlt7iXWxcqWUtEUZ04KsOAf5EUgKVxNDGxsmtvJiJYwELgSE8Zz/MfpVP7FbnURfTs/nJEUMe3iXPAyR04PNdBNGBGs/lIxYksN2UDj72B064YflVN45GIeZGZTxwu33yMdaYFCe08zT4rhNxaHEbk9cD7p98dM1kXmiQ3axXMEjw3rEgmNCQWzxnHIJ7npXQLfxRXUdoU2+gIyJ1PBAIHOR09CKja0ayuJNwlUMrqhIILZ+6PqaQGXY2mpJcBb233yJ8i3CsCMejY/T3qJ/D7WUt9OsyBbhGGQf8AVEg5z6Yzmp4LG40+7hk1W9lF1KwENjbsM+27soHer0Ummz3V+8IlaWRfLmjH3SQOCB64oA5rTbiB7wW0BlmEYBaVzy3t7A1KZTdqEJ8nb8++JQCSeCfc/wBKbJfJp5Ns1slmXH7u5hQMpHqPr34z9KmfYUhuLZ1kwMtIAACf90dKAK7WtwI91wYi8WQsgIUSL6FTjn6UyKKa4O3ydtuB8oIwB6EE9ST19a0LyKGZFupJWEKx7iOpAGBx6e9VVVpAi29y0YZd8L9en8JHemBkTGCFd7xth5Ah9ACM5FBjKB3aZo3j+85XIKnpkd81rXNjFPBKt/JFAOGaSNiQGzwehxknkdqq3kTxT+VLI5QY6YIAxxgdPzoAyJ9PjuF3hPLJGd6AlPx/u1kXNlLbn504PQjkH6GugliRLy0hQs0Use3PQlwTycd6cIyUL/KsX8SOuVJ+nUGgDkypFJW5NpscxLW52t/dPQ/Q/wBDWXNbPExR0ZSPWgCvRQVIooAduOMGmmiigBKWkxRQBetJdsO3PQ0T3rFdiH8aph2AIHem9aADrS0UtABVy2h3cucDtUUMQzlqn3baAFkhaM+o9ajzVyGfGQec06a1R/njOM847UDKYYghgWBHII6iugt/Ei3UH2XWYWnTgLdR8TR4Pfs/48+9c+yFDhhikzSA7VfAbanCL/QtTt721JOY5XEckRPbB4yTwOlOl0TUtJvbTTZ/KAnLKolO1WJAJUkZIPTj16Vyen6neaXcefZzGNiMN3DD0YHgj616PY+MtD8VLFbeJYfst+uRFfRPt2g/3W7c9m496BkNp4Un1TOlSTSyBHSSGa1TzDARkMjbipXqMfTNZba41lqMtlrCXLxpIyQ3/k7JyoOAWU8OPxz6GvRrzT7zSIIdSiuX1eBSAJY4lM8bY+VuMbx6qe3Q1xNvY3OsSXOnanqLyXUu6RTejy9rjJDKT0U9Cvb3FAEwIa2FykkU9qxwtzEcx59GzyjezY9iaWsWCG50PUrfUU+02sE5AniCcHsykEEHnnB9eK0pdQsP7RktFdLO4BygZiLedT0KseY2P905XPGRTuKxZGM08elQGQJIySBo5EGWSTggevuPcZFVdO1uxv5pI0kZNndx1HqAOaYi+cGRqAMjNRTSiGKWTYzY5wOpq/4V1y1YzWN/EptbrAfHVT/Cw+lICuOKeKt6ppkul3hgkO9SN0Ug6SKehH9apCmA4DvThSAmjNAD6M0maM0ALRSZpaAFzS0lFADqz9YUtp0nDEAqzAd1DAn9Kv0EAgg96APP4IUj8ZWyptaM3KkY7g8irmqRNp/iGa6TgLOxU+nNKbNLPxxYxxjCNMjAemavaxZgXV3smV1Lsj5zuXJ4J7YzxSY0LpuoaXe68b6/LLMy7VCfdL4wDn0NRWs7f2pdR3Fqvno5Jtz/AMtEPUD19RXOWahL8wOrEYO7HVcd61hrEAmt4Y2hu5TjEsiYaM9huODxSGa141jesgt5kntuB9kucrJGfRW9u1JqF7cwWkllbOn2WONv3QG1lBOSGB7g9CKtBJL+G5SayRpp4Vl327DAIH3vUNjris3Sl1O61Pc0iJN5ZtyJYifOQ/wsO/HOaAF+HE89z490xCWYCTpuOB05r6z6LXh/wk8DGx1ifUrxkaWHgLG2QhPQZ9e59OK9wPQ0xMKKKKBBRRRQAUUUUAFFFFAC4orM1bXdL0S3M+p6hBaRjvK4BP0HU/hXmOv/AB70i03RaJYzahKOBLJ+7j/Xk/kKAPXya+avjZqiX3jH7KrxH7HGIwAvzZPJ3HvWRq/xS8WeIZ/Kk1T7Dbvx5VqPLGPQt1P51x135j3EjM5Yn5iXOST9T1oArspU4IpJEUE7TkZ4p4cMnPXGKaeKABVrodCtSbeaeB4ZpUOGtZQf3g9VI5B9xXObsVo2SXUa/bbTzf3RG6SMHMZ7ZIoA1zbC8D/ZXImU5Nq7YkGO4PRx+tex/Au6mk0zVreVvkimQhT2Yg5PtnAryGAXPiG6WR4IXmccmOQI+4fxD37kV6B8M/E0/hrXJ7DV5rJLG6QubyRwrB0GACf4s5x60Ae94FLXL6f498N6qbn7HqcUiWxAlk6KM9Dk9vfpXRxSx3ESyRurxsMqynII9QRQBJRRRQAUUUUALRRRQAUUUUALRRRQAUUUUAFFFFABRRRQAUUUlABS0lLQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACUdqKoahqttpyZmf5uyDqaALpYKCWOAO5rndV8Uw2+YrPEknTd/CP8awNV1+51EmMHy4f7o7/AFrJAoAlubqe7mMk8hdj61GKO1Z+pazaaZCXnkUHsnc0AX3kVFLMVAHrWHcavPezG20pN5Bw0x+4v+NVLaO78SRpczv5Ont92NDyw9yP5VtotvYQiGFMdljTqaAMS48EremOe4vXkkHMoLZ/L0rcstPt7CEQ28aqg/X61Zs0fzvNuZ1Qr/yyBHGfU1oG1Td5hdfL68UAVIoXkbC/n2qyzQWUW93wR3P9BVa91OK1UJF87HgItYdyJ5gbm8fIH3Y92KAH6hrU95Mba0DAdz3P41yuq6xa6QSDIlzc/wBwNlVPv61kavr91Leta2Bf5vlIQck+grX0H4fTXw+0aq7ruHEaHkZ7sf6UAYdrZat4tu/MywhzgyH7o9h6/QV6NpHgnTbG0CPDvkJBMh4fI9D2/CtewsLbRLGON3UiMY37cfkKzb7W5bkmC0DAeooA0L3VbbT4vLQqzDjArDb7Vqcu+QsI/SprXSyzeZP8z9a2I4AvAFAFS0sUhAwKvrGFGTxTiUi4PJ7CoLrzBGSXU+w9qALiRlsEHAH61mtbN5pKO7Sg8ZbgVZhkCwRs55xnO7iqtzKTJ5cbfO3UhsnmgC0kLwW4Dv8AvCScgZGT9KI908yoQ/lxjkuuM1M6Axxox+4AWJ9qIpklVlTfk8ZegCGS6RpdiRu+3j5OMVNBbJGd6bstzz1qqkiQCQuVWPPy4+8f8aA89221A6D174oA0d6BtoK59KeRkVBb2YiYuSzOe55q3swKAIVUb8Gpi6Rjnr6VDNcJCpbcoA6ua5DXfGNrYKypJ8/r1J+lAHQ6lq8VpHuldc9kFeZ+JPGzyFobcqfo3A/xrmtb8TXOpyuqlljJ6buT9TVC1svNZWk5LdE/xoGAa4vrjzpjlM/MX6Y9Ks7FeTy7FGOep7/n6VcNh8oMrqkQ6Ad/oP61r6VZy3MDQwweXajrM7EBT657/SgDIt7BIZBuX7RcHoByAf61vJpOwCbU3weot06/j6VZE9npMZSzGZcc3EnX/gI7fzrGudQkuJCqFjk8n/69AF691ZI4/JiCrGvSNOn4+tZDtJctuc4FCRAHc/Jq9ZWT3bHPyoKBFeCJ2ISFM5rZsdLETeY/zN3x2rQs9PSKPOMAdfeiS6y3k265PTigBZJoYo8JzmmxSXNxIPJGAKtW2k7mD3PzE87f8a2IbVFYBFUD0oAS3uSkIVztYdRRVwIMfdooA53S1Iv083dC6HOyQbT+GcZrqZdQ/syPVb9CjfZ7WGFQeQZWJYKcdcZHFea6P4zuZZktpvmVvXkfkc1rareww2d7CjbIre+t7kRg4H75CrDHscEelDBFycBBFqNqNkMrENGP+WMo5ZPoeo9uK3IGh1rTvs0jqr5zE/8Azzk/wPQ1zGn6pBAzLcfNZzgJcIOuOzr/ALSnke1XG83SNRMfmKy8MGH3ZYzyrD6j9aANHTbqbT7z7NcDZIjYwf4W/wAD2pNe05YXGqWo2xSt++Uf8s5D3+h/nU+uRrqeipeRAtdWkiyF0+80Q+8PfHXHtRpWp+eht5QsqMvIflZF9xTEWtKvUvLbypH2EkEOOscg+64/z0qK30drfU9Xv1REVLCcPCOiyFcnA7qfvD0yRWfrdoui6nYNYO4gnhZ2RmyBggbVPXv0NdFb3AuYDMnzyCFkZB1liI5H+8M5H5VI0Zmo6fpVpFbR3Ml6JPIQeaJN+fkB6HNUU0/SnGU1O4X/AK6wg/yIq5rEZvdItblDveJVjcj1UYz+K4NM8Nw6fe6Wbea2Wa/R384eYVcpklWQZAxjA+vWmIoXWmparHeQahbTrbSxzFCrKxCsGOOo6CtnWb2exvBLA+Y5CTn1B5ByPUGqNv4ZvZCTPC8MWT/rMZC54yemcdaXfFdyyaHpu28S1gD+c8oRYvmxtDEHcAfy6UARtqNnc/8AH3p8TE/8tE+VvzGDTbe/s7bSbmwjIOy4aUR3OD5yPg98ZIII9elOGgX5GDLZr9bgn+S1NZWR0m6na5e0ZbuNEWT7yxupOFYsoADZ6+opgUYYrd3EllO1pNnIjkJMef8AZbqv45Fafh/S01OOC8mgiN3qBkkYuA0cMcbBSVXozMe5471mSTwrO6T6fbGQHB8vMZz/AMBNamhai+mWJENpKxtfNMEUmcyxPhmAYgZKMMn/AGTUsaNaK1tzPPbag6EpM6QymCIgoMYyAoK4zjPT6VnXmjtos51C0gS63sTGhlJjkkPT5jkqSexJB6Ag1q2uyOYPqELTJJCNsu3BLsxdiPxIA2knAqullpuq6i9qn+kW32lcnJBG1N7/ADDB+8VXnkc0ijndNs7+YtqRm2mRi8txK20FuhznGMdMdulVdR+zPq/2m1PnQ+UBLIqkxRSZ6Kx4G4cnHGfrVzVbO1j1HzLp5jG8k0JIQyAyRMBv254JVhkjuPepLCbSrafD6nF5L/LLHNE6AoeCDkY6VSJKCpIrBlDZHOQprZvZ7qTw++o280tve2S5aSPhmiyNy8g8YO4ehFZ+neIbn7CiRC0kjjZ0ikdGZmRWKqW+YZO0DPFTtqN3qYksbm6SC2uFMb+TAqkg9snPBpiMe4LTSbrqZ5z63EhfP0BOPyFWLPUH0+UGJ1Abgp2YemOhrUmuRocos7a0hS48sN9pCZaVT/Fk5IOeCOgNVRr0j6Xf2urQXeovMJBFh1MeCPlyDgoVPcZpAdDp/wBivHMjbkS7jEJlX70bg5Td64YfKx5B+U9RWXrkS3IlWeFhdQMUmxExjkI6lSARgg5x2PFVfC7NxZXTs6SRBJT3J4Bb655HuKu22p3txo99vkxfWV/PFNs6FsghvxAoGYNvd3dovlW166xr0ikxIo+gbJH4EVct9Q1a91bS7IXcMKzXOC0dsoIAVmPUnOQMY75q5a6hfaiJtmni8WHHmkRhsZGR1yScdhVNrq1uFimsbJY76ORZLeSNiF3g9CM4weQeOM0CM7W/AMOoTSyWj25mJJIjby255+6SR+tcTdeHNd8P38ckUdzFNyY5ItytwMnGPYZ4Jr1HUgtyE1K3RlV/llQ/eicdVb0P8+tVf7RvkgjRJz+6dZEBGcMvI/A9D7GnYdzlLD4jNcQ/Y/E+npqEDYzcRqEnGOhJGA2PwNdbFp3h/wARaVMNEkiuP3ZxDD+7uVz16nLH6nFZetaDo+pwW16+mzWct5CLgNaSLIo3c4KnB49q4e/0C/0i7SSxmmkwC6yRI8brjrkdj9Cake5buvDF1o99513czJZ+WXDODHKSDhlZcnBB6nv2qjNqVnfkwKFdTwAev5mt2y+Ic80AsPFOnpq9qPl3SfLPH9G7n6/nRN4K0nxA32zwbqiSSA7m066PlzL7Lnhv8807hY4q905reV2RHWMYxvOSfyr0jwZrcMXhW1guFlk23wj2RlQTuQkAk9AdpHrXBa1Z39pqki3MMyPBtDRPlWHHUA+/cVo+H7uJ7LULAh2e4CyRSAhGWROVYZOMg5GM85oYIs6zrGp6v40ubi4tJZYhuthbQoX8lAMLgYwSPve/NNh8O6xDDLdLpkk1qgw77CFlHZ2UjIYeowaqWmpapYazcNeSTQXLtmbeNrBuo47e1ezaH47tLnSCL9wtwi/MQvyycdeP1pXA4vwTqWiLdrb31siSyNgSnJb8WJOf0Neia8+nvpEunPs+zSIYymSBg+hHIPvXjvinWrLUnSS2tUguQ3MkfAIJ4z7j1rG1jxHfPM6xySxx8c7SOg96Vhm1qtrpOkLHNqNzca1KnMUAURRqpPHmN1Y+oXrW/wDCnUG1PxLqd9Ja21ulrpxWOO3hCKql8gE9WxjqcmvJ7idpn3GdpSeuc/1r0j4UySwaZ4ruVRWCWIB5wR8rnNMRxugRm88aaTH18y9h/wDQwa9b8T6Jb638SZ5r2xmeG0WHEibsTfISU+oJHT8a8u8Bp5nxD0NPS9jP5DNd/wDFG/1BNWheyeI20UsiTfN83mHnBA5A2gdKARQlsdNj1mW2nsnuNcsLVpoY/mCSMB8qD+9jrkcE8Z4qvpdzL4q1Dw/Pbr5MNvqMRngGQGbcvznnBI6D2PtVe0t9NhvIvE41RZYrf5J4UdmOWGF2kkFVOeh6H2NXNEt7GDxv4ZXT7TCyTIftSMR5hBYkMucZ2gfjmgZT+MMgfx7OB1W2hB/In+tcA64iJ9j/ACrt/iq4k+IWo/7KxL+Ua1xkw/dlR6GgXU7v4sQA6poVqjnzE0qIxDsck8fXjiuKMf21ppVnxNJlpLcqclhycdj/ADr0D4p2JvPFWlxIjtIulQtiPrgMQfoeeK4W5lkCIJLo+YjfK8ijerDsT1wR0PNNCZb0LxTq2nWptCsV/phzvsrvDx4HXbnlT9K0VsfD3iDB0W7/ALJvm/5cL58wsfRJex9A1Ydjb2ovg+qu1sjc52EpJnuCM/4VlSoscrKj748kK+Oo7HFFh3N3UbO+0q6+y6vaSwS9vM7j1Vhww/P61Qls1fLR846joR9R/UVoaV4zvrC1FjdpFqemHraXq71A/wBluqH6GtmDRNG8RgP4Zvvsl4ef7Lv5MEn/AKZSd/YHmgLHEvG8fXkUK9bV7aXOnXZstWspra5HaQbW+o7MKoS2QYFojuHt2+o6igREkxFXbW/lt5A8UjKR6VlsrocNSq+KBneWHihJkEF/GrDs/v6+o/CultNSubYCbT7kzxcZjLfNj2PRvx5968kSYitGy1We0YNFIw9u1AHtNpqmlawQLlPs90v/AC0C7T9GHp+Yqxc6ay/OR5iMMCSPPI7ZA615vYeILW7CJeLtYdHDYwfYjkV09hq99YHdbTm6t+8ZwWx9Ojfhg0CNeONEhkikfiTGT2VhkBuPTIz7ZrOmaf8A4SE20hwkkQAHYHHP61pxXWka0Q/zW11nBwxUE+jD/EZrRubaOSVFnsoljKkLNHgAe3HOPx/EUAYEcRaLzMRSeQwIkkfEcQHO4k8ZB6d60Y4pJr0sLpGtDFnYPvRvjIYHtkGoZ2klgktZhCERiot0T5I1GcZHqR3rOWG4Gu+dEimOSELg/dBUY2n2/oaQFRooXvY5p4YZDG3EuMNzxnOea5rT0lt/EMsILAuzrvHVWHKnH1rtZ7VCA6BljcZAPUeoP0PHvVT7BELn7e6MZceUvYErgE/iKAZhOkd3FkwI9td5/dHjyJx95Af4c9R7VWttFfT5/PhukSNwQYrgc+4OM/gRXUQCHzpY50UfaCCZAvG8Z2kgd+cZ9DWZeRzG6aNg+N7bFII6nsKAsMgsftK3NtGUeKSMg4P+rJwOP0qCLSn0y0ihuhDiIljI67ic9AorTt4V0qaNrkS/aZcqIlXhRjJLN0HTgetRMNPk0LdbSSzQ7yy55aPJ5UZ7A+9AzGM6KxVIF8ps7w/Jkz6/4UX9s04W4QLt2qAiL0AHGPp0q00NrbSRtNOmG6CQYVsjnHPb+dRyzbGVFO23/wBVI/8AdJ+5IP8AZzwaAuZ8MGIvM+XcCTCD/exgkVWz56lJBknnI6k/4/zqTW5Z7VrB2TYyE7gOm5Tg/nyfxqee0kVxLHGwVsMpC5HNAjMl8vz5oI49phxkHkyKepPvUUkUbYiZvMDDIjk+8qnod3atiaxjE0kq3O2+aDJXbzsBxuHqeOapNaFjbTSvKsjc7HTggcAHvmmBgz6ZuBe33N/0zK4Yfh3/AArMeIqcEYNdJbZmhVpd+4MyZyQAe30pt3bxs+y4ZXkHG9F5H9D/ADoA5kgiitK5054ssnzoOpHUfUdRVBkxQAyiiigBMUtFFACgZ4q1FEqrubkn9KhjIBzVhWFADQcGkY0GmnrQBNFkmr8LHbtI6HmqERAq7BJ82KAL32ZZIsSDKDH15qlc6W8eXg3OnoeGH+NbFuBNCNp2unQ/StCRovKBf5Cwz9Mf55oGcRyDg0bq6jUbKxltfMmOyZh8rp7eo78VypIDFfQ0AdFoPjDVdBBhhuXe0YYaEv2/2Tzj+VegprcHii1I0T97dgAi0upEDxsP7uRyPcGvHc1JHI8MiyRuyyKcq6HBB9iKQz1yyW5jgvdL1u4W0a4VJEDzIYlIOMMBk89hUl58PJNQsfsk91bgpcq8EsZyRER86kHB9CO2a53wr8RIrHzLTW7KK4hmBWS4VBvOeMsP4vr1reu47dV+2293LJpDcxi05Eaf3Sx+79OvtSA4vxLLHo9/c6DZTXEsNufL/wBIYPhsclDgFevIHBrK0bRr/UJ99pJ5MsZG13YryPcVseKb23vLsTWGoxLbEALDgswIHJJIFUI9TLW0ab8OnBIbhvTjtTA6XS/EN5Yyy2upO915JdJC6goyeqt3Pcd6j1mC1tfs+paSZjazZ3LKwzG4PYjqpHeqmi29jcymXUJrlbdiMmPBGc45z0+taev2Nlpc6pZXf2q1lBcxSoN0Tk46e9AHTeHn/tvQhbX+7DNttrk8iN/7nsD79ayJo3t55IZBiRCVYehFZmixXYmZ0MrxOP3ltHOI2IHQkHuDyOKvtuDHcrKe4LZI+ppoTH5ozTc5paYh2aWkpM0AO4pfpTM0ooAdmlpAaWgBaKSloA52OxubzxVbagRthS5SNR3IGRn6ZrW1TSJbh55NjSRSStnZ1VgeAf6VI0ko1TTI40YhrlSx7AAE1Ri1a5h1vVoIJ8Fbp8Rg8nHQ/iOKljRQbTbhVNvK4MUpETSIuJImPTd6isxfDNzZ3YE+wMGwOcA4PXmtpfEkU17JO0amV9u7zfVfb1qSTW4hdC5V8P2k6lT3BU5BFAzanMOjW09zIXTyrPygflO5yOmRwetcbp3i4xyzW0/mpa3AAaaPHnRkDG4diPUUzU/EKaiy2k0bpZLn7nB3n+I+v0pum6FGW8xAl1E3McjsEX8iRkigD6d8B2ml2XhOxj0if7Rbsu9pictI55Zm9ya6YjNeAfDzxEPCl7NpouvtSyQvcvAnKxFTyikE84r1zwt4y0fxdZG40y4y8fEsD/LJEf8AaX09D0pks6OiiigAxS1RvtW07S4jJfX1vbKBnMsgXj8TXm/iD45+HtN3RaXHNqkw4DJ8kWf948n8BQB6rWZquv6TocJl1PUba1Uf89XAP4Dqa+bdf+MnizWd0cN0mm25/gtV+bHu5yfyxXBXN1PdzGa5mlnlPJklcs35nNAH0Zrfx18P2cbDSre41GQcBseXHn6nk/lXmmvfGrxVq26O1lh0yE9rYZfH+8efyArzYuelJmgC1d31zfTme7uJbiY9ZJXLN+ZqAvTM0lAEgYg5qd5S6j1xioVTA3N+FKRnkUCGngZpOTSqharCJGIndu3AA7mgZCkTucKKuaZqV5pF551rNtyNskZ5SRe6svQj+XaqWWPtT0Q9aAL8l4z3bTWyfZ8tuGxj8v0NVpPmy7TbyTk9+aAmRyaikiZPmHIoAv6NrVxomopdQ7XQjZLE/wB2VD1Vv6Hsea9m8I+KP7OiGo6O73Ohuf8ASrBnBktGPVlHbH93oR0rwMmtPQL67s9Wga0vks5HYIZZT+7AJ/jGCCvrkUAfaFpeQX1pFdW0iyQyqGRx0INTmuS8JLdaR4eVdb+wWfPyC3mzEQf4lJ6Zz0ya6qOVJV3I4Ye1AD6KKKAFooooAKKKWgAooooAKKKKACiiigAooooAKKKSgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBPwqOSRIoy7sFUdSegpxzg4615x4h1DULi9eK4dliU4ES8D8fWgDe1TxSoBisee3mn+lcpJK80pkkdnc9SaZH0p31oAMd6a8iRqWYqAPWqGqazaaVCXuJFB7IOprz7VPEWoa7KYbcNFb56DqfrQB0Ou+M4rbdbWH7ybpv7CsXStCvfEM5u72ZhFnucn6AUaX4fwQ8gyT611+nwmy+7909RQBfstOSytBa2m1E9+efWrSWi20mEfdKRmST+I+w7CrFvbtJ87NhP1qdhEzfK+SvJx3Hv60ARIiqDyqoR7cHv8AU0juHtSqSbivR/WmFneQZVl56dvbNSeSygfeByOccGgDHaxl803E7quOgLVOtn/aURgmVxGBtBTg/XvWjKkKkzttOOuW4FRLqigPJIiQxD7uOtAFbTPCmkaS/nQ23749Xkbc369Pwqe/1m3swUTa0nYDpWVd61PeMYbRCAeM0tnpWD5k/wA8h9aAKxF5qku6UssfpWpa2KQphQtW44VUegp7zRwyCMhmJGeOgHuaABY0QZcqKkjBYFim0D1/xFZ13cLcKAm0Do3z5bH44FWIw1vGCu5I+2//APXQA2aK4Zt6OGXqMkZ/DjNQmOWc7WRlB/jd+cemB2NSz3JkAWLcXPOdhIx+FMhSeaIu6ICPujdxj3zx9M0AMeN5VEaRoMevTA7gE8/lVy3iEWI8JuHzEhcdTTYrZwTLLsGBgIMcfjSwMgaaVodoC8ktnJ/M0ATtGoJl39QAR16fSqrymGJ9xZpZPuj0pRNc3GBCPLGOpUfpirVrp4RvMkLPJ6mgCvbWTySCSYJ04xWmkQUYWpMBRk8Cq91fRW8RZ3VUHc0ASsyR9eTWLq2v22nxlppFLjogauW8ReOorZXjtzg+vc15re6pe6vOcliD2z/OgDpfEPjma6kKW546ewrjbh7i4PmvvO49T3q3Dbww/NIyySDr/dH+NXDbtL+8uXZI+w/iP0HagZk22nyTNtUbn9Ow+prdtbZYMIi/aLg8DAyAfYd6vadpU17HmILbWQ+9K/Q/1JrWE9npMJSyX5sYaZ/vn6egoAgg0iO3IuNWffL1FsG6f7x7fSotQ1hmAQbVjX7sacAfhWfcX8twxEffqagSL94B8zyGgQrNJcHLnC+nenxxNICkG0kdq0INGuJgGZ1j9utakWmJbsJHCh8YyOhoAqaVYyOH8+NcHoO9bFppwtiSu7yz1BXpVmwEUseUKnB59aff3SQwsi/fYYoAtxQKy4Iz/Wo7ewjtpHKjljnPfHpUeivMLcR3HX+F/UelazAMMf5FAGFe3FyJ/JgTb71YGoNbRR+bzLj5gK0jAGIcD5xVb+zYZbguRyPyoAuRXMc0YdW4NFQIxiBSSRMg/wB3tRQB45oKj7SJH4Hr7d6n1jVheec4LAXM6sB/sRjC5rKku44oPKjf733j6j0FUnnaSTeewwB6CgDZttUki4zkV2Ok6uuqeH5bVzm604GW3z1aI/ej/DqPSvN1k9a0NKvHttTt3QsAx2MB3U8EUMD1Dw9r6LLGm/huVz79voau39p/ZV1Hc2vFpM2Y/wDpk/Uxn27j24ryf7ZJZXLRqceW2B9AeP0r0jw7r0Oq2Jsbs/urhdh/2XHRh7g80COo8qLxBpItWdUmU7oJP+eb+h/2T0NZOkX8tldG2lDRSRttIPWNx1FZmk6ybG9e2ujtljkMcvbkdGH1HNdBr1mL63/tW2GbiJf9IQf8tIx0cepH6imBqiNFmEsKAwXR2vHuwBIedvtk8g9jkdDXP6toioTPAu6ME+zRnuPUH1FXND1JJojBP88bLtYZ6r2I9x61qXO2XXLCKeZxI8MqsYzt+1AKGQn34IPcGkM4aRC4xM7uPSWRmH5EmtPREurTUEvILOaa32NFNtiOPLPJx2yCMitC61S00yVlttNhSQcmR13H1zk5qveanrD2C30omS0kAKy7Pkweh9gfUigQ7U4pkP2iGZ5baX5kdWOMGspyZVKSfMh6g8itHSkvIbO+uVdTbLG8kkUv+rYgZJB/hJ6ZHU9av29hp8NnFqV5slE6rJFDGSYlB6Anq59ulO4FbRJJY/D8VnZhIZoSyqQmPPUHIYN/EwzgjPaqcX2/U9SSJp5UkhYO0vI8kD+L69gO9Tapqz34EajbEuNuOCMdNuPu/hzUWk3KJZzaZLO6yzyvKLlssZCR91z14HQ9MelIDWsvE/k3DWMg3RneQfJLxSAckso5Q+pXK+wqRNXgs9SivbS80tLYpIGWS7GA7bcNnbnA2j5T+BrH0u2a21q6csjC3sWYMjAg+Y4UdPZTUOnItxHfWDopM0X2hMqOXXhh+KkH8KVh3Lt1c/bNQitrGYf6KrtGbiHAvJJDukYKe3TA645qqbmydtl3A9lKD/rI8vFn3HVfxBHvUcS/2lbCxm3G5hGbeQnmRR/DnruXsepH0qeG5UW4udVD4t3UG6jQk8nA3qOvPG4fj60xA97pT6zc8tPbvBGWmtcOFl+YNwD3XaTjPNTx2FtdnNhfQzH/AJ5k7Xz/ALpwf0qtZrp99Pd7d1ncXFy8kKSIEWRTgKMjgMcZwcHnjNR3ljJbybLmHp3Iz+VMDX15rq0stIukKw3aXDQnzIg4ZGjLMMHqNyg1nnW7o/62x0uf3MLRn8wSP0qW6tLb+xLTV3SVxZyos0JmfaY2Owkc5BDEHgjI4NQXd5pMKbzpjj5lX5JmTkkLySxGMnnNIC3p2tQrfRMdFRH3A5huRg89PmUfzrTsbIxadcX80flyTzym/j6mMmQsjnHXAOCRwQc9qxtV0iXT7d7tBKFhw00UmC8akgbgw4ZRnnvjmtGC/uZdJeSKZopkAilI/uEgA+nysQfpuFA0Ug9z4b1KSWKNpbafaZIwcHK8qyt0yAe/DCo7a/0q1Z5UttQkkdmciRFU7mJZvmLYGSewp1jqD32nzW13YsHiC7o4m4iJk2ZUn+EN1U8r1BI4pw0QW7H7dewwbSQUj+duPc4H86AItLkj1XxNfHUEcfbIA0EMVwyICmAUJGNx24P51K0mkwsynSmJBIPmTu382prro0IDwm4a5Q7objeSY3HRlAwPzzTLi9truz+13e2zuvunKny5j0+Q+p/unn60xEkd5bvcW1gdOVrZ32W487YIWOTtJIPyk9O4Jx0pkl5fSS/ZYLK2tCsgwgQyPkdixwMHocDpT7fQ2uIvN1J3tbXhhGOJZMcg8/cHueamfWX+3CGAQiSY+WuoyZ3R5HG5ehYngNwMnJFIDLuPCGjaw09nJIltfwMdxHzeWTzhiOWHP3sHHQ4rhdZ8F6vod2HiDSLy0U0L5yBySpB7exz7V6PbWjabp2qSyIyyxQ7YyfvCRmC7geu7JzmnSSkWNhLMlvcxyStHMbmPcQwGVOFK7sgH72elIq555beOria1/s/xJZRaxZjABl+WeLHdZBzke9WbnQ7LxNJb3Hha9SS5jXDWN0wjmIHQgnAc/SuhvfD9hrN1PBqYWHU85E0OI45EP3So6AY6Z4PIODzXJaj4F1rTmluLWI3EMOW8yLh1wMnK9VIHJ/MZFAXM/XjqNrAbXVLK5We3CiKWSMhowTyjMRyvpzx24rnk1GdPuu2OhrudO+Ieow2b6brUKatp7rsaO4OJApHZuv0zS2ngzwz4h85tD1iWOYruSwulVZd3orEgMP1oAxNLgmv4pp7CG5Yqv8MW9Qw/vHB/SrcTaiblLiaxuLkhghAmLruA6NHx27Hipzbar4G0mdi1xA0ziN1dGRlzkggE4PA6jPWq8GpWesvG2qAySr9y6jBEqn/aAI3D9RQM5W6t5I5mdlwHZv4cYOeQR2x6V6f8Ol8j4aeNbru0WzP0jP8A8VWBPoT3jxk3ttNbbuZTMAVB6k5Oc/rXR+E18j4MeLeV5nK59eIxQxHJ/DaPzPiVo6+lwzfkjGu3vtUsJfHmq2Mkfk3kOo+bb3CMTuYBdy4PGcDHFcp8J4/N+Jen/wCyJm/KNh/Wtn4gteHX4bzS5ohvmlZcOoZZFcqxBOAenPXr70MEbR8PWOt2cUtrc+XZSztNObdFUyk7lZecYyAM5446VZ0XT9J8P+J9G0i2uftFz9qOxJGBkjjCO25gOAedv0rnNB8S22gyiDXNLmt57zcGuYeUODgMEBxkN1x1/GrPhXUzrPxZsEudOit7u0EuZldsyqI22kg+uQQeuKQzmviSxf4hax7TKv5IorlH5YCul8esZPHmuN/0+OPywP6VzhXfOijuQPzNMR6V8TYT/wAJlZpBY3zSJZRAS2zHvn5eQQMVyeqQT2l6tnNpH2mCSISIiBzKF6ElgMgg+2Pauk+J9vdT+MZVsbq4zDZwKwQuELFTj5hwCe2etc1HrF7nK3chmkiEUsF7Kfmx0KPkYPPQkU0JlP7Be/2XJe6XczxwDcWgaUbgF+8Rg8468gHFZUGqK48u+gW6iPOd2yRfcMB+hBFdDpeiXExuVS2ubaQRPh5X+XJBB24HPHU5xXOQ6Ysly8BuUR9uYgejHsM9vrQBZ+y6NNyuoXNsT/BNBv8A/HlP9Kk1SXTUt7WCwmeeSMszzeX5YOcbQAeePWux0H4U32pxC6u7kQ42gpGu7II6g9DVy/8AhhplhEYW1GYXIJwZF+Xn1AU5/Oi47HM6d46vFtBp+tW0WtacOPJuuXjH+xJ1U/nWgnh7Tdc/feE9RY3HJOl3zhJx7I/Rx7VC/wAO5SP9H1NJn/upayAfmf8ACsu68G+INNPmPZOFU5WQOATjuBnI/nSAguoHhuXtb+1ltrpfvKybW/FTwfqKoTWJVdyFWT1HI/HuPxrpIvF9yYRpviixGqWqcD7R8txF/uydfwOam/4R6DUwbjwrqDX2BlrGfEd1GPQdnH0oCxxZDIcMKUMe1acsYMrwzxtFMvDIUKkH/aU8j8KpzWbKN6cg9COR+dMQkc5U9a17DWZ7VgUfgdqwDlTg05ZCOlAz0S1161vtguRsmHSVGww/H+h4rpNO1m9sIsh/tlqew5OPdf6j8q8fjuCDy1bNhrVxasCj8elAHsFrc6Tq+JInW3mbjr8pPpn1+tJPZS28p3hkz12Z2uPX/P61wtnrNpenMm6C4/56R4BP1HQ/jXT2Ov3tkoWQLd2w7jnA78dV/UUCLyw7LeSJmQbmDLn+Fz8ufYHgfrUExd9X+y4UARAr8uMYHIrQRbHVoSdPumgkcEFHww564B45qeXT7Z7xVWO4ScqVBcnDD+7u7N70gM60gdruOSNM+SQ7HjCj1JPAqxLBczS3LtIoWNc28yL86jHI5z9c+tJN/pll9mMapFzi3DHtnliDkt7nNUYw41O5KK7RyRq+zJ5A46evX8RQMz5GVlWSCdnjQbSN+dwOd2R6+tUdKsl0tHjmmUwSynCH+KM8H6Edc+vFb8tlEszzIGMjj74+UMD6468eozVVrKJ5CzjaOTjGcgdeaYjMv9KDs1jON0bHg+now/D86w47e6sWkSB01C1GRJGhyyj3XqP5V2Vz5NzZoVfLRj5TnO5e447jqPY1lXEdysUZsYFa6nYoSFG449cf1/GgGVL3SX13SYDAmGQ7gZMhiBgHPHYcfUVUvtmmyG5uZ32MgWK2jbBbHUk9hmt5YkTTprG/1F5JE/eSyx8+Q2chQe/TJqrriWEd5FdPZrdHyldnHUAcZA4z746UgM83G1IWeNNzxnB2/dB4IB+nWqs9pdCQxsUkhbGWPytGR7nhh+NWkeO+EhE0UihT5QRNrj0BqQI1zpzwh2EidfRl96AMbyp/NMMUKnDEyY5DE85J7VWvkjgE0wG+SMYIPIPTn8v8a0ZIowJYI5H+Rgk2w42sejD6VGNOmkDJdTI0bgoJQCGZB3K9eOxHWmBlgIRv3OF270k/2ccg+uKpy2kdyu7G1zn54wcfiO1alxbCJYvLkL2rKu3YeCB3+pP5VSnREECJv8l5GU56hj05FAGJcWckPLDIPQjkGqpWukC/ugSVjx8rIRkEj2qlNYxzAvF8hB6Hp+fb8aAMeippYHjOHGKhIIoAXNPWTHWo6KALO4HpTe9RBiKkRwTg8UAPU4NW4WAIquAMZpN+00AdBY3IRhzjFWJ5dnLSKQGyAewPpXOpc7e9JNePINuaALF9fNOAi8KuazdhzkU7dmrVrC0hxigCtkpjd3708GtSfSlbTpZFf95F8+PUDrWLvx0oGT5q7p2rXuly77WbAYjdG/zI2P7yng1nLIDweKl6DNIDoLrUtBv0UvpD2dy7l5ZYJMpz2CH+HPbt2rc0/wALabqluZdLkeTJXzYw25tpPzFVwMY68muCq1ZahdadP51pO8L9Dtbgj0I7igZura6lo+oXen3RYW+OSeBIpPykfXFXLa+FtqltNdwMwceZCZGwrYOMgnIOPQ1G3iG116OKLVS1vNHhVlj6Ff4hk5wfTPAp39iavPvsNKZdRsEPmIyyIxUHvjccN64oA6O61WRZdtzZIARuCfe3RkfeU5I+q9qo2dwiwbXtrlizkxCFd6qh6AnrmoIYtU0JobfVrV1ti+YJThwDgZAIzwQelb1stnbSyz2l6YYZlPyROFkjPsD1XPTv2oAoRzo8uw70f0dCDU4Oarz6fcWqLdXN65hlGUufMwZB/tKclT9MinwzRyp+7nWbA5Ibn8elNCZNSUUtMQmaN3akpPcUASA04GoweKdmgB9LTc0D0oAnttouYmbsw6Vxl5p2oL4wv7uGBTtvHA8w4HXr9Oetdlbn9/H9RXJ3mtrB4k1S0uAk8ctwVxLwBg44btkUmNDtZ0WGJRqiTxI3mKJYj0Zv9nHr3q5YxWN4s9zpNss8yYPlOc+WD14747VdgK2+l3FgbKKeJJN5jkAdlQ8hu+4Y9DWDPbO32abRF2yQzbQY8qORuwzH6dDSGZ408zyXDzbEWLmTe2DyccL1Jp0s1ndCJ5DKv2ePyzHgFCPqema7mc6RrUECa3Dt1ZlwZLZCp/E9D+Oa5K48O3lidQt50Rlb7mHznHT8fagBfCEqz+KBNbowit7aVmwO2zA6e9W7HXBauPENnqKWGtWhRBEVwlxH3Jx94EdR1/nVDSmey8Gay1r+4vo5oxO/RvKPACnt83WuRMuTk8mmhM+g7v8AaB01LCJrTSbqa9KjekjBIlbvhuSw9OK891z4xeLdY3LHepp8J/5Z2g2nH++cn+VeebmNGT3NAi5eX9xfTebdTyzyf35XLH8yTUBlJXbUWacOnNADd1GaKSgBaSjmlC8ZNAC04A0+BEeUK5wnc06TBYhBhO1ACKVH3+QO1K0u4bVGBTRH61IFAoAaqtUgXAxQKWgdgAFOApBS5oGLThxTacvNIBs1mrpvibkdVP8ASqOCprUzUE8Yb5u9MVh41O4urSKzubiVoYuIg7kiMegHp/KvT/Bvja9knhtnvmg1mBQkPmvmG9QdEYdN2OARyeo5ryHGDxUgnIXa3OOh7j6UCPsrw54ht9es2dUaC6iO24tpPvRt/UHse9bdfPXwr8VarqGvQJNZXd5JGBA17DyAh5AmzwcYJDda+hFoAdRRRQAUtFFABRRRQAUUUUAFFFFABRRRQAUlLSUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAJXLeK9LEkX2xByOH/AMa6qopoUniaJxlWGDQB5QXWNSWOAO9cd4j8d29hvgsisk3TPYVZ+Kun67pMhMTN/Z79Gj/rXjrks2WOTQB1dgLnxFMZ55mc55G7pXZaboqQKMJXKeCLRkc3iTZUnbJGO2O/1r1WxsWuAGXhPXtQBTtrMsQqJkmtmKyhtY/MuCvHr0FF1eWWjw8lTJ6dzXJX+tTXkhluI5hapyMLhCPc0Ab1xq0lzIY7Laq95H6VXs0mNw8s86BAOdhwSK5u38ZWcd3Da2yIzOwUBFZuTxk444rsIbJXn82Y5QckBcY/AUAX0ESDeXZgxGMY/rRcNI0qbNgQckuN2KowalbTah9mCNsByhdeMjsK0QnLSI7AcknG5fxFADNhMZ8za0bDLSdiT7dq5W9tpZtWa0km2IoDL/tL7dvrXTlkc7pPJkI6BMhvyzVTV7A31qs1su26g+aLPf1U/WgBlnaxRxgIi8VcPlxLuk4ArK0y+WZRIOOzIeoI/wAK2iquMEZFAEcytJF+7dUBHJIyaiaAyxhEmRiOokT+VPnRpTsjmWM989ce1J9kaSUK86sF7kEH8xQAoBjO14cBcYJQAZ9qG+zXTASwsSOnyGkktrWOQNLdPkdN7f4mpJQ5iRLZ2Iz8xHcUAMiFsZHS24fuOc/rUpgGcCZhj+A9M/Wo42uAd7w7QON7kcCo2d72QpFtMIwDkdcelADZf3EZDzKWYnbGjZOTTo7Oe4C+a6iMfwIuBV230yGLDBFz61dbbFGT+QoAjht1jUAU6SVI145Nc8PFsBaSO5RrcqSuD3IrmNf8bRxqyRybQP4B94/X0oA6jWPElvYq+XVnHvwPrXlniDxlcX8pSKTjOM9hWPd395rMxI+WMfgB9TVdXgtDiFfNm/56HoD7CgY+XT5FcyX0m0H0OS30pYxLc/uLOPZEOp/qTVu1sHu5lN/Ntz91CcEn3J6CtS00ue8X5dtrZKeZD0P09TQBnWtqIpFit42ubk9MDOD7D+tbtvo8Fm32jVSs1x1FuDwD/tH+lON7aaTCY7FdpPDTP99v8B9K5+91ZznBoEbWo62zkKNoC8KqcAfQVkhZbl98hYD0pttbt5ImkGSea19MgWVN78PnpQBQEW35EFbGi6cRIZHHJ6ZrQiskyHdV+tSecI5AtuN8noKALwVI12gZc9qdfW5ey8sDLn0qxbi4aMNIixn35NRXN6bVd0aM56Z9aAK9vpk0XyRyRR5H93k/XFOWwis2826ffJ79P/r0+xguJtQ+2zFoVx/q8/54p2pzw3E8MQ+ba3UUAWbV2uWki8lk28g1fXbEu6QqWA/CmadbGKKRmcsWI69h6VJKUiEhc5UjoaAGRXscsmxZlJ9BVlo2ZCRwfbvXP2GmpJP5yOy4bIOa6C4ukhjZ2PAGB70AZbai6uyxRttU479aKdba9bpFtaHBBP8AD196KAPnvOaUZo2saBGxOMNQA4Edzip4biOGQOq7iORmmJbM1WEsGIzhvx4oAjkuXuJzI/Vq2dHuZICfvAEgj61nrFFEfnkiX9asJe2UByN8x/75FAHReI7jebbU0ODOoV/94dDW34X8VypGgZ8mI4Oe6+4/SuCv9aa9tUtlhWKNSCMNk8UaVcvDdZXowwaaEz0e+uotK1WN7f5bW4HmQY6D+8n4Hp7fSunt5k13TohbzKl3C3mWkv8AzzkH8J/2T0NefyS/2n4cuIDzNbfvYfXI6gfUZrK8P+I5tPvAC58tzg8/kaQHplwkfiGyllSPyryHKXNseqt0b/gJ7Go49Utm0lNM1Zb1BDGsPmW67vMjH3VYA5BxwexqaLU7a5WLVC/kvuEdzJH95SeFk9x2I6EfSrOoWlvNIUu/KglHS4j/ANU317ofZuPQ0DsZWpa0LyD7DaQNb2AwSHxvlI5G7HQZ7d+9O0SW5JkshbPdWWDIyDGYSf4lJ45/u9+1JHoVzcaj9kheJsAM0gIKxqf4mx+g71d1G/ttMt/7P0/aAD88h6s395vU+g7UCIm0iK7Bk06dZwODHn5lPoR1B+tSeHoRbw3WqvxlWt4D7A/O3/stVvDkEN7qk9tOimR4T83R1JyAQeCKg0LVYZNCttKv3WAImyGbopBOdrehz36HvTuBW3st7JeW223nkPLxqBkHswxhh7Gr8kl6dG0/V4o7FLrzSS4VxjDMjKV5GCPfvTrjRbqJ/lCsh5BDdamghkTwtc2sgw1vdy8egO1x/OgCnDPJqfmadb2kNncyxmRZvOLcqQTswAdw6jPaonvL57iGPUJvtFpFMGlt0iWNpSM4JYdSDyB0JqANJFLHNA22aJg8RPZh/Q9D7GtTVEjuYYdUthiKcfMndW6EH3B4oAj1HT40Rbm2fzrOYZV/6Eevt2p2nahclo7OZPtULcBJG+dR/sseo9m/Aim6RLLHO0PltPazczRDr/vr/tD0/i+taVtYWNkW1F72EWS5Ky5GMfTqT2xigCjrupWSaQ2h2Dv9pmlUTh0KmFAwbBB6knHTPFVzqVvejy9VslyeDc2y/wDoUZ/Xafwqwt7Lqcd9qUcFvPaz3DEwyIGaMABVDA8gkLn8aqwQWl9PJBaw3FtMiCRvLzLEFJ25Kk5UZ/unj0pAOk0V7nT3TT9Qmu9PyC0MdwxQY5AZTyPocCtPS4Wt9LvpJhjML8H1I2qPqWPFc9LHPY6i8Tlre7iUOJInILIejKwxlTgjnvwRXQ3N3c2lvHqFxM17bxr9oityioDIhHLsB8wAYMPoaBlTU5I9M8Vaiw3G3WZknReT5ckaM+PcN849xVjVojewreKUeVMCfy+QcjIcf7LD5h9SO1Yxad5ZZrh99zNK0srDgFyecew6CrFlftYAI3+pAIBOSFUnJVgOdpPII5U8jjIosA6xtbXUdEhjtgo1WKQfaC0p3DDncSvQqVxjA696fqt+kcVtp+mzIZLWVbiWYYZRIv3E9DycnH0pL7SLa+H2m2jR8Dc0RUMwB7jHBX3HB/SqVraSXNxHZ2sa+a2cDbhY1HVm9FHegRt3Eh120+12xYSJ/r7UtzG3r7g9j3HvWFdh1heMR7pZP3ccZXq7cAYrY1A22n28VpYO6zod5uk4kZj1JPoegU8YqA3Lw6nomq6rJC1p+9TzI4yvlzAAq7jJGAM8jpTuBY1eJ9OhsdNjuHuI4PLa4jlbd5xXtu6qCc46jgVXu7rTz4dvhbvcgxSRyCKSFiyyBgQNwBBBXdzmp9Ttbn7U1ww3xvysifMpXtgimaQS8mqWf/PazWUD/aRyp/RxQBXvxaNo3nRX1vc3NuQbZI3/AHkikjdGV689RkcEVK1xFqFxZWFtc3kUxDgvMpiWZAvEJwcMSeQPY461nrM9neW+owJumtW347yIRh0/Fc498Vf1izgEwkgO60uFWaBxxwfmBBHQj9DRYDI1DQ7K6zDc2vK8ABtrx/7ren+y2R6YrmL3wJqCrJcaU/22GPLME+SaMAZ+ZT0x6g49K9Ft5f7YUW10VGoqP3UvQXCjsf8AaHcd+o7ikuLAw+FdXlu0ZMRHyjnDBwQFAI55PGO9A7nmtl42vo7P+ztbtota07p5N19+P/dfqp/Orlv4a0DxAsx8MaubS7kH/IMvztYkHOFk6H2rpdV8L6Dqmdkz2FyBgi4GVz/vAAj8Qa4TWvCGo6S4bYskbDdFJGwYMB3VhwcfmPSpHczdXsNe0K6NtqltNbyHgGVAcj/ZbBB/A13OgK8HwN12Ri3768AAPf5oxn9KxtG8d61p8IsNRji1awHBtr5d5A/2WPI/HIro/FXjSfW/B89tHpCWNjI0ewl8uQpz90AADI4NAGL8GE3/ABEif+5bTN/If1rTutQ0yz1670q/t5ZZGvnuIorqEIqsXyp3BiRkH7wGMAZFVvgfHv8AG1y/9yxkP5ugot7azl1zUL3XIWtZJENwwkUFlVT99e43DsQPbigCpcXs/iW+exksoY7vTbmSSGyuWEfmRHAMe4Y+cEAj16jpXXeFLKOL4i6L9xbqLTphNGGDGNRgKrEdWG4j6Yri9V15dQ0ZdasrKCG6W6C3JdQzSIMeUeef4QCB/I1ufCSR9Q+IUuovIWee2laQFCADuTIU9COfqO9AzkPFsnm+MNZkH8V7L/6GRWTBhdQtt3QTR5/76FXdcfzvEGoP/eu5T/4+1VbSET6xaRf354l/NwKBI7T4oancaZ8Tp5YGdIzbwhgjbRIu32/SuZurmz1cyHyIhNKOZC5Vg394oOCfXaefStr4sM//AAsm/TYpAjiUD1wg/XmudOkE2S3CXKJk8iRWwMngZAJz+FMTGW+j6is+IY1uI8bfNS5CqAffcNvvkVNcWkukyRKzpNEQQY9wbyzjHDAc47YP1rPupJEXYxtpgCR5qRAv+JIzVF7maVvnmdsDAyT09PagDvUTU9TY3el6q+OMQxzNGYgOigA4wO1WG1rxnpabJNRmeNTuIkmV+vfJ5pr2Jj0iw17RJnmfyVF2iIAd4HzHA/X867XRL4a9pQePa0gX5wIkcn14NSUcja/EO/j4vBcyDuYp1Q/ntP8AOrsPi3QpzvYOkxGM3fmH/wAfRj/Ktafw5FNN5YgZZH5GLMRdP9ocfrWXc+ArZWO91DHPE10B+gAoAsG/sbxdsa7x/wBMtTRx/wB8yAGsrUvD1hcSLOiahayp83m29shIP1RsH6jFcx4h0iTRxE4n0yRJGwsdvcGR/qVPIHvWRALq5u47eyDvM3G2Ncc/4e9MDvL24drMDXIW120TAW5NtJb3cQPcOVw2PQk1jt4b+2RNdeG75dTiHLWxUJcxj/aQ8P8AUVi6s8tg8dob3zrlV/feXKSqt/dBBwSO9ZkVxJFMJY5GWVTkOjYYH2I5oEX5Yo3dkkRoZkOGR1IwfcHlf5VSmtHTkcjt/wDWPeulh8Uw6miweJbX7egGFvImCXMX/Ahw49m/Opn8NvPBJdeHrtNXtF5aNRtuIh/tRHr9VoCxxuSOtSLIV5FXJII5cgDYwOCDxg+hzyPoaqTWskRwQ1MRYiucc7q2dP1y4tWBDsQK5gEipUlIoGek2Wt2V2d2/wAi49QuMn3HQ/zrqbPxHPawhLsLPD2kHIH49R+PHvXi8dx71tafrtzZkbZNyehoA9ogS1vQZrOT535ZH6nPv3/Wq7QmOZlf5S4OUPr3wfeuH03WbacgwTfZpu6fwk/T+orqrTxGRsi1OFXGcCXPB9w3c+xwaBFmRQlvvA3eUOm75imckH3HOPbNIxWaW4jCKvksAD7djVpbRJ5hcWN2zxEjzI35YL3xn+R/Oj7Jb77ho3uCFA85P48AfwkgfQ0gKdlbRw3kkkcDh3jJkKDEceT39CcY45welJJFcLZTz3ARJS2wvFwWQngj0P8AOp7ra/2dZF8qGJxJ5cb8AjHLD+L3J/CsuwMkkMlvPv8AlnKnOcKx6H0Azx7ZoAxdQtIrfQbtLbzQWlUyb2ycHjr3qOyd73SbZB/x9IW8knu69UPsy/qK2Z7VSHjkVuQUYfXj8weaiS1j063W2A3SKwdiRj5h39Qc88UwMB9KW7kF1a/6NI3zBwvyE9wQOhH5GrdtDIP3kkluJlzkb+JRjkfXFal2qGITW5KsWLMEXBVj19uTzVKKxkuMsNojTlpD0H+J9hSBIoyaPcW+o3V4I08meMAiQ5Acdc+1VJ5FjceTtBUY3/0APQV0qTWdzeRyLPKTJCw8krjzVyRkr2IPNYl1b2ccXnGd40zgh0ztP50DKMsD3luDuVducIFwM9/zqlBbNiR2VQBwN4439vyrXmceUJrLZIEO8+X91hjDLjsSOR9Kx9cEo0+1kjkLL5hIk/vKfmUn3HQ/SgVyoxLuVmGexPQ//rFV2ffdTQ+XjyTgAd0759fWtQQPeW8VyiZMi/MB6jqabJYwAscv9raEsc8BlHXB7kd6YGUyoG8kLuBGdjcgfQ9RVObTlYnyD83/ADzJGfwxwa13smhUB42SaRCu/d0Y9vTpiq0MLSQxM8agN8rHbzuBxn60AYEkTISGGD71FjFdDMI5SUeNnVeN/wDEPfP9DVC401lBeJt6dSO4+ooAzaKcyEU3mgB6yMvHanFw3SoqKAJN1KOaZn1qSMZNAE8MRZhXRabaopR+mOtZlpEMZAroLGNnxgdaAF1+GNdAnuUGCwVfTksMn8q4pFB616ZNph1TTLjTRIsZYAxyEZGQc4P41FZ+CrPTJI2m/wBMmPTeuEB9l/xoA4uz8P3t+UaGFvLb/lo/C/nVe+R7V/sbHIiJYHvk8H+Vew3qxyWEZUspTGNg4+gArynXFjm1iYW218ZVinTPtQBkrL61LkHpUctrJGu4o1RKzr0oHcs9Kt6Xql5o99HeWMximQ5BHQ+xHcVTfKxxSMoAkBxznODgnHagUgPZND8a2Hiy0k0zWFSCV8tgZ+8O6emDzjr2rD1rwzNa6jNNbB7iDO+Ybtzw5Gc89VPUN+B5rzhSVIZTgjoR2rttB8eSwy20es77iKEGMTAZcxn7yN/eX0PUUDLU9loC2fmXepajFJtVVit4DsMhP3iTx07VWieLSbprCaZbScYeOU5a3nQjKkjqmR3GR6ivTJtY0O50sz2dlFPG4BVxgjjHUjoxGR2NcJ4nt9C1EWu1bmzns1SFoo8NmPcTwDzwDkHn0NAD4L1JWETjyZyM+WWyGHqrDhh7irGa4uc3WhypbzolzYSjzIQ2djKejKwwVYdDjoa2rHUvtGPssj3QHJt5GAnUex6SAe2G9qdxWNzNJioYLqK4XfFIrAcEdwfQjqD9amBpiACnA0lFADqUGm0tAE1uu+eNAcFmAz9a5W60G9tfF1xcXUMU0LzOHXO7cDxge/pXV2bYvISO0i/zqn/ac0niu+S2KNKJpPKErfKSGJAHvxx/OpY0cQ2qXmkzy25hm8tCRHFcbgFGeDjjt+FatlqNzr9ldwu6QXhkWSD5/KWVuhXJwMgdKt33iC9iuI/t1srxxyET20vJZT/tHP4Uye40O2mDBIdQsLgZijLFDFzyGHZhzz+NAyyt7qML22mahbS/apYwyRgKSrD+LI6DjnJxVi7SRZg0ur6dDLnMgkuQ2eOxwSuKhltrKSKxg0OFrQaxcGEuX3MsaDDANz1OTWTdSaPaTSQw+HBcWyHaZZJnErYOCQRwKAN+5sLW88I65Jp90lzcBUe4ki+6dvO0fhk5ry3BAr0/w3plrAJ7/SrqZ9PvIjDLbS/eib+JX7HHY9683vYGtr2a26mN2X8jihCZEB6mg4zTcP0PFKF9aYgzT2+cgKMcUz6UoPPNACmNgMnvQAKc7mQ5pNtACCnbc0oFOFACKmKkAFIKUUhi0tJRTGOooooAKdTaKAHU9PWo6kU8UgH1FOcJ9aloitGvpmiR8Oq5HvQBnGjANSSxPC2xxyKjpiNPQPEWqeGdUj1DS7p4Zl4PdZF/usvRh/kV6lefHPWtQ01U0ywtrW62/vWLFyD6qDxj69K8axQkjxOGQsCOhFAj6Y8DfFa3121jh1MJDdxqBORxg9N+P7h7n+E9eOa9PVgygg5B6Gviyyuz9piuLeZrW/jOVlBwrH+hPQjoa9z+GnxLhvJItB1XFrdfdiyfkLDspPTPZT07cdAD2OiiigAooooAKKKKACiiigAooooASlpKWgAooooAKKKKACiiigAooooAKKKKACiiigAoopKAFpKKzNea6XRbo2Uvl3IQ7Gxk59vegDTzmlrwfQPiLrOjXMtrdubqFWJEcxO8nPIVuo9s8V7JomtWmvaXFf2bho3HIzyrd1PuKANSiiigAooooAKKKr3V3b2Vu1xcypFEgyXc4AoAnrC1PxZomjXkNnf6lbwzTHaqO3Q+57D3Nea+M/i7hZLPQW2ryDcnqf8AdH9a8XvLua+umnnkZnY5ZmbJP1zQB9lo6SRh0YMjDII5BHtUtfPHw0+Jz6GYtG1mbOk8iKYgloCeQDjqn8vpX0BBPFdW8c8EiyRSKGVlOQQehBoAnooooAKKKKACiiigAooooAo6lptrq1k9pdxLJE4wQe1fNvxE+Gl54avTcWUbz2MrfLtXJBPavpqaaK3jLyuFQetcb4h8SwGIoVUQ543jJJ9vSgDyrwB4NvdO3X+pv5MUi/8AHufbux7H2/Ouo1PxKkB+x6cN0nQELwPpisTU9Q1C6BkiR49PPG51IX8W6VzV74nttLjMVjte4PW428j2X0oA29RuYNPjN1qc4e5PIhLc/j6fSuUvtX1XxTdCzso2EX/PNOFA9SadpHhnU/FFx9puXeO3J++eWP0H9a9L8O+Ho9F04QukPmLnLp/F6E+9AGP4T8DwaQyX11J5l3g4I+6M+g7/AFNdXc3kVpCTI+1fTdyao3+tJBmK2HmS9M9qzYdOuL+Xzrx2PfZ2oAr3N3d6hMDZR7I1Oc9Px966a0mM8CP8wl6SbCBg+uD1ot7RIVAQYqu4bTrsSKP3Mn3vagDTd/KiygikP8RcBfzpxYC137FQnnjpUTIZV3AoznG3zFGAPapFtZmBaebJ4A4wAPoKAOc1WH+zL3+0Ih/o05Czgfwt2b8e9bFjcB1C7s8cH2qWYW9yJLdtrRyAhh61zdnJJpWpHSrhvu/NBIf4l9PqKAOmnhSQfMitj8P1qvaoyzlVg2IBwS2cmrcTeZGGp7ukKl3fAoAoLZkF3kDeYQT5h6D6Z4qNZn5RJJpsD74AwD7U8Qz3w52eUfVTn+eK1be0SJAAFoAz4dOnuADczOw/udB+lasNukC4UYxTndYhz+VZeo6xDaRkyuo/2B1oAXW9Wk06z823h8wg4I/wrGufE6G1LxBtwGWL8BfrmuX8Q+MGMZQPtH8MY6n/AArj7++1TUkX7QWt7U8jPAx9OpNAF7WvFc091IltsdnOPMAyfwFYRZgu69fceoT+L8T/AEqN7qG2BS1GWPWQ9T/hT9PtUumMtzJkA/c7mgY4+degJDtSEcY6AfWr1rbJBKIrWNp7k/x46fQdq1rXSzdwB8LbWi/8tZFwD7KvepZr+10+JorBNm770p++349voKAGw6ba2H7/AFJ1uLnqIQ3yr/vH+gqtf6zJcPgHheABwAPYVmXV475LvtFZFxe5ykf50CLd3e8/MdxrOV3uLmMAMcsMAVCAznPWun8G2CXWr7n58tcj6k0AdIbFFs4ySq4GcHitTTtIj+WYjJxxUF5ZyXFwU+bYOMCtS1tnsrUebMwiA4HegCtLa3c82wDEY9Oa0LGzSyO4jk9Saht9XRrsQqmEwSSO1WIrpru72RpmMcMaANNYwecVG8aDjZn26024le2gAUqT7t0qzYvHLCHUqz9GPvQBg6lLdeZ5KRsuak07S9pE1weeuTW/LAJSDt5HSop7IzwGMvtHrQBKqIVAAwPXNZR02SS5ka4nYxA/Kg6mr5aK2hCs+4IoGC3JxWJcatLPN5cKMB0GOaANVJbeKSO3QKP9jjFUNVge7mVIHbA+96VDHFHY/wCl302zA6d/wrlde8YM+YbU+XF7dT9aAOle80uxbyJZ13r16UV5NNqFzLKXy3NFAFXfaoeSzfQYpRcWufuPUK2+T0Y1L9mx94KPq2KBDHv5OkYVB7Dn86izNMcszH61OIFY4UoT7EU8+XAu6T8EHegCsLdj1qRLQn+8aja9cn5ERfwz/OkM9zJwXf8ADigC4tttGWCge7YqYSW0PMk6/ReTWX5Mjct+tSLak8ZoA2U8RLaROlrD87DG+Rv6CsiOVialWwfqUbHvxU8cUEfLzRL9G3H9KBnW+HdT8yM2lx/q7mIxv9exqpca5fWs1tfRXTrewbrSZx/FtPy7geCCKxk1KC0O6IFpByHf5QD9OpqncX6zRTbmZ5ZZA5IGFBH/ANagEew+H/EtpPBFKoitxqcYiuNihQtyvAJx0DDj64qldb9P1GG42eYYLhZfLf8AiwclT7/1rzLTdS+ys8cnzW8mNw7gjow9xXc2mvRXcYtdSkUSAYS5P3ZF7bv8aAO4k1nT45LnW01CGSU23lQQ7gJS5yVDL1yCeewArjShjsTGvJEewe5Ix/Opxpc7sJIVWQY+Vxh+PY9avW2mzf2hpyTptWS6jDAgjgHceo9FoEamsvLolzb2unTvAIkVGQYZCQoBO0gjJOemKjuL+XT/ACrpw19aanzKDtRo5FUDC4AHK9j6dap6vdJP4iU3D7YGnjEzjqsbN8x/Wr2sWS22j38KbzDbzw3EHmYLA7grDI6jDHB70AJbPoN1IwWSWNkXc0ckb5A6Z4ByM+hp1xqEFvA8llYveaW5HnEERGOTpuVTztIwCTjms3RZTb+ILU7sJOHtG+jj5fyYCrcAvINf+yWyK0kpb925AUrtLEHPGMZoAns/ENrFPFBYaZMl1M22L7QyLEH7bmBJPsO54qg9nZsFtr6FIL2Ik/agmQzE5JZe/PcYIqvqVnFFJGYgwtLnfsG7mJ0OGjz7HkH0+lXN8mv6VMjOo1W2TDv/AM9VPCyD8eD6H60AUwt1pOob0dY7hl4I+aOdPcfxL+o9qvRGPUZ/tmmO1lqsSkPCcMGQ9cZ4dSfxHfHWrFvFYaxaeRG6WjhAXhd8NBKB/rFyfunvjgjrzWDaq12LWRSySnayvG2GVj3U/wCeOtAFm5N/qGpiS78ozLEIUjii2Kq7tx6kkknk84rpp4kTRFil248m4OT/AHBAyk/TJApbVbuW7kj/ALM+1TJEjfaBKsaMGzjcOoPByF4rH8Q6kC01k7/vGUJPMEZI1jB3GKMEcgkAlj17UDRnRktFGz9Sqk/XFPBrTtdAmnhVpJJVkdQ/kxKuYlIyC7NwhI5AAJqwfD8IjCiZ/MHdb5GP5FAP1p3CxhBZITutZmhIOQB93Prjgg+6ke+a29BWWTRrplkzqF3KS8j4zuA/1WRjAwNy8cg+orPvNLu7LDFWkQ9NyCNj9Dko34EH2qS0s7qGVLhbizhyAHikuSfMXrtbapAI6g5yDSYBpaWf9u/Z9WjYxyQssfmOVHm7hwTkfMVzjJ61Nq8BsNG0+wl/1puJZ9j43CPG1d31z+Nat9Y2uoRHz5IZ4yP+PmMhwPaQDp/vYwe+KwpvDs9nl4YQ0bY+ePnIHTnJyPoaAE8Pi5iu7uC1umhgitvN8pwHj3lwq/Kenf7pFWRqc6G11qW0t2ig8y3vBa7vMCSABX2k4KhhzzTNGVrey165cMrqYrYZ9QpY/wDoQp2jxPDHY3ZKT2l5PLZXNvg5VRkZJ6HOPbGRigQsNrpV4xNrq9vxyUkIRx9VYgj8qa97o9pbvp0l6xs4pB5V3GhkijkbJaLcAenUY4GcVkyxLpupRvMiTnTbzkuAdyK5Vs59VPPvWtemDR9SurCSFZdPnfaYUXhlbldoH8XcEc0XAZLf6Oum3dtpd02oX0yYBhB/cruGZCcDbjt71V1PS57lGuv7Ru9UtEO4CSU74MdyoxnHXcOR6VCftGl6jE9vOszovm20x6XEJ4Kt/wCgsOxwa0ZJLeG5sdTtppoNMnl/eyR8vBwQykc8q2M+3NAECXkOr2/2XUpFE0ilIr3gCUEYCuRwG9G6Hvg07UbzUk04aTc2NuJVkiP2oPtwEP3tmPvEcEg4Io1jTlt47a7BhMd4zxyrGwKM4G4SKBxhlByBxnmrOmkyaVML1Jbm1t2RVMalpogSAAO7rk9Oo7ZFADBoGna3oeLq2VZEuYhFJGuG+eQKQPrknHTiuU8T6XqDaXLbLD5phldC8fQ7WK9O2cZx0rvm1qx0+VY1hbNq3mpacGaaUAhdwGRGoJz8xz7VgRTyhhvfdcOS7ImSSzElsAcnk0DMf4HWzx+JdW8xGVlslXBGDy4/wrJFk2teM7uZ7W526iWjYIc/Z3PDK4I6DHHTj3r0K3t77TX+3A2+kSuMeZcuEMig5wYwCW/IVzXiCB7vxFLqwdvs1xEqTPZQvGRIBjzBkg5759qQzmtKsrXSdAa7vmW4guHdJbctjAD7Fcd2Ut1xyMAg123wx1At4tl0hNOhs4bK0kceU7N5rM0Y3kk9wK82VbePR4X12a4nV53EEcYxKFVssd7fwsx6YPPORXpXwq1OLWdf1S+jt/IW2tBEO52GQsoJ7kYNDGeT3p36jO396aQ/mxqxoUYk8VaSp6New/8AowVTc77gt6sT+ZrW8Jpv8caIuM/6bHx9DmgSOx8VWUWofEXXJHSF9jxqAZdr4Ea9AcBh6c5BqobT7N91HBOBscYY+gx3+v611GuQXn/CR6i11dia2kmzDbCFD5YGAeSCT/Ks9reAalaxrBtj2k+XISVZwRzgBgDg9gKQzlNW0SKKB5DbeW55wRjPO7NcLPGqOQPU19D/ANnw3Fh5d1GtvbYJLvE20D1GVGOa8k8ZeG4NL1Ym0nWeGUbsjopzyP600Byttd3NsQ1vNLGQc/ISOfp0r0XwTq0MmonfEqCdQJ48YUMDyQP7pBz7VkeHPDf22W2Z4SUabDk8jaMMwH4d/evV9Y03SbeO38u1hS8txGSUXGEkycE9DkrQwRC9haQyDZbW/DAF7fUjGP8AgSk9PWsq7n0tGk23ugqp5O9Gkzj3rfEa30qFBEcHI8zTvOXgdmBBGPSsq6t4rmRkig8NtNuKhZreSIlgM42nvikBmTeELHW9Iju/s9p5rswW5tQ0alMZXaD1HXk/hXMm1j0eQ2sISMEAtgdc+p6mvRb6/FvZxxTfZtyKG/cjgv0CqDk4H1rkNaiWKYBfKB8td56jd3yQDyDwaAMmCW1Vtsdzp8HrvhH86syWsF7bMHh0u9wOkUa+YPoQwIrONxcb9q2mmXaehfB/MgYrQt3sLnFrHp32a+XJ+xTOU8zPUxydie3ODQByetaEbTbPaw3CROMmKZCGU+zEDcP1rPt1u7WKLULOco0bANJGxVoSTxkjkA+vSu6sZr4XEsOi312l5Efm0y9IMjY6hc8P/ukAmohBY+J0L6akWl+IEDKYQNsN36pg/db/AGTwaYjPbX7bUgE8T2PnOPlGqWeEnHoWHRx9cE0k3hm6No95o08Ws6cvLG3X95F/vxnlfqOKx4/ssNztn+0WVyjYeJYhIjEHptJBXnscirq2k+nSW+qaTdXNpIQcFPvhgcMpA6eu054phcyWt4bgZjOH/uk9/Y9/oeapy27xEgjpXYSa/pWsStb+KdPa2vgcHUrFAr/WSPo31HNMvPDV5DZm9sJodY0xf+Xi1yzRj/aX7yfqKAsccGxUyTMtWntI5+Ym5PY9fw7Gqj27x5yOlAi5FdYIOcGt7TfEU1t8kv7yI8EHnj8etciGIqZJmXvQM9Q0/UVdhPptz5bD/lkWOPwPUfqK6uz8RpLthvo2ilIwG6E544xwfwrxC3vmiYOjsrDuK6Wx8St5flXSrLGeuVz+lAHqi2rEeZbTLcQHtwD+Pr+PNUjH+6dFLKAeezcdj9K5/T9UYYk0+5/4A7/oG/o3510MGt2t8fIvA0E2Pvjg/wD6vpkUCHzKGYSPtaRVG8D+9tyD+OOfesyMNdWi3AHLcN9a3LfT9spd51kt3UgSR9SSRjJH09Kqw2dvDCz2qPcRrJ/qjwAx4+bvtHpSAjtTdxaXILeFSSSYw3AlzjP5dAfeq1+ggNuXnaFZE2xxZACsfvcjqff0p2ph3+1Sq7i5MGyMh+OMNgDseO1I0J1KwjFyGY+WrHeu7K+3cEH+dAzBnspZ9TtJkfJh3Ry5OD5Zz834VJdQboRcqVkQ/JKcYBPuPcVant4tscSB9i4G9zuYDuDwOlOhltIjJZO+fMA3A8ZHYj1IoEcpLpbWM8Vzp12kcknzLDI+M4PQHof51dGnzajps1nJa/ZpCdw8zgK3X5fY9a0hbT27zBUziNjFleC2RgjPr7VDa28loxi1O6aS7njYm3GCsagZOfc9KAsYd7bW+lQWsc++a5iUhYVztLHqT6j09aUSTJbRB9waQHzPbnBA+lbVzIi6XaSWKLPGMopl53YOduex7DP0rHj1GC6MsUplWboIpcYU+xxx9KYFc6e6ZIm/0XPKSsTtPqp5NQJBLJN8rKY1JXPQEdenvWtHB58LWsnEi/MpPb2qg32SaWeFUYJbttlB6sOm4e+aQGbqJe1G4DjzQJCP4lI4zUM4W3Qg7SynEZDcgHnJx6elbLWUcYKys95tTIjP3vVV3Y5P/wCqqV7bJFcltikSDJRk6ZHQj1pgZtxBDLzIFUkDDjqc+o71m3FjJENw+aP++ORWy0DvcOyDAaIMPm6MvGKHDoV8tGDv1z3PcYoA5oqRSVtT2kEvI2wyf+O59Pas2e0kgbDjHofWgCvTgxU5FIVIpKANbTtQijkCXHAPG/rXe6dBGYo5oyrxHGHTke9eW1d03VrzS5hJbTMvOSh5U/UUAeqhCku1GYA5xnsR2rXjf7REA3DYx+PrXIaT4usNTKpc7bS4z0P3GPs3b8a6uBAVYg8gg/p1/GgCpr8z2fhnUJ0OJI4jj6nC5/WvLYysIROnTJ9Sa9dv7VNT0u6snLDzUKE46Z6H8DXkVxaSW8zWlyGjuIjhge/oR7GgC+QhjyOUPWsW9tRAxKP8nUD61qJKI4AjcBeeay7+5WUgLQASQW40KG5VmNy1w8bgngKFBXA98mtXw1og16aW2E3kyJAZFfGRkEDB9ua50u2zZn5c5x713Pwz51e6z2tsfm4oA5/UtIvNJujBeQlD2PVWHqD3qpXt99Y217amG5hSaI9Q/wDT0Neb+I/Cg0xWubWbdbg8o/Vfx7igZiaZrF/pExlsbl4c/eTqjezKeDXRJ4gtPEM8Md4WsL8fJFcxHMR9FYHkA/iBXGM3pSJ1yaQz3fSTFaW+ladr02n3MyTExF2DBe4ABXAI9KzNR0pxe6pYzadZm7eBpbWaOMhZ8/ewoPDgfMAO4964/RfGBFpHpurIJY1ZfIuyv72Ajpz3Arv5ZIdbs7C1ubrJVwftcTlXh7K65zvHqBSA4S+hbSbfT7y+vXaW7UlXj5lVBwGYnhxngqeR61at9WCxh7h4ngJAF1FnZ9GB5Q/Xiuj1rQNR1jwjc27JDc6pBfFUwyp5igZ3qD/EVPI7nmsuL4ZalaabbTJqsUF9PjzbeQbUVT13Nk5x6EU7gTKysAynIPQjoadmuR1BdZ8JX+y4hRIHJ2BTmKUA4LL3Ge1bWma7a6kAqny5j/yzfqfoe9Mk1qKbS9KYE9nt+326t0LqD+dcxLZ6r4f8QXGoSBpoTJIfMimK4JPQkDI9xXTWmPtkO7pvXP51S8Z2i6np10LSbN3YykyIj4zGSQSR3OcVLGh1rNpesaDCt/ZLNcSSElwcOpJOcN/Q8VkeK/Bq+ERbajb3RuLO5yqsUBMbYzgnoa2tIk8PLYRw2AvGkixumlULkkZKt689MV1dzFa6l4UlgMO+NSD5b/MATx36Uhnk+i6xBLZDTbm6+ySwXH2qxuyMrFIfvK3+yfXsa1p7mJrK61O5S2tLuNgV8m5Dx3BJ+bC87T3zTNT0Gx0795E9tEcAtFKc8Hoe+PxqrrWhWZ0hL+1tMRxMGnlicMrZxlRgkLjtTA0/D+oo1hr1+AILZ2jxk8FxnOOgJxXLeIsJqUlwmMXADA+/Q12UgtrnQrQ6VDCdMc7EjKbgsnXbIOu70PeuS1mMXGmxzIiIYjgpGxIA6cZ5GDihCZzvWl2mhafxTEM2Uu0CnUlAwooooAWlptLQA4GlNJSGkBJQKQUCmA6lpKWgYUUUUARvkmljnK/K/IqyLf7RHuVsSDt61XaJhnK4I60CLG9SMg5FWdCmiTUi9x/q2Ugduc5FZG4jgNQsuzvQK50HiRIhteJMBjnNc8AT0qWW+lmhELHMYOQDzj6VLYwG4mEY6kUBcrYorY1bSRZW8Uo6sSp+orHoGJ05FbfhHX7fw54ltNTutOjv44DnynOCD2ZT03DtnisTpSdaBHtGu/HTVZZo20exhhtAwJeVtzMB1U4+7mvQ/C/xIsddhjllxHbyEKJt3+qkP/LOQfwn0boa+WIbh4GyvIPUHkEe4rW0rVrnSr/7ZpbbSwxLbyfMki91YfxL+ooA+0KWvK/hp8QbLXCNLeYQTBf3dtKxLrjqFY/fX07joRXqfagBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAQDio3dY0LMQoHJJ6CsrX/EmmeG7I3OpXARf4UHLufQDvXgXjP4map4lke2gLWmn54hU8uP8AbPf6dKAPRfGXxbtNJMlnomy6ul4aY8xofb+8f0rz7xH8YPEOrQRx2Lf2YBHibyWDGRvVSRla4Bmyclqjb5uKAPcfh78YY9Q8rSPE0qQ3f3Y73okvoH7K3v0Psa9eB80hz93sD/Ovi/aq++eD+NemfDr4p3uhNFpGsCW700HbFKPmlg9v9pf1Hb0oA6b4peAr2aZtb0SPcSM3EUf3hj+JR/OuD8F+Nr/w9qXmb8qx2yxtkI4HY46H0Pb6V6P4n+JMl1GbXRz9njcEG5dfnI9lPT8a8YvIys0siv8AfyZBu7+tAH1L4f8AEth4jshNaSYlAHmQMRvjJ9R3HoRwa2yM18k6Jrdxpt5DJDM6SRnMUiHkeo47GvoTwd44tfEtsVfbFOmBhnBJ9+354oA7PFFVL2/ttOtmubudIYUGS7HArxrxp8YWkElloBMacg3J+8f90dvrQB6F4r8e6T4YiZJJBPeY+W3jPP8AwI9hXgfivx9qniW4JnmxCD8sKcIv+J9zXL3l9PeTPLPIzMxySTkk+5qmTQBO0hdsscmmnHSo80oNADs87j26CvQ/h98Trrww6WGptJc6SRgRg5aDnqueq+q/lXnnWk9cdT3oA+0bK9t9QtIrq0lSWCVQySKcgg1ZIr5U8C+P9Q8HXmxf3+mSSAz25/Isno2PwPevpXQNe0/xHpceo6dP5kD8c8MpHVWHYigDXooooAKKKjd0jUu5CqOpNADwMVnajq1vYIQx3Sdk/wAax9Z8VQwRssEiqB1kP9K8x1rxNLdmRIX2p/FIWoA6HxD4vbzCqvvl7IOgrh7rVI7djdavJ5knVbcN1/3vQe1YN/4ijt8pa/PKesp/p6VpeHPh9rPiUxalqQmtdMdxvlI+cqe4U8496AKxv/EPjq/j07ToXaMcCOP5Y419+wFX7n4TaxpurWyyTW/l8O0v3l9wFPWvYrP+xvCmmix0aBERR8z92PqT3Ncnq/id5pzHAWmmPfsKAJy9ppNuA5UEDHucVjz313qzbIR5cP8AOmwafLdS+ddvuJ5xWzDbpGAqjFAFOz0yODBxlu5NakcXHFNklS3i3sjvnoka5NRMzTMWV2CKM7D19qAJGlcTbBC4T+/2NTSQrcQlG71VDy/aFREc/wB4j7oFWYpMyOvocUAVLN8FrSfqvT6VfR0SPYkmQDg5Y8Z7c1WvoCQJ4/8AWL+tPSdZLXfs3E8YC55oACGjdyiIBgfw8k/Ws3xDp39qaf5tqcXdud8L4wTjqK1CCYRuh3e2cYpXnRQpTlhxjoaAMXw3rC6lbEt8s0Z2yp6N/ge1dF5aSDDjIriNUsrvRtfj1WwgZ4bk7ZoY1zyfYd+4rsLecog84YOM0AX0jVRwMAVFcXccEZZnVQO5rF1TxJb2alVdXYdh0rz/AFnxc9zIUJZj2RKAOs1jxakW5LdlGOrlq4S/1i41VzFYCWSTrJKfuj8+3vVGe2Zx9p1WZoI+qwj7zf4fjWZda07RG2s08iD0Hf6nvQMtySWWlku7rd3fqeUU+w7n3rLuLq71Obc+9j6DJ4qW10t58TXL+XGeeep+g/rXZaVoS/ZRIx+zWx/j7t/if0oA4qz0y5u7gQwwu8jdABzXW2eiWWjKJL4rcXI/5ZBvkU/7RHU+1X7nUrTToXg0+MRA/efq8n1P9BXL3mobyWd6ANDUNXluZOX4HAA4AHsKwbq+EZPOXqpcXzSZVOBVLY7nJ5oEOmuJJmyxpFjz8xqVIQPc1YSEk4HNAEUaZ4xWzoF82kaklw3+rPysPb/61UQixgY5PrRtZ2wvJoGeyW6w3kcdxGVZGAOR3FPvrFruEKjbfrXF+DdbNpMNPuZMxufkPYH0rs9RkuoTE9sN8eeRQIht9JtrdSHG71POPzq+phtok2J16AVlXmpSMvlRxsM9R9av6XETbxRyffUYGfSgCrdI15cBBMuD1Abke1aVnZJZj5Op6j1+tMe1tLW48wqolbsOSaZfXjxqERNpPYf1NAGmH+XdlSKoXeqJbggbWPoOlVja3qxDfIo3c7PTNVJxaacvm3siluyd/wD61AF1JXv7M7/lznPoB71iahrlho0ZS22vN/fPQVz+u+MXdTBbfJH2CVxsj3N/L8zE5oA0tV8QXV/MW3sc96ow2bzPvc59zV+y0ksPmGcdSeFH1Na9q1hEXUXIEo6SFCyg+w/rQMzo9Lbyx+6Qf72Mn3oqzLpf2iQyNqluxPdnIP5YopXCxxZe4k43t+HFIYnP3v1qy8scfA3E/l+gqH7UB91F/wC+aYhEtmPSpPsL5Bfge5xUYuPVB+VPCpODtGH7e9AEqQ268GZM+gUmn5hUfKkrf8BwKrG9KDEaBPp/jTftsx/jb/vqgCwZ9o4hC/UE1G97OOFfH0wP5UguLlRuO/HuvFJ9qWQ4ljB9xwf0oAiaeRzlmz9eaZuf+9Vie2VY45EbKucc9RQLUHrIn/fR/wAKAK2T3pwarH2Njwro30P+NRPbSx8sGAoAQORV2z1KW1+UbXjPWN+RWeQR1pQaAOjg1u2XH+jyx/8AXKUj+RFbmla+H1GG6ge5aa23Hyppi3mIw2sFyeDiuCDEVJHO0bh0dlYdCOtAXPVLmM3UZvLKTzIm655x7MOxFNiub6eBLSS5u2tlZSId+5Mjke+B6E4ri9P8SzW7hmZ4penmxNgkf7Q6H8c10Vv4qgkAMk9iZD/HJbkfnhhQFjpE0y9nTMEeJRhk+bncDuX9QK1dc8z7Za6raFonYLNGdv3SRkgj05KkVzCeNrW3G1tRmdf+eWnQ+Vu9ixyf1rU07xxoZiFssk1rGOkN6hkjHrhxkrn8RRcLC6hqZv4LG0j0/wCziGeS4lcOCpYqVwo64JOTml0yC6bUYZrPaJojwT91geCjex6fr2robOLTdT/eW1qtwD1NrNFKP1II/EVpPAunW7Sk22mRqM+dcyh3X3VRxn05NFwsc9eaHa6vOZbbyT8xDRSMpaJwcMpB7g8e9WLTSoNObfdzJuH3Y48M59lUZOfc8Cs2TWZprmK20uyhaFiIrWK4tleSY9TIxOCCTk9eBXTQ3DppdxBa2to1y0ckTXUIWGAMQRgMcl8HrgYpXCxS068naxm1O6mNpa3TZhjhI3tGo2rljnC/Qckmqja3Y7jtTUD7i8kHP54/Sm36m806G2tj+8soo45rfPzLtXaGHqpPII/nVTR7Oyu7S/W6S4e6hcMqROQ3lFR8yqAdxDZzwaYXLOn6g1zaXGkRzy+cGkuLd5Gy0yn5mDHuy+vdee1ZVtb3V/qP2SHb5wjeRjK+0KqkAknnuRTLu3NtNEYp2IYCa0uUGCQOjD0YHgj+hrQM8l2I9bsSlvqFsSl1EF3KNw5O09UcD8PqKBDBd6hpVw9rOcggExuQ8cqHuOxB6exq7p+s3g8LRi2kWN7KZ7aWNeBgHcrYGOqsPyrOIa58PSzSspmsbjzBsBAEMjbWUZJO0EgjntRorBdTuLF/9XqEBAH/AE0jBYfiVLD8KALK+IlkkxeQwysp++PkkH0YYOfxre0y6tLnmMy4/i8tyky+5xgSD8M/WsHR9TksYjo0lj5vmXjMZPlKMjH5g4POR0GKqyq2mavPFbuwFvcMkZ3c7Q3A98dKBnYy6XB9nuRBcS6hbXUnmygOrSq+ANy4xuGAMr19K5w6bqOnSS3GkXbNEx/ebFDDI6bkYcMPXg1PY6h5HhWyuXhileaadpPkAJJlfnOM8fWr1te21+ysGfzQP75SYD/Zfv8A7rZHvSA5dbO6fcJ0mnkkZmld0wWLH5iccc5q/efan0nS70For+zZYmc84kiPyk+oK4z7Gt3UdQg0yKOVrl5hI2xW/s4Owf8AuNhgA31AzWVp2oT6lrUsdvJcsbnG77RFE8IKrjLInMYxxnJ96dwsZuq6qNUW0UadLbzR3DzPIXVowrLhlUjnBbB5AxiptIilDzgp5lhKP9IH/PNgOJFHqBwfUfSpdX1TSdMuEiu9PiuJC20/2dMsu3/eU4I/Wmv4ldoRBpulrb27/LM10+HKd1Cr0B789KAHReF4DMJ0n08jJxLHKnQ/iMVfg1jSLG7h0+G+i8q3lW4vJR824qcrGoGSxLckgYAFYF5DLrchNxbwyKSP3cMCxoMdOgyfxNWbXQHTAURQD0RefyH+NADftGnQyS/ZrO7ukZ2cfapBDGMkt91AWbr3IqSPU9SkzFasLVT/AMs7GERce7csfxNa0Ph1IovOnXbEOTJcOI0/XFKdR0e1jxE73hH8Fqm1Pxc4H5ZoC5lRaRO7mR9qO3Vz8zn6nk/rWtYaP5UTySeUNg3SSy/Kqr+NVG13UpjssoLezH/TFPNk/wC+jwPwFQz2GsX8Hkz6hcmIyK7LcSeYGKnIypHTPpinYRLqn/CMX0XlTQtqL9M20IVP++2wPyqzpevWmiWEdhDo3k2iwlPOhdXbhTt3KFBP15qO10ctNtl/eE42ojH/APXV920/SW23N9bWsg6xId8n/fK5P50WC58/PFLDPsmjdJAMkOpB/WruhapFovijTdTmRnitZ1kZE6kDrj869b1e80nVkMDaK10v/Pa7fyvxCqCw/MV53qvgW+eZ59Mh8+EnIjjLMV9skUmNM9BvLW2s1vdbNw1xb6jK16t0c7Y4iBtXnpjJrI8S68fDWlx3doEe6uMxQs27bg8swwRnAx+dcPp/iTxD4S3afIjG0f79hexb4pAevB6Z/wBk1vXWp+GfGNlZ232l/D99bbvKjlHm2pzgbd3VRgYGRxSsUcpd+L9fkQvNPFhjjPkJ+hxVaLWtQ1OWQXMrS7EL46YCjnA+lbGu+C/EULxI+nedFj91LZgNCwP8QYHGfrisfT9I1CzvpJZLeaNrYbidobaPUjPK4647UxHbaHfyLaxNbSKsckUnXrG4HB/EcVdsfEE+l2J0q9KPlEEF2ykheDiNic9M8Ht9K4eV5LXY8KqIfM3q8T5QA9vp9a6GIteiJ7SF7mFhi8iQjlDyCVznI7EDpUjH/wBr3TyXWmXU01vKzAFw/RgQwVgOxxwR147VetNdm03xNeedM8lvKyHzfvfLgbSMfd9P51Hfxs5tE2Q3F1Gm1wcCZWU/LIAcFlK4UrzyOBVzRdBTV7jclzLYzNg4kjLRtzg4xg8HHegDoNQj0vV7MXun6oNP1S3UhPNQlCD6qc4Jz1XkV5he6vqMF7JDfJDJKpxvVcq2O6sMZz65r0V/Bwvxc276tCbqJm8tBEUDL2KtuJB/OvONd0jWNDvNkkjSRMCV+YMCByR6ZpgQpqNlfZBgPm9SA2Gx3KsMHj0YGrlwqx2kEF3cb7C53G0vf4oJB/C3t0z7HI6VkW9tDqq7rJTDfx/MFj6OB2A67h7darTXckunzWzqpLyK49mGQSPqDyKLCub8Gr/25AthfzCDWbU/6He7sFiOkbMPX+Fux68VYvhceILCTxDZBk1ezKpqlugwZMcLMoHc4w2OhGe9cSISi7lZsg49ulanh3XrrQ9YW8ik++DHMCeGRvvZ9fX8KAudHJq9t4wtvLne3ttbACeZNgR3Q9Hb+F/Ruh74NZAW7stQktr4vaXiEb0l+UnHQknhh7+nTNQeJvD8ulTG9gDNYTSELJnO1+pVv5g9xVGLWLmSO3t7w/areE/LHI3IXuqt1APpnGe1MDX1fSdRuI/tH2WJzksZoXZvMB9CeD+FYdlqF/pN551ncTWtwh6xsVOR2I7/AENaeord6aYbuwvp2s5MeUwkOUI/gYZwCPyI5qNdX0/UHJ1mykaU/wDL1aMEk4/vKflb9DQLY6XyY/G3h291S2jht9d0yMzXscabUu4v+egA4DDv61zdldfaIT5se8A4yW5H0P8AjXV/DhbZr7xBDZvIYbjRLldsuN6kYIzjg5z2qj4J0Uapo12+3JSZR+aZpDMObTkmBe3O726MPw7/AIVlyQSRN8wrprq1tlvp7ZJ9ksLmPEny5YdQp6ZqrOssZ23MbMPXo359/wAaLhYwQ+KlSYr3q1NYpLl4CrAdR3H1FUGjeM4IpiNS2v3hcMjsp9q6Sx8S5UR3aK8fr6e/qK4cPU8c5X+KgZ6zp2tSxgSWFzvHeJzg/n0P4/nXSWmt2l+HhnDWszDDfL1+oP8APkV4hbXzxMGjdlNdLY+JBIFjvE3AdH6EfQjkUAek3GnXEcYOVeLqrx+/0qvB5n2mKUgcceikdx+PNZNhrFzDFvs7nz4jyY3+9+XQ/oa2rW/0/V4vL8xrWcHp0BPuD/I0CKepSfYbYSRLmJpVBb1Qg8fgQRTGsVZ9skCzBDuiA+8WOMAGteewt4tPMV+juM5zEpC4PQ4Hp3qNx9kkdYY1xJHuNw7/AHs8BV9B64pARS/am1FSTEsIVi3ILRydip7jsRXPPBBJeSzeSomaN08xHPJI7g8fSrrRPBfWDQIwVQ8bIckAklsnPY+9Ld2m0hgjKGGcHsR1H4GgZyujsBY3Vpc7vJDBm9QDwWHuDzU8+mJeEpfJiaIiNpo+GPHyt/tAithLOK2MlyEXzbnJx6A5B/DP86kZRPYvGsbGZQAAg6gHj8qYrGHHALZQpumeWM4UumCMdj61N/Y4hvrm+SdN0yEug6qD1bb74qeDTXmkLPuWJThiPvZ/uqO5qX7RbreWqyWvk3EsbiMyfwqDjBPuOaQznrhmeXcu4AHI9c+p96JII7iAOxxMQSSTwcHn+dXbwR26PvtHaSMnfsfnH94D0qobhNSBa22GSPL7DwzAjDKR3yOlAFKWBYgLVjiaQZxn05Ax71V+d1ZM9VIBPY44q7f2I+0RXsbu0YgRlI68HBzn0HWrDw20txvgftuMYHAz1IPpQIwYCfsqu7KCwwyHqcUyMq0OX24Jb5MZBx1wO1b93bopjlkCQBZDwnIKgZHXpn+dZs9u88kWHVijBgfVT97mmBkT6crfNF8pPOx+/wBDWbLA8bbXDAj1rqHhYQERHfz26kfSoXtt37qUCTGcYb06gH1FAHMYIorUuNMYAtDuYf3Dww/Cs5oyDg8GgBtb+ieLdQ0chN/2i2H/ACykPQf7J6j+Vc/gjrRQB7NoviWw1uHZbzeXcdTDJ94fTsR9KqeL9OhvLLzHjUyoDtfHI/GvJldkYOhKkcghsEV09l40uvsjWepD7TGRgS/xr9f7386AOad3HBPQ4qHOetT3ABmkZOVJyDUFAC13Xw1yL2/bsIEH5v8A/WrhK09J1i+0iVnspNhcDcCoIOORnNAHtl7qFtp9i1zdTLHEnUnqfQAdyfSvJvEviifXJjHGDDaKflj7t7t/h2rP1bW77WZg93JkLwsY4VfoKi03TLrVboW9rGzt1J7KPUnsKBlQZzxUwrWvNKTTXETHdJjl/f29qx5cq3FAEgrW0XxFf6HMr2sm6MHJik5X8PQ+4rGWQHrTgaQHsfhnXvD/AIhItrktb3ckm4xySMAz44cNngjpx+VdRqOhFZrSV5/PsFDieOXLtIGGPvA9BXzsrEEEdR0rt/DXxM1fQoxbXIS/s/8AnnN94fRv8aLDOz8ceGbzV9PtLzQLaFo4VYS2xxvlHqB0OMdOteVRWFxO5hFnLbspbIHGHHYA4IPtmvR7nxZpGrWbXejjULS6UZa3iwQp67tueV9SOlSy3lpf6h9nmSaa7t4ophfSQAxSBgGUSAckE8A4yKAOEs/ENzpxEN/+/i6Bx94fn1x6HmultL62vovMt5g47juPqO1Zs2gvB4vhS/2NYX8xeTykIjBOSqgnOPm4yK5m3tL7z2k02C4WSM4OOoPJx79DTuKx6NaHN3D/ANdF/nTtYnlu/CE1+9s008E5w8XBEZOPmyB/Wud8Na/JeXsMV2ih1ZST03DOCMHvRo2so+lahYSTq7SSlfL8wRsYwxPyk8HPpSYI1fBdhFf2l1bSzLHdynfCONu4fwk+9bVtcvZWUyXRWKKJi0xk4xjjH1JrgrHVFtrwyW/mxiN8Yk+8CPXHGa7XxA9x4i8H3Go2saGSLabyHHzSRjpIPoetAzi5bG58V6vLKmoWkNu2d2X2gKq5HHc+lV9ZtZfDdi9laSM8F2oMsqsSsmO3pxVW9vbG107TLe0hT7QiM1zKnVmZsqM+wwK2tL8TzyWrWdzDFOcjCSDLBR1GD6j1oAqfD2/ePWpNNc5tbyJtyHoHUblb6ii6SKe6uhIihmDKH6FgThSf72Dj3HetnTtPsrLxFdanbR+RDbwSO0ecrGx+UAe3WsdoEe9ig2efHJIrLJGdwXPP6fqKBM5Igq2DxTqn1OMxajMCFXLEgDpgnt7VXFMBaKKKACiiigApaaKWgB9JQKKAHGgUhpc0DHUtNzTqAFpDwM+lOpUOGBoAjSYg5FXFlSZcP19e9Jd6cU/eRjhuRWeXaP73BpARzjZM6A5APWohzTuXk9Sadjb14pkiKvrVy2nMDB0OCOhqmZAKQsTQI39T1xb7TY7d0xKjZynQj+hrOs4UuHKv/dzx2qiMmtPTJlt5w7cjBH596BleaBomIPSocVr6koMCTLtKk9vWs6GB7hisYyQM4oGQ0qsUO5DgilZWU7WGCKbQB0fgrxfP4R8SR6qlrDc8FJY5VGdp6lW/hb379DXuOo/HHQ4NKjurC0ubmWQf6sgKEb0Y5r5qxUsFw8Dccg9QehHvQI+qfDfxL03XLW3nkH2dJcI7E/LDIf4H/u57N0Nd2CCMivjbS9SutLuTfaW6spG2e2k+YMndWX+JfQjke1e9fDbx7aa0qacHZWVeIJXBkhI/hBPLp6N1HQ0Aeo0UlLQAUlLRQAUUUUAFFFFABRRRQAUUUUAFFFZGu+ItM8N2JutTuhEv8K9WY+ijqaANN2VFLMQAOST2rzLxf8XbDSPMstGAvLsZUy/8s0P/ALMa8+8a/FLUfETSWdkxtNP6eWh+eQf7RH8hXnxkz1NAGjrGt3+t3rXd/cvPK3dj0HoB0ArNLAU13qIkscDrQBITk0hcKCB19aZvwML+NKsTHk9BQIEVpm+XuM1q2UgtHO0KWPf0qkh2Daq47UjTFeF69M+9AzXl1AKd0rsT0rf8G+BdS8dTPepNFaafExjeQtuZiOqhR39z+tcISXySeSMj61reG/E2qeEdXGoaXIAWGJYn+5Ko/hYfyPUUAei+PPh5beGrG1vtJSUwL8kod9xV/XPHWuH+1XOmSx39s7rG33tjYKt35HQ11Pir4tXXiDSTbW1qltbTgCaOXDtu77W4wPTjNcrbSC4geIDcko4Ho3b/AAoAf4g8S6vrkEbXGp3F1FGMBJD936gdT7muVMhLc9atzJJaTOo4PQg9/Y1TlIJ3Lx6igBC340maZmlzQA/PrRn1pmaWgB4NLTAaUdPQUAOzz/Stzwz4q1TwlqgvtNlwTxLE+THKPRhn8j1FYO4Z4p2Ttx60AfWfgvxrp3jLTTcWmUuIgouIG6xsR29Qexrqa+QfCOs63o2uRTaCZTcthWiC5WVc52sPT37V9GSeL5pLGMm0a1nZRvDMG2nvgjj8aAOlvtSt7BMyNluyDrXA694nuLibyY13H/nmDwB71iarrhvbpYIpH81jzIHJ471UvRHYac0ka7mcHMm758D2oA5rxDq0yykz3OVB4Rflrl4m1TxDex2OnW8ssjnCxxj9T/ia2dF8L6l431s+dJ9jsI/vTTKR8voqn7zV7XpFhoXg7TvsukRIZMfvLhuWY+pP9OlAHJeB/hrpuh77/wARqs2pRN8tq4yieh9Gz69BXWa34oSOEjd5cQ6IOprndb8UtLMUgLSynjNY8FhcXsvnXbMcnOKAHT315q8pRN0UP86u2emxwAYGXPU1dhtViTAWpWZIhlm57CgByRqoqK6uXjiAihcliR27fWmpdb7gR4bnoKluHLKAQq464PT8aAKXmtdSrG5ZZOdyD0HarEZQLOwdicc9OD2FLZyxSmURxr8v/LT1P1pfs5ugu95duegxt/kKAC3idQZiclhge/5UxGeOYRqHLk5YkEfzp9yZJZxFHJ5MY4yOpx6VNG4gh2vI7gd364oAtDLLzVAr9knP/PJ+vtUi36yyokAZgepwRj86syxCWLaaAKsjSSRsqhXXgglsf5xSgKoEshVSOM+/t61HGYljMVwcBPfHFZ91qavlLKPOzrNI3yr/AEoA0Li+t9Oty9zNyefvZJrg/E/i9ZVMUcjIg6bOtZ+t+KILWR0gka6ujx5h6A/7IrlJEe4nN3qUm3Jzs/ib/CgDRin1DWSRA3lwD700hwB+P+FOOpafosLRWoWe7PJuGUEg/wCz6Csq61e71Dy7W3DLEOEjjX+QFWbfSIrYebqBzIP+WIP/AKEf6CgZBBaX2tzPM74iz80kjcD/ABNakOnWyzR21jbNc3P9888+uOgFadrY3GpxRuxa0sV74xkeijvV2S/tNKtjBZIsSfxMfvt9T/SgBbbT7bTD9ovnW5uxyIx/q4/r/eP6Vmap4hknlKqdx6AdvoKz7m7nvyQnEfrUMMQWTYiszHv3oEIyzTtukfHtUyaEl6vyTsG9+RWnDpM7R75EwKvWGlOswdOCOvvQBiW3guV3/ezov0BP+FSX3g65tbczROsygZIAwR+Fdc+h/aV3C5mWT/eGP06Vb0xZ4M290d46K56/Q0AeT/ZzGfn4p27I2jgV2Hirw6baf7Tbp+5fqPQ1yu1In/vEflQMRIwBluAfz/CnK4XhBgfz+tOZDLh0HX9KkRETPIL9vSkA0R7SHDbR1HrXpHhbXk1K1+zXB/fxjHP8Q9a80Yu79yasQTS2MqTRuVlXkYoA9jNrETltv1NSGNPKIUZrzmPx9dxxhWHOPTNRx/EK9S7DyfPGeoxTEd1BYyRah9oOXABA/GtB3h2+YwQbf437Vxf/AAn9sYtywIT6Z/pXNa14zub7McZwvZE4FAHYa74wgswyWr7n/wCeh/pXnWoa1c6hKTvY57mqSJcXsuX3Gtmx0kbfMfasa/ekfhR/ifYUDM22015yGk3c9+9a5gtdNiDXBw/aJPvn6ntSTakkR8nTEZpOhmPX/gI7VZ0/w7JMDdahMsURPLyd/p60gEju9Ou4hHLNcQgfwBBt/nn9aux2GkyriK9iJ9G4NSnw/ZPC8kF3E0adX9M1gXdn9n1GKBXzucDI+tAGtc6HDHLtaeJTgcZ/+tRSa2zPq0/zdDiikB55JE4560lvtWb5gvTv0zV8xsOlQvEG+8PxqhCFFbIfavv0qtE5jl4PfrVnYwGD8w/WoJIlHK/l3oAW6jGRKvRuvsahiIWZSegYZ/OrixyNC6OmMjuQPpUJs5cZUKfowNAizJJ5U7+YWx2xz/niqErK0zsgwpPANW3JeICQbZFGDnjI7VWELk/cb/vmgC1dsVsbZPUk1R3se9WrwlkhXDfKDn5feqqglgvqcUAWEhmZQ44B6ZbGad59xbEBtwz+RqaeZYbz503KoKgemOKr3E4nKIiYVc4zyTmgCdWhueGGx/UdPxFQXFu0L4PfkY6H6VNBGkMfmSnAH5k+gqFrwyy75FU9gOwHoKBkOSKXdVoT2zjDw7T7MR/jS/ZY5RmGTJ9Dx+vSgCsGqRZCKidHjbDjBFJmgRbSY+tWI7kis0PTxKRQBv2eoLG24hc+vQn8Rg10VlqlqzqzwpvHRzyR9Cc1wiy1YjuGXoaYHruhSJdy3csbkSySw6dBIP4TLlpGHuFGK0fEGpCGVbeA+TBEuFxwFQcAD+tcH4R1kRf2XAz7QdbEpPQcxFV/WrGr6v5l5NDKWUEAA+45/Q0hm1JHcW88U/8ApFtcdYpShU/hkYYHuOc1oRGDWm8tgtrqg5XYxVJj6qQcq3+zn6ZpknjaPUbNHkWWS4Yos9o8XmQS5OGZWzlOOR6GmX2nxG2N5aFprEn5gT88BzxuPXbno3Ud/WgRYsbA3llJou3Zcw5ks9+ciQA5U55ww4P1zUOhWt/eXBvtP8qGOMeXO9znyypGTGwHLMOvHQ1f0u0W5sI9c1q6mnDZFtGG2EopKhnYYLEnp7dajttTtLRprIRtb6XcMWyCcwSHgv1JKnv3B56UASXNk+naRqixXFtcyT2zp5exkx34yTnHapF0S0tzZXP9sMsqmO5hkSFdhOMjGWyQQcGs69spbGcxyBTnlXHIYdiD71NpjfarCbSX/wBZADNZn1jJ+ZP+AsePY+1OwF5Ui0y+l1i8KT2kQMmbYE8jsVPKg9M8gVhTee0xubiN4jM5kJdCoyx3cEj3ps6tJaXFruYJMjIwB65H+NdPda5ci00+6X54rq2jdkdsgnYuRjp1zRYDEkEo+H+kBNomeKR1+rO5Wp5ItKutOuLnRjNHc2lsLht7v1AyyODxuIBwV9u1S3AtLmyE8c32e1tRumtu0afxMnsM5K/lzxVO+1W0l0+TS9JRxbSgC4uZUKtIvXaqnkA92PbpSA1tOvE1WI2lxGskjrtw/AmXqFb39D1U9OK56bTb6Jf7PQ3EtgHkltTG2I5yeWjcDo+MkBv4gR0IqTT7WfUNRjtbeRodo3zTD/ljGOpHueg963JNZsYLwQw2qpBFhY5IuGGOhJHU9yTnmgdzE0qyt7pMJIqR4BARcZHrXSR6HbWcAuLn7PbxYz5tzKB/P/Cue1Tfp2prfw7PsVywIlThVlJ5Vl/h3dfTOcdcVsXCJrmkKyQiS8tQWgB6kdWj+vGR78d6BEr6zo8AxALjUWH/ADyHlxf99HGfwBqq3iDU5vktIbezB7Qp5kn/AH0R/IVX0uO1ul3P87dRluCv0/nW3cPp2k4jvr2GByAwt4gXkIPQhR6+tMDE/su6vJfNu3Lyf37hzI34A5x+GK0rTQxI3yo9w3+78v8An8arz+JY0+XTtMye016f1CD+pFVJW1rVV23V1MYj/wAsx+5j/wC+Rgn8SaLgblxNp2lLsvdQhhYf8u8P7yT/AL5XP61myeJ1UlbDS+B0lvn/AJIv9SKhtdCVPkHGf4YhjNa8eiLaw+dMIbaIf8tblwuPzpAZceqeJZt7w3aAkEKHtkWIZHYdfxzVOw0GW2iijlKAhf3skZyWbHJOQCcnua1ZdZ0mDKwfaNSYd4h5cX/fR6/gDVd/Ed+5+S006GL/AJ5GJpCfqxIP5Cgdi9Ho8NtD584hgh/563LgD9ev4CgaxpNuP3JuL1x0EQ8uP/vo4yPoKwb69uNa1Y3d5aJEIYUihAfevGSzKD0zx15pR70BYvahqEOrwtb3WmaesTdjD5rf99N0P0Feb6v4AuIQ82myLcR5J8vow+g713X1qJ763tW/eXUUZHZ3A/SkM810rxH4g8KymCGZ1h6SWlwN0TeoKnp+FdPpGseDNWnH2u1fQrslj+7dmtmZhtbOOVBHUHiti7m0LVk8u8e0dj0feA3P5VyureBJY0afTJPtEXXZ3x/X8KBieKfC1zoySXVra/8AEtAHl3UExlhmU/ntI9DXO22+5QQojPt+ZdjDcPpzkj6VZ0vXNb8MTFbO5liQ/fhcbopPUMp4Nbi3ng7xPhdRtW8Paif+Xq0Xdbs3qydV/CgQ5XeLw8s2q2832gkrbv8AKHYKcE4JzgevrUtp4ja3s5LxJllKHyytwmSQ3XcAcHBAIINZer+Ddf0aIahFt1KwA+S+sn81NvuOSv4iq2jaiX07VI38oFYjOQygqwGAVIP14xyDQM6ebWHuSLq5kRHb5g6NlivZto5GPatWW6txoTaxO7avaQDzFESq22XGFLEnKqCecivOJLZD9lvlmbyixjMZbmNgNwGeu0g8d+ordtPEzzsllYoi/wCjSRNFEuBLuHbJySOTjv2oAj1rXLTU/D1vdPp0trqqTjbdxKREyc5BPsenoawp71NXwZoVF8vJuI+N2O7joT/tDB9c0hkSK7lTzHNsePLDcFsehyOta+krp4iYHekjDAIAxn/a4yR14o2FuY1tFGBLCZFJbBYjuM4wP8a9B0CDR7yI6SdMs0W6URedsO8ZHDHJ5I6/WuUuvDFzHLFdwLcTxsSWIhOB6AEe2KrW+sR2V20d3DcRhWwQnUEevQ/kRQGx0s/21fClzJ5f2p9xD4XqgZkDgdCRszXm0u0ncrda9e0W/f7CqabGq2i2w2u8HylVJbHLAk5PJwevWuOg0G+uNci1SDQnbT5ZA/l3CBF5+8dpP3QTx24oDcueENOtbvRbq0lkW4+1Jv8AJ4/dyKSFHqCR/SuLvLdbW9lhQkojYG8Yb6EdiOhr2PSvCBtJpbiGHy3nIbYMnpzkDoBXK/FGzgh18yCBUlkAcTR4CTKe5HZgcg+vWgA+EQ3eLL2I9JdLuV/8dFaXw0uHtfBvim5ijEk1oqTIjdCQjDnHPas74QH/AIr6FP8AnpaTr/45mtX4TSiCw8ZKwyI7MPj/AHTJQBhanZzavplzqmqWx05pJPNTbCT5kjYA4JyFPdj7datafoty2ovp8wXaLfdjfu2yLtDAd8EHPpmoTf3M+k6rHdbPNaJpFkZuJUOGVhnqQOmDn8quXlw1nMuvadPi+tjGL2I5KzxMAN+CM8jGfUc9qBnJ+ILV9J1qS26bQrD2yKqi6ScbZlyf744P+BrsPizYrb+L4yowJLONvyLCuAZSMkdqaJLktpkF4juHt/UdRVQhkOCK9f1bwD4R0nTLRJfET2V/OqtFLcOGDZAOCoHC571xeu+F9S0TD6hbh7Z/uXtsd8Ug7cjgfpQFjllc1PHORRLZkDejZT1HSqpDRnBoA3LPUprZt0UjD2rprDxBb3OFvF2ydpBw351wCyEVZjuCOtAz2Kw1u7s0Gx1vLY9RxuA+nf8AD8q2kuLDWY4/s9y0EqElUPTJ6ivGLHWJ7Ugxv07V09jrttdyh5C0M+PvDgk/yP40BY7ye1ubeUM4+Y9xyD71EsLG3liO0FiWjz13/wCB/nVG18QXNqAlwq3Vt/f7j6jqPwrWRLDVY/NsLlkc9Yy3B+lAjELNPfSQMioPKWRPbjkfgat6fbyxvI25VjYbN5bqw5GPUitKexs2vRstpVlZCCDnDAHJXJzyetVLgB5IpXRDHHyABxF2wv50hlK/hEdnFdOzzuFYMY8kcgAuQO2OprGNvDLNBLv/AHURZonHO5SMFfxrU0yCVLM283VZWRTuxweRj1BqOSEf6tIVHc8YzjvnjrQIoywrLZrcRZ8yDCyAnJ2/wn3x0rHm0KC4njmtHltpjlmYDMSkepzla6NriO3lQGNo4ScFyuRz/CT/AI1DdaReJDOiRshmUohyOCSMZ9MDmgClBaRraF7m8iKQyb2khY4BxySOoz3qpqlvcLcRw2sOYyFaWUABdvUAY7YqxHNbaYJbW2jV44ITJcSleZn4UD/d5/Gk1QvfwWs9rJ9nBQBcH5QT0Vh6HoD2oArPCDKxLIyP0IbIBNU7i1hgHkRQsCfm46AnqAKdb3slvIbO+hETZ6gYyf8APpV++VzYTSR7fNjibD9wPUfyoAyGFtHvijz5pG5S3G4DsD0yKpi3eOW5Vg5iLB4z05PXr+tbFpELuCN/LSYTJv2H++P/AK9TFPOEiNMjQiMgxbej9cqe4oA52QGNZJpUby9ylTu7n0x2FRzWq3HEgUn++nUf41ba2MyvAf8AlpwPY9j+BqZYUUvAC2/aFJ7EjrQBzNxp0kQLp88fqP6+lUWjI6V1LRiFmVT+8UAyewPQj196q3FlHKNzhUkPO9Oh9yO1MDneRRV25sZIOXGQejDkGqZQigABIoIDdODSUUAJjHFSKcdaZiu08K+CJtUKXeoq8Vl1WPo8o/ovv19KAMrw94avPEFwAm6O1U/PMV4HsPU16zp+i2ekWQtrOPYv8RPLMfVj61dgtorOFYLeNI4UGFRBgAVK656UDPM/FlvtndsdD/OuLnHNeleLrbc/I615zcrgkUgKQNPRyKjNFMRaVgacTngVWXmp0V/TNA7kscrwSCSJ2SReQ6Ngj8RXX2N/fauVltrtry6aAQXVlM4SSRAc5jbgNjqP4gfWuNI+bGc49KUErgjgjke1ID1HSb238uPT9YYT2plKwzXHymNh/Cx/gkB454YVan0XUbad9Z8PahHdRRkD7OY8uQTyD2yCcg15baajd2lwZo5mLv8AfEnzLJ/vA5BrqtO8ZW8ckf7mWyus/wDHxDKfLH1X0/lQM2dP8LWNxqkcN9OsbSb5lljfZJE/XawbHRuh6EZFEul2k17cW2r2yWt/bDfBcCPicr0G0DDA9TUmr3CTzC4u7RdZv0AVBblsGM8huMZpbHxY7addx3FreQTKR9neZt3lMOwyAenHOeKAMLfpDi7NnA/lySBt54+YccA9BntWlH4j1rw1YxvLaN9kRtieag6MOU/3SOx71JBoOra2JNQjaGOZz5kccg2+bnnKkcKc9M1Qt/EV9DPfadqFrDKYixuLe4XPmHvgjgH+dAEDS2N/tk0M2lvdMfmiuQFJB7KTxmotautY0pJYf7KhWGUjbceSDJkc53A5z9aiv30eTQxfWGlS25SYLKkkwdOc/d7irtl4gmfTPLg3locGIOwPyjqpHfigDWh22/h2FYZkD3WHmuI1BQsRjZ7YrHIigWSK2ntNzn94I9yOSPrx+VUND1lT4rtkjRRBeMIriIfcbdxnb0yDzVvUrITtMwG14pWi3k9cHof6H86aEzC12IGOCVRgjMbfUHI/Q1jqa27iKee2mhmOGhXeAepx29+Kw1ODQIkopKWgYUUUUAFFFFADhRRRQAUtJRQMcKWkpaAHUoplLuxQBrQXWFEb/MvoajvbFLiBpITyo3YPWqSS1K8+2Fue1ICvpZjhvYnkX5Qean1uK2W6BtnDAjkj1rPjnVaZNMZCMcUySLFSKtT2NstxdRpIcBjiptTsH0+YJnIYZFAFPgU7zdvSock0oU0ASmZ3G0nj0rT0YJ9qO9to25/WssKBU8c/lkc4xQBpazFGrKydyeayamur97mNUP8AD3pbaLzcqBk4oAr4oqee3eEgt3qGgYscskEgeNyrLyCOCK9I+FeseFE1xB4htEiv/OElrfFyEVsY2sAcDnkHoe9ea5pp4oEfbGoa9pelwefe39vBHjOWccj2qXT9VsdVt457G4jmjdA6sjZyp6H6V8bLqTX0EVteyEiIbYpDyVHofaup8K+L7zwtexW1xLLHbA7oZgMmEnqcfxoe6/iOaAPq+isHw74gg12xSUMgm2gssb7lI7Op7qex/A81vUAFFFFABRRRQAUUUUAFNziqGq6vY6JZNeahcJBAg6s3X2A7mvCfGvxZvNaMtjpBe0sDwX6SSj3P8I9qAPQfGvxT07w8j2lgyXmoDjg5SI/7RHU+wr571/xDqWvXzXl/cPJIx79APQDsKqu7OcscmoZMN1oAjSfjB4pxb8qjRFJIJxkUYCnBO6gB+S3PQUoUvwvCetOUd2ViR2q3DaqRvkbCA5AoAiWILEG65FNLAdfSrN5dRhdkW3C96oEk/gc0AOZyenpSDJz+dAGOvY0u4jp2/kaBDuF59OR9KRiQDt7HI+lN6deccH8ad0xntwfpQBLGVaKWMDlsEDt9ee9W9PmIYwueRypLZzWesjRMHXqpx+BqyrkqXH+sRt2z0+ntQBoanEs0QuQ6ktww7hh3/GsOZCMnv3rdjdJYXVi3luP/ANRrMmjKuynqvB9x60AZnINANTSptNQkY5FABmnDmmA0u7FAyUYHXrSF6izU9raz3lwtvbQvNK5wqIuSTQAwEk8V0vhvwdqPiCRXRGgte8zjjH+yO5rsPDPw1hs40vvEBViORbBvlH+8e59ulbN34mjjuPsNs3kQxj5SF4x6ACgCxaafp3hG08qwjSa6I+Yn/WSH3PaoZLi/1SUJJA9svU56fzNVY9e0y0myzs7ORy8q5OfTPNdBFYSXF0r58uIjJy+cfiaAKUGlocpbxu0nQv3P/wBatWw017TKSv1OcbRx+FQXWqIm61sUYleN6fd+pasqXV2s4zHE7T3L/efrzQB0d9qVtp0eZJGZsd+tcvcX97rEm2PckNJbaZPey+feFjnnFbsFqsahUCigCjY6XHbjJGW7k1pqqxLuYqo9+KdmKM4eZFI6jdz+VUZbmWW88uGTKLyd68EegB5P4UAXXcrGXUZx74qpBeoPMcOuR3dgMfj0q0JSyjKMCPTgfjVO6ubcTbbkKSnQunGT6HHOPSgCNJWlu3cFCAMZBJ3fTA7ULI9zK0CyRAZ67DwB75x+FOeRBFsfb8vREUnnt6VYKutriOOJWYAnPHXqcUAOjiFra7U3PkjcaGZmu4wszDPREUbcVZMiwgs7qoABPy+tUWuzLIRbM0jNxvxhVHtQAtxIomygR3X5QC3Q+vrTo7Cadt9xMTnqg6VZs9PRAHcbpOuT1q98q/LnmgBkUSxjAHSkLFhleg6ntT5biOCPdIce3c1wHizx1HZg29tIu7kFU/qaANPxLr9hY2r7pEaTHArzHX/GlzqUf2eACCAcbI+lZ0/23WZTc3D+XEehfPP0HU1QvtOks7rySVc9imec0DJ7a6t4ED7WaY9X7j6en1p6RXGoHIG2L++f8fWi105IxvuPwSultNKub6JZLp/stkB8o/iYf7I9PegDOtmjsx5NlHumbgyfxfn2H0rdtNOtbMC5v3Fxc9RH/Av19TWfeR2locwJtRegPJP1qibi4vOMeXHQBq6hrryyeXH8x6ADsKyTEWPm3L89cU+NAp2QJukPetex0RpGD3HzN/c7CgRBaWP2uIYdkB6YrasNKitRwnz9yasYSyj2kJ+f9KhElzeErCGAPegDbtYAVz1FR3drPBGTbfxdqk0m0uLYbGZnU/pW0EDjB4IoAxNHtbm1gf7TI2ZDkD+6K1h5crJHnfj8cfiKytTvXs2MUCMZT1Pc1Z0t5xBhn8yQnP3doXPYmgC5cJbXqy2T7WwOR9ehrzPXNDbTL5kb/Vk/KfUV6bFBapdGVnBuccndzj6VV17ToNV0uQjaWUZU0AeRyzFfkUbR/P601AzDcxwPWp7hEimKuMyLx7UiN537tuvY0DGNKTwvA7+p+tSxjdCQ/QdCaYI0jOG+Y5/CpZF3AOvIPSkMrkL0UUjWy43djU4iAJzyfSk2M56UCK32dGyF61Lbab5kgGGJJ4AXJP0FaFvYFk85yscI6yv0/wCAjqTTZNT25t9MRhnhpj99v8B7CmBK62mljbOFeXtCjd/9oj+QqJItQ16YLhhEOiIuFA+lW7PQkhiF5qc2xDzg8s30FWbnUZWgFvYx/ZrZuN/dvqaQDo103Qlxhbq79B91T7nvTg9xqEjyXDscJwDwIz7DpTrbRbeCy+03jkA8jAyx/PpVR7wzZi0+NlVBvJf0HegZasgIPDt44/5aXAQH2H/66yEdX1+1aV1WNZQxJ6AA5rZtYRdaJDbJPEJS7PIHcA8n3qG48OXRbcE3cdU5oEyabT2uriWZJ4WV2JB80UVlnRroHGw/rRRYDm1ZHGVKkUFay7gG0uCscmR1BFTRag3Rxn3FAFsx+lMZPUZqSOaOQcH8KkIFAFN1LfdfH1qu3mxnmtFowajMZFArFclpLVs9QNw/A1B9of1q9tUAgDGQR+dUXt3XpyKYWHrdSDgnI96lCRzRmQhU2jO8e3tVeOIs2KlunCKIEPu3+FAgZWuhvJQE84Johg8pt8gyB6c1WDEdKnhlkz8tAEM8zTNluAOAOwFEMDzEheg5JPQVduoEa381gEkHT/a/D1qF28q3VEDDIycjGT60AOS2hbKqZW29XCjAqGaN7WUYfIYZB9RTYZZYyfLLDPXvUwgnuW3yFj7n/OKAJoJkugIp/wAG7j/61VLiFoJijdVP/wCo1ZX7LBy0m5h2Tn9elPk2Xsm/eqvgKAe4HqfWgZnU7NTTWskJww+nofoar0APBpwds4FR+w6mtaG2jtLUTyjMjDKg+nqaALdhGY4YWkm8iNJRL5hXJLDptX09zXVNqUF/Hi6tknTHM1v1+rKefyzXnzSXF7KQm5sc/Qe9SI93Z/vFLADuGyP0oA6x9HfBudLuVljz0B5H1HWtDw94h1ay1mGzjgWSWXKlJD8hTHzFj/dA61y1l4iZJxJNuWQf8tY+G/Pv+INbNnq3mS6hq7OpcILeJwmzIPLHA4z6kdaBHoB1fT7C1tdGvplmto48Jc24I8rknDLzlRngjnHUU250144RPC6XFs4yssbblI/CvJP7Uned5WLZY/pW1oniq90ibfbSKYnIMtvJzHJ9R2PuKYHf2moJFbDTryOWa2c7bYxrukhc9FUDllPp2+lFpZ3g1iNFP2Ce3iN0r3CE5UHayhR97IPIzwKqRatAkVt4gU/ZppMyW1ocFRGwK4Zuu4jJBGMdamkn+2eXq+mTM0lvJu8uT70TEcowHVSOM9COaAJZbe5n1cQ26207yq8qtFLtXC43ZDcqec45qabdBoFhZXMbQ3MBdVBIZWTcxBVhkH5SMjrUXnwW19Y6tb7haGTcQeqocrIje4BP5VIts0niOy0q5PmW0d6XaN+VKojt+R4pAVrW1+3ajaWLDKTyjzf+ua/M34YGPxrc1e+sr69a3vod0faROHhz02nrwO3T2rOhvLGzmm1CCFIJo4XTyt+I5EJBO0fwtxxjg9KoNKZ5ZJC2SxJPsfoaYGjKG0TTfsEQ3yXTM5u0H+uA6Dj7pUcbfxFU9LW2dpra50y4uZ5Zh5MqEhI49oyCQRtIOTnBzmtHT49RurMCOSK2tUkDrdSpvIYf3FJw3XBJ4xUGo33iC0ZY5dQRIpM+VNZQJGJB6bsEhsdRx7UgGBILLVr/AEa6P2mwLCKVJOThlDYJ/vAnGfUA9agsZLjQ9Tmsp3dhA2FlPBlj4KuPcAjPvVOCBjJ5cKO8jEkhMu5J6k9SSfU10l5Z3Oo2SrNarHcpAAhMqmQyKTghR2KkqQTnpjpQBX1KH7NOuq2u0QyyDzQOkcp/i/3X/RvrU9/B/bOnR3FsM3lupAUdZY+rR/UfeH4jvVDQtTiuIDa3MeYpAY3ik4PoyH0IPT0NSoJtC1ERNM0kLAPFL0LJng/7w6H/AOvTAn0VrW4ijKbEPAaTbng/xevNaF9q2kaVdyWohu9Qu4iBJGg2opIyMscDke5rK1SAafdJqtoFFrPJiZB92OU85/3X/Q/Wrl1brrGnJc243XsEeQB1miHJQ/7Q6j8qQFebxLqs3yWwt9Oi9IEDv/30RgfgKypVNxMJbh3uJf8AnpM5c/hngfgBSqyuoZTkHkGlpFB3opf0qjmS+YBC6wk4GzhpP6gfqaAJ5b22tztmuYkJ6B3AP5daRNQs3O1LqEn/AHwP51raf4WkaLfhIE9Rgc/7x6n86uTeEIpYyrzbh/tRFh/6DTFc5smS9nNvAWCA7WZDgs390Htjua3tP8KAReZ8iJ/fGFBP1OSxq1o3h5LK48tSnlAf8s8DaoyW47E1o+INXfTtlralUuCuZJAOY17KuelAFCXwkjxks8uPeJmH6rWDceErqzkNxpU+wryyQ4KH/eQ/zGDSveXMhLNdXBPqZmz+eaki1TUIn3C6eQf3JvnH4HqPwNFmBhXy212pi1yxVJOn2qIcfj3H/Ah+Nc3qXgaYR/aNMmS5iIyNh5x9P8M16ZHfQX0Xl6pGiyn/AJaZwD9G4x9GA+tUZvDDQsbjSZ2ic8lBxn3K9D9R+dAXPKNN1fW/C12ZLC6mtWz8ydUb2ZTwa6I6/wCGPE4ZNdsf7H1CRdp1CwX922f78f8AMit+8WKVTFrengdjcRLn8x1H45HvXOaj4I82M3OkTJcRHkIG5/D1/OkMreIfB2sWmnfarKSHVdH+8Lqxw/PqwHK/yri4El81GUlMSKPN6BTng57Y61u2l7rPhi/Mllc3FlMDzjgH2ZTwfxFdfp3j7TtQiNtrlilpI55u7KEFGJ7vEeDnuRQBxmqKo1y5CASRO/ykdCT3GMdTk1Y075rvZJtRFY8lgFGD0ya2/EnhWYwRaroMEN9p+CZZLFt6RnPB2/eTI6gjANcrPtIj8vec/wB/rnv+tAHpVhfbBHZ3k63ti+CUiX5eSMAEgHJzzjmuS1zRIF80JC8WxnCcljwcheeWGOh6+tLo3iR9EtHjTe8rMDgn5VGMZ+v4UanrdwyxvFCzP5WZPMTKkEZ4x+tAy54cuI9J0TypZi95KZFEKZ3Rq6bcfXPNel+Hbe9k0i0/tbm+WLBYtnp0JXoD/hmvHfDkt3qOs27KH2pIP3iLtWNieMAd69UuNVkNmjRSLumZypReqqxUAj3CZyKGBfvvGen6PqtvZPbyC2lIEuoFgEWTnAb24+grwzxHfS3mqTqk73OnxXEv2WUqcFWcngnrXWXsus5WyuDFqFpdXEcgcgeZGFbcVYDoOxJ4I6U+9sNQ8Rapc75Eh0/ATGAeRg/KvQH37UCZS+Eb7fiVpY/vLKv5xmui+FCmLxD4utwAWFtJgHoSrsOfaqHhXw6+g+PNDuoroSRG7ETbl2srMjYGMnIIHWtT4bIIvip4os2/5aRXK/lKP6GgDnDa3WraVcrdwxKHSRojG4ZSyDdkc8Y6HPrU0VodN06W7mm864OngGAuSOFYAZOeAD0HpV/U1VbaTRNLOwmFUL5HLlR8vHKg8DPestraW0tF1ZZFjxBGs1vMhOJM7c4yMDHJNAzS+LJ8yXw5d959KQn8CD/WvOHX92T7GvSficRdeG/Bt6Nv7ywK8dOAh4rzuRf3aigR6Z8SNJfV7Tw5cW53XR0kME/56qoUkD/aGcgd64/wtrOv6bp9/cafPvsbVA9xa3A3wupYLgqeM5I6YrtvEF1LHoXw8v4jtbyPLL9uiDB/KuV124+w+CbO0sE2x3DSLfkfeDq+QregJ5/DFAx6N4Y8SsDaP/YGqN/yykO62lb/AGW6pn0PFZWsaHeaTMIdUtGti33ZU+aKT3BHH5flWHY2V3qVwLeztnuJSM7Yxnj1PoPrXdaVF4z0C2NtNaQ3unYzLY3M0ci47gDJKn6UCOHmsmQbl5B6Ecj86r/Mpwa76LTdB8RsToV3/ZWot1029P7qQ+iMf5Gue1XR7nTLk2up2UtnP23D5G91PQj6E0wMVZSOlWY7gjrUc1lJFz2PT0P0NV8lTg0COjsNcuLUgLIWT+4a6mw1m1vCCrtb3HqOM/UdDXm6SGrMVyVPBoGeyWXiOe3UJfxrcQj/AJaJ1H9RWmltb36+dp90x3cmJzn+deS6b4hmtmAcsy/71dPp+pwXDCS3nMEvXjp+IoCx1JtpBmFkb5TnB7fT+lM1JXazlmifEhUZPTJB5/PmiDxGUATU4FePp5yf4/41sW8cDo81nsuUccAkbh+fagRhQQSy7dnVow53/dUnozdsfzqyHt7mePfeMyMrqR0WTB5cA9CDV9xJtjJ/cxYO6LA4bt+AHTtWVNbyS3lsVbmPcD7qev4g0gMy7hkuYLi33Z80bQSBkHPy8+maoabaSf2aqTp+63tG/wDe2noQPVWGfzro5IS6+YPm7MduAW/+uOabcwKI40VMYGT/ALxxmgDClsESMpdR+Y/K4fDKSOhHcZFS2phaSOB4FHXaeT24H0rXSza9tfmkRTG2AX+gxk598CqckLW0Esce5Z+BJKF6A/wjPb1NAzPuISbN5La1NuN5jkTbyMc4H+yetZtvbv5ofG3B5zxweK3IbiSWa6h8xlkjm/dEe4yufY9Kyr37YpaWP95jIlif09R3oAqSGNys3lsspDj23qDkH37j1FZ0G66s47pWw4Ox/Zh0/MVqWkttKyvlgGIEyv1Vhwr/AJcH2qvp9ncWzalbRpnLJ5QfgH5uv4LTEVzp89xL9sQLxFtYHv6GqkkLxxPcq6EiJiE5BOOhHt1rfSWASCVtr+SxXcnA3EHIwO1VJYJiyReWrKeVkTnb7MPQ+opDMYIs0YaNs7huO/kEH1H1qhNp8U+WgO1x1B+7+B7fjW9FC3+oWBQqAj6c88+ntVGRV81IUTG6NmU+pB5H5UxHNT20kLlZEZTUmnWgu9StrVyyiaVUyq5IycZxW9LGhAjKFlI3bH7ewPUVT+yyW9wk9hMyyxncAGwyn2PegDtPD3gG3sLs3GoyRXTIf3SKDs9i2ep9uldzgYHtXnmieOGRxBqwyc4MoXn8R3/Cu7tbuG6gEtvMkkZ6EHNAywRk89KcRxTWyVLDqKcDnigDm/FdsJbHcq8ryK8svly5PfvXsesp5lk3tXkuqx4nYhcA0DMFxg0lSSDBqKgk19G0W61q6FvZBWkxuYk4CqDjJrsNb8IQ6N4aMySNNcqy+ZJ0GDxgD0zXO+BL37H4stAThJw0J/EcfqBXq+t24vNJuoOu+I4+o5FAzwltyyEBsV3+teFbCXwDpnijTcxlokW6h3ZXePlZhnkfMORXCXC7ZTXqvgJv7c+GOt6IxzJAXMY9mXcv/jymkCPLiG8veOVzgn3porW0vw8+pWN60U7LfWpJa1MLHKAZyGGcHPGCPxrMeKSI4ljdD6OpH86AL+n6rJa7Y5N7wDoA5Vlz/dYcj6dK6NZk1GKN7aZ7gxdIn5bHuM5/LNcaKTzWjYMhZXHQhsGgD1zSteitbZbHVDFFFLIpw6bGB6bt49PQipNe8PDWmlMc1oLtiBZ3acLdR/3Wxxn0NebWmtvdR/ZtUnlli/5ZyuxYxHGPqVPcV3mgxabpFlbT3WoO0F3CVESupVXz+ZXoR0PtQM5V7GTQrv8As/V0F3asxE8UL7XiI9M9+47Gq8WgJDfKBqiRQybhEduWyOQrKOmfWuz8R2i65eNbJbINZhUSpcxEGO4ULwremVxj1rkIwssF8bm2ltbizUFUfPLbsbeec80AWpdBex1zTtQitpYIxcxCVWYEKxYcqR2P6Vc1NkOt3YcbIraaR5Qiknk43DH9eKrxX+pYhW5hUCS5jYknkncOcZ/lVnXFuxql7FGVXfJ8uFzkAk8sOnXvQhMxvNae3SRV/dtlRvH3T0IB9PauauYWtrqSF+GRiD+FdTdsbdA1xMnl+XiOOIgqG7jjj6Vi64mbmO4Bz5sYJI9R/iMGmIzweKdTFORTqBi0UUUAFKKSigB1FFJQAtFFLQMWikoYZGBQApYCm5yaiOVPNODZoEPDYps0hKbaaz7RUBfNAC4pQKbuNGTQSXIXCYYHBFLfXb3DhnfeQKp80Ac0ASW8D3EyxoMs3SnzRSW7bXXBqxZSfZ545h/Cc1PrV5HeSxuibQBigZllyelHJpvFSDAFAhFFX7KdYJNxGeMEdKoFx2pN5NAzY1We3ljieE8/xIeoNV7e1FxCxzhh09DVEZJrX0yVIg4bb82MZoC5mujI21utNxVzUlC3H1FVvLfbuxxQMix6Vetr/ZELe6DTWw5CbuVJ7qe306VTxQaBHq3wd1C7TxPHaWWsWyWxY7rO7yGZD1MR6bvVc++D1r6LmuILWIyXEqRIOSWYAfrXw7G7wyB0cqykFSDggjoQR0NdBdeJtR1yKOHUtRuHKDaDJKSp/wB4evvQB9iQzRXEQkikV42GQynIINTV8yeCPiHe+GbqLTtQkKWy/LFJJkooP8Lf7J7MOV9xxX0TpOpwarYpPAxGQNyEglSee3BB7EcEdKANOiiqWoalZ6VZvd31wkECDJdzj/8AXQBb6VwvjP4maZ4XRraBlu9RxxEp+VD/ALZ7fTrXn3jf4w3GpeZYaCz21tyGuOkjj2/uj9a8okmeRi7uzEnJJbJNAG14i8U6p4kvTcajcM5z8sY4SMeiisMtTDJUTyYoAlZ8VA8maYzk0iozHFAD1LEE/lVgRfclHRx+RHUU1UHToBUiv8joO/zD6igCRNkA3Nyx7VDJdPJxnjpUTlmz+dRnIz69aAJBk4/KnAgfjxUe48gcY5p3r+YoEOBB69+KcCDj34pnr+Ypev48j60AKOce4wfrS5z17jB+tHXP+0Mj60df+BdPqKADOBn04apbYIZNjuwI6H1HoahJHDHo3B+vrR/F5Z2k44NAGlaOiO8anKN0+tS3sZeLzxtLpw2O47GsmNzHjAwe4rWiuYpF+VssVwyHigDMlQMMjoeRVMjFaDoY5XjIwOoqrMmHyBgH+dAEBHpTOasQxPNKIkRndzhUAySfbFek+GPhnhRf+IPkQfMLbd/6ER/IUDOQ8M+DtT8STBoE8u1Bw1zIvyj2X1NesWGl6J4Ks/3Kb7kj5pX5dj/QewpNS8RWum2httOCIkYwCFwqj2Fea6v4lmuHfypGYnrK/wDSgDpvEPjFnJTfyekaf1rgr7UZbiXfI/PYDoKpmeSWTCbnkc49SSa77wt8NZ7rbfa5uhhPItw3zN/vHsPbrQBH8OvCcmqXY1m8T/RoW/dB8ESMO/PUCvVb28tLG3xKVx6Hv+FZNxqdppFsljpsKDYNqpH0FZ8NjPfS+deOxz/BQBcsrtdRlfEeyJT8oAwD/jVltNgWbzVjUE+i1PBbpEoChRirPBXFAEaRoqgsyqPemXDzAhLeFjzy+4AUSRLIUDs2Ac8HFQ3BmMoRIGkHXJwFA+vrQBBLbt5xDwzKZD8zptIP49RU8NvbiQPvdtuQBIQcGmxfbWJEcPyd0kGR+Bp6Wd03EsESx9AkfQe9ADZLC5mlLrPEI85CbcfnSLYom1VjZnU5PzdT688U52gQvD86og5fzSMH2qxE6LAcvuKjJPfHuaAIWlWORWeBQxOAHUFgPriormW3Moa5+XHIQKdzfl/WnfbbmdiLbyig6GQHIqzaWL7jJM7PI3JNAFWGB76UzSIyg9B04Fa0VsidsYqRVWMY6VPb2k96SsScDkk8AfU0AQswUccAdTXOeIfEsWkWhkQxGQEcOcZHfHvVPxv4t0zS9OFtBcS/2iM74igwD9R6eteTTG81pHury6ZYweQ+ckeo9aANnWPHV/q5NvYxlN3HBySPrWAsMUEhe6dbi5POwn5R9fWoHuwn+jWEbDPGerNViHTI4f3t+WLnpEG5/E0DLX2o4zB88nQyuvC+yjtS2lvNeXPl20bT3B6v2H1PYVp2miS3MYmvD9ktOqxgfOw9h/U1auNSttPg+z2qLHGOydT7saAH29hZ6X+9uSlxdDufuL9PU+9Z2pazNcSFYtzOe57VTklnvW3yHatOj2AiNOCe+3PNAhixEkNO+5j69KtGzuGi3qq+X6jmrFppT3GROm0g8Oneuo0/TUghCIGCj15oAoaXpaJAhUckZJ9av3nmWsSLDHktwMVoIIbaaOIlVL9B7itHyFcAkcg5HsaAOetNGeZhLeliTz5Y/qa34YIliEahAPbtUV7FN9lIgbDjrWdbLJpccjyuzPJ/AenFAHQGJtu1OPSnwxtEMSPufuap6bd3F1Due2YY6E8A0Q3f2vUZLNw0OwZx3NAEV5LE94g+U44rViiCxhXZV44FJPZxDZKsa709B2qpby3U2olymLYA8ngk+3rQBRutMniuJHifIb17fjRe3C6RoXlyP+8cVr3UyRuozlyQMVx/xDytujI+HwM/SgDgbmdZ72R2bgn86l3YjGz7h/P8az7eNnOArE1fQLEm0/MT27CkMRYy+CenrUhfC7E4FNXex3FsYrQhsAIftNy628P99urf7q9/r0oAqQW8k0qoiMznog61elNnpi/6Qy3Fz2hT7qn/AGj3Pt0qs2oyzE2ulQtHG3Bfq8n1P9Kv2uh2+nxC51Wbaeoj6sf8KAKUVrqOv3G6TIi/JQP5VqBtP0b91aot1djjft+VT/Wkluri8tz5Q+y2ajOBwWHT8a0NA02C2tDf3C5J+6H5zjvRcdilDpGpapMLm6RnDdN52j9cVbv9Hu4wHaBfLUjOORge/r70l3rt1NP+6DhAcAheCT2zXQ27XcNoJJtpDDkjtSApQiDULQ27jOxfyrLk0WYRMkcfA5UFsBs+3HStJ9RtrZiQUBP9xcE1g3+qXt/JIsD+TCh5Jb+tADTpF2N5kh2EDKjcOfxzxVZHvbV9qSTRn03GnafBO9wZl2ybeskn3R75NOuL6KCV2t/3sxPMp+4Cf7q/1oA6vSLwjT1+3zfviSRuXnb2org3knuHMheV2PU7j1oosFzlra0WaNpHPy52j6//AFqmiit4F+ZA7+p6flU1tDJLpsSxhuGYk9s1XuUltgoxhzyX/oKoksko4/49sj1CY/UUxCS5Eb9P4H/xrPFxcIciR/zqaO7klcCQbj0DbeRQBdMuw4kDIffp+dP4YZBqO3YT5STkDgist3ltJ5I1dhtJFILmsVzUZi9Kqxaiekg/EVdjmSUZUqaBjAu0g45Heq0lsGJZTyeeavlQaaUFAGelqxbDL0qRriOEbYwrP69h/jVphwVIyDVSS1Vj8hx7GmKxVeVpG3ucmp47lwNr/MnoeaVbRs/MVUDuaXbbJ96Tcf8AYU/1xQIkceXCZ4AuBjcCM49x7VSknlnPzlj7dvyq39qj8too42G4EZdv6Cm7DaQbkVWJ/wCWg7e3tQBALWUjLDb/ALzYpfs0oGV2n6MCajZnfk81bF0oIZLbDgdj8v5UAJbXzL+7l+aM9QaS7t1TDx8xt0/wNVvLdmJI6nNXsn+zZN/UFcfWgZQU/OM+4/Ota+lWa2jkBXlAuN3QjqKoQwebdJGvVj+XqanuLlIZSkcabR6gEn6mgCG2uGtpCQNysMMD3FWGureSLYfNVCckcEf0qMSKy72tFKeoUj+VJvs2/wCWMq/R8/zFAD1Sxb/lo4PpsOT9MGrsU0SadHZzF4MEk5HBJ+nSqunCMT3Dx7iUjzHv657n8Kgn3vKT8xoEaQ07eN0Lq47bGDfp1qCS2ki4PU8Ad8n2qgodTuXIPqODV+1vJWkd5p8yxjbEZG+7nqQfWgDpv9H1DT7O1e+S2vraFYjDN8gJAx8rHg5+tFpNrGg6nbNGAJZT5QD8pKp6q3TK9/bqK5YWc8zEq6SE+hBzU8LXNq7RSl1IjxEhY4UsduQD04oGenC7sLey2NYzahHNcCOaZJzFD5pBB2qASVAGCe5ree2W1kj1WMPgRMsdwJjcQbWXbljgOvHG7kDvXnE+pCxt7GGCT5YZkkA9FjBYn/PrV/Tde1TS4Y5nmYeaVLeSwU5Y4wUIKtjPUYNSM6nTVitHa+1BUNxAR9ntshgzEZEmRwy+mPxp76lBdbrjUESS4gPmqDn98B1jYDlvUe49K5xLsSw33nSI8MUs13Yyxjy8iMr50eBkBXGSAO4yK2ofEMMaBrG2is0YAhghLEHkfMck/nVCaLuo6tJqHCHEOMAD07dOMewqbSY01SCbSLrmKUAxn+6wPykfQ4/DNQKLK8vbaV7yK3S5t2klCIG/eKwGcZG3IP5irkS6TaSiSPULlnXoU2r/ACU0xEMut3qWwiis4rIchkjTAJBKn68g9c1Vjs9Su9OttUidZvNXzFjjlzIAOvGAMjuAc1ZvpLuwjjuba5F3p9wxPl3a+ZtkOWI3DBGeSPfPFV7XXWsZC9tpVnHKTuDiV9u71K45/OkBW1F457T+3omxIm1b3Z/Ep4WX6g8H2+la1tImtad9lkdVuUO6GQ9FfHX/AHWHB/OqPh3ab4W03zxTo0cvHDBuvH40XdnLo08FzAmLaUtGAOkciEq8Z9jjcPy7UDLulXSlJdOvoflbdFLDJ/48h9x1B/EVHbtLoOrfZnmLRnEkE395egb6j7p9+e9SajGLy1Gq2xbzY1HngdWjHR/95Oh9R9KnRI9e00WzuiTod0MvZXI7/wCyw4P59qYiHWrFIWGoWw2207YlQdIpT/JW6j3rMrW0a+Vlm07UIWwQYp4m64HBH+8OoP41nXlnLp17JaSvvwN8UvaWM/db69j6GkUmUb9zHp1yy8EIefrx/Wt/wzZRzXbuwyI+APYf48CsS4t0ureS3fgSKV646jFa/gm/LEB/9Y8YJH+2OGH/AH0tAmbOva5JYS/ZLIhZlGJJtoJX/ZUHgVzEl7dTktLdXDE+szf41a16J4tXuA3OWyD656fpWZnvTSBl6DUr2IqVnLhSCEm/eLx9eR+BFMvLuW9u5LmZVEkhyQhJA+mearqeeKCeTRYQmaTcAyg/xMFH1PSlqexaEanZG5OIftEe8+g3dT7ZxmmAwNipILqW0kjS3dlDsf3eNyZwT93tnHUYNSahbNaX00LptKMRip9BSOXX7FJduCz4z3YI20fnS6DLH9o2t2uy/hVCP4zyv4N1H/Ah+NULjwzGZPtOlztHK3OEYDd+HIb9frUc6NFPIjjaQSMUkTvC26CRk5yQOVP1U8fj1pWAzr6Muht9c0/zkHHnRjkfUdR+GRXNX3ghLmI3GjXSTIf+WZPP09K9KTVIp18u/hUjpvGWX+pX/wAeH0qrc+Gre4/0rTZ/Kkbo6MPm/HkN+OTQFzxu3m1rw1qHn201xZTqfvqSufqOhH1roo/FOh+ICE8UaX5F1jA1LT12t9Xj6N+FdTfQzoDb6zp63MQ482NPmHuR1/In6Vzl74Ltr2M3GjXSMP8Ank7c59P/ANeKQ7lTUvCNzDG+q6LJDrGmAAmS0yWX/eQ8qfXrWDcQSahbyPDeSqUHFtIhGfYMDj8DinquseGr/wA2GS4srlP44yVP49iPrXTaf420y+lA8S6Wvnf8/wDYr5ch/wB9RgP/ADoAk8OXGnaa9rHP5tusYEhkdB5YYckE5ySfXGKdLd2V9ZCxihmn8rzAJk3KnktIWU5OM8kjpVf/AIRWSe7OqaderrukoTJ5cTs0seOQrRHnj6EVtadqlt4ylZbWKW01iwiYrFMwKTxc7kIGMD1HbORQMsaJqMMUTQNp1ntIxmP9yDgdOMjJ9sZq1beJ7WaX/Qra2zGu0Yug2MdyAM8Vy93pk0gZ7Sea0uYG3FAxEkD/AO0PTH8XQiuRubDXY9Sl1A+dNcCXJuY+SzH+IDqffilYD0H+27S58VaKsd3E8p1WBiInGAPmB6dOWqx4UcW/7QWrRDgSy3S4/Jv6VwkOp3E2taVJqmnol0l5E32gIIvMQOMhlAAY/wC11rtoIjYftHFJH3b7ljnp96IkUxGfqGgXlvdXV5bTwwTglYDGykSFXIZZO+f5Y5qV9O1N5ykifaYiY1nuSoCS5AyGXqCfu56Z9Kj8RTSeF/GmrRyz3f2a9aSdBCMgMW3BsHg4wQe9dK+sQXOg3NxfXdvbboV825tsPgMflk28kckZGOtAzC8dQRp8OfC3kurxW8s1sHBznBYDn/gNedPyoNehavGr/BDS9kyTpbapJGJVBAYFpMEZ55zXnrf6oUC6nouu35tvhV4MnYBoz5sLqehHOPyxWCLoFDe2g3Z/18J5B/2sdwe/vzWtrkTXfwH0B1Gfs99IpPoMyCue8KRvcQFk48qRM+4JwR+XNAzq9HuNOk0+9m0y1itbxoyRGBwZAp2/gT07ZrzuyvZDepcXhlmKSZnjLHcynqfqD2rvdFs3s/E11bwoksAOGB/uk8EfTOT7VU8aeEJk1db3So5S8k/lMsSEtuIyGwOmRnNCEzkLiOG4FtAl3bylrjaJgW3hSeNwIGMe1dVo/iS7ht5dLu5E1uxV3RbG7Tc52Y+44yVJ7fSsC/8AC3iGNHlmtNxiOT5e3f8AXaOTVDT797Od7iOS5husMS8QB4wck55H17UwOyi0HRfEal/Cep+TcnltJv2wc+iOeDXLalpM9jdPa39rLZ3I/wCWcowD7g9xWGkjI4dGYMvIIOCD7Gu703xfqs2jrFr2nLrujLlcyczQ47q45GPf86QHFzWrxHpUQYjrXfjw1Y61E1x4R1AXYAy2mXeEnUf7JPDfhXKXdiYp3t54Zbe5ThopRtYH8aYigshHSrcN48ZBBYEVTkt3iNRhsdaAOx03xJLAQrncnSujstTjyJbC5aCTqU6r+Xb8K8wSUjo1XILx0IIbBoGe0W3iOC42W2rwKM8CUdD9D/8AqNaD2KNEJLWQTp16/MK8nsNfITy5zuQ+vIrp7DUQuyTT7rYw/wCWZPy/4igDqWiAEke/JbrjqoHfFUlV1vLpZEc72DrgHqeML61ai1u2u1WLUYPImPSTsT7HvWk6zTp5iyI0YGFeL7ynuTSEZcsUduskc8bujKzSkfdUDoo9W/SmXsrTskkAUxsAADwScde4zipJfLfJTgAFSO5HcVFbxBFjiBY7lx5m7AJHT3GRxQBnSQbNwXA3Y5HXA7fSnXsLBkuQm1iAWH+16/j1q3JEFbB+WRiQB34HP86ZawM8gW4m3QuCMdDuz93P60AYE0ESS24EDXF1K37rYu09cbTjqB3JrXvraSYTo2xY/KyJIz82c/Mue4PY1Pc26pbF7V9rtuijlkU5jH8QwOQT6+lYrs1lqFpAzqyPbqh7qTn/ABGKA2M2C2tI43tbeZpXaTLeZ8rKfpU6xtJbywFsFeh9QKde6VDMSyuySY3xSDkkA4ZW91PHriltLa7DBZ3h8xfuSbxhvrn1pgUJIXj3wRXLIVYCTuFYjPI7g1A1kpdXuZkSZZPl8tflDHjB/u59+9bD6bcJdS3iQ/uZVUEO3AYdz+FV5yqTM0YXBbc3+03r/hSGYTWoV3SQN5gyoJY8ZGKZFEXgj8yPAxzj+8ODWxdWbyN5iHcXOcfXpVadQR+7fcIz5f4jn9aAMiSBZwS6cdAe/wCfem2N7f6RL51lcsVHUD+TKauyqWuIVBUI4YH2I5Bqq3NwSCuAu4uOOnU0xHcaJ43s78CG9221x6/wE/0rqY3DRqw7ivGJbdZAXZGTniQe/TIHrWlpfiLUtDIQt51rn7h5H4elAHpF+qm2lVzwR+teY+IIPLuip4B5H1rurPxDYa3CEhk2THrE/X8PWub8VQL5YOPnHI+negZwM64NV6tzrzVWgGTWdw1peQ3CdYpFcfgc17utws1ukmVIYBuOmDz/ACrwKvWPDuofafDdkWPzJH5Z+o4/lQCOA161NrqlzFjG2VsfQnIrrPhBqH2bxXNZMf3d3btwe7Idw/TNZPjG3/00Tj/lquSfccGsvwtqA0vxXpV6xwsdym7/AHScN+hoF1Ou1u4u/A/ifVL3SNThMkxaOS2mQl9pO7p0IHY1HLomr67El/4nupRbiJXhMO0sVOD9MY7dc1s+M9HtfF2u3KaffKur2WYpbKZdu4KfvK3cd6tabcatF4UsNNW0ik1Cyl8m5srjgtECQrA9cDjkZpFHmuvW9jY6m0WmPLNaKoKySdWyMnPpjpWctvNLbNdLGxhRgjP2DEZAP1r0PUPC6ah9ruNOgmgubbBudNkwcg8/IenIyQehx61neBo4odfv9FnKyQXkBABXqV5GQehwTQI4sDFXbHU7iwJEbBom+9FIuUb8O31HNOu9Oe3u7mD+KGVkI+nT9KpFSpweKBnd+GvEtlHMsVyim35xE3+sjbHBR+pUH+E9q6HW9EbxDpgiim2zYxHcfeWZgM+Wzdm9K8jBrpdA8Y32j7IJS1xZ7t5iL4OcY4P+NAFCE6lb6raQXyTIIrhBiQHqGGRXW67A66jdDf8AJJI7gcgg5xwR+H51p3V5o3iDSZZbWR3kMqTYx+8icEZDL6Hn5hWfq00tz4guGjG15ICoBwcEHAbHocY/GhCZjJCkcoURvHLOMK7gNGX6gEHoe341jaltuLMPmLzI+ojXbgA4II9q20nle8iMRb96wOx+ckHlT/tDsfSql75Ut7cCdEyd3lSfdLKcjnscfnTJOVU4NSZqNgVcqexxThQUPopBS0AFFLSUAPpDQKDSAKKKWmMOlJupzAkZqMjFIQ/5W4NOaBBEGUsT3FRjJ6VdSFWt+X96YGdNAVG4cg81XrZaEtbrlfX8sVnNENgb1z+lAiCnZFDIwG7t603FADtwo300ClxQIeJXHApGZnPzUYpRigCSGMblLdM81qa3YwwRxTRFcSc4HpWakqqME0TTmRQgJ4oGVwKeFFWLC1W5u443bAal1CzkspvLbuMigCDIFOE5X7tQDJpwWgRIZXkYFznHArc0qGKSBw55yeKwgAKswXz27B0OCP8APNAwu0WO6kRegPFQY9aklnNzcmUjBbqKtfZWlgRgO3WgZQpuMdKeQVJBpKALdvqDJGILkedbjopPK+6nqP5e1ew/BgeInkFxp89tcaMsphmglm/fQL1yFHQHsOh5xivE+tXtH1vUNAvhd6bdPby42tsPDKf4WHcUCPsDXNafT7aQWNq17eBSVt0cA8euT/LmvmTxp4s17X9Sk/tR3i8tiBbcqsXsB6+5rLTXNUk1FdWtr64N3E2/DuWK+vBOCp6H26108stn44sjIm2PUo1zJGWyy++erx+/3l6HI5oA4NJezcH1p5fjjpVmTTksb6S21Iy27ocEBM/5+vNaNpp2gOd0+o3HltnAi2lh+B60AYTSelMAZz7VqX2lW0TGaxu1u7bPXo6/7y/16VVEe0c0AS2Wl3V75n2aFnEYyx9Ka0f2fhuJPTvWvpc5/sqaGGOUl2xIUfAI7ZpL24urXTBavZpFDKc58leSO4bkg+vPNAGGXJ6dKVM+YDnHPWkwB1rRWXTTp0Mcw2TEkNImT9Mj/CgRM+lRTWMlzazrLJGcPEB8yg9yOmPcflWMV6Z+lb629wLVbe0u4Uw+7zVlAbB9cc1Yh06C4y9ylusyH+OUBZm9wMY+o4NAzlfu4z24pwJXGe3Wug1rS0t4RILFrYt/zzm8yPP48j8zXPEZ/wCBfzoEPBI/A/pTuRx6cj6VErdCeh4NPUjgntwaAH9M47fMKMDov+8KQEr77Tg/Q0uMZHdTkfSgAB3HI6MOR70qgNJk9D/+qjgN9CDj60vQgnpnaaAAEAbV3ZU45xSg/wAaFgRRMdsquzZ3DB9qam7cAAxLHgdTn0xQBae4W4hRH4lXoT0I9M1oaJ4d1DxDMYbWP930eVvur7+59hXT+HPh3Jf+Vd6rvggHzeT0dvr6D9a7K91mx0S1Fpp0cSBBjj7q0DK+jeGtH8G2/nPia9I5lcfMfZR2FYfiPxfkFC+B2iRuT9awNY8TT3kknlSZ9ZXbj8K425nMspw7O5/jPUmgC5qGrzXknzHC9ox0/Gn6Pomp+JLsW9lCxUfekPCRj3P9K6Xwt8OLnUtl3qoe3tjyIujsPf8Auj9a9Fa603w/aCzsIUBXgRxrwD7+poAoaD4Q0jwnALq5Kz3oH+tdeh/2R2/nT7vVrrVJPKtAyRdzUIt7rU5vNui2D0Sti2tEhUIgUUAVLHSkh+d/mkPUmteOEAZ+UAUu0RAsw5HQep9KqtK1ztSVFQkkfu3zwKAJ5WldkS28po+5J5/CplBxg9aosJGMaR7NzHOXbGBVhZT9pdM520ASsoqrKrxkulz5YP8Af5FXRzTGUdCOKAKcVyDbyyJJ5xQ444BNMU3abZHd5mY48uNgoH5ir5VILdnCLtHP3etZzyyXUgS3/dxn72EIP5mgCSa4jRAXs/MyegwxyPxpoS4vSEEa28GfuDqfrVu302GLDBFyO9aCKinFAEVvarGoAHSraQuxCRpuc9MVLbQCWeKNnEYkJAZuBxyce9UfFHj/AEHwbC0Vuy3N/jAQHP5ntQBalOnaU0sus3iWwjGdrNhmzzx6ivM/F3xXuNQ36X4ajaG35HmD7x9//rmuO8Q+ItV8W3JvNUn8m2Byqe3sP61htdbv9G0+FgD3HLN9aBj5WjgczXUn2m6PODyAff1psENzqUwkmdo4AeW9B7etTQ6dHARJdN50x6RDnn39a6KHRXeNJ9Wf7Pb/AMNunDke4/hFAFK00t/O+zaVD5xx81yWGMHvkcD6VsQWlhov72R1ursf8tG+4p/2R/Wqt3rkVvD9ms40hhXpGnH4k96xleXUMvI/ye1AF+91q4vJmCbjn+MtVNYcfPIdzmp7G2aUEIPkBxmtKPSBJJkFsfrQIqWFg1/Juf8A1a8Y9a6e20mCOMDyU/IUlhbxWnyMduehPrWskQMgZ5lYDoid6AMq9V7QqkKZZume31NXFv44LcPJtEmOUB3frV2a2ibM8x4QH5BzxSWFvp99B50Vsg2kgEpg5H86AM2K1uNSuVuHGyMHKk8flXRQv+72t94cGshrmSaZokma3cMFX5Mlq3FRYmDHmQjHXAoAkSJjywx9ahmtQzKrIpX19Kp3msw2lxsuIzn35/StaIpPArptMbjI9KAKkcQSUmEtg4BJ6D6VYjsoy3nHcZf7+7+lU7uK8n3xW4VIxxvLYyfQVLZKdNtSk8+85z7D2FAGkTtjyxxjvWPf6ykX7uD55TxkVHdyXOpNtgPlxD7zngY+tYt7rWm6GpEBW4uf+eh6A+1AGhH/AKJ/p+oybe6xnrXAeKteXU7wgHK54HtVDWPEl1qUz5dmzVG1s3lYPJ3oAlgdiAFGB7VoW9nLdy4jj3Ect2A9yegFWEsYLGHzb6TyUxlYx/rG/D+EfXmojc3urEWlhB5NvnAROp9yepNIZLLc2WmuFj23d0P9n92p+n8R+tLBpmo69cG5u3xGOS7cKBV6DStP0NRJqBWWftCG7+9TPPcancRRT/6PbuMxxR9ceuPT3oAI5rXTsW2lQ+fcnjzivQ+1NbSJLmbzLqZmc+vqevXsK6exs7W1jMdpEofADOW/rVS4lVZAh+aWXgBCOaVxnP3Klxa6e0+0LkhHJ5yeMYB/KtnVFeHTljjDJ5ajA246d6gt9NuX1ZJZkVY488A5xg5/WtS+jV4ZJ7k+TEOhPJP60Ac6+ooLaJRbxM38QBK/y7VPF4gvJP3bPFFD/cALEj69vrUJks28yHyNkI53yOc+xAHT65qCJW/eGNEhtz96WXDDI/unvQBJJIkinYq+ZIfvheRz0J5zn86SX7NaQhLhcdxCmNzem7HAHt1qtLqKwgrZ8Meszj5j/ujoBVaC0luQZnfy4f4ppD/L1P0pgFzez3ZEajbGOFij6f8A1zTmht7BfN1CT5sZFun3v+Ben86r3GrwWQMOmJl+huHX5j9PQVz9xd/vC7vvkNAjbfxJeBsW22CLtGq8CiuZa6kJzRTEaEUjpa2yAMvfH95Seo9aWVgd8btuGCRn2pdQhaLTLQP95IR+Gef61Rt1eWQD5qACZtoGdqjtVeGdoZCRtZD1B6VYnj86Y46Dj8qQRpGPkHPrQDLcFzCi7hBg/wC9xVCcCad5X5LHJxVlIZJOg4qVtOnMZKAH8aAM0wr1AagJtORuFbCx5gVJIdrD171We5sFkdPIfg4yDQBFFcSjgtu+vH61ZS5Rjtb5T7/41EGsH6PKn15oNnvBa3kSYeg6/lSAsnBpCgNU4xIrbFco3oeh/wAKl+0lDtlTHuORQMkKkdOahe3R+q7T6irKusgypU/SlIBoAzXs3XlfmHtVixy5MTjIbKkGp9mOnFA4Oeh9RQKxmrcNGNgjQ7SRkqc/zpftcnYIP+AVbNrGozHGp+tVpJHj4MCD/gApgRm7n6h8fRRUkU4lcLcFnHb5v5e9TmOMhSPJYEc9Fx+tUZwiTkRHKev86BF2BRb38bE/IwIV/rVW6Ro5zuFW0HnWUit1Vd4/Co1D3ULlyuI8DeevPb3oGS/2gj/Od6uewwQPpk1AJbZOkLt/vkD+VNEVsPvzOfoop2bNRwkrfV8fyFAFiykM11uVEhjiUsxTk4PHf1pst5IkpVdmP9wU22nSOc7IWETrtYAkn2PPpUktvCWL+cu04PqefYUCGDUJMY8uE++yn27ia0lQlVffvY44weB09Kj820iHyxvIff5R/U0R3m2WQmNVjkUKQnbByDzQA0QWykE3S59kP+FISqNI0ZdsbSC/BOKssYYIkljgaTdyHfgfkOf1pg33I8y5fZEvoMAfT3oGAmkOHfcSevcYHOPp6+taE2rzXAjBGCv3MdC54B/AZP1rJiEhilkQ4jQEnPp/jTcyFftIzhSAcYHJoC50l5eLDbx21uciO3eNQPV8KPxIzSW/iTWbICIXcyhAF2egHHSsCQSmFLhH3KDlgOqn1Pr9anh1R3xHOizJ056/getANnfWHiGW/wBCubi82yzW8kZhcIN5JYKV45OQTxV5NShU7Jy0D9hKpTP5gV59d3clq0VvBM3kfLc+WVHyycgZPU+ozWraeM72JQk6LLH3B5H5HIpiPTbJI7/w3qVsZMBUM6lSMgrhgR19CPxqAxaI33bi+T/f2n/2WuWtb+01rTrqO1tUs7oxuFlh+Q7gu7BAOCpxggir1h4r0fWbKNdT/wBBuygH2mNSYmyOrAcqfzFIDftYrC2u47i31T5kOQJYRj8cEGptXnuo7GIzulxYSzyyzpFwJBI4KsuckMjcDng47NTrfSI7zRQqpaDyYCY9RtpNyS4BOW54zjn35FR6HNHe6VPaXI3xBPM2+qEfOo/4Dkj3AoGQaddS6XfCGSRXVsPHKPuyKfuuB6HoR2ORUt1F/Y99FcWwxY3BIQdo26tH/VfbjtVBbd1MmlXBUzRSukEpOAJP7pPZZBg+zEHua1dJuYdQs5dOvg4Vxtkzw8ZB4YejKaAHa1CbmBdatWzNCo+0Y6yRjpJ9V6H1X6VZiEXiDTEt8ot1HlraQ9FcjlCf7rf/AF6p6bc3Oj6k1jdkCWMjn+FgejD/AGWH5HIqO6t/7C1FJbcN/Z9yT5Q/55nq0Z+nUeo47UCM/wCblZEZJFJWRD1VhwQfoaq21w+k63HKrYhnbcD2Eg+8P+BDke4rptXthf2v9q2/zSoo+0oP+WiDpIPcdD7fSueuIY7q3aJ+UbuOx6gg+o6g0itzrdYtU1CwW9g5ZFGR6p6/h0PtiuV5Bwav+Gdce2n/ALNvivmL9x+0i9N2P0I/+tWjrGiptN1ZDMXWSPqY89x6r79u9NMRz+Mcikye9KVx1oBAxkMR3xjI/OqEJ+OOn6c1ZjiF3DKUjHmRANIB0KnjdjtzwR2yKJbN0gFwhEkLHHmJ2PoR1B9jTLW6ks7qO4hbEiZGCMgg8FWHcHvSAtxagrxLDfo88aDCyr/rYx6c/fA7Z5HrU02mPFbRahYzrPBuBjmj6qwOcEHkEHsaspa6XrRxZyfY7tv+XaX7pP8AstUGmyz6FrJtrtNtvOwjuI26HPCv9Rxz3FIZfKweJYDLEqxanGMyxDpIP7y1zsiNHIUdWBBwa19ZsptD1RLm2JG1t8RHt2P16GrviyzRWhvkTb52Nw9yM0IGc4rU+OWSFi8MjRseuzofqDwfxFWtMsYryC/llkZfssaMAO5YkD8OKz8nOcKM9hkgfnTEa8Wrb0Ed9CrJ/fRcj/vnkj8CR7VDceHrO+/0uwn8uU9HR8fhkcH6H8qohvSnJI0cm+N2STu6cZ+vY/jRYZUvbe7hU2+rWK3cI4EgHzj8P8D+FczfeDbPUFabSJ1yOsT8Ee3P/wBavRYdYYqIruBZI/7yLk/iv/xJ/CobjQtP1QfabGby5R/GrYIPpnqPow/CkFzxd7fVfD98JYzcWtwnSSNip/MV1+g/EqOLUYrnX9NiuLpAVGoWyBJwD13Do4ror20vbZfI1OzW8t+m/Zhx+HQ/gR9K5m+8H2GoxtLpU+2QdYn4I9v/ANYFIdzv5BpXjaYXWmXUQeJAUvbVytzEw4KyRkcqfbIrKfT7mDNrquleZNu2rd2UXySg9CcHCH1yMV5VcadqmhXgkxNbTJyskbFSPoRXVaV8S7r7N9i8QRS3cR6XVu/lXEfvkcNj3oGTeILSOC2nDAeXbskoPHyuGGMH36Vp+ImEH7QelzjpNLat/wB9JtrGvfDF34sKPoHiZdWhZsm2u5fKmhJ7sp4OO5Fa3jYfZvi54WlZlJ2WYYg55WQg0AXfF9oLnxRd3UlzLEtpcFZFRwRJGVXjaTwecZrkpX0S4vJJLDTdm9mEgkB7EfIVBIG4dPT1rb8X6b53xV1Ge3dFMZi3xnH73cihhgnuDwelY0FvZaFYSanEzTRNfeRNGpGYBz175OMD9aAOm1jSl0/4M3VsmRHDqSyqG5KqxUgH6bq8sPMIr0/TrqXV/gx4oknffKl60hPXjMbDr2xXmQGYaBdT0vRksrv4Kql/G8kFtqhyqNgkluPwy1Gk3toXNu1k9taxAsDbxgAoB83bOR+dReD4G1H4SeJLEBGKXaOBICRyEPbntXG3+uyWaRaYEhhNqzZkj3M2TweSc0AehjV7TTrPzrC2R4SWKzRNu5Yf3ufmI/hYDnpWnp979i02W8uzh8BpR6seQg9+gryGx8QLp14tzYmQsT+/WXG2dT1UqO36jrXoMMEuuXAuRJ51gV32UabgADwc4ONw5B3HPpSGc5qA1A6v9tSZxvJJwOVYAngjt2qrOlhqKXkjXlvY6hKvlzearBCAcswwDgkDkY5rQ8Wte6TbAQyfupsj1wPTvjI4rC0q50yXU7d5ZEjhkcB4ZUZipJ5CkA7lJ6A4xTQHOXdrJZ3DQsVbGMOmcEHkHmp9OubnTruKaMvHu6Hpkf1rq9T0z7Le6jeuivEyH7KOoyQRx7jgVzNpqElkoRsS2znBjlXcPfHofpzTEaCXkdj4mScDyobhB5oj+XaGHJXHTB5GOldTe61cRwx23iixXWdMx+6vl+W4jU9CHHX8eDWRceHYNWtYJ4L6KDI2xvLnae+wnsw7Z6irmj3dzoEMtrq1pcXNr91XR12L7jIIP0zikMjfwumpW73Phu9XVYFGWtZPkuYh/u/xY9vyrlprMGR0wVkU4aNxhwfQg12OqeG90C6/4ZkddhywhJVlP0HKn26HtVf/AISu01NFtPF+nfaJAMR6lbYS4UdiccOPrQKxxLwyRnkUgkx1rtLvwncyWb32i3Sa5p45LQ8TxD0ZOox7Zrlntkkzs6jqDww+opiIUlIq7b37xEFXxWc8Tp700PigZ3Om+KAFEV0qtGeueR+IrptN1SWIebp90pTtE7cY9j/Q15OkxFXrTUZbdw0chU0Ae122rWOoER3kP2a5/wCeg4P/ANf8KmksnhhMyq08YPBj5B+orzfTvFCOohvY1I9e3/1q6vS9WuYG32Nx50XXy5Dz+ff8aAL81ncXcEd1CvKzbjk4O3oePp/KpigiiL/uvOOCoflYwe/H8RH5VZg1ay1FxFIXtLnIJGcKSPai/t3gcyeSWibJzGoPNIRm+e135sLvjyZmCgdx2P1x/Ks69077W0QDYkRsq/8As5yR/WtBAqeaUjXLBMP/ALvI47GrEpWGKW6w2xFyPUZ4NAFCOCBp3TLIfM8xSW434w2PTPpWdcQvJMYUXcQxVcdxmtSSNchgNycEe47fnV2WEvJceXCsICAiQLyRn5lx2I9qBmNEYYpbeOS5cyRsYiE+6xIztPrj1rMuktrfdLIXTa3I25x6ZFal/E1wVxN8vmiRD1xg4xn6U28gS4i82P70fyNleq9if5UAYbyF5RPE6+VwpdOgwflbHbB4PtVDyfJ1i6gc7YpVMg9iOf0q82lPDPv06TGV3SRv/q+Ooz2+hq+dPiuVtppJlWYqYwiMCDnquRxnHSmIyYokCZREnDEc9OD1x71Snijj822Wbdt3bo+5UnqPoK0Y4rrzrjzIPs9v5YWIHAAwcjH1ppgjkjPmpuaHLD1ye+f50gMe3gZIhH5ysyrgemM8VB5TR8F13fxD0z0J9q01VYc3DWzsScqg7jvj1Iqq9s090Lm2HmRKpRvlxkds+hpgUjZqGDq3kSjBBB+Xnp9K24z/AGnai1vrlra5X7k0g3RN7EjlfrVExkB5U25VeRu9MdvTFT20pRjtbKHqj8gg0AYOtaPfaVLtvIGQN9yQcpIPVWHBrDIwa9c0+aJ4HtfkaJvvWlyN0TfTPQ+4rD1jwNbXTmTR3NtP1+w3L8H/AHHPB+hoGefiux8J3hXTrmDd9xww+hH+IrlbqzubG4e2uoHgmTgpIMEVpeHZ/KvpEzjzEP5jmgDo/EP+kaar/wBx8/mK4dgQSB17V3Ug+1abNEeuDj6jkVxMy7ZSKAZ6LJeCP4k+H9XVsJq1rC0h7FnTymz/AMCFWtc1+50fW5LTWIPtNnvZre4i+WeEE9A3fFcpLLJP4F0q/j/12k3z25PcK+JU/wDHg9dR4/sI7+OHWo7qJIrqKMrGclpAx3HaB6A85pAb3hqY3Wtf2hbapDe2jwJCcjbKpV8rvHfhiM1izrb6r4ptdT06ZYLuxmMc1u42uwDEN9TyRmuHe2uNO1HdpF1LPtBIkhBDYAycr1471eumu9Qu7ZpbjBmk81poovmDtjceMdeuB3oGaXjK3S38UySp/q7uJZB9Rwa5O/UqwYd63ry7uNQ0oPdSNNNp9wI/MIwzROPlJ/EVkXikwk9xQBSjieSMuo4XrTeatWRLMVH8QI/rT9KsBqGrrYmTYZdwU9fmAJH54piuVoZ5beYSwSPHIvRkbBrsg811b7Hkif7QjZmc4OSOx6fewSp5FZNr4evLS/jmvY2itU+fztgdDjpxnnmuglut0BZ5rGWEtt8sp5ZJxnsBtP1oEylJPP8AYhcpJs2N80Y4KkAA5HXg/oazrhY4j5dxG7rKyshDZxnrz6+/cVposby7oUheRc/u7hsMAeDhhww+tVjDPawkW0MIckEvDLuIx6Ken4UAc1q0Bg1GYbNoLEgDp1qoprW1Y+bZwyHl0ZlYnrz8w/rWOKBkuaWmCloAdRSZpaAFFLSUtABSikpyHDD60hirKM4PFSGDeu9Ks3FtDcfPCNhxyO2aqRC4gnCrwc/hQA3YDwj4cdj0NT2RxIUkbb7HvVi8sNqxt5ilmzjHQ1Rcuh8udG49eo+lMk0Z7hBAxX/dFR2tjHM0EczY3k8frVL5jsJLPEvbv+NWILvN1FIf4GBH50AGpyIP3aIqovAFZO0npWxrUYW6f/eNVrW2EkTt70AZ5yKbk1emtlAyOR6iqjxlDjr70AN5NLikzijdQA8ClHWmbjRk0CL0Mmwh1OCKbfXb3UoZzuOMZqpzT0HNAFi1spLkSFP4Bmq8oeKQo4wRwa1NPuzY3HmKFYEYIPcVQvpxcXckh4LHNAyvkmlANCgnoM0pfFAEiLtOTWlb3qxx7HPyfqKyd9HJoETF98r9xV6exAgSWLPTJBrPjHPNbyXazWXk7lDKmPrigZgGinAF2wKc8Tx/fHWgZHHJJFIHjdlYcgjqK1Le7jeUXMc32G+i+ZZI8hWYdxj7h/Q+1ZdNIzQI9Ljn0vxP4dtrnxKf7Pkt22Le2oDfaFH8JXkKw/yKt6fpXgi80m5n0yS5MtswEn2pfMG09HIAyFPqAceleYWt7Lab1U5ifHmRn7rY9R6+9a2n3V1p9wNV0d23xf6xNu7CnqGXup/L8aAOh1vw9bWtxJJYTxBvL8wwnGdp/iRujKfauTJLuAe5xXoejXej+KPKggRoLhyRJZHGYWPV4c/eU91H1xnkw3Hw90rT76SPVvFNvbsrAiKOP5gD0ySR/KgDK0DTWhluVu1dZAF8oRkEbhVTVdVu7S6XfGPJJw0ZwysO4bt/Wul1vw5fpqU0ej6rbylIw0qSMY3C4+9jkMvuuffFU7fw/bQ6e8eo6tbh2O4lEEoHuM4waAOSvkiaQT2wYQSjKg9QR1B+lVGwRz25rvbf/hDtNEm4TXm4fNG8m2PI7hR0P41Bd694fWDZbeG7REPAJHP5kk0AYei2VrKkst/b+cGPyPubt16V0o02wliC22gO+3pI6mLH/Aic1Rg8TagsHkadZIo9YoAD+JwBWdd3mq3GWu7jYD2aXoPoMnFMDqLgST2pt767060tsYKBjLIB7dBkfWsddO8FW5Imv9TujnP7sKg/QE1h7rFRuk1FXPpFCT+pxUJvrdGP+jPcD/bkIH5ACkBt3yeE5IXSxtbyGT/npJN3+h4/lXPXFq0Z3od8TjG8Lxu96edZniU/Z7a3hHqIwzf99HJqnLqV1c/LJM7Bu2eOPagBytnBP+61b2geGL3W2DKDHbLkNIe49vWqnhuGwufEFtDqXEExHPQB+2fYmvV7y9XTmSwsIPPnbhYoxwB6nHSgCO20HQLDSTZXccRhlABlcgNu7EHrmvOPFWg/2JqbwJJ5lvMu+GT1A/rXqdrpQaZLjUU+0zDnYMbI/bngmn+IPDVp4i+xPMWtxCTu2Y59vx9qAPINK0e/16Vbawh3PwWc/dHY7jXqegeDtK8MRi6vClxef3yvAPooP8+tatvFYaHaC00+BUP8I24BPqT1JqreM8ED3Ux3y9FO3KigDK8T+JpYl2D91F0GDyfrivN9T1uW4JTd8h7A/wA6XxBq51LUvJTcVU4wnOT68VteGfAF5qU63NyGgtAQQ5XJb2Cn+dAHNabpmoa7eC2tIHcnrjoo9Sewr1bw74F0zw8gvr0pPdKM73+7Gf8AZH9a2A2l+G7TyLSFFf8AuJ1J9SazCt5q8vmXBZYuye1AFm+1me+Y21gGWPoXpLPTFjO9/nk7k1etrJIYwqjAHpVwRrGu5qAI4oQO1TtvWNjGF+6evrVW7knjiL+XtTt83NTJK5hAL8kfc29PxoAzWa5A2SBZN/REz8ue/NXUiWCWPELBFGPM/hA/xqOC4db1YYeR/EasNAlxJJvRSme7Y5/DrQBFbwBh9o+bknb/AJNIYZlkCpGxDHLybgR+lLfqrSRxF9kIwPk7n0qRDFZRYQbB1wTnigC1GDjmlIqql7JNIqxQMsfcv/Sro55NAFO5g89lBdgF7Doatww4HAp3ldzVbUdUttOgLTOq+g7mgC5JIkQzuU49azYPE2h2+oONSu/KiRS2R0Yg/dzXnviPx2ZA0UT7R2Qd/qa4mV7i/Pn3knkQdQO5+n+NAHovjX4qTa2G0rQINloP+Wm3k+49PrXnEjw2rGS4f7VcnnlsqD+PWovtUkp+y2ELBD6dT7k1Zt9PitpB5y/abpukacgH+tAyFLe61I+fcP5cP99/6DvWxpunzXWYdLh2xj/W3MnAH1P9BWnDogiAudbk28ZW0Q8/8CI6D2qK91wugt4AkcKcLHGMKPy70ATodP0JCYT9ou+9xJ2P+yO1Y17qstw5d3bB7luaoXd6o5c5PYVkTXckxx0FAie5vM/IldbaaeRpkRX+ID9a5bRNNOp6pFbn7n3m+gr0K6EluotrYKu0YoArabp1yh+QqIu+V/lWg92kR8uEb5OnFaOmO5gRHgw6jBP8NXILWzMpYBDJ3IwMUAZ1hpJvf3t4WPpGGwK2/seyLZCFXt93nFRxyIk2IznHUD0q80qKAp5dvugdTQBjS6K8txvnuX8oc4HB/E0tzqUFnF5FsFGPSteSxe4hPmSMuRwE7VkQaKkU2+U7mJwM0ASaRE8rCR05zu5rVubi2iGZdmQcgHOc0lndRK0kSBl8rGfl65p1zbW920clxGx2HgM2M59fagCgsf8AaVx5pjYxKMZxWm1xFaW4AG1EGAnf8qqy6hBDJHCu1iWAwi9PpUt6qXkWH2pGvWQtgD8aAKNrrNxLJJGsG4Z+V+30pl5c21iPO1CbMnURD+vpWTq/imy0iIwWG0uBgy/4V51qWtXOpTEh2OT1oA6XxB42kmBgt/3UQ6Ilce8lzfycluT0qa101pZBvDEseAOSa2fItdKTdenMna3jPP8AwI9voOaBlTT9GZvmwuF+9IeFX6n+lWJNSgsT5VgPPuP+exXgf7o7fXrSINR8QSrBDHsgH3Y0GFArYgstO0UcL9rvB2HKg+/rSApWWgz3ub3VJtkPUtI3X6etXxqiIDZaLGsfBzK/DNjsD2oktrnWLOW5muEJTO2FGwAByfyFU9JJ+2xW7fLC7gNz/LPQ0DsMa1lt5hcGbdLkMCeeT1yDXQjxApg3Ijx3OMYOCi5wBg4zyexq/efYo7KRZILcDHykKAQfr1rn3tmuJSEK/Mo4OCQw57c/j0pAad3fPpOnZuZFnmYbvvDqT0XHAA74FW7CwmS3Gp3Kb7qQDagHEa9sCuck0q+vdQjN9NtiQ/fl+Xp6A966y51Y2kCpAVm4wPLXP69KALFspSNpJOp4VD278j1Ncx4ivjdTpZR/OM5YJ/Kp768ubiES3DraR+u75j9B3rnZrwLvS23RqfvOWy7fU9vpQBO32azG642yzDgQq3yr7E9/pVSSa51CZU27j/DGq8D6CkS0xF59y/kQ9i/Vv90d/wCVV7jWCENvp6eVF/FIfvt9TTFctSNZ6b81yVuLntCh+UH/AGj3+grHv9UnuzvuJNsY+6g4AHoBVGa6SMnad8h71nvI8rZc0xE8t2X+VOB+tVie5603vxUixs9ADdwoqyLY4ooA2tcdrm4jhWNgyjDD+VMS1W1tt7nDngn/AAqzNeK85kZFBxgZb+dU5X818f6xuwHQUAU5DnnGB2FSiJIY/On6fwp3NWPs6R/fPmTH+AdqkXTpLqQCTgtnHtQNIxZ7uWU7V+VfQUrQ3sUuwiUNjONx6Vsf2PbSySwpPumiwSgHr6Hvir9xLaxTwCW5i8yIhWPJOMYI4GKQHO217clirncF/v8Aao5oPNucoMbgDV67EY1KV4irRPghx096rT7ftqwg4UsFz7HvTEVzZHbkSLUeye2YONwx3BrR/tWOBjGLZGA4568VOptrmIzRDaB95O1AEVtLHq0TQyDbdKpKv/ex2NZjg5z0Iq1NGltdJPC+0A4OP6e1RvE7sWxw1ADVSRhlflk7EcA/4Glj1B0O2QZ/Q1KJtwCOUG3IyByaqGPzoiw++nX3HrQBpxXMMw+U4PoalxWNaQiSQq7MBjtV2432QVkkZ1Jxh6Qy0VzTGBAweRUUWoRScP8AKfzFWRtYZUqRQBTe1jkPHymq7Wjo3PI9a0ygNJtxQBWkxBbmMfecYPsv+JotWVo5YR/Hhl+o6ipJollGG4qsbZ4zvRs4596YiCMJFdDzlyo6ircrWzL80ykZzhE+b+mKcxiuI/3y7JB/GBwfrVcWZ2h3dUUjIzySPpQAPeEDZAnlj16t+fb8KlsWzDLC3G8gqexI4IzTFkgiIWKHzJPV+fyHSmTvcySBZgwJ6Arj8qBCvbsrbcVMsMVuA85yeyDqf8KJpXtoogJGMh5IODgfjSvFHcR+ZCzH1B5IPv8A40ARC7kVjsCiNv8AlmeV/wD11M4M8WbktGAcKQvyDjpgc5qKC2dpgpFOvJxIwjj/ANWnT3Pc0APdUW1S3SeI+Y3J3cADnB9MmnwwGG1nWd0EbxnB3g8jkYx71StxmZc1NfoBdAgKN0SHj6UAOtFSJRIblACOVwSfoRiokuI4p2aKNdhPBcZx9BmltZjDMD270l3beVODGMxScr7eo/CgCdrbz182KRpJTywfqfp/hVdGbdsxz6VJbQT7gU3D9atXbxxoHcr9qBGMc8d93+HWgDUsb0aTp7XHSQAiMf3nIx+Q71iwSbFRUfOAB70lxm8YOtzuccBGXaAPQY4qo2+NtjhgfegDatr9oTx0zkjsfqOhr0XwNraz6iz3AVYUibzH7BcHOfoK8mhkd3CD5ifX/Guoj1JbTSG09ofIh4N2+/c04PKxrjoCOtAzvLu5iur1jlT5lrbOw9zGB+oAqQzsx+1qf9LhAM//AE1jHAk+o4VvbDeteYrrdy13Jcs+HkIJHYADAUewAAFb1j4odJI3LKsqHKv1GehBHcEcEdxRYD0hhH4htYYlkSK+h+WF36EE8o2OdpPIPY1Dp15HqVlLpupxuu4tG6Z+YOjFcq3ZgRwe9Yem6nbRy215btttZtwCZyYmH3o8+3BB7gitbWoPLk/tq35ieRTdAf8ALNzgLKP9k8A+h59aEImtJrvw7qVvbXLpLDMHNvcp91gMBgy/wnkZHT0pmpaQ1t5l3p8bT2B+Yxxrl7fuQV6lR2I6Crl5atr+kw/Ztv223ZpIEPHmZGHjP1wCPcCo/DWoocxy52FWRgeG2EbWGPUHtQBzk8MN/Ajxyf7UUsZ5B9Qf6d60tJ8UT6fLHa6oMc4S4T7rfj2J9D+tV2s5dPc2M+3zYAFyn3WXHyuvsR+uRUc8KzwSQuOHUr+YpFHWtplnq6mW1ZUJBOR93oTyO3Ttx7Vy8qeVNIoOQpIz9K2fBN+skIjfqVyR7/xD881natbG11GaM9mNNCG2F7JZTlgnmRONs0R6SL6fUdj2NXJdFkuF+0ad/pFsx4I+8v8AssOoIrIFWba6uLRjJbzywyHjMbYz9expgS3Gk3tvEJJbaRU9dpGPx7GttkbxD4aaVvmv7P8Ads/eRcZBPvg5+oNVrPxbqEDBbnbdxHhgwAb8COD+IroNN+wzedc6e6iC6ASWPoY3wcZHbOcfyqWNFu+it7hrRLgqSJVOM8k4/wAah1uyl1aAW0BUFG3MX6Z7DNPkiWYXEUm1ZHAO4pkxgqMHjpg55rK1q5kPhuMIWyzFZj0JI+9n69fpSGTaJ4dksmu3uypjnh8ogHIIznP4Vx1wqrPIiHIViAfWmCRghQO4U9g5x+WaAGYYUZ+lWkSIABTwNxCjkmmbWHJDCrdhdW1n5rzWss8vymIo+3bgkk98t0wOlAhnlyKm4p8oOM9s+lKjFZBIhZJB/GjYP+fat6zW4aAuZFniuNwyuNsi4LKCB0YZOM/SsW9t/s8x2lWRiSCnT/PNJMbRdh1dwvl3UazxnugGfxXp+RH0qO50PTtUBuLWTypV/jUkFfqeCv48VQD09WZWEiOysOjocEfiKdguVLuzv7KPyb23F5bHvtG76+h/DBrm73wnpmqBn06byZu8T8fz5H4/nXfQ6tKo2XCLNGepCgH8V6H8MGmz6PpurL5sBVJV7jIK/wAiv8vekM8dOgalpuoffa2ljBcSh9pAH90jkk9sVQtLqZvFVjczzSyS/aomMkjlmJ3gnJNesX2n39nF5d1At9a++NwHseh/Q1zN14Zsb+4Fxp8uy5jYP5MvyMMEHv8A596AOg+JSGDxzBLb7Id7IbiU4yGKAIx46cED3rkbHQE/sfUVnZyYJkePccGUsoPQ55A7/wBK7nxjq2manbRasiTWmtW5SM20yZE0ZfkejAE5BHSuH1S8u9S1e7tJ5EtmHlyrLGmNw2hcYHXK9+ORSGdh4dvbfU/AnjGwggkie3t/nEuA7NsY7iB0+6B+FeULzDmvXPBotrq98VW8J3THTvLnPUMwU7Txxnkg/SvIYT/owB7CgXU9Q+Ekf23QPFNhux5gjI/EMM/pXBeN9Jhs9Xufs8m+SOZxNj0z8rccc/yxXcfBSYjUtdhXq1mjge4Yj+tX/GHhlvFFtFrGmbv7SiVkmiOGE2GPybh1YDpxjFAM8NTPWvR/Bl5cyadtsgmYWHnw3EuIpT/Cy45RscE9DXPnRLaPw4bnDrfLcMHR848vpjHqD1rM037Wl6DZbjOASFB5YAZIx34HSgEei+L7htd0ZrWNJYtUtsSNZSxbZGXHJXHDDBzxXmhaSCTcEUNtG+M9CPX/APV0r0CPUoTodre3t79otmkGEDYnt3PRomPPBHI6YqvrsGn6hqo1WHyry0mhWCc42mGTGN5UYxnqD0zQNnHC6lkcx3MsywyyK+4fMVYcBh68VoeJ7VbVYz53nLMiNFIItoY4+ZugHWtnR9IeKC/06UqpiV2kc99pypUkd8j8619ZsmuvD9sl9BKLS1BOwONzISBvUn+IH+E9RRcVjjNCuLyCxlYwPNaH5ZQUJTHbOOmOx7V1ml3FrFZo1nbJIFBEkZmIlYH1ByrjHsK5JI73Q9ThiiumMM2GgmQkK6E4Bx9eCOx4rsdBs/tGoTwRWUIvGiOJEyFyeenQfgKGNFa0u30zUv7R0aSVbFf9dDnmPPUMvdfQ1e1zwvbeJNNutS0OPzJUIkMUPIXI+bA68nnA6VTube4sNTeN0S3v1XDRg5SVT2HYg+lTDUbqC5hl0HdbODme1i4eJh0wCfnXvx9KQGX4g0fUfCNxYano5ube3NpDvuYsgCXb8wb3J5weOaWPxNoniFRF4nsvIu8YGqWICufd16N9a6uHXLjX7G60TxPGEDjAMLeVIcYYAg54JArlNb8AmCwF/ot415D5fmSQyLiWMcgkY4cDBzjnHOKYEOpeEr+2tft1m8er6YeRdWnzFR/tL1Wuaa2WUEx849Ov5Vb0DVdR0W7N1Y3z2siMBKic7lPfaeGHqK6671Pw5rcyx69a/wBk6k6hl1KyQiOTPQvGeRTFoeePE8fvSLJXX6x4U1LTLcXhEWo6a33b60bcuP8AaA5X8fzrm3tUkG6Jt306/iKBESzEVo2Oq3FpIHikYY7dqyGjdOvSlV6BnoVl4ptruMR38fP9/wBPcHqK6rTdWurcZtZ1urbr5ZPP/wBevGElK1p2Or3FmwaKRloA9ogvtO1JsIwt7jGCj9Ke9jctJJFcsqxyKR5nVSOgxXn9p4mtrxI0vY9rj/lonBH4iuqsdXuoIgYZFvLTuh5YfhQI0oLcWaxFX89hHiEhflwBy319Kz1mmXUUgR5TDLCwwTklhzz7kVrQXdjqUHlW0iW8hJ/dSLgZ9vrVWeC4t5cyIqscjj344I6UgKElvCpLKmfMweOBz7etMWaC2+VtwZuG3LwPbj1rQVNkcRYKdpwPXH/1u1U7VvPhzNtJ3GOUdzg9qAKs+m3EiTW0QaNJ8bZD0wDnn8M1VW6tbCArbR5ht50RnfqxPJb656V0yFpJIY5pETgrJCG4yAOh9QMVhajA9zZz2wO7fypxzkdOfpQMx9VhvBctJbnzQ37zyTyrD1X3HQiqcN4ly3mRoyXCf6yL274rbhgmudNtQR8zAlCezr2+jLwffFMu1S3mDrChkABy68/n+lMRi3DMupou/bDNDviPZWXr+YqWYIs4BdkBKsNgwRgd/XP8q2bZVntoj9jQ5k2sem0H+Jfb1FZk+nzm6csGcBuSGH9aQzPlgKzyMEwpJK014jEwTGEAAx6VqyYjlELDcAMg9zjqPrjkVRhVp5bq2bmSNiwPqDQIWCZS7xEfOigrnoy9xWna3U3m/ZnRZ4SAVDdfoD9azUtUKx+azi4yVUdAe+M+/arlsVUhpNuGwp9vSmBbuba01eAwSxpdwr0jl4li/wB1uo/UVx0/hebT79biwd7iFGBaIriZV75XuPcV1VtA0V4YZZPlUEq47/SrskqMQtyjMBzHKnDD/wCuKAOXiXyZthPysK5PU4/Lu5AOxIr0fUbFnUSA+enUSJ1/Ed64nXLRxIZFDEHr9aBsu+EkbUNI8RaOiNJJPZrcQRjkmWJwePfaxrp18+8+GunSmFxdaZM0EgKfMFBOMg+xH5VxXg/UTpXizTronavm+W/+6w2n+deteINQuLLVFRdPle2li3G4txuII6hl6MPTvSYI8stpriy1uKWMOkyvuHB55/UGuo8SaHHdRLPA82n3RXIilQrG3+6e1WJtIttcmheGf93nYJYuRHnuQeRg9Qa2p31TT9E/s3W0iubaPCKZDyMdCueaBnmumQzQXt7plw6t9st2RSDkF1+ZT+YIqsD5sAJ7ipb5otI8TW91byM8KSrKN3JAB5U/hU9/bC01K7t05VJSY/dD8yn8jQCMezmNteJKMfu3GQehHf8AStPUIG0fXkuYS4jXEsZ747j6jpWRdqFlI6bq6TUHGoaDaXRVvMWJVYnuwG1vzwDTEzd1jZLfE/a2EsUCkIMlRxn5l/iUg4PpVCJ7u7uBNBBvkGEYbehAOCSeDj17qaZBLHd2kMhRTdMiqPmCybl+XKseDkDlTT7tpILe3VS6DG4vggCTphh2BFBIm8ojQGGJdsqsA/BUMvIVu2GGB25rMu7XzSZ7csJVJyBwTjqR/tDuPxq208sFq6SRkwbg4jk54PUeoIIyKc7fZpxapOzebcMzkpnkgYBx09cimBiu81/byxNtLKpcv3bbzz71iDg108jIlyZcZYEjen8XYhh0z7iuduI/KuJEwwweh60hoaDS0gNLmgY6lptOoAKWkpaQC05etNzSZpjLqS4qU3bKpUBST0PcfSs9XIqUMG60gL1tGzHe3JXn8aW5dHiAkRd57elVBe/Z+SfwqtJfC4l3ONvoRTJJGiaP54nyP1H1puVchvuSevY0hl/hHU9x3q7HaGeyV0GSNwPtigRWvbl523SDD+3T8KWwl4dOncUxg8XySDcv+ehojAjO+L5h3HcUDLYhj83PzBT94dvwqGWNFkKAN83Iz3+lPWRJiGB2kdR71MrI/wAuVL9eccUgMmWAZ+T8qrlSDg1uSKmzzPLyF9OPxqjPGjDenIP6UwKORTtwp0kDoM44qPFADt1G49qMCl4oEG9+maFXJ5oyKUMBQBqaWIEuttxxG4xn0qlqCxi8k8r/AFeeKZ5/HSo+XfJoGCgUpIFaemacl5HKp+8MH8KypU2TOnocUAO3+lL5jkYzxTAtP4FAiaD7wJ9RWlfKhsg67SM9RWOJdvSnG5kZSmcKeooGPWNnBZRnHWojWhp6qXIY44qK8iVJTigZS61La3M9lOk9vIySL0I/zyD6UzbR0oA2VvNJvJEmuo5rScHk233SezDPKnPb8jTNS+1XU32i5uZbvIAWWQ7mKjpnPNZBGasWd61udkiLJEeqHt9D2NAjsfDniG3u0ttM1SUwXNuQLG/Bw8fojHuOwz1HBroZrjStLtJdRvfDa3dxHLsmk3HyweoZUJwoPp0zXnc+mRXUP2mwd3H8cZ6r9au2fiLWTpFxpbXKTWxXaY5V3MAPRuuB70Ad5Y+INL8V2Rgg0HTmv7diRbFFR5I/VGHGR3BBHtUMeh293FcXlxrcVlbQNtaEWarPF/st2HsRkHse1eYGSeynjnhkaGaMgq8bYII7giu70bx5Z35hj8QIY2UbJLmKMMkyHqsijnn1XoeQBQBY1bQdAl0uG7tta1BklyPOkXzIwR2bbyv4A4rl59AvYZ0g+V9/zIUy6yKf4lYZBB9R+NXW1u28MeIrldJmW+0aVgTGVxwe4yOGHr3rtfD17FdyB/Ds6mGbPnw7gGhc9HCnkc9ccH60AcfpvgPXr/JGmOnXHmuse76AnJqnf+H76yn2tZSh0/1kZ5/InqKNb8R+IrLxBKJ76Vbm2l42dAQfTuP510Ol6qPEcbtp15LZ37DdcacJcJLjq0Wc4z/d7fTmmBwpurdAymNlc8EdB+VRIiqwceuOa9DudQ0XwpNFcxaH9olmxIt1L8+7nnrwCOhGODUmo20HiWE6hHBDcWc33ZrWMJPC391lGA+O4IB7gmgDzvGMZ7Nj869i8DS21x4YWdRKboHyp5ZHJPHcN124rC0D4dwyP9t1S8t5LQ8xRxy483Hck4IHqOvY13n9oaZZLHbwSW6xLhAkbAAfhSAsqEUBn24X7pOOh7DpSqUkEkSI0YOeuOvr9Ka0byy5aRAnBwVzwPT0PvUwgjjiJZMxg7uHO6gDGjsldjJcO3mpnCAZwat29iZ4jHcxq8Z6JIx6Hrn0p11cxW6vfIF29GJGcEdOnr0qg3iCWONpbubCt92MelAGnbaZpekqWt7K2gPcogH45rL1DXpJpDb2IYk8FqzzPeay+FykNbVjpkdtGNq89zQBRsdJOfOuTvkPPPatqKIDgbRinNshjd3PCDJA61WmvUaIoiKkjDI8x8fyoAurkttSNiB/H2P5VWuVuHkDxIs0Y9McfQ9f0qCwVo4PMQvzncTnBP4cfjTrm8QIAhQyE9DkD35AoAhleS7+RRMB3JUKNvfGec012kkkjWDzjx1RwOOnJ74/WpY2lmi80wygdNgbJx68c1YjMkUJlnZoVY8DJbH58CgB1vapFkxBvMI++5yc/jTJonaOFZYXMmQByRg+vBqSCaKC1jZ52IbJDnjNVZbma6bNtA6swx5sh6D2AoAdczosuxCvmJ04JGT9O9Oh00SHzZ3Z2PXLVPY2Ihj55Y8knua0gioPmoAijgAGAKkOyIEsV46k9Kqalq1tpsBed8eiDqa8s8UfECa4L29sdqdMCgDs/EHjO309XS3dWkHV+wrzDUta1DWmkaAO4zhpOw/wrIZbi6/f30hSI8gdz9BTobi5mDW2mwYixhsL2PdjQMbm3sDucrcXPv8AdX/GlS1uL8/aLt/Lh6jPU/Qf1qza2EcEwRU+13rHhAuQD9O/410lvosMBFzrEnny9Vtkb5R/vEdfoKAM3TNLuL2IrYxrbWg4e5k6H8epPsK11m0/Q4itgN9wRhrmT75/3fQVX1PW2f8AdgqsajCxpwqj0AFcvd6iM5J3GgRo3movMzO78HqT1rEuL/HyRdPWqstw8/U8elMVPWgBG3SHJ5qRIcDPWnqtWootoy/Q9qBmp4UuI7LWonl+64KZ9CelennT1lm3gKQ/rXj4LZAUYFepeD9X/tLTvs05/fwgDnqR2NAF3VTLDCkdumAeOKp2elXah5A6iRxjJ7D2FbR1GGK7Fo52yY4z0z2qve6kltsWI75CwBy2QQTzQInsdNWyQyyuzueST/hVGa9Z70NawqZAevStCaKWTdIm0yAfuwelJpsEyq7zworE5yDwPx70AaUMhkiDEbeOQe3tUhjTO5qovfwW8mHdcHqT/hVe71cfdhGT2P8AhQBpSKBkpGvrywFYN1cXl5dGCHfwcY7fmKntFuVLXV1N5cRH8Z/p3rA1zxnbWSNDYdTwZD1NAGrcSWGir515MJpx0jHQH3rh/EHjO4v2MaPtjHAROgrnrzUrrUpyS7HP51ZsdHeWVAUZ5D0UdaAKKRXF9JufP17VtWOkARec5WOFessnT/gI/iNWJ5bPSl2S7J7gdIV+4v8AvHv/ACqvDbap4iuMtuEY/BVH8gKQxZdWWL/R9Kjbc3DTH75+noPpVqy8PBUF9q83lxnkA/eb6CrUS2Gifu7ZFvL3+9tyin+tMntri9mSa6nWYk/NFnDLj/ZPOPpQFi8kl1eYtNNt/stqeC5wGI9exNbltpMFtaPBHC0pYfPJyAffntWfaiOCMJFtjAHOF2n8T1qz/aJgiRbaG8bJJAjXIJ9dxz/9akMakN1Eht7ay8lACDKSFBz6A8Y9654QPbXvlDcs2eYpAGB/EcH+dbzaZr9wjzDyYs84PL/XPPNZkGl3WnO+p6g/3fuoW5YjtwelAFe7uJY794IBiT7pJYsB64BFV3tUs2E7zbpgcqR69un61PczSzutzI8MMsig7Bku2ehxgjNJ9ljtv39/I/mHpH1c/X0oGIsJvX3yw+ZM/JCSlVjHv1FTvfpaB1STz5T1+Y+WuOOB3qjc6hJMnlxhYYv+ecff6+poNolrEJtRk8hDysQ/1jf/ABI+vNMVxCLnUpztVpJByT2A/kBUc1zZ6Z8q7bq6/wDIan6dz9aqXusyzxGG3UW1qP4E7+7HqawZr1U+WLk/36BF6+vnnk867mZieg/+tWVPdvL8q/KtQuxY7mOSabnPFMQnFABJ4qRIi3NWFjWMZNAEcduTy1T5SMY7+lRyTgDjgVWaRm4FAFkz80VWETsM7TRQBrboEPzOW/So5bt8EQIETue9RqDFOUlGCPWrUrxvaSBRg46f4UDHeHD52pHeck4HPoTzVk6nc20zxuqspL+WTwV5INY2m3Js79JO3etu/hS4/eoylX+Y47E9fzoAyJbtTciRDsI7ocGla6aTqysPQrUkemCaUxg4fGRVGS0khujETyKBFpAhPyu0b/mDTbqOV+VjYNGA2RyMetWrCxEkLzXE2yIHaMDJJ/p9angWC3ujiaUqw2EHHTrQBiXKpIfNj/i5I9D3qWzkVYJIy6qWIznuK0JrCKcyC3KrMvVM/K309Krw2T3gLRwYdOGA7kf1oAgvJEMKRxncc5J/pVMbwu4FsdKtXELRpghhSCDbGDvyG5oAjt/nyh6joakgK+bIT1K4AqW3tzCjTScDHA7mkFsTaeZt5BP5UAR/NGd68EU6eZriLYyLkdCP896rXCtHKUJyBSwMzkJ1z0oAaLaSnr5kB4kVT9ame2dujbcdiajFhKZEU7cE9QaALEd84/1ibh6pVuOeKX7j8+nesiaB7eXGfYGmhGzycGgLm6VBpjRis1bq4t8bjuT35q5DqEMnDfIffpSGSbB3Gahmh80BAegwPwq4QCMimmOgDKQTWs2/ZnGR+dStejA2R4I6bznGfQVdKEVXaFGOMYJoEU445LqY45J5YnoPc1YkljtlCQffHWTuT/Qe1SSv5MHlRDA7+pPqarWsqRTFpBkEcHbnB9cGmItW80txBMHmYYRjkY7DPPFMiurdwizQ7e2Y+f0pslzEsMqRbi8vU42gAnnAqOwjEl7EG+4p3H6DmgC5ILS3kw/mg/7g/wAaSV4pP37RsYgoUEMCwx6/Wq8ge8vxGp5c9+nPNTxQeTFch3zF5Z56ckcDnvmgBsX2WaTYglLHJA2jtz60rXiCRI9jIisGycEnt9MVnxSNDKki9UINXNRiCy70+43I+h5FAC3L3Pll0uXeE/3Plx9QKbbzxyRi3n2gD7snp9f8atxtYxEPHMoiIwyOxJORyMYrJVCz4Qd+KALq26Wl3m5TdFg47jJHB9xReSQtEI4zvfOQRnCj055qwM29sqXJ3Rt0H8Sj1Ge3tTBdWMPMdtvPrI/9B/jQMSwtCCZpfljXlnPQDv8AjTNQvWubhmA2gtux6ADao/KmT6hPONpO1eyDgD6Cq1AEyyVMkxHQ1TBp4WTqA348UAbmmXsn+kWiu2Jl8xPaRRkH8VyDXY+E/GLqv2a5VZUC4KNyJIzwVIrzq3uI7aZZmdmdDkInTPuafZ3bxXSyIOQeg9PSgD2ODUYdF1JLdZGNlOoktpCednTGf7yHg+owa0NZi8uQa9Z7cEj7YqdMngSD2PRvfB9a88nujf8Ah+WMPiayP2mHP93o6/lzV/wl4slgzbXG2aIDBQ8iSM8Mp9RTEd9JCmuafGIji7iB8gn+IHlo2+vb0P1rAByvO4EcEHggjqD7iq8Grw6FrUlizt9mYLJBJuzmNvun6qflP0rd1eKO4iOrW5UocfawnYngSD2PQ+/NIaMK2un0jWI5U4imbIPYSfxD8RyPfPrXXavbJqdhHfW3zELz64/+t0P4Vy00MdxC0Ugyrdf/AK3uKt+H9ak0u6+w3pZo2+6/94ev1HcUDKuCKePu81vajo6zg3Vj84PJjH8x6j9RWZDdG2TyntopFJyc5DH8R/hTuKw7R9NOq6iltv2qFLM/oB/jmulg8NT6dP59leBs/LJFIpAkXuCQT+B7Gs6x1zTdMt5XtrS4+1OMfvCu0e2R2/CoNMvNT1bWI421C4QHc7eW+AFHUAdPak7gdZ5z+ZvCsZowQVPDMnX6bgeffnFVruxS5SQZCCRVO4co2CBuOefoas3MT8Bpg7j7p4WQY9Bn5vp19KrS77ckuiozdcj5W/H+E/p6ioKMV/DAF4Q0ipFuwfmPAHOemcVtQWdrBGqC2RmRQxyOWB9O3A5qxuViVYrHIx58xcZU9Ac9egGRTmeI7l8xOoIj3ZGO2MfiPpTuIqT6XYXW7fHsZip/dZXC9zjpwc5zWHf+G5YlMlq/nLuICbcPjOBx3/SunWOYgZjcqeudoJ/UZz3oaTcGG5w+BvBOGBOegIOQPb+dFxnB2V5Lpsz7UDxOwMsD/dYj+TD1rdnshqtos1i6tE6/LGRghwcFAfb0+nNXNT0WG+iM0aCKXOA4wIyOnzDrzzz1zVPTbpdJs7m2ebZOpMgQsoIbAB254IPUHvTuI5tlaNijjBBwQeooBIx/tAN+YzXTS6fHr9hHf2YRLhshos/xD7y/1Hsa5ryZoLn7NONuDhSeMH+6f6VVxWHAnvQG5DAsHHQhsEfQjmkdWRircEU0GmI1LbV5oxtuE8+PueA34jof0PvS3Gk6VrKbotqSjnHIZfqOo+o496y92OtOz0YcMOQRwR9D2pWGQXmm6nYR7JUW+tewfG7Hs3Q/55rIbT7K8iuY7TCSzRlXhuFIcdwVPXIPIwcV1ttq9xCNsn7+Pvnhv8D+PPvSzWOk60MKFjmHONuGB9cdfxBNICD4fWGh6BYy2r7rbU7lWSaSZiFlUFtu0njoeRwc14ki7TIn90kflXsNzpmoaeDuRbu1PZ+Tj69D+P51z134e0rVCRBus7o/wHj9P8M0hlH4U6vaaX4xaO8mEMd5btAGPTeWBUE9s4OPequuz+IPCnjOWF7plkibfbvHwhQjhlB46dfesjUvC2r6LKLlA0ixsGWaLnBHIPqK37T4jLqFoun+LtLi1S1XhbhBsnj9wf8ADFAzKtbtLlQly7+XIX809Tyd27nvmnS2kMcMDWMy/abOQmKYjb5qhsjcO2a6aLwxY6taGfwlqkd9GoJawuMJOue3PUjpWIbXUbbz1l0p7aeNf3QmXaS3JOM/5xQB1+jeGIr+CO/ubdLfzpiwOzOFIHIHuc8+2an1HQbKHVrUwxpZxlArb/miljJw0bkZwSOQememK5nSvGGr2e+PUGdrdY8qlxhm3BdvDeh6fyq7oXi6KXUpYc4jkPyxovy8/ewOv60MLl/w9pHkeKNTgeRbuzghIhk67kHKq3qQeD64rH8Q6zqVtqQMEMV99rUfw85PGcdBkcAdB713eqS2+n2dxq0LeVJKMNnhchSwbB5B4ANeX2euR30kF8sZh1CIyv5nRefmwo9AQRjtmkBFf6W0F9ceGrvbBMr+bYu78RyEDMZbjhumexANdn4Eu4wk6TIyXYxHcmRDui24yp5GM+o5rC8R2Evim3OpWivLdIE2nj96CVz046MKp6R4kiS6+zao8sd7bt5YvYcFmC8BXU8OB0B6gUAdB8SNFZxbalDu8uMbZQjAmJCeGJHUZ71zljr1tPAlj4ggW4g+7DdqmJFPuepx+dddqmo3UkVhq9sPtttC0i3EMWSskRUA7lx8pxnjpxXI6v4bF3azah4euUutPXEjW4P72EE8jbznH50AXbi6l0u8SynCahZ43iK6fDKuM/K3UEjoM8+lacMKSR2l9pV9m2JKC2mcgqxBLLnGBx/9aua0C6ttWt5dK1K6lYxsZI5gAV8sDJGTySfemy3S6TGNR02Sa40+Vts0Ey8xc/L83RgexHQ8UwMe4s4rTX2h1aO5sJFbeNiAkqTlT17juKTXLZnuzIlvcIhHyPJl/MHXduGRz+ldNp99pWoXUl1MNPuh5DRLDdLiSLI7Z4bvg9RWd4ih0Wx020t9N/tESKWExaYkSLtBzt5AwadxWMrR9c1Xw0ILvTb5kSfcHi+8hYHG1lPHP8q6f7X4Y8Slf7QjGgapIAy3NtzbSk92X+H3riLdUSRVUedGxUmGQHJz0xjn8q29W+z3mmW3kQuhgcLN+7KhQRwF7kDuTQFyfXPC+q6IomvIFuLNvuX1q2+Nh6kjp+Nc69qHG+M7h6j+orb8P69ruhSqun3am2klMZtpvmibAyQVPAz7VvzxeGdcm2XMb+GNZYZ5BNvKT3/2c0gPO2V0PPSlV66fW/DOq6KA99a7rZvuXdv88TD1yP64rn5LQEb0OR6jpTEEczL3rSsdXntJA0UjKaxSrx9aVZKBnoln4mtrwIl6myUdJo+D+NdVaa3cQQjeVv7T1/iA+n+FeMRzsv8AFWrYaxcWbBoZmHt2oA9ljWx1T99YT4fvE/X3xUr2ccMry29uqSJ82G4AJ4LD1IFef2PiK1vCPPP2a57Sp0P1rrbHXbmOHy75Fubfp5qckD+lIBL2J3eKRNuUlEi44JB4I/LmnXMIkBdDnB2sRx8w/wAa0/sVtdxCSwm3gc+Xu5H0qmwNuHRy26QjIPHA7/WgRQu4vmARj8uAMdiOv60s9l9s8qaWRFMi5KDqQPvED19aljhc3s0exmEjB1O3A5HzdewNWGeOzkjDR7xHIoEhx95s5IHYUDOd1h5Es5DbCWOVFR4kDcAKclcepHX1qLULdLiDz9rCKUAyBDgoSMqwrSu7S4a6kYOphJ3YPG3twe4otoJHeUy7WiddhAxzjpge1Ajkke9sCFuka4tmI8u5VclSDwfw7itNtPlnl+3W42pLCUJ7AngN9KsXANnHM+zfJbITER6E88d+tTWjukMTX0wRtwJReMK/AVh7/pTAzoLld0v2fcwhIXzD/Ee5/wAKVIE2b952nOVPIz/SnajKLScq8brbrJ/rIuD9GFPjKOSyFTDKcqR69qQDZRujTD5I6A+tWQyzrGyEmR/mKOOAeMkexHWouJ2lXy8GIhSPVCOD9asxqQPLZlXy1yqd8emfp2oAaJktncBGx0U+vTk+1Vrm1ttQj52wzc4PY1cuII2UFDlHGCPTvVUAIG3rwAT9eKYzkdV8PPA5LJt9GXpW9pPi+4g8pL26MM6LsMso8yCfHTcByjY43D8RWjZMZdijdLEw+4RkHPp6Gob/AMNRXKGWAfZ5CSNr/dJ9qALeivKfHV1Pc2nk2GqW+3fG4eKR1UZKsOORk84NYMet+Kn1GbShG+ofZg4eGZdxkjUn5hnn7vp2qmj6v4auMQO8IzkxPzE3vjp+Irp9P8VaXqc0P26P7BqEf+ql/h3f7LdRn0PFIZyl5a6XrcuxN+m6gBgwTghSfQE9Kl1i1nhXTpp02yyWoil/34ztz+K4NW/GHh/U9QvhfQOLoFQvGA+O3sRV3VbaZvBOnvcri6siqv8ANng8Z/EGgDhrxACH9O9bfh5/tmjXViwyUZpFPsV5H5rmsu8jDRMD2pdCvfsN20iDIaF057E8A0xM1NGENzC1pNB55ibzEQNgsCMMB7jgj8q0J4JDbB3+0WphHl/6Sh2spJIyfSsHQ7o2muxI+3y5W8sk9s/dP4NiugvbRbaae4nuZfJfkQnkFSfmAyeob0+tBJl3Cv8AapGUqWGHwHzx1pqTxlpJnR5JfK5TsOcZ46cVauHRHMVvDKgiUsBnfuHXPIyB3qnIr+awZPLlDHKI2ASDzzzQMZF5Sr5McJaJ8uZTyw+nbjuO9Z+txHzopz/y0QA46bhwfzGDWz5VxdjMjsItu5THjcvqMd8/lWZqCxvp22IysIjwZE2nHT/6xoAxhThTAaeKBjqUUgp1ABQTgZpaVV3Hb60gI/MzShhSSQshpqo5BI6DrTAlzTGkfkLxTc5HytTo2AbB5oEV2JJ5pmcVPKAScDioiMUATLJu6da09PlfypYg7DcMj+tZETBZBmtGEmCUN27UCBmkgOyQboz6/wBDUbBMeZG/4dxWg0YcExbXU8mI9v8AdrPliVcsh+oPUUANZ9wyw59aiVyW4poyxqzbwgzIX6A5NAzZsLdWs4iP9Yd3B7ioHOCyA89T8o4pTLszJCVGeFQ+lV0nV5DvTbJnJoARopSMSbSfUdxVKS1blunNbDg439ZDiq8sDMu9HUAcMj9D9PegDEZSpwaMVpSWgzgleRxzzVKSBk5HSgCMClxTMml5oAfxTlYA1FinYoEW0uzEd0bspIwcVWwZJCT35pMVJEQDzQBcXTHfTvtKHIGc/hWdyav/AGx47aSBZP3bdRVOGIzSog43HFAxoFPAxUl3ayWjgP36VXyTQBZS48vpSzXL3MiluwxVYA09Rg5oA3bCzS6sv3m3GePWsiWHazBeQpqzbaj9nOOo7iprEia6kK8BgevpQBlEUVev7ZY5wqd/SqrROpwwYH3oGFvczWkwmgkaORehRsUktzNLdG5Z/wB6TkkDHP4UwilUGgRq297a3i+VfIqSYwJh0z/tDt9aluNOudHt4dSttrQuShyA8bH0I5BB96xiMUokbbsy23rjdxQB0cE+g60Al2X0y4PAOS0B+hOSn45HvUk3gzULa4eSwK3CRR+dvjfaSg6lSOv4Vy2RVyx1W/0w7rK9mgyCMRuQMHqMdOaALJtzqhcwPvm6sHJ3n885rNaKezmDgsjochw2CCO4I5BpA7iXzFdgwOQRwc1prq6TxPHfwefkcSI+1s+/BB/SgDW/t/VfEeh/2Zdzw3Plt5ilov3/ALncMbs9zg+9ZWmatqfhm+MlsWCNjzIpFOyQD1HH4EcispGeKUSROyOpypDYIPsRXS2viWC7t/sut2yzcYW6jT94P94DAb6jn60AdHD4k0rXtFurGK1u7a9kPm4BDordyGGDg98iuIu7XULZyymbYD15qbW7TTLQ2txpGp/aPMXLDlXib06D8Kl03xVc2qeTexpdWzDDA8Nj2Pt70Adt8O/GEtzMNL1F3Myr+4mAy20dVb1x2r0aKSR5S3nL5XQZGCx9eea8x0aLQBe2OpW4u7e6gfMqSwlAyEcEHJBIz1B5r0lGafKJvYEFt7+/TBNAEzs1xD5cm0qRhiFxwa8l8Xpqmh6nGzus1uW3KSv3sHoa9aQP5PlJuZVIHC/nzWZ4i0RNf0WS3lCiXGY39GHT/CgCXRLi2v8AS7a7ttvlSxhgPT1H4GtAToJjCo+YDOewrzL4e6vNpupXPh2+3K+5mhB7MPvL+PWvTCQqlm6d6AIfLAld5LmXJHB2fKP5ihIHg5ZEkDHl0iAGPemLbqylxe7ix5+UYx6bf/r05tPtViAkuXVe+GIH6mgBXa0uV8icMo9silhhtULpZvk9CN2cf4U5iY4cWcm9jjBHYUkMkhl3vBKD08x1A49SaAHNbzhcxvvC9ERQGzVeeSaIRpMGeRv+WaPgn3JoLPfS4hLJCpOJEfBP4VZttMjhkLjc0h6u7ZJoArQwT3U/mXKKqAYWMc4HvWpHAowFGKkWNVGaqalrFppUBkuJFGP4N3NAF4lIELMVAHUnoK5HxB44tdOjZbd1MnTef6CuK8T/ABCmvWMFpuCdAB/nmuUXT7q9P2jUJ/Ji64Y/Nj2Hb8aALWo+IdR1298mBnd3Jx61cs9AtdPX7Vqc6iQc46n8B/jWdbXMkV0ItEtlJH3ndNxI9yeg/KtFLF5iDL/pF85/1afMq/QDrQMzLu0+0zm5d3S1J+Xf94/QVtaLplzcASQIlnYLkNLIufMz1AHVjV2DSrTTz5+p7biYciHdlV/3vX6CqmqeIJLlgkRUjoAPugegAoAvzT6fpcJSwj25+9KeXY/Xt9Kw5r2a6YiLgHqTVbY7/PO7c9jV6zs7i6/1UeE9TxQIqLpYuH2vO+8+mKRvBl5JN+7miMZ/jfIP5c1sLpUhnEbI27P+cV06aRJNbrG11LGwGMoBz9c0AcJP4I1C3gMkXlT7RkhGO78AawBA6OQ4xjrmvXdPgvNPu/IuZvPiPRz1H/1jWF408O4H222ThvvAetAHBbkH3fzqzGhlXjqOtNitAhzMce3+NTNJ/AFwB2FAwCqg+Xlv0q7o9/cadqUd3FuYofmHqvcVU2hPmcsPQDrTTK0nygYHoKQHsyx2ur2kV0qq4ZeD3x/9aoE0KPzw7FjjpmuL8LeJDon+j3e42znI9VP+BrtD4s0oRb/Pl+gUfzxTEaMkrWFuSsfmAdPaorTztQt/PkfaWzhOwx602y1XTtUiDW8yehR2wfoadNd2GnRbnuYo1H8CNk0AMttMRJHa5VZ5T0AbIX+lZupavpejbiAjz+ifdH+Nc74h8elg9vZfJH6jqfrXCy3F1qMpLljmgDc1zxfeai5QSMR6DpWHFaz3j75DxWlpuivO3CbtvLE8KvuSa0Zr6x0gbLYLd3f9/HyKf9kd/qaBjLXSY7WD7TdutvDjIL/fb/dX+pqKfVpbj/RNKhaOJuCert7k/wBKkttI1HXJTd30jJD1LyHAFaUd1Z6d/o+jw+dc9DcOvT6elICnbeH7ewQXOrSYY8iIffP+FX7b7frbfZNPh+zWi/exwMerGoFaPLvcHzrpuAX3DGevUc+2BXWNGNK8PpHbj944BJHUkjNIZlxeHdMssCe7Z5O/lr/U5/lSnSNJaXzI3dyOP3oBAJ9apWlrcC+827Eyr1xIuBtHU4qoJP8ATSLKZ5YyQCDk4yeAc9DQBpt4e24SG+Qx55TfgfTBwa3YLC58oIJHSMDH+uAH5AZP51lPq9rpMK4jW5uVOJGRcgE9ue9RN4quJZMR2TmR+Blhz+FAHQSX8Ok6cVd90i993X+dc3c3c2oWpMnlKAQRvXKKDz82R1PoOaztRmkEhkvZ90xORDG2QP8AeI/kKz5rm5vWSM7m7LGi8fkKLAWZtRSAkW/zyng3DDn/AICO1V7e1nvS0hZVQH95NIcKPx7n2FLJDbaaN9+++Xtbxt/6Ef6Csu/1We8/1hWKBfuxpwo+gpiNGXU7bT8pp6edcdPtDr0/3R2/nWDd3hMhkuJDLKeeTmqUt6SCkXA9e9Uy3OWOTTETT3LzHk4HpUJNIfm6VLHATyaAI1VmqxHAByakASMc1FJN2/SgCVnVBharSTEn1phZpDgVZgsmb5m4FAFZI3lPHNX47RI13SHJ9KsKioNsY/GpY7ZnPrQBAN2PlHFFaIt0UYZ+aKAIb+2R7iJIz5hSFVdx0LDPT6CqcjRpbhldTg16leeH4JopBbMsEjjGdmetcZd+Ar9CfKeKQdsNg/kaVyjn4GtnyzRr5nYkkD8qvLJHBDIZJFyFJGDyT2/WpF8G6uGx9jf67hj+dalt4Cu5GHnSJGMc5OT+QoEc/DewsyStG3mL3UlT+YpHM13el4LYncAuApY8fSvRtP8ABml2YBkjNw3rJ0/75Fb0NtDbpthjSMDsigfyouFjypNE1qeIJHYXAXt8mP54qOTwrrkYybOXH1H+Neu4FHy96VwsePpoGsRnd9iuOO4TP8qmRruy5kgkTnJJQjn15HWvV/LjzkBaZJArrhuR780XCx5a8UF8xkina1mPX/nmx9cdjVeTSb5MYuYmz/cI/wABXoV54fsZssbZA57p8p/SuevvC8rZ+zXW3/YkH9RTuKzOYewMZ33U44/2smrUMS3EEoCAbBnH+zVHU9E1O1fM0bsM/fTkfpW1p4+ywu023fhc59SMEUXHY566tC4G0MzKMHHcdjVW3U213G0iYGe9a3mKZSqvjaSAR7Gk1GL7VBHtRUlHX0k98+vtTEQ+eluGeWDzjnpuwAPXinJ9nu4y1sWjlUZ8s9PwqmLa6jUq4dR6HODTbS2uDdDykY46+mKBkwfzmKMvzn+YqoeBvPrV8JvvZJf+Wackjpx/jVOeJiRH36/nQIjeRWjIHJIxVrT9Iluf3j7li9e5+lauleHjxNdDjqI/8f8ACt7yQBgDilcdjIFqsahEGAKjaLFazxAcms2RnuZvItU3Me4pDKMzEt5cYy59KsHSpEtTIxzP1CfTt9avCC20mLc/724I6/4egrPF9L9qErn2wPT2p2JuUGmJUnYmfXbmq5niY5eFSPbj/Gr9/CIbnev+rk+Zf61lzJtf2PNMCcNZH+CUH8KktprRJ5cOyB49oJXPfnOKoZoxQBovprlt8UiNnkFHFNeyvZsCR2YDpl84qgOORThvI6cUAXFsEiG64mRR6A5J/AVIt1amMQzRs6pwrhsNjPGe1ZpHqy/zpQQP7xoA0C2mjkJMfbgf1oN/FEMW8CL7v8x/oKogZ+6mf1p6wzHoMfpQAksktwxd9xNN2+pUVOto7nk5/WrEelSHkxtj1PA/WgDPO0d2P04pRz92PP1ya1PslrCMyXMK+wO4/pmkNzp8fCCaX8kH9TQBnATMdqjH04qaOxllOBuJ9smrg1KBTuSx5HTL5+mRiqz3t9N8rTOM9k+UfpQBKNKZBmX5B/00IX+dPQ2dt96dT7RDcfz4FVUs3lPO5mPpyasjTtgzKwQf9NHC0CHvrTrG0VnCsIdSrSP8zkHqPQUaZ5kE6yZ2hRjHqMVH5ljD1n3H0iQn9TgVJHqtnB88dtK7Dp5jDH5CgDf8QStNoWn3ThllglaLPrGwyPyIrQ8I+KJbYiKY+ZGvyyIed0R4INcNeajealJmVmKg5WMfdFWtNWWKcOOuCMDnOaAO/uNTj0vVJdPlfMaEGFz/ABRkZU57+n4VdL29/Dt357ghsEEdCPeuP8SSmTTdNnkO24iBiYdyh5H5Y/Wsm01S4tiCkjYHakUep6XrtzpMohufmiJ4fop/wPt+VdDL9h1kh43VZMc4xuz9DgH9DXltp4mWWPy7lNwI5yuRWpasqt51hesucHypWJUf7p6r/L2oA6m70i5gJKDzEHPy9fxHUVf0C90vS2luZ5JhMwKCPyycDqTkccmsS08U3FqUTUI8Dsx5H4OOPzxW5HLpmqjIKrI3fgN+fQ/jn60xGRqNz/aF9JcsD8x+XPUAdB7VesPEF/Yp5ZdbmDp5U2Tx7N1/PNJcaJcRndF+9T0HDfl3/Ams/ayuVZcEdQeop6BqdBL4nt/J/wBGsZY5wCBvcGMZ9s8/gBUNx4sufMspoEVDGcz24XiXscNjoR09D1rDbrS9QKLILm/aeKZTdXysWIuAGtSwxtccFMHuRyPcVc0fXbaa0SHU711vBIwEkiYA5+XDAY5HXPHauRZVf5WGQeoNW9Pmtba63Xdq9xEFwAhAwc989eKVgud4C4kQmP8AeqTu2HJJHfnrnOf5VS1Gyg1OEtIF84f6mXgADtuHJGef6Uyz1Wxvo91ukqNEuwxy8NtPQq2ecH36VeY4kDB2PlqcA4VunY/T/wCtUlHBN59hdh45GWSKTcBu43jjJ/kfUVvtHa+JIZp4oFt7gYEqNlgxIJ7djjr+dM8Q23mIl1uy7/KcY4wARkDvjgmq3h69MV3DaMsSB5WdZjndu2fcI9Dj9aroLYsXWiSyLbRw/vZtpRiWHzFRkHPfK4+uKxvsdx9r+ziNvM+YbT1yOcfXGa6n7bHcaisEEiFnUSRENw2M8L05HOR3yRU7uJrmN2jiUFsYdfmDdirDn19x6UrhY4WYfMI2DbgQ4A4JCkEgH1xV68sprYRvGrTwTbfJlReJATgfQjuK7CWzt2uInktopbxGzvUlVz/tHucdRjmniN4oQC6xwZOY4YwAGzx1BPI9qOYLHHW9i01ncmNGFxbyEzRlTu2NyGx7HIPtiqfDDJGQCQD7jrg+3tXfCN0md1uX8wcZ+VjjG4dskY/WqdxpImgmtnS3Ys5kUxgq24j7y4yOcc8Y4ouFjnLfU7m3wHbzoyP4z82P97v+INSy2ulawNhVY5vQrg59h3+qmrOq6aVtbZ443WRYRGQcYk2jAYEcZ9c4rP1LT3sbgW7DzAwUqQPvZx0/HimIrT6ZqOnZaNvtNuOxOT9N3X865zUNC0fWWIaNrO9PoNuT9OjfhXXpd3umzPBL8xQ7WjdskewYf1yKmePS9YXbIixzHsVAJP06H8OfagDx6/8ADGr6JOLq2ZyEOVmtyQR+XIra0/4jzywCw8U2Cara42+afknjB4yG7n8q7a50jUNPBa3fz4R1Rsnj69R+Oa56+0jR9WOy5gayuj0O0DJ/kfw5pDuQX/hjTvFGlK/g/VFuZoyCbG5IjmChcYHTJH6+tcPHbnRZrm31aC8tbxcCJMbSDnkknt9M1q6h4R1XSJRc2js4Q5WWFiGH4jkVft/H0l1brpvi3TItXtF4Ekg23EfurcZoA39OvJ9X06e2bXrO5trZQSZFLtk/wHOMqehI6dua4fVNL/s64kubGdHtupjD7jGCOOR95T0DDr0ODXWz+HLPX7EyeCNQtHwPnsbhRHcD2BPB/wA8151PZahpmpmyvYJraYny2jkBBwTj8RmgDufC/i+KztbS28teH2zDfgFR0HsK0/EHw9bW4x4g0xo4PtbNI9sW6HPQH1IryyO3vISJvJl272TIUkFl6j6ivUfBHj6G2sRYX8mxw2YrgrnGezDuPftQFyraW8ukQ3N1pc80b2ig3dldHJ2jqQeNyntkcV1GmG016K217QUit7+KaNbqLdtzGeGVuxGOQcVn6ot5Z3/2pQrykkjLApJGR80Z/wBkjofUetaXhVtJtGur22E1u17EqGFxjbgknC47Zzn8qQzF8c+FYNIv21qyKrbzFku4wMBdwxvGPXPT1rmtA1yCz36VqcK3NjJ8i72yI8/0P6V3HjTXLO70GayiKEhGaQo+4HAJyAe/A7968u017TUnWC6dYGJAS4H8Lf7Q7j9RQBY1zwfNYt5+msby1Yu2+McRLn5UYnGWHfFUidUuAmnSQO0jKRgJ+8IHY9zgdq0fEmNP1CMyQ3CxsXWQZx+8B5Kk9R3punyXVxDZ6lIkvlR6lFGt6+QYxkHr/OmIxVW5iEWzMcseCkgOM4ORj3FWVubpXkubiSWFp2Cvj5RIcZzzxxW74k8N3mo+PriytESJbuUm33ttRm25IHoxIP41pr4SjutE0+xae4LSXiJdggF7WXDBl+jcYJ45FO4rHGxwXLyyI8LyRSNu4OCD6g0zW7p57uJGdnMUQTli2PbJ61NYx2UtzEJjcQQ5w8ZkJIAPOD29+K3r3wjpp16VNNvlfTMh1y+ZACOnPUA8bqAsY+h+MdY0D93a3O+1P3rWb54iPTaen4V0C3vhHxI26RH8O6i3WSJd9sx916rmrlv4R8MXAdbq5urcqAA8bj73qQc5/CnXfw7RPE1mtku7Tw2bl85QKuCSD7qeR60ijC1nwjqelQ/aJ7dbizblb20PmREepxytczLacblKkeo5FbcHiDVPCeqzR6VezJCHb91ID5bDJ4KHgjHcVsrrPhXxLk6lanQ9RbrdWi7oJD6snagVjgzvTgrTlkxXVav4R1DToPtOyK7sG5W9tD5kRH+0Byv41zMtqV+Zeh6Ecg/jTESR3BWtrTdeurBh5Ujbf7h6VzR3KcGnrKRQM9R0vxBbXRBST7Jc+o+6fwrqYta86JI9ThWRe0yc/r/jXh0VwVP3q6HS/E1zZ4Vzvj9DQB7CyC7jdknWSFl2/J95f6896yrxFlBUhv8AbTGDx0Iz3FYOnavb3TCS0n+zTem7j8q6GLWo5AI9Uh2E8CZOhoEKYvMSQYx/EvvnqP61SmtradniIceWwB2HBzjhga3PszNbeZbFbhP76HkfUVG+mlZjcCRQHQCQIvIxznH6UgKEltC8UMaw7yDsaTGPmHdvUE9awNVsdlpfqd4uGKu2Tu5B5x9M10eoyywwmaJmXYUIj/6Z5+b9Dk1TuoMnzAM7iTz6/wD1xQMxTI1xbRzlcloh5vGQyjg/Ug8/Q02Oyt7NnAD4cYkRH+UH1APT1FaSwfY4bdIh8ijdg9we1PuLJ55I5o02xOB857DHf6UCKqQQb1kjDtI8eeejbecH3HpULIZBu5EgNW57j7DJE0RXyS6JjbyFYHk+5OKr3P2hY8wSYI5TPqDyD9DQMjnlCmQD7wVtv1xkfpTImSWOOXPykjPGevWo7S6SWfy7iNoZuhQ8Z7gj6Vcs7GWGUxN8kKOztv8A+eeM/wA+KBCW0cdhDJHbO8zfNHGRxt4JP41VhnmnZ7Zi/knDAHna2MGtASRygrEiqJcMrjqMDAHvxVaSHaT5jrmTrjj8fagAkfzLcwXMCyQjgh+o+hrmNZ0NrDEifvLST7rnqCezV09w7/Z/NjPmbCN4HfBwf0NaQtom03FzueGb5RHjPBzkn6djTGcBpuo6lp8qxWcvnRscfZ5W+Un/AGT2Nak2uQX4mhuJ5bQG3kSayli534+Vg3cg+tZ+paedOuzA/wA0bDdFJ/eU9DWiFi1qw+0XEbPdWgAmxw0sZ4DZ/vDv/wDXosFzlsiWBG67hWfAdk5Xp2H9K6S60GWxKJbO11byKZIZEHJXrjHqO4rm7sCKc/7XNA2TX0boySoGBbkH9RXeJeRXccc+xWjcq481MgOVGSPcGuRnmvLrQbdU+a3gGzhRkEfr0Na2iajeJoSLGqv5bFJFcbgy9QCPUZ4I5xQSWojfx2t2ZjvmllFvkJlo93Iwf7p7VUe2kWO1Ri4llWSRi/J3AkH/ANBz+Nadpc21zPFOI3DwHd5QPzDHpn7yg84PIqpbQSL5KP8AM0UrsTuycn5l2/7JAxQIoTCNI7d97AxYPmAfdydwyP7p9fWob1HaaW3hfMTtu8twOAf7renPSrzJaGITeewiBBRCmTsbJ2Htjriq9yzzzyvbptcLtIfBUp0UH3xjB70wOTdSkhVuoOKUGrWpWz211tfbuZQxw2cEjkH3qoKRQ4U+mCnigBafH9/NMpc4pAXQQ3XmoZmTyzGqY3daYshFPyrEE9qBssWtpFHB+9HMnSqU8KRT7Ubjt7Gr8l/GsJ3bd4Hy1kxzCQkSHrzmmSTSoGG/oe//ANeqzIRVlnwNp6/zpBGTGSOaBFTGDVmO5IXY4yKhZeeKZyaAL6vkZQ5H6inyTCVcP8x7P3/GqCkqcg1Y4Pzk5oAlEITY7D5WGRUkhKR72DEdBjoKFugYEikjVgpznuAaszPakl4JtpI6HkH8KAIPKae3VwOTnp7VAWK/u5Uz/MVajvfKhZEjxu6/Nxz6VL5G+AGTn+lAytFM8RDq3mKOM9wPcVYEqPGSpy45xVRoWQ74jkD06j6imBwTu+4/qOh+tAFxohImyXgjnI659qjktwwJL9PWiO4Csok4/UGrGUPzr1NAGXNbr1Tbg9x0qqwK9a2RBEpclWCHpzxmq0kUbAqAw28EHrQBm7zS7jUjwMCdvIFR9KAF5oxSbqXJoEOAqzFhQGHUGqmTTgW9aALuo3sl4Yy+0lRjiq8FrLMCyDOMfrUajJ5rT068FjKSyK6MOR/KgZmMWVsEcikyTUty3nXUjKMbjmmywyQnDDFADQuKsQ3BhOVqtkmlwMZoA0JbsXLKdmCByfWrV8gFpG/U1kq4Q5FTteyPD5R2lT+dAAsQZC3eolIzirEMqKMMe1VjgMcUASyRYjDjvTIojI23OKsb0a22k80yE4cGgLjHh2EqeoNN25FWbhUaUuO9TWap5oUqvNAXM/ZQE5qWcMkzp6Gp4bYuoYkLn1agZWCUjRGtWHT0c4a4QD6Gt+0sdHSHa6+c+OSQaAOJYALzVcnJrb1HR5UnfyIZTHng7DjFUDplyOTBL+RoEeneDtEg1nwpE1xIz7lZBz/qyDjj6V0fhG9l+z3OiX7sbuxOzcerRH7rf0rgvC/jR/DWlrYyaU0qCQkyeZt6n0INdLq2vaXFq9lrdhexGZP3d1CDzJGeo6ckdvegDvHSeMRpGGxyW2dSfTmiFlyQ0zkpwyfwgn0rjLr4o6NCPLgN5PxjfGm0j2OetWvDfjKz8QXT6daWVzDJjzPMlKkMAec4PBoAw/iLostndweIdP8AlmhZTIR6j7rfj0NdpoOrQ63o1vfRdJF+Yf3WH3h+Bq5qFpDqFnNayBWRlKMPr/hXmvg+8k8NeKrrw/dnEM7/ALnPTeOn5j9aAPQpoWWcNFaISTzJuxgd+KlmtDNdb5Y2eMYCgLwPc9qsgjG5jgVTkeS7ldLYqYuhJyM/kaAGCVoCYxO8j8nCRDC+nSpUsry9jC3U/wC6P/LNBjP171esbBLeIKBz3NXtoUcUAV4bVYUCIoVRUzGOJSxZVA6k1R1PV7LSoS9zMoI/gzzXkvin4iXN80lvatsiHHFAHaeJvH1ppamK3O6T1H9K8o1DVtR8SXjeXvfjPlg84qhHaXF9m5upGjh6l36n6DvVq3uSsgttLh25PzSYyT7n2oGRRQxaY/mTPvueyI3Cn61NDBc6iwluXaO1B5Pr9M9avLo4tbsJKn2y7flY48lee/v+PFb0WmQWuJtWKzzDlbZD8i/7x7/SgCjYWMt1Fss0W108Hm4kHLfTuxq9LdWmkQsloCHIw0p5dv8AAVS1LW3l+SPkjhUH3R9AKyfLeQ+Zcv8AhQIdc3E+oNhWZY/50W0UccoiTc0h6kc1ZtbKe9OEHlw+vr9K6Oy0iG1iyAq+pPJNAFGDRY5GErhmx1B6flXQ2lsgIQbR6AVQe9JPlQhWb/YqxZaZdvOs7yMrg5AH8qANWayzGJUGJEqrY218dRN1LJtiAxjdwx7YrfiVyoDriqep3I09A4GSeF9B7UAWdqMoR3V93b0/KnzwQXMMlm5ySvTvz0NZWk3NxIWlmIYv92PZ8wwepPvWrHZp9r+1SSN5wGMBuMH270AeTa7o8mnalJEwwucg9sVmM6qRt5Pr3r1fxZpSahpxnQfvIx27ivLGhSKQhjkg9KBjVVnQ7unr6GnReWjYHf8Aj9KSXcSMdD0qSOEJlpO3YdfxpDGGJ5JCPzJpksOxco7c+h4qSSVn4HAHYU9ECL+86HkDvQBVtftMDFreRh6+lLc3V5KdrSZ+lW/NyCoTCHsKtWWlSXIMh2xwr96WThR/ifYUCMa2095mBfPP45rpbXSreyCG7+Vj0t0++/1/u/zqzpd1Yx6iLW1jaXCs0tweDgDov90E+nNRaBF9t8Rlm3ECTjPtQBX8SzSxXS6dbOqRDrGi4Ge/ufqa0tO0C00uGKe6T7RdSDcsW7jHqxrK1QrJ4pZn+6JP61v6jcWeoPGJ45lEYAWSLDrgdMqaAKeo/wBoajiNg8ajgQqnA9OnrU8UlhoFsqywpPdN1D/MF9sevrUqtNLAYbW9tpiRheTBIP8AgJ4P51lNZ/Y58XcDG4ALEyncMeyjr+dIZ0FxYjUbeK5t4FLcHbHwR7j2rVld10yFJ0ZXUDntx2zVHT5bex0kXksiiJuYhzwvpzWNfa+2qt5EKyiLkF//AK5oA1NdvXn09I7Vco4/eS7eOOwPeuctT9gJmk3JmMhcsPmyRx3qxIZJcRW8OJGAyB1GBjscAe5xVa4ltrQYkK3FwOAo/wBXH/iaAHIgmd5TN9ntf7+45Yew6n88VHLqKQKYrBPLBGDIfvt9T2FUlFzqE+xQzMegHQf0AqxI9lpQ+fbdXX9wcop9z/Ef0phcbBZSTKZ53WG3/wCesnf6Dqahm1dbYNFpg254a4f75/wH0rP1DU5bpvMuZuOyDp+ArHmu3k+VflWgRanu1ViSfMkP5VQkleU5c0zcKaNzHimITPpTljZzUscHdqmLpGMDk0ANSFUGWoeYKMLx71DJOTUPzOcmgB7yljxTobd5W4FWrayHDScCr7QmPCKMD270AVo7eODBxub9KsKjyGp4bMv1q4qJF8qDJoAgjtQq7n4FScn5UXaKnWJmOX5qdYgOtICotucUVO95BE2xmXIooA9Cgu4bhQ8UisPZqnBrzCMXNs2+3mKn2OK07fxJqkA2uBIP9oc/mKVhne8f3aUGuMHi+YfftFz9T/hQfF10/ENkuffJ/wAKLMZ2m4CqN3q1lZ8TTop/u9W/Ic1yUl/rN8CHlW3jPUJx/Ln9apGKxgOZpmmfv83+H+NFhXN+58YwKcW8Dye5O3/GqTeK9Qc5S0QD/dY1nf2vBCNsNvj8h/Kom11+0a/99GnYLmoPFWpr96CL/vhh/Wpl8Xzjh7VPzIrD/ttyeY1/76NOXWImGJIePzosFzpbfxZbSHE0LpnuPmFaMN7YXuDDMhPoeD+Rriw9hP8A3VJ/CkawON8M2fTP+IpWC53MlojD5dtY2p+HYL6ExspTnIMbY5+nQ1jW2sajpp2s7NH6N8w/xrobDxLZ3nyXA8hj6/d/OgZwd94bv9MJdE+0W/qnUfUdar2spkXYeU7g160bZJBvQqwPQg1iah4bt5pTcRxqkzDBI4DY9R60JiaODs7w2960TyFIycAnOMeuM4qWeR5e7SKSRy5xx7CrepWjabCwmiXzekeVzk9vyrIttPv9Xutoyx79lA/CqFqOG+SVYV2s+fljReAfwrodK0EWx+03Kq855A7L/wDXrV0rQ4NLh2oN0p+9IV/l6Cr5SpbGkUjGKhkCoCzcAd6t3EiQR734H8zVGGyuNXfzHLRWinr3P0/x6UhlDyptVmMVv8sI+9Ien+fap55bfSYjBbLul/ic+vv/AIVYvb+G0h+yWA2qvBcf49z71yl7erGSq/NJ39qtIlsbeXPzGSRtzt+dZ32hjKHPT0pjszncxyT3phpiN2IC7szbn76jdGay5Yy0fPBXrTrO5MfOcFOfwpL27Ek7mJNqNg0hlLjsKVVcn5R/47VyGNGCnC896szNa2spjkMruvBCKFH5mgDOEMzddwqRbMk8nP0qwdQReIrNPrIxb9BgUn229fhW2D/pmgX/AOvQA6PS3Iz5b49TwP1p/wBlt4f9ZPbp7btx/TNVjHPKfnkZj7kmpU0yUjcQwHrjA/M0CJDPYRjgyyH/AGF2j9aYdQjH+ptE+shLfpxR9ntov9Zcwg+mdx/IZpftNhH085/9xAo/XmgBDe3zjCnyx/0zULUP2eaY5eRmJ9WJqY6pGv8AqrJPrIS36cUw6hfSDCHyh/0zQL+vWgCZNLfGSjAer/KP1pfItYf9ZdQr7A7z+lUWhuJnzI7Mf9ts1PHpcjDcQ2PXoPzNAEzXdhH93zpj/uhR+uTRFeWLt+8SaMdfvBhTfs9rD/rJ4QR2Dbz+lKklgx2+e6+5jwP0NAEMt9c3LFYC0MPZEbHHuR1pkVhJK3G5j7Lk1pR2Sth1mRoepdD2FZt1qUkpKQEx246AcE+5oGWxo5QbpWVB6yOBTT/ZsH3p/MPpEmf1NZJJPJOfrSUAajanbLxFZZ95HP8AIYqN9Yu2G2MpAPSJMfr1rPzRQBI8ryNvd2ZvUtk/rT1mIqCigC+k9Xba+eEgo7CsUMakWUigZ21h4ldR5c3zKeCDyK2LWW1l+e0mNs/XYOUJ/wB3/DFecpOKuQXrxEFHwRQB6raeIL/TwFuk3xf30y6/j3H45roINV0zVkUvsJPAJbP5MOR+f4V5PY+JJYsLIcj1FbcF9YXh3q7QTHrJFxn6jofxFILHdXWiHHmWrqw7IxA/Jun54rKlikifZLGUf0fiqlnq2oWIBV/tEPrH1/Ff8D+Fb1p4gsNRi8u4CAdwRkA+4PIP5U7iMkdaUjKVtTaNDKvmWk2B12H5l/PqP1rMntZrY/vY2APQ9VP0I4p3FYdYajdadIPJmXysljFNyjZ6gd1z7V19tex3UWxH3g9QxB4GMYPTcOn4VxPRKEd4zvjdkYdCpwaTQ0zvGijuoZo5QzZAQ4+8B3z7jk5rk7/RZ4P3kW5oScrInYjvxyCK0tH1hbjEVwgS6j5V0PEoPUbT19wDkdRWskRWyMIkSOQlgUJ29eMjOOcdR360thnIW9r5YEsglNu5BLjOYXHUD8eeK6yJ2W2HmyeexwRMUwGU/wB4d89j+uasW8KoAWmUMTgx8OWA9hnOffpTbiQzRPLGu3YCETIBKg8kduueDSbGNhBWFQgzDcAkRluY3HXDd+OlWkJZ967cgDy2zxIMHg++Dj8Ky/tkkkP2e5tmxGSxEYClcHquODjPNXELMJMhH8zDK6DiRDjkj8PwP1pAP3KsQlG5cdMrkxnkhTjsQeakA2Mq/N159FPQg/7ODxWVqWtW1iTCN01wDysRGAOcBieOM8d8Vnx+KCud1ixPABEwJxjGDkDI9KdgOlPl4Bfe4BLEAf6tvoO3bBqnPp6yeXtRUeE7whOVyuC2Mepx0qla+ILGYrE7PBI5AYXC7RIOpywOM9sntWxxj5AxPJUjjJPAPX+70oA5/VtNlvdRWSFNss7fNGT0Y9SD3U+vasGdGiMiOmSuePcV38lujx48noR5XzYYAcnn+HgVnf2fAuqi6n5DK21yON5GAzdu/X1ppiaOat7+6tiNknmxrxsfJH0B6j+XtVlzpmqjy7iNYZW/v4GT9eh/Q1mSxyW9yygYZWOQeh55zU8kKSWyXEIYxsdrIeTGw6qf5g9xTER3Gh6jp+XspvMiH/LN8kAfzH6iuev9L0jVjsvIGsro8B9vyk/Xof0NdHBf3VoMRTHaP4H5H4dx+FWTcWGpjyr62WORuDIMYJPqeh/HFAHlGpeENU0eX7TaMzonKyxEgj8RyKuQ+Obi4iitPE1oNVhi4BlAWVcHIKuOcj9a9Al0C/03MmmT+ZD18o5ZcfTqPw4rn9QsNJ1QldQtTY3R480coT9en54NIdySDSbPW4Bd+Ebu3lkTzJGsLk7JlkIxuHZs9/WvNtf0u90nUR9qtWtJZh5nlnsx+8B7A9q6HUfB+paU4u7GRnROVlhJ3D8RyKu2/jyae3GneLNNi1i0HAeRQs8fuG70BYg8KeKJp5INM1a9t1sQdoNymQqnsGAzj2NbWt6TewM13pl3Fe2Azh4TuaH2wOo9/wA6gsfA/hjxBOZ9D1eaS3KsZLBtq3EZx8u3PDAHt39a1bLVdJ8Bww6RGLuW7mkHnubcodp43c8fL7UhnFi8jv7y30y4PyzN+9nPDSIBlY/ZSR+NUf7Os4LyG/sJPMhSfL2snUKrdyeOcdD2Nd34p0LSNTtjcWE+Z4lymWxLuBzjAGAv1/OsxNCsJ9RNxC7x+bFgxFQWyV5B55NNMTOh1vR7DxL4j0zVXH2nRLgF49jfLGxUsVb0yQAB65BrkbXxO+q6/Y6Xb2kUWkSZtpLRBtjMTMCSf9odQ3YitXV9VsPCnghNK01LiSeeYSNLIh2qehJboGwMAAY71y+j21zot/aXGpwPbRyA4kfGQCODjrQB6DoNv4ke5ltI5rSYW7OlvdXGS5QEqCcdeBWveLf21nt1WyQ+emDfWhwV/uyMnUYPIweKq+HHubG+lvpb6FrFIVkAPHTIBB6EEEgjrnFdFa67b3ljFcRjdFIp8snoU3EYP0OR9KQzwnX7GHSdOt/tAMmq3EsrtKJPl2BgFOBwc8mp/Cniq30uaRr6FXAjYL8gOc8EHPtnFM+IGmnT/EBeAlraQb0HVYssflHtxkfWsy1Wy1GHy7xfJmP3Z416H/aUdQfbkUxHoSX2mahArwafb3FqWDA2znzl9mUnIz0OMjFXre6n0aHzYBvtJSxiiR23wnILKQRyCOM1yXhrwvbSLG00/mSln3CNmAjC+w5IIOQR2BrobLTtNvvMa233G8/KZZmWSEj+6w7HsCOaQyhq+taTqVxDcXWlubNyFmilbndnqrDleO1SSaf4SvARbWEa24UnesxRwcdCSx5+vWtiay1LRVMlxpqarp55ZwmyQD0ZTkH6/rWTqEukXNiZrS2yFPNrLa4CqTz8wHNAGLZrPokv2vw9r/lxH70VxxwezDkN+VSSar4c1ueSPUYF0y7JI+3WSZhlPq0Z6Z9RVKTQNM1GQtb6gtg/eGbLL+DdR+NWpYPC3hyz2TbNWvnHXJ2x/kcD9TTAoav4Wu7GD7Uuy6sW+7d2zb4j9ccofrXOSWzJyOlb0OvLosn2vQr2a3LnElpIu6Mj8eo9iM+9X4tS0PXwftkK6Tek/NNEhNvIT/eX+D6igVjjcsp5p6ykV0mseE77ToftDxrNaNyt1btviI/3h938a5yS3eM/dpiLUF28bBkdgR0Iro9O8VXMSCKfbNEez1x28jrUiSkUDPWdF1mMyb7G8aCQ/wDLJ+ldVa63BLIFvo/s9x0EqD5Tn+leDw3bIQVOCK6TTfFE8KiOf99F6P1oA9YubOQxF02SQtkh4+cZ9aqhPNV1bb8wHI7YrB0fXVb57C62v3hkPH0rft9UsL9vKula0uR0ccA0AZ8iObmZH4ChWX6HjH51ZVI4TClxNtMZww7Ybop/nWm1isRSVEVn24E27KnHTP41kagrvCUzl9ynJ/vDnP0NICldRiaeSHyc7TkHqCvYH0pRCZYZC3GWyvpzV+ZDIJApyUOD7qen5dKrPFJu2RScqSACMgn0P9KBFC8aJbfzZIPPm3BVQ/wt2x3Ga0JI3lhlW7dN/lA+X6MOTg+nrVn7PCJ42jV3kwX9V3Dv9QayLtvs0trKHZhl1lL+5yaAI0nikkMf+r2DCxlcEfjUsqq0LTMFO1Tke45z+Ipk1msrhCmQCQHHDKR1GfbqPY1atrePzI4Xmdg3yk7MfTPvmgA0qExzSTxJgmP7/Yjrk/TpTtUjk8q2mL7dy8bOgIPOKkKpZ6eYbd2dCxVn7jH8JqGII8BikPyMRtz2Y8frxQMzb7SvtuhmNdzTQgzQ+u0nlB/Sud0nUTpdy7mHzUZDHLGeCVPXHvxxXWvdPHexwpuRjEGQ9OR1XH4VFqWhrq1p9tt0SO8GQU6CXHb2NMRSjW3OnLbQXLBZJvNsrj/nm+OUb+62fwP41keIdEi1BEvkRY5vu3KJx5cnfI/unqDTdOnW3mls7sEW8p2yg9Y3HRvqO/tW25kjZ5JE8y4gXZcRj/lvEejD1I6/X60xHKaRdJpv2qwvY18mTkZXIDDjOeo4o0TXI9NvpoGVXtpRgpjK8dDj6fjWlqFnHb3EU8e2aE4eM9RJH/iO9R6r4TNxfyXGlvFEGiWZY3OAc8EL24P86QGo1lZaji50+dYZhyBnjPs39D+dQyQMs0YuIVWeJgwQ/Lk5zlT2z6dPSuS+03uk3ZjuEltpge68H+hro7LxJHPCIL5EePse34dx+FAyJLZz5lskO6N3wMMA8QJJGR3APII96qPBK2IjC9tJEjK0qfMjAdR+PYetbklksyiS0K3CdREzYcf7rDrWTNcWUIeGS0u42P3j53IP06H8RQBjaxCklv8AaY9x2yBCT3G3g/jg1iDrXU3Fsk+n3KwTLLGVDA9GVhyNw7ZGRnpXLd6AJKM0gpWUkZFAxc5pc1DkinhxjmgCTOKRnbblaiLZ6VLE6lCrfWgRVc7jzTBxUrioyMUAWFcFeOtWIN7ZRT1H51SRsMKuwny5B+YoEV3BBIP5GmY7CtWaFZV3gbvb+IfT1FZ0ibOhyKAGjA96cGCjimBSTxT1j9aAAM2cipVLnrTlT8KduWMen86AJIExIC/QVPd3McY4PLenas2S5bovFVWLMcs2aBml5pX5s/Rx/Wnwos4kJGTjPHWqEbrjB61dtZninRgfY0CGkPD8rrlD2NPjkKjMfzD0PUfSlnaWKVi3zKx6HoagOw/PGcH0NAFwSJN8+cEdqmMkZGcrhf8AazWcZC3LdR370xXLNjbQMuzKo5CcN/GOxqjLAxbGMHrW1aRI9kqk7XJP5VWYytjYeR2PQj0oAxCCDg0ZFX7yHaA+PY1WkhKoHXlDQBHmlyaaDS5oEAJp29jxmm5pc0DJ4x39DWhqN1HdQRBI9sg+8fWspXI6VIr8gtQBOtg8lkblf4TzVZYWKl+w61dS7eOCSFH/AHb9RSLKEhkQc7xigCj/AMCo/wCBVZiiUrzUOwbqAGjNOwameEfZ96nkVAA9ACg1IjY606KBpMgdaiLbSQV6UAS+ZUkc7RtuA6VXDrU8Pls3ztgUAEnmTuX28n0p6QXLdK07dLZukqfi2KvolrFy06D8aQ7GXbaZezEBXx/wKr8umXekSQyzvvjfuCce4rRt9S022O5pmY+ympdU8QWF/p5tUglyDlXOODTuwsie08SJaQiJXVgOnymtK28ZuWSNSo3HH+q4/WuIjhkk+4hP0Wr1vpt25G2Fh7ninYk9HvbSLxB4fkEsaGZQRkKM57V4/eWghleN3cNGcHdyMfz4r1nw7NNEVjnXiRdrdxmuQ8W2EcOst2D8nHY0uo1scxYi0lm23dyq8YBAOf8APvW7oWuW/hy8umR3ufNAXYcowwc5B5Fc/NA+djp8q8hBwSP9n1qWIhgn+lMY0+6hxuHt60AepeHvGk+tav8AZVsmjhRMvvb5lPYk8Zqj8SNCd4YtYtdwmtyCSnXAOQfwNTfDyxkitp9RldXW5+WPH8OOu7612k8SXVnJBNGrRsCD3BWgDL8K6yuv6FBdj/W42TJ6OOG/PrXRW1ukYwqqPpXBeDfD+p6Dr9+ny/2XJ8yEtyx7FR9ODXYahrVnpcJeaRcjtu/nQBqtIsSFmKqo6k9K43xJ47ttMgdbd1MnIBPU/QVxfiz4hfbgbaxZyO7Zwv4DvXFJaz3Z+03szJEecv1P0FAFjU9b1HX7w5d3LHgColgtdPG+crcXP9wfdU+/rUkTPcE2umQbV/ifv9SavWdhFBMI4o2vb5ugC5UH/Pc0DKy2lxf/AOkX0nkQdh3I9h/WtvS9Jluoc24WysB964f70n+73J/StGLS7ay/0nVpBdXXVbcN+7j/AN496oapr8lw3lp24UJwAPQCgDRlv7XSrX7PZbgAMGV2y7fj2HsK56a7nvJCE3BPWqwh58yYsc9quQ2882AIJREeN44IoEQIIIW2see561pppEhmhlc+ZExySB0+taGnaRkKtxtlIPBNdRb2iIo4oAq2lgqqCoqtJZXt7dNEx8uFep/wrZ0+4hmmlhTrGcY9vWrzRc7h1oAoWVjbWICIFVzxl/vMa04olGWXqetYV1pDzXplkkcqvIA68Vbsr+7mvSkcO+LGDnjHvzQBqpC6yl2kyp6JiquqTRhUjbaxyOKS9vZbW4t4Xj2JM23zByB/hWhLp8LW+3ZuI5BPWgBllD+4BARQeQT3rIvbG7iu5JYnVww49QPrV5Li5l1FEjj/ANHXiQlenFXLh1jaMNzk9KAMTUrj+ztBEUz5kcV5RJme6kZOFyTntXcfEV3ifdG/Xgj0rzuB5CwYNyKBot+d5Y2x8n1P9PSltw7MW7d89KeYYw+9u/8AB6U8gsABwPTsKQCfKjfJ83uafHA9zMI4kZ5G6ALkmrltpZMYuLp1t7fs56t/ujv9elaLuI7ILYQvCJPlBAzJJ7s3Ye1AFUw6fo679QdZ5+1tG2QD/tsP5D86ZaSXuv3YMy7LSPG2JPlGO2B0x61DZaGLnVGWR/MRDhjnqf8APr1rs7SxhsbfyoxgDkD0zQwMqO2itJrry1VUitCAB23N/wDWqh4dufsU0suVEnO3IyMn1qe7/tMatI2kyMZFCho0wSR/unqKl1G8SxhhfUdISRmHzSRoYWB+o4z9RSGJPb2d7MZZrR0kJyZLV8j67Tn9KhXSC+9rDUEkKAsUlUxuAPzH60rvaPpB1KwmuEQNs8qZQcH2YVq6bOzeH7u6l5YLgE9efemIxtFdr/UltrhUcZwSRz+fWpNXMdz4iaBy4RjsBTt6Z9vXFR+ENv8AaUkzFQACcnpmrE2l3w1Jrp08pN24TMw2fXIzn6CgaI9baaYwW+xhaxgKqdMgd8+9Ojjls4g91ctaQ4+WGNQXI/EZH1qGXUoLLi2P2i4HHnydv91e31qhFBd6pM7/ADMOrSO2FH1NIBbvVXlBhtk8mE9QOre7HqTSRaYsSC61Gb7PCeQP42+g7fU05ryx0ltloi3N2P8Alq6/Kp/2R/U1hXuovLKZLmRpZT23UxGpd6wfJMFki21t3I+83ux71z896qkiP5j3c1VmuZJj8xwPQVCTTEOdi53Oc5pme1AyatQwg80AQpCz1YCJGMmllYxcBce9VHlJPFAEsk/GBxUBdnOBSxwvKwAGc1pQ2KRjdL+VAGcsRPWp0jxVl4QSSi8ULEc0APgyWCmty3gDRBX/AArMhtiWyRWvawlce3SkApjfJToKkjhAxmrTnIBcLn1rJvtVigBSP53/AEpgXZZooF3M2AKxbzWGfKQ/KPXvWbPdy3DbnOadb2ct1JhAxzQBXeV2cnfRXSweGmMQLdaKAMuO7dOFdqspqMi4ztNYYLCniRx3agDoBqpH8C5oGqydF2iuf81+zUoaQ/xNQBtS37vw83Hpu4/Kqb3qDoWNUirGmmNz2oEWWvgegpv2z2/Wq/lP/dpDGw6igC0Lxc8inC6jPtVLYcc00gigDTWZW6Gpo7mWLlHYVjZIqRLh04zn60AdJDqzfdmRWHrU3kwXI3W77T6f/Wrno7lG4PBqxHKyEMhwfagdzorHVr7SZAobMfdDyp+npXZaZq9tqkeEO2UfejPX8PWvP7bUElXy7gL7GpWhktJBPbu3ynII6ik0O56Fe6XbX8JjmjVgfz/D0qO20yzsYBFHDsH988kn3NUPD/iRL4C3uiqz9j0Df/XroiBjrx6VIzOazHJU5rN1C4hsY90nLfwoOp/wFWNT1VID5NsitcnjA5APvjqfaqlvYJaf6fqj75TysZ55/qfboKaQFa305r3/AE7VPkhUZWLpx/QfqaranqpmTyYPkhXgAcZH+FJqmqvdElm2QryEzwPr71yV/qTS5jiOI+57n/61WkQ3cL/UOTHCcnu3+FZJ5pxyTUyQ4+ZqARCsZPJ6UyVQvIqeR1UgM2BTZocrvRty+o7fUUhlWh/uxn2/lS4wcHrStzCp9CRQBZtmzF9KXVB/pxf++qt+YplocqR71LqY/wCPZ/70WPyJoESWqLIYweA2AaS5vFt7mSFbZCUYjMjE/oMU21bCIfQ/1qLVhjVZsdyD+YoAX7XeyD5H2D/pmAv/ANemGGeY/vJGb6sTV6xCtPCrjIYgEfWqk2o3QlkSN/LUEgBABwD69aAHrpjkZIbH0wP1pfsttF/rJ4R7Z3H8hmqEkkkpy7s31Ymo+lAzR8+yj+75r/7iBR+Z5/SmnUVH+rtU+shLfywKoUUAXG1O6PCOsY/6ZoF/+vVaSV5Tl3Zj/tsT/OmZowT0GaACipFt5W6I1SCxuG6RsaAIAzDOCwyMHFJVg2FyOTG1Rtbyr1RqAI6KCrDqKkjtpJfuigCPijNaiaWkfzXEip7Hr+Q5qTfYQcBXc/go/qaAMkI56JRsk/umtX+0IF+7ap+OT/Wj+04+9rFj/d/+vQBk8jqtJmtj7daSkb7VB64yM/zoa2sJ+Yi0efXkfmKAMgGpFlK1Yn02aH5gu5T0I5B/GqZVl4IxQBbSfFWorpkOVOKygaespFAzqrLXri3Iycit631mzviPOGJezodrD8RXnyT4qwk/cUgPUrK+vbU77S5+0R/3Sdrf4H9K6Cy8UW858m7j2SHhgRgn6g9f1ryC01i4tyCr5HvXQWviK3ulEd2in0z/AENAWPTf7OsLxS9u7Lu6eXyB/wABP9D+FZ15pdzZxmQqskQ6yR8gfUdR+Nc/aXMsWHsbrcP+ecjfyb/HNblp4qKsIr+Nkc8Anv8AQ9D+f4UxFM4dMHkdq3rHxHNGIob4efGjZ80ffxjGD2P160422m6mPMgdY5P9jAP4jof0qjJo93GxCKJcdl+9j/dP9M0aMDr7S6tpYWa0kiddm9lj46cYPccDoe9JcRrGki7VKltoPuB3HYkcfWuClLwgsA4cdQnB+naur0FriTT2md/MjilDLIHLbgODuX2/lSasNMuKirFvQOfLI5jOT064/wB3gjvUesXwtNIilt3xKxdFYLjGfvcdsGrJBEkcmEGehDkZXrtGB1FQ3Fmt5EsNyWEcXaP++T3PUg/SkM4mON5TiNGY9eOa6fRdNhtYI7+cFhgEgDdtOTxjocjit23SxtJY7WPyoZZM7UHWQAZyPXj1pl5d2ttZPc73kjLHmFs4PI9QOCDTbFY5/UtHmnNy6WqIIiB97llx1I6ZHcisnT9SutMlHlOXhGMwu3GB/dP8J5PtXQyXthdTQyLrDxSQ4cb8hSQc47DB6Ec1z2qI0eoylo/L3HIQdAD2poGdnY3ttqEHn2/KLjPOGiOOVOef/wBdPfZJH5Y3fNuQg/Mqg5I3Y+nrXDWd7cafP9otjhyNsiHpIvof6HtXYWt3Bq9qk0SfLkRsnR1IGSCBx1/Sk1YEyo9hbRDc0aPEAQHyd2R1GR29yOtYsbLDJLc2SNPDwLm2l4bbngnHH0YdO9ddvCiR5SrcHIL7SBnHJ6H29zWPM9tY2Mr2oRZnw0Ep56/w89R/OkMgg0/R74b474Rn/nlMoDD26jP1pLvw+lvbyvHM8jDDJ5eCrL0YH3HXr0osP7F1aQJc2stvdYy0cZYxtjqVxnA9q1H0bTePsUxtJQQRgnaSPVTxTuI5qzeaCC4aGd1kgZT9nK5Uxkfe9RhuDj2qdriw1MbL6BY5Dxv7H8en/fQ/GtC70i7kdp7MLHdwdBxhgR0HqD0IPBqr/ZiX0QkttsF3jM1lI2GVu4XPUelFwsZU/h+908mXSbndH18vquP93+qmsG/tNN1LKanZfZJzx5yfcz7nt+OK6NJbmwkMYZkIPMbrxn6dvwq015ZX6bL+BVY8eYP8f8fzpiPKdS8G6hp0n2vT5mmRfmV4iQw9wRzVrT/H11HEth4lsU1W1XgPLxPGP9lu/wCNd7L4dntf3+k3Pynny+oP/Af/AIk1galYafqDbNVtPs03Tzo/u59/T8fzpDuamiJomo6eU0CeLUMMX+wXzBZlB6qpPXHbmsLXdMTUNYeKGeLSr+Fv3IuQYt35ce2RxWDqHgy+sm+06dJ50a8rJEcMPxHNWrLx/fwxJYeI7GLV7ROMXAxNH/ut6/Wiwy9Pqnifw/CLbVNIs7rjKyyIWVvT5hwfxrjdS1Jtb1C7v7sutyEB2A/LncFwB2AHYV6xoV3a6/dW0eieIVSxzibTL2NTLGvopP3hUPiLw14UvL26sEsn0y/8olbjb5aMx6ZXOCCetAjhNM1uMWH9k3zukSnYrjqoJzj8Ccg+9dBrWpQ2VjpXh3RTL9oTchkL9d7dM9+STXF6xompaIm7ULdFwdiTBwd2O3B549arHVZXmtb0I3nwFSH7MVOQaAueheJY7B9Ui0KaZZXn0zZJIf4Z1X5H/HaM+xrmNFi0/TdFm1G/svtjCVLeOIsVGTksc+wFXotQsNX1IX8MedRaMhIi+NzYwASSBxnj2rI1y7vpVjto9OliitBmX+LLn+JiOPpQM6fT5k0nxCZoI3a1djLaYbCsp5Cn0wDg1Dqeq2ukC7msXto7qQqWSJiyc87eQMZ71BokdxN8PtU1FgyyabOGhf8Au5Ckj9TTdV+y63ounyeXKfNdY2eHGRMB8yMPQjDKfrQBDaeO9amZI1usSYK+YeeO2c9x0z3HWuusvL1KykurYRPexfNcRpmLOOpwDhge3FYukeCtOu2khktL23lhA3b5OGz3DdPwFdfp2hR6ZLFcR73jjhWPY/8AFGDkD3xmkByN1o+la5DK/nPZSRZEihNxU/Trgdx+Vc6dC8O6ZCbi91SW+TOFS2iKKT7sa7HW9Ivf+EliuLCRbeV1LtvXAwD1Pbp1zWRdtpq2SafdpDauZ2kLL88cgOd3Q8ZJGM9KYHI3lnp6L9tsS72jko8THLxZ6EHuKk0DA1KIyPC1sg5Z32/KOzD+L6V0DaXZ6XZS3jwRfZ4428kg7vMYjaG/XiuRj0+WJ1jeHd5qghCdrqOzc9M+9MRr22r6n4euZ7jR7vOnmQ/uzgxsCejIen5Vu3CaBq0UT3sY0O7uBuimj+e0mPfjqhz16YrkpxHHblpZ3aQYDRNDtkB6YLdMUul6vi3OmXaLLaSHoeqt6qexoBF7WPCeoaXGJpoQ9s33bmE74mH+8On41z8kDRnit7RdY1XQdZ+w2l7tieXy2jmXdEwPQsp4/Kt138N63cS2twE0PVFco38VtIwOOO6ZpBY4AEipklIrW1rQ5tH1GSxvE8qZeR3DKejKehBrJe3ZORyKYi3BePGwZSwI9K6TTvE7oBHdDz4/fqPxriwxFTJMRQM9e0nXJAA2nXW9f4reQ1uw6hp+pfJMn2S69D90mvD7a+kikDo7KR3DV1Wn+Kg6iHUE8xe0g+8KAPRZraWzXzNm5CMZHIwfpUcdpIl1I23EMu1w56AgfMfyrL0vWpYo82c4ubfvFI3+Nb1rqNjqR8sP9nlxgwy8Kc+lIRWll+ySxJHtWMy+WwHUAjgn6mqV7aLMCjdGwQf61o3lvKkm+aHPOSexA6fyppYSDe45D5/A9qBlYKomKkYBAXpzkcClgtSsrtOGUR84bqT2oRXuJCy/6zzCp+oPX+tXpCXEzNMjSKNyjt6HHsaAM8XhlMlrsRZTtkBHIY9xWTdzSo37xC0H8Wwcj6/41oRsCxlRGBK7clen+SKmlG5fPTgSDDAdmPp9etAjMlVrtLK9g3SNDNtdx1Kn+I+/r71qJILWIxXG2Wc5kCDjCg8H60unu0bXNvANqqo3TEAfMenA4J5qDVJo7ORbj5WaWLa0oGcdunpQBkeItMS+h/tG0TE0YzKn95PX6jvWfpN293Esan/TLYZhz/y1j7ofoOnt9K6SCdFcPCytGcfTJHI+hrm9b09tK1OO6tNyRSHzIiP4T3H4GmgLTQxPEkKHFtcEtbk/8sZP4oz7E/r9aitWkmWOyE32a5gLCGQ9ADwyH29KnWa3urUzOMW1wQlwg/5Yy9nHoD/npS/vEm+0Om69s8ecP+eqfwyD+tMRTu9On/si4huJ7S72gGFA4kPHLDHXp/KuQh0iWeOWWzliVo+WikbHB6bT09sGuyh0pZ9SuZ4bpbaNFW5iPlFtyNznA7DoaZf6nbafFHb21jbtazZ86SNiTJ/eAJ6eoFAHGWeq3FnLtBZCDyh6Gukj1ey1SMQ38a+Z0D9D+Dd/oas3mk2WqxGaO18uL90kOFwzBlODn1yMH3rlbrRr2xVpIh50K9QOSopAa0+hvBKJ7c/aIAcsg4bb3BH+GRXJXSKl1IFGBuOB7Vs2Osz2zBQ7cfwv1H0PUVW1yeO8ukuUGCy4kGO47+9AzMWrEeMHPeq61LnHSgZIbdHPpUl/bpHpsbAqWBxkcZFRrJVa7Yu+M9KAZWQ/NVuE7Wz+f0qn0qxG4YejCgRLLEAcp37VWIqyZMjb+lJJH8oI7jNAit3qxFMAux+ahIxTQCaAL4kOMg5A7jqKSVxJjdtJ9R/WqqEqetSjCnPU0ASNEUO08U+OMkgAYycZp63KlozJGpC4Bx1qeSS3GZLeTaf+eZ5BoAjniWBCGf5/SqG4k5app1N3cIU3DOKkuLB4F8xTuA/OgZVlCkbhUBFXfLDRfTuKrshHP60CIQcVoxDMYlTn1qgRUsE7wk7eh6igDYJSaIEMoPTnofrVCaHyydq7T6H+h70LMkhynyk9QehqXzcrscZHof6GgCkMk4qaGL94M09IflLj7oPNSltseQPlB6UAXJJU2F4+H6AeoqubpFGTwT1Heg27TWqSp+P1FVnyPkmX8e//ANegBJ5xMNoXip7G3aYGFhw/T2PY0yCKNWDOcx/3xz+Yq8dQjRTHbDaDwXPU0DMd7YqcMMHkflUEsbRnBrXcBpI064BJ/GoLuIFgPQUAZmaXNSPCR0qIjFADt1LuplFAh+6nb2qKlGTQBOsrgYHegMc0kQzxT3RloAeJGKhacoc9A1QByKlSbFAFy286Jt/lnj/ZqTybSWQtIJEJ/wBkGoYb5o+juPpV+LWpF4abP++maAEj0qxl+5dY+qEf41ZXw0ZOYrhG/H/ECpYtajP3o7ZvqmK0LfV7UkZtYT/uvigehnDwrdjkcj2x/jVqHwncN9/cPxArdg1a24zA4+hzVjXDcRaMbuwkZSuCR1+U0XDQy4PCMYwXdP1NaUOhabbAGaZFx9B/OuMj1DUbzL3F3KIh15x/Kqk85vJxDF0zjJbrRqGh6MLzw/a/L5iOR1AOf5VC/jTR7VtkFuzN7Rf1NcFcOLGLyYx85+89P02ANFJcfekXoP60AdneeP54ItyWqLnoC2D+QFchrnim71ba0iIjDkEdfzrLuZHlnO896qPlpMUWC5ZOq3bR7HkVk9CgP9Kl0yKfU9St7ZSzPIwUfjU+l+E9d1gg2WmXDof+WhXan5nFeleF/hrc6Ld2uqXGoKt1ESWhjXKkEY25Pr3oA6/R9Lg0uxWzhP7lRznGfxq6skUeZWdtgGB2/IVVu9SsbaEtGEJA+Zzwo+teZ+JPiGdzwaYdz9DMeg/3RQB1viHxfaaOh3v838EKff8Ax9BXk+ra3f8AiK+L/MAeiL0A96Zc6dcGX7TqFypVwG8wNkyZ54zSwLNdAwWEflwj70h4/M0DIkjtrDBbbcXPp/CD/Wra2Uk3+k6pI0cfURj7x/wqxaWyW84hsYWurx+N+M4PsO31rci02100/aNUkW7u+ohDZSM+/qaAK2n6Vc6jCGRV0/TB1kK4Lf7o6k1oSahp+jW5t9OTywfvSk5eT6nt9Ky9T8QS3JJZ+BwB0AHoBWLab7+Z3flV/WgRemu7i/OF+WPPWmpGkIwvLfrVyxgE8zIW2helbMelo7BnReKAKGjaeLljO4z2X0rqorZIyqfxmqcM1vZSBGG4NxhBn+VbEMUaHzEhdSR9+TP6ZoAybpJI7/8AdybFTB4Td+lTvfXF232e1gff3J4x9c9Ks3Oo21sh+yhJrrunc/StK7kcacJh8sxAIB569qAKdhp32IGZn3Sd8frWzEGmGV6eprM0qFZJXuHSUOFCtvbIYn0q7cXLx2kkkCKwj6oO1AFswoy/I6kiqzQIcDZmVTu+TgA+9RaNqp1LejxvHJHzyOCPUVavIrlxstQoLfec8AD1oAVLOOfmY+YR74x+FXVG2PDHGO5rGsLCTT5pLi4ulYsMbB0+pzT7qebUP3dsWRR1c+lAE17qixfu4Tvk9qo+Z9kQ39/JjHKoe9Z1/rWn6GpCOs9z3PYGvO9c8U3WpTEeYx/kKALfinWDql2QHG3OT9KyYdoj/djB9e9VbazmmfzH3Y966S30lLZFlv3aBD92P/lpJ+H8I9zQMp2llNdy7IY2Zup9APUnoBW1bWFtZ2s9yTFczQAZz/qwx6AD+I+54pmvSGyt7Wzsz5AlUNJGPU+p6k/Wtm2t7ay0fT4ZRxPLubPfHSkBz1nFqOpfaLqaN2mGPIDrx+GeCRWjHb3slqltJPciViQTG20kn1Hf6VcuL+/m8QR6e909vZsNw2RAZA5AA5HNLBMi3BJLRsj8BwASc46D+lIZc0/RbXSbUJJM80p5Yx/KOfck5qxPbJLCfs0zpJjgS4x+Y6flVq1vLdowwMTkk4789O2KdOwuLJvs6Ksq8gbtoPtycUgOX0ayvrDUb67uwoKwkLJuB568ex71zWt6vf6k+4eckZ+VuflYj9K2daudZSMRzWktvBIwDPw2fXkcfhV77TpwsI0ZFkhfKLsUAs3pknj8qYGFIGt/CdhCfvSs0h/FsD+VdHEiN4a+x+fFHNNyPMOAQPes65k0m7ENpLdPYz2yhNksWUyPUg8VYNndPCBA9veRD/njIGP/AHycGmIz4tH1TTI3JtWkibqY8OpH1GaimkWSIRRxyCQnAQEkE/StO3X7NmWV5bJU6vkqfwHes7VvE7T/ALu0JOBg3EmN5H17CgCH7Pa6ePM1F90na2jbn/gR7fQVQ1HW57qMJlbe1X7sUfArHuL0KxbPmSetZ7zPK2XNMCzNfZykQwPXvVJs5y3WlPB4pQrPQIbnNSJET1p6oqjJpyHcwFAAIuau2gVZNrd+lPSEMtJtPTuKALVzZiaI461lJY84JrctJfMXa3UVHcW+1t69DQBTUrCNsS/jUsUDynLbiTUkUKM+TxWjGyLxGPxoAjjtEiXc4yfSmC0TcWUYB7VbWNnOT1qc+XCu5yoHvQBBFbgDJouL2C0XLPz6DrWbf6zjKW3T+/WG8kkzZZmJNAGpLrMlw2w/LH6CmzWvmAOiswP6VDY6XPdMNqcetdvo+kx20YSQbs+tAHM6f4fkmIaXhf1rqrXT4bVAqJ0q/wCUqkhRgCnBaAItlFWBHxRQB5jFaSTHCxsfwrUtPC15dMAkLV9Eaf8ADPTbUDzNpx6DP866O08M6XZqNlurEf3qAPnfT/hlfXRG5G59K7DTfg22wGVMf7xxXtkcUcS4RFUewxT/AMaAPKx8HLMDJZM+3/6qhm+ENuASsSsf9+vW6Wi4Hg2ofC+W3BZLTgf7RrkNU8G3NsdwgdQM5/ir6nxVK60uyvFInt0bPfGD+YoA+SX0hwdnyH6nB/Wqs+lbMg7fw4r6O174aWWoqWgID9g3B/MV5Vr/AIA1PS5SPLLJ2z/T1oA81eydT8tV3iZPvDFdQdOkhl2XAKA9yuRVe4tApIO0r69RQBzfIqeKdk4PIq1NYHqn5VRdCh2sMEUCNFJVcZWtGx1BoDtk+aM/pWBASJQBV3BzQBt3caxoLmI46Hj37ityz8Q399aQ2an987bPN7kf571h3h26bGp9EH6Ve8JxiTVrXPRWZz9ACaRR0iQ22ix73xJdEf5x6D3rC1HUjIXmuJMAdPT6CmanfeX5s8hZstx756Vyl1dSXMm6Q/QDoKqxN7j77UHujgcRjoPX61SwWOByTT1RnO1auRwrEM96GxpEUUAQbm5NMkkAP3lx3yQKlEizSmIFgTxn3rMEJJO+RFIJBznPFIZLdPbvF8u3zeMFP6nvS6cxM4Q8huoqLyoAPmnz9E/xqeF7eIkJJLuYY34AxQSVpgBsx2LAfQHimn/UH2YVNPAy4A5wOD2I/wAagXmKQewNAyW0PLCreoDdZWr+hZf61StTiX8Kv3XzaV/uTD9RQIgtj+6PsaXVx/piP/fiQ/pTLU/fFS6sMraP6xEfkaAHWjbTC/oR+hqjeLsvbhPSRv51Ztz+5FRamMajKf72G/MA0DKlFFFABTkiaQ4WkQFiAK27WJLS289wpPRQe57k0AVodNAXzJjtX37/AEHU1N51lb8JHvPq/A/IVSubuSdyS7Gq9AGk2rMPuIi/QD+uaYdXuu0jD/gVUKKBF/8AtW5zkyP/AN9mnDV5ejHP1wf5is6igDVW7tbgYmgTPqnyn/CmS6gsS7IEVPp1/E/4Vm0UDHvK7t8xpgyaUDJxU0ahZdrenFAENFPkiZG56UygQlOV2Q5VsGgAnpzSEYoAtwahJFwTge3T8R0q4Gs7sfOmxvWPp+K9fyrIoGQcigZfn0lgheF1kT1Tn8+4rOeN0OGFWob2WJgQ5yO4ODV9b6C5G24jUn++Plb/AANAGJmnCQitaTS4p/mtpFY/3Dw35Hr+FZ01pLCSGDcUAKkxHWrCT+9Z/wBacCRQM3rXUZoCCkjY9K6Cz8SpInlXKKwPryK4ZZSKnS4560AemW08Zw9jceUf+eZbI/DuK2rbxLcW2I7+PKdn6j/vrt+OK8mt76SFgyPit+x8TMg2TdOnqKQWPWY7+w1VAr7GJ6bmw34MOfzzVeTTLm1nS40yfkSK21zgsAeQcEBs/nXDwXVrP89rN9nkPPy/dJ9x0rbtNbv7NcTASRDuvzA/h1H60xG8fEGoRSyeZBD5pbO0ghcYx931981WbXtTIKiZEBOcxptYc5wDmp7fVtP1SILJt+j8j8D1H50640WNl32sy4/uyN/Jv8aNAMyK7uEvI7pZibiM7ld/mOcEd/Y1e0nUIooLqxu5HWK4wUcn5FfktnAJGev1qjNbS2z7ZYyh7ZXr9PWmR9/pTsANGHmMcT+YDkAjjIAyevsKdd3M9wIjJh3jjVBjjIHAz70Ws621zHKyMwGQQOuCCufwzmthNHs72NpLTUUYoQGDgryfTvj0OKAM24s2htobpNxtphlHK4PHUEeoNN06/k0y/S5j3MBxLHn/AFi9PzHY10n9hONEa1JE00bs0RBwACRkc9/QVgajpUuneUZHR/MXd8mcD2yetK9wOxiniu7OGdWimikXKh+AD6fUH+VVJtKgZt4RSGyCj8HJ5yOgFc/oWpDT7xI52/0KY7Wz0Vjj5vp6/nXWSStuEbbd5ZQuemATgn9QfepasMo2zxaSJ5fIZpFQHZGBu2jv7g+tXbW/ubi18+a2h8pm+UB8hkxndnke1QXLl1kaFFMiEkFOsecdScZFOgzbqsSosSkFgE5AJO5sD0/iH40DK8bztLKgSX5G3ADqoPIxjse46U5HstZi2SIzSx5JAbEsf+0rdSP5VckP7xXV1VxwXRcEnGR7YIqvfWSTLJcIy294qnJDfK2OTzSAjm037TbeTdzefx/o90B8xGPutjqf51zr6XeRzLG0DhnYqoI5JAz/ACrqbUq5DW8m2TqYpeMEjqMcN7VYENrH5kXnOJQwckklgwH3h7Y/CmmKxxbJe6dLhhLCeuCvB/Doas/b7W8Xy9RgHTHmD/OfzzW7ewSypK06pcxTDEUi8CIgccc8cVyk1tPbsBLGy56HsfoehqlqJj5fDUsA+0aTc/K3Pl8EN+HQ/hWBqdhYXgMer2X2aXp50a/Ln37j8a3ILia1YmGRkPcdQfqDxWh/adveL5eoWw/66JyP8R+tFgPKNT8FXdji6sJvPiHzLJG3I/EcirVh4+vraEad4jsk1exxtxcD96o/2W/z9a9Dk0AoDcaPdcHnYCMH6jof0Nc9qmmWl5uj1Sz+zy95olyv4r1FIdzbguPDHjLSRaWJhuGEYQ2tw3l3AA6AMeuPWvL/ABNokOieIp7OK0vIIVIIjlfCnjnax6jPSptR8HXdli6sJPOiHKyRN0/Ecirdj4+1G0hXT/EVlFq9iONtyP3ij/Zf/GgZheH2t7PUjcNdKIosyGIw7zx1xwQMetSalqsDz3C6fdusc+xjvzw65x+BB/OvRNHj8O6teC98NXUVvdfZjA1jcAK7KSM8nhuOPeuZvPBSTSRR3iJpVzFGiSgRfLKxxyMnGTnkj0p3FYl8Iax9oe60m3kh8y7yYUkH7uV9vMbA9MnOD74rVn1PSYPBMLWekRW0s96sV5CPl8mWPLAnvyOB04zXG33huPSNetksb95YyocSlNhWQE5H4YyDW5q185gntdQs9uoXmz/SIuFkdTwzKejc4OOtIaO58PassnlR4R4yojYIp2hh90++VP44qHW4bjU9NFpCW+26fIeIn2loz91x6gjj8CK4XR9XdUEbPtUYATuWHQ119r4jhPiDSp5ghkeCVJHxgk8cNjg9DikBkT/ubMPqk9yJpo8RhFJfHcnsfpW9BLonh7wt9oSFbmQYMouU+Y/QEDA9vWm2OiXra7dm/v2ltZmLw3EUnAB6Ag8ZHcVieMry0Z107WbqaCMEhLyGLPmqD3XPX6UARah43urvRl/svSIhatCxYpblhFIG5TOMDjnPeuRl1Ox1C7iv57bZP5WJdrnaDgqpC+gOCR710Gqay+pTaXovhiWW0sbWLBllJjEhx95vw/Mk1V1vwbLa6KdYe+89SUXYVxIc4ywxwVGR1piKkOpQNpfnajpVtd4KweaxKtnHyg4PzDHQisaXR2k1S1FnGwinYYXrsOeRn0rr7Lw3azeD2n8l7sIHkjjLbX3heDgHpk9KPA0sbSiefaTC2eexAwePof0oAxfEmjPH4ykg0+J7gRxQuwRckZRcmofEGjeb4rWxt5FkkufL2nYRksBnP0PWvVv7Qt1S/W3hTzlk/dAttDc4yzeg/SuMkhuNC1CTxFr1zbzXfWCGEgjcOgouMs6ratceDYra8uEu7rQdVjtGuMc+VIuNpPXAPH4VzWt6KNNvfsxkCyMCVR+MjOOKu+EZptV0XxlaSZLXVmbxT/00jfdx74Na3iwR69LpqC22m40pbxLkc9RlgR7Mv60AcBNaHdsdGDVRkhkjPTpXb6PoR1JJ7ZZPO2QpJG3fPRh6gAkEVyclw8U0lvcJuKMVOeoIOOtMmxSD4qZJiO9SNAk3MRyfTvVZo3j7UAatnqk1q4eJ8EV1Nn4nhuQEvUwf+eg6iuAD1KkzL3oGez6drtzFDiKVLy2x/q364rXgnsNSA+zyeTL3hk459q8TstUntZA0UjKRXTWPiWOYhbpMP2kTgigD0o2q20sjJvUnmTf6d8fkKzmR5LmEow6FSfY8j8jVSw8QTJEAXW9tiOh+8K04VsdRXfYTKsg/5YvwR9KBEJQPEGU7UClvoR1/Wks491zIJJB5Ug+btg/w81O0JV/srh1Lg9fcYpbSAwW6PcbRKq7Sh7sB1x3wKQxs6x+VCizMsQl3F9ud2DjB+lZdk7SwyWkvMkUpC56c9j7H+eK1HufPWW22qQjAjHoRn9eaqxW6JJNcDqVVfrzwTQIpW2nx2zGRt6husafdPocHkGrurWaaj4WlCLma3HmD1yOo/GrE6faYUnVG352yAdMjv+NallBGqyQAcLEdx9yKBnnEKDTHMhmS5tWCpcIvXawyDg+nUGtExzRsghcSXEC77d+1xCeqn8OP/wBVY06mO6uIgEKEnBK8gA5wD6e1X9LuTIiWTPslU77SQ9n7ofY0xMklAdYfsszQpKW+zS9DGTw0TenOce/1qLWYtOt4YtKk+UqpMtwOfLkPT6gdDVyZYGSQypstLltsyf8APCXpu+h7+2DUMFpHZ31pNKi5tz5c+em4AhJPoeM+4piGXF2y6lp2jwOjQ+XFh07SAZBB7jPUVK1vJPI7QfPH57RrInzAHqufbGVI9MGmTWskmoyXd1JCstldR4lj4U5ORnHGPftWftkj1PVbKN2VyzXVsUPVlJPBHXK/yoAi8QWOnXmoslx+4kfBiu41+UkABlYdwCMA9R3rmNY0W60qQxyP50XVJAOorqLjyJ4Udztsb7LZ6/Z5x978D1I7g+1Xrm3e0s7JrlEuVjhUTp1K4JCtjuMcH8KQHmqIcbqdiuxv9KsbvT7ieyZc28mMgY3IRkAj1ByK5aWEqcGgpFYnHWomjLoXHrV2azH9ni4DchiCP5VXhf5XXGe9AmUz700HBqZ+TzUJGKAJ1IPSrKK8sJUNnbziqcRGcHvVyByjc/8A6xQIrMOzU3HpWjNEHG8fMPUfeH1qjIu04DZFACAqOKduxwtMCk9KkVPxoARS2eKkUOeP5VIiDv8AkKcZFX5Rx7CgCzZIsb7n69qivblYmESnI6mqcly4yF+WqxJJyTmgZoqwUblOU/lUWN8ny96rxMOh4qdWIOUoERugB9KiOatXG4nceh/KoMc0AIARzVhMOo3P07VFgLyeTSh1645oAuQ3BiDKERkYYwasebbtGCm6N8YI6g/UVl5ZjUy7u5oAsmf91sQbQeo3cGp4IVkgxNznkeuPaqiLzzxU815GE2r9/GOKBkEkflHdG+R0/wD1imjaxyvyt6Hof8KjDM5yfzq9Pbo0auXUbuh/xoEMtifOw/Dnrn0p8hjkkJ3VVbfH8soyOx/wNIVYHeh3j9fxoGWvKRgcjmqjW4Z9p4z0NSrOpAyKAfNkGzdkUAUHhdGKkc1HgjrWxdw5m4/u5/KsuZCrUCGAZ5p+0qQ3Y0QgE7W71ZjUZMT8Z6H3oAZt2MGH3DVkKrr9ajC+UTHJ909D6GlVzBLtfp7UARtCFbBqWKJGOCVq2bU3aZi5+nNRnTZx/doGWI9Ojf8A+s1Wo9CST+NxWatjcqcqPyNWEF/FyvnD6MaAuaa+FTIPkucfVKo6jo8ulyojur7hkEcVJHqmqQdJpP8AgYz/ADFLc3V9qYVpk37OAQuKNQ0J9FLRzqzhmiJHI5Ax6+ldxq919m0d5I4VkjK89cYI9q5fTdMu4o49QsDnB/eRH9RjvXbRCS7t4kRFQvgEFc8d+BQwR5pAy3dq0Sp8ynIAXOQe1R2/h3WJJBJBp9wY+z7CB+Zr222stOt7cRJHtCcMY12nnscjnNXGAIRLUtHGvDHjaB+lAHlp+H2u6jGheCK3bA3GR8/jxmrVh8PbywX99qEQmk4EKIWyPUnPFeih576RhC+23zjeOG49D71es9NgtmLgMZD1dzkn86AOLsPhVpRfzb6e4nJ7Bgg/Sur0/wAM6JokZWy063Qnku43H8zmtZnwOKytT1q30+Jizq0n9zPT6mgC7NcRW8Rd2VVUdeg/CuA1/wCIVlavJDA7Nt646n2Fcp4s8byX7vb28jN2yjfKPp61yKWTEeffO0cZ5x/E34dqANHU9e1HxBKYIhst858tOF+rGqaR29iQEH2m6PTC5APsO5qaFJ72Py7VFt7QdZD3/Hua1tK0ySeUw6TB5kg/1t1Lwqj69B9BQMgtrA3eX1adhIq7oYdw5x1U+57YrZttEllgSfUn/s7Tx92EcSSD2HbPqamH9meH/nRlvtQHW4kX5Iz/ALKn+dYGoaxc38rO8jHPVz1/CgC1d6pHZ3DppYaKIjbszkkepNY91fsMtI+T6bqo3F6FBSPr3NUAXdstzQIlnu3mJxwK7HwxYH+yGeYbN5OCfSuSsoFmvYIjyHkVT+Jr1a705hHDbxjbHjtQBW03RY5gJTtYA8Y9qszw30k/kpD5cY/j9vwq7YWD6da73mZIjzszn8j71btNXt5GlRRtRBnPY0AJpGnRW6ncN0p6uetaMtpHKAr9PT1qpY3Rup3eNPkU/n6iryTfapzbpuQKPmcrg/hQBltBp2kM8wRfMPTPJH09BWc17c6tcBYg3l5/CumvNItri38sxqT1z3z9arw2v2CAtbw7nUZA9f8A69AFy1gktbTLoxA5OOv5VQfUTqEht7NN3Zj0A+tasU0rIMx4OBuBbpntVOe9trNcRhRgk4jUDJ9TQBNbxpZ25zt95ByPpxWfJrFx9tH2ZN8fQj29au2d1Jd2m4DAbO4npj3rC1TX9N0GJhblJLnueoB9qANS6lWKM3OpTKkfURjgn8K4vxB44/dm3s9sMPoOprltZ8TXeqzFt7HJqnZaXNdygvk7jwByT9BQBHLNd6lNgbjmtHTdHSWdIQUMx5O84UD+Z+grft9CSG2KyTJbkjgZ5z7n+g/OsNHn0vUlkVtssTZBpDOrt9Nj027tY/LZ5JG5kdcbVAydo6Dp35rGtozqPinksyCToWz3rYna81OGKbTo0CyjDSk5MfqpJ6CoYZ7LQgRbf6VfH70v8Kn2oAqeInE/ifaOEQ4H4Vq6i0GoWsMEiTKsQG2aHDY+q/8A1xVBnttRfzbzT28w9ZbV+fxU5/QVPFp/DyWN8H2DJjmUo4A/n+dACpBeSRbYmi1BQQcwttl45GVOD+VWpJXuLaY3EayR5AaMxFZo/wBMjFQaRcJqt6sUkal1bBccH8CKsW9x5/iPVN0n7pIcEFyB145HNICCxhezSN9L33dq+TLGfvKfUf16n2ro7WaG8gRkLbT2xg/l/SuVi1CyshuhuYUOcnZkufYjqfqa6LS4kEIkEawB/n2EnO49ev4YoGZ/jJvI0VVjP3nwuO9czY6Zc7Uknn2PAAyoiAcj14xn3re8Y6pFaywR+Qs0ijIB6ZPrj2FZ32qd7RJdR3WcRGQnms7sP9lT936mhAV72xEkfkpA0l5Kd2e6j/aPv71WjFjomGkdbq77Ro37tT7/AN7+VQXuts8RgtE+zW3c7ss3uxPJrnp77GRFyT/GaYjT1PVpryUy3c2fRB0H4VjTXbzZVflWoCxc5c80zPNMQoODmk6ninqjN1qUAL1oAYsXc08kL0prP/8AqpEjeY4UcUAIX9OTUsEb7tzVZhtAhywyaupaE/MeBQAkPTBqSSLjcO1MLxrJsTnFWk5WgCohKMGWtRWWaL1BrPkj2Pjsadby+TLtP3TQA5kMchBq5bBSOaSVPMXI6iqjhjGUVsE0AW7vU4bZCkZ3N7VgXN9Nctl3YjsOwprLwVIYyZ/KtHTtEluSHcYSgDNgtpLhgqDOa6LTvDuMPP8AlW7Y6XBarhUXNaSJxigCtBapCoCIoqyq96fsIpQMUgHxANlW6npSFcHFIKcMsetAC80UpBU4ooA+haKKKYBRRRQAUUUUAFFFFABUM9vFcxGOeNXQ9QRmpqKAPOPFPw7S6ikn077/ACTEf6GvFdY0a60+eSN4yjDqDX1ga5/xH4TsPENsyzIqT4+WUDn8fWgD5RdnhOGHB7dqz7sGSTeBxivQvFvhC80O5aOePKdVf+Ej2NcbJbBTgjFAGRDnzkHvV/qaHsikiyJ0HWlQHzAPcUCNjUvltI19wPyFa/hUCOdpcZKW0h/E8Vj6t/qox/tf0rb0BfKsL6T0hVfzNIroY2uNi2iXuX/kKw44mlPtWvrrEm3X/eP8hTNHsJ75xFDGzyO2FRFySapkorqixLgfjVKe+xJiMKQOua9iHwrhl0ZftF60V4eygMmT/D2Jx3PSuJ1r4baxpgaRLb7TbjnzLb5v/HetIo5O2mZ5CERFOODtJ5/OqYeEjDo4cdSDnn8avCL7KxREcykHAIK4x65ql+7T5ZIWJ7nJBoJFQ2yEsH3ZGMPFnH64ptzLHK6eVHtx1PTP4ClAtT1aVfyP+FS29vDJKAk249gVI/xoAfK2IrdG+9yx9hiqcXJYeoNSzu7fO3VuD7Y7Coov9Yn5UDEtziYVouM6dcj02H8jWbEdsw+taiLutbpR3iJ/I5oEUrU4cj1FWdRGbK1b0Z1/kap2xxKPer14N2lof7s38xQBXtT+6x70mqD/AEqNv70KH9MU20PDCpNTGRat6xY/JjQBn0UUUDJ7Rcy59BmtLVGMaxRDosa/meTWZatiQD14rR1P955bjuin8uDQBmUUUUCEpaKdGgc7TQMaKDTipU4NLGm9gtADoU3PyOKY6gMVHarUjCFML1NMgi3fO1ABBDj53/AU2Q+ZLhOccVaK7hg9KhaRIvlQc0APfHl/N6VRqZvMl6jigWz0AOhAaMp0NROhRsGrEcBQ7mPSmyAzOAi8DvQBDHE0nTpTisYzl2JFTP8Au4wi9TxTPJ2rkDJoAYyg/wCrDY9aj6VfG49T26VVnj2ncO9ACJPJH0bI9DV+HUgy7JkVk9H/AKN1FZlFAGu9pa3IzG+w9g/T8GH9ao3Gmz255De1V0kdPutV+31SWJdjcoex5H5GgDMIKnBGKATW0Vsrwc/uie45X/EVUuNKljTzEw0fqORQBUWQipknqs0bocMKTNAGtDduhyjsK2LLxDc25wz7hXJrKRU6TkUDPQrbVrK8O8nyZT/EnB/H1res9TvrIBo3E8Y/udcf7v8AhXlEdxzkNitWx1m5tSNkmR6GkB7BZeI7a8j8qbA/vIRkfiD0qy+l21yhktJFjJ7btyf4j9a83ttetbvAuU2t2kHB/Ot2yuriA77a68xT+Df4GmI1LzTrm3UiRGUHpInK57EEVuaVf2MFrJJFb7r+UBWt8/eUfeCseMdx3rNs/Emf3d0mM8E/4jvV5rSw1FN9s6xyA5Bj45+nb8KGCNR9Sks9OjuJUt4ptu6JD1bP8B9wPzqkniqGZQl3YqoBzmPDjP0ODWXqNlqLSedcu1ztGPMHOB79CPyrNA55oSG2dfnRtXTcpiMikAhDsaQHsVOOlXLi0Z0AjcmQZ++eArehPPpjJ4NcGUViCeorTstcu7FSj7rqLaQEJ+cfRj1+hpNBc6q0mke6ZHhRZD/fycqoAO3tgnrUcxMONm4tubA4wVXqBjoR27Gm2uo2mrwh7d2EoyxTo8Rxyfw706+sp7mAKk6tKDuBHykevTgg9aQx6yPKAVfeFHUctj3H8iKp6kXltJQjsxibzFJcDIbvjoSDkGiyeVSsVyiJIhKq54OeoXI9eoPtineVJbzS3KyNM0WZAfQHhuDwfegDJ0WG/uLW4O/zrdG2rv5eNsZ+U9SMEcVsCC+xHJbz7pFA3JuP4rnggegNT20sVnYTCNlEzZcIEIJOAMgd+n0rkbTWLywvQ0ha4DkjDkjBPYMOmT0zxRuI6i0MhIS2R7eVMiWISZyPUKa0MxyRNHJB5kB+cyIBjr1x2I7+lU7uOW6itZFDxyn6Fhnp8wPYiqsExWZ7Zz5c7YOxzsEnqAegP1/CkMpahoTrNmF1bfvfe7cEDnr68/jWOySRHbIjKfQ11IZoIjFJGJGwSf4TuB+XI6E8ckdq5q6tpYLplVtx+82MlRnsfTFWmJobFK8LbonZG9R3+o6GtAanHcKI9QgVx0EiLyPw6/lWcsTSRu6Lyn3h6Z7/AEpob1piLLaBG5NxpF1gnnYGAz9R0P8AOuf1TS7e5Ji1WxaKT/ntEP5rWxuKSI6uyMehHBq2t/I6+XeRi5j7HGHFKwXPL9Q8FXUI+06dIs8Q5Vo25B/DkGprHx1q1hCLDX7RNWsOnl3Q+cAf3W65HvXoh0e3uHM+l3LQy90zg/T0P41j6lYJIzw6vp64/wCe0Sc/Vl/qKQ7lH7LoXi6Fn0HUfJv9q7bG+bawI7K3fPTvXKeJrfV9NuooNWhmSdZd4kbO1sjs3Qn6Vd1HwM5j+1aPOtzGOcI3zL/WksvG+taVD/Zut26arYdDb3oywH+yx5/OgZh2i3bxm5ggmaIOIzIiEgOei8dz2FSvqQXypUfBRtwB9Rwc/Wu5sZPD2taeLfw1qH9lXxuVuTaXTbQxAxtVuhHGR6VQ1TRAkqW2p6QloyzByThI5QEwSHHXPcUAanhfxJaT7DNdM0eCPKJA6joe/wDjVzU/Bel+LI449L1WWFIZfMa1k+fOfvbc8g449Kzn1PQ4dOtVs9Psfs9rsW8LRozAt/ED1+gpYPFdhpk0yhIrZlz5EttGGE6noQ2eMj8qQFrUvBVzptjeXVkkTmeU4SYhUijwSwJ59KyvCsk3iWCbQLgyywyWZWCby2KQ4B4Y8cggEfSp5viO1xLJaNIgttxy8yYBBAypA7EisjUPiBe22q2q6RdJBb+Ssc3lcoW3kswBAxximB0OlX8nheefRdVkiaWHHmGPJ8wHlWbj7oHTvXOavbLo2uGeB1Nre/OpHTd/F+BzmvQNZ02TVdX07X9KfH2y1BJk4U7ePm78g9qxdb8M2q6P5MMz3vkTMzx28w8yBiOdqnqM5+U9qQGLZ3bMZvM2upPzAruz9RXM6neaPLcMl5pksUqMV3xSnacHtniujs/DV3NMj2l5DcoMZDnypF/3lP8A9cVB4o8O2El08aaxFJdxCNJRs/d+Y2QoyCTkgc8Y9aYMn8F6/pi63peiafZOkVxJLHLJK+TIZIyoH06VqxGY/DvSiXZZNKv5bK42rlvLBI4+gOcV5jpF0+ka/ZXbcNa3Mbn/AICwz+let3kd3aT+NrDTyilL2DUYS33THLw2fbBoA5GIz6Tq2n31jJuMbnzVi+7LGJCrgj+8B1Hpg1keP9NTTvGupxJ/qnkEqY/uuAw/nXTvYp4fubrzCq3EzCS2t+ojkZDuBz/CSDj2xWL45827TQtUlDCS609Fkz/z0jJVhQDI/BFlos1hr15q1q919htkmjjRyvBbaxGO4JHParC6BY68hl8O3X2hwMtYXDBLiP8A3SeJB+tU/AmJNduNNY4XUrGe0/4EU3L+q1gwWk0F3lZmBiZT5kTHO3uykc8enWgQ680yW3meKSF4pU+9HICrD6g1nsjoeVrvY9eubm08jxBZrrFrDx9oT5biJezK4+8PrUMvhi31aI3Phy9XUYgMtbPhLmP6qeH/AApi9DiRJU6TFakuNPeKRkIYMhwyFSCD7g8iqZDxnBoA2bPU5rdg0cjLXSWfiKOYp9pGyQdJY+D+NcIr1YScigZ7Dp/iOZIgLkC8tx0kH3x/WtuOSDUovMsLlZpRn5JGw4B6ivFLLVJrZg0chWukstfikkDOWgm/56R8Z+tAHebZYWkUoySNj64Bz+VTYYywhE/1kilx6AdazLPxK5iCX6Lc2+OJo/vD+tbdvqekxQmWKbzCefc/U0CJbkWdtcG3+2fZpHAJG3CmoL3UbPTNPmKTo8rjsc/jXMaxctq120jDKA5H/wCusz7MN3zMT7E5oHqyCWMzMZOhJz+dX9D0Uaneh5iVtrYh5WHGSOQoPv8Ayot7WW6uI7aFcyyHavp9T7Acmul1CSDRdMSytjnbnL92Y/eY0A3Yxr6eGfXfIjh3i7PlzRDkHJ4IHqP5VG8csDyWrbZbm3UhQek8PdT7j/A1e020TS7WTVrn/j5lU+SD1WM9W+p7e1UtEt5NT1aTVrrcttak7f8AafH3foAcmmSQvYxJ4cv3t5MxytG6o/3hgnIP09anHlxXUTvBmK2tkmt5EGC21V3AHp0PIqpPcBdRZrYZiXLlD0xj5vwP86niF0JLNrctc6UJD8m3LRhhtYHvgCgCsum2NvpszvI7W0/lTiE/wknCkMPbINTXpaW6hjT9wi4iic/NtYfKyMO+Rz70+yhSSOa3uJNv2SNreaM/xRF+GHuCciliiR4b6O7fEkIVbhPYHaJF9xkH3GaAIvsUVlY6lGsLCRsB4xyAcZBHfae35VkfZtMsdItnvbV5GnLbthAK4OD1robGyvoTcS38/neZ5cavuzuUMCPw2/pWVf3rz6pJHcQqIYZ8RPswquDwGPTB6H86AMHUbSxis0+xGVobiPq/QOpwfpXL+XLaz4kRlYdj3Br0aMpFeb7WPasrMREQBtlUYZP+BKeKy7qwguvNSNF2uqXEG/japO1h7AE/pQBxk8SH5l4z+RqmwI610OpaJc2RlVfmCH50HJGehHqD61kTQ/uwwHbNICnVmKYEAP1HQ1XxQRQM0FlI5zj3FIQJpBwuTxx3qnGx6E8GrCNsIK845oEPCAEhuo7VPDA8rBVXGehPSr8aQbDcyJyV6djmkhVGbdazbe5if+lAyldRpCpQPmQGqKHBy1O1JfLvpAD3z+dRocjP50ASThM/LVcj0q4yh4wT1A61WZcf40CIc1bgBdeOoquRUkchiYMPyoGacYSSHbnB9/5Gqk0Xl9Bj/PY05Z1kOU+UnsehpWfAKn8j0oEVApNSLF61N5REQcdM4p8cZY4A+tADAmOvFLuVRxTrry4/3YfL96rK5BwKAGyTseBwKgDEHNSOMk0wj0oGWFcuMmr/AJfnWgydrrwc9PbP+NZtuyiTDdDWjFMbc4PKH16Y9DQIgLSQfu5Uyh7H+hpm8K25Swq6wjkXbGcf9Mn/APZTWfKhWQrhh7GgBHfJ4q/ppMMvmtyACPzqmkW761ehnSCLY/QcnPc0DJLx4xKWRuSoGPQ96zbkqZcL2GKa8xZyR3q/p6xs8gmjVoyvPsexFAjJ6GraMsy7WOJB096NStktrkrEd0ZAIz157VVVSeTxQBYkmLr5bD5x3ohJSRHPzbDnYeQR3FRFuwp0ZIfNAzrZ0GnQRX1gfKilAyOTjIzSReIZ/wCMQy/X/wCuKk8Oy22o6ZJpV3Jtwf3WepHXj3BqHxF4TfRI4riOZpIJBw57fWgC5HrNtJxLp6H/AHNv+NWVvdKf71rMn0U/0riRn+GWpVacfdkoA7VW0aT7t0yE/wB7/wCuKv2cWmw5YTwzenzD+VcAt3eIMB8/8CpTeTuCrhB74GaAPQYtc0uFZFV/KbrgoRn3Hb9ateEtTl1rXdhKeXCC/wAmRk9B1rzJZp1BUuSDXp3w8024g0ySaG02TXB/4+JB0T0WiwzuZ7uNz5bWzXRHOUOOfQ80iae10d91Gqj+GNG4A/rV20tI7aPgLv7nuasHPWgQkESwx7UXAFJLOkUReR1VR1J4rN1XxBZaZEfMkUyD+DPA+teUeKfHc15KYbeRZB04ztH0oA7PxJ48trBDHG5GQeR95vpXluo65qXiCZo4tyW+ckZ4+rGs827SH7TqUzAHkJ/Ef8BVqKKe9ixGFtLEfxnv/iaBkEawWjBLdPtN2eAduQD7CrQsljYS6g7TTt923Tnn3x/KtLSdLmu98elQ7IRxNey9B+Pf6CtqP+zfD6lrb9/d/wAV1L1/4CO386AK1vobeUlxrT/ZrfqlpHw7Dtn+6P1pmp6+EgFnaRpBbr92GLgfUnuay73VLm+kfZk56ktzWbLZX8ikxJu9g3NAhLm953yPk+lZc108x64FSppt/MSy2tw3uENRGFopNjowI4IK4xQAzZnk1KsRZeOMVIkRPNTKuwcdfWgYyKNkcOvBU5HrkV7F4ev49b0eG4I/ep8rj0Ydfz615JEjM2V7dzXU+ENXj0rWEiYt5FyQregb+E/0oA9Cv9Oa/hRY3CAevpUNto9nFGEZdxPX0P8AjTr+W9tL+KWNPMtSDuQHBFZ8mpTXt7CiIwjjkDY+lAjcaWKKJ4rYKZFHAA4/Os6wt76a7S5eTaFOdnqPQ1pNpaXlpgF1Dch0bBpQ8Fjb7C/mFfy/E9zQBoocgGiSWOIEkhfc8Vix6zMQ5SB2Q9HxwKqNDdamxZ32RDqTwBQBf1KabyQIXVyxx8ikH8881QNrbWEP2nVZtvcRDrVDVfFllocHkWzrNMg27z0H0Feb6r4hvtWuCWkJyaAOs8Q+OmkjNrZL5MI6BOp+tcOzXWozZbcQTU9lpcs0y7gzSN0Qck10kVlBYJ+8j+0TdoU+6P8AePf8OKBlDS9CJj81yEiHWR/u/wDAR/EfpV19WTTpNllZeYvRpJcgt+XQewpv9tatbuWls7d4T/yzkhDDA7A9vwrXs/E+kXIEV/pn2cnjfFyPyPP60gM1NUhuvmjsrtrg9InlLxg9sZGfwJIqfV9MuZNOju7lFS6A/eoPTsa1tQ1G10cvBplt++PWV1+79Kz4ZDC/2q9meeSdceUOSynuaQzP8PXoV5dPnkZLe54zu+6/Y/0NaElvp1izQzw3Nyc4eS3+XafYEHP41h6pYPYXZx9w/Mp9jXU6fqb6hpG9Ui+0RDZM54OOzHuf50xGVf20NjZR31heymKQkCORcMMHHPb+Vatnck+GLq6m5kYBQe/PvWDfFtTNtp1kC/lcM46E5yTW9c2iLoMelRXVv9qzuZHkxn2z0zQBW8ExfvpJz1wzD3PaqFveXWj69PNcQSqJeGBXDfUZ4NTxRXek2n2e6tJUU/8ALTGR9Qw4/WtHT7+WdRbR3LTIf+WMyCVfyPIoAZZto8zuWKtC5BAl5aNz17cA07ULkT2psrAKETGZt+2OLHTDe1VtbGi2YzcQQi5H/LK2dtp+uen0FcpqGsTXMYRmWC3X7sScAD6CkM1rjVLazYeXI19eqMC4lXhf90f1PNc9e6k0khkuJGlkPYtWdLeMfkjG0eveq3XnvVCuSzTyTHLHA9KiyAMUmT0FPWPPJoENAJqVYwOWpeF4FMZvxoAcWx04pgLMcJzUsVu8rfNwK0oLMLwo5oApQ2RPzP8AlWnDakjCCrK26RDdIfwpPNd/liGB60AOCRwDL8t6VGxkuPl24SrMFiznJ3E1pR2aRDL/AJUAY0ent97H40cRybMqa15yzpsjXaK5+6mjhfAOT3xQBeZVkSqTKeQetWIJQ6jFLKm4bh1FADrSfI8t+op9xF/GKoElSGXtWlBIJY80AR2cMLXQZ05Peunt1CgbRXLuDFJkfWtzTboSptY8igDYUDipkWo4eRVgfLSAGwOtMAyM09VLtgVa2pGvNAFIClHFPI5z2puM0ALk0U3BooA+hqSlopgFFFFABRRRQAUUUUAFFFFABRRRQBmazo1nrli1peRhlPQ91PqK8c8QfDG/tWke2j+0QjJBX0r3QGl4NAHyjdaJPaMVkjZPqKyptPeC4ifZ+7Zhz+NfV2o+H9O1SMrc2yMT/EBg15j4t8GR6SmICJoX5KfxL74oA8h1Y5eJB7mui04eVoFyf78qJ+QrntUjZNXSBh93A+uTmu50LQ7nVtNgtoBjfK0jOeFVRxk0DexyY0W813WreztIy7lfwA6kk9gPWvYPDHhKz8PWQVds10/DzdM+qr6L6nqa1NJ0az0eCOG2jyzcvIR80pA6n0UdhV8SB5Ao5HUnpnH8lFDYiGQNLJlvujgAd/YegrhvG3xAWwEumaNIpusFJrlPuw+qr6t79qz/AB34+DCbSdGmwM4nuozjJH8Kn09T+VeXF2PLDK0ATT3El1FGkrs8cbMw38nLYyc9ecetU3hV1+bp/t8j8xyP1qQyBuvAHandeTwOwoAqzM625Qw7lxgHdkD9KjsI/L/fvwq/qaunru6H2qEx7nBkdyP1/DPFAFCVuAp65JP1NMU4YH0IrQXSpbgn7KGmI5Ixz/hVGa3kicxujK47FcGgBjfLM/sa17Qq0m0niRWX8xWTIP3pPrg1ftsER+hxQBRQNHIAwYEHkGtKVDJpVyqjJDq2PoeaqX0jyXsgc/6tio+gNX7NyheQFsIhYgd8dqAMy0Pzke1W7uCSeyieMbvKLbh3weaqSSefc74o1jz2TpWlbBxl/M2hRln6ACgDEpKu31xbzn91Dh+8nTP/AAEcVSoAVSVORWvA6Xlt5R/1i5K++eo/wrHp0crRtuU0ATSwvE5VhUdacFwl6PLmRi398df/AK9V7u0FvIVD5xQBUpyNtYH0pKBQItSoJEDLVeNtkgJ7VNFvQZQqw7inEwyfe+U+9AxZU8xART4QQmCKaiov3ZF+m4VKCD0oGHaoxGobd1NPNKKQCYp+R2ppORUDztG+AOnrQBYIz1qJ94HyDmmJdbjtIx9KbLI6NwetADNspbcetSBzAmDyTURuJDxupqAO+GOCaYi4kyucDj2NJMN0ZFRoqxcseakVw4zQMpGipZYyjZHQ1FQSFFFFACqxU5U4NWYL+WFsgn6jiqtFAGstzbXfE8fJ/jXg/iOhqOXSPMBe2dZB6DqPw61m1NFcyREEN0/z1oGRSQSRHDDpUWa201OOcbblA/ueG/Mf1pJNNt7jm2k5/uPwf8DQBkByKlSciiaxngbayNxVfoeaANGO5960rPVZrYgo7VzwcipEmIoGd7aeJFlcC45Fb1vebh5llOpb0Jx+teWJcVfttQlgYMkjA+1AHrtl4qlhcJeIwPYnj8mHFbQbTNWH8KynuPlb/A15RZ+Jzt8u5QSKeCf/AK1bVncwT/NY3RQ/88m5H4DqPwpBY6+60O5h+aH98vsvP5f4VnFSD8wwfSi08R3dj8tyjGP1PzL+fUfjW9FqOm6qoEu0MehPB/Bh1/HNO4jFtriSznW4g++CCff2rs7DULe+tRgorHjngZPUH0PtWBPoL/etpg4/uHg/gehrMxcWVyfvwyjqCOo9weooauB1bWskbvHI++MnywXOeQNyjI9ienQj3q7aEnEszxeXHlGyMNnGMN2P171gJ4kK2f2ebT1ODkPC+3BHRgD0NXrfWba6syiB2dsPJvZdw5xnaPQj+tTYZZ8sm3iWNt0kQ3RJ6qOOf9kgEVkeJNPG8XsSMVk/1h24XJG4Y/Dr71vxzq8h8suWX7ybNpUn+8T0zxUd7aJdWgtyVwNqbw5BVhxyvQ0IZzml2FxDpd3eSXb29vLsMWG54JB259e3rWlDcaXrsRsgXkmjjBeSRCpzwDn05I6VR1S2uYNHsLd/mS2DRsU6ZJyp/FTWZJp1xFpq34nW3E2VjKORIcH07jimI3Le4vNIiktUT7bDHuC+ZxJF7HuVrYsNQiu4A7QpBLLhZAQDkk459R6ZrPtZXu7FnvE+z3MEYAI6tnGCDz19OxqvCdTs3jZv9Jt5SzGJNrMVAyT7UhmjfafFHKb/AGIFUOk/l9CpU849c4/nXHg1093qGnwxRLKspeYBmRG3GP2b8K5l5EYsQMc/Ljpt9PrVITAjeMU4NkZ700HFIPvOvv8AzqiSTAJDHgjoRwauRXz7RHcBZ4v9vqP8+1UgcdWwPU9qU7z8qffPA+vakBPJo1pdN5+n3H2efrjOD+f+IrH1TTzIDDrOn+aP+fiIYf6kd/wrVeOWCby3XEy8EA9/QGrcGoOo8ucedGexH9P/ANVIZ5hqPgdZYzcaRcrOg58voy/h1qKw8Y6/4fj/ALP1GFb6w6G2vV3DH+yx5Feoz6NYX586zka3uBzkEgj8ev55FY2o6dOqmLVbFLuI8eaF+bHr6H8KQ7mJbt4Q8TySfZpv7Nu5xte0u2/dSdxtYdCD0rlL7wbqWj3Y/tNmtYIgZPtA/eRkAjbtI4JOa2r/AMDW94ryaPdKWHWGTqP8KzrTX/E3hImyuFZ7Q8G1u18yJh7Z6fgaAI7bQ0u5r25890tnDRb2hO0se+fQGqljoVpp93LLqOoWckCxSAJG5LbypC/LjPBrsIda0PxLYLY2163h+6ClVt5ObaRjzw3Uc+tcx4y8L6ppEVlPd2+4GI77mHLxE54+b6c07hY9R+H2tNrXghoZ32SWGI1dPvKpU4OPasq5s5ZdSkurV9moHrLbcebweqnjJNc78Ptft7GXdBAizwwrHNHK+EnUnG72IYjH1rpbGF77xDLqCQNabdsnlPkrGc8tkdsipGc7qF3LdyTWeqJLHMih7aa6CpIrjnaxHDKensa5SdSL2NzJ5Ec7K3mnkRyA8E+o559q9T8S6P4Ve4ebVX+yzE7g8W7c2ecYBI5rlta0ezXw1qVxZwOLZSnkl/mI55Ofp1FNAclcWdlJNcxpNtdJVRWP3CCv5gFhwa9e0OSO+8Q6TJcfNHrPh37PLg/elhbDY98CvFEKzSK7FiSACPUAY/TFemeENQB0PwxfHd/xLNce0cnqIp14z+JoYkY+oO2uamt+8U0cM0iq10h3+VJGcL8p6fLkHsaf4hZtT8EW9y53Tafqc1vKdm3hxuHHbmtKbw5MdWv7W3vnUNPKNobKZyflZexB59TWhrGki38Pa3YyFTdyWsd4QBjLQkKXx7g0DPLdJvW0vW7G+XrbzpJ+AYZ/TNX/ABJp8mmeK9QsonaL/SGkt33YBVjlefoax5V+cj1r0e/s4PElho8sm3zruwjy56hkOwnP+8AD9aAOc0PVTb3oW6spllRlEkMbBVl9SVPT6jg1SfS3TU7qW2ufs8qu0kBUlW5OQuOCD29Kk1KGKwZv9Oae5tuPJuEPGDyATVSLWZZLhSLq4IYfdUZ8ok9B1OBTFY0U8WregWviSyW92fL9qj/d3MeOPvDhsehp8/h2LUITcaHcrqcI5aNRsuYx/tJ/F9VrE1DRb+3/ANIEM08UmWEgiYde5BFQaRO8V4DHvWYcq0bFWGOeCKQEc1i6E7OdpwR0IPoR1FVTuQ4Nd82qwanAH1e1+2qox9ttsR3MQ/2gOHA96o3Phr7ZbvdaTOmqWw5JhGJ4/wDejPP4rQFjklkqeOdl60k1i6ZZOQDg+x9D6fjVY7kODTEb9jrE9qcpI2O4PSuksdWsbsjzAscnfsDXn6y1YjuCpoGesqI2j2Jt8thxioDb5liQyKgd1XzCuQATjNcNp2u3FoQFk3J6HpXo/haSDVIftt8ipaxno/SVh0H0HekBsWemJoMNxPNNE9y2VzH0VB9e5PWsu0tTrN9Lc3H/AB5wnLD+8eyD69T7VNd41bWYUUymGRx5iRN1Hdvw6mrGrX1tpGnrZW3yoM4y3JJ6sfc1RJlavLNrepx6fbNyxwT2UDqfoBU2szwafYx6XafLCgx7+5PuTVqxtl0bTJLucYu7gcg9VXqq/U9T/wDWqjpdo19qQu7jBiViQj87iFJ/IHH40AVoLSzsNODXyzGS7UiQKQdqhjgY/XIq3NPY6RDDAjvgKD5kfoehx6EUk9xFqW258hHNs26a37NGTywH8/zqK5aKNop7YNizZSA/OYicj6gEkD2IoAfd6daalF9pSRyW+bzVy2QfX1H6iq2pabat9naa6ZHKiNpivykjoD36d6nmna0t7j+z2ZFilV9u3gK4yce24cfWqWt2U2o2okZGhuo0EhiDZWRD/Gv9RQMllMMmrabZ207bFUIxH8QHA/rVK5vIxPfBYfORiRPCWwTg43r+HUdjzWDbSta3EcoOGRgR9RXSzW1nc380izNBNOoubebd8uCPmUjvg9e9JCKv2VYLZmuZlEXmxtDKeDIANytnscHB+lU/ttpJMXkSWA2wdWj+8CjE7ueuMn8Km1bEWlQ291HsDSMCUbIjPUFf9nnP0NZNukhuAjRl5Ix5UoQFvMjbgMMdcZ/lTAux7k1DSGaRZZG3ITnOVJYLn14NV7rS7XWbWTyo/s9yueBwCc4/FSeh7HijTrqTT4Zo3Tzfs0ytgryBkgkehyAavSTG30wXJmifEu6ADqUYksD7H9DQBwV3o9xZkO4zG2QHHqOoPoRT7W3juIpEk4KDIPtXdXkSTQfacN9mnAZzt5HZZB7jofUVjzaeiNJcJGqzRfJcxDpz0df9k/oaQHKTWTwnIZWQ9HHSmqpJwKuSK0bso4HIxUCfK2cdO1AG1GEMIhbkCAgj37VhSSshwpYfzFXXaZLUygNhj9+qJbznAbntmgCs+5juJyadE+089KdJHtNRUDLiyBV4bjsadGglDce9VY2XGDVmBmEgwcZ4oEQOpQkUzBNWJ0dZDv79+xqECgBAKnUqQD39Ki3Y6U7f6DBoAtRzugKjaQeqGp3uYZTkoyHsU6j/ABrOUMTkVKoY8E5oAWaMTS716989zVo2KpFuzgjrUcK7GDMM47U67u0X5U53cmgZXlQfX+dV2XuORUxfPyjke9OKAxZDdKBFT61Kk7px1HoaYwpAvrQBbWVSMr/3wf6U+NvOkAY8dOaqpkHOM+1SF/wFAy55EqkjY2F9PT1qjPkydc1ZhunjB2v16gt+tRu/mvubbn2WgRFHH3rRgMaxBX4PUkUy0iGfM25xUWoFBLhOD3A6UDIZpt8pJ5q1p8ENzIUmDBCp5HUH1qgqFua07dUSLaTtY8k+3pQIq3Nn9mnKF1YdQR3FLHFn/CmXEpeXc/WtHRQjXeHdQNvGRkf/AFvrQBXQhSNy8A1vz+J7i60n+z5hvhHTfyR+NV59FnmnLRngnstSReGJ3+/JgflRoPUxPLizkjj0rafSbG5s4WsvN+0sceWmWLd+B1z9KtDwxbr/AK29hU/7cv8AhU66Wqy25stXtEuYJRIn3sZByORQBhS6Ncwn5/NTH9+Jl/mKu2vhbULu2EqW0s6MflMTAn8q9ytpjdWkbt1deRuzg9xU9lYpaqc8ljkngfyoA8/8O/DWL7JHNfyXCSk5MXy4x79x+dejQW8dpbxwxBVRBgD2FStIsY96xtW1+102EvM67wPuD+tAGpLcJBGXd1AHUmuD8RfEG2td0EEh/wB8cn8K4rxR48udQleG3fEXTiuWtUiuS9xeTNhT9wdT/wDWoA1Jby78S3siiUxx4J3vkrn/AGj2zVWIR203kWcf2m66b9uQPoO31qcRzXMQOVs7FeR2J+g7/Wty00qO+tUk0wpZ2h/180ud24ddueWz+lAzGiskSdDc7ru9c/LCvIz7+tdHFoiJsuNdk6crZRn8txHT6Cp1uNO0OApp6N5hGHuZeZG+noPYVz93qUtzIQhbB6n/ABoA1tT1/MQghCpEnCRR8KPyrAdpbg75jx6UQxBpNvWQ1qW2kbpg8z5TqAO9AinbxMwyicDvitPToJTdBVDMB1G39RXQWViFAARQnSnOh0zzCke4t90+lAFZ11OzcS/ZIpLfvsfkUniHQ4NW0kX0Mf75Fznbgkeh+ldDppnubGM3MChnB3J04zwavQWyJ5iLzGTjB7UAeHbWB2njHapdqADzO3auj8V6P/ZWomSNP3UvII7VzZiJYhunrQMkjbzAYwMY5GKkWII4DH5uwFNTEYwnfvSFctktjHekM9Y8LaquuaMiSH/SYMLJ6kjofxFbMdrCh3HAPt1rxqw1m60q8+0WT7X6MOzD0NdEfiXqAh+4qn3XNMR3WppcyRGO1d19VHAP0q0tpaz24jdGGMcDrxXLaZ8RdMu4AbuN4Jh1CMOffmqesfEWCKJ005GDnjzHbLUCOt1LU7HSLQLcOqqo+WENz+PpXmfiLxzcXpMNsdkXQInTFc3e6ne6tOWd3O41Z07RZbiTasbPJ1x2HuT0AoAorFcXsm5y2PU1u2ejrHEJrg+RD/z0cct/ur3+vSp5prHRcL8l3djoBzHGf6n602LUpLhvtF2kMgPQlgzZ9AAePypDJbma5jtCul2rRw/xy8GRvc98fpXS6SmsxWcZu9RtJJSu5be4TJC/73b8KwLWxRLw3Ku+8543dj29xVs6e13dC/Gor9pikDNF5Rwq9jjoeKQzWTVra7lube80zybiBctsPynnA6+tYd7a2z6xaLFGq7pVJ+gOTWhL8011Ju3F2jTIXGcDcf51mG9htddinud3lRhui5OSMA/hmgTJtZkV4JHHBkc/kTVzyvMmiuLARXkaIq7Y5V3jA/unGfwqzHpmmaxCFtdRimOPuFtrfkf/AK9UZfBF2k2YXKep6YpgLfot/BJbyFBdrl4405KrjlSe571gaVfHTdRzIm6FvkmjPdT1rfFvY6G+Uf7Vff3t3yqfr3rO13TSixX0aMsc43Eeh7igDUuhLAwttKg8qOUBhIOWZT3z6VnS6beWb+ZLZSyJ3fbvB/EZFXfDl6t9ZnS7ify/Ly0ZLAAr3Uk9u9RX+saZobOmnF55z1+c+WD7Dv8AjSAv2kq2tp9pluZbGPHA3fe+in/9VYOq+KvOJi06NYQRhpsAM3uSK57UNTnvJTLdzMzHotZkk7ynaOFp2C5ZnvQHJzvkPUnmqMjvIcuc0jDbzScnimITPGKUKTT1QDk0/djpQA0Kq9aC34Uihnbai5Jq/BpTsu99ufSgCksbydBwe9W4bMLy3JrWhhjaLyiMH0qVVitxj7z0AVoLNiNzfKB3qx5iRfJEMn1pdstwcdF9Ku22nE84oAox28k7Zfcc1qQaeqDc+0CrIEcAwg3PTHYkbnOAPyoAeZFUbYxj3qrcXMduC8z/AIdzVC/1mOMGO3+Y+vasCa4knbe7sTQBfvtZknykXyR/rWSz5OTQSTwKnhs5JucYFAEtjcENsP4VsI24VUi04pyBzVgI8XDd6AIp49vI6Go4JTDLz0PWrZUOuDVKVNp2+lAGowWWPI/Co7eZ7eYMO1V7OfB2N+FWJk3DcvWgDrLK4WaNWU9ats2eK5TSdQ+zy7X6H9DXRxP5h65zQBehbauR1NShSTzzUcaZGac8wX5V/OkAp2IPm/KoT1yKTdu60mfWmAtFJRQB9DUUUlAC0UlLQAUUUUAFFFFABRRRQAUUUUAFFFZuo6klpGUU5l/PH/16AGapqsenwkjmU9B6fX/CuVbzb67Z5D+843F+Qo9T7+go3S3k++Q8gnLnny/8WP6VYgAjiGBgElgDz1PVvf2oA5XVvANjqmpx3cbtblSAybc5X+97E/lXT2lnbaZbpbWqKsSAcHqe+5jVhQFyT3y3P8z/AEFRGaOOIySFQnLkycAAfxN/QUAKpctI8u1cjPLY+X1b0FeV+OvHjXEkmm6LPi1AKT3A4M3qFPZffvVXxr4+k1Mzafp8jrYlv3kw+9Pj+S+3euBBLne/TsKQAz7jl/lHYUvBGTSnHU0n86YCEZ+9+Apu0rzn8KdkjrzSEgc/xGgBMnqetJn8acUYAN69KbwOO/vSAlt7ue0YvDI6E9cdD9RWrbajJqs0VpLYpdSuQFKL8w56/wCSKy7OznvrgQW6ZY9fQDuSewrUe6S1H9k6FumupflnvEX5mz/Anovqe9MDN1zTLOLVvs1lO0r5PmBRkKT/AAg98Vcu/COu6RaRzXenTJEwysm0kYPPXt+NereAPhxFpCx6jqsayXpwUiPIi9z6t/KvSiAyENyD1B5BoA+QNRRlv5Tt4Yhh+Iq1Yndhf78bL+lfQPin4ZaL4i2ywotjdKf9ZEvysD1DL/IiuA1T4Saxo7edYSJqECHOE+WQD/dPX8KAPKYDiVK0pjnSrlfdD+tVLuznsb6W3nheOSNsFHBBHpxV4DfY3a/9Mgw/Ag0AYtJTqSgBKKWk/ioA2tNVYYJJ26qOPqelULqUvIctn1+taEBDaawHZlP4dKy5ARIQfWgBtJRS0CFVipypwam3JKMN8rVBRQBIYHHQZpux/Q0gYr0ZhTxPKP42oGSIZl/vEe9WQOMmqYnkPA5/CpY52Y4bb/KgCxUUkKyc96eKf+tIZnsjRMM0+ZWbDjkYqxOAYz+dU1d0+6xFMQzFLUhmcjDbT9VFR0CDNORvnGfWmU+Ngr5PPpQBeKhhg8ioWt0PTil89QKlXBGQ1Ioh+zJ/tUfZwD8pqwAPrRgUAU5YCuWHSolUscCtBgG4NRiFFfcN1MVikeDRV5reN+SWBpptVJyDxQFioVK4J706OaSP7rcelWJ7Zz8yLu+lV3jZDg0AX4NVYLslAdPR+R/iKlaCyuxlD5LehbK/n/jWUyFMZ6mkVyhypwaALFzpk9vyRweh7H8apEMpwwxWlbanJD8p6HqOo/I8VZLWV2PmQIT3Tp+R5/KgDFDelSLIRVybSXCeZCd6eqc1QZJIzhxQBbjuCOtWobtlIKvgiskGpFcigZ2Wn+J7iABZv3i+9b1re2N6N9tMbeU9QOhPuDxXmqTkVaiuipBVsH2pAetWus39hxJ+8i7lMsv/AHz1H61uw65YanCEuETPTnnGffqK8ksPEl1akBj5ijsetdBbatpuokMSYJ/74baf/r0BY7u40hGG+0nVgeiSNj8m6H8cVlz27wtsmjZJB68H8D/hWda39/Z/NC6zw99nX8uhrds/EVtdr5VwMequuR/3yeR+FMQW2u3lpLF5szTQjKyZXc5Q/wA8ds1qR6k8F0rXv721lwLe6iGVA7Bh65656VUk0q2ulMlpJs74Lbl/PqP1qpEuoaNKWC4if7yH5opPf0zSGdnuB89htlHlYIONpOeAcfU1yuvXpne2tVsWt0tzyhI6Zydv9K17TVvtcFr+8hHmkj7MMBuOR0+mcVYnuNMu7gWc5R5do2oV29R2J70hmBca+ftpkt7VBbDpCcKTj3FXRqNhNB5sN2sFxF1D5UsMDcB3IPQUXvhhlJktXz3KP/Q1jT6RfJJte1mz2wM5+hFPRi1IZp2nmkdmxvJOw8kenP0qMHjFdFZ6Oltpc1xeQqS6g7CMPEQx6E+tYEiR8yQlmizj5xgj2Pv/ADqkxNCZ5x3x0o3Zkz6j+VLCyCXD7QrjaXP8J6g/T1qU206yyRumJIs5/wB09CPUYoESQES2dxAEUtkSA99oGCP1Bq1ZgR6b9vx+8s7hMD+8vDY+vWruh2cMemy6nNyNroE2/gaksUWHSIpQFImIYjqdwPBx3x/Kk2UkZGqCRL6SQHlmEsb/AN4HlT+PSo5UaN1LDAcBh9CMiuxNta3vkJKUZ4yTgArkHPGD6Gsq+shdaszThhapC4HlNgqVxhT9RyKSYWMJXIO5SQR3HBq9FqcqrslAkU9c9f8AA1mOUV9qZAJONxyf0pwaqJLkulafqbeZAfs846Y4P+P8xWZf2V3bReTf2q3ts3G/AJ/wP6VZBz+FXINSnh+R/wB9Geofr+f+NKwzz3UfBOn6gHl0ifype9vJ0/LqKy7TWPE3g9jbOWe0PDW1wPMhYegz0/CvV5dN03VWBi/cXPUAcH8O/wCRrNvdPvLaIx3cCXlseCSAT+fT88UDucIq+E/E0hMDnw7qcgxtJ3W8pPbP8OaveRr3g+KNr2AyRIwMN3E3mR4J5BP93vz0puo+DLG/3Ppk3kzd7eT/AANY9nq3iXwdMYUkc2/RreYb4WHpg9PwpDOnbXZ9XvLpYoEkjgOyU8d8+vXJBxW5DrmlXvhy5025nZZp4dnlPFjy2AwuP8a4+K/8L+IWlLb/AA9qM67XdGLW02f7w/h578YqTxD4b1XT9CsrqKE3MkMhBuLd/MWRDyrZ9jQI4TVbdLG9a2gkaZEwDIOhbvt9u1dZ4Kd5vC/im0Q/vYoItQiH+1C+Sfyqle+IFudOaHVNItJ5yo23ATy5V+pHBP1FWPhpcxr41gtHOIb+GWzYHuHQ4z+IFAHUeK7j+yfGY1C3huPs2qRR3Zli5CsQCzAd+Oo7g1rSa1oWteJtOS1LvNfwTWXmgfIVKHKHPcNgj600QwT6FoN1flN9rbbAHbDGWFzGwUfxZXqO+BWBpV/bXSHVJPJEtjcJc5j+UqUYDleo4/MGgZ59cxNFIY34aMlCPcHBrs9N1JLTwTpV9ICUsdSltZtnXy5VDjH0YZFZXjmwFh4w1WFF/dmcyx/7rgMP51r+AXtrvRte0y7ginjIjulWUZUEEqTj8RzQB18jaR4g0f7ULKK+WRv3o3hT0+8M88+lcvfeGdJ0wm9067u7LP3do3EZ+tUNM1tfDOu/ZZB/oE4wV/u4OAQfaur1LWNFhv7OLUt8dvcbhFL1UNkctjkDmkB53eap4k/tCONNTubokhYsZBPYfLT9R0y9tdRinvo4oLoHLmMja2e5x0Yd6lsvEg0/VraCcJcwafNK1vcAZkKlSFBPcA4PtVKz1XS0sZZL9Li7v52JJ8wqsY/qTTAWNJbabMB2v1Uf3h7U5Q08wurCRrXUEOf3bbdx9sdDSTRmeziuIc4hycdwDzU6QG9j+0wf65Rkp/ex6e9AEg8S2uot5XiG0P2gfL9vtQEnH+8vR/x5pl34aee0e702aLUrUcmW3XDr/voeVPuMimvZw6xFsOIbtTgSHv7NWVpy3un6pIiXMtlfRg+WQ23LDnBPoe3agTMuRdjYoV8V1B1zSdb+TxBYbLk8fb7IBXz6uvRv51TvfCl1HA15pc0WqWI5Mttyyj/aTqv8qYGOJCBwa63UNcuYbO2toX2RCJRsH+6M/rXHRqzyqg6sQK1NUkzLtHQcflxQJnoeia9BoOjrcPJ5txcRhif7qHoo/rWzoFzDr12+qSBEiiGAJFyGbGABn06mvKLuVhp1uucful/rWrda5c2un2kED7FEKZ2cdufzoEeg6jPJqd75edoGSx7BR1b/AAp2mk3WpxeV+7it1xGD0298+uQDn3rhrXxQbW2Ns48x3ILyZ5wP4fpXS+HNXtpNRUrJtEmVKH3FAzQZrm0lFsiL5kcpktz2ZD95Ae4Pp/WpLS0NxIXjGLM5Uk/ejyPmTB+8B+nWkaFYbMLdzbojO0eAPngcdCPUEckUt+lwmm27rdLJNGzT/u+pXO0P79OaYiJIoFimWK7SS3EZhJl+UjPKg9jgjg+nFS2jyn7LFNGoMEcoJPJ4TjnoVK4I/GqMawyS78KttfAxuO0UvUfhu5HsTUlleraaZ/pEbExytCSG+aNWXt9ORSAoXdtb6uheCFIb1F3GIdJVHdfRh6d6IRa/8I5GZIPOCTFWG7DLnnKntn06GtOKOG10qV968Sh4LhBz93KkexIII7VFNbq0gITy7fUo847RyjofYE/zpgY85tW0mSN5HuLZGXaD8skQOf5H8Kr6cr2um3DxOz4YKsyfeCYzjHseSPQ1sTR6XJEsRjW2mdTHMiZABBxhgeOTyGHTvWdDbW9s1zards2079gQrJG69x2PGcjvQA2QJeQyrP8A6yXbiUcsueMH+8oYd+xFVp4YrC1hsL3aQzuTInVQTwwPp61p2sNtLbOdyFSf9YikYbOenUDjp2qtdWH2+OQSzKkhmbye4wRlhjrz296QCyTPLe3CKEFpCoQxFuQpGNwHcDv7c1DNBLDcBjGskoiKDt50YGCjf7QHIPepVvLWC1dLcJdTRIVMki4O0jbkY6gehotrhLrSIkuNylGEfmbSCpH3WB9uhpgYUtpCl1DMvz2k3crzt6EH3Hes+90xP7SktrZGEikgIWzu9MH3FdHLCqmRLjakUjYmx0ilP3ZB/st3qKXS/tE6TSSLDLbcT5/2ehHrmkBzKTXNlIYXQjBw0cg4qvqUCwXIeEYjkAcV04trG7jAT5zM74GcGMAZx/hVTWtGmj0iKeL95HEcH1ANAzmJ95w56HvVVqvIcrsPXtnv7VXkjwSMYoEQDircZ4Ddu9U/anxymM+o9KBmrxKuCVz79G/+vVOaMKejD2NOjlDfc/I/0pxk3DZ1Hoe1AisqE1IsY+tTmLAQnkN0qWGAyNtG0Y7dDQBAEA6/lT94Awv6VDcyKsrxqGwD3pkbEMMNjPFAwlmc8dBVcnmppFZX57/rURFAiRHJ+WrEKeZlfXp9apqcHNXo3x86fiKAIJImXnqPUVH0rQaRJPmPB/vj+oqrJEQ38P1HSgCEZzmn7S3NOVBTvlHJoARU/GpQAOtQtMB92oy7PQBoRXHl8JyO4qKXbLJvKAevNQKp71OievFAE1rFmTOeB1pLpQj7UPXtT4Z1hGOoNK08RO7ycmgZnYZjzWpaIUj3o3zHt7VEsC3Ep42itFriCyii2Qp5q9zk5+tAhp1C9C7EkcKOwqNp7mQfNJKfqTVpfEUgOfKt/wDvj/69PHiaQf8ALC2/79//AF6BmeFY9iakQOpBAII9KvjxRKP+WFt/37/+vSjxZOP+WFt/35/+vTA29H8YahpoCOrPGPzxXWW3xDt2H71FB91Irzr/AIS24/5423/fn/69OXxZdtwsdt/35/8Ar0gPSdQ8TPPp8k1q6RDBJd2/ka8slvNQ8Q3MqvOsNuhyz+vPQep9qlu9U1HVoPs4C5bjEYwKfb6fHpFri6k3M5yYk6k9s/5zQMVdDs7tfKtLfAXrJK/Oe5Y9APakFhaRMltYwPqF6D99F+RfoOn4mrNvp97qcpZy1pYjhstjA/2R1JrXN1babafZbBfLjx80h+/J9aAMg6K4YPqE6yyj/lkh+VfbPekn1CK2j2RckcD0FQXOovMxSHn3rPKIkvzv8x7mgRLI0twd8hwKnt7aS4+WMYQdTUsWnyeZE0hVonIwR/Wurs9PVVG1MgDgUAZVlpawN5gTd6k9T9KsvdhJAkCZPYVYFveahMY0HlxKcFzW7p+kW9snEase8j9f/rUAUdGW8Vz9p5jfoO4roDbpIAxRSV9aRYXhixCmXx371atw4X9+FVyMkBs4oAxptTC3AtrdHmlb+4cHHfHar8qPNB9msziTIJd24B681TtbC0k1GV1jyST096tap9psYYjaxsiZ52DkfnQBB4h0o6lpTI4UyoMivIplkWZ43GCpxj6V7DY38tyty83ESrnkYry7XbiMaxIyDhj81A0Z+9Y1weT/ACpYy1wDH36rTfs43Es/y9qkchAFj4Qjr60gEWONG2swZz09M1AyvPJg8n0qZID9+QkD9T9KkZzISEXaD19T9aBkDWyRjDct6U1LEuwY8CtS2sprs5RMheGkP3R9T6+wq35v2H5bG3a4uV6yyDAHso7fzoEQRabBYQ+deFoEPRf+Wjfh2/GryWer6taBdOgS2sSMj5wC3uT1NRPqNldSg6pp0ttMOk1q5B+u05BrUiu3hs5Lm1vob2CPBYOvlSr9QOGP60AV7Pwd9n3vefPkDHP5g+/v0NaNroFhLl3t0EaHHHBPPTNJF4ntZ4D5kjKQM4dNp5/mKvW13DPYrJG/7ts+wPNIZNLFC1uUS1hUY+UogBH49TWNb3Ns17cskipJGgxJHgg+uTgdPTFbVzu4VdwwMZ9qw/IaHUruIQKUmi3B/T19qAJ5r+00+cLfxytHK3mLNFgg5GDweoq2NP0XWo/9DuonY/wHhvyPP86qR3Z0/TzHqEKXkIwMPzjPQDrgCsnXtJtoZbS40128u4K7cMeCT2PXFAEuo+FJrMmSN2Xb/nrVzS5rybw75c11Md0zKuXJ+UAcfnVzVJvKguuW67R+AxUWnQo2jWwMiIqIzPI/QMxzj3OKYjLsnEX2ybYrGEKsWVz8x7+5q1czrBpMp1KRlaX5gj8vkdDjPFZlzrNvp5ki0weZKxJNw69/b0/nXNXd+XlMk8jTSn8cUAWCcxl2favr0rMnukBKxf8AfZqCW4kmPznj0qE4HSmIUk5yxyaQtnpQAWqQKF60ANVCeTThhelBaoyxPAoAeXFN+ZqckRbrVlIsUAW9LKJ8rhQc9a3EQHp3rAjjPatrTpGGI5OnY0AW/swft+NO+wliGYZ9/wDGtKGEYq2kQHFAFGK2iiUM/J9qV5Gb5V+UVanWKKPe5UD0rltT1aQ7ktgyr696QGjeahb2Sncd0n9wVzd5qk92SCdq+g6VRd3c5c570gV3O1R1pgBPPqakigkmbCir9npLyEM9dFZaWEx8lAGNZ6P0LjJrft9MUY4xWnDaKgGVq0sXpQBmGyVR0qldWmVxXRNF8tU5rfcOlAHKEFXKt1FRzR7xkdRWtqNnxvQcr1+lZg9DQBnnKnPStG3mEq4PXvVWePncO9QxSGOTOaAL8i+WcjpW9ol6jrskPI/lWIGWVPamJI8E2RwaAO7FwGG0cCm55rLsLsTxAjrWirZGOlICUHvS0xT607NAC4ooooA+haKKWmAUlLSUALRRRQAUUUUAFFFFABRRWNquqeQhhgOZW4yO309/5UAO1TVUtY3jhO6bpx2P+Nc87vNJIxfBUHc/XB9B6t70zy2LO7PkFsM/fJ6qv9TUyqFjz90ZCjHOMnoPU0AKkKwwY6befpn19WNK7LBH6EDv29z7015cRkqOnT2z/NjVK/u4bG1knu5lhiTmSQ9FH9SaAJJZglu0krrHFGu+R3bAXvlj6+1ePeL/ABu+sg2FiWj09Tz2aY+re3oKZ4y8aS69L9mtt0OmRn93F3lP95vX2HauPOSct1PQUAP/ANpuvYUwjJz3pNpAznn0oLEcUAO5FJuAODTdx7cml5PA5NADjgfd5Ndb4M8D3HiS4E9wGhsE5aToZMdlPp6n8queCPAcmskX2oDydPU5x0Mv0/2ffvXsVvFHbr5MMax28aqFQLgAe/8AQUAVTo+lDT49NbT7d7SNcLFIgP4+o/nXC+JfhroqW0l1aXjWGFLbZPnjP0zyo/E13ep6lbaXBLc3kiqqgHDt175P+FeN63r2reP9ZTT9ORzAzbQg/i929BQBDo91apPcWOnJutYbeR5rhxzM4GB9FB6Ct74RaTC+s3ly8asYYhtyvQk9RWrqvg+18G+BJOfNv7ghZZOwGOVHtWn8HbRTY6pcMv3pUQH6An+tAdD0JVAp1TNDjlWzUDZXrxQICcVS1K5WCykZmbsOOvJ7U+6u0toi7ngD/OK5rUb57hhzgABsHoo9/U0AUr/RNL1i4D3+nwzSCPC7+qg/3iCM1yt78OLGRZZdNneCIqUIl+ZWJ4+Ujn+ddfb4kaRm3eT8o5+9Iev5VoBHkkhQ7Q5b5U7RqOcn3pDPnPU/Cur6VPNFcWU37rkuiFl254bI7GsUqR1r6pmdIreRY8+Uc+Y/8Up9BXKa94J0fV3Lyad9nuSAP9FwrL7kdCaYHgFIa77U/hnfxM50ydLsD/lk/wC7l/75PB/A1xt5p13YTGG6t5YJB/BIpB/WgCTTrhVbY/3W4P0NJeW7xyEnn39R2NUeVORWjbXyMvlzjK/yoAoiitB9PEuWt3Vh6d/yqlJDJGSGGMUAR0tGKKBBRRRQA5QxYBeDVuMNt+deRVKrFuTzz0oGi1Rn1pM5oNIZBcvj5R361Vqxcochx0qvTEwpKKWgQLjPPSpxBn5kPFNiRHOG61YSNU+7QMBGCBuUZ71IFCjAo+lLSGKAcZppFBPHWgZxQAn1opCwHJ6Cm+dH/f8A0oAfS00FW+66n/gVO5B5FAC07ORhgpHvTaWgCKeASHK8exqq0TB9grQppFAGcy7TjNICR0qeWJUG7qSaiCMei0xE8F9LA+5WbPqGwavLeW90MTxqSe4+Vv8AA1jnijFAGnLpaSZa2kVvbo35VnSwSRHDBuKfHcSRnIPSr6akJQEuFV/r1/P/ABoAyd1PEhFaslhb3A3QPgns/B/Poaz5rKaA4ZTQAJORVmO45yDg1m8jrTw5HSgZ01jrlzan5XYj0ro7XxBZ3oC3Ua59e4/GvO0mIqylx6GgD1S1uLiH95Y3XmJ6O2D+f+NbVn4oAPk3kbITwcrjP9DXkdnq89sQUkNdFaeKI5kEd3GpB/GkFj0z7Hp9+PMtn8mTrmLjn/d/wrNvNLuoG3sGmjUAeYjZ4HTPcfjWDayo2JLC6ZT/AHC2R/iK2rbxNc2pC3kOR037v6j+tMQ+y1Ga21C3nkeZ4ozkgPnIA4GDxiuo0nWG1Z7ktCqCErjYxLc88+3uKyo5NK1dNyOqSn0wrf4Gov7OvdOM32Z/Nhlx5sXILYyB05HXtmkxo3bm/wBM1CYWTz+ZuOTGOhIPQnvj0FZWpzx2kMlvbae67iwUOPlkUkZA75z09K5+RFdirRsig8IWyQfrxV201S6tJw7v9qjXkRzEnaw7qeoOOtFguVZ0MUpVv85rVni8zwzY3iHE8TNEJB1wrfL+Q4+lZ9zMl3CLrYscpkZJY06bhyGHsVIrQ0K6t3WXS704guCGifptkAx17Z/nT6CJ7a8MXkxhM2l9zszgBzwyg9iGH4cVoRyWMMAs4p3ZodzMjrhlU9cj2PcVPZaLJpqyeXMsybt4iZOM+o9G+lYurSTp4jhuowBuZUXPSRGIBB9jnn0NSUdBBciSFA4W4ixwerAf1pJZEiiMluEmABBD/fVT169QPSqMcA0+YW0Y3xn5Gw3+rfGcCrUkjKRvh3vjcB9Opz1xSA5z+z0uNTdWf9zGr3AdO4A4499w4qjmt/VBFaLHeRp/q2Kyp/eQkhl+u05H0FZGo2T2F40T8jqr+qnofyq0yWQg07NRA07ORVCHgg8mrkGpXEHys3nR+j9cfX/HNRutlFBC2JpDJErlxKFAJGcAEdqr7kaYJGXOckB1w3H04P4Uhl6a00zVQMfuLjsOhz7ev4GsrUNHuooylxGt7AOOfvD8f8anIIU7422jHJHHNT2+oXFvhUdXTskjZ/I9RSsB55qXhCC5JbT32S9TC/B/AHr+FZOn6x4g8I3BS2neOPPzQyLuib6qen4V6/LHpeqALKnkS+/HPsRwf0NZWp+HpvLIkjW8t/U/eH0br+dIdzk49b8KeJnkGrWjaPqEyFGurbmJs4OWXtyP/r1HZfD7XbDxBp9/pkltfWqXCSx3dvKNgAYHLDqOPrUOo+EIpSWsXZZP+eUi4b8Ox/Csmzl1zw0xuLO7ltirYKIeCf8AaU8YoGel+INLhurO9haNnTTtbacBP4Ypk3k+wBNcNq9wpW6g0q1+/NJb3SjB3LIEKsD9V/P61s/DzW7jWm8WQahI00t5ZGZvXKgqcD2DcD2qlptpBosI1ybY0RgMLRM3HmA4V8+4wRnvQAzx3C80WhalIjLJdabGkuevmR/K2ffpVD4ezbPF8VqzYW9gltT9WQlf/HgK3df+1al4Bhubw757K/I8wLgSRTIGVsduePrXC6denTNcsr1Wwbe4jkz7BgT+lAE3itmee1JGCiFW/wB7JzV+e9t9X8F/6ROn22zK/I7fNIB8uV9flIz9Kt+KtGl/4SXWrJ0+XMl5ZSDoVxvK/QqTj3FclZmJoZoZEyHXcpHUMOn4etAHQ6FqH9nWvmJDE0zYO90DED2rH1u2M+pu9tAoM3z+VGMBTjkAfrUmnSNESpjZ9m3AHoeefxq9cvbxSmQWrq8pAdASSD6Z7UCNfwZqtxJLJeXMisibLcggc5B28eny11OqeGLK401tR05ltrhQzCKP7rEckY7EV5nBp1zNJ5ekTGdJCG8rIViR068E816b4evrptH+yXVrcxl4ynzrwZMY/A0MZx11brdRC/tRtmHLxjuR1P1pC1trVusNztSZP9VN/Ep9z3HtUljKySGNhhlkIOOv0/wq3ceHpLmym1CwOZYjl4h/EDzlaAON1CxawvJbe5TbIpVuOQVPUr61Z0ZPs8F9e219NDdWpQqYWwShyCfcA4zW4k8OsWa2F+/kzxN+4uSuWjP91vVT+lYN3bwabfXDxahF50ZICBTncOoIxgg9DQJm2txY6vfQQamltHfTqGt9Si+QFzkKsyjjk8Fh061z2o6ffW91JFcwMksbFXQ9mBwarX+pHUZYm+zQ2+xdoEQwPr+deqatYNr/AIUtNctQhuZ7eMtnu4+Vh9SQaAPNtUKvLHFGcj5VH5YpNWfbKIh0QBR+AxVuK1FzcRo0D293k7UI4LLyRz3rI1EyLdyJLxICc/WmJoR5cvke1XdOuJheReUW3hgRWSuWfArUt3+zxlYxulI6+lAHdTeJbu0vTLNCrQyhdwfDIxA55/ya3Ypv7U062vtORUMLOBEHywAwWwD1GT0rzC31W6s8pKN8bdY5BkEVbuNWNqlslmXtyjGUAOcqT2BoEekWCWbR3VwY1fkA2rjIGBuYr79cflU11ZxXEUqJiOa4cKNg/dsw+ZTjtlW6iub0zxgs+mA36IH+0YM0a4YEqMP79OR3robW6E8sRikU7WDrGMbcKT0PXnJx6A0AQmaC2trKwuBgGLMmeqsXLcj09addaif7RkhmgYWwGwxbclVH8Qx6dc9DReWkd9FAnmL9pJZgT2TP3D7jqKtfaIYYWWxk+03MESK0g7qG7eo7GgCvqEsVtDHNJp9vcyE4812PIIypwOuRWE1vNqmofa7EIswO6QBuFYd+f4T/APWrfAXUbW6tvJe3jBzEJP4VJ4GfQMfyJqtcTtPLFCgQbx5UtuI9rxEcHBHUdxTArtPNGftDzQmF3Cw7EA5HJ6eh4NSXUKEJI9tD5qksodsJgnggj0NULazFvdym5O2KFkcnqAdw5A9COvtVyN28oqki7AGIJXPAOePUbSDjuM0h3Mj7IINZhuopIbdmIaS3kfaRnhgCeCPTmpp5NQeWZLybzIR96PH3k7suOMjrinapZQ3FlCHbyGEjou/lFJAO3P8AdPUHtTIrXURaRNFJsuQrRSRMQDIBwCM8H5T/AIUxEKB38y3lCvc267SP+e8R549wOQaYAWj2A+bIkWYyf+W8Pof9paW+WWHTrC9+ZLqAtE2eDgHK5H04p2VuYY7m3PlB33Kf+eM3p/utQBm2UCWJubptzQrt246kHI/MDNdBp6LeaWI3+ZJUK57H0P8AL86z5kSe1lGPLimbEsf/ADxlH8ge1LpE81r9kt5NygSyQMD2JwymkwRy66bC2p/ZbncqMxTeOqnsagvNCvbCSVJEZkXguOR+NdF4rtDbakJ0GBKA30Nb1tMl5ZW10ed6bZPqOD/SgZ5FKoWQigRswO0ZxXomr+EbW9zLb/uJj6fdP1Hb8K5GbTbnSrpFuIWUZxnqpz6GgLmMODVpGB5XrWheWlu0pX/Vt1B7HP8AKqBjZG2enWgDRsMspDAMFPA+tI1zDJIROnQ8OOCPrT7EhYgO7MD+ArPvW8y5cocgkkUARahNHLdlo+QaSPBG4fjUBHrSo7IeKALjYZf84P8A9eq7IRz2pdxPsKl+RovcUCKnSlVmU5U0rCkA7CgZOsxP19RUsT4bc1V1GDw3NPJx7mgCwI1Yth1GOmarzIwbBFKspznvTx83JH50CIY4Wc4C5NS+Uy/eGKvWseAXXr2+lVb+RmlwBgCgYwyInSo2mZunFMCE9amSE/SgQxA/XOKsICeTz9amhtJH+5Gze9Wk026P/LFqAEtjGse1u/erxXR5wiTfaA/TzEb+hqFdIvT0gapRol+f+WNAyC88PXMX7y1P2m3PIdOTj3ArNNvIh2suD78V0tppes2xzA7J7Z4rTQa6MF/s74/vrz+dAHDiBqUQn+8v/fVehG4kFux1WO2MYHQDrXA3qC+1OQWMHlx56DOBQBH5X+0tTRRFQe5PHFaNvptvpyCW6fdJ6f4Vp6bbX2pnzoylnYLkNLIuQ2eoAP3jQFjMhuhbYS1SVrojBxzgfQVvaVZQWsX27UXM122dsPQR/wC8aX7XY6LG8OnLt3femcfvG/HsPasOa+uLuQpFuAzyaANfUdZ3McnJHAA6fQVkuZbk75jtX+5To7cQDe+5nPer+nWhu59zj5V7UAUEt5H5jgcx+y1qWWk+aoWX94mcgHqPxrpIrRIlTPBPAqrdxvBegRSLGBhiduf096ALdpYqkewhcDjjpWjpximV1QqTG20/UVjS3s9xN5NjCzE9T0A9/atfS7BtOBeR1JlOGx0z2oAveSVDhNoJzj61kW4urW4ku7mZtgBXZ2NdFHG8xJVeB3pLi1DMEePcr9fQUAQ6fqS3NqXMMvyZzhT09RUtlOmqrI0L4hDFW5+Yn60RK8eY7Z229s9F+lT6fYLZySOjsfNOXyuPm9aAKREWhrcTKWYkjaC3OPatATefaJIQxDKGw/UZp16sSjzJ0iwvR35/SsR9UmvZvs1mMrnBPrQBV8WXUlhombf7snMhHBryfzjNK0x5bP5V33jvVI0tlsUdWKLhseprgbRAp3P37UDJF3yH1qUOI1CgbiOntUux1bgbQPyqaCy3DznKxwjrK/T/AICO9AEMSySnaVZix4A5OfYVeeK10xd9++ZO1tGef+BEdPoKrtqhybbSoWDtw0x5dvx7D6VoWXhyK2AvNZm27uRH1dvoKQFH7Zq+pMrWge3ii5RIVKhfyq/B4m1izIW+jS8QcZmTJ/76HP61o/8ACSXFmIk06yhW1ZtojkTJbnGSfWjX72KVZ1NtErKcAp+tAFiS90/UdOinNi0JmDcbgQMcZ6VmRR20ejzK8nlpcTEAhc9OAPzNPYeRYW0Z/ggUn6n5v61C9umqabbW9ndW4uYeTFI+wlic8E8H86QF28t9EgsLe2vCxZFEf2hW/wBWR0BHT86hl1HT9N00QW9ys5AwoFYX9m3Nnq0X9qxyorN83mghSD3zyD+Ga6LVLSxjhaKZLe3kwpj83I49iB+ODQM6HT9z2NsrlWYKu4njnHPXFJqGlO08d1C7kp99Ey25T2ABxx9K5HSdd1mCURQQ/a7dWwPmK/TDdvxrrf8AhLLG3ZItQ/0abuHIkAP+8M/qKAM/+y5xqMLv5P2ZcnYit82ePmBPXFQ3pQatp1okjOsMm8pt7DkY9R2qB9SvA80jTJb2RYlZTw5H+yucfQ9Kwr3XpJN8dnuRD96Z2+dvqaAOi1TUtPsoStyftFwST5SngEnuR1rktR1i4ux++k8uEfdiTgAfQVlTXgXOw75D1c1QaQyHMhzTsK5YlvGk+SP5VqsTg0hPpShS3JpiEJ3HgUoTuacAB0pC2KAHZHamF/SkyzGpUi9aAIwpap44hU0cVWI4SaAIkiq1FAW7VahtM9qvxWwWkBVhtOeRV2K329KspEMVDd31vYD5zl+yCgDRt5vJGHbCep7UXOsRRriEhm9e1cVfaxPdkqG2x+gqrDevCRznHamB0lxcyTtvd2/Gsq7vE+7EMsP4vSo2nlvsIgYDvWhZaPyGYZoAzLexluWy3ANdDY6Uirgj8a07TTgMDFblvp2xcsPwoAzrewVR0q/HCAOBVgwFO1OCGkBEqc1MqE9KeEHWpFFAEYiH1qKSLtirgWo5GVR6mgDJuLYFfm6Vy9/biGYlfuHpXYTo0nXpWZdWAljKsMe9MDlyAy81Slj2mtCWMwylG6rUMqbhigCG1nKnY3SrUg3rkdRWa6lT71dtpvMXaeooAt6feG3mGTx3rqIJgyBhzmuNmTadwrV0m/6ROfpQB0obNSA54qrG+eamDUgJc0U3GeaKYH0TS0lFAC0UlFABS0lFAC0UUUAFFJWDquqko1vbnHZn9u+D6e9ABrGr+UvkWxzIwIyP1wf61hwI0skZYsUIOXHVsdl9B6mmRoLi653GNVA9Cx/+Jq2G/eHHPAUY/kPb1NIBTw2BwBwuP5L/AI1XkmdZo1A+7kDHY46D1PvTpp8NtXuMcfyX+prNvdTtNJglvb6ZUhjXr3JP8KjuTTAt3t5a6bZm5vZlgt4/mZz29l9WNeJ+M/GM/iGcRRo0FhGT5MO7kn+83qf5VF4s8XXXiW9DOPLto8iC3Q8KPUnux7muaIbdk8k0gEAIGc808Er1GTTdwHB6mnjH1oAUEH60HjgUhw3FIiM5wnSmAm0Nwv516P4H8A/a/L1PV42W1BBihccyehYenoO9XvA/w/CpFqusx8cNFbHrnsWHr6Dt3r0ofP8AN92Menb2HqfegCNZFSLYqhFU7Qg6LjgfU+1Z+qa9aaJp0lxcyKpGTg9j6n1NU9d1+z0DTzPO4B5KqOTk9h6mvLra11v4ka6ERSlqpzz9yJfU+poALm51r4ja8LW0R/ILZx2A/vMa9q8IeD7DwrYiOEK9y3+tmI5J9B6CrPhrwxYeGtPFtZpyQN8p+9Iff/CtscUAed/FufbpNpAD96Qmr3wlg8rwm8h6y3Dn8gBXO/F24zeWcOfupn8812nw9h+zeCNPB4Lhn/NjQB1Dnis2+vEt4yznPoO5pup6pHYw5Y5kP3UHU/4CuQN3LfXDz3D4jx278/dUUASXt5NdebKx2xgHB7Aeg9TTGt1IiLQsIgB5UPdj6tUzQDycyqu4bRDD2XJ6n3p88pSaQq+T0aT09lpAQxRlZJHd13bvmI6RjA4HvU7kCa33BvLO7CD7zcd/ao7NA0YfZ8zMxWP8erVP9+4wDhwCZJfTnoKAGzsxjOCpkxyf4Yx/jTmMkpVbcqq7t26QbjIR+XFE6okSLs+VmULH3bnqabNLaiaPz0aQjJYqpKxnt05H1oAbcPvRlurTp/Gi7x+XUVm3lpaahZyRyJFcx4x5cwDgfQnla2G3Swl7W6SVcdJDu/8AHhyPxzVK4SLys3MDwyblAkByOv8AeHI/GgDi9Z+F+lXRaSwebT5f7j/vYvzHIrz/AF3wJrehfNPbeZEQSJYTvBA7kDkfiK99WaRVeVHWcDpvx/6EKpwl5ZZZHbfIy8v/AEHt7UwPm1XkhPyluKtx6mWG2dFce/Wva9X8I6Lq2TcWqpMf+WsS7G/Tg/jXnWrfD+aCaQafcLcKBkCT5G/Pof0oA53yrS5GY5Nj+h/xqCWwmj5AyPUVHc2V1ZSbJ4XjOe/Q/Q9DRDfTwHgtj0oAiKlTgikrQF5b3HE0eD6jimtZJIN0Min26GgCjTkcoeKke3kj4K1DjFAjQQgjI70vPeqkEu07W6VcBpFDSM8VXe2yfk/KrXBowPWgCiYXH8OaWONxyB+Bq5jFJ170CsNXp0wafScU73+WgYlLSUYoABk0tNORSZoAjnz5ZxVQmrE0owVHWq1MTFzUizyL/Fkeh5qLNGaBFyO4DnB4NWP0rLBq5bylhgtyKB3LFH0oopDGMoPXmq8+4nao4q19aikXcCo4zQBSpKldFXjOTUdMkOcZ9aSpY3UDa4yKf5aOPkNAyNJHjb5TirsOpFRscKy+h5FVpUO0Y7VDg0AajR2d2MqfLf8AMf4iqlxpk0I3qNy+o5H51WBKnIOKtQX8sPG78v8AODQBRIK8NxShj2rX821uxiRFDHunH6VDJpLEF7d94HYdR9RQBTWUip0uKqPG8Zw4xSA0DNq2v5IXDI7D/gVdFYeK3QBLgbx71wyyEVOk/Y0AenWt1ZXZ8y2m8iTrgdM/Styz12/sQEnXzovUcj/EV4/DdshBR2BFb1h4luLfCu29PQ0gPXYdS0rV1AfasnqT/wCzdfzplzoTgeZburD0PH5Hoa4a21Wwvu7QS/3w2K3rPU9QsR+7kE8Xp7e470wJJbd4GO+Pa/f5fSouv41tWviDT9Rj8i5RUbuCOh/HkVJPo0cq77WRfbnIP4/40XEUItTv7d4zHdyAJ0DtuH0weooudUe41CK6lhiXyz86RrkHpyAe461DcWs9vgSxlf5fnVYoRkdjTsguzp729W1mvJooVW+YxmTfkow6bgPQ8Z9M1DFrguJJDJC6OEydvOcYDY9u9LZI2s3ltdxuhkTal1C/XbjaxA7qRz7Gnw2SaZcalI8LkwRfuPM6MGJBPv2qRj73UJYtWitriCHyWKJIHGVbJ4cenJpPEUhmnitNm+6B/hHQH+H3NNu5LbUbS11C9Kwq5MZAJ4IJB/DvWreTW0LSaibbeVQATJ1JIxnHpz1oA46SCaFisiMpHXNNRwpR9gcAg7DwD7Vd0+G3uvt0lwJT5MQkAQ4zliOv1qk8qSvlIVhQAAIGzjHvVCN/w89v5N0skKbS+Pn5Cg9vXBp91o8cF3BNCCqo3zD7wHB2sP8AZPQ+lYVrdyWkpkj2ncpVkPRgex/oa6BdZSK0haCTc4QERvxwMkqT3NS9BouGOwul2P5siEBiACRn8OhFU102Gyklkihae1lj2jKbipBzx39j3qGE296zzQnySSDNFJLsZSf4lPpWlCXWKPEcqTE5yh4J79eOf1pDMHULARGOWAM1vKu4A8lR3B+lV4Lqe3I8mTA/uPyP8RXTRzGTek8CRtIpXzAvBPuO3vUQto0j8gadFOwzkZCkDvgnr6j2NO4rGJM1hf8AyXcIhkP8Y6E/y/lWVqHhxypYIl1FjHP3gPr1/PNdDLY2gYPMJraMMN0UoDAgc8HuDWd5jQXRWxL+WWPloeeBz07celMDltAsIvDfiSPVLZG+48cts+FLKw/hPQkEA0msRWV1rEptZxawTXKAWtwm1HR1w6kdsMMjtXXSy2N+nl3sOxz/AMtB0/z9az7zw+xiIj2Xdsf4JOSB7HqP1FILmBolrq2p+GfFtve2jRRLCjQg/wAMkZLbQOv3eleZzrl/YivZ9P1C40ee4A/fxXAVZYblsNgDaNrdCcHGDXk+r6VdWMrq0DhQTjPYZ4oGd5qfk6noOj6oN4uZ7BYy4b7zRgoy/wC8AM1y2keHXubfW5oCyW9rAsik8/Pu+UfzqLwz4lgsbWfSdYE0mlzEOvlfft5QeJFz+o713EcCN4R1VtFvl1bzUAEcSBZAD1LKOdwoA8302Rpr9mj+USqOPcda2pIZZkDlGEbNtLj+8B1P0rm7JZI7guwdWRiMdCGHOK62DUFjQO5Vl+/kepHzUAZMlk+m5uE3LLgsMdAQcZFdl4U8UapawO91bO0EhILuhK5XqT6ED1rmbu6Q28aRrmTjZjkYJOQc02LxnqFpPa28sf8Ao0eUnixt89CSGDepwSAaAOtvLCyn1N7q3KhZx5gEbg7XH3hn9R78HrXReHrdY9QtQyKonAY44Vjj5uOxOeR61iXP9m/YoktpHs7uBvkSWEbWU4wGHBzjg1p6Rrj3dzCjpCzRMf8AV5RSOmR36UgGePtF0m22SwWU0l2/RLZefxxXl2qafpxubN52uYDcK6zPLwYpAcDcO2OMjuORXpni/wATaz4clgu/s0LWVxuVZXTcwYdFb8OQa4fWPFdhrksV+1rEdQJC3EMmfJmVR8rA9VYdPcU0JnLyeH76DGVRn+cbVcE/KcHHqD2xXpPgqMa38OdW0S53pLZSmRccOoOJBj8Vb8688vtQF9ethfsyFt0XzkhSQARn0OBXd/DnVpF8bva3DJ/pFksbbOQzIAQT6nbkU2CEvZJrzULbVI/JkDW8c1uh/wCWjI21hu7MV/8Ar1znjyygkubDWLNWFrqEAcZGCHU7WU+4710Gsw3Kanb6XFJttra8NsNi8wktlc+qkH8jVrxVaW914N1C1tx+90a9RyB2SVRux7bs0hs4DwroR17WPsf2lLcCJ5DI65wFGTgeuKlvtA1DT4JZ7Z4tQsT1ubb5wP8AeHVfxpfCd6NP8VabPJxEZhHL/uP8rfo1Q3i6l4X126W2kmt3gnePzE4BAbHPYg+9MVikk+yICY7gcED0/GlcJcHek+WJ6OvNb4l0rXrL7Xqlk1lLu2tfWS5Td28yPtn1FUb3w5d6bbm8iKXdmfu3Vs29Me/dT7EUCsMiv1t1+zeTvtx1yOc+ua09I1V7eQJHI6R5zg+lc0ZpAvyu/P61JHP8oADb6AR6Pp/idIpDItqk0mcnOeMfxDFalrDPc3Uep6bsYOS4TcFPX5kwfy+leXRTXEcMd1bzMrLIV46g/wCBq/fazc/6NCwVPKBb93wCx5J9qAZ6dqWo6hZajJFL5r2g+8giH3DzyQOo/nSSTxRXkkxTdc+RvgkQfLKp/i9mA61k6L4gvp9GsnTLiIt5qZ/1gDcZ+o4rRimjurOaS0Rkazm8yFJOoRuqH26/nQBRXz5tMm3vEI9yhS/3iRklQfTmooZTaQWGEUxidwQ/Ucjoe3DYParkpia3CrCz27uDEf8AnkxPzIw/lT9RtFlmurQIix27FxJzkA4XB/Mc0xFC/njmtUjMOy3jZonTOWjOcq39PwIqrHG/9nzQ3PzxwFSrjn5G4yp9jgj8RVu6tZ4ZI55I2aOdQswHPTjcMfgQamjjk03TgjzKMzEAjkAEcbh6E9vTNICnc2T3OmiDz/PDoZIHLZbcv3kz3GORXP6ddCzuninG62l+WVPY9/qK6mws1/tSK5jTywpJliP8PUZHqp7H8KxpNMNxr728YyA+SO2M9PzoAs3ETWkzM/74BAJgP+W0R+64/wBpajn2/ZhbO65BV4bgdx/C307GtTWm8iW1trYK0yELH754I+hzzVG6so4Lt9KZ1O7LW3orH70f0Pb3+tMCxr1v9v0IT4zJFhjj36/rms7wxL5tpc2THlf3i/Tof8a29IHm6abZ+cZQ56/Q/TiuWsGOla+iv9xXKN7qeKQzq1BZAT17/Wori3imiKSorqeoIyKuNGUkdPxBrN1UyLANgb3I7e9MXUwdW8L+f+8s3wQP9W/Qj2NcldWdxaS+XcRujDsf8813tpIGt3SVntphkbsnYxAJ/Ws65hnkgRn/AHsZJVo252sOf1HQigZy1tGJgU89Y3I+UnvVWa2ltJwLgdehHQit6XRROrtZHLjkwv1x6qe4qnd28kmljzEZZIGK/NwcGkIxZwu7jn3quRir7ossQbow4P1qm6lTg0DBCS2DV232nKt0bg/WqA45qzG4YZHB9PWgB8tvg4X8j1/+vVc5HHSrwl3DY43Adj1FQSRqz5QsfY9aAIAKeIyetSKgpS6p9aBCrF36VKCkfJ61WadzwOKYAzUAWxdEn5aaXZuCc/rUSRk+9TrHjr+lAE1pApfe3QUXLRwzYT8adHOIl2jpUkU9s7Ymh6/x0DEGr3IGA7Cj+2LvtK//AH1XSaboGhXzp5moTW68Zyob8iBXY2Pw98IXAG3V5ZSe3nKv6YFAHlf9r3x/5by/99ml/tK8brM//fZr2GX4beFoIXkM1z5a8tI9wAoH5V5t4ouvDNpJ9m0OCaQocNcSSkg/QenvQBim9uTz5j/maWO5nkkAZ2x/vGqcBurqXZEGJP5VuJYwWMHmX8rNIf4BxQBDBDLcyHb9wdSTgD8atQnbcLZ6XA1xdNwNq55/z3NT2Vhc6wm8uljpidZH7+yjqxq+99a6ZaSWelpsjfh5j/rJfqew9hQMamkWemt5+szrd3vX7NG+UU/7Td/oKp3+tyXL4BAjXhQOFUegFZt1dA/efA9KyZ7ov8qcCgReac3NwkSHLOcZrXSAQPHH0XPzGszwzZSXOpiXH7uMEsTXa2un217fuqSZCgEp/ntQBHb2Cyj5fmjP+etXVkgsSABuPTCVYu7K6BWC0RNgHROoFXdI05LZi8g3SkcE0AOt0RsTiB1JGcycAfhUdxeW1qd8AWe7JGU7ke1a8lqkyBX3YJrOlWw0qZpgi+YenqP8BQBY1OR47RZoQolbGQev+RT7GFPs8kzmZ1c/dlPQjr+FYsc9zq94mwN5YNdS7pp1qr3PCAYztyOexoAoX+qf2aI2mjZ4X6bORWpbtDd2aSR8xSLkVk+Y+ryjyoP9GTucAH6Zq9Nf29jb7VG3aMLGeD+IoAq3I1O5wlrC0MYOA+4DOO+OuKuzapFYWo+0SK0oUA47t3xWamtXF/bJDHasbg8E9seo9qyNTv8AT9KUvcyLd3naMN8i/X1oAluJbrUQbm9nW3tQep7j2HesDUvF8dhbta6aNingyn7zfjXMax4kub+U5kY+gHQCsuK1mum3ufl9TQBJNdzX02W3HJz71oWlnJKyoELSH+AdTVu00tLWD7Rcn7PDj/WN95v90f1NI2o3F2PsWkwGOJuC45ZvqaBliaWx0tB5zLc3I6RJ9xT7nuf0qK307U/EMvmzNst17nhVFXbbR7DRVFzqzrLMeRCGySfenS3mo64RDBBLHZDpHCmeKQEou9P0Vfs+mxrc3fQylflB9qjj0rVL+QX9zllPIeRtqj6E+lOECR39tbmF4lU4w64z7fjWnr1jcXcUc8c+IOAwDdB/hSGUklgkvrW0j2kxyB2I74BJP51Q1N3kiOFYliSfzp6TDTp43toYgY1KkSHJbPXd7n2rVtdW0a4Hk3tq1mx7jLx59u6/n+FMGQ2etaJdkLfRzWspAXOdycDA9CP1q1c+Gba9i8+xkilT+/EQcfXHT8QKS/8ADVncQ/aLW6hkV84Ifrjt/wDrFZGgwNaXd8yO2I4MfixAFAi9p/8AadlqMNjLP51pITuilUOMAZPByKrxiXUZLiBbOGS3VyFO07l74BzgD61bt3f7VKzNkxQMF9dx4AFZC2UViJLjVLp4w5z9mifDN6buw/nQBuwxC2AhjgXzFwz4YbRjkbmHA57daxLm+sNPunlRFvb9jnzHXKKT6D+prOv9bluIRBCFtbRekacf/rrCku8AiL8TRYLmlqGpSXMplu5Gdz/BWVLcvNwOB6CoC245Y5pOc8UxChtp4pPvHilCk8mpOnSgBoUDrSlsUwv6UBWegALE8CnJGW61IkNWUioAhSKrMcOTU8Vvmr8Fp3K0AVYrUtWhb2o6EcircVuAPu1PsVPnPbr9KAGRwY7U+V4rePfK6qB61n32uwwLsg2u/r2Fc3dX0105aR6ANa/14tlLZdo9e9YkkzyksxyTUXJ4FXLWwkmcZHFAFZI3kOFFTtp8qAMw4NdJYaQFHStldIWSEow6igDH0W3V4gVHPQiulgtEUAkVz9tv0y+KScITg11cDKyAjmgCaEeWQR2rQjbeuRxVWIIW+b/61Xcog9qQAUBGKjMWKR5zn5adFLu+VutAAqEnFO+RPvdaeVO3K1EIy3vQA1nZ+nApBEetS7FUZamPITwvAoAjcIg55NZ10WYHsKuuM9aryx5oA5fULQsTIvUVl89K6yeEbTmucvrfyZdw+4aYGdcR5+YfjVZGMbZFaDfMOaoyptOKALyOJYxUYLQy5HFVoJfLbaehq4w3pmgDodOuxPGB3Fa0YzXFWVy1vOD27119rcLLGGU5BFICzzRTsMeaKAPomikpaYBS0lFABRS0lABScAZPGKCQoyeAK53VtXDqY4DiIdX9f/rfzoAdqurBgYYGwvRm9f8A6386xbdTOdzcoWOAeN2O59F9qLaPzNsknT7wQ9/9pv6CpoCBFuHOck5798n0ApALGMNI3Xex57sB/IU13KqSoyWJ/IenoKVjthDn03HPfv8AgKxdc8QWehad9pu33OwxFEPvSn29FHc0AS6pq1lo9jJf6hJiM5Con3pSOiqPSvEfEniS98QaiZ5W2RrxFbj7sa/4+pqHXNevtavTPdSbiOI4x92NfQD/ADmsnOTgde9ACAkH5uvrTgR0BpDx0pMDtxQA/jGB+NNwP4eKbkj6VZs7aa9uY4IIXeRzhURckmmAyKJ5ZFREZixwAOSSewFew+CPAUemLHqeqor3R5hh6hT6n1b9B9at+D/AkeiRpe3oSS/YZA7RfT39T+VdmwAXJ5JXA7Z/wUfrSAUn92O/GOO/sPb1Nc74q8V2XhvTtrOrTsNoROp9gP61S8Y+NrXQIHhicSXTDACf54ArhvDXhPVfHOqnUtTd1tQeXPp/dUUwK+jaFrXxE1rz5yyWiH5nP3Yl9B6k17xomh2OgaatlYQqkajk/wATH1J9al0zTLXSbGK0s4ViiQYAHf3PvV2gAFBOBSZprt2oEeK/FGfzvFPlj/lmoH8q9E0+/j0nwvp8A2+YtsnHYZGcmuM1Xw++seLJb+7PlWfnbRk8yYzwPatuaM+UgKc4/dRfT+JvYUDJVcXZFxdMzhjlU/ilPbjsKlht3kvmJC+bt/4DEP8AGm2aiKJXL5lAG+U9FHoP8/WrMWxGlk+bymYBU/ikIHU+1ICK5Tbabn3eW0qjP8Un09BUbozSDCqCOg7Rj1PvUk3mXd3CN+3GWL/wqAOg96fdSRrblIwwixn/AGpTigBlmFS13B2EePmk7sT2FOh4uyxTkRjbH2Xk8n3qNHIVP74UcfwxjH86WN1HmMdwRiAT3bjoKAHzncQokxlstKeOnOBTV80DMUaBVHCPkHnvntTHH+kQrIMyHJjjHQcdTTZGt45Sr3LJc8ZfcV/AdvwoAV47WSXfMj2z4/1nI5/3hx+dNZrlJ4E3rcqZlx0ycAn7w4/Sp45LoLvOyeMjH91v8D+lVJPs8d5aMC1pIZic/c42n1yDQBJO0QMmYXhkI6OuM/iODUSv5cbnr056fzqzdvc+WRKYnQdwMN/hVVseTkd2/QCmBVmcgHKNxzjIP9KwbiC71K9jijKrAR+8JbB47V0DJu4O76Y/nQkKLswnT6ZFADYtLsTZfZXtYnhJwUcBs8deRXMat8NNKvovMsC9pLnoPnQ/8BPI/A12IxHHuzgAknJ7eua5vUtWk1PNtaOyWeSJJRw03sp7L79T2oA8q1DwrfWk0qw7LuOI7TLb5ZcjqPU474zWMTLCxHzKR1FeyxxpFGEQKFAwAO1Vr3SLHUFxc2qOez7cMPxHNAHlkWouo2uFYe9TD7Lc9Pleul1DwGCpewuM+kUvU+wYf1Fcrf6Nf6ZJtubeWL0JXKn6EcUAJJYyL8yfMPao1lkhOG5HoabHcyxnrVhbyOQYlRfrQAqTo/Vtp9+lSDkZHP0pjWsMo3RP+FV5IJYjyrUAW6TPaqQmlXje1L58uMZoC5cLCl3jFZ5ct1bNJmgLl0yovVqeHU9GrOwaVSVORQFzQ71VllcEr0qWKfzDjZg+1OkjEn3utIZRNGKfJEye4plMkKKKKACpYDiUVFUtuMy0DLwpetIKM0hhmmOcDNPxQSoHIoAz2GTkCngxNy24GpXBMoyflPalWKMt91uOfamIj8qNvuPQqlJgB0qcIu4lR1p4Ug9KBjHcoMgZHeovPTvG1WSB0pu1aQEIkib7wxQ0CuNyVPsU0oG0YA4pgZ5Rg23vU6TzW5Bz9Pm5/OpnXOGUciopITj5dvNAi6l2l0uJ4w3v0b8+9RyaZFPzayAn+4eDVXYxj44IqFJZEPDUAE1rNASrIwxUOa1ItSOzZKquvo3P69RUjWtndDdG/luez9PzoAyhIRUyzkUtxp89ueRketVuQcEYoA0obpl5U4rZsfEFzakYckelcqJCOlTLMR1oGej23iCyvgFuUUP/AH+4/Gtq0u7u2/eWF35ydfLdufz/AMa8ljuCDkGtSz1i4tnBSRhSA9itfE0Ew8i/h2MeDlf6dDVp9Otrxd9pMv0ByP8AEV5vaeKYplEd3GrA9flzW3Z3C5EunXu0/wDPNjkf4imBty2NxaSb/nTqBInH6ircGuXcduLa523NseGV/v7fZvX0zVa08SyxOI7+FueN/UH8f8a0vs2naiN1vJsY9h/hQITU7rTH0iG1sndtjE4ZTkA9d2e9QWGvT2Nl9kngW7tgNoBbDBP7vvjtUV1pVzDyF3qO6f4daoHgEUWQXOvsLPSltLq6tZ90U8W072H7sdceuc+tcgeGOPU0zK9cc96MhlPzU0guXYdOvZkDx20rBhkEL1FOtYVdJbKaHlAzKTw0bA8j3GDyK0by6u7aWS0uXMS+UohZGwI2GB19D/OoLgXLxJdfdubQqspPVnHRvcEYqbjsWoNMS4gAbE5GPLxwdo9T7dOatwWAjjxumCdBG83yjPbjp7VQt9ahX5iNpcfNGeBuOQSD6U2fULy0jZUTcDhQ78jANLUZomF7mFnEjLIh27G5zkU5JFXyo5i0EwUbXLZDD69qzEvUa0uLhXlR3lQMh+byzjIPuufxqK11U3dyLa5dPmYrHIeVBPY55APaiwG6Lpl8yK4kyRzygZSp6dKjLGMb7fTnYAbh5Z+XcPTPas61u0hvpLK/tmTOQuzJz7YH51obI4phJazOImxmPnjHdf6igDIurWOVZLi0dmC8zROMPET6j0qrBM8HzRPt9uo/KumvLZbnFxDIq3K/6q5T/wBBYeh96w57hoXeOXToUuD1kCkfjtPFNMTQNc2l5HsvYFGf+Wg5B/z71m33h5mizaSJPEekUnI/DuPwoeSYOCoV9zBcdDyccdqkSd4j+7dkJ7dj9RTsI4DVvCsTSEGNraXPQ/d/Bun54rnJNP1XQroTwPNDIvIkiJB/TrXta39tf24F7ACD/EP69x+tULvw6rQmTT5UmiPPlPhlP0Hb8MUDuedx+LrPVoxb+J7DzmBGL61ASdSOMsOjVOPCrahEZ9F1OLVLcDOE+WZR6Mh/pVrVPDFnNndGbOb0b7mf97qPxFcxc6Pqmi3IniMqMvKyxNg/UEdaQyzcWV3bxn7QXiVcY3ggjFZ+qTzlo3kg6gfvQMhu+QRxW9B43N7CLPxLZ/bYhwLmNvLnX8Rw341s21taXVkzeHfs2pHHNs58qcD3Q8MfcUAU9O1VPE6SCTI1RFyMvgTgDkHPG7HQ966Vb9rWHT7iKD93EFT7Q6YYcYZWHQ45we+K8ovWls9SZDZNZSLwYipBB/Hmu30PVpn0GV0dpokINxb9SuTgMoPUeooYHbXLN4x8GXVrfpjyjG/mRoFIYscbQf8AZ/nXiep6Lc6ZM0E8e18nyzkfMPw716/4fvJNVV0ZVNvt25VSpA6YIz/+qtHUbDSPFentHA6NPAWjyOuVx19frSA8GsrsJMFn2tGOzjcD7VvaZd22leJ9I1Ky/dxidfNQtkDJ2tg9cFTVLxL4fn0a+kDphc/5wfSsZMnjNUI+gvEtnBZvfahFsS+nVGjy3EkkYOPx461z+hSw6kZbCWNxNrVrcRhzjDY+eLGO45HrV7WZZNa8F6FrNurySw+XIyA4DAja4b23KTmsy3lu11mG8XTTaWkUq3kJB3CMqfnXI6ZGc9iDSGeWyo8UhU8OpI+hH/1667xPbXWsalb39vIm2+sYrnZI4VSSNrjJ4yGBqn4700aZ4v1CGMfunl82L/cb5h/Oni5EvgzT7iSETf2ddy2rAjIEcq7lyPZt2KBMo6PHPbzR+T82JFiuIzyNrNjDdip6Z7GkM1z4e8XTWunXjwRrceXlPmBTd3U8NgetXfDV3bW+o3ljOi24u4lVXPSNgQykZ7E1Y1OxtdK87UJ42kvJZSVcn5Y89wB1oAr3k2h6jfXFtfwrpt6jlPtdqmYZMHALJ/Dn1WszUNEvdKAupAtxZsCI7q2bfEfxHQ+xoitkuUurgzbZIf43X5ZVJGBx396tNqF7pLRXWlzeWWiAuok+ZCw67lPGCMdqYGI08lsdkfAbB9efWrNvL9ogENyrcZMbjrgdR/hWp9p0LXebmNdIvz0liBa3Y/7S9Uz6jiquo6Re6T5VzNGHgYbfOhbfEwPdWHH4HmgViD+1biKaNraRofKGF2N29/WultPF1zHpskjWyCWT5GkB4bHP3e1cjIsRRQvy46v6j6U2a7DIIo/9Wv60AdtpHiwRPiZFZGxuQ9Gx39iPWusW8tb+5luUfFreAxs+c+U3BGfbI/KvGkkI6V0Hh3WpbKcxtuaGT7yHoT/T6+tAHoZd4p0eJ+ZoNoI6eYh5x9dv61AwZ5pE2PMJPnVD1ZG+8v8AvBhke9RW9zaz2Yihu0Mvmh4B0YEjlT75A/Grb3TajJ8kfl3UQDrjnLAfN+eM/wD66YitbzS29/Z2p+cK48uT1jYYKken8iMVp2enx6fJd3TNk7m2/wCfYfzpmn+dJdXE09sokjYPFkHAZjggfU8/Wk164mhUWEMbySFcl0Gcknnp70AUNLxcahc6rN/qrbITP98/4CqWmQtqeuS38nMVvzz/AHj90fh1rQ1lf7L0aLT1/wBYQN+O7nlv14pZohoWgJbH/XP80pH94/4DigCpa3Jk12R4yqxdJPcngfjWb4stPJ1FLpRgSjP4iri6dMbGCSN0Vy5dgTz9fwrQ161N3oW9h+9h+Y+vvS6j6D7K5F3plrc55wFb6jg/pipy6wzwkjq2MHuD1rF8KTq8NzZPzj51/r+laOrRu1juXdvTnjqCMHP6UCLl7ZRnDKijfyMdOPas2a1Vk+RNpJG78DwauNeTSWZ2ckruUuCCGApn7ySESuMEgE/WpLMW5sAl2kg3KAAMp29D9KjkhttWga2ul2TDg44PHcVtJtZDu6ev9KpT6fHOwOMOAcP3H0ppiaOJ1Lw5eadmSL9/D3IHOPcf4Vh+WHR2HGO1en20sqN9nuSpb+F/73196qal4dtrwNJGPKmI+8vQn3FUSeXlcHFHIrSv9HvLKTdLH+7JxvHTNQ28SNIEfo/GfQ0hkCSsflNTRsVIZqJ7J4TuQ7lzjI7fWmL1xQBd8qIqXBbntVaaIH5lOR79auSRf6HkdQuaZaRpdQFScSqe/cUAUkjz0qxFAWP3c1aW0WLO9warq8inCsooAsrZSkfKBUo0y4PXAqp5s56PVi3t9Qupkht0lklboiKST9BQBKNIl7utPGk9mkUVej8I+KZh8mkXp/4Bj+ZqG+8Na/pkBnv7KW3iHeTA/rmgBsWltEcpd7fpx/WrsSXEeM6iuPfBrncuRnzKIg88oSNyzey0Adkl+YoWE14ki45Qd/wzWHcWCatd+eqRQWy9SmBmoltlsyGvuAeiAjP4+lXrKwu9bPzOtlpydZCvBHoo7mgY2GVUlWw0a2ae5bgFBk/5960o9EtdNf7RrMy3d71+zI2UU/7Td/oKsyX9ppVobXSY/s0JGJJf+WsvuzdcewrmL3UlbIzxQBp3+qvcHkrgcKg4VR6ACsG5v1XOw7jVKe8eQ4XgVCq560CFeRpW3MaUIB1pyR54FTJD3bigDufCVoH8NSyR/wCsMjZ/CtzTNJjiD3UjldvccH6VzHgXVUg1N9OfiK5+7n++P8RXpBs1nge3PyjpkfzoAyrfWE+2CFExGAcnv9as2ty95dZRG8sfx+9OttJs7PPmfPk8uVwM1eeeGINBCMOB12gKD6UAKsz3F0bZA0YA+Zz39hRfaPbT2nlmNcjnPes7T7S7lvBdSTYCHIAbg/WukUZAJ9OtAGPBE9hFGLaDdlgp9QD3FX5yk1q63MKmHqd7ZBx6ipJpY48L5iRk+vf8K5vWGvb+6FnbHd3ymQPxFAGlLrFtHKlvFtIYgYRQAB7VW1L7NqDLNdSJbwRf8tHOGb2A71h3l9pvh+M73W7vgOn8Cn+tcHrHiS61K4d3k3HtjoPpQB1ev+NYYIXs9MHkw9C4+81cDLcXOoTYy3Jp0NjJOd8xwD3NbcVhDYwiW6P2eLqB/wAtJPoOw9zQBRstIaSXYqeZJ1I7AepPatJ7mz0sBIwLu9HQAfu4z7Dufc0xJL7WHFrptt5Ft/s9T7se9a1vZaV4eXdOBeX3aMcqD7+tIZQtdFv9YY32pzGO3HJeTp+Aq/HfJHmy0K2X5Rh7gjoDxn2qeW21TWR9ov8AdFbEfuoxwMeuBVWe2XTVcRO0IlHlluTwT3A5oHYdpekJdXctzdyNJFF1c/xH0+lX5fEnk3AtbONQq4AQcUrRmx0IRx9QN2cHnPfntVNNQ0+OG3keGUSfeIh2nDD2PIFIDppybq2Ed9C/mcbXBG+P6HuPaonubSzsttxiWNQcmTqfqPWq8mujVbHY1q8AXAjk3Lub6DPGPesKa1E04SSd5Yww5K58zvgDPB9QM0ATS69pVxcIY9LVpA3Eocx/yyP0pLmwgubh3tAyg/NKS2Y4yeo3cEfTH0pslvb2p332wY5jtYsZ/wCBkVUmvbnUJUtYI8L/AAW8S4H6fzoAuNfW0FvHZW77ljLMZOgLGnaQita3UzuqRyTLukfgBVHb1OT0qjKllpS7751uLntbRn5R/vHv9BxWLqOqy3j7p3VIh92JOAB9KYjc1HxDbws8ekpmQ8NcP1/D0rlbm7zIXkdpJTzzVSa7ZxsT5VqtupiJZJXmOWP4VEGI6UYJ6VNFFuPHNAEYUmngBelSyQug5HFQEtnA4oAUsBTCxalCHvU0cfNADEizyatxx1KiKTxxV+C1DdfzoAqxw5q7Da7u1WYrUA4q9FBgUgK0Nttq6kIFOcx28ZeR1UD1rC1DxAeY7Xgf3+/4UAa11qFtYj5zl/QVzeoazNdkqp2R+grNkmeRizliTTVjeQ4ApgBck+pqWG2eduBV600pnILiujs9LVQBigDJstI5BYZNdFaaaoUHFX7ezVOoq8kWKQEMNuqDpVlVFKBgU8CgDH1qwE8XnJ99Rz9Kh0S8LD7PIfmTpn0rfZARjHWua1G2ewuxNHwOooGdMvSpNxPXmqNlcrcwK69/0PeroHegQ9V3HAp/lEH6U1Tg5FSiTdyaALEZ+UCnFecjg1Ar4OanUgjIoAgkQ5yahIxV8rnrULxDtQBUI9ajYZHFSOCDimmgCjNHWTeW6yKQelbrrmqM8VAHHSxtFIUbqKilUMMVt6haM6b1HIrGPHWmBRcEfWp7ebPynqKJkzyKrco+VoAvSD+IVq6NqAik8uQ/J2+tZEbh1pOY2yKAO7W5LDK9KKwLXWAtuoY8iigD6vpaSigBaKKKACmuyopZjgDkk9qSSRIoy7ttUdSa5XWNYa5zHGdsQIH157+/oKAH6tq5lBjhOIvX19z/AEFUBCRCWf74GQD2/wBpvf0FNWIAb5OoIHP8PufU1OZFchW4ycgHt7n39BSAHRRCVHAA7/zP9BSkqIWCjjHfv2yf6Co5zu+QHABBOe3fJ9/QVg+KPFVt4csAW2vdOcw25PJ/229AKYB4v8S22g2WXO+aQfuof4pCO59FFeI6vrV5q9691dSbpW49lHYAdgKbqur3Or30l1dzNJK55J/QAdgOwqgWB6UgG59Kdx0popSRTAXbnpxTM4PPSjd61YtoGuZQijJJ4FADbeF7iUIibmPQCvRPBur6V4XVl1TT7iO7k4N3gOgX+6Mcgevc1kaZawWAKyxsZD1kHP5dwKvGZPNXyysg54PWkB6zYaxYatD5llew3CMR9xx+o6gD0rk/G3jiDSozaWjeZdycDH8z6CuF1K8stEmR7a3H9qOMhEOBHnuwHBPoK1vCHga41ESa9rRfygDIA/3pCOc/T/IpgYkmnf2t4+tbeTc2+WNJM9+ma+irWzgsreO3t41SJBhUHAAFeIeC4/tnxFt5G5AldyfpnH8q91yD0oBjqCaQmoncCgQrNWTqGohVZIzyOCR6nsPeo9Qv3bEcG47jjI6n2Ht71QQl5ZETYbgttL/wwrwPzoAguJHOo2qCNWkXJjj7Lx95vU0l3GFiuDHJuwD503ct/dX/AAq3PBFGwCuyxhSxk/jlYkDis6WTDeWBzkBU/hjyRyfekMRCVRUCfOANsfYe7VatYv3MkzyZAYhpf6LUO1QCzFvLJ5P8Uh9qcjSTLFEo5ySsfZQT1b3oAc80b3SoI28tFJSPuTwMn2qK4DYkJbLlcM/Zc9hT0RUnmKuuxVUPKe5J7U2Zt3l7Uwm4bY+556tQBaSJIot0n+r7J3Y+9M3ebKXUKZWJ+kYHFQFpJ5XJfGP9ZIeij0FS2oQWsbEMIyTgfxSZP8qAFlSM3EQDsI0UlpO7EkDAqAzSJLMXtFMJIUAONwx6g8frVhnEd65KbpVUbR/DHnPJrPDNIZRHdOSWJOcMM/7vb8DQBMVtAALad7eQnmM/Lz/ung/hSGSeW7gikhScIsjcYB6AcqeO/rULGQLtlg3r6p8w/FTz/OpLKBZLwGzuNsiwMSh+YDLDjaeR0oAWVbZFIgDpJkZi5X/x08flTJC3kAfKASeP/wBVWLk3ICpcwoCOhR8g/geRVS4ZBEuPc47/AKUwIlR1fOWwePnUn9ckU+eSK2iaaaRUjUZLnAAx7+lVLnUrfT7eS5uXZY1HP17D61yF1qVzrl2JbgMlspzFD/It6n27UAaV5qUmsHYA0dgDkJ0M3u3ovoO/eiNQoAHYCmQ/dqRegoAd2zQdzfKoyT2oyWYIvJ9PT3NWURYxjqe59aAEih8pQWOX9f6CiZVkj2OFIY8grkH86kJx1qNjuIHvQBz1/wCDNKvgWjRraQ/xxdPxU8fliuR1HwTqVqXa2C3ca94vvD6qf6Zr04ZcYXp3P+FORQC4A4zQB4c6TW8hRw6MOoKkH8jUsd86cPyK9lvNMstRiKXdsk2ehdefwPWuC1vwvp0MxWwnlD/xI/zKPYHrQBzwe2uOG+VqY9kwGUO4e1FzpV1bZJTI9U5qqk0sRwC1ACshU4IxTauJfq3EyK1O+zwT8xPg+hoAo0YqeS0kj5IyKhwR1oAeirjcTiphMgG3dVTFLQBeVg4yKaYIzyarxPtPsasOxQBvmI7igCM2xwSGzUTROoyVqf8AdzDILKfSopHwCgbPvQBFUttjzPwqKpYldZAcUAXRSgimDNOpDFBpGzigUpK46UARmMOcnqKftpBx0p+84oAQDHJoz/tUDJ6UEEGgAzxk0bveqs4ctndkU1IyRuY4FAFwEdTzQT3FRqV6B1J/3qeoz0oAbk9qUpuBFPCsKA3ODQBCkbKu09aryxlWJ7Grp56U3AzzQBSZSvXvSKWU5U4qxMgZcjrVY5B5piLcGoSxcE5Hp1H5VYJs7tfmTY/qnI/LrWVSgkcigC5LpcijfCyuv+xVEqynDCrMN5LC2Qf8aui8t7kbZ4wSe44b/A0AZIYipVlIq9LpSyDdbSbu+w8Gs+WCWA7XRhigCzHcVdtr+WBg0cjA1jBqeshFAzuLDxZLHhLkb1robHVLS7INvN5UnavLEuCKtRXTKQynBHcUgPZ7bX72zIW4j8yP++OeP5itWO40vVhkFUkbv0P5968h07xRdW2Ec+ZH79a6Wz1XT7/5o38iY9xx/wDWNAWOxutEliBeH94vbHX8v8Ky3iI3xuGGQQfxpbTV9RsAPmW5i9uePp/hW1Bq+l6su2cKknv2/qKdxGfHqlz5sj3CJP5kYVg/TIGMj0yOorWs760nhhhlKhZMRtG/3hj7ufXB6Gqt3ou1fNtpPMHp3P0PQ1lyIyZjlXDjsVwaLJhc0NQ0treRzH+8tzyO5A9x7dKrWtwI4ZoGbbuGYieQrDqPoR+ta+lak76eyOvmTQNlXc4yvcE+v16ipJbDTdQaNo5kheYMy4746gjpkUrjMp1W2a3uAuYbiIMyBuNp6gH2PSkuNJb+0XtN2eOv+yehrVs7B7ZzaXUKXFqp3RvnBXPUj1U9xVyeFBetdbvmJ2HPTHH48EUXCxWngE8lrLJuE0ICse5YdCT7jv3q+joYxtf5SMhx05647/UUyQJNIT8rEDHXt/Xjmo2ge2mQZdozyNmMgj2PBzSGNuVuY3L2gQytyVbgMAOVP1Hf2rPmvbiK5txHPvtJyCu9QzKpOCM+oNaiyhiUaRCG4+6Q2D2waqyWgM5VAEEZzGAMHp/CehoAgm0+baXnZPtEc2BgdQMEE4460+50+G4kuphDKrvhlG3hSTz0PIJq5BI0bBXRgzHb5hXAJ9CO3tVs8nADcHoeCPp60XA5260tQtpFA37wqFkJXAznIb6dqzS0lndOikpIpIOzpx69sV2P3g49TgYbsPTP8qry2cTRTBdqGQYLhcg/4GncVjB+3w3KhL62Vx/fAz+nX8qp3PhyOWEvp0yNE3PlHBU/h/8AqNXZ9JkEscNsrsWHLnHUnqPYVUkLWd3IiSMkiMQSPb1piOL1XwxbOSs0LWkvqclD+PUfjXLX2gX+mSiVN3HKyRn+RFeyLqKzJsv4EmT++nUfh/h+VVJtAguI2l0ycY7xPgr+IP8A9agdzzODxfPLELTX7KLVrZeAZhtnjH+y45/OrtvpdleyG58Kapicj5rC8ISUj0Vuj1p6v4ahdyt1bNbS/wB9ASn49x+orkr/AMOXlj+8RfMj6h05/IikM2xqN3YSeZcafLHdQcTQ4KPg9GHqKxdS8TXLX0N1YXtxA0JJjTaFK568jrnvnrViz8XX9vGLXVI11K1UYEdwTvjH+y45FTz6Xo3iGMNot79nvu9pfEAt7K/Qn60AdPZeKtO8d2B03VIYrbVRFiKQcLM44wPQkdvyrza/sX0/UJLaQMCp4z3FQ3lhfaReeTeW8ttMpyA4weO4Pf6iuquEj8Q6Hb3mQb1V2sR1LDqG9yPm/GgDsPhxeXN94VutKjdVeNpI4pDztLrvUEehKsPxq/4fvzqthfaeZIsiExMYsGSIt8rLtPv09elcj8NL17PWrq2I5ltzIo/24mDD813Cun1jRtPs11nUJXaCQMJbd4/lJOQwYEdQRjI7HNAHN+Novt2iaFq6zLMTA1lNInQyRHaD+K1keF28+21rSm5+02ZliH/TWE7h/wCO7q7K90nzfh1fbUcYddRRHXBUn5ZMD0OQ1ee6JejS/EVjeN/q4pl8weqH5WH/AHyTQBD/AGq7W8ENwqSwoy4JHzKAex64IrctWjglga6dL2yY/wCrfnjPHWrd54JsftUlvFqiqTKyhCnIOcqOfUVUGl/2ZrCaTcb5htVlcjsw/lQBm+J7pf7Ukgto/JtXw0cY+7g+h9jWLJdTNOG3t5oGN/c/Wuv13QXYhZGTERJEkQwCpHX9ATWJc6JLaNc3EcZcWwWRkkXOYyQN3uMkfnQJohstHudWkS30+B5bo5Pl8A4Azxk807Tr3VNIaZLaRlCrumt3G5GXodyng470W7k3Iv8ATN8c0H7xokJyAOpU9e/SurtNIsddMl5cvcsDEGP2fCyLkdTn74+lMEYBbQdcP7z/AIlF4e4y1tIfcdU/UVR1HR7vR9j3Nqphb7s0b745PcMOKn13w3Po0pZZFktXOYZNw3Mp9QO471V0zWNS0oOLd8wMcPDIu+KTPYqeP60AQ/unQPsZfYd6sx3i220NbYB7mtHOh6sY3X/iT3p+6Hy9tIfY9U/UVm32l3umzKb6PCOfkdTujb3VhwaBGrb3TOcxnKsMgE4x7VsQ+LJ9P5lVDIPl3lPm59Tnv61xlwCiowk+bP3B6VNcStNYxbtxkGQT7dqAPV/D2vSXdoLi8fagJ2DsfT8qmsJXvdYmvUkYQ26Ebx6kYA/ma8zOoSw6DHErsCpb8ia128RHSdKisrXd+8UMznuSBQI6XTo21jWXnupE8q0IYof4uuP1HNF/nVtX8jOY0+Zvw5ptjdQab4dkS4ZVu7j5mHccfKKp2919hWGUtzM4Lf7nT/69MA1O4Lak1xEFBt3xgcArng/j0NbOlOtzDPbSOzBh8pPdWHyn+VZWrMr6nNKqLvT76j/lpGf4vrjr+BrQ0m0a3EMhnQ7lPljuYzz+Yz0pDOdsmbSdeUNwEk2N7g8V2c8APmx9Qw49/wD9Yrm/FNp5d8twowJlz/wIda39Ouftek21x1IXa31HB/T+VACLBHFDGkDu0eOPMOSPUGsvVUu4IY7i2jYwqcM45K49R6Vq3VzNYEShVe1JPmptyRnuO9XEvLcWi3AdHjYAJnOCD6kUrDuc61xGbxI4nzFMoMchXgk9j+NPkZowNwwVPFW7yziltlWzgSORJN4BbK4PUD055qOW2PkgSbcHqN2cE9cUDKtxCJBzyhOR6ii3lLgxv/rE/UdjUVlpTf2gxN44G3CA5Oeeh9verN1E0DLIo5RsMO+D1FC0E9Sjf2a3MU1u4yJlyP8AeHI/z71w1rpf2mSeJW23EQ3qP72PvD6969JmTMYdeq4YVyWrR/2Xr8V9GP3bESYHcHhh+WaZJzV1xMWQ4yBn696pBdrV1WoWsNnrGXRXtnIb6xn/AOtUUnhe5nvbiK0eIhMEB2wSp6EUwMZZ0KbX5FOWdEB8qHBNah8NavbqT/Z/m47owNZ9wb2zO2eyli/30I/WkBFEsrks/Soiih9y1YhlknYK3A7464rR1Dw5cW1kup2b/abB+DMg5jb+5Iv8Le/Q9QaBmW8JkHmxff8A4k9feprO+khZSsrpIhBVwcMpHQ1XjmKNg8H/AD0p0oRxvB2uKAPUYvi3LbaBtnsmfUUG0yD/AFbDs/qD6ivMNc8R6n4huzPfXLPz8qdFX6Ci2knkbybdPMJ46cCryadZ6YPNvSskvURigDN07Sprsb5H2Qjkk8VrLPHARaaVA0kp43hcn8KhPn6gyKSttbHpngY/rWwjWWl2+20TEne4P32+noKBkMeiLb4n1Cbzrk8+WOQvs3vRc6oD+7j3MV4AHQVVluprrhPlT+/61DCjPJ5VujF+5NAhzRtOd1w/4CnjQre+jIiOyQd/8RWgNGnFsZAcuOSK1dF0pGhEkr4EvAG7BP8A9egDAXwPPJEXiuUaQDhCuAfxrnpbWWCcxSoyspwQexFenw6Q+j6jHcW80zW0nEkbncB+NZ3jbRBgX8KdR82KAODwIhkDJ9aWON5WyO3c9BUqIACH/KpcF4yi8Y5AH8qBiCVbWdJLc/vUYMH7Aj0r1j7TLrOg2mpWE2yQ7WYdRkcMD9DXkywDkv1AzsrtPAGtCO6l0qcqIp/mh9A46j8Rz9RQBuXl1f3bCFoWUcZA+7XQDTY7yzfzhiNuW+bBBHvUyQJGeQxPb5TVLVIJbpRHGjJyOd2Mjv8AlQIeLi0sLXbGWfb0J6f/AFzVc6xfGA4gdUPKyHuK0Gis54lheDe64xGGxj6+1Yeu+KLHSU2s6TzoMKg+5H/iaAIp7aRl+3X9z5ES8jPU49BXNa1438qF7WwXyoz1fdl29ya5rWfFF3q05ZpGbPT0/AVRtdPluJEaQMS3QdSaAI5J7rUZj945rSsNJ3HKrkj7zNwq/U1oG2stKiDXpw/a2jPzH/eI6fQVHDBqXiGTy4E+z2i84HyqB70ABv4LM+VYj7Vd9PNdflX/AHR/WrcGguy/2hrlzsQ8hD94/hU6NZaEjx6fGt3dqMtLjKr71Dawy6rc772RpZJMqI+3PcHPBHfPakOxag1Ge/lGm6OiWsJ6yHgkDqc962NH0p7ASSvGjEN8splVifcnoB6VmXulXtg8U8WxBuyJInB2tjofT+VPtr03crXN9IvmQKGiAUKuTnnHqP50hm3qUyRRGWadsqPmBUjnsKxrm0u7+WFsIkTEEpk5x7k1PoUL6/cXF1M+6CBsQxv0LY6t71sQ28kEhe5C7gOBuyDnvkfpQBVvI1khJnKw244Ulck+wGR1rn5ILCSB/I85PLJO9gMkdxgckD86m1u6fUbwWVujTheSE9fc9gPWqxW3tDunK3d1nOwH92p9z3P6UAFtCJYPMT9xEOGuXbCt/urjLUS6kltGUsl29jM/3j9P7o+lQH7bq1yQPn29T0jjHuegpJr6x0l9sG28vB/y0K/JGf8AZH9TTELHYsYvtN9N9mtzzl/vyf7q/wBTVe515Y4nttKhW3hP3pTy8n1b/IrHvtRluZTLdzM7nturMkneb2HoKALU96ATs+dz1c1SZzIcseaZmgAmmIOacqZpQoHWnZxQBIsQNW4FEcgJ6GmQrlKsKoxg0AWZIg8eQOlZs8AHzDrWrasGXaeoqOeIA47GgDIWIk1chtyR92pIoMvz2rTtrfOM0AQW9l0yOtaEVqAeatRwhRUV3e29muZHXf2QdaQFhI1Uc8CqF5rlvagpFh5P0FYd9rU93lEOyP0FZ3zMfWmBYur6e6bLvkenaqwBY1LFbyStgLWzY6RyC4oAyreweYgkYFdDZaSqgHFatppoXtWtDaqlAFK2sAoHFaMcKqOBUqoF608DNIBFAHvTgKUDFPC80ACjIp4X1pAMU7rQAD0qrfWgu7cp37fWrgFLimBy2mTtZ3Rgk4BOPoa6WM5FYutWRUi5iHP8VWNJvfPj2OfmX+VIZrYxSjjmgYIpcetAh27IxUkblahzTs0AW/NGMk01pOMjvUKfMwDGrJUY2+1AFJ/mOe9NxU0i4OKYu3PzUAQMhNQyQ1oNGOoqNkFAGRPBnrXMalaeVJ5iD5T+ldnLGTWbeWqPE4foRTA40jPFVZU2fSr9xE0ExQ9jwfUVWkG4UAVY5Cj89KuEh1qi6kHBqWGX+E0AS7mXjNFKTuOaKAPs0UUlLQAUyaaOCIySHCjqaZPOlvEXkbAH+cCuU1HUnvZe4jBwqDqSf6+p7UAP1TUpLw8HbEDgDvn+p/lWakJ82NpGxtO72XH8zU+wrINzcgdui57D1PvSSqu7LHbGg5x6nsPUmgCQyDzFGMKMnnnHufVjSF8ShgMEZA9s9z6mmRuHkLt8oQAADnbn+bGlU75i4+VFwM9cf4mkAi/607uFTkk9j6n1NfP3im6kvtdvLnzHbfK2N7ZOAcDrXvV/dCGxu512r5UTHJ6LhTyfVjXz9dKXJJ5zQBjl26NRv9Kkljxk9KgKmmBJuNKGFRcip7aB55Qqhj6kLnAoAntrZ7mUIv4k9B7mulsLGO18xVCSA4y/+eKSxRII9lu6t6gjnP4c1YtlMr4WN1d2ONnQ9uo/rQBIqb3CpvVjwEK5z/n2qvqGpLpUnkW+yXUyMEgZWHP82/lRf6u1sTYacfOu2+V5k5Ef+yv+16mu58CfDpbQR6pq6bp2+ZIX7d8tn+X50AUfA3w7ad01fW1Ztx3pE/Vj6t7fzr0jXpEsvDd4yBVCxbQBwBmtULgYrlfiDc/Z/CkyjrIwWgDi/hbB53iae4P/ACygY/iSBXr+ccg4rzL4TQYj1O4I67EH6mvRpJVQFmOAKAZI9wUBLbcDkmsC/wBUkuiYbbdsPU92H9BUWoXs13OLaAcck/T1Pt7VArAQGGI4RiFll/ik56LQBoGPK7I3UFRiWbsv+yv+eKjhiWG0jDIxBJKxDrISc5b2/wAmrZIQxxiNQQP3cPZR/eao4dggzv7fPL3P+ytAFK6mbdIWKtcADc/8MQ9B71QZdvl5DEM2Qn8Uh9T7VpMqqzvInIYbIR7Dq3+f1qhuaW4ypweS8p6AY6L/AJ/WkArM+cnBlAPP8MQ/qangRFhTBZYeMn+KU/4Uy4CLBt2YjA+WPux9W/z9ao6hq62WAjLJdEYVB92MUAaSgyXUwCKZRtCx/wAKjHU+9V3YI23fwWG+X1x2Wq+hSzzaZM8rttknbL7stIemB7VduQWmiRUUFQSEHRR0BPvQAIPMCK6YUcxxDv7t/nipkc7AqupYAZY9FHoKqPKqRvsfAwd0nf6CrsUSFIwUYR8bIu7H1PtQBWO15JWbcIsgf7UhxUE728kUQuLXZz/rOCAufUcirMhYTzxptMxkIJ7RjjgVSDXEcQCyRXAUYwflbH1GR+YoAs+Q43NZTrNH6SNuH/fQ5/PNMW4gkupFvIGhkSJB5g+YAkk53DkVSkniSXc6S20v9/7vP+8Dg/jU1i1wZr2cbbgCRFO9tpyE7Ecd/SgCaV/NAxc+dGvQk5/X/Gs/UZNuxcqPl9f8K0JXEhL+S0Rb7wIHX8OtY2sP/pW3piMUwOb19/Oto8ndiUfxA9B+lU7QDiptbkciFDtxuOcdyB6VDadqANZDhT9KegabhOg6n0pkS+YNv8Hc1dRFSMKowBQAIqoVCjoM/wD16mAzUfQ4A7U9nCqSeKAGP09PWmqhk9o/1P8A9akwZDvfgdh/jU4BIzQA08YA7U0EAufenO6RqXcqEXkk1zOo6s9zvih+WEnr3b/61AFnU9Y4NvbHjo0g/kP8a595FUZNNklCD3qq7Fjk0AEjmQ89KgltIJ/vxqffoamxTgPWgDGn0VvvQvn2f/Gs2W3ntm+dGU11mKcIVf5WG4HseaAOUjvZY+DyKsCe2n4ddp9q1rnRbaTlMxv7cj8qx7jSZ4SSu1x6p/hQAPZZ5ifcP1qs0bocMMUgeWE8FhjtVuPUj0lRWHvQBVV9nZT9aGlZ+tXjHaXPKN5beh6VXlsZoxuA3J6jmgCDecbRx9Kbmg5BwaKBAKuRMu0c5qnRnFAzSBpc1Win3EK351YpDFJH0pKUgduabzQA4YxSfSiigBQacKjFLQAr4PFU5yfMxVl5FQZPX0qqUZ/nbjNMTGDNWopggCP1qBfLQ5zk0wsS2aANDeCPlNGe5qCKUHqealJpDHBj2pPemeYFYKfzp4IPTaaAENQShCcE4f8ASppdxGE61A8Tnk8mmBEylTzTakI2xkN17UygkSloHJ4o9qAJI7iSPoePer8WpK4CzhXH+3/Q9ay6DQM1XsbW6+aGTy3PZun4GqFxp9zan50bHr2piSsn3TirkGpyxjYx+T06j8jQBm5x1p4kIrWYWN2PnHlv6pyPxHUVVn0maMb4iskfqnNAECTEVaiumU5DYrOIZThhilDkdKBnWaf4lubXAZ96jtXS2uvafqGDMNkn98cH868yWUirCXBHekB7Da317ajfazrPF6E8/wCBrUg16xvh5N9DsYeq9P6j8K8fstaubUgxyNj0ro7XxRBcAJewqf8AaHUUBY9HSxkhJn0y5Vg3VHwyn/Pv+dZU8dxFLmTfC27dgcDd6jt+VZdleSLiXTr3cP7jnn8/8a27fxMjYt9SgxnuVHP9DTEXLDxHPbuIrzbJbsQC+3lQeO3UetbXmRyI5BbCk7vUY6H8fX1rDOnWd9GXspl5/g6j8uooF1qGnYWdN0apsjlHJHORk9x2Oe1JoaNmG5iljM3mRPgbXCenY49+9SYnc/IFcxgD72AyjoRnuOhrGg1exhZpVsnhmbqUwVP1BqzZ63bnYryeW53Ao68AEdm6cdgaVhl24le0VZCOCfuZzj3J6cU2NRDEZM5GCQC2Bz0/X9akVMRu6Db90Kf4Wz69iKjlhLB0jK7JP4CvyDnPHp/jSAmWZZEySoBUcHkjnGG/HvU+DuwQx+bv1H09azmEvnOp2tGRkmNfm2gdPw4NUdR1owL9ntnXI5Mg9+w9PemB0BKLsLuoycnoM/4GmrhuQVYMcnHf8u9cE8rytud2JPXJzSJcPBIHileNx3UkU+UVzu3UuCXfanUHp+P19a57U9KwDJAJpZGOT3z/APXqxp2sM4QX6Y3cLNjAYjse2a2GAcfPwOyA8/h9KWwHENDNbhDLG6Bum9cZ/ClDFZN6Flk9VODW3q2nyylrlppXIHyxlc5Htj071z4OKtO4noaSamWQRXcInj6ZxyPw/wAKgl0S3uVMmnTqhPWM8qfqDx+YFVt+etKrlWDKWVh3DYNFgOc1fw3bvIUu7VrWXtJGuUP4dR+ork9R8LXdoDJFieLrvTmvXotTJjEV1Gs8X05H+fbFRPo1rd5m0ufyZOpjPT9f60h3PJLbxJfW1uLHUIU1GxHH2e6ySv8Aut1U/StrQbbS7q7K6RfeQJ8B7G7YBlYfdZH6Ng9jg4yK2tY8OwyErf2RgkP/AC2iXKn3I/wrjtR8LXVqvnW+J4eodGyP/rGkM1Pn8P8Aiaw1GWBrZ0nBmibjgHa2PYqfyNegeIdFfVrKOF5Ha1gikjkTI2hozlW9clcD3FeZaf4s1CxEVtqUEWpWsLBliuhloyOhVuo/HIrtdM8UL4rsdehTfaCPyriPbgtsGFYenOBQBX0u9dvGsemSTzNpVzbtawiViRsmj3Lg9ODxXnd7btbXUtu4w6MUb6g4NeiwXM3hTRmthbpd3UoMts7/AMUSndgDqGHUeozXN+PrZIvEs1zEP3N4qXUf0dQ3880AbbSvf2mk6lF/rJ7ZQ7/9NIzsY/U4B/Gn+IVlu7FNVshm7tlKSxg5I5Gfy5/Osnwyn9r+Gb3Tmn8h7OdbiNzz8jjaw/76VT9TUPh3VUsL6aJ3ZhOQsofoSD1oAveF9Rl1BZRdvv2DoR+ntWho6/YRLa6pDusJRJGspyV8p8ExkjOMEBh+NYFojQ6vfrDuh2zNwWxwT0Ptiu209PNsRb/aUAmYFTH8+cEjoeM57e9AHCXukJ4c8Rxy2Unm2rHfES2Q0Z4Zcjrj1+ldBLI9zcCTSwkEqKF+zOwKyAdCo7Ej0NXNS8PyYOYWZA4DpH8uUP8Ay0UH7rA9exrJm06a2heOT/SEVv8AR5o22sMHlW9D/kUAPF0xhktri0VJTkh5FysbH1B6A+3esPW9H1VYEjkgieQfvA9vjaVIBGOldU7XZ0gXE/8ApaR4VndQJIiR0P8AeXtz1rH+2Pd6bLbRW000OQTCGBMZ/wBkE5x9PpQBwc28RqMMBk5Hoe9anh/VTb3K2F2zSaXcsI54SeFBOA6+jA8gio73T2swZhJmKQkNG6srK3uCBTDoN/Fa/abiB4Im4DyLgc9KYjd1rw6dLnJDr94qCemRxWIRPAJEuF4I4+vavSpEXX/DWmSzQ74rnaly46xkAqWB/wB5f1FYs2i29zZ3dvCc3EC+akZGGwBzx/vA/nSuFji7qXakcK84A/Opr6XdJDHnlQq/lgVQDlrsM3ABz+VWXtbrzYrmaCVYJPmWRwQrY9DTEamrXzy3qby2AeajOszPKCxygGAh9B2rJubgtNnPSmwZllVB3NAHpkr21+ls1tN5d8YQwjLcMPRT6gdj1rSeV0limmTZvjV8dlfHUex5U++K8vnvHN2vllsx4WMjqMV1ia1eII/Oj86BlQE5B2uBgn8e470COr1uFb3QvNQhjF8wPqO9UfClyDHc2b/9dB/X9M1Z0fUre9jktmKq7cEHoc8fzxWFZyPpXiEA8BJdjD1BpDZ2gQSxbH54Kt+FVzp7SaeLaCZxsDK0fGDk7sfUHoatDCzOo5B5B+n/ANbFNd2t2MwVjGRiTHX2YfSmLqYyzXFrKVmj/dgn5+2fQ+lK+ow7QXKhG6VoKl1dW4MvkzIQcSRchh7jqD7etZtzFZ3en21rHG2Iw37z0IPIB79eh5pWKHhSPmZsbckEdRjnIp9xJFIFEzNvf5cv0J9M+tMSBY7URF9wAwD3HaooJljsvs15A0sTjaXTlgR0b8OxoGLbmSJvs0/PBMR/vKOoPuKyvEFmZ9NdguWt2z/wE9a6KKxIDQTSJcSRnMcg4dT2yOx9exqCaESNtf7sgMcg9D0pknGFft3h+KXrLat5Teu08qf6fhWlpl1NDZxXcL7ZYv8AR5eAcr1XIP5VBpUIttWn0+XhLgNF/wACHKn+n40/TxsuZbF+POBT/gY5X9ePxpiN2HW7zjeLdx7wgfyIq3PewX9oYrmyXjoYnz/46f8AGsa0srqaASKiEdPv4ORwa0IrW6XrbMf9xgf/AK9AHN6hoWn3eX0qZY7lTzEQVP8A3z/8Tms7Sdd1DQrqVI3WOR1KSxyDdHKD/eU8H2rS1+AxXJbDJu55BBBrGupX1CCNXCtdKcLIf4h6N7+hoAx72WRbpy8aDccgJ059B2qxZaPNd/vrk+TCO54zWncWlnprLc3O15nUMsQOQKuWukX+rxrdahJ9h03+HK/Mw/2V7/WkUVIZWMgsNFt2klbjegyfr7fU1qxaJZ6UPtGpMt9fnkQhv3UZ/wBo/wAR9ulWJNUtNLtDa6ZGttCfvP1kk92b+lYEtxNdngskfr3NAiS+vDcXPmNtMo6BOFA9APSqbnJ3zHc/ZB0p2NkiwojBmPBPetmw0cA75OT6mgCjY2lxPKGcbVPQVuxW8dudz7RxzStLGhEFvH5kp6Ac81cj0O5dRNcnj/nmOv40AaWmxQXNuHj2sp4P1qodBke68tpGW1Rtw/n8vvW1p9ilnFt+Vc87Km1NpLWxNxCm9hxx157igCQICvltuJfgoemKZeWsN7ZT2a7S0Y6enHFUdEN20bzuJTJIcYf7gHY/WtS1tLWG4luUk3TyYWU78g46DHbrQB4zf2LWl9LE3Cqe9RFgEwq4GM5711njzTPI1JZ0HD9a5Myxxjb98j8h/jSGIiMf3hO1Aep7/SnLceVIGg3Lg5z3/OgF7hMdXX8sU0bYwWXDOPyFAzaXxrr1rD5bXTFez9Ca3NL+JSNCI9Utm81R/rIztDfWuDCvK2Tz71MYFKhQMv60xHR6549lnEkVgiwRv12HLN9T1rj8XOozbm3HP5Vfg0kSyAMrM56IOprTllsdLjCyKksw6Qx/dB/2j3+lAivY6MscfnSMqxjrNJ938B3NOk1MK/2bSY2MjcGY/fP09B9KkgsNS8QyedcP5Vqnc/Kij2rUtpLSwkFrosKT3R+U3MnCgn07CkMq22h22nqL3XJvmPIh3fO319K2LZbnWIchFstMH3Yx8pkx7iqVhZztdy3F9I7XIOCpxkfnk/p+NdAtyygtMIXA6RlgoHuckZpNjsZ9tBFp1tcRmaJo5MjygMtjHbPJ5rF0hha6mJGTEi5AR1KlgR29/TPWr91rWlJcvJJaxPIOC4QsPz6fpVTTXh1Ce4KjybeMfIH5Ck9Bk84z2z+VAF2/1TKBm+RQeYyfmP4dxVJ7fTrvTxPJdfZ4wdh3odw74446dOmaqQ/Z4DI9yd8oOABzgg/59jVm0t7m9YzxoscS/wAb8Rr9c9T7UDNmwutOttM8rSXlLDPLsB5h7nmobqW6eH/iZXQgj7JHzIw9B0x+NUmure0ObYJNcDrcsgUD/dUcD61BDDPfFrhn2wj71zN0/wCA+p+lAhZLwpH5FuGgiY/cXl5D7nqaY8ENmnmalL5I6i3Rvnb/AHj2/nUEurwWW+PTEZ5TwbmT73/AfQfSueubsmQvI7SSHueaYjUv9cmuYvJhC21ovRE4/P1NYkl1xtj/ADqCSV5eWP4VGDimIcTk5Y5pMnPFAGaeBQA0Lnk0/gUZ7Dk0bSTk8mgA5PT86B8vI/OnbSeopdpoAuW7AqDVrHcVn27FGwehrRTkUACt5bBx071dZRImR+FVQBjb+VTW7/wHt0oAgX5WDfnWvHLFDD5kj7R71nzptOR3rHvvN3jJOztQBqX3iEkGO1GB/f71hPI8pLuck1JbWktzJsjRia1ZvD8tvCjuc5oAx1VmOFrSs9OdiCa0rDSlwGxnNb9rYKACRQBn2mmYxkZrags1UZIqxFCq8AVYCgUgGpGAPSpQB2oApwGKAEwKUCnhaXGOlAAo9acMdKMUoFABigClpcd6AHDrQB+VHandaAI5YlkjKsuQRiuWlhk0zUNw+5nj3Brraz9UsxdW54+deR7+1AE9rOk0QZehFWiAelcxpF2YJjbydCePrXSI2RQA76UvXmijpzQAA4qzCxK5NVjQDgUAWZAHGR1qqwwaduz0oZSACe9ADUkK8HpTyAwyvIqEnFKkhU0AEpwOBWbPG0n3q1TtcZqpMVTPHNAHN6nYB4S/QryK5wjHBrsbtWkzmub1C1MbGReh60wMyZM8jrVU/KeKvY+Wqsqd6AHpL8tFVqKAPtqoLq7jtITLKcAfmT6CmXt7DYwGWU4A6DuT6CuUuL2XUZd8o4z8kY7/AP1vU0AF7fz38oY7gh4SMf5/M0yJBGST80h44/8AQV/qaVFYyFjzk4yO+Ow9hTk5y2OSSOPT0X+ppAHIJYnvjj+Q/qagK+YSznCA4wP5D39TUi5I3Hpz0/kP6mqVrf297LNbwSbjBjzSnOCedoPr6+lAEoOd4HyjcQAO3bA9/ep4ztUZOMEn/d+nqTTFCrz8owO3b2HvUnliOLLHDKOT1C+w9WNMDB8T3H2bwzfZXll2qn93ccZPqxrxq5CZPG2vUfiFL5GiwQZZWllBKD0AzlvfJrzCQlujZoAypcn5etRFBV6aNSQcYOaaITnOFIoAqiFiCcVsabD5XAO0nsaqmIBMDcuSBzWxaRu7BHVCvUueAAO5z2FACz2k91GUhhzMfuuD09yeMAVE17MsSaTpcjzzN8s1yO5PVV749+9PaebVZv7K0RHaJyA8ozul9h6L/OvVvBnge38PwrcTor3xHXqI/p7+9AFTwL4Bh0dUvtQjV708qh5Ef19/5V6EtRqMU8HHNAh5Nef/ABSudul2sH99i35V3bSV5x4/trjV9Ys7G2GSFyT2Ge5/OgaL/wAN0W28MSTN1lmJ/IVu3M73ZREKrySxPRQO596ydNgXTLGOwhmzHCPmlPQE9cVfskWWGMuh8peY4u8hz1b/AD9aAGSiLzMLvW3EfX+KYk/nj+f0p1opa6jOV8wcqn8MYHc+9OuzumlyV835QX7RjqQPeli8mJgWRvLx8qfxSt6n2pAWZNqQStvby2zl/wCOU9Bj2/z0pSzKQDtMqqMJ/DEMd/eoLtyFIZ13jAZ/4Yx6CoJJo367hb54T+KY+p9jTAj80SW8jqW2tIQ0veQ5wAvt/kUwbVugNikhfkj7D3agSkWwb5TJ1A/hjB/rXM6rra2SzbZcbsBpOpJ9B6k+nQd6QF3W9cis4pQJFaXHzydh/nsO9chZNeapdG9aOUWMb48w9Gk7AnufYcD61qeHPCt74uuRe3u+30tWyPm5lPovr7t+ArsPGMNtpuiWNjaxpFChcrGvAAC0DE8PRiPQ7Yq26VwXJPSME5/Oprh8AhCwjcYJ/ik57U6yUQ6JaI8e2NY1xGPvSNjnPt/k0yVnaZ94/eqABj7sa9fzoERhGyuRmQkBY+y89/8AP0rUluBCxVHUykfPL2Uei/5/WsvK8FXYLkfP3Y+1WUQH55Rk/wAEQ9fU0AVJMLaGaUMIjuIjGd0h5PPf8Pzrk4L4JLmCRreTug4H4qeK6vUCYdPlOVaYQHk9IwR/n3NcN5pChZ4VYY6hdw/LrQBvQ6xcCXMsaSZ4Jj4yPdTx+tLp15bKbsxyPbSyTsdqHZkcAfKeD+VYMTRyEeROy47btw/I8in2eoPCrJPGs0W9icLuByfQ80wO2SR5Yw0jqzgdcbePcCsLV2zfygDONo/TtWnpzRy2EbwJsjdflx9enNY+pOpvp+f4iOvTFAHM6uy+fCuFB5460WmXOBwg6+9M1U7ryJTyAucDpyams+T+NAGzBtwF6YqznAqpF61Mz/wryfSgB5dQxJ9BSnJ5PYcD0pEjx87ct/KpG4jYn0oAbGOn0p09xFawGSR1CD9fYVXuLuO0i3yHHYDua5q+v5L2Tc/CD7qdhQA/UNUkvWx9yIdE/wAfesuWULwOtJLNj5U6+tVjk80ABJJyaMUopQBQAYpcUdaeq+tAAq5p5YL9386azgD2qB5C3sKAFkkzwOlQFqGNWNN0y91m+jsrCBp7h+gHQDuWPQAdyaAKn2T7bKkKQNNLIQqogyzE9AMc5rQ1L4eXVrJFbQ3MU2pFd89mnItx2DP03H+7XoOgaJHpxNtosizXhyl3rG3KxescAPU9i1b7aZbaXarBbJgYJJPLMxPLMTySfU0AfO9/o+oaXJsu7WWA9i68H6HoagivJ4TwWxX0E8ccsZjkRXjPVGXIP4Gub1D4faLqchMKPZSnvDyv4qePyxQB5Ut3bz8TRgH1HFK9ikg3W8gb2PBrodX+G2t6dvktkS+hHeH7+PdTz+Wa5J0ntpSkgeOQdUcEEfgaACSJ4zh0IpuasJfuBtkCsPepQtrcfdOw0AURVyFvlGQ/PftTJLN05X5k9RUXmSKAvzDFAF046HmmEOB8pb6Gqolf+83NWYm3qM9e9AxvnlfvJUiTK524p21SMN0pqxIDxtyKQDwRSn8qQEduKcTxQBSYfv8AHXmnyxs2COlI8gQnA5qEsx6tTESExpwBuqPqeKVUZulSho4+ByaAHxRADJXr61MFzUIuVPHSgXCfSkMawkU/3gadHKASpGKiklDcD86iJJOTTEXg692pHkQqSG5FUs0ZoC4pOTRt+XdSYJPFSsAkW09TQAka/wAR6CmNySR0qwoWSLGelNKhFKk5zQBBSgEnAqRY8ruP5Uqg9TwKAIyDnik6UrEk02gQAkcirEF7NA25HYH2qvRQBrC8trv5bmFcn+NOD+XSopNIEgLWsiuPTv8AlWbU0c8kZBVulAyKSGSE7XRhTQ9ayamJV2XMauPfr+dNewtrgbrabaT/AAPx+vSgDPWQip0nIqGe0ntzh0YVFkigDYt9QkhYPG7KR3DV0ll4qcqI7uNZk9+tcMshFTJORQM9PsLy2mO+wu9knXy3P+TW/a+Jri3Ijv4dydN//wBf/GvG4rsqQVZgRW9YeJrq3wrnzo/R+v50gPWli0zVV3W8ixyHtwP06GqN5pVxb5+TenqPT6VyVprFjdkGOT7NL6ds/TpW/Za/fWnEu24hHcc4H8xTuItWGpXGnygKWeE8NC5+Ug+noa2f7as9oxI4jPWKRcEfU9xVOO70jV1yWWCU9/8A6/8AjUd1olxEN8Z81OxHWjQDTPiC0hl2jawwCJNuRn39yOtQ3EGmavGJbZ9tweoDenqK5x1KHawwR60xWYMJEZldeQRwR+NFguWprC6t/O3xn92QCfr0P41HPbSW08O8gByrAn0yK0bXxFJkRajiSBhtd8c47E49PWrXiKBbqzh+y7ZEiUsSGydp7+4ouwsSSLHNcXKPGYQ3yeWRkYPIZfcd/amaZqLWdybK5fIziGU9j2HPY1iW+s3AnLXH76NivmDocgYDA9mx+dad7bNe2KXttl4eeCMOPUEd8UrDOjdcnKvhl7noCP8AOPpWJq1rJdeX5MESAFvkTjJP8zVnRr17uyCtzLDhSe5H8JP1q3LEm6JiZQzHITgjPTpS2GcoLC4EPnMFjjDbS7nGDUU/lpO6xurKDwfUV1d3B9stfsz5XLbt5THI7VSh0K2i2+exMhbCgtwc9Kq4rGTHYzvZ/aVXIzjFVw5Rgw4I7jqK6wpHbwxv9pRIsYG/px0wfb+Vc3fiEzs9vIrqNucdic0JiaJotUlC+XcILiI9Qev+fyqOXS7C7zJp9z9lmPWNuh9j/wDXqju/SgEHBDe4NOwGRq3h8ElL+18onpNEuVP1A/pWf4W0p/DviWK7kKTaXMjwTuOVCMO/0IHWu1tdSniBjdFni5JR+eO9K+madfEyWU7Wc56g/d/H2/MUh3Of1eRtJ1iK5eSPULC0hke0j3gsp3AAgj7w2nkVjeIxJqngrQtVeHy5E8y1YY/hDEr+GK2bzSfs15Gl5GtvcA5huYvusT7dP8auahHdap4UudNdEuLtWMsRiULuAOThfUAnI/GkM4HwfceXrwtGOI7+F7Rs+rDKn8GAqvZCKS+kiuXEEmSG3IT8w6g45BzWZKJIJ+A8bK3HUEEf1FdG0uk+IpY7l7pdO1kgeaZVxBO4/i3D7pPU570ARajPJG73CjMqqFmH95f4XH06Guw0vXZLr4fXN5CkTX2myoS+wA+WTjnHocYNZOr6Zf6jqNgkgis0MXlFzyuB3DDhge1afhPSbfSrPXYb2+id5oXhntx1WM/dkX+8AcE46UAZUOv+Ibq9tLwFhtcEnjaydGBH0rrrtdNu0mS2Plu5LRuj8HPIyvT2NecXts1jZx3JkVxuKSBGKnOMhgP6jvTtM1RrZc72aEHgHikB3NhHFZ27G9PkSOQALkEoVPVcj7ynqD1Fczq9lp1nqklpJOsJYApcW7cYPRWB6EdM10+neIbC9hOl6pIoDjMTn5cHocHp9Kwtet0tvFWjR3cMU9srJl0XBkjzxux1x1oAxNVLW9kySarNMhjJjS4jyspyBhTz0Hf1rOkuJtQ0dzJM7eT82CSR1AIr0cacmgWGs3Z+x32nqwubKKVQxicn5l9uPT0zXLQ3g8W/a2kgSBGdQ0kCBWGezL0IOOD1pgX/AADdNf8AhjU9HDsJYwzRH2YZ4+jL+tWGvzqOsWlxLD5d3ZKhuAVwGAX5hkdQVBP1rK8LtFoPxBWxQuIZgYT5jDO7GV6f7Q/Wt3VbNdGi1W7k3GJyYwg7rnOfwViKAPPPEmmDS9fvbYcqkp2+6nlT+RrVOs30XhOwlgkDR20r2s0UihkYH5l3A+xI/CrHiy3S5sNM1GGZZw0RtpJR3aPgE+5UjNUPDhMsOoWGxGMkIuIlfkGSM7sH6qSKAKRh0nWPmt3Gm3Z/5ZSsTCx/2W6r9Dx71RvLO70mYRzQPGx6E8hh6qehH0q9eNpzWVxMsMsMkrfLEMFIz1OD1x7Uy11W7srCNJNlzYyFgbeYblyPTup9xTJILW5cDc4i6fKSoBzSxu6SbhNnPUA1eNlYamsb2En2WZultct8pI7K/T8G5rNntLm1ujFPA8Ug6h1x+P8A+qgLG7pt66zR4bHzA59gc07UtZaXU5Hz1x+dYn21bdNqHLHjNVVdpZPUmgGez6VqKXemWlyT8xADfUf/AFj+lTawL1bdJrDazowLIeQy9xXntrqT2dhDbK/IJJH1GBXcadrKPGiyHoADQhMmso57DUkdJFW1nByjsdofGR+Ofzppgtbe4l/eSwXMzZMLplMnurDjnt+taW6K5iKnayt1BqON3tB5b/vrX0P31/xFDQJlN4zu8txg56mqLRW02oJC5UyYIaF2Khgf4lI/iHpWjqMUhtHaN1YEBlI6HHY+9c/cBNTji2PtmjHOf8fY0kUzZWwt5Z0it7y5guY12+Z/EVAyFI6MMdKmlifJV8Mzxhw6dGI747Z7isfY82yGWeUShQRJ/Fx3B/nV+xmnkn2O7FIRtGev/wBcGmJmF4kgaK6hvouC+DkdnFVtWbNzBqEHAnRZVx2cdR+BrodVtBcafc24HKfvU/rXNWjfa9DuLY8yWrebH/uHhh+B/nTQjag1K8hfNnM6w3Ci4CAAgE/e6g9DWxp99qtzJhijIOpkhX/AVzvhy8RWiEp4tpgW7/u3+Vuvo3NeoTWdrYwNIz7nx8oOAPrgUnoNIxdSsBeWYae1Vkxy/wB3j2ByK8/1Tw9EfMNg+XXkxHhseoB/pkV2FzfX00kwEbERfeBbnB/p71yMyOrmc3LThTkxvwwQ8B1PXg8H0oAh09dNsLWG5l3X1+o+X7QmEh56Y/iIPc1S1HWp7ycku0kp49h/hUeosblw+/Zuzu+XBJH9fX161Vij3MkMHU96AIyFDb7iTc/p1AqaO1nvJCi7kIwVcfdINb1pocSRb3j3Z4JPerQiFhdRQxwMwODhOoHf8qAGW2hgwxCTczRnIbvkV0EFgjw4PRh/OoLu9itItqFWkPQDk1a8PecbIrOGzuJXP909vwNAENvZ22kwPJjn+Ju5/wABV3S9RhvFLhNgVtvPQ56GrctskyvGwyGGMVmy6Y8kcccJ2xr94Dr+FAG21rG0geSPcUPHYVT1S6ItNqIzD+JwvAPpUGrNLHpqG0uZWmGCUk/iHfOOQa3BGklkFjRQHUECgCGzh22aLK+0kZ2bemfWsNNMuYrwokm6Iybs9Djr171sW0F4t5LNcnbEy4EfofX2pks6PBd7X+RUPzf/AF6AOC8earFLN5SHcFIX6461xiQqp3OeOwHU0/V5HfU5lL7gp4PtTIUdxnt6mgYsrOT5a8L2A70+ONU5fn2/xqbacADtxmrFtaSXDbI0yR19APc9qAK0aHduHyp+mKvxWYSH7RcP9nt+u91+Zv8AdX+pokvbLTvkhVbu89cfu4z7Dufc0QaTeaqxvtUn8uHqXk6fgKQEL6hPdv8AY9KgZI2OC/V2+pq9DpNjo4E2pv5tyeRbqcnP+0aet/HD/ouhwkE/K1wVyx9cY6fhS2sCtcrayxs1zKwBkkHOCecEdvrQFixBZ6n4iXczpZ6enA7L+AHWr66Bp8MBgj1eVSRg7IuD+uam1v7SwSx0+GUqiYCRDPTisa4iW30yI+e6XWSw3cFh0I7857Uhl7+ytRhjBsJ/tMaAjEjk5PqAeR+tOt7H7QgF5DDPOeSkm4EH0AA5x9aXT7hBafab+Ty4s/Lh8FvX3xVw+J7BEPl3arHjoGJJ/E0AX41gi0gie2hjjIIMewACuf1M2kv2aC0jRbU/MIo0OZiOOMcEZ61Fc38+onzJd0On5zvPVvoO+apXer4jMdovlxngyH77f4D2FAEhhtbNvNvEhMva1t+FH+8c/oKgnvrnUZVgjRmP8EEQ+UD6dPxqGOyIh+0X032a3PIz/rJPoD/M1XuNa2wvb6fH9mtz95urt9T1piuXZRZ6Z818Uurntbxn92p/2j/F/KsfUtWuL07rmTbGOFiTgAegFZk14ATs+Zz1c1TZ2c5c5NMRYlumcbU+Varg+tJkijBPWgApQvrS9KOvSgBelLyfYUAfjTwmetABEm44FXFQquB3qGNdpyKvxrvXNAFUJmlEJNaENoXXOKtRWWDk0AZsVmzdqmwUNa5ijgiLuVUAd6w7m/iM22MfJnrQBYOeoppJBDjtSxtkUH09aALnE0VUJolf5XHQ1PbPtOw/hT50/jFAGvpkEKRDywvIrUkt1ngKN36fWsDSbnZJ5TfUf1rpImBFAGTp37id7dxgg8Z/lW4qjFZWpwFSLmPhl64q/ZXAngVx3oAtgU4A0KM1IABSAQCpBSYxSgdxQAo5604Cj3paAEApQKdgdqMUAJSijFKPagBQKM4o60o9KADOKCMijPalHHBoA5nWbMwTC4jGA3p6960dMvRcQDP3hwf8avXMC3ETRv0b9D61y8Tvpt8VYYGcEetAzrgc80tV4ZVdAynIIqcnNAhciko96DzzQAqsUNTbhIuKrHkUK5U8UADr2qPFSO4Y+9RnNMBC3pUUgzUhqNuaQFOVayrqEOCCM5rbkXNUZo+OaYHIXELQyFfyNV3G4Vu39t5kZwOR0rEYEZBoAqlMHFFSkc0UAfTV5cSajNufkf8ALNO2B/QfrTbfCx7vvFyST03fT0WkUbYTu5LDJ7Z+voopOq4HIxj6/wCC0ASq48vd1yCeO/fA9BUQIWHJPAGM+v09vekmcCPaOhI9t3+C151408aJJFLp1hJmM8TTDv8A7K+3qaQE3jHxwEEmn6XN22y3Cfqq/wBT+VX/AIdRGLw3LcsNvnzEB/4mA4wK8iy0kgLdPQ17d4bt/sfhrT0+b5k3Z7kn5sL/AFNAG6zEyZxgD5eOw9F96mcnaT8qhfyXP82NV4zuZSXxtB5Hb2X1PvUzPwB8q7ecHkL9fU0Aee/Ee4dr60txwFiL4PXk4yfc4rgnHqPyrp/G0hn8SXBCOBHtTJ68DnNc/GHzldrY9aYEmmtHFcSM+1v3R2g92J6Vb+w21zMqmNoZJELfuvujHbPT9KTTtOF5cF5EZEGBkdz+FdnqMOn2PmySqkaxqASOOgx265oA4tdHYxpMlzCbZTlpHbAAHX8quSvZv4Vne2jxGxZFd+rcgbsds9hWbi58TXgsbCPyLBDnHQe7N/h2rS1eK3s9Gjsrc7o0kCh+m7GSSPxoGjq/hLpccWm3N4Y18wsFD7eR34r0sCuV+Hlr9m8Jwk9ZXZv6f0rq6CQprNQzVVuLgRKT/wDWA+tABPOE+UcsegrCvmSWaJ8qqFvnlHVj2Ve9NuLsXFx1bysYBH3pSew9FqZInW8h3oGlCkgfwxDHFAxYkCy7njywyYofT0J9/wCVWFYRRhEfLdHlHv8AwrULhliLqWCOcF/4pSew9qsKmxuAvmAf8BiH+NAECx5YBY+dzFYz0H+01K2wSHL9AN0vrz0WlRkS3DHcI26n+KUn0po3NcuxCmUYCx/wxjHU+9AEOocQqWHO4GOLPp3b/P61BvKxSSb18wAlpD0X2WnajIkcQZ5MISS8p6tjsPauJ17xGZSLa3Hy8BYh1PoTjnnsOppAWda8Rxw2whhysWMcN80hHXnsPU1Z8K+CbjW5Y9V1wMtoMGG2+6ZB2yOy/qe9aHhLwG3mpq2vJvnOGitn6L6Fh0yOy9B9a9E4UUwuNSNYYkRFVUUYAC4AA7CuH8eS+Zc2tuOvlH82YCuk1nW4NLhwfmmYfLH/AFPoK86uLuTUtWje4k3O8qAjvtz2A7UAjt0TbHsV8sqhXmPSMDstUZGTzJFUN5W75U/iYjuf8/WtFgqiMtHx/wAsoR7fxNVKNWeSR0ZTKzEvIfuxj0H+frSAZED9riDBWlOcJ2Uds1ekZUhlEcmNoJlmPbHYf54qoohMuQXWFVJL/wAUpPp35/WpJUDw/vhhcfurcevQFv8AOBQBkaoMaDK7loYioUZO1mJ964yRXX7kiuPRuv5j/Cu18VSfZ9LdW/eSllBA6KPbP/6zXA74mJVHaOT06f8Ajp4/SgBXeMuDKjKR/H/9kKit5HWEMkmc5Pz8/qKSV5UVj8rYB9j/AIVXUxeVHkNG+3/dJ4/I0wPTNJDHT7VSOdin865++b/SZiOu5ueveulsVEen22duRGvVuuAK5S5JaRivGSSfX+fNAGFfqx1DDHJCjOKntBjj3qtdcajL7YHP0qxaZIwv4mgDUR8LtHLmrUSgc9SaqRgAIF7nk1cQd/SgCYEAVT1DUI7SEr96UjgD+ZqK/wBUS1BjT5pv0H1/wrnJZWkkMkj5J5JNAC3NzJcSF5Xyf5VSlmz8qfnSSyl+BwKhxQAlLRRmgB1AGTSCpBgDJoAUDAyaazBetI749zVdnJ60AOeQk1EWpGauh8P+F31OH+0b+RrPSUbBmxl5m/55xL/Ex9egoApaH4fvNeuZFhKQWsI3XF1LxFCvqx7n0HU16Ro2iwvZGx0tJbbSX/19y/y3GoEep6pH6AcmtLT9FW4t4Y5bRbPS4Dut9OHOT/z0mP8AGx646CugCheBQIit7aK1hjhhjVI04VEXAA9qqan/AOy1o+n1rM1P/wBloAxt1S23+uBqLHc9KsA29jaHU9Tm+z2ScD+9K391R1JNAGhHCHWSWSRIYIxuklkOFUD1NcZ4hutP8ShYIrFP7PjYYuZEAmnPTIYjKr+pp2pahd+IMNcxta6ZGcwWI7+jSHufboKruCYsDsR/OgZyV/4Diky1hcsh/wCecvI/MciuUv8AQNS035ri1cL/AM9E5X8xXrqgZp5wVK44oA8TjuZYjwasi8imGJo1z6jrXo+peGdKvgXkg8mT/npF8v6dDXKah4GvYFMlo6zx9kPyvj6dP1oAxDaxyDdDIp9jTFDQclGz+hqKa2ubOUpLG8cg7MCDTkvGXhxuFACtOTJv6fSmvMznPSpc28/T5Caa9s45XkUAQgkHIarSTbh820fjVUqQcEVIrqoGUzQBOyK4/rVd4WU8c0/7QP7tSRyK4zQBVIZeu4UhzV4kdDzULLGCdwxxQFivRmg0CgQUU6RdrZHQ02gBKkjj3n2ojXIOelPg+8aBjzGV4UfjQIM8s2alFOAxSGRrGq8haVolY5PNSAincUAQMrAYVajEZbl6suwAzVNGkz8ppgKygcBKj2gfeqRpF7tmoyy4+UUCAlcYAptJ1qQRHG40AMoqQFOhRv8AvqnCJJPubh9aAIcUquynKtipTbSCmmCQfw0AWYNTkQbH+eP0PIqcxWN2Mq3kv+YrN8tupXFNBIPFAFifTJofmHzL6jkVUO5ThlxVyG9liPBq0s1pd/LKm1z3T/D/AAoAyxJipkmI71Yl0l9peArIvt/UdaovHJGcMKAL8dya1bLWri2I2SMR6HpXNBqlWYigdzv7XXLW5I84eTL/AHhxXSWGtX9mA0E/2iH+53/KvJI7kitGz1We2YGORh7dqQHssGtaXqo2XUfky/5/EUT6FujL2ciuh7bv615zbeIop8LdRqT/AHhW7YarcW2JLK73L6E0wLt1BNbNsljZT7itPQroHMBLAoGZfm7HqAPz4og8V21yBBqVsoz/AB4qx/ZVtcMl1pdyA6ncAOxHt1oEMvrfT/Nm+7HK0WVP8LHqCv17irGm6tb2+kiNucNlhu5XJ5OPQVWuriSMbLvTkKBWVSnAUnv/APWrBPy9aErjudULc6brccsMi/ZZ2MTEdFJ7H8cEVelQhctHwrfMA2ee5GOa5KzvjbFo5iZLaQASJnkY6MPcfyrsIFESo3m+YGGUkHdT6nv70mCMTVENrdwXLTS4Lbtgc9QeRn3FVtVvo55YZLWaUFQp5bODjP5g5Brorlf3bZRHR8bg/I9iK5u70i5Fy+1OCey9M0IGEBu7vTfsxtndEcusie/UH8arTWlzb/6yF1467eMV1+n2wtrKOMhgQOSOtWSM/wB0gnB9KLhY4FWINX57VY9MtLkdJAVP1B/wra1HRYbob4l8uU88LwfY+/vWJDIRC+mXR8vD7onfornqp9j6+tO9xWJdMtJp7iIiN/KbKl9vGCCKp3EZgnkhccqxU/hW7O1xbeG4UTdHMkmyTHXv/OsSS2uwplkhlIPVyDQmA1ZwIDbXSfaLFuo/ii/2lNVsS2N5HEZmzxJbXI/iA6H6jvVy8txbiBlLbJYlfPuev61Xjga7gksN3zjMto/91x1X6GgC1e2Wn6zaSXlzZJIOFvYUGGU9pE/mPXkGuP1P4cSpI/8AZl0lyHXzIFPHmpjPyn+9jt3rotP1CWCSO5jTLgFZYj0YfxIf6eh5rZi8kCG3SRhY3Tb7OXo0EpP3T6Zb8m+tAbHkdpq+q6GXtH+eAHD2lyNyfkeh+laCzaPq4H2W4bSL7GBFM5aBj6K3Vc+h4rvdc0qDWoJLmS2QX9v8tzHtxuHZh7H9DxXD3ngxpyradIp3nHlyNjBPbNFh3MrVbPVLGBYr4zJlgoBO6KRT0ZWHBqDTb2Eym1aNH5+VHO0MR2z2NWIdR1jw7LJZSpmFSVe1uV3xnHXg9PqKvxnw1rRwR/ZN2wIAb5oGJ/2uq/jSAy71rue8EItpUlHKxleQB/Suq065vNY08WCvE1/AuYHPOUPUA/TnHtXNy6RrmnT28beaGxiKXdvjOTjCsMjBHOKl0aPVrO7iu7R4ZCGJWSOUZBHBGDjB9qAuV9Xn1HTNSeG5V4pAefRhnr6EGum+H62tx4lu0+SO1uYgMbsEODkY/WrXitoPE2hRawkIE2ny+XdRd1VsDP0Dflmr0mj2VxpttFYWyWl8vMMsY5k9yRwRQM5zxtpNzo2uxajCchCro/c7W4OfwGa6rxDc21z/AGXcXM6waffRHMx5WNioZcj0wcVi6nfvfaXLZaqFW4tcguPQjg4/3sVPos9vqfgAw3A3HT5Co74UngkegDYPtSAqzxJf6DqttGIiISl1EYvukj5Xx9Rhq4/S7v8As3WrW6b7sco3D1U8MPyJr0+wtre30y2ubeHfC1x5d5jjy42Uo2R7EjOK8y1ixaw1O5tX/wCWUjIfwOKYEus6V9h1m6s4v30SMXUPw2ByMeoIqXTNEOpRTRSZS1jxIGHOMjpzVrV4Tf6PpmtIzeaEW3m9nU7c/iMGtfT9TiawksW2xiRQN4HAcev1oFYwLkQx/wCiLCjwwfMzjAIzwcg9Qc1nW+tz28f2e4jW8se0U3OB/sN1U/St690NLGCc3VyhublQ0YDcBSex/CududNmeaOOEs6k4GOpJoAmOk2epnfpN1iU8m1uGAf/AIC3Rv51SaN7CUxPG6zL1DjBFPbT3sp/KvY5YXXnO3qPocGrUWtnHkXka3tsOF83iRR/st1H0ORTENtnbfvk5NbFvfMmCr1SSxgvhv0qdpG6m3lwsg+nZvwqoWeGQo4ZXB5BXBFAM7Gy1uSLHPHpXR2WtQ3IAc7XrzSKc1dhvGUgg07iPRzKsUZUlmtW6gdYye49qINJjwzxoh8wcSbuGrlbDXXTAc5rYguUkO6CdoHPYcr+VJoaZeayZJWwcrF1z1Udzj09abGUM8bIexU+/elS3d/3r3TtL2dOP0pLayW3ckPnnONuOTRYLkswxJHL1AOG+hrjZVGj+KCknEDsUb02Nxn8Otdsw3Ar61y/iy1860huwPnQ+XJ/SmxGdCBp2tmGfiMlopP91uM/hwa6y51a8m0eFX3CSEm3cj1XAz9SOa5O+P23TrTUOrsvlS/768fqOa1LW9ndbeWCTa9ziOT5uPMX5ec8cjB5oYGwGMCoy3KzxLjbKn3o88jjup9P5HiqN5YR28st1MWRGBbyhg4DDDfgf04q4YZ4JB9qtkXJCvJD0AJwdy9P5VDrWXugBuW5QkKT0kHTa3uenoaQGBd20ZttvmLMFIKPtwSCOhHv2+tO0/TYLYrM3Trk+lIjxwXGzevRl8s54U84/A9K09I05L2bbNIzQoOEz1/+tQM0Y4opvLInVo+oRO/40t1aWqSpc3smEJ2ADgD8RWmbMpCI7YLGOnA7VnrpKW80k99c+ZHnIjPT8fU0ALJaWWkzo6RsXmO2Mu2cE9gT0rRtEuJwklwEhKsVOH3BlrFur5dTu44ETKqa6YQKkMYWTYsYGPlz9R+NAEqSRA7IzFx1zyajkURyl0VU3fef0rB+xvd+I0uLSZ0CsGYD7px1z9a6K5W2nMdvPJtWU4wGwTQBDYX1o921vE7ebgnP97HXmr0txDawlpjgDoO5rLK6fpkh+zQKJiMHYS3884qrfvEsYu9Vk8tRysYb5j+HagCSe7udSk8uAMkX9Pc1z/iHXbbTdPextpFaRv8AWsOn0FYmv+N2dHtbIeTD0wnU/WuLZri+lyxY5oAnRjLdNK3JY9K0I0J5P0p2n6ZJIhZdojX70r8Kv49z7CrsV3EtyLbSx5k3RrqQcL67R2/nQMkg0shS9y+zapfyt2HKj1/uj9alsLyWa2nnREgtoEO2NF6seASe5qpJbvaWd/NLI0kz7ELn1OWOP0rTs9Pmm8Kvb2gV7mQg+XuAZgPQHGfpSAzPD1pG63V9NH5nkgsAehPatFom1mNJ2n/dHjEq7EXHpjIwelUdMvp9I327iW3mBycqVYH6HtWvHNaXzl5YNsrDBuLL92/P95ejfl+NDBFNdTi0JBb2G2a5P+tkT09AfSt6a2e+e1u4thkG1mLnBHv71z8qpo129vbzeYRghwmxyScbSTn8uQa1r7U4NFhisow8846IOWyaQzUkvIoNUy6qGZSABzu7jGOtcjqBubvVfPlBQgjGF/1Yzxk9qlEssjSXN1C6u3GG4x3Ax7+3NMkEmHk1GVoImAxCn+sYenP3R9eaAFmLX1wFt08yTBVgV/dqvQ554HfNRE2mnnnZeXK9Pl/dRn2H8X41BJePNi2tIfKiJ+WKLJZj79yaV4LWwXzNSkzJ2tY25/4ER0+gphcbm81acuTv2/ekdsJGPc/0FNlvrPTjttcXV2P+Wzj5VP8Asj+p5rP1DWJrpRGStvbL92KPgYrHkuiRtj+UfrQIuXl88spkuZGmlPqeKz5J3m6nA9Kiz/epPpTELnFJ1pQKd0oATGKM0cnpT1WgBoBPWkfI4FObcnI6GoiSTzQBZjGRVhEqrbNltta8EG7BxQBFHCWrStIMHDd6dDbVPNcwWa5kOT/cHWgCzHEqjiqd5qsNqNifvJP0rJvNXmnJVDsj9BVAb5DxuJNAE97fTXcmXf8ADtUUNpLcHEaMa2tM8OS3JDz/ACj0rrLXS4LaMKiAUAcgtnNbQoZKDzXU31oskTLtx6VzDKyMVbgigCIkjDDqKuRuJY/rVQ06B9ku09D/ADoAflopQQeQciun0+4WaMOO4rnJl3AMKs6VdeTcbG+43T60AdW6rLGUbkEYNY9ozafqLW8n+rY8H37VrRSblqvqdr58AkQfvE/lQBqJgrkU8ZrO0y5M0O1z844NaINIB45604U0CnigAHpTqQCn4wM0AAFHfmjk0daACjpRS9qAD3oNFFAC9eaWkHvSkgDigBPrWRrNkJoDKg+df5VrZpGAIxQBz2jXpDeRJ17f4V0CncK5fUrV7K78xB+7PI9q2rG7W4hDA89x70DNAehozikByM0daYgphFL1pCaQBmkJoNNJxTAQ00inGmMaAGMKrSLVo8jioXx0oAzLiPIzXO6lBtfePxrpbqWOJSXOBXMalqKSZROnrSAztwoqHNFMD6fkORjdnJ79/c+g9BUTyqkfznH8R38cDufQCmXFwkGXkdVRFLyO7cD3Y/yFeV+L/GT6gzWtg7JaDhnP3pj6n29BSAt+MvGn2sPY6fIwgB+eYdZT6D/Z/nXnzyO53Pz6CmtIzNl6M856mmBb0+Fru/hgUZeSRVA+pxXvuzZJFEORGgQY9OmF9uOteP8AgW0F34qs0ZN4Ri5HsBn8s17MoE0sjZzztyO+B91fb3oAFJSU4PIGOOi57D3oBImKttXaMnPRfc++KcG8hXYbd4O3eOi+w9TWZqFw0FldS/xrGzAFuBx1Y+tIDzK/na91G5nV9++Vjz9aZBbrJzJG2B3HP8uagKfNudG+o5/+vV2KeOztzcST7YB1zySfRe+aALtrJDYWv2qSQLbo24l/rwPcn0rJd7/xnqZxuhs1JY54VR/eY+tMs7O88VXYZ/8AR9Pg55+6o9/VjWvqmq2mi2a2FjGpPG2LuT/ffH6CmMW+v7HQNOFrbD5WHCjh5yP4mPZf51j3U09zZWP2j/Wy5cjGMZOAAOwAHFdJ4P8AA8+s3X9q6wXMbHOD1b/AVQ1aNLrxiLaFFEYlCKo6AA0AexeH7L7J4esIR2gUke55/rV1wV6ip1CxRKg6KoX8hisbV9XW0jKRHMnTPXGf5mgQ67vUhyoZc9yegHvWNqEzzRW6EN5bEsI/4pPdvQe1K0LhfNk+aVhlYvT3Y/59qZBG0jb2ft88p/ktIBlpG4vZWbaZAAC/aMdcCr+5UyXDEEHan8Uh4GT7UlgikMyJ95iUQ/luanNsYynPyHary+p67VoAazO00O91355f+GMAZwPen3EqRQF3DBSDtj/ikPqf8/Wo7mURXcIZFyqExQjt7tVCaUytmR/vEK0ntnotAF6ESSNvcruC4z/DGPQe9SQhXjLf8smY4/vSH/CoXmyBGqYjGdsfc47tQs3l26pG+ZNuZJT0jB5wPf8AyaYHM+NZ382CFJF3KrEgciP8PX61p/D/AMKW8Fjba5djz724TzE38iIHpjPViOp/KuZ8UOovJggYbYTjPUkjqfrmvWNKgFpo9nD08uBF/JRQDLXArB13xFHp6GGAq9zj8F+vv7VT17xMIt9tZPlujSjt7D39689u717iRljfAHLyluB689zQCRNqGoTXNw2GaS4c5Zn7e5/oKs+HrZm1yy8sq8jtuLnvgE5+gq54Z8JSazsmuQ8GmZzjo9x/UL79TUumRQr4nkCfuoIvMCiPj5c4Cj6+1IZ1U8qxqwR89pbg/wAh/nis8y7oo02MI/4Yh1Y9ct/h+dWbhxj94nIH7mEduPvNUEKtHIdhVphwzlflUD0/z9aBE9uCL0l0Vp9o2p2jB9f85NLcxmGN2R8uMK0p7c9vp+QpbR08uZy+I8jdIercf1//AFVDfSM4hRkKxlx5cPdsc5P+HbvQBz3ilpH0yPydqhpckyKSZDz+nvXFyTEjbcQNgdx84/x/Suu8Zy7o7USybHLHCI+AAB+tcc0kynA2yD3+U/mOP0oAjkRDBI0MzAYPG7I/I8inIziNEdNwwOn+BqtcSxtEwkjZHPTK/wDswqe3VzPEEkyCyjD89x3FMD06PbHB8p6RYx/wGuVkUHj07d66iZlW3mI2/LG3Rs9RXKuCw2jgd8cZ+tAHPTgnUJlXgBv7uK0bVcDAHFUHx9smA7OR+VaFsdq5NAF+Pgiqt5qYiUxW5y3d+w+nvVa6vi/yRHCevrWbJIEGTQAOwGWaqsjlz7U15C5yaTPrQAhFMdgp54pzOF6darTk5BNAEm8dqUHJqruNOVzkAUAXBgfWmtJjp1qLfgcUxnpAOZ6jLd6EV5pVjjRnkchVRBkknsAOpr0bw74RTSJ43vYEvNbIDx2T8xWgPR5iOrei0wMzw/4PUJb6hrkMpjm5tdNTiW692/uJ6k9a9LstJdpY7u/8ozRrtghjXENqn91F/m3U1Z0/TPs0klzcSNc3k3MtxJ1PsPRR2ArQxigQwjFMNPY1GTQAen1rL1PqP92tNQWIAGSTXPa94jt9MuHtLARXerqOc8xWvu3q3ovWgCC+uLPQYUn1FDPdS/8AHvYR/fk92/ur7msCY3Wq3o1DVXV51/1MKf6qBfRV9fU9TTYomE8l1czPc3c/Ms0nLH2HoB2AqYMQcHoaBjpCCvNQOMQk/T+dPbPJqN8iJjntQAu7HANNZ1QZ+Y9gPWmFmYlEGX/l9ae0YSM85Y9/x7egoAVVYyb36joOoX/69KTk8flSkkHijjtQBXubS3vYzHcwpMh/vjP5VzWpeB7KUGSzma3f+4/zL/iK6vOeBULseUUZc9v6n2oA8w1Hw1qem/NJAzx/89I/mH6VmpPJHwD+FewtIlvH5sz8gdf6AVzGq2tnqcxdrZE6/Oq7WP1IoA4sXSOMSJS+RHIMxvWjd+HXTJt5FYej8H86yJbee2bDoyH3oAc8Lp1FNRtpznGKcl06cNyKnWS2n4f5T6igCF5Q3ao2YscmrD2h6xlXHtVdlZeCuKAEp8IBfmmUAlTkUCJWV3bnikYBRtHJpyzHPzdKfsR+RQMaq71GOnenxqE49aaIyhyOaeASPmoGPFOOcU0EdjTgAe9IBFFOOB3qB3YHC0zcQfmfFAEj/MME1C7gDalNcjs7Go6YrhSg4OTzSUDrQIuIVfDYWnEdqhCsqfKelIsj42tuoKJBGNxY1KoUcCkVWI521KFX+/SAbj04o+Y07CnvSYHrmgCN0VhzTDFEOcVOcY6UqKGFAEPlRMOEqMIqtlYyCKtsu3gVH09qYFcXE0R3o+PpxVkahHONtzGpJ/jHBqGSNW6moWgx0agRbfTop/mtZFJ/uHg1QltpYDh0IoDvGeDjFXYtSfZsmCun+3z+vWgDODYqRZTWkbayuxmJ/Lb0fp+dU7jTp7fkplexHSgBUuCO9XrfUHiIKuwrGyV4PFPWUigdzrrbWhIAs67hWxZag0bCS0uWUjsWrz9JiO9XILx0OVOKQHr9l4rZoxHqEO4ev+etXjp2n6pGXsZ1ViPuH/CvKbXXpE+V/mX3rcsdWidw0cxif/ephY6O90i7sslkbYD1HNWdJ1o2Ui21yc2p6HvGT/T1qK18VXFqoFyFuIT36/rWiE0TXE3QyC3mPbgf/WoEbjzRRpvLgRHAyeOv9DSBRFJ/rMRP3+8B/wDW9KwZrTVNPtTA3+k2noecD271l2usXWnnZGd0J48qTkD6dxSsO526yBsANuPIz0OPepTyCfm7nPfHeuVPibbFtjtmQ98PlT/WmHxI8lq8ToyyEjbJGcYH+P8AOlZhc6CK6hcx/ZpmcOrfKevH3gO4I64qnrunpc2v2qPczoOT13L/APWrBt9V8vUIbt0yyNlgOBJxjOOgPrW/perxzxYlkUFpGATuvcZHoc07WDcr+H7k3EMtnc/vEjwVz1Cjp+R/Q1L4ivbi3a2W1kaNGj3ZTvz/ACq/DYR2t1JPb/KJOCD9c8fWp1s4zMGIUqpIX2z1xSvqBxU97cXgTziG2DAwoHH4VCkhhkSZPvRMGH4dfzFaMurxCeVksYllIKiQHseOR0rKDd6tCLWr2MlpeS39qnnWkxDMI+qsRyfxpbS/tjZvZXYzYysWWZOsLHqT/s559Qajt7ma1YNE/wAvQxnlSD1FQX0EcYN/aKwtnOJ4v+eTHvj0pAdB5909y+/Z/alunP8Adu4j1/HpnH1FZeoQpFsvbX/j0uOo7xOOqn0IP+eaWwle5iitFk2XUB32Uv058s+vHT1HFXVnglhluWj220p8u+t+phkHRwPT+Y9xQBQni0zUwl1qNrFJHMvlSucjypB0YkcgEVzF34HsboSPpeoFSql/LmG4AA4wSPTIwcYIrpIFm0fWvLYJNBKPmD8rIg5DgjuBT7vWxDcvG2mW6PGxUmJzuK9GAz6jp74oA85gv9b8MTGESfuc8xSfPC34Hj8sGrUk2g69AY9i6Rf8FDk+QW78jlQffpXobaYklobxpLdrVh8ryKGWRT0OD6fmOa4/WPBKr5ckU0UM0xJ8nsB2/D0oHcbbjU9M1K0k1SyX+znVbS8uI/njniOcMxBIJA79ePWtdGt9J1GDR9Pe4NtPua2u5vuyHqEVumMcCuNjutc8LTtCCyxg/NDIN0TfgeOfatj/AISWx1vRhpcki6XKsnmQ7stCsgOcqw5TJ7dKQyTxS/k6gDdARSzxHekjfKQegJFSeA7i1h1m600Orw3lsSUD7l3r1APupNQ3Kajc4j1WFSzptFxkNHLjo6sOOuMj8a5q01L7Br1peJD5Jt5F3gcZAOG/MZFAHoENhqcejXdml+VsmlaEyOB5kUh+62R1TIAPse9c34ytZTPbX8ke2S6hUyj0lX5XH5jP411Ot2FxqGrW32fZHgb1lBI80ZCsjY+oIP1rH1y6TVtHvY03FrKdH567XUK35MvNAzG0SQXHh/ULRy2LZ1utg/iQja4/D5T7UzRprY3eZJMxyE5Q8MAOme2fpVfw1MsOvRRSPtiug1rIfZxgH8GwafNogtxJcJqlu0cZZW8wNG+QeQQQRx7GglnRXN9pd63kSxzFYoyA46hT157EHkVi2htrHSbrzJFuTyynBBA6A1DDqEttaFo2hIHGRzuz7/zFQtM0tnciJN0bLmSLjMfI+YdyM/8A16BkVul5fbFu2mltWHyFvmwc9ATyM1uaZ4IsrsXC3k8tm/lsYQSCdwGQD7GsL7aYLVYMuMYIHbHce1a9jetHLHI7s53DdHIThlIyCPT+tAGVqfhm+06dVt91yBF5jSQ5wuOp9RUUeryv/o+qQNdxoP8AWdJVHYhu4+tdzc3FvfQTNvbynUrOkbHfEhPBBPUCuZuPDkke+bT5/tVrBFvMwZd0a55BU9R7UCZR+yCVTNp063UYGWiPyyqPde+PUVU+2BDjbjFZyzyW96JreTEiNuV04/Sum1axiltLW/jhby7pQ3ydFY9R/wB9ZFMLGcmpAdf0rVs9SIwUfNc5dWrwIJOsbcA/Sks5mSbAPBoFY9EstaIwGNbtvqEcuOcGvNYrrFadtqLJ34piPQwQRkd6pX0C3MVxat0nTK/7wrHstaIABb8DWq1/DLEsgbayEEZoYI5jR1MsF9pcn3yPNiH+2vDD8R/Kl0xt6XFoVyT+9jH+0vUfiv8AKnasTpXiKK/iH7tiJV9wfvD8qNQX+ztYE8PMYYSp7qef1HFAG1BcRv8APBDeMxHUSB+COh46e1aV2t1dwB4oPPt8Bgjr8ynuAeoIrPsVuEkuLa1RHgfEgJONqkZVgQRj09OKtp5eDbS3e+R/uuCcKw7FvekBylxYSvqLqdqNy2N3PryOoJqzY3s9vcQzxuqiFtso9V7/AJ9jVi9tGtZo42ktzdlgAZB8yr/vZx+dUHZDOxG3a2V37cAjODQM9DWdJ2ENqVeTGfYDsTWfrWjzSwqyXLls/MO2PaqXhp76O4iR9piRdjP3K/w/lXXlVkG1+9AGJpFlaWG1G2tM4JGe+OtWtQsf7QaJo7ryYxnzPLGdw9AOx96uPBDHGMIzYzyBkjPXFYd5qsk83k2gx2Gzqe34UAa1vb21uBCgVMDJP8Wfc96x7izu9QvX3PiNDnzN20ADpj0pyNb6JDJc6pOqs44iDfOcc1xPiLx1LeA21oFhhHZOp+poA6XVPEthocZS2dbi6HWU9Afb1PvXnOq69earcu7yFix6n+lUliub5yzbiOpPatmy0lViEzlY4h/y2k6H/dHUn9KAMu206SVxvDF2PCDljW5BYW1sdk5VpQN3koeAB3Zh/IVLfCSwmhs7AYMygyyjLSAH1wOOOwqe2guLeaeJoEhjAwrum8sf7xB+8COlK4zJuVvtTuorbzEELcpHGpChfXHpXTaboi6ZaOTzJ6juOoz9M4qXR9GubcveX9zbq8mPL6/d7YUD5R7Va1id7DT2mO2SMDG+PkZPr6UhnPiax1JptNuLmW2m84ssmzehPAAIHParvk3dlagTwJc2i9Li3O9B75HK/iKhtJdPXRIry7g8y7ZXKOG2nYDjaeecfnWRoF35eq3M9nPcJbrC7sjHnngA44PJ60xHU20q6oFgk8q8hA5S45eMf7LDkY+tY2hJG/iQpEGEMbEgFs4A96m8OyGKw1K+fspwfrxUHheWJGu7iUtlwR8n3uTyQD1wKAHQTrPr9xM8cTANuUv/AAsD8pz6Z61GlpdT301yJk4+9cu3yqe+09z+GadNHpli0hE73hY5Eewxj/gXP6A1TaW81Wbyok3BekacJGPfsBQMsz6klvxbzS3Mq8faJmzj/dHb61VjtZrkG5uZvJhPWaXqf90dTSSS2Ol/eIvLsdFH+qU/+zfyrI1DU5bqTzLqTceyDoKBGlNrEdqjwaVHsB4a4f75/HsPYVgzXY3Fsl5D1c1WkuHl46D0FQg4piHtI0p+c0zkdKKXbQA3rThQTim5JoAduAqRE3jd3qNUJqxCNrc9DQAxVz1qQLTnTZL7GpVjJ7UAQFCwxVZkINbMdtmq97a+WQwXrQBQjJSQMO1dNbPEsAkYqqkZ5rmcc09nYqFy2BQBs3etfwWy49XrKZ3c7nZiTTrW1luZNkaMxrqtL8MgES3PJ9OwoAwLDSLm+kwiYX1NdhpugQWahmG6QdzWxDapCoRBip1j5oAiRAvCipdtPC4pwHakBUmiBFczq9oUbzlHXrXX7AeKoXtqssTIRwRimBw7Uw+tWbiFoZmRuoNQMBQBZhkDrUbgo3H4VDE+yTB71akG5cjqKAOh0u886EZ6jg/WthSCK4vTro29wM/cbg/0NdZbvuQHtQBTKGx1DcP9U/8An9K20IZQRVG6h+0WxUffHIqHSrsuPKf76cc+lIDZHvUg44NMB4zS9aAH5Apehpgp3WgB3SgGkooAU9eKKM54ox60AAp1NoFADie9JmjNJmgBetGe1IfUUvUZpgVL22W5gZCOeo+tc7ZzPYXhjfdjOD/n2rrDzWBrlkxxcR9R96kM2onDDIqTIzWFpN7vTymblf5VtKwYcUCFY8033pe1NJxQAE5pDzQTim54oAM00mkdgOc1lX+twWoKqdz+gpgaTypGCzHAFYOoeIIospD8x9e1YN9rE923zHA7AdKzJJcnk0AWrq/luWLO7GqLvn3pjOTULSqtAE+TRVMzsTnbRQB6R4s8WS6pM0Nu7paA8RnrIR/E3+HauReQsdzdaGYk5brSfzoAB65yaAfzowKTDZ46UAegfDG3U6hd3Lbj5cO0AdyTyPpgc16bHJtiDL95xyR7/wAK/wCNcL8O7YxaDcTkY8+XaoHVsDH4DJ5Nd2iYjdiVGPlJH/oK+9IB5XZACduVAPtHn+ZrnPFUqQeH5QS2+ZgoQdcE5JaujnYhdoG3aMhOy+59TXE+Obh44bS3XA3lpMHqccZNAHDT3sdqN7yHHp1J9hSaZZT+Ir5Jrp/JtE6DsB6D1NRz2TahLHGo75J7CtG41E2sa6ZpfzTfdMg52/T1P8qYzQ1XWotPhXTdNjXzR92Mchf9pvVvQVt+DPAUk7jVdYVmLHcsb9WJ7n2q14K8CLbFL/Uk3zH5lR+fxNekgBRgUCbI22W1pIyhQI0JHYDArx3w3H/aHj2F25/fFz+HP9K9V1+cw6JdsvUptH1PFcb4S8PvphbUbwEXT5WGEcHkck/n+FAHaapqZij2Rcu+QCOufQVheW8k8a/K0xYHnlYx159/5/SpVEkt3KAVJRQpk/hjzyQPf/JqWONBJFGobackD+KQ+p9qQE8kai0kAdsN1kP3pD6CmlSoKhFBUfKnaMY6n3okZnbl1GwjJ/hjA7D3qldTZjKENiQ8IPvSfX2oAlt51MMcalvKbl3H3pSew9qsiQoXKIGmBIVP4YgOMn3qvbIYlLHaHXhpD92Meg96jiOE+cN5bksE/ilOeCaAGOha5Ls7be7fxSE+ntRNE6y2+5BvzlY+0ajuferaShXkdgpmACj+7EMZ/OqM0m6VS5IjO4/7Uh/z/nFAEk9wqWkuxuoO+XuT6CnW8beXGhTJx8sXv6t/n9agkDy/JhfMPAH8MYPf61ehVArssm2If6yXu3svt7/lQBwWvkya3MjupzPHGT0H3hn8OK63X/E/nqbWyfbbqMNJ0LY9PQVxckqNf3Mz7VQOTk9AMnFQyO92yjY/lMQI4kXLzHtx/T86Bjp7wzhwr7Lfo0ndvZfr+tdd4Z8GNdeVe6rD5dsuGhtD1Poz/wBF/Or/AIY8HC2aK/1VFa5HMVv1SH3929+3au1Apg2MbbDCxHAVSfyFed+GFeXVLuVY9zbeCeiknOTXe6nKIdKu3/uwv/KuH8KKRa3krvsiLKpx1Jx0FAkas+EWUh8k/wCsmPf2H+eKSNEIAYMsOeE/iY+/+H50twwLQh0wM/JEP5n/ADxSlj5hCbTIer9l9h/n60ASDy97SON0hb93EDwuBjJ/x/Kq8haW5Vl+aTccvt4HHQf4fnRaxF4yyuyxZYtIerc9j/X8qn2xySIpXZbxgkDuxPHT+nekBxPi67jjnt7fy3cAMzPtyCScfjXJnyJMmCTafRDx/wB8muo8YrKNZBAQYhGIz2GTxkcZrkpvLZszR7T6nkfmKAI7hpFXB2sCQOOD19DVrTBC+rWqD5XMq8dCef1qhMpxGEkY5YdeR+daehB5Nds0ePgyg5HI4GaYHol2dmnXLDj5Tz9TXLsB1Jxn06f/AK66XUWxptx2ztwMeprmJHIz7ZP3sf8A1qAOf3r58rDpuP8AOpXnLAJ0T09apNIyTSKV5DH8ahlvCgwnLUAWp7hIl569hWe87OdzGqzSOxy5yaTfQBZ300ydh1qsZCTgVNEhNAEqKWOTTLpMMB7VfghJNQ6hFtlQf7NAGcaTOKkYYOKjIzSATdVjTtPvNXvorKwgee5lOFjT9ST0AHcmreheHb7xBeGG2VUijG6e4kOI4U7szfyHU16poGhW0NkbPSvNhsHGLi+dds977L3SP6cmmBQ8NeH4tIlMGmulzqoG251PbmK19Uhz95vVu1dxp+nQafB5cIbk7mdzlpGPVmJ5JNPtbWG1URQxqkagAIgwAKtDigQvSmsaCajZqAEY01EaRsL25JPQD1JprtHHDJcXEiQW0QzJK5wAK4nWNfl14G0tBLbaR3P3ZLj3PcL7dTQM0dY8TrLHJZaJI2c7Zb4dB6hPU9s9BXNQ28VvG6RooByx9ST1JPc+pqVFSOMIgVVHAA6AUjZO4D0oAXJwNtOFNXkDtmlJCrnOAKAEPPAppUyKUU4ToXHb6UIfM5wyp+RNOYjoOAOgoAUBEG1OBn8z61E4+Uk08H1pr/dI9utAEjEZ9KjPFObPX9aZJloiw4TBxjgn/AUANZmbKx846v2Ht7mq8s8NlHluS3buTUd5qCWyeWm0yDsOg+tYUszyyF3LEnuaAJ7i6e4k3OcDsB0FVmkx061G0npTM0APznrTXRHBVwrD0NGaQmgDOudFtpcmPMR9uRWRcaRcwZZV3qO6f4V05PFMwTQByCySwngsMVYF6rjbMin3rpJbKG44kjU+/f8AOs+48OMV3W8i/wC4/wDjQBm+TDLzE+PY1FJA8f3l4pJ7K5tDiSNl9+350R3csfB5HvQBHTldkORVhJLWbiQMpPcUPZk/NC6uPyNACJMpHzcGnqyv91qrMjIcMMUnNAXLBT5MK1RgyJ8opiuynKnFPE5z83NACsTjLn8KhwWPAqRmQ9mphJ6DigBSjYzio8U7JNIVI60CEqSIqGGRzUeKUcHigC7uzwaCRnio1cqMuagdyz5oKLDzhTgU03AxxUO35cmljUF8noKBEyzknHapfOjP8QFUyxY8UBH9KAL4ZCOGzTuDwGqqiFAT3qJmcvkbhSGXifWmMw6Cm7iyhj1xzSgZOKAGOu4YqFNyNhulWwpHUUjLxxTArSQsTlR1phgYdeKkeSSPvxTfO3cNQIjIKcqfyqzBqE0J4OR6Hp+VVmC54ptAGoZbG8GHTy5PVOn5VBNpMiLviKyR+qc1RqeG8mgbcjsKAIGV0OGGKVZCK0xqEFyNtzCuf76cH/CmNpqTDfbSK3+x3/KgCokxH8VWY7kj+KqMkEsJw6MKaGNAzpLPWri3+7Jkeh5FbdprFtIfnHkyHunT8q4VZSKsR3JHekB6rYeI7+yI2P8AabY9uuPw6itlLzRdY5lRbaY9+2fr/jXkFrqUkJykhWtq11xH+W4XB/vJwaAsd5deHbqLMsOJoj0KGstkaKQqwwehBqvp2vXtp81pc+dF3j3c49weDXQw69pOrDytQg8ib++O31HUU7isYTNjBqW1uDb3McyBSUYNj1x2rXuPDjPF5tjMtxF1GGyaxXgkgcpIjKw9VpiOvsNUN3CZIk2hTiSL7wX0I74rTjnMi7gVPuOB+vevP7e6ns5xNA7JIO/9CPSt6x8Qx3EyrdRrC54Micpn1K9qlopMdqFlpc0hS1uUS6z/AKsnhj6ZPesNopFZkKNlPvDbyPrW7qljFe+cEjWO9jG5ox0kX+8tYi6jcrC8JkyGG0kj5sem7rTQmRhxUkc3kSbyN0bDbKn95T/UdqqghelODnGKoRFJC1jdvbb28sYkt5B129Rg+oNbcF604OopGrTRgJf24/5bRno6j9fY8dDVPULV5fDVvfdZLeVlB9Vz0rPs72WzuY7iDaWXPB6Mp6qfYj8utSPc6Oa0SeGOzSTKMPMsLge/Pl/4D6iqGmXflSPc3qKkka/Zrltm8xMCNkm30IGD71ZD23koN7DS71t0TnrazdwT2Gevvg96hvUusyXcYUajajbcxFcrPGf4iO4Pf0PNAhEhutQtHndE27/3RQbQWI5G3pyOR7j3oS2t9Pihe6k2GWCQLnJAzkAH056Gq+pX76nBYw2kflwOdiojf8tA2Dz6g4xUuorM03lSXa3V1DGMkLjcB146Ejv6jmmBHNHGigXMaFWRWKyjIGflOT6Buvs+R0rA1LwfZXsEclo7Wk7M6Yl+7uGCEY9AeuG6EYrqJ2VtZigK+ZbPCDFCeMbkBKqe2TkAHiqUk63FvC03y2hHlMEzmJx91iO52/mMjtSsBwm3xD4UzlHW2LFWjdd8TEc4ZTwMj/61MaXQ9bYM5/sm7Iwer27H+afqK9EFvcSaZfQ3Ua3MVuiyR55DLnkA+hU5HoR9awv+EP0jUrFJ7ebytz+WZOAY2P3Sy+nYj8QaLDuareY3hmMJdQveW1uil4n3KVPy5J/AGqMcFnl0njdNQ1BWid05RmIJG70O5cg981l+FdMvdI1+6029GyG6hkhLA5Accr+ufrW7dztaajNc6lD5awWygsPuSEH5HX0IZeR7+9Io83uFeG5yvyspyPYj/wCvW/rWpPBfW08aKbS/iWbHUZbhh+DZqt4pt0h1aWSL/VTYnjI6bWG6kiCX/hVWdv3mnXH/AJDk5H5MD+dAFpdNgW9SKBngjkiVxk5U565B681o2drd6fPJef2fLfaZDuSR0UfuwQVYfTB71oaH4ck8T2EM9uU83BG53IwB0xjv3966rwtbvodtc201zb+Zj96knRh34oA83vfD8815LLpjpPFDGrjd/FGTgH9cEHpT7TQ7i/spJF2QPHgum/oOxHt2I7GtS8kuLPVpfsGwwyFg9vG2FkXOcr6H196s6horuItS065ZXeMOsZ43KR+R9CDQBXtNEuF8q4gvYpdgIlj3fNjHbPXHpWcEjh19Lm31FY4vM/fRxxlW2nqMHg/Sn2dvcz6gtq8L2lw4ZonBIVmAzjHbPb34qDXbZ47iGWR0u9wUeYilUYf3dx6EUAY+pane3N2bW8t7YxKT8yQKhI/vAitjwzLHd6Hc20zqUsnMmG5BRux9g1ZSXkIvXs7iBxATxHM2SPbdVrR4U0jXgjHdZXgaBs8kBvu7h7HHNAixfQRXZurExhZZgZoccjeBll+vGRXHQRstwik7csASewJrubW1calKzcfY4xKhA5ypwQT69j6g1zeu2aW2qSiP/Uv86H/YYZH6GgGi3e6PNZTyRxyJc+X97y/vD6r1qkkxBq7extd2Nvq8UjJMIgshDYPmJ8pP4jBqhFqkVyuNQjyen2iJQGH+8OjfzpisXorogjnFWZdUZYvLBznFZc1tMsXnWjLcw/34+3+8vUVWWYQfO7Zk9KAN/VNVL2dpETkxjBrRSZb/AMN28xP7y3kaI56leCPyziuHeaS4lz1JrUN2bbThao/U5b60IR3fh6Rbr7NG5w0bGE57o/3c/RuPbitA2yWE80VzH85/1TuhZTj+Ejsc1w3hzU2ju5Vct5ZhbJHYggqfwIr2rTtQ0nxHo9tdvJEJZIxvPo44IOeOtDGcTqVtbTm2W5dIZgRg4wpUnO0nsfSsiRlNzdQ+SqRxZwCuOC3B/M9feur1XS/sN75zHzbc5PmD5tpI4bHfHWubeZUtokl/0kzvJCso4IUEY69R7HpQIv8Ah+++yTGKZ8jb2btnjn2P866Rtdt2gygzJ0I9K4mGFlmiuUKvEQVdx3HTkdR/iK6W91bSLG0juJRFuKgrbxY6j1I6UDLVpcXl1NJJIdtsFOSeFFYGseLbDRlkj07a9wfvTH19q5XX/Gt3qOYYW2RDgRx8AVz0NpJdnzbiTavvQBLf6tfarcks7sWP1NSWmkOZArhnlPPlp1/H0Fa2l6YksywRFVGzeZB1IHXBPAxWw6Q2ADQEGFFdpCPus2MKCT1OaVxmZY29sb2K1O2eYsMxr/q4x3JP8RH5VNp0H9r+JnDuzW0J4QtwAKXw8n2ex1LU5BghGwfc8U/wnKkQuXbY0kmRsLYzntntx0oAuXeqT2Olyahp+1ZZLhlkk2A4GcAZ/pT5WcXXlS3PnSBVZnKbRzzwOmKamnvaSSLp53rIS0lhdMA2T3RujfQ9aVvLaykS5h8uGJRGxlUrNA2eOD29+RikM3mjgk/eSFssM8MOKp6ne2+mRbZ3RYpVI+bof9k1m2JOmyrZ3MzTRuN0Nxzgg9VJ7EVuGPfHh0RohgFHUMPxBzxQByEiweI59kaMtlbjAwOCcdsU2TRLjR9Nv2WOUh9ig7DkIRkk+nPFUhdva6tdw2ELLuJAijX5Sff0x2q68uqW0Ud/YzvbyQxDz8v949Cccj2x1piLmi3VsmjfZjHFOHP72ORiAw+o5BHrTJdGsGfdYXT2cp58m65Q/wC64/qPxplpfRayjzXumJA69b63fyhn/aHRj9BmqNz4gW1D2+n/ADv0Nwy/yHQUAS3UJgG7VrkIF6RRsGdvx7D9ayr3WZJYfIt1W1tB/AnU/U9zWZc3mZC8jtLKecmqDyvIcuaBE0l1nKx/n3qrkk5bmjoeKXk0wG/SnYo4FITigBelNLelADMalSE96AIlQsasLDipkixU6pQBXWI08R4q0sRNSi3z2pAVGTfF7ip7ZQ6hqc0Xl9eKp3DyQ5ROEamBqSTwWyZY5J6AVl3N81ycDhfSqm1zy1WrTT57tgETj1oAqn1FKvXBq7faa9iwVuhGQao96AOq8MzR7zCwXceR/WuwReK80sbhredZFPKnNejWU6z28ci8hhmgC6q8VKOBxUa5qQCkAAZ5pwFKB3p3TpQA3Hc1FKm5anA7GkZc8GgDktcszgTIORwawDXe3cCvGUYZBGK4q8t2t7l0PTt9KYFFxjmrEEgdcHrUTrUcbGOTmgCw42tmuk0S9EsHlufnXj/A1z7AOvFOsrk21yH7dD9KAO4RgazL2I2t0tzH0J5x+tWreVXAZTkGp5Y1nhZG70gJ7eZZYgVOQRU4IrB0+doJjbPxg8VuK2aAJAcU/jqKjBNOXmgB9LSUdeDQA73pD60g44pe+KADtQaOlFABR1pKWgAz2NHSg9cijrQAE1FKodSjDIPFSGmZpgcncxPp97uU8ZyDXQWdys8IZT1/nUWqWguIDtGXXkf4ViaddG2m8tuATSGdVkdRSE5FRI4Iz2qG5vobVd0jqKBFhiAOTVC91S3tAfMfn0HWsDUfEjyZS3+UeveueluHkJZ2yfemBsah4gnuMqh2r6DrWLJKTyzVC0vpUDvjktQBI0pPSondV5JqJpj0WosMx5oAe8zNwtMCE9akRKsLAByxwKAIBFxRVwBAMYooAudaMDoKaOlKSRQAhz+FTQIZJFGOCQPSi2t5LqZEQZJOAK7rS9CS0j2lIp2kwGJ5xnsM5oA7Pw/YDT9FtLUbSTy7pzuJ5wp9Pet4nACjaNp6jovsPeq0SRxBUU/cXaXA+gCrVhn2kEbQQOnaPP8AM0gGyD5SP42OQh7d8tXmnjmfzdcjX5iIoVG/1J5/KvR7jH7sjdzng9T7n2rybxNdNc65fOXVgGK5C+gxQBhNqkkg+y2f33OGkHX6D/GvVPAPhC3tbWO/uArzt0z0GK8u0K3SO68wjvX0JoEQh0W0ToTGCfx5pg2aSqFGKbIyqpJOAOaWSVUXJOAKxLm8mmn2IP3ag5B6DsC39BQIku7jzpFjVN38QB9uhPoKqowlUFZO5Ms3TPP3V/z+tKArq2wt5X8Un8Up9vb/ACKlhjEESKQvmAcJ2jHqfegYibY43XZhCx2xfQDlqrR3B8+WTOBgAyf0WnbgYizlirE5PeQ+3tVZNxlfhfMzgD+GMY6/WkBPuLMECZfqsXYf7TUxkQASM+MsA8vf6LUPnCGSQoW2kYJ/ikJ9Pb/PSoxLLLcIDtJUEhP4IxjqaAJJpzIdqphVBKRf1arMcbCMu0n3YwGl7D2Ws4MPKYKW2OcF/wCKTPp7VoEuQsajLj7sY6L7n/P0pgNh2EcoxBYmOIdWPq1NlVmnZiVMgXDP/DED2H+frT7YCKDcH2hh+9mP8lqNArKTsbBbEcPdiB95vz/yaAGALhAVYRM3T+KQ9f8AP+FWbqQwWsjsAZEjZgg+7GMdT70yKFnugVdTKoYs/wDDGPQe/wDk1FrrrBoV2yfLGUIyeshNIDzyxjm1SeFI4WmkfmG2Xv8A7TH09zXqnhrwrFpAF1clZ9QYYMmPljH91R2HqeprA+G9oo1HVLjHKRRQg/gSRXolMGGKKKKBGT4lk8vw/eMO67fzIrmvC6IumO6jfM0rbU7DGBk/5ya2vGUmzQGX+/Kg/rWdoMLLoEOf3MTbmZ+7ZPQf55oH0HXDKrbd+W3fvJfTAPA/zxTSweIZG2Adu7f1/qaddIfMiYx4j58uPv8AU/54pFDISSN82CQOyj/PfvSAkSTbbxbx2+WIf1/zgUkEpjupWYb7gRjA/hXJ/T+ZqNA4GxDmTjdIeg9h/h+dWIoUlkl8t9tuADLLu5Y9+f6/lQB554obzdduGWdjIAob5gRnHTHaucklkU/Mm7/c4P5GtLxLJby69fMsexBLtQlSvAAHX/Gsgq5GUmyPfn9etAEEzwySxgfK27P909K2/CyufEVtufcg3HpyML+X6VhSZE0YkRSMHpyPyrofByxtroeM/chc4H4Dp2pgdnqzBdNcAcsyj0zXNtktwVx710Osv/oKYOcv1/CucJ/h3LgdjQIx7u23zSHvnrWRNblSRiupaMNvPvWbcQAmgZgFCKYwrSlgxVR4yKAKyJlq0rWAmo7eAlulbVpbcdKAH29uOOKpatHi6x6KK6GCACsnWE/05h6KP5UAYDpzWz4f8KvqytfXc32PSojiW5K5LH+5GP4mP6VtaL4VQxRahrKSi3kP+j2kf+uuz7f3V9WNeiWOlO8kVzfJEHiGLe2iGIbdfRR3b1Y8mgClpmipJaRW4tfselRHdFZdWlb/AJ6TH+Jj6dBXRqoUAAYAp+MCkzQIaPvn8KcTTCcMfwppYmgBWaq97e2ml2RvL+TyoeioOXlb+6o7mq+saza6FCDOPPvJBmG0RuW/2m/ur7muGuLi71G/N9qMiyTkYVB9yJf7qjsPfqaBkuqapda/Mr3Q8m0jOYLRG4Hu395v0HamAjp6VCp4BPqf51KvPSgA5oPOQKGOKYzY+XqT0FACBzt3dqZIrMpL8Y52dvx9alSLy1y5zJjqOg+lK4zGfcGgBGbJpDtx7U3I60Eg89AKADJPA6U2Rv3Zz6fnRli3yhifQf1pzCOCNpZ3UEdXPQfQf5NADsFyGfhOoT/GsjUtXDF4bY8dGk/oP8aqajqsl0TGnyw/q319vas1mxQA5n7k1Gz56UwuW60goAdS5pooJ9KAHE1Gz9hSEk9KVV4oAcATzT1XNKiE1OoCDigAVAvJoZ/Smls9KekZPJoAj8vzBhhkHsaqz+HbW5yUDRt6p0/KteKKrccVAHC3fhi/twXiTz0H/PPr+VZR82BtpyrDseDXrMcPtS3GlWl8u25tkk9yOfz60AeWpqDY2Sorj3p2y3mHyPsPoa7G+8AQy5ewuWjP/POXkfmOa5nWPCusaGInvrR445cmJwQQwHpjn8xQBnyW0ickZHqOajIpY7maLoeKmFzBKMSx4PqKAK+aM1ZNqGGYXVvbvUDI8b4cYoAeiqvPep9gYYbmqwlYHNKLhgedpoAdLblcsvSoOh5q4H3Ju7U3dG4yaAKrMWOTT4hl89ql8qNuRTlQKMCgBjbD2zTcM3yhcCrAUCkoGIkYUU4HFLQBSAUbe9KFDUh4p60ANIwMUDApxWgqB3oAaX4qtMW38VPgKSc5pAi5zQBWD7l2lc0CPeflXAqztVfurRuNMRVML7sYak8l84w1W9zUu5v71AFRoio+7zTChFXtxoJB6haAsUCCBk0qStGco2KttFG3NRGADkc0BYsR6ozDZcIsi+/X86ka2tLsZhk2Of4H4/WsxlIPNA3LyOKAJp7Ce3PzJxVcEjg1ch1CWIbc5X0PI/Kp91ld/fXyZPXqKAM9ZiKnS4I706bS5oxvj2vH6pzVIhk4YYoA14L10IZXYGtm31osAk6CUdj3H0NcishFTJOR3oHc9E07WJYX32F6yt/cdsH8+h/Gumg8WwXKi31i0BJ48wLg/wCB/CvIIrwr0atW21qRQEba6+j80AepvolnqC+bptyrf7OeR+HWsS5064s5D5kbAfpXPWeqqsgeCcwv6Z4/A9RXVWfjCVVEeowpcRH+Lv8An/jRcVi4uou1jY3Scz2TbJB/eQ9M+xHFZ1+ImunltjmFzuX2z2PuK2I7fTNTG/T7kQykf6t+M+2OhrKvtJvLJmZk49R0oQFBmKqSFycVqr4fv2WNlRSrgMCD2IzWUiPMwjXlm4/Gut8Q6lc6LpNksD8qoUp64ptiK+urHp/hyOxyu4nn+tcYhxGB7Ut1qNzqUvmS7gPemDmkUjV0a5xcGxmjaa0vCEljHJDHgOo9R39R9K11NzBcmzaQHUbQN9nkfkXEQ6o3qQPzHPan6HpyaZZjULji4kU+UD/Cv976mq1okmu67G6MyW1owkMo6gg8YPqf5UCZRuo4YSLqN2h0+6kDMeptJx3Pt6+owe1aJM7z6jNc2qW8y2efMibKyMDxIp7ZpbyS3/t+S3hj3294Qk0Q9SeGX3H8sismW8m8Nrc6Tcbp7ViVjPXEZPzBT2xjIHShiJo5ZL6K1ubqdBcvKyQyBMYKYxuA9fUVcGmyNqMyRxrNZXGN/lMG8vd8wIx6NnHtx3rNjkg06HE0jF4pFurG4TJSXJG5T6ZA79DU6Zs9Rv4LNHbEqXUUceQWjP3gMeqsPyp3AuXJTSNJ8s3btLHNhgmQY0JIPsQGAI7Z471lSWCX1zHLFGtvdlslE4jmAOSye/cr+IqwglW5ksoj9qD5lgEjf6+Fxllye/Q+zA1SaS4tBFaRjzraSVZLaV8h4yDyPqOhHb6UgNjULGPUNdVUk4eMEkd8HGQahlt7WS9Wwk1TzoT8jW8yZzzkDPf5gMdwa0dTuI7CC5mKfvFlKps425ww+vJrl447fUvMcHy5Z2zBIWxtlHWNj78FTQMq+K7WK+tYZLJQ/wBmjKOIx0UdDjrgc59K5PSb2K1nmgugTaXSeVPjqozkMPcHmu/til4dpj2XdwSYpBwY51GGQ/7LcEfWqtxplpryQl7QCeSJiZIcJIHU4bA6Nxg4PPpRYdzY+HzXGhy/ZJh51rJnbcREFGBPBB9wenasrxZqd1a+JbqK5R15zCSuAV7EetczJY614blE9jO01s/zLJFlkYe69j2I6itq38c22p2pstcgwcYjkK7ljPqO6/qKQzKW7uJJoriGZIXhkDZ3cg+oB6/StCfXnuoFh+WPy1Kr5fAwSSf1Jq4vh7T7zS7iWzcfaAN0Ekb7kb/ZI7H0NcXc3DwT+VJG8cynBD9c0AemeBoItVg8u+d3gt23gA9G+vUZ+uKPFulrMLmy02Z2glKymFASu7POQOme9cLpmr32iSZdZbffxl1IB/xFdtpOpXUV1JdLMpSZceZFzmkBxmp+HWX5UuFEyfdik4OPTNVLmOd9FS6aNhNBIEY9CMHj9a7XVNEj8QSSSm7aOWJCVlk7EcgMOpB9R0rjdQ/tW3ibT5f9Y+PMjP3vUEeoI6EUwOukkSbw1Lqdu8SPdBG5OMMR82PX5hzXL63Gtxp0UqRshtz5LIeSFPzL/Mj8Kk8PTNeaHPabd8tpL5yRk8MjcMp9s1rrpsl7YXLFFHmRAY3Z+Ycrz+Y/GgDltMxd6de6e7svAmXHtww/EHP4VW0zRLm7luYkjeULAZN6DOMcjiizm+w6nFK/+rVsOP8AYPDfoavWl/NoOpX1shYb18vf3AByCPrQIwQ9xZTBkLxSDuODVyKaHU5kjnjRLhzgSx8AntuXpz6iu0vrHTfEOmXGozN5V3hGQIuASR82fxrB12C3aztJLOyEbxxASSRjnI/iP19aAMmWJ7IGPy8MOCaqrulfA5JrobqNdRWG8T5HeINIe3B2sMex5+lZuoWctsQYo/kcZJHPPpTE0RrOtpCyIcyN1rrfC11dW2lgJM6gsWADcc1y2maVNc3CM8bCMHJJ4rvbTS3EW6zgZoQAPkwee/AoEbNprbkhLr50P8Y4Yf0NN1vSYr2xjezkRHRjJHsGAWOCf5emR6VQS0ui20QS591Nb1lp3kxAyPiQjlC+F/H1oGcVciUJv8g20kZAb+6VbqQR23flmuW1KO8a7Ns5b5eK9F1W3kRpBDJEBJ/yy3/e+gPWudisba7mEbJL5vaMyhFJ7KWPQHt37UBYwtO0h55NsSbyPvP/AAr9T/Sum0+1021I8zbdyt8oA6DPHA7Y/Oq17Z+IreRPKhayjj/1cUXGB698/Wp7aHXNUjJmW3tkxia8KBHYD+8R1pDM4l9I1jDDIhk3Y6gxnr9eDWjfadGly0uqXatbA5gt4cDzF6g4HABFRa1FatbW89rO1wlvi2lkK8njIP8ASprMRXemx3Jtpbm6tR5LRx4+YDlWPfpxx6UAKmr3ImjSExWdsvAi2hs/7wPWpn02xu5FdE+wXLHiW3y0LE+q9V/Dj2pjOt05t9W063EexnhmtzskjAGcH1H1zUHhmYma4lZ2MMKsRv8AYUAS2E882o/2ZcGKXY2N6cj8D1FWROJdX1jdIRBDbiP74Hfjk5qh4WDT6rc3QXLZYqPU9hVXTL1bPUdQttVjeBbobZC8eTGc9SD296ANmGOCK3jsHk2W023yyjiQhuoZc5+WteSY2unyNIdoVMgn196xLPT9Ps8Xe9EuIRgyIwAZezEA45HXFVL6+ub2C5WR0g0yQ/LLJw/uFXuD+VIZFp1zp0G5baZZ7ssTxuDEn0ABDD8RUd/e29vCqXu1nBJ+zROSCx7u3f6DgVkSalHawtb6ZH5ER4aZ/vyfU9voKxZbsAnZy56uaYrmjqGpz3ePPfZEv3Yk4AH0rJkuGf5E+VahLMx3McmkPPSmIOnNHWlA9aXIFACYoJxTS3pTo4XlOB+dADck9KckRY1b+yIi58xWbuKkSMAcUAMihC1OE46U9EqZY80AQKhqwkRNSpDzgVK4SCPfIVHt3oASOHvUVxfQ23yr88np2qrcX7SfIhwntVMAk8daAFmnmuX3OcCrAHnW/B+YVZstImuSGcbUrSl0xYIcIORQBT0rSEnUTSHd7V08FukSAIMCsDSJvIvTA33X5H1rp1XigDN1exW6tGwPnXkVxMke1yPSvSwgK4rjNdsDbXZdR8r8igDIj967DwzfcG3Y+6/XvXGjOc1dtLp4JkkQ4KkGgD1GPnpUy9Kz9Oulu7aOVOjD8j3FX1pASAU/tzTBxTxigAI70mCaWnYxQBWlTcK5vXLIyQ+Yo+df5V1LDtVK5hDAhhxTA8+69aglXHIrS1G1NrdMuPlPIqixyKAHW8gI2+lK64O4VWXMbAiroIdKANfRr3K+S55Xp9K6GNsiuEhlaCcOvY119lOssSOpyDQAalARi5TgjGf6VesLkTwqwpcK8ZRuQRg1lwM1jemJj8jHigDoBgdKdmoo23Cn9qQEgbH40oOeajB9acDigB/XkUdqAaBwaYCnkUmKXpRSATIFBOeKQ0maAHA0nTrSZFGRigAJxTSfelPIpjuqDJOBTADg1zGtQC2l89OA3X61a1HxFBb5SE729ewrlL3UZ7yTdI+R6dhQBrP4heO3CIPn6bzWJc3ktw5eR2OfWqrygdOtQs5PWgCRpfTmo2f1aoJJgvTmoizufagCR5h2qH5pDzUqRZqZYfSgCJIalFuQeeBUwAVcjmmnJNAwCqo+X86OtSJGxqaO33HCjcf0oAgCnHSitqLw9eSxh9oXPZutFAjMB7mp4YnnPyDOOT7CmW0RuJAB+J7Cujhjit4PLgk++QD7560AW9LhSzh/d7WZh8x7/T6V0Wi7ZtThTyWHzZIHTjmsXHmMAYcn1Fbvg54rnV5443LCFMs3XJJxtX+poA7WNQkjsxUHoXH8PsPeoy+WwBgA8D09z706NTJKT8qqp/BR/U0kuFHA56qnfHq1ICtK+0SMQxCrnHdscn8K8jvn3+bIRguSfzNen6rdCDQbtw+0yAqZfXPAC15Zek7AoORmmBa0G3Ms0SD+NgPzNe7tLHaWw3HaqAD8hXkHhKzdtUtX2MVRgx+gr0i+b7VOFaTbFH95/c9h70ASG8luZQ4O1Vbv0XjqfU064QPBGuGWFnHyfxS9yT7VGsf+kRK8OI1XdHD369W/zx9auSkGQEnnBLP/AOyrQBHISocrtyeM/wAMYPH50kxjhjLMGOclUPVj6n2ptw/lmNCOc5WL+rVTupS0ZYvncQC/r7LSACzE4Lr8oAZuy+w96iAyqjY2xySqHq3PU+1K25VyByOVTsPc07P7sKhySAGk7n2FAFfaWaRs9Dgv2XHYVEAgkK7W244Tux9/b/JqxnywF2cgnbH+PU1U3N50pB64DSdh7CgCWJXluhtK+YDkk/djAFWY3xExBYRnOT/FIf8AP+cVHDCQqAjAbJWMdWPqfarfl+UuzcpmOAXP3Ywf8/40wGSF5ZFVkXeoGyIdF929/wDIpiyiK1kKHqx3zdzzjC0r4KyMpYQnO5z96U+3tUMSSOYVKKWVcrH/AAqPU0hk9qwZzuTEaKNsQ6nJzz+VUfFL40eQH5pHZVOPuqCeg9zWjZp/x8yB/lJAaY+w5C1l+LGIs7KJRsjeYbUP3jjnJ9PpTEjQ+HUWNO1Kc/8ALS8I/BVArs65b4fx7fCcLn/ltLLJ+bGupoBhRRTTQI5Tx1Lt022T+9Ln8hUmlxtHplkr7WZYl2RjoB6n/H8qoePHJa0iHXDt+eBWlEdtqkcL4RVAaUnrgdj/AF/KgYkqtLPtV9zgfM/ZfYD/AD70x41js5WjO2MZLSHq3rj/AB/KkztL79yW6heO8mf1wfTqaju7rzGjVxhcgLEPb1/zgUAMODFuddsI4VO7fh/T86uWvERaVMsz/JD9AOT/AJwKrFyZ/lTfKe3ZQf8AP1NSw3IigYIVa5ZjvkK8Ljp/9YfnSA8n1WWVtSu3dFOZn/1bZ7n1xWS3ksTs+Rvb5T+VXLppTNK4fduZjhuvJ9R/hVNpARtkRv5imBATJ5+CVbC9+Dyfaun8EgSarcuUYFYD+rDvXLgI0zeW+MAdORXYeBIyZtQL7T8kYBH1JPFAG/rYKWluny7yzH0Gaw2BBx8hAA5HPPetnXnH+jJ/sk88d/asTpISSv4rzQA9Y8w59c1Tmj61qKo8gYqpIgNAGRLFVN4Oa2ZIqr+Tlhx3oAgt7bB6VsW8OBTIYcGtWys5LiVIoYy8j8ADqaACGEkgDk1ox6FDa6qJLmBbzU2AaGxP3Ih2kmPYei9TV+1T7NObXTCk1+nE93t3RWvqq9nf9BW7p2nQ2ERVNzSOdzyO2XkY9WY9zQAljpxhle7uZPtF9IP3kxXt2VR/Co7AVoY4oFFAgJphOKUmkVGkOF7cknoB70AMwzNtUZJIwBWLrXiSPSpDZWGy41P+I9Y7fP8Ae9W9vzql4g8RviTT9HnVdwxNepyw9VT0/wB78q5uCFYItsYwD69SfUnuaBiBHaaSeaR57mQ5llk5Zj/h7dKdkk8U5hjpSAfPigBqZII9yBUqnbxUakAH3J/nTSzScIcL3f8Aw/xoAczksVQcjqew/wDr+1OjQDnqT1J6mo41AVVXsKnXAFADiCRimMAI3J9DUgpkoxEQOOD/ACoArBjjPb0pvzHese3I6k9B/wDX9qEVpsFdwjPfufp/jUGoalBYQmJFUykcIO2e5/zk0AWLm7g0+Dc55bt/Exrmb3UJb2Xc5wo+6g6D/E+9V7i5kuJTJK+5z+nsPaqzP6UASM+KjJJPNN69aUUAOooo9zQAuaTOeBSHk08LQAgWpUSkGB1pwfNAEoIAwKTljgUKC1Txxc0AMjjyatxw0+OGrUcVADYoatRxU9IqsJHQAiR1Zgt3mkCRozMxwAOpqxY6fLeSEIFWNBmSR+FjHqTQdV88vYeHSwj+7PqZHLeoiHYf7X5UASzXEGjSi2hjS+1jqIt37uDPdz6+3WoYNKluLo3+pTNc3b/ed+gH91R0Cj0rR0zSYLGHYicnlieSSepJ7k1cZQOBQI57UvBuiaqCZrRY5T/y1h+RvxxwfxFcTqvwsvocvplyl0naOT5H/Pof0r1aloGfOl7puoaVP5d3bzW8g7SKR+R6GmLesBtkG4e9fRb2Meop9mltkuEb+CRQw/XpXC+LfAvh2D9zp0kqankF44mzDED/AHs8g+gByfSgDzEC2m+62w+lMe1deRyPar9/4a1CyJYR+ZH/AH4+f061mLLNA2Pm47GgBTI4GzpTMkVYF1FLxKnPqKDbo67opFPsaAIk3E4BxVtelVNjxnDDipFnCcUASSeYfu1GjyBsOODS/aRngVIrq43CgYZB6GnUwbc5FSAikAoOKCaTcKOKAFz6000ErTDIg/jWgB1JkUwzoPemG4HZaAJ6KqGZz/FTS7HqaYrlzP8AtCjPpiqOaM0Bcv4oqkHYdDipEnOfm5oC5ZooXkZFFIYxlFVnDE81bIzSND5nemBTpM1PJb7BndUBoETQXk1uco7Crq31vc/LdQrn++nB/LpWZRigC/JpYlG+1kWQenf8qoyRSRHDBhSpK8ZBVsYq9HqYcbLmNZB6nr+dAGcHIqVJiKvNZWt0N1tJtP8Acfj9aozWc9ucOjUAWo7kjvWhbanJF0fj0rnwxqRZiKB3OyttVjJBBaCT1HI/Kuo0/wAWXlvGEuFW6gHHrj+ory6O5Iq7b6hJGco+KQHsVlrnh9v34jEMvX7v9RXP+I9aGq3gWPiNeBXIQ6nHIf3yc+o4rTgmgYfu260wsSM2BXSeH9BmmnF3fwOlkiiQE9JcjKge3rWTpunS6pfx20K8seT2Ve5P0rpPEWoDS7KKytJuIoxGM+gGM0A2V9XvrjV9STT7Pl5DjjoAP6CtG/ktvD2kixtT8x+8/dmPUml0zTo/D9nNdzzJNdSj74/hQjIHPc96ztPt/wC2dRlvbr/j1gOSOzHsv+NMks6Zbppti2p3f/HzMCUB6qp7/U1iW9vNr181zJC720eTjsWHRf8AGpdd1GXVtRSxtBks232//UKXUzJpVjZWUU3lRl23SocgZIzk+2c/SkA/TrCeCzkikhSW2J4t5SBtPcKx5BqW7tXlmtbzTRLbXFsqoBNjBIJwhwfQ4HY1S1qO9FgFuJN8tqdspQ8SIThZB+IKn3xV6BfsEcymR7u08jzoHzgtGMbkPuAQwoGVHt9SvYJZ2ga0urWYyxEcAKxyygn0Y5GexIq3p1tqcS3dzqUeDu85N6DHmfxEDpyM5xUSaqLmylj0+SZ5FGfssr5OO5X+8PasdNVuzb+UJlMTe3OPzoA37wte6Otz5iwSOysjyHjzAcjPseBWFchXuJZYbZ4ixVLu1xtKsTw69uG6H19jU7uLvw20TuwFtMGY9fkbgnHtxT7W2uLTU7W0uXWaOQ+XBdJyChHKNnqCOmeQcEZFAjRNlb2lxNd3kjwzB4pSIwGXzBwXx15bOR71SfT5THHcWd1FIWuftCiPKtHzhsA9R60moXDy3k11C3mGPKzxHup4J+h9exqmCY4ZEjdsRkXEEnQ4PBHsehx6g07AXBcra6jroVFmhjbeI+3L4OMdM5rG1PR7DU4TfxxP8hImC/K+O+R2Yd+xHPrW3K0N1ZRqkaQ3d5b7fMHAkZW+63oSRwfwpt5eJp1/Z+ZOr3EsQju3RRzz8rEf3h39RxSA499G1XRpI7rSbh57aVS0ckf8QHUMp/iHcVLa+ILC8lxrNigkYbWlC5X2OOqkHoR/KulZU0u4ktpyU0+5cHIzm1l7MP8AZPr6e4NZmu6THOXuJIFFxEQt2i8deki47Hv6H6iiw7mhY2Qv9BuLa4uLe7gKMYSnzFSAcAdx2IrC8NJaWU4a4u3jDdkY9PoOv0qpbaPqVvIbnRZ3eReTHGcOR9OjVLa67bxXUi6jZfZrk/eljTGD6sp7/SkVc6+5it9RGbHUNsuMFXyRj2zzVHxBpr61bJLaRpFe2UOWhlbcsir/AHWPII7DPIpkd3JPb+bHdpcIOQyoDj8sEVDLc3Eunz+eyjYjFZFwT+X0pAcp4bvzaeIYnbaiTExPjoN3Q/gcGu1tb5PsWp2M96kN1asJI0RcNIwO7p3Bx0HrXmrBBiSKRjg4JPBB7GvRfNsL7QRq09qrTCNWEg4YOPlPI56/0psEcfr1ukeoGSL/AFMwEif7rDI/wra0vSYfENvbyMm6Yx+SxDchl4B/FcUviK2t5dOiktyreVwcdlb5h+uam+GmrJp/iEwzDMcqkjPZh3H4UAW9Y0+fSLSIPAy20NsoEpb/AFmDxx6+tcjdXj67eRx2223Re/IA+pr2PXdQstT026tpUVkDkr9CMnH415Dq9qbW7Sxi2LHJ80MqDG4HoG9weDQgL+gKYPtOn3RA8thISPmBQ8NjHUdDTms2MN2bd98a/vIz6MPvAD0I59iKw9Nmm0/V4XuN2wsYpCf7p+U/lnNd9JYrpujw/c3XFwXB3cAgbW/A9D70AcSt/cldhfA9q7PwRrCLOLe6CuF6E9duefy6iuOvrYWt/JEOmcj6HkfpV7QCw1m22d2wfoetUQe9NY2Ih8yQfKB/fbH865XXblU8wWW0KOuF5C+uOuK2N8snh6Eg5K4/HqB/SuKhRpJpna58m5jOVR+Mg9eT78EYPvUopmFqULrcpFfbp9mXjeNsebH3APqOo/L0pN5kiKI3mbVOHOAzRkZGfXjPuCM1qXdvHJGzzqsEcUqlgP4X/wBnrwR1FZ4NrFO58uWNoirKQ+4FM5/Ec/kaZIzRdY1fT763s3f7RaSngS/MAo5JB6jApLq+u9cvUhuJvLtiSVjj4UKOSTSSfurqaXaqgQt5Se7HHH4GqsF5bWeoFbwSiJojHvjxlc9Tg9aRRq2sEd3ZXFtFbtHbOmEkblpH7Z9Pasvw/dPbar9nY7PPBjbPOHHK/rxV5tTh06FNuppcxcGKO2Qq0g7Bgc7foKzNWR0u47yNGj+0Ksy+quOo/A0AWLu+nu55LO0V7qd8o0gUqoGew/mat2lra6dp82nTXyR3VwMFgCyx+zYpbi5ubpI2sIUt4rqPzJZY+CW6MCT0wap/2ZKiCfT5ra82n94I3y4PupwT9RmgBYrS+0KE+fB5lo5z9ohO9D/wIdPxwa2re4TV7by7mOG9t0H/AC2ba0Y/2ZOo+hql/aMGj2xkvC8Fywx9nhcfMP8AaHQfzrldR1ua7Uou22tskiKPjOfX1oA0L2fS9NunWy826ccKJHykftxgNj6fhWDfajJcS+ZcyNJJ2TsKpSXRPyxjaPXuarn1oEPlneQ/NwPQVEfajk04L60wG4Jp6xk1JEgarSR7o8dxQBTeMgZFQEk1p7MjFUpI9klAE1nZef8AMx4HarcpWIGFEUCq9tM0LZWri7LyU/wuRx9RQBXUVMi96GtpIjh0Ye9TxKaAFSPNWAoQbm2gD1qtPdR24xnL+grMmu5bg/MePTtQBo3GqrH8sHX+/WbJPJM25zk0kdu8r7VGSa39P8P7iHn/ACoAyLWyuLttsaYHrXTafoMcADyfM3vWtb2cUChUCjFWlxQBEkKqMKMCmTwAjNW8fhQybhikBxl/A1vPuTjB3Ka6WwuhdWccg78H696p6na+ZCSOq/yqhol19nu3tXPyScr9f/r0wOpUVna1Y/a7NsD51GRWlGOKkK5TFAHlUqGOQqe1CNitvxFp/wBnujIg+RuRWF0oA6/wrqO2U2rnhuV+v/1xXaocivJ7KZoZ0dTgg5Br0zTbtbq2jkHcc+xoA0lxThTB604H0pAOpccUA4pelADSMioJFyKsGmMKAOb1qy8+3ZlHzLyK5FgVPNejXEfWuL1ez+z3JKr8jcimBkuM8inwP/DTTUf3WyKALUo7itPRrza3kuevI/qKzEYMtNUtDKGVuR3oA7yJ8ioNRg86Hev3k5/CoNOuhcQI4/EVoggigCLTbrzYgG+8OtaINc9Jmxvww/1Tf5/StyGQOgPrQBOM0o5popwOKQDhTtwPFR55pc4NAElJzTc0vXkUAHtRQfWkJoAM00sAap3up29nH+8dc+neuV1DxDNcZjj+RPbr+dAHRX+u21nld29vQf1rlNQ124vCVJ2p6CsqWck5dqrvIT04pgTSS92OTUDSk1E0gHU1A0pbhaAJWkC9agaR34FKsZbk1IseDxQA1It3Wp0hycAVIke35n4p5fsOKBgI1QZPX0pCSaeg3jbUiwHo3Ht3oAjRS52jnNWEt1BweT6CtTTNCur7iJNsXdzwB9TXVWWjWOmx7yFnlHWSThB9BQBzNloFzdAO48qEfxngfh61tRW1lpcOUVc/89ZOp/3RVp7uS7lKWsZnK/x4+UfQVVbSbuWXdcozZ9aBFR9ULOTHZtKv99sZNFaUOjBEx5m3npRQBwttPJbHKn61rWVzJfXEcYhzjJJzwB6k9hRRQBroJ9QmGn6bvcOdrSDOW9h7V6NoXhpPDNmSzr9omUea4/hx0Ue9FFAGp5hiA4UEDIHZfc+9RSqDHu+bY3r1k+voKKKAMHxbL5Ojxwfed2UHHQY5wP61x1hodxqt4iRJtiXBdivCj/H2oooA9Cs7W3sLFYrYMAPvP/FIewFaOnqc75ApkBOyPsuOMn3/AMiiigCyG8yRnyxUkAuOrH0X2qQswlBAUOigKP4Ywf60UUgKjmN5d7lvLA6/xSnpx7VXlYvOhO3PYdowB/OiigBzKHj2jdsY495Cf6VOQLb7u1pgPwjFFFAFGQII95dvLPV+8hPYe1MikEpZwig5/dx9lA4yffiiigCxaM26Zw6gAANKf5LUzqrLDuVhEzErF3bA6n/P1oooAS5l2q4Xa02ME/wxA/1pNiCEgFlh4DP/ABSn0Hsf1+lFFAEtspbaWCnktHEOg9z/AJ+lc54unxeQq77jGjsfQEDiiigaOz8JwfZvCemR9D5Ck/jzW1RRTJCmk4oooA8/8Xy+f4gtoV+bEaggc9Wro3jUMGkH/XOIe3c/5wKKKBlWVybiU43zHgD+GMY6n/OTUEkYhPkI26V2XfL2HfH/ANbtRRSAEABKxOyxA/NL3b1wf6/lUE8sUelksNkCqzHPG7r+OP1NFFAHlDLFIMxyMuf7hyPyNVisi/3Xx+B/woooArHY0jbxtfgc8H8xXb+A4tsF85diCyDr6A/40UUwNTXeZ4V9Iuv1NZBDBssygfkaKKBF6NcwL9KrulFFIZWdKYsWZFHvRRTA2NO0yW9mKRhcKNzO7YWNe5Y9hW3aobpDaaS7x2Z+We/27ZLj1WLuq+rdT2oooA37CygsbUQW8axxrwAKuLRRQIfSE0UUARyPHDC9xcTLDbxjLSPwB/ia4rW/EE2rA21sr2+mjqOjz+7ei/7P50UUDMaJQJGChcYFTZAGKKKAF65PamlgoLngDqTRRQAxIWlGXGFyfkPf6/4VKfzxxRRQAxPuj3FSg4xx0oooAViFXczKBUBzMfnGI+ydz9f8KKKAMrU9bEWYbUqX6F+y/T1Nc48hYlmLEk8k9TRRQBAzZ4FFFFACinCiigAyB1o5PWiigB6rSlgOBRRQAwtSwMWnC+tFFAGnHFVuOGiigC3HFirUcdFFAFhI60orOC2tPt+pzfZrIcD+/Kf7qr1JNFFADJRdeIFETwtZaOpyloG+aX/akPf6dPrWtaWcdtHsRFAHHFFFAF0DFQyDn8aKKAG1PBbNMC7FUiXlpH4AFFFAGJq3ihRC1to5ZIed91/HLjqEz0H+0fwFcsZFHypx/F68nnJJ5J9zyaKKAGYLtux9Knt9E03Vmmiu7RJHKAqejA555GP1oooAwtR+GcjAvpl2jDr5c3H5MOPzrjtR0LVNHkxd2ksPo+3Kn6EcUUUAVVupF+VxuFPHkS/xbDRRQAjW7gZX5h7U3zGQbQMUUUAR72AIB60odwMAmiigQ4TOOrZ+tPFzx8y/lRRQMa1wxGDtqMAsaKKAH+ScZBzUZBB5oooAKKKKBBQATwKKKAJVt2PJp6W3PNFFAy0ihRgUH0oopDGMaVelFFADW5GKqGNs9KKKYhPKf0pREx7UUUAL9nk9KY0bLyaKKAD5k5BxVuHUpUXy3+eP0fmiigCYpY3Y+X9xJ78iqk+nzQ/MBuT1HIoooAq5ZTg8VIspFFFAEyXBH8VXrO4llmSOIMZG4AFFFAz03TNWtfDmkujur3Uo/euO3+yKl8PQLrl8+rXnFrCcoj/8tG7D6Ciigknvp5Nc1qO0gdxEzDeU9AeT+Ap+tX1vp2nrp9geVG33LHqx9zRRTYFPTI4tLliWVM3MpDSP/wA8weAP15qnFB59tfSXE6vEJWS4hK/NHj7si+pHOR3AIoopDY63kkWUWlzJvkiUo0e7IuIWA+ZD3YAA474B65p1tbXVjcQpdQzeVCxjD7Ttlhbg49xnPrg+1FFAiC3NrHo1zG+1Lm0nbyrhF+YMcFckc7TtYexxU2o6XBJcB7aZUupo0nNueFkLDJ2+hJycUUUAVdEYNqQgcYEoaKSN++exqffClx5NnfYMPy/Yro4+6cjY3TIPI5oopgS38EX26RrSZYbtct5Z+7KCMnaT1yOqmq2o6lLbz/ZoP3USx7AHUHKnkH3APBFFFDAh4v7WOK2TZJHGJPL3cc8Ntz3DDp+VadxFp0+qh9mbi1UP8n/LYBc8/wC0OvuKKKAKlvcPqMZa7MU9tckoNn34nycI2fXqD6/WnW22R4bOX572BGEW/hbuAj7mezAcYPp7UUUgKMEI07UIZ4HdrSUlVLcMp7o3ow7U2/t0vbw22qebcR9FmC7pFB5DA9SMdQaKKYHPSaVPZXckukXMxVDjJGx+Oxxwal/4SF5ImtdThKSEFfOjTDDPqvf8KKKkpGPdaS6xfaLfyriHj97Dzj/eXqK3vCU4uLOSykOBDKJeemw/Kw+lFFAyxcW7S6lcx2yK1hMhjUj/AJZuDuUEf72QPrXM2s7afqcNwNw8twT9O/6UUUAd9oSvf6/NZs7SQxqzbJPmMg2kjBH4EVz+vWUt5Gi2yMZo5P3YHXn/AOvRRSApaxptxpenRG9kVpZ2zLER80bgYP6YOa2Yb1tT8PWgM6I8W7d5mCGIwCOfUc0UUxFb+zPt9iih8XUPyL6SL1Xn1I4H0xUnhOD/AInTJIGDojHB6g5AoopoTPWbk40oInTpj6KSB+YrjIhfXMRlxFPFuKhJWBbI7Doe/rRRSQ2OuQjaSFkhciQsGQtypXgAH1A6Z7Vz6RJHD5kk26KM/u3RcnafvKR2x19vpRRVEixSxxyQzCNLpUUjZJ3x29jjofXpWssvh/xHFtjdbe5A5il4P4N0P6UUVLGjMcaPo0m20Rb28B64yi/4mmX8V1eaTLJeFftMTCVU/iCHgjHYelFFIoq6NeWjW01jfvttwfNjcvgDPUYHX6VDqXiG2iZ00e2WM42m5cDeR7elFFMRylxeZcsztJIermqkjM53Mc0UUxDCc0AZoooAd0pCaKKALcKkAEjrVtB8w7Z4oooAbjBINQXMeRuFFFACQlDGVYc9jViA+XKHHUUUUgLj3xCEuVx6VnTX5YFIxtH60UUwKoVnPPNaVjpUlyQSMLRRQB1FlpsNsvyjJ9a0VXiiikBMEqVUxRRQA5VHenEDFFFMCpPEPrmuT1C3a1uty7gVO5TRRQB1Om3aXVpHKvccj0NXh1zRRSAzdasRd2TAD515FefyxmOQqe1FFMZGjYNdh4Y1LD/Z3P3un1/+vRRQB2iNkVKKKKQhw6U6iigApD0oooAhkXI5rE1ezFxbsoHzjkUUUAcZIhRipHIqFxkUUUwCB8HBqdxkZoooAu6TdmGfYx4b+ddZC+4UUUAJeQC4tiP4xyKr6Xcnb5T/AHl9fSiigDXDZ5p+aKKQC5pc54oooAM0bu1FFMCG4vILWMvM6rXNaj4nZspa/KP7560UUAc3NcvKxd3Yk9c1UeXsKKKAIGk9TUDzHotFFADQrNyalSP2oooAsJFgZbgUu4Kfk/OiigZLguM9xT1gOMv8o96KKALllaT3lwLazhZnPcLk/wD1q6/T/C9rY4e+Iml6+Uh4H1NFFAF+61GK2i2hFIA4jThRWI8t3qs6oSwUkYA4A/CiigR2NtaR20AhT5YoxzjuabOyLjbx9aKKQFSQAOffmiiigD//2Q==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_enhance.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "##### 增强效果方法列表\n", + "\n", + "* class PIL.ImageEnhance.Color(image) 调整图片的颜色\n", + "\n", + "该类可以用于以类似于彩色电视机上的控件的方式调整图像的色彩平衡，增强因子为 0.0 给出黑白图像，因子 1.0 给出原始图像。\n", + "\n", + "* class PIL.ImageEnhance.Contrast(image) 调整图像对比度\n", + "\n", + "这个类可以用于控制图像的对比度，类似于电视机上的对比度控制，增强因子 0.0 给出实心灰色图像，因子 1.0 给出原始图像。\n", + "\n", + "* class PIL.ImageEnhance.Brightness(image) 调整图像亮度\n", + "\n", + "这个类可以用来控制图像的亮度，增强因子 0.0 给出黑色图像，因子 1.0 给出原始图像。\n", + "\n", + "* class PIL.ImageEnhance.Sharpness(image) 调整图像锐度\n", + "\n", + "此类可用于调整图像的锐度，增强因子 0.0 给出模糊图像，因子 1.0 给出原始图像，因子 2.0给出锐化图像。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JPEG (1924, 1080) RGB\n" + ] + } + ], + "source": [ + "# 读取照片基本信息\n", + "\n", + "# 导入 PIL 的 Image 模块\n", + "from PIL import Image\n", + "# 打开一个图片文件\n", + "img = Image.open('files/test.jpg')\n", + "print(img.format, img.size, img.mode)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "Mode 的解释\n", + "\n", + "图像的模式定义图像中像素的类型和深度，s当前版本支持以下标准模式：\n", + "\n", + "```\n", + "1 (1-bit pixels, black and white, stored with one pixel per byte)\n", + "L (8-bit pixels, black and white)\n", + "P (8-bit pixels, mapped to any other mode using a color palette)\n", + "RGB (3x8-bit pixels, true color)\n", + "RGBA (4x8-bit pixels, true color with transparency mask)\n", + "CMYK (4x8-bit pixels, color separation)\n", + "YCbCr (3x8-bit pixels, color video format)\n", + "Note that this refers to the JPEG, and not the ITU-R BT.2020, standard\n", + "LAB (3x8-bit pixels, the L*a*b color space)\n", + "HSV (3x8-bit pixels, Hue, Saturation, Value color space)\n", + "I (32-bit signed integer pixels)\n", + "F (32-bit floating point pixels)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'jfif': 257, 'jfif_version': (1, 1), 'jfif_unit': 0, 'jfif_density': (72, 72), 'exif': b'Exif\\x00\\x00MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x02\\x01\\x12\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00&\\x00\\x00\\x00\\x00\\x00\\x03\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x07\\x84\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x048\\x00\\x00\\x00\\x00', 'dpi': (72, 72)}\n" + ] + } + ], + "source": [ + "# 读取照片信息\n", + "\n", + "# 导入 PIL 的 Image 模块\n", + "from PIL import Image\n", + "# 打开一个图片文件\n", + "img = Image.open('files/test.jpg')\n", + "\n", + "# 显示照片的信息，存在一个字典变量中\n", + "print(img.info)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'ExifVersion': b'0230', 'ComponentsConfiguration': b'\\x01\\x02\\x03\\x00', 'CompressedBitsPerPixel': (3, 1), 'DateTimeOriginal': '2020:05:04 12:09:28', 'DateTimeDigitized': '2020:05:04 12:09:28', 'ShutterSpeedValue': (255, 32), 'ApertureValue': (64, 32), 'ExposureBiasValue': (0, 3), 'MeteringMode': 5, 'Flash': 16, 'FocalLength': (22, 1), 'UserComment': 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+ ] + } + ], + "source": [ + "# 读取照片的 exif 信息\n", + "# 我用了一张佳能微单拍的照片来做测试\n", + "# exif 的信息还是比较复杂的，这里可以简单的看一下\n", + "\n", + "from PIL import Image\n", + "from PIL.ExifTags import TAGS\n", + "\n", + "def get_exif(filename):\n", + " image = Image.open(filename)\n", + " image.verify()\n", + " return image._getexif()\n", + "\n", + "def get_labeled_exif(exif):\n", + " labeled = {}\n", + " for (key, val) in exif.items():\n", + " labeled[TAGS.get(key)] = val\n", + "\n", + " return labeled\n", + "\n", + "exif = get_exif('files/img_3845.jpg')\n", + "labeled = get_labeled_exif(exif)\n", + "print(labeled)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "draw image ok\n" + ] + } + ], + "source": [ + "# 在图片上画线\n", + "\n", + "from PIL import Image, ImageDraw\n", + "\n", + "img = Image.open('files/test.jpg')\n", + "\n", + "draw = ImageDraw.Draw(img)\n", + "draw.line((0, 0) + img.size, fill=128)\n", + "draw.line((0, img.size[1], img.size[0], 0), fill=128)\n", + "del draw\n", + "\n", + "# write to stdout\n", + "img.save('files/test_drawline.jpg', 'JPEG')\n", + "print('draw image ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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pKdTTTJYmM0DI6UZpQapMhxTJ4Lt4wYz80bfejbof8D7ipcpFKJ7SR43TDAE4YH/ZYf8A1jVUqQeaQdcU7k8p2mm+N1nt2steha4ikUqZlALDPALKQQSPUYNO8S6Zov8AwjNtqun6vDc3aFY54/us2fukKTnIHB/OuKZSBupjOWGD2ppg9CTzAw54NS2zhZgz9Oc/jVQHBqePIOfSpYloySeELk5U56VVPvVqRcnI71AwoQvQjqWLB4qMjFCkg5FMDQgcZKv0PUU6aEEbs5H9/wDx/wAarK6uPR6lSVkODwf0NIXqV3QqcGm+9W3CsPl49u34VAUweaLjGqhY1N935RyaRc5CIMseK1DparDtSTNyB8wPQn0BpSkluNRb2M0ssQyeWNNWVZOG4NRzxyxylZAyuOoNQ5p7glbRl9ZGjPzfg4qfKMd2dp9exqhHOV+V+RTjMqjKFh7daLBY0ptTllj8lnyv9/b8zY9TVNosndGcN6ev0qFJlYbXqXJQc/Mnr6UJW2FJ9xyTuDgnaQeh6E/0NOgl8sSxumdwzz2x6fXpSMqyjn8DUZ3R/JIMjsRT0FqtSVgrDaBnjoG4qIxkgYFL9fuHuO9PVyo544/IU2JW6lNk7imYq06DtTWiJFFhpoqminMuKbikW2FKBRRmmRuLRmm5paQ0gzRmigCgbdiVFzya6m00y3v9NiXKrsDeYO+ex9a5eNh0NWvtTIuAaTRN3e5UZSrFT2phqQsG69ajIwcUXNGrK4CnAUg6UtUZtjgSOlSiYgVBmkzSKUe5K0js25ixPvU9tG1xMiDvn8gCT+gqoOa19FvTpeoRXYjSYpuBjfoQRg/jjpSa00K57MbcaXcwwiby2MRUNvA4APr9OlUTkV7Tpuq6dq+nR3KxxThZdsoPymMN8xyo9FXHofUivGpSHkLgYDEkAdgTmsoyb0Zvy3V0Rg1La3Utldw3MB2zQyLJGcA4YHIODweR0NRYxRWqdjOUT6N8CfGaw1sR2Gv+XYX5wqz9IJj9T9xj6Hj0PavWAQRkcivhnJFd/wCFfix4i8N6W2mxSRXUGMQfassYP90jkr/snp2qmk9UZtH1TmivBPB3xpuo782fiHY8Mr5juOnl5/hP+z6Ht39a9ys7y3v7Zbi2kDxN0IpONhNFmiiikIKKKKACiiigAooooAKKKKACiiigAooooAKSlooAxvEHhzT/ABJpslnfwqysOG7qfUGvmjxx4A1DwjenejTWLH93MBx9D719XmqWp6ZaatZPaXsSyQuMFWGauMraPYuMuj2PiojFJXpXxC+GF34ZmkvbBGn01jnI5MXsfb3rzcqRTcbarYUo21WwylooqRC0obFNpaALMU5U12fg3xvf+FL4PCWmspD++ti3B919G964MHFTxTFTQnYpOx9iaB4h0/xJpkd9p0weNvvL/Ejd1YdjWsRxXyX4X8V6h4Y1Jb3T5uuBLCfuSr6EfyPavpLwn4v07xbpwubNtsi4E0D/AH429/b0PehrqglHqjpK5bxlMVtYYx3JNdTXD+Mps3sceeFUfrSQo7ninjubdqMcY/hFciOtbni+fzNck56cVhpywHrTir2Q3ufTPwotPsvgKzOMGVmk/M4/pXb1ieEbX7F4T0uDGCtumfqRn+tbdE3eTJluxaKKKkRwHxD+H1t4qsGubdVj1CMZV8fe9jXzTqOn3OmXstpdwtHNGcMhr7T7VwHxF+Hdt4psWurVFj1KMZV/73sauMk1yyLjK65WfLxpKuX9hc6deS2t1C0c0ZwyHsRVQ0mmtGJpp2Y00lOptBI09KbT6TAoAYBT6MUtADSKaRTzSYoAVWxR0OTTaMnpQBI6gjIqW2jx8xqANip4pMcUAals/wDCaZdRio4nwQRViX5480DKDIPJwBzVJ2IODWieDVS5i/iWhiKxJoFJSgUAOFIRilHNKw4oGNopKWgQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAtKKSrunafPqF0kEKbnY0wLOh6RPq+oR28Q6nk9gO5NexRSW+iaZHZW3CoOT6nuTWFplnbeHrHy02mZh88nr7D2rN1HVS5fL8VEnfQpKxLrOt7I3ctz2+tcDcXDzys7nJJzU+oXzXMv8AsDpVGqSBi5ozSUZpiHg4r0f4XeP28N6gNNv5GOmXDdT/AMsXP8X0Pf8AOvNqUNSTKTsz7YjkSaNXRgysMgjoRUnavEvhF8Qg4j8OapN8w4tJWPUf3Cf5V7YDkUmrClG22w6iiikSFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFMmmitoJJ55UihjUu8jsFVFAySSegA71j/2nqOq/wDIFghjtT93Ubv5o5B2aKNSGkU4IyzIMEMpcGk2VGLepoX+pWelwLNe3CQq7bEB5aRyCQiKOWY4OFAJPYGs/drGr/NDJ/ZNi3KSeXuu5B2O1xth6dGDkq3IjYYFqw0a3sp2u5He71B12PfXCoZmTIOzKqAq8D5VAGeSCSSdGjV7lXjH4df67f5lWw0620yBobZXAZt7vJI0ju2AMs7EsxwAMknAAHQAVaoopkNtu7CiiigQUUUUAFJS0UAFJRRQAUUUUAJRRRTASiiimB8F04MQeKSkqbFJlhJh0apeDytUs1IkhFCbQmicyHoetbNrao+imVUbzt7DPt2rCJya2dLvJLWLClTH1ZH5BpSvo0EUrO5Ta2Yh2IxjvVcFkNbl7rEUURjtrVRI4+YvyB9KxwySDDcGqjK+6sEo2W9xnyvz0NPWTaNkgyAcimPGy89RQHx8rcim0Rc0LeZGMY+VQDnjoaeURVCY3DO4/XtWem5PmjOfap1vwoyRyKzkmnoax5WrMslWeQb48k9BinND5jOX646A5NU11N8nPOangkubtjHCnQFjhew78UNS6grX0M+7j2SYquOuDVu5ikSTEgYH3qqVINWLdXFaEgbh0qMMUOVJB9RV21cDhhkdCPUU27tDCwK8q3Kn1FBKfVFfcj/e+VvUDj8qayFcdCD0INNxinK7JnB4PUdQfwqXpsapqXxfeNpM1IQj9MI3oTwf8KjZWQ4YY/rQmEoNK/QKAKAKeBVpGbYAVKjYqOjOKZJZDgUj3AA45NVi5NNzS5ilHuPD5PNKRnkVHmjNRY0UhSOacOKfFE0rAepxWld6K9sAofdJjJHb6VSaW5D956GYDRmhlZGKuuCOtMJzTbJSFJzTaKTNTuXsLmpIY97YqMDNSqcU0hOR6iNesb3TCtlZQs0cCW6pJyYkGBhl/iHfcv41z3iK0to7jej/ALpEwmOjZ7q3fHpXMwXTxSq6OyyKcq6HBH4itxddhuLOO31BFEccpm3woB5rY6MB0ye4/KsnDld0XGfMrM566RYpdg6gc/jVenSzNNM8j/fckn8abVD0EqWCPdJnsKiHJrQiTZGB370N2Lpx5mWAlvcLslGxuzjp+NVLrTprb5iMxno46VNV6y1GWzOMJJEfvRSDKkVCk0bSpqRFaS6deW4t7+N4ZgAscyEAf8CBrN1LT30+42OdykZVx0IrpJtJtNWjM2k8SgEtaH7w/wB0/wAQ9uv1rm7mWdY/ssu7YjZAccqfb0+laRaOWrDlKVTxkMOOoqAigMQciqMi6GBGD+VIYmMRlH3AcZqHcWAJq3ayDy3icZRufepemw0u5TIphFTyhN2E3AD1qIiqENyQalWXIw1RkUAUATBjnGeKsSymbYcY2qB+VVQT+NSoAOTSDyL2m+VFcB34P8JPQH1NXo7eT7WXd8R4zntjuax0Zic9BVsSTG0eNXYxHggdR/8AWqWne6KTi1Z9CvqF19suy4+4o2r9BVJhWhZ2ys0iOVw64jbtnPr2ou9Nlto1aQY3Zx68e1NNL3Qbb1MzBxS1bWJXiAzgj9arOm0nHSmK9htSRzMnHUVFSUDuXVI+9GfqKkDK6kYyO4qgrlTkVN5oYZ6NQS0S4ZJMJ8wPanAhgdvPqO4qXTXT7QUmKqJEK5PQE9Kfd2D22CZE8wngZ5I7Gjm1sw5dLohHOSPT+VK8f7vevBx+J/CogwbhuHz36GniV0IycVaJRDtz8vpULoVNaKvvVydp/TpzUToCCR3osFyjikNTvGQNw6VCRzU2NLqwlAFApaZNxaXNNzSZpAkP3UmaSigrYUZNXrBIvtkRnGYww3Z9KpDg1ZjcA5oIcrmprOlpawC4QrlnwMcAqRkflWEatz3JcBM5A7bulVyARlaWxpFX1GZpQM0lO+lMiTFAxUiSFajzSZpiSuXY9QuLbcbaZ43dTGxQ4yhGCp9QarrITxUOakiGTU2uaKTirIti3JtxJnklsfQY/wAagxitm1njls4rRggKE4JH3gTk89j7d/rVC8tmtpSjjBDFT9R1/nU31saJ3KlHNLiimmEok3mhoQjDlTwfr1rvvh18S7nwlex216Xn0p8LInVoh2ZfXHp6dK88qxptxbW2pW093b/araOVWlty+3zVByVyOmatPoZONj7Us7y31CzhvLaVZoJlDpIvQqeQatVzPg7xPonibRIptFlRYolCNa7QrwccKy9vYjg9q6apasyQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIJ7eK5gaGaNXjYYKnkV4N8R/hPJYNLq2hRl7c5aW3HVPdfb2r3+msqupVhkHqDVRm4+hUZW0PiF0KkgjBFNr374j/CeO9EmraFGFuOWlgHR/ce9eDz28lvM0UqMkiHDIVwQRVuKteOw5R0vHYgozRikqCRaUHFNpaAJ45Stbeh69e6HqMV/p05inT8mHdWHcGudzUkchU0k2tUNStsfVvgnx9YeL7PaGWDUYx++tief95fVf5d6yfFNysmpTsDkLx+VfPen6lc2V1FdWk7wXMRykkZwQa7fTfF32rT5Y7x8zhTyf4qJPTQvTdHHa7OZdWlc9CxqPTITc6nbQrzvlVfzOKkuVE0rs46nNbHge0F5430qADjz1J+g5q6fxIS+I+qLaIQ2kMQ6IgX8hip6SlrNmYUUUUAFJS0UAedfEf4c2/iiza8s0WPUoxw3/PQehr5svrK40+7ltrmNopoyVZD1BFfa9eb/En4cQeJrRr6xRY9SjGR2Eg9DVxlfRmilzLlkfMhpKtXlnPZXUltcxtHNGSrI64IIqrSatoyWmnZiUUUUCCiiloEJSYp1JQAlNIp9IaAG0qnB5oxSUAW4ZcHmtKJg8eKw1Yir9pcbXw3Q0DJZVwaR13w1LPgjIpkXzRGgRlyLg0FxjGKszR8mqpXBoGKPanlMikjwDzVkpUSlY0hFPVlEjBoqeSPvUBGKpO5EotC0UlFMgWiiigYUUlFAC0UlFAC0UlLQAUUgya3ND8PT6rKGPyW6/ekPT6D3oBIq6XpNzqlwIoEz6nsB6mvQbGytdBtdkW1piPmk7n6e1Kv2XSLT7PbIqgdT3J9TWJe6gXyWOBUvUos32otITg8VzN/f+YSiHjufWoru/aXKJwn86o5zVALnNJmjNIaAFzRTaM0CHZpc03NLmgCaGZ4ZVkR2V1OQRwQR0Ir6T+GHj+PxPpgsb2QDVLdQHz/AMtV/vD39a+ZhWjpGrXWi6nDfWUhjnhYMp/mD6g96fkWmvhex9oUVy/gvxdaeLtEju4CFnX5Z4u6P3/A9jXT1LViGmnZi0UUUhBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRWXea9a2909lbRzX9+mN9rZgM6ZGRvYkJHkcjey7sHbk8UN2GouWxqVjza3LNPJbaRYvfTRsUeZ2MNtGwPIMpB3HIYYjV8MMNt60z+xrrUvm1y782I/8uFqSkAHo5+9N1KncQjDGYwa2IYYraCOCCJIoY1CJGihVRQMAADoAO1LVl+7Hzf4f8EyodCWaeO81mRNQvI2Dx/IUghIPytHEWYKw67yS+ScMFwo2KKKErEyk5bhRRRTJCiiigAooooAKKKKACiiigBKKWigBKKKKAEopaSmAUUUUAfBe2iraxgw5HU1AycZoQEWPSgUuKSgdyaMHgmr9sQPkboaoxEEbTVhCUOD+dO3Yi+upYcBfklGR2eoJICvzL8w9RVxZEkG16a0TxHdHyvpQmnvuFmtUUllZTg8inlFk5TipGRJeV+V/SqzK8TelGqHoxcMh9KdlJeG4PrTlmWQbXFMkULyDkUXuKzGFCjYPNdLo0r6bA06PtmlG3jrt9PxrH0yBZbkPJ/q05Pv7VdvpV+9Hw7HoKnl53y9C3Lljfq9iwxh1S+2uNoQYyOh/w5qnq+izabNGjpxINyn1FWLBPspzLxnkmrU0U1/L5k0iog4XPJA/kKqpL3kkRTj7rf3GBFbukgb5f++q0IoPtVnJAfvp80dW1trNDhpnkx/c6foP6043NpanMcKq3q74P5cms3IuMZdTlpFwT2I7U1kIUMe9a91aedK8qhRvOePU9BzVC+CrMI06IMU1LmHJOJTNPRyowQGX0NPWLdEznoKj2nGadrgpNO6HhVf/AFec/wB0/wBPWkIIOCMEU3NO8zIw43AdOeRRdor3Z+T/AAEJptPMeQSh3AdeORUeaL3E4OO4tJRmiiwmwp6jJpoFSLxVJCbLUPyEEdQc111vd2d/CGlKCQdQ7YPvj2rjVcd6kMqjq1JxT3FGTT0J9ZaOfUJZIvuk4GO+BiswipJZd7cdBTdwPWpl5GsdtSOgDNOIoHFNEy0HgYFFIDQTWhmLuxTS5PHamk5pKhsuK6jqSkzTlBJqS0T26bm3HoKtimxoEjCinis5O51wjyoKUGiigpj45XikDxsyspyCDgin6zfSanHDJNChnXKtMFwZB23epHrUDMFBJqJZn8okHnvVRT3Ma0ly8pRYbTg02pXBz83WoiK0OMdGecGrMR2MPXtVSp43DDa3WgGWpI1YbhyP1FVWQr15FTLIynB/OlIVum0E9uxpB6FQinD0FPZCDihAF5NMBVXHJp4w3LdBUkMD3OSvRarzM2SmMYpXCz3JPMVjtBp6SvEck/j/AI1S+tPWVlHPIosHoaXnx/eZOT3DcH6jvUDTMW3MWI6A9eKpvJu6cUgkcDGaLDLLS7vlyvNJnPDcGqu4k5qRZMjB5oBisnOBUZBHBqaNsMGPNPlVByO/NAIgSJ5ZERBl2OAPUmlw8UhRwykHBB6g1JJC8YDMMBhke4qyt2l0BHfZJHCzqMuvoGH8Q/Uevai5fKRQjEgPcc1PNHuO8d+n+FOls5IVViVZD/q5U5Vh7H+YPIpEm42Ov1Q96PNENNOzIWw3D9fX/Gk3MmFfkdiP6VM6Kwyu4j9R/iKgJK8dUP5U0ybDy2Bk8joMU6FHu5xGDjPU9gPX8KrDLHagzmtXTrhNOilYj9642nPp3H40pN20HFLqaEkds14sVhaW7Er0kG7oMZHPU1jXzRs7I0KRyKSCEQqQR1BGa2tPheCGS/QruGWKHt9PpWFeoWupXzksxOfXPNTHewWb1fUo4xSVYSAuCc4A7mo5ImTr+lWUrEVFLQKZLYgFOxRTc0Ba4/OKTcabmlpDskLmnICeaYBU0ZAOKBOXY3tIsYr2xmhATziwOSMnb6D/AOtWPf2ws76aBTkI2AfapYrkw8qcfSq8kolkLN1PelsOOujIDRSsMUACmhy00ACng46UlJmmRuWFmAHNOnvZrgx+YdwQbVz1xnPJ71UzSjmla5cXy7G3oOnJq2qLbOXEQikkYpwcKuR698VLr3h650W4KP8AvLcn5JkXgjtn0zS+GNZbQNUW9WETZQxsOM7SQSVzxnjv1/Wu71vVrXVPC0t7b+TOohdZWK/MGC8ZX+Elm/P161jKTjJWWhrCXMtWeVE0lKaTFaJjlE0tE13UPD2px6hptw8FzH3HRh3Vh0ZT3Br1ef476nc21ubXTbeKaMBp9zlhIR12jqFP5ivFsU5HaM5U1d7mTi0fW3hD4gaX4xsw9s3kXIwJIHYbkY9vf2PQ/pXX18WWOoT6dLHf2Fy1vdRkDhuuT6dxxyDxX0P8NPifD4rjXTNS2w6ui5XH3Z1HUr7juKHHqibHptFJS1IgooooAKKKKACiiigAooooAKKKKACiiigAooooASvNviD8MLTxLDJfWCLBqSjPHAk9j/jXpNFOMnF3Q4ycXdHxTqWmXWl3stpeQvDNGcMjrVI19YeNvAOn+LrEllWG9Qfu5wOc+h9RXzP4h8Oah4b1KSyv4GSRT8r/AMLD1Bq2lJXiW0nqjGpKcRSYqSAopKWgCRHIq5DOQQQcEVn05XxSGdJbql/8gKpMB0PQ/Suz+FGlOfH0LyDIhid89s4wK80gnORzgjvXc/D7xTNoOuCUp50TDbIO+09wfWqi7MpH07RVDTNUtdXslurOQPG35g+hHY1fqLGbVtGLRRRQAUUUUAFJS0UAeZfEv4bQ+JbVtR09AmpRjPHAlA7H39DXzfd2s1ncSW9xG0csZKsjjBBFfbleYfE34aw+Ird9S02MJqUYyQOBKB2Pv6VcZX0ZonzLlZ81UlWLq2ltJ5IJ42SVCVZCMEEVBSatoyGmnZiUCiigBaSijPFAgpjEinbs0YzQA0Upp1JQA2nKxBpCKBQBfiuAV2t6VNa/MHArMD7TzV3T5kWfDdDQMsSQZ7VWmtWUZxW8qI2DSzWyyRHipuPlOVwVPNW4XDDaaLq3MbHiqqkg0SV0EZcruXJI6pyx45FX433rg9ajkSslJxdjqlFTjdGb0pafIu01HWydzklGzsLRSUUxC5opKKAClpKKAFoUEnApyRtIwVRkmu10LwwkCrd6iPdYj/M0eo0ijoHhh7vZc3e5LfqB3b6e1dVc3sNpAILcKka8ACoL/Uwo2JwBwAK5q91AISWOWPQVO+4yze3+Mu547Cufubt529B6VFNO80hZzUWaoY7NJSZozQIXNJmkzS5oELSZpKKAFzTs03NAoAfTgajpwpgdN4O8VXnhTW4r23JMZ+WaLPEidx9R2r6p0fVbTWtLg1Czk3wzIGB9PY+4r4zU17H8EvFLW+oS6BcyfuZwZIM9nH3gPqOaLXRfxKz6HvdFJS1BmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRUF5e2un2r3V7cw21umN8s0gRFycDJPA5IH40AlfRE9UdQ1ex0vy1u59ssufKgjRpJZcYzsjUFnxkE4BwOTxVL7TrGrcWcP9l2Z/5ebpN07j1SLomQQQ0hyCCGiq7p+lWum+Y8Sb7qfBuLqQAyzkZwXYAZxk4HAUcKAABSvfY05VH4vuKX2bWNW5vJv7Lsz/AMu1q+6dx6PL0TIJBWMZBAKy1qWdnBYWqW1smyJM4BJYkk5LEnlmJJJJySSSSSanooSJc29OgUUUUyQooooAKKKKACiiigAooooAKSlooAKSlpKAFpKWkoAKKWkoAKSlpKACiiimM+F4XZV4PHpTJZA3yhcUR8cHvSOvODQkK5DimkelSEEdaQigBqttOauRypImD1qkRRkigN9zQG5OnIqaK4I6H8KoRzkcNUwKvyp5p6PcnVbF1kSYFk4eq77k+VxkUxZCp+b86m80MMPzRdrcej2KjoOq8iiBGklAJwO59BVgWrSRvKgby14z70tu/lqcd6W+w9tydgLfIQ5UHgio4iXmEj9ulatraolr5k6KfM6IewpJbGOVd1vw/wDcz/KpVSy0B023q9Sve38bW4tQevLGs6SWZAAzsyDpzmnT2zIx457g9RUCuy8HkVcYq1xOT2JlundtpOR2zzirFvsmmGI1f5gFHT9aplFflOD6VYsZzBLztWTouenPU0pbDjvual7KtvGctkqT+LnqfwFc8ytI2epNWbq5E8u0E+WvA9/Umn2Nvvk3t9xaiMeVWHOXNK42aEpahAOvJ/p+tOFsFhCsOcc1qTRrhYyF3k7pPYDoKo3RdIw4KgNnAxzgcZqoyVrsUotPlXzMyWDaeKhxU7IT82etMKU2VEiBIIIOCKfvV/8AWZz/AHgOfx9aYRikqbXKUmtBzRlRkEMvqKQClRmU5U4/rUmUfqAjevb8qeq3BqMttH/XX/P7xooJpWVkxkcHoeoNRk5p3IcWnZji/pTc5opKGxpC0maKVRzSsDZJGhf6VsDRk/s6Kcli8nPHQDtWbFxg1u6dqscUAt7hWMY6ELnj0IoadtBKV3qYNzbPbMA44YZB9ark1sazdx3cieWMRoMDPFZBFF3bUaim9BtFLSUimgFTwBRIN3FMjUE81IV9KTLiupcFLVNJWj46irKOrjK1DR0xmmh9GaOtMkfYpY0wbS1IZ5ATs/OmI5U+/wDOqzMSST1pyvkYNaJWVjhnJylzFhlDnCc+1QsuKntH2XCu3an3ECx5bzFIPSi+thW6ookUnSnkUhFMRIkp6NzT923p0qvUie9AFqN08iVX+8cbajWNnYL68CmrknipgQgyaVuw2y9MrWkQjQZIP6mo74IbcM4XzDwD3p8FyxwjjcD09R9KguEM12sh5iDAMnQqM88VK7Md+tygV7Gm45wOa6C80OUTSeRGzxYLLjrt9R6is6zgTzJEcrh1wCexz39KfMrXFbWxnlfSm1o3Vi9uAW25J4GecVRIz14NPfYNtxlANBGKKYEydj61ZKF1HGe1VYmA+Vuhq7bbmnjjHOSMGkwSu7M62ys7a5077NJCrA4G8ryOAOD26VmTeF5Le8+9vtxG8gfoflGcH3zitmyysgUcCtfUCkWkXMj9CI4hjrlnH9BWTlZHf7NI4WSVrEOylHhdtrRydG+oHQjsw5qF7eO6jMlruYKMtEeXjHqP7y+459RVvWoYhabwG8xnyDzgr9elYtu8yNvj3fJ82RwR7g9jRB3VzCUVsx4do/vfg9K+2QgDhyfwOaukwX2VlZLe5P8Ay06JIfRh/Cf9ocHuB1rPmt5rWYxzRskg7H0Pf6ehrRO5hKLj6E4hFrJIkwZZF7+hpQRK3mXB28fKcZz9aS9vW1C4M8kcSswAxHkDI7+9OszH9qje4DGIHJx/npR0uxWu7GtDa3JsRibbJIp/dP0Knp9DWNdb0uCHRlcYyDXRmA3F0LsXObYAkkdgPWuf1C7NxeyOwzEeFHcAdPxqINs0mratFmyjjltJCRkqwJHt3qO5t0BJZ9oyAM5HXn3/AFNR2Mnkz7w/7og59/Y/WtCYpLI6MOGUcbvYd/aqb94lWcW0YtxbASYQ5JGfrVYqRwa1VtGY7Vfeig4ODxnt7/hVaS3OQgDcA/P2OKq5K7soGinuhU4IpmKaHJhilAoozTIHUZpmaM0ikh+6kzSU4UA5EsUe5gG6Gug1XQ0S0lu7cIsSKpUDuO+f51hRtjBHarU2ozNa/Z/Mbyz/AAbuKTXYSZnGkqQhSMrUYFNalyXKKBmnDiijNMzvcekhX6VI1y/luiOyo4AYBsbgDkZ9cGq2aM0ioq2pKjFjirHlMRkDtn86ghXPWuv0OO11aeOzk8pJGXaIz8vmEYAIb6Dp1+tRJ2VzSMruxyRUjrQK3PEVh9l1AIkaoFhjLY6BiNxH5EViEYoTuaOIh9q9Q+FXj3QfDMos9X0uGF3kJXVETdIuez9TtHTK9B1HevL6OlWn3MpR7H2/b3EN3bRz28yTQyAMkkbBlYHoQRwRVivk7wJ8TNV8FzLAv+l6Wxy9oxxjPVkP8J9uh/WvSbX492jaxi60qWLS3wFkDBpYz6sBwR7A5HvT5exFj2iis7StWsda0+K+026jubWQZWSM5H0PoR3B5rRqRBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA3tWB4p8Jab4s01ra9iG/HySj7yH1BroKShNp3Q02ndHyN4x8Fal4R1EwXKFrdj+6mA+Vh/Q1y5FfZ+taJY6/p8llfwLJE4xz1HuK+bPH3w4vvCVy00QefTWPyyhclfZv8a1sp7bl2UtVucCRSU8jFNqCBKWkoFAD1YitzQJvLut7d6wkUuwUd61bXMBFJjR6r4Y1+40e/jnt5P3LMBNGejL3/AB9DXukUqTQrLGdyuAQR6GvlWz1MwRls9q6vwJ8UJ9DuRY6o7TaW7fK3VoM+nqvt2oV3oU1zI+haKrWt3BfW0dxbSpLDIAyupyCDVmkZtWCiiigAooooAKSlooA818ffCuz8VMb6ydbXUMctj5ZP97/GvAfEXhHWfDF0YtStHQZ+WUco30NfYbukUZd2CqoySegFeJ/E7xxbatDJoliFkhBxLKe5HpWkW5aM0i+ZWaPCTS1Ndw+RKVqvmpM2LR1opaBiYpaKKACiiigQlJTqSgBvWlVipyKSk60Ab+n3okUI55FaqtkcVx8UrIwIODW7aXwdAG61LRVye7t1kXI61hzRFGreMwbiqN1Er8ihAzNWRs5qxvDrVZ1KmhWqZRuXCo4vUSYZqseKtPyKrOMGiPYKivqhtFFFaGQtFJS0AFT21rLdTLFEjMzHAAqXTtOn1K6SGBMk9T2A969A0/TrTQrb5NrXBHzSH+ntQ3YaRX0bQLfSIhcXW17nqB2X/wCvTr/U2clVNVr7UWkJ5wPWubvNRLZSI8dz61Nu40WL7UQpKo2ZPX0rGd2c7mOTTSSeTSVQx2aSkzRQAtJRRQIKKKKADNFFFAgpaSigBwp4qMU4UASCtTQtRfStasr6M4aCZX/AHn9Ky1NSx9aqO44uzufaVpOt1Zwzr0kUMPxFT+tYHguZrjwhpcjdTbp/Kt+okrNoUlZtC0UUUhBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFY83iCKSeS00qB9Su42MbiEgQwsDgiSU/KpBxlRukAOQhFM/4R77f82vXX9p/9O3l+XaD/ALY5O/op/eM+GGV29KV+xfJbWWn5/wBeof2zdal8uh2nmxH/AJf7oFIAPVB96bqGG0BGGcSA1PZ6HBFdJf3p+26kuSLiUEiIkYIhQkiJccYXkgDcWPJ1KKLdwc+kdAooopkBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAJRS0lAC0lLSUAFJS0UAJRRRTA+FFOODTjgioUkBGDS7j0FMWq0HBGPTpTSo7cVct1EkBUdcmojbsM0kxtWKpHrTSKkPoaaR6c0AMxSqxXpRiigZOkoYYNLk5wpqsKnDDFO5LRpW15HDYywsGLO4PtjFLpsEc92N/8Aqwcn/Cs9AWq2jCMA5xUuLaaiVzJNORt3ruxOwdBhfSls0YjzJeNoyapwX5wEmG4evep7mXzbXy7Z1HOT7+1SpNR5LByJy573Rk3l3JPdtIOB0A9qZlJuvyv61I+1iUmG1h3/AMagkiePk9OxFacvLoiXLmd2MZHjOf1pd6uNr/nSrMVG1+RSx27TguisIxwT2B69aOZC5WQtEVORynqK1bO5iijGz5sYOzuW7D8KzvniOKUBXO+M7ZKJRvoEZWdzaRcjcTlict9arXcLzQwhBkhmQfzqrHeugKSfK+Dg1Ye9AuocnESKAAPTHNKXRRKprVyk9yG9tDEy453KOnqOtRSQbFf5c471o29ykoMaNsfHys5zx7UjRPLHKFfeVYLv29R36VlzPaRtyreJiGIjk1Cy+lbjwrNEREc7eD2P5VQa3Zck1UZEyj1KNJmpmT1phXFXchRBXK56EHqCKXCP935W9CePz/z9aZSGpsaKelnqhWBU4YEH0IpKejnG1huXsCen0pwjDf6skn+6Rz/9ehabiceb4fu6/wDB+X3EYFOHFFBNaGRIr4pwmAqtuozSvYaRLJKX4FMBpKKlu5a02FopKeoyaRS1NCwsPtULlXUSfwoe9QSwvAxVwwIqSKQpjb2rSjuorpfLuxuGMCQdR/jWbbudcYxcUupjYDUg3xtuU4NaN1pjwjzIyrxHo4/r6VTEbtximnczlBock+7huDTbkOy5I4FJsZCrkcU643yYG9jjoKpRszCdTTlKB96BkGnkdjTOlWZE8ZIIP41alUSrvHTv7VSRs8GrEcrIaQETIV9xTCPSrZCydOCe3Y1A6YJ7e1AEQFPUFuKFXJqTheOpoAcAEFKOPmem8L8zdagdyx5oF6lrzGByOntVpLlJAPM3ZHRx1rMWRl6cilMq9QMGiwzWl1O4jtDbQ3MvksfmA4H6dPw61T+0KPmIUn1qkJWBzmkZy3WkoopvoWC5LZPIoYK45/OoFcin7s9KZNxCpA9qjIq7GFlg2E4IORVaRcMQO1CY7EdamiL5l8uedgLYrLrd8OQM8krjrwopS0RpSV5pHXaZaiQCWTnknHb2rW1Cwnv/AA47WzxNLDcC58onDSJGvO0d8FuazILqOCPDuijoC7AD9ag15ryJtFvbTcLdIXZbgfd3M5DDP+6BmsJHdIp32nM9pEhTHmJhd2MqRyOhOfr19qxUszZRXTXBQF1CqiOGO7Oeg6VtySyrFaypMVIlwrvjbuAyAD1GQe/T6Vjz3hn1Kb7E6pEMtyMKT3yOeCe1KN9jKSS1KNzcW3kKsQlEq+uMe/1p1verJbi2uo2khHAA4aPPdGPT3U8H261ZuNJkvD9pswrRHmRARmI9wR6ehqAwKIAG27CMg/X6d60UlYzadxsth5CefHIs1sTjzQMYP91h1VvY/hmoVLNz0UfrVmN5LMgrIrblG7IyCp/hYHhh/LtUr28d20i267J1+9alufqhP3h7dR71akZSp9iJDIYpER22PwyBuo/kaTT7ZXvNjlSjKwjOf4uw571Wy8XXcR69xU/mo3zsMn1Bxn607EXYlxZ3FqA5RlzkH0OPaiC6XBHcjp1x9M9R7UxrmR2BkdjjhSTnj0prxrIcr8r/AKGjfcm1vhLyyu9woZ2eFCOegP8An0pslvMshaHcXVmyOxAOB+NVIrh4pNr9e+eh+v8AjWhBIr5LvgDkgLk0PQcVzK1ynLCSoVkb5sHB6jPBqjLB5bYBVh7V0CneZUjj4Vc5LE84/XjtVCRYFl8sBgSON+MHPv8A40JvYbSWpkYpDVpocruHB6Y9x1xVUiqBtdAoxQKXOKZAtLmmZpaQ1EdupC1NpaCr2HDJPFamj2EV3dsk3OI2KjdjLDp0rMTg1bimeFg6HBHOR1FBLkO1KwfT51Rv41DY647Yqiat3V093LvmdmbGMmqrDBpFW05hKcBQMUVRDY5WI6VMJ8D37exqvmkzSGo9WXJNQup5JXnneRpSC5c5JI4BP4CrWm2qXs0iuWWNIWckeuQo69ssM1ljk1uaHqC6ZcSSFFYSR7DvXO35gwOO/IFTJaaGqqO9jPurZ7aZ4252kjI6HFQV0GsoskKXKbGEi5Mg7kEKeB0yTWARmpjK6NGhMVLDIAwR/wDVkjNR9KBirTM3E6rwp4y1TwTq/wBo06fzbZyDNbOf3cq+/owHcdPcV9OeE/Fem+MNITUdPdgAds0T/fifGSrf0I4Ir46JIPFfS3wf1zwxP4ag0jScwahGu+5hmI8yV8fNID0ZT2x0HBAq3qrmbXc9QopKWoEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVW8s7e/tpLa5iWWJxhkYZGDVqigD5w+I3wquNCeTUtIRptPJJaMctF/iP5V5WwIr7cmgSeB4nGVYEEH3r5b+Ivgq88Na3NKICbGZy0UgHyjPO0+lar315mnxrzOENABJwOtOdWHOKs2YVAXYc9qggntbfyxufqanI9KYsu6lkkEUe5jUlDLmcxxbAeTVWGYg81BLIZZCxpoamhXPSvAXxDu/CtwIJi9xpbn54epjz/Ev9R3r6L0vVrLWbCK9sZ0mgkGVdT+h9D7V8YwzlTXZ+DPHF/4TvvNgdprSQjz7Ytww9R6N71XxblaS0e59VUtY/h/xDp/iXTUvtPmDxnhl/iRvRh2Na9Q1YzaadmLSVVvb+00+Az3lzHBEP45GAH61x2pfFjw3Y5SCWa9kHaBOP8Avo4FNJvYai3sd3+FUdS1O10mykuruQJEgzz39hXnN18RNfuLNry00qGztAM+ZOxd8fQYFea+IvFmqeIJf9NunaNeiD5V/IUJK+rK5bbs3vGHxH1HW3ktLR/s9hyML95x7muAcknJOacTUZNEpX0Ww2+hU1C286HevVawCSpwa62CKe5mS2toHuJ5OFijXLE/Ss/X/CmuaDtk1TTZrVJPulsEfTIzzTUXa5Li3qjEVs1JUA4NSBqLEj6TNJmjNAx9JSA0tAgpKKSgApKCcUnWgAqWKUocg1HRQBqxz7h96pfMyKyo5MGrivkUDCZQeaq4wasOwC1XznrQAp5HFQuKlximMM1NhqXQgooYYNJVCYtOjRpJAijJJwKbV3TCi3qO/ReaBHa6XFFpGnBRt81hljVC+1MHLMcCqd5qBf5VbisSedpG5PFK3Urcmur57g7RwnpVMmiigoKKKSgBaKTNFAgzS0lFMQUtJRQAUtJRQAtFJS0CFFOFNFOFADxVmBd8gUdziqy10HhLS31bxDaWyjO5xn6Zq4RvJIcVqfUfhG3Nt4T02JuCIF/lW5UNtCtvbRQrwEUL+VTetZt3bYpO7bFooopCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiqt/qVnpcCzXtwkKu2xAeWkcgkIijlmODhVBJ7A1nf8T3Vf+oLan/cmu2/9Cij5H/TXcrfwGlcpQbV3oi1f63Z2E62pZ57513pZ26+ZMwJIDbR91cjG9sID1YVV/s3VNT51a9+ywH/lz02Vlz/vT4VzyARsEeMkHeK0bDTbPS4GhsrdIVdt7kctI5ABd2PLMcDLMST3Jq1Ra+5XMo/D9/8AWwyGGK2gjggiSKGNQiRooVUUDAAA6ADtT6KKZmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFJS0AJS0lFAC0lLRQAlFFFACUUtFMZ8ECpgcjAqKpYxxmhCbLcI/d7h2FNnmkZcZ49RToX2H2NPkiK/MnKmiyHzPdFGj6VKyA9ODUTAg88GmLcQ4NNIp2fWkxSAAKkVcmmquasoiqu5qA9RyAIu49KFBkO5vuCkUGQ7m+6KJZgowtVsibNskZzjC0sU7IeuDVBZWU5zVhZEkGDS9RtNbGl5sdwoWVcHsaY0MkAyvzx9xVMFk9xVuC6K8Z3J6UJuO2wtJb6EDwpMP3Rw39w1r6IgOnXVk7KssjAqD3wMVUaKC4+ZTtf2/w/wAKVEaM/vJFYDtjn/GlJKSvFlRk46SRINGudso2ZCAnFZLxOnzDpV+51W7ljFu8zmIfr9T3qBWB6UKUt5A4xWkWVw4YbXGR696Y0RUZTlKsPCrfd4J7VD88Zx0qtGTtoS2RjaXEpXjoDwDWqqwwWrI82wyNuXYM4x/SscqkvT5X/SliuHt2QONyA8Z/pWU4tmsJpaNGsV+y2kk0brIXOSdvHNQTRTvEu6Plu45p9pLG8rSfKYtp3R+p+lSCdnkijjTyInBIw2Sfbms9bm2jRly2+xcHdVQrjkdK2bmOeeGJHDeYWPB447ZqlcwfZ/k8xWfuB2rSMrmUlbYpmHcu5ahII61qRx4Ur+P51DPCNrN6D+daW0uZ82tmUBS0mKKLgSmQNxJk/wC0Ov8A9emsmBuVgy/qPqKZQrFTlSQfUVO2xopc3x6+fX/g/P7wop+5X4YBT/eXgfiP8P1pGjZRnqv94dKV+43DS61Q2loopkoUVcis3e381Rxkj8qqKCTgd67zR1thpsdnIFYAc+oJ61EpWR00IKTdzivmU4PFSJJXS6p4eIQyw/PH6jqPrXNTW7wEgjipUrmkqbjqti/a3zwNwcr3B6Gt7TP7KnkLvujYjGOoGev1FcgslXrZZGiaZQ3lqQpPue1OMHJpIHWUYtyFkUeY4HIyfyqzd6UsWjQ3oLYYohQ9dzKTkH046VWYfKT7Gt3WP3fhq1i7tOh/KM/413VYrlPH53zI5OSBvL3t0JwD61VIIrURPNIgPSRgPoSeopdQ0i4spFDplSgcEcgqc4P6dK5TaMk3Yy9jbd4XihZOzVt2lgx0wXJ2mNmZcd8isyS3STLw9e6d6Sdy3poxgbHTkVLCVlnjR+hIBqqMqcGrlhbPeXsUCFQWPU9sdzTewJajZ1MUzqBxnimY2jd1Nb6W0Jm8i7DMo/5aRjJH+P8AOo75dNcx2+muzeWMO8i7VlP+yDyMe/WpUug+W92jAOW560hWrUtswJ2Bg46oev8A9erNvp/maQbsbiwmKMnoMA5/WqbsJamVj0oxmrM1syH5euM471aax3WkDxozb1+b2ai4W6oySCOtFTyxmNijDpUJXFMVxKehPSmUqnBzQMtRH9aa6en5UKf4h+NOZww96QiuRVi3nkjjKI7KCecNiiaHy225zkA/nV22sk8sM/JPNTJpI2oQlKdolQuWOSa7XTQdS8PWFr50yyECGMbNyZWRyWx143rkjsT6VlaZor395DbQQ8ysF3noM9T+A5ru7aFLm6tIYnW2t8yxabIjY27RjrwQzA7z1BFYzlfRHa4OGrZyOl3Qk0s2b7lkMrMrnna4wAKxb+0GnXnmNzDKDjHof8DW3Y2qzIS24SbQ5kHADgdcHHBIyD70/WxBFplrHfWT7eR5qyAmMk5GAOMY/hNTF2lZGcldGJZxxyQCeQJIrlkA3FTuGOCR6jp2qreJJDEsaOxiIyB6c9K07G6ghs7mzsoXn3MjgkfPnpke4HSpGit7hpILrclzyQARnknjHTPtVc1nqQlpYwVuP9HaKTdzjBHfHr+FXb+3try7NzbajbqTjaj5VhgADn1p7WFtGvmS3yeUvH7sFmOfUdjVePT7edvNS5YQg4+dPmJ9AOlXzLdMlxvoTuZHXF+Asn8N0nzK3s+P/Qhz65rLulkhk2PHsPXg8EeoI4I9603tPs5byXdSV3fgOoI75z+FRlcKIZEWRDk+Wp/Mqex9uhqoyRMoX3M2ObA2tyKnU4GV+ZfTuKZPZ7IzNAfMt84L4wVPow7H9D2qukjIeKsxlGxf+SVQG5HY9xUeXg4PKHoR/SmK4b5k4fv70vm7sIeATzQSaFtdBcnOeCAe3I9OxqVkS4AQxs59BgAZ9W7fSqt5ZtaXEix7iEHXsRTI5/MXbnBAOPb3Hr/OlbqhuWnKyzNaokW8Ou9DjO7IB+oH6+tUZrQlA7PknqR0/wDr1oQkGEQk5TcG3+vBDf4U0WuzKqchu3XB9jRfuFtrGM8bISrdRUZrVuICpKuMnoMdsdeaoSxMnPammPRENGKWiqJuAFLSZpM0gsOz6Uu80ygUD0Q7NTRRb2APeoQKsRNjn0oE5HQaposC6fLewsgjVUMYH4Ag1zJq7LfO0Plb22dcbuPyqodrDI/KktCoq4ygCgYp9US2A4qVJNvWoScUmaASuWXu5DEYldhExBKdiR0NIrjyyT1A4qvmrMAHU0rFc7WiO51XwBImmRXOnF3mjVVuIX6l9oyVJ9SSNvtx6Vwzo0bFWDAg4IPUV694d8b/ANrxy2tyyLfsuYo5OUkcZxt9yT0PPHcV5r4juVu/Empzrt2vcvjHAwDj+lYxlK7UjfRq6MnNTWt3PY3UVzbTPDPEwZJI2KspHcEdKixRWsZW2M5RPoHwL8a7O7txY+KZBbXUa/Legfu5sdmA+636H26V2mjfEfw/rd1LBBd7GTDDfxuQ8bvbB6+nevkoEqcitDTZpxdpPbXJguosNDIGwd2cAD+vt14qtGQ0faoIIyOQe9Orxr4afExdSlj0u/dUmbhY+gz/AHk9j3TqOoyOB7Jmk1YTQtFFFIQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAmKo6ppVnrFjJZ3sCTQuMFXGavdaDQnbVAnbVHzP8AEX4c3HhdnurUGbTWPD9489m9veuH8gJbxuOR3r6V8W6X4h8WTyaNbLDY6T/y2upPmeX/AGVX09zXkfi34aar4Ri+0o/26w/ikVcGP6jnj3rVrmS7mrV7dziMxqu8dqzZ5mmkyenYVr3nlNZlo9v0rEIrO3ch6CUUhopiAGp45ipqCigDrfCvi6/8L6mt9YyccCaEn5ZV9D7+h7V7nF8ZPC76Ytw8swuCmTbeWSwb+7np+Oa+YFkIqRZTnrRe+5d01Zo9F8U+L7zxTfmaYstuv+qhHRR/jWZp5giuoprs4hByaxrOdDHlz06CppJy/APFJtsGzqvFXxAgudOXS9KRhHxvkIxn2FclG1zgPPC6bum8EZ/Ou2+G3h/Tp57jVr7ym8ltqJJjAOMk81S+IfiK0vtU8q22GONcKU6ZpaJ8qA5wniq1xcpAvPU9BUM16kMPBy5rHmmeVt7tQkS3Y9i+E2pafourySaoqCS6AWGc9Ij6E9gfWvbtb0Sx8R6TLY3sayRSrgH09CK+QtL1RomEcpynQV7l8PPiAYli0rVZd0BwIJ3PK+isfT0NaNt69St9tzx7xp4KvvCWsyWs6E27EmCfHDL/AIjvXN+QwGSVr6b+LGnQa3pUNqCDIp3gjqPSvm2+tJbG6aCXqp/Ok2nqJ66lM5FJzUlGKRAzJHWnZpCKTpQMdSZo60BeaBBjNKBRSE0ABpKM0UAGcVNHIRUFKrYNAyxJJnimKc0pww4pY0GPmoAdnIxTTSFhnipYk80cUCIXQEVARirjRsh5FRsoNAFYCrMI2HdTAgXk0pegCZpCahpu6ihlRYtFNzRSKFzRmikoAWikooELRRRTAWkoooEFLSUtABS0UCgQ4U4U0U8UDHqMmvcPgt4XYNJrNxH0GI8+teY+DfDNx4k1mK3jQmMEbj2r6s0fTINJ02GzgUBI1AOO59a1fuRv1Y9lc0KKKKwICiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKxP7e/tL91oMf2zdx9uxm0i/wBrfkeb/F8sefmG1imchN2KjFy2NiaaK2gknnlSKGNS7yOwVUUDJJJ6ADvWP/aWqanxpNl9lgP/AC+alEy5/wB2DKueQQd5jxkEbxT4fD8Uk8d3qs76ldxsJEMwAhhYHIMcQ+VSDnDHdIAcFyK2KNWVeMdtX+BnWGiWdhO10Fee+ddj3lw3mTMCQSu4/dXIzsXCA9FFaNFFO1iHJt3YUUUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkpaSgAopaKAEooooA+CiMU+NsHBpp96THpTAtrxyOlTpKQOOnpVJJSvBqUENyOKe4tiyyJJyODVd0I4bkU7eR96n78jB5Bo1Qb7FUr6U0KasrD5jHZ2GTTFUbiD2peg9tx0aADcelPUGVs9FFPt4XvZ1hTp3PtWhNp5RSIDnHbdzSclHRhyykrmZK4UbVqox3HNTSo6sQ3BHY1GQD14qt9QtbQjxScinketJikBLFOV4apwUb5lODVPGelICVprQTSe5e3MB8wzUctyyjaHf6E1XErY60w56mh6jimupZjnB+VqmAI+ZD+FUKkjnZOOopKVhyjcvrIG4PB9DTiAww3P86gVklHvT9zJ97ketNWeqId1oxrwFfmXkU1ZP4XXIqwGB5H5014kfJ6H9KObowt1RDsZDvgfpStcSTyJnqBgAU0h4m9KflJPvfK/qKHHqNSaL9sHkjcSiViCCDuxj6k9qGt0aUysWcsc/dwv596prI8R2y/Mvr1/MVfiuAQOVINKMdbtjlU0skIVHLE8nqelUr07IcHq/P4dv8AGrzlCeG+QDLD+Q/H+VUFVr27+f7g5b6U5NJWJinJ3KcMZZiT0AJqMrlsCtO52RxnYMFufw7f41VihIYO3QAn/Cl0uy+rsVCMUlOfqabSGncKcrFTkfqM02loKi2ndD/kfoNjfof8KRlZThhj+tNqWNiBjqv909KnVbFpxlvp/Xb/ACJLZR5m49v51qQXDxMCpxVFEG3CZ+hp4YrUN3OumnBHWadrXIR2rQn0qw1Jdyny5D6cqfwriUlxWpZao8BAJytZ2N07kk/g+7859gxGoLGTqu0dTn+lXNatYbHTrS2gTagCE+pYoCxPuSa0Rqr3GnzRw8u0TDB+lVPFhAvI0Xou4D8AF/pXbg9ZNs8nMbJxS6nMuMxke1bniUBdOsUHeZz+SKP61jbckD1I/U1teKgRDYL05lP6qK6a3ws4L++vmYFkCb62A/56r/Ouo1cK7WsSH5tkYmx/CpUFSfQZzzXN6cudSth/tZ/IGuivbOR/EEVyCjRCGKN034YKYwCcdSOc1x6faNot83umakzxWX2Uwps8wthOoPRvqR3HHqKxpbdgdw+U9jWrDPL5xhmjhkljkz+8Q5kI45IIOR29ahmuJJiZLh2KOcZK/dIGMFfTH402or4SuaUtZGLMM/fGH9fWrFjP9jDuPvkYz7VYuLQlQxRgD91+qn6Hv/OqLRsrfN0FS1fQuMrG7pWoJZQ3N1chXyjHy36FiML+Wc1z6sR0OasSW0lxbho33Y5aPuKoZaM4PFJLVsrS1jSjulcBJvmHr/EKvWd89kzSQzo8bfejdeG/3h/XrWFvB56GkaQ9BQ4p6DT1ual5qsmpXBkuAgA4jEa7RGo6Af8A16Yl69rG5t5m3n/ZH+c+9ZvmCmlznNHKrWDmd7ljzTJkud2eTnrmo3THKnINNDA/WpYHAmVm6Z5pk7lcikq3cQlcvlcE8VVI9aaYCqxBqQDJqGpo+RjvQBfDK0Ku6biOAfp61prrKRgiO1hGfVc1kwOpUo3AP6GogSTxWcl0OmhK12jsPDGr3E/iS0jP3G8z5I1wT+7bGPf0q3fzSRC00uzvkms0WVFkyTkuRklTwvHyhlxkZPFYHhO0vJvEdm0FrNMEf97sB+VCCrEntjdT7xh/bn2OJ/JK4jPmjb5brxg+3A596yktdDdy6yLli0n7t43UyFfLQ8ZGGGFGfXBx+VP1iaQWOo28obHmoYwcAjuSVHT8Dx3qeW2/s7xH9m8tJ7aGNTMZkxHIwXLEYwMFjx74oupk+0SySxtaeZGF8v70RzjgsSSvTOec561n1uO+hy2nSIt0Q5ZIpFKM4/h7g/ga1NK061l1NftEk2xWzhGGT/wI56/Sq0mm3twd9zPb2qD7u9gAfpjNNtVubGTL8lWwCDwR2IPoa2b6oxs9jt9Z0vRtC1EPFZSzaffReaTJ+8B5HygDByDkeveuJuE8uBbaGMh2leQR9WCHhc++K7yNp9b8IXcIO65slN1BGg6gD51z1OV5/CuCWa1eSSRpniMgG7OWB9MEUtdxx2KUcV6bwIiSvKBjZyTiobiSaG7+YeXLERx0OR6+9b9rdxeaYonaOKSIr9oAywJ6E99o9BVGTSoUvFSS+iuQ/LSRM39RkmqUlfUmUXbQhvEuLbUftFoj4mRZCAm5cMMlSOhGexqJraG+ybZPJuh1tj0b/cJ/9BPPpmtWaS/hszFYXMzxRAHKLyFPZsZ6fyrMS++2EQX/AM2ThZujRn1z3FOMnYmUdbMzOUbB4I6ipV+bp3rQmAlla31HckqHaLoDOfTcB94f7Q5+tV3s5baTypRjcN0bA5Vh6qRwRWiZlKLWqL32+5NkIg6nIwXIy2B2z6frVFlWQ5HyyfoakicDKOPqP6iiSMEbgcj17j6/40loTLVXGpOyttfgg/n9f8a0FuD5RWHhhjP49/wrLY/wv+B7ikEpj4BzjoadkxKTSsaF3eEkKh3OAAS+D0+tOnsJxa2ty+wiUfcHbOcZH4Zqvptulzdhp/8AUry309PxNatuZprxJMs8MbbQ49emT7dqnbQJN/M514flDjgntUJFbt88B1KST7N5aMxC8nAxwePWs+W1B39iPVqq/UpaaMoGinMjKcMMUlMlsQCnUmaTNAWH5pN1NpaB6IM09F3GmgVLG2DQJyOg0Oztrq0uIZCiytgglcnA6gVjajbpa6hNDGcxoxCn2pBceWPlaoml3k7u9IqOu5EaAM0u0s2F5z0xTiCvysMEdjTFJ6gBinBivNMJxTd2aZNiwbhgPlOD6/4UkZLNzUAqeEhTzSsVzW2L8tpIII32dUBH4jP9apsjKcEYNa+nX0Kyql3uMP3S6cso/r/P+VVdQWFr6VbaRZok2qJB/FhRk/QnNRdp2ZtC0tihSgelOZcdabyKaY5R7lzSzbHVbUXtxNa2vmr5s0I3SRqD95R6ivsHw3dWd3oNnJp+pNqVuIwoui4ZpCByWPr6gjNfGVb3hTxhq/hHUhdaXc7QxHnW7cxSj0ZfX0I5FWndWMWuh9kUV5xoPxi8NatpqyXczWN2MCW3k5x6srdGX9fUV31td295EJbedJIzghkbIwen50mmhNNFqiiikIKKKKACiiigAooooAKKKKACiiigAooooASopoYriFopUV42GCDyMVNRQBw+v/DPw/rGkPZwWcVpKMmOWJcEN/WvnHxT4T1LwrqTWl9CwGT5coHyyD1B/pX2LWN4j8N6d4m0ySx1CAOjD5W/iQ+oParUr6MtSvoz40IpprsvG/gHUfB98fMBmsXP7q4A4+jehrjiMUNWFKNhtFBpKQgpynmmE0oOKALkM20hd1XkesUMQc1oQyblBoAuSSS+SyI7gN1AJAP1HesmQurfOcmrU12VGxetUSxzk8mgLjijsN5qJlPWrAuCV2dBUcpTotICLpW3ourGCVI5T8vY1hmjOOaYkz27TdUe5hAlmaTgAFzngcAc1574whb+1pGI4PQ1D4d8QNayiC4f92eATXZX9pbaxZ4O0uRw9Lrcps8sxT1AJwa6fR/A+oa54lTR4GWJmVnEjg42jr+NVfFHhPUvCepGzv4+vMcqfdkHqP8ACq5WCizKaFFiyDmqjLT8nGKQ1IEXIpc05hmmdDTEGaaTmn03bQIQU+pIbcyd6mNowGc1DlFaNmsaVSS5kimaQVIwwcU3FWZsmjwoy1G4sMVED609WwaAGc5xWjZjb171TAUkGrsZGBigRamiV46z3hxmtANujqrIOaEBnOSDg0zNWriIkbxVXpQAopwpop4FAxuKWnEcU2kNBRRRQUJRRRQIKKKKAClpKWmIKWiigBaBQKcBQAorV0TRbrW9QjtbaNmLEZIHSpPD/hy/8QXyW1pCWBOC+3gV9K+CPAtn4WsUYqHuyOXPY/41qkoLmkNK2rJvA3g628LaWi7B9qYfM3ce1ddRS1jKTk7slu+oUUUUhBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRVHUNVtdN8tJX33U+Rb2sZBlnIxkIpIzjIyeAo5YgAmgaTbsi9WXea5BFdPYWQ+26kuAbeIkiIkZBmcAiJcc5bkgHaGPBg+zaxq3N5N/Zdmf+Xa1fdO49Hl6JkEgrGMggFZa1LOytdPtUtbK2htrdM7IoYwiLk5OAOBySfxpasu0Y76v+v60+8y/7GutS+bXLvzYj/wAuFqSkAHo5+9N1KncQjDGYwa26KKErEyk5bhRRRTJCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApKWigBKWikoAWkpaKAEooooA+DOnWkp5BpmO4pgFAYrSfWigCZZc8NTicfdNV6mUjHvTTE0WreYQ7ye64qI/PIW9ajB5xViFTuDenNLzH6m3ZQrZWm4j97IOfYU2OXfKyru+WkivUlASfg/3xVuGJAd7FdvXPrURlGKfNuE4ym1y7FLVVRLQF0XzH+76/WsDOOGrY1FpJZ2kdMxngegFUGgD8xnPsetVGLSuVKV3YrE4460bD2/KpbeLN5GjjgnBq5c2brNhU47U07uxLVlczCPwpDknHerk0Pltsdah8grIHHIBHSgERNEy8HrTORWrLbicb0Oap3EHlsFPpQmgaZWx6UUpUim59aLDUhQzKcirMVznh/zqtSUh7mhj+JDj+VOEozhuDWfHKyHg1OZPMHAqr30ZDjbVF+zt/7QvorYOq78gE9MgZpl1bCOYoBtIOPY03T5mt7yKROTGd3PrW876dOfPmPkkctG39PWo5lGWuxbi5JW3OcEjxkowz7GlXj54Tj1Q1LO1tPcSGLeqk8b+tVnjeM/yNaJ3Rm1Z2HSXLlQijB7+5rQt3SK0KEcscuf6Vm71f7/AF9aV5H2bScj1qZRvuVGVtifabq45OYxyxoupAFIXgn/ACBUcMnlxEZ47+5/+tVaSQu2aT1dwvZWQw0w1IFLdKaRzQCQ0UtFOxSLQgqVRimLgVIKTZpFdSRWxUquG4bkVBQDSsbxk1sWcEHIOaeklQq/atDTNPl1a/S1gXLnJZs4CgdycH/9dQ77s1jJPyNvRrW5AsZQnyXtwtuoIzuTPzED6jr7VL4lbzLyNvUO35tU+pTw2kFtLGZLaazQmCOVMb16DBHBPPYmsObWE1CaMXBwUXaJP8fWu3DzikeRio1JVLyIol3Twqe7oP8Ax4VreK3B/s9M9I5Wx9Xx/SqdrEf7RtANpBmTBHQ85qfxXj7bZqO1oD+btWtZrlOZX516MzNLAOpw/wDAv/QTWjrkkT6tJvn8towF+43ZVxgiqOjqDqkXsrn/AMdrb1G4e313eYUfY2Yw65DD+JD9e3oa5LvodFN6v0M6PU7cj/SQty4GFk8khgfc5Gfxomv7KWIhra7kOOhUAfieuK3gLq7vMWt3aQ200ZlgL2yZKj7ynj7y9CO/WqZvNvP/AAlFuP8ArlGP6LTUOtwlPpb8zHmnFzCkSWNwI0AVQh/HnI71AlleSSfJaTY7Erk/jxzW01+g5Piub/gEb/0UVWkuLBx+917UJPXCP/UinyLuHO2rW/AxXheKXcu5JB+FHlx3p8uRNk3Zx0P1rTaXRBEyCS+c9iYlyD+LVLo1hDdy7/ORQucvJ8u0fTucVMrLqEW2/dOTnga3lKN1HpURWtm/ht113MEy3EMbqCQpGQOvBqS60rcJZ0eLycZV84H0+vtUKS0ubuLvoYBGKSpnQrwwqIirJAHFSL1zUVSIc8GgCy4DqDVdlIODUqyFeKcE8wMwHCjP0pAVTxSqxU5FOZcUzFMDS09rZpw8+/aAchOp/Otf7XpsX3IHb6msvTobMQeZcyYck8DOcVp29xpS3kKJbNKDKo+fuCQKyk7s76C5YX7nR6HrMAsPLXfapLfwRM8ON2AGYDnqC2AfasLW1nvfFV1fPud5py8o2bGjYj5lK9sEY49Kh+2/abfVJo0SJobuK8SNF2jALLgAdPvLXRy6Vc6p9pv7aOWQsoVpDzlmI2n3JVhn6Vm/d1G/eepWurxLvSJ4E+zpNC5mlJdg8ilRtXaTgrjnI70liq3kMbCFWiVgsiOdqsCvHzHuecfStfWfCxubuGKJMzxQRh0RF5JwMsxIxjB5rm9BmvbiC7tvOZoIDuSPr8xPOCP9kcfWs1FNaA5W3OavIHinkVhjaxGPQZrStfNOnBZI3XjgkEZXOQRmuj1PSU1KxUJBtvImHVtuY+49PcGsi/l1c6tcLKHMcRVFjf7oBGFA7dK05uZW6mfLZ3NrwlrDaffRSn+A8jsR3B+oqLVPCtj/AGldCwvka0eVmgGOQp52n6ZxmsK6xaFGk3wSEE8DOcfjViae4jgEiXQkjLKh+X+IqGx74BpXl9kaSvdkdxZjRrcpPueV87AnQDsc1mC+zDh93mBQM/TgVrzzSX9mscpYlSdpHOOOT6jpzWbqOnxW1vFIjuXfnB9Mcn86qLT+LcJeRdinntrjRbW3maOJ41lfHRyzHdn14GKoO1ncWsz+WsciklcdeT0PrT7WaG7tIba5m+zzQ5NvcdgCc7Wx2z0PatNbbUUYTLo1ncy4+W4j5BP97AOM/gKexO6MrWXkimtImLFlt49yejEfzxipVgvbdGxaPNYN83lvwQccsvdT79+9StBDaTyXOqTrJfSHcI0OfLPqxHGfQVT1S5d70SRl1IUEkE9+aaeyRLW7Ys1ujxG4gdpIR1OMPEfRh2+vQ1XWRoyNzY9D2NWll3lJ0nS1vk+Vg/AkHvxjnuDwac0K3PypGsFyesWfkl90PY/7J/D0q1LozOUL6oqyBXGV4Pp2/CoYoS8mDwD60jB1kMfzDB5B6jFaTtaT2NoiXKx3IDLKHQhcZODu+lVexlYjCmSTyrZNr4AYA5yfb3NWra7dIHhjDBVI3kfw4P8AjWersv7iMKxJwCOTn2+tbQ/4lscUCDdI5y5Hc9/wFTLsiopL3mQfJdFwRu3EnHbk5z/SlksFMRG9SQBwHBwOoJqO8ljt7jZGdjMMsn8IJ/kaktZEJnZurRnIOOoxjHpSu7XKtHmsZ01ox3tIc47gjGfTis94io3DkHvWzcwBZFmjkVSwzg9G/AZqCazeKMOUbGDhME8HpV3IT1Mg0VYkt2ADAfeGcVBimDYgFOpM0maBWHZozTaWgeiDNKOTSAU4cUCcjT0mRLbUIpnHCHNWNcis18t7WTezsxY9eD0rKWUAU152albW4RvcjNAFO++eBU89nPa4EqYyAfXGRkZpjlvoQijNNJxSZoEokvmNjFIkjIwdDgjvUeacKCua2xu6Cj32uwK0ayE722FQQSEY9OnWna9pQsb2QQoyxdcddoPvVCzuXtZUlhdkkXoR+v4Gta+1OO80v5zslQbfL3cHJGNv65zWck1JNbF0530b1Ofxiil4NKQRVJlyjcEkZDxXZ+EfHGseFl+0Wkvm2SyBZLZzwM/3fQH06fTrXF0vQVakZOLWx9heEPF+m+MNKW8spAJVwJ4C3zRN6Eenoe9dJXz98C7XQv7RlvJNVdNbBaNLLfsVosdf+mhzzjPy+lfQFKSSehLQtFFFIQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAUdT0y01exks72BJoJBhkYZr5g+JngeLwfrCC1nWW1uMtHGT88fsfUehr6N8XeI4/DeiSXhAaY/LEh7tXzL4kvbzxBfSXt7M0kzHqegHoB2FaRdo67Gkb8uuxyJNNJqea3eLrVfFSQFLRikoAWpYZNh56VDRmgCaSQE8VHTKM0CHUGkooASilpcZoAbXSaB4he0YW85zEeAT2rnCKTpQO57/4H1SwstfgvLh1Ebo0Yl9Cemf8AGvS/FHhfTvF+jPaXaKcjMUo6o3Yg18seHvEDWTC3nOYj0PpXu/gjxkYIo7O9k32jcRSHkx+x9v5U9ehS8tzwvxX4Vv8Awpq8llextjJMUoHyyL6j+orBNfYPirwrp3i7RntLpFJIzFKOqHsQa+WfFnhe/wDCeryWN9GcZJilA+WRfUf1FDXNqhtc2qMI0w4NIWpM1JFxenFAo60dKYiSNyhyKuGcNCfm5qhS5NZygpG9KvKmmkDHmkoorQxYUUUUCFDEGrEUuKrU5WxQBqxNkU2VT1qrBNhgDV58NHkUAV8b4iKoOuDWhDzkVBNHzQMqlsjFSRRtI21Rk1GVxV/S5kjm2uOveolJxi2jbDU41asYSdky0mks8PPJrMuLZ7eQqwrt7VFyD1H51LqOkw3dqThQ4FcEcU1L3tj7GvkNKdFey0kvxPPqSpbiFoJnRuxqKu9NNXR8dUhKEnGS1QlFFFUZsKKKWgBKWilxTEFLSCpEQuQqjJPahK+iCwgFdl4M8A6h4pvF2xslr3kPpXR/D74VXOsvHfamhitQchT1NfQOm6ZaaVarbWkSxxqO3f61r7tPzY9I+pmeGfCmn+GbJIbWJd4HzSY5NdBRRWLbk7slu+rFooopCCiiigAooooAKKKKACiiigAooooAKKKZNNFbQSTzypFDGpd5HYKqKBkkk9AB3oAfUF5eQWFq9zcvsiTGSAWJJOAoA5ZiSAAMkkgAEmsv+17rVfk0OLER5/tG6hJgx/sLuVpc8YZcIQSQ5I2mez0G1t7pL25kmv79M7Lq8IZ0yMHYoASPI4OxV3YG7J5pXvsacij8X3f1t/WhB9p1jVuLOH+y7M/8vN0m6dx6pF0TIIIaQ5BBDRVd0/SLHS/Ma0g2yy482eR2kllxnG+RiWfGSBknA4HFXqKLCc3ay0QUUUUyAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkpaKAEopaKAPg7FNIFWGRW6cGo2UjrVWC5CR60mPSnmkxQA0CpAOwpAM8VLGhNIB8UeTzVh3EY2ryajLCMbR1pVwo3Py9NK2pLd9B6lkGWPJoNzIqlAzeWeoFVJZmJwOlIk5HDUnruNXWxpQ3bKMN8yH1qVreKf54HwfT/PSs4bT8yHBqRZSp53KfUU0raxByvpIteVNnDw7vf8A+vVv7abWPbKnmRgdzyPxqiL+QLyUb6//AFqpzXjSthtuB6VMteg4q3UuS3bXUm91UDoAOgFRlY85BYH2aqysvVTg0NIQMsFNNK2zFe+6JZJNpBQsD69zUOd33+pquZCWyOKlSUNw1C8xu9tB7LgVA646VPu2jg5FEKiSYAjg0PQFroViCKSrc0JEmAOO1VmXBI9KYJjKkjJztHemU5G2sDSHe5bhO3Djt1FXpegdhuibr7H1FUhkfOvI7irUEwxtPKHtT21I30IJLfA3ody+oqMSsvyvyKutCynfbn/gFV2CScEbG/T/AOtRZPWIXa0kQtEG5Q/hUYZk4P5U90eI8cUeYknDjB9aL9x27EbBW+7wfSmKvzfNT2XaevFTWsBubhE6Anr7UO24LXQ0tKiSMPczQq8eCoD9ye9VbqCOW6Ii4AHP1q7dmOEfuflA4A7E+tU4yIwWfgnk0oqy5n1CUm3yx6Ge0ZRtp60qpmpHfzpXkPGen0oUgdahs6Ix11ImUigHFTkA1G0fcUrmjj2EBp1R8inodxx3NBN7DhknArt/Ccdzo2p/Zrq2aM3QGHdccjkAHoQfbvUWgaEtnp76jfowuJg8VrGUztfbneQeuBzgZ9Mc1dS4TSrKUtviMnzwTQky20rAfdCnIXPocFT3qnTTVpHPOs7rlexW8awYt4ZxMu6KJEEW3k5OCf0rhw655G0+o6fl/n6V2niR3mlSRRiGeGJu+OQSAfcVytxajBdacntc2pU3KDlHa5b0nUJLS8ieVfMijbcFPRjjA/nWrrM0Or6iZbB94ihSPyz97gZYj1GSenNcz5ohjwn3j1Hp/wDXqON8MGjYxuOnp+f+frVOTSszCVKM3eP3HRaIpOqAeiP/AEFbk8dzNrV9Y3cb/YzJJMkpGPLUHBZT3BPGPU1k+Hb+O41RPt+UlIEayhepJGS/rgDr1ra1CZp5pZ3u8WEodSYvmLAOdu0dAxBHJ4xSVr2uYqEot6DYEFusY+1L9lmkDQXKf8sZh91/YHoRTo7Kxe7kludPizMxiZCSBBcd1OP4X5Kn14rNs7XyElLXtt/Zso5Er4Y/8BGSGHQ9ver8UqyxOJS0+2HbLs63FsD8si/7SH8eKaT1ixtp6mfcX1pbzPEui2wdTg78nGP+BVasXuLxd8Gn2iqO4hDY7A88frk1Fqdq08TSMVe6gVTI6dJoz92QfXv6GoooLu68LyR26M2yXdhOpB4246k4GeM4FEorlVnqXFrV8qLtzrFvFePaQQzL5WQ0kUMbdOpK7c4HfB4qG8v4BILWa7vJN6jlEVkORwQoAOD1HWq7PCYoLu2vXFwvyLNJwwOP9XIRxg9A34H1qISTXN1hEZLyOTfCiIQ0cg5MZA6qeoI6H2NJpMlSa2Kd1p7FfPQrdxnJ8yMnPHX3+oNY1zNvIVQwQdveuqnnu1uLgKktnJcKGA5RhKoyQc+q5Geh471WmgTUdIjvrqRHfnzJY4wskfzbRuA4kHIJ6MM96XL3L5zmQ/GG5FJ5LMhdPujirN9plzYyuskbBB0kAO0g8gg/SktGUrJE+7DY5+lD0GtSgR60VNOEEhVDnHGaipgOUkjFWbZiGK9QwwRVNetWUYqwI/Ckx3sLOFDbVDACoCKvOqyruUZ9R3H0quIWaQKnO44oQW1saFvpZNrG8kyLu5ALDpVyys7JLqNnuuVdcbBnJyMfrUcVhujHzOe3A9K2NB0e0mupbm8SU2dnEbmbD4LbSNqg9ssQKxlI9KNPljsW7/8AsZdUuYtK0t1ks98NxAZSRexA7Wde6sCN3HbntXTaDrCWkMc0sbWsV1M1xFC4PyxrHsiBxzyV/KuSfU4Lsvczwy2873RuYpI/lMSEkkKw5Oc+n55ra1LTtRGoyGd5Zt7jyJiM+Yg+6w6cY9KyntYUTI8avcW7WaNe5vjE0l15TkAl24Axg4CqOPSjwlCU055T/wAtGY/gMKP60uv2WiW8tze3V7cXN80Y3WvK/vDgZDnrhfmIx3q/pVu1vaWyIMQ/ZwTv+9uJ3Dpx0PNa0tbIyqaRbLyudgDjI/lT2ZjFsMj+WMkYPAzjOR0PTFJjigDiuipSjNa7nPCpKBh69oct1EskLxNs4AJILEjhVJ78Hg1hXVokUNo07shhi/fFOu7OAB2zjiu58tSMEKR6Hkf/AFq5bxLpNwV+0W3mvEgyybslSO+O4rk5ZwajI6eaM1dFKBnadFtd6ZB2lz6DJ9OvWrYu2km8m8Dw7wDkJ09Dt4ypz096y7a6heJ0Z9rdjt5z6itU3b/2fa3MkLvHAGVZHYuu4DAHqoHHXtSkrMaZhzxGxvJETbJGDlSVxkH27fSplDz6XLNGNsv2hI1SMn5sqxb8sCmrK80ht7kfvHKhS/qfftmtLSRGl29jew+QN6vHvBADjjn6joematuy1ItrZFePSTcbHnhcSOoDRr1z0zntmrkmlziPd9lQqQB8788HjP410p22I+VP3g28k+g64Puefx9axNW1USAIHYOpHPbIrLnlJl8qWpiX+mljJK8Lws5Bz95Qe/T1qreQvBM32dVNvGinPYg8ZPvmuhsLpJoyjlSRnI9cY5qK4so2Dm224cfNEejd+PerVRp2ZLhpdGOtxHdKI77crkDbMFywHbcP4l/Ue9V7mwuLSQeZtMb8xyRnKSD1U9/5jvU9zZGWV5y+Acs5xwoHAX69qisNVktAYZEWe1c5khk6E+oI5Vv9ofjkVvGXYylFPciUiMj+/wBsda2LfVMKWuI/MkQZVx69t3+NQTadE1u97p0jT24/1of/AFsOf7wHVfRhwe+DxVFJGjOG/A9qHFSMdY7k0MD3UV0zDfcZDj3BznFRv5lrJ5coYccHvzUiypGd67lcdMNxn1qMzGZmMh3Oxyd/f3oXYT79S5FdsvzRDEmOuMlvYHsPan7XlhRX3eZ5vzHPPK5GPcYrN2vFl4/mXuKtQXIMYUHgH8R2oatqgUm3Zg8UnR/mI+YP/wDFe9UriFOSODk4HqPWtlpUTDgLIwI6t1J6cDt75qK8REy5RmHBJQ84I680KWo+Wy3MBlI4NMrQnVJAZBymB06g9KqSwtGcGrQm77EWKdSUmaBWY7IFJmkoxQPQM04c0mKdQJssQhQQTW/f6pb3GkvC0K/aGCgyfTpXNrJtoaVm4J4pNDimDDFM60u4txVuDT5p7VrhNu1WC47kmheY5PsVAPWn0MGRirDBBwR7ioy1URa5Jv29KRnLdTTKcBmkVdIu6dB9pvraAnHmzImT0GWAr0a6+Ha3OmmWC6i+0oGy44TI5we+ea80iYLxXZ6T44kstNms7/fOPKYQSjl1baQA2eo5HPUe9Z1FLRxLpyV7M4wr3FJ7UoYFQB2AFOeGRFR2GAwyD69qaNZJdBqO8UgdHZXU5VwcEEdwR0Neu+CvjdfaaIrHxEj31qPlF0v+vUf7Q6OPyP1ryGkq0+jMpRsfW3h/4keHfEdxLBaXTRyx4IWYbdyn+IZ7A8H0rsetfE+mXEqXsPlzPHKGzFIhwwbtg+549Mda9w+GnxTS8ki0bWZkSdjtikJwpPop7Z/u9u3HAfLfYi19j2mikpakQUUUUAFFFFABRRRQAUUUUAFFFFABSUdBWN4n1hND0G6vWOGVSEHqx6U0ruw0ruyPIPir4gOpa59gifMNrxx3avOJSAMmrd7dPcXEtxKcu7FmP1rnb++MhKIeKc97LoaN20RFe3IkbYnQVSNLSUGbCkpabQAhptKaQ0CYuaWmjmlzigA6U6m9aOlAx1OU4602igRIzAjAqKlooASuo8M+JnsJkt7g5hPGT2rmdvrTTx0oGj6i8IeLFhiitrmTfatjZJ12e30/lSfGRtIfwTK95Gkk3HkOOqsehBrw/wAHeKmsLqO0vH/0ZiAHP8OfX2r3DUvClp4o8Oi3kmZsoPLIPTuMfSi6TTZd9VI+Xz1pK7DxR8O9c8MrJc3EHnWSnH2iPoPTcOorj2zQyWrAvWpSMimKKlxQIj6HmlpxXNM6daBC0UUhNAAaQHml60uKAExRS0UAAJFXrefjY3SqFP3lelAF6HHnlR3q21oZOgrKgnCzBj+NdTayJJGGXmhspIxLqwaIZrOIKNkcYrrp41kUqa5+8tTGx9KSdw2NfRL9ZY/Kc/OP5VsyzPGmAeD6dK4iyjle8jSH/WMcD3rvWEEluI5HUSgAc4znAJwPSvLxNNQnddT7zIsZUxFFxlvHS/c47VYg0hcVk10d/CVJU1gTJskNdeHleNj5/OMPy1nNLciooorpPFClopaACgUAVsaD4fv/ABBfLa2MLOSQCccCqjFydkJK+xQs7Oe9uEgt42eRzgADNe8fD34TR2qx6lrSbpDgrEe1dL4G+G1j4Yt1nuEWa+IyWbnBrv6tzUNIb9/8gbS0QyKJIYljjUKijAA6CpaKKxICiiigAooooAKKKKACiiigAooooAKKKKACis6/1m3sp1tI0e71B13pY27IZmTJG/DMAq8H5mIGeASSAav9majqv/IanhjtT97TrT5o5B3WWRgGkU4BwqoMEqwcGlfsWoaXeiHza6s08lno0aaheRsUk+cpBCQfmWSUKwVh02AF8kZULlgQ6JLNPHc6vfPfTRsHSFFMNtGwPBEQJ3HIU5kZ8MMrt6VqwwxW0EcEESRQxqESNFCqigYAAHQAdqfRbuHPbSP/AAf69AooopkBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHw2VSTleDULKy/eGRTQ/rwak38YPIqrtC0ZXKj+Gm7TmrAj82QKnVjSyQmNtpFK/Ydu5GkealJCDavU0hYKMDrQBt+Y9aa8yW76IBhPmbk1C8hJpXbceaYR60XuO1hv0pMU7B7Un1pDAMy1Is7dDzTMetIV9KYh8jlhwFFQGngkUpUN9aGNOwzJpM0pUim0igoopaQD1PGCakQncCOPSoV6ip1Hbv2pk3syzJM4X5vmOOtVCSTnrVg5IyOo4qEgE8cGhLsDfcjIBphyKkIxweDSH3pgSQXGz5W6VZ4PzRn8KzyPSnJIyHii9hNJmjHOVOPuvUpKTcScP2NUlmSQYbrT9zKP7y0W6oL9JEro8Yw3zR/wCfyqu8atkofwPWp45sDjkUscC3M21Nq4Usfwocv5gS/lKiKWOKvGMW/wAyOrIQDkdqgRvKkZcdOPeprWB7+7WCPhCeT7dzSemr2GtdFuRLKZJAzfcFLdTrLiJOnUmty80a2lyls/lyLxg9DWI+nXFtIRKjD0PY/Q1DqKRrCk0yt04HSipWX1qMqRSubctgViKkBB6dajoFIaZL5YfrXUeF9PiFzFc2/wC/vYm3mI7cmMDkpnOWBFc/p9pc6jdra20bSStkgDsByT9BXoVvP4emsobGe2e3kgXYshGxwR1JYE8k9c4q4QlLVIyr1IqPLfVkB83ULiK1uZvtEgUtFIX2mVCdyyRHorg8FehqpNqJsLOaB7RJpZm3JMg/dz84YSKeM+uOQa6e38K2F3ZRQQXTMI2LxSCX5489dpA5BOD6U2y8Oy6C0rpPNcCVgFQ7AA5P3s5POM9vrSdWF+VvU5fZVGuZLQwfGBSOx0yBE2EKHKBeFygwN3Unk/hiuRcZQium8ZXYudXRFC4ijHzhywYnnPPp0/CubfiOs225anrQioUHy9jOmts8rS6dpN1qd59mtkUvtLHJwMDrzU7vjrWz4ayk2pz/APPOwkOfrWlSXKro8uhFzdnsZF7aXejyiC4jZXIOM9CPVWHBqzpevzWAO0JKpP3JRnHGBt/HB49BWfZapPbxeRKq3Fq33reXJU+4PVT7irD6ZFeq02lO0mBlrZv9dH9P76+459RU2vpI607eZ0cDxa5tgkvZZ5T0hmRVk3dijDIYk9jjNRWflRlIrS+/0mKbdBHcIUdX6Mh6gg9CM9a5aC7lt28zf+8jbKkfeUjv7YrWtdT067jWLUYHjYAAXlp98Y7uh4b6jFaud9zklSu7w1/P+vQ6FW3RQy2qYwzeRG/8L/8ALS3b/ZPVagkhE+mvDYF1WQ+fbHkMHH3oyexFWrmNIlN0HZ7S62iWSPOCQoYSL6MD8xHXGabvaIzPIVAcgXOPuhz92Zf9l+h96bfMrrdEpcuktmULp7aSNbkSSnzFxJdCLkN3SdRwwP8AeAB+tXJJ7iysbOKwuVmCxedLEmR5isMkr1JA6Z6j6U64tVuNZkWC1ltpniabzrVwuQPvBkJwxBPQYzVaY2Emm2z/AGp3mhZg1zCuCuWLKSnGB24xg/Wrp25tTOWxVguZIiVE++2nyIpJcP5b+hBzj0PqORUKRx3lvdxziK2kjwzFUIXOdpYqOhBIBx1HbitGJZFtLieIW80kjBPtEJDHaVPPlngnI5GM+lVHuxmZ5YIS7wlQI/k+XowBA5BHIJHBGDzVylG7TQoobcLc3GqRxRzqtrPCvzv88TbI/m4Hfj2NZtzpD21wwi/1iYMkXUhSMhlPdTn6jvWnbKlhFcyMfOjMSPHFvwwLEEMwHTCkjPQ9KZG1rY2k19a+bNLxGolx+4BH8XqOwxwe+O+co9Y7FxlZnOzwYJ3DGO/+NU2UqcGuouIftltxC8E2zd5UiHLDH3kYgFh3K9R1FY9zanaCkbFccnrg/hUWZpddDNpyP2agoc4qd7R/KDpyMc0ikriK5XnNaGnTwpeCaVGbaD0754rJVivBqaJsHilLYqn8SOlbWo1BVLZcdst/hWppWoSS+G9edEhUhYUwc/MCzMR9fl4ri95re0KYxaRqcmxXSGa2ldTyCoZ1Ofb5uaylHQ73M7XTpre68E2UUv2f+04JXa1D/KxRRu3E46dQPU8Vp6V4vWUCDWY97ouFc43rnup4yD6j8KzNM0i1bRzfqYrq3/e2q24mIcB8FdvBwRlskjHGa4/QL+4j1KG2mgaa3im2RgrveFix2hTjk57d+cc1jy8yYrpFbXJ5NT8SXUat8slztUDpk4XOPw5rum2+YwThdxx9BwP0ArlLXRbiw8aRW93CyiOd5M8kMEyxIJ64IwT611S9Bn0rqoq8jKvpFIcQKRR60opwrpOUbimHrkdfWpqYQKmUVJWYJtO6MLUNAguCZbYJBcZz935GPv6VAtpe2MRS3nVMghoXXKncMHqMYrpNtRXECzwlH6HuOo+lclWlKOsdUdUKsXpI5L+w7m7l3GBSSMEmUAHGMEHsaurDNHIIdURXjfCiYqNynHGSOqn3pLmG8spMyyN5KYcTICQwB5Vh2P14Na8cMUIt7p3Sa3ugxMafNgdw2eAT16dRWLk+ppZdCjLBeRWsts8zpEGCwSS89c5Ue3FcjfK6MVkLBgcA9j9K6/xBoVxO0X9lxqYVjAbfMCq5OeST344HSqiaQhli+1XVuApJAALjd2B9qqLUdWEk5aFCM2WnEKJnOYwWyPX/AD0qeG5hmwqSbuepBHvTrzTGEcvlJ5zswVnGDz14I4GfrWVbxTWb7J0ZD2B9DRZSV7i1jobV5Yeb8wO22mwZSemR/U1z97pqpJI6SKkY5VCuGI+nOPzrqrWJJbR4zxuBAI9a5vUreWaZGiSV5WHIReBjj8880U5O9rinFWuV9PS5hvIjbTNHKchJEbnOM49wehB4NWY2t9Y4hEVrfn/liflimP8AsE8Ix/ung9iOlVtLuEh1BPtLqmxXVXPQMRgZ9hUdxo15DH5ixrNF/wA9IiHX9On41unrZmLWhWuBLBM0TxvDIhw0b5BBHYg0qTLJ8r8HsavxalDfRJa6xvYINsd2gzLF6Bv76j0PI7HtVTUNLn08o7FJIJeYbiJt0cg9j6juDgjuKsycV0HLIVI3fgexpzJk74/lk9OxqnHOR8r8ipt+0ZU5X9RQQ10LMM+GwflfuOxq95+/p/dA5OOnqT29ao2kAu4rl+siKCB684phZ4GCyDGQCCRnj0I70mrjTtoaS29sokGUZjGWIjU4AHJ6mqBijJkTLeY3zDeOvfj1qeB0SSR0LF3BBz02kHOKnESTBQibnTHJPoMcHsKV7bj1aVkY0tuVG5Rwaqmt+a0ZRuR1LnIIGMDvWdPZlcdnPY96aYSuUcU7pSlWU4NMNVcVh2aTNJRigNgzS0Yp1Arj4wM1r6ZqRsNyFA8bYJQ+o6GsYNilMrGhgkye7k+0XUs/yjexYge9Vj1oDHtV/TNP/tC8ELOyggk468DoKNipNWsUgtL0qe7tJLOQBwwDZK564BxVYtQmTysdu9KNxPWmZpQKYXtsSwglq6KSzjn0638ubcRCpI28Agc8+uSa51G2VpWOpvYTCVNpHdH5B/Cokm1oVGVnqVJo/LkKen9ajqxc3Ud5dyyhPJDHKpuzgAYAz+FaNh4cu9S0efULba3kyFTF/EwAXJHrgsOKV7as30elzGqW1MRuoluXdLcyKJXjGWVc8lR3IHSo2UoSCMEU2rjKzM5RPsvwrqWk6l4ftH0fUPttrFGsYkLln4GPmzyG9c1u18W+HvE2q+GNRF7pV29vL0YdUkHoynhh+vpXrFp8fLqSa1N1pCLGvFwInzvHdlzyMdcfrTavqjNo96orC0PxNp3iC0jmtJ0bzF3KAc7l7lfXHQjqDwRW7U2EFFFFABRRRQAUUUUAFFFFACV4x8XvESyXcWkxP8kPzy89/SvYpWZYXZBlgDge9fI3jHULy58R363QZJRMwYHrweP0rSC0cuxcNLsyb+/MpKIcLWaacaQLk1Im7jaSrH2c7SwOcVARikISkpaSmAwjNG2n0lACYoxS0UANxRmloxQAmadTcUdKBDqAcU3NGaAJlG6h4SF3VGrlTkVJJcNIu3GKAIc4r034dfEqXQ5E0/VJGex6Rynkxex/2f5V5mFp4bb0oeuhUZWPdfEHi1PH0yaLphdNMz/pd2/yhUzzjNec+LtJ8N2l0sXh6eadUGJHkJIJHcZrn7XWby2s5LOKZkhkOWA4J+p9KjimIPJ4NC0VhuS2RXKlTg04CrU0SyDevWpNN0q+1a6FrYWstxcEZ2R8nFCTeiEkUSD0FIyEda7SD4feK4+W0C5P4D/GsHWNMvdNuzDe2kttIP4JBg03GS3G4O1zG6daXGaey5poBpEiYoxTsUUCG0UuKTFABTafSEUAN6dK0dOvjBIFY/Iaz6AcGgZ16yhxuBqG5iWaMg9aybO9ZBsY8VfFxmpsO5lSLJbTh43ZHU5Dg4Iohv7iKcSmRiehO7nmrtwFlGazJEINDinui6derStySa6m/JcJeQbxtz7VjXUfemW87wnGfl9KtSMsq5FcypulLTY96WMjjaVpfEZRoFPkGGpgrpTueDKPK7MWlAoFaehaTNresWun24zJM4X8O5qox5nZCSubfgrwNfeLr4JEjLbKfnlPTFfS/hfwjpvhawWC0hXzMfNJjkmp/DPh+18N6PDY2yAbVG9u7H1ra7Vc6mnLHb8yW+iCloorIkKKKKACiiigAooooAKKKKACiiigAoqC8vbXT7V7q9uYba3TG+WaQIi5OBkngckD8ay/tuo6x8mnRzafa/xXl3b4kcdCIomIZWHPzSLgEDCOGyE2UoN69DRv9RttMgWa5ZwGbYiRxtI7tgnCooLMcAnABwAT0BNZ3l6rrPFws2kWX/POOZTczdirsuViXryjFjlSGQgg2rDRLOwna6CvPfOux7y4bzJmBIJXcfurkZ2LhAeiitGize5XMo/D95VsNNs9LgaGyt0hV23uRy0jkAF3Y8sxwMsxJPcmrVFFMhtt3YUUUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEopaKAPgwSetPDehqCnoe1O42i1bOFuI2bsc1dniMo3I2afptir2kly/qFX39acqDz9iHDfpSV224ik4q0ZGesL+bjYxNTzWrRRgycFu3pW3Fc2tmQ97A+4A42Ywx/HpVK41CLUZ3aaNI8/dx0H40ua720KceVXbMZ4WXnqPWmcj3rTktXi+ZPmSqxiSTp8pq+XqjPm6MqEjqOtS+SWXJGKRrd1kGV4zW5qNqw2PGnyHHIXg8VN9bFte7dGA0bp1HFMJxWvLbvHCHZOvbHFUWhSTp8p9DVEpj4bEvZfac5GSMfSqzRHG4Vq2UyQWptrkN5ZJYOOxPtUtxHYQWhJkWSRxlRG3T61mpW0ZbjfVGFnsaQx55WpyisMjg1EVKmtNyCEjFJUpwetMZcUrDuIDg5q0hDrx1FVKejlTxQmNq5cVh0PBppUuwQdScfnUfmBh706NirIx65B/Km+4l2HPG0fyP19DUJX+7+VacqR3J3qceoqhMFEp2npSTTG4tEBHpTTUx5+9+dMIx70yRnIqSOZl47VGaBSGWdwPKcGrNtcC2kLEbjsI/E1SRyBgU5WJOBTeqsC0dyY/vJNx710mlQpZ2Jm+UyyfoPSufhTuau2148RPlvkdwehrOUXJaFRkoPXc0IVnkuGDcgnioNcvlR0s4znZguffsKvWt/AQSnyykYAPTNYV7ZSpKXkDEtzu9c+9TKSnJK2iN8PSlFOV9WRCRZPvfn/jSNGQM9Qar4ZDU8M+OOx7HoaDa6ejIyn92lihkmmSKNGaRyAqDqSavpZm5yYBkgZKf4VuaBp62cMup3siwRNGY43CFnBJxkDgDOCMntnHNONm7MireEXIm0TSNX0fU1e3jQvt3SSlx5JjP+10x+ue1dPqmlW2twm70+aFrteJUjPysfbOPwNUYLS1TSZLbUNTiFrMQ8SRZZgezDpgEdRz+dV7i9i0KIW9tAy3MYBjmG0LIhOctj7wI/I+hFdPK6b5oM8xz5rqRnWt/e6Rdfu3eNkPzRtnH4j+oruNP8SW+r2WyQqlyhBKHk+hI9Rg9fzqrFaWHi/TUuETybpRzjGRjr7H6Hr1GKh07SbnRI7tWC+aWBVx3AU4BB54PbpzmlVnSqq7Vpo0pqpT2+FnJeIJUm1GNkOQIEH45NZEv3R9avamGW9MbHLRoiHHqFGf1qhN0Fc0neq7Hpr3cJZ9inN1ro/Clu11FrMI3EvahOFycEnJH0rnJeWFbujIT4c8SSLkt5ESDHXJY0Vdjjwu9zLS1TTbxoL2PcduBhQ6yAjgqc4PsRVW3g86TNm8onT5lQn5uOeCMc1YstVu7Py4Yx+9iyYSVy0WRztzkDP0rLjnltbsP/wAtEbnPr70JPU1ckrGr9utdSyupfuLrtdxp1P8A00Udf94c+uaqXeny2TxvMnyScxyRtlHHqrdMfqKvfZ7K6s5r6481ZnYARxdM9+DnOSelVLe+utLkktpY1aIn97azrlT36dj7jBpJ9glFPUmt9cubZViRj5SusgTquQMDj6cV02n31rfCUWi/vIQ7JayniWA8tCD3wOQDyO1c0dPttQBl0uRw4GWtJDmRf90/xj9faqMEyWlwsqly6H02/hVxdneJnU97Sf3/ANbndiMPGlusz/Kvm2dx0coOP++l+6w7jmq0dprK3caz2NtNCzAzCFYgZFPqQQffHFZena+xsGtrlTmJhNbzK2GjI649QRwR9PpXRTWsOp29tqECx3IibmNWIWQDqh5BBGcjP0q+azMXTaWmqMt/7NsLyVJ7KbBBGC+9COocdCMY45yKmRZkLO1vJGXXIzMj7jj5cEEEg+4z61Atxo8HkwSedcqZZGYSq8YgyAFXrlsHrg0yfaNUiSfTIUkjASGKNWwWJwueSWGa0nH2q03M6cnTaZZurSNf9JhCfaZIjGqSMcKMYPHsOB2qg01vb2h8mC2kaQFZiJGk2jI4HYA9icmr4uJvIhiu0VbmQyrlFGRt4xjPJIPFV1leOcTBYsucfOo+633cMMc9xuyCehB4rGk5xdpGtX2ckpRGzXNzeQ3EyT24imlV1eaXY0Eg5wvbp+YqvNaxS3sUrPEYpNvnGFwyxsTg8jpnqKsTeeBJbXoie2aULLNCihgyngkjgH6joaY4trAXDpDNaMp2+TM5eO4UdQG2gbu4racbarqZQlZ6mFfWDwXd2saOYopCpfbnHJxk9Oa0ry3SHToJLfgvEjH5sjOMGtR1uS32uxdGEqoJI5HCiZSDt4PGRgg+9ULbT/MM1ukd2kirvMMqDgZA4PGeTxjB9qy5btNGjkrNMwZI0m7bJf0NV1DKxQ9q2LmxIO3GRnGduCD6EHkfjWnH4cu0wvloT67hSk7G2Hg5vfY5kKx6BjXTaLYanY2Opyy6fcLb3enSojyRkK2NrAgkdgCR61raPoGNSg+2lBbq2+UdRsUFmz7YFTX+sa9/aeoR3d3iKVvK+zIyvEsbZPGO+3nOcjv6Vi5HZKCWiY7Q7Vby3No101tJ9mMiuhwc5C4/75JNZGo39rp9yXtIXMcyhFkLgEvGQBIFHQkevrnOa6bw3bx3v2e6CLsMDW0kfckMcEjHcFcnsDzVXxHpu+0caq9slyHP2WOPaHVTyqs3AACqTt9TxzWUWr2Y7XM/wteXF7JMszsYbOBhEOuGkcAnn2zmt0etZHhCHZot9ckYMs6oPoq5P6tWwPau3DrRs5sS/eSFp1NFLW5zin2pMUCnUDG4IpcZpaO1AETIGUqQpDcEHkEehrD1Owuoo1fTQrRp1tivI7/Ke/0rfIpnGa56lFP3o7msKrjo9jz/AFDUbswRMJhhxyV4IxwVPpg1ZsbtJ2upWfMkgG2L2A9frXQapoNtqRLN/o8zHJkRchv94evvWBZ6Zd6TqTyKjCWHMkZKgoVHUknAx61ztJJxasza7butjRNsDpdrJEkzSS5MkfOTjsMkDkc+wrFn1KK7ljCQeWwzzkYx6cV1Ola7YSzwQ63avACZEJt2BiaN1wRtJyvJyCtVbqy8IWMqumoy3flrt2BNgJzksWyCT7VEWk9UW07FewupAwCpvU8++O/fp/Kql+zy3TWyTpGGYn58gHIzyewrorS+tpdPQWds0CAHYhbOD0znqQe9YP2ZJLu6ncr8rFeOOgpRerYNaGPFp0k0m0ooQH5pP4cev+FReRcWtxvtnljJYgHODtHc4rs7IwRackkyKxcbjvHA/L8KrznTpoJCo8lxk4HIOegx/hV+1fYj2ZzQuLbUBsvo1ilPAuYxgZ/2h/WmpLd6JK9tNGk9pLhnhk5jlHZgR0Pow5FXZLBreYRlFeKThT7+tIbiJ7JYrnLWwbyyRyYm7Mvt6itFK22xDj95UuNKhurdrzSHeaJBultn/wBdAPUgffX/AGh+IFZasBwRVuWO50m+R4ZmSRDvimjOMg9Cp96umSz1s5k8qz1E/wAQwsM59x0Rj6/dPtWielzKUblKzleCcSI+D0NTXU800peY7weMdMew9KZNbT2kzJNG6Spw8brg1IrJIuc4Pr2PsaNNzPX4SBWMfKHcn8quQ3ZCgKfkHX/64qm6FGyvB9P89RURk2ncvyn2ptXEm1saU10sFusa/MxOTvX1659c0afZPdwzOZFVIsFQe7c8A9uKyxulYVvmNGit7S0f5iv709m9T+J4FJg3pcyXtnJEZRsgZ37fxxVKSNkPIro9QV5jEqDy5IAUbPQkEHn6Z4qjNbttIZM5wT3Az15oT6j12Zj0tSyQ7cldxSoiMVVw5WJmkzRRigWwtGKMU4UwuC9av20720oljOHHeqGaXe2OtIVrmjqV++oTK0pXKrgYGKzWGDSZJqeKMOyhuhNJIvmtGxEAKdW5rWkx20X2q32rCSoUDnOR1rAOaadybDt1JmkpwHrTC9th8Yy1eheDNZtorNNKcIsrSOQT/wAtAwxgHsRxxXngOOlTLcbR71E48ysOMmpXNXxEI11ZxHD5Y28puz3I6/QVkGlnu5rmYyzyM8hABJ68DA/SrNlZveS7IxkgZOOwyB/Wkk0tTdyTKtOVipyKt3+l3ensBcQugboSODVMGmpEuNzqPD/i2fw1Naz2juyE7ri3JIAYHAdW/hYjuOCBg5r6a8H+KLbxXoy3cDOJUwJUaNkKkjIyD6j0JHoa+PwzIyOpwVII+o5Fe+eAPjPBqDRaX4m8q2umwqXyALFIegDDoh9/u/StN0Q10PaaKYCGAIOQehp9QQFFFFABRRRQAUUUUAJXmPxI+GMXiaJ9S00LFqaDkdBKB2Pv6GvT6SnGTi7oabWx8S39jc6ddyWt3C8NxGcMjrgiq69Md6+rPHPw803xhZF9qwagg/dXCjn6N6ivmjX/AA7qXhnU2stRgMcin5X/AIZB6qe9W0pK8Smk9UU4oiY9ofBNU3XaxFX4J4liJfrVGRtzE1mhMjoooxVCEpKdSUAJRS0lIAoopaYCUUtJQAlHFLilAFArDaM0EUUAOHIpCKdG2DUpjzyKB2K4qVGppXFWrDT7nUbyO1s4HmuJDhUUZJpxi5OyBK5LA5JCgZJ4GK9u+EvgLU9O1AeIL7NsrxFI7cjllPds9PatT4efCW30NYtT1kLPf9Vj6pF/ifevVQAq4HAFVKUYLljqyrqOw415v8WPCq6xof2+CLNza5Jx1K9xXpNRTRJNC0bjKsMEVmnZ3JTs7nxZcW4XJXj2qoRXe/EXwy3h3xDPEqYtpiZIT2weo/CuFYc1TVnoNqzGUlSKhY4HNK8Dp95GFIRFSU6koENopTTDmgBaaaUUGgBUbFXYZ8jaao05XxQM0d+ahkx1pqPniklbtQIjI9KdHIV4PSmA07Hek1cqMnB80QmGRuFV6tYyuKgZSppJW0NKlTnfMIK9N+B8MEnjstNjfHau0Wf72QDj8K8zUZr1j4QeENSudeg1s7oba3JOTxvyMEfjWsE3dij5n0bS0lLWRkFFFFABRRRQAUUUUAFFFFABRRWXea0kd09jp8P2/UEx5kEUigW+RlWmYn5FORxgsRkqrYOBuw1Fy2NSsT+3H1T934eEN2P4r6Qt9mQdMowGJmBz8qED5WDOhxk/sWfVPm8QTQ3UJ5/s6OMfZh6b9wLSsMnk4U4VvLVgDW3S1Zfux83+H/B/Iy7PRvLukvb+7mvr1MlGc7YoiRg+XEPlXqwDHc+1ipcitSiimlYhyctwooooEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfB8lu0Z5pbaHzZlQnGTyfauyn8KzjR575HVlSVkCHqQMDIP1rlDE8EpDjbj1oWquht2dmbNzOkMKpD91BgD1NS6FGDdo8oyGPOfTv8AnWEjtJJlvuitOTUo7XTyI+Zn4HtTb5IcqJUeed5B4ovba61qVLTi3jAUf7w+9+tZAJX3FVCSTknmpEcilFWVi56u5oQXTx8Kcj0NWc29z975JP5/41mBlb2NP3kfe5FVa2qIvfSRoeRPGcLtYVdju5LWIB9pXGSh5FYyXcijCyNj0PNQz3sj/KTx3qZe8rNFR913TL91q8144UbUiXpGOn4+pqIyROMuMGqIdW68U7fgdVNOMUthSk38RZaWNEIU9fVqrZ3NuU1XeQtSBiKOoWVrIshvwNKzcYNQiQNwaN3PtTFqSGBhGHbgHke9QnIrWVUuLKNFPzqORVOeHy1APWpjK6KlGzKZANNIxTypFJTEKj7eKnQEnPpVcDmra9A6/jTSuJu2pZkVfKT06ZHaqjxlevTsRVuF1xg8qaJIGQbo/mU9utC7MH3WxR5HWk+lTMitynyn0NQspBweDRawbjDzSgYopQM0AL14FSxID9Kai5+lOLbvlXgUbg3Yl3F/kThO5pHuEiGxP/11A8oUbEpIkLtk0SkVGF2XYGYDex5PNadvfYGyUb4z2NZQyKkV6wZ6ULJJGydJtr1d1tJgn+B6x7rTLi1J3xsB69vzqzDcvEwZDityz1ZJBsmC4PHPQ/WpUmi3FSMLQ5b4alFBZR+dNIdojPf157cd69SinsF0mRfMwJolMQ2HzQwUqoHy4ADDg/1Nc/pmnx5uH0xF88AZAY7tpznaf6A5rXuhbxXVhZyRssTQg7z0Ik4ZGB7Bh7YNbRpc8VJHnVqzhJw7FDyornIuZ7i4QKWQHBf5eWCuBg4HJVgpGOlSxS20lp9nsLlWkTLCO6tgxK4yQOo468UptDaG4mT7Q0ibo5TwD5ZyquwBO5hxz+ZqM3UaXdvNex+cDIDHe2y7SxHZl6E+oODXVTi3FxZwyVtUSaZqFzYQ/wBpO+nNCjYlS2QK4UkDdkYB68r1xXS+IZ1m0YTxlmBhleLYc/PtAXp6ZJH4VyN3pqz6tJbNZQzOsjL5tpiN17gMp4YY6HjPrmtSC7+zeFbpYpFklsFAxINrKS2PmU+gx3OccVySpxc1J9DenUkoSgnucFcyyXF1JLN/rXOW+uOaqzjGBVyZzLPJIerMSfxOaq3XDAe1RH4z08S+XD28kUm/1grotILReDdfmQsrGa3RSPXNc6f9ZXT6eBD4CvJTwH1ONT34C+neiqzjwy0OdvIp7aW3m8jAWJQG4G7OT689etQmGN5YrqeN5IZcgmPsw6g4/P6VvgW2twwWd2723kcRGPndk/MSD6AcYpb+yks4BGsL2umnczYbc5xwGPY5yOenNTGfTqbShf0M2GNbaezuXDG3EZkAHdgxXA9803z7vU7i5uE05LlmXPzJnaFGMgcE4Aqa3vUfRbZJtpMLvtxywzz+RycUW1/vnSCEtGH+QSJw0anrjHt60ru+w7K25gxrK0u+P5WU7sj5duO+e1a0Go2+qEQ6ptSdsKt8oGc9vMHRh/tdR71c1/SFsNFtbq3kWSF5DEx4DBgM4I75HOa5fs30NaJqSuiGuV8rL97a3NldS21zJtkQ4IHQjsRjqD1FSWOotpyt5MjhnyDjIJHIIyO3Tjoa0/FiN9stCn8NlCP/AB2ufKt5QdhwR1FNPmimzOzjN8vQ7HStUttRlM0tqZNQt48xvjKuc4BZRyzA4xgHrnjFS2Elw1rcapdzrO1uxmEJOWRgRznH8QbI5xkVxMUwjbKllJGDj3roYdUsr2MxakHhkbj7ZB/HnP31H3uTyep9aqLlHYmcYT12f9f1p9xftYjNqUMhfzrWab/Whfuufukj+FgcZ9ajtyixSOI3MluWYoCPuk4OBjlQ3JB6dqljV7SxiS2nSaFL75bhSREQwXIY9V7ZBx7ZwamZEh16RCjiVVaaMoQfOUjJUqeCSMg4Izjpmt1OMtzmlTlDRlJgRKHEypcSAtHcJwkwJ5DDoDn8OxHeprpZoF+1PJdwmaNXm+RZIQfu4ZPT5evNStbWarIguWa2lXzUiCMSBnllbBGAOvsORmluHied3SGWCSBFCvE+ZBGB9/bkq6HqQOlVUlG1kTFO+oxoHlik0+7hhh2xEr5QO1kJ3b1yT0Pb3qs1yIRFbNN9lCLtmik3yRSjGNykZIyOfrVy3thBrVsty/klAXHl5aGWM/eKjkqfVemfSqkLwSTXNs9tDcyLn7OJs4HcpwR/+usNb6GtronskvHlgL7Li1kwElkYGRUJ+ufwIOPaupAGOa53S43kktna0tIohG8kfksxMbZwytnoc8kV0AOAM+lRVlzM7cJG0Gwjt5biK9SEM0rQGNQnq5C/+gkms1vDTadDIbpHCtEPKfoASfmP+0SDjGeBzXXeGUZpZSqf6x44x+GWb8eRiupv9ZsImGnzxpJEwwUOCuB2+vpkZFcs5O9jpe5574btWhk1MRxt5ME0ZJMuDGrxkt83A7DI9DiuJ8SzS3UkMs77mkJPHI2j5Rj29K9Mt44b3Q/sdoEaa4uJZSmcNIVbaDnpwpFcvrekwx2139pS3zbboWMT5eHgY4/3iAPXkUov3rjXYs6Jbm38IWCHh5i82PZjx+lT4qeS3+yCG2/54wxx49NqgH9aiIr0aK9xHDWd5sSloxRWpmApaSloEFLR3pRSGNphHepDSUgI9tNuIYbuExXEaupwefb/AD1qXFIRUzpxmrMqMnF3Rxev6Jc2ZM6BpoMZzgZUe4HUe4rnZ4lQRyoxaN84yMHivWBIVXYw3J2B6j6Vj3mgWN1jYixk9gdo56n2Ncsual8Wxumqm25maTcIbWNd6o6jjPQnqPp9akhiRtHmcHdPubdj5sseOTWMZX0jUpbN0YxqcDeuCV9R6/WtS1mTzJHjOBIQSM9MVhKPU1i+hX1xntdItkU9eO3bj6/nWFatJdEoeg5J6dK2dfvI3t4owqkDjHT/ADisayOYpbdBl5eFOcYz6/WtYfCZzfvGvYTtMturlmQS8/XoKilsV/s+aFnYFJhkhc55IH05PWi0trnSgTdRq0THDRBxu47gjIB9K3oNVsziZoGkSRdsh4Emcchh0Pse9TJ2d4lR1WpyVpuvYGsHDF48tC/Xae6n2P8AOqslhcqXxBKUBI37Dg/pXQRQCW3SeH5GfMMuOobJKt+PQ+9NS9k8qR5r6UyZ4Hm4IPToO1Wpu+hLp3Wpm2eqqIks9Sjea2XiOQf62H/dJ6r/ALJ49MU+8057ZEuYZEmtZDiO4j+6x/ukdVYd1PP1q3N9mu4B9vKlj0uEUB1/3gPvD3qgrX2gTll2SW8w5RxvhnX0Yd/5jsa0jK5lOFlqQrKGGxx07dx9KieJnY7OQBnP+Nab2ltqsRm0kMJlG6Syc5dfUof41/8AHh3B61UsboWUsjPD52+JkwTjk45/DFV6GTj3HRhIotiBXLgZHcEd/wDPWprUSQ3caQPvlkHXuPr6VV85Iyr2+8SY+bfg8+3t9a1dIixbz3KFXuTkBN3IHc/jUydkEY3YpDpM63G8EDJHQn3/APr0/PmrKqIoiRVbHqSRnk+xou5nTTEW4CvK/wB0f3R3IP6VTguQIyF7ggmoSdi+ZXXmQ3IdCVZFCv8A6uQLgde/8qpTwrlyvGDjHrxWy5XOXdkAUZKDlvTJPH0FV7tVfbIIcjaGwDyAc/pxWiZntuYZGDg0VemhEvzpyG/n3qmwx1qrglcbmkzRQBTFsLQBS4ooC4oGKnRhj0qvmkzSCxclvJJIhEzsUXoM8VXbB5FM5qWNM80WKUklYaFIG7HB70ZropGsrjQ1DviaGIqqe5Oc1zrDFK4lG+ohakzQATTgKoV+wq9a6PwzrP8AYeom4CblkTy2OMlQTnK57g1zgp4lKdKUkmrMSbvdHpvjvUra/wDDdrJbPE0U86spQckhG3An1BIyK81I9KYZnfAJbGc47ZNb/hi3NxeXB+Q7Ldj87YGCyg/pms1HkidCmnoYWaK0NUsDaXDYGEJ4/Gs+qjLqglE7bQPih4k0TSf7KivPMtRxH5uS8Q9FbOce3btXqvw/+K39rFLDWXQXajG4DHmAfxr6+69R1GR0+dCKnhuWhGVLLKpDJIjYZSO4I5Bq077mbutz7dR1kUMpBQjII5BFSV5J8HfF2ta7aPaX9hNNbxZC6iuAm4AEow/vHIORwa9apNWJasLRRRSEFFFFABRRRQAlYHijwnpvivTGs7+EE/wSD70Z9Qa6CkoTad0NNrVHyN408C6n4PvzHcIZbNj+6uAOD7H0NcmRivtbVdJstZsJbO+gSWGQYKs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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_drawline.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "blur image ok\n" + ] + } + ], + "source": [ + "# 图片滤镜效果，模糊\n", + "\n", + "from PIL import Image, ImageFilter\n", + "\n", + "img = Image.open('files/test.jpg')\n", + "\n", + "img2 = img.filter(ImageFilter.BLUR)\n", + "\n", + "img2.save('files/test_blur.jpg', 'JPEG')\n", + "print('blur image ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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xkDkxjHAXGaIQXUJN2NLwZC1xrxSXlSynNfT2m2kVtaoI1ALAEn1r588GWkUWrNxhsKRk19F2vNtH/uiuuD00OSvpYmooorQwCiiigAooooAKKKKACiiigDz/AOI3hePVdMmudoJAyRXzReWv2O+lt2/gJFfZOrW32vS7iEDlkOK+UfHGly6fr0sjKQrn9a1g+aNh7o5tkJ5HpUZBBxUglABFNaRQahkDdzLW5odh/aBCk98c1hM4boDWtod5Jayhl6ZzTTsBoap4ektDlVPJr2/4V+H4rWy89k+ZFA59a87tdUtrsxicqDu7+le2+Dpbd9NIhYHOCcUN6FnT0UDpRUCCiiigAooooAKKKKQgooooAWiiigAooooAKKKKACiiigAooooAKKSloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKQnigApryKikscCqd3qUNqpywJrltR155mKoTSbSKUWza1HXEhUqhrlLvUZbpjyQPrVWSRpG3MSTUZYDk0tytEOqK4uo7dCztis7Utahs0IDAtXI3F7eavNtjyEJ60eQtWaeq+JGkYw2/JPpWbBot1qWZJskelbWleHUiAkmGWro4oAoCIuBVJWBu2xx9voi20gG2uhstO4Hy4Fan2FAd7Y/Gq11qcVsuyLGabYiwWgs0+bGfSse/wBYLAhThRWTqGq9Wd/wzXL3eqzXcvlQAnPfsKW4WNHUdYVSVU5Y9u5qhZ2V3q1yA6MELYxW5oPhR7grPcc55ya7GKxs9OT5Rg+lOyQ27FTSfDtvYRBpFBYVbur2G3G2JQD7VUu9ReU7I+ntUcFm0h3SZ/Gk3cREfOu35zitC2sFTkjmrMNuqDoBU+9EzyM0CuCoqDtSl8jHSq5kZnAAXGac3CHHXNAyaNhnqDmrCtuqkCVGc1JBLn86ALLrVeSEMPug1aHNMK80AQJDtTkYFVnKLwMA/Wrc7Nt2g0yC0LHc38qAIIrZpMcHHqTWhBZKgBIzU6osa1Xub5IVJJApXAnaRYh24rF1PW4rVCS1YOt+K44Ayq+T6V53qmuz30hAY4+tLctR7nQa14uaRmSJvyrkp7ua5ctIxx6VAqknJ5JqxFbM5GRRaxXoRorOcL+dX7WwLHpk+tXbTT84yK1P3NonzdfSi49tyO209Yxubt3NPmv47ddsQ59ao3OoPKSq8D0FV44zK/NBDkaNlqk0dyGJODXoel363UC884rzuCwYkcGuo0pja4XNMh6nXUVDE6vGGzRQIyp4F1GN5l+bDKQfbgGqsTvYXRjb7pP5irVnqn2ifcR8x681Y1K0W4jEiD73I9jTqqUJXezM6aU4cvVFW7g6TRfUVPDIt7bbG++B+dVrC43g28nBz37GmyBrO53rwuea2hLmVjKSa16orIz2F3jnYTxWvPGt9a7lwWxxUF5Al5b+YvXvjsar6bdtFIYZDgj1rKcbFwn1M6OEw6hEp6EsB9cV0SErpVuV/wCeJX8c81V1O03KJ4xyCCcdjUun3Ky27QMeQdyZ9e4qJTbabOhRXK7GZY+bPNjLnLEYHtT5ZI4pCjttIOCCKguG+x3RaJsqT0B5BpzXi3B/eSRtznDrg/nWhz9Bn2WOS6dgo+RQQVPHNatwIoAIQ5z0w3SqUU8S3ChTB8wIPPanXsUsypIu1s9MHHFaU0mxSvYgktSrbojg+hpRdeYghYfvM46U1LiSHiVTt9xUtntYRSHfgKxJGK1m+VCirsbFbbQ5HmGNiQc8CgwLyxSPeoGdp5xmnyylzlW49u9Q/wDLdRgliMYA6iudze5duxLdK0yyxfvgSqgZGRTNPRftIMv3RznHFOthKk6SMkgCgq2R6UkTbrmFEYqfLOcdOTTjVFKF9ya+u0MoQchuTg9h0qZJlmiIQbj6E8is2eJjcggglm2+mRWpZxw2w3k5cdq2XK4kO6asZEyTSXmxz977vsKvJZBIy4GCvO5nxT2dLq4kKFsseqdFqOeCaa44K7CABu6VzyjZmmhZtLoFzC5DfLn8KpapZQksXQEYzn2q1DZLFKJZJU3DjCelTXyLPAwjXp03e9KL5XcpO6scyfB8V5HI0agsqrjnnJxXPXkepaFctGhMkQJ+R+a9K0rcqujAgsikfVay9TtIri8kEqKw4xurOym3c39rKDSORt9ct72PyZsRsRgpIOD+NVbvQUc+baS+Wx6K3KmtbVfCaSyFrZFI3Y+U1kLp+r6Wfky0QP3H5FTyyj8JqqsJ6Pcht4nQPBdRbTjqOQay7yyktrvfbylVI3BexrobfWYWmEc6iKQcFHGVNSXGmRXh/cy+XwdvGQaTm+pXs0yq9uWsH+TDKFJ+prkLl5befGWAB9a9F+yzW9vIJUJEsSuCRxkf/rrmr6x852kwyAe3Bo5jGCtdGHBebSDnoc10Wk+IXt3XEjYz0rlZrJ0nZUcEZpVgni5IbpnIolTUkaKbR6VqrQ63p4f7xJwQDXDvYJbh1KxgoSCN3NTaTq3lOIpGO011q6Emop5iMoc4bhc7hUxlyaMbXO7o5Oxu0iXyWwVYgjPOKi1SyGWnjXaGOcg5Fbup+HYbFy+1Mg44POaz7gr9l8tW5HJqrpu6NFC8XzGVCjKm8qrbSPvHGavKI7uMIQkZLYK5yDzik0mznufkCBvm4yM5FbcHhi5tZZJHH7kMDtI/Gt+eMUcbpyb0OW1XRXshI4jxsk2nnPatjwImZ7xsfwY/nUvii7Uh4goLuQfl7Y4qx4JheOO6dkZdzAcj6f41jJ3iawVmV9cHm+N7ZPQj+tdXrsEQ0iAsASzFuT6H/wCtXLT/AL/x8v8Asn+n/wBeum8QLGbOyjfJKyMevUHNRLoaU9bmLJ5c0MpQZZNpDAY69am0mOD7d9nZcoHyv0rLgnMEvlqHY7skHnIFbWk6cZ9bikbKiRCwHpUtXRexz3jWxW2nYoOC+BXIBSSABya9B8exhUg9Sx/rXGWMIk1G3UjgyL/OtaekNTlm252QDSL9ofNFu5T1FVDG6khlIPoa9N1W8s9N0nBhbd5m0FT04rGWOx1GIMdrH+8v3h+FUp3KcGjiwGHanBiDXRXOjtGC8RE0fsMEVmSWqnoADVJxexD5kVkmIPWr1vfyREFWrPeBkNMDEUnEqMzsrHX+QJM/XNdRZayGAKvmvKkmIq/a6hJCwKtSTa2KajLc9jt9TSRcN+dW1iSYZjb8K80sNfBwHOD610ljrPQq+RT92W+5DhKO2x0zI6sEwenHpWc0E4kUleM9B71btNUjkI3/AKVoFIpoyY15HbPUVLTiQknuZsUGyXOAxPGM8ClZ1lyCRwcAg1PMuwlSgAHQCsvyCkxxwA+cVKj1G5u1iG6dhOwUlj0PPQVnGyxIXwBlsgntW9PArZOMn2FU3h5zgD9apOyshcrbuyncW3mRswHDcismbTNxZosZHYmt/wAwkmMLkEY47VFNZygEqAScH6UotxeoSiqkdDFtLbrGzjn+E0yXTFhO9c5Bzj1q+iPBcO7gFRyWcfyqdp45owm5TjkY6029boUIpRtLc5+GRy7qVGCemKkkG9iqryeOaSaRIJm+UuM5IJxT0dHO5ODnpScbK6CNTVxZVkgC/fyM9sZwaYsO4H69c1pTJujz74qsoLZ2rkA4bFO9x/DoZU6bHxxnGRUZi3ICVBDe9bJshKV+VmPQZFUZrcRgKFAHvSvYfK3qZM1mkgOPyPWsyayZDkDIrfkQqzbQOGz+FEkAI+Zc+9WpdwOVKkGm1uT2KvkgfjWZNasnbI9qrcPUrUZxSlSKSkMduppoooCwlLSYopgXreXatE12SMA1TDECk60CuBYscmiilAzSGkKOtWoI89ajij7mpw23pQUnYV4iOlRnircUgPBpZIAwyKTRpGfcgguJLd90bYNbdrq6ThY5sA9CG6GsJo2X6UzNQ4pmqkdjF4cjvyGiYo5/u8g1YbSrmyYxsZABjkVzWm65dadIpSRioPTNd5pfiW11QKk7LngFW71lK6GrbjYNHmaPeMD5DkSHAb0qg9zLYXHkyKuMDKhtwrtJLdEsnazMYLL0LZBrlrjTmuZXlltkTBAzGc5FTGbiaNKW46KVJxmM8/3T1p/es6OyuLKdk4kRc7STzVmS/j+UtypHJHVTXVTr9Gc9ShbVFkU8VUa5RYjIGDL6iobbVoppTHlc/WujnjY5+WVy8etSLE7LkKSKimDqm9RkeoOataJfxSv5MnDDpmplVSHyMhHFOFXL+08p/MT7h/SqQrRNNXRI8ClFNBpc0wH0UmaM0gFopM0tAC5paSigB1UdSj3xgEDGCKu02RBIhU0gOBtoV+3lcf8ALM5q5dL9ku0kX+4M4qRrcJqr/wC6RVnVrfKqcEHYCK5cQ7SRvSV0MtLuGfJbbuHc1LFKPtgYZ2HowHQ1zsCvFOXxgZ4NaUeoRrMFCspzjAPWsHE1jLubMw3sMh0PXeh4NVrydkg+RTknG7PWrUab7ZXQkFSRhh1zVGKymnaSNlbZu3ZDdKhI0exd8AzzzeICpJ4YV9RWoxaRA/3RXi/w48JhL0XJU8nOWFe2KNqgDoBXarWRxVXrYdRRRTMgooooAKKKKACiimPIkalncKPUmgB9FYGpeLtL05TunDsOymvPde+MMNuGSCREPovJp8rYWZ61cXMNvGzSyKgA/iOK+afilqFteai3k87nJyDxVPU/iTqOoyMIy2D/ABO39K47ULme6m82Z9zVSXKPRIqEc01l6U/cCMU0mggFWt7SoIygOCCRncDWAG5rTsPOXDR4I/uk9aBo1pUdDnJU9mHQ17L8Jrqd4YkkYkFDz9K8ZgczEgu0ZXsRkV3ngXxNBodxtld/kONo6EGi2hSPoOiuVsvG+nXkiqpxu6c5NdBa30F4m6JwfUdxUWaFZlqiiigAooooAKKKKQgooooAWiiigAooooAKKKKACiiigAoopKAClpKWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEo7UEgDJNZt9q0NspwwJoGlcuyzJCu5yBXP6n4gWMFIzWLqOty3DEITisclnbLEk1m59EaRglqyxdX01y5LMcfWq4pCQBk1l6hrMFopG4FqEu4N32NGa4SFcucVkz3sly2yPIB9Kq2zSam5cE49TWrDCluMIMt3Y1ZNrGVL4ee7bfIfwrUstLhs1ACjd9KvWiMxyQSD3NXjCo+amK5WjhJ+lSySxWyfMRn0qC6vVhBVMZrEvJpDG0jkgUAP1DV2clQ2BXK6nrCQZw2W/nWff39xcXJitlJOetaek+EJ70iW4ySTn5qdhoybWzvNamHBCHtXdaT4Ugs41klADAd60rKwtdIhICqDVe61J5jsi6dKL9AbuW576K0i8uPqB2rKZprt++DUkFk8rbpM1qw2yoAAKQirbWITkjJq+sYUU7hBUE74AwaBXLIUFTiqcsTF+oANSxzYQ5P6VE7s8g2g/lQBKsQRc4zSqCxJIGPapP+WYzgmlVhtxhfwoGRu4J2jA+tSxRhfmJFVgwR+mT70qmSU4GMUAXhIucDmpCMioYLcrgmrOABzQKxEEyeRUhkWNe1V7i7igUlmxXI614shtwyo4J+tS2Uotm/qWtRWsZJcCvOde8XlyyROa53V/Ek97IQrnBrOt4GnbcxJNKzZdkhz3E11KWkY4PaphblsbRVpbMDAAJNa1jY8Avx7VWwamZa2DMeBWzDYrEu5uB6mrDvBaL2J9BWZc3zzHA6elJ6hzWLU98kK7YuvrWa8rzHJJpBGzckE1dtLMzMOKEibleG3ZzgCtiz0/ZhmH51etrBYlyRT2Ys2xBQSxGkWMYA5p0LSO4IBxU9vYbjufj61rQWqJ0FMB0FwUiCs2CKKtCMAYxRQFzB0yKRZwZI2TnkMMEfhW+97HFbxAnhnZj7LXnej+JXuplEp+duCQTWvd6nuSDL/6yF0+mK0xN5WTMcNZSb6l9nJlMyeufqK1I3W+t8H74HPvXNafqCSIiFuSPl/wrQinNtMsifcb9DWavTdmVNc3vI0raU2kpik+4abqNoUYTxc9xjuKkuwtxAssZAJXB9jSaddiZFgl6jj6V0fGvMx21WxZ0+6W4h8t+TjBFEGmFJ0KkjdOpB9qo38TWNwXTGA4GRWzbXPmWysD0Ib6EVy1YvodFKai7MpX8EQVzIYj+8bgjFUBZ27j/Vxn/darusxNPBLt65LCq9gYTbyK8vlnaNvy8Zq47Ey+JkCWcUV1G4VwCCvByKvXU4W2jOwEKoXp6VWFpH5xJuVZQcjbT2dbjESnbl9uAM5rWm0nqZyi2iE3MMiEbipI6HmljliZeEJLJtynYik/szB5aT8FpYbeK0GXMnykk9sg1pUs1oKOhWdvLiZiCQB6YNa+l6cs6y5JKwgAAdSSPWs1h5m4RGQKR1IrU0+8aEMrOAZdu4jsRXFWvbQ6qFr6l+PToEk2GPHy8/N3rN1CyS0JdHOSP7vIFbluxlYyv3ckj2qCRftBY5xtiJwRnP8AniuWM3F7nXKEZLVHMwBXm8yVsgfdAHOPWo7wySMFUnbv4GeSKv6hbvbzvGhBCBT6HBquojijcmBi4GVO7PNd1Kd1c8+cOWViK3hnRg0anFalwkgtlcxj5uPm7GqK6gqSuuyXGAcZ/wDr09Lr7Syx+WeDkbnrrleUdjFKz1KC3F5cLgEjsdi4/U1NA01s4aRCw9S2atTKLeMqqu5JPGcYNV3nbydphSM8dQc1zPsaeaN/T2ikTemNy/Nj1HeqWqWpkO6MDBGVIPIFU9KllWOYgEEbmXjtV57gOWHlgholZc1lGDjI0clKGu6MPbPG2Xg3kfxDg0qstzJLHJbkgR5wze9XYZVlGcbevf0oWVfP+Vs8bWBHatTJdzE1rwjDI2U3IcDryK5ptM1XR2LQSFo16qemK9DuZnVF/e4YdMnqKoyXMcv+sjVt3ykj3rSVKM43NKdecXY5iy8SqjiC8UxsOMNyprofK0u/tMRMsUjd25U1V1PQre6hZlMbFTtw64P51zE2m6lpTl7YMEB5UnKmuOVNx1R1qcKmj0Y/UPD89lM8zxq67uq8rWTNNBK/lhQpFb9t4lYr9nvVaI9DxlTUVxo1leOJ4SEY/wASHINXCrHaSInRlumchc2/lyBlYH6V6L4Y1gQaewJTcLcsN3t/+quK1PS7y2K7l3Ic4ZRkVb0q7aGBVJCsAVwehBpVoqSugoScZWZb1S5uNSvxHGQVOHBJxk1KPDt+B5pX5W5AxnFZ1rctZ3yvJGjopwD1GK9R0zxPpx0kxvHH0/EVhKo4pJI6FFSldnP+GXtre6CXSbWHqK7bV7mxksNsbLuIyDXmWtanDJqDPaEjngVh3uuX7RIm7OAeN1ZuEpu9zV2SNLVks7WeWX92hY/eJ3MT7VoeFJYp7KZ0DYaYcseuCK88ubqaZ/3mQa7rwbuXRQeRlyen1rolFqOpzXT2KGnjzvHcp/uk/wBK7fU9OM4tS8oXKZwR0rh/Dp8zxddue2f512Xi+8ljnjW33Z8tSMDtipqX5kiqS0Mq6Wzt3Z0kTHmBSR1qbRrn7VrbIjYwmFOO2RWclsWUvOhDPhgWwBWrojA6xLJC/wAvGV29P84qdkWzD8fnElqn1/lXK6Sm7VrUf9NBXTePTm9tx6A/yFc/o4/4nFp/10rRP3Dngr1Gb/iziwj6czsD+tcuiSxKsiblK9GU113ieISWMPzKP37Z3fU1y00ZiUjy+3JU8GtaUbxIqTtKxatNbZW2XYORx5ijmtB0tL9S6lWP99OD+IrEtYPMVj8rf8CwaqzmW0uS8JZO+QacqdtUOFS+jNa406WIFlAkj9V/qKzJLdW6cGr1n4gG4Lcrg/8APRP6itdrS2v4vMGAT/y0jH8xUqTW5Tpp7HIPEyHpSBiK3rrS57cElRJH/eXmsuS2B5WqVpbGb5okSTEd60LXU5YCMMcfWspo2Q8g0K5FJxLjM7rT9fViAzYNdLaayQAQ/H1ryaOcg8GtO01aWAj5sijmaG4xluey2uoQ3CgOQCexqw9qGXMTD6V5xp2uI+PmANdNZa0VI+YEUaSM3Bx22NoKEYK3XPfvWXMHWTGw7d3pWrDew3BBZgKtPBE6FkbJHOMVLTixL3jIS1USB2UjPoOTU22N0ztwwPHuKklIR8DeMc7ieazlEiSY3naH7+9TrIL8pUvoRI8oywyOB2rnk3xXjHHAxx7V2M0GSW6kdapHTkVskD1NVGWlmTVg27oxbm0EzEMpDKevqtVo9NMDnIYjOcqa6aKEEkBckDGPaqV1azebgLgY65o50lYFRbdymLXz4tuSDnvUYshbiQ5Y57DtWlGn2SJixXAAJJNOnZbhT93djBxWabtobcsbq5hmUeaOScHjHao7q38xAQDxnrVpoo4gSy/kaY8u/Ij+b+If1pqLtcmVWKdkZv2dVU7hzjmo8b2x/Opr9mjlIGdrYoWEsqMCMng5q2tLkwmm7GcwG9VH8WfwNQywqwO5QD6itiSxwgYJ0frnvVGe0ZUkO1ePQ0J9gaaepiXFh3GBWdJA0Z5FdMiMzMMDGAarz2yNwFxn1qr9w9DnCCKStKexK8j8qovGVOCMVQXI6KKKQ7hRRRQAoGTVqOMBeagTAqwG4oAM4NNZqD1pp60ATRGr8JzgVQiOKuQPyKARdFqJF5HJqpcac68qMfSti2w4FXCqBMEA4pNFqTRxro0ZwwxQkjxsGRipHcGujv7aDySwxx2Nc05CuQKTRrGSZ0+keLbm0ZEmbcg7tzXXx6xaX8ClWSJz1aMcGvKM1btb6ezfdDIV9qylTT2NYzseq2dr9oVQ8YlwSu4Lgmkk8MW8pD7mUiMhg3HPasHw/wCMFBWO5kcN2yeK6NtTErCWGVcdw5rBqUWac3McTrvmWe2EBCSM5TisrT7KZ3MuSOpwK1PEM0cs+7ymLjqc4FUre/8ALQqnFbJvlMtLnQWGoLFiGZVwQMHPNSX22KXzoCoYHPyelVdIhjupgZPKJP8AeOKvanBHGAsQGenyngiou7l2VjodKuUvrHErrvC8qe9UJ0EUxVTleorO077RAM7tg7ErmrjuXfJYN7gYrroTvoclWNtR1GaaDS10mQ7NLSUZoAWlpmaUUAPzRSCloAWkY4Un0FFKelIDAWxkk1FnP9wmtK805pgmwjIQdfSpWLLJMVA4iNUm1ZorlEPOIh1rixN+bQ6qGxmyaQclAh3YJHcGqlvoTR3ytzgnJ9q1f7ZVZgrcbc9PenPqgJyATjtWCnLY05VuaMrJYacpbaSX3EH0rnrDXY31EIQuN/foRUOo6w92xhJ5PGD6VWttOiB3M5jyegGTVwVtZCk29j6Z8DvbSacJI2UyH+EdhXW8V4D4O8UHR/lLEqrhASeteuaN4qtdSkMLMFkzgc8GumL5tjkqQadzo6KQEYpsk0cS7pHVB6k4qjMfRWHfeK9KsVO+4Dkdl5rhte+LdtZhlgdE/HJo5Wwsz1GSWOJS0jqo9ScVh6h4v0rTwcziRh2WvnzWvipf37MIS7Z7uePyrjL3X9RvyfOuXwf4V4FVypbjske/618XrW2VlheND9cmvNta+K99elhE0jZ7scD8q82MhPUkmm5p3S2Fe2xrX/iLUdQJ864YKf4VOBWWXJOSc0yjFK7FdskVyGyKlaTcKiVacVOKBDTScmnAZqZUAWgdiEJnvVu0ujbnayhl9e4qrk5OKeqk9aTZSi3sX2v5N25HPIwaiku52O7zGB9jioQvFMdCKOcv2Ukrmpo+tzWFyMyuFznO7pXsXhXxcxkj3z/Oejf3q8CYkVtaBqE8V2sW9ymc4XqPpTvcjVaH2Hp96t7brIOD3FXK4HwhrVpb6dF5t27FlHDjkGu0ttQtrlQY5VOenNS0JotUUZooAKKKKQgoopaACiiigAooooAKKKKACiiigAoopKAFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEzUFxdR265dgPapJSVjYjriuA12+uftBTJA9TSbsioRuzX1PxBwVjP4CuZnupbhyXbj0qBSWGScmlOByalaot+7ogxUU9xHAm52xVDUtagskPzAt9a4i/1q51GUpETg0KOom7m3q/icLmOE/lWXp9hcatMJJScdcVHYaM8jB5ck11um25twFUVaQr9i9ZWP2eERjmra220gkd+BViCIkBjUrGP1GfehCI1QAD/ABokYlCBxTTlmwORUgiI/KmBjvbMZMnJqb7CbqPYwAB9a0JFCqCc/hVf7fHCPcetK4ble28N2ds/mybSevAxU1zqEVuuyIDj0qjc6lLcNtj6UlvYs53SZo1YERM94+TnFXraxVMEjmrcUCoBgU9pBGOAKAuIFWMc1IhDDg5+lVJpGkOBu/Lino5iUbjn6UAEyMGz0qA7mOC2cdgKllnZl+X+dNjV2HJPvQAwqx+Xc1WYI1BGck9OaSOPaM5PrTlfbyW7+lMCZo/l6VWLbThRTizzEgDIzVqCz5ywpAV4LVpDls9e9aKQKg4AqQBUFU7zUYrZCWYCk2BYd1jGSQKwtV8QwWaH5xmuZ1/xkkYZI3yfY157f6tdahISXO2le5aj3Oh1zxfJOzJEx/CuPu5ri5Ys7danhgBOTyatrbBiBtyaLFX6Iy7ezZyCQQPWt2zs2wAox71cs9MJwWH4VpNJBZpjgt6UXDQigs0iXfIce5qO51BYwUi496qXN68zcHiq6RNK1BLdxXkeVskmlSEsRgHNaNvpTvg7SRWjFp4gXJH50xFXT7AuBvBrZtrAQnOBU9mIyPSn3c4C7U5PtSEWo4FdMCkSxWN92Kbprvj5+9aZUGmBjXEkiyAKDiporwx4B61oNag84qBrEM2TQItRTK8YJODRUKMIhsLqAOgIopiseNaNJ5Teae1WtQ1Ikqqt/q0I/E1kNdLEmEbI7Gqbzlzyeppt8zV+goqyb6s2rbU3i2jcRjpXXafrCXVqN5+98p9jXnCyVfsbp0Z0U8EZH1qq1pK/VCp6Oz2Z6hpOroxMEjDGcGtCeMwSiaPp3x3rywalJb3YcMeeTXdaRrQvLLYxy6jI9xShNqzM5Kza6HVnZqFpnqwHPvVKyuGs5/Jk+6enuKzbHVltLzyy3ytyta9/ClxEJ4SOeQR2NaTjfVEJ/ZZreWsqcc8cfSsa/sXt3LwvsJ59jVjSL/OI3OGB6Gr90VYy8DblSM9vWufVS8jdWlHXc5bzL2QlAzH/AHUq1p0E9tKXc7cncCW5zV2/uIbWUry2TgAcVVmnkMOVSPB4x3rQzLF68zksjc9xmsmWSaUbdx3dsdav2okMKySg4ZsAjrV2G2hVGm67SQSeprojUTVrEcttSvAuy3ZpEJyAclulVIjI8hdVXZjjHen314GDJEMZGKSwkLqw2gkKFIHGKznSdrlxnroadtrAQmFlzjAODgipDexpI4CzksR0A6DH+FYkcC+cwYHLzYww9KVQxc7i42uVPzHoa5HQVzpjiJJWZa1C5W6lZ2QkSEA4PKgdKrp5ZkAYBx6dDUaow3ADBzg1MqbEPmIDlTgMcflWsI8qsjCcuaVxiSQq6IFA+duHPVeasrFF5gYBkOe3IqrBHBMV4BAjA+9nBpzW8tuQ0Tttz2NdkL2sZSsW9UeJInOQ/wC7DcNgg1SGoIAMidfxzV+WF5YgWcfOvBC85FVWmjYR5kA3DqyVzzWpaemhJb6im2RRKcspHzx//WrYitFdWcEEbFCH+YrBkhREjnjlj+foVP8AStGC9kksAU543DHYisJ826NabS0kVpN1k7YA2nIyRnrTEvYkYyfuASMcVJDObw+UxAZiQNwqMadGnzspIIzk8Cri7+pE4uL8hllcCacAuoVGIJK54NWp2iicoZvphKiLw2+SjID3UDqKR83C8Ju7jBxit6clsyG77Ab1WZgZMcZOUzmo5olnbaUd0K53EYFS29jtIlcgnHfpillvEXMS7vlH3QODSnDqthqX3mXN4ZtL+M4TkrXKX2h3+jTlrWRwoP3MEivQYkaNUl3Z3OT16AVHM/nwylg3K7hsPUVxuDv5HZTxFlqeew635biO8jML+uMqfwqWa0tbr95bkRknIZOR+VdFd6Da3nRQARwG5Oa5y58P3mlP5ltMUGfunoaHBpaGiqU5PzM7U7G8hhEnlqy85ZT1rno9TnjO0sQBXYRa3tUw3sAjYHG8DIpG0PT9RUyhY1Zj99Tx+VJNLSSG4SveLMrT4JL1FceWxznlsGnTw+UHjkKJx3U/zrUawm0ODckaSxgEhhyKqW2oreZBIXPBRsEUlbdGkuZxtfU5m9tyk5YDjivQ/Ckezw3E3+yzfof8a5+8sYZEdwWHHQDIrq9HRYPD0aj/AJ4k/oKKkk1oZezlFXZzPhMb/EN8/sf5muz1W6Rr+OKcFk8jaytxjpXI+BwDqt456Z5/M1ueN4SNUlMKNkAZx3WpnrIunormr/YlvfAxpgEIFUO3Aq1plrbafeSrgZEXJHQnmsDStem06ArJICVAwrDn86saTqJv9Vun8yQowGARwDg1mlIttHNeOjnVYh6If6ViaKM61aj/AG/6VseNznWwPRP61k6Eu7XLUf7Wf0rf7KOel8bZ1mu/Nb2q+bCA0rcOPc1zt3AyqHEg64JB+WtvX0mNtZtGsed7nkc9TWFLdsIzHOiZB3BkH8xW9HY5q1ue5TntwY2bMZYY5UEVnRXKhyj/ACkHHqK241E0L5uIyGYcBcGskacZbxgFJy/JFaT01FF3JFgt5WB8uNvdXxU0139iBNsfLbgABs10mj+B4btAZJE5z99sVrXPw+iWPMSRPkfwOM1g6sdjpjTluclZeIo5MLcr5bf316H6ir8tna3qeYhVSf44+QfqKmm8BTKCVhZR6mQVmHRb/TJSYLjGOq44rO63TNmu6K93p0sI+dd6dmXkVmS2vda6a3vJSdlxEEY8blIKn6ii602KUbgvlMejDlTVxn0ZlKl1RyBVkPIoDkVr3VhJD/rEyvZhyKz5LbutXozL3o7hFcMhBBIrXstZkiIBYke9YBUqeRSq5FJxLUz0Oy1xXAw+D6V0ljrTJjLV5DDcshGCa2bPWZI8BiSPehNrR7DcVLU9diuoLsfMeaWS1IyUwy+tcJYa0rYw+D6E10tlrXQMwp2UtjNxcd1c01T5gGznoapufmA7bsGtKOWG6Kk7cjn0qVrMBN4KEA547Vm1yi1lsUIrcB9zYAPAzSyQxSBSAM9CDVpyQSFJyP4sVQTzQ4VmyNxB4qVG+pUp8uhl3sYlEkZUAnpxVW0ttkjBgclf1rcktRnOe/I61AbYhtxB4q9LWRmk73ZkXtlvGQcZHashbeSCYYDLzwSOK6+VBKhVRk96y7m1cArg4Azz0oi7aMVWHN70SpNp/wBqRXbtxxVJ0NoqIQCQeWIrftCsaplVGTjBPWqWoxxhc7ujEAUlroW0kubqUNwZTkEAtVWeAn5iCAc+4NWQ6OcBSpB7nip9m9MDFD91hFqaMQRANwe3FQXMartJ7nrWi6HcFXG7BIzUP2Pf94Nwc4PrT82JdkZeA0eSB6EVUmtUcHH5Gta5tTFkYA55zVKVCC20DIAPFNPUdrLUxJrRkJwMiqpQjrXRvECp3LnvVKezDDIFUmmBj0VYkt2XOATUBUinYEwzT1fFR0UhljdmkqINipFcHrQBIpwatRNg1XGMUm/aaAN+0nA4z1qee4AGc9qwEuStJLds4xk0DLF7emTKg1mlSTml3FjVq2gMhxigZV5XrTw2a1J9PCwBiMZGKxidpI9KTRanbcnBIORWjaavPBhHYsn6islXzUuCBmpcb7mkZJnQvf29zgNKrcY2svP51qWmj215D+7dA2Dj61xWav2Oq3Fi4KNlRzg1nKLtoaKxtGCbTZ1DdM8EdquW10RLl3yoODjnFV11ePUV2y7cnrn1pI7Q3UqiGQK546d/zqLdw9Domvh5H7pmxjGR2NMhmeVmOY+nO445qvBZXVoQLgbhjntWghttgyELf7XINQpOD0KcVJWZEsnzYO38GqYGqcywxyFgydfuYqaCZJBheK7qNRyWpy1aai9Cekopa6DETNG6kpKAJAacDUYNOBoAfS03NKKQEkaBhJxyUPX6VyV3aSDU1YsdhTnAz3rrY2wHPop/lXL3Opbb9Y8lcL1XmuSvfmOii1YLjTAFWYK7DbnIFPgiimtwu7DYx061oxzRzW4DSEllxx61mvHJBPuTIUjOGOa5U+50WRlz2BjnDSqy5PHFBuNiEIygZzkHmuyFvFdWBXGDj+I1y95o5iOUJI56CrU09GKSfQZpUzSebISWBdeprcsvEA0vUjJGxMW4k4boaxLeP7LZLx1Rsf7wrlJLmR3Yljye1b0nZ3Ry1W1oe8v8ZFi05U81A4GMjljXD6x8U9QvXPlFzn+Jz/SvOdxNGTW6Zhexs3viXU70nzLpwD2U4rLeVnOWJJ9Sah5pw6UXYrthupM0UlAC0lFOAoASnAU+JQTzT3AzgUh2GqcdacWzwKaEp4XFJySLjTbEVTUgHFApalyNo0UtxAop4FIKXNQ2bqKWwtOAz1ptOXmkMZLbArkD8qqozwSh0JVlOQRWhnjFQTRgjNXGVjGpTT1RpweKNTAVRcsNvpXoPhTxxNGESWXcmefVTXkP3TxVi3vZLeQPGxDD9a1UrnM7o+wdA12PUYUBcMSPlYHrW/Xz18M/Ec890sJmjUE52P6+1fQMT7olJ6kUmiWiSijIoqRBS0UUAFFFFABRRRQAUUUUAFFFFABSUtJQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFGaACiiigAooooAKKKKACiiigBCMiuW8RaaGUyKPeuqqvdwC4gZSPpQOLszymaeO1UmRgMetcdr3jCOBWSJvyrT+IthqFm7tArba8auHleQ+aTuzzmmoFy7nW2txNrM2WY4NdTp+jLEASozXMeEIMuuOe4r1Oyst6AsMCmTuVLWxzgAVqrBFbJufrSXF3BZIQuC1czqOsvks7Kq+55qbgbk18z/LHnHoKitmk80by3J71zdl4hSa58mPJPc5xXWW0JaMSkEZGetCGX4wowcDOKJCWOFBP0qol2BLsOB261dwCMjB9OeaYrDHXKEHIPYVzd3G5ucHO0nFdNls9HA9+aoaja708xR9aTWgLcis7VFUdzWgFC4rLsbghvLY8itcAOAamL6MckMkG4Aj+dQtED94Ee4NSSjtg4pscWTxkfjVCETYhHIJ+tS4DjsB9aayKGwdpPtSyKMfKF6UACxLnG3PvT2j2jOAPcVCjCM5LD6Cly87jAP4UwGMx6LuPvT4rZ5OcHHuauRWQGC5JqeUrBCSMcCkAkNusa9KdJMkS5JAFYU3iEROytg4rldc8XBQwjOT65qWxqLbOk1fxJDaI3zgGvNtZ8UzXkpjiY4Poax77Uri+kJZztPvVJRtPHJpJXNEkh0iPId8jE59aVIi3AFWreAzH5s1qwaaAeaY/UoWtm78AYHrWzb2UcC75DgepokuIbNcDBb0rGvNVeV8buvai5LZrXWpqoKQ8Cs795O2TUcMTOm85rWsIA/BFBBSFu3QCtfS7D5gWFXY7NF5IqxGdrYjFAF1IQoAApbi3MiACp4EZlywJps8jpwBQBTgtCowCxP1qU2oibMlPtVkL73PGaddzLLJtU9qYE9qfMxtxir4xGuT0qKwgEcakgDinXLKIyCeaBXCO8jd9o61ZZPk3Cse1tWa4354rVnuFhgwSM0AUXv5Y3ZFQnHU80U+LXbeJSjR8g9cdaKB2PnnJIoBpdpPagRsTVXIdhwNTxT+WcimpbMe1SrbAdSKT1HewjTNK4NbmjXj28qHPsayUSNTzk1Zju44cY2j9aNtERJOR0WpXDRqJUOCpyPpW1oHiHzIxDI3ysP1rhLvVDcJtBJ4x0pdNuHicckYNb03pZmNWOl1uj0ee++y3AlB4zhq6G1vo72DzFYE4ww9RXAy3AubINnkjBqnouvS2V15bNkA4rKS1Gm7KR6Lc2/2lQP4lwyt60kd2Io/KkSI8lvnGOaLG+iuEVkP3hlQf5VLOlrdAbSGJ/hPUGs/aK/Kzf2ba5omfdaiGjEcZGf8AZ6CpdPeV0xIcDpk96bDpYeYYDBAehGM1auZ0tk8mMZcjpWidtUZNdyK6tYsMw+XHoOKksbRYY2ZxnB3H+lV7YSm3kLFl+YcHkdalhvlM1xC3OWGR+FaqpeyZPLZXRn3E7LMzgkgHODVx1KwyMPOwFGR1ol00yqxiOcg8VaWJha4bq0YP5VrKMXawlN21KEVyZxnc7MpwRtx+dRnck5jkjC8YDE5pkTGGfPYnDVeu4fNh3gHcP1FCppMObQZNahFEkGAQO1Ps7gyvscYPr2osTIwKuDgdCauBbe0ZpCDvx93FNtJai1GaldmKNAkXCqSNvcmsy2uI2jSPdhlHCv8A41ZYwzzDaGBRMAg9f84qEQrcvsyjHdtwww2a5W7s10Wg+SJpAoV0UjpuFa2k2flJHHJ0Ofywc1gZmtLhUJJUtt2t2Naxkkitt6qEJ+UsDng1nN6aDiuaRWeUW93Ey/xAsPzqfUM3NsXjJ6ZHP6Vku5mfccgAbVz2xVq2ujH8jcg9q09k4xUkVKak3EfGsLZErkDsAuabcXUcQUqn8Oz0/Glmt1kG+KRlJ9DVaC0aWb5iSB1JPWktUZ2sahc3Noqr8oA4PqKzTA/2pEPBzkn2q/PItpHtQgnGNpqBcq8zlWH3WGTkbe+K2jUsrMnlu7k1yTFCu4Lls4I7ZpQweCDCKflKkq2Mj/OaLtDctuhOVA+73FRWsICqskYyspXkdj/+um4Ra0Gpa6jF2GOMhWViMPnoPSh5jPA0UiR9AMDkg+tVwojujG5by2+UjJwD2qzcRYCzxqAw+VsCkqVmHMlsZF/oMEhPABbv2Nc7c6Bc6fme2kaL26qa76Ei5iwRx0OexqUwRRRxRtKhyxyOvGKKkINamkK04s8zTXXUmG8Uxkcb1HB/Cpjp1nexiSMKG6+ZH/UV0t74bt7uDdsVixOCnXrXJ3mgX2luZbVpFANcTptao7FWjPczrzTdVtwRCWmj7FD/AErsrGCe28NxmdCp8k9fwrDsNamhlX7bbMCP407/AFra1vX1m0hI4icBDkBcDmod20rFyaUdzH8Ax+bc3f8AtMBXQ6xqCJrEwJXd5e3KnNYvw6Vt0zKOTKB/Kr+pNBFq8s0jKzI2BgYzkVMviHT+EoSuL+6XKuqugHmKueR3re0O3hja7KHJ3jBxjPFc3fahGpY2pf5Y8Lt4571p+DnuLiK4mlDYXGCT9KGnYrQ5/wAZNu15h6L/AFNUPD2P7ets+pq14rbdr83sB/WoPDibtft/bJq3pEwo6yZreI702y2XyqRluo96x5bmO5UFXZW9AKv+K0ZhYoMnIY1mJpknl71R84/hNXCVkjGrTblcZEkytjzyik5/1WakCi2uBJuJYn7w4zVSae4g4HmqQOjGqaXcklzH5hyu4ZzW8pJomMHdHc2t5eyIghcr16jOanku9UiG54d/HUAiqloY4LVXjkUMGyBntXY6ZJBqNptlUFvdsV50nZ7HopaHN2/iWaE4khRP99Cf6VfXxNBOMSFT/wBcwP5Zq7caFaSOwAQfV81nXHhWFcspbP8A0zjNF4hZiSXOl3H3mjBP9+LFVJILZDm2mtyD/AzEA1g6xDNpzsI5rsY/vrgVmwT3tyw+b5fVgDVqL3uS5paWOom+zKuHCwMe2/ch/Ssu406GX5kIjY9xypqhe/ahHtWIkE8fIKow3N9ZnIVwvcMMitFclpPcsXNg8X+sTjsw5FZ8luRytb1pq0E42SARseoblTU0+nQyjdEfLY9uqn8atT7mMqXWJynKnmnrIV71o3Vi8TYkTHv2NUJLdl6citNHsZ3cdyzDdshyCRWzZa08ZAY5HvXMcr1qRJSKlxNFO56TYa4pAw+PbNdNY62pABI54rxuG6ZCCCRWzZ6y8eAxJFHM1uNwT20PY4pYblc5GTUb2+05xkZzmuJ07XcgYeumstaWTAZsg0cqezM2nHdXLpUE9ev86h+WQKD3BzVtBFcMCpH51J9l2bWIQeh7VPw7k25tjPSBYwSxGM8A9TSTRIUODkYz83ap7rPlvsIzjGSKpRvJISpPGwYqdWrlXSdjE1BCkauCCQpxiq9v+/tmDrkd/wAa2ri08wbMcHkVElukAPHDDFUpLlM5QfMYT6ewY7cnHbvVi3h2/Kd/Pt0rWmt2aPKJnjGaqJbyBt7ZHtmpnNNWKp0nGVzMfTykquWJwx6elVp2wSvJ56V0UjptaNtoIYc56Vl3cCLKW2g9+DU6uyNfdjdmZLD5sZOD75NUTbbM7uv9K1XdGXahxkbcGs7Ui6AuBjgZFWo2ZlKpdaFJlG4jGB2qqWBcDPB/nWjHGJIwc4Ipz2QCZwoG7Gaa0Y9JK6Mh0Rjzj0yKqT2YPI/MCteW32qxDBsNnAqFYm3NknrkVSYjn5IGTqOKixiuglhDcYAzWfPZY5AxTTTC9jOop7xMh5FMoHckWQilL5qKigCTdSjmmA1JGMmgCxBFuYcV0Gn2yjBIrNtI+hrfsYicCgaF1WAJZqV9zXGRRtK+ApJJ6V6U+n/bYFjIz1BGajsvCsdowZlAGfqaaBq5xsOhXDrvZdo9KqXkRtm2Ec9K9auLaGGzO1FA2/jXmWsqJbo7fWkGxkLJzUgYGmyWzqM1CGKmk0WqjW5ZDFTkHB9q0tN1u5sJg29mX0JrKLDB5zg0o5qWu5rGdz1vSvEtprECxXAG/G3JqlqGkypMGgf5CcDHQ15xb3EltIHjYgiuy0LxYFkWK4xtJBIbpmsJU2tUaqXc1ZNPuHgCtChyScs3OKy1nFmw3cqeMdCprvEvtLvbdGj2eYOT61yuvW0Fy5C+XuZiQTwQaUZ2ZVlJCQXqSDrkeverIYEZByK5XyLy0cMsb47lea07e6lGPMRkJ744NdkKy6nNUotao2aTFQR3CvgE4NTg5re6exg1YAKcKSimIdSg02loAljQyB1HUqa5a90aX7czqW3bcg+ldXbPsZ29ENZ819vucdcKM4rjxDalodNBK2pzE9xe2kp+TK+q1ZtLxpkQsrLgFWyMkVavpo1uGEsPykcOOlMju7aGQOjNEw+8QM5rnvpsbWJ7W829GUhWwWwR+lTTXEL43GZxjHyriln2yhn3iQwxhyduOTWfcyvHKsT3cgYjOAPlGaSSbHdpGhcW9vNpiNDwRuABPPNebyxmOZ1PUMRXo9jZs1k/QsPnBz1FcLq0Jj1CTAwG5rei1sctdPcpAUHFNwaUCug57gOtPPIxTKcpwaBAUxSAU8nNJilcpRbEAp23NAFOFS2aRp9wVcVIBTRSik2aqCQ6lpKKk0HUUUUFBTqbRSY0Op6io81Ip4oAfUMxwKmpFgM/Qd6EKWiM89aTFTTwNCxBFQ1aZzNGxoGvzaJdrIigqD6civS2+LtxJbIoklYrjgcV45T0kaM8dK0Uu5m00fSng/4jR6gBDcSYYnjca9Mt50uIw6EEGvjXTtSktZRJG54OcDqK9v8Ah949+1FLWVi5HHvTavqiLXPYqKajqyKwPBGaXI9agQtFFFABRRRQAUUUUAFFFFACUtJS0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUZoAKKKYzqv3mA+poAfTWYKMkgD1NAYMMggj2rJ8QNMunOYQSw9KARqpKj/dcH6Gn5rxSz8b3em6oYrolfmxkdMV6lomuQatbhlkG/wDnTsVKLRs0UgpaRIUUUUAFFGaoajqttp0ReaQAjtQBcd1RSzMAB3NYF34u060uDEZMkdSDXnPi/wCJWN8Ns/0ANeR3+t3t7dGZp3Bzxg00i1FdT66s72G9hEsL7lNWQa+f/AXxBktZVt7lyeQOTwa90sNQg1C3WWFwQe2elDViWi7RQOlFIQUUUUAFFFFABRRRQBlazotvq1q8cqAkjg4r598a/Dy4s7xnto+Ca+j7i6jgUljzXE+IdZtZsoyq7dqalbQqL7nl/g/w89igluD0OcmuovNYWMGKDgDvWRqd9KgOCET0BrmL3XVTKplnPah6gza1DVUhBeR8muamuLrWJfLhQhSfvY5qWy0W91qTfJuweQO1d7o+g2+mRbnXnjrQkh7GX4c8JrbATTLz15611FxdxW0e3jgdKq3eohRsi/SqUVrNdPufOKG7iIJJpJbjegOPat+ykLxBWIz7imwWMcaYwKaVa3l46VEnYqNmaBwg6MPpzTiVaPB5B4waiBDoCMj6GpI4vlz2+tUS0YF5A1tMHX6j3rSsblZYwaluoVuIimDkdDWHFK1ld7WyFJwalrW6GtUdDKmRkEioEV8kbs/hVmBxIgOac5CLwOaoTKbx4JLNxnpTFZzwqqOfWpfLkmf+ID2rQt7RUGSOaYFOG0kk5IAFX4rdIVqRnWNewrI1DWYrdT83NTcCbU9R+ygYOKwb3xGq2xLsBx0z1rm9a8T+axSP5jXIXl/NLkFsCpu2y1HQvaxrzTysIw351gtMXbc5OfrUUsyr7mpbKE3DjIzTSSK1FVGk4AIFXrayLY4rUt9OUKC35CnzTw2y4XBYfpQ2GiCGCO2Xex2+9VrvVOCqHA9az7u/ZydzVjz3ZJwDRuS3ct3V8TnmqlqXuLxQOaq/M55rpPC9ks1wCQPvUxHQQWarapuHJrXsNPXZuBpktm5dVUYFaNvGbWLLUhED2sjvgZxV60s1iILDJqKPUFLkdeatRytLJwMCgC+salfamPArds02aYQqMnFWLSRJV96BGLeeaDtQcU+yssEPJW5JbI5ztFMe2yBjtTC45VXysKe1UHtWaTLdKts/2ePn1rInvpJnKp0ouFjQjlijO0H8ap6gu85JAWqpnS2BkkcZ+tcxrXif5mWI5NK/YaR0Y1DTLcCOaYB+44orym41C5lnZ9zc0UWYWKeYlPykEeuMUvmoP7tQiH1P507y0H8X6VpZGdwe5x90E0wSSN04+lPCRnv+lPykY4xRoK7IvLdupNPEGOpAqNrjJ4BNJ5jnoAPrRcepZVEHVqnjljj5A/Os8LK3c/hTxbu3XP4mlzMXLfc1TrOyExq3HoKz0uHafzBnJOaaLYDqw/Cp40jX1P6UrhbSyOs0fVHS2I3Hch3LS3euTW903zZj3CRRnt3rnUv1gUhSBnjAqtPe+YQeBgY5qJRUmaUm4Rses6Nr8c1ttLlnQ5XJzlTTZ3CXwlY5RxjNeY6Zqklu64Ygjp9K7Ky1iO6iCtyPT0oXuOzFUXN7yOuN3bfY1CIysWBY9uKxov8ASNRVhkbpN3HoKqARueJyB6EkVoWUaJcFvMU7E7H1q7mLbuaU9w1vDEDh9xJ54701ro28YJAQIO3IINV7qRXu4omIwE7+tWrqKM2tudyfPGVbB+lVzBa5JbmC4+bYrZOMqaWe4WH5AyoV7EbuKxtNClmUqCSA4+orQkISd88R43cD1q3ORKSHpqcRRow0ecc7RzTLgtMG3SOVfopqpf272s7SAYZMZ46g1YikW4jQ+hz9Kltvcq1tioha0PBLR+h6iraSJJ864PO4HuDT5PLNsRKSXVuCB1BqjbRsLplj6BunY1IPQkMM010pZGA378k5ya3LiMR6awb73yj8eadZxfKhLhck8beeKpapcSFljXbjPALjJPqazlJX5UaU6cviMtsebJjpvbH50nB4zWla6R5kO9tuB1Zj1qZtOgCkeYn4x4H8q61XSVtyHS1vcx2eREIHI9619NVTAeBuVcAD+dU5rBo+UIx/snIq1YWc4O4CUr04AGKzqyg1eJcIyvZkSIRNIJmwd/XGeKdqjQFZhE+RsVQQMc1qz2ayKDh0fvuFZFzpsuc+aSByA3FZJqQpRcSHT5JGALKHHmbc5wcVaM3zyqpkG4qy7+mRUdlE0FtGWxna8nFOghMqXKjJKp5qknr7fpV8zRK31HMltcuWZGUnk7eae0iAbDDuyCCpOD9azTEI9RKK7okqdA2MGp5Y0fysruYpjcx5yOKv2rFyosLPCoQRxrHg87m61SniuCqnKRsM5AHBquYFtLjc0ZCMQGBOdp7GtAuXDYwxC/dPeocm2P0IIJVZBHt2spztzS3T7owjb3Tngrnr70l9HCjF4mIZQGHB/EVf09DKiFwBlSTu6HFIWuyKkPh62ubVW2YYRMSD7Vy+vaVItt5UQx8gOMetd3JqEcUDRx43Ou0kdADWY4kuZGcx/IeAW4GBTp2crvY1kmo2Oc+HlnNabjImD5pPNUtfgkk1K4DuiiM7huGN1dtZRw2b7kDSc5Kxj+tc7rlpLLvaGM7wSRvOcj0rnqQ9+8djqp1Eo+8YcdpDaPIjN0AZSOevJroPDNwhtLkpuIkcDLDGMYrmJlubaaSSWPbhQSTyMmur8LBpNKkbyyF37s4wOtZzTS1NVJPY4TxIc69cfhT/AAuudeQ+iGoteO7XLn/eH8hVnwqudZY+kZq5aIxobs6bVNMMzWBdXA8rPTI7VB9hQDYAD9K6O6tkE1ttTBEQy2/+lRLBvmOOfmHQdBXK231OtJJXOQ1PRMQGQISMVxFzF5UpGMEV77JY201uUlGTjHp/SvLvFGix290WiAA3Y61vTnbRmNSF9Ujm7S+ukIRSWXpjFeheEruZWVJA6jPBxWP4b0FJrhHkxtAFemLpdna28bQg9CDj1FTVknoVTv1JJBt+fzJORnBtwf1rNu5iSF2X7E8Dbha01aN4gCz9MfLNtNUZbaGaYbTfnHPyyBhWPQ1vqZt74eF9bM7vPjaDtl5rl5NPjsJSqoSc4zXoUlwkEPkjeCTg7+uK5LVlUsWwxJY9KrmewuVPUyPJhkbdJBM59Q1Peys3j4WaM+rHIqo06Rvy88fvjitGK5VosmbIP8eM/mKq7QnY5jUtEkyXhCPg9UbrVW3e8sm2fMB/cfoa618A5kijZT0ki4qK5tobmIpjd3HqK1hPpIynB/ZMy3v4blRG3yMescg4pJ9Kjk5iPlt/dbofoaqy2jQzFJURx2Zjg1Zjaa3XEbJIg/gJ4xW7grXizmdSztMyrqxeJisqFT6461QkgZOR0rq47+3nHlSbQSPuSdPwNQ3GlRPzA2xj/A3Q/Q0lPpIbp31izlgStSpMRVy4sWjba6FTVN7dk+lW0nsSpNPUtwXjRnKsRW9Ya4yEBz+NclkqakSYioasaqZ6jY6yTgh811FnrCOm1sGvFbXUZISNrV0Vjr3QMcGnzdGJwT1ieolUmBZSBn3qAw+W4O0VzljrfAw/Fb9vqcc6ANg0ct9jJq26CYBYdw7dKqFPNLAdula0dskqnB79CaT7KInJwFyOtZvTQau3cpQoiqquevB9qrXSCOMnaDzkkd6sXLOoQrjAfJBHNReWZ4vnIyCRUqNtWVKpfRGFNDuldlOFYhhj1p91aiSM8dsir8luQSBimqQE2Fee1VLXYiNlv1OYlsXRiVDMO5FT/ZPtMKh2zjg4Fa0tlJyVB5Ycg9qrR4g3FgFCt1PU1XOml3J9m1LyMia1e1jZUHBPJPWm4YqSR3rYvvLZZGxlhzxWT9oU4VlUf7Q70Wurg58jt0K0lsx+Zhk4/hquIvmI6ZGa2EQSJ7VT27pBGpGSOKSdy5JLUyrpNrALzlePrUB+6vXkcjFbZtRkFgFyetULmAIR6A9qfN0BRvqZkkCSL05PY1nzWbLyo/CtgxEsPQNih48DBwQKq9hHOMhU4IptbU1oJBwM1ny2rIeASKe4XKtOVtppCpFJQO5r6deKrgN0rttM8mVAUYZx0NeZg45FaVhrE9m4yxKj86BnqKK0bggdK1IyHTBrj9L8SQ3IVZCDxjOea6u0kjlQNGwIxSC5U1wmOwAH8RxXmZlDTM7c5Y16rqdsbqxKgDK8ivL7+ye0uXDL8pOQaYMkMayR9BWReQbDkCtFZ1RMZrOvJw5IFJbg7MV0XyCcj7gYfWtLRNKOpLgDJ3bawDI2Me2K7jwHzMv/AF1qnsKLszE1DRrixkOVJUVndPavar/TILyM7lAY9wK898QaAtkS44+lZnUncyLLWrizI5LKPfmtyDV49SYISCf7rcGuOZuSBSwyPFIHU4IqZQTKU7M9q0+0tJLaQSQyEMgwRVLUNNihCmIyKhbBBOcVyuieJypRJHcEDBG7g13CXUepWzeURu2ZCnnNc0k4s1TTOQvLeXTzKxU4U55PBzTrTU1bAyf91jXQ6to8d5brHvKhotx781lJ4MaK0Lnc2BwynJzW1OtYiVJSLEUySjKkfQ1JmuWu/tWkTAMXZR13DBFadhrEdwoDnn1rthUUlc4pwcXY2KKYrBhlTke1OrQkkjUuJFHUqcVzl9HLp16ZS+0EAYYV01p/rj/umq3iK385YiwwjL1YZ5rixDtJI6aC0EthbaqhViASMdODWL4g8NNp8DTRqSMZ4PatrRnhS3UHyg3oK6i6hi1HTwjkE7cZxXI5uMvI6bJo8u03VNwIZC2U8t17kVdkurUou9mfggh0wV/Gn32l2tpMDuYZPVeKgngjljjcXO5SMbW4xWl09UZu6NHRrki3hyG24YHjtXKa4FM28A8Eg11cIRtPxCW3qMnae3tXOaxGGVhls443CuilG2pyVpXlZnOdTS4pB1p9W2JQQ3bRinUlK5SigooooKQtLTaWkWhwNOptFIpD6UU0UooGh1LSUtBaCiiikBG5IpY5cHBqfyg61C8JWmtdCJJrVExkG2relSqs/wAx/irILEcUJMUOQatQ0OepWubutLGQWTB47VggZqWS8eUYNS2UPnOB70JWRPtLsq4xRWvqGnfZ4t3tmsilc0toAJU5BxXQeFtcGj6j5zSMnI5Fc9RVRlYzcex7Tc/F6VpIhHI7KBg7eBXe+FfH0WqJGkkn7zurd6+Xo5ipw3StrSdbm0+dHVztU8EdRV3TIZ9jQzJNGHQ5BqWvLvAHjU6oqQybmYcFhXp4PFS1YTQ6ijNFIQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFISAOtABVa7vILOIyTSBQKx9d8VWejwvmRTIB69K8R8VfEK51GZ44JDjpmmkNLud/4m+JkNjJsgfgHovU15/qvxRvbgDymZyGyM8YFcFPcSTuXkYsx7k1A3NO6RV0j3DwR8TRcYgu269QT0r1eGaDUbbcjB0Ydq+OYpHt5BJExVxyCK9P8ABHxEuLRhDO3QfxHg0NdSbpvQ3/iH4Qdd11bjAHINcd4X8V3Wi3ghmc7VPrXbeJvHCX1q0S4YEfdB4ryK/INy0gwCT2pKSehcrqN2fTWg+KbTVIVzKoc9/WujBBAINfKmg+IrrTbhSsh2g8g17n4U8ZQahBHHLKN2O9DRmnc7ikeRY0LOwAHc1m3+uWdhbmSSZTxwAa8k8X/E0tvhtn9sCkNK53XiTx1Z6VE6xyDcO+a8S8SeO7vVJWWORgh7561y+paxc6hMXmkJz2zWaWJNMrRFiSdpWJZiSfWmZqLNKDQIejtGwZSQw5yK9J8D/ECbT7iOC4kwM4ye9eaUoYqQQcEelFwufYukaxbatarLDICSORWnXy94O8dXWkXMcc0rbBwDmvoPw/4ltNat0ZJF8zHIz1oaE0b9FJmlpCCiioZp0hXLNQBKSAMk1lahq8Vqh+YD3rI1nxNFbqwDc+ma831vxO0zH95x6Uh27m/rviln3LG/HrmuHu9bCszu/wCJrEutXkuJPLiBdz0ArZ0PwZe6nOs12PlxnaelUo9R6v0KkMd/4hl8uCIrGf4scmrA+H89vdJJNvbnkGvU7K107QLRQqr5mKw9U15rhyqfpTbS0RSsiO2W30u0WNQNwFVJrqa7fC5xTIraW5bc+cVqwWqxjgVNiWVLewx8z8mtOONUHYUjOI/7o+tRCQuQR3PagCXed2ART5YvNj9xVcOdx+7VmN8kD2oBFaByj7CcVe3HGDnmqtzDg7xTo5MoB3HvUrTRlPXUcQFYNz19ay9YtPMj85F5x82K1c8cnH4UjlGQoRwRVk3MjRL8v+4kPzDpmugCCQc1x9xZTW1/vh6Zzmult7sJArysBxSfcbNJEVBxUFzfR26ks1YupeIobdDtcfnXD6l4lmumZYicetS2Ci2dLrHilI8qH/AVxeo6tNek/MAvYVmTyFiXkfJ9SelZs1+SdkQyfWluaWSLU1wkIyx59O5rOklmuj8i4WprawkuZAXyxPauqsNFijjBkUH2NFgatucnaaTPcOPlwO5NdLaafFYR7mIB/U1dubm2tBhAC36CufvdSLkktQDn0RdvNS4Kp8q1hXV9jPNVZ7xnOBVMqznJzTSJ1FluGkNNVCeTUiRVYSH2obBRb3IkjzW3oN19iulz0JrPCBRTgrEgjilcvl0PXrFo7uJZFxmrF1aeamB+lcZ4W1gxOIpD7c12VxK5AePoR2pmbjZkMOnJEMtxV1BHEpK/nWbPcyuoAHPrV6yQvGd/emIrTZnl2jNaNrB5CAk0zy40k4bJpLiV/uqD9aAL/mqBnNUbnUQnCk5qAxylMkn61QuZ4bUFpG5HvSegF9pRLGS5wPc1gajrFtYqwUjNYer+KMAxwmuUlmuL2Qkk80h2Zo6nrs925VWO30rPitpLhsnJq3badgbn4rVgSJE27M/jipc0ti1FlGPTCUBEafU45oqxJpYuHMn22CLP/LN5CCv6UVHtPMrkOM3ytycZ9aMSHufwqZ3ROn8sVF5+Txmt7nKKsb/3jTjFn7zfpTROR608SF+9MehIkC4+6fxOKfsC9Ao/GqjT7eDkn3pouCTwBS1DQtlj2BP0qF5mX+HFNDtjJUfnSCZWODkUWHdDTcOe+PpTDKx6k/nU8kIxkelNESkd6pNAQ7jQGqYwL6n8aaYCOmDTuhWbEEhHQ1ettQaEjJIx3FZ7KR1FIDQ7SWoJtM6qDxFKn/LcH/eWtew18yyEPJHh8DKrjFcAGxU0Ny8TZU1Dp9ilNdUepTSm5YSq2Hp0c902EYLwMZwc1xNj4geIBS2R6NW9a+IIsAlJB/uuKOa26IdO+sWdXZ2jpJHIThRwc8dau3ULrGGQbto2tj07VzUfiqOID5EH+1K+f0rVtPFdtccSCN89WiOD+VTz+Q1Sdt9R19dvcq6kksUVPu88U+wt5BIzYOw9fatO2udNucHz0U/9NErQMtjDF8khlPoi4H51TqRSJ9lNszZtMMp481TjnaODUlvp32WMyBHJ9WGOaqNqTtdFUMgwcsUfp6CtzzlNovms4wQ21zk8VnKpJaWNFRju2QwXBgBLYOwYJbpk1nXN/BIW5g3Hp8h/xpb6UTRuitgsxINVLOCPYomZFJXHzDvVqC3Zm6kr2Wxah1LfGQu0cYOO1UQ9y9w6mVxhgOOetQSr9mlMiAYBwwFWtxkiYxkkkZUg4OataLQl6vUbcXE1uGV+SO4GCDV+LUN8LkBjuQOCWP41SkDXS7nBDSJtOT/EKrWDnyip/gP6GiwNvZM04NTlYZxIB7nIrYsryKdSrso/3hkVh27xrPLCwH+qPX1FU7uV7Vm2EjdHkj3qJR7FKbj5nXS2EXl4VVQhdowOCDWBc2MsG4KowRjk/wBavRag0cMrMzYATGTkd6ltr+K443Lz69Klc0VqW3Cb03Of+x3RkEmzBXBGOelXJ4GCMdm4Y3AD0PWti8kitYywt04GT+8wDWPBeRzPlhCqIP4G+bBqlUW4vYyRQvLgzoY/lJ8sJgLzkVZsbaV0AYNx90/0qS9vbaCTCN7/AL5cUwaxkBYyW44EQ4z9aq91dEONnqSy2MauTNJMq/3WWrCX9vHG0aJncuxAePxrNdbq4xteVPdmyTUlvoUj/MRI/ux4oaclYOaMXdaiTzxCfKtGuAABGu48Uiu8jZWIn/alOf0rSj023g++65/uoMmpTPbWw+RFU+r8n8qI0+gOrKRRSxurheSxX0HyirsOmwRp++wQf4QQSahkvpZuFV3H+0do/Ko/Iu58YVRxj5ARWypO3YzclfVjL+y06VceWkfOfnOSfwpFlitNKuIbUAs2CAVwKt22jNGAzgjHVm5NWSbSIFSjS4/vcCiVGDXvMqNea0ieLa1aXK6ncSvEQrN1HPam6DeraamdxA8xdmT2r1DUobK7BSNVRj2iTdXDap4QuGlMkIYHrlhisKlNdDpw9ZRvzbs7xCt1BDcIQ22MRlV659axde1ZdPtmKg5HJA61y9vf6rpG1LiGRo1PEkecitG51G012BkkCykgZIO1xXJ7JqV2drlePunN3Xiy+znay9+uP6VSj1ea8cGTsccnNaN94bklRmtpAy9w33hWXa6a9vKVdhz2IIrflg1oc15p6nY6ZeeXCyKcOrL+Vay63KFZWySSflzXELctDKAH/wBkg1sQ3CS7SXVWGCpI71zyhY6YyTNKPV5/tC/KCvPVak03VHguoQ3YEYzjvVedIlG5Sy85BHIIPWr+maOt4wJVXx02tg1DtY0Rt6nLDdWRkwWJGeG5FedX2pSpORlyo6ZOSK72XQl8rYpdMr/fz/WuA1vSLi1uGO0uM9RVU7PcibaV0QR6gJGA37T71PJMbdGlXAdeWXswrN+yPtyyFT05qC5mlVRHITwpGfUVsoX2MZVLbmzb6xH8xX7v8SZqyT5iiS3YEDlTn9DXGKsi/MDyRWvo1zcrIYmjcxv0OOhq5QsTGpc1Xu7e+i2SLh16+oNU1DwtgnfGeARUWrWE8Mgu4CVYjJArOh1BnlAk+RjxuFOGi0JqRUtzQvrPz13Ry844U8YqjDqN5YOI5PnT+63P5VcuJrhI/MQBwByCOtVo72CVcOUU4+7IMiq0kjOPNDTodEkMWq6aJowcN8o5+61YcOS7RuOVJB4rf8OlP7PkCbcLcqfk6c1Npmj/AGq7ujjpvasnLlNeXnOYms1bkcGs+S3eM9OK6a9txbT7CMcZ4qjJECM8EVUaie5MqTjsYe4qalSYirE1srdODVN4mjPNaWT2IU7PU1bXUpISMMfzrobDXsEbmwa4gORU8c5FTaxqpJ7nq9lrvA+bj1rorXU454wrYPvXi1rqUkJGGrotP17BALYNPm6MThf4T0iW2D5ZDkemagRCpKtwKyLPW9yjLZ/GtqC8iukw2MkdaTj2M/h33K14ArDjkr+tQwW6uVlyRnqe1bX2RXUbSDx361WkTyo9oOMHAAHSobewcmvM9io+wAgckEDgdqxtRi3iQbj1BwRWnP5pmlO7gkN+FMubYnJ69jSty2Y7890c5bF3lZTyTnFI+nBjuA255ANbAs1h+butSmISR/KB361bmlqjKNJyVmYkUAjO3aACOxo/s9UIkweDVxbQo25iAR0p8kqqJI9+QpHSs7tvQ2UYxSTMKcESEAU2WASJzj8qv3e2Ni24YPqKo+ekjBclT0PpVRjfUmdVRdihIqx5A6g1WkUhSMcg1ozWjGUMf75z9KlFuCFJXB6Zpu1gjd7mH5bLvypyOlMaMHIZfTmtu4tk2kgFvl71nSwBmOAQMYNNO+omraGVPYhgSB+IrOlt2QniumaL5QOnFVnt1cc8GqUgehzmCKK1LiwxyMfhVCSFkPIqhpjUkaNtyMQR6V0Gk+J57NwHY49RXO4IpKQHsOl+I7a9QB2UEjrVLxLYwy2zTRgeuRXmVvdzWzhonI9q34fE0klsYpSQcd+hoGYNw7JKyZ6VWJz1qxdsJJiw71WpksWu68CcSx/9dCa4QVqabqkmnsCnOD2ND2CL1uex3+qQ2UBZ2G7HAzXmHiDxA19KyIcj1qhqGtXN6NpZgPrUVhpU982Qp2+vrU2Nr32KIyTUorVu9M+xrhhg1ky/K1K9y7NK7JFJUgg4NbOl6/PYuMsceoNYKvTwamUb7lRkeyeH/EsF8FWSYA7QM4rqpLRpcyROWU4IKdB+FfPFvcy20m+Jyp9q7PRPHs9oAlwzAdM54rnnSa2NVM6zxloy3sMjI53nBzivODp13bSkYOR7dRXoZ8VxaiERZIGDnndxmoA1vcxj5IVMhZNqtz+FOMpRVgcFLU4+11aW1k8uTI+tb9tqMNyowQDWVqGjkSSyBWIRQFycnrWW9vdQXLGEgoMHH4V006pzVKPY7iJ9m5h2WnaqRJZnY2Mx9G55rmbHVZBHLFLkPtxirv8Aau3KSKQoTIJXIziortSaaLo3Ssy34chjljyQNwGDW7LO2nwMHzjGFxXFWGpPa3gKEEHn5ehrqdTV9R0pXTcOOCOxrCUOY0UrOxyd0rapeH5pgoPH41DqltdWdtsTdIq5Az2qGe4mtjKsXmBt4AH0rRsb5riMLcxSYIOWxVaqwPUoeGr6QCWOXOUPf0NTauqmaRQTjOcVo2unoMyIPvsBkDsKo3Y865lYEjgjBFbU5XZyYiNrHITLtmYDpmkqzqMXlzj0xVUVbGhaKKKRQUUUUDClFJRQykPopBS0i0OpRTaUGgaHUtNzTqCkLSHpS06MBmANAWGJMVarAdZEOaJrM7Ny1SctHkGlvsJ3itdiKfh+Ki5NPw0jcClKlDyK3R5893YRRzVy2n8hwaplqQsaehOpuXuqi4g257Y5qhbReaPxqkMmtGxkEZ59ahxSRpGbvqQzwGNjxUGK1b7YwLA1nxxGTpUmt0RU5XK9KWSMocGmU0xOJ0/g/wASDQ9SWRmZV3A5U8fjXtUnxctxaIweJW4yc9a+bqminKnDHirTT3M2rH1P4e+IFvqx2mRS2enqK7iCdJ4w6HINfHuk6vLYTI6OQByCO1e6eBfG63wSCZ/n6ZHOabSexNrnqdFNVgyhh0NOqBBSUtFABRRRQAUUUUAFFFFABRRRQAUUhOBWBrnimy0eJi0ilwOmelAGvdXcNnEZJnCqK818WfEqG0R4bdwD7Hk1w3iz4jXGoSPHbucdM54FeeTXMk8heRizHuadrFWSNfWvEd5q0zGSRghPTNYjPTWeoi2aHqDZIWyaaXxTMmlVCTzRsQ7scil2rStGWHGe1UkIUUNMR0qW76FJKJryahtU81v+FPDY8RXa5ctnnb0FcKXLda3fDnii60G4Uq7eWD27U4xsDnfRne+KvAw0i33oFz1yBXG22oXWmOHhcjaa39d+Ib6taLGxLsBjI44rlklFxEc9acrkNroWtV8U399AU37R32nrXKSSu7EsST71buVaJyOoqjIcnNA4yurCFqTNMzS5oKH5ozTM0uaAHg0uaYDSigBwNdL4b8W3miXKfvGMWR36VzGadmmmCZ9WeEPGEGvWsYZ18wjg56112QBya+UfBmo6hY38ZgB8rdnPpXu0fihzpyebIC2KTQ2jqL3VIbVD84z9a4fWfEjylljc49ayNR1uS7lKhsj0rOvRILYsmWbHTtUglY53XtakV2y5z7muftrbUNal2wK209WrbsPDd1rOp5uEbZu6A16lp2l6b4ftQSqGQDpVqyV2VZdTnPCngeGxVZ70AnGcnrXT3+sW1lD5NuqgAdqxtV15pWKQjA9qyorea6fdJnFJyuFx893PfScE4NWLawC/M3Jq3BaJEowBU/3egpEtipGEHSo7iUxjjHFIs534NOmdduf6UAVQ7Sk5GR6mp1GxQMAGiAq319KkEWXJwox69aYDY0OCeOtKpcSZ4x9abLnIUDNSoyRAZC59qALW3emDVNl8p6lW4ZjgDAqWWPenTmpauCdiu0hIHOPoKFA29abvEQO6szUNSWNDl9o9BRfQdi9c3kEKEsFYgd64jXvFG0lIiAfSqGs+Ic5jRvwBrk5HMknmPk/jS3KSNN7q4u/mlc49BUUt5Dbpt6t0AFZ5upZf3cAwO7VYtrH5gz/Mx7mlYpXYxYpr18vlU9BWjb6WDgKMD6VoWliSOQAKtS3UFmuBhn/SgbaQW9tDZRbnwo9+pqle6wWzHFwvtVK6vJbljycVBDAzv0yaZm5NsRjJM2WzzUi6V9pGB1rTh0t2UHFaFnp7I2T0piuYEPhgFvmz+JqS68LNFFvRT/OuwGntIuUKEirdrbsFMcq8UDuzyh7ZoXIYcilHoK7HxFoZRjKi8deK5QoI2wRUM3jqNVMdaeuM4ApxXcKcqqoqS0l1Hws0MokU8ivQPD2sR3MSxSEV502SfarNrdSWbh0NNMJwUkevC1iY7gAamMaqmFwDXnMfi+VI8HJx702PxnIsvOQPrV3OVwadju1gcTFmPGaszSwRLuc4xXHf8Johj+8c49K57VfFctxlY2PNGorM6rWvFEcClI2HHYVwWoazcXshAY4NUsz3kmWJOa07fT0iAaTj2pOy3LUUUYLGSdst+taKQw2w6An1PSnvNz5cK/gBVm20oyndOSfYVEpdyrdhIZ4pBjbH9c81ajtbaQgchj6GpTo8BTKAjispY2gvwFY4APGazsnsyrtaMuzaRAzhjOikjox5/lRVa5LNKMnooorLnkiuVHDPE3fmmxLhvm9avmOomi5rrU+5zuHYYyKQTUCNtfHarOw4qF4iDkVSkmQ4tCTpkZFRQ/6yrQXKAE/pUXkYOQf0qiScsFA4GMVTY5kGP0qywymDzUKxHdnihATzNthP0FVA7HgVYnVihAFQRKfNGR70ATIsmM7jTjKyfe5p5k8sAZx3qGaXzmOOc+lAE6FZhioZoSh4qSMCJdx61G1xub2pDTIsEUVZEiMOv5igwqwyKq4FcNUiykdz+dMeNkPtTM00xFtZueeasx3G05HB9qzQ1PEmK0UrEtM6ew125tyAtw4HvzXQ2/iGWcAS3DkenSvPElx3qzHdMvQ1a5G7tCbnsmeuaFJFPJExxt2tM30HArR1G+dNkaH53H/164Hw9qxQSLnn7IFH51qX+qul3FKVJUYP4Yrl3k2XU0ika5S6T97gMp5IBzU8brPFtDfp0qrb69azwJEViGAcODgipXjJQTwkb8fg1VczsuhOyL5TGVgezH1FMsbKRmZMsU6jb1q9Y2hWGRpAqYHzE880kd+ElKKwLYxnHUVcYN69AbS06jpIUiEa/vF/eZJzntSW+mxrG0pjHzblPz89frVW7STBdWbaf0NPtZzLFhmPPyt9fWtfZLcnmZZ2LEcr99hsAc1l38M8++RUypwowfenvLJFPGrnISQdfyrTM0UdntMY+VyMqPXBpSpa6BzaaleTCQybuBuUEH6GljXFpHIMcuV6flUkhjuAARu39Pc1VurjyYlUR7cdApyKylFrRlLXVFuCeO8tmjlIyMrk1mT2s0MztHgDhWyOR7il05HaTf8Awg/ma1nv4VbyXIYkYbNYyg+hrCpZWkZVraC5l2zku+f4zW4mnW1smXLH/ZRaxrhzBcCWInAPI9q1HuDfWIKyMARhgK0j0uZSXvX6Er3tvbj5FjjP+18zVXk1CWb7iO/u3A/KoLC3Qy7H4bpnvWs4t7RQTGWPTLHArp5IrVmfO27RRnJBdXBwWYD0QY/WrMelpD80zKnuxyajm1cn5Yj/AMBiH9aqh7uY/KoTPf7xqXUS+FC5W92ahltIFyqFz/eY4FU5NaJOyNv+Awr/AFpI9Imm+aXc3u54q0tla24w77iP4YxUObe7KUV0VzOae5mXAiYZI535NEemXE7s8vmNk8Bugq9Jfw23CKkf15NVjrTbww89sdwcD8qzc0axpTaLS2NvbDEhOf7qCnm8t4FwEjX/AH/mNYs9/NdkKZX27ix4waYqgdBUOZtDD9WS6lFZ6kjKY3LHuPlFcJqnhSSKUzW25G6gg125IUZJxUZulGRtLj6VF2zpjaGx57Dquo6Y+y6jLqP4wORW9YapZamCsro5I7qA4rWu7W0vFIaJlJ+mK5bUPDQV/MtmKt2xxSt2LUoyF1nSTIfMtJfNBJJQrhhWFDcT27bGYxsOzrVxdQv7BhHdxmVB37j8a0obqy1JNoZXP9x+GFS2+ovZ21QsN6qQczISBlh2q3ZaxJCoKKRnnKGsi90e4IP2abI7xMMGq1s7wIkdxGyNnZzxUOCeqKU31OoXxA0jg+a+c4Kmr9zIktkZXVVc8HceorgZCyuzknaDu+taSa4slsbcsSNwILHOKSp22D2mg/UNQt2STydmNvKHrmsFn+1R8Aj2Ip1w489tyryc5x2rSsIYHXG1STWt+Qya5zKtYkWZFcdDiux06Oy+wliz70G4ehrFutBmjLToqhRyOc1Xt9SNrMEZl9OaJNz2JjH2erOk1O0Y2siIpKAcfqcV5xPGwuGXYQwPQV6la3i3dm+QHLNz83rXPHw5NNqbkkAHj5eaIPl0YN+01SG6JZpc2JRx868DPcVy+qWbWl6yFSAelev6P4bMGx2yUC8547VxfjezhglGwjIOCD1BpU53lY0nBcqK/hE/6DcD0mQ/yrrtEYxTTbByzOhx9DXIeEj/AKJej0ZD+tdhohK3c6gZPnMB+INKfUmOjMC8snvLndnc2SvJxgCo9O077RtQnJL46dquz3ZW9w64USkYAqXTW3XVuqHcrs20g9CKhs2t3OQ1q3fT7nbzjJFUVmSQYPFdP40gJSGUjBMjA/rXK2Fv9o1CCE9HbFdEH7tzklZysD24blcVXZGQ816jJ4asYtIjd1RmYEELwQa5W90AgFrZvMX+4eopqomDptao5lXIqeOcjvSzWjIxBUgjsaqlWQ81TXYSnbRm5aapJCRzkV0mm64CR82D9a4FZCKsxXBUgg1NrbGnMnoz1+y1ocbmraW6hu1x8uTXjtnrEkWAWJHvXS6drgJGHx7Zp3T3RDp/ys7qS0IBKjIqMqMDd16H6Vn2muA4DNWvHLDc4OQDUyi91qTe2jVjFbLOF5PzFasQwiMHcDwc4xWo9oo+fIIDZyKpXikxuqMQSMcd6h62Q17ivcrXcahX2YA44NY0kRd2cjBOM8Vrxqz7g/IKg4xUMkAU+1VsS3zFCe3EqHjIPTisiTTRuLLtwDjGcGuiwfuAAnHrUM9gxCkEYzkjFJPleoSiprTco21orqF2sSRj5qqXcIikG3CjJAHerqu9tt37vmfC89BUF6TcRMYxn5vxoSu79A5rRt1K3kl1UNu96rTW+09sdKfBPIjBW3NVu5UNCrBScmizi/IUZKcfNGUUwuMdeOKqfZ5AVY9wQa1oIMhmYFSrYp7xKVICnOeD7U27aFRjzaswSMY5HIPGKge1SZcgYz61pSQEyY9DQsAVQvHWmnpcGtbHOT2JUnaKovEynkV1MkYBwRnJ4qpNZq4OPyNUpCOdPFFXp7JkJx+VU2QqaY7iZpCM0UUBa4mOakXPQCiOJ5XCopJNdp4c8JSXDrJMv59BTBLWxmaH4cn1CZWdDt9K9LsdDgsLbAUF8flWhZafDYxhI1Ge5qy65WpbubRVjzbxNBiQmuJnGCa9J8UQcMcV55dptY1K3NZaxKIOKer1GetFWzmTaZZVhTs5qBc1Mimk0WqncsW909tIrKeAc4NdNZ6gHjGdseG3q68jPvXI96minlgzsYjPX3rNxubRn2PS9NvYZyUlKlivO3vU1xo0bviKPG8cnNeb2+pzRyBmJIHccGur0zxWfliMgcYxtcc/nWMoSTujVTT3NKLR40ld5fkfhdpGQalbSY1ZjGC8ZyCA3Skm1MeWJoZvJB5555pItZWWILKInJBB28E5qbyYNIypIEjmEeFDqCPwq7HqM9nalCu6NlydtPTR5LhDcIjgAHlaoSX5t8Q3CsF+6rAVSZDS6kEr217LvKPk919feo5Y5ITH5Ycjvg4FLczxKDJDOuduRtXBpYtZjmtgJNrHGDxg09Q0Ni3KtZbcEuBgKTg1mTlYiciQHtu5FUbLU2kupIiSQoyM+1XNShzJuViNyB8fWt6KV7M5a71ujB1iMHcw5/iFYymtu6idovmBI6ZrEI2uR6GtJR5dCYSvqPopKWoNQooooAKKKKGUhwpaQUtItBS0lFAxwpaSloGLmnocMKjpd2KB3NSGcFdrVFeWyOm4VVSWpJJyI8ZqUrPQuUk4O5BYoizEP0qTU0j6pxVMT4Ymo5ZS/cmt7Hl3syPHNOC1Ys7cTSYNS31r9n5FO9geuxU4FKJSvSosk0oFMlomM7uME1o6YBu+b1rLAxU8U/lnIpNXQ1KzNHU4lXJUVlYqee7MoxRBF5gqLWNudPQgxSVYmgMZqClcq1x6StH9PSvS/hprdpa3iCWfYMbWTFeYU+KaSCQSRsVYdwauMiGrH2LJ4t0q3t0YTbhgDir1hrlnqC5ikHXGDXyKuv31zEInuXx6ZrqfC3i+70u4UPKWTPIJzmqsmRY+pARilrmvDPiKHV7RCjgnH5V0gPFQ0SLRRmigAooooAKKKDQAZqGe4it4zJK4VR61max4gtNJhZpJFLjtmvFvF/xImu5Hitnz24PAppDSudx4u+ItvYRPFbyc9Mjqa8K8QeJ73Vp2LyMEPbNULq7mu5TJM5Yn1NU5MGi9ithqzZ60peowtGMHFBDY/JNAUk09EzVuGFRgtSbsCTZCIdq5IpC22p7idAu1aoli1JajukPL0gyaQClzVJENtjulIT0pOv40tMkljOVAq7ZTFGwfxrOVtp+tWY3GQ3qMUNXROzL17CHTcByKxZUxW1G3mx46kVQniw2MdaS7A3Z3RlnikBqaVMGoSMUGilfUdmlFMBpd2KCyQECgtUec1dsNNuNQlCRIcdzQgsVkVpGCqCSewrqtD8J3F9Iryrhc9K6bQPBcVrEJ7oAcZy1a0usWti3kxBQOhobSGkWLLSrXSIRtQFx+QpzXEt02NwI6cVTg1y3mnVN6sx7AVv29v5gEuOPYUtwuVI9NLcnH41o21jHjEgDD3NMuLnyhtUAfWs6bUpVG0ZBoDU1prq105SIVVW/2axZ7y4vpCATimw2s10++TOK14LNIlwBQGxTtdPA+Z+TWkkSxjgflS7kT1qpJOWlwBx7GgNy47ELxVXzsH5lP4VOJBswT271UkdC2FB59DQA0MS+7DDv0pcvK2MPj60oGeiNz3NTIqqV+9nNAySKHyhwO1JkhvujHck1KJFWMZGSfeq7sXbCrgfWgBJXy2FJyeeBT4rV5Dkjj3qzbWn8TVdACjGKLgQxW6xr0GaWRwiktwKSe7it4yzsABXB+JPGCRho4Dk+uaTYJXNLX9ftrNThsEehrzTV/FT3BKRsee5qpdS3OpzEuzEE9BWVcWTI+Bnn1pblpWLCXQfLOcsakEbznLcL6UlnYkkcEmuhtdN+XLChsdu5nW0aR4UDn6VuW8MUUfmSHAqjcrHbt8pBb2qq1xLLwScVKuwbtsaV1qnBjh4FZp3SNliTmnRws54BNa1npTPhnGBVJEMgtbAzLwta9ppscPLYqdVjtlwOv0pQJpj8o4piNS2t0dcDFJdWjovyCpdOgkjwWrX8sOvQGgDI06KROZO3rWmiqxyABWdfzPE2xR+VT2O8pudccdzmgCW7hiu4jERyeleea3pDWk7Nt4r0hDGr5Ld+lUtcso7u0Lgcik0mVGbTPJ5HK8CkXd1NWbyIQzsvvUa/MvTiodjdXtcYW7CpV5XmmhQrVIVyOKVzRIgZQTimmAdanC4pQjMcDNNXFKyKvk5GBmprfTjI2SOPU1djtlj5k/wC+aVpmY+XEuT6DpRzdjFtCgRWq/Lgn1pY4Zrs/3U9TU8FgAd85y3p2FWJJto8uIZPtWcpJeo4wcgiSCzXtn1qQyvNESPlX271JHYYi86Xk+9U55+Cq4x04rFyudMaaSL0LNFCMseEJ6+tYqSZuyzZ6YrYj2SQsrEg7QOlVX0oNykgz+VVTsr3M60XpYn/s43H7xJIwpA4ZwCOMUVQk0i6ZycFvfmiq9n5ke08jmVdH+6cilKis64H2aUqjbgD1FPhu2OBVuD3RnzItlKYY6lSQEcginYBqbtbha5UdWxx+lQkuvc1fKVG0frWiqMl00VuSuffFQeYRxjpV5k+XFU5ITuJrSM0zNwaATZ6ipgq43VBHES3Ip8z7V2irIBlEnHPFEcW3JqAOw7mrETMxwRmnYCCd2LY6CkjiLcnpVyaJCo9ajIKRnaPyqQFSANwNufrUUgaBuDTY3kB4GakMZc5fAoAliYTDB61Xni8tuOhqxGUiIIx+NK+yf+dLYaZRpc1NJblRkcioCMVadxNDgakQsxwKiUFmAFaccKwxbmocrAkXtPfyGDHk7duAccV0S3lvcRhC+GAxh64gyySnCnAqRJ54P4sj0NRbqNu+jOrktCH3R8H6Vf0vU7hJDbyltgx+dctZ660fDHj+6xrTgv18tZMbSSXPNUtdDPltqj0T+2YQGjY7S5GVY9aY8CSjfA3I5x3ryuXVpZZyxYnmtjTPE00DKsjFh0B7iuqK5VoQ/eO9huGOYmA3jru6YpIoSLhs/MrNtIQ4/GqEWpQFPNfa3AVmPrVoXiXTb0KjdwdtZSqdEO1tx13EzSOu51KkAhhnmrdy2yKVCQ2cHgc1XWU+YwL4Yrg89x0pzy/aJpJSQcxDkeuTSVV3Hy3QyxBe4PJwg6e5rRuktwFgQBWAwfc1EzxwzNGhLHI7cggVRneR5tzAgk8VrFqbuyXdbbF1kNvG3kjPHCjtUVlbSuR5xWMbDu3jvU8MUkiZlVmOMbQcVVut0H+rt+nHztkisZRs9Cl5iHEjrFn5ipIIotZGtLgAj91IOnpUEENw8qzEdO54GK07iNGt87xuVs4C8YrNsaVxZlMbrNGffjuKfdot/bb8kjqRnoaq2VxuzbSnkfdNTLm1mOf9WxwRW0XzKzM3pqiTTIomfy3GWHbpV+5vorEABYowehPJrNnRoZFmjPuPcVJLDFeW/mqBn+RrOd1oXT5XLUim1hpT8qs/uxwPyqpJczy8NIVX+6nFRlSjFWHzDikrFtnoRhFbCBQDnHPqaCQoyTgUMdqk1XVJLiXao5/lQlcJzUFckNyo6Kx+lH2pR1Vh+tatp4caZQ0m4/jgVNJ4cgUY3AH2enyruY+2k9kYab7uQKmefbpW3aeHDIgaTJz6mrWl6PHDMDnd+Oau6vqItY/Lj+gGaeztHcz1m7y0RmyeHLcD7yg+0hrLutCkjyYyJF9D/jT31G4Y9VH/AAGkTUZh1Gf92tvY1EieaC2ZhXemJICskfPoa5q+8NAMXtyUYdq9KV4LpSJFqrPpJIJiO5fSsZR6M3hWa80eZJfajpreXcxmaIf3uo/GtS31Gy1FBGcE5z5cnBH0NdDdaYjZWSP8CK5y/wDDKsd8OUas3C2p0RqRmUdT0MshNm5Df3HP8qw7XTb6C7/eQuBg5HrWv9o1LTT5cyedEOzf41s6drthcqI5Ixuxja5wR9DQpW3FOn1RyVwpVo85VgpzkVc091Ewya2NX0qK5cvaS4bPCP6fWufuIbi1bbIpRh6VMkpBG6O9sZFmtyixqDtwC2Ca5vXdKDSllAJOOgpmm6ybZFRsE4x70mp6sZIy3ABPAB5rOKalY0k04mho0ht4DAAM/L17kV6NosCGB5HVQrNu6V5LoUkst4ZCvyjnBr0ifUfIsjEDjaoXB9T/APqoq72FTNHU9eS0imhQITtO1AOTXj/iu5ubvUWkZNquASM10s7Ty3gcxyAKpB9DmqzaC96I18kkgcnNVT5Y6szqOT0RkeEG/d36/wCyp/Wu00ghNQuOv+tHT3FY+m6A2mzXg27SbcsADnpmtnQ8HWGDHgvGf5U5NSu0PlatcyNQt2kvJipYBZR1GOtO0qFba4gAOcXJAP51oa7KovpEjOW8zOOnHFZcUEkE8LOWb9/uBU44zWaehq0O8crmzZsY23B/X/8AXXGaZ8uqWrf9NBXc+MsTaVK4B4kU8/QVwtt8l1A3pIv862T90wjH94d34ltpZND8yOR12TdQemcf41z9heXcaqt4hddxUOOCK6nVSzaJcrjK5Q4/75rA1iTZbSeQuXAUqPqOamL6GktxvmWWqIfuye44YVm3eiyKpaH96g/MVk2NleSzboy0RB+8a6i2maAAXc24jjeq4NXdx2M+VS3OUltSpOBgjsetViGQ8iu6mtbS/Uk7WP8Az0TqPqKxb3RJolLoBLH/AHl6irU09GZODjqtTCWQirMN0yEEHFRyWpXkVAdynmqaBTsdHZ61JGQGORXT6frgZRtf8M15wkmKtQXbRkFSRU6rYvSS1PYbLWh0JHPrWkTFdDIKg15RY64yEBzmun0/WQcFX/DNO6e5DptbHV/Z2jbOOOnFRXEJKrtXOD2plprCsNrH8DWrE8Ey5DYPpUuNtVqS9dHoY8Ns23e6dDj3qw+NjIVAxjGetXpogqt8vfg1mTRbpHK5wcHrUWbYXUdDL1C1VgdqYIOetVLa0dVZTnOMiuheAE9OtRvGoX5TzTT0sKUE5Jow5LFFO7AHcEVJEisjKckCtf7KJo+QeR2qs9v5Q4UEYOAalty0LjCMNTOubbarBMHjNZohZSxatmNy6gFByhyKzbxHRjtGB1BFNR1sxSn7t0UbnaqOwwcYNVVUyOxUZwcj6VfC+Yrow+Yjg+tRwWrQyIzAhOcmrskrMxUm3cpPZlhk54bNV5oDHnIb71biOpjKg4yM4qpcxb9x5x6GpT6GstNUY7RiRTuXocVQnsVbJAreEH3uepqpKuyQj0YA1aZPmzmprVkPTIp9hZG6uPL46Z5OK25YkOQ2M5xkVWEJt5fNQc1SaZWx2OheEIoWSWRcAgE55rtoYI4IwkagAVwWjeKGi2xzcADGDXa2eowXaAo/PpSdy00y5jmnY4pvYUoPFItHN+JYN0BI9K8vv0w7CvXtZTfbtx2ryvVotszfWkzaOsTn2GDSVJIMGoqs5ZKzNjR9LOozqgJxnHHeur1PwwLDTw20A7Tx71heDrsw35X3DCvTtdQXGmlh9fzpNtMpRTizxZDsuk3fdDDP512154djfRDcJgkcggc9K43UIvKu5F6YavSPDs39o+G/Lzk7P1FTU01Ck9bM81PHHekUlWBBwRW+vh+WeeVI493zEAhu9ZF1YzWUjJMoGDjrRoa6l2y1WSNgHYH/AHhkVuRXUUxBGxCe44FceOlSJdSQH5G/A1EoX2KUu56xpOrJbxhH/ukZDAg1HqWlW+qIHgLEkfMvcGvP7PVWlKq5Cn27132iXKRIW3qzBgeepFYSi4O5qmpHJz2T2DtDIWXI+UuODUEenr55KgkZGRniuu1qOPUEMkRJXkYI5WubMU0DIJGz8xGfaqUm0S4pD4tH8q7LqytmNiMelWL1mxGRn5o1A/Cm2N87yqpfICMBxS3y5EJwcbMnmtqMve1OfEL3bozbhTIHGMH2Nc1dRmOY+9dOfkwFUk55z6ViapHg7sYw3etpJ31OanJbFAGnUxTTqg6Li0UUUDClFJRQxodS0lFI0QtFFLQMWikoYcUD6AWFNzUZyDShqdjJyd9SQNimSyZHWms+KhL5pqOpFSpZWQYpRTc0uTWpylyCXyzkUXVyZhg1UyaAMnmkNOxJDE0rECpJoXgOGFTWTiKTcal1K4SYcYzRqhqzepmlqOTSAZPFSYwORTE1YQCr9nMsTDPrVAtSbjQ0hK5rX0ySL8vX2qCG381MgVSBJIya1bCRVUA1DjZGsamtmZ8kZjbBpmKuX2N+R61XEbEZFSaaMjGQcirkF3j7xw3Y1Uxg80hqlIlxPWPhtrhiv1SV5Ap6MjcZ9699fXNPghRpLpDkDoc18ZWN/cafMJbeQqR6d66A+MNTuoghuCuPTrV6MzZ9bWl/b3i7oZAwq3mvm3wh49urGdUuJCyEjOTXvOh63Bq1sskcgYkVLVhWNqjNJkY61latrtppMLPNIu4D7uaQjSlmSFC8jBVHc1wPiv4hWumxPHBIM9M55rhvGPxNe4Z4bV89gAeBXlt5qE97KZJpCxNO1ikktzc1/wAW3urzNmRhGffrXNs2TkmmF6ieSi9wuSM+KheTNMLE0ioSaQbj17VOsXGfSmqmBUgfAx+FJvoK1tSQFYxUT3JPAqJyxOKj5FCXcHIkyT1pc4qMN/hThVkMdmlz/jTRSigkdRSD+VL0/CgA/wAipYSO/SoqTOOmRQFr6GnbyANipLiMMuR36VmRuVbOa0UmV49p/Cp6icdLGdKmQaqMMGtGVSG9jVSVO9URCVnZlcikCszAAZPoKuWdjNeyhIkJ969B8PeCUiVZ7oD6mj1N0czoPhK51GRWkQhPSvSLPTLDQYAWCFwOnpSXmq2mk2/lQAAgY9zXB6x4ilnZhu49Aalu+xodFrXivIKRNgdK4W81OWeUlWOSaroLm/m2xqzE/pXb+HfBRfbPdDjrlqaXULD/AAXoU91KJ52bHXnjFekXF1DZweWp5AxxWULiLT4PItvxNQRwS3b7nzihu4tyzBILmUnHXuasHT49+4jP1qW3tVhXgVb4IpBchjjRBimzPjowAx6U9lJPXFV5WYH5WA/nQBCyMxB3KQPWpIoVXJOAfYUDzG6n8RT0hYnl2/GmAx4C7ZBIH1o+yjO4kfXFSNIy4Ge/pUqyBU3EYoGRBAp+7nA6jpTHkIPyj9aGkaVsBW/CrEFluOXX86AIYoXmYZ6CtCG1SMDgZqVEWMYAFTmIiIyOQqj1oAhd1jXJOBXM654lgsEOJOcdqreLPE8FrE8UBQtjAIPNeWXd5PfuXlc4PPWobKUdNTZ1XxhcXwKxucetc6ZPMfdKxJNQEnO1Bk+tWbayd2z1agaJxLtTAGB6DvUlvaPcsCVwtX7XShwzjP16VYluorRdsYBb1qSm0h0NtBaJl+PbvVS81Fm+SLhfaqstxJO+STQkTN0Qk00u5Dk2MGX5erUdk0mNoJFWbXTmlYZ6e9dLZaeFQAgGnYVyhpumDaCV5rRnheJdqCr0Yjhfb6VoC3R1BwDxTFcwrbT2kO+Q4Hqa2baCAABeTSXULeXhBVKIzW/LZ4oA2vK+XAFSIuwZNVLO5aRTlqdHcGWcKeecUAQXCrJPjaetaMMCCLAHNLJaKBuHWoIjJ5uDkDNAinc2cvm5Unr2qtqV0LOxYOecVtyTJ5gXiuN8ZyYg4NJsa1Zw95OJrtm96AeOOtU40Z2JNXFGwYqGjqV0gC5OTTyx6URqztgAnNXVt1jG6Tk+lJ2QObZXhgaQ56D1NWi0cC/L19fWo/MkmbZEuf5CrcNkkQ8yY7m/lSbdrvYi93ZFaK3mumy3yx/zq/HFFbqFjXLUSOdmIwPTNaGnWaIhml5PWspVOxrCj1kV4rCe4YFzsX0xViTSnjKlckD2p0moSCYJGoAragZmttz8/WudzZttsU4oRPbeWwwelZs+mCPOCFz7VoyXscBJAP41iXmozTP8mefSnFSbugbsRCFlPyTEnPbNME9yr7chvqKWBps5lcgenem3F0qnAx9B1q7O9huSW512l3cK2SieRVOflGO3/wCvNFcDKZpJC37wewJ4op+ykZ+1j2OVhgEqZJyKnjiRDTrOIta8Ef5NR3CNGD3rueh5yjJvyJxtx8pA/GkBIPT8RVDzZF/hFSxTMT0xUtXL5XEu78dcGlDK/Q1Gn71SMc4qhLK8MuO1Q6fYcal9GaZXNRtHVWK99auJOj96hqUTS6YzYBUEsG45q9gGmlKam0JwTM5bfByelPaVYxgVbZeKqyW+45rWNRPcylC2xWaZmPtUkcoPBFOFr9TTtqp1GPrWiaM2mKygLuAqr5rucKMVaM6ldmR+ApuwIuQPwpAV/KJ5LZNOEbLyCaN7lsjI+lTeY2zGSfqKACK452v1pLiIY3LUPluX3Yq30iAb0NLbUafQpxHEoPuDWjO++AAA9c9KpwxhnyegqaWUIQnJP1ovdjehDDIYW5GOfSrDXCyDnaaYORnkUhKHjK/iKCRyxo4+UD8DV6OVVjKccjHBqnaovBwOvOKZOkhc4XHNUl1E30LnkRv0bB96QQtHIpB4DAmqaNOnQnHoaswzMy/MpGeTir52kLlOjS4WW02Enk5ypzT7O8e2kMfmlo+pA6iuXDN5m5JGQ+3FWre5kMqK8gYFuTjsKyk9CoxTkj021vx8hMqoxTdjZubFanlrJblkdWLYJXbtavPDqZWHAOWZMdfU1qW2tTRxbJDlduAG9h61xtzvc71CFrWOojZEkd5CctyxPapopVlfckbBT8tc3DqO9o5HJUEBHXORz0Na1tfIyKMGQg7T84HSuqlUb3OKtT5HpsXri+cqEQFSKls5POj3SHlOufSoFjjcktGcK4Ay3Y1djWCEkKsQzwcnNdykpRtY5mrO5XuJTbhkjQybSMHOetRjz3Vn3MAvBBXin3vyEybMHHWM4yKrjVW8ho/NlZW4IK/1rnasWrPUjmjKkMg+bqPr6VfgnW9tufvAciq1gwuJJN4OMhhntU1xavp9yzJ0HzH3BqOblYKLkmye3bObeT/gJpI2NnPhh+7bgiklUSossfXqKlXF3AeP3i9RW796NyNmR6haZHmx4J6/UVmjmte1k4NtL1/gJrPvLcwSlgPlJ59jXLKPU7aNS6synO2Ao9T/AErY8N2qzbXYdST+tY1wuYs/3TmtjwzcBGaLPIJx+NEXZMK61V9jW1bVBar5cY+gHFc+2ozuc5UfhVjWlYXAPOOlZddVClDlu9Tnq1JKVlsaEGpTRtkn8VqG5uWuGBYk49arqaCa3VKMXzIzdSTVhKATn8M0VNaqrTMG6lDjNVOXLFslK7sIDj1BxU8V1JGSB8wAovIdjKy/dKjn8KWyRZDMCOdoxWM3GULs1ipRnYtmWC4G2VRn9apz6WrAmFgR6Gn38RjumxnHY1FHcSRnrke9Zex5oqUSnOzs9DJu9LVgVkQD6iua1DwwjZaIFW9q9IW4huF2yAH69ahm0tJATC2f9k1hKFnqbwrNeh5P5mpaWdkimaEdmGa0rXVbO9Xy5MAnrHL/AENdZd6UDlXjx9RXN6h4Zjky0Y2t7VnKHU6Y1IyKN1o0TOHtn2Nn7j9PwNZ0+nXBKrLBtGeWzxU5GpaWdrAyw+jc1qafrlrKpikARjxskGR+Bog+V3YVINr3R2jNBbwuhEbSMCoAPzVq3l2zyN0RW28Hk5FZRso3uvOicRH+6eh/Gtu2iF0hi8leTjco6H1zWdS17odO6WpJp99BGmJZHyOfmANWhrtmJTGJDk+i9awtR064iZ1wckY54yK5Sa01G3uM5YDH3ST/ADrONNS3ZUpOPQ7s6ij6hKmxl3Wrgbh16/41DpkgGqHB/wCWUbZ/z9K5vRZJn1VFcuA0bDBOa3dPQ/2nGOm+3A/LNWo8qsS5c1i1q+mtJrTFGL5bJVjjj2qr9hnEaGQFQMsF7jBFWdduZ7HWkY7WGQDubnFW5NVtptLKAEvzhW4/Wo10NLopeLogNKuAMn5Ef9P/AK1edqcOh9GB/WvR9dxPpNwwBG+3DYJzjrXmxOFB/GtY/CYr+IeialcCLRLgsP4UbP4VgJOk6KzfMuODWzqqed4cmI6mBT/OsHQLOWWMCRGClT1qWla5Sb5rG5a+QNPZok3SD2rlbjUbs3s8UkhCn7oIrsNJsW+0qpVgrDn0ql4i8LPLNvt1HK76KU0pe8KrGTWhzDzPHcNJEZI22Zx2JFblhqsboBcRhWCgmRDxz6isK40SeEcOQxHQ5xVRWkgRkkVD0HzGumXJLYxgpRWp1tzYWd9kjaG/56R/1FYN/ok9uC20SR/31rIj1G4trppIZNpz0HSup07XFuV2zkRyeuODWfvR9C3GMjlJLdl+7moclTzXc3OlW94N6ARuf405Brn77SZrYkumV7OvIrRTUjJwlHbYyllI71dt754iMNVJ4WXkdKjyRQ4gqh2FjrpGA5/Guls9bxgq+R9a8vSYir1vfyRkYY1KumXeMtz2Sz1tJlCu1X9kUo3RtgmvKbHWuQHPPrXUWOs9MPkVWkiHBrWJ1TRFOCO1U1hkDqGGBgiprXVI5VG45q8BFKuUHPU5qGmiVvqUgRDg5AwcAVXuNrj5exPIqxcoT8pHfNRImNw469KSVtRSbk7Gd5ISRTjpTbm3Bj7HtV2ZAM89s0kcJYkMvQ/pQ+401sYa26pNkDao4PerU0CNbqB1zgg9K0buzVRwvoxArHmlkjkZWJ+/n8KNZMlpQ+ZmlEjchsAgYGO9SLHuUL1qW4st53p+IptvCyHDbvfIqpLS5MJNSsyg8RzgKCSDUDWe7Pyk5Hc1sGyKuG3ZwT27VBIuHA5JxipcuxqoO+phTQlf0PSmCIgN9a15rYNg46CqsijGAOSM00xSSuZMluCcjg1Laajc2Mgwx2+5qaVDlsA8Yqu8ZB6HGcc1aegup2uk+Ko5lCSnn3rp4LiKdN0bAivHvLYHdG2CO1amna/cWTgOxwKNGWp23PQ79d0RFeZ69BslY4712lvr8F9GFYgNjrXPeIolZSykEe1Szopu5wc64Y1Xq3cLgmqtUtjGqrSL2jzeRqUbZ68V7DBMLrRgCc/JivEo3KSo47EGvUtAvhJp5XPvRLYqk+hxHiCHy75j6iui+Ht2A727HgNj8DWf4og/eFwOhqj4Vuza6ueeGU/pSlrAzWk7HU+IB/ZGqDbC2HbIKnjNV4dGbU1Fy8KhWY53Vqa/ax6yIJYhhiocMrZ5rRtlkt7J7eUsqlcq4HQ1g5WXmdMY3fkea6tbLbSBUVQc4IWqMdrLMu5RnjOK77UPDwvUDgK5YHDr6+9ZekWfktJBPHhkbvVxnoDj73kciAVPoRWha6pNCQGdsDoQelTatYeRfuqjg8istlKnmnpJFcrjqd1ouvxlwJW+Y45PIP4Vt6pp8Wo2rT2yqT1wOMV5WkjRsCpINdLoviiS0YJKeCNuT6VlKm73iNS6MbbW95a6n5TjK7Tgitm8jYxwkddgGa1UvbC+aKbEayBSD6Gsu8fCxrwQjdqul78tTDEPlgZjxEg7iH2nOM4OKydTjGCu1h6Z5raO7zGyowARnNZ98FKkc9OR1rsmrHBB2dzmRwcGpM0kw2zNj60grFnWmPopBS0iwopaSgpDh0paQUtItBRRSikUg6Um6lKkimEYpoltjsBqcYRjjFMXrVxIwye9Nbmc2rGfLEQM1XxWxJB+7biqEkWCa1ORtlelyKc0TKM4NR4ouDHZo3UgFGKYh4kIoLFqSlGKALFtGDKM9KuX9vGkIK9cVRSQLTprhpF2k5FJodysBTwKsWlv5rkGlvbb7O3HSi6HvsV8gU4TlehqEZNOC0ybEpkaQ81sWMSSRHIGcViggVZhvDF0qWroqLsxbtAkxAqsQaleXzpAasfZ90ZIqbWNVNMo0AlTkGnsu1sUyhMbjctw3hQ5ztYdGFewfDHVJTOFkuZFAAYcfKa8UrV0rxDe6V8sMh2eh7VaZDVj6q1rxRb2tmyxXUYkA5714D4y8T6ndXUkbOwjJ+8D1rJXxXqE04kklyp6jFakr2+s2/zbVkx1osJaHGiYsSWOTSmSrs+mm0nxIoKnoSauW1lbSD7kZPvRZiZhs9NALGtO9sY0c7F2+3UVTCBaluxSjfUlt7BpU3ZAFOa1eIE7GIHfFauny4hCqVzjjIpt4rCMkqWb1zVKN1cUnbYxC5zToxluRxTSMNyK0Y2hMZXvjgGhIzbbHtp6vbhwMZGck1kyRsjEEe9basgTClsYxtxkVYt7SGTGS/PonSnyhY5kgilBrodT0zZFvU7wO5XBrnmGD9KGrCY4HNOqIGpAc0hDh/8AWo//AFUn9aUc/jQIX+lAXJqRYJSu4RsRjrimjhv1oFcQ4z2pysVORTXGOQKltreW5lCRIWJ9BRYaZIZd64xzWtpPhu41J13IQldDoPg3dtnuQAPU11FxfWWjW+yHGQOtK6Q+RN3INN0Gx0SAPKF3jtWXrnipUUxxMABxWJrPiSS4JAc49BXJyPcX0+xAWJPQVOrNSxf6vLcyHDE5NTaVoN3qswyhCE/ia6Dw74JeYrNcjjrk9BXdxJaaVCI7dAX/AL1VsBn6R4as9IgV5lG8dqvTXjzny4RhegApmya8fLE4rStrNYh0pbjK1rYZIaTk1qRxJGBmglY0zmoTP5hIDEgeooESu3zYBb8KmQcDNVN2GwGPA7VKkvIBoCxMy1VlUrztz9KuDmmOgbg0AVkkbbwuCaYJJFyThR6sKtMqRL7/AFqo2+V8AHH1oAGnU9G5+lCRvM3circNn3Y5q7HGq4AFAyK3tVRRxzV+O2Z+FHFPjRYsvN8qDmsfX/G1jpMBS3dWfHUU9gSbZdur210tS9wwz9a868T/ABAaUvBaNx04rlNe8S3Orzsxdgp9654u0hwvJ7k1LdzVJR9Sa6u3uJDJKxLE1HDE8z4PCntU9tYtI2cZPrW3baesS7m7dzSuFurKlvpe7BxgHtitVIoLJMyYHsKr3OqR2yFYzk/3qzGmkuiWJP40tWTfsXbrU2lOyPhfQVT2luWqe0tS7VpR6YWIyKErEsqWFiZ2HFdHbaTEoGRzRZQx2+AcZ9q14kzyAcepqhGXNH9nYbRwPSrcV1sixhifcYqzOkQG5iM1JDbxyoWOeBmkBQiiknm3nNb0HCBazEY+eERMjvg1rxLsjBP1pgSCMHrUUtsrHkVBLqBilC9ia0Ym86LdQBVSMRDAwBU8NshbepGfSqtzFI5wufwqS3c2yfP1xQBfd1RPmxWVdX45SIVHeTtJkk4WsG/1i3sUPzDd6UmwSNF7pbdTJK3P1rhfEesi8mKqcgVS1TxBNeOVQkL7VQtrSS4fJBNL1LSsOgJPQVoRWpf5n+Vakjgitly2C36CnL5102IgQP71Q32LcgMkcPyoOfbqakitJbkgyZRPT1q3b2cVv8znc/qauRJLNJhEIUHkms5TUS403LfYiigSEbY05oksZZuX4HpXQ21nDHGC3LVBOy7mVe3tXPKq5M3jGMdjD8vE3lqvcdK0rkGG0UDv1psNi7XXmHJ5z0q/eIqxAPjgVMpFXuzA+1CME8gjvjNTR6tJJHtw7DHXpULyRMcBsn0AqAhUb5sn2HFWrPct2toWJG85u/4moWeOFeoz6+lV5boL8qjJ7AVDsZzumb6LWtuxyt8r1Fad5TiLp3Y0wmOBSzHLepqC5v44BtXr6CsO5vmkPJOPSrjC5nKobDeIL2FvLt5dsY6DFFc01y+eOlFaeyiZ87Lts5jtxkAAgYpZHDgZxmnTwPb2UYbhiqn/AMdFU4dznJq2gpVOZDZsLnioYn2t0qxNGWOKaIgtCRNSaTsWo51Vev6VSnAkkzVhYievFONuDgh6d0jJKW5RMJ64pVDL0NaqRKE5warvLGrbSV/EUmyoXbsRxzkdasJOD1qINE3938KQxKxypINQ4xZp7yLWVbpSFBVUb0Pzcj1p4uADgnNQ4NaopTT0ZKy1A8AarKyK3/16cQDUqTRTimZjWxU5FTRA+WwbtzVor+NJsHPFaKp3M3TvsZ7uFkYANSeYf7jVceAcmqzLhuhx9atSTIcWiEysP4PzqSKXfwcCpdse3ncKqyYVxtqyC4i7WOKguPlm39qsR/NED3zio9nmLyD3pIrdCrcIYwpC/jTPMQHOVH0FNEaA9B+dPwq+g+goJJbZg5OMnJwM0k0wWQ/KfzogfB4PGc5xT3iVjuAzVITIvtAKkBW/OpYgHXhT0wOaj3on90H86SOcbgOTg55psESKrA8A/iaazOjKTgdelSEKFyMZ9BUZGQSRj61DVyouzJY7l8g5zirw1AvtQtgZ5rJAOM9qQFs5GKhwubKodDLfkRYVuWcH8BSQ6vNDyY1IJLZyawi7kbgeamt7rewQjFOELGVWXMd/pur+fFHuypYHILcVfguneNWK7sjqpzXnj3xtyCmM/d4PatG08QhAFYYA9OK6aTaRhNWPT7WeGa3jEhwc7cOMdqh+zwqu1lhOCR+tczba55lqJElYhSCVNXLPWreUNA5DFX6E4PrU1G73CNmtDegjiiLERgbgRlWqfUWaZWIB5RVUk9MVVR7OS1LJcPkt/q2HIqzZsLmFoiSSCQD9KymkzWEuV27layuAh2t9xuCPQ1abdbTCReR39xWfIpjlYd+pHrWjaSCeIxMfmH3T6iqpya3JqQSH3MYkVZ4vqMdjUilb21O4fOOGFRQN5Ehhk/1bdPY02QNZ3AlXp39xVzjfVbGcJOLM6aIxO0bdO3uKr2c7WV8G7Z5+lbt3Al1AJY/qD6VhTxFh0w61ztWZ33VWJ1F7Ct7aCVMHIzXNupRipHIq9ouqCM+RMfkPr2rQ1HTllXzYuc8git6NXkdnsc84cy80YHSinPGyEhhg0i4zg4ruvdXRy21sxAcHOKswoXClDyOB9aQ2xKbl6+lNt5vIkIYfKTz7VlP343iaQ91+9sWknyfLkG09MHpU622xvOiHOOV9RUnlQXseeCfUdaLRZLafyHJZD901xuVtV9x1JX3Jykd7Bg/eA6+lY08LQSFWrblt3t7yOSMfJJ1FJrFp/oyy45xTo1eWSV9GKrT5lfqjCB9amjuZI+hyPc1NZ2omiVsAliR+VU3+WQjnAPeuy8Jtxsc1pQVzRS6jmG2RQfrUcumxyjMLY9jVINmpY7h4z8p49DWUqHWJUaq6mfd6V1Dpj8OK5rUfDUUuWVdp9RXocd6kq7ZBn60ybTopwTEQD6VzSg09UdMKzXXQ8jaPUtKOBmSL+63Irf8AD3iy3s58Sgx5ILI/KmukvNIxkPHj8OK5rUfDcUoJVQp9RWMoHVGrGR2b3Wn60mY2COTzG/3T9DVZ9MWMFSU2Yzh+1ec+TqmjvugdmQHoeRW5p/jWQr5NztVumJBlTWLg1sapmkttbwazaEGMFmI+UjpioYPk1S2x2R1/JqijijvtUguAwgCtnKcqasOIotYghSQOQ79PQmnHQzmWvEYt57iGSTYxaENyMYOBWE0i3Ti3WYkAcAdBmr2uxPI1qWdgjRH8MYrMZYrPcylTJ5G4cdTQkVfQ6bULMJoCFed9sVOPb/8AXXlhH7s/SvU4Z2vfD8LMMDyMD9K8wZcbl9CRVRvYy/5eHpmleXNoXzkfNbdx/n1qtpktrHH8u3GMZNO8NqbjQoQC3ELDj8K4++1Y2rPCS5zx83HSnGPMmhzfLI78ahArlYWBc4GKvrdxi2ZpQu4ggA9hXltjrpS5XeykHpjqDXb2DPqUKMrEdm5rCdNo1jJPQxtUlme4dwAYx29qy5raC5TAiZiCT8taviNHsRyvA71haZKk0x3hi2DgqcVtSjdXMqjtoc9dWrwykAN171ZsHmRtrKGToR6VvahZw7UZWBzyaxopfKuDvTAzwRW17oytZ3L9pfy2lwBG5GT0PQ10JuUnG7bsLdR1U1kNpkd7D5yA5HPynmr2muIQIpijL/tLzWDasbx/AgutJguMsn7lz6cqawbzS5rdsSRkDsw6GuvubKRVMlm49dh6VRj1KJiYLgCN+hRx8pq4VGROinsca8LJ9KaGK11t1o0Uw3QERsf4T90/Q1g3WnywNtljKn17GtlJSOdxlAqpMR3q/balJERhjWa8TL2pgYihxKjM7jT/ABAOA5wfWumsdazghsj615OkxHetG01OWEjDHH1pXaHaL3PZre/iuEwxzU5gBUtERyOhrzfTvEI4DNg11VhrW7HzAij3X6kuDitNjUmtXduTj5cYpwTZEM7vu8471ag1GOVQG24xSXEW4AxsG/GocXs9ibrdbmd5nnfLg8r3qjcWXmS7iOMc1oIhR1zngnipGUKi5xknFC0egPWPvGcluAgVsEhcGqs0DmXC5ArTVd8m765qfylKqc5OSCKhtt2LjGKVzJ5WNlZV5XPJrPukVH3YBBFaNxGDLnHIG01HParNCBjtmqta1yXJyukYm8SttX8jVIoVlXcDwdtaElk0TbuwOMjrVn7CssTMFJxzk1baRgk5OxmfZ1IIOR1qlcwgdF64Oa0vKdJMbs85NRvCGBPc8UlobX5lbqZCxcEY6moXiGSCD7VqeXsbqKqyQMz7unORTuLyKsSvE+6Nj9K1UBvoNrANx0B5FU2jwR161PbOVYEjBHcVSdyk3F6GJqWkTRZZAXXuO4rBYFWIPBr1aHybqMCdQf8AbHUVj6v4WSZTLEAR/fQfzFNJdCpSvvucBXY+G7wiJVz1XFc1d6bPaMdykr/eAq5os5jfHoaUloVSdpHRa4POgJ9RXKWMv2fU4JD0D4P0PFdZc4ltz7Vx1yhjnYehpR2CsuWdzubCV43CqxyjFa1dQ1s2V2jZ2bwMhvumue0+bzGSTP8ArIw/4jrWprViL3T4JCrNtzkjjHpWM0rmtN6G1pN/by7nPyYYvjPykVVuY7dtXZgNquAcg8VxMDXVq6rExAPBVjVmSa4RyxyrqMYLcGo5LPRmiknuXvEUASVHGDg7SRXL3a4ORW7PO13GAcfPHnrn5hWRcjdGD7VcdNGar3oOxSSMsuaToasW+SpA7VLa2hnndMcjFdPJdXR5/teWVmNsrmeOZVQkgn7tdTG0ptw7q4wemOKzIdFNtdHcQ3OAucGtwlY49oSVcL1zkVMUlqTWkpaFOTABGOSOtUSDI2cjOCDWkw3A8yAeqiqc5VT99icdGXFXdtGHLZnOahCY5AfbmqimtfU13IxI6gEYrGFSdCJc0tMFLmpLQ6ikzS0FoUU6minUi0FKKSpIsbxnpSKQgbBwRT/KDjIqxJAroCvWoYo5FfGDii9waaWpEUxxip7Ztp+bkVLNbDbletVWDxnnitklY8+cpXZemkUptHemwW6uWY/WqQYk5qzbTZl2+oxSaYotdRl+qR/KBWXtJNauornDeoqGCAMufai+g7XM8gim5NXpYBniqjRkU0wdhvJpcGk6UZpisOFKOtNzRk0xF2CXyzkU26uDMaq5NPTk80rAnYnt7RphkCopkaJtpq/aXAgBqneSiWXIo1RSsyvkmnAGhUJ6CgnbxTJJE4NX47oLGVNZe+lyTSauCbRZLCSYe5qzJZkRbwKpRHDg+9a/2hDa7feokrbGsJ9zGIwcUlSsu6Q49aRoiozijYu6YkcrRt7elalnqDQEOhJGeRnpWRSHNNMlxPRbeWy1O2Jl2OdudsnGDWhZadptxGEUxZK9EPevM4b2RAEZjitbT717dwyN8v1rROxDN3V9JWBCyh8A4rmCpabZ3ru7K6t9Uj8mZwrkcNnrTP8AhGrCK8SQru3dzJxUtJu4XdtDI0e2EcaszLjn71Qajc4mK7dy+oFbt/pOyNPJDqnQFeRUMWkxPxNG7v0weKtJWJscncJ5mGUcjrUHkSnkRv8A98mu9S0t7JBvh2YPUJk1Vu7u1AIXzCfoBRYLGZotjcMFLeYoHUEVu+UsQBN0EI7Dmsj+1Y40ICgf7zf4VVk1KRj8rAf7q0K49EdBM1vcRbH82T3PyiqAstPibJS2X6nJrHa5Zhly3/Anqq12qn7wH0GaGrjujcu4LWSPEXlN/upXO3NqYmJAAwemaV7/AB0Dn8cVVe8eQ4KgUnYiw5QWO0Ak9RXRaN4cmvZFZ1+U4OKo+Hkhe9AmI4Pf0r1CJ4YYVS0A2kfeqbpAo3ILfw/YxW4iYZl7ACuD8R6R/Z9xuRSF3YIr1K1tGYiSRio/U0apoEGokkgMOOtJPUpxR5PpehXGpuBsYL06V6Povhe10yFXmX5gM4NatraWmmRhIwu/uaiv5mW3ZyTjGcDrQ32FGNjJ13XhaIY4iFA7CvPdR1qW5cgMSat6w95qN2UihcjPUjFaGg+DJZ5VkuV47g9qSXcqxgabpF5qkwAVgpPUjrXo2i+FLXTYlluAN3XHrWtDBZaTCFiUFwOtRZnvHzyFp3DclmvCw8q3UKo7Ckt7Isd0nJq5b2Sxjpz71cCBBSAjihVAOgqZwRHxnr2qCaQrjB/SnpKDFk4+uaAKhMrSY+YDPU1L9xTubk9qRJQZTxmrBClxubH4UARherFsCmgEyZVgfTinTA5wv5mnLMsSjLBj9KBluMHaM04iqqXDO3+FXFGRQBVljLntip4LdUA4FSbAOTVHUNXgsojlxkCi4F6adIEJY4FZg8T2VrIWkKnB7muC13xi0hZIWJ7ZzXF3F5NcOXkdufepu2ylG256H4q8fNdExWjFR0yprzu5vpbhy0shZj2qvuaQ4X86t2tg8jZAP1NMteRXSJ5jz09BWtaaYTgsMD0q/bWEcCb24x3NMuNSSIFYRj3pBexMTBZJ82M+grKvdVZwQDgegqhd3+SSW5rJluWkOAaLEOV2WpLoyzKuc5NdPZWJa1DY61zei2Ru7sE9jXemAwRpGoJpiZDY2Dqc7TWgzFPlCkGr1iuIfn4Bq1HDbl85596QFSxsWmYM3FbH2TamBk0yF1D4SrvmjGO9MVzJksm3bnIA96jku0gTy4+T0rZltWliOMVmJpe2Uswz3oFuP06Eyv5jLWnPMsIAOc4xSWpEYC7RzSzxo5LMwH0oGyiLcXMoYr3zWg8q2sBFQLdRxOEQdqW7kjMQMjYGOlAEEWoPI5A4FVrzUYrZSzuPqTWNqviG3skKxkbvauF1HWbi+kIDHHpSGlc39Z8Vk7khP41ysk097ISxJzT7ewknbc34k1ppHDaLxgn1NJtIpIrW2mhBvl49qtGZUxHEvPoKdHHPeNwCqeprShsorWPdjJ9e9ZuVtWWk3oirBYtJiS4OB/dq4Jo0Hlwrk9OKkeKSeAlQyqOpqnpy7Lsq3JFYyndHTSoLd7jmhnEokkViPpWrDeRmJYwi59R1q9N9m8lcElu9YZVDL8hwwJrFS5jdWaszVkv/ACUAU4LHAAq3ZWryxecc49+9c6kJa6XzH4B4ArpftywWyxp29TSkrLQzsW0KxLucgY5rntYvTM5VGwOmafd6gzKfmz7dqwZ7gySYHzN/KinTbdxOSjuP8xYVznHvUDSSTE7cqvqaYVAO6Vsn0qrc6gqAqvX26V1KFjCVVstNJFbqTnn1NZV3qZbIU4FUbm8LEknms95S5Naxh3MJSJ5bncTzVZmJPNN709Yy3atLGdxu4UVZW2JFFAzd1ffNLtK7TwMfhVZbdYYuetW7i6WWUsQQfSqsjmQ+gofmZwmoxtFalNxl+KcECLk9aspEp6VKLFpQc9BUOXRG9Ki370jHedmfalLi5UZByPpWqlhEsq5AHPc1bZYUTaUJBFS35GvIu5hRTybtrjH0qKWLe+fer0wQzKV49RVZ22yfhmuiEU43OSTanZEP2NsZCmmDzYTwSR6Grv25I8KSfWpwY51zxzUppu1jVppXIYpUnjOeD3qjMCJCvXmrEiCJiV71C3ztuzS5XF2IlJNXQq7wuecUqXhRsNS7xsIANV3TeTjvRyKQRm1uaUdyjjqKlGD0NZNrGS+PfvVuVmg5/lWEoWehundXLRFRsgPaoor1WHNWFdX6Gps0PRlZ4S3Qmq5tTvrSKUm2mqjRDpplVsRRgU2FgUx35BqeWLeKr+SY2yK0U0yXGysiELslO6p2MWMhe3c05lDpyOagNuASTk1d7mTTW4jS5+VRk+1TQklQH+lMUqOB+lId+7gYqkIVoCW46U9Y1jXJ60NIEHOKVl85M96pO4iPz/mwOR6ipsbk5A6fjUcVuQ2T0pbhz90ChoLjyFCFVB4FCIscYBJzn0qCDJkGc9afc7sAbmxk0NBfUfEgAYbSRn1qLzFSXH6CkgI3bW70s8B3ZUUWC5Myeau4cemKiUMHCmnwRup5IA96nkKIAcduaadhb6GhDci2tChblsZ/Cs/7WWmaQEgk561BI5lQADHHY5qudyUpO4lFJHR2uu3cQC+exX35rt/C+rFsFyeZF69+a8rt2LuBXS2+pm0gGxstj5cDGPeoltYag3K5311exSXAZGH32A+mBUkM4RgyHjqD6GvNE1mRZAdxIXNbVn4gA4c8HrW3s70/MHL3rM9DZ1vIWcD51GSBRFIJgbeQ5ZQCpPcVh6ZqsfmZ3fXnqK1p0BZJ4iDxwR39qVOd/dZEo2Jo3NlI8Tk7CRkHtmor6y585OPr3qzOovrPeOu0K3timWUzNC9rLy3BH4VNSNmVTqOOxhSRksWUEOp5FaOm608P7qb5l6YNR3UHlXDsB8rnP0NU7iMFN2OQc5rB9mdsoqS5kdU9pDfQGVAOmcVz1zF5MzJ6VreHbndG8LHmqerxGO6LdjXTh5NT5b6HJU1he2qIbW42nY54PQ+lW3sBctujYKx7GsoVbgvJYBgYYejVvUpveGjIhUT0kWRpt3akSR9vTvW1aQ/bPKJGD1/Ss221sbtkqBQe46V0OnSQybTGQGByPcV5+JlO3vo7KCg/hZOsKGFA45CnnHvUWq2yT24AOF24GKsSLlQRnjtnrWdqtw8UDBDggZFcdNy5lZnVOyi9CtZWMFvAczD5SWGa56+VRcvt6Hmg6hdEbTLn8Kj8uWT5trEn2r2KNNwk5Se55tWoprliiMU9QWOBTWidfvDH1q1aypHgMVGcg5HWtpzsrx1MYwu7PQT7PJjkUsczxnhj9DWrbqk0ZIHbp3rOvIDFISAQPeualiFUk4TR0VKHJHmiyxHeI42yKPxpk2nRTjMRAPoaohqljmZPun8DWk8Ot4mMavcoXekkZDx/pXOaj4chmBIUA+orvUvVYbZBx702WxhnGYztPpXNKDW6OqFZrZ6HlAsNQ0ss0Er7OmByKbo95czeJ4ftDZPI6V6FeaU65yuM9x0NYK6PHBqUd0V2lSfmAyKztpsae0U2tS14khdre3WLcSYwqgfhn+VY0dk22N5jjdGeG7ACuv1J45bKAbVkAUncp6GuM+1PI8A2t5YBUgmsNTqSR02mTRt4fiiXb1YDb+P+FebXK7LqdfR2/nXomhmOTRQqdpCRjt/nNcBqY2andL/00NVDqZP40eg/D1PtWnxxdcK4x+BrivGml/Zb58LjPI/Ouv8AhpceWq5PAkIP41d8Z6Nb6h9odGTIORzyKI1OWVmFam5ao8VTKt9DXofhfU8RhTL16EDkfWsO50YQRuG2khB09aoafNcWs3yMQoODxVztJaE004vU73xITf2gwobIxvU5/OvOnjltJPmBBycEV31pcKoXzYiHAyf7rCs7UrCO4H7tDGwYuAOhHpUUpcujLqw5tUc9az3FzLGrAMvTB4yKk1awNvapIItuM5+bNbmm6WryKRHuJb5a0tT0tZbVkIXLcA47itPbJOxn7FtanJaPczBAocD05rqbedJYyJGbJ7lOK5ZIntbgqEG3oRjpXW6NatcxDaX2k9MVlU7o1p7WYyG5aK42HBUnFWbrQIdWeB1wrE4bNVbuL7Hc7XJ2FuMjkVN9untVymSv8LHkGsk2ndGjRnalp2o6HbKeHQSN8jentVa11W0v4/LfaCescnT8DXWJfrqVqqTqpA7sMiuX1nwqkKPLBhcHgoeDWsZX0e5DWhVutDSTLWzbT/cb+lYdzYyQuVkQo3uKvadqVzaTCCb96vox6fjXQm7sb6MIdrE8GNzyPoa3vKCu9Uc0lCTstGcG8TJ2pFYiuqu9CDZa1bP/AEzasK4smjcq6FG9CKpSjIhqUdyBJyp4Nadlq81uw+bishomQ9KRWIocSlM9B0/xEr4BfBrprLWQwGWrx6OYqcg1q2WszQEfMSKV2U4xZ7Gl1DMOR+IpxtfMX5fmUHIA61wOn+IFfHz4P1rp7LWgQPnwfaiya03M3Fp+Rqi2EKt8uOcnNUnkfzWGBgOPyrRW7iuE+brjqKry2xBJXLD1qLNbg30RTlh+Y896jAwNu0H0xVyQYjc85xmq8ah5T8pyAGFC1WpLfLsVZLNm2t2HUVXDGDar5y6E1vHaI2Uj0IJrH1GAZyq4I9/WktXqVLRXW5jXu87ZV4GMZFV4nZiAy5HrWzHbkoVbsensarz26wfwnOe1W2krMyipOV0ZTxt5vKZAfB9wacUVDtCjoRzWwsSSI2Tg7cj3rOntgJgSwAIzyKzUmzo5VHVlF4CWHpTPL2H8c1osBswOfpVQDewB4yMVaTsZOauOt5trLzjJwa0re6YHJJHYkVnpb4yQBxzVqEAZXPPWmmyrWJbuxtr0HKhXP8Q6GuZn8PPa3RZBtB7joa6eAFZM9ielWyUPHH0PSqT6ME7anLeW6REMO1cvqabZyR3r0K8s1YEoMe1cbrFk6sTtoSNKs1JLuWfDzNPbQqMbo5Sp+hrrp7OZtNmhcdVyvNcT4VuPs2pmOQfK+Dg+1en3lyHiUwhCrdVNYVW0zSjqec26PFfhZFG0nBB7V0Gq6M01is6Rqy45KHkVafTEu7jKIqyDnB74rUki8mzKEYyP4ayctmbJHnFuTDlScmN88jsajuE2mRPQ8VZvAF1Nl/vgr+NNuRny3/vpz9RW176lUd3EzrPH2sIejcVqRRGC+hkHRztNY7t5Vyjj+Fga6BjvtFcYyjbq6IysjhrQ99mvcT+bbxvtByTniqgBOQWKr169qZbyI0ADMRtb6jmpZWMaEJt64/CrhHmOObaYbiozluVPIPpWfcpK43KxIxnmpxKWYBiNxBGRTWdVU/KPuAdavl5XoJSvuZE26SMhgQRxWOy7HK+hrpJgp3Be4zg1gXSlZsnvWU2mzpp3sMBopAaXNZmyHUtNp1BaCnCm04UikLTlODTaTNKxVy7HLgU8zD8aoq5FSBqIrUqc1yal+Lc4yTTbjYQQarC78oVA955rda0S1PPlrsOZNvShG2tu9KZ5uRVtLcPEWBx0rW99DPYr3EpdR6Cn2knGKY6FSQaRCEORxUyjpoNPuWzGCece1QyxDPT8qeJAetSBoyOagozJYCO1VypU1tuikcVTlgLdFppg9yhS5qR4GWosYqkxWFzRuNGKOKYh280KNx5pM0obBoA0rJYxneBVW+Ceb8tRiYgcGmZLnmk0NMQClyBWja2qSRZJ7Vn3CeXKVzRcLCb/AEpwlbsajAp3Apklm3Pzgmr1yqmEEdcVlCTb0p/2hmGKlq5cZWFCFhxUbDB5q9ZqGHNR3UQVuKlqxspXRTqe3uGhbplfSo/LOM4NN6UXE4m9a6jsO6J0DejUl3fXUrq6ykbTnCnisE1atroqdrniqTIaaOx0TX9yGKRzyPmUmuikuUgt/N+0Ept3KAMn8685aDd+9hYK45yKtpqs3kCCRTkdCDVXsJHawarp2pIUEkikrjOcjNKdAhYB5LYsCM7jJ8przmO6nsJ/MjzgnJB712Gj+JI7yEwSAHI+6WwQfancC5d6NA8Ti3ig3KOin5q519FvJWIjDEemcYq3castnfLuUqD3zmuhs9Ss7iFnJUSAZBI60+YWhzVv4XlY/vZ1B9AM1Be6HJbk4ZGA79Kn1LxLc2Wo4iKiI/3fStSy1SHVUQTeW3YORyPrRcDkPKkdjGkRc+wqGS0lhb95EyZ55Fd9cLbaYQ8oVl6EKMContbXVYgbcFGx91jkGhiaOIhd45laMnd14r2HwrGZrJWmUE7QV4rndI8KW8UolnJUDk7h0rs7XUNMslWGGQZ9TUNAtDUUBRzxxQz7kIBBOO1MjZbgBkJKnuDVpY9gznnGOlIrcyfszPIfc1YWy85dshIHtTrljCNwXpWY2tiPjv8AWkDLh0jTbUmR13N1+aqlxfj/AFVsoVfaqvnXF8/GcGtS005UALDJoAq21i8p3y5rVihSNemBT8LGBjGagluCyELn04FANlpGUnAYfSoLjcGyCRUULMgJc06W5JU7QSKAIJHdyM7OKTDk7QUxUiBmGeAeucVKqFVyQGP5Uxj4oMLnIz7Ur5Vj84ApyzCIMSBn0qvLK8zYBGM9AKQCSOXwFBOalhtGfBbgVLa2f8TjJrQVAgoAiit1QDinSyxwJudgBVHUdZgsYySwyB615p4g8aSTO0cDE/Q1LYJXOs13xbDaoyo4z9a861PXLnUWOHIX0rGmuJrhy8rE/WiMyONqdPWixaSQwtg85JNOSF5mGQfpVq2sGdskEepNb1pYRwJvbCj1NA/NlCz0snBcfhWmzwWSc4LDsKr3mppCpSLgevc1zt3qJYnmjcTl2NK91NpDy3HoKw7i9JJwarSXDSHg1GFzRYQMzSHJNOSKnKlWY48DmhspRNbw1IsF2A3GSDXpsNok4WQdPavI4XaKUOnUGvTPC+qLcQLGzc0J3CcbI0byF0TbGKrW9lOepb6VrSXCK211yM1DLfIsgVOh9KexmSW9sIFLOefSq7zubgBc4q0RJNACM89cUWkARsvn8aBGjbsWiG7rUpjXqapfbEibaO3YVBcagzjagOaBl1/LXlSMj0rIuJJpJNo4X1pyXBiBeZ/wrnta8TwwbljI3e1K4JampcXkFghd2G71JrjtZ8VSTkpCxx61h3up3OoSn5jg0610xpPnfgeppbblqJW23F5JliTmtG305IRul6+lWGeG0XCDn1702KGe8OTlI/1NQ5dtikgack+XCuT6DpVm30/nzLg5Pp2FTRRQWi4GM/rSsWlfksq/SspTs9DaFJy3LcCh2CRdPYVtx6ephy7AnrWfaPHEgAIzV83UgQ7TF9TXPOUmzZJRVkVpY9iMm/g9sVjBNt0cEHPdTW09rdToXQufccCs77DLbSF5MlvpTTXUqLaK9xPKSqEtj8qgy0RyGPPbrUs+ZnJVXODjJ6CmbVT5mINUkaqyV2So8rAMZCij/Z5NOe98pcZ/PqapvcNIdsQJ9z2pmEi+Z23N69hVqF9zlqVVe0R8jy3HLEqlVpbiK3XAP+Jqrd6mBwnJ9axLi8LE5JJraMG9jllMvXeoF8gHArKmuS3SoXkLdTTOtaqKRDbYE560BSTT0iJqdUCDJqiRiQ9zU3yoKjeYDpUDOW6UtwuWDPg0VX8lzzg0U7AaoZV4Dbveo5piBkZpmxopMSAg56VNLtMXArM6IU4rYfpbmVm3VcnvGgkZeQMDNY9hKYbgjsTWtdRiddw61pBRvqRNyWxmy3reaWBPXvTWufMPJOfrUn2IMTzVSS2aOTFX8jJq3UsKdxz1psiFiCCCOlWbS1BXLDnryamURq2DsxnNNtW0MXNJmLMn5jipraTYoBz0q9PBG/zKcioUsmcHBPrWVram0ZqehXuZMrx6VVDNirU9sytt70gt2C9KblzPUvk5URRHcOaemAcH1qRIdo96ebcnJFEXZkSWhDkI2RSyy+amMmq825TSwsWOKbabHrFDREQetPWRo+5NTNCx/wDrCo/szMx5NJpCUyeO8x1P51aSdGrLkhZDTV3A8GspU09i1U0NvAboaaUrNW5ePrVuK8V8A1m4SiaRkpEnliopoyykCrYwwyKQpUqbQOKZlBGicmpHuMjp+Qq60YPUVA0Kk1tGdzKVO2xUVGmbJ6VMX8tdq8n0qSQeWuFHNVozhvnB61qjIsRyEpkrjnvRHIjuykgnPemPMoXCgD9ajtlLOzGncRcPlxEH5PzprsrjOR1zUEwEku3sKl8vy4gCe/rRdgkhIzGST8nFOeYBtpweMVTibE5HYk1YmTJRvzouwsEjNjKAD9aSOUSfK+c+lTgoICCCTVPy2aYlAaQFhIvLYkHg0XJDn5V9qlBCJh+vvTfPiQZBH4Ci47BZwGL943UdKbdXJZgoPAGKjluy3C8Cq2cmmlrdj22JlkqdJiO9VBmlHHcfhWqnYlxubtjqckSj5vuHH4V2GheIt/8Ao0zcZwD6GvOYpRGD057mrNpdutxuBIBrF/FdBNaeZ7Bb6qttMCSNpOGFXbgKSs8DcHkGvPWvmntFfJyRtYj17Vf0DxA0g+zTN1O3nsa2T54+ZjK617ncsEvrbcAN38Q9DWRJGUYxuP8A64qC31pbW7Usflc7WB9a1bkRXUQliI56f4VzzR1UKlvdZl2dy1jeA54zz9K6S8iW+tRKmCcVzUsYkXjhhV7SdSNu3kyn5T61KlbVF1IWd1syqyFGKkcinhGK5wa2ruwS5XzYsc88VQU/ZztkDD+Vd0a6lHzOV0rPyHaZp5vZSCvGcV0sGlGycbWAA9H6Vk22qQ2sOVaPPsOadYX9xfXhIClc45rir+1nd7I66Xs4W7nS7sABu/f3qtcWq3IC9eTT5ZIocKzhW79xQrMQGVtw9QelecrrU7dGYn9g/vQcE9+la8GnQRIAQeeKsowbGcnHp61Jt4GFP1rSVeclZshUoxd0ijLpcMq8AdD19axb7QjGCyDHHbpXTqp9VpXwRhgCKcK84PRhOlGW6OHt7iWzlCSbgBxmtWa2+2wGRQdw6n2q7f6THOpKgZxyMc1Wt5f7Oj2yLxjafpW8qqlaUdzJU3G8Xsc7NE0MhVs00E10Fzbw30bsrDcOePSsGSB4ZijZrtpYlSVnuclTD2u1sGaekrIflOPamMpH0pgNdek15HNrFmlFfAjbIB+NLLY29yCYztb9Kzd2KkSVk5Vjj9K554dbxNY1O5WutKeIkhce46Vj3Omq6cqVYHIZeldZFf8AG2Qf4Uslrb3IymFJ9Olc0qbW6OmFVr0M7S7aCy0a5yQ/QrtHI5ry3WhjV7k4IBbPP0r1OfTpYQdpIB7r0Nc9qGhR3O4lcse9Y8jTubKcZSXQy/A1+sE8kGfm37hzU/iS61GzvJJFVmtpRznmsSbRLvTbkXFtnI7VqW3iRJVW3vVaJxx8/KmocWnc33KC3DXm8uo5AIFPFpH5x2Jw/UehrZfTrG6QyQkRu3RlOVNUpbG5tAHY7l3Z3KMinCLlKyJnJRV2dPo+gpcW6vKxYBBkZqzc6HEWZIvnA7Dgg1zFh4lms4yjnABxleOK0tL8Tx3V6okIPOMk4qKtOVN6ip1VNFvTdOMNwrFCuWyQfWs3Wbh2vWtIyygMa6zVJYrbSzPHhuM8HJ5rgRrCS6hI7FSNwGD1qYa6ly7FC6iZHjeUfM3Dcdfeuz8IGOEIj445U561iajZvqESTQgDbnOPaqmj6yLGZYpdoOPuvx+VOXvR0BKx0PjTT2LNNERnr9TXNadqKqfJf92/dW5FdTd3i38LIrAqyjGT0NcxqmjvI4mjRkl57cE0o6qwSutS01ysDh8lFJxuj6flV+Q/abWUK2SRnI71z+nygoY7lFVlXaQx71PLJLYOTHjavdDkGhx1BSTRjXFsU1FnKq6k9weDVPUVzho5E+XqF4xXSQXdnekGfej7w2Vxg1W18QmJzGYGDY24TkGuqFbTlkjmnR15kzLs9ZuLb93KDMikAZ649q30ks9Sg+YBv9l+GFcjbgmYZ/MGtW7AaEGIhXK5Uhuap009VoT7S3xE15oDDLWzbx3RuorBmtGRirKVYdiK2rDWJ4yI7keYoUHd3FbciWV/EMlJQffDCo5pR+IOWE/h3OAdGQ9KA+K6W80BxlrY+Yv9w/eFYU1myMVZSrehq04y2JfNHcSOcqeDWrZ6zLCQC2RWEyMlCyYNDiVGZ6Lp3iFWwN+D6E101nrKtjLYrxyK4KnINa9nrMsJAJyKV2U4qR7Cjw3OM4Ge4qU2iooYHIPGRXA6d4gBAAf8DXVafrIkAUt+BpOCeqZFnEknUmTOTgrgikaHeqkjtmtApFOMqQGNQtE0WAV4xjIqWmuhMXuzNdNpG0D3oe0Eg3FSdpqwsDll3AAYwalZggKk/eBOB7VEtWXF2Wph3WYXjVduMkN+NVJo5XtQQfmHTjrWncwGRw+Dj2piRDZtY4xyM1drK6IXvN32OajMsU+D68g1d+xkgP0w27FW7mHB4Xh29KfauChUjhgQM+1OUtNCIwtK3QzwSDhgATk09UJO7vTrkhJAygHcO9ELq2MdQOlFrK5ald8rFY7SD71YQbl5AwO4qFQJCoB6jI+tWYlUYyOp5NDY0rsb5uxQGwfXNU7q0hu0PGc1fniBGe4qqR5anmmnYGctdaRJbTiaEElTnFbGna4VQQ3EYdQenRhWlEgnLBgOD1qvfaGkgLoRnGcr1FEkp7lwm4kmn3LyMzQyBmjbIB4OKqX9/fG/kRlKxkjDL2qgGu9Nk3Fd6f3h1rbsNatbgHzFVieuawnFxe2h0RkpHPXkKyTo0mCysDuU802/tDBA2DlVkyD7GrusaWbi5Wa0Oz5s/L0q3eWrHTCH5LL6UcysawupHEXaYya19MlE1kVP93aaz7pcpmk0+4MKOmerA1vT97QwxatK6NqxOZHQNtP3Tx6VcmQNnJjbPPydRWLp91jUHycbuRXQTQMzI4VcEZyBXRH3JWPNkuZXMiTdkEZwOOlMDAjacDnFXJVZhkAHPf0qky/Nngj3olKSl5BGMZR8wyQ25j7YrK1OELkjoDWv5eV5ABB5zVLUIj5Z+VenY5qZJbounJ7MwxThTRwcU4VmzqQ6lFIKdSZaCgnFLTgu7ikUtSLfShqHhIPApoRgMmmS7xepJmmFz2pOT0FKh9apLqY1Kl9CFyT1pgODU0g5qEiqMSZHzWnayt5eKyYj82K0IDsIPanbQVxZNwc5FRMR2q86q4z0PqKpTRleaaWghhfA4NNWRi2ATTOSasW8YD5NJjRrW0YaFt3oKhkRQxAX9aDN5a4BPTmo1m3PkmpasNO7GPDkHiqclvitbhlGDUMkO7t+VSmVaxispBptaMluO+aqSQlatMVrkQFLScg0U7iaHcU5WANR0uKYi4lyY1wtV2zI+TTKkjIBpDuXPsX7gvWeQQxFXjdkRbRVaOMyyEUBuRgUowKmuLdoBk1WyTQmhNFmOfyzxSvN5p/Gq4FPXg0WBOxs21qkkBJ64rOng2yEAVNBfGNSuamgKzvk1m4tM2U00ZZXB5pKv3lvtfiqrRMoyaLlrUWK5eLgcg9jSG4YuGAx7ZqMigLTuS4mnBPFOm1/yNSSW8VsTIrZA5wOCKyeVOQcVKbqV02s2R05FVzC5NTet3tb9Ar7X+vDCp1sJbVTJa3JRc42nmuVDFTlSQfUVej1a6jjMZIZT60KQnEsXUbXMhErfOvGQOKqJ9p0+XdGxx6djUQupPOMnr1FWvtyyRbWOD7jNPmQnFmp/ah1G38t0YORgndwais7680lwHDGLsRzisVJmhlLR4xnp2rcs9TgmQRyqFJ6g9KE0JxZ1CeKBeWQQTxhtu30NcrdSanHMZElLAHI5qHUVjtiZIF2nPbkGlsdZUERyjBJ69qaYNM7rwX4mllYQT9QcEMa9EDlhkjjrwa8z0WO283zlEWHAwVPevRLeQTQqykY4HFJ2AnuIxPCyhfpzXmmvi40/UlYhhHnmvTlBA7Vi+I9IXULByAC4FSCH+HZYbmwRlxuHWtjfjjHtXnXhfUZNOvjaTHABxzXofBXcOc9KGgZGyhjyFxn15qMJGhJzj2LUvlqzfdwakMQXG4jPvQAoxIMACkFumcFc+9BQKo2rSI/ljLFR7ZoAc8Wwdl+tV3l6gEfXFSlpJ2wp4PYirEVmcgsc+wFAypBA0rc5x3NacVuqjoKlWNUHOKzdT1y20+MlnGRSbA0ZZorZNzkCuQ17xhBaoyo4z6A1yXiDxtJdO0UDfrXJv5tyxkmcnNS2ylHuaGoa5d6vOyqTtNJBpscal5TuY+tUoso/wAgOPUVqxQS3K8khaC7GXPbeZNtQA+y1q6dpmOZFz7elXIbWG0TdIQB79TVa71XIMcAwvt3ouDa2LlxLb2vTBf09Ky57ySc4yce1VSXkbLcmrtrYySdAaLENlQWZnODjn1pp8MvIwPzYPpW5Dp0glFdBDp5eIKNpPuaok4WXwrLHEWXf+VYslo9vIVkGMV63bWskTFJF+U1geKNB+UyxL70MpM4IY7VZRCy0iW+xiG6ipc44FS2aQTuG0KK0tGvns7tTnCk1n4wM0iuSflpGjs9D1+1EWo2yyKfmxzTl0o+ZuNcV4e8QGxwkjce9dcPFNptBzH+dUmc0otM1Hb7NDgUyLdOjdqrwazZ3i4Lj8KdNqtpaIWD5+tFxEgs1TLSMVHvWPqWrWlipwwyPWsLXPGOdyQtk/WuKnu7m/lJYk5NF2xpNm1q3iea5YpESF9qxY4J7yTLZOauWml5w8vyj36mtBpYrZdkS8+g6mockti0iK3sYrZQ0mC3p2pXnknby4Fz7joKkjs5rn5pjtT+7V1FigGyJcn0FZylbVmkYuWxVh05U/eTHc3v0FaFtA9wdsYwvrTVDyuiSgqCeldEkItrANGo3EcVhOozeNNR1M5NLgjOZCpb/aNPNlC3yqyj2U1V8ufczykYqt5rG5wp4HvUWb6mqTZojTGMgKhvyrVgsNqAEKT/ALvNZw1RbWIfNjjoTUf/AAkEkq4V8D6VL5mKxuz36WkBTIye1c9eXv2gEtnFZ95es53E59zWf5sk/CcD1q4Um3dkSnGKJprpV+Uf98g1EFaT5pTgdlFMPl243E5PqazbrUychDgetdMYdjnnUb6mhPeRwLtGPoKxbrUGkPLcegqjNdliecmqjOWPNaqHcycuxLLcFjxUBOaTBY1KkJPWtDO4xULVMkQHJqQBUFRvL6UhjyyoKheUngUzLSHAqzDak4LU0ibldImkPSrkdsFHIqwkQUYAqdIC3WgCuAwGFHFFaCwRgctg0UDI720IC4YSYCjeOhwoBqjIVQEGvT7/AECCZT5ceAecA4rkr3wjNvYrvGa5adeDW52yjbY5uFozjJ/SronVI+pPPerKeFrkPjd+ladt4Qlkxv3EVbqwWtxJO2xgxXBLnb6+macbK6uXBWNuucniu+sfCkEAG8AewFbEWl2kQ4iB9zWUsakrIz9hd3Z5qui3rrjgVE/h28HOf0r1dYYlHCIPwpTGh6qv5Vl9bY/q0DyVNEvUPY1YjtLm3+9Hn6V6ebeI/wAC/iBUEun28gOYl/DimsY+pMsLF7Hmc1sJvvR/0NVHsAvQED3r0S40OIjKZFYV7oUvOwH6g1tDFQe5m6FSOiehyhgVDyatRwiSLjpUd3pN4kw5JHoauWyfZrXEuQcGtJVE7OLCNGWvMYt1aksQPWqkcbQud1a7yo0xIwQeaju4RKmV/wD1Vab3JUk04siE6qAdwAxThIkq5BBqqLaVhgYakht5I3OeB6Un3KUFYc5zkHtVZxtarpiJbnvUEkDSybUGc8Vd7K5go3lZFcneAAOau2OmSOwYitPTdEPDuPzrcW2WNdqiuWriFsjsp0erMkW2xcUxo9vWtSVAgJNUXRp32qOKxg3NmskoooMC7bVqU2W2PcaumNLVMnG6qX2vfIVP3TXbCk7HHOsr2KEh2kgg8VWLrnB/Wr93HzuA+tZkq4OaqK1sErNXJgYz/cqSFkBAwDz2qjQM1pYgvPCu4srYPuKa0bNwX/Sq4lkA+8aXz5P79KwWJlt0TnJJFTCeM/KSB9aoM7N1YmgA+lIC+Wix1SmGdFHy8/TiqoVjTlhdj0NId0JJK0h68UwD61ZWzkPUY+vFSLaKD8zj+dUhNlLHoKcFY1oCCFexb9KXfEnQIP1oFczxE7HABqdbSQjnAHvVk3KgHB/Jagad2b5VH480WYrii1QfecH6CpU8qHnb+LGoxHMwyzbR+VKIIx958/QU+UVyd9RYpsUkjOcDgU/TnkWbcM5JzVbMUf8ACPqxqaO/SMfKV/4CKafLsKS5lY3tWuD5LOGAYqGH1FaHh3X2kTyHb7w49jXE3F7NcnaOnT3q3polhkVuRgg5PFQ9RONrWO9n1VYZgx4D9frVhbmK4G5GAauO1e6R4mKuCeGGPWqFrq0sJHzVk0+h3QlzR1PU9P1d7VtjnK1rmW1v1ypGTXmdrryyABzz61r2t+QQyP8AippxdnfZkSptfDsdRcaaynK9KvaZKtihLIueuc96xrPXXAw/zCtSOezvF6qD6GrnKU48stjKNou9tSvfalNNcFkkIA7VNaaxJERvz/vLTJtN/ijIxVN4XjPzKfqK3jClOHLYhzqKXMmbz+IF2jDr+Awagk15ssQCc9CT0rDPWggEdKFgqY3i5m7H4hfy1B6jIb3rS0/VklLAoORxg1x+PTj6VbsZ0t5Q7MqkHqRWdXBxUW0XDFNuzO6EgIyG4PTNUb+yWeMleOORTrK+W5QBSpzzlTVpuRnA5PY15esZeh6CaktDjTJPp9zgk7ehz6VpxxrqUaMOSBnIFJrNqCC6r05qrpN6LdmDFuFAxXc/3lPmjucmsKnK9ie60ohHA4xyKy5LGaNyCpwO+K6U38bTYDcA7TUxjR2ztJ46joRWUMVUpqzNJ0IT1RxzQSLOqsp9Ksvp84jGFwccg11T2UD5YZAI9Oal+zIePLDe7mrljpbomOEijk47CVogxU/MuQR6iq2yWFuhU9a7MWkYGBEuMdBVeXTUbDBWBHBzyOKUca7+8tAlhV0Zzsd4yjEg4p7wW9yMj5SfSr13pWPlXGN2eKzZLSSJl2ZBOc1tGdOez1MpU5Q3WhTudMIByoZa57UNAhuAcxgH6V1y3Lw7RJyCM5qQxW90MjAJ9KHF21HGo4vRnlcmm6jpTl7SRig/gPIq/Y+KChEN2hhbPORlTXa3WlnBO0MtYF9oUNwpBjGfpWfL2N1VjLSRR1Oxs9Tty8LLG7L95OhNc7Bp93pc6NIqumTlhzmr8uk32mOXs5G2/wBw8ipLfW08wR3sZhfucfKamUpPR6lqCWsTZtL0XunKkySqGBHXiuP1KFLe4Z4nOc9fWulurW2urbNrJJGx6MjZWuOvtP1K2dvMzInUMOaIRijKo5N3Or0HxFEitDIw+ZcDIrV1Hwkuo4vIMkKB8wFeaRQXUMnmKOgDfga9I8MeLgkH2Sc8ldpUms6kJRd0XTq82jIoLeXT0RJQrjOMrXR6XDFqK+UdxUqRg+tZ98FfMqDMTdQOxrS0GeGzCszKCjE+/NYOXU6GjB8UeHBZx+dGMMVDAj1rndPvo3LQzDBbPOK7fxfrMU1kQDnBwK8305o7i7KDkgnj2rWN2rmUnZj9V0zyJjLbS+XnGASeaoOJpVKtyR3zxWtrDz2QC5U8AqCM8VLpNtLNcK0kYCtCSVx1Nbw2uzCb1sjm2RreQhsZ9KnV3EQyqlQuR7Ctu+0K4u5IXjUlVUb9orUuvDLW9lKWjUMkWVP94VqqsbGUqMrnIQPtlVxIq49s1He3bbgY22tuzleK0reFE1HyJHjwAMHHqKvnQI7iWN1Uk4wcDipdSK3HCjJSujKsvEs0WEuV8xf7w4at1Lix1WPHySH0PDir9t4as2KrII899y1FP4SVWkltnHyj5dvB61g5Qb0OvldtTGu/DxIJtnDf9M24aufubGSFyroyt6EV0l7qF5pd15VxF5sYAGe4/GrUV7ZapFtYB/8AZkGGH0NaxqNLUxlRT2OIKsp70qyEV013oKtlrV8/9M24NYFxZSQuVdGUjsRVpqS0MnzRdmOiuWQggkVtWOuSQkBjketc0QymnLIRQ42LUz1DTPEQcAb8+xrp7TVI5gOfwNeJQXbxsCrEV0GneIXiIDt+NF3swcVLVHramKQAoAGBzz3qpcxNvGR9DXOWGurIo+fPtW/b6jHIuDg+1LkW6Jle1mPWLKKD9DVYxZYAEfjWtGscoHltjPammx2sGKnIOeajbcm19jPktFMZHAOM1i3kBhMZBGFyOPeuhuAUZQAOQQT71nzwGaMFu4FJaO45aq3UxUh86EIy9Dj6U5LfyiMkde4rSEIjJOOpApXtd5B9O1OU+iFCnfVlZYQFLKBwf50xkO/6VPKfJ+XackE/jVe4LFd6r160ku5cpJbEckm1R9eaYq+cGBH60yItJlHGc9DVuK3MZLkZBUAY9appIyi3J3ERBCmQoGRk5qBbiVpMHAOSOPSrzbcFQDx8tVjFsbOOlJa7lvyGSxJOu1wMnuK53UdKeBjLHlT7V020uQFHWrclqstoVlADY6mnfSzKi2nocRaalcQuAxJA71fvNSaW2w6A/MCrhu30qtf2ptLguo4zyKsRWsV9BgADd09jQqUZM1nWcI8yOcuNrtJt6bjiqEZ2y4966K60l4wzKMdmFc9dRtbzjcMEGnGLjI0q1I1aakWhG8NxHLj5Qea622cz6evBLBSPlNcuwae1JVOeua2NKnkEBG09M8dq0m+c4Y3jdWJ2Tb/BgBd3JqrLDjzDwcEDj0NaPmiWN1JAYrjJFQyjLyFuhVcccVon0ZhJWehTYEMy8c8Yqhdx7kGEwcYyK1XRd5baDgdc96pTASYVcYz29aqdraExbucvKuyQikBq1fRbHyc9cc1UFc7O6OquPHWnCmCnikaIWpIzzUdOBxSZpHctjaRzUcgH3RTFkp4Izk0le5dVxcbk0NqoTLVUljCScVbku0WLFZ3nb3JNao8+RI6ArVZkqwWyMUbMjNUQVAMGrMc+Bg1E64qPvQBoLMexpzSBhjpVFGINTg5FFwsTCHHOKeflHGKWOdeATUztGUHAP0poCPYzoSO1QnchqylwANop5iDqWPFK/Rjt1RBHMRxmp/NDD0qo8e08U0OQaThfVCUnsy5s396ieAEcj8qWOb15qfKsOKl3RasZcsIHSq7IVrXMY7kVXlhB7CncRm5pc1K8JB6VERiquKwvNHNJmjNMQ4ZqzC4jYNVTJpwJoAt3Vx5y4qKK2aQZAqNeTzWha3CwqQaQzPcFGwaTJNTznzp8rUbwtGASKLhbsNAqxDceUeKrZpRTA0PtHnMMnvU9xGPJyBWWj7TmpzdFlwc1LiVGVgEeVzUY+9ipo5BtINQsRvzRZBzuxI8Xy5pkcW41PvBjxTYiA9HKg9oyN4gppoQnoKsygMRUkCAgg0uV2L9qilsxSBOamnG2XAqWKIMOalGjlHqVwlBhPUA/lWpDaIzDIP51tQWlqYSuRn60+WRPtII45yQMEmoQ2HBz0NdBfaLO8hMSAj61U/4R++PIhJ/GqSM5SvsegeGdLF1pjPGoDrhhgda6vQ7kxyfZ34DdPrXEeHtaudGURTgopGMFc1rza/arKJ043fMD6GgXqegOpUEjH40JhwQQuDXFzeP4liAxCDjncc1d0PxQmqSgB4jzj5aLMDI8U6WbK9W8hGFJzxXTeHtRW/09VJ+dRirmr2S32nshGTjIrhdEvH0jVzBISFzj8KN0CO9ZGWTh2x6YpHiYgEtj1zVlHVow45BGRUDl5X2gEfSkBECRwij6lqmjtZJTkrj3NXLazAGWGT71d2qi+goArw2yxjHU0+aaK3j3O2BWdqmu22nxsWcZAry/xF44luXaKBic+lS2NRbOt8Q+NYbRWSJxn615lqOtXeryEiRgpPWs1zLdOZJ2Jz2qe3znYg4oLSsQrGIW55b3q1Cks74PTsKuRaY8hDnIB6k1sQWkNtHubCjuT1NJlLTcqWlhggsOfSrk1zFaJjIZvT0qrd6kACkPA9R1rMJeVstk0Etj7q6lumOTxSW0BdwMZqza2DzMMCt6202OBQz8mmlYlsq22l7sMwAFblnbxJhRjNVvMZvljU1asrOYyBiOKANCSyG3co5qG1gmE3PTNbUKfIFNVb92t0OwUCuWVRWPTkd6ZdwRXMBiOM1TsHlkOWVsepNaEaIHyWHWgDyzXdLa0umOPlzWOSBxXqfibTo7m2MigV5nLCIpipqXodFN8yIgMjmnIADQwJ6U+NAOam5tyoQqSc9KbJ5gXAansxJwKcowOad+wOK6kdvc3cHKuwH1oudQu5hgyOfxqUcnaoyatQ2AJ3S/lRzWWplKKuZdvYy3L5IJrfsNOijcA8nuT2qa3aOJHAUAjAAp9irTXr55CjtWcqjexPKkU9RcrKsUIwWOM1fs9Mjgi82U/N6kZqneYTVUz0WtZ7pJokVXQ+zcVLbUbjjZysyC4WST5Y8Kn97pT7d7e0ADHLe1SnmPaqOp+m4VnvGVlHmPnJwABisXqdcUtkbgtUudskfBz3rU3CK3UPggVRtLhLO13N2Hes271Z7p/lAwDWfLKTsgehb1W5RoNqYHsKwYT5TEnDHd+VWXdymC3A71TnnRFxnH8zWygoq3UmNXfsSMwkfc/Pse1RSXQHyxjJ9B0qqokm/2U9TTnkitU68/qauMO5nOq2tB3lljumbJ/u1WuL9IRtXt2FUbvUy2QpwKyJbksTg1tGDOZzLlzfM5JZqz5Ji/eoy+etNALGtUrbGbd9w705Yy1SJF3NSllQUCGpEFHNDyhRgVE82elRDLmiwD2kLdKdFAznnpVm2tQeTV4QbR8opiK0cCpVpIy3QVPFaluTVpYgg4FAEEduFGTUgXsKsLGTUgjVRk0rgVxCSM0VKbyBDtJGaKVwud/FqFvN9yRSOxq0CGHUEV5rFNNa/Kj8DtWjBrk8fUn8K8yWFktjuVWLO6wnoPypwIrjl8Ry46t+VDeIpm4DNUewqFc8TsHlRBlmAHvVC41e3izg7jXLSalPN1Y/iaqPKM5d61hhG9zOVeKOgn8RNkhOPpVJ9euW6b6xzewp0wajOpJ2H6V0LDRRk8SuhtDXrkHnf+dSr4ilH3i35Vz39pKe36U5b+JuuKHhogsQjqIfESOcOQfrxV1L+2n6nFcaJIZO4pyhl5icj6Gs5YXsaRrxZ2clrBcDICtWVf6GJoyE44xgisqDVLi2YbskeorcstdimwshH9a55U5wd0aqSkcRe6DPauSuaqxiQEq6kGvU/Jgu04ww9DWPfeHo2BKD3xiuiliukznrUE05R3OGikIfDJkU6Z0UnGF47Cr+o6e1rk7D+FZlpp9xeSjCkLmuvnha5yQhUk7CRRPO+I1Jz3rd0/RgmHcfnWnp2jJbIC4y1aJjwMAVw18TzaRO+jh1DV7lLyQowBgVDKVjGTVu4kEQ96qLbPcNufpWNKnKbNpyUVdlLynuX6fLSTNFaJgY3Vau7mO2QqmM1zF9eliea9ijh1Fanl18Q5vliNvbssxJNZ3nkvUcjlzk1H0rZ+RlGHc2kYTw/oaz54sZFOtbnZ1PBptzcB2FYzWt0bQelmVMc0oRj2q3BErmpmMcOPlQZ9eaLiv2KKwMx6VMtm+OmPrUxuhj5ST/ujFMMzseF/M00mK4C0Ufedf51IIYl7M36UwCdv9kflQYCeXkz+NOzFcl3xJ0VR9eaabkdifwGKaI4l7E/pS+bEnRUH1OaLLqMb5rt91CfrzThHO3+yPpig3gHAz+AxUZndjwn5nNF0thakvkf35f60ojhXsx/SoNtw/wDsj2GKX7Pn78n65o5h2JjLEvRUH60qTp1wh/DFRCOFR3P6U9TCeNq/g1K7DlI5J2ZsIB9TQsMz8s2B+VWVjiUFgcY7GqdxdkNtTGR3pXGlcmFpGOWfJ9hSkQR/wj/gRrPa4lbq5/CoiSepzSuy+RGmbyNPut/3yKibUD/CCfcmqNFIaikTyXUsv3m49KFmIqCigtOxeS4I71oW2oyRHhqwwxFSLKRScS1M7O01tWwJPzrctdQDYKSZ/HmvN0nx3q/b6g8fRjU6rYdoy3PVLPW5YjhjuHoa2YdQtboYbCtXllprnQSc/Wtu21NJANr/AIE0+YzlRf2TuJ9PRxujP4is+S2kj6jI9QKpWmsSxY+bj0NbUGq29wAsowfWtYVpRMZQX2kZlKRkVryWMM43RkH6HmqUtnJH2yK6oV4y3MXSa+HULG6a3lHy+2VOK6q11COZBlgDXHEY9jT47iSI5VjXPXwqq+9E3o4jk0Z2ksaTxbSR681zd5pckchaPj1FXdP1QSJsduvGCOlbBRZI8kFgQMHrXBGc6EnE7Wo1Umcva2tysoJ+96Z610cQKRDcu09CM1LDaxgE+XnHOelE+0JhR7nFZVKjmy4QURyZQ5bIKnr6ipQQV69faqEdwScAgZ4+tWo2KqNxIAGDn1rJFkp6nH86UHOelZN7q0cHAK/Vqpx68zcBgfYrW6ozkrpaGbqxTsdE6q/UAHiqstqrvv69uBVSHWEY4cD8D0rTjmjlAKOD61m4yiUpKRz+o6eWPHRBjOKx5I3gkwCQevFd00asMk9ueOKyrnTlKSEgZ4rroYpxXLLVHPVw6k7rRnPxXjqdripmS3uh2DeoqrdJsumIHyZ/KneWSu5ThgO3euppSa8zn1WhBc6U2CVG9awL/Q4bgENGM/SulS8kThhkVIPIuhzgMamVNrdDhVa2PNJNJvtNcvZytt/uHpTodYUS7LyERMepxkGu/uNKOMqA4/WsK+0aKcEMgJ+nNYuC3OmNVS0kVHs7C/tzs+RmAXegyMVyGr6Ve6dcGZAzKrYDKO1bsmk3mnuXs5GA/uHpT49aDfur6MxN0JIyppc0lvqinSjvEg8Pa9537m5Vzx2Nbl7ayOvmWYcAjJHrVKHQ9Ovn8yNo43JGHB+U10cUg0qMwypHJDjAbduFYTSvdFxbSszhrm5kLSRzggqMAE5GahtLaG01ZJM/LjG4dDmuw1PTLW82XEMYG4/MFGRWMmlRYVJGAIbqeK3p1IKNmY1IT5ro1buwt7uxNxwDEoByOozWJ/aCNfzW0C/cwVf344rc1Oaa204wQoH3Lt+U1x+nA2140kzDexHFQttSrK53umWkksvCS/MoJCnitXUrOVbKVPmyBjZIM/hmqmjXq26CdiQC2M5rpJdTgvIJVYr6fjXO27m54pr8aWlxMUYAjaAoHOaXQddW2ws5bBPc1J42tZFvjKoyu7HHesS1iS4hwBkgdK6klKOpztuMrI9ATV7W5bKXDI5GMMOKvR6mLZcmQNgDJI4Nc1ommFFc7yFUDAbnrXQw2vmv5JcArwQF4Nc8oxT0N4ttamdevDd3G8ouc5DIKZNZWZh3BmR+OqcVrvpotuT8vbHUGqV4iFMKpQkfeV+v4UXt1HoYcl0LdyrjzUH900gurPUB5eA/+w/3h+NQzaeszfOWPqQcGnMLGwh+/sI7962iZtJ7lK80INk2xyf7jdawp7OSFirIVPoa3X1vZ1Cyp2bODVuCe21RNpUPx0JwwraM2lqc86avozj+VNPWUjvXQ3nh8kFrc7sdUbgisGa1khcqykEdiKpWa0I95blq2v5ISCrH866Cx8RMMByR71x+StSJKR3pNNFqVz1jS9fBxlsj611NrqscybSwYHsa8Mt7+SFgVYiuh0/xEyEb2wfWi/cbinserSxJN8yH8DVfysDBGMHvXP6d4hV1G5sj1rfttQiuAMkMKTjfYzs0UTEWkVQOpIqwF2qQR94cfhWj9nRgWjYHnOO9Z90hEg4IwDUNNsd+VaGfcoHcEdCKasPyj2FXREGjXjtUWzLdOO9PoRtqyi0SpIu0bT1JHpVpcGBSc5B61aNsDGTgZ6e9Z92Skrr0HBAFG+g7KOqIBIquQR35NSOqsuc96abfzCGHGasxQfKVIHr1qpWWpMG27CWUQxuPI3U+/TKsE6Zqd4zCh2rjjIqAMWA39azbbZtpFamVf2AuLctgZPyn61gWsrWUhhk+Ubsg11bzbG24BXdyKr6ho6zws6DtmtU7ERknp0KyyRzrvKhsrh19fesXV9GSZCyDnqvuKmtnktp/LfqvQnuK1CFZAM/I33T6H0roXvK5zVE6b02OTs2+zR+U6kkcVJYalHb3RRlwM9607iyQ3AZhgE4b2NU7zQfMRZUzkEqSKjl1sNSk1e5reXBcDdGRk+lMlt2CNnOCMZArm1lvNPfuyj0NbFnraTAK5walpoSnbSSI5I+Sd2MDPTg1WYYbhlYZyAeDWyypIN0bbSe45FULhzHw7Rn/AIBTV3oXyxeqMHU4cpn/AGSaxR1rprpUeHI4IJ49q5p12SFfQ1LTRtHTRDqM0CgqSOKk2XkKDTs1Dkg07JAyaBc1iTdihmOOKiL5qVCCuKpKxnUqXVis7E9TTFODUrjk1ERVmJOrZFTx5K4qmh5q7CcGjcRDIpVuRUWOa05I1dc9DVGSPaelFguRinhsCmBSakVKAEBOalXJ9aVUp24LTESwDDZNTz3KquAazpJyOlVmdmPNS9yltY0POz/gafGnmDiqKPxirds+DincQrKUJzTklx3pZid2SOKgJHWm7NCV0y6JA3WnbkIqh5nHWkWRi2Mmo5S+YtyICOKpy256gVrRIDESeTioJE+YjHFFrBfUxmUqcGkq9cQgc1WaI7ciqTEyPNLTaXNMQ4E07caZmlzQBYhOHDGrdzKkkWBWcHxT1fJ5pNAm0WBZlotw9Kr+S2KtJc4QLSeYuyizKvcp49qKsBAQTURX5qWoXQ0E0tTNGNmRUIU09RpqwoJp6NinJCWHSo2yjYNJtglFku+npKVNQBqnhwWGaTcjRQh3Bg0jZqWOGU9M1egWLAyKvR+QvNZ8zXQrki+pRtrCeVgBmtA2U9mdzZx7irttfQwkHaPzqW/1NLqHaoXOMcCrp1Kjla2hFSnS5XrqLb6pBGg3xAmtC31a0dwpt1P41yiWkrnhDWhaaZceYGCnr2Fd6px3Z57clsztbrTrW/04vFGFbGRXmmo28lvcMhJxngZr1DR0mS3McgOPeuR8UWIS6ZgO9cU7QqWWx2UnzU9Tl7aJD99PzNb+jamukzK6KFwc5ByK597dlbIB+tOjC55UbvrWgrnquieJZNSm27sgnHHSqvi3TDDMt7EvfJpPBOniNFk2428/Wuv1C1W9tHiYZyOM+tZt2ZRm+F9QF9YiJm+ZB+ldNBAqnNcToOlz6dqGSWC56Y4xXSahr1tp8Z3OM0nZBbsa01zFbRlnYDFcb4h8aQ2sbJE+T7GuT8R+N5LktFA3XjiuLdprly8zk57VDbZaj3Lmp65d6rM3zkJ9apRxKnPVqkSMnhRgVoWmntIQcYHrT2LSKkVs8pAwfpW1ZaUFIZxk+npV2C0itY9z4Uep6mql5qvBjh4FK4NpFye7gtk2/Kz+g6Cse4vJbh+TxVfEkjZJyatQ2cr8ACmkQ2QxxksARnNaVvpju4baQKu2OljcC4wa6WG0SKLkDgUyblWz08JEMLzimtZyyyhcHFatnKjvtGKvNCOwpAZltawQEByC1a0MSbQVAx2rJmtJRLvHSprW4lWQIxIxTGaqxlWyRVW+KOQNpNLLcsJduc8dKuG2V41k74oEQ2lunlcA5xVK6tpxKNpYDNW0MqybeQO2KtO67sHGcUAc/q10LewdXPOK8wun825Zge9dn41mZEwprz+JnZyT3qHqa0rosGTbxT4st97pQIx171KqFjhRzSsbc1hOAaljt3mPTC+pq1b2IBBfk+lX3jEcR27cgZyTwKhzS0QrtlNRBaJk43epqS0828k+UEL61Dbae15cgu2Qa6u1sorWLjHSs5ySXmEU2zK+xrHHhRlmf+VQ2DtbTuXRhk0+48yS6CoMjcadcSrZx/O+w+hORURb69RziOnjjupC29SfRx/WopNPdYy4QgDupyKVpFeEOojPGcrV23cf2aWxjPNVd9DN72ZQ0p2N0y5YbfQ0+9YNeqvB2/NzUOkMDcSsT1NS3UCtdGTeCPTHNFSyaN8PrdXEvGkdRGh9zioo0jt1yyqzepNE96sY2jr/AHRVQrLcfNIdqelKCZU6kUrBPePK+2IZPr6VGsAU75W3N79BSS3ENqpA/IVj3mptJkZwvpWsY9jmlM0brUUjBVOTWHc3pdiS2TVSW4ZzwagJzWygkZOTZI8jP1NR8npQMk1Zhh3dqokhSImpgqoKfIDGOlVHkJNAtiV5sdKhLMxp0cLSGr8VoqjmmBniMnrUyR4q20HPAoWE56UCsOgzkCtm3iV1FZ8Nucite2jK4pMBfJK8AVIsQHJqdiqrkmsy81FYwQppDLUs8cI5IrIu9TzkKaoTXckzdTTreykuGHFAJFWSV3ctuxRXSQ+Gy8YY96KZRlR3bLxnNWEvvWsYFgKcJGp2MtTdF8uKBfqOgrC8xqevmN0Bo5AvI2H1FiODiqkl6SeTVQxTH+FqYYJP7pp8tiXFvcna8z3pn2r3qHyH9DSGJh2oDkLAuveni5B71T8tvQ0wgj1pBymktx6HFWI7x1/izWKGI71Is5HWmL0Ohivw3D1YBV/mRsH2rnkuAetWorhl5U1m4JmkasonS2mqT2jAMSV9a6qw1aG7UBiMmuBgu1kG16tRvJbsJIjx6Vy1cOnqjrp1lLc7u70uG7XJAplvpMNsvyqCfpVHRtcWUCOU89Oa6FmQpvyMetcUuaPus6EzPe2A56Vm3cqxfKnLVavb7JMcRJqksQQeZMea0o4dzZNSsoK7II7ff+8lPHvVO+v1jUpHwPWl1HUgAVU4FcpfX5ckA17dHDqCPHrYmVWVo7BfXxYkA81ksS7ZNOOXb1qRYsckVdSaii6VIhEfrTJFAqw7BTjFMkTcMiuZSbdzdpJWK2SKH6fjQRinN90/SqZCLVo3zLTboYZT6EiktTyKkvRgE+jULclbj7aNX6jpSTziJgoJ5HQCnWZ+YD1qteD94h/CqbEuo7zXc8L+ZpwSdvYflUttjYx9BmoLi52y4C59yahsaTYpt/78n65pRFEvqaqtcSHvj6Coy7N1Yn8aVy+QvF4k7IPqc003ijoT/wABGKo0UXGopFprwnov5momuZD3x9BUVOCM3QUD0Qhdm6sT+NJnFTLayt/DUgsZT6UWDmRCJpAuN3FRk5PNWTYyDuPzprWko7ZosHMQUU5o3U4KkVJHbPJ24oDmRDShWPQVoR2aLy3NSZgj/uj9aNBXuZoic9jS+TJ/dP5Vo/aoh3NH2yP3oFdmYUcdQabzWsJ4XPJ/OhoIZfu4P0oHdmUGqRZCKnlsWXlarMjIcEGkNSLKT471bhvHQjDGskHFPWQik0axkdVaa26YDHI963LXV45MYfafevP0nx3qzFdFSCGpW7F3T0Z6laarJHgq/H5it231qOUBZgD715JaaxJER8xxW7aa2j4ycGlfuZypLeLPSxBb3S5RlP1qlc6c8XK5+lc9aamy4ZH/ABFbttru5QsuGWtY1WtmYzg/tIrKzxPkZU1tWGslPkYkcYxmmBbW7TKEZ9DVaTTmU5XOP0q5OnVVpKzJi5Q1izq4L2CVRliDnHzGnOQwG1s8Z4ri2a4tiMEge9dDpFx50ah5PmK4wRXDXw3s1dao7aVfmdnuXgijrjn1FRapceTasFGDgE81aIyMDd06YzUUtstwfmxz/e9K54W5k3sbSvytI5Dybi6kL7ScnvW/p2lBUJkUZ9zWnCljApHDYGSfSprieKJPl28Doa66mJclyRVkc9Ogovmk7nPX+njIdH2nHbtVG11Ca0nCyHv1rbe8jmyAi4bjg1z1+u2UEcjpzWtBc65Jois+X34s7Czv0uoh8wDY9OtSzgMGBx6+9cXZXr2rjk7c5+ldTZ3i3cY+b5wM/WubEYeVJ+RtRrqotdytcWSKGY5/HpWX5e2XAGB2I6V00iARlWB/LNZNw0MIyAT6gCohUexcodSulrbSj51w3qDTJtNhVQY2XPIOetWdOVbk8Nxnr0Nasunkryd4x3FaOvKD3IjSjLocxC8iABwDkHn3oKwXWQww361o3GnYwV6d/aq0un/IS2QR0cVrCrGTuzOdJpaGVc6WQMqN6/rWFe6RHOpV0/AjmuoLzWxw/wAy+tO3W9yMMMGtXDS+5lGo4vQ8zn0i7sHMlnIyj+6elOttcMMnl3sZif8AvYyprvrjSsglcMKwb/RI5gQ0f4EVk4I6Y1k9GXbC8sLiMEv5ZK43JyprO1TT5pGDxB3Q9Wj5rCm0e7sHMlnIy/7J6GpbPxHNZyhblXhb+8v3T+FYunZ3RsmmizIbi2iLFJXA9OtcrfXcs86qwMYDHGVwa9Jsb2w1aT9/EhJHEiNgH60ut+HbSSANBsfJ/hGaqE0n7yMqkG1ocXpWss6rA74B9a6GTVwJFijdfnYE49q43UtJm02QyJvKAkgbap2+psk6uW6fpTlTTd4kwqNK0jsdbMUtmAwzIJdw91NZthZQWcpWRtgIZs4z64p0N4l9GMt0xk4zxWdq1xK7MI3B3HjtwKSjK1hucL3Z0NhMFbII2sMNxz1pdS1T7DtlQP0P3uKg0eN5LCGRhksnzD8qjubb7dCRtwyg8OD0qORX1Kcm17pBB4uluD5ci7kz0NdPaJDc228Ltz071naP4VEyRvmIg9CFrqrXSk09SrMqgnNTNxWxcU7anK3mmxXG5Rjd9cVhXGkR25ZpUA75kbNdPrFoPOdkbKZx8pqhNarcoyiN5TsC+hBqoSYpLscbd2sXLRgccggcGjTTGsj53Ke2PWunn0KRLU4jkJYjgjGK5V4XiuXwxX5unauqDT0OaaaNVtUkhuFWQF0xjPcfjWmYrXUbbdgSY6g8OK5qeKSOHJjPswORTbPUWbKElXHQiipFLWIU5X0kXbvw+TlrZt4/ungisOa1khYhkII7EVu22rTR3GyZTIm3cCOtbD/YtQwhAcsuRnhhSU2twlTW6OFBKmpUlI71ranpBtTvUFoz39KyXgK9KvRq6M+ZxepdttQkhbKtXRad4gKEbm2n1zXGZKnmpEmI71NjVSueuWHiAMBlvxFb0d5Fcp8xB968UtdSlhYbX/Cum07xFggM200XvuJxvqj0byf+eZyKijtm3At2yCKybHXFfGW/EVvwX8Uyc4+oqXF9Cba+8V5TsYrgklc8+tVZ4PMdWxV+aHJ3IdwxxUZ+706LStb1C999irHEFUJjmpEt9p3HHPpToxulBHQDNTuVWIAjHY1LvccWkroptMG+XBOMrzWbOZEk4HT1NXwMSEn1p0kKSDkA88ZqlaJnJOSM/wCzvcElVxlcj61oxSCKMBgRwFOfWnWmE25AwT09qivWVD04DZxmh6uwl7q5jG1nTxIDLF1HQjsaz7KfeDHJxng+x9a6GKVJl2H+LPFYWp2jWdx5qj5ejVrTk1oy5JTiSzRZU5HzL19x601CzBkGSD1xUkMvnRjBy6jI9x6UqoueDhX6H0NdHmjkXutxZnX1lC8ZLKwfnPGK5s6XNy6q+OxFdeLdpnDSFmUnawLdMVHczx2z7PnyDjnpRNa3Jv3OSg1C4s5Nr5wPWtiK/t7xMNgE1auNKju4BIo+YgkmufuNLnt5Mx59RWTWtkXyO3NFl+fT/lYxtwR+FcvexNHOQRzWzBqU1uQkwIFUNUdZjuUd81Mr9TWlN3szPWp4wD1qBalBxWbO2G5J5AY0t1EiRcdcUivVa5kZuM8URbuFZK1yup5qxFVXoanjcYrVHEyWVO9VyKsb88Ujx/LmmIrdDViOXHWoSMU0CgC+JvQ0jEOcVVQkVMpwQaLhYf5RHapI4ixxTo5lJ5NTtIgAIA+tAEUqrGtUt2TVhw074zxSyWmxQaVx2KrjioCKubMrUDJg0xESnBrQgHQiqBFTRTFODRewGqdsiYPWqcsW3kdKRZ896eZARzTQrlUZJqeCPDgmnLFk5AqToKBloyhUABqEzgDJoMZZCc1WZT0NJq4J2CWXzOBUsFuzxnI6U2FFDZP5Grn2lFG1RSasO9zMmttrkCq7xlK1W/eMT6moLiIHtQmHUzc0ualeEjtURUincbQbqXdTKKLkkm80vmH1qKlGaLgTrIcUu45psYzT2QimhDw5IxSqCfSoQxFSpIRTAu26MPSrAszI2Sin8aqRXO3stXIr5B1jH4NScW9gUkt0SppIb/lkfwNTLoWeisPwp0OoxDHDj6GtK31aJf8Alow+oo5ZofPT6lFNAlH3SatRaBIfvNW3a6nHKwAdTn2qfVWkS08yMdu1ZyqTjuioQhPZmTHoMS/ff9aux6fYQjLsDXMjUbosS3GKrS3888m0Sn8KOeo/IpxgjuUuNOhGVUcUxvEVpE21FT864eeZ44/mdj+NFpGJF3nrS5ZtasE4LZHYXPi5YV+QnPtXK6v4ga9Ynk/Ws26djIRUKWN1cnEUDt9BR7NXuPnbVkgN8zDBX9au6LC1/fqnbPPNWbHwhqF0w3LsHpjJrv8Aw74G/s5vPljzkDljitNiUjq9DsRZ2KAj5iK0XlSJSWIHfmqV7qtvYwfM+No6V55rnjJpZDBATz2FQ2NJs6XXPFcFkHEbDd615nqut3WqzHDkJnqar3QmuG82ZmOT909qbFCTjjApFpWI44sdiSe9W4bdnYcZPpVm3syxHateGCG2Tc5A/maLl6LVkFnpnILj8KuzXVvZLgYZx+lZt7rIRCkXyr7VlwM92+4kkUbkOVy/cXct055OKiERHUEmrdnAGcDFbEemp1bpQSU9MsPNYFhxXSW9pEowFFV7QLEwVF/GtdFCruYj6CgDNeJ1mGxCfxxU7STOoQjAqd54zIqqpPNW3CJbqwUA0AQWNsImDE81tRgsOazrBWk+ZiCM54FXLmVoY8rkUxMstAGXpVfyFQkjC/WmadeNcHDAYqe6iZgQufwoASOKOZhkjI9qvfLGmCeKyraJ7Zt0hPXPNOurhpB1wtAD7i7UEiNRn1rPnvFt42eRhnFZuoazb2KHLDd9a4TV/Ec145SM/L7UgsWPEerC9uCqnIHFY8KjFRW1rNcyZIJzW/Zaeiuqtgt39BUSaW5tEqQWjyAFuFrYhsVjgDKDk9MdabfYhwsYGScA1sWypFblpDzt4rGc21dFrezMSK1uZRyNqc55q3FaPIPL2p+NTtNiXEcSkdM5q0jeVICyjbjqKxcupqki1Y6YLZNxCgn15qa6gLQnaBn/AGasW13FKnBJwB0qS5IMRKkisW3e7LSVtDl7aKS0cyOcjdnBFYWtXE93KyrGpXPGBWxqM1wkmCwZR7YpY3tjCNwUOMMc1vB2szNq6M1QY7Yr04C1twhW0wJz93tVOZ7eUY2qwJzlWwacpXAWOV1PoRVt3Rk00yrDaSW7kxy8578Gn3D3LJhiMdzirn2gwj98yP7EVj6nq8XIRVB9F6U023YNrtOw0eXBlmOT6mqF3quMrH+ZrLur9nJy34VnvMzmtlDuZORYnvGdjzk1TdmY5JpD1pQpNapEMaMmpFjzTgoFSIQWxQAixe1XbZQCAackQK0u3aaV7hqiee1EkeQKzRZ4fkVtW7hl2mmTQYORQnYbVyiiBBgDmrMULSGnRxAtzV+MKBwKdyWrEaWqqvNAtQWyBVtULdelPZ44VyxouBFHAFGTTJ72OBTgjNUb3U8ZVDWNJLJO1AJGnJqrSvtB4pj2z3AyvNMstMlmYHBrsNM0xY0G8DNA9Ec9Y6IzsCwrpbXTo4FHAzWgIFT7op4WkJsiCADiirIj4opC0PNYNOluACkZwfatW18KXVwRiJufavfrL4b2doQuxWA710Np4XsrYDEa/gK6Pcj5lXR4HYfDe6nwWjI+orqtP+FfALr+Qr2eKxt4h8sYqwFC9ABS9pbZC5jydfhXAF+5+lRS/C+IDhcf8Br17FGBS9o2HMeEXvw3eMEopP8AwGuU1LwZcQE/uifwr6feGNxhkBrPutEtLlSDGOaOZPcOY+UZtHeEkOjL+FUptPVRxX0ZrPgCG4VmiXB9q8z1zwPeWjMVRiPYVPIt0Nu55g9oRnFV3hZe1dXJpcsLkSJ0qjcWq56VLuhcqZz3INTRTFTzVuaz6kVRdChwaE0yHFovxzZ6HmtK0vMEK1YMLHNX4v8AWJ9abVyLuL0Nt2MJEsZxW1Y6xLdJ5OT0rAlOLUfjV3QhzI3oAK5pwjKSO6FRqNzpPkt03ucseaxdR1POeePSl1i7MchGegrk7q6aZjzxXpUqMYRueVVqzqyt0H3l80rEA1QClzT1jLnpVpIgi5NKrVUV5m9GiRJCFGTTHlQHGald9ykDtVB1O8jLfga5Pek7s6nKMFZD53Vl4/Wm2zEqwPbpTfKBP3c/U1LHtTjaoqzB6kVwAJGx7VH/AA/hU06E5b1NQj7tBY+2PIq3eDMbH2Bqlb8N+NX5/mh+qUGfUitD860y9GCp9GNFqeVqS/Hyn2YVUthLcW2PysPY1Tuv9aD6irVoeRVe7GGU/UVDLg9StRRRSNAp8cTSHAFNRdzYrXgiWGLc3XrT2IbbdiCOyVRl8VLvhi9PwqtcXTOcDgVWJJppNhsX2vUHRc/Wm/b/AEUVRoquUXMXvtxPYflSi9U9UFUKKOVBzGms8LDnj602S6RBhOazwSOlJkmlyhzEsk7v3NR8mhVyamVMcGm7JArtkNFSSRkHNR0J3BqwnNPWRl70gFIRTEXIrw9G5+tWQYZxjjPoayqVWZehqXErmvuXZbAHlP1qlJA8Z5BqzFeMvB5HvVtbiKUYbj+VTqh27GPnFODkVpyWccgyvH0qlJaOnIGRRow5mmCTEd6sR3BByDVAgjrShsUmjWMzoLXVJYiMNW5aa4r4DnB9a4hZSKnjuCD1qGjRS7nplrqXQpJn6Vt2muOmAxyO9eT2+pSREYY1uWmug4EnNF31JlTi9j1OK8tLtcHCsaUxSQNuiYlcdq4W31JXwUf8M1t2esyR4BYke9O90Zcs4O5vNqlxEgVvMwFxkVWfWJmPAb8TTodQt7rh8BvWpJdPjlG6M59xVU400/eQpTnLZlA3kz7sscMMEA4q9bag0iES4Yjg+uKoyWckfbI9qiA9a6nSpTXumPtKkH7xI+VnLJkYOetJLIWYK3Hzd6IGCyFW6N61rppyXoDKhy3PBzUznGnoyoRdRXRmSW5WHd3HUU6wvWtZQM/Kf0rof7KLW+05GQAeKw7vSZYM4R+Dis4VoVU4SZcqUqdpROrgnS7hyCQe+DUE9gHycn14rD0q7lt5dkgI9M103nq2GHQjNebWp+zl5HdSnzxuUIo47P5to69TV2G5MqMQWzj071FMokCjnmnQEqMHJ571k9dWWRGWQPnjGO460KySHbkI3T2NWmQf3eOvNQTwIgDgjGc0XGV5tPSSNsAA9x2rDk02RZWCkcDPFdNbuGXDEH3HNWNkfDEKeMcVvSxE4aIynRjPc5FluLX73zLQJILgbXABreu7dZFIXAZeo9a52eymjdiFOK7KNSFX4tGctWm6e2pHPpIYEphhWFf6HHKCrRgn3Fb0d1JCcHkVaE8FyMOBn3q5U2vNEwq22Z5nPol1YSGSykZD/d7VYsPE09lMq3QeJgfvDoa7yfTFkX5MMPQ1z+oaCkoIaMH2IrCUEzpjWT+I0ftula5aBZH2Oedw5Brz3XtBktZd0SxyRnPzItWptHu9PkL2cjIR/Cafb668MgS+iZD3YdDURTgzSUVNaGRpEMkMpbyWPO3O7Ap2pXyi4UNEMoSOnY12EMelX8JIUAtIH3qfpVC88JrcStIFMsbtkEHp0rV11s0c/wBWb1KmgatH9mELA4QYOOoFdJcXFvbIQrFise5CR94GuMbSG068BiXHOCA2citO7vBHbRqXB2A8N1wa556vQ3ppxVmdrod9G9uAoweoPc1PqzyTg+ScBwMZGea4LSNVMDAA9O+e1dPBrKOZo9x5ZSBmsWmmalUpNkRvsLBucnFaUv2SwsvOuFw56FTSx2AuLozMqlR1Vh1rK124hjmEW2MopyUZsYqlq9BtqxW1LWvMgJVG2q4xl8ZFclPeKS3IGGyARWzdTx3KeTFEhDsBxzior3wx5cCShcE8nniumLUdznlFy2Ka3Fu8RBZkyNwAGRVG10w3GqI0YyueT7V0ltopbSDKuFbaBwM8U/w4sSXTrKMEZApSmlewRh3MiTQbgXVqVjwCWBNV59NmW8hKbldAScH0r0qaW2aMFVG7aeQM4rjb4w2rSyktk92GKiFS5Uqdi+9il7aRRlSHntS4Gf4hXM6jpot40YqcMByK3/Dl69zPazt9yObyv+AsMVJrVq0tsy/88rkxsPaq5nFicYyVmcJJa5GRyKpyQsn0rsbfSxJE4OAysQRXO3o+zXbxOMYraM1LQ5503HVGaGK1MkxB609o1kGVqBomQ1TV9hKdtzWs9WltyMMcV0+n+IgSPnwa4AORUsc5U8GptY0Uk9z2Ox1xXADNj3rZiuIbgc4+orxe01aWEj5iRXSad4iwR8+PY0aPcTguh6QYAi7lwR2xVCUku3JxkHFULLXVcAFsVqrJDcDOcE96XLbYh+ZE0YwSBzmkt0yeQeOlTvC2w7cEZHIp8UIjDZzyc1L21C2t1sRXUWxH2Lzt456VmqfPJRhnKitN5BJxg8jHNV4oNjq2O5NEWluTOLexTjtTC+SR7Z61a1GzW6swcdeDVow+aAOPXmrIiH2Bx+VT7T3kaQpuMWcFCht32HcDk7c+1Xhhl3Y+RvvD+6az9Ryl84BbAPY1YsbkEbX57MP611wlZ8rMatLmjzLcmdSx2sPTePX3qK4SKR0UDcxYce1WpEPGD86jKn+8KgFuC7tknIyh9PUV0LVWOReZEZxDmPaV+919Km8qN3UNjAQMQfSmNAksu5d21424PtVUSyRvG5yUC7GyO1Zu8SrlPUNNgkZVRR05U1zmoaVJANyKSPSuqlVnkGeZI2x/vLVie1TyWDA4LfLmotzMqMrO5515LL95SKMV2F5psU0O9FwSvT0IrmZbZlJ+U8VjOLi9T0aU4yXmVCcVGyFhn2q7LbfuyR6Zqsh+XFOKtqZVZ3dkVGGKQHBqSQfMaiIqjEmU5qyql1qpGeauQtj6UxPQrupBwaZir0kYYZHBqo6lT0osCY0GnbqYFqRUpiAE1IqsacigU4uB0oAs2yqvJpl1cjOAeOlU5J2HAqszFjk1NmUXlcYphG41BG/qamQ807gRumKiPWrUqkjOKgxzQIRcjpVhDnrUXSlDUwLkcwTAOetTmRGU9DWaCSalXNGwMtmY42g1IkQZMtVVMAgmpnulCbQanqNaoikXY2BTKRXLOM9KsvEuzI61V+4rDYWwcnpUjlWNVuV6U3JznNJrsEXZ6lkxqRVZ4AScVKJsDk05P3j8YqEmmaSaaM54SpqMjFas8PHTvWdMhBq2Z30IwM1IqUkYzxVlVB4NADFXBzVkKHWo9u3inRttb1oWoPQjaLaakjjUnmri2puB8o5o/su4XorfhT5b9QU7bix20bY+YfjVuPTI5O6fnVMWlyh6N+IqdPtKen4ik6U+jK9rB7o0E8P7/u8/Rqp3mnyWR6sOakjvbqP+AH6GnSvPeH5lPNOEaqer0FOVJrTcn0Vma4UE5+tdhqJYaaFUHp0rndN02RGVip+tdjbQC5iWORc4FLEu6QYZas4D7HcXG7bCwzxzTLfw3ftMGAA5r1220a0hQFoVyeeeaueTbQ8rFH7YHNKOxcviueYjwZcXMQLFj9BV2x8Euh2bGYepOK9GQPLhVX/CrsFmFILkE+mKL6CSszjLH4eQsQ8ixr9RmuhtfCenWgG5N5HpxW6zrGvUACsXVNdgsoiS4GBSbH5InkNjpsRKxxpj25ri9c8bxQlkjYZ6cGua8R+Lpbt2igbg1x5V5XLytuY+tTuUo9zU1HXbrUXOGYKe9Z8ceGzyTnqakjhLEcVrWels+C/C+g70bFpENpatcgDGecGtWDTEhyXwSO56Cpi8Gnx8kA/3R1NYt/q7zZUHavoKLhdInurtLeTEe1iO9Zd1qbvks5rPuLsnvzVIuznk0JENtk0tw8z4GTmut0OyxbbnHauZ0uBZb5FPTrXpUWnkWcYQdQKYrDbDTVYllNWntZS21ckVbtYDZx7n5qzFfxt2BP0pCbCwsFGDIMn0rRktVKEAAD6VXtpWd84wKuLKJHCkjOfWmBlTRxWh3nJIqos8t5KFUfLXQ3WnLMnJqvBYi2xtHfrigLlm1hMFuCcdKrSytO2zaQK0FlzFhzx9KpTXEMJJQDcfWgCe3hW2j3BeaqNfyGYqoNWFuBJCTIcLXP6rrlrYK20jNK4W1NW6vkgTfI3OO5rjda8WBdyQtk+orndV8Q3F+5VCdvoDVG00+a6cFs496XqWoiT3NxfyFmY4NW9P01ZpQGrYh0iOODgbmqgjPY3uTnaTUOad0ty+VrfY3o7COzjYkD5V6D1qpbhp9SwPuqO1XGlN7ADuPA5AHWmRzR2XzdCfzrnTf2i2tNCtqJC3kSnoK0XlE8CqjIT/AHTxVTzUvHyZFz/dcVObVkj37Dgd1ORTvdWZLTi7gsbIwyGQ46MMj86sEZjUH5TjqG4NM0248yR1BOAOh5FODhpgmBjd2FRJWZpCdxLctE5O7GPyNdBayiVccA4rnTcCKVkCOcnHPStrT2XbvZvbpUVFoaxKPiZf3W0KCdvpXM2lnJs3N0bg5at7xBfqW2xnPOOKyVuwseWbcR6jGK1pfARJq5Hc2gZQQ20KMZFRR3ZsYz+9OPfkmql9qw5AYE/oKwLm+ZycsSa2UHLcwlNLY077VmlJwcCsaW6Lng1A0hfqajrWMVFaGbk2OJJPNJjJpypmpAAtURcaqetOJCikLelKsbSGnYBpYnpUsCNuBNWIbT1FXEtgoyaBXCEcCnvHxkU0MFOBVhRuWpeha1K8ZKNWgpEsfvVORMGnQSlGwaT1HsOZCjVagKgZJprqHXIqtKWVCBQmDVyzc6gkS4U81i3F9JMSAeKa6O7461oWGkPMwLCqJtYz4LSS4bgGt+w0LozrWzZaZHCo45rTSIAYApXFcqW9kkKgAVcVcdKfsxSgYoEOQAjmgrg0DinAZNABzRSkEUUAfQtFFFUIKKKKACiiigAooooACKp3WnwXaFXQc96uUUAea+JvAyTI0kKDPqBXkGt6Fc2ErB0OAeuK+p2UMMEZFcz4g8KW2pwsVjG70qrp6Md7ny1JujJBFZtyu45FekeKfCM2mysRGdufSuJltCpIIqeWzG72MeIYetGDmVKa1oQSQKktxidaZkzSuOLdR7VqaGuIWPq4FZVz/qkHtW3oy7bVD6sTWK1mjovamzN158ySGufjiLmtjWm3O/uaXRdKlv5VVFOD1Nd9WTjHQ48PFSepQEYiXJFVJ7rBI5r1yP4fLNYhig3EevNchrHgO4tNzpkAdnrjjFSd2zslJJWiccku5SeahdwXOSPxq7NaPa7lcAY64OapNwxJP6VozH1BSg5I/I013DOAh707ywRnC06KJTkgdPQ1IDpG/dHPpiq3r9alnODj0qBe9K2hWw6LiQ/Wr7cxL+IrPTiWtEcwj60yHuU7c4NWb0ZjY+wNVYuJCPQ1cuRuh+qVb2F1ILQ/MtMvRjHsxFFqeRT74cMf9qoZUdyhRRRUmpYtE3Sr9avXjlYgB3NUbR9so+tXbwbowfQ03uRHdmbRRRWpAUUU9FzSbsNK4yinsu2ljXc1F+oW1sLGmetNZfmwKsPhFxSRJuOTUc3U05egkUWBk0NkvxVnbxULEKeKhSuy3GyFfHl81UPU1M256QQE1UbLciV3sLGAVqN12mrCRFKa672o5tQ5dNSFIyeaftGcYFSHCjFII+9HMHKRshxUeSpq6qfL0qvNHg5ojO7sEoWVwjuXj6E1civFfh/zrNo6VbimQpdzVaCKYZGPqKqS2DLyvIqFJnQ8E1civuz81LTRWnQzmRkPINICa1yIZx2J/Wq01gf4KV0O7RVWQipknI71A8TxnBBpmcUmilM14L54yMNWta6464DnIrlVkIqVJsVLiaqZ6Faauj4w2D6E1vWervHjDnH1rymK6ZTwxrWtNYkiIBJIpaidOMj1u31eGcbZV59astawXC5jYH9DXnVprKPj5sGt2z1RlwVc/nVRlbZ2MpU5R80bE1jJE4YDIB/GtfTrlLeFULbSO9ZttrCuAsoBq6qwT4aMjPoadWTnG0hUmoS00NWTUxDEWLscnBxVH+3IZ2xIQ3+9xWfd207DAOB6VmtGyHDAiijhYNavUqriJp6LQ6pHspmUg7CR35xV0xAoGQgj1FcSjvGco5FaVprMluRvOOevaprYOaV07lUsVFuz0OlgJAw2eBSTsI2OcYwKitb+O7XjAb+dPu7dmQkAjj615zTTsztTTHq+4enb2qnfuzREKwBK4OOaW2ZlBQknNPeFs8j5W4pxdmDRm6dHcFsncp6nHQitEo5B55z2NS5ENo21ecY61zyanPb3zB/lGfrWig6jbiZ8yhudCjtnEjBgehIqw0cMqFTtDEY5qmZkuIi8cikn8KYLho1w549Rzis9Ysu6ZQv9MAZnTgcdBWQ6bHK5BxXVFmkjO1iQT1rAurScylghIJwOK78NXl8LOSvRj8SIY7h4+hyPQ1ZE8U42yDB96qeS/cEH3pmecGu7khUWm5xtyhvsTT6bHIPlwR71gahoMcgIKA+xFbizSR/dPHoalEySDEi4/lWE6Mom0K3Y85n0S5spDJaSMhB+72NSWniC6spAl0rxkfxL0P1rvpbFJV4wRWLe6LHICCmfY1zygmdUK99zPd7PU0WQFQ/Pzp/UVz2sWF5GpZV82PbjK9avXWgz2khktHaJvTsaiTVprZtl9CV7eYvQ/Ws+VxNdJLRmLbR3B+YAD5c4PHFTm8lglDMrA4HNdbBPpt5CDsRj5ezco5FVpdGQ7XU+agUDKjJH4UnJN6oFF23NbQNYeWECVGAx97BNXdQ8PQ65MZfLDErjcOKxxc2dtHsBlDkHbjjpT7XxVBbEbHLZHIZsYrKzUrxL0a1Jrzwk2l2iPGCDGvVByTmq+mQXmoRvbypgrJyGbk8UT+OPP+RwSCOimsqHxVFDq8flRZzLnLda0XM1qjP3VszobfGnO9pIjeW5IG49K53UF+x3hkj4weRntXZT6X/adstwV+6+Tg8jNZ+q6TG67dyrIVAG8cH8am6KSdzEtr8lM7iDjIxWLquoebLtlnDL6MK2odIMMoEu6Me3zA1na5pdq0YZF5OSCG5wKqklcVWTSHaRqkH2a6hiZSwRWXaOmDXV6jEs8d4VQESwrOpJ74rzTQQINaEJ+7KhT8xXpNsGu7bTQckSwPbtj1XpV1I2ZMXfUwLNW/tFo1+7IMHPY4rm/FNsUuIJSMFlIb6iuqVfsLwyOcuD8xPoDWP4siZ45HI4SbI/3SKIOzCpG6IvC1tbyW8bSuq7nKksM1Jc6daXbPsKo2SAy8qaz9Bf/R5Y+8ciyD+VRSefY6rMIXIBYttPTnmtFdvQzkoxWpXvtHntiSVyvZl5FZbRshrt7a7WZAJF8tiOc8qar3ek21xkpiJ/blTVc3SRly31icgHIqeOcr3qzeaVNbN8ycdmHSs9lZDyKqy6CUmnZm1aarJER82RXR6f4gIwA5Hsa4NXIqxHOV71NrbGiaa1PXdP15GIywBPY9K2hcxXKddpI7dK8btNVkiIBORXR6fr5UjD/gTT0e4nDTRne+UyMCRketSwx73A7AVj2GupIAGIB9DW3Hqdske7gNipdNvYi/LuSStDbhQ4H51WvdSghtSqNzWFqdy91LuRiB7VmMJG+85NVyQjvuNc815EFyDPM0nrV7SdNZ5DKwwMY/CizszcTBQPlHWt6422VsI0HzsMVdOLnK4Vqipw5VuZVzhrjyYz/q+hqKQBDtJxu/8AHTV2OBbaJp5OvU/Wq1rC1zK8sn3W/QV0OVpWR51tNRiw4MJY/MuQaiSBfs43dWfa2e3TFFzNmcqp+XOOKDHJKsmBhio5+lDd3ZmnLpdCpaosquQuVJXP0qOQecqx/eHJIqwAGY7uC5Dg+/emqE+UA4JJ2H+lNRsxEBtFW2QKQcg4rPlsYoVk3hQRjqOtbdpDtwWxtG44NZl0++YKV+Urzz1qHDmnqWpcsdDDv7MGJvLVcqe3oa5h7d4ZCGXiu+2RAYIyAAM+1ULrT453HAByUOP0pygugKfc4yVARkVUYYrobzSnjxtHWsm4t2jB3KQc1i09jSLW5SBwasRy8VBjmggjmlctq6Lyy8etIQJDxVVGPSrETYce9O5Fh4jwelTRQbzV2KJDEGPrQuwk4x9RSuOxSuFWMY74qopy3NOvmPm9c1ChzihNsGrEkoGKrkVbK7lqBlwaYiLODVqHkVWxUkcmw0DNJVVkwRVWWPb0pyzg96Vn3UxFXBNPVKnEWRkCnpHzzxSuBEFAp2QBxSzlUGBUAbNFwsNklPQVDvOck1I45qIigexZjfOMVeCtJFwazYDg81oRShRRYVyI7l61GX+lXJNsgzj8RVGRCGp2EIWyav2Qwckd81TSPNW0lWNCO9A3sTXEikj61mzfN0Helkm3PweKt2aK+A3rSaF0MsEq1WUcEU69hCPlfXtVZQRzQCJ3kzxmn22DMu7oeM1WJp8ZOaLDudTs8m3EijdimR6rH/ECKsaPLFdxLDLjJGKj1jQGs4zLH93rxWbcU7NmqTkr2LEWp27dWH4irSXVq/eM1xodwepqQStT22ZNk90dmEtJP+WaH6GrUC2cS5K4/CuGW5dehP50/wC3S4xvf86G5tWTBRgnsd6NWt4YyFAOPQ1e8O35vbs4Dbc45rzNHllYADJNen+CtMkSJXbOMdB61Kp63bNFKy0R2rSD7qfKenSnRWrOc7jj1Iq1b2wQZbk1OxVBkkAVZAkMKxLx1plzeR2yFnYDHvWVqniK2sYzlxmvMvEHjGa6Zo4CwHsalsFFs6zX/GkVsrJG4JrzbU9dutSlJLnaazXaS4k3ysWJqWOEkgYyfQUJdWWkkRpHnnr6mrlvZvKcKv41fs9KZyDIPotajfZ7GP5sZHRRQ2UrIr2mnJCu9u38Rpt5qsdupWE892NUrzUpbg7VOF7AVly208gyCc0bkOTYl1flySWyazJLhnPFWf7Ku5D0FQSWctu2JExTtYW5DtJ61IseaesWamVNopNlKItmTb3CSehr1rw7dR3loqkgkDivKY0JPPSuq8L6mbW5WIn6UJlShpoegXdn5y7QfyqKDTo4fvHFE9xIAJEJ2nkVW+1TTT4x1GKDKzNJ2jgiwvGe5NVrSORpy/b1q6LQPErMyggd6cHW1j4pjNFOVGfSmSyxRjnrWYL6UjgcVUleR23yNtA96Qi7dyO6/usgfWsuSSK1UyTOCfc1Q1TxJDYxFFYE+1cDqevXOoSEKSFouNI6TWfFuAYrdvauPllub+bLFjmpLTTpbhwzA49TXRQaW0EW5Y/x61MpJFxjcz9P0RsB3Qt7AVfaVrM4VCMegpPtVxbvzHIFHcHFaNnqNrOwjkALf7Y5rGUpPfYtW6blaK7nucYK/ULg0zULEPCXBye1al3Nb26qYkX5ugFVkZplKsBgmsrpO6NFqrMz9IvGU+Sx5H8q0pLYK24hADyN/NYl5A9rcCZQQM81uWlzFc2eGBJ6jvTn/MgjbZlS8iFuu4KvHoeKvQyj+y93TIz1rNug91KVA2rnvWi6RixEQLZxjgZp393UzcXzaEWjLkTOTyTUTGaK+ZsBkzwQaW1WW2X926t+lXYdspzNAB/tKcUNhF23GQvbNIGkBDe1S3l6I4wI32r6dzVPUZ7a3XMcjZ/2q5e81ZjkKx+tJQ52W58pp3eoIqndj6CsK71NnyAcL6Cs6a7ZyeSTVZmLHJNdMaaiYSqNkss7OeDUJNJzT1T1rQi40AmpFQDrS4ApCSelArik4FIAzngVJFAznmtGCz9qYmU4bUtgmtGG1wBxVhYVjHNOBJOFFFwtcQIqD3ppDScCrcVmz8mr0dqsY5ApBsZK2RHzGk4RsVqXBAQ+lYdxOqydaHqNMtsoZaqspU1NBKHWnSJkZqU7F7i28vG00+WPPIqnko1XoXEiYNDXUaYy1hQy/NiuktY0VRtArnDmN8itrT7neoBoTJkupsKKmRajh5FWBwKZAEDFN2k9qcq7zVjaqrQBVxQOKew5puKBC5NFNwaKYH0NSUtFMAooooAKKKKACiiigAooooAKMUUUAY2taHBqkDKyDdj0ryXXvh5LHI7QoQM+le5Ux4kkUh1BHvTTsNM+W73w5c2pIeJvyrHbT5Ipt208V9R6h4asr1TlACa828T+FYrUsYl5HpQ7DsmeSXR5UfSt/Tl2WSeyE1i6hA8d7sKnrXbaFosl5CoKnaFA+tZQ+O7LqL91ZHMQ6HcardgBDsLda9V8OeF4dMgRnQbgOmK0tL0W3sIwSg34/KtGa4jgRnkIVRXRUq82iOanBxRHK6wpudtqAda4Hxh4ugELW6BWHYdc1W8Y+MsEwW7ewFeZTXMk8peRiWNY2NGOupvtEjuUVS3pVN4Q3OPyqXdSjmi4inKmFwOv0pkEe0NnvV1gD2z9aiMecjAFO40Up33OcVEvX8K0f7OablcD8arSWM8LfMhx6iqWwXuyuDhwa0YjuhI78Gs9lKsM1etuh+lLoTLRlZlKXDZxg1dZd8C49CKq3BzKvHUZq3A2IcnpnFVuge5QgBU7Twc1auYjKjEdwKryNunO3sauw/KmWGR6VLHtLQyGUqcMMU2rt3IjEhT+FUqk1FVtrZrUidZ4tpPNZVPilaNsijchpp3JpoSje1Q1pwOLkYOKr3NuIzxVp9AtfUqU+NsNTaBVMlFl13rkVEnyNzUkZwKcdp61ntoab6iuN4yKfEMCmqVFSh19aiW1jSO9xT0qPywTmn5zSioTaK3ECgdqdSHpUDylDTSchNqJYIzUTjA4pi3G7imySEVSi7ick1cYVYnmpBJs61CZTSA7jzWnKZ83YtpKGpJhlajXalSBgwxWbVndFp3VmUyMGkqaWPBzUNbJ3RjJWYUUUVRI5ZGU8GrMd6y8NyPeqlFS4pju0aiyxTDBwPY0yWwV+UOKzwxHQ1Yiu3j78VLTWxV7kMtrJEeQai5Fa6XccgwwxTZLSOUZXH4UvUeqMwORUqTkd6WWykTJHIquVKnmk0WpmhFdEHg4rUtNXkhI+biubDYqVJiKlxNFM7uz10MQGOK37bVeAUevLY7kjoa0bbU5YiMMfzparYbjCW563aa6QAsmGWtNJLS8XggH0ry2010HAc4NblpqYYhlcZ9Qaal8jN0mvhOun0sj5o+lUXgePhlOKSz1yRMBiGFa8V7a3a4OAxreFeUd9UYSgm9dGZtpcNbSAgnFdZZ6glzDtLfNisObTFcFo/0qmBPZyZ5wPSs61KFbWOjNaVSVPR6o6ZomD7wO2704qzGmYyCG5wRzWFHru2MKz84xhhV631aJwF3KOwx0rgnRnDdHZGrGWzLzRKzn5flY4NYWsWIQB1wGHT1rehdXUEBcE5zuzTL6Dz4io69cYpUqjhK46kFNWMKxWWGNHlIYLnINXI7iG6TblUfOBg1BqFvMkMixg4bBFZiWktqwdQ25TkgjiunkjUTlfUxUpQfKbKyvaNzkZ7jkGtO1aK4U703Y5zWdaZmtFDDcT6io4y8EpI+XGBk9K57atdTbpqW9Qs4lhdkPUBlBrmZsee+B3ro7y/UQspZSRxzXNyurSEjpXdgnK+pyYpRtoNPNOB4xTQaQHkV6R55OjsvKmp1lSQYkWqozjpTlVnOFrGcIPc1hKSJZbFJFOzkelYt9oiSAgoB7EcVsAvCRyeeasJOr/LKormlTa22N4zseb3nh6W2kMlszRN7Hg1HBrN5YOFu42x/z0T+telzWEcynYR9DWHe6GrAgpj8OKxcEzqjX6MwftFlqdv8Af+Ych0+8D9Kwp/D05neSFvPjJwAODWre+G2jcyW5aNxzlaoi8v7BgLmMyKD99OCKlXiynFTWjKq6E7IN0e0jnG7BqmthHZytIfN3hwRurrI9StNRg2bEZiRlhw4rB1vRbjDyW0jSoHBCk84rZVIyVramEqUlrc9L8Kaol5p7xLzuBOKoagHad1D5AJ+SToK5jwlrIsE8mVymW9OVrqbKQX16JBIHUkgnFcM1aTOuD905u71B7clN4Q5IITkY/GubneV7glnZwuR9RXp2raZBJtbbk4xggCuX1bR/LtElRRwDkirpySZM4uSscnb+XFe2s3GVwTzzXpWkzLb6fI4P/Hrdhxn+64ryQMVyM4K5r0jQJ/tdpdxZz59kHH+8tbVdVczptrQp61ul1KaMOjeXkhDxkGoNai+0acwCY326uOc8jrWxNYC61BHUKd6KeevSpdR05LbTYlI+47RfgawUkbtaHnGjti+MfaVCv49RVnVY5BPDcYyHQBvqOKooTa6ijdPLkwfzrtzYR31hwPmik/nWvNyu5Moc0Tn9OuVMZikKEMpwWHIpHWeCbzYWJTAypPFP1Cxk087vIKqedyms6G7kuXMaouc45bGa25oyjc5YwcZF2LV4Wcwy/IfRuVNFxplvcjdFiNj+Kms2+0i4UmTCD23ZpunXVxaSbGOF7q3IrPZXTNXFS3ILvTJrdvnQj37GqTKyHkV28dxBcJsOEY/wtypqjd6PDKT5f7p/7rfdP0NVGonoyJUmtUcusmO9WIrgqcg0+602a3bDIR79jVMhkPINXa5Clbc3rTVnjIDHIrpLHV0lADOa8+WTFWobpkIIYip1WxfMnuepxPHKmVOTQtqZZtoBPBPFcNY63JEQCTXoOg3CTRiaVucflSSbZcpKMbmpb2sNhbrI2QSucN61Xjia6nMr9O1SSP8AbbjZG4KKMAmpb2UW8XkxDLsADgV135I2W55km5y5mZt0ftU4hT/Vqefei8YW9uIYx85HOKtxRrawGVuvaqcSedLJM+eBTj7urJ5XJ6EEVqiD95tzuyMjrVrzY4FIUKQTjBp9yFllZeQvQH0NVZUWRgGHDDBx2NNxUtWWpNaLYmNoJ8PGACP4aqXFskDDJA2tuAzzU4lZUj2jO5dp+oqjqMMtzbh1OR0yByKh88VpsVeMtxxuVJdEIIHNVbl0EyJwGx8rY9ayrZpbe6Cy554Oa1zGksSSHt8rDHPsaVLdtinJNWRBsWOPe39w5HpVRrhXztKksRg9ORVnUWKwsFboOKx4oWlVtnQ/MDnoRVSk0zK+pckAkadtpK8H6etQ3+lpcq21R0FOidojiQEb1IznvVwSqAHCn5lw3pntTVpFJtbHC3ekS27nAPHOKbDbB1ww5ruJ7cTxBioJHSsiewVVJTjPKn+lZuBoqj2OXlttjHH5UkSneB71duEfeeCarL8rUONik2zUUj7OR33VlzStHI208Zq2d/l5XnjpVBsuxBGDUDTK8jFzk0Rtg0+SPbUPQ0DvcuBxilVA4qujVYiJ6CmIgddppmM1YmQ5ziocYoAFyKmU1FmnbqALUcwUjNTecpHGPxqgMmpVUntTsIc6ea/+FT/ZQqZpsWEOSaWe6GMA1L3L6FeRAKrstTF91KVBWmSVRwalWYjrTGHNIBQBbWWnr855qqnFS78dKLgkWhF3UiqkwbdjpUyXBA55pjEOc4NMRHHGSavxsI09D70ltEOpqG8ODgVN9R20Ip5dz1YtIVlBBx1qmqFjWhDiNM1QFa4txHJx0pYot3QZpJ5Sz9au6Yqu2GGefWk2CQ2CV7aQMpKkVsT641xaiJzk461DLpnmNlcAe5p0ejr/ABOKlqm9WWnNKyMpVUy9eprWXT0njwhUtjPPFTppVuvVifoKv2VvDFdIQrlehyacpK3ukxi76syG8OXwAIhyD3DVZtvC13Ngbdp9GFet6daWdzYIpiQsBj0rVtdOgh6IuPTrSTRVrHCaD4EUbZJkjOO9egWdjFYwCNFA+gqdnSJeMACsPVvEMFlExLjP1obGatzexWsZZ2xiuG1/xvHCGSJgT061yHiLxnNcsyRM2K5WJ3upN8mSTzzU3uUka17qdzqkjM7nbn1qj5OGI5+pqWJArjjNbVtp0cwDN8zDoB0o0RRmW1k8x+UYX1Nblrp8cC72wB/eNTPJb2Sc4LjoB0FY95qMk7EA8elFwvbY0LrVEhUpBx6t3rGkmedsknmkijaV+cmtS10wlgSPzoSIKcFs7DIU1pWVq7PgrW5aacmzAGala3NscovP0oAqLZ+U4+Tg+q1Fq+iR3FmZUQA+1dNZr5sZ39O2ati1RoihAwRTA8UkgaKVkI6GlwAK6fxLpRtrguo4rmShJ5qGdEGmiSP5ulTQMYJlkHUGokG0cUpznJqbmqjoepeHdQjvrQROQTjjNbsdjGpyFrx2w1eXT3BQnFb48cyKBuyfxq0zmqU2ndHf3KyBlC4AqWO3DwguRnuTXKWHjaCSL94UJ/2qqar43QIViYZ9BTuZWZ02oanZ6fE3Klh3rz7WvFrysyQHj1rAvtWutSlOWOPrT7PS2kw8hwvqaXmy1Eq4uL6XLEkmta10tY8F1yfSpS8FmmFH+JpYr0zfL84B7AVnObtoaRimyR3w6pEQW6Adq3NPVVi3SxvuHdW/pWbb2zgh9vy9uKvRwSNICAwXH96ueUrmyhbYu+fBKQjYOTgbhzWPPBELtmQcAHpV7Zt8s7cEKSeazzL5c53jqR1og2zOpFKzJrrajRA/wgU6KSHy1zIUOSfmXirpitL5eTsJ/GoG0N0+ZJ/k+uadlbUSk07kF55d1CdvOBgmsqxna1uTC5IBPFbaxJbnaTnPWs3VLIjMy4yPSnF290ctdTRlXcokj5z1PpVURz+YGWbJ9M4qXSL6Jo9s2B2NJqWrxWwKqyH/AIDzSs07WHo1e5bFxHHHm4EchHqMfrWHqOuouVh49geKw73VZJycHC+1ZUkzMetbQpa3Zk52Ll1qDysSzE1nySM5601qTk1sklsZN33EpwUmlVadkCmJsAoFG7sKVUaQ4ANXodPY4JBpgU1hd+xq1DaE44rTitlC4IqYRrH2ouKxWhtQoyRVkEKMCnKjyHgVdgse7UhlOOB5W6cVoQ2SoATVlUWMcCmSTBBljii4DiVQYGKqz3iRDkiqF7qioCENYk908pOTxSBIu3mplyQtZTyFmyTRyx4qxDZvIRxTCxJZzkECtVDuWq0WnlcHFWRG0Y5pNFJ2IZkxTIZSj4zVll3Cqki7TSTvoN9zROJEyKLeUwyj0zVW2mxwasSDIyKWwLU6myuBIgq6WrldOvTE20muihl8zBFURJWZeiOBUuCetRxrxStKAMCgQ5sAVCetBYmm5oBsdmim0UCuz6GoopKoBaKSloAKKKKACiiigAooooAKKKKACkJAFBIArM1DUBFGwU0AN1HUlgUqp5rl5s3zHzOV9KdNI9zIxbOKliQLn8qTC5y174NgursSbAe9dDYWEdhAqIvzYq5kKM1Wu72K0haWRgABx70mxuTHTXCW8ZklbCj1715h4w8YsztDA3fjB6VB4s8ZSTSNDA3TjAPSvP3kaWQs7Ek9SaEhXFmneeQu5LMTyab2oIFFDFcQrTcYp2aTrwKAEzSZq0dOu/L8zyH2+uKqkFTggiiwyaC4aE5Cg/WtK1kS8J3Aoo6k9KoWto9wdxB2DvWpDE8ki29unzHjHpR6FqPVmdd6V9puVjt0yx9Kvf8ACI6jBEJAhYEdK9P8HeCRHturpcseeRXoT6baSwiN4EKgY4HNaJdyJNXPky/tpreVRKjKRwcipIj+4YehBr6C1/4eWep5MaA5HQ9q4DVvhtPYxuY1ZeO/Iq7KxL1tY8w+7cOPerzH9wuPQ0l/pl1ZXZEkZx6gUoGYFyOhIrJoezMikp7DBI9DTak1EoA5paF+8KaE9jWskCRbvWqt1KXc1bgObcAehFZ0ow1C3JXwjKSilrUgcrEVLkMKgozUtXGnYlKns1AD+tMEhHpThKfQUrFXJU3g81ZXpVMSk9qmSQng5rOUTWEieopIt1SDmnVmm0W0nuUChRqV1LDip5gNtVd5HHWtou+pjJJaDSjDtSdKeXBHT9aYTWhm/IXcfWnxMd1RVJGcGk9hxepc2hl5phhBNAlqVTkVhdo3smQ+QPak8gVZAo4o52PkiU5IcVEFzV9gGqMRAHtVqppqQ6euhTIwaTGavGFT6Un2cZp+0RLpsqYK80+O4eM8E1NJBxxUJhYdqakmhOLT0LkV8rcOKkaKGcZGD/OswqVpVlZDkE0cvVCv3J5bBhynNVGRkOCDWhFfHo/NTEwzj396XqNeRkBqkWQircthnJSqbwyRnkGkUpFiO4I71dgv5IzlWNY4JFPVyO9JxNFNnY2WvEYEldBa6okoBV+frXmiTkd6uwXrxnKsRU6ov3ZbnrNnrEsRGGyPetiLU7e5GJOGryez150wHORW/a6vFMBhwD6UJmcqL+yd1LZRyrujP5VSaGaA5Ukisq11SSMja5x9a2rfVYpgBKoz6itFUez1RlKNt9x1rqbxNt249MGte01BpZRg/VSazXtILldyMD9OtRpHNasCRvA6HoRWdWnTmrx0ZrSqSWj1OtKrJEpZMDNc/qVwyzHnK+wq9a6khBVtwK45JqSWWznwrYGT1zXLC8Zao6ZWa0Zhrqmzy1BPHUirzahG8GSMkim3OkROcxOpz6VnPpU6thSMV1clGet7M53OpF7XILiVpnJwSB3qEda34tMWC2DSdRn6GseaA7iVXj2rqo14J8iOerSm1zEIJ/Wlzz+NLHycc56intFjnsa3dWKdmYqk2romiG+MYzwNv41csockccsh/Op7GzVbVmbGcg1cgjXcxHY4FcNesteU7KVJ6XMW6jkLxnaeRtxULoyAFvpzXXC1ikTcUI44781l3tkCW2jgMOo9amli1syqmHetjGSYocqatLdqwxIKpT4jmZQRx6U0NXe4RmrnFzSi7FySzimGUI+lZN7pCsDuQVeSRlOVOKspdg8SDPvWE6DWxrCr5nA3/hoBvMgyjjkMvFZpnvrIhbqIyoP4x94V6i9pDOMoQKyrzR1YHcn4iueUTrhW7nExta3wBQhmz24YVo2JubJt0MgkXBypGDUeoeGgGMkQKMOQy8Vl/ar7T2C3CebGP4h94VlKJsmnsbj6wLp4xMSrdcNWu19Zz6WbcouQeCG61zUF/bXyhcq5HYjDCn32mv5TSWcu7IzsPBFJwi7W0ZinOnLXY43WbR7a6dljKozEjjtXXeBJtz2Ic8eY8DfRhWJd3W2Hy5VljccENyDUnhW88qaYDrHIkw/A81ck+WzLi02dbNNLp1xbkkHyXaMg+gPFXNR1FbvT50KgMm2XHvVvWY7dUvGcZ/eBxgfwsKxtOEE14YwGAkQxfNznisDXocHrUPlanOB0Y7h+NdZpV7stXbBIkt1kH1HWsLxLDtnhlH8SbT9QcVreFjHcWUcbk5AaPitHqtQWx1KQ2Gq2qtwj7fTNYGoeHYbbMixRSEc8nFQWeqHStRMBY7Q2Bk1tanqlvJaSN8hOAazXMnZbCst2cRP9pM3lC2RUHXvj8agms1U5znI6Y6Vdl1DM9yYIm/eIo9himS6oHmZZCqYwFBXk1rr0M9CqCUPPI7+1TrcSovyNuX+6eaGgknBkjXIb0pVgYDkFSOxqG7GiuOTUYpP3UoC5/hfofoahudKhmUtCdjf3WPB+hpZrNbhCMfNiqcMl1YEhvniB6H0rWD7MyqQT3RlXVtLbSFZEK1Cr10y3lrdZifAP9yT+hqjdaMjHdA2w/wB1un4GtOa+5jyOOxQtpMXEfpurphrEkNskaEjJxmuYS3lguQJEK45q3O5Hlrnmrg7amVTVWZ6DpeupZ229zk5xz3NbWn3kd+fOlPfI56V5aZ2CICTjfWiusSWtsFjJGSatO8rswlF9D0O9uBM+EPyL0ptqPM3oDweuK4q18RlVAdua39I1ZJdw3DcRnNKcuaXkVG0FbqzWm/dyt1wRhvY+tCQbxlzg4z7GppV3OzAZJUMPQ0y4/dxttBVSApraUrIzsROipwFKtu3DHNODgwzHkrlSKpjftOCd6cjHcVYSYKjIejLuGRUKbe4Gff2kc7OyA4DdfSlRAkJBbDbcjjrWlGqbRJtzuHzCoZLchc4z5bfmtNqzTRSVzGuCskQyArEHnsar2iqig4HQnBFbU9tGUaMAkA5XIrOWERMF2EduvUU38VzNxITEsyABcEA8dqYVWBsMTjAwDWnDblR90ggHiqdza72BYE7VyajnTlY0lSlGPN0GIxdUVQcE5zjjNRTQbVOQQpOfoalEqxReWmQ2cgA1OC0kb71xkZxmtN1czSOemslaZsjms27s13grjBGeldK8WGBHJHI9x6VXksl5cdD92oe5absc4oMbBHAIPcVWu4thDj6V07W0UrEDAKgDp1qrqukssJZR15pTkjSF2ro5aQEiqzCr2MAhhVeRPTmpGQrwatRGqp4NPSQrSRTVzR+V15qtLHjpSpNnvTi4aqIK4SpFjqcQnsKkSH1BpDIAoFO3DHFMnfYwHFRK2fei47CyStnAquWJPNSyKc1ERQIkRqsxqWGKpocGrkTgUIL2I5IipqLpV5mDiqzx88UwuRAmnYJpwTFO4WkAirUgwOtRGXHSmby1MRfjuAgxUchEjZzVdAanUeppWQ7smt4gT0ouAV+7ToplSlaVWPWhbjb0KGCW5rTs0ZQCoNRRxCRwav8AnR269SaG7Alck866AwE/OjzL09FI+gpi6pGOqt+dPGrRD+F/zpqVvsicX3G4vW6l6kiS8RwwD596BrEQ/gf86cNaj/uP/wB9VXtf7ouTzOm0nxBe2gCsrYrqrbxbIygMjV5h/bcf9x/++qcutL2R/wDvqspWlqkWrrS56XqviMi0Z8kHHGK83utRuNTuiGZiuelJLfzXqeWhYD3NOtbb7OOOW9aySd9TaysNOmROvzuM98CnppoC4jAVf1NWoLWWSQFjxWg80NonZn/lVBojLXT9i5b5R6nvSvfC3Ty42x9Kiur5pWIyaobGZxkE5osJskeV5mySant7RpSOKmtrB2YHace9dJaWASIELzTsQzNttPEIDNxVreSdqDPuKti1lmk244rWs9NiUDcRu9KAK+lJLkFga22t0ZdzClWARj5R+VWFGIW3Hj3pgzJN6scojXCj6VfUs6qR6dhVaG1SW63AirF6JLcZTnigChrum/aLEkjLAV5ZdwtDcMhHQ160t0z2p80Y/GvNNckQ3zhMdalmlN2ZmbwopUO8U3ywWBNSEbR8vWoOhMQKq5qFoi7dOKmWM5yafjJGBTSByS3IfJ2DgkUR2TzPjBNaUNk0mN2B7E4q/wDZ5LYAhOP9nmpcrEOz6FKGxS3wWALe/SryWctwMRk5+lBnaQBZeR/dkXFaFvdfZolMatGD/dOQazlKW4RUSCDw4w+eYkn3rSttHjBBLH8aYuuK0eHUemQav210kq5Fc85z6m8YxHPBFHFgZ4rLhdfNC46seDWvKN8eByDWG0RSUfNghsVENSnoWvOWI5KqRjaQae9vY3q4PysahV487JMgsaqT27w3SyQuVAySK2irepjPUbd6XNZgyQSnHUc1ctbm4NqwcjKoM8d6fcSubGJWOSV5pIjGtvIZDjcQMe1XJ3Wpgk09CjCWeQE5JJJJpbu6QWxViCRxVG91OO3JCEemBXO3epPLnJwPSnGm5O5XNZalmS4MbMUbbz2NZVzdF2OWJNQSXDOcA4FQE11JWMW7jmck80mfSkC5p4UCmTcQKT1p3AoLYphJNMBxakwTSpGSaspFQItaeEBG6tyJAQMdKwokIPFbVgx4BpMZaFtnoKeLIk9K0IIgQDVtYhjpSAoRWyxr0pXcKPQVZuGjiUkmua1HUGBISgErly61COFTgjNYN1qTzEgHiqUszyMSxNNSNnOAKASEZix5OakitnlPQ1ftNMZyCRXQWelhccUwZkWmlE4JFbdvpqqBxWpDaKo6VbWH2oE2ZZsgB0qnc2vBwK6FouOlVZrfNISZyjKUbBqGVNwzWxfWmMkCssjHBpM1TTKOSjVehlDrg1XmTvUUUhRqN0LZl9gUbIrc0m8BADGsRWEi0RSmGQEHihDeqO4E4I4NJnNZNldCRBzzWkrZFMzZKKWmKadmgQuKKKKLAfQtFFLVAFJS0lAC0UUUAFFFFABRRRQAUhIAJJoZgoJJ4rG1HUQoKIaAHahqSxjaprBkka4OW5yaa2+R9zelPVdv86VwFSIKPrQ7BV/GkZ8ACs3UtSisITJKw4HApNiJL6/jtLdpZGx6CvKfFPi6S5keGF/bjtUXijxbJeSNFC/Hc56VxbEuxYkknqaAHsxclmOSepNMIowaTJoAWjNN3U5EaVgqgkn0oAcFLEBQSTXZeFPCEt/OksyHbngGrXhHwdJcyJNOhx157V6xYWUVjEscSgepp3sOxnr4Y09bYQlPn7sK47xL4OsbdDMdjc9uDXb6trEGmwF3cbsE15jf6jfeJ9Q8i33eVnk0J3KSe7MaEIkUiIvO8KMdhXT+AdKjuNTMrruzIeT7VavvDKaLo6vIAH25981tfDS1DKjkdi1EVZms5Lk0PQI41RQqjAHan1M0WKgbitDlAnFUdQmVY9rbTnsaddXawryRmudvb0yt7ZxUydikjJvdCsb8lioUk54Fc5qfgZHjcxKCeo28Guxt2LFeeefwq6qjn0xz71Ckyj5/1HwlfW8zMke5SeKwZrWa3YrLGykeor6dubO1kULJCjH6dK5bVfB1peoTGF5yfmHFO6KueC0hrv8AVfAM0BLIjJ7gZFcneaHeWhO6Mso7qKA3IrObjaaW5h53L0NUxujboQRV2G5Vl2vR1uLbQpEc0Vee2STlTVd7d07Zq1JEuJBS0bSOtFMkKKKKYDl61aiAqoCRViFicVnUWhpTepaozSA5oNc50Fed+1VaszoTzVY10U7WOepuFJRSgZqzMVevNTLFnkUkceeoqyqbazlK2xrCN9xqxj3qQLilpaxbbN0khQOKaaCaKQCUUhbFN81f8mmk3sJyS3H0opgdT3p4PvQ0NMdk0YB60lLSAhlhDdKrNEQav01lB7VcZtEygnqZzLilVyvQ1NLHiodprZO6MHFplmK8ZODyPerSzxTDDcfyrKPFKCR0NDj2BPoy/LZo/K1TktpIz04p0dy6HrVxLpHGGGKl3Q15GZyOtKHIrTe2jlGVx+FUpbN06cilox8zQJOR3q1DdMhyrEfjWcVZTyKUORScTSMzp7PW5IsBjke9dBZ61HIAC20154kxFWo7kqQQamxfMnueqWupMuCj/ka2bbWQ4CzAEeteTWmsSwkfNxW9aa8j4DnB9aL9xOknrFnpa+TcrmOQZPYmqc9pPHJvRuc1ztrqQOCkgrattaYAK53Cqi7O6M5KS0kWINRubdz5hA47iugsdTjutrPgjB6ishJbS8XsD70C0e3GYjxzgioqwhPXZlUptO3Q257+3kzH8qgcZrLvAmAsZV+OSvUVjyiZZd5Jz60+O9aPO8c+qinHDSSUolOum7SIZA0bA8g9a1mjU25YdMBsVnXMqzkkegIzV7S7hZEEUmOBtOe4roqJypp21RzwajNpPRli0ldo/LHO3r9K0fNjjBBUAtjv3qS2sIozvGCCMZDVkXiMb0Rjdg8n2xXnu027HcrxSN2CZSCB8p/Si5wYyDkZHbpVKBioXnlgDgjrVpjxjOK59ma7o5ue28yV36EkLg1VkASRlHY4roLu3LNtUH5+n1FYl3A0ZEhBG7r9a9bCVubRs8/EUktUQg07NRA08ckA9zXe+5xIkSQqcq2KtJedpBx600RjyiQF64xtqsclyAB+Fcspwk7M6FCUdUXJLeGdcrgGse+0RZAfkHPcVfKSR89PpUkV0x+8pI9QKylT0vF6GkZtM8+1Lw2UYyRgo3ZlrPj1DUNNYLOhmjHfuK9VaKC4U9Mmsa/8PxzAlVA+lZOBvGt0kcnHe2GqxbJNrHOdrjDCk0rw1GuqO8ExVHQqUYetJqXhhoyXRSpHQrVBLzVNKUjc0i47/wCNQ1K1kapw3R39/bma0jzJzJZbW92WuPNyY7yAW7ZKlXGOxBGaveGtXm1C2RZQdySFQPY//rpr2QtbufOS/mkr9D2rFe67M0TUloZ/iW0ZbEsw5SUsPo1Z/hm4MTTLn7rK4/rXSayPt+nPIq4WSDIHoy1xujSbNTCdpUK/jVLVNDiWfFTmLU5XT/noHH0pWvI7rTlDE5I6jtUut2xmjguG6FQj/UVgQhkJjDH5WI61cVdGT3NfTJIVTEilu3WszUox54kjGAp71PYMVcDPBJ61bnAK84Ix3FUt7mE29kX/AAxPCdsUwGDlutdfe6NA9r9ohAKnrXnNukizq0bbcflXommSXL6Q8bFXwAePSsKi1ujppt2szlp7coxK9VNQ+XHdKUIw/Q1ffIvJUYHB5xUsujyOgmhBzjIxUp2dzRo429sGgcHBKnPNW9MldUjRirqwOVfnmtiSITKY7iPDAYIPGawbhEsZBtVtvXrXVCSktTjrXhsai+TOrJsZWBwY2H8qzLywkiulPJjPKn2qrHqWL2JwzEBxnJ7V6DPpyXeiJIo+ZCenoaJS5BRhzrU4KT70a46ZJqO6faiD8a0zYyb2yu7A6+1Y2oo8MuxgR6VcJXZlOm4kXmnjmtTSbuWK5VgTjvWImWatKBvLUEcGqsZtdTt4tbdFj3MSFGMjqBW6l7HdwkmTqATkV5rFfPuw/I9RV463JAMo+QQBitXK6sZ2sejWkIwpcEfKTuHen3FqskY2jIUd+uK5rSvEIaEBsg+X3ret72OdSykcjHWk3GKuxxi5OyHeYtuSjdm4+lJ5xZlVRkev9KfPAs8hYA/LgcetPXykVU2gHJPNOLQndMhuikSbgXHHQCsZ7cyyeYQwTpyOtdAI5JkQuoOBziqMqbFaNFZcchs5Bq7aWFJ31KsbKmFw2QpJ5qaWNWjyGYAgA4FVlgkWTc5JzmrIbcvAOBjoaxlTblzLc1hVXK4yMaS3VJ2LbiOmakKltvlrjI4IPerGoW7FGZeoAPBqsYnELHORkMD6Vqt7M5xn3uDxk8exqN1yjL0B6+xqS6Xyld+Npw3FNRhKm4ckDn3FLrYb02KMUJhfc3fOc1tIq3dlJ0JHNUpo98WByyjg+oqXT5Gid0P3SAwqKuxdLe3c5mfTh9teMADPIqrdaNLH8yrlTzXSazCbe6SZRwD+lalokd1aYZQcVKexUXa6PK54zHIQwwaaIyw4r0HVPDUc4LxgGuZk0ia1lIK5WrlTa1HCqnoYYDIeeKnQ5q9cW6EA4qls2txU2aKbRo2vzLk+lK0yo+OnHSi1YKhz6YqjdnL8UbBe5DdurPxSR1Cw55oRyppAy22CKgZKUPmpcBkpiKvSnKxFDDmgCgZKshNSoc9arrxT92KNRaFjaDVeVSD1pyyGnD5jTERpCWqTytnWrkCADJqvdkk8UitCIuFphlJ6U0IWNTJCTxigQxdxqdFPep4rN2HYVZSwf1FPRiegluVXGavbYZhtYp9DUC6e/wDfFPGnNn/WAU1CPVg5PoRT6STzEQPaqp025Bx5ZP0rchhkQYaQMPerSLGDlg34E0muXZjTvujmW0+4UZaNgKhKBTg5B967YvaLAc7s+9cneRNc3RERO3NQqutjT2d0VQm44GTVqK2cYO3j3q3b2iwL83LelaVtZec2ZFJHpmm5gqRSt/M24jUfXFbdlCqIXnwMepqOWS2tBjaGYds8CsyW7knfC8D0rPqW2krGndako+WLge1ZrPJM3OaEgbG5hmtGwszM4yOKdiSglnIx4ArStNM3sCwwa6CCxjUBdoNRvCyTDYhPtQBJb2aRx89hnmtSyEb/ACgdqynadsJjaK1NNtzDhicnFCAtvbhUJVeaz186CQkjjOa3UTfg+tNmtlbrTEMtrstGWIFSxP8AayV74z7VGsCquzaoBq1Z2nktuAx9DQBWKfZJFOMk+1Wd4nhy4HXFF60KDLsfpWUb7e2F4QUrhuUfEs/2S0by+OK8slmM0xY9a7fxbqaNCYw2Sa4aBQWJNIuCW5Iu5vapQccVIseegzmplgVPmk/KlotWW22tBkMTy9sD1qwBFbjIwT603zWkIjhXP06Crtvp6r+8uG3Edj0FQ5CV+hWQXk53QxEr6mrMN7LZtiaFk9xWjFeRwSiNUJAGcimahLFOsXBy3XNZqUZO1i5RlFXJ5pLe5tWb5cgelVUREAXso7mmdLY8feao5ws0bffUnjI5qNdkLS6ZZZbMRbVZck52+tO+1pbqqopGTWLb2NwbgZbegHBU81rXUESQKwEhIHOPWiUbaPU1izpNObz4Ax70t3prMdyjryPrXK6ZqdxBKFG5F9cV0q+IEeIoQJGx2GKxlTlF3RammU2sWeU7y34npUN1tEr4AA2gZzUN1qhWUkk9Pu9qwb/V9zHBGfQdK0jGUiJTSOgvtRghjwpHAAzXK3msO2QjED1rMub55CSzE1nvKzHmumFJJanPKd9ixNdlyecmqzMSeTTSfSgLmtkZ3DrShacABSFqYmOyAKaWpvJNSpHmgQwKWNTRxe1TJF7VZjhz2oAgSL2q3FAWqzDa5xxV+K229qQFWG19qvQwbSCKnSNVHPFV7m+jt1OCM0rgaUNwsIG80T6rGFwhrj7vVXlbCk1WjvnB5JNBVjorm8Z8ljxWTczh+F5pokkueFq/aaWWILCgd7GbBZPM2cGt6y0oDGVrTs9NAxha24NPCqOKCWzNtrFUA4rQjgCjpVnyNtOCUCIlSpljp4SpFFITI/KqKSKrmMVHIRQBkXNuCpzXNXsHlyEgV18yF81lXllvQmmVF2OYYbhVSVNprRnj8qQiq0ibhSTsU9SK3mIODVp/mGRWcwKNVuCXcuDTa6guxdsroxSYJrpbecOgOa5BxtO4Vqade4+UmkhSR0gbNSA1Ujk3DNTq1MixLmim4zzRRYNT6JpaSiqAWikooAKWkooAWiiigAprMFUknApHcIpJNYmo6l/CpoATVNT2goh71iqXlcs570wbppNzdSc1YXCkAUrgOIAqu8nzECnSy9qx9S1eHTomd2G/sKTCxa1LU4dOgaSRhuxwK8f8V+KZNQneKJ+OhIqPxN4pm1Kd0jc7M8muUbJOTQkAcnk04GkzSgigB2c0HFIakt7aW5lEcakkmiwhsULzyBI1yx7V6L4R8Glys9wvbOTVzwj4L2hZ7hefUivRY4Egh2RqFUDFN6DTsR28UVpCI4lwAOtZ+r6/BpluxLDdjjmqmva/BpsLgON+MVwNvbX/AIq1DOG8nd+dSk2Wl1YskmoeK9R2R7vJzXqfhbwlb6Tbo7xgyYqx4b8MW+kWyfIPM+nSujAxWlkkRKd9Dzv4kTbYGQdlxV74cQeXZbsdIxXP/EeffOYx3bFdf4IjEWlMfYCo6mktII6pzWXfXiQqeeaTUdSWBCFPOK5aS6kupuSetNyMkh91dPNIB2zUIhJXOec8n0qwsYypPTnJpsj7YwAO3SouVYZCm0jHp+dTkkFvwpsA3ZJPQdakwGbHvwKBiu3I9P501jkcgnjoO1PcBSOe3JpN+B8uB0/GiwX0InUFexHoayLvSLK7IEkQVj/EtbkpRhyMH2quUwQQQeCaV9STh9T+H0V0m6FUfPdeDXC6r4Mu7Fm8sMcHoRXugcLGOMHHaqskMd2MTIGXPQiri2ymz52dLmzYrIjLj1FPS7U8NxXs+p+FLK6ViqgH0PIrgNX8HG3Ysi7Rn+HkVTSA5spFKOMVC9oeqmpZtKvLckqjMo7gVAtzJGcOPzpaoTImjZDyKZV4XEcgwcUjQI/K1Sl3FZFKnxvtNSPbsOnNRFSp5FPRi1TLyHIp1VIZMHBq2GBFYSjZnTGSaGsuRg1A8GelWqMUlJoHFPcomEilSIjrVzApMe1V7Rk+zSYxFAqSil/OobLSEpaSikMKWmnikLYoC5HN0qmTzViWTPFVq6KasjnqO7HByOhqRZiKhoqmrkptF2OfcanBzWYpwauQSZ4NZThbVGsKl9GWKKKKxNhjKDVaUEdKt1FIuRWkZWZEldFE0lSuoFR1unc52rC44pBmpEYd6k2BjxSbsNK+w1JnToTVqO8B4eqzxnHAqLY3pS0aG7o0mSGccYzVWWxI5WoFZ0PGasx3jDhuR70WsJFNkZDyDSBiK1N0Mw54+tRSWIPKUirtFRZSKsJcEd6rSQPGeQaZkik0Wpm5bapLERh63rPxCDgSGuIWQjvU6Tkd6hxNVPuem2mpo+CknP1rdtNZdMBjke9eRQX8kR+Vq27PX2XAc8UepLpxeq3PWIry1uhhsAmkm01XG6M8e1cRZ6xHLja+DW9aavJGBhjirjNrZmcoS6q6LEtrJF1BxUKMyOCpwRWrDqkFwNsq8+tPksYphuiYH6VvDEdJIxdPrHcrRanPH1wR7Uq35nulJLEnpmoJrOSPPGRVdCYpVf8Aumm6NKeqBVakNzoWvfLjhbOQARxSWupNO6qeTnHNVrZI7pNjHHGVPvVtLUWpSX5SCckr7V57pwV4vc7YzlKzWw57o/aEVt2eSMdqj1TEsY2LgEd6lLJJLvP8Jz+dWp1T7GGXB68GlFqElYbjzJnKPGYzgkUKcZzkcda0IwJ3LnaMuR06VQlYFyAc4PXFenTqOXus4J01HVG3pPlTKFc5JH51PNpiiYuoBB/CsK1uTA45IGa3P7UXySWHfk15+Ipzpy02Z2UZxmtd0WvJt3UKQikcc1XGnxo4xj72etRLOshDFvoQuavJKpjI3owx0I5rmU5xVrnRyxk7mPeWTxsWUEEHHFV1llXh0LAe1dCWSRSrHIBziomgiGcMVz7ZrohitOWSMJ4e7vExHSGdcY59MVj32gxzKdoxmuolsBkvtRuOCvFUXMiPjbkAc1rGalsZyhKO5yem6WNKuXLLhWYMCPWm6uDPJ5irn5CdyHoc11jrFMCrAA+lZ0+kKW3x/KfbpUyp3dy41raMwLO3lm02HJ3Ku7PHYiuAbNlqoJ4MUv8AWvViJLSzeEoVGOGUV5vrthMbuWZBkMc8CojFpnRCcWdTPFHdaMwG07Hz07EVgQaKzIJ2UHdIc/Sm6JrRhAguNoI4O/owrqWe1fTQsG2Jjk4JyKnWLG7M4S1IW6KcfIxFa8lqZE6Dhc/hWQLe4trxmlTAY5z2IrdhvUEQBPOMUTb3REUrlE28lm25PXpXWeH9RlWPEyDbjHvXOXd0rgkYP1ptnrLJcCAxgbyME9qTTkh3SZ1txaxTXIkCnO7acjtXQ6NZKCUkGV6c1hfaImt0lwGLrk4bnNa2lX7FtoDjIAyeaxexqM8U6FEgL2yJnHJNeY6tpjB5FZV3AgqB3HevSPFOqzafmSSN3C4JB6YrhL/XrW7+cfKAMqV6g1vRbObEK60OZm0vy3JViBwRketem+FZml0yLfg/Lgg8gmvPpL95ZU2sWA4PHWur8G3ytut+cBywB9DXRWinC6MqM2nZlma1jtrnDoQI5Sjn2PSuc8VadtQyKvMbYJ9Qa63VoXk1Z0VTtkXecnrjioNbtVuNKh4+aaAg/wC8K54Sszoqx5kcDo2mLelywJCkDAPrVq+0lra4aOJiHU42PTNEdkmnhyQWTI+oqfVbm4t74SMRJHIobB9a35m3oYOmorUyyWgbEqbW96T5ZCOOnoa3IFg1KHGVJx9x+D+BqlNpBQnyDz3RuDVKSbszJ02tVsMhuvKUKDg/WtfT9XeI8tkfWuakEkTbXTaw9RTkmKg8gUVFdWCl7ruei2mvrnazkZGOK04HSSfez7c9c815nDM4bjnjNaLa7cwr8jsMkdauGkbGVa0pXR6O0royFEXB77qZIy7N2MfNhge1ZGm6wtxCwlK5IGCexrWV0uEBbHzrhseo71q9kyFZ6FKXMgJIYHB6dqiVjFvzn7oINX4x5jhc4O0hh61HLBuEWM5YbSKb11RDXQp3kwmX5TyVDA4/SoYVdkXHAYc1PJaNjap6HipRGIozkZ+TP41KbuKxQuYhLbt2I4Ye1ZFo7wXPkt1B4ro4rcv5mT8pHFZpsDLqgIHAoqPldykm0PkjIAYDAbkexpFZVdWxjsRWhdxD5YRnpk+1UJYtu3I+b+KnNXiKErO5LqdsLjTww54xVTQ5eBGeo+U1rwJvtHiPXGRWBBm11Rk6BuRXPHa3Y1qaSUl1OlC8EVWubSGUZdQD61dX5kVx0IqjfiTadnWuyE707s5Z02qlkc7qehq4LRkfUVy9zYywOcgEV3qNL5RDAHvjHNZd5aeY7ZT3BFZOpF6HTGlOO5y0Sblxg1XmgMbEnke9bsumH7y8H1FVby1dYjvHUZzRKKtdApWdmYUwFQHirzRhlNVHXBqCwQ5q3CARg1SBwasRuKSBkksOORVcjFXBICOaidATkYqhIgGc08KTTwlBYLSuA5Y/WpAyqKrmUnpTfmamItC47Cms288io0jNTKmP/r0tB3JraAE5Ip0xEbcChJQgqWJ43cByPxpWZWwxL0oMBRTxqUg7Ct/TdLsbphvERz74rrrPwnpLqM+Sv4ZosuoWZ5oNTm7AflTv7SuD6flXqkvhHSYYy++A4HQJzXA+JHtLV2jiKA+irR7g7MyDqVx/epyahcsfvn8qoQRy3D8LxWvDbJCoyAW/Ol7q6BySYqvNKP3jnFTQwFj8i4HdqntrMytlgfpVuSeG0XC4Lj8hUM1Wm4iW0UCeZIQPc9TVS61QKCkXyr/OqN5ftISWYmsia5LE4NFhOVzQ89rmbbnNakFvsIyOTWboNo0029gcZrs7WzillUHAwcUyWRQWSsoLDFX7dUhYBBViWycDbHux61csLALgyjJoAfEvy7mI+gFNZ4jKoCnFarWylcAADFZs6xWp3HJIoEh8yxpErBQDVqyjZo1ZtucdqyoXlvZwAPlroABawDK5PpTGVprxrZgB6960IZPPg345FZbL9skyy8Z71aluksoNtAiKcS+bweB14qdtRFvb7f46pnUmlRudq+tYWp6rDbRsWcA/qaTYWLN5fNIxaR+PTNc7qXiRLZCkZy3sa5/U9flnYpGSFrLit5rp+QSTSXmMluL2a+mLMSauWto5AJ4HrU1vZR2ygyYLelTb5JzsiXj17Ck32LQ8yRwJheT69zSRWctydz5RP1NWYLSOAb5Tuf3qwPtFw2IUAX1NZSkl6mkISl6DR5NooRF+b9aTyJZTvlfanoKlSzeKcGVQffNX762kaEeVwMdhWDnfc6oRUdigrIdwUZO3HSob0ukoIHCYpoY27ndvJJyeK0oL23uPll2uffg04uzuFWDlHQgsr6B4lil2HH4GrL2EMoL20pU+lMvdHgeMyRblxzVTTTNEwTzCQX4z6CraW8WcibvyyQ9FeJ5PMQEqOGXimSSLPOEaRlwOlWWLSh/9p/0rNZxbTPJuBOepHSlHUpPlNSNIreIsGGMYyRzWVeXkUbE7unGAeaz77WWbIVix9aw5rsuSScmtYwb3JnUXQ0LvU2fIBwv1rKkuC/Q1CzljzTfpWyikjFyuLu9aTGTShc07gUyWwC4pScU0tQFLUxXEJJp6oTUiQ1YSH2oAhSL2qzHDntU8VuT2q/Da+1FwKsVsT2q/Ba+oq1FbgVMdkIyxAqQuJHAFolmjgXJPNUbzV0jBCGufub+Sc9TigaVzUvdY6hDWNLcPMcsTUIDOe9XbaxeQ8g0WHsVkiaQ8A1OLF1IJBrorLSgMEitb+ylaLpzRcLmRpFqrAZAzXTQWiqOlYcCGzudpHFdNbOJEBFK45omhQIelaMfK9KqxAE81bDKooIFKAimGPFI03pTkkB60xCBKdwtSFeOKi2EmkMaWJ6Unlk9al2haYz+lAiJwqis655BxV9+arSx5FMDmb62JJYVlEYODXVXEIIIxXP3kBRyQKGi4voZk8feoEYo1XWGRVSVMGlcbRcRxItIjGKQEVVhkKtirJwy5o2Dc6HT7oSKATWvHzXG2dwYZQM8Zrq7S4EiDFBMlYtc0UoBPSiqJPoqikpaYBS0lFABRS0lABTXcIpLGkkkWNSWNYGp6mTlUNADtS1PqqGsZN0rEt1xSKDJuZj1qVRgHHtSbAFUIfoKR22g/SlkOFJ965/XNfh0+3cBxv+vSlcCTV9Zg06BmZxvA/KvHvEPiGbUbh1Rzsz1z1qLXNfn1Gd/nOwn86w85NACZJOTS5pDSUAP4pMU3Jq/punT6hOqRoSCeTTSFYjs7Ka8mEcakk+1eseEfBqQRrNOvPUk1Z8LeDo7SJJZk5/nXbYSGMDhVFF7DBUSGIIgCqK5rxL4ng063aONgXqp4n8WR2UZhhbLniuX0XQL3xHfCe5DeWTkA0JXLSSV2Q6bpV94o1ASShvKJ4HrXsOheH7fSLVVVBvx1xU2j6NBpVsqRqNwHJxWpV9DOU22IKGOATRmobh9sLn2NAkeQeNZfP1qJM5zJ/Wu00i+W00QKOpNc3qejvdax5rclefpWoYTHCsYPAxk1mbSaskWmla6ckn/61JFCC3HTP50WyhV56dhVmMhSDxkD8qSIY2VdhAJ5Cn8KruhKA84x+dTyEySHPTgUs5CqFGM9PpSbQ1sNhGAemfT0pUPz5z/F1qONjt49fzpVJyOOaSQmx8zZP4dKjwpIBOTSN948/U0rEqOF4psEKEbHBzz0qFm/eEdMLUqsCO4NRS5DsT8w2iktQY+UMqgdR7VArbR6VNM6tjAIPpUB4T8K0SsJMqzyHBPH5ViywyXFweDjPpW4y7+OD+NCQqpJHFUBUGh2c0OJEBbHUCue1nwPbSRtIoUjGemDXZSSpBGWc9qwL/UXuHKIf/rUXsNXPK7/AMNTW8h8sMB6GsmSG5tTh0YCvXvJUg7xuJ6k1SutFtbgH5QpNK6KsjzBLvswqXMUorp7/wAIZy0S/itc5daJeWhJC7gPSl6C5Ss9tjlTTQzxnkGmiWSI7XBH1qVZ0fhqPUL2HLMD1qQMvrUZiRxlSKiaORehzU8qZSnYtZ96TNUzI69RR5xo9mV7QtlwKPMXHWqJkJpN59afshe1LvmgGnCQGs/J9acrlT1odNCVUvk8VWlcg4p8c27jNOdA9Qvdepb95aFInNJUrxEGosYrdO+xg01uFFFFUSFTQH5qhqaAfNUS2Kj8ReHSlpB0pa5jrENMfpUmKQ4xQmIouDupVwfSpGA3U5Yx7VtzKxjyu5H5YPahVKtU4QA08JU85agRscCo9w9BVkqDTfLX0pKQ3G5CMHtSNCGGRU/lr6UoGKfP2FyXKW1lPFTJcPH3JqRkBqN07801JPRkuDRZSWOUfNxTJLON+V4+lV8ELxTFneNupppPoS7ISS1dOnIqHkHmtFLtW4cfjT2himGRj8KfqF+xmq5FSrOR3p0tmy8rzVYqynkGlYpTNOC8dCCGIrZs9dkiwGORXKByKlWYjvUuJrGZ6NZ63FJj5sGtu11R1IKvx6g15NFcspBBNa1prMsJHzZFLVA4xluevW+sJKoWUZ96naC3uRmMjNec2evo+Axwa3bTVM4KPn6GnGTWxnKk1tqjofs1xanMR464qaLUWB2Sgr79qqWusbvlkwwq/ttroZGM1blGXxozTlF6D766jeA7cBiByDUNpqmxBFISAfxqGbT2XlOlUnRk4YGtYUaco2uKVWonc6SMWv2UuCofOeB1rnpwPtDBe54qMTyKu0OcelOg+edPrV0qLp3k2TUqqpZInWxkZC2VA96lhjIbax6nHsRVpJ3jijCqD8xzkdRTHVtu8DhuRiuSpXlN8rOmFFQXMi3b2ZC8MU9atxwsq/eQ47hazoNSCptJ5Helk1F87g2R14rklGbdmdMZxtdGgVL4wQT1yKekhVTkhufyrKjvW8pTxkKaiivzI5U5BxU+ymHtI9za81G6qMdODg1EyxEHDJzxgiqsFziUBjke9aAQhgQoIqdYlaMx7u2+bdt2nsR0NVkkkjO0qT+FdBPArx/KPqv+FZEysoKjeQO1d1GvdcrOWrSs+ZEeYphhhg+lZWoaHFcKSFGfUVYk3iTGCO9PFw8YGea39nfVGHNZnA6p4WIJZUz7gVh+TqGnE+WS8Y6o3Nevb4bgYcYP0rOvtEhnUkKCfUCspR7m8avc88t9WglHlyqI2xgrJ0NTNp0VxzE3lE9icqfxrS1PwvkEhM+461z7Wt/prZhclB/C1ZOBtGUXsS3Gn3FrguuR6g5BrNkMkcqyomdpzitm219XAjuB5Z7gjKmr7rYz25wxjY9wMqacbdQkmyLS9SjuIY0kIUKcEEdK6aCcwRsY+T1wD1rze6jurK4LoGZCfvL0rqdJ1TzLdQ5G7HB9axqQ6rYqE+jOy1BF1vTNrqAxUqA3NeQaros9hM2FO32Feq6NqClQGOwDnB5q3qun2GpQsnyhyM9fWlTqcjswqQUjxC1lCS4fP1FdJoN0kOsLtbIYVS8Q+H5tOnLBDt7GsnT7hoL+CQk4Vxn6V1tqUdDCKcZantupxwrYW9z1bYQfbNYYkEtlcKcfuGVlHsetaILXmiqQwwF2n29Ko2NvLLLtY7t6mJ8jGT2rj2OrdHn8yf2f4hI6KJP0P/66v6rbefZo5GPLkKEgZPqKTxVamG7jl6EjafqKmWd5rCQKxBliVxj1HWumEtUzKrG8Slp1szu0fJG0lTjuKa11LFdqjDzEMe4g9R+NXdIuNrhpATgk59jU1/aRRnehB+XgAc0Tl79mZwVo6FWQ211tTAYlc7G6g+xrOm0plYmDnnOxuDT0g8yRJGUjJ6A8ippb5oUw/wC8UNwT1x9avVbGTtJmPJJJC20qQR2NTxS7xlsZHY1pCW2v12nDkdjwwqtNpzKCYG3DrtPBqlJPRkSptajE1OW3bahyM810Ft4jaO2ALHgZxiuVkAVjuGD6GmPcHbtFW3pYzcdTuNM8TKJgZDx610sF9HchHQglPmx614+krKepFdFomqyRsFcnHTNEZcvoLlvoegudrEYBCvnn0NQSI7EK3Haobe+idQxBztw3NWlczKNoyOQTW8WnqjNxa0ZDFvWR0AwGXke9XxaLbr5zDBK5osrd3kXeOuQfwpdRdnKxLgAnB57VMleSHzOMdOpmp87vK3f+VVYozc3LSn7vb6VdukKQrEPvN/KmlBa2fH3mGBRfmkYvRWIYZv8AS9gzjoPpWdrcHk3KzL2OauRw4fzMkEirOqW3nWIPU4xXPJ/vNNjr5b0l3JdPlE1qB6VYOzfhwORWPoE3SNux2mtS8UqVYdjVxlZOJnbmakS3FlGUDKO1Z8tovSraTubdQexwcGm5ypyeQa8+baZ6kFpqY0tmYzlQPXBqCa2iuIyhABxW7tDDBFU5rTJJTj2rajiGtJbGVfDKavHc4rUNCkgJaMHFYktuy8OuDXpiqDlJFyPeqGoaFFNGWjA+ldyUZq8TzuaVOXLM81ZcGgZHStW/0iW2kbg4qpFEGOD1rI6NCBZDU0Z55pZrbYcj60xAdwFGpGhc2Dbniq0sdW2TEOaSCLzVwRRew0rlJY81YjhzVj7OEFRbirEDNF77DtYmW296kFpn+KoBM+cCrNvHcXD7UIH1ppyFZALEdzT1sk9a1o/DGqyKGDLg96pX2mXNgP30y7vQU+aQOKEiiMXR/wBa0IL2aE8Sv+DVzhuH/vGpIjPMeCceuaTbYKKOv/tqXytrSP8AnWBdWqXU5kYk855qNAY+Gck1ftLZ7hwWyR6Vi97m0dtBkFuSNsS4HqavpZpAnmSnHuetSyzwWKYXDSD8hWFfao0jHLHNPcd7F661BUUpGdq/qaxLm+64NUprtnOATUHLcmmkTuPeVpDSBaVUzUyxjvQ2NI7jw3aK9plRztrfs7EwZkb9a5fwlqIjmELHp/KvRTAksQAI5GaEyZKzMyPUBvKgZ5q7BI8kmcYFEWnRQPlsDPvViSSKKL5ABn86AJFlEjhMj6U+701ZU5JqhZxO9wXHQ10Cj5OaYGRb2otThR2z0q5NJGY1MrAHHTFPmnhTIJyawtRllmbCkqlJsVrl1b+GJtkQ6VUv7iA/NI3Pp2rDu9Sg0+I5cBvrya4zUvEE925WNmCn3o3Gjo9Z8TxQKYoCC3tXFXF5cX8pLMSDRFay3L5bJrThtorYZPzN+gpNpDSKttpucM/A9TWirJCuyJef1NKiTXLfJkL6mtO2s44BnG5vU1MnpdlRV9ijFYyS/POdq/3RVkSRRrsjxmrht5pxgfKtVntfIcKME5zWDq30R0wopayJLS0ad/MmPHXHpVxb1IplijjHXqRmg5htNxzzVOO6hifedm4H+KsXds3SvsdEY0khV2VVOPTg0yS7hghIZgfYVV/taO5gCB16dMcVlSI8zdRj2PSkoNkuSWhLJqEMku3cQD225qGe1jfbJwPTjBqPyYLf5h17saiaeSc4jyF/vmtOXoh+0UFdFt74pE0YY5IAxmls2SLDynGFPHuaz2eK2UsTz/ePWsi71QtkISB61pGF1ZHJOpeXMza1DV0iG2M/gK5q6v3lJLNx6VSmuSx65NVixJ5raNNIylNslklL1ECaMZqWKIselakEYXNPAxUzwso6VXbOcUCFLYpuSaUKTUscfNAhqRE1Zji9qljjBxxV6G2B7UMCtHDntV2K1z2qzFa89KvRQADpU3ArQ22O1XEiCjJpJJY4Fyx5rGvtY6qhouG5p3GoRW4IBGawL3VnlJCms6a5eVssaakTSHgUWGlYRpGc8k5qaG1eUjg1etNMZyCRXQWelhQPlp3G2ZVnpWSCRXQWmmqoHy1ft7NVA4q8kQHalcTdyGG2CAcVZVAB0pwWngUhGPqlluG9RzUel3JU7GrbkiDoQawLuA2s+9RxmpehrB3VmdEh7ipAxIxmqFjcebGOeavKKpMzas7D1GTUgTBpinFSh80Ek6HjFOK+lQq+DU6nNAbkEimoSMVfKgioXjpgVCKjcZFSuMGmHpQBRmjzmsi8t96njmt6Rc1RnizmgDkZozG5Haq8i5Fbl9ankgVjuu0kGixondFFxtNTQy54NJKmeagGUajcWzLreorV0u+2MFY1jxuGXFOVjG4IpDep26XJKgr0orCttWVIQpPIopkcrPq6lpKKsQtFFFIAqOWVYkLMaSadYUJY1zOpamZH2IaAJNS1Mu2xDWeELgFu5piKThm9M1MGHT0pXGJtAUfWnKQFFRyN/Kua8ReJodMt2RHG7byakkXxN4ji063ZVcbuec147rGtz6jK2WOzPT1pusa1NqU7MzHb2FZTMDTSsMTOTS00UpNAhSKb0ozVm2tzO4GDTAS3t3nbCg49a9A8KT2+mlTKsT4/h6GsbTrWOBQTgn0NaDCORsFce4qWw62PUrTXrKdAM+WQOhFc74i8WRxSCCFwfWuIuNRktgYbaZznjHWtLTPDdzd27311nOOM0WuaxST1MqzifV9biMxJBJbH417xo2nQ2FhEqKAdoycV5D4ctl/4SGRVHCEKK9tTCoqjsAKuIq2+hJSE0hNRSShBkmm2YoV5AoyTWXeXuflWo7y9OSFPAqgh8xhnPT86VykitK2+Rj0B6n1pLhBhcDnPAq1LEF57k9PSqTyfPjNQUMUkKefXmrcC4ALenSqwGFz3x+VSqxbIHTpn1pIY4yguQMcnBNRyfMw+tKqbWXI5ySBSO3zZHp1pWC5NGqomT1oG1m46D9agLMyAdscCrEK4z6/yqtgSuMdRuPAznp6VAzDeMtyBmp5HA78HP41SkYl+VAFIHoTswC5wDUO4SOwBIOQKYAexqWKM7ySP4xQmLcknBA5wR61XY/L+FWJhgnn8xVWZsCrWwNWI1UlvX6Gm3FxHbJliM9hmqt1qKWsZY4Ldq5i41KS+mOG+X1qri3NK6v5Lpyqnj+VRRrtqOFQqcVMOKllJD6TBbgUDLHFWUQKKRQkce0c1Dc2cFwCJIweOtWiajY5oA5y+8KwXCkoAfY1yt/4UmhJMYI9j0r00c0BFIwwBGO9O4jxaazu7Q/Ohx69aalyRwwr1670eznQlkCfSuJ1fQYVc+WFPuOtGjCxzgaOQdqY1uD900+fTpITxn8ariSWI4YUaoloRomXtUeMVaS4U8NTykcnTFVzdxWuUqKsPbEdKhKFeop3TE00PTiphKBVXJpKTjcpT5S6CHFIYQahiYg1Mz7Rnis2nF6GqakrsjaEComjIqxu3DtUTvjiqTZEktyGpoOtQ9TU0SkGqlsTHcuilzTBmnVzHTcXNI1A60pPFAEezJp4XFIOKdmm2wSQAYpc0nWgikMQsB1pN61BKGzxTVB7mrUFYzc3ctgigmolYYqRealqxaYnNBUkU7FANK42iMIQDVaSM7qvHmoyozVRk0yHFNFIqRSrIyHgmp5UyOKrEEVvF8yMZLl2LUd4ejc1OfJnHbPvWZTlYjoaHESZZksu6VVeJ0PINWIrtl4PIq0s0UoweKl3Q0ZYYipFlIq7JZI4yuB9KpyW0kfbIpWTKUrE8dwR3q/banLCeGOPrWJkinrIRScTWMztrLxDyA9dFZa2r42v+teWpOR3q5BfSRnKsRU2aKfLLc9ktdZIwGO4e9aKT2t2MHANeTWXiF0wJDXSWWsxygFXwfrQnbUiVLrE7CfTBjMZzVExSQOG28g5qO01d48fNkVrxX1tcjDgA1vGtJb7GEqavqrMoxXpXaGJG1sg1q29zFKhQ8HqDVWfT0cboiOaotHJCc4Ix6VMqVOorx0Zcas4b6ou3dgVO+Lp7VXjlKbVkHQ4P0rU0+7WSIK/IIwR706W1t58j+IcVj7Rx9ya26mvs7rniZ6wknA9cUi2ZE6OvHNa1paGOQEkgfnmrJgVXUgdOamWIUdEUqF3qVDbL/EB1q/Hs2gYGaaUD4HIzTChjPb8a5HJvc6FFLYSfcgzk7elZkrM0pUx/MOCela4O4YIIH14qpJBuk3DJJ6804z5QlG6IFsgzK+04KZP1pW04OsY+Xjg1fiyODggdqs4GOg6dqr2090yfZx2sc/PppVWKjvkYNUSZoDjBK4711ewHGR29KrTWMcijK5NbU8U18SuZzoJ/Duc/5kM3yuMGqd7osNypIUE+tadxprK7HgDOaptK9uQucjHTNdUVGprA5Zc1N+8cbqXhfO4hN3061zcmn3unuTA7Y7qa9ZE8U3DjB96p3ekQXK5wDUSg1o0awrHmcOqIG2XMZib1HKn8KvKkDqHjIUdQycj8q19S8MZBKpuH61zM2lXdjIWt2Zcfw1m4m6lGWpsJd3NnGSrAoejDkVk3HiW5guvMTGO+Gzmmwau8J2XMZQ55YDg/hT7u0s7+MuoUE/xx/wBRUqKvqOV7aG/Drlprun+VNgv79RXEalZG0u22/cJ4NJ9kutNnEsZ3oDyVrbkjTULIt1Yc0/g22FH3t9zr/CN8Z9JKnLYUMR9OtacN9FGzK6lWSTeNw6iuV8EXH2e48hzwH2n6Gup1GyW2Mb7x82VYE5rGSVza1jmvGFqskdwyDhXEq/RqwNJlH2VQ3/LKTB/3TXaXlos+kBy6sdphODn6VwWm/JeyW7fxgj8RVwemgWurDGn+z3ckQBVlJHBrXtJVkIMhPTrVy48PRXkiTCPl1Bzu796rR6c1rMIzwM4605yUvUyhT5WZOsTiORfKHA6msczs3y9R6V12o6N5uGUZyO/rWM+jvFtkxwSVyOxrWElaxjUpu7KcFqZwWVwhHerQuJ7RxHcjeucBu9LZSOso2EHJ2nIro7fSlv48yLGrk4+fkGnNpPUVNNqxhbra+XHDfowqnNppjy0LFx6EcirOq6HLZzF4Q2R12jpVWG+uIcC5QsPXoRSi+wTgupX24OCMH3qxDcLEcZ5q/m2vIyflfjtwwqjNYsrbozuX9RWiaejMHBrVGpbXxUghyK1YvEH2Zec/nXIs7JjZmpJHLx0WtqmHNfSSPUtJ1pJYjI+Bx1qdZluGaUAHJyM151BfvHEqA4BwK1ItceKBYwfmY4rWLSjruYVF7x1EQN1eM7A7ece1NuD59wFXkLwKr2l/FFZBi3zEcVFb3arOTnPBx9abfLC/VkwV567CXUjRSLGpIBH61o2r/arTacZx+orNuyszptP3uh9DWnpsITk5Gev1rBptXNYv3rdDFgH2TVGXoGORXUFFmiRuMGsHW4DDcLMP4W/StrTZRNa49KemjFHrEUW21ThR17CqN5FKj8dPStCaQwtnB9uasebFJB8y9etZypKM7vY6oVnKFluc+srYAK4IPIqVmA78EVbuII5TuQk5GOBzUD25MWDnOPSueqo30Omm5W1Kkmx+VI3U+JsjaabBZuZeQMZ5qeaHynGO9bUZ+zktdGc+Ip+1i9NUZep2aywsdoyv8q48aeBORjgHBr0R0EkYz3GDXK31v5F37N8prqfxeTOFSfL6HM3MTIdhGSDiqYXDdK6ae2V5VY/xDH41F/YLXTK0Yb5vQd60cdrDjPuZKSZXGKcCVHy1qt4cuoh/EPqtUp7G6t/vR5HtUNFJlcB25aoSoD1ZhJeQKwxn1q/NokjReZEPmH8Pr9KjqWtjLMeRkVZsb42kyttBI7EdargtExRwQR1BprletDBXPSYfHiQaZ5apHnHHyjNcBrPiC61WdgBtU+gqKFHmG3bx61ajs4oecZY1BrFFG0smb5pOK041A+SMfjTQjvJtwQK0lMNrGOhb0qG+hokkrjEsgq75Tx70Pe+WuyLAFQTXMk56/hUUcDu+MVSXchyuKd0p5NKNKW4HWtBdLk8sNg1qaZYKBtY4IqiTnR4YDxlk5I9DWLc2T2sxRx0r1GHT5IZs/KVPQisbxRo+Y/ORf0oY09ThQu0cU9ELHJ6VIEwcHtUgXI4qGzZRe5JZXAs7uNxxzg16hY3L3dgjJyVGK8pEfc/nXaeENWCMIZDwDg59KaZM43VzovNuHdB6Vrx2ymBS7YYe1TxwxHDjYQfeorkOWXa2FqjEQyi2TC4qBr+dlwPyq0FiWEGRgOOSawNY8Q2ljEVQjPr3NJsY65nMeZJX59M1zGr+KggMcTbiOOKwtU8RT3zlIyQue1Z9vZSXL7n6dyaVhDZrq4v5dzMeTVu208KN0vA/U1djhgtV4wzepp8cEty2cbV9TScuiKS7jVYAeXEn5VZisuN8x98VMIUtYsquTSWxe9k2k7QDWbmo7G8KMpavYkimVm8uJenpWxZ2SxuDO5LYyQRWZNYtZHfGTnHQjFSxaiyx5diOcHJzWEm5HSqajG6Nm6kWNQqKBnpistrd55g2GIHrUmnKdQfJP41rCNLYHLYUVj8PqIozxolsqP2HSsaaOJuF2MT2FWdUuXuJCEO1emfaqIKwp1xx+JrSCZalyq4+MFBhm/4CKSS6P3EBZvQdqgJeX/YT9TUU11FapgcfzNapPqc06ibuSlSTumbJ9OwqpdaokY2pyfboKzLvU3kyAcLWXJMWJwa2jT7mEplu6v2kJLMfpVFpC/emE0gGTWqVtjNu4d6cq5pQKcOtMRIkWatwIFNRwrkVZVcUhk7xB0qhNBjoK04SCMGo54uvFJOzG1dGSsZJq5Dbk0+OEFq07e3HpTuQQwWntWhFb7cVYSIIMmoLm+igU4PNAFnCRrliKo3eqpEpCmse71V5SQp4rOLPIckkmkOxZutQkmJ5IFVAC59amitnkboa2LPSySCRQMzLeweRulbtnpQGMrWraaaFA4rVhtQo6UXE2UrawVQOK0Y4Ao6VKqBakApCEVQKeBQBTwtAAozTwtIBinUAFU7+1E0R45q8BQVyMUWBNrU5uzka2n2NXQxMHUEVj6nalG8xal0273AKx5qVozaSUo3Rr04cUi8ilqjAdmpEfFQinZoAteZxSM3FQpyan2grQBUfk03FTSLg0xcZpgQsmageLNaBQEcVEycUAY9xbgg8VzmoWhRiQK7GWLPasu9tgyHI5pgnZnHsM8VWlTFaFzEY5DxxVWQbhS2NdGitHIVarWQy1TcYNSRSY4oaJT6E24jjNFHXmilco+yxS0lLWhkFQz3CwISSM024uVgQkkZrmr2/aeQqp4oAdqN+8zFVJrLVDvJapgvr3pHwDn3qGx6Em4Dj04pCeSKYjA/nSg5OfxpAQXchjtpZPRTivDfF13LPqTqW+Ve1e0a3ME0yTrzXh+sHzb6Zv9o00JmAWNJvqWWPBquVNMRJupQ2aj5qzawNK444oAntbRp2+7xXQWVosIXOQfeo7WNII+VwfUVfg+bLbhtA6mpbDfYkEZb0wO9V5rg7vJgBJJ/OlluGncQW65JOOK7fwl4MJK3V0uT15pxjfVmqSirvcq+FPBslzIt1dr7813GuRxWGiGOMbRW5DAlvEI41AUVzHjefytP25x8pNW9iItykcl4Ji8/VpJMfelr1ktivNPh5D8yyH1LV6HLMEUkmhaIKush8tyI1yTWNdX7SyAL0qC+vGlYgHioYvvqOp6/SpbJSLZQvFz1PaiOPYSe/r6VKMCLr9TThjB4+gp3GkUJ3JAHOP51RYHcT3x+VaEgAOfbrVIndJjHHYVLGBJ2gduPxqxCMc8Z/lTJMKBzz3PpWZe6qkERVWx1/GobSHFOT0NVcNjB45yahclW6duBUOlTNMNx5+X8qszLkk/rQtgas7CryP5mplIA6f/XqtvwQB69KtwoShJ685PpTBFZskg98flUbbSxw2aknYLxnAA/OqRlUkjJBpXsxO5Z2r8vY0Qy/MNpz8xOKq+c6nggiktHDsmTg7SaegK5clcseQRWZeybQevWtFuorF1J+ce9aJaCu+pz2sSFhjP8ADVGzAAFS6pJyRz0qK17UmUjVQ4UVKgL1HEu4CrqIFHFIoEUCpcUwHn8aeWwKAGOcUiqSaTljUyjigBuMVGzqgyxwMU6aVYkJY1zl/qRdiqmgCxqGqdUQ1gTTliSxyabLNycnJqq7FjQK4kpEh5GarSWcbjpirGKcBQIxp9L6lR+VUXt5oTxkiuoxSG3ST7yimBy63LKcNUyyRycH9a17nSkYHABrJm05oz8uRRoFhrW6nlahaF17ZozLEeRxU0d0p4YU02hMrg7DzmgyE1cKxSjjFQPakfdNNNPcNbEO/AphOac0bKeRTaqxLbFHWrUTDFVKcrEUpK5UZWZog0uaqxTZ4NWQQa55RaN1JSFJpKDikqSh1JRRQAoNOzTBRmgAcA1UlJBwKsO4Aqsw3HJrSCs9TObutBqk56mrMcuODUAwBTC3zZrRrmIT5TQ35FJUET54qbOaxcbM2Urodmio9+DTwc+tK1gvcQ1XkAzU789KgaM9auBE9iFlxTalPA5qOtk7mDVhKUHHSjFFMRNHcuneraXaPw4rNopOKZSkaj2sUwyuPwqnLYumSvIpiTOh4Jq1He54cZqGmik0Z5DKeQRTg5FaZEM47Z96ry2HdPypaFXaIUmI71bgvXjIKsRWe8bxnBBpA5FJotTOssvEEkeA5yPeujs9cilA+fB+teaJKRVmK6ZCCGIpNGnMpLU9ettVZQCr5FakOowzDbIBn1ryO01uaLALZFb9nr8b4BbBpXsS6SesT0QRIVJiI59DVWVbmJ9wyRWJaav0Kv8ArWzBq6OMSAH3FOLSd9zOXMlqXrLV2Rgjgg471s+cJIw49PWsHyre5w0bDdUiPLbjbglc5xWdanGWsdzWjV6M2Y5VLbVDA5yM1Icv3IBPpWMuorGuCw+h61Zg1VCSCOOOQa5nRmlexuqsb2uW5Dt4HX8qEOBuPU+1OVknAO4NnOfUU1kPGO3vzWexoSo6sc47kZqUEbc5AGKoKrrjAbj1qlf6m0I2Dg9MVcKbnKyJnJQV2bTXEKZy4zSLPC5+VxmuMe9mkOd5/CkS+nVuG3exrt+oStucn1yNzs3jDDgDmsLUdOYksu2rFhqLEAP+VanyTLlcH6iuVOdCdzoajVjZnFvE8JwxB+lOSZ0+63Hoa2NSsSwJVFH0rDdHjJDAivXoVY1o+8edWpuk9C4LiOQbZFwar3OlwXKk7Qai3Zp6TOh4NE8On8JMa3c5zUvDIbO1Aw9+tcvcaLc2che3ZlI7V6qtxHJxIuD61FcabDcqeAa5ZU2tGjqhXPJxfPG2y6jKN/fUfzFa1gbeRScj2ZP6it7UvDSuDtUMPQ1ytzotzZSF7dmQjtWUo3VkdEZp6o17W1ktdULRfOjDIZfzrtNQgN3axzEqudrAn3GK83s9amspw1yjow43L3rrtN13+2LSCBCDw0fPHPasZRe5rzJ6FWN7h4Z7dFBVW35X2Nchqa/Y9cMgHy7w4+hrvbUpYzgSDGTsfHYmuQ8UQMGjlIwQSh/A04spaG5ay4tg27iN/wDx00uqBZoBJCQW+8CKzNHlNzp+OpaMofqKbZXpjkeCQ5ABxUuOty0lYs6VdNdRtHLw31rVtrUSF0dQQcH8awrc7b1WXow7V2NnbmeAbo2JAxk8UpO2xktdzibnT0stSLquFJ5GOhrXEoMSlDwecjsa0b/Ry5+8M7cjPrWabSW2YYOw/wASnpTc3JWYlFLUWbfPHtlywPHHeue1TTNkbSJkLjHzda6jaRb7gNrY7cg1l3F4ZkMbOityOR1p05tOyFUimjh5C8bHa2GHcVp6TetcTiGbBbqrUl7psgYsFyT0IPBqO2sntpo7guODkgV1PU5Y7mxqej+WgmQHaw3cVj7GTHBxmvQoIBe6ZgHmMfL75rIvtKRLfJXB6Eiso1GnZlzpJq6ORab51APepVnxJH7ZNUpVaO7ZD1U4q19iuP8AWAAgccV0N7HI4tl6bUHVEAbgClt9Wfz4wScZ61jXEpDbTxj1pLdsyA+lOTuSonosNzDLHGSw+/jI7VrwSFFX5sgHt3FeaxXk0cybSQM5NdFbaydiB3GQTTi9NSbNy0Ot1WIXFnuA5IxVTQbjH7tjyPlNOstQjurUxk544rOt3+zamQDw/IrKDUk0aVY8kkzrZIhKhU/UVDJa5RtoHPIOanicOisO4pJWMYOD9K3a56foZxfJUt3MndLbsQ2cUSXYx82auOq3CseeOxqncRJIzKCN2RxXHKnH7R6EajewIw++OlPlO9Og49Kj8opCR3pqSDJQsA3bNZQir2LnJrUSM4yD0rL1q23x7x1x+tb0UcZG4HGex6VBeW++J0x05FdqbcVfc8+cVzO2zOQ2edDkdcbh9RWjp8pjj4Gf4hzjnvUKR+TO6EdDuH070+P91KyjoDkfQ10J80TC1mbMOog9Ub881NOLW6i+ZFzj+JapW9lI5O0nHXp2q9HZTAYIz9RQkitDmNS0uAZMYwe2Kzo7+W1jMEql07HuK2NXt57diyqfw6VhSETNg5X1pThdXHCVnYybyQy3BKA89qmtrBmw8h49K0fsioBtBZs+lXrbT3kOXHHp2rnudaSKsFuzYWJcD1rRSxjt498rY+vU1LJdW9mm1AHcfkKybi7kuHO5qkL2EuJULHyxgfrVclnNOCMz461r2emcB3HFCRLbZSs7RnbJHFbMNvFCQSOalROdkYq7DpUkuC2eaYF2zt0uISAO1MXTZFudw4Faljai3UA5qbUN0cOU4JpiuLDGmxVO0lR1qHUIIrq0MY5ODUWneawLPgAc8da0Io4ycFh7DFAHj+qWZtrxlI71W6Dius8YWISYyL0rkvMCjFZtHVB6XAKepqSG4eCXdGeaZncvoKbgKKEU2uptp4suoAAQTj3rb0/xyPLxIR+IrhdpkPNP+z5HHFVcwcLvQ6bWPGTS5WFiTXKySXOoS5cscmrUOmlzubhfU1dBhtlwgy3rQ2kRyle302OFd0v/AHzU/mM7eXCv5dKljt5bk7pPlT07mrqLDboQvB9BWcp9zSNO+xHb2OPnlO5v0Fa1vbtIuEU/UVUtYTNIC5BXrjNbCXMVvHtyy/QZzWFSo+h0RpKO5Qkt44Y2DKxcDqaz7RzDcu3OM9u1aN1dkgkLIV96zU/e5crgn2qVsaxlbcu3upeZld27j8KreWkq5K574BqoSBKRzjtmpY4C/wB/hfTvQlYvl0NqzuFt4dseA2Ow6VDcXbtncST6noKoGZLcbUyP9kHNRMWk+aZsDsopqnd3MJ1IrQc0pdjsAZv7x6CoWdIvndtzep6Cq9zfxwjaMfQVh3V88h5PHoK3hDojmnUb3NG71XqIzz61jzXRckk5NVnlLVHmtlFIycmx7OW603JoHNPAqibiBc07pRShCe1ACUo4p200baBFu3ORVrFUYCVNaCcipZSHIdrVZYB0zVfFTRN2NJjREo2vWhHcRxR5JFU5UxyKy7tpc4B4oQpI0bzWeqoaxpZ5JmyxNPtrKW4YcVpNorxpuIpjSMhI2c4ArStNPZiCRWhZaYDzit62sAuOKLiehQtNNAwdtbNvaBQOKsxQhR0qdVAoJuJHGAOlSgUAU4LSEJilAp4WlxQAiinijFKBQMMUoFFLigQopcUCnUAQzwiWMg1zskbWdznoCa6is7UrUSxlgOaUkaQlbRk1nOJYwc1b6iucsLgwy+WxroI3DKCKUXcU42Y+iloqyBQcVYjfjmqppwYikBPJg1WYYp+4mkZTjNACK+KccGoScUqyYpgJL0rPnjL5rTJDCqk2BQBzeo2Y2k45rn3XaxFdfdqXBHaucvrcq2QKGrlJ2MuRc1XPymrhHFVpU5ov0Ka6j0l+Wiq1FFhXPtmq11drboeeaZe3iWyH19PSuZuLx7pzycVZAt7fSXDkA8etQRrjrQq4/KnevPaobGBPf2qJsnn2pZZFjRmdgAPWqkF9HdPtTnPApATD0HpUqnCn6U0KP1qXZx+NMkwPEkwjssZxnJrxy7+d3brkk16h40m8u3YDsv8AOvLpOeopAzMlHNQ7K0HhLn5RmmeQQeQQfemCZUWHJFa9hAFAJXNQLFgjjNalrHtTcwIAoewWb2HyREqCGwveiEzXLCCFMk8cVLCk+pTrDApPbp0r0vwt4Rjs41mnXLdeacY31ZorQXmVvCXg9YQtxcrluvNehRRrEgRBgDsKYiBFAUYAqQHFaGUpNjya8/8AiDcYhZAe2K7tnrz3xbbSahd7edu6olsXS+K5a8EAW9lvPGErfuJzKxAzisPTITZ2YQDkjpWrbKWYk/ifSpvcJasikXB7bifyp1uoLk/r606baSoHTk49adAAv1zz7UwRaYY2jv8AoKjaQLET25/GmTSgYPb09aqvLlMd8flS2Bjncs2D6cD0qsHVec9+TSySqiMc4Hr61zWp6x5R2Ict2AqWyoxbLes6ykCFVPPYVzKC7upDI4wG9fStfRfD9zq90JpwSP5V0et2EGnQCKJRuwoJxRyaXZvGcYvlQ/SE2WzHtwPrU05IPTnP5UmnjbacHknr6UjtuI9Ov1pWOeTvJjYl+cZ6dSauvKEgAA7cCqIJ3D1/lVhUJXn8SaaAp3jMsbvnnjn0rm3v5FnbncAa6HVGC2rdgW/OuMkkHmuc/wAVJktmwupAqcnHHerNpeEMuMEbMVzhl+Vs+lS2t2Elxkjgd6BJ2Z2cMgkXIGBWFqTfOOfWtWyk3wls5x/hWNft+9x7VrHYbOb1JvnPWktOSKZqJzMfrUtn2pMpGzBgAVaBxVSLoKmLZ6UiiTdyPrQckUiJ3qQjAFADUH86Li5S3jJJ5qtcXawIeea5+9vmnY88UAPvtRaZiATismWXrzTZZewqAkk0CBiWNJilFKBQIMUoFGKeooAFWnFgopGbAqB5M0ALJJmoGOetDGrmn6ZLfODgrHnk+v0ppAU4NMN/JtjXA7t2FW7nwXIIS8aNjoHPf8K7/R9AWJFzH/up6/WuhlsFhXc4Bb9BVJEuVjwG60K+sySELAelUhPLEdsikH0Ne83Gn21yCJIlz6isC/8ABVteA+WFb2YYNNpC50zypZo5ODQ9ujDK8V0uqeBLm1YmIMvselc3PYX1if3kTBR3HSp1RSVys8DKfWoyMVYW57MKk/dyDtTTE0U1ODVyJjjkio2t8fdNMJdO1KS5kVF8rLefemnI9KqiVs1ZjbcKzcWlc0Uk2J5hHWnLLupxUGmhFU0rpoqzuSClI4poZfWnE8VBRUbJelce9EjYNQlya3SbMG7DyQBTMbjShcnmpAVWqvYm3NuSRx4Gal28VCJxS+eKyak2bJxSsNcEGlSTHU1G8uTUW41ahdambkk9C6JBSPICKp7jRuNHIhuoxWOTRt4pACTUpwBVXsQlfVjVXAyaRhmplAZaayhRU82pXLoQU4LmpBH3oAxVcxKiyMrik6U5jzTKaExwdh0NWIrxl4PI96q0UcqYrtGos0UwwePY1HJYo4ynFUASKmjunTvxUuLRd0yOS3kiPTIqMMRWml2kgw/FJJbRyjK4+oqRptFFZCO9Tx3BHeo5LR06cioDletDRambdtqksJGHOK3rPxADgOcGuIWQjvUyTkd6lo0Uk9z1Gz1cHBV/yrctdZzw+GFeP29/JEcq5H41tWevMpG/8xS1B04vbQ9XBtrteMA1WmsGT5oz+VclZ64rAbX/AFrds9cOQGbI96uFRrYylCS3NC2vprZ9snA9a1V1VDgkDPqDWelza3YwcZpk2m5G6I/lRKFOo9VZhGpKOxqf2xEOCMjPOTUFytveKSpGfesGaGSMkMDTI5pIjlW/A1SwdvehLUTxN9JrQtvp0qOuOQelRrbtFMpYHHIq5Z6qC4RxjB4qzqYSWPzIevpQ61SLUJj9nTa5ohGgaGMrgnbnI61NZ3pil2P09Kw7e+eGQI424yK0poWlj85BhgM8dKwnSkpWlszWM01ePQ6BgsqZwCprE1K0Dltmc1b0258yPYx596tSKvP3TxXNzSoz0N7KpHU5j7C6cvnGM9KrvgEAZrp7hY5U2BlBxjg1UTToYzlnXpmu6njesjkqYRfZMxbN2j3Y7VEJHhbAJHtXSMsMURGMj2rn73YXyvHWro4h1JNSWhFWgoRTT1JFukfiQY96iubGC5Q4UN9KqbqcsjKcgkV0Tw6+yYRqvqYuoeHFkVti7h6HrVHRNNbT7yReV5DqD6iuyiuQ/Eg9s0s1nDOuRgn1HWuWdNpNM6qdXqc9rE4Sd3j3KzyKTxWd4kga5tJ5NnHyuv8AWti7sG3HLNIndW7U69hSfTPLRdo24IPNczg1sdcaiaucFo1yYlljBwVIcf1qYgLqTkKTk5XnsaoS29zp15uaM4HX3FattJbXJjdgG2nqDyKpxtuaKom9BLt3tirAEDOVP9K6rTdUEmmKybtzqR1zyKxLnTxdwsIi0gB3DHWtLSraCzsxGxIIO5ST37is2lYzblzeRUttclmutsrAJjBB7GuleWC6tUIZS23BBFcPqQNvNK0bYBO4dDRp2qyo4JJqZQ6oalrY7eC12xFHXchB+6a5rUYkim+UHBOCsgresNZt541SQAN061lalbK98+0tsboSeAamO5Td0Yd3ayFA0VqG56xnoPpVCc3Btgrx7M8dK7ZoLO1t4ZX3bym18HGeKwoRFqb+WikAA8PzWsZkKKuaHhK+MtmqnBI4IPtViQCeWWD+Fy2OfxrI0T/QL9oMqCfmGK6O/t1tZo5lA5+epno7lJaWPO9dtTBerIB94c/UVeF0psnxEDuQOCvX3q34ht/OgeQKQQd4+hrP0p1MID5whKH6GtVLmSI9mkmyo8UV4uSNx9RwRVOWzkg+aP519uoq9diOBiEK7kyM9zUMd4QD5q9MfMK11Ryq07leKXj5gc1Mty4brx2zVt7ZLiMuB/wJf61QktJYzkHco7inzXViVTadzd06/aJ927gClm1bF3Gc8g1hfavLXA61CkjSyg570QjYiu+d2PYNIv0mtRkird6rSBdudrDt2NefafqTW6KoPYCuy07VlkiUOR+Na0NW10Ma2iUupYt1mjQ+ZuNSNbgvvVFZf7wPNWlkjmX5Tg+1NChHyVwfXsadak7aFUcQm9Sm6YHPTFU2CebgnAzw2M1oXqkJvUdOtYcgaVyVznNcNONpanozlzR0NcIvGChUnpmpGj2gc5AODWTGJAOg4NXbZ3Y7X7mt4S5pWOapBwjzGLqkPkXAcdAcH6GqcpwUf0O0/Q1v6tAJYM98YNc8vzxlG6kbT9RXRTdnynHUtujWtLqXYu1AxHynn8q27F5pHAeIj6Guf0CRXuRFIB84xz6ivQxDbw2ayqFUkZOKirUVN2NadNzVzI1DToZ4DvwD6Vw2p6SiPlCAc9RXYXt7I4ZVbg+1crdFxKxIxn09aUKk+uwTjBbFWFIbaMmYBfoc5qnd6mzjZENqe1MvtzEnBqrDF5j7QM1L3NU9BmWc5OTU9vavOyhRx3zW1a6QCoL9atxwpbSgcD3IoGR2mjgKGYCt6GwVodoxxVeS4HkhYxk4rQ0vewJk4yKQFcWcdrlyMntV2yu0lOGx0q1NbCVelURYlJfkGOaYGtsTAYMADUF4zOoVRkVHIsiiNVUY6EitGGHdaKWHzjrQIgtYFNqQFAJ4rPMEsVyZCSFA9a0I42iPzHgHNQ30y/ZXIoDY4Txbfq5EQIziuORATk1a1uZpdQbnPJqtEjEDNS0awdhWz/DTkTjmpfL9qtQ2jOMkELSG2ytHEWbCrmriwpFy+CfQU9pVhHlovPoOppY7R5fml4X+6KlyBdiIySXDbYhx69hVqKzSEb5Pmb1NSoAPkhTJHpU9vC73KiccDnArKU+xrCl1kOgtZLkcZC1cXS4Y1yFQt6l+amvEkSBUhwM9SazZt8Kncct04NYOTZul2LD2kiZ2tjJ7VZgtZmXJaRR/s4qOxu/LjHmNk9wRVk6xCOFjy1Jtg1Zln7LaRxfvVJb1J5rHvEQbvJBHbdnpTbnUHlf5j+ArPnvC3yINzfpThGTE5KK1HkRRfMWJ/wBpqjM7yjEY2p3Y1BjndK24+nYVVuNSWPhME/pW8YGEqzasi60sdupYtz/ePU1k3eqFshDgetZ9zevISSxJqk8jN1raMO5zuRNLcFicHJquWJPNJQBWhNwpQKXpRTEKOKWgCnqmaBCxpk1aEeB0qONcGrsabl6UAVvLz2pwhJPSr8druq1FZ89KAM2O1PXFTBSnFabrFCnzVjz3amTANJjW5YpAdrZpsb7lpxpFWLXDx1TeENIM9KmhfHBp8qdxS2Y90aem20SoCBzWpLbiSEjFYmnXG1gCa6KJgwFNkapmZagRTbWrZRRjis68hKPvUVbs5/MTGeRUoqWquWwKeBQozUgFMgQCnijFOAoEAp2KBS0AJilxTsUYoASlFGKUUAKKKKKAFzSMoZSDS0ooA5zUbYwy+YvY1d0678xApPNXbuATREY5rnkLWl1jtmpaszdPnjZ7nUqcilqtbTCSMEVPmqTMWrbi5ooooFcFO01NkMtVzQr4pgDrg1HjFSM2aYaBDSxqKTmpDUbUDKcy9ayruAODW1IuRVKaPg0COSuIjG9VXGRW9fW+4E4rEddpwaGjRMqleaKlI5oouFj6bvbx71i3QHsPSoowFU/lQgIQFjknnpigU2yOpIW4NQyzpBGXcgCmXFwlvHvdgABXnvinxX8phhb2AFS9R3JvFHi0AGGFu/ABrU8Gl5IfNkYk7fyryVHe7vF3EsWYV7H4chMGnZxjOBTeisB0Ibkc+9TZ4FVk5qV3wp9hSEeeeOLjc7Jn+LFcKykniuo8WSNLeAZHUnFc6qnuDQxMdZqm4BhyWq81ksgLBeAcc0/SbHzrpCemc10d5Ha2dopYjdjNF7FxjfQ5WTT0hJZwRjtVycR/YdqnnAJxVZvO1G5IUtsz1q3ewi3gWP8AiI5pSvdG9OKVzqvh5pUbATOuT97mvSlXAwBgVyvgi28nT846KBXWVsjmm9QprNQzYqncXIjXrQ2TYWe4CDrWHdBZJiQMt29qWW5aaXAPf8qcsfUnof1rNu5aQRxjH8zVkMFU4H0FMZcMP5elPC4TJ/8A10kmJobt3MST260cBh9elPDYJ9f5VEPmOe3XPrTKIro4A57cmqbyrHCCxwPT1qXUZ1gUsx6DgVyGoarLdS+RbZYngkdql72RcIXJtT1pmYwwnc5PGO1XPD3hia+mE9wDg8kmrnhrwmZCLi5HuSa7+GGO2iCRqFUCtIxtqxVKiS5YkdrZw2UIjiUADvXJeJZhJeqgP8f8q2tW1mO1jYKwz61w321r7U1bqBk1M5dApQfxM6mzXFmmR6kD1qGQ/Njvj8qtRLstF/3eTUBQFskcY4HrUkvcjjA3nPT+dXJiFjGB9BVdR8/YnP5VMV3HGe/JoSC5j6pn7Oc8nJP0ripT8x+tdnrziO1AHAwfxrhXckc0mQxHchTg0kMmZgD6gVBK/H40trkzp3+amkI9A03/AI8WNY9+czmtmxwumE5rBvWzO3NaIo5+8GZfxqe1GMVWuD+9FWbbmkykacbcAVZjFVYxVxcAZJpFEwOKo3t+sSEA81FeaisalVNc/cXDSMSxoAdc3TSk5PFUJJewolkz0qCgQHk0UUZoEOoAoFPGBQAoGKazYpHfFQM+aAFd81GWpOWYKBknoK6TRPDslxIryJubsvYfWqSApaVokt5IryIdp6L3Neh6RoYh2jYC4HTHC1oaVoqxKAo5/if/AArfihSFNqDFMhsjt7RLdfVj1JqrqHT8a0qzNQPB+tNEsyCeTUkH3qiwS2AMmpDKloNzEGT09KGQk2y7KIo4t04BHZT3rjdbtLK/kZUjCsR0HQVcvb+a8chDx3b/AAqmEC9PzqWzeKschf8AhBXBaNQfda5y60G7tSdgJA7d69WQfKKSWCGZcOin8KVy3qeNl5oG2yKR9akWdHGD+tejX3h22nB2AZPY1zF/4Skjy0YI+nSgmxgmJGGR+lCgx+9E1jd2p5UkeoqJZyOGFGoK6HtKc0xpSak3I9NaH0pqwNsjEhBqwkmRg5quUK09WCjrRKKaHFtMmZAwqBoiDxT/ADsVIjhqhNxKfKyttYU05q6230qBwtVGdyZQ8yCjNDdeKVRk1oZiUU5lxTaSdwaCpI03GkVeOaki60nKy0LirvUcY8dKURZ61KKcKx5mbcqI1jxSmPNSDFLxSux2RAy4FMCZqeQ4Wq2459qqN2iZWQMuKjwKeXFMLZrRXMpWEJFNpdpJ6U8RnFVdImzI6KlGPaneXu9KTlYFG5BT0ldDwakMBpphYUcyY+WSLEd4Dw4qUpFOOMVnmMjrQGZTwTRbsF+5PLZEHK1WZWQ8g1aS6YcNyKsK0UwwQM+9S9BpszQ+KmSYip5bEHlTiqjwyRnkGla5anbcvQ3bIQQxFatrrMkeAxyK5kMRUqzEd6lxNI1Dv7LWw2MPg10VnrjLj5uK8miuip4Nadrq8kWATkUtRuMZHr8WoW90uJMZol0+OUbomFeeWmuA4+Yg1vWeuOmCHyKqM2nozOVJ+qNC5tJoTnaTjuK0NMudw8uQ5B4INRQa3BONsuKsrDBI2+FgD7GtKlRVIWkjOmuSV0F5bxKN23+KrFlfRLGYWIwRiq10JBGQS3WsV2ZZCQSOamlSdSPK2aVKihK6R0kWEl3RnturSYmTlWzkelcpZXzLIqs3Toa6q1YPEGHTuK5MTSlB2Z0UKkZLQzboyxTBhjHQ8VQvr93QAEZ6cVv3MKyrgg+2KwJdMd5gV6e9XhpU7+8Ksp290WJ7m5ixuxlcVWks7hOetdTYWyxQjIGfcVO8MbD5lFP60oTfKtCfq/NFcz1OHIZDhgRV2SALCzDtg1sX2lJIhZAKyctH+7f0wa61ilUSa3OZ4dweuwW1o7o5OMYzUNxmGYhWx34rZYFLUiIA5XIrJa1md1Z8cmqp1lKTcthTpcqSjuQGTzOH4b19agDmGQnGUPDLVq5tzEnIwQ2KrEb0yeo4b6UVKaa5oihNp2ZJPpFpfx4wFY/dP9K5fUPB7xEzW4ZCDyV7GujtpzC7ROflzwfStdJCwLYywGHH94VlBqWjNJOUHdbHlgvL7TJtk4bjo69atnUBeR58zf3yvDD8K7XVtEt7xMquA3KmuNu/DUiuzRblde61EqPY2hiFJWZl3dvOyEoRImM8dR+FVrO48s7XDKfpVx5LmycLcxs69nUYNTqbS7QkEM3qOGFR5Mtq6vEovPJ5waKQYB6g10dlO19DsO4tjn61gSafKWdodsiZ6Dg/lU+mzSWF5ukZ0xjKstTKF1oRGpyv3w1S4vrSYRTMxjPGK1/CAVpF8wMCGJ5HGKuanHBqiRucZdcggdxVqztIxZB7dyWA+YelZSkuWx1R9536GdrlgbDWo54/uFsce9at3OJ9NVmUsYwOB1xWbqt0z2+yU5bqM+oq3p0sd7ZFM8MPWp1smVaxSuYUms1ChsYKHd+lcnYgx3jwNxuBH4ivRo7JDYyopwTFlc88ivP9WT7NqazKOpDf41UJXGl0I9Stw0ySdN4wcc81YsdIaeMM65GMc1ZngEtuGXGFO8fQ1paddI1qsQHzf41tKTUTkhTXO7mFcbLbKxsVbpVEagBIRIAP9oVv3mmiJd5zgE9a5ybT3kY7PrmqjJSQTjJO/QfLaw3XzjAP95elQG2ktucZX1FOSCW0+YOCPTNSpfxlsONp9R0NVrExSjN6C28hyCTWxb3zR4wazjHFKNylVPqvQ1GyyQnkZHqK0hJLYwq03fU6+z1tkxlq37XVo51wxFebRXBHer8F6yEEE1uqhyyp9j0UyCRccc0yKyiY5DYOelcxZ6xwAxrZgv8AzB8rA1hVoqWsTooYhwVpFt7dEbJIoj2hzt69aVUMi9sHsTSxW+xsn+dRTw0oyTNamLhKDiLcReYjL6jiuQuUMF4y/wB7kfUV2rdPpXNa9b7WEqjp8wreouWVzlg+aHoZ0Mpt7wSJxnDj6jrXWf2lJPAEVsgjcBmuQbmIOP4DuH0rStJy0YTqF5H0p1YKSUh05taGyjEgAggt1Bqjd2qjk459auW5+ZcSZHUBqh1NWaPp8pGTjsamEUypt20MG5tkfOFxngjPen2NnHG+5qaXCsQc9jWrptsJ5OSBk0ppLYqlJvRmhBCGXIUj3NK1tEZAOM9a0TZBY9q5PHrVI2whffIcY96zNxskCQgMTnjPStKzXcMjO3GemMVlef8AarhUQHA4rdRVigG709aBXJ1mh3bCTups22Mbj0rISOSS8DKcr0rZlaPYEdgCBQBFbTxTPt2g81dluY7dccZrOxHCcxtuPXOMVSvr6OJS0jjI9aALFzdtKSScLXNa7rkdvbtGr5OMVj6z4qxmOA5965GWae9l3MScmkNK5Izme4LnnvV6GAvwootbIRqGl4Hp61eRg3yIPwFQ5JGkVcI7ZEQuTkjjNSo37htoJyeppkkciIgY4By2KuxW2+wyDg4POKxcmy7WKWnWomlaUjOTV6SPfuQjywPTvVS0aWyGHTcvqK0oJY7lSiNtz/C/+NKon02KpSjsyvDLHayBVTIzjmtiKBZsS4wfYVitCkd0qlOWPrkVoy6mLeAKhAJFYy8jpsX5LmONcHBI9RXOXzGS7L/wZ9etSefLK24sD34qF3AOWwSO56CqjTtqyPaWegOTJGFyVX2PJqJjHAO4z78mozM0hxHz6segppMcQ3M2W/vGtFEipWWyFYvL975E9O5qGa5it0wOPYVRu9T6iP8AM1kS3JcnnJraMDmlO+5eutQZ8jO1fQVmvMXqMsT1ptaKKWxm3cXNHWgClqiWwxRR1p6rQA0DNB4pWBWoiTmmBYjGRVhEqvbnJxWrBBupMBkcJPatG2gx1FOigA5NSSXMduvUZpXGWlRY1ycCqlzqKQghSKy7nU2c4U1SVZbhuATSBImur6Sc4BOKZBZyzMCAa19O0J5SGcV09tpUUCfd5pjdkcqlo8KfMDSGumvbQGM4Fc5KhRyCKTGnciztNWkbemKqmnRPtbFDV0CdmSqxilBro9PnDoOa52QZ5FW9NuTHIFJoQSXVHTyRiWIis2Bmt7jaema0YZAygiob2DI3rSa6ii1szSjIZQRUgrOsJsqEPatEGhMUlZjxThTRTxQSKKWkAp2KAFAooopgFFFLSAKKKKAFpaQUpoAQ1kapabl3qOa1s02RA6EEUNXKi7O5g6bdFG2MelbqNuGa528ga2uN44Ga1LG6EkYGealO2hpOKa5kaNGaQHIoqjADTTQaQmgYUhNBppNMQGmEU4mmE0AMYVWkWrR5qGTGKAMu4iyDxXP38O0kiuiu544wckVzWoXiuSBQUjPLDNFQlsnNFFiz6ec9eSfc96rXF3HbRF5GAAFMvr2OzRnlbn3OSa8y8UeKnmkaKJu/btRe5GxZ8U+Ky5aKFvbArgJp3mcs5JJpskzSuWYkk0zNGxLNTQoTNqcQxwDmva9Pi8qxhTGMjJrynwdbiS/L+mBXscaAIqj+FaGylsOQegqG7l8u2lb2qckIPwrI1efbZtzjPFIDz3WJBLqD54xxVaGAMMjBouCXu5GBzlqf5628eWALdqm+oKN2X450sFLn72MACqLPPq04znyx+tR29vLqM25shKvXFzHZR/Z7YZk6Eiq39TZK2gTTxadF5UIBk9fSqgZ7q6VWB5YCt3QvDEt6GurkHAG7moGtkXxDFCnRTk03G25UZLVHqXhu18rSVOOprTf5RTNOAg0yFTx8uao6jqIU7F6mqucrV2FzeBOAaybudpS2D7Z9KGDOm8+vWo1QnJI+gqWx2sMtYzuU8+v1q+Dgj1z+VJboAeew5NPxlx+JxQkO4wsSx44/nT5ZAigZ59fSoJpAmcHvyarvKXk56DnFAFpDuUsemfzqVByT6D8qrbz5YA9smn+bhCB+XrSuM5jxVcMd0cZOSwH1rU8JeG42Q3Ey5w351haoTNqUCk5zL/KvRdE2W+kK7EAEk04b3NKrcYJLqaICQpgAKorA1rXUt42VGqrrfiBY1ZEauDvL2W6m6kkngCic+iCjQvrIl1HUpLqQktx2AqbRYi1yWbOBjirek+HZZ42nlXkLnnoKs2dsIdRaNOcEDms+V7s6XONmkdLMwSILjsMLVLzCznHXufSp5jkYz9TUCDHOOOw9atnFuTQLnGc4z19akuBswfXkCkgIBGRzjgVHcyEufYcmgDnPEbt5Jzn7vQHpXGFu2f6V1niV8REAfwjqK455O3SpZD3Ek7Z9alscG5j/AN6qjv09Ks6Zzdx/ieKaEz0C3ONMWsG75kkOfWt1eNNjHPQVz1yctIfr1rRFGBNzOKv2q4AqjJ/r/wAKvQOEXJpMpF9SFGTVa7v9q7VNVri7J4FZssvUk80gHTSlmyx5qo7kmmvJuNNzQMQimMwFOZqrTE5oESbxShs1V3U4PzQBbBAprSVF5nFMZ6Yrj2eiON55AkalmNLa2st7KEiX6nsK77w94YVEDuuAerd2p2C5R8P+GGdhI4y3dj0Fei6bpKRRgKu1O57tVmw01YkXcgVR0WtPGBTIbIwgRQoGAKaaexqMmkISs6+UucD1rQJAUsxwoHWud1jWY4wY48gfqad7Ds2QXFzHaKdpBbu3pWK8kl42SSI/1NNJadt8vQdFqQHFS2aRikBAVNoGAKjI6U5qY3SkUIDwKC/0qMZbpUm3GM0ACjPNKwB4I4pR0FHFAFG40y2uB8yAH2rn9Q8JxyAmNQfpwa6wmombJ4p3A8yu/D9zbElMnHY1nOJoDiRGH1r1ibyvLIlVW9jXNahY29wx2DH8qLoTSONEyt1pSiv0rRutF2klQfwrNe1mgPQkUCsNaEjpSLlDyKVZyvDCpVeOSnfuKxG0tRsxNTtCD92oWjZe1NWBtsZT4xzTKUEg03qiVoyVlJNIQFpVkp+0PUao10ew1RkVIi7aaEK08CpkyoruPFOPSmAinAgisyxRQSKhdiDTN5qlFsTmkSOQagdgOlI7VEa0jGxjKfYCc0qnBpKVRk1oQW0CkU8gVCFYLQrNmsGrs3T02HiMZqVVApFGRzUgUVLZSQlGKdgUnSpKI2QGmeUKnIpVUGmpNA4pkHlDFM27TwatFQBUZFVzEuKIvtDxn296lW5jkGG4+tROgNRGP0q4tMzkmWHtY5BleKqyWrp05FCyvGeCasx3atw4qmmSUcletPWUir5hhmGRj8KrS2TL905qSlJoWO4I71ft9Skj/irGYMhwQaVZCKTiaRqHXW+rBupwa27LWHjI2vkV56k5HersF86EYY1Nmi3yyWp63Z64kqhZcGrjQW14uVIBry621gjAJresdbKkYeqjNxehnKl2OjuNLliO5ORVrTtSkgcRygg571UtdfVgA/NXwbS9GRgNWk6iqR5ZmcVySutDeFwjxhgeDzxSIUf5lw2Kw5IZoo8RseOmDVFdSnt5MSBvqK41hW78rOp10krnZK46DgelOdgqZJFct/b3y8MCfpzSNrrOmM545BqfqlXsV9Yp9zoPtKmRlOMg9qparY71MiABvasKPUSrOd3UVu2epR3OVbnKirdCdFqQlVjVTiR6RKXISQdOKXV3MeRFGDg4q/FZokm5R78U+e3Qxs7jPGaz9rH2nNYp03yWucjLLJKMFCKjhIWYBuhGDV28uo0kaME4z0xWYz7nLdM17FJucbW0PMqJRle9yW6tjhXU84wamtL7BCk4kAwQe9RJOcbX5U1Dcxch0PzDkGuapTcHc2hUU1Zm0siMgHPlsf8Avk1Suo2ik3gHK9fcUyzuQy5PQ8OP61dIEieWTlgMofUelUnzLzMpLkZmSWNtexkEfMORx1FYF/4UG5pIYypzwUODXRKDbzE/NtHTHapJL0NIEQyMGHQjvUJJuzK55Q1TPPma8sZSssTSKONwHzCrH2xLpR85J7gj5q7W40u3uIy0qEOeuOMVzGpeGl3NJb7sjoR1pTp8uqNoVo1NJIj08SFjEkpdfvqDwVNbwmaB84QAgblHauOMl9YOBIhkUdGH3hWmuux3CDK5kAwSOGrlnFs7INJWQ/XeDkZZd3BWk8OXCYaIn7r8Z9DUEubqEkPvCnIHQisqwuTa6xsJOGPeko+60a30O8ZXtrjGAMfN14I71yfiSyMabv7jHH+6a6iZpLiKOZXO3AVh6VkavILm2SM/eaMqfqKlBHcx9Nl82zVTg5Ux8+val0+ZIrg7uNpqjpj4eSI+zgfSrl3pvlzNKiSKGwwwcjmt170eU56qcZ8yNq7uEuoGUbeffvWUQsCMQuD09aqpchVxhifftTWLyqDtOc9aiMXHQcpXV0QpG13K24IOa1rLw1DcR5yjnPOKykuGhO3aM5xWtYag6EFmwMdvWnOUug404pGZqGjT6dIxhbaofAB71Uiu8kq4CnOPau3mkjvIQDgnqC3OTXNXmkKWZlADM33cdadOd9HuZVU1qii0aHlfkPtyKgadomw3WoQlzaXQV43WMnBB6VrXNj5tt5igbh1rVycWY+zU1dFKPUMEc1q2epkYw1c7cWrxDOD6021nKt1rWMznqUl1PRLPWOgJrbt79JB1Febw3RGOa07bUWQj5q3jUOWVI9CV1YcGs/UoRJbkEcr/ACrKs9Y6AmtT7bFMuCR8wwaKtpRuFK8ZWZzcK7d0TfwkqfoafZuYZtpONp2n6Gi6XyrwEdG+U/0pknEqP2cbT9amm+aNi2uV2N+3nc7QWiO3jlcVYuVEkOchTyMg8VQsm3IjZOcYPGea0A52FAP0rOOjszR6o5W5gPnlsk4PpV3T7hoLgKc4P86lvYvKdifM247dM1QIYMMAg9amd0/I0p8rXmd9bTi4iXb1PH41W1LT5JBwOKz9AeTf3wTkV1xQSDkVJexiaZZJbjLKcjrV27t/NJwOPc1ZkjjjUtx+dY9zdySOUTP17UDL8KRxqdpy1Zt0jmbczFVqM38dhGXlfLDnnpXHa54uaVmSBj6ZpXCxv6n4ggsYioYFgOlcJqWu3N/IQrEL6CqJ+0Xsm5yTmtOz0xdyh+SewpNpFqOpnW9jJO25unqelbFtZJGuQpPbOKsSW0inEUZIHTsKkitpTtVwzMewOMVjKpfY1jAz2imnmMcasB0JNbtnpQtbfzGHzYzzVjTdLEb72V/WrWps8duRGDz2NYTqX91GsYJamG7LPJsKKQBjrg1YgfyBtilx6q1Qs8EUEryEI3A5GeazLe9klL5KupbAq4q6M52udABFcI5KbGHdOlZ9p8t3Jg5AHpUto5EDtzgk1DpssbzvuI+Zu9VG+qE7aCvIDeEZORxxUckO6UM+do6LUtzsgkMgAQnuGzVFpZZuhKp6nqaiMWdEqkYpdSea62DYiqD/AHUqsQX5lP8AwEUx5orZc5x7nqayrvUmcEKcLW0YnNKdy/c6gkI2rgn0HSsa5vXkOWb8KqSTljwaizWqikYuVx7SF6jopcVRNxOtKKQnFJyaYDs09V3CmKhNWIlwcUCGBeaeFp7Jg1IkeaAIWQkdKruhBrXjt89qr3Vvs5ouBThO1xXQW00aRAk81z2MGpDK+3ANDGa1zqfVUNZzSSTN6061tJLhhgV02m6AOGcUhpIxLLSZblgSDiur0/RI4VBYDNa1vZxwqAqirSx0hN9iGKFUACjFTbaeFxTgKBFSaLcp4rm9TtNpLAV1xTNZ1/aiSM8UDWhxLU3oc1auoDFKRjiqzDihFPUnjfcuKATHIGFQRvtbFWG+Zcih6Maeh0Om3W9ACa1uHTB71x9hcGKQAmupt5N6A0ENEG0wT56DNa0Th1BFUrmPfHuHUVHY3BDbGqNmV8SNcU8UxTxTs5qiB4NLmmCnUCFpQaSimApooopAAp1NooAcaTNGaM0AFFBooEU763E0Z45rDt5GtbjaeBXTsMisLVLQht6ipa6mtOXRmtBKHQHNTZrD0274CMea2FbIppinGzHE02lppNMgCaQ0E03NMAJppNNkkVBljisi91hIgQp5oEkactwkS5YisK/1lUyFIrFvNWkmJAbisqWYsck5osUl3Ll1qDzE88Vnu+TTGeoWlApjJ8miqZmOemaKYHpvibxX9unb7LvWMnA3Hn0yfr1rjJJWkJJJJNDMzfe603g1K00JYUA0YoAO6mI7/wAB2+XVyOr5r05X649cVwvge22W6Njoua7qJeAefWk9yh7D5MnvXO+I5fLtvopPNdDIflxXE+LrgKrrk/3aTA4i4u/KySMk07T7d76Xe+dgqF7YTyZY/KKvxO21be2Xk+lJb6HQoWVy/PdrCotrQZY8Eiug8MeEpLmRbm6Ge/NWPC3hIlluLlcnrzXokMSQRhEUACtYqxjUqdEUrqGOx0mRYwFGMV5ppo8/xLLIeinFej66WaxMadTXJ6JpBgned/4juJNKW9xwdonXXd75UCxIfugCsfDTSbmzj+dSndM4JzgnOPWpUTDE9/5VBCRL5YEag9R29KhYBYuvX9akdufw/Oqk0hOFHYflSQMmil5bH/6ql3gDGe3X1qtGu2Mk9+3rSqx3k56cfSqbDcjdGkkzjv8AlTZE2sfp19asq67R+f1qrM5Ln/OKVx2HSSYQDHPYUsXMbMT6kmoj8xHp/Op2Hl2jnHO3gelIEcjdSBdYhYjhdzVt3WvlNOiiQ4O3oK5q+kH25ieSKhRJryURxAk+vpUJvZHbyRdmx8txNdzbVyzk9PSup8PeF2kYSzDPqxq54e8LrGBLKv1J6mu0iiSJAiDAFbQp9Wc9bEfZiUrmCO00x0jUAYxXHWC+ZqLsem+ux1l9mnt7muS0bmdm6nJNKpuRS+FtmjMMEZH0FMjHc9fX0p8zAsf1NMZsJ6DFTuJtIlXao4PQcn1qCX5ifTHSnRISSTz6CpNoZiByTx9KbWhNzi/E8+HZTjqB1rk5GB/+vXS+KFH2g9/nPIrlnHJwamxL3IZSMir2kfNdr9KzZT834Vp6Gubv6AVSQmjvpPlsYx7Vzc/3XP1rpLo7bWMf7P8ASuYuDmNvpWhRiyNic04zHGKqTuVmOarS3RA4qSrlqa4CjrzVF5ixqu8rOcmm7qQE++jzM1W3knFTRKWNMCVFLGo51xV+CEnHFQ3kW0/jQBnGm5xUjLioyKBBvq7p2mzX8gwCI88tVrSNClvpFZ1OwngY5NenaJ4eS2C5jUv2HZaqwXsZ+g+HUgRC8fHZe5+tdzZ2KwgMwG4dB6U61tEgwcZbuTVwcUXIbuA4pGNBNRs1IQjGopJEiQvIcL/OmXFzHbJvkPPZa5LVNYkupCkZH9BRcpK5e1bW94McX5CucKln8yQksf0pyrjJJyx6k0h7fWpLtYXNKKRelDECgaAmmkZoU5NOJxQMAoUe9NP3hSg0hPIoAd/DTDxS9AKa/TJoBDWOfaq01wsIPPNRXN8sakKeaxprhpGyTxQK5PcXTSseeKqM9MZ6ZmkA/Oahkgjk+8tPzSE0AZtxpSOMqAayptMeM/LmumJphUNwRmmByeZYj8wOKkW4VuGronsI5e2Ko3Gh8EqD9RTCxmFEfkUxoSOnNPls54Dxk4qMTMvDg002iWhm0jrSq5Wp0dH64/GkaBT93inddRIVZQetO3A1A0TL2puSKlxT2LVRrcsEZ6UzLKaiDkGniT1o5Wh86YrGozknrT2YHuKYW9KpIltCFeOtMp+SeKQqRTJG1LGRmosU5eDQ1dCWjLoIxSHaKiV8Comck8Vko6m/PZFhpsU3z6hI45oUVXKiOZ3J1mOeakEoPrVQ5J4pQjE0OCGpSLwcGl3CqygqKYWfdWfJdlqdkXCaYxpoJK0oGamxd7jGBI4qIBg3NWttMYCqiyWiB4i1MMRFSM5Wm+bnrWibsZNIYGZDwanjvCOH5quxplVa5FzT3QzjHH41BJY5GUNVAxHTNTx3bpweRRyvoO6IXieM8g0iuRWgtxFKMNx9aY9qkgyvFSylJorpMRVmK7ZCMMRVSS2kjPTIqMEjrUtFqZ0dtqzLgMc1u2WtYxtc/nXBrKR3qxHcle9K1tjTmUlZnqdn4gIIV2yK10uLW8XnGTXk1vqbpgE5FbFnrOMfNg0JuL0IlSv8J3M2lBvmib8KoSW7wnDKfrVWz8QOmAzZHvW5BqdteJh8ZrohiJLfYwnS7oxmbmp7W5MMm7JxjFaM2mRTAtCwz6VnS2U0B+ZDiupVKdRWZjyzg7o6mx1Lz1UbsnHHatPzdyFWyMjoa4KC6e2b1HpW5ZayJDsLfgeteXiMHKL5o7HoUcVGS5ZbjtSghZicc+tYrQMvIGV9a276ISKJEY4PfPSsR52TKMvIPrXThZT5dGYYhRvdkYbtUituGwnr09qrFuSaUPXdKHNGzOKMrS0ELtbzCQdDwwrWtphKoUNweUPoaq3dsWtjJjqoP41n2l15RwxO08H2rzX7kjvUfaRt1N+4TzIzIB8w++v9aow4RmVlLHIZOcZ9qtxzl13DBdRyP7wqGeMDDgZjPKn+6a1spK6OR3i7Mc4ldcBmQY6HmkMXlq247vnFQSXbyv5acMOTn0p+wu0IZSAzetVFqS5WJ3WpXurOGXcXQcsRkVh6j4cR1aSLggAjacGuhkYogLDBDckUyVyXZU6g5B9RQqWti1Vkupw8kd7pxO8GRB3HBxUSTQ3UyOw3MrA+jCu7e1S5t13quGyp9qxJvDtvKr7CFlA3AetZVKKT0OmninbU1LaWOSwWOJmOWPDdelUpYR9mfOCVO4A9ap6Pb3FjdBJHZlVwyg+hrZlRVlC8MryED1FcM4OLO2FRSV0cFKPs2qf7JbB+hq/POQqKSeG2HH6VFr8WJRKowMlfypYSLi3DHq6f+PCrRoyZLZXAZiDk81pWemOeFAAbnHWr2j6MdTtSYy3GD8orp7C1isIGiuFJOOD3FROo1oRyq+h59eaQZZvMVSpZjkDsadb6buChmOfpW1eShNRIQnbuyCe1Ld2UiweYpxzzj1pObaGkkVYLBY1IJYelU7ndDI4KsRjjLZGasWy3LOwLl1BAI9qq6paPAxbBQcnk5/SnG9xSs1qYWoSK8jFEYcDndkZra0WVLmDa/O9dv41kCZJH2sVJIxuAq3YD7NMwU9w4raV3EwhHllpsSX8MbxEAfcypzXNxWxW+MJ9a7RbbzZixHyyHHPvXOanEbe8WUD7pwfwpQnbQdSlzMdLaiJEIJUsOhqIO8ZwwIqzNuNpuDZCnIB9DVGG7RztPHsa2jNnNOmr2Rfiuyp61aGpsCoDdKyJFz/qzg+hqAytF97OatzurGXsrO7N281PcnXnOa0EnWe03Z5Khh9a4wyvM464rRW9MMG0HtirpvlZnOJ3Wiy/aA0anlhuH1HWtc2skUgbDEH3rhPD2qm2uEdjwHHX07165p09lqVlg43L057VnWnyyvY0pw5la5yd5Csu44J4PfmsiRCHUc8966rVdP8su0fpwK5+5IidwV2YAbBrSE1JWZnKLgy5o16IHGfXvXSvqeY8KD7VxVvtjnDFiBkGtufVbO1tt275h1JNRNWZrB3RofaiQWmIC571z2s+I7e03CMgt2ArnNX8UyTkxwHA9awEilu33ybmyagtIsX+rXWpSEbjt+tNtdNdzubp6mtLT9PjZ+R0OMVqXUSWsTrgBzgAVnOoo6I1jC6uULOCNN3AAUdT1NWLZC87dgB2p9pbMYnk2k5NP0uRBNIJCVJPcVm229Sti5OY1kVETftUd6SGT/Vu0YQEketOltzv8zP0dOn40FNygZwxORjoawaNos2YTG8SgOMCq9/IscR3IXAPaqNlK6N14zgjPStG4hjntmZ8kYzkHFZtWeppe6OYdBqLlF+XcehqP+y/s6L5ZUnkkZ71C0rw3m2IDAPJNWJ55gmXAcFsgAV0pPSxg7NFywkWKExzbkJ6HFNewVsvEAw/vJwfyqC3u0aP55Nn+w4zVO81hYsqh/AGmk76GenUdMI4WO7kju3asy61ILkR8n1qhd6g8xJZvwzWc8pY1tGHVkOSWxYnumkYkkk1UZix5pKK0sZthRS9KaTTEO6U0mgKWqZIT6UARKhY1OsNSpFip1jouBXWKnhMVaWLPapRb5pAVSuRmp4FGOaV4wnBqrM7J92kM0GuI4l4IrOuLrzTVYl3PJJq3badJMehpgkUjSr1q7d2D2wyRVLoaB2Om0B49wBHNdjEowMV5xp1wYZ1NegafOJoAc80hyRoKKkHSmLUoFIkQDNOApQKdigQ3FQzJlTVgCkZcimK5yer2eMsBWAwxXdXtuHjIxXH3tuYpTxxmguL6Ge3BzU8T7hio3WmRttajdD2ZYOUbIrodJuwyBSawDhlzUtlOYZgM0IGro7RSGGOxqhPGYZtw6VLazCRAanmQSR++KTVyYuzLFtMJIx61ZBrEtZTFLtPStdGyM0kxyRKDT6iBp4qiR9LSUUhDqQ0gopgLRRRQAUUlLSAM0dKKKAAmoJ4xJGVNTGmGmxHMTo1pcbh0rZs7kSRjnmo9SthIhIFZNnO0E2xjUbM3+OPmdNmkJzUMcoZAc1XudQigU5YZqjG3QtswUZJrNu9UjgBwRmsTUNdLEhDWDPdvKcsxpjSNa+1p5CQpNYs1yzkljmoGkqB5KdgJGkzULyVG0melR4LUw1Y5pCelNCk09I81YWIUmyoogEfFFXAFA6UUuYvlfYt9aMU0dKXmgwsB61bsbZri4RQpIJ7DNNs7R7mQAA49a7bRNIjjkiLAgjmnewXOv8O2vkWIODyAK6IcLj2qlZRrHAigVbLAVJQjnAHtXmvi64JmPPBbOK9DupAsbtnoM15V4lm825K80DW5irdtLKIk616d4N8MoY1uJlyevNeb6LbBr9cjOWFe8+H4xHYKcVolYqdR7GpFCsSBVAAFJI4QZollVFPrWRc3LM5HbNDZla4t5OJAfQcVXQAggDAHAFBUuPc/pUiqERv85qWUhBhBnuB1quJfn/HpT3PX/OKrA8+386hhqicvuJ5/Go2UA5PYcCoTLtP4/lURlL7vQ/rT2DcnaUsQAfqamRf3eT69Kpg/OP8AOKtZJiwPzpFDo+SfpyajkUE+2fzqRRsU5H0FN+9k+2SaQyNfv+/8qNQfytPcjPOBn1qRI8ufTOMetQ6yQtqFPJz+VARV3Y4mG2l1HUJFQHG7BavRNA8Nx2sSvIg9cetYfgm0SSdJGUHLs/NeigYrWEVa469WXwIQKFAAGAO1LRRWhymP4hfbY/ma57RF/dsegx1rY8UPi2A/2TWdo8eLYnqeBWM9WdMNKY+dsHp24FMJyPf+VPuUO4889zTAvy+gpEEqthSB+JpI5Mc9Bk0xevPT0qQIPLLHqAcD0oA8/wDEcwe5HTqcVzkj8/41r+IGP20AE8A5rDbnr1pMlohkfJ/Ctnw6N10ePSsNxhz9a6Lwyubj/gYFNCOy1E7YQPRTXNOMg9a6HVW/dH/drnm+hrQZj3dvubIFZM0BBrpnj3dqzri3yTxUlIwihFRstaUsGO1VXiOelAysiZatK1gyRxUUEBJ6VtWlt0oYD7e36cVR1GLDdO9dFBBjHFZV/A0kwRFJYnoKSGc+0RY4Aya3tF8NyXMqvKhJ7L6fWtjRfDhdw7rlu57LXe6bpKRRgKuF7nuatIluxT0jREgUBVBbu3p9K6KKFIVwoqREWNQqjAFLmghu40dRTiaYTg0mSaQgLZ6VTvb+KzjJJBf+VV9S1eK0jKo2W7tXHXV3LesSxIT+dIpK5Pf6nLeysFY7e5qqgCjAqMcEgdKlXmkaIO9IelKaYTngUBcTdSEEkGpFTAzQw4oGMBwAKU03pQTmgAzSFuRQMnpSSOkKZY/hQA4sEXcx4rJv9R/gQ1XvdRLsVU1lvJk5J5pCuSSSljkmoWfNMLZpBQA7NLmkFITTAcTTC1ITmhVzQA4ZNSKuaETNTABRQAKoFKzUhOaVUJoE2RNbpN95RVabQkmHygVsRRVbji9qLjRw9z4fmi5TI+tZzwXNufmRseo6V6ksAYYIBHvUcujW1wD8u0mncNGeYrcjo3FPIR67O88F+cSYUyfVa5rUPDtzYuwVtxHUY5o9BWM1oT2qMqR1FSN50BxLGw+opwlR+DVJslor0dasGJW6GozGyn1ppoVhyLUwjBFVt+KesxB71Ek2axkloEkOOlRbSpq1vyuTTcqaSk1owcU9UViTT41zUmxTT1QCm56Ao6jCKbtJNWNopMDNRzFco1YwBTwB6UvagCpbuWlYUYpdgPakxT1pDGkAUDinECkIFK4WELVXkY54qbFJtB61aaWpLTZXySKTyyas7VFHA6Cq5+xPJfcqmE5o8ls1azRn2o52Hs0VTCRTDG3pV3j0owD2oVRidNFHBFPjndOhNWWjBqJocVSmnuS4NEyXitw4xT2himGRj6iqDIQaVXZDwaq3Ylkkloy/dOar/Mp5Bq2l2ejc1N+6mHapsNNlFZSKnS4IpZLI9UNVXjeM8g0rFqZr29+6dG4rWtdXxj5iprklkIqZJyO9S0aqaejPRrLXZIwPmyK37XW4J1CyYryaG+ZCMMRWnb6qc8k59RRsJ009j06SztrpcxsATWfLp81s4dRkA9RXOWeuSRkFXyPrXRWfiFJAFlrWFaS0MJ0u6NBLoGNlbjcM89jWbdkOwYcHGDWqPst2nykc+lULrTpU+ZPmWrpSSldaCn70bFADc6r6nFalvo8kiq5JxmqNrBI90gKkYbNdBqd82n2IAHPWtq9VqyizGnCybaINVKW1j5eecYrkQ3X61JdajNeN8x4qKNGkYKvJNcUnc7aEX8TNXTJpJH2A/MvIP9K1HKoSrD5H+8P7po06zSztzLJjjmkhU3l20mPlPGK1pXRzYmUXOyKUiNFPvAyy/wDjy1MjZgSRWLhZOh7e1S3SCSXykPKDhhWdcXLWjiNv4uePWqqLldzGOpNGyyySAg7VGSM+tPW2DkfPgqM/N6VXtW34bGRICjjuPQ1JDPITEN3DK0Z3dj2qo1WkDiXZ8W8RUKCCvr3rPgQvKrq52Y/EUSF3OJeCRtPsRTIfNjuwuCPlIbHQ+9TKTlqFrFx7Lc6HGCp61FMkXmAOg3KdwZWq7cS+Raox+8Uyc1g7/NfeScrwR6isbc25s6vJZRKPiOzSWNliB5+YZrl7VpLdvJkUqQ2VzXcqF8sq3zBByD3U96qXWkwTb1U7WVhjPoan2SS0OiGLe0jW8HXqQblSVfmH3TVbxHfXMVzvTOw9dprnGiv9LmzFkhT0q6mvx3KiO6QB+/Y1zzptO51RnFrQzzes8xZixyKuy60zw7M9cE5qybK1uLctDIM91PBrlrpZoZjHtJ54IoSTKvbU9D8LJDeMWl2BmGMmpNf0sTyKIVUkZXdmuL0u/u7HDYJQ+ldRp2qC4wzEMc9HNRJSi79Bqz3OR1DSDbEMwww7imKrRmNi3GK63U7AXgLrgEn7oNcfqUctqGQg7VyAa1hPmE+SKOmgaM6bEwPzoD0/Sue1WPzoi2M7vmFWNGuXmhEZYjcu0/WtGXT2ezyQflJ6jtUW5ZDTvqcxat5tuEP+4ar2+lSSs23GQ+MGpFUwXckXr0q7aagbS5jZlBVmyePwrbmaWhlUgr3MWYXFlM0bDIB4BqRD9oG1gw9mFdg9jbXsEcpVc5Oaxr6CK1wVPRuh9KFUT9TN0312MiWHyOnfpVcbnbmtw2/2mEd9pxVC5tGT7nX0rSFTozGpStqiNJfKUKvWux8P308Uedx6Vyun6e0kwaQjFdvY6efKURkZ+lbQs3qc7T3NyDU/OGyXoeOaj1KyinjLxjqMHmqqWM24ZAH0rat7VkhO8nH0qqsYpXTCF27M46dJkTaGzx+PFcxfyXEsmCxIPau81ZY0bqcjvisMWiSMWJQf7WM1zud1qbRp2ehhWWlNKQWGPr1ro7fT44YijLsyOCaryWNwp3QDzR3NTQwSggzbxg52lsisJSb2Z0RSWjKmTDeYU/LJxx61bePcVkwckclvWmajCAm9Fw33xiprQ/aYcBxz8wzUt3VxrewwXIj+Rkcr69KsLbpKpaJt2BnaeCKhlYLExLupBwQw4NR2cpCS5xjoMUlsKV07Ms2dwwuCh3DA71JuDyRr/tk8CqdlmSeZgfYU1XliujvUlVbIINDV2ENPQ0IZImlwCGYE9RitG4cQ2JB6fWsqEwAswk3buQp7Gqd/fl8IzHgfdHSs3HmZspWQ2N4WkdwmM8ZPIqG7v0RWwfxP9Kx57xIchTlvQdBWTcXjSH5mz7V1RgzCVQvXOok5EZwPWsuWcsTzk1Czsx5ptaqKRi3cUkk80nWlAoziqIbDFBOKaTUkcLOehxQBHyaekRY1aFsAOlSJGBQBHHCB2qcR1IiVMseaAIFSp0iz2qZIae7JEOTzSGIsYUZNRzXSxjAxVae8JyFqqFaVu5NAJCyTtI+al2b4+RVu00t5eSKvyacIo+lJj2Kunaashya6O3tI4l4ArF06UxTbT610qDKgikxMy9UsxLCSBXHTxGOQqfWvR2jDoVPeuO1qyMUpYCmmUjIj4Irr9AveAjGuOBwav2F0YZlOeKGPc9OjOVzUy1m6ZdCe3U55FaK0iGSCn0wU+gQYpMZpaXFMRXlTINc5q9nkFgK6hhmqF3AJEIoGmcEy4JBqCRcHNaeo2xhlJxWe3IoL3FhfIwacwwciq6na1WgQy0DRsaVd9FJroYmyBXEW8pimFdVYz+ZGOaCJKw+7i2sHWrdlPvTB60OokjIqhGxt58dql6Mpe8jdFOBqGJ96g1JTIsSBqUGowacDTEPopAaWgQtJilooGJmjNIaSkA7NJSUZoACaaTSmoZZkiXLEUCHOAykGua1RVgl3AjINWL/W0jBCGuXvNRe4Y5PFDVzSDaNRtbKRbQax7m/kmJy1U3l96gaSmlYG9bkrSZqJpKheXFRFi1MVrkjy+lRcuakSPNTLDik2VGPchSLNSiKpgoApDk0rmigmIFApeTT1jJqzDaPIcKtLcrSJVCnHSityPw9dSoHXbtPTNFVyk87MoGrNvbtMw44qO1j85xnH4V0FtEkQX5Rmmcxa063SBASuDXT6Qxkn+9kCsBCBGDuwK6HwywlmGAOWxUjUXudjEoRR7CkZjmnhSW/CmS4UGgClfyFbRznrXl2rtvupDnPOK9H1ebZZEY/CvMr1jJMx9TQXFXZe8Nwb7+P2Oa9mtZVtdPjHfbmvLfCto4nEhHFehuxkCKPQDFXeyJlqydrppXI7YpXXJz3x19Kiij2t174Jq2wBGPwpLUWxGF2qo9vzpJGCp15pJn2n+tVJpCcCkxiO+QfeozkEn070c7RS8sp4/CpSG2QFcmoxwe3Wp2O3P86q8lhx+FAIkUZJPbH51cQkEDuP0qGGMn+pq1tCjnp6etAXI3bcp547n1pN4UY9uBRIDt9/5VEoLFic4z19aRVyxbOWbPcnrVDX322/sFJrRtkwV9geKx/E77YHHU7MfnQ9h0/jNPwPDtiRvSLP512dcz4Qi2WrH0VVrpq3jojGq7zYUUU01Rmcv4qk6L7AUmlriz9ATyag8TPuugv+0BU9mSLRB3POKxe507QSJJVDnpx6etNkj2qM9fSkDFXBPJyfwqOSfccD8TSuQMGfxqU/LZykdozzUW4beOBj86LuYJYS8YGzilcLHmetkG/f6d6xpDWlqsha+kP8qzGNLqSVict3611HhRN0q+71y55YfWuv8HoN0Z/2iatAkdDq3Ct16CsEj61t6w2Nw98cViY5Gc1Yx3l5U1Tmi9q1AvyVVkTNQUZEsPtVRoMt0rYkiqDyctQBXt7bkcVr28OMcUyGHGOK17O0MnJ4UdTQAttbNIcKKvW+jIZA2OM8sep+lXrW2GAqr8p6Dua3rSzEYDOPm7DsKpIlyI7LTljRcrhR0WtMAAccUCimRe4hNNJpSajd1jUvI2FoBCMQBuJwo6k1h6rriQoY4jx09zVXWtbz+7iyewFc8AztvkOW/lUlxj3FlkkuZN8p47LTD0p5FJjg0ixqjJqUHApmQppjEtQFxzNk05FpijrUyjFAxSMimsMLUgqOU4WgCvnk0gySOuKApJJPSqt7fpAm1Tz60CJ7i7jto+vzVz13fvOxAPFVri7aZiSTiqzPSAez1GWyaTOaBQA6iiimAuaTOaTqaeFoAQLUqJSDApwagCUYAo60igmp44qAGpHk1ajhp8cPtVqOL2oAbFDVqOKnpFVhI6QCJH7Vbgtmk56KOpNSQ2wC75eF7Duae85dhHGv0Uf1p2Few5nSFCsfHYnuahGmC8O6dBs9Mc1p2lhj55fmb+VWygWq2IcrnM3vhCzukIQAH0YZFcfqvgCSLLRKy+68ivVaXtRcSk0fP11o1/Yk5Qso7rVPzipw4wa+g59FtL0Eyxqo7sOK4rxH4UsgCYAh46kfMaNC17x5n8jimGHn5a0bvQZrdvkz9DWeyTwH50OPXFF2gcRhLKMYpmTUwmVutKY0YcU1YHcjjJzVpelVghU08S7aiSvsXB23JHz2qNSwPNL54p4YMM1OqRV03oOzmlFMBHrTwwqWWhwoJpM0ZpDFzTSaCcCozIBQkxNofRmojMBTDNVKDYnJIsZpMj1qqZSaaZDVqmyHURb3r60bh61T3n1o3n1p+zF7Uugg96WqQkNSJL61Lgyo1EyzRSLyKXFZmhG6iq7jBq4RTDFuq4ytuRKN9inQGI6Gp3h2iq54NbJpmDTiWI7t14PIqys8Uow3HsazqM0OIXL0loj8pxVSS3kj7ZFOS4dOhNWku0bh+KlpjTKAYipUmI71beCKUZXH1FVZLV05HIqbJlqbRZiuivc1oQaiy4yawMsp5p6zEUmjRVDtbPWXTBV/wzXRWXiLICyGvMI7kjvV+DUGXGTkVOo3CMvI9cttWsgN5xurE1vVBevtTla5C31IHALYrRiuI37jNNsUaLvvoTgchQOScV0GjaYcefIRjOPpWdpdkbu4VsfKpzXQ305giS1jHzN1xRBXHiKvJG0dyO6mN3cC3i/1anBxVmZlsbYRpjzGFJZwJZ2onkGGIzg+tQRKbqczSfd7ZrspxSXMzy5auw6JVt4GnlPNZEsDajK7kgc8Gp9RuTczeREfkX0pspa2TYnLFRwRWU5czLho7j4rZIHBlYDB4YU+eBWVmjKMCc/L2qvK0johVsCQcZ7H0qa1m8m3VmYKd2xhWdmjVyUnqRGCWZd2QQy7W+tS2UTIqySYJHyn6USagTGcKoI/iUVlvqUxyjVaUpaIOWK1ubd2yPFluQowQPSsWOLM0aoeOzD09DVlZWuYQoPLrj8ajskMTlsdBllPrQouOjJm03dF2K1jSJC21iVZTng1GbdfMJG5R5W05OQeOKZeyb2VM8EZFQI8yANg4PysK6PZK1zHnbHggmBZVyWBUk1lXujxXXzoApPT61tZRlAPJSTofeoZGSLMIXgtlc1hKFnqawqPocw9peWWTGSQOqmkjvY2bbMuHHIzXTspQjcueOnqKyNT0uKQb4+AeVP9KiVJSV0dNPFNO0iwkNpc2jFBtcfhUGlK1vdujDcA3GazYra6jGEY7fQ9KlivGgfE4zz0JrmnTkjvhUhLqde4ztZVwAc8GsfXbFZrRn689VHSmwXkbr+6+U+m6m3k8n2Vs5GT0NYRvF6Gk4xlFnL6VOYLySIkkg5H4V2kd2jRpujbbIpGT0FefGXytQ8zoQ1dxZeTLYxneQRwR7VtUWzMqbVtDldViMNwJAPutj8q19I0pdTUBeSrBhx2pNZhSWAso5PP4ip/BGopbXJSQ8A4/Cpu+XQ1aTRe1HT5tLgzzjfnAHSuQu53u2ZcHHuK9c1u5tr+32YGSvb1rynVIPs07+WCOec0UmpPzMaiaXkWtKKhdjHORj8amuLYkbtvTg/SsawaRLnaxyG+YV2z28Ysi/XMf6mrd4yuxK046HHC5eJioGCK7XwjqYeZY5T1OOa429i2Tbh0NaHh9mW9BXPUV6ChGVO6PMlKUZ2Z7gtrbfZxLhRkZ6VzWsXC5KRNz7GtUyOdL64O2uTkDtKSwJB7ivPpJuTu72Oqq0oqytcwr15fMJLEA8EHsag8z5SUO1j6eta11AHG5ucg4OKzlKAgBlIIweO9dfs+ZaHIpuLI7XUpoZZBKudvcU6e8eeZgowBjJPqaY67mGATluce1QpIY5TvUfM+75u9cU4WkdsJXV2X/s5e3IzuZevsKz7GY2txJGRkKdwHtV5bxQ2I4j83BLHIFZ12DFOspHAO1vpURvqmbSto0Wb2R5nKBdidR70+2iRLfYFJP15NMLB4gRyynHPpTGtnLB8yqfUjihdhS11HW8UlpIWRs5/hYc1oK0EsZaUGFvUd/wAKqyajFDb7ZGWQjuw5Fc3e6yzMwiYjPerScjPRamlf3qW7EJIAPYcmufutRZ8gHAqlNdM7EliSarMxJyTW0YJbkSm2PkmLGojzR1pQK0M2xAM1IsZp8aZqysYxQBUaMgVAc5rSMeRiqkke1qdxNEtpa+ZyatOoj4FQW8pjq2uJ/rQBAOamRKcbZkPtUsa4HNAxUjqb5Yxkmq8tysY4rPlunkOAeKQF6e/C8LVB7h5T1NEVs8zDANblhoucFxQMyraxlnYcHFdHY6MEALitO2so4V4WrqqKVxN9iCK3VBgCmzwBlPFXAKR0yKQHJXUZt7gOOnet/T5xLCBnkCqupW25ScVS0u4MM2xu3FPoM6ZRWbrFn50BIFacfzAEU6RA8ZU96Qr2Z5hcRmKUqfWmI2DW5rtj5chYCsHpTLOv8O6hhgjH2rtI2DKDXlWnTmGdTmvRtLuhNAOeaWwpLqaopwpgpwNBA6lpBTqBDSOKgkXINWDTGXNMDm9Ws96FgK5ORCjkGvQ7qEOpGK47VbUxyFsUFRZkOKfE/amkUz7rUbj2LDjuK1dKu9pCk+1ZSMGWljcxSgj1oG1c7mF8qKhvIsjeKq6dciSMc81pHDrg0nqQnysjsZ8jaTzWiDWC263nyOma14JRIgNJFzS3RZFKKaKcDVGY8cUuajzzS5oAkpKbmlzQDCigmmswAyTSAUmo3lVBljgVRu9UigU4bmuavtceQkI1A7XN+91mOAEKwrmb7WpJiQrHFZU908hJY5qq8uadh2SJ5ZyxyTVZpaiaSoWkJ6Uw1ZK0lQs5Y8UoUt1p6x89KLj5RqxlutTrDT0Tb1p+fSk2axQBAoop6qWFSJAWbABJqS0l1I0QtVmG1Z2AVSTWpYaJJMQWUgV0lvplvZoDJgH0HWmkS5JHO2miu5BcfhWuttBZpyAT6CrzMzjbEoVf1qu2nyyE7jgU7GTlcpPqLKxCWXmAfxcUVpxaQNv+s2+1FFkI4O3lMPXkitiwme6m2JknGfYD3PQUUUPYUIp2NeyV9TuUtrcF2J2gKpNeiaVoDaLCGmADD3BooqnokVU02NUvsznjjJ+lRS8orno3SiipZJgeJpPItgG4B+Ud8muT0zRpdRuDtQsEGW4wB+P9KKKEOL3O5srOOztgVAzwBx3/AK1rWiktg9ef0oopdSWWFIbDr93dgfX29aeSQxXuME0UUAirJIDhyeCeP/retV5CDJt9qKKbAfjcFPZjgVIyCPKHqBkjNFFJDRUlYBPN42k4H+e9RRFXIA5OOf8AP4UUUhli2bKs+flQ8nPH/wBerDfwE/8ALQfJ7/hRRSEhkrqpKf3OWx2+tAGIfMI4zgf/AFvWiihDJrdSWAByWFc14omCTNE5xyCe/wAo60UU3sXR1nY7bw3EU0xZcfLMA6H1Ujg1sUUV0I5ZO7YU0nFFFDEcLr86/wBspAx+csGAHPB4HStqKEwQxI4w5HTqaKKw+0zql8EfQrud0piHVRlsVE6AS7CORxj3Pv0oopGbGqMxlhyBxnt+dVdRkA0yWbopXOTxwPrRRQNHmN1KlzM8kbB1Y8GqTRnJAFFFLqQyqSFfaTgg967fwZHviLrgrHwx9CaKKtCRqaxxJtPVvmH0rIK7Tk8fU0UVYy6i5iz2NQulFFZllZ0pqRZcD14oopgbOmaa927BRkRrufkAAepJ4A+ta+nRC9hEluu6PJVTggHHcZ6j3FFFNAzftLRYRubBf1q6tFFMyY+kJoopiIbiZLa0kupSFjjGWZjgD8647Udde8GYCdp6EgjA+hooqTSO5jJl3JLEnqalziiikaB1Gaax2ozHgDrRRQALEzcn9aUgAED1oooAF9fXkVIOKKKAFdgi7iePrUJJfB55oooAxr/VkUtFG3I64rAmnaVizGiikBAWyaSiigQopwoopgBOKKKKAHqtKWA4oooAYWpYW3She56UUUAaccVW44aKKQFuOLFWo46KKBlhI60WgTT9OGoXOEiJwpc4JPoF6k/QUUU0IVY5rxVdAQrD7zDGB9DzWhaWKQdBlu5ooquhnIvBcCopBzRRSIG1PDAXiaUj5EGSScD8zRRQNGDfeIY5Ij9mbcvIBAwOOp9652W5aaTczbmPP0oopGy2IWjEuNygg+op1voVtfN5YGH6kY7UUU0BlXvw/eZibdR/vK4A/U1yeraBf6K+LiORVPQlDg/j0oopsa1MwTEcNTg8b9xRRSIloONucZUcfnTCxUYooqlqGwzzDQJGooosTzMcJiOtP+0cUUUOKGpMa0+e9RjLGiijZBzNsf5RIyKjIwaKKUXdlTikJRRRWhmFKBk0UVLAlWBiKekHNFFZuTOiMUWUXAxSnrRRWJqhjGlHSiimT1GP8wxVZozmiitaZnPUTym9KBETRRVczJ5UO+zt6UxoivJooou7g4oFd4+QcVYS8/vj8RRRVKKZK2JT5E3GRn8qhn0+SNdwB2+o5ooqBvRFMkqcGpFkI70UUmVGTJkuCO9aOn3DzXKRKSSx4AGaKKlmsZM9CtNUh0e0xKVDgY655/Cr+jg6qGvzzHnG48flnrRRWkDhqNym7kzzf2jfraW/z46Dmm6leRWcQt1YBzwcc4oororaSUVsYp+7cqRR/ZnZZBiVlGAfc9/TrUL/AOkfaJs4+zcOc8qR1GOp6Hp6UUVzU9bnTVilZIZuPntaMQJNolUA9eM/L/e49M1LNa3UIkWWNwsihhlDjdx0OMHrRRWiMyJJrePzZGPyIVSYdQrH/wDUeabc2AZ4oRjznQOi7gCVPI//AFdaKKFJxegdCHTTvufswP7zfhQeOfTmrIubd9Se23IsikgncF/DnGfwooq6j9/5C6L1HTIn2tYGI8wLuwTjPGePXjsKp3lz5DiJ1w2Qx7cf1ooq6knZGa2H2kn2qYBTyF3n125x+PI6dasOsbu0QO6ZVLFeuBjPH4c4oooj70dRy0asQxyLdxLJEcr5nlocjlvQjqM+9PVUmuRZY/eTJ5iKePyPT8OvBoorNaMb2uUwnklZRjy5WKqSOCw7H0Psar39nHJOVaIK5AO1QSDxntRRV04qU3Fl8zjBSRjmznR2NupKocEc8GmSavsjaCcMCDyrKaKK4JpJnqUJOUNTOuNOeS3N3B88R67CGx9ccj8a2/DVwLlXiJzsj+ftjnGaKKibvFmkFYnuLeS4UeTlsPjjpn09q56GcWd653YU98GiipjsaI7fSTPqF09oo3XCoCVU9sZznoePSsfWbM3V2RDlsnHAOfyooqFpPQh6x1M3UtPk0ZI2uF2ODtAzzx6jqK1re8+2aZuDjb0BLAdMZ60UVpJtr5mcFZuxCbA3gZUG5jjHPcjj86m8MwD+1DCfvocsCMYANFFd9Ft0mceISVU9RnDLp2FHOMD64P8AhXKpFJcbpoj8u4qCrAZPpjr3oorjw+t2aYpWshl0h+y7unJUHHcdR7VgMFWIzdU7kc4P4UUV6CdonBLcdBMmQ4Pyknp7df8APerkE+mawxgR180ep2fzxRRXBiHZcx2UNXyspTPZ28zQRvuYcYX5v1FLe2zPbMzJt2x5ZSeQOxx1oorDmfKpHU4pIqabqEKKRK6DC5OSBkVXvddj2lYJGYenQfrRRW3InNkczUTmbrUXmc7m/AVTdy3OeKKK2WmxzczbI80AZooqgY7pSE0UUCLcKkAE96tKOg9elFFJjWwhGGqCePjNFFAxkRGKtQnY1FFBJZkulVctiqMt7uyFNFFJDRW+aQ8mtOx0t5yGxxRRTGdHaaakKgkCtNECjgUUVImTBKlVMUUUAPC04gYoopgU7mEMpHrXM3kZtLoMRj1oooW40dDpdyLi1Vwcj1rQFFFITMzV7H7RAxC5IHNcDPHskYelFFBcdiNH2tXX+HdRBkWHPzHgCiihj6HaRtkVKKKKDIcKdRRTEwpD0oooBEMi5FYmqWfnIcDnFFFIpHHTJskK+lQOOKKKZTCJ+cVOw4zRRQwRd0y78uUIT34rqoX3LRRQRIS6g8xMqOcVFp9x85jzyKKKl7lw1izVVs1JuoopkhmlzmiimSGaXdRRQIrXd9HaRlpGA+prm7/xCHyI2oopGiijn7i9kmJLE1SeXmiiqQEDyVA0uTgUUUxCAFuakSOiiky4k6xYXJ6UuQDxRRUs2jFNkoG4U8QkDLcD34oopDlpsXtOsZL65WCFWdz2UE111p4fWzYCbaHxzyD/ACooqkZTbRamu4bNNseBjvWdHLLfXCqM4J9KKKog6e1s1ghJZRuHUntSzFB6CiikQio4Ab60UUUCP//Z\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_blur.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "Filters\n", + "滤镜的效果有下面这些\n", + "\n", + "```\n", + "BLUR\n", + "CONTOUR\n", + "DETAIL\n", + "EDGE_ENHANCE\n", + "EDGE_ENHANCE_MORE\n", + "EMBOSS\n", + "FIND_EDGES\n", + "SMOOTH\n", + "SMOOTH_MORE\n", + "SHARPEN\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "edge image ok\n" + ] + } + ], + "source": [ + "# 图片滤镜效果，边缘强化\n", + "\n", + "from PIL import Image, ImageFilter\n", + "\n", + "img = Image.open('files/test.jpg')\n", + "\n", + "img2 = img.filter(ImageFilter.FIND_EDGES)\n", + "\n", + "img2.save('files/test_edge.jpg', 'JPEG')\n", + "print('edge image ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image as img\n", + "img(filename='files/test_edge.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 16 - \345\207\275\346\225\260\345\274\217\347\274\226\347\250\213.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 16 - \345\207\275\346\225\260\345\274\217\347\274\226\347\250\213.ipynb" new file mode 100644 index 0000000..bd40866 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 16 - \345\207\275\346\225\260\345\274\217\347\274\226\347\250\213.ipynb" @@ -0,0 +1,617 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Lesson 16\n", + "\n", + "* v1.1, 2020.5, 2020.6 edit by David Yi\n", + "\n", + "\n", + "## 本次内容\n", + "\n", + "* 闭包：将函数作为值返回\n", + "* 偏函数\n", + "* 高阶函数 map/reduce/filte" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n" + ] + } + ], + "source": [ + "# 将函数作为值返回\n", + "\n", + "def lazy_sum(*args):\n", + " \n", + " def sum():\n", + " ax = 0\n", + " for n in args:\n", + " ax = ax + n\n", + " return ax\n", + " \n", + " return sum\n", + "\n", + "f = lazy_sum(1, 3, 5, 7, 9)\n", + "print(f())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 闭包\n", + "\n", + "在这个例子中，我们在函数 lazy_sum 中又定义了函数 sum，并且，内部函数 sum 可以引用外部函数 lazy_sum 的参数和局部变量，当 lazy_sum 返回函数 sum 时，相关参数和变量都保存在返回的函数中，这种称为“闭包（Closure）”。\n", + "\n", + "一个函数可以返回一个计算结果，也可以返回一个函数。\n", + "\n", + "返回一个函数时，该函数并未执行，返回函数中不要引用任何可能会变化的变量。\n", + "\n", + "闭包和函数的作用域等有关，掌握和理解闭包，对于后续 Python 的深入开发有很大好处，特别是装饰器。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 偏函数\n", + "\n", + "python 的 functools 模块提供了很多有用的功能，其中一个就是偏函数（Partial function）。要注意，这里的偏函数和数学意义上的偏函数不一样。\n", + "\n", + "在介绍函数参数的时候，我们讲到，通过设定参数的默认值，可以降低函数调用的难度。而偏函数也可以做到这一点。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12345\n", + "8\n", + "26\n" + ] + } + ], + "source": [ + "# 进制转换函数\n", + "\n", + "print(int(12345))\n", + "print(int('1000',base=2))\n", + "print(int('1A',base=16))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "虽然默认参数还是很容易使用，但是如果我们在某个场景需要大量调用的话，还是有点不方便，特别是对于有很多参数的函数来说，会让程序显得复杂。还记得之前那个 max min 的程序举例么？我们可以用偏函数来解决整个问题。\n", + "\n", + "通过 ```functools.partial``` 来进行操作。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "64\n", + "85\n" + ] + } + ], + "source": [ + "import functools\n", + "\n", + "int2 = functools.partial(int, base=2)\n", + "\n", + "print(int2('1000000'))\n", + "print(int2('1010101'))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "12\n", + "18\n", + "9\n" + ] + } + ], + "source": [ + "# 偏函数举例\n", + "# 原来的一个函数\n", + "\n", + "def func(x=2,y=3,z=4):\n", + " return x+y+z\n", + "\n", + "print(func(x=3))\n", + "print(func(y=6))\n", + "print(func(x=4,y=10))\n", + "print(func(2,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + }, + { + "ename": "TypeError", + "evalue": "func() got multiple values for argument 'x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 会报错，不需要再输入 z 的值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: func() got multiple values for argument 'x'" + ] + } + ], + "source": [ + "# 构造偏函数，设置默认值\n", + "\n", + "import functools\n", + "\n", + "f1 = functools.partial(func, x=2,z=3)\n", + "print(f1(y=3))\n", + "print(f1(y=2))\n", + "print(f1(2)) # 会报错，不需要再输入 z 的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## map() 函数\n", + "\n", + "python内建了 map() 和 reduce() 这两个功能强大的函数。\n", + "\n", + "我们先看 map。map() 函数接收两个参数，一个是函数，一个是可迭代的序列，比如 list，map 将传入的函数依次作用到序列的每个元素，并把结果作为新的 Iterator 可迭代值返回。\n", + "\n", + "举例说明，比如我们有一个函数 f(x) = x*x，要把这个函数作用在一个list [1, 2, 3, 4, 5, 6, 7, 8, 9]上，就可以用map()实现如下：\n", + "\n", + "![](map.png)\n", + "\n", + "> 如果你知道 Google的那篇大名鼎鼎的论文“MapReduce: Simplified Data Processing on Large Clusters”，你就能大概明白map/reduce的概念。在 hadoop 时代，map/reduce 的理念是高效并行运算的核心。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "4\n", + "9\n", + "16\n", + "25\n", + "36\n", + "49\n", + "64\n", + "81\n" + ] + } + ], + "source": [ + "# map 函数举例\n", + "\n", + "def f(x):\n", + " return x * x\n", + "\n", + "r = map(f, [1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "\n", + "for i in r:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "7\n", + "12\n", + "19\n", + "28\n", + "39\n", + "52\n", + "67\n", + "84\n" + ] + } + ], + "source": [ + "# 这个 f(x) 函数可以比较复杂，包含更多逻辑\n", + "\n", + "def f(x):\n", + " y = x * x + 3\n", + " return y\n", + "\n", + "r = map(f, [1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "\n", + "for i in r:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8, 22, 36, 50, 64, 78, 92]\n", + "67\n", + "487\n", + "1299\n", + "2503\n", + "4099\n", + "6087\n", + "8467\n" + ] + } + ], + "source": [ + "# 进行 map 处理的数据也可以复杂一些\n", + "\n", + "def f(x):\n", + " y = x * x + 3\n", + " return y\n", + "\n", + "list1 = [x for x in range(1,100,7) if x % 2 ==0]\n", + "\n", + "print(list1)\n", + "\n", + "# 主要的程序还是很简洁就可以了\n", + "r = map(f, list1)\n", + "\n", + "for i in r:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12, 7, 6, 17]\n" + ] + } + ], + "source": [ + "# map 函数也可以同时作用在两组数据上\n", + "\n", + "def addition(x, y):\n", + " return x + y\n", + " \n", + "numbers1 = [5, 6, 2, 8]\n", + "numbers2 = [7, 1, 4, 9]\n", + "result = map(addition, numbers1, numbers2)\n", + "print(list(result))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0]\n", + "[1, 2]\n", + "[4, 4]\n", + "[9, 6]\n", + "[16, 8]\n" + ] + } + ], + "source": [ + "# map 函数更加复杂的用法\n", + "\n", + "def multiply(x):\n", + " return (x*x)\n", + "\n", + "def add(x):\n", + " return (x+x)\n", + "\n", + "func = [multiply, add]\n", + "\n", + "for i in range(5):\n", + " value = list(map(lambda x: x(i), func))\n", + " print(value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## reduce() 函数\n", + "\n", + "再看 reduce 的用法。reduce 把一个函数作用在一个序列[x1, x2, x3, ...]上，这个函数必须接收两个参数，reduce 把结果继续和序列的下一个元素做累积计算，其效果就是：\n", + "\n", + "reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n" + ] + } + ], + "source": [ + "# reduce 举例，一个加法函数\n", + "from functools import reduce\n", + "\n", + "def add(x, y):\n", + " return x + y\n", + "\n", + "print(reduce(add, [1, 3, 5, 7, 9]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13579\n", + "\n" + ] + } + ], + "source": [ + "# reduce，模拟一个字符串转换为整数的函数\n", + "\n", + "from functools import reduce\n", + "\n", + "def f(x, y):\n", + " return x * 10 + y\n", + "\n", + "def char2int(s):\n", + " return {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, \n", + " '6': 6, '7': 7, '8': 8, '9': 9}[s]\n", + "\n", + "def str2int(s):\n", + " return reduce(f, map(char2int, s))\n", + "\n", + "print(str2int('13579'))\n", + "print(type(str2int('13579')))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 \n", + "3 \n", + "5 \n", + "7 \n", + "9 \n" + ] + } + ], + "source": [ + "# 拆解上面的函数，先 map\n", + "\n", + "list1 = map(char2int, '13579')\n", + "for i in list1:\n", + " print(i,type(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13579\n" + ] + } + ], + "source": [ + "# 拆解上面的函数，再 reduce\n", + "\n", + "list1 = map(char2int, '13579')\n", + "print(reduce(f,list1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## filter() 函数\n", + "\n", + "Python内建的filter()函数用于过滤序列。\n", + "\n", + "和map()类似，filter()也接收一个函数和一个序列。和map()不同的是，filter()把传入的函数依次作用于每个元素，然后根据返回值是True还是False决定保留还是丢弃该元素。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 5]\n" + ] + } + ], + "source": [ + "# filter 举例，在一个list中，删掉偶数，只保留奇数\n", + "\n", + "# 判断是否是奇数\n", + "def is_odd(n):\n", + " return n % 2 == 1\n", + "\n", + "print(list(filter(is_odd, [1, 2, 4, 5, 6])))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'B', 'C']\n" + ] + } + ], + "source": [ + "# 筛选一个 list 中为空的元素\n", + "\n", + "def is_empty(s):\n", + " # strip() 用于移除字符串头尾指定的字符（默认为空格）\n", + " if len(s.strip()) ==0:\n", + " return False\n", + " else:\n", + " return True\n", + "\n", + "print(list(filter(is_empty, ['A', '', 'B','C', ' '])))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 7, 11, 13, 17, 19, 23, 25, 29]\n" + ] + } + ], + "source": [ + "# 返回一定范围内既不能被2整除也不能被3整数的数字\n", + "\n", + "def f(x): \n", + " return x % 2 != 0 and x % 3 != 0\n", + "\n", + "print(list(filter(f, range(2, 30))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 高阶函数小结\n", + "\n", + "使用高阶函数可以让代码简洁优雅，好的函数，可以使程序修改和调试都变得容易。\n", + "\n", + "函数式编程，相对传统方式比较难理解，但是这样的确比较 pythonic，对于程序项目来说比较容易维护。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 17 - \345\274\202\345\270\270\345\244\204\347\220\206.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 17 - \345\274\202\345\270\270\345\244\204\347\220\206.ipynb" new file mode 100644 index 0000000..a0c8bcd --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 17 - \345\274\202\345\270\270\345\244\204\347\220\206.ipynb" @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Lesson 17\n", + "\n", + "* v1.1, 2020.5 edit by David Yi\n", + "\n", + "\n", + "## 本次内容\n", + "\n", + "* 异常处理\n", + "* 日志处理\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 异常处理\n", + "\n", + "异常是一个事件，该事件会在程序执行过程中发生，影响了程序的正常执行。一般情况下，在 Python 无法正常处理程序时就会发生一个异常。异常也是Python对象，表示一个错误。当 Python 脚本发生异常时我们需要捕获处理它，否则程序会终止执行。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:a\n" + ] + }, + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'a'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 输入字母会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a:'\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 4\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'result:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'" + ] + } + ], + "source": [ + "# 输入字母会报错\n", + "\n", + "a = int(input('a:'))\n", + "r = 10 / a\n", + "print('result:', r)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:a\n", + "except: invalid literal for int() with base 10: 'a'\n" + ] + } + ], + "source": [ + "# 防止输入字母会报错\n", + "\n", + "try:\n", + " a = int(input('a:'))\n", + " r = 10 / a\n", + " print('result:', r)\n", + "except ValueError as e:\n", + " print('except:',e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:0\n", + "except: division by zero\n" + ] + } + ], + "source": [ + "# 防止输入0作为除数\n", + "\n", + "try:\n", + " a = int(input('a:'))\n", + " r = 10 / a\n", + " print('result:', r)\n", + "except ZeroDivisionError as e:\n", + " print('except:',e)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:0\n", + "except: division by zero\n" + ] + } + ], + "source": [ + "# 如何捕捉多个类型的错误？\n", + "\n", + "try:\n", + " a = int(input('a:'))\n", + " r = 10 / a\n", + " print('result:', r)\n", + "except ZeroDivisionError as e:\n", + " print('except:',e)\n", + "except ValueError as e:\n", + " print('except:',e)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:0\n", + "except: division by zero\n", + "finish...\n" + ] + } + ], + "source": [ + "# try...except...finally\n", + "\n", + "a = int(input('a:'))\n", + "\n", + "try:\n", + " r = 10 / a\n", + " print('result:', r)\n", + "except ZeroDivisionError as e:\n", + " print('except:', e)\n", + "finally:\n", + " print('finish...')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "except: name 'c' is not defined\n" + ] + } + ], + "source": [ + "# 防止错误举例\n", + "\n", + "a = 100\n", + "try:\n", + " b = a + c \n", + " print(b)\n", + "except NameError as e:\n", + " print('except:',e)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a:3\n", + "covert ok\n", + "result: 3.3333333333333335\n", + "finish...\n" + ] + } + ], + "source": [ + "# 对 int 进行判断\n", + "\n", + "a = input('a:')\n", + "\n", + "# 对输入值转换还有更完善的方法，设定一个转换结果\n", + "try:\n", + " a = int(a)\n", + "except ValueError as e:\n", + " print('except:', e) \n", + " a = 1\n", + "finally:\n", + " print('covert ok')\n", + " \n", + "try:\n", + " r = 10 / a\n", + " print('result:', r)\n", + "except ZeroDivisionError as e:\n", + " print('except:', e)\n", + "finally:\n", + " print('finish...')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 日志处理\n", + "\n", + "对于防止错误比较有效的一个方式，就是打印日志，平时俗称的log。我们在学习 python 的过程中，经常会用 print 打印出一些变量的值什么的，其实也是日志的一种方式。打印日志一方面是记录正常的操作，另一方面很大的作用就是用在出错的时候检查。日志大部分时候是一种文本文件，便于存储、查询和归档。python 中也有比较完善的日志处理模块。\n", + "\n", + "实际操作中，日志文件的数量会非常惊人，其实在大数据处理中日志处理也是重要的一项课题。\n", + "\n", + "python 自带很完整的日志操作函数包，不过用起来还是有点复杂。一般情况下的日志使用，我们推荐使用 fishbase 函数包中的日志模块，对于日志文件名、文件名日期自动变更都做了一定封装，方便调用。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log ok\n" + ] + } + ], + "source": [ + "from fishbase.fish_logger import *\n", + "from fishbase.fish_file import *\n", + "\n", + "log_abs_filename = get_abs_filename_with_sub_path('files', 'fish_test.log')[1]\n", + "\n", + "set_log_file(log_abs_filename)\n", + "\n", + "logger.info('test fish base log')\n", + "logger.warning('test fish base log')\n", + "logger.error('test fish base log')\n", + "\n", + "print('log ok')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 18 - \347\252\227\345\217\243\347\225\214\351\235\242\347\274\226\347\250\213 tk.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 18 - \347\252\227\345\217\243\347\225\214\351\235\242\347\274\226\347\250\213 tk.ipynb" new file mode 100644 index 0000000..84f5eef --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic Lesson 18 - \347\252\227\345\217\243\347\225\214\351\235\242\347\274\226\347\250\213 tk.ipynb" @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 18\n", + "\n", + "* v1.1, 2020.5,6 edit by David Yi\n", + "\n", + "\n", + "## 本次内容\n", + "\n", + "* 简单窗口编程" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tkinter\n", + "\n", + "top = tkinter.Tk()\n", + "top.mainloop()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import tkinter\n", + "\n", + "label = tkinter.Label(text='Hello World!')\n", + "label.pack()\n", + "tkinter.mainloop()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import tkinter\n", + "\n", + "btnHello = tkinter.Button(text='Hello World!')\n", + "btnHello.pack()\n", + "tkinter.mainloop()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from tkinter import * \n", + "root = Tk() \n", + " \n", + "list1 = ['java','python','php','js']\n", + "list2 = ['mobile','desktop','web']\n", + "\n", + "# 创建两个列表组件\n", + "listProgram = Listbox(root) \n", + "listPlatform = Listbox(root)\n", + "\n", + "# 第一个小部件插入数据\n", + "for item in list1: \n", + " listProgram.insert(0,item)\n", + "\n", + "# 第二个小部件插入数据\n", + "for item in list2: \n", + " listPlatform.insert(0,item)\n", + "\n", + "# 将小部件放置到主窗口中\n", + "listProgram.pack() \n", + "listPlatform.pack()\n", + "\n", + "# 进入消息循环\n", + "root.mainloop() " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, I am computer\n" + ] + } + ], + "source": [ + "from tkinter import *\n", + "\n", + "root = Tk() \n", + "\n", + "def say():\n", + " print('Hello, I am computer')\n", + "\n", + "btnHello = Button(None, text='Hello', command=say)\n", + "btnHello.pack()\n", + "root.mainloop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic \347\273\203\344\271\240\351\242\230A.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic \347\273\203\344\271\240\351\242\230A.ipynb" new file mode 100644 index 0000000..c26fd62 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/Python Basic \347\273\203\344\271\240\351\242\230A.ipynb" @@ -0,0 +1,802 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Basic 练习题 A\n", + "* v1.1, 2020.4, 2020.5，2020.6, edit by David Yi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 题目1：两个正整数a和b， 输出它们的最大公约数。\n", + "\n", + "例如：a = 24, b = 16\n", + "\n", + "则输出：8" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "a = 24\n", + "b = 16\n", + "for i in range(min(a,b), 0, -1): \n", + " if a % i == 0 and b % i ==0: \n", + " print(i) \n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "while b:\n", + " a,b=b,a%b\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "print(max([x for x in range(1,a+1) if a%x==0 and b%x==0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 题目2：输出 9*9 乘法口诀表\n", + "\n", + "\n", + "\n", + "程序分析：分行与列考虑，共9行9列，i控制行，j控制列。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 x 1 = 1 \t\n", + "2 x 1 = 2 \t2 x 2 = 4 \t\n", + "3 x 1 = 3 \t3 x 2 = 6 \t3 x 3 = 9 \t\n", + "4 x 1 = 4 \t4 x 2 = 8 \t4 x 3 = 12 \t4 x 4 = 16 \t\n", + "5 x 1 = 5 \t5 x 2 = 10 \t5 x 3 = 15 \t5 x 4 = 20 \t5 x 5 = 25 \t\n", + "6 x 1 = 6 \t6 x 2 = 12 \t6 x 3 = 18 \t6 x 4 = 24 \t6 x 5 = 30 \t6 x 6 = 36 \t\n", + "7 x 1 = 7 \t7 x 2 = 14 \t7 x 3 = 21 \t7 x 4 = 28 \t7 x 5 = 35 \t7 x 6 = 42 \t7 x 7 = 49 \t\n", + "8 x 1 = 8 \t8 x 2 = 16 \t8 x 3 = 24 \t8 x 4 = 32 \t8 x 5 = 40 \t8 x 6 = 48 \t8 x 7 = 56 \t8 x 8 = 64 \t\n", + "9 x 1 = 9 \t9 x 2 = 18 \t9 x 3 = 27 \t9 x 4 = 36 \t9 x 5 = 45 \t9 x 6 = 54 \t9 x 7 = 63 \t9 x 8 = 72 \t9 x 9 = 81 \t\n" + ] + } + ], + "source": [ + "for i in range(1, 10):\n", + " for j in range(1, i+1):\n", + " print (\"%d x %d = %d\" % (i, j, i*j),\"\\t\",end=\"\")\n", + " if i==j:\n", + " print(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 x 1 = 1 \t\n", + "1 x 2 = 2 \t2 x 2 = 4 \t\n", + "1 x 3 = 3 \t2 x 3 = 6 \t3 x 3 = 9 \t\n", + "1 x 4 = 4 \t2 x 4 = 8 \t3 x 4 = 12 \t4 x 4 = 16 \t\n", + "1 x 5 = 5 \t2 x 5 = 10 \t3 x 5 = 15 \t4 x 5 = 20 \t5 x 5 = 25 \t\n", + "1 x 6 = 6 \t2 x 6 = 12 \t3 x 6 = 18 \t4 x 6 = 24 \t5 x 6 = 30 \t6 x 6 = 36 \t\n", + "1 x 7 = 7 \t2 x 7 = 14 \t3 x 7 = 21 \t4 x 7 = 28 \t5 x 7 = 35 \t6 x 7 = 42 \t7 x 7 = 49 \t\n", + "1 x 8 = 8 \t2 x 8 = 16 \t3 x 8 = 24 \t4 x 8 = 32 \t5 x 8 = 40 \t6 x 8 = 48 \t7 x 8 = 56 \t8 x 8 = 64 \t\n", + "1 x 9 = 9 \t2 x 9 = 18 \t3 x 9 = 27 \t4 x 9 = 36 \t5 x 9 = 45 \t6 x 9 = 54 \t7 x 9 = 63 \t8 x 9 = 72 \t9 x 9 = 81 \t\n" + ] + } + ], + "source": [ + "i=0\n", + "j=0\n", + "while i<9:\n", + " i+=1\n", + " while j<9:\n", + " j+=1\n", + " print(j,\"x\",i,\"=\",i*j,\"\\t\",end=\"\")\n", + " if i==j:\n", + " j=0\n", + " print(\"\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 x 1 = 1\n", + " 1 x 2 = 2\t 2 x 2 = 4\n", + " 1 x 3 = 3\t 2 x 3 = 6\t 3 x 3 = 9\n", + " 1 x 4 = 4\t 2 x 4 = 8\t 3 x 4 = 12\t 4 x 4 = 16\n", + " 1 x 5 = 5\t 2 x 5 = 10\t 3 x 5 = 15\t 4 x 5 = 20\t 5 x 5 = 25\n", + " 1 x 6 = 6\t 2 x 6 = 12\t 3 x 6 = 18\t 4 x 6 = 24\t 5 x 6 = 30\t 6 x 6 = 36\n", + " 1 x 7 = 7\t 2 x 7 = 14\t 3 x 7 = 21\t 4 x 7 = 28\t 5 x 7 = 35\t 6 x 7 = 42\t 7 x 7 = 49\n", + " 1 x 8 = 8\t 2 x 8 = 16\t 3 x 8 = 24\t 4 x 8 = 32\t 5 x 8 = 40\t 6 x 8 = 48\t 7 x 8 = 56\t 8 x 8 = 64\n", + " 1 x 9 = 9\t 2 x 9 = 18\t 3 x 9 = 27\t 4 x 9 = 36\t 5 x 9 = 45\t 6 x 9 = 54\t 7 x 9 = 63\t 8 x 9 = 72\t 9 x 9 = 81\n" + ] + } + ], + "source": [ + "print('\\n'.join(['\\t'.join([\"%2s x%2s = %2s\"%(j,i,i*j) for j in range(1,i+1)]) for i in range(1,10)]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 题目3：编写一个函数，给你三个整数a,b,c, 判断能否以它们为三个边长构成三角形。若能，输出YES，否则输出NO" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no\n" + ] + } + ], + "source": [ + "def func1(a,b,c):\n", + " if (a+b)>c and (b+c)>a and (a+c)>b:\n", + " return 'yes'\n", + " else:\n", + " return 'no'\n", + "\n", + "\n", + "print(func1(1,2,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "yes\n" + ] + } + ], + "source": [ + "\n", + "def func2(a,b,c):\n", + " if max(a,b,c)<(a+b+c)/2.0:\n", + " return 'yes'\n", + " else:\n", + " return 'no'\n", + "\n", + "print(func2(3,4,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "# if else 的用法\n", + "\n", + "x, y = 4, 5\n", + "\n", + "smaller = x if x > y else y\n", + "\n", + "print(smaller)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27\n" + ] + } + ], + "source": [ + "# 匿名函数 lambda 用法\n", + "\n", + "a = lambda x, y=2 : x + y*3 \n", + "print(a(3) + a(3, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + } + ], + "source": [ + "# list 的 count方法\n", + "\n", + "l = ['tom', 'steven', 'jerry', 'steven']\n", + "print(l.count('tom') + l.count('steven'))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + } + ], + "source": [ + "# os 用法\n", + "# 计算目录下有多少文件\n", + "\n", + "import os\n", + "\n", + "a = os.listdir()\n", + "print(len(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "204" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 获得路径或者文件的大小\n", + "import os\n", + "\n", + "os.path.getsize('/Users')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "164\n" + ] + } + ], + "source": [ + "# 列表生成式用法\n", + "# 在列表生成式后面加上判断，过滤出结果为偶数的结果\n", + "\n", + "n = 0\n", + "for i in [x * x for x in range(8, 11) if x % 2 == 0]:\n", + " n = n + i\n", + "print(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "byebye\n" + ] + } + ], + "source": [ + "# 简化调用方式\n", + "# 省却了 try...finally，会有 with 来自动控制\n", + "\n", + "filename = 'test.txt'\n", + "\n", + "with open(filename, 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "# python 有趣灵活的变量定义\n", + "\n", + "first, second, *rest = (1,2,3,4,5,6,7,8)\n", + "print(first + second + rest[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "letter: t\n" + ] + } + ], + "source": [ + "# for continue 举例\n", + "# 以及 等于 不等于 或者 的逻辑判断\n", + "\n", + "for l in 'computer':\n", + " if l != 't' or l == 'u':\n", + " continue\n", + " print('letter:', l)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 4 5\n", + "count: 1\n" + ] + } + ], + "source": [ + "# 找到符合 x*x + y*y == z*z + 3 的 x y z\n", + "# 多种循环\n", + "\n", + "n = 0\n", + "\n", + "for x in range(1,10):\n", + " for y in range(x,10):\n", + " for z in range(y,10):\n", + " if x*x + y*y == z*z:\n", + " print(x,y,z)\n", + " n= n + 1\n", + "print('count:',n)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 4, 1)\n" + ] + } + ], + "source": [ + "# python 内置函数 \n", + "\n", + "print(max((1,2),(2,3),(2,4),(2,4,1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 amy\n", + "1 tom\n", + "2 jerry \n" + ] + } + ], + "source": [ + "l = ['amy', 'tom', 'jerry ']\n", + "for i, item in enumerate(l):\n", + " print(i, item)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 7, 9, 11, 13, 15, 17, 19, 21, 23]\n", + "18\n" + ] + } + ], + "source": [ + "# question 1\n", + "l = [ x * 2 + 3 for x in range(1, 11)] \n", + "print(l)\n", + "print(l[1]+l[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/yijun/dev_python/python_beginner/python_basic_L2'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# question 2\n", + "\n", + "import os\n", + "\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 4 5\n" + ] + } + ], + "source": [ + "# question 3\n", + "\n", + "first, second, *rest = (1,2,3,4,5)\n", + "print(*rest)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "appendleft() takes exactly one argument (2 given)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeque\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappendleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: appendleft() takes exactly one argument (2 given)" + ] + } + ], + "source": [ + "# question 4\n", + "\n", + "from collections import deque\n", + "\n", + "q = deque(['a', 'b'])\n", + "q.appendleft('b','a')\n", + "\n", + "print(q.count('b'))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "yes\n" + ] + } + ], + "source": [ + "# question 5\n", + "\n", + "x, y = 3, 6\n", + "\n", + "if 1 < x < 4 < y:\n", + " print('yes')\n", + "else:\n", + " print('no')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "# question 6\n", + "\n", + "d = dict([('a', 1), ('b', 2)])\n", + "print(d.get('a',2))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n" + ] + } + ], + "source": [ + "# question 7\n", + "\n", + "a = [0, 1, 2, 3, 4, 5]\n", + "n = 0\n", + "for i in a[::-2]:\n", + " n=n+i\n", + "print(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a---a\n" + ] + } + ], + "source": [ + "# question 8\n", + "\n", + "a = ['a', '-', 'a']\n", + "print('-'.join(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n" + ] + } + ], + "source": [ + "# quesion 9\n", + "\n", + "from functools import reduce\n", + "\n", + "def f(x,y=2):\n", + " return x + y\n", + "\n", + "r = map(f, [1, 2, 3])\n", + "\n", + "s = reduce(f,r)\n", + "\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "small\n" + ] + } + ], + "source": [ + "# question 10\n", + "\n", + "if 2 in [1,2,3]:\n", + " print('small')\n", + "else:\n", + " print('big')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "False\n", + "False\n" + ] + } + ], + "source": [ + "# quesion 11\n", + "\n", + "s1 = 'abcde'\n", + "s2 = '12'\n", + "s3 = '12s'\n", + "s4 = '#s'\n", + "print(s1.isalpha())\n", + "print(s2.isalpha())\n", + "print(s3.isalpha())\n", + "print(s4.isalpha())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" new file mode 100644 index 0000000..6634c1a --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 01.ipynb" @@ -0,0 +1,1268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - 面向对象编程 01\n", + "\n", + "* Python Senior, Lesson 10, v1.0.0, 2016.11 by David.Yi\n", + "* v1.1, 2020.5.4, 6.13 edit by David Yi\n", + "\n", + "\n", + "## 本次内容要点\n", + "\n", + "* 类与实例\n", + "* 类的属性\n", + "* 类的方法\n", + "* 创建和使用子类\n", + "* 构造器方法和解构器方法\n", + "* 调用父类的 super() 方法" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 面向对象编程\n", + "\n", + "#### 类与实例\n", + "\n", + "类，是一个定义；实例是真正的对象的实现，创建一个实例的过程称作实例化。\n", + "\n", + "所有的类都继承自一个叫做 object 的类。继承的定义之后再说。\n", + "\n", + "类的一些操作方式和函数很像，在没有面向对象编程方式的时候，就是面向过程（函数）的开发方式。\n", + "\n", + "类可以很复杂，也可以很简单，取决于应用的需要。面向对象的编程方式，是现代流行的开发方式，博大精深，需要慢慢理解体会。一开始有些不太清楚，也没有关系。\n", + "\n", + "#### 类的属性\n", + "\n", + "类可以理解为一种称之为命名空间（ namespace），在这个类之下，数据都是属于这个类的实例的，我们称为属性，用实例名字+点+属性名字。这样的类比较简单，在 c 语言中称为结构体，在 pascal 中为记录类型，python 的数据结构比较简单。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "5\n" + ] + } + ], + "source": [ + "# 申明一个 class MyData\n", + "class MyData(object):\n", + " pass\n", + "\n", + "# 实例化 MyData, 实例的名字叫做 obj_math\n", + "obj_math = MyData()\n", + "obj_math.x = 4\n", + "print(obj_math.x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### 类的方法\n", + "\n", + "光是把类作为命名空间太简单了，可以给类添加功能，称为方法。\n", + "\n", + "方法定义在类中，使用在实例中。可以理解为实例是真正做事情的代码，所以在实例中调用方法完成某个功能是合理的。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "Hello!\n" + ] + } + ], + "source": [ + "class MyData(object):\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + "# 实例化\n", + "obj_math = MyData()\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n" + ] + } + ], + "source": [ + "# 类的方法的直接调用，其实还是实例化了\n", + "\n", + "MyData().SayHello()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "9\n", + "4\n" + ] + } + ], + "source": [ + "# 在上面基础上，复杂一点的例子\n", + "\n", + "class MyData(object):\n", + " \n", + " # 初始化方法，双下划线前后\n", + " # 实例化的时候，需要传递 self 之后的参数\n", + " def __init__(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + " def Add(self):\n", + " print(self.x + self.y)\n", + " \n", + "# 实例化\n", + "obj_math = MyData(3,4)\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()\n", + "\n", + "obj_math.x = 5\n", + "\n", + "obj_math.Add()\n", + "\n", + "o1 = MyData(1,3)\n", + "o1.Add()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello!\n", + "7\n", + "11\n" + ] + } + ], + "source": [ + "# 再复杂一点，创建多个实例\n", + "\n", + "class MyData(object):\n", + " \n", + " # 初始化方法，双下划线前后\n", + " # 实例化的时候，需要传递 self 之后的参数\n", + " def __init__(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " # 定义一个 SayHello 的方法，self 可以理解为必须传递的参数\n", + " def SayHello(self):\n", + " print('Hello!')\n", + " \n", + " def Add(self):\n", + " print(self.x + self.y)\n", + " \n", + "# 实例化\n", + "obj_math = MyData(3,4)\n", + "\n", + "# 调用方法\n", + "obj_math.SayHello()\n", + "obj_math.Add()\n", + "\n", + "# 再创建一个实例\n", + "obj_math2 = MyData(5,6)\n", + "obj_math2.Add()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0]\n", + "[1, 2]\n", + "[4, 4]\n", + "[9, 6]\n", + "[16, 8]\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 我们建立一个有趣的简单的模仿游戏的类来说明面向对象编程的概念\n", + "# v1.0.0\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "n1 = NPC('AA1')\n", + "n1.show_properties()\n", + "n1.fight_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "---\n", + "\n", + "这个 NPC 的类，在初始化这里定义了 NPC 拥有的三个属性，name、weapon、blood，其中 name 需要创建实例的时候设置。\n", + "\n", + "NPC 类有两个方法，一个是 show_properties()，显示 NPC 的属性；另外一个是 fight_common()，我们称之为普通攻击。\n", + "\n", + "#### 创建和使用子类\n", + "\n", + "通过 `class B(A)`，即表示从 A 开始创建一个子类，我们称之为继承。\n", + "\n", + "面向对象的编程方式的有点已经可以体现，我们可以通过继承来构造一个对象体系，比如这里现在 NPC 这个类中，定义了游戏中人物的最基本的一些属性和方法，可以理解不光 NPC 是战士，还是巫师，对会有这些属性和方法；然后我们把各自独特的属性和方法定义在从 NPC 中继承的各个子类中。我们先来定义一个战士 Soldier 类。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类，继承自 NPC class\n", + "class Soldier(NPC):\n", + " # 暂时什么也不干\n", + " pass\n", + " \n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "\n", + "# 调用方法，因为 Soldier 中此刻并没有任何实际的方法等，所以实际上自动调用了父类的方法\n", + "n1.show_properties()\n", + "n1.fight_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "在子类中，可以覆盖父类的方法。\n", + "\n", + "比如 `__init__()` ，我们需要在 Soldier 类的初始化中加入一个只有 soldier 才有的级别 level 属性。\n", + "所以，我们先调用父类的`__init__()` 方法，再写 Soldier 类需要的代码。\n", + "\n", + "而 `show_properties()`方法，我们先用简单的办法，完全覆盖，也就是使用这个新的`show_properties()`方法，来显示 NPC 标准的三个属性以及 Soldier 的一个独立属性。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('soldier_level:', self.soldier_level)\n", + "\n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "n1.show_properties()\n", + "n1.fight_common() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "再来看看 `show_properties()` 这个方法，总感觉这样有点笨重，因为不管是否是面向对象的编程方式，都不应该有太多重复的代码，现在 NPC 类中也有 `show_properties()` 方法，用来显示标准的三个属性，而 Soldier 类中同样名字的方法显示四个属性，如果 NPC 类中增加了属性，那么两边类中的这个方法都要修改。\n", + "\n", + "我们来看看有没有更好的办法，可以用 super(需要知道父类的类，self) 的方法来访问父类的方法，这样代码就简洁优雅了。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + "\n", + "# 创建一个 Soldier 类， 作为 NPC 的子类\n", + "n1 = Soldier('AA2')\n", + "n1.show_properties()\n", + "n1.fight_common() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### __init__() 构造器方法和 __del__() 解构器方法\n", + "\n", + "可以理解构造就是创建，解构就是销毁。每一个对象的实例都是有声明周期的，从构造到解构。\n", + "\n", + "类别调用的时候，对象被创建的时候，python 先检查是否实现了 __init__() 方法，如果没有，则什么也不干。如果有 __init__()方法，则执行。\n", + "\n", + "python 作为高级语言，具有垃圾对象回收机制，当 python 发现对于该实例对象的引用都被清除掉后，会执行 __del__() 方法。通常不需要自己去实现这个 __del__() 方法，python 会做好这一切。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 先整理一下上面的代码\n", + "# v1.0.1\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n", + "\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 创建两个 soldier\n", + "\n", + "n1 = Soldier('AA1')\n", + "n1.show_properties()\n", + "n1.fight_common() \n", + "\n", + "print()\n", + "\n", + "n2 = Soldier('AA2')\n", + "n2.show_properties()\n", + "n2.fight_common() " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level: 1\n", + "Fight Common\n" + ] + } + ], + "source": [ + "# 连续创建多个 soldier 的实例\n", + "# 并存储在 list 中\n", + "\n", + "s = []\n", + "\n", + "for i in range(3):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " n.fight_common() \n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[<__main__.Soldier object at 0x1063f2a58>, <__main__.Soldier object at 0x1063d7e48>, <__main__.Soldier object at 0x1063d7240>]\n" + ] + } + ], + "source": [ + "# 看一下存储了对象的列表\n", + "\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "AA1\n", + "AA2\n", + "3\n" + ] + } + ], + "source": [ + "# 可以和一般访问列表一样访问列表中的对象\n", + "\n", + "for i in s:\n", + " print(i.name)\n", + " \n", + "print(len(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "AA2\n", + "2\n" + ] + } + ], + "source": [ + "# 可以删除一个实例\n", + "s.pop(1)\n", + "\n", + "# 显示列表中的实例\n", + "for i in s:\n", + " print(i.name)\n", + " \n", + "print(len(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "Fight Common\n", + "Wizard Magic!\n" + ] + } + ], + "source": [ + "# 再增加一个巫师 Wizard 的类\n", + "\n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def wizard_fight_magic(self):\n", + " print('Wizard Magic!')\n", + " \n", + "# 创建一个 wizard\n", + "\n", + "c1 = Wizard('CC1')\n", + "c1.show_properties()\n", + "c1.fight_common() \n", + "c1.wizard_fight_magic()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: AA2\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "name: CC2\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "AA0\n", + "--\n", + "AA1\n", + "--\n", + "AA2\n", + "--\n", + "CC0\n", + "--\n", + "CC1\n", + "--\n", + "CC2\n", + "--\n" + ] + } + ], + "source": [ + "# 创建复杂的 NPC，3个 wizards，3个 soldiers\n", + "\n", + "# 创建多个 soldier 的实例\n", + "s = []\n", + "\n", + "for i in range(3):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(3):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "for i in s:\n", + " print(i.name)\n", + " print('--')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'fight_common', 'show_properties']\n" + ] + } + ], + "source": [ + "# 显示类的方法\n", + "\n", + "print(dir(Soldier))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'fight_common', 'show_properties', 'wizard_fight_magic']\n" + ] + } + ], + "source": [ + "# 显示类的方法\n", + "\n", + "print(dir(Wizard))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 当前版本的代码\n", + "# v1.0.2\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "NPC created!\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "\n", + "NPC created!\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 在 NPC 的 __init__() 加入显示创建的是什么角色\n", + "# v1.0.3\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " # 先简单的显示\n", + " print('')\n", + " print('NPC created!')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: gun\n", + "blood: 1000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 但是在 NPC 这个父类中没有显示出具体的子类名称\n", + "# 所以我们用下面的方法来显示子类的名称\n", + "# type(self).__name__ 来访问类的名称\n", + "# v1.0.4\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " print('')\n", + " print(type(self).__name__, 'NPC created!')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "面向对象程序设计（英语：Object-oriented programming，缩写：OOP）是种具有对象概念的程序编程范型，同时也是一种程序开发的方法。它可能包含数据、属性、代码与方法。对象则指的是类的实例。它将对象作为程序的基本单元，将程序和数据封装其中，以提高软件的重用性、灵活性和扩展性，对象里的程序可以访问及经常修改对象相关连的数据。在面向对象程序编程里，计算机程序会被设计成彼此相关的对象。\n", + "\n", + "面向对象程序设计可以看作一种在程序中包含各种独立而又互相调用的对象的思想，这与传统的思想刚好相反：传统的程序设计主张将程序看作一系列函数的集合，或者直接就是一系列对电脑下达的指令。面向对象程序设计中的每一个对象都应该能够接受数据、处理数据并将数据传达给其它对象，因此它们都可以被看作一个小型的“机器”，即对象。目前已经被证实的是，面向对象程序设计推广了程序的灵活性和可维护性，并且在大型项目设计中广为应用。此外，支持者声称面向对象程序设计要比以往的做法更加便于学习，因为它能够让人们更简单地设计并维护程序，使得程序更加便于分析、设计、理解。反对者在某些领域对此予以否认。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 2000\n", + "wizard_level 1\n" + ] + } + ], + "source": [ + "# 继续优化，根据 NPC 类型来设定 blood 和 weapon\n", + "# 将代码尽量集中，是有好处的\n", + "# v1.0.5\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 2000\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" new file mode 100644 index 0000000..0139f1d --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/python_basic_2/Python - \351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213 02.ipynb" @@ -0,0 +1,1324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - 面向对象编程 02\n", + "\n", + "* Python Begginer, Level 2, Lesson 12, v1.0.0, 2016.12 by David.Yi\n", + "* v1.1, 2020.5.4,6.13 edit by David Yi\n", + "\n", + "## 本次内容要点\n", + "\n", + "\n", + "* 更加优雅的程序设计思路\n", + "* 类方法\n", + "* 静态方法\n", + "* 类属性\n", + "* 类属性安全的访问方法\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: gun\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: short gun\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: short gun\n", + "blood: 2000\n", + "wizard_level 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n" + ] + } + ], + "source": [ + "# 修改战斗的输出，战斗不能只喊喊口号，需要对对方有输出，\n", + "# 在 NPC 的 fight_common() 中我们设计返回值表示输出\n", + "# 再在主程序中增加一个 调用 soldier 的 fight_common()，来检查输出\n", + "# v1.0.6\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'gun'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'short gun'\n", + " self.blood = 2000\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + "\n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "print(s[1].fight_common())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "soldier_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "wizard_level 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "wizard_level 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 在 soldier 中增加专门的战斗方式\n", + "# soldier 和 wizard 的战斗中都增加输出\n", + "# v1.0.7\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = ' sword'\n", + " self.blood = 1000\n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # 建立 soldier 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.soldier_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Soldier, self).show_properties()\n", + " print('soldier_level', self.soldier_level)\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 建立 Wizard 的初始化\n", + " def __init__(self, name):\n", + " # 调用 父类 NPC 的初始化方法\n", + " NPC.__init__(self, name)\n", + " \n", + " # soldier 自己的初始化\n", + " self.wizard_level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " # 通过 super 来调用父类方法\n", + " super(Wizard, self).show_properties()\n", + " print('wizard_level', self.wizard_level)\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码 \n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "# 测试一下各类进攻方式\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting\n", + "Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将子类中的 show_properties 去除，显示不同的 level 功能放到 NPC 类中\n", + "# 修改子类中的 solider_level 和 wizard_level 统一为 level，也放到 NPC 类中\n", + "# v1.0.8\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " self.level = 1 \n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " self.level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " def fight_common(self):\n", + " print('Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "print('\\nBegin Fighting')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "程序是否功能越来越强大，但是也很简洁，并且很容易扩充功能？这就是面向对象编程思想的好处和强大能力！\n", + "\n", + "思考一下\n", + "\n", + "* 给 NPC 增加一个 MagicPoint MP 值，应该怎么操作？\n", + "* 考虑程序的发展，如果把武器 weapon 和伤害值不固定写死在类中比较好，应该怎么操作？\n", + "\n", + "我们还可以继续来增强功能\n", + "\n", + "* 引入类方法，来简化不同子类继承后进行的操作\n", + "* 战斗输出放在子对象中，如果我们要修改输出值的话，有点不方便，我们引入类方法\n", + "\n", + "类方法和静态方法\n", + "* 类方法是为了访问类属性更加方便\n", + "* 类方法和静态方法可以通过类和实例来访问,效果是相同的\n", + "* 静态方法跟普通函数没有什么区别" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Fight Common\n", + "{'blood': -100}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 引入类方法，修改 NPC类的 fight_common() 来完成统一的并且能区分角色的战斗输出\n", + "# v1.0.9\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " if self.npc_type == 'Soldier':\n", + " self.weapon = 'sword'\n", + " self.blood = 1000\n", + " self.level = 1 \n", + " \n", + " if self.npc_type == 'Wizard':\n", + " self.weapon = 'staff'\n", + " self.blood = 800\n", + " self.level = 1\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # 使用类方法，传入的参数是 class，因此通过 class的 name 来获得具体名称\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, 'Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_common())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "{'blood': -100}\n", + "Soldier Attack!\n", + "{'blood': -200}\n", + "Wizard Fight Common\n", + "{'blood': -100}\n", + "Wizard Magic!\n", + "{'blood': -300}\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将 NPC 对不同角色的初始化值从对象定义代码中剥离，便于调整数值\n", + "# v1.0.10\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'Soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'Wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " self.npc_type = type(self).__name__\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # 使用类方法，传入的参数是 class，因此通过 class的 name 来获得具体名称\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, 'Fight Common')\n", + " output = {'blood': -100}\n", + " return output\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def fight_shoot(self):\n", + " print('Soldier Attack!')\n", + " output = {'blood': -200}\n", + " return output\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def fight_magic(self):\n", + " print('Wizard Magic!')\n", + " output = {'blood': -300}\n", + " return output\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].fight_shoot())\n", + "print(s[2].fight_common())\n", + "print(s[2].fight_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "Soldier Fight Common\n", + "('blood', -100)\n", + "Fight Sword Fight!\n", + "('blood', -200)\n", + "Wizard Fight Common\n", + "('blood', -100)\n", + "Wizard Staff Magic!\n", + "('blood', -300)\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将战斗输出的值修改为从外部数据读入\n", + "# 战斗的方法的名称修改\n", + "# v1.0.11\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " print(cls.__name__, fight_output_dict.get('fight_common').get('name'))\n", + " output = fight_output_dict.get('fight_common').get('blood')\n", + " return ('blood',output)\n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " print('Fight', fight_output_dict.get('sword_fight').get('name'))\n", + " output = fight_output_dict.get('sword_fight').get('blood')\n", + " return ('blood',output)\n", + " \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # 定义一个巫师专用的战斗方法\n", + " def staff_magic(self):\n", + " print('Wizard', fight_output_dict.get('staff_magic').get('name'))\n", + " output = fight_output_dict.get('staff_magic').get('blood')\n", + " return ('blood',output)\n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n", + "\n", + "npc count: 4\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 加入类属性，npc_count, 计算创建了多少 npc \n", + "# v1.0.13\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " npc_count = 0\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # npc 计数器 +1 \n", + " NPC.npc_count += 1 \n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())\n", + "# 查看 npc 数量\n", + "print('\\nnpc count: ', NPC.npc_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AA0\n", + "npc count: 5\n", + "4\n" + ] + } + ], + "source": [ + "# 类变量容易引起问题，每个实例会和类拥有同名的类变量\n", + "print(s[0].name)\n", + "s[0].npc_count += 1\n", + "print('npc count:',s[0].npc_count)\n", + "print(NPC.npc_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n", + "\n", + "npc count: 4\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 将类属性修改为私有，__npc_count, 并通过方法来获得创建了多少 npc \n", + "# v1.0.14\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " __npc_count = 0\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # npc 计数器 +1 \n", + " NPC.__npc_count += 1 \n", + " \n", + " # 显示 npc 数量方法\n", + " @staticmethod\n", + " def get_npc_count():\n", + " return NPC.__npc_count\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())\n", + "# 查看 npc 数量\n", + "print('\\nnpc count: ', NPC.get_npc_count())" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "soldier NPC created!\n", + "name: AA0\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "soldier NPC created!\n", + "name: AA1\n", + "weapon: sword\n", + "blood: 1000\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC0\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "wizard NPC created!\n", + "name: CC1\n", + "weapon: staff\n", + "blood: 800\n", + "level: 1\n", + "\n", + "Begin Fighting:\n", + "('Soldier', 'blood', ('Fight Common', -100))\n", + "('Soldier', 'blood', ('Sword Fight!', -200))\n", + "('Wizard', 'blood', ('Fight Common', -100))\n", + "('Wizard', 'blood', ('Staff Magic!', -300))\n" + ] + } + ], + "source": [ + "# 进一步优化\n", + "# 战斗输出内容从外部数据读入了，代码就显得很重复，我们继续简化\n", + "# 通过 静态方法 fight_output() \n", + "# v1.0.12\n", + "\n", + "# npc 的属性字典\n", + "npc_dict = {'soldier':{'weapon':'sword', 'blood':1000, 'level':1}, 'wizard':{'weapon':'staff', 'blood':800, 'level':1}}\n", + "# fight output 字典\n", + "fight_output_dict = {'fight_common':{'name':'Fight Common','blood':-100}, \n", + " 'sword_fight':{'name':'Sword Fight!','blood':-200},\n", + " 'staff_magic':{'name':'Staff Magic!','blood':-300}}\n", + "\n", + "# NPC 类\n", + "class NPC(object):\n", + " \n", + " # 初始化 NPC 的属性\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " # 将类名称转换为全部小写\n", + " self.npc_type = (type(self).__name__).lower()\n", + " \n", + " print('')\n", + " print(self.npc_type, 'NPC created!')\n", + " \n", + " # 初始化值统一放在 NPC 类中\n", + " # 从 npc_dict 中读出\n", + " # weapon_list 保存武器内容\n", + " self.weapon = npc_dict.get(self.npc_type).get('weapon')\n", + " self.blood = npc_dict.get(self.npc_type).get('blood')\n", + " self.level = npc_dict.get(self.npc_type).get('level')\n", + " \n", + " # 定义方法 - 显示 NPC 属性\n", + " def show_properties(self):\n", + " print('name:', self.name)\n", + " print('weapon:', self.weapon)\n", + " print('blood:', self.blood)\n", + " print('level:', self.level)\n", + " \n", + " # 定义方法 - 通用攻击\n", + " # fight 的内容从外部读入\n", + " @classmethod\n", + " def fight_common(cls):\n", + " return (cls.__name__, 'blood' , NPC.fight_output('fight_common'))\n", + " \n", + " # 静态方法 - 战斗输出\n", + " # 从外部数据读入战斗输出的值\n", + " @staticmethod\n", + " def fight_output(fight_type):\n", + " name = fight_output_dict.get(fight_type).get('name')\n", + " output = fight_output_dict.get(fight_type).get('blood')\n", + " return (name,output) \n", + " \n", + "# 战士 Soldier 类\n", + "class Soldier(NPC):\n", + " \n", + " # soldier 自己的战斗方法\n", + " def sword_fight(self):\n", + " return ('Soldier', 'blood' , NPC.fight_output('sword_fight')) \n", + " \n", + "# 巫师 Wizard 类\n", + "class Wizard(NPC):\n", + " \n", + " # wizard 自己的战斗方法\n", + " def staff_magic(self):\n", + " return ('Wizard', 'blood' , NPC.fight_output('staff_magic')) \n", + "\n", + "# 执行代码\n", + " \n", + "s = []\n", + "\n", + "for i in range(2):\n", + " n = Soldier('AA' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + " \n", + "for i in range(2):\n", + " n = Wizard('CC' + str(i))\n", + " n.show_properties()\n", + " s.append(n)\n", + "\n", + "\n", + "print('\\nBegin Fighting:')\n", + "print(s[1].fight_common())\n", + "print(s[1].sword_fight())\n", + "print(s[2].fight_common())\n", + "print(s[2].staff_magic())" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\210\227\350\241\250\345\210\207\347\211\207.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\210\227\350\241\250\345\210\207\347\211\207.ipynb" new file mode 100644 index 0000000..b284a23 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\210\227\350\241\250\345\210\207\347\211\207.ipynb" @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 列表切片\n", + "\n", + "列表切片的语法\n", + "\n", + "a[start:end] 从start开始到end-1结束；\n", + "\n", + "a[start:] 从start开始直到末尾；\n", + "\n", + "a[:end] 从头部开始直到end结束；\n", + "\n", + "a[:] 复制整个列表；\n", + "\n", + "从左到右时候的位置关系：\n", + "\n", + " +---+---+---+---+---+---+
\n", + " | P | y | t | h | o | n |
\n", + " +---+---+---+---+---+---+
\n", + " 0 1 2 3 4 5 6
\n", + "-6 -5 -4 -3 -2 -1\n", + "\n", + "a[start:end:step] step 表示步长，正负号表示方向，\n", + "step 默认为 +1 ，所以默认 start 和 end 是从头到尾，如果 step 为负数，则 start 表示尾，end 表示头。\n", + "start 和 end 是根据 step 来确认的。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Shanghai', 'Beijing', 'Hangzhou', 'Tokyo', 'Osaka']\n" + ] + } + ], + "source": [ + "a = ['Shanghai', 'Beijing', 'Hangzhou', 'Tokyo', 'Osaka']\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Shanghai', 'Beijing', 'Hangzhou']\n", + "['Shanghai', 'Beijing', 'Hangzhou']\n" + ] + } + ], + "source": [ + "b = a[0:3]\n", + "\n", + "# 省略start，表示从头开始\n", + "c = a[:3]\n", + "print(b)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Osaka']\n", + "['Hangzhou', 'Tokyo', 'Osaka']\n" + ] + } + ], + "source": [ + "# 省略 end，从-1 到 end\n", + "b = a[-1:]\n", + "print(b)\n", + "\n", + "b = a[-3:]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = list(range(100))\n", + "print(a)\n", + "\n", + "b = a[0:10]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = a[-10:]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = a[0:10:2]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# step 错误，不输出结果\n", + "b = a[0:10:-2]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = a[10:0:-1]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = a[-1:0:-5]\n", + "print(b)\n", + "\n", + "b = a[-1:-6:-1]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 列表反转\n", + "\n", + "a = ['Shanghai', 'Beijing', 'Hangzhou', 'Tokyo', 'Osaka']\n", + "a.reverse()\n", + "print(a)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 使用切片来完成反转\n", + "\n", + "a = ['Shanghai', 'Beijing', 'Hangzhou', 'Tokyo', 'Osaka']\n", + "b = a[::-1]\n", + "print(b)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 字符串切片操作类似列表\n", + "a = 'abcdefghi'\n", + "print(a[1:2])\n", + "print(a[3:5])\n", + "print(a[:-5:-1])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\215\225\345\205\203\346\265\213\350\257\225 unittest.ipynb" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\215\225\345\205\203\346\265\213\350\257\225 unittest.ipynb" new file mode 100644 index 0000000..825a43d --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/draft/\345\215\225\345\205\203\346\265\213\350\257\225 unittest.ipynb" @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 单元测试\n", + "用Python搭建自动化测试框架，我们需要组织用例以及测试执行，推荐Python的标准库——unittest。 \n", + "unittest是xUnit系列框架中的一员，如果你了解xUnit的其他成员，那你用unittest来应该是很轻松的，它们的工作方式都差不多。\n", + "\n", + "官方说明文档： https://docs.python.org/3/library/unittest.html \n", + "The unittest module is actually a testing framework that was originally inspired by JUnit. It currently supports test automation, the sharing of setup and shutdown code, aggregating tests into collections and the independence of tests from the reporting framework.\n", + "\n", + "Unittest框架支持以下4个概念:\n", + "* Test Fixture –对一个测试用例环境的搭建和销毁。 A fixture is what is used to setup a test so it can be run and also tears down when the test is finished. For example, you might need to create a temporary database before the test can be run and destroy it once the test finishes. \n", + "* Test Case – 一个TestCase的实例就是一个测试用例，通过运行这个测试单元，可以对某一个问题进行验证。 The test case is your actual test. It will typically check (or assert) that a specific response comes from a specific set of inputs. The unittest frameworks provides a base class called TestCase that you can use to create new test cases. \n", + "* Test Suite – 多个测试用例集合在一起，就是TestSuite。 The test suite is a collection of test cases, test suites or both. \n", + "* Test Runner – 是来执行测试用例的。 A runner is what controls or orchestrates the running of the tests or suites. It will also provide the outcome to the user (i.e. did they pass or fail). A runner can use a graphical user interface or be a simple text interface.\n", + "\n", + "我们使用PYTHON的单元测试的主要方法是:\n", + "* assert-基础断言，允许用户自己扩展断言；\n", + "* assertEqual(a, b) -检查a和b是否相等；\n", + "* assertNotEqual(a, b) -检查a和b是否不相等；\n", + "* assertIn(a, b) -检查a是否在b中；\n", + "* assertNotIn(a, b) -检查a是否不在b中；\n", + "* assertFalse(a) -检查a的值是否是FALSE；\n", + "* assertTrue(a) -检查a的值是否是TRUE；\n", + "* assertIsInstance(a, b) -检查a的类型是否是“b”；\n", + "* assertRaises(ERROR, A, ARGS)-检查当A使用参数ARGS被调用的时候是否会引发ERROR；\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 一个简单例子， 将该脚本保存为 mymath.py\n", + "# 建立一个自己的脚本，定义了 add substract multiply divide 四个数学功能，我们用单元测试来检查它们是否与预期的功能一致！\n", + "\n", + "def add(a, b):\n", + " return a + b\n", + " \n", + "def subtract(a, b):\n", + " return a - b\n", + " \n", + "def multiply(a, b):\n", + " return a * b\n", + " \n", + "def divide(numerator, denominator):\n", + " return float(numerator) / denominator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 将以下脚本保存为 test_mymath.py，并运行\n", + "\n", + "\"\"\"\n", + "构建unittest基本使用方法\n", + "1.import unittest 导入unittest库\n", + "2.定义一个继承自unittest.TestCase的测试用例类\n", + "3.定义setUp和tearDown，在每个测试用例前后做一些辅助工作\n", + "4.定义测试用例，函数名字以test开头\n", + "5.一个测试用例应该只测试一个方面，测试目的和测试内容应很明确。主要是调用assertEqual、assertRaises等断言方法判断程序执行结果和预期值是否相符。\n", + "6.调用unittest.main()启动测试\n", + "7.如果测试未通过，会输出相应的错误提示。如果测试全部通过则不显示任何东西，这时可以添加-v参数显示详细信息。\n", + "\"\"\"\n", + "\n", + "import mymath\n", + "import unittest\n", + " \n", + "class TestAdd(unittest.TestCase):\n", + " \"\"\"\n", + " Test the add function from the mymath library\n", + " \"\"\"\n", + " \n", + " def test_add_integers(self):\n", + " \"\"\"\n", + " Test that the addition of two integers returns the correct total\n", + " \"\"\"\n", + " result = mymath.add(1, 2)\n", + " self.assertNotEqual(result, 555)\n", + " self.assertEqual(result, 3)\n", + " \n", + " def test_add_floats(self):\n", + " \"\"\"\n", + " Test that the addition of two floats returns the correct result\n", + " \"\"\"\n", + " result = mymath.add(10.5, 2)\n", + " self.assertEqual(result, 12.5)\n", + " \n", + " def test_add_strings(self):\n", + " \"\"\"\n", + " Test the addition of two strings returns the two string as one\n", + " concatenated string\n", + " \"\"\"\n", + " result = mymath.add('abc', 'def')\n", + " self.assertEqual(result, 'abcdef')\n", + " \n", + " \n", + "if __name__ == '__main__':\n", + " unittest.main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/IMG_3845.JPG" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/IMG_3845.JPG" new file mode 100644 index 0000000..4138905 Binary files /dev/null and "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/IMG_3845.JPG" differ diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/baidu_logo.gif" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/baidu_logo.gif" new file mode 100644 index 0000000..639f863 Binary files /dev/null and "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/baidu_logo.gif" differ diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/calc.py" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/calc.py" new file mode 100644 index 0000000..5b30cfa --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/calc.py" @@ -0,0 +1,128 @@ +# ----------------------------------------------------------------------------- +# calc.py +# +# A simple calculator with variables -- all in one file. +# ----------------------------------------------------------------------------- + +tokens = ( + 'NAME', 'NUMBER', + 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'EQUALS', + 'LPAREN', 'RPAREN', +) + +# Tokens + +t_PLUS = r'\+' +t_MINUS = r'-' +t_TIMES = r'\*' +t_DIVIDE = r'/' +t_EQUALS = r'=' +t_LPAREN = r'\(' +t_RPAREN = r'\)' +t_NAME = r'[a-zA-Z_][a-zA-Z0-9_]*' + + +def t_NUMBER(t): + r'\d+' + try: + t.value = int(t.value) + except ValueError: + print("Integer value too large %d", t.value) + t.value = 0 + return t + + +# Ignored characters +t_ignore = " \t" + + +def t_newline(t): + r'\n+' + t.lexer.lineno += t.value.count("\n") + + +def t_error(t): + print("Illegal character '%s'" % t.value[0]) + t.lexer.skip(1) + + +# Build the lexer +import ply.lex as lex + +lexer = lex.lex() + +# Parsing rules + +precedence = ( + ('left', 'PLUS', 'MINUS'), + ('left', 'TIMES', 'DIVIDE'), + ('right', 'UMINUS'), +) + +# dictionary of names +names = {} + + +def p_statement_assign(t): + 'statement : NAME EQUALS expression' + names[t[1]] = t[3] + + +def p_statement_expr(t): + 'statement : expression' + print(t[1]) + + +def p_expression_binop(t): + '''expression : expression PLUS expression + | expression MINUS expression + | expression TIMES expression + | expression DIVIDE expression''' + if t[2] == '+': + t[0] = t[1] + t[3] + elif t[2] == '-': + t[0] = t[1] - t[3] + elif t[2] == '*': + t[0] = t[1] * t[3] + elif t[2] == '/': + t[0] = t[1] / t[3] + + +def p_expression_uminus(t): + 'expression : MINUS expression %prec UMINUS' + t[0] = -t[2] + + +def p_expression_group(t): + 'expression : LPAREN expression RPAREN' + t[0] = t[2] + + +def p_expression_number(t): + 'expression : NUMBER' + t[0] = t[1] + + +def p_expression_name(t): + 'expression : NAME' + try: + t[0] = names[t[1]] + except LookupError: + print("Undefined name '%s'" % t[1]) + t[0] = 0 + + +def p_error(t): + print("Syntax error at '%s'" % t.value) + + +import ply.yacc as yacc + +parser = yacc.yacc() + +while True: + try: + s = input('calc > ') # Use raw_input on Python 2 + except EOFError: + break + parser.parse(s) \ No newline at end of file diff --git a/python_basic_notebook/list_dump.txt "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/list_dump.txt" similarity index 100% rename from python_basic_notebook/list_dump.txt rename to "Python \345\237\272\347\241\200\350\257\276\347\250\213/files/list_dump.txt" diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parser.out" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parser.out" new file mode 100644 index 0000000..eb8c40a --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parser.out" @@ -0,0 +1,395 @@ +Created by PLY version 3.11 (http://www.dabeaz.com/ply) + +Grammar + +Rule 0 S' -> statement +Rule 1 statement -> NAME EQUALS expression +Rule 2 statement -> expression +Rule 3 expression -> expression PLUS expression +Rule 4 expression -> expression MINUS expression +Rule 5 expression -> expression TIMES expression +Rule 6 expression -> expression DIVIDE expression +Rule 7 expression -> MINUS expression +Rule 8 expression -> LPAREN expression RPAREN +Rule 9 expression -> NUMBER +Rule 10 expression -> NAME + +Terminals, with rules where they appear + +DIVIDE : 6 +EQUALS : 1 +LPAREN : 8 +MINUS : 4 7 +NAME : 1 10 +NUMBER : 9 +PLUS : 3 +RPAREN : 8 +TIMES : 5 +error : + +Nonterminals, with rules where they appear + +expression : 1 2 3 3 4 4 5 5 6 6 7 8 +statement : 0 + +Parsing method: LALR + +state 0 + + (0) S' -> . statement + (1) statement -> . NAME EQUALS expression + (2) statement -> . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + NAME shift and go to state 2 + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + + statement shift and go to state 1 + expression shift and go to state 3 + +state 1 + + (0) S' -> statement . + + + +state 2 + + (1) statement -> NAME . EQUALS expression + (10) expression -> NAME . + + EQUALS shift and go to state 7 + PLUS reduce using rule 10 (expression -> NAME .) + MINUS reduce using rule 10 (expression -> NAME .) + TIMES reduce using rule 10 (expression -> NAME .) + DIVIDE reduce using rule 10 (expression -> NAME .) + $end reduce using rule 10 (expression -> NAME .) + + +state 3 + + (2) statement -> expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + $end reduce using rule 2 (statement -> expression .) + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 4 + + (7) expression -> MINUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 12 + +state 5 + + (8) expression -> LPAREN . expression RPAREN + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 14 + +state 6 + + (9) expression -> NUMBER . + + PLUS reduce using rule 9 (expression -> NUMBER .) + MINUS reduce using rule 9 (expression -> NUMBER .) + TIMES reduce using rule 9 (expression -> NUMBER .) + DIVIDE reduce using rule 9 (expression -> NUMBER .) + $end reduce using rule 9 (expression -> NUMBER .) + RPAREN reduce using rule 9 (expression -> NUMBER .) + + +state 7 + + (1) statement -> NAME EQUALS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 15 + +state 8 + + (3) expression -> expression PLUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 16 + +state 9 + + (4) expression -> expression MINUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 17 + +state 10 + + (5) expression -> expression TIMES . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 18 + +state 11 + + (6) expression -> expression DIVIDE . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 19 + +state 12 + + (7) expression -> MINUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 7 (expression -> MINUS expression .) + MINUS reduce using rule 7 (expression -> MINUS expression .) + TIMES reduce using rule 7 (expression -> MINUS expression .) + DIVIDE reduce using rule 7 (expression -> MINUS expression .) + $end reduce using rule 7 (expression -> MINUS expression .) + RPAREN reduce using rule 7 (expression -> MINUS expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 13 + + (10) expression -> NAME . + + PLUS reduce using rule 10 (expression -> NAME .) + MINUS reduce using rule 10 (expression -> NAME .) + TIMES reduce using rule 10 (expression -> NAME .) + DIVIDE reduce using rule 10 (expression -> NAME .) + $end reduce using rule 10 (expression -> NAME .) + RPAREN reduce using rule 10 (expression -> NAME .) + + +state 14 + + (8) expression -> LPAREN expression . RPAREN + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + RPAREN shift and go to state 20 + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 15 + + (1) statement -> NAME EQUALS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + $end reduce using rule 1 (statement -> NAME EQUALS expression .) + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 16 + + (3) expression -> expression PLUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 3 (expression -> expression PLUS expression .) + MINUS reduce using rule 3 (expression -> expression PLUS expression .) + $end reduce using rule 3 (expression -> expression PLUS expression .) + RPAREN reduce using rule 3 (expression -> expression PLUS expression .) + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + ! TIMES [ reduce using rule 3 (expression -> expression PLUS expression .) ] + ! DIVIDE [ reduce using rule 3 (expression -> expression PLUS expression .) ] + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + + +state 17 + + (4) expression -> expression MINUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 4 (expression -> expression MINUS expression .) + MINUS reduce using rule 4 (expression -> expression MINUS expression .) + $end reduce using rule 4 (expression -> expression MINUS expression .) + RPAREN reduce using rule 4 (expression -> expression MINUS expression .) + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + ! TIMES [ reduce using rule 4 (expression -> expression MINUS expression .) ] + ! DIVIDE [ reduce using rule 4 (expression -> expression MINUS expression .) ] + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + + +state 18 + + (5) expression -> expression TIMES expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 5 (expression -> expression TIMES expression .) + MINUS reduce using rule 5 (expression -> expression TIMES expression .) + TIMES reduce using rule 5 (expression -> expression TIMES expression .) + DIVIDE reduce using rule 5 (expression -> expression TIMES expression .) + $end reduce using rule 5 (expression -> expression TIMES expression .) + RPAREN reduce using rule 5 (expression -> expression TIMES expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 19 + + (6) expression -> expression DIVIDE expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 6 (expression -> expression DIVIDE expression .) + MINUS reduce using rule 6 (expression -> expression DIVIDE expression .) + TIMES reduce using rule 6 (expression -> expression DIVIDE expression .) + DIVIDE reduce using rule 6 (expression -> expression DIVIDE expression .) + $end reduce using rule 6 (expression -> expression DIVIDE expression .) + RPAREN reduce using rule 6 (expression -> expression DIVIDE expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 20 + + (8) expression -> LPAREN expression RPAREN . + + PLUS reduce using rule 8 (expression -> LPAREN expression RPAREN .) + MINUS reduce using rule 8 (expression -> LPAREN expression RPAREN .) + TIMES reduce using rule 8 (expression -> LPAREN expression RPAREN .) + DIVIDE reduce using rule 8 (expression -> LPAREN expression RPAREN .) + $end reduce using rule 8 (expression -> LPAREN expression RPAREN .) + RPAREN reduce using rule 8 (expression -> LPAREN expression RPAREN .) + diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parsetab.py" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parsetab.py" new file mode 100644 index 0000000..4bee864 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/parsetab.py" @@ -0,0 +1,40 @@ + +# parsetab.py +# This file is automatically generated. Do not edit. +# pylint: disable=W,C,R +_tabversion = '3.10' + +_lr_method = 'LALR' + +_lr_signature = 'leftPLUSMINUSleftTIMESDIVIDErightUMINUSDIVIDE EQUALS LPAREN MINUS NAME NUMBER PLUS RPAREN TIMESstatement : NAME EQUALS expressionstatement : expressionexpression : expression PLUS expression\n | expression MINUS expression\n | expression TIMES expression\n | expression DIVIDE expressionexpression : MINUS expression %prec UMINUSexpression : LPAREN expression RPARENexpression : NUMBERexpression : NAME' + +_lr_action_items = {'NAME':([0,4,5,7,8,9,10,11,],[2,13,13,13,13,13,13,13,]),'MINUS':([0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,],[4,-10,9,4,4,-9,4,4,4,4,4,-7,-10,9,9,-3,-4,-5,-6,-8,]),'LPAREN':([0,4,5,7,8,9,10,11,],[5,5,5,5,5,5,5,5,]),'NUMBER':([0,4,5,7,8,9,10,11,],[6,6,6,6,6,6,6,6,]),'$end':([1,2,3,6,12,13,15,16,17,18,19,20,],[0,-10,-2,-9,-7,-10,-1,-3,-4,-5,-6,-8,]),'EQUALS':([2,],[7,]),'PLUS':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,8,-9,-7,-10,8,8,-3,-4,-5,-6,-8,]),'TIMES':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,10,-9,-7,-10,10,10,10,10,-5,-6,-8,]),'DIVIDE':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,11,-9,-7,-10,11,11,11,11,-5,-6,-8,]),'RPAREN':([6,12,13,14,16,17,18,19,20,],[-9,-7,-10,20,-3,-4,-5,-6,-8,]),} + +_lr_action = {} +for _k, _v in _lr_action_items.items(): + for _x,_y in zip(_v[0],_v[1]): + if not _x in _lr_action: _lr_action[_x] = {} + _lr_action[_x][_k] = _y +del _lr_action_items + +_lr_goto_items = {'statement':([0,],[1,]),'expression':([0,4,5,7,8,9,10,11,],[3,12,14,15,16,17,18,19,]),} + +_lr_goto = {} +for _k, _v in _lr_goto_items.items(): + for _x, _y in zip(_v[0], _v[1]): + if not _x in _lr_goto: _lr_goto[_x] = {} + _lr_goto[_x][_k] = _y +del _lr_goto_items +_lr_productions = [ + ("S' -> statement","S'",1,None,None,None), + ('statement -> NAME EQUALS expression','statement',3,'p_statement_assign','calc.py',67), + ('statement -> expression','statement',1,'p_statement_expr','calc.py',72), + ('expression -> expression PLUS expression','expression',3,'p_expression_binop','calc.py',77), + ('expression -> expression MINUS expression','expression',3,'p_expression_binop','calc.py',78), + ('expression -> expression TIMES expression','expression',3,'p_expression_binop','calc.py',79), + ('expression -> expression DIVIDE expression','expression',3,'p_expression_binop','calc.py',80), + ('expression -> MINUS expression','expression',2,'p_expression_uminus','calc.py',92), + ('expression -> LPAREN expression RPAREN','expression',3,'p_expression_group','calc.py',97), + ('expression -> NUMBER','expression',1,'p_expression_number','calc.py',102), + ('expression -> NAME','expression',1,'p_expression_name','calc.py',107), +] diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/calc.py" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/calc.py" new file mode 100644 index 0000000..5b30cfa --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/calc.py" @@ -0,0 +1,128 @@ +# ----------------------------------------------------------------------------- +# calc.py +# +# A simple calculator with variables -- all in one file. +# ----------------------------------------------------------------------------- + +tokens = ( + 'NAME', 'NUMBER', + 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'EQUALS', + 'LPAREN', 'RPAREN', +) + +# Tokens + +t_PLUS = r'\+' +t_MINUS = r'-' +t_TIMES = r'\*' +t_DIVIDE = r'/' +t_EQUALS = r'=' +t_LPAREN = r'\(' +t_RPAREN = r'\)' +t_NAME = r'[a-zA-Z_][a-zA-Z0-9_]*' + + +def t_NUMBER(t): + r'\d+' + try: + t.value = int(t.value) + except ValueError: + print("Integer value too large %d", t.value) + t.value = 0 + return t + + +# Ignored characters +t_ignore = " \t" + + +def t_newline(t): + r'\n+' + t.lexer.lineno += t.value.count("\n") + + +def t_error(t): + print("Illegal character '%s'" % t.value[0]) + t.lexer.skip(1) + + +# Build the lexer +import ply.lex as lex + +lexer = lex.lex() + +# Parsing rules + +precedence = ( + ('left', 'PLUS', 'MINUS'), + ('left', 'TIMES', 'DIVIDE'), + ('right', 'UMINUS'), +) + +# dictionary of names +names = {} + + +def p_statement_assign(t): + 'statement : NAME EQUALS expression' + names[t[1]] = t[3] + + +def p_statement_expr(t): + 'statement : expression' + print(t[1]) + + +def p_expression_binop(t): + '''expression : expression PLUS expression + | expression MINUS expression + | expression TIMES expression + | expression DIVIDE expression''' + if t[2] == '+': + t[0] = t[1] + t[3] + elif t[2] == '-': + t[0] = t[1] - t[3] + elif t[2] == '*': + t[0] = t[1] * t[3] + elif t[2] == '/': + t[0] = t[1] / t[3] + + +def p_expression_uminus(t): + 'expression : MINUS expression %prec UMINUS' + t[0] = -t[2] + + +def p_expression_group(t): + 'expression : LPAREN expression RPAREN' + t[0] = t[2] + + +def p_expression_number(t): + 'expression : NUMBER' + t[0] = t[1] + + +def p_expression_name(t): + 'expression : NAME' + try: + t[0] = names[t[1]] + except LookupError: + print("Undefined name '%s'" % t[1]) + t[0] = 0 + + +def p_error(t): + print("Syntax error at '%s'" % t.value) + + +import ply.yacc as yacc + +parser = yacc.yacc() + +while True: + try: + s = input('calc > ') # Use raw_input on Python 2 + except EOFError: + break + parser.parse(s) \ No newline at end of file diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parser.out" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parser.out" new file mode 100644 index 0000000..eb8c40a --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parser.out" @@ -0,0 +1,395 @@ +Created by PLY version 3.11 (http://www.dabeaz.com/ply) + +Grammar + +Rule 0 S' -> statement +Rule 1 statement -> NAME EQUALS expression +Rule 2 statement -> expression +Rule 3 expression -> expression PLUS expression +Rule 4 expression -> expression MINUS expression +Rule 5 expression -> expression TIMES expression +Rule 6 expression -> expression DIVIDE expression +Rule 7 expression -> MINUS expression +Rule 8 expression -> LPAREN expression RPAREN +Rule 9 expression -> NUMBER +Rule 10 expression -> NAME + +Terminals, with rules where they appear + +DIVIDE : 6 +EQUALS : 1 +LPAREN : 8 +MINUS : 4 7 +NAME : 1 10 +NUMBER : 9 +PLUS : 3 +RPAREN : 8 +TIMES : 5 +error : + +Nonterminals, with rules where they appear + +expression : 1 2 3 3 4 4 5 5 6 6 7 8 +statement : 0 + +Parsing method: LALR + +state 0 + + (0) S' -> . statement + (1) statement -> . NAME EQUALS expression + (2) statement -> . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + NAME shift and go to state 2 + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + + statement shift and go to state 1 + expression shift and go to state 3 + +state 1 + + (0) S' -> statement . + + + +state 2 + + (1) statement -> NAME . EQUALS expression + (10) expression -> NAME . + + EQUALS shift and go to state 7 + PLUS reduce using rule 10 (expression -> NAME .) + MINUS reduce using rule 10 (expression -> NAME .) + TIMES reduce using rule 10 (expression -> NAME .) + DIVIDE reduce using rule 10 (expression -> NAME .) + $end reduce using rule 10 (expression -> NAME .) + + +state 3 + + (2) statement -> expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + $end reduce using rule 2 (statement -> expression .) + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 4 + + (7) expression -> MINUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 12 + +state 5 + + (8) expression -> LPAREN . expression RPAREN + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 14 + +state 6 + + (9) expression -> NUMBER . + + PLUS reduce using rule 9 (expression -> NUMBER .) + MINUS reduce using rule 9 (expression -> NUMBER .) + TIMES reduce using rule 9 (expression -> NUMBER .) + DIVIDE reduce using rule 9 (expression -> NUMBER .) + $end reduce using rule 9 (expression -> NUMBER .) + RPAREN reduce using rule 9 (expression -> NUMBER .) + + +state 7 + + (1) statement -> NAME EQUALS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 15 + +state 8 + + (3) expression -> expression PLUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 16 + +state 9 + + (4) expression -> expression MINUS . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 17 + +state 10 + + (5) expression -> expression TIMES . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 18 + +state 11 + + (6) expression -> expression DIVIDE . expression + (3) expression -> . expression PLUS expression + (4) expression -> . expression MINUS expression + (5) expression -> . expression TIMES expression + (6) expression -> . expression DIVIDE expression + (7) expression -> . MINUS expression + (8) expression -> . LPAREN expression RPAREN + (9) expression -> . NUMBER + (10) expression -> . NAME + + MINUS shift and go to state 4 + LPAREN shift and go to state 5 + NUMBER shift and go to state 6 + NAME shift and go to state 13 + + expression shift and go to state 19 + +state 12 + + (7) expression -> MINUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 7 (expression -> MINUS expression .) + MINUS reduce using rule 7 (expression -> MINUS expression .) + TIMES reduce using rule 7 (expression -> MINUS expression .) + DIVIDE reduce using rule 7 (expression -> MINUS expression .) + $end reduce using rule 7 (expression -> MINUS expression .) + RPAREN reduce using rule 7 (expression -> MINUS expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 13 + + (10) expression -> NAME . + + PLUS reduce using rule 10 (expression -> NAME .) + MINUS reduce using rule 10 (expression -> NAME .) + TIMES reduce using rule 10 (expression -> NAME .) + DIVIDE reduce using rule 10 (expression -> NAME .) + $end reduce using rule 10 (expression -> NAME .) + RPAREN reduce using rule 10 (expression -> NAME .) + + +state 14 + + (8) expression -> LPAREN expression . RPAREN + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + RPAREN shift and go to state 20 + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 15 + + (1) statement -> NAME EQUALS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + $end reduce using rule 1 (statement -> NAME EQUALS expression .) + PLUS shift and go to state 8 + MINUS shift and go to state 9 + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + +state 16 + + (3) expression -> expression PLUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 3 (expression -> expression PLUS expression .) + MINUS reduce using rule 3 (expression -> expression PLUS expression .) + $end reduce using rule 3 (expression -> expression PLUS expression .) + RPAREN reduce using rule 3 (expression -> expression PLUS expression .) + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + ! TIMES [ reduce using rule 3 (expression -> expression PLUS expression .) ] + ! DIVIDE [ reduce using rule 3 (expression -> expression PLUS expression .) ] + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + + +state 17 + + (4) expression -> expression MINUS expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 4 (expression -> expression MINUS expression .) + MINUS reduce using rule 4 (expression -> expression MINUS expression .) + $end reduce using rule 4 (expression -> expression MINUS expression .) + RPAREN reduce using rule 4 (expression -> expression MINUS expression .) + TIMES shift and go to state 10 + DIVIDE shift and go to state 11 + + ! TIMES [ reduce using rule 4 (expression -> expression MINUS expression .) ] + ! DIVIDE [ reduce using rule 4 (expression -> expression MINUS expression .) ] + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + + +state 18 + + (5) expression -> expression TIMES expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 5 (expression -> expression TIMES expression .) + MINUS reduce using rule 5 (expression -> expression TIMES expression .) + TIMES reduce using rule 5 (expression -> expression TIMES expression .) + DIVIDE reduce using rule 5 (expression -> expression TIMES expression .) + $end reduce using rule 5 (expression -> expression TIMES expression .) + RPAREN reduce using rule 5 (expression -> expression TIMES expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 19 + + (6) expression -> expression DIVIDE expression . + (3) expression -> expression . PLUS expression + (4) expression -> expression . MINUS expression + (5) expression -> expression . TIMES expression + (6) expression -> expression . DIVIDE expression + + PLUS reduce using rule 6 (expression -> expression DIVIDE expression .) + MINUS reduce using rule 6 (expression -> expression DIVIDE expression .) + TIMES reduce using rule 6 (expression -> expression DIVIDE expression .) + DIVIDE reduce using rule 6 (expression -> expression DIVIDE expression .) + $end reduce using rule 6 (expression -> expression DIVIDE expression .) + RPAREN reduce using rule 6 (expression -> expression DIVIDE expression .) + + ! PLUS [ shift and go to state 8 ] + ! MINUS [ shift and go to state 9 ] + ! TIMES [ shift and go to state 10 ] + ! DIVIDE [ shift and go to state 11 ] + + +state 20 + + (8) expression -> LPAREN expression RPAREN . + + PLUS reduce using rule 8 (expression -> LPAREN expression RPAREN .) + MINUS reduce using rule 8 (expression -> LPAREN expression RPAREN .) + TIMES reduce using rule 8 (expression -> LPAREN expression RPAREN .) + DIVIDE reduce using rule 8 (expression -> LPAREN expression RPAREN .) + $end reduce using rule 8 (expression -> LPAREN expression RPAREN .) + RPAREN reduce using rule 8 (expression -> LPAREN expression RPAREN .) + diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parsetab.py" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parsetab.py" new file mode 100644 index 0000000..4bee864 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/ply/parsetab.py" @@ -0,0 +1,40 @@ + +# parsetab.py +# This file is automatically generated. Do not edit. +# pylint: disable=W,C,R +_tabversion = '3.10' + +_lr_method = 'LALR' + +_lr_signature = 'leftPLUSMINUSleftTIMESDIVIDErightUMINUSDIVIDE EQUALS LPAREN MINUS NAME NUMBER PLUS RPAREN TIMESstatement : NAME EQUALS expressionstatement : expressionexpression : expression PLUS expression\n | expression MINUS expression\n | expression TIMES expression\n | expression DIVIDE expressionexpression : MINUS expression %prec UMINUSexpression : LPAREN expression RPARENexpression : NUMBERexpression : NAME' + +_lr_action_items = {'NAME':([0,4,5,7,8,9,10,11,],[2,13,13,13,13,13,13,13,]),'MINUS':([0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,],[4,-10,9,4,4,-9,4,4,4,4,4,-7,-10,9,9,-3,-4,-5,-6,-8,]),'LPAREN':([0,4,5,7,8,9,10,11,],[5,5,5,5,5,5,5,5,]),'NUMBER':([0,4,5,7,8,9,10,11,],[6,6,6,6,6,6,6,6,]),'$end':([1,2,3,6,12,13,15,16,17,18,19,20,],[0,-10,-2,-9,-7,-10,-1,-3,-4,-5,-6,-8,]),'EQUALS':([2,],[7,]),'PLUS':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,8,-9,-7,-10,8,8,-3,-4,-5,-6,-8,]),'TIMES':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,10,-9,-7,-10,10,10,10,10,-5,-6,-8,]),'DIVIDE':([2,3,6,12,13,14,15,16,17,18,19,20,],[-10,11,-9,-7,-10,11,11,11,11,-5,-6,-8,]),'RPAREN':([6,12,13,14,16,17,18,19,20,],[-9,-7,-10,20,-3,-4,-5,-6,-8,]),} + +_lr_action = {} +for _k, _v in _lr_action_items.items(): + for _x,_y in zip(_v[0],_v[1]): + if not _x in _lr_action: _lr_action[_x] = {} + _lr_action[_x][_k] = _y +del _lr_action_items + +_lr_goto_items = {'statement':([0,],[1,]),'expression':([0,4,5,7,8,9,10,11,],[3,12,14,15,16,17,18,19,]),} + +_lr_goto = {} +for _k, _v in _lr_goto_items.items(): + for _x, _y in zip(_v[0], _v[1]): + if not _x in _lr_goto: _lr_goto[_x] = {} + _lr_goto[_x][_k] = _y +del _lr_goto_items +_lr_productions = [ + ("S' -> statement","S'",1,None,None,None), + ('statement -> NAME EQUALS expression','statement',3,'p_statement_assign','calc.py',67), + ('statement -> expression','statement',1,'p_statement_expr','calc.py',72), + ('expression -> expression PLUS expression','expression',3,'p_expression_binop','calc.py',77), + ('expression -> expression MINUS expression','expression',3,'p_expression_binop','calc.py',78), + ('expression -> expression TIMES expression','expression',3,'p_expression_binop','calc.py',79), + ('expression -> expression DIVIDE expression','expression',3,'p_expression_binop','calc.py',80), + ('expression -> MINUS expression','expression',2,'p_expression_uminus','calc.py',92), + ('expression -> LPAREN expression RPAREN','expression',3,'p_expression_group','calc.py',97), + ('expression -> NUMBER','expression',1,'p_expression_number','calc.py',102), + ('expression -> NAME','expression',1,'p_expression_name','calc.py',107), +] diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test.jpg" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test.jpg" new file mode 100644 index 0000000..d9e820d Binary files /dev/null and "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test.jpg" differ diff --git a/python_basic_notebook/test.txt "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test.txt" similarity index 100% rename from python_basic_notebook/test.txt rename to "Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test.txt" diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test2.txt" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test2.txt" new file mode 100644 index 0000000..2c87d95 --- /dev/null +++ "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test2.txt" @@ -0,0 +1,3 @@ +Hello, World! +Hello, Shanghai! +Hello, CHINA! diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_blur.jpg" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_blur.jpg" new file mode 100644 index 0000000..2799849 Binary files /dev/null and "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_blur.jpg" differ diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_crop.jpg" "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_crop.jpg" new file mode 100644 index 0000000..9d6da1c Binary files /dev/null and "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_crop.jpg" differ diff --git "a/Python \345\237\272\347\241\200\350\257\276\347\250\213/files/test_drawline.jpg" "b/Python 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python_basic_notebook/imgs/python_install_5.png rename to "Python \345\237\272\347\241\200\350\257\276\347\250\213/imgs/python_install_5.png" diff --git a/python_basic_notebook/imgs/python_use.jpg "b/Python \345\237\272\347\241\200\350\257\276\347\250\213/imgs_old/python_use.jpg" similarity index 100% rename from python_basic_notebook/imgs/python_use.jpg rename to "Python \345\237\272\347\241\200\350\257\276\347\250\213/imgs_old/python_use.jpg" diff --git a/README.md b/README.md index 4f4ac10..acc0a54 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,52 @@ -python 培训教材，试图做到实用、快速、清晰。 +随着 Python 的飞速发展，已经在越来越多的领域有着广泛的应用。从 2015 年开始，我们在学习 Python 的过程中，参考了很多书籍和网络资料，觉得对于初学者来说很多内容写的不够循序渐进，且 python 语言生态圈最近几年发展非常迅猛，网络上有些代码示例还停留在 Python 2 的时代，有些教程比较传统。因此为了公司内部培训的目的，开始编纂这套资料。 -其中 \python_basic_notebook，采用了 jupyter notebook 形式，便于初学者循序渐进的学习，大约是每周一次，每次 1-2个小时的学习量。 +我们采用了 jupyter notebook 形式，便于初学者循序渐进的学习，对于基本概念讲的比较快，更多体现 Python 的实用性。有些也通过一题多解和比较不同版本差异的方式来教授一些知识点。还设计了一些练习题和思考题，使得学习者能够迅速的掌握开发思路，有实战能力。 -从 2019 年开始，按照专题形式增加内容，比如 section 01 是讨论 Python 在计算过程中的缓存，一些算法优化的方法，包括简单介绍怎么使用 Nim 来为 Python 写扩展，以及 PyPy 的初步介绍等。 +目前内容还在持续更新中，会不断加入各类专题材料，包括 API 开发和调用、扩展编写、性能优化、数据分析等。希望能够对 Python 爱好者有所帮助！ -自己和团队在学习 python 的过程中，参考了很多书籍和网络资料，不过觉得对于初学者来说很多内容写的不够循序渐进，且 python 语言生态圈最近几年发展非常迅猛，国内网络上大部分代码示例还停留在 2 的时代。因此为了公司内部培训的目的，在之前一些自己其他培训资料的基础上，开始编纂这套资料。 +今后，我们也准备用视频等方式来进行推广，敬请期待。 + +从 2016年底开始，有不少同事和朋友参与了这套材料的编辑，特别感谢张怿檬。 + +--- + +Python Basic 入门教程 + +Python Basic Lesson 01 - 简介.ipynb + +Python Basic Lesson 02 - 变量, print, input.ipynb + +Python Basic Lesson 03 - 循环 for, 字符串.ipynb + +Python Basic Lesson 04 - 列表 list.ipynb + +Python Basic Lesson 05 - 字典 dict, 元组 tuple.ipynb + +Python Basic Lesson 06 - 随机数.ipynb + +Python Basic Lesson 07 - 文件目录操作.ipynb + +Python Basic Lesson 08 - 函数简介.ipynb + +Python Basic Lesson 09 - 日期函数.ipynb + +Python Basic Lesson 10 - 函数参数和匿名函数.ipynb + +Python Basic Lesson 11 - 集合库 collections.ipynb + +Python Basic Lesson 12 - 列表生成式.ipynb + +Python Basic Lesson 13 - 完整的 for 语句结构和 pprint.ipynb + +Python Basic Lesson 14 - 访问网络 requests.ipynb + +Python Basic Lesson 15 - 图片处理 Pillow.ipynb + +Python Basic Lesson 16 - 函数式编程.ipynb + +Python Basic Lesson 17 - 异常处理.ipynb + +Python Basic Lesson 18 - 窗口界面编程tk.ipynb 遵循 GNU GENERAL PUBLIC LICENSE Version 3 协议。未经授权，不得用于商业用途。 diff --git a/python_basic_notebook/basic demo.ipynb b/python_basic_notebook/basic demo.ipynb deleted file mode 100644 index bdf3e82..0000000 --- a/python_basic_notebook/basic demo.ipynb +++ /dev/null @@ -1,52 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 3, 'd': 4}\n" - ] - } - ], - "source": [ - "x = dict(a=1, b=2)\n", - "y = dict(b=3, d=4)\n", - "z = {**x, **y}\n", - "print(z)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python_basic_notebook/python_basic_lesson_01.ipynb b/python_basic_notebook/python_basic_lesson_01.ipynb deleted file mode 100644 index 4ee09bc..0000000 --- a/python_basic_notebook/python_basic_lesson_01.ipynb +++ /dev/null @@ -1,665 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 1\n", - "Python Basic, Lesson 1, \n", - "* v1.0.1, 2016.12 by David Yi \n", - "* v1.0.2, 2017.02 modified by Eamon Zhang\n", - "* v1.0.3, 2018.02 modified by David Yi\n", - "* v1.0.4, 2018.12 modified by David Yi\n", - "\n", - "### 本章内容要点\n", - "\n", - "* python 简介\n", - "* 准备工作\n", - " * 使用标准的 python 和 IDLE\n", - " * anaconda 介绍\n", - " * jupyter 和 notebook介绍\n", - " * Pycharm 介绍\n", - " * vxcode 介绍\n", - "* 基本变量概念\n", - "* print() 和 input() 用法\n", - "* pycharm 用法介绍\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## python 简介\n", - "\n", - "#### 定义\n", - "下面的定义来自维基百科，https://zh.wikipedia.org/wiki/Python\n", - "\n", - "Python（英国发音：/ˈpaɪθən/ 美国发音：/ˈpaɪθɑːn/），是一种广泛使用的高级编程语言，属于通用型编程语言，由吉多·范罗苏姆 创造，第一版发布于 1991 年。可以视之为一种改良 (加入一些其他编程语言的优点，如面向对象) 的 LISP。作为一种解释型语言，Python 的设计哲学强调代码的可读性和简洁的语法（尤其是使用空格缩进划分代码块，而非使用大括号或者关键词）。相比于 C++ 或 Java，Python 让开发者能够用更少的代码表达想法。不管是小型还是大型程序，该语言都试图让程序的结构清晰明了。\n", - "\n", - "与 Scheme、Ruby、Perl、Tcl 等动态类型编程语言一样，Python 拥有动态类型系统和垃圾回收功能，能够自动管理内存使用，并且支持多种编程范式，包括面向对象、命令式、函数式和过程式编程。其本身拥有一个巨大而广泛的标准库。\n", - "\n", - "Python 虚拟机本身几乎可以在所有的操作系统中运行。Python 的正式解释器CPython是用 C语言编写的、是一个由社区驱动的自由软件，目前由 Python软件基金会管理。\n", - "\n", - "#### 创始人\n", - "Python 的创始人为吉多·范罗苏姆（Guido van Rossum），是一个非常可爱的荷兰人，不像其他很多目前常用的语言的创始者都退隐江湖那样，Python 的创始者至今还是把握着Python 发展的方向。在 2018年，Rossum 宣布不再参与 Python 的发展，而是改成一个小组。\n", - "\n", - "#### 版本\n", - "Python 2.0于2000年10月16日发布，增加了实现完整的垃圾回收，并且支持Unicode。同时，整个开发过程更加透明，社区对开发进度的影响逐渐扩大。目前2系列的最新版本是 2.7.12，2.7版本将在2020年被停止支持。还有不少以前的项目使用 Python 2.x 系列开发并且维护着，不过这几年很多新项目已经都不支持 Python 2.x 了，所以建议大家不要再学习 Python 2.x 了。\n", - "\n", - "Python 3.0于2008年12月3日发布，此版不完全兼容之前的Python源代码。目前 Python 版本是 3.7.x 系列，发布于2018年10月。3.7.* 系列相比之前的 3.6.* 系列，新功能增加的频率快了不少，\n", - "\n", - "#### Python 能做什么？\n", - "\n", - "![](imgs/python_use.jpg)\n", - "\n", - "#### Python 的优点和缺点\n", - "优点 \n", - "1. 软件质量 \n", - "注重可读性，被称作“可执行的伪代码”。比一般脚本语言高很多可维护性和可重用性。极简主义的设计思想，尽管实现一个任务可能有很多种方法，往往只有一种方法是显而易见的，明了的解决办法胜于“魔术”般的方法。\n", - "\n", - "2. 提高开发者的效率 \n", - "代码大小只有C++、Java 的1/3-1/5，意味着可以输入、调试和维护更少的代码。 Python 代码是可立即执行代码，无须传统编译语言或静态语言的编译与链接步骤，意味着修改代码直接看到效果，具有快速调整能力。\n", - "\n", - "3. 可移植性 \n", - "大多数 Python 程序不做任何改变即可在各类平台上运行。\n", - "\n", - "4. 标准库/第三方库的支持 \n", - "有众多成熟的功能模块，包括网站开发、数值计算、服务器应用、数据库应用、游戏开发、Web、人工智能等各个方面，特别是最近几年 Python 在人工智能、机器学习、数据统计方面大放异彩，这也归功于一些非常优秀的第三方库。\n", - "\n", - "5. 组件集成 \n", - "轻松与其他应用程序进行通信。可以调用C/C++的库，也可以被C/C++调用，可以和Java集成。\n", - "\n", - "6. 简单易学，非常适合编程初学者\n", - "支持所有现代编程语言的特性，但是也适当做了简化，在学习曲线、程序效果、程序性能上做到了较好的平衡。\n", - "\n", - "缺点\n", - "1. 执行速度不够快（与 C/C++,Java 等相比） \n", - "Python 标准实现方式是将源代码的语句转换为字节码，再将字节码解释出来。但没有将代码编译为底层的二进制代码，所以速度比较慢。\n", - "\n", - "2. 在服务端还没有非常强大的框架。\n", - "\n", - "3. 在很多领域，优秀的第三方库太多，选择困难。\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 设计哲学\n", - "\n", - "Python 的设计哲学是“优雅”、“明确”、“简单”。Python 开发者的哲学是“用一种方法，最好是只有一种方法来做一件事”，也因此它和拥有明显个人风格的其他语言很不一样。在设计Python语言时，如果面临多种选择，Python 开发者一般会拒绝花俏的语法，而选择明确没有或者很少有歧义的语法。这些准则被称为“Python格言”。\n", - "\n", - "在 Python 解释器内运行 import this可以如下的内容，称之为 Python 之禅意。\n", - "\n", - " The Zen of Python, by Tim Peters\n", - "\n", - " Beautiful is better than ugly.\n", - " Explicit is better than implicit.\n", - " Simple is better than complex.\n", - " Complex is better than complicated.\n", - " Flat is better than nested.\n", - " Sparse is better than dense.\n", - " Readability counts.\n", - " Special cases aren't special enough to break the rules.\n", - " Although practicality beats purity.\n", - " Errors should never pass silently.\n", - " Unless explicitly silenced.\n", - " In the face of ambiguity, refuse the temptation to guess.\n", - " There should be one-- and preferably only one --obvious way to do it.\n", - " Although that way may not be obvious at first unless you're Dutch.\n", - " Now is better than never.\n", - " Although never is often better than *right* now.\n", - " If the implementation is hard to explain, it's a bad idea.\n", - " If the implementation is easy to explain, it may be a good idea.\n", - " Namespaces are one honking great idea -- let's do more of those!\n", - "\n", - "#### 提高编程能力的体会\n", - "\n", - "* 世界观和人生观\n", - "* 天赋和勤奋\n", - "* 阅读\n", - "* 实践，实践，再实践\n", - "* 会问，会搜索\n", - "\n", - "#### 如何运\n", - "\n", - "* Python 交互提示模式\n", - "* Python 自带的IDLE\n", - "* Microsoft VSCode\n", - "* Jetbrain Pycharm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 准备工作\n", - "\n", - "* 下载安装 Python 最新版本。\n", - "\n", - "* 或者下载安装 anaconda 最新版本。anaconda 是一个强大的、开源的 Python 整体解决方案，包括最新版本的 Python、jupyter等各类工具、精选的 Python 第三方库等。目前 anaconda 已经包含了 Microsoft 开源强大的代码开发工具\n", - "\n", - "### 使用标准的 python 和 IDLE\n", - "\n", - "首先到 Python 官网 (www.python.org)，找到下载链接，目前 Python 进入到了 3.5+ 系列，不建议再使用 2.x 系列了，还有不少项目习惯用 Python 2.7.x，Python 官方已经明确除了安全补丁以外，不会继续支持 2.7.x 系列，所以如果是初学者，不用任何犹豫的选择 Python 3。\n", - "\n", - "目前 python 最新版本是 3.6.4，而3.7 版本也在测试中了。\n", - "\n", - "从 Python 官网的 download 链接开始，以 Python 3.5为例，简单介绍一下如何安装。\n", - "\n", - "![](imgs/python_install.jpeg)\n", - "\n", - "windows 操作系统只需要下载32位版本即可，对于 Python 的学习不会有任何影响，mac 电脑下载对应的最新版本就可以了。\n", - "\n", - "以 windows 操作系统举例，下载后，执行安装程序，如下：\n", - "\n", - "![](imgs/python_install_1.jpeg)\n", - "\n", - "一般情况下不用做任何修改，几分钟就安装好了，如下:\n", - "\n", - "![](imgs/python_install_2.jpeg)\n", - "\n", - "安装好后，在开始菜单找到 Python 3.5 程序组，看到其中的 IDLE(Python 3.5 32bit)，运行后就会出现下面的 Python 命令行界面：\n", - "\n", - "![](imgs/python_install_5.png)\n", - "\n", - "可以在这里进行输入 Python 程序，正确的话，会执行。\n", - "\n", - "![](imgs/python_install_3.jpeg)\n", - "\n", - "不过在这个类似 shell 的环境里面编辑复杂的 python 程序会比较麻烦，所以，可以在 shell 的 File 菜单里面选择第一项 New File，这时候，一个编辑器窗口就出现了。\n", - "\n", - "在编辑器里可以编写比较长的程序，选择菜单 Run 里面的 Run Module，就可以运行程序，结果会显示在一个 shell 窗口中，就像下面这样，注意在运行程序前是需要保存程序文件的。\n", - "\n", - "![](imgs/python_install_4.jpeg)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "### anaconda 介绍\n", - "\n", - "![](imgs/anaconda.png)\n", - "\n", - "对于初学者，Python 的安装也许并不容易，尤其面临 Python 版本的问题，会浪费很多时间，甚至让人放弃的感觉，anaconda 是一种简便的安装方法，可以完美的兼容 Python 2.7 和 Python 3.x，并集成了许多 packages（第三方包），免去配置环境变量的烦恼，\n", - "\n", - "我们觉得 anaconda的优势如下：\n", - "\n", - "* 集成很多第三方库，省去一一下载的麻烦； \n", - "* conda 除了可以安装第三方库以外，还可以将 Python 环境作为安装内容的一部分，因此一台电脑上配置多个 Python 开发环境非常容易；\n", - "* Pycharm、VSCode 等都直接支持 conda 配置；\n", - "* 自带很多强大工具，最新集成了微软的开发工具，VSCode，并默认安装好了 Python 扩展；\n", - "\n", - "anaconda的下载地址：https://www.continuum.io/downloads \n", - "\n", - "anaconda 安装过程大致如下：\n", - "\n", - "---\n", - "\n", - "### Jupyter 介绍\n", - "\n", - "![](imgs/jupyter.png)\n", - "\n", - "Jupyter Notebook（此前被称为 IPython notebook）是一个交互式笔记本，支持运行 40 多种编程语言。在这里我们将介绍 Jupyter notebook 的主要特性，以及为什么对于希望编写漂亮的交互式文档的人来说是一个强大工具。\n", - "\n", - "如果安装了 anaconda，只要执行`jupyter notebook` 就可以启动了 jupyter 的服务端。或者启动 anaconda-navigator 这个图形化导航工具，来执行 Jupyter\n", - "\n", - "如果没有安装 anaconda，可以执行 `pip install jupyter` ，来安装 jupyter，其实它也是 python 的一个第三方扩展包。\n", - "\n", - "我们的整个教学实践都会在 Jupyter 环境下进行，因此各位会渐渐熟悉起来。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pycharm 介绍\n", - "\n", - "Python 的开发环境大概至少有几十个，一般的学习、调试用 python 自带的 shell、IDLE 即可，用 jupyter notebook 也可以满足很多需求。\n", - "\n", - "真正的日常开发还是需要专业的编辑器，推荐 PyCharm，分为商业版本和免费的教育版本，免费版本用在一般的开发已经绰绰有余。\n", - "\n", - "![](imgs/pycharm.png)\n", - "\n", - "Pycharm 目前最新版本是 2017.3.3，下载链接：https://www.jetbrains.com/pycharm/download/ \n", - "\n", - "### vscode 介绍" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 基本变量概念\n", - "\n", - "Python 中的变量赋值不需要类型声明。\n", - "\n", - "每个变量在内存中创建，都包括变量的标识，名称和数据这些信息。\n", - "每个变量在使用前都必须赋值，变量赋值以后该变量才会被创建。\n", - "等号（=）用来给变量赋值。\n", - "等号（=）运算符左边是一个变量名,等号（=）运算符右边是存储在变量中的值。\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "counter = 100 # 整型变量\n", - "miles = 1000.0 # 浮点型（小数）\n", - "name = \"John\" # 字符串\n", - "name2 = 'Tom'\n", - "bool = False # 布尔值\n", - "print(type(bool))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 2 1\n", - "Hello Hello\n" - ] - } - ], - "source": [ - "# 多个变量赋值\n", - "\n", - "a = b = c = 1\n", - "b = 2 \n", - "print(a,b,c)\n", - "\n", - "s = s1 = 'Hello'\n", - "print(s,s1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 2 3\n" - ] - } - ], - "source": [ - "# 多个变量赋值\n", - "\n", - "a, b, c = 1, 2, 3\n", - "print(a,b,c)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 1\n" - ] - } - ], - "source": [ - "# 交换变量\n", - "a, b = 1, 2\n", - "a, b = b, a\n", - "print(a,b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 动态语言\n", - "Python是动态语言，即变量本身的类型不固定\n", - "\n", - "与之对应的静态语言（如Java），在定义变量时，必须先指定变量类型，赋值时如不匹配则报错" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 理解Python变量在内存中的表示\n", - "当我们输入 a = 'ABC' 时，Python解释器做了2件事情：\n", - "\n", - "1 在内存中创建了一个'ABC'的字符串；\n", - "\n", - "2 在内存中创建了一个名为a的变量，并把它指向'ABC'。\n", - "\n", - "也可以把一个变量a赋值给另一个变量b，这个操作实际上是把变量b指向变量a所指向的数据" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ABC\n" - ] - } - ], - "source": [ - "a = 'ABC'\n", - "b = a\n", - "a = 'XYZ'\n", - "print(b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "### print() 和 input() 用法\n", - "\n", - "print() 用来显示内容，input()用来输入内容，在类似命令行的操作中，这两条语句非常有用。\n", - "\n", - "print() 也支持格式化输出，类似于 c 语言中的 printf。\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100\n", - "100\n", - "100 200 300\n" - ] - } - ], - "source": [ - "print(100)\n", - "\n", - "a = 100\n", - "print(a)\n", - "\n", - "print(100, 200, 300)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sssss\n", - "hello sss\n" - ] - } - ], - "source": [ - "name = input('ss')\n", - "print( 'hello', name) " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "small\n" - ] - } - ], - "source": [ - "a = 10\n", - "if a > 10:\n", - " print('big')\n", - "else:\n", - " print('small')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n" - ] - } - ], - "source": [ - "for i in range(13):\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "h\n", - "e\n", - "l\n", - "l\n", - "o\n" - ] - } - ], - "source": [ - "for i in 'hello':\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The length of hello is 5\n" - ] - } - ], - "source": [ - "# 支持格式化的 print\n", - "\n", - "s = 'hello'\n", - "x = len(s) \n", - "print('The length of %s is %d' % (s,x)) " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello from English\n", - "hello from English\n" - ] - } - ], - "source": [ - "# 更加好，更加 pythonic 的写法：format函数-增强的格式化字符串函数\n", - "# 关于format的更多细节，参见 https://docs.python.org/3/library/string.html#format-string-syntax\n", - "# 通过关键字传参\n", - "print('{greet} from {language}'.format(greet='hello', language='English'))\n", - "\n", - "# 通过位置传参\n", - "print('{0} from {1}'.format('hello', 'English'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "print() 格式化输出说明\n", - "* %字符：标记转换说明符的开始\n", - "\n", - "* 转换标志：-表示左对齐；+表示在转换值之前要加上正负号；“”（空白字符）表示正数之前保留空格；0表示转换值若位数不够则用0填充。\n", - "\n", - "* 最小字段宽度：转换后的字符串至少应该具有该值指定的宽度。如果是*，则宽度会从值元组中读出。\n", - "\n", - "* 点(.)后跟精度值：如果转换的是实数，精度值就表示出现在小数点后的位数。如果转换的是字符串，那么该数字就表示最大字段宽度。如果是*，那么精度将从元组中读出。\n", - "\n", - "* 字符串格式化转换类型。\n", - "\n", - "转换类型 含义\n", - "* d,i：带符号的十进制整数\n", - "* o：不带符号的八进制\n", - "* u：不带符号的十进制\n", - "* x：不带符号的十六进制（小写）\n", - "* X：不带符号的十六进制（大写）\n", - "* e：科学计数法表示的浮点数（小写）\n", - "* E：科学计数法表示的浮点数（大写）\n", - "* f,F：十进制浮点数\n", - "* g：如果指数大于-4或者小于精度值则和e相同，其他情况和f相同\n", - "* G：如果指数大于-4或者小于精度值则和E相同，其他情况和F相同\n", - "* C：单字符（接受整数或者单字符字符串）\n", - "* r：字符串（使用repr转换任意python对象)\n", - "* s：字符串（使用str转换任意python对象）" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 3.142\n", - "pi = 3.142\n", - "000003.142\n", - "3.142 \n", - "+3.141593\n" - ] - } - ], - "source": [ - "# 格式化 print 举例\n", - "\n", - "pi = 3.141592653 \n", - "\n", - "print('%10.3f' % pi) #字段宽10，精度3 \n", - "\n", - "print(\"pi = %.*f\" % (3,pi)) #用*从后面的元组中读取字段宽度或精度 \n", - " \n", - "print('%010.3f' % pi) #用0填充空白 \n", - " \n", - "print('%-10.3f' % pi) #左对齐 \n", - " \n", - "print('%+f' % pi) #显示正" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python_basic_notebook/python_basic_lesson_02.ipynb b/python_basic_notebook/python_basic_lesson_02.ipynb deleted file mode 100644 index 1cd9f89..0000000 --- a/python_basic_notebook/python_basic_lesson_02.ipynb +++ /dev/null @@ -1,1587 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 2\n", - "\n", - "Python Basic, Lesson 2, v1.0.0, 2016.12 by David.Yi \n", - "Python Basic, Lesson 2, v1.0.1, 2017.02 modified by Yimeng.Zhang \n", - "\n", - "\n", - "### 上次内容要点\n", - "\n", - "* python 简介\n", - "* 准备工作\n", - " * 使用标准的 python 和 IDLE\n", - " * anaconda 介绍\n", - " * jupyter 和 notebook介绍\n", - "* 基本变量概念\n", - "* print() 和 input() 用法\n", - "* pycharm 用法介绍\n", - " \n", - "### 本次内容要点\n", - "\n", - "* 循环语句 for 和 range() 用法\n", - "* 常用数据类型 \n", - " * list 用法\n", - " * dict 用法\n", - " * tuple 用法\n", - "* 随机数介绍\n", - "* 举例\n", - " * 中文分词介绍\n", - " * 小程序练习（猜拳游戏）" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 循环语句 for...in\n", - "\n", - "Python的循环主要是for...in循环，依次把list或tuple等中的每个元素迭代出来；python 也有 while 循环，一般不常用。\n", - "\n", - "可以理解为，`for x in ...` 循环就是把每个元素代入变量x，然后执行缩进块的语句。\n", - "\n", - "如果是简单的按照次数的循环，一般用 range() 函数来产生一个可以生成迭代数字的序列" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\n", - "c\n", - "d\n", - "e\n", - "f\n" - ] - } - ], - "source": [ - "# 按照字符串进行迭代循环\n", - "s = 'abcdef'\n", - "for i in s:\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\n", - "c\n" - ] - } - ], - "source": [ - "s = ['a', 'b', 'c']\n", - "for i in s:\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n" - ] - } - ], - "source": [ - "for i in range(3):\n", - " \n", - " print(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 循环语句 while \n", - "while循环是在Python中的循环结构之一。 while循环继续，直到表达式变为假。 \n", - "\n", - "表达的是一个逻辑表达式，必须返回一个true或false值。" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The count is: 0\n", - "The count is: 1\n", - "The count is: 2\n", - "The count is: 3\n", - "The count is: 4\n", - "The count is: 5\n", - "The count is: 6\n", - "The count is: 7\n", - "The count is: 8\n", - "Good bye!\n" - ] - } - ], - "source": [ - "count = 0\n", - "while (count < 9):\n", - " print('The count is:', count)\n", - " count += 1\n", - "print(\"Good bye!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "##### range() 函数\n", - "\n", - "range() 函数产生一个等差序列，range(x,y,z)，表示从 x 到 y（不含 y），z 为步长，可以为负。\n", - "\n", - "修改下面例子中的 range() 中的起始、结束和步长，来看看各种效果。" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "4\n", - "7\n" - ] - } - ], - "source": [ - "for i in range(1,10,3):\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n" - ] - } - ], - "source": [ - "for i in range(10,2,-1):\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 Mary \n", - "1 had\n", - "2 a\n", - "3 little \n", - "4 lamb\n" - ] - } - ], - "source": [ - "# 同时获得列表中的序号和内容，可以这样写\n", - "# len() 是获得列表的长度，可以理解元素个数\n", - "\n", - "s = ['Mary ', 'had', 'a', 'little ', 'lamb']\n", - "for i in range(len(s)):\n", - " print(i, s[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 Mary \n", - "1 had\n", - "2 a\n", - "3 little \n", - "4 lamb\n" - ] - } - ], - "source": [ - "# 更加好的写法, 使用 enumerate\n", - "\n", - "s = ['Mary ', 'had', 'a', 'little ', 'lamb']\n", - "for i, item in enumerate(s):\n", - " print(i, item)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 字符串\n", - "\n", - "字符串即有序的字符的集合，用来存储或表现基于文本的信息" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spam\n" - ] - } - ], - "source": [ - "a = 'spam' # 单引号\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spam\n", - "spam's log\n" - ] - } - ], - "source": [ - "a = \"spam\" # 双引号\n", - "print(a)\n", - "b = \"spam's log\"\n", - "print(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "multile lines\n", - "this is an example\n" - ] - } - ], - "source": [ - "# 多行字符串\n", - "a = '''multile lines\n", - "this is an example'''\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a\n", - "b\tc\n" - ] - } - ], - "source": [ - "s = 'a\\nb\\tc' # 转义字符串\n", - "print(s)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\n", - "ew\text.txt\n", - "C:\\new\\text.txt\n" - ] - } - ], - "source": [ - "# 原始字符串\n", - "a = 'C:\\new\\text.txt'\n", - "print(a)\n", - "b = r'C:\\new\\text.txt'\n", - "print(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ABCabc.txt123\n", - "ABCabc.txtABCabc.txt\n", - "A\n", - "t\n", - "txt.cbaCBA\n", - "AB\n", - "10\n", - "abcabc.txt\n", - "ABCABC.TXT\n", - "True\n", - "True\n" - ] - } - ], - "source": [ - "# 常用的字符串表达式\n", - "s = 'ABCabc.txt'\n", - "print(s + '123') # 字符串拼接\n", - "print(s * 2) # 重复\n", - "print(s[0]) # 索引\n", - "print(s[-1])\n", - "print(s[::-1]) # 反转\n", - "print(s[0:2]) # 切片（前闭后开）\n", - "print(len(s)) # 长度\n", - "print(s.lower()) # 小写转换\n", - "print(s.upper()) # 大写转换\n", - "print(s.endswith('.txt')) # 后缀测试\n", - "print('abc' in s) # 成员关系测试" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", - "\n", - "#### 列表\n", - "\n", - "列表 list 是 python 内置的一种数据类型。list是一种可变、有序的集合，可以随时添加和删除其中的元素。\n", - "列表在一般的 python 程序中是最常用的数据类型。\n", - "\n", - "列表的基本概念：\n", - "* 创建列表\n", - "* 访问列表中的元素\n", - "* 增加元素\n", - "* 删除元素\n", - "* 排序\n", - "* 多维\n", - "* 保存和载入\n", - "\n", - "list 中除了保存一般的数字、字符以外，可以存储 python 中各种复杂的数据类型，包括 list 本身、对象等。" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['pig', 'cat', 'dog']\n", - "cat\n", - "dog\n" - ] - } - ], - "source": [ - "# 直接创建列表\n", - "\n", - "a = ['pig', 'cat', 'dog']\n", - "\n", - "print(a)\n", - "\n", - "# 用序号访问列表中的元素，支持双向\n", - "print(a[1])\n", - "print(a[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['bird']\n", - "['bird', 'snake']\n", - "['sheep', 'bird', 'snake']\n" - ] - } - ], - "source": [ - "# 列表初始化\n", - "a = []\n", - "\n", - "# 列表末尾追加元素\n", - "a.append('bird')\n", - "print(a)\n", - "a.append('snake')\n", - "print(a)\n", - "\n", - "# 列表指定位置插入元素\n", - "a.insert(0,'sheep')\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['sheep', 'snake']\n" - ] - } - ], - "source": [ - "# 列表删除指定序号的元素\n", - "a.pop(1)\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['pig', 'cat']\n" - ] - } - ], - "source": [ - "# 列表删除指定内容的元素\n", - "\n", - "a = ['pig', 'cat', 'dog']\n", - "a.remove('dog')\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4, 3, 2, 1]\n" - ] - } - ], - "source": [ - "# 列表排序\n", - "\n", - "a = ['pig', 'cat', 'dog', 'snake']\n", - "# a = [1,2,3,4]\n", - "a.sort(reverse = True)\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['cat', 'dog', 'pig', 'snake', ['cat1', 'cat2', 'cat3']]\n" - ] - } - ], - "source": [ - "# 列表追加另一个列表\n", - "# 使用 append 一个列表的话，这个列表会以一个元素的方式追加到原来列表\n", - "\n", - "b = ['cat1', 'cat2', 'cat3']\n", - "a.append(b)\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['pig', 'cat', 'dog', 'snake', 'cat1', 'cat2', 'cat3']\n" - ] - } - ], - "source": [ - "# 列表追加另一个列表的元素\n", - "\n", - "a = ['pig', 'cat', 'dog', 'snake']\n", - "b = ['cat1', 'cat2', 'cat3']\n", - "a.extend(b)\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# 统计某个元素在列表中出现次数\n", - "\n", - "a = ['a','b','b','c']\n", - "print(a.count('2'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# 列表内容保存\n", - "\n", - "# 使用 pickle 模块\n", - "import pickle\n", - "\n", - "f = open('list_dump.txt', 'wb')\n", - "a = ['pig', 'cat', 'dog', 'snake', 'snake']\n", - "pickle.dump(a, f)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['pig', 'cat', 'dog', 'snake', 'snake']\n" - ] - } - ], - "source": [ - "# 列表内容读出\n", - "\n", - "f = open('list_dump.txt', 'rb')\n", - "a1 = pickle.load(f)\n", - "f.close()\n", - "print(a1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "#### pickle 序列化\n", - "\n", - "Python中可以使用 pickle 模块将对象转化为文件保存在磁盘上，在需要的时候再读取并还原。具体用法如下：\n", - "\n", - "```pickle.dump(obj, file[, protocol])```\n", - "\n", - "pickle 的格式和 python 版本等有关，不同版本之间不兼容。比较好的序列化方式是用 json 格式。\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['pig', 'cat', 'dog'], [1, 2, 3]]\n", - "cat\n", - "3\n" - ] - } - ], - "source": [ - "# 两维列表\n", - "\n", - "a = ['pig', 'cat', 'dog']\n", - "b = [1,2,3]\n", - "c = []\n", - "\n", - "c.append(a)\n", - "c.append(b)\n", - "\n", - "print(c)\n", - "# 第0个元素的第1个元素\n", - "print(c[0][1]) # cat\n", - "\n", - "# 第1个元素的第2个元素\n", - "print(c[1][2]) # 3" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['pig', 'cat']\n" - ] - } - ], - "source": [ - "# 列表生成式 初步\n", - "# 过滤列表中的重复元素\n", - "\n", - "a = ['pig', 'cat', 'dog', 'dog']\n", - "b = []\n", - "for i in a:\n", - " if a.count(i)>=2:\n", - " b.append(i)\n", - "#print(b)\n", - "# 如何取出只出现过1次的元素？\n", - "\n", - "\n", - "b = [x for x in a if a.count(x) == 1]\n", - "print(b)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", - "\n", - "#### 字典\n", - "\n", - "字典是另一种可变容器模型，可存储任意类型对象。\n", - "\n", - "字典的每个键值(key=>value)对用冒号(:)分割，每个对之间用逗号(,)分割，整个字典包括在花括号({})中，格式如 `d = {key1 : value1, key2 : value2 }`\n", - "\n", - "字典中的 key 值不可以重复（定义后也没有办法重复）\n", - "\n", - "字典的几个特点：\n", - "1. 查找和插入的速度极快，不会随着key的增加而变慢； \n", - "2. 需要占用大量的内存，内存浪费多。 \n", - "而list相反：\n", - "1. 查找和插入的时间随着元素的增加而增加； \n", - "2. 占用空间小，浪费内存很少。 \n", - "\n", - "* 创建字典\n", - "* 访问字典中的 key-value\n", - "* 修改字典中的 key-value\n", - "* 获得字典中指定 key 的 value\n", - "* 删除字典中的 key" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Tom': 95, 'Tracy': 92, 'Jerry': 90}\n", - "95\n" - ] - } - ], - "source": [ - "# 定义字典\n", - "# 访问字典中的 key-value\n", - "\n", - "d = {'Tom': 95, 'Jerry': 90, 'Tracy': 92}\n", - "print(d)\n", - "print(d['Tom'])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Hugo': 85, 'Tom': 95, 'Tracy': 92, 'Jerry': 90}\n" - ] - } - ], - "source": [ - "# 字典增加元素\n", - "\n", - "d['Hugo'] = 85\n", - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Hugo': 85, 'Tom': 97, 'Tracy': 92, 'Jerry': 90}\n", - "True\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'AAA'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# 获得字典 key 的 value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'AAA'\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 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m# 获得不存在的 key 的 value，默认值\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'AAA'" - ] - } - ], - "source": [ - "# 修改元素的值\n", - "d['Tom'] = 97\n", - "print(d)\n", - "\n", - "# 是否存在某个 key\n", - "print('Tom' in d)\n", - "\n", - "# 获得字典 key 的 value\n", - "print(d['AAA'])\n", - "\n", - "# 获得不存在的 key 的 value，默认值\n", - "print(d.get('Tommy',80))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Tracy': 92, 'Jerry': 90, 'Tom': 95}\n", - "{'Tracy': 92, 'Jerry': 90}\n" - ] - } - ], - "source": [ - "# 字典删除 key\n", - "\n", - "d = {'Tom': 95, 'Jerry': 90, 'Tracy': 92}\n", - "print(d)\n", - "d.pop('Tom')\n", - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'id_21': 63, 'id_27': 81, 'id_26': 78, 'id_1': 3, 'id_24': 72, 'id_22': 66, 'id_4': 12, 'id_18': 54, 'id_5': 15, 'id_3': 9, 'id_11': 33, 'id_13': 39, 'id_9': 27, 'id_29': 87, 'id_23': 69, 'id_8': 24, 'id_6': 18, 'id_28': 84, 'id_10': 30, 'id_15': 45, 'id_7': 21, 'id_2': 6, 'id_0': 0, 'id_19': 57, 'id_12': 36, 'id_14': 42, 'id_20': 60, 'id_17': 51, 'id_25': 75, 'id_16': 48}\n", - "30\n" - ] - } - ], - "source": [ - "# 获得字典长度\n", - "\n", - "d1 = {}\n", - "for i in range(30):\n", - " d1['id_'+str(i)] = i*3\n", - "print(d1)\n", - "\n", - "print(len(d1))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", - "\n", - "#### Tuple 元组\n", - "\n", - "Tuple 也是一种有序列表，在存储数据方面和 List 很相似\n", - "Tuple 一旦内容存储后，就不能修改；这样的好处是数据很安全\n", - "\n", - "应用范围：在我们需要使用 list 功能的时候，但是又不需要改变这个 list 的内容，用 Tuple 元组功能会很安全，不用担心程序中不小心修改了其内容。函数传递多个参数时候，就是采用 tuple，保证参数在被调用的过程中的安全。\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Tom', 'Jerry', 'Mary')\n" - ] - } - ], - "source": [ - "# 创建元组\n", - "\n", - "t = ('Tom', 'Jerry', 'Mary')\n", - "print(t)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Jerry\n" - ] - } - ], - "source": [ - "# 访问元组的元素\n", - "\n", - "print(t[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'tuple' object has no attribute 'append'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 元组创建后是不能修改的\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Someone'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" - ] - } - ], - "source": [ - "# 元组创建后是不能修改的\n", - "\n", - "t.append('Someone')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'tuple' object does not support item assignment", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\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'aaa'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" - ] - } - ], - "source": [ - "t[1] = 'aaa'" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(['A', 'B', 'C'], 100, 200)\n" - ] - } - ], - "source": [ - "# 创建复杂一点的元组\n", - "\n", - "l = ['A', 'B', 'C']\n", - "t =(l, 100, 200)\n", - "print(t)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(['A', 'B', 'C'], 100, 200)\n", - "['A', 'B', 'C', 'D']\n", - "(['A', 'B', 'C', 'D'], 100, 200)\n" - ] - } - ], - "source": [ - "# 变通的实现\"可变\"元组内容\n", - "print(t)\n", - "l.append('D')\n", - "print(l)\n", - "print(t) # tuple的每个元素，指向永远不变，但指向的元素本身是可变的" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "(1,)\n" - ] - } - ], - "source": [ - "# 创建只有1个元素的元组\n", - "\n", - "l = (1)\n", - "print(type(l)) # l成了一个整数，因为这里的括号有歧义，被认作数学计算里的小括号\n", - "\n", - "# 1个元素的元组必须加逗号来消除歧义\n", - "l = (1,)\n", - "print(type(l))\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "#### 随机数\n", - "\n", - "随机数这一概念在不同领域有着不同的含义，在密码学、通信领域有着非常重要的用途。\n", - "\n", - "Python 的随机数模块是 random，random 模块主要有以下函数，结合例子来看看。\n", - "\n", - "* random.choice()\n", - "* random.sample() \n", - "* random.random()\n", - "* random.uniform()\n", - "* random.randint()\n", - "* random.shuffle()\n", - "* random.sample" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "45\n" - ] - } - ], - "source": [ - "import random\n", - "\n", - "# random.choice从序列中获取一个随机元素。\n", - "# 其函数原型为：random.choice(sequence)。参数sequence表示一个有序类型。\n", - "\n", - "print(random.choice(range(1,100)))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "b\n" - ] - } - ], - "source": [ - "# 从一个列表中产生随机元素\n", - "\n", - "a = ['a', 'b', 'c']\n", - "print(random.choice(a))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[35, 98, 29, 20, 31, 73, 22, 50, 91, 77]\n" - ] - } - ], - "source": [ - "# 创建指定范围内指定个数的整数随机数\n", - "print(random.sample(range(1,100), 10))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "16\n" - ] - } - ], - "source": [ - "# random.randint(a, b)，用于生成一个指定范围内的整数。\n", - "# 其中参数a是下限，参数b是上限，生成的随机数n: a <= n <= b\n", - "\n", - "print(random.randint(1,100))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n" - ] - } - ], - "source": [ - "# random.randrange的函数原型为：random.randrange([start], stop[, step])，\n", - "# 从指定范围内，按指定基数递增的集合中 获取一个随机数。\n", - "\n", - "print(random.randrange(1,10))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5265744262403391\n" - ] - } - ], - "source": [ - "# random.random()用于生成一个0到1的随机符点数: 0 <= n < 1.0\n", - "\n", - "print(random.random())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "38.8422507789255\n", - "35.132838608034035\n" - ] - } - ], - "source": [ - "# random.uniform的函数原型为：random.uniform(a, b)，\n", - "# 用于生成一个指定范围内的随机符点数，两个参数其中一个是上限，一个是下限。\n", - "# 如果a > b，则生成的随机数n: a <= n <= b。如果 a b:\n", - " return a\n", - " else:\n", - " return b\n", - " \n", - "print(my_max(3,6))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 5, 30, 14, 25)\n", - "100\n" - ] - } - ], - "source": [ - "# 函数的不同参数形式--下节课再讲！\n", - "# *number理解为可以传入任意数目个参数，包括0个参数\n", - "\n", - "def my_max(*number):\n", - " print(number)\n", - " return 100\n", - "\n", - "print(my_max(1,5,30,14,25))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 5, 14, 25, 30]\n", - "30\n" - ] - } - ], - "source": [ - "def my_max(*number):\n", - " a_list = list(number)\n", - " a_list.sort()\n", - " a = a_list[-1]\n", - " print(a_list)\n", - " return a\n", - "\n", - "print(my_max(1,5,30,14,25))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 5, 14, 25, 30]\n", - "(30, 1)\n" - ] - } - ], - "source": [ - "# Python的函数返回多值其实就是返回一个tuple，但语法上可以省略括号，写起来更方便\n", - "\n", - "def my_max_min(*number):\n", - " a_list = list(number)\n", - " a_list.sort()\n", - " my_max = a_list[-1]\n", - " my_min = a_list[0]\n", - " print(a_list)\n", - " return my_max, my_min\n", - "\n", - "print(my_max_min(1,5,30,14,25)) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "* 写一个函数，输入一个数字，判断其是否是偶数，是偶数的话，返回 True，反之返回 False\n", - "* 写一个函数，输入一个英文单词，返回其中的元音字母的个数" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please input a number:3\n" - ] - }, - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = int(input('Please input a number:'))\n", - "\n", - "def oushu():\n", - " if int(a/2) == a/2:\n", - " return 2\n", - " #print('这是个偶数')\n", - " else:\n", - " return 3\n", - " #print('这是个奇数')\n", - " \n", - "result = oushu() \n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "plese input a word:elephant\n", - "3\n" - ] - } - ], - "source": [ - "a = input('plese input a word:')\n", - "b = ['a','e','i','o','u']\n", - "c = 0\n", - "\n", - "for i in list(a):\n", - " if i in b:\n", - " c += 1\n", - "print(c)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 scissors , 1 stone , 2 cloth , please input:0\n", - "human: 0 scissors\n", - "computer: 1 stone\n", - "computer win\n" - ] - } - ], - "source": [ - "# 优化的剪刀石头布程序\n", - "\n", - "import random\n", - "\n", - "FIRST = 0\n", - "SECOND = 1 \n", - "BOTH = 2 \n", - "\n", - "t1 = ('scissors', 'stone', 'cloth')\n", - "t2 = ('human win', 'computer win', 'draw')\n", - "\n", - "table = {'01':1, '02':0, '10':0, '12':1, '20':1, '21':0, '00':2, '11':2, '22':2}\n", - "\n", - "def which_win(i1, i2):\n", - " s = i1 + i2\n", - " return table.get(s)\n", - "\n", - "# 显示规则\n", - "for i, s in enumerate(t1):\n", - " print(i, s, ', ', end='')\n", - " \n", - "human = input('please input:')\n", - "h_index = int(human)\n", - "human_choice = t1[h_index]\n", - "\n", - "c_index = random.randint(0,2)\n", - "computer_choice = t1[c_index]\n", - "\n", - "print('human:',h_index,human_choice)\n", - "print('computer:',c_index, computer_choice)\n", - "print(t2[which_win(str(h_index), str(c_index))])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# 五局三胜的剪刀石头布程序\n", - "\n", - "import random\n", - "\n", - "FIRST = 0\n", - "SECOND = 1 \n", - "BOTH = 2 \n", - "\n", - "t1 = ('scissors', 'stone', 'cloth')\n", - "t2 = ('human win', 'computer win', 'draw')\n", - "\n", - "table = {'01':1, '02':2, '10':0, '12':1, '20':1, '21':0, '00':2, '11':2, '22':2}\n", - "\n", - "def which_win(i1, i2):\n", - " s = i1 + i2\n", - " return table.get(s)\n", - "\n", - "mark = True\n", - "computer_win = 0\n", - "human_win = 0\n", - "\n", - "while mark:\n", - "\n", - " # 显示规则\n", - " for i, s in enumerate(t1):\n", - " print(i, s, ', ', end='')\n", - " \n", - " human = input('please input:')\n", - "\n", - " c_index = random.randint(0,2)\n", - " computer = t1[c_index]\n", - "\n", - " print(c_index, computer)\n", - "\n", - " who_win = which_win(human, str(c_index))\n", - " \n", - " if who_win == 0:\n", - " human_win += 1\n", - " if who_win == 1:\n", - " computer_win += 1\n", - " \n", - " print(t2[which_win(human, str(c_index))])\n", - " print('human:computer ', human_win, ':', computer_win)\n", - " print()\n", - " \n", - " if (human_win == 3) or (computer_win == 3):\n", - " mark = False\n", - "\n", - "if human_win == 3:\n", - " print('final human win')\n", - "else:\n", - " print('final computer win')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now you can guess...\n", - "please input number:50\n", - "too small\n", - "please input number:60\n", - "too small\n", - "please input number:70\n", - "too small\n", - "please input number:80\n", - "bingo!\n" - ] - } - ], - "source": [ - "# 猜数，人猜\n", - "\n", - "import random\n", - "\n", - "a = random.randint(1,1000)\n", - "\n", - "print('Now you can guess...')\n", - "\n", - "guess_mark = True\n", - "\n", - "while guess_mark:\n", - " user_number =int(input('please input number:'))\n", - " if user_number > a:\n", - " print('too big')\n", - " if user_number < a:\n", - " print('too small')\n", - " if user_number == a:\n", - " print('bingo!')\n", - " guess_mark = False" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now you can guess...\n", - "please input number:20\n", - "too small\n", - "please input number:40\n", - "too big\n", - "please input number:30\n", - "too big\n", - "please input number:25\n", - "bingo!\n", - "your guess number list: [20, 40, 30, 25]\n", - "you try times: 4\n" - ] - } - ], - "source": [ - "# 猜数，人猜\n", - "# 记录猜数的过程\n", - "\n", - "import random\n", - "user_number_list = []\n", - "user_guess_count = 0\n", - "a = random.randint(1,100)\n", - "print('Now you can guess...')\n", - "guess_mark = True\n", - "\n", - "while guess_mark:\n", - " user_number =int(input('please input number:'))\n", - " \n", - " user_number_list.append(user_number)\n", - " user_guess_count += 1\n", - " \n", - " if user_number > a:\n", - " print('too big')\n", - " if user_number < a:\n", - " print('too small')\n", - " if user_number == a:\n", - " print('bingo!')\n", - " print('your guess number list:', user_number_list)\n", - " print('you try times:', user_guess_count)\n", - " guess_mark = False" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now you can guess...\n", - "please input number:99\n", - "too big\n", - "please input number:40\n", - "too small\n", - "please input number:50\n", - "too small\n", - "please input number:60\n", - "too small\n", - "please input number:70\n", - "too big\n", - "try harder, please input number:65\n", - "too small\n", - "try harder, please input number:67\n", - "too small\n", - "try harder, please input number:68\n", - "bingo!\n", - "your guess number list: [99, 40, 50, 60, 70, 65, 67, 68]\n", - "you try times: 8\n" - ] - } - ], - "source": [ - "# 猜数，人猜\n", - "# 增加判断次数，如果1-4次，以及以上显示不同提示语\n", - "\n", - "import random\n", - "\n", - "user_number_list = []\n", - "\n", - "user_guess_count = 0\n", - "\n", - "a = random.randint(1,100)\n", - "\n", - "print('Now you can guess...')\n", - "\n", - "guess_mark = True\n", - "\n", - "while guess_mark:\n", - " \n", - " if 0 <= user_guess_count <= 4:\n", - " user_number =int(input('please input number:'))\n", - " if 4 < user_guess_count <= 100:\n", - " user_number =int(input('try harder, please input number:'))\n", - " \n", - " user_number_list.append(user_number)\n", - " user_guess_count += 1\n", - " \n", - " if user_number > a:\n", - " print('too big')\n", - " if user_number < a:\n", - " print('too small')\n", - " if user_number == a:\n", - " print('bingo!')\n", - " print('your guess number list:', user_number_list)\n", - " print('you try times:', user_guess_count)\n", - " guess_mark = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_lesson_03_exercise.ipynb b/python_basic_notebook/python_basic_lesson_03_exercise.ipynb deleted file mode 100644 index b4fed99..0000000 --- a/python_basic_notebook/python_basic_lesson_03_exercise.ipynb +++ /dev/null @@ -1,171 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# Lesson 03\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目1： 给定用户名列表，输入开头几位字母，程序自动补充用户名" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "please input name : A\n", - "Do you want to find ['Adam', 'Alex', 'Amy']?\n" - ] - } - ], - "source": [ - "# 补充用户名字，利用切片的例子\n", - "name = ['Adam','Alex','Amy','Bob','Boom','Candy','Chris','David','Jason','Jasonstatham','Bill'];\n", - "i_name = input('please input name : ')\n", - "\n", - "wname = [];\n", - "for n in name:\n", - " if n[0:len(i_name)] == i_name:\n", - " wname.append(n);\n", - "\n", - "if len(wname) != 0:\n", - " print('Do you want to find %s?'%(wname))\n", - "else:\n", - " print('%s not find'%(i_name))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目2：编写一个函数，给你三个整数a,b,c, 判断能否以它们为三个边长构成三角形。若能，输出YES，否则输出NO" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no\n" - ] - } - ], - "source": [ - "def func1(a,b,c):\n", - " if (a+b)>c and (b+c)>a and (a+c)>b:\n", - " return 'yes'\n", - " else:\n", - " return 'no'\n", - "\n", - "\n", - "print(func1(1,2,3))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no\n" - ] - } - ], - "source": [ - "def func2(a,b,c):\n", - " if max(a,b,c)<(a+b+c)/2.0:\n", - " return 'yes'\n", - " else:\n", - " return 'no'\n", - "\n", - "print(func2(1,2,3))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## 题目3：编写一个猜数字的程序，人在0-1000范围内设定一个数字让电脑猜，每猜一次人反馈电脑是猜大了或小了，为电脑制定一定策略最快猜到正确数字" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "# 猜数，机器猜\n", - "\n", - "min = 0\n", - "max = 1000\n", - "\n", - "guess_ok_mark = False\n", - "\n", - "while not guess_ok_mark:\n", - "\n", - " cur_guess = int((min + max) / 2)\n", - " print('I guess:', cur_guess)\n", - " human_answer = input('Please tell me big or small:')\n", - " if human_answer == 'big':\n", - " max = cur_guess\n", - " if human_answer == 'small':\n", - " min = cur_guess\n", - " if human_answer == 'ok':\n", - " print('HAHAHA')\n", - " guess_ok_mark = True" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_lesson_04.ipynb b/python_basic_notebook/python_basic_lesson_04.ipynb deleted file mode 100644 index a750428..0000000 --- a/python_basic_notebook/python_basic_lesson_04.ipynb +++ /dev/null @@ -1,848 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 4\n", - "\n", - "Python Basic, Lesson 4, v1.0.1, 2016.12 by David.Yi \n", - "Python Basic, Lesson 4, v1.0.2, 2017.03 modified by Yimeng.Zhang\n", - "\n", - "### 上次内容要点\n", - "\n", - "* 列表的切片用法\n", - "* 函数用法\n", - " \n", - "### 本次内容要点\n", - "\n", - "* 日期库 datetime 用法介绍，datetime、time 等库的介绍，获得日期，字符串和日期转换，日期格式介绍，日期加减计算\n", - "* 函数不同参数形式介绍和举例 \n", - "* 匿名函数 lambda\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I guess: 500\n", - "Please tell me big or small:big\n", - "I guess: 250\n", - "Please tell me big or small:small\n", - "I guess: 375\n", - "Please tell me big or small:big\n", - "I guess: 312\n", - "Please tell me big or small:small\n", - "I guess: 343\n", - "Please tell me big or small:big\n", - "I guess: 327\n", - "Please tell me big or small:small\n", - "I guess: 335\n", - "Please tell me big or small:big\n", - "I guess: 331\n", - "Please tell me big or small:small\n", - "I guess: 333\n", - "Please tell me big or small:ok\n", - "HAHAHA\n" - ] - } - ], - "source": [ - "# 举例\n", - "# 猜数，机器猜\n", - "\n", - "min = 0\n", - "max = 1000\n", - "\n", - "guess_ok_mark = False\n", - "\n", - "while not guess_ok_mark:\n", - "\n", - " cur_guess = int((min + max) / 2)\n", - " print('I guess:', cur_guess)\n", - " human_answer = input('Please tell me big or small:')\n", - " if human_answer == 'big':\n", - " max = cur_guess\n", - " if human_answer == 'small':\n", - " min = cur_guess\n", - " if human_answer == 'ok':\n", - " print('HAHAHA')\n", - " guess_ok_mark = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 日期处理\n", - "\n", - "datetime 是Python处理日期和时间的标准库，用于获取日期和进行日期计算等。\n", - "\n", - "需要理解 timestamp，UTC标准时区，时差这些概念。\n", - "\n", - "Python 的日期相关的标准库比较多（略有杂乱），有 datetime, time, calendar。\n", - "\n", - "datetime 库包括 date 日期，time 时间， datetime 日期和时间，tzinfo 时区，timedelta 时间跨度计算 等主要对象。\n", - "\n", - "获取当前日期和时间：`now = datetime.now()`\n", - "\n", - "日期戳和日期的区别，日期戳更加精确，日期只是年月日\n", - "根据需要使用，大多数情况下只需要日期即可\n", - "\n", - "Time 对于时间的处理更加精确，用时间戳的表达方式\n", - "\n", - "时间戳定义为格林威治时间1970年01月01日00时00分00秒起至现在的总秒数，时间戳是惟一的\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-07-07 12:38:54.969064\n", - "2017-07-07\n", - "1499402334.9690642\n" - ] - } - ], - "source": [ - "# 显示今天日期\n", - "# 显示今天 datetime 和 time\n", - "\n", - "from datetime import datetime, date\n", - "import time\n", - "\n", - "print(datetime.now())\n", - "print(date.today())\n", - "print(time.time())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# 各种日期时间类型的数据类型\n", - "\n", - "print(type(datetime.now()))\n", - "print(type(date.today()))\n", - "print(type(time.time()))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n", - "1499402337.276191\n" - ] - } - ], - "source": [ - "# 连续运行显示时间戳，看看时间戳差了多少毫秒\n", - "\n", - "for i in range(10):\n", - " print(time.time())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time: 0.014995813369750977\n" - ] - } - ], - "source": [ - "# 用 time() 来计时，算10万次平方，看看哪台电脑速度快\n", - "import time\n", - "a = time.time()\n", - "for i in range(100000):\n", - " j = i * i \n", - "\n", - "b = time.time()\n", - " \n", - "print('time:', b-a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "#### 日期库-字符串和日期的转换\n", - "\n", - "字符串转化为日期：datetime.strptime()\n", - "\n", - "日期转换为字符串：datetime.strftime()\n", - "\n", - "日期字符串格式，举例\n", - "\n", - "```python\n", - "cday1 = datetime.now().strftime('%a, %b %d %H:%M')\n", - "cday2 = datetime.now().strftime('%A, %b %d %H:%M, %j')\n", - "cday3 = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - "```\n", - "\n", - "#### 日期库 – 日期格式说明\n", - "\n", - "* %a 英文星期的简写 (shorthand of week in English)\n", - "* %A 英文星期的完整拼写 (longhand of week in English)\n", - "* %b 英文月份的简写 (shorthand of month in English)\n", - "* %B 英文月份的完整拼写 (longhand of month in English)\n", - "* %c 本地当前的日期与时间 (current local date and time)\n", - "* %d 日期数, 1-31之间 (date, between 1-31))\n", - "* %H 小时数, 00-23之间 (hour, between 00-23))\n", - "* %I 小时数, 01-12之间 (hour, between 01-12)\n", - "* %m 月份, 01-12之间 (month, between 01-12)\n", - "* %M 分钟数, 01-59之间 (minute, 01-59)\n", - "* %j 本年从第1天开始计数到当天的天数 (total days from 1st day of this year till now)\n", - "* %w 星期数, 0-6之间(0是周日) (day of the week, between 0-6, 0=Sunday)\n", - "* %W 当天属于本年的第几周,周一作为一周的第一天进行计算 (week of the year, starting with Monday )\n", - "* %x 本地的当天日期 (local date)\n", - "* %X 本地的当前时间 (local time)\n", - "* %y 年份,0-99之间 (year, between 0-99)\n", - "* %Y 年份的完整拼写 (longhand of year)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-01-02 18:19:59\n", - "\n", - "\n", - "\n", - "2017, 19, 05 11 12:46\n", - "\n" - ] - } - ], - "source": [ - "# 字符串转化为日期\n", - "\n", - "dday = datetime.strptime('2017-1-2 18:19:59', '%Y-%m-%d %H:%M:%S')\n", - "print(dday)\n", - "print(type(dday))\n", - "print('\\n')\n", - "\n", - "# 日期转换为字符串\n", - "\n", - "day1 = datetime.now().strftime('%Y, %W, %m %d %H:%M')\n", - "print(day1)\n", - "print(type(day1))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sun, Mar 19 11:28\n", - "Sunday, Mar 19 11:28, 078\n", - "2017-03-19 11:28:19\n" - ] - } - ], - "source": [ - "# 日期转换为字符串，各种格式\n", - "\n", - "cday1 = datetime.now().strftime('%a, %b %d %H:%M')\n", - "cday2 = datetime.now().strftime('%A, %b %d %H:%M, %j')\n", - "cday3 = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - "print(cday1)\n", - "print(cday2)\n", - "print(cday3)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time.struct_time(tm_year=2017, tm_mon=5, tm_mday=11, tm_hour=12, tm_min=47, tm_sec=34, tm_wday=3, tm_yday=131, tm_isdst=0)\n", - "time.struct_time(tm_year=2017, tm_mon=5, tm_mday=11, tm_hour=12, tm_min=47, tm_sec=34, tm_wday=3, tm_yday=131, tm_isdst=0)\n", - "\n" - ] - } - ], - "source": [ - "# 格式化时间戳为本地的时间 converts number of seconds to local time\n", - "\n", - "print(time.localtime(time.time()))\n", - "print(time.localtime()) # If secs is not provided or None, the current time as returned by time() is used\n", - "\n", - "print(type(time.localtime(time.time())))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-03-19 11:02:10\n", - "\n" - ] - } - ], - "source": [ - "# 本地时间转换为字符串\n", - "\n", - "t = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime())\n", - "print(t)\n", - "print(type(t))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "#### 日期计算\n", - "\n", - "对日期和时间进行加减实际上就是把 datetime 往后或往前计算，得到新的 datetime，需要导入 datetime 的 timedelta 类。\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-05-11 12:48:38.858528\n", - "2017-05-11 02:48:38.858528\n" - ] - } - ], - "source": [ - "# 日期计算\n", - "# timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0)\n", - "\n", - "from datetime import timedelta\n", - "\n", - "now = datetime.now()\n", - "now1 = now + timedelta(hours=-10)\n", - "print(now)\n", - "print(now1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-03-22 12:47:45.586054\n", - "2017-03-22 23:07:35.586054\n" - ] - } - ], - "source": [ - "# 日期的各个变量的计算\n", - "\n", - "now = datetime.now()\n", - "now1 = now + timedelta(hours=10, minutes=20, seconds=-10)\n", - "print(now)\n", - "print(now1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### 函数不同参数形式\n", - "\n", - "#### 位置参数和默认参数\n", - "\n", - "位置参数：必须按照顺序准确传递，如果数量和顺序不对，就会可能造成程序错误；调用函数时候，如果写了参数名称，那么位置就不重要了。 \n", - "默认参数：在参数申明的时候跟一个用于默认值的赋值语句，如果调用函数的时候没有给出值，那么这个赋值语句就执行。\n", - "\n", - "注意：所有必须的参数要在默认参数之前\n", - "\n", - "默认参数的好处：\n", - "* 减少程序复杂度\n", - "* 降低程序错误可能性\n", - "* 更好的兼容性\n", - "\n", - "#### 可变长度的参数-元组和字典\n", - "\n", - "可变长度的参数，分为不提供关键字和提供关键字两种模式，分别为元组 tuple 和字典 dict。\n", - "\n", - "可变长度的参数，如果是提供关键字，就是字典 dict，需要提供 key – value。\n", - "\n", - "将字典作为参数传递的时候，可以直接传一个字典变量，也可以在参数列表中写明 key 和 value。\n", - "\n", - "\n", - "函数参数小结\n", - "\n", - "* 位置参数\n", - "* 默认参数\n", - "* 元组参数，一个星号\n", - "* 字典参数，两个星号，需要传递 key 和 value" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "110.0\n", - "120.0\n", - "130.0\n" - ] - } - ], - "source": [ - "# 函数默认参数\n", - "\n", - "def cal_0(money, rate=0.1):\n", - " return money + money * rate \n", - "\n", - "print(cal_0(100))\n", - "print(cal_0(100,0.2))\n", - "print(cal_0(rate=0.3,money=100))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "61000\n", - "62000\n", - "52000\n" - ] - } - ], - "source": [ - "# 函数默认参数\n", - "\n", - "def cal_1(money, bonus=1000, month=12，a=1,b=2):\n", - " i = money * month + bonus \n", - " return i\n", - "\n", - "print(cal_1(5000))\n", - "print(cal_1(5000, 2000))\n", - "print(cal_1(5000, 2000, 10))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# 画一个三角形 ，n=高度\n", - "'''\n", - " *\n", - " ***\n", - " *****\n", - " *******\n", - " *********\n", - " ***********\n", - " *************\n", - "\n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - " *\n", - " ***\n", - " *****\n" - ] - } - ], - "source": [ - "# 函数默认参数\n", - "\n", - "def draw_triangle(n=5):\n", - "\n", - " for i in range(n+1):\n", - " print(' '*(n-i),'*'*(2*i-1))\n", - " \n", - "draw_triangle(3)\n", - "#draw_triangle()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5\n" - ] - } - ], - "source": [ - "# 函数可变长度的参数 元组\n", - "\n", - "def cal_2(kind, *numbers):\n", - " if kind == 'avg':\n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " return n / len(numbers)\n", - "\n", - "print(cal_2('avg', 1,2,3,4))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5\n", - "10\n", - "10\n" - ] - } - ], - "source": [ - "# 函数可变长度的参数 元组\n", - "# 输入类别，输入要计算的数字\n", - "\n", - "def cal_3(kind, *numbers):\n", - " \n", - " if kind == 'avg':\n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " return n / len(numbers)\n", - " if kind == 'sum':\n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " return n\n", - " \n", - "print(cal_3('avg', 1,2,3,4))\n", - "print(cal_3('sum', 1,2,3,4))\n", - "\n", - "# 如果已经有一个list或者tuple，要调用一个可变参数怎么办?\n", - "# 在list或tuple前面加一个*号，把list或tuple的元素变成可变参数传进去：\n", - "print(cal_3('sum',*[1,2,3,4]))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5\n", - "10\n" - ] - } - ], - "source": [ - "# 函数可变长度的参数 元组\n", - "# 简化程序逻辑\n", - "\n", - "def cal_4(kind, *numbers):\n", - " \n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " \n", - " if kind == 'avg':\n", - " return n / len(numbers)\n", - " if kind == 'sum':\n", - " return n\n", - " \n", - "print(cal_4(kind='avg', 1,2,3,4))\n", - "print(cal_4('sum', 1,2,3,4))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10\n" - ] - } - ], - "source": [ - "# 传递元组和字典参数的最合适写法\n", - "\n", - "def cal_5(*numbers, **kw):\n", - " \n", - " # 判断是否有 kind 这个 key\n", - " if 'kind' in kw:\n", - " kind_value = kw.get('kind')\n", - " \n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " \n", - " if kind_value == 'avg':\n", - " return n / len(numbers)\n", - " if kind_value == 'sum':\n", - " return n\n", - " \n", - "print(cal_5(1,2,3,4,kind='avg',max='ignore'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "### 匿名函数\n", - "\n", - "Python 允许用 lambda 关键字创造匿名函数。\n", - "lambda 匿名函数不需要常规函数的 def 和 return 关键字，因为匿名函数代码较短，因此适用于一些简单处理运算的场景。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# 等价的函数一般写法和匿名函数写法\n", - "\n", - "def add(x, y):\n", - " return x + y\n", - "\n", - "lambda x, y: x + y" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = lambda x, y=2 : x + y \n", - "a(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a(3, 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "44\n" - ] - } - ], - "source": [ - "a = lambda x : x * x +40\n", - "\n", - "print(a(2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "思考一下\n", - "\n", - "* 计算一个正整数的因数\n", - "* 写一个寻找列表中最大数的函数（不用列表排序方法）" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_lesson_04_exercise.ipynb b/python_basic_notebook/python_basic_lesson_04_exercise.ipynb deleted file mode 100644 index 2ddf87e..0000000 --- a/python_basic_notebook/python_basic_lesson_04_exercise.ipynb +++ /dev/null @@ -1,297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Lesson 04" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目1： 计算倒计时，现在距离2018元旦距离现在还有多少天" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "177\n", - "\n" - ] - } - ], - "source": [ - "from datetime import datetime\n", - "\n", - "#日期格式转换\n", - "newyear = datetime.strptime('2018-01-01', '%Y-%m-%d')\n", - "#获取现在的时间\n", - "now = datetime.now()\n", - "#时间差\n", - "timedelta = newyear-now\n", - "print(timedelta.days)\n", - "print(type(newyear))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目2： 计算一个正整数的因数" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def func(number):\n", - " b = []\n", - " for i in range(1,number+1):\n", - " if number % i == 0:\n", - " print(i)\n", - " b.append(i)\n", - " return b\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "5\n", - "10\n", - "[1, 2, 5, 10]\n" - ] - } - ], - "source": [ - "print(func(10))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目3： 写一个寻找列表中最大数的函数（不用列表排序方法）" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n", - "-3\n" - ] - } - ], - "source": [ - "# 找到最大数 #1\n", - "\n", - "def find_max(l):\n", - " \n", - " # max = float('-inf') 表示负无穷大\n", - " max = float('-inf')\n", - " for x in l:\n", - " if x > max:\n", - " max = x\n", - " return max\n", - "\n", - "print(find_max([-20,1,6,7,20,5]))\n", - "print(find_max([-20,-3,-6,-7,-5]))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "# 找到最大数 #2\n", - "# 使用递归\n", - "\n", - "def find_max(l):\n", - " if len(l) == 1:\n", - " return l[0]\n", - " v1 = l[0]\n", - " v2 = find_max(l[1:])\n", - " if v1 > v2:\n", - " return v1\n", - " else:\n", - " return v2\n", - "\n", - "print(find_max([1,6,7,20,5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目4：函数可变参数练习1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.0\n", - "2.5\n", - "10\n" - ] - } - ], - "source": [ - "# kind 中 增加 max key， \n", - "# max = ingnore， 则忽略最大值\n", - "def cal_6(*numbers, **kind):\n", - " \n", - " if 'kind' in kind:\n", - " kind_value = kind.get('kind')\n", - " \n", - " if 'max' in kind:\n", - " if kind.get('max') == 'ignore':\n", - " numbers = list(numbers)\n", - " numbers.remove(max(numbers))\n", - " \n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " \n", - " if kind_value == 'avg':\n", - " return n / len(numbers)\n", - " if kind_value == 'sum':\n", - " return n\n", - " \n", - "print(cal_6(1,2,3,4, kind='avg', max='ignore',min='ignore'))\n", - "print(cal_6(1,2,3,4, kind='avg'))\n", - "print(cal_6(1,2,3,4, kind='sum'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 题目5：函数可变参数练习2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.75\n", - "2.5\n", - "10\n" - ] - } - ], - "source": [ - "# kind 中 增加 min key，\n", - "# min key = double, 则最小值计算两次\n", - "\n", - "def cal_7(*numbers, **kw):\n", - " \n", - " numbers = list(numbers)\n", - " \n", - " if 'kind' in kw:\n", - " kind_value = kw.get('kind')\n", - " \n", - " if 'max' in kw:\n", - " if kw.get('max') == 'ignore':\n", - " numbers.remove(max(numbers))\n", - "\n", - " if 'min' in kw:\n", - " if kw.get('min') == 'double':\n", - " numbers.append(min(numbers))\n", - " \n", - " n = 0\n", - " for i in numbers:\n", - " n = n + i\n", - " \n", - " if kind_value == 'avg':\n", - " return n / len(numbers)\n", - " if kind_value == 'sum':\n", - " return n\n", - " \n", - "print(cal_7(1,2,3,4, kind='avg', max='ignore', min='double'))\n", - "print(cal_7(1,2,3,4, kind='avg'))\n", - "print(cal_7(1,2,3,4, kind='sum'))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_lesson_05.ipynb b/python_basic_notebook/python_basic_lesson_05.ipynb deleted file mode 100644 index 9e473ff..0000000 --- a/python_basic_notebook/python_basic_lesson_05.ipynb +++ /dev/null @@ -1,1568 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 5\n", - "\n", - "Python Basic, Lesson 5, v1.0.1, 2016.12 by David.Yi \n", - "Python Basic, Lesson 5, v1.0.2, 2017.03 modified by Yimeng.Zhang \n", - "\n", - "\n", - "### 上次内容要点\n", - "\n", - "* 日期库 datetime 用法介绍，datetime、time 等库的介绍，获得日期，字符串和日期转换，日期格式介绍，日期加减计算\n", - "* 函数不同参数形式介绍和举例 \n", - "* 匿名函数 lambda\n", - " \n", - "### 本次内容要点\n", - "\n", - "* 文件和目录操作之一：文件和目录操作\n", - "* 文件和目录操作之二：读写文本文件\n", - "* 搜索硬盘上指定路径指定类型的文件" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter a number: 10\n", - "The factors of 10 are:\n", - "1\n", - "2\n", - "5\n", - "10\n", - "0.0\n" - ] - } - ], - "source": [ - "# 寻找数字的正因数\n", - "\n", - "import time\n", - "\n", - "def print_factors(x):\n", - " print(\"The factors of\",x,\"are:\")\n", - " for i in range(1, x + 1):\n", - " if x % i == 0:\n", - " print(i)\n", - "\n", - "# take input from the user\n", - "num = int(input(\"Enter a number: \"))\n", - "\n", - "t1 = time.time()\n", - "print_factors(num)\n", - "t2 = time.time()\n", - "print(t2-t1)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n", - "0\n" - ] - } - ], - "source": [ - "# 找到最大数 #1\n", - "\n", - "def find_max(l):\n", - " \n", - " # max = float('-inf') 表示负无穷大\n", - " max = float('-inf')\n", - " for x in l:\n", - " if x > max:\n", - " max = x\n", - " return max\n", - "\n", - "print(find_max([-20,1,6,7,20,5]))\n", - "print(find_max([-20,-3,-6,-7,-5]))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "# 找到最大数 #2\n", - "# 使用递归\n", - "\n", - "def find_max(l):\n", - " if len(l) == 1:\n", - " return l[0]\n", - " v1 = l[0]\n", - " v2 = find_max(l[1:])\n", - " if v1 > v2:\n", - " return v1\n", - " else:\n", - " return v2\n", - "\n", - "print(find_max([1,6,7,20,5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## 文件和目录操作之一\n", - "\n", - "Python 的 os 库：有很多和操作系统相关的功能，还有很多和文件、路径和执行系统命令相关的函数。\n", - "\n", - "##### os 库常用函数\n", - "\n", - "* os.sep 可以取代操作系统特定的路径分割符\n", - "* os.name 字符串指示你正在使用的平台。比如对于Windows，它是'nt'，而对于Linux/Unix用户，它是'posix' \n", - "* os.getcwd() 函数得到当前工作目录，即当前Python脚本工作的目录路径\n", - "* os.chdir(dirname) 改变工作目录到dirname\n", - "* os.getenv() 用来读取环境变量\n", - "* os.putenv() 用来设置环境变量 \n", - "* os.listdir() 返回指定目录下的所有文件和目录名 \n", - "* os.remove() 删除一个文件\n", - "* os.system() 运行shell命令\n", - "* os.linesep 字符串给出当前平台使用的行终止符。例如，Windows使用'/r/n'，Mac使用'\\n'。\n", - "* os.mkdir() 建立路径\n", - "* os.rmdir() 删除路径\n", - "\n", - ">注意：我们在 /Users/用户名 路径下建立一个用来测试的文件 test.txt\n", - "\n", - ">不同操作系统在路径和文件处理上有一定差异，这里的举例是基于 macOS，在 windows 下面会有较大差异，但是用法是一致的\n", - "\n", - "##### 关于文件系统的延展阅读\n", - "* 文件系统介绍 https://zh.wikipedia.org/wiki/%E6%96%87%E4%BB%B6%E7%B3%BB%E7%BB%9F\n", - "* windows 文件系统 FAT、FAT32、NTFS 介绍 https://support.microsoft.com/zh-cn/kb/100108\n", - "* linux 文件系统介绍 http://cn.linux.vbird.org/linux_basic/0230filesystem.php" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\\n", - "nt\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "# 操作系统路径分隔符\n", - "print(os.sep)\n", - "\n", - "# 操作系统平台名称\n", - "print(os.name)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 获取当前路径\n", - "os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 切换路径\n", - "\n", - "# os.chdir('/Users/david.yi')\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class')\n", - "os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['.ipynb_checkpoints',\n", - " 'imgs',\n", - " 'list_dump.txt',\n", - " 'python_basic_lesson_01.ipynb',\n", - " 'python_basic_lesson_02.ipynb',\n", - " 'python_basic_lesson_03.ipynb',\n", - " 'python_basic_lesson_04.ipynb',\n", - " 'python_basic_lesson_05.ipynb',\n", - " 'python_basic_lesson_06.ipynb',\n", - " 'python_basic_outline.md',\n", - " 'python_basic_outline.pdf',\n", - " 'test.txt',\n", - " 'test2.txt',\n", - " 'tmp.ipynb',\n", - " 'Untitled.ipynb',\n", - " '~$讲义.docx',\n", - " '讲义.docx']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回指定的文件夹包含的文件或文件夹的名字的列表。这个列表以字母顺序。 \n", - "# 不包括 '.' 和'..' 即使它在文件夹中。\n", - "\n", - "import os\n", - "\n", - "# windows\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", - "os.listdir()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# 注意返回的数据类型\n", - "print(type(os.listdir()))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "22\n" - ] - } - ], - "source": [ - "# 计算目录下有多少文件\n", - "a = os.listdir()\n", - "print(len(a))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "python_basic_lesson_01.ipynb\n", - "python_basic_lesson_02.ipynb\n", - "python_basic_lesson_03.ipynb\n", - "python_basic_lesson_04.ipynb\n", - "python_basic_lesson_05.ipynb\n", - "python_basic_lesson_06.ipynb\n", - "python_basic_outline.md\n", - "python_basic_outline.pdf\n" - ] - } - ], - "source": [ - "# 可以指定路径参数，来列出该目录下所有文件\n", - "\n", - "# list_a = os.listdir('/Users/david.yi')\n", - "list_a = os.listdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", - "\n", - "# 可以判断各类情况，比如第一个是 p 字母\n", - "for i in list_a:\n", - " if i[0:1] == 'p':\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\r\\n'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 操作系统换行符\n", - "# 在一些文本文件处理中有用\n", - "\n", - "os.linesep" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ok\n" - ] - } - ], - "source": [ - "# 建立路径\n", - "\n", - "# os.chdir('/Users/david.yi')\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", - "os.mkdir('test')\n", - "print('ok')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "##### os.path 常用函数之一\n", - "\n", - "* os.path.isdir() 检查给出的路径是否是一个目录\n", - "* os.path.isfile() 检查给出的路径是否一个文件\n", - "* os.path.exists() 检查给出的路径或者文件是否存在\n", - "* os.path.getsize() 获得路径或者文件的大小\n", - "* os.path.getatime() 返回所指向的文件或者目录的最后存取时间\n", - "* os.path.getmtime() 返回所指向的文件或者目录的最后修改时间 " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\n", - "True\n", - "True\n", - "False\n" - ] - } - ], - "source": [ - "# 检查给出的路径是否是一个存在的目录，存在\n", - "\n", - "# os.chdir('/Users/david.yi/Documents/dev/python_study/python_basic')\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", - "s_dir = os.getcwd()\n", - "print(s_dir)\n", - "print(os.path.isdir(s_dir))\n", - "print(os.path.isdir('C:\\\\Users'))\n", - "print(os.path.isdir('C:\\\\Users222'))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 检查给出的路径是否是一个存在的目录，不存在\n", - "os.path.isdir(s_dir + 's')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 文件不是路径，所以返回 False\n", - "os.path.isdir(s_dir + 'test.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test.txt\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 检查给出的路径是否一个文件，存在\n", - "\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "print(s_file)\n", - "os.path.isfile(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 检查给出的路径是否一个文件，不存在\n", - "\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test222.txt')\n", - "os.path.isfile(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 路径不是文件，所以返回 False\n", - "\n", - "s_dir = os.getcwd()\n", - "os.path.isfile(s_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "True\n" - ] - } - ], - "source": [ - "# 对路径和文件都通用的检查方式\n", - "\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "\n", - "print(os.path.exists(s_dir))\n", - "print(os.path.exists(s_file))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "17" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 获得路径或者文件的大小\n", - "\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "\n", - "os.path.getsize(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4096" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 获得路径或者文件的大小\n", - "\n", - "os.path.getsize(s_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1490680273.8670404" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回所指向的文件或者目录的最后存取时间\n", - "\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "\n", - "os.path.getatime(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2017-03-28 15:01:40\n" - ] - } - ], - "source": [ - "# 返回所指向的文件或者目录的最后存取时间\n", - "import os\n", - "import time\n", - "\n", - "# 将日期格式化\n", - "dt = time.localtime(os.path.getatime(s_dir))\n", - "# print(dt)\n", - "print(time.strftime('%Y-%m-%d %H:%M:%S', dt))" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1490681200.9609938" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回所指向的文件或者目录的最后修改时间\n", - "\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "\n", - "os.path.getmtime(s_file) " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Tue Mar 28 13:51:13 2017'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回所指向的文件或者目录的最后修改时间\n", - "# 使用 time.ctime() 方法来格式化日期\n", - "\n", - "import time, os\n", - "\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "\n", - "time.ctime(os.path.getmtime(s_file))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "##### os.path 常用函数之二\n", - "\n", - "* os.path.split() 返回一个路径的目录名和文件名\n", - "* os.path.abspath() 返回规范化的绝对路径\n", - "* os.path.isabs() 如果输入是绝对路径，返回True\n", - "* os.path.split() 将路径分割成目录和文件名的二元素元组\n", - "* os.path.splitdrive() 返回（drivername，fpath）元组 \n", - "* os.path.dirname() 返回路径的目录，其实就是 os.path.split(path)的第一个元素\n", - "* os.path.basename() 返回路径最后的文件名，其实就是 os.path.split(path)的第二个元素\n", - "* os.path.splitext() 分离文件名与扩展名，返回(fname,fextension)元组 \n", - "* os.path.join() 将多个路径组合后返回，第一个绝对路径之前的参数将被忽略\n", - "* os.path.commonprefix(list) 返回list中，所有路径共有的最长的路径" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test.txt\n" - ] - }, - { - "data": { - "text/plain": [ - "('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic', 'test.txt')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回一个路径的目录名和文件名\n", - "\n", - "# os.chdir('/Users/david.yi/Documents/dev/python_study/python_basic')\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')\n", - "s_dir = os.getcwd()\n", - "s_file = os.path.join(s_dir, 'test.txt')\n", - "print(s_file)\n", - "os.path.split(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\tt1211.txt'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回规范化的绝对路径\n", - "# 会自动补齐完整路径\n", - "\n", - "os.path.abspath('tt1211.txt') " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n", - "True\n" - ] - } - ], - "source": [ - "# 如果输入是绝对路径，返回True\n", - "\n", - "print(os.path.isabs('test.txt'))\n", - "print(os.path.isabs('/Users/yijun/test.txt'))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic', 'test.txt')\n", - "\n" - ] - } - ], - "source": [ - "# 将路径分割成目录和文件名的二元素元组\n", - "\n", - "s = os.path.split(s_file)\n", - "print(s)\n", - "print(type(s))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/yijun'" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回路径的目录，其实就是 os.path.split(path)的第一个元素\n", - "\n", - "os.path.dirname('/Users/yijun/test.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'test.txt'" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回路径最后的文件名，其实就是 os.path.split(path)的第二个元素\n", - "\n", - "os.path.basename(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\test',\n", - " '.txt')" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 分离文件名与扩展名，返回(fname,fextension)元组 \n", - "\n", - "os.path.splitext(s_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\test.txt'" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 将多个路径组合后返回，第一个绝对路径之前的参数将被忽略\n", - "\n", - "# os.path.join('/Users/yijun', 'test.txt')\n", - "os.path.join('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic', 'test.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/yijun/'" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 返回list中，所有路径共有的最长的路径\n", - "\n", - "l = ['/Users/yijun/test.txt', '/Users/yijun/test/aaa.txt', '/Users/yijun/bbb.txt']\n", - "os.path.commonprefix(l)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\.ipynb_checkpoints\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\.ipynb_checkpoints\\python_basic_lesson_01-checkpoint.ipynb\n", - 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"C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\.ipynb_checkpoints\\Untitled1-checkpoint.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\1.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\anaconda.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\jupyter.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\pycharm.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install.jpeg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_1.jpeg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_2.jpeg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_3.jpeg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_4.jpeg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_5.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_use.jpg\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\lesson1.txt\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\list_dump.txt\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python6.txt\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_01.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_02.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_03.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_04.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_05.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_06.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_outline.md\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_outline.pdf\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test.txt\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test2.txt\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\tmp.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\Untitled.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\Untitled1.ipynb\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\~$讲义.docx\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\讲义.docx\n" - ] - } - ], - "source": [ - "# 遍历一个目录下的所有文件\n", - "\n", - "import os \n", - "\n", - "def list_dir(root_dir): \n", - " for lists in os.listdir(root_dir): \n", - " path = os.path.join(root_dir, lists) \n", - " print(path)\n", - " if os.path.isdir(path): \n", - " list_dir(path) \n", - "\n", - "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", - "# list_dir('/Users/david.yi/Documents/dev/dig/doc')\n", - "list_dir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs 4096\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\anaconda.png 834508\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\jupyter.png 105782\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\pycharm.png 192052\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install.jpeg 21799\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_1.jpeg 28779\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_2.jpeg 22304\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_3.jpeg 31846\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_4.jpeg 15478\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_5.png 33686\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_use.jpg 102903\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\list_dump.txt 52\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_01.ipynb 17697\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_02.ipynb 30474\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_03.ipynb 21840\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_04.ipynb 25646\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_05.ipynb 41047\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_lesson_06.ipynb 24430\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_outline.md 1271\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\python_basic_outline.pdf 63096\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test.txt 0\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\tmp.ipynb 1275\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\Untitled.ipynb 2468\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\~$讲义.docx 162\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\讲义.docx 47848\n" - ] - } - ], - "source": [ - "# 遍历一个目录下的所有文件\n", - "# 显示文件的字节数，用 getsize() \n", - "\n", - "import os \n", - "\n", - "def list_dir(root_dir): \n", - " for lists in os.listdir(root_dir): \n", - " path = os.path.join(root_dir, lists) \n", - " if lists[0:1] != '.': \n", - " filesize = os.path.getsize(path)\n", - " print(path, ' ', filesize)\n", - " if os.path.isdir(path): \n", - " list_dir(path) \n", - "\n", - "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", - "#list_dir('/Users/david.yi/Documents/dev/dig/doc')\n", - "list_dir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# 遍历一个目录下的所有文件\n", - "# 过滤 . 开头的文件，一般是系统文件\n", - "# 显示文件的字节数\n", - "# 显示指定后缀的文件，引入 endswith 用法\n", - "\n", - "import os \n", - "\n", - "def list_dir(root_dir): \n", - " for lists in os.listdir(root_dir): \n", - " path = os.path.join(root_dir, lists) \n", - " if lists[0:1] != '.' and lists.endswith('.txt'):\n", - " filesize = os.path.getsize(path)\n", - " print(path, ' ', filesize)\n", - " if os.path.isdir(path): \n", - " list_dir(path) \n", - "\n", - "# 注意不要挑选目录下过多文件的，否则会耗费电脑资源\n", - "\n", - "# list_dir('/Users/david.yi/Documents/dev/dig/n_query')\n", - "list_dir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_use.jpg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install.jpeg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install_1.jpeg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install_2.jpeg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install_3.jpeg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install_4.jpeg', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\anaconda.png', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\jupyter.png', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\pycharm.png', 'C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\imgs\\\\python_install_5.png']\n" - ] - } - ], - "source": [ - "# 写一个可以搜索硬盘上指定路径指定类型的文件\n", - "# os.walk() 返回一个三元tupple(root, dirnames, filenames)\n", - "# 第一个为起始路径，String\n", - "# 第二个为起始路径下的文件夹, List\n", - "# 第三个是起始路径下的文件. List\n", - "\n", - "import fnmatch\n", - "import os\n", - "\n", - "images = ['*.jpg', '*.jpeg', '*.png', '*.tif', '*.tiff']\n", - "matches = []\n", - "\n", - "# for root, dirnames, filenames in os.walk('/Users/david.yi/Documents/dev/'):\n", - "for root, dirnames, filenames in os.walk('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic'):\n", - " for extensions in images:\n", - " for filename in fnmatch.filter(filenames, extensions):\n", - " matches.append(os.path.join(root, filename))\n", - "\n", - "print(matches)\n", - "# import os\n", - "# for root, dirnames, filenames in os.walk('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic'):\n", - "# print(filenames)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## 文件和目录操作之二\n", - "\n", - "读写文件是最常见的IO操作。Python内置了读写文件的函数，用法和C是兼容的。\n", - "\n", - "读写文件前，我们先必须了解一下，在磁盘上读写文件的功能都是由操作系统提供的，现代操作系统不允许普通的程序直接操作磁盘，所以，读写文件就是请求操作系统打开一个文件对象，然后，通过操作系统提供的接口从这个文件对象中读取数据，或者把数据写入这个文件对象。\n", - "\n", - "##### 读文件\n", - "\n", - "函数 `open()` 返回 文件对象，通常的用法需要两个参数：`open(filename, mode)`。分别是文件名和打开模式\n", - "\n", - "在做下面的例子前，我们要创建一个 `test.txt` 文件，并且保证其中的内容是如下样式，包含三行内容：\n", - "\n", - "> hello\n", - "\n", - "> hi\n", - "\n", - "> byebye\n", - "\n", - "文件保存在可以访问的目录，我这里就保存在和 notebook 同样的目录\n", - "\n", - "> 使用 jupyter 可以直接新建 Text File，来完成建立和编辑文本文件" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test.txt\n", - "hello\n", - "hi\n", - "byebye\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "# 获得当前路径\n", - "# os.chdir('/Users/david.yi/Documents/dev/python_study/python_basic')\n", - "s_dir = os.getcwd()\n", - "\n", - "print(s_dir)\n", - "\n", - "# 拼接完整文件名\n", - "filename = os.path.join(s_dir, 'test.txt')\n", - "\n", - "print(filename)\n", - "\n", - "try:\n", - " # 打开文件\n", - " f = open(filename, 'r')\n", - " print(f.read())\n", - "finally:\n", - " if f:\n", - " f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello\n", - "hi\n", - "byebye\n" - ] - } - ], - "source": [ - "# 简化调用方式\n", - "# 省却了 try...finally，会有 with 来自动控制\n", - "\n", - "with open(filename, 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "['hello\\n', 'hi\\n', 'byebye']\n" - ] - } - ], - "source": [ - "with open(filename, 'r') as f:\n", - " lines = f.readlines()\n", - "\n", - "print(type(lines))\n", - "print(lines)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello\n", - "\n", - "hi\n", - "\n", - "byebye\n" - ] - } - ], - "source": [ - "for i in lines:\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello\n", - "\n", - "hi\n", - "\n", - "byebye\n" - ] - } - ], - "source": [ - "# 更简单的按行读取文件内容方法\n", - "with open(filename, 'r') as f:\n", - " for eachline in f:\n", - " print(eachline)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "##### 写文件\n", - "\n", - "写文件和读文件是一样的，唯一区别是调用 `open()` 函数时，传入标识符 `'w'` 或者 `'wb'` 表示写文本文件或写二进制文件。\n", - "\n", - "r 以读方式打开\n", - "w 以写方式打开\n", - "a 以追加模式打开（必要时候创建新文件）" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\test2.txt\n", - "Hello, World!\n", - "\n", - "Hello, Shanghai!\n", - "\n", - "Hello, CHINA!\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# 写文件\n", - "import os\n", - "\n", - "# 获得当前路径\n", - "# os.chdir('/Users/david.yi/Documents/dev/python_study/python_basic')\n", - "s_dir = os.getcwd()\n", - "\n", - "# 拼接完整文件名\n", - "filename= os.path.join(s_dir, 'test2.txt')\n", - "print(filename)\n", - "\n", - "# 换行符\n", - "br = os.linesep\n", - "\n", - "# 写文件\n", - "with open(filename, 'w') as f:\n", - "# f.write('Hello, World!' + br)\n", - "# f.write('Hello, Shanghai!' + br)\n", - "# f.write('Hello, CHINA!' + br)\n", - " f.close()\n", - " \n", - "with open(filename, 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "##### 操作系统和文件系统差异处理\n", - "\n", - "`linesep` 文件中分隔行的字符串\n", - "`path.sep` 分割文件路径名的字符串\n", - "`curdir` 当前工作目录的字符串\n", - "`pardir` 当前工作目录的父目录字符串" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "##### 使用 glob 包查找文件\n", - "\n", - "glob 是 python 自己带的一个文件操作相关模块，很简洁，用它可以查找符合自己目的的文件，就类似于Windows下的文件搜索，而且也支持通配符: `*,?,[]` 这三个通配符，\\* 代表0个或多个字符，? 代表一个字符，[] 匹配指定范围内的字符，如[0-9]匹配数字。\n", - "\n", - "glob 的主要方法也叫 glob，该方法返回所有匹配的文件路径列表，该方法需要一个参数用来指定匹配的路径字符串" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\*\\*.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\anaconda.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\jupyter.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\pycharm.png\n", - "C:\\Users\\yimeng.zhang\\Desktop\\Class\\python基础\\python_basic\\imgs\\python_install_5.png\n" - ] - } - ], - "source": [ - "# 使用 glob 来遍历指定路径下的指定类型文件\n", - "import os, glob\n", - "\n", - "# 获得当前路径\n", - "# os.chdir('/Users/david.yi/Documents/dev/')\n", - "s_dir = os.getcwd()\n", - "\n", - "s_find = os.path.join(s_dir, '*', '*.png' )\n", - "print(s_find)\n", - "\n", - "# list_a = glob.glob('/Users/david.yi/Documents/dev/*/*.py')\n", - "list_a = glob.glob('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\Class\\\\python基础\\\\python_basic\\\\*\\\\*.png')\n", - "for i in list_a:\n", - " print(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", - "\n", - "思考一下：\n", - "\n", - "* 修改上面搜索制定类型文件的程序，当搜索到10个文件的时候即停止，并显示\n", - "* 想一下，如果要搜索指定目录下的 .py 文件，如果py文件中有指定的单词，则显示，并继续搜索" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_lesson_05_exercise.ipynb b/python_basic_notebook/python_basic_lesson_05_exercise.ipynb deleted file mode 100644 index 912247e..0000000 --- a/python_basic_notebook/python_basic_lesson_05_exercise.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "** 题目1：文件和目录操作练习**\n", - "\n", - "1.在桌面上新建一个名为'lesson_5_test'的文件夹；\n", - "\n", - "2.现有如下文件filename_list = ['task1_20170701.txt', 'task1_20170601.txt', 'task1_20170501.txt','task2_20170701.txt', 'task2_20170601.txt', 'task2_20170501.txt']，在对应的task1、task2文件夹下新建列表中的文件；\n", - "\n", - "3.搜索'lesson_5_test'目录下七月份的文件，并在文件中写入‘hello'。" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop')\n", - "os.mkdir('lesson_5_test')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "os.chdir('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\lesson_5_test')\n", - "filename_list = ['task1_20170701.txt', 'task1_20170601.txt', 'task1_20170501.txt','task2_20170701.txt', 'task2_20170601.txt', 'task2_20170501.txt']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "task1\n", - "20170701.txt\n", - "task1\n", - "20170601.txt\n", - "task1\n", - "20170501.txt\n", - "task2\n", - "20170701.txt\n", - "task2\n", - "20170601.txt\n", - "task2\n", - "20170501.txt\n" - ] - } - ], - "source": [ - "for i in filename_list:\n", - " dir_name = i.split('_')[0]\n", - " file_name = i.split('_')[1]\n", - " print(dir_name)\n", - " print(file_name)\n", - " if os.path.exists(dir_name):\n", - " f = open(os.path.join(dir_name,i), 'w')\n", - " f.close() \n", - " else:\n", - " os.mkdir(dir_name)\n", - " f = open(os.path.join(dir_name,i), 'w')\n", - " f.close() " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import glob\n", - "ha = glob.glob('C:\\\\Users\\\\yimeng.zhang\\\\Desktop\\\\lesson_5_test\\\\*\\\\*07*.txt')\n", - "for dir in ha:\n", - " with open(dir,'w') as txt:\n", - " txt.write('hello')\n", - " txt.close" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/python_basic_notebook/python_basic_outline.md b/python_basic_notebook/python_basic_outline.md deleted file mode 100644 index eb493ae..0000000 --- a/python_basic_notebook/python_basic_outline.md +++ /dev/null @@ -1,47 +0,0 @@ -## python 初级学习大纲 - -最后更新日期：2017.5.26 - -#### Lesson 1 -* Python 简介 -* 准备工作 -* 基本变量概念 -* print() 和 input() 用法 -* Python 开发环境介绍 - * Python IDLE 用法 - * Anaconda 介绍 - * Jupyter 和 notebook介绍 - * Pycharm 用法介绍 - -#### Lesson 2 -* 循环语句 for 和 range() 用法 -* 常用数据类型 - * list 用法 - * dict 用法 - * tuple 用法 -* 随机数介绍 - -#### Lesson 3 -* list 的切片用法 -* 函数用法 - -#### Lesson 4 -* 日期库 datetime 用法介绍，datetime、time 等库的介绍，获得日期，字符串和日期转换，日期格式介绍，日期加减计算 -* 函数不同参数形式介绍和举例 -* 匿名函数 lambda - -#### Lesson 5 -* 文件和目录操作之一：文件和目录操作 -* 文件和目录操作之二：读写文本文件 -* 搜索硬盘上指定路径指定类型的文件 - -#### Lesson 6 -* 集合库 collections 简介 - * namedtuple: 生成可以使用名字来访问元素内容的tuple子类 - * deque: 双端队列，可以快速的从另外一侧追加和推出对象 - * defaultdict: 带有默认值的字典 - * OrderedDict: 有序字典 - * Counter: 计数器 -* 错误处理介绍 -* PEP 8 规定以及 python 编程基本规范 -* 单元测试基础 diff --git a/python_basic_notebook/python_basic_outline.pdf b/python_basic_notebook/python_basic_outline.pdf deleted file mode 100644 index c218981..0000000 Binary files a/python_basic_notebook/python_basic_outline.pdf and /dev/null differ diff --git a/python_basic_notebook/test2.txt b/python_basic_notebook/test2.txt deleted file mode 100644 index a4008ba..0000000 --- a/python_basic_notebook/test2.txt +++ /dev/null @@ -1,3 +0,0 @@ -Hello, World! -Hello, Shanghai! -Hello, CHINA! diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache.py" deleted file mode 100644 index ba540c1..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache.py" +++ /dev/null @@ -1,134 +0,0 @@ -import pickle -import time -import inspect -import base64 -import hashlib - - -debug = False - - -def log(s): - if debug: - print(s) - - -caches = dict() -updated_caches = [] - - -def get_cache(fname): - if fname in caches: - return caches[fname] - try: - with open(fname, "rb") as f: - c = pickle.load(f) - except: - c = dict() - caches[fname] = c - return c - - -def write_to_cache(fname, obj): - updated_caches.append(fname) - caches[fname] = obj - - -def cleanup(): - for fname in updated_caches: - with open(fname, "wb") as f: - pickle.dump(caches[fname], f) - - -def get_fn_hash(f): - return base64.b64encode(hashlib.sha1(inspect.getsource(f).encode("utf-8")).digest()) - - -NONE = 0 -ARGS = 1 -KWARGS = 2 - - -def cache(fname=".cache.pkl", timeout=-1, key=ARGS | KWARGS): - - def impl(fn): - load_t = time.time() - c = get_cache(fname) - log("loaded cache in {:.2f}s".format(time.time() - load_t)) - - def d(*args, **kwargs): - log("checking cache on {}".format(fn.__name__)) - if key == ARGS | KWARGS: - k = pickle.dumps((fn.__name__, args, kwargs)) - if key == ARGS: - k = pickle.dumps((fn.__name__, args)) - if key == KWARGS: - k = pickle.dumps((fn.__name__, kwargs)) - if key == NONE: - k = pickle.dumps((fn.__name__)) - if k in c: - h, t, to, res = c[k] - if get_fn_hash(fn) == h and (to < 0 or (time.time() - t) < to): - log("cache hit.") - return res - log("cache miss.") - res = fn(*args, **kwargs) - c[k] = (get_fn_hash(fn), time.time(), timeout, res) - save_t = time.time() - write_to_cache(fname, c) - log("saved cache in {:.2f}s".format(time.time() - save_t)) - return res - - return d - - return impl - - -@cache(timeout=0.2) -def expensive(k): - time.sleep(0.2) - return k - - -@cache(key=KWARGS) -def expensive2(k, kwarg1=None): - time.sleep(0.2) - return k - - -def test(): - # Test timeout - t = time.time() - v = expensive(1) - assert v == 1 - assert time.time() - t > 0.1 - t = time.time() - expensive(1) - assert time.time() - t < 0.1 - time.sleep(0.3) - t = time.time() - expensive(1) - assert time.time() - t > 0.1 - t = time.time() - v = expensive(2) - assert v == 2 - assert time.time() - t > 0.1 - # Test key=_ annotation - t = time.time() - v = expensive2(2, kwarg1="test") - assert v == 2 - assert time.time() - t > 0.1 - t = time.time() - v = expensive2(1, kwarg1="test") - assert v == 2 - assert time.time() - t < 0.1 - t = time.time() - v = expensive2(1, kwarg1="test2") - assert v == 1 - assert time.time() - t > 0.1 - cleanup() - print("pass") - - -if __name__ == "__main__": - test() \ No newline at end of file diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache_fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache_fib.py" deleted file mode 100644 index 8b42916..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/cache_fib.py" +++ /dev/null @@ -1,18 +0,0 @@ -from section_01.fib import cache -import time - - -@cache.cache(timeout=20, fname="my_cache.pkl") -def fib(n): - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) - - -if __name__ == "__main__": - x = 47 - start = time.time() - res = fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/diskcache_fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/diskcache_fib.py" deleted file mode 100644 index ab4ba03..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/diskcache_fib.py" +++ /dev/null @@ -1,20 +0,0 @@ -from diskcache import FanoutCache -import time - -cache = FanoutCache('/tmp/diskcache/fanoutcache1') - - -@cache.memoize(typed=True, expire=None, tag='fib') -def fib(n): - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) - - -if __name__ == "__main__": - x = 47 - start = time.time() - res = fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib" deleted file mode 100755 index 8d484de..0000000 Binary files "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib" and /dev/null differ diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.nim" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.nim" deleted file mode 100644 index 8646a18..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.nim" +++ /dev/null @@ -1,16 +0,0 @@ - - -import math, strformat, times - -proc fib(n: int): int = - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) - -when isMainModule: - let x = 47 - let start = epochTime() - let res = fib(x) - let elapsed = (epochtime() - start).round(2) - stderr.writeLine(&"Nim Computed fib({x})={res} in {elapsed} seconds") \ No newline at end of file diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.py" deleted file mode 100644 index 27281eb..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib.py" +++ /dev/null @@ -1,30 +0,0 @@ -import time - - -def fib(n): - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) - - -def cache_fib(n, _cache={}): - - if n in _cache: - return _cache[n] - elif n > 1: - return _cache.setdefault(n, cache_fib(n-1) + cache_fib(n-2)) - return n - - -if __name__ == "__main__": - x = 47 - start = time.time() - res = fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) - - start = time.time() - res = cache_fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.nim" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.nim" deleted file mode 100644 index ecd3021..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.nim" +++ /dev/null @@ -1,7 +0,0 @@ -import nimpy - -proc fib(n: int): int {.exportpy.} = - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) \ No newline at end of file diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.so" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.so" deleted file mode 100755 index 92c5f5a..0000000 Binary files "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/fib_nimpy.so" and /dev/null differ diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/lru_fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/lru_fib.py" deleted file mode 100644 index ad56bca..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/lru_fib.py" +++ /dev/null @@ -1,20 +0,0 @@ -import time -from functools import lru_cache - - -@lru_cache(maxsize=None) -def fib(n): - if n <= 2: - return 1 - else: - return fib(n - 1) + fib(n - 2) - - -if __name__ == "__main__": - x = 47 - start = time.time() - res = fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) - print(fib.cache_info()) - diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/more_fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/more_fib.py" deleted file mode 100644 index 76981e9..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/more_fib.py" +++ /dev/null @@ -1,33 +0,0 @@ -import time - - -def pow_fib(n): - return pow(2 << n, n + 1, (4 << 2 * n) - (2 << n) - 1) % (2 << n) - - -def for_fib(n): - a, b = 1, 1 - n = n-2 - for i in range(n): - a, b = b, a+b - return b - - -def list_fib(n): - - list_f = [] - f = 1 - list_f.append(f) - list_f.append(f) - for i in range(n-2): - f = list_f[-1] + list_f[-2] - list_f.append(f) - return f - - -if __name__ == "__main__": - x = 47 - start = time.time() - res = list_fib(x) - elapsed = time.time() - start - print("Python Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/nim_fib.py" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/nim_fib.py" deleted file mode 100644 index e61df0c..0000000 --- "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/fib/nim_fib.py" +++ /dev/null @@ -1,9 +0,0 @@ -import time -from fib_nimpy import fib - -if __name__ == "__main__": - x = 30 - start = time.time() - res = fib(x) - elapsed = time.time() - start - print("Py3+Nim Computed fib(%s)=%s in %0.8f seconds" % (x, res, elapsed)) diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\200\351\203\250\345\210\206.pdf" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\200\351\203\250\345\210\206.pdf" deleted file mode 100644 index d51d1b0..0000000 Binary files "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\200\351\203\250\345\210\206.pdf" and /dev/null differ diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\211\351\203\250\345\210\206.pdf" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\211\351\203\250\345\210\206.pdf" deleted file mode 100644 index 103c96b..0000000 Binary files "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\270\211\351\203\250\345\210\206.pdf" and /dev/null differ diff --git "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\272\214\351\203\250\345\210\206.pdf" "b/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\272\214\351\203\250\345\210\206.pdf" deleted file mode 100644 index dcac978..0000000 Binary files "a/section_01_\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214/\350\256\241\347\256\227\347\274\223\345\255\230\343\200\201\344\274\230\345\214\226\347\256\227\346\263\225\345\222\214\345\212\240\351\200\237 Python \346\211\247\350\241\214 \347\254\254\344\272\214\351\203\250\345\210\206.pdf" and /dev/null differ