diff --git a/README.md b/README.md index dd3bf57..d3c1b1e 100644 --- a/README.md +++ b/README.md @@ -30,9 +30,9 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， ## 使用说明 -- 使用前请将matplotlib升级到最新版本V3.3.3(2020年12月)，否则可能会出现报错！ +- 使用前请将matplotlib升级至V3.4.2以上(2022年1月)，否则可能会出现报错！ - 本教程独立网站已上线：[https://datawhalechina.github.io/fantastic-matplotlib/](https://datawhalechina.github.io/fantastic-matplotlib/) -- 使用时若发现任何问题，或是你对项目内容有好的建议，欢迎留言交流 +- 使用时若发现任何问题，或是你对项目内容有好的建议，欢迎留言交流，联系邮箱skywateryang@126.com ## 目录 @@ -41,31 +41,23 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， > 和matplotlib的初次邂逅，赶紧拿出画布，画笔，一段奇幻的旅途即将开启 -本回作为引入，介绍了matplotlib可视化绘图包的特点，以及如何用最简单的几行代码画出一幅可视化图表。 - * 第二回：艺术画笔见乾坤 > 挥舞起手中的艺术画笔，发挥想象力，在画布上自由地绘制图形 -本回作为整个matplotlib宇宙中最重要的一个环节，重点介绍了matplotlib绘图的核心API，以及使用matplotlib绘制基本元素的方法 - * 第三回：布局格式定方圆 > 没有规矩不成方圆，你应当开始学会如何合理地在画布上布局了 -本回介绍了常用的两种绘图布局方法，让使用者可以自由地在画布中进行布局 - * 第四回：文字图例尽眉目 > 为了让你的画流传更久远，快来学习下如何在画布上题字吧 -本回介绍了如何在图像上，坐标轴上绘制文本，以及如何在图像上绘制图例。 - * 第五回：样式色彩秀芳华 > 下一步你需要学习下怎么样绘制出更加花样繁复，色彩绚丽的画了 -本回介绍了4种修改matplotlib绘图样式的方法，以及6种修改matplotlib色彩设置的方法 + ## 致谢 @@ -76,7 +68,7 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， | 成员 | 个人简介 | 个人主页 | | ------ | ------------------------------------------- | -------------------------------------------------- | -| 杨剑砺 | Datawhale成员，**项目负责人**，数据分析师 | 公众号：口羊的数据分析实验室 | +| 杨剑砺 | Datawhale成员，**项目负责人**，数据分析师 | 公众号：口羊的数据分析实验室 | | 杨煜 | Datawhale成员，数据分析师 | 公众号：BI数据可视化 | | 耿远昊 | Datawhale成员，华东师范大学在读 | Github：https://github.com/GYHHAHA | | 李运佳 | Datawhale成员，上海交通大学在读 | 知乎：https://www.zhihu.com/people/li-yun-jia-68-9 | @@ -98,6 +90,12 @@ Matplotlib可以说是python数据可视化最重要且常见的工具之一， 第五章：杨剑砺 +> 《Fantastic-Matplotlib》V1.1 ：第一次全面更新 + +全部章节的更新完善：杨剑砺 + + + ## 关注我们
Datawhale是一个专注AI领域的开源组织，以“for the learner，和学习者一起成长”为愿景，构建对学习者最有价值的开源学习社区。关注我们，一起学习成长。
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diff --git a/docs/_sources/第二回：艺术画笔见乾坤/index.md.txt b/docs/_sources/第二回：艺术画笔见乾坤/index.md.txt index 5533e6a..086486c 100644 --- a/docs/_sources/第二回：艺术画笔见乾坤/index.md.txt +++ b/docs/_sources/第二回：艺术画笔见乾坤/index.md.txt @@ -6,8 +6,18 @@ kernelspec: display_name: Python 3 name: python3 --- -# 第二回：艺术画笔见乾坤 +```{code-cell} ipython3 +import numpy as np +import pandas as pd +import re +import matplotlib +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from matplotlib.patches import Circle, Wedge +from matplotlib.collections import PatchCollection +``` +# 第二回：艺术画笔见乾坤 ## 一、概述 @@ -36,54 +46,39 @@ Artist有两种类型：`primitives` 和`containers`。 `container`是容器，即用来装基本要素的地方，包括**图形figure、坐标系Axes和坐标轴Axis**。他们之间的关系如下图所示： ![分类](https://img-blog.csdnimg.cn/20201122230916134.jpeg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYwNDk2MQ==,size_16,color_FFFFFF,t_70#pic_center) -### 3. matplotlib标准用法 -matplotlib的标准使用流程为： -1. 创建一个`Figure`实例 -2. 使用`Figure`实例创建一个或者多个`Axes`或`Subplot`实例 -3. 使用`Axes`实例的辅助方法来创建`primitive` +可视化中常见的artist类可以参考下图这张表格，解释下每一列的含义。 +第一列表示matplotlib中子图上的辅助方法，可以理解为可视化中不同种类的图表类型，如柱状图，折线图，直方图等，这些图表都可以用这些辅助方法直接画出来，属于更高层级的抽象。 -值得一提的是，Axes是一种容器，它可能是matplotlib API中最重要的类，并且我们大多数时间都花在和它打交道上。更具体的信息会在第三节容器小节说明。 - -一个流程示例及说明如下： - - -```{code-cell} ipython3 -import matplotlib.pyplot as plt -import numpy as np - -# step 1 -# 我们用 matplotlib.pyplot.figure() 创建了一个Figure实例 -fig = plt.figure() - -# step 2 -# 然后用Figure实例创建了一个两行一列(即可以有两个subplot)的绘图区，并同时在第一个位置创建了一个subplot -ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot - -# step 3 -# 然后用Axes实例的方法画了一条曲线 -t = np.arange(0.0, 1.0, 0.01) -s = np.sin(2*np.pi*t) -line, = ax.plot(t, s, color='blue', lw=2) -``` +第二列表示不同图表背后的artist类，比如折线图方法`plot`在底层用到的就是`Line2D`这一artist类。 +第三列是第二列的列表容器，例如所有在子图中创建的`Line2D`对象都会被自动收集到`ax.lines`返回的列表中。 +下一节的具体案例更清楚地阐释了这三者的关系，其实在很多时候，我们只用记住第一列的辅助方法进行绘图即可，而无需关注具体底层使用了哪些类，但是了解底层类有助于我们绘制一些复杂的图表，因此也很有必要了解。 +| Axes helper method | Artist | Container | +| ------------------- | ------ | ----------- | +| `bar` - bar charts | `Rectangle` | ax.patches | +| `errorbar` - error bar plots | `Line2D` and `Rectangle` | ax.lines and ax.patches | +| `fill` - shared area | `Polygon` | ax.patches | +| `hist` - histograms | `Rectangle` | ax.patches | +|`imshow` - image data | `AxesImage` | ax.images | +| `plot` - xy plots | `Line2D` | ax.lines | +| `scatter` - scatter charts | `PolyCollection` | ax.collections | ## 二、基本元素 - primitives 各容器中可能会包含多种`基本要素-primitives`, 所以先介绍下primitives，再介绍容器。 -本章重点介绍下 `primitives` 的几种类型：**曲线-Line2D，矩形-Rectangle，图像-image** （其中文本-Text较为复杂，会在之后单独详细说明。） +本章重点介绍下 `primitives` 的几种类型：**曲线-Line2D，矩形-Rectangle，多边形-Polygon，图像-image** ### 1. 2DLines 在matplotlib中曲线的绘制，主要是通过类 `matplotlib.lines.Line2D` 来完成的。 -它的基类: `matplotlib.artist.Artist` matplotlib中`线-line`的含义：它表示的可以是连接所有顶点的实线样式，也可以是每个顶点的标记。此外，这条线也会受到绘画风格的影响，比如，我们可以创建虚线种类的线。 它的构造函数： -> class matplotlib.lines.Line2D(xdata, ydata, linewidth=None, linestyle=None, color=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markevery=None, **kwargs) +>class matplotlib.lines.Line2D(xdata, ydata, linewidth=None, linestyle=None, color=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markevery=None, **kwargs) @@ -97,7 +92,7 @@ matplotlib中`线-line`的含义：它表示的可以是连接所有顶点的实 + **markersize**:标记的size -其他详细参数可参考[Line2D官方文档](https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#examples-using-matplotlib-lines-line2d) +其他详细参数可参考[Line2D官方文档](https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html) #### a. 如何设置Line2D的属性 有三种方法可以用设置线的属性。 @@ -110,7 +105,6 @@ matplotlib中`线-line`的含义：它表示的可以是连接所有顶点的实 ```{code-cell} ipython3 # 1) 直接在plot()函数中设置 -import matplotlib.pyplot as plt x = range(0,5) y = [2,5,7,8,10] plt.plot(x,y, linewidth=10); # 设置线的粗细参数为10 @@ -118,23 +112,16 @@ plt.plot(x,y, linewidth=10); # 设置线的粗细参数为10 - - - - ```{code-cell} ipython3 # 2) 通过获得线对象，对线对象进行设置 x = range(0,5) y = [2,5,7,8,10] -line, = plt.plot(x, y, '-') -line.set_antialiased(False) # 关闭抗锯齿功能 +line, = plt.plot(x, y, '-') # 这里等号坐标的line,是一个列表解包的操作，目的是获取plt.plot返回列表中的Line2D对象 +line.set_antialiased(False); # 关闭抗锯齿功能 ``` - - - ```{code-cell} ipython3 # 3) 获得线属性，使用setp()函数设置 x = range(0,5) @@ -153,42 +140,48 @@ plt.setp(lines, color='r', linewidth=10); -绘制直线line常用的方法有两种: -+ **pyplot方法绘制** -+ **Line2D对象绘制** +介绍两种绘制直线line常用的方法: ++ **plot方法绘制** ++ **Line2D对象绘制** + + ```{code-cell} ipython3 -# 1. pyplot方法绘制 -import matplotlib.pyplot as plt +# 1. plot方法绘制 x = range(0,5) -y = [2,5,7,8,10] -plt.plot(x,y); +y1 = [2,5,7,8,10] +y2= [3,6,8,9,11] + +fig,ax= plt.subplots() +ax.plot(x,y1) +ax.plot(x,y2) +print(ax.lines); # 通过直接使用辅助方法画线，打印ax.lines后可以看到在matplotlib在底层创建了两个Line2D对象 ``` + - - + ```{code-cell} ipython3 # 2. Line2D对象绘制 -import matplotlib.pyplot as plt -from matplotlib.lines import Line2D -fig = plt.figure() -ax = fig.add_subplot(111) -line = Line2D(x, y) -ax.add_line(line) -ax.set_xlim(min(x), max(x)) -ax.set_ylim(min(y), max(y)) - -plt.show() +x = range(0,5) +y1 = [2,5,7,8,10] +y2= [3,6,8,9,11] +fig,ax= plt.subplots() +lines = [Line2D(x, y1), Line2D(x, y2,color='orange')] # 显式创建Line2D对象 +for line in lines: + ax.add_line(line) # 使用add_line方法将创建的Line2D添加到子图中 +ax.set_xlim(0,4) +ax.set_ylim(2, 11); ``` - +​ +​ **2) errorbar绘制误差折线图** @@ -210,32 +203,28 @@ pyplot里有个专门绘制误差线的功能，通过`errorbar`类实现，它 ```{code-cell} ipython3 -import numpy as np -import matplotlib.pyplot as plt fig = plt.figure() x = np.arange(10) y = 2.5 * np.sin(x / 20 * np.pi) yerr = np.linspace(0.05, 0.2, 10) plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)'); - ``` - - - +​ +​ ### 2. patches -matplotlib.patches.Patch类是二维图形类。它的基类是matplotlib.artist.Artist，它的构造函数： -详细清单见 [matplotlib.patches API](https://matplotlib.org/api/patches_api.html) +matplotlib.patches.Patch类是二维图形类，并且它是众多二维图形的父类，它的所有子类见[matplotlib.patches API](https://matplotlib.org/stable/api/patches_api.html) ， +Patch类的构造函数： - - -> Patch(edgecolor=None, facecolor=None, color=None, +>Patch(edgecolor=None, facecolor=None, color=None, linewidth=None, linestyle=None, antialiased=None, hatch=None, fill=True, capstyle=None, joinstyle=None, - **kwargs) + **kwargs) + +本小节重点讲述三种最常见的子类，矩形，多边形和楔型。 #### a. Rectangle-矩形 @@ -244,7 +233,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别 > class matplotlib.patches.Rectangle(xy, width, height, angle=0.0, **kwargs) -在实际中最常见的矩形图是**`hist直方图`和`bar条形图`**。 +在实际中最常见的矩形图是`hist直方图`和`bar条形图`。 @@ -266,27 +255,23 @@ hist绘制直方图 ```{code-cell} ipython3 -import matplotlib.pyplot as plt -import numpy as np x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 plt.xlabel('scores') plt.ylabel('count') -plt.xlim(0,100)#设置x轴分布范围 -plt.show() +plt.xlim(0,100); #设置x轴分布范围 plt.show() ``` +​ `Rectangle`矩形类绘制直方图 ```{code-cell} ipython3 -import pandas as pd -import re df = pd.DataFrame(columns = ['data']) df.loc[:,'data'] = x df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True) @@ -299,7 +284,6 @@ df_cnt.sort_values('mini',ascending = True,inplace = True) df_cnt.reset_index(inplace = True,drop = True) #用Rectangle把hist绘制出来 -import matplotlib.pyplot as plt fig = plt.figure() ax1 = fig.add_subplot(111) @@ -309,17 +293,18 @@ for i in df_cnt.index: ax1.add_patch(rect) ax1.set_xlim(0, 100) -ax1.set_ylim(0, 16) -plt.show() +ax1.set_ylim(0, 16); ``` +​ +​ **2) bar-柱状图** -> matplotlib.pyplot.bar(left, height, alpha=1, width=0.8, color=, edgecolor=, label=, lw=3) +>matplotlib.pyplot.bar(left, height, alpha=1, width=0.8, color=, edgecolor=, label=, lw=3) 下面是一些常用的参数： + **left**：x轴的位置序列，一般采用range函数产生一个序列，但是有时候可以是字符串 @@ -339,23 +324,18 @@ plt.show() ```{code-cell} ipython3 # bar绘制柱状图 -import matplotlib.pyplot as plt y = range(1,17) plt.bar(np.arange(16), y, alpha=0.5, width=0.5, color='yellow', edgecolor='red', label='The First Bar', lw=3); ``` - - - - +​ ```{code-cell} ipython3 # Rectangle矩形类绘制柱状图 -#import matplotlib.pyplot as plt fig = plt.figure() ax1 = fig.add_subplot(111) @@ -363,17 +343,19 @@ for i in range(1,17): rect = plt.Rectangle((i+0.25,0),0.5,i) ax1.add_patch(rect) ax1.set_xlim(0, 16) -ax1.set_ylim(0, 16) -plt.show() +ax1.set_ylim(0, 16); ``` +​ + +​ #### b. Polygon-多边形 -matplotlib.patches.Polygon类是多边形类。其基类是matplotlib.patches.Patch，它的构造函数： +matplotlib.patches.Polygon类是多边形类。它的构造函数： -> class matplotlib.patches.Polygon(xy, closed=True, **kwargs) +>class matplotlib.patches.Polygon(xy, closed=True, **kwargs) xy是一个N×2的numpy array，为多边形的顶点。 closed为True则指定多边形将起点和终点重合从而显式关闭多边形。 @@ -381,14 +363,13 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 matplotlib.patches.Polygon类中常用的是fill类，它是基于xy绘制一个填充的多边形，它的定义： -> matplotlib.pyplot.fill(*args, data=None, **kwargs) +>matplotlib.pyplot.fill(*args, data=None, **kwargs) 参数说明 : 关于x、y和color的序列，其中color是可选的参数，每个多边形都是由其节点的x和y位置列表定义的，后面可以选择一个颜色说明符。您可以通过提供多个x、y、[颜色]组来绘制多个多边形。 ```{code-cell} ipython3 # 用fill来绘制图形 -import matplotlib.pyplot as plt x = np.linspace(0, 5 * np.pi, 1000) y1 = np.sin(x) y2 = np.sin(2 * x) @@ -396,22 +377,22 @@ plt.fill(x, y1, color = "g", alpha = 0.3); ``` +​ - - +​ #### c. Wedge-契形 matplotlib.patches.Polygon类是多边形类。其基类是matplotlib.patches.Patch，它的构造函数： -> class matplotlib.patches.Wedge(center, r, theta1, theta2, width=None, **kwargs) +>class matplotlib.patches.Wedge(center, r, theta1, theta2, width=None, **kwargs) 一个Wedge-契形 是以坐标x,y为中心，半径为r，从θ1扫到θ2(单位是度)。 如果宽度给定，则从内半径r -宽度到外半径r画出部分楔形。wedge中比较常见的是绘制饼状图。 matplotlib.pyplot.pie语法： -> matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=0, 0, frame=False, rotatelabels=False, *, normalize=None, data=None) +>matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=0, 0, frame=False, rotatelabels=False, *, normalize=None, data=None) 制作数据x的饼图，每个楔子的面积用x/sum(x)表示。 其中最主要的参数是前4个： @@ -425,52 +406,51 @@ pie绘制饼状图 ```{code-cell} ipython3 -import matplotlib.pyplot as plt labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] explode = (0, 0.1, 0, 0) fig1, ax1 = plt.subplots() ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) -ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. -plt.show() +ax1.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle. ``` +​ + +​ wedge绘制饼图 ```{code-cell} ipython3 -import matplotlib.pyplot as plt -from matplotlib.patches import Circle, Wedge -from matplotlib.collections import PatchCollection - -fig = plt.figure() +fig = plt.figure(figsize=(5,5)) ax1 = fig.add_subplot(111) theta1 = 0 sizes = [15, 30, 45, 10] patches = [] patches += [ - Wedge((0.3, 0.3), .2, 0, 54), # Full circle - Wedge((0.3, 0.3), .2, 54, 162), # Full ring - Wedge((0.3, 0.3), .2, 162, 324), # Full sector - Wedge((0.3, 0.3), .2, 324, 360), # Ring sector + Wedge((0.5, 0.5), .4, 0, 54), + Wedge((0.5, 0.5), .4, 54, 162), + Wedge((0.5, 0.5), .4, 162, 324), + Wedge((0.5, 0.5), .4, 324, 360), ] colors = 100 * np.random.rand(len(patches)) -p = PatchCollection(patches, alpha=0.4) +p = PatchCollection(patches, alpha=0.8) p.set_array(colors) -ax1.add_collection(p) -plt.show() +ax1.add_collection(p); ``` +​ + +​ ### 3. collections collections类是用来绘制一组对象的集合，collections有许多不同的子类，如RegularPolyCollection, CircleCollection, Pathcollection, 分别对应不同的集合子类型。其中比较常用的就是散点图，它是属于PathCollection子类，scatter方法提供了该类的封装，根据x与y绘制不同大小或颜色标记的散点图。 它的构造方法： -> Axes.scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs) +>Axes.scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs) 其中最主要的参数是前5个： @@ -487,27 +467,26 @@ collections类是用来绘制一组对象的集合，collections有许多不同 x = [0,2,4,6,8,10] y = [10]*len(x) s = [20*2**n for n in range(len(x))] -plt.scatter(x,y,s=s) -plt.show() +plt.scatter(x,y,s=s) ; ``` +​ +​ ### 4. images images是matplotlib中绘制image图像的类，其中最常用的imshow可以根据数组绘制成图像，它的构造函数： -> class matplotlib.image.AxesImage(ax, cmap=None, norm=None, interpolation=None, origin=None, extent=None, filternorm=True, filterrad=4.0, resample=False, **kwargs) +>class matplotlib.image.AxesImage(ax, cmap=None, norm=None, interpolation=None, origin=None, extent=None, filternorm=True, filterrad=4.0, resample=False, **kwargs) imshow根据数组绘制图像 -> matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=, filternorm=1, filterrad=4.0, imlim=, resample=None, url=None, *, data=None, **kwargs） +>matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=, filternorm=1, filterrad=4.0, imlim=, resample=None, url=None, *, data=None, **kwargs） 使用imshow画图时首先需要传入一个数组，数组对应的是空间内的像素位置和像素点的值，interpolation参数可以设置不同的差值方法，具体效果如下。 ```{code-cell} ipython3 -import matplotlib.pyplot as plt -import numpy as np methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'] @@ -522,12 +501,13 @@ for ax, interp_method in zip(axs.flat, methods): ax.imshow(grid, interpolation=interp_method, cmap='viridis') ax.set_title(str(interp_method)) -plt.tight_layout() -plt.show() +plt.tight_layout(); ``` +​ +​ ## 三、对象容器 - Object container @@ -535,7 +515,7 @@ plt.show() 比如`Axes Artist`，它是一种容器，它包含了很多`primitives`，比如`Line2D`，`Text`；同时，它也有自身的属性，比如`xscal`，用来控制X轴是`linear`还是`log`的。 ### 1. Figure容器 -`matplotlib.figure.Figure`是`Artist`最顶层的`container`-对象容器，它包含了图表中的所有元素。一张图表的背景就是在`Figure.patch`的一个矩形`Rectangle`。 +`matplotlib.figure.Figure`是`Artist`最顶层的`container`对象容器，它包含了图表中的所有元素。一张图表的背景就是在`Figure.patch`的一个矩形`Rectangle`。 当我们向图表添加`Figure.add_subplot()`或者`Figure.add_axes()`元素时，这些都会被添加到`Figure.axes`列表中。 @@ -547,8 +527,11 @@ print(ax1) print(fig.axes) # fig.axes 中包含了subplot和axes两个实例, 刚刚添加的 ``` + -​ + + + 由于`Figure`维持了`current axes`，因此你不应该手动的从`Figure.axes`列表中添加删除元素，而是要通过`Figure.add_subplot()`、`Figure.add_axes()`来添加元素，通过`Figure.delaxes()`来删除元素。但是你可以迭代或者访问`Figure.axes`中的`Axes`，然后修改这个`Axes`的属性。 @@ -562,12 +545,13 @@ ax1 = fig.add_subplot(211) for ax in fig.axes: ax.grid(True) - ``` +​ +​ `Figure`也有它自己的`text、line、patch、image`。你可以直接通过`add primitive`语句直接添加。但是注意`Figure`默认的坐标系是以像素为单位，你可能需要转换成figure坐标系：(0,0)表示左下点，(1,1)表示右上点。 @@ -588,10 +572,6 @@ for ax in fig.axes: ```{code-cell} ipython3 -import numpy as np -import matplotlib.pyplot as plt -import matplotlib - fig = plt.figure() ax = fig.add_subplot(111) rect = ax.patch # axes的patch是一个Rectangle实例 @@ -600,6 +580,9 @@ rect.set_facecolor('green') +​ + + `Axes`有许多方法用于绘图，如`.plot()、.text()、.hist()、.imshow()`等方法用于创建大多数常见的`primitive`(如`Line2D，Rectangle，Text，Image`等等）。在`primitives`中已经涉及，不再赘述。 Subplot就是一个特殊的Axes，其实例是位于网格中某个区域的Subplot实例。其实你也可以在任意区域创建Axes，通过Figure.add_axes([left,bottom,width,height])来创建一个任意区域的Axes，其中left,bottom,width,height都是[0—1]之间的浮点数，他们代表了相对于Figure的坐标。 @@ -617,16 +600,16 @@ Subplot就是一个特殊的Axes，其实例是位于网格中某个区域的Sub 会在下面章节详细说明。 **Axes容器**的常见属性有： -`artists`: Artist实例列表 -`patch`: Axes所在的矩形实例 -`collections`: Collection实例 -`images`: Axes图像 -`legends`: Legend 实例 -`lines`: Line2D 实例 -`patches`: Patch 实例 -`texts`: Text 实例 -`xaxis`: matplotlib.axis.XAxis 实例 -`yaxis`: matplotlib.axis.YAxis 实例 +`artists`: Artist实例列表 +`patch`: Axes所在的矩形实例 +`collections`: Collection实例 +`images`: Axes图像 +`legends`: Legend 实例 +`lines`: Line2D 实例 +`patches`: Patch 实例 +`texts`: Text 实例 +`xaxis`: matplotlib.axis.XAxis 实例 +`yaxis`: matplotlib.axis.YAxis 实例 ### 3. Axis容器 @@ -663,11 +646,7 @@ axis.get_view_interval()# 获取轴视角（位置）的间隔 - - - - - +​ 下面的例子展示了如何调整一些轴和刻度的属性(忽略美观度，仅作调整参考)： @@ -694,12 +673,12 @@ for line in ax1.yaxis.get_ticklines(): line.set_color('green') # 颜色 line.set_markersize(25) # marker大小 line.set_markeredgewidth(2)# marker粗细 - -plt.show() ``` +​ + ### 4. Tick容器 @@ -720,10 +699,6 @@ x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴 ```{code-cell} ipython3 -import numpy as np -import matplotlib.pyplot as plt -import matplotlib - fig, ax = plt.subplots() ax.plot(100*np.random.rand(20)) @@ -734,27 +709,36 @@ ax.yaxis.set_major_formatter(formatter) # 设置ticker的参数，右侧为主轴，颜色为绿色 ax.yaxis.set_tick_params(which='major', labelcolor='green', labelleft=False, labelright=True); - - ``` +​ + + +## 思考题 + +- primitives 和 container的区别和联系是什么，分别用于控制可视化图表中的哪些要素 + +- 使用提供的drug数据集，对第一列yyyy和第二列state分组求和，画出下面折线图。PA加粗标黄，其他为灰色。 +图标题和横纵坐标轴标题，以及线的文本暂不做要求。 + + +![](https://img-blog.csdnimg.cn/20210523162430365.png) - - - - - +- 分别用一组长方形柱和填充面积的方式模仿画出下图，函数 y = -1 * (x - 2) * (x - 8) +10 在区间[2,9]的积分面积 +![](https://img-blog.csdnimg.cn/20201126105910781.png) +![](https://img-blog.csdnimg.cn/20201126105910780.png) ## 参考资料 [1. matplotlib设计的基本逻辑](https://zhuanlan.zhihu.com/p/32693665) -[2. matplotlib.artist api](https://matplotlib.org/api/artist_api.html) -[3. matplotlib官方教程](https://matplotlib.org/tutorials/intermediate/artists.html#sphx-glr-tutorials-intermediate-artists-py) -[4. AI算法工程师手册](https://www.bookstack.cn/read/huaxiaozhuan-ai/spilt.2.333f5abdbabf383d.md) +[2. AI算法工程师手册](https://www.bookstack.cn/read/huaxiaozhuan-ai/spilt.2.333f5abdbabf383d.md) +```{code-cell} ipython3 + +``` diff --git a/docs/genindex.html b/docs/genindex.html index 836f45b..fd17083 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -190,9 +190,9 @@

- By Datawhale
+ By Datawhale数据可视化开源小组
- © Copyright © Copyright 2020.
+ © Copyright © Copyright 2021.

diff --git a/docs/index.html b/docs/index.html index e7fe5e2..bcff182 100644 --- a/docs/index.html +++ b/docs/index.html @@ -205,7 +205,8 @@
  • 一、概述
  • 二、基本元素 - primitives
  • 三、对象容器 - Object container
    • -
  • 参考资料
    • +
  • 思考题
    • +
  • 参考资料
  • 第三回：布局格式定方圆
      @@ -247,9 +248,9 @@

      - By Datawhale
      + By Datawhale数据可视化开源小组
      - © Copyright © Copyright 2020.
      + © Copyright © Copyright 2021.

      diff --git a/docs/search.html b/docs/search.html index c8e9504..009cd7b 100644 --- a/docs/search.html +++ b/docs/search.html @@ -209,9 +209,9 @@

      - By Datawhale
      + By Datawhale数据可视化开源小组
      - © Copyright © Copyright 2020.
      + © Copyright © Copyright 2021.

      diff --git a/docs/searchindex.js b/docs/searchindex.js index 46ce2be..d539329 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/docs/第一回：Matplotlib初相识/index.html b/docs/第一回：Matplotlib初相识/index.html index 05c339c..41c3b4c 100644 --- a/docs/第一回：Matplotlib初相识/index.html +++ b/docs/第一回：Matplotlib初相识/index.html @@ -397,9 +397,9 @@

      - By Datawhale
      + By Datawhale数据可视化开源小组
      - © Copyright © Copyright 2020.
      + © Copyright © Copyright 2021.

      diff --git a/docs/第三回：布局格式定方圆/index.html b/docs/第三回：布局格式定方圆/index.html index 90a3528..7244d72 100644 --- a/docs/第三回：布局格式定方圆/index.html +++ b/docs/第三回：布局格式定方圆/index.html @@ -473,9 +473,9 @@

      - By Datawhale
      + By Datawhale数据可视化开源小组
      - © Copyright © Copyright 2020.
      + © Copyright © Copyright 2021.

      diff --git a/docs/第二回：艺术画笔见乾坤/index.html b/docs/第二回：艺术画笔见乾坤/index.html index 39237e6..81190dd 100644 --- a/docs/第二回：艺术画笔见乾坤/index.html +++ b/docs/第二回：艺术画笔见乾坤/index.html @@ -201,11 +201,6 @@ 2. Artist的分类 -
    • - - 3. matplotlib标准用法 - -
  • @@ -293,6 +288,11 @@
  • + 思考题 + +
    • +
  • + 参考资料
    • @@ -307,7 +307,21 @@
    -
    +
    +
    +
    import numpy as np
+import pandas as pd
+import re
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D   
+from matplotlib.patches import Circle, Wedge
+from matplotlib.collections import PatchCollection
+
    +
    +
    +
    +

    第二回：艺术画笔见乾坤

    一、概述

    @@ -332,52 +346,58 @@

    primitive是基本要素，它包含一些我们要在绘图区作图用到的标准图形对象，如曲线Line2D，文字text，矩形Rectangle，图像image等。

    container是容器，即用来装基本要素的地方，包括图形figure、坐标系Axes和坐标轴Axis。他们之间的关系如下图所示：
    分类

    -
    -
    -

    3. matplotlib标准用法

    -

    matplotlib的标准使用流程为：

    -
      -

    1. 创建一个Figure实例

      2. -

    2. 使用Figure实例创建一个或者多个Axes或Subplot实例

      3. -

    3. 使用Axes实例的辅助方法来创建primitive

      4. -
    -

    值得一提的是，Axes是一种容器，它可能是matplotlib API中最重要的类，并且我们大多数时间都花在和它打交道上。更具体的信息会在第三节容器小节说明。

    -

    一个流程示例及说明如下：

    -
    -
    -
    import matplotlib.pyplot as plt
-import numpy as np
-
-# step 1 
-# 我们用 matplotlib.pyplot.figure() 创建了一个Figure实例
-fig = plt.figure()
-
-# step 2
-# 然后用Figure实例创建了一个两行一列(即可以有两个subplot)的绘图区，并同时在第一个位置创建了一个subplot
-ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot
-
-# step 3
-# 然后用Axes实例的方法画了一条曲线
-t = np.arange(0.0, 1.0, 0.01)
-s = np.sin(2*np.pi*t)
-line, = ax.plot(t, s, color='blue', lw=2)
-
    -
    -
    -
    -../_images/index_1_0.png -
    -
    +

    可视化中常见的artist类可以参考下图这张表格，解释下每一列的含义。
    +第一列表示matplotlib中子图上的辅助方法，可以理解为可视化中不同种类的图表类型，如柱状图，折线图，直方图等，这些图表都可以用这些辅助方法直接画出来，属于更高层级的抽象。

    +

    第二列表示不同图表背后的artist类，比如折线图方法plot在底层用到的就是Line2D这一artist类。

    +

    第三列是第二列的列表容器，例如所有在子图中创建的Line2D对象都会被自动收集到ax.lines返回的列表中。

    +

    下一节的具体案例更清楚地阐释了这三者的关系，其实在很多时候，我们只用记住第一列的辅助方法进行绘图即可，而无需关注具体底层使用了哪些类，但是了解底层类有助于我们绘制一些复杂的图表，因此也很有必要了解。

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Axes helper method

    Artist

    Container

    bar - bar charts

    Rectangle

    ax.patches

    errorbar - error bar plots

    Line2D and Rectangle

    ax.lines and ax.patches

    fill - shared area

    Polygon

    ax.patches

    hist - histograms

    Rectangle

    ax.patches

    imshow - image data

    AxesImage

    ax.images

    plot - xy plots

    Line2D

    ax.lines

    scatter - scatter charts

    PolyCollection

    ax.collections

    二、基本元素 - primitives

    各容器中可能会包含多种基本要素-primitives, 所以先介绍下primitives，再介绍容器。

    -

    本章重点介绍下 primitives 的几种类型：曲线-Line2D，矩形-Rectangle，图像-image （其中文本-Text较为复杂，会在之后单独详细说明。）

    +

    本章重点介绍下 primitives 的几种类型：曲线-Line2D，矩形-Rectangle，多边形-Polygon，图像-image

    1. 2DLines

    -

    在matplotlib中曲线的绘制，主要是通过类 matplotlib.lines.Line2D 来完成的。
    -它的基类: matplotlib.artist.Artist

    +

    在matplotlib中曲线的绘制，主要是通过类 matplotlib.lines.Line2D 来完成的。

    matplotlib中线-line的含义：它表示的可以是连接所有顶点的实线样式，也可以是每个顶点的标记。此外，这条线也会受到绘画风格的影响，比如，我们可以创建虚线种类的线。

    它的构造函数：

    @@ -393,7 +413,7 @@

  • marker:点的标记，详细可参考markers API

  • markersize:标记的size

    • -

    其他详细参数可参考Line2D官方文档

    +

    其他详细参数可参考Line2D官方文档

    a. 如何设置Line2D的属性

    有三种方法可以用设置线的属性。

    @@ -405,7 +425,6 @@ 
    # 1) 直接在plot()函数中设置
-import matplotlib.pyplot as plt
 x = range(0,5)
 y = [2,5,7,8,10]
 plt.plot(x,y, linewidth=10); # 设置线的粗细参数为10
@@ -413,7 +432,7 @@
    -../_images/index_3_01.png +../_images/index_2_01.png
    @@ -421,13 +440,13 @@ 
    # 2) 通过获得线对象，对线对象进行设置
 x = range(0,5)
 y = [2,5,7,8,10]
-line, = plt.plot(x, y, '-')
-line.set_antialiased(False) # 关闭抗锯齿功能
+line, = plt.plot(x, y, '-') # 这里等号坐标的line,是一个列表解包的操作，目的是获取plt.plot返回列表中的Line2D对象
+line.set_antialiased(False); # 关闭抗锯齿功能
    -../_images/index_4_01.png +../_images/index_3_01.png
    @@ -441,7 +460,7 @@
    -../_images/index_5_01.png +../_images/index_4_01.png
    @@ -451,18 +470,45 @@

  • 绘制直线line

  • errorbar绘制误差折线图

    • -

    绘制直线line常用的方法有两种:

    +

    介绍两种绘制直线line常用的方法:

      -

    • pyplot方法绘制

      • +

    • plot方法绘制

    • Line2D对象绘制

    -
    # 1. pyplot方法绘制
-import matplotlib.pyplot as plt
+
    # 1. plot方法绘制
 x = range(0,5)
-y = [2,5,7,8,10]
-plt.plot(x,y);
+y1 = [2,5,7,8,10]
+y2= [3,6,8,9,11]
+
+fig,ax= plt.subplots()
+ax.plot(x,y1)
+ax.plot(x,y2)
+print(ax.lines); # 通过直接使用辅助方法画线，打印ax.lines后可以看到在matplotlib在底层创建了两个Line2D对象
+
    
+
    
+
    +
    +
    [<matplotlib.lines.Line2D object at 0x000001EBFE710A90>, <matplotlib.lines.Line2D object at 0x000001EBFE710E20>]
+
    +
    +../_images/index_6_1.png +
    +
    +
    +
    +
    # 2. Line2D对象绘制
+
+x = range(0,5)
+y1 = [2,5,7,8,10]
+y2= [3,6,8,9,11]
+fig,ax= plt.subplots()
+lines = [Line2D(x, y1), Line2D(x, y2,color='orange')]  # 显式创建Line2D对象
+for line in lines:
+    ax.add_line(line) # 使用add_line方法将创建的Line2D添加到子图中
+ax.set_xlim(0,4)
+ax.set_ylim(2, 11);
    @@ -470,27 +516,8 @@ ../_images/index_7_01.png
    -
    -
    -
    # 2. Line2D对象绘制
-import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D      
-
-fig = plt.figure()
-ax = fig.add_subplot(111)
-line = Line2D(x, y)
-ax.add_line(line)
-ax.set_xlim(min(x), max(x))
-ax.set_ylim(min(y), max(y))
-
-plt.show()
-
    -
    -
    -
    -../_images/index_8_01.png -
    -
    +

    ​
    +​

    2) errorbar绘制误差折线图
    pyplot里有个专门绘制误差线的功能，通过errorbar类实现，它的构造函数：

    @@ -509,9 +536,7 @@ pyplot里有个专门绘制误差线的功能，通过
    -
    import numpy as np
-import matplotlib.pyplot as plt
-fig = plt.figure()
+
    fig = plt.figure()
 x = np.arange(10)
 y = 2.5 * np.sin(x / 20 * np.pi)
 yerr = np.linspace(0.05, 0.2, 10)
@@ -520,21 +545,24 @@ pyplot里有个专门绘制误差线的功能，通过
-../_images/index_10_01.png
+../_images/index_9_01.png
 
    
 
    
+
    ​
    
+​

    2. patches

    -

    matplotlib.patches.Patch类是二维图形类。它的基类是matplotlib.artist.Artist，它的构造函数：
    -详细清单见 matplotlib.patches API

    +

    matplotlib.patches.Patch类是二维图形类，并且它是众多二维图形的父类，它的所有子类见matplotlib.patches API ，
    +Patch类的构造函数：

    Patch(edgecolor=None, facecolor=None, color=None, linewidth=None, linestyle=None, antialiased=None, hatch=None, fill=True, capstyle=None, joinstyle=None, **kwargs)

    +

    本小节重点讲述三种最常见的子类，矩形，多边形和楔型。

    a. Rectangle-矩形

    Rectangle矩形类在官网中的定义是： 通过锚点xy及其宽度和高度生成。 @@ -542,7 +570,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别

    class matplotlib.patches.Rectangle(xy, width, height, angle=0.0, **kwargs)

    -

    在实际中最常见的矩形图是**hist直方图和bar条形图**。

    +

    在实际中最常见的矩形图是hist直方图和bar条形图。

    1) hist-直方图

    matplotlib.pyplot.hist(x,bins=None,range=None, density=None, bottom=None, histtype='bar', align='mid', log=False, color=None, label=None, stacked=False, normed=None)

    @@ -561,28 +589,24 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别

    hist绘制直方图

    -
    import matplotlib.pyplot as plt
-import numpy as np 
-x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 
+
    x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 
 bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... 
 plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 
 plt.xlabel('scores') 
 plt.ylabel('count') 
-plt.xlim(0,100)#设置x轴分布范围 
-plt.show()
+plt.xlim(0,100); #设置x轴分布范围 plt.show()
    -../_images/index_12_0.png +../_images/index_11_01.png
    +

    ​

    Rectangle矩形类绘制直方图

    -
    import pandas as pd
-import re
-df = pd.DataFrame(columns = ['data'])
+
    df = pd.DataFrame(columns = ['data'])
 df.loc[:,'data'] = x
 df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True)
 
@@ -594,7 +618,6 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
 df_cnt.reset_index(inplace = True,drop = True)
 
 #用Rectangle把hist绘制出来
-import matplotlib.pyplot as plt
 
 fig = plt.figure()
 ax1 = fig.add_subplot(111)
@@ -604,15 +627,16 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
     ax1.add_patch(rect)
 
 ax1.set_xlim(0, 100)
-ax1.set_ylim(0, 16)
-plt.show()
+ax1.set_ylim(0, 16);
    -../_images/index_14_0.png +../_images/index_13_01.png
    +

    ​

    +

    ​

    2) bar-柱状图

    matplotlib.pyplot.bar(left, height, alpha=1, width=0.8, color=, edgecolor=, label=, lw=3)

    @@ -635,20 +659,19 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别 
    # bar绘制柱状图
-import matplotlib.pyplot as plt
 y = range(1,17)
 plt.bar(np.arange(16), y, alpha=0.5, width=0.5, color='yellow', edgecolor='red', label='The First Bar', lw=3);
    -../_images/index_16_0.png +../_images/index_15_01.png
    +

    ​

    # Rectangle矩形类绘制柱状图
-#import matplotlib.pyplot as plt
 fig = plt.figure()
 ax1 = fig.add_subplot(111)
 
@@ -656,8 +679,7 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别
     rect =  plt.Rectangle((i+0.25,0),0.5,i)
     ax1.add_patch(rect)
 ax1.set_xlim(0, 16)
-ax1.set_ylim(0, 16)
-plt.show()
+ax1.set_ylim(0, 16);
    @@ -665,10 +687,12 @@ Rectangle本身的主要比较简单，即xy控制锚点，width和height分别 ../_images/index_17_01.png
    +

    ​

    +

    ​

    b. Polygon-多边形

    -

    matplotlib.patches.Polygon类是多边形类。其基类是matplotlib.patches.Patch，它的构造函数：

    +

    matplotlib.patches.Polygon类是多边形类。它的构造函数：

    class matplotlib.patches.Polygon(xy, closed=True, **kwargs)

    @@ -682,7 +706,6 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 
    # 用fill来绘制图形
-import matplotlib.pyplot as plt
 x = np.linspace(0, 5 * np.pi, 1000) 
 y1 = np.sin(x)
 y2 = np.sin(2 * x) 
@@ -694,6 +717,8 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
 ../_images/index_19_0.png
    +

    ​

    +

    ​

    c. Wedge-契形

    @@ -719,14 +744,12 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边

    pie绘制饼状图

    -
    import matplotlib.pyplot as plt 
-labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
+
    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
 sizes = [15, 30, 45, 10] 
 explode = (0, 0.1, 0, 0) 
 fig1, ax1 = plt.subplots() 
 ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) 
-ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. 
-plt.show()
+ax1.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle.
    @@ -734,29 +757,26 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_21_0.png
    +

    ​

    +

    ​

    wedge绘制饼图

    -
    import matplotlib.pyplot as plt 
-from matplotlib.patches import Circle, Wedge
-from matplotlib.collections import PatchCollection
-
-fig = plt.figure()
+
    fig = plt.figure(figsize=(5,5))
 ax1 = fig.add_subplot(111)
 theta1 = 0
 sizes = [15, 30, 45, 10] 
 patches = []
 patches += [
-    Wedge((0.3, 0.3), .2, 0, 54),             # Full circle
-    Wedge((0.3, 0.3), .2, 54, 162),  # Full ring
-    Wedge((0.3, 0.3), .2, 162, 324),              # Full sector
-    Wedge((0.3, 0.3), .2, 324, 360),  # Ring sector
+    Wedge((0.5, 0.5), .4, 0, 54),           
+    Wedge((0.5, 0.5), .4, 54, 162),  
+    Wedge((0.5, 0.5), .4, 162, 324),           
+    Wedge((0.5, 0.5), .4, 324, 360),  
 ]
 colors = 100 * np.random.rand(len(patches))
-p = PatchCollection(patches, alpha=0.4)
+p = PatchCollection(patches, alpha=0.8)
 p.set_array(colors)
-ax1.add_collection(p)
-plt.show()
+ax1.add_collection(p);
    @@ -764,6 +784,8 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_23_0.png
    +

    ​

    +

    ​

    @@ -786,8 +808,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 x = [0,2,4,6,8,10] y = [10]*len(x) s = [20*2**n for n in range(len(x))] -plt.scatter(x,y,s=s) -plt.show() +plt.scatter(x,y,s=s) ;
    @@ -795,6 +816,8 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_25_0.png
    +

    ​

    +

    ​

    4. images

    @@ -809,9 +832,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边

    使用imshow画图时首先需要传入一个数组，数组对应的是空间内的像素位置和像素点的值，interpolation参数可以设置不同的差值方法，具体效果如下。

    -
    import matplotlib.pyplot as plt
-import numpy as np
-methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
+
    methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
            'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
            'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
 
@@ -825,8 +846,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
     ax.imshow(grid, interpolation=interp_method, cmap='viridis')
     ax.set_title(str(interp_method))
 
-plt.tight_layout()
-plt.show()
+plt.tight_layout();
    @@ -834,6 +854,8 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_27_0.png
    +

    ​

    +

    ​

    @@ -842,7 +864,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 比如Axes Artist，它是一种容器，它包含了很多primitives，比如Line2D，Text；同时，它也有自身的属性，比如xscal，用来控制X轴是linear还是log的。

    1. Figure容器

    -

    matplotlib.figure.Figure是Artist最顶层的container-对象容器，它包含了图表中的所有元素。一张图表的背景就是在Figure.patch的一个矩形Rectangle。
    +

    matplotlib.figure.Figure是Artist最顶层的container对象容器，它包含了图表中的所有元素。一张图表的背景就是在Figure.patch的一个矩形Rectangle。
    当我们向图表添加Figure.add_subplot()或者Figure.add_axes()元素时，这些都会被添加到Figure.axes列表中。

    @@ -862,7 +884,6 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_29_1.png
    -

    ​

    由于Figure维持了current axes，因此你不应该手动的从Figure.axes列表中添加删除元素，而是要通过Figure.add_subplot()、Figure.add_axes()来添加元素，通过Figure.delaxes()来删除元素。但是你可以迭代或者访问Figure.axes中的Axes，然后修改这个Axes的属性。

    比如下面的遍历axes里的内容，并且添加网格线：

    @@ -872,7 +893,6 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 for ax in fig.axes: ax.grid(True) -
    @@ -880,6 +900,8 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_31_0.png
    +

    ​

    +

    ​

    Figure也有它自己的text、line、patch、image。你可以直接通过add primitive语句直接添加。但是注意Figure默认的坐标系是以像素为单位，你可能需要转换成figure坐标系：(0,0)表示左下点，(1,1)表示右上点。

    Figure容器的常见属性：
    Figure.patch属性：Figure的背景矩形
    @@ -895,11 +917,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边

    和Figure容器类似，Axes包含了一个patch属性，对于笛卡尔坐标系而言，它是一个Rectangle；对于极坐标而言，它是一个Circle。这个patch属性决定了绘图区域的形状、背景和边框。

    -
    import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
-
-fig = plt.figure()
+
    fig = plt.figure()
 ax = fig.add_subplot(111)
 rect = ax.patch  # axes的patch是一个Rectangle实例
 rect.set_facecolor('green')
@@ -910,6 +928,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
 ../_images/index_33_0.png
 
    
 
    
+
    ​
    
 
    Axes有许多方法用于绘图，如.plot()、.text()、.hist()、.imshow()等方法用于创建大多数常见的primitive(如Line2D，Rectangle，Text，Image等等）。在primitives中已经涉及，不再赘述。
    
 
    Subplot就是一个特殊的Axes，其实例是位于网格中某个区域的Subplot实例。其实你也可以在任意区域创建Axes，通过Figure.add_axes([left,bottom,width,height])来创建一个任意区域的Axes，其中left,bottom,width,height都是[0—1]之间的浮点数，他们代表了相对于Figure的坐标。
    
 
    你不应该直接通过Axes.lines和Axes.patches列表来添加图表。因为当创建或添加一个对象到图表中时，Axes会做许多自动化的工作:
    
@@ -921,15 +940,15 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边
 ax.yaxis：YAxis对象的实例，用于处理y轴tick以及label的绘制
    
 会在下面章节详细说明。
    
 
    Axes容器的常见属性有：
    
-artists:    Artist实例列表
-patch:     Axes所在的矩形实例
-collections: Collection实例
-images:    Axes图像
-legends:	  Legend 实例
-lines:	  Line2D 实例
-patches:	  Patch 实例
-texts:	  Text 实例
-xaxis:	  matplotlib.axis.XAxis 实例
+artists:    Artist实例列表
    
+patch:     Axes所在的矩形实例
    
+collections: Collection实例
    
+images:    Axes图像
    
+legends:	  Legend 实例
    
+lines:	  Line2D 实例
    
+patches:	  Patch 实例
    
+texts:	  Text 实例
    
+xaxis:	  matplotlib.axis.XAxis 实例
    
 yaxis:	  matplotlib.axis.YAxis 实例
    @@ -967,6 +986,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_35_1.png
    +

    ​

    下面的例子展示了如何调整一些轴和刻度的属性(忽略美观度，仅作调整参考)：

    @@ -990,8 +1010,6 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 line.set_color('green') # 颜色 line.set_markersize(25) # marker大小 line.set_markeredgewidth(2)# marker粗细 - -plt.show()
    @@ -999,6 +1017,7 @@ closed为True则指定多边形将起点和终点重合从而显式关闭多边 ../_images/index_37_0.png
    +

    ​

    4. Tick容器

    @@ -1015,11 +1034,7 @@ x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴

    下面的例子展示了，如何将Y轴右边轴设为主轴，并将标签设置为美元符号且为绿色：

    -
    import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
-
-fig, ax = plt.subplots()
+
    fig, ax = plt.subplots()
 ax.plot(100*np.random.rand(20))
 
 # 设置ticker的显示格式
@@ -1036,14 +1051,27 @@ x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴
 ../_images/index_39_0.png
 
    
 
    
+
    ​
    -

    参考资料

    +

    思考题

    +
      +

    • primitives 和 container的区别和联系是什么，分别用于控制可视化图表中的哪些要素

      • +

    • 使用提供的drug数据集，对第一列yyyy和第二列state分组求和，画出下面折线图。PA加粗标黄，其他为灰色。
      +图标题和横纵坐标轴标题，以及线的文本暂不做要求。

      • +
    +

    +
      +

    • 分别用一组长方形柱和填充面积的方式模仿画出下图，函数 y = -1 * (x - 2) * (x - 8) +10 在区间[2,9]的积分面积
      +
      +

      • +
    +
    +
    @@ -1064,9 +1092,9 @@ x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴

    - By Datawhale
    + By Datawhale数据可视化开源小组
    - © Copyright © Copyright 2020.
    + © Copyright © Copyright 2021.

    diff --git a/docs/第五回：样式色彩秀芳华/index.html b/docs/第五回：样式色彩秀芳华/index.html index 7679d63..a0dfd10 100644 --- a/docs/第五回：样式色彩秀芳华/index.html +++ b/docs/第五回：样式色彩秀芳华/index.html @@ -289,7 +289,7 @@
    -../_images/index_2_01.png +../_images/index_2_02.png
    @@ -349,7 +349,7 @@ ytick.labelsize : 16

    -../_images/index_9_01.png +../_images/index_9_02.png
    @@ -365,7 +365,7 @@ ytick.labelsize : 16

    -../_images/index_11_01.png +../_images/index_11_02.png
    @@ -377,7 +377,7 @@ ytick.labelsize : 16

    -../_images/index_12_01.png +../_images/index_12_0.png

    另外matplotlib也还提供了了一种更便捷的修改样式方式，可以一次性修改多个样式。

    @@ -389,7 +389,7 @@ ytick.labelsize : 16

    -../_images/index_14_01.png +../_images/index_14_0.png
    @@ -538,9 +538,9 @@ ytick.labelsize : 16

    - By Datawhale
    + By Datawhale数据可视化开源小组
    - © Copyright © Copyright 2020.
    + © Copyright © Copyright 2021.

    diff --git a/docs/第四回：文字图例尽眉目/index.html b/docs/第四回：文字图例尽眉目/index.html index 9f9fc56..5049942 100644 --- a/docs/第四回：文字图例尽眉目/index.html +++ b/docs/第四回：文字图例尽眉目/index.html @@ -391,7 +391,7 @@
    -../_images/index_5_02.png +../_images/index_5_01.png

    ​

    @@ -493,7 +493,7 @@ ylabel方式类似，这里不重复写出。
    -../_images/index_9_02.png +../_images/index_9_03.png

    ​

    @@ -523,7 +523,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_11_02.png +../_images/index_11_03.png

    ​

    @@ -557,7 +557,7 @@ annotate的参数非常复杂，这里仅仅展示一个简单的例子，更多
    -../_images/index_14_02.png +../_images/index_14_01.png

    ​

    @@ -909,9 +909,9 @@ ax.legend(loc='upper center') 等同于ax.legend(loc=9)

    - By Datawhale
    + By Datawhale数据可视化开源小组
    - © Copyright © Copyright 2020.
    + © Copyright © Copyright 2021.

    diff --git a/notebook/第二回：艺术画笔见乾坤.ipynb b/notebook/第二回：艺术画笔见乾坤.ipynb index fc7fa59..c516817 100644 --- a/notebook/第二回：艺术画笔见乾坤.ipynb +++ b/notebook/第二回：艺术画笔见乾坤.ipynb @@ -1,5 +1,21 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import re\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D \n", + "from matplotlib.patches import Circle, Wedge\n", + "from matplotlib.collections import PatchCollection" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -54,64 +70,31 @@ }, { "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-31T08:16:02.613781Z", - "start_time": "2020-10-31T08:16:02.591813Z" - } - }, + "metadata": {}, "source": [ - "### 3. matplotlib标准用法\n", - "matplotlib的标准使用流程为： \n", - "1. 创建一个`Figure`实例\n", - "2. 使用`Figure`实例创建一个或者多个`Axes`或`Subplot`实例\n", - "3. 使用`Axes`实例的辅助方法来创建`primitive` \n", + "可视化中常见的artist类可以参考下图这张表格，解释下每一列的含义。 \n", + "第一列表示matplotlib中子图上的辅助方法，可以理解为可视化中不同种类的图表类型，如柱状图，折线图，直方图等，这些图表都可以用这些辅助方法直接画出来，属于更高层级的抽象。 \n", "\n", - "值得一提的是，Axes是一种容器，它可能是matplotlib API中最重要的类，并且我们大多数时间都花在和它打交道上。更具体的信息会在第三节容器小节说明。\n", + "第二列表示不同图表背后的artist类，比如折线图方法`plot`在底层用到的就是`Line2D`这一artist类。\n", "\n", - "一个流程示例及说明如下： " + "第三列是第二列的列表容器，例如所有在子图中创建的`Line2D`对象都会被自动收集到`ax.lines`返回的列表中。 \n", + "\n", + "下一节的具体案例更清楚地阐释了这三者的关系，其实在很多时候，我们只用记住第一列的辅助方法进行绘图即可，而无需关注具体底层使用了哪些类，但是了解底层类有助于我们绘制一些复杂的图表，因此也很有必要了解。" ] }, { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2021-05-23T08:29:15.890803Z", - "start_time": "2021-05-23T08:29:15.508211Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# step 1 \n", - "# 我们用 matplotlib.pyplot.figure() 创建了一个Figure实例\n", - "fig = plt.figure()\n", - "\n", - "# step 2\n", - "# 然后用Figure实例创建了一个两行一列(即可以有两个subplot)的绘图区，并同时在第一个位置创建了一个subplot\n", - "ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot\n", - "\n", - "# step 3\n", - "# 然后用Axes实例的方法画了一条曲线\n", - "t = np.arange(0.0, 1.0, 0.01)\n", - "s = np.sin(2*np.pi*t)\n", - "line, = ax.plot(t, s, color='blue', lw=2)" + "| Axes helper method | Artist | Container |\n", + "| ------------------- | ------ | ----------- |\n", + "| `bar` - bar charts | `Rectangle` | ax.patches |\n", + "| `errorbar` - error bar plots | `Line2D` and `Rectangle` | ax.lines and ax.patches |\n", + "| `fill` - shared area | `Polygon` | ax.patches |\n", + "| `hist` - histograms | `Rectangle` | ax.patches |\n", + "|`imshow` - image data | `AxesImage` | ax.images |\n", + "| `plot` - xy plots | `Line2D` | ax.lines |\n", + "| `scatter` - scatter charts | `PolyCollection` | ax.collections |" ] }, { @@ -121,7 +104,7 @@ "## 二、基本元素 - primitives\n", "各容器中可能会包含多种`基本要素-primitives`, 所以先介绍下primitives，再介绍容器。\n", " \n", - "本章重点介绍下 `primitives` 的几种类型：**曲线-Line2D，矩形-Rectangle，图像-image** （其中文本-Text较为复杂，会在之后单独详细说明。）\n" + "本章重点介绍下 `primitives` 的几种类型：**曲线-Line2D，矩形-Rectangle，多边形-Polygon，图像-image** \n" ] }, { @@ -130,7 +113,6 @@ "source": [ "### 1. 2DLines\n", "在matplotlib中曲线的绘制，主要是通过类 `matplotlib.lines.Line2D` 来完成的。 \n", - "它的基类: `matplotlib.artist.Artist` \n", " \n", "matplotlib中`线-line`的含义：它表示的可以是连接所有顶点的实线样式，也可以是每个顶点的标记。此外，这条线也会受到绘画风格的影响，比如，我们可以创建虚线种类的线。" ] @@ -159,7 +141,7 @@ "+ **marker**:点的标记，详细可参考[markers API](https://matplotlib.org/api/markers_api.html#module-matplotlib.markers)\n", "+ **markersize**:标记的size\n", " \n", - "其他详细参数可参考[Line2D官方文档](https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#examples-using-matplotlib-lines-line2d)" + "其他详细参数可参考[Line2D官方文档](https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html)" ] }, { @@ -186,17 +168,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -209,15 +181,14 @@ ], "source": [ "# 1) 直接在plot()函数中设置\n", - "import matplotlib.pyplot as plt\n", "x = range(0,5)\n", "y = [2,5,7,8,10]\n", - "plt.plot(x,y, linewidth=10) # 设置线的粗细参数为10" + "plt.plot(x,y, linewidth=10); # 设置线的粗细参数为10" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.160728Z", @@ -227,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
    " ] @@ -242,13 +213,13 @@ "# 2) 通过获得线对象，对线对象进行设置\n", "x = range(0,5)\n", "y = [2,5,7,8,10]\n", - "line, = plt.plot(x, y, '-')\n", - "line.set_antialiased(False) # 关闭抗锯齿功能" + "line, = plt.plot(x, y, '-') # 这里等号坐标的line,是一个列表解包的操作，目的是获取plt.plot返回列表中的Line2D对象\n", + "line.set_antialiased(False); # 关闭抗锯齿功能" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.287757Z", @@ -258,17 +229,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[None, None]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -284,7 +245,7 @@ "x = range(0,5)\n", "y = [2,5,7,8,10]\n", "lines = plt.plot(x, y)\n", - "plt.setp(lines, color='r', linewidth=10)" + "plt.setp(lines, color='r', linewidth=10);" ] }, { @@ -301,15 +262,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "绘制直线line常用的方法有两种: \n", - "+ **pyplot方法绘制** \n", + "介绍两种绘制直线line常用的方法: \n", + "+ **plot方法绘制** \n", "+ **Line2D对象绘制** \n", "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.415785Z", @@ -318,18 +279,15 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[, ]\n" + ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
    " ] @@ -341,16 +299,20 @@ } ], "source": [ - "# 1. pyplot方法绘制\n", - "import matplotlib.pyplot as plt\n", + "# 1. plot方法绘制\n", "x = range(0,5)\n", - "y = [2,5,7,8,10]\n", - "plt.plot(x,y)" + "y1 = [2,5,7,8,10]\n", + "y2= [3,6,8,9,11]\n", + "\n", + "fig,ax= plt.subplots()\n", + "ax.plot(x,y1)\n", + "ax.plot(x,y2)\n", + "print(ax.lines); # 通过直接使用辅助方法画线，打印ax.lines后可以看到在matplotlib在底层创建了两个Line2D对象" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.527324Z", @@ -360,7 +322,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -373,17 +335,16 @@ ], "source": [ "# 2. Line2D对象绘制\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.lines import Line2D \n", "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "line = Line2D(x, y)\n", - "ax.add_line(line)\n", - "ax.set_xlim(min(x), max(x))\n", - "ax.set_ylim(min(y), max(y))\n", - "\n", - "plt.show()" + "x = range(0,5)\n", + "y1 = [2,5,7,8,10]\n", + "y2= [3,6,8,9,11]\n", + "fig,ax= plt.subplots()\n", + "lines = [Line2D(x, y1), Line2D(x, y2,color='orange')] # 显式创建Line2D对象\n", + "for line in lines:\n", + " ax.add_line(line) # 使用add_line方法将创建的Line2D添加到子图中\n", + "ax.set_xlim(0,4)\n", + "ax.set_ylim(2, 11);" ] }, { @@ -414,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.623411Z", @@ -424,17 +385,7 @@ "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -446,13 +397,11 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "fig = plt.figure()\n", "x = np.arange(10)\n", "y = 2.5 * np.sin(x / 20 * np.pi)\n", "yerr = np.linspace(0.05, 0.2, 10)\n", - "plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)')\n" + "plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)');" ] }, { @@ -460,15 +409,15 @@ "metadata": {}, "source": [ "### 2. patches\n", - "matplotlib.patches.Patch类是二维图形类。它的基类是matplotlib.artist.Artist，它的构造函数： \n", - "详细清单见 [matplotlib.patches API](https://matplotlib.org/api/patches_api.html) \n", - " \n", - " \n", + "matplotlib.patches.Patch类是二维图形类，并且它是众多二维图形的父类，它的所有子类见[matplotlib.patches API](https://matplotlib.org/stable/api/patches_api.html) ， \n", + "Patch类的构造函数： \n", "\n", ">Patch(edgecolor=None, facecolor=None, color=None,\n", " linewidth=None, linestyle=None, antialiased=None,\n", " hatch=None, fill=True, capstyle=None, joinstyle=None,\n", - " **kwargs)\n", + " **kwargs) \n", + "\n", + "本小节重点讲述三种最常见的子类，矩形，多边形和楔型。\n", " " ] }, @@ -515,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:16.766398Z", @@ -525,17 +474,7 @@ "outputs": [ { "data": { - "text/plain": [ - "(0.0, 100.0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -547,14 +486,12 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np \n", "x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 \n", "bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... \n", "plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 \n", "plt.xlabel('scores') \n", "plt.ylabel('count') \n", - "plt.xlim(0,100)#设置x轴分布范围 plt.show()" + "plt.xlim(0,100); #设置x轴分布范围 plt.show()" ] }, { @@ -566,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.129308Z", @@ -576,7 +513,7 @@ "outputs": [ { "data": { - "image/png": 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vSY0z6CWpcQa9JDXOoJekxhn0ktS4/wVUzzhnBRuDogAAAABJRU5ErkJggg==\n", + "image/png": 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Z9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/plhBHtekrhcAAAAASUVORK5CYII=\n", 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    " ] @@ -588,8 +525,6 @@ } ], "source": [ - "import pandas as pd\n", - "import re\n", "df = pd.DataFrame(columns = ['data'])\n", "df.loc[:,'data'] = x\n", "df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True)\n", @@ -602,7 +537,6 @@ "df_cnt.reset_index(inplace = True,drop = True)\n", "\n", "#用Rectangle把hist绘制出来\n", - "import matplotlib.pyplot as plt\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", @@ -612,8 +546,7 @@ " ax1.add_patch(rect)\n", "\n", "ax1.set_xlim(0, 100)\n", - "ax1.set_ylim(0, 16)\n", - "plt.show()" + "ax1.set_ylim(0, 16);" ] }, { @@ -641,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.257348Z", @@ -651,17 +584,7 @@ "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+ "image/png": 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q37BRi733kjwT+ALwzqr6xbTzACQ5DzhQVbdPO8shbAFeAnyiqs4AfsX0TiU8xfB89S4W/wN6LnBMkgunm2pltXgdc++uZU7yHhZPV14z7SwHJdkKvBv4p2lnWcEW4DgWT+3+A/D5JDncN2zUYu/1OIMkT2Ox1K+pquumnWeJs4A3JnmAxdNXr07y2elG+n/7gf1VdfCnm2tZLPq+eA3w46p6tKp+C1wHvGLKmZb7WZLnAAx/XfVH9vWU5C3AecBfVb9uoHk+i/9hf3f4b+NE4I4kfzzVVE/aD1xXi77F4k/bh31zd6MWe2/HGQz/J70a2FdVH552nqWq6sqqOnE4UOh84OtV1Yujzqp6BPhJklOHi85lrUdAH5kHgTOTbB3+GZ9Lj97cHfoKcNHw8UXAl6eY5fck2cniKcA3VtWvp51nqaq6q6qeXVWzw38b+4GXDP9O9sGXgHMAkpwCHM0qkyg3ZLEP34Q5OM5gH/D5KYwzWMlZwJtZPBq+c/j1+mmH2iAuAa5J8j3gT4F/nm6cJw1/krgWuAO4i8V/O1O7DT3JXuAW4NQk+5NcDFwFvDbJ/Sz+hHFVj7J9HDgWuGH4b+KT08h2mHy9sEK23cDzhpdAfg64aLWfeBwpIEmN2ZBH7JKklVnsktQYi12SGmOxS1JjLHZJaozFLkmNsdglqTH/B8ZqG4OFyLXnAAAAAElFTkSuQmCC\n", "text/plain": [ "
    " ] @@ -674,14 +597,13 @@ ], "source": [ "# bar绘制柱状图\n", - "import matplotlib.pyplot as plt\n", "y = range(1,17)\n", - "plt.bar(np.arange(16), y, alpha=0.5, width=0.5, color='yellow', edgecolor='red', label='The First Bar', lw=3)" + "plt.bar(np.arange(16), y, alpha=0.5, width=0.5, color='yellow', edgecolor='red', label='The First Bar', lw=3);" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.385378Z", @@ -691,7 +613,7 @@ "outputs": [ { "data": { - "image/png": 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bR79P88DLRz+7G8nngPMBkpwBHMsYM1muebmP3lR5apqCQ8Cnx5imYBLOA97Mwkj4ntG/10861CZ3JXBzkm8BfwD8w2TjPN3olcUtwAHgPhZ+JzbEJelJ9gJ3AjuSzCe5ArgeuDDJAyyc4XH9JDPCsjk/AhwP7Bv9Ln1soiFZNueGskzG3cDpo9MjPwXsGueVkNMPSFKDvEJVkhpkuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QG/S+Vr7lMxW37agAAAABJRU5ErkJggg==\n", + "image/png": 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XVNXW0e/TPHD26Gd3PfkqcB5AklOBoxljJss1L/fRmyrPTlNwAPjSGNMUDOFc4BIWRsJ3jf69behQG9zlwI1Jfgj8PvB3w8Z5rtEri5uAfcA9LPxOrItL0pPsBm4HtiWZT3IZcB3wliQPsPCq47ohM8KyOT8NHAvsGf0ufXbQkCybc11ZJuNO4BWj0yO/CFw6zishpx+QpAb5hqokNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ36H+GCu0abe7v5AAAAAElFTkSuQmCC\n", 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    " ] @@ -704,7 +626,6 @@ ], "source": [ "# Rectangle矩形类绘制柱状图\n", - "#import matplotlib.pyplot as plt\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "\n", @@ -712,8 +633,7 @@ " rect = plt.Rectangle((i+0.25,0),0.5,i)\n", " ax1.add_patch(rect)\n", "ax1.set_xlim(0, 16)\n", - "ax1.set_ylim(0, 16)\n", - "plt.show()" + "ax1.set_ylim(0, 16);" ] }, { @@ -721,7 +641,7 @@ "metadata": {}, "source": [ "#### b. Polygon-多边形\n", - "matplotlib.patches.Polygon类是多边形类。其基类是matplotlib.patches.Patch，它的构造函数：\n", + "matplotlib.patches.Polygon类是多边形类。它的构造函数：\n", " \n", ">class matplotlib.patches.Polygon(xy, closed=True, **kwargs) \n", " \n", @@ -742,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.511490Z", @@ -752,17 +672,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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75tfnvZUEgGQkyeU1b3yK6BaXVy+7Vj5kW4KtT+CgUqXE3Lr7o/+aBQNBytWyJyoGeOsq5zGX1y67VlZiO176FNENytUyM9kZ1+cPbJYIJ7i6ZusTOGUlv4Kqd0b/NQjC4rr7Cd9bPxUPqVQrzGRmPNOB2BAKhChXy6zl19wOpSus5FY8NTy4oS/Sx9W1q54rV+xXC+sLnvsdA8TDcU8sSOS9n4xHrOZXXS1AtpOlDVvH2AmLG4vbL6zqolAgRLFSJF1Iux1KV7i8etlzH+rAOwnfEsE2ljaWPHmBAIgFY0xmbB1jJ1xZu0Ii4p1242aKemacuZ9VtcpUZspzzbxQqyycL+fJFN1dkMiRRCAiD4vIORG5ICJPbPH6PxGRNRF5qf71B7s91i1X01c7up7pXqQiKU8ud+c3qspUesr1OlLbiYViTKWn3A7D99bya66vP7ATtwvQtZwIRCQIfAb4MHAn8KiI3LnFrt9T1XvqX//bHo/tKFXl6tpVz14goqEo68V11ovrO+9stpUupClUCoQCjizU57hUJGUjxBzg9WbUSDDCdGba1RicuCO4D7igqhdVtQh8GXikA8e2TbaYJV/JEw6G3Q5lWyJizQYtWsmv4IHyQtuKhWKkC2lypZzbofjadHaaeMibd/dQGxLu9h2+E4lgAmju9p6sb9vsfSLysoh8Q0R+YY/HIiKPi8hpETm9sNDeCVWNoWZeFpSgLVTToqnMFNFA1O0wbsgSfuuurrm3uuBuxENxVvIrFMoF12JwIhFs9Zlq81X0ReAmVb0b+H+A/7qHY2sbVZ9U1VOqemp0dHS/se7KTGaGcMC7dwNQ7ydIWz9BKybXJj19gQDv1a33m43SBulCmmjIuwlfRFBVV2eSO5EIJoGjTc+PANc0eKlqWlWz9cfPAGERGdnNsW7w+icIqE04ml+fp1wtux2KLxXKBZZzy8RCMbdDuaG+SB+TaRshtl9ud8LuVkACrk4scyIR/AQ4ISI3i0gE+BjwdPMOInJI6hW9ROS++nmXdnNsp5UqJZZyS56air6VxuQYL0xP96OV/Aoi4plCc9tJhBPMZGasAN0+ebFMzFaS4aSrE8taHi6hqmUR+RTwLSAIfF5VXxWRT9Rf/xzw3wP/QkTKQA74mNYa4bc8ttWYWrGaX0XVO5Uod7K8scxIYsTtMHxncX3REwvR7CQYCFLRCmuFNYbjw26H4zuT6UnPjv5rlowkmcpMuXbtcWTcXL2555lN2z7X9PiPgT/e7bFuWsp5e6hZs3gozmR6kttHbnc7FN+ZzEx6cqbpdlZyK5YI9qiqVWazs4wm2tun6IRQIES5UiZdSDMQG+j4+b1/z9Rhk+lJzzcLNaQiKa5m3K9T4jeqynR62nOF5rYTC8WYytjEsr1ay69RqXq3TMxmKu51GFsi2MQPI0kaIsEIG8UNm1i2R5lihmK16NmJZJs1Ks6avVnJ1woK+kUkEGEmM+PKuS0RNFkvrpMve3si2WaCWIfxHq3kVjxbR2orsVCMtfwa+XLe7VB8ZTo97flRYc3cTPiWCJp4fabpVgISsHHmezSbnfXN3UCDiCX8vfJbP1A8FGc5t0ypUur4uS0RNFlYXyDgsx9JMpK0ZoM9mkz7p/mvmRcWMPGLQrnAan7VV3cEjYllbiR8f1312syPF4hkOMlsdtZWLNulcrXMwsaCZyvLbicZTlqH8R6s5ldB8c0w8LeJO5PgLBHUNYaa+WXEUENjnLktYLI7jQuEHyYZNWskAq/XwPKK5dyy75p5odY85EbC99dfQxulC2lPr0h2I4o7t5N+5JeSA5uFg2GKlSLZYtbtUHxhMj1JIuSvD3Xg3p2fJYK61fyqr4aaNYsEIsxmZ90OwxemM/4aSbKZJfzdmc74Z55Is2goSqaQ6XjpcUsEdTOZGc+XJN5OMpxkcs06jHdjKjPlywsE1EqPL2y0twR7N1gvrpMr54gEI26Hsi9uDAm3RFA3lfbvBSIejrOYW7RKpDsolAus5dd8e0eQDCdt6cpdaPQD+ZWIdHxVNUsE1EaSLOYWfXuBCEgAtDal3mxvNb/qi0Jz20mEEzZCbBcWNxb9N1qoSSKc6Hg/gSUC6hdQH44kaaaobztCO8WvI0kagoEglaqNENvJVHrKVxPJNnNjhJh/r3wOWsn5qybJVmKhmHUY72A6M+3LkSSbWYfx9lSV2eysb5t54ecjxNZLnashZomAWskBvzYLNdiEo535uaO4IRQIWUmRG8gWsxQr/ikouJ1Odxg7kghE5GEROSciF0TkiS1e/6ci8kr96wcicnfTa5dE5IyIvCQip52IZ6+mMv6+lYTaHcFKboVipeh2KJ6UK+VYL677diRJg5UUubFuultaWO/cCLGWE4GIBIHPAB8G7gQeFZE7N+32FvCPVfUu4H8Hntz0+gOqeo+qnmo1nr0qVUqs5FZ8f0fQ6ByzDuOt+b2juCERTrCwvmBLV25jcWPRl5NCN0tFUkxnOrd8uxN3BPcBF1T1oqoWgS8DjzTvoKo/UNVGT+aPqC1S7wmNTxB+HmXwNpfqlPjBcm65K37HAQmgqqwVLOFvxU8LS91IY4RYpzqMnUgEE0DzMlmT9W3b+U3gG03PFfi2iLwgIo9vd5CIPC4ip0Xk9MKCc7dMKzn/lZ7eTiwUYzrbuU8RfjKVmeqKCwTURojZnd/1GvXC/N7MC7W+oFKlRKaY6cj5nEgEW11Gt0xjIvIAtUTwu02b71fVe6k1LX1SRD6w1bGq+qSqnlLVU6Ojzq1BOpOdIRb0d7NQQzKc7OjtpJ9Mp6e74gIBEA1GbYTYFrLFrG/rhW2nUwnfiUQwCRxten4EuO5qJCJ3AX8KPKKqb0+bU9Xp+vd54GvUmpo6xq81SbYSC8VIF9IUygW3Q/GUjdIG+Yq/Vp67kUQ4YQl/C93UUQy1eSOLG51Zg8KJRPAT4ISI3CwiEeBjwNPNO4jIMeCrwMdV9WdN25Mi0td4DPwKcNaBmHalWCmyVvBvyYHtWPvxtbrtAhEPx1ncWLQO400WNxYJSvfcDSTCiY6NEGt5sK2qlkXkU8C3gCDweVV9VUQ+UX/9c8AfAAeA/7feYVeujxAaA75W3xYC/ouqfrPVmHarW0aSbLaSW+Fg8qDbYXjG8sZyV/2eAxKo9RMU1hiOD7sdjmdMpv21NOVOkuEkc+tzqGrbBzo4MutCVZ8Bntm07XNNj/858M+3OO4icPfm7Z2ymvNv6entNDqMT46cdDsUz+imjuKGxpKGlghqqlplLjvHaMK5/kO3BQNBytUymWKG/mh/W8/V0zOLp7PTXdNR3JAMJ5lOW/txs5nMTFd9UoRah/Fcds7tMDwjW8xSrpa7qqMY6Fgxyd5OBF3UUdwQC8XIFDPWYVzXbR3FDdZhfK3V/GpXzBPZLBjozBoUPZsIipUia/k1okF/LkazE+swrum2juIG6zC+Vrd1FDckwomOrEHRs4mg0VHcjZ8ioNb/Ybqvo7ihucPY+L/09HaaO4zbqXcTQW61a2YUb2YzjH9uOjtNPBx3O4y2ULUZxvDzGcXdNiAA6h3GlTLZYrat5+nZRNBNM4o3sxnGPzeTmSEVTrkdRltEg1Hm1q3DuBtnFF9D2t/E2bOJYDoz3ZWfIKB2gUgX0j1fknqjtEGunOu6juIG6zCuaaww2K0CBNo+w7gnE0Gjo7jbZhQ3iIitYUz3XyDi4biVpKZ7Sk9vJxlp/x1+TyaCtfwaSvtn67nKSlKzku+eyrJbaZSk7vU1jLul9PR2OlGSuicTQbeWlmgWC8aYXe/tCpVT6amuWKP4RlR6e+SQqjK3PtfViSAUCFGqltraYdyTiWA2O0s01J3zBxqs/bg2IKDbJgxuFpZwT69hnC1mKVfKvl+jeDfaeefXk4lgJjPT1Z8goDaEdDW3SqlScjsUV3TLGsU7SUaSHZlw5FVrhbWubv5rEKStHcY9lwhKlRLL+WXioe4cW94gIiC9O8N4rbDW3X1AdfFQbYZxVatuh+KKxY3Frm/mhfYPCe+5RJAupEG7ZI3iHahqz84w7pWO8mAgSFWrPdth3E0rz91IIpxgJjvTtg7jnksEa4W1ris9vZ1eXtJwJjPT9Xd9Db06w1hVmV2f7fp+IIBwMEyxXGSjtNGW9++5RDCTnenaQnObJSPJni01MZPtvtLT2wkGgiyst79CpddslDYoVoo90VEMtLWp15FEICIPi8g5EbkgIk9s8bqIyH+sv/6KiNy722OdNpPu/pEkDfFQnOXcMuVq2e1QOqpQLpAupLt+ZFhDMtybCX81v9rVEwY3C0iA5Y3l9rx3q28gIkHgM8CHgTuBR0Xkzk27fRg4Uf96HPjsHo51TLlaZim31LUzijcTEQTpuWaDXukobkiEE8xl53quw3gpt9RTv+d4KM5Upj0jxJy4p7oPuFBfdhIR+TLwCPBa0z6PAP9Jaz0dPxKRQREZB47v4ljHpAtp8uV8T91Gz2RneHnuZW4ZusXtUDrm3OI5ZrOzhKRHmgyATDHDmbkzDMQG3A6lY87OnyVbzPbMSm3laplZbU+fnxN/KRPA1abnk8Av7WKfiV0eC4CIPE7tboJjx47tK9C+SB8fuOkDPdNZDDCyNsKPp37MzxZ/5nYoHZMupjl1+BSH+w67HUrHXFm7wtPnnu6qNXt3JPD+o+/vmSZAoG2VdJ1IBFvdm22+0m63z26OrW1UfRJ4EuDUqVP7upKHg2HePf7u/RzqW7cO3cpsZpaJ/gm3Q+mcNDxw/IGe6QsCGIgOMJudZaKvN37PhXKBQqXA+46+z+1QuoITiWASONr0/Aiwuedqu30iuzjWtKAv2kcwEKRS7eJ67U1KlRKRUKTrZ45vNhAb6KmO0/XSOscG9tcyYK7nxKihnwAnRORmEYkAHwOe3rTP08A/q48eei+wpqozuzzWtCAgAQ4mD5Ir59wOpSM2ShuMp8Z7qhMRancECG1f0tArcuUc46lxt8PoGi0nAlUtA58CvgW8DnxFVV8VkU+IyCfquz0DXAQuAH8C/MsbHdtqTOZah/sOs15cdzuMjlgvrfdU30BDOBhmODbcMwkfhaH4kNtRdA1HhlWo6jPULvbN2z7X9FiBT+72WOOs0eQoZe2NuQSKciBxwO0wXHG47zDnl8/3RLOYivbUCKl267mZxb1oINpbfzC99u9tONR3iEK54HYYbVesFOmL9PXMfKBOsETQA/qj/QQIdP2Eo3K1TDgQJhXpzsXqd9IrCXC9uG79Aw6zRNADgoEgI8mRthWs8oqN0gZjqbGe6yhuaDSVdHuHca6cY7zPEoGTLBH0iIm+iZ5IBL0yjn4rkWCEgdgAhUr3Nw8Nxayj2EmWCHrEweRBStXuXq2sUq0wkhhxOwxXjfeNd/0IMUUZjA26HUZXsUTQI3piwpHQ8yNJDqcOk6/k3Q6jbYqVIvFQnHi4N9aa6BRLBD1iIDqAiHRth3GlWiEUCPVsR3HDYHzQ7RDaar20bv0DbWCJoEcEA0EOxA+QK3XnhKON0gZjyTEC0tv/pbt95FCulOvpfqB26e2/mh4z0d+9HcbrpfXeKqy3jWgoSl+kj3y5e5uHbEax8ywR9JCx1FjXjiipqHUUNxzuP8x6qTs7jBXt+rseN1gi6CGNfoJuJIhdIOoOpw535R1BqVIiFoz1VHnxTrFE0EMaI2q6rcO4Uq0QkAB90T63Q/GEwdggsuVSH/5mHcXtY4mgh4QCIUbiI133aXGjtMGh1KGe7yhuGIwNdl2yh9qM4l6sLNsJ9pfTY8b7u2/CUa+Wnt5ONBSlP9rfdQXoqlrt2cqy7WaJoMeMp8YpVopuh+GoilYYTfbQWr27cLj/MNlS1u0wHGf9QO1hiaDHDMYGt14p2seso/h6h1OHyZe6pwnQOorbq6VEICLDIvKsiJyvf79ugK+IHBWRvxWR10XkVRH5rabX/q2ITInIS/Wvj7QSj9lZf7Qf6J4OY+so3tpgbLCrRohZR3F7tXpH8ATwnKqeAJ6rP9+sDPxrVb0DeC/wSRG5s+n1P1LVe+pftlJZm3Vbh7F1FG+t2zqMe72ybLu1+tfzCPDF+uMvAr+xeQdVnVHVF+uPM9TWJrbfqIsm+ie6psPYOoq31o0dxsOJYbdD6FqtJoIxVZ2B2gUfOHijnUXkOPBu4PmmzZ8SkVdE5PNbNS01Hfu4iJwWkdMLCwstht3bDqUOdU2HsXUUb2+if6JrOoyt9HR77ZgIROQ7InJ2i69H9nIiEUkBfwX8tqqm65s/C9wK3APMAP9+u+NV9UlVPaWqp0ZH7Q+/FQOx7pphbBeIrXXLDONG6elEOOF2KF0rtNMOqvrgdq+JyJyIjKvqjIiMA/Pb7BemlgT+XFW/2vTec037/Anw9b0Eb/anMcKmqlVft603Sk/3RayjeCtD8aGumGFszX/t1+pV4Gngsfrjx4CnNu8gtY+efwa8rqr/YdNrzcMAPgqcbTEeswvBQJDR5KjvS1Kvl9Y5lDrUVXc3ThqIDaCqvl/DOFfKWWXZNms1EXwaeEhEzgMP1Z8jIodFpDEC6H7g48AHtxgm+ocickZEXgEeAH6nxXjMLh3pP+L7CpXrxXUbSXIDkWCEwfig75uHFGU4bh3F7bRj09CNqOoS8KEttk8DH6k//j7bTGFS1Y+3cn6zf92whnGVqnUU72Cib4I3l9/09dKO1lHcfv5tIDYt6ZYKlXaBuLHDfYd9vQZFoVygL9JHLBRzO5SuZomgR/VH+wlIgEq14nYo+1KqlIgEIyTDVnLgRgZjg77uI8iWstb81wGWCHpUQAIcSh3y7dKV66Va/4B1FN/YQGwAxL8lRfLlvI0Y6gBLBD1son/Ctx3GVnJgd0KBEKMJH48QU6yjuAMsEfSw0cQoFfVn05CqWm36XfLrCLFGk1ZjZT3TPpYIethQfNuKHt4n1lG8W2OpMV+OEMuVcwzHh4kEI26H0vUsEfSwZDhJJBihVPHXRaJQLpCKpHw9JLKT/DpCbL24zsSANf91giWCHiYiTPT5r58gW8raTNM98OsIsUKlwHjK1iDoBEsEPe7owFHfjRzKl/JMpCwR7FZjhJjfEr4gDMV83HzpI5YIetxwfBjFZ+PMxWrT79WxgWNki/4pSV2pVggGgtZR3CGWCHrcUGzIVxOOGuPhraN4b0YSI776PdvKc51lP+UeFw1FGYz5pzBZrpTjYPIgoUBLZbJ6zlB8aJuKX96ULWY50n/E7TB6hiUCw9H+o75pNrALxP4kwgkS4YRvVqarqhUU7CRLBIbD/f5ZyaqsZQ6lDrkdhi8d7T9KpphxO4zdEayjuIMsERiGYj5ayUqtf2C/JvomyJe8n/CLlSKJcIJkxAoKdkpLiUBEhkXkWRE5X/++ZQoXkUv1BWheEpHTez3etNdAbIBgIOj5cealSolIKGJLU+6TX0ZaZYoZjvYfdTuMntLqHcETwHOqegJ4rv58Ow+o6j2qemqfx5s2CUiA8b5xz48zz5Zq/QNWcXR/BmODiIjnK5HmS3krKNhhrSaCR4Av1h9/EfiNDh9vHHK0/6jnE8FGacM+KbYgFAhxMHnQFxMIraBgZ7WaCMZUdQag/v3gNvsp8G0ReUFEHt/H8YjI4yJyWkROLywstBi22exg8iDVqrc/KaK18fBm/24avIlMwbsdxlWtEpCA9QN12I6DsUXkO8BWwzR+fw/nuV9Vp0XkIPCsiLyhqt/dw/Go6pPAkwCnTp3yz8wYnxiKD3l6hnFVq1Zx1AFjyTFPNw2tF9cZS40RDATdDqWn7JgIVPXB7V4TkTkRGVfVGREZB+a3eY/p+vd5EfkacB/wXWBXx5v2i4ViDMYGyZVynqzquVHa4GDyIOFg2O1QfM3rCT9bzHLH6B1uh9FzWm0aehp4rP74MeCpzTuISFJE+hqPgV8Bzu72eNM5xwa9W48mW8xy08BNbofhe4lwgv5oP4WyNxe0r1JlLDXmdhg9p9VE8GngIRE5DzxUf46IHBaRZ+r7jAHfF5GXgR8Df62q37zR8cYdE30Tnp15WlW7QDjl2MAxz04sU9SWpnRBSwVbVHUJ+NAW26eBj9QfXwTu3svxxh1e/gO0C4RzJvonODt/ducdOyxXyjEUHSIWirkdSs+xmcXmbX2RPmKhmOdWLMuX8/RF+kiEE26H0hUOxA94ciZ5tpjlpkFr/nODJQLzNhHh2KD3mg0yhYz1DzioP9pPJOS9JUqL1SKH+w67HUZPskRgrnG0/yi5cs7tMK5RrBY5MmAVR50iIhzr9+bAAGv+c4clAnMNT07YUrtAOO3YwDFPzTAulAskwgn6olZHyg2WCMw1BmIDhANhytWy26EAtUqU8XDcCs05bCTprYRvw4PdZYnAXCMgAY4OeKdufaaQ4abBm6zQnMMGY4OEg95J+LlKjmMDx9wOo2dZIjDXOT5wnFzJG/0EubJdINohIAGO9B/xTMIXFSs05yJLBOY6I0nvLHQuiDf7LbrA8UFvJPxipUg8Ys1/brJEYK4zGBskEoy43mxgF4j2Gk2OeiLhpwtpbh682Zr/XGSJwFwnIIFaGQKXyxXbBaK9vJLwC+WCNf+5zBKB2dJNgzexUXZ3eKFdINqrkfDThbTboVjzn8ssEZgtHUwe9ESzgV0g2uv44HFX5xPky3n6on2kIinXYjCWCMw2+qP9JMIJ16qR5ko5+mP9doFos4OpbRcF7Ih0Ic2tw7e6GoOxRGC2ISLcOnQra/k1V86/Vljj1iG7QLRbX6T2adyt9QlKlRJH+q18iNssEZhtHRs85todQaVasQtEB4gItw7f6ko/gaqCwGhitOPnNteyRGC2NZoYRUU73ldQ1SoiwmjSLhCdcGzAnYSfLWY5lDpENBTt+LnNtVpKBCIyLCLPisj5+vehLfY5KSIvNX2lReS366/9WxGZanrtI63EY5wVD8cZS46xXlrv6HmzxSyH+w8TCUY6et5eNZoYRUQ6vqh9upjmtqHbOnpOs7VW7wieAJ5T1RPAc/Xn11DVc6p6j6reA7wH2AC+1rTLHzVeV9VnNh9v3HXb8G0dn0+QLWbtAtFB0VCU8dR4x8tSqyrjfeMdPafZWquJ4BHgi/XHXwR+Y4f9PwS8qaqXWzyv6ZDDfYepaKWj57QLROfdduC2jtYdKlVKxENxhuLXNSIYF7SaCMZUdQag/n2nsWgfA760adunROQVEfn8Vk1LDSLyuIicFpHTCwsLrUVtdm04PtzR5SsL5QKpaIqB6EBHzmdqxlPj0MGuoNX8KrcN30ZArJvSC3b8LYjId0Tk7BZfj+zlRCISAX4d+IumzZ8FbgXuAWaAf7/d8ar6pKqeUtVTo6PWidgpAQlwYvgEq/nVjpxvJb/C7cO3W1mJDhuMDZKKdm4YabFa5PjQ8Y6cy+wstNMOqvrgdq+JyJyIjKvqjIiMA/M3eKsPAy+q6lzTe7/9WET+BPj67sI2nXR86DhnF8525FwVrXBs0MpKdJqIcPLASV6afYlDqUNtPVelWkEQDibdncxmfq7V+7Kngcfqjx8DnrrBvo+yqVmonjwaPgp05mpj9mQsOUZAAlSq7e0rKFfLhANhG1fukmMDxzpSgC5dSHPT4E02KsxDWk0EnwYeEpHzwEP154jIYRF5ewSQiCTqr3910/F/KCJnROQV4AHgd1qMx7RBOBjmlsFbWCu0d5bxSm6F24ZvIxgItvU8ZmsjiRGioWjb+4M2ShucPHCyrecwe7Nj09CNqOoStZFAm7dPAx9per4BXLf8kKp+vJXzm845ceAEF5YvQLx95yhUClZ3xkXBQJCTB05ybvFc2ybzVbUKgo0K8xjrsje7cih1CAlI25qHytUyoWCIseRYW97f7M4tQ7dQqLSvwzhdSHNs4BixUKxt5zB7Z4nA7Eo0FOXWwVtZLay25f1XcrXRQuFguC3vb3bnYPJgW5uHssUsd4zc0Zb3NvtnicDs2u0jt7etdn2hUuDEgRNteW+ze8FAkHeMvIOl3JLj712pVggEAhzuO+z4e5vWWCIwuzaeGiccDDs+sqRYKRILxaxZyCNODJ9oy+ih1fwqtw3dZkXmPMgSgdm1cDDMnSN3srTh7KfFpdwS7zz4Thst5BEjiRH6o/3kSjlH33ejvME7Rt7h6HsaZ1giMHty+4HbKVadLVlcrpZttJCHiAh3j93NSn7FsfcslAskQom2T1Yz+2OJwOzJSGKE4fiwY5Uq04U046lxhuPDjryfccbNQzdTpepYaeql3BJ3HbrL7vo8yhKB2RMR4d7xex2rPZQupLl77G5H3ss4JxlJcvvw7Sznllt+r6pWUZQTwzYYwKssEZg9Oz54nHAw3PIQw0K5QDQUtdpCHvXOg+8kV269n2Alt8Itg7fQF+1zICrTDpYIzJ5FghHuOXQPi7nFlt5ncWOR94y/h1CgpQnupk0OJg8ylhxreT3jjfIGdx+yuz4vs0Rg9uWOkTtQ1X3PNC5VSgQDQW4/cLvDkRmniAi/OPGLLdWYWs2vcjh12CqNepwlArMvyUiSuw/dzfz6jSqPb29hY4F7x+8lHm5j8SLTsiP9RxhJjOx7udJMIcN9R+6z9SU8zhKB2bd3HXwXCHvuKyiUCwQDQX7h4C+0KTLjlIAEeP/R9+9rKOlSbokjA0dqq58ZT7NEYPYtGUny3iPvZW59buedm8xtzPH+I++3wmM+MdE3wfHB4yxu7L5PqFKtsFHa4P6j99vdgA9YIjAtuXP0TobiQ6zld9eOvJxb5lDqECdHrB69X4gI9x+7n2KluOu7v9n1We4dv5cDieuqzxsPskRgWhIKhHjwlgfJlrIUKzeecVwoF8iX83zw+AdtYpHPDMYG+eVjv8x0dhrVG69yv5JbYSg2xHvG39Oh6EyrWkoEIvI/iMirIlIVkVM32O9hETknIhdE5Imm7cMi8qyInK9/H2olHuOOkcQIDxx/gJnszLbFykqVErPrszx4y4MMxe3X7Ed3jN7BO0ffyWR6cttkkClkKFaL/Optv2olxX2k1TuCs8B/B3x3ux1EJAh8htri9XcCj4rInfWXnwCeU9UTwHP158aHTo6c5AM3fYDpzDTrxfVrXssWs8xkZ/jQzR+ymkI+JiL8o5v+EbcfuJ2r6avXNRMtrC+QK+d45OQjDMYG3QnS7EurS1W+DuzUGXQfcEFVL9b3/TLwCPBa/fs/qe/3ReDvgN9tJSbjnrvG7mIoNsTfX/57pjJTKAoKA7EBPnrHR60OfRcIBUJ88JYPcjB1kOcnn6eitXkkqsrRgaN84KYP0B/tdzlKs1edmNI5AVxtej4J/FL98ZiqzgCo6oyIbDvrREQeBx4HOHbMShJ41dGBozz6zkdZ3FgkX84TD8cZSYwQEOuO6hYBCXDX2F2cPHCSxY1FytUy/dF+a/LzsR0TgYh8B9iqduzvq+pTuzjHVrcLN+5t2uoA1SeBJwFOnTq15+NN5wQDQcZStshMt4uGokz0T7gdhnHAjolAVR9s8RyTwNGm50eA6frjOREZr98NjAP7m6ZqjDFm3zpxv/4T4ISI3CwiEeBjwNP1154GHqs/fgzYzR2GMcYYB7U6fPSjIjIJvA/4axH5Vn37YRF5BkBVy8CngG8BrwNfUdVX62/xaeAhETkPPFR/bowxpoNkp8khXnTq1Ck9ffq022EYY4yviMgLqnrdnC8bymGMMT3OEoExxvQ4SwTGGNPjLBEYY0yP82VnsYgsAJf3efgI0Npiu+1jse2dV+MCi22/vBqbV+OC3cd2k6qObt7oy0TQChE5vVWvuRdYbHvn1bjAYtsvr8bm1big9disacgYY3qcJQJjjOlxvZgInnQ7gBuw2PbOq3GBxbZfXo3Nq3FBi7H1XB+BMcaYa/XiHYExxpgmlgiMMabH9VQiEJGHReSciFwQEU+sjywiR0Xkb0XkdRF5VUR+y+2YNhORoIj8VES+7nYszURkUET+UkTeqP/83ud2TA0i8jv13+dZEfmSiMRcjOXzIjIvImebtg2LyLMicr7+vePLi20T17+r/z5fEZGvichgp+PaLram1/4XEVERGfFSbCLyr+rXt1dF5A/38p49kwhEJAh8BvgwcCfwqIjc6W5UAJSBf62qdwDvBT7pkbia/Ra1EuJe838D31TVdwB345EYRWQC+J+BU6r6TiBIbR0Ot3wBeHjTtieA51T1BPBc/XmnfYHr43oWeKeq3gX8DPi9TgdV9wWujw0ROUqtZP6VTgfU5Atsik1EHqC2BvxdqvoLwP+5lzfsmUQA3AdcUNWLqloEvkztB+cqVZ1R1RfrjzPULmaeWf9PRI4A/w3wp27H0kxE+oEPAH8GoKpFVV11NahrhYC4iISABD9fla/jVPW7wPKmzY8AX6w//iLwG52MCbaOS1W/XV/DBOBH1FY07LhtfmYAfwT8r+xjuV2nbBPbvwA+raqF+j57Wu2xlxLBBHC16fkkHrrgAojIceDdwPMuh9Ls/6L2H7/qchyb3QIsAP9fvdnqT0Uk6XZQAKo6Re0T2RVgBlhT1W+7G9V1xlR1BmofRoCDLsezlf8J+IbbQTSIyK8DU6r6stuxbOF24JdF5HkR+XsR+cW9HNxLiUC22OaZsbMikgL+CvhtVU27HQ+AiPwaMK+qL7gdyxZCwL3AZ1X13cA67jRvXKfe3v4IcDNwGEiKyP/oblT+IiK/T63Z9M/djgVARBLA7wN/4HYs2wgBQ9Sal/8N8BUR2eqat6VeSgSTwNGm50dw8Xa9mYiEqSWBP1fVr7odT5P7gV8XkUvUmtI+KCL/2d2Q3jYJTKpq4+7pL6klBi94EHhLVRdUtQR8FXi/yzFtNici4wD173tqSmgnEXkM+DXgn6p3JjrdSi2xv1z/ezgCvCgih1yN6ucmga9qzY+p3cHvujO7lxLBT4ATInKziESodd497XJM1LP2nwGvq+p/cDueZqr6e6p6RFWPU/t5/Y2qeuKTrarOAldF5GR904eA11wMqdkV4L0ikqj/fj+ERzqymzwNPFZ//BjwlIuxvE1EHgZ+F/h1Vd1wO54GVT2jqgdV9Xj972ESuLf+/9AL/ivwQQARuR2IsIdKqT2TCOodUJ8CvkXtj/Irqvqqu1EBtU/dH6f2aful+tdH3A7KJ/4V8Oci8gpwD/B/uBtOTf0u5S+BF4Ez1P7OXCtPICJfAn4InBSRSRH5TeDTwEMicp7aKJhPeySuPwb6gGfrfwuf63RcN4jNE7aJ7fPALfUhpV8GHtvL3ZSVmDDGmB7XM3cExhhjtmaJwBhjepwlAmOM6XGWCIwxpsdZIjDGmB5nicAYY3qcJQJjjOlx/z9lTrGPSZ625wAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
    " ] @@ -775,11 +685,10 @@ ], "source": [ "# 用fill来绘制图形\n", - "import matplotlib.pyplot as plt\n", "x = np.linspace(0, 5 * np.pi, 1000) \n", "y1 = np.sin(x)\n", "y2 = np.sin(2 * x) \n", - "plt.fill(x, y1, color = \"g\", alpha = 0.3)" + "plt.fill(x, y1, color = \"g\", alpha = 0.3);" ] }, { @@ -821,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.607540Z", @@ -831,7 +740,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -841,14 +750,12 @@ } ], "source": [ - "import matplotlib.pyplot as plt \n", "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n", "sizes = [15, 30, 45, 10] \n", "explode = (0, 0.1, 0, 0) \n", "fig1, ax1 = plt.subplots() \n", "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) \n", - "ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle. \n", - "plt.show()" + "ax1.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle. " ] }, { @@ -860,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.703570Z", @@ -870,9 +777,9 @@ "outputs": [ { "data": { - "image/png": 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e2cnaHtHls870NZi+Bjf3B0BrKkJLd4HmrjTJznTZ9wd4PsLlX/UwMVyj25gqaGZijq7N7UGXIVJ2oQ789HymLlfFzM7DxFCBiSGAJqCJ5rYIrd2Q7MqR7MrQ1LlQ8tTQQjbGxf9I1dw69tUyNT6rwJeGFOrAz6YbY6okwMJUgYUpYCACNGORZlq6Pjo+IE9T5zxNbWu/39xCE+ff6mRhujFn4hRjYS4ddAkiFRHqwM8sNE7gr+QFmB0rMDsGi0smtRKNGy2ppf0BXZ/cH5CZSTLwZju5hfCGPUAhX3+/+kSKEerAz2Wrs/OzVuSzzvRVmL4KH+8PaDE2pIxkV4GWWIy9+xv3S7BYGzs1wpfGFOrAj8VD/fYByM4543PO+CWIxXPceV+B7XfOkEiEOPhj2n4vjSnUiRdLhPrtf0IuC6d/FeHMe23svbfA7rtmaUoGfxxA9elzIY0p1J/suAJ/VV6Ac8cjDJzYyM67YM89s7RsqPET1JZTpDXoCkQqItSJ19xan2doqhZ3uHAGLpxpZcueVvbdO09bR20ft1AW1hJ0BSIVEerAj8VjtLa31PzBV7XgyiBcGUzSvTXJ/kNpOrsb+ETvphG+NKZQBz5AW3ebAv82jA3D2HATHT3N7D+UpvuOGcwaaRpjAqwt6CJEKiKch1Iu0969MegS6tLEqPPuLxL88v90ceVSO4VCg3yUot1YI5+9RkKtQf5KS9ezPRV0CXVtZhKOvhbj1Z92MjTQQT5f5x+piM54JY2rzv86168ttZHWdu2kW6+FWXj/rSivvNDJ4OkOspk63VoY1Vr40rhCH/gAW/ZuDrqEhpFNw6m+KL94vp2zxztJL9TR6QIjXVhEB11J41LgA1v2KfDLrZCHs8civPL8Rj440sXcXB1MgY3uDroCkYpS4ANtXRvp1rb8inCH86eMV1/YwLG3U0xPJYMu6RZiENsVdBEiFaXAX3Ln5/YEXULDu3wOfvliC32vpZi4UWNz3WN7MauDXyEi61Cne9bKL7Wlk847Ohi/OhF0KQ1v5BKMXGqma3Mz+w9l6NoU9Fz+GMTvCfD1RapDI/xl7urdF3QJoXLjGhz+eYI3Xkpx9XIbhUJA899j+zGr1U1NIuWjwF+me2uXZuwEYHrc+dW/x3ntxS4un2+v7lx+2wDxe6v3eiIBUuCvcPDhu4k3xYMuI5TmZ+DYGzH+/X93cuFsJ7lctPIvmujFTFs2JRwU+Cs0tzRx8OG7gy4j1DILcPJwhFee7+DcyS4y6Qp9AUd3Y9E7KvPcIjWoqMA3s0fN7IyZ9ZvZd1a5/4/N7P2ly1tmdn/5S62e7fu3sP2urUGXEXq5LJw5avzi+TZOH+1iYb6Ms2gi7ZB4oHzPJ1IH1gx8M4sC3wceAw4CT5rZwRXNBoHfdvdDwHeBZ8tdaLV95pEDtPdo1cRa4AUYOGm88vwGjh9OMTvTvL4ntAQkHsFMm+4kXIoZ4T8I9Lv7gLtngOeAx5c3cPe33H186eo7wPbylll90WiUB75yP4lkHS0NEAKXzsJrP2nlV79MMTVeyhpIBomHsciGstcmUuuKCfxtwKVl14eWbruVPwV+ttodZvaUmfWZWd/o6GjxVQYk2dpM71fuJxqrws5DuS1XL8Ab/zfJ4V+kuDF6G+Edv1/b7SW0ign81SZHr3qUjJn9DouB/+3V7nf3Z9291917e3p6iq8yQJ2bO+h99LMK/Rp1/Qq883ITb/4sxcjwRtw/ZS5/bD8WP1C94kRqTDGBPwTsWHZ9OzC8spGZHQJ+CDzu7tfLU15t6N7apdCvcZPXoe+VpROyXFzlhCyxezDtpJWQKybw3wX2m9keM0sATwAvLm9gZjuBF4A/cfcPy19m8Lq3dtH7VW3eqXUzk3D09Riv/qSLS+c6Fufyxw9hibqeOCZSFmsGvrvngG8BLwOngH9y95Nm9rSZPb3U7K+AFPADM3vPzPoqVnGAurelePjxL5DcsM5ZIlJxC3POicNxrl39DSy+clKZSDiZezCLVvX29npfX31+LyzMpTny/44xMTIZdClyC7FEjM//50M6haU0HDM74u69pTxWR9qWoLmliYd+/wG23qnZHrWoY1M7j/zhQwp7kRW0iEiJotEon/vyffTs6Obkm6fJZXJBlyTAnvt2cuCL+4lENJYRWUmBv07b928htaWDo6+c0Fr6AUokExz6rYNs3lUf031FgqDAL4PkhiS/8fu9DLx/gbNHBsjn8kGXFCq7Dm7n7gfvJJ7QUgkin0aBXyZmxr77d7P1zjs4ffgsw/1Xgy6p4W3s2sB9j9xD5+aOoEsRqQsK/DJLtjbzuS/fx857tnPyzdNM35gJuqSGk0gmuPNze9h1cLu21YvcBgV+haS2dPLIHz7ElYFr9B8dVPCXQbwpzt77d7HnMzt1AJxICRT4FWRmbN13B1v33cG1C6P0Hx3U3P0SxJvi7Lp3B3sP7dR2epF1UOBXyeZdPWze1cPY8A3OHR1k7PKNoEuqeW3dbey+dztb992hEb1IGSjwq6x7axfdW7sYH5nk4gdDXBkcIZ/VHP6PWCTClr2b2P2ZnXRuag+6HJGGosAPSOemdjo3tfOZ3zzAyMUxhs5eYfTSdbxQCLq0QLS0tbD9ri3svGcbTckynspQRD6mwA9YNBZly97NbNm7mUw6y9XBEa6dH2Hs8g0K+cYN/2gsSmprJz07uunZkaK1rZSzV4nI7VDg15BEU5ydB7ax88A28rk8o0PXuXZ+lJGLY2QWMkGXt26t7S307Ohm085uurZ0EI1qu7xINSnwa1Q0FuWO3Zu4Y/cmAGan5pgam2ZidIqpsSmmrs/U/JdAvClO5+Z2jeJFaoQCv060trXQ2tbClr2bP75tfmaeybFpJkenFv8dmyIzX/0vgWgsSmt7Cxu7NvzaJdmq8waI1BIFfh1LbkiS3JD8+FcALK7VPzc1R2Yhu3TJkE3nyCxkyCxkyS7dllnIkk1nf+35LBIhEo0QjUWIxqJLl1//fyweI7mxmZa2FlrbkiQ3Jmlu0U5WkXqgwG8wzS1NRQewu5NNZ7GIEY1FtUyBSINT4IeYmZFoTgRdhohUiYZ0IiIhocAXEQkJBb6ISEgo8EVEQkKBLyISEgp8EZGQUOCLiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIKPBFREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgiIiFRVOCb2aNmdsbM+s3sO6vcb2b2d0v3v29mny9/qSIish5rBr6ZRYHvA48BB4EnzezgimaPAfuXLk8Bf1/mOkVEZJ2KGeE/CPS7+4C7Z4DngMdXtHkc+Adf9A7QYWZbylyriIisQzEnMd8GXFp2fQj4YhFttgFXljcys6dY/AUAkDazE7dVbePqBsaCLqJGqC9uUl/cpL646e5SH1hM4Nsqt3kJbXD3Z4FnAcysz917i3j9hqe+uEl9cZP64ib1xU1m1lfqY4vZpDME7Fh2fTswXEIbEREJUDGB/y6w38z2mFkCeAJ4cUWbF4FvLM3WeQiYdPcrK59IRESCs+YmHXfPmdm3gJeBKPAjdz9pZk8v3f8M8BLwNaAfmAO+WcRrP1ty1Y1HfXGT+uIm9cVN6oubSu4Lc//EpnYREWlAOtJWRCQkFPgiIiFR8cDXsgw3FdEXf7zUB++b2Vtmdn8QdVbDWn2xrN0XzCxvZl+vZn3VVExfmNmXzOw9MztpZq9Vu8ZqKeJvpN3M/sXMji31RTH7C+uOmf3IzEZudaxSybnp7hW7sLiT9xywF0gAx4CDK9p8DfgZi3P5HwIOV7KmoC5F9sXDQOfS/x8Lc18sa/cKi5MCvh503QF+LjqAD4CdS9c3BV13gH3x34G/Wfp/D3ADSARdewX64reAzwMnbnF/SblZ6RG+lmW4ac2+cPe33H186eo7LB7P0IiK+VwA/AXwPDBSzeKqrJi++CPgBXe/CODujdofxfSFAxvNzIANLAZ+rrplVp67v87ie7uVknKz0oF/qyUXbrdNI7jd9/mnLH6DN6I1+8LMtgF/ADxTxbqCUMzn4i6g08xeNbMjZvaNqlVXXcX0xfeAe1g8sPM48JfuXqhOeTWlpNwsZmmF9SjbsgwNoOj3aWa/w2Lg/2ZFKwpOMX3xt8C33T2/OJhrWMX0RQx4APhdIAm8bWbvuPuHlS6uyorpi68C7wFfBvYBPzezX7r7VIVrqzUl5WalA1/LMtxU1Ps0s0PAD4HH3P16lWqrtmL6ohd4binsu4GvmVnO3X9SlQqrp9i/kTF3nwVmzex14H6g0QK/mL74JvA/fXFDdr+ZDQIHgP+oTok1o6TcrPQmHS3LcNOafWFmO4EXgD9pwNHbcmv2hbvvcffd7r4b+Gfgzxow7KG4v5GfAo+YWczMWlhcrfZUleushmL64iKLv3Qws80srhw5UNUqa0NJuVnREb5XblmGulNkX/wVkAJ+sDSyzXkDrhBYZF+EQjF94e6nzOxfgfeBAvBDd2+4pcWL/Fx8F/ixmR1ncbPGt9294ZZNNrN/BL4EdJvZEPDXQBzWl5taWkFEJCR0pK2ISEgo8EVEQkKBLyISEgp8EZGQUOCLiISEAl9EJCQU+CIiIfH/AaeC+L5yRtcNAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "
    " + "
    " ] }, "metadata": { @@ -882,26 +789,21 @@ } ], "source": [ - "import matplotlib.pyplot as plt \n", - "from matplotlib.patches import Circle, Wedge\n", - "from matplotlib.collections import PatchCollection\n", - "\n", - "fig = plt.figure()\n", + "fig = plt.figure(figsize=(5,5))\n", "ax1 = fig.add_subplot(111)\n", "theta1 = 0\n", "sizes = [15, 30, 45, 10] \n", "patches = []\n", "patches += [\n", - " Wedge((0.3, 0.3), .2, 0, 54), # Full circle\n", - " Wedge((0.3, 0.3), .2, 54, 162), # Full ring\n", - " Wedge((0.3, 0.3), .2, 162, 324), # Full sector\n", - " Wedge((0.3, 0.3), .2, 324, 360), # Ring sector\n", + " Wedge((0.5, 0.5), .4, 0, 54), \n", + " Wedge((0.5, 0.5), .4, 54, 162), \n", + " Wedge((0.5, 0.5), .4, 162, 324), \n", + " Wedge((0.5, 0.5), .4, 324, 360), \n", "]\n", "colors = 100 * np.random.rand(len(patches))\n", - "p = PatchCollection(patches, alpha=0.4)\n", + "p = PatchCollection(patches, alpha=0.8)\n", "p.set_array(colors)\n", - "ax1.add_collection(p)\n", - "plt.show()" + "ax1.add_collection(p);" ] }, { @@ -924,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:17.799637Z", @@ -934,7 +836,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
    " ] @@ -950,8 +852,7 @@ "x = [0,2,4,6,8,10] \n", "y = [10]*len(x) \n", "s = [20*2**n for n in range(len(x))] \n", - "plt.scatter(x,y,s=s) \n", - "plt.show()" + "plt.scatter(x,y,s=s) ;" ] }, { @@ -970,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 87, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:18.406235Z", @@ -980,7 +881,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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1o/8GzffjebsPALw878H/pMLu//IB2L2M+ylgSPPaPmQQsl9Qbi5M9r3hRBS7Le23vAe0e1zX7icRlBdvZn+dVr4r7zl+Q5ykAHZ0dHR0dDyXITtkzGlAjLfKgCt+ez8DZOe5qpSVZ8rUzrkngg4RwWs+TpfcqyGCSmSfTX4e+TEPIwC7e5nVTmC1tXmI0PoXlclUwHrBpjgFaZEH9EU2EF35oi3Jaf4EQ5DAyFAwGIkUGWL7qkhkKrOAkKBmcTUV8LZI4BLFMev3X6l5DtT7DZ0RaSUCMQFClRQymxpYvrD//kTstVDRiUEsULHf8Ckq4Bo6Aezo6OjoeMGgJRZM2hBCQSKt5A5ihEODIMZ+JXv5AAk8VvU7BkQ6D+9q1T0nF/Gg5vWWdATh2/1wNIrdUsGi+pk8Z0v+1f2JVhIY594ACsKow8ylDpiLNvlFCFIh6ZX8BREUsDISFJkEDELyL8LafFcngfHZp6KdJFCjAu6QPaDao9iKoA0ZV7cDMYx024eW+05EUGIj5cL2m3DyR0T+k9VKAk+LrFhFJ4AdHR0dHS8YtIqcKJX4P4Ah6nF8hGtJYEv8Zvt7/tbcVXwCQSrEIvZbtWh5LpxQVMWuKnx2rBA3JyBG7uyrKhFA6mTDExSCkBCV84LwzUjg7oUf/x0XMBcwIWuaHTf1zu4jkxRCuF2hKksVMJOCoUUFDLTET1BdxNfZKMgfkzbkz18kv99uryB8hRyWB7kq60SPYcof2401pbVRANmZIqMkmqgrhCSCOpfhmm1yAxWwE8COjo6OjhcEFIRRjFSEupRBpvwVMmgsJ4OQQOAgQUeQwDVEEoj9LX4wYtGiUZGoVfYa1YmwON4qhA0ZISb7Kk4uac0N3HymXRhmzPMwCXwwqCfNLCENceMgamBsMBUSmCDIUGxR359cBSxu+1YFROW19e8ECdxVcfe5+ou6urZtFdmwBdfn4HD9spNEAiWuyio7yVOFpmQuX2KAnc7mbK8DlhTC8OP2bR6UBHYC2NHR0dHxgoACmJRNCdIgglqUv+zqECs5ETM1cJIEIcIGNdaqJYH24UYWHoobONS/oto1JLIc30/62uNrcYAgmC80OF+ogAmgDCABmhsSmHgW5xdfc8aNbuAFViVcyQYAGoUWGIEmEzsIYJA4cTVwQfyUMQaVoYykhDHsV67RPiN72Zfr0GaLt0SdGoWvJXk6swk19gQ0ETRb3J+6HayEjBlHE7sSy/4VsrmCE0yZDRLoGcGtGkhIgNIDZQYDnQB2dHR0dLxAoEqYhGexW6UEjJO+IINBBIXUSvPACAhDkVQgRE4knSwuiCBQlaSoxdiiZCCvEA4uhGI9bs+I24L4AZVk8IL0sZGASN7QUPEU0ATjP65EmaPYNDFzMfpnL0lgK5t5/J9GQN0Nkw8EhEsngEvFjf2z25I8mRty3iDpEF+ogoAzlUoC/RhQbdJmDc/+9p6SPwAKqcZs6+5abghheZDbSX2fgMTmuWV31aeIqZzfTwJqRru6mqtivwdXDUntN6kZNTGEUL/zETbqBLCjo6Oj4wUBhal5wHIwNyUH6vsN8UtQDHD16TqXMLDjDy2FuNFuo4zJfrWpdf8GEaxKkpM/LBS/hvgZqWhj/ioJsfixqgLapRtpCK8jmAovLMQvOW+QIBheC1AWSSCtT/oBYAqg0Y9lNnYkgbR1GUUIQrxbuC7MQva+Nh4QCoxkiSItQVza5JCKy15smp2QV8I9J+ct+UOzrwnQ7HZKFu+nifz+hrEAVd25mySufYpPPtaIICpZJHIVEMkUwfYD9ySMdALY0dHR0fGCgIIwxYCuVQkMVYmpFn/mRgEEo+xvOBeX8FINTBCIphlpOSXhg6E7maUUS7GUB+26b3fcvPP9Svzc80mh/JkKGCtQKNNMMIt9IxHuDrYbBhV3RJdkB8xVpRu4gAWVAJZ7UzJ+5+5XAMjEyF4iJoig1QbcgnVOyOuXyvXLNcigYrNSO7AhgWGj5BOEcAHTTP3z+x3qX+zPVEDP3HXyp2LHoBYLqAwgKch+fLuXSnH/1VeK2SWCGueFGujfaye2tBPAjo6OjoePcBmKEhLZ4MKUF+dYSZEMtqB2P/dBcLC48AGhplUv7NyFLy2OM11f960tdlveA4szO/zOW4UqMObkl1KTLWLLrvAtiaBkwsDZ1CIJFzFhQMZOljBQnzvaotLHoBA/FL43t9WC+CnVy1D2fV7sa7h/XR1SMoKhjTux+fgdEqhkFfQa9U89TLKSjFtyASvhfj4rz1u1NrlN4jiTQogg/reDCJoaeFZVQKBm//qXyiQ4U4GAyu9gSf6uQxsHGISs2AZVtdWZKlvVWRkILCgqIJQgA8P5ul9JAqaGrxLVuoDqq7WsEME4t7iF/fxjs3Y6Aezo6Og4gKyMp8e1dUvXMXBGEsVAGRvJGNiC1zecy7JwG56s21cb3DaUPdvRiGLyEe2MgK0CZwBAKCUyABTyMnveqBXE6vFHWgLQNbkakTB7yGBjBlG4DgmUnZy4okQZYOLiDlRRMJEHs4u/J1sSwhRv9hIYdwQFYSs1+WO2Le48BROX+zewLd1n3GeuBtq+ztRAJp3VqAOws6KIrfs7dy2uEfW5GzgSDPz+Mqrt2DNHl27gqDXXkEME4fO1Z0112pXCZnTf3bsEbohf4wbGgvNFEeMHhIDwzHQ2O1btJbMl/NhV2TOeMGrCOU8Qj+G7oMnIIRhCoyuDvi42WfawuYfVJlnQpgyMrTDStqmAuZOrAhmEnVhMvfN2hcY28/i/hRKYABVbB5pKbGZ1AysEFO5gv9mFCGap9lkSQWDVLXysbToB7Ojo6DiArISnp/Ojzx9IMHDGJrbiWzUCuOGMMSffnzwgnedB7jSVgreI7Z5lx5jqYBUqBbOaWFXcUlrjkRIgCaBkg5AMCpIgfArOVBcpYIAnuOJBEPY1ZcnWARZiq2NGZIHsDB8gTcEgIssovSO0CuCSAFbyp4UMDiwQIQyUIUT2/Bo1MGrTleQQnOYGrtdRn0fCRyV/4XJs4gBD8ZNd9c+IXiV+qoBdvpe+0dbZ21wLjESSaCV44iQliCBczW0Z4A1NmpXxjkWbWqp+ra0GzjjnZG1H63rLI0244NGWkiMjdBfEAG8Lu81gnHnbyhFTqHy8AhhtC9XtXt3AWuv7tXF/Zd+TcxoCKMpguCt38MzfCdDBJmAQI30K1NVYFnGYhQgCc7cwUGMAjkAngB0dHR0HIGA8u1ArDiEI4MCCQUz5G0RwzhPGhggKUwluXw1wD9Bkqx5Q3huw3patSCwlroy4VSpQVKVQJWQwZU9SqH0+kOSFGhgqYGypUQMnU0CYvFDtQg2cLXH1kKGwLOBAkC3AxuxWJR1YrDwHKYQJAxmpG1h21ECw1UoJ9/HSFRxr1ZoSuJ8dLWOzKI6FahukQggRT0aN3UoB50ZhQrh8I4FDGxcwXC0aFJjWSWB5GkyTnWzAVCYy/zAUoST66TcQdkUJT2+NAB5Wau1xxhO2LDjjCeecMHGCJEJmLmqgNK7hLIQLGksNQQFjQ9Os8HQk6+zPCF5MqooKGG0KxUazSRUTNCkkObFORvxU7LYynKgnT7N2EojsDSy7ap4JSlYcmorb123jhaPJJWGN4+pkMOGo5fo6Aezo6Og4ABHCO7bHK4CblJFIMPiANXDCGWdMyjtE8Jwni2nysaAlglHwlsHYUN5btiK1qhac9LAdE1YPQlegdf0OzssGgLIN7Jydz5AtV0VZnRBiRgDZY53QqoHZSBTlRg1MBJrkbgmgAttpGQNYiWDZAsjCSGxqkypBmMDEEBWIE/hQA00llKIGBhGUpiYdYOqOwGrNia7XnKtqUlxTKEtaFEA7ESgJHzPC5zzMiYe5fG0rKX5KUfSaILDYRwzqJKO6kymjZBBDyZbNCCIYq4HA1b8ghQU3cAEL4R1X5ztxmq19WgJ2xgM2KeOME7YpY0qMURlPpBGjJnP5Mhc1/YJ8YsUjMqyETKkBuUcFjMlVKgrkQolkMWW9qLFO7oK0U21fQQZF/D46MWeNyjrs7RTAVL+7iXexdTVQdE4E2Q3OZCuCiBRFEODqBu4EsKOjo+NmEBAup+O7ylEYGxYkFkzMOEsZkzDOEheV6Rw1qF3aAR8oWY2MIBwep3RNf966FkOpIA9EtxHVY8US1VhAMTKoYjFvlF18cgWQs3qInyllhRDGeUwgEhATOAuU2JRDlrq8mNxAKjoRUQew3BPf1thIuz+hkpaEHSYMICs7wl52RPN+NZBg2cAPmOIyj9esKpPFbcJIu5M8S/6g6gKOnJTUEkGqMX9L929LAv05Qb1gsRMMWRBBdRUyPhNO6oH6O7yhAnh/a3UA2yLLM6WtsdXICRvJGDlhK9aeLlLCJAlTGiGJnHhHpjBBeAQkMognnCEjwwh8ICt7GZ89yjpaJbK668NOhQTGxCr71tuYOKdXV2XNJjVvxQrXRLux9gfSuRoYirC48qjqbmCtMzLhehzwgtHX26ETwI6Ojo4DEKmD1TEYUkZORgAz2wCzYRtkztJUkgTOIggsRgTfDwJ4RoxRB1/w3gaOUDgCpZxJo1SEqsXckkCtA1MZpGzAksH+vpE8iztiatRAcTWQCBzEz1UpntQGr7JIvY94TP4I1+PdQAFMCwXQOOjclZeFkFihDREMFVCVkFjQjtTcxAMGISxrCi+Q97iBWzvF9RWiw2okk6iS9ob0IeL8krt0g4sFIVQAKYhexZIEUih/Ai/5UtXASCQJwkKxVJyGbDV3Ad+IAAphe9XQD1dCAY9fbe5LuOuHlDAmS6yahI38Daa4ipd2yR5vKty4g5s7kohqDB6MgK0lgdQyMLVNpVAA2VbjQNLyGy+JHtyofym86ZV4x1rFMxLo5A+xLrVoUQHtBM/4zZX8FTIoC1WwqH/NdzqQsd0JYEdHR8cBqBK24/FdZRbGlAVDypAhG+FL1BC/afdNnm2aVMBqxYm3OoAhyGTKRmRGArslLNqBaunuFFYbrIQqERRXAQePTxrgih2ApRpYsoNhA/WKGohsqgtN4kSxUTXujv8BSpgmLn9zVmplQSpU50RQ4TF8rgbGvqjFdILRFIy24tGxqghg5Uv2LTO2zACucW81DpBYrVBzuIFJS4wfeV2+cD2aO9DdjR5XFm5GXQz4hQRGvZnm85GphAWqmqILiXgy+y6FDPr9LbiJYZUwLQggKOIh5/YiVkxJMAwZU2acDYwsXNZgDld7TvPJEQB3B9fs4DNkgCZkpFlG8PJ9QBD2eYJVO7FSD63QRE78FBSqeoK3LVMBC5FuSSA1kymqMbWa/bcgRvpU4GxRjQwKnJyb/XRB/gohPAKdAHZ0dHQcgCowjrsL16+B4O5DJ3yqhJyszl9mNpWpIYNLJBKwKBIrNjpZHJOXtADyTFkqS2U1cVQ78UrNYAXyOEBpXFURohecQKr7t6iB2cYvDpInCzXQVSh73x418I5gy6XOXcA1ycITPgRgFhPYWMogbYSviVec++rKsZoUwq6uxd+rsWX77NtmIM9iEoPwALPEHft49ZhLlGxfauL/iis4xVWE6ldvgoAKGY/7UGysDRFUVwS5EklS1DKIzcc+mPO73iq9TPMP9XhHuLs9kpiIFTkxcmbkIRv5G7jEWU4SNRg9hg8y/1utHcshKTn34TYOEhgFvwEj9QNb6aYU6rpfE5r42qKuD3NVHRqEz79guOCL2uvNIzLsJ88Iz+oTgIYIRmKPIH7oAPs6wktlEDio/AU6Aezo6Og4BCXIeHzdCxGymDpxAijixM+GnJYYxECdSMGquJQNmE0JHHUAq1oCiDLyiuJSVk2AlkfyeCUjgVxJYPJBJJQjjytiIXf3oqqAZetqoDRqYKaStBCDE1Gsl0s7aiAdEYx+eyDI5KU1KMpkNNcLgFggksAeZ6dsK4JoU9w5yLtlU2JGAgcWW27OYwGhRuwOZf8CrhTqnJzO4t1YISwgTZUEChAZ3EUFTICoXXOQPl3MT4LwMZy8+32oRYyrGmjLiMGXiaOSbEqKkmBCHGrg7biAoQS6au5Xua5IqKj76gq2DgTJDNm4qh6k/Yh05MSC1Ng3gcsEal85mFb9S2xJXYk9wYoFktgzgBcqYKOqB/GbkUAn36F0FsWzqbdJnvBBOep4qk3SsrjLX4tbWAv5Q6PUHkcCOwHs6OjoOAQFdHucAggAOogF4TuJ0AHuXrTX2wGrLX0RysNICRvNZcmqrFTcwFFouAU3WYuhKCUWEFJ5HqsShCKnTmxs7LCBP8qH7ZR8mdUIXFcDI4FgTQ1cLILycKGATiaVKah4KZWsLqKVS0vuAhaol9BQX4oLgBO/XOOojiSBx2DmSvT9xIos6nGK4Q6url5tV/MoK3wEGTPlbiFwVfIO+0wP56yqX/ytcCWH0hQKY0MGoaiZwKjxhzcigAKk+1Q/w8vbmOrlrm5CUdjAgGSCDp6x7ZOr5Qondo9lFi4Rx2Y3iaZCxkV5x3U/U9M9BjE1D2YGJ0VO7oZtkqqq+tf+ZJwEOvkuE5Iggt5mIsM+2k0QQXP3KsDJCZ+RQa8pU+M02zjArgB2dHR03BAKYDxytCMAwhAh0CAek4X5QDUAvFKsOEjcoFIK3cYKIdehLVlBhVhYEgqxgpIVj1WJmmEwAihU4sgoVL94lJjA2O5XAy1buNYLLGpgMI+7gsLcmc4stFG8VGr2JrENyVY0WYuyZdGZnmJ7KglEVXSX7t+S/KG75KKNLWNWv06aqYBlrdlkBIwQql+N1ZuRwIZYmTvRT3CbqhM/I/CwZeDYn/vvNcgg4ISwJX+YhwOeClIgXVFx/2pR/Ki6gqP0TWStC0NF0eZC7LQtNAkcod6RIImrd6qeADIYCVwgwc8nW72HSaz8T6MEJhakJMiTgpI0rl9TaFVqbO38J0OzmFRQ4wr2dqNM4Cls3hBBj8M0ZbZRhlsyGCEBD4sAPvOmJ/HvvvQ3nfKWo/Hrv/gND+VzAeBt/80zD+2z3/7KD3hon40ve7C3dTvtotvp9vC42QlK4O3xLmBTKdRDcmJVhnXFgqHA4GRCGZMmTJIxUsIoA4SmUqZCaF2pSAsSObB4vJK5qlIyl3QErcNXJgj3nnickjLA2ZXAdhm4mSIYg5XOjitbyZg4R9kSJeOcO0MhgIHG7VnKqWhNiHFSoJ5yWcdO33ESSB6XFsvCFdcjwV2/ds/3xf2FalsygWFkIuIOMxNYyWISkxMwhrk+hWqsn8txkUlav7RfCxo3YtlWBbe1qRE+t6dqsWO4fsnJhPHWOfmzn9yDG5YESJdhn0oAjQTGo6rVksgIlpOsCLPAWpuieo/LxCj2VZAwAJjKcov7loOrmcC+kg9nJBqQWJGSgJOtIqOhAg6WsKFKEFdqg/wVVy+7C9i/I3Ml3szu0mVyu6hNsJK6Eut1Nz0L3Mh6hHQ4YY+agwjF9jC6AtjR0dFxCArw9rjBrlR8UJhT1zvh8IISKUaq6h9RQhLBNltNuYEEAyUMvoD9qMmWuQLvXQUEwKxkxUytILbg8qICBgl0N6IPLOLuUsv+pbkCGAOzoJSHYSLfalGWpCEaZTm5fMcEEASaqmuxZtTCy6pU8hd2MoFzkSmQxLOdGWABC3v5mFpexBZxECd9lQTuQykuXOLz2jIjChFXALXGAtoKH9q4YY0QhYu2QufEt7Vfk92tofo5CQzCReKEMxSmQgCpJn4UYuG7N7GrAumqXmtVAFHqVmqsXpMAGtRcwBLeVdNaQ8OrdQMVAw8z1Y7J4mhHGTBSRoIpfFsFzlbiE9rYv4FiSUd7bFLGKIwpM1ISSGboIEb8hCGDsXB2MhbtiqmIdtZOPKYz6jxSDmLobUi02KQkZmlL0I0IUtZis5k6GObqMYAdHR0dDw5SgMfjzlUylUmCBG5QKrPl+DDY2Ll1XjJ43b6BBVeSsGFXATnN4gBjwF2WrGgTQCLDNLGAhC0RRKmqgMlkPxWPeYuR3EWkyL5sSV5RkwiF1NVtxAG2SqEPZkUxvF6JuDW0CmATY2UkI74kGpJeIQ0J9FwWd9kZ+WOxuC/SyOJmiCuC9kH7SWAt/eFuSDKiR64yZdJiK3VXMDxGEV66B3AyhDbTd0ECUcl7xGq223A9FnevoGT5FuUvYgFbJWlJ/nBDF3AogMU2zba4fishlEzAxt3Q8Co1fgcyKUCW1JMmz9aN5Rgl4VwSrmgwEqhW/oVVcUYTMtZL91QXsPrnCAbKGEiwYcGUBJMIZMie6R9EvVEB4ZMhqmtrg2tbsuMRixkqOmoJmFDfBaCEEo9ZyKC6ouhk0Jbta37fca97HcCOjo6OB4QCfGwMIADZ2EBcFlNAJYFCplrUZA3GmJMpEtmUi1ESzriuUDDqAIEtadW6qqrCIU0sYY0FDDewiNUC5BTZyFLciqWUiMKVPF/6a6kGusqn3KqAvs3kJEML+WjVQLrDlUBigARQlL9ILiAnamWt3fqWZp8h5NnLRBBhMAmycCkpw2wu3dYVvKzz18LqBEbJGLhLkcq6w1F4WjwTWeGuQTWiIqlxT4cSCLgCWEmgETwt9qBc3Y8Rs2lxn77PjSLITijCNdy4gVv37+xr3lgBjA9FVf9C+Qvyl+pvlMK1GiSQwl5GoMbRVbuUMUrCVgYMIriSjIGN/I1iS8qF+343pEKQtKqzG8rY0mDreXMqMYBDyhgyW0mnLL60osfkDfDajHGdRgJnLnqP6bRYT5hrl6say0UFRCGD0EoGoVRej/2YyFXFNnb2G6oTwI6Ojo4DIAF4e8IbPFtTtM1CBZR8+Tdi5KyYWMGZkZiRJDVxgLbKwcjJXcB00P0bCBVxUEEWQeaoPxjxZVTLjCRXl9AoOqXAM0xFa9XAfaSiKRa9Tw0k2XPBDwnhTrMn8TDVK9QSq5W2IIHUKLhOADN5PBaRx9dbfb9QAS0G0M5fWxauLQ7Nnr0RKuDAAs00iwWMJcM02U1ThWl9qqXkS7lewFfx8FgxP7aPuLeKUiGCDHMNF/VPq7rUqIC3TQBJgOEqlqNbuoC1IX9kK2sISkFlJQVFhrcTRmFGpoQpCbZTQiLFhjPOmHHlKuDIyQtHsy0Pp0bul/F/7EkgG7JYXFP+EgYSnHG21X2YMXl9TxnMfjSIJaXEZA/w8khuk8jAzi0BjIdPmNgy7XMbn1nc9qbMlnjAJku7qrfh+jUDXRcH2AlgR0dHxyGc4AKug6KrM0SlLK2CrY5rcS8aGRhZy4A1CWOUhJHZXb91nVLR9TjAqgIKmLi4gcO9GCqgugIIFWgyd6WCTVUabGBSgq9DqrtqYGF+2FUDw8UoNSsYzWt3hlAAw/UL2DZi/pjgBWIsoSJOatzE4uyWqLqCU6h+ofgVVySDXXI0JXD3kswl7PGFOo8FTGyJBEUBZDH1yElqcrcikkZlkfar+q8sfmtw22GXuM/i/epDC6FwEtGSwYXrF2gI4eJaTgUpkLb2Kcv4v4j9kwQrgZPmpZPUEylAZEvgJoJOVmB9mowETkmKCngmGaOytS2fVG00IxOXRBDAamqKq+xBAhOMCE5sKuLAGYlTiQXMYoq6eEKKlKzy+KI+QQqi7okfWBZRj4cs3PPlUbO2Qw3dIX/tthjoMEvvBLCjo6PjEIIALtWP5QhI9fxyIAaqQjAI6okZOdvapVNm5EQYJVkWsGZ3/ZpiEev/RuzfbDWQZsWCWvrCAtazmusysZWqECWkJKV0hgAWswQAk5O/IApramA2glTWDA41kBsXoyccFDUwz7ytDx3OfQzhLiXYl+OgfCsksKhQQaCoDOQK+JJxlQAW9++BDJe2JExLAkuCATKEyAoSs9lEEWSnsuZKuniXBJJd8g5xpwVxLwkgmBNBRbP+75wMRpLJmhJYbvaDQhVp6ypnqLPh/mV11Y9sGVyJP1xd3aboKmiCLb/GBM0MyZaok8VCK6Y0V9SzT6Iitnbfd0geYbhx13Ekg5yxkcEsjPOUS2a/qrUZHfxeKVf+VQpwWzsSbtTAUMoLAaxE0JI/moQdxUIFRMneXibrHIhImKETwI6Ojo4DID3RBQw06pO7rMLVFUVupyCBiuxxgBsWX+TeBq1WAQwFZG3N0tlqIO4GntTrmTXKUmIjGCnF4M6QwePLAM8e9OtdqIEq5mK0Fem0DmyhBjaD2VINvNuVQFzZCmHPt+ZKNdY+I4FO9EoCcOO+h5DH4pGX9qhuYKCqUmtxgK07uOwTAFiix+C2Hpz8QQCZyYepxAOmWQ1yDjMheGL5PoX8zYl7m/nbEkFtCMfM5btQk/a6gW/oAuar+B2ZK7hN+jDy5zUnywzCzuME6ATbMoFiGbZs5WFy9knVQLYutzDGph2FG9hiAOuKIICrszB1dsMTRMhqcRLhPE0YlXGWJmufsGXocuMKjvtkNuKZukkM0EQllrQQPUZxC1cCGMeaeo0rii15LG+1l5ZrKPf6QPPrBLCjo6PjEBRI21NITOOuIngAuLrLh2o8ILPF6bkKOCZz/06Siho4aiorgKxlKwbmCSGMgTIm4pJkoACGNH9/JgWmZAMXeV01d38WNZABzR5kTnCpz0lFqwa6sgSy2smUMSsgfWdwoa0Ic06UKIjQkgSSfy81Mmu8OEptzGs3BgmMR3s715JAdo6FiMW+pnNw+bjP6gqRktUfXIAITr4ZmsknF1xIIDyuDBml1E1Ztq8hgkVJiljAIA/OX66LASyHbqQAAmkr5X4rqX0nVshQSaq5VrUosxEbyAzoRKDBCyVnI4CayZR1dfLntTXFCVtbTimDsWmU1uQBdOEGlkYFzGAMmnHutpM02u9goIUjoFIqcaNp1IEJcuvXS+HqjXqAhfRhlvkbxH2uBOos7q+qfrRL1A+gE8COjo6OA6jxSseiGbCcFIX7N4K/zWVlWaahAuZG/Zu0LnYfioWVHamjbmoyfiMGMBHPlEBLLqC9pAIAKFuRaRWFEpvLLVaMCBejx9WpNKSCYJH5QfKaUiOzYsR3nQTiA2NRAYGS8BBFuaNuWsRTRYHleCwXUzhmvdnZsn5uk8UZjSdTbPBtSWCCBxwCyGlmrxBjQy6M7FdAPJGiIe5B3qXuq1Qi6H7m4voNFWmfCtje1/K0emQfCBRFjf2DNUhgIgvXTE2pmyhbE9myuUmOcDKFom6S1eNzN3AWLoQ92hOwu/4vw5JBEokTQ6swmNlVRWJvR/XzJvY1iT28IgtBh6aNFZc2l7YBL25dyXp1C0MWRLDYx2y45goOMrgb+9cJYEdHR8eNQScogKHuRcyPhvtnIqvtxh64zmTuumxZujkxxsw4S1zdwJKKG7gdsNYWr18uf2XkL0PchRmkQniXjYWyJCE9iKsWDamrGaX+3ZpEC3U3b1kTuC0gXWK47hARu4a6vi+hkrm5suUKky7fr43yZ4eP8WTXVVlk9tz2s5MQBoMwAqskMFzmHEWnAVDzOUSE7Ct8CDEkyHpL3GtJw6LghvuQnCiSFwKP4sER91cI9IJUqG8Xt/mBQVNIjvb7JDdF/FzsK3jRbE/2mBO/IELVPYripjd7FcUWDXnbw1wLCYTNFhiCDbKtwEMMIcLIGYMyBncFC1wlTuvrEgtZWaFC0j0+s6iBoQiS3fcdIjgjfYv9MmGpmfYz0t4JYEdHR8cNoQCPR7qAS8yPEb6oaQbyASwvXVYMTQoRc9XmJt4sXFYxYO0rBVPWmHXyt+EMyeQkUObK0srlmjfRSIU6qTCXHJc4sxLHFERjTQ2MzFNaFJC+voLNrYJC/dOa5tEm+64mNCyfn+Dxny015jYAUAj5/OIqEYRYksZsEd92f/Y3uPytiWxpupy9YB+xue9b4t4ke8TqJ1Ul81IvHn+m7m5UZ05FuCwqoBPqB7w/qwjlyu+JfTlXyXMo6PM6eLQgROWadh67yy62Cm5rk0Tm7A2inrzFWVq8vzcIIBPOMe1Vg5fHzUYK8vfO1ECpSqDFMeoqEWy/e0niQaMMLtTapWp7nZ06Aezo6Og4BAJkc5zcoQTkjWUDyoYgG0AGe+hgKzrEPpItJs/JFpdPrNhEoVm2Reg3lLGhjATFxhevL9nAxCWBoCUNHEQxD0CaipJUlCVSjO46HrNtM5vSIsIgUVviisVWYGCPD/QsYHisWlGYiiqzGLwSFVfWcwKnuC1Xzoscjep2XyF4iDpy87VoAS8TA5QEhDYWkNW0p7LiSLNcYHs5RtaDBAI5M8gVpkLcY53cJREMt3CQP61uYG1cinF5VfWL9YCb+7jnHp0ED9Ssihj5falxsmU5OG6U6HggFPf58SjWnVjnbYmzLedGGRc04YJGb18TUmPHTIQzzdhqQsKAxB5eIUbwY4UQoFmCEbECj52fJsXEjCkxcraHMENjSbvsaiATMIQ3oKqbCLUz1NnyoKIALskfsLJ/DToB7Ojo6DiAIHVHgQDZ2PmF+CVANlbaQpz4gdUC2JMtLD/46gKJrdTEGWec81QI4IYmnFGGNIHr4qpQDomNPSNYNgAbWWRJzSohyclfMnWCbNDKomDP8hVWj3myLRFbIL6sEEGpRNDUJFdsPHNRResSVo8CVGvM2XPM1MDd8xdPg3w1x2eZvuW+1pVXYjtQriShlOqx92VSJ4Nsq0SoYJI0J/PuDp4krkELcSdXAGNFmch8te/LRpbkABEsPlKak8GGUNRYSbvmNdX0Ri7gQtyqCmaZwFQz5UtWcEMC20eCFfT25yDYb5AFPJtIZZynCQNlXPCIDU/Y0IQLHp0ETrNM4KwWE7vRjNGJ4EZznYzRmdkZgqtQfRuyP7BNAMbEmHLCxJ6R3BBBGRoPgDSEUL2Itxd+Rkv8ghA2BJBaO62Rv64AdnR0dNwATuqOgfq58dBk3iQNBdAfGEz9S/4Ykq0xuvFis+02Bh4mmWUtSkv8VBolcAT74rEMBaslhmxFq8qEhMlJxeQKIJPVu8uuLkkpxRGB9StEUFwRdJeWSl2iijLVNWbvEOru6J1jLRq3dasoxbEgfUSVfBFpKeHRLutWCzvP3cCtUjRXAbO59xsiaCqgk0gJJdDsBJgdW3tNxMVe08JeIrRDBKFBBsNe62TQYv+C+FVSiOUtPUVNXQMRdGBEclSESETIhAxBBhsSmABJVJaHm5HAVBX1lJyEsxVzPksZZzzhnCec02TqH49OArfYIM8IYJSHEWInfwNGzUga6wOLE0HBIOJ1NzMG3mBgW9IxkXhBd0EeyGoSMiMnT04py8i5vYIEtluNtoedJJAS96cAtQlMOJ78AZ0AdnR0dByEnkwAG9fvxtS/SgR9oBoEvFD/Nin7YGUDVlX/qgLYYqcIsRPBEQmsjYtKUlGnJq8/x6RIYrFNyTOQY9myKSdbSi4yKJ1Y5GxEUNUGJau9xh5tT2WpNRvELK4sBq47RSh/wMw1qM12np3dxKIVQqhOBOujdf3OniMIhxULHjgX8hdEMFBVQLZ1fp0IsgomTe4KDtKeCrmcOCEJF/XW3PZe6FsUU1bPWOWyJm0QCynkryp+hbRrHPMLDEIYN7AhFjurit2AACqM5IW7NzJ9lb2YMluJFXEyGEq6hqrOPqHiRbvyNrUZrD1dpKmSPzbid4+vcI+v8CRf4UnagklwhnnbymTxtxdqJHBEwka8LUptl+dk7fRKBmxIcM4Z93mDszRhmwdsU0IWxjYljDk17ctqCEqQQSELu1CysIuS0QxXcKkp10NAUQIJO+RvRtQPG6kTwI6Ojo5DODEGUIriZy7fcP3GIBUDFa+pf5QLkViSv4gBDOR92RWNK5FjYXtXAVtiMREXYrEkgpMwsmhRBIXV1hKWhlgIQzkvXI2WZIDkyQYxiN0hSq7MSnxYSYrYec3d8q74hXeyVf2WJNBsJTMSyCSF/G1aN/CCBVfyV4kgkxZX8D410FzMRgRZeH5tQhBXc7UQd/asX1fX1MihLslgZEIHIWxJILAgFXF/byDtEiAbbkh4FIKu25b82aOqf5UQKrSEVEQsrWCTzO17liacezjFPd7inMfi+o1HxNi27n1RX4GHCBtNGHUwNV7NfbyRZoLGE65kU4kgZ1zlAVecMSljmxPONWGbrabnmFPJ9M/iRauVnAjOyaB6eEUl7abQWiznvHwReQb0PAv4sI06Aezo6Og4gNMVwFD/FLpZuH5TM1At1L+BzV11vsf9m6DIEJzFuOyKH6spSOUhFgRvx90VqaYCDkqYSGxtVEqYVFaJ4BADlBfUzU4mskpRLcjdi6EwlUzUcDUG+btLF3Cr9i2fO9Er+6zmPlwQQeKaSBDrNbNvw/3bqn8bzk0msM7IX3KXMFBJYPb1g4VohwhGQshkhfDcjo0a6AkiTDoj7eHGj3hOEStbEjGdImzirBMJYXcLN2QwngcJBBpS6E937vUN7CTxQyYnfsUNDF8HuCp/MlANrXDFT5rQCgwKHgTDkHE2uJKeMi7SiCfSFvfSFhc84km+KsTvSdriHo+eYCU7iSA5MvEpY9QJoyacabYHZVzoiEvaYCPnpZ0+K2cYRHDGA85lsILuiXGVB0yJsfXtmJPV+hT2NYWtDJR4mytE0B9FeS+kPeyFhrT7xe+z1wo6Aezo6Og4hBMIIFAVQBlsIJNN6/q1gYqTDVabIRf174zNXbVxxSLcS0sFcKuoJHCBBM9+VCmPEQkDBJPYuqYzt7AkDCR7ieAquSAFs+wfpFrVIgarO0QkBAAoSR92TF0BREkeaNU/I391S/49LbtTihudSDG0RbgLCZS569fJX0sCAYDdlR+FjlsiyK6+xueOynM1t1EDJ1KQNC7pcOO7rbKY4hcKbqiCqmSqYJCIRhmsSR/UkD+du3+1ErcHtxEVVV2DAO7E+62Qv3ADNxMrDHVSNSTB2TDhPE24SCPOOeOJNOKcJtxz8veku38tEzhjA0Ui+zlEpaTsGRaj2u3fgjGSWDKITkb+dIMzMjJ4qRs8Q+fFLXzFAy5l48vQJTyREq7ygDGNmCRhm2zFn0nXyWBMuiIMoyq6VIhgKXcDNGSwsd8R6ASwo6Oj4xrICT2lzuL+bIAqrt/BMhSHwdS/zUL9swXnp+JaasnfpokBzFi4FCFIGDBG0gDN1UABldjAjWYMZEvNDWSrhVynCEoogUUR5NU4wVCZlmTwrlA4S6MAaqh9hBn5K8vAOSkkVwMLoWJz/yaeu38HLwtSV2CZq38JUkhf2CzUPyax0i8wssHQHSIYZC/UwCDqUyi9CzVwEiOZmU21nRNBRuKGDDbu4aiVp574MScVOiN/FDc37rPSzQggeWZ9G5vJVf1DqIAzFzAgZ55RHwr7oMAgSAv178IJ4BOu/N1LVyX+zxTACfd4wj1SnBE5+SO3EUGc8Y6kyGoKYVbBJRgbElw6EawuYVMAL2mDc97gSjY4pwkj23KOoyY8wYwr2Vhbk4RRGVsxRXCNDE5i04aWELbLEba2C5toJ4AdHR0dt4dTXMCAx/0VF7DHKLUqxeAlKprYv1D/ztMy+aNm/9oKBXFRA9DEBCYvBJjIXMKjppkamJXBrNggY5QVIrjiGhamMjBNwoVgqCeSLN3DojIjgyW78Ub1Qk5EkLxQ9vxYqIK1bIgrgOzxY80Dxf1ryh+RxeNRqHqL5I+yDB8WqqBnipZVQYor2EhhBhVC2BJBADM1EKiu4DZGsFUDBYRJ7O9KJPo0iT3JySA7YcjEnu+xJIELQggUxW9ux2W2wel2ymdLAogms5dmsX5tQpVsYO1qo8BGwBtBCvI3THhiGHExjHgi2eMeb/1hyt89vsI9mnBBigsibIiRQNhQml+fKjYQz9a2EjwbFYwq2EAwUnZFMGMDVwbJlMEggkH+RhmcDI4Y1WIAjQyOhQy2yqDZk8t6xjHRyg35q9tqOwA7BakPWakTwI6Ojo5DIBt4jkUp+jxUhYIGKa5fc1WZUlEyf139GyiXbMXW/Zs8KaQtv8FesZchGDGASTDqUNRAiysTr2eWsMGEUYYZEQzXcAZjIsGgDPHg9VFSyRoWJkw+cJmqtKsKxnqoESvYJiLcJTSZreaZvqjJHwvyV0mgx/s1il+QwKr87cYAlmLAjfoXj4jdBOrycIFaFNqPN/utGgix2oEQIKV1NdDKyZhb2PYrERzUyETiqtgmVgvTDBcj5iRiqSzFsRbLJPSTbERAPo8PcuLnNQHnCqC9VshfM7HCoCAnf5tNxvlmwsUQyt+c/FW374gLyrggwT0iXFDChhIGJKRlUhXBYl6hEBKMmp34GRG8ciJ4BsFFcuInmxkRHDVhq8NMCVwjg6MmXMkwI4NBAtv9WBpSFkQQQLFj4Jj1qzsB7Ojo6DiAyOw9+vyN1kGqxP1pUf82ydSKTco76t/5ovxL6/49cwUwuU8ueZB+cmVq1AEJihEW32cqoBQiKGruwCCCm5QtK5FszeGREga1/XAPT8rIRQmcq4JBAJcKxZIMyl2mAbdqX3ED13i/kvRRiB+q+uexfxwPMgW1JXszt2/j/l2qfyXxw2sDxv7u9YpzPN1RAwsal7Ao1UQRSQ0h5OIWFuU5EYSt8qJKRREs5CFiAxsVqSUSSxJo+37pN8kCZksC0RlBX6qAqFm/Lfk7U+hGnPwJNhtT/s488/ciTZX8pavG7Wuu3ydpwgUBF5RwTpu58rdAoqjEaCRxQ7kQwTNSXKrgTAWXmnAGL+JeikdPEHgZGR2uIYNpRgYnTRh9LfCWEAbxa58DKMeXttq37nHgJALIL9viRX/8Dae85Wj89y9500P5XAD4/f/l//bQPvsH/vOLh/bZH/VlD/a+bqdddDvdHh43O4GM1B2LpUIRLqrBSd/ZkHFeBqsRF16q4qypVXbOIy7I4pc2yDiDZZWeqbmkoEB2hWnUBFZ39xEh6YCsjA1NGHVAVsIZMbY6YKMTMhgj2TJkGYRRBosRVC9ToQmiFr8UA83oJCPrnAAu3VWzfa1rG98lNGlRSWe1/kLgaclfksb9KzPyx674pcYVPFC7TF/EAspe9S/QEsH5xfpFkcwUQCib+79xCcc2ysWkpBiLS9jJIylEZYcIJpKiHCW3ScSYLZWkIISxv29N3ZtACZiiqbcuYJoTP03z5RML+TsT8FnG5mzC2ZBxsTHX771hiyeHLZ5MV3hRupqpf0/SFvdowjkB9/h68reEkUHeIYKmCPpzFVzohEsdcIGxtikYCdxHBkUZl7KBJHJiGMepvD6q2bSsE640e760T1cAOzo6Om6KE13AQf6K6zcZ+Ru8QO3S9Xu+KFS7VP/OQhGEmGswgtVBSOoJAMQ7RDAUwUw2UNiglZwcZq91tksGR62kL+L+zrgGroeLOC8IYCiHS1dVvksXMAFIWBR3jtf2xf1V1y83y4jFWrL7kj+AuhYsgL3q35L8JUhZz5mX7t89LuERCRtkq/3oWcGTJGw4W7kYWHY2xMkiA6K1wLSoZxo3dglVsLgT/fpEd+PJ5iTQb+dNFECyhA5gHgMY8ZkzFdBjajE05G9jyt9mqK7fIIA7ZV+4qn8XJBb3h3QS+WuxJIKXOnkYhoKRPVvY4mm3ZKRti1D5jASuKYPnPDrRS4UY1gmZtdVJGBnhArbfUEsGq416EkhHR0fHzUF6kgIY5K8qf1GeopK/c1+hoLp+x0L+znnEWZSAQbiABWckyDFSkiCpryKxIILiqxcEEYxlrUIVjNfrsV0yOGoqpFJ4TgZtIKokUZQwsitOml0ZjOd8a6rRcdASAwgAs6Xeiit4Qf5YZq7fSPhgmi/9xg3ZAyrhi/1jUNYF9m0GN6/ptSQQKrNC3+ESLgTA91m13HtWUwyFaEYE1ZWjZTxZctVQYZnKrf1UqZRKuVEMILcxgE2GdksEXf3T1JR7cUV92EzYbKYZ+dtN/LiaFXw+p4wNARtinNPNqU8QQQZjRMZItlzcVtWfC86VMPq6wjYpqyTwguYkMIMLGTTlb7BJWVEArQ0a4avtKgghgNmxY9AJYEdHR8chEIzUHYsgfx7zNzQZinPyl2eu3yB/G8plkfpQ/xLUu3QjCaxU1MAlEQzXcKh6GQwBz8jgRjMyyM/x4rPgvYPPPjJ4xlX5axXBIIahDt4ZIr4v9oFKAr2wM0q8H3bi/mJVjVD/Bn/M1v9tsn9vilAD2/IwvEICM6KUDM9JIDEGexWAYICpgSNscBcQJjAGzm4PLkRQlEBKrh46+QOcJFZCEYSw3Mtyq2+oAJ57so4/b2s1aoKV7klO2geL+eNBnfxlnA/Zkj6GERdpxJNpW8hfWfGjUf/OGvVvJ+HjBthQsrYHQkJ1CxsRVJxBsAV7KSZBJisqvUWaLTU3euhGSwbb59GeZ9uZCtgogEfW6OkEsKOjo+MQCMBw3GBPhFruZdPG/dUg9TPOJVC9df9W8redu4AhvjScCz++TFgGFSK4gZUVEYiTPhvss68yEYOGZTa6IhhKAmwgySAjhkoYNRdlIchgqwwKCCOnokQUN5XXNJPUkMB0lwogSravGQSm+MXWXyOP86vkz12/TeLHUv0DsKMCAlXNWyLD6suFastUXb9rCuAhpBkpZGR11VEFiFVECAhpcAPBaFeMwUMHjPTpQSIIoBwHAPJtwm5tuZtYVQnIZ3M3vZbknMjUdvIX9TN9MrXZWMzfeeP2Ndevkb/lUm82mZKi/j2o6/cQWjXQp1RgX7Vn63UER4VvrZZgqIKx1FymsZl4DaVNRhxvbadcFPuYNOQSA1h/T/kIC3UC2NHR0XEIpODN8QTQYv6kkL+LzTxD8WIYze2bplKo1gYtKx8RxC/WKd2QYAMrWJvVliULIrghhQBHkUH48UOEcNTsg8zYDDzjjhJh+1WFiLjBrIyRnfhxVRDvDAQruu22aGsBElciuEv+jPC1K3+0ZI8PKH7ZSRZgyouguknFid9yvyV+y/tzzP2ympDV9VdVQ7sKgD3rOFRDLBhbez5KKRlTAU1NjoQTuyaaqX/2/NrL3A8G9Exrgs7STR9xmknByWxkGb8Tzods5G8z4nyYShLVOY9lInVRQiqmpg1ZsWe+0YUfRiGXCiQiZCgY4lZQCLwEE8hJOptC6KEXZYu80ibnij2AQgqB+ps6JeSiE8COjo6OAyAC0pCvP9HPHYaMlGQv+Ys4pXD9ntPkg9fcbbVU/xi2SkECChEE1slgJYHkRC/i/DxGiGhOAsPF1KgQWccSrN4OPPb6XIU4RAjvNAaQACoEsCF/oQACIBYj6sXli4b0oah/QQDbZId9sX7h7k5OqMvSb07MBEbSshfoXmKp4CxdfPvApMh7PLGMuSp4HQlcvIIELaof07w0jd2jm7iAFXrWFDa/Jj4zkqjOnPydN8u9PeErfjyRxpn6F+QvQe1BBAbfqvt3DUECBeRqPWFUKRO3rV/P6FtTihViuiwETQY/CJuGDC4Ve6D9zfi2E8COjo6O2wGRYrM5jgACqAkfwy75i8SPM56qu8oHLUv8mGaxf6H+JZj7StT2mZp1S1WxISDDBpgNjBS0hBDNdkkK47VCCoMEeszgRTP42KA07o0dbGOVSvzgXSqA0Kr0AUW1CnduXeUDZbm3cP1S89hH/FrlS5QhoZy5sgoBNpwLAWZlSJSEKfFa+9EO6sv4rpZQ10zQByfX9p2CoM7VvlAE4x6o1lVKbgUE0JmU+Ewq24jLrCV5UvJlEz2JKly/VkJpnNXPjDCKWD0nyid5XsmdYUMJo6JMAGwCpxhVZkr+6OEcRgi5EMGtK7hLxb6N5wWqeh/7cW+B7gLu6OjouDGIgLOjFUA1AugDVYn5G6rqVwPVF8ofzdWLVv3bePC6xJqyUR9OFRt3NW1QyUX21+2qjQiiUYvKeWUgCRJjpCLTNAs4r0HpY40tXKgRh1TCuwRzJSpB+my/kr5Q/pilxvrReozfElHfkBFbLit1RHJGKGZliTdljKhrAu9DS/rieRvgvyR/bUmQKAdyU5SVRZrkjygZc2sgRTrLjUqrM1W2JeaDr5yzYZkpfxFHe7ZTQqmungPUAtzlN6/y0FVAAO5qbsr7uNt+VLEfo8dtjgq0yV0jcan3OUvsKqTQ3NlZreQTYAldmyB+/ls5ZvXKTgA7Ojo6DoBIcb6Zrj/Rz42BasO5FnpuBqsgf/e8UG0hfsX1m3FBk5Wt8Ni/DTESEZLrfuzLMQQhNJfvnBSiPVYu0BRDew2IlULFyaEArvwtCGGrEGKu9BUieIAU3hWIzMUb+7adE8Al+Ysiz+WBXVdvSZxQ9SSJUP+M2EEseSK2jIZEoapno6a9JHAti3Ot3Mfu80r+pDl/rVjwslDwWu245RbYTQC5KYgVmzNrU60bfrbusmdiJ/Y1s3cy6GsC1aZkyxtp5D06q8CWdrv9NJDjkUAAFDGlTPbUj5GXA4pjWBDIuoRgoozs35PLxIHLWtPHoBPAjo6OjgNgKC6G4wngGXvyRxpxxtmyfdOEJ4rqZ+Qvkj7mbuBcshbPQv2DqX8DIraoxv9ZOkBDAks2qA12rQoQZBBkJBGL18TJoe1XQmgxgk4myJNK1AjhUiXcRwrvEnMFMLa6QzRaohfqXyDKn7TP26zZKHUDhpVMYRvYRa3kSlLLvIVn60bWL5Mt0bcPLelqg/rXa77NiV85dw8ZLNe+eP/ybxwifseWF7kOBGAz5HLPq+u9xmNGke3EUtrUGU9lQnWe5ssmtiuytMggjCDLxvVfJYMeqgpY1hCGIKtvoZBoXyd81qw+JOZlg8JNHCu9LImvXDP56gSwo6Oj4wCIFBfDePT5Z+1AxVNRLZarfZyXLMX5I4LWGTXOLzVVd3ezGGsnzw35Swudg1vS15wHVAVR0AxQhRBidrxmFrcJJQuVkJoyFXeYAwLMCSCwRwEESpzfoRUtJOrkoaqAk7t6GQrJZDX2xFS+dh9AISXAvFzMmsLYYp4lPF/1AcBe0mfPdxW/Sv7q5+xT/LQhjfuuDwD0BoblRlVvSWAsvRe2GdiW1RtYTPlLGQNnW5KPsp+/JHxVhd5SwkYtEeQK2W0wQSA4x2lLwV2HNdJn1zMnfrVNoaruaFR31PYEYGeSBcyThexvt+rx8cS2E8COjo6Oa3CsisVQTGLROtsVYlEWgffHFW1wqSOelfOZG7jGAmZcUMaFXJUYn/3XePj1Y5IPSr2xZhCaJSYslD57z64LOI7H4HVXWBK6XTfw9bRFUYshG12wMi+TsBM/gmRT+pi0ED5T99hdkG0JmbpsXGAtoWKprrWErpyjNCOB8Xp7fKnmXaf06eK97d8q92TNBXyTnBBCKbXjTwHUGMx4LZWl93ZjM+N7j5LAbMrqlWxqjUWy36IQY6QRI03YUsYZCc5JLNEK5Jn1kSFsV8IHSFQhdhGG4eqeXdOc6AGYkT3gMOHbR/Z22tYJxO9Q39UJYEdHR8cB6CJ26hAEBPJluFh4dnyUhHOefKAacM4bPEt5rghKxllTA/CCt5bN6MHtD4pj4vDagcau+TCpa8tOLOuRLQvV3iWWpK/db4nGUvkztc9zOSIRwp9PnqGpmYoyVYlfWzMw1TWBFwQQmJPAQ8gzIrYSp3eA7MXrS/UPmKt7e5W/PSSwheKm/K8qfcCuCtjew3bJPbsmhqhYm9IEVgWr4ko2NSMWjJFsktWWVYp2VROsBMl/3Ymqln6dLhhRdjWxZPF81o7aSdSc6JVz94RP2Gfzon3NJwbLtt3LwHR0dHTcIk4pt5GVfSQYbKBSwqSMgcS2IthwxpUM2FDGs3w2i2Xa8Dy26azUM7v50mMHr/sIQgdUorisWRffvS1bUu7HHWKN+AFz8rcPLekrz0mhZHGRQUZyNkI4IyxBZjAnfkuScwrWyNjSHbzmyl26cVdJ4JIArvzNfckf7d94EBBQltgLLO8hrdy34tpuYjDDWKKEDVvR5EsdcEETLmmDjeRSFDomUhtYlnBJHGlsFvtr9RqXaEutrNVytO18xY7bmGDF961/e72NXTcB6wSwo6Oj4xD0tCzIcBVqttUwbD1ciw/bSsJQFCNTIAbOVu7CY5o2zTYGKVt/Vm5p/dn1gW2NyLX7y1Iksb+WlQrULNS7xlpM39pVqFIpbyK++gXgfEIJ6iphdhK4dFPG31oSvCCAxxSQvi4WcO34Grlrt8cQvDW1rx6b/+19v/0bVQV0F3Bg7d4t3b7l+xDhKg/Ivr5xZluLeqCMjWRcspG+ZyMz2NtQlIiJRJENTV6Dr7arhHlM4TKpYifecEGwWjds+9p8wnTaRGttf/n5D5qc0wlgR0dHxwFETNixYBgJjPiwLGwDmqQyqAXZ23V3ySyGLJ7H+dfhOpXwqBp3M3K3S+bWSo2sHa+fcfckMLD2l0PVA+YkMKuTtiBM7ksmsqD99t4t1an27/A1xO9Q0skSO1m4+2L0FseWBG9J7nTPee1n7buG644fA8KuYgrs3tdybYSSBdu2LfG1da3NDDaZktpeEiyBxEieT6Kw2NK6Ajhz3a+0q2PI+3LJv30rvgDrk66y30yyln9rX/s6JuyjE8COjo6Oa3AKiWndhhn7VKJhNtjN1pxdySA9VKB43woNa+fvUxCTK11tZimwP1t0zQ15KLngLnHMX2zt2Q6TLanRcv/mn7hG4Hjljx4ieqe4g1czcGcEYPf4DgFceV89d/1zDxE8PVEVX8OaUrpEtKUggRIxf/47nojnbSTXiROAncnU/NjcRd+W6mnt0x4vx05U4vOiXS2J3HLiZduWBC7c/639D/zir1PgOwHs6OjoOAA90ZXJTqbiPWvvXBKAY5SidUK3OGdBBq/7jFUX20rCQBw/xs14KKngUWONauy17aNwX7d//ppz95G1NUJ3iOzNSkJeQzbnx6+5wGtwrBK6Vp+QV2r4HWoLy4nPmkq7G8O5nsW9fH3t89e/xzp520fuDpXqac9vP+tUdALY0dHRcQ1OUTvmGZzHDajLuKtj/uY8xmz9uL22nhSxRihv6kJcKk23vYLEKTiFQC2fL8nNIWXsEBFannuK+/c6HLyONaUP+15vj1//dypu4gI+/Pu7rt2Me2oULt93yIUeOOTaP5SUso842rFFQeY98Xr7CN6xcZ7Xfbfr0AlgR0dHxyHoaZ1rS5bWiFJLkpYqzPLYqkqDWurE9nXn+N5M2JX3xXaNtOkewrd7vL0Bi3Nxt9j3965TzI76jmv3qOzsJ5A3Qdjs2s/c48rFim3qeftfu9ZwDzcpfe7mXqhhqnVVkzb+MY7bdt6elm1vDYfKBcVKJXV/lxS271vDvljLYydey2u/6QSrE8COjo6Oa3BKR9sOPNIMSNocr/sA4hhiHyiZx/G6LP5+GZTmz23fN3FsRvp2z9/rdlwld76J+7FK/HaPPSrsU/nWSN8O4W7JODCzTXlvYRRUv/c+8nUqjr11a39jeezQNT2ovW6Z2V8XetC2p9w8z2L7WUyDE2Go1q16+9lpY2vfobSdeF6zv4nUntP+9aXLecCq2nuM6nxoIrb+GTt/5mh0AtjR0dFxABbXdvz5IjwftKTdAuoDlYoRCNtSeY52CwACUEs2gnuR1vGLFlu/bixJ4JIAtsfXXIF7laR2f4U0XONWfJhYI+vHKK1Hk/FQAoMMtvv+vvkff4AvcRPefA0hpGOu75hrvkVyv1TY27WLl8QvCzvhs33xtiXCts1cCb0QIN62xK85tvu+5/Jrkdox2t2n5hjFa0A9vsTqb7N9EsceQMF9AJxEAK/GAT/xppfd7C/uwcf8tg9+KJ8LAOlXv99D+2xsHiaH/pIHele30wq6nW4Nj5udABtcjkXWZjDyB2IwK4NRMyj5cyhAzTESWwM+9pcEsBLBeK4rx1bO3UcKA0cQPdp3zuL5LYa8nYx96kohe+U5in1mpE+a50HKg+wFOfdzC0FvSSGuuU/7sManT+Bae+/5IeXvuveuvech2DYyx1vyN/mEKQsjC2PKXNpYzpX0ibcpzQRkqu1KvF3lpk1FW1sqge19pqa9lP3d4/GQWIN6TZU/+KX3GPfAvQ4Sf1MK3hXAjo6OjgNQPT0GsJC/zJX45Ur+6gBVSR/5AAUhUPbxJcMHLaoxVw1xm5E/UB2kdl5vjhWZYn7eznh1aADa+dIHzn2ECuDRil+rGDXkb0c1CsLXqLTUEL+6T/V773M3HsKOCnXCe5c4lhCe+Gduy67LskH7yN+YGSKMyYlfzlyJXyZoZmtXmUCZgOxtKte2RRJtKiZcfhErruDadqg8V66kT4ms4Lufj+b1nXa3D9eR8J02efgDT7VJJ4AdHR0d1+CUfrVV/kTr4DRXJmJwsgGqkD4foLgct2McK8oDVXVAo0oE+UM9ZzYI7Zy/PKcNMpoPMquDym2oSQ8J+8qg7HP1rrril8RPMCd9RUVqjjcEsBBB1O0p92NnnF8b9w9xgVOUvPYjT7HZLdj3OvIXsX4t+ZumhJxdZZ8YOjXEb4pHS/68PU1YmVjt+R6L9lK2TPPn5Xj7us7bGbDfVke633cnZzfV/gydAHZ0dHRcg1OSQKoCaKRPmgHKiJ4PUq0ysRiwSACOASv7vgciqisSa6SudVO1z5dksH2uBOwboY52J5547sPEWpbk0apf4zacEb/ZttkHyrFQBlcVQWB2H5b3avXndYBAPOj4f5KL9zrcwK6K45S/veQvJ8hERv4mJ38jWzsZCZwBmsieTzaBotzu65wA7iOBC7JXCGAhfHWrBFMEG8Vw9TMf8D4+jAlVJ4AdHR0d1+CkLGAhS/SQxi01sRGFqRJAzpX4xUDVEj87pvV59mtZxiRhTvx2yF24q4CdQW323vJlD3+/WyURt4zCtRriF8/XYv2Oicek3BC/7ASvEMHmvDi+Qv5oSTIOqE5rz5fq7s73vubneTR5OMF2t0FIgvzNnrsyGzF/c7fvgvyNXFW/0doRjwCPpviV501bqu1JK3Ff+S5rKp8uSd9iv7iFl22x3LTrbsj64YelpncC2NHR0XENjs22a7N+zfUbyh/KQMVTHZyKSlEInw9ak5aBiwsJrEHmNsDQqsI3I33lNcVSzdghjEfg5IHorslgo+wBmO+7qmRuXieJa+RvJWmgxmfOn8+OafN8JybQrmEnLjBwSOHbs39LXsBVHGXnG9q2JX+h/rWZvvYgT/ggT/jgXfI3EnikQvx42xC/EeCxaUujWntyFZAnXbXPbILUqH7KNHP3VuIXz7W+vpxYPai9HmIb6gSwo6Oj4wDCFXXUuaEuiatLTWySqRI0J36NQmHb3cEqjQoeFTTFKEULArdwSxXipwuVj3ZVjaVCuADdZkXjO8K+en5Hk7/iql+QvfZ5ru5DbmI1Z1mmRREMVnEcsVojfzP7nKoq3TbiZ3jDQtCH6vzZA0UBlKKou6oeSnpL/rZO/kYgbb39bIHkJDDaUSGBo1ibUm2IeW1jgNsiUdPGCJpolwimlhBSVQqBWyHtXQHs6OjoeBRwBeJYRNbvTszfFC6qxj21XRC/EWWgSqOCtwreCtIooFEaEheuJpqThCB1TDuk71qy+AJBq/rdiPzllgTuT8xZbkkb1+LSxXhNQsg6+fM3PqgSuPb6bZj7lpJAZit4IOxHtZ6mUEn6aDN90SR8FOVvBNIVkLZO/rZqj1HBV4q0FfBWQJOAJwGNOWYMuyubMOCVnq2dBPFLbO1rQQZjP9qlJv+cma1WjPGw7HMEOgHs6OjouAYnJYE0tf5KvJ8nffBIIFf40jYIoLpqoVWtuBKkKxuseMzgywk05hJjNBuYZs/h5M+J4R7CGCpFSxQf6L7ctfp0BJZLtp1C/kq8X0P+eCVBJ2Iy63PddQnPXMG6SgRn2EfqliogrZyzeP/eexMfcQsE4yafoYukD1k8sjSveUytNCEVpqijTKhm5O9KCwlMW8VwKeArm0TxVQZdZfB2AsYJNGVAxAng4gsx1XaV2NsQA4mhzE4IGRi4IYINUZxNzprPXN6LI+x205KC+9AJYEdHR8c1ONoTqoRSKFjIa5Ghkr9pTv5mSsWoSFeu+F3ZYMWXkw1WV1vQ1QhqBiWQDzjNc/hAdYgomkqxQhJbHEMIj6iNvc+1/FChTc2/8jzc80EQ7fg84eN68seNPesxnRFDI39aYwJLLKDOB/IDcYA1prNxRTYq4KGkkDsj5TckkW1tzaX6V0ih1JhatJOqeCxDKEZF8vY0XCnSpbWldJnBVxPocgJtR9B2BMYJuh0BFSC7/KeNDEhcSWBKIN+CGTQkaGKQE0IwQ4eWGDppBKp9mrbw0G10ZLvrBLCjo6PjOpwQA6grqlJN7qjB6UH+hit3U125UnEl4KsJfDmCLkfQ/Svo5RX06soHpJWBKdQJ3yeuz5WpnpPqOaskcQ37iN6+8x8F6XOUci/lADWED02BZyouv3l5l+YxKySMJmu7KSWyIIY8Ky+iTXJIEwd4TezcPHZM9yt/h9zAd2CDW8sC9ovfp/7txtQ25DvKKS2SPqI9FfJ3OZqKfv8K2I7Qqy1wdQWdJmjOQM7QhQJITVujlKBkW2wGgBMosRHCIRVlkFKqCmHTtoDFZGitTT2CdtMJYEdHR8cBtEkF154LoNSPa5WkKETbKBXF7RuD1aWpfulyAt834oerLfTZ+5BnnzUCSDYoEVfyp1zdU5T4eoLoJDCOh3tr9g3XBqMV99XqeY+QAAJYJX+RnBO2qQogStYvzbZus1nyR0sEtZK+qbqAObv6FypghpO+PYkgi6QDv+wKQnnDDhFcSTKo562ws9s2yy1kAQO76l98dLS7ElPb1lpsiXmTOZ9GS/pIWwWHin41VfJ3/xK63UJjO037r68h6uWrcgJtBiOEKYGGwdpT7AcRZK5uY7cbte1n2UZusc2corp3AtjR0dHR8cLBLAN4j+u3xObVfSyUQFxH/pqiwjw16l9xCWshf5YRrAdiAJsDK3FjSgBBZy5has7FbH9BGNdwC3zjtjLEmRS5Yb2tG3iG1lZBBGVunyjtYjX/FLzNoO0EbEdT/pz8yf37J8R1NJAMvcpVDRwGI3+bwcjkMLhb2BXCQtrnW11Opm5KAJv3n/JJnQB2dHR0dLygsCR/Rf1bksCF6keLY+Gy5ZZgzNy+DfnLpkKF8kdiiQUUpWAiGUTai2yw4iK0OE1d2Y/z9imHBFqR6G419uyG/I9Ir1XWdWE7UprXXFQU0s2tTUaxMi+jZfqSu3318gpyefVg5G9xYTpN0GkCDUPZUpqgrYs4bMoRD+hq4MzWDymU4oj3dwLY0dHR0fGCQVH61o4F8cNC/SuqUksCF2rgUhkspMPJ3xjkT5usYNsvCSBOBAOhos3ctgQgo5C4krkbMZvlfMzIYRnuPXawfPfW87h2w/YRkOcIokrL0lYlnnIWtxmFnhWUBZQzMLoCOG4tjELy7V7fNAHTBORsimDOxUVcQjGAGprhmJHAvfG3R2RaLdFdwB0dHR0djyUi3O469a/ZJ6HCm5bqX0suQvnjogQ25C8H8WhJoJO/UAOLC3iRcDB7Ut28gMWOFYJI2CGGRe1ruIKuuQT38YKsq4evjSW7HQ/w/o8Pu9nFNHajmV24KcVT1/xVU/7GDBon6DhaItWBmL8bX68nlNCwAVKuGcNFBWyIIBZu4NVYWobNBI7EAxD5TgA7Ojo6Ol5Q0FlM2doJKAkZZbk2AXbUwKXCtHjUpI+F+hcKVHEDu4wVmccHXJBBvCiySEUt2Wfp9l2ogGiLlbeZJoeI3AGBac2FPHv9rlaJ0fn+bHm9ha1IFDSJq38KTNkf00Mlf/X6FDpuAR3qD69NwAJmqt61rmA6QQHsBLCjo6OjowNV4Wv3D7l/G3IxL+KMWV2/WZZvifdDUfxa8kdZrdBwiQPUSgzWysGwC3XkWa9U1b+WEKKJB6wDfy0ZM08QaYjIEm2m63PIE7ybABJbmtmKGtK+rMMIJ4I6TdDxjghgXOY0GXFnp9GN+5caIli4LdFc7Csk8XDNoBmBfADPdieAHR0dHR0vEOzJHp09x/7XlWaZujOSsVAIK/HQXVLYkL+IAQwFkFoSuERkMBe1T+sgrzQ/DhhxaD+qVf5mCSK0/28uhMSjcQcC4GqSSOu6L/e/Jezhfhcj317n784h2UrJBFnPmBPCLPtjAuN6r4kBtM96cObeCWBHR0dHxwsLJQ5wcWyfIujHinuxnOOv6+IRpMPr/BU3bzaXrz0a8pd1RQFcMCiuJK3Ubsx+yWxfZn7c4wK5KnykqITAY/tKCZn276zcq9Xb+IiLes8PrJDUmXKrvg5zc5+zrfKh+bCS9lChCmj2XZrF9lEN9ISu2egAcaVjbHMNgewEsKOjo6PjhYeZsrf/tFltvsbtuzw2ew5Xmjy7l0J5arbw1UB2yF8QlCWyNnFi/t7I7g3Cxzo/DliRZKASQdWZKkTL7I+sR7t7C3G8w0xhWVycNkTdrmldrZzHcjb32ZYReYhXfAKCDEaiT0YhhLNC0cH7DsQA6jG2uUb57ASwo6Ojo+PxwRoZ3Eco2tcWamB9rzbnVOIxc/Wukb9DSRTBV8pqHwRSNbIX5DBeij8tC/Lo71urN3jI3btKDpdq5aNCSwSX7vj29SCBfs+Xy7w9cszsY8ZW2SV7xNcQV+Ib2aYTwI6Ojo6OxxJ7idC+cLmWTOnivHasbgnaGtHbGwOolbSV2L+GLMTfaRTC9u9oJI8w1t+771iDY2IBn0sJIwUtCd957TmiAC4xs3OkiFci2BJXWlP62u91SsZwvEVPSOUmol8C8DMn/5WOB8UrVfVlp76p2+nO0e30/EC30/MDD2QnoNvqjtHt9PzBqq1OIoAdHR0dHR0dHR3PfzzAOiMdHR0dHR0dHR3PZ3QC2NHR0dHR0dHxmKETwI6Ojo6Ojo6OxwydAHZ0dHR0dHR0PGboBLCjo6Ojo6Oj4zFDJ4AdHR0dHR0dHY8ZOgHs6Ojo6Ojo6HjM0AlgR0dHR0dHR8djhk4AOzo6Ojo6OjoeM3QC2NHR0dHR0dHxmKETwI6Ojo6Ojo6OxwydAHZ0dHR0dHR0PGboBLCjo6Ojo6Oj4zFDJ4AdHR0dHR0dHY8ZOgHs6Ojo6Ojo6HjM0AlgR0dHR0dHR8djhk4AOzo6Ojo6OjoeM3QC2NHR0dHR0dHxmKETwI6Ojo6Ojo6OxwydAHZ0dHR0dHR0PGboBLCjo6Ojo6Oj4zFDJ4AdHR0dHR0dHY8ZOgHs6Ojo6Ojo6HjM0AlgR0dHR0dHR8djhk4AOzo6Ojo6OjoeM3QC2NHR0dHR0dHxmKETwI6Ojo6Ojo6OxwydAHZ0dHR0dHR0PGboBLCjo6Ojo6Oj4zHD85YAEtFPE9FH+/7nE9HfftTX9EJHe8/v8G922x6BR2Gb60BEH0FEP/aor+O5jkdlOyL674joO+/6776Q8CC2I6KvIKI/+7CuqeNucd1v4Lk8hg2P+gJuA6r6pbfxOUT0bgC+BcD/BUAC8KMAPltV/3VzzvsC+GsAfiuAKwB/R1U/5zb+fscubsu2HXcPVf0eAB/wqK+jYx2q+jUAviaeE5EC+FWq+uOP7qpe+FDVT3/U19Bxd3guj2HPWwXwIeEdAP4QgJcBeGcA/wuAf0pEAwAQ0RmAfwHg/wDwKwG8J4DXPJpL7ejo6DBEH9XxeKLb/7mJ57pdnhMEkIg+l4jeSERPE9GPEdFHEdGriegbiegf+fEfJKLfsOf9ryai1/j++xCREtEfJKKfJaI3E9EXNOcyEX0eEf0EEb2FiL6eiN4FAFT1UlV/TFUFAAHIMCL4Lv72VwH4OVX9S6r6jJ//uod4a56L+GAieh0R/bLb5oKI3pmIvo2IfomI3ub77xlvIKLXEtEXEdG/dlt+p6utx9jrFNs+QUR/36/hR4noc4joDXd5cx4xHtQ2X0xE30tE7yCif0pE70pEX0NEbyeif0tE79Ocr0T0GUT0n9yWX0RE7+fvf7u3pzM/9yPb+++uks9eXmPz+ucQ0c8T0c8R0R/2v/X+d3TvHjV2bAcARPRxRPRDRPSU3+MPijf4/fxcInodgGeI6P39nn0aEb3e7f3pRPQh/tlPEdFfb97/KiL6V77/3X74h/138EnX/f2OOYjo1xDRTxHR7yeibyCiN7k9v5uIPrA57+8R0Rf7/rt5m3yKiN5KRN9DROyvvZyIvsnb7k8R0Z9sPiPGx9cQ0dthY1NHAyL6jWS84WlvU//Q+7ryu2/OLX0NEX0sEf1/vD97PRG9enHupxLRz5Dxhy9YvLZjF2rGMD/nw70tPeWf/6qHdhOuwSMngET0AQD+BwAfoqovBvAxAH7aX/54AN8AI2BfC+CbiWhz5Ed/OMz99FEA/hwR/Ro//icA/B6YC/flAN4G4MsX1/Q6AJcAvhXA31bVX/SXPgzATxPRt5ORj9cS0a8/7Rs/7/GJAH47gP8MwAfBOh4G8HcBvBLAewO4D+CvL973yQA+DcC7AzgD8NmL1/fZaw37zv3zAN4HwPsC+L8B+JRTv9zzHA9qm98H4FMBvALA+wH4Pn/Pu8DCIP784vyPAfCbYO3hcwD8Ldi9fi8Avw7A7z/xGkFEvx3AnwLw0QDeH8BHHvulXyDYuS9E9BsB/B0AfxTAuwL4SgDfSkTnzft+P4CPBfBSAJMf+1AAvwrAJwH4KwC+AHZfPxDAJxLRb13+cVX9Lb77G1T1Rar6j478+x0AiOg/B/AdAP6Eqn4dgG+H2eDdAfwgGlf7Ap8F4A0wr9OvAPD5ANRJ4D8F8MOwdvlRAD6TiD6mee/HA/hGmO33ff5jCZ+EfjOAr4b1Y98A4L858u3PAPgDsPv6sQD+GBH9Hv/cXwvgb8L6y5fD2sV7Lt6/1y5E9ErYb+P/BbP5BwP4oWO/123jkRNAmMp2DuDXEtFGVX9aVX/CX/v3qvqNqjoC+EsALmCDzjH4n1T1vqr+MKwRhXr46QC+QFXfoKpXAF4N4L+lRqpV1Q8C8E4w0tLOFN4TNlj+NZjx/xmAb/Ef2+OCv6aqP6eqb4V1UB+sqm9R1W9S1WdV9WkAXwIj2C3+rqr+n6p6H8DXw374LfbZaw37zv1EAF+qqm9T1TfA7PQ44Sa2+QlV/WVY5/QTqvpdqjrBOs7fuDj/L6jq21X1PwD4EQDfqao/2bx/ef7Ba/Tjn+jX8R9U9VlYu3ycsHZf/u8AvlJV/42qZlX9+7C44w9bvO/13q4CX+Teie+EDWZfp6q/qKpvBPA9OGyfFsf8/Q7gI2BiwR9Q1W8DAFX9O6r6dDPG/AYiesnKe0cA7wHglao6qur3qKoC+BAAL1PVL1TVrar+JICvgo0/ge9T1W9WVVnYv8N+oxsAf8Xv6zcC+LfHvFFVX6uq/1+/r68D8HWofeZ/C+DbVPW73bZ/FoAsPuKQXT4ZwHep6tf5db1FVX/oAb/jjfHICaAHHH8mrJH8osu0L/eXX9+cJ7CZ0suXn7EHb2r2nwXwIt9/JYB/4vLrUzCFI8NmX+11XfpM7vOoup7vA/hXqvrtqroF8BdhM4BDatULDTv3lYjuEdFXuiz+dgDfDeClRJQOve+6zz3lGnz/5Wh+M4v9xwEPaptfaPbvrzxf2uLU8w9eo+9321XEfXklgM+Kvsr7q/fCvA9cu083sU+LY/5+h4kK36uqrwUAIkpE9GVkYUZvR/VovdvKe/9XAD8O4DuJ6CeJ6PP8+CsBvHxx7z8f83HqcWsjp+DlAN7oZDrwM8e8kYg+lIj+pbvefxlm37DdrJ9S1WcAvGXxEYfs8l4AfuLA63eKR04AAUBVv1ZVPxz2o1dY8gVgNwuAxe7BFLifu+Gfez2A36GqL20eFz47XsMG5lIEgNf59XXM8Vkwl+yHquo7AQh3Et3xdfw85nL8e+078THCc8U216HbbhevB/Ali77qnk9MAw+zPzrm73cYQXhvIvrL/vyTYW7AjwbwElhYCrDS5lwl/CxVfV8AvxvAnyKij4Ld+59a3PsXq+rvbN/+kL7PCwE/D+AVRNTe8/f27TMA7sVBIvqVi/d+LUzRfS9VfQmAr0C13c9jzkvuwUSgFofs8npYmM1zAo+cABLRBxDRb/O4kkvYDDUk1d9ERJ/g7tnPhLkfvv+Gf/IrAHyJ++JBRC8joo/3/Q/zAM0zsoSCz4XNuP6Nv/c1AD6MiD7aFZTPBPBmmIr4OOPFMLs9RZZQs4wZuyt8PYA/TZb48ApYbOnjjueKba7D1wP4NLJA+nsw18rjjq8C8OmuSBARPekB6i9+SH/vF1Anu4/i7z9f8TQsfvO3ENGXwdrcFUwZugdgbxkQsiSb93ei8sswb5QA+AEAT5Ml+TzhquKvI6IPedhf5gWC74PFxP5JItoQ0ScA+C/8tR8G8IFE9MFkyVavXrz3xQDeqqqXRPRfwAh94BsBfFzwBABfiNN41NcA+Ggi+kQiGsgS7j745G93S3jkBBAW//dlMCL1JljQ7J/2174FFsj8NljQ5Sd4POBN8Fdh7P47iehpGKH80OZavhzWcN8I4HcC+FhV/TkAUNUfgwW7f4Vf08cD+N3uDn6c8VcAPAGz4fcD+OeP6Dq+EBYm8FMAvgvWWK8e0bU8V/BX8NywzUGo6rfDYjb/JcwlFhO9x9Z+qvrvAPwRWNLO22D35VUP8U++GsDfd5fjJz6Cv/+8hao+BUs8+x2w5I+fgY0h/xGHRYtfBeur3gEjLX9DVf+lqmYAHweLBf0pWPv92zBFseMa+Jj8CbDf61thPOIf+2v/J2ys+C4A/wnzOH8A+AwAX+j84M/BJqfxuf8BwB+HqYQ/D2sXR1eaUNWfhfGKz/Lr+iEcjnd/qKC5i/y5A7LU6/dX1cctk7PjlkBEfwzA71PVnazHjuc2yDK7fwTAuSejdHR0dDwwiOjvAXiDqv6ZR30tzxU8FxTAjo5bARG9BxH9V2S1Hj8ANsv6J4/6ujqOAxH910R0TkSlCHsnfx0dHR0PB50AdryQcAarVfY0bLWWbwHwNx7pFXWcgj8K4BdhWXIZwB97tJfT0dHR8cLFc9YF3NHR0dHR0dHR8XDQFcCOjo6Ojo6OjscMJy1U/OJ3GfRlr6irABEAakrexD5B7TXS2XkEgOM1AFGix56vlyVT/0wFIKoQEASAgJCVMSHZVnm2zcLIShBhqJIl1ottSQGyDyn7pP5cFFCAVP11BdSOQdWuJJ5juY1z2kJAisWBo/E03vZmVX3Zqe974qXn+k4vf/Lkv0f+n9mqsR2p202rLf0YUM9v3x9QEOxWMjKqjSZNthV7iDBUqNrIH9Bm67e/XCswv69ui3Je2LHdB9x+S5uufeBxePv05odup7aaVdvObBs2mdsi7ARgYSsAzXu33g2EnaJt2cPbmTBEGSLkdgKgNGtHMxv5beTcfInWPqjnUfxxXZxzqJ1FOzwBd2GnFsv2dJ2daj9Z9w+2J2VMWLQlTchCTXsCIPTA7am2meb5qp1aG7V93t21J+DBbEXNzlrfR9A6bs2ez9tf+xmq5LeJ3FbU9H/VXtkfR/V9WOy3OKbvm/V//qa1dnUk7uensZXLB6onus9OtNhZ5xfNc4rnbfux53Hu7ieEXajYSdT6PVGqW39os1Uhv6e+Xen75jZA08cB0IWNgMYeKDYq7Qrz47M2tbTZjunqgX1t6iQC+LJXnOOL/7Gtac1kpfoSFIls2EgkSFAwBGeUwSQ4Q8aGMjYk2EBwRoINWXXlRIQNCEyEBAKDkZqRLqtCIMhQXKlgq4pnlfCsDHhGN3hK7uGp/CTeOr0Ib52exNume3jr9h6e2t7D268u8PbLczx7eYbt5QZ6P4EuE9J9wnBJ4CtguA8M9xXpEhiuFOlKkS4F6UrA21weNGZgnEDjBEwZEIFOE5AzkAXIGaoKiPgoKv48DCO+0bJ/zOD1XfqNR1UuX+KdXv4kPulrPub6ExskCJgUTIoEwYYzGGq24wkXNGFDk9tywgWPZmO39YYmsz0JUrMyTgZj1IRn5BxPyxPFVr+0fTHevH0Sb7l8Em999gk8c/8cV8+cgZ4dwM8yhvtwWwF8BbPNCFDWStiB0shIFJRty5OCJtvyJKBRwKOYHScxO+YMmrLZM2eoiNlLZf0GHcB3/NJXPnQ7nXPNhQhbWbszGyUSt02e2am0w8ZW0VbtsxRvnN4ZAIqdnpUz/PJ0D2+fLvDU+ASe2t7DU5dP4GlvT+P9DfQyge8z0iUhXRJ4C6QtwFvY/c9ml4u3xe99106UAZoEnM1elM1OmASUM2iS0t4oi7Wv3LSx8bSKUHdhp8Bae6r2WbfTzGZNH7qvPT2V7+Gt+UV48/hivHV8Em++svb01P0LvOPZC2yf3QD3E9KzbG3pPiFdAenS2hOPbqtlexItJMRsaQ8eFZxre7K2lEHZ+8fJ+8NpAlShWe60PQGn2SruK8ckaU/ft6GMCxpty2Njr2k23tlnmL1GHZBBuNQNLuUMT8sFnsr38MvTPbxlfBJvvnqR9X337+HpZ89x9Y5z0LPVVukS1vdtgbRV0GSTKcqK6AqCJFAhjVraHo+y0/dZ/5dr31faVK7j1Qn2+r63fdPxhllgaaf2N97aI15r+7vZNjiH93/l3KaPC2Tl0n5EGZc6YJQBVzrg2XyGKxnwzHSO+3mDZ6cz3J829hg3uL/dYLtNmK4G6DaBtgzaEnhL1o5i2zzSWNsYT/D2Y+0obBRti7KAJjEBKitIpNlK7QODyLf7wTuAOe8Aij33tamTXMAKn8XA1QBlZFcLYkaaY8YDqq83zNrOtwhvAMgL2ppXiJH4sdac+chLp5X5idYphO235yyfLz+MXbUkAsivgfe8Ye14vGftwjo6Ojo6Ol7gUOcEgeAVAMrx9nXR4BmLrXJzzvF0Jp+4EFJ4Uk5dP0mX3GL5WntsH4+I89vXmRZuocVr5ThXzrGC0wigmkqQlbDV5Gx6wKgJW02+bzOf2AZZzIX8hQs3FD4jeBmm9sGPt+rfg4KWWjnpXgPuEkEje0r2AFE1wMrNJiKA2d/Hxb1thrLbTOX9nQR2dHR0dDzeaF2tAFZJ4HViT4hN7fsfNnSHW6ydE1uaEUFdG/ep4Ra8ON6SveV7g3eUpw3vOAInuYAVhK0ORa4thA2EBHbCFoZs9LrWEV/2xfeNlG3UX8OuBJ2hq8pgi+LSamLTyp8nXTBx+2NB+la3bIYitn0wmWzpZLAYRp3gsYAEUGaTZJlB4QpmAiRIoJgrmNjk2TBYz8bu6Ojo6HhMsKvY+dgfAknrjo7xEgwfaOsWQKKMDALDySNx4RyJBFnnxLL+xRuIMNQ89h2bET/nFFCnDWQUgBWQ+pq9jwBuXKXBOaCVMzAqXQreoQoiqrzjGpxMAEdNGJEAAEml+OnPMFUiqIwN5fmbFyQwQcFO/qBq11pIYIU0xCgrPJj2+i9WElBa6XZB9OAGQRDEeO7KHxEWhM9/dOwqnyqU2Awl+0kgUH6ngDCIIyaw+aGv+qqv/ZodHR0dHR3PKyiAURmpUdJEExgKUc8xWBLBPSQwwV26yhCPDWyRr1EH84rrmGlXiCpDNK0QlTXit9x3rtHyEGo4CJVjtLMN4qfsiXftnSyE0HnH7tXtxUkEUEB4Vs6QEEGaghTxeUQWfKmCDU3u06/xgFnZGGtLaigXBRCqEAJYFam5fHMNVyJ8CGmhHppyqjNDVFl2j/I3I4i089BkxK/cbCbAf4KrJDAlD9RtiGCjBgINEezo6Ojo6HiBwzJrueQrBOHKICRSI4OqhQhmNcFJiLBBRksCi8ePBOzJpC6trf7tY2MFWxGp3Veg8ARdcAr4a/F89eGXDiKoEz8vDeBeRZiopNRsydQ+ovp+oKp8rRIIFCJ4HU4jgEq4kk153mbijJpKJs6oCRvKyEQQYmRiS2cSQBZGyRCcuTs4qyLBsuFbdbPECgIlkUS8TMUS7BlBPDMaGubt0mjL2DlYOKBJoUz1kcjT8wkUqfrMRlxTcmFTsEYCkVLNDm6JYHKJFgkQBSWsZ18dw3o7Ojo6OjqeZ5h8/DbVz7yKTK0CiB0iCBWMSDPSN/p7AUCgkKiv0ngogXk8oSwSSAK8iO1rn+96E4NTuFKHOdGjlmMsHkX1a9zAqigq4Y4KyM6InGUWEhhcpCVMwCwu8BBOVAAZT+cLADU1G0ApQRGEsJY5SBg9XT6DcEYZAivdYEkhuXENi32HhggGMtz9i+Pcv0A1XEv+lLQYS7l5zJ4b6y5xgEw1wYPVXlMFNT+eGQkkNxJZSj3BlcNwZRMZEQScGDZkEJincHd0dHR0dLzAoCCMUkf5tqwcALASJsBLKYmViVM1TyMUG2SMSBAlbDgXEsiuAhpDwkxYCfJ3agbwDK1rd3ZMq4oXfIII6lxCXe0zrkHFM1l4RtRUZUCdzRmvYBAEmt0VnNg4gshcCQQ8eYTs9SNzC05WAJ+Zzouxot7O/npXpgSesxX6FRoBNgNcoGHjsBIxGxKIaiGCgayRNUx+HTwzYns9y20UWAVrMcyMifMaGTTlT7ORP2UCXAkkNca9jAIgwGIJNQzgRFDUeKF6nbk1VbDcYCOEHR0dHR0dL1QoqgJoB8yV25I+IHIFCNIQwcHJX1IpyteGMwSEUQY7RrmSwAY1W9heWI//21UB24iwguAQaESk4J4rHAPBLUShKd5UtCLTgNRdwiuuYIXnFDDgrNFELVVfwMIfiRrid5hPnE4A85ndFGhl5555O3BGEsHAVQW84BGjJpzziExW6+fC3cIXTggF2f8RNhCPAdC5Gxi1QvchBBllaPHdUxNgWXz33DxnZ+pBApMdk0SgwU4mN4YyQAOACbs5K+HiXRJB4LAqGP76lGaf1dHR0dHR8UKDKGGbq9s3EGN3EMJK/CoRBKOogXDVLLYbyr6qR4gwy5CzpmTMNbGAbRjZjBSSFsXvkKs3OAY1z+OSVBsV0L2OpDCPYHMNpncKCGy8Ap6DAJm5fNXVv0IEW6Z6gEucRACzMp4ez8sNCWMxqVVPz0YCNyK29XjACx7txjsBzES4YEsQCWXQGPlkSqDX/4sModwWkY5HKT5tXzRiAmLmQH59BPOvh9GULQbQCJ8FVMIfpvwBmp0EiqmAiFhAZZNlpxUSSGTVucPF2xJBYK4KAubnL9lNc1Krnfw9Fhg14RcvX3zUuedpan7jUfleStxtTLpspYKMc9/aCgYThCwzX8D2XBkMqQXZvdDqGhiKxAJmAbOCkkCZoYNCBgJtFCRUF75hsiXgBMhn4YqAr1xgnRRn29KUIFl9NRCGJvGq+OzbBJrEVmppqt+TKGg4qfvqeNh4DtQ1XRYYPgTxsBuG1gRC4pmiFGQikxGSrFbpIkN95SprQVYBw8crHEcwZpglK9qo0goWRHMyYd8VLia0maX2vJQciRp0Ll8RmydrXsbMFmJtExSPvOgTzp1DQdjKYMQqEkGaMTz4BRRgYgzNqh+SycSm+PsLEhifs4lDKxm98wLStHNsLfaPnEO0YhKKsocqLjUq4Fxcsn3oUgVUDwWrd3WVBGYAyfmdE0KUFVzsoTP1D0YID7TLEwkg4R1LAtiof0yKgYL8Cc7ThFETrmTAvZQwkj0f2YpGX4QqyNUlfOZaoC2xo0jQIteOasu42EJJ3NT24Vk9nxL/1xqyZeaHXMCtCpg97Tr5GoDuCia11SAp2Wdr3GigGqQlgsXAVJfbSTRzEdt7I4X77jvSHJlTAEDsgbe5ed2yqzZOGcwNr6uN61QcWbOyBNqW/SVPnsnz1oyWnSDFH2zrKwHu6seJHeDdQqIrUPKkMNopbJAXJZgyGAkMuMKeZp9nJPAU7BRXd7TZcPXkxk7u0Yi3t7a8DmHDaFN3jVNIBWB9TtueEpldGDFhrtmKGzxYppdcU/B2twj+Yr99YNGeqD6ZxRlRfb3Yz35a80GG2ZbJbOqf3hXMtZiuPQ+oRMEq2qYSW1b6v8Zeogz4BKrNzssrbXCJQzFn4Z0q11/uMWZjVVsyRJvxTZtzQVVVskTHWm8uRI5C/pblzUQqCbwDqAJjTrPfaQ3d4kZcMjJoVUa4cA1LKPXkB6CSP9VSps4+a10FtLdQ4Q372jc35K+Ek63kFBjZ86SO1hW8kmvQqoBIpvwpTHwKWJZwvfQggRaGprP9GRGMmxv3eU9/HTjZBfyO7ZwAhtI2kCCxuX8HVySuZMA5T4UI3uOtEUBNEK5qYAYVl7CphFTWWEwNFw71z0hgGHGParE0XFH/4K5c2k/+ijJojFqFLPgy7jUDZBWHivqn5D+lpUFi0BLxQax1D4elw2jHdVzXQUGY5PTPWgbYBpJKya7KxGUG3A5eWdvZMO+U5DkGVBqV1qzsiNlcxG7a9/R9qcfbukqaTGVa7QQjqcezuZHznXaAgP3p6cg/yGJZ9lEmYZIEcAaDMcJdIiAI1SWSzA1SbRJkL5f6Wfs7h7awermGJYlY2Z+pFf6a+kgVz4NgKM2DoSnsHJOtolREHExlmda53s1E6RRSEW8w9xVVVaO51/vaEzsNqTQjRox5iasy8b2WBDaTXTSD0OxBxf0UdirtKUQGIq+UYP1mZC9GXHSpjxpB6lEhIec7bU+ADarjCX1fxJUB1obAqLFlsHYgauNUZJGaGmi2ShBI09+J8tGT4pb8LPu+ZZmyYksfo0I4i/GnkJJCMBrxIjeJjEFa2IQMa9QhV9VyZcd9geNPXcIUwLkLuGxRecWSDA4sECEIWb5BUf+A+f7sOuffaV8FkSV2cgkIlWCTFpJtbaPajBo7VSXQRSUlSDJ3tqo1OhkAnhpvIWDMbFn/jth4TKMAmjfYiSCjKIGLL7z3O55MAJ/dWhkYMxCKoRJb7F6QwA1nnHHGNpkC+EQaMXHCE2nry8n5oszhCiaGMGPEhAsngBtMGIFGWvcl5rSuOxzbNcwMVyTaSi5aI1Wptkq2MpDXniGwu69MNYG5gYME+g9MiYzoFT/8gggCtZHFDLt1EQceYAH1pZ2u5HT3WMRzCpGpSCsNi9UWO2cIMlEZsBDbIztA9gW9a6wmSifWzn6XwbVaKoY7yq4dn82qdK7gkpM+ZYBSZHO3n3ViB3hDKAjbfJyd2GcebVxMlFXacLbBC3UGbEXarURC2KsQjcY+h0jEMhgaWHSABLufThDaNhVEQiOYmWC/8fLnbMBpg6Ej7MIaGFXFHQyNCVRkwUUtzjuAKuHqSDsF2vjo69oTgLl9YAVyZ6TvyEkVl34ZjVIxn0gtJ7xhC/uyaJj6oj0NDFWZe0SY/XXrpWmCjSq5KUh7p20KuDqSAKamTYW9RAgDVWKRwj7qcWi07gbeN6kqRYgXhGNJLpZ939xW4Tr078huL8VO/wcNT0YVL4ivSWQkreFLJ+HBGWAogIHl/YjnA8mMDIqah1GCkHP2LXmJuXFGAgUEVp4Vh27XEjb+sNuPzK4nvm1xAWud2PBi8tqMVcV+7pYt7U0JmiKyz6xgn1csUjAb7YjsGynV5I/sBDSIn2fPzsa1A93kaQRQGM9enhUm3LLiQgJZMaSMDZsieJ4mnKWMrSRcpNHJ4BZjSkUNHHWLLQ/YRrwgMc4oY6sJadGotr4SiWC9jo9938Y9TQCzNAMWGiPFbKgydEkAJYDE6vMYa7eOD2CQCmRwEihOAmPtXxH/vDCOu39bZr5GBs1i829wA1eXgPDMdHbSe9oBa6CEiTMGzUbQ43q5EkAApQg4Q0onmDwIN37H7Wwr6/4Og5oOcOaGbx/l9tXPMfdivJdmNSTDXQplsCdhs2ddR1NTAJRjiq136rJSrbPgazHZJlwiAwsmd4cU90X94kYAfdAaYe2oVZk8bgHAYbu0f7O2d7idKvlbxtSGolSaqMLeOJuNUrETYDEwktylpk72ABTnt8A6vJg83RGxENTkt2Mxi43GgIEzznXCROyTXsI5hVGB5F2xKVKuAjb+IlN/9/d59t5F8ht7lmD0eU3/ZuQgao/ZZKR4ONT79ExFhI3vJC5DrQot8MFKXHFyonFbno1jIEq4zJvrT8RccWrjakdKEJ6sTJmdABZFYvU2NXjio8wmwAlSVNtYBOGYv7/a97UEfaH6KZu9WltZu2xSBvw1ZYI2iYwhZBMAZF/xqoldPzoep3zIg0GbJJDlvQjSxWQKeetlFCZMyr7vcZpB7hiAbLAkgSApq4wAUWClbpewCXJaJKc095nhyp+P5TN7qYV7RdnCYkPjBZKq4wkuUiBsNgEoYUsAeekXIrVSKKoAsW3ynAgW4ucfru13u60kEFVgvBoQrjZQJFlYcHg8EicjgUkw5oRNypgGxjYPuBhGTMqYNGFixphSUfGEqhoY9QMtJnCuWLTMPTrFtRnWjKT6zbEGpG6QpoGllngQRNT6Xn/I4I1qINAEI4EToIMNSgoByNVAZyoajapN0w6DAQfr9dygbZlSezIBrIG3Ecd5zkbmRs7zWVK4R5wMjv4zOiMr7J2gO66QfZ0hUSQY+IDVZGPXGZTbxDPDtQqo3iashhKJuseT6qCl7tHygSvEl0L+iKBeMoCylFT8u4CAcH86brASLw9UbCSm8J01HWBWgngHnkjqoEWCrQ4zlSluQMyM94ZSlMBsnbUplHAKNOqs7SP5PRcjHEBLLKiZQALckEC77bamOKuz2SgcTwpkckLkg0W+G2KRlUvs8zGo6kUG04CBBWc6YZKE82SrJJ1jqoQdaNpUO6lSt1Gela9o458Bt3UzSWayfrioSKn1bFCZUIkPPiQoMgMpyioFQtaewkZoJlemnLVf2t4TE2Flqd6QO4TieAIIBPmrfd/kfZ8o4bzthWeTKil931IFTB6mcfBvNsSikgon6ytKkpEG8ze1YbDa2ComwaEqVb1hMQFGVZTMVrQ7Th2LGyT9iBLGqbbfNtGi9SwSWTIok0LY1Lwggpakk63PDo8XA6xpprIXpa30kzy7jkOIyUHE/zErMtVEkAgrI4bX/WuIn6J6RCJ5w0l4ieeGta8ggSQwd3D5DTlHiK6PIqzJiaBzDXKRSVs1/4j58Wl+DSHI5YC5XK0gZhDbRRApOAlSStgmwSZlnA2MLIzzYcKkbJKtEnIiJ3BuzBIXyDVzGB5vQXVB51EHdyPvBmfHjwVYDFosqMWcqXaKMSt24qdJTcgTQIQKCQS8Qfn1INs+ZZTBiVjtXHEmLlLcVdqogDMyGB8MHPTVn4I2W/sYtAW9mRRnnDFwxsQZU2Kc6bR7n1k9UDqys+aqRZDAfTPhWRJRQ9RLse1ksRKU3EaDD0SkLQeoxEJMtS0Tk+icCODRY+Co1pjkkLKKWqE2aN0hRAmX03FNMEIFljG3kyac8bQzYLErPzFoAVVlKpj5F/avlbm/rJIT9siYT5hHL7BNkKBVcAxRi4TAuXaAlQdZrykQMDEweQeYxeNfQqmFpfndAUQJz4ynK+ptXPSWEs5SxqiMc06Q5H1XAiCh/MlsUpWoCa3APBQGWHErLm3ElahrxDMH+RticuTZ2C2pUFcflu0pmlQQjhBaiEIiqW3KVyjQNjj9DpCVj7bVMubM4teNAA4sGJXxRKpjTJDFWMd+2ffBlfV2rNrnqWpJZ+37KlkvfV8IELA+y9qX20ub9uQTq1AAy99B8ABuHQQeEiA+bnEZm/SOCCAAbKc0+4g20aKdbNokVpHUl4ljwgCzS0x4pVEuS9xyGxPYqICyaD/tCiGBts/bGaeAMk61CmBV/LRm9aZG7XO7LvwaMxIIWEyg8Qq/xxHHHmpgDGIeQwhfRYRKWws18HobnEwA6Spm5CjuBRsI7KZQUkgi5MRgFuSBMQljTFayZRIjfLb1R1P8uC0Xc0G23645HOeUTGCsxwC2zJ2LRB4K0zwJBIS5e6SQPyODUHNLxQxKwD5jcYVCPC5AWyLoimDR4rWSwUYFLGTQLvpW1CdRwjPb4wesmH0l7wSvIoazuO4TJC0biFR3iKon7XBRLdrZVlu2Z+czmgbPLMicrBNMCh0ImgHJKIM9EZXOD7AtqZFCytEIbV1qNINW6QjJlFshiw3ERObCFwEGPmrWdFtQJVxuj2MxeeBK1BfxthMzJneHlLJIZDFkl3Hj2BIJWldwJBjsU//aIu8lzMOJRZSCwUw5bwKZg6cNVNWlIBoxqaJ9BAMgsskakZd8oXBvtB3d3UDktPYEYBYXvUkZAwkmnTAxQ5IR9tKmEpBEdiZVow4ATSWGqaypjiaT0Z+3hCImwUyKzLUtyQTQYGU0aHCVlp3ote3JFQWSpj1FuAxVL6ES23NW0Ohtk8lCKtp+7w4hSnh2PEEBbPqfaE+JBGcpm62Ui52i/ZjKVPu+ApoQJFD2jEuBtLAVkQJJKwlMBB2snYSbXlwFElCJ8YrJcPR/s74vviMaEshGlsxGbCJFbmx1Sv93AwKoSphcAWyXbF2GlhGpeeNIkZitTSkhC2OTculvZBk3H+Kik0CrnKCznIo14rf+NZcTK7G4Su+7WhdveBaDDEZsM0pTMCWXfQxrSWC48Iuim22rFOV7FMh27ygS48L925BBu8Gzuf1enEYAFeD7XBTAWaZmatx3iYGkkEEgIsiZIRur+Zc3E9QNKK4Gxgwrh2vDZ8VCBOERG2TIIiD6YN0yrxk0iwV0hbLI67GNxhbxfhEb4yTQ7qK1wCDeRDB/fVEojNSRrhDBpmGtksGW9N1SZylSk3WORXSCQ6qqxfkwYUrVRmGn6AgTfJALtwjJbCYM7CcXQO0EB48XbYlFVSq0xDcAPmDtEEB3AbOWQQsR2xlSPVkHEEvzMCtoIlBy12/mW7fDdRA5gQBqLQ4UsbaFACabRAloNpkKpELWK8EAjFzkRXbrvvUxC/lr25ITdZSBKtz0ZNlrWj0zNudpXPXibWlB2INgtEnARuyTu/i1xMceMcG9FYgSnr06bUJVXFcs2KRkdlLGlhMmSUaMHbOSF4cmVaghMGuILMkgM5zEkp0KCaxejYg+YYIR8vgQdULonoywtfqAxz5hBoVKwdCsYCJwFss4zVasloJY3CFECM8caas1pSmxYMOCUfKs7wPmiuHVou8LV3BZzwvYCVUKpELHauw8ef9XxqNF3xfiaiRbkydORV9YSLuX5Fnr+0AKnfw7M9nfFPN62eecZq9rvKeH36tADhdwEEA4GXQCyD4BFN9mUSQmJGYMKfskyuMAuVI7JgFLqh2Ec4lZvccDCAWxnVQtY6CL275ZsUOZ3JMI6wcjua1VAv2bqhqHMk+iq7fZEsYoaw2VcW9khJRZuJOWMKgdMmgz+nqTr8FJBJAESFf2BeA/rhqr4G7VBBsYBoVOhDwwdJOhQpCNZ+4IQzbO3FFr8bQQZpxbsTNksuK1s9dR4/9atDFLbSfMLMUtoj7bKoSvKf4sCaZ0ii1KHTdZGhIIuII0WWZiDExmJPWU7ErygvztJYPAXNG4YaepSri8f4Ji0cy4UhKk5MRCPEZz4EZp0FlHaIOWqxZq2aZtJwjMVcCYFUdDHChXmd8VZB3MNSHuhrJgdXMTUiriIiILjnwGjGQFiMknIjwBy3I/PAFgtrUVk1gBYluAunaCdwRVYNwe6wK2bWsnI+zZJkPCyFrbSPLfflHxMA9it6ySodjh4GSqmUiZnZq2FO6OcP8OMDe9K0vqXy9EYW0IBrF6oDPKoMVOLpRhtpwIlKx9UYaptq4C3pVYK0K4ujx+QhWDGLPZaWza05mrFm2/V9y/i0nVhqa5exE1BlB0TiraNhkTBGYFJ0WO/ji7oj7YgBF5GtTE1EaoVBBAMJXwHmVAp1jTlAoZpMnjCzOBJi39YdRJvds2Rbg80lZtLOqyTUXYUggVgTQ4wXaFtk6oAlPpoA6pgMWdHJO5pMhJoIndVnVCBScIMfmNobCSP29TCTZByiFQUCHrhbQzQ5OCJ/V2qPO+7xRT3cQFrAQZw5sYnVu0HesbJKP0M+z9jvEHG5tTxPQ3TJRJsKWmT/U/UUqWLfyiLcFvMU8kNZU4+j5m9hqEMc7DlL8ED3kIImjtrB3iW/cv5eoOjvhNcm8Ie2iZlvh2J/cax4xEkmJGBu0rav3Qa+x5sgKY7tcfVSGARfnzwXbwjmKwmAIRGAFUgkhd6iRi/+KGr8LvUJRBiKD17OqhfU4lJwBmLpCaCezSbYp1fn2mRZjHW4QLuHA4AqAep2TTZmJYoCYxOKsXQoX9EITKm0u2DmOH/LXPgdZouDkBFMJ0dWIZGG9oU7If+TDYgGXkb33A2nDGRicvNTLgTLMnhNROENiNgwkbRiMLBTAlSwaRRlkKFzzgs1ZXltoBC6iDlmaAsjUgZS0kMBqThuI0WeyqCozID2iKeZ98yx8MSshXxyUyLAngxGKDhriyPuRZR1ZUpVAZSGZB7IYJIJ7Fwth23p4q+ZOSsMNJINGWxG2lHstC0SlpmejN1YpQ9awtURNIDc+iZ7cVc3Nu0uZzTHW6EyhhOtJOQBDa69sTsDvQ2OpJAzZq21ABlwHsa64r6/dk0Z4Ektj6aCcWpU8jU2pL/Xe3D1BtpKm2J5rU+3tvR9622Ik8JvgkLNQ/zMtR3AFUCNOxZD3IRkxYqMavjzljGix5sfZ7NWFkQ5u5YkuLDPsGeaVdtY+wFSUt4on1e9aeLBmHPC5zzrsKeRebMMUkeLn6RNiMsvV9mrRJnGtsdYq5blKHUwFdIYDatJ0gguYtsP4/eXuyLkCMJKdW/WsmQhTkDPWYSjmnRVV5PTSm+TwrPm3tlwCbALO7ZZN6/LOW+otIc0W9NIF5bgqAmtgTE2F2t6+5+12JD+9ITNYieic+W6n0ieYsoKNDZB5QAUSZpWvrbkvGeiVXVU0yNe4hwuQ3esbafaCKG74DdgZPc6ViLf6vVSxKRxhukWSzilmsxYAawJ6dvIbqVxqEE5DIxonZ1BRurKoCkqtVxf3ByckeiltkRv6SHd8p+3KTjlMAvTw9Q1JZgUEhSSBZzXUvbEKmn2NFvjcWLyh1zeeNl+2JoOhZcsFCrQDmnWCixk5JwAPZ76aQfCfek4K8btyMALakIqEMRlGVnT2RINYHj98q+XJkHEW+T5DObwUC4Gp3IF89tZnUmavcMtVzJshgbWutTVk7yBglYaQ0q2cGHUpoxY59UCdTQGS0tnGHpvBQ0lpOxH/SZgsv6xI9TEsAFbMYs2Kn4p6v5K+QwFB+w05KzSzgIUMAnNCewgzqA3qEwoiQ9yv13LivG864oowNbcoSmhudSgmfZXtqEa6tQiZdBWQnNDSYa1bViIXFhJEPtCjtSYFyf6OMRQmpCDtNbqcgF5MrEtkEAFOgYk3SStbvDKf2faQuZJitKDFyqn1fbgSLCC3aUFUBW8U2EKsfRkF2oBKMMvmlGm+YGsIuYauhEnWCq7Cpti0A9f4GKfDkAc21DS3j3VtSb2PWg9vqJi5gKIDRxSTEFpXsuE3Uq0OY+pmgomBfVUsTQVdq5jIpttHI0lRY14AIEp+/Z18dwLXkt8SCLGxCRfGAWMcXsX7Qht9oWcnXv18T2kLwSZiRXWTMiF+EyrTPrYA3Ks+Q+JsKgEo/DA8Fuq6LPJkADvf9e7n83w6oVkrFAow1go0zIGJ17bTEmw6zware9PC5Swlij+OBQz78Nmg9BsChJRccM2J3Vwt24y3iBsbsC1p+pMuA9Yi5KDMzL9Bp7g/31cfMKiusSGcz2xJtOt5Qn26hsxQCP3tihJR3FkiAJkbeCGRjhUSDEBNqprDVC8wzAnhG4ZpStLy8ZC2uJIEMFIXDBUPKGAaGZC7qsW6s0j58Bmg/+LhXDamIhhJtPBlB5+Lmr2qgJkssYbedDN7AZoT/DqAEvjydACphFmerQS6a81PEwVJkLlZX8CYZwYjEs+uyfzds5UwSSQmpSEmQs9UYs06H663L9jsnBSQiEYK0hztjRgCDZHg8y4RZ8oGt6BIdYTMbuSMzQchin49FeEgimSkR8oahGyokMBDtacObpj0FCcxIpNhqKqQC8CS4FdfVbNLrrkyLDxWzkxB0U1UIYfIJVb2nc5WWyqRKva0wA5o9KSS8PlMlHqUvjNUJ7rI9Aaf1fTNPlhZ7ZV/jWjJDzqq9UvRVFMudbopiG67gBMYWKEuYRrJii5L84cpS9H0pJeQhJgqWGiDucYp7iiAG0QZcAYy+D7ZAEDTN+z4LbzJ1sPR93p7Ixz2KRIIjsSdq5Mg3E3jL9c+5Ghtu66jRG0kW6hNO86zZb1mESjZ9O6li6JzZNCSQNZaV2z/OLtdcb2OgqxuYTIlk8t+OfaeI9RMoeCK/Di1xfkXp89+ekUHv3ygIH3aIX0x6qdgbO5Nqu69+O2X2dC9OI4AKpEudNZyl61d8llLJnxEraQYJgSW4jA2zHnios1YfaGLQupJN0U0Ftb5cmwFXiIm7Jgd/JE4W1JsEkwhyErDYLA853NTwH1WNNas/hkoCI1aJ3J3IuRnAQoFSzFi6kbxICAnih+q/n3W+tzNTNjud2OkyrI6Rd37qbvupmc2UUgksOOOMMx5wJQMu2Ap6R625pYxudRp3B6vWVZ9YrG5kEqQheydoF7bsCI2gNzOcMhM2VxTHwEXeETZuK+aIkSGfCTcNrgTS3g1MUT/WTuEuQZm8wNX1rPOGTqS4ykMhBOdi628PKqX4ekBQO8N9GfXAnFxskmDKgpTYFzWvCrbGpXq8n2yoSEuVAFJtL4SGAKJk17XJB7VD9LilOxSUAG9PR9vJYCTdB9+NFvVtHq+E0p6sTU0456kpkG8qYKnd6NifqCOVVKSMTUoYk/d5g62ioE0fXNpTm2QVNlJ47KUTwdZGHl8W6nq4FucEsA5Cd2kvUmC4f5ytioiBOVHSjalwWRv1L4ial4o5TxPGxlam1no9VAAgKf3eqgcEpsyHQLFJgnHIyJnrpApcw6xapS7TnAACpe8joRIDWFX0ed9XbBXtqZCJE/u/G8UAAjR6Ylur+sXDf2sREgKGL8mq9jtOvCMiWfKYZQtTTlURj4QQgS0fp/4Edv5qDGAzhu2464khHpdoxZ2tXUEtJjHcsBpJGYMfbPpW8dsXZFDF1ECdKX+7RDBcv5W0w0Snojj6348u/jYVQAgwOAGMoodFZk5O+mK22MZvqRm4RH45850ogVkxkuKKBYmtaOoggqvsawqrxy55UdtwgO9bbqyoFlQ71lAAB7bOULI0JIe98LPfDa1xnYGoexVB0xwuD1YjG0EAi0TvXzlY+4IMxmyrJYQAdt3ADwo5vhMEUDsZXlFt1UIcRzZbXQ0Zmzzgkgecp2E2WIla2Z4oXxGIWbDN1Jr4CnjMEuVZzbScBJLM7RWNUwk2uyoDlt2rpWIR5UjYXcA2WHlfFapSGbQA8lkxwka4Q4KhAB9JLMolFcXdJy/KZdJi+S3WnraeebrNA644YxBTl0ZKGGVAYi0TG0Ze7QSBeUjFMqwipao82thkto3fE9RcwHE/1duIRgcXMYC5IRfFVjX5YNkRnhSndBsQj30+FmVi7OVXsrnrY66XKWEkIxVDStjkAWc8YZuGsn66rXcepa7miQZtCawWJat+rT1JbnLOuBB1bZUEoLiQigLoiQU0EWI5zUgICUXd4mmbvtEHq1moxl3hBFvNvVhedirBPBBnWqJ0JlJs04BLr2u7TQlXecAZD3MVkLSSdd2vrJeVlGhOAgcW5MESJiOcAl5qx9yMVFyN0e+1IRHWVrSq6Q0JjL7PJshOBKVOrh5IAbwB/yOFJ6oAgPUhEVqlbF624rZ2j051rxrZkkGAUkoGmLKCKJV2MCmDxeu/qoBdOTfPh3GIdfIXGfge+6c1TInEytuxEpgJwgLSVJYWhZoQRvDJNVriF+IZ1TV+QwWUuo3yL/HbbPu/EkIzc9vPS6PN2tw1nOJkBXC40rpINdeHDB7/4aUGMOuoqTBgIZ+ROIsOEjhkwciCLScMlDDQUAatTGyL3PsfbSvjF6NBiwpYlstqyMXG45ZSWqiAHnQLhbs7q88+ulwrSeGp2qSlfEUQjsgkLQ0piKA2ZDAM5S5MUpoRQru/t6gAXl1/XqDa0+uDbbSqtrAA8swJU1JcjQM2LEb+JGErg8eXDRjJgtdLMPQBJFcKU0MqWqVWBvEZXrY1HsnUpjKhiFsVqfZBwDMsg5hhMYNM5lJ0UkL+Pc39qzUmpiHrd4VZTO21Jzd2SjaDj2Ql+2nxbFK1ncxOW07YSpqpgAJrS+ztSWbTnYo2pCLaEnvA+5AEWTycQgEMBDHHh12Ud1Y6aI0t8zZWybgNVsxU418IMzXQ2pkuBirgLl2Lp7an6qa3WC7aWDsX5ZInlhkYWbFNGVc+mbrKA55I7YSKZyu4tFjWA9xx1xc3sNtJvP0MMicWriCYchFymNspyiqR708oxKLEQbtKE8k8VVWfD0p3hVPaVCGAYavJQoHI+z5xsWLiBE6CbUoYB+vzrmTAE96mWhUwwxcKIFP/1og6MJ9Y7e/7pFTYgFdDgI8bxV5SxYYS28cAnER5IY0yCW5DLUqbkgWROPpmn3Duzs1vNAK/tqIAipO0+E1FqZWmlIo1f68Mkdk4EylYpNQbTsRgsj5vkmQ5AK7+mfK3KyQl0uJOLgkglDERe8ymCVEiCk1uJ1fWEUog3BZAzezNNQ5QyROwXPmblfcp24Xq1/Z/Pnmeu4DrmDgngIeNdGIMoCJdqX9BKwVQ4v6yggbyVTS0utTKm40EWq0chrJAJ4YkxTRZfawhWbD6pAmTZkzNoLVRI32JMjKoVMVPMCVDSLCBkcWJrDDupIyBfSm6kOOF6ozYyWowd/FBpayn2DYexlyZkCCBaNxU1UVVDJZQns9ma9GYw2CR7n8LHSYJkC6PPz8Um3Dfl/idQtjdTqN1gKMwxmzEYhQfrJxUWLa2zYKtPtZ+NwiA0glW92LGlBk6ZG/sVhRSCdAc5K+SZm2fS50xUkYJnrYOhhadYEMwSowF7pQAQgHeHnkqUzNYoRZddqUgivJmtvY0JTY7SbJyPmrLL4pnbXPUJKBcFtTYu7Z2E0+YWgUw4mm9E8TAtiydr6MdBDC+q6r9rjTUJe/whWrdK0YcQ1NvK1zGETZhdlqnrQ8BpxB1oE6OE2ZZ7ADVyW9SZLZ2NEq29uTK0hPM9pqTCiuMP29PLdrEglAtmCzWM/o8SxICZu0pii0u2xJgnSFTIYI82e9PFsSiVQWLnaJ/fBQE8ASyPlMAYyEAqaVX4h7pxMiTtaOrnLDNCdPAptbKiMwWOtGqgMklnpphvxAs2onVwktlLkUASNaeyOxQCv7G5MpJUY2rgrfpECyu6fuC/EVM4Yn9302TQOI3FZ9FZOJSW0S5rW+nLq4Ub6e7B8TLooiQJ+4wMln7ErXsXU6KrDbptd9uqHO7sOxuarbWprIYCcyegCLqcYAJNbkradUmgEgfBSIOEGaH0udFBREgGGPZahmLUV3D0f8tJsNLF/DyXu/D6WVgrprOg7TETlAE0gtmBZSjWHQoTBE8LMk6Q0m+BJwwppyKCnjGVtw2Bi1b5YC9k6uGknKMSsX8gQWDZgyUcMYZE1sF/sxsMTEiHvPH0EHcNcKlUQVrnzWeQgAXHd0sjqxRARdkcGYsme/brfLB8SSD7LcTb0/7JPWA8Cg/YB2gDwKJoIkhgxX1npz8TcKYlGeKhbjEnr0lxyx4DUt1aaCqWlh6f7QM7whjJtwqpT4jhpoq1saOgBSY6lqzpeBmSzBcyY3YJwC3psReB1IgHUsAo54ewbPVvYRAdGgMn5DZygWTk/QpZWxlwCSTkXW2tbeFQpGy3/muO9HdVJBSUzA6wmGmLjFSGbAAC/FQD2wBdPDPDQUw7nN0djn6B7sOq4+FWaB0GdyoZrmR6p5f1e3DSMXxvwl1Jite0L5tT7w1dTpqpE5TwjREe0oYm/YUE6qs66rSrLRSE9MUyW/RnhKb3XRYtCeXXbSJASwEw4kRZZSQnZhUzdpSqYTQTLy46QfvuAzMSX1fELxwMQ6hMLmtmKyc2WRu/GlKyBubVG2zLeeXwaXvy0RldQlfzBBADWNp1drwgLQu+42767NIieGizBD3fphruE6ubPbn7axpK6XwM+Z9387kqiiA83Z1NG5CAAFftx2FH8AngSUuvkpm8YWNIGaPzcze5/lDhJHV+iTxmoERdiRqXEFc+Uvlj1eUuD8naAkKifg/NIW/xQSsxOQCOpkaWPyGlVoqlWZWv2f2ma73eaW9hfrX2CbI3o6dZD4ZLmbz7bFE/mQXMG8FsxhArwVWiY0bKyTOQv48ZsRlamT/QpkhohBxdU6jVAJhlLrOaXbDzIw1M1q2TlfIXMZMhZBMiTGpLdZ9JhYzpUBTlsH892VsIgvIFJ+VRAmRSCqgyKKKji6FGxj1Psi8E5z771H2Zwa70ZSqsdMDKRbuvolrIJQ4M5rIi8iyE0Aj7OH+DfvMBiySmVIxK1obro/SCc7VpU3KyEJIzaoWlM2mukgCUG1IIfl+mSHWGIzSLMNtQG3wbTOris+8CyjA43EtlUeqnUmpJ+UzS/LY22R2EpnbKYh6u6KLkQoLlziUBbx8XrLrfSY8JEtPSE3lDSEqnZJ67T4AtfMin1zk6Njb+12f23esnWjrBlGEMnAH0OOVWgCIzEVatqeoxTYBOjF00mKnMSdMypjcA1IHrtrvLZW/JWbhL54AF+0p+tQSHD4lm0y40td+16I2xeS0GaDL7420KClrhH1ZnPaucNKkKmL/GLO4OyILS1j2fSJU2pSoqeptezISaO0qXPaHlhpLjb1aVT3aVLiBAZQkOG0UwFJVIlymPt7au2rfp04WBWRxd4ATEZSxuU7QTrDXDUwb4VHlc9pHCUXwviNh1ldoECcnT1FjmBVetiyXLPlSw1bXCz4vEXyCSTAAyEQYyFYaEZCHvZh9hlKOxmxk4tEKCYRNnLTwIuv7FNGvackEjslucfuqt6UQjZp21cZDhws4uuxju8YTk0AUvLXWrqEMSe3Yq0phX9KqxXtdqMgQK/WHULIYY9m1HOsEN+6qNbdUdf+6SkGCKIIkxEYE1ZaRE5A11FR/BAoXjTY1xd/uZS1lEenZpq7UIOhZmREnghaYu0zZbgespRJoU7YgHHsDOB8UCqQjiQXQkHmxN8dzZg/iH8gzx6w0grl06/1cDliZeBaztM8NAmBVXbJ1HmW3MxJCSTTwQ6SYdYLqncpaIG5tluRL8BTOiDKPuEvBQlE75GtAk/e3HmIX3UwUSI62ZTai0p5iQhVu4FhBIgp7L22y6qpCW1RaSuZ+FFlPZNlw6hMpbolDQwBDDSuzYcRJDQmMDr95C+0MEH6qXN+h3wZOIRX2BlP/2vYE7zOsSD5m7WmSZp10WNuKiVQbVhGqUlsEv0WotW3pimhPqVEsAiLmji5+B3cT21rmRnrKYLW0VyGTPqhN1VYlqacZoO4MJ7QpZdSQisjYbAQLmlb6vtmEysapaE9LrK2us3Q7LhctWMaYlZYe6p82VSrCZU/zNqXwfjHZJClCRWYxdNRMrmL8acaho+7f8aeugtwTENfbElg/A2pTPY9ccF4hQYZR7kGUhYmSZe1jDVlr9YN2oivu+k0+emxCzQdB1NaHHkBFZdRGpAjCXkggWftCbvpDV2ij5EurBhaXr5O/Qvz840qsZyHrmBF3iq62ZZ7X4PS1gEfxrCT2AHouKoyAy48q0rpL+rln3bZ18yKjs9T00UoC96Elf6EiZTA2mOyv+806914gg/GEG6lVQKJwrm7q3zKu5kaLOITIJmVtCGxDBIf6fUKmLWRP4OofKjsPlTSeN1sAt+IyIRzfCQKAumKhSqUERwSw80SQ7GpN9g4o4iz2DFiAdX7hRgSwatNlvcZI5IkFvxN7JrKTCyNr5m6kevHlupUUs5lXmneEs0ErOsW4YTGu3eGARQrQkXaKjDlVoPBdwmxiFao6ojMEbMBSLuQ8ZsNZ2RKs9sT97fz9ZpAy9a8SC1v4hioJJFP0CRaTCKCONEI1vmxmALdJ2GePvNAOWItljB8eFFZM/Fj4hCLCIcLFSAk2kRxgtXvCA1HIupGLskY6ar+VwftuCYD5IFazgQXTgfYEAEqN+z7akn8H/P/Z+/do27YuLwj79T7mXHvfc7+vqOIhWmVVES0l8Rk1CC3B+AioSbCRlkDEgMEIRNMSI4lgmohGIyCaBhEtoySoTUEQQUIiioCNoCHhIURJoqi8qiihqqjX99W995y915xj9PzRex+jj7Hm2nutvffd537fnf20fdb7Nfvsffz6rz8G6TiUehhSe56zZS0co66zURc6u/nqQdVlH6gMoIIGDaoEcccFzub7zK/3BILbUbOnDC+r6Fn17Q0L+p16XF+L25l1mkrYIcoBD7uPE2pogDw4dj9naMBYZ9i8RwcslK3m0UnGJwDAZ6WAfY00wOdYwUFMBwJJankOAjjStV6zP55Bi6Bvi/3LQpioDbh3qfOFbau4CTpU0VPBk9UaTqKp5TnsPlJlAogYOSsTWNyGPK9rTaM+TaTOS7V6aMUMAQgahtiu+UNjA9E/Fo/xY3LlfmECrAW+J6GeTKoJBgAWFIYNomxDJluxaQBGXUGrhzBNogMEYENofRL7iplyV6cEAo5ie5turGlFGCU9stqSaL0FwaJ20gLTbNGgsy0VyLYUMMXfJ/F6G96o98s2+KsA8AVYDYHuRHKhkNVWFfgA6/73OKAVK+53sN6M63TB8sDoXPNHBIcukbnQGKMgF43UhG0XA4/8/Kd6FFjYdCbteBZjXsSGd9eo0ezcjafmGl95sQKwcRjOPE+ZJP9+HoB0NmUAVvXku7hQp5vcOUMGWx7mXAMI0Mot9Lp01z0VqN3xunAVm5TfbVROEvRmIHAoE4EzsIS6qEVGxo+XA5TXyi7S1QCQ6uJau5gzDfbUsh++aI0Bat1JIvzOc/bkMrK1oz1FsA7Y+eIYXaCcS9E6Rb0TjUFy4M2oepVwf7M7IOLVVw2qcLlNwX2H2NQHJ24Gm6JaezXo6YkIaPR9405YdbcJEm2WFK5+T/10AQWgLpb9ILM5sBmR1UN7hqRLITuQcrvy8+CVwbqjPQLQNUDYVb9dD3Wxc7c753rG71y6d9TX6YYTgrYvIisIbBldFCEcrmBWiHQsWrHfIeG3ebNO3/RBgT2XWkbhvqKmg11XYRxQvc+Pq3+HR/R5dQ0gFQ3tug1OyE68MP6BfAByBT7oIwxT3LkzrkZI1PbuU2q2bR82U26b2VuKxIdxJvaB0haNhV+6dXIQCZZFe7cKCwqzpT5082xKNv6goM7HQhi+2YPbBpoQfj8ZKDkP/twTX6OVbbnYCeoHA6AOUNR6hPg7AIzU+kNs7bXs0ngfgJpq1K6p+oXDM92DSAV1baiodZVZVG+sfAc66m1711fGf1ctViRtJAJ1549WmDz25aOunJ2Kcm4ItAtbtOO2qfc1PflG5kThaJJ9eQA1vzOAu3bbEj6jbpz18+e7vBIABK63J63pCY1g/hfsCt2i9bAtAaf2tMUqjdcjcxvBurCuasVXJ6gOa1bR2cuqHzRdGHMW7ekEsAM1qHpNPVV/dYnU7yXt9ww6i8GkA7AeaPR70mei2lWvjTrnf/w4gsRtKF4n0TKqUp9qjRKw1GgFfAYAjBXz3yeDHcH0B+nt6rXVBDRw4mdt9b80AFJnOqN/E095Pu73XOr+wFuPBXvR9y8dCCyZMFkdYJGs9eiiBNHJ7zLdZS+hIDaQHsoqvFbdwGUd9G31qH39n9h53Wqray20g794IDeO7Tm5kgFEg5mWj9cfIJrqlL7W7YRSrkDn4Y+IyvB0hoI9wUwrDsYAzrTWlLCOgCEcRPekvRMd/uiScACm2DzSO0taVGmrjdEgU3qjaqFA0OoZEYBgrfsrTUkOBKNDOgGFIxCsnvM9yZWffUlR7WMyOsEIBluhOSq40F4PubxY+dzTfMF6n2KB0iVCXuhtEXys/+gWqtHmEIOpYE+cK6s+Wx65iDZQzaJbxelA9bbV332ZccMZt2nBcZpwzDYH0hoYvD7K6w4B4ONJ9dstnLXsg5UBy4xiJQbIZOnsVitct8AaghFe37P+HpCqF+CsXs5Jsbq/58oWIBzFAUe0J/LgSXwaQqgdHA75CXD4DKjkUpvyTvUYoFdbGlJuAK72j6l2brdxZUv96LZP9yRcL7Ns1T6zDRom04kHDn4fUEeFExB35SGzu4rsoZkejvWODiKuXXueq2s7eSqA2XrP8TvJ8PwHSKQovS2EGYwIW71FMokELIIFCRMysi1ExVLBOnZfZR1si4oHVEDODCKtCxRCPyYrNuGwWE+EAUFnBO1x1c9QTiYNEHbY6nJM/AQAeKmMigzRe035hNCYuG227K3xM+duv9kbXnDLC25paQDQWEHA98hkzDLhIBl3MuvWcJQ7hSfSvTe9nundOiOR4G7VXRRSYqyrIGdCSUmBoNfAWR0cZdIdDTJVsEdW3xQXqpgGroorvWPpFgoM972mXGDQMUo9t6i4JCoXs4CXvJ9/fl2Y9B4ANpx8i2Hyp0Dvd4ap/tTAMG2QYp++XPqBDzBfEmzrhCV7QM7NwXJJpFvHNRapVBbwwfcl0fVzS09xcau0XpDoG3CqkKqj17aPK04Mr2iRUSfj+XhGtIv0lMYa7SkFQFFfGzIm8f30sbb1Jkw3cZtH/3IVCET9bJ1jDg6dWYos0/uyJ4TjfsHzJHzneN1TdP1a1WJG6mzCj29bi6KuEtpImEQFLKf6rTaG3r/WDEil62Lc6uBHH/Mdq8C2VaSDC2rD1/3pwjpCSoBaVvFe7GrDBXya0myhnIC/qkffGYkYMzIWJMxUajNIEwOBtgezvq9gyUnJJOh5opNJ9LKygYW0ZKk4w2H24mUhJcxMtTIRT9k7D0ClDdnvgLHb9AW6vB4AekGjnVhgMifQmj5qd5U3UoyG5XUJdp1sXz3vCK2z/IyduOVFwR/ZXwWBq6WE1Zicgl+MBZzFwGMx9pBXzHSDqfiuBm0GXaIDEtu0d5tHuJYABAtbZ5zSuHUvVqHKBlZgFwGhAHVKdwSGAcGPEeZLgL8rcBe88Hn8q07Q6xWqEzTHhN4JdnUsXp9ZvYvNMpNUneAILKITTDZmhMjqynKq9WWeqgo/wE5FHVFR01aeDvEoKtT61UGj9tRYgPxqQlAncIG0RYrCooT6WwXtPoSF/uQYo0+3uyPU78OAtMUKaCAwSup0384Fn5mlnXRSmdv4g1Wf5vO8u3So2xRqW0FB9HGC1cHYXb6IvZZcRXZXe6LeF3a2hEFPfbrWbYijHaG3JxizFHdBAnBiTzWdiAYqEonODQ41tX7p+uECS3VJ1U0971gqg1ZZB1E9OQPx6vZkIhtpue3nodMTRp0FIOjgrwuAQ0rRiYgICF1XBQl1Vi2o27c+TkFw8OdMfSHb/hQF8OZIamUWzgBaVZbeV0FgAxaou2lIrf+zI9BA4MAkXXT8XgK4PRDYbt4XAisdBzOwb8F+AHR+qukl+r8G/mJtZiKARSrgS1yAMuO0u1JvO+vMRYdzrcRgAnIhrMTawFgI2WYXFiY914z1q2OXwm4v9T5RVrB1AVNdsyDtEDX9WaDw0gBQHZh12rLuoSsc/tJwacZUkp50JY0gQ6MV3Q6qYLK9Fg8p48AZN7Y5egV/gQF0AOipYQA2iJORacEik4HA1Z63Yi43CghzYxUnvsHEMyYuuMsz7tcJx5QqEFxT1rSWzcAr2TZVN8VJoUrdwjova5OL74c5pBMcGEK8jsMPcDvWzwKBhK49/TK9BucX/1K/cDn+T1w6Z6XOTJ1g0h6o7vuoI6TqCJMBj5NBw6St9jrUW1CsVkk8NcKeUiS7hKU89BNJBJzs0Ft9THNw5gSr53LrMX1cU+f1QnIpUPedQEC9fjBeAjWFNwJ1HxGi+pGmL2MrPGWlO83a1Hzr5p4p6/BoYqzBqU6k9TChJbQuVq5XwDJqpHrShYtBXKxyFwYgSMfGIIx/EE8/2mLlNnTFsXu2PMWeUrCfNNhVBVAw36cBz1RBWwuovOZ5y56iLTmrNFPGSspYrBbcFqaaWnS3sgCqJ7he7Ltbin7TnpJrAtqEkPTxyOoKWXrRi+dfGQH6sb/4uXWN6u2oXofpKoA/374yllZENt39XwG3GnQLrMoA2HUItJZbtDQwV3sFu2FZ48fgA2MgXLwu39aWFkB5VzBtkPcCKypsf68lFci169v3SQuaEO7z5w/A3IXpPFGha7/6wplz1Z+DQG+QK6S+LRMDWE5AYAu6NFOyUKrrYuZS2cBCgsxkjCBrY4cDwXIKBCvxUkEfGhgU6UqAvEEpHpZL08BXMoAETNYlm1hPLCYd8TJRHR1Skju+9lcm/UFlkuoYwQBNBZwypqQDMA9WY/RBWvBBOuJNOuINH/GG7/Eh3+MN3QcQmEMELHWj5wWMIguOYNzJjLsy445m3PKCtwYCb8uCt3zAxAU3vOKdfe5dmnGXZxyzbXOWdeuzNScdAiqkQLDoYNCSbbC00bbdtHaf0i7NwGr3symNotG9EAAU6PG+RkqiOqesTNT0lXRANGzfZOYC9p0gbNGaKFcDq2n5+AOMutFxPbqln+9bmsGYJOPAbaSCttq3Nns3MhEd9tnAn15mK5TWegtByVy3JRMwsKLOqITnp2wHCq31lS6yei0RulxPktCBP9dPBRosVUeUSs+o17KKxgrNlAMrqJfFF4/gaIulQTJx1XErhrbATkrXLecsoAhhSqXOLRQhbbKy6mci2zquwhufpg84CATZ+xYruahsU9tb89MWISDP9PgT42vM5/mfTKarSXQbv2mwJx8CTAUz+cK0gqngQGsNshxMgLTuGVAd+fD7DMYBq467EMYB1qjAzZ78lxQW5NLbkggp0OeQuor2ZLlEgeIGy2rZMGgNdgtLKHN5CZroCiGEHYQeFg9qfb0qidra1fk+qF0FvxdZdZ9I4aSC+78EHUh8oBVHTDWwcsA+U8ZCSUudzKamUjpd6ViNAqY2uoyCzorVBOqfGuE4jNiZ2W7skrGb2lUqrfn1Gv/3TNWeZjJO74tsbAXjHYuOABA1k+ilZF2a3jCC7pXdGFgHf8meA6C7XqBryyLJAtwFbBEGQ8C2lq1SVMckSKS7jq3GBmYm5CLgQoZTSh3WfwIEnUgKU1IqKwhUUgnVD/qBlNNmkJdmAEFAmdlSHIHpm0hBw6yXkqiCCL2NCi7cCWISYCqgJEhJMKWMm5RxM60d+/eGj7ilBR/yEW9IQeAtrXhDKw5UMFNgtEnTTlkKCgFHYRxQFNjJjLflBgfKuKUFn9BNNdi3fFAQmA+1yF1BoLKBdT/VnHRga066a4nPwvM5hjZ+QxDBIAwQUq8wqw2sOrpCaRfpabrcOquhJdPX5EDQAbwuWJQEnEod2KzbFzU2dTYg6N3ZNX0F3cvUpRAjM53s2rKSOr/YXFJrKoCuuaCEBcsnwHt0lfXJADnDQcDahnwLh0UrtNjD50m9llAEpg+LTMFJ+gI1tUXK9eSMekrKqEdd8bBIOXte01g20hYyKcCDzgoENAIuFI3NJE9AWsFFN15fSwN/BbpTCNAWq2SgQ/VunXJAG5lAp3OzUNpfBekhPfMaclVARUE/UwiKwyUYCtRNR7PrycDAaFezDYxkTSg+bE+2oNTRPhNquZKmGZWtyEI6HDrYm+vOdWRqqPZUS32s7Ae+R3WdlTpkPC6lIl5QLg6qvMTFwJ9EoB5sSpLUMiWuQD1jHsBfY9c1EC7uaKTUUWVAD9hnUZbphjUwPqS1O6+JBGyMugAG+FSvtXu8qM4zqR3VOXQVBFpg5W/L0OHE3mQg0KkWvj5dKs8xPweggfmr/q0rQZJaKtbVZYayJOKmH7K0OQEBqDeW1tm/eJ/rsB931crKIjC8w1wD3UQCLqleMgmOOVU/6MxjLozVGEAHgnFkVsUPmW0uZSOUGnaQmr7v9uw2X6jXqbe1TwUAHhJqLRIDMnEFDv6XZ+oi3zKr4yuTNDYwCWgSXaimjMOUcZPWnv0LzF/9owVveMUtCWYAMxESCEwODESjVgBHKbiVgjsDggfWxpA7mqtTnUlTzDekgPO+TPqXFhzLhPs84ViS7afKFQzmwljqQGQbjuwLn3c4Cmxf5DbuwanbxgoGGj8q9RkiBOTDla9hGJAP+prFnKIxS1OpwEJT9SsOvOoCZgvVgdaaiqqpRcpdCqt43VsAE2NHsRvnsSSrCeQGLEYQCFQwvmZnVRg5i4318fpUDVjApFPxbYB369h+XQYQuByolwnNORpwlMn044wSq46YXU8Zc9JGqpuk53cPKrLpStFBXbBwOusq23B07YTrgaA7Py6CidrOMACwpNTpy0FHLgoEyaL2nLkHGUxablI8CkZzdmY7r5UCFgbKFfZUz7VktlT/xHSmwS9PgmkK9sQrbgz43Vh5i9uTp+oBIEMetScA/WzHCeBcsHICZUHmNiOyG5xrQEPtSZBL0ZIMs6dCjML6mcJs+7FC9ZSo2ZSlruoBeSUR0mN9kVSwQRuMrTSygpvvm5MGU4eUDaw38OdryqEGVrkCdkAD4SKkA9gNsN/YVqfrEPiObFLUlXedOiDMRdPwa25pxkwCIguGwRAqwEp19IjXRHdz5a70f89Vq3D/HhWQ11S8nJRNeMesl49pGYUCvlij6evHZP0Enrlw9q/zgQb+Yj+BS7H7kpTKAi5IWsMu0rGAkQ1cibFyQiq2MxbzWSCo5wJX1r0UXaOKgT+SAAadQApZRMcLEoHghXJlDSChHNiQugHARJXlq+CvOjy7PgEyOxgUyFyU/ZsK0lRwmNbK/t3G1G+6xxtufw7+3pDglggzMWYkzBQKG0hJ8CyChTIWKZgNCN5Ixq2s+MTqAm/5qKngcqMp5XKDuzLjXia9LBOOSUHgIlxB4Fp083bfZWEcf+GgUKTtbezgz4GhRGUCLQVzjULOCQPlcJ11VgbQ9FUOqjPVly5YDv4OSQGFM7WntZmtQ5uD84vfr65fG4t4rNeYSrb9UfUYd2yFhaCrbc69FsaUlKFdSUf6OLjItiWhZLZL0gXL5n5JBBevJNcsVhUAOgM4SVuwZlRWnZIgTY1Vmllrar2hauZszVANAB6c5TNgURcsS1XViDeMVWLxqNh2nBDGQqonn5YPKAAEgDiRPxdG9i3KipyA9kIG9n1LMq+NEaDu+wwokH8NISBfY0802JPZUdQVTQJOGYnVnmbu657nE7C+VgDI4IvtSe1oqno6WgPcKsl2Hen9T90pJvi0qTBWZqyp1UJnSso+m02hQG3Kd3wKo4pelQIkXXcukQg4OgA4AHY4UeFsbQXra5tOQQsOBv5cXwWs29ja78+QTV25DmKHKpPoWmM2FQHgHLc1FUKxhrkIMlYW3YWHRIHFquwSzvo+6H/X+L8XAIBAY/4q+AtgL9agj82JYGf/oPXElqKP+yp7+ne20oo+TV8G9vYMAAShhHOYWSoATChgSSdsoPrEy4DgJLrHsO+C5gQSBwKpSASAjxFIwd4eMb3rGEBW4xKytC5HEOgs4AD+Bgcos7FJc0GaMuZ5xWHKuJ2WwP4t+EK6wxs+4kM+GvN3rODvQ2Lc0IQbmpBoiwZQljJLwUoZt5JxJxmzFBwMEN7JijtZMCPjQLnWCH5SbnBfZrzhYwWBS0q4LxNWSRUMRlDizrRtu9W2tPNourFXqDs0VL2FKHzU31PkagYwOsGk4K8cJIA/Ac8F02xM7bRWB3jDq7GnrSnnlo9Wm2nNA2BkeDpY6m4tEVy41FpCTrjPBQu3Y912SWgv8AVrDVtpZS5YktVtDgtXyRpl1aLb0MnttRSvJlcwS2VGYCxko5wiMLTGqM9WUzvqyhetA601XaVfhzEDWDCBqWDx/DQDLKXO1UxUcFdmW6S0iH0VdXq+Q0I2Pa3TgrhDTAUXIXBy0J65VF2VomlNKaHGVgwIwmwkP3MFulCErmcAwS3dmw+iPvBgwe9c1J6mgpt5DYHvEkZd6bxTbX471no/ANbBWwysn9pT4r7RQBmPgnuaMHPWLIbkbtcRoDGGBdT5syUn5ETaFOf2lEprirNdC3Q6gtpSLX+pB+R1RBjINxc+eQAWDbCPvi8bUdFIipsQ/I6surOAQEYGKWCngqN3p2z4vtjBfV8UsC+UUDif+L5afwu0tcZ0tuTUgmBmG2mmtlUKhykWg56qjq7wf89Il3gg69cbAJRqP71uWi26WK2z1zsTaRmZg/Ranz6UVTB5YDzqK6Tvh99UUDqfyFKwlEnBo5TKBs7S+8FV+EEgOKdmY7mSRpoVkdC0VQp1l3FHGqkHDwHEo8MSj8nVDOB6y6FA04FgAH9TAH+HaFDuAAWYFVDMc8bNlHE7rbhNKz6cjvggLfhiBX/3DfxRA39veMYNPU6dJNJIeUJSYGJAUJnAjE/ExszIgrd0gzuZcUsL7lgbR97whDuZcF9mLGJNIQEMLgZMlqJpLgeEukk4d8bZRWzGDAI4SWteq8BNoSucoH+mR1uTNJ3ZgkWHjDRlTOYANVW/2oLVnOCtg0AD1S7ZjChBkImQZIKuWRalSanNPG3o8FSZCj/ODipcqhM05xdB+VIScspYzCmumftu7jqImE6c4GttXSUE5EsZwIP0hdFJgymZBTIpo+4g3Tvpb9KK28kXq6UyS85W1DIIAxcO1pOBv9jRnaDbLCYW3MkEhhZGL5QwSalA0BeoeH6PAHAtqS1YXlLBBUvhyjblwsiJUUppzq8ExwegrK+UA35OQOXMX/B9Nfidgp7SWtP0N2HsVbQnpmLnvwKLuusRlc6e7mTWBa2UalP3NGFiZdM9qIpA3SVu8ejB7ZETsjAWC6xyYRzXhGzXqz3lMJj4BFi8klzr+zoGUGrtc9OXZqmUqGi+b3NChenL68yzddHPpLPkImDnCtqb7/PASvVVUBJ1vg8A4rab/daBuuasSW1q6YLg0vk+KaWmF7s69Wv93nPMzzMZBIDkhAVsIDA0jTr4s4Y34laXHtm/ObUZwlNl/soGUG+AvY3lKSff07uyi5Dqj0V1WzTDNEODKmcFfd3ygHgRZ3IVCBZWXcX0fmTcc2FjBRUMRuwQB+r7dQAnLOClGOI6AMiWCom1EyHSLYksxdGYvxr9htQvH3Kr+zOjemPg7wvpvoK/L/I7S/8u+JALbokuBn9RRiA4y2pM4IpPSjmpD7yTGUdOuCszbuSAhY+62EnqwaDdp+AvVSC4SA8CRyM9qb1BDwCfu8OGMFCuBoBSGUCp4E+AQ0EKYD2yFV6nqU7w2M1p9LolwAZ0g5EgODpdzgWzTLgTQbIi6Zky7mSqqUo/xoXJjjN3BdLRKbqTdBB4zFNlL+pIn8JYjF1aC2/War6qXMEsVQaQQjQ8GfizgGqaersa2T9nlmZboDwVcstLBQEZpY7gcbDOUpBMT2w77Gh5RcJSpqajkur2Vyes0sgMOns+gvbCFWR4KUVk0KPTy+l1AOC19iTU7MlBoBwEciggB+lzPmH/PmAtfbmlVe2JN+yJAqtkLPsCnZHJMlc9HQ3Az5QrqFgkYWED3ImrTxptykFGzXQks6dk9lQSJks7ekPc6ntPezNcYCteU65ha8WaDCIDqAGw6guHgnRQfTlR4euU+75bWoOejhWsK3C35inh2sGdqGAW3awgiQZR3jCnI8syFk64KeuJ7wNwAtrH9aaWKCW1pWNJOtHC9ZRTl2asvs8O3lXkwzMLpsvUgloAdexObQ5xG2LRmu0I/pLW0Hq980nTG7dSpZu0YjLGdgv81TpAtOkV4xaz2njlO4RoIJZYbS0Ld0BwsRnElREUwkpF9cRNT4WpZQ8HVtBJokoYVZtqdbqxc7/N8ryOSLq6CSTfUGUhnKbtR4jEOorA/B0s+j1o9Hs4rLiZV3wwL9WoPjTwV2v+jP37kFYFf3Q9+IvSAUGs+scFd6E+8E60Y/goCXc0Y5Ej7kRB31GmDgz64ucgsL9Ux+qgMBruGMEBOFkwnyUE5JtrqPzALE3OKg0O0HT1wbTgzaQL1pvUFqkP+aiRLy84INfuUQDqCK2r1FkMtmhLF6vUFipZsdCEW1usFjH2wY7tuEVWTStCHZ8vWktaqiM8Ft22LBsgXD3aKnySkn82+3qNEFAu1FNkALs0iKUTtZyiLVTO/vli1TFL3Oo0D5RxQK7AIi5aFawb+JhprdvEHcXOfdLuxUWSvg66G4+DiqgvBxxxwVoCAHyotjZbKrgEALiWV2QAn2JPyVL1szPpFvzOGTfzgttp7ezJU8BxxyPXz0P2dJCMI5Ixt20IfoIWrlfw57bEqfomYEtH2/Z0LAoCx4Y4n44QG+LGEpdXEwby7YW6Cn4PrEwtUvN9fMiYbZ26MV1p5uNYfZ+vVbc2rWIO+lJ78hIY7aqOuoq+by6501Vh7nwfgKqXLv0L7uwq6sybF32kWd+4eOr7gOt0xc8YxC6E1q3tzJ+zgaEJJF52Y64q+BNrzsm16W22wFdHlGnNq2Y/evB3Y4HVHMYsxd1cgLbFrM4X5grYMzFYSvWHM1YsZcKcctWbZ0YydMLFDQKjy05W6NZyHgjP3Kf0Y8ZwzBaO/nCckHGJXM0Arreobf8gZYzAqCNDWudvSH149GvO73DQhSoCig+ne3wh3eOL6Q5fTHdD6hd4Qwlv+MrW1jOSiPGGDpglYZZFdwwJ9YG3stYZguo4daj0Yotevc56mUEVEPoCGAFLdKox6o6AEEAXiT9H5BonGF4DbkXPNJsDnDNuDws+OKiuvjDf48NJ/2KX9q1dv6UFByjlHkUXrBK21ln1uCJjpoRFcr3PAYaCaT2+Rbhe1vfsouJ2zH2xO5YJS0m4L6k6xBFkjPWarylCypBfIuWm9HOvrIs+gr/DtIYU/YIP07EC9Tfp3hjaY8dWJCq4tRrAXEFbD9ZVLwosDtQARq768WPf6ygP53P24w1j/Ow1vmBpacUpMyhCVVfRAS6vBADlKQDQFqzaQX/IyqQboLidHfwdh6kHRxt1FXY+orWzJwfqPlq4B4Lb9uT20dlTOm9P8TWuq1gDHRviXE+1Bnewp+dmNK6Rq3yfgw721CKsOdFKlA4rDvOKNzdHfDgf8YX5Hl8z3+EL6R5fSHf4gq1TUVcKANX/+flfKptEJ77vlnSd8ftuZdn0ff76ItzpCcCm74v25JMstnTl9lRBxRXHOvEzJueTBUbUbkd9VODnt33EVRKwTTqYamd2xpwKbsIc4X4jibV21nvw6zXQbRJCYwFHmQEd3eNMLmlQmjDZ3umqOwd/6hu5z4zYXEAvk1E2MJ9krmKJjJdh+EitsYxs1NnY0HWJXL0TSL6lIXJC2+HDRlK0zl8J0W90gEsAf8cK/rzr95YWfEieCim4JX4W83dOZkrQHQ9s5EJF9BlHYdxSxp2zfQ4CYU7UFHwUreg4ymR0fTNad6KRFYmL4Mj+vcQG8AC08+vmCuN0YOH0utW9THPGYV5xe1jwZl7w4XxfmQpfrDT6NbYCGbeUMcMHoUp1go21oHq/puPteIbdWxqonhQkQ40vHjMXvY+7Y94Y2nbdwaA7xC1ji0b1KsJQZvwSmaUNPA2OMKVSwd9tx9Ieq55uaG2pKl46tiIFsO46SqIDgB1cZNOZL1IOCNUGNMId9QSg1swA/eJVQMjcdOUL1wdh0YpO0etqR9bcGaxPXVhQbq9c7Cygqs05UwB/Zk8fTAs+NP/nIP0c+IsAcIalaomgU95aYDUCwSzLiT05aAe2fU/1XSANapPq567M+IBPweCop1jm8upCV+iqph5bapGnAk5Sfd+bm2P1fR+mY03Ta/pXWcCmM9MVCg4BADYgSJgtFei+L6MBiOMA0H1tccAXddauN9/n+oo+8L5MWFOzq6XT1XZTyaWSnpMCJiWJqh4qAJR+PfJRL6m0mr8B/B2slnb2sVdcaur3Jtanc6t7jjXQPmLJm+HSBgxWHrc18yRw3XxisZE97u+cMfT0cGUFqU1ImELpWI4p4gAMi1CbkSs9XoigMOptBH+P6fPqLuB8q1clFmxafr7uTlA7fos6wENpEdWUN6LfpRlVMKgbyrgl4IamftTLC4qzgYtkJCyYUXBni+Iiasxa76Q1GUekapwLPEo+BSwexY0AZSuKixu8vwgLSACuAYBAa6n3bqpZi9Q/OPSpqg/TsQfrPCxW5vx8EsPsAEC8e9EXLUIG4YCCo6URj5RxGxxfdIDViQ4p8i3w0YPAqTrCGh2n1KUhRyb21YSUGb/oqYcMkM25YmjNl4G/OWmKfkz9euPHyV7aIVXFA7hgMWbf9FNQKliPKaoZWaPiet6f6shrZvS+BjRGgLFIS0lGBuOcjtxGjvmVACABuFBPVXzRSmINVNbxO68V/EWQ7gPvnZ1tafqCW9Ll3je4yALMPvS+6slAuqWsHFxoELqYr1o6e4p6cvG62srscqr6etOVvyTc83TC4tZFLOjpVeUaXZHOkKvDhNMwl9ZsqstSWeYjbk7gvu/W9KXBkvq+qCvA2duCBTqb1lnB0fflShpEAHhqX6O+nIRwPd15zfrA5G6VJul7XwEAn8UAGjtueqinSghywaafMOiZU6j541b3l3zkFWvts6d+veu3batoY1/gW5E28Oc1tfX32RxbFZ1nexTgQP3IrERi/qw1i3jXsNuO7pVOrWZQ9Hk1hUwFU/B3U/B37XbQ0wD+ntpEej0DeGgK6wY11qnpaDVkkxY9e8fvPGk68TbUU1RAUY0qGpSyf58W+IsS2UC2+YGLzQ9apOAIRqGs6Q9iNVak6mC3AEsEhAA6FssXQuDUsJ8tLOCb/PjzghA1UDFNpYKKD+YFH87HytS21G8D629sdxYF7Ar+Ro3NBGQABYKMfvGaUWzR0sXrNh6/kOYoGMFf/1g87q1m8xQMbtVsAv0YjFcRAujCxSrN+jydd6XOcPJRL6Huz0HFDWuXttcpxaYCbwJJ5hRvSRcqoAcWkbV1FlBBu+rJz+EjsoHGtnDV7x2KqQF0YH2R1DGBC52CwbGmtmv8eaUmkCfZEytYd/AX7allPo5a95xO2XTf6lJBhbJ+Ls2WDGSYPTm75IGVg4uoqwgsgD5N34LSUx/WQIVdBruKqeKtMpdXlSt05XtmswFABxYxoIq+r4H1VvZSJyAY+JsN/Ln/80B41NcBtqYYKzj6vqgvoNeNvt+276vkhOnpDfeMoIP3rdIk4Lo1aBo7Zq8Rgu37jb60xa5X4EfG+g06cvDnjN/BhnLXMS+ca2dvG4nke8+Xti/6Rto3dgLX6+K3Sa8T0KZp620HhEdMmLFa+4jYSC31YQzBYjWgbjdszUDeXMI1iNLdnach+H2odyCuXZeA+ScwgE1JtWOnbkcVi9Nb6sPp9MgmxQJ1Zyhi+uPGHB8TvQoABJQNvMGEBEKCpccgAQjCjJU7wOLov0Zw5I72NOLeWghdXioFTARM8+ULlk5PR+2oiqCiLlYpsBVDqspTv878HUj3aU01BeKdVai3t8DgDD3hs+17mWntwPFYU6bv1adCIht7lNQ7RG6Osaa3hvT8qwpprdElMs25TblndYiHSQufb0PdX5ylqeDvWFO/senD65QYvqOOLlJAWKgiaytUrzsr6LWCc60htKCG2gLVR9ENXLjNRHDRpYWHIvhxoQKA+2s3vH6iEAHpKntC1dMI0nt7Op4Ff64fB38z9ed+tSWROg+7GANYAPNLjNlGUWSzK6C3pdOmKr0dmahzJS8RVGyWvLyHFPA1vs91VHXljFJ6wPedK3uhglvSnawPRJhBiNv1jfpqgP2M7wNVO3KbAnCit0giuO/zrFRXrx4uvfbT9QX09YSXysnIlGulYwD1Olndn2c69LIFvR3zl3I3+NnHvoxbvvU7fTwtbV13tfItbjZBIAOie3frnt3xORlxmHSxTFi9boO/c+gydjadQbpjSAB7sW9Ar/vWdZ8iABQauhFjp05s0Y5DaY35i3R6N0YktfRH35YtZkivlOYx8U5h/ZexIFemxHdVTMgDI+LRnC6GNXojc4TUMyPn0povBwAFh8Ppdl4PCZshtW3ecsdURPAXU1Ua/Wrtyw0158e2RR/QJtzrb1UHqEXsPRiEMVG+gOVwkmc6PZlbWrE5SwcWFTwgbYLBmC45aS55JQaQCEjTZYvVPFt9ioHAqCefTabzyTI+4BZU1eHcNfW7dnVlmgIxsGaflW2mQAQWI2vrrCAotwUL6ECGvqd0gHC0Bz3+ywnTtNVYMgL1G77uHH+qqD1dxwD6XLIp6U5H0Z4iSK9jlIZSCmXTFZzfEtetLqOc2JJIx7RHMLhlS1sLhLKFp0xULMsYdRTLXaLO9DNeN6i6xvcR6RZi7GOpwk4fvk7dxprndG/NAxlj2ctsgVQK/o87NqbZVNSXgsFmX0DTVxlsqr3Xqd/T9WYJvm/pfN9Y/3lOT9f4vmcBQAIoSRslY6BPL1GD3bgHswe+vh+zg7+23VvY9zeWtTzQ2qIz/h7/HY8BYx4CXb1PoNvioALF2Z67lAm+V2IhiwhYB4QXEBZ484nUFP1Elu4dwCAo2DJdl8G6fi/gWweAQ7GmAUCeSldLMaVSwZ/v9hFr/3xngtuwWGkti27BkjYc32sIw5hHARbKmEWNdzGjXUT3etQ0C9di7MiMeOt/NTQKlD2FiIsate/yHDaKSHCYLl8c2Qwu+fZhFVQsdeBzHX0QmwkqqFAH6OBvJoXP7Vg2KWYEGYIigpmod34GCAGxEz289uSHRmBhoMTS9F6I62n6vk6zZ6C2Cq1fQ4gE84VsxZxy1RPbpPs5gr9x5Ets/LBxIm3qvVTwxwDmwWE48NtkLQYwCLQ0McJ98LElg9K6dCQRtInELqU1lTiAf2ix8tT9py3X2pPqSGukJi5dZ3ZnT3WG5lifaXW0UOaPNwLhgmJg49SWxuBqtKUINEapC4sBwSLUlbyMNbrnmhbGJpPXEiLBnK5gAE1PRFJ9n2/L5wFVHaJOujdzXKuqrqgHf1FfriuggZFzgbA+Xyp7GM2n2ZbdHkC9B2Ke9t9urmvB1rkGk0slnXrky4WUJAIsFe/3ARX4jQwtkyjrx1Jty+v/GA34RUA3glQPVFr9nrZ3KFtnKOwMXvQ1ffMxD3g70IfKCura386JRHFDZg5gUd9Ha3m1TpDBAfTZvsPmcxPpNIYarFua+VK5MgVsRetO2Rr4o9BA4LN5YurjUCOqxlIcfAcJbtuIHawr8ak07UtKMsVpzl/nnTVKVwy0+fUCrl15sYuSkESQK+hhYw3ttmg3mII/349VNXnhdpabwiS4na9jRwiooGK2VnpfrA5h6n23Q0EYeTCTAj0Hf4moA4E+CiEhKaCAgkGgd4q+iOlrYIbTnw+9ezfwGNhDr60Z0/TRIY5F1u+Lsbi0kPpgTKGmNNQBHmyf30PSrfkmn3PFeVikdO/rmvpFK1Rn4IRdYhCKSN3HVNncnhkc08RAqxt0yUJ4w0u3qIwBUmwqWShhNlZwprWCigNtg4q6Vd2nLETAzZX2lAJIH+0p7qDjg57d/83WRV8Bhe137vYU7QhAtSX1R+ViW3JgOEpxXQYm3ktebmV9EAzeDuUu+jmvzQA2W3n8uVZbBnSNBB5Q3aRxiHrv+9ruRX3mw/Xl4j4P8K2Ai+lzGwwCrWwm/pKC0Q823UZmPnYa69w67+Dvg63n6um567TPEaTKAkpg/5RFr6VJlfmTyv4RyQD8Nrp3hZVhgx4jNgClg52lmUAFbAXZbCt2A2/VYsZ1o33e6TQEF08f543ANVXwWVDq5zsgdLCon2xHT0kn0fRxY/2uA+VXM4B0q6dkpWu5FWh6jj5xqfVJN8ZSxLq/OJvHR1F4B0486E6Tv1JG7kFJoGqACgrFWVvA0lwnJxOARNkWVFWkFp3adHjyVLIVygu/iLskAPMVHVqVVSLV3W1a2hT14AA9AtZFygeeKvuX0MCfN9REYTuptSgWdt2iYnOODgid1QDaQhYlD45wZA/H2hrvuIvdxlsp+vr+rwQCvY7vEnF91lQ9tU3pfbuj0a7qmINa/1IqgHTwl4ATtgLQczx7pGlgMDKDp2niBgjrdyZpQRNU362xpLHlPlvLh+T6rMFCrADeU1zG2Pr7LXJdWvapwhDcXMgquYw6ilsndp3ZJ8BCGSVnZpNlQSbTEXe6irZUkJCqLTkgPGdLow2190R9fEwlH8EnDVvebRzrn/X176GmFgBBgfcl4ulCcrBujQSHpDZ1cHJi3Ds2+D7XlQdSrq/RkzOdCYI3wKA/H+hBYZSY7gdOyzS8PMnLl+J9sYN/bK67RmfXME2jEAGcSrgtfeo3XI/gz/1X1N1DQFSHKLOmXFlBWEHB0aCPlznMWAEk3UnH1qEFgI/Ccokd2Vvd2FsM+DgJ5DEZJydE8dEzdUOFyhKKMYJDkPCSY2CIdT6S5+2VnkWXn3eK1uuTDmzbUgWWwmfzVAA4DDkFGvjTESz51RpB6veQokYlntoN0ZY0uv4hiXPwxtuuwHifH4fnOs5r0iAujMYqTbZ4dazSACp8b1LtsFLA0JwfKYM6SJYyAEN9jjvHE1YDDRSO33UUj6IdnLQ0vYIQn5m2oJ9v16Xo0VjZVxG6nAFMAQA6UJ8M0LXNzsXSuz7eZW31q17vZ3+AHquxxKJfuEr9ngACYaSsYEE73soWtoWrvUNv29mdlddIWYG0M+X6vHapW54xfOzCTO+nruyp9uT1Sa6j0Z7qjjg2nsLT8gfyOrJWTnFiUxLrnFIFhFtAA+htKQLDUbZsSYNdfeej+OgLDo1BbXA40BbW1xYiXKwrX2gnKhUE1v1jWQHexKXaUwp/takAZkcG1rn+21p4w/Gg82DQAy8ANfiK4qw8sF2mwSErBdJ152jZpyNSzUL5PsWxs/iazNPW0ORrZGQAT0AgWn0mUwt+t4BfvF6kjSKqu3f4SJYaSPnxL7q+W5NbgXbstjRy6t4XaGxpBH16+zzw89dv1TNvzQfeanrrPlPa55yTSzrwrwOAJJitwLZ2uvlfoGe7WorUZvPc2HTuaFRRdFHW7pejJTx0n9gFAF4FBDrw05NCsCCjSIiuxCPjkCZBK9r1upmuPmrDKW4V3b5kxHxNdBbrypwlmjnX7XG6Fnr0DlC3CsNFrj4uYBEMFkhd5GK9DACbNj7+NtTnth8BAyiiJ6cxx4sAByo4+vsMtReAljQwUFnZ1xSiy/TkDi46v8jobekKQNvjsrLsp/oaQWBLV7EBc6qLji5Ivkjp+V9fv5Gq77UnFTA0trzXR663V50sHzruFJBE5vz1AMZT7WmymWSuo7rjQB1J0TIfrh+f9/dYQOX3ZfFSCtfjKdDQ92upyOFZAHr2l62uMAJBtzG3J+0ip6q/gwCZitVYbQ/UfQ25NDXJlkJ08MeQ2kwwBR3VjlKUTV116V4HZoNuADzu76R5tvh+I1AfwWGOPg+oZUoe/C7EOEhBttuZPLByZrA8Kfv0nMQcGWbwn96BQDQ/N9ZpVlAY0r96LBow8vEpiySg6GtrTTHpNmxJBIU0aEliflSaz4ScAtxtJrAHYuN4t61dXGIj22Pgr3TP2wZ/417e18iVAFDrK6KyXDmJS1dHlqh04E/n8pzP03vL+iwJd5ia8ygFmVYsVOpA6JcGghH0aZ2EXA36AFTg9xDoOzfXqX6XF8p3XwosRnHDirevev0TFmUO6fVY5wSYA5Tt7xCdqH5XBRaxViYRUKxOzQ2mnlsBBOaBwXgNSVTwhcPxoufeJA28XD9TYGpdXxmMuzLb4q21dHdyqDMAvXuxDj21Be5DWh74ZBourztHY6NGHGHR2YfXYqIfKr01Nkmvv5+GneeIj6bw642FLc8ep5GIT4DGU+2p4Px3qbWGn1F70q8nF8+niwSGMrU5BFatqzRKzRxYvd0ipltCZWS9IfAhGXXQM+XnmdmT94Gc/aRL7LSCwI0u1k9bvMHDxcEdgA78tesP22Cdkwfb5zrg6QxnOwmlWDaIUiOiBNUn6vXz+htJHL2vB2cdBjDQF6/HMUkPbRerrzkd1B0fa7//VH8vuhMIkdRi6FhA64biKY/aoWOTuH3RGvPTPosoQbdXYZk912AHhrCgYEHGgoI7OWLeKIw+FyFvycjweXr3EsCnrz9l+rYA37jAAYEqfgQEvk8ZJ/hvbb2WJY650bZ072Sr6Xo5b0BjMXPn/B5wjFsS65vO6c/B+qgzBydbQ7m/0sQ3CweCc7DfW2qaW7cp4siA0nW/+drj0zHcZwKhOEB6BH9jbU3/Pl85uvKFya9HMOzNSXW8jpgtQZkaeAPayXtulEIM9hLt6VJbArbtaQm+cKl6Om9PX0m2FHdWAJxJojaYXPqRKncyB4YzAxDdIUJ8fiyfBXLtM2KTwZYuz0vuXnuenHA9+ezTSwaCXyrP5XdjVsPFwZ7ejw740fDcAgKZ3ytgFNHSkgr+DEvo781YwZi42JaJyuou5CnhNi/Q5RyDHY9VBwIDWzfezgHAjdvAPgT6zgG+rRTwUzYwuA4AQnBIrRsR6KOoVGuSAvjDdofOIkmpV1FK/U707C2kJ+VCE+5oxS0tuOu6TYttYp/r3CVXQZ07dzI3yRmSVscHbAM9fd5pSrfeXxenU2av6xDaAn4DyOgjiJcFgddsB+MGV0KBfRYHTbp/5EJT3anBHeAsK+5RzNAKdIkRm534+OefNHOcpBAfcaDxuWdAe1yojtH5nXGEwOuBQBkWnYekdnuZjurexfYbdWsh3eNzQcJCqdoWx+NaDsi0tpSwlKu2dLr2PB1HtYyLztZOBlvb+23tWftaY2CAp9tT3G4rG6Bwe7qjGXPRmto7mZGK1OC3ICOjIGNBEgLT2tnUpSzRuYapcxK7hB8DFXX8C9KmLX3WAaDup6rCnc4sjRhAYPtT38elt5uMYjXHBiYeCIDba3o5y+SdcYNxvdLn9WtW9Hcnkw9GXT2B/XsOcUF0Wv8cgZ/fPtfo4f5QzMZWcDsgDJRMyJzr83wo+uLpXhTc01Sv+/s7G39JyUefem3HYvRVI9jz62N6dwR85wY/x/ccr2/dfkiuTgHPAwD0Goo4hHGi3N0ev9xaWDtypFTQl0GVkj1KUucYGg5a630rlvbJ3l6PAfhIijPIfQMtnwN47fblIG8cJ/JQsai+f29A74vRiI5QQYSAkbBS0U2qKeFeJqRysMaP4PiYcYsFS7GJ+CjmBFEbAx6S7Q63y59bX7MB2iMzEWfLjQ5wBOafRTkxcjDWAnAy4CcCFgbb1kJ3RQeyuG0V0oJotadUazj9vZ4jDx23cRcQv2+0ky3At9U9Fz9reaWdQPS7X35usIEmBewNTKwlDfbU9iIFoPVJICxhb9m3VKpNPSTX2sx5nQ1Do8/YUjf3bwPIA9spqdeQS3XFkOr7Iou0im7HNQljMsB+b+sRF/EXoxTGQgsWavs2J9nOdo1yrlbrku/eM1DnyYkt8DcOvj+3Rl0iz9vmT07KKiLw09uPp3xBAAoDXLCCUQqhiJJPpRBWwwgz53qdrSYX8PR9a7AD+vTvYx3GUeJ6vrW13rk6vi2wNwK9c1u/+fs+9L0ekqsZwJkHABguI9u3BfxiXj4X/aE+t8w3TJ55xVs69GCvdsgpRXsI3XMA6ggZYNjHbwMIjo7vsT0W/fppjr8tWu05Pc3bP3+L9Xs4f/8cuZYO9kVrFY2kmAT3Zdo0gFZfNuNO5rDDhO4v61204/G/BGBdC8IeAutj1Ou7gWwBDn3d6wPASz9TjEFy57iCMQE4ZqAwdY7BSyt0k3G1rbqAdbak73Un87mPfbZsMYBAq3sZdfZQp1x8vb7ms8fUAtCmIkENdLls1+bkxLX++Y5n3PKCt+Um7Nnsszbd557CuUv8xnUp/lO/9ViNpu8w4a+Per30O76UXKUrCiBwYJEYCUfaLltyVjDOcNSsVAm+72l1nZcy7FvryFZwG31eHK5+ylK9MgOI04kBwMO1toJm8wzU8iMmqXV9RILCpAFxwCPHkjrSKu4WEsGgy9a6589/6FidS8k+1MARwV3nxx8BgcBpZuLadf8qAMjUUsD1Pi82Dig6AkD9smxbmxTdiJq05XpFwsIZqTTQOBfrkvOcvIHCOMqiKg6NzgUifdsb37lobIxgtujwh4xlZCZi0ae+X9/t0z73POB4iVTw1QsW9PtNKJuLVks16ibwdzzjLk24LSs+ots6I7COh3mi83uKbKXXR5AXt6o6BzT8GLyqyOWpxfrdhCCiOzsUIkxMKJlRWAucJypYhTGVgiNPmCjjHc218360LQD4mG+v+tqXbJ1Uv/fGQjUWR2+NQtDn0cnt+PrX1Nez7AkTSkgJehp1kYQlKRv4thxsyLoO7Xab8qD3tTpqz2UwYrB0skXfANj1Ne/JpnCdrnwf1sgije4rB//vgdXbYuPMSpuNGnV1bWPPUxswzumrbncZ/J7XAZ5rTrhW1hcC9lug72zKF80v6O4ZBCGpQBCiv4UhHZM4ElV6vZx81vi5Txl2fS4lO9aYAucBoK8L50BgfM65z71Ers6hTBtjMh46gAVkGVnGvVjbtTVsMARsaRynZaNyIriLSnwoZ7+Vw98yxriIbSH6PnW17dTGTh59L9q8b/yc57RuPyqiDOs1QiRYYdvIFMAXrSKEVdhqKCYc04QDr/g432CmXAcPe/T70GJ1DXC4Vh6bt1TTv9Uh9np6H+xfjGgfkzXo052YSJsE76CPqWDiVDsb2epmY+pjTHtcMvfwIUd4Scecy1bxs952EPhwLUz3Hq8FLq4A6gAqUxvtqZAGUWthTKyB1JEnvMsHG40V5gOyzwjsN7K/Vp5qbw+NrnhobIW+tq9tenW5KqgKYqlED6x8YfaO0rUkHNOKe55qhioOiD4lJy7T13N84hhcbenNm1ge8n36Xtfran1mDe5DbF8FeIhgqBcJz3UZM5O0gU06vDLo6TQtff479rMHzx+/LdB2DtiJ0Ol9j7zXJd/hnFyZAj5HjW6lCQcq1EsnznTrbhVd9sAyjjLoKdum2Ieec1le/8HfcQIAzy9M13TrvDT4uAZYtBe1SKrYDhkLBIkTJi64w6yjR3LpgMVk8wLHZp9LUiDn9PCcqKt3cqegIt725z/U9fxpy8WfFwCgkDa8jBHuMtTj1scG3Zxc4tQxjjIuVOdY9dGOx3P9XGFzkTES7lMgW0XP14Cy58hL2BOTIBfGkcyeQq10m0HXbMmvA+d84wPjWjZ0c21K8jFwfg6sP5Sueg25SlcGMjyVCDRbKkJYibFKwkQZdzThkHOtJ+uDq1O/d4kPO/eca3R1rtHgGv/nci1gfykG8Jyc6HG4rYPnL1srHgKbW++xdSSesi65dMBt4/4I/CpIDE+UMz7v3Ll+6Te9egxM3Gbn3IdvOWu9fhoZnn/uZSfjJqp/gOa9JArYkq3vfHrZ/75PM3f/mFzLKhLa94vHiMopmDgHMvT6w87rMYr9Ur3UjliTrWMenWC7TV0U9r4WKkBT9ZfqKR6J8TXx1mPH95J0y9b9D0XKDwWF59Ih53QwRsBbUb68B509haU/x0yMwN2P7VbKKr72nIy6ORfsPqbnLf87gsCH7Oh0AXsPDCAu11X0eYAeBwcVC1Lf3LjR7HhNKnGUh2zrofozlzFrtRVMbfk/f+7IRMX3uFSW8rJd+JcEdGU4NBl08rqXYMaAM8DwAn86ykPfb2T5toCeN/Y/9jvjcy+Vq1PAvucccN5xjE4j3l+dPIbbfj28z+hIHjpBxs6heNtfFR1rbC1/DHScY+weWpTibzn33K3HXkKeUgNYX3vl6675nHictwAEbTzvMQCzZUxb51F/Punr3vdCBVx+vJczKf3+95/ev3V8LpVz+hrHNYyPx9dufeYY4Y66OOcMR+f2egwgXV1S8ZA8dEy2n9/fHovnN23pAfuK9z32HSOjNrIU5+xo1N9rynN834uX4mxIZ1PDfefsbXwsyjnQ7XY0BlWut/iap+rpuTZx7nPH870HQ+fPtdFPxPvG+6+V3v89/PiWnIK308c6/3YGL5x8yiO/5TFA+KQ5CnXj6g1gdI51qTUIJdSNmIJyYeQyAEEhSHAwfv2hH3W6rUy/ubQ/FreUifcBp851lK1FNl6/BtGP8mIOU55mnFsAyR3JqIf+OuqJOJ6sW7+oGsugJ78enzPef/67978hniv1OwLWRNHr6DEj+tTkCj2dA3fX6EccXI3nor/3CbDwKz2A2NIfxsfOyKYdhN8z6qR3iuff61MVuR48nwO5o75KOa8r+2h7g3iQh6DIL4Netnxh9xw8rKtz4PtSW7qWiXgxeYLvG4OoJ9kX8Lgf2QAR49rUX4aXntHVQwBnc90c9frwN35Qcn6+/Y1gDzg9986BvRHoPeTvmo4GvzF+/gN2hnP6eMphOIMjuu9zDjOcO8+eoMwn1QBuOcMR/K0G9CLwW4t2ZjrgK3a7FEbODBGoQyysyit2cEoAGXHBOvcl/dKNiwRwkEGo9xEedpoPyWMLGcaveQEAfKlGPwGQy9MWLAnAvJiOVCfmDO066iWA4nqh9gXib6Ez14Gqm/rYeNufg8tBYPc96vfZuC8+9z2IRuiXPXfNLd0yLkilnLGdQur8XE/RfuLvHrP24/Gv90u7nzbuO/earWO8Bew+gzryr/EUe4oAounoSnsaj4XLOZsadfSQzrBtU5uLzTmdfIXr6hzYc2CuPlD94KauJFzGLzHKY7b0gJ4e83v6/eONDT/8aejrGQDQfd8pM3YK+rYAbXe9NLAnZjvV9424YcQQT/n9pN3Ipzq98n1OwGe8/sCbPbRmXIkhnj1JdaveYC2sgE+4Aj+/Lxd7PLMBv2BcmSCZmxM0ZVIh3dO8kNrEOacIdAuRREBht4WlN7BogGec5FnZMvqHnOe57/ziTlOP6TUSF6rm9Ew3phdZSY0+EygTOKvzo6z6IYHePnPoJOjGL6shRX0h3u5fs3WoRgyypQsa7wtvdImqPx25XE9lwwm6jhz0lWyLk+kIpekHZkNbdtSVF42n9GhPQLWVLX0Cg47iwR2UR1t2saWz4f5z7/fpyQvZk/k8Kejtqag96R9MT9RsSU7P0QdtyW8Hv9Y9/wFb6t7iAf1sP/aAfl9NnmBTCIB9w/eVlVVPK1d7YtNVtasKMNCvUfqV9HO2dBBun9VXvDz3q8/YytV+7xqdPYsBPNXTJaCvBDKoiIG8SBTFIMoDgQKQAz8D61s44rHz9SGdnPV5j8lD+njs9hM/MsqLjdI/B/6WwlhywpodADLWlaszLNkWLDcyBxTnwEVB7xBHL0YyGBrp/uTuILkZmt+/BRbr6x+Thxaxa5T7giJyfT2LG5gCc1usIvBbGbSS/i0EzgCt5gxXqDMUnDjDLaZC4jFHuO76qtfj86Vd35DT43vm2D8GKl5RRC5PLebM9hpn/XrgV0GfA/Q1AAq3oXPgYpwCc2bBijqqj5/Y2/Abz9AXl+rkIb2+ljzVnhxE1CB3tKestuR2RBlmS6638wAQ6AOkbVuy439ib3K6mJ28eX+z19f78WuXyDW66ti/h4Je930LgRfTzQrw2gD76PuojKCgXZ7oCghrEXV67Z5f3+t8UDU+fJVtXSnPmeo1+r66ZEpfyzeWSTjwq2ysoLKyKOb/pPm4LvD1+11H/sEdnnjoBz8CAuMTh8cePhgnH/Pocx69/0K5GgCe7TYawN+Skw6kNPC35IScDfwVRlkZ4qDPFyw3qDUsWF1UjFOQEQ8C2X/BCY4G1i6pA4FCADjUIF0KAs9FXo88Xp/3KTnMa+qjYioxLlZlScBKwEqghcELwAvVS1qh1w0A8iqdnjrZAn8bC9QpKHcd0uUGhY3Px2dvsQIuB4DuBAH06cNMm3a0uVC5HeXehnjtP+scmKiPnQPs8bVo73EiFxz3s3bxnnT2XHuq/m60p5VAbler2ZLbUwYoy4X2RNt2s3kfndfX+DGXsESv6NcukUsZwDGd2JESo66O6vfS0fQVdBX1VHW1pa9zPpA31qzxOsJrH3CEl/i4l9LNc8e6dkvjBvCLwe4J4xfS8TXbIYoXcIZJ1+yHXw9AMBIW/tu2MPZweTEjGMXf+MwTnuz3XoMB9M8YW8frjCGhyvyN4G9dE/JqtX7rw6xSXbBWVRqv/cKlSpRHI2N3ihH89deNHcQ28ACAB2zt9MCE6++bdSpX1sHUSCumPcz50ULqAI8AHwnpCL2+AOkooBVIywgA5fRc75gj00FNeVAHxk/0ATy6aD3q2D4DgG+US4GFO8HqGD2NmHWRQm6gQgGg2lEF6BWkh8XKnF46Dsf0LNh7GMDH19Tfd8HP+6yBvS25xp70+dyzSVv2tKDZ1RpsahHzgwAHAHhSTtnpQTD6vFOggU09nWdpH1HAc4D8pyiXNqD0fo9OwF/V1b2Bv3vVVfN/UoHgie9zcHQCFqjTi+pEdC16AMB373GlfFo6uGB+/INyvmGvb+iQwrW+r6vBtCyhsnwERMLoXNZjzCI6MES4z663H2p3xXXoDAh8XEdPVGJ8hxfU5/UMoP2AWPe3ijd4nKZ9O/C3aiSMhW3BMqaiRr8WXWXYQmbR1SoWZQWFRgA4KGvL8VVj4wYEa+Q1AEPg1PgelWtAIDYef0FRp3ZlzVJ1glaHWZ1gA3/pnpDuAb4H0r0g3SsATIs5w0VAWcBZtrsbqpMLzs51kLZAIJ0A8qc6wS15nywFAKsRuxQAthPzIfDHawPqEVTwKmpTDgJLA4Hp2A7EKfu3vTA13Uh7LL7OxU/DpxzrzwgAFFwO1AF0oEJKrCGjzWAq3cPsS2pQlRYxnUlI17tj6QOpE+AX7Ebtibqg6hT8vcyBfhQwvoJc4/tiE0GpdkVVV1gbUE936vtS8H28BN+3SmAB9ToQbMF9H4CuLIlIfV9ci7bsDngJ7PCi8mwGMAA/v73Z3LHF+gUA2NXPxvKJyARaOcWYsu8yigA22VugI4Q2fd2Gji7KhnQH5JHH8fJr1pNqAMfUr49tOdfskbPVljn4i7S6pxHj9aVFV8ouyUlKxBcwBYFxAbOjPICKyPi163IK/rqFjnqlbSnwDOXeOcMz1PKnKdf44kq1u9F16UR0TEW6A9KdOsDpTnSxuitIxwI+FlAuoLWcX7CYwmJF6uhSuy0Eu907ybpgXeLbP2OO8qyEtMejT3WgKOh0hGIp37UHfyNLG22qsRV6me6bJ99iKjqAkfy6nKQVTxYsXKivIO8blG+KAbqLnx7tydNTOWQ6lgD+oj3dmz3dC/ioNkWrqE0NAVVjXltQBfdrbjtMPbMUQEbVaXVYL3OoPgtyFQPYlVNw3+TmJISDwHvV1XRnIPAoSPfq93gV9XtFHgCApzqTRCEIdqBOp3YX7ehT0tU5JvicPB8A+iXVy4vA39DgRvnhOlrOAMrpJYmcsIHnsorAKSm0SUx0gRW2dXXuML+HIPm6FLBsdP1upH5zYaxW85czo+SEsjTwx0cFf7RqTUVlKUIKxBesLsJalV1SEKh/MMq9ijvCADIcVAjrY2WiDTCIE8Bxwm48IluM36OdjJ+SXMdYQGn2GF15t5vVkymQaNHv/E4wvStI9wXpXQYvGXy3gpYMlALkwTu4QTABzHqMGe266UcSd+AwLmr6Po/8lkt+9mPDHl9RLgbq5hjHomeK9bNrqCUze0pHUb0dpdpRZWpXZdHTXSgC9MXJrrstdaySA/QtgBjSjIDp8YXlJVngiz/zifbkqSrXE1uNc9XPfQumpjsLpu4L0n0G36s90VrUpqKQ68DsiAjCHGwKaksOAFMIvKjp8jFbuPhnXwkePk25JqhSm3LAgRNgUVO8C8BHqQzgdFeqrviYwUsB1gLKWcFf1NeGrlrgG/zeY7pyudCmrtfddQvUGJRcK2dTwBH8hfuwsT5tgr+1NSY24sj/Wqr+tK9AttdtjKzfoJPhuj4/YhJsX7/oIF35/CvlSU0g47BnkQYKnf3Llf1jFKv381SVd755TUWNro6iTnHRRYsXUWZpEfBSQEuxhUsdImXxs6b/kmcMTFIAGkwVCFaQkRxsBOAX2avuM3CinB7syfb9OH3dpyFXZWOE9CtVcNFHULUeyVil6V5skSpIb1ekuxV0t4Luj6BlBdYM5KFAhLnqBUwgMiDIBGIGEg9gsF/McAUIrLKxKL0P8PCQXMsASnCAKFCbMkfGkbFdmh0pCDS2wm1p9QBKkN4t7XMqUEdYuFCBudvTFjAc01wAdOzStfIZ0xHwNHvq01Woi5XXZaZgT9O9gr/pXUa6WzWYul9A94va0lZA5XZU/Z3Z2KW2FPzbmZ+xLQ+BvU93e9iL5CoG0J9fwZ/Wk7XUoQGLzqbEQGAG32fQfQYfm9+jXPS6AzU7XjToiexSmDf1VG3O3mO0rUfljC6uZfrOyXPY+ppxHcGf9HqpNiRuRwP4C6neCv4WhCkVre65AsFaG9jKYMa69UubGON99Xr3/PNr1jn7es0syJUAMGzzBjrD/tkQaBslErsUa/pjacxfdYJ30sBFWKzSfQYdC/gYouE1g1ZjmmSoN3OjM8Ah5gyRWA1sMiDIjDJZ1DU1UOg0/PY4kocNZzPt+x7Sv/q5lxt5jYKNYXJj62h0Sx0mq/VL94Lki9W7BfTuHrg/Qo4LsK5AzhA7HhTZPzKwRwxKrI+lVBctuHMM18fIefvnnvm9oxMcn/cZYi4eFHOO1REK1fmYHVg358aRRXd7ui9IFkjxYqxSAejdsX4MxUXLjj1FpnZrgRrTkLEM40oG8LMG0Ks8wZ5qPZGggYqwWHEE6HcF053Z09sj6M5salkgy3IaUBGbPdGpLRkIJGZIvZ97HUZWaku2wMPWcz+LQdY1XyD6PPfP3lgQfF+vs9LWpRGoGwiU1Vh1sgMZwDpx7+tcNxR0RIlOfR9w3fHesD0hern46jkMYGD4gAb+uuDJQLnEgNdsCdGeHNB5KUzsIVil12GW0BgS2cDtkrL+K1uwy04QBT8XgF8P1IdSAHvOZ0We1gUcUr8j+1eKD3i2oadWV1HHU3hKMaR9a0OBg78xDXK3go6LskvLCqwrJBegGNO0tVlmdYTJwEQCTUkNbFLAQSlp5DWZ4QV2cEwlA08DgCcn0zPrJi6RuP3PRc9HiIJjHYQvXIJAocPSiFbrt2TguADHBXJ/D7k/6qIVGAsBOtZPo9+kxzklgJM6P9eVp4kdDLqTBM4CuPHXnuiqq6G5bCF7DbmcrfAr23+1Oz52vsW6WS+fMPDnqXrKAjo2BrAyfHa96oOK6o4BsOhjuVQwSIFVonAsZbriPHxscXpPjvPp9tSAehxF0UZc6YLEq/0dC+g+g+6VTZfjEbi/hxwXiPs50020pQYm7DKlCga7QMrBIDfwSPpm279j6/4L7OgztL49KJVlisGVXcYOUSqmJ2eRVrOj4wo62pq0rOr31hXIpQLAGPxKBO3V13GfATHgjkxNt37Mz4DAeOsxndFL+rlnNv1sgr8QPNW0vLOz0ZbiEO6MOpi7Tg8x8FdH9XhHfQcCw2VkAA1PnEyxGJsYIZUg2qxTr7NrwzEfDv/7DpaekALmvhMYqrQcwWDtgENXBF1TVKGuItYopaPVlRm1nt5pZEV3RwUYxwVYjtUhyrLCxoD3X7JGxQosJCUFe/OkYGNKwDTpY5M95gxhpOJDCixGU1tG1gG9AEjPpYU/kzIwF/Ucj3USkT73ho81K+u3NicoOevvrU7LnZ9d2mNUBEgCEVYD9OiYRY+p6yC+F/AwYONhESJqwDtEZfqT/Zmfcd0APUsBVKBO9lgFGAIDg9KcnIFAKgJ4CcVqtrMau+TgolB/3IsuRGTXhaEMhRCkFLsO/WDW4+vHlYYZg/GnjEI1arrQK34G0o1nZcgAdEXmQw0SOajIWkNWmSTzebKskHXp355Y2Vpj/yQb8MsEpKLBk9tT4aY/UjBf08XA2eNNwCmLNDbDRLA/2Nb7kGvBur4Ivd8LwS+qLaHaUe349WyUg79lARb1fY0BdADYwDolVpAYg15JQHKfV0LwWxpojO+HM+uQX4m28YDOTg7FKwbCXRewM4IIOvS/MLOvkhIhiBq7fSNj2/CGBVlBhzBgj5oKDn0FWxJ7C+qlPxayIOgZwbaYnjm+7xEEPmkMzBb7J5b+dQDonVVdKjHUKXW1SsfI/AXw9+6okfC7O42E7+5Rjosyfw+JCCAZUjLE/SYRaJrVWc4zaJoUBE5JWagptZRJiJA7Gt7e58HPbQeqymdhPMKT5Ez6uh/BI4CIAQW9FN3k0d6j5VWkMIiLLkjGKIkISIoZurQPirWdRP2xHW+PEg2Y6cH3eZ+68WaBi2S153l6avHmHJ1Tlu68SxGY3klt0pk+yZjeZfDdoql6D6aWlqpff/Dj+jEtBdxSUs7YOrNOrNcbWO/tpdX/GYMxyiZL8cgi9ZWSqg/SsbYmp1tzheu2+OgiVDS4LaUFU500e5KMyuQJ86k9lXJ67l963o+L4cgAxlrny97xsyObARWdsrWBTVcgUcBL1pKkZdXSF2NsxctfDAC2NGED7K3cpREUMQsS6wPrGrQBAk+O90nW44GA+SHQf6E8twkEGE5JoAPifVaqsX9qI9TtDlb1E7JUJ+DPUvg+qozCHxz8SVivh9IyIdLDRj3YOwGGQAB87fV6e4OsuvaYvaChvehewBL/hmGNY1s2B4qWF7EOq9KDv7t7yNs7yN0d5N27FlU9RUQgy1EB4f29soPT1INBTwuHNKRGbUP0taW00aF6/Vt5gtPdZZdddtlll69iaQESnTSDjNmoDgh2TLriCkhkBGUA7AFn2LgejhkRz2YJal000BM3tRZ6yArWshdgExj6a8+zgHId6o6vfAEg+GQA6Onf8T6JyN2p5xPKNlCzPptssXb6ey+qPVbwVz55+zjrd42IaJpyXYHjAponBYEptYYEr6PxOqhH0iXxvTevj2Mcdtlll1122eXzLCH1e4Joamo+pOc9AzKUU3Dur9dUb0YH/tgmidBqlz5jMwuoNpVuZO0iqAt4YASFVJk+qsDO6y5luF3lwiTQmD5+CSLw2Qxgy9tTvV0R/bD9SuzY0eJnbyiwcS/HbEXQC3B3D9zfvzz4G6VkyH2GHI81RQxnAb2wuuuMfERbEoDembrAXXbZZZdddvlcyxaFFdO/fhuWmj+p0WzPr6nhLLVJJNb7daBvLWC7jrUo8OsAoGDM1jnDp5lBq6VlNJLL0/UGCDtGEKh16ZvD10d4czY9v40hnsMEPnknkPG2pc9rXWDdnNnZv6H7jTqErl1VtBTtSDwukPsjyru7Txf8RYkpYk7a1egsYGQAt2qaRinnQOAFLCDzzhbusssuu+zyVS+R/bs4/VsHOFOb4zfWbJZQ61ekdnFX8Gd/DQCWOlaONgBgrWtOWr9ODgZDnwABOBlX5qAwowHD8H7208/LBWO0tp5xaTPPkwZBx+vxdj/XBwGdx+JNdLWAlJWWZRspQssKWWykyHLEe5GSlchbV6NyQwGvy5Ziunq/MAZlZ/922WWXXXbZpcqj3dpj+tcvgY4BPOmq3yScrObPmcAI/lYfiVVQa9jO7GXvnfTCRnLVMjG7XlABIWCQIbKEGFhA3gBwEbydww6PALxzbOEoT04Bj8rrbvt1U94pQm8DGNkKMcm20anjD5ZnNHy8pNSO4uH+SxpBdtlll1122WWXTjaXyrH7d+Oxhi1wmgYe6gIb1gh/WdO9KBvgz5nA2gUcFn1m1M56pn5U1gAG6xgyMgBbx2JJA3yOHwo2sIR+/sMsXuxQfuBpj8izawCBvv6vG6oJBATfFNfAoOXrVy3CRC7Kuq2nc68+c7KDvV122WWXXXZ5ssT07/aDVv9ntysLKMPzBGE8mRjpZHVpJXT7Fq31I8cbI/izkUsy1gD67ZQUy4QUMEkAghX0oT0HofHDsVLEdlvbZV4yoszf8xlVci8CADdlpG2H4k0duoiab6eiA4WlNCXssssuu+yyyy6fMzlhAOnkMQqXNOANOBj0esFsQ7azp3gfAH++i1Us47JmUMoZdYeWR4GgtOdA+g0Q4mza7J8RUeFwALZYvocw0qdVA3hO+qne8YGWs+8V1G7XoksRHaI57nu5yy677LLLLrt81cpjNYEUsUUoL9Pbp2RT296tbfFGNuYFEXOE65JtAPvG4HVkaGMooKDRG0IL+u32in6huqWm78DjePJMyrbu1LMl+fLGjnpALpAXGQRdP3KkZbekQ+1+8GEbPT9QgLnLLrvssssuu3xVyNlVPtb5bTxx3EzjZKed+LfxOhLPOkYQWDTt6+BvxCDe3VtEt8IEHgSBvs2p+Ou2dgnj4f5zO1x5Q8kDjN9Tt/B7FgAcx8GcyMgEnlEo0JSyyy677LLLLrvsciGR1bODW/fH8SRbu3adwx5StJW3iAG8AiA98p1DetdvE51eOsiMTGF83bn325CnbmlKY7Hjg08m+h4A3/6kT9rlKfLNIvIjrn3RrqdXl11PXxmy6+krQ56kJ2DX1SvLrqevHNnU1VUAcJdddtlll1122WWXr3x5xgSZXXbZZZdddtlll12+EmUHgLvssssuu+yyyy6fM9kB4C677LLLLrvsssvnTHYAuMsuu+yyyy677PI5kx0A7rLLLrvssssuu3zOZAeAu+yyyy677LLLLp8z2QHgLrvssssuu+yyy+dMdgC4yy677LLLLrvs8jmTHQDusssuu+yyyy67fM5kB4C77LLLLrvssssunzPZAeAuu+yyyy677LLL50x2ALjLLrvssssuu+zyOZMdAO6yyy677LLLLrt8zmQHgLvssssuu+yyyy6fM9kB4C677LLLLrvsssvnTHYAuMsuu+yyyy677PI5kx0A7rLLLrvssssuu3zOZAeAu+yyyy677LLLLp8zCgXp3wAAv6ZJREFU2QHgLrvssssuu+yyy+dMdgC4yy677LLLLrvs8jmTHQDusssuu+yyyy67fM5kB4C77LLLLrvssssunzPZAeAuu+yyyy677LLL50x2ALjLLrvssssuu+zyOZMdAO6yyy677LLLLrt8zmQHgLvssssuu+yyyy6fM9kB4C677LLLLrvsssvnTHYAuMsuu+yyyy677PI5kx0A7vIVI0T004not7/v7/GVKkT0bUT0Ez4D3+N3EdHPft/f4ytJiOibiOhjIkpPeO0/QkS/5omfW19LRD+KiISIpqe81y4AEf0CIvpV7/t7fLXLZ8XXfdblqwYA7gr/6hcR+VdF5G98399jl11eW0TkT4rIF0QkAzuI/koVEfklIrLrbZfPhHzVAMDHZI9ad9lll1122WWXXVQ+kwCQiL6RiH4TEX0PEX0fEX0rEf35RPQ77fb3EtG/SkRfa8//1QC+CcC/aWmSvz+kK34WEf1JAL+TiJiIfiERfTsR/Rki+leI6IfYe/jz/ydE9B1E9ANE9HcT0Y8hov8PEX2JiL71/R2V9ydE9FcS0X9IRB8R0W8gol9PRL+IiL6OiH6L6ekH7PqfG17XsbJDOumWiH6N6fNLRPQfENGPtMf+DiL64/Z5f4KIfnq4/3eH9/sVpqsfJKI/SER/zfBZ/7rp+CMi+o+J6L/2GsfrMy4/hoj+E9PXv0REtwBARD+JiP4j08X/i4j+Mn8BEf1viOhP2XH8z4jov2X3/9VE9Afs+H83Ef3y8JofZ+/zJSL6Q0T01732D/1KELORn28+5hMi+heI6EcS0W+14/3vmp3V9CsR/WIAfw2AbzV/9632Xn8xEf0OIvp+08cvCB91OGcLRPT1RPRvmB3/CSL6X77yYfiqlC27oe2U+s8koj9Juq79g+H1iTRl/MfsPf4gEX3j+/tFX3liPur3mB/6TlIscQiPC+k6/0fsOf8sEVF4/OcQ0R+24/+fkK6Ff6vZnf/dE9Hvsuf/ELOz7yHFGb+QiNge+xYi+veI6Mum61//6gdkFBH5TP0BSAD+EID/A4APAdwC+PEAvgXATwRwA+BHAPj3AfxT4XXfBuAnhNs/CoAA+FfsfT4A8HcC+KMA/jwAXwDwmwD86uH5/7x95t8I4A7AbwbwZwH4BgB/BsBf+76P0Svr4wDg2wH8vQBmAP99AEcAvwjADwPwPwDwBsAXAfwGAL/5AZ38IwB+jV3/uwD8m/baBOCvAvA1pqsfBPCj7Xl/DoC/2K7/HQB+d3i/n2HfYQLw9wH4LgC34bPuAPx37P3/cQC/930fz/esy28D8P8D8I0AfiiA/6fp8a+wc/vH2rH6mfbcGwA/GsB3APh6e48fBeDPt+u/B8Dfbte/AODH2fVvAPB9duzZ7Pb7APwIe/x3AfjZ7/t4fBb+7Dj/XgA/MviY/7fp5BbA7wTwvw3+ado6hmZ/32l2cGu3f6w9dtYWTD9/EMA/DLX1Pw/AHwfwN4XXus1232H/e1Cvm3Zz5nj+n6Hr018O4B7Af8Ue//kA/r/2XmSP/7D3/du+Ev7Mrn4CdF35cdA14kcB+MMAfm54ngD4LQC+FkoifQ+Av9ke+6kA/hSAH2PH/1sAfPPwOV9j7/l32e1/BcD/1ezvRwH4zwH8LHvs1wH4B83mbgH8+Pd9nD6LDOBfDeDrAfx8EflERO5E5HeLyB8Vkd8hIvci8j0AfjmAv/aC9/tH7H3eAfjpAH65iPxxEfkYwD8A4KdRnx7+x+wzfzuATwD8OhH5MyLypwD8P6CO+fMkbjz/tIgsIvKbAPx+ABCR7xORf0NE3orIRwB+MS7TCQAsUPD2LSKSReQPisgP2mMFwF9CRB+IyHeKyH+89QYi8mvsO6wi8svQAIvL7xaRf1u0bupXQx3o512+VUS+Q0S+H6qvvw3A/xTArxSR32e6+JehC9GPA5Chx/UvIqJZRL5NRP6YvdcC4FuI6IeLyMci8nvt/p8B4N+2Y19E5HcA+ANQALLLqfwzIvLdwcf8PhH5D0XkDsD/BZf5nJ8E4LtE5JeZ//pIRH5fePycLfwYKDD/34nIUUT+OBSQ/LQX+3WfT3nIbkb5R0XknYj8ISj54br52QB+oYj8Z6Lyh0Tk+17hu3/ViK0rv9fWiG8D8Ctxukb9UhH5koj8SQD/dwD/Vbv/ZwP4J0XkP7Dj/0dF5Nv9Rcbs/VoAv0tEfiVpg9ZPA/APmP19G4BfBuBvt5csAL4ZGhTciUjNZr0v+SwCwG8E8O0issY7LS3yrxml/oMAfg2AH37B+31HuP71UDbL5duh4OZHhvu+O1x/t3H7Cxd85leTfD2APyUWwph8BwAQ0Rsi+pVGdf8glJX9WrqsU/FXA/htAP41IvrTRPRPmqP8BMDfCuDvBvCdRPRvEdF/eesNiOjnGT3/ZSL6EoAfgv6c+K5w/S2AW9prQaM9fDtUv98M4O+zFMiX7Fh+I9RR/VEAPxfKXPwZs8Gvt9f/LAB/IYD/lDSF/5Ps/m8G8FOH9/vxUDZ3l1N5CZ/zjQDOAQzgvC18M4CvH3T1C9D7xF2ulEfsZpRRN67vx3S6yyNCRH8haWnSd9ka9Utwihueevx/MZTp85KJHw7Nko0Y4xvs+t8PZRJ/v5Vh/J3X/p6Xls8iAPwOAN+0sVD/Eihd+5eKyNdAWQYKjwu2Jd7/p6EOz+WbAKzoHe4uvXwngG+IdRFQwwA03fSjoammrwHw37T7/bmfQFO8Ln+2XzE28R8Vkb8IwH8dymD8j+2x3yYiPxEKGP5TKCPRCWm9398P4H8I4OtE5GsBfBn9ObHLqcQaom+C2sR3APjFIvK14e+NiPw6ABCRXysiPx5qOwLgn7D7/4iI/G3QEol/AsBvJKIP7f1+9fB+H4rIL329n/lVL6O/+w5o+vZa+Q4Af2LQ1RdFZGdrnynn7OYK+Q5o2niXp8s/B11D/gJbo34BLl8jzh5/Ivpp0OzJTxGRxe7+XjSWz+WboGlkiMh3icjPEZGvh5ZA/R+J6Fuu/D0vKp9FAPj7oaDjlxLRh6TNAv8NKNL+GMCXiegboPURUb4bjzvAXwfgf0VE/yUi+gIUVP76kW3cpZPfA01n/C9IC9B/MjRND6hO3gH4EhH9UGitUpT/CJpin0mLzn+KP0BEfz0R/aXGFv4g1HCKMb0/2YDEPVTnZeN7fREK3r8HwERE/zC0HmOXh+V/TkR/runrHwTw66EA++8moh9LKh8S0X+XiL5IRD+aiP4GIrqB1pG9g+mDiH4GEf0IESkAvmTvX6Ds/N9CRH8TaSH7LRH9dRQahHZ5toz+7rcA+HOI6OcS0Y3p7sde8D6/H8BHpA0LH5i+/hIi+jGfyrf+nMhDdnOF/CoA/xgR/QVml38ZEf2wF/+yX93yRej68rFlkv5nV7z2VwH4eUT0V9nx/xYi+mYi+isA/DMA/ntWjgYAsPKKfx3ALzb7+2YA/2uoPwQR/dTgA38AGhRce068qHzmAKAdxL8FWnD5JwH8F9CU4D8K4K+Esjz/FrSBI8o/DuAXWhrj5515+38Rmnr89wH8Cahh/j0v/Ru+mkREjtDGj58FXeR/BnSxuQfwT0GLl78XWsj+7wwv/4egEdQPQPX3a8NjfzaA3wg1zj8M4N+D6oahRvOnAXw/tF5jy2h/m33efw6l2e/Qpzd32ZZfC+C3Qwv9/xiAXyQifwDAzwHwrVBd/VFoww2gdUy/FKrj74Kyff+APfY3A/iPiehjAL8CwE+zWqbvAPCTodH290D18vPxGfQ3X8HyKwD8FNJu7n/aanB/ItR3fheAPwLgr3/sTczf/iRo3dOfgOr5V0HLKXZ5ujxkN5fKL4cCit8O9ZP/AtTf7nK5/DwA/yMAH0ED3Ys7b0XkN0DTvL/WXv+boc1zPxnA1wH43dQ6gX+rvezvgWa+/jiA322v/RftsR8D4PeZv/y/Afh7reb2vQn1pV277PK4ENHvA/DPi8i/9L6/yy677LLLLrvscr3sEfkujwoR/bVE9GdbCvhnAvjLcMr27bLLLrvssssuXyHyee+I3OUy+dHQVMSHUGr7p4jId77fr7TLLrvssssuuzxV9hTwLrvssssuu+yyy+dM9hTwLrvssssuu+yyy+dMrkoBf+HrDvJDv+F28zHaGMOnw3YEBIBIH2cIyP8Idj1etvfyYT3+zsUeERCKEDJY/4SxynBZGEUYpRCkkDZbC4EKgAKQoL8ufl0Av23XIdJuI9yPc5fhuQAayyqn07sekI/wA98rIj/i8leofPC1t/LFr/9w87FRTxSutOPejj9B7DEAQVfda4O0X0pVT3poGUVMb0KqG7tdhCBiehIAQlUvpvB6/eQ+DPdD+ucAQV9ouvT7u8vhV3SH6rzifnD5nifp6YtfN8sP/4YbvUF+0R97twtAwKTXGWJ2BBARGADZsyhcb99c6i8QCAqAIoIC0j/Ry2w60ku1owzSS7OlqrNCqicBYHprNqQfRgX1/s6+gp11trVlVw8detp67AE9rd/7JD19+HUH+dqvf/Pgc6pdDbbij1X/RrKpW7LXjj/F/Z2YriToqupFCKtdup46e3L9BH10fq+zK2mPA8EH2vcc/SBwqjMZfsDJ9fbaLW19JN//JD0BwA/7oSzf9I26rD1kE+9LCgSrFCxgLJJwlEkvS8JSkuq0rln6F9epuHZRASgLqAhQ/LIAxWypFLX9rYNMBCIC/I/D9S3ZeI93y5dxzG+fdGC/5odO8iO/YdavYh9wah/NDzLU17ku+Zn6VD8oKCLq46Dr0opkOCJhvRJPdLrZ8nfV70VbeQBDPAoW6MGbUc75vqsA4A/9hlv8fb9BR8AxnY6vSWGkDdsqm1Aw04pEgplWHChjphUzMm55wQEZM2XMVDCjIJEgmcKT/aCsxw5HYSxg3EnC23KDHyy3+Kh8gO9fv4DvXz/ED6xv8P3HN/jS8Q2+dPcBPrq7wdu7A5Z3M+Qugd8x0h0hvSOke2B6B6Q70ev3gnQU8FHASwEvfplBawHZJdYMlAKyS+QC5AwpegkRvSwC8dulQHIBpECKAFI6gHhO/l35jd/+6JM25Itf/yF+yq/+b2/qial9ruuLScFFQtFLKmDo5UwZTEV1Yrf9eaPOAVQQsZhjuysz7mXCXZnxyXqDT/IBn6wHfLzc4O1ywNtlxrvjjPv7Cev9BLlPoCOD7wm8kF4eAV6BdARohepm1fsoA7wKOKsz5FX/KAO0FnAW0CqgXFSPuYCymA5Nf0X1Qbk0IOJO1PVU7Hdu6O3f+c5/9kl6+mHfcIN/6Df95aoDO/f9mM6UTQ+l2Qxl3NKCW8r2J7glwgzCTIyZEhgMBiHp/uPIot97RUYWwb2suJOCOwHeSsInMuFtucEncsAn5QYf5Q/wUbnFl9c3+Djf4EvLG3y03uDj5QYfH2/wyfGAt/czjvcz8jFB7hLoSOB7RjoCfE96eQTSPcymmn2luwI+FqR7Pf58XNW2lgys+cSu0PbcVJ3oCQtAFwRxXbnI4JeCvn7b9/6fnqSnH/L1b/B3/fq/ZvMxNiedyG2pmA6l2o7bTPN/2Wxrteet1b6iFAPgi0w4SsKdzLgrM96Wm6qjH1xv8dF6iy8fb/HRcouPjwd8cn/A/f2M5W4y/TD4jlUfZk/prukmHYG0iNmVXR7NdpZidlWAtYByVvtxP+j247bjOjN/B78spkezNbenrgTJ9Pg7jr/2SXoCgG/8xgm/87f+WaYDAoORiDAhVZt4n/K2HPHd+Yjvzh/gT61fhz+9fB3+i+PX4U+9+1p8792H+P53b/DR21vcv5shb6eTNUv/BPMnwPxJwfS2YP5oQfpkAb+9B93dQ+7ugXu9lHVt65ALJ9A8gaYJdDgAhxk0z8BhhswDJHhgnfo93/YvP/k4/FnfcMD//jf/BUi+tqDU9WbEBLdUMBPU1xHjlqZn6zNLwTs54qOy4iMhfFRmfKl8gO9ZvwZfym/wvesX8b3LF7bxxN0EeTepTUXdvAOmO0G6M393L0j36uv4PoOP+kdLBpZVfV1Wv4ecT20G6H3bKOysAQ+3R2DI+G1/5p/btKknN4EU6Q8+U0G2jHJCQRGqIKGAkZA33yNTAYMwQxm+FFBvfhwjPUuE+uuygaDFog4Q6XU29L8VKREDEn4nsyoYUOUUBjaOw6cpridfXKJeLpEsvAn2X1XsUIuzK2duY7y++V6BNqJgMJfWwl7z3EdEQDjKhAS1naI8qZ1H/nlARgFkQoLYM4pFrYIjBEwGRCRjJgDg6kCKvVEWsdcJMvrpo87y+bnSLtvBbGxt+P7POQxP9d38mIIHeSF95cHfOeArIDAEWRiJCkqwlwxlZ4tQC2bButgN750od699MbnmcMXnMqkDjvbCUFYKoo8LQZhAQu350ef5+4x+j0ifV0oD8f7cZ4oAuJeiXxXq65TVFsxImC/apfLTkwUZCwhHJCyif8ouUWP/KuOKytxGthbh8bOu/DFDrQFuac8dmd2HXu9f7omijLZlGlCQiDqf59cTCRbDBYsoObQgg4nxHE0WCHLEGuYD9fqVzmk8bT8bZLPKIyD5KgAo0AM1SoJ0QKMeQGmLmTo9RoZYVKbv5qJOUpDt8qWEquX4HTgFDCe3aXjNiKgDcDBnRiI11QZigEtzoMEpEhOkqHt6KSBxiUT9OAjM4ArW6/eWAoABaotUsQWqvg8BkHIVkBzl4teSYNOiHjAyMf3psQaIRU81ptOIyhcuYBss+H22YJ193hNEQFgkodiPqfowgKf3+aVCPzZWkKHOEOYUUYFjNva2HSAHf4sUWMbCmCVNQ2XRd6wlFehTwv5Xv7dsFXzYoXlsYfoKlRJOOAd8gALB+lgEiVQAYRRj0lW3LRDOwuYH7fYQ/D5H6OTKxu3helVv8Ica/Ep93J9Sg+KTDw524cBPWgCshyTY0RYIfKYUEdzZezEESYBEhBkFCzJmURD4PoBgloJ7KbizzMjRLhdPNxbWFH9IM46gj+pfS8f77Zq5MB8nIppxGkUKMMKnmpqUU9/2KaxTAuCuzJZd8qCWcXBMYCDQbSIhA2S3Rb0VgCfrsaBY+hfIW8zPM0VOcMUZDLH1WLWdB4LBFwiWgKsZQDph/vTu8EU7J4gKAhv4U4UD2SJkRhINc7JH06Z9fydfsK4RJql1h933idfP3K7nA6N3hjUCRot8+w/VF1vAS9QWSiLSQ1MYxOVTBYEKB/rv5qkqZxlGELglyv61BWorloivvzpyeug3nFP3mUUtLlDbr2th5eYC5o+7MZp6IHIeBD5TBFAWAMkcmwL0JJoKKWCwFBTimjZMUpCgaZPk9IBRBgUFc2A9XLLVuiwQHEWwCLB4fZ/BSa0nM0Aaasu8RhBArdW0g9iUJBRqME/lIjC4dTy32KRr5YWAeh7BnUmRVG2r3l/BYfNxjGygT/1fOmNXm/71QuGHAKQFUtL5uT7QHTMiTiajGMNXPOCVdj3+uf2YfYnfNr8HLj0IDDZFL2BPgPq9j0rTSSLBLGKXCthnWTET44bmVwWC7+SIT4ooABRN5d/LhPsy4ZgnLFnZQClstX9UawDHvwgITwFbeRhQV+bP6wVbGUz1b4DWFH5KUoRwJzNYrOxLNLgtaHXHxctYPGiCF7ECCLu3XqvDLAXZjo8GxFaTbgGw+7/4Xc9Jd4QIp+tWvI/JbM7sg03HAGod5pgg3CItuvcf0r9XynUMoOiCpZ/XTpRs92mKKjwW2KKW+lAgmInAwraI5Qr+Yhp4C5YUOQNCL/0NQIcUxtSvsK33ZECB1GlW8Nc5PDTQ50olQw7OAoo5O2aQF+VGecGUYpQY1SSSHhBKg9YOAmu0EVhApqyvq6CxLVznxJsIzklcpDYB+gjMN+4XIghJVUFMBavO5DwaDAuVqo2qDoRJ65vG548gEOgc5VOlCONtvunqx5JIZdGTsawFqx5TI2ejZBBAWuMzW1ojbYB1raFV8HcUruyfFqJ7Omqqzq8BwdCkA2P/HouYHzqdT3QbbOlMEfqjDNFDTvKlgEWXDncfuA38nP0DYKUtCs43A2FR+4TAWPfttFSULfvy7+KX1bbGwNZ/RrCper/ZhLgaSBoLCH+8lcRspoELWqBU07+OWPxt6JQJfCHJIHxUDnYs1J60zlJZ8wMVzCSYpeCOMr6OP3iV2sBFMt5KxltJ+Kjc4m25wdtyo0xgmXCXJ6yFkbM2GiDrsaQMBQkW6zmFr0DQ2D4HgWPNsoO7DZFQ40y5AJO0RpJLGcBnLFsFjE/KTas7txrAhTJmSrilpYLBWywAnATKQE3frpYvKVfVBLo9LpCa/NkimK7BGmPwNN73IDfSsYGBNfe1OPq3x4De+F6PyJUp4JAnDwem1cNYClhSA4PQ1VlTVs35ad2LIMOZP0IKaeBK9wYZWa0o/h0SSQUZ5CChOjP7HZ3TQ3WC3X1M9TUUot9aBygESZr6jTUt1emxKNtH0pwds0VZkQX0I4cXA4JycuI2J9CBQeH2GPUgUCszHPzlCv6y3RfTwM9l/iiAue534AEm0J8TwN/mYxSYDl/M2N84HO8I9Fy0gOv0sQgEnyEFhHuZlPkLzQQMwczeIJCxUMJMGZmsW41Y/5hxiwVFqBZMz2hNVP1noYE/MI5gq0OaapPBEv60kcc636wbrnVsV9IxsBB+0J99WBoQjMD7PYoYM+riICuL1zsnswPzQ8IVBLLVOEMmzAbku3IY8nQ/VxA4iqbkrS5z0OxWKUUEgTXgHYCg+7ruMjw3AkGp/k9qINwFT103qX2Al8VU9k8qCwigMYHAiwRTLlkY31c+7BoLvMHGmwscEN5SRsIdvi493OH9/O9U8OVyhy8V4KOizVY/WD7A23LAJ+sN3uUZS0lYsgJAyXq8KVPrJq3AT1oa2IGgiJ6MWyncs+DNAGLOQGJILqBU8FCz22mQ9XS7LEJ4a0B9bJg6UMJCE25pwUyp+rqDZBQiZMq4pYKMglsDg/OFNZ5ZChbJWIwFVL/YSl+yUC2BeUw2gR71l+KBkl0nhtpIZNSBvoyssubSg8BRnsn+AU+oAYyO0KWrj/ECclJKF8KG3+1xc34LJl3sRBezJIJMgtrerRmLKmOefrsWMdDXwTGSgz/qF3hn+cQYI+EWAYtHv2QRbgV/MFZPgtMzBw40pyfNGca0B6UEWyr0O8oGEIwH/IkyMha1IUcCQ0vo2MAIAostSiMLiIEFvLap5GIZIqr456B+synE8ak16lSmkAKgdxaw6ongjT31tS4RBAKnIPEZUoTw8XrTsTceEc+Sui7smdT5FWhqpDCjFL2+0IpbrFrXB9Fu+uHk8Zq/DKopKK9DcvbPO7cbCOQ6CmFkAWtdUpRrT4POcT58TEcW8GzKMAYyLyTR73U1swCKfY9WYmEpeJCC9zJZKUm2VLCcYQFbbaAHs16b+ZhspX7r4bFzqwN4w2UFgWw2ZlkQxBpaz3QUZe9IcrOdyALGD6+pLj1y6gjJnFAJxOnLMXCrJHzf+gWbJlHqBIOx69o76jOOyPjkU2MCF8n4uNzjy0XwUTngS+UNvpTf4ON8i4/yLd7lGW/XA+5XTQHnNUFWVvCX0f+dpIJFawGzXY5jYB6ygzENXDJQknagxudsva5ef/pxyWB8nHWkHBsAdH8329SDhZMFwBMWJNOXA7SMGQUFglu7dDZwnIQQdVGsFjRDsEAD4mwp4NHeHmT/tta8DZty6ZjzMdPhtuXXnTUfQWD9nDPf60r2D3gCA3hfJvue7QAstlgBwRGCAFEFxpk9Cf76gkUm7fKx1zsL6IzhcxtCdLRRZHlgjs7BW7zPAAK328ICSZqnl2QOThhSCigpmyfJf505t5RAOUNSsugqaeSccwcCUUqrCUyAF+ZuFu0+QcYIpgiFRSpZnZilSUgaG2gnW7bLBZ7u2mYBxwXRxQ11S+K5wydAXf8IQT/1Ce2vB4IO9Brg22wE8VomQZ/ej8wFcAoCEzXH94LYooDwcb6xj1DmmskAIOV66Y5xYQVqR540YiXG0dIlC011YUuiDEf3WRbZLpJwhBaf30n7WwYGcC1pYP1CKlgQagHx6EJwcnownTpEu96lE41JqmN6HkoPxzTJC4NAAWEtagcr+hKYytx64ERWymIBIEM2U/eMAne/BwKOopeeHdmSx9JRdYbakP512xgZPwd8LfhV/9d8IZlNUguemazhwPxgDYL1SFAuQGIFEuYDKwjsFjbWPgQpqBW5L+D7ViR87/o1ALA5viqO4plpxZI+xiL3WOQtfnj64MVqAn3MyFvJ+KgIvq/c4EtZwd+X84f4cv4AH623+Gi9wbt1VgC4TCiZgFX/aDUWsMDGXBnwyzCwh5ay7f4U2D22loivTzkrMVEKyBnALVb2BBA+XV9ZGD+wvOmDXsqYOVcQeCeL+jbWgPVDvlcwSAsWWnBLK5aaNhbcQlP7TIQZCWlYf7JoB/EiBYuVwxTziT5uyachuA1uNcF1ckJUUD0uI8vuZRabf0ALmOJxdb+2Begi8/cE8AdcXQMYUiE1Ja1XFkndXDkHdVm0gN0doD/HazOO1gO0QMdcZKuHcUfqzF8xQLEFKmI9YlxEAbR6sEEZ1QFyc4T1saRGhuAIUQILmOzJieuhqN/KHF8HAi0CJim6yOWslC8AkBfsJq3B6OZDXKOdJgLCUnpH1lJNlgaugJAbEARhQtYTyBctKSjU9O4sYK1zop559SLaa2ULqI+pK3/shAUcrsc6wK00cE1lCWlqamQusAEC64G8+qedlSKMT1YFgA7E9fwVzKzdvBPrwjVxUVaOjxaxEhZu4O+Wj5gl4WCM4UFOT54jlNWrM+WMBXQwGMdSLMJYil7Phe3S6v8qAxhYnwtA4OggezaeWqmFS0z/bqWCx/tGEFjf/HlgsIgGvt2IFith8fT9imTpXi1oL0SKdfy8LkBiCfVHExSuKBsI8iL3/gSLg7gfkpGF78oq3D4AoAK6U+avLVDu8zQAhsB8Hqrj1CBYR/QSoH6PrCxmBIEiCixI9Pwplr8Mfg/ASWPqU2SRhO9evubsTNPIqN/Soqx3+hiLvEPGO3wtT/gCb292cPl3yPhyucNbEXxSGB/JAV/Kb/D9+QsGAD/Al9cP8PF6wNv1gHfLjPs1Ia+MsrKlfxvYY5vbREUq+PPRMLVRzf6u6qYWaSRFzkDm3ibH9xoB5XMYQCF8tOpxnixbOFPBFADgDc+456UBQVF/94bvcaSEIy+4xYJFGDeUsVh95y0ICwqSEDj8Hu361XEyd9JqoSv4g28u0Ybjb0q1K1v5Y2aReltyssmbPzxr1WUXfZHarJ1F7xKKnKZ8z4G/C1LDVwHAEhhAfX+pJ0FCwQqu93knYnSA4NDW7d2MVKwLsuCIZGiqZw1HubQjmCoIhCkJTVnG9DUGEDX6leG2Zi8aCyhgIAkIYyrYDv4IAt3hZWjqA6kNRU3JpoSXOvn6uSMRBMA6LBjcpa085ehDOBUIqpISWKTdZOhtG3dRAvjz2WWXqGMrgqog3e8IYC+mfLeYv5q6N7JH0+56/cE0sAM/8ZYju8xoi5YfR9Yv1XXDvWBNWhbCx8tNY5NIwd/kLCBncFYQOJeClRkLJ7xJqWMDb+mIxdh2ZzjuzLb6z2MsVvfnIyju5NAN7V5KYwB1hwkdUyPANvsXLi8l62XQs0e+VWpULOgc41ba16PmcZZcp7Pno/bVHYKJgr52DnsA5YBw4owihIlD/e0ARHU8jKaCIZq48qjjKSNh/Pwh+2s+b2Aj/KdQY/t6H2hBcFFdVxbQbadIAH8FEr11KT0IFFGb8uwH/DCaX5Tm917CrtbC+P7jh3Y8Ssesz5QxcUwz5hr43KUZR3yMe7nHnXyCH8K3T2ID35YjvlyO+FJh3MuEHxRt9nD27/vXL+DL6wc6tHtp7N+6JuTMwMqglcBrnwJm++vSv3XHD7S0L/S6PJYCNpFaB5hAuUCSNNt5YKi63XH18XHJwvjy8baes+73Zs6YqOCGV9zwgnueToDgURJuecGtLPiQ7xXMY8EtMmYpBgSBOAZIP1OLrxYBFlCthXbw13an8h3GLqgFHNepel8oLYtrFxuOKOjrAEPGo6udleD3PNNRP/vpwM/l6hrACAAB1BStp0LiYuZRWKlFjaholkUdH0tzeMoA9qlgf8xR+dm0ojEn/tnNGaI5OHI0HgDG4AyFpTpAKTBlWURsqSkCGoCQxgJCJDhCAzce+WrZn50F0tjAyHCM7/FEESGs5XTR41FXQigkDQja7ZmzprxGndnrZv1hqidIHS2jP5v7y0fSwB1IBwLY88WrB+QUQGAH3ostVGzHVGzsjgiQqJIN1YiKp4yhl876+qIVUisSUsAU6wGfKUUIn6yHrmmJSTBxqSBi4oKpqGOMnbtvUsJCq962WplbOuJACXeku4fw4MB93It3/t7JAfdlxp1MuLd5ZPdlqqDPU8A+qNY7gPVcH0DgGRkBfNMpVYeoRdGozvCkqxTASfd1vB0A5EsOFXYpINznIfCt11sJhfsgpoJSCBPlzn4iAGQqOErwpbQCoqyg3j6tAxybrdIQOIxCQEPlzup1ad92WYNfli4Ibue/vWnNfJhlG4vegUAR62fxwDYEys4GVrsMenqJFLAwvu/+TXdc2ECgskulu7z34EdsLl96i0/kDm/lHb5IjC/wzUVAULt8j/hyyfioJHyp3OBOZt1dp3yAj/Itvmzs3ydZd0R6ux5wt044rgnrorV/yJb6LQDnNgIG9lcBYNbj5XWA4wzAi2VoBsG4a8i595PyTAaQ8eXjB1U3iTXLMRlIP7AygDdpxYFX3PCKN3zEPStre2fgbxHNfiyUsNBiYFCBYN1VLPyegtYItwRf2BrhPA0cCJOt9G8EfH5INnydZ56EoOd7DlvwMavtiCiOqNmLUDvr63gEgt33eBj4PTZe6ToGUAjv8lxvn4K/EA2HyKuQUqu3vNRUSHWMzjLBdkGo37yliUFnlLAhEeR4JEyApvrcqdnfJvDrnKFeL4nAnv4w54ekOxLKZOcBE5BLSwdTU6aDuj79EYCgPw/PZ/8AtctjZGqDpY5sk7K1HNKNgpIJE+cTHY3vV6C/JQ4CBxpD6/ddmr6qYB0YDCkAd9ZzvgJ3P5yG22AMlYJ2f8zZQqqpLAmLmMBG9CRo+qVYnRKoAUHTo6ThPHyGvooQ3i5z9SNM6gg9GiaazCnqYnWbElZOWFOqKWAHhDe8aKrea5toqkXw/WdydXQeUMW5fx75+viXLbuj8YZjwc7x9de7gGsAguTOsKbow1/IhOhBCufSyA6eqRN8rk0VIdzlabN2lc0BR/CXzJ4yEwqxzjE7CYA32BkDgdpJ3D9UB4IPtjRuv6l/fvi8HtaPa2PyTkpgtv4sYFWfqZ6t2o//oMlACwUm0I63uO8VYyMdpAQWnWKt2aUU8gOyFsb3vvtCZULrNpdUOltSkLFitf1340B0ZYLeYeEFC+7whhI+oMPZJhEHf29Lxp2EJquuvnZq3fXGrte6WqCy6x2rbodNL9vAZxoe6zp/fQbg1jiXLRHdspS8E7h25gzn5yYQfIbvK4RPjtYFzOqHJy5IbCyg6ec2rRUE3qcJb/iIO57xJt3XlPBtqA9U36dA8CA+Yqb/nq0ZzhrfoCDQx864npwRfEi2MlSnBJMGs+rrUH1bV/ZC1DJQJ2w5GhDckgH4XTNT80oAyHi7Hk6iTUfx9bqdRLOlslbOOGDVBYYZBYSbCgbbrCZ3fOwO0LtNpQcVvhH6uQ65sbGgOULAa2Fqbj44O8l+XVmkNu7CjPMEDZUGAsP8OCHSFAgRapcVEShnnNTBgJuDRFhcn1G3VIRwzKdjK/Q61/tivSSTGGthoCGAP69nAivrEDvBnQ18imylrOKCVUvNbME6qfXjBvQ64FfZC+vqrcDNjr1lrX0RqyDQ9FCvA23h0gN7+iOeBQAZ7462IXpduBUEJl+4LDJ2VnadrG4lUevWhdaJaU3gajsd2F7CD7CAseu33/njgjpOZ2jrbZwAwdMUPtXXCamdUUZ1hkikkbBYAwIM3I0gsH6mfeBD40TOgMJrRIRwZ4HvOG8vZj7GdFYBoVDbKcQ7hnMd37D1m1Z4ba1LK0x//HfQYE9g6QPeCMBPmL9wvbLm0nzfWtR+Jvuao/2gaIdwbvYi5gNrunK0p1E3zwTruTC+/M66S0nAXMBk64zZVTKGaU4ZxzJVJsjXlmNSQHDEWxxlwZFX3FHGGxsc3dr+tJ7zTlbb4QMG/iarrz0YszQZ2Ejdrh8FBgILN1a9gjqcgMETqY9Lf1nKdeuH6F71xLnTx6OB0zNUVYTw8d1NPVeT6SqxYE4ZMxfMacJdzrhNCw6mq2Oa8AFrycs9z3jDR2viMTDIfUPcQdpe9gBawAsyJnEOo7B0Dmr0hVs/tU0VQQhaUQmmmFVs91Mjm5KCPhIDf8zGApKBOU91jWz5+QN+4uMu9HlXM4Bv10O9veUMI8MUU1k3nHDDa88qRMdesdURKMDBtx6ztLCLK3BLHHxyOKkI0A5Qc4hiIw064Beul+IHnlBqWjDCPvui7sRJdGgnFYuESZlAd3xkjwUWqdb8CbdozX+SxAPyNNluAukXrBYdcwcGCxOYuC5cxeoyAE3bLyVh5jYaRt+TarddZf0eSGQzWsDgUpnamvpFt0ghGlZg//wSTjCkNlC8XYZjI4DV3/cgkAhSihomeQ2NwJsSyOsiX6j+D1C13x3nLg3OFg0nc4YOABdOyGnFKozbZLP5OKEkrmzgjdj8LF5qsbuXYbTPdOdmILBoysNZiWhbm+zfEPzFDlM/JQgt+m1AMKQXLchCluoQqzP0SBho2vP6WFEWqj+IaIySNRy8tIwpYOA007CVblyFFQg6o8o9ELQ32Pg8bdiKi5aL6y7qyZtP+tIXqSxg7ayvLLoBvILeD8bbDGgViD4PIsDk+1VDbSj7D3D70UBYUABKyiaJB9pmTyI9GHR5Ib2VQnj7VhuriATEph8WpGTsespILDikXHff8JrXGhAlq5flt1hwxIe04o6OuCXSQetEJ00FbyWFEUuHWld7X2YsZbKmqmTsOtcGKycZlNWjnggdrxvjR+PxGkHgNeL1gsXZw6eTD5dKKYT7u7kGKsztL3HBlDLmpL7vfppwk1YcU8J9mvSvLPggHbGkZCn8BW/4qMCdtRnulhbcodTGuPrZAQAeu0H4qTHB0nZBimUKW5sXRMavB30O+ASyGmHhaWDLeIjV/GlJmXu30pe+mD7oofrzLcB3wXilqwBgFsLHx5v2/nXhGurvzClOls468IqVM9bU8u4ZXLsaAatvMWyVqW3zkkm7ebwO0Otgal3MsEjFTkpnUNwRVPCQfOEJzi8RpIg6QVHjZhDKHPm+AAJ9n9+s768perFNNDSliGyOr1gjQXCCWuAZwCBgyn068HMRAe7XMwvWoLNRX6sIJsoobDWX7Ht5hgNQ0/bhZPQ8LNClFR9K/zLa2ApfrBT0RaBuo1z8NgHwdV5Qmb1KLHSXajCeyoJ/9RV65hfoIkYMb7XXCSKlAT/TmeAMAHmGSCEcj6qnehzCguXOUB1iwVIYh5LrgnVMSTvgeFJnKBpk1YYQ1igY0jcVtDQHhzEIXJnAB8ceIOgpMrWjA3Twx2T6HMBGpuYQfTwPc2uu8mPkIDAEUJ1EYDgySi8FKixV7xKbl2L9XfV5ECROtaZpTYyDrBW4FV769yfuzF7Bn4Ktdt/jMwFjcJcqC4iOAeyCXgrAr+hCVSoYaS0eENMIqX8qBvh0MdRgWMSCZQuIic0XlmZDngKOYBDAy245Vgjrx3M4PwGwgJLaFrOAU0Gyv2NOOJZUmcD70pi6O5mxpAl38g5v6Yg3vOCWdP6cV4Jk0YaCOwN/b8sNPrH6v3G0kv5xHbGk6wxO07/+1a85LM9luQ2UU9x68VP6LACqp3daP+UEDZmemAtSmpBSwZwy7tcJh2nFuzTjg2nBXZrwLs34cNJt9O7SbPWBM97wbM0iR9zRXBvjUmABAXQAUOehTj34s3XrfP2fVPDXERXO8tVgN2AN83U0soAS/R0aERJLJoDr/NmnxQB+cvRUSPysFnWyMTkRgB05GYWbah1TEUYZ66mAE/yToOyCn5aO3sdtkpKxHfVt4ndiG/vB0sDf4BBLrf/qnaCeMzoAmqHRO5GmFfTjWKPrDedXEXveiIBHMAi82IIlQwrYjwfQIpgtPfkQWgV/BYWbMyhC2kQRQKA2JoTPGRpBtiTWifptT306+GlG1Rekk+kKMJtxZlYIJcW9o539gy1itjiuDQSS7dVMJAbmTScBCOqxbAvVmIY8icKvFSHke/1BXv9Idq7q8ShgZqRUsJaCNTPypKxBlrA7R1JmfU2pgsBbXlAKtYHrYwQcwN846+pc407bWedUTwoyTpsITv9aTZlE8Je47mva8bZEkDoyZAMsSHCQJ4zSy4B1EcJdSNXHSz93o//zNKOm7rn6utplCB83YQGTnWwOBNWPrYgF3lqX6XWap79rbIBrfk8amuj0NLB+SW2ca+lL6+8VUY1QcZBpAZMzgFms9ELvrwy6+0JRG/MSi6of06XwywJA/iS1xdl+s1hQWZIAScBTASdBzjreKBdveEpYJWk3/Jw0fcsz7tI73MlR04y0dqy6kxp3MuMTOdTxShVEDsPVHw2yLjgccm6Br7ZwJZHgzSCXND6/xDolBNwb089AncFrOspJ9bNO5v8yY5mUsV3mhGPRbMh90cY1rw9cUsIba4zTnURWHCjVNHAcsu4gsNVnNnJKn/OA/yDgZL5m/S0hwKJmX+r79A+iLKCuY2PQi9OSiUuB4JXg/PomkPtDl7LSy9ZsEWu6PIU1c8JSMo6cah1TQaNYcxpO1nDzYNAvpkMeY5WiE0wW9ZE7QAfczgR6KiQ1G3Bs1hZCaUBitSiYBbTab832vgIQe1cVGaAojRHciIArGHR5AeMqQrhfTgd2x8VrXLhS4QAEFfy5fg4gpc3yBCS0rqoBBJ7uYtB03OloYCFj3ZIDdV2wqDEXyaJUH+RsjorqdV+w/KudAYG5nrRtEXMwX4xxLFLVQGGhGn2qAM/TVwFgALCOtLFLjYYJhYGSC3IqKFNR8DdxB9oU/FntSrjPB6uytHlofmxcP1l8A3QyvW2DQNeTH7oK/DZYwG3gFyNjve5BFwuDpFi3KVt9mYFAPw/MIcpWGv4hJulF7AnVnh7yfZ5tcL+XSLAm3cZqFcbKqwJB05OLN8fVwMoyIGWMOM6I1ouWmgpurLrV9sbsR2X+xFhYsy1joAJR1xhAaKDEmWrGnaxcrE6wENtHmyzdLAEIDtf9Z5H7vRdM21MGpk+o+fla562ZAJmUBCiJUSatey6FsGYbe1SaHWWwpm4n65rno40dWetgaQCVPffUr4O/0511qNX/SRysPizakVEf7jflqrLi7frHyqQ/tQv+of2ZX2j6gX4OkN5x35HuYD3pBgtlKsgrg6eCnFnT9dOKLIQlaer+mCask+EKC4AjCPR5j+r/SrexRBY6AX8VnG+UmrVMFcKxd5+3QSx12Q4/Bwky6YvKxBpwIQG2Ia6LUMAHY+D0glmo68bAhJQVwteIBfw18vQ0Fonl85UFjMxFFkI2FnCcWQYea18CgwGuCL57SXCE3k5eAQbr7h3CGgFWStabPyZ06V/7Cm0hDIr3sj4yXKSLoaeFuaHIEqNg0ih4Kx0CNCf4As5QACxr6uoVxgXLflK3aDEJkrTutFxKx9KWMGA75sUdBKaQBnZwcU46pqKmrBoT6AYDZ/5sVIt3v8U68vYpZ0AgoUZq1UeuvtWfLWbWUFKZX697cuZTbD77C5IVEAIdyQ9IjSobY0FAEq1rLIxSMqZJuwZ9D/gGAq3JqjJNjIUzZvHht3JiY94MshYHgdv6auwStEaRuDpCd34UWCXkFmSpQ7f0opaGaX3t5IEWQ8SaC7o8qBmZSAUWeswGBZR2f/36L1wH6H6v83dA5/Nq/aZdJhJMqaiPMwBYWcAtB27gr08F88mQ9XEUzNiZ3Eo7AA6pNbHsB1JYlJwBlOayGvPnX6cxgQXKeOhnUgUfhVsQXMsqhCoQrEYaACBJGFr8kgCwGAAEetCbgDJZEDKJ+vtJJx4cDQQCCgp8jNZaEtbZamWnhEXucCdzYJZs+z7zdXWwurGAXmPbwF+qwP8E9G3+mP6v1tKSrV92/IVZd6ZK7VLvT0C5cDeB2pg0+ICuXvVKVvGhjytAuqOuYQxeL2f+Qia9zBNDZkJeBXlWAL1OWgqT57Bd5UAq+XQE30tdd0kayioCCPSSJccm3feth8ODXieTjJkz99UFuzXI8tFyXncbuuknt6wAAq20DCI9EARe3LddBQBRCPndFI5GZAT0gGgaT28zab3FmhhrKpq+EuoioG7RCd8moXTOMFFfFH1uc3R9bqtt87Eang7RE8xOMgZKMqZF2ue3yNfPfzJn30CEA79CZBPaDdwVgVhKpAOCMjjB4AhfEvzp+xDWJVBkwAAAGxDMhRsI44LEjGzpq5KsEcf0dDstuI/NJQMI1DztedZiHEkydgFXcOGLVTYG0FLPzrh1znPScj3Wn63F2V2kpp2m3hBSmQwDfnCDLNqFRVnfn2zshQZftuCNP+3ZKWCA77k5E0Jf75hEGyWSICdbtAqjFILMegwii1C7Tc2J3YB0/2BbtGfqFwN3gLX7F167ebo4VVYJqOePRuxSU78U0sBUGT49zjXQSoRiOxm0OEgpAA4IqNbTFlQgqD/Ovo/fHhnBF3aQ+p6EcuztCX4M6nmmDVVa0M5gFqylYMqMPOXGzKamtzyWwAwg0KrtKsiLHdoxuNKdLvq6Zw/smEUrTAKr3tgJ0dq/glpXW0GfGAiswZOBv4zaq0PlTCDlPq9Q9YHVjoqeJyfg74X6DqgA8ydowKLWeev5KJMBwQkoE1nDLGEthHdAHckSmSBn+JYp4Y2ngXnpxiy1HXZ0tuZ9mVu3ffHmgn60yNaZ6nvQ9+wSTmpp9bdZOjGRsX6sjVApAfOsg52lPG4TZONIfH6QSwB8XZeps4vPYQQFSO+o/kbYWgzzG8qUif5lBeoya6CorK2l7oU0o2j2tZSEPAWdScINrbUeevSBtRkusLRjkNWOgXQ+sE6rGNm/hA4MlhD0aonZhr9zEEhFiSIvHxuzhsCL+rjrAKCg5u0bCLQTuUPE0Fw+CygxMos6QVfa5AWwLT2YSMDZmYbYBaPpX+2MK3V7OR+N4KLzt0roaG1pmMSl1cN4GthZQHMCLe3rH2uRr6/7Xi9GbeCwd81TaQzSCRAUQMKm3TENUutjgF6pz3SGIkBew0kc7TWwf1QZCwODzLZ46ELlznD2CkxvnqjHKNBvBgKLpJNUI9CKaWuqqrJKrWDddUSWAhUfQSH6/mJs3LD3QD1kbe4u6YuoLVautwr+tHY9DFWFpfDV8LQr1Y5VLfAIh/QFqHgSIB2Bbmuu4DiQlAHUaf1iYK90C8dWCimm3H0Wne9MMY5w2qpDGhupTv642EJPtX5Hh5yiayyAl1YkXWRlMhawOOjTY8pud6ROUSwIoNIAebQZIKgiBlDAiwGJTgSQLb9HzdcRQQMPA1zMmrYqBv5y6VN+DtaTnf+ABr3sB4N9xBLgkwHOLUwuYxdwCtmYyqon05UBPim+LWSXhVb/Rz2bXus/8yOBVGDURWzv4CyoQ6MdQ0ca/2W24FUA+LEEUOGsXwB+yW7PGgCXIl51ibsNe8pCKLMBjDRhSfe4E20wOFi63kspfLC6M0peU9Y3Fviib9/ZAlYYsOtSwNT8gXSggsCWTpSJIRMDUwLlBEyTEhCz+m1Zl/OggUj3pk9JL5kb2KtAL5x3XfH/ExTkLy1Auut/H1uWhxNQJmXPykqQWddSyYScCXIwfyLN/414otXdKgN/C/WDC6U6scKf5wzuOAoryjhfc3O80gj6UgB/FvSWiQLW2A56Ww1tqJuNtuLyAkDwagYwvdMv6yenfvMI/NDVMymVa7UWWSfke8Gtix7cMF5G2xZ1hll1hisOhBPFjOJjL/po2EYAcEFJbJGvgj9N/2ok7JkKX0Na5AuL9O02+/wyAxFxo+4NIIiihdSa/m2RsLf06wENkfBznWExxiIa6CZbS23hMra2OPhzo0oKHCo7alulJRKwsNZycNFC9mIpqTHVuLFwxQ5k1VMDgaVjl2BsmLEGbuCWjor1SGx6ctZC7zcQH/VX1CjrVH0nLsXey+3NPqtipmBwJM/wfi4F4GMAfxRSBQ6kEhQITuoU/NTxPvnNVJLpyllA8AoU1P21T/dufmS0kjHpVFjnqhnDpQFegRRWkOpgT9ThsbFLxTuuC4Emj4CbLvVz3O64D6C2bEZ/eH9px3PLdp7drFMIdORT8AeqAZD7P7HzVhdo8ytT2QTqcfciIAx15rZrCFu2w0csnd0P3Q6MbyPoc9U0qLJ9e51V9+C3tLIKD7TKNIBANDZd2b8LAqkzjHpjAQEtqbCRJ75DzwsIZWD+JHadW/lBAspsAf+sv5NyC0JgS/E6vN8I3JxZuuVFtyrDXMFErSUrfUepZrz6QLh+X/O9fVoRqLVwXgc9TqywSz3PFADSPOkezbkY6eB1SgTkrHvQdx/OBvoUBNI0KXsInLJ8xD34i489QagA090Acs1/lATQqiCdJmf/oGytMFrn9Cnw6/VGdUxWEcIc9hmusxylNcSNO4BEHXW16t6saKUVSNSCq9rx63oKQa+tOQHWVBAopJm42kzqGcPaO+Bq2PB7o1xhS1cBQJKQtwdatKJHKXTzUDuBfQbOrGmrbjspkwrWSEeQ+BDbZAuP7xt8xNAMMtbDVNbCJr67MwxpYLZ0GrLXGdiCNYVGD5iCaIh8zaGwAYuOBQxOcASCEHT7NurQRwc0wwL1EiMRBMASGUBbrALQaClHXbCIjQlgBekePdXU+DoBU9PVfUWEK1jCoETpC5nOMUyury79TIJs7BJbqlAkHi9j9ryRA+13wdi/WJiuNUl9SrjqrJwCQYjpsS4K6LKPkTwTtOc8VUgAPqLpxdMIGZX91BSds2CAQEduFGqLFZF3Eocv5LqylL3Pc5yQu4j+XCdiBOiJC1jY2HRptlSdIBSsO7MizqgL6v7BEyp4RQXXpj+gBljCmgnoA6hTm2nH8ALb6bHm9SIA31P9qlKDCdebwFNBShMMaXs/Bp7688DCfJXPxaz+S6QCwDpofaPmGUD1d3GWp+96oQ1wBSkpy9Wx6jb2yjt/a9/12oNAD7KYpAG+rUAqbwBBwQmjTnYek+lRqg5fIKCC2vX8SQldl1ruUyagLIQ8C2gFaLYgXhcv06vC6NXAWGykqYwRqDby+LilWNoygr+84QuZSlerWY+vA4rQKFUBUVL2iIqB2QSUmUAro6wCyslKj9QACQbEvSkkZ93tKAaxmv4x9s/Sx9wAYGUC/Xl+vb3B0xUlQHrXAG8NfCsDCJSsIL2mTYuWJihvQtCE4XRZPSXaqDlvVtzKVI0y1qq3cjIEkmKjp8Ani7jOSvgdiPYFBeIeALDaJeVipRJUccOjdbNPyH5cyQBa3h4IYKJd74Ys24JWPI9fWCncA1WHGGVkhKJD832DDwDGUSOuOAeGWutkjSCc67YyU9J5XCUXlMmKMUXrqbzcsBY7m1F6PabXNnl6UhhgYwGFRa8PbJIzSUgtGoaDDgnRMNAGrZrQhm6vEiHQsmGozlzUGhOrNzGHQ6lYXYkv3AGoTwDn1OunpFp/pEpBAIEPS9yFJHEBG7uUktZ3SDF2SVjZAj/vjcWqgJY1amL/TaEBobKzzhSygDOQGWGfTTHwZ7cNoHSXQLdQPJtRchGgjoRjt6HeGeqCTQ04wUCgMWa2kYZ+r+qsEihLdXD+/q4fFsFYj1mfRgUsZEEU1W7wXAoya1PDZKxwKW7LqifxSNXS9Q3sxd9M7Q4DEh48sukLK5rdWBnFSWe2v53/jEEnJ2D9GaJAvdlQC3pRFzCQAw70aftCkKnURau+pwdB4TICDb1TL2Z4QNoGrUepQS9ntUlbsGJQxTZ7TMzfKaPsQYX+SA92eW2lE5VVd/+XzwRSGz5wi1FXu5LGoBe15Wf7PD+uBZg/WW3wOCATq7+fgDwTeCXklVCyIGeKrwRIQYJQwkpAwPwnUoRww2tllVzieCV/noPArXIKLU8qtra0jJlkzU6VSVPuWj+GulFByaQA6UAomUE6x6fCTUIAeKuhqZz7Tnm2xS2ps6wgsBZQ+3PCbbuUsV7wCXqa7pyIMF9nQKpMAE0EnpS1pWzZnIOeN1pHqTVzY4vLWOLSy4I7aOreGwRdxmkVsVRprK1l9mxiY/5qT8Fk0bmgkgfFbWEIep1U9dFyHWnE1BNE5zKG+uVVnpA5vI4BLEC69xsNANYSqTi2ww4MZYKshHJoKSxH7r5mJy5I61xZuynPlar14sxkXzVBO+N8z9IoPguQqdRt6CZu0XBKOk6DCxsLKOqsJz0BWDQd6mCiMkrm4COQaADDFq9sNUsOANNpNKwgY4iGgbYeOiAM9z1JbMHqDk8F677oQk8yCqyFAS9J0p1rLiO4ULaCddFhQTb2AtS6F083sN+uL3N2qRRbsBIpHPeoR5rpeDMHZd00W9O+ISVcHmEDC1AcHBqD4SwtdQuV68WPq195mQWLxGsA0QPAOpzXgZRYnVxbkbQbkFFIkC2CXClhYY3+Jy44ltScmBQwrLtN6YETEMi1blK3ZiwDCzgj15qayR2gp2GKGFOpx60AYKHKAvZg0ByhO39bp8QcYqslIx3gawESCbYBQ7je2dBLSQHSwABq8OR6oxP91fpNT9vXF9r3dCbd9hieii84U2UBZ8l1keLAAm6BQK8l7HweaSfyWixln0RZoBLAup1X7EeNzL58V7AHAillBPtA6oRRT+G6s4ISTGkss3i2rgTpkwWS9GSSqaBMjDIzKGtzpdbZG7MSGy6MdStJ57OtLDhyqCUP6XVAfdsNVmRq468AnOzCFCWCijrknYs2SSYGpdaxrU1fVqbkpRTmn/Is1X/lzMaU2+gvQH9XYtCaQGtWFJJzxwACAHnziI+OYbaALAC+zpHiBAw+RUgCADTb9+YPLamSSs50fqOyxdoiJcQ6MIAEKzPul6kGP8cydWuMaaclqM4GwdLbk+GSmgFhLZdqOMK7l1HLKhS8w8579YMl+D4hquuS2pRuoVjrZ8UbSbGZ/ThXP3utHT0hBew3GgCsSnSHOLAYZRZAyJCwurRCwEo6quSeJyQSY+uUtbsvUwOBNnMpeQJ9GMjmYGMEFlVxrBPF18w6Ty3b4jVJd1C1464HE63b1xyhsUda8N2co0bE2/VlFfjZgk5e/2TO0L3hi9SVAfq+SwNHUV/eyOK6UkdhaTxzijr4FUAirNDXENCBC59nxUiYqWARBkvb9/ShNGNMW01UsJqOEhdkplpjJt795EwgGgiM5556ADRgW+y21WmWcRHzBcuZCteVBDBYI60IAE/186yFSwBepAEI/ysKIoo/R0JDEjng8NcwCgM5q2NaszbyLMbWrpzARcBISElQhJFojJvRwKABwwkA2GppwkxIt6W6kX1Nb3qQw12Q2u8UGw+cAT/yRYBU55VZGsBDppYqDOD8RAcvxc7GryqWqgeC36Pq95yJruDdR6yYd3UutmgXi9W6JmgDp9rAkaZqCyuVGvy6MJXNFHCCBb32KZOltjwNvNaOfmNsLfWLIvX7AQb6gAAE6fGyihpEnQLByKg3m+ovgbYGv5TaqAj4bgGYbf1h0KxBKxVWwFQBtAW+xtzyZGvCUY9TWXS7xWOaMKWC+zxVcO3A3KdVjA1W8XZCsU78gkTciA5qIN13JvE5ceJpw9zWz3rO2zHNhcIxtd/EEyQxOBFoYmAtwLLq3vRiTQWVSm/gThz8+Uxev998U2QBfQh1BYlP1NN0V4z9M7v3dHcNzKX65WbvNmWANL3tuCOTgDiZD0y4jzjA1isWxgSuJQ9AD9wBA39Sqi1NxqwnTnWLuinrbE/mPpso2a5P0vnFHvjBwJ+twyHzQdwCXp8icpIxrPbimKE/ro/u3zzI1Sng6c4ibAd+FQC2NujafeVF7EZbQzS9ClNcZgExY0kJx6wGdkgZ9znjwFMdZDuLtnAfZdKuq40Uo9e+OAvoYNIB5ULJomFjAY2V8G6i9hPJiDLL81OLhh3waVTcmgwiCKzAr0jvFDtwgS4iBnRxE+BFqAsSA6761lVf3g0reqFpX9ZCdXjrun0HsQgLxMhZa+giuEhUsJJud6XgT8FFJqn6Gel4Zza62zEitghrYnOEooBZgZA0/0cKCMQXKrIozH4XldMGkfo3MH91waosreuH+gXL5SXxhenJu0lr0MTNRxfbt7iOtwFZvZDbG2l6jhgl6aDoNScNqGodp42zEKos4Dg/rknBDGCBbffKPVjsm4PQUptSAGlgXwGPZZ5lAIEh8vXaOV4dBKJmDhooF2BqC0EE5y7Nj79QENX96JCqH/1etaON+s167qKl7RVXYWU935fESCUhlYKpJPVZwpgk1QHePp5nk7HFqR35e8SUVSytEE9RC2oTiP+24nYTMiE1kIr244GUp4Wr3ztl1MlTYhbsxszHi9tWKaC7Rf32lLRDtghQkp03NpvI9KHAQyATgRcF7ZI0ayWLDiNeV12jlqTDhY+cMBlTO9FY/4yqk+4SWppUqCBzxsH29V5FM1kLJ5Qpo2TWkoGipRWlOLXsoa+pyoiEtdYjMBIpcE020oUSay3Z7PWBnpMU/5L2ZgRh1t/AfAL6vJyr7j5SSYVn2FoB+L5YyVir1SRLdcfzBhhG41jGCqylCQoidZ1a14TVxpnpLEf9WyhpgFVSbVrkIYHMtnY5WGdSHLEK48AZmW2oeyrIklEKNRaweLMrmr4cuAbw2rbRbOCvgsAKfs2fVYDfsh99Zqqtieds57FypacxgIMD7CJgW8R8265KW9e0j4JAVTojc8KaBAsXLCnpvn+86t6MvOoMJZoqC5jRHGGcAVhrAGH7cRY1OI/Y5pSxFEtdTaVFw5OmVoKO9ORfCTW1yNQcXwWA/hccYcF2RDyAvw4Ioh2fTlnPdIa8NuOszJ9fN59CBoDVeaFS42KgQywCLEazr8yYCmMlqQO918KYSGsyKktkH701dgRwsM6VsZ24dDVmmQkshGQdpYCCm7bkhVOqwOaQGZAb2UCiUCwHozTsdYOeXCddmqoa28voJQoJtCDdmSQzdj+AfnaXSb8/EVXATgY0tKvT07CtcUcArIWNcWDgpFpGJbIUgH50Jo00y4buvAA+yskYGvjuJgEEUgCB5jfYbWltZRTi4NztSGQAEA4E23c7+ZovCdLt/WuqHggLEU6zHozauKNTBRrorWl71i3IFk6YcsFCggNnHTxcUscCZuHK7m2JLloNBPqCNZXGMM1JgURJ5vfgJUWNVe/KQxB8XyirqJeRUa9lLxIWruADq84k6A7DQvaCyhIB3R8VyOQMKtp9xCIoNIFNB5rG0zWKV0JZoWntVTuXadWmxbIyytSAxLEkzGXCQbIBuNQCW+oHd9frxDpKi1ut2UoFB16xGqiYJ6urnbPph2pdbWWQgm7sjQEYCLTzMSU2UEvgmUFrsdRiQWxEbCeQ6z0APQ63a3kD1XPYP/pZDKAI0n0x29HP1CYXMnCLdmIGnBFTxsqYQZm3VdfzzNp1PRXbMtMHenNpgTAYUzgI1bZIvZWC9YxCjBJYwDk1wJ6tu7+UrB3KQi2o8kDYQaCrCurDfW2qTaWW+ehKx4qlgW2o+knW0M71djzDwe2a4R7W0fU1gEc/WBH8uRL9uhdzEjq/ZT9e6gwxQ+6JsU6MY064mVjBX1l1T8a6PQsjmyG1+Tm9VBYQpUXSpdHtMyv7l0vBNFE9oN1gTgqAIrcOuOogvfYlgkCnb3MAgg9ExJvskkV4L+IMDVjEww5QA39FAWFtlhBdxCVppEUwEJh9wdLvXgpXFnAtjJnNmQljrgxTYwFHlklrWwsS6KTZp3Z7F0Zige8OkVJRQBa6GCSwEs5s6n1mXJnq/QoMN9jAsFhJWKwaCyidfloDwgvoJwjb7Dwr8bLUrzR9edAxdFmKzZfSWkG99G2tio1ZisCsjrOgth0Sk9SxSd138sVKqKLQdOEPF1NOMeMQoJvFKCQa7a5t0fIUiHixd9EaM5yk5RF0037bpw0APVUPtIVwEwD6qI5iwQ/6XWhqjXQmFNb5qGtmTIkUWBioWIR1iLextyAFDmNlmbODnraaqWBF6Aa2wFcbd2wBdGY96QJV3U8N6NX3cdWP1LIXkNuK12ra+buVAjayqeluBPFSj+2mDp+sKwGOi3a2ltouBgBgbuBIko2y8RFem3/aNFMK21ZkjENqo10yt60X4zrLg/9j5BY0mT3Foe1dOYX9hFW0y1UAQNgyZ2o/EXgJkdom2e9iAU+CdNRGF15ZtyytEymkO9YtqFEAVUGeL48pgsF2MCtL+GQ9AbxkPd8MC1AxHxWCg/Z9xMCpgiReVYe0QkccZYJktrErVIdE++4g8ZhvieKGAAYZ0DwIqh0WoQrY86RlMDLrQRRPBYPDODkHe+RvFbIe0Fr6Fa3usag9eXaQCkChjhAI61KNKtHur+JKelwNVw6CljMAkDoGsNiWJ3VrLf9O9lxOooh9IhvuyHVT7iUn3TlEGEtJKEw2TLOBwITSsX8KIgqSqPObbQusI02YN+h2H8rqjAmJbX7kv6mOc9DvXIjqcOIK9Ia0cNsIoy1iFWS4Qt0JemDYgQy6SGGXCIl+v/5O+7NIXjekhkUp7UlEOp8LbEDKFjQpsetT/3JhFFb2z6fch1zciajeUtXZbMOJi7EWlQ2sn+EgUADXORVL93I7pwggmHMsDbzX35xD5OzA0cFUBIIBlIsEQAicMbRniqgTqPowUcZMukG73fe1c6hmnIoBL0/PBvAXhw5HYSqIMzPTEFAxidZ4GkDnjcaDUZxlztRsyVmvmkK0xYXModPagESbp0l1kHSrA0JdGE6bp6445k8RsRQw6Y2a9q2+D/BTsyToDFO0ZiUmbzryNDGj+EIlug9ttqBqNZtaSsKBbYs+A39xr+0oscaspZAZk2iwO9uuPrm0GZ+QYpywLXkeiJje/LxzO2o1fyEILvq7UE6BYB/wRiCIGmlvserP1qUIZM3mWxPIshjEbDPyOPjmVqbD2Ud1uJ2RNVa04Kptkbk9O9PtSUuRYr5C1xCWvgQGQAdOSrBZlckbSmvGzFnzlgpFBRTJulF5UfKFVxuptAZgPgBAwNfxHuRVEJh6QFgv0fusp+iJ7rNuiUaEMmk0pWPu7LhGbJFJgWzSGmHJUU/6I8R1VPqNJrbmBAKoo2BiFzfb9UQFd1YME/1nBOx1P+dZwaADdkEb7+w10LXm2VO+GTXzEQNeyQ0jRL9Xr4N6HVYfGH7fFTZ0fQr4qA6onhRj+sM6+IoNVk4eg/kPX/WkokS1Q1gmBYFrTsiyGm3r+/T5TCVbREQpXHeEvjOId/8msrExpDMFJ8v9R7o9pwKRXIEM4OeQecBMHbjVOpfWAaejldQRdvuelgEIhgVMNpxgLG596RSjl26Naat6PXyHbjZUZNEYWtsQAHM2B7gW1hqzEMkCoVkgnI9MYf6RpUvauJHWsLOSDqKeK5ABUv0BeoDduEBFjcX1ZE+h3BxVnZFCeoClaC2dsxgjELwkBXzpzKlLhNC+BwJXURlBOgV8FOppWy5Pr3fn84bT6+fFSQf+fOGqzxVlZX0/WraGhLio+Xud/C5KACn7Vczx1QCrllAQJBsbaCMvatp3qCmrNlMCAN8Cf58SECTxWk3/fTht/rCZbXVXE3seM+nAbB8b5V2D2VJWmeuwdfdz46KVhU+2sPJ9t6su0epoZ7OliTJW5sqsT8kGUsf62qnY4sTqy4DmK2J9rZ+LFIJg1xU3IBiDKunY2xDsun1Fnb2U7gRA0TQqEQG5gLgY+BPU7kqzIYqgVNzWGvjz4Gprfq1LtKU5AAhvxnEdFqI60zY2LgKn7JQHUyslW5sYwqXV/lrA6+DPd7XiScATgVc9Zyn343o69jUethHkcVwDI/ijuo5cEBOeFSUpNJIQ1hpyEQNOpL+XVm2K4JVQEirw88HKlKk2Soj9ttZ40QD7KLFxymcOx/raJIopwMCC3u89xCJCp6w2EGhwotY5EwGr2otkKJPpTLPpqK5Nj2Q/TgFg+BqfFgBEiamQdvJ5WrdMhtbFFinYYGWyyMRz95PVWWQzqkIVZOTitWUJS3CIutUO6WBUEzZWKFmzQhJVaCZ1mCsxVs64MQbwkHId0KknSG6pPfhipQ0qHhFXNtBBnu1PW3zLMqNvi4OqE0fYR8SjE6wKe9EouAEh8h+HtnAJGuBRLbkzsRPWGL8WAatnaJPXHXv0C9W5ocJAqzXzItsEsjEjbd7c5NEVE6bN99KaDE8DC4nS/gbw6kKVg3P0cSKwhYo8RbUBBDfA35gCBl5APy6CAAD1DiFqYMdsqYLA8P3qbXsfhMXp3CKll6Vj/6oDtMWqOjpq2yMtSJjs+auByKkksI0DijPnEgsW0i7XdRVkSii2a4isDHjTioP1OvJF04w+NqmLgGMk7MB3y/GF4/qiIqh73lbGudgCWNB2shAK7B9q6t63+KxpxdSCKmcs6l7B6IOqMb3oOnT2qTWJkNUtaR3Ujb1PBZTjvsMmlLnWOtUAuHg9mNmF6co33ZbaQU+tLCFmOobbWyngE0b9JXVWpCnoGsb+yu/AVk7hgMJtyUGgX8ZdXGbRhsQW+OYG4NGAvE9eYBatuU6MsiQLNLR0ynea4Ek7aHkCeFYGkFZnmWAMpzNLoQkn/vQB4DnLjXhJ4RLPA4AQAS1ZKbEk9c28Ro5tKDZnKHYIY2EiKIp+IQbsp7ryLvlcs0/aZKoNpim8MJPaTZLS6aq+lz23Gw5Nfl9Ctu0gNU1uJVQr1HaSgfLV/F8MeHNjoGuw6zZV7aYHf+eIo0vXqOsYQAC0+g/VBdjz9xAtKW6bQlj0SGQ/uv2Q6iTM2SMyTE6vY3ve1SiMApBGWDpVn6oRLpxwwGrplFxz+L7LRYwQMgmApFFWpjNsYHOG5Gnh7ONgeiBYHb5EhZ46wS1G4yV8YQcsgtF2gAf6vQRmeL5PZ/2Oxg4K1cjqKQxYa94xEILGAs7WZOIsYGHChBZtq34a++HT6aWQNhZ4Sjh0LIr9Zr3t56Ffd6MT+82oQLADfzi9rh/8smtVD+J68NkxJVE6B0CQM98oHm/gdFacj1mKi1aMhH2bpDllm8XpDlHn1aXq/Aru8tw5Q91FwcYyrGwskWiARWz1O1rETas1jU1SQVLvCNHbDfrr3TH5lIQKQJCQJqUW8Nn3KVB/B2fLAmsrzihV9pZqWUX0RbFre0tcP3Fsz0xas5kLnxSvH9KKgha8CdD83mSb6uisp5quF/cR3jjlmR0fc+XMexFLZ20HUzHzcY5VlxcsfQkHCXUM1UanavXtwS/qcx9/ayV0GtvqgC0CCt9rtl3aGgUlNHwHkZnmDjy4LXn3duKCe55wZMHCCTkJysIK/hjKBtZhxIAs1swyGwBcQ7nS1kgVCT/KLiPI8xTzCAA7gPhUEdGhjDAfZjXnxEVLQ4rXLVItJSNbL/smkehDewV2AC1kPdzvOfibrcF0HOOz2J7ONTimvtt+a5i+dvnqNqyFWqc5mZ7YfpsDwaaf/k8xhIP285hhCwBeQ1BcXQPIubTFlpzpA1DzimS0p9dXtEvEGosT5B7YpaDIukHzYJ2xCYTRjKyYQ3QWsBDVzjhv567F0OH93E8U6/DQSfJKT1c20EGVUbo1LWxsTe0IHuvMBNsLmfQG+VLgr753uEkSvpPd6cDDv4/YayqDa3rp6wQH/WwAwujQandpAIFpYAFjLaCzGaWrL0kQK3hzAFicqTA2sISmkJMGkQy9g6xG0G7CKXcvaDY9+fGrx+2FQd+Dcg702WOXCKHpYGu7MXd4IwD0RcvFI+HIXNyVtmj5AugjMY5laqlILkir4GizPlcCyLrJJQmwBqbdx75kKKNuA1CbMzxNhYzH48VY2TPHs6bkxQ+wATr/KuQAyJrcguOODG5nT82PX8Si17KX6jAaCKy1TAxkK8VYjf078Np1b5ekYP8YG8UKoTjDXoYAuNY7wm4HfRmwR8h2VOBrNrXt90Lg49/hJXTo65LvVNF1tVIDNQ7i7XdFkNOAjjrEugfswPh4s81DgOIQu/GgRIVubjDVkqWZ5jq6TOs42yYGbfhwsREnjDIllCSQMOtQbIQNrf//9t5uyXEd1xpcICU7d+0+55uYiLme93+suZ6rOd27ypZEYi4AkCBF2c5MZ9bur40Ip2TZaUuCCS4s/BDgTXJrc5NScTCGunvnyY5+W36a/fajYiAwhhLxkHnIGcDe4bvl+BWbh4GePEivawK/0ar7oqdIXJrlr5wqWOep0Wu/Ugg1D2nztIVYIh/WZzEpGRZc5APG0A7Y837MNJiBm8v+0Fh6HwAEamhKAofiwWVDG1QQKjtgMzzRZv/+L8nnGtX8P3+VG8ATZtqQHAuYSQxgYq0KUkPY9zOzu5EzISVIhkBAGxqxvDgL7ZihsFwR64kVOkOoYSJTaFNsgG7f5BnG0O473C3295267+3Pa/d5x2DP9n1vst17uILA2Vi9AOREmAr4Sw34y0yATlhEUoks+9r7jqRnloSFuRj6wvZ5rzULi2E6Y9JJSlewkKhR610ac9Hf0y+TnpXoXuu3FroHccNO9BOW9cScwj4EEhU8+NAwgBJ+XHlCItLVRLiwgaUBsRrEKedS0X1Nk05eGcumK8WkAAoBOalhTNI8FcmF6psQsBpFcONAYaeTz85Ed8S+038do9o/e4+pxcZ4d7y85o57Z+qIXfe5mXXJSw1xdiyEDwWfQq2EPPE2BJdbkt9JSrYYfai91Xw6DFCKD4ojHGxMuXFkQJDV9nX2bjeh9/fl00JSAaxr3NoqFxyCtEsyQDjIXa/gp3ZIMNsRQl0HewquNcgBoLD5x3LTq34CFo7IWDHnuXHCLKViVvb2J51KQ+8pRiwxYp0iti1jC9GFhaXogzX0ay1tGjbdqktHwAEtuGtCvR04BvFzQsC91qwifdTapP9hULvPzg5ScIAd0PunS8LqvT7ThnNY8UYr3kIFgNWxkk3CWlhA01UpMqV0uPpODIyrNaUmYW6NtdU+W0iTc6KSRNqq7XNpQDZ2jJjo7svwHr1jIL0fAH5CGg+iUaIHC+3/BHgWyRB9zb2onxEgsYy66DMgDOKsoCL7JOsuJyaQGMMUpHJPenXJL8qMRmUDueQF+hwm8jkybhIrhjAfDMDBgPxS+eh86VilW1KKCnbFBdJ80yfWBhZwgixgW8DfhrLIdwSQIhATgjJIgLBTgsstFCzhxewBn4F0SE5gKRDRkBxp+oGlWXE25wWNTpo1XL9ZR7s5+4buzPABnQdMdWmjEfPnGcGmylS/a3ae8EoaDiZttp4zppww5wm/bIlAW96M6lrPng1MQXMDI5f2DaXiPHIdM30IuJRE9rfiaejhrnh9lJ5oHxH9V68vA+wACru0f15Z2kgJ0mewXnwOpGNJUiuya+TtU2o8GLTfTc66rJaygU06TKCaDsOoqRMJFQgWO8fdBEYlDAy0oLCcw3foz9JHPPvXgYn9/3ATRSbHKEVqUynqY8PJAzs3T2UKZTyFkDFzKqFi70y1ObsMKuFiIT7aUCOD11BCjaRLkdFmETjnUA3AuL/2lgGsnT2KTYXbfyIArP0FlWzpnfdu3z9vH1zAuoGyKUgR6EwJ57DhzYG/CgI1dK+6ypDIR2IBgTMlzFl0+jOfiq6k0DSXFXisD3EME5aQsWgKTApR+nBu3umlUlXvmVpkdSt30cOWnPgtAJANOOkgEqPtqPWI2hrBvadXnve2vOIsodwbPGEsNgTKONGmlW5bE66SMHD7i8wUcKbtcJFkHyZbiRGDNo4kRuKMEKS7eM7St8u6lcuEpddQ1gJEieXDvGIGwL4djFNgr7wBU/BpGYEI2r/WD6iybyfintuERehBRi4PEwN/lRWs/bBWxNIcVSasUELBCEDmrKyp/m8OheECZKBIkS81bCCRhI+lQlh1RVwnr+wfFQgyU8loIKBjAKsyntkFBhgYMi8jHfXPdez4/y0J5F4/UGMVcgP87HGibexYQdq6zLwVQ3jNsxR7WH6MYwMN9E1ZljcjYsQcxRtXNnANlRHMIbRAMENbIahumEqF3yiM+C3SjYvjyUft3fD4eOKq2TSOrXWhpQLYXbK6MbUJodbGU5al47KwVUOZgJAcuNAVY7YcEDNjSxJqFL2IA0ykjGBWGy+l3Xsg6KMd7MCg2cBeb/pjNh2WM36mTguYIGH/SI+5MHAT5nT7jd0roMLCs7lhlDz7Z2Di5FglGVcypqQaWMDFJZ+wsoLEfEYEu/XslVUPNeVi0iVT1xhuhhpZO2zQhNrKR53bIfhr7plj/ro5umEC9b2f1k/U/IJQx04F6FTY2aofKnrhwI3OSBnaEGoPzFPYFPzlAv7OQfT0I1xFZ7Q2jK2J5WwuHPGGBRc64cKVtX3LK/5Jb4UFLiuPpVl/K7KE4BojVl1RJsUoLYU2WzpOwSCbnlzj+2L3LHzP9ZbfYNO/DAD2ORRQ2pwdG1YbQkvptlHpJVnVqPVmv1VcpddH3lX1sGxgJQ6YISAisPQDRJYJJ1AulXhegqPcY5aBvaYoxlCBYAoZW5QehTkLO5izJLA3E1bQG6JVPSWElbzyugHolNhMZM8IZ9mg8M8Hk1HvOVUPzwZWBefVlooX2lSuOYbJJ9r2rRDk+mrYyoPADNKKyg1IExA3hBxgP9HCkGhvOunyztLwU1dXkPB9qLmBGsIvE1Z296YDgmAXBvaTk5FLnq34vIZ2ujGjZwatPHfOU6tHbiYpyUHlJlQVKVcPVUO/ngE8a/jjRFsZV7HXF2TSSkQ4ccLCspTSLo9J2UABGBlznsr3XtKMiSQsLA6eLNxuY6sBggYwLK2isBZOR5pe0uvlq4RRx1OdFN1k5UKJ+9CijiO1cwhcdEfe4SVbezSVyd8mFgHZNXez6BFJ86OzQkEgWh8aPd/qnE1N0QIg4bE1RwTtQzhygHMWIMhZ8s1yUv0E23IF64659W2vdo4vUD2pZ7OAhBr+jVIwYSCjVM5qy54SDqaqO1Cvt3ZuKiFZBRR+LBmj5AGg6czIikRUgMUcJBoyZ9GipWF4tmmmXJjGU9gwh9mlVzAWCzUGARc8OXCRpLkyEtd0iqKHFoCjHpJiQAOB9tslaOTL3+tPKMzAn+YxwkL00UL1pgPZz9F01j0iCwkTGRQzQqz3K1LGKSacQ8IfccE51NCvgb8/dWvOlUUZa3GotKJbdK6SZWk3nGjDhQQM/gwnTCmrQ5BxCgI8f4YTrmnCEiOWbcISIpICwRyDLs0ISYXJVHoAsmdqS+EYduOoqKDgiTozPaqa9zGABOSZigKrpyBVPDtlRUKegKyLPMs6i9AEVlVeYOn1E3XJtoLcpX2LoXbxsBbMtOGN1gYAgtSzQsBMm3hVyFhsPU0wZp5KonQgxpwnzJTxi2QNzi3LGo/WhmZNUdZoTBEpSrPWPNWm1RwJWXNeGgWqB1x6tjlE3zIYtAOC9cnnpQeAIwZjP1HB5Xy04KIwtECZsEYFBj6U6JtsGlA3FnDXV8nOV0FgyNGFQSK2LJ+/KQi08+iBIBF0NQxt7EpBCnuMFbTGoWb0GbXww5hAHXAeYDSG8kmgw4M9r4/hhFRed16v6SlwAef2KKAiZM1ZqYCiZ/7avKWWWZeLh4ZDAmaesCoQXLmu033VHBnT/1XDIrK+bcaFZvXKZbnHNUrfTwOCBt5tJRMzgtYiClzH0Ci/+KslN5EP7MePi3zYGuhMqJOWvT/KJEpRQXrMiIExm/Or+vLgwlgLz9SamONrvQJXjoUNjJSHRTv2XJY2k2b5a46lv+cUa3PqlGWlppxziYTIsldUGYwC2hUMsoJBN7ZuTlxAM3l9TgiYJgF/k+QA8qSPqFWzUfTZzE1uazq0uYlCdXAFiFX9FEaJFmUAlzKeTkgFWPQVpjtgQRsuvDbAwh6TAoolTjilhMs2yziOCZc4tbmBmxaJWK9JA+zNHORAxeD2NZG5fqtkAHow+G41EThGBXsC+vIcZV6Noq88Vx2xe+RJscRkxwwAyniap4TztOEtCvtn4O9HvOJHWPBnuMrDdEab2ket3Nb7kGF2b8OKgAutWDnir3zGG624hBlvecU5n3EOK/4V3nAOG36lE/5KJ5yi6OqSJqzThus2YUkRmy4tWJzfLA5WHtg8VjzR2D6TZjwR6qSF9j035N0AkCc5gaZT+ID1y5OCwEn+hycHAicgT7J4MiIjTBnTlDHFStueVXketb8FQewzkgBAqg01ZZIKmqDOml8xIbLmonFWL1oM4pWShsQSljzhmiacWNd8ZAWDHLDFJCBwSmIUIxXlyRJBVJafGbIXWVLFrQ1MbfHB2Hlhz5rMFJiLnpzudL8HGaWZrQ18Bz5gzJLzgg1g1Gq15B612XDfZLOs4axViwC0mlfPMVheYEnglL5qmREQsZG0BpioshUEAz6hhNGMqTUgWBLbQ68jbliMRi+NZ+Xx+VP5P73v1IIJ5/l6rxfdw3RlTFKwicqBP8tPmcKIRd+aKjjvVPUgMEGYixMJ+PNAMGj/zWueS5K0zw28JjmHS5qw5KkwT7L+tzaAz6RA0IENNYwMVEBYdFP1MALkn0nP23+YAgL7WmrHUE2DcZNUmbDYbXVsKagIUfUUUxlP1rT+CKwfFRYkkl6CEVNhnGLgXdHORMnpZcKkK5AseRK7pjYwhYy1rKcq42kr6wmr3QtB+7h2QP2AuQUce7uzeexu8Gd0RcAUlUmK4HkCz1HAxRSQ54A8ac/aSHV+snlL9VVYpSCgopmbdE6yrQCKBW9hwZ9KUghhkTCXtApPdQILB2RKWFk6U5xYH5TwF53bsZqTAou5hDRPacYlzTiFhF9hxpIylpAFXGwaxk/aV7UDgVz0Mv6tG7ijYM6ltDoDOSCrDPZnhGdtmhlkvWWOBJ4N+JED7C2hVPTkSaQpN3qaQ8LbtOKPKI8fYSng74eCP2EBkz4YM4BI1HARiRmJGCtnvFHCRXOhLzzjkueS61mYYKq/j1/xhF9xxq80F9C+5og1RazZ2b0c3HxFCgS1VZNiBraOA6o79jawIyvk2GPj6J0hYCCd9PaYUx4AUAV+Rq8b2MszIc8VtedJDaKCP5pyARanKKzfKaaiNInVL4WqtYenbAGboAgnJCSSxM0Lz4WynXP1qOew4ZpFgb/SjDVsOIWpNJ/esixHt6RJ2sdEWZopxVSM4qoAcMuVtSihEmcUwQcDzylwp7wnSO7zHh1z0Yc5xNv1YIMrtW4Nr1140Yd6S+UTtQ1RfZEB0IK/qOAdWRlAC8kCdT9sBQwGCBs4MWGjiI3DDghuCvxSZoQs7WUKENR8M7b8QM9aMJcQMMrkxO0AagbWExVFqKFEzyT5R8PM6tjRJqkWVpTwb5YF7lUfPpzYtz84l8q3Cgb7PJghAKSg4D03QDByBRmBcmEFZ5qro5Xl+3+ljIkiFg0Rzxyw6vJ/qxrE0hhZwSCrwTPDyH7cfIMwif0qOmvGEI11F+AYCgWQCiwoiq5kwkoS9QjV8bV8JatWNIbpVmFBH6a3FhaeDZxDKoygVZrakpsnTroUXcSibODMobCCWw6YFAzuWMEOqHv2trd37EO/w0nqk1olAp9mZQADOEawgT8FfoVZ6h6VVar2L8aMaUqqp4S3uJXIlDFKxvz1jNIbJcxq3iIEXAACKs6UkQFl0TPeWBjAv/gkbCDJ9hxWnPOKn+mMt7DiZzoJGAwSJbuECXNMuKYJ1zhhzQFLjAouKrNuy6RxAQ96tzsdkEZ+zOk3u28Opi80u1cMeEs4CNjjGEqrHp4IeQ66VV1NQJoHOvIk0mQkUsJpSjjF5Ng/zfeL18r8FRC44QclvBEwE2GmAFmnvlKbmTISGJkZKxgX3nDhJA9accrJ4ZKTbPMZP8MJ57ThFE7lPH6lGUsS27emOs56R6suhUpu+VU43VHRXU9MDKenG2p6JwMoihmHEi38642egj9T4MxlyzMDMyPMGfOccJ43pW0VtQelbcMi9HpYHQjcHF3rqHWQ9i4jLCThqQvPxbM65YQLbU35vTAXE85hw8qxAYFrXItn7A3jLkS8m7ToPoOhSmO3/6jS7kkB5kBNWOv05Su8ap4m14nKvL+oIcVYKzotv8JXQFmBwRySroXZ9pYrAFAvLICaQoIQuKyVaUuPxQL6ctFJDwQjZ1kf+g4QDJoj2OjFe1e9Z9X0ojoAg0+QHNExSXBhKgcmfFjRZhQL/wZU8BesCq2GE/tkdQN99rCUCgv/lrC9u1hzsGqaxVTZQtZ0C0hV44Xn8jmzC2dZWPgaIpacsITYhCBnvx4uUxlXWQ2drPGpanBA8MvFAKA53CVpvY6dHOvWwF+eWHUoE1UTqpqShKuMWdK0l5OyB3NIJafspOzsqFrRxAoLVp60ylT0EBSc+zC9APMJU0hi50Jotk0kJMXKCmZbS706wMlNVGb7ZEypZXaAo2dvD23fZyQQ+Dw1od88R+RT0AchzVSJiQYMqoM118hUnGQcnWMFf3+EBT/igjfa8KZMUs8ozeACLCIEXPjfU4KwSpmAMydcFQjK+Flx0VxCCwu/0Yqf+YwzbbjypCzTCb/CXIDgOQqru8a4IypsbqpAgsZgAWZLalV6VLviW0zZ8Q8LAflkIWB55ElAYDoJC5hmIM3UEklR8YORSBOD5sr+nSbRkbF/xvzJ44ofJLoy8PdnIJwpYEbETPLoJXFGJhawjg1vVIHgCRkXXostNSfNHLdz2vAjTPiZTwUEXtOEbaoE05pkLjNni4GyIAarHfSLVkigyjnCAzD/qG38AAMINCX0DZvkgJ+FQgz8nWSfDfxNjDCnBrWfo8TtC20bFamHpSjujTacKeFEWTyr7jpXlsTNmQNWZaNaJlDzanjFhWacw4xrnjWpPQpFq/sCBtcCChswGBUMThUMJofkvQKHKF5v6FexGew1W3TFLQjscpM4AugmqqD5mSHUUFWZsNQr7kNV/tGE6VF7mgmPVKsXI+fyWKFhRbaWI4yQI2YHBC10Jau7RMQsYd7RZJVyKC1+6mTF9ycrr5huQD2DCGRSb9Y5UeidKB1TuQklQhhA01FQA+hWEDiFdBhONEDhHxa2OpUiAwPqLK2UAAWAWfpsggsQXJAwk6RerJzKeqe+SMTY4pAy5hA1LDwJY5XiEGzIEpBmBANyv373dwLAkvu8t3vY2T9jJ2SSyrMxFbmGqqY6jjz7d9YQ44+wNIUFvsDAO1QmpWE3pGF35AlBAcXKEQtPqp+6CsXKUpG95SBbtX/GCm6ccFJQuJledHuKYWjzKlBPHeDATebiWcIE8HmuDZInQpoF/KVzaMBfOlWCgvVRdDVnhMhlfvKgwkJ8NjcZA/QnbXijhDMBJ6rAIhJh6qoQMxiZMlZOOBPjpLp6U3bpL5axamHGC814CysuecaFZxdqnPGHhhmveSppFjaWrLCxHUdw4GF//82RtOUdJ037mbQ7hzF/8TMMIBHyOWqebA31GvOXZrQg3aKIszpXHkd07N+PacEfccU/4hU/4oJ/xEvH/q0F/P2giDPNQ+BnEikgApgp4swTrrxhxoYZGXNYceaEmdtKcMsTfKMNF55wThuuecIfYcI1iq62KRQwaA5XcX7ZL1krYWJApqPc2cB2YQZ3jx8YW+8DgAHYztSySgYifOVOl7CZZyCfxBDmE4PnLKh9EvZvnhL+mNeiuD+VWv+v0CrujTb8CNswXl9uAAELs+ZXJCwccELGov2XZiQp6c5C1154xoXm0vBx1dy/AghVKQISx2CwJHUOwIcxGXUd3Wr8Rij+KUIyAdl+0ZUPAZdJjAvQ8OCvhqqyJqq7ZPUj9m/AMvnVQLwIsJDqxQhuewMqG5iZCis4UcLGsTCCGYSVogBAztg0PLlxQKSAFFQXxE2FY9IKxsS5TFBZG0GPJioAXzZZGbPUrEjgE53t+cQNGLTQvOUo+fC8hRNLSwKtWmyZvz0LaOBvLpXbPA4DQxoLR3ABgkFZwBhYWScJUa6cBIRQncwiMq55KmHhLUdMNB2CjezGUWECv5P9g6g92yLkIwDodReqvvLEAi5KuktllWLIOE2SyP/mHF8JLS4179n3K6NVWmEh7QGg9S2jIKBaQbnlac4swH/hqTCBxcaF6vjmIMUJezAYqr3jsb0zXY3sXc86jQDIU2xgIKSzMEs5CpNkrFI6EdKZkE4V/CUlJiQ61YGKWfUzSXTKIlM/4tIWEyioeKOEHwS8UcBM4SawiPr3TLMySwlnSrgqEJw5N2HGC81lrrrwXEKNlzDjZz4VIuMaJ1xzHEauyipLTjdA60gFx+5Jik0uwM86CZQq8k8ygOkcShqM1QzUsK+CvhM5HQFJccSIRDpPG/6YVrzFDX/GpTCAvujjTwXqBv7+oBMi9SjiWCIF/KATzjzhzBveeMNfnHHiRXTmwsJ/0dmBQQGClnp2zYI3thhxzZMuPVfHV10WMpTnpitmt6pPp8PeLt5zkt9dBJIOACBCZwSDxey5DrCTKu6UEU4J05xwmjb8mFf8MQkA/HMS1G7s348Sr1/xI2z4QYwTEWal1UMHATMyzpq0uUJyXU6cCxB8i2sZQBeeMauyFm1rsXIsxSTCCE5iIHktYHBlUVqTM8hjFL9pqLGfwExZR4r7lJBjAD2R5cFfYMdgcAkBIwpTEaZc2L+Sq6SPnv3zFYs90OjzlUwigvYxUyBIUqhji3BH1qphxwTWhsQCACfKZeDkEIoOcqCiBwvZG73eM0oSYsy7SaqfoPrJ6Sn6IjF4VSdd2NfyXjyrFGvxlLB/vGP/rPnp7Io/rCinVupuWn2YGvDnK+F6dl2KPRirVnEbELSlGG0/wPoEJg0PTxUoulw0CQvPu1CkH0t+fdw0MITfIgYATU9Ao7Pq+HqgruBvzoAxShaqKqyS5TwLm3PqwV9owV/P0nrJOsYSSFqLdOxsDwQveXb2jrCS7GdQjYKEOAwRl3HG4RCsGyBktxW97cfWM5lcJkJ6iztGKc8kAPAkc1g6A+nsyQnZsoUUp4x53iQ1KbrUJAcqTDcGKgz8nWnCmaaHgYWFHg0IFnZJt6cgYUYPAI0R/Cuf8ZbXAgRtblryhDVHXLXDRQaVeSlzBYPl96P7tRdlalY8sbzioG1pAGCijwNADoT0FprohweAxvylU40ein6M/WtJpF0KWXRhXw39yr7o6SPgz4sBwZkjZl5x0Y4If+WqLwnhz6Vi+K98xhqWAgJ7kskcr8RUdJZMXwr6vP2r+3IN/XK5j4yrdzOA+VyNoHm/LRPYMhdZcyryzOCTgL94qkp7m4Va97TtP+JF2b+lIHcDf29EeNMBY7S6V2LijAzGiqQUe8ZFJzcPBI0RfNOCkREAXNjAXw8G90rz3rKwUtVIes/rHoJ/hjC5pHWgtnRxOjMQWKtJtahgypL3Z615NAdmjrmyS1TDi6fQFtcEbSXSF4HsmgsrIElMWDGVFjIrpgYMFtAHyRObYKAvYqKEzAET18koBdJJSlnBEHaMhVHqfa6FTU79xOQZ22cKdyHgPpRYHCjPAk6V/fMAPZSO946Z9aH50LF99kBtU1H7zdUOD56/sFV6hLEVBrC2/+GGDbxwLmHIkAWYGhtoDNSF5hLKtxDkNU0FhGycmnFjgNB08+iY6cHSR/RkANCeVwaQq97M/lnOn+YoCVOREWLCrE7vedqU/VvLpGWFH2+0NSFfX/jmc599CC6ZPQGVVIlRmL4AQaqh4ZUjMm07MGgg0MZbkx+tWwMWPchgrmsR80BnduZPD+MHIP0RS6sXqxxNMyEr85fOLago4OJUyYn5tOE8Cfv3x7Tiz2kR9m8AKs4a9n2jgB9hxpnm++c5EAOCBiredBxdOOPMqRSK9EzgX3TGWVuSrBwbhsnrzQMJAxbAXgc1ciBb601o/V19H8nP6MmiiQYAS+Goz/lrmD+np5kbEukUk5JI645Esn5/AtIZP4Iwrx8Ff16qzlbEvGIOGT857/I5Pel0oRk/QtRC1QlrnpAjlRQ06RMpzz1gTw7wFTuo4z45HfoVf+7JuxnA7a3umyFsk9i5mcRYaXWeMzAz4jlhmjec5g1vpxU/NPT7Z1zw39MF/4jXNvRLC36o4v58wLvy8fpk4SdOwggSY+GsAyvgjaQM3/oxrazhX1gYeFIjqeAwVJBo773yVJT2GRT/XsXd0xPPdXIQPbmQcEDb1NP1Y5Rmmgb+lPXT3ArrzyjNNa130lEO4FYqg/3yVSbGCkaEsgC3NPKW0K9NUJkDZmxYs1S7JR0MfqCcy2AJygamJnSVFfAZgPCMxSM5FuU+PnuyUmBRAGATBuYaBtbiKUzG0EqIyuf+WTGBVNNvDTMbnQG3VXSCsa7K+s3IOFHGqGqxB4OJpRn4ygpCGCVkbGygvF+fB8aFWYBht4rIyhErRUycseVUQv2nsA3Gz7Zzpo7ks6Bvp6dTzZ8FekcKtZWShugLm27M31TBXx+uEvBXux5UBrDml416lXmGNun4st5lMzJWBYJiA+MQCL7xqmvTBiysoShyzKADEhYe7kHFxqGxdyOmacTcfoSxuCccCOuP4KJQVKpH08lAoADAdFZG6cTI5yy6Opmeqo4KOTFdXV76tYCKP0PGn58Ef14aRpDXEhJ+OwCClhu4hIgLn9qc9kJeVHuZmco+sJ93WsexRg+spZc5ehOl0ek/JEzA9kYddkAtHC1A0IH0MyOfqp6mqZJIP+alRBCNRBKmdsF/hwv+pFULdAhvNN3M+fuInGlGCKHo6+zyOS+mLwWAa5hEXxzxQ3GFjzpmDmWea7Y7vSn4wxjIP4In3l0Eks91vxQVOA+4aVfhwlXG/E3zhtNpwx+ntXhX/5ivh6HfH2HFnyHjjQhnmvAjnB4+XwGDARPM401qGBkzZwWFBGmZELBoHplV01XDWIGfN5btABNFenCy5eAGXTj0vLwhTM8AGaRMUWEsHPgrISxuViegUEOKobQ+cH2VNORrTUlrjplnmGqbCr9c3ygMbMv2pQIaQtnPFDRsmEvrkTmmZnD0YHCCMEmT3u+NYxMi3kgWz/Ih4mIMc8AcW1B+FJ56NmORp45RsjYvBv6mCv7Y5WZSE56vYV/f+69ZlQWsfeS2UqVd+sXBmgSbbtr82lh+NPY7IomJE2C13an82FB+Y80yWFnXiA4ZF2ZhBrUAqIAMBwQTQsn17B0oP4a+RUgAA7vnJX/WAXdE157HUikU/E2T5Pz50K9n/qxdRWkqHFbJV3bMnwB1RnQg3WQmWfAIgEZAKitruZojRtCaeyfImumJxR6WCUlZdu/0jphB08uWI3IcO739xNWsTYznjCsOAiwKqJhcWLGwfsooKbBgDyrmVEK/o7x0AxUGyg1UCDHxefDnZaaIAEKANltXZul0AAQXjrjwWgCGrdYj89kYWACyiEL7vW0brz6f25i/aZDa86hwANIbun7CKGBdIoeaOmYg8JRb5u8kenrTps9/qq6sQrvWDqyOpY1P15OJ11fEJvYOOnZLbqfMY33aWe+E2WoxCdRuu3Bv/9wfe0TexwAG8ZqavLIC/NqcstL0dMqS/DxX8PemSvtzXvCP6dqEfm2A1U7dqYR9z/TupYsB3AaCC1u4OGlzTtIBs5WBUwfR7VCxGcoUqaF1fe7SLQTvj31OFHgDLfNnWwN/VIsIpJVILqDC8v7aPmXyKJNRx/5F5OGyYsEZjaIT2MogLfjrgaD0N6sNvqXNTzsgDBwKWKSSLzhRLCHivnCkgEEmWd3gYJJqKqyeNEkVLbnQIpMLJ/o8Mg/+dCxZx3uf+zcVHQk493qxsHzwgM89vAizNAJ/HRA0EAgBH2AWvWlFY3OrGEBYEHlSxLjsGhZb3ufMUsVq1are691YIE8PAPsk9meL6Imb/D8DgIVRt33XmNbavUyTrE4wN90O1rLaUQv++vVkW/A30x6gm8xQBrBhaFMDAH3hjhSLRJz0PSsnBXgVABrg6xmKBhCG6kyZHRwBd9Od6GzMWnxaVwHY/lD9WDcKzyb5gsSzFRNk0CkjGqjQokTP0NbczMUxs9LnT/LRn8somViumaxutSpTz7goEKzAQoD9G9dw4yPAwiS5AesjOZYv3Dv0cm6fAIBU6wkaBrCvHZg0AqLgj06p5v25EL0Bdaue/+HAnwB1I5G+BvyZmL4iEwJvWszDiJbPqelnhisWjk3E0XBGYmm5lTg0DF/dD43OTJfpnXPU+9vAnFs2qYQXXT4MyFeSSrhqNs9KlWaUrS/Xrsm1dYC9EeNMz4nZ3wKCiaVgJLEaURLmKFG6CwbteWMwg2cTxTh4DwxokfpTwQUBmLpJWPVUlvEhlA7vsq3NaWNh/erSfJOGFv2qH22rF80PcQUBHvzVtiJS+yv6sMnJqoHHQDAiODawNiT2YDBkFvDOockXNFbQAKCxgi0YvM1QjHTzlIlLgUUTSjSDWBg/tOBvqgDds3+mI2vMbU2g/b2/1bZht/wbWvAXiJBdJUwACgi0pWclHJyxUkBkRgYDtMk/KDOYujEcSNlAfay6Uoh5yh4AzgpMjkJXXyYaArZ90RdXRt2BP2HSc2nPM89J+5Mp+NP8P2t2f/IL1Pt1ZHV5sBOyFgQI+Dsp8IsghG65k8wsLTwNJIKxQop3ohYC+cKdDHHA+hYyM4cS+cgcdlGP4uhqiNgYpZUjzrwNQ439WPpoztI9YQKSZwCbbhRWmOMKEpX5i1PG6SQh+j/mtSv8WHCmrYTo22bPUoz4VQDQxNglm7cCElYwBB6w5n1KpWlpBn4DWAB1/vEEBICy3KBvPO77TxYA+AkGEIUBxO3UF1fxS6eEOGfMp63k0fr6AV+h3RZPSWrLd+jJ5EwzAgKuWGELJMi949KibtHUjOpsraqz1YE/cuAuVAfqhg7fMze9mwFkBYCNAQRKKBEBZQkZySWrHrDRtTVeLzH7supHSdZcGnrdmjQ+SwwIBgQdOKks9yJrZwubsSIhA1igoUZkJEuWdsUjxVgOjKYh+B69Ay5x+9kTGQE0MZoFbBXwyRYCBgG3goS1ErGK3wosCrNUmL92ofqa85d1lZC8A38+BNyGg/XaKdfwIetqIQMg2FQOQ3sJQppKe1YwBFadyQCTAHAoOWaJWzDYh+ZlokrleS9PyVci1AbDPqRoubQdi24rSATH/k0K/Kw1TwCX8K9V/wbKdxO3raADkN+//Ei4gMB8o0dHgNwpzzBlMBJlgAMSZUTVzWyAUMVSAaD/7/MDbf3oUQ5MZgI+kYP0LiGuObXe7tl+OGbSDfwZ8OtDv30FvfVkPBXnytah1f7sqODPA3RAWMEE1tBMZWnDIFQfNExsQNBayESWcWKjS/JytSBL83IbVlBDxD0Y7HUG7AveeuDxFDEGEG0eegktajEiT1kKPrr8zDetJv0xLaqnpaQm2Xq/hZVV2/NdoMLPWwA0HCvs+coZS5nPti6vfQ8sAOxYwHILKZdlIX2DeOvpGjub8hERMgmN3ath+9pDs/TPPGnF70lyaP84rfhjXrWFUq3QPg+q5t8oaeeQ79GTSf1drADEQV6YcS1hYQGCVrkvpNOEpESGNXcHoGOqB30tfhiNp3spZe8sAmHJl9B9bwipM4SBahVpDLmAP6PVm4bPWln1Z7iirk7w9fS65MAQAokZjEhINnmBcSLpKWjM4Eq6ZJUCB4/gZzWMbyTg761D8LfQO/BkY0gagqL2GKDqIgGH0u1dJyzVWQ/+JvL5ZS37Z5P0iD0CsAN/PfhIHMoEFMEFBAZSlpBDORapzxccA0FrLi1GMe6AYGYqhSb7vMw0DFcBX8Q2kXi53gtuWvKQer5dcU4Ff7ms/evXZe6bb/f33OMGAX6EwIRIXP/LgcCRlHUygYNvggJBmagEZGREkmbSJgsrk0BiJi1AY8yUsVYBwvAGluegLwIQIyEAs2PUHZNOyv71S2dZ70zJ+5OKX1vf/ORC9W/Nsny21i8XVr1UV8MV5Tjw1yxbBQHaKGobh+pBGYHlfZEJidSJYgXdGqIKbnRZOoYx8damqSnWihUM9vlmcn5t5OPpFcCoeepMqAWJHgRqWx5y4K+05enmqAIqBpXzMyqr1Lci+2ppgIWmW4RiG+tvZkY6BBYAGnDhJVLGjJoDaMt71kp/m0s+kW4RgPTmoh+mr752QHv9RevLOCW8ndaSSlHYv2is31ZSKAyoq09dxs93ykhXAJf0DM/KJ2V4Lbrlw/XFiUJQ2+d0RxVTmBSd3rnkd1cB0ymVD/WMkq0RSIVNqhOVhT/+mNYmXv+HUbXOCJqHFdToBZIJ6qvEqoY9EEwKLEouGgR8RAWGRVnIOwTvwaAh+EfQuxx7jiEhAmJ007IN2KInpy8FgJE0ZBW7XnKxtn3xa/7KvauL0heGyU3uSZk8m0T29z4PQaAdMzYQBhwZAAWArRrtNhAE6koWCCjgIRBjFKK6maf0mZYHR+JCwJ4FtHBiU/DhinOs7UvRU2n7UnVUwr8DgG4NnQ38RaaS+xq45vhWELgXA38mVn16T2TsCMtkOsyo7G8BgRyd3s0RCAiUSqg/4JsYwADQnOvapwcsehlLCsznqfbNFPDXVtD37F8klzZhTMvBJJscO1tP0xzZYxAov2jVMWcB1fob8EAQVHUl36dpGiTL/hkQXN3+UXpGYShsPLnf1HsS1h8SBRYlP91AhWeTtDJ7KsU5tZig7/nnezHWYojaC+93ScM8OmABByxWtamLbo3lBdocsn7euQX8fAuiz2iupJN5u2eOb3CO75SbPNo/TivOpeXL0qyeY8uvGYNuLa6EPf9+8GdypCvPyhd97cZh2ukNwE53OwD4INh9NwMYG0PIDajwTJKtHDHFWvVmMfuSV+EWPPfrXJ4gLSkkj+V7FBcNWEDWm81uMswaRokAFgcEV684G2AgZxD36D31xm+A3p8hwQFA+8p2IW/RF0E6uhNxs/xPDN0SXtonLqghOKL/Uwf2akgwHJFJNyVaGBEkYFNBYGJyIBEFGHrQWNJ9lakwFglZ3w8AnIVdVFAIVFYl+z5rX8BWlNCiY5ZQWEA1gIGbsG/T9sU1fw6Uq34GgM+uwViAhIAV0qDZDI7dt8xi3C0dYiSm4ZIza0yqA5ejCWYEEoMCQqCyhUH1vgOB/f63CCNO8n3GnvfOVHF61ZGKgds1fmPCSRnAnkWvobW8GzulXyisuIPUHtnx/X1INwZabIAh6phs9jNODCxlLG0IHDSEBZwcGw/gJis/AwUMzjqZmdixZ4oHFrJcYgsqwpxkiTfNSZ/jvjL7La4FWLSdDSrDBrRV2N8tkYKOU7XHLAskCLsEnNRWCsA3QoMqO6v2MSEU1snErzbjl4bsVwj6LAOY33LZl22tG7CIRwF/sTK0b9NaiqgslcJIJHOcDKj/HeRIVxJdBKyjwsghA1DBICBpNahzlOkidXZgxmPO1bsAIAXGfJIcnh2TBE3o7tYRtN5x1qW7X/OyUZxjlExuGbNnS6SAxFkZR2MdpBWCGc0TkeZIQb0tdgNNFBdJ89FQc2ssxOipWzN+zwaARNwwgB4A2tbrKxAKkCghRQv9ki3/k8t2JMam+by9Cs7sf46vsw9HjMLk3khZ78B7EnrQwEFyF5UNNOaiAEEoy8SSbG/f6QHv0wp2CNLbz6VTSEjRgT/ag78pVuBnejJQ3gNzycNSlpMkIbxU8ulbZWKQFSZKlagLJZV7iTbc64FlLpNLBX+WJ+ZzYuX+td5sL4EyErfTqzkCdu8Ls/sNQgRMc9J9N4YM/NnjwPbJdp9De8TQAhU0F6ZWnVAwI1NlswE0xSCWq9n0BXSh+h6w3xILzbepGNTk6oK2m3m6Saeqysi7c/0K2x5YgIUytAioy1pObU66hef9OvRvccNZQ/RtFX3aRTES5H5nkkSH3yVRHYUMKQLKandPJAsfANgBfLNtUeckn57jUxCOwN+nZyxi4GwAsE0ha/KcjUVvQHpNITvt8mi3YZ5zAtd5+5tFFqcYrYYlY7Fl5fVFvw+0zi+AYon1esrRZo6871y9DwACmCf50NAYQi4ggjQENbkJygzgKSScdSWJtrHkvlmwGD5GZsZGqUkW/2rpDZOFVDwbEgBlB1FAIPSYH2iiMJc/pAqKzhu2lijPFAlJtddhoA+oeouOsbXXbfHve0m+Bi4SEYIa/QL+2Ng/C/DmJmBn4V8vHgjfSnTtcymfIRYW7p8bgGw85GdNXCShRdmvuWQFWFjRhwsreoBhejPPshdJQzAwNgmIQMaKKC1Z3G81FR6ushwGAo+k6sazfTX0Z9WHlgJRKxErM+6rEn2bitG1/C4hYkxTap7bWOntn40jq8punKnQOlO9WPFF1nuwQlofrcUZ5cLKlokekEIP7HMxfZj+CPz1wP2ZEoiFlQd29k+OfU1aBU65OlN+rWzHJvnm9rIcX8LbJMxf30Jpv4qRst0khTRSlft9FaZACyreQ5JUxohKnq1Frx763hLR+aQERjibU4WmE4Vf3lKKqDbMITdRxJ6hrSRSZaZtbloZoifr/PGNegIETyQWh6g4aF0KzUfFihtNTKePyvsAIDFOnSEMxQCKUTTwUCoTQ9awRy5rx1p12yhUVRIgmVRp3zPAEltEnuvA4poL6KsgvTf9qJiivIL8wBsVSHxGbILxE40PAQdq9ecntKP/B1DZJMsb04rA/txnZQYs9ACdzEzWwb0b9zXyoLAFfqMeSUfNM29VJ9r/t9f5Da4idcU6BrpLSLGGFqeOXTJgcQTSS44jB2mtEqTdCnACsAAZSBSkqh2x9N+LXPt9gSuDOsojrN/V5hOV1AcH/Hw7Cl8UJcfCQ/oDsNv/DiESuyb71YHqx5AH5d7+TcEKpsYMugfDixZfBHWKxZHSPKAGnLc66XgB+Vxuj/kwvT0/Au99vzGfxyyf3Ra4jWS/UsHtVICnSGCEU5IMD9NN195qirm05JktPK8M7UR510LJjzG75pUCVra0C+M58S3gYuUkdtV62LIuzaigwgP9XnxqxiO947IWC9l+0DkrEoM/oUNj1W+lUfhixALSlfUr/U6VSALqXFW6b5D8vq06+soZUStyv0NPiTM2pEM9jZwy75ABt/XVzl9dDuCDunknAASmWAFgcMDBA0EbPJP3fl2OUi/SB2eS5YmQcNEwFQBEltuTwJg5vmuR7Xti6wYDKAOq7NtxbQlzFEpZDFR04S/5370xlesldw7Pn8RIJyH/3MQmLdlvdVYeTke+51pmbabMUfIjszCNjscurT4SpFLQQo21Evgeq+hDRCNG0BVmoJ1c/ARWjt0Af773375VRc9OPn/CKsU6BVRU5rYYRKr5tCXsqyHfqscKBEtbG4SqJ+g4ypAKwABkbFg44kTaE5OkD1+kjMgzSgsfz3oP2FCgDd97wCf3bV8Nb61EPgP8eoD+tRXBjCnux1Pv/BZGlly+zwE7a+DcWHQpppia+236EuZUV3RhF5Zz723WBW7GUJ1Iyvd2gM+/3gN4+YwO0Hf6fKRprf8Of+zZQgRMpwosRh0OjPnz4O9kSyeGyibVc6VyjStP2v0h4SLlgxAafcWbcDyYEJ82R5nYXGXzVIa0fvFz04rbgMLnAAJjIAG9osgSUk5qA0oIn+q8+Jk4CBHjfBYwNkpH8kWIkXKjJ59GUcdYLnqyAsyVJ1x0/F2YIXqSZWHPuhzcs4Gg15M0VOeCIdYD/ADsmfgR6BsBvs9iiXeGgOsC5GFgBE0ZxgCOwh3ZGSIDE/YQ73dW0KcGKieslLBQwokyrryV0vtYQo+3J2cP8oAayh0BPTmO8r7UPH9scJmyGmaky22z6xvJM0ChZ/r64z4MbJOX/59eSrNkyLJrQXu1yYtADoScCSsmzEF6UPk1I82YBh8qvyN+gr/1w/fPfbXhvX5kR+thjlrA9N/9NCFucsv6nNpRcY6NN7/UW3OOqEtyxZAbPSUKSIGQsjTBlhYeuan6K+1HdsA9D3V3lLPZs0M9SHgv8POtQ3qwfiTPYnFJJ6X63Nk+jB3goxSKzDUnc+WIa1kdBVLBmgPeSPM1aXI9ATcFf9LiqORiuQr5PoXGf6c/dgTMjgCfvfYo6BuNyaNzerYQMX68LQCwy2+ey9axfkpOnDU3vYlWaQjY9FhZpRqetyuTqk5Zs3emhMChNG/+DBgcAT8PKjwxcU/K+R7kk/lCrBHjH2FpMcICfgYABmK8zVvZ76OIFuGIIeNUliCty5GaXqJzShMFBekRF3YrfmRItEod3TMxVqyYHZb4qK6O9LNyHpJG6x2mb1SdPcIRclnjsfVlS8GZcoDWCNrWwJ8Hg0e5LoWhyIwramuRbEqkCSutWGnFpTTetAXrs4ZAXG+sB87fAzs5j/549xzHuTL3wN6ICSnX/8WhEJmU3POGAWzB30gyCGRhXkgFUyYBf5JsJA1fM8nDlu2aQ8KaorayYEfNtwblVnXWMP+ru0e7thI3WCLP9LUAsG370rR8cffhSJ5RFUwApqlenWf1YqhjyfLKfHpFD9Z9Q+ut6GlGQip6yoGwJtNVbe5awV+71ueItW3a/PTMwTtC9HLsPuh7dAlFfx++Qvrx4nU2coL7c7LxI85TgKwBuOKCWRjZLDZF2Niofee2opuSM23MbEnGHySX38uvPXBKR3m3fc7tCMDb/4zY9/r9x2H7Z7LrMWT8eV4avUyaJ+vBhKUmzZQ1lCjbc1f8IddiBVSSO3txeXRvkFWiFra5SdaZj0QIvMI37TbSAmj7N9b74MbWAyTFIwSFl0ecgrpfQWKGFmYpCLTfw+dCwIy3aQ8ADdRZLvpEyUUSUymk6kkl+/0tPMmY0KiUzdULIk6c8EYbLvoZMq7k1z9aYeeoA4nXhz2/V2z1SJSwYfv6semONcc/iSXe3QfQe8IF/KEFFbeAxZpjayCd8Vt5whKmspbhTAlvYcGMNOhI3vYm8udxS5pFk9FOKP7YEQX7aHjE/tczIPZ+L1+VxzRi8zz4Gwkz6WBnMBO2HErpp6yZqz/gIFWyK0Vsmt+0hoSYKytlgAKAAxQf9xl3xqwDbD27Z8f6hs/yHgcmuuPl+/qcwK8A6iSVogbWb4UW+8nM3ufPNxd2AsUA5qzggyTPL5CFEqcC0svWsbYA9iwTxss/9UDsHkvrgXu/v9fdSL97YFHO5QsAoI98mPQRkCPxywxKxCMU3RSAzrbEVyytYeZQm0JLS4sKzntg/p4luUY5tXKePQi8zeodMe52zaNmz/vfyfN1FYnx3+dLR0J4QJHV6anAz5ygKeQK/ho2VX6zC2IDLGaOhaWdKeHE8j0zcqmYlbFVCQpPWgBj4qLN42xzz/3rBjLkNefMujnskZA/0I7R0rYLKG1Houpqdef2WQbwj0lDwJ5E0oflYZq987ryIv1AueQ5G9u3kiwbOZPTERIuZO1tbrW5sfnqWEb3HdgDPXvPLaDXg7x7PYN7TCHnMyBOHsAW7w4B+0nHAz9gXzAA1Al5gzd8UzGMWw6YNFR1pYQ5n3ad8UeLUZsnDGDnDT+ySPUtDxk4CIsMwJ0Phcj/9eHGfaz+owmbn5Ej3fjcMUDUs+VQmQwdUIEkB3CjjIkDFg1DXnOU0GSywVubpAYHJkbn8OjEdY/pyc3EcwzyPMvXpCI0n3U7TPVMIEgEnKZ0k1mS9+1TLFrwJyh9y1L1O1kIkaV320QSkl9oaiZEA+nBGz0XVrHx3Y+nIyB/CNK78MQjoL1e1xike9223/k1Y6n/7d57DtRz3BQ4ZJLf3QauYyjnsqTiFITtm0JGTLmCdcqNLm4D9Ds5toPf7yhM24O74f4grUI+Y69DYDx2nq2vKWT8n+efADCwRVzu9WgM+IICO/cMLqBHqrQ3LCnKUmnULZWGXI77JdNq38A6bh8nLY4d0XZOGZMYRzmezTEHOoAW5NnpjZjmz4TxAzHO0+ac2Wp/+giiAXaxTa3Tu7r2OzmT4gVxpC48OwZ9jyHG6xt7B3g8rvoxdCulYsTgtXjB62i8Wtgo5eVZhVXvYwCx/8He837BAVuWgbkhICdhkDYOmCjiShlzzriGqRi/ugC1Vc9Zwmeb+9JMWl5ZuwnrMaBxxM494g3L6/tQVrkP7nn5vi+arO4JMxW9jUAgmMDEyKTLb+lg3EiBYebiSQP7QWvHZOv10k2aH/Ahjyb8I2BQ2BcPDDvmt9/aPbr33Z+VAMY5ttk7o3B9z7SX87FzJCBrwUegXHQGxJ0h7SfEYuwGoN2Dmh6sH7H8u3NTGVVcj/Iuj/T4SJh+NJ6ePcb66+7HkVVNBtZz1nFhOtmyAL2Nw1A3Nq6iY6/se5tcWrf1urmnlyMZTy7tuOiB3ZFuHl1K8WsYwIz/Y/4FALvfsb9/o/vZgD+duwAgwCp/I66Yy9xjn2HgYpdDCxRw4UmKHXFxh2F/j9zKl+6jVnZsFE5873e9VwiMtyhQsyeV/NwRuzFiYuAvavPkktNMdR1xY9Lbvoat7npGvf4mHneq6v3YOz2jCOARrvD/24eIm9cOnKoj/d2at94NAIHb4cXypW5yAgcsKRSv68j4jZ+3npp919Hg9ZOkH9AjsDFsGLljBvdgrk9I94byqJq0VdRtw/cVCdLe2FqIt7zm3sfd+4+YqCtQ2Ch732hbvrO7/7fyAA9TCIYT/H4weH3YPnesn22b+3Cw7+UzYY9eLAR8JKPfrAccJl5XZdLqQGM/GR6By8YYH4D30Xkdjv9OPgLgx9v9uNqF7Z8EMAjHv8mRM2UgSPRBYOayqkwgxpLjwGFq7duRXnrAPnpPOf5AJGQkt0C4ve5fu6cj/xnt93wVAPx5+NojItckQMKKqI5Y8T0pMSh8QwV29wBH7D9/AAgfvY6jnLFHU5RGgMKDvu2TDKABwF6ObI1FOrzDuyGWsXPFtMcJqY1s7PTX5aoDH2PWR3IU9TsCdH0aRfPaIGrit/6198i7AeCtSlEA1Qt2x4thUuBncjQ5+f0R+/EeYzhiOOrn3B5IIwN2xFIcGUf/vvq5x4bvWUbRA5tRwUD7Zsd8HTAcwID9cPujCXJ07BHn4dZr91gePpiM2O/3r5Xn7eeOWMBnCgGYQwsA7+m/P6fUGIAHv3cHFI6fj8arHH8fI9jLfkwcA7qdLm+Mr0Pg/oW69J+dOkDo5ZExci/CcqibB+zbe3R0BLRHOnlUH/d08AzbFynjf00tAGwm36MJ9s6Ee3R+I5Z8xCg2QKMDHX3erQ9BjljeETg8aq81CinadbXM7lG+2R7M18/+DAMIzA8A2b39Dzs73b5/BJiPx9goRanXY78/YmkfJS3GkZAjcLePkDTHDyIh70mr+BADeEtGA340MT3LI+wNX59QOnrPERty63weMYiPTFCjz/5quW946/69PIJnTKRjEHj/PY+cj10L97pwQHD0er/k7VeDPwClqMPL+Dd3+3d1BGz7fZP+zh5d6ZEBvOUUDEHOgzmD94DDCNQfvbc//hVy+Pk3zqG/E0P9PHDeRzqgwTHP4HsZ6Wp3vg/e+9Fvr9XF8bU8c6xFZPxXuJTnR31CSzES1YrpNccCjHx0xxeReQcfGM9jI7JC9vf50UfRLc9YjdjHR9iqw+4JPu+sXNtxhMv2/RbAbgGA9wgR4xw3PYfR/DgASjdSC96TFnJLxgTVHjCOCaX3O1j+fD8bDfHvfVQ+BQCPBu6RF++NRGYqrAx37/GT+MiwjMQbudFqF/Za2cd9ANhfg9+/ZwA/yio9wxjy4Hvvfcdo4rwFlvz7j0CUf+0I0BH1z28zICPpr6c/n935jc59+MFfDwInt15jVoZ8NH48+Cvjh2k3luy9o7Hkt/6zb8kR8Bv1mbzJFn9Aj7fG0j2Af/SZHxF/b0fSgJ7BcdNH3W/P3/Rnz0fjaXQdrT72x62fJHBfj72MxndzTd1r7fuPz/mrHatAjP+KAgCbXoXaxiWDAY4N8LPuFCtHZJa2VhtHrDli41BaK2VI0aIHhSNgNDonv9+H/QP5vp6306HsM3zxCtCyhUcyYpo80JVje7B71CnhUwAQe4DlgZCtmGXfb8f9MTv/Xh/ePtrnjvR0b0wdMez30p7upTv1bOqjUY8jINhfy3tA77sB4L3k+FuAL7lJK2XZT1luV86hAIusr0nzblelaccG4pfTKrvE8pzaRrv2Wv/80es+Bhj3jeDR+T9bHjG+/cTlr8N0we6eMxPYdFP+0fTUfcduJtbjo/t8ACKOnBmiG/fxaNIcgr/9sdHnfJUQhAGsevC9t8bjbMuhAX6jMcXueK9LoAXw7/k92hirY6Z/vgckj4CNkdxzMI4Y3I9c13vliNU7AuDsdGTjrLdzu7Hlbd0NQAWMxwwR6goz/j2d7nb/b195y2719x7713b/Uw4OL+FpEpHxZ7jCN2028AdIhWsAIenzxKGAv5UjljxhzRHXHLEpAFzShI0DtixgcssBSYGgJzGO2FNg3ynjcAUZY/5o3xLFA76+WMg++5Eeq55Z6lnNI5DbgypAukV8VMT2peZ8RnavP8fNtqqbrBjC28bNMIXZwhyGThdw++fYO0/vIZf8//X7zfUdALfeocfwOZrn/f4j8ikG8FYoqmcmUg4N6MtqAFMKYghVeZwhhjATkEnvhG6PwIWJv3ZitXz7fXLHyF7Dno3ysjNmtwDPrUnonQr6mNDuh3DEUoyAgU1KOZNORG6iUl2wvgYDf0wFv+yu8RF9+beWyer++29+z0AnD7+//+ov0BtRTVXwY0i83+pdm/fnDZwBPBtX3pEyYCFbyNgCGrDOpjs9Lt9z/3xlp9UPuef9expnDO1rd2UEAgevDx2Q3Zs/J4+CvrqPoYNb9YPW5rHYO85uTPV2b3RdNLhMrx8aPHfvuWXzmq88GksdSN29PjrnLxQC441WXU8+YMVUcuUs3LkiInNoVqG65gnXPGHJE65pwpIFDC5JmMA1Raw5YEuxjLfMHsRXIA8cjyXvGPk1cJtWT24FE7/GdKBxw+RRQZFs5XkLMirr2QM7m89HQCrpMfkMlPd9RiIyEkJj/2ytYQN8PftqwK8H46nYwoAt1TB+awtvEBg3dLVzpHbkUQWHO5LJfd4thvaRqJs9v+0QH37FUN4NAG/RlbeAXzYlpSD7SY5xJnAicApAInkwQEkABSWSlWsYIGccR3ZLwIM82IE8Jnc8eDAIqc4rin7wJvB4vwEJ/hy/yfiVr+P7IbF7oC+zgr8ekGcFegogqDyXY6YrQMfNvWv3t2ykB+pee1B2Y+1AZ8P3Dt7zVRLAtWJUxY8rC3eMDF0/Ed10ptjpzbb+OvkxkFuKhByYKGfuHS173r3X3+xHwMcQePSgwx+/B0Q+KP3P4RG2rxzP/hGa8cSpG1O5jimzd35M2TXZbWwu0d1vbu55aw/tWDv2jn/wxNSaxhtj6b33/x2k8EMSoI2zOdTkf57aXn4O/Bnw+5VOuOaIS5pxSTOWFAUIpohlm7ClgDVFpOTmrkTj8QWMgTFwTEoEeYAA0rWLoeCwrg8OBF0VaAQUR+lNXvyc4FlLD+x6gmYUkTMAldLnikCACgLLOTpSaeUW8G0cCyBPOWBVO7gmAeZZ8UVKMsYy01hHtwgl2x/MQe8llXoWvnzcHcbdg1N5rR6867i/0+Z9iAF8BPj17ERSz7coSEEfb1SAHyUCbQRKFfhREgNYjSJ0stqfVwMgCGAiIFTjxwEAkTwPA8AIPGaRuptMox9Qv38gz5uiWmG0Pyq4562negf4pVAHjelAJy0yfdi+/VC53S/X2t2PdvJqtzsw2N2oo1s7vJ+3JqmDczuUJ05Ydq4GAk18SKaGd/de7k2HKuu4cky6B+uNM1V01YKL3aUPgN0h0LAL9EDRAxDgfbZqMK4KYPW/M2/Yb1zLR6TPubnH+A2Bn9eNjaPO2S3PHfgjBy6O9UPlohunt+xXANDqpfugIbrcv1y/+M5z4Mvs3OiLot0wBuAaBd8Cf7/SjEuacEkzfm0zrpuAv+sq4G/bItIWkbeOrBgA9mZMmTCNxw1BSAmdp4ygSIEBIys6cFgYp5AVFHLDQAFoWKijdIp9igjKHNA4KbtInH5Y/pxW+44hJjX0HAr4W/KENUUsysZuDvhtiimy2sKc1LEqhFIdYzZX+TF1k6jwY8QTSqE9Js/d63Cv9TbxloycWtt2BJPf9uTTo/IuAMiONgbgPIcK/kpuBEtO0pZtggpImypoI/AWAAV/tMkjGPjbUPcTEHSBvSBrbu9ZJn9vdoPLFEY6wPT9wb2nmajooYnpI0Zw+H9fJLmj548o5IYW71kjByCaScoBdAPlhyD90esdAb8RWPiozXkv6PsmPfXiDWLKYgA9+FvVyG02porhGzDpppN0DNbLWPJO1a0BQIwyRo7GXIkN+9fk/1oD6T/34PsOHCrqDCN1BrHYh+MreVjM7o1ycUasnzEmhYkYTUr5hsM7Gl9HwKLcED1XD/SO9NPvA91guHPXBr+Pu/bwGyVQBjiU7hNlPVamAv4ueVbWb8KvNOPnNuPndsI1TbhsE67rhGWbsK4K/NYAXkMzX/V6G4KK5j7sx4UAPpL5qIBAAEEIC9s3gFHqB3qAWLbtD98A4TCfVMeOAb9yvgqazH6QA7cNG50+N7p68Gf5fpvmZY7AX8/IbpvYv5QCeBNiqeCK8nBOVT9fPTCuqq1TPZmDFVBJJmBANvn/7wzS6NbtbJi8qTm3xk4P3td/1h15fwi4YybsWJ+MbiFfD/6KkjYCtiCDaFXwtwJhA8JKAgALENRt4nagPaAwUwKXgUXtIPPbbkJ7WEbn8F6w8QVyL2H+rsfnPabNDaTNT1RUGdqEylgcTVj9PegAQA8MjnTyLuao/8p7evhGPRVHunOsSiK2Ht+6UEcq4E/BhRm+JOOqAHWbpLKbpBqjjkOH6tgj7hykI3DRvMZiKN1rzfvQ7R/dKLffAkB3rDeGT9Jnz/gB2LF+Ndyr7N8BMCedpMS2OUfX7TfRDz+2muuVncr8oWy514ezg3vwJ3/eM64+mjbxXXYwQSqAc2H9Jqw84ZJnBX/1cUkTfm4nXLYZP9cZ13XCukWsy4S0BfASZL5aSOcnUn05/Tlg4e3ecJ5q5ijq5iiAI4SwKCCQHLhAOY6AmtJkALEfVyNpwIRL2zHg6tno4jjab9CBpkcbj944Bd9qx/L9DAQuKQ7B37JFrJuBv1gJpTXsx1Y2/NCNreaa2nvS62o3nkZkUiGaZDz6VLPilJH7+CMd9Y7rzr7V473D/hEn7H0AkLFj/QB8DPytAv7CIsoKiweB+tgYtAFxBSixgMIMUObx4NoZPipeVDPAzLM6AIDsk5MenZj8aXxXqe+B2GRUnh8AP8vlGOYkmcdk4M+MnoFAM4CetdgZjAM9efGAQJ83E1r/uvuf3fE78u7J55vVWCrvVDc+728H/no23cBF0VcL1s0A7r1gNCCQeoPjb0cPMHT/EHCULbX/N9Lrg+Ns5+UOPOJngz8wOgYQDevH2Bd5NOCv149FPBJAqwI/c3ptXKkDTJlLBKRJs2juBQ/Zvp0j7PUxAOwfdaz+Tk6V6Kqu2544YDHWjydceVLgd1Lm7yQAcD3hsk24rBOWZcK2TkhLAJYAWhX82Vy1ylxVQSCcjrjqKQ9+i50DVOcjBXn2iHKsn6sEbAzAIep75Ht4P6YcYGjSdfQ1bwsa574DTDbmPgMAgbZHo4V8xfmNJedPGMAB+Fuj2EAFftgItAbRx6p60nFV5626LxFFPrR9RQ6cqB2ZtMMVdGwT9XMP5S7gc6d3ZK8fHHMfZgBHJfBD8OcTZh34C4sCwBWIBv4WIC5AWLmAwLiyPN/kIT9KBjIfA8CC0KkqoxtQtl+VSm5wcvOZD8mNG/7d7B/cZDVsj2Hgj11lry/0GIC/wsgqQ0tZwbpNVklA+ghYHE7IPZDz95/2xw8nqg9OXPXLP/n/n5C2Gi+4hOdQEp23FGU83XKoBqyS6MbpKwE9wyQ64hZc8PFv9h7wG4GLPQBpP2P32QMZGrfOKPZht2eOOwv32n4P/nzC/CHzpxGPAvh8xKM8B2jjOt76cSXeXTumdmDaMRSNHmxS4v3kBDysi16+3b7dEAYE8CEq6xcL+3c19i9X8Pdrk5y/X+vswF9EvkZhlK6hkBPxSgiL6sm2SlKEMsbY2b7BHAW4+04OUDA4UgcAbb8lMkDumNdzE3YcK/AoT7sHdm06grMRDix1Pew/JFbkVkL0JfSreX6a8+fBX2FmV2X9NirsbBlTaz+mVDfe0Rrpyn5EqidgpKs9KC868+CwYQWd3t1nN3NXZ+N2ts3ec+u10WfdkHfmAIqY4QPQgL+SB+MSoDkHyStLoQ37evBnwO8KxIURF9mGlcs2rBlhy6BNwV/KxRgWKQUepLF5UvBHFfzpfqFqowFF7kBId/H3ShZ/M+vXy60myIfgz+eOpTH423m+ZgQdsGgGVmNgBvfI5yv13rEbJLswF0Y6+swd6+Qb1GnV2tbXysBg6vL+tuRy/pT526VSFFBBDVBvgMTWGr4mHNJNWEfGEDgGfR5cjHJsRyCxfF73HeMbpm/rz40xZJufxQSKo+vH0p75K9WSvt2EG1NNnrMbS03EY+X6fGMF8FxBYKrX2YP0PTAX21b2Xe5ScYyPJiUcA4hH5HdGQBhUwN/CEReeceEZP/OpCf3+NODXg78lIi8RWALCNSAo6ItXkjlqMaKCGz2FjQvAKBEqG08eXABujnK6iHUuykZWOBCYdb9hCT1D2IOMzrnSm1O2zTjvmT1zQDqgVG0Ff5oBZGX6AJTQ79o5wAX8pbhj/gr4M+C3KjhfK5lUxtRa56lGVzquel01UpwmVzy6I5S6SGP38Mzh0VzW66iMoUed3N5p/woACLTgz7c68OXjVlXKBgLNC1ZWKSioCAUEKvi7MqarAcCMcGXEa0ZYEsKSQGsGpSRKy9mscnuCREAMorBIQAjNlhUUwu1z7JQMjBH6v5EcAr+y34E/pfhlMPjkWTTAghLcxMVNCKSZrEp+hQ2wwzOtYAEdmABag4YO9H2Qsfj7iDVdrY1MbQyV8G+mUu1bc/5onEdbvF0HLgpQN0DhjLpLqTjUFzenK4cKs2S6Yqcn6ozdCHDwTscPjbOBgetzrVoAyPv/+6D44qm2obNjAK31hObONt0NfBqFB386URVAoaAiWtRjVT1tWXXEGgEZnGTwOnHAwumlMIE7HepnFD3ITfvouPpdrCCDcMknJFDJ97s2eX8n/LWd8HM74a/1hKsP+y7K/DnwF6+EeBU9TRcgLIxoRIVFp4qusoylTeYlyqwAo3jjcGW6MjZiaIkKfeRIQADyRI4B3AONAgyd09XaS9nZAYoOTPhcuJD2zkfYuIa4DdRun1Ny0+RZAaE0e9a+i6X3onY+sMiHB38KAAs7WyKJLFsFgWU8LaKTsDJCykBSPTVRRTMglZzgGMrY6bGD6YoDib5ceH4UZTzqhnArlaVxcDs9tu+tOnlkDL5/JRDdDnNiAPWE62oF1vqgGkK0RtAUZuDvypguAvziNSFcE8JlAy0bsG6gLQFbUgCYxwAwBCAEUAwK/GSLGMCTHWtBYQF/WtUDdF7wvxnA6Fe56MGfDfyyr21gStWoFnkEK/ZQkOcnqZFnVcIFLmzQhKy8hxXIn6LqgBtAUAZNsF+6O24y0M2/AyAUO1rBn3W6X1Ns8/5S2Cc8O/BX2L4brJJna0NSXeXKWJTc2oYFHFuQBjSEAcDYsU8dQOyYig8zgKiTljfct/IYPyZj8Neu5IGmmIodm45+/JguLPKh4E9AhU5Yi0Q7LPpBKbeAgjsQWHK/qi1rJi1ziA0Amu78GDxwruT1f4MBBQEVF55Lpa+xfz/zCf9K5wL+LtrqZQj+LgFxIcQLKTEBxAt0XqrzVFgZ8SrERFiS6GiTB3IGUi66oo5ZMp2UeSkEYNL5KAZ9CNjIMQiAmEIFFp6BiuOQP+DBxTEAbBw/hjodmnK1CVBqIm9KvFD6eAyYUZd281W/S5pK3t+agzB/KWJdo9i/JVbwdw1NaD4uqqsrFxBoj7BkGUtLkkjimu7qysZGxRQVMyAE1Qftt52uxBFz425EZjg5Cu8WEJj3x8pNxfts3of7APomku0+KrvEhL5/XK0m9YBCDJ+FfuM1I/5KCNcN4bKCLitoWYF1Ay8rsG1ASmBxv1vPKgQQERCjMIExgkIApgmIARQCeIoOHKoySQab3MEOmIRWU/8WxtD9ukbgr67kQW1/p5Lw69pQOCaQUgf+VnYgkDsQaD9YrgZoMGm1RQItuAC4BQ76vvpr95c80MvfXFW2nNEo9Ovz/morEU14TgPwZ3lJxqqvvZ4cUC9hEPcwRt0MS3aACu14qGFcBwANYJS2Fm2Y64h9aj77SF8e9B0ZxScYxCPxTXTbVkoW8hXaszadlYtq2iY5Z6rmALbgLy5ZAOGSEdcs4GLNwJZB2RiLbIiznqCfrIiKPeMQZJyFMASFnjUE2rFYbv3ffAx5ySzM36rh35/pjJ9a8OHz/tqCjwosguX8Xffgb/ql4O+SZY66aGRqUYJiSy1BkRI4ZYCzzFVeiABSkqLMUVH0o3MXTwEcI2L04KIDhYOUplaXnhFyINClfTQgMDs2Mx0ApWzOyCcAoAN/VvVbHGBr9Jyi9PnbouTVWrHHVvP9SgrZtYK/eIVGEjPiRcfS1elqTUImraIzpAS2iGJObcBA9YRAoqPosMMUBRhOUfTjyCWOYZd+Vpje3tbZ1n9xA/66VALudNnPre9gAT+1FNxIan4Z6RJHepKuR5wHga0hVM9qyQL+rpuAv+sCvlyBZQUvC9gAYErNxcoVEyjGZnBhnmQ7TaAQQAoGzfsqbKEZTgAIqhdH2Q+v928MBtlNWvWgO+6YwML+uRYhks9Xq8GCr1A0ILjVQp0CKoxdGoGK/hyJ9D4DPQs7AobyP/rPDpgzAdSghPt6+d0Tm+8dZ8saWei3afRcGqdT6XFlhnAXUjTwZ6zS6reqozUrCLSQlXr1DbjwRsaQlDlGop8GbNwDGQ40+rCkfB72xnB4w9ptUxDRG8fyns+jv1Ek2VfUl7Q8N5b8MpYlvO7zZLfO/pkNXCztJSOsSSZgnYQNWJBjK4p4O+XSYIpdCwEUqQJ11VUD5t3ntKkWnxwo4f5bniXsGMBrnnHl2uz5oi1frtrnr/T4K6ySgL/oAYWBP3v8EuAXL5tEpq4r6LrK3LSuMjetm8xNHgB2DCCFDgDGWOeoSfZpii3ICKGA+jgFDFOa+pC+/86R45Qc66XHyf3usKU9UDLyZftcGXABfxb6terfUvxmDjCJ87up3Ss5fwrSlw78XXKNIhqRdBWQTpdF9LOu4MX0tbZ6YgdsKex11eEJ01mjq0jgGKteBjYRGOOHBtCZfettmg9XdwDwPTbv0wAwO+PYeMb2Bq7sUt/YtBQMJGMDdXKyfD9l/vhyBa5X8OWKvKxAvvHDYxalAuAVQIigbQLFCJomUcqUqjJNaSSKIjOgBQg6BY2UdXD8I/IsMNms9FE/3IWFgcL+NSAQaMIDXl++zUGqYFAYP8uryA0A3BXr+B+qDoBy/xT8EVAmJXKgkBwwr/eKccwAtq/t7hHdfPlbhIFmIfPdUm/JiqmCy6MVVimMQoqO+StbF1Is4cRSUKWefWGXalpF8e5HDpZuzZiRhUcUaMCAhrHqZHm21ISMD9knYOwVo/V8y3MLX/eORsdgPkNG7F+Zbcu4QnF6qXF+XV6t5mGG4kipjhbRTVgT6JoQfOpLSjVUlZKeg16jB4AhVJ1oCgy5cCOpPkgjH9SFj4EBgAjHo+Xv5ARnEK7KAP7MJ2H/sq30MeOqAHDZlFlaA+Bbkq1whR8oKUkG/qZfG+LPDfRLiAm6LOBlKfMTb1uZf26JYQxe3cEgQJDmSUmKPSBsUpoUaLSRLFRQ34kfF2W8ZLROX86VHVvlWnqgJMRLBt+ah+9dP2rxR1nn19r35NDZQG33omkvpcrX5dHGxYG/X4x4SZgM/P1aAdPV5QIsK/L1Cl6321gCADgd60r1REYyTaq3SfTYpJ458F6cZ7ht853tXEnFyxw45hYJ6J31fv9AnsIA7pccc8cbo0gF+PkqxD4UJZOUeh7XOrjy9frQRTWSE/iawCGC5iQDLCUFghMoJRlowZB+50kDNRfAjt+SjxpDm1ifKDvwN2T/9kCwdH33D6ezWpUojF8JLxr4s1wYY5W6H3CRfuLx7KsHhd6gkd0rbicpPdZ8rr8XnV4O7/U3z2UW+mjBn6umT24VCV/xa+14SlWpPlwVfamkN1Z97Vgl8/BTruBCWXUuhqUL81g4hKiCCs84haAhKUvFEONHhSFs2cKaB9ohcq+vxqi5t3hPOOe9sQTGxRIfkN7q9DYPLgWmsXkl1IZSbdnYv+JMcWHVSe1fAX86GWPbhKnIaacbNhRnNizGkgpDFukgKpMSbVTBoGd0gcYWNePmgMnbDZnfCAjZh4DzXFb6uKSphH6v2kokLxF8VebvEgTw/ZKij+kXMP1izD8V+P2VMP3cEH+uoJ9X0M8L+HJB/vkL+XK9DyQekZzAOYHXpRyiaQKdTgoAp5rWZEDDUpo84HcME4ja8ePHiDkTGtIt6VTXpTJkiwLcJzDprfTgr7J/YgepsH8+n7a2uKIG/IVFwr7TxYG/y4rwU4Affqm+fl3A1+vnT99wxfUq42WahRmcJgGDIRbgXlLPdAt1hJtx0utpBOzYR9RugL+c28+5IU8PAdcvdqBCn3tWCQMQGBT8YcvVA9Ef4ofAnxdVGKYJnJIMrJTA8yQDwWL8waipqiC6BwDfa/C+wUA2DOzuRdrph/R507/Ph+39xKXh4KaViOaV0SYMBrKyS9wlrntJ2APuAAmf6cREQd7nASEA0RfcBOVpHv+5KjvA2MtvmLRsiTGr/G3Wzi5VvwMD2FXRl56ZpZJUk58V+ElFvVXSp31BlbEWKbepFd6QmOj4YEu10PCIAAwJk0gKBtX0CgMefUjShZHtM2+GC31kZuQV26QGtEbyi6R+BbVf4xjBnQNVgB8XFtAS7sMqY8Yc4AL+jIEpqS+VqS06sokFUL1s4uRumwOBEZRtAgqSt1iA/N7xLc9NjnTzmbHz5HHHIFx5wppjCf1edY3fGvqtK3zQqtW+CxAve/A3/5Ux/UyY/loR/rUg/PUL/PMX8r/+Qv7580t/XwBaRtGARgzgeVYQOMmYmqYy5oqt7FjbIXjwIV11AgX0rQ0Qffp1AV0P1Jo6lkrvU8mttehH20YJO9tnDHq8OPD36wr+dQH//AX+9eshdvb9F8PgdZH75cAg96Bdx+CHAKBLd2uc895Rz+49D8hTAWCxtzvvGG3FUTGIrh/Zhsoeaf4E1Og90wOx/EFkBqLcVIrCAgoLEYoxpN4YBmcBn8UE3gitfFo6tq8wss173LYJZaHRW9Pl3jUFpeRyyZLmJxn4s5yyUc6SiWcdAu3BX3kuJ1ZAQ+aG9Wt+clo8Um+D6rP53t3NunUnv0a4rp3tCz/KerI5NGsyW+5s6dHVFVJZrl8D/q4G/jbQNYGWfUFVyam13KWSvHtDdKxQaNmm8gikIa3gXg8VqARq94E2jDm8XwcG0rxj/zurXZs/qST/lY7lG74BrWPVPFxRVQcGfU8ySiwO8JYFoGsYDj4M50PAFuYuAC6Aw6Y5ZVnvexYAnjMo2SSUKxh04Xz5jI4JtM82kutR2/YbnKqs7V9sDdlrjpL3lyYs2lrEij7I95FTEDhdgGgFH78q+Iv/vIL+9RP8r7+Q/+dfXwqODqUADQCXi5AY0yRg0PLdQ2wZ+v7/PWCwvDfv+GX+tmuzdleF/ePQtMJKrqF6sX8ZZclE340iuvzZaJ1DDPz96y/kX5fnsLT3xINBC+nHAMxzcZLNNgIYz//FdnUAL5v+qvNXgJ4DgU2+aR/B6eTLGMACBH1OjDOQ7XJUtRdPARR9ouaTT47XBeBJTtQbS2M0gkILqoYVSDuF7QbZSB4Gg0/Mlr5BdzVhKsCxgLJPdswDwX7iKjozBtCBwFvl9X729CF2EobLJqQhGPTvgWP/+oIdBYfla0Yg0ctXgvAbwkDpgN/3/CuN1L3x69Zk9utlV0+4FnwU8HfZEDRRna4S1vEFVQ/lwuxOnlHyY7atjBVLZq8sYA2DNInUhfGlyjqNUi6cNK00RmGQW+Hrr2Jq2CrsMQB/1f75djUtI+j6/Cl7buyfRUAeKX5r7Ly/7xYGVhtXgGAIgAHAGCTH1zu8XhdHwO9vCASZCdc8YctRQF+u7N+yRS38CKWVSFyoNHmOCv7mX4z5V8b8LwV//3MB/fMn+P/7H6R//vPLWb9HpbCDl0vNSVOds6Vg+Pf34MHy+b4DGPXnXpi/uvRlcuHf0lS9tCXr+9KiMIG9vaPrCvp1BS5XYWu/gakdSgnpA7heq230qTOjOd+3tzOQp/aMPfP3BHkaANzlxDix8KLsO1apBxfq/UrFkYAHXr+AsrVz3jalwYWpYqeUNiwSAEiYGJ2hHaqB7gC5I8AxCrl9Una/k0HuUh+qN6EbOpMKMhRgJ1WOymBYo27XMmAYkjNmjuukU8AgV4BA+ryGf5X9YwV6aXBpobtwon0xgBnI9PhgenblsAd/vodmYf/K8nwta1QrsYGmw702Ow2LNVDfJJfMg79fF/Cy3C+oeq8wl1ClhYi5q3Rkxw4W3XqDiBtO1Y4BzC3g87lx5i0DXzSubv8QyPKd3dgpzXZLmgUXu1f6MGpBDqVcIyD2eBSomx4yy732LCkHeT1EICgQtHEENIwsiGrPsXeAvyMAP75Rzx1Q1lD4mqX6d0lRVpTQtiJpi+C1KyYoK3xo/uyVEbXaN/xcJN/vX3/9rcDfTiwnDSiOmOxb1Zxjh54IID4qDDSrH9nWgKABxGr/HAh0zHmxewu74illzK8L+Nev336tAFrbCKCJoPRv7RnAL5QvYQB3hQYYgL5iELluFYiVvIRNQ1JfKb7vD7OCDRZ2KUERurKDCXvwNgR7lpdzBPTsf7/WMy6/HUYb/u2Yv/oPdUu93oBmQpOHMrZdOK4875OLfViOqH6wCz9RBtjf0qwn5MFC0ksI7iI9IAQcKLR7bN81ukF4WBdPrShFNXasYWDBNVpd6ntoNquz0A4INpX01urFcsk8+LtKMdXXJHb7i3PV+CmCNCetesGSm1YAf596MfxIA3SdgbTQ1Sg80i/t9NRLtHHkzpdHz1v7J6t5OEDY2z8LyakdxLp+jKXNSX9HzsnNUcZkZCnQySxA0LN/Doy3KTCPgcBD0PgNwoAwgCzNhJc8df3kaN9CyVXQR23yHK8ZQQs++C/J+ftbAIlHxI29v7v4HMCSCwixgdZXs5BEDLc4gQeBWoDoikd5XcHX69/3PvgIym+UrysCGcnQGKK0DDEQQZm1MWP+HnpaQSAxSTWdAUGgDPoCJLgFfDRCBH3OTC8FNP5Gg3IA/qpuaKivHQvIwK760lqJ9ODPM4AerDmQJ0COWjbQQr8+BRNQIGiT1uBz7X1e/P+MbsA39iwzYF5W0nE5ZuVWuWIcXzxVjSA3hpCsEtu63Vs1vYUSL98A/nrJauis4t6Hhz3bruPiJoPkwR9QCyJKY3hhOJ7NdNxj/PaFIGgcqv54CQMXBn3AnmvBR6n8/diJy0SjT83tKlcTWCZaD8D9GPIhqv4e3AJ4xjzeki8AiMzSTPiqK0qsSfPLSj5ZkJQK14ORfENua5l0TQjXFXRZkH/+/D05f/+bi2f9DPhZJMT6CBtbXvPQ67ZxgJ3NgxVOLS+d3ZMnF4HUHlltcUF9vmOWvEFk1klOPd+vZv+8mLdMXIFgICClCgZDqOdkOTL2/95QdiHFPaPRXddvykEzoV5nI/Fkmemq6M8APPThEEwP/vpwnL9vPcgbsYEdQCv8XvZMxY0LMaawv7wGjH6fGPgrhjB34V/LIWO5Vg8Gd5WlGlIMSSuxLZ1iUzbd8sh+F5MxCE2S5iv5fJhbebW7sK6rYPyu8NY9MNi+uW77kHDvRBl7XopZfHL+ZyVL/jKnDgSyAnFWWp0C2DPunb0DUMfsLQa9T6u45XQ9WZKGFDeOZb3ZxFQaqtuKVGGzogKXSlF60SqIuDypbchLdlJYP1QQCFRnynpskmumXlPGXEFi0uXqNG3Mp0285LZ8KwN4uNixHbMlw/TxaCnz00QpF84BFLK2EwkVhPRg0EtKhx4tj97f/C/fnPQ+Ike37l231E9W5Tm3r8Fv3ZJvPdvnw3MmRHLMh5zunbQBNCsK0dcKEDS20N7Tf19/A0oe4vt/a59tgFtuq3q/NxkkCwUX5sjt+2KCXFkkyrltIPwsMPFZ8aHJGAsQ3LHtvTS/gy45erQq0DfIqB9g3ZdNb/easTNymMpYyXX7jHNNCRTlnEntq4Rrk4bggTJ4cgWCQMek32gP9LuFgbKihFWZWnV9TatwY6fJKVNG3YcSv6II8SU76cHfuKDK2b7OBsLyz3OuqRMvuSvfGwL24oyjhULkuDfyv4GpaEAgqRGuTVZL531vBEcesUlhlm4bzW8Hu0fyyGm4czW9tWDeLX6e/YTWfbiFmgwEAgcsIO3/52CfLH/TPgvu81y+4Oha3huSes4yY7TbNp9qxs8/L8dRQHnxjhM3jBKY6/JNaaCD3yU2zhLGqRdAxw5n969cbMPDbWu+W/rTGZkGB/J7nRlzzt6R+vQ5MTizpK1QRqkWzQCC2rlyy7OAQmVrmxVHRk7UUaHNLdv4JVILCnxvuXIbbTw5Fr2y6XX5SgnH57+Hw/S/oVgPVKAFf3f+qW7N3hXmXLdJ0j9eentMPuW23VfYeydU3cn8e8BfJ9zlG/lzagCb5SsOP+SGAS95jl8Yd/zgbexTGx8COx+ZrN7z/pFzMPj/u+d6ix79DUDi0AB2FdleJz0zK8fqPWkKc6C/19+dcdxLf06Zx+NKxwezq/gF6v/+3cCfk50FPAKCv0NG923UQ8zZ46KD/nN+p407kFpYoGOrOEzyul87ulkCU8HEt6ch/QfKfhWxCtrlgGya1AnYsSM7/jezc39jofcwT0T0/wL4f77udF7Syf/NzP/Xe//ppadvl5ee/j3kpad/D/mQnoCXrr5ZXnr695Ghrt4FAF/ykpe85CUveclLXvLvL3+PzN2XvOQlL3nJS17ykpd8m7wA4Ete8pKXvOQlL3nJf5i8AOBLXvKSl7zkJS95yX+YvADgS17ykpe85CUvecl/mLwA4Ete8pKXvOQlL3nJf5i8AOBLXvKSl7zkJS95yX+YvADgS17ykpe85CUvecl/mLwA4Ete8pKXvOQlL3nJf5i8AOBLXvKSl7zkJS95yX+Y/P89djkChwx9RAAAAABJRU5ErkJggg==\n", "text/plain": [ "
    " ] @@ -990,8 +891,6 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", "methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',\n", " 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',\n", " 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']\n", @@ -1006,8 +905,7 @@ " ax.imshow(grid, interpolation=interp_method, cmap='viridis')\n", " ax.set_title(str(interp_method))\n", "\n", - "plt.tight_layout()\n", - "plt.show()" + "plt.tight_layout();" ] }, { @@ -1104,8 +1002,7 @@ "ax1 = fig.add_subplot(211)\n", "\n", "for ax in fig.axes:\n", - " ax.grid(True)\n", - " \n" + " ax.grid(True)\n" ] }, { @@ -1146,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 90, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:18.740285Z", @@ -1156,7 +1053,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
    " ] @@ -1168,10 +1065,6 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "rect = ax.patch # axes的patch是一个Rectangle实例\n", @@ -1214,16 +1107,16 @@ "metadata": {}, "source": [ "**Axes容器**的常见属性有： \n", - "`artists`: Artist实例列表\n", - "`patch`: Axes所在的矩形实例\n", - "`collections`: Collection实例\n", - "`images`: Axes图像\n", - "`legends`:\t Legend 实例\n", - "`lines`:\t Line2D 实例\n", - "`patches`:\t Patch 实例\n", - "`texts`:\t Text 实例\n", - "`xaxis`:\t matplotlib.axis.XAxis 实例\n", - "`yaxis`:\t matplotlib.axis.YAxis 实例" + "`artists`: Artist实例列表 \n", + "`patch`: Axes所在的矩形实例 \n", + "`collections`: Collection实例 \n", + "`images`: Axes图像 \n", + "`legends`:\t Legend 实例 \n", + "`lines`:\t Line2D 实例 \n", + "`patches`:\t Patch 实例 \n", + "`texts`:\t Text 实例 \n", + "`xaxis`:\t matplotlib.axis.XAxis 实例 \n", + "`yaxis`:\t matplotlib.axis.YAxis 实例 " ] }, { @@ -1251,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 95, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:18.867939Z", @@ -1262,16 +1155,76 @@ { "data": { "text/plain": [ - "array([-0.2, 4.2])" + "[]" ] }, - "execution_count": 20, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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p91TgEtZyCw8z/Z0M3kvLZGBca164eSSJXdr4HUskKFTgEpbMjAWrd/P7hWs5VFrBXeP7kXx2T5po8gRpQFTgEnZ25x/ivgVpfPptLiO6t+OxyUPo3VGjBErDowKXsFFZabyyYjuPLVqPAdMvHciNY3to9htpsFTgEhY25x7knvmpfLVtP2f1ieYPkwZrmFdp8FTgEtLKKiqZ+fkWnvxoI80aN+KJK4dw5QgNPiUCKnAJYem7C5g2P5WMPQeYmBjL7y4fRMdWGnxK5F9U4BJySsoq+PPSjTzz6RbaRUXy9HXDmTg4zu9YIiFHBS4hJWVbHnfPT2VLbhFXjujKby4eQNsoDT4lciwqcAkJRYerBp+avWwbnds056VbR3F23xi/Y4mENBW4+O7Tb3O5b0EaewoOcdPYHtw1vh8tNPiUyElpLxHf5BeX8tDCdcxfvYueMS34++1jSeqhwadEairQGXnaArOARMCAW81smQe5pJ5blJbJA29nsL+4lJ+f15ufj+utwadETlGgR+BPAYvN7Mrq2el1Z4WcUM6BEn77dgaLM7IY1Lk1s28dyaDOGnxKpDZqXeDOuTbA2cDNAGZWCpR6E0vqGzNj3qpdPLRwLSXllUyb0J+pZyXQWINPidRaIEfgCUAu8IJzbiiwCrizep7M7zjnkoFkgPj4+ABWJ+FqZ14x972Zxucb9zKyRzsenTyEXjEafEokUIEc/jQGhgNPm9kwoAi45+iFzGyGmSWZWVJMjC4La0gqKo0X/7mV8U9+xurt+3no8kG8njxW5S3ikUCOwHcBu8xsRfXn8zhGgUvDtCmnkGnz01i1fT/n9I3hkUmJdG2nX5GIeKnWBW5mWc65nc65fma2ATgfWOtdNAlHZRWVPPvpZv73H5uIahrBH68ayqRhXTT4lEgdCPQqlF8Ar1ZfgbIFuCXwSBKu0ncXcNe8VNZlHuDiIXFMv3QQMa2a+h1LpN4KqMDNbA2Q5E0UCVclZRU8+dFGZn6+hfYtInn2hhGMHxTrdyyRek93YkpAVm7N4575qWzZW8SPk7px30UDaBPVxO9YIg2CClxqpbCkjMcXb+Dl5dvp2q45r0wZzZl9ov2OJdKgqMDllH28IYf7F6SReaCEW89I4L/G9yUqUl9KIsGmvU5qbH9RKQ8tXMuCr3fTp2NL5v3kdEZ0b+d3LJEGSwUuJ2VmvJeWyYNvZ1BwqIxfjuvNHeN607SxBp8S8ZMKXE4o+0AJD7yVzgdrsxncpQ2v3DaaAXGt/Y4lIqjA5TjMjDdSdvLwe+soLa/k3on9mXKmBp8SCSUqcPmeHfuKuWdBKl9u3seohPY8NnkICdEt/I4lIkdRgct3KiqNF7/cxv8s2UBEI8fDVyRy7ah4GjXSbfAioUgFLgB8m13I3fNSWbMzn3H9O/LwFYl0btvc71gicgIq8AautLySZz7dzJ+XbqRl08Y8dfVpXDa0swafEgkDKvAG7Jud+Uybn8r6rEIuHdqZ6ZcOpENLDT4lEi5U4A3QodIK/vTRt8z6fAsxrZoy88YkLhzYye9YInKKVOANzLLN+7h3QSrb9hVzzahu3HvRAFo30+BTIuFIBd5AHCgp49FF65mzYgfx7aOYc9toTu+twadEwpkKvAFYuj6b+xakk1NYwtSzEvjPC/vRPFK3wYuEu4AK3Dm3DSgEKoByM9PkDiFk38HD/H7hWt5es4d+nVrxzA0jOK1bW79jiYhHvDgCP8/M9nrwPuIRM+Pd1Eymv5NBYUkZv7qgDz87tzeRjXUbvEh9olMo9Ux5RSXT5qcxf/UuhnZry+OTh9AvtpXfsUSkDgRa4AZ84Jwz4Fkzm3H0As65ZCAZID4+PsDVyYmUVVTyq9fX8F5qJr8c15s7L+hLhG6DF6m3Ai3wM81st3OuI/Chc269mX125ALVpT4DICkpyQJcnxxHaXklv5i7miUZ2dw7sT+3n9PL70giUscCOilqZrur/8wB3gRGeRFKTk1JWQU/eWUVSzKy+e0lA1XeIg1ErQvcOdfCOdfqX4+BHwDpXgWTmjlUWsHUl1JYuj6Hh69I5NYzE/yOJCJBEsgplE7Am9WDHjUG5pjZYk9SSY0Ul5Yz5cUUlm/dx+OTh3DVyG5+RxKRIKp1gZvZFmCoh1nkFBw8XM6tL3xFyvY8/njVUCYN6+p3JBEJMl1GGIYKDpVx8wsrSd1VwFNXD+PSoZ39jiQiPlCBh5n84lJueG4l67MO8NdrhzEhMc7vSCLiExV4GMkrKuW6WSvYnHOQZ64fwfkDNASsSEOmAg8TuYWHuW7WcrbvK2bmTUmc0zfG70gi4jMVeBjIPlDCtTOXsye/hBduHqlhYEUEUIGHvD35h7h25nJyCw8z+9ZRjEpo73ckEQkRKvAQtjOvmGtmLqeguIyXpoxmRPd2fkcSkRCiAg9R2/YWce3M5Rw8XM4rt41mqMbxFpGjqMBD0Kacg1w3azml5ZXMTR7DoM5t/I4kIiFIBR5iNmQVct2sFYDxWvJYjeUtIselKVpCyNo9B7hm5nIaOXgteYzKW0ROSEfgISJtVwHXP7eCqMgI5kwdQ0J0C78jiUiIU4GHgNU79nPT8ytp3awJryWPoVv7KL8jiUgY0CkUn321LY8bZq2gfYtI3vjJWJW3iNSYjsB99OXmvUx5MYW4Ns2YM3UMsW2a+R1JRMJIwEfgzrkI59zXzrmFXgRqKD77NpdbXviKru2a89rtKm8ROXVenEK5E1jnwfs0GB+vz+G2l1JIiG7Ba8lj6NhK5S0ipy6gAnfOdQUuBmZ5E6f+W5KRRfLLKfTt1JK5U8fQoWVTvyOJSJgK9Aj8SeBuoPJ4Czjnkp1zKc65lNzc3ABXF97eS83kjldXM6hzG169bQztWkT6HUlEwlggs9JfAuSY2aoTLWdmM8wsycySYmIa7hjWb329m1/MXc1p3dry8pRRtGnexO9IIhLmAjkCPwO4zDm3DXgNGOece8WTVPXM31N28h9vrGFUQntm3zqKVs1U3iISuFoXuJnda2ZdzawHcDWw1Myu9yxZPTFnxQ7umpfKmb2jeeHmUbRoqis3RcQbapM6NPvLbTz4Tgbn9Yvh6etH0KxJhN+RRKQe8aTAzewT4BMv3qu+mPnZFh55fx0XDuzEX64dRtPGKm8R8ZaOwOvAXz/exBNLNnDR4FieunoYTSI0YoGIeE8F7iEz46l/bOTJjzZy+Wmd+X8/GkpjlbeI1BEVuEfMjCeWbOBvn2zmyhFdeWzyECIaOb9jiUg9pgL3gJnxyHvrmPXFVq4ZFc8jVyTSSOUtInVMBR4gM2P6OxnMXradm8Z2Z/plg3BO5S0idU8FHoDKSuP+t9KZu3IHt52ZwP0XD1B5i0jQqMBrqaLSmDY/lXmrdvGzc3tx1/h+Km8RCSoVeC2UV1Ty679/w9tr9vCrC/pw5/l9VN4iEnQq8FNUVlHJr15bw3tpmdw1vh93nNfb70gi0kCpwE/B4fIKfjHnaz5Ym839Fw1g6tk9/Y4kIg2YCryGSsoq+Okrq/h4Qy7TLx3IzWck+B1JRBo4FXgNHCqtIPnlFD7fuJc/TBrMtaPj/Y4kIqICP5miw+VMmf0VK7bm8fiVQ7gqqZvfkUREABX4CRWWlHHri1+xavt+/nTVaVwxrIvfkUREvqMCP46CQ2Xc9PxK0nYX8L/XDOOSIZ39jiQi8m9U4MeQX1zKDc+tZH3WAf523XDGD4r1O5KIyPfUusCdc82Az4Cm1e8zz8we9CqYX/YdPMx1s1awZW8Rz94wgnH9O/kdSUTkmAI5Aj8MjDOzg865JsAXzrlFZrbco2xBl1NYwnUzV7Ajr5hZNyZxdt8YvyOJiBxXrQvczAw4WP1pk+oP8yKUH7IKSrh21nIy80t44ZaRnN4r2u9IIiInFNB0Mc65COfcGiAH+NDMVhxjmWTnXIpzLiU3NzeQ1dWZ3fmH+PGMZWQXlDD71lEqbxEJCwEVuJlVmNlpQFdglHMu8RjLzDCzJDNLiokJvVMSO/OK+fGzy8grKuXl20YzKqG935FERGrEkwkbzSwf+BiY4MX7Bcu2vUVc9ewyCkvKmXPbGIbHt/M7kohIjdW6wJ1zMc65ttWPmwMXAus9ylXnNuUc5Kpnl3G4vJK5U8cwuGsbvyOJiJySQK5CiQNmO+ciqPpG8IaZLfQmVt3akFXIdbOWA465U8fQL7aV35FERE5ZIFehpALDPMwSFBl7Crh+1gqaRDRiztQx9O7Y0u9IIiK10qDuxEzdlc8Nz62kRWQEc6aOoUd0C78jiYjUWoMp8NU79nPTcytpE9WEuVPH0K19lN+RREQC0iAKfOXWPG55YSXRrZoyd+oYOrdt7nckEZGA1fsC/3LTXqbMTiGubTPmTh1Dp9bN/I4kIuIJT64DD1WffZvLLS9+Rbf2zXk9eazKW0TqlXp7BL50fTY/eXk1vTq25JUpo+jQsqnfkUREPFUvC3xxeha/mLua/rGteXnKKNpGRfodSUTEc/XuFMrC1D3cMWc1iV3a8Mpto1XeIlJv1asj8De/3sWv3/iGEd3b8cIto2jZtF7980RE/k29abg3UnYybX4qYxI68NzNSURF1pt/mojIMdWLUyivrtjO3fNSObN3NM/fPFLlLSINQtg33Yv/3Mr0d9cyrn9H/nbdcJo1ifA7kohIUIR1gc/4bDN/eH89PxjYib9cO5zIxvXiBwoRkRoJ2wL/68ebeGLJBi4eEseTPz6NJhEqbxFpWMKuwM2MJz/ayFP/2MikYV144sohNFZ5i0gDFFYFbmY8vmQDT3+ymR+N6Mqjk4cQ0cj5HUtExBeBTKnWzTn3sXNurXMuwzl3p5fBjmZmPPzeOp7+ZDPXjo7nMZW3iDRwgRyBlwO/NrPVzrlWwCrn3IdmttajbN+prDSmv5vBS8u2c/PpPXjw0oE4p/IWkYYtkCnVMoHM6seFzrl1QBfA0wI3M37zdjpzVuwg+eye3Duxv8pbRASPbuRxzvWgan7MFcd4Ldk5l+KcS8nNza3Ne9MrpiV3nNdL5S0icgRnZoG9gXMtgU+BR8xswYmWTUpKspSUlIDWJyLS0DjnVplZ0tHPB3QE7pxrAswHXj1ZeYuIiLcCuQrFAc8B68zsj95FEhGRmgjkCPwM4AZgnHNuTfXHRR7lEhGRkwjkKpQvAP1GUUTEJ7oHXUQkTKnARUTClApcRCRMqcBFRMJUwDfynNLKnMsFttfyr0cDez2M4xXlOjXKdWqU69TU11zdzSzm6CeDWuCBcM6lHOtOJL8p16lRrlOjXKemoeXSKRQRkTClAhcRCVPhVOAz/A5wHMp1apTr1CjXqWlQucLmHLiIiPy7cDoCFxGRI6jARUTCVMgVuHNugnNug3Nuk3PunmO83tQ593r16yuqZwMKhVw3O+dyjxiZ8bYgZHreOZfjnEs/zuvOOfe/1ZlTnXPD6zpTDXOd65wrOGJb/TZIuU46Ebcf26yGuYK+zZxzzZxzK51z31Tn+t0xlgn6/ljDXEHfH49Yd4Rz7mvn3MJjvObt9jKzkPkAIoDNQE8gEvgGGHjUMj8Dnql+fDXweojkuhn4S5C319nAcCD9OK9fBCyiatTIMcCKEMl1LrDQh6+vOGB49eNWwLfH+H8M+jarYa6gb7PqbdCy+nETqqZMHHPUMn7sjzXJFfT98Yh1/ycw51j/X15vr1A7Ah8FbDKzLWZWCrwGXH7UMpcDs6sfzwPOd3U/UWZNcgWdmX0G5J1gkcuBl6zKcqCtcy4uBHL5wswyzWx19eNC4F8TcR8p6NushrmCrnobHKz+tEn1x9FXPQR9f6xhLl8457oCFwOzjrOIp9sr1Aq8C7DziM938f0v5O+WMbNyoADoEAK5ACZX/9g9zznXrY4z1URNc/thbPWPwIucc4OCvXJ3/Im4fd1mJ8gFPmyz6tMBa4Ac4EMzO+72CuL+WJNc4M/++CRwN1B5nNc93V6hVuDh7F2gh5kNAT7k/77Lyvetpmpsh6HAn4G3grlyVzUR93zgV2Z2IJjrPpGT5PJlm5lZhZmdBnQFRjnnEoOx3pOpQa6g74/OuUuAHDNbVdfr+pdQK/DdwJHfKbtWP3fMZZxzjYE2wD6/c5nZPjM7XP3pLGBEHWeqiZpsz6AzswP/+hHYzN4HmjjnooOxbnfyibh92WYny+XnNqteZz7wMTDhqJf82B9Pmsun/fEM4DLn3DaqTrOOc869ctQynm6vUCvwr4A+zrkE51wkVSf53zlqmXeAm6ofXwksterfCPiZ66jzpJdRdR7Tb+8AN1ZfWTEGKDCzTL9DOedi/3Xezzk3iqqvwzrf6avXebKJuIO+zWqSy49t5pyLcc61rX7cHLgQWH/UYkHfH2uSy4/90czuNbOuZtaDqo5YambXH7WYp9ur1nNi1gUzK3fO/RxYQtWVH8+bWYZz7vdAipm9Q9UX+svOuU1U/aLs6hDJ9Uvn3GVAeXWum+s6l3NuLlVXJ0Q753YBD1L1Cx3M7BngfaquqtgEFAO31HWmGua6Evipc64cOARcHYRvwvB/E3GnVZ8/BbgPiD8imx/brCa5/NhmccBs51wEVd8w3jCzhX7vjzXMFfT98XjqcnvpVnoRkTAVaqdQRESkhlTgIiJhSgUuIhKmVOAiImFKBS4iEqZU4CIiYUoFLiISpv4/L7k7zvtI2V8AAAAASUVORK5CYII=\n", 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    " ] @@ -1309,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 96, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:18.963976Z", @@ -1319,7 +1272,7 @@ "outputs": [ { "data": { - "image/png": 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2GLpmdYyxMzBefSenGgNS01Bbd6FrVQ/+KGTOl6ojKjgbAJ8Pc+RE1NJ1+NbPurTHDJScQxu7T2c4fAyz05OoT/6N9e547Cd6Yr3xDLp1HHbbZoGto2Z1rKf7oTu3dU4XXpuO+iLJuQ72/XGMpWuhYnl0zshkUJQvC5lZGLNzNtimCSlnMJaugyoR6Eb1Azp73bkt9sCHUYd+wHzqNUj61llf/Ucilg3RNdBlr28wKDDXAS8fLr/W0PnO/2JOW4Jaug4a34quVhm0Rm37Ct/GBdC4QfBquYx532NQtRK6RlWM+X/F9+ki58JvkGpRm3dgth8AVSOwFk1At7nn0hedmVX0DVNhl8mBIxhL1mK8/Z4z/4hwdHg51MGj+BLnQJNfFK2OwtaitXNnzgt/xlj4PvrhX6MjK6H2H0Zt24Vv/Ry4w4V15WouG403Ji3AmLcKMrOwpr6Ijm0IWdmYE+ag/vm5s65EVi70R7sbwOxsSE2H0+fyXhfxD7Hn59RZ1DffOSf9GZnoqEjsJx8p+rWmG6klh9nlKVTiFihfFt/Hc+HOhkGvRX2yFWwbfV8z925MuJFlkpUNx1Mw5v8Vzp6HmyOxu3fIc+kmaLXsPeCMHSxeA+Hl0E1uw/rjkOBcl7wshGrlRxgL/4b66F/OAE2FcnAhHd/f38m9TFFY7gUwNQ2z10jUwaPw7ffolneiE9pgj+jnvO/z5b24frVrfG5c+7veWvy0Bp8PY9QkjNUb8a2e7gyCBLOWy1dAN6+DXm8d/r1RIC7+F2Vd8fmcy1imCWGmc9NCUVxIx3hrIfYz/ZzR5Wu5vI7MLNQ/P0clH0ZXrID+VSzccvN1z96dAGZcdG4ZqlYZu/v9UDkCY/Ii1LeH0bENsd6f5nyJ/gV7+Urm/4L9rxX1C3ejlj37ncsRtap7v1zcCECofT9uybiI2fYx1M7/oh/piDXzlWsHOgA3hrhyJ4z6+N+QegHr3fG5521W6ziMmcsxpizCbN0Xa9PinyxQY/h49B0N0P0fvLSQi3pza1FqadwA3b8b3H5rkWpwpRb/cnHhCw+Z78fFZYLPhzHmbdSps+ie8aj3N2BmXMRa+Eb+Icy5NgnAkeNQs4gb5xyuxFkdO+Ecz9fNGRXLyoZK4djD+2C/NhS19wBmpyed9/xb0e+OOAt9zkrnkMIlRapl7kpId+/SR3FZLsWlDldqcev7OfIjRuIWdOMGWBOfxZo0CpW4BfOx0XAxM5/CnQ2P2WMEZr8/gEtNel0JoL6tLpw+h9q4zXmhZAln61W2DHbfztgj+6N27cUYP9t5XymoWxPf1mVY70109a52qaX41uFKLW7d8xkVifXS77HmjoXqVdDd7sd+8xnU+s15Q3jlnVCVK6K+2ufa42runAOeS8Xs/BTcVAJr8mhomHNR1H8IceY8Zp9RcPoc1qZFgX2+TGopvnUUt1r855T+881zqRj/uw7j+T+j41thLRgHpUs50546C5UrOj+nnHFuEneBO2eU4eWd++G278GYtgQOHHFeN01nuDmiAvboQagvklC797kyS6nlZ1hHcavFfz7rH3ENL4/9SMdLe8LfveRcdz14FHPkRIwxbzvTufi7Ktx7HCm2IdZfp2L+ZhAYynnCPabupS3YmfNwc1X0ZY8hBYzUUnzrKG61XCm8PHbPB0ApjFGTMHs+4zwatXEb1pb3XJ+dq2Oqum0zrMTZGItWYz43EfXhFueN74+j1m+GihWC9hSz1FJ86yhuteQtTDt74Uc6Yo8agFq/GfWvnc4dLm7cmXWFgNyKprZ/hTF0HOrLb5ynqkuWhDPn8K2ded13CkgtoVtHcaslj0M/YI6YgNq0A9+mxZfOVV0WuJ4wKWdQn+1Ebf8KXScK3bb5paHnYJNaim8dxa0WcO7tHDgGtewDfNv/EtD7TOV3QwiRn6RvnZHZABx2Xk4CKISHpDO2EB6SAArhIQmgEB6SAArhIQmgEB6SAArhIQmgEB6SAArhof8Dp+0ZzvsiiiAAAAAASUVORK5CYII=\n", + "image/png": 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dPQ9VbkVH3RG4OtLp7m3RtWtgdh4MCbudg6BlOftL0SLYj/0OzibBkRP5el1vbsTXvws7dgC6aiWMN2dgvP5+ejUGJCWj4hPQVSsGtxUSMt9YHVHaCaDfj/nyRNSi1fjXfJj1iRkI6ae6dvd2cOgYZtt+qC+/wfp4HPZznbHGOy1tdssmgashQ5WKWH/uiW7XEvb9gvHGB6hvdzodJ345jrFoFZQJQ6e3TgZcWElIScWYucz52TQh8QzGotVQPgJdt2bAS9CN6mG//AyElcTXaSB8u9OpI+N007adxrxSt+TrdQNzH/DKJvPrNZ9v34U5bSFq0WqIroWOLAtao7b8gH/9R3B3reDVcgXzoV5QoSy6UgWMuSvwb5jn3PwNQh1q0zbMR3pDhQiseW+jWzTOOuVMSXXnoJTXbbL/MMbCVRjvLXBqiAhHh5dCHTiCP24W1L8r8HVo7fTKeeUdjL99hv7DY+jIsqg9h1BbEvCvmQXRLuwneaxTLY3D/Mtc2HsQe/wg7GYNwOfDnDoftfZr/BvnZ95eywt3A5iWBkkX4fQ55xorg2Vd++b6qbOoXfswPlzqnGZUjsTu16Xg95pupJZ05hP9UXGbIawk/rWz4d46Qa1DfRkPto1+qIm7nRJuZJukpsHxRIw5y+FcEtwWid3x0Wy3boJSx+79TrvB/JUQXgpdvzbW2BcDf08ywxWdIdQ332F8/Cnq48+cT+fyZcCy8S+bDPfUztfLuhfApGTMp19GHTgC+35BN7sXHdMCe1BPZ77fn/1G5bV6d7jR6yO/tWTQGvx+jGGTMD5f7zQxF+T0Jr91XLkDut37Jb+1ZBz13e4AUJD9xO93bmGZJvhMp9NCQV24iPHu37AH93RamK/nqm2htv7gtAQXL4a+pzZUvjXfq3cngJcuO92Gbi2H3bEVlIvAmDwPte8QukEdrM+mOYVnbNwrd7SMXypjWkHfcDdq2bEHwsOgakVv63Br5w+V98eNOtx06TJmy16o7bvQXdpgzXjt+qHOw9lTfrlyiFVrv4GkC1gTXkb37oRu/zDWiqnY/bqg4hMwH+jhbMCrNqoxcBxq7nLnKJfxixW0c2tBapmz3JlW786Chc+tbeLSDhcq748rdbjF78cY8x7q1Fl059aoz9Zh9nwVLqfkvLzWWdvgJ/fGB3UngMdOOOf0NdJbxVLToGw49sDu2LEDULv3Y7bt58zLOIr+fBhjxhKMWZ84pxUuKVAtsz+Bi+7c+ig028TFWgpch0vvDQCH/4sRtxl9dy2siUOwJg1DxW3G7DUi5xCmH3jMJwfg+30f1JYEV8pwJYC6dg04fS6r/2TRIs4RrGQJ7O5tsYf8CZWwG2PcTGe+Uk4PgvjFWAsmutqzPVRqCZU6QqmWAtdRKpdrtPyoHIk16nms2bFQsTy6QyvsCYNRazZlD+FVPaH0ffXgXBK6Ut5bOq/HnWvAc0mY7fpDsSJYk0dAnfQboxkX0GfOY3YfBqfPYW2cF9jny0KlllCpI5RqCZU6MmRcV2Zcc55Lwvj7aozh76BbN8f66C24pbizbOIZp3M4wNnzUMadTgjuNLOFh2G9+wpq6w6MaQth/+H0VzecJueI0tgj+qK+3Yn6/n9dWWXI1xIqdYRSLaFSR4aM69mMVtfwMOwubbI+Cf80yrn3euAI5pC/YIxK/1smLj6s7d5Ddw3qYK2Yivn7vs5zbC92g6gaWUexM+fhtgroKx5DCphQqSVU6gilWkKljmsJD8Pu/DgohTFsktP1TCnU+i1Ymxc4y7h4e8jVrmi6ZROsuJkY8z/HHDoR9cVmZ8Yvx1FrNjkf20F6kjlUagmVOkKpllCpI+fitPNJ3KUN9rDeqDWbUF9vd3pDudEz6yoB6Yqmtv6A8dJbqO9+cp6qLloUzpzDv2pGvnsKFJZaQqWOUKolVOrI0cGjmIPeRm3c5nQvy7hedVngxoRJPIP6n+2orT+gq1dGt2ya1fwcbKFSS6jUEUq1hEodV0pNw+wzBrX4n/i3LgtoX1P52xBC5GTnPucWSQBOO68kARTCQzIythAekgAK4SEJoBAekgAK4SEJoBAekgAK4SEJoBAekgAK4aH/A/oCIQEQRxXBAAAAAElFTkSuQmCC\n", "text/plain": [ "
    " ] @@ -1348,9 +1301,7 @@ " # 调用y轴刻度线条实例, 是一个Line2D实例\n", " line.set_color('green') # 颜色\n", " line.set_markersize(25) # marker大小\n", - " line.set_markeredgewidth(2)# marker粗细\n", - "\n", - "plt.show()" + " line.set_markeredgewidth(2)# marker粗细" ] }, { @@ -1387,7 +1338,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 98, "metadata": { "ExecuteTime": { "end_time": "2021-05-23T08:29:19.075005Z", @@ -1397,17 +1348,7 @@ "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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zl1TwT3EN83mOss3GqMfF5EK4Id5XiWSKwNr6lgJit1nY427lvHJhlYQSkE08dX6Zlwx20NJ0bfZGpWtB4skNZgJr7NWFC+BNRweZVjUhiizMBeK02a24WrYeZZuNEY+TUCzJgp7+Ws/4I7lrQDIZ7m5TFkiJKAHJIBJPMn45kNV9BZoLCypXjX5xeQ0pr/TuAbj7pl047FZVE6K4hrngGn1FWB+gtXWHxuiJtRQyqtBzx0BAE5DzqitvSSgByeD7F1ZJpuRV9R+ZtDXb6GqzV8wCMX4dGQE/Yw33HO7n68/NspbYqMg6FPXBXBFFhAYjeirv5Hz9B9KNwtvu7SyQHi2Vd6UK/e0aBSUgGRw778ci4OiQO+cxWiZWpQREs3SGMlxYAG86OqBqQhTXMB+MFxX/AOh1NtPeYmuIpopGI8XebV1YqitvqSgByeCp88vctKcDZ3PuUfGDbgeXKhREn/ZHcOlWTya3DXepmhDFVaRSkvlg8RaIEILRPldDuLDSjRS3SOMFzQIBlcpbCkpAdOLJDb5/cTVr/UcmA+5WLq2uVaRv0JQ/ylCPAyHEVdstFsEbbxnge+eWGqr9hKJ4liJxkilZtAUCWhykEQRkKRzHYbfisOf+IQhXUnlVMWHxKAHROXkpQCKZ4tYcAXSDgS4HiWQqbSaXkwvLUYa62rLuu1IToqwQBcwHiqtCz2TE48QfSdR9e49cs9A3Y6TyKgukeJSA6BgDpG7NwwKB8nflTW6kuLgcZajbkXV/uibkhKoJUWTOQi9NQKD+M7E0AdnafWWgUnlLQwmIzrHzy+zvc14Tb9jMYFpAyus6mg3ESKZkTgEBrSZkyh/lhKoJ2fGUUoVuYIy3rfeWJkuhravQM1GpvKWhBATt1/6J6ZWc9R+ZGMWE5a5Gn8qSwruZdE2ICqbveOYCa1gtYtvU1a3Y3dGCw25lss6bKi6F43m/DkPdDoKxZFVGVTcCSkCAidkQ4XgyZ/1HJi1NVnqczWW3QIzA3vAWAmLUhPzfk6omZKczF4jjcTVjtYjtD86BEIIRj5NzddzWPbmRYjmaoDdPF9Y+PRPrvHJjFYUSEOCp836AbTOwDAbcrWWvRr/gj9Bss+DZYqYBqJoQhcZ8MFZSBpbBiMdZ1xbISnQdKdlyFkgmhoWv4iDFoQQELf4x1O3I+wM42OWoiAUy1O1Iz2TPhaoJUYDWyr2U+IfBiMfJXDBGMFafLp2tZqFnY7CrFYuA80sqlbcYdryApFKSp6eWt82+ymTA3crM6hobZawFmfZH2JsjhTcTVROiAK0KvZQUXoNRjxZIP1enmViFCkizzcruzlZlgRTJjheQc4thVqLreQXQDQbdDtY3ZLp9ttmkUpILy9F0q4XtUDUhO5twPEk4njTFhWU0VazXliZXBCS/GAjo89FVLUhR7HgBeUqv/3hZAQIyUOZU3oVQnNh6iqGe7S0QUDUhO525QOk1IAaDXQ7sNkvd1oIYnXgLyUYb6naoavQi2fECcuz8Mn3tzVfN3NiOchcTprvwFrAmVROyczEExAwXltUiuK6nrX4FJBzHbrXQ3rJ1G5NMhrvbCKyts1LnFfjVIP9XuQGRUnLs/DK37eu+pt/UVuwx5oIsl8cCmc4jhXczd9+0i9975HkePnGJsQLiOYr6x4wiwkxG+1w8e7E+f4hoo2ztBX2ejc/ZlD+Ce5tC4nIifGIYuF165Rcytv0M8EFAAj+QXvlWfft9wPv1wz4svfKhLNfrAr4KDANTwM9Ir1wRPiGATwH3AFHgfumVzxSz5h1tgVxcXmMuGCso/gFa4K2vvblsFsiUP4LNItjdmf8XgqoJ2bmUMso2GyO9Ti6trNXl+2gpHM87hdcg3ZW3ioF04RPvBB4HHhQ+8V3hE7uET4wC7wNeKb3yRuDX9WO7AC/wMuA2wCt8ItsMivcCT0ivHAWe0J8D3A2M6n8PAJ8udt07WkAKrf/IZNDtKFstyPRylAF3KzZrYf97VE3IzmQuEKPT0ZR1DHMxjPY5kZK6LCjMt5FiJkYq71SVUnmFT7gAH/A24APA/UAEeAfwJ9IrVwCkVy7op7wO+Jb0ymV937eAu7Jc+l7AsEweAl6fsf2L0iul9MongU7hE/3FrH3bbyghxANCiONCiOOLi4vF3KNmeXpqmU5HUzrzpBDKOVhq2h/ZsoVJLlRNyM5ktoRJhNmo5/G2hTRSNDBSeatogaTQXFRdANIrp6RXhoD9wH7hE98TPvGk8AlDJPYAFzPOv6Rv20yf9MpZ/fEc0Ffg+duyrYBIKT8rpRyTUo719vYWc4+a5dh5rf5ju2K9bAy4HVrDw42UqWuSUjK9lLsL71aompCdyXwwZkoA3WCouw2rRdRdU8VUSuIP599IMZPh7raqZWJJrzSsjY+gubA+IXzCgRajHgVuB34O+HPhE51F3kOiiZSp7FgX1nwwxpQ/WlD6biaDXa1spCSzAXNrQVai64TiyaIsEFA1ITuRuRImEWbDbrMw3O2oOwsksLZOMiWLaig53OOoai2I9MpHgTcDHwN6gfegWQaPSq9cl155HjiDJiiXgcGM0wf0bZuZN1xT+r+GCyzf87dlxwqIMf+j0AC6gdGV12w3lmFG51tEuBlVE7KzWN9IsRQufhZ6LkY9rrorJiymiNDASOVdjVY+lVf4hFP4xJD+NARMAC7gH9GsD4RP9KC5tF4EvgG8VviEWw+ev1bftplHgfv0x/cBj2Rs/0XhE0L4xMuBQIarqyB2tIC02a0c6m8v6nyjFsTsQPoF3YwuxoVloGpCdg4LoThSmpeBZTDicTLtj5JImuuiLSdL+iz03iJdWADnq2OFNAGfAb6Gll11N1qa7TcAv/CJU8B3gN+UXumXXrkMPAg8rf99SN+G8Im/ED4xpl/3D4EfEz4xCbxGfw7wGJoQnQX+HPjVYhe+Y+tAjp1f5uhwV8GZTgb9HVrmRjksECGuWDjFoGpCdg5GEaGZLizQMrE2UpIpf4T9+qCpWidtgRSYxguaCwu0Gqyb92bLiC0feibVXdnqQIDf0P82n/N54PNZtv9yxmM/cGeWYyTwrpIXzg61QFajCV6YD3HbcPFvFLvNwq72FtNrQab9UXZ3tJaUktnWbOPum/r5uqoJaXiMGhAzg+hwZbxtPbV2L7SRYiaDXQ6EqJoFYrAKPFvNBRTKjhSQp6c0104+A6S2YqDLwSWTq9G1LrzFWx8Gbzo6QCie5JunVE1II5O2QEx2YV3f60SI+hpvuxSOY7UIOlubCj632WZld0d1u/JKr1yVXvls1RZQBDtSQI6d92O3WTgy0FHSdbRaEPMtEMOcLoWX7etiwK1qQhqduWAMu82C21H4l+ZWtDRZGXTXVybWUihBd5u9qLR80LrynldNFQtihwrIMi8d7Cy5cnfA7WA2GDMt0BiKreOPJPKaA7IdRk3Iv59dYkbVhDQsc3oRYSG9n/Jl1OOsLwEpYBZ6Noa6HWouSIHsOAEJx5M8PxMsuv4jk0F3K1Jq0+DM4EoTxdItEMioCfl+USneijrA7BqQTEY8Tl5cjJheLFsuiqlCz2RfTxur0eqk8tYrO05AnpleYSMli67/yMTsWpDpdApv6RYIwN5uBy/bp2pCGpn5YIw+k+MfBiMeJ4mNFBfLPL7ZLJbCiaJSeA2G0l15lRsrX3acgBw7v4zVIrjFhFS9wS6jrbs5b7jpZc183muSBQJaMP38UoRnLqiakEZDSq0TghmDpLIxqqfvTs7XfiBdSllUJ95M9umxRzWdMH92noBMLXPT7nbamksvgdnV3oLVIsyzQJai9DibcZqwNoN7DvfjsFv52nEVTG80VqPrJJIp01N4Da7v1X6R10NFejieJJ5MleTCGnBrqbzVbOteb+woAYmtb/DsxVVT3FcANquF/o4W06rRp/wR0+IfBm3NNl492suTL/pNva6i+pg9SGozrpYm+jtaOFcHAmJUoRdTA2LQ0qSl8ioLJH92lICcvBQgkUyVXP+RyaDbYZoFcmE5aqr7yuCGXS4uLEeJrauiwkYiLSAdxX9pbseIx1kXFohRRFhKFhboTRVVDCRvdpSAHNMHSN1aQgX6ZsyqBYmtbzAbiBU0xjZfRvucpCS8uKh+WTUSV4oIW8t2jxE9lTeVqu0kjKVQ8Y0UM9HauqvPSb7sKAF56vwyB3a56HSYN/d4wO1gPhgv+de9EYgvpYliLkY9ejC0jqqKFdszF4ghBHhKCBxvx6jHxdr6Rs3Pl1mKFN9IMZPhbpXKWwg7RkCSGymemV4xLf5hYGRilVqsN2VyCm8mwz0OrBZRV0Vhiu2ZD8bobmumqciGoPkw2qdPJ6zx8bZLoThCQFdbiRZIj0rlLYQdIyCnZoNEEhumC4hRC1Jqrvx0iXNAtqLZZmWo28GZOkjHVOTPXDBW1vgHwEivLiA13lRxKRzH7bAX3V3bwPj8qYr0/NgxApIeIGVye3PDAik1DjLtj9LeYjPVvZbJaJ0EQxuRx5+b5fe/fsr062ptTMoX/wBwt9npcdpr3v1ZahW6QY105a0bdoyAPHV+meFuBx6TUx49rhaarKXXgkz5I2nzuRyMelxM+6PEkyoTq9L8zbEL/Pn/O59uvW4WlbBA4EogvZZZKnIW+maMVN5p5cLKix0hIKmU5OmpZdPdVwBWi2B3Z2vJ1ejT/mhZ4h8G6QFBS+qDUUmklIzPBAH4zumFbY7On9j6BqvR9bLVgGQy6nExOV/bmVilNlLMZLjHoSyQPNkRAjK5EGY1um5q/UcmpdaCrG+kuLy6xpAJc0BykR4QVOOuiEZjLhhjWc8QesJEASnXIKlsHBnoIBRPcq6GA+lLIXNcWKAlsqgYSH7sCAEx6j/M6MCbDa0WpHgBubyyxkZKliWF1+D6XicWUV8T5swiFFtnUa8TqDTjlzXr41B/O/8+uWRaMeesXgPSX8YaEIOjQ1rd1PHp2uyntpbYIJLYMMWFBbCvu42V6DqB6Lop12tkdoaATK3Q39HCgLs8H7bBLgdL4XjR42Onl8uXwmvQ0mRlb1d9DQgyC98/neItn/3Pqtx7fCaIEPCuHx1hbX2D/zSppcx8BarQDfb1tNHVZudEjQqIUYVeag2IgfFDThUUbs+OEJCTl1a5Za+7LEN3gLQwXV4tLr5QzhTeTEY8rh3pwnrmwgrnFiNpV1IlGZ8JsK+njTsPemhtsvLtCXPcWEYVeiVcWEJo3atrXUB6XOa4sPala0GUgGxHwwvIWmKDC8tR9uutqcuBISAXi5yPPrUUpbXJSm8ZK4pBC6SfX4qwXicDgsxgLbGRbo733OVAxe8/PhPkxt0dtDRZedVoD98+vWDKbJbZQIw2uxVXi7mjbHMxNuzm/FIk/WVdS5jRSDETI5VXJZxsT8MLyLnFMFJeqagtB4PpwVLFveEuLEcY6naUzUIyGPU4Wd+QOypF8YX5EEby0HOXVit675VIgsura9y4ux2AOw94uLy6xum50q3A+WCMXWWaA5KNMT0O8kwNWiFmNVI0SHflVRbItjS8gBjV1/vLKCA9zmbsNkvRgfQpf7SsAXQDoyfW2R3kxpqY1YLYrmYbJy9V1gI5pd/bEJA7DngA+LYJ2VhzFRaQm/Z0YLdaatKNZTRS7C6xjUkmQ90OJSB5sAMEJEyTVZQ1QG2xCAY6W4uaC5JKSS4sR8vShXcz13v0AUE7KBNrYjaIs9nGjx7wVNyFNT6j3e/G3R0AeNpbODLQwRMT8yVfez4Qq0j8w6ClycpNe9prU0DCcVwtNlqarKZdc7inTc0FyYOGF5DJ+RDX9TjL2nAOYKCruFqQuWCMRDJVljkgm3HYbQy4WzmzgzKxJmaDHNjl4iWDncwGYiyEzK0G34rxmSD9HS1XNfi744CH719cxV9CLGEjJZkPxStSRJjJ0SE3Jy8Haq6bwVKktFno2RjudqhU3jxofAFZCJc1/mEw4C6uGn0qnYFVfgsE9J5YO6SpYiolmZgNcbC/nSMDmhXwfAWtEC2A3n7VtjsP9CElfOeFxaKv6w/H2UjJss1Cz8XRoS4SyVRFX8N80IoIzRYQlYmVDw0tIGuJDS6ulDcDy2DQrf1iCceTBZ13QQ9o7y1jFXomo30uXlyKkNwBmViXVtYIx5Mc2t3Oof52LIKKxUGiCa1y23BfGdy0p52+9ma+fbp4N9ZcBavQMzEKCmvNjbUUjpuWwmswrFJ586KhBeTsgp6B5amMBQJaVXkhTPmjNFm1flqVYNTjJJFMldx+vh4wgtgH+9tpa7Yx4nHyXIUEZGI2hJRcY4EIIbjjgId/O7NEIlmciF+ZRFhZAel1NTPU7eD4VK0JiDmNFDPZq1J586KhBcTIwBqtgAVypRaksDfctD/CYJc28KkSGK/FTnBjTcwGsQi4Qf9vPrynk5OXA6bUYWzHKSOAvqfjmn13HOgjHE+mRwwUSnoWeoUtENCskGcurFTkNcyHRDJFYG2d7jZzBaSlyUp/e4vqibUNjS0gCyHsVkvZK7xBKz6CwmtBpv3RsjZR3MyVpoqNH0ifmA0y3NNGq13Lzjky0MFiKM58sPzFcOMzQTodTezOYiW8aqSHZpuFJ4p0Y80FYtgswrS6h0I4OuRmKZyomVoif8TcKvRMhnvaOK8EZEsaWkAm58Nc19tW8pSyfOhus9PSZCnINSSlZNofKWuK8WaczTZ2d7TsiJ5YE3NBDvZfcSEd1gPpJytQUGgE0LMVh7barfzQ9d08MVFcVfpcMIbH1VwxqzWTsSGtIWmtNFb0m1yFnonWlbc2hLJWaWwBWQhVxH0Fmm97wO0oyALxRxJEEhsVsZAyGelr/J5Ywdg6F5fXOJQhIIf627FaRNnrQdY3UrwwF7omgJ7JHQf7uLAc5dxi4b9w54Mx+ioc/zAY9ThxtdhqJpC+aPTBKoOA7OtxsBxJEFirTCqv8Ilh4RP3Z9n+RuETUvjEWMa29wmfOCt84gXhE6/Lcb19wiee0o/7qvAJu769WX9+Vt8/XOyaG1ZAookkF5fX2F+BALrBYIFt3Q3/aiUtENC+BM4u1PaAoFI5PasJZKaAtDRZ2d/nKnsm1tmFMImN1DUB9EyMqvRiigpnA7GqxD9AK5rVGisWF78xG6MK3ew6ELjyuaxEHET4xDuBx4EHhU98V/jELn27C3g38FTGsYeAtwA3AncBfyp8IlsV5UeBT0qvHAFWgF/St/8SsKJv/6R+XFE0rIAYLppK1IAYDLgdBQXRjQyPSrQxyWTU4yS2rg2xalQmMjKwMjmyp4PnyhxIN+oktrJA9nS2crC/vaghU/OByrYx2czYkJsz8+GK/TLfinQjxTLEQIyuvOWeTqiLhA94G/AB4H7AuOmDaF/wmRWw9wJfkV4Zl155HjgL3LbpmgK4A3hY3/QQ8PqM8x/SHz8M3KkfXzDbCogQ4gEhxHEhxPHFxeKLnyrNmXlDQCrjwgIY7GolGEvm/cGaXo5iEZrwVBJDVBvZjTUxG8TtaKKv/epfpocHOljWmxyWi/GZIK1N1vQXUC7uPODhxPQKq9H828yHYutEEhtVs0DgSj3IMxeq78ZaCsdx2K047DbTr23UZlUgDpICJNAFIL1ySnplSPjELcCg9Mqvbzp+D3Ax4/klfVsm3cCq9MpklmPS5+v7A/rxBbOtgEgpPyulHJNSjvX29hZzj6owOa9lYFUyw2mgwK680/4IuztbsdsqawiO9GqieqaBe2JNzGoB9M1B7MN6Wm0560FOzQQ52O/aNsh9x0EPGynJv57J/4fZlUFS1ROQl+7txGoRnKiBehBtFrr51gcYXXlbyt4TS3plBHgH8BE0F9YnhE84gD8C3lPWm5dIw7qwzsyHKpaBZWDUguQbB5nyV6aJ4mY6HE14XM0N21QxuZHi9FzoGvcVwIF+F01WwckyBdJTKcmp2eCW7iuDlw500t1m54kChkzNVnCQVC4cdhuH+mujseJS2Pw2JpkMdbdVpBpdeuWjwJuBjwG9wG8CNwHfFT4xBbwceFQPpF8GBjNOH9C3ZeIHOoVP2LIckz5f39+hH18wDSsgkwvhirQwyeTKXJD8BOSCP1KRJorZGO1zNmxb9yl/hHgylVVAmm1WbtjlKpsFcmE5Sjie3DKAbmCxCH70gIfvvrCQd2uZufQs9OoJCGhurGcvrlZ9OJm/DFXomQz3tDFVZheW8Amn8Ikh/WkImADapFf2SK8cll45DDwJ/JT0yuPAo8Bb9GyqfcAocCzzmtIrJfAd4E36pvuAR/THj+rP0fd/Wz++YBpSQCLxJJdW1so6AyQbnY4m2uzWvALpgbV1VqLrFU/hNRj1uJhcCNdMRbGZnMqSgZXJ4T2dnLy0Wpb/9vEZYwbI9hYIaHGQYCyZ96/5+Sr1wdrM0SE3a+sb6Wy3alFuC2S4uyKpvE3AZ4CvAV7gbuBTuQ6WXjkO/C1wCvhn4F3SKzcAhE88Jnxit37obwO/IXziLFqM43P69s8B3fr23wDeW+zCzY881QBGBtaIp7IWiBCCwTzbul9polh5FxZoFkg0scFMIMaeCvXhqhQTs0GarCJddb+ZIwMdfPnYBS4sR01PoX5+JoDNIti/K78fLz+8v5cmq+CJ0wu87Lrt45hzwRidjiZTZ18UgxFIPz69nC7QrDQbKclyJEFvmWIgcKWp4rQ/wpGBzrLcQ3rlCnCXXo9xu/TKL2Q55vZNz38f+P0sx92T8fhFNmVn6dtjaO6ykmlIC6QSUwhzMeBuzSuInm7j3lM9CwQasyfWxGyQ63udOZMTjEB6OepBxmeCjPa5aLbl9wXvbLbx8uu6864HmatiDUgmuztb2d3RUtWK9OVIgpSEHlc5LRCjK29FKtJXgWcrcSOzaEgBmVwIY7dZKl6gB+jV6GvbukeM4qRKtXHfjNGhuBFbmkzMBnO6rwD297mw2yymV6RLKTk1E8gr/pHJHQc8nFuM5JXtU+lRtltxy5C7qjPS07PQTW6kmIlRo1WJ6YTSK1elVz5b9huZSEMKyJn5ENf3OqvSK2jA3Uo4vn0tyLQ/isfVXJb89Xxwt9npcdobLhPLH9aaJWYLoBvYbRYO9reb3hNrIRRnKZwoWEDuPNAH5DcrfS5Q+UmEuRgbcjMbiFWtIHUp3cakfC6sliYr/R0tai5IDhpSQCbnw1VxX8GVWpCLy1t/qKarlMKbyYjH2XDFhBNGAH2bL/Ejezp4/nLQ1HYum2eg58vebgejHue23XkTyRT+SLzqAXSDsWGtsWK10nnTAlJGFxZobiw1Hz07DScgkXiSy6trFU/hNbhSC7K1z3TKH6l4C5PNNGImVq4WJps5PNBBOJ40tV33+GXj3oW/9+446OGpF5cJxXJbrguhGFJWt4gwkwO7XDjsVk5MVacvVjk78WYy3ONQXXlz0HACMpnOwKqOBXJlLkhuCySaSLIQildfQPqchGLaWhqFidkgfe3NdLVt7dYwZqSbWQ8yPhNkuNuBq6Wp4HPvPNBHMiX5f5NLOY+phSr0TGxWCy8d7KxaIH0xHMdutdDeUl438HB3G/5IguAW4r5TaTgBuZKBVR0LpKO1CVeLjYtbWCAXlo0mitV3YQENFQc5NRvc1voAGOl10tJkMTUT6/mZQNYJhPlwy95OOh1NW1alzwU0oa+VGAho6bwTs0Ei8eT2B5vMUihBj9OedeaKmbzl1r2ceP9rcDU3ZNVDSTScgEzOh2i2WaqW3QRaRfpWFohhDlfdAvEYPbEaIw6SSKY4txjOS0BsVgs37u7gucurptw7EF3n0spawQH0zPXcvr+X77ywwEaOuEw1R9nm4uiQm5SEZy+uVvzeS+F42eMfoLX+6XY2l12o6pGGE5Az8+GqZWAZDLhbt6xGT88BqVIRoUGP006no6lhxttOLoRY35B5CQho9SDPXw7m/MIuhPHZ4gLomdxxsI/lSCLnl/FcYA27zUKno3AXWbm4ea8bIaoTSF8Kx+nexlWpKC8NJyBnF6qXgWWwXS3IlD+K29FER5W/CIQQ7Pe4GqYn1sQ2LUw2c2Sgg7X1Dc4tli6gp9ItTIqzQAB+ZLQXq0Xw7RzZWHPBOP0dLTX1S7ijtYn9HldV4iDlbmOi2J6GEpCwnoFVyRkg2RjsamVtfYPlSPY5Dxf8UfZWOf5hMNLn5Mx8Y2RiTcwGaWmybDuHw+DIgHkV6eMzWvC+lC+0DkcTtw67c8ZB5gOxmknhzeTosJvvT6+YYsnli5RSa6RYAReWIjcNJSBGW47RKmVgGaRrQXLEQab8kao1UdzMqMdJYG09PdmtnpmYDXJD3/ZzOAz29Thps1t5zoSCwvGZQEnuK4M7D/Rxei6UNQ18LlgbbUw2c3Svm1A8WdGaosDaOsmUVBZIlWkwAdFcEdXKwDIY7MpdC5JIpphZXat6BpZBuidWnbuxpJTpIVL5YrUIbtzTUfJskLXEBmcXwtxUgvvK4I6D2qz072yqSpdS1lQbk0zGhvXGihUcMFWJKnTF9jSUgJzRM7AGq5iBBaS722arRr+0EiUlqeikxK0wxtvWe0+s+WCcleh6QQICWkX6qZlgSXMtTs8FSUk4ZIIFcn2vk309bdfMSl+JrpNIpmrSAtnb5aDH2VzRvliLIc1i7lUWSFVpLAFZCDPiqW4GFoCrpYlOR1NWC8RI4a1WF97NeFzNuFpsdV8LckrPgipUQA4PdBBPpkr67x83IYCeyR0HPPzHOT/RxJXaCmOQVC1aIEIIjg5VtqAw3UhRCUhVaSgBOTsfqrr7yiBXLciVLry14cISQjDaAD2xjAysAwW2ETFmPJRSDzI+E6SjtSndxqZU7jzgIZFM8e8ZVelzQe29VItBdICxoS4uLEdZCMUqcj/lwqoNGkZAQrF1ZgKxtEum2gy4W7NWo0/5o7TZrTX1xh/1uOrehXVqNshgVyvtBbYRGepy4GqxlZSJdWomwKH+dtPSa8eGu3A1267qzpuuQq9BCwS01u5AxdxYS+E4VovA7aidz9FOpGEExCiGG63wFMJcDLhbuZylFmTaH2Gou62mcvlH+5wshRM5047rgYnZIAd3Fe5CslgEh/d0FD0bJLmR4vRcyDT3FWjt5l99Qy/fPr2Q7hY8F4whhOZyrEVu2tOO3WapWCDdH07Q1WbHUmV39U6ncQSkilMIszHY5SCeTLG4qVHh9HK06i1MNnOlJ1Z9urHWEhtMLUUKjn8YHB7oYGI2SDy5UfC55xYjxJMpbtxjnoCA5sZaCMV5Xm8RPx+I0eNspslamx/ZZpuVlwx0cOJC5SwQlcJbfWrz3VgEZ+bDtDRZGHTXxpez4Q/PrAXZSEkulmEOd6kYhZf12tLkhfkQKVl4AN3gyJ5O1jckZ+YK/+9/XrdcbjIhAyuT22/wYBGkiwpna7QGJJNbhtw8fzlAbL1wIS6UxXCiptzAO5UGEpAQIx5nzZi0hpBlZmLNBtZY35A1U0RosLujhTa7tW7jIEYbkXxbmGwmXZFeRCB9fEarfr+u11zLt6vNzi173ek4SK1WoWcyNtTF+oYsy6z5zSyF4iqFtwZoGAE5uxBmf43EPwD2pAdLXbFAjBTevTUmIEIIRvpcdZuJNTEbxNVsKzoLasDdSqejqajZIOMzAQ7sai9L6vgdBz08dznAfDDGXDBGf40G0A1u2dsJlL+xopRSa6SoLJCq0xACEoytMxuIVb0HViYOu43uNvtVFogxV7nao2yzMepx1m0tyMRskAP9rqKtTyG0QHqhv5yllJyaDZoaQM/EmJX+9ZOzBNbWazYDy6Db2cx1PW2cmC7vhMJwPEk8mVIxkBqgIQTE+OKrdg+szQx0Oa6qRr/gj2K3WWrSlz3qcbIQihOI1tfUtVRKcnouVHT8w+DIQAdn5kMF+e8vLq8RiiVN6YGVjf19TvZ0tvI3xy4AtVsDksktQ25OTK+UtTnnUoVG2Sq2p0EEpLpTCHMx4G69xgLZ2+WomThNJumWJov15ca6tLJGOJ4sWUAO7+kkmZLpmer5MD5jzAApjwUihOA1Bz3p2FQt/vDYzNiQm5XoOi8umTdrfjN+o4iwRlOadxINISBn5sO0NllNqwQ2i0G3g8ura+lc/ml/tOYC6Abppop15sYqtoXJZtIz0guoBxmfCWK1CG7YVb4fLncc7Es/rnUXFlxprHiijPUgqgq9dmgIAZlcqK0MLIMBdyvrG5L5UAwpJdP+aM20MNnMns5WWposdZfKe2o2hEXADSVan/0dLfQ47QXFQZ6fCTDqcdLSZC3p3lvxsn1dOOza9etBQK7rcdLR2lTWQPpiWDVSrBUaQ0DmwzXTwiSTgYxMrMVQnLX1jZpporgZi0Uw4nHWnYBMzAbZ19NGq720L3EjkF5IJtb4TJBDZXJfGbQ0Wfnh0R7aW2w4m21lvZcZWCyCo0NujpcxkL6kF+d2qXG2Vaf235HbEFhbZy4Yq7n4B5BuK58ZB6m1IsJMRj0unnrRX+1lFMTEbJCXDnaacq3DA53865lJookkDvvWH42FUIzFULxsAfRM3v/jh7L2VatVjg5p9SsrkQTuMnzJL4XjuB1N2Gq0Kn8nUff/B4x53rWWgQVXzwWZ0oOKtTIHJBsjHiczgRihWH1kYgVj61xaWSs5/mFwZE8HKXmlMHErzG7hvhWDXQ5+6Pqest/HLI4ajRXL1NZEtTGpHepeQM7UyBTCbLQ0Wel1NXNpJcq0P4rVItIFhrWIIcL1UpF+Wm/hXmwF+mYOFzAjPV39XgEBqTdeMtCJzSLKNh/EH04oAakRGkBAQrQ2WdO/9muNQXcrF5fXmF6OsqeztWab4cEVEa6XOMipGXMysAz62lvoa2/OKxNrfCbA3i5Hwe3jdwKtdis37m4vWyB9KRxXKbw1Qt3HQIwAeq1lYBkMuB18/+IKkUSy5rrwbmawy4HdZqkbC2RiNkRXm52+dvO+TA7v6eTkpdVtj3v+cpCbTO7A20gcHerir5+aJpFMYbeZ+6NpqUEbKQqfGAZul175Bf35bwC/DCSBReC/SK+c1vfdB7xfP/XD0isfynK9LuCrwDAwBfyM9MoV4RMC+BRwDxAF7pde+Uwxa67dn8N5MrkQqpkZINkY7GplZjXG+cVITbYwycRqEVzf66ybtu4Tc0EO9rtMna1yZKCDF5ciW8aBgrF1LixHKxJAr1eODrmJJ1OcKqAwMx9i6xuE48mGc2EJn3gn8DjwoPCJ7wqf2AV8HxiTXnkEeBj4mH5sF+AFXgbcBniFT7izXPa9wBPSK0eBJ/TnAHcDo/rfA8Cni113XQtIYG2d+WC8ZmaAZGPA7WAjJQnFa98CAb0nVh1YIMmNFC/MhYoaIrUVhwc6kPJKkDwbKv6xPUZB4fEpc9N5jfk6jWSBCJ9wAT7gbcAHgPuBiPTK70ivNNLvngQG9MevA74lvXJZeuUK8C3griyXvhcwLJOHgNdnbP+i9EopvfJJoFP4RH8xa99WQIQQDwghjgshji8uLhZzj7Jh/FKuxRoQg8zq+FpO4TUY9Ti5tLJGNJGs9lK2ZMqvDXIyK/5hcHiPXpG+RSC9khlY9UpfewsD7lbT4yBXqtAbygJJARLoApBeOSW9crMb4JfQLBSAPcDFjH2X9G2b6ZNeOas/ngOMtgb5nr8t2wqIlPKzUsoxKeVYb29vMfcoG2fma2uMbTYyB1zVahuTTAwxPrdQvl5GZnBKz8AyW0B6nM3s6Wzl5BaB9PGZAL2uZjyu2q8MryZaQaG5jRUbsZGi9MoI8A7gI2gurE8In0h/WQif+HlgDPh4CfeQaCJlKnXtwjozH8Jhr90MLID+zhYMF/1gDdeAGIwYPbFqfDbIqZkgTVaRHsdrJlpF+uqW91bWx/aMDblZDMWvmolTKo3aSFF65aPAm9HiHL3AewCET7wG+F3gp6RXGvOxLwODGacP6Ns2M2+4pvR/Fwo8f1vqWkC0AHrtZmCBNit6V3sL/R0tZe2ZZBZD3Q6arKLm4yATs0FGPC7TM3xAi4NM+aNZW9vH1jeYXAgrAcmDW/SCQjPdWIYLq7uB2pgIn3AKnxjSn4aACcAlfOJm4DNo4rGQcco3gNcKn3DrwfPX6ts28yhwn/74PuCRjO2/KHxCCJ94ORDIcHUVRH0LyHy4poZI5eKGXS7Tit3KTZPVwr6eNlO78j5/OcD3zi6Zdj3QBORgf3n+3xudeZ+fudaN9cJciI2UNH0GeiNyYFc7bXarqX2xlsIJXC22uvgxVgBNaELxNbTsqrvR0mw/DjiBrwmfeFb4xKMA0iuXgQeBp/W/D+nbED7xF8InxvTr/iHwY8InJoHX6M8BHgNeBM4Cfw78arELr9s6kEB0nYVQbWdgGXzqLTdjYqZp2Rn1uNKzLkplMRTnFz73FJH4Bo+9+1VpF1kp+MNxFkLxsomyEUg/eSnAK0eubiFyJYCuBGQ7rBbBzXvdHDextftiuPFmoeuZVHdtrgNB+9LPdc7ngc9n2f7LGY/9wJ1ZjpHAu0pbtUbdWiBn0j2wat8C6WhtqquK5RGPk+nlaEHT+XLxwUfHicQ3aGmy8Dt//3x6NkopTJQpgG7Q6bCzt8vBc5dXr9k3PhPA1WJjsKt24261xNEhNy/Mh0zrr7YUauhZ6KvAs1VeQ0HUr4DUQQpvvbK/z4WUcG6xNDfW48/N8vXnZnn3a0Z5/48f4tjUMl95+uL2J26DMTWwXAICWhwkW0+s8Zkgh/rbTS1ebGTGht1ICd+/sGrK9Rq5kaL0ylXplc9Wex2FULcCMjkfpq3GM7DqlfR42xIC6cuRBB945Hlu2tPOA6++jjePDfCK67r5yOMTLARjJa3v1GyQvvbmss6DOLKng0srayxHEultGynJ6bmgcl8VwEsHO7EI8wLpS6qRYk1RtwJyZj7ESJ+5bSwUGsPdbVgtoqRA+of+aZzA2joff9NLaLJaEELwB284TDyZwvvoeEnrm5gNlj0p4XCWEbcvLoaJradUBlYBuFqauGGXOY0VE8kUgbV1JSA1RN0KyORCmP01OAOkEbDbLAx3O4quBfmXU/P847MzvOtHR65yM+3raePdd47y+PNzfHN8rqhrx5MbnF0Il9V9BXBTuiJ9Nb3NyMoy9inyY2zIzYnplXQNR7EY1mCPq2FjIHVHXQrIajTBYihekzNAGoVRj6uoWpBAdJ3f+YfnOLDLxa/ePnLN/gdefR0Hdrn4vUfGiwqsnl0Ik0zJsgtIe0sT1/W0XRUHGb8cpNlm4fre2m9JU0v84iuGWN9I8fFvvFDSdRq0jUldU5cCYrQwGVEB9LIx2udk2h8lniwsE+vBr5/CH0nwiTe/JGuRX5PVwh++8QjzoVhRXyjlzsDK5PBAx1UurPGZIAd2udQo1QIZ7XPx9lcO89XjF3n24mrR11lUAlJz1OUnwcjAUhZI+RjxONlISaaW8p/F/d0XFnj4xCV+5Ueu29LN89LBTu57xTBfenK6YN/4qZkgLU1asWO5Obyng9lAjIVQDCkl4zMBDqkAelG8+zX76XU283uPPM9GkancSw3YibfeqUsBmZwP4Wy2sbtDNbMrF6MF9sQKxdZ5398/x4jHyX+/c3Tb4//n626gv72F9/39SRLJVN7rmpgNcsOudqwVaF9zZKAT0CrpL62sEYwlVQC9SJzNNn73xw9y8lKArxaZyt2IjRTrnboUkDPzYUY8TpWBVUau623DIsg7E+sPHjvNfDDGx990hGbb9m0mnM02PvzTN3FmPsyf/eu5vO4hpWRiLsihMrUw2cyNu9sRQqtIVy3cS+enXrKbl+3r4mPfOM1KRnp0vvjDcVqbrLQ1120DjYajLgVkciFcFy1M6pmWJit7uxx51YJ87+wSXz52gV/+4eu4eW+2wWjZueNAHz9xpJ//8+2zed1nLhhjNbpekfgHQFuzjZFeJ89dCnBqJoBFaP2dFMUhhOBD995EKJbk498sPP6lzUJX7qtaou4EZCWSYCmsMrAqwYjHlY435SIST/Lbf3eSfT1t/MaP7S/4Ht6fvFFvc/Lctm1OKlGBvpnDAx2cvBzg+ZkgIx4nrfaGauJXcW7Y5eK+Vwzz5WMX8po9n4kqIqw96k5AjC+0csyBUFzN/j4n55cirG/kjlF87J9Pc3l1jY+96UhRHVJ7Xc387o8f5NjUMl89vrVv3MjAOrCrcj8ejuzpYDEU58kX/aoC3SR+/cdG6W5r5gOPjBfUG62R25jUK/UnILqrQ1kg5We0z0kyJZn2Z59O+NSLfh76z2nue8Uwtw53FX2fnxkb5OXXdfEHj23d5uTUTJDBrlZcFWxMeVgPpEcTGyr+YRLtLU38zj0H+MHFVf52mx8NmWgColxYtURFBOTsQoiZVXOmkk3Oh3A12+hXGVhlJ52JlSWQvpbY4Lf/7iSDXa381l03lHQfIQQfecMR4skUH/yn3G1OKtHCZDOH+q9kfB1SAmIaP33zHm4ddvPRfz7NanT7gPpGSrIcUS6sWqPsApJIprjv80/z9r98mqAJLZ0n58OM9KkMrEpwfa8TIchakf6/vvkCU/4oH33jERz20rNijDYnjz03x7dOzV+zP5pIct4fqWj8A6DVbmVUd5fe2K9cWGZhBNSDsSSfyCOgvhxJkJIqhbfWKLuA2G0WPvrGI5xbDPPOvzpRUM5/NiYXQuyvgxkgjUCr3cqAu/UaATkxvcLnvneet71sLz90fU+OswvHaHPygX98/po2Jy/MhZCysgF0gx8e7eHG3e10OOpnpks9cLC/nV94+RB//dQFnr+89QAzf0RVodciFXFhvWq0h4+84TDfO+vnfX//HFIWV4m6HEmwFE6oGSAVZNTjYjIjEyu2vsFvPfwDdne08r57Dpp6ryarhY+84TDzoRif2NTmxAigV2M08HvvPsg//OorK37fncD/+LH9dLfZ+cAjWw8bWwoZRYQqBlJLVCyI/uaxQd595yh/98wlPvXEZFHXuDJESlkglWLU4+TFpQhJPRPrU09Mcm4xwh+84TDOMhR03bzXzX2vGOaLm9qcTMwGcTXbGHBXfv6L1SKy9vVSlE5HaxPvvfsg37+wysPPXMp5XLqRoktZILVERT8Vv/6aUd54ywD/+18mefhE7jdLLibTPbCUBVIpRjxOEskUF1fWOHlplc/+24v8zNgAP7K/t2z3zNbm5NRskAP9av5LI/KGm/dwdMjNRx8/TSCaPU6qOvHWJhUVEC3b5jA/dH037/27k3zv7FJB55+ZD+NqtrGrXWVgVQrD2hufCfCbXztJj9PO7/74obLe09ls48HXa21OPvOv50ilJKerkIGlqAwWi+BD997ISjTBH30re0B9MRzHbrXQ3qLamNQSFbfL7TYLn/75o1zX28avfOkEL8zlP7RociHEqMrAqihGwebvf32CF+ZD/MFPH6ajtfzB5DsP9vHjR/r542+f5V8nF4kkNqoSQFdUhht3d/DzLx/iS09OMz5zbUB9KZSg22lXn/0aoyqO3Y7WJv7y7bfRarfy9r88xnyeM7In58OqgLDCGF2PZwMxfvrmPdx5sK9i9/b+5CFamiy8+8vfB6qTgaWoHO/5sRtwO+x4Hxm/JtFGVaHXJlWLDO7pbOXz99/K6to6b//LpwnHk1se7w/H8UcSKoBeBQ7t7qDH2Yz3J8vrutqMx9XC79xzkGAsiUVofZQUjUuHo4nfvusAx6dX+PtnLl+1zx9RVei1SFVTS27a08GfvO0WXpgP8a6/fiad6ZMNYwrhqOqBVXE++sbD/NN/eyWdjsp/gH/21kFeOdLNTXs6iuq1pagv3nR0gJcOdvKRx09fVXi8FFJV6LVI1XMTf/QGDw/eexP/emaRD2QxXQ2MwUbKhVV5up3N9HdUPn0WtMSLz99/K3/zjpdX5f6KymKxCB689yb8kTif/NYZQJsD44/EVQpvDVJ1AQF468v28s7br+fLxy7w6RzDhc7Mh3C12OhrV2+inUazzVqWmhNFbXJ4oIO33raXL/7nNKfnggTW1lnfkHS3KRdWrVETAgLwm6+9gZ98yW4+9s8v8Mizl6/ZbwTQVRaGQtH4/ObrbqC9xcbv/eM4i/os9F5lgdQcNSMgFovgE28+wm3DXfzm107y1Iv+q/arKYQKxc6h02Hnt+46wLGpZT7/vfOAKiKsRWpGQEBzVXz2F48y0NXKA186kR5zuhSOsxxJpNuLKxSKxudnxwZ5yUAHXz6mzQxRAlJ71JSAgPbL4wv334bNIrj/L4+xGIpn9MBSFohCsVPQKtRvwvBaqzTe2qPmBARgb7eDz91/K0vhOL/80NOcvKRVpqoMLIViZ/GSwU7eetteOlqbcFchjVyxNaKQ1upjY2Py+PHjZVzO1XxzfI7/+lcnsFkErU1WfuB9rQqiKxQ7jI2UZCVa33UgQogTUsqxLY/xiWHgdumVX9CfNwNfBI4CfuBnpVdOZTnvLuBTgBX4C+mVf6hv3wd8BegGTgC/IL1y+/GPBVCTFojBa2/chfcnDrG+IVUGlkKxQ7FaRF2LRz4In3gn8DjwoPCJ7wqf2AX8ErAivXIE+CTw0SznWYE/Ae4GDgE/J3zCaBnxUeCT+vkr+vVMpeaT6+9/5T4kMOh2VHspCoVCYTrCJ1yAD7gLOAJ8F4gA9wIf1A97GPg/wieE9F7lNroNOCu98kX9Wl8B7hU+MQHcAbxVP+4h/VqfNnPt21ogQogHhBDHhRDHFxcXzbx33rz9lft4zaHKNfFTKBSKCpICJNAFIL1ySnplCNgDXNS3JYEAmjsqk/QxOpf0bd3Aqn5e5nZT2VZApJSflVKOSSnHenvLN0RIoVAodiLSKyPAO4CPoLmwPiF8oi5cLjUdA1EoFIqdgPTKR4E3Ax8DeoH3AJeBQQDhEzagAy2Ynkn6GJ0BfZsf6NTPy9xuKkpAFAqFoooIn3AKnxjSn4aACcAFPArcp29/E/DtTfEPgKeBUeET+4RP2IG3AI/qx31HPw/9Oo+YvfaaD6IrFApFg9MEfAYtbtEDXEALfvuBLwmfOAsso4kDwid2o6Xr3iO9Mil84teAb6Cl8X5eeuW4ft3fBr4ifOLDwPeBz5m98JquA1EoFIpGoJg6kHpAubAUCoWiNlgFnq3yGgpCubAUCoWiBpBeuUqdCYiyQBQKhUJRFAXFQIQQi8B0kffqAZaKPLcSqPWVhlpfaaj1lUatr29IStlwhXQFCUhJNxLi+HZBpGqi1lcaan2lodZXGrW+vkZFubAUCoVCURRKQBQKhUJRFJUUkM9W8F7FoNZXGmp9paHWVxq1vr6GpGIxEIVCoVA0FsqFpVAoFIqiMF1AhBB3CSFeEEKcFUK8N8v+ZiHEV/X9Twkhhs1ewxZrGxRCfEcIcUoIMS6EeHeWY24XQgSEEM/qf79XqfXp958SQjyn3/uavjFC4//TX7+TQohbKri2GzJel2eFEEEhxK9vOqair58Q4vNCiAUhxPMZ27qEEN8SQkzq/7pznHuffsykEOK+bMeUaX0fF0Kc1v///YMQojPHuVu+F8q4vg8KIS5n/D+8J8e5W37Wy7i+r2asbUoI8WyOc8v++u14pJSm/aE18zoHXAfYgR8AhzYd86vAn+mP3wJ81cw1bLO+fuAW/bELOJNlfbcD/7dSa8qyximgZ4v996CNvhTAy4GnqrROKzCHlt9etdcPeDVwC/B8xraPAe/VH78X+GiW87qAF/V/3fpjd4XW91rApj/+aLb15fNeKOP6Pgj8zzz+/2/5WS/X+jbt/1/A71Xr9dvpf2ZbINp4RSlflFIm0Aa637vpmHvRxiuCNqbxTlGhYedSylkp5TP6Y6NtsulTusrMvcAXpcaTQKcQor8K67gTOCelLLaw1BSklP+G1qk0k8z32EPA67Oc+jrgW1LKZSnlCvAttJGiZV+flPKbUqYnxT2JNquhKuR4/fIhn896yWy1Pv1742eAL5t9X0V+mC0gucYrZj1G/xBlG9NYdnTX2c3AU1l2v0II8QMhxONCiBsruzIk8E0hxAkhxANZ9ufzGleCt5D7g1vN1w+gT0o5qz+eA7LNQ66V1/G/oFmU2dj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    " ] @@ -1419,10 +1360,6 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "\n", "fig, ax = plt.subplots()\n", "ax.plot(100*np.random.rand(20))\n", "\n", @@ -1432,83 +1369,49 @@ "\n", "# 设置ticker的参数，右侧为主轴，颜色为绿色\n", "ax.yaxis.set_tick_params(which='major', labelcolor='green',\n", - " labelleft=False, labelright=True)\n", - "\n", - "plt.show()" + " labelleft=False, labelright=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 思考题\n", - "1. primitives 和 container的区别和联系是什么？\n", - "2. 四个容器的联系和区别是么？他们分别控制一张图表的哪些要素？" + "## 思考题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 绘图题" + "- primitives 和 container的区别和联系是什么，分别用于控制可视化图表中的哪些要素" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "1. 使用提供的drug数据集，画出下面折线图。PA加粗标黄，其他为灰色。 \n", + "- 使用提供的drug数据集，对第一列yyyy和第二列state分组求和，画出下面折线图。PA加粗标黄，其他为灰色。 \n", "图标题和横纵坐标轴标题，以及线的文本暂不做要求。 \n", " \n", "![](https://img-blog.csdnimg.cn/20210523162430365.png)\n" ] }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "ExecuteTime": { - "end_time": "2021-05-23T08:29:19.106129Z", - "start_time": "2021-05-23T08:29:19.077010Z" - } - }, - "outputs": [], - "source": [ - "# 数据导入代码\n", - "# 导入包\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# 导入数据集并转成方便作图的格式\n", - "Dataset = pd.read_csv('data/Drugs.csv')\n", - "group = Dataset.groupby(['YYYY','State']).agg('sum').reset_index()\n", - "df = group.pivot(index='YYYY', columns='State', values='DrugReports').reset_index()\n" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2.分别用一组长方形柱和填充面积的方式模仿画出下图，函数 y = -1 * (x - 2) * (x - 8) +10 在区间[2,9]的积分面积\n", + "- 分别用一组长方形柱和填充面积的方式模仿画出下图，函数 y = -1 * (x - 2) * (x - 8) +10 在区间[2,9]的积分面积\n", "![](https://img-blog.csdnimg.cn/20201126105910781.png)\n", "![](https://img-blog.csdnimg.cn/20201126105910780.png)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 参考资料\n", "[1. matplotlib设计的基本逻辑](https://zhuanlan.zhihu.com/p/32693665) \n", - "[2. matplotlib.artist api](https://matplotlib.org/api/artist_api.html) \n", - "[3. matplotlib官方教程](https://matplotlib.org/tutorials/intermediate/artists.html#sphx-glr-tutorials-intermediate-artists-py) \n", - "[4. AI算法工程师手册](https://www.bookstack.cn/read/huaxiaozhuan-ai/spilt.2.333f5abdbabf383d.md) " + "[2. AI算法工程师手册](https://www.bookstack.cn/read/huaxiaozhuan-ai/spilt.2.333f5abdbabf383d.md) " ] }, { @@ -1535,7 +1438,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.9.4" }, "toc": { "base_numbering": 1,