Jekyll2020-07-08T10:02:30-05:00https://asyncfor.com/feed.xml神烦小宝用Jupyter Notebook写博客Python数据科学分享——5.推荐系统2020-07-08T00:00:00-05:002020-07-08T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/07/08/data-recs<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-07-08-data-recs.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/markmap.png" alt="map" style="max-width: 700px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># %matplotlib widget</span>
<span class="kn">from</span> <span class="nn">matplotlib.font_manager</span> <span class="kn">import</span> <span class="n">_rebuild</span>
<span class="n">_rebuild</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"whitegrid"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">mabwiser.mab</span> <span class="kn">import</span> <span class="n">MAB</span><span class="p">,</span> <span class="n">LearningPolicy</span><span class="p">,</span> <span class="n">NeighborhoodPolicy</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<table>
<thead><tr>
<th style="text-align:center">时间</th>
<th style="text-align:center">事件</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">1891</td>
<td style="text-align:center">美国Sittman与Pitt发明老虎机</td>
</tr>
<tr>
<td style="text-align:center">1987</td>
<td style="text-align:center">学者提出多臂老虎机问题(multi-armed bandit problem,MAB)</td>
</tr>
<tr>
<td style="text-align:center">1997</td>
<td style="text-align:center">使用Thompson采样(1933)解决MAB问题</td>
</tr>
<tr>
<td style="text-align:center">1998</td>
<td style="text-align:center">ϵ贪婪算法(epsilon-Greedy Algorithm)</td>
</tr>
<tr>
<td style="text-align:center">2002</td>
<td style="text-align:center">UCB算法(Upper Confidence Bound,置信区间上界)</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">Yahoo!提出LinUCB算法</td>
</tr>
<tr>
<td style="text-align:center">2020年2月</td>
<td style="text-align:center">美国康奈尔大学与阿里巴巴共同提出了PSLinUCB</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="EE(Explore-VS-Exploit)问题">EE(Explore VS Exploit)问题<a class="anchor-link" href="#EE(Explore-VS-Exploit)问题"> </a></h1><p><a href="https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-">EE,Explore VS Exploit</a>:一种运筹学概念,任何profit-seeking系统的基本条件,平衡探索新模型(exploration)与维护旧模型的收益最大化(exploitation)</p>
<pre><code>1. Explore:A/B实验,惊喜猎奇,新颖多样;强化学习(reinforcement learning)
1. Exploit:物以类聚、人以群分,尽力迎合用户,使收益最大化(profit maximization)
</code></pre>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/ee.png" alt="map" style="max-width: 500px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="A/B测试">A/B测试<a class="anchor-link" href="#A/B测试"> </a></h1><p>A/B测试分两个阶段进行:</p>
<ol>
<li>Explore阶段:用户分A/B组进行两个模型的对照实验,一段时间内检查两组实验效果</li>
<li>Exploit阶段:假设A效果优于B,那么将A组模型覆盖所有用户,放弃B模型</li>
</ol>
<p>问题:</p>
<ol>
<li>流量从Explore阶段直接切换到Exploit阶段,缺少平滑过渡,风险较大</li>
<li>在Explore阶段,为了收集足够多的对照数据在B组上浪费资源,在实验时间上没有弹性</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Bandit算法">Bandit算法<a class="anchor-link" href="#Bandit算法"> </a></h1><p>解决问题:在任意时间任意A/B配置空间做出至今最好的决策,使目标函数收敛</p>
<ol>
<li>根据实验进展结果,平滑地减少实验数量,避免一刀切</li>
<li>尽可能将资源集中在效果好的实验A上,从而降低资源浪费</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>概率论的<a href="https://en.wikipedia.org/wiki/Multi-armed_bandit">多臂老虎机问题(multi-armed bandit problem,MAB)</a>。</p>
<p>假设有3个老虎机在你面前,每台老虎机中奖概率不同,玩哪个老虎机可以做到长期收益最大化?</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/bandit.png" alt="map" style="max-width: 700px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/bern_bandit.png" alt="map" style="max-width: 700px" />
</figure>
</p>
<ol>
<li>摇臂(Arm):一种选择对应一个摇臂。例如,用户点击哪个房屋?一个房屋是一个Arm</li>
<li>收益(Reward):选择结果的量化指标。例如,用户点击了3次房屋A,那么得到的收益+3,优于点击2次</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Bandit</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">probas</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters:</span>
<span class="sd"> n:老虎机arm的数量</span>
<span class="sd"> probas:每个老虎机arm中奖概率列表</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">=</span> <span class="n">n</span>
<span class="k">if</span> <span class="n">probas</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">probas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">probas</span> <span class="o">=</span> <span class="n">probas</span>
<span class="bp">self</span><span class="o">.</span><span class="n">best_proba</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probas</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">generate_reward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> 选择第i台老虎机,计算收益</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">probas</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mab</span> <span class="o">=</span> <span class="n">Bandit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">mab</span><span class="o">.</span><span class="n">probas</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([0.63347486, 0.14875777, 0.27710719])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 试验10次(10个时间步)的收益</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="nb">print</span><span class="p">([</span><span class="n">mab</span><span class="o">.</span><span class="n">generate_reward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">mab</span><span class="o">.</span><span class="n">n</span><span class="p">)])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>[0, 1, 1]
[0, 0, 0]
[1, 0, 0]
[0, 0, 0]
[1, 0, 1]
[0, 0, 1]
[0, 0, 0]
[0, 1, 0]
[1, 1, 1]
[0, 0, 0]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="累积遗憾(Cumulative-regret)">累积遗憾(Cumulative regret)<a class="anchor-link" href="#累积遗憾(Cumulative-regret)"> </a></h1><p>假设每个老虎机每次的收益的服从伯努利分布,取值1或0</p>
<p>$
\begin{aligned}
R_{T} &=\sum_{i=1}^{T}\left(w^{*}-w_{B(i)}\right) \\
&=T w^{*}-\sum_{i=1}^{T} w_{B(i)}
\end{aligned}
$</p>
<p>其中,</p>
<p>T:表示尝试的次数<br />
$w^{*}$:表示所有老虎机中的最大收益<br />
$w_{B(i)}$:表示第 i 次试验时被选中老虎机的期望收益</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Solver</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandit</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters:</span>
<span class="sd"> bandit (Bandit): N个老虎机arm</span>
<span class="sd"> """</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bandit</span> <span class="o">=</span> <span class="n">bandit</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">actions</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># 选择结果列表</span>
<span class="bp">self</span><span class="o">.</span><span class="n">regret</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># 累积遗憾</span>
<span class="bp">self</span><span class="o">.</span><span class="n">regrets</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># 累积遗憾列表</span>
<span class="k">def</span> <span class="nf">update_regret</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">regret</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">best_proba</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">probas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">regrets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">regret</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">estimated_probas</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="k">def</span> <span class="nf">run_one_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""返回当前进行试验老虎机arm索引"""</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_steps</span><span class="p">):</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_steps</span><span class="p">):</span>
<span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_one_step</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">actions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_regret</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="ϵ贪婪算法">ϵ贪婪算法<a class="anchor-link" href="#ϵ贪婪算法"> </a></h1><p>ϵ贪婪算法(epsilon-Greedy Algorithm,1998年提出)是一种在当前作出利益最大化的选择(exploit)的贪婪算法,使用ϵ参数调节以实现探索(explore)的效果。概率分布如下:</p>
<ol>
<li>1 – ϵ:Exploit维持旧模型</li>
<li>ϵ / 2:Explore尝试最好或最差实验</li>
<li>ϵ=1:均匀探索所有arm,导致资源浪费</li>
<li>ϵ=0:停止探索,仅仅维持旧模型</li>
</ol>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/epsilon-Greedy.png" alt="map" style="max-width: 700px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">EpsilonGreedy</span><span class="p">(</span><span class="n">Solver</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandit</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span> <span class="n">init_proba</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters:</span>
<span class="sd"> eps (float): 每一步探索的概率</span>
<span class="sd"> init_proba (float): 老虎机初始概率,默认为1</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">EpsilonGreedy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">bandit</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimates</span> <span class="o">=</span> <span class="n">init_proba</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">estimated_probas</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimates</span>
<span class="k">def</span> <span class="nf">run_one_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">:</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimates</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">generate_reward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">counts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimates</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">return</span> <span class="n">i</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_results</span><span class="p">(</span><span class="n">solvers</span><span class="p">,</span> <span class="n">solver_names</span><span class="p">,</span> <span class="n">figname</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> 绘制bandit算法效果图</span>
<span class="sd"> Parameters:</span>
<span class="sd"> solvers (list<Solver>): 算法实例</span>
<span class="sd"> solver_names (list<str):算法实例显示名称</span>
<span class="sd"> figname (str):</span>
<span class="sd"> """</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">bandit</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="c1"># 1. 遗憾随时间变化</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solvers</span><span class="p">):</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">regrets</span><span class="p">)),</span> <span class="n">s</span><span class="o">.</span><span class="n">regrets</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">solver_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"试验次数"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"累计遗憾"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.82</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.25</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="c1"># 2. 算法参数估计</span>
<span class="n">sorted_indices</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">n</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">b</span><span class="o">.</span><span class="n">probas</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">n</span><span class="p">),</span> <span class="p">[</span><span class="n">b</span><span class="o">.</span><span class="n">probas</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sorted_indices</span><span class="p">],</span> <span class="s2">"k--"</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">solvers</span><span class="p">:</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">n</span><span class="p">),</span>
<span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">estimated_probas</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sorted_indices</span><span class="p">],</span>
<span class="s2">"x"</span><span class="p">,</span>
<span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"按收益率排序的老虎机"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"估计收益率"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="c1"># 3.</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">solvers</span><span class="p">:</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">n</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">counts</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">regrets</span><span class="p">),</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"steps"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"老虎机"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"中奖占比"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s1">'5.data-recs/</span><span class="si">{</span><span class="n">figname</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">experiment</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">test_solvers</span><span class="p">,</span> <span class="n">names</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> 使用K个带随机收益率的老虎机进行N次试验</span>
<span class="sd"> Parameters:</span>
<span class="sd"> K (int): 老虎机数量</span>
<span class="sd"> N (int): 试验次数</span>
<span class="sd"> """</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"老虎机收益率:</span><span class="se">\n</span><span class="si">{</span><span class="n">b</span><span class="o">.</span><span class="n">probas</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"最佳老虎机: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">probas</span><span class="p">)</span><span class="si">}</span><span class="s2"> 收益率: </span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">probas</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">test_solvers</span><span class="p">:</span>
<span class="n">s</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">test_solvers</span><span class="p">,</span> <span class="n">names</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">tag</span><span class="si">}</span><span class="s2">_K</span><span class="si">{</span><span class="n">K</span><span class="si">}</span><span class="s2">_N</span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s2">.png"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">K</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">Bandit</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">20000</span>
<span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.04</span><span class="p">,</span> <span class="mf">0.06</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">test_solvers</span> <span class="o">=</span> <span class="p">[</span><span class="n">EpsilonGreedy</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
<span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"$\epsilon$-Greedy(b, </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">)"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
<span class="n">experiment</span><span class="p">(</span><span class="s2">"epsilon-Greedy"</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">test_solvers</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>老虎机收益率:
[0.63347486 0.14875777 0.27710719 0.95064444 0.01736183 0.08938155
0.95135302 0.85254521 0.68298702 0.50005527]
最佳老虎机: 6 收益率: 0.9513530154669764
</pre>
</div>
</div>
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Thompson采样"><a href="5.data-recs/1933-thompson.pdf">Thompson采样</a><a class="anchor-link" href="#Thompson采样"> </a></h1><p>Thompson采样(Thompson Sampling)1933提出,1997年学者开始使用它解决MAB问题。</p>
<p>由于Beta分布是伯努利分布的共轭先验分布,因此假设每个老虎机收益服从<code>Beta(成功次数α, 失败次数β)</code>先验分布,例如</p>
<ol>
<li>α = 1, β = 1:表示成功率50%,但不是很自信</li>
<li>α = 1000, β = 9000:强烈认同成功率10%</li>
</ol>
<p>迭代步骤:</p>
<ol>
<li>每次从老虎机的Beta分布抽样,选择随机数最大的老虎机i计算收益,</li>
<li>如果收益=1则,$\alpha_i=\alpha_i+1$,否则收益=0, $\beta_i=\beta_i+1$</li>
</ol>
<p>缺点:计算后验分布困难,需要借助Gibbs采样、Laplace近似和bootstraps算法计算,<a href="5.data-recs/ThompsonSampling.pdf">参考教程</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">ThompsonSampling</span><span class="p">(</span><span class="n">Solver</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandit</span><span class="p">,</span> <span class="n">init_a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">init_b</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters:</span>
<span class="sd"> init_a (int): Beta(a, b)初始参数a</span>
<span class="sd"> init_b (int): Beta(a, b)初始参数b</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ThompsonSampling</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">bandit</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">=</span> <span class="n">init_a</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_bs</span> <span class="o">=</span> <span class="n">init_b</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">estimated_probas</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_one_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">beta</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">))</span>
<span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">generate_reward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_as</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">r</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">r</span>
<span class="k">return</span> <span class="n">i</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>随机探索让我们有机会探索未知arm。然而,这种肆意的随机性可能导致我们因为陷入一个收益差的老虎机而终止探索(陷入过拟合)。</p>
<p>因此,一种改进方法是随着时间增加而缩小ϵ,以降低随机性。另一种方式是优先探索不确定性高的老虎机(类似梯度下降法中使用Momentum摆脱局部最优解),</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="UCB"><a href="https://towardsdatascience.com/recommender-systems-using-linucb-a-contextual-multi-armed-bandit-approach-35a6f0eb6c4">UCB</a><a class="anchor-link" href="#UCB"> </a></h1><p>UCB(Upper Confidence Bound,置信区间上界,2002年提出)算法持续跟踪每条臂试验累计的平均收益,并计算每条摇臂的UCB。UCB表明了我们对每条摇臂收益估计的不确定性。</p>
<p>如果有一个摇臂的上界非常高,那么说明这个摇臂潜力具有高度不确定性,因此选择手臂,也许说一个更佳的探索机会。</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/ucb.png" alt="map" style="max-width: 600px" />
</figure>
</p>
<p>虽然摇臂3取得的平均收益更高,但是UCB算法会选择摇臂2,因为它的潜力不确定性UCB更大。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">UCB1</span><span class="p">(</span><span class="n">Solver</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandit</span><span class="p">,</span> <span class="n">init_proba</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">UCB1</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">bandit</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimates</span> <span class="o">=</span> <span class="n">init_proba</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">estimated_probas</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimates</span>
<span class="k">def</span> <span class="nf">run_one_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># 从置信区间上界选择最佳arm</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimates</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts</span><span class="p">)))</span>
<span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">generate_reward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">counts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimates</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">return</span> <span class="n">i</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="贝叶斯UCB">贝叶斯UCB<a class="anchor-link" href="#贝叶斯UCB"> </a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">BayesianUCB</span><span class="p">(</span><span class="n">Solver</span><span class="p">):</span>
<span class="sd">"""假设先验概率服从Beta分布"""</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandit</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">init_a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">init_b</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters:</span>
<span class="sd"> c (float): 置信区间上界标准差扩展系数</span>
<span class="sd"> init_a (int): Beta(a, b)参数a</span>
<span class="sd"> init_b (int): Beta(a, b)参数b</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">BayesianUCB</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">bandit</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">=</span> <span class="n">c</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">=</span> <span class="n">init_a</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_bs</span> <span class="o">=</span> <span class="n">init_b</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">estimated_probas</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_one_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">)</span>
<span class="o">+</span> <span class="n">stats</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span>
<span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandit</span><span class="o">.</span><span class="n">generate_reward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="c1"># 更新后验分布</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_as</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">r</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_bs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">r</span>
<span class="k">return</span> <span class="n">i</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_solvers</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">EpsilonGreedy</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),</span>
<span class="n">UCB1</span><span class="p">(</span><span class="n">b</span><span class="p">),</span>
<span class="n">BayesianUCB</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">ThompsonSampling</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="p">]</span>
<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
<span class="sa">r</span><span class="s2">"$\epsilon$-Greedy"</span><span class="p">,</span>
<span class="s2">"UCB1"</span><span class="p">,</span>
<span class="s2">"贝叶斯UCB"</span><span class="p">,</span>
<span class="s2">"Thompson采样"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">experiment</span><span class="p">(</span><span class="s2">"epsilon-Greedy"</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">test_solvers</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>老虎机收益率:
[0.63347486 0.14875777 0.27710719 0.95064444 0.01736183 0.08938155
0.95135302 0.85254521 0.68298702 0.50005527]
最佳老虎机: 6 收益率: 0.9513530154669764
</pre>
</div>
</div>
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>然而,由于UCB算法未考虑用户关联特征(上下文),包括用户的历史行为、人口统计信息等。因此,</p>
<ol>
<li>2010年,Yahoo!提出<a href="http://rob.schapire.net/papers/www10.pdf">LinUCB</a>,引入特征向量,对UCB进行改进,提升Yahoo!首页12.5%的点击量。<a href="https://github.com/appurwar/Contextual-Bandit-News-Article-Recommendation">github代码实现</a></li>
<li>2020年2月,美国康奈尔大学与阿里巴巴共同提出了<a href="https://arxiv.org/pdf/2003.00359.pdf">PSLinUCB (Piecewise-Stationary LinUCB, AAAI20)</a>,解决了LinUCB没有考虑用户兴趣会随着时间发生变化的问题。</li>
</ol>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/PSLinUSB.png" alt="map" style="max-width: 1000px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="LinUCB示例">LinUCB示例<a class="anchor-link" href="#LinUCB示例"> </a></h1><p><a href="https://fidelity.github.io/">MABWiser</a>是一个MAB算法快速原型库。它支持上下文无关(context-free)、无参数上下文(non-parametric contextual,例如UCB1)、参数上下文(parametric contextual,例如LinUCB)bandit模型,支持训练和测试并行化、超参数调优、仿真等实用功能。</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/5.data-recs/online.png" alt="map" style="max-width: 700px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Arms</span>
<span class="n">ads</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
<span class="c1"># 特征</span>
<span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="s2">"ad"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
<span class="s2">"revenues"</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
<span class="s2">"age"</span><span class="p">:</span> <span class="p">[</span><span class="mi">22</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">39</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">37</span><span class="p">,</span> <span class="mi">52</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="mi">42</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">57</span><span class="p">,</span> <span class="mi">38</span><span class="p">],</span>
<span class="s2">"click_rate"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.99</span><span class="p">,</span><span class="mf">0.68</span><span class="p">,</span><span class="mf">0.15</span><span class="p">,</span><span class="mf">0.23</span><span class="p">,</span><span class="mf">0.75</span><span class="p">,</span><span class="mf">0.17</span><span class="p">,</span><span class="mf">0.33</span><span class="p">,</span><span class="mf">0.65</span><span class="p">,</span><span class="mf">0.56</span><span class="p">,</span><span class="mf">0.22</span><span class="p">,</span><span class="mf">0.19</span><span class="p">,</span><span class="mf">0.11</span><span class="p">,</span><span class="mf">0.83</span><span class="p">],</span>
<span class="s2">"subscriber"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="p">})</span>
<span class="n">train_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ad</th>
<th>revenues</th>
<th>age</th>
<th>click_rate</th>
<th>subscriber</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>10</td>
<td>22</td>
<td>0.20</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>17</td>
<td>27</td>
<td>0.60</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td>22</td>
<td>39</td>
<td>0.99</td>
<td>1</td>
</tr>
<tr>
<th>3</th>
<td>2</td>
<td>9</td>
<td>48</td>
<td>0.68</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>4</td>
<td>4</td>
<td>21</td>
<td>0.15</td>
<td>1</td>
</tr>
<tr>
<th>5</th>
<td>5</td>
<td>20</td>
<td>20</td>
<td>0.23</td>
<td>0</td>
</tr>
<tr>
<th>6</th>
<td>3</td>
<td>7</td>
<td>19</td>
<td>0.75</td>
<td>1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"age"</span><span class="p">:</span> <span class="p">[</span><span class="mi">37</span><span class="p">,</span> <span class="mi">52</span><span class="p">],</span> <span class="s2">"click_rate"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="s2">"subscriber"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
<span class="n">test_df</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>age</th>
<th>click_rate</th>
<th>subscriber</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>37</td>
<td>0.5</td>
<td>0</td>
</tr>
<tr>
<th>1</th>
<td>52</td>
<td>0.6</td>
<td>1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 标准化预处理</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">train_df</span><span class="p">[[</span><span class="s2">"age"</span><span class="p">,</span> <span class="s2">"click_rate"</span><span class="p">,</span> <span class="s2">"subscriber"</span><span class="p">]])</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test_df</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># LinUCB模型参数alpha=1.25</span>
<span class="n">linucb</span> <span class="o">=</span> <span class="n">MAB</span><span class="p">(</span><span class="n">arms</span><span class="o">=</span><span class="n">ads</span><span class="p">,</span> <span class="n">learning_policy</span><span class="o">=</span><span class="n">LearningPolicy</span><span class="o">.</span><span class="n">LinUCB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1.25</span><span class="p">))</span>
<span class="c1"># 基于历史广告点击和效益数据进行训练</span>
<span class="n">linucb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">decisions</span><span class="o">=</span><span class="n">train_df</span><span class="p">[</span><span class="s2">"ad"</span><span class="p">],</span> <span class="n">rewards</span><span class="o">=</span><span class="n">train_df</span><span class="p">[</span><span class="s2">"revenues"</span><span class="p">],</span> <span class="n">contexts</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
<span class="c1"># 预测展示的广告</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">linucb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"LinUCB预测结果: </span><span class="si">{</span><span class="n">prediction</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>LinUCB预测结果: [5, 2]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 每个arm的期望收益</span>
<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">linucb</span><span class="o">.</span><span class="n">predict_expectations</span><span class="p">(</span><span class="n">test</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>1</th>
<th>2</th>
<th>3</th>
<th>4</th>
<th>5</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>-1.309401</td>
<td>0.105144</td>
<td>-1.912977</td>
<td>-10.291130</td>
<td>9.665161</td>
</tr>
<tr>
<th>1</th>
<td>-4.524479</td>
<td>14.652897</td>
<td>4.524684</td>
<td>-3.257354</td>
<td>-8.684806</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 上一轮决策产生的真实收益</span>
<span class="n">test_df_revenue</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span> <span class="mi">13</span><span class="p">])</span>
<span class="c1"># 在线更新模型</span>
<span class="n">linucb</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">decisions</span><span class="o">=</span><span class="n">prediction</span><span class="p">,</span> <span class="n">rewards</span><span class="o">=</span><span class="n">test_df_revenue</span><span class="p">,</span> <span class="n">contexts</span><span class="o">=</span><span class="n">test</span><span class="p">)</span>
<span class="c1"># 增加arm</span>
<span class="n">linucb</span><span class="o">.</span><span class="n">add_arm</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
<span class="n">linucb</span><span class="o">.</span><span class="n">arms</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[1, 2, 3, 4, 5, 6]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>参考资料:</p>
<p>1.<a href="https://en.wikipedia.org/wiki/Multi-armed_bandit">multi-armed bandit problem,MAB</a><br />
2.<a href="https://lilianweng.github.io/lil-log/2018/01/23/the-multi-armed-bandit-problem-and-its-solutions.html#bayesian-ucb">The Multi-Armed Bandit Problem and Its Solutions</a><br />
3.<a href="Bandit_Algorithms_for_Website_Optimization.pdf">Bandit Algorithms for Website Optimization</a><br />
4.<a href="https://cosx.org/2017/05/bandit-and-recommender-systems/">Bandit 算法与推荐系统</a><br />
5.<a href="https://towardsdatascience.com/recommender-systems-using-linucb-a-contextual-multi-armed-bandit-approach-35a6f0eb6c4">Recommender systems using LinUCB: A contextual multi-armed bandit approach</a></p>
</div>
</div>
</div>
</div>Python数据科学分享——4.数理统计2020-06-08T00:00:00-05:002020-06-08T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/06/08/data-stats<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-06-08-data-stats.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/4.data-stats/4.data_stats.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Probability theory is nothing but common sense reduced to calculation.——Pierre Laplace, 1812</p>
<p>概率论就是把(不确定性)常识精简成计算——皮埃尔·拉普拉斯</p>
<p>统计定义:</p>
<ol>
<li>“A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.”(Webster's International Dictionary)</li>
<li>“The science and art of collecting, summarizing, and analyzing data that are subject to random variation.” (A Dictionary of Epidemiology).</li>
<li>处理数据中变异性的科学与艺术——方积乾(中国统计学学家、中山大学教授)</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Python概率编程">Python概率编程<a class="anchor-link" href="#Python概率编程"> </a></h1><p>(Probabilistic programming)</p>
<table>
<thead><tr>
<th style="text-align:center">首发年份</th>
<th style="text-align:center">名称</th>
<th style="text-align:center">简介</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><a href="https://docs.scipy.org/doc/scipy/reference/stats.html">scipy.stats</a></td>
<td style="text-align:center">2001</td>
<td style="text-align:center">Scipy概率与统计模块</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/pymc-devs/pymc3">PyMC</a></td>
<td style="text-align:center">2009</td>
<td style="text-align:center">纯Python概率编程库(PyMC3基于Theano)</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/dfm/emcee">emcee</a></td>
<td style="text-align:center">2010</td>
<td style="text-align:center">MCMC采样器库</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/stan-dev/pystan">pystan</a></td>
<td style="text-align:center">2013</td>
<td style="text-align:center">基于Stan(C++)的概率编程库</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/jmschrei/pomegranate">pomegranate</a></td>
<td style="text-align:center">2014</td>
<td style="text-align:center">Cython实现的随机模型库</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/arviz-devs/arviz">ArviZ</a></td>
<td style="text-align:center">2015</td>
<td style="text-align:center">贝叶斯推断(模型无关)统一接口</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/google/edward2">edward</a></td>
<td style="text-align:center">2016</td>
<td style="text-align:center">基于Tensorflow的概率编程语言(edward2, 2019)</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/tensorflow/probability">tf-probability</a></td>
<td style="text-align:center">2016</td>
<td style="text-align:center">基于Tensorflow的概率编程库</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/pyro-ppl/pyro">pyro</a></td>
<td style="text-align:center">2017</td>
<td style="text-align:center">基于PyTorch的概率编程库</td>
</tr>
<tr>
<td style="text-align:center"><a href="https://github.com/pyro-ppl/numpyro">numpyro</a></td>
<td style="text-align:center">2019</td>
<td style="text-align:center">基于Nympy+JAX的概率编程库</td>
</tr>
</tbody>
</table>
<blockquote><p><a href="http://www.math.pku.edu.cn/teachers/lidf/docs/statcomp/html/_statcompbook/sim-bootstrap.html">《统计计算》</a> 李东风(北京大学数学科学学院 副教授)</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># %matplotlib widget</span>
<span class="kn">from</span> <span class="nn">matplotlib.font_manager</span> <span class="kn">import</span> <span class="n">_rebuild</span>
<span class="n">_rebuild</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"whitegrid"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.max_open_warning"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_rows"</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">scipy</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="kn">import</span> <span class="nn">pymc3</span> <span class="k">as</span> <span class="nn">pm</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">tensorflow_probability</span> <span class="k">as</span> <span class="nn">tfp</span>
<span class="kn">from</span> <span class="nn">tensorflow_probability</span> <span class="kn">import</span> <span class="n">edward2</span> <span class="k">as</span> <span class="n">ed</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="概率基础"><a href="https://github.com/seeingtheory/Seeing-Theory">概率基础</a><a class="anchor-link" href="#概率基础"> </a></h1><p>以下介绍取自美国布朗大学《看见统计》,中文版bug多,而且前端资源网络问题,调整了一下放在服务器上运行</p>
<blockquote><p>推荐视频教程浙江大学<a href="https://www.icourse163.org/course/ZJU-232005">概率论与数理统计</a>,<a href="https://www.icourse163.org/course/ZJU-1001640007">概率论与数理统计--习题与案例分析</a></p>
</blockquote>
<h2 id="基本概念"><a href="http://10.2.3.100:8000/basic-probability">基本概念</a><a class="anchor-link" href="#基本概念"> </a></h2>
<pre><code>随机事件 / 期望 / 方差
</code></pre>
<h2 id="古典概型"><a href="http://10.2.3.100:8000/compound-probability">古典概型</a><a class="anchor-link" href="#古典概型"> </a></h2>
<pre><code>集合论 / 古典概型 / 条件概率
</code></pre>
<h2 id="概率分布"><a href="http://10.2.3.100:8000/probability-distributions">概率分布</a><a class="anchor-link" href="#概率分布"> </a></h2>
<pre><code>随机变量 / 离散型和连续型随机变量 / 中心极限定理 </code></pre>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="统计推断:频率学派"><a href="http://10.2.3.100:8000/frequentist-inference">统计推断:频率学派</a><a class="anchor-link" href="#统计推断:频率学派"> </a></h2>
<pre><code>点估计理论 / 置信区间 / Bootstrap方法
</code></pre>
<h2 id="统计推断:贝叶斯学派"><a href="http://10.2.3.100:8000/frequentist-inference">统计推断:贝叶斯学派</a><a class="anchor-link" href="#统计推断:贝叶斯学派"> </a></h2>
<pre><code>贝叶斯公式 / 似然函数 / 从先验概率到后验概率
</code></pre>
<h2 id="回归分析"><a href="http://10.2.3.100:8000/regression-analysis">回归分析</a><a class="anchor-link" href="#回归分析"> </a></h2>
<pre><code>最小二乘法 / 相关性 / 方差分析
</code></pre>
<h2 id="推断(inference)与预测(prediction)"><a href="https://www.datascienceblog.net/post/commentary/inference-vs-prediction/">推断(inference)与预测(prediction)</a><a class="anchor-link" href="#推断(inference)与预测(prediction)"> </a></h2><table>
<thead><tr>
<th style="text-align:center">对比</th>
<th style="text-align:center">推断</th>
<th style="text-align:center">预测</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">模型选择</td>
<td style="text-align:center">对数据产生过程进行归因(Reason),选择假设最合理的模型</td>
<td style="text-align:center">对各种模型进行评估,选择性能最优的模型</td>
</tr>
<tr>
<td style="text-align:center">模型验证</td>
<td style="text-align:center">拟合优度检验(goodneess-of-fit tests)</td>
<td style="text-align:center">通过测试集的损失表现验证</td>
</tr>
<tr>
<td style="text-align:center">模型应用</td>
<td style="text-align:center">解释数据产生的过程</td>
<td style="text-align:center">预测新特征的结果</td>
</tr>
<tr>
<td style="text-align:center">统计日常</td>
<td style="text-align:center">咋了?啥样?为啥?</td>
<td style="text-align:center">让我们期待明天会更好</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="scipy.stats"><a href="https://docs.scipy.org/doc/scipy/reference/stats.html">scipy.stats</a><a class="anchor-link" href="#scipy.stats"> </a></h1><p>scipy统计学模块,包括常见统计分布、极大似然估计、置信区间、假设检验等统计方法</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="numpy.random随机分布"><code>numpy.random</code>随机分布<a class="anchor-link" href="#numpy.random随机分布"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="均匀分布(Standard-uniform)">均匀分布(Standard uniform)<a class="anchor-link" href="#均匀分布(Standard-uniform)"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1024</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[0.64769123, 0.99691358, 0.51880326, 0.65811273],
[0.59906347, 0.75306733, 0.13624713, 0.00411712],
[0.14950888, 0.698439 , 0.59335256, 0.89991535]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="标准正态分布(Standard-normal)">标准正态分布(Standard normal)<a class="anchor-link" href="#标准正态分布(Standard-normal)"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[-1.87529904, -0.56850693, -0.06510141, 0.80681666],
[-0.5778176 , 0.57306064, -0.33667496, 0.29700734],
[-0.37480416, 0.15510474, 0.70485719, 0.8452178 ]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="带配置参数的分布">带配置参数的分布<a class="anchor-link" href="#带配置参数的分布"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="离散型分布">离散型分布<a class="anchor-link" href="#离散型分布"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([13, 8, 9, 7, 14, 9, 8, 11, 11, 9])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([5, 5, 8, 4, 6, 6, 6, 6, 8, 4])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">negative_binomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 5, 7, 9, 14, 15, 6, 14, 7, 9, 7])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">geometric</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([3, 3, 6, 1, 1, 3, 1, 2, 1, 1])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="连续分布">连续分布<a class="anchor-link" href="#连续分布"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[ 0.88169829, -0.97506843, -0.3934696 , 0.27655528],
[ 0.60227603, -0.77024602, -0.40896008, 0.5647249 ],
[ 0.95862598, -0.66679706, 0.77273023, 0.14810595]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[113.23731981, 101.39008994, 87.03052898, 61.86347929],
[111.61655881, 82.21339344, 102.73634972, 87.05919982],
[107.51795522, 121.83893846, 94.13730255, 111.72173917]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">standard_t</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[-0.22458417, -0.72277945, 0.49479094, 4.08936976],
[ 0.09470278, -0.42810122, 2.73248456, -0.27440016],
[ 0.04688723, -1.85904818, -2.00913033, -1.51370431]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">beta</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([0.67257261, 0.0744057 , 0.35638654, 0.37640555, 0.75432552,
0.25481084, 0.47389881, 0.99394393, 0.04577132, 0.15859972])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="多变量分布">多变量分布<a class="anchor-link" href="#多变量分布"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">pvals</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[0, 0, 2, 2],
[1, 1, 0, 2],
[0, 0, 1, 3],
[0, 0, 0, 4],
[0, 2, 1, 1]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]),</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[ 9.73763159, 9.49225394],
[11.40208466, 10.01617814],
[ 8.35730988, 11.13369124],
[ 9.82584874, 10.64592839],
[ 9.21820763, 9.10287525]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="scipy.stats统计函数"><code>scipy.stats</code>统计函数<a class="anchor-link" href="#scipy.stats统计函数"> </a></h2><p>假设<a href="https://www.wanweibaike.com/wiki-IQ">智商(IQ)</a>服从均值为100、标准差为15的正态分布</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
<span class="n">dist</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><scipy.stats._distn_infrastructure.rv_frozen at 0x7f5a9232c510></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="生成随机变量">生成随机变量<a class="anchor-link" href="#生成随机变量"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 92.66369207, 75.57944739, 134.27787431, 101.97179807,
111.14296519, 120.19113585, 113.88208261, 116.91381411,
95.80518317, 110.87883135])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">xs</span><span class="p">))</span> <span class="c1"># PDF</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"PDF"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">xs</span><span class="p">))</span> <span class="c1"># CDF</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"CDF"</span><span class="p">)</span>
<span class="n">cdf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cdf</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">cdf</span><span class="p">))</span> <span class="c1"># P-P图</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"P-P图"</span><span class="p">)</span>
<span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="mi">100_000</span><span class="p">),</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Q-Q图"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAssAAAF+CAYAAAB051eAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeVyVdfr/8dc5wGEXEBUVcE9BzCVX0kxztKaCmTaXzNIstabsN9OYs7TMTKtt0zjNtzAdTTPLMS0d28yyhQRz31BTcUNFBNnhAOec3x8Uk8lBQOA+5/B+Ph484Nxc3FyX5/jhOvf9uT+3yeFwOBARERERkQuYjU5ARERERMRVqVkWEREREXFCzbKIiIiIiBNqlkVEREREnFCzLCIiIiLihJplEREREREnvI1OQKSxrFy5kscff5zAwEB8fHy48847sVqtzJ8/Hy8vL+Li4njsscfo3r37ebE/uueee7j33nsNrEBEpHnZu3cvs2bNIjc3l1tvvRVvb2+N2WI4HVkWjzZ69GhSU1P54IMPeO+999i+fTuTJk0iJSWFgQMHMm3aNMrKys6L/fFDg66ISNOpqKhg5syZzJw5ky+++IJvv/2W5ORkjdliODXL0iyEh4czYsQIvvvuOwAsFgsPPvggZrOZlJQUg7MTEZGtW7fi6+vLtddei8ViYfTo0fTv3x/QmC3GUrMszYrZ/L+XvMlkIjY2lsOHDxuYkYiIABw4cIAuXbpUPb755pvJysqqeqwxW4yiZlmahczMTDZs2MCQIUPO2x4QEEBxcTEA69evZ+jQoQwdOpR//OMfRqQpItJs5efnExAQUPW4VatWREdHnxejMVuMoAv8xKP9OJj6+/szZcoUcnJysFqtVd8vKSmpGpxHjRrF3//+d6NSFRFp1ry9vavmIwNs2rSJ//73v4wePbpqm8ZsMYKOLItHGzVqFMnJyXz22WdMnDjxgu8fOHCA7t27G5CZiIj8VIcOHTh27FjV45SUFEaNGnVejMZsMYKaZWmWysrKSEpKwmQyMWjQIKPTERFp9oYNG0ZGRgbffPMNhYWFfPTRR1UXZWvMFiNpGoY0O0uWLGHZsmVcccUVzJ8/H29v/TcQETFaUFAQ8+bN47HHHuPs2bOMHTsWs9nM/PnzNWaLoUwOh8NhdBIiIiIiIq5I0zBERERERJxQsywiIiIi4oSaZRERERERJ9Qsi4iIiIg4oWZZRERERMQJl15/Zfv27fj6+hqdxkVZrVa3yLM+VJv78uT63KU2q9VK3759jU6jyWjMNp4n1waeXZ9qM56zMdulm2VfX19iY2ONTuOi0tLS3CLP+lBt7suT63OX2tLS0oxOoUlpzDaeJ9cGnl2fajOeszFb0zBERERERJxQsywi0kyVl5czY8YMp9+3Wq1Mnz6dxMREZs2ahe5hJSLNkUtPwxBpaA6HgzMFVnKKykjPscKpfNqH+hPi72N0aiJNqrS0lNtuu40jR444jVm9ejUREREkJSUxffp0kpOTGTZsWNMlKSLNhsPhoMLuwGb/4bPNgc1R+dj+k88OB9gdDuw/fHY4Kn/Wwf8ed20dhMW74Y4Hq1kWj3euqIz3t2fwxf4s9mTkkV1U9pPvZgAQ3dKfyyND+GWvdozuGYGfj5cxyYo0ET8/P9asWcPo0aOdxqSkpDBmzBgAhgwZQmpqqpplkWbKbneQX1pObnE5uSXl5JWUU1BaTkFpBYWlFRRaKyiyVlBcbqOkzEZxWQWl5XZKy23kFhRh/vQs1gobZRV2yirslNvslNsclNvsVU1yQ5k0pCNP/rpXg+1PzbJ4rD0n8/i/Lw6xbm8mZTY73SOCuCamDXHtWxDRwo+MjAzatY/kSHYRe0/ms+XoOT7cdZoQfx9u6hfJ/SO70ibYz+gyRAyTm5tLcHAwAEFBQaSnp9cYb7Va3eKixtLSUrfIsz48uTbw7PqMqs3hcJBntZNZWM7ZIhtZRRWcLa7gXImNnBIb50oqyCu1k2+1cbF+1s/bhJ+3GT9vE77eJny9zFi8Tfh7g793BT6+Jny8vPAxe+NtNuFtBm+zCa8fvvYyVX7tZQazyYTZBF6m/31tNpkwmcBsApMJTPzk8Q85mExweUTDXmCtZlk8zpmCUl78ZD//2XKCFn4+TIrvyG0Doohp2+K8uDTvXGJj21U9ttsdfHsom+Wbj7M09Sj/2Xyc+0d2Y+qwzjrSLM1SaGgoBQUFABQUFBAWFlZjvFbDMJ4n1waeXV9j11ZabuPgmUK+P1PAwTOFpJ8t4nBWEcdziikqs50Xa/E20ybYlzbBvsS0bEHLQF/CAy2EBVoIC/AhNMCHEH8fgv18CPbzJsjXm0CLN2azqdrf7S7Pm7MGW82yeJT3t2Xw6Pu7sVbYuGdYZx645rJaz0c2m00Mu6wVwy5rRfrZ7jz7YRovfLKf5ZuP888J/egdFdrI2Yu4lvj4eJKTk7n22mtJSUlh8uTJRqckIrVQUmZj98k8th/LZffJPPaczOdwVmHVkWEvs4kOLQPoFB7AkC7hRLcMIDrMn/ahlR9hAT6YTNU3vs2RmmXxCCVlNv6yeg/vbj7OwE5hPH9rHzq3Cqz3/jq3CmTenQP49uBZfv+fHdzy2rf88ZexTBnaSQOIeKTjx4/z9ttvM3v27KptiYmJrFu3joSEBGJiYoiPjzcwQxFxJq+4nNT0bFLTc9iUnsPeU/lVc4DbhfgR174F1/dqS4+2LejRNoiO4YH4eGlBtNqqsVm2Wq3MnDmTU6dO0aNHD55//vkLGgVnMbNnzyY9PZ2WLVvy6quvsnfvXh544AEiIyMBePrpp+nSpUvjVSbNxum8UiYv3MT+zAJ+M7Irv/1Fd7wbaBC4slsr1s68ilkrdvC3/+5l67FzvDS2D77empYhnmHdunUAREdHn9coA1gsFpKSkoxIS0RqYLc72JmRxxf7zvDV91nsOJ6L3VE5faJfdCgzru5Cv+gw+nYIpVWQ6985z9XV2CzXZtmg6mL8/PyoqKhg+fLlTJo0ieTkZLy8vJgwYQL33XdfoxYkzcuhrELuXLCJ3OIyFk4eyIgebRr8d4QFWnjjzgG89uUhnv94PzlFZcy7cwBBvjoxIyIiTcNmd5ByOJsPd53is7RMMvOtmEzQJyqUB0Z2Y2i3VvSJDtU1No2gxr/2tVk2qLqYW265hbvuugsAu90OQH5+Pp9++inr16+nXbt2zJ07V6ez5ZLsPJHL5IXfYQLemRbP5VEhjfa7TCYT94/oRkSwH4+8t5MJ81JYNGUg4XrHLiIijcThcLDzRB4rt55g7a7TnC20EmDx4ururRndM4KRPdoQFmgxOk2PV2OzXJtlg6qL6dSpE1B5es9sNjN06FD27dvHQw89xIgRIxg/fjybNm1i8ODBNSanZYiM56q1pedYeeSTUwRazDw9uh3eBSdJSztZp33Up7aeAfD4yAie2ZDJbf/6ijnXtSPI4prv4l31uWsInlybiEhecTn/2XKc/2w+wf7MAny9zYyKbUNC7/aMjGmjo8dNrMZmuTbLBjmLWb9+PYsXL+a1117D29ubyMhIunfvDkBkZCTZ2dkXTU7LEBnPFWtLP1vE4+9tJNjfwn9mxBMVFlCv/dS3tthY6Nwxi6lvfsdz3+azZOogAiyuNyXDFZ+7huIutamhF5G62Hsynze/PcIHOzIoLbfTJzqUp2/qxY292+tOswaq8SqoH5cNgsrpFtUdCa4uJisriwULFpCUlERQUBAAixYtYu3atdjtdg4cOFDVOIvUxam8Eu6Yn4rd4WDJ1MH1bpQv1fDurZk7vh/bjp1j+pItWCtsF/8hERGRn3E4HCQfPMuf153i+rlf88GODH7dN5K1M4fxwW+GMnFwRzXKBquxWU5MTCQzM5OEhARCQkKIjo5mzpw5NcbEx8ezatUqsrKymDp1KhMmTGDFihVMnDiRlStXcttttzF69Gi6devWqIWJ5ymyVnD3os3kl5Sz+O5BdGsTZGg+v7y8Hc/d3Juvvz/Lo6t243A03K06RUTEszkcDjbsP8NN//ctE+enkp5Txqxre5D6x1/w3C29iWvfeNfhSN3UeO64umWDarO00LRp05g2bdoF+1uyZEl985Rmzm538PDyHew/nc+/Jw+kV6RrDCJjB0Zz4lwxcz8/SI+2wdxzlZZDFBGRmn176CwvfLKfbcdyiQz15+mbetErsJA+vXQg0RW53kRLkWq88tkBPt5zmkdviG2U5eEuxf/7RXcOZBbyzIdpdG0TxEgXy09ERFzD/tMFPPdRGl/sz6J9iB/P3HQ5t/aPwuJt1jUOLkzNsri8j3efZu7nBxk7IIqpwzobnc4FzGYTL4/rwy2vFTPz7W2seXAYnS7h7oEiIuJZcovLeOnTAyxNPUqQrzd/uj6GO+M7aVULN6F7HYpLO5ZdzKwVO+gTFcKTv+7lsmtzB1i8eePO/pjNJh5YtlUX/ImICHa7g2WbjjHyxQ0sTT3KnfGd+OqRkUwb3lWNshtRsywuq6zCzoPLtmICXr39Cpe/xXRUWAAv3daH3Rn5PLNWp9NERJqzg2cKGDdvI39cuYvLIoJZO/Mq/pIYR2iAbiLibjQNQ1zWsx+lseNEHkmT+hPd0pgl4urqFz0juGdYZ+Z/k86QLuH88vJ2RqckIiJNqNxm5/UNh/jn5wfxt3jxwq29ubV/lMueGZWLU7MsLunzfZksTD7C5Cs7cW1cW6PTqZNHroth89FzPPLeTvpEh9I+1N/olEREpAkcPFPI75ZvZ+eJPG7o3Y6/JMTROtjX6LTkEmkahricnKIyHlmxi5i2wfzx+hij06kzi7eZf4zvi83uYNaKHdjtWn9ZRMSTORwO3vz2CDfM/ZrjOcX86/Yr+NftV6hR9hBqlsWlOBwO/rxqF3klZbw8tq/Lz1N2pmN4II/d2JPkg9m8ufGI0emIiEgjOVdUxrQlW3hi9R6u7BrOJ78dzg29NQXPk2gahriUVdsy+Gj3aWZfF0PP9i2MTueSjB8Yzbq9mTz30T6uuqwV3doEG52SSBWr1crMmTM5deoUPXr04Pnnn79gTmVxcTEPP/ww586d44orruCRRx4xKFsR17T5SA4PLttGdmEZj9/YkylDO2lusgfSkWVxGZn5pTyxeg8DOoYxbbj73wnPZDLx3C2XE2Dx4uH/7MSm6RjiQlavXk1ERASrV68mPz+f5OTkC2LWrFlD3759eeeddzh48CCHDh0yIFMR1+NwOPj3N+mMn5eCr7eZlfdfyd3DOqtR9lBqlsUlOBwOHn1/N2UVdl64rQ9eZs8YcNoE+/GXxDh2HM9lYXK60emIVElJSWHo0KEADBkyhNTU1AtigoODKS4uxmazUVpaio+PT1OnKeJyissqmPnOdv72372MjGnD6geH0SsyxOi0pBFpGoa4hLW7TrFubyZ/uj6Gzh5297vEPu1Zs+MkL366n9E9I+gY7ln1iXvKzc0lOLhyalBQUBDp6Re+mRs9ejTz589nzZo1XH311XTo0KHGfVqtVre4ZW9paalb5FkfnlwbGF/fmcIK/vrFaY6cK2PKFS25tVcAGekHyWiAfRtdW2Ny99rULIvhzhWV8cQHe+gdFcLdQ13vdtaXymQy8eSvezHm5a/4w3u7ePvewTpVJ4YLDQ2loKAAgIKCAsLCwi6ISUpKYsKECdx222387ne/Y+vWrVxxxRVO9+nr60tsbGyj5dxQ0tLS3CLP+vDk2sDY+jYfyeF3K7ZQVmFnweSBjOzRpkH378nPnbvU5qyh1zQMMdyTa/eSV1LOnFt64+3lmS/JdiH+/PH6WDYezuad744bnY4I8fHxVfOUU1JSGDx48AUxRUVFWCyVdxuzWCwUFxc3aY4iruL9bRnc/kYqLfx9WPWboQ3eKItrq7EzsVqtTJ8+ncTERGbNmoXDceEFSs5iZs+ezdixY5kxYwYVFRW12pc0P98eOsvKrRnMuLorse3ce/WLixk/MJpBnVvy3Ef7OFtoNTodaeYSExPJzMwkISGBkJAQoqOjmTNnznkxEydOZNmyZYwbN47S0lLi4+MNylbEGA6Hg3+u/57/9+52+nUI5f37h9KtTZDRaUkTq7FZrs3V0tXFbN68mYqKCpYvX05RURHJycm12pc0L9YKG4++v5sOLQN44JpuRqfT6MxmE8/c1Ivisgqe+dB9526JZ7BYLCQlJbFmzRpeeOEFoqOjmT179nkxUVFRvPPOO7z77ru88soreHm557rnIvVRYbPzh/d28dK6A9zUL5LFUwcREqCLXJujGpvl2lwtXV1Mq1atuOuuuwCw2+213pc0L0lfHuZwVhF/+1Ucfj7N449wtzbBTBvehZVbM/j20Fmj0xERkWqUltuY8dZW3t18nAdGduPlsX3c9iZZculqvMCvNldLVxfTqVMnANatW4fZbGbo0KEsXrz4ovv6OV1ZbbzGqu1kfjn/XH+C4Z0CibBnk5aW3eC/42KMet5Gt7fzXpA3j7y7lX8lRmHxapyL/fS6FBGpu7zicu5Z/B2bj57jb7+K4874TkanJAarsVmuzdXSzmLWr1/P4sWLee211/D29q7Vvn5OV1YbrzFqczgcPLfwO3x9vHjh9iFEtPBr0P3XlpHP23OW1kxe+B3fZFn4zcjGmYKi16Xx1NCLuJesAiuTFqRyOKuIVydcodtWC3CRaRi1uVq6upisrCwWLFhAUlISQUFBtd6XNA+f7s3kywNZ/HZ0d8MaZaON6NGG6+La8s/Pvycjt8TodEREmr2TuSWMS9rI0exiFkweoEZZqtTYLNfmaumfx8THx7Nq1SqysrKYOnUqEyZMYMWKFdXGSfNTUmbjb2v20iMimLviOxqdjqEeS+gJwFP/3WtwJiIizdux7GJue30jWQVWFk8dxFWXtTY6JXEhNU7D+PFq6Z/6+dXS1cVMmzaNadOmXbC/n8dJ8/PahoNk5Jbw7rQhHrumcm1FhvrzwMhuvPjpAb7+PkuDs4iIAY6cLWLCGymUlNt4+94hXB6lW1fL+Zp3tyJN6mh2Ea9/dZhf923P4C7hRqfjEu4d3oVO4QE88cEeyirsRqcjItKsHM4qZNy8jVgr7Lx9jxplqZ6aZWkyT/43DR+ziT9d7/oXZjUVX28vnkiM4/DZIhZ9e/EVYkREpGEcyipk/LwUKmwOlt07hJ7tPfvGWFJ/apalSXx5IIvP0jJ5cNRltGmmF/U5M7JHG66JacPc9Qc5U1BqdDoiIh7vyNkibn8jBZvdwbJpQ+jRNtjolMSFqVmWRldWYeeva/bQKTyAKUM7GZ2OS3rsxp5YK2w8//F+o1MREfFox3OKuf2NFMoq7Cy9dzDdI9QoS83ULEujW7zxCIezing8oafugORE51aBTB3WhRVbTrDt2Dmj0xER8Uin8kqY8EYKRWU23rpnMDFtNfVCLk7NsjSqrAIr//jse0b0aM01MRFGp+PSHrimG22CffnLmr3Y7Q6j0xER8ShZBVYmvpFKXnE5S6YOIq69LuaT2lGzLI3qpU/3U1Ju47EbexqdissL8vVm9nUx7Diey6ptGUanIyLiMXKLy5i0IJVTeaX8e8pAekeFGp2SuBE1y9Jodmfk8e7m40y+shNdWwcZnY5buKlfJH2iQ5nz8T6KrBVGpyMi4vaKrBVMXvgdh7OKeOPOAQzs1NLolMTNqFmWRuFwOPjbmr2EBVh4cNRlRqfjNsxmE08k9ORMgZX/23DQ6HRERNyatcLG9CVb2JWRx6u392PYZa2MTknckJplaRRrd51i05Ecfj+mByH+Pkan41au6BDGTf0ieePrdI7nFBudjoiIW7LZHfy/d7bzzcGzzLmlN2Pi2hqdkrgpNcvS4ErLbTz74T5i27Vg3MBoo9NxS7Ovi8HLZOKZD9OMTkU8lNVqZfr06SQmJjJr1iwcjuovKn3jjTcYO3Ys99xzD2VlZU2cpUj9OBwOHn1/Fx/tPs1jN/bk1v5RRqckbkzNsjS4N746TEZuCY/f2BMvs8nodNxS2xA/7h/RlY92nyblcLbR6YgHWr16NREREaxevZr8/HySk5MviDl+/DgHDx5k+fLlDB8+nMzMTAMyFam7l9cdYNmm4zwwshtTh3U2Oh1xc2qWpUGdzivl/zYc4pe92hLfNdzodNzavcO7EBnqz1/X7MWmpeSkgaWkpDB06FAAhgwZQmpq6gUxGzduJC8vj4kTJ7J582aionR0TlzfouR0/vn5QcYPjObhMd2NTkc8gLfRCYhnmfPxPmwOB3+6PtboVNyen48Xf7w+hgfe3sbyzceZMKiD0SmJB8nNzSU4uPLOZUFBQaSnp18Qk5OTQ8uWLXn99dcZN24cW7ZsYcCAAU73abVaSUtz/alDpaWlbpFnfXhybXDx+r46UshzX54hPjqAO2K82bdvXxNmd2k8+blz99rULEuD2XrsHKu2ZXD/iK5EtwwwOh2PcMPl7Vjc6SgvfrKfG3q3o4WfLpaUhhEaGkpBQQEABQUFhIWFXRATFBRE586Vp7CjoqIuOg3D19eX2FjXf6OclpbmFnnWhyfXBjXX9+2hs7z0zREGdApj4dTB+Pm41x1jPfm5c5fanDX0NU7DqM0FIM5iysvLmTFjRlXczp07GT58OBMmTGDChAkcPnz4UuoRF2O3Vy4V1zrYl/tHdjM6HY9hMpl4PKEnOcVl/HP990anIx4kPj6+ap5ySkoKgwcPviAmLi6O3bt3A3Ds2DGio3XBrrimtFP5TF+8hU6tAph/50C3a5TFtdXYLNfmApDqYkpLS7n55pvPi8/Pz2fChAksW7aMZcuW0aVLl4avRgzz/vYMth/PZfZ1MQT56oRFQ+oVGcLY/tEs+vYIh7MKjU5HPERiYiKZmZkkJCQQEhJCdHQ0c+bMOS+mX79+hIaGcsstt9C5c2d69+5tULYizmXkljB54SYCfb1ZNGUQIQE6AycNq8auJiUlhTFjxgD/uwBk2LBhtYpZs2YNo0eProrLz8/n008/Zf369bRr1465c+diMtW8UoLmvxmvNrWVlNt5as1xuof7Euuf7zb/Fu70vP2qi4nVO+AP737HX0fVbq1Qd6qvrjy5tqZisVhISko6b9vs2bMviPvrX//aVCmJ1FlecTl3/XsTxWU2Vsy4kvah/kanJB6oxma5NheA1CYGoEOHDjz00EOMGDGC8ePHs2nTpmpP+/2U5r8Zrza1vfDJPnJKbLwxeTBxHS+c9+iq3O15+21hAM98uI9MczgjerS5aLy71VcX7lKbGnqRxlNabuPeJZs5ll3Mm3cPokfbYKNTEg9V4zSM2lwAUpsYgMjISK688sqqr7OztXasJzieU8wbX6dzU79I+rtRo+yOJl/Zmc6tAnnyv3spt9mNTkdExDB2u4OH/7ODTek5vHBbby1VKo2qxma5NheA1CYGYNGiRaxduxa73c6BAwfo3l1rH3qCp9buxctkYvZ1MUan4vEs3mYevSGWQ1lFLN541Oh0REQM8+xHaazdeYo//DKGX/WNNDod8XA1Nsu1uQDk5zHx8fHV7mvixImsXLmS2267jdGjR9Otm1ZMcHdff5/FJ3syeeCabrQN8TM6nWbhmpg2XN29Na+sO8DZQqvR6YiINLlFyem88XU6d8Z3ZPpwLRYgja/GOcu1uQCkupgfrVu3rurrNm3asGTJkvrmKS6m3Gbnr2v20jE8QLcSbUI/LiV37d+/4vmP9/H8rX2MTklEpMl8e6yIpzYcZnTPCJ5IiLvoQgEiDUG3u5Z6efPbIxw8U8hjN/TUepZNrGvrIO4e1pnlm0+w/Xiu0emIiDSJrcfOMeerM/SJCmXu+H54mdUoS9NQsyx1llVg5R+ffc/V3VszKvbiqzJIw3vwmm60DvblL6v3YLdfeLMgERFPcuRsEfe8uZnwAC8W3DUAf4sO0kjTUbMsdfbcR/sorbDxeEJPnQIzSLCfD3+4Lobtx3NZseWE0emIiDSanKIyJi/chMPh4MlftCM8yNfolKSZUbMsdbLlaA7vbT3B1GFd6No6yOh0mrWbr4hkQMcwnvt4H3nF5UanIyLS4ErKbEx98ztO5ZUy/64BRLbQ3fmk6alZllqrsNl57P09tAvx48FrtJqJ0UwmE3/7VS9yi8t4ad1+o9MREWlQNruD//fuNrYfz+Uf4/vSv2NLo1OSZkrNstTa0tRj7D2Vz6M39CTQt8aFVKSJ9GzfgklDOvJWylF2Z+QZnY6ISINwOBw8+d+9fLInk8du6Ml1vdoZnZI0Y2qWpVayCqy89Ol+hnVrxfWXtzU6HfmJ343pQViAhcc+2K2L/UTEI8z/Op1F3x5h6rDO3K3lScVgapalVp75MI2Scht/SdS6lq4mxN+HP10fy7Zjuby7+bjR6YiIXJLVO07y9IdpXH95W/58fazR6YioWZaL+/bgWVZty+C+q7vSrY0u6nNFN18RyeDOLXnuo326s5+IuK2Nh7L5/fIdDOrUkpfH9sWstZTFBahZlhqV2Rw8+v5uOoYHcP9IXdTnqkwmE0/f1IvisgqeWZtmdDoiInW2/3QB05ZspkN4APPu7K8bXonLULMsNfrP7lwOny3ib7/qpYHLxXVrE8z04V1ZuS2Dbw+dNTodEZFaO5lbwl3/3oS/jxeLpgwkNMBidEoiVdQsi1MHzxTyzs5z3Ni7HVd3b210OlILD1zTjQ4tA/jzqt2U2exGpyMuzGq1Mn36dBITE5k1axYOh/OLQxcuXMjkyZObLjlpVvKKy7nr35soslbw5t2DiAoLMDolkfOoWZZq2e0O/rRyF37eZp5IiDM6HaklPx8vnrnpctLPFvH2jlyj0xEXtnr1aiIiIli9ejX5+fkkJydXG5eRkcGqVauaODtpLkrLbdy7eDNHsotImtSf2HYtjE5J5AJqlqVay747xqYjOdw7IJzWwbq1qDsZdlkrbu0fxYrduew9mW90OuKiUlJSGDp0KABDhgwhNTW12rinn36ahx9+uClTk2aiwmbnwWXb+O5oDi+P7cuV3VoZnZJItWq8s4TVamXmzJmcOnWKHj168Pzzz1+wbJizmPLych588EFef/31Wu9LXMPpvFKe+3AfV3YNZ3Q3rX7hjh69IZbP9pziDyt3sur+ocYyalAAACAASURBVHjpinL5mdzcXIKDgwEICgoiPT39gpg1a9YQExND165da7VPq9VKWprrX2BaWlrqFnnWh7vU5nA4mLvxLOu+L2DGoHC6WfJIS7v4jZXcpb76UG2uq8Zm+cfTdElJSUyfPp3k5GSGDRt20ZgBAwZw2223ceTIkTrtS4zncFSuflFms/PszZdTfOaY0SlJPYQGWLhvcDjPfnmGBd8cZtrw2jU70nyEhoZSUFAAQEFBAWFhYRfEbNiwgZMnT/LNN9+Qnp7OW2+9xR133OF0n76+vsTGuv66uGlpaW6RZ324S20vfrKfj78v4IGR3fj9tT1q/XPuUl99qDbjOWvoa5yGUZvTdNXF+Pn5sWbNGtq2bVunfYnx3t+ewWdpmfx+TA86hgcanY5cgqs6BjKmZwQvfnqAg2cKjU5HXEx8fHzVPOWUlBQGDx58QcxLL73EsmXLePnll4mLi6uxURaprflfH+bVLw4yYVA0D4/pbnQ6IhdV45Hl2pymq01MXeJ+Sqf0mlZ2cQWPfXCCnm18iQ+vrMlTaquOJ9cGlf9/7urly8ZD8MCSFF68rr3HTMfw9OeuKSQmJrJu3ToSEhKIiYkhOjqaOXPmMHv2bKNTEw+2fPNxnlqbxg2Xt+OpX1+u6ZjiFmpslmtzmq42MXWJ+ymd0ms6DoeDe97cTLkdXp00hC6tK+cqe0JtznhybVBZX7/YWJ4yt+Shd7aTfNaX6Vd7xnQMd3nuXLmht1gsJCUlnbfNWaMcFRXFokWLmiAr8WQf7z7NH97byVWXteLlcX085s27eL4ap2HU5jRdbWLqEifGWLHlBOv3neGR62KqGmXxDIl92nNdXFteWneAA5kFRqcjIs3Qhv1neHDZVvpEh/L6Hf3x9dZNrsR91NgsJyYmkpmZSUJCAiEhIVWn6WqKiY+Pr9W+nMVJ0zuWXcxfVu9hUOeWTLmyk9HpSAMzmUw8dVMvWvh589A727FW2IxOSUSakdTD2UxfsoXL2gSzaMogAn1rPKkt4nJqfMXW5jRddTE/WrduXa3ixDgVNju/Xb4ds9nE38f1xazTYh6pVZAvc27pzdQ3N/Pypwf44/WuP4VBRNzf9uO5TH1zM1Fh/iyZOogQfx+jUxKpM92UpJl7bcMhthw9x1O/7kVkqL/R6UgjGhUbwcTBHZj39WG+PXTW6HRExMPtOpHHpAWptAy0sPSeIYQH6QZX4p7ULDdj246d45X135PYpz2/6htpdDrSBP58QyydwwN5ePkOzhWVGZ2OiHioPSfzuGNBKiH+PiybNoS2IX5GpyRSb2qWm6m84nIeeHsbbVv48eSvexmdjjSRAIs3/xjfj7OFVn7/nx04HA6jUxIRD7P3ZD53zE8l0OLFsnuH6KyluD01y82Qw+Hg9yt2cKaglH9NvEJzyJqZy6NC+PP1sazfd4b5X198vXMRkdranZHH7fNT8PPx4u17hxDdMsDolEQumZrlZmhh8hHW7c1k9nUx9I0ONTodMcBdV3biuri2zPl4H1uPnTM6HRHxALtO5DFxfiqBFm/enRZPp1a6C6x4BjXLzcyWo+d49qM0fhEbwdRhnY1ORwxiMpmYc2tv2ob48cDSrWQXWo1OSUTc2Jaj57h9fgrBft68M20IHcJ1RFk8h5rlZuRMfin3vbWFdiH+vHRbH91mtJkL8ffhtYn9OVtUxgNvb6PCZjc6JRFxQ8kHzzJpQSqtgnx5d3q8pl6Ix1Gz3EyUVdi5b+lWCkormHdnf0ICNE9ZKucvP3vT5Ww8nM1zH+0zOh0RcTOf7c1kyqLviA4L4N3puphPPJNuo9MMOBwO/rJmD1uOnuPV2/sR07aF0SmJC7mlfxQ7T+Qy/5t0ekWG8Ot+WkZQRC5u+ebj/HHlLnq1b8GiKYMIC7QYnZJIo9CR5WZgYfIR3k49xoyru3Jj7/ZGpyMu6NEbezKoc0seeW8nW47mGJ2OiLgwh8PB618e4pEVO7myazhL7x2iRlk8mpplD/fZ3kyeXLuXa+MieOTaHkanIy7Kx8tM0h39aR/ix72Lt3Asu9jolETEBdnsDv72370899E+Evq0Z8FdAwny1Ulq8Wxqlj3Y7ow8Zr6zjcsjQ3hlXD/MZl3QJ86FBVr49+SB2B0OpizaRF5xudEpiYgLKS6rYMZbW1iYfIS7h3bmH+P6YvFWGyGeT69yD3U0u4jJC78j1N+H+XcOwN/iZXRK4ga6tA7i9Tv6cyynmHsWf0dJmc3olETEBZzJL2XCvBTWp2Xyl4SePJ7QUwdgpNlQs+yBzuSXMmnBJmx2O4unDqJNCz+jUxI3MqRLOK+M68fmo+f4zdtbKdeSch7JarUyffp0EhMTmTVrltNbn8+ePZuxY8cyY8YMKioqmjhLcQW7TuSR+GoyBzILSZo0gMlDtUa/NC81Nsu1GUyri6lu286dOxk+fDgTJkxgwoQJHD58uNGKas7yisu589+bOFtoZeGUQXRrE2x0SuKGbujdjqd+3YvP953hkRU7sdurb6TEfa1evZqIiAhWr15Nfn4+ycnJF8Rs3ryZiooKli9fTlFRUbUx4tnW7DjJbUnf4mU28d59VzK6Z4TRKYk0uRpn5f84mCYlJTF9+nSSk5MZNmzYRWNOnTp1wTaACRMmcN999zVeNc1cXkk5k/6dyqGsQhZOHqRbWcslmTi4I7nF5bzwyX4sXmaevflynXb1ICkpKYwZMwaAIUOGkJqaesH43qpVK+666y4A7PaLn2GwWq2kpaU1fLINrLS01C3yrI+Gqs1md/DvLTms3JtHXBs/Hh3RBlNeBml5GQ2QZf3puXNP7l5bjc1ybQbT6mJOnjx5wbbY2Fg+/fRT1q9fT7t27Zg7d67uINeA8krKmbQglbRT+bx+R3+GXdbK6JTEA9w/oivWchtzPz8IoIbZg+Tm5hIcXHnmKSgoiPT09AtiOnXqBMC6deswm80MHTq0xn36+voSGxvb4Lk2tLS0NLfIsz4aorYzBaU88PY2NqXnMWlIRx69MRZfb9e47kXPnXtyl9qcNfQ1Nsu1GUyri6luW4cOHXjooYcYMWIE48ePZ9OmTQwePLjGpHWUonYKrDb+vO406eesPDoigvbkkJbWMGvlGl1bY/Lk2qDh6rsuykFW71CWbT7OudxcZsa3wsvghtnTn7umEBoaSkFBAQAFBQWEhYVVG7d+/XoWL17Ma6+9hre3lgjzdN98f5bfLt9OQWk5fx/Xh5v6RRmdkojhahz5ajOYVhdTVFR0wbbIyEi6d+8OQGRkJNnZ2RdNTkcpLu5UXgkzF2ziaG45SZMGMCq2YeeTucu7wfrw5NqgYet7JtZB61YHmPv5Qbz8AnllfF9DjzS5y3Pnyg19fHw8ycnJXHvttaSkpDB58uQLYrKysliwYAHz588nICCg6ZOUJlNus/PyugO8/uUhurYOYsnUQbrbq8gParzA78fBFCqnW1R3JLi6mOq2LVq0iLVr12K32zlw4EBV4yz1dyirkFtf28ipvFIW3T2wwRtlkR+ZTCZ+N6YHj94Qy0e7TzNl4XcUWrUygjtLTEwkMzOThIQEQkJCiI6OZs6cOefFrFq1iqysLKZOncqECRNYsWKFQdlKYzqUVcitr2/ktQ2HGDcgmtUPDFWjLPITNR5ZTkxMZN26dSQkJBATE1M1mM6ePdtpTHx8POXl5Rds69atGw8//DBvvfUWo0ePplu3bo1enCfblJ7DjLe2YALemTaEXpEhRqckzcA9V3WhZaCFWSt2Mvb1jcy/awDtQ/2NTkvqwWKxkJSUdN62n47tANOmTWPatGlNmZY0IbvdwZsbj/DcR/vwt3jx6u39uLF3e6PTEnE5NTbLtRlMq4upblubNm1YsmTJpeQqP1j+3XH+/P4uosMCWDB5IJ1bBRqdkjQjN18RRctACw+8vY3EV5N5487+9OtQ/XxXEXFNB88U8qeVu9h0JIeRPVoz55beWpNfxAndlMSNlNvs/G3NXh55bydDuoSz6v6hapTFECN6tGHl/VfibzEzbl4K7205YXRKIlIL1gobc9d/z/X/+Jr9mQU8f2tv/j15oBplkRro0mY3cTK3hAfe3srWY7lMvrITj94Qi7eX3uuIcbpHBPPBb4Zx/9ItPPyfHWxKz+Gvv4rDz8c1lpgSkfNt2H+Gv67ZS/rZIm7s3Y4nEuJoHexrdFoiLk/Nshv4bG8mv1+xgwqbg39O6EdCH80pE9fQMtDCW1MH88pn3/PqFwfZcSKXuRP60T1Cd44UcRWHsgp59sN9fJaWSZdWgbx59yCu7t7a6LRE3IaaZReWX1rO39bsZcWWE/Rs14J/TbxC0y7E5Xh7mfn9tT0Y2Lklv3t3OzfO/YaHx3Tnnqu6GL4es0hzllVg5R/rD7Bs03H8fbyYfV0MU4d1xuKts5IidaFm2UV9vi+TR1ft5nR+KQ+M7MbMUZdpgBOXdnX31nzy2+H8edUunv1oH5/uzeSZmy6nR1sdZRZpStmFVuZ9dZjFG49SbrMzcXAHZo66jFZBmnIhUh9qll3MiXPF/HXNXtbtzaRbmyDeu+9KrTQgbqNVkC+v39GfD7af5C9r9nDD3K+5e1hnHhp1GYG+Gm5EGtOpvBLmb87mo2VHKS23kdinPTNHXUaX1kFGpybi1vTXy0Xkl5Yz78vDzP/mMCZMOl0mbstkMvHrfpEM796aOR/tY95Xh/lgewa//UV3bu0fpQtTRRrY3pP5/Ds5nQ+2Z2CzO0jo054Hr7mMbm3UJIs0BDXLBisps7E09Sj/+uIg54rLSejTnj/8MoZI3ehB3FzLQAtzbu3N2IHRPL12L39YuYv536Tz+zHdGdOzLWbNZxapN2uFjU/2ZLJk4xG+O3IOfx8vJg7uyIh2NkYM7G10eiIeRc2yQfJLy1my8SgLvkknp6iMYd1a8YdfxuhOfOJx+ncM4737ruSTPZk8/8k+Zry1lW5tgrh/RFcS+rTHR0eaRWrF4XCw52Q+K7ac4P3tGeQWl9MxPIBHb4jltv7RhAT4kJaWZnSaIh5HzXITO5BZwOKNR1i1NYOiMhsjerTmNyO7MbBTS6NTE2k0JpOJ63q15Rexbfhw92n+74uD/G75Dp7/eD8TB3dg/KAOWu9VpBoOh4PvzxSyducp1uw8yeGsIizeZq6Na8tt/aMY1q2VztKINDI1y00gv7SctTtPsXLrCb47cg6Lt5nEPu2ZfGUnHUmWZsXbq/K1f+Pl7fhi/xkWfXuEl9YdYO7n3zMqJoJb+kcxokdrHW2WZq2sws7mozl8nnaGdWmZHM0uxmSC+C7h3DOsCzdc3o6QAB+j0xRpNtQsN5K84nLW78vk492n+fJAFtYKO11bB/KHX8YwdkA0LQMtRqcoYhiz2cSo2AhGxUZwKKuQt1OP8cH2DD7ec5qwAB/G9GzLdb3acmW3cHy9dUdA8Ww2u4O0U/mkHM5m46FsNh7OprjMhsXLTHzXcO69qgtjekboltQiBlGz3EAqbHZ2n8zn6wNZfPV9FluP5WKzO2jbwo8JgzpwU79IekeFYDLpdJnIT3VtHcRjN/bkD7+M4evvs3h/20nW7jrFu5uPE2jxIr5rOMO7t2ZYt1Z0bhWo/0Pi1hwOB2cKrOzOyGPH8Vy2Hc9l+7FcCqwVAHQKD+CWK6IY3r018V3DCdKSiyKG0//CesoqsLIrI5cdx/P4au8p9i87SnGZDYBekS2YPrwLY+La0jsyRPPJRGrBx8vMNTERXBMTgbXCxrcHs1m/L5MvD2TxWdoZAFoFWRjYqSWRfmX8wjebXpEhaibEZZ0rKuPw2UIOnSlif2YBBzILSDtVwNlCKwBmE8S0bUFi3/YM6tySwZ3DaRuio8cirkZ/ZWrw4xGA9LNFHDlbxKGsQvadLmD/6QLOFFQOdiYTdAq1cGv/KAZ2akl813DdJUnkEvl6ezEypg0jY9rgcDg4kl1MyuFsvkvPITU9h49yS5i/OQWTCTq0DKBHRDAxbYPp0jqITq0C6RQeQIi/j45C18BqtTJz5kxOnTpFjx49eP755y/496pNTHNVbrOTXVjGmYJSTuWVcjqvlJN5JZzIKeFYTjHHcorJKymvivfzMdOtTRAjerSmV/sWxEWGENe+BQEW/RkWcXU1/i+t72BaVlZWq21GDboVNju5JeXkFpeRU1ROdqGVs4VWzhRYOZ1Xyun8UjJyS8g4V4K1wl71cxZvM90jgrjqstbEtgumd1Qoce1bcOzw98TGxhpSi4inM5lMdG4VSOdWgUwY1AGAjVt3UxrQhp0n8tifmc++0wV8lpaJ3fG/nwv29SaqZQDtQ/yICPGjbQs/Wgf70irIl1ZBFsICLIQG+NDCz6dZnv1ZvXo1ERERJCUlMX36dJKTkxk2bFidY9yR3eGgpMxGabmN4nIbJWUVFJfZKLRWUGS1UWStoKC0nPzSCvJLyskrKSe3uJyc4jJyisrILrSSW1KOw3H+fi1eZqLC/IlqGUDvqBA6twqka+sgurQOJCosAK9m+DoT8QQ1Nsv1HUxPnTpVq22NMehuSs9h1baMqsGvuMxGUVnFD4Nf5cBX9MN0iZ8zm6B1sC9tW/gR0zaYX8RGEBXmT6fwyj/U7UP9NdiJuIBQfy9ifzjy/CNrhY3jOcUcziriaHYxJ84Vc+JcCSfzStl2PJecorJq92UyVTbWLfx9CPL1JsjXmwBfbwItXvhbvGjh58M9V3UmKiygqcprEikpKYwZMwaAIUOGkJqaesGYXJuYS3XiXDELvkmn3GbH4QAHlWf1HI7Kptb+42d75de2H7622R3YHZWfK+w/+2yzU2ar/Fxus1NWYafMZsdaUflRVmEH0muVn8XLTGiAT+WHv4XL2gQxuHNLWgX50qaFL62DfGkb4ke7EH/CAy3N8o2XiKersVmu72B68uTJWm1rjGZ554lc1u3NJMDihb+PFwG+XgT5ehMR7EewX+UfxBZ+PlWDX1iApfJIU7CFlgEW3YpXxE35envRrU0w3doEV/t9a4WNs4VlVWeSzhWVV51hKiitIL+0nILSyjfWecVlnMq1UVJuo8Lm4Nq4th7XLOfm5hIcXPlvFRQURHr6hc1jbWJ+ymq11vmmGDtPl/D+1jPY7A5MJjBh+uFz5QEM8w9nIM0m8DKb/rfdbKrcZvrhsxm8zSbMJhM+ZvD3NuFlMeFjNuPjZcbHbMLHy4TFy4TJYSPQ14KvtwlfLxN+3mb8vE34+5jx9zET4GMiwGImyGLG4vRvgg0orvwogLMFcLZOlTeO0tJSj74xiSfXp9pcV43Ncn0H09puu5j6DLxDW8HQWyIvElXxw0dJ5XiXB9l5kF2n3/Q/7v4iqIlqc1+eXN+l1OYDtAPaBQABQDiAGfD94aMaZWdI++EiQ08RGhpKQUEBAAUFBYSFhdUr5qd8fX3rPCUtNhbGjazTj1yytLQ0j50658m1gWfXp9qM5+zvSo3Ncn0H06Kiolptu5j6DLxGcJcXQX2oNvflyfW5S22u/GYlPj6e5ORkrr32WlJSUpg8eXK9YkREPF2Ncw5+HCihcrrF4MGDaxVT220iImKMxMREMjMzSUhIICQkhOjoaObMmVNjTHx8vEHZiogYp8Yjy4mJiaxbt46EhARiYmKqBtPZs2c7jYmPj6e8vLxW20RExBgWi4WkpKTztv10bHcWIyLS3NTYLNd3MK3tNhERERERV2ZyOH6+UqTr2L59O76+usGHiLgnq9VK3759jU6jyWjMFhF35mzMdulmWURERETESFpUWERERETECTXLIiIiIiJOqFkWEREREXFCzbKIiIiIiBNqlkVEREREnFCzLCIiIiLihJrlenjjjTcYO3Ys99xzD9nZ2dx+++0kJCTw4osvGp3aJSkuLua+++5j/PjxPP/88+Tk5HhMbeXl5cyYMQOoXEdx+vTpJCYmMmvWLBwOR7Xb3MVPa4PKGweNHTuWGTNmUFFR4da1wYX1ASxcuJDJkycDeNTrVBqPxm33ojHbPWsDzxyz1SzX0fHjxzl48CDLly9n+PDhPPPMM4wYMYIPPviAr776ivT0dKNTrLc1a9bQt29f3nnnHQ4ePMgTTzzhEbWVlpZy8803k5ycDMDq1auJiIhg9erV5Ofnk5ycXO02d/Dz2jZv3kxFRQXLly+nqKjIrWuDC+sDyMjIYNWqVVWP33zzTY94nUrj0bjtXjRmu2dt4LljtprlOtq4cSN5eXlMnDiRzZs3c+LECa688krMZjODBg0iNTXV6BTrLTg4mOLiYmw2G6WlpWzbts0javPz82PNmjW0bdsWgJSUFIYOHQrAkCFDSE1NrXabO/h5ba1ateKuu+4CwG63A9XX6y5+Xh/A008/zcMPP1z1ODU11SNep9J4NG67F43Z7lkbeO6YrWa5jnJycmjZsiVLly4lMzOTnTt3EhwcDEBgYCB5eXkGZ1h/o0eP5uuvv2b06NF07dqVwMBAj6ntp3Jzc6vqCgoKIi8vr9pt7qhTp0707t2bdevWYTabGTp0qMfUBpVH0WJiYujatWvVtnPnznnk61QajsZt96Yx2z1rA88Zs9Us11FQUBCdO3cGICoqisjISAoKCgAoLCwkLCzMyPQuSVJSEhMmTODzzz8nLy+PI0eOeExtPxUaGlpVV0FBAWFhYdVuc1fr169n8eLFvPbaa3h7e3tUbRs2bGDjxo387ne/Y8+ePbz11luEhYV55OtUGo7GbfemMdt9a/OUMVvNch3FxcWxe/duAI4dO0bnzp1JTk7GbrezadMmBg8ebHCG9VdUVITFYgHAYrHQr18/j6ntp+Lj46vmU6WkpDB48OBqt7mjrKwsFixYQFJSEkFBQUD19bqrl156iWXLlvHyyy8TFxfHHXfcUVWfp71OpeFo3HZvGrPdszbwnDFbzXId9evXj9DQUG655RY6d+7MnDlz+PLLL0lMTGTEiBF07NjR6BTrbeLEiSxbtoxx48ZRWlrKq6++6jG1/VRiYiKZmZkkJCQQEhJCfHx8tdvc0apVq8jKymLq1KlMmDCBFStWeExtzkyaNMkjX6fScDRuuzeN2e5ZmzPuOGabHO62JomIiIiISBPRkWURERERESfULIuIiIiIOKFmWURERETECTXLIiIiIiJOqFkWEREREXFCzbKIiIiIiBNqlkVEREREnFCzLCIiIiLihJplEREREREn1CyLiIiIiDihZllERERExAk1yyIiIiIiTqhZFhERERFxQs2yiIiIiIgTapZFRERERJxQsywiIiIi4oSaZRERERERJ9Qsi4iIiIg4oWZZRERERMQJNcsiIiIiIk6oWRYRERERcULNsoiIiIiIE2qWRUREREScULMsIiIiIuKEmmURERERESfULIuIiIiIOKFmWURERETECTXLIiIiIiJOqFkWEREREXFCzbKIiIiIiBNqlkVEREREnFCzLCIiIiLihJplEREREREn1CyLiIiIiDihZllERERExAlvoxMQuVQrV67k8ccfJzAwEB8fH+68806mTZtW59i7776bw4cP4+fnV+3PFhcXc8UVV/DKK680Wi0iIs3B8uXLmTt3Lna7nd/85jdMnDixzrEas6WpqFkWjzB69Gj+/ve/k52dze23386gQYPo27dvnWK9vb2ZM2cOgwcPZsOGDaxfv54nn3yy6uc+++wzPv7446YqSUTEIx0+fJgXX3yRZcuW4ePjw6233sqQIUPo2rVrnWI1ZktT0TQM8Sjh4eGMGDGCTZs21TnW27vyvWNBQQHPPvsse/bs4eabbyYuLo6NGzeeFyMiIvWzYcMGhg0bRteuXenQoQNXXXUVX375ZZ1jNWZLU9GrSDyS2Vz794E/jbXb7Tz44IMEBQWRmJiIyWTi0KFDxMfH89lnnzVGqiIizcrx48dp37591eN27dpx4sSJesVqzJamoGZZPEpmZiYbNmygffv2vPHGG+d978YbbyQuLu6C2Jdeeqlqm9lsZvbs2QQGBjJu3DgcDgdLlixpsvxFRDyd1WolLCys6rHFYmHXrl0MHjz4vLgbb7yx2ticnJyqxxqzpSmoWRaPsH79eoYOHYq/vz9TpkxxerHIypUrL4jt1atX1fezs7OZN28eFRUVTJw4kZiYGJ566iny8/O55ZZbmqocERGP5e/vj9VqBeDLL7+krKyMrl278uabb14Q++STT14Q+9ML+jRmS1NQsyweYdSoUfz973+/5Njw8HCeeeYZIiIiWLhwIS+++CJ//OMf6dOnD5s3b2b37t0NmbaISLMTHR3Ntm3bAHjmmWfw8vJy2theLFZjtjQFXeAn8gOHwwFAREQEL7zwAp9//jnjxo0jJiaG0NDQ82JERKR+RowYQXJyMgcPHmT8+PGkp6czYsSIOsdqzJamoiPLIj+oqKio+nrWrFkcPHiQVatWceTIESIiIi6IERGRuuvUqROzZ8/mrrvuAiAgIACLxVLnWI3Z0lRMDr3tEgG0wL2IiBF27NhBnz596hyrMVuaipplkR/k5eXh7+/v9AiHiIi4Do3Z0lTULIuIiIiIOKEL/EREREREnFCzLCIiIiLihEuvhrF9+3Z8fX1rFWu1Wmsd6648vUZPrw88v0ZPrw/qVqPVaqVv376NnJHrqMuYXV/N4TVWE9XffOtvzrVD09TvbMx26WbZ19eX2NjYWsWmpaXVOtZdeXqNnl4feH6Nnl4f1K3GtLS0Rs7GtdRlzK6v5vAaq4nqb771N+faoWnqdzZmaxqGiIiIiIgTapZFRERExO0tXQqdOoHZXPl56dKG2a9LT8MQEREREbmYpUth2jQoLq58fPRo5WOAiRMvbd86siwiUoP1aZmcLbQanYaIiNTgz3/+X6P8o+Liyu2XSs2yiIgTe0/mM/XNzXz9fZbRqYiISA2OHavb9rpQsywi4sTS1KP4epsZ2aON0amIiEgNOnSo2/a6ULMsIlKNQmsF72/L4Mbe7QkNQqNFQQAAIABJREFUsBidjoiI1ODppyEg4PxtAQGV2y+VmmURkWqs2pZBUZmNO4Y0wGEJERFpVBMnwrx50LEjmEyVn+fNu/SL+0CrYYiIXMDhcLA05Shx7VvQNzrU6HRERKQWJk5smOb453RkWUTkZ7YcPce+0wXcMaQjJpPJ6HRERMRAapZFRH7mrZSjBPl6k9invdGpiIiIwdQsi4j8xI7juazecZKxA6IJ9NVMNRERoy1dCqNGda26M9/991dzpz6HA775pvKb773XoL9ffwlERH5QbrMz+72dtA725aFfXGZ0OiIizdLSpZU3Ezl6tPJiPYcDoHJVoqNH4bXX/hdbcfQEB6YsJv/3i2hx+nsIDISBAxs0HzXLIiI/SPryEPtOFzBvUn9C/H2MTkdEpFlZuhQeegiys/+3rbJRPp8vpfyKD5jCQkazDq9yO6nnhjN44Z/g1lshKKhB81KzLCICHDxTyNz1B7nh8naMiWtrdDoiIs3K/ffD669X3xxXcnAFW5nCQm7nbVpyjmNE8wx/YhGTSS/rin1y4+SmZllEmr2SMhu/fXc7/hYv/pIYZ3Q6IiLNxtKlMH06FBVV//3WnGEiS5nCQnqzixL8WMVNLGQKn3MNdrwA6NiIS+KrWRaRZs1ud/D/3t3G7pN5zJs0gNbBvkanJPL/27vzuKir/Y/jL5BFEZBFRVxY3BA0twyXSC3DbNFIUeGiyFi5lNqvrLzdrnVbzJvd671dKyOrIRVRyiVpdalMSdw1FXBlUVFAFhmWYZv5/UFSJgMjMgwz83k+Hj2C73znzOc4zNe3h/M9RwizV19ItqGSB/kWBUoe4StsqWIfgcxhJesJ4xo3rn/fVDv16SJhWQhh0f75XSrfn8zmlUcCCA7wMHY5Qghhtp566sab8/4sgJMoUDKdNXiQwxU8+C//RwxRJFPzWz93d5g7Bb75BjIzwcurJigbYjOS6yQsCyEsljIxjY9+Pk/kcG8Ud/sYuxwhhDArsbEwcyZUVOg+x4UCwliPAiWBHKASGxIYjxIF3zEOjbUtGg14elbwzjt2Bg3FukhYFkJYpJU/nePt71IZG+DBK48EyE59QgjRRBoaQbammjHsRIGSx9hMa8o5Rn/+j/8QSwRX6QDA3LnwwQc1z0lJOYe/v38zVH8zCctCCIui1Wr517ZTvP/jOSYM6My/pwzAppXszySEELejb19ITq7/nB6cJYoYZvAZ3bhIPq6s4kmUKDjCIOD3QYs/BmVjk7AshLAY5VXVLN5ygviDFwkP7MabIXfQytpyR5QrKyuZP38+H374Ib/++ivz5s2jS5cuACxZsoQuXbqwYMECLl++jJ+fH8uWLZMReCHETezsoLKy7sfaUsxkPkeBkpHsphprvucBnmM5W5lABTfeVO3oWLOEnDGmW+giwylCCIuQXaQm7KMk4g9eZMF9PXnrMcsOymq1mokTJ5KYmAhAUVER4eHhxMXFERcXR/fu3dm6dSseHh5s3bqVoqKi2nOFEALg/vtrdti7OShruYef+RQFV+iEkpl04gov8RZeZPIw3/AFk28KynPngkrVsoIyyMiyEMICJJ3PY0HcEYrLq/hw2mDG9fM0dklG17p1axISEggODgZqwvK2bdvYuXMnnp6e/O9//yMpKYmxY8cCMGzYMPbt20dQUJAxyxZCGFl90y26coEZfEYUMfTkHCocWU8YShT8wgj+OM3ij9zd4d13W15Ivk7CshDCbFVUaVi+/TTRP5/Dx70tqx8PpE8nZ2OX1SJ5eXnxzDPPMHr0aMLCwti/fz+FhYU4OTkB4OjoSFpaWr1tlJeXk5KSYtA61Wq1wV+jJZP+W27/jdn3r75y5sUXPfk97P4eeltTRghbUKDkfnZgjZYfGc3rvMJGJlFK2z+09Pv2fGFhBbzySvYNr1Nf94zZfwnLQgizlJxVxIsbj3HiUhHhgd1Y/EgADnZyydOlS5cu9O7du/brvLw8XFxcUKlUAKhUKlxdXettw97e3uB3q6ekpBjtjviWQPpvuf03Rt91jyJruYsDKFASxnpcKSQdb95gMZ8xgzS619ne3LlWf7hpz+23//TTHP3XFcblbw4hhFkpq6jm3Z1nWLX7PC5tbImeficP9O1k7LJavJiYGHx8fHj00Uc5ffo0c+fOpbS0lMTERB544AGSkpKIiooydplCiGag6z7ejmQzjbUoUNKPk5TRmo1MQomCH7kXrY5b4VrSyhaN0aRh+Y93VgMsWrSItLQ03NzceO+996iurpY7q4UQBqHVatmWnM2bXydzIb+MKUO68reH/HFxsDN2aSYhIiKChQsXsnbtWoKDg+nZsydeXl5s376d8ePH06dPH4YPH27sMoUQBqIrjtlQycN8jQIlD/M1NlSzl2HMIpoNTKWIdnU+b8wY2LHDgAU3oyYLy2q1msmTJ5Oeng7AwYMHqaqqIj4+nunTp5OYmEhOTg4eHh5ER0cze/ZsEhMT5WYRIcRtS71SxBtfJZN4No9eHR1Z9+RQRvRob+yyTML27dsB6NixI2vWrLnhMTs7O6Kjo41RlhCiGdQ3XtmP4yhQMo21dCSXy3Ti3ywkhihSqX86hKmPJP9Zk4XlP99Z3b59e2bMmAGARqMBkDurhRBN6kJ+Kf/dcYZNRy7i3NqW1yb0JWKol2wyIoQQOtS3moUr+YQThwIlQzhEBbZsZQJKFHzPA1Q3EBsDAuDkSQMUbWQGm7Ps4+MD1IxaWFtbc/fdd7N69WqD3VltCXfImnsfzb1/YP59bK7+5ZZU8fmJQr49XYQVVkwMaMeUfi44ty7jzOlTBn1tc38PhRDmSdcosjXVBLMdBUpC2II9FRxhIAt4l3X8hTzq/y2diwsUFBig4BbEoDf47dy5k9WrV7Ny5UpsbGwMeme1Jdwha+59NPf+gfn30dD9u1hQSvSu82w4cBGNVkvond145v5eeLZrY7DX/LNb6WNzhuqSkhJUKhWOjo58//33jBgxAk9PWU9aCEtW3zSLXpwmihgiWU1XLnEVdz5kDkoUHGNgg21rtQ2eYjYMFpZzc3P55JNP+Pjjj3FwcABg+PDhcme1EOKWpVwuInrXORJ+vYy1FUwe0o25o3rQzc3B2KW1GPPnz2fOnDls3boVd3d3NmzYQHx8vLHLEkIYga6Q7IiKKcSjQEkQiVRjzbc8yDO8y1c8ctOOenWxpJB8ncHC8ubNm8nNzeXxxx8HYNKkSUyYMEHurBZC6EWj0bLrdC6fJqax+8xV2tq1QjHCh5lBvnR2ab6RZFNRVlZGYGAgn376KW+++SZ79+41dklCiGakKyBboWEkP6NASShf0JZSUvFjEf9kDdO5TOcG2za3G/ZuVZOH5et3Vs+aNYtZs2bd9LjcWS2EqM+1sko2HrrI2qQMzl8twcPZnhce8GPaUG/aOdgau7wWy8vLi5CQEEJCQti0aZNMwRDCQugKyV5k1G493Z00inAilgg+ZSb7GIquraf/yBJHkesim5IIIYxOq9Vy7OI14vZl8uWxS6grNQzycuHdsIE8dIcntrK6RYPefvttCgsLcXFx4fLly0yYMMHYJQkhDERXQG5NGRPZhAIl9/ED1mjZyX28wutsYiJlNDx1TQLyzSQsCyGMpqCkgi+PXmL9gQukXlHRxrYVjw3qQsRQb/p1qXuhe1G3kpISvvrqKwoLC+nXrx8qlap2+2ohhHmoOyRrGcq+2q2n21FEGj68xqt8xgwy8GmwXQnI9ZOwLIRoVpXVGn4+ncsXhy6yIyWbymotd3Rpx5LH+jFhQGecWstUi8Z45plnGDVqFHv27CE4OJjFixezYcMGY5clhLhNvwfkPjcc9+AK01mDAiUBpFBKG74gFCUKdjFK59bT13XuDJcuGaZmcyNhWQhhcFqtlqMXCtly5BIJv14mv6QCt7Z2TB/mw+QhXfH3dDZ2iSavuLiY6dOns337dvz8/LC2lqkrQpiqukeQrbClgkf4CgVKHuRbbKgmkRE8wSrimYKKhq+lMop86yQsCyEMQqvVcjq7mIRjWWw9lkVmfil2NtYE+3sQMqgLo3p3wM5GAl1Tufvuu4mMjOTixYu89NJLDB061NglCSFuka65yP05hgIlEcTSgatcojPv8AIxRHEaP73alpDceBKWhRBNRqvVcuqKim+OX+br45c5m1OMtRXc3bM98+7ryQN9O9GujUyzMIT58+eTmprK+fPn6d69O3369Gn4SUIIo9MVkN3I4y+sQ4GSwRyhHDu+5FGUKNhOcINbT4ME5KYiYVkIcVu0Wi3HL13juxNX+PLwRS4VpWFlBUN93Zgxoh/j+naig1PDC92L2/PSSy/Vfr17924Ali5daqxyhBD1sLODysqbj7eiirFsQ4GSCWzFngoOMZh5rCCOcPJxb7BtS9h+urlJWBZC3LLKag370/LZnpzNtpNXyLqmppW1FXd4tGbufX6M7etBR6fWxi7TosybNw8AtVrN7t27yc3NNXJFQog/0zWK3JtTKFASyWo6c5lc2vMBT6FEwXH669W2jCIbjoRlIYRerpVW8tPpHHam5PDTqRyK1FXY21hzT68OPBvcm/v9PbiSeQ5/f29jl2qRunTpUvt1jx49eOONN4xYjRDiOl0B2YkiprIBBUpGsJcqWvEtD/I0M/mah6nETq/2JSQbnoRlIUSdtFotZ3OK+SE1hx9P5XAgvYBqjRa3tnaM7duJ4AAP7unVHge73y8jV4xYr6V77733ar/Oz88nPT3deMUIIeoMyVZoGM1PKFAyiY04UEYy/rzAMtYwnWw61dOiluu77klAbl4SloUQtUrKq9h7Lo+fTufwY2oulwrLAOjTyYnZI7szxt+Dgd1caGXd8DaponkFBgbWfm1ra0vfvn2NWI0QlknXKLI36UQRwww+w5d0CmnHaiJRomA/geiz9XRycir+/v5NW7DQi4RlISyYVqsl9YqKn0/nsut0LgfS86ms1uJg14oRPdrz1L09uNevI51d2hi7VNGAP4ZlIUTz0RWQ21DKJDb+tvX0j2iwYidjeJklbOYx1Oh3Xb0+ipyS0kQFi1smYVkIC5NTpGbP2avsOXOV3WevkqsqB2pGj2fe7cvI3h0Y4uOKvU0rI1cqhBAtk66ADFqGkcRMPmUqG3BGxTm6s5jX+YwZXMBLr/ZlmkXLImFZCDNXXF7F/rQ89pzJI/HsVU5lqwBwdbAlqFcHRvZqzz29OtCpnaxeYYqmT5+O1Z/+5tZqtVhZWbF69WojVSWEedIVkj3JIpLVRBFDH05RggOfMxklCnZzT4NbT4ME5JZMwrIQZqa8qpojmYX8cvYqiefyOHahkCqNFjsba+7ycSVkUB/u6dWeAE9nrGXusclbs2aNsUsQwqzpCsh2lDOeBBQoGcd3tELDboJYxot8zmSKcdKrfQnJLZ+EZSFMXGW1hl8vFrL3XB57z+dxML2A8ioN1lZwR1cXZo3szoge7Rni40prW5laIYQQDdE9zQIGcqR262l38rlIF/7JX4khirP00qt9CcimRcKyECamslrD8UvXSDqfR9L5fA6m51NaUQ3UzDuOGOrNiB7u3OXrJltLWxCVSsWePXsoL6+Zg56Tk8OsWbOMXJUQpkVXSHbnKhHEokDJQI6hxp4thKBEwQ7uR4N+AxESkk2ThGUhWrjyqmqOX7zGvrR8ks7ncSijoDYc9/ZwJPTOrgzv7s7Q7u64tdVvEXthfubNm8eAAQNITU2lZ8+ess6yEHrSFZBbUcU4vkOBkvEkYEclBxjCU7xPHOEU4qpX+xKQTZ+EZSFamLKKao5kFrAvLZ/9afkczqyZVgHg5+HE5Du7MrS7O4G+brR3tDdytaKlUKlUPPvss8yfP58XX3yRsLAwY5ckRIumKyT3IQUFSqazBk+ukEMHVjCfGKI4wR16tS0B2bxIWBbCyApLKziYXsCB9Hz2p+dz/OI1qjRarK3A39OZvwz1YqhvTTiWkWOhy9ChQ4mNjcXNzY1XXnmFiooKY5ckRIujKyA7c40w1qNAyTD2UYkNX/MwMUTxNQ9TRcNT2iQgmy8Jy0I0s6zCMg6k59f8l1ZQu5SbbSsrBnR14cmR3Qn0ceNOH1ecW8ucY6GfRYsWUVlZiZWVFQcOHODZZ581dklCtAi6ArIVGu7jBxQomcgm2qDmBH15jn8TSwQ5eOjVvoRk8ydhWQgDqtZoOZ2t4mBGAQfT89l7JoeckvMAONrbMMjLhUf6e3KXrxsDu7nIahXilo0ePZrAwECCgoIICgrCzc2N4cOHG7ssIYxOV0j25Xzt1tPeZFKAC0oUKFFwkCHos/W0BGTLImFZiCZUVlHN0QuFHMrI52BGAYcyClCpqwDo6GSPX3t75t7Xm7t83OjTyQmbVg0vVC9Efb7//nsOHDjAL7/8QkxMDNbW1rXBeciQIcYuT4hmpSsgO1BCKF+gQMlodqHBiu0Es4i32UII5TS8KZMEZMslYVmI25BTpP5t1LiAQxn5nMwqokpTc0Xt1dGRR/p35i4fV4Z4u9HNrQ2pqan4+/sauWphTuzt7QkKCmL48OEcO3aMb775ho8//phdu3axefNmY5cnhMHVt/X0CH5BgZKpbMCJYs7Qk5d5k9VEcpFuerUvIVlIWBZCT9UaLaeuqDiUkc+hjAIOZhRwsaAMAHsbawZ0q9kAZIiPK4O9XHFxkJvxhOHFxMSQlJTEuXPnGDBgAEFBQfz000+0b9/e2KUJYVC6QnJnLtVuPe3HaYppSzxTUKJgD0HINAtxqyQsC6FDkbqSo5mFHMoo4HBmAUcyCyku/31KxZ3erkSN8GGIjxsBns7Y2ciUCtH8SktLefrpp+nXrx9W9W07JoQZCAjwq/O4PWomsBUFSsayjVZo2MVIlvISXxBKCY56tS8hWdSlScNyZWUl8+fP58MPP6S8vJwFCxZw+fJl/Pz8WLZsGRUVFTcdk4u7aAm0Wi2Z+aUc+m2e8aGMmlUqtFqwtgK/Ts6EDOrMnd41Uyq6uraRn13RIjz11FONfq5cs4Wp+P3H7o8/f1oGcxgFSv7COtwoIJNuvMXfiCGK8/TQq20JyKIhTRaW1Wo1kydPrt01auvWrXh4eBAdHc3s2bNJTEzk8uXLNx0LCgpqqhKE0Ju6spoTl67VBuPDmQVcLa5Zl9bJ3oaBXi6M69eJO71dGdjNBSdZwk2YGblmi5au7n+XWdGeXKaxFgVK+nMcNfZsYiJKFPzAfXptPS0BWdyKJgvLrVu3JiEhgeDgYACSkpIYO3YsAMOGDWPfvn1kZWXddEwuvKI55BSpfx81zizgxKVrVFbXXC193B0Y2bsDg71cGeLjSq+OTrSyltEzYd7kmi1aIl2/uLChkgf5FgVKHuErbKliH4HMYSXrCeMaLnq1LyFZNIbB5iwXFhbi5OQEgKOjI2lpaXUeq095eTkpKSl6vZ5ardb7XFNl7n1sqv5Va7SkF1aQnKMmOaeclFw12cU1c41tra3o3d6eR/s4E9CxNf4dWuPS5vooRCnaglJOF9x2CTrJe2j6zLWPzX3Nbixz/fPXl7n2v2Yu8vWk/HtiDuAkCpRMYy2dyOYKHvyX/yOGKJLpq6M17Q1fJyefqv3OlP/ozPW915cx+2+wsOzi4oJKVbMzmUqlwtXVlZKSkpuO1cfe3h5/f3+9Xi8lJUXvc02Vufexsf0rUldy5Lcb8Q5l5HM0s5CSimoAOjjZM8SnPXd6uzLY25V+ndsZ9UY8eQ9N36300ZT+Ymvua3ZjWcLPWH3Mqf+6RpHbUUg4cShQEsgBKrEhgfEoUfAd4/TYetrqDyPIVoB5/HmZ03vfGM3Rf13XbIOF5eHDh5OYmMgDDzxAUlISUVFRdOvW7aZjQtyKP96IdzCjgMN/uhHP39OZiYO71i7fJjfiCXP30ksv6Xxs6dKlercj12zRHHRdjq2pZgw7UaDkMTbTmnJ+5Q6eZTmxRJBLR73al2kWwhAMFpYnTJjA9u3bGT9+PH369GH48OFUVlbedEyI+lRUaTiZVXMj3sH0mvnGuapyoOZGvEHerjzYz7PmRjwvFxztZTVEYVnmzZsHwPLlyxk5ciT9+/fn5MmTbNu27ZbakWu2MCRdIbkHZ2u3nu7GRfJx5WOeQImCwwxG1kQWLUGTJ4vt27cDYGdnR3R09A2P1XVMiD+6VlbJ4YwCDmbkcyC9gGMXCimv0gDQza0NQT1rplTIjXhC1OjSpQsAGRkZPProowD4+voSExOj1/Plmi0MRVdAbksxk/kcBUpGsptqrNnGWBbyb7YyoYGtp7VcD9ASkkVzkWE4YVSXCss4mJ7PgfR89py6QkbhebRaaGVtRd/OzkQM9WaIjytDvF3p6FzfBVQIyzZmzBhCQ0Pp2bMn58+f55577jF2ScIC1bf19D3sRoGSyXyOIyWcphcv8RariSSLLnq1n5ycatHzdoVxSFgWzUaj0XI2t5j9aTXh+GB6AZcKa7aLdrS3obe7LY/d2Zu7fGvWNnawkx9PIfQ1d+5cQkNDycrKolOnTnh4eBi7JGFBdIXkrlwgktUoUNKTc6hwZD1hKFHwCyO41WkWJnTPrDAjkkaEwVRWaziZVcT+tDz2p9VMrSgsrQRqtou+y9eNJ+/xZYiPG306OXHm9Cn8/XsZuWohTNOpU6d47733KCsrY/To0bi5ufHQQw8ZuyxhxnQFZHvUhLAFBUqC2Y41Wn5kNK/zChuZRClt9WpfplmIlkLCsmgy6spqjl0oZH9aPvvT8zmUUUDpb0u4+bg7EOzvQaCvG4G+bni5OcgqFUI0ocWLF/PGG2+wZMkSQkJCiIyMlLAsmlx90yyGcBAFSsKJw5VCMvDiDRbzGTNIo7te7UtAFi2RhGXRaOrKag5nFJCUls++83kcuVBIRZUGKyvw83Bi8p1dCfR15y4fmW8shKFZWVnV3uzn4OCAnZ2dkSsS5kRXSO5Idu3W0/04SRmt2cgklCj4kXvR0vC69hKQRUsnYVnoTV1ZzeHMApLO5ZF0Pp+jFwqpqNZgbQV9O7cjcpg3Q7vXhGMXB/mLWojmNGvWLEJCQigsLGTq1KnMmTPH2CUJE1ff1tMP8zUKlDzEN9hSxV6GMYtoNjCVItrp1b6EZGEqJCwLnSqqNBy7WMjec3n8cu4qhzNrRo6treCOLu1Q3O3D0O5uDPFxw7l1QzsqCSEMacyYMdx3333k5+fj5uYm05xEo+n60enH8dqtpzuSy2U6sZzniCGKVD13yZOALEyRhGVRS6PRkny5iF/OXSXxbB4H0vMprajGygr8OzkTOcyb4T3cuctXwrEQLU1cXBzh4eG4u7sbuxRhgnQFZFfya7eeHsIhKrAlgfF8yky+5wGq9YgREpCFqZOwbOEu5Jey5+xV9py9yi9nr1Lw22oVPTs6EnpnV0b0aM+w7m4yrUKIFm7Hjh2MGzcOV1dXY5ciTER9W08Hsx0FSkLYgj0VHGUAz/BfYokgj/Z6tS8hWZgLCcsWRqWuZO+5PHafucruM7mk55UC4OFsz319PAjq5c6IHu3xkBvyhDApd911F5GRkYSFhdG2bc3SXCEhIUauSrREukJyT86gQEkkq+nKJfJwI5rZKFFwlEF6tS0BWZgjCctm7vrUil2nc9l1OpfDGQVUabQ42LVieHd3Zozw4Z5e7enRwVHmOAphwjw8PJg5cyYAWkks4k90Xd4dUTGFeBQoCSKRaqz5jnH8H/8lgfFUYN9g2/LjJsydhGUzdK2skt1ncvkxtSYgXy0uB6BvZ2eeHNmdkb06cKe3K3Y2DS/pI4QwDY899hgAKpUKW1tbWreW3w5ZOl0B2QoNI/kZBUpC+YK2lJKKH4v4J2uYzmU669W+hGRhKSQsmwGtVsu53GJ2pOTwQ2oOhzIKqNZoadfGlpG9OzC6dwdG9u5AB6eGRwiEEKbpyy+/ZNWqVWg0GsLCwrh8+TKLFi0ydlnCCHSF5G5kMoPPiCKGHpynCCdiiUCJgiSGcatbTwthKSQsm6iqag370/PZkZzDjpRsMvNr5h77ezozZ1R37vXryCAvV1pZy9QKISzBmjVr2Lx5MzNnziQyMpLQ0FBjlySaka6A3JoyHmMzCpSMYSfWaNnJfbzKa2xiImU46NW+hGRhySQsmxB1lYbvTlxh28kr7EzN4VpZJXY21ozo4c6TI7szpk9HOru0MXaZQggjaNOmDUePHgXg0qVLtTf5CfMVGwvTptX1iJZA9qNASRjrceEaafjwGq/yGTPIwEev9iUgC1FDwnILV6Su5IeUHL49cZmfUnMor07HxcGWMf4dGRvgwT29OtDWXt5GISzdG2+8wTvvvENeXh5Lly7l1VdfNXZJwkB0jSJ7cIXprEGBkgBSKKUNXxCKEgW7GCVbTwvRSJKyWiCVupIdKdl8/etlfj59lYpqDR7O9ozt5UR4kD+Bvm7YtJKb84QQkJWVBYCdnR0vv/wyWq1WVrYxQ7+/pX1uOG5LBY/wFQqUPMi32FBNIiN4glXEMwUVzg223aYNlJY2fc1CmAsJyy2EurKaH1Jz2Ho0ix9O5VBRpcGzXWumDfPm4f6dGNTNlVOnUvHvqd9i8EIIy7BixQoA0tLSKCoqwt/fn1OnTmFvb8/GjRuNXJ24XTf/u6fmQH+OoUBJBLF04CqX6Mw7vEAMUZzGT6+2ZRRZCP1IWDYijUZL0vk8Nh+5xLcnrlBcXkV7R3v+EujF+AGeDOrmirXcoCeEqMfSpUsBiIqKIjY2llatWlFVVYVCoTByZaKxdP1iwI08/sI6FCgZzBHKseNLHkWJgu0Ey9bTQhiIhGUjSLtawsZDF9l0+CJZ19Q42tswrl8nQgZ2YXgPd1nBQghxy9RqNT/99BN+fn6cOXOG8vJyY5ckboGugNyKKsayDQVKJrAVeyo4zCDmsYI4wsnHXa/2JSQL0XgSlptJWUU13xy/zPoDmRxIL8DaCkb27sBLD/nyhv4vAAAVs0lEQVQTHOBBa9tWxi5RCGHCli9fzqpVq4iLi6Nz587861//MnZJQg+6QnJvTqFAyXTW0IUscmnPSuaiRMGvDNCrbQnIQjQNCcsGdjZHxdqkTDYevohKXYVv+7a8OM6PiYO60qmd7LAlhGganTt3lhUwTISugOxEEVPZgAIlI9hLFa34lgeZzwq+4hEqsWuwbQnIQjQ9CcsGUK3Rsj05m89+SWfv+TzsWlkzrl8nwgO9GNbdTe5UF0I0uSeffJJVq1YZuwyhQ31bT49iFzP5lElsxIEykvHnBZaxhulk00mv9iUkC2E4EpabkEpdyYYDF4j5JZ2LBWV0cWnDi+P8mDKkG+0dZatpIYTh+Pv7s2PHDu6//35jlyJ+07cvJCfX/Zg36bVbT/uSTiHtWE0kShTsJxDdW09rax+TgCxE85Cw3ARyitR8mphObFIGqvIqAn3d+PvD/tzv7yHrIQshmsWRI0f47LPP6NWrF23atMHKyorVq1cbuyyLpGsUuQ2lTGTTb1tP/4AGK3YyhpdZwmYeQ40+O7Bq0Wrlt5NCNCcJy7chq7CMlT+dY8OBC1RpNDx4hyezR3anf1cXY5cmhLAwa9asMXYJFk337Dotw0hCgZKpbKAdRZyjO4t5nc+YwQW89Gr/+ihySsopwL8pShZC6MmgYbm0tJSFCxdSUFDA4MGDeeKJJ5g3bx4qlYpRo0bx/PPPG/LlDebKNTUrfjhD/MELAITe2ZU5o3rg7d7WyJUJISxNdXU13377LSdOnKC4uBgnJyfuuOMOxo0bh7W1/GbL0HSFZE+ymM4aoojBn1RKcOBzJqNEwW7uka2nhTAhBg3LCQkJDBw4kNmzZzNr1ixeffVVRo8ezRNPPEFISAiTJk3C19fXkCU0qcLSClbuOkdMYjoarZYpQ7oxd3QPuro6GLs0IYSF+tvf/kZlZSV33nknrVu3pqysjG3btrF79+7aDUtE09IVkO0oZzwJKFAyju9ohYbdBDGTT/icyRTjpFf7EpKFaFkMGpadnJzIysqiuroatVpNcnIys2fPxtramsDAQPbt22cSYbmyWsPapAz+u+MMRepKHhvYhWeDe9PNTUKyEMK4MjIyWL9+/Q3Hpk2bxtSpU41UkXl66ilYubLuxwZypHbraXfyuUgX/slfiSGKs/TSq30JyEK0XAYNy8HBwXz88cckJCQwatQosrOzcXKq+Zd127ZtuXbtWr3PLy8vJyUlRa/XUqvVep97K45klbJyfx4XrlUyyLMNTw7piK+bPcXZGaRkN/nL1ctQfWwpzL1/YP59NPf+Qcvro7u7O++88w6BgYG0adOG0tJS9u3bR8eOHY1dmsm7/37YubPux9y5SgSxKFAykGOosWcLIcQQxXaC0dDwRlMSkIUwDQYNy9HR0YSHhzN58mSee+450tPTUalUABQXF9OlS5d6n29vb4+/v343MqSkpOh9rj7yistZ8nUKm45cwdvdgVWRA7jfv6NR10hu6j62NObePzD/Ppp7/+DW+tgcoXrZsmWsXbuW+Ph4SkpKcHR0ZMCAASxYsMDgr22uWrUCjaaO41Qxju9QoGQ8CdhRyUHu5GneI45wCnBrsG0JyEKYHoOG5ZKSEuzsanYcsrOzY9CgQSQmJhIQEMD+/fuJjIw05Ms32tZjWbz65QmKy6tYcF9Pnrq3p2xHLYRokdq2bcvs2bONXYbJq28UuQ8ptVtPe3KFHDrwHvNQouAEdzTYdufOcOlSExcshGg2Bg3LERERPP/886xbtw5PT0/ee+89FixYQEJCAvfeey/e3t6GfPlbVqSu5JUtJ9hyNIuB3VxYFtqf3h763ZAhhBDCNLm6QmHhjcecuUYY61GgZBj7qKIVX/MwShR8zcNUYdtguzKKLIR5MGhY7tq16003nqxbt86QL9loRzILmLfuCFeK1Dx7f2+evreHbCgihLAov/76K/PmzaudIvfqq6/yn//8h8uXL+Pn58eyZcuMOhWtKdW1u54VGu7jBxQomcgm2qDmBH1ZyL9YyzRy8Giw3bVrISLCQEULIYxCNiUB4g9c4O9bTuDRzp7P5wxnsJersUsSQohmV1RURHh4OHPnzgXg888/x8PDg+joaGbPnk1iYiJBQUFGrrLxdE218OU8UcQwg8/wJpMCXFCiQImCgwxB99bTNWxtoaLCMDULIYzPosNyZbWGN79K5rO9GQT1bM97fxmEi4OdscsSQgijKCoqYtu2bezcuRNPT09sbW154IEHABg2bBj79u0zqbAcGwuzZ0NJyc2POVDCJDaiQMm9/IQGK7YTzCLeZgshlNO6wfZlmoUQlsFiw3J5VTXz1h1he3I2T97jy6JxfWTahRDConl5efHMM88wevRowsLCOHHiBKGhoQA4OjqSlpZW7/NvZbnPxtJ36b7XX/dg/XpXbhwV1jKCX1CgZArxOKPiDD15mTdZTSQX6VZHS78n4lattBw/fqr2e2OsINjSli5sbpbcf0vuOxi3/xYZlksrqpi95hC7z1zltQl9mTHCx9glCSGE0XXp0oXevXvXfp2cnFy73KdKpcLVtf4parey3Gdj1bV0X2wsPPMM5OXV/ZzOXCKS1UQRgx+nKaYt8UxBiYI9BFHfNIs2bawoLb3+nRVg3KURLWF5xvpYcv8tue/QPP3XFcYtbii1tKKKGZ/uJ/HsVd4J7S9BWQghfhMTE8PXX3+NRqPh9OnTLFq0iMTERACSkpIYOnSokSusERsLPj41205bWcG0aTcHZTvKmUw83/AgmXixlL9xhU5EoaQTV3icT9nDPegKyi4uNdMsfg/KQghLZVFhuVqjZUHcUQ5lFPC/8EFMHlLXr9yEEMIyRUREsGnTJiZPnkxwcDCTJ08mOzub8ePH065dO4YPH27sEvnqK2dmzYKMjLoe1TKYQ6xgHpfxJJ6p9OUkb/E3enKG0eziM6IowbHe15g7FwoKDFK+EMIEWdQ0jLe+SWFHSjavTejLI/07G7scIYRoUTp27MiaNWtuOBYdHW2UWmJj4eWXITMT3H7bGK9m9Pjma3d7cpnGWhQo6c9x1NiziYkoUfAD9zW49XRAAJw82fR9EEKYB4sJy2v2pvPJnjSiRvjI1AshhGjBYmNh1qzfp0DcOMWiZtqEDZU8yLcoUPIIX2FLFfsIZA4r2cBUCql/fvWYMbBjh2HqF0KYF4sIyyezrvGPhGTG9OnI4kcCjF2OEEKIerz8su65wgGcRIGSaaylE9lk05F3eQYlCpLpW2+7c+fCBx8YoGAhhFkz+7BcVa1h0cZfcXWwY/mUgbSyNo/dp4QQwlxlZt74fTsKCWM9M/mUQA5QiQ1f8QhKFHzLgw1uPe3uDu++KzvrCSEax+zD8sd70jhxqYgPIgbTzqH+C6oQQgjj8/KCCxnVjGEnUcTwGJtpg5pfuYNnWU4sEeTS8abnSSgWQhiCWYfltKsl/Gf7acYGePBgv07GLkcIIURDzp0jYWAMLpmf0U17gXxc+YTHUaLgMIP541JvDg7w0UcSjoUQhmXWYfmVL09gZ2PNGyH9sLKS6RdCCNEiFRfDF1+AUgk//8wd1tZk3TGWp7P+xadXJ9DW/betp/PA2lqLRmOFtzcsWSJBWQhheGYbllOvFLH7zFX++mAfPJxbG7scIYQQddmzB8aNg5IS6NUL3noLIiPp3KUL7wPv/+n0lJRUi97FTAjR/Mw2LMcmZWJnY81U2XhECCFaLg8PePppmDABRoyo2ZJPCCFaELMMyyXlVWw+colH7vDEta2dscsRQgihS69e8Pbbxq5CCCF0MsvtrrccvURxeRURw7yNXYoQQgghhDBhZheWtVota5My6dPJicFeLsYuRwghhA6xseDjA9bWNf+PjTV2RUIIcTOzC8tHLhSScrmIacO8ZQUMIYRooa5vaZ2RAVptzf9nzZLALIRoecwuLG8+fIm2dq0IGdTF2KUIIYTQoa4trUtLa44LIURLYnY3+D3YrxNBvdrjaG92XRNCCLPx5y2tGzouhBDGYnaJckTP9sYuQQghRAO8vGqmXtR1XAghWhKzm4YhhBCi5VuypGa76j9ycKg5LoQQLYmEZSGEEM0uIgI++gi8vWv2IfH2rvletq8WQrQ0ZjcNQwghhGmIiJBwLIRo+WRkWQghhBBCCB2stFqt1thF6HL06FHs7e2NXYYQQjRKeXk5AwcONHYZzUau2UIIU6brmt2iw7IQQgghhBDGJNMwhBBCCCGE0EHCshBCCCGEEDpIWBZCCCGEEEIHCctCCCGEEELoIGFZCCGEEEIIHUwqLJeXlzN79mwmTJjACy+8QF0LeehzTkulb+2LFi1iypQpzJkzh6qqqmau8vbcyvujVCqJiopqvuKagL79W7VqFVOmTOGJJ56goqKimau8Pfr0sbS0lLlz5xIWFsayZcuMUOXtq6ysZM6cOTofN+VrjTkwxetDUzDl639jyWfNMt/3PzPmZ96kwvLWrVvx8PBg69atFBUVkZiY2KhzWip9aj948CBVVVXEx8dTUlJiUv0D/d+fS5cusXnz5mau7vbp078LFy5w9uxZ4uPjGTlyJNnZ2UaotPH06WNCQgIDBw5k/fr1nD17lnPnzhmh0sZTq9VMnDix3s+XKV9rTJ2pXh9ul6lf/xvL0j9rlvq+/5GxP/MmFZaTkpK4++67ARg2bBj79u1r1DktlT61t2/fnhkzZgCg0Wiatb6moO/7s2TJEhYuXNicpTUJffq3d+9erl27RkREBAcPHqRr167NXeZt0aePTk5OlJaWUl1djVqtxtbWtrnLvC2tW7cmISGBTp066TzHlK81ps5Urw+3y9Sv/41l6Z81S33f/8jYn3kbo71yIxQWFuLk5ASAo6MjaWlpjTqnpdKndh8fHwC2b9+OtbV17QXEVOjTx4SEBPr06UOPHj2au7zbpk//8vPzcXNz48MPP2Tq1KkcOnSIIUOGNHepjaZPH4ODg/n4449JSEhg1KhReHl5NXeZBmfK1xpT8o9//INTp07Vfp+Tk8Ojjz5qkteHW/Xnvt91110899xzJnv9byxL/6yZ+t/7t6slZAKTCssuLi6oVCoAVCoVrq6ujTqnpdK39p07d7J69WpWrlyJjY1JvYV69fGnn34iKyuLPXv2kJaWxtq1a5k2bVpzl9oo+vTP0dERX19fALp27Wpy0zD06WN0dDTh4eFMnjyZ5557jsOHDzN48ODmLtWgTPlaY0r+8Y9/3PD9woUL2bt3r0leH27Vn/sOpn39byz5rFnm+35dS8gEJjUNY/jw4bVzdZKSkhg6dGijzmmp9Kk9NzeXTz75hOjoaBwdHZu7xNumTx///e9/ExcXx/Lly+nbt69J/UWoT//69u3LiRMnAMjMzKRbt27NWuPt0qePJSUl2NnZAWBnZ0dpaWmz1tgcTPlaY8pM+fpwu0z9+t9Ylv5Zs9T3/bqW8Jk3qbA8YcIEsrOzGT9+PO3ataNbt268/fbb9Z4zfPhwI1V76/Tp3+bNm8nNzeXxxx8nPDycL774wkjVNo4+fTRl+vRv0KBBuLi4MGnSJHx9fenfv7+Rqm0cffoYERFBXFwcU6dORa1Wm9TnsC4XLlwwq2uNME2mfv1vLEv/rFnq+96SWGktcQ0WIYQQQggh9GBSI8tCCCGEEEI0JwnLQgghhBBC6CBhWQghhBBCCB0kLAshhBBCCKGDhGUhhBDCDK1evZrw8HD69+9PeHg427ZtY8WKFc2yA96mTZtuOpacnNzolRw2bdpUZ5uGVtdr7tq1i127dt10njHqE81DwrIQQghhhiIjI4mLi8PDw4O4uDjGjh3bbK+9efPmm44FBAQQGhrabDU0hbr6MWrUKEaNGmWEaoSxSFgWQgghLMgvv/xCWFgYEyZMIDc3l7KyMhYsWEBYWBivvfYaULPF9OzZs5k6dSpLliwB4OLFiyxcuJCXX36Zl156CYC0tDSmT5/OxIkT2bJlCyqVivDwcJKTkwkPD+ejjz6qfd19+/axYsWK2u+PHz9OWFgYISEhxMbG1p4TEhJCaGgoO3bsuKV+xcfHExISwvPPP8/06dMBav//x6+/+eYbJkyYwJQpUzh69CgAYWFhvPjii4wfP56PPvqI8+fP39CPLVu21Lajzyjyzp07mTJlCqGhoaSmpgIQGxtLaGgokyZNIjk5+Zb6JoxLwrIQQghhQdLS0mpHmpOSktiwYQO9evVi/fr15ObmkpqaSnR0NA8++CAbNmygqKiI3bt3A/Djjz8yefJkli5dCsA777zDvHnzWL9+PatWrcLR0ZG4uDgCAgKIi4tj1qxZOut4/fXXWb58ORs3buT06dMA5OXl8cEHH/DPf/6T9evX692nqqoqPv74Y+Lj44mIiKj3XJVKxbp163j66adrR46PHz9OVFQU8fHxfP3113Tv3v2GfoSEhOhdi0aj4a233uKTTz7hzTff5L333gNgw4YNfPTRRyxfvhy1Wq13e8L4LGuDcSGEEMLCPfbYY1hZWdG5c2cqKytJS0vjyJEj7N+/n6KiIrKzszl79ixhYWEADBw4kHPnzuHr60tQUBADBw6sbSstLY0VK1ZgZWVFdXU1RUVFtGvXTq86ioqK6Ny5MwB///vfgZqg+dprr+Hh4UF5ebnefSooKKBjx47Y2dnRt2/fOs+5HlDLyspYuHAh7dq1w9q6ZszQz8+PgIAAAJydnfV+3brk5+dTWFjIU089BYCdnR0AL7zwAosXL8ba2pr58+ff1muI5iUjy0IIIYQFcXBwuOF7X19fZsyYwZo1a5g/fz6enp707NmzdorCsWPH6Nmzp87nLl26lDVr1hAWFoatrS0A9vb2lJaWUt8mwc7OzmRlZaHRaAgJCaGsrIz//e9/vP/++zdMn9CHq6srOTk5lJeX8+uvv9YeV6lUAJw8eZK8vDwqKyuJi4sjOjqahx56qPa8tm3b1tludXU1QL39+DM3Nzd8fHxQKpWsXLmydn7zsWPHeP/99wkLC2PVqlW31D9hXDKyLIQQQliwKVOm8Ne//pXPP/8cZ2dnli9fzuzZs3nxxRdZt24d/fv3JygoiIsXL9703OtzmIuLiwkMDKwN01OnTmXGjBk4OjqiVCrrfN3Fixfz7LPPUl1dTWRkJG3atOHee+8lNDSUHj16cO3aNb37YGNjw6xZs5g6dSp9+vSpPT5o0CD++te/4urqioeHB7a2tvj5+TFp0iR8fHwoLCyst92HH36YqVOn4u3tzbJly/Sqxdramrlz5zJt2jTUajVPPPFE7fFJkyZRUVHB888/r3ffhPFZaW/ln0tCCCGEEC3c9OnTWbNmjbHLEGZCwrIQQgghhBA6yJxlIYQQQgghdJCwLIQQQgghhA4SloUQQgghhNBBwrIQQgghhBA6SFgWQgghhBBCBwnLQgghhBBC6PD/GoP/k53b60MAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="统计日常">统计日常<a class="anchor-link" href="#统计日常"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>1</strong>. 姚明智商132分,有多少人智商比他高?</p>
<p><img src="/images/copied_from_nb/4.data-stats/yaoming.jpg" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"超过姚明智商的人数为:</span><span class="si">{</span><span class="mi">100</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">dist</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="mi">132</span><span class="p">))</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>超过姚明智商的人数为:1.645%
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 仿真方法:随机抽取1亿人,统计比姚明IQ高的人数占比</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e8</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"超过姚明智商的人数为:</span><span class="si">{</span><span class="mi">100</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">samples</span> <span class="o">></span> <span class="mi">132</span><span class="p">)</span> <span class="o">/</span> <span class="n">n</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>超过姚明智商的人数为:1.646%
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>2</strong>. 有人说爱因斯坦的IQ是千里挑一,那么他的IQ是多少?</p>
<p><img src="/images/copied_from_nb/4.data-stats/einstein.jpg" alt="" /></p>
<blockquote><p>推荐:《上帝掷骰子吗:量子物理史话》——像武侠小说一样精彩的量子力学科普</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mf">0.999</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>146.3534845925172</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 仿真方法</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="n">samples</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.999</span> <span class="o">*</span> <span class="n">n</span><span class="p">)]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>146.3653368563009</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>3</strong>. IQ在(70, 90]区间的人有多少?</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"IQ在(70, 90]区间的人数占比为:</span><span class="si">{</span><span class="mi">100</span> <span class="o">*</span> <span class="n">dist</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="mi">90</span><span class="p">)</span> <span class="o">-</span> <span class="n">dist</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="mi">70</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>IQ在(70, 90]区间的人数占比为:25.23%
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 仿真方法</span>
<span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">samples</span> <span class="o">></span> <span class="mi">70</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">samples</span> <span class="o"><</span> <span class="mi">90</span><span class="p">))</span> <span class="o">/</span> <span class="n">n</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.22976765</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>4</strong>. 测试了100个用户的IQ样本,计算参数$\mu$和$\sigma$的极大似然估计(MLE),以及$\mu$的95%置信区间</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">110</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">loc</span><span class="p">,</span> <span class="n">scale</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(111.18547994292243, 15.446518813564095)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>95%置信区间</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">itv95</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">interval</span><span class="p">(</span><span class="mf">0.95</span><span class="p">)</span>
<span class="n">itv95</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(80.91085938181644, 141.46010050402842)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s2">"stepfilled"</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">xs</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">itv95</span><span class="p">,</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"r"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAFjCAYAAAApc128AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3TUVcLG8e+kk0IKgQAhBEIooSiCCAgqIk0UUAQVUbHsAq6iq7uIa6+r2HbXhlgRC4ooGorSLaCAICAlQIBQEiCk90wyybx/jC+7KA6BlDvl+ZyTEzO5zjy/65E8/HLnXovdbrcjIiIiIiIn5WM6gIiIiIiIK1NhFhERERFxQoVZRERERMQJFWYRERERESdUmEVEREREnFBhFhERERFxws90AGc2b95MYGCg6Rj1xmq1evT11TfNX+1o/mpPc1g7mr/a0fzVnuawdjxt/qxWK927dz/p91y6MAcGBpKUlGQ6Rr1JSUnx6Ourb5q/2tH81Z7msHY0f7Wj+as9zWHteNr8paSk/OH3tCRDRERERMQJFWYRERERESdUmEVEREREnFBhFhERERFxQoVZRERERMQJFWYRERERESdUmEVEREREnFBhFhERERFxQoVZRERERMQJFWYRERERESdUmEVEREREnFBhFhERERFxws90ABERt2a3Q0kWZO2EwsNQWQqV5Y7PtnKoLIPqKghpAiFN/+cjGkJjICDE9BWIiMgpqDCLiNRUVSWk/wQZP0P2LuIPbIIvD0F5/snHW3zAPxiwQEXRycdEtYPYnv/9aN4N/IPq7RJEROT0qTCLiDhTkAF7V0DqMtj3LVgLHI8HR2MPiYOuoyG6IzTtABHxjoLs38jx4RsAFotjvM0KJdlQcuzXz1mO5z6yGfZ/D1vnOsb5+EFMV2g/BLpcAc06//c5RETECBVmEZHfyjsAm96HnYvh2HbHY2EtocsoSBwM8f0gpAkHU1JISkqq2XP6BUJ4rOPjZAoPO+5cZ2yEQ+vg++fhu2ehSXtHce58BcR0UXkWETFAhVlEBKDKBqlLYMM7sGeFo5jG94PBjztKcrOk+i2rjVs6PpIud3xdfAxSFsCOL+D7F+C756BJIpxzPfS8GRpF1F8WERE5gQqziHi3ggz4ebbjo+gwhLWAi+6FHjdCeCtzuUKbQa9bHR/FWbBzAWz9DJY/Ct89D+fcAH1ug8h4cxlFRLyECrOIeKfCI467tj/PhmobtBsIw5+DDsPA18X+aAxtCufe4vg48gv8+Ar89CasnwmdR0HfKdCqp+mUIiIey8V+KoiI1LPSXFj9L1j/hqMo97gRzr8TotqaTlYzLc6C0W/AJY/Autdh4yzYPt9R+Ic8BTGdTScUEfE4Tguz1Wrlzjvv5MiRI3Ts2JFnn30Wy2/W8P3RmGnTppGWlkZUVBSvvPIKO3bs4I477iA21vGGl6eeeoqEhIT6uzIRkf9VXghrX4MfXoGKYjjrGhhwn/sU5d8Kj4UhT8CFUx2l+fsX4PV+0GMCXPyA4660iIjUCacn/SUnJxMTE0NycjKFhYWsWbOmRmM2bNiAzWZj7ty5lJSUsGbNGgoLCxk3bhxz5sxhzpw5Kssi0jDsdtj8EbzUHb55GtoNgL/8CKNnum9Z/l9BjaHfnXDnJjhvomN3j5d7wOp/O7ayExGRWnNamNeuXUu/fv0A6NOnD+vWravRmOjoaCZMmABAdXU1AIWFhSxdupQxY8YwZcoU7HZ7nV6IiMjv5OyF2aPgi9scO0z8eRVc84FjxwtPExwFl06H236E1n1h+SPwSi9IWWg6mYiI23O6JCM/P5+wsDAAQkNDSUtLq9GYNm3aALBs2TJ8fHzo168fO3fu5K677mLAgAFce+21rF+/nt69ezsNZ7VaSUlJOZPrcgvl5eUefX31TfNXOx49f9U2muz6iOjtb2P38eNYz3vJb3cFFPpAYd1ds8vOYY/HCGk5nGabXyLok/EUxA/laI+/UR3Q2HSyE7js/LkJzV/taQ5rx5vmz2lhjoiIoKjIcZxrUVERkZGRNR6zYsUKZs+ezYwZM/Dz8yM2NpYOHToAEBsbS05OzinDBQYG1vxQADeUcjqHHsjvaP5qx2PnL2MjJN8FmVuh0+Uw/DlaNG5Ji3p4KZeew6QkuHA8fP8C4d89R3juLzDyZWg/2HSy41x6/tyA5q/2NIe142nz56z8O12S0bdv3+PrlteuXXvSO8InG5OVlcXbb7/NzJkzCQ0NBWDWrFksWrSI6upqdu/efbw8i4jUieoqWPVPeGsQlGY7ll5c+6HjMBBv5evveGPjn1ZAUAR8OAaSp4C1yHQyERG34rQwjxw5kszMTEaMGEF4eDhxcXFMnz7d6Zi+ffsyf/58srKyuPXWWxk3bhzz5s1j/PjxfP7554wdO5bBgweTmJhYrxcmIl6k8Ai8NxK+ne7Y/eL2dZA0wnQq19GyO0z6Fvr9FTZ9ADPOh7TvTacSEXEbTpdkBAQEMHPmzBMemzZt2inHTJw4kYkTJ/7u+d5///0zzSkicnKpy2H+RKgsgytmQPfrTCdyTX6BMPgx6DgcvpgM742AgQ9C/3vAx+m9ExERr6c/JUXEPVVVOo6J/vAqCI2Bid+oLNdE694weTV0vQpWPgFzb3DsUS0iIn9IhVlE3E9BBsy6zHFiX48J8OeV0LSj6VTuIyAErnoLhj0Du76CNy+GYztNpxIRcVkqzCLiXg5vgjcHQuZ2GP0WjHwJ/BuZTuV+LBbocxtMWOC4w/zmQNj+helUIiIuSYVZRNxHygJ451LH7g+3LoWzxppO5P7a9HO8ITCmC3w6AZY+5NhxREREjlNhFhHXZ7c7jnr+5AZHsfvTCsdnqRuNW8JNi6DXn+CHl2DujY43UYqICKDCLCKuzlbh2Dt4+SPQ5Qq4aSGExZhO5Xn8AuCyFxzrmncugtlXQGmu6VQiIi5BhVlEXFdZHnwwGja9DxdOhave0Xrl+tbnNhg7y7FW/J2hkHfAdCIREeNUmEXENRUddaxXPrQOrpzp2DNY+wU3jC5XwI1fQHEmvD0YjvxiOpGIiFH66SMirif/ILx7qePz+Hlw9rWmE3mf+PPhlqXg4w/vDoe9K00nEhExRoVZRFxLdiq8MwxKc+DGLyHhItOJvFezTvCnZRAZDx+Ohe3zTScSETHC6dHYIiIN6uhWeP9Kxz/ftAiadzObx03llVRQbLXV0bNFYLniM5ouuIGAebeSU1RGWYcr6uSZq4IiOJRb6nRMaKAfkSEBdfJ6IiJnSoVZRFzDoZ8cx1wHhDruLEe3N53IbRVbbczbmF6nz+nf+jlGFd9N7Ne3s2TvMXY2G17r58zOziI6uqnTMWN6tlJhFhHjtCRDRMxL+w5mj4JGUXDL1yrLLqjSN5gvOv+b9PAeDEt9lM7HFpqOJCLSYFSYRcSs/Wvgw6shorWjLEe0Np1I/oDNtxFfJv2LgxHnMST1cbpkfmk6kohIg1BhFhFz0jfAR1dDRBxMWABhzU0nklOw+QbxZdIL7I/ow5A9T9Lt6OemI4mI1DsVZhEx48gWx6EkIU3hxmQIdb6WVVxHlU8gC5KeZ19kfwbtfVqlWUQ8ngqziDS8YymOo5cDG8OEZGjcwnQiOU1VPgEs6PQs+yL7ccneZ+iQtdR0JBGReqPCLCINK3sPvDcSfAMcu2FozbLbqvbxZ1HHZ8ho3J1hqQ/TJm+N6UgiIvVChVlEGk7efpg9EuzVjjvLTdqZTiS15FjT/CLZwe25fOc0Ygs2mY4kIlLnVJhFpGEUZTruLFeUwI1fQNOOphNJHanwC2V+l5coCmzOqJS7aVq8y3QkEZE6pcIsIvXPWgQfjYWSLLj+c53g54HK/CP5rMsrWH1DGb1jChFlB0xHEhGpMyrMIlK/qiph7o1wdBuMfQ9a9TSdSOpJcWBzPuv6KgBXbbudUOtRw4lEROqGCrOI1B+7HZKnwN6VMOI/0GGI6URSz/IbxfN555cJrCrmyh13EWArNh1JRKTWVJhFpP6seBy2zIGLH4AeN5hOIw0kK7QjCzo9R2TZQUbsvBef6krTkUREakWFWUTqx/o3YfWL0PMmuHCq6TTSwA5F9GJ54gO0LviJS/Y+7fhtg4iIm/IzHUBEPFDKAlg8FToOh+EvgMViOpEYsKPZ5TQuP0zfQ29SEBTL+rhbTUcSETkjKswiUrfSN8Bnf4JW58JVb4Ov/pjxZmvj/kx4eQb9Dr5OYWALdjYbbjqSiMhp008yEak7Benw8XUQGgPjPoaAYNOJxDSLhWWJDxJacYwhe56gKDCGjHDtlCIi7kVrmEWkblSUwJxxUFEK130CIdGmE4mLqPbxZ2Gn6eQHxTEyZSpRpWmmI4mInBbdYRaRWskrqaC4vIImi/9Mo8xtZI+YTblfPOSWmo5W76qCIjjkgteZU2w1HeF3rH6N+aLzv7n2l5sZlXI3H531nulIIiI1psIsIrVSbLWRMf8h4tIX802bu9mU0xFy0k3HahDZ2VlERzc1HcNtFAa1JDnpecZuncRlu+7nrZiHTEcSEakRLckQkVoJ3vU5fdLfYWuzUWxqOc50HHFxR8O6saLdfcQXrGdY1lum44iI1IgKs4icufQNRC2/h/TG57Cy3TRtHyc1siNmJD+3uJZ+eV/SOTPZdBwRkVNSYRaRM1OQAXPGURXSnAWdnqXax990InEj37W9iz3B3blk7zO0KPzFdBwREadUmEXk9NmsMPdGqCwla8R7lPtHmE4kbsZu8ePjlvdRHNiMETvvJcR6zHQkEZE/pMIsIqfvq2mQsQGumIGtSUfTacRNlfk2JjnpBfyryhi5cyq+VeWmI4mInJQKs4icnp9nw8Z3of/d0Hmk6TTi5nKC2/FVh8dpXryDQXufBrvddCQRkd9RYRaRmsvYCIv+DgkDYKC2BJO6sa/JRfwQN5HOWYs56+hnpuOIiPyOCrOI1ExJNnxyo+PY66veAR9f04nEg6yLu5V9kf0YkPYCMUXbTccRETmBCrOInFqVDebdDKXZcM37ENLEdCLxNBYfvm7/GMUBzbh85zSCKvNNJxIROU6FWURObcWjkPYdXP4vaNnddBrxUFb/cBZ2eobgyjyG734Qi73KdCQREUCFWUROZfsX8MPL0OtP0P0602nEwx0LTWJVwlTi89fR5+CbpuOIiAAqzCLiTM5e+PIOiO0JQ582nUa8xLaYUWxrNoI+6W8TlLbcdBwRERVmEfkDleXw6U2ON/eNnQV+AaYTibewWFiZcC/HQtoTtXQK5O03nUhEvJwKs4ic3JL74egvcOXrENHadBrxMlW+QSzs+CwWe7XjVEmb1XQkEfFiKswi8nvbPoMNb8P5U6DjpabTiJcqaNSKnCEvwZEtsFT7fouIOSrMInKinL2QfCe0Og8uecR0GvFy5QlDoc/tsH4mpCwwHUdEvJQKs4j8V2UZzJ0Avv4w9l3HZxHTBj0KLc+BL2+H/IOm04iIF/IzHUDEG+WVVFBstRnNUBUUwaHc0hMei1w5ldDMrWSN/IDyqij4zfdPJqdYa0ulnvkFwJh3YOZFMO8WuPkr/WVORBqUCrOIAcVWG/M2phvNkJ2dRXR00+Nfd8xawvDdH/BT7I2szukIOWbziZwgKgFG/Mdx4uTKJ2Dw46YTiYgX0ZIMESG8PJ1L9j7N4bCzWBN/m+k4IifXdTT0vBnW/AdStT+ziDQcFWYRL+dTbePSXQ9hx8LiDk9it+gXT+LChj0NzbrA/IlQeMR0GhHxEk4Ls9VqZdKkSYwcOZKpU6dit9trPGbatGlcffXVTJ48GZvNVqPnEpGG1/fQTFoUb2N54gMUBbUwHUfEOf9GjoN0Ksvg8z9DdZXpRCLiBZwW5uTkZGJiYkhOTqawsJA1a9bUaMyGDRuw2WzMnTuXkpIS1qxZU6PnEpGGFZf/E73S32Nrs1GkRg8yHUekZpp2gMtegP3fw+oXTacRES/gtDCvXbuWfv36AdCnTx/WrVtXozHR0dFMmDABgOrq6ho/l4g0nGBbAcN2P0xeo9Z8k/A303FETs/Z46DrVbDqaUjfYDqNiHg4p4sV8/PzCQsLAyA0NJS0tLQajWnTpg0Ay5Ytw8fHh379+jF79uxTPtdvWa1WUlJSTuuC3El5eblHX199c+f5qwqKIDs7y1wAu53xR/5FkC2fWS0f4WheMVBsLo+bstlsZv87urmazF9BQRjFmQdO+j2f9pNpu28NzLmRtKGzqfYPqY+YLsud/wx0FZrD2vGm+XNamCMiIigqKgKgqKiIyMjIGo9ZsWIFs2fPZsaMGfj5+dXouX4rMDCQpKSk07siN5KSkuLR11ff3Hn+DuWWnrClW0M7+8hcOpeu55u2d1PZsg/RxpK4t99uzSenpybzFx4eTlxbJ2vrI2bBrOF03Ps2XDmjbgO6OHf+M9BVaA5rx9Pmz1n5d7oko2/fvsfXGq9du5bevXvXaExWVhZvv/02M2fOJDQ0tMbPJSL1L7oklQvT/sOukF5sajHOdByR2onvCxf8HbZ8BNs+M51GRDyU08I8cuRIMjMzGTFihONv+XFxTJ8+3emYvn37Mn/+fLKysrj11lsZN24c8+bNO+k4EWlYvtVWLt39IFa/UD5rfjdYLKYjidTeRdOgVS9YcLeOzhaReuF0SUZAQAAzZ8484bFp06adcszEiROZOHHi757vt+NEpGH13/8q0aX7+LzzfyipiqCR6UAidcHXD0a/Ca9fAJ9PgpsWgo+v6VQi4kF0cImIl2idv44eR+awqcXVHIg833QckboV1RYuex4O/gDfa6s5EalbKswiXiCwsoChqY+R06gtq+OnmI4jUj/Ouga6joFvnoZDP5lOIyIeRIVZxNPZ7Qza+zSNKvP4usPj2HyDTCcSqR8WC1z+IjRu6Tg6u6LEdCIR8RAqzCIeLilrMR1yVvBD68kcC+1kOo5I/QoKhytfh9w0WPqg6TQi4iFUmEU8WOPyDC7e9xzpjc9hY+z1puOINIw2/eH8O2DDO5C6zHQaEfEAKswiHspir2LY7kcA+Lr9Y9gt2jVAvMjAh6BZF/jydijJMZ1GRNycCrOIhzo3YzaxRVtYmXAvRUFOTkoT8UR+gTD6DSjLg4V3gd1uOpGIuDGn+zCLuJK8kgqKrbbjX1cFRXAot9RgojOXU2yt1+dvWryLvgdnsqvJIHY2vbReX0ukPtXq/5WABML6TCNizRPk/PAepUlX112w0xQa6EdkSICx1xeR2lFhFrdRbLUxb2P68a+zs7OIjm5qMJFr8q2uYFjqI5T5RbKi3X06zU/c2qpdWbX69y32yxjTeCFNV97PF7ltKAxqWUfJTs+Ynq1UmEXcmJZkiHiYvgdnEl26l2WJD2L1DzcdR8Qou8WXJe0fBWBo6qNY7FVmA4mIW1JhFvEgLQs3c27G+/wScyX7o/qZjiPiEgqDWvJNwt9pVbiJcw7PMR1HRNyQCrOIh/CvKmVo6qMUBrbguzZ3mY4j4lJ2NL2MPVED6HdgBlGl+0zHERE3o8Is4iEuTPsP4eWHWdL+USr9QkzHEXEtFgsr2t1HhW8wQ1Mfxafadup/R0TkVyrMIh6gTd4azsr8nI0tx5MRfo7pOCIuqTSgCSvb3Ufz4hR6ZcwyHUdE3IgKs4ibC6wsYHDqk2QHJ/BD/GTTcURcWmr0JaRED6X3obdoWrzLdBwRcRMqzCJubuC+52hky2NJ+8eo8gk0HUfE5a1KmEqZXyTDUh/Bt7rCdBwRcQMqzCJurH32CjplL2Fd3J84FtrJdBwRt2D1D2dZ4oNEl+6lz8E3TMcRETegwiziphpV5DJw7zNkhiTxU+xNpuOIuJX9Uf3YGjOKczPep0XhL6bjiIiLU2EWcUd2O5fsm05AVQlL2j9MtY8O7RQ5Xd+1+StFgTEMTX0Mv6py03FExIWpMIu4oQ7Zy2ifs5K1rSeSE5JoOo6IW6rwC2Vp4sNElh+k/4FXTMcRERemwiziZoIrshm471mOhHZlQ+z1puOIuLX0iHPZ1OIazjnyCbEFG03HEREXpcIs4k7sdi7Z+wz+VWUsaf8IdouWYojU1ur428kPasWQPU/gV1VmOo6IuCAVZhE30inrKxJzv2VN/G3kBbcxHUfEI9h8G7E08WHCyw9raYaInJQKs4ibCLFmcfG+5zkcdhabWo4zHUfEo2SEn/Pr0oy5tNLSDBH5DRVmEXdgtzNo7z/xtVf8uhTD13QiEY+zJv528oLiGJL6OP5VpabjiIgLUWEWcQOdsxaRkLeaNfG3k9+otek4Ih7J5hvE0vYP09h6hP77XzYdR0RciAqziIsLsR7jon0vkN7Y8StjEak/hxt35+eW4+h+dB5x+T+ZjiMiLkKFWcSV2e0M2vs0vvZKliY+BBb9LytS335ofRt5Qa0ZvOcJ/G0lpuOIiAvQT18RF5aUtfj4UoyCRnGm44h4hf8uzTjKBQe0NENEVJhFXFaINYsB+14go3F3LcUQaWCHG5/Nzy3HcfbRz7Q0Q0RUmEVc0q+7YvjZK7QUQ8SQ/y7NeFK7Zoh4Of0UFnFBnbK+IiFvNatb/0W7YogY4lia8dCvu2boQBMRb6bCLOJiQiqyuTjtBQ6HncXmllqKIWLS4V+XRHU/+imxOtBExGupMIu4ErudS/Y+jV+1lSXtH9YBJSIuYE38X8gPimXInifxqyozHUdEDFBhFnEhnbK+pl3ud6xpPZn8RvGm44gIYPNtxNLEh4goT6ffgddMxxERA1SYRVxEcEU2A9Je4HBYNza1HGc6joj8j4zwnmxuPpZzjnxCy8LNpuOISANTYRZxEQP3PYd/VRlLE7UUQ8QVrW5zB4WBLRiS+gR+VeWm44hIA1JhFnEB7bOX0z5nJT+2nkhecBvTcUTkJCp9g1mW+CCR5Qc5/+AM03FEpAGpMIsY1qgyj4H7nuVoaBIbY8ebjiMiThyK6MWW5lfR4/AcmhdtNR1HRBqICrOIYQP2PU+grejXpRh+puOIyCl8Hz+FosAYhqQ+jm+11XQcEWkAKswiBrXL+YZO2UtZF3crOSGJpuOISA1U+oWwvN39NCnbT5+Db5qOIyINQIVZxJDAygIu2fs0x0I68FPsTabjiMhpOBDZl23NRnJuxgfEFO0wHUdE6pkKs4ghA9JeJMhWwNLEh6n20VIMEXfzXdu/UhoQxZA9j+NTXWk6jojUIxVmEQPa5q6mc9Zifoq9iazQjqbjiMgZsPqFsbzdP4gu3Uvv9HdMxxGReqTCLNLAAmzFXLL3abKDE1gfd4vpOCJSC2lRF5DS9FJ6pb9L0+JdpuOISD1RYRZpYBem/ZuQimyWJj5MlU+A6TgiUkur2v6Ncr/wX5dm2EzHEZF6oMIs0oBa56+j27Ev2Rh7PZlhXUzHEZE6YPUPZ0W7+2hWsptzM94zHUdE6oEKs0gD8beVMHjPk+Q2iufH1hNNxxGROrS3ycXsajKIPofeoknJHtNxRKSOqTCLNJD+B14hzJrJ0sSHqPIJNB1HROrYqnb3YvUNZcieJ7DYtTRDxJOoMIs0gFYFG+l+dB4/txzHkcZnm44jIvWgzD+SVQlTaV68g54ZH5mOIyJ1SIVZpJ75VZUxeM8T5Ae14ofWt5mOIyL1aHf0YFKjBtD34EwiS/ebjiMidUSFWaSe9Tswg4jyDJYmPojNN8h0HBGpTxYLK9tNw+Yb9OvSjCrTiUSkDjgtzFarlUmTJjFy5EimTp2K3W6v8ZjKykomT558fNwvv/zChRdeyLhx4xg3bhz79u2r40sRcT0tCrdwzpGP2dx8LBnhPU3HEZEGUBoQzTdt/0bLol/ofuQT03FEpA44LczJycnExMSQnJxMYWEha9asqdGY8vJyRo8efcL4wsJCxo0bx5w5c5gzZw4JCQl1fzUiLsS3qpwhqY9TGNic1fG3m44jIg0opeml7IvsT78DrxFelm46jojUktPCvHbtWvr16wdAnz59WLduXY3GBAUFsWDBApo3b358XGFhIUuXLmXMmDFMmTLlpHerRTzJ+YfeIKr8IMsTH6DSL8R0HBFpSBYLK9rdR7XFj8F7ngB7telEIlILfs6+mZ+fT1hYGAChoaGkpaWd0RiA1q1bc9dddzFgwACuvfZa1q9fT+/evZ2Gs1qtpKSk1OhC3FF5eblHX19dqwqKIDs76/jXNpvthK9dSauynfTI+JCfwofxsy0BXDCnK8+fu9Ac1o6nz182PiyK/hOjM//D4fUzSWkzvE6fXz9Dak9zWDveNH9OC3NERARFRUUAFBUVERkZeUZjAGJjY+nQocPxf87JyTlluMDAQJKSkk45zl2lpKR49PXVtUO5pURHNz3+dXZ21glfuwrf6gqu3vwyJQHRrO80jWi/UNORTspV58+daA5rxxvm70CT8RywriXu5+dp2WcMRMbX2XPrZ0jtaQ5rx9Pmz1n5d7oko2/fvsfXIa9du/akd4RrMgZg1qxZLFq0iOrqanbv3n28PIt4mt6H3qJJWRrLEh+gwkXLsog0EIuFZe0eACyw4E7QckQRt+S0MI8cOZLMzExGjBhBeHg4cXFxTJ8+3emYvn37nvS5xo8fz+eff87YsWMZPHgwiYmJdXcVIi6iWXEKvdJns73Z5RyIPN90HBFxAUVBLcjv/xDs+wZ+fs90HBE5A06XZAQEBDBz5swTHps2bdopx/y/ZcuWHf/nZs2a8f77759pThGX51NdyZDUxyn1j+TbtnebjiMiLqSk6w1E7V8MSx6EdpdARJzpSCJyGnRwiUgd6Z3+Dk1L97Ai8R9Y/RqbjiMirsTiAyNfduyWseAuLc0QcTMqzCJ1oGnxLnqlv+vYezXqQtNxRMQVRbaBwY/B3hWw6QPTaUTkNKgwi9SST7WNwXueoNwvnFVt/2Y6joi4snNvhfj+sOQBKDxsOo2I1JAKs0gt9cqYRUzJLla0+wdW/3DTcUTElfn4wKiXoaoCFvxVSzNE3IQKs0gtRJek0vvQ2+yMHsreJgNMxxERdxCVAIMegdQlsOVj02lEpAZUmEXOkE+1jSGpj1Hu15hVCX83HUdE3Ml5k6B1X/h6GhQeMZ1GRE5BhVnkDP13KcZ9lPtHmI4jIu7ExwdGvQo2Kyy8W8vNeUUAACAASURBVEszRFycCrPIGThxKcbFpuOIiDtq0g4ueRh2fwW/fGI6jYg4ocIscpq0FENE6kzvyRDXB766V0szRFyYCrPIaTo34z0txRCRuuHjC1e8BrYKHWgi4sJUmEVOQ3RJKn0OvcXO6CFaiiEideP/l2akLoEtc0ynEZGTUGEWqSHHUozHf12KMdV0HBHxJL0nQ+vz4av7dKCJiAtSYRapoV7p7xJTspOV7aZpKYaI1C0fHxj1yq8HmmhphoirUWEWqYGmxbvonf42KdFD2dNkoOk4IuKJmrSDQY9C6lLY/JHpNCLyP1SYRU7Bp7qSoamPUe4XoaUYIlK/zpsI8f3g6/ugIMN0GhH5lQqzyCn0PvQWTUtTWZb4AFb/cNNxRMST/f/SjGobJE/R0gwRF6HCLOJETNF2zkt/j+3NLict6gLTcUTEG0QlwODHYe8K+Pk902lEBBVmkT/kW21laOpjlAQ04du295iOIyLe5Nxboe2FsOQByDtgOo2I11NhFvkDfQ++QZOyNJYlPojVL8x0HBHxJj4+MOpVwAJf3g7V1aYTiXg1FWaRk2hR+As9Mz5ga8wVHIjsazqOiHijiNYw7GnY/z2sf8N0GhGvpsIs8ht+VeUMSX2M4sBmfNfmLtNxRMSbnXM9tB8Cyx+F7D2m04h4LRVmkd/of+AVosoPsjTxYSr8Qk3HERFvZrHAiJfALxC+uA2qq0wnEvFKKswi/yMu/yfOOfIJm1pcw6GIXqbjiIhA4xYw/HlIXw8/vGw6jYhXUmEW+VWArZghqY+RG9Sa1fF3mI4jIvJf3cZA0khY9RRk7jCdRsTrqDCL/GpA2guEVmTzdYfHsfkGmY4jIvJfFgtc/i8IbAzzJ4GtwnQiEa+iwiwCJOR8S5djC1nfagKZYV1MxxER+b2QaBj5Ehz9Bb6dbjqNiFdRYRav16gyj0F7/0lmSEfWxf3JdBwRkT/W6TLoPh5Wv0hQ9jbTaUS8hgqzeDe7nUv2Pk2grYgl7R+l2sffdCIREeeGPQONWxG77lGoKDGdRsQrqDCLV+uU9RXtc1bxQ+vbyAlJNB1HROTUghrDlTPwL86ApQ+ZTiPiFVSYxWuFWo9y8b7nyGjcnZ9jrzMdR0Sk5tr0J7fjONjwNuxZbjqNiMdTYRbvZK9maOpj+NirWZL4CHaLr+lEIiKnJavbJGiaBF/cDqW5puOIeDQVZvFKPQ7PoXXBBr5JuIeCRq1MxxEROW1230AYPRNKc2DR30zHEfFoKszidZqU7KHfgVfZEzWA7c1Gmo4jInLmWpwNA+6D7Z/D1nmm04h4LBVm8Sq+1RVcuvshrH5hLE+833EYgIiIO+v3V2h1Hiy6BwrSTacR8UgqzOJVzj8wg6ale1iW+BBl/pGm44iI1J6vn2NpRpUN5k+G6mrTiUQ8jgqzeI1WBRvpefhDtjS/irSo/qbjiIjUnagEuHQ67P8efnzZdBoRj6PCLF4h0FbE0N2PkB8Ux3dt7jIdR0Sk7p1zPSSNgBVPwJEtptOIeBQVZvEKF+97ltCKbL7q8Dg230am44iI1D2LBUa8BMFN4LM/Q2WZ6UQiHkOFWTxeh6ylJGV9zdq4W8kM62I6johI/QmOgitnQPYuWPaw6TQiHkOFWTxa4/LDDNr7Tw6HdWN93M2m44iI1L92A6HPX2D9G5C6zHQaEY+gwiwey2K3MWy34w7LVx2ewG7xM5xIRKSBXPIINOsMX/wFSrJNpxFxeyrM4rF6H3qX2KItrGh3H4VBsabjiIg0HP8gGP0mlOdD8hSw200nEnFruuUmHqll4RZ6H3qLlKaXsqvpMNNxRMTL5RRb6/w5q4IiOJRb+scDAhIIPf8BIr9/hLxvZ1B81k118rqhgX5EhgTUyXOJuAsVZvE4AbZihu1+iKLA5qxMuNd0HBERVu3KqvPnzM7OIjq6qfNB9mFcEfE1cd8+woL8NuSEJNb6dcf0bKXCLF5HSzLE4wzc+wxh1mN81eEJKvxCTccRETHH4sPS9o9g9Qtl+O4H8K0qN51IxC2pMItH6XRsMUnZS1jb+k8caXyW6TgiIsaVBjTh6/aPEV26j4v2/9t0HBG3pMIsHiO8PJ2B+54lo3F31rfSFnIiIv/vYGQfNrQcz9lHP6NdzirTcUTcjgqzeASf6kqG73oAOxa+av84douv6UgiIi5lTfztZIYkMWTPk4Raj5qOI+JWVJjFI/Q78CrNi3ewLPEhioJamI4jIuJyqn38WdzxSXyqKxm2+xEs9irTkUTchgqzuL22uas59/CHbGl+FXuiB5qOIyLisvIbtWZVu3uJK/yZXumzTMcRcRsqzOLWQqzHGJL6GFnB7fm27d2m44iIuLwdTS9jZ/RQ+h58k5aFm03HEXELKszitiz2Ki7d/TD+1eUs6vhPqnwCTUcSEXF9FsuvJ6C2YPiuBwiqzDedSMTlqTCL27o452PiCjeyst008oLbmI4jIuI2KvxCWdTxaRpV5jE09VGwV5uOJOLSnBZmq9XKpEmTGDlyJFOnTsV+krPo/2hMZWUlkydPPq3nEqmp2IKNXJwzh5Sml7Kj6WWm44iIuJ1joZ34ru1fSchbQ8+MD03HEXFpTgtzcnIyMTExJCcnU1hYyJo1a2o0pry8nNGjR58wvibPJVITQZX5DN/9ELn+zVmRMA0sFtORRETc0pbmY0ltMpD+B16lReEvpuOIuCynhXnt2rX069cPgD59+rBu3boajQkKCmLBggU0b978tJ5L5JTs1QxLfYSgynw+bvkPKv1CTCcSEXFfFgtLEx+iKDCG4bsfILCywHQiEZfk5+yb+fn5hIWFARAaGkpaWtoZjTmdcf/LarWSkpJyynHuqry83KOvr65VBUXQdfertM37gS+b3c4hv3j8srNMx3JbNpuNbM1frWgOa0fzVzt1OX8fNr+XiQf+zsXbH+CD2Ied/uauoCCM4swDdfK6punncO140/w5LcwREREUFRUBUFRURGRk5BmNOZ1x/yswMJCkpKRTjnNXKSkpHn19de3YlmV0zv6AndFD2Zc4Ab+cbKKjm5qO5bays7M0f7WkOawdzV/t1OX8WWnKap87GZD2IkMqlvFz7Pg/HBseHk5cW884IEo/h2vH0+bPWfl3uiSjb9++x9car127lt69e5/RmNMZJ3JSRUdpsuQ28hq1Znni/Vq3LCJSxza1uJY9UQPof+BlmhdtNR1HxKU4LcwjR44kMzOTESNGOP5GGRfH9OnTnY7p27dvjZ7rj8aJ/E6VDebdgqWimEWdnqHSN9h0IhERz2OxsLT9QxQHxHD5zvtoVJlnOpGIy3C6JCMgIICZM2ee8Ni0adNOOeb/LVu2rEbjRJxa9SQcWEPe4JfIKW1nOo2IiMey+jVmYafpXPPLrVy6+yHmd/4Pdouv6VgixungEnFtu76G1f+CHhMoTRprOo2IiMc7FtqJle3uJT5/HX0Ovmk6johLUGEW15V3AOZPgubd4NJnTacREfEa22NGsa3ZCPqkv02bXJ2bIKLCLK6pshw+neA4rvXq2eAfZDqRiIhXWZlwL8dC2nNp6sM0Lj9sOo6IUSrM4pq+mgqHN8EVMyAqwXQaERGvU+UbxMKOz4K9mst33odvtdV0JBFjVJjF9WycBT/Phgv+DkmXm04jIuK1Chq1Ykn7R4kpSWHAvhdMxxExRoVZXEv6Blg8FdpdAhffbzqNiIjX29fkItbHTuCszPl0zkw2HUfECBVmcR3Fx+CTGyCsBVz1FvhoKyMREVfwQ/xkDob34pK90wnI3Gw6jkiDU2EW11Blg09vhrJcuOYDCI4ynUhERH5lt/ixqOM/KQmIpsnCWxw3OES8iAqzuIblj8CB1TDiJWhxluk0IiLyG+X+ESQnPYePNQ/m3gi2CtORRBqMCrOYt3Ue/PgKnDcJzr7GdBoREfkD2SEdyB30Lzj4Iyz5h+k4Ig3G6dHYIvXuyBb48g6I6wNDnjSdRkRETqGswxVQuAN+eBladIceN5iOJFLvdIdZzCk+BnOuc6xXvuZ98AswnUhERGrikkch4WJYdI9jdyMRD6fCLGbYrPDJ9VCaA+PmQGgz04lERKSmfP1gzDuOXY0+uR6KjppOJFKvVJil4dntsPAeOLQOrpwBLc42nUhERE5XcBRc+xGUFzhKc2W56UQi9UaFWRreutdh8wdw4b3Q5UrTaURE5Ew17wpXzoT0nyB5iuOGiIgHUmGWhrVnBSy5HzpdDgP0DmsREbfXeSQMfBC2zoXVL5pOI1IvtEuGNJzsPTDvZmia5Lgj4aO/r4mIeIQL/g5Zu2DF49CkvaNEi3gQNRZpGGV5MOda8PFzvMkvMNR0IhERqSsWC4x8BWLPhfmTHFuGingQFWapf7YKx6lQefvh6vchMt50IhERqWv+QY43ATaKgjnjtHOGeBQVZqlfdjssvBvSvoNRr0CbfqYTiYhIfQmLges+hrJ8+Pg6qCwznUikTmgNs9Sv1S86dsS4aBqcfa3pNCIiUks5xVbnAwLa0WjIy0QvuoXSuZPIGfYaWFzz/px/SLjpCOImVJil/mz7zPEGkG5jtSOGiIiHWLUrqwajunFu/BQuSH2Z7aXhrG4zpd5znYnBiWGmI4ibUGGW+nFwHcy/DVr3hVGvOt4QIiIiXmND7A00th6mV8ZsCgNb8kuLq0xHEjljKsxS93LT4ONxEB4L13wIfoGmE4mISEOzWFiV8HfCrJlcvO9ZigJjSIvqbzqVyBlxzUVF4r5Kc+Gjq8FeDdd9CiFNTCcSERFD7BY/Fnd8iqyQjgzfdT/NilNMRxI5IyrMUncqyxxbCeXtd9xZjk40nUhERAyr9A3mi84vUuYfwRU77qZx+WHTkUROmwqz1I0qG8y7FQ6tg9FvaPs4ERE5rjQgmi86/xvf6gqu2PFXAm2FpiOJnBYVZqk9ux0W/w12LYJLp0OXK00nEhERF5MbnEBy0nNElB9iRMpUfKtPsT2diAtRYZba+/ZZ2DgL+t8DvSeZTiMiIi4qI7wnS9o/Slzhzwzf9SAWu810JJEaUWGW2tk4C775J5x9HVzysOk0IiLi4nY1Hcqqtn8jMfcbLtnzjOO3lCIuTtvKyZnbudhx7HXiYBj5kvZaFhGRGtnc8loaVebRJ/0dygIiWRN/u+lIIk6pMMuZOfAjzLsZWnSHq98DX3/TiURExI382HoywZX5nJc+i1K/SDbFXmc6ksgfUmGW03d4k2Ov5fA4GP8pBISYTiQiIu7GYmFlu3tpZMtnwP5/Ue4fQUqz4aZTiZyUCrOcnmM74f3REBQBN34JIdGmE4mIiJuyW3z5qsMTBO4oZEjq45T7NdZpgOKS9KY/qbncNJg9yrH84sYvHEdfi4iI1EKVTwDJnZ4nK6QDl++6j1YFG01HEvkdFWapmcLDMHskVFnhhi+gSTvTiURExENU+oUwv8t/KAhsyagdd9OicIvpSCInUGGWUyvJdtxZLs2D6z+DmM6mE4mIiIcp84/ks66vURIQzZU77iKmaIfpSCLHqTCLc2X58P6VkH8QrvsEYnuaTiQiIh6qJCCaeV1fo9wvnNE7phBdkmo6kgigwizOlBfAB1fBsR1w9fvQpp/pRCIi4uGKA5szr+trVPoEcdX224kqTTMdSUSFWf5AeaGjLB/ZDGPfgw5DTCcSEREvURgUy7yur2HHh6u2/YXwskOmI4mXU2GW3ysvhA9GO/ZbHvseJF1uOpGIiHiZ/EbxfNb1VXzsNsZsu43w8nTTkcSLqTDLiU4oy7NUlkVExJic4HZ83uUV/KvLGbt1ku40izEqzPJf/78M4/AmGPMuJI0wnUhERLxcVmhH5nV9Dd/qCq7eNonI0v2mI4kXUmEWB2sRfDgGMjbCmHeg80jTiURERADIDunAvK4zsNirGLttMlGl+0xHEi+jwixQlufYOi59A4x9FzqPMp1IRETkBDkhiczr+joAY7dNpknJHsOJxJuoMHu74iyYNQIOb4ar31NZFhERl5Ub3JZPu75OtcWPMdtuI7pkt+lI4iVUmL1ZQQa8eynk7IHrPtaaZRERcXl5wW2Y23UmVT4BjNn2F50IKA1Chdlb5e6Dd4dB0VG44XNIHGQ6kYiISI0UNIrj024zqfANYcy222iVv8F0JPFwKsze6NhOeOdSxxv9JiRD/PmmE4mIiJyWgqBWfNLtLQqDmnPljjtpl/ON6UjiwVSYvc3hzY5lGNjhpsUQ28N0IhERkTNSEtiUuV3f4FhoJy7fOY3OmcmmI4mHUmH2JntXwazLISAEbv4KYjqbTiQiIlIrVv9wPu/yCociejF0zxP0yPjQdCTxQCrM3uKXufDhWIiIg1uWQJN2phOJiIjUiUrfYL5MepHdTQZx0f5/c/6B18BuNx1LPIifs29arVbuvPNOjhw5QseOHXn22WexWCynHFNRUfG7x7Zu3codd9xBbGwsAE899RQJCQn1d2XiYLfDDy/BsoehzQVwzQfQKMJ0KhERkTpV5RPA4o5PUr43jN7p7xJSkcOKdv+g2sdp1RGpEad3mJOTk4mJiSE5OZnCwkLWrFlTozEne6ywsJBx48YxZ84c5syZo7LcEKqr4et/OMpylyvh+s9UlkVExGPZLb6saPcP1ra6la7HkhmVcjcBtmLTscQDOC3Ma9eupV+/fgD06dOHdevW1WjMyR4rLCxk6dKljBkzhilTpmDXr0rqV2U5fHYLrJsBff4CV70DfoGmU4mIiNQvi4Uf4yezNPEh4gp+4uqtEwm1ZppOJW7O6e8p8vPzCQsLAyA0NJS0tLQajTnZY61bt+auu+5iwIABXHvttaxfv57evXs7DWe1WklJSTmjC3MH5eXl9XJ9vtYCYtfcR0jWJjLPnkJu/HjYtavOX6ehVQVFkJ2ddfxrm812wtdyejR/tac5rB3NX+1o/pz71rcv6bGPcd3hp7h68wRmxz7G0aATf7td1TbYo3tGfauvHuOKnBbmiIgIioqKACgqKiIyMrJGY0pKSn73WGxsLB06dAAgNjaWnJycU4YLDAwkKSnp9K7IjaSkpNT99WXthjm3OU7xu+ptYrqNIaZuX8GYQ7mlREc3Pf51dnbWCV/L6dH81Z7msHY0f7Wj+Tu1guihzI1ux5U77mJi+jQWdnyGg5F9jn/f19eXpETP7Rn1rV56jEHOyr/TJRl9+/Y9vm557dq1J70jfLIxJ3ts1qxZLFq0iOrqanbv3n28PEsd2rsS3hrkOJDkpoXQbYzpRCIiIkblhCQy56x3KQhqyZU7/kq3o5+bjiRuyOkd5pEjR7Js2TJGjBhBp06diIuLY/r06UybNu0Px/Tt25fKysrfPZaYmMjf/vY3PvjgAwYPHkxiYmK9X1xt5JVUUGy11etrVAVFcCi3tE6eK/SXWUR8+yCVUR3IHvEeVSFxkFtKaKAfkSEBdfIaIiIi7qgksBlzu77JZbvvZ9Dep4ku2cO3be8xHUvciMXuwu++M3mr/1BuKfM2ptfra9TFr9MsdhsD9r1I96Ofsi+yP4s7PEmlX8jx74/p2Yq4qODaRnUJv/1vol9H1o7mr/Y0h7Wj+asdzd/ps9ir6H/gVc7NeJ9DjXtSfOkrJCXpEK8z5YlLMv7oerQ5oRsLrCxg+O4HaZO/lg2xN7A6/nbsFl/TsURERFyS3eLL923uJCs4kcF7nqJ60RiI+hRiupiOJi5OJ/25qabFuxi/5UZaFWxkaeJDfN/mTpVlERGRGtjZbDhzu72BpaoS3hoMO5JNRxIXp8LshjofW8i1W2/Fx25jbrc32R4z0nQkERERt5IZ1oW9l38KzZJg7g2w8imorjIdS1yUCrMb8amuZODeZxia+hiHw7rxYfcPyAzTr5FERETOhC04Bm5aBN3Hw3fPwgejoVh7W8vvqTC7iVBrJmO3TeLso5/xU+yNfN7lZcr8f78vtoiIiJwG/yAY9SqMeAkO/AgzL4CDa02nEhejwuwGWuev47otNxJdspcFHaezus0U7Ba9X1NERKROWCzQcwL8aTn4BcG7w+GHl8F1NxKTBqbC7MJ8qm303/8yV22/g3K/cOacPYs90QNNxxIREfFMLc6CSd9Cx0th6YPwyfVQlm86lbgAFWYX1bg8g6u3/pleGbP5JeZKPjp7NrnBbU3HEhER8WxB4XDNBzDkKdj9Ncy8EA6tN51KDFNhdkEdspdx/ebxRJbtZ2HHp1mReD823yDTsURERLyDxQLn3wE3LQbs8M4w+GY6VNXvCcDiulSYXYhfVRmD9jzFZbvuJyc4gQ+7f0hq9CDTsURERLxT694weTV0GwPf/BNmDYfcNNOpxAAVZhfRonAL128eT9fML1nf6iY+7foGhUEtTccSERHxbkHhMPoNGP0WHEuB1y+ALR/rDYFeRoXZMN/qCvrvf5mrt07Ex25jXtcZrIm/nWof7YIhIiLiMs4a67jb3LwrzJ8E826B0lzTqaSBqJUZ1LJ8D9ds+TfRpfvYGnMF37b5K5V+IaZjiYiIyMlExjsOOvn+Rfj2Gdj/PVz2InTWibueTneYDfCpttH74JtMPnA3QbYi5nf+N8sTH1BZFhERcXU+vnDRVJj4DYS1cByr/elNUJJtOJjUJ91hbmAxRdsZtPcpmpWksjnsYn7o/CBWv8amY4mIiMjpaN4N/rwS1vzbsYNG2ncw/DnoMtqxy4Z4FN1hbiD+thIG7Huecb/cTHBlPsmdnuPTllNVlkVERNyVrz9cOBUmfw8R8Y51zZ9cD4WHTSeTOqbC3AAScr5lwqar6X5kLptbjOW9c+ayt8kA07FERESkLjRLgluXwaDHYM9yeKUX/Pia9m32IFqSUY9CrMe4OO152uesIju4HQs7PcPRsG4NmiGn2Nqgr1efPOlaRETEw/j6Qf+/QudRsHgqLPkHbP4ILn8R4s6r95fPK6mg2NqwBb0qKIJDuaV1+pyhgX5EhgTU6XPWBRXmeuBbXUGPwx9y3qF38aGa1fG3s7Hl9Ua2ilu1K6vBX1NERMRrRbWF8Z9CSjJ8dR+8PRh63Oi4+xwcVW8vW2y1MW9jer09/8lkZ2cRHd20Tp9zTM9WKswez24nIfc7Ltr/byLK09kTdRHftfkrBY1amU4mIiIiDcVicdxpbjcQvnkG1s6AlIUw8EHoMcFxN1rcitYw15Go0jSu3HEno3b+nSqLP591eYUFSc+rLIuIiHirwDAY+pTjTYHNkmDRPfB6f8c6Z3Er+itOLQVV5tP70NucffRTKn0a8U3be9jSfKxO6hMRERGHmC6OA09SFsCyh+CDqyBxMAx5Epp1Mp1OakCt7gz5VZXR4/BHnJvxPv5VZWyPGcma+L9Q5h9pOpqIiIi4GovFcSJgh6Gw/g349jmYcT6cezNcdB+E1u1aYKlbKsynyafaRtfML+hz6C1CKnPYEzWANfG3kRucYDqaiIiIuDq/QDh/Cpw9zrG+ecM7sHkO9JnseLyRbry5IhXmmrJX0yF7OecffJ3I8kNkNO7Ogk7TOdL4bNPJRERExN2ERMNlz0PvSbDqn/D9C/DTW3D+ndB7MgSGmk4o/0OF+RQs9iraZ6+gz6G3aFKWRnZwO75IepG0yP46+lJERERqJ7o9jH0XLrgHVj4FK59w7KpxwT1w7q3gH2Q6oaDC/MeqbXQ6tpje6e8QVXaAnEZtWdThKVKjL8Fu8TWdTkRERDxJ825w3cdw6CdHaV5yP6z5D/S9w7HOOTDMdEKvpsJ8Mts+p/nyJ4jL30dWcCILOz5DapOLwaJd+ERERKQexfWCCcmQ9p1jmcayhxyfe092LN+ox8NP5I+pMP+WrQKSp2Bv3IbkTs+yN+oiFWURERFpWG0vdHykb4TVL8K3z8APL0OvW6DP7dC4hemEXkWF+bf8AuDvu8ksgr0/Z5hOIyIiIt6sVU+49kPI3AGr/wU/vgprX4euox13nWN7mE7oFXTr9GQCQvSGPhEREXEdMZ3hqjdhys9w3p9h52J482J4eyhs/wKqbaYTejQVZhERERF3EdUWhj0N9+yAYc9A8VH4dAIt3uvDuenv0agi13RCj6TCLCIiIuJughpDn9scd5yvnYMtvA0XHHiFP2+4jMt2/oPW+evAXm06pcfQGmYRERERd+XjC52Gk9VsACu//56umV/Q+dgiOuQsJz8olm0xV7C92eWUBkSbTurWVJhFREREPEBucFu+a3s3a+L/QmLON3Q7Op/+B17l/AOvsz+yDylNh7Mv6kJsvjoM5XSpMIuIiIh4kCqfQHY1HcqupkOJLN1P52ML6ZT1NZflPYDVN4Q9TS4mpelw0sN76DC2GlJhFhEREa+UW1LBodxS0zHqRE6x9aSP5wW3YU2bO1gT/xdaFfxMUtZXtM9ZQZdjCykKaEZqk4GkRg/icFg3nTvhhAqziIiIeKUlW9OJPnLyoulxLD6kR5xLesS5rEyYSrvc7+iUvYSzjn5OjyMfU+wfTWr0QFKbDORw4+668/wbKswi/9fe3cVGVeZxHP92+mLLzGyntQhY3ovQWBbLEgulQMjGmk3c9sJU6bQ2vsWIJsWsxhivuFET8eVmMYYLrLAkbdhit62uCYqrsF1aVKq4bFkkVBEsw0DLzHRKZzove8FmgpNyemqhMzq/z1XP0yczz/zyTM5/JmfOX0REJIWE07M5OfNeTs68l6zQMIuGurjj0gF+62pn5cBe/Jn59Oet43T+Os44VjOWPiPRS044FcwiIiIiKSqYYYtd75wZHmHh0L+44+IB7rh0gOUXOgilZXI2dxWn89fTn1eBN7sw0UtOCBXMIiIiIsJY+gy+LbiHbwvuwRIJcbv3KxYPHWLR4D/5/enXgNcYzJ7PGcdqzjjK8IUXAjMTvOrpoYJZRERERH4iYsmIXfN8cNGfcFz5nkWDXSzw9FByoZPS83/lPiy4XCWcyS3jB8fdDNhKCP9Kb1mngjkJ/a71Hcr/8mey6BnDlAAAB2FJREFUrvw6frkrIiLySxfMmcHhhkaO1jyW6KUkxOWcBfQWLqC3sA5LZIw5vmPM/PEfFAePU3a2iTVndxJOy8Blu5Ozv1nJudyV/Gi/i2CGLdFLvyFUMCehVa3vqFgWERFJIllXRljV+k7KFszXilgyOZe7iq/H5vNVwUxuCXm53fs1c7293O79ilU/7qHs3C4iWLhoLeK8bTkD9uWcty9nMGfhL/L2dSqYk9CXNY/pG2YREZEkEsyZwZcqlscVyPgN/fnr6c9fD0BG+ApzfN8w19vLHN83LLu4nxWutqtz0624bHcyYF/OBWsxbttSPLcUQlpaIl/ChFQwJ6GjNY/pE6wJFy+6KShIjR8b3AzKb+qU4dQov6lRflOnDG+OUHoOPzjK+MFRdnUgGiH/yvfM9v2b2cPHmeP7hrvP7sZCGIDRdDtu61Iu2JaRXfBHyL8vgasfnwpmEREREbl50iwMzljE4IxF/GdWFQDpkQAF/lPc5v8vM/0nuW34BHed34fl73+DFWcgPTPBi/4pFcwiIiIiMq3Clltw2Utw2UtiY2nREDV33cbcJCuWAX55V12LiIiIyK9ONC2DaGZydhVUwSwiIiIiYkAFs4iIiIiIARXMIiIiIiIGDH/0FwgE2LJlCwMDAyxbtoxt27aRFnefvPHmBINBU2PxjyUiIiIikmwMv2Hu6Ohg1qxZdHR04PV66erqMjXH7JiIiIiISLIzLJi7u7upqKgAYM2aNfT09JiaY3ZMRERERCTZGV6ScfnyZex2OwA2m43+/n5Tc8yOTSQQCNDX1ze5V3QD/WHuTX6Cufnw/y438jMov6lRflOnDKdG+U2N8ps6ZTg1NyG/Ydf39Llu6EOaFggErvs/w4LZ4XDg8/kA8Pl85OXlmZrj9/tNjU2ktLR0wjkiIiIiIjeT4SUZ5eXlsWuNu7u7Wb16tak5ZsdERERERJKdYcFcXV2Ny+WiqqqK3Nxc5s2bx6uvvmo4p7y83PSYiIiIiEiyS4tGo9FEL0JEREREJFmpcYmIiIiIiAEVzCIiIiIiBlQwT5ORkRGeeuopamtr2bZtG4ODg9TV1VFVVcXrr7+e6OUlPY/HQ0NDA7W1tbz11lsEAgGefPJJqquref7559GVRcbGxsbYvHkzwLjZKU9j1+Y33rHyMxaf1wsvvMCDDz7I5s2bCYVCys+EazMMhUJs2bKF2tpaXnzxRUB7cCLxexCgqamJRx55BEDn5Alcm9+xY8fYsGEDTqcTp9PJ6dOnU2L/qWCeJp2dnZSWltLS0sKpU6fYunUrGzdupL29nYMHD5q6L3Uqe//991myZAktLS0cPXqU9vZ2dY40aXR0lPvvvz+WkTpxTk58fvHHYK4raqqKz+uLL74gFAqxd+9e/H6/9p8J8Rl+/PHHFBcX09LSgtvtpq+vTxkaGO89e+7cOdra2mLHu3bt0jn5OuLz83q9OJ1OmpubaW5uZvHixSmx/1QwTxO73c7IyAjhcJjR0VF6e3tZu3YtFouFsrIydT6cQDQaxe/3xz61vvTSS+ocaVJ2djadnZ3Mnj0bMN+dU66Kzy/+GMx1RU1V8XkVFBTw8MMPAxCJRADlN5H4DNevX8+jjz5KKBTC5/Nhs9mUoYHx3rMvv/wyzz33XOy4p6dH5+TriM/P6/Wyf/9+ampqaGxsJBqNpsT+U8E8TSorKzl06BCVlZUUFRVhtVpjnQ+tVisejyfBK0xu1dXVeL1eGhsbyczMJBwO/6RzpPIzL77rpsfjGXdMzFN+5i1cuJAVK1bw0UcfYbFYqKioUH6TZLVaycnJwel0cuuttzJv3jxlOAmdnZ0UFxdTVFQUGxsaGtI52aT58+fzzDPP0Nraitvt5siRIymx/1QwT5MdO3bgdDr55JNP8Hg8fPfdd7HOh8PDw6Y6H6a6V155he3bt5OVlUV+fv6kO0fKVeN15zTT1VOuT/lNzoEDB9i9ezdvv/02GRkZym+ShoaGCAaDtLS04PV66e7uVoaT8Omnn3L48GGeffZZjh8/zp49e8jLy9M52aTCwkLWrl0b+/vSpUspsf9UME8Tv99PVlYWAFlZWaxcuZKuri4ikQhHjhxR58MJfP7552zdupVgMMiJEyd44okn1DnyZ1InzhtP+ZnndrvZuXMnO3bswGazAcpvspqamvjwww9JT08nOzubQCCgDCfhjTfeoLm5mTfffJOSkhIeeuihWH46J0/s3Xff5YMPPiASiXDy5EmWLl2aEvtPBfM0qa+vp7m5mU2bNjE6Osr27dv57LPPqK6uZuPGjSxYsCDRS0xqGzZsIBAIUFdXx9NPP01tba06R/5M6sR54yk/89ra2nC73Tz++OM4nU5aW1uV3yTV19ezb98+Nm3ahMPhYN26dcpwihoaGnRONqm+vp733nuPBx54gMrKSpYsWZIS+0+d/kREREREDOgbZhERERERAyqYRUREREQMqGAWERERETGggllERERExIAKZhERERERAyqYRUREREQMqGAWERERETHwP2dbqR581xR/AAAAAElFTkSuQmCC
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="PyMC"><a href="https://github.com/pymc-devs/pymc3">PyMC</a><a class="anchor-link" href="#PyMC"> </a></h1><p>Python的概率编程库,包括贝叶斯统计建模与概率模型,MCMC采样与变分推断。其中,pymc3基于Theano实现,pymc4(Pre-release)基于TensorFlow Probability实现,开源教程<a href="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers">Python概率编程与贝叶斯方法</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">d</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">pm</span><span class="o">.</span><span class="n">distributions</span><span class="p">)</span> <span class="k">if</span> <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">isupper</span><span class="p">()])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 AR
1 AR1
2 Bernoulli
3 Beta
4 BetaBinomial
5 Binomial
6 Bound
7 Categorical
8 Cauchy
9 ChiSquared
10 Constant
11 ConstantDist
12 Continuous
13 DensityDist
14 Dirichlet
15 Discrete
16 DiscreteUniform
17 DiscreteWeibull
18 Distribution
19 ExGaussian
20 Exponential
21 Flat
22 GARCH11
23 Gamma
24 GaussianRandomWalk
25 Geometric
26 Gumbel
27 HalfCauchy
28 HalfFlat
29 HalfNormal
30 HalfStudentT
31 Interpolated
32 InverseGamma
33 KroneckerNormal
34 Kumaraswamy
35 LKJCholeskyCov
36 LKJCorr
37 Laplace
38 Logistic
39 LogitNormal
40 Lognormal
41 MatrixNormal
42 Mixture
43 Multinomial
44 MvGaussianRandomWalk
45 MvNormal
46 MvStudentT
47 MvStudentTRandomWalk
48 NegativeBinomial
49 NoDistribution
50 Normal
51 NormalMixture
52 OrderedLogistic
53 Pareto
54 Poisson
55 Rice
56 Simulator
57 SkewNormal
58 StudentT
59 TensorType
60 Triangular
61 TruncatedNormal
62 Uniform
63 VonMises
64 Wald
65 Weibull
66 Wishart
67 WishartBartlett
68 ZeroInflatedBinomial
69 ZeroInflatedNegativeBinomial
70 ZeroInflatedPoisson
dtype: object</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>分布函数</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Normal</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sd</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span><span class="o">.</span><span class="n">dist</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_latex output_subarea output_execute_result">
$\text{None} \sim \text{Normal}(\mathit{mu}=0.0,~\mathit{sigma}=1.0)$
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>随机采样</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([-0.33071042, -1.03612079, -0.34140955, -0.07609122, -0.74469107])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>对数概率——适用于各种极大似然估计场景</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span><span class="o">.</span><span class="n">logp</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array(-0.91893853)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="A/B测试">A/B测试<a class="anchor-link" href="#A/B测试"> </a></h2><p>假设实验A点击率(点击用户数/总曝光用户数)是0-1均匀分布</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">pm</span><span class="o">.</span><span class="n">Model</span><span class="p">()</span> <span class="k">as</span> <span class="n">model</span><span class="p">:</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(</span><span class="s1">'p'</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_latex output_subarea output_execute_result">
$\text{p} \sim \text{Uniform}(\mathit{lower}=0.0,~\mathit{upper}=1.0)$
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>按照多重伯努利实验抽样$X\ \sim \text{Ber}(p)$</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p_true</span> <span class="o">=</span> <span class="mf">0.05</span> <span class="c1"># 真实点击率</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">1500</span>
<span class="n">occurrences</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">bernoulli</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">p_true</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">N</span><span class="p">)</span>
<span class="n">occurrences</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([1, 1, 1, ..., 0, 0, 0])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">occurrences</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>81</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>实验A的点击率</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">occurrences</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.054</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">occurrences</span><span class="p">)</span> <span class="o">==</span> <span class="n">p_true</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>False</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>将样本传入PyMC3的<code>observed</code>变量</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">model</span><span class="p">:</span>
<span class="n">obs</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Bernoulli</span><span class="p">(</span><span class="s2">"obs"</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="n">occurrences</span><span class="p">)</span>
<span class="n">step</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Metropolis</span><span class="p">()</span>
<span class="n">trace</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">18000</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
<span class="n">burned_trace</span> <span class="o">=</span> <span class="n">trace</span><span class="p">[</span><span class="mi">1000</span><span class="p">:]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stderr output_text">
<pre>Multiprocess sampling (4 chains in 4 jobs)
Metropolis: [p]
Sampling 4 chains, 0 divergences: 100%|██████████| 74000/74000 [00:11<00:00, 6576.51draws/s]
The number of effective samples is smaller than 25% for some parameters.
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>$p_A$的后验分布如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"$p_A$后验分布"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">p_true</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"$p_A$真实值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">burned_trace</span><span class="p">[</span><span class="s2">"p"</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s2">"stepfilled"</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实验A--VS-实验B">实验A VS 实验B<a class="anchor-link" href="#实验A--VS-实验B"> </a></h3><p>我们需要对比实验A和实验B的差异,假设</p>
<ol>
<li>实验A样本量1500,真实点击率$p_A=0.05$</li>
<li>实验B样本量750,真实点击率$p_B=0.04$</li>
<li>两组实验差异$\text{delta} = p_A - p_B = 0.01$</li>
</ol>
<blockquote><p>贝叶斯推断对样本量差异不敏感</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">true_p_A</span> <span class="o">=</span> <span class="mf">0.05</span>
<span class="n">true_p_B</span> <span class="o">=</span> <span class="mf">0.04</span>
<span class="n">N_A</span> <span class="o">=</span> <span class="mi">1500</span>
<span class="n">N_B</span> <span class="o">=</span> <span class="mi">750</span>
<span class="n">observations_A</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">bernoulli</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">true_p_A</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">N_A</span><span class="p">)</span>
<span class="n">observations_B</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">bernoulli</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">true_p_B</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">N_B</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"实验A: </span><span class="si">{</span><span class="n">observations_A</span><span class="p">[:</span><span class="mi">30</span><span class="p">]</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"实验B: </span><span class="si">{</span><span class="n">observations_B</span><span class="p">[:</span><span class="mi">30</span><span class="p">]</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>实验A: [0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]...
实验B: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0]...
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">observations_A</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">observations_B</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(0.04933333333333333, 0.044)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">pm</span><span class="o">.</span><span class="n">Model</span><span class="p">()</span> <span class="k">as</span> <span class="n">model</span><span class="p">:</span>
<span class="n">p_A</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(</span><span class="s2">"p_A"</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">p_B</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(</span><span class="s2">"p_B"</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">delta</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Deterministic</span><span class="p">(</span><span class="s2">"delta"</span><span class="p">,</span> <span class="n">p_A</span> <span class="o">-</span> <span class="n">p_B</span><span class="p">)</span>
<span class="n">obs_A</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Bernoulli</span><span class="p">(</span><span class="s2">"obs_A"</span><span class="p">,</span> <span class="n">p_A</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="n">observations_A</span><span class="p">)</span>
<span class="n">obs_B</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Bernoulli</span><span class="p">(</span><span class="s2">"obs_B"</span><span class="p">,</span> <span class="n">p_B</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="n">observations_B</span><span class="p">)</span>
<span class="n">step</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Metropolis</span><span class="p">()</span>
<span class="n">trace</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">20000</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
<span class="n">burned_trace</span> <span class="o">=</span> <span class="n">trace</span><span class="p">[</span><span class="mi">1000</span><span class="p">:]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stderr output_text">
<pre>Multiprocess sampling (4 chains in 4 jobs)
CompoundStep
>Metropolis: [p_B]
>Metropolis: [p_A]
Sampling 4 chains, 0 divergences: 100%|██████████| 82000/82000 [00:15<00:00, 5137.09draws/s]
The number of effective samples is smaller than 25% for some parameters.
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>三个随机变量的后验分布:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p_A_samples</span> <span class="o">=</span> <span class="n">burned_trace</span><span class="p">[</span><span class="s2">"p_A"</span><span class="p">]</span>
<span class="n">p_B_samples</span> <span class="o">=</span> <span class="n">burned_trace</span><span class="p">[</span><span class="s2">"p_B"</span><span class="p">]</span>
<span class="n">delta_samples</span> <span class="o">=</span> <span class="n">burned_trace</span><span class="p">[</span><span class="s2">"delta"</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">311</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
<span class="n">p_A_samples</span><span class="p">,</span>
<span class="n">histtype</span><span class="o">=</span><span class="s2">"stepfilled"</span><span class="p">,</span>
<span class="n">bins</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"$p_A$后验"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"#A60628"</span><span class="p">,</span>
<span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">true_p_A</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"$p_A$真实值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"$p_A$, $p_B$和delta后验分布"</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">312</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
<span class="n">p_B_samples</span><span class="p">,</span>
<span class="n">histtype</span><span class="o">=</span><span class="s2">"stepfilled"</span><span class="p">,</span>
<span class="n">bins</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"$p_B$后验"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"#467821"</span><span class="p">,</span>
<span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">true_p_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"$p_B$真实值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">313</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
<span class="n">delta_samples</span><span class="p">,</span>
<span class="n">histtype</span><span class="o">=</span><span class="s2">"stepfilled"</span><span class="p">,</span>
<span class="n">bins</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"delta后验"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"#7A68A6"</span><span class="p">,</span>
<span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">true_p_A</span> <span class="o">-</span> <span class="n">true_p_B</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"delta真实值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"AB_test.png"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>实验A样本量<code>N_A = 1500</code>大于实验B样本量<code>N_B = 750</code>,因此$p_B$的后验分布比$p_A$的后验分布宽,说明$p_B$不确定性比$p_A$高。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实验结论">实验结论<a class="anchor-link" href="#实验结论"> </a></h3><p>$\text{delta}$的后验分布大多数>0,因此可以推断实验A效果大概率比实验B效果好。</p>
<p>实验A比实验B效果差的概率:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">delta_samples</span> <span class="o"><</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.29823684210526313</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>实验A比实验B效果好的概率:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">delta_samples</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.7017631578947369</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="高斯过程回归">高斯过程回归<a class="anchor-link" href="#高斯过程回归"> </a></h1><p>(Gaussian Process Regression,GPR)</p>
<p>假设从正态分布抽样一组带噪声 $\epsilon$ 的样本</p>
$$
y \sim \mathcal{N}(\mu = f(x), \sigma=\epsilon)
$$<p>对于线性回归问题 $f(x) = ax + b$</p>
<p>高斯过程可以给出先验分布 $f$</p>
$$
f(x) \sim \mathcal{GP}(\mu_x, K(x, x^T, h))
$$<p>其中,$\mu_x$是函数均值,$K(x, x^T)$是核函数的协方差矩阵,$h$是数据平滑参数</p>
<blockquote><p>高斯过程可以生成任意曲线(曲面),必要条件是噪声能够近似成高斯分布,经典的线性回归模型都可以看作是高斯过程模型。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="生成模型示例">生成模型示例<a class="anchor-link" href="#生成模型示例"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gauss_kernel</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">knots</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">((</span><span class="n">x</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">h</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">knots</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">hs</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">hs</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">stats</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">cov</span><span class="o">=</span><span class="n">gauss_kernel</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">)))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"h = </span><span class="si">%.2f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">h</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="模型示例">模型示例<a class="anchor-link" href="#模型示例"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span>
<span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
<span class="p">]</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="n">xp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xp</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">xp</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsYAAAD2CAYAAADVjUExAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deVyVZf7/8ddhR0BAZRFERZFFxCRXRE2blBa3rCzbnTQtt8pxHGe+86uZpmlxqqm0smW0rLQyF0wLtawURVNxRxDFDRRBZRWOLOf3h+WMZSpy4D5w3s/Hg0d0e3PfH8+l8uY+n+u6TBaLxYKIiIiIiJ1zMLoAERERERFboGAsIiIiIoKCsYiIiIgIoGAsIiIiIgIoGIuIiIiIAOBkdAE/2759O66urobc22w2G3ZvOU9jYCy9/sbTGBhPY2A8jYHx7GUMzGYzXbp0+dVxmwnGrq6uREVFGXLvtLQ0w+4t52kMjKXX33gaA+NpDIynMTCevYxBWlraJY+rlUJEREREBAVjERERERFAwVhEREREBFAwFhEREREBFIxFRERERAAbWpVCxChLU7OZmZROTkEZQT7uTEuIYHhssNFliYiISD1TMBa7tjQ1mxmLd1FWUQVAdkEZMxbvAlA4FhERsTNqpRC7NjMp/UIo/llZRRUzk9INqkhERESMomAsdi2noKxGx0VERKTxUjAWuxbk416j4yIiItJ4KRiLXZuWEIG7s+NFx9ydHZmWEGFQRSIiImIUTb4Tu/bzBDutSiEiIiIKxmL3hscGKwiLiIiIWilEREREREDBWEREREQEUDAWEREREQHUYyxSZ7TVtIiISMOiYCxSB7TVtIiISMOjVgqROqCtpkVERBoeBWOROqCtpkVERBoeBWOROqCtpkVERBoeBWOROqCtpkVERBoeTb4TqQPaalpERKThUTAWqSPaalpERKRhUSuFiIiIiAgKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIkAtgnFFRQXjx4//zV83m82MGzeOoUOHMm3aNCwWy7XeSkRERESkzl3TOsbl5eXcddddHDp06DfPSUxMJCAggDlz5jBu3DiSk5Pp06fPtdYpYnOqqi2UlFdSWFZBUXkFhWUVlJorqbZYqKy2UFVtOf951fkfCt2cHXF3dsTdxfHC501cHGnm6YKXqxMmk8ng35GIiIh9M1lq8Sh34MCBrF69+pK/NnXqVAYNGkRCQgJz587l9OnTTJ069TevtX37dlxdXa+1lFopLy/Hzc3NkHvLebY2BtUWC7kllZworuBkaSV5pZWcLK0kv7SKk6WVFJRVcbaiGmu9D+LsYMLX3REfN0d83B1p5u5IgKcTQU2dCfJyJqipM02c667zydZef3ukMTCexsB4GgPj2dMYREVF/epYne18V1BQgJeXFwCenp5kZWVd9nxXV9dLFlgf0tLSDLu3nGfUGFgsFrILyth3vJiMk8Vk5pac/+/JEsorqi+cZzKBn6crQT7udGnjg5+XK03dnWnq5oS3uzPe7s40dXfGw8UJRwcTTo4mHEwmHB1MOP70JNhcWUVZRRVl56oor6ym7FwVpeZKTpeeI7/ETF6xmbwSM/kl5zh4vJz8kuKLam3h6Urb5k3oEOBFp+CmRAd5ExnohZuzY61fB/0dMJ7GwHgaA+NpDIxnL2OQlpZ2yeN1Fox9fHwoLj7/jb24uBhfX9+6upXIVSsxV7LzaAGpRwtIPVLA9qNnyC85d+HXA5u60SHAk3t7tCE8wJM2zT1o5etOQFM3XJxq9sR2aWo2M5PSySkoI8jHnWkJETXaIrrUXMnhU2c5fKqUrFOlHM4/S9apUlbszGHB5iMAODqYaO/nQXSQN11CfOgR2oyIAC8cHNSWISIiUlN1Fozj4uJITk4mISGBlJQUHn744bq6lchvKjVXsjnrNOv257PhQD7pucX83DzUroUH/cL9iA3xoWOQN2H+nni7O1vlvktTs5mxeBdlFVUAZBeUMWPxLoCrDscerk50DGpKx6CmFx23WCwcO1PGnpxC9uQUsSeniOTMfJakZgPQ1M2JHqHNfvpoTnRQU5wdtQCNiIjIlVglGB89epRPPvmE6dOnXzg2dOhQVq9ezZAhQ4iMjCQuLs4atxK5rKpqCzuOFbB+fz7r9+ez7cgZKqstuDg50L2tL5OjOxDb2ocuIT74NHGpszpmJqVfCMU/K6uoYmZSeo2eGl+KyWQipFkTQpo14eZOLS8cP3bmLJuzTl/4WJN2EgAvVyf6hrdgQIQ//SP88fMyppdfRETE1tUqGP888S4kJOSiUAzg4uLCnDlzanN5acBq20ZQE+bKKjZknmLV3hOs3ptLfsk5TCaIDmrKmL7t6BPWgm5tfa3Si3u1cgrKanTcGlr5NqGVbxNGXN8KgJPF5WzOOs36/fmsTT/Jyl0nAOjcypsBEf78LsqfmGBvrYYhIiLykzprpRD7ZY02gispNVfy7b6TJO05wXfpeZSYK/FwcaR/pD+DOgbQt4MfzTzq7onwlQT5uJN9iRAc5ONebzX4e7kxuHMQgzsHYbFY2Hu8iLX7TrI2PY83vt3Pa9/sJ6SZO7fFBBHtZSbSYlFIFhERu6ZgLFZXV20EVdWWC720SXtOcPZcFc09XBjcuSUJ0YH0DmuOq1P9PRW+nGkJERf9cADg7uzItIQIQ+oxmUxEB3kTHeTNxBs7cKb0HGvScvly53HeW3eQymoLr6QUcFtMS4ZcF0REoJchdYqIiBhJwViszpptBD8/6VyyLZtlO3LIKzbT1M2JYV2CGd4liG5tm+Fogysw/PwDQH21k9SUr4cLd3UL4a5uIZwpPcfcNalszbPw5neZzFqbSUywNyO7hzD0uiCrTUgUERGxdQrGYnXWaCMoNVeybHsOH286zJ6cIpwdTQyI8Of22GAGRPrXa7/wtRoeG2wzQfhyfD1cuCW8KU8NiyK/xMzyHTl8tuUYf126m398uZdbOgUysnsIvUKbaxk4ERFp1BSMxepq00aw70QRH6ccYUlqNiXmSiIDvXh2WDSDOwfha2DPsL1o4enK6PhQHu7dlt3ZRXy25ShLt2ezdHsOrZs14f5erbm7W2u8m+gpsoiIND4KxmJ1NW0jqKiq5ruDJfz1uw1sOXwGFycHBnduyX0923B9ax9NCDOAyWQippU3Ma28+cttUXy9+wSfbD7CP1fu49XV+xlxfTAP925LhwD1IouISOOhYCx14mraCIrLK/j0x6PMTT5EdkEZbZs34S+3RnFn11Z6OmxD3JwdL4zn3pwi5m3I4vOtx/h40xH6hLXg4d5tuTHSX20WIiLS4CkYS707XljGvORDfLLpCMXmSnqGNmPs9U158KauClc2rmNQU1668zr+dEsUCzYfYf7Gw4z5cAvt/DwYf0N7hncJrvHW2SIiIrZCwdjO1edGHAfzSpi1NpPE7TlUWyzcGtOSsX3bcV2ID2lpaQrFDUgzDxcmDAjj0X7t+Gr3Cd767gB/XLSTf6/OYGy/dtzTvTXuLrY/QVJEROR/KRjbsfrYiAMg82Qxb3ybyfIdObg4OfBAXBt+Hx9KSLMmNa7XVpc/s1fOjg4MvS6IIZ1b8l1GHm+tPcDflu/ljW8zGd27LQ/2bqvl3kREpMFQMLZjdbURx88ycot5/Zv9rNh1HHdnR8b2a8fYvu1o4ela42vVV4iXa2MynV9Ob0CEPz8eOs2bazN5eXUG7647yKP92vFwfCiervrnRkREbJu+U9kxa27E8b8O5JXwyqoMVuw6joeLI4/d0J4xfdvVaovmug7xYj3d2zZj7uge7Mkp5NXV+/nXqgz+k3yIx25oz/292qjFQkREbJaCsR2zxkYc/+tEYTn/XpPB51uP4ebkwMQBYTzSJ9QqK0zUVYiXuhMd5M17D3Vj+9ECXl6VznMr03hn3UEmDgjjnh4hNrN9t4iIyM8UjO1YbTbi+F+FZyt48/tM5iUfotpi4YFebZh4Y9g1tUz8FmuHeKk/XUJ8mP9ITzYdPMXLqzJ4OnEP7647yLSECIZ0DtKkSxERsRkKxnasphtx/FJ5RRVzkw/x1neZFJsrub1LME8ODK/xpLqrYa0QL8bp2a45n47rxbr9+Tz/1T6mLNzOf9Zn8edbo+jZrnm91KAJnCIicjkKxnbuajbi+CWLxcLKXSf458o0sgvKuDHSn2kJEUS1bFpHVdY+xIttMJlM9Av3Iz6sBUtSs/lXUjp3v5PCoI4BTL8lkvZ+nnV2b03gFBGRK1EwlhrZnV3I35fvZfOh00QGevHJmJ70DmtRL/e+lhAvtsnRwcSdXVtxW0xL/pOcxZtrMxn06g/c37M1Tw4Mx6eJ9Xc+1AROERG5EgVjuSoni8v5V1I6n289RrMmLvzz9hju7h6Co/pDpRbcXRyZMCCMkd1CeO2bDOanHCZxRw7TEiKt/udLEzhFRORKFIzlsiqqqpmXfIh/r8ngXFU1Y/u2Y+KNYTR106YNYj1+Xq78Y3gM9/ZowzOJe/jzkl0s2HyEZ4ZG07WNr1XuoQmcIiJyJQ5GFyC2a3PWaQa/vp7nVqbRs11zVj15A3++NUqhWOpMx6CmfDquF6/d04WTxeXc8dYGpn62g5PF5bW+9rSECNydL14iThM4RUTkf+mJsfxKfomZ51fu44ttxwj2ceedB7oysGMAJpPaJqTumUwmhnUJ5qaoAN74NpP31x9k1Z4T/PHmCO7t2eaa2ys0gVNERK5EwVguqKq2sGDzEV76eh9lFVU83r89E28Mo4mL/phI/fNwdeJPt0Qyslsr/rpsN39dtocvtmXzz9tj6Bh0bSugaAKniIhcjhKPAJCRW8z0L3aSeqSA3u2b8/dhnQjzr7uls0SuVjs/Tz56pCfLtufw7Jd7GTJrPWP6hDLlpg76oU1ERKxK31XsnLmyitlrD/DWd5l4ujrx6t3XMbxLsNomxKaYTCaGxwbTP8KPF77ax5wfDrJi13GeHdaJAZH+RpcnIiKNhCbf2bGth09z2+vref2b/QzuHMSap27g9thWCsVis3yauPDCHZ359NFeuDo5MHrejzyxMJUzpeeMLk1ERBoBPTG2Q8XlFcxMSmd+ymGCvN2ZN7o7/SP01E0ajp7tmrNySl/eXHuA2WszWZ+Zz9+HdeLWmJZGlyYiIg2YgrGdWb8/n+lf7CSnsIyHe7flD4Mi8HDVHwNpeFydHHlyYDg3dwpk2qIdPP7xNm6NCeRvQzvh5+VqdHkiItIAqZXCTpSaK/m/pbu4//1NuDo5sGh8b54eEq1QLA1eVMumLH08nj/eHMGavScZ+Or3LE3NxmKxGF2aiIg0MEpFdmDjgVNMW7SD7IIyxvQJ5Q8JEbj9YqMDkYbMydGBx/uHMahjANMW7eSJT7ezctdx/jkihhaeenosIiJXR0+MG7Gz5yp5JnEPo95NwcnBxGfj4vi/wR0ViqXRCvP3YtH43vz51ki+S88j4dUfSNpzwuiyRESkgVAwbqRSj5zh1tfWMW/DIR7u3ZaVU/rSvW0zo8sSqXOODiYe7dee5ZP6EOjtxrj5W5n62Q6KyiuMLk1ERGxcjYOx2Wxm3LhxDB06lGnTpl2yj2/nzp3069ePUaNGMWrUKA4ePGiVYuXKKquqeXV1Bne+vZGKKgsLxvbimaHR2ghB7E5EoBdLHo9n0o1hLN2ezc2v/sCGzHyjyxIRERtW42CcmJhIQEAAiYmJFBUVkZyc/KtzioqKGDVqFAsWLGDBggW0a9fOKsXK5WXll3Ln2xt57Zv9DL0uiK+e6Etc++ZGlyViGBcnB6YOimDR+DhcnR25971NPPvlXsyVVUaXJiIiNqjGwTglJYX4+HgAevXqxaZNm351TlFREatWreLOO+9k0qRJmh1exywWCws2H+HW19aRlV/KrHtjefXuLjR1cza6NBGbENval5WT+/JgXBveX5/FsFnJ7M8tNrosERGxMTV+f72goAAvLy8APD09ycrK+tU5rVu3ZsqUKfTv35977rmHzZs307Nnz8te12w2k5aWVtNyrKK8vNywe9dWYXkV/96QR8rRs8S2dOepeD9aOBeSllZodGk10pDHoDGwl9d/VLgj7dwDeHVDHre9vo6x3ZpxW0RTm9jt0V7GwJZpDIynMTCevY9BjYOxj48PxcXnn7QUFxfj6+v7q3OCg4MJDw+/8PmpU6eueF1XV1eioqJqWo5VpKWlGXbv2thwIJ8nl2znTGkF/3dbFL+PD8XBwfhv8NeioY5BY2FPr39UFNwaV860z3cye1Me+wodefHOzoYv62ZPY2CrNAbG0xgYz17G4LfCf41bKeLi4i70FaekpFzySfC8efNYsWIF1dXVZGRkXAjJYh0VVdXMTNrHfe9twsPViSUTejOmb7sGG4pF6pu/lxtzH+7O00M6si4zn5v/vY7vM/KMLktERAxW42A8dOhQcnNzGTJkCN7e3oSEhPDiiy9edM59993H4sWLueuuuxg4cCBhYWFWK9jeHT19lpFzNjJ77QFGdg3hy0l9iA7yNroskQbHwcHE6PhQEifG08zDmYf+s5nnv0qjoqra6NJERMQgNW6lcHFxYc6cORcdmz59+kX/7+/vz/z582tXmfzK8h05/HnxLjDBrHtjGdw5yOiSRBq8yMCmJE7sw9+/3Muc7w+yOes0r98TS0izJkaXJiIi9UwbfDQA5RVVzFi8k0kLUukQ4MnKyX0VikWsyM3ZkX/eHsPse68nM7eE215fx9e7jxtdloiI1DMFYxuXebKE4bOTWbD5KI/3b8+n4+L0JEukjtzWuSUrp/Ql1M+T8R9t469Ld1NeoTWPRUTshYKxDVuSeoyhs9ZzstjMvNHd+ePNkTg7ashE6lJIsyZ8Pi6OR/u1Y37KYW5/cwNZ+aVGlyUiIvVAKcsGlZ2r4o+LdvDkpzvoFOzNysl96R/hb3RZInbDxcmBP98axdzR3TlRWMaQN9azYqdaK0REGjsFYxuTebKEYbPX8/nWY0y6MYxPxvQk0NvN6LJE7NKACH9WTO5LeIAnEz7ZxtPLdms7aRGRRkzB2IYs35HD0FnrOVVyjg9G92DqoAic1DohYqggH3c+HRfH2L6hfLDxMHe9vZGjp88aXZaIiNQBpS4bcK6ymmcS9zBpQSpRLZuyYnJf+oX7GV2WiPzE2dGBv9zWkTkPdCUrv5TbXl/H6r25RpclIiJWpmBssOyCMkbO2ci8DYcY0yeUhY/2UuuEiI1KiA5kxaS+tG7ehLEfbuHFr/dRqQ1BREQajRpv8CHW831GHk8sTKWiysJb913PLTEtjS5JRK6gdfMmLBrfm78t38tb3x1gx9ECXh8VSwtPV6NLExGRWtITYwNUV1t4bc1+Hp67mYCmbiyf1EehWKQBcXN25PkRMcy8szNbD59h8Ovr2Xr4jNFliYhILSkY17PCsxU88sGPvLomg9tjg1nyeDyhLTyMLktErsFd3UJY/HhvXJwcuHvORuYlZ2GxWIwuS0RErpGCcT3ak1PIkFnrWZ+Zz7PDO/HyXdfh7uJodFkiUgvRQd4sn9SH/hF+PLN8L1MWbufsuUqjyxIRkWugYFxPFm87xog3N2CurGLho3E80KsNJpPJ6LJExAq83Z1554FuTEuI4MudOdw+ewOHtFueiEiDo2Bcx85VVvPXpbt56rMdxLb24ctJfenaxtfoskTEyhwcTEwYEMa80T3ILS5nyKz1fLtPS7qJiDQkCsZ16GRROaPeTWF+ymEe7deOjx7piZ+XZq6LNGb9wv1YPrEPrZs14ZEPtvDvNRlUV6vvWESkIdBybXVk6+HTPPbRNkrMlcy6N5bBnYNqdb2lqdnMTEonp6CMIB93piVEMDw22ErViog1hTRrwheP9ebPi3fx7zX72XWskFfu7oK3u7PRpYmIyGXoibGVWSwWPt50mHveScHdxZElj8dbJRTPWLyL7IIyLJzfFGTG4l0sTc22TtEiYnVuzo68PPI6/j4smu8z8hg2az0ZucVGlyUiIpehYGxF5RVV/OmLXfxlyW7iw1qQOKEPEYFetb7uzKR0yiqqLjpWVlHFzKT0Wl9bROqOyWTiwbi2LHy0FyXmKobPTubr3ceNLktERH6DgrGVHC8s4+53Uvh0y1Em3RjG+w91x7uJdd42zSkoq9FxEbEt3do248tJfQgP8GL8R9v4V1I6Veo7FhGxOQrGVrDl0GmGvJFMZm4xb9/flamDInB0sN5SbEE+7jU6LiK2J9DbjU/H9eLubiHMWpvJmA9+pLCswuiyRETkfygY19LHmw4z6t0UvNycWDohnvKKKuJf+JbQP60g/oVvrdIHPC0hAnfnizcCcXd2ZFpCRK2vLSL1x9XJkRfuiOHZ4Z1Ytz+f4bOT2a++YxERm6FgfI3OVVYzY/F/+4mXTohnT05RnUySGx4bzPMjYgj2cccEBPu48/yIGK1KIdIAmUwmHujVhk/G9qK4vILhs5NZteeE0WWJiAharu2anCwq57GPt7H18Bke79/+QuvE5SbJ1TbEDo8NVhAWaUR6hDZj+aQ+jJ+/lUfnb+X+63z5e4QFByu2YYmISM3oiXENbT9awJBZ69mbU8Sse2P5482RF/qJNUlORGqipbc7n46LY8T1wXy04wzjP9pKibnS6LJEROyWgnENfLH1GCPnbMTZ0YEvHuv9q/WJNUlORGrKzdmRl++6jke7N+ebfScZ8WYyh0+VGl2WiIhdUjC+CpVV1Tz75V6mfr6Drq19SZzYh45BTX91nibJici1MJlM3N7Rmw9/34OTxWaGzkpm3f48o8sSEWFparbVFxWwZQrGV1Bw9hwPz/2R99dn8XDvtnz4SA+aebhc8lxNkhOR2vh5Y6DApm489J/NvLfuIBaL1jsWEWPY4867mnx3GRm5xYz5YAsnCst56Y7OjOwecsWv0SQ5EamN1s2bsPjx3vzh8x38Y0Uae48X8c/bY3D7xbtRIiJ1rS4XFbBVemL8G5L2nOD22cmUVVSx4NFeVxWKRUSswcPVidn3Xs9TA8NZvC2bu+ds5ERhudFliYidscdFBRSMf6G62sJra/Yzbv5WwgK8WD6xD13b+BpdlojYGQcHE5N/14E5D3Ql82QJQ2etZ9uRM0aXJSJ2xB4XFVAw/h9nz1Uy4ZNtvLomgxGxwXz6aC8Cvd2MLktE7FhCdCCLH4/HzdmRe+ak8PmWo0aXJCJ2wh4XFVAw/snR02cZ8eYGkvac4C+3RvHyyOvU0yciNiEi0ItlE+LpHurLtEU7+fvyvVRWVRtdlog0cva4qECNJ9+ZzWYmT57M8ePHiYiI4KWXXsJkMtX4HFuy80QZLy5KpqKqmrmje3BDuJ/RJYmIXMTXw4UPRvfguZVp/Cc5i/0ni3ljVCw+TS69So6IiDXY26ICNX5inJiYSEBAAImJiRQVFZGcnHxN59iKTzYd4c+rjuPbxJllE+IVikXEZjk5OvD0kGheurMzmw6eZtjsZDJyi40uS0RsQENbb9hcWcWBvBKjy/gVk6WGi2ROnTqVQYMGkZCQwNy5czl9+jRTp06t8Tm/tH37dlxdXWv+O6iFiioLdy88REc/F2b0b4mHizpLjFJeXo6bm/q5jaLX33g1HYO0k+U8uzaX8spq/tjPn14hHnVYnX3Q3wPjaQyuzbcHi3l9Qz7mqv9GOldHE5N7t+DGdl41ulZ9jMGps5U8uzaXwwXn+Oyetjg7GtNVEBUV9atjNW6lKCgowMvr/Ivs6elJVlbWNZ3zS66urpcssK5tmBFGzuFMojt2rPd7y3+lpaUZMv5ynl5/49V0DKKiIK5LGY9+uJW/r83lD4MieLx/e5tuW7N1+ntgPI3BtRmz7NuLQjGAucrCJ7tKmHBbjxpdq67HIPXIGZ5avJVScyWvjbqeztGBdXavy0lLS7vk8Ro/IvXx8aG4+Pxbd8XFxfj6/nops6s5x1b4erjgoG8kItIAtfR25/PxcQy9LoiZSelMXJDK2XOVRpclIvWsoaw3/MXWY9z9Tgpuzo4sfjyeBINC8eXUOBjHxcVd6BlOSUmhZ8+e13SOiIjUnpuzI/++uwt/uiWSlbuOc+dbG8m2sW+GIlK3bH294cqqav7x5V6mfr6Dbm18WTYhnojAmrV41JcaB+OhQ4eSm5vLkCFD8Pb2JiQkhBdffPGy58TFxVmtYBERuZjJZGL8De35z0PdOXr6LEPfWM/mrNNGlyUi9cSW1xsuPFvB6Hk/8t76LB7u3ZYPf98DXw/bXU2nxj3GLi4uzJkz56Jj06dPv+I5tmhpajYzk9LJKSgjyOc40xIi7GpJEhFpXAZE+rN0YjxjP9jCve+m8Ldh0dzXs43RZYlIHfs5u/w307jbRKbZn1vM2A+3kF1Qxkt3dGZk9xBD67kaNQ7GjcXS1GxmLN5FWUUVANkFZcxYvAvA8D9IIiLXqr2fJ0smxDNlYSp/WbKbvTlFPD0kGhcnrboj0pjZ2nrDq/fm8sTCVNxdnFj4aC+6tmlmdElXxW7/pZyZlH4hFP+srKKKmUnpBlUkImId3u7OvP9Qd8bd0I6PNx3h/vc3kV9iNrosEbEDFouFN77Zz9gPt9De35Plk+IbTCgGOw7GDWUGp4jItXB0MDHjliheu6cLO44WMGxWMruzC40uS0QasVJzJRM+2cbLqzO4PTaYz8bF0dLbNiYAXi27Dca2PoNTRMQahnUJZtH43lgsFu58ewPLttv2blgi0jAdPX2WO97awNe7T/CXW6N4ZeR1uP1iQmBDYLfB2JZncIqIWFNMK28SJ/Whc7APUxZu5/mv0qiqrtGmpyIivyk5M58hs9aTU1DG3NE9GNuvXYPdbMhug/Hw2GCeHxFDsI87JiDYx53nR8TYVOO6iIi1tPB05aMxPbm/V2vmfH+Q38/7kcKzFUaXJSINmMVi4f31WTz4n8208HRl2cQ+3BDuZ3RZtWK3q1LAf2dwagtKEbEHLk4O/GN4DB1bevN04m6GzV7Puw92o0OAbS60LyK2q7yiij8v2cXibdkM6hjAK3d3wdO14cdKu31iLCJir+7t2ZoFY3tRYq5i+Oxkvt59wuiSRKQBySkoY+ScjSzels2TN4Xz9v1dG0UoBrA5FkgAABr9SURBVAVjERG71K1tM5ZPiicswIvxH23llVXpVKvvWESu4MdDpxk6az0H80p598FuTLmpAw4ONesnXpqaTfwL3xL6pxXEv/AtS1NtZ1KwgrGIiJ1q6e3Op4/2YmS3Vrz+bSZjP9xCUbn6jkXk1ywWC/M3HmLUOyl4uTmzdEJvBnYMqPF1ft5gLbugDAv/3WDNVsKxgrGIiB1zc3bkxTs68+ywaL7PyGP4rGQyTxYbXZaI2JDyiir+uGgnf122h37hfiydEE+Y/7XNTbD1DdYUjEVE7JzJZOKBuLZ8MrYXReUVDJ+9gaQ96jsWkf/2E3++9RiTf9eB9x7shre7c62uV5Pj9U3BWEREAOgR2ozlk/rQ3s+DcfO3MjNpn9Y7FrFjGw+cYsgb5/uJ33mgK08NDK9xP/Ev2foGawrGIiJyQUtvdz4dF8c93UOYvfYAo+f9SMHZc0aXJSL1yGKx8J/1Wdz//iZ8mjizdEI8g6IDrXJtW99gTcFYREQu4ubsyAt3dOb5ETGkHDjFkFnr2ZNTaHRZIlIPyiuqeeLT7fz9y73cGOn/Uz+xp9Wub+sbrDWORedERMTqRvVoTWSgF499tI073trA8yNiuD22ldFliUgdycov5cmV2RwurGBaQgSP3dC+1q0Tl/LzBmu2SE+MRUTkN8W29mX5pD5c18qHJz/dwdPLdnOustroskTEytbszWXoG+s5XVbFB6N7MGFAWJ2EYlunYCwiIpfl5+XKR2N6MqZPKB9sPMw972zkeKFtzCAXkdqpqrbw8qp0xny4hbYtPHh9cDD9wv2MLsswCsYiInJFzo4O/N/gjsy+93rSTxQz+PX1bMjMN7osEamF06XnGD3vR974NpO7u4Xw+fg4AjyvfSm2xkDBWERErtptnVuybGIffD1cuP/9Tbz5Xaa2khZpgFKPnGHw6+tIOXCK50fE8OKdnXH7xWoR9kjBWEREaiTM35NlE+K5NaYlL32dzqPzt1JYpq2kRRoCi8XCvOQsRs7ZiIODiS8e682oHq2NLstmKBiLiEiNebg68caoWJ4e0pHv0k8y5I317M7Wkm4itqzEXMmkBak8s3wv/Tr4sWJSX2JaeRtdlk1RMBYRkWtiMpkYHR/Kp+PiqKiqZsSbG/go5TAWi1orRGxNRm4xw2atZ+Wu4/zx5gjefbAb3k3su5/4UhSMRUSkVrq28WXF5L7EtW/O/y3dzZSF2ykxVxpdloj85Iutxxg2K5nCsko+HtOLx/vb51JsV0PBWEREaq2ZhwtzH+7OtIQIvtyZw9A31rPvRJHRZYnYtbPnKpn2+Q6mfr6Dzq28WTm5D3Htmxtdlk1TMBYREatwcDAxYUAYH4/pRbG5kuGzk/nsx6NqrRAxwP7cYobNSmbRtmNMvjGMj8f0xL+pm9Fl2TwFYxERsaq49s1ZObkv17f25Y9f7OSJT9VaIVKfPt9ylCGz1nPm7Dnm/74nTw2KwMlRke9q6FUSERGr8/NyZf4jPXlqYDjLd+Qw+PV1WrVCpI6dPVfJU59tZ9qincSG+LJycl/6dGhhdFkNioKxiIjUCUcHE5N/14GFj8Zhrjy/asXc5Cy1VojUgd3ZhQx+fT1LUrN54qYOfKTWiWuiYCwiInWqR2gzVk7uS7/wFvxt+V7GfriVM6XnjC5LpFGwWCy8vz6LEW9u4Oy5Kj4Z04snbgrHUatOXBMFYxERqXO+Hi68+2A3/t/gjnyfcZJbX1/HxgOnjC5LpEHLLzEzet6PPPvlXm6I8OOrKX216kQtOdXkZLPZzOTJkzl+/DgRERG89NJLmEy//olk586dTJw4keDgYACee+452rVrZ52KRUSkQTKZTPy+Tyjd2zZjysJU7n0vhXH92vPUwHBcnPScRuzD0tRsZialk1NQRpCPO9MSIhgeG1zj66zbn8eTn+6gqLyCZ4dFc3+vNpfMZFIzNfqXKDExkYCAABITEykqKiI5OfmS5xUVFTFq1CgWLFjAggULFIpFROSCmFbefDm5D/d0D+Ht7w9wx1sbOJhXYnRZInVuaWo2MxbvIrugDAuQXVDGjMW7WJqafdXXMFdW8c+VaTzw/mZ8mziTODGeB+LaKhRbSY2CcUpKCvHx8QD06tWLTZs2XfK8oqIiVq1axZ133smkSZM00UJERC7SxMWJ50d05u37u3L0zFlue309Czcf0fcLadRmJqVTVlF10bGyiipmJqVf1dfvO1HEsFnJvPPDQe7r2ZrEiX2IDGxaF6Xarcu2UjzzzDOkp/93sJycnPDy8gLA09OTrKysS35d69atmTJlCv379+eee+5h8+bN9OzZ87KFmM1m0tLSalq/VZSXlxt2bzlPY2Asvf7Gs9cxaOMIb9zakpeT8/jT4l0s33KASXF+eLs51nst9joGtqSxj0FOQdlvHr/c77vaYmHZ3kLmbjuDh4sDf/tdID1aOXHoQIbVa2zsY3AlVwzG/2vq1KkUFxcDUFxcjK+v7yW/Ljg4mPDw8Aufnzp15QkWrq6uREVFXU3NVpeWlmbYveU8jYGx9Pobz97HIP56C++tP8i/kjKYuOIEL4yI4aaOAfVag72PgS1o7GMQ5HOc7EuE4yAf99/8fR8vLOMPn+8gOfM0N0UF8MIdMbTwdK2zGhv7GPzst8J/jVop4uLiLvQVp6Sk/OZT4Hnz5rFixQqqq6vJyMi4EJJFREQuxcHBxKP92pM4KR4/L1fGfLiF6Yt2UlxeYXRpIlYzLSECd+eL3w1xd3ZkWkLEr861WCwk7sgh4dUfSD1SwAsjYnj3wa51GoqlhsF46NCh5ObmMmTIELy9vYmLi+Po0aO8+OKLF5133333sXjxYu666y4GDhxIWFiYVYsWEZHGKTKwKcsmxDNhQHs+33qUW15bR8pBLesmjcPw2GCeHxFDsI87JiDYx53nR8T8alWKUyVmJnyyjckLUmnv78nKyX25p0drTbCrBzVars3FxYU5c+ZcdCwkJITp06dfdMzf35/58+fXvjoREbE7Lk4OTEuI5MbIAKZ+tp1R76bwSHwof0iIwM25/nuPRaxpeGzwZZdn+2rXcf5v6W6KyyuZfnMkY/uG4uSo5QzrS42CsYiISH3p2saXlVP68vzKfby3Potv9p3kpTs7071tM6NLEztgrfWGr9aZ0nP8v8Q9LN+RQ0ywNy+PvI7wAK86u59cmn4EERERm9XExYlnh3fi4zE9qaiqZuScjTyTuIdSc6XRpUkjZo31hmsiac8JBr76A1/vPs7UgeEsfry3QrFBFIxFRMTmxYe1IOmJfjwU15YPNh7i5td+YENmvtFlSSNV2/WGr9bJ4nIe/3gr4+Zvxc/LlWUT+jDpdx1wVuuEYfTKi4hIg+Dh6sQzQ6P5bFwcTg4O3PveJmYs3kWRVq4QK7vcesPWYLFY+OzHo9z08vesSTvJtIQIEifG0zFIm3UYTT3GIiLSoHRv24yvpvTl1dUZvLvuIGvScnlmSDS3xgRq1r5YRZCP+2+uN1xbh/JLmbF4FxsPnqJHaDOeHxFDez/PWl9XrENPjEVEpMFxc3Zkxq1RLJ0QT0BTVyZ8so3fz/uRo6fPGl2aXMbS1GziX/iW0D+tIP6Fb+usZ7e2arLe8NWqqKrmre8OkPDvH9idXcg/b49h4dheCsU2Rk+MRUSkwercyoelj8fzwcbDvLwqnUGv/sATN3Xg931C1adpY36e0PZz7+7PE9qAOl3t4Vr8XI+1VqXYdPAUf122m4zcEgZ2DODZYZ0I9HazZsliJQrGIiLSoDk5OvBIn1Bu7hTI08v28PxX+1iSms1zt3eiaxst7WYrLjehzdaCMVx5veGrkVds5vmVaSxOzSbYx513H+zGwHre6lxqRsFYREQahWAfd957qBtJe07w9LI93PHWRkZcH8yfbonE30tP54xW1xPabElVtYVPNh3mpaR0yiuqmDCgPRMHdMDdRRvU2DoFYxERMZS1N1JIiA6kT1gLZq/N5L11Wazak8sTN3Xgod5t1V5hoLqc0GZLth4+zTOJe9mVXUjv9s35+7BOhPmrj7ih0L8QIiJimLraSMHD1Yk/3hxJ0pP96N7Wl3+sSOOW19axfr/WPjZKXUxosyXZBWVMXpDKHW9tJLeonNdHxfLxmJ4KxQ2MnhiLiIhh6rrvNLSFB3NH9+CbtFz+/uVe7n9/EwM7BjDjlkjaaTWAemXtCW3/q763b/5fZ89V8vb3B3nnhwNYLDDpxjDG39AeD1dFrIZIoyYiIoapr77T30UFEB/WgvfXZ/HWdwcY9OoP3NuzNVN+14Hmnq5WvZf8NmtMaPslo1a7qK62sGxHNi9+lc6JonIGd27Jn26JpJVvkzq7p9Q9BWMRETFMffadujk7MmFAGHd3D+G1Nfv5eNMRFm/L5vEB7fl9fKjV7yf1o75Xu7BYLKzbn89LSfvYnV1ETLA3s+6NpVtbrYDSGKjHWEREDGNE32kLT1eeHd6JpCf60atdc176Op0b//UdqzOLqaq21Nl9pW7U52oXO44WcN97m3jwP5s5U1rBKyOvY9mEeIXiRkRPjEVExDB12Xd6JWH+nrz3UDdSDp7inyvTeCU5j6UZ3/PkTeHcFtMSBwdtL90Q1Me7DgfzSvjXqnRW7jpBMw8X/t/gjtzXqzWuTlp+rbFRMBYREUPVRd9pTfRq15xlE+J5P2krn6WVMmlBKrPXZvLUwHAGdgzAZFJAtmXTEiIu6jEG673rcPhUKbPXZvLFtmzcnByY8rsOjO3XDk9NrGu0NLIiImL3TCYT8W08GD2oK1/uzOHV1Rk8On8rnVt58+RN4fSP8FNAtlF18a5DVn4ps77NZOn2bJwcTDwY14YJA8JooYmajZ6CsYiIyE8cHUwM6xLMbTEtWZyazWtr9jN63o9EBzXlsf7tuaVTSxzVYmFzrPWuQ+bJEmavzWTZ9mxcnBx4uHdbxvVrh39T7ZxoLxSMRUREfsHJ0YGR3UIY3iWYpanZvP39ASZ+kkpoiwzG39CO22Nb4eKk+euNxfajBbz7w0FW7j6Om5MjY/q2Y2zfdvh56QmxvVEwFhER+Q0uTg6M7B7CHV1b8fXuE7z5XSbTv9jFq6v3M6ZvKHd3D8HLzdnoMuUaVFdb+GbfSd794SCbD53Gy82J8Te0Z0yfUK1tbccUjEVERK7A0cHEbZ1bcmtMID/sz+fNtZn8Y0Uar67O4M6urXiwd1vaaye9BqG8ooovth3j/XVZHMwvJdjHnb8O7sjd3UM0qU4UjEVERK6WyWTihnA/bgj3Y+exAuZtOMSCzUf5YONhbgj34+H4ttzQwU9Lvdmgg3klfLLpCIu2HaPgbAUxwd68MSqWWzoF4uSothg5T8FYRETkGnRu5cMrI7sw45YoPtl0hI82HWb03B8JbeHB3d1DGHF9MP5emrRlpIqqalbvzeXjTYdJzjyFk4OJhOhAHohrQ8/QZlppRH5FwVhERKQW/LxcmXJTBx7r356vdh9n/sbDvPDVPmYmpXNjpD8ju4UwIMJPTyXr0YG8EhZvO8ZnW46RV2wm2MedPwwKZ2S3EK0wIZelYCwiImIFLk4ODOsSzLAuwWSeLOHzLUf5YtsxVu/Nxc/LlTuub8Xw2CAiArz0pLIO5JeY+XJHDktSs9lxrBAHE9wQ7sf9vdrQP8Jfy+zJVVEwFhERsbIwf09m3BrFHxIi+HbfST7fcpR31x3k7e8PEObvyeDOLRncOYgwf03Yq40ScyXf7jvJ0tRsvs/Io6raQseWTfnLrVEM7RJEgJ4OSw0pGIuIiNQRZ0cHEqIDSYgOJK/YzNe7j7N853Fe+2Y//16zn8hAL4ZcF8SgjgGE+XvqSfJVyC8xs2ZvLkl7TpCceYpzVdUENnVjTN9QRsS2IiLQy+gSpQFTMBYREakHfl6uPBDXlgfi2pJbVM7KXcf5cudxZialMzMpnVa+7twY6c+ACH/i2jfHzdnR6JJtgsViYf/JEn7IyCNpzwm2HD6DxQKtfN15IK4NCdGBdG3jq1YJsQoFYxERkXoW0NSN0fGhjI4P5XhhGWv35f3UcnGMDzcextXJgd7tm9O3gx89QpsR1bKpXQW/E4XlJGfmk5yZz/rMfE4WmwGIDPRi8o0dSIgOJKqlerXF+hSMRUREDNTS2517e7bm3p6tKa+oYnPWab7dd5Lv0k+yNj0PAC9XJ7q19aVHaHN6hDYjJti70WxJXV1t4UBeCalHCvh2Zx6ZX50k82QJAM09XOgd1oI+Yc2JD2tBK98mBlcrjZ2CsYiIiI1wc3akX7gf/cL9gGhyCsr48dBpNmWdZnPWadam7wPA2dFEeIAX0UFNiQ7yplNwUyIDm+Jh4zu3VVRVc/hUKftzS9h7vIjUIwXsOFpAsbkSAE8XB7q2bc7Ibq3oE+ZHZKCXNkuRelXjv0EVFRVMmjSJt99++zfPMZvNTJ48mePHjxMREcFLL72ktztERERqKMjH/cIScACnSsz8eOg0248WsienkDVpJ/lsyzEATCZo06wJbVt40La5B22bN6HNT5+38nXHuZ7WUa6sqia32MzxgjKyC8o4lH+WjJPFZOaWcDC/hIoqC3B+m+3IQC+GxQbRJcSX2NY+lOcdIbpjx3qpU+RSahSMy8vLueuuuzh06NBlz0tMTCQgIIA5c+Ywbtw4kpOT6dOnT23qFBERsXvNPV25uVNLbu7UEjg/Me1EUTl7sovYnVNIRm4xh/LP8mPWaUrPVV34OpMJmjVxoYWnKy28XPDzdKWFpyvNPV3xcHXEzfn8h/tPH27ODliAqmoL1dUWqiwWKn/6vMRcSVF5JUVlFRSWVVz4b16xmZyCMk4UlVNt4aJ7h/g2oYO/JwMi/QkP8KSDvxdh/p64u1w8wTAtXw/RxFg1CsZubm4sX76cgQMHXva8lJQUBg0aBECvXr3YtGmTgrGIiIiVmUwmWnq709LbnZs6Blw4brFYyC85x6FTpRzKL+XomTLyis3kl5z/2HrkDPnF5yirqLrM1a/M1ckBb3dnvN2dae7pQlz7FgT5uBHk406QjzvBPm4E+zT5VQAWsVWXDcbPPPMM6enpF/6/e/fuPPXUU1e8aEFBAV5e59cR9PT0JCsr64pfYzabSUtLu+J5daG8vNywe8t5GgNj6fU3nsbAeI1xDDyBTh7nP85/y3cCPC78enllNeWVFsyV1ZgrLZirLGw8Usqi3QVUVP/3Os4OMDLGh+6tmuDu5ICnqwOeLg64XLI9oxoohapSKk7BoVNXX29jHIOGxt7H4IrB+Fr4+PhQXFwMQHFxMb6+vlf8GldXV6Kioq7pfrWVlpZm2L3lPI2BsfT6G09jYDyNwXkvvvDtRaEYoKIavjts5rlR8XV6b42B8exlDH4r/NdJJ35cXBzJycnA+baKnj171sVtRERExMpyCspqdFykMal1MD569CgvvvjiRceGDh1Kbm4uQ4YMwdvbm7i4uNreRkREROpBkI97jY6LNCbXtODh6tWrL3weEhLC9OnTL/p1FxcX5syZU7vKREREpN5NS4hgxuJdF03Mc3d2ZFpChIFVidQP214JXEREROrV8NjzaybPTEonp6CMIB93piVEXDgu0pgpGIuIiMhFhscGKwiLXWocG62LiIiIiNSSgrGIiIiICArGIiIiIiKAgrGIiIiICKBgLCIiIiICaFUKERER+YWlqdlark3skoKxiIiIXLA0NfuiDT6yC8qYsXgXgMKxNHpqpRAREZELZialX7TrHUBZRRUzk9INqkik/igYi4iIyAU5BWU1Oi7SmCgYi4iIyAVBPu41Oi7SmCgYi4iIyAXTEiJwd3a86Ji7syPTEiIMqkik/mjynYiIiFzw8wQ7rUoh9kjBWERERC4yPDZYQVjsklopRERERERQMBYRERERARSMRUREREQABWMREREREUDBWEREREQEAJPFYrEYXQTA9u3bcXV1NboMEREREWnkzGYzXbp0+dVxmwnGIiIiIiJGUiuFiIiIiAgKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIoAdB2Oz2cy4ceMYOnQo06ZNQ6vWGaeiooLx48cbXYbdmj59OiNHjmT8+PFUVlYaXY7dqaysZPLkydxzzz3MmDHD6HLs2ty5c3n44YeNLsMu7dy5k379+jFq1ChGjRrFwYMHjS7JLr377ruMHDmSMWPGcO7cOaPLMYTdBuPExEQCAgJITEykqKiI5ORko0uyS+Xl5YwYMUKvv0G2bNlCZWUln332GaWlpRoHA6xZs4bIyEgWLlxIXl4eaWlpRpdkl7Kzs1myZInRZditoqIiRo0axYIFC1iwYAHt2rUzuiS7c/ToUTIzM/nss8/o168fubm5RpdkCLsNxikpKcTHxwPQq1cvNm3aZHBF9snNzY3ly5cTGBhodCl2qUWLFjz00EMAVFdXG1yNferbty+jR4+msrKS4uJiPD09jS7JLj333HNMnTrV6DLsVlFREatWreLOO+9k0qRJehfXABs3bqSwsJD77ruPLVu20KpVK6NLMoTdBuOCggK8vLwA8PT0pLCw0OCKROpf27Zt6dy5M6tXr8bBweHCD4tSfzw8PHB3d2fUqFE0b96ckJAQo0uyO8uXLycyMpL27dsbXYrdat26NVOmTGHRokXk5eWxefNmo0uyO6dPn6ZZs2Z8/PHH5ObmsnXrVqNLMoTdBmMfHx+Ki4sBKC4uxtfX1+CKRIzxzTff8OGHH/LWW2/h5ORkdDl258yZM5w7d46FCxdSVFRESkqK0SXZne+++46NGzfy1FNPsWfPHj766COjS7I7wcHB9O7d+8Lnp06dMrgi++Pp6UloaCgArVq1UiuFvYmLi7vQT5mSkkLPnj0Nrkik/uXl5fH+++8zZ84cvYVvkLlz5/LVV1/h6OiIm5sbZrPZ6JLszssvv8yCBQt45ZVXiI6O5v777ze6JLszb948VqxYQXV1NRkZGYSHhxtdkt2Jjo5m9+7dABw5csRu372y22A8dOhQcnNzGTJkCN7e3sTFxRldkki9W7JkCXl5eTzyyCOMGjWKRYsWGV2S3bnvvvv44osvuPvuu/Hx8aFPnz5GlyRS7+677z4WL17MXXfdxcCBAwkLCzO6JLsTGxuLj48Pd9xxB6GhoXTu3NnokgxhsqjDXURERETEfp8Yi4iIiIj8LwVjEREREREUjEVEREREAAVjERERERFAwVhEREREBFAwFhEREREB4P8Dl2v4hnbSZ5sAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">pm</span><span class="o">.</span><span class="n">Model</span><span class="p">()</span> <span class="k">as</span> <span class="n">gp_context</span><span class="p">:</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">Gamma</span><span class="p">(</span><span class="s2">"h"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">gp</span><span class="o">.</span><span class="n">cov</span><span class="o">.</span><span class="n">ExpQuad</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">h</span><span class="p">)</span>
<span class="n">gp</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">gp</span><span class="o">.</span><span class="n">Marginal</span><span class="p">(</span><span class="n">cov_func</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
<span class="n">ϵ</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">HalfCauchy</span><span class="p">(</span><span class="s2">"e"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y_est</span> <span class="o">=</span> <span class="n">gp</span><span class="o">.</span><span class="n">marginal_likelihood</span><span class="p">(</span><span class="s2">"y_est"</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xs</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">ys</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="n">ϵ</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">xa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">7.5</span><span class="p">]</span>
<span class="n">betas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">alphas</span><span class="p">,</span> <span class="n">betas</span><span class="p">):</span>
<span class="n">pdf</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">xa</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">b</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xa</span><span class="p">,</span> <span class="n">pdf</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"$\alpha$ = </span><span class="si">{}</span><span class="s2">, $\beta$ = </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"f(x)"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]:</span>
<span class="n">pdf</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">cauchy</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">xa</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xa</span><span class="p">,</span> <span class="n">pdf</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"$\beta$ = </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">b</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"f(x)"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">1</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><matplotlib.legend.Legend at 0x7f5a56da6350></pre>
</div>
</div>
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">gp_context</span><span class="p">:</span>
<span class="n">trace</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">tune</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stderr output_text">
<pre>Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [e, h]
Sampling 4 chains, 0 divergences: 100%|██████████| 6000/6000 [00:04<00:00, 1338.14draws/s]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pm</span><span class="o">.</span><span class="n">traceplot</span><span class="p">(</span><span class="n">trace</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">gp_context</span><span class="p">:</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">gp</span><span class="o">.</span><span class="n">conditional</span><span class="p">(</span><span class="s2">"fp"</span><span class="p">,</span> <span class="n">xp</span><span class="p">)</span>
<span class="n">ppc</span> <span class="o">=</span> <span class="n">pm</span><span class="o">.</span><span class="n">sample_posterior_predictive</span><span class="p">(</span><span class="n">trace</span><span class="p">,</span> <span class="nb">vars</span><span class="o">=</span><span class="p">[</span><span class="n">fp</span><span class="p">],</span> <span class="n">samples</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stderr output_text">
<pre>100%|██████████| 100/100 [00:07<00:00, 13.40it/s]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xp</span><span class="p">,</span> <span class="n">ppc</span><span class="p">[</span><span class="s2">"fp"</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"grey"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">pm</span><span class="o">.</span><span class="n">gp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">plot_gp_dist</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ppc</span><span class="p">[</span><span class="s2">"fp"</span><span class="p">],</span> <span class="n">xp</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s2">"cool"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="tesnorflow.probability"><a href="https://github.com/tensorflow/probability">tesnorflow.probability</a><a class="anchor-link" href="#tesnorflow.probability"> </a></h1><p>基于tesnorflow的贝叶斯推断和统计分析库</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="随机分布">随机分布<a class="anchor-link" href="#随机分布"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tfd</span> <span class="o">=</span> <span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"."</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tfd</span><span class="o">.</span><span class="n">distribution</span><span class="o">.</span><span class="n">Distribution</span><span class="o">.</span><span class="n">__subclasses__</span><span class="p">()])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 Autoregressive
1 BatchReshape
2 Bernoulli
3 Beta
4 Gamma
5 Binomial
6 BetaBinomial
7 JointDistribution
8 JointDistribution
9 _Cast
10 Blockwise
11 Categorical
12 Cauchy
13 Chi2
14 TransformedDistribution
15 Normal
16 LKJ
17 CholeskyLKJ
18 _BaseDeterministic
19 _BaseDeterministic
20 Dirichlet
21 Multinomial
22 DirichletMultinomial
23 DoublesidedMaxwell
24 Empirical
25 FiniteDiscrete
26 GammaGamma
27 GaussianProcess
28 GeneralizedPareto
29 Geometric
30 Uniform
31 HalfCauchy
32 HalfNormal
33 StudentT
34 HalfStudentT
35 HiddenMarkovModel
36 Horseshoe
37 Independent
38 InverseGamma
39 InverseGaussian
40 Laplace
41 LinearGaussianStateSpaceModel
42 Logistic
43 Mixture
44 MixtureSameFamily
45 MultivariateStudentTLinearOperator
46 NegativeBinomial
47 OneHotCategorical
48 OrderedLogistic
49 Pareto
50 PERT
51 QuantizedDistribution
52 Poisson
53 _TensorCoercible
54 PixelCNN
55 PlackettLuce
56 PoissonLogNormalQuadratureCompound
57 ProbitBernoulli
58 RelaxedBernoulli
59 ExpRelaxedOneHotCategorical
60 Sample
61 StudentTProcess
62 Triangular
63 TruncatedNormal
64 VectorDiffeomixture
65 VonMises
66 VonMisesFisher
67 WishartLinearOperator
68 Zipf
dtype: object</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">x</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><tf.Tensor: shape=(3, 4), dtype=float32, numpy=
array([[ 94.99498 , 107.465 , 92.16038 , 119.55264 ],
[105.00224 , 99.26422 , 106.99145 , 73.55863 ],
[105.61177 , 92.737144, 121.1416 , 90.87714 ]], dtype=float32)></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">xp</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">50.0</span><span class="p">,</span> <span class="mf">150.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xp</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">prob</span><span class="p">(</span><span class="n">xp</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="随机采样">随机采样<a class="anchor-link" href="#随机采样"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dist</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><tf.Tensor: shape=(5, 4), dtype=float32, numpy=
array([[2.325097 , 4.3809695, 6.011235 , 6.6105776],
[2.8294497, 4.795865 , 4.3433237, 5.784731 ],
[3.8394353, 3.9012687, 4.900641 , 5.8869133],
[2.7562547, 3.839128 , 4.772919 , 6.0769143],
[2.497485 , 4.127298 , 4.343773 , 5.853659 ]], dtype=float32)></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xp</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">9.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)[:,</span> <span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">xp</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">batch_shape</span><span class="p">),</span> <span class="n">dist</span><span class="o">.</span><span class="n">prob</span><span class="p">(</span><span class="n">xp</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsEAAAFjCAYAAADcjdYMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXhb93Xg/S92gCu475REUSLBVZIXSZZiJXHSJYnVNM1SxU7aZNq6bztpO8m4ad+ZJ/N0Op3ncTvN20mnU3vSNpk4iZ02cVI5XdKsjiVb8iKLG0BSC8V93wCS2HHfP8BLW5S4A7hYzucfPSYv7+8QBsmDH845P52iKApCCCGEEEJkEL3WAQghhBBCCJFokgQLIYQQQoiMI0mwEEIIIYTIOJIECyGEEEKIjCNJsBBCCCGEyDiSBAshhBBCiIxj1GLRq1evYrFYtFgav9+v2doiuclzQ2xEnhtiI/LcEJuR50dy8Pv9HDly5I6Pa5IEWywWHA6HFkvjcrk0W1skN3luiI3Ic0NsRJ4bYjPy/EgOLpfrrh+XcgghhBBCCJFxJAkWQgghhBAZR5JgIYQQQgiRcSQJFkIIIYQQGUeSYCGEEEIIkXEkCRZCCCGEEBlHkmAhhBBCCJFxJAkWQgghhBAZR5JgIYQQQgiRcbY8Mc7v9/M7v/M7jI+P09DQwJ/+6Z+i0+luu2ZlZYXPfOYzzM/Pc+zYMX7/938/bgELIYQQQgixV1vuBJ8/f56ysjLOnz+P2+3m4sWLd1zz/PPPc+TIEZ599lmuX7/OjRs34hKsEEIIIYQQsbBlEnzp0iVOnToFwIkTJ7h8+fId1+Tm5rKyskI4HMbn82EymWIfqRAiIRRFoWtkkXBE0TqU5OBbxOwe1DqKpDGwOIAn4NE6jKSghMNw4waKIj8rQqSiLcshFhYWyM3NBSAnJ4eBgYE7rnn3u9/N3/zN3/D8889z5swZamtrN72n3+/H5XLtMuS98fl8mq0tkps8N8AbjPD/XZzmxcFljlbY+IMHS8mzGrQOSzPWuV6qL/4BdStTTI79NnMNH4V15WCZQlEUnp94nq8Nf40icxH/8dB/pC67TuuwtON2w+c/Dx2d9H772/Dbvw02m9ZRiSQjf1eS25ZJsN1ux+OJvur3eDwUFBTccc1TTz3FuXPn+NCHPsSnP/1prly5wrFjxza8p8ViweFw7CHs3XO5XJqtLZJbpj83bk4v8TtPv86N6WU+eE8156+O8envTfHUx+6hpSpf6/AS742vwY/+A2SX4Kk8SVnHX1IWGoGz/wssOVpHl1ArwRU+99Ln+N7w93h79dvpne/lc67P8bmTn+MX6n9B6/ASztvTw+gf/r+EZmZQ3vkO+MkLWKamqP7Lv8S8f7/W4Ykkkul/V5LFRi9EtiyHOHny5Fod8KVLlzh+/Pgd1ywvL2M2mwEwm82srKzsJVYhRIJ93znJL/yvi8ws+Xn63x3nf3yonX/4zZNEFIVf+uuX+NbrI1qHmDihAHz30/CPvwW1x+GxFxg5/T/gXX8Ezn+Ev303zGZO38Oge5BH/vkRvj/4fT59z6f5wju/wDfe9w2Olh7lP1/8z/y3S/+NYDiodZgJs/Cd7zD40UdQFIV9X/sqfOpT1P7NFwlNzzDwwQ/h+dGPtQ5RCLFNWybBZ8+eZXJykocffpj8/Hxqamp44oknbrvmkUce4ZlnnuEjH/kIPp+PkydPxi1gIUTsRCIKn/9+P7/+ldfYX5zN8586zan6YgDaa+w8/6nTHKst4DP/0MF/+cduAqGIxhHHmXscvvxeeO1v4YHfgUe/DdnF0RKI078Hjz4Hngn4P++A/u9pHW3cvTD8Aue+e44Z7wxPvutJPtHyCXQ6HYXWQp5895P8avOv8o2+b/DJ732SqZUprcONKyUQYOK//jHjf/CH2I4e5cC3vomttRWA7Ace4MC3vom5tpaR3/otpr/wlyiRNP9ZESIN6BQNKvq1fHtA3poQG8m058ayP8S///oVftw3zYfuqeaP39+C1XRn/W8oHOGJf+3liy8OcO++Ap762D0U5Vg0iDjORl6DZz8K/iV4/19B8y+ufeq258b8IHzjUZjohHf8JziTniMhn+x4kr+6+lc4Ch38xTv+gsqcyrte96+3/pXPXfwc2aZs/uc7/idtJW0JjjT+QnNzjPz7T+G9coXCT36S0k//B3TGaDXhW58bEZ+PiT/6ryx++9tkn3mQ6s9/Hn12tpahC41l2t+VZLXR/wc5LEOIDPXFF2/y475p/vj9LfzpB9vumgADGA16/tN7m/jLc0fpHFnk89/vT3CkCRCJwHO/DkYL/NoPbkuA71CwD/7dv0Hrh+DHfwJDd07MSXVXp67yV1f/ivfWvZev/PxXNkyAAX5u/8/xtfd8DYvBwh+++IdElPTbAZ3+whfwdXVR9fk/p+z3H19LgNfTW61U/Pc/ofy/fI7lF37K7Je+nNhAhRA7IkmwEBnIHwrz1UuDPNRYysdO7LvjAJy7ebi9kvcfreS5K6MsrAQSEGUCXfsezN2M1v2WNW19vckG7/sLsObD5b+Of3wJ9rTzaXLNuXzuxOewGq1bXn+o4BC/d+z3GPIM8eLIiwmIMHHCCwssfucfyfuFs+S95z1bXq/T6Sg4d46cM2eYf/ZZIoE0+1kRIo1IEixEBnq+Y5yZpQCfOHVgR1/3iVMH8AbDPPvqcJwi08il/w15VeB4ePtfY8mBYx8H53lYTJ/GwfGlcX449EM+eOiDZJmytv11D+17iNKsUp52PR3H6BJv/h/+AcXno/BjH9/R1xV8/GOEZ2Zw//M/xykyIcReSRIsRIZRFIUvXRzgcFkOp+qLdvS1joo8TtYV8ZWXbhEKp8nb3pM9MPBTuP/XwbDDg37u/w1AgVe+GJfQtPBM3zMAnGs8t6OvM+lNnGs8x+Xxy1ybvxaP0BJOCQaZ/9rXyTpxAmvD4R19bfYDD2CuP8jcV74ih2kIkaQkCRYiw7wyMEfPmJtPnDqwrTKI9T5xaj9jiz6+1zMZh+g0cOmvwWiDY7+y86+110Lj++D1L0NgOeahJdpKcIVv9n+Td9a+k4qcih1//QcPfRCrwcrXXF+LQ3SJ5/nBDwhNTFD48Y/t+Gt1Oh2FH/s4fqcL7+uvxyE6IcReSRIsRIb5u4sDFGSZ+MWjVbv6+occZdQWZvGli3eeHplylmeg8++h/Zchq3B39zjxW+BbgM5vxDY2DXz35nfxBDx8rGnnSR+A3WrnfQffx3dvfpd533yMo0u8ua88jam2lpwzZ3b19flnH8aQn8/c//1KjCMTQsSCJMFCZJDhuRW+75zk3P21G06D2IpBr+NXHtjPa4PzdI4sxDjCBHv9SxD2w/Hf3P09ak9ARTtcehJS+G3viBLhq66v0lTUxJGSI7u+zyONj+AP+/lm/zdjGF3iebu68L7xBoWPPoLOsLufFb3Nhv3DH8bzwx8SGBmNcYRCiL2SJFiIDPJ/X7qFXqfjYyf37ek+H763mhyLkS9dvBWbwLQQCsCrfwsH3wmljbu/j04X3Q2e6YMbP4pdfAn28tjLDCwO8Kjj0V2VyajqC+o5WXGSZ3ufJRhJ3ZPk5r7yNPrsbPI/8IE93afgkY+CTsf819KjRESIdCJJsBAZYskf4huvDfPzrRVU5Nv2dK9cq4kP3lPNdzvHmHL7YhRhgjn/ETzjcPz/2fu9mn8Rskuj9cUp6mnX0xTbivm5/T+353s92vQoU94pvn/r+zGILPGCk1O4/+VfyP+lD2DIydnTvUzl5eT97M+w8M1vEllO/bpxIdKJJMFCZIhvvT6Cxxfik6f2x+R+v/rAfkIRha9eGozJ/RJKUaJj0Yrqof5de7+f0QL3/Rpc/z7MpN5khJuLN7k4epGPNHwE004nZNzF6arT7Mvbl7INcvPPPgPhMIWPPhqT+xV+/ONEPB4WvvOdmNxPCBEbkgQLkQEiEYUvv3SLIzV2jtYWxOSe+4uzeaixlK9eHsIXDMfkngkz8iqMXYnWAutj9Gvw3k+AwQyXn4zN/RLo666vY9Kb+NDhD8Xkfnqdno82fpTOmU46pjtics9Eifh8LDz7DXLe8Q7MtbUxuaftyBGsbW3Mf+VplEiajBYUIg1IEixEBvhJ/xQDM8t88vTODsfYyidPHWBuOcD5q2MxvW/cXfrfYMmH9p3Nwt1UTmn0KOWrXwdv6kxGWPQvcv7Ged5b916KbDubG72Z99e/n1xTLl91fjVm90wE93e/S3h+nsKP7+xwjK0UfvzjBAYHWX4xvU7UEyKVSRIsRAb4uwu3KM+z8vMt5TG978mDRTSU5fJ3FwdS50CAxZHoKW/HPhY99S2Wjv8mBFfgSuqcmvbctefwhrw86ojNW/+qLFMWv3joF/n+4PeZWJ6I6b3jRVEU5r7yNJaGBrKO3x/Te+f97M9gLC2VcWlCJBFJgoVIc30THi5cn+FjJ/dhMsT2R16n0/HJ0/vpnfDw8s3ZmN47bl75IqCsnvYWYxVtsO80vPJ/IByK/f1jLBQJ8fXer3Nf+X00FDbE/P7nGs+hoPBs77Mxv3c8rFy+jL+/n8KPf2xPEzLuRmcyUfDRj7L80kv4r6Ve3bgQ6UiSYCHS3DdfH8Zs0HPu/tjUN673C0eqsGeZ+IfXRuJy/5iKRODq16DhPVCwtzFxGzr+G7A4DAMvxOf+MXR5/DITyxN8tPGjcbl/dW41Z6rP8J3r3yGiJH8t7MK3nsOQn0/e+94Xl/vbP/JhdCYTC899Oy73F0LsjCTBQqS5F/qnuf9AIYXZ5rjc32oycOZwCT/tnyYSSfKSiIlOWJ6OHnUcL/XvBoMFrv8wfmvEyIXRC5j1Zk5VnYrbGg/VPsSsb5a+ub64rRELSiTC8oULZD/4IHqLJS5rGAsKsN17D8sXLsTl/kKInZEkWIg0NrbgpX9yiTOHS+K6zpnDJcwuB+gZc8d1nT27/oPovwffGb81zFmw74E310piF8cucm/5vdiMe5sbvZkHKh9YWyuZ+XqchOfnyXnb6biuk3P6bfivXSM4kRp10kKkM0mChUhjL16bBuDBOCfBbzsUvf9PV9dLWjd+BOWtkFsW33XqH4qeILcwHN919mBsaYyBxYG1JDVeSrJKaCho4OJocifByxeju7PZp+K3Kw6Qffr06nrJ/XgIkQkkCRYijb3QP015npXDZTGegrBOSa6F5so8XuhL4iTY54bhy7E5HGMr6ho3krckQt2ZPV0V351PgFNVp7g6dZWlwFLc19qtpRcvYG1qwlgUuzFxd2M5fAhjaSlLL0pJhBBakyRYiDQVCke4cG2GBw8Xx7zT/W7OHC7hytA8bl8w7mvtysBPIRKCgw/Ff62SRsitTOq64JdGX6Isq4y6/Lq4r3Wq8hQhJcQrE6/Efa3dCHs8eK9eJfttb4v7WjqdjuzTp1l++WWUUPJPEBEinUkSLESa6hhZwO0LceZwaULWe/BwCaGIwkvXk3RU2vUfgDkHao7Hfy2dLloScfOFpByVFowEuTR+idNVpxPyAulo6VFsRlvSlkQsv/wyhMPknI5vKYQq522niSwu4u3qSsh6Qoi7kyRYiDT1Qv8Meh2cri9OyHrHagvIsRiTsy5YUaKlCQceBGN8pmTcof4h8C/C6GuJWW8Huqa7WAouxb0eWGUymDhefpyLYxeT8lCV5QsX0WdnYztyJCHrZZ88CXo9yxeS80WBEJlCkmAh0tQL/dMcqbGTn2VKyHpmo56TB4t4oW86+RKd2euwMBRNTBOl7u2g0yfllIgLoxcw6AycqDyRsDVPVZ1idGmUQfdgwtbcDkVRWLrwIlknT6AzJeZnxWC3Y2ttZemCHKEshJYkCRYiDc0vB+gcWYj7VIj1zhwuYXTBy82Z5YSuuyW1NjcR9cAqWwFU3ZuUdcEvjb1Ea3Ereea8hK15qjJaapBso9ICAwOExsbJOR3/euC3yj59Gl9XN6H5+YSuK4R4kyTBQqShF6/PoCjEfT7weup6STcl4voPoPAgFB5I7Lr174KxN2A5eeqk53xzOGedcT0g425q8mqoza1Nurrg5Reju7Hq6LJEyXnbaYhEWHn55YSuK4R4kyTBQqShn/ZPY88y0VZtT+i6NYVZ1BVnJ1ddcNAHty4kthRCVf8QoMDNHyd+7Q28PPYyCsrazmwiPVD5AK9NvkYgHEj42htZunAR84EDmKurErqutbUVfX4+S1IXLIRmJAkWIs0oisIL/dOcri/GoI9/5/96Dx4u4dLNWXzBcMLXvquhlyHkTcx84PUqj0bLIpKoJOKlsZewW+w0FTUlfO3TVafxhrxcmbqS8LXvJuLzsfLqqwnfBQbQGQxkP3CS5RdfTL4aeiEyhCTBQqQZ17iHaY8/4fXAqjOHS/AFI7x6a06T9e9w/QdgMMP+xCc66A1Q947oZIokSHQiSoSLoxc5WXESg96Q8PXvK78Po96YNCURK6+9juLzxf2o5I3knD5NaHoaf3+/JusLkekkCRYizailCImuB1YdryvEbNDz0/4kKYm48SOoPQnmbG3Wr38XLE3CZLc2679F/3w/s77ZhNcDq7JMWdxTek/SNMctX7iAzmwm6777NFl/7QjlC3J6nBBa2DQJ9vv9PPbYY5w9e5bHH3/8rm/ZXL58mXPnznHu3DnOnDnDt7/97bgFK4TY2gt90zSW51KWZ9Vk/SyzkfsPFPJCMiTBi6Mw5dSmHlh18J3Rf5NgVNqF0Wiylaj5wHfzQNUDXJu/xuTypGYxqJYuvEjWvfeit9k0Wd9UVobl0CE5QlkIjWyaBJ8/f56ysjLOnz+P2+3m4sU7X70fP36cZ555hmeeeYaGhgYcDkfcghVCbG7ZH+K1wTnNdoFVZw6X0D+5xPiiV9M4uPGj6L9a1AOr8iqgrCUp6oJfGnuJhoIGSrK0e36oDXkvjb2kWQwAwfFxAtdvJOSo5M1kv+1teF9/nchyko0VFCIDbJoEX7p0iVOnor+wTpw4weXLlze81uv1Mjg4SGNjY2wjFEJs28s3ZgmGFc3qgVXq+pqXRFz/AeRWQGnim8Buc/CdMHQJ/EuahbAcXOaNyTd4oEq7XWCAwwWHKbGVaF4SsbRagpCoo5I3knP6FEowyPIrr2gahxCZyLjZJxcWFsjNzQUgJyeHgYGBDa+9ePEiJ0+e3Naifr8fl8u1gzBjx+fzaba2SG7p8Nz4x8szWIw6cnxTuFzaJaCKolCUZeC7r92kLUejHa5IiMPXf4Sn6kHGe3v3dKu9PjeyzIfZFwky/OLXWarSZufxtfnXCCkhakI1mj/Pm7ObuTB8gR5nD3qdRq0p//wvUFTEzWAQ9vB47Pn3RnY2WCyMPP88lJfv/j4iKaXD35V0tmkSbLfb8Xg8AHg8HgoKCja89sc//jE/8zM/s61FLRaLZmUTLpdLSjbEXaXDc6Pzuz/mVH0J7S0a73wCDzUF+dfuCQ4dbsBo0CDRGX4FAm7s93wA+x7/v+75uXGoDi7+PjX+PnD8xp5i2a1vXfoWNqON99/7fkyGxBwPvJH3WN/DT376E0IlIdpL2hO+vhIK0d/dTe7P/gyVTXv7WYnF742hE8cJ9PRQn+K/f8Sd0uHvSjrY6IXIpn+ZTp48uVYHfOnSJY4fP37X6xRF4ZVXXuHEicSdQy+EuN2tmWUGZ1c0rwdWnTlcitsXomNkUZsArv8QdProiDKtGS1w4EHN6oIVReHC6AWOlx/XPAEGOFlxEh06XhrVpi7Y29lFxONJ+FHJG8k5/TaCg0MEhoa0DkWIjLJpEnz27FkmJyd5+OGHyc/Pp6amhieeeOKO67q6ujh48CAWiyVugQohNqeORtO6Hlh1ur4YvQ7tpkRc/wFUHoOsQm3WX+/gQzA/ALM3Er70kGeI0aVRzeuBVXarnZbiFi6MaTMVYfnCi6DXk30yOTZuslfrkpdkVJoQCbVpEmw2m3nqqad4/vnn+bM/+zNqamr47Gc/e8d1bW1tPPnkk3ELUgixtYvXZ6gusLG/KEvrUADIzzLRXmPn4vWZxC/uc8PYlTfHkyUDdUzbzZ8kfOnL49GmZi1Ho633QOUDdM90sxRIfLPg8suXsLW2YsjPT/jad2Pevx9TVRUrL7+sdShCZBQ5LEOINKAoCleGFrhvfyE6XeKPSt7IvfsK6BpdJBCKJHbh0ddBiUBtcuz0AVBYB1nFMPJawpfumO6g0FpIbW5twtfeyNHSo0SUCN2ziT1EJBII4OvpwXbPPQlddzM6nQ7bsWOsXL0qRygLkUCSBAuRBkYXvEx7/ByrtWsdym2O1RYQCEVwjbsTu/DIa4AOqu9N7Lqb0emg5n4YeTXhS3dOd9Je0p5UL5BaS1qBaGyJ5O/tRQkEsB1JfEPeZmxH2glPzxAaH9c6FCEyhiTBQqSBK0MLAByt3XiCixbUeK4MzSd24ZFXoKQBrMnxdvea6nth9hqszCVsyQXfArfct2graUvYmtuRZ86jLr+OjumOhK7rvXoVAFv7kYSuuxU1HjU+IUT8SRIsRBp4Y2geq0lPQ3mu1qHcpjzfSkW+lTdWk/SEUJTobmsy7QKrqu+L/jv6esKW7JyJ7rRqMYpsK20lbXROdya0BMB7tQNjRQWmstKErbkd1obD6KxWvB2JfVEgRCaTJFiINHBlaIG2ajsmLebxbuFYbUFid4Jnb4B3HqrvT9ya21V5LDq2bThxp4N1THeg1+lpLmpO2Jrb1V7SzoJ/gSFP4kaDea9eTbpSCACdyYS1pZkV2QkWImGS7y+mEGJHfMEwzrFFjiZZPbDqaK2dkXkvUx5fYhZUa27VXddkYsmB0uaE1gV3TndyuOAwWabkmBryVmqJRqLqgoNTUwTHxrC1J18SDGBrb8fvdBEJBLQORYiMIEmwECmuZ8xNMKxwLMnqgVVqcn41USURI6+CJQ9KGhOz3k7V3Bcth4jEf2JGOBKma6YrKUshAA7mHyTblJ2wumBfZzTZTtok+MgRlGAQv9OpdShCZARJgoVIcW+slhocrUnOneDmynxMBt1a817cjbwCVcdAn6S/3qrvA78bZvrivtTNxZssB5eTrilOZdAbaCluSdhOsPfq1WjZQXPylYYA2NqiybmURAiRGEn6V0IIsV1vDC1QZbdRmmfVOpS7spoMNFXmryXrcRVYhsme5CyFUKmxJaAkQk0u24qTMwmGaGz98/2sBFfivpb3ageWJgd6sznua+2GqawUY2WFNMcJkSCSBAuR4q4MzXNsX3KWQqiO1drpHFkkFI5zCcDoleghGcnYFKcqqgerPSHNcR3THeRb8tmXty/ua+3WkdIjhJUwPbM9cV1HCQbxdneTdSS5RqOtl3XkCN6rkgQLkQiSBAuRwsYXvYwv+pK2FEJ1tLYAbzBM74QnvgutNcUl4Xg0lU4X3Q1OwMlxndOdtBW3JdUhGeu1Fifm0Axffz+Kz5e09cAqW3s7ofFxgpOTWociRNqTJFiIFKY2myX7TrCapL8xHOe64JHXojutWYXxXWevau6H6V7wLcZtCXfAzY3FG0nbFKcqsBawL29f3JNgtcQg6ZPg1Z1qKYkQIv4kCRYihV0Zmsds1NNUkad1KJuqLrBRkmvhjcE41gUrSrQpLpnrgVXV9wJKXA/N6J7uBkjapri3aituo2O6I66HZnivXsVYUoKxsjJua8SCxeFAZzJJSYQQCSBJsBAp7I2hBVoq8zAbk/tHWafTcbTGHt+d4IVBWJ5O7lIIVdU9gC6uJREdMx3o0K2VGySz9pJ2Zn2zjC2PxW0Nb0cHtiPtSV0aAqA3m7E2N8tOsBAJkNx/OYUQGwqEInSOLibtfOD1jtYWMDCzzNxynA4CGFbrgZO4KU5lzY/OMY5jc1zHdAcH7QfJMefEbY1YUXerO6bik/iF5uYIDg4lfSmEytbejq+7G0UOzRAiriQJFiJFucbdBEIRjqZIEnxMPTRjOE4lESOvgikbSpvic/9Yq743GnMcSgAiSoSu6eQ9JGO9QwWHsBltdM7Epy54rR44ySdDqGxH2lH8fnx9/VqHIkRakyRYiBSlzt09ti+5J0OoWqvzMeh1vBGvQzNGXo0ekmEwxuf+sVZzP/gWYPZGzG896B7EHXCnTBJs1BtpLmqOW3Oct6MDjMakPSRjPWmOEyIxJAkWIkVdGVqgPM9KRb5N61C2JctsxFGRy5V4HJoR9MJEZ2rUA6vWDs2IfUmEegxxKjTFqdpK2nDNufCH/TG/t/dqB9aGBvS21PhZMZaXYywtxSsnxwkRV5IEC5Gi3hieT5ldYNXRmgI6hhcJR2JcAjDeAZFQatQDq4obwJIXl5PjOqc7yTXlciD/QMzvHS/tJe2EIiFcs66Y3lcJh/F1dqZMPTBEG0ltR47ITrAQcSZJsBApaNrjZ3jOy9Ga1KgHVh2ttbPkD3FtKsaHZqgNZqm0E6zXR6dEDMc+Ce6Y7qC1pBW9LnV+xa81x03HNvHzX79OZGUF25HUSYIh2hwXHB4mNDurdShCpK3U+Q0phFij1gMfrU2tnWB1kkXM64JHXgX7Psgpje194636PpjqAf9SzG65HFzm+sL1lCqFACi2FVOVUxXzJFidt5sqTXEqNWmX3WAh4keSYCFS0BvDC5gMOlqq8rUOZUf2FWVRkGVaS+JjZuS1aKNZqqm5H5QIjL0Rs1v2zPQQUSIp0xT3Vm0lbTFvjvN2dGAoKMBUUxPT+8abtbkZjEY5NEOIOJIkWIgUdGVwnqaKPKwmg9ah7IhOp+NobQFXYrkTvDgCnrHUOCluvap7ov/GsDlO3UlNhUMy1msvaWdyZZKJ5YmY3dN79Sq29uQ/JGM9vdWKtbFRmuOEiCNJgoVIMaFwhM6RxZSZD7zesVo716eWWPQGY3NDtbEsFZPgrEIoOhTTk+M6pzs5kH+AfEtqvUsArO1ex2o3OLy4SODmzZQrhVDZjhzB292NEgppHYoQaUmSYCFSTO+EB28wnHL1wCo1eb8aqyOUh18FoxXKWmJzv0Srvi/a2BeDQzMURaFjuoO24tSqB1Y1FDRg1ptjVhfs7Ywm06nWFKeytbejrKzgv3ZN61CESEuSBAuRYt5YTR5T5bjk9dqq89HpiF1d8MirUHEEjJSGPY8AACAASURBVObY3C/Rqu+FlRmYv7XnW414Rpj3z6dcU5zKZDDRVNQUs51g79UO0OuxtqReaQhIc5wQ8SZJsBAp5o2heYpzLFQXpMbg//VyrSYaynJjMyEi5I/OCK5JwVIIldrQF4OSiI6ZaLKUik1xqvaSdpyzToLhvZfLeDs6sBw6hCEnOwaRJZ6puhpDUZE0xwkRJ5IEC5Firg4vcKTGnnKNPm91pMbO1eEFlL2WAEx2Q9gPVSk0H3i9EgeYsmF070lw13QXNqONent9DALTRmtJK4FIgP75/j3dR1EUvF1d2NpSc1ccVg/NaGtbK+sQQsSWJMFCpJAlf4iBmWXaqlOv6emtWqvzWfQGGZn37u1G46s7ZJVH9x6UVgxGKG9983vZA9ecC0ehA4M+taaGvFVzUTMAzjnnnu4THB0lsriItSVFa8VXWVuaCQwMEFle1joUIdLOlkmw3+/nscce4+zZszz++OMb7tx88Ytf5MMf/jC/9mu/RiAQiHmgQghwjbtRFGipytM6lD1pqYwm8T1ji3u70XgHWO1gr41BVBqqaIfxTohEdn2LcCRM71wvjiJHDANLvKqcKnLNuXs+PtnXE02irU1NsQhLM9amJlAUfH19WociRNrZMgk+f/48ZWVlnD9/HrfbzcWLF++4Znh4mOvXr/P3f//3PPjgg0xOTsYlWCEyXfdoNGlUk8hU1VCei0Gvo3vUvbcbjXdEE8gULg0Bot9DcBnmbuz6FoOeQbwhL47C1E6CdTodTYVNe0+CnU4wGrEcPhSjyLRhbYrujKtJvRAidrZMgi9dusSpU6cAOHHiBJcvX77jmpdffpnFxUUeeeQRXnvtNaqrq2MfqRCC7lE3JbkWSvOsWoeyJ1aTgUOlOXTvZSc4HITJnmgCmerU72EPJRFq0pjqO8EQ/R765/sJRnbfHOdzOrHU16O3WGIYWeIZS0swFBdHk3ohREwZt7pgYWGB3NxcAHJychgYGLjjmrm5OQoLC3nyySf5yEc+wuuvv869927cqOL3+3G59vYqf7d8Pp9ma4vklgrPjdcHptifZ0j6OLejJlvh1cE5nE7nrpr8LPPXqAsHGFVKcMf58Yj7cyOi0KA3M9/9I6aMzbu6xYWhC5h0JgLjAVwTqf38yPPlEYgE+OEbP2Rf1r6d30BRoKMD7r037j8rCfm9UVvL4htXWEyDn/tMkwp/VzLZlkmw3W7H4/EA4PF4KCi4czZpTk4OBw4cAKC6unrLcgiLxYLDoc1uhcvl0mxtkdyS/bnhC4YZXhzg4aO1OBwNWoezZ6fmrHz/hpOi6jrKdrOz/cbrAFTd8x6qiuM7DSEhz42LLRQFhina5TpTQ1M0FjXS0pTajWAA1kUrX7jxBXz5Phz1O388gpOTXHe7KXvgAQrj/P8tEc+NqfvvY/aLf0NDXV3K72xnmmT/u5IpNnohsmU5xMmTJ9fqgC9dusTx48fvuKa5uZnu7m4AhoaGqKmp2UusQoi76J3wEI4oNKd4PbCqpSr6fah1zjs23gHmHCisi2FUGqpoj35PuxgbpygKrllXytcDq/bl7SPLmLXruuB0aYpTWZuaIBzG37+3sXFCiNttmQSfPXuWyclJHn74YfLz86mpqeGJJ5647ZqjR49it9v5pV/6JQ4cOEBbCs9lFCJZrTXFpfhkCJWjIg+djt03x413QHkb6NNk0mNFO/gWYWFwx186sjSCJ+hJi3pgAL1OT2NhI665XSbBTifodFgbU/8dE5DmOCHiZctyCLPZzFNPPXXbxz772c/ecd0f/dEfxS4qIcQdesYWsWeZqLKn5klx62VbjNQVZ++uOS4ShokuOPYrsQ9MK29tjivYv6MvTaemOJWjyMFz154jHAnveO6xz+nEXFeHPisrTtEllqmqEn1+vjTHCRFjabKFIkT66x5101KZn9Inxa3XUpVPz27KIWavQ3AlPSZDqEqbQG/c1YQI15wLo87IIXtqjwN7K0ehA2/Iy6Bn5zvjPqczbUohIDo2ztrkkCRYiBiTJFiIFBAIReib8NCcJqUQqpbKfMYWfcwu+Xf2hWqimE5JsMkaPUJ5N0nwrIv6gnrMBnMcAtOGuqu907rg0OwsoYmJtEqCIVoX7O/rQwnufmycEOJ2kgQLkQKuTXkIhCMpf0jGempS3zO2w7rg8Q4wWqH4cByi0lBFO4xd3VFznKIoa8clp5O6/DosBsuOk2CfM3p9OibBSjCI/8buD1QRQtxOkmAhUkDPavOYOlEhXaiTLnZcFzzeAWUtYNiyrSG1VLTDygx4xrf9JZMrk8z55tKqHhjAqDdyuODwjpvj1JIBq6MxHmFpRk3qpTlOiNiRJFiIFNA9tkiOxci+wvRo9FHl20zUFmatJfnbEom8eVxyutnFyXFrTXFpthMM0e/JNetC2cHOuM/pxFRbiyEvvUqHzPv2oc/KkrpgIWJIkmAhUkD36CJNlXno9enTFKdqqcrb2U7wwi3wu9MzCS5vAXQ7S4LnXOh1eg4XpFlpCNG6YE/Qw8jSyLa/Jt2a4lQ6vR6LNMcJEVOSBAuR5MIRBee4O+3qgVXNlfkMzq6w6N1mw086NsWpzNnROucd7gQfyDtAlim93iWAnTfHhRcXCQ4Pp2USDNGSCF9vL0o4rHUoQqQFSYKFSHI3p5fwBSNpc0jGemqdc892d4PHO0BvgtL0e/sfeLM5bpucs860qwdWHbIfwqgzbrsu2OdKz6Y4lbWpCcXrJXDrltahCJEWJAkWIsmppQLp1hSnaq5cnRCx3brg8Y5oAmy0xDEqDVW0g2cMlqa2vHTGO8OUdyot64EBzAYz9QX1294JfvO45PR8PNaa46QkQoiYkCRYiCTXPerGatJTV5ytdShxUZxjoSLfur26YEVJ36Y41VpzXOeWl6bjSXHrOQoduOa21xznczoxVlRgLCxMQGSJZ6mrQ2exyIQIIWJEkmAhklz36CKOijyMhvT9cW2uzKd7OyfHuUdhZTa9k+Dy1ui/41uXRKhlAo2F6TUO7K0cRQ7mfHNMrkxueW26NsWpdEYjlsYG2QkWIkbS96+qEGkgElFwjqVvU5yqpSqPmzPLLPtDm1+41hR3JP5BacVmh4ID22qOc826qM2tJdecm4DAtKGWemxVEhFeWiZw61balkKorE1N+JxOlEhE61CESHmSBAuRxIbmVvD4Q2nbFKdqqcxHUcA1vkVd8HgH6PRQ1pyYwLRS0b69JHjOldalEACHCw6j1+m3bI7z9/WCoqT1TjBEk+DI0hLBke2PjRNC3J0kwUIkMbVOtjntd4JXT47bqiRivAOKG8CcfuPAblPRDguD4J3f8JJF/yKjS6Np2xSnyjJlcSDvwJY7wW82xaX3CyRpjhMidiQJFiKJdY+6MRl0HC5L37e7AcryLBTnmOke28ZOcDrXA6u20Ryn7oym+04wRL9H59zmSZ/P6cRQXIyxtCRBUWnDcugQmEzSHCdEDEgSLEQS6xlbpKE8F7MxvX9UdTrd1s1xnknwjGdYErxxSUQ6H5e8nqPQwdTKFDPemQ2viTbFOdDp0u9UxbfSm81YDtXLTrAQMZDef1mFSGGKotA9upj2TXGqlqo8rk0t4QtucBrWxOquaCYkwdnFkFe9ZRJckV1BgbUggYFpQ93t7p3rvevnIz4f/hs30r4eWLXWHLeNsXFCiI1JEixEkhpb9DG/EqQ5TQ/JWK+lMp9wRKFvwnP3C9SRYeoIsXS3RXOca86VEbvA8OYIuI3qgv39/RAOZ1QSHJ6fJzQxoXUoQqQ0SYKFSFJqaUBLZXpPhlCtNcdtdGjGeAcUHgRrZjweVLTD7HXw3/miYCmwxC33rYyoBwbINedSm1u74YQItTQg3ZviVDZpjhMiJiQJFiJJ9YwuYtDrcFRkRtJXXWAjz2qke6PjkzOlKU5V0Q4oMNF9x6f65vsAaCrKjJ1PWG2Om7170ufrcaLPz8dUVZngqLRhaWgAvV6a44TYI0mChUhS3WNu6ktysJoMWoeSEDqdjpaqfHruthO8MgcLQxmYBHPXkohMaopTOQodjC6Nsui/8/mRKU1xKr3NhuVgnewEC7FHkgQLkaS6RxdpzpBSCFVLVT694x4CoXWnYa01xbUlPiit5JZDduldj092zbkoshZRkpXe48DeSi39WF8SoQQC+Pv7sToyZ1ccVpvjenq0DkOIlCZJsBBJaMrjY8rjz5imOFVzZR6BcIQb00u3f0Kdl1ueQTvBOl006Z/ouuNTmXBS3Hpqc1zfXN9tH/ffvIkSDGZMU5zK4nAQmp4mNLPx2DghxOYkCRYiCbnGo81QTRlSD6xSv1/n+kMzJrogrwqyizSISkPlrTDdCyH/2of8YT8DCwNrSWGmKLQWUppVeseYNJ8r+t9WR2Y9HtbG6IsgX2/fFlcKITYiSbAQSUhNAjMtCT5QnI3FqMc5fpckOFNGo71VeStEQtFEeNWNhRuElBANhQ0aBqaNxsLGO5Jgf68LndWKef9+bYLSiLUx+v/f37v5cdJCiI1JEixEEuoZW6S6wEZ+lknrUBLKaNDTWJF3e3Nc0Asz/VCeQfXAqvI7j09Wk8BMaopTNRY2MrA4gC/kW/uYz+nC0nAYnSEzGkhVBrsdU2UlPqckwULsliTBQiQh57g743aBVU0VeTjH3G+ehjXlBCWcmTvBhQfAlH1bXXDvXC82o42a3BoNA9NGY2EjYSXMjYUbQPRURV9v71ppQKaxOBz4eu9+ip4QYmuSBAuRZFYCIQZmlmnKsMkQqqbKPNy+EKML3ugH1AQwE5NgvQHKmm9Lgvvm+mgoaECvy7xf340F0bpfdTc8ODpGxOPJuHpglbWxkcDAAJGVFa1DESIlZd5vUSGSXO+EB0XJvHpglToWbq05brwTLHlg36dhVBoqb40mwZEIESVC33xfRtYDA1TlVpFtyl4bk6bWw1odmbkTbHU0gqLgv3ZN61CESEmbJsF+v5/HHnuMs2fP8vjjj7/59uRbdHZ28uCDD3Lu3DnOnTvHzZs34xasEJmgZzX5y7TxaKrG8lx0ujcfh7WmOH2GvmavaIOABxZuMeIZYTm4nJH1wAB6nZ6Ggoa1MWk+pwv0eiyHD2scmTbU5N/nkrpgIXZj078q58+fp6ysjPPnz+N2u7l48eId17jdbs6dO8czzzzDM888Q11dXdyCFSITOMfc5NtMVOZbtQ5FE1lmIweKs6MTIiJhmOzJzFIIlfq9T3StlQFk2ni0t2osbKRvvo+IEsHX24t5/370NpvWYWnCWFmJPi9vbUycEGJnNk2CL126xKlTpwA4ceIEly9fvuMat9vNv/3bv/HBD36QT33qU3fdLRZCbJ/aFJcpR8DeTXNlfrQcYu4mBJczOwkubQKdYS0JNugM1BfUax2VZhoLG/GGvAy5h/D1ujK2FAKiR41bGxvxyZg0IXbFuNknFxYWyM3NBSAnJ4eBgYE7rqmtreV3f/d3efvb384v//Iv88orr3D8+PFNF/X7/bg0evvG5/NptrZIbsnw3AhHFFxji7yvIU/zWLRUbPAxuuDl+qV/oh646c3Fr+HjofVz40DuPoLXXuK1smIqrZXc7M/csjPLsgWAF17+LvePjeN+VyHuDH5uUFYG3/seru5uyLAxcalA8+eH2NSmSbDdbsfjiZ5c5fF4KCgouOOaqqoqDq/WY1VVVTE7O7vlohaLBYdGr95dLpdma4vklgzPjWuTHgLhAU637MfhqNY0Fi293TDNl668gtU7DnoTdff/PBjNmsWj+XPDeS/WWxcYLfBzf8X9mj9PtXQwfBCj04h+egiAmgfPkKPh46H1c2Ph1AOMP/88dTYbloMHNYtD3J3Wzw8RtdELkU3LIU6ePLlWB3zp0qW77vB++ctf5p/+6Z+IRCL09/evJcRCiJ1TT0rL1PFoKnUyhjLRCSWNmibASaG8ldnlCaa8UxldDwxgNpips9fhdTqBzDsueb03m+OkLliIndo0CT579iyTk5M8/PDD5OfnU1NTwxNPPHHbNY888gjPPfccH/rQh3j3u99NfX3m1qoJsVfOMTdmo56DJTlah6KpklwLpbkW8hd6M7seWFXeSp85+kIgU8ejvVVjYSPmm6MYS0sxFhVpHY6mLHV16EwmOT5ZiF3YtBzCbDbz1FNP3faxz372s7f9d2lpKU8//XTsIxMiA/WMuWkoy8VkyNBxYG9xsixE3shcdERYpitvo9cSPUJbPTAikzUWNlI69hz6hiNah6I5ncmE5dAhOT5ZiF2Qv7RCJAlFUTL6uOT1TueMA+AvbtY4kiSQXURvdj7lOjN2q13raDTXmF1H1Qws1hZqHUpSsDga8fX2ynQmIXZIkmAhksSk28/cciDj64FVrYZBAG7o92sbSJLos1hpDIa1DiMp7J8zYlBgsFymIQBYGx2E5+YITU1rHYoQKUWSYCGShHN8EZCmOFW1/zrDkRK6ZzN3XrLKG/JyiyANS/MQ9GodjuZMN4YB6CpY0jiS5KA2B0pdsBA7I0mwEEmiZzQ6GcIh5RAAZM+76NXtp2dsUetQNHdt/hoRFBx+P0w6tQ5Hcz6ni4DVwGv6Ia1DSQqWxmgSLMcnC7EzkgQLkSSc4272F2WRY9m0XzUz+JfQzd5gNqdhbWxcJlOPS24IBGCiU+NotOfr7WVlXxm3loZYCa5oHY7mDDk5mGprZUyaEDskSbAQScI57pZSCNVkD6AQLmvBNe4hEsnshp++uT5yTblUGbJhokvrcDSlRCL4e3sxNR5CQaF/vl/rkJKCHJ8sxM5JEixEEvD4ggzOrtBcma91KMlhdbczd989LPlDDM9n9m5f73wvDYUN6MpbMz4JDg4PE1lZobj1PiD6AkGAtclBcHCI8NKy1qEIkTIkCRYiCbjGo8eTy3i0VRNdYCvgQF30BMqescwtiQhHwlybvxY9Ka68FSa7IZK5UyLUuteyIyfIM+fROy8lAPBmXbC/Tx4PIbZLkmAhkoBzTCZD3GaiE8pbOVSei0Gvw5nBSfCgZxBvyBs9Ka68FYIrMHdT67A043P1gtGI5VA9jYWN9M5K0gdyfLIQuyFJsBBJwDnupijbTGmuRetQtBcORScglLdhNRmoL8nJ6OY49e3+tZ1gyOjmOF+vC0tdHXqLhYbCBq4tXCMUCWkdluaMpaUYCgqkLliIHZAkWIgk0DMWbYrT6WQmLrPXIOyH8uhxyU2VeRk9Js0158KoN3Iw/yCUOEBvgvHMTYL9TtfaXFxHoQN/2M+ge1DjqLSn0+mwOhz45fhkIbZNkmAhNBYIRbg2uSSlECo1wVvd9WyuzGPS7Wdmya9hUNrpm+uj3l6PyWACoxlKGjO2OS40M0NoehrL6lv/DYUNQPSFgogen+y/dg0lGNQ6FCFSgiTBQmjsxvQSgXBEmuJUE51gsEDxIeDNZkFXBpZEKIpC71wvDQUNb34wgydE+HqjpSHWxmgSfCD/ACa9SSZErLI2OlCCQfw3B7QORYiUIEmwEBpTm76aZSc4aqILSh1gMAFvNgtmYnPcjHeGOd9ctB5YVd4Ky1PgmdQuMI2oxwJbG6MvCkx6E/X2+rXDRDKdHJ8sxM5IEiyExnrG3FhNeg4U52gdivYUJboTXNG29iF7lpkquy0jx6Spb/PflgSrj00GNsf5nC5MlZUY7Pa1jzmKHPTO9aIomX2gCoD5wAF0Vis+qQsWYlskCRZCY87xRRrL8zDopSkO9yh459ea4lSOiryMnBChvs2v1r4CUNYS/TcTk+De3rV6YFVDQQML/gUmVzJvZ3w9ncGA5fBhfL2yMy7EdkgSLISGFEXBOSbHJa9Ra13VUWCrmirzuDm9hDeQWYdE9M71UpVTRa45980P2uxgr824uuDIygqBgQGsjY23fVzdJZe64Kjo8cmyMy7EdkgSLISGRua9uH0haYpTjXcCOihrvu3DzZV5RBToncis3eDeud7bSyFU5W0ZNybN398PirJW96qSCRG3szY5iCwuEhwd0zoUIZKeJMFCaEidf9tSla9xJEliohOKDoIl97YPq02DmVQXvBRYYsgzhKPQcecnK9ph7gb4MufxUI9LtjY13fbxbFM2+/L2SXPcqjdPjnNqHIkQyU+SYCE01DPmxqDX0Vieu/XFmWC8I5rgrVNlt2HPMmXUoRlqUuco2iAJBpjsTmBE2vI5nRjsdowVFXd8zlHowDUrO8EAloYGMBjwu+TxEGIrkgQLoaHu0UUOleZgNRm0DkV7y7OwOHzXJFin09FSmU/3aObsfKpv7zcVNd35SfUxGu9IYETa8vU4sTY13fVURUeRg7HlMRZ8CxpEllz0ViuWujp8PbITLMRWJAkWQkPdY26aK6UUAoCJ1YTuLkkwQHNVHn0THgKhSAKD0o5r1kWJrYRiW/Gdn8wth5yyjEmClUAA37VrWJvv8oIA1kpGpC44ytrUhM8pSbAQW5EkWAiNTLl9THv8ckiGSk3o1o1HUzVX5hMIR7g25UlgUNpxzbnu3hSnKm/LmCTYf/06BINr9a7rSRJ8O2uTg9D0NKHpaa1DESKpSRIshEbUJi9pils13gn5tZBVeNdPt2RQc5w35OXm4s271wOrKtphug+C3sQFppGNmuJUdqudiuwKemelOQ7efJx8UhcsxKYkCRZCI92j0SYvmRG8arzjtpPi1ttflE222UDPaPo3x12bv0ZEidBUePekD4gmwUoYJtP/bW9fjxN9djam2toNr3EUOmQneJV6oIiURAixOUmChdBI99gidcXZ5FiMWoeiPZ87OvKr4siGl+j1Opor8+nOgJ1gddLBljvBAONXExCRtnxOJ1aHA51+4z9ZjiIHt9y3WAosJTCy5GTIycG8b580xwmxBUmChdBI96ibZimFiFJPP9ugKU7VVJmHc8xNOJLep2G55lzkW/KpyL5zHNgaey1Y7WlfF6yEw/j6+rA0bfKCgDenaPTNy8lxANZmaY4TYiuSBAuhgfnlAKMLXmmKU02snn62STkEROunvcEwAzPLCQhKO2pT3N3Gga3R6aKP10R6nxwXuHULxevdsB5YpTYRyqEZURaHg+DoKOEFGRsnxEYkCRZCA87x1aY4GY8WNd4RHfmVW77pZS1VanNc+tYFB8NBrs1f27weWFXRDpM9EA7GPzCNqLuZWyXBJbYSiqxFOGdl9xPe0hzXKy8KhNjIlkmw3+/nscce4+zZszz++OMoysZvQ37pS1/iV3/1V2MZnxBpSW2Kk53gVRucFLdefUkOFqN+7fFLRzcWbxCMBDevB1ZVHIFwAKbTN9Hx9TjRWSxY6uo2vU6n0+EokuY41VoSLHXBQmxoyyT4/PnzlJWVcf78edxuNxcvXrzrdaOjo3z729+OeYBCpKPuMTdVdhsF2WatQ9Fe0Bsd9bWNJNho0NNYnpvWJ8etNcUVbicJTv+T43xOJ5aGBnTGrRtIHYUObi7cxBfyJSCy5GYsKMBYWSF1wUJsYssk+NKlS5w6dQqAEydOcPny5bte9yd/8id85jOfiW10QqSpntHFtbf2M96kMzrqaxtJMEBzVT49Y4ubviuVylxzLrKMWdTmbTwObE3hQTDnRGcspyFFUfC5XFi3aIpTNRU1EVbCXF+4HufIUoOcHCfE5rZ8ab2wsEBubi4AOTk5DAwM3HHN888/T2NjIwcPHtzWon6/H5dGQ7x9Pp9ma4vklqjnxkowws2ZZU5Vm+W5CNiv/ysVwPXlLILbeDyK9Cu4fSF+8moX5bmm+AdIYn9vXBm5Qq21lr7e7U052JdXBzdfZjAdn0sTE+DxsFBQyMI2vj+TP/p8+FHPjzCUGuIdHZDkf1OKS+CHP8J15QrYbFpHk5GS+vkhtk6C7XY7Hk/0mFKPx0NBQcEd1/zkJz9hbGyMCxcuMDAwwFe/+lUeffTRDe9psVhwbHD8Zby5XC7N1hbJLVHPjVdvzQG3eMeRgzgay+K+XtK7NglWO/X3vDM68WALwdwF/vLlGXy2EhyOTUaIxVCinhvhSJihK0N84NAHtr/ewEl446s4Gg6DPjGJX6K4B4cYBfY/9BC2bTwejUojua5c5s3zCfs9n8x/UzxnHmTk2WfZB2QlaYzpLpmfH5lkoxciW5ZDnDx5cq0O+NKlSxw/fvyOa/78z/+cZ555hs9//vM0NzdvmgALkenUpi6ZDLFKbYrbRgIMcLgsF4NeR3caTogYdA/iDXm3Vw+sqmiH4DLM3ohfYBrxOZ1gNGI5fGhb1+t0OpoKm9bqqjOdtakZkOY4ITayZRJ89uxZJicnefjhh8nPz6empoYnnngiEbEJkZa6R92U5FoozbNqHYr2wkGYcm67HhjAajJwqDSHnjQ8OU6dbLCtyRAq9bFLw3nBPpcLS309evP2G0gdRQ6uzV8jGEnfsXHbZSwtwVBcLHXBQmxgy3IIs9nMU089ddvHPvvZz9712urqar785S/HJDAh0lXP2CItMhotaro3OuJrB0kwRA/N+EnfFIqibH6gRIpxzbqwGCzU5W8+Duw2JQ1gsESPT279YPyCSzBFUfD19JDz9rfv6OschQ4CkQA3F27SUNgQn+BShE6nw9rkkCRYiA3IYRlCJJAvGOba1BLNUgoRpY722mkSXJnHzFKAKY8/DkFpxzXn4pD9EEb91uPA1hhMUNaUdmPSQlNThOfmtjwkY73GoujJcTIvOMrqaMJ//ToRf3r9rAgRC5IEC5FAfRMewhFFxqOpxjuiI74KtzdZRtVSFX0RkU6HZiiKgmvOtbNSCFVFe/SxTKOxcWod606T4H25+7AZbVIXvMra1AThMP7+a1qHIkTSkSRYiARSm7lkJ3jVeCeUt4J+Z7+KHBV56HSkVV3w6NIonoBn90mwbxEWhmIfmEZ8LifodFgbDu/o6wx6A42FjfTOpe8pejthbV49OU5KIoS4gyTBQiRQ96ibfJuJ6gKZ2UkkDBNdOy6FAMi2fgZpiQAAIABJREFUGDlQnJ1WO8Hq2/dNhTvb+QTS8uQ4n9OF+cAB9NnZO/5aR2H0+OSIEolDZKnFVFWFPi9PkmAh7kKSYCESqGcselJcOjVz7drsjehor10kwRAdMZdOO8GuWRdGnZH6gvqdf3FpM+gMaZYEO3dcCqFyFDnwhrwMugdjHFXqiTbHyclxQtyNJMFCJEgwHKF3wiOlECo1YStv29WXt1TlMbrgZX45EMOgtOOcc1Jnr8NisOz8i01WKGlMmyQ4ND9PaHx890nw6pxlqQuOsjoc+Pv6UIIyNk6It5IkWIgEuT61RCAUoVnGo0VNdERHe5XsboyV+mIiHXaDFUXBNeva2SEZ61W0p82sYHXX0tq0u8ejzl6HSW+SuuBV1qYmlEAA/80BrUMRIqlIEixEgqydFFclO8FAdNeyrDk64msX1BcT6XBy3LR3mjnf3O6a4lQV7bA0CZ6J2AWmkbUkeJfHzZr0Jg4XHMY5JyUAIM1xQmxEkmAhEqRnzE222cCBop03+qQdRXnzuORdsmeZqS6wpUVznPq2fVPR7t7+B9KqOc7ndGKqrsaQv/sXjI4iB65ZF0oajY3bLfO+feiysiQJFmIdSYKFSJDu0UWaKvPQ66UpjoXB6EivPSTBkD7Ncc45Jzp0NBTs4YSz8hZAlzZJ8G7rgVWOQgfugJux5bEYRZW6dAYD1sZGSYKFWEeSYCESIBJRcI67pSlONb5au1qxu6Y4VXNlHgMzy3h8qd3w0zvby768fWSZsnZ/E0suFB1M+SQ4vLREcHBo1/XAKrW+undW6oJhtTnO5UKJyNg4IVSSBAuRAAOzy6wEwtIUpxrviI70Km3e023U+mpniu8G7/qkuPXUk+NSmN8VLQ3Z607woYJDGHQGemZ7YhFWyrM2NRFZWSFwS8bGCaGSJFiIBOgcWQCgtVp2ggEYuwKlTdHRXnugJsFdKVwXPOudZXx5nOaivb0gAKDiCCwOw/LM3u+lEW9XNwDW5r09HlajlYP2gzhnpQQAwNoSfTx9Pd0aRyJE8pAkWIgE6BheJMts4FBprtahaE9RYPR1qL5nz7cqybVQZbdxdXghBoFpo3smmpS0Frfu/WbV90b/HX197/fSiLerE1NVFcaioj3fq7W4la6ZLmmOAyz19eiysvB2dmkdihBJQ5JgIRLg6vACrVX5GKQpDuZuRpviqvaeBAMcqbHTMZK6SXDXTBcGnYHGwsa936yiHXT6lE6CfZ1dWNti8IKAaBLsDrgZ9gzH5H6pTGcwYGtqwteZHrOkhYgFSYKFiDN/KIxzzM2RGrvWoSSHkdei/8YoCW6vyWd4zsvskj8m90u0rpku6u31e2uKU5mzo2Um6mOcYkKzswRHR7G17q1hUtVS3AJA54wkfgDWtjZ8LhdKID1OWRRiryQJFiLOesc9BMIRSYJVo6+DKTt6zG8MHKkpAEjJ3WBFUeia6aK1JDY7n0D0xcXo69GykxTjXd2ltLXHJgmut9djM9rWSk4yna2tDSUQwNfXr3UoQiQFSYKFiDM1OWuXJDhq9HWoPAp6Q0xu11KVh14HV4dSLwke8gzhCXhiUw+sqroHfAvRspMU4+3sBINh1yfFrWfQG2gqaqJrRupgAWyrZSbeLtkZFwIkCRYi7q4OLVCSa6Eif2+TENJCyA8TnVB1LGa3zDIbOVyWy9WR1JsQ0TkdTUbUt+1jQi0zScG6YF9nF5ZDh9BnxaA0ZFVrcSu9s70Ew6k9SzoWjBUVGIqL8XVIEiwESBIsRNxdHVngSI0dnU6a4pjshnDgzSkGMXK01k7H8ELKTQHonunGZrRxMP9g7G5a6oiWm6RYEqwoCt6uLmxtsSmFULUWtxKIBOiflxIAnU6Hra0Nb5fsjAsBkgQLEVeL3iA3p5elHlg1eiX6b4ya4lTt1XYWvUFuza7E9L7x1j3TTXNRM4YYlYYA0TKTyiMplwQHbt0i4navvWUfK2qpiZRERNnaWgncvEnY49E6FCE0J0mwEHGkHpLRXi1JMBCdWpBTBnlVMb2tWm/dkULzggPhAK45V2zrgVVVx6JHU4dSZwqAb3V30hqjyRCq8uxyiqxFkgSvsrZGn28+2Q0WQpJgIeJJTcraauSkOCC6O1l1L8S4NORwWS5ZZkNKHZrRP99PMBKM7WQIVdW9EPZHy09ShLezC11WFpb6GJaGEC0BaC1plSR4lW01CZZDM4SQJFiIuLo6vMjBkmzyrCatQ9GedwFmr8W0KU5l0OtoqcpPqSRYTcrisxOces1x3s5ObM3N6AwxLA1Z1VrcysDiAJ6AlAAY8vIwHzggdcFCIEmwEHGjKApXhxdkNJpqLD71wKojNXacY24CoUhc7h9rXdNdFNuKKcsqi/3N86shuzRlkuBIIIDf5YrZSXHrqdM3ZF5wlK2tFW9nR8o1kgoRa5IECxEnY4s+Zpb8HJUkOEpNyOKwEwzRJDgQjuAad8fl/rHWNdNFa3FrfKaG6HTRCRwpkgT7e3tRgkFsbe1xub8kwbeztrURnp4hNDGhdShCaEqSYCHiRK0Hlp3gVaNXoPgwWONTH73WHJcCJ8e5A25uuW/FpxRCVXUMZvrBl/zzk///9u47Pq7yzPv/50xVGfVuSbbcZMm9N1woMY4Bi94CGxJKTJ7NL7sJYQlk2SdlkyyQ8GwSEmNqQkIgJAQwJMZUd2RbLrJsZFuWZVtyUS+jMqMp5/fHkYyr3GbmHmmu9+s1aBgdz/21fDRznTPXue/e/tRAzwzRK94WT158nvQF9+idhk76gkWkkyJYiCDZXt2CzWKiIDNedRT1dN2YGSJIrRAAgxKiSHXY+0VfcO8ZyYAuknGq433BW4M3RoC4ynZgTkvFkpkZtDHGpRoXx0kLANhHjUKzWunaUao6ihBKSREsRJBsr25hzKB4bBb5NaO1BjrqgloEa5rGxNxEKYJ7DeppO+kHLRFdpTuIHj8hqAvKjEsbR0NXA7WdtUEbo78w2WzYRxfikjPBIsLJu7MQQeD1+SmraZX5gXsd7wcOXhEMMDE3gf31HbR2hfcSuWUNZQxNGEqcLS54g0QnQsrIsD8T7GttpfvAgeNTdwWLLJpxsuhx4+natQvd51MdRQhl+iyC3W43S5YsoaioiIcffviMHyN5vV6+/e1vc8cdd/Doo48GLagQ/UlFXTtdHp+sFNfrcAmY7ZARxDOffNEXXFYTvn2wuq5TVl8W3H7gXtlTjJ99GLcAdO00zooHqx+4V35SPlaTlbJ6KYLB+HnrnZ2491WqjiKEMn0WwcuXLycjI4Ply5fT1tbG+vXrT9vmo48+oqCggNdff536+nrKy8uDFlaI/qL3ojgpgnsc3gpZ48FiC+ow43vOvG+vbg7qOJfiWMcxGl2NoSmCc6ZCey20HQ7+WBfJtWMH8MVKZsFiM9soTC6UM8E9ei+Oc5XtUJxECHUsfX2zuLiYq6++GoCZM2eyceNG5syZc9I2c+fOZf78+Xi9XpxOJw6H45yDut1uZcWyy+WSQl2cUSD3jVVl9ThsJjrrDlJeH7w+x37B72VUzRZahhVRG4LfvZx4K+vKa7gqyxuw5wzkvlHcVAyAw+kI+mtRVHcyQ4Gaje/gzL0yqGNdtA0bIDubvTU1QR9qkHkQn9Z/yq7Pd2HSAtMN2G/fU3QdYmM5umYNR8eMUZ1mwOq3+0eE6LMIbmlpIS7O6FlzOBxUVVWdtk1sbCwAt956K2lpaeTm5p5zULvdTmFh4cXkvWTl5eXKxhbhLZD7xoGV9UzOS2H06NEBeb5+7dhO8LlIHreA5BD87k3f4WZNRQMFBQUBu9AqkPvGP0v+ic1kY+HkhVjNQV5J0DsMPrWRwzEIw9c9Xdep2F+FY84cBoUg3zzbPN6vfR9rlpX8pPyAPGd/fk85NHEi3kPVDOun+fuD/rx/DCRnOxDp81A4MTERp9NYZtLpdJKUlHTaNs3NzXR3d/P666/T1tZGcXFxAOIK0X91dnvZW+uUVoheIboortfEwYk0tLs50uoKyXgXqqyhjIKUguAXwAAWO2SOD9uL47xHjuBrbCRqwviQjDc+zRhHFs0wRE8Yj7uiAn9np+ooQijRZxE8a9as433AxcXFzJgx47RtXn75ZVasWIHZbCYqKgq32x2cpEL0EzsPt+HXjZkKBEYRHJUIycNCMlzvjBylYThVmtfv5fPGz0PTD9wrewoc2Qb+8JsFoKusZ5GMcaEpggfHDSbOFid9wT2ixo0Dnw+XfFwvIlSfRXBRURG1tbUsXryYhIQEcnNzeeKJJ07a5q677uLNN9/k9ttvJzEx8bSeYSEiTe9FWTI9Wo/DW4xCLIhzwJ6oICsOm9kUlvMFV7ZU0uXtCu78wKfKngKeDqjfHboxz1NX6Q40m42oUYFpTTgXTdOMRTNkhgjghJXjSuXiOBGZ+uwJttlsLFu27KTHHnnkkZP+PyMjg1deeSXwyYTop0qrW8lNjibFYVcdRb3uDqj7HAquDdmQdouZ0YPiw7II7v0YfnxqaM58AsYMEWAcjGSE1wVQXWU7iCosRLMFd9aQE41LHccLZS/Q6ekkxhoTsnHDkSUlBWt2Nl0yQ4SIULJYhhABtr26Rc4C9zpaCro/ZP3AvSbmJlJW04rX5w/puOdS1lBGvC2e3LhzX0AcMMnDICoh7FaO071eXLs+J2p8CA8IMIpgn+5jd1P4nRlXIWr8OFk5TkQsKYKFCKB6p5vDLV1yUVyvmhLja4iL4Am5CXR5fFTUtYd03HMpazAWyQjm8sCn0TTj518TXkWwe98+9K6uoC+ScareVhTpCzZEjxuP5/BhvI2NqqMIEXJSBAsRQLJIxikOb4HEIRCbGtJhJ+YaM9mEU0tEp6eTfS37GJcW2qIPgOypRltKd0foxz6Lrp5FMqJDfCY4JTqFbEc2O+qlBQCMGSJA+oJFZJIiWIgA2nywCatZY2y2zAyBrsOhYsiZFvKh81JiSI61UXIgfFaO29GwA7/uD20/cK/c6aD7vjgzHwa6tmzFnJSEdfDgkI89Pm082+u2o4fxctKhEjV6NFitdG0Lz2n0hAgmKYKFCKBNVU1MyEkkympWHUW9pv3QfgzyLgv50JqmMS0viU0Hwucj3i21WzBpJialTwr94LnTQTPBwQ2hH/ssOktKiJk6NbStIT2mZkylrquOGmfwV6kLd6boaKLHjqVzc/gcIAkRKlIECxEgnd1eympamT40WXWU8HDQmGOcIaEvggGmD02huqmLIy1dSsY/VcmxEgqSC3DYzr20fMBFJUDmuC/+TRTzHDmC5/BhYqZNVTL+lAyjR72kVgo/gJipU+nauVMWzRARR4pgIQJk68EWvH6dGcNSVEcJDwc3QEwqpIZmDthTzeg5GNlU1aRk/BN1+7rZUb+DqRlqij4AhsyBms3gVb+gUWeJUXzGTAt9qwzAsIRhJNmTpAjuETN9Gni9dJWWqo4iREhJESxEgGyqasRs0pgy5PTlxSPSwfUwZHbIFsk4VWFWPHFRFjaGQRG8s2En3f7u42cglRgyG7wuY/U4xTo3l2CKi8Oer+YASdM0pmRMYUtteM2YoUr0pElgMklLhIg4UgQLESDFVU2MHRSPw97nGjSRoaUaWg5BnroVJM0mjWl5yWysUt8X3HvGUXkRDHBgnboMPTo3byZmyhQ0s7re+amZUzncfphjHceUZQgXZoeDqNGj6dy8WXUUIUJKimAhAsDl8bG9ukX6gXv1XoDVW3gpMn1oMvvrO6h3qm0B2FK7hZFJI0mwK5w1JCYZ0kcrvzjOW19P94EDyvqBe0lf8Mlipk6lq7QUf3e36ihChIwUwUIEQGl1C91eP9OHSj8wAAfXGRdjpY9WGqP3oGTzAXUtER6/h21125iSrvAscK8hs6F6I/i8yiJ0bjFaEGKmqi2CRyaOJM4aR8kxKYIBYqZNRe/uxrVD5gsWkUOKYCECYFNVE5oG0/PkTDBgnG0cPBtMaqeKG5edQLTVzMb96loidjfupsvbxdRMtUUfYMzU0d0Ox9RdANW5aTNaTIwxP61CZpOZyRmTpS+4R8wU4yCt96JFISKBFMFCBMDGqiZGZcSREGNVHUU95zFo3Ke8FQLAajYxZUiS0ovjwqIfuFfvdHUH1E2V1llSQszEiWhW9b8rUzKmcKDtAA1dDaqjKGdOTMSen0/nJukLFpFDimAhLpHH52fLwWZmytRoht6eUwWLZJzJjKHJ7Kl10tKpptdxS+0W8uLzSI0O7dLRZxSXASkjlPUFe5ubce/da0zJFQZ6p6yTs8GGmGnT6Ny+Hd3jUR1FiJCQIliIS1R2uJUuj08uiut1cD3YHJA5QXUSwOgL1nXYrGAJZZ/fx9bareFxFrjXkNlwaAP4fSEfumursTSv6n7gXgUpBURboqUvuEfMtKnonZ24Pv9cdRQhQkKKYCEuUe9iDFIE9zi4AXJngDk8poqbkJuIzWJS0hdc0VKB0+MMj37gXkPmgKsV6kJf6HRu2oxmsxE1fnzIxz4Tq8nKpPRJbKmTM8HwxcGJ9AWLSCFFsBCXaFNVE8PTYkl12FVHUa+zySiuwqAfuFeU1czE3EQ2KZghovdjdqUrxZ2q999GQUtEZ0kJ0RMmYLLZQj722UzJmEJFcwWt7lbVUZSzpKZiGzpUFs0QEUOKYCEugc+vs7mqSaZG63V8fuDw6AfuNWNoMjsPt9LuDu3UYCXHSsh2ZJMZmxnScfuUmAsJg0O+aIavvR1Xebny+YFP1duqIn3BhpipU+ncsgXdF/p2GSFCTYpgIS5B+dE2nG4vM4dJKwRgFMGWKMierDrJSWYMTcGvQ0kIzwbrus6W2i3h1Q/cK+8y499K10M2ZNfWreD3EzMtPC6K6zUudRw2k02K4B4x06fhdzpx792rOooQQSdFsBCXYKP0A5/s4DrImQaW8GoNmTwkEYtJO96/HQr7W/fT7G4Or1aIXkNmQ2cDNISu0OncXAIWC9ETwuOCyV42s43xaeNl5bgex/uCZQllEQGkCBbiEmyqamRwcgxZCdGqo6jnaoVjZWHXCgEQY7MwLichpPMFh2U/cK/ef6ODoZsvuHPzZqLHjsUUExOyMc/X1Myp7G7aTXt3u+ooylmzsrDm5EhfsIgIUgQLcZH8fp1NVU1yFrjXoY2g+8PqorgTTR+azI6aFrq6Q9PrWHKshPTodHLickIy3gVJHgaOzJAtmuHv6qJr586w6wfuNSVjCn7dz7a6baqjhIWYqVPpLClBD2G7jBAqSBEsxEXaV99Oc6dHiuBeB9eDyWq0Q4ShGUOT8fh0tlUHf77gE/uBNU0L+ngXTNOMg5UQ9QV3lZaC1xs28wOfanzqeCyaRfqCe8RMm4qvuZnuykrVUYQIKimChbhIvfPOzpSZIQwH1xsXxNnC7+NugKl5yWgabNwf/JaIamc1dV114TU/8KnyLgPnEWiuCvpQnZs2g8lE9OTwumCyV4w1hjGpY6QvuEfvxYvSFywGOimChbhIG6uayIyPIjdZ+oHp7oAj28K2FQIgPsrK6Kz4kFwc13tGMSxnhuh1vC84+PMFd5aUEFVQgDkuLuhjXawpGVPY1bCLLm+X6ijKWXNzsaSnS1+wGPCkCBbiIui60Q88Y1hyeH7cHWo1m8HvNVYjC2Mzhqaw9VAzbm9w+4JLaktIsicxLGFYUMe5JGkFEJMS9CLY391NV2lp2E2NdqqpGVPx6l521O9QHUU5TdOImTZN+oLFgCdFsBAX4UBjJ3VOt/QD9zqwHjQT5E5XnaRP04cm4/b6KasJ7upgYd0P3EvTYPCsoC+a4SorQ3e7w/aiuF6T0idh0kzSEtEjZtpUvHV1eA4dUh1FiKCRIliIi7CpyugHniH9wIaDGyBrAkTFq07Sp96DlmBOlXa0/SiH2w+Hdz9wr7w50HIQWmuCNkTvR+rRU8K4NQRw2BwUJBfIxXE9jvcFl8hBgRi4pAgW4iKsrWgg1WFneFqs6ijquduhZlNYzg98quRYGwWZcayraAjaGMVHi4EwnR/4VHk97Sv7VwVtiI4NG7Dn52NJSgraGIEyLWMa2+u20+npVB1FOduwYZhTU+lYH/yecSFU6bMIdrvdLFmyhKKiIh5++OGz9gY98sgj3HbbbTz44IN4vd6gBBUiXHh9ftbsreeKUWnh/XF3qOxfBb5uyF+oOsl5uXxUOpsPNNHm8gTl+dfUrCE9Jp38pPygPH9AZYyFuCzYuzIoT+9zOuncuhXH/PlBef5Am5szF4/fc/xAJpJpmoZj7lza161Dl/d1MUD1WQQvX76cjIwMli9fTltbG+vXnz6xeklJCV6vlzfeeIOOjo4zbiPEQLLlYDNtLi9XFqSrjhIeKlaCPd7oL+0HrixIx+vXg3I22OPz8NnRz5iXM69/HCBpGoy8Gio/BW93wJ++Y/168HpxXN4/iuDJ6ZOJtcaypmaN6ihhwTF/Pv62Nrq2b1cdRYig6LMILi4u5rLLjI84Z86cycaNG0/bJjU1lXvuuQcAv98fhIhChJdP9tRhNWvMGZmqOop6ug4VH8LwK8BsVZ3mvEwenEhCtJVPdtcF/Lm31G2hw9PBvOx5AX/uoMlfCN1OOPRZwJ+6fdVqTAkJRE+YEPDnDgar2crsQbNZe3itzIoAxF42GywW2levVh1FiKCw9PXNlpYW4nrmdXQ4HFRVnT6pel5eHgAffvghJpPpeNHcF7fbTXl5+UXEvXQul0vZ2CK8ne++8X5pNaPT7NRU7QtBqvBmb97DMOdRjjjG0dqPfq8mZtj4aNdRdo2xYjqPM7bnu2+8ffBtrJqVxLbEfvM6o3kyyTdZaS5+jTp3AD/d8Pvh009h/Hh2V1QE7nmDbIRpBB92fsj7W94nLzbvnNsP+PeUggIaV35A4zXXqE7SLw34/aOf67MITkxMxOl0AuB0Okk6y4UNH3/8Ma+88gpLly7FYunzKQGw2+0UFhZeRNxLV15ermxsEd7OZ9+oburkYMt+/uXaERQWhvEcsKGy+j0ABs27h0GO/tMecoMrjtV/KcUbN4gJuYnn3P58Xzd27d7F9KzpTBo7KRAxQ6d0LimNJaQE8LWxa8cODrS2MqhoMQn96DU3LS+NpVVLqbZVs6hw0Tm3H+jvKY3XXEPdk08yIj4ea3a26jj9zkDfP/qLsx2I9NkOMWvWrOM9vsXFxcyYMeO0berr63nxxRdZtmwZDocjAFGFCF+f7jE+Qr9C+oENe9+HQZOhHxXAAPPz09E0+DiALREH2w5yoO0Ac3PmBuw5Q2bkQmisgMbKgD1l+6pVYDIROye8F1A5VWp0KmNSxrC6RloAgOP93E5piRADUJ9FcFFREbW1tSxevJiEhARyc3N54oknTtrmrbfeor6+nvvuu48777yTv/3tb0ENLIRKn+yuY0hKDMNSZWo0Ohrg8JZ+MyvEiZJjbUzKTeTTABbBvRdTzcvpR/3AvfKvNr5WfBCwp2xftZroCRP6xdRop5qXM4+y+jKaXMFfYjvc2YYOxZqbK33BYkDqs3fBZrOxbNmykx575JFHTvr/b3zjG3zjG98IfDIhwkxXt4/PKhv5yozB/ePK/2Cr+BDQ+2URDHBVYQZPrdxDXZuL9PioS36+NTVrGJYwjNy43ACkC7HkYZCab0yVNvObl/x0nro6XJ9/Ttp3vhOAcKE3P2c+S0uXsv7wehYPX6w6jlKapuG4/HJa3ngDf1cXpuho1ZGECBhZLEOI87ShsgG31y9To/WqWAmODMjsH1f+n+qKUca/46o99Zf8XB2eDkpqS/rnWeBeI6+Gg+uNxU8uUcca46x4f5ka7VSFKYWkRKXIVGk9HPPno7vddJxhhigh+jMpgoU4T5/sriPGZj6+9G5E83lg3ycwcgGY+ufLSGFWHFkJUQGZKq34SDFev7d/F8H5C41FTwKwelz76tVYMjOx5/eDBUPOwKSZmJszl/VH1uP1y0IRMdOnocXESEuEGHD657uXECGm6zqf7q5jzohU7Baz6jjqVW8Ed6txQVU/pWkal49KZ92+Brq9lzbH+ZrDa4izxjExfWKA0ikweJax6EnFpa0e5+/upmP9Bhzz5/frtqF5OfNwdjvZXicLRZhsNmJnzaJ99WqZP1kMKFIEC3Ee9tQ6OdLqklaIXntXgslqLJLRj11ZkE6728vmAxd/AZRf97O2Zi2zBs3CauofC4ackbnn33PvB8YiKBepq6QEf2dnv1kq+WxmZc3CYrKw5rC0RAA45s/De+Qo7n4057MQ5yJFsBDn4eNymRrtJHtXwpDZYI9TneSSXDYiBZvFdPzf92KUN5VT31Xfv1sheo1cCO3H4GjpRT+Fc9UqNJuN2JmnT6nZnzhsDqakT2FNtRTBwPGDmvZV0hIhBg4pgoU4D5/urmPMoHgyAjCLQL/XfAAa9vTbWSFOFGOzMHNYyvH5ny/Gmpo1aGjMye5f8+Ge0cgFxtdLmCqtffVqYmbMwBQTE6BQ6szNmUtlayWH2w+rjqKcNSMDe2Gh9AWLAUWKYCHOobmjm62HmrlKzgIb9vYUSPlfVpsjQK4qSKeqoYOqho6L+vNra9YyLnUcKdEpAU6mgCMdsqcYZ/ovgruqCs/BQ/12VohTzc8x/h4yS4TBcfl8urZtw9fSojqKEAEhRbAQ57Cmoh6/Lq0Qx1WshOThkDJcdZKA6O3zvphZIhq7GtnZsLN/rhJ3NiMXGougdDRc8B/tPUvomH95gEOpkZeQx+C4wVIE94ibPx/8ftrXrVcdRYiAkCJYiHP4ZHcdKbE2JuQkqo6iXncHVK0dEK0QvXKTYxiR7rio1ePWHV6Hjj4w+oF75V8N6D2LoVyY9tWrsY0Yji0nO/C5FJmXM4/NxzbT5e1SHUW5qHHjMCclSUuEGDCkCBaiDz6/zuq99cwflYbJ1H+newqYqjXgcxsLKwwgVxaks7GqkXb3hc0Ju6ZmDWnRaRQmFwbtnYiOAAAgAElEQVQpmQKZE4xFUC5wqjRfewedJVv6/awQp5qbMxe3z82mo5tUR1FOM5txzJtLx9q16D6f6jhCXDIpgoXow7ZDzbR0emRqtF57V4LNAUMuU50koK4YlY7Hp7Ou4vxbADx+DxuObGBuztx+PR/uaUwm4wK5fZ8Yi6Kcp44N68HjGXBF8NSMqURboqUloodj/nx8LS10le5QHUWISyZFsBB9+Hh3HWaTxtyRaaqjqKfrxqwBwy4Hi011moCampdEXJSFT3bXnvef2Va7jXZPO/OyB1ArRK+RC43FUA4Vn/cfaV+1GlNcHDGTJgUxWOjZzDZmZc1idY0sFAEQO2cOmM20r1qlOooQl0yKYCHOQtd1/ll2lBlDk0mI7seLIARKzWZoOwyjrlGdJOCsZhNXjErng89rz3v1uA8OfoDdbGfmoJlBTqfA8CvAEgWfv31em/u7u2n/+GMc8+ahWQfe78oVg6+gtrOWHQ1y9tMcH0/MtGm0rXxfDgpEvydFsBBnseVgMwcbO7lx0sC5yOeSbP8zWKKhcLHqJEFx46RsWjo95zVLRLevmxVVK7hy8JXEWmNDkC7E7HFQcC2U/Q287nNu3r5qFb7WVhKuLwpBuND70uAvEWWOYvm+5aqjhIWExYvxHDxE1zZZUlr0b1IEC3EWb249TLTVzKJxWaqjqOdxwa6/GwVwVLzqNEExd2QqqQ47f99ac85tV9espq27jeuHXx+CZIpM+Aq4Ws5rzuDWd5ZjTksldvbsEAQLPYfNwZWDr2TFgRV0+7pVx1EubuFCtKgoWt95R3UUIS6JFMFCnIHL4+O9HUf48thMHHaL6jjq7V0BrlaYcIfqJEFjMZu4YeIgPt1TR1NH34XO8n3LSYtOY2bWAGyF6DXscmOWiNLX+tzM29RE++rVJFy3GM0ycH9XioYX4ex2sqp6leooypkdscQtWEDbihX43ef+pECIcCVFsBBn8FF5LU6Xl5sn56iOEh5KX4e4LKMwGsBunpKDx6fzbumRs27T2NXIusPruG7YdZhN5hCmCzGzBcbfZlwM2cfCGW3/+Cd4vSTccEMIw4XezKyZpEWn8W7lu6qjhIWEG67H39ZG+6efqo4ixEWTIliIM3hzSw2Z8VHMGj4AlsK9VO11xsIJ42+DgVz0AYVZ8RRmxfNmHy0RK6pW4NW9LB4+MHujTzLhTvB7jd7gs2h9+23shYVEjcoPYbDQM5vMXDfsOtYdXkdjV6PqOMrFzpyJJSOD1rfO7+JJIcKRFMFCnKLO6WJNRQM3Ts7GLAtkGAWQ7jMKoghw8+RsdtS0UlHrPOP3l1cupzC5kJFJI0OcTIGMMZA5/qwtEe6KCly7dpF4wwDujT7B4uGL8epeVlStUB1FOc1sJqFoMe3r1uFtuPAltoUIB1IEC3GK5duP4PPr3DxZZoUAoPTPkDUR0gfQqmh9uH6icfDz5tbDp32vormC8qZyioYPzFkQzmjCnXB0O9SVn/at1nfeAbOZ+GuvVRAs9EYmjaQwuZDllTJLBEDC9deDz0fre++pjiLERZEiWIhTvLn1MBNyEhiRHqc6inrHdsKxMpj4FdVJQiYtzs78/DTe3nYYn//keVDfrXwXi2Zh0dBFitIpMO5W0MynnQ3WfT5al7+LY+5cLKmpisKF3vUjrqe8qZy9zXtVR1HOPmIEUWPH0vqOHBSI/kmKYCFO8PmRNsqPtnGTXBBn2PE6mCww9mbVSULqpsnZHGtz8VnlF72fPr+P9/a/x5zsOaRER1CvuCPNWEZ5xxvg9x1/uKO4GG9dHQkR0grRa9HQRVg0C+9VytlPMM4Gu8vLce3ZozqKEBdMimAhTvD3rTVYzRqLJwxSHUU9n9cofEYuhNjIOdMH8KXCDOKiLCddIFd8tJj6rnqKRkRQK0SvCXeC8yjsX3X8oda338EUH4/jiivU5VIgOSqZOTlzeG//e3j9XtVxlIu/7lqwWGh9W+YMFv2PFMFC9PD5dd7efoQrRqWTHGtTHUe9/augvXZAzw18NlFWM9eNH8T7O4/R7jYKneWVy4m3xTM/Z77idArkfxmiEoyp8gBfewfODz8kftEiTHa74nChVzS8iPquejYe3ag6inKWpCQc8+fT+t676F45KBD9ixTBQvTYcqSLhnY3N0+RVgjAuCAuOgnyF6pOosQtU7Lp8vhYUXaUTl8nnxz6hEVDF2EzR+ABkjXKaIkpfxdcbThXrkR3uSKuFaLX/Jz5xNvieadSzn6CMWewr76Bjg0bVEcR4oJIESxEj48rnSTFWLliVLrqKOq5WmH3P4zCxxJ5Z/oAJg9OIi8lhr9vPUxxUzEunysy5gY+mwl3grcLypfT+s472IYMIXriRNWplLCZbSwauohPDn1Cp69TdRzlHPPnY05IkJYI0e9IESwE0Nrl4bNDnRRNGITNIr8W7HobvC6YEDmzQpxK0zRumpzDZ/sb+ah2FXnxeYxPHa86ljo50yB5ON2rX6Fz0yYSbrgeTYvcebSLhhfh9rkpbipWHUU5k81G/LXX4vz4Y3zOM8+vLUQ4knd7IYB/lh3F49elFaJX6euQmg/Zk1UnUerGSdlo1ib2de6maHhRRBd9aBpMvJO29bsASCiKwAsETzAudRx58XmsblitOkpYSLjxBnS3m7b331cdRYjzJkWwEMAbJdXkJlgZl52gOop6jZVwaINxQVwkF31AbnIMeXmfg65xzdDIWBCiL/qYW2ipiiEmPwNrdmQvJqNpGkXDiyh3lnOw7aDqOMpFjR2LbdgwWt/8u+ooQpy3Potgt9vNkiVLKCoq4uGHH0bX9TNu5/F4ePDBB4MSUIhg27i/kW2HWrh2VHxkn+nrtf5/wWyHiXerTqJcp6eTDvtqvO0FlB6QfcO5eQ+edgtJg6rB3a46jnI3jrwRq2bl5Z0vq46inKZpJN1xB13bt9O5ebPqOEKclz6L4OXLl5ORkcHy5ctpa2tj/fr1p23jcrm46aabzvg9IfqDZz7dR6rDxpdHygpxtFTD9tdgyj0Ql6E6jXJv7HmDLp+ThK4F/OaTfWc9ERAJdF2nYemz2HKziEtvgC1S+KVGp3Jl2pW8U/kOxzqOqY6jXOKtt2BOSaHh2WWqowhxXvosgouLi7nssssAmDlzJhs3nj4nYlRUFO+++y6ZmZnBSShEEG071MzaigYemDsMu1wQBxt+bXyd/W21OcKAy+vi97t+z6ysWdxVOIHyo218srtOdSxl2j9dhXvPHlK/9W9oIy6HDb8BT5fqWMpdn3U96MjZYMAUHU3K179Gx/r1dO3YoTqOEOdk6eubLS0txMUZZ8ccDgdVVVUBGdTtdlNeXh6Q57pQLpdL2dgi/Pz842PE2U1MTXLhcnVH9L5h6WpgeMnvac1bxLGj7XA0cn8WACuOraDR1cjChIUMtljIdFh48h9lZOmNkdc2o+vw9NOQkcGR4cNpabyNIfv/D8dWPEXzyFtVp1PKoTuYlzqPv+75K5dHXU6iLVF1JLUmTwaHgwNPPQWPPaY6jXJSc4S3PovgxMREnD3TnTidTpKSkgIyqN1up7CwMCDPdaHKy8uVjS3Cy87DrWyq2c9DC/KZMn6k7BsrfwC6l6TrfkRS8jDVaZTq9nXzz53/ZHL6ZG6ecTPl5eV8e0Ehj71VRqM1jbkj01RHDKn2deup3rePzB//iKSxY0EfA5V/JHPf62Re832wROACIj3Ky8t5aM5DrHp7FRs8G3howkOqIylXf+/Xafj1bxgKREXyaypSc4SLsx2I9Pn576xZs473+hYXFzNjxozAJxNCkd+t2kec3cJXZ+epjqJeRyOUvATjboUIL4DBWCK5trOWJeOXHH/s5inZZMZH8ZtP9ilMpkbDs0uxZGaScMMNxgOaBvO+B22HofQ1teHCwOD4wSwauoi/7PkLza5m1XGUS777bkwOBw3LnlMdRYg+9VkEFxUVUVtby+LFi0lISCA3N5cnnngiVNmECJqKWicrdh7jntl5JERbVcdRr/h3Rn/nnO+qTqKc1+/lhbIXGJsyllmDZh1/3G4xs2T+MDZVNbFxf6PChKHVuXkzXSVbSLnvPky2E874Dr8KBk2CdU+Dz6suYJh4YNwDdHm7+FP5n1RHUc4cH0/SXXfhXLkSd2Wl6jhCnFWfRbDNZmPZsmW8++67PPXUU+Tm5vLII4+ccdsPP/wwKAGFCIbfraokymLm3jlDVUdRr6sFNj0Ho4sgvUB1GuVWVK3gcPthvjH+G6f1/t4xbTCpDhvPfBo5Z4Mblj6LOSWFxFtvOfkbmgbzHobmA7DzTSXZwsnwxOEsGLKAP5f/mbbuNtVxlEu+56toUVE0Pidng0X4ksvhRcQ52NjBO9sPc/fMwSTHRm4v43Gbngd3G8z9nuokyvn8Pp4ve578pHzm584/7fvRNjP3zx3G2ooGtle3KEgYWl2lpXRs2EDKvV/HFBV1+gb5iyB9DKz9Bfj9oQ8YZh4Y9wDtnnZe3/266ijKWZKTSbr9dlrf+wfdhw6pjiPEGUkRLCLO0lWVWMwmHpgrva+426H4t5D/ZcgarzqNch8d+oiq1ioeGP8AJu3ML493zxxCQrSVZyKgN7jh2WWYExJIvP2OM29gMsG8h6BhL5QvD224MFSYUsi8nHn88fM/0unpVB1HueSvfx3NbKbx+RdURxHijKQIFhHlcEsXb26t4Y5puaTHn+HMVqQpeQm6muUsMMZiEM/teI68+DwWDF5w1u0cdgv3XjaUj8pr+fzIwP3Y21VeTvunn5J0z1cxO2LPvuHoGyBlBKz5hTGVWoR7YNwDtLhb+Ovev6qOopw1I53EW26m5e238Rw9qjqOEKeRIlhElOdWV6LrsGT+cNVR1PN0GQseDLsccqepTqPc6prV7G3eywPjH8BsMve57ddm5+GwW/jtqoF7Nrhh2XOYHA6S7z7H8tkmM8x9CGrLYO/K0IQLYxPTJzIjawa/3/V7XF6X6jjKpdx3H+g6jS++pDqKEKeRIlhEjH117by2uZqbJ+eQnRitOo56G56Bjjrj4qYI5/F5+NXWX5HtyGbR0EXn3D4hxspXZw3hn2VH2Xpo4E2J1bltG86VK0m66y7M8fHn/gPjboXEIfDxj8DbHfyAYW7J+CU0dDXwh11/UB1FOWt2NgnXF9Hyxhu49wdmwS0hAkWKYBERfH6d//hbKdFWMw8tzFcdR73az2H1EzDmJsibozqNcst2LGNfyz4em/EYVtP5TZn3zcuHMyghmof/WorL4wtywtDxu1wcfewHWLIySXnggfP7Q2YrLHoC6j43LpKLcNMyp7EwbyHP7niWiuYK1XGUS/u3f0OLjuboD36A7hs4vyui/5MiWESEl9dXsfVQCz8sGk16XIT3Avu88PY3ISoBrnlKdRrlyhvLeaHsBRYPW8y8nHnn/efioqz8/KZxVNZ38L8fDZxCp+GZZ+iuqiLrJz/puxf4VKMWwfjbYe0v4eiO4AXsJx6b8Rhx1jgeX/84Xn9kz6NsTU8n87FH6dq2jaY//lF1HCGOkyJYDHhVDR08tXIPVxWkc8PEbNVx1Nvwazi63SiAY1NVp1HK4/Pw+PrHSYpK4pHpZ54DvS/z8tO4fWouz62ppHQATJnWtWMHjS+9TOKtt+C47LILf4Iv/w9EJ8Pb/wd8nsAH7EeSo5J5bMZj7GrcJW0RQHxREY7586n/31/RffCg6jhCAFIEiwHO39MGYbeY+NlN405b/CDi1O2GVT+HwiIYc6PqNMq9UPYCe5r38PjMx0mwJ1zUc/zgukIy4qP43l9LcXv770e9/u5ujjz2GJb0dNL/4z8u7klikuG6/2dcJLf26cAG7IcW5i3kS4O/xG+3/5b9LftVx1FK0zQyf/wjNKuVIz/4AbrMKy3CgBTBYkD7w2cH2HygmcevG01GpE+J5vPCO/8HbA649pfGil8RbE/THp7b8RzXDL2GKwdfedHPEx9l5Wc3jaOirp3ffNx/Z4to+O3v6N5XSdZPfow5Lu7in6jwOhh7C6x5Co7tDFzAfkjTNH4w8wfEWGN4fP3j+Pz99yApEKwZGWR8//t0lWyh+dU/q44jhBTBYuA62NjBk+/v4fJRadwyJUd1HPWKfwuHtxhtEI501WmU8viNNoh4ezyPTn/0kp/vilHp3DIlh6WrKymraQ1AwtDq2rmLxhdeIOGmm3DMnXvpT7joSYhONA66IrwtIjU6lUenP8qOhh388XPph0246UZi586l7umn6a6uVh1HRDgpgsWA5PfrPPLmDiwmjZ9LGwTU74VPfgqjroWxN6tOo9zLO1+mvKmcx2c+TmJUYkCe8/FrR5MSa+Phv5XS7e0/H/Xq3d0cffRRLCkpZHz/wvuizyg2xfi04WgprP9VYJ6zH7tm6DVcnns5z2x/hqrWyJ4mTNM0sn78IzSTiaM/+E9pixBKSREsBqRXNx6keH8T/3ldIVkJET4nsN8H7/wrWKPhuqcjvg2iormCpaVL+XLel/nSkC8F7HkTYqz87MZx7D7m5JlP+09bRMOzz+KuqCDzxz86vzmBz9fo642+89VPQF154J63H9I0jf+a+V/YzDb+a/1/SVtEVhbp33+Ezk2baPnLX1THERFMimAx4Hx+pI2fr9jN3JGp3DY1V3Uc9VY/CTWbjHlc4zJVp1GqvbudR9c+SrwtnkdnXHobxKm+NDqDGydl87tP91G8vzHgzx9oHZs20fDc8yRcX0Tc5ZcHfoBrfgH2OPj7A+AauEtMn4+0mDS+P/37bK/fzrIdy1THUS7xlluInT2buqd+gWv3btVxRISSIlgMKPvr2/nqSxtJiLby5C3jpQ1i4zJY/T8w4U5jDtcI5vK6+NYn36KypZL/vuy/SY5KDso4/3fxaIakxHD/H0rYURO+06Z1lZVR8+A3sQ0ZQsZjjwVnkNhUuHGZcSb4tTuNpboj2OJhi1k8bDFLS5fy5/LIvjBM0zSyfvZTTPHxHLrvftxVkd0mItSQIlgMGIdburj7hY3oOvzp/hnSBrH9z7DiP6DgOih6JqLbIDw+D99d9V221m7lZ3N/xtycAFz8dRaJMTZevX8miTFW7nlpExW1zqCNdbHcFRVU3/8A5uRkBr/0IuaEi5se7ryMXGAUwgfXwxv3RPSyypqm8ePLfswVuVfw800/Z3nlctWRlLJmZjL4pZdA1zl07314jhxRHUlEGCmCxYBQ73Rz9wsbcbq9vHLfdIanOVRHUuvz5UYf8LDL4ZaXwGxRnUgZn9/Ho+seZe3htTw+63EWDV0U9DEzE6J49f4ZWM0m7nphI4caO4M+5vnqrq7m0L33odlsDH75JawZGcEfdNwtxvzBFSvhrSVGn3qEspgsPDX/KWZkzeDx9Y/z8cGPVUdSyj5sKINffAF/ezuHvn4v3oYG1ZFEBJEiWPR7rZ0e/uXFjRxrdfH7r09jzKAgntXqD/Z9DH+7F7Knwh1/BotddSJldF3nJ8U/YeWBlTw05SFuzb81ZGMPSYnlT/fPoNvn564XiznW6grZ2Gfjqa3l0Ne+ju7xMPilF7HlhrBnfurXYcGPYdff4b1/B10P3dhhxm628+srfs241HE8vOZhNhzZoDqSUlGFheQuW4anro5D992Pr7X/TTMo+icpgkW/1uH28rXfb2J/fQfPf3UqU4YEp8+z3zhUDK/fBWkFcNdfwRarOpEyuq7zi5Jf8GbFm3xj/Df42tivhTxDfkYcf/j6dJo7PNz94kaaOtS1Anibmjh07334WlrIff557CNHhj7EZf8Gc78HW1+BD/4zogvhGGsMv73qtwxLGMa/f/rvbKvbpjqSUjGTJ5HzzG/o3r+f6iUP4u/oUB1JRAApgkW/1dXt4xt/LGFHTSu/+cok5oxMVR1JrSPb4NXbICEb/uUtY7GCCKXrOktLl/LK56/wlYKv8K2J31KWZUJuIi/cM5Xqpk7ueWkTrZ2hXzzC19pK9f0P4KmpIffZpUSPGxvyDMdd+Z8wfQl89owxfVoEF8IJ9gSeXfAsGTEZ/OtH/8quxl2qIynluOwyBj39S7rKyqj+1rfwd0X2hZQi+KQIFv3SriOtXPebtWyobOSpW8azcEwET/2l67DpeXhxIUTFw1ffAUea6lTKtLha+M6q77C0dClFw4t4ZPojymcJmTkshWfvnsLuY21c8+u1bD7QFLKxO7dsYf+NN+KqqCDn178iZtq0kI19RpoGX/4fmPAVWPVzeONfoDN0P49wkxqdynMLnsNhc3DPint4fffr6BF8YBC/YAFZP/1vOos3UnXLrTJ9mggqKYJFv+L367y4roobf7sBp8vLH++dwU2TI3hJ5I4GeO0O+Of3YOg8eOATSIjcn8emo5u4+d2bWV2zmu9N/R4/uewnmLTweJm7oiCdvz44G4tZ4/Zln/H/PtyL1xe81bJ0r5f6X/+Gg//yVTSLlbxX/4Rj/vygjXdBTCa4/rdw9X/Dnvdh6WVQtVZ1KmWyHFn8+do/MzVzKj/d+FO+/cm3aXY1q46lTOINNxgXy7W1ceDW22h65ZWIPjAQwRMe7w5CnId6p5uv/34zP3nvc+blp/H+v8+L7BaIyk+N4qHyE/jyE0YPsCNddSolPH4Pv976a+7/4H5iLDG8es2r3DPmnrApgHtNzE3kH9+eyw2TsvnVxxXc/lwx1U2Bnzmiu+YwB//lqzT87nckFBUx9O9/J3r8+ICPc0lMJpj9/8H9H4EtBv6wGD7+MfhC3y4SDlKjU/ndVb/jkWmPsP7Iem5efjOfHflMdSxlYmfPZug7bxM7Zw61P/s51UuWyMwRIuDC6x1CiLNYtaeORb9aQ/H+Rn5y/Rie/+oUkmNtqmOp4e2GDx6HP94AUQnG2d+ZD0bsPMDVbdXcs+Ieni97nhtH3shfrvsLo1NGq451Vg67hadvm8iv7pjI3mNOrvnVWpaXBm5+1NZ//IOqG27AXVHBoF/8gkH/83PMjjC+QHLQRPjGaph0N6z9Jbz0ZWiKzIUTTJqJu0ffzWvXvkacLY4lHy7h6S1P44nQAwNLcjI5v/stGY//J53FG9l/w420r43cTwxE4Jl/+MMf/jDUgzY0NJCWpqZnUeXY4sKV1bTyo3d38dTKveQmxfDKfTO4sjAjKD2eYb9v+Dyw4w1jCdo9/4Sp98Jtf4zY9oe6zjqWlS7jvzb8F23dbfxszs+4f9z9WM3WgI8VjH2jIDOexRMGsfFAEy+vP8C2Qy0MSogiOzH6gvdvXdfpKinh6A9/RNPzLxA1ejSDX3qR2KlTA5o5aCw2KLjGmNVk+6uw6QVwtUD6aLCH95zfwdg3UqNTuWHEDbS6W3m1/FU+OPgBsdZYhiUOw6yZAzpWuNM0jejx43FcdRUda9bS9Ps/4K7YhzU3F2t6+H/yFfbvKxHibP8Omq6g0aa8vJzCwsJQD6t8bHF+dF1nQ2UjS1dVsm5fA3FRFr42O49/vWIEUdbgvQGE7b7R3WFMKbXhGWirgfQxcNXjMCr4iz6Eo4NtB3l558ssr1yOT/excMhCvjPlO2Q5soI2ZjD3Da/Pzwvrqnh+zX4aO7qZMiSJb84fzpUF6ZhMfRfDut9P+6pVND73PF3bt2NOTibl3q+T/LWvoVn66QIpLdXw0f+FXW+ByWIs+X3Zv0HKcNXJzijYrxurqlfx622/pqK5gqzYLO4Zcw83jriRGGtM0MYMV36Xi4Zly2j+45/wt7cTO3s2KQ/cT8zMmcovfj2bsH1fiTBn+3eQIliEDZ9f54Ndx1i6upIdNa2kxdm5f85QvjJjMHFRgT+7d6qw2zc6m2DTc7BxGXQ1weDZMOc7xjK0YfqCH0y7GnfxUtlLfHjwQ6wmKzeOvJF7xtxDblzwF3wIxb7R1e3jr1uqWbZ6P4dbusjPcPDg/OEsnjAIq/nkzjXd46H1H/+g8YUX6N5XiTU7m+T77iXxppswRUUFNWfINO03Dvy2/Ql83TD6epjz7zBokupkJwnFvqHrOmsPr+XFshfZWreVRHsiXyn8CneOupPEqMibCtHndNLyl7/Q+Ic/4KtvIGrsWFIeeIC4L12FZg6vM+Vh974SoaQIDoOxxelauzysrajnk911rNpTT1NHN3kpMSyZP5wbJ2UH9czvqcJi32jYZywtu/d9OPgZ+D2Qv8h48x88U222EPP4PWyr3cbqmtWsqVnDgbYDOKwO7ii4g7sK7yI1OnQXRYZy3/D4/Pxjx1GWrqpkT62T+CgL8/LTuDrbzpTa3VC8nva16/A7ndjz80l54AHiF325/575PRdnLWxcCptfBHcbpIyE/IXGbfAsCEL7y4UI9evGtrptvFT2EqtqVmExWZiSMYV52fOYlzOPvIS8kOUIB363m9a336HxxRfxHDqEOTkZx9y5OC6fT+ycOZjj4lRHDI/3FSFFcDiMLcDl8bGvrp3PKhv5eHctJQea8fp1EmOszM9PY9HYTBaMzsR8jo+Bg0HJvtHRAEe3G0sd710JTZXG42kFxpv8+DsgI3wv8gokn99HtbOa0vpS1tSsYcORDbR72rGarEzLnMb8nPksHr6YOFvo39hU7Bu+jg4++3gzVR+sImH7JoY3HMCEjjMmHueE6aReu4gRixcQa1dbBIaMqxVK/wJ7V8CBdcbZYXs8DL/S+F3JnQFJeWAK7ZlAVe8pFc0VvLv/XdZUr6Gy1XjdGBI/hLnZc5mTPYfClEKSoyJjBU3d58P50cc4P/yQjrVrjWWXLRZiJk/GcfnlxMyYjn3ECEz20C8hLzVHeLioItjtdvPtb3+bo0ePMmrUKJ588snT+m7OZ5vzDRMKskOGRle3j2NtLirr2tl9rI3yY052H22jqqEDf88eV5AZxxUF6VxVkM7E3EQsZrWTlQRt39B1o7WhtRrqyqF2J9TugrrPob3W2MZsN+b5zV8II6+GpCGBzxEmPH4PDZ0NVDur2du8l4qWCvY27WVfyz5cPs4Ly6AAAAvQSURBVBcAadFpzMuZx9ycuczKmqW8/zGYrxu+9g68dbW49+3DvbcC9549uPbuwXOo+vhqalFjx9IxaQYbMwtZ3hlH6WEnYHTFDEmOoSAznoKsOAoy4xmR7iAzIQqHfYCeGQZwt8P+VT2fmnwA7ceMxy3RkF4IGWO+uCUNhbjMoJ0xDof3lBpnDWsPr2V1zWo2H91Mt99Ynjs1OpX8pHxGJo4kPzmfEYkjyIrNItGeGLY9tJdK93rpKi2lfdVq2levxr13r/ENsxnbkCHYR+UTNWoU9vxR2IcNxZKRgSk6Omh5wmH/EGf/d+jzVXL58uVkZGSwbNkylixZwvr165kzZ84FbyP6H6/Pj9vbe/Ph9vhxeX20u7y0uTw4XV7aXF6cPffrnW5q21wca3VxrM2F0+U96fkGJ8cwKjOOa8dlUZAVz4TcRLITg/fCE1C6bszM4O0Crxu8LuNrd4fx8ayr7eSvXc3gPAptR8F5BJzHjLNWvcx2SC+AEV8y3qTTR0PudLCF8TRWJ/Drfrp93bh97uO3bl83HZ4O2rvbcXqctHe30+5pp627jWZXM3WddcdvTa4mdL449k6OSmZk0khuHXUrIxNHMjplNPlJ+f3mTVr3etHdbvxuN3rPze9243c68Tmd+Nvbja/OdvzONrz19Xhq6/DW1eGtrcXf0fHFk2katiFDiCooJKGoiKhRo4ieMAFLz1XNY4H7gDqni22HWth91MnuY23sOeZk5efHTlqB2GG3kBFvJzMhioy4KNLi7cRHWYmPshAXZSXuhK9RVjN2iwm7xXT8vuqD0j7ZHVB4nXHTdePA8mgp1H5u3N/zT9j2xxP+gAaxaRCfBXE9t9hUY4pBe7yx0qI93vh/mwMsdrBEGV+t0cbvrCl8fx45cTncWXAndxbcSaenk9L6UiqaK9jbvJe9zXt5bfdrxwtjAJvJRlpMGukx6aTHpJMWnUaCPYE4WxwOqwOHzUGcNQ6HzUG0JRq72Y7NbMNuthNljsJisoTt76dmsRAzZQoxU6aQ/tB38Rw5QteOMtx79+DasxfXzl04V7x/0p8xxcdjSU/Dmp6BJSMDS2oqprg4zHEOTI44THEOzPHxmGIdmKLsaHbjZrLZjPsDtRUpAvR5Jvihhx7i6quvZuHChbz88ss0NTXx0EMPXfA2p1JxZOTpdvPezdOIafeee2NxSU58aQzT10mDfvw/fW1wETTjpvXcjt839dx6Hrto58512hYn/Jqfek8/4fu9/+19TO+5b7xM9Pz/hXRQaRoWzYzVZMVismI1W7GajJvNbCXKHI3VdH5vIPrZ/t5ni9Ob8wxfdXR6/mIn3XTdD34d/P6T7nvcbiyahu7zoXu94PV+cd/nO/8fh9WKJS0NS3q68WabkY61574tbyj2EcMv+qxUV7ePvbVOqho6ONbmorbndqzVRW2bm3qnm+4LWKHObNKw9NzMJg2r2XT8MZNJw6RpmDQwaRpaz9fe+2BMbaXxxa+B1rPPH//+8R/KmX8XzvYbcl6vKbpOor+ZIb4DpPvqSPI3kuJrJNlv3FJ8jTh0J2bO/+fhwYIfM17NjB8zPsz4NDNeXUPXLOiaho6GHxM6GjomYxdDO/49vee1oXeX1Y//LbWe/+/jr3T2n8g5s/vQOWbRqbb6aTTrNPXcmk+477qAGl/TjTNoZt1YbMCsgxkw6xqmnkSmnu9pJ970L+7DifdP/K+xXZ/jX+Djp7J162Q06KS06MS3Q1y7Tny7cT++XSe2EywXsJijTwO/ybj5TF/c95vA3/OX9GugH7/17AMaZ/0Kp+8P+gW+bVzo9sHW6bBw3ZubsdrCpy2lz3eflpYW4noayx0OB1VVp09gfj7bnMrtdlNeXn5ewQPF63HjtZvwuMNsr+iLwqhnHFo78+OBj3lpz3jeJZrW11gnPK5ppzym9by49L6bm3rGPaHoDbKLHUE7nvmLZ9FOePyk+5rWc7+3mDH+X0ND0zRMmDBppp7/N2HGhEkzY9bMmDUTZs18/Ptn4+25nW/6sz/VWb5xesX1xeO9P4MT75s044yfZjLu9xy0eHU/2OxgNoHZ/MXNZAa7DaxWsNmMW+/9mJieWyzEGvd1mw0PcNalDw4cOO+fxpnYgFFRxo10ExDTczN0+/x0dPfcPH46e752+3Q8Ph23V//ivk/Hp+v4/eD16/h0YwYXn1/Hj3Hc4NeNAyQd8PUcRBw/1ujZ5sTfR73ngZN+R8/wC3u246wLOSxtJZYdpjFgGnOWJ9OJwkWs3kGMv9P4qncSpbuw0Y1V92DDjU33YMWDVe82Sl/dKIFN+LDgw+T3GrsK/uOlr4Yfk+4/XvZyvATWj5fAX+yxvf9/9r/dWXf7CzggTfIat7PxotNl0unSdDpNxv1OTafbpOPRwINOt2bc79Z0o/ADfJqOD6PI82E8rvPFza/1Hj6f8PgJRd5phd45/h5nPea9kL3DArWZxu3MT6Zj8YHdDVFuHXv3F18tXrD4dOOrFyw+sHh1zH4w+Y0C3tRz3+wHTdeN4v+Em6nnL65xwldOLv5PPRA414HBqS50+1Dw2k3s3l2OxRr6Ivhs+iyCExMTcTqN3jOn00lSUtJFbXMqu92upEdm3N+2S3+OOCvZN8TZyL4hzkb2DdEX2T/Cw9lOvPb5AcisWbNYv349AMXFxcyYMeOithFCCCGEECKc9FkEFxUVUVtby+LFi0lISCA3N5cnnniiz21mzZoV1MBCCCGEEEJcqj7bIWw2G8uWLTvpsUceeeSc2wghhBBCCBHOwnfOFyGEEEIIIYJEimAhhBBCCBFxpAgWQgghhBARR4pgIYQQQggRcaQIFkIIIYQQEUeKYCGEEEIIEXGkCBZCCCGEEBFHimAhhBBCCBFxpAgWQgghhBARR4pgIYQQQggRcTRd1/VQD7p9+3bsdnuohxVCCCGEEBHG7XYzceLE0x5XUgQLIYQQQgihkrRDCCGEEEKIiCNFsBBCCCGEiDhSBAshhBBCiIgjRbAQQgghhIg4UgQLIYQQQoiIExFFsNvtZsmSJRQVFfHwww8jE2KIUz3yyCPcdtttPPjgg3i9XtVxRJh5+eWX+drXvqY6hghDzz//PLfddhv3338/3d3dquOIMNHZ2ck3v/lN7rjjDp588knVccRZREQRvHz5cjIyMli+fDltbW2sX79edSQRRkpKSvB6vbzxxht0dHTI/iFOcvjwYd566y3VMUQYqq6uZt++fbzxxhvMmzeP2tpa1ZFEmHj33XeZOHEir7/+Ovv27aOyslJ1JHEGEVEEFxcXc9lllwEwc+ZMNm7cqDiRCCepqancc889APj9fsVpRLj56U9/ykMPPaQ6hghDn332Ga2trdx1112UlJSQk5OjOpIIE3FxcXR2duLz+XC5XFitVtWRxBlERBHc0tJCXFwcAA6Hg9bWVsWJRDjJy8tj/PjxfPjhh5hMpuMHTEK8++67FBQUMHz4cNVRRBhqamoiOTmZV199ldraWrZs2aI6kggTCxYsYO3atSxYsIDhw4czePBg1ZHEGUREEZyYmIjT6QTA6XSSlJSkOJEINx9//DGvvPIKS5cuxWKxqI4jwsSqVav47LPP+O53v8uuXbv405/+pDqSCCMOh4OhQ4cCkJOTI+0Q4rhly5Zx55138sknn9Da2srWrVtVRxJnEBFF8KxZs473eRYXFzNjxgzFiUQ4qa+v58UXX2TZsmU4HA7VcUQY+eUvf8lrr73G008/zZgxY7j77rtVRxJhZMyYMezcuROAQ4cOkZubqziRCBcdHR3YbDYAbDYbnZ2dihOJM4mIIrioqIja2loWL15MQkICs2bNUh1JhJG33nqL+vp67rvvPu68807+9re/qY4khOgHJk2aRGJiIjfffDNDhw5l/PjxqiOJMHHXXXfx2muvcfvtt+NyuaTuCFOaLvOFCSGEEEKICBMRZ4KFEEIIIYQ4kRTBQgghhBAi4kgRLIQQQgghIo4UwUIIIYQQIuJIESyEEEIIISKOFMFCCCGEECLiSBEshBBCCCEizv8PS3HGMmudXVgAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="高斯混合模型">高斯混合模型<a class="anchor-link" href="#高斯混合模型"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gmm</span> <span class="o">=</span> <span class="n">tfd</span><span class="o">.</span><span class="n">MixtureSameFamily</span><span class="p">(</span>
<span class="n">mixture_distribution</span><span class="o">=</span><span class="n">tfd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">probs</span><span class="o">=</span><span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]),</span>
<span class="n">components_distribution</span><span class="o">=</span><span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span>
<span class="n">loc</span><span class="o">=</span><span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">],</span> <span class="n">scale</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">gmm</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">xs</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="TFP高斯过程回归">TFP高斯过程回归<a class="anchor-link" href="#TFP高斯过程回归"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xs</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">])</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="o">+</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">xs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">xp</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">9.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">kernel</span> <span class="o">=</span> <span class="n">tfp</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">psd_kernels</span><span class="o">.</span><span class="n">ExponentiatedQuadratic</span><span class="p">(</span><span class="n">length_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<span class="n">reg</span> <span class="o">=</span> <span class="n">tfd</span><span class="o">.</span><span class="n">GaussianProcessRegressionModel</span><span class="p">(</span>
<span class="n">kernel</span><span class="p">,</span> <span class="n">xp</span><span class="p">[:,</span> <span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">xs</span><span class="p">[:,</span> <span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">ys</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ub</span><span class="p">,</span> <span class="n">lb</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">+</span> <span class="p">[</span><span class="mi">2</span> <span class="o">*</span> <span class="n">reg</span><span class="o">.</span><span class="n">stddev</span><span class="p">(),</span> <span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">reg</span><span class="o">.</span><span class="n">stddev</span><span class="p">()]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">xp</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">ub</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">lb</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xp</span><span class="p">,</span> <span class="n">reg</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">c</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xs</span><span class="p">[:],</span> <span class="n">ys</span><span class="p">[:],</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"k"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Edward2"><a href="https://github.com/google/edward2">Edward2</a><a class="anchor-link" href="#Edward2"> </a></h1><p>Edward2是谷歌推出的<em>简单</em>概率编程语言,基于NumPy和TensorFlow生态系统构建,可以将模型编写为概率模块与模型计算结合,从而实现灵活的训练和推理。</p>
<p>Edward2借助GPU提升计算性能,谷歌使用<a href="https://papers.nips.cc/paper/7987-simple-distributed-and-accelerated-probabilistic-programming.pdf">NUTS</a>(No-U-Turn Sampler, HMC变体)采样实现贝叶斯逻辑回归(Bayesian logistic regression)的运行时间对比。</p>
<table>
<thead><tr>
<th style="text-align:center">模块</th>
<th style="text-align:center">运行时间 (ms)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Stan (CPU)</td>
<td style="text-align:center">201.0</td>
</tr>
<tr>
<td style="text-align:center">PyMC3 (CPU)</td>
<td style="text-align:center">74.8</td>
</tr>
<tr>
<td style="text-align:center">Handwritten TF (CPU)</td>
<td style="text-align:center">66.2</td>
</tr>
<tr>
<td style="text-align:center">Edward2 (CPU)</td>
<td style="text-align:center">68.4</td>
</tr>
<tr>
<td style="text-align:center">Handwritten TF (1 GPU)</td>
<td style="text-align:center">9.5</td>
</tr>
<tr>
<td style="text-align:center">Edward2 (1 GPU)</td>
<td style="text-align:center">9.7</td>
</tr>
<tr>
<td style="text-align:center">Edward2 (8 GPU)</td>
<td style="text-align:center">2.3</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="随机变量">随机变量<a class="anchor-link" href="#随机变量"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">normal_rv</span> <span class="o">=</span> <span class="n">ed</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
<span class="n">normal_rv</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><ed.RandomVariable 'Normal' shape=() dtype=float32 numpy=-0.8224109></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">normal_rv</span><span class="o">.</span><span class="n">distribution</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="mf">1.231</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><tf.Tensor: shape=(), dtype=float32, numpy=-1.6766189></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dirichlet_rv</span> <span class="o">=</span> <span class="n">ed</span><span class="o">.</span><span class="n">Dirichlet</span><span class="p">(</span><span class="n">concentration</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">]))</span>
<span class="n">dirichlet_rv</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><ed.RandomVariable 'Dirichlet' shape=(2, 10) dtype=float32 numpy=
array([[0.31023756, 0.07827895, 0.08835499, 0.03265208, 0.04359685,
0.02597943, 0.07192924, 0.15645337, 0.025865 , 0.16665252],
[0.31551656, 0.08522676, 0.1547178 , 0.10970751, 0.06513554,
0.03616604, 0.02903147, 0.1352999 , 0.01480891, 0.05438948]],
dtype=float32)></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="基本操作">基本操作<a class="anchor-link" href="#基本操作"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">ed</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">20</span><span class="p">),</span> <span class="n">scale</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">20</span><span class="p">));</span> <span class="n">x</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><ed.RandomVariable 'Normal' shape=(20,) dtype=float32 numpy=
array([ 0.7987103 , 0.14509809, 0.8323622 , 0.29810244, 0.08135905,
-0.49820969, -1.5680871 , 0.9793448 , 0.8511595 , 1.2923892 ,
-0.16361478, 0.61047137, 0.21112299, 0.20411171, -1.4496089 ,
0.95102966, 0.83335084, 0.7262526 , 0.03912232, -1.5042037 ],
dtype=float32)></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="mi">5</span><span class="p">;</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="n">y</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(<tf.Tensor: shape=(20,), dtype=float32, numpy=
array([5.7987103, 5.145098 , 5.832362 , 5.2981024, 5.081359 , 4.5017905,
3.431913 , 5.979345 , 5.8511596, 6.292389 , 4.8363853, 5.6104712,
5.211123 , 5.2041116, 3.5503912, 5.95103 , 5.8333507, 5.7262526,
5.039122 , 3.4957962], dtype=float32)>,
<tf.Tensor: shape=(20,), dtype=float32, numpy=
array([ 0.15974206, 0.02901962, 0.16647243, 0.05962049, 0.01627181,
-0.09964193, -0.3136174 , 0.19586895, 0.17023191, 0.25847784,
-0.03272296, 0.12209427, 0.0422246 , 0.04082234, -0.2899218 ,
0.19020593, 0.16667017, 0.14525053, 0.00782446, -0.30084074],
dtype=float32)>)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><tf.Tensor: shape=(20,), dtype=float32, numpy=
array([ 0.99932045, 0.6202987 , 0.99951476, 0.90341896, 0.38574815,
-0.9863742 , -0.99999964, 0.9998883 , 0.9995977 , 0.99999523,
-0.6740202 , 0.99554545, 0.7839798 , 0.770094 , -0.99999905,
0.9998518 , 0.99951935, 0.9985982 , 0.19315422, -0.9999994 ],
dtype=float32)></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">base</span><span class="p">[:],</span> <span class="n">x</span><span class="p">[:],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"r"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"x"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">base</span><span class="p">[:],</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)[:],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"g"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"x + y"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">base</span><span class="p">[:],</span> <span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="n">y</span><span class="p">)[:],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"b"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"x / y"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">base</span><span class="p">[:],</span> <span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">)[:],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"tf.tanh(x * y)"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
</div>Python数据科学分享——3.数据可视化(2)2020-05-29T00:00:00-05:002020-05-29T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/05/29/data-viz-2<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-05-29-data-viz-2.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">from</span> <span class="nn">matplotlib.font_manager</span> <span class="kn">import</span> <span class="n">_rebuild</span>
<span class="n">_rebuild</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"whitegrid"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
<span class="kn">import</span> <span class="nn">pandas_alive</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">ssl</span>
<span class="n">ssl</span><span class="o">.</span><span class="n">_create_default_https_context</span> <span class="o">=</span> <span class="n">ssl</span><span class="o">.</span><span class="n">_create_unverified_context</span>
<span class="n">iris</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">"iris"</span><span class="p">)</span>
<span class="n">tips</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">"tips"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_covid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s2">"3.data-viz/timeseries.json"</span><span class="p">)</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">df_covid</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">_</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]))</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"日期"</span>
<span class="n">df_covid</span> <span class="o">=</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">_</span><span class="p">[</span><span class="s2">"confirmed"</span><span class="p">]))</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">top20</span> <span class="o">=</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
<span class="n">df_covid</span> <span class="o">=</span> <span class="n">df_covid</span><span class="p">[</span><span class="n">top20</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="seaborn统计图"><a href="https://seaborn.pydata.org/">seaborn</a>统计图<a class="anchor-link" href="#seaborn统计图"> </a></h1><p>面朝大海,春暖花开——海子(原名查海生,1964-1989,安徽安庆市怀宁县人)</p>
<p>2012年,美国斯坦福大学(Stanford)Michael Waskom(目前就职纽约大学NYU)用高级接口在Matplotlib基础上为数据探索和模型拟合创建各种统计图</p>
<h2 id="频次直方图、KDE图">频次直方图、KDE图<a class="anchor-link" href="#频次直方图、KDE图"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="p">[[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="n">size</span><span class="o">=</span><span class="mi">2000</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="s2">"xy"</span><span class="p">:</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>除了频次直方图,我们还可以用KDE获取变量的平滑分布估计图。Seaborn通过<code>sns.kdeplot</code>实现:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="s2">"xy"</span><span class="p">:</span>
<span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">shade</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>用<code>sns.distplot</code>可以让频次直方图与KDE叠加:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="s2">"xy"</span><span class="p">:</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>如果向<code>kdeplot</code>输入的是二维数据集,那么就可以获得一个二维数据可视化图:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>用<code>sns.jointplot</code>可以同时看到两个变量的联合分布与单变量分布:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">sns</span><span class="o">.</span><span class="n">axes_style</span><span class="p">(</span><span class="s2">"white"</span><span class="p">):</span>
<span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"kde"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>可以向<code>jointplot</code>函数传递一些参数。例如,可以用六边形块代替频次直方图:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">sns</span><span class="o">.</span><span class="n">axes_style</span><span class="p">(</span><span class="s2">"white"</span><span class="p">):</span>
<span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"hex"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="矩阵图(pair-plot)">矩阵图(pair plot)<a class="anchor-link" href="#矩阵图(pair-plot)"> </a></h2><p>用<code>sns.pairplot</code>探索多维数据不同维度间的相关性,例如费舍尔鸢尾花数据集记录了3种鸢尾花的花瓣与花萼数据:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">pairplot</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">"species"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAysAAALaCAYAAAAx7sxUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydeXxU9bn/P3Nmz2SZTCYQQgABA4GqxQWw0FsI0gKCAvVeLSCL1ba4FMHbctXbi5faciu91yBFG623LBrUWkGsYWm5RH/uCBpB2cKehYRMJpN1JrOc+f1xciaznDPrmS153q+Xr3jOnOU7h+f7PfM95/k8H5nb7XaDIAiCIAiCIAgixWCS3QCCIAiCIAiCIAghaLJCEARBEARBEERKQpMVgiAIgiAIgiBSEpqsEARBEARBEASRktBkhSAIgiAIgiCIlCRtJis1NTVhb3vx4sX4NSTFGCjfNR2/ZyQxGwvpcm3SoZ3UxsTFrRCpfv1TvX1A6rcxHu0LFrOpfj1CQe1PLune/v5C2kxWnE5n2NtardY4tiS1GCjfNR2/ZyQxGwvpcm3SoZ3UxsTFrRCpfv1TvX1A6rcxHu0LFrOpfj1CQe1PLune/v5C2kxWCIIgCIIgCIIYWNBkhSAIgiAIgiCIlESRrBO3tbXhF7/4BVpaWnDdddfh17/+dbKaQhAEQRAEQRBECpK0Nyt79uzBHXfcgV27dqGrqwvHjx9PVlMIgiAIgiAIgkhBkjZZ0ev1qKmpQXt7O65cuYIhQ4YkqykEQRAEQRAEQaQgMrfb7U7Gievr6/Hss89i5MiRaGpqwrp166BUKkW3r66uhlqtDuvYNpsNGo1GqqaGxMW6cfBcB4ZkKXFDgTZh5wUS/12TRaK+57hx4yQ7ViQxGwvpEgPp0M50bKOUMQskLm6FSPXrn+rtA1K/jTabDTfeeKOkxwwWs6l+PUJB7U8ufPulHmeJyEiaZmXLli1Yv349MjMzsXXrVuzatQv33HOP6PZqtTrsYDl58mRCA+vlD85j08cXoJTL8MHaGSjISVzHTPR3TRbp+D0jidlYSJdrk8x2sm4WZpsZdpcdKrkKBo0BjCzwxXI6XMt4tzFRcStEql//VG8fIN7GcPtAItonNcFiNh3+zYKRru3n463L1gWdRpe0eIuVdL3+/Y2kRU57eztOnz4Nl8uFr776CjKZLFlNiQm3241tH1+EQaeCw+XG375qSHaTCILwgnWzqGmtwZLKJZj11iwsqVyCmtYasG422U0jiIRAfYBIJN7xNu+deRRvRMwkbbLys5/9DOvWrcMtt9yCtrY2zJ07N1lNiYkLpi7UtVqx/DsjMCxXi7+faEx2kwiC8MJsM2PVoVVo6OIeJDR0NWDVoVUw28xJbhlBJAbqA0QioXgjpCZpaWA33HADKisrk3V6yfj4XAsAYMzgTIwbko0PzprAsm4wTHq+KSKI/obdZffcNHkauhpgd9mT1CKCSCzUB4hEQvFGSE36JRCmGCevtCNbo4BBp8aofB2sdhfOm7qS3SyCIHpRyVUo1BX6rCvUFUIlVyWpRQSRWKgPEImE4o2QGpqsxMjpxg6Mzs+E2w1ck6cDAJxoaEtyqwiC4DFoDNg8Y7Pn5lmoK8TmGZth0BiS3DKCSAzUB4hEQvFGSE3S0sD6A263G6ebOnBbySC4AQzJ0UIG4ExTZ7KbRhBEL4yMQXFuMSrmViS9EhJBJAPqA0Qi8Y63dK8GRqQGNFmJgcZ2GzpsThTlct4qKgUDY5Ya55ppskIQqQQjY2DUGpPdDIJIGtQHiETCx1vzxWaMGDci2c0h0hyarMTAqcYOAEChvs8Icki2BhdIs0L0E0J5M4Tj3cBvk1GQAZPVlJQnbKniMUEQ8UatUcNkNYUd607WCZPVBIfLAaVciTxNHtrsbdRXiJgId9z3H5tzVDlosbV44tGoNULB0E/VgQ5FQAyc6Z2sDPEygRyco8Gn51uS1SSCkAy+Vj5fgpLPOy7OLQYjY0J+Hs4xUuF7EER/gXWzsGXa8GDlg2HFupN14kzrGaypWuPZvqy0DOXV5aiqq6K+QkRFuGOu/3Yrxq3AnNFzAuJxTO4YmrAMcGj0iYGLLd3Qa5XQqZWedXk6FTpsTnT1OJPYMoKInVC18sOppZ8K9fZToQ0EkQjMNjPWvL8m7Fg3WU2eH4b89muq1mB+8fyw9icIIcIdc/23WzBmgWA8mqymxH4BIuWgyUoMNFisKMjRgGXdnnXGTDUA4EqbNVnNIghJCFUrP5xa+qlQbz8V2kAQiSDSWHe4HILb56hywtqfIIQINw79t5PL5IL7OVhH/BpLpAU0WYmBBosVg7PVcHuty9NxdcTrW2myQqQ3oWrlh1NLPxXq7adCGwgiEUQS66ybhYJRCG7fZm/zWaa+QkSCaBwyKpisJjR0NsBkNUHF+G7ncrsE91MyShADG5qsRInb7UaDxep5k8KT17tc29qdjGYRhGSEqpUfTi19vVqPstIyn23KSsugV+tT5nsQRH/BoDGgbFpZyFjntQI7T+zEs9OfDeife2r2BN2fIIIhNuZ2ODqwpHIJZr01C0sql6DD0eGz3dtn3ha8X1AVO4IUS1HSbnWiy+5CXqbvE6dcnRIy0JsVIv0J5c0QjneDpceC8upyrJ20FjmqHLTZ21BeXY51U9Yl7AZEHhPEQIGRMdB0akLGurdWoM3ehhdmvgC5TA61XA2j1oh1U9bhcdfj1FeIqBDyWWFkDBa9u8hHj7LyHyvx2rzXfOI1R5WD7XO2w8E6oGSoGhjBQREQJfUWbjKi1/pOVhQMg1ydCvUWWzKaRRCSEsqbIdTndpcdVXVVqKqr8ln/uOtxydoYDuQxQQwUemw9IWPdWyuw+9xu7D63GwBw4K4DnnKxBBEL/j4rDZ0NgnoUm9OGwkzf1K8CXUEim0qkAfS4JEoaeicrhozAXN48nQqNbTRZIQjSixBE6kH9kkg0FHNELNBkJUoaeqt9GTIFJiuZKqoGRhAgvQhBpCLUL4lEQzFHxAKlgUVJfasVSrkMWZrAS5inU+OLyxa43W7IZLIktI4gUgOh3GXKgSeI5EI6LiLRUMwRsUCTlSipt1gxOFsDIHAyos9Qwu5k0dHjRLaGSu4RAxv/3GWCIJIP6biIREMxR0QLTVaipMFixeAsteBn+l4dS3NHD01WiH4N62ZhtpnpSRlBpAhqjRomq4n6JJEw6D5AxBuarERJg8WGCcOFvSL0Wm6C0tzRg9H5mYlsFkEkDN6rgS+ByucgF+cW042KIJIA62Zhy7ThwcoHqU8SCYHuA0QioEiKAoeLRVOHDUadcBWLnN7JSlM7VQQj+i/eXg0AV4Zy1aFVMNvMSW4ZQQxMzDYz1ry/hvokkTDoPkAkApqsREFjmw1uN2AQmazoM3onK1S+mOjHeHs18DR0NcDusiepRQQxsKE+SSQaijkiEdBkJQp4jxW9gMcKAOjUCsgZGZo6ehLZLIJIKGJ18xkZg4bOBpisJrBuNkmtI4iBB/VJIt6wbhYmq8kTTyqG/FOI+EOTlSjgPVbyBDxWAICRyaDXKtFMkxWiHyNUN7+stAwbPt2AWW/NwpLKJahpraEfRwSRIAwaA8qmlVGfJOICr09ZUrnEE08djg7yTyHiDgnso6DBwqV3CbnX8+RolTB10mSF6L/4181nZAw2fLoBVXVVAPpylyvmVlC5SoJIAIyMgaZTQ32SiAtC+pSV/1iJ1+a9Rv4pRFyhyUoU1Fus0GcooVIwYN3C2+gzaLJC9H+86+Y3dDZ4fhTxUO4yQSSWHlsP9UkiLojpU2xOGwozC0X2IojYoclKFNS3WlGQrRGdqABAjlaFSy3diWsUQSQZPl/e+2ZWqCuERqGByWpCRkEGTFZTwFO3WGv0U41/YqAhFPMA9+RbN0SHxq5GMGDAyBiUFpX6TFhIT0CIEWosVclVKC0qxfzi+chR5aDN3oY9NXuSEk807g8saLISBQ0WKwr12qDb6DOUaO22w8W6IWcCXe4Jor/Ba1i86+2Xf78czd3NojX4Y63RTzX+iYGGWMyr5Cqs/MdKz7r1U9dj54mdWDlhJQCgqq6K9ASEKOGMpXq1HisnrMSaqjWebcpKy6BXC3vOJbOtRP+C/lUjxO12o8FihVFEXM+j1yrBugFzF71uJwYG3hqWA3cdQMXcCmQps4LW4I+1Rj/V+CcGGmIxX9dR57PuqY+ewvzi+VhTtQZP3vqkp0/SDzpCiHDGUkuPxTNR4bdZU7UGlh5LyrWV6F/Qm5UIabc60WV3iVYC48nJ6HOxz89SJ6JpBJF0vDUsAJczH6wGf6w1+qnGPzHQEIt5rUIbsC5HlYOGrgawbpY0BURQwhlLU2W8TZV2EImDHq9ESH2vx0qwSmAAoNdynzeTyJ4YwIj5PvA5zqE+j/X4BNHfEIt5q9MasK7N3kb9gQiLcMbSWMdbf4+WaEto07g/8KDJSoSEMoTk4V3sG9usQbcjiP6MXq1HWWmg7wOf4yzk1RJJTn2s+xNEuiEU87/97m9hUBt81q2fuh57avZQfyDCIpyxNJbxVsijJVrPHxr3Bx6UBhYhoQwheXK03GSlqd0W9zYRRKpi6bGgvLocayet9VSPKa8ux7op62DUGgO8WiKt6hLr/gSRbvAxv33OdlzpvAJzjxllR8sAAL+69Ve4JvsaKOVKMGCwbso66g9EWIQzlsYy3orpTKLx/KFxf+BBk5UIqbdYoZTLkKVRwh2kdLFGKYdGyeBqO6WBEQMXu8uOqrqqAK+Hx12Pe/7fX+cSKbHuTxDpBl9Jb9n+ZT7rH/q/h/Dune+iILsgSS0j0plwxtJox1updSY07g8saBoaIQ0WGwqyNQinGLFeqyJjSGJAE1ZuMcsCnU2ApZb7y0aXx0wQKUccY1usbzFuuq0TfqTAGEs6EyIWkjqq7dy5E0uXLsXSpUsxf/58rFu3LpnNCYsGixWDc4IbQvLkaJVo7qTqFMTAJWRuMcsCV08AL88ENl3H/b16giYsRPoT59gW61uONockxyf6CSkyxpLOhIiFpKaBLV68GIsXLwYAPP3001iwYEEymxMW9a1W3DQ8PAMkfYaSNCvEgMY7t7jL1gWdRuebW9zdDLy+CLBc5pYtl7nlBw4CmYOT13CCiJU4x7ZY3v7pptMxH5voR6TIGEs6EyIWUkKz0tTUBJPJhOuvv150m56eHpw8eTKs49lstrC3jQQn60ZTuw1q2HH58qWQ28tdNlxtt+HUqVNwBxO4xEC8vmuqkajvOW7cOMmOFUnMxkK6xIDNZkO3phvNaPasG21UQcXfRHksl2G3duFcbeK/UzpcS/82ShmzQOLiVohUv/6RtC8Zsd2M5rS4hlITLGZT/XqEItb2J3uMDdZ+73tBqsK3X+pxloiMlJisVFRUYNGiRUG3UavVYQdLvAKr1twNNy7gmoI8DB+eF3L7YS31+PhyLa4ZXQyNUi55e4D4fddUIx2/ZyQxGwvpcm34djqdDphsJjhYJ8yMHHk/3Io2jQb2DANU3WYYvqiASqvDuHGjktbGVCbebUxU3AqR6tc/ovZ1NgH64X1PtAFg7FyoVCqMG6IEFCqw2jyY7ZaAJ82sm4XZZg57fdRtTALxmDgEi9lUvx6hiLn9QnGoHw5VRjbGDXMBTjugUAEZ+QAj/VsO7/aHE79O1gmT1QSHywGlXAmj1ggFk7yfqukeP/2FpL9/Y1kWn332GSZPnpzspoQkXI8VHr2Xiz1BEIDT6cCZthos378Ct+++Hcv334eaoSXYbjmJWf/3Eyw59hxqZv47WG3ohwEEkdJk5AM/eo37oQgAY+cC09YCW+cAm64D++5jqGk9E+A74WSdgn4UYuujNdYjBgj+cagfDty7G+hoTKiOJRyfFSfrxJnWM1i+bzl3f9i3HGdaz8DJOuPWLiI9SPpk5ciRI7jhhhsgk4VTXyu58B4ruRFOVq7SZIUgAAAmmwlrqtb41NpfU7UGC8Ys8Cyvem8NzHZLMptJELHDMMCg8Zw2YPXXwO0bgb8s9TzhNt+0BKveWxPgO2GymgT9KMTWm23m5Hw/Ij3wj8MHDgLqLGEdS3f80rLEfFa849dkFb4/mKymuLWLSA+Sngb24YcfYuLEicluRlg0WLhcW4NOGdb2OVpuUtPcQSJ7ggAAB+sUrLUvl8l9lqOtvU8QKQXD9ImYLbU+qTj2DINgX3CwjojWU18hQuIdh0BALHLrLnMpYXEiHJ8Vh0s89omBTdInK4899liymxA29RYrcjOUUMnlcIUhmOffrDS20WSF6KewLPc0LkjeM5+nnFGQAQWjQKGu0OeGVFpUCgWjwNZZW9Fmb8Oemj1Ue5/oX7AsIJMBPz4AdDUDH22Cqtsc0BcKdYVQMkrB9QqZcN9hZAwaOhs8GgCCCMB/nFZqBXUsUMRv3OV9Vvzj2nusV8qVKC0qxfzi+chR5XjuB0omvAfERP8l6Wlg6USDxYqCHA3YMCt75WiUkMlA5YuJ/kkY9fu985TnvTMPO0/sRFlpmafWfmlRKVZOWIn7D9yP+w7ch42HN2LlhJXQq8MrD04QKQ/fT7bOAf48CzjwJDBjHQwXPsHm6WUBvhNGrTHAj2L91PWifWf5vuU+GgC1Rp20r0qkIELjdEcjp1vx1rH86DXuYVOc0Kv1PvFbqCtEWWmZz1ifp8nDygkrsfHwRp/7QZ6GNIwDnaS/WUknGixWFOZoEW4RYoaRIVujJM0K0T8Jo36/f57ytpPbuL+zt8HJOqFgFFixf0VAjnLF3AoYtcaEfyWCkByhfvLOI2Du24fizAJB34ni3GJsn7MdVzqvwNxjxh+++AOOmY7hUsclbJ+zHaybBSNjsHzf8gANwMszXk7ilyVSDrFx+v6D3Fgd52pgPJYeC8qry7F20lrPW5Py6nKsm7LOM9a32dsENSt0PyBoshImbrcbda1WfLsosie++gwlVQMj+idOe8i8Z6E85W0nt2HR+EUYlj0MDZ0NlIdP9G/E+onbDUauEPwRxpcpXrZ/mc/6qroqPO5+HIWZhaJ9h5VRdTDCC7H4c9kB/bCENcPusqOqrgpVdVU+6x93Pe6zDd0PCCEkn6ycPXsWhw4dgt3eF1yPPPKI1KdJOOYuO7rtLuRnRfaKPUerhKmTOhqRHoRTB9+DQhUy71klVwXkIFc3VoMB0NB+GYyIhkUGGWrba1Oizj5BhERMuyWgVUHdEa6MsUzGCZ1F/Fa8c/xvMN6AH1//Y4zIGgEZZKjrqINcJkdpUanPj79CXSEYN2V3E16IjdNKLefBEurNil9si3kDhULoXuCvTxTdhlHBZDX5nBNA+PcqIu2R/BfA6tWr8ZOf/ARDhgyR+tBJpbaVK1uclxmZAC03Q4WTV9rj0SSCkBReX8KnbfE59MW5xcI3Ab5+P59iIJD3rFfmYOWElZ5X+3ye8obPfoequiqUFpWirLTM8zmfh8+nhvHbj8kdQxMWIjXhNQH+/SC/BGg+5bv+zi1AzUHg+rs4DYvlMtiSeaiZ+e+eMsZ8vxutH43NMzbj+S+fx+Lxi7HzxE4sHr8YD//fwz59A+DeuPD7OdocAOnsCR5tHnD3K31ls/XDgcVvAh1XgNcX+8bsoPG+Exa/2BaLVdF7hBd6tV7wXuCtWRHbxuay4f4D93vWlX+/HHaXPfx7FZH2SP6vmpeXh3nz5mHSpEme//oDda3dAIA8XWSTlRytEuYuO1g2XKULQSSHcOrg+yBUv9/vZmcR8VWZXzwfAPcjq7y6HNtnb8WBuw7giclPUJ19Ir0Q0wR0NgpqVTD5p2H5rVh6LCjOLcaTtz6Jpz56CvOL5+Opj54K6BtrJ63Fjtk7sH3OdhTnFqPHRmnHhBfWFuD9jcCsDcCKSu5vt6lvogKI+6z4xbZYrIbj9WPpsQiO7ZYeS8htLrRd8FlX11FHnkMDDMkeVb799tsAgJKSEixbtgxz585FRkYGAGDBggVSnSZp1Jp736zoIksD02co4WTdaLM6kBvhRIcgEklU+cL+9fv9jyniq5KjyvEsV9VV4fGJv0Bh9nDUttdSnX0ivRDVBDiE17POsPxW7C67R7vC9xmh7ZysE8v2L8OBuw7QU2UiEKcdOF3J/cezojI8nxW/2A4Wq6EI5/4ito1WofVZp1VoSdsywJBssuLuLedbUlKCkpISz7p0cKYPh9rWbui1SqiVcrgieEui7zWGvNrRQ5MVIqUJpw5+SPzym1UCmpRCXSHa7G2+55BxQ5FSLuwxQXX2iZRFoeI0KBMWAdpcwNoKVL8GMAphrYBc6bNezG+F73d8v2yztwlu53K7Iu+nRP/FXz8lF4hPIDyfFT+9S6hYDUY49xexbaxOq8+xrE5r7PcqIq2Q7DHMwoULsXDhQkyfPt3z/wsXLoRK1T+Cp9bcjUK91jMpC5fcXmNI8lohUh2DxhDg77B5xubwjeYE6vkbXMDm6c8G1NbfU7On7xzTn4VBy+lcjFqjYC1+KltJpCzaPGDaWs4/Zdtc7u+0tcDJSk6j4u9lkVnA/e1db/iiQtBvhe93fL/cU7MH66eu99nu2enP4u0zb0fWT4n+i5CnirMnMD4zjMCPdob2WeF1iWHGajDCub+IbVOUVeSzriirKLZ7FZF2SK5YffTRR7Fjxw7P8s6dO3H77bdLfZqEU9dqxUijDpFKTwy9b1N4zQtBpCq8v4OQ70NYCOTuM1ePo/j8B6iYsQV2RgEV64S+7jjWTXocj0/8BVQyBQzafDAKblKvYBQYkzsG2+dsh4N1QMlQNTAixbG2+GhQYLnMLc/aABz6NfdXlw/kFAFZhb5aL6cdjEKFYm2eaL/j++W6KevAsiy2zd4Gl9sFuUwOFaPC8uuXUyUkgkNIP2U6BVT+q++6nf8C/KQqtM9KhLEajHDuL2LbAAhrHfWB/otkvwAOHz6Mw4cPo76+Hlu2bAEAWK1WZGdnS3WKpMGybtS3WnHrqMhn7QadCjJwhpIEkeowMib6txhCufvKDDCfbIbxk80+q42rv+57queHglGgQFcQXRsIItGIaVa0uVyZ4jfu5dat/rrvx6Cf1osBgva7mPolMXAQGYMF49NhDc9nJcJYDXqoMOJYbJtw1xH9E8kmK0OHDsWkSZNw8OBBTJ48GW63GxqNBuPGjZPqFEnjakcP7C4WgyL0WAEAhZxBjlaJBgulgRH9EO/8aJksMA/a0Q3H7I0wfWsunG4WChkD4zeVUHrlRkfk7UIQqYaYjwWvDeCX/fUA6It9lmXBggXrZn36gFjfoD5DCCIUi45uEe2UKjyfFT/CjT0n64TJaoJuqA6NXY2ib8gplolwkHSyMnToUPzwhz/ExIkTpTpsSlDbm8KVmxGd/iYvU4WGNnqzQvQz/P0lxs4NqOfvKJiAGkMR1njVyC8rLUOxJhdKROHtQhCphpDf0N2vcOVigb5lbZ7Pbnzs8z4qfFlib5+Vc5ZzAX1DbD31GUIwFnNHCfth9XQAry4M7rPiR7jjtZN14kzrmQC/FH+/LBr/iXCRPBquXLnis3z06FHs3btX6tMklFpz72QlympeBp0KTe1U+57oZ/jnR5+u5H6g3bfP47tikrmEfVNsLQCi8HYhiFTD32/ovn3A8be46ku8r8X7Gzltixd87Av5p6w6tAomq0mwb4itpz5DCHpfGUYFrssq6JuoAOI+K36EO16brML+Wv5+WTT+E+EiuWq1vr4eP/zhD3HnnXdixYoV2L59O5xOZ1qL7D0eKxG61/Pk6dT4poFc7Il+hlB+9OlKYM4znlxop4hvipN1AojS24UgUg3vvH5LLfDJZuATv23mPOOzyMe+mH+Kg3VEtJ76DAFA3PvKe52lNjyfFT/CHa8dLvHYjeZ4BCH5ZOXq1avYtWsX7rnnHqxYsQIWiwVKZXp7JFxq6cLgbDVUjByuCEsXA9wkp9vuQrvNgWxNel8LYmDjk1+sUCBn9ka0jLkNDrkcSpcLxtMHoZDJuJuhQgWFiM+KglGgobMBjIzBinErMKFgAnJUOWizt2FPzR6ql0+kB/6eFto8Ye2WgGYllH+KQqZAaVEp5hfPxxDdEGSrsiGDzNNntp3c5rM99RkCQGBMCmlRxHRWAroqb1RylScmfcZrRgWT1eTRnQTzy/LeTqPQBByvurEajIxBQ2cDaVgID5JPVoqKinD//fdDqVRi+/btuHz5MoYNC6PiRApzztSF4YaMqCYqQF/54sY2G01WiLTFP794xbgVmFM8B2sOPeybl/zZS1B8/BygHw7jfX9HWWlZQO7yf332X6iqq/Isl1eX+yzrlTmhG0QQycRfs8VrU46/xfmrvPOIrx7Az8OC95R4/svnsX7qeh/Nyvqp63Ho0iGsnLDSp++sn7oeO0/sxMoJKwEA205uI48Jog+hmBTSomjzAvSFQroqf/RqfUBMlpWWweay4X4vXWL598uxqXQTVlet9qzbVLoJVqcVK/+x0me7Byc86LNdWWkZNny6wXM/IA0LAcRhsvLf//3faGtrg1arxbFjx7Br1y4cP35c6tMkDLfbjQvNnbhtnMBr1TDJ03FVxBosVowZnBXJybmKMpocgJFHfX6CkAL//OIFYxbgoYMPBeQlb5/xPAo+fg6wXIZy6w9Q/LMPsG32NjhZJxSMwjNR8d5n7aS1qKqr8ixXzN4GY+aQpH1XggiJkKdFKH8VL4T8Uxq7GmHuMeMPX/wBP77+xwF5/0999BTWTlqLNVVrsG32Niwav4iePhN9CMXk64s4nYp3Gpi1hdNRzdrQ52r//kbgjjLhFLJeLD0WQS3Kr279lc+6lf9Yidfmvsb5ZbkcUMqVUDEqLKpc5LNdXUcdfvPpbwKO530/WHVoFSrmVlCZ4gGO5JOVs2fP4tChQ7DbuZzDTz/9FI888ojUp0kY5i472m1OFOZooj5Gfm/J44stXeHvdOkT4G+PAqbTgDoHmPII8N3HADmZ4xHJwT+/WC6TC+cly70m1pbLUPZ0YIh+GE6ePImcYTmeiYr3PjmqHJ9le6+mhSBSlmj8Vfzw9pSoba/Fsv3LPJ+JaVn49U63E8Oy0jtrgZAYsZj016I47Zy+8HSl73o/XZU/YhoTrUIbsGeTYzwAACAASURBVM7msqEwsxAnT57EuHHj0NDZELCvVqEVjXHvZdKwEJL/8l29ejUeeOABFBYWSn3opHDexE0wBmVH7rHCo89QQimX4VJLmC72tYeBVxYCWYOBKY8CV74Cqn4L1H8B3L0jZF4pQYgSIp+ZdTlhtjbDzjqhYnrd5XsnyP75ygpGIZi/rGCUaHjgAFTdZhi+qADjFa98nr5/LnObvc1nmWHkaGivhYpRQK8xwuJoozr8RGrhnfdfdAswdTX3JkWj55brjnDlvL00XKzGALPNBBbg/nO7wTAMGDCe/sRP5sW0LLmaXJQWlULJ+KYU83qyjIIMmKwm6icDEYWKi7kJi/remFS/FvibQaEC+51VME9cBjujgIp1wvD5DjBKbVDvFbHxm5Ex2FS6KajuUGhfq9Ma1v1Ao9D4aF34lEfyZxk4SD5ZycvLwx133AG5vH+kLV1o5iYr+VnRv1lhZDIMytKEN1mxdwO7fgpk5gNzywC5Eij+Ppdz+tkLwLurgQUvRN0WYgATIp+ZdTlR03oGq97ry0fePL0MxbljwMgVAfnKj9/yuGD+8qHL7+N3R37H7T+zDMXaPE+NdD5P37uuPq9ZAdCXs/zZ71BVV4XSotKAc1AOM5ES8J4WVRuAyT/z1ajcuQWoOQhcfxewdQ5guQz2O6tQc/OP8PxXLwb4qvhrUarqqrCnZk+A3mv91PV47uhzWDlhJQzqPo0K+VUQADjNybS1IbUorMaAmpt/hFWHHvEa659Fsb0bzPa5onqXHFVOQEy+MPMF2F12PPHBEz5jul6t9zmn0NhflFUU8n5Q/v1yNHc3B8S2Sq7y0b9QvPdvJJ+slJSUYNmyZZg7dy4yMjIAAAsWLJD6NAnjvKkLSrkMBp0KUerrAQCDstQev5agfFYOtF4A5j/PTVR4Sm4HrGagugIYPQO4/p/DPvf5tvN47uhzaOpuwneHfhf3X39/wGtbYgAQIp/ZbG32TFSA3pr37/XpR/zzlQuyCgTzl9dOWuu7v1e+MZ+nXzG3wvNETK/MwbrJT+Dxib8Ew8g9ExUAmF88P+AclMNMpAS8p8XtGz0TEgDc33ceAVbsBbbd7llvnrgMqw49grWT1gb4qvhrUX456ZdQMkrkafICtCzHTMdwuvU0ts/ZjgJFAXdsEb8K6icDDGtL30QF6NNR+WlWzDYTVr33mN9Y/xgqZjwPYxC9S4utBeXV5Vg7aa3nLYrZZsavPvxVwH3AP/aExn7+DYnP/UCtx7op6/C463Hu7YwbWPKPJQGx7a+ToXjv38RlslJSUgKAE6enO+ebOzEsNwMyALF8m8HZGpyqaYbb7YZMJhPeyN4NfPI8MHIakDsScLO+n397EZcS9u4aYNhkj5dFMM60nsHSvUshZ+QYlTMKLx57EZ83fo6XfvAS1PLoU9uINCREPrOddQrXvBfxRAmWU++zv1++sXeePg8vpm9or/XRtIidg3KYiZSAYbhCKEL9inX6rLcziqC+Kvx6N9w+WhQn6/TRsvDbe3tWkF8FASBszYroWO9fyMdvX4fLgaq6Kp8xeuusrWHHntDYDyDwfuC1LKR1EdPJULz3XySfrCxYsADvvfceTCYTrr322rTXrlwwdWGYIQNsjPOuwdlqWO0utHTZYcwUmSR8sxvoNgHX3x04UQG4imD/9K/AOz8H9v4SWPx60HPanDasrlqNDGUGfj3l19AoNPj0yqd46dhLeObwM1j3nXWxfSki9fHWqITwf1CJeKKomD7NivfnYjn1/vnGkfg/+Lehzd4mXNefPCWIVIBluX714wNAVzPw0SZOq6IfDjAKTj9wuhJs0S1g5ErsmL0D2eps0X7D9xdvPyN/LQu/vbdmRUxLQP1kgBGmZkV0rGddvsfz815RypUB47Ebbkljz8fLq9eLRej4VqfVZz+K9/6N5Ml9q1evxmeffYY33ngDLMvil7/8pdSnSBgu1o1LLd0Yqo9er8IzqFfzElS38sUOwDAaMIwS3yarALjhHuDMPuDswaDn3HlqJ2o7arFqwipoFNz5bx1yK2aNmIU3z7yJ6qvVEX8PIo3gNSovzwQ2XQfsXcvlL+uHc5/7+T8YtPnYPL0MhTruAQOvWTFoez/vzTnmP69urEZZqe/2ZaVlqG6s9t1f5Zu7HAz/NlQ3VmPlhJXYeHgj7jtwHzYe3oiVE1YG5EMTRMLh+9fWOcCfZwEHngRmrON+LN65Bdj3b8C0tZxWZfbTWH7gfizbvwzPHX0Oz05/1qffrJ+6Hntq9mDzjM3Qq/Woaa3BksolmPXWLKzYvwIrJ6xEaVGpZ/uy0jKfp8/+fZO8VwYovGblwJPAtrnc32lrAzQrBo0Rm/1icPP0Z2GQ60TvDwCQp8kLGI+ljD1ee8XH/pLKJWjubkb598sDjl+UVUTxPoCQ/M2K2WzGc889h2XLluHmm29O61SwutZu2F0sCmIoW8wzOLt3smLqws0jcgM3sFwGaj/lqn+5XYGfezN+PlDzd+7H523bBTfptHfi5WMvY0rhFAzPGQ7W603N/Gvn4/Omz/HM4Wewc+5O8bQ0Ir3x16jwZSrv28elrvhVe2HkChTnjkHF7G2C1cD8c44ZGYMNn27wyV8ury7HkzeuwqLCqVw1sIO/BTPv2aC1+73xbwPDKLB8/4qQ+dAEkXCENGDvPAIsfhN452HuDUvTcZjvP4BVB+7zxDD/hmTb7G1www1GxlUDWzdlHQwag6D+xF/LYtQaoWD6bt/efbPL1gWdRkfVkQYiYWpWGJsZxUdfR8WMLb7VwKY8wm0rUg2szd4WoCF86OBDeG3eawFalGhiT1R7dXtFSK0LxXv/RvLJyjXXXIMnnngCzc3N2LJlC6655hqpT5EwTjd2AAAKsmMXow/KVkMmA2qaO0ROto/7O/Sm0AeTK4Fb7geqnobh0l7gW9cHbLLn3B50ODpwx+g7fCYqAKBRaDBv1DzsOLEDn1z5BFMKp0T6dYh0QCh/+XQlV0tfRO/EyBVBzRi9c44bOhsC8pcB4PGxi1H48qy+FbN/F1Gzvdsglq9MuclE0hHTB3SbuIlK77Ld7QqI4aq6Kjw++XEUZgamSYvpT/y1LP7wfbP5YjNGjBsR3Xci0psIfFaYTzbD+Mlm3/WTfxpUCysWmzanTTCWI0VUe8XaBY9PD6wGDpJPVp5++mkcPHgQo0aNwsiRI/Hwww9LfYqEwU9WhkjwZkUpZ1CQrUFNU6fIyfZyKWC6QaHfrADAsElA/ljkff0y8IPVgKJPB+N2u/H6qdcxPm88CnWFcAkcb+rQqXj3/Lv407E/0WSlv+LtA8Hjl4McCv/8Ye+nV6J58t1mn/OxSi3MnVeQPVQHU+cVn7c1oaBcfCIl6NV+jTaqOB+KjHzx/mVt7fNdyR4KlUzuieEbjDfgx9f/GAY1149YNxvwNDiYl0VDZwM9RSZ8CUOXyCo1MHc0wO52QiVTwKDSgYni3uDvtSW1hpDGe0KMuIx2M2fOxE9+8hPMnDkzrVOMTjV1oChXC7VCGs+YoXotzjULTFasFuDih8DI74U3UQG4QWnCUii6rwJHt/l8VN1cjYvtF/GDET8QnKgAgJJRYlrRNBxpOoJL7Zci/CZEWsD7QATJQQ6GUP5wTWuN502dQZkjnPf8RYXnfOySv6LGdhVL9q/A7N1zsWT/CtS0ngHrCs+hnnLxiaTjpf1SPX8TpwG7eoLTAfj3r7tfAS4f5rQrB54E/lQKQ+UvsXna/6C0qBQ/v+nn2Hh4I5btX4bl+5b79CceoZgvKy3Dhk83CPZDYgAThi6RXfJX1FibseTAfZi1ey6WHLgPNd1XwC6vjPjewHttxUtDSOM9IYbkb1b6E6eutGN0fiZcEuluinK1+LLWAruThUrhNU88e5Arczn0lsgOOOTbsOmLofl/vwduXAqoOF+byvOVUMvVGJ83Puju3x36Xew5twd/Of0X/HJi+hZCIETgfSCC5CAHI5R3A9PVhOKDG1Bx06OwZxg4jcrR18HM+i1w64OAtRVmpQqrDq4U9W4J+RVEavPTU2UiYQTzJ/LvX9o8QGf08V1hTr2LYgBPzn0Gyw/cH9IbQkwbxqdbkqcE4UFMl7j4TS4dUXQMfgwVs7bCGOG9wd9rS2oNIY33hBiSTVaWLl0a8BaF9xTZsWOHVKdJGDaHCxdbuvHda6W7GQzNzeitMNaF4sFZfR+c2Q9k5AG51yAiNxeZDJaRc1Hw5Sbg85eBqavgYB04cPEAphZOhZJRwh3keLmaXEzIn4A9Z/fg0ZsepVet/RGGCVvc7k9I7waXA8ypd2E89a7vjjcv4yrRALCvrg7q3RIOYrX5CSIhBNMBCPUvAd8V5tS7YGf/Jio/Cl4bFs5+xABDTJf4nYdCj8FuJ5AVmc4kEX4+NN4TQkg2WXnllVekOlRKcPZqJ1ysG0W50jm9D9VzxzrT1NE3WXG7gQsfAMNuRTS2kz36YmDIBODjPwCTf4Zq0zFYeiyYWDAx6ESFZ/qw6fji6hc4dPkQZo+cHfH5idSGdTlhtjYLVvcKhWj+MGSApdbHR8LD2LlcAYgVlYC1FSo3hHOcmQiGHu+c7AjfDhFEzESi/eLTG/22Z0vmeXxWzD1m/Pn4n3HMdIzTokAGtv0KGIYRjG3K4x+gCOmk/Me9YLqpXlSsS8Q/SwlT5xXfewMAdDYCLgc3jmcWAF73i3hrVghCjITd8e128Zn3f/7nf+LQoUOJakpYSCmu5ynUayADcOqKV0Uw83lucCi4IfoDX3cX0HUVOPYGPqj/AApGgdH60WHtOj5vPPK1+Xjj9BvRn59ISViXEzWtZ7Bk/wrM2n27NHqR6WUwvPsLLj962+1cDf+x3BM8jJ3LLW+/w1PjX88ohXOcNWE+OfPPyeb1Aizl6xMJQpsX6E909ysB3hVwOYGmrzl/lTu3eLZnS+ah5rYnPD4rGw9vxM9v+jlKi0qxfup6bPjsv1DTfgHsu48Jxjbl8Q9AxHRS/uOekC7x7lc4I8jeZYNMJaAtLEOHs9v33mCpAdtylkth3DyB+9v0dd8EHPHXrBCEGAnTrDzwwAOC6WBHjhyByWTCjBkzEtWUsDjV2A6VgoExUxPF+w5h1Ao5CnI0OHGlvW/lxQ+4v/nF0R94yASuktiHm/DhiGtwvfH6kClgPIyMwa1DbkXl+UqYrCZ6/dqPMFubseq9NdLpRSCD4d1fgOHTvvga/iv2ArN+y71p2Xa7T26/xXQaa77cKJzjrAgj1oLpBaJMbyOIiLC2AO9vBGZt6HMFf38jcEeZbwx2NvZ5XHQ1cdvr8mHOHe7js9LQ1YCnPnoKL8x8Aes+WodjpmM43XoaFTc9CqNAbFMe/wAk3HFPSJeozeNic84zgEIFJiMfxawLFbO2eqqBMYwSi/bd63tvqFqNihvXwujv0XLfPiCniFsVZ80KQYiRsMmKkDmkw+HAr371K0ybNg0HDx7EzJkzRffv6enByZMnwzqXzWYLe1sxjp5rRFG2EnV1l+FipTO2NGqAb+pbPe0rPLYXmRoD6lsdcLdEXpXLbrfjUu1lZBRMg+v0dtRYHFhYtBAXL10M+xjD3MPAgsWrn7+KWfmzQu+QBKT4Nw2HcePGSXasSGI2FsSuTfZQnYhexBFVu0YbVX0TFR7LZdgdDpwz9WC0UQWVX/60XS3chi5bF5ovNod1Tv9jwnIZdmsXztVKf20TFWex4N9GKWMWSFzcCpGK13+0UQXV6UrfdEcA9pm/8YnBksFqyPhYrTsCvHEvt91jxwX7QKutFcdMxzzL9gxD2LHdDPG+k4rX0BubzSb5MYPFbKpfDyFiG/fMIZaD3BvUuoBzul0OnOq9fhkFGRGP5+l4/b3h2y/1OEtERsImK0IljN9++21ce+21eOCBB/Dqq6/iypUrWLp0qeD+arU67GCJNbDcbjcuvFmLqdcaMbRoeNTHEaLEJMfxo3UYNqoYmSo5sPc4MHwyhhcNjep4ly5fwojhI4CiIvz16n4AwKQREzE4syDsY4zACBQ1F+Fwx2Gs/t7qqNoRb9JxsIgkZmNB7NqYOq+I5ipH1a7OJsH8aJVWh3HjRgl+rurpEsxx1ml0fcZ1wTQpoc4pMekQZ/FuY6LiVoiUvP5iMahSYdwQZV/MdjQIbydTCPbDNnubr++KMgtsybyYYzslr6EX8fjhGixmU/16CBLncU/03tDT5buhfjhkciXGjRvJ7Wc1hR7P/UjL6+9Fure/v5DU98gnT57E3Xffjfz8fNx555347LPPktkcD3WtVrR2O3DtIF3ojSNkRB53zNON7UDrBe4GNyQGvQoPI8eHxqEocDoxusMU8e6ThkzCcdNxXOm8EntbiJTAoM3H5ullgZoTbXg+KwGE8m0R+FxvKBbOcVbmcNuE0qTE6BVDEDEjpgvYu9Y3ZnWDBbUtBq0xoB+WlZahurHa13fl0MOomfnvYP21MMTAI87jnkFjDNSxlG6CIWtoYJx7PfjUK3OCj+cEEScS9mbFaAzMZxw+fDhqa2sBAMePH0dhYWRl9OJFda0FADDCEI/JCueF8nV9G27WfsKtNI6J+bgO1olPnRbM7nGhoPoNnB1yXUT7TyqYhF01u7D3wl7cf/39MbeHSD6MXIHi3DGomL0tqmpggQcM4dsi8LkFbqzZL5DjzOtmQuVmx+gVQxAx4xWDdmsXVCoVN1Hh08K8Y3bwdVyOv1c1JcbaguKDv/XxI9IffR2GKQ9i+f4VgZoyyv8n/GNOq5N03GNsZhQffR0VM7bAziigYp0wfL4DzD+tCYhf72pgFptJWLMSpg6SIKIlYZOVZ599NmDdP//zP+PJJ5/E3r174XQ6sXnz5kQ1Jyhf1VqgUjAYkiOduJ4nT6eCTiXH1/XtgOZzQJ0NZA0BYnQjrm4/jy5XD76tHwP9mQ+httShR18U9v6DMgZhVM4omqz0Mxi5QtqbSCjfFr/P7e21wX1WgnlYhHtOgog3vTF4rvYkl/rlp1/xxKxc4REje3DaBf2I2Ft/EnfPCiKN8Y45qVNenXYwn2yG8RO/31yTfwroh4nuZmedMftmEUQ0JNXBPjMzM2UmKN4cq2vDuIIsyGQySGRe70Emk2F4XgZONXYAyiNAwfWQ4iQfmL+BQiZH4cjb4D77CQYfewuXv/doRMeYVDAJr59+HRfbLuKanGtibhPRD/DTkzg1ephsLXCwLigZOfI0eWhzdIhWKVIxwvn6Hp+VSDws4gjLutHSZYc2rxDNHT3I06nAMIE6OyIQ/trZnS6oFPL+de34+GdZwO1C8aDeUvY/+wCwXAI+2sSJ6YPFrEiMi/YN8qxIKCkbv+H4rABcaeEg3iiCRDnuisWsklGisasRDpcDSrkSRq0Riki8tELAulmYbWaqhjeAkexfe+nSpVi2bJnPf/y6dMLpYnG8vg0lBdmQsAiYDyMMOtRfNcHd9A0w+FuIxgzSnw9bv8F1OaPBaHLQUnQzjKcPQG5ri+gYEwsmQgYZ9l7YG3N7iH6An57E+fHzONN2Dsv334fbd9+O5fvvQ03bOfz6419j1luzsKRyCWpaa8B6vSUMqZtJAU0Ky7pxuqkDC1/4CNOf/RALX/gIp5s6wMZrAOhHeF+7qc9U9a9rx8f/39YApjPA1jlQ/GECV57b2sp5WcxYx/kLBYtZkRjP1hhQVhqoZclWZifoCxIpG7/h+qzw3j5BvFEEiXLcFRzPSzeh3dmF5fuWc/eFfctxpvUMnBK9bWHdLGpaa7CkconofYbo/5CDvR81VzthdbgwOg7iep7heRlodZ6FTM4CedfGfLzGnlbUdDVg+fA5ANxoHD0N+Zc/w6Bv/oYrN98b9nFyNbkYkzsG+y7sw0MTHoq5XUSa46cnMd2yDGsOPRyQr7x20lpU1VVxOfeHVvnk3PvqZhxQMUpf3UwKaFJauuz4yY4jqGu1AuAKbPxkxxHsfmgq8rPUCWtHOtKvrx0f/7M2AO884qureueRvvX37QOyCsVjViTGW7qbUF5djrWT1noqK5VXl+OJyU9gCOX/J4SUjd9wfVa8vX347fy8UQSJctwV0kG6ZQrc6+fZsqZqDbbP2Y4CXfhVScUw28xYdWiVr7bL7z5D9H+SmgaWinzVK64fbsiI2zlGGDLAMDXcQm7suagfmU8AAEoyucHJll2AtkElGPT1HjROuBvuCNIKJhZMxKsnX8XZ1rO4Njf2iRSRxvjpSRxyuWC+co4qx2fZP+ee182IloBMsibF7nR5fqzw1LVaYXe6ktSi9KFfXzs+/rW5wroqfr3bHXpyLRDjTtaJqroqVNVV+az/5cRfStF6IgxSNn7D0fIBXOqX0HYuR+hzRDnu+usga0V0iQ42jDaEgd1lJ20XIf1kxeVy4ZtvvkFPTw8AoKmpCfPmzZP6NHHjq7o2ZGsUyNOp4IzTW8ai3AwYmbMwq4tgUGXELK7/wPw1BqlzUaDMhtvNDbKNo6dh7CcvIq/mEEwls8M+1s2Db0bFyQrsvbAXq3JXxdQuIv3wyQ1WKGAomecxglS6XKJ+Ed7LKsgAS23f0zogvNzreH6vIHnpKoUcRblanx8tRblaqBRyyc7RXwl27YSuB4DUukbBPH74vH5ra19+f9EtwNTVgH4EoNUDjx7ntnU5OZ1AsON5n9bNQiGS/y9lrj8RHLH41arkaO7o8cRprlaJVqsjcXGrUHHphRMWcZNiPu3QX1MiVwZuZ6kHGAVgvsD9zSwAFMq4NVUpV4rqWExWEzIKMmCymqLWmajkKtJ2EdL7rKxatQqvvvoqysrKUFFRgV27dkl9irjyVa0F4wvjp1cBAJVchpvl53CKuTbmiYrD7cKnltO4JXecZ6ICAO35Y9CdXYjBX70ZkYA/R52DEkMJDlw8ALfU1QWIlCYgN3j/CtTMfBJsCfewwXhkh2CO/Z6aPZ7lzdOfheGjF3z9J8znQ+dex/N7hchLz9Op8Kdlt6AoVwuA+7Hyp2W3eH5cS3GO/orYtcvVKgOux8WWrtS6RuF6/FS/Bty5hftROGMdcOBJ4MV/ArbN5byy9v0bpxNwOoIfjz9tbz/beWInnvXzuigrLaPUlgQiFL87fjwJTe09PnF6qqkD/777WOLiVpsHTFvLxdq2udzfaWu59d7oBvtud/kwMOJWTle1uVdfdfUbLjbjhFFrDLgvbCrdBKvTiiWVSzDvnXkx6UwMGgM2z9jsq5OZsRkGjUHS70GkNpI/wmltbcXmzZuxevVqbNq0CYsXL5b6FHHDanfhdFMH7r11eHwnK90NMKIVW3tGYUqMxzppq0eXy4ZvZV3j+4FMhsbR0zDqy9eQXXcE7cMmhn3MiQUTsePEDpxpPYOxhrExtpBIFwRzg997DBU/2ALjrQ9CYW3FmPpT2D57a181sK//hnXDZuPxsYuh6jbDcHADmG//CPgEfXnWc/8ndO51HAmVl84wMowdnIXdD01Fl9UGnVYT8ZPTlM19jzPe1877qbPQ9bjU0o3/2PN16lyjcD1+7ijjJhxznuF+/AlpV/6yFFixNyydgXc/a7O34YWZL0Auk0MtV8OoNUIpj99TcMIXofh1w41lL3zsE6crXz2K/5g3Hn8/cTUxcWttEdai+I+bNrPvdjcuAXb+S+B+K/YGLUkcCwpGgTG5Y7B9znY4WAeUjBIqRoVFlYsk0ZkwMgbFucWomFtB1cAGMJJPVoYMGYJ9+/ZBpVLhxRdfRGdnp9SniBsnrrTBxboxypgZ1/NkNVcDAN63jsJPe9zIUUf/Ovmo9TwUMjmuzRgc8AbFXHQjik7uRUH1XyKarNw0+Ca8evJV7LuwjyYrAwjR3ODuFu6pHbgBo2D119yNz3wB2L8WAbeeWx/s+3/LZUDpp/8Syr2OI+HkpTOMDPlZapjqzuMaIV2NBOfor/DXzhuh65GhkqfWNYrU48dSG1y7wjrD0hl497Pd53Zj97ndAIADdx2giUoS8I/f+tZuwTjVa5U+y3GN23A1K/7bMXLh/eLsg6JgFD5i+obOBkl1JoyMoTeOAxzJp6a///3vMWXKFDz11FPIy8vDpk2bpD5F3Kiu5XLv+VfC8SLT9CWcjBqn3MNxsiW2Ae+o9TyuzxnN6QT8cDMKXB35XeTUfwlty/mwj5mtysY4wzhKBRtg8LnB3hTqCqHqNvet8K7FL1f2lb70/tza6rvs6A7cJoE+KnxeujfRaFKSfY50Quh6dNtdqXWNeE2KN+H4pfhvz2taGEVYxxPtZ5SDnxKI9WWL1eGzHNe4DTc2/bdjXcL7JVgHRTFOSE1c3qMdPXoUu3btgsFgwMiRI+NxirjwVa0Fg7PVyNbE9+lWlqka3bklcEKBr5ujf+LR2NOKi45mTMgphlskF/TqNbfCJVdh8Fd/jejYEwsmoq6zDqfMp6JuH5FeCOYGTy+D4YsKbgP9cODe3ZwtkKUWUGjALvkrTEt3oeGBAzAt3QV28Ztcjj+//Y92Arkj/er570yoj4oUmpRUOEc6IXQ9RuRlpNY1EvKauKcCkMn7dCYsyxWFsNQCMjlcd1f4bn/nFi7e736FEzLfuxtY8iawopL7e+9un1hn3SzgBl76wUt44bYXcIPxBsrBTzGEYrf83pvx1tFaz3Lc41bMB0Wb1xePnU3csvd2X1Zwsei9Hx+b/njHdmeTpDpCg8aA8u+X44XbXsDWWVvxwm0voPz75RTjRNRIPt1es2YN8vLyMHbsWLz//vuorKzE//zP/0h9mrjwVZ0F44dkwxVHwYrMZYeu5WtYiu9CTjPwTQxvVvpKFovnorpUOpiGT0L+2UOon3w/HLo80W29uWnQTXjlxCvYe2EvxuVFnhZDpB+MGyhm5ai4cS3sah1UPV0wyDPBzH0WmP07QKkFOhqBVxcClstgS+ah5vv/jlVfbkRDV4PHIKx46s/BfOch7o2K1gj0tHG6FWUGt45R9haWSEzOsZiuQspqDgcRjgAAIABJREFUPok4Rzohdj0ApM414jUp9x8E7J2A+RxQuQbovMr9AMwvAZpP9elQxs4Fc9s6YMEfe31Vep+sz/xPQKEBZDLAaQMq/5Xbnv+B2QsvrOf1KrwQebBuMPRqPeXgpwhCsZurVeK3C2/AU3ckKG69fFDs1i6otDpuYuIdj3x85Zf0+aXIlVzK15K3uHh0u7l1/vDFJfyPNWi8ZJUa7S47fvPpb/ruDTM2S3JcYmAi+WTFbDbjueee8ywvXbpU6lPEBUu3HZdaujH7WwUS+MmLk9F6Egxrh1U/GiOzgVPm6J9mfNj6DYyKHAxWZoENUtO8afT3MOjCRxj09duon3x/WMfOVGVifN54HLh4AI/d/BhksoH5o2tA0d0M5tWFMHrnPOuHczdC/TDu6ZuXgNh80xKsqlrjK6KsWo2KGx6Fcecibv+HP/cVfPLHDGVaJjFCuop0PEc6IXY9UuoaMQwgA/DKAt8YfX0RF6PegvkJiyDb+S+coP7tB4VjOojAXqiAxeqq1aiYW0ETlRRDKHYTHre9eqlztScxbtyogPFXsICDpda3CATAxaa/wD5c08koISNHQmokHyE1Gg1eeuklfPTRR/jjH/+IrKwsfP7551KfRnK+quP0KiPy4mcGCXApYADQkzkCI7OBc61uOKN4k+Ngnfik9RS+nTk66EQFAHp0RrQOuR6DvvkbGIc16Lbe3FJwC650XcE3Ld9E3D4iDQkl6vT73J5hEBZRZni96pfJojctI4hEIBb3/oZ7vJBezCRSzKCvt/+QuR0RE+GI7sWKPPgL7MMV8EcJxTohNZK/Wfn2t78Nu92OL7/8EgAwfvx4fPbZZ5g4MfxqVMngq1oLZDKgSB/fyUpm85dwZAxGjzIHo7KdsLPAhTYWxbmRifWq28+jy2XDGG14T6ebrp0Gw5VjMJ7+O65eNz+sfW4adBN2yHZg74W9uM54XUTtI1IUf9M6jQHoauJ+aDEKYNYzgH5on8HY5cO9E45a7i9vjgdA1W0WNuvyFuS73T77AOCWY6h6lAzzxYFo+Bgp3tdIJpNBLgMYhkn9a8WLlL1NH7OHcv3hZx8AlkvAmf1AhhH48QFAoxeOaUbBfd7VDHy0Cag74iOKJnO71MTpZHG1swcOFwulnMGgTDUUihR409U7VnvMdJVaYaNIpZb73GnvK/IgFJveeMe893YSFT6hWCekRvLJyiOPPIIzZ86gqakJhYWFKCgogE6nk/o0knOszoKReTqolUxcPVayTNWwGa8D63JhZA637huTK+LJyvvm41DKFBgmD0+w1mkYic7cazD4qzdxdfy8vnzrIOiUOlxnvA5/v/h3/OKWX1CqQrrjn6f8nVXA9Xf11ekfO5czGPNf3jqnb/nuVzyfG76owOaZZVj13pq+vOTpZTAc/C13Pv1wLpffa5+ggs+wvgJnvsh7ePBi17GDs+L2gzgZ50w3hK7RM3fdgO0fX8Ca749N7WvFi5mrNgCTf8Z5p/CxeucWbsJ+ywN96Yxj5wL/sgN4c5lvTO/7N+B0Zd9+n70IlD7pEdjzBSy8NSskrE8uTieLU00dWPnqUU/clt97M0oGZyV3wuI1Vqs8mpKdwG3r+uJQPxxY/CbQcQV4fbHgGN033g7yPT4f8/6aFYkKn1CsE1Ij+WTl6aefxtWrV1FXV4dHH30Uv//971FeXi71aSTF7XajurYNk0ca4jpRUdhaoOm8jPZR8wC4UZQJKGTcZGVBcWTHer/la9yoHwPGFX41scZrp+Haz7cj9+JHaB31vbD2mVwwGS8dfwlHm45iYkFqvx0jQuCfp+xvIDZhka/BmP/y6Uru7337ALcbjEKFYm2er1mXSg9mXq8gX6Hibn7ZhcB9++B2OSCTK7mJijy6oScZ5osD1fAxEoSu0b+9dQz/MW986l8rXsx8+8a+iTnQZ/q4+E3ffsL3gxV7ub8yGbB3bd96fr/79vUK8bkfvWRul3pc7ezxTFSAPgPIv/zsOyjUx9fCICiCmpLFgQa7bZf7CjoAfTG4+E2g28S9fXl/Ixfb3hpBLwG/5y17Rr5k4nrvWO+ydUGn0VGsEzEheeScOXMGf/jDH5CVlYXp06enhSlkQ5sNps4ejM6P7xugzF69ii17BABAyQDDsoATEVYEu2S9iovWJtyUOxYsG/6+rUOuhzVzEAo/39FbjSk0Nw6+ERq5BrtrdkfURiIFCWUg5p+LL5Sbf7qyN7VrGJA5GIxcAaPWiMLMQhi1RjByBSfQ7P0cDMNNTHKKcKrJxt0wo5yoAMkxXxzIho/hInaN9FplelwrhuHiWiiPX8ho73QlpwPQD+P2438keu/ndgf8+OPN7Tz9hX68JRWHixWMW6dLujK+USGmKfE32FVmCMdmt4kz8n3jXm5ZSCPIG556j9USwsd6d2M3xToRM5JHT25uLrZs2YL29nbs3r0bRmPqV344XmcBABTlxllc31wNt0wOm66vKsfIbOB0hBXB3m85DgAYlzk8xJZ+yBg0jP0+MlovIvfc/wtrF7VcjYkFE3Hw8kF0+5v7EelFKAMx3txObBmILq+5t56/J/c6wnr+TieLBosVl1q6AASatgoZtLGsG80dPahv7UZzRw9Yv1em3sdssFjhdIq3iQwfQxPMSI9f73SyEV33hCNmxCdmtMfrrsT2k8kk9a4gpEcpZwTjViH3/WnkcLhQ39qNSy1dqG/thsMR58m3WEz534Md3eEZ84qVL46TzwpBSI3kk5UNGzYgKysLEyZMQFtbG37zm99IfQrJOV7fBgUjQ2GuJq7nyTR9CVvuGDjR9yNnZDZgsgIt1vAHivdbjmOUrhB6eeRpFeahN8KaVYChR7ZzN+EwmFI4BVanFf93+f8iPh+RQvgbjfkbiPHmdmLL0eQ187nXL8+E6vmbgJdncsth3hj5nPK7X/wE037/Htb/7Rv88d6bgxoL8vqJhS98hKnPVGHhCx/hdFOHZ8Lif8y7X/wEp5o6RH84k+FjaHK1SpT7/bs8c9cNeOtoLZ656was/9s3ONXUgYvmrrCve8IRMuK7c0tooz2x/faujSjWicRjzFAGjCd/vPdmGDP6ftw7HC6cutqJe176FNN+/x7ueelTnLraGd8Ji5gpZO4o33W5owK3u/sVX2NeIY2g17iMTddFPC4TRKKRXLPyxBNPYOHChbh06RKOHz+O9957D9u2bZP6NJJyvL4do/J1UDJxFNezLmS2HEPnNbPg9pokjMzm/p5sYfHdotBzx3ZnN75oP4t/LroNrmjKAMoY1JfMwrWfb4fh7Hswj7kt5C7FucXI1+Zj99nduGP0HZGfk0gNhPKUNQYut97l4J6+6Qb7fq7Niy2vOcZ6/v455X8/cRUA8MZPbwUAwcpcoTQmwfLUhS8bGT6GotXqwOb/O4P/mDceg7LUyMtUo8PmwF03D8N/HziNL2stOHGlA0/Pvy719AE8/v1DJuPc7AeVePqJoO6K3+++fUBbHVcN7NCvuWpgTccl864gpMfU7cC71XXYumIi5IwMLtaNvx65jLypo1Co4v59r3b24EG/8eLBV4/ijZ/eiqHxysYQMoXkHxL5j8f+6zQGTqMy67fcmC6kEYyzzwpBSI3kkxWLxYIZM2agoqIC//u//4sf/ehHUp9CUtxuN47XWfDda41xFddr285C4eiENcdXST+qd7LytcmF7xaF/uf42HwCTjeL67JGRt2W1iHXozt7KIYe2Q7ztdNDVgZjZAymFE7BO+feQW1HLYZlDQu6PZHC8HnK3vibM/p/HsvNK8Z6/kI55X8/cRW/mjsew/OENWahNCbR5KmT4WNw7E4X/n7iqs9k8p6XPvXZpq7VigyVPGBd0vUB3gj1D56cIpw6eRLjxgmMvbzm5c+zfNdL6F1BSI/DxeLFDy7ixQ8u+qxfcus1nv93sm7h8SKePxiAQFNIHqH4DDWm+xNnnxWCkBrJJys6nQ4PPfQQrrvuOrz//vspX7a43mJFa7cDI43xbWdW8xcAAFuWb35pjhowqIETJieA0D+G3jd/Db0yE0VqPdhoDZZ6364UH/4zjGf+AVPJ7JC7fK/oe/jb+b/hjVNv4BcTfxHdeYmBR4z1/Pmccu8fC0I55d6oFHL8YPwg3HXzMOi1SlisDrx1tNajMYnmmERweM0Kf00tVofgv0G33Td1pl9d9zh7VxDSo5QzgnHqHZMKRiY8XqTzm1WKVSLNkPwu8dxzz+Hhhx/GmjVrMHjwYGzatEnqU0jK1/Wcc72/yE5qsps+g0ObD5sqL+CzkdnAqTBE9i43iw/M32CS4VtgY3QAtxR8C136YSg8sgOyMMof52pycdOgm7D77G7YnLaYzk0MIMRyr8PUvQzKVAdoIcrvvRmDMsUn9rlaJVbdNgZPv3sC97z0KZ5+9wRW3TYGuVpl1MckguOv6/niYgt+7vdv8PPbxmD0IF3/ve4xxjqRePJ1KsE4zffSow3KVAvqWtI6bilWiTRD8jcrarUa3/rWtwAAJSUlUh9eco7VceL6ofF0rne7kX31MKyDbhQsNTwyB3j7vBt2lxsqufjTmq/az6PN2YUbckYDiPEVtEyG+pLZGPPpn2A8vR/N4+eF3GXG8Bk40nQE+y7sw8LihbGdnxgYiOVeh6l7USgYlAzOwl9+9h04XSwUYThMt1odgpoUXrMSzTGJ4PjremQyGe5+8ZOAPP9dD07pv9c9zt4VhPRYbE5BPcruh6YiX9n7JlYpR8mgTLzx01vhZN1QMDIMylRDqUzjaoAUq0SaIflkJd04Xt+G0fk6yOUyuOOUgqrurIO6uxGWPGH9zshswMkCZy0sxueJD4AHTdVQyhQYkzEk7EpewWgbVILO3BEYcrQCprGz4BYqb+jF2NyxGJo5FDtP7cSCaxdAJkvj1+BE4hDLvQ4ThYKJSIAdji9KpMckQuOt66lv7Rb8N3C42PiJklOBYJoXIuUI10NJqZT3v7ilWCXSiAE9jXa73fi6vg1jBmfFbaICANlXDwMArDnXCn7Oi+yDmUO63W4cNFXjFsM4KKVqrEyG+pJZUHc1w3j6QBiby3Db8NtwynwKhxsPS9MGIr1IQm1+f88Up/P/s/fm8W3Ud/7/a0a3ZPmSJd+Oc/iIc5ALCAmQNGHDEdiSlhZKjm1KgXAuWTbQLe2PFsp+y7ItLcsR0m5DCaENdxtgyxVCgNzOSRI7t+Mjvi9Zt2bm94cixbJG1jWyJOv9fDx4BM98ZuatmbfeM2/N5/1688P2UJGiL0qoPi1EcHheAMMwo6M3zRB/V6tTeOoPEfC9VsjF+6yknJ+GgnqqEClOWicrTT2e4vpxcS6uz2zbDbcqC3aV+HzQYp2nm/2RjuDJypGBBpx3dOPynBoIYXafD4d+Y5Xv7QoTRh3M3KK5yFHl4IUDL0hmA5EiJECbX6xnSl2bGY+9e0i0hwoQe1+UUH1aiOB4z92fvz6NF5fOCJjnn61OoZf5Iv5eqjTTg16KIva9HrC5A+rXUs5PQ0E9VYhRQFonK0daLhTX58b39a6nXmUGuCDBQcYCY/TDv1n5pPMA5IwME3UhJAkjhWHQXLUo7LcrCpkC14+9Hvvb92NP6x5pbSGSm2Da/NaOuB1SrGfKqtdq8d2Zpb6/73x1L7osF5XxBtdPfP3ot/DuvXNRla8Puy9KsD4tg49BiOM9dzPKDXh+ywn8/MYabLprNn5+Yw3+57Pj6Eilcyji7/I3l8XV34n4Ifa9XrF+N2xOLrX9NBQJiNsEITWj6OeDyDl23gyGAYqy4te5XjXQBLW5Af3j/xnDzTUbmwnsaechCEJALYggCPikYz9mZFdBBUDq30P6/WpXroMwtIHUEK4uuRofnPkALx54EeuvWy+xNUTSkgBt/mBzyrM1Cr+/h84xj6UvSrjz2IlAvOcuW6Pw67vi5WeLU+jXXOpFMaoI9r3mBQF3b6j1W55SfhoK8mNiFJDWyUp9qxlluVrIZfHrXJ/Vsg0AYMmZOOy48VnAJ43AeYuAogz/ZOW4pRmN9g58u2Q+eCEOD0wMg5bKf0Llrj8i59QXIbvaK2VKXF9+Pf5a/1dsa9qGq0uult4mIjHwvOcXNzGFmBHS5ud5AV0Wp09VSqwPQq/t4pRFsTnmg/ehlMuQpZKhw+IMS81naM+QYMcgAvGeu6F9VvgLP8IIAFp6bZAxAMuyyNEo0GNz+a6TSi3RD0fD+XG4UC+KUUWw73WGSo5PVl/t18FeLmPR0usRhFDIWORpFei0unx/mzJUYFnGL8YYdMqw395GTTR+TX5MjALSO1lpM2N8ni6unetzWrbBqSuCQ5UHDNPPpDLb8++Bdg5FGf7B5+PO/WDBYJKuFJCwXmUwffnVsOnzUXDgr+iuWACEUPpaULYAXzR9gf+36//h8sLLoZJR4WnK453b7J0y4NXeN9V4bohebf6h6yXU5vfOK/dO17j7qnI8sLDSJy/qnVP+/oEmAOJzzMX2ceO0koB9VJsyRBMWb82Ld/tIa17SmWy13Hd97l9QgXs37oMxQ4VHrqvCv7950Hc+n/7uVGyrb8NN00p8MtMlORqsXTodPC/E9tAXyo/DRcTf3d97DXLqRZGSiH2v/3Ln5ei1ubHylT1+sUEuA77z4o6g8WPtspnQKmVY8afdfjEikummEROtX49A3CaIeJO2NSs2J4ezXRaUGeJXr8LwLmS2boe1cDa4EI0Xx2V6iuxrWwPHfdq5H9OyK6Fh4ni5GBat4+dB130G+ub9IYfLWTlur74dTQNNeOWbV+JnFzFyhJrbPFib/6FvPP9G+gAYgqHzymeUG0T7INwyqyzoHPOh+7hlVpnoPtoHHKI2xFrzks50WJz4n8+OY/mcsbh34z5PjdH88Vjz1iG/8//o24dwy6yywH44G/fHXhsk1Rx9EX9vdOqpF0WKIva9ZhlGNDb027hh48eq12rR0GX1Wxb3urZo/XoE4jZBxJu09dYT7WYIAlCUFb9eCxkd+yF3DcCaNzXkWIUMGJcF7G/zn+Z1ynIep62tmG2YDJ4P3Wk+FrpKZsKl0qPgwKawxk/Km4RLCy7FusPrcLrvdFxtI0aAcOY2e7X5s0s9/0p8wxs6rzxboxCdZ95tceLWdTtx94ZafHy0HW6OD7oPGcuI7sM9zCtVb81LcY4WRr2KEpUwcXG853rwgu+cB7uGwa5LzLVBUs7RH+Lvdrt4gkukBkO/14P91EtTjw2Dv+7B/FSrlAUsi2tdWyx+Hee4TRDxJmHTwNxuN6655hqUlnpUfX72s5+hqqpqxI5f12oGABTGMVnJbtkGgZHBkjk+rIbzVdnAx+d437x6APikcz8YMJiUUTZsgb4UCDIF2sZeiZK6/4Om+wxsuWNDbnNb1W2o667Dv239N/xl8V+gkVOjvRHjwvzl8XlKj3Z+OPOXE1CT4q0f0RiK0GF2BMztHlqjcvdV5ZhRbkC2RoFcnRKLakx+hdp3X1WOwmwNtjw8zzfHXMYyaO6xQimX+XoneB8wOF4QnasupwREErzXj+d5yFkGHz10FRQsg7/dNxet/Xbwgvj5D3ZdYq4NGs6Ph/q/xgDYuqiLd5oiZ8Vr4hgGeHn5TGRrFJDLWFE/tTr9E5OSHA00Shk6zA5p6lg4NzDQiup8FdDXBMgUVHtCpC0Ji8r19fVYvHgxNmzYgA0bNoxoogIAdefNUMvZqBWDwiG38WNYTdPhYobvDO+lOgewc8DRrou/En/SuR9TssZDx4xMXtk+dg44mQL5B98Oa3yOOgd3TrkTp3pP4de7fh1n6wgfg7TzlS/MCE87P5Tevnduc3aZ528J5jYP7m0w/7dfBfQsGdr74Bd//wY3TivBk+8fxa3rdmLlK3vwwMJKLKoxAYBv/vjtf9iJBb/5Aitf2YMbp5XgkyPnL/ZOsLv9+qy8tfccXhLppWDKoDqrWPFev8fePYSTHRb8cvMRdA543np9+4Wv8eT7R6GQsfjTD2cFnP/zvZaAHhdrl06PvTYomB9rDIH+3/YNsHk19Z9IUwwaJR5YWOmLN0++fxQPLKyEjGV8y371/hHR+DHBpPNb9uqPLkNbv0Oa/kyc2+Ob668H89x0YP31gK0XuO11SeMzQaQKCXuzcuDAAWzduhW7du1CZWUlnnjiCcjlI2dOfVs/xhkz4rZ/Te9xaPtOom38aghh3vwmGzz/7mh2Y6pRhgZbO45bmnHX2JvB86EbNkoBp9Shu3gGck99jnNz7wGvDN0wc3LeZNw47ka8c/IdlGeVY+XklSNgaZoTbP7yjz/1vOaPZpvBc5sl+qU5WM+Sd++dC6NeFbD+uzNLReeQr//hpbjjynEozNbg9j/sFF0P1Hl6J/xpN/5+/1y8e+9cPzWwTXfNDksNjAgf7/X7+Y01ePTtQ75/B1+fhzYdwH9/7xL8/MYa36/X//PZcTx+0yTk69V+18nc2QKWzYnNqGB+LOb/bywHrv1PoP6D8L5DxKiiy+YUjTdPfnuyb5n3re76H16KbovT57+/WjLFz3cFCFjx4vagsS4iBlo9vjnYV1//HnDHJ5LGZ4JIFRKWrEyZMgXr16+HyWTCI488gi+++AILFwaXzHU4HDh27FhY+7bb7SHHHmnqxbRiHRrONURkd7hMOvtXCGBwjimBub0t7O0K1AZsPePGdbmteLN3JwCgyK1Ha5v4PtxuV9B10WLLrMLV53ZBtvstnCmdH9Y20xXT0ZDVgN/W/ha9nb24Lv86SW0K55pKwcSJw0tMR0IkPhsp4/OUUIrMX3baLDjVKH7MaLYBumOyU2MoEp3vbbHZ0dl0OmB9qBqVLQ/PC1r/MPjvfosdtq4W37LOIXb1B7F3pPwsFobaKKXPApH5rff6ea9bsOvHAAG9LB5bXIMTJ+r9lsXv/HcH9X9ocvz+Hu77kIr+kWzY7XbJ9zmczw53PrSmsrDqUT4+2o47rhyHW9ft9C376XUOvxgTKtZFQnW+CoyIrwouG+raBp+/2OLzSJDs/hgKr/1Sx1kiMhKWrFRXV0Op9Lzunzx5Mhoahk8aVCpV2M4SyrE6BxzotZ9GdUkeyspM4RsdAeMObIfVNAMZeSXQ8uEX3U0zAV+dB0pKyrC38y+o0Zej3FAQ9M1Ka1sbCvIl/hXQZIKlsQSVzdvgmrsipIyxlwfKHsDLh17Gnxr+BEbP4P7p90POSuNiqRgsIvHZiBloE52/rNToMHHiuMi2USoxsVAByJXgNQZ0O3vh5JxQypTIVeeCjUGFrsPsCJjvvajGBIVCAcZUBjnL+K3vtblE54d7+6oErT+Rsdh012zfnHOdRo3yKM59KvhZvG2MxG+919d73YJdP7H5/XKWCThOXD9bEP+Hrcfv7+G+Q6Hs4wUe3fZuyb4/0ZDsPhyPB9fhfHbw+XC7ebQPOHz9UhCknkrMX4f2dhoaY8Rindi4sOhrEvVVRqbAxImha0mlJFafTnZ/DEWq2z9aSNj7wzVr1qCurg4cx+HTTz9FdXX1iB27/kJxfVF2fIrBNX0noe07DkvJVeAjSFQAYEoeMOACtrR04MjAOVyRN2XEpoD5YBh0lF8BbU8DdG3h31jkrBx3Tb0L80rm4X+/+V/c8dEdON1LKmFxIZr6ErFtvr8B+PAR4HeTwb//bzjRcxxLP1iKa9++Fks/WIoTPSfAx9DbJ0ej8KtLWFRjwgMLK3Hrup2Y98xW/HKz/3zwt2sbA+oYXlo2E2/XNgIIXn+ycccZ35zzBxdWIkcTXp0YERve3hVv1zbi6e9O9f3rd32WzoBJr0x8zZDG4PH3of5/4C8X/46hBoAXeJzoOSHp94eQDrebR12bGd9/eQfmPbMV3395B6wuLrBuatlMlOVqApZ5Y1Cwvkve78Lg7aLuz6TLF/dV3chOTySfJpIFRhDiLDEVhOPHj+Phhx8GACxYsACrV68ednwk2W2osf/71Rk8+f5R/O+/zIJWKf3LpbJ9T6Po6B9x6po/woHIbsh9DmDpx8C3pmzHHvff8bspDyJzmF8x4vJmBQDrsmPax79Ez9ircGbhTyLefuf5ndhwdAMcnAM3jL0ByyYuQ42hBkyYb2mGkoq/bsTd5gvKRk6bBUqNLnI1MIbxJCr1HwAAOm//C5Ye+j1aLBenNhTpirBx8UbkafKiMrHD7MBj7x7yqe0MrTkBPAnM4zdNAuDpMj20q3m2Wu7pPs/xkA/qJu3meMhYBq9uP4OXvzzr219Jjia6eeJIDT+Lt42R7n+wGpibF9Brc0EpYzHgcKPd7MDbtY1YOXcs7C4e44y6YWuG4v5mZfNqYNoPPFO/bD3Aud3A7Ls9Soth1AAMZ1+nrRNLP1gq6fcnGpLdh+Nh33D79K5r6bXh+y/vCHjz8eLSGdAoZH4d7O+4ajwYhvHFoKExKZjK12Blw5jUwAbagK+fB6YvBVgZwHPA/o3A3PtHtJ5KCp9Odn8MRarbP1pI2DSwyspKbN68OSHHrm/tR65OCb1aAU7i9vUM74Lx1NsYKL4KTlYTsbJMlsqjCnbQdhCVOaXIlqlG/s0KAF6hRlfJTOSd/gLnrrwPnEof0fazC2djkmESPmn4BJ80fIL3T7+PCdkTcPOEm7F43OIRvXmPWi5o559qPBZ86leQbQAAvY2+RAUAnNpcv5sSALRYWuDkom905nRz+Phou69IVazm5OOj7fjZ4hqUGS6KOQxNNIa+BS268CNDc4/VL1EBRqDfAeGHt3cFADR0WbD4ua8Cxtxx5TisfGUPtq2Zj+Kc+DXiHRa30+Pvg3weAHD5XZ7+EzHi5JySf38I6XBxvGhNic3J4Z+f/9pv+dLZ5X7xCAiMSWIM/i7EhNsJ7HjO899gLr8r9n1HAPk0kSykpYxEfasZE4wZkicqAJDd9DmU9k70l10TtgrYUCbn98GpaMAlmQmYAjaIjjGzwXIuGI5/FtX2eqUe36n4Dn4777dYOWklZIwM/738l8aCAAAgAElEQVT3v3HNm9fggc8ewMGOgxJbTESEtx/FBZTWbhTpivyGFOmKoJRFLyWrlMtw91Xl+GT11djy8Dwo5axPhtiLt+YkGG43j5ZeGxq6LGjptcHtvvi9UsplvmkXg/cXc68OIioUMs/1fXn5TGy6azZeXj4Ti2pMvloWlmX8riHPC+gwO9DcY0WH2QGVWh0/44b4OwBJ+1QoZUrJvz+EdCgu9EsZTLD6FLVC5ueXUckPx0KcfTVcyKeJZCHtkhXuQl+AMYb4/LqXf3ITXBoTzJkVUe9Dof8GAMCZx0tlVlRYs0tgySpGXt2HMe1Ho9DgqpKr8Ohlj+KpuU/h+vLrcbDjIJZ/uBy/3/d7mv+aKIbUsOTu24jn5j/ruzkV6Yrw3ILnkKvOjfoQ2Wo5bpxWgpWv7MGC33yB29bt9Oub4q1fMAaZ1y02z7yuzex72JV0njgRM3laRUDfivsXVGDf2S48c8tUPPD6ft81dLk4vx47S178GlZFdvweDOPQR2gwuepcPLfgOUm/P4R0GHXKgHq3l5fPRMmQ+pT1Ky9F54BE/VKiJc6+Gi7k00SykLBpYIniXLcVdhePslzpkxWVuQHZzVvRPemH4Ljop6Ecdx8C4yzAkaY83Di2J/QGcaSz7DKMOfwutJ0nYM2LPgHzUphRiO9UfgfXj7seb9S/gT8e/iNaLa146sqnRlw1J+0Z0o+ClStRoTFg4+KN0qmBWcT7GHj7pnh7Fjy1ZCqMIjUM7QMOrBqy/arXavHG3VegKNvzS31Vvt6v30FMXaOJmOi0ugKu970b9+GVlZdhzZsHsb+xFwCw6rVabLprdkAPnlUb90ddbxSSOPQR8ts9w6Iip0LS7w8hHb12N/7ns+N+/X70ajl+9f5Rv2VdA078+5sHpemXEi2DfDWimkSpzSCfJpKEtEtW6ls9HRYKMqUPOoXH1kNg5egpujrqfXS6elFvb0ApuwiH2hRwuAFVAq9SV8lMlB7ZjLyjH+Lc1f8q2X41cg1W1KyAQWPAOyfeQa46F2suXSPZ/okwGVzDAs+rVinriYLNE/f2TfHy+E3iyX2w7d3cxbdxks0TJ2Im2PXqGnD4EhXvMjcviI6Na73REH+XfPcMS/V4ScrQ+jnAU0M3dNmmu2aPvF+KEU1NYjzMIJ8mkoC0S4/rWs1gGOlli+WOXphOvglz+XVwMtHve+fAIQDApdnj4eRYfNOe2IcwTqlFT9FUGE5uAeN2SLpvhmGweOxiLCxbiFePvopPGj6RdP9E4gk2T3xozwIAojUpwbYfrsaFSAw8L/j65gxmuD4rVG9EjBRi9W28gIBlVidHfkkQSUba3fHrzpvj8rBjOv46ZJwNPWOuhxBDDcaOgUMYqynBpCwWajmPnU2Jn3vfUXY55E4Lcs4EqvzECsMwuLXqVozNGosndjyBbnvyd+QlwseUoRq2b4r3719uPiJakyK2/dpE9OgghoW/UAv46vYzeHHpDP+6gGWBdQHeOqWh9UZrl06neiMiLojVt+lUbEAdS0muBuuW+y+jOjiCSCxpNw2srrUfFcYMSFkrx7osKDr2JwwUzYFNafBookdBq6sLpxxNuCX/ekBwoirXgn0tWnC8GYn8IdmcNx52rQHGox+gu2Kh5PuXs3L8aPKP8Ivtv8Bv9v4GT135lOTHIBKDXM6iOl+PN+6+wtc12qhT4qklU31Tv365+YhvGsbQmpTB23v7rJgyVJDL0+53lqSmy+L01Z/0WN1Y/8NLIWMZqOQs1EoW//H2Yb+6AG+d0tB6I3NnC1g2J9EfhxiFiNW3uTk+oI7lmX/U4Vc3T6E6OIJIItIqWbE43GjotmJhtSn04AgoOL4RCkc3zld8P+KO9YPZafZMAatQlwIuATVGKw6263GyW4GqvMRJGINh0Vl2GUrq/g+qvhY4sopCbxMhxRnFWDRmETaf2owVNStQlVsl+TGIxCCXsyjK1vg11/IW0zd0WfzmiwOBNSne7YnkxenmfPP836htwhu1TQCArx/9FuxOPqAuAPDUKQ2tN+psso+c0UTaMdTfvPFnqG/+bDEX0GeFIIjEkVbJSl2rGYIAlEioBMa6rCg68gcMFM6BRVMEcO6o97V94CAqtOVQ83K44UK1wQqWEbCrSZnYZAVAZ9mlKK77B/Lq/oHmy38Ul2PcMO4GfNH0BZ6tfRZr/2ltXI5BJJ7BXZ7lLINFNSZfh/temwtv1zb6TdMM1RVasq7RRFiInW9vPUBTjw3fn1mCO68eB7nMcw0UF2pThnYOpxoAItF4+wINjT8KGYsOs4NiCkEkCWmWrHiUwAqzpWs8ln98IxSOLpyv/B74GBKVZmc7zjlbcWvujXBznsREo+AxPseGPc1qrJhmkcrkqHBpstGXX428+o/QfOm/AKz0Dxo6hQ6Lxy3Gm8ffxJ7WPbi04FLJj0EkFm9tg3fK0N1XleOBhZU+uduhfVeGjvfOH6/K14NlmZDrCWkJdr4rjBn4w4pZeG9fIxZfUoyVr+y5eD2XzsDLy2bg7tf2+W1DNQBEojFoFKLxB4yAJS9+TTGFIJKEtJr4fex8P/RqOXI10twkWZcVRUfXYaBgNiya4pj2td18EAwYTFD572dSngWtAwo09yf+V8jOssugtHYhq3Fv3I6xsGwhctW5eLb2WQjCCHcNJuLO4NoGAJhRbhDtw9Jrd4uO9/Y86LI4w1pPSEuw891jc6EqX48Vc8bi3o37/K/nxn3QKOV48tuTse2Rb+Hde+fSgx+RFHRaxftAWRw8xRSCSCLSLFkxY4IpA1L1Sy+oewVKexe6q26N6a0KL/D40rwfNRkToOD8L0mN0fNGZVdT4tWPegsmwaXKQN6x2DraD4dSpsQNY2/A4c7D2NO6J27HIRLD4NoGAMjWKIbtaTB0fKTrCWkZ7nyzLBO0dwoArHxlD2QMYNSrKFEhkoJg/jrUPSmmEERiSZtkhecF1LeaMT4vA1L8YC9z9KH46DqYi6/21KrEQJ39LNrd3ZiVORUc75/0ZKs5lOjt2NOc+GRFYOXoKpmF7IadkNt64nacq4qvQpYqCy8dfClux0hXeF5Ah9mB5h4rOswO8FLK4oXB0F4HvTbXsD0NxHojRLKekJbhzvdwfVY4XqDrQiQdwfyVYYCXl8/Eprtm4+XlM7GoxkS+SxAJJG2SlaYeGwYcbpTlSqMqVHR0HeTOfnRV3Qaei+0Xl639e6FhVRirKBBdX2O04lS3Ej22xF+ujjGXgxU4GI5/GrdjKGQKXFd+Hfa27cWB9gNxO0664a03WPLi15j79OdY8uLXqG8zj2jCMrTXwdu1jQF9VAbXM4j1RohkPSEtwc53jkaB+jYzfrn5CJ7+7lS/9S8unYG39p6j60IkHaYMVUCflbXLZoLjBTz5/lHcum4nnnz/KB5cWIkcjSLB1hJE+pI2BfbftPQBAIpyYk9WFLYOFB57BX3l18GqiL6vCgDYeQd2DXyD2TnTwbvFp5JNyrPg49O52HdeiYXjEivtadfnYyC3HMZjH6Bt6i0AE5/pHPNL5uOD0x9g7cG1pAwmEcHqDd69d66fnGc8Eet1kKNRBO1pIDY+kvWEtAQ734N9q8PsxM9vrIFBp0RhlhoqOYsfXz2BrguRdCgUMlSbMrDprtlwX3gzqFaw+PYL2/3i5KrXakc0ThIE4U/aJCsHGnuhlKhfQ8nh58HyLnSP/y6EGBIVANg5cBgOwYlLdFUQOPFqmoIMJ3LULuxuUiU8WQGAjrLLMPbAG9C1HYOloCYux1DJVVhUvgjvnHgHR7uOggE95MRKstR3DO11AGDYhwCx8ZGsJ6RF7HwP9q39jb24e0MtAE+flTy9dOqLBCE1CoUMxTkX2xk091iTIk4SBHGRxM8rGiEONPaiKl8PWYxvAlTmRphO/BW9E74Nm1wf074EQcAnfbtQqDQiD8H3xTBATZ4F37Sr4Ii+jl8yuoumgZMpYTy6Oa7HWVC6AFq5FusOrYvrcdKFkajvEKuJ8S7TGIpE62QSXUdDxI5SLsOiGlPAPH+GYeh6EkmNy8WhuceKhi4LmnusUCtYqoMjiCQjLZIVN8fjcFMfJhboEet9s/TQ7wCGRc/YmyDwsemKHbefwylHI+blzoaLG14WcWKeFU6OxeG2xP+CzCvU6Cq9FIaTn0Nu7Y7bcbQKLRaWLcRn5z5Do7UxbsdJF+Jd3yFWE3O2y+JbNv+3XwXUySRDHQ0RO9lqOR5YWOk3z/+BhZX4+JsWup5E0uJycahrH8Ct63Zi3jNbceu6nTjf58CrKy+jOjiCSCLSIlk52TEAm4vDWKMupv1oeuqRd/o99FZ+H3bEnjR82PsVdKwG1eoxIceOy7FBJeOxuzk5Ambr+KvB8BzyD78b1+NcM+YaqGQq/K31b3E9TjowuN7g60el73chVhPT0GWlPilpQIdFvF/F3AoTXU8iaWkfcAT47arXaqFWyuIWJwmCiJy0SFb2nvXI7JblakOMHAZBQPneJ8EpM9FVtijmhoXtrm7stnyDeYbLwbtcIcfLWaDaYMW+FnXMb4ekwJFhRG/hZJiObAbrsoXeIEr0Sj2+VfotfNn5Jc71n4vbcdIFb71BcY5W8n4XYjUxWqWM+qSkAS6OF72OMpah60kkLcH6rLg4Pm5xkiCIyEmLZGXH6S6Y9CrkZUT/NiSn6VNkt25H95Q74ORjn7v6j97tYMBghnYiBCG86WQTjRb0OWQ41Z0cEoqtE+ZD7hyA8Uh8a1euLb8WMkaGPx7+Y1yPQ8SGWE2M1clRn5RRyuBaI+qvQqQCQ+vjgvmtnJITgkgqRn2yIggCdp3uwowxOVG/kWA4B8pr/xP2rPHoMV4es03d7n580r8TV2RPh4wL36hqgxUsI2BPkkwFG8gdiz5jFQr3/wWs0xK342SpsjAzeyb+furvOD9wPm7HIWJDrCZmgkkX0Edl7bKZvp4F1CclNRlaa/Tq9jMB/SqovwqRTIjVx/GCEOC3Ly2bCVMMP2wSBCE9o166+GT7ADoHnJhclBn1PgrrXoHa3ICmq56GO8YGkADwTvdn4AUe87JngXeGvz+tgsfYbDt2NanxgymWeLU4iYjmidejZtvvUHDobbTMWhG341yRewX29u7FHw//ET+/4udxOw4RPWI9OAQIeO79o/j5jTXI1ijQa3Phuc+O46klU33TK6hPSuoxtNbo5S/PAgA23TUbHC9AxjLUX4VIKsTq437wh13Y/MAcvz4rpgwVFAp6E0gQycSoT1a+PNEJAJhgzIhqe5W5ASUHfw9zyTyYdWMBLjbt4FP2RnzWvxvXGOZC4WIQqZ7YVNMA3q03oqFXjvKcxOsYW3LK0FM4BQUH30THxMVw6QxxOU6WIgtXFl+Jt0+8jR9U/wATcibE5ThEbAztwdHcY8XHR9vx8dF2v3GP38QF3YZIfsRqjV7+8ixWzBmLMkNsQiYEEQ+C1cdZHbxfnxWCIJKPUT8N7B9HWjHBlIHcaF7rCjzG7/gPgJWjo2Yl+BgTFSfvwsvtbyNbrsdc/SXghcjf0kzNH4CMEbD1bPI0WmusuREM50LZ18/H9ThLKpZAI9fgiZ1PxCxwQIwMVJMyOqHrSqQa5LMEkbqM6mSl3WzHnrPdmFeRBy6KgpXCY+uR1bYTHdPug02I7ZdfQRCwvvNvOOdsxe2F/wzOGZ2Up07BozrPiq8aNHAlicCOI8OIlspFyD39JbIadsbtOJnKTNxSeQv2t+/H306RlHEqQDUpoxO6rkSqQT5LEKnLqJ4G9o9vWiEIwPSy7Ii31bfvQdm+p2Eu/RZ6cmcAfPSZgSAI+GvXR/i8fy9uNC6AUdCDQ/RvaS4v6seRDh12NqpRoQk9fiRorZiP3Jb9GPv5Mzhyy1q4MoxxOc6VxVfi6+av8cyeZzC7cDYKdAVxOQ4hDYNrUiw2O3QaNdUwjAKo1ohINchnCSJ1GbVvVnhewJ+3n0V1gR5GfWRTplTmc6jcdj9c+hK0TroTfAyJSqerF79t3YC/9W7Ft3Jn41L1RHB8bNPJKg1WmHRO/L1ei2SZDSWwcpyatQKs244JH/8SDBefJnAsw+JHk38EJ+fEv239Nzg4R1yOQ0iHtybF1tVCPQtGEfHs2UMQ8YB8liBSk1H7ZuWL4x041WHBT2+ojkiyWGlpRs0nS8HyLjRd+h/winUJgoBmVzuO2s7gpP0cuty96OMGwICFgpFDy6qRIdNCz2qRIdPCJbjR4GjBEdtpyBgZvldwAyYryuGW4CGeZYCry3rx1jETDnRmozBJXi7Y9fk4M/0HmLDnFUz46Jc4uehxCHLpX7Hn6/Lxo8k/wosHX8RPtv0Ez8x7BnJ21LoyQRAEQRBE2jIqn/BcnICnP61DYZYaU4qzwt4uo2M/KrfdB7lrAE1X/xcsrB7trm58bT6AL8370eLqAABkyfUwKQ3IV5sgQICLd8PG29HpPI8BzgoLZ4WckaFQZcSivKswQzcRcrcANxe6U324zCo0Y3tjFt49WYh5VT3QKpLjFUtP0VScnfY9lB94ExX/9xhOX/MY3JrIp+GFYlbBLNxmvw1/rf8rVm9djV9f9WvoFKRCRBAEQRAEMZoYlcnKqwe6UddqxlM3TwYQ+jUv67Ki6OjLKP5mLVzafBya8zi29B/B9oFDOG5vAABUasfiNsPlKFXmI0NQgePdAYpULMOCZWUQLhxVEATPlC8XB6lTCZYBbq7qwNraYjy3Mwtr5vZCliST+jrGXAGelaP8wJuY9MZdOHfV/egZexWkbgyzqHwR5Kwcrx97Hbe9fxsev+JxzCqYJekxCIIgCIIgiMQxqpIVq9ON//7oON76pg/fmV6MCpMeXLCiDoGHtqcehob3kX3yTZzl+7G57BJ8qdXhyLnnIUBAqaoQS/IXoUo1BmpeBo7nADfghvgbEl7gwXORdk6JnvJsB/6p7Bw+OjcG/7ktB3df2g+TLjkkwrpKL4U1swTj9r+OCR8/AathPDomXo+e8rmSFt8vKFuAIl0R1h9Zj5UfrcR003TcVnUb5hbPRZYq/LdqBEEQBEEQRPKR8slKc68Nr+1swLkuK7462Yk+mwvXV2XjtstKwfECGM4B5sQG7B84Co6zgXP1w+nsQa+rF90Mhxa5HGcLtOAYLYB2lPAFuMm0EFXqMdBDDZfbCbgBDsmRBAxlhrEDmToN3q034sEP8jDR6ERplhvjc1y4utye0C73tqxCHLn6IRiaD6Lg1FaM+ep5jPnqeTh1RthyyuDMMMKlzQUvV4OXq9A9fh5curyIj1NtqMYTc57AV81fYUvjFjz65aMAgAnZEzAmcwyKMoqgV+ihVWiRp8nDteXXUo0LQRAEQRBECsAIKdJd78CBA1CpxHudyOQKcIwMTk7AgJOHxe6Cm7uYXChlLPIylZDJGAAcGM4NQXACvAMcZ4XTaYedc8AFN1wuB1ycE5LP24ozDMNALmOhYBnIZYxn8hsv/fSzWGBkMrCsArxMAYFVAIwMAmSe6WEMAzcvRNUPZyhKuRIqmQoqVgUlq4SckYNlWcggAwMGnIsDx4knn3K5HBUVFTHbAAzvswQhFVL6LEB+S8Qf8lki1ZDaZ4nISJlkhSAIgiAIgiCI9CJJSrIJgiAIgiAIgiD8oWSFIAiCIAiCIIikhJIVgiAIgiAIgiCSEkpWCIIgCIIgCIJISihZIQiCIAiCIAgiKaFkhSAIgiAIgiCIpCRlkpUTJ06EPfbs2bPxMyTJSJfPmoqfMxKfjYVUOTepYCfZOHJ+K0ayn/9ktw9IfhvjYd9wPpvs5yMUZH9iSXX7Rwspk6y43e6wx9pstjhaklyky2dNxc8Zic/GQqqcm1Swk2wcOb8VI9nPf7LbByS/jfGwbzifTfbzEQqyP7Gkuv2jhbglK52dnbj55ptF17ndbsyfPx/Lly/H8uXLUV9fHy8zCIIgCIIgCIJIUeTx2vHTTz8Nu90uuq6+vh6LFy/GmjVr4nV4giAIgiAIgiBSnLi8WdmxYwc0Gg2MRqPo+gMHDmDr1q245ZZb8NOf/jSh0w4IgiAIgiAIgkhOGEEQBCl36HQ6cccdd+CFF17Afffdhw0bNgSMOXToEAoKCmAymfDII4/g2muvxcKFC4fd74EDB6BSqcKywW63Q61WR2V/qpEun3WkPufEiRMl21ckPhsLqeIDqWBnKtoopc8CI+e3YiT7+U92+4Dkt9Fut2P69OmS7nM4n0328xEKsj+xeO2XOs4SkSH5NLB169bh9ttvR2ZmZtAx1dXVUCqVAIDJkyejoaEh5H5VKlXYznLs2LG0cax0+ayp+Dkj8dlYSJVzkwp2ko0j57diJPv5T3b7gOS38dixY5LvczifTfbzEQqyP7Gkuv2jBcmnge3YsQOvv/46li9fjmPHjuGxxx4LGLNmzRrU1dWB4zh8+umnqK6ultoMgiCShPrueuxr25doMwiCIAiCSEEkf7OyceNG3/8vX74cK1euxLPPPovVq1f7lt933314+OGHAQALFizAnDlzpDYjpeF5AV0WJ5xuDkq5DAadEizLJNosgoiYTXWb8KtdvwIA3Fx0M56c+GSCLSJGCopjxGiBfJkgEkvc1MAA+OpVBicqAFBZWYnNmzfH89ApC88LqG8z485X96Kpx4aSHA3+sGIWqvL1FByJlKLJ3IRf7/k1Liu4DAIE/K3lb/hh7w8xPnt8ok0j4gzFMWK0QL5MEIknZZpCpgtdFqcvKAJAU48Nd766F10WZ4ItI4jI+PORPwMA/mXSv2DZxGVQsAqs/2Z9gq0iRgKKY8RogXyZIBJPXN+sEJHjdHO+oOilqccGp5tLkEUEETk2tw1/P/V3LChdAK1cCwECqjOq8Xnj53DxLihYRaJNJOIIxTFitEC+TBCJh96sJBlKuQwlORq/ZSU5GijlsgRZRBCR80XTF7C6rZhTNAcCPOroNfoa9Dv7sef8ngRbR8QbimPEaIF8mSASDyUrSYZBp8QfVszyBUfv/FiDTplgywgifD4++zHyNHkozyz3LRunGwcZI8P2lu2JM4wYESiOEaMF8mWCSDw0DSzJYFkGVfl6vHvvXFIeIVISjuew8/xOzC2aCx68b7mCVWBs1ljsbdubQOuIkYDiGDFaIF8miMRDyUoSwrIMjPrEdJAmiFg50nUEZqcZNYaagHWVOZX46OxHsLqs0Cq0CbCOGCkojhGjBfJlgkgsNA2MIAhJ2dGyAwwYTMieELBuQvYEcAKHuu66BFhGEARBEESqQckKQRCSsr1lOypyKqCRawLWjckcAwA43Hl4pM0iCIIgCCIFoWSFIAjJsPQ04FD7fszpbERG876A9TnqHGSpsnCk80gCrCMIgiAIItWgZIUgCGngOex/byXcEHB5Xycq/vH/QdXXEjBsjH4MjnUfS4CBBEEQBEGkGpSsJBCeF9BhdqC5x4oOswM8LyTaJIKIniPv4kDfSbBgoJn9IMC7UVi7IWBYqb4UjeZGuHhXAowk4g3FNSKVIH8liOSH1MASBM8LqG8z485X96Kpx+bTbq/K15MkIpF6CALw9e9wUJ+N8RklgCYbnaWXwXjyczRdcTfcmmzf0AJdATiBQ2N/I8Zlj0ug0YTUUFwjUgnyV4JIDejNSoLosjh9ARIAmnpsuPPVveiyOBNsGUFEQethcK2HcUghQ5V+DHiBQ0f5bLC8G9ln/ZtAFmUUAQBO951OhKVEHKG4RqQS5K8EkRpQspIgnG7OFyC9NPXY4HRzCbKIIGLg0CacVGlgFdwYry0EANgyi+DQ5iLn9Da/oQW6AgDA8Z7jI24mEV8orhGpBPkrQaQGlKwkCKVchpIcf2nXkhwNlHJZgiwiiCgRBKDufRwsmggAKNMYPcsZBr0Fk6BvOQSGu1ifopFrkKvOxeleerMy2qC4RqQS5K8EkRpQspIgDDol/rBili9QeufKGnTKBFtGEBHSUQ/0nMVBnR45Cj0Mcp1vVb9hPGScE7r2er9NCnQFONt/doQNJeINxTUilSB/JYjUgArs4wTPC+iyOOF0c1DKZTDolH4FeyzLoCpfj3fvnRt0DEGkBCc/BQAc4gYwMXMseN7tWzVg8BTQ65v3A8arfcsLdYXY3rIdgiCAYcjnRwtD4xrDMJAxntoAim9EohG7L9N9mCCSH0pW4kC4CiMsy8CoVyXQUoKQgLNfYSCnHGftHbjSNBPARelPtyoDVn0BMs4f9ktWinRFsLltaLO2+WpYiNEByzIw6JSkskQkFcPdl+k+TBDJDU0DiwOkMEKkDTwHNGxHXUEVAKBEkxcwxJJdCl3XKU9tywUKMzxF+KQINjqhGEgkG+STBJG6ULISB0hhhEgbWg8Djj4c1eoBACWq3IAh1uxSKOx90Dp6fcsKdZ5k5WTPyZGxkxhRKAYSyQb5JEGkLpSsxAFSGCHShoavAQBHGSeMymxksIHTKSzZpQAAw6CC+kxlJtQyNRXZj1IoBhLJBvkkQaQulKzEAVIYIdKGs18BOeU4amtFhb4MPO8KGGLLLIQABln953zLGIaBUWtEk7lpJK0lRgiKgUSyQT5JEKkLFdjHASmVvkKpihFEwhAEoGE7LOPn46x1P+bkXSI6jJcr4dDmInOgGeZBy00aE5oHmkfGViJuBItRpLJEJBPBfBIAOswO8lOCSGLilqx0dnbixz/+Md577z3R9T/96U9x6tQpzJs3D/fee2+8zEgYUih9hasqRhAJofs0YO9FnT4PglVAqTqwuN6LLbMAenOL3zKj1ohDnYfACzxYhl7ypiKhYhSpLBHJxFCfpHssQaQGcXtCePrpp2G320XXffzxx+B5Hps2bUJjYyPOnj0bLzNSGlIvIZKapr0AgDq556ZerDYEHWrPyIfO0g6Gu9iDxag1wsW70G5tj6+dRNygGEWkMuS/BJEaxOXNyo4dO6DRaGA0GkXX7969G9dffz0A4Morr0RtbS3Ky8uH3aDWDE0AACAASURBVKfD4cCxY8fCOr7dbg97bDKjMRSJqpdYbHZ0NnkkX0fLZw3FSH3OiRMnSravSHw2FhLlA/nffIJsuQb7+9uQKdPC2tkHMyd+k5cLGhQKHHrqa9F3oa+KYPFIGe88thNVuqoRs3s4UuH7NNRGKX0WiMxvw4lRkZDs5z/Z7QOS38ZgP2LGwnA+O9z5kNp/40GyX89QjBb7pY6zRGRInqw4nU68+OKLeOGFF3DfffeJjrFarcjPzwcAZGVlobk59Lx1lUoVtrOMFsfqMDtQkqPxC6YlORroNGqUX/h8o+WzhiIVP2ckPhsLCTs3X50BCqagBb2YoC+FMS8XgxtCDkapcgKnPsBYpR09Y8YAADRWDdAIuDPdmFiZHNc2Ffws3jZG4rfhxKhISPbzn+z2AclvYzweXIfz2eHOh9T+Gw+S/XqGguwnpEDyaWDr1q3D7bffjszMzKBjtFqt79cVq9UKnuelNmNUQOolRNLidgCth8AbJ+KkpQVl2nwES1QAzzQwAYCm++KvlbnqXLAMi3ODVMKI1IJiFJHKkP8SRGog+ZuVHTt2YNeuXXj99ddx7NgxPPbYY3jqqaf8xkyePBm1tbWYNm0a6urqMHbsWKnNSFrcbh7tAw64OB4KGQtThgpyuXjOSIo6RNLS9g3AOdGUaYKt14niYYrrAY8imFWVDXVPg2+ZnJXDoDag0dw47LaCIGDn6W6c6bRgXpURxdmaYccTI8fgGMXzPDjBc726LE7RWEXqhkQyEe49NpL7NkEQ0iN5srJx40bf/y9fvhwrV67Es88+i9WrV/uWX3PNNbj99tvR3t6Obdu24Y033pDajKTE7eZR12bGqtdqfcoja5fNRHW+ftiEhRR1iKSjqRYAcELpCSEmVXbITcwaAzIHJSuAp8h+OPlip5vHI28dxHsHPEpiSjmL33zvEtx0SVG0lhMSw7IMDDplSFUlUl4ikpFQ99ho7tsEQUhLXL9pGzZswIQJE/wSFQDIyMjAhg0bcMkll+DVV1+FXq+PpxlJQ/uAwxfwAE8h36rXatE+4EiwZQQRIc21gM6I464+MGBQpMoJuYlFkwt1fysgXJz2adKY0DLQEnSbpz44ivcOtODHV47Fs9+fhnF5Ovz7mwdx7Hy/JB+DkIZwVJVIeYlIRei+TRCJJ2E/C2RlZeGGG24Iqhg2GnFxvKjyiJujmh0ixWiuBQqm4oSlGcUaIxTBy1V8WNQ5YHkXFJYu3zKj1og+Zx8GnAMB47843oE/72jA7ZeV4Z9q8lGQpcZD11RCrZDhyfePSvlpiBhxujnR2OZ0cxGNIYhkg+7bBJF46B3mCKKQsb5CPi8lORrIZXQZiBTC1gN0nQCM1ThuacZYXRF4IfQDp+XCVDF138VpX3kaT63L0KlgTjePX/79CMYYtLjpkkLwF5KhLI0C/3xJEbaf6sLO010gkgOlXCYa25RyWURjCCLZoPs2QSQe+raNIKYMFdYum+mnPLJ22UyYMqgmhUghWvYDAKzZpThn60CpxhTWZha1Z6qYqrfJt8yXrJj9k5W3aptwutOCO68aC8C/nuGaifnIVMvxp6/ORPsJCIkJR1WJlJeIVITu2wSReOLSFDJdcbk4tA844OYFyFkGpgwVFIqLvxrK5Syq8/V44+4r4OZ4yIOoiqSLYg4v8Oi2d8PJOaGUKX1StmLLiSSi2VNcf1qlhgABRcN0rh+MXZUJnmGh7ruYrBg1nmmg58wX5Yudbh4vfH4SU4qzUGHS+96qeFHKWVxZYcRHR1rROeBAHj00JBwxVaUcjcIXxzRKGdy8AL1ajk13zQbDAAADo07pF+tUanWiP8qoJZJ4yzLp8zum2P2W5wU/9a+KPB023TXb795OxfXD4/UrbYEWnbbOoH6V7v5HhAclKxLhcnGoax/APYMUQ15aNhPVpoyAhKVoGOnVdFHM4QUeJ3pO4MEtD6LF0oIiXRGeW/AcxmePx6neUwHLVWp6IE0ammqB3PE4bu8AABQoQyuBAYDAsHBqDVD1Xyyo1yl0UMvUfvLFHx4+j+ZeG+6ZNy4gUfHyrSojPjx8Hu/ua8adV4+L/rMQkjFYVWlwHDNmqPDIdVVY89YhX0x7+rtTsa2+DTdNK/FXWVo6HTwvjKpYlwxEGm8rcirS4oFR9H67fBYUcgY/XL8HTT02LKox4YGFlX739tF4T5aSYP421K/CHUcQ5A0S0T7g8AUzwFOAd08UiiHpopjTbe/2BSgAaLG04MEtD6LT1im6XJGlSKS5hBdBuFBcPwUnLE1Qs0oYFBlhb27XGaDqP+/7m2EY5Gny/BTB1m8/i7EGHSryg6sEluRoMcagxQeHzwcdQySOwXFs1fzxvkQF8MS0R98+hFtmlQWqLG3cP+piXTIQabzttncn0twRQ/R+u2EvGrttvmXfnVkacG8fjfdkKQnmb0P9KtxxBEHJikS4eUFcMSTYT8NBSBfFHCfn9AUoLy2WFrg4l+hyniHllaSgrxGwtAN5lThhaUG5rtBPijgUDl0eVOZWT9JzgTxNnu+a7z/Xg4ONvX5F9cG4tDwXBxt70W62R/VRiPgxOI5laxSiMU3GMmkR65KBoPGWF4+3Ti49HsSD3W+1youzIYL5L/lpcIL521C/CnccQVCyIhFylhFXDInwNXG6KOYoZUoU6fwb+xXpiqCQKUSXswK5alJwoV4FuWNxxtqKMm0+hDCUwLw4dAbIXTbI7Rf7pORp8nDech6CIODP289Cp5Lh0vLQdUqXludCAPDJkbZIPwURZwbHsV6bSzSmcbyQFrEuGQgab1nxeKuUpYfoQbD7rdV5MaYF81/y0+AE87ehfhXuOIKgJ0CJMGWo8NIQxZCXolAMSRfFnFx1Lp5b8JwvUHnnquZp8kSXu/pciTSX8NJcC8hUMGvz0O7sQ4EqvOJ6L3adR/1L1X9R/cuoNcLmtuFkVxs+OHwei6cUhiULWpqjQV6GEp/Xt0f2GYi4MziOrd16Cs/cMtUvpj393al4a++5QJWlpdNHXaxLBiKNt+kiaiJ6v10+C6W5Gt+yt2sbA+7to/GeLCXB/G2oX4U7jiCowF4iFAoZqoz+iiFGnRIKhcxPbUQhZyFnGdic4kpfYqo6o1ENjGVYVORUYOPijQEqIGLL69vqE20yAQBNewFTDc7aPQlCviq84novjgvJirq3CZb8GgAX5Yv/su8gXJyA+VXhNYplGAaTi7Kw60w3OF6AbJR9R1IZlmUw3qD1xUMFy+Bv982B3cWDYRjIGKAyfwJyNAq/WGfubAHL5iTa/FFHpPE2XYqbWZbBBBGlL4Zh/FQ787SKABXP0XZPlpLBfmWxW6BT60T9Kt39jwgfSlYkgucFnOqyBqh4VRgzcKJjwG/5M7dMxX/9ox4dAw5RVZHBqjqjGZZhfQ+q4SwnEozb6emxMuX7OG3xFLablFkR7cKh9TyIKgcpgnmv9Uf1RzG99DIYdKqQ9SpeJhdnYevxDnzT3IdLSiNLnIj44XJxqO+whFRHBOAX6zqbqP4oXlC8DcTt5lHfPuCvSLdsJqrz9T7VznRR6JQar191nO3AmIljQo4jiOGg9FUigql4tQ84ApaveesQVs0fT6oiRGrRdhhw2wFjJc7YWiFnZDAogit2iSHIlHCp9FD1t/qWeW9UHbbzuKbGFHaiAgCTijIBANuOd0RkBxFfpFJHJIh40j7gCFSkG+Kn6aLQSRDJDCUrEhFMVcTF8aLLszUK3/+TqgiREjTu8fybOx6nra0o0ZjARKAE5sWhzYVy4GKdiUaugQxaKNQ9mFoS2duRbK0SZblafHmyM2I7iPghlToiQcSTYPdnN3cxrqWLQidBJDOUrEhEMFURhYwVXd5rc/n+n1RFiJSgaTegLwQ0OThjbUOJ1hSREpgXpyYHqkHJis3Jw2XPgSF7AHI28pA0uSgTB871wu6ih4dkQSp1RIKIJ8Huz4MFPtJFoZMgkhlKViQimIqXKUMVsPyZW6Zi7dZTpCpCpBaNe4DCaXC57Wi0daBYHd08Y4c2B0pLp68/y55TTnDOXDCK6BqBTSrOgpPjUdvQE9X2hPRIpY5IEPHElKEKVKQb4qfpotBJEMkMFdiLMFi9S0yNK5i6V36mCu/cOwcuN++33WB1L+/452+fPmqUvniBR7e9m9Q8RjPmVqDvHDDlezhn6wAHHvmq6FSbHNpcsLwbCms3XLo8bD9uhwq56HfVQQAPJsLfUCYWZELGMth2vANzJ1ChZrwJFR/dbh4dFifyMhTYdNdsn1KbKUMVUFxPSI9KrUKnrZPisQhivlspouIpl188X+mi0Jlq0HNHekHJyhBCKX+IrY9K3Us3wh8sTvACjxM9J/DglgfRYmnx6aRX5FRQ4BhNNO72/GuYgDM2T3G8URGZEpgX5wVFMJW5DT2yXBxocGJidR4aBBfMrh5kKiLr3aJRyjDWoMXus9G9mSHCJ1R8dLt51LWZRdWVBj8AEvGBF3jYM+y454N7KB4PQdR3l8+CQs7gh+v3DOuv6aLQmSrQc0f6QVd1CKGUP8TWp7O6V7e92xcwAKDF0oIHtzyIbjs9OI4qmnYDMiWQXYLTVk+ykq+KLllxaDwNv5R957HnlAMcD0wwehKUbkfrcJsGpTJfjyMt/XBQ0WtcCRUfw1FXIuJHt70bq79YTfFYBFHf3bAXjd028tcUg5470o+w36ycPHkSW7ZsgdN58UH8/vvvj4tRiSSU8kew9emq7uXknL6A4aXF0gInlz4JW1rQuAcwTQIYGc5Y22BS5UDJsOCjUAO7+GalBV83OmDMZFGWlQu0A5328yjPmBTxPqsKMvHhN634prkfM8dQU8F4ESo+hqOuRMQPisfBCea7WqUsYBn5a3JDfp5+hP1m5aGHHkJ+fj4uu+wy33+jkVDKH8HWp6u6l1KmRJGuyG9Zka4IShkVH44aXHZPM8jCqYDA47S1FaXafPC8O6rd8XIVXEodmJ5WHD7nxPRyOfQKT4LRYW+Oap+V+RkAgN1nuqLangiPUPExHHUlIn5QPA5OMN+1OrmAZeSvyQ35efoR9jfSYDDgxhtvHPXJSijlD7H16azulavOxXMLnvMFDu/c0Vx1boItIySjeS/AOQBTDQRB8MgWq40x7dKpzYWjsw28AFQXs1CwSujkmeiwn49qf9laJQoy1dh9hqYBxJNQ8TEcdSUifuSqc/HsvGcpHosg6rvLZ6E0V0P+mmLQc0f6EXIa2HvvvQcAqK6uxooVK7B48WJotVoAwM033yy6TW9vL44cOYKJEyciNze1nCeU8sfQ9VoVC6uDx+9umwY5y0CtYNHYY4XmguqNi+ODqoeEUtVJBViGRUVOBTYu3kiqHKOVs18BYADDBLQ5emDjHSiI8abg0ORA1d6ObC2DPL0AXgCylLnojDJZAYCqAj32n+uFIAhgmNT6HqUKw8VHt5tH+4ADmWo5Nt01GywL8DwgYxn0O1xw24QApURCWliGhXpATfFYhGC+63JxfmpgeVpP4t3S62nqrJCxMGWowhKIGA339FSAnjvSj5DJiiB4Og5XV1ejurratyzYw0BfXx9WrVqFefPm4de//jX+/Oc/ByQsbrcb11xzDUpLSwEAP/vZz1BVVRXTB5GSUMof3vViyjcvLp2BL+raMWtsLta8dUhUMQcIraqTSrAMizwNScaOWs5+BeTXAAoNTptPAwCMysg6zQ/FrsmFwX0Mk8pk8DY1z1TkojPKaWCAp8j+i+MdON1pwXhjRkz2EcERi49isfClZTPx/oEm7D7bi0euqxo2HhLS4bA7KB4HYajvulwcjndacM8gv12/8lI4XTzujlDRbjTd01MBeu5IL0KmoUuWLMGSJUswf/583/8vWbIESqX4VKf6+nr85Cc/wT333IMrr7wSR44cER2zePFibNiwARs2bEiqRCUSxJRv7t24D9+eUeK7MXuXD1UJC6WqQxBJgcvukS0ungUIPM54lcCUmTHttlHIg5px4RLjgG9ZljIX3fYO8EJ0tTBVBXoAoKlgCUAsFt7zWi1umVWGVfPHh4yHBJEI2gccvkQF8PhmU7fNl6h4l4WjEEb3dIKIH2Grgf3rv/4rXn31Vd/fr7/+Om644YaAcd5alj179uDQoUO47777AsYcOHAAW7duxa5du1BZWYknnngCcvnwpjgcDhw7diwsW+12e9hjY0FrLBVVFxEEQXS5xWZHZ5Pnl2mNoSjkmHAYqc+aaEbqc06cOFGyfUXis7EQz3Ojbd+HMZwDnfJ8WM414FDXSehYNaxd/TBHqLzidrvQ2tYGAGjozsLlAIrdJ9HUXuIZYFeAB4/6hsPQsZFPMxMEAToFiy2HGzBNb4l4eyA1vk9DbZTSZ4Ho/DZYLJSxDLI1irBjXbKf/2S3D0h+G+12u+T7HM5nhzsfWlNZgG9qlTJRf3Vx/LDnVap7+lCS/XqGYrTYL3WcJSIjZLKye/du7N69G83NzXj++ecBADabDZmZwX9ZFQQBH374ITIzM0WTkClTpmD9+vUwmUx45JFH8MUXX2DhwoXD2qFSqcJ2lpFyrJZez6vewQGqJEcDhmFEl+s0apRfsKvD7Ag5JhzS5UuUip8zEp+Nhbiem/PvAAyLvIrLkSdXoaNnAGMyCmHKy4UAIaJdtba1oSA/HwDw7j7PW5AxKjtcJs8y+0A5dpkBpYFBmX5MVOZWFzlQ12WP+nykgp/F28Zo/DZYLOR4Ab02V9ixLtnPf7LbByS/jfF4cB3OZ4c7H8091gDftDo5UX9VyNhhz6tU9/RI7E8FyH5CCkJOAysuLsZll10GvV7vUwG79tpr8fvf/z7oNgzD4PHHH0dVVRW2bNkSsL66uhomkwkAMHnyZDQ0NMTwERKHmPLNi0tn4G/7mvDMLVODKuYAoVV1CCIpOPUZUHgJoPD46RlbG0o1pogTlcH02Fjs7i8AAGRY2nzLs5Setymd9ugaQwKeupXGbhs6qanbiCIWC19aNhNv7T2HtVtPhYyHBJEITBkqvDTEb0tyNXg5CkU7uqcTRPwI+WaluLgYxcXF+M53vhOWXPG6detgMplw8803w2w2Q6/XB4xZs2YN7rnnHlRUVODTTz/FqlWrorM+TogpevC8gPYBR4A6SKVR56ckolaw+OfpxVDKWLxzzxzYXJxv/GDFHBfHw6BT4p175gyrGDaMkYC1A+PzlMBAG6A1gmc8nV2dnBNKVgmWZWF320kpg4gOSxfQvA+4fBUg8Oh3W9Hp7EeB2hDTbg+1KTEALewyDTKsF5MVvSIHDBi025qi3nf1hbqVvWd7cN3kgpjsJMJHLmdRna/HG3dfARfHQ84y0CpZrJgzFrfPFqCWs3jz7ivgvLBOJWfRZXEiR6NAj83li7UqtTrRHyU2LsRluJ2AXAlojQAbedzlBf5iLJcpka3KRq+jl5SPJEahkKHC4H8PN2iUkMmYAIUwsfv/YEIpiaYEEvlvTCYM8X2vHPHQZeT/6UXY0sWZmZm+//ciJl1866234qGHHsKbb76JiooKFBQU4Nlnn8Xq1at9Y+677z48/PDDAIAFCxZgzpw5MX0IKRFT9PjLnZejz+b2U7pZu2wmKo06HO+wYNVrtTBmqAIUb565ZSr+6x/16Bhw4A8rZmFCng717QMB+wmlMiJiJNB+FPjrD6DsPQdkl4Ff9i5OsBwe3PIgWiwtKNIV4Vdzf4Xf7fsdOm2deG7Bc6jIqaAvOBE+pz8HIAAFUwEAZy4kFvkxKoHVdyqhkfOwqXOgs3b4lssYGfSKbHQ6opcvHpung0LGYOfpTkpWRhi53PMQV9dmxuYDTVh8STHu3bjPTynxtR0N2H66C09/dyq21bfhpmkl/vFw6XTwvJBaD3heBsVlXIjLuO0vgKkmogc+XuBxoueEXyx/9lvPYu2Btfi86XNfTwmK57Fjt7txostfDczzpkWFW9ftRFOPDXdfVY4bp5X4jQl23w6lJJrUSOS/MZkg4vvPLXgOSpkSqz5Z5beM/D+9CHmlBUGAIAjYvHkzamtr4XA4sH///oDExUtWVhbWr1+PjRs34he/+AUqKir8EhUAqKysxObNm7F58+aAdYlGTNHD4RYClG5WvVaLDovTt1xM8WbNW4ewav54nyqImGJOOCojAVg7LgYUAOg9h+6+Bt8XHABaLC342dc/w4+m/AgtlhY8uOVBdNtJJYmIgJOfAepsINtTP3L6ghKYSZkV027rOhSYkOuERZXll6wAQJbSgA5b9NPAFDIW440Z2NvQE5ONRHR4Y9wts8p8iQpwUSnxzqvHoanHhkffPuRRChsaDzfuT131JJG4jL/+wLM8Arrt3QGxfPXnq/Htim/7/qZ4Lg1dNmeAGtg9r9XC6uB9y26ZVRYwJqr7drIjkf/GgpjvP7jlQTSZmwKWkf+nFyHfrCxZsgSA5w3Lk08+6Vu+YsWK+FmVQJxuLkDRg2UgqvLh5i+qfgVTvMnWKETH++2H4yMz0u28GFC8dqt0vi+zlxZLC7IuPFi2WFrgjFC9iUhjeN5Tr1J2BQCPf56xtkLByJGryAB4V1S7HXAyaPz/2Xvz+Daqe+//M4tGu2XJ8hrH2XCchCUhQFnCkgDtDdAWemmhbLkNpU/D8koJPOW2lHtz85CnvS0/cG7aS9PlYUlIKbQUAoW0XJYADVsSAgkhi2Mnjpd4kXdrsTTL74+RZC0z0mgkOZJ93i94OTo6c2YkffWdczRz3mfYgLOrvfAFS1E11AIKEiTIv6SXGJzo8Ok35wCywviv+07CHxRg5pis2iJkRkgQoxYwNTtY7L+V6gR5YcKON6co5GUMnpDLMyAoBFPm8shjks+zR/WcLI7PyVOL04zP24VOjuI3G9Ri38yak8pI/E8tNF9Ds9vt+NnPfoYXX3wRv/jFL2C1WvN5XKcMjmWiE+QiiBKSymqdZrA0FS2PGG8S6wz6Q4r149phMryUyXLyJdrY4x7zosZaE1dWY63BUHAo+m+OIRP9CBpp3yXPhaq7MFp0zNeFWnMFKEn/SfqIRx68T7f74TU5wQpjMIfG11op4VwYCvaDF/WfiBoq7RBECXvbyNWVicbA0FELmFKuE8KdwFR1OLZIB5gKeRmldXJ5BnAMlzKXRx6TfJ49qufkmNsQ1eI04/N2oZOj+M0Gtdj38/6kMhL/UwvN37YNGzZg4cKF6O3txYIFC1LawIoZJaOHkaWSTDebbj0H5VYuWq5kvHnkm2dh047mqBVEyZijxTKShKVcvpc0klhK6+ByzMDGyzdGv+iROStP7H8ieo9nZKIagZCWgy8DDAdUnxUtOubvxnRLJSRJ/y/fhzwcaErCtJIAvEb5l2Kbvyf6vMNQBgkSBkP6bz2or7SDAvBRC7lNYKKJ5Lg/7z6Bx29ZnGRK/N27Lah1mvHz68+STWGJ+fCWs4vXnqSQl/HtZ+XyDHCZXEm5vHFZI7Y1bYs+Jvk8N5SZuSQb2K9vPQcWIx0t+/PuE0l1dJ23C50cxW82KMX+xss3otZem1RG4n9qQUmSpN9BOoFk4rrO1oudygbGCyLYGBtIKCTI5aIEA0ODpSkEwgYwzkAhEIw3fUVsYInt6DhIwNeLoN8LzmxNsoEZaAMgSQgIYzDQLNwmN1jWoNocL/Lw+D0ICSEYGAPcZjdYWvOaoXmnGF3nE3XMOd+PJAEbzgTK6oFL/zcgiQiKIZz3j3txw/Qr8RXn6bqa7eruxuMH5iPAA3ctboNj9CS+8tnjePe8H6Kl6gIAQLu3Gc8d+xXuP/P/Q4PjXN0v4V9f2IdpTjOe/d4FGW1XDHGW72PMtv1IThRECQxNgaLkkDIZaHiDAhhKLgMolFs5DAb4aK4d8XRi9qxZOXstuSbte5NoUzKXAf6+jO1KajYwURQhSAIESQBLs3F5OrKNN+CF1WQtWGNSPuI3VZvp9hcI8OjzB+NsYBzHxPUBSk0ser3BuPM2TVNJ/YR8iCEmNCcJPDDaBQghgDEAtiqAya4fEHv8SqavxBjVagMTJXFC+izFcE6YChROb7SAUDJ60DSFmtKE28NECUc93jhzWKIBrKHSHpfAWJZOakfnQQK2SjS3HcT8+bPlIgBusxuiwKNp4AhW71gzbs9Y2oh651zQComHF3kcGTiCNW+vibPPzHXOLagBC2GC6PwEGGoDzrsDCN/ydcLfCxESqjin7mZ5kcLRfgMuqxuFBMBrkq1idt/4hPoSQ3itlbGTaND/CtBQZcfOo55o54IwMSjlxEgeBICekRHF5yI50tOe+9XNJ5RwXgaQlV2Jpmi4ze64slJjqWqepila0aJEjEmpEUUJxwZ8ijGZ2AeIPW8rWUOVzvdFhSgCvYfyZgNTM30lxqhS7AOIKyN9lqlH2gj82c9+BgC47bbbsGLFCqxYsSL676mOkjks0QB2Ksw2/f7e6EAFCNszdqxBv1/51hqP3xP90kfqr3l7DTx+z4QdM6GA+OJlgGajymJg3ARWbizR3Wz7qBkhgcIMh/x9CbFmBBlT3ForNoMDNMWg19+p1owmGirt8AUFHOoayaodQmYo5cRIHkz13KQkx3alVHlazaJEjEmp0RuTkzKW82wDy2WMkj7L1CPtEPTHP/4xAGDLli15P5hiQ8kclmgAOxVmm6DIKxo1giKvWD8khBTrh3QanwhFjCQBX2wD6i4CWBMQnp8S1RYbSqJXWzLl+LAFADDNPv6d8ZlKYfOOz1mhKRoOgwu9gewGK5HFIT8+1o8zpmWnWiZoRy0nRvLgpLJ/pSPHdqVUeVqUROWcT4xJKUkXr7nerqDJsw1MzfSlJ0ZJn2XqQa4PZ4GSOSzRAHYqzDYczSoaNTiVy6MGxqBY30Crz3EhTFK69gMDx4DZS6MDFUAerFSbymCg9Mdz+6gZJUYBduN4u6NGJywJV/xKOCc8Af0LQwJAmc0It43Dhy19WbVDyAy1nMixTMrnJiU5dElDHwAAIABJREFUtiulytNqFiViTEqN3piclLGcZxtYLmOU9FmmHpoHK9dffz0eeOABPP3009i9eze8Xm8+j6soUDKHJRrAToXZxmUux8aljfH2jKWNcJmVrR5usxuNyxqT7DNK940SJjkHXwYoGqg5O664xdeFOksVRJWrc1poHzGjzhFCrNPDZyyF1ecBhfGyEoMLnkC3UhMZ0VBpxycnBlAkDpFJgVJOjOTBVM9NSnJsV0qVp9UsSsSYlBq9MTkpYznPNrBcxijps0w9NM9E+v3vf4933nkHf/vb3/DYY4+hpqYG27dvz+exFTw0TaGh0o4X71qCIC/AwMo2sF/dfHZe7SBpj4thUe+ci63Ln0JQ5MHRLFzmcsXJ9QDA0izmOufi6aueRkgMwUAXng2MMAFIEnDgJWD6BXG3gAmSiOP+bny1tB6Avo5/SABO+kw4s2o4rjyy1oopNAq/Qb51y8GVYSQ0hKDgB8fol1E0VJVgZ3Mf2vr9qCuz6G6HoJ3EnJiYB1M9N+mgaXly8h1vZGwDUyJdnq531mPrNVsL3gZWSKSL11xvV9DkOF6TmqfoaIymsoFpgfRZph6aP9nly5dj8eLFWLZsGf7t3/4N06ZNy+dxTQhKimKapuLKDWGTkD+sI06lGqZAodTMwWXNU8JK1GKm+MWDZli4bdXyZgKPfn9veOBiAM1wCAiBuGTB0iyqrFVy/Vh1IM2BpmkE+AA4moNLAuiQPy6RadEREoqA3kNAXxNw5rfibgHrDPRhTAyhyqj/V9r2YRaiRKHaPhZX7jXKRrASf/f4YCVsBOsPdqPKPFP3PqPzVo73kcFKHonkS1EUwYsSaEpeSFdSqBPJtdUOc3F37NRQUhcDsrBCCAHDHSlzZ0RRrJZLI7lalMRox8zj98TlakJmhEICgrwgr1rPCwiFBBiN6btGStbQgkWt75BYFmuzU2sqrj+h/kNoJLYtVRZ4/J5oLKe7+hESQvD4PeBFPqrnNjDJt3fF9lkIkx/Ng5XXX38dO3bswPbt2/Hwww+jtLQU//jHP/J5bHlFTT1YX25DU++oqo54063nYF6lHSxLT6y+UEWDaTKl7oQpaYzXL1mPDZ9sgMfvSVIHKukF4+pfuA71238CerQH+PazEMvnoWmomSgzJwNfvAyAAqYl3wIGAJXhgYUejg3IJ5tKS8JgxSSrkK2+XqDkNADyKvYA0Dd2MqvByjSnGVaOwYctffjmOdN1t0NQJ5IDG//nMP7loll4+v1j+JeLZuFfX9gXlxONLI0VT3w8eTSvSijl6Bu2APtfAOqvBF6+Jy53J+bOZbXLsGrRqjgda2wuTczNSvVT5XZCMmNjPI54vLjzmT3R2Pz1redgrtuqacBSFCjF5a0vAnwgY02x1mURtGqKEwkJITQNNiUpietL6xUHLISpg+YstmbNGjQ1NeHrX/86XnnllehAJRgsTtuImnqwZ3QspY541TN70DM6lrKNvOgLVbSCldbUH6GSxvihnQ/h9jNvV1QHKukF4+p/sBb9l94f3X+/v5coMycLX2wDas8FDLa44mafPNm9gtNv1To2wMLICHBZ4m0tvvAAyO4dX2vFwcm/RvcFupANNEVhbqUdu48PZNUOQZ1IDrz+nOn41xf2Rf8m5sTWPt/k0rwqoZSjn78NOPuW8YFKpFwhd15bf22SjjU2lybmZqX6qXI7IRmPLxgdqABybN75zB54fJMoNpXicqBFl6ZY67IIejXFRElMUEPzTwdPPPGEYvkdd9yBzZs35+yAJgo19WBIENPqiHlBTNlGXvSFKlpBlkqtkVXTGDvCHc9EdaCaXjCuvsUV3b+qJpkoM4sLz1Gg5wBw6QNxt4AB8pUVN+eAmWYh6Pxcjw0aUGMNgJKkuNuDQqwJQdYctzCkhbHBQHPoCXTo2lcsDVV2/HFXG/pGx1BmK5JbNoqISA4sNRvi/sbSPuCHhWOSyopa86qEmvqVZhTLE3Ong3OkzKWJuVmtvlpuJyTDi5JivPLiJJJyKMWlwaJLU6x1WQS9mmJepX0+C7ELYXKQ9fXhYjXtqKkHDQydVkccWRF7QvWFKlpBXkr9EappjIeCQ9F/x6oD1fSCcfV9/dH9q2qSiTKzuDi4Tf47bXHSUy2+LszIwgQmSkDrIIsam19xer7X5JRvAwtDUVR4rZXs9MUAMK9KXsRyTyu5upIPIjlw0B+K+xtLrdMMX1BIKitqzasSaupXUVAsT8ydQ8GhlLk0MTer1VfL7YRkWJpSjFd2Mt2eqBSXIZ8uTbHWZRH0aopZlfbJxHlC1oMViirOL7WaerDCZkypI9506zmoCP9CO6H6QhWtYLc39ZUVJY3x+iXr8cT+JxTVgUp6wbj6F66D691Ho/t3mcuJMnMy8MU2oPpsIGGFekmS0OLrQq2lEpLOxSC7RxkEeBqVFr/i815jadxgBZDnrWS71goAzC63wsBQ+ICst5IXIjnwhT1t+Pn1Z0X/JubEGWWWyaV5VUIpR9+wBdi7Ffj6r5Jyd2Lu3Na0LUnHGptLE3OzUv1UuZ2QjNvC4de3nhMXm7++9Ry4LZMoNpXi0jlbl6ZY67IIejXFRElMUIOSsrw0smLFigm5DezgwYOYP39+Tuuq2cB4XkTP6Bh4QYSBocHQFARRjBpuzByNYEhCUBBhNsi/DoYEMf/6QgWjx8HDh9O+Vp4PwRPwICTyMNAsTKwZPt4XZ5sR+ZBs+JB4mBgTRIpCMKwElCQJY8KYbOZgLTAEhifcBpbJ518oTNQxZ72fgePAfy0ELl4DzLki7qmusQF8+aOf4M7Z38B5thm6mn//hAmNH5Ti+2ccwpzK5EmSC49tx5yu3dh69R8ghX/8eLPzBRwZ3osNF7yqa5+xrHvlABiawqurL0lbtxjiLN/HmGn7qjYwCaCo8P+gwNBUWqtiob//qscXm5spCqAYeaKyyQV4uwGBl28Ho1n5Nsvw87zJBc9YH0JCCAbGgDJTGYaCQxBFESJEiJIImqJhoAwISaHoYxo0aJqOt4eFbWAj/pGCVhfn4zNO1Wbsc0rn/GBQQJ8/CF6UwNIUyswcTKbC+SU/J++XwAOjXbKRjjEAtir5CzraBYi8HJe2KoBNP4k9sT/hNrnBKmwX6RfEqrQBpO0rBPkg+gJ9URtYmakMLMOeMuNooeekqULW30i3u3hHvErqQVGUkmxgm2//EsZ4Ed/bvBvlNiMeWN6AH/553HYTawjL8wGn1QomIkoimodbUlo5RD6EpsEjWL3jvhjDx2OYXToHTUMtSWaOuY76uOSkRUdIKGAOhQcEKreAAUBFFiawtmEWFCSUGf0Akk9qXpMTrBiEOTQMX/h+ewfngo/3IiCMwMTYde8bABZUl+ClTzswEgjBbiJGmVyjlEdDIQGHekbjLEuxVsVJZQNTMTWifJ6sA0+0g73zC+DwqxDnfRXNV/4k3qx0+UbMKZ2D5sF4S9j3F34f98Xm55gcnph7u491Y8Z8fT8sTGaU7J3Pfu98DPr5JBvYvAobDIZJcpuiKCbH4a0vAiEv8Nyt8bFZeQagsh4boK0/ESESm73HezFj/gxNhjBREnFs+FhSHY7hsOp/VhHj6BQm60/6sccey8VxFAxKhq/WPl+0bNXSOdGBSuT5WENYoaHFyiEbPu5LMHzcB0+gX9nMESBmjknFodcAdwOgMOBsiZjADPpNYB3DDCqsAigVGYTXKOuL7f6eaFmJIaIvzs4IBgDzq0sgSiBWsAmkZ3QsybIUa1WcVDYwFVMjRruU7WCLbgIA9C++Jdms9NZqePyeJOvXfYn5mZi+Mkbp3D7GS4o2sEI9n+tCzQYWGahEyp6/TY7ZFOi1fGndVq1O+0g7if8pDhmWJqBk+LJwTLRMzXYTMYQVGlqsHEFJzcAhKJaHiJlj8uDtA068D8y+LMkCBshXVkpYK6y0/nu424dZVNtDEFXuOI0sDGnzjQ9WHOG1VjxZ6osBoL7SBpam8I+jZJA9UahZlmKtipPGBqZmARNCyuVmeXAetLhU8msoI0sYQRtK53aaArGBRYjEbAr0Wr60bqtWx8yaU25HmPykvQ3stttuS5pEL0kSKIoqSmVxOiJ2m9gE5gsK0bKI7Sb2+VhDWKERsXLEJoAkAxjFKtZhaUax3EDMHJOHpr8DkgjUnKP4dLO3CzOtVZAUBjJaEETg5AiL+W7lyfUA4DOlWmsl+0n2RpbBaRU2fEgm2U8YEctSYp6MtSpOGhtYxLYU2/krrZPnBiiV++UrfJyvXyW/GuLKI9avVDmckB6lc7soQfl8PhluT4ygFJ8RG5hSzKZAS38im23V6vj5+PMHif+pR9oe9pYtW7B58+a4/yNlkxElw9eMMku0bNOOZjzyzXjbTawhrNDQYuWQDR+PJRg+HoPb5FI2c5jI/JRJw6FXAXs1UKq8wnuL7ySmW/QPVnq8DHiRQrlF/VcwnjFizGCB3dcdLTMxFhhpc07WWgHkeSsHTw5jJJD6l0NCbqiwGZMsS7FWxUllA1MxNcJWpWwH+/RZAIDrk63JZqXLN8JtdidZvx5LzM/E9JUxSud2I0sp2sAK9XyuCzUb2I3PJMemrSplU3otX1q3VatTa68l8T/FydtP5IODgzhw4ADmz58Pl6twgirWBmLmGPCihBAfb/JqqLTjxbuWxBlDBEHEc//rAvCiBAND4y93XhQ12xhZCt0jAW02MAWjF2g63tZhCF/yFELx1i2Bl41dIg+ONoBmOFiqLPD4ekFLEgLCGDiahctcDjo8SY6maNQ767H1mq0ICkGYGBPGhDF0jHTIdi/GDMPYCObYavH08icREoWoMaw3IP/y9/Typ5LMH7EGMBNrgiiKCIrpTR16tyPkgaAPOPomcPo35KsrCfQHRzDIe1FjKtO9i/ZhOQ7LzWNAivGO1+iENeY2MEC+FSwX+mJAnrfyl70d2N06gGUNFTlpc6qTaFZymg0Y8IcgiCIEUYLDzOK5/3UB5HQo28D+69uLwIZtYMU4ud5kMgKj3XIeF3l5DRWGBdxzgZXbAVDyd4migZGTgKkEWLkdIsOhHwKCkgT6a4/K/0sSZtNGPL38KfCSAIaSrzR5/B7MdsyO5mwKFBiKwVPLn4IECRzDwcE50OPrQUgIgaVZcDQHiZLgMrlgNBnh8XvSmpMmwuJYSNA0hdPc1uh5nKUpVNiMKLdIcWVlZg4MQ6N3ZCzJFFqQJPYpzGWAvy++j+FuAL7zWrz5C2JCWQVEmkZ/YuxIiLZPsxxmlszAU8ufitq60sWXpcoCj98Dl8mFWSWz4rYtC59bYuM1sY7b7AZDM9Hvw1SIVUIymgcrgiDgwIEDGBuTJ551d3fjq1/9qmLdoaEhrFq1Cpdddhn+8z//E08//bTigOXBBx9Ec3MzLrvsMtx11106X4J2Ym0gSlavWENNrN2G50Uc7hnFqhhjyOO3LMY7h3pw7iyXahsKB6BsjXE3yKuHP38bYKsArvgPYNtdcXVEdwOaBpvizDHrl6zHhk82wOP3xP1749JG1Dvnxg1Y3GY3QkIITYNNSXav03qP45i7Dqt33K/YtpKxI2L1cJvduHfxvXho50NpTR16tyPkiZYdAO8Has9TfLo5PLm+knPq3kVHeLDitgThHVGv5zWWotQXP6ekxODKyZwVYHzeys4mDxms5IBEs9JXFlRg9RVzsfHNI/iXi2bhX1+ItyVaDAxWPPmxtjxZqIgipnMjwCsPAed/H3j5nniT0v4XgPorgY9+E/e8OO+raEqwfq1bsg4723biqtnLsSbG8rVuyTr84Ys/YNWiVTjNcRqO+o4m52vHaUl5/LGlj+FvLX/DPzf8M8bsY7jl1VtS5lUtZqbJhtJ5/A/fOx9DCTawJ1eeB56X8L0tuws/XpX6FDHGOVXz143PAIwJ+MM3o2XizX9CE0slx4TIgH7mG8DgCQSvfxLNNfMUY5KLWVBSLb5YmsVdb9wVt20JV4Lv/v276PR24jvzv4Or5lyVbCB1ziXG0SmO5qy0evVqPPPMM2hsbMTWrVvxl7/8RbXu4cOH8aMf/Qh33nknLr74Yhw4cCCpzuuvvw5RFPHcc8+hra0Nx48f1/UCMiHWBqJk9VIz1PSMjkUTXKTuXVs/wbWLazW3ASC1Neb52+THS+4dH6jE1JGNXfHmmId2PoTbz7w96d+rd6xBv783afcev0fR7tVXuyg6UFFqO5Wx4/Yzb48OOCLbqpk69G5HyBNNrwOcDSirV3z6WFhbXM6VKD6vhfZhFqUmAUY2tYDCa3LC4u8FhfF68pWVbnk9gCwh81ZyS6JZ6fpzpmPVM3tw/TnTowMVYNyW2Nrv054nCxVfL9g/3SrbvCIDFWDcpHT2LXJ5wvNK1q+1O9fiurnXRQcqseXX1l8r5+VAn3K+Vii/b8d9uG7udWgface9O+5Nm1ezsToVK0rn8aCCDay93x8dqETKCjZelfoUMcY5VfPXc7cCQ61xZf0jHcoxEVOvb9YFqjEZi1p8dY52Jm0bO6n+urnXKRtI/USOMtXRfGVlYGAAGzduxL333osNGzbg5ptvVq37pS99CQCwa9cu7Nu3D3fffXdSnY8//hhXXXUVAODiiy/Gnj17MHPmTNU2x8bGcPDgQU3HGggEFOuay2rSWr28/gA87S1x5Zby6Yp1JUnZeKPUBgDMcXPgFAwckhgCFSk3OxUtHWrGLkd4XYrEfwfFUNJ7YJtmy8j6FdueN+BF73F5AGSpskTrq5lqYutH0LsdoP6Z5ppcLv6UScxmg673RpIw59DfwJcvRG/7CcUBwad9TTDRHIL9o+jiA7qO7XifHeUmP/oHBiDwPAYGlfXBvSKHeSIP/8kWDIXXVaHGDAiKYzjSdgAmZLfWCgBMs0h4+9gI9uz7AhaD8i+kExVn2ZB4jLlesExL3MbmUmA8n6rlVQvHJJUp5clCfv+j+VslR4Nmxm1fMc+rWb8YilHNu3JeVrM0KpczFAMza9aUV2Nzcap6+SAQ0JdLUpEqZiMxpXQeV7KBxdo/I6Q6r+ebVN8JtT5FxDgHQN38ZbDEFQWNVmVbl9EafazWV+BFPu4Y1eJLyeoVeyVP7TsREpL7MxNF5P0nC0OeWjQPVqqrq7F9+3ZwHIff/OY3GB0dTVlfkiS89tprKCkpAcsm78bn86GyUl7g0OFwoKMj9URao9GY9Qr2vSNjaa1eVrMJMxO27Rz0K9alKGXjjVIbAOR7nRUMHBQdY43xDyjWUTN2DQWHFP/N0Yak9+Dk6MmMrF+x7VlN1uhCYx6/J1pfzVQTWz+C3u2A4lxFNpOYzQZd701fM+A9Ce6cFZgxvU6xSu+gF7Os1Sh3uyDqmGAvSUCP34zzp3nhLHViYHAAzlLlW8okcRpwEphpEdHlkPPC6PAM7B4FjGUM6qzZL3J3ATOEN1sOYtjoxjkqt4IVQ5zl+xi1xG1sLgUQzadqedUXjI8ftTxZ0O9/JH+r5GiIguLzatYvQRJU866cl9UsjcrlgiTAz/s15dXYXJyqXj7IR6czVcxGYkrpPK5kA4u1f0ZIeV7PMym/Eyp9iohxDoC6+Svki2uKG/Mq27rGvNHHan0FlmbjjlEtvpSsXmLMfEm174SBSe7PTBQFnZOmEJpvA3vkkUdw0UUXYe3atSgrK8OGDRtS1qcoCmvXrkVDQwPeeuutpOctFkv0FxafzwdRzP86JbE2ECWrl5qhpsJmxKYEY8jjtyzGtk/aNbcBILU15oYt8uOdG4BrH0+qIxu74s0x65esxxP7n0j698aljXCZy5N27za7Fe1eZe2fYuPSR1XbTmXseGL/E1i/ZL0mU4fe7Qh5oDn8naxcoFolYgLTM1ABgH4/DT9Po9ya/vYJrymy1sq4ESyiL+7PwcKQQPy8FUJ2JJqVXtjThk23noMX9rTh59cn2xJnuCza82ShYikH/61nZJvX13+VbFLau1UuT3heyfq1bsk6vHTkJTQmWL7WLVmHbU3b5LxsKlPO1wrljy19DC8deQm19lpsWLohbV7NxupUrCidxzkFG1ity4zf3XZuccSrUp8ixjinav668RnAMSOuzGWfphwTMfXKjn2oGpOxqMVXja0maduIrhiA/J1QMpCS+SpTHkqStN0QLooi3nzzTXR0dKCurg7Lli1LWn8lwm9/+1tUVFTguuuuw8MPP4ylS5fikksuiavz0ksvoa+vD9/97nexceNGzJo1C1/72tdU95/J6DZV3ViDjYmjEQxJCAli1FDDssrjN54X0TM6Bj5c18LR8I6pG8VUUbOB8SF57orIA5wVEIKyDYwxyIMZhk22gVE0AkIAHM2F/z0GjmLgkiiAMaCfgmzaojm4JIAO+REy2uER/OOmDdoIQ2AIotGGfomPM40FhICqeUPV6kVzoGkaAV55W702sGL8dWOijlnXfp69CejaD1z3GwDJPxSM8H5c9P79+M6Ma3CpQ3lOSzr2dXF4+B0X7jqnEzNL/SmvrDBCCNd/+H+wb96N+KT+BgBAUBjDLw/+CNfPugP/NO1WXceQyLpXDoClKfx19SWKzxdDnOX7GLW2n84GJogSGJpCuZUDyzJxddXy5Cl7/9XycgLHjrVgVrkVAAXwYwBFyZcQJQGgGPnWmpAvplw2g4msUbaBQQJN0aBBg5YElILBYLicAgX5PwpusxsszYIXeXj8HoTEEFhq3Prl4BzoC/QllbtMLhw/fhwlVSVRc1KpsRSDY4NJJqVTZQPLx2ecqs3Y5xLP4xU2I0RRRK83GLWBZRKvE0Ha9ysxdk0OYLQ3xvJVDoAZ719EbWBS2GwXKauEyLDJMRFjAwPLIWByYCAwEO1DOE1OmFhT8mGF48sb8MJqssJlckEQBXj8niTTV+w+Y2PbQBui34VTRTGcE6YCmiNgzZo1KCsrQ0NDA9555x28+uqrePTRRxXr3njjjbj33nvxpz/9CfX19aiqqkJjYyPWrFkTrXPllVfi5ptvRk9PD9599108//zz2b8aDURMX4k2m3TGD5alUVMaf79lqSWpmpYDAGyV8WWiCHgOyxPlVGxgqFgAmmHhtlUrG0C+9TQQ8gMvrYJoq0DTVf8Xqz9YO27iuHAd6rf/BIbRHlR/+1mgfB7QeyjaBl1aB3d4P0on6aSXETaMxb0MDYYZpe0IE4wQAo69CzRcDaWBCiCvXA8AVcbsTWDl1vRrmwiMAT7OgZLR8cv/HGOEmbGhN9CZYsvMmF9dgpc/7cRIIAS7KfUCaITUJFoTAfmKi1peTaxbMKhZGhVyYSAwBlhmyvXf/qmyFSzWxPStzcC7j4A+/CrcKu2myoYszaLCUpGRuWssMBbNsely8lTLxYnncVGU0NTrK654TSS2T8GPAT0Hx4U9kZi01wBPXT1eduuLAB9Iinm6YkFyTFCIts+LPFoGjijauhIHFJH46j3eG721kGZoVNuqk15C4j6rrKnXeyFMPTT/jNLf349///d/x4033oh169ahp6dHta7D4cCTTz6JrVu34j/+4z9QX18fN1ABAJvNhi1btmDhwoXYvHkz7PbsJ9BmQqLN5pQaP2KNHio2MPh6letH6vg8wEurZKvHpfdHBypA2MTxwVr0X3p/vIFMyUzm0z+5cioaZoqS9l1AcBSoXqRapSVHJjCLQYTVwGuqP2p2we6NX1fFwbnQm6O1VgB5cUhBkrC7VXmiPyE7CiqvakXN0qiWCyP11axgsSamP62If6wjx2aTV0lOTk1RxmsqRnvGByrAeEwKgfiygRZd5381oyixdRHyjebBislkwm9/+1vs3LkTv/71r2G327Fr166sdu5wOHD11VejvDx5fkW+CfKCovEjyOu7Pz8r+OB40lAzzfBB5foRYowfavaZoMU13p4QSr+fDIlVEMbtVyjSxD9ZaX5LXrSufJ5qlRbfSXA0izKDTfduOkYYVNu0rxg/YiqDzduF2OuaJQYXPP7czFkByLyVfFNQeVUrSvk0VS6M1FfL1bEmJqXHGebYbPIqycmpKcp4TYXIK8ekmPB61AxhaWIzJISUbV2i9jxPIOhB82Bl4cKFCAaD2Lt3LwRBwIIFC/DRRx/l89jyCscy0Ql0EeQJd4zKFnmE5cYnukVMMrGU1sl1lOpHiBg/MG6fiaXGWgPO1z/eHmNIv58MiZ0oF7dfpgAnJk5lWnYAVQsBQ/J9xtEqvi7UmauST3IZ0D7MojKDwcqoqQzG4Cg4ftxS4zS60TfWA1HSdnUmHZH1Vt5vJuut5IOCyqtaUcqnqXJhpL5aro41MSk9zjDHZpNXSU5OTVHGaypoVjkm6YTXE9NfiKuXJjYNjEExngw0uaWWkF80D1buuecefOUrX8HChQtx1VVXYeXKlbjnnnvyeWx5JdFmc0qNH7FGDxUbGCzlyvUjdSxu4LpNstXj3Uex8cJ18SaOC9fB9e6j8QYyJTOZRf9VrqlomCk6gj6gcy9Qc7Y8+VeFZt9JTLdU6jaBjQYpDAUYVGowgUUYMctx4vCN/3Ln5MohSkLOjGAAcHpNCQ6eHMaQj/wamGsKKq9qRc3SqJYLI/XVrGCxJqZvbY5/rCPHZpNXSU5OTVHGaypsFeNmUWA8JhlTfJlztq7zv5pRdKrNfSJMPJon2D/88MPo6elBe3s7fvCDH+CRRx7Bpk2b8nlseYWmKTRU2vHiXUtOvfGDpiGWz0P/Ha9HbVyu218HzQcgsib0syyCvq5xOwdNy5M073gDQb8XnMkCSBJEikL/d/+OoCSgnDHh6aueRkgIwcAYwIBGxw1PwUCzcDNmsCMnAXsV8N03ZPNYrAFHoxkn6WVQNOqd9dh6zdY421eXtyutGWwibTRTmo7d8q0C5Q2qVfxCEJ2BfiyrOE//bsKT691m7YOVUbOsvyzxdqGn5DQAgNMor4fSE2iH21Sr+3hiOWOaAy980oEPWvqw/AwykTOXFFRe1UpMPlXNeeGceFqlDRiWb9DOAAAgAElEQVTpBEwlwNW/ABgO+M5rsg2MZgFrhVy+/KdhO5gZuOrnEL/6GPohIigJoH3doCkGNE2Pm4/CeVrJfERTNGY7ZuOp5U/FWZTUcqXRZITH74nm1Tmlc6I5meTheIoyXhNJPF9XzJNjMtYGRrHAyu1xhlFRksL9BR4cxcJlLpf7FilgaRb1pfWKsRgbcy6TC6IkwuP3wDrNii5vV7ReYqwBSBt/UzlGCTKaBytHjhzBli1bsGLFCixduhS///3v83lcE4KSzeZUIEoimoaa440tF67DnN1b0HzubfFWr4jJJWwA8Qy1oSbUBfHdR9B08d1Y/eE6uM1u3Lv4Xjy086HoduuXrMeGTzbA4/egceljmLvzcbDH3k2202RgxlEiYgBJZ6HRYg4j5IHWDwBQQNlpqlWO+bogQUIVp98E1h4ZrFgyGKyYXJBAoWR0fIFYJyf/0tfla8WC0gt0H08sp5XbYGJpvHuklwxW8kCh5NWMULI0RojkxLd/CkOi/Ss2NybmzoZrgEt/CPHzv6Bp8Y1Y/c790Vy3bsk67GzbiavmXJXWrMSLPJoGmzQZmERJRMAWwJ2v3qkpr5I8XKTxGkEp5i57IMEG9gzAWYBn/jlaJt76IpqoIFbHxNTGpY2od84Fzah3C0VJRMtQS1y8bPryJgSFYFIMsTSLu964K1r2+JWPgxd5TdvGxh+JUQKQwW1gTqcTv/rVrzA8PIwXX3wRbje57JcrFI0tH6yF55I1yVavBJNLpUUCnr8N/YtvweoP16HT24nbz7w9OlCJbPfQzodw+5m3y/aOHffBc8ka7aaxPBhsiKXmFHHiffmqSor5Ks0+2b5VYXTo3k3HMAsDLcFp1j7XRKQN8Bnj9cVm1gozY0Wnv1X3sSTCMjTmV5fg/WYyyZ6ggVT2r9jcmJg7F90E/GkF+s9bER2oAHKuW7tzLa6be50ms1ImBqb+QD/WvLNGc14lebjIUYq5JBvYrcDAsbiy/qHW6EAFCH/uO9ag35/6PK8UL+0j7Yox1DnaGVfWOdqpedvY+CMxSgAyGKz89Kc/hd1ux6JFizA0NIT169fn87imFGrGlhDDpDW50JJs/4g1gDk4h+J2Ds4R1zYAbaaxPBhsiKXmFCDwQNsuWVmcYi3YJm8nDBSbpbaYQZUtBErbmrNRRk0u2EfjVcUuYwW6fW26j0WJ02scON7nw8khf/rKhKlNOvtXJDcm5s5w/SDNKuY6hlLO74lmpUwMTJnmVZKHixyVmItj8IRs/4ohaLQqf+5i6h+XlOLFzJoV2zKz5rT11LaNjT8SowQgg8HKj3/8Y0yfPh00TWP//v1FPbm+0FAzthgEIa3JRaRk+0esAWwoOKS43VBwKK5tANpMY3kw2BBLzSmg6zMg5AXKU6/Ge9TXiRmWKlBZmMA6hllU2XhkNlQBRsyyvjiWUq4cXf4OlS30ccY0eSC28yixghHSkM7+FcmNibkzXJ8TecVcJ0jK+T3RrJSJgSnTvErycJGjEnNxlNbJ9q8YuDGv8ueeZqV4pXjx837Ftvy8P209tW1j44/EKAHIYLAyODiIyy+/HK2trXj00UcRCATyeVxTCkVjy4Xr4H6vMdnqlWBy6fZRwA1b4PpkKzZesBY11ho8sf8JrF+yPm679UvW44n9T8j3Oy99DO73GrWbxvJgsCGWmlNA6wfy3/K5Kasd9Z7EDGu1bhPYGA/0ehlU2jL/5WvUVAZjyAtTaDRa5jSWYyjYj6DgS7FlZkx3WVBiYvHOEfXFbQkEAKntX7G5MTF3fvos8K3NcO3ajI2XPRqX69YtWYeXjrykyayUiYHJZXKh8bJGzXmV5OEiRynmkmxgzwDOWXFlLscMbEyIqY1LG+Eypz7PK8VLrb1WMYZqbDVxZTW2Gs3bxsYfiVECAFCSpO0+jVWrVoGmadTX12Px4sXYvHkz/t//+3/5Pr4oBw8exPz5qX8R1lM3b2Ro1BIFHv3+3nEbmESBDvkQMjngEcfC5g0GblMZDKwx2n7QPwrOZA3vkkc/TSNIASbGhKAYREgMwUAbQIPGmDAm2zsYMwxjI+rHpdMGlvSa0hg8MjF8FMRnmiETdcya9/PHW4Cu/cB1v1bVFo/yflz4/v34lxlX4zJH6kGNGscGWDzwuhsrF/VgftlItHxgcADO0tST9mv6DuLiQ3/A9kv/E92OegBA09A+vNz2JNae/TtMs9brOiYlfvlWE450j2DXT64ERcn2n2KIs3wf46l8Dwru/Rd4YLRL/ksz4GkDWDEk278oRrYt0bSySZGiZFuYEITIcOiXeAQhgqLkW3ApikKpsRQDgYGoWcllcmIkNJqUE3mRh8fvieZzJWtYhJZjLSipKtFsTppo01I+PuNUbRZcTGkl9hxvtqmfh6MxGjZ9Wd3AaG+MDaxSXgQ4to6tCiIQ0+cI28AoOu25XykWRVFEX6AvGsdlpjLQtGwIizXdFaMNrGjjZ5Kh2Qb2X//1Xzh69ChOP/10HDp0CBs2bMjncRU3mRq1RBF07yG4E+rz7no0DTUnW2Acc8B6moA/3gQupj5dsQBuWtm0FWsDS2vSSGXGyYCIGUzv84QcIknAiQ+AmZekWV9FvgWr2qj/V6uItrjMPJbxtiNRffHJ6GDFaZR/7esOtOd0sHJ6jQPvN/ehuXcUp1XYc9YuYZIg8ED353FmJeaGLcD+F4D6K9WtYAq5kwbgSsjLy2qXYdWiVUn5fdOnm/B2+9tx1iOWZlFl1WauGwuMZZRXSR4uQGL6EFyqPoQoAr2HUtvAvv0swJqAZ74RV0ZXLIDbVq24T7V+iyiJaB5sTjJ6BfgA7n373rg4ri+tR5W1KqmzrxRr6eKPxChB89DUaDTi9NNPBwDMmzcPdjs5uauSqVFLpb4n0KdsgQn0pWxfyZ4RawMjJo0piOcI4OuTTzwpOBqOmQpOvwmsfZgFBQnllswXXfSG9cWOGH2xgysDQOGk77juY1LijBp53sp7R4gVjKDAaFeSWYl6/jbg7FtSW8FUSMzL19Zfq5jfr62/NvqY5OopitY+hBYb2B9vAgZaMm9LoZ6aDSwyUImUqdnqCAS9EEl1PsjUqKVSPyQKKhYYIWX7avaMWBsYMWlMMVrfl/+Wpb4ycdTXCTNjhIu16t5V5wiDCqsAmsp0ej0g0ix8xlLYYwYrBpqDw+BEl+9Eii0zp6LEhMoSI95tykzLTZgiCCHlPEszuoyJiXk5nbUx8pjk6imI1j6EThuYprYU6mViA+PTmMUIhEwgg5V8kKlRS6W+gWZULDBMyvbV7BmxNjBi0phinPgAsJanvb2vyduJmZYqSDon1wPylZVqeyiVHTklw2Y3HCPx9i+nsRxd/tzqiwH5VrCPj/eDF9RvjSNMURiDcp4VBV3GxMS8nM7aGHlMcvUURGsfQqcNTFNbCvUysYGpzakiEPRABiv5IFOjlkp9t6lM2QJjKkvZvpI9I9YGRkwaU5DWD4CaxQBSd8qbfbIJTO9gRRCBkyMsKqyZ3wIWYdhSAfvoSdAYP4ZSrhzd/o6U68Po4YwaB7xjAvZ3DKWvTJha2KqSzErSDVuAvVtTW8FUSMzL25q2Keb3bU3boo9Jrp6iaO1DaLGBfftZwDk787YU6qnZwDYs26DJVkcg6IUMffMBTctzA+54I7VRK9YcY68CvvsGIARlgwzNgB3twVzrNDy9/EmERAGGsA2MZY3R9oN+L1hLCfopIOjripoy6p312HrNVtmeQXOgaRqPXPbIhJs0UnEqDR9TiqF2YOgEcOa3UlYbCI3CExzGNFNmmupYerwMeJFChUX/rSvDlnIwYgh2vwdDZvlKkMtYgYDgx6gwABubu87b6dF5K704uy61qYwwCUllPmRYoPIMYOV2+bEkQRQEMBd8X87RK7fLg2eWA8xlaS1KNEXH52WGg8NQgqeXP4WQyMMQtij9+0X/jh8JP4rLiROZK0leLgBoGiifB6zcDkkIgmI4efCc2IegacDdAHzntXj7V0LfQ5RE9N/xerz5S6mtNP0WpRh2mVwQRAFPLX8qagNzm90wMMnrAGmBxB9BCTJYyRfpjFpq5o3yeeN2D1sF2Cv+A1Xb7lK1zrT3tiDE9MXZOSIGmUL+ZUPJWJbWUkbQR2R9FXea+Srh+46rsjCBtYdNYG4dJrAIQ5YKAIBzpC06WHFyYSOYvw02e+4GKyVmA2aWWfBukwerr9SnaiYUKVqsjQwL2Gui9RilehnYH5WsRlWxRiYAbjb++YnMlSQvFwgxli8qVUwJPNBzIN7+dcMWeZDNyLlYlEQ0JRi8VD9TDSZQpRimGRrVCXGs62WT+COoQD79U4WaeWO0a7x8yb1AZKASWyfGzmFwGJLsHMVgkFGyihTDcRclrTsBox1wTE9Z7ajvJACgkivRvauIttidzZWV8MJkpcOt0bIyk6xtbfc2625XjbNqS7G3bRAjAf23rhGKEL3GpcR6mdofM2QicyXJywWC1phSMNbh+dvk8jDF9JkW07ESJhYyWDlVqJk3Yg00amaPGDuHSImKJo5CN8ioGcsK/biLkhMfANWLAKSe73HU2wk7a0EJY9a9q/ZhFk6TABOrf24Jz5rg4xwoHRmPfRvrgImxoM17VHe7aiyaXgpBlLDzKFFtTin0GpcS62Vqf8yQicyVJC8XCFpjSs1YJ4z/8FJMn2kxHSthYiGDlVOFmnkj1kCjZvaIsXPQEq1o4ih0g4yasazQj7vo8PXLtxNUnZW26lHvScyy1kCS9Csn24ZY1NhDELOcCD9kqYBjpD36mKIouE3VaPe2ZNWuEnMr7bBwDF7/ojvnbRMKGL3GpcR6mdofM2QicyXJywWC1phSM9bFzBcpps+0mI6VMLGQwcqpQs28YasaL9+5Abj28ZR2jtBQKMnOUQwGGSWrSDEcd9FxIjxfpbwhZTVJknDU14nplkpIKVa4T4UoAR3DDKrt2d9ONWwph32kE1SMvcxtrEantzXnRjCGpnBWrQPvHO6FlOO2CQWMXuNSYr1M7Y8ZMpG5kuTlAkFrTCkY63DDFrk8TDF9psV0rISJJecT7EdGRrBmzRqIogiz2YzGxkZwXPyomOd5XHnllZg+Xb6H/qGHHkJDQ+rOVCGhaqtIZZZJJMb2ASEk/xJisAAjJyGW1MSYOwxwfe9t0CG/YptjgTE0zGxIsnNAFOHxd8fbPxj1j3uiDRxqVhEyiS7HtL4vm4tKZ6Ws1hscwjDvwzSTfilDr5fBmECjwqJ/cn2EYXM5WDGIEn9vdJK921SNQL8fA6FuOLmqNC1kxqLpTnzY0o9jgzwW5LRlwilBSy7WYm2MtGOrAL7zGiSRB8UY4s1MMe2Iooh+GghCAjfWn5zTwu3xoOCBIFvAGAPcZrfquhRKubLUWJqXfE3ycoEQE1NBvxec2SrHpiQCQ53jfQZb1bixLlJmrQT8fdGYpi3lyp+pBMDbnba/ktg3KDWWYnBsML6/ASTFY6TMUmWBx+/RFEck/ghq5Hyw8vLLL2PlypVYsmQJ1q5di/feew9XXHFFXJ3Dhw/jmmuuwQ9/+MNc7z7vqNoqHHNARyxeaYwwckPjto9o/Wsfh7jvj2g69zas/mCtZhtGop1DFHg0DRzB6h1rxttY2oh651zFAcupMnAoWUUIOebEB/ItYAyd8opEU/g+4Uqjfn1vxARWnsXk+ghD1rAFbPhE3GAFADq9x/IwWCkFAHzc7sM1OW2ZMOFkYOdKaT+KtPP2T4Hzvw+8fI+6mYmmIVrLU+fRcHv8vudxZPGNWLPjvmi9xmWNmOucm3LAEsmV+c7XJC8XCOHYbG47iPnzZ8vmr+7Plc1fjlp5G5XYpysWxH+mGr8jibG2rHYZVi1ahTVvj/ctNn15E4JCMCkeOYbDqv9ZlXGMkvgjKJHznugtt9yCJUuWAAAGBgZQVlaWVOfTTz/Fjh078M1vfhMPPvggeF7/PfITjaqtwp+hEUbJ9rHtLvRfcm90oBLXfgY2jH5/b3SgEm1jxxr5GDN5TcTAUdwEvcDJz4DqhWlvnTrsleeH1HBZDFaGcjhYsVRCAgX30PiEerdRHqy0eZuybj8Rh9mAOeVWfHjCm/O2CRNMruxckXYW3QS8fE/a9tLm0XB7nnNXRAcqkXpr3l4Dj1+b4IHk6ymKBvNXzgx3YRJj7dr6a6MDFUCOvfaRdsV4bB9pJzFKyBl5W2dl7969GBoawqJFi5KeO/PMM/Hkk0+ioqICDzzwAN55552kqy+JjI2N4eDBg5r2HQgENNfNFEuVRdlWISpbOYJ+L5rbko9ljpsDp1SfZhXb9wa86D2efLJVeq0l06yqx6j0vqi9JrV9ngry+ZnGMn/+/Jy1lUnMZoPae2Pt+gh1Io9epgK+E60KW47zSe8RlBscGOkbwACv7zauI93T4eBCCHj74RWT570IPI+BwQHN7Q0aXbD1HkZ36fjEdxvjwNG+Azghpn49ejjNQeHvRwP4x57PUWZhct5+rkj8vHMZs8DExa0Sufieq+ZWlVycth0VK2Nie+nyaKS9EMMo1gsJyvk5kXT7mahcqZdAIJDzNlPFbKG/H+mIHP+8SqN8ZS+WwROQhBAOhV+f1tjXWi8x1hycIyn2zKxZMR7NrDmprJD6FFqJvP+5zrOEzMjLYGVwcBAPP/wwfvnLXyo+P2/evOg8ljPOOAOtrek7HkajUXOw5DOwPH4Paqw1cV/OGmsNODps5YhNAKV14MxW+RJuIqPdyvVFXrF9q8mKGfNnJDWj9Fo9oydVj1HpfVF7TWr7PBUUY7LIJGazQfW96fwzQDEon3u+vLJxCtp7BlBfUgd3mRPpFMdq9H1mxzSHAEeJQ7GFgcEBOEu1X7kZttegwtuJyorx23Qq/bXoFTpRV5f7uPxyiR9/P/oZmgJWXHxO6jk+p5J8fxcmKm6VyMlrU8utark4XTsRK2Oa9tLm0XB7BkFQrGdglPNzIun2U+i5Mh8Dh1QxW+jvRzqixz/UrhiHFGPA/PnhfKU19jXWS4y1oeBQUuz5eb9iPPp5f9zrKLQ+hVaKPX4mCzm/DSwYDOIHP/gB7r//fkybNk2xzg9/+EMcOnQIgiDgjTfewLx583J9GHlD1VZhztAIo2T7uPZxuN7bgI0XrsvKhuEyl2Pj0sb4NpY2yseYyWsiBo7i5vg/gKoz5An2KQgIQRzzdWGGpRp6ByqiBLSHTWC58mkNWmtg8ffBFBqJllWap6PL144xIfe3a00rNaPKxuLVfSdz3jZhAsmVnSvSzqfPAl//Vdr20ubRcHvu3ZvRuPSxuHqNyxo136dP8vUURYP5K2eGuzCJsbataRsal8X3LWrttYrxWGuvJTFKyBk5v7Ly5z//GV988QU2bdqETZs24fzzz0coFMKaNWuide6++27cf//9AIDLL78cF110Ua4PI2/QFI16xxxsXf5UsmkrnVkmriEacDcA33kNEHn5l2+jHbRzJuoNZmy9eiuCoj4bBs2wqHfOVT5GtddEDByTi6AP6NgDnH2bbJBJwVFfJ0RImJ7FpEaPTzaBVVqzN4FFGAyfhMtGWtHhOgMAUG2eAQkSTniPoL7k7JztK8LplSa81TyA3pExlNuNOW+fMAGksnyls4TFPk9RgKUMuPoX8oB/5XaEQiEYTBbF3J42j4aPi73obswFhaeXP4WQxMNAp7aBJb08kq+nBgIPjHZhXqVRvqpiqwIqTo/vM9iqgNjzuhbDXQb11Ex0SfZRQLXMG/DCarKSGCVkRc4HKzfffDNuvvnmlHXmzp2LV155Jde7nhhEEXTvIbjVLBpqZplEBB7oOaBo9qAZFtm6MGiGhdtWrb0+MXBMLtp3AWIIqEwv4j00Kk+ur8picn1beHK925y7lYYHrXL8lg21RAcrlWZZd35s5Iu8DFbOrDThzeZRvH6gC7dcUFy3KxBiUMrF6QxISs9//VfAR78Blj0IVCzA0cOHx2+5UdptujwaPi4WQDY+O5KvJzkx5q+oge7GZwCDFXjmG6ktd1r7IRrrKcWaUuyplfUe7y26W78IhQcZ5mZKrkwzWsweBIJeWncCFA245qStemi0HVbGBCdr1b279mF5QnouTGARxgxW+LgSuAabo2Vm1gonV47m4QM5208sVTYW1Q4TXv6sM31lQnGRLncrPf/yPbINTE+OJxD0otQ/eO5WYKAl+74HgVCEkMFKpvBBRTMM+Aw7aYKyPQxC9qt/Ewg4/g+g8nSATX8r02FvO+bYagFJv0K8ddAAp0mA2ZD6lrNMGbDVoGygOa6sylyHYyOHcrqfCBRF4YLZZdh1vB9dQ7m3FhFOIelyt9rzERtYpjmeQNCLWv/AYEkuI3FJmAKQwUqmsNz4pLQIpXVyeSYwBuV2GEN2x0cghAJA+26gZnHa+SohUcCh0TbMsU6DlKZuKloGDJhRGoQo5mp6vUyfvRZ270kYYybZV1nqMBjsx3BQ27oUmXJpfTlECfjznra8tE84RaTL3WrPR2xgmeZ4AkEvav2DkC+5jMQlYQpABiuZkivTjBazB4Ggh/ZdgDAm38uchqO+TgTEEGZZtc9vSiTAU+gcYVBrz/0vfH02eY5KRczikNVm+f7nY6Nf5Hx/AFDlMGFelR3P726HlGYxTUIRkS53Kz3/9V/JNjA9OZ5A0ItS/+DGZwDn7Oz7HgRCEZK3RSGLDVGU0OcNIsgL4FgGZVYONE0lV0y0aBjMgCgAwx3pDWCxMGx6s0d2Lyi19YYweWl5G6AYwD03bdX9w8cAALWmMt27ax1kIUkUauy5v21qwDYNEihU9B9Bm1ueUF9hmgYDzeGLwV1Y6Lo05/sEgMvmluM377ZgT+sAzp1JdJta0JxDTxU0DZTPA1Zul2+zYQxyzo3kxcTcTlHy9+hrjYC5DPDJCztitDu1RYzk26LnlMdyTP9AEnlQkf4BzaQ3fYUtYnExnqt+BYFwiiARDDkxHe4ewfc270b7gB+1TjN+t+JcNFTa1Qcstsr0dpnUOwU8h/Vtm/4F6T8uQvFz9E2g5mx5vkqaW7v2jRxHqcEGF2OFKOq7MnJsQL51scqW+8EKzxoxZK2Eu398jgpDs6iz1uPz/t1ABmv8ZcIFs8vw9PvH8cddbWSwooGMc+ipQBSB3kOp82Iaixin1SJG8m3RUhCxHNM/oBJjKpXBK8YilmgZJQMWQjFDMimAPm8wmpgAoH3Aj+9t3o0+b5rOWzZmsFxZxSa6bUJh4/UAJz8Dpp+fdqACAPtHjmO+fRakLCbXHxtgYecElHCC7jZS0WerRdlgc9zrmWFrQG/gJDyBjrzs02RgcMHsMry67ySGfER6kQ7dOXQi0ZsX9VjESL4tWgoilvXGFLGMEiYpZLACIMgL0cQUoX3AjyCfpvOVjRksV1axiW6bUNi07AAgAVVnpq06wvtxzNeN02y1WU2uPzZgwIzSEKScrV0fT7+9FlzIC6dvfGX5mbZ5AICDg7vysk8AWH5GFfwhAVs/as3bPiYLunPoRKI3L+q1iJF8W5QURCzrjSliGSVMUshgBQDHMqh1muPKap1mcCyTesNszGC5sopNdNuEwqb5LVm16qhLW/XzkVZIkDDDXKF7dyEBODHMorYkiHzNRffY5ddS1T++tkop50YpV4b9Ax/mZ6cAZpRZcdY0B57YeQxjhdTpLkB059CJRG9e1GsRI/m2KCmIWNYbU8QySpikkMEKgDIrh9+tODeaoCL3qJZZ0ySGbMxgubKKTXTbhMJFFICm14G6CwGkv1Kyd+goKFBZTa5vH2YhiBRq8jBfJcKI2Q0/Z0d1775oGUVRmGFrwMGBTxEU8rfva86qhmc0iG2fkkUiU6E7h04kevOiHosYybdFS0HEst6YIpZRwiSFzLgCQNMUGirtePGuJZnZPxLtMZlYYLLZFoAo8Oj396JkmhWe0ZNwmctBU/S4kcZeBXz3DUAgdpopQ/tuwNsL1F2kqfqHg4cxz14HE0VD1HkbWD4n10ehKPSUzEKl5wtAkmRLE4C5JYvwWf/7+HzwAywuW5aXXZ85zYEZLgs27WjG9YtrwRTKZPECQ3cOnUj05twYi5gkBEExnDaLmK9XtX1REtEf6EdQCIJjOLhMLjl/E045BRHL6WJODYaVJ9MnGu8UJtdH+hBBkQdHs3IfgkzCJxQoJDLD0DSFcnv61b4VNkxt58jDtqLAo2ngCFbvWINObydqrDXYuKwR9RIH+plvECPNVOXQXwHaAFSdkbbqKO/HvuFj+Nb0KyCK+ifXN/cbYGZFOE183m4DA4AexyzM8OyD09eJAes0AMB06xzYDaX4R9ereRusUBSFaxdNw8a3mvDi3g5885zavOxnMqA7h04kenJujEUsycwUO2CxlGuygomSiKaBJqx+a/V4/r58I+qd9WTAUiCc8ljWEnNqMCzgSJ2nFPsQSxtR75xLBiyEgoRkxiKk398bTTIA0OntxOq316B/qJUYaaYqkiQPVuouBJj0tyvsGToKASLmWadntdvDHgNmu4LI60gFQI9DdhRXe/ZHyyiKxpnOC/D5wG50+/M3Cf782S7MKbfikb8fRiBE5q5MObSamTTW6w/0RwcqQDh/v7Ua/YH+fL8SQrGQZ8OcYh9ixxr0+0l/gVCYkMFKERIU+WiSidDp7UTQaI2vSIw0U4few0B/CzBziSZl8QcDB2GkOUw3uXXv0hukcGKIxezSQJ48YDH7MjnhMzpQ3ftZXPlC1xKwlAGvtm3O275pisLNX6pD93AAT+w8lrf9EAoUrWYmjfWCQlA5fwskVxPC5Nkwp9qHyOIqO4GQT8hgpQjhaBY11pq4shprDbgxb3xFYqSZOnz+Z4Ci5cUgNfDh4GGc5ZgDWtJ/paCpzwAJFOoceZyvEoGi0FV6Gqp694MWxzWcFtaGc91L8WHPmzgytDdvu19Q48DiOif++62jODnkT78BYfKg1cyksR7HcMwPOaIAACAASURBVMr5W8MVUcIUIc+GOdU+BE1uASMUJmSwUoS4zOXYuLQxmmwic1ZcjhnESDMVkSRg33PyxHrOlrZ6m9+DZt9JLCyth5TFYOWQhwMFCTW2iem8dzobYOD9qB44FFd+nvsKuIwV+O2h/4NOb/6ufKy4cAZ4UcKDf9kPKc+3vREKCK1mJo31XCYXNl6+MT5/X74RLpMr36+EUCzk2TCn2IdY2giXmfQXCIUJGUYXITTDot45F1uXP4WgGAJHG8ZtYDrtYoTixezZJ98icO4dmm4Be93zCQDgDNuMrPb7eQ+Hmc4QOEbM95QVAEB36RwIFIvpXbvQUTa+6CXHGPH16bfjhdZf4/9+9n2cX34lykzVYCkDeCkEM2NFfcmZmG6rz2r/lSUm3HjudGz+sBUvfdqBb5xNJttPCWJsX0G/F5zZqpxbNdrGaIpGvbMeW6/ZSmxgBGW0xpze5uP6EMQGRih8SGQWKTTDwm2rxsGDBzF//vzxJ/SayQhFi6P1b4DBAtQs0lT/bz27Md8+A6WMUbcJzBei0NRnwPI5IxMyUAEAgeHQUzobtV27gAUrowpjACgzVeKm2fdil+d/sNuzAwHBl7T9gtJzcNtpD6DMpP878k+nV+HDY31Yu+0AvjSrDNNKzek3IhQ/YYtYc9tBzJ8/O229tM1RNNxm/fPFCFMArTGnt/lwH4JAKAbIYIVAKGbGRlDS+ndg7pfltR3STHX/YuQEDnnbcefsf85KWfxFDwdRojDbmTwoyCedzgac0/IKnN4ODNjir2zYDaW4vPpbuKL6BtCUBFESQVEU/LwXR4Y/wztdf8XP992F+85oRJWlTmUPqaFpCqsum4OHXvocdz6zB39adSGMhbRKO4FAIBAIkwxy3ZlAKGY++yMY3gc0XIN0AxUAeP7kezDSBiyyz8xut10cOEZErX1iJ5t3lM2HBApzOt5TrSNBgiABEmiIEgUjY8OZziW4adYPwIshPH7wJwjw+gdZ1Q4zVl06B/vah7D+rwd1t0MgEAgEAiE9ZLBCIBQrkgR8/FuMueYnm2MU8ASH8Er3R/hK5fnIxikjScDuThMWlI+BodLPkcklAc6O7tI5mNX2TsZru5SZqnD19NvQ4+/AluafZzVJ/rxZLnztrGps+bAVTxGdMYFAIBAIeYMMVgiEYuXom4DnCLyzlwMabun67Ym/QYCIK8oXQ9IwEV+NlgEWHh+DMyu9eV9fRYnW8oWw+ntROXg4422nW0/DxVVXY1fvO9jbvyOr4/j2eXU4b6YT6175Aq/tP5lVWwQCgUAgEJQhg5VsEUVgtBsYbJP/ihP7SzNhiiJJwDv/CZRMg7e0IW31I94O/Onke7i66iI4KENWu/6o3QSaklDvHM2qHb10lM0HTxtQ3/a2ru3PKVuGSnMtnm3+Jfy8N/0GKtA0hXuW1aOhyo57//gpPmrp090W4RRB8jehUAnH5hw3R2KTMOXJy2BlZGQEd9xxB26//XbcfffdCAaVV1198MEHceONN+Lxxx/Px2HkH1EEer4Afn8lsOEM+W/PFySpEPLP0TeA9l3AubdDFFKvlTImhvDQ4c2ws1ZcU3lBVmurCCLwbqsZC8rHYGb1t5MNPGNEu/sMzGx/D1wo88EGTdG4ovqbGA4O4K9tT2Z1LBxL474vz0VFiRHf27wbn3cMZdUeYQIh+ZtQqMTEJvffi0lsEqY8eRmsvPzyy1i5ciWeeOIJuN1uvPde8mTY119/HaIo4rnnnkNbWxuOHz+ej0PJL75e4I83yWtcAPLfP94klxMI+UIUgLceBhy1wPTzU1aVJAnrjmzFwdE23D3nenBZGMAAYH83hz4fgwtqhydMWazEkeoLwQpjmHfiDV3bV1tm4CzXhXiz4y9ZLyRpNxnwwD/Ng8nA4Jbff0QGLMUCyd+EQoXEJoEQR17Uxbfcckv03wMDAygrK0uq8/HHH+Oqq64CAFx88cXYs2cPZs6cqdrm2NgYDh7UZt4JBAKa62bDHDcHLpJMIgyeQNDvRXPbxFiCJuq1nmom6nXGrVmTJZnEbCaUHv0Lqk9+hv4v/W+MnGhFMBhE64lWxbpbB/6BV4Y+xrfKl8I+JKBL7M5q39sOzITVwKOC7sDAYGZXVgSex8DgQFb7jzAAEzqt0zG3+RW8YTsPEpW5PngufR4O0Xux+fAjuKnsAVAUhWAwiBMq72U6Vp5dgt/tHsC3f/M+fvaVapxWZtTVTjoSvwu5jFkgf3GrhYnMZ3rydzHk20I/xkAgkPM2U8Vsob8fShRC3yJXFOP7H0vk+HOdZwmZkdd1Vvbu3YuhoSEsWpS8WJ3P50Nlpbx4lsPhQEdHR8q2jEaj5mCZsMAa7ZYtTLFJpbQOnNmal0WclJgqX6JifJ2ZxKxmvB5g22+BuovgWnAFXJKA1hOtmFEXvxq9JEn479a/4tmhnVhedSGuqToPohjKatfHB1h83ufA1xuGUOooyXj7gcEBOEudWR1DLM11l+KSg1txSfALHJ5+pa42LjZcjTdPvoC+uuNY7F6KEydaUZfwXmqlDsDa6gDWv3YQ//ZmD55ceR7Orsvd642Q7+9CXuJWIxP6PdeRv4shDxX6Meaj45oqZgv9/VCkAPoWuaIo3/8Yiv34Jwt5m2A/ODiIhx9+GD/96U8Vn7dYLNFfWHw+H8RivBfTUg58+9lxbWxpnfzYUn5qj4swOZEkYNs9QMgHXHAnoDL3RJKk/5+9Mw+Posr6/7eq9yRkbUgIMWwGQTZxAQVHFlEQkO0VVDAuyDgI87LIwKAyIg7IKAoBESODyBYQcEEddfyBRlBfBVERxLDLEmJCmpCEJL1X/f6oVK9V3R1SSXcn5/M8PKErt27drjo5de89G1ae+QhvnvsMw9P64YHWd9R7ocLzwNbDcTCoOdyarox1pL78kXQdLsVn4oaCrVA7rq7eS4/kvkg1ZGD776/D6qx/zZhW8XrMH9YFeo0KE/69D3uOk9tGxEL6m4hUSDYJwosGWazYbDbMmDEDs2fPRps2bSTbdOvWDT/++CMA4OjRo7LtIhqWBVpdD0zeDcz8VfjZ6nrhOEEozf41wPHPgNufAgzJkk14nsfy3z/A2vOf497Wt2Nc69vBOa31vvT/ndfjpz/0GNGpAmo2PIH1fjAMDrYdAoO1HD1P7byqLliGxYC0MSizluLzwjxFhtUqXo8F916PtAQ9Hl//Az48GNhqTIQJ0t9EpOIhm7ZpP5NsEs2eBnEDe/fdd/Hbb78hNzcXubm56NOnD+x2O2bNmuVqM3jwYEyYMAEXL17E3r17sX379oYYSsPDskBcarhHQTR1TuwG/vs00HEQ0L6/pFXFyXNYcnIbtv3xNUan34HRrW4F57Si0srgxyI9fv5Di+Iraly2sNCoeBhjOLRPtKNnmhVdW9mgk9EGJy5p8Mb+eHRMsuGW1pFhVRG5FJ+Jsy1vQNcT7+Ncq5tQmtSpzn1kxHZAt6Te+PT8FrRK64BMXJ0bmCeJMVrMH94Fy3Ydx4x3DuJ3UzWmD8oCyzL17ptQENLfRKRSK5unzhdEnesXQShNgyxWJkyYgAkTJgRsExcXh02bNuHbb7/F5MmT0aJFi4YYCkFEP+e+B7Y/DLTqDPSdJblQsThtePrYeuw2HcT9GYNxj7EXzpc78dHReOw9a4CDY5Ckd+KaBBvaxDthczAos6ix61QMPj0RC62KR880K25Jt6J7mhUpBg4VVhZ7fjdg25E4JOqceKRnMRCWMpCB+anDMBivnEX/n5bhoz8thU1bd10yIG00CqtP4YNLb6BHu1sQo66/PorRqjF3SGe8/e3vyNl9Ar9eqMQr43ogMUZb774JgiAIornQoAH2wUhISMCwYcPCOQSCiGwKPgbemwzEpwN3/ROQ2JkvdVTimUPbcfjKGTzZYSySbNfhpb06/HBBBzUL/CmzCje1rkRqrBW8T75hh5PB7xUGHL0Ui18vxuCHC3q//nukmjGuy0XoVPVLe9xQ2NUGfJ91Hwb8+jaGfP9PfH7rc7Bp4+rUh05lwD0ZD2Hb6dfw+m/PYHrXl6FTGeo9Nq2axRN3dED7lrHY+N1Z3LV8L14c0x13XU+7+QRBEER4WLx4MZ599tlwDyNkwrpYIQhCBksl8MVC4Ie1QOuewOAXANY7PS/P8/hv6Y9YVLQVDobDiMSHsPP7Tjh+SYNYDYdhWZXonX7ZVbxRqi6KWsUjK7kGWck1GHEtUFytw/lKPWrsahg0HDokmtEq1hLWmiqhcCk+E//X5UH0LdiKYf83H1/dOAvl8XVz50qPaYfbE0bi64oP8fpvz2Dydc8hXlv/bF4Mw+Du69NwXWoLvLn3NP688QDu6NQSc+6+Dt0zEurdP0EQBEHUhWhaqAC0WCGIyOLyWeCXd4B9bwDmy8BNjwHd7oOn+5WDd+LrsiNYf343fqo8iXg+HfaiB7ClshVSYx14oFsZerSqgJqpW4Y9hgFax1nROs47ID/SFyoifyR1wjddH0af4ztw79d/x/G2d+FI++Goik0LuY9MXWcMy4jD5xe24YWfJ2FMuyfQu+Wd0LD1d91qmxKLF0Z2xe6CEuw8WIR7V32DW9ol4b6bMjCwcyu0auFv1SIIgiCaNxaLBTNmzEBVVRUSExNx7bXX4siRI7BYLEhOTsayZcugUqnwj3/8A2fOnEFycjKWL18OlmXxwgsvoKCgABqNBsuWLUPLlkJGuezsbGzatAmAsPEpde7cuXNRVFQEtVqNVatWhTVcgxYrBNHY8Dxw5mugpgywVQMVhcDl34ELPwGmY0KbDgOBXtn4tNyKX458jSqHDZfsFThv/QNFjpNwMBbw9gRYL41CTXlvdG1lw7isElybVO3n6tWcKEloj897TkXPwi9x3ZnP0eX3T1HWIhNlCR1Q3iIDZl0S7GoDLiZ3hkUnbdXonHgTWhraYNeFd7DhxEt47/dcdEq4AdfEdkSb2I7omdwXDHN1gfJqFYuh3Vrjjk4tsedYKb46fhF/f+8wAKCDMRZdWsejvTEWxjgtEmI0SDBoEKtV4+Z2yVBRcD5BEESz4+TJk2AYBnl5efjiiy/w/fff4+abb8aUKVOwcOFCfPHFF1CpVHA4HNi8eTOWLFmCr776CgDgdDrxzjvvID8/H0eOHMGAAQP8+v/iiy/8zr3xxhtx7NgxfPjhh9i/fz+uXLlCixWCaFac/grYNNrjAAO0SAWMnYHr7gHa9gMMSTh92Ya//fY8VPpiAADPs+BtydDYu8HIX4/r4jqiQ2cHMvRFSIqPgfDnHMFuRbwKLeIaQ9kloiD5YZzpNBKtLx5CiqkAGaZfcG3hV64WRRn98N2tT/udaYuJRQudDi10bTG5xVycqzmJX8sO4HRlAX66tAcqRo2c295DnDqxXiNMMGgw8oZ03NuzNc5eMuNocSUK/qjEocJy/PfXYjh9FpyvjOuJ+27KqNc1CYIgiOija9eu6NSpEyZNmoS2bduiRYsW6Nq1KwDguuuuw4ULF+BwOPDzzz8jOzsb1dXV6NixIyoqKtC9e3cAwMCBA2XrGZ4+fdrv3KSkJIwdOxaTJ0+G0WjEM88802jfVwqGj5Jt2IMHD0Kn04V7GEQTR61WIysrS5G+AsmsimWh1woxKDwP8DwHjuP9fK4YloHGYADDMuB5HjzHw8lxwv+j4083ImAYBizDgKm1Tjg5HjZH6G5yDAC2tsYBZ+fANdS9Z4TaLwzLeFlvnHYbOKd0fRslZRYgXUs0PCSzRLShtMzWhYKCApSUlGDAgAGYPXs2/vjjD/Tr1w/Tpk3Dc889hzvuuAMA8Ntvv2H69Ok4cOAAGIZBeXk59uzZgxdeeAEfffQRTp065Soh4ukGtnv3br9z09PTsW/fPowePRrLli3DNddcg3HjxoXl+wNRtFghCIIgCIIgiOZEZWUlZs2aherqami1WnTu3Bnnzp1DdXU1jEYjXnnlFTAMg+eeew5nzpwBwzBYunQpUlNT8fzzz+PkyZPQ6/VYunQpkpOFgtKeixWO4/zOTUlJwezZs3Hx4kVwHIfly5eHtXg7LVYIgiAIgiAIIgp47bXX0Lt3b/Tp0yfcQ2k0aLFCEARBEARBEEREwoZ7AARBEARBEARBEFLQYoUgCIIgCIIgiIiEFisEQRAEQRAEQUQktFghCIIgCIIgCCIiiZrFyokTJ0Jue+bMmYYbSITRXL5rNH7PushsfYiWexMN46QxNp7cShHp9z/SxwdE/hgbYnyBZDbS70cwaPzhRcnxcxyP0itWXLhcg9IrVqG2WiNRUFCAgoKCRrue0kRNBXuHwxFyW7PZ3IAjiSyay3eNxu9ZF5mtD9Fyb6JhnDTGxpNbKSL9/kf6+IDIH2NDjC+QzEb6/QgGjT+8KDV+juNxrOQK/rzxAAovm5GRZMC/H74Z16W2AMsywTuoJ+JCpUuXLg1+rYYgahYrBEEQBEEQBBFtXKq2uRYqAFB42Yw/bzyAD6b2Q8sWuqvu12KxYMaMGaiqqkJiYiJeeuklPPPMM7h06RI6deqEBQsW4NVXX8WuXbsAAB9++CE2bNgAm82GefPm4eLFi0hNTcWSJUvAcZxXXytWrIDVasX06dNhNpvRtm1bLFmyRJH7UVeixg2MIAiCIAiCIKINm8PpWqiIFF42w+Zw1qvfkydPgmEY5OXlYezYsXj33XeRlZWFvLw8lJaW4ujRo5g9ezaeeOIJPPHEE9iwYQMAYPv27cjKysLmzZvRrl07vPfee3591dTUoLS0FNnZ2Vi/fj0KCwthMpnqNd6rhRYrBEEQBEEQBNFAaNUqZCQZvI5lJBmgVavq1W/Xrl3RqVMnTJo0Cd988w1+//137Nq1C9nZ2Th//jxKSkokzzt58iR69uwJAOjZsydOnTrl15der4darcaOHTvwt7/9DRUVFbBYLPUa79US1sXKli1bkJ2djezsbIwaNQrPPfdcOIdDEARBEARBEIqSEqvFvx++2bVgEWNWUmK19er36NGjuPHGG7Fu3TpUVlaibdu2eOSRR7Bp0ybMnDkT6enpAAC9Xu+Kv+F5HllZWTh48CAA4JdffkFWVpZfXz/++CPeffddDBkyBK+++ipiYmLqNdb6ENbFyoQJE7Bp0yZs2rQJN998M8aNGxfO4RAEQRAK4eR4HC2uBM83XsYbgiCISIRlGVyX2gIfTO2Hb/8+EB9M7adIcH2bNm2wadMmPPDAAygtLcW4ceOwd+9eTJw4Ee+88w5at24NAOjbty927dqFBx54AAcOHMC4ceNw8uRJTJw4EWfOnMGYMWP8+urWrRv69euHNWvW4JFHHgEAWUtNQxMRAfYlJSUwmUzo3r17uIdCNCIcz6HMUgab0watSotkfTJYhjwTIwl6RsTVsmX/Ofxj56947cFeuLdneriHQxBEFNCU3zksy9QrmF6K+Ph4vPXWW17HVqxY4dcuMTER69ev9zq2bNkyr89ardavr1tuuQX/+c9/lBlsPWD4CNj2WrZsGfr27Ytbb71Vts3Bgweh04X2kC0WC/R6vVLDi2ii9bvq9DpY4iyYtWcWiqqLkB6bjuX9l0NfpYfVYvVr31jfU8m0fnWR2frQUPemrs8oXONUkmgco9KpKJWS25e/voj801UYeX0SnrwlKaRzIv3+R/r4gMgfo8ViQa9evRTtM5DMRvr9CEZzGr/S7xwlEMcfrSl/mwpht6xwHId9+/Zh1qxZAdvpdLqQhaWgoKDZCFa0fleT2YQnP3kSRdVFAICi6iLM2jMLecPzYDQY/dpH4/esi8zWh4a6N3V9RsGIhmdIY1RObmv2lgMAKhyqJqO7I318QOSPsSEK0wWS2Ui/H8FoTuNX+p2jBNF+/5sKYbetHThwAD169ADDNHxRHCJysDltLoUkUlRdBJvTFqYREb7QMyLqQ1GFEMxZVB6e7DEEQUQX9M4h5Aj7YuWbb77BLbfcEu5hEI2MVqVFeqy3H3t6bDq0qvplxiCUg54RcbXwPI/iCmGRUlJJixWCIIJD7xxCjrC7gT311FPhHgIRBpL1yci9KxeFVwphUBtgdpiR0SIDyfrkcA+NqCVZn4yVg1Zi+pfTXf7DKwet9HpG0RIMGS3jbCrU2JywO3loVAyqrA5wHF/vrDcEQUQPos6NSYuByWyS1bmeulmv1gd95xDNk7AvVojmCcdzsDgsWPT9IpdSyhmYA47naBIZIbAMi6ykLOQNz5Oc5HM8hxOXT/i9WLKSsiLqGUbLOJsSVVYHAKBVCz0ulJtRZXMgXq8J86gIgmgMQtW5Uu1y78pF3rA82LgmuLHEcUBNKeCwAWotENMSYCPvuy1evBjPPvvsVZ2bnZ2NTZs2KTyiCHADI5onJrMJM/NnegXSzcyfCZPZFOaREZ6wDAujwYj0uHQYDUavl0aZpcz1kgGEZzj9y+kos5SFa7iSRMs4mxJXLHYAcKXprDTbwzkcgiAakVB1rlS7KbumAAwk3zlRDccBF38D1g4GcroJPy/+JhyPMK52odKQkGWFaFREk6/daZcMpLNzNKmJFkINhgzVHSDc4ySU44pFsKwY4wRf80qzAwgtezFBEFFOIJ1rMptclvpmpZtrSoF3HgTKzwmfy88JnyfvBuJSr7rbN954A1lZWRg8eDDefPNNtGrVCvn5+bh06RI6deqEBQsWABAsHt26dcPx48fx1ltvwWKxYMaMGaiqqkJiYiJWrFgBtVrtaitaR6xWK+bNm4fi4mLEx8cjJycHKpUK8+bNw8WLF5GamoolS5ZAq/WPK7LZbJLtfMcSCk1kyUpEA6LJd+InE2HjbJKBdBqWXEWihVCCIT2f+YiPRmDiJxNx4vIJcHzj7SZR0GbjIy5WRMtKBVlWCKLZIKdznbwTEz+ZiCHvDcHETybCyTubj2522NwLFZHyc8LxejB06FDs3bsXAPDDDz+guLgYWVlZyMvLQ2lpKY4ePQpAqEXUq1cv1+Lg5MmTYBgGeXl5GDt2LGpqaiT737ZtGzp37oytW7fi7rvvxokTJ7B9+3ZkZWVh8+bNaNeuHd577z3Jc+Xa+Y4lFGixQjQanibfDb9uwLIBy1yKKj02HcsHLg9bLnWi7ogB+J7P0DcYMhJcsEIZJ6EsbsuKsFgpr2mCO6UEQUgipXNXDFyBpfuXer0Llu5fihUDVzQP3azWAomZ3scSM4Xj9aB9+/YoLi5GVVUV4uPjUVxcjF27diE7Oxvnz59HSUkJACArKwt3332367yuXbuiU6dOmDRpEr755hvZwp2nT59Gjx49AABjx45F9+7dcfLkSfTs2RMA0LNnT5w6dUryXLl2vmMJBXIDIxoNT5PvB6c+AACsHrwaWlYLjUoDo8EINUsiGS0EC8AHIsMFK5RxEspSZRUsKcmxwotYXLwQBNH08dS51ZZqxOpjwXEc8gvzvdrlF+Zj/q3zm4dujmkJPLDV7QqWmCl8jmlZ76579OiB9evXY9CgQTCZTOjRowf+53/+B/n5+UhPFxaCsbGxXuccPXoUN954I5566inMnj0bP/74I2677Ta/vjt06IDDhw/jtttuQ25uLlJSUpCVlYWDBw+ib9+++OWXX5CVlSU5Lrl2vmMJhSYoEUSk4msa/uDUB5i6eyoMGgPSYtNcCxWO52Aym1BUVQST2dSoLkNE3QgUgA+QC1ZzRVycJBmE51xjo8UKQTRn1Cq15LuAZQO/Q5oMLAu0ul6IUZn5q/Cz1fWKZAMbOnQoNm7ciAEDBmD8+PHYu3cvJk6ciHfeeQetW7eWPKdNmzbYtGkTHnjgAZSWlqJbt26S7caPH48jR44gOzsbv/32G0aNGoVx48bh5MmTmDhxIs6cOYMxY8ZInhtqu1CgbWyi0Qi1bodUykOdXhfGkRNXSyjPvKGh1MWNj7hYiY8RYtBqbM5wDocgiEZETufm3pWLKbumNN8aKixbr2B6Oa699lrs37/f9XnFihV+bXzTCcfHx8vGjHi21ev1kv0tW7Ys6LlarVay3dWkNqbFCtFohOKOIxfjsHbQ2nANm6gHUu4AjW3ml5OpvOF5FCPVQFyxOBCjVcGgVgEgywpBNCdkde6wvObh8kUoDi1WCEUJVilcdBuSQy7GgWPIFSwakHv+RoMRpWdK0bZL20YfUyTEzTQ3qq0OxGpVYFlAzTJkWSGIJkKwdzwQQOdyNqTHebuCEUQo0JKWUAzPNLViasK6pqmVi3FgeRLVSEeJ598QUNxM42N1OKHTqMAD0GlYWqwQRBMgVB1POpdQGpoBEoqhRJpauTSz9gqq0xDpREKaYikodXHjY7Fz0KlZgAf0ahXMdlqsEES0E6qOJ51LKA25gRGKoYS7jVxcy7GSY0oPl1CYSHW3otTFjY/V4YRWxYIHoFWzMJNlhSCinlB1fCTEKhJNC5IcQjGUMv0GS4dLRCaRbPonmWpcrA4OWrVwj3W0WCGIJkFddLyoc2uKa0jn1tKYZRkWL17c4OfW5xp1haSHqDfiH6DNacPaIWsxMGMgAGBgxkCsHbIWNqdN8g+T6qlEP57PEDyQe1eun+k/UZcIk9mEmLSYBnnOJEeRh9XBQVebCUynVqGG3MAIIuqRc+8SdbwSOjhUfR5ter+xYzqfffbZBj+3PteoK+QGRtQLqXzqKwauwILbFqDUXIrJn0+WrG1BtS+iH7lnuHXEVlgcFmhVWiTqEnGq/FSDPWeSo8jEYnci0SDUWNGpWVhosUIQUY+US62SOj5UfR6Ner+hUui/8cYbyMrKwuDBg/Hmm28iMzMT99xzD7Kzs73qmWRnZ6Nbt244fvw43nrrLVgsFvz1r39FRUUFMjMzkZWVhSlTprjaiue+9tprcDgcOHDgAKqqqrB27Vq0bNnSr53VasW8efNQXFyM+Ph45OTkgOM4TJ8+HWazGW3btsWSJUuu+ntG5lMl6k1j7TqUWcrw+s+vY27vuXh7yNuY23suVh9cDTtvx4z82NCAawAAIABJREFUGbKBeJEajE2Ejtwz5HjO5W5Vbi0P+px9ZdXBOUKWXZKjyMTq4KAR3cA0LAXYE0QTpdwSXMeHSqj6PBr1fkPFdA4dOhR79+4FAPzwww/o37+/ZLuDBw+iV69erkKQp0+fRlpaGrZs2YKzZ8+6FipSnD17Fnl5ebjrrrvw/fffS7bZtm0bOnfujK1bt+Luu+/GiRMnUFpaiuzsbKxfvx6FhYUwmUxX/T0jwrLy/PPP44477sCgQYPCPZQmQWPuOnAchwnXT8CCbxe4rrWw30I4OEfAP8xIDcYmQieUZxisjZSsLh+4HLkHc5FfmB9UdkmOIhMxwB4Q3MAoZoUgoh8pfb3m7jWK6eBQ9Xk06n0x3sdz3ErEdLZv3x7FxcWoqqpCfHw8YmJiJNtlZWXh7rvvdn1OTU3FkSNH8NBDD+Hhhx8OeI3Ro0cL401Ph90unZn19OnTGDJkCABg7NixAIALFy5gx44deP/991FRUQGLxVLn7ycSdsvKgQMHYDKZaKGiII2568CBcy1UxGst+HYBVIwqYCBeJAdjE6ERyjMM1kZKVmflz8KorFGuz4Fkl+QoMrHavQPsyQ2MIKIfKX19rvKcYjo4VH3OMqx0PbYIdQEDGjadc48ePbB+/fqA8+jY2Fivz19//TWmTp2Kbdu2YeTIkQH7NxgMQcfQoUMHHD58GACQm5uLHTt24N1338WQIUPw6quvyi6iQiWslhW73Y758+ejf//+2L17NwYPHizb1mq1oqCgIKR+LRZLyG2jHanvGpMWI7nrUG2pRumZ0qB96vQ6aBI04BgOLM/CXmGH1WKVbAM1JK8FDljefzlm7Znl3jHvvxyVxZUotZRCp9cF/H0o37Mh6NKli2J91UVm60O45D2UZ6jT65AzIAczv5rpapMzIMfVJiYtBkaDEXN7z0WCNgEVtgqsO7wOCdoE13UCyW5d5SgY0aA7fMeopMwCyshtjdUOm7kG586dhdVcjRqbA0ePHgXP8wHPi/T7H+njAyJ/jPXZXZUjkMxG+v0IRiSNX2pukftLLnIG5mBm/kxJHew5/mBzC51eh9WDVqOopggGtQFmhxnpMel++jwhPQEL+y308+jgHJzi90ocf331bEOm0B86dCgmTJiAL7/8MuRzunTpgj//+c/YtGkTUlJS8OSTT6JTp05XPYbx48fj73//O7Kzs5GYmIhXXnkFhw4dwsKFC/HOO+8AAEpKSpCRkXFV/TN8sLdHA7Jjxw7s2bMHCxYswObNm2E0GpGdnS3Zti7CooRgRQtS39VkNmHiJxP9zI2hBHKF4kLm2WZu77l4ef/LktdK1iejzFIm+4fJ8VzA3wf7npFOY405nPcm2DPkeA5nK8+i8Eqh6+WT0SIDbePbgmVYlFnKcOryKcz/dr5L3hb1WwQePB7/f48DCC67dZGjYESDnDX0GJXo/7r5n2HUDekY0ysD2344h49+KcKpF4eBYZgGv3ZDEunjAyJ/jA0xvkB9Rvr9CEYkjV9ubrF1xFZwPCepg8Xx13VuEciF3WQ24YX/ewGjska5Nrk+PPEhnuv7XL2C1aWIpPuvJNu3b8d//vMfaDQaqNVqTJo0CX369An3sGQJq2WloKAA48ePR8uWLTFy5EgsX75cdrFChI5obvT9g0/WJ8tO7DyPB8tY4WkKXnd4HZb2X4oKa4XXZFTsN5DiCPZ7ovGp68Q/2DMss5Th/WPvY3Sn0VAxKjh5J94/9j4e6f4IjAYjOI5zLVQAQd7mfzsfi25fBCA0UznJUWTB87wQYF8bs6JVq8DxgN3JQ6sOvFghCCJ8BNP/cnOLRF1i0A0iOfd0z4UOy7AhZcxK1idjWq9pknMcIjTGjx+P8ePHh3sYIRPWxUpmZibOnz8PADh8+DDS09ODnEGEgpy5EYDkrkXHxI6u1IOLb19c56Bpm9OGRd8v8uqTiD4aIjEDwzMY2mEopu6e6upz2YBlYHhh0mrjpAMlW8e2xuf/8zlVm49CrA4he5trsVL70+JwuuJYCIKILELV/1qVFvNvne/anAw1NkUqKN5oMKKkusTlQrZx6MaQAucb0qWKiEzC+mTvu+8+7Nu3DxMnTsTWrVvx+OOPh3M4TQqpit1yOxsms8l1vMJWUaeg6UndJ+HZb56NqhSChDQNkZjBxtnw1FdPefX51FdPwcYJLx+5gEq9Wk/V5qMU92JFWJCKCxQLZQQjiIglFP1fZinDlF1TMPWLqXjs88cw9YupmLJrSkjvCCldP6XnFNdCBQDKrGUhB+tLzXGIpktYLStxcXFYuZJ24RsLuXR/dqcdfdL64JFuj0DNqvHvu/+NV354xZU6dsXAFeA4oRZGsj7ZyxScoE2IuhSChDQNkQ5SLoW1g3cAkHYryL0rFw7OgfOV56FRaWA0GKFmIyLLOhECVoewKHG7gdUuVuyRXWGaIJozSqSiD0SyPhm5d+V6xS92TOzo1d+6w+uw+PbFrg3QQO5dSsYqEpEPzQCaEXJ5vmM1sbi/8/1erjo5A3Pw7K3PwsbZsHT/Ur+aF54m2IbIHU40Pg2RB16j0kj2qWE1XtcV3QoMagMsDgum7JriVXelU1InWrBECVa7j2XFww2MIIjIJBT9X993hK/L+IqBKzAwYyDyC/NdbQwqQ1A3s2isYE/UD3r7NwGkdhjEquB2px0alQYqqGBxWvD20Lex9betWF+w3vUHbnVa/Vx1ZubPxPqh6zH588mywW5GgxEcz8kG8xPRhZyVAzxQVFUkuXslVpsX5SxFn4IKW4VLFlP0KVg+cDlm5btTC68evBoqRoWiqiKwDIvlB5a7XlY7R+10LZoBd92V9UPXgwdPO2hRgGhZUbuKQgo/qTAkQYSHUKwQgRLz1KWNHGWWMrz+8+teaepXH1yNp299GuMuj4NBbUCyPhk5P+Z4LV7SY9OxbcQ2WJwW93yGUYUUiE80HWixEuXo9Dq/HYbcu3JhcVi88p4v6rcIOT/lwGQ2YfnA5Xi026PgGR7J+mQUVRXJuuoEM/lSoFvTwfdZ6tV6lNaUYuKuiZK7Vw7OgeOXj3stRJYPXI7PTn3mtRjOSszChns2wO60Q6/Wo8xShoc+fcgrP/4lyyUcMh0SFjESMldcXYyH//sw7aBFAaK7l4b1iVmhwpAE0eiEaoUI9V1+tQH2HMdhwvUTvGqjLO2/FBWWCi9ri+f7AAD6pPVxbVqJbdbcvYbcz5sZ9LaPcjQJGr8dhsIrhV5Ba2I62EndJ7n+6O283RWUJldtXs2oQ64mS4FuTQPPZ8nxXMCAS5PZ5HqBiL+flT8LozuN9mpfYatAWmwaqi5UgWEYvz4XfLsAk7pPAgA4eaekzJVZyyTHQEQeYoC92idmxUyLFYJodOqSOCXYu7w+AfYcONdCRRxHhbUCM/JnyL4PAODRbo/6vWfOVZ4LORCfaBrQrDLK4RjOb4fBoDZ4Heth7IG5veeiY0JH5AzMgdFghN1ph8lsAsdzUDEqLOy30PXHL+5uMAyDFQNXeB0nF6/mQ7BgSrvTLvl7FaPy+szwDIqrixHbJhZmh1nynGSdIFM7j+/E8oHL/WRx3eF1kmMgIg/RDUzru1ghNzCCaHSUTJxic9rQJ60Pdo7aiY9Hf4ydo3aiT1qfkPri+OBzFXFs4vsgPTYdalbt1yb3l1zkDMyhuUkzgtzAohyWZ/0C3swOs+tYD2MP/O+N/+tlel3UTyi4N/GTiVg5aCVS9CnY8tsWL1/SLb9twaisUfjwxIdYO2QtVIyKXLyaGcGCKeWC5528e1L6aJdHUWIuce2MrRuyTvKc1nHuuioJ2gTBbYyzQ82osWTfEpdLgO8YiMhDDLBX+wTYk2WFIBofJROnxKhj/JLxLBuwDDHqmODjYP3H4TlX8Ryb5/vA4XT4tTGZTTAajOR+3oygJxvl2CvsWDloJdJj09HD2AOr71yNtvFtsfbutRiYMRCTuk/yM73O/3Y+TGaTyxzMsiym9ZqGl/e/jMc+fwwv738ZE66fgHWH1+GS5RLOVJwBx1Pa0eaGGEwpt3tlNBj9rCDLBy7HzuM7XZ8fvP5BLxM+z/NY8qclXucs+dMS4YVa63qgUWmQFpuGa1pcg9TYVMy6eRZW37kabw95G6vvXI3cu3JpBy2C8bWsiAH2NWRZIYhGJ5gerwsWh0WybpbFYQl6LsuyWNZ/mZcubx3T2s97I/euXKgZ9z56iiFF8j2TpE8i9/NmBFlWohyrxYrr2l2HrSO2elWCFdMPJ+oSJc2s4u53UXURLA6LK7DO7DDj+OXjeO2n1wDAzypDwc3Nh2ABl2pWjU5JnVxWEA0rZANr1b0VHrz+QWhVWpjt3m5fsZpYOHmnV4CmmlXD4XTIjsM33eXKQVSbKZJxxazULlI0FGBPEGFDySQ4dk7a9dfO24Oe63A6YHaavXT54tsXo318++BJXcQkLbXvGaq91fygGWcTgGVYcDznF1Q/M38mGIaRDESrsFW4/q9VaV2BdQa1AS/vfxmHTIckrTIU3Ny8CBZwqWbVLitIWmyaq4ijp5XEU/70aj3m7JnjFaA5Z88ccJC23NUlOJSIDMRFiZatjVkR66yQZYUgwoJSSXB89TngXzdLDg6cq9gjIOjyZ7951pXsJ1BSFzFJi/ieoYVK84MWK00EuSA6sQ6Kpwl1Ub9FWHd4XcA86umx6VSdnvBDrN9TVFXkStAQCF9XMbkAewfnkOxTyeBQonEQLStiYL34s4YsKwQR1ci5/qboU4K+F6QC7MU5igjpe0IOWp42EeSC6DSsBmpW7XK74cEjIy4DS/svlTQH+5qMqTo9IXI1VYO9XMWcdqhZtaRM/V7xO6Z+MdWvTyWDQ4nGwTfAnmUYaFQMuYERRJQj5/p7uuJ00PdCKLpcKgg/PTYdWpb0fXOHLCtNBLkgOgCYunuqy+0m58ccnCw/6ZWxCfDeMS+zlCFZn4y02DTFAvOI6Ke+Llk8eDBg8NaQt/wsfbm/5Er2qWRwKNE4+AbYA4J1hVIXE0T04+v6W2GrkHwvlFvLYTKbEJMWA5PZhERdYlBdzrKsZAIWlqWpanOHLCtNBLkgugtXLriUiFQa45WDVqJjYkecKj8luTNC1ekJkasx0ctVud82YhtqHDUAgDl75nilJvbsU8ngUKJxsNg5sAzAMgBfe0yrYmFxUEZBgmhqSL0XjAajX8Ifca4RSJc7nA4vT5BQErAQzQNarEQ4YvFGB+eATqUDwzCwc3ZoWS2SeaCjUQtUlQAxLcGyQhAdIFhKyixlAAMh3esvubIB8xvu2SC5M5I3PI92sAkXWpUWAzMGYlTWKFc9nuIrxWDA4HzleVdwvWfwo8lsQu7BXK8aPrkHc/F0n6cBCIuRFH2K13V8XQPE4FAiOrA6nNCqWTAMA752taJVs+QGRhARhDhH8Fo48ABqSgGHDVBrgZiWgI9Vw/c8vVrv916I18b7JfwR5xSBdDkHDnP2zPFzA9swdANMZhNtWDVjaLESwdiddpwoP4FZ+bNgNBgx88aZmP/tfPdOxW0LkfXZs0DVReCBrUCr6wGWlYwtWNRvEbRqrXTaQZl0hDanrc4xCkTTJUETjyk3TPGzkizZtwT5hfmuz52SOrkWLDzPY8L1E7yseQv7LQTHcxj6/lDXOQC8+kjUJYbzqxL1wOrgoFOrvI7p1CparBBEhCAbf8ipwG4eA5SfAxIzveYVgc6bcdMMr0KRa+5ec1WB8nJB+HbOjkf++wjNQ5ox9KQjGJPZ5JoYTuo+ybVQAWp3Kr5bgLI7ZguK5Z0HhR0RSMcWzP92PloaWkqmHVQzasnjLMNS2ljCxSXLJa8Cj0XVRZiVPwujskZ5fTaZTV7n+VrzFny7AHytg5B4zoybZuDtIW9jbu+5yD2Yi3JreSN+M0JJrHYOOjXrsqoAEALsyQ2MICIC2fjDirPCfALwm1cEOq+oqsjr2LnKc7JzikCwDCt5nueGKs1DmidhW6w4HA4MGDAA2dnZyM7OxrFjx8I1lIhDDHZ3cA7XH6hcGmFzaheYJmwFF9dKMN1C8CE1GozIGZiDt4e8jZyBOTAajGAYBgv7LfQKXlvYbyG0rFYy8I0FS2kEo5y6php2cA4UVxfjfOV5FFcXw8G5fYXtHvIoUlRdhARtgtdnO+cuEMaDlzyn0lbp9fmy5TIe+/wxzMyfifzCfJKxKMZS6wbmiU6tgpUsKwQREcjGH+pivRuWnxPmFVUlQPl52OzSqefjdfFe841dZ3Zh2YBlXnOKxbcvBhtkysmClZyjmB1m/7HSO6JZETY3sGPHjmH48OGYM2dOuIYQkXiaWVcPXu1K41dhq5BM6Xe84jRePrQCK+9ZjCyNASyEwnu+LmOL+i2CmlFjy29bvOIHtvy2Bc/d9hyyOBXyes2FTRcLrbUayZwKZbW7HJQ2Njqpa6phuWB40a1LI5N2WCwwKn72LBAml4ryj+o/AvZBMha9WO0ctGoWHoYVIRuYnSwrBBEJyKYI9jSHAoIrGOcA1g4Dys9Bm/2+5HkJ2gTM3TPXa75hNBi9AuUNKgPUqsBTTpZlJeco464b59WO3hHND8UtKydPnsSaNWuwatUq1z8pDh48iK+++gr33XcfnnnmGTgczTvbg+cO+MWaizAajNjw6wbX7sS6w+uwqN8ivx2HdYfXwWgw4iJvRxFngclsgpNz+rmMzf92PgBgWq9peHn/y3js88fw8v6XMa3XNCTzALt5DIybxiJ97RAYN40Fu3kMknlQ2tgopq6phj3dDsX2nm5dRr1grfMtCPbhiQ9dn0UrnkgyD6zst9j7nAHLvM7x7YNkLLqxOpzQ+VhWtCqWLCsEESGwLOs3n1jUbxHYxHbCAgUQft6fB3z+rMs1LDn/Raz807+8zlsxcAWWHVjmN98oqSmBjav19uBsePPQm+C4wBsWyfpkyTlKRosMmoc0cxie911K148RI0bgz3/+M1q3bu061rt3b792hw4dQlpaGlq1aoW5c+diyJAhuPPOO2X7PXjwIHQ6XUhjsFgs0Ov1dR98mNDpdbDEWTBrj3tHe2G/hXjtp9fQMaEjHun2CLSsFjqVDk6nEw44cLz8ONYdXgcAfumI19y9BiM+GOF3nU9Hfwp7mR2aBA04hgPLs7BX2JERx0P7+o1+7e1/PQhwNlRcKXRZXBJaZKCwWgOLxdrg98WTxnqmXbp0UayvushsfZC7NzFpMRjxkb8c/Gfkf1BTXON3PK5NHIZ9MMzv+KdjPkXVhSro9DogEbhQfcG1W9Yuvh2Ka4SMYGaHGW1i2wDlgLVWPjqkxqHIXoZCe6XrnPaGVtCrDbBdKYLWWo34pA4oZTRwwOmSSWsjy5dINOgO3zEqKbNA/eV23udFsHEMHu3lTpLwzqFyFF1xYP3/XBPw3Ei//5E+PiDyx2ixWNCrVy9F+wwks5F+P4LREOOPS4/DvG/mYVL3SS4LxrrD6/DS7f9CstUJNcPBwbPQqFXQrOzmOs/R6yGcvWMWiqr/cOnzjokdMeS9IX7X+Gj0R5iya4rXnCYzJhMVRRV+bT3R6XV+cxQAfsca6x0h3n+l9SxRNxR3A0tJScGIESOgUqkCtuvcuTO0WsGM161bN5w9ezZge51OF7KwFBQURJVgmcwmPPnJk35ByHN7z8XM/JnYV7zPK+WfyWzCy/tfRlF1EXIG5vgFMIvBbX7V7FVqXNPeZ7KQDMEfNTHTHVgHAImZ0Gg0wNsjYfQ53n7ybiCuQ4PcCzmi7ZkCdZPZ+iB3b0xmk6QcxOpj0bZLW7/2xVV/SMsNq0aXLl1gMpsw8ZOJfr8X5VT87CWrVX9gypdz/c7JG7QK6WtrX3CJmWgzeTcQlyF8DuOGWTTIWUOPsb5yq8ovQxzLIDPTLWNJZ504V1ketN9Iv/+RPj4g8sdYUFCgeJ+BZDbS70cwGmL8pitCDKOotwFBL+tZFWJaCXMELeA3NzD9aRamfjHNS5+vvnO15Huj8Eqh35xmwz0b6v5dpN4HjfiOiHb5aSoo5ga2c+dO7Ny5E507d8bDDz+MLVu2uI5JMWfOHBw9ehROpxO7d+9G586dlRpK1CEX7JagTfA2eXIcUFWCZLsduYPfwOo7V6NjQkfM7T0XPYw9XOfm/pLr566zsN9C+Ycd01JIUehr/gWAIS8CGTe724oBd0TEU9fq70aosNwnKHL5gGUwQth4kCv+1TGho1ciB8/ARxt4yWQPNusVdydicojy88LLMYirABHZWB2cV/V6QIhZsVI2MIIID7VzB1HHJoPFylsXeL8bbl2AZF/d6zM3sKvUfvp815ldfvONnIE5yP0l16urouqioAleCEIOxSwrojdZ586dXQsPnufBMIxk+2nTpmH27NkAgEGDBqFv375KDSXq0Kqkg93S49JdhRlZHsDF34RUgnGtYBv+EhZ9v8jPbeyQ6RBMZhPMDjPevOtNVNgqYDKbhED62kJ8frCskEt98m5h0sg5BD/VY58ISmrkKuDLF4DCA8JnNQW2RQN1rf6uBo9OP23DhkGvw65SQeN0wnhgI9R9pwHwl9Mexh6YeeNM/GXXX7wCK/Uqt8uCXLIHva3WDS3jZuDO54H1w2Rz+xPRhdXB+WUD06qoKCRBhAWOc88danUse38eso58hrweM2CLSYa2pgzJ37wO9p6XvM/1mRvo1TpJfZ4ak+r1nmEZ1i+FPQXFE/VBsdnAmDFjMGbMGAwYMMD1/zFjxrhcvXzp1KkTPv74Y3z88ceYNWuWUsOISuR2wNNi02A0GIXJZU2pS9mU3TEb07991s/EOqn7JNfC5dUDr+Ivu/7ichmb1nMKkg0tgw+GdwKXTgDVJcLn8nPAR38F+s10W1zEXRraAY94xOrv6XHpblmSI6Yl1D3GI23jGFyz/AakbRwDdY/xwu4a/OV0Ss8pkokcOKfVtYPHySR74BLaAI9+Aox6A/j+Df/c/leKyNISpVjs/qmLtWoWDo6Hk1M0RJIgCB+riZ++9Jg7ABB+bpsI9ta/wMioke5wwMiowQ76BxCb6t8XywJxqUDiNXDy0vrc5rR6vWcSdYmUnIdQFMVjVmbMmIGNGze6Pm/ZsgXDhvkH7RJuQtoBd9hcysYWkyzpNia6hIkWFgDolJiFvEGvI5nRyk9UJXZevKwp5eeA1G7Awx95W1xoB7xp4WthU2uFhUrt8/WVU45zytThsQI5PYHETNge+490m5pLwPrhblmrLhFkDRDkraIQWDeE5CwKsTo4aHzcwMTsYBa7E7G6sGXMJ4imhdS721dfOqze8aiA4Hpbcwn4ZLbHeVuA8rNAgAr2crW27Jx3NlfPd0W1pRqx+tiAVn2CCIZikrN//36sWrUKFy5ccKUsXrp0KeLj45W6RJMm6A64WuvyG9XWlElWeT1/5Txm5s90LVTSY9NhKP4VxpU3gd0w3KsSrRdSOy+iNQUQrqvSABtHCgsVsY1PdVuiCeCxi4a4VL8Fgqec6hmVpBxqmdrkGuXnoDWdlG5TddHVxkvWAEHeqkvdvyc5iyqsdqffYkWMYTGTKxhBKIfUu9tPXzLueFSR/n8Htj3kc94E4PLpgH2JtbY8EZOw+CK+K2qKa4Jb9QkiCIpJT5s2bdC7d2+0aNECffr0Qe/evTFkyBCsWLFCqUs0bzwC3ZL3voqVt3lXeV054FVkxLb2D5jb+6pwvhgY72kyrjYBV0oAW43/zkv5OcCQ5N5dYVTSbSjYvtmSzKixsv+r3jLX/1UkW6rdbfJfxEqf4MuVff/plktAkKPYWhdF0dLybY7370nOogarg4POZ7Gi8bCsEAShEB4eFy7KzwF2s9uVi2GB+9YDE3cIrrcTdwDG66TP08T4H/PQvUa9EcsHLvertWXUG0EQDYli9vg2bdqgTZs2GDt2LG655RaluiVEPFx0WIcNWRoD8oblweYwQ+u0I/nzfwBVxcgb+AxsKR0EV7JtD4MVXWsSMwGV1itIH3c+D3w4Vcj4JZG6GIltBZegmJbC7opUGwq2b7awPI+sn7Yhb9Aq2Fg1tJwDyT9sBJvprqvExqYii1Mhr9dcV62eZF7tvUuSmAnoE4UXaYwR+OIFt0uY+HuSs6iA5/laNzDvxCo6WqwQhPKIHhe+72XTcSBvnPD/xz4DnDZvl6/xG4Hrhrs9JcTz7D71t3x0r1qtQaeELGwYuh52zgENq4ZRb4RarWngL0o0dxR3Hv7jjz+8Pv/4448oKSlpUnErHM+hzFIWUoYl/5M5YeLvGxPgeVylBViVsDvi2UZ00QHAOh0wVhULGdc2jHIpK+OmsYKCefRTIVgOcAfG85zbZDzkRWGhUn5O2MUeuUpwx/H0VY1v43YDEi07vr6xMSEE7RPhQ07e5JrXRbZZFdjOw2DcONYtExN2ADUmYeFhrwFSrgW7cZRfrR4Mf9X9Mr1/M2C5LPzOchkY8Heg5LC7z4c+AHgIO4UhfAcifIjpidUybmAWOyVLIAjFkHovj1oNfPG88Pvyc8DlM8DOJ73du7Y/DDz8obeeHbcBMCQLlhdNjKC/kzqAM6SgzGzyeiekxbWWGxFBNAiKL1YuXLiAsWPHYuTIkXj00UexYcMGOByOJrNY4XgOJy6fwPQvp7tS960ctBJZSVnBFyxywXAtOwOlR6UVTtVF/4A5pwMo+RXYng1m9BvS5tzKC8CAecKk0GERAuNvm+pua0hy/7/wgBBMP+RFIZBeG+M/IQwSfE1EIKEEX3o2r6tsO+3Cwnr4q8LLLbGtIHfiizExE8jeKS2fxk7AzF+F861X3P7T4uLk8d3CbqDGAFwpDhj0SUQO4mJFKhsYQJYVglAU3/cyALz7qLdlGpDWwTzcutteI8wJrFe8LDDcQx/gRMWpq5vvEISCKC5tFy9exPvvv4/PPvsMAFBeXg6r1ar0ZcJGmaXM9YcLCJkwpn85HWWWsuAnywXDVRX7H/9wqhB0LBUwV1XQoquFAAAgAElEQVQMbM8Wfme+7B88JwYob5sopCIWA+M92/qeV3gA+PwZYaEiEVgNIGjwNRFhhBR86abOss07hRdj3jghs5etGnj/z97X45zS8smoBDli4F6IiOdsHiMcT7xGuEYdvgMRXqy1ixG1jxuYuFihAHuCUBjP9zLDCBucnthrpHVw2Um37s4bJ5Qs2DbBS9eWVZy9+vkOQSiI4rPNjIwMPP7449BoNNiwYQPOnTsHm63pBMfKVZv3rNoti28wXMbNgjXDaZeuFG9Icv/fM8DYaXf3I7pweVafH7kKOP5f/749236bI5h9xaC7Rz8RfFsdNqpt0VSQCr4MUC1eVrYdFukc/jzv3b/tiv/17NXS8skwQp8OmzAmTzzlXS6AlALuIxLRsiKXDYzcwAhCYTyT5vAAHtzmHUwfnyG4gXvq4Ps3A3t8CkBqYvx0rU0Xe/XzHYJQEMXdwF555RVUVFTAYDDg0KFDeP/993H48GGlLxM25KrNh1SZ1TMYLuNmYNBz3nEivpXizbV+/L4BxiqNux/RhWv4q0DKtUDJEeDwdqD7eOm+xbatuvrnWQ/kekZEH77Bl0GqxcvK9sVjgBgL5SkXvv1fKfYP9qwuBQ6sFxbMhiRBpve9Cdz9AvDaTd5y55kMQpR3uQBSCriPSKwOwXKiYX0D7IV01uQGRhAKIuXqO36joHPFemj3vQ3EpHi7fMW2krfAeOharbX66uc7BKEgis9ET548iW3btmHNmjX4/vvvsWXLFvTv31/py4QNuWrzIVVm9Ug/jH4z3YsJwL9S/KjVgvVDKpA9Lg0Yv8m9U1J1UVA+KgOg1gF9pwNOq3vHWux7zJtCfvWkDgCrFtzEQnU9I6IPT3kDhGf/4VTvZ+7xnCVl+7aFSM5/UbK9X/8HtwIT3/Xe1UvpBPT9X8HFcP1w4eetTwI1l919fjhVGBvgL+++16DEDhGNaDnxs6yohcVLjc3hdw5BEFeJlKvvnqXAnc8JOnjIi8DXywQXL0+Xr+9We88hEjOBhNrCkB7HkhPaUiV6IiJQ3LIyc+ZMTJ48Genp6cEbRyEhVZuXPdkjGE6utklqNyG4mFUJudGlAtlVaqHdY5+Bd9rBqDRC5i/TMW9LiW8V+ivFwu8f2CosauRqq4j/J1eb6MY3+JJ3BnSp8pNtHkjelu1Of+3T3q9/bSxQcd5bBu/PAw5u9rasfPE8MHihd59iwL2vvFNih6jCZVnxi1kRLCtmG1lWCEIxpFzL+/wF2DLOex6g9ynO/d1KoO9fhayhnEPYvIxrBbAaL13LxrREFoOrm+8QhIIovlhJSUnBvffeC5VKpXTXEYNYmfXqTq4NhqsqkXZvEQPcg6FSAwkZOFpQgC5d2gv9SVWhH/KikGlJdCsTd8cf+0z6+nKuZ0R04pHuWlbmPJ6zl2xXlfi7CvjKhWf/FYX+VZG3TXSnKfbsQ5Qz8bPGIC/3ntcgIhqrPXDqYjPFrBCEcvi6ycp5bEx8z/u8xEyAswtB+b746FoWuPr5DkEohOKLlc6dO+Phhx/G8OHDERMjVEMdPXq00peJfnzzo183HBiyWNjRqCkT/EeddiE+JS5NWJxI1cwQkQtEFqvQi1YW8Tijks/PTq42TZO61sqRah+o5oln4geR8nNCLJVn7v6ETKHwI0Cy1sSQrbMiZgMjNzCCUI6YloJOvnxa0K+xraR1sErtXtQkZgouYLG1m6ZksSaigAZZrHTu3BmAUM2YkMHTvYXjhEDkjSOB9ncAt0wWijZ5KpZWXQU3L5+Jpl4vLAhlA5HFAnyiO5h43Ne9RixEKed6RkQ/dXWp8m0frOaJSkYGGZWPa9hmYORKwP4SyVoTQ3QD0/osVlQsAxXLwOIgNzCCUBSHxa1fH/1Exnqur61kX7sBKrqNh1iDiyDCjeKLldGjR+Orr76CyWTCtdde22RjVxTB0yVMDHa/7X+FXWdPH/89LwP3/Au4UiT4lZafc7lzpU74WOhLbte8RTpgqXS783juZJN7TfOjrs/c143M19Uw/0Vg2MtCGmOGFdJh73jE21p3pcjHNeyhWjdECRcEIqpxB9gzfr/TqlnUUMwKQYRGrSdFR6NW0L1Smzq+AfY8J+jgGpPbkh1jFHRzvMdcTEqXv/OgsDFFcwIiAmmQAPvWrVvjwIEDePrppzFnzhxs3LhR6cs0LTxduDQGIUDON+0wzwu7Jz5B82qm1gc80K45BSgTSiAXzPn2PW5ZHZ0LjHpdeDlKBdMDtcUiyR2oKeIOsPfXLzo1S3VWCCIUPFISawNZPnx1sq6FkLzH05I9Otdf31L9KiLKUHzGWlZWhnnz5iEmJgY33XQTuYIFwukQgpLBe1SY5aUD5BhGsLbse1MIosu4GZi4A2qWAapNwJUSoPKCcE58G+8K81R5vvniWTCsqkSQOc/PvsU/A7UHvCshSwVz7pwCWCqEFJnbHhIsevYa72uIrmFEk8NVFFLtr2O0KpbqrBBEKEilJJYqJyC6f7s+GwQd7KuTfedhvucBlFSHiGgUn7W2a9cOTz/9NEpLS7Fq1Sq0a9cuYHuTydQ8A/CdDqDkV2FX+t3HBHeZxEzAXC6941FTJtSo6PMXILmDUNzvk9lgP5gMXCwA3hoM5HQD1g4WdmSoAj0h7s6t9ZCNkl+Bj2dJy4pv+49nCe3Fz5/9HRi30f2Si20pLauxtcHyYupiQ4p/BXtWcaMuEQGI2cC0EhsiWjUtVggiJEK1fBhSvOul2GVKIvA+8wGqX0VEGYrPGP75z39i9+7d6NChA9q3b49p06YFbP/SSy/BYrEoPYzIp6oY2J7tjj/54nkhEF7M3uUbIFdx3m1lefRTYOsDwuchL0oX+iPfU0Jqd257tiAzxz7xlxXf9jc86JZRQDgHACbsEHyiW6RJy2p8G3fNFEYFfDTdv4L9vcsb7z4QjYa4GJGLWaHFCkGEAMPIJCvx+bsyXxJiWkX9qomROc9n84Dcw4koo0G2NwcPHhxSu++++w4GgwEtWwZfzVutVhQUFITUr8ViCbltQ6HX65Aay0LNOOHgVSip5mCxWF2/75yqA+OpUAoPCLUonvhKsLKICxCJtMM853Cfa0iS3Emxmatx6nx474GSNNYz7dKli2J91UVm64Pcvelo1Ar+zp54Fv6s/SzKil97Kdk69glw1/PC/xmV4A8tuh3UyqqdA06WVAEQ/g6uuePvUO94yNXGMW4zzpdWwxKB8hkJuiMYvmNUUmaB+sntheIysAxQVFQIh9N7N5d32FFRxQfsO9Lvf6SPD4j8MTbE5mQgmY30+yHFtalx0Ixc5Re76oAKtotnXPMKDctBc+wT90ZSxs3CfMHnPJ5hYPY4z3c+IlDWIN8lGu+/J+L4ldazRN0Imy+GzWbD6tWr8frrrwe1vgCATqcLWVjCLliiO80WYZdam5iJ9g9sBdp6BMdVFErvgOhaCKkIH/kPAB4wHfdLO8x4pog1X5bsR2uIRZcuHRrtKzc0YX+mV0FdZLY+yN4buSKQPgUZXbJyxae9jGzh8hlhYZ2YKWSeuXelkA6z1mqiuXe593g4Dpi8GzZzNbSGWKhjWqJ9hO7gRYOcNfQY6yO3LU7/Bp36Cq7JyITTx0++xa814Hg+YN+Rfv8jfXxA5I+xISaugWQ20u+HJFUlggXa0yJ9YjfUhiSoazOHahMzhXmCp44uPCCcJ1q/a3Uyc8+/ELPlXvn5SAMSlfffg2gff1NBscVKdnY2GB8TJc/zYBhGMhvYmjVrMGHCBMTHxys1hMhBLjjO0zUrLk2oN7HNveOM0bnAB38RFE5iJvD4LiFgzjPt8KjVgFrnTlP8bY6/JYZ8TwlAOp31+E2C2wDgX+RRpQHGrAE+eEJof3ArMH6jd80fsXAoIBzb8YjgvrhptLzs1SZ4OHW+oEktoAl/LHYOOjULHv6JVbRqFuVmexhGRRBRRkxLYOAz3rr74Y+Az5/1XsCYy/zf//3nCOUPjn3i1vn7/k2u4kRUo9hiZdOmTXVq/91332Hfvn3YsmULCgoK8Oyzz2Lx4sVKDSe8hBIcx7CAJlaY6In50NV67/Z2szAx9FROXzwvFG6s9Te1mauhjYkHHt8NOMn3lPBAyi/ZkCLEi9zzknSRx/vWe1tKflgrxEgBAHghGYRo5QNqK9RnuWNUSPaaNVaH01Wt3heKWSGIEPHQ3aJFGhznX9bgofeBT//mPUf4Ya1Ql23IYiGRiVoHfLfSu39KU0xEGY3mBmaz2aDVutPi5eXluf6fnZ3ddBYqgHw1ec+0gDWl7kmiZ5shLwrWlsRMYcJYdVH47NuP1261seG/ExGdSBWBDFTk8d1H3TIICPJ253PCORWFbiufiCinCRkN+jWI6MDq4KBVsxJ2FaHOipjamCCIIPhapCsK/VPFX/5deo4g6mxA3h2Y0hQTUUSjbYFOnjxZ9nd1tcpEPKGkBXTYhGr0928GHv1E+BnXyp0N7IGtgqsYpRckGgo5GfRMPewpb3Fp3mkyRReDuLTwjJ+IOCx2J/RqFlKrFY2KhZUsKwQRGrU1r1wV7BmVv8fGnpeE9PCB5giUpphoAjSaZaVZFYcMJS2gxiDUSvH0NR21GkjuKJxH1eeJhkZOBpPaSbt1qdRAajfgsc8Ap12wqMSlCccJAkLMilajkrSsaNUsLGRZIYjgSFWwvz8PuG64O/MXIFhV4tMDzxFoHkE0ARptluEbfN/kkXK/8YRz+tdH+XAq8Ohn/oqGguCIhkBOBh/fDSReI32OSh3Y5YvjBBdHeik2S6wOJ3Qq6eetU7OwOThX4hWCIGSQStKzbaIQZF9y2DuZjiHZW8fWWmT8dDDNI4gohrZEw4VTJgi//Ayw80lBCbVqnNSCRDNFTgadVxl46bEb6PUyJTluNojZwKTQ1i5irA4Oeo2qMYdFENGFXJIeVh3YQkI6mGiiNJr0Go0UBO6FGITviVgDQ0wtWFManrERzQM5GbzawEu5lN0kx80Giz1QNjBhgWK2UdwKQQQkkG6OSxUs33Gp/gsQ0sFEE6XRFivLli1rrEs1LqLJtfy88JML0SdbKuht5CqhbkrGzUJGJltN3fokmhe+AZh1lZNQAi/rIt+hpOwmmjQ2RwDLSu1xi4MWKwQRkJiWwANbfHTzluBB8aSDiSYKuYHVh/qYXD2D3uxmd6V6ABj0nHcudTLjEr5IBWDWVU6CBV7WVb5DSdlNNGksdqfL3csXcbFClhWCCALPAazGuw4bqxGOB9pjJh1MNFHCVsG+SRBKpfpAiEFvHAdYq4TMHkNe9M+lTtVmCV/qK3sigQIv63oN0VLju7ihFJnNBquDg0bGsiIG3pspfTFBBKaqGNgyzn/R8dhngROckA4mmihhq2DfJFDK5Oq5w22rITMuEZzGMPfX9RqUIrPZY7EHyAamIcsKQYSE0y6T/MQe+DzSwUQThSS4PigZoCzucGtjlA16JpomSgfHK3UNUY7lAkCJJo2ltoK9FGIGsGparBBEYFQaad2r0gQ/l3Qw0QRRPGbF6XTiyJEjsFqtAICSkhKMGDFC6cuEF7GWBMcBE98T0g2LfqVJHepnciUzLhEKVysnvnVQDCmA+ZL0LhzJIlEHHE4OTo6HRsayIi5WqixBdocJorkTlwZM2AFUnHPPLRIyheME0QxRfLEyffp0xMbGorCwEK1atUJlZWXTWqx4Bh3HtQLuWgR8Mtt7MlcfyIxLhIKHnNjM1dAaYoPLiVTA/PhNwJ6XharIvgH0JItEHRCr02vU0gUf9bUWlysWR6ONiSCiFqfVe25x/+Zwj4ggwobis47Lly9jyZIlSElJQU5ODiwWi9KXCC+eQcf9ZgIfPKF8TnMy4xKhUCsnp0zW0OREKmB+ezZww4Puz77yS7JIhIilNnBeq5Iu+ChaVmpstFghiIBUFQPbHvKpYP+QcJwgmiGKzzxat26Nzz77DFqtFm+++SaqqqqUvkR48Qw6NiRRMDwRPcgFzBuSvD+T/BJXgbXWsqJmZSwrohuYlRYrBBGQqw2wJ4gmiuKLlaVLl6Jv375YsGCBy7rSpPAMOjZfpmB4InqQC5g3X/b+TPJLXAWiZUXODUyjYsAwQLWVAuwJIiD1CbAniCaI4ouViooKJCcnIz4+Hvfddx+OHj2q9CXCi2fV729zgFGrA1cAJ4hIQapi/fhNwMGt7s8kv8RVYrXXxqzIuAoyDAO9RoVqsqwQRGDi0gTd7KurKcCeaKYoHmA/Y8YMryKQW7ZswbBhw2Tbl5eX48iRI+jSpQuSk5OVHo7y+AYdawzA47sBJwUgExGOVMC8IQW4dzlwz0skv0S9sDhEy4q8/OjVLGoodTFBBEalBlK7AY99Bt5pB6PSCAsVleJTNoKIChST/P3792P//v24cOECVq1aBQAwm82Ij4+XPaeiogJTpkxB//798a9//QsbNmyIngWLVAVv37SwNPEjIg0p2fX8zHFAVQnJMFFnXG5gMqmLASFupZoC7AkiOCo1kJCBowUF6NKlvXCM5hhEM0WxxUqbNm3Qu3dv7N69G7179wYA6PV6dOnSRfacY8eOYd68ebjhhhtQWVmJI0eO4E9/+pNSQ2pcpNLCeqaBJYhIh2SYqAfBAuwBYbFClhWCuApIPxPNGMUkXFysjB07Fr1790bv3r3Ro0cPaDTyAWG9e/fGDTfcgB9++AGHDh1Cr169lBpO4yOVFlaJNMYE0ViQDBP1wBqCZUVHbmAEcXWQfiaaMYo7QGZnZyM/Px8mkwnXXnst0tPTkZoq4TJVC8/z+PTTTxEfHw+1Wn44VqsVBQUFIY3BYrGE3FYpOhq10EqkGrSZq3HqfMONJRzfNRw01vcMZAmsK3WR2fqg1L1paBmOBlmNxjEqKbPA1cvtqbNXAACXL12Eo1KmkdOGiiq7bP+Rfv8jfXxA5I+xIWqvBZLZSL8fwRDHH645Rn1pKvdfaT1L1A3FFyszZ85E69atceDAATz99NOYM2eOV8C9LwzDYMGCBcjJycGXX34pG4yv0+lCFpawCFZViWCW9VQmiZnQGmLRpUuHBrtsc/kjisbvWReZrQ+K3ZsGluFoeIY0xquX20NV5wCUok3r1kiMkU5/nXTChqIKs2z/kX7/I318QOSPsSEmroFkNtLvRzBc4w/THKO+NJn7T4QVxR0dy8rKMG/ePMTExOCmm24Cz/OybdesWYOdO3cCAK5cuYIWLVooPZzGQyotLKWBJaIJkmGiHlhqUxdrA2UD07AwkxsYQdQd0s9EM0Zxy0q7du3w9NNP4+LFi1i1ahXatWsn2/b+++/HzJkzsWPHDmRlZeH2229XejiNh1RaWMrUQUQTJMNEPQglG5hOo0INZQMjiLpD+ploxii+WFmwYAG+/PJLdOjQAS1btsS0adNk2yYkJODtt99WegjhQy6lMUFECyTDxFUiZgPTqljI2dP1aiEbGM/zYBj5rGEEQUhA+plopii+JJ89ezb27duH+Ph4/Pzzz/jb3/6m9CUIgiCICMNid0LNMmADpi5mwfHuhQ1BEARBBENxy0pZWRlWrFjh+pydna30JQiCIIgIw+rgAsarAEKdFQCosTld/ycIgiCIQCi+WNHr9VizZg26du2KQ4cOoUWLFvjhhx9wyy23KH0pgiAIIkKw2J3QqdmASVX0GmExU211IDlWOmMYQRAEQXiiuBtYz549YbPZ8PPPP8PpdOL666/Hvn37lL4MQRAEEUFY7FxQa4le7basNEmuFAMndgGVReEeCUEQRJNBccvKX//6V6W7JAiCICIci8MZMLgeELKBAUD1/2fvzMOkqK6w/1ZVr9OzTw8MwybLICCyiMoWBVyiqBGJO4j7gktQ5HP93AjGRE3AEJOg5kNgBKNiArgRo4gLQREERRlgZIcBZh9m6Z7u6qrvj5rq6aWqu7q7eqZ6+vx8fJiuurfqdNU5t/rUvfe9XU0RTBSBr/8G/PdpQPACrBm4/C/AyOs72zKCIIiUhzTvCIIgiIRxeXywWThEylbkYWAtrV2sZ+WTZ4D/PA70nwRcuhAoOh1Yex9wZGsnG0YQBJH6ULJCEARBJEyLh0eGmYvYsyIPE2tu9XaMUR3BN68AG18CRlwH/GIO4CwBJj4C2HKAjx6Sel0IgiCIuKFkhSAIgkgYlwaFL3nOSmNrFxkGtnc98NEjwMALgZEzAbFNktniAE6/Bji6FTi8uXNtJAiCSHEoWSEIgiASRpIjjiZdLO1vcneBZKX+MLDqNqDwVGD8bxA2/m3AeVLS8vVfO8U8giCIrgIlKwRBEETCaFk7xdZVJtjzHuCdmwGfB5j8JMAoPErNdmDABcCuD4CW2g43kSAIoqtAyQpBEASRMC0ePmqyIi8a2Zzqw8C+eBE4ugU47wnAnqNerv9EQOCB3R91nG0EQRBdDEpWCIIgiIRp8UiLQkaCZRhYTSyaU1kN7PgO4KsFwJDLgaLhkcsWlACObsCP/+oY2wiCILoglKwQBEEQCeETRLTyQtQ5K4A0FKw5VReFFAVg7WzAnguccVP7hHo1GAboOw448DnQ2tQxNhIEQXQxKFkhCIIgEqKlbQ6K1RR5GBggTbJvSdFhYFmH1wMV3wHjZgMmq7ZKvc4CfF7g4MaYztXsbcaRxiM43nwcjZ7GOKwlCILoGui+gj1BEASRXrjaekqiDQMDJPnillTsWRF8KPzxNUn9q+doRFz9MpBuQwHOApR/DAy6SLWYi3fhvb3v4eMDH+Onmp/Q5A3uiTkl+xT8uuTXuPbUa5FhzkjgixAEQaQWlKwQBEEQCSEnHxZO2zCwlExW9qyDtfEgMO45aE5UAClR6T4M2LdBtcjOmp2Y89kcVDRXoF9OP0zqPQkFtgJkWjLhE3w46TmJHdU7sGDrAqz+eTUWnbcIfbP7JvyVCIIgUgFKVgiCIIiE8CcrmuassP5hYynFpr+CdxTB1GMkgChzVULpMQLY+jpwsgLILg7a9VP1T7h53c3ItmZj3rh56JPdB6JCMnRB3wuws2YnXvnhFdz40Y1489I3UZxZHFaOIAiiq9Gpc1YaGxtx++2349Zbb8W9994Lj8fTmeYQBEEQcSAnH1p7VlJugn11OXBwI5r6X4qYExUAKB4l/bv3s6DNte5aPLDhAeRac/Hbcb9F7+zeiomKzNCCoXjkrEfg5t2459N74OJdsdtCEASRYnRqsrJ27VrccsstWLJkCZxOJ7788svONIcgCIKIAzn5sGiZs2LmUq9nZcc7ABg0F0SRKlYj7xTAlgv8/N+gzS9tfQnVrmrMHT0XFpNF06GKM4sxa8Qs7K3fi5e3vRyfPQRBEClEpw4DmzFjhv/vuro6FBQUdKI1kREEETXNHnh4HywmDgUOC1iWibkMQRCxoUdcUWwml0a3FwCQYY7+SLGaWP+E/JRAFKVkpc848Ky2hCIMhgWKTgcO/k86HsNgd+1urP55NX5d8ms4M5wRe1RCGeYchsm9J+ONnW9gSr8pGOYcFp9dBD3bCSIFMMSclW3btqGhoQEjR45ULdPa2oqysjJNx3O73ZrLasFqs6HFnItZK7bhSJ0LvfLsWDxjFDK89Wh1uzWXSQZ6f1ej0lHfc8iQIbodKxafTYRU8YF47NQjrmI5Ripcy1Ab9fRZID6/3b3/JADgZF0lWhsi/+j2uJrQ4vFh165dEMXgska8/raanehXuw+1fS6Gp9WNg4cOxnWcTEtPFDR9iX1bP0Groxde2PMCHCYHhpmH4cDBAzEfb4xtDLaYtmDeF/Mwb/A8MIz049mI1zAQdxKeh5F8NtL1MPKzXYv9qUBXsV/vdpaIjU5PVurr6zF//nz85S9/iVjOarVqdha9HauqsRU3/m0jjtRJ44OP1Lkwa8U2/PueCeifZdVcJhmkSxCl4veMxWcTIVWuTTx26hFXsRwjFa5lsm2Mx28/r9wLoBr9+/QCx0YeCtat5ij4/c3oP3BQ2LAxQ17/dcsBzoL8oeej8dhx9O0TpwpXDgfs+Sf64wgO9TwVWzdvxfWDr0ff3vGrek0zT8PynctxIvMEJveZDMCg1zCAZPxwjeSzka6HkZ/tWuxPBch+Qg86dc6Kx+PB/fffj7lz56Jnz56daUpEPLzP31DJHKlzwcP7YipDEERs6BFXFJvJ56TLCxPHaJxgL5VJiaFggg/48V2g30TAnOAP0+yegD0P2PcZVu5aCY7lML54fEKHPKfnOShyFGHRtkVhvVREdOjZThCpQacmK6tWrcLOnTuxePFizJw5Ex9++GFnmqOKxcShV549aFuvPDssAas1aylDEERs6BFXFJvJ56TbiyyrCUD0cfy2tuvenAqT7A98CTSdkJKVRGEYoGg4Wg9uwpqf12Bir4lwmB0JHZJjOVza71L8XP8zvjxKAjWxQs92gkgNOjVZmT59Or799luUlpaitLQUl1xySWeao0qBw4LXbjzT32D1yrPjtRvPRIHDElMZAOB5ARX1LhysaUZFvQs8Hy6DKQgiqhpbcbSuBVWNrRAEemNGpA+B/s+xwGszo8dVJBRjc+aZ4FhQjOnESRePTJtJ0yRxuWclJRTBdrwDWDKBojhVwEIpOh1fiY1o8jZhXI9xMU2qV2NMjzFw2pxY/P1i6l2JEbXndp7dHLUNCixDbQhBJJdOn7OSCrAsg1O7Z+Hf90yIqAZiNbGYP3UYMizSCs3WkPHYPC9g14lGzHpja/tEvRtGY3D3LJjaygqCiN0nGnHH8i3+Mq/deCZO7Z5F6iNEl0fJ/5fechb+ePUIMIBiXGkhNDbNJgaPvvsDPt5ZGRRjRHxIPStmaPmtbDVLb6SbWg0+jMbrBna+Bwy8AIgyD0czRafjo58zkMvZ0D+nvy6HNLEmXNTvIqwoW4EtJ7YgE5m6HDddUHpuH6prwY1LNqu2QZlWDuVVTfScJogOolN7VlIB+S1vVaMbHt4HnyDC6xP8b1G8Xh+O1rXgcFcT0FcAACAASURBVF0L7BYOBZnSG1+PT8AfPipDTXP7QpeVTa3+RAVom6j3xlZUNrX6y9Q0e/wNoFzmjuVbgo5DEKmEHEP2gmJUNbaC54WgN5KBn4+fdGPhf3cH+f8L63b5e1I8PgFvbT6I4yfdmt9o1jR78IePyuDxCf5jvLBuF64c3dt/DoqxxDjp8rb1rEQnQ05W3AbvWfn5v0BrA3DKubodsiWjAF84MnCeaNMyYk4z5/Q8B9mWbLzy/Sv6HTQNqGn24KejdSjpnomiHBtKumfip6N1OFjTErENOt7Qqvicrnd5qLeFIJIA9axEQH7Lu/C/u3HT+H545N0fgnpESpwO7Kluxt1vbEVhphUPX3wqHlrVXub5K4dDENqHeXl9guJEPd7XXoYm8xFdidCekl8O7YbZ5w8K611c9Okefy/H81cOR1WjB9sO12NU71zcNL4fbln6rb/+feeV4JpXNml+oykIQlj8Pn/lcGTb2ps/irHEOOnm0SffHr0gALtFSlYaXAZPDne8A2Q4gcLBiGvVegU+r/0RLobBlKoK/3oremDhLPhl319iVfkq7HfuxxCQepEWzJyIUwqzcd2rX/vbhr/fMBoFjva2IbQN6pVnx99nnIHCTGvQs7ow04pj9W7cFdC2UW8LQegD9axEQO7luHJ0b/8PHaC9R6S6xYO72xqmWZMG+BMVucwj7/4AX8CLFTPHKk7UMwUo6NBkPqIrEdpTeOXo3oq9i4G9HI+8+wNmTRoAAJg1aUBQ7F05ujfuWfFdTD2PPhFh8fvIuz/AZm6PKYqxxKhv8SDTatZU1mE1tdXxJtOkxHDVAbs/AgZdBL0SFQBYV7UVTtaGs05Ww1Z/SLfjAsDkPpORYcrAv479S9fjdmWaWwX/MxyQ2oa739gKUWxPLkLboCN1Lty94jvMPr8k6Fizzy/xJypyOeqxJQh9oJ4VBeTVals8PI7UuZBrNwe9QRnVOxezJg0AL4j+7aFlAKmx8voEVJ6UFo4SRBFv3jEWxxvcEEQRLR4feufb0S2zXRJTnvAXOhY2lgnFBGEUQnsKc+1mFGZa8eRlQ5FrN6Pe5cXiDXuRa2//oXukzuX39wKHBeP7F+COc/uDYxmwDKOp59Hr9aGyqRW8IIJjmbC3oEfqXGhpk84NjLHqtv3xrFidrqtc8z4BNc0e5GVoS1Yy2npW5FXvDclP/wZ8HqD/ZN0O2ci78FXtT7i0YCS4vXuQdfR7uPPiX2MlFLvJjvP6nIcP9n2AffX70D9XnzkxXQmeF1DZ1AqvT4CZY8ELomJ7xAsiXr/5LGRYOBRkWhXL9HM60CvP7n9O93M6aFQEQSQJSlZCCBy28uRlQ9Erz456l9ffKI3qnYv/c9GpeOTdH/z7j9S5gsrI9Mqzo7a5FW6vEDQ87MWrhuOFdbtR1dSKxTeMDrMh2kR9gkgV5J5COS4EUQwbLvniVcMhBMzM7pVnR3GuHRsfmQyricUN4/r6h2C8fvNZinFmDogRr9eHXZVN/jemgTG37XC9v063LCs2PjI5LLGIR+QinYUxqps8EEUEJZyRsJk5MAAajDxnZfubgHOQtDaKqM+Pzc9qvodH5DHKeTo89vXIPvodqoZdrsuxZS7seyH+c+A/eG3Ha/j9Ob/X9dipjpLAzapZ4xTbI7uZw5NrfsSROhfW3jdBsUyWjQsS3REhKrZN1GNLEImT1smK0pvQmmYPVn93GK/ffBZMHIM3bhuDN785gJenj0Jdsxe98+2obvLgxauGI9Nqwv+76UxU1LuRm2HG8lvPxh8+KvOPvX/xquHIsVvwmzc3B3UNP7RKSnQWb9iLqsZWZNlMyLCY/OeXVUhkeuXZ8e97JqCwA1bLJYhECI2pPLsZy285GwdrW5Bh4VCYZcUfPioLiofXN+7Ho1OG4K07x6LF48PAbg6YOAZeXkQrL+Dl9eX+8os+LceLVw0P+uGw8JoREAURB2uaYW4bUhk6tOOhVT9g/tRhuGXpt/7YtJpZ5DvCY0pJ5GLhf3fjmcuHQRRFxV4TNWGMdIjbqkZJICRbY7LCMgxsFg4nXQbtWan+GTiyGZgwR7dEBZCGgBVZC9DHWoCTBQORc+wHXeetAECWJQujc0fjw/0f4r5R96FnpnEXW+5oKptasejTPUE9JD5BxOsb9wdte33jfjxz+TD/trwMS1AbJLcnb981DsW5Nv/xBUGkUREEkSTSNllRexPaPduCS0f0DJpM98oNo2E1s7hv5bagNyulmw7i6jN7+d/A9Mqz428zzsBvzitBRYMbL6zbjT9dM0Kxa7hbltXfQxN4/vwM5eFk1JVMGB2lmHrzjjFo8fqCYkRpAr2coPsn4L/2jWL5bYfr8cK63XjzjrGoqJeGWZpNLK5c3D7hvvS2sxVjqH+hA2/dORb1Li9eWLcbL08fBSisyRc6dE22MdKk/nQWxqhslIa5Ztm0P04cFg6NRu1Z+W4ZwLBA37G6HbLe24RNdWW4ouckCCKPRucAOI9sga3uINz5p+h2HgAYlzcO39Z9iyU7luDJcU/qeuxUhoEYJrTx9l1jFcU3RIiY//5OxTYIaB/iHYjWJQ4IgoidtB1fpPYm1OURwibw3vXGVhyudYW9Wbnj3P6Y8/b3QdvvWfEdKhrcuKt0K6qaWuETRMUJ85lWU9ikvTuWb4FPBE2wJ1ISpZhq5cWwCfXRJtBHKg8AVU2tEAFc++rXaPb4/C8R5PIHqlsUY8jDC7j21a/9sakWU6EiF0oTbEMnzqazMEal3LNi0/4GOcNiMuacFU+zlKyUXCQtBqkTn1Z/D14UMDp7IADgpFP6N/vodt3OIZNtzsaE4gn498//RrWrOnqFNEFQENpQ2vbIuz9AEBCxDeqVZ4dJIQlhWQaFWVb0zMtAYZaVEhWC0Im0TVY8vA/j+xfgv3POxfq5E7F+7kT85fqRQZPmZY7UufyTQgO3cazyZN9cu9nf+/LaF/vw4lXDg1a/ffGq4XB7fSjMtOKVmaPx1p1j8crM0SjMtEIURcUVdakrmdCDwNXh9V4HQKl3gWWgGCOBE+hDJ+BHKt8rz47Xbz4THAOsnzsRQ3tkozAzeJjVok/LsfiG0UEx9PcbRmPVlkP+z6/dqL6Cfeiq1qE2yjYF9pqorYSdDnF7ok1AJNuuvWfFbuZw0og9K9tXAu4GYOgV0LTCpUbWVW1FL3shepizAQCejHy02vOQdfQ73c4RyJR+U8ALPF79/tWkHD8V8Sk823m15QQEIWxbYBv09xtGBwnjEASRXNJ2GJjDygVN3JWTiMO1LYqT5GTloMBtYtu/oWV75dnxx6tHQBRFXDm6FwRRxB+vHoHiXBsYMDje4EZehll5Yp+FQ48cO3UlE7qT7EngoZPpAWk4vlKMFOfY8PlDk2BmmaD9akIV3bJtWD93Iswcg3oXj1uWfh0UN4GT56uaWtEt24q37hwLXhBhYhkUOiwoOncgbhzfD2YTiyY3j8tf3hh2HYDw4RwMwyjaFNhrks5DQA7WtKAo2wYTy8Kn8Qd+hoUz3qKQnhbgiz8CPc8EcnpDL8nias9JbK7fjWt6XwhBbPvODING50DkHNsBiII07ExHuju6Y1LvSXhrz1u4bvB1pAwGwNS2dEBwcqIc2yEjvPyiH+vnToQgAlYTA45L23e9BNHhpG20tSgM93po1Q/gWAbPXxncEyK9pbWF9Y40ub2KvSaVjW6IooiHVv2Aa1/9Gg+t+gE5djMyrSZc/9rXuPqVTThQ0xK2LstDq34AL4hBXcnypHtaEZdIFLWhj3qtA6DUu2Azc4oxYjWz6FvggIljg/a/u/Uw/jbjjLBekWff/wnn/elztPKi4uR5ec0DOV7z7Rb0zMtA3wIHeuZlwGIx+WOKARMkYiFfh+Mn3bAXFPsnjMvli7JtmnpN0nUIyP7qZvTOtwcpukUjy2ZCXYvB1p/4ZjHQdBw463boubbKx1XfQYCIM7IHBG1v6DYI5tZGOCp363auQKYOnAorZ8WLW15MyvFTjW6Z1rAeV4eVC29vZpwBE4ewNssnCDjvT5/jggWf4/rXvqH1UwiiA0nbnhW11eRZhsEL63bhycuGYnBRFjy8gNe+2AcAeP3ms8CxDGwmFiaOhcvrwzNrtwcpibywbjf+fN1I2Mwc/nX3eHh9gv8t67GG9nkvZo5VPL+Xb39IprMcKqE/yZ4ErtS74OF9eGHd7rAYeXn6KACA2xu+/41NB/HPO8eCgdRbk2sz4ZnLh+GJSwWIUB5W1r/QgS8emgQTx6JbphWmCHLfatehot6FqxZvCouzdO41iYYgiNhb1YQLhnRDLK9RcjMsqG32QBBENHt4ZNm0KYkljdp9wOcvAAMvBHL76qoC9n7lZgx09EJ3cyZ8Qvs8nYZuQyAyLHL3b0Rzd/1XnM+2ZOOy/pfhnT3v4IsjX+DcXufqfo5UwmRiMbh7Ft6+axx4nwATx6LF48Mbmw76n+0+QcRrX+zDzHF9w9qsP10zwn+sdBHPIAijkLbJilmhS1heU2Xb4XrMf38n3r5rHGb84xt/mbe3HgmSEa6od6GqqRV3lW4NOoaJY9Et2xZ2zsBhMmrDXQKHlqSzHCqhP0rDtPSeBC73LshUNbYqxoh8To5lFPffjxL0zMvwbyvOld5yVtS7lNdZ4Vh/mWioXQf5TalSnIV+L0Jif00zGt08+jsVZNUikJthBi+IWLn5EJ5Y/SNeunYkrhjVSTK7Ph5YfS/AcsDZd+qaqOxvOYEdjQdwR7+pQYkKAPgsGWgs6I/cA//D0bG363bOQC7oewE2VWzCUxufwuqpq5Fry03KeVIFkym4nTha14L/7avB21uP+Lf1yrNjyuk9wtoknxC8FlQ6iGcQhFFIu2Fg8gRjC8fg7yFdwi9eNRyLN+z1v1ntlmmNOPyDYxA2ZOz5K4eDU3nhGjhMZvGGvWHDY0KHlqSzHCqhP50xCTzaOU0Kwy6fv3K4otIOoDyUY3GMk12VbHr+Sin2ZSjOtLF5fy0A4JSC2JSzcu3S/X95/c8AgLe3HNbXsFj45Gng0P+ASY8BZm0Jr1beO/ENWDAYma08Z6S+6DRk1B+C5eQxXc8rY2bNuP3029HQ2oDHvnoMPiG9fTpUYMRmZsN+Byy+YTR65dujCnSkg3gGQRiFtOpZCR1Wddc5p+Cfd46FIIgwcywsZgYvTx8VNMwj0vAPlmWx7H/BC0ot+99+/G7acMXzhx7PbuHwr3vGw8sLikNLOuJNOJE+dNZwJquJxfypw5Bh4dDi8cEaMEQr1hhSGsoRbdhXKEoT6J9Z+6N/gj5AcaaVD3ccQ48cG7pnW+GLYRyY3Et1vE1J7KeKkxB1VN/SzMZFwKaXgVEzgeIzdO1VEUQBH1Ruxpl5Q+BguPbJ9QHUF52GPj+uQd7+/+HEiCt1O3cgfbL7YPqQ6Vi+czme//Z5PHb2Y2B0XIgyVVAaVv3PO8di6/5qrLxjLERRBMMwWL/zGH41smdQGxMo0EHDQAmi40mrZCV0WNUrXx7ABz+eCB5W5VBe2V6pYSpwWDDnwlNjWrE2luEk8htgWhGX0IuOHs5U0+wJmswOIGgoZTwxFDqUQ35bGksCFngdBEHEnAtPxc5jjRRnMVB+ohFfllfj9l/0iylRAYC+Be1D/CafWojPdlf5E5cOY9sbwH+fBE69FBh+va6JCgBsbfgZFa21uK7PLyEIyspnrQ4nWrJ7IG/vZ0lLVgBgUu9JqGypxJu73oRX8OLxMY/DzHbyPKEORmlYden/9uNXI3th+mvt6oKLbxiNHJsl7AVIoZleXhBEZ9HpyUp1dTVmz56NlStXJv1cWoZVxTKpPdlvqmliL5HqRIu5RH1cDxGKQBuaXW447DaKMw38bcNeZFg4nFPijLmumWNx4dDuaHB58YuBTny2uwr7qpqRnwQ7FSl7H1j7G6DfucC4+3RPVABgZcUG5JgcGOroDQjqC2DW9BqN3jvfh7X+CFpze+luh8zVg66GmTVj1Z5V2FO3B7+b8DucknNK0s5nNJTaos0H6nH1WX2Cen4zLBzFPkEYjE6ds9LQ0IBHHnkELpcremEd0LLKdKzyrsmWK01XOVSia6Al5hLxcb3kmGUbXDUVFGcaOHHSjTXbj2LqyOK4h8vdOqEfHrxgEIpyJP/Yc6JRTxPV2f8FsOpWoMcI4NxHkpKoHHXXYH3197i4x3iwKr0qMjW9RkMEA+fu/+huRyAMw2BayTTcPeJu7K3fi2lrp+GP3/4RJz0nk3peo6DUFs0+vwQ3v/4tbln6La599WvcsvRb3LhkM8kSE4TBYMROGSgs0dTUBFEUcc8996C0tDRi2e3bt8Nq1TZ8xe12w2YLV+Oy2mxoMedi1opt7V2+M0Yhw1uPVrc0BMFeUIxJC74Kq7vhwV/AVVOh6fwdidp37Wp01PccMkQ/CdFYfDYRjOwDWmIuEfSOVyNfS5lQG/X0WUCb3366txF//KoKj03qgdwEXVwURTz96QlcdGoObjk9I6nX3171Pfp88QB4RxGqz3oY3tDV/6Lg8XhgsUQfHvha7ad47+R3eKbPDTC1qveqyIzZ9TYy3Q34cNLzCS8QqcXGJr4JX9Z+iW9rv0WWKQszes/AROdEsDovTqmE2+3GqFGjdD1mJJ+V40WpLSq97WxM/uPnYXWM9LxPhTYpEl3Ffr3bWSI2OnUYWGamdgUZq9Wq2VnKyspUywqCqDDkJM+/v6qxVXFSu8NuwykGdNZI37UrkYrfMxafTQSjXxs55oKHWOVFr6gBvePV6NcSSL6NWvx2yY/fI8duxoiSvjGtr6JGz7xGVLrZ5P4oOLoVWP0QkF0MyyUvopiLfU7SwUMH0bdP34hlKlvrse7Q97iw+9nonVcIUUPPTaP3F+i2tRSns1U42efsmO0KsvHgQfTtG9lGADhtwGm45OQleHPXm/j7/r9jY+NGPDXuKZyaf2pC549GWVmZ7seM5LOB8RL6/BchGv55nwptUiTIfkIP0k66ONqQk86QdyWIrkwyh1hRvHYOm/bVYFTvXF0SFQDokWPDgeoWnY6mwLEfgNJfA/Zc4OLngTgSFa28eugj+EQBvyoaqylRAYC6HqfDY8tB0fa3kmaXEn2y++Dhsx7GHaffgUONh3D9B9djRdmKzlFm6wBCn/9OR+TlCQiCMAadPsHeaNCkdoJIHSheO57DtS04UufC1BHFuh2zONeOjXtr0BqrrJgWDn0DrLwGsDiAS/4ImJM3JOXHxgN459hX+FXxOXCAhQhtw8xEzoQT/c9B753vw1G5C83dBifNxlAYhsG44nE4zXkalv64FH/Y/AdsOb4F8yfMR6YltvVzUg1qPwgiNUi7nhUt0KR2gkgdKF47lk17awAAJd2zdDtmj7ZJ9hUnI09Gj5k9/wGWTwUy8oFLFwDmjOh14sQjePHUnjdQYMnBZd3GQBRjmw9Tecp4eC2Z6PW/V4BO6NnItmTjN6N+g+tOvQ7rD6/HdR9ch5/rfu5wOzoaaj8IwvgYIlmJNrmeIAiCMAab9tUg32FBjxz9eiiKc6VjHW3UKVkRBOCLPwJvXgc4S6RExeLQ59gKiKKIeXtWory5ArMGTAMXQapYDcFsw9HBFyH7+A7k792gv5EaYBgGvzzll3j4zIfR0NqA6R9Ox7r96zrFFoIgCBlDJCsEQRCE8RFFEZv21uCMPrkQdHz5X5RjAwPgUL0OkrGNJ4B/Xg+snw8Mvgz45e8ANnkjnkVRxML9/8baym9wU99LMMAS/2oxVX3HoimvL/p+8RIsjSd0tDI2BuUPwpNjn0SfrD546IuH8Pzm5+GNIwEjCILQA0pWCIIgCE2UHWvE8ZNujOido+txrSYOzkwrDiaSrAgCsHUZ8NezgL2fARMfBcbcDTDJG9bT4G3Bw7uW4PUjn2Bq8TmYnHcahETWbWE57DtjOhjBh0EfPAaTq14/Y2Mkz5aHuWfOxUV9L8IbZW/g9v/cjn31+zrNHoIg0hdKVgiCIAhNrPvpOBgGGFasb7ICAAO6ObDjeAuEWLtsvG5gxyrg7+OB92YDhUOAq14HTjknKQs+AsBJvgWlR9Zj6pbf4r9V23DrKZdhWrexEHTofWjNLET5mFthbTyGof+6F5nHduhgcXyYWBOuHXwtZg2fhV21uzBt7TQ8879nsKt2V6fZRBBE+kFqYARBEERUfqpoQOmmAxg/oAAZFpOuw8AA4OxTCvD1vlq8s/Uwrj2rT/BOHw94GqXExNME1B8EavYBhzYBez8F3A1AwUDgl88BxSMBUQDiFFYWRREuwQOP4EWrwKNV8KDacxLHW+uwvW4P9tevwXcNP8Mj8hiVOwiP9LoQhZxNl0RFptE5ELsm3IsBW5ZjyJo5aCwahrp+E9DiLIEnqxt85gz4LA6InFm3c0bi7B5nY3DBYKzbvw5r967Fu+XvYmDuQIzuPhqnFZyGIkcRumd0R6YlE1bOCgtngd1kj35ggiAIDVCyQhAEQUSkrtmDy1/eCIeFw03j+oJhGHA6j64aP6AA728/hKfX/ISpI3vCZubad/7jPODY9+GVsoqA/pOBgecDBYPakhQADBdeViO/LX8Dq459qbiPBYMBmb1wWc9zMSbvNPS05sLnk4eu6fs4dTtLUHbBEyjcvxF5R7agz6ZXgvZ7bbnYceM7ABd8XhNnApfA91cjz5qH6wdfj8v7X44tJ7ZgW9U2vLf3Pby1W3ltmPtG3oe7Rtylux0EQaQfjJgiqz9t374dVqu1s80gujgmkwklJSW6HIt8lugI9PRZQN1vWZYFOAt4n0+3xSCVMLEsGMELQWiX/mUYBnarFYAIBgIg+PxDvERBgKizRWabDQADQIQIEYIowCefD2KbbR376GTkBIRh25IxBq08D1HvLq5YbGIZsIw0mpxlWMj/MQwDwSfA41Geg9RRPksQeqG3zxKxkTLJCkEQBEEQBEEQ6QVNsCcIgiAIgiAIwpBQskIQBEEQBEEQhCGhZIUgCIIgCIIgCENCyQpBEARBEARBEIaEkhWCIAiCIAiCIAwJJSsEQRAEQRAEQRiSlElWysvLNZc9cOBA8gwxGOnyXVPxe8bis4mQKtcmFewkGzvOb5Uw+vU3un2A8W1Mhn2RfNbo1yMaZH/nkur2dxVSJlnheV5zWZfLlURLjEW6fNdU/J6x+GwipMq1SQU7ycaO81sljH79jW4fYHwbk2FfJJ81+vWIBtnfuaS6/V2FlElWCIIgCIIgCIJILyhZIQiCIAiCIAjCkFCyQhAEQRAEQRCEIaFkhSAIgiAIgiAIQ0LJCkEQBEEQBEEQhsTU2QYQyUEQBdS6a+HxeWDhLMi35YNl1HPTWMvrVZcgQuEFHtWuanh9Xpg5M5x2J0yselNF/kekC5F8Xd6XU5yD483HIYgCxYMGvD4BJf/3Izxy8WDcPWlAZ5tDEIQCuicrjY2NmDNnDgRBgN1ux8KFC2GxWILK8DyPCy64AL179wYAPPHEEzj11FP1NiVtEUQB5XXlmL1+NiqaK1DsKMai8xahJK9E8aEVa3m96hJEKLzAY0/dHsz5bI7fnxZOXohBeYMUExbyPyJdiOTrAFBeV46/bvsrpg+djqc3Pk3xoJGfK5sAAC+vL6dkhSAMiu6t19q1a3HLLbdgyZIlcDqd+PLLL8PK7N69G5deeilKS0tRWlpKiYrO1Lpr/Q80AKhorsDs9bNR667VpbxedQkilGpXtT9RASR/mvPZHFS7qhXLk/8R6UIkX5f3TS2Z6k9UQssQylTUS+tosCzTyZYQBKGG7j0rM2bM8P9dV1eHgoKCsDLbt2/Hhg0b8M0332DQoEH47W9/C5Mpsimtra0oKyvTZIPb7dZcNtVR+q4ZRRn+h5VMRXMFmt3NqDpQFXaMWMvrVTcWOuqeDhkyRLdjxeKziZAq/q7FTkdPh6I/eX1exbp6+18qXMtQG/X0WaDj/FYJo1//zrQvkq/Lf+dYcjqkPU4Et9ut+zEj+Wy0e1a+X+pZ8fA+Q/qe0WMiGl3Ffr3bWSI2kjZnZdu2bWhoaMDIkSPD9p1++ul4/fXX0a1bNzz88MP4/PPPcf7550c8ntVq1ews6eRYSt+12lWNYkdx0EOr2FEMh82BvkP6hh0j1vJ61Y2FVLynsfhsIqTKtdFi5/Hm44r+ZObMinX19r9UuJbJtrGj/FYJo1//zrQvkq/Lfzd4GjqkPU6EZPxwjeSz0e7ZjqbDACoBMIb0PaPHRDTIfkIPkjKItb6+HvPnz8dzzz2nuH/w4MHo1q0bAGDYsGE4ePBgMsxIW/Jt+Vh03iIUO4oBwD9uOd+Wr0t5veoSRChOuxMLJy8M8qeFkxfCaXcqlif/I9KFSL4u71tTvgbzJsyjeIiBFg8PABBEsZMtIQhCDd17VjweD+6//37MnTsXPXv2VCzz0EMP4e6770ZJSQk++eQTzJo1S28zugzxKB2xDIsBuQOwbMqyIEUltXpB5QUvzGzk8qF1S/JKsOLSFaTGlIbE45+R6phYEwblDQryxQJbAepb6xXLk/8RqYzW+JEV8rIt2Vh68VKIEMPKl+SV4KnxT8Hj8WDZlGWkBqaRFq8PACBQrkIQhkX3ZGXVqlXYuXMnFi9ejMWLF2PMmDHwer2YM2eOv8y9996LuXPnAgDOO+88jB8/Xm8zugTxKh0JooC99XtjUgOLpXwoLMOqvvkmui7x+KeWOibWhCJHkeby5H9EKqI1fiIp5AWWk+Og7AANW4kFt0dKVnyCCFEUwTA00Z4gjIbur1umT5+Ob7/91q/0dd999wUlKgAwaNAgvPfee3jvvffC9hHtxKt01JFqYET6Eo/fkG8ShIRW345VIY+IjZa2ZAUA3F6hEy0hCEIN6hs2MB6fR1HZxePz6Fov3vMQ6U08fkO+SRASWn3b6/MqK+QJ3qTbmA7Iw8AAwMNTskIQRoSSFQNj4Sz+iZIyxY5iWDiLSo34LedKQwAAIABJREFU6sV7HiK9icdvyDcJQkKrb5s5s2I5M2tOuo3pgCugZ6XV54tQkiCIzoKSFQMTr9JRR6qBEelLPH5DvkkQElp9O1aFPCI2ZDUwgHpWCMKoJG2dFSJxYlU6ClSWKbAV4M3L3oSbd8PCWsCyLI43H1c8hpJ6mIW14Hjzcf/bOzfv9quKmVh93EZWuAlULNPr2IQ+RFIr0uqf8n129HSgsqUS/bL7BflanjUPJ5pPgBd4mFgTnHYnzJzZf45YlO0IIlUI9G2xTTZXhIjKlkqYGTO8otev6FWSW+JXyDMxJlhYC+pb65FrzQ1TyrParKh2VcetjhePwl8q4wqYp9JKyQpBGBL6ZWhwtCodqSnLDMgdEFXpS0kN7NkJz+Kl715Ctas66G9ZhSbRpCKSwg0lLMZADyUutfu8ePtifHbkM0zuNRmzRs4K21+SWwIzZ05YqY4gjIrs23/d9ldMHzodT298GhXNFZjcazLuGnEXHtzwYMS2XCl2Fp23CKYcE2Z8MCOueIlXgTKVcVHPCkEYnq7Z+qQhasoy1a7qqIozSnWf2PgEbj391rC/9VKhIYUb46OHEpfafZ5aMhUAMLVkakQ/IDUwoqsi+/bUkqn+RAWQYkJOVAD1tlwpdmavn42Kloq44yUd4y1QDYySFYIwJpSsdBHUlGW8grKSTKDijFrdHEuO4t96qNCQwo3x0UOJS+0+y/6UY8lR3M8LvG42EIQRkX07NAbUYiK0LVcrZzfZw7ZpjZd0jLdWXkCWVerN9/goWSEII0LJShdBTVnGzCoryQQqzqjVbfA0KP6thwoNKdwYHz2UuNTus+xPDZ4Gxf3yUEBSAyO6KrJvh8aAWkyEtuVq5Vy8K2yb1nhJx3jz+gRkWDkAwcpgBEEYB0pWughqyjJOuzOq4oxS3WcnPIslO5aE/a2XCg0p3BgfPZS41O7zmvI1AIA15Wsi+gGpgRFdFdm315SvwbwJ8/w+vqZ8DRZMWhC1LVeKnUXnLUJxRnHc8ZKO8eblBWRYpJcjrV5KVgjCiDCiLENicMrKyjBkyBDdy6Y6gd9VTcUlSHWLNcNmsqGFbwkqE1S3TT3Mzbvb1cB80t96KnZ5fV5Uu6oVVaAifc9UoaNsTuZ5oikDaVEOCr3PBbYCnPSe9NfJNmejxl2j6gd6qBNpPUYq+FmybezMa2D066+3fbJfCoIAAQIEUQDLsGFqYIGqXyzDggULAUJYuXxbPg4cOIDsomzN8RIaG0oKY3pOrk/GPY50zGjnO/PZT9A9y4qfjp3Ey9NH4bLhxaplOwOjx0Q0yH5CD0h2qQuhpMwUTekrUOmlI3s1BFHAvoZ9aaU6k4pE8gstykFa73OPzB5x2aCFdFQ4IlIDLb6t5L+Binqh/tzqbtUcLxQb0jAwu0UaBuahnhWCMCTp0RqlMdGUvjpL6SUdVWe6GlruoRHusxFsIIh4UfLfQEW9RPyZYqNtzkpbsuImNTCCMCSUrHRxtCh9dYbSSzqqznQ1tNxDI9xnI9hAEPESrQ2XP8fjzxQbIT0rlKwQhCGhZKWLo0XpqzOUXtJRdaaroeUeGuE+G8EGgoiXaG24/Dkef0732BBFEV6fiAyzlKzQCvYEYUwoWeniRFP66iyll3RUnelqaLmHRrjPRrCBIOJFyX8DFfUS8ed0jw15XRUb9awQhKGhCfZdHJZhMSB3AJZNWQav4IWZkdTAXpz4oiY1MJvJBkEQ4BE86uVjUIwJrFeYUYgVl6wIOzaRGoT5VptaXOA9ZBkW/bL7YenFS/1qX/m2/IQVxmK1sySvBCsuXZE0hSOC0JPAGGAZFt3s3aQYEnmYGBMsrAWPj30cT+AJCBDAgkWtuzamJEM+R541D8umLAtSFEuX2PD6JDFUu1mes0IT7AnCiFCy0sVRUgNTUmwKVYR5dsKzeG/ve/jVgF/hiY1PBNUdkDsg6jHVbEl35ZmuhBbf8vq8+LnhZ8z5bA4qmiswuddkzBo5y/85tE6yfKSj1e4IIl6UYmDBpAV45ftX/Opf8ybMw8qdKzFr5KwwVTCrzRrXOdKxLfa29aRYTCxYBvB4qWeFIIxI+rRKaUq8ik1PbHwCNw27yZ+oBNatdlXHpSBDyjNdCy33s9pV7U9MAGBqydSgz6F1yEeIdEcpBh7c8GCQ+tfTG5/2x1KoKpg5R3mtqmjnSMc487YNAzOxDMwc6x8WRhCEsaBkpYuTiGITx3CK272CNy4FGVKe6VpouZ+8wAeVybHkRKxDPkKkO1rVv+RYCt0uMNF/cFOcScjJiYljYeIYki4mCINCyUoXJxHFJp/oU9xuZs1xKciku/JMV0PL/TSxpqAyDZ6GiHXIR4h0R6v6lxxLodtZMfpjneJMQp6zwrGAmWXBU88KQRgSSla6OPEqNj074Vks+3EZnp3wbFhdp90Zl4JMuivPdDW03E+n3YmFkxf6y6wpXxP0ObQO+QiR7ijFwIJJC4LUv+ZNmOePpVBVMG+DN65zpGOctQ8Dk3pW5OSFIAhjQRPsuzhalJDCyrSpgc0aOQs2k01RsSsedSVSZepaaLmfZs6MktySIDWwAluBah3yESLdCY0BlmFhZsx4fOzjeFR8FCzDggWLp8Y/hVxrLp4a/xQe9T3qj5XdJ3bHfI50jTNZqphjGZhY1p+8EARhLChZMQhq0sF6SbfKSki8wKOypRJenxdmTpKaNbGmmNWS4lVXCqynJlGrtJ3oGGKVDRZEAbzAwyf4wDM8BFEIK8+xHMycGR6vB2azGSbOBKfJGXSMald10Dn1Vu7SWw6ZIOIhVJKYBQsBAsyMGV7RGyQfrDUGtJZTigGn3enffrz5eNrFhpyccAwDE8tQskIQBiUpyUpjYyPmzJkDQRBgt9uxcOFCWCzhY2Eff/xx7N27FxMnTsQ999yTDFNSAjXp4Je+ewnVrmrdJCV5gceeuj1BsrELJy/EoLxBMLEdm7eqSWeqySJrkeMkEiNWOVMt/hTtmB0hoUoyrYQRUPLDeRPmYePhjbi4/8V4cMODHR4D8crQdxXkYV8mjoGJY2hRSIIwKElpjdauXYtbbrkFS5YsgdPpxJdffhlW5uOPP4YgCHjrrbdw+PBhHDhwIBmmpARq0sG3nn6rrpKSoTKyFc0VmPPZHFS7qhM+dqyoSWeqySJrkeMkEiNWOVMt/hTtmB0hoUoyrYQRUPLDpzc+jSsGXeFPVOTtHRUD8crQdxXae1ZYcCzNWSEIo5KU1+kzZszw/11XV4eCgoKwMps3b8aUKVMAAL/4xS+wdetWnHLKKarHbG1tRVlZmabzu91uzWWNQEZRRkSpyormCjS7m1F1oCqsbizf1dHToSxF7PN2+PVS+85qkpo++DrExiFDhuh2rFh8NhH08ne1e6Lme1r8KdoxYz1nPMRyjlRoO0Jt1NNngY7zWyWMfv0TsU/ND9Uk4uONASUb1c7t9SnL0OsZf0r26U0kn410z/YeaQEA1NVWwef1oKlFNJz/GT0motFV7Ne7nSViI6ljf7Zt24aGhgaMHDkybF9LSwu6d+8OAMjJycHRo0cjHstqtWp2llRzrGpXNYodxUEPjUBJymJHMRw2B/oO6RtWN5bverz5uOJ5zJy5w6+X2neWJTVDt3PgUuqeArH5bCLo5e9q90TN97T4U7RjxnrOZH+vVGg7km1jR/mtEka//onYp+aHskS8XjGgZKPauc2cOenxp2Sf3kTy2Uj37JBwHMBxFHXvjsy9LrAsYzj/M3pMRIPsJ/QgaYNS6+vrMX/+fDz33HOK+zMyMvxvWFpaWiAI6TtWVE06eMmOJbpKSobKyMpzDPSezKwFNelMNVlkLXKcRGLEKmeqxZ+iHbMjJFRJppUwAkp+OG/CPKzesxoLJi3olBiIV4a+qxAsXczSMDCCMChJ6VnxeDy4//77MXfuXPTs2VOxzLBhw7B161aMHDkSu3btQr9+/ZJhSkqgJh384sQXYeNs8AgeHG08GqTeFQ01dbGijCIsm7IMXsELM6v9ePEQSYEpknSm0nYtcpxEYrAMiwG5AyT/CFCLC5xo6/V5Ue2q9ssQ98/pHyRLHOpPgfez2d0Mh83R4VLFJNNKGIFAPxQEAT7RB5/ow/Sh08GxHJZevBQiRL9KWK27Ni4/tdqsYep6sba36RIb/jkrLGBiGTSTGhhBGJKk/EpdtWoVdu7cicWLF2Px4sUYM2YMvF4v5syZ4y9zwQUXYPr06aisrMQXX3yBt99+OxmmpAxKUsDxqnd1lLpYJLQoMKnJH8cri0wkhiAKEZWBvD4vyuvLw/xx8fbF+OzIZ6pKQvL9rDpQpTi0pCPuN/kUYQRYhkW+LV9RFWzlzpWYNXJW1HiKhCAKcGe6cfcHdyvGMLW3wcg9KWZOnmBPyQpBGJGk/FqdPn06vv32W5SWlqK0tBT33XdfUKICAJmZmSgtLcWIESOwfPlyZGVlJcOUlCZe9a6OUheL1YZ0UplJRaLdMzV/nFoyVbE8QRDhqKmCTS2ZmnA81bprMefzOdTuaqS9Z4WhYWAEYWA6dVHInJwcXHLJJZ1pgqFRU2rxCpHnb6gpagWqi3l8Hn2N1WhDss9LxE+0e8YLfES/Ci1PEEQ4kdrnROOJ2t3Y4NuSE2kFe+pZIQijkh4DU1MUWaklkGJHMcxs5DVHZEWt0HqB6mIWLnyRTj1RsyHZ5yXiJ9o9M7GmiH4VWp4giHAitc+JxhO1u7FBK9gTRGpAyYqBiVe9q6PUxWK1IZ1UZlKRaPdMzR/XlK9RLE8QRDhqqmBrytckHE/5tnwsnLiQ2l2NBK9gT8PACMKodOowMCIyJtaEQXmD/OpdNtYGiD5UNh2DhTWD5SzIKMpAdUu1X+1LVpIptBcGqTSZWBP+cM4fYObMsLAWHG8+HqT8Ekm5KxpqymOFGYVYcckKeIT0U5lJRYLUwALU4uR7ZubMGJgzMMiv8mx5eHzs43hIeAhm1ox8az4qWyqD1MQEQUCNuwaZPTNxrOkY8m35aPQ2+n0t15yDenc1PAIPC2tCvr0QLEdNE9F14Hkvqt2Sih7HmpBvy8fSi5eCYzj4RB94kcdjYx5Dga0AT41/Co/6HoWFsyDHkuOPJxNrgoW1QGRE5FpzUd9aH9ReA9KclVxLLpZNWQZBFKT4subG3bZ3dQKli80cAz6Nl1AgCCNDvwgMjok1ochRBMHHo7xuD2ZvaFdiClT4CvxbVpaZOXQm3tv7Hn414Fd4YuMTqspgA3IHRFSBioQRlMcIfYimBiaIAvaf3O/fP7nXZMwaOSuiOljplFJUu6vDyny09yMsLVuqrCg2aSFK8gZRwkJ0CXjeiz0NwSp68ybMw8bDGzFlwJSg7YHxpqQGuWDSAmw/sR2jikYFbV984WJ4fJ6w2E2kbU8HeJ8AhgEYBm3DwKhnhSCMCLVWKUKtq8qfqADhCl+Bf8vKMk9sfAI3DbvJn6go1Zu9fjaqXdVxK3cZQXmM0IdoamCh+2X1okjqYLzIK5a5YtAVqnVmb5iDWldVx31xgkgigck60K7+dcWgK8K2R1Pfe3DDg5jYZ2LY9iONRxRjN5G2PR3w+ESYWAYMw4BjWfgEEYJACQtBGA1KVlIETxQlJqW/K5orwDFc1HpeQVl1TIuCjBGUxwh9iKYkFLpf9rHQ8oFqRj7Bp1iGYzjVOhXNFfAIfOJfiCAMgFel7VZrm+V4U1ODFEQhbLvdZFdVjiR1MHV4nwAzxwKiNG8FALw0FIwgDAclKymCJYoSk9LfxY5i+ERf1HpmVll1TIuCjBGUxwh9iKYkFLpf9rHQ8oFqRhzLKZbxiT7VOsWOYlgiLHpKEKmEWaXtVmub5XhTU4NkGTZsu4t3qSpHkjqYOl6fABPLQITUwyJto54VgjAalKykCPn2QiyaFKzyEqjwFfi3rCzz7IRnsezHZXh2wrMRlcGcdmfcyl1GUB4j9CGaGljoflm9KJI6mIkxKZZZvWe1ap1FkxYi317YcV+cIJKI0xauojdvwjys3rM6bHs09b0Fkxbg80Ofh23vldVLMXYTadvTAa8gSj0rkCbZA4CHp54VgjAajCiKKfEaoaysDEOGDNG9bDwkopyV0Hl9PGpdVW2qSZIaWKO7EQ6rI0wNjGGkt0Runxs2zgYA8AreILWuIDWwoGPHpsikpgam57VJ9j1NBh1ls57n4QUe1a7qIDUwU0AvR6jvh6oS5VhyUOOuCaovCD7UuGv9CmL5tjw0epsMqQaWCn6WbBs78xoY/fpHs0/t2eBXAxN5cIxJap9FEQWMBQ2iBx6Iiu1lYDyaGG1qYM3uZjhsDl2UHpNBMu5xpGNG2vfQO9/ji/IqLLpuFD7eeQL/76v9+Obx89E926arfYlg9JiIBtlP6AGNtYgRJfWrjlJXYTkTnJk9gradOHACfYf0TezAggC2ahec/7weqD8E5PYBrnsT6DYUYKN/J5Zho679QhifaGpggPK9Dv1c5CgKOKgAVO9CjxDfsob4VqhfE0SqEenZYDKZUST7uCAAlTuBtphwBra3Ic8QWQ1SCaU212l3oupAVdAzgdpndfignhXpBR/1rBCE8dD86/rnn3/Gq6++ipdfftn/fzoSTTEpJWmp8j84AUj//vN6aTuRNiTFt8m3iDRBc/xQTBgGj3/OCvxJC61iTxDGQ3PPygMPPIA77rgDPXqk9xvQaIpJKQnvaX9wytQfkrYTaUNSfJt8i0gTNMcPxYRh4NuSFYigCfYEYWA0JysFBQW47LLLwHFc9MJdGFkRKfChlPLqKiaLNDwn8AGa20faTqQNSfFt8i0iTdAcPxQThsHrE2Fq61HhZOli6lkhCMMRdRjY6tWrsXr1agwePBg33ngjVq5c6d+WjkRTTEpJMgqlMdO5faTP8hjqDFJkSieS4tvkW0SaoDl+KCYMgzdgGJhfDYySFYIwHFF7VmSxsMGDB2Pw4MH+bbLaVLrBMixK8kqw4tIVnauu4uOBpuMY3N0KNBwBMouAQAUlQZDGQPMe6Y1dRqH6ZHmWlSZ33v6JtvIhGE1thogPTb4di18BAMtCKByM2ts/RqvghZU1S2pfkeqEnsNeALhq4vJNgugoWIZFSc4ArLh4abuync0JtlkhXuT2VhAA0QeIouTzGn2b2lx98MqLQiJgGBhNsCcIwxE1WZk2bRoAoK6uDnl5ef7tH374YfKsMjidrq7i44ETPwJvzwQjKyxdUwp0HyYlLCFqM5rUvVgWyOwesymdqY5G6E9E347DrwRRQHlDZIWxiOc49VJg4sPA2zPjUqojiA5DSVXxmlLg8xeA3R+E+25GYeztNKjN1RPeJ/qHf8kr2FPPCkEYD80t2/333x/0eeXKlbobQ2ik6Xj7jzdA+vftmdJ2oEPVZrqkOhqhTBx+FbN/hJ5j5PXhvk7KSYQRUYqPt2dKPix/DvTdONtpanP1w+sTYGaDF4WkOSsEYTyi9qxs3rwZmzdvxtGjR/1yxS6XC9nZ2Uk3jlDB51VWk/F5pb87UG2mS6qjEcrE4Vcx+0foOex5pJxEpAZq8WHPC/4s+26c7TS1ufrh9Yn+4V/+nhWe1MAIwmhE7Vnp2bMnzj77bGRlZeHss8/G2WefjYsuugh//vOfO8I+QgnO3D45Uya3j7QdaFebCd2fBLUZWQEnkJRXRyOUicOvYvaP0HO46jrMlwkiIdTiw1UX/Fn23TjbaWpz9cPrE/xJitnfs+LrTJMIglBAc7Ly61//2p+sDB8+HGazuSPsI5TILJLGQgeqyVxTKm0HOlRtpkuqoxHKxOFXMftH6Dm2vxnu66ScRBgRpfi4plTyYflzoO/G2U5Tm6sfvEA9KwSRCkQdBiZLFGdnZ4fJFV9xxRWq9aqrqzF79mzVuS0nTpzA1Vdfjb59+wIA/vznPyM/Pw0aWzU1JS0qSwFlhPx+qL3tP/CIPCyMCfl2pzS5sumEtD+7OHi/KQPsyaOA2Q4IPsCn8fxR7DKMOhqhiODjUeuqalcnsheC5SKEfaT7raAaJ9jyUdtyIlj9yF3r389mFKIkux9WXPR6gK/mgz1ZIQ1b5MyAozsQUAeFg4OV6ewFcSvVEURSUIsT50Dg5g8BgQdYE2DLBaY8D1z0uzZf7wY0VkAAUMuyELK6Qbj9YwiB8RnFt6nN1Q8PL4ALUQOjCfYEYTw0Sxe///77KC4uxrBhw7Bz504cPHhQNVlpaGjAI488ApfLpXrc77//HrNmzcL06dPjND0FUVNTKhwMVO2KrAoTUFfody7KJ9yN2RvmtqvBTFqIEi4T7LJLVfb/CSU/fQi2zzhgzT3azq/FLhhAHY1QRPDxKK/bg9kb5gT7Sd4g5YRFi9pXgGqc8vEXoOST58Duel+qf8O/wHpa4Hz7BnV1r2tKgR3vApsWqSsixaFURxBJQS1OnAOByl3tvq3i68KOd1F+2hT8dfc/MX3odDy98emYVb2ozdUHXhD8SQrX9m8rT8PACMJoRH0VM23aNEybNg08z2P+/Pm49tprMW/ePAiC+tsHjuPw0ksvITMzU7XM9u3bsWrVKkybNg0LFiyIz/pUQ039pel4dFWYgLq15zzgT0SANjWYDXNQK3oi7J+L2lHXtycqWs6vxS7CsNS6qvyJBBDgJy6V+xejOpHy8R9E7Rkz2uvX7QfkRAVQVvd6eyYwKqAO+RhhZFTb8apg31bx9dqzbsTsr+dhaslUf6ICkKpXZxA8wb5tzgoNAyMIwxG1Z0UmKysLv//97zF48GCUl5fD4XColo2UpMice+65uOeee2C323HzzTdj165d/kUnlWhtbUVZWZkmW91ut+ayHckApwUWBfUX0eeR1ksJ2e5xNWPv4bKwuh7WpKwGw3KR90NUVJ9RO78WuzqKjrqnQ4YM0e1YsfhsIqhdm+yeDmU/ELyK5dX8U+1+qx4/I2A4pzlDm7pXm+9GO2eyMWrbEUiojXr6LNBxfquE0a+/2+2Gx9Wk3I4L3uD2UsXX5fY5x5KjGD/N7mZUHYg/WU+Fa6g3kXw20vVo9fBwu1pw6NBBeNoWgzxeWYWyslbdbYwXo9/PaHQV+/VuZ4nY0JysvPTSS/j4449x5MgRDB06FA888EBCJz7jjDNgsUjqJUOHDsXBgwcjJitWq1WzsxjWsZpOSEMBAh9guX3AcBbF7Ra7A0OG9A+raxF4FDuKgx50xY5iWASp+1p1P5iYzq/Jrg7CsPc0ArH4bCKoXZvqpmPKfsCale1S8U+1+616/JaAN8PeluBjyupeIeeA4Av63Bk+BqSGnyXbxo7yWyWMfv3LyspgsWcqt5esWZOvy+1zg6dBMX4cNgf6DumbkI1Gv4Z6E8lnI10PAQeRm52FPn16ghcEACeQmZOHIUMG6W5jvBj9fkaD7Cf0QPOMPJPJhEsuuQR33nknLrvsMn+iES+33XYbKisr4XK5sHHjRpSUlCR0vJRATf0lsyi6KkxA3fwvX8KiSX8KVoOZtBD5jCXC/j8hf9ubwNS/aT+/FrsIw5JvL8SiSQvD/cSucv9iVCdSPv4C5H+3or1+Xj/gmjciq3tdUwpsC6hDPkYYGdV2vDDYt1V8Pf/b5Vg09mmsKV+DeRPmkapXJ+L1Cf65KhwjT7CnOSsEYTQ096wkwqZNm7B3717ccMMN/m333nsvbrzxRpjNZlx33XXo37/j36ImFTW1mBA1pajbZQLKsLwHJRYHVly8FB7BCxtnhyDyOC54YbntP8hnTChhTW3721RmWCvYEddLamC3fSKpgcnKYI3HgKwi4JZ10nbOApis0nZHIXDHZ4DXJW1nOeDkUVJlSgFYzoSSvEHBfhBJDUxJ7ctegNrW2nbVIUsuWFcNwHvAmiwoyR0YovRVAHbK88Avn5XUjzKLANEH3PwhRIEHw5qkH3WBikmZ3YCxdwFn3dpeh/yKMAIh7bjNZlVur235QPMJIKuH5NsMA4giIArBn1kT2DF3ooQz46mzH4XAmrBsyjIIohBR1UsQBdS6a0n9S0dEUQySLmYYBmaOoTkrBGFAoiYrv//97/HYY49h5syZYNrePIiiCIZhsHz58oh1S0tLAQDjxo3DuHHjgvaNHTsW69ati9duYxNNVUlJ2Uhtu0oZFoATQGXlMVSxlZi94cFgRabcQXCaQt7QBc4lULJx6t+AH/4JDL9Ou2JYqGoTYShYzgRnZo8YKgSofYkCyuvKMXv97GA1sU9+J6l9nXop2IkPwxmq7PX5C8DuD6TPN30AuGqBt2dK4/mV1L9C65BfEUZAoY3sffUb0vbA9trHAyd+jKwCdvVy4IsX/T7OXvcmnBp9XDEONaqGEep4fVJSIvesAICJZUm6mCAMSNSW7rHHHgMgJR7Lly/H8uXL/X8TKsSoqpQIjF3wJypAgCKTmuJTJBvX3AOM+01simGk2tRlqXXX+n8gAQFqYrLal5qy18jr2z/z7ujqX6F1yK8II6DQRpreuSHcN5uOR1cBe+fGuH1cMQ5JNSxh+DZF06BkhWPgpWSFIAwHvZZJBrxHWfGI9+h+Kq/oU1ZkEvnIFdVsZDnl7T5vh30nwhh4fJ7Ial9qyl72vPbPDKNJ/SuoDvkVYQS0tuOhbaOWuIjBx1Xj0EcxkgjycC9TUM8K41cFIwjCOGhOVq688ko8/PDDWLZsGbZs2YLm5uZk2pXamCztkyplcvtI23XGzHD+CZoyxY5iWJgoI/zUbBR8yts5c4d9J8IYWDiLsm/Jal+y2lEguX2k7TKiqO5nanXIrwgjoLUdD20btcRFDD6uGoccxUgieP09K+0/g0wsSz0rBGFANCcr//jHPzB+/Hhs2rQJt912G6666qpk2pXaxKiqlAiii8WiSQvCFZnUFJ8i2Tj1b8Cmv8SmGEaqTV2WfFs+Fp23KFxNTFb7UlP22v5m+2eTLbr6V2gd8ivCCCi0kfzVb4T7ZmZRdBWwq5fH7eOKcUiqYQkjJyVcwK8gE8fJXX4BAAAgAElEQVTA46MJ9gRhNDSrgV188cU444wzMHnyZDz55JPo2bNnMu1KbbSoewHSxMym49IwAnMGIHilvy0OgG8NUEsqAkxmxVPV1NTj1JKSNkUmHywMh3yRAeuqlYbaBKp4eV3BthQOBm75SDqnrAA28dFgxbBYFMsI4xPoc7LyVqA6WID6EWuyoCRnAFZcuqJdhcicE6z25XCGKHsVAlOeBy76XfvnNoWkdjWw7sCEHsCYOyU/shcAv1oo1SO/IjqDaOqNYAC+VZK3PXlUUrhjOCkGfF5piFeg6pc1W/rsL2eR/HvKH6R2NgYfZxkWJXklwXFIamAJw/sn2LdfR45l4KVhYARhODQnKx9//DE2bNiAjz76CPPnz0dubi6++uqrZNqW2kRT9wpUkMnsBpz/jDSxvd+5wFm3A2/fGKyw1O00xYTFZrOCrd4D52fPAWPuAtbeF6zu9ekzQFNl8N/xqntpUSwjjEuoapHsW92HSQmLgvpRkGpR6H4l1aNQZa9rSqUfcssua1cDU/Iz8iuis4im3mjLByp/Aj5/AUxoGxui8oXLXwa+eQU496Hg7Qkq3LEMC6fdqfMXT288Sj0rLE2wJwgjornlnDNnDsrLy3H55Zfjvffe8ycqHg9N8ouLQAWZCQ+0K3CN+017ogK0qyU1HVc8THcHKz1kR17f/hCV6625Rzp26N+k7pWehKoWhfpWNBW70P1a1MDenin1EpKfEUYlmt/LcaPUxoaqfK29T/qcgPoX0TEo9ayYOZIuJggjorlnZcmSJYrbb7/9dpIxjodABZlA9Rg1NS5BWd3LxPjalWYiKdCE/k3qXumH2j33eaW/o6kfhe7XqnrEMOFlyM8IoxDN7wVeWxsb+JkU7gyP3INiCulZ4WnOCkEYjoQHvYoiBXZcBCrIBKrHqKlxscp5JS9y7UozkRRoQv8mda/0Q+2ec23DC6OpH4Xu16p6FNpGkJ8RRiKa37MmbW1s4GdSuDM87RPsA9TAOFIDIwgjknCywoS+NSW0Eaggs/GldgWuTX8Brlkerp6UWaR4mBPNgjQeevub0njpUHWvjS+F/03qXulJqGpRqG9FU7EL3a9FDeyaUmlCMfkZYVSi+b0cN0ptbKjK1+UvS58TUP8iOgZ5BXs24DcMxzL+7QRBGAfNw8AIneFM0qR5WUnJZANuWScpcMlKMrLCksUBnDyiqN7kdrcCfQYDl7wgbbj5QwAiwFmlIWVXLW1XA7tqKal7pTOcSZpM71eAC/EnJZ+w5QONFe3lnYOC60dVA+smqSHd8hFEnwcMZ5HOSX5GGIVIbaEgAO5aILsYmPI8RIYBc/OHgCgADCvFwJTngYt/L31mGOmzOUNqky/9k6QIJorSnBVqYw0D39aDErAmJEwsgybqWSEIw5FwsuJ0kkJJXAgCUL07XIFGSaUrUMkrUL0JkhpYzKpeMqTulX5wJiCnl/r+QJ9QUw/b8S6waZGy+leo7wUoLUVUAyOIzkSpLZR9N0BpkdGqghev4iLRYXj8c1YCh4GRdDFBGJGEW8wFCxboYUf6oaZAo6TSFajkFaIM5lcDI7UlQm/U1MNGzQj+HEn1KJrSEkEYFdl3Q1XAtKjgkeKi4ZEn0rMBXSsmlvWvbE8QhHGg1zudhZoCjZpiU6iSVxt+NbDQ8qQ8QySKmi+yXPDnSKpH0ZSWCMKoyL4bqgKmVRWMFBcNTbsaWGCyQnNWCMKIRB0GNnPmzLBJ9KIogmEYkixOBFmBJvBhFqjSFbo9VMmrDV7kYFEqT8ozRKKo+aLgC/4cSfVIzc/JPwmjI/uurO4l+3DoZ0A5DtTih3zfEHgFKSkJSlZIDYwgDEnUZKW0tLQj7Eg/ZAWa0PHMskqX0pwVBWWwE80C+ikdh5RniESRVZBCx+ZvWyHtDxyrL38O9T01Pyf/JIyO7LufPSepfMlDwWQVPKU5K0Dktpx83zDIc1O4oGSF1lkhCCNCamB6IgjSeGQt6losK03ADFRWMmcAjceArCLgtk8kZTC5F+XX/1BXA+tLql5EnIT6rC0faD7R7pPdhoaof3UHJvQAxtwplbcXAL9aKCkgKflegNKSx9UMi91B/kkkn1ja4kj1Mwok32ZY4OYPwft4mKwZkt8HtrlqcUCKi4aFFxSSFZahnhWCMCCakxWfz4effvoJra2tAIATJ07gsssuS5phKUeA6pEm5RdBiKz6FYtqDKl6EfEQ6rNqKkcB6nMAwn0tmu+1+efew2UYMqS//t+DIAKJtS3WUv/yl4FvXgH+f3t3Hh9Vdf9//DVrMknIQkhCEtZAIOw7CqJUpFVB3MANify0ahU3/Coo1Fbtt9Zav/2CSrHViq1B64KotaKiIGIpX5AYFoWwyRK2bGTfZv39McwwM5mZTJJZM5/n4+FDZnLunTP3nnNmztx73/eSxyClr/sx110/kLE5bOnPHUFRq5wvsDfKZEWIsOPzTzwPPvggq1evZtmyZbz55pusXbs2kPWKPO1NPXJX3jH1S1JjRKC5tkFPKUcO6XNChL3OJtC5W/6f98PoW1C/N0/G5S7CaHJ/GpjJAiaznAomRDjxebJSVVXFs88+S2pqKsuXL6e5uTmQ9Yo87U098lTeMfVLUmNEILm2QU8pRw7pc0KEvc4m0Hkbm2Vc7jLsaWAKh/usnJu4yKlgQoQXnycrmZmZfPrpp2i1Wv7yl79QX18fyHpFHltyjCNvyS+eyjumfklqjAgk1zZoSzly5JI+J0TYa+9Y7Ovytv4h43KX0GKwTkg0LqeBwfkbRgohwoPPk5Xnn3+eyZMn8+STT9qPrggHtuQY24dcW8kv7spfsxK2LJfUGBEcrm3QlnLk2CZd0ueECHvtHYt9Wf7qFbDzHxhvWC3jchehN5lRKpxvCmmbuMhd7IUILz5fYF9TU0P37t0BmDNnDuvWrSMnJ0ovlnVMmtHGg6HReqpMfA/4+RfWf3c0DWzO30Cjs97LovYkqLTWm/AZmpzXea4OA3poob5UUmaikS9twDUVSZcKTZXnH/cYBP9vHZiNoFRDQnrr9C/H8q7LS7sT4cZbCpfJaL0Gy96+06GhzLm9N5+F2CRrP1CowGKy/n/GHwAV1J0Ci0Xaf4RrMZrRqpTW+8hZrNeoqGyTFYkvFiKs+DzKPvTQQ06P33rrLa/lKyoqmDt3rse/GwwG7rnnHm6++WbWrFnjazVCz5YU89fp8PXvofoYvH4lvDgaXvsp1J2BbpnWBBhvH2K2NDDbsq9fCbWnrMvWnYHXpsPy4db/l+2DNf/P+pple60fuOfqoP3T2PPPm+XXoKjh0A49tgHHtrp8OHz8MJR+f/6xbZlPH7O2wb/NsLa1hJ7QvT90y4KK/c7lS7+3rsdxeWl3ItzYUriSe58fi01Ga/t1HHPL9sK6xdbH6xZD2Q/n2/vrV0JjpbU/NFbCusWoKw9Yn5f2H/FaDCa0aiUWy/mJie00MLlmRYjw0uZkZfv27axYsYKTJ0+yYsUKVqxYwfPPP09iYqLHZWpqanjsscdoamryWGb16tUMGzaMt99+m88//zxyroFxTIqZ9AC8e1vH0pM8JdbUn2k7JcxdGUkPiy6+JB75mvY1+hbnx7b26+41XMtLuxORov6M9/bvrn84jrejbzl/Y0jHv0v7j0h6kxmt2vkrkO0Ce7lmRYjw0uZpYNnZ2UycOJEvv/ySiRMnAhAbG8uQIUM8LqNSqVi+fDkLFizwWGbbtm08+uijAEyYMIHvv/+eCy+80GP5lpYW9u3b11Z1AWhubva5bHsN6KFFa/uwUqrcpsZYTAaK23h9p/U4LatH0UZKmKcy+qYGDpcE5n2HWiD3qSNv7bq92tNm28tT+3FsA63KeEr7srWtc49t7dfTa7iW97XdBWsfdkYk1tGfbRYC227bEsjtn5cR431s9dA/7OOth7+H27gb7m04EEmi3tqsp+1RVlGFEgslJcftUcXVZ60/sB7+8Sj6CpXf69kR4b4/29JV6u/vcVa0j0+TlezsbK6//nr7ZKUtCQkJbZZpamoiI8N6s6ykpCQqKyu9lo+JifG5sQS0YdWXWi+4rD5uva7E9m+b5D4oVBqGDOnv+3qcltW6fd4xJcxTGa0uvsvedC8SB4v2tNl289B+nNqAaxlbmpGntnXusb39engN1/K+trtI2IdSxwC32zYE9L3VnPDenj30D/t46+Hv4TbuhnsbDsQXV29t1tP2iC1qIq7GTK/efWyXrFBJFeyqJi0zmyF9UlotEwrhvj/bIvUX/uDzNSv5+fl89dVXvPfeexQVFVFaWtqpF46Li7P/wtLY2Ig5Us77dUyK2foS3PhGx9KTPCXWJPRsOyXMXRlJD4suviQe+Zr2tfMfzo9t7dfda7iWl3YnIkVCT+/t313/cBxvd/7Dmgom426X0GI4dxqYw7X0MRrr0ZQmvSlEtRJCuONzGtjChQvJzMxkx44dLFmyhEWLFvHGG290+IWHDRtGYWEhV1xxBcXFxdx0000dXldQuSbNaOOd05MSeoLKh83qLbHG8XlbGticv7kto29qQKuLl1SaaONLG3DXxnSpzo9ju1tTji5/pnX79bT8rGVw5XOShiQii0oNGcNd0u7Sndt/fIbnMXnWMgx6PZrbP5U0sC6gxWhqdc1K7LnH9S3GUFRJCOGBz5OVs2fP8sILL3Dbbbcxbtw4pwSNtmzdupXDhw8zb948+3PXXXcdd999Nzt27ODQoUOMGjWqfTUPJVvSjF13P62njefdlDlcsi+sTkEQQeRLG3DXllwfJ/Vq8zW8Li9EpFCpW7d318dexuRDckpIl6E/F13s+E0m9tyRFZmsCBFefJ6s9OvXjyVLllBWVsaKFSvo169fm8sUFBQAMGnSJCZNmuT0t+zsbFatWkVhYSEPPfQQKlV4XMwmhBBCiK6txWgmxvXIyrnJSkOLnAYmRDjxebLy5JNPsnHjRnJyckhLS+O+++7r9ItnZGQwY8aMTq9HCCGEEMJXLUYz3WKdvwLFaqyTl0a9HFkRIpz4fLLtI488wrZt20hMTKSoqMgeOyyEEEIIEUn0RhMalfNXoBi1nAYmRDhq9zUrNvn5+QGpkBBCCCFEILUYzWhVCqfnVEoFGpWCBpmsCBFWfJ6sxMbG8sorrzBs2DB2795Nt27d+Pbbb5kwYUIg6yeEEEII4Vd6o7nVkRWwXrci16wIEV58Pg1s1KhR6PV6ioqKMJlMDB06lG3btgWybkIIIYQQftfiabKiVtJokCMrQoQTn4+s3H///YGshxBCCCFEULS4uWYFrDeGbJQjK0KEFbmblRBCCCGihsVisV6zola0+lusRkWDpIEJEVZksiKEEEKIqNFsMGOxnE//cpQQo6a60RCCWgkhPJHJihBCCCGihu3Iie0mkI6SdRoq6luCXSUhhBcyWRFCCCFE1LBFE8doWn8FSorTUNVowGy2BLtaQggPZLIihBBCiKhhiyaOcXOBfbJOg8lsobpJTgUTIlzIZEUIIYQQUaNR7+XIik4DQHmdnAomRLiQyYoQQgghokaD/tyRFTcX2CfHaQE4U9sc1DoJITyTyYoQQgghokaj7ZoVdeuvQL27xwGwu6Q6qHXyu7pS+HghvDoN1i2G6uOhrpEQHSaTFSGEEEJEjXr7ZMV9dHFGYgy7TkTwZKWuFF6bDjvfAqUadqyCP0+BHzeFumZCdIjPd7AX7pnNFiob9OiNJrRqFanxWpTK1jeaEkL4h/S5yCP7TISTRttpYG6uWQEYmJbAd8ersVgsKBQR2E4/+S+oL4drV0JCT6g7DV89A2/dBPkfQN/Joa6hEO0iR1Y6wWy2sL+0jutWbuGi577iupVb2F9aJ5GHQgSI9LnII/tMhBvbfVbcnQYGMCwribMNeg6W1QezWv5x8Eso/hdMvBsSMgALdOsJP/stxPeAt2+F2lOhrqUQ7SKTlU6obNBz1xs7OFHVBMCJqibuemMHlQ36ENdMiK5J+lzkkX0mwk1DixGVUoHGTXQxwNCsRAC2HzkbzGr5x+Y/QFJvGPhT5+djk+DSX4KhCd6/E8zm0NRPiA6QyUon6I0m+wewzYmqJvRGU4hqJETXJn0u8sg+E+HmbIOe5HMRxe6kd4tBp1Hxw6maINbKD45vg5JtMOoWcHf2WlJvmPBzOLYFdq4OevWE6CiZrHSCVq2iV4rO6bleKTq0bi7aE0J0nvS5yCP7TISbino93eO1eDoRUaFQ0Kd7HMWn6+zP7Th6llc3/4jFEsanL+5YBTGJ0G+K5zK5P4OMEbD+V9AcYZMxEbVkstIJqfFaXr1tvP2DuFeKjldvG09qvDbENROia5I+F3lkn4lwU1HfQkq8Fm/zjl4pOg6V1WOxWLBYLMz581aeWbePonCNNG6ph33/hEFXgMLLVzuF0np0pbkatrwUvPoJ0QmSBtYJSqWCwRnd+GDBReiNJjRqJWqlgtM1TZJ4I0QHeUuOcu1z0s/Cn+s+UygUqBTWa1lk34lQqKhvYVhmktcy2Sk66oqNVDboaTGev75j24+VjO2TEugqtt++j8HQCP0vabts6kDoOwX+byVc9ID1ehYhwlhAJitLly7l8OHDTJ06lQULFrT6u9FoZPr06fTu3RuAJ554gsGDBweiKgGnVCpI6xZjT7yxXUhq+/VwcEY3+TAWwke+9CNbnxORQ6lUkBqvlTFShIXKej1JXq5ZAchKsh4JPFxWT6Ph/PVVR8obAlq3Dtv1D+s1Kd1zwOLDxfMj5sCxf0Ph3+GiBwNfPyE6we+nga1fvx6z2cw777xDSUkJR48ebVVm//79zJw5k4KCAgoKCiJ2ouJIEm+E6DzpR12X7FsRDhpajDTqTSTGev+tNivZOlk5VFbPoVJrhHF6txhOVDd5Wyw0ak7Ckc0weKZvExWwHl3JGAHbXgazhF2I8Ob3Iyvbt2/nyiuvBGDKlCkUFhbSr18/pzI7d+5k06ZNbNu2jUGDBvGb3/wGtdp7VVpaWti3b59PdWhubva5rL/oUrPcJt40NDVTceLHgL1uKN5rKATrfQ4ZMsRv62pPm+2MSGkDvtQzVP3IJhK2pWsd/dlmIXDt1pd9G+7bP9zrB+Ffx+bmZr+v01ubdd0eh8+2AKAyNnD8+DGP6zRbLGiUCgoPnqS+xUhyrIqe8QqOV9QFdfv6sj9T971BOhZO6wai9/KeXMWlTiBt7ypO/Pst6tLGd7aqboV7e2yLrf7+HmdF+/h9stLY2EhGRgYASUlJnDx5slWZESNG8Prrr5Oens7ixYv5+uuvueyyy7yuNyYmxufGEoqGVV7XQq8UndOHca8UHfG6WPoFsC7R0oki8X22p812RqRsG1/qGap+ZBMJ2zLQdQxUu/Vl34b79g/3+kH41zEQX1y9tVnX7XFg50ngJHl9M8lI1LldxiYrpZZyvYq6FgsDMxJJT4xl/75S8vLygnZn+zb3p8UCGzdC9ngy+w/1/cgKQHYmHHqHXmVfwSX5na+sG+HeHtsS6fXvKvx+GlhcXJz9l5PGxkbMbm48lJeXR3p6OgDDhw/n2DHffwkIV5J4I0TnST/qumTfinBwuLwBpQIyEmPbLJuVpONwWT0HS+vomxpHaryWFqOZ2iZjEGrqo1PfQXmxNZK4PRMVAJUW+l0MxZ9Ac21g6ieEH/j9yMrw4cMpLCxk9OjRFBcX079//1ZlFi1axL333ktubi5ffvkl99xzj7+rETSOyUXp3bS8+4tJGExm1EoF6QkxrS4cNRhMlNW3YDRb7GU0GpXHdUrakYgm7tK+kmPVnKltxmAyo1EpSU+IQa32/juLax9K0WmoajI4rbO8Qd+udbYl2vut6/tPilFR3qDHaLagUSmJUSvoFqvmnbsvRKkACwrS4rVOy8TEtv0FUojOOFRWR3aKDqVCgbmNW6ZkJcey9cdKAPr3iLengpXXN5MU5/0C/aAp/Dto4qD3xI4tP+AyOPAZ7P0Qxt7m37oJ4Sd+n6xMnz6duXPnUlZWxubNm1m2bBnLli3j4Ycftpe57777eOSRRwCYNm0akydP9nc1gsIxuSgtIYbFVwxm0Zrd9qSbP88bR15GN/uXIIPBRHFZPfeuLrSXeXneOPLSE+wTFkkVE9HOMe3LaDRTXFrHPQ59xrVfuXLtQz8bms6Dlw1yWsfL88bx0oYDrN9b5rTOjor2fuv6/n9xcT+uGt3Laax7fs5I/vDZfsrrW3hu9kg27y9l1uhezvv21jGYzZao2GYiNA6U1tO/R3ybExU4f5E9QO/ucVTUWa93KatrYWB6x8cLv2mph+/ft95bRakBj7e59CItDxJ6wvdrOzRZKWssY/W+1Ww7vY3q5mqSY5MZljqM6X2nc0HPC9pfHyHc8PtpYAkJCRQUFDBq1CjeeOMN8vLynCYqAIMGDeLjjz/m448/bvW3SOKYbnPPTwbYJypgvXD0ntWFlNW32MuX1bfYP7xtZe51KSOJOUKcV1bfYv8yC+77lSvXPjR7XO9W67h3dSGzx/X2eZ1tifZ+6/r+54zv02qsW7RmN/f8ZAAnqpp47P3dzBnfp/W+fbMoaraZCD690czRigb6pMT5VH7QuR8wNCoFGd1iSYqznrJYWtvxscKvfvgA9PUw8Gd0aKICoFBAn0lw9Jt239F+84nNXPvRtRT8UECMKoa81DxiVDF88uMn/OKLXzDrw1l8VvoZjYbGjtVNiHMCcp+VpKQkZsyYEYhVhxW90WT/oE3Wadwm3RhN588hNZot7ss4/MTjuE7HMnqjRAuK6GMwmdvsV65c+5CnvpnscJ+FttbZlmjvt67vX6VUeN3mJ6qaPJaJlm0mgu9IRQNGs4WsZN9ON+yREMPjV+SR1k2LBez3Zimr9X+iWbtZLLDtL5CaCyl9wdKJftN3Muz9AA6sh5E3+LTIp0c+ZfHmxeQm53L/6PtJjEnEcm7CZDQZ2VWxiy+OfcGqY6t4//T73Dj4Rm4cfCM943t2vJ4iavn9yEo00apV9otFq5sM9n/b9ErRoVad38RqpcJ9GYdTHhzX6VhGq3a+rkWIaKBRKdvsV65c+5CnvlndZPB5nW2J9n7r+v5NZovXbd4rReexTLRsMxF8B0rrAOiZ5D0FzNGo3slkJVuPxMRrVaiVCkrDYbJy4DMo3QNj8js3UQFIGwy6FOuRGh8UlRXxy3//klFpo3h84uN0i+lmn6gAqFVqxmWM47EJj3FnvzsZ1H0Qf93zVy5//3Ie2PgAm0o20WIKk6NTIiLIZKUTHNNt/rzpMM/PGemUdPPneeNITzh/p+30hBhenjfOqczLLmUkMUeI89ITYvizS59x7VeuXPvQ+4Ulrdbx8rxxvF9Y4vM62xLt/db1/a/ZcbzVWPf8nJH8edNheqXoeG72SNbsON563946Jmq2mQi+42etpyOld+tYX1coFCTHaaioD/GpiiYDbPgNJPeBXuM6vz6F0noq2OGNYPB+08s6fR2Lvl5Ez7ieLBi5wGuEs0KhIDs2m3tH3csfLvkDs3JmsbNsJw9sfICL376YhV8t5L0D73Gq/lTn34Po0gJyGlgkckyy0aiVqJUKmvTnU30At0k/uWkJ9gQwnUbllAaWFq91ughYo1GRl57AO3df6DENzHWdtqQiueBU+EMoEqtsr6lLzaK8rqVdr6lWKxns0md6xGkpq2/xmOTlLlEsKUbVah1PXT2cJ2aaUfshDczda0ZTGphSqWBgj3jeuftCFArrGSpmi8X+GAvEaJSsmDsGsJ4m379HDmnxWqdtVldxCqUyJbRvRnRZJ6ubSInToFWpMFk6do1Hkk5DeSeub/OLfy+Hsr0w84/WzuYPfSbD/nXWCUveTI/F/mfH/1DeVM7vLvodGrXviWipulSuGXgNM3NmcqDqAHvK91BYVsiG4xsA6JfYj4uyL+KSXpdwYeaFKBXyW7o4TyYruE/ycUyueeOOibQYza2SfnLTEjhYXu9xOXepRRqNimwvF/eZzZZW64ymVCEROKFIrOrsa5rNFg5VNHhNmXLXzzqbKNYRjq8ZbYxGM/vL6nlxwwHmT+7PY++fT0V8bvZI/v6fIzz808HEqJXctmq7x7ZQcSIMTq8RXdap6iYyEmMxd+ILfnKc1p4KFhJ7/wlfPQN5V0Ha0M6fAmbTczhoE6ypYB4mK0VlRaw9uJabBt1ERnyG06lfvlIr1QxNHcrQ1KHcOPhGShtL+aHyB/ZW7uW9/e/x5r43GZA0gIfGPsSlfS7t7LsSXYRMXXGf5OOYXHOsstFt0k9ZfYvX5TqSMBTtqUIicELRtjr7mr6kTLXVzzqSKCbax7aNZ4/rbZ+oAPbkr9njenPXGzs4VtkoY5sImVPVTaR3i+lobhZgDewISZu1WGDLC/DefMgaDRfc47+JCoBSDb0vgIOfg7H1+zNbzDz/7fOk69KZ3nd6hyYqrhQKBT3je3JZn8t4YMwDvDjtRe4ZeQ96k54Hv3qQxV8vliQxAchkBfCc5GNLronTqjwmErWVeNPehKFoTxUSgROKttXZ1/Q1ZcpbP+tIophoH9s29pa8dqKqiTitqtXfZGwTwXK6urnTRz+TdBqqG/WYfLlRi780VcM78+CLX1vvqTL9N4F5nb6ToaUOjm5u9adPj3zKnoo93DrkVlTKwIRgaFVaJmZO5FeTfsX1udfz+dHPmf/ZfMobywPyeiJyyGQFz0k+tuSaRr3JYyJRW4k37U0YivZUIRE4oWhbnX1NX1OmvPWzjiSKifaxbWNvyWu9UnQ06k2t/iZjmwiG2mYDdS3GTgc4JMVpMFugsiE4R2bVjWXw6jRr+tfFj8KkB6wXfQVC1hhQ6+D7D52ebjY2s/y75QxOGczItJGBeW0HaqWaq3Ku4sGxD3K05ih3rb+LquaqgL+uCF/yaY37JB/H5Jq+qXFuk37SE2K8LteRhKFoTxUSgROKttXZ1/QlZaqtftaRRDHRPrZt/CRso+kAABu0SURBVH5hCc/Ndk5FfG72SN4vLOHV28bTNzVOxjYREqeqrUf8uneyvSXrrMuXB+O6lZZ6+nz9ENSfgatXQM6l/j31y5VKC70mwP5PwHz+dVbvW82ZhjPMGzLPL6d/+Wpk2kgeGPMAJXUlLPhygcQdR7GovsDeYDBRVt+C0WwhSafho/sm02wwE6tR0mIws/zm0WiUCjRqJQlY7GlCtudO1jSRmaR1el6tUrL85tGolQpiNdYyunNpX7b0IoAmg4mEGBUtRkurVCPHVCFbMtnpmqaoSxgS/hWMxCp3aWMDUuOckrhSdVpO1zQ5Pa5s0js9rjOY7Ovon9J6edfHrulggNNzueeSqhxT+Bwvrm8rsSwUKWrhynVbJMeqKW/Q0zslhidnDSNWo3Ta1koFPDVrGGqVkiaDiXfuvpA4rZImg4W0eK3TumJifbtZnxDtdbrGGt7geDPYjkiOsy4flPjizx5HW3sUrn4RknrR4bvUt0ffydbTwI79B/pfTFVzFa/teY0p2VPondgbsyW4p88OSR3CXSPv4k87/8Qz//cMT09+2mtcsuiaonayYjCYKC6rd0oVenneOHJT4zlY2eD0/LIbR6FRK7n/rSLSEmJYfMVgFq3ZzeScVOZN6suCN79zmwa28taxfF1cxvj+3Vm0ZrdTmR1HzjI1L91pWceEorRuMSFJbxJdWyATq9y113/cdQHVTcZW/eylDQdYv7eMp67KY1z/Hva//2xoOg9cNshjeU+P/7XzBH/55ii9UnT87fYJNBvMTulfrss49qO2+pn0w/Nct4VtfxUeqWBc/x78a+cJZo7KdhrXbGlgt1/U3z42vjxvHFX1TZxN0DmntN06BrPZEnXbVQTeGdtkJa7z16xAEI6sHNsKRQXU5d1EYkoOQZmoAGSPsx5h+eED6H8xr+x+hUZjI9fnXh/0iYrNuIxxXJVzFR8c+oARaSO4YdANIamHCJ2oPQ2srL6lVarQvasLqWzSt3r+4Xd3UdVgsKYI/WSAfeJx1yU59g9lW1nHNLAFb37HNWN72cs7lrlmbK9Wy7omFEkymIgk7tpri9Hitp/NHtcbgGlDM53+Pntcb6/lPT2eM76P/XHJ2aZW6V+uyzj2o7b6mfTD81y3hW1/2fbjnPF9Wo1rtjQwx7Hx3tWFDEhPbJ3S9mZRVG5XEXhnappRKCBJ17nfaG2TlTO13m+e2CkWC3y+BLplUpd9KUGbqABodNYJS/G/KKk5xtv732ZG/xmkxIT2/kfXDryWET1G8Oy2ZzlQdSCkdRHBF7WTFaPZ4j4hyMPzthQbx7QbT8lEjmlgZov79Vk8PO+YUCTJYCKSuGuvSgVe+4hr//CWJuXtscrhl3hP6X2uy9j6UVv9TPrhea7bwra/bPvR25jYKinRw1gbjdtVBN6ZmmZS47WolJ372hOrURGrUVJWG8AjK4c3wqkimHAXRnMI+kPfyVBfyotbnkStUDOj/4ygXqvijlKh5OfDf45OrWPJN0vQm+RHjWgStZMVtVLhPiHIw/O2FBvHtBtPyUSOaWBKhfv1KTw875hQJMlgIpK4a69mC177iGv/8JYm5e2xY4yop/Q+12Vs/aitfib98DzXbWHbX7b96G1MbJWU6GGsjcbtKgLvTK01trgzN4S0SdZpA3sa2NY/QXy69WL3UOgzicL4RD4rL2R27mx0al3bywRBYkwi84fN50DVAVbuXBnq6oggitrJSnpCTKtUoZfnjSNVp231/LIbR5ESr7GeU73pMM/PsabdvLr5R1beOtZjGtjKW8fy0Xcn7OUdy3z03YlWy7omFEkymIgk7tprjFrhtp+9X1gCwMa9p53+/n5hidfynh6v2XHc/rh3d12r9C/XZRz7UVv9TPrhea7bwra/bPtxzY7jrcY1WxqY49j48rxxHC6rbZ3SduuYqNyuIvDO1FgnK/44PpAUpwncZKV0LxzeACNuCFxEcRsMSjW/Sc8gy2jisvTxIT+q4mhM+hguyb6EVd+v4rvS70JdHREkCovFDz8zBMG+ffsYMmRIp8s6JtnExShpbDE7JQRpNCqnlDCNSolapcBkNmM2W4+mxGlV6I1mDGYLOo0Ko/l8opdaAU1GMxqlAq1aSYPeRKxaiUKhcEoDazaYiD+XBmY0mVE7pIF5qq+7FKL2bJdIFonvM1h1Dqdt4669GgwmKhr1XtO/vKWBddOo2izv+Dg9IQaFQmHtw+f6Vlq8lupmo8d+ZKt3Q1Mz8bpYUnQaqpoM9vKuj0OZBhbo/d3W+h33sUKhIFajoElvTVFsNpjRqBQYTBZMZguqc2lgFgv2NDC1UkF8jBKDSdFqu9ZVnCKnf/+AvbfOCqe+5km41zEQ9fO2TtvfRj29nsvy0rlhfO9Ov97yLw9QWtvMpkWXdnpdrXx4H3z/Pty0GtQxHDt+jL59+vr/dbx4+dgnrDz2CX86U0bfsXdSNuLaDq/r2LFj9O3r3/o3GZt46j9PoVFqWHvNWuI18X5dv6Nw70/RIqrSwHxN9dFoVGSnxLldzjENzF2KV3texxeBTG8Swt9c26vRaOZgRUOrZC7H9C53fSM21nloyvbw2Ftfy0p2PnUhTeP59CJbvStO/EifwXmS/uWFUqkgNV7rdhsN7BHP/rJ6p/1tSwN78LJBTuOkjWN7qTjRHOy3I6JAk95ETZOB7gn+OWqXEq9l94kaLBaLf2N060phz7sw7HpQh+Zzv7DmEH8+to6fZkxkXNU2lN9/QNmwWRCgu9Z3hE6t484Rd/Lc9ud4dtuz/HbKb0NdJRFgUXUaWEdTfRyXc0wDs61DUryEcK+svsVtMpdjeldn+kYg+pr037Z52kbu9rctDcx1nBQiWM7U+uceKza9knU0GVoHb3Tat6+CyQCDZ/h3vT4601LF4n2ryNalcVPmVE4PvBRdzUlSjmwJSX28yU3JZUbODD46/BGfHf0s1NURARZVk5WOpvo4LucprUhSvIRozWAyu+0Ljuldnekbgehr0n/b5mkbeUr4so2bjuOkEMFiu8dKUpx/Jiu9u1vPvDhQWueX9QGgb4RvX4OBl0F8qv/W66MaQwP37llBo6mFhQNvQmk2UJU1kqaEdLK/fd3pjvbh4uoBVzMgaQBP/+dpTtWfCnV1RABF1WSlo6k+jst5SiuSFC8hWtOolG77gmN6V2f6RiD6mvTftnnaRp4SvmzjpuM4KUSwnKhqBCBF55/TwHql6FAA3x2v8sv6AChaDU1nYdh11ou8guhUcyW37fojx5rKeTzvNlIU5067VSg5MXQmuuoS0n/4Z1Dr5Au1Us1dI+/CZDHx+DePYwrDCZXwj6j65Ohoqo/jco5pYLZ1SIqXEO6lJ8S4TeZyTO/qTN8IRF+T/ts2T9vI3f62pYG5jpNCBMuxykZU56618oc4rZrcjAQ2Fpf5ZX2YDPCfl6w3Y0wJbsDEpsrd3Fz0HOX6Wp4a+nOyVXE43oSyuudwatIH02vba8TUnAxq3XyRHpfOvCHzKCor4oWiF0JdHREgUXWBvVKpYHBGNz5YcFG7Un1cl4vVKnnvF5MweEjx6ujrCNHVqNVK8jK68e65/qI5l8zV85KB3Da5f6f7RiD6mvTftnnbRrb9bTSZ7WlgT1093G3aoRDBcLSygaykWJRKBWY/HbS4oH8qBf93jC2HKrhoYI/OreyHD6DmOFz0EFiCc6pkSVMFy458wBcVRQxK6M2CnOtJRNk6plih4OjoGxn21R8ZsP5piq9Zjlkb536lITIpaxKHqw/z+vevk5OUw7UDO55eJsJTwCYrS5cu5fDhw0ydOpUFCxZ0uIy/dTRdq73LSYqXEFZqtZKsZJ1TBKS3ZK72CkRfk/7bNk/byLa/hQgXRysbyE6J89tEBWD6kAy+3FfK/W99xwcLLqJfj3gMJjNbD1fSv0e8/bqWNhn1sOn3kDYY0ocEfLJSbahnVcl6Vp/chEqhZH7fmUxNGQpmA3i4n4pel8KP424ld9tr5H72aw5e8Zuwm7DcnHczZxrP8PTWp8mKz2Ji5sRQV0n4UUB+5lq/fj1ms5l33nmHkpISjh492qEyQgghhBAdZTRbOFBaT99U/3651qqVLLp8MCazhfmvb+f7kzXMX7Wd21ZtZ/r/fk2Rr9ezfPtXOHsYJt4b0IlKlaGeF458xOXbf8XfTmzgsowJLBv5AD9JHnxuouJdTcYQjoy9hW6nd5P30cNo60oDVteOUCvV3DvqXjLiMrh/4/3sOLMj1FUSfhSQm0L+9re/5eKLL2bq1Kl88sknNDc3M3v27HaXcbRz505iYnz7pbO5uZnY2NhOvYdIES3vNVjv0583f2pPm+2MSGkDkVDPSKyjv29YFqx26064b/9wrx+Efx2bm5sZM2aMX9fprc3uO13Hf60v554L0+mf5P+QjGPVev664yx6kwWNUsHsEd1Zf7CaOI2KlbOy0ag8nz6qqSshZ/1ttPQYQfmwn2MxG1uV0ev1aLUdv9amxtTIB7Xf8q/aQlosBiYnjmBa0ijijQrMbl6vLWk1Rxh/4CMsCiW78m7iaK8p4OVeM52tf3vVG+tZfWI1VfoqFvRfwOTUyZ1an60/yY0hQysgp4E1NjaSkZEBQFJSEidPtr4oy5cyjmJiYvxyB/uuJlreayS+z/a02c6IlG0TCfWUOgav3boT7ts/3OsH4V/Hffv2+X2d3trsv4q3ATAyJ5uUeP9Pwvv0gQF9mvjhVA3j+3Wne5yWPj2r+MPn+9laGcMvpg5wv2BzLfztLlBr0U1bTB+N+wlmR+9g/2Pjad46uYl/lm6j2WxgWvo4ZmZMortSi9nSidSsjAz2ZufSb9d7jP/+bwyq/I4TF95FQ4b77R+IO9i3ZUmvJby862WWH15OqaaUhWMXEqfp2JG1cO9P0SIgk5W4uDiam6255o2NjZjNrQ9t+lJGCCGEEKKj/nO8gb7d40hNiPHrNSuOspJ19uu0LMCYPimM7ZPCCxsOcu2YbDISXSYitafgnXwo2wcz/wgeJirtVWNoYEPFTtaV72Bb9X60SjXT0sbzs/QJ9klKpyYq57TEp7J/0t2kHdtG9v7PGfrBA1T3mUjpiOuo7TUOFKEN0kiMSeS/xv8Xaw+s5e3it9lwbAM/H/Fzrhl4DfGa+JDWTXRMQCYrw4cPp7CwkNGjR1NcXEz//q2j+HwpI4QQQgjREf8+WEHR6SbuuKhfwCYqnuRf2JdF7+/ikXd38fvZI8iKaUFZvg/2r4MdrwMWuOI56D4QTxe2u2OxWGg0tXDWUE+5vpqjjaUcbDzFdzWHKa4vwYyF7NgezO87k0kpecRYLFgsZr9MUpwolJT3m0Rl9hgyjv6HjB+/YfAnS9DHpVLd9wLqMkfR1L0vaoPBei1OkCcwGqWGm/JuYnzGeNYeWsuz259lWeEyJmVNYkz6GIamDiUrIYuMuAy0KomlD3cBmaxMnz6duXPnUlZWxubNm1m2bBnLli3j4Ycf9ljm3XffDURVhBBCCBGFlnywm+xEDT8dkkGwg8czk2K586L+vPrNER57/kUKtL8HLKBQwaDLYcw8iE0+dwNI97X7uHQb/1OyBk4qMFpMGC0m9GYjRpeJR4xSw9DE/tzS53JGJubQU5OI2WzANkNTBPCWehZNHGdyp1M64CeknN5DcukPpB76ivR96wAYDli+VGDW6MBipj5zJAdnPhuw+rgamDKQReMXcaTmCNvPbOf7yu/5quQrpzIapYY4TRxX9LuCJy58Imh1E74LyAX2ADU1NWzZsoUJEyaQlpbW4TI2obzoU0QPtVpNbm6uX9YlbVYEgz/bLEi7FYEXrDarUCgwq2IwmEJ3mrlCoUClUJAQq0Shb0Bp0gPWIyS+UMfEolAq7cuYMWOxgBkTpnMTGJsAfZ1rN4X9gnsFZoUKlCr7RfgWFOgNxvYcTApQ3UClUNn/U6BAqVDS3NTsdjl/t1nRPgGbrAghhBBCCCFEZ8jthIUQQgghhBBhSSYrQgghhBBCiLAkkxUhhBBCCCFEWJLJihBCCCGEECIsyWRFCCGEEEIIEZZksiKECKjq6mq2bNnC2bNnQ10VIYQQwify2RU+uuRkpaKigmuvvTbU1Qi4p556io0bN4a6GgFVU1PDXXfdxfXXX8+vf/3rUFcnrNTV1XHnnXdyxx13cN9996HX60NdpVZqamq455572L17N/Pnzw/7QT+cxw6j0chPfvIT8vPzyc/PZ//+/aGukl9EQju2Cef2AeH9mRCKsXzp0qXcdNNNrFy5Miiv528VFRXMnTs31NXokEjq1+5E2mdXV9clJyvPPfcczc3ub+zTVezYsYOKigqmTZsW6qoE1EcffcSsWbNYu3YtDQ0N7NmzJ9RVChv//Oc/uf3221m1ahU9evTgm2++CXWVWtm/fz+PP/449957L1OmTOGHH34IdZW8CuexY//+/cycOZOCggIKCgoYPHhwqKvkF5HQjm3CuX2E+2dCsMfy9evXYzabeeeddygpKeHo0aMBfT1/q6mp4bHHHqOpqSnUVemQSOrX7kTaZ1dX1+UmK1u3bkWn05GWlhbqqgSMwWDgiSeeIDs7my+//DLU1Qmo5ORkDh48SG1tLadPnyYzMzPUVQobt956KxdddBEAVVVVpKamhrhGrU2cOJHRo0fz7bffsnv3bsaMGRPqKnkU7mPHzp072bRpE3PmzGHp0qUYjcZQV8kvIqEdQ3i3j0j4TAj2WL59+3auvPJKAKZMmUJhYWFAX8/fVCoVy5cvJyEhIdRV6ZBI6deeRNJnVzToUpMVvV7PypUrefTRR0NdlYD68MMPGThwIHfeeSd79uyhoKAg1FUKmHHjxnHq1CneeOMNBgwYQFJSUqirFHaKioqoqalh9OjRoa6KWxaLhXXr1pGYmIharQ51ddyKhLFjxIgRvP7666xZswaj0cjXX38d6ir5VTi343BvH5HwmRDssbyxsZGMjAwAkpKSqKysDOjr+VtCQgLdunULdTU6LZz7dVsi4bMrWnSpycorr7zC3LlzSUxMDHVVAmrfvn3ceOONpKWlcfXVV7Nt27ZQVylgVqxYwdNPP839999PTk4Oa9euDXWVwkp1dTX//d//ze9+97tQV8UjhULBk08+yeDBg8P2fPpIGDvy8vJIT08HYPjw4Rw7dizENfKfcG/H4d4+IuEzIdhjeVxcnP2UvcbGRsxmc0BfT7QW7v26LZHw2RUtutRkZevWrbz11lvk5+ezb98+fvnLX4a6SgHRp08fSkpKANizZw9ZWVkhrlHg1NbWsn//fkwmE7t27UKhUIS6SmFDr9fz0EMP8cgjj5CdnR3q6rj1yiuv8OGHHwLWCy7D9ZfCSBg7Fi1aRHFxMSaTiS+//JK8vLxQV8kvIqEdh3v7iITPhGCP5cOHD7ef+lVcXBy2baurioR+7U2kfHZFC4XFYrGEuhKBkJ+fH5aHwv2hvr6epUuXUllZidFo5MUXX7Qf7u5qdu/ezZIlSzh16hSjR49mxYoVxMfHh7paYeGtt95i2bJl9i+tt9xyCzNmzAhxrZzV1NSwcOFC9Ho9ubm5PPnkk2E/4QzXsePAgQM88sgjAEybNo2HH344xDXyj0hox47CsX1EwmdCsMfy+vp65s6dy6RJk9i8eTPvvvtuRH7hDMf25otI69euIvGzqyvrspMVIYQQQkSvmpoatmzZwoQJE8IyGEEI4RuZrAghhBBCCCHCUpe6ZkUIIYQQQgjRdchkRQghhBBCCBGWZLIihBBCCCGECEsyWRFCCCGEEEKEJZmsRKj8/Hy/lvPFiRMnnG42tnbtWrlJo+iQzrTLZ555xu3zju1R2qrwxrV9eCNjrQgngWi77sg4K8KJTFaEz06ePMn27dtDXQ0R5Xy5IZ+0VeFNuLePcK+fCJ1gtQ0ZZ0U4UYe6AtGgubmZhx56iPr6epKTk3nuuefsN/AaNGgQTz75JC+99BK7du2iubmZ7t2787//+78oFAoWL17MyZMnSUlJ4cUXX0Sj0XS4HhaLhV/96lccPXqU7t27s2zZMj766COKi4spLi6moqKC5cuXk5uby6OPPkpJSQkZGRlkZWWRlZXF2rVrqaurY/v27bzwwguA9c7At912m33ZQYMG+WuziTARivZ7++23s2zZMi6//HI+/fRT7rzzTvsvdo43STMYDCxcuJDa2lo0Gg1XXXUVf//736WtRinXdvjHP/6Rp59+2mnMW716dav2kZSUJGOtCKlQtF0ZZ0WkkCMrQXDo0CEUCgVvvvkm119/PWvWrCE3N5c333yT8vJyiouLARg/fjyrV68mNTWVDRs2UF1dzdSpU1m9ejUJCQns3bu3U/XYsGEDRqOR1atXk5mZyaZNmwDYs2cPr732GnfffTcbN26kpqaGs2fP8u6773LixAmWLFnC/PnzWbp0Kddddx0FBQV0797d7bKi6wlF+x0yZAibNm1izJgxfP311+Tk5Lgt98UXX5CVlUVBQQFZWVkA0lajnGM73LhxY6sxz137kLFWhINgt10ZZ0WkkCMrQTBs2DAGDRrEHXfcQd++fTGbzRQVFbF9+3Zqa2spLS21lwMYPHgwJ0+e5MILL2TTpk18/vnnVFZW0tzc3Kl6/PjjjxQVFZGfn09DQwMDBgxArVZz1VVXodFoyMzMpKSkBJ1Oh16v54YbbmDWrFle1+m6rOh6QtF+hw0bxscff8zUqVP57LPPmDRpkttyJ06cIC8vD4Dhw4d7Xae01ejg2A6PHTvWasxzR61Wy1grQi7YbVfGWREp5MhKEBQXFzN27FhWrVpFbW0tffv2Zf78+RQUFLBw4UL7LxW7d+8GYO/evfTp04f169eTm5vLihUryMjI6HQ9cnJymDlzJgUFBSxdutQ++Ol0Oqdyu3fv5qc//Snvvfced9xxh/352NhYmpqaAOtpDu6WFV1PKNrvkCFD+Oabb5gyZQpbt25l6NChbstlZmZy6NAh++vaSFuNXo7tMC0tze2Y59o+ZKwV4SDYbVfGWREpZLISBNnZ2RQUFHDzzTdTXl7ODTfcwObNm7n11lt5++23yczMBKyHT/Pz86mrq+PSSy9l7NixrFu3jltuuYWamhr7L9gdNW3aNMrKypg3bx4vvPAC2dnZbsvl5OTw+uuvk5+fzwMPPMCOHTsA68B25MgR5s6dy7p16zpVFxE5QtF++/fvT3JyMr169SIzM9Pjh+jll1/OkSNHyM/P5+jRo/bnpa1GL8d2ePXVV7sd81zbh4y1IhwEu+3KOCsihcJimw6LkHrppZeYOHEiF1xwQairwqZNm/jrX/+KRqMhJiaGWbNmMXPmzFBXS4SxcGq/InpFWjuUsVbYRFrbFSKYZLLSBTz88MNUVFQ4Pffqq68SGxsbohoJ4TtpvyJSSFsVkUrarohkMlkRQgghhBBChCW5ZkUIIYQQQggRlmSyIoQQQgghhAhLMlkRQgghhBBChCWZrAghhBBCCCHC0v8H/7o1BvoJo7cAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="分面频次直方图">分面频次直方图<a class="anchor-link" href="#分面频次直方图"> </a></h2><p><code>sns.FacetGrid</code>获取数据子集的频次直方图。例如,饭店服务员收小费的数据集:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tips</span><span class="p">[</span><span class="s2">"tip_pct"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">tips</span><span class="p">[</span><span class="s2">"tip"</span><span class="p">]</span> <span class="o">/</span> <span class="n">tips</span><span class="p">[</span><span class="s2">"total_bill"</span><span class="p">]</span>
<span class="n">tips</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>total_bill</th>
<th>tip</th>
<th>sex</th>
<th>smoker</th>
<th>day</th>
<th>time</th>
<th>size</th>
<th>tip_pct</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>16.99</td>
<td>1.01</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
<td>5.944673</td>
</tr>
<tr>
<th>1</th>
<td>10.34</td>
<td>1.66</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
<td>16.054159</td>
</tr>
<tr>
<th>2</th>
<td>21.01</td>
<td>3.50</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
<td>16.658734</td>
</tr>
<tr>
<th>3</th>
<td>23.68</td>
<td>3.31</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
<td>13.978041</td>
</tr>
<tr>
<th>4</th>
<td>24.59</td>
<td>3.61</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>4</td>
<td>14.680765</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">grid</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">FacetGrid</span><span class="p">(</span><span class="n">tips</span><span class="p">,</span> <span class="n">row</span><span class="o">=</span><span class="s2">"sex"</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="s2">"time"</span><span class="p">,</span> <span class="n">margin_titles</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">grid</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">,</span> <span class="s2">"tip_pct"</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><seaborn.axisgrid.FacetGrid at 0x1a1ddd0f50></pre>
</div>
</div>
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="分类图(Categorical-plot)">分类图(Categorical plot)<a class="anchor-link" href="#分类图(Categorical-plot)"> </a></h2><p>展示分类数据分布情况:</p>
<ol>
<li><p>Categorical scatterplots:</p>
<ul>
<li>:func:<code>stripplot</code> (with <code>kind="strip"</code>; the default)</li>
<li>:func:<code>swarmplot</code> (with <code>kind="swarm"</code>)</li>
</ul>
</li>
<li><p>Categorical distribution plots:</p>
<ul>
<li>:func:<code>boxplot</code> (with <code>kind="box"</code>)</li>
<li>:func:<code>violinplot</code> (with <code>kind="violin"</code>)</li>
<li>:func:<code>boxenplot</code> (with <code>kind="boxen"</code>)</li>
</ul>
</li>
<li><p>Categorical estimate plots:</p>
<ul>
<li>:func:<code>pointplot</code> (with <code>kind="point"</code>)</li>
<li>:func:<code>barplot</code> (with <code>kind="bar"</code>)</li>
<li>:func:<code>countplot</code> (with <code>kind="count"</code>)</li>
</ul>
</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"strip"</span><span class="p">):</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span><span class="s2">"day"</span><span class="p">,</span> <span class="s2">"total_bill"</span><span class="p">,</span> <span class="s2">"sex"</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tips</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
<span class="n">g</span><span class="o">.</span><span class="n">set_axis_labels</span><span class="p">(</span><span class="s2">"日期"</span><span class="p">,</span> <span class="s2">"小费金额"</span><span class="p">)</span>
<span class="n">g</span><span class="o">.</span><span class="n">_legend</span><span class="o">.</span><span class="n">set_bbox_to_anchor</span><span class="p">((</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"swarm"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"box"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"violin"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"bar"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_factor</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">"point"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="联合分布图">联合分布图<a class="anchor-link" href="#联合分布图"> </a></h2><p><code>sns.jointplot</code>画出不同数据集的联合分布和各数据本身的分布:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="s2">"total_bill"</span><span class="p">,</span> <span class="s2">"tip"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tips</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"hex"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>联合分布图也可以自动进行KDE和线性拟合:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="s2">"total_bill"</span><span class="p">,</span> <span class="s2">"tip"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tips</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">"reg"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="pandas-profiling"><a href="https://github.com/pandas-profiling/pandas-profiling">pandas-profiling</a><a class="anchor-link" href="#pandas-profiling"> </a></h2><p>Pandas + Matplotlib + Seabron实现的极速EDA工具,<a href="https://blog.csdn.net/wangyaninglm/article/details/101025067">中文显示设置方法</a></p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/3.data-viz/pandas-profiling.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
<ol>
<li>类型推断(Type inference):检测Dataframe字段类型</li>
<li>基础统计(Essentials):数据类型、惟一值、缺失值</li>
<li>分位数统计(Quantile statistics):最小值,Q1,中位数,Q3,最大值,四分位距(interquartile range, IQR)</li>
<li>描述性统计(Descriptive statistics):均值、众数、标准差、和、MAD(Median absolute deviation, 中位数绝对偏差)、CV(coefficient of variation,变异系数)、峰度、偏度</li>
<li>高频次样本(Most frequent values)</li>
<li>频次直方图(Histogram)</li>
<li>相关矩阵(Correlation Matrix):三大相关系数——皮尔逊(Pearson)、斯皮尔曼(Spearman)和肯德尔(Kendall),ϕ相关系数(Phi coefficient, Matthews coefficient=MCC)</li>
<li>缺失值处理(Missing values):矩阵、计数、热力图(heatmap)和树状图(dendrogram)</li>
<li>文本分析(Text analysis):文本数据的类别(大小写、空格)、字体(拉丁、西里尔)和字符(ASCII)</li>
<li>文件和图像分析(File and Image analysis):提取文件大小、创建日期和尺寸,并扫描截断的图像或包含EXIF信息的图像</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pandas_profiling</span> <span class="kn">import</span> <span class="n">ProfileReport</span>
<span class="n">profile</span> <span class="o">=</span> <span class="n">ProfileReport</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"EDA报告"</span><span class="p">,</span> <span class="n">explorative</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">profile</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="s2">"iris_profile.html"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>open iris_profile.html
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">profile</span><span class="o">.</span><span class="n">to_widgets</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>字段较多时,相关性分析会比较慢,可以通过<code>minimal=True</code>设置参数</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">profile</span> <span class="o">=</span> <span class="n">ProfileReport</span><span class="p">(</span><span class="n">iris</span><span class="p">,</span> <span class="n">minimal</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="声明式图形库">声明式图形库<a class="anchor-link" href="#声明式图形库"> </a></h1><p>Matplotlib的缺点:</p>
<ol>
<li>样式不够丰富</li>
<li>web/交互比较差</li>
<li>大数据渲染速度慢</li>
<li>API是<strong>命令式(Imperative)</strong>,语法比较啰嗦</li>
<li>数据可视化最大的挑战之一是图形的可移植性(portability)和可重复性(reproducibility ),创建一个图形并导出到PNG或PDF后,数据就很难再提取出来被再次利用。</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="altair"><a href="https://altair-viz.github.io/">altair</a><a class="anchor-link" href="#altair"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>2015年,美国华盛顿大学天文学家、UW eScience Institute主任Jake Vanderplas(@jakevpd,目前在谷歌开发基于Numpy的自动微分器<a href="https://github.com/google/jax">jax</a>)在可视化语义(visualization grammar)库<a href="https://github.com/vega">Vega</a>基础上开发了altair,一种Python的声明式统计可视化库(Declarative statistical visualization library),将图形打包成描述数据和可视编码之间的关系的<strong>声明式(Declarative)</strong>JSON文件,从而实现将图形与JSON互转,增量更新无需重新绘制</p>
<p><img src="/images/copied_from_nb/3.data-viz/altair-logo-light.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<table>
<thead><tr>
<th style="text-align:center">命令式(Imperative)</th>
<th style="text-align:center">声明式(Declarative)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">关注怎样做(How)的过程</td>
<td style="text-align:center">关注做什么(What)的结果</td>
</tr>
<tr>
<td style="text-align:center">必须手工配置绘图步骤</td>
<td style="text-align:center">自动完成绘图细节</td>
</tr>
<tr>
<td style="text-align:center">配置与执行是耦合的</td>
<td style="text-align:center">配置与执行分离的</td>
</tr>
</tbody>
</table>
<blockquote><p>“声明式可视化让你专注数据与联结,毋需深陷技术细节</p>
<p>(Declarative visualization lets you think about data and relationships, rather than incidental details.)”</p>
<p>——Jake Vanderplas 2017</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">altair</span> <span class="k">as</span> <span class="nn">alt</span>
<span class="kn">from</span> <span class="nn">vega_datasets</span> <span class="kn">import</span> <span class="n">data</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">column</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">iris</span><span class="p">)</span><span class="o">.</span><span class="n">mark_circle</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="n">alt</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="s2">"column"</span><span class="p">),</span> <span class="nb">type</span><span class="o">=</span><span class="s2">"quantitative"</span><span class="p">),</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="n">alt</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="s2">"row"</span><span class="p">),</span> <span class="nb">type</span><span class="o">=</span><span class="s2">"quantitative"</span><span class="p">),</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"species:N"</span><span class="p">,</span>
<span class="n">tooltip</span><span class="o">=</span><span class="n">column</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">properties</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span>
<span class="n">row</span><span class="o">=</span><span class="n">column</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">column</span><span class="o">=</span><span class="n">column</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="p">)</span><span class="o">.</span><span class="n">interactive</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-d116ff01c1384151a4bade6da861ab6a"></div>
<script type="text/javascript">
(function(spec, embedOpt){
let outputDiv = document.currentScript.previousElementSibling;
if (outputDiv.id !== "altair-viz-d116ff01c1384151a4bade6da861ab6a") {
outputDiv = document.getElementById("altair-viz-d116ff01c1384151a4bade6da861ab6a");
}
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "repeat": {"column": ["sepal_length", "sepal_width", "petal_length", "petal_width"], "row": ["sepal_length", "sepal_width", "petal_length", "petal_width"]}, "spec": {"data": {"name": "data-a4bd480ea976f54878a155ce12ba18ce"}, "mark": "circle", "encoding": {"color": {"type": "nominal", "field": "species"}, "tooltip": [{"type": "quantitative", "field": "sepal_length"}, {"type": "quantitative", "field": "sepal_width"}, {"type": "quantitative", "field": "petal_length"}, {"type": "quantitative", "field": "petal_width"}, {"type": "nominal", "field": "species"}], "x": {"type": "quantitative", "field": {"repeat": "column"}}, "y": {"type": "quantitative", "field": {"repeat": "row"}}}, "height": 200, "selection": {"selector001": {"type": "interval", "bind": "scales", "encodings": ["x", "y"]}}, "width": 200}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", "datasets": {"data-a4bd480ea976f54878a155ce12ba18ce": [{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.9, "sepal_width": 3.0, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.7, "sepal_width": 3.2, "petal_length": 1.3, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.6, "sepal_width": 3.1, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.6, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.4, "sepal_width": 3.9, "petal_length": 1.7, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 4.6, "sepal_width": 3.4, "petal_length": 1.4, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.4, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.4, "sepal_width": 2.9, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.9, "sepal_width": 3.1, "petal_length": 1.5, "petal_width": 0.1, "species": "setosa"}, {"sepal_length": 5.4, "sepal_width": 3.7, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.8, "sepal_width": 3.4, "petal_length": 1.6, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.8, "sepal_width": 3.0, "petal_length": 1.4, "petal_width": 0.1, "species": "setosa"}, {"sepal_length": 4.3, "sepal_width": 3.0, "petal_length": 1.1, "petal_width": 0.1, "species": "setosa"}, {"sepal_length": 5.8, "sepal_width": 4.0, "petal_length": 1.2, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.7, "sepal_width": 4.4, "petal_length": 1.5, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 5.4, "sepal_width": 3.9, "petal_length": 1.3, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 5.7, "sepal_width": 3.8, "petal_length": 1.7, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.8, "petal_length": 1.5, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 5.4, "sepal_width": 3.4, "petal_length": 1.7, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.7, "petal_length": 1.5, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 4.6, "sepal_width": 3.6, "petal_length": 1.0, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.3, "petal_length": 1.7, "petal_width": 0.5, "species": "setosa"}, {"sepal_length": 4.8, "sepal_width": 3.4, "petal_length": 1.9, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.0, "petal_length": 1.6, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.4, "petal_length": 1.6, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 5.2, "sepal_width": 3.5, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.2, "sepal_width": 3.4, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.7, "sepal_width": 3.2, "petal_length": 1.6, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.8, "sepal_width": 3.1, "petal_length": 1.6, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.4, "sepal_width": 3.4, "petal_length": 1.5, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 5.2, "sepal_width": 4.1, "petal_length": 1.5, "petal_width": 0.1, "species": "setosa"}, {"sepal_length": 5.5, "sepal_width": 4.2, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.9, "sepal_width": 3.1, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.2, "petal_length": 1.2, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.5, "sepal_width": 3.5, "petal_length": 1.3, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.9, "sepal_width": 3.6, "petal_length": 1.4, "petal_width": 0.1, "species": "setosa"}, {"sepal_length": 4.4, "sepal_width": 3.0, "petal_length": 1.3, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.4, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.5, "petal_length": 1.3, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 4.5, "sepal_width": 2.3, "petal_length": 1.3, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 4.4, "sepal_width": 3.2, "petal_length": 1.3, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.5, "petal_length": 1.6, "petal_width": 0.6, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.8, "petal_length": 1.9, "petal_width": 0.4, "species": "setosa"}, {"sepal_length": 4.8, "sepal_width": 3.0, "petal_length": 1.4, "petal_width": 0.3, "species": "setosa"}, {"sepal_length": 5.1, "sepal_width": 3.8, "petal_length": 1.6, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 4.6, "sepal_width": 3.2, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.3, "sepal_width": 3.7, "petal_length": 1.5, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 5.0, "sepal_width": 3.3, "petal_length": 1.4, "petal_width": 0.2, "species": "setosa"}, {"sepal_length": 7.0, "sepal_width": 3.2, "petal_length": 4.7, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 6.4, "sepal_width": 3.2, "petal_length": 4.5, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 6.9, "sepal_width": 3.1, "petal_length": 4.9, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 5.5, "sepal_width": 2.3, "petal_length": 4.0, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.5, "sepal_width": 2.8, "petal_length": 4.6, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 5.7, "sepal_width": 2.8, "petal_length": 4.5, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.3, "sepal_width": 3.3, "petal_length": 4.7, "petal_width": 1.6, "species": "versicolor"}, {"sepal_length": 4.9, "sepal_width": 2.4, "petal_length": 3.3, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 6.6, "sepal_width": 2.9, "petal_length": 4.6, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.2, "sepal_width": 2.7, "petal_length": 3.9, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 5.0, "sepal_width": 2.0, "petal_length": 3.5, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 5.9, "sepal_width": 3.0, "petal_length": 4.2, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 6.0, "sepal_width": 2.2, "petal_length": 4.0, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 6.1, "sepal_width": 2.9, "petal_length": 4.7, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 5.6, "sepal_width": 2.9, "petal_length": 3.6, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.7, "sepal_width": 3.1, "petal_length": 4.4, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 5.6, "sepal_width": 3.0, "petal_length": 4.5, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 5.8, "sepal_width": 2.7, "petal_length": 4.1, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 6.2, "sepal_width": 2.2, "petal_length": 4.5, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 5.6, "sepal_width": 2.5, "petal_length": 3.9, "petal_width": 1.1, "species": "versicolor"}, {"sepal_length": 5.9, "sepal_width": 3.2, "petal_length": 4.8, "petal_width": 1.8, "species": "versicolor"}, {"sepal_length": 6.1, "sepal_width": 2.8, "petal_length": 4.0, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.3, "sepal_width": 2.5, "petal_length": 4.9, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 6.1, "sepal_width": 2.8, "petal_length": 4.7, "petal_width": 1.2, "species": "versicolor"}, {"sepal_length": 6.4, "sepal_width": 2.9, "petal_length": 4.3, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.6, "sepal_width": 3.0, "petal_length": 4.4, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 6.8, "sepal_width": 2.8, "petal_length": 4.8, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 6.7, "sepal_width": 3.0, "petal_length": 5.0, "petal_width": 1.7, "species": "versicolor"}, {"sepal_length": 6.0, "sepal_width": 2.9, "petal_length": 4.5, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 5.7, "sepal_width": 2.6, "petal_length": 3.5, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 5.5, "sepal_width": 2.4, "petal_length": 3.8, "petal_width": 1.1, "species": "versicolor"}, {"sepal_length": 5.5, "sepal_width": 2.4, "petal_length": 3.7, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 5.8, "sepal_width": 2.7, "petal_length": 3.9, "petal_width": 1.2, "species": "versicolor"}, {"sepal_length": 6.0, "sepal_width": 2.7, "petal_length": 5.1, "petal_width": 1.6, "species": "versicolor"}, {"sepal_length": 5.4, "sepal_width": 3.0, "petal_length": 4.5, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 6.0, "sepal_width": 3.4, "petal_length": 4.5, "petal_width": 1.6, "species": "versicolor"}, {"sepal_length": 6.7, "sepal_width": 3.1, "petal_length": 4.7, "petal_width": 1.5, "species": "versicolor"}, {"sepal_length": 6.3, "sepal_width": 2.3, "petal_length": 4.4, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.6, "sepal_width": 3.0, "petal_length": 4.1, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.5, "sepal_width": 2.5, "petal_length": 4.0, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.5, "sepal_width": 2.6, "petal_length": 4.4, "petal_width": 1.2, "species": "versicolor"}, {"sepal_length": 6.1, "sepal_width": 3.0, "petal_length": 4.6, "petal_width": 1.4, "species": "versicolor"}, {"sepal_length": 5.8, "sepal_width": 2.6, "petal_length": 4.0, "petal_width": 1.2, "species": "versicolor"}, {"sepal_length": 5.0, "sepal_width": 2.3, "petal_length": 3.3, "petal_width": 1.0, "species": "versicolor"}, {"sepal_length": 5.6, "sepal_width": 2.7, "petal_length": 4.2, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.7, "sepal_width": 3.0, "petal_length": 4.2, "petal_width": 1.2, "species": "versicolor"}, {"sepal_length": 5.7, "sepal_width": 2.9, "petal_length": 4.2, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.2, "sepal_width": 2.9, "petal_length": 4.3, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 5.1, "sepal_width": 2.5, "petal_length": 3.0, "petal_width": 1.1, "species": "versicolor"}, {"sepal_length": 5.7, "sepal_width": 2.8, "petal_length": 4.1, "petal_width": 1.3, "species": "versicolor"}, {"sepal_length": 6.3, "sepal_width": 3.3, "petal_length": 6.0, "petal_width": 2.5, "species": "virginica"}, {"sepal_length": 5.8, "sepal_width": 2.7, "petal_length": 5.1, "petal_width": 1.9, "species": "virginica"}, {"sepal_length": 7.1, "sepal_width": 3.0, "petal_length": 5.9, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 6.3, "sepal_width": 2.9, "petal_length": 5.6, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.5, "sepal_width": 3.0, "petal_length": 5.8, "petal_width": 2.2, "species": "virginica"}, {"sepal_length": 7.6, "sepal_width": 3.0, "petal_length": 6.6, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 4.9, "sepal_width": 2.5, "petal_length": 4.5, "petal_width": 1.7, "species": "virginica"}, {"sepal_length": 7.3, "sepal_width": 2.9, "petal_length": 6.3, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.7, "sepal_width": 2.5, "petal_length": 5.8, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 7.2, "sepal_width": 3.6, "petal_length": 6.1, "petal_width": 2.5, "species": "virginica"}, {"sepal_length": 6.5, "sepal_width": 3.2, "petal_length": 5.1, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 6.4, "sepal_width": 2.7, "petal_length": 5.3, "petal_width": 1.9, "species": "virginica"}, {"sepal_length": 6.8, "sepal_width": 3.0, "petal_length": 5.5, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 5.7, "sepal_width": 2.5, "petal_length": 5.0, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 5.8, "sepal_width": 2.8, "petal_length": 5.1, "petal_width": 2.4, "species": "virginica"}, {"sepal_length": 6.4, "sepal_width": 3.2, "petal_length": 5.3, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 6.5, "sepal_width": 3.0, "petal_length": 5.5, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 7.7, "sepal_width": 3.8, "petal_length": 6.7, "petal_width": 2.2, "species": "virginica"}, {"sepal_length": 7.7, "sepal_width": 2.6, "petal_length": 6.9, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 6.0, "sepal_width": 2.2, "petal_length": 5.0, "petal_width": 1.5, "species": "virginica"}, {"sepal_length": 6.9, "sepal_width": 3.2, "petal_length": 5.7, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 5.6, "sepal_width": 2.8, "petal_length": 4.9, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 7.7, "sepal_width": 2.8, "petal_length": 6.7, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 6.3, "sepal_width": 2.7, "petal_length": 4.9, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.7, "sepal_width": 3.3, "petal_length": 5.7, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 7.2, "sepal_width": 3.2, "petal_length": 6.0, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.2, "sepal_width": 2.8, "petal_length": 4.8, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.1, "sepal_width": 3.0, "petal_length": 4.9, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.4, "sepal_width": 2.8, "petal_length": 5.6, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 7.2, "sepal_width": 3.0, "petal_length": 5.8, "petal_width": 1.6, "species": "virginica"}, {"sepal_length": 7.4, "sepal_width": 2.8, "petal_length": 6.1, "petal_width": 1.9, "species": "virginica"}, {"sepal_length": 7.9, "sepal_width": 3.8, "petal_length": 6.4, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 6.4, "sepal_width": 2.8, "petal_length": 5.6, "petal_width": 2.2, "species": "virginica"}, {"sepal_length": 6.3, "sepal_width": 2.8, "petal_length": 5.1, "petal_width": 1.5, "species": "virginica"}, {"sepal_length": 6.1, "sepal_width": 2.6, "petal_length": 5.6, "petal_width": 1.4, "species": "virginica"}, {"sepal_length": 7.7, "sepal_width": 3.0, "petal_length": 6.1, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 6.3, "sepal_width": 3.4, "petal_length": 5.6, "petal_width": 2.4, "species": "virginica"}, {"sepal_length": 6.4, "sepal_width": 3.1, "petal_length": 5.5, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.0, "sepal_width": 3.0, "petal_length": 4.8, "petal_width": 1.8, "species": "virginica"}, {"sepal_length": 6.9, "sepal_width": 3.1, "petal_length": 5.4, "petal_width": 2.1, "species": "virginica"}, {"sepal_length": 6.7, "sepal_width": 3.1, "petal_length": 5.6, "petal_width": 2.4, "species": "virginica"}, {"sepal_length": 6.9, "sepal_width": 3.1, "petal_length": 5.1, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 5.8, "sepal_width": 2.7, "petal_length": 5.1, "petal_width": 1.9, "species": "virginica"}, {"sepal_length": 6.8, "sepal_width": 3.2, "petal_length": 5.9, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 6.7, "sepal_width": 3.3, "petal_length": 5.7, "petal_width": 2.5, "species": "virginica"}, {"sepal_length": 6.7, "sepal_width": 3.0, "petal_length": 5.2, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 6.3, "sepal_width": 2.5, "petal_length": 5.0, "petal_width": 1.9, "species": "virginica"}, {"sepal_length": 6.5, "sepal_width": 3.0, "petal_length": 5.2, "petal_width": 2.0, "species": "virginica"}, {"sepal_length": 6.2, "sepal_width": 3.4, "petal_length": 5.4, "petal_width": 2.3, "species": "virginica"}, {"sepal_length": 5.9, "sepal_width": 3.0, "petal_length": 5.1, "petal_width": 1.8, "species": "virginica"}]}}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">source</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">movies</span><span class="o">.</span><span class="n">url</span>
<span class="n">heatmap</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="o">.</span><span class="n">mark_rect</span><span class="p">()</span>
<span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="s2">"IMDB_Rating:Q"</span><span class="p">,</span> <span class="nb">bin</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="s2">"Rotten_Tomatoes_Rating:Q"</span><span class="p">,</span> <span class="nb">bin</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Color</span><span class="p">(</span><span class="s2">"count()"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Scale</span><span class="p">(</span><span class="n">scheme</span><span class="o">=</span><span class="s2">"greenblue"</span><span class="p">)),</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">points</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="o">.</span><span class="n">mark_circle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">,)</span>
<span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"IMDB_Rating:Q"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"Rotten_Tomatoes_Rating:Q"</span><span class="p">,)</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 支持&(垂直)、|(水平)、+(有序叠加)三种Infix notation(中缀表示法)实现图层排列</span>
<span class="n">heatmap</span> <span class="o">&</span> <span class="n">points</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-2d328dbcc8f74b6baa9d66fd4f262234"></div>
<script type="text/javascript">
(function(spec, embedOpt){
let outputDiv = document.currentScript.previousElementSibling;
if (outputDiv.id !== "altair-viz-2d328dbcc8f74b6baa9d66fd4f262234") {
outputDiv = document.getElementById("altair-viz-2d328dbcc8f74b6baa9d66fd4f262234");
}
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "vconcat": [{"mark": "rect", "encoding": {"color": {"type": "quantitative", "aggregate": "count", "scale": {"scheme": "greenblue"}}, "x": {"type": "quantitative", "bin": true, "field": "IMDB_Rating"}, "y": {"type": "quantitative", "bin": true, "field": "Rotten_Tomatoes_Rating"}}}, {"mark": {"type": "circle", "color": "black", "size": 5}, "encoding": {"x": {"type": "quantitative", "field": "IMDB_Rating"}, "y": {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}}}], "data": {"url": "https://vega.github.io/vega-datasets/data/movies.json"}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">heatmap</span> <span class="o">|</span> <span class="n">points</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-4b275b3f47014c4bae3d0f9bc9794ea4"></div>
<script type="text/javascript">
(function(spec, embedOpt){
let outputDiv = document.currentScript.previousElementSibling;
if (outputDiv.id !== "altair-viz-4b275b3f47014c4bae3d0f9bc9794ea4") {
outputDiv = document.getElementById("altair-viz-4b275b3f47014c4bae3d0f9bc9794ea4");
}
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "hconcat": [{"mark": "rect", "encoding": {"color": {"type": "quantitative", "aggregate": "count", "scale": {"scheme": "greenblue"}}, "x": {"type": "quantitative", "bin": true, "field": "IMDB_Rating"}, "y": {"type": "quantitative", "bin": true, "field": "Rotten_Tomatoes_Rating"}}}, {"mark": {"type": "circle", "color": "black", "size": 5}, "encoding": {"x": {"type": "quantitative", "field": "IMDB_Rating"}, "y": {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}}}], "data": {"url": "https://vega.github.io/vega-datasets/data/movies.json"}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">heatmap</span> <span class="o">+</span> <span class="n">points</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-0343b2bd4cc24565847fab4177d9c7c5"></div>
<script type="text/javascript">
(function(spec, embedOpt){
let outputDiv = document.currentScript.previousElementSibling;
if (outputDiv.id !== "altair-viz-0343b2bd4cc24565847fab4177d9c7c5") {
outputDiv = document.getElementById("altair-viz-0343b2bd4cc24565847fab4177d9c7c5");
}
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "layer": [{"mark": "rect", "encoding": {"color": {"type": "quantitative", "aggregate": "count", "scale": {"scheme": "greenblue"}}, "x": {"type": "quantitative", "bin": true, "field": "IMDB_Rating"}, "y": {"type": "quantitative", "bin": true, "field": "Rotten_Tomatoes_Rating"}}}, {"mark": {"type": "circle", "color": "black", "size": 5}, "encoding": {"x": {"type": "quantitative", "field": "IMDB_Rating"}, "y": {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}}}], "data": {"url": "https://vega.github.io/vega-datasets/data/movies.json"}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="pyecharts"><a href="https://github.com/pyecharts/pyecharts">pyecharts</a><a class="anchor-link" href="#pyecharts"> </a></h2><p><a href="https://echarts.apache.org/zh/index.html">ECharts</a>声明式Javascript可视化库,由百度前端2013年发布1.0版本,2018年进入Apache孵化器。pyecharts是Python对ECharts的简易封装,相比js语法并没有太多优化</p>
<blockquote><p>参考论文:<a href="https://www.sciencedirect.com/science/article/pii/S2468502X18300068">ECharts:A declarative framework for rapid construction of web-based visualization</a></p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pyecharts</span> <span class="kn">import</span> <span class="n">charts</span><span class="p">,</span> <span class="n">options</span>
<span class="n">bar</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">charts</span><span class="o">.</span><span class="n">Bar</span><span class="p">()</span>
<span class="o">.</span><span class="n">add_xaxis</span><span class="p">([</span><span class="s2">"衬衫"</span><span class="p">,</span> <span class="s2">"毛衣"</span><span class="p">,</span> <span class="s2">"领带"</span><span class="p">,</span> <span class="s2">"裤子"</span><span class="p">,</span> <span class="s2">"风衣"</span><span class="p">,</span> <span class="s2">"高跟鞋"</span><span class="p">,</span> <span class="s2">"袜子"</span><span class="p">])</span>
<span class="o">.</span><span class="n">add_yaxis</span><span class="p">(</span><span class="s2">"商家A"</span><span class="p">,</span> <span class="p">[</span><span class="mi">114</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="mi">125</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">105</span><span class="p">])</span>
<span class="o">.</span><span class="n">add_yaxis</span><span class="p">(</span><span class="s2">"商家B"</span><span class="p">,</span> <span class="p">[</span><span class="mi">57</span><span class="p">,</span> <span class="mi">134</span><span class="p">,</span> <span class="mi">137</span><span class="p">,</span> <span class="mi">129</span><span class="p">,</span> <span class="mi">145</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">49</span><span class="p">])</span>
<span class="o">.</span><span class="n">set_global_opts</span><span class="p">(</span><span class="n">title_opts</span><span class="o">=</span><span class="n">options</span><span class="o">.</span><span class="n">TitleOpts</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"某商场销售情况"</span><span class="p">))</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bar</span><span class="o">.</span><span class="n">render_notebook</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<script>
require.config({
paths: {
'echarts':'https://assets.pyecharts.org/assets/echarts.min'
}
});
</script>
<div id="f4baae48d1534ed18e0c628198e4c193" style="width:900px; height:500px;"></div>
<script>
require(['echarts'], function(echarts) {
var chart_f4baae48d1534ed18e0c628198e4c193 = echarts.init(
document.getElementById('f4baae48d1534ed18e0c628198e4c193'), 'white', {renderer: 'canvas'});
var option_f4baae48d1534ed18e0c628198e4c193 = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"color": [
"#c23531",
"#2f4554",
"#61a0a8",
"#d48265",
"#749f83",
"#ca8622",
"#bda29a",
"#6e7074",
"#546570",
"#c4ccd3",
"#f05b72",
"#ef5b9c",
"#f47920",
"#905a3d",
"#fab27b",
"#2a5caa",
"#444693",
"#726930",
"#b2d235",
"#6d8346",
"#ac6767",
"#1d953f",
"#6950a1",
"#918597"
],
"series": [
{
"type": "bar",
"name": "\u5546\u5bb6A",
"data": [
114,
55,
27,
101,
125,
27,
105
],
"barCategoryGap": "20%",
"label": {
"show": true,
"position": "top",
"margin": 8
}
},
{
"type": "bar",
"name": "\u5546\u5bb6B",
"data": [
57,
134,
137,
129,
145,
60,
49
],
"barCategoryGap": "20%",
"label": {
"show": true,
"position": "top",
"margin": 8
}
}
],
"legend": [
{
"data": [
"\u5546\u5bb6A",
"\u5546\u5bb6B"
],
"selected": {
"\u5546\u5bb6A": true,
"\u5546\u5bb6B": true
},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"textStyle": {
"fontSize": 14
},
"borderWidth": 0
},
"xAxis": [
{
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": false,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
},
"data": [
"\u886c\u886b",
"\u6bdb\u8863",
"\u9886\u5e26",
"\u88e4\u5b50",
"\u98ce\u8863",
"\u9ad8\u8ddf\u978b",
"\u889c\u5b50"
]
}
],
"yAxis": [
{
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": false,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
}
}
],
"title": [
{
"text": "\u67d0\u5546\u573a\u9500\u552e\u60c5\u51b5",
"padding": 5,
"itemGap": 10
}
]
};
chart_f4baae48d1534ed18e0c628198e4c193.setOption(option_f4baae48d1534ed18e0c628198e4c193);
});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bar</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'/Users/toddtao/Documents/reader/data_science/data_science2020/3.数据可视化/render.html'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">IFrame</span>
<span class="n">IFrame</span><span class="p">(</span><span class="n">src</span><span class="o">=</span><span class="s1">'3.data-viz/render.html'</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">700</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<iframe width="700" height="600" src="3.data-viz/render.html" frameborder="0" allowfullscreen=""></iframe>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print(bar.render_embed())</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="webapp">webapp<a class="anchor-link" href="#webapp"> </a></h1><p>将可视化图转换为webapp发布,解决方案有<a href="https://github.com/plotly/dash">dash</a>、<a href="https://github.com/voila-dashboards/voila">volia</a>、<a href="https://github.com/streamlit/streamlit">streamlit</a>、<a href="https://github.com/holoviz/panel">Panel</a>、<a href="https://github.com/bokeh/bokeh">Bokeh</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="plotly交互生态系统"><a href="https://github.com/plotly">plotly</a>交互生态系统<a class="anchor-link" href="#plotly交互生态系统"> </a></h2><p>加拿大plotly公司开发的可视化工具,有企业版授权,dash解决方案,支持Python、R、JS、Julia、Scala。plotly + pandas = <a href="https://github.com/santosjorge/cufflinks">cufflinks</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">20</span><span class="p">)))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">20</span><span class="p">)))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"plotly图形示例"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="ipywidgets交互控件">ipywidgets交互控件<a class="anchor-link" href="#ipywidgets交互控件"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span><span class="p">,</span> <span class="n">interact_manual</span>
<span class="kn">import</span> <span class="nn">cufflinks</span> <span class="k">as</span> <span class="nn">cf</span>
<span class="n">cf</span><span class="o">.</span><span class="n">go_offline</span><span class="p">(</span><span class="n">connected</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">cf</span><span class="o">.</span><span class="n">set_config_file</span><span class="p">(</span><span class="n">colorscale</span><span class="o">=</span><span class="s2">"plotly"</span><span class="p">,</span> <span class="n">world_readable</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<script type="text/javascript">
window.PlotlyConfig = {MathJaxConfig: 'local'};
if (window.MathJax) {MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}
if (typeof require !== 'undefined') {
require.undef("plotly");
requirejs.config({
paths: {
'plotly': ['https://cdn.plot.ly/plotly-latest.min']
}
});
require(['plotly'], function(Plotly) {
window._Plotly = Plotly;
});
}
</script>
</div>
</div>
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<script type="text/javascript">
window.PlotlyConfig = {MathJaxConfig: 'local'};
if (window.MathJax) {MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}
if (typeof require !== 'undefined') {
require.undef("plotly");
requirejs.config({
paths: {
'plotly': ['https://cdn.plot.ly/plotly-latest.min']
}
});
require(['plotly'], function(Plotly) {
window._Plotly = Plotly;
});
}
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@interact</span>
<span class="k">def</span> <span class="nf">show_articles_more_than</span><span class="p">(</span><span class="n">字段</span><span class="o">=</span><span class="n">df_covid</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">阈值</span><span class="o">=</span><span class="p">[</span><span class="mi">50_000</span><span class="p">,</span> <span class="mi">100_000</span><span class="p">,</span> <span class="mi">200_000</span><span class="p">]):</span>
<span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span><span class="sa">f</span><span class="s2">"<h2>过滤部件:显示</span><span class="si">{</span><span class="n">字段</span><span class="si">}</span><span class="s2"> 超过 </span><span class="si">{</span><span class="n">阈值</span><span class="si">}</span><span class="s2"> 的行数<h2>"</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_covid</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_covid</span><span class="p">[</span><span class="n">字段</span><span class="p">]</span> <span class="o">></span> <span class="n">阈值</span><span class="p">,</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">columns</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@interact</span>
<span class="k">def</span> <span class="nf">correlations</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">df_covid</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="s2">"number"</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">),</span>
<span class="n">y</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">df_covid</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="s2">"number"</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]),</span>
<span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"皮尔逊相关系数: </span><span class="si">{</span><span class="n">df_covid</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">df_covid</span><span class="p">[</span><span class="n">y</span><span class="p">])</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"描述性统计:</span><span class="se">\n</span><span class="si">{</span><span class="n">df_covid</span><span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">]]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span>
<span class="n">kind</span><span class="o">=</span><span class="s2">"scatter"</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"markers"</span><span class="p">,</span>
<span class="n">xTitle</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">title</span><span class="p">(),</span>
<span class="n">yTitle</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">title</span><span class="p">(),</span>
<span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">y</span><span class="o">.</span><span class="n">title</span><span class="p">()</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">title</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Voilà基于jupyter构建webapp"><a href="https://github.com/voila-dashboards/voila">Voilà</a>基于jupyter构建webapp<a class="anchor-link" href="#Voilà基于jupyter构建webapp"> </a></h2><p>将notebook直接转换成web页面,可以通过命令行<code>volia 3.数据可视化.ipynb --port 8880</code>运行notebook,也可以通过notebook插件运行</p>
<blockquote><p><a href="">papermill</a>可以将直接运行notebook文件,支持自定义参数</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="dash基于flask、reactjs构建webapp"><a href="https://github.com/plotly/dash">dash</a>基于flask、reactjs构建webapp<a class="anchor-link" href="#dash基于flask、reactjs构建webapp"> </a></h2><p>由于dash运行方式与flask相同,因此不能直接在notebook上渲染,可以通过plotly开发的<a href="https://github.com/plotly/jupyter-dash">jupyter-dash</a>在notebook上渲染</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">jupyter_dash</span> <span class="kn">import</span> <span class="n">JupyterDash</span>
<span class="kn">import</span> <span class="nn">dash</span>
<span class="kn">import</span> <span class="nn">dash_core_components</span> <span class="k">as</span> <span class="nn">dcc</span>
<span class="kn">import</span> <span class="nn">dash_html_components</span> <span class="k">as</span> <span class="nn">html</span>
<span class="kn">from</span> <span class="nn">dash.dependencies</span> <span class="kn">import</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Output</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"3.data-viz/gapminderDataFiveYear.csv"</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">shape</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(1704, 6)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>country</th>
<th>year</th>
<th>pop</th>
<th>continent</th>
<th>lifeExp</th>
<th>gdpPercap</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Afghanistan</td>
<td>1952</td>
<td>8425333.0</td>
<td>Asia</td>
<td>28.801</td>
<td>779.445314</td>
</tr>
<tr>
<th>1</th>
<td>Afghanistan</td>
<td>1957</td>
<td>9240934.0</td>
<td>Asia</td>
<td>30.332</td>
<td>820.853030</td>
</tr>
<tr>
<th>2</th>
<td>Afghanistan</td>
<td>1962</td>
<td>10267083.0</td>
<td>Asia</td>
<td>31.997</td>
<td>853.100710</td>
</tr>
<tr>
<th>3</th>
<td>Afghanistan</td>
<td>1967</td>
<td>11537966.0</td>
<td>Asia</td>
<td>34.020</td>
<td>836.197138</td>
</tr>
<tr>
<th>4</th>
<td>Afghanistan</td>
<td>1972</td>
<td>13079460.0</td>
<td>Asia</td>
<td>36.088</td>
<td>739.981106</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># app = dash.Dash(__name__)</span>
<span class="n">app</span> <span class="o">=</span> <span class="n">JupyterDash</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">app</span><span class="o">.</span><span class="n">layout</span> <span class="o">=</span> <span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="s2">"graph-with-slider"</span><span class="p">),</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Slider</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s2">"year-slider"</span><span class="p">,</span>
<span class="nb">min</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
<span class="nb">max</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
<span class="n">value</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
<span class="n">marks</span><span class="o">=</span><span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">year</span><span class="p">):</span> <span class="nb">str</span><span class="p">(</span><span class="n">year</span><span class="p">)</span> <span class="k">for</span> <span class="n">year</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()},</span>
<span class="n">step</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@app</span><span class="o">.</span><span class="n">callback</span><span class="p">(</span><span class="n">Output</span><span class="p">(</span><span class="s2">"graph-with-slider"</span><span class="p">,</span> <span class="s2">"figure"</span><span class="p">),</span> <span class="p">[</span><span class="n">Input</span><span class="p">(</span><span class="s2">"year-slider"</span><span class="p">,</span> <span class="s2">"value"</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">update_figure</span><span class="p">(</span><span class="n">selected_year</span><span class="p">):</span>
<span class="n">filtered_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">year</span> <span class="o">==</span> <span class="n">selected_year</span><span class="p">]</span>
<span class="n">traces</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">filtered_df</span><span class="o">.</span><span class="n">continent</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">df_by_continent</span> <span class="o">=</span> <span class="n">filtered_df</span><span class="p">[</span><span class="n">filtered_df</span><span class="p">[</span><span class="s2">"continent"</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">]</span>
<span class="n">traces</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="nb">dict</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">df_by_continent</span><span class="p">[</span><span class="s2">"gdpPercap"</span><span class="p">],</span>
<span class="n">y</span><span class="o">=</span><span class="n">df_by_continent</span><span class="p">[</span><span class="s2">"lifeExp"</span><span class="p">],</span>
<span class="n">text</span><span class="o">=</span><span class="n">df_by_continent</span><span class="p">[</span><span class="s2">"country"</span><span class="p">],</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"markers"</span><span class="p">,</span>
<span class="n">opacity</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="p">{</span><span class="s2">"size"</span><span class="p">:</span> <span class="mi">15</span><span class="p">,</span> <span class="s2">"line"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"width"</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span> <span class="s2">"color"</span><span class="p">:</span> <span class="s2">"white"</span><span class="p">}},</span>
<span class="n">name</span><span class="o">=</span><span class="n">i</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s2">"data"</span><span class="p">:</span> <span class="n">traces</span><span class="p">,</span>
<span class="s2">"layout"</span><span class="p">:</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">xaxis</span><span class="o">=</span><span class="p">{</span><span class="s2">"type"</span><span class="p">:</span> <span class="s2">"log"</span><span class="p">,</span> <span class="s2">"title"</span><span class="p">:</span> <span class="s2">"国家(地区)GDP"</span><span class="p">,</span> <span class="s2">"range"</span><span class="p">:</span> <span class="p">[</span><span class="mf">2.3</span><span class="p">,</span> <span class="mf">4.8</span><span class="p">]},</span>
<span class="n">yaxis</span><span class="o">=</span><span class="p">{</span><span class="s2">"title"</span><span class="p">:</span> <span class="s2">"人均预期寿命"</span><span class="p">,</span> <span class="s2">"range"</span><span class="p">:</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">90</span><span class="p">]},</span>
<span class="n">margin</span><span class="o">=</span><span class="p">{</span><span class="s2">"l"</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span>
<span class="n">legend</span><span class="o">=</span><span class="p">{</span><span class="s2">"x"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
<span class="n">hovermode</span><span class="o">=</span><span class="s2">"closest"</span><span class="p">,</span>
<span class="n">transition</span><span class="o">=</span><span class="p">{</span><span class="s2">"duration"</span><span class="p">:</span> <span class="mi">500</span><span class="p">},</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="c1"># if __name__ == "__main__":</span>
<span class="c1"># app.run_server(host="0.0.0.0", debug=True)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">app</span><span class="o">.</span><span class="n">run_server</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">"0.0.0.0"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Dash app running on http://0.0.0.0:8050/
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">app</span><span class="o">.</span><span class="n">run_server</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">"0.0.0.0"</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"inline"</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"3.data-viz/country.csv"</span><span class="p">)</span>
<span class="n">available_indicators</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Indicator Name"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Country Name</th>
<th>Indicator Name</th>
<th>Year</th>
<th>Value</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Arab World</td>
<td>Agriculture, value added (% of GDP)</td>
<td>1962</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>Arab World</td>
<td>CO2 emissions (metric tons per capita)</td>
<td>1962</td>
<td>0.760996</td>
</tr>
<tr>
<th>2</th>
<td>Arab World</td>
<td>Domestic credit provided by financial sector (...</td>
<td>1962</td>
<td>18.168690</td>
</tr>
<tr>
<th>3</th>
<td>Arab World</td>
<td>Electric power consumption (kWh per capita)</td>
<td>1962</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>Arab World</td>
<td>Energy use (kg of oil equivalent per capita)</td>
<td>1962</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">app</span> <span class="o">=</span> <span class="n">JupyterDash</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="c1"># server = app.server</span>
<span class="n">app</span><span class="o">.</span><span class="n">layout</span> <span class="o">=</span> <span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Dropdown</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-xaxis-column'</span><span class="p">,</span>
<span class="n">options</span><span class="o">=</span><span class="p">[{</span><span class="s1">'label'</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">:</span> <span class="n">i</span><span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">available_indicators</span><span class="p">],</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'Fertility rate, total (births per woman)'</span>
<span class="p">),</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">RadioItems</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-xaxis-type'</span><span class="p">,</span>
<span class="n">options</span><span class="o">=</span><span class="p">[{</span><span class="s1">'label'</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">:</span> <span class="n">i</span><span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Linear'</span><span class="p">,</span> <span class="s1">'Log'</span><span class="p">]],</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'Linear'</span><span class="p">,</span>
<span class="n">labelStyle</span><span class="o">=</span><span class="p">{</span><span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">}</span>
<span class="p">)</span>
<span class="p">],</span>
<span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'width'</span><span class="p">:</span> <span class="s1">'49%'</span><span class="p">,</span> <span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">}),</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Dropdown</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-yaxis-column'</span><span class="p">,</span>
<span class="n">options</span><span class="o">=</span><span class="p">[{</span><span class="s1">'label'</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">:</span> <span class="n">i</span><span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">available_indicators</span><span class="p">],</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'Life expectancy at birth, total (years)'</span>
<span class="p">),</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">RadioItems</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-yaxis-type'</span><span class="p">,</span>
<span class="n">options</span><span class="o">=</span><span class="p">[{</span><span class="s1">'label'</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">:</span> <span class="n">i</span><span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Linear'</span><span class="p">,</span> <span class="s1">'Log'</span><span class="p">]],</span>
<span class="n">value</span><span class="o">=</span><span class="s1">'Linear'</span><span class="p">,</span>
<span class="n">labelStyle</span><span class="o">=</span><span class="p">{</span><span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">}</span>
<span class="p">)</span>
<span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'width'</span><span class="p">:</span> <span class="s1">'49%'</span><span class="p">,</span> <span class="s1">'float'</span><span class="p">:</span> <span class="s1">'right'</span><span class="p">,</span> <span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">})</span>
<span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="p">{</span>
<span class="s1">'borderBottom'</span><span class="p">:</span> <span class="s1">'thin lightgrey solid'</span><span class="p">,</span>
<span class="s1">'backgroundColor'</span><span class="p">:</span> <span class="s1">'rgb(250, 250, 250)'</span><span class="p">,</span>
<span class="s1">'padding'</span><span class="p">:</span> <span class="s1">'10px 5px'</span>
<span class="p">}),</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-indicator-scatter'</span><span class="p">,</span>
<span class="n">hoverData</span><span class="o">=</span><span class="p">{</span><span class="s1">'points'</span><span class="p">:</span> <span class="p">[{</span><span class="s1">'customdata'</span><span class="p">:</span> <span class="s1">'Japan'</span><span class="p">}]}</span>
<span class="p">)</span>
<span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'width'</span><span class="p">:</span> <span class="s1">'49%'</span><span class="p">,</span> <span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">,</span> <span class="s1">'padding'</span><span class="p">:</span> <span class="s1">'0 20'</span><span class="p">}),</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">([</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="s1">'x-time-series'</span><span class="p">),</span>
<span class="n">dcc</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="s1">'y-time-series'</span><span class="p">),</span>
<span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'display'</span><span class="p">:</span> <span class="s1">'inline-block'</span><span class="p">,</span> <span class="s1">'width'</span><span class="p">:</span> <span class="s1">'49%'</span><span class="p">}),</span>
<span class="n">html</span><span class="o">.</span><span class="n">Div</span><span class="p">(</span><span class="n">dcc</span><span class="o">.</span><span class="n">Slider</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s1">'crossfilter-year--slider'</span><span class="p">,</span>
<span class="nb">min</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
<span class="nb">max</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
<span class="n">value</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
<span class="n">marks</span><span class="o">=</span><span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">year</span><span class="p">):</span> <span class="nb">str</span><span class="p">(</span><span class="n">year</span><span class="p">)</span> <span class="k">for</span> <span class="n">year</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()},</span>
<span class="n">step</span><span class="o">=</span><span class="kc">None</span>
<span class="p">),</span> <span class="n">style</span><span class="o">=</span><span class="p">{</span><span class="s1">'width'</span><span class="p">:</span> <span class="s1">'49%'</span><span class="p">,</span> <span class="s1">'padding'</span><span class="p">:</span> <span class="s1">'0px 20px 20px 20px'</span><span class="p">})</span>
<span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@app</span><span class="o">.</span><span class="n">callback</span><span class="p">(</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Output</span><span class="p">(</span><span class="s1">'crossfilter-indicator-scatter'</span><span class="p">,</span> <span class="s1">'figure'</span><span class="p">),</span>
<span class="p">[</span><span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-xaxis-column'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-yaxis-column'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-xaxis-type'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-yaxis-type'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-year--slider'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">update_graph</span><span class="p">(</span><span class="n">xaxis_column_name</span><span class="p">,</span> <span class="n">yaxis_column_name</span><span class="p">,</span>
<span class="n">xaxis_type</span><span class="p">,</span> <span class="n">yaxis_type</span><span class="p">,</span>
<span class="n">year_value</span><span class="p">):</span>
<span class="n">dff</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">==</span> <span class="n">year_value</span><span class="p">]</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s1">'data'</span><span class="p">:</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">xaxis_column_name</span><span class="p">][</span><span class="s1">'Value'</span><span class="p">],</span>
<span class="n">y</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">yaxis_column_name</span><span class="p">][</span><span class="s1">'Value'</span><span class="p">],</span>
<span class="n">text</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">yaxis_column_name</span><span class="p">][</span><span class="s1">'Country Name'</span><span class="p">],</span>
<span class="n">customdata</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">yaxis_column_name</span><span class="p">][</span><span class="s1">'Country Name'</span><span class="p">],</span>
<span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="p">{</span>
<span class="s1">'size'</span><span class="p">:</span> <span class="mi">25</span><span class="p">,</span>
<span class="s1">'opacity'</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>
<span class="s1">'color'</span><span class="p">:</span> <span class="s1">'orange'</span><span class="p">,</span>
<span class="s1">'line'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'width'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'color'</span><span class="p">:</span> <span class="s1">'purple'</span><span class="p">}</span>
<span class="p">}</span>
<span class="p">)],</span>
<span class="s1">'layout'</span><span class="p">:</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">xaxis</span><span class="o">=</span><span class="p">{</span>
<span class="s1">'title'</span><span class="p">:</span> <span class="n">xaxis_column_name</span><span class="p">,</span>
<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'linear'</span> <span class="k">if</span> <span class="n">xaxis_type</span> <span class="o">==</span> <span class="s1">'Linear'</span> <span class="k">else</span> <span class="s1">'log'</span>
<span class="p">},</span>
<span class="n">yaxis</span><span class="o">=</span><span class="p">{</span>
<span class="s1">'title'</span><span class="p">:</span> <span class="n">yaxis_column_name</span><span class="p">,</span>
<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'linear'</span> <span class="k">if</span> <span class="n">yaxis_type</span> <span class="o">==</span> <span class="s1">'Linear'</span> <span class="k">else</span> <span class="s1">'log'</span>
<span class="p">},</span>
<span class="n">margin</span><span class="o">=</span><span class="p">{</span><span class="s1">'l'</span><span class="p">:</span> <span class="mi">40</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span> <span class="s1">'t'</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span>
<span class="n">height</span><span class="o">=</span><span class="mi">450</span><span class="p">,</span>
<span class="n">hovermode</span><span class="o">=</span><span class="s1">'closest'</span>
<span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_time_series</span><span class="p">(</span><span class="n">dff</span><span class="p">,</span> <span class="n">axis_type</span><span class="p">,</span> <span class="n">title</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s1">'data'</span><span class="p">:</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">],</span>
<span class="n">y</span><span class="o">=</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Value'</span><span class="p">],</span>
<span class="n">mode</span><span class="o">=</span><span class="s1">'lines+markers'</span>
<span class="p">)],</span>
<span class="s1">'layout'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'height'</span><span class="p">:</span> <span class="mi">225</span><span class="p">,</span>
<span class="s1">'margin'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'l'</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s1">'t'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span>
<span class="s1">'annotations'</span><span class="p">:</span> <span class="p">[{</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">:</span> <span class="mf">0.85</span><span class="p">,</span> <span class="s1">'xanchor'</span><span class="p">:</span> <span class="s1">'left'</span><span class="p">,</span> <span class="s1">'yanchor'</span><span class="p">:</span> <span class="s1">'bottom'</span><span class="p">,</span>
<span class="s1">'xref'</span><span class="p">:</span> <span class="s1">'paper'</span><span class="p">,</span> <span class="s1">'yref'</span><span class="p">:</span> <span class="s1">'paper'</span><span class="p">,</span> <span class="s1">'showarrow'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">'align'</span><span class="p">:</span> <span class="s1">'left'</span><span class="p">,</span> <span class="s1">'bgcolor'</span><span class="p">:</span> <span class="s1">'rgba(255, 255, 255, 0.5)'</span><span class="p">,</span>
<span class="s1">'text'</span><span class="p">:</span> <span class="n">title</span>
<span class="p">}],</span>
<span class="s1">'yaxis'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="s1">'linear'</span> <span class="k">if</span> <span class="n">axis_type</span> <span class="o">==</span> <span class="s1">'Linear'</span> <span class="k">else</span> <span class="s1">'log'</span><span class="p">},</span>
<span class="s1">'xaxis'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'showgrid'</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@app</span><span class="o">.</span><span class="n">callback</span><span class="p">(</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Output</span><span class="p">(</span><span class="s1">'x-time-series'</span><span class="p">,</span> <span class="s1">'figure'</span><span class="p">),</span>
<span class="p">[</span><span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-indicator-scatter'</span><span class="p">,</span> <span class="s1">'hoverData'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-xaxis-column'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-xaxis-type'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">update_y_timeseries</span><span class="p">(</span><span class="n">hoverData</span><span class="p">,</span> <span class="n">xaxis_column_name</span><span class="p">,</span> <span class="n">axis_type</span><span class="p">):</span>
<span class="n">country_name</span> <span class="o">=</span> <span class="n">hoverData</span><span class="p">[</span><span class="s1">'points'</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">'customdata'</span><span class="p">]</span>
<span class="n">dff</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Country Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">country_name</span><span class="p">]</span>
<span class="n">dff</span> <span class="o">=</span> <span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">xaxis_column_name</span><span class="p">]</span>
<span class="n">title</span> <span class="o">=</span> <span class="s1">'<b></span><span class="si">{}</span><span class="s1"></b><br></span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">country_name</span><span class="p">,</span> <span class="n">xaxis_column_name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">create_time_series</span><span class="p">(</span><span class="n">dff</span><span class="p">,</span> <span class="n">axis_type</span><span class="p">,</span> <span class="n">title</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@app</span><span class="o">.</span><span class="n">callback</span><span class="p">(</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Output</span><span class="p">(</span><span class="s1">'y-time-series'</span><span class="p">,</span> <span class="s1">'figure'</span><span class="p">),</span>
<span class="p">[</span><span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-indicator-scatter'</span><span class="p">,</span> <span class="s1">'hoverData'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-yaxis-column'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">),</span>
<span class="n">dash</span><span class="o">.</span><span class="n">dependencies</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="s1">'crossfilter-yaxis-type'</span><span class="p">,</span> <span class="s1">'value'</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">update_x_timeseries</span><span class="p">(</span><span class="n">hoverData</span><span class="p">,</span> <span class="n">yaxis_column_name</span><span class="p">,</span> <span class="n">axis_type</span><span class="p">):</span>
<span class="n">dff</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Country Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">hoverData</span><span class="p">[</span><span class="s1">'points'</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">'customdata'</span><span class="p">]]</span>
<span class="n">dff</span> <span class="o">=</span> <span class="n">dff</span><span class="p">[</span><span class="n">dff</span><span class="p">[</span><span class="s1">'Indicator Name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">yaxis_column_name</span><span class="p">]</span>
<span class="k">return</span> <span class="n">create_time_series</span><span class="p">(</span><span class="n">dff</span><span class="p">,</span> <span class="n">axis_type</span><span class="p">,</span> <span class="n">yaxis_column_name</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">app</span><span class="o">.</span><span class="n">run_server</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">"0.0.0.0"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Dash app running on http://0.0.0.0:8050/
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">app</span><span class="o">.</span><span class="n">run_server</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">"0.0.0.0"</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"inline"</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1400</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">700</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="网络图">网络图<a class="anchor-link" href="#网络图"> </a></h1><ol>
<li>Networkx:复杂网络绘制与图算法工具</li>
<li>daft:matplotlib基础上构建的概率图模型</li>
</ol>
<h2 id="Networkx网络图"><a href="https://github.com/networkx/networkx">Networkx</a>网络图<a class="anchor-link" href="#Networkx网络图"> </a></h2><p>复杂网络绘制与图算法工具,可以与<a href="https://www.graphviz.org/">graphviz</a>结合使用,类似工具推荐<a href="https://gephi.org/">Gephi</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"B"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"B"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"C"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">spring_layout</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edge_labels</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">edge_labels</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">get_edge_attributes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">"weight"</span><span class="p">))</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nx</span><span class="o">.</span><span class="n">shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s2">"weight"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['A', 'B', 'D']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pydot</span>
<span class="kn">from</span> <span class="nn">networkx.drawing.nx_pydot</span> <span class="kn">import</span> <span class="n">graphviz_layout</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">balanced_tree</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pos</span> <span class="o">=</span> <span class="n">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pos</span> <span class="o">=</span> <span class="n">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"twopi"</span><span class="p">)</span>
<span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"equal"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>scikit-learn与graphviz结合,可以让<a href="https://scikit-learn.org/stable/modules/tree.html">决策树实现可视化</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>gini不纯度(gini impurity)是CART (classification and regression tree) 决策树进行分裂的衡量指标之一,表示按照当前分裂规则随机抽取样本是错误分类的频率。</p>
<p>鸢尾花种类是$J=3$,那么第$i$种花在数据集中的占比(概率、频率)用$p_i$表示,则计算公式为:</p>
<p>
$${I} _{G}(p)=\sum _{i=1}^{3}p_{i}\sum _{k\neq i}p_{k}=\sum _{i=1}^{3}p_{i}(1-p_{i})=\sum _{i=1}^{3}(p_{i}-{p_{i}}^{2})=\sum _{i=1}^{3}p_{i}-\sum _{i=1}^{3}{p_{i}}^{2}=1-\sum _{i=1}^{3}{p_{i}}^{2}$$
</p>
<p>如果gini不纯度为0,则表示每个叶子节点的所有鸢尾花都有一个明确的分类</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">"classic"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span>
<span class="n">clf</span><span class="p">,</span> <span class="n">feature_names</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">target_names</span><span class="p">,</span> <span class="n">filled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="daft贝叶斯网络"><a href="https://docs.daft-pgm.org/en/latest/">daft</a>贝叶斯网络<a class="anchor-link" href="#daft贝叶斯网络"> </a></h2><p>Daft是在matplotlib基础上构建的概率图模型(probabilistic graphical models),贝叶斯网络之父朱迪亚·珀尔(Judea Pearl,2011年图灵奖得主)2018年出版了《The book of why(为什么)》介绍贝叶斯网络的因果推断。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pyreverse -o png -p daft /Users/toddtao/opt/anaconda3/lib/python3.7/site-packages/daft.py
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>parsing /Users/toddtao/opt/anaconda3/lib/python3.7/site-packages/daft.py...
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/3.data-viz/classes_daft.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">daft</span>
<span class="n">p_color</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"ec"</span><span class="p">:</span> <span class="s2">"#46a546"</span><span class="p">}</span>
<span class="n">s_color</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"ec"</span><span class="p">:</span> <span class="s2">"#f89406"</span><span class="p">}</span>
<span class="n">pgm</span> <span class="o">=</span> <span class="n">daft</span><span class="o">.</span><span class="n">PGM</span><span class="p">([</span><span class="mf">5.6</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">],</span> <span class="n">origin</span><span class="o">=</span><span class="p">[</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_plate</span><span class="p">([</span><span class="mf">1.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">],</span> <span class="sa">r</span><span class="s2">"$D$"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_plate</span><span class="p">([</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.95</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="sa">r</span><span class="s2">"$N_d$"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_plate</span><span class="p">([</span><span class="mf">4.6</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="sa">r</span><span class="s2">"$K$"</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">"bottom right"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$\alpha$"</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">fixed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"theta"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$\theta_d$"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">plot_params</span><span class="o">=</span><span class="n">p_color</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"z"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$z_{d,n}$"</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"w"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$w_{d,n}$"</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"beta"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$\beta_</span><span class="si">{k}</span><span class="s2">$"</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">plot_params</span><span class="o">=</span><span class="n">s_color</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"eta"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"$\eta$"</span><span class="p">,</span> <span class="mf">6.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">fixed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="s2">"theta"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"theta"</span><span class="p">,</span> <span class="s2">"z"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"z"</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"eta"</span><span class="p">,</span> <span class="s2">"beta"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">"beta"</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
<span class="n">pgm</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"lda.png"</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/3.data-viz/lda.png" alt="" /></p>
</div>
</div>
</div>
</div>Python数据科学分享——3.数据可视化(1)2020-05-22T00:00:00-05:002020-05-22T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/05/22/data-viz-1<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-05-22-data-viz-1.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/3.data-viz/markmap3.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/3.data-viz/1pic.jpg" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="目标与原则">目标与原则<a class="anchor-link" href="#目标与原则"> </a></h1><p>以最小的复杂度展示足够多的信息</p>
<ol>
<li>目标:如果不能为用户提供有用的信息,那么就没啥用;如果信息展现形式太复杂,那么就会被噪声干扰</li>
<li>原则:<ol>
<li>对比(Contrast):让页面引人注目,避免页面上的元素太过相似。如果元素(字体、颜色、大小、线宽、形状、空间等)不相同,那就干脆让它们截然不同。</li>
<li>重复(Repetition):让设计中的视觉要素在整个作品中重复出现。既能增加条理性,还可以加强统一性。</li>
<li>对齐(Alignment):任何东西都不能在页面上随意安放。每个元素都应当与页面上的另一个元素有某种视觉联系,建立清晰、精巧而且清爽的外观。</li>
<li>亲密性(Proximity):彼此相关的项应当靠近,归组在一起。如果多个项相互之间存在很近的亲密性,它们就会成为一个视觉单元,而不是多个孤立的元素。这有助于组织信息,减少混乱,为读者提供清晰的结构。</li>
</ol>
</li>
</ol>
<blockquote><p>美国教育家、设计师Robin Williams《The Non-Designer's Design Book(写给大家看的设计书)》</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>随着HTML5、SVG/Canvas普及,尤其是Mike Bostock于2010开源D3.js,python数据可视化受到冲击,Facebook于2013发布react.js后,Python数据可视化开始向web组件化发展,重点方向是机器学习与Web交互</p>
<table>
<thead><tr>
<th style="text-align:center">首发年份</th>
<th style="text-align:center">名称</th>
<th style="text-align:center">简介</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2003</td>
<td style="text-align:center">matplotlib</td>
<td style="text-align:center">基础绘图工具</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">networkx</td>
<td style="text-align:center">复杂网络与图算法工具</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">seaborn</td>
<td style="text-align:center">快速统计图</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">bokeh</td>
<td style="text-align:center">交互式</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">plotly</td>
<td style="text-align:center">交互式</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">altair</td>
<td style="text-align:center">声明式语义</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">dash</td>
<td style="text-align:center">基于plotly的web app</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">tensorboard</td>
<td style="text-align:center">tensorflow and keras</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">ipyvolume</td>
<td style="text-align:center">3D交互</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">Vaex</td>
<td style="text-align:center">高性能渲染</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">streamlit</td>
<td style="text-align:center">机器学习web app</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">volia</td>
<td style="text-align:center">notebook web app</td>
</tr>
</tbody>
</table>
<blockquote><p><a href="https://pyviz.org/">pyviz</a>网站整理了Python数据可视化工具</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/3.data-viz/pyviz.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="matplotlib基础图库"><a href="https://matplotlib.org/">matplotlib</a>基础图库<a class="anchor-link" href="#matplotlib基础图库"> </a></h1><p>Matplotlib的设计哲学是让Python程序员完全控制可视化应用。Matplotlib中文字体显示问题,请参考<a href="https://blog.csdn.net/wangyaninglm/article/details/84901376#seaborn_381">中文设置方法</a></p>
<ol>
<li>模仿MatLab,上手简单,理工科同学上手成本低</li>
<li>许多渲染接口</li>
<li>功能齐全、文档完整</li>
<li>测试容易、源代码质量高</li>
</ol>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/3.data-viz/matplotlib-logo.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">from</span> <span class="nn">matplotlib.font_manager</span> <span class="kn">import</span> <span class="n">_rebuild</span>
<span class="n">_rebuild</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"whitegrid"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
<span class="kn">import</span> <span class="nn">pandas_alive</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_covid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s2">"3.data-viz/timeseries.json"</span><span class="p">)</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">df_covid</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">_</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]))</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"日期"</span>
<span class="n">df_covid</span> <span class="o">=</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">_</span><span class="p">[</span><span class="s2">"confirmed"</span><span class="p">]))</span>
<span class="n">df_covid</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">top20</span> <span class="o">=</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
<span class="n">df_covid</span> <span class="o">=</span> <span class="n">df_covid</span><span class="p">[</span><span class="n">top20</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="绘图环境">绘图环境<a class="anchor-link" href="#绘图环境"> </a></h2><ol>
<li>在Jupyter(IPython) Notebook中画图:<ul>
<li><code>%matplotlib notebook</code>会在Notebook中启动<strong>交互式</strong>图形</li>
<li><code>%matplotlib inline</code>会在Notebook中启动<strong>静态</strong>图形</li>
</ul>
</li>
<li>在.py文件中画图:执行使用matplotlib的脚本后,会看到一个新窗口,里面会显示图形</li>
<li>在IPython shell中画图(在图形界面系统中启动):在IPython shell中启动<code>ipython</code>后使用<code>%matplotlib</code>魔法命令,每个plt命令都会自动打开一个图形窗口</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> notebook
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'测试'</span><span class="p">)</span>
<span class="c1"># plt.show()`会启动一个事件循环(event loop)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div id="42ac6c75-4983-40d4-aa06-6419dc8c6a51"></div>
<div class="output_subarea output_javascript ">
<script type="text/javascript">
var element = $('#42ac6c75-4983-40d4-aa06-6419dc8c6a51');
/* Put everything inside the global mpl namespace */
window.mpl = {};
mpl.get_websocket_type = function() {
if (typeof(WebSocket) !== 'undefined') {
return WebSocket;
} else if (typeof(MozWebSocket) !== 'undefined') {
return MozWebSocket;
} else {
alert('Your browser does not have WebSocket support. ' +
'Please try Chrome, Safari or Firefox ≥ 6. ' +
'Firefox 4 and 5 are also supported but you ' +
'have to enable WebSockets in about:config.');
};
}
mpl.figure = function(figure_id, websocket, ondownload, parent_element) {
this.id = figure_id;
this.ws = websocket;
this.supports_binary = (this.ws.binaryType != undefined);
if (!this.supports_binary) {
var warnings = document.getElementById("mpl-warnings");
if (warnings) {
warnings.style.display = 'block';
warnings.textContent = (
"This browser does not support binary websocket messages. " +
"Performance may be slow.");
}
}
this.imageObj = new Image();
this.context = undefined;
this.message = undefined;
this.canvas = undefined;
this.rubberband_canvas = undefined;
this.rubberband_context = undefined;
this.format_dropdown = undefined;
this.image_mode = 'full';
this.root = $('<div/>');
this._root_extra_style(this.root)
this.root.attr('style', 'display: inline-block');
$(parent_element).append(this.root);
this._init_header(this);
this._init_canvas(this);
this._init_toolbar(this);
var fig = this;
this.waiting = false;
this.ws.onopen = function () {
fig.send_message("supports_binary", {value: fig.supports_binary});
fig.send_message("send_image_mode", {});
if (mpl.ratio != 1) {
fig.send_message("set_dpi_ratio", {'dpi_ratio': mpl.ratio});
}
fig.send_message("refresh", {});
}
this.imageObj.onload = function() {
if (fig.image_mode == 'full') {
// Full images could contain transparency (where diff images
// almost always do), so we need to clear the canvas so that
// there is no ghosting.
fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);
}
fig.context.drawImage(fig.imageObj, 0, 0);
};
this.imageObj.onunload = function() {
fig.ws.close();
}
this.ws.onmessage = this._make_on_message_function(this);
this.ondownload = ondownload;
}
mpl.figure.prototype._init_header = function() {
var titlebar = $(
'<div class="ui-dialog-titlebar ui-widget-header ui-corner-all ' +
'ui-helper-clearfix"/>');
var titletext = $(
'<div class="ui-dialog-title" style="width: 100%; ' +
'text-align: center; padding: 3px;"/>');
titlebar.append(titletext)
this.root.append(titlebar);
this.header = titletext[0];
}
mpl.figure.prototype._canvas_extra_style = function(canvas_div) {
}
mpl.figure.prototype._root_extra_style = function(canvas_div) {
}
mpl.figure.prototype._init_canvas = function() {
var fig = this;
var canvas_div = $('<div/>');
canvas_div.attr('style', 'position: relative; clear: both; outline: 0');
function canvas_keyboard_event(event) {
return fig.key_event(event, event['data']);
}
canvas_div.keydown('key_press', canvas_keyboard_event);
canvas_div.keyup('key_release', canvas_keyboard_event);
this.canvas_div = canvas_div
this._canvas_extra_style(canvas_div)
this.root.append(canvas_div);
var canvas = $('<canvas/>');
canvas.addClass('mpl-canvas');
canvas.attr('style', "left: 0; top: 0; z-index: 0; outline: 0")
this.canvas = canvas[0];
this.context = canvas[0].getContext("2d");
var backingStore = this.context.backingStorePixelRatio ||
this.context.webkitBackingStorePixelRatio ||
this.context.mozBackingStorePixelRatio ||
this.context.msBackingStorePixelRatio ||
this.context.oBackingStorePixelRatio ||
this.context.backingStorePixelRatio || 1;
mpl.ratio = (window.devicePixelRatio || 1) / backingStore;
var rubberband = $('<canvas/>');
rubberband.attr('style', "position: absolute; left: 0; top: 0; z-index: 1;")
var pass_mouse_events = true;
canvas_div.resizable({
start: function(event, ui) {
pass_mouse_events = false;
},
resize: function(event, ui) {
fig.request_resize(ui.size.width, ui.size.height);
},
stop: function(event, ui) {
pass_mouse_events = true;
fig.request_resize(ui.size.width, ui.size.height);
},
});
function mouse_event_fn(event) {
if (pass_mouse_events)
return fig.mouse_event(event, event['data']);
}
rubberband.mousedown('button_press', mouse_event_fn);
rubberband.mouseup('button_release', mouse_event_fn);
// Throttle sequential mouse events to 1 every 20ms.
rubberband.mousemove('motion_notify', mouse_event_fn);
rubberband.mouseenter('figure_enter', mouse_event_fn);
rubberband.mouseleave('figure_leave', mouse_event_fn);
canvas_div.on("wheel", function (event) {
event = event.originalEvent;
event['data'] = 'scroll'
if (event.deltaY < 0) {
event.step = 1;
} else {
event.step = -1;
}
mouse_event_fn(event);
});
canvas_div.append(canvas);
canvas_div.append(rubberband);
this.rubberband = rubberband;
this.rubberband_canvas = rubberband[0];
this.rubberband_context = rubberband[0].getContext("2d");
this.rubberband_context.strokeStyle = "#000000";
this._resize_canvas = function(width, height) {
// Keep the size of the canvas, canvas container, and rubber band
// canvas in synch.
canvas_div.css('width', width)
canvas_div.css('height', height)
canvas.attr('width', width * mpl.ratio);
canvas.attr('height', height * mpl.ratio);
canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');
rubberband.attr('width', width);
rubberband.attr('height', height);
}
// Set the figure to an initial 600x600px, this will subsequently be updated
// upon first draw.
this._resize_canvas(600, 600);
// Disable right mouse context menu.
$(this.rubberband_canvas).bind("contextmenu",function(e){
return false;
});
function set_focus () {
canvas.focus();
canvas_div.focus();
}
window.setTimeout(set_focus, 100);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items) {
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) {
// put a spacer in here.
continue;
}
var button = $('<button/>');
button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +
'ui-button-icon-only');
button.attr('role', 'button');
button.attr('aria-disabled', 'false');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
var icon_img = $('<span/>');
icon_img.addClass('ui-button-icon-primary ui-icon');
icon_img.addClass(image);
icon_img.addClass('ui-corner-all');
var tooltip_span = $('<span/>');
tooltip_span.addClass('ui-button-text');
tooltip_span.html(tooltip);
button.append(icon_img);
button.append(tooltip_span);
nav_element.append(button);
}
var fmt_picker_span = $('<span/>');
var fmt_picker = $('<select/>');
fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');
fmt_picker_span.append(fmt_picker);
nav_element.append(fmt_picker_span);
this.format_dropdown = fmt_picker[0];
for (var ind in mpl.extensions) {
var fmt = mpl.extensions[ind];
var option = $(
'<option/>', {selected: fmt === mpl.default_extension}).html(fmt);
fmt_picker.append(option);
}
// Add hover states to the ui-buttons
$( ".ui-button" ).hover(
function() { $(this).addClass("ui-state-hover");},
function() { $(this).removeClass("ui-state-hover");}
);
var status_bar = $('<span class="mpl-message"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
}
mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {
// Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,
// which will in turn request a refresh of the image.
this.send_message('resize', {'width': x_pixels, 'height': y_pixels});
}
mpl.figure.prototype.send_message = function(type, properties) {
properties['type'] = type;
properties['figure_id'] = this.id;
this.ws.send(JSON.stringify(properties));
}
mpl.figure.prototype.send_draw_message = function() {
if (!this.waiting) {
this.waiting = true;
this.ws.send(JSON.stringify({type: "draw", figure_id: this.id}));
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
var format_dropdown = fig.format_dropdown;
var format = format_dropdown.options[format_dropdown.selectedIndex].value;
fig.ondownload(fig, format);
}
mpl.figure.prototype.handle_resize = function(fig, msg) {
var size = msg['size'];
if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {
fig._resize_canvas(size[0], size[1]);
fig.send_message("refresh", {});
};
}
mpl.figure.prototype.handle_rubberband = function(fig, msg) {
var x0 = msg['x0'] / mpl.ratio;
var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;
var x1 = msg['x1'] / mpl.ratio;
var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;
x0 = Math.floor(x0) + 0.5;
y0 = Math.floor(y0) + 0.5;
x1 = Math.floor(x1) + 0.5;
y1 = Math.floor(y1) + 0.5;
var min_x = Math.min(x0, x1);
var min_y = Math.min(y0, y1);
var width = Math.abs(x1 - x0);
var height = Math.abs(y1 - y0);
fig.rubberband_context.clearRect(
0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);
fig.rubberband_context.strokeRect(min_x, min_y, width, height);
}
mpl.figure.prototype.handle_figure_label = function(fig, msg) {
// Updates the figure title.
fig.header.textContent = msg['label'];
}
mpl.figure.prototype.handle_cursor = function(fig, msg) {
var cursor = msg['cursor'];
switch(cursor)
{
case 0:
cursor = 'pointer';
break;
case 1:
cursor = 'default';
break;
case 2:
cursor = 'crosshair';
break;
case 3:
cursor = 'move';
break;
}
fig.rubberband_canvas.style.cursor = cursor;
}
mpl.figure.prototype.handle_message = function(fig, msg) {
fig.message.textContent = msg['message'];
}
mpl.figure.prototype.handle_draw = function(fig, msg) {
// Request the server to send over a new figure.
fig.send_draw_message();
}
mpl.figure.prototype.handle_image_mode = function(fig, msg) {
fig.image_mode = msg['mode'];
}
mpl.figure.prototype.updated_canvas_event = function() {
// Called whenever the canvas gets updated.
this.send_message("ack", {});
}
// A function to construct a web socket function for onmessage handling.
// Called in the figure constructor.
mpl.figure.prototype._make_on_message_function = function(fig) {
return function socket_on_message(evt) {
if (evt.data instanceof Blob) {
/* FIXME: We get "Resource interpreted as Image but
* transferred with MIME type text/plain:" errors on
* Chrome. But how to set the MIME type? It doesn't seem
* to be part of the websocket stream */
evt.data.type = "image/png";
/* Free the memory for the previous frames */
if (fig.imageObj.src) {
(window.URL || window.webkitURL).revokeObjectURL(
fig.imageObj.src);
}
fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(
evt.data);
fig.updated_canvas_event();
fig.waiting = false;
return;
}
else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == "data:image/png;base64") {
fig.imageObj.src = evt.data;
fig.updated_canvas_event();
fig.waiting = false;
return;
}
var msg = JSON.parse(evt.data);
var msg_type = msg['type'];
// Call the "handle_{type}" callback, which takes
// the figure and JSON message as its only arguments.
try {
var callback = fig["handle_" + msg_type];
} catch (e) {
console.log("No handler for the '" + msg_type + "' message type: ", msg);
return;
}
if (callback) {
try {
// console.log("Handling '" + msg_type + "' message: ", msg);
callback(fig, msg);
} catch (e) {
console.log("Exception inside the 'handler_" + msg_type + "' callback:", e, e.stack, msg);
}
}
};
}
// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas
mpl.findpos = function(e) {
//this section is from http://www.quirksmode.org/js/events_properties.html
var targ;
if (!e)
e = window.event;
if (e.target)
targ = e.target;
else if (e.srcElement)
targ = e.srcElement;
if (targ.nodeType == 3) // defeat Safari bug
targ = targ.parentNode;
// jQuery normalizes the pageX and pageY
// pageX,Y are the mouse positions relative to the document
// offset() returns the position of the element relative to the document
var x = e.pageX - $(targ).offset().left;
var y = e.pageY - $(targ).offset().top;
return {"x": x, "y": y};
};
/*
* return a copy of an object with only non-object keys
* we need this to avoid circular references
* http://stackoverflow.com/a/24161582/3208463
*/
function simpleKeys (original) {
return Object.keys(original).reduce(function (obj, key) {
if (typeof original[key] !== 'object')
obj[key] = original[key]
return obj;
}, {});
}
mpl.figure.prototype.mouse_event = function(event, name) {
var canvas_pos = mpl.findpos(event)
if (name === 'button_press')
{
this.canvas.focus();
this.canvas_div.focus();
}
var x = canvas_pos.x * mpl.ratio;
var y = canvas_pos.y * mpl.ratio;
this.send_message(name, {x: x, y: y, button: event.button,
step: event.step,
guiEvent: simpleKeys(event)});
/* This prevents the web browser from automatically changing to
* the text insertion cursor when the button is pressed. We want
* to control all of the cursor setting manually through the
* 'cursor' event from matplotlib */
event.preventDefault();
return false;
}
mpl.figure.prototype._key_event_extra = function(event, name) {
// Handle any extra behaviour associated with a key event
}
mpl.figure.prototype.key_event = function(event, name) {
// Prevent repeat events
if (name == 'key_press')
{
if (event.which === this._key)
return;
else
this._key = event.which;
}
if (name == 'key_release')
this._key = null;
var value = '';
if (event.ctrlKey && event.which != 17)
value += "ctrl+";
if (event.altKey && event.which != 18)
value += "alt+";
if (event.shiftKey && event.which != 16)
value += "shift+";
value += 'k';
value += event.which.toString();
this._key_event_extra(event, name);
this.send_message(name, {key: value,
guiEvent: simpleKeys(event)});
return false;
}
mpl.figure.prototype.toolbar_button_onclick = function(name) {
if (name == 'download') {
this.handle_save(this, null);
} else {
this.send_message("toolbar_button", {name: name});
}
};
mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {
this.message.textContent = tooltip;
};
mpl.toolbar_items = [["Home", "Reset original view", "fa fa-home icon-home", "home"], ["Back", "Back to previous view", "fa fa-arrow-left icon-arrow-left", "back"], ["Forward", "Forward to next view", "fa fa-arrow-right icon-arrow-right", "forward"], ["", "", "", ""], ["Pan", "Pan axes with left mouse, zoom with right", "fa fa-arrows icon-move", "pan"], ["Zoom", "Zoom to rectangle", "fa fa-square-o icon-check-empty", "zoom"], ["", "", "", ""], ["Download", "Download plot", "fa fa-floppy-o icon-save", "download"]];
mpl.extensions = ["eps", "jpeg", "pdf", "png", "ps", "raw", "svg", "tif"];
mpl.default_extension = "png";var comm_websocket_adapter = function(comm) {
// Create a "websocket"-like object which calls the given IPython comm
// object with the appropriate methods. Currently this is a non binary
// socket, so there is still some room for performance tuning.
var ws = {};
ws.close = function() {
comm.close()
};
ws.send = function(m) {
//console.log('sending', m);
comm.send(m);
};
// Register the callback with on_msg.
comm.on_msg(function(msg) {
//console.log('receiving', msg['content']['data'], msg);
// Pass the mpl event to the overridden (by mpl) onmessage function.
ws.onmessage(msg['content']['data'])
});
return ws;
}
mpl.mpl_figure_comm = function(comm, msg) {
// This is the function which gets called when the mpl process
// starts-up an IPython Comm through the "matplotlib" channel.
var id = msg.content.data.id;
// Get hold of the div created by the display call when the Comm
// socket was opened in Python.
var element = $("#" + id);
var ws_proxy = comm_websocket_adapter(comm)
function ondownload(figure, format) {
window.open(figure.imageObj.src);
}
var fig = new mpl.figure(id, ws_proxy,
ondownload,
element.get(0));
// Call onopen now - mpl needs it, as it is assuming we've passed it a real
// web socket which is closed, not our websocket->open comm proxy.
ws_proxy.onopen();
fig.parent_element = element.get(0);
fig.cell_info = mpl.find_output_cell("<div id='" + id + "'></div>");
if (!fig.cell_info) {
console.error("Failed to find cell for figure", id, fig);
return;
}
var output_index = fig.cell_info[2]
var cell = fig.cell_info[0];
};
mpl.figure.prototype.handle_close = function(fig, msg) {
var width = fig.canvas.width/mpl.ratio
fig.root.unbind('remove')
// Update the output cell to use the data from the current canvas.
fig.push_to_output();
var dataURL = fig.canvas.toDataURL();
// Re-enable the keyboard manager in IPython - without this line, in FF,
// the notebook keyboard shortcuts fail.
IPython.keyboard_manager.enable()
$(fig.parent_element).html('<img src="' + dataURL + '" width="' + width + '">');
fig.close_ws(fig, msg);
}
mpl.figure.prototype.close_ws = function(fig, msg){
fig.send_message('closing', msg);
// fig.ws.close()
}
mpl.figure.prototype.push_to_output = function(remove_interactive) {
// Turn the data on the canvas into data in the output cell.
var width = this.canvas.width/mpl.ratio
var dataURL = this.canvas.toDataURL();
this.cell_info[1]['text/html'] = '<img src="' + dataURL + '" width="' + width + '">';
}
mpl.figure.prototype.updated_canvas_event = function() {
// Tell IPython that the notebook contents must change.
IPython.notebook.set_dirty(true);
this.send_message("ack", {});
var fig = this;
// Wait a second, then push the new image to the DOM so
// that it is saved nicely (might be nice to debounce this).
setTimeout(function () { fig.push_to_output() }, 1000);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items){
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) { continue; };
var button = $('<button class="btn btn-default" href="#" title="' + name + '"><i class="fa ' + image + ' fa-lg"></i></button>');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
nav_element.append(button);
}
// Add the status bar.
var status_bar = $('<span class="mpl-message" style="text-align:right; float: right;"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
// Add the close button to the window.
var buttongrp = $('<div class="btn-group inline pull-right"></div>');
var button = $('<button class="btn btn-mini btn-primary" href="#" title="Stop Interaction"><i class="fa fa-power-off icon-remove icon-large"></i></button>');
button.click(function (evt) { fig.handle_close(fig, {}); } );
button.mouseover('Stop Interaction', toolbar_mouse_event);
buttongrp.append(button);
var titlebar = this.root.find($('.ui-dialog-titlebar'));
titlebar.prepend(buttongrp);
}
mpl.figure.prototype._root_extra_style = function(el){
var fig = this
el.on("remove", function(){
fig.close_ws(fig, {});
});
}
mpl.figure.prototype._canvas_extra_style = function(el){
// this is important to make the div 'focusable
el.attr('tabindex', 0)
// reach out to IPython and tell the keyboard manager to turn it's self
// off when our div gets focus
// location in version 3
if (IPython.notebook.keyboard_manager) {
IPython.notebook.keyboard_manager.register_events(el);
}
else {
// location in version 2
IPython.keyboard_manager.register_events(el);
}
}
mpl.figure.prototype._key_event_extra = function(event, name) {
var manager = IPython.notebook.keyboard_manager;
if (!manager)
manager = IPython.keyboard_manager;
// Check for shift+enter
if (event.shiftKey && event.which == 13) {
this.canvas_div.blur();
// select the cell after this one
var index = IPython.notebook.find_cell_index(this.cell_info[0]);
IPython.notebook.select(index + 1);
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
fig.ondownload(fig, null);
}
mpl.find_output_cell = function(html_output) {
// Return the cell and output element which can be found *uniquely* in the notebook.
// Note - this is a bit hacky, but it is done because the "notebook_saving.Notebook"
// IPython event is triggered only after the cells have been serialised, which for
// our purposes (turning an active figure into a static one), is too late.
var cells = IPython.notebook.get_cells();
var ncells = cells.length;
for (var i=0; i<ncells; i++) {
var cell = cells[i];
if (cell.cell_type === 'code'){
for (var j=0; j<cell.output_area.outputs.length; j++) {
var data = cell.output_area.outputs[j];
if (data.data) {
// IPython >= 3 moved mimebundle to data attribute of output
data = data.data;
}
if (data['text/html'] == html_output) {
return [cell, data, j];
}
}
}
}
}
// Register the function which deals with the matplotlib target/channel.
// The kernel may be null if the page has been refreshed.
if (IPython.notebook.kernel != null) {
IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);
}
</script>
</div>
</div>
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<img src="data:image/png;base64,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" width="432" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'测试'</span><span class="p">)</span>
<span class="c1"># plt.show()`会启动一个事件循环(event loop)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 保存图形</span>
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'sin_cos.png'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span> <span class="o">-</span><span class="n">lh</span> <span class="n">sin_cos</span><span class="o">.</span><span class="n">png</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>-rw-r--r-- 1 toddtao staff 23K May 28 16:13 sin_cos.png
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 用IPython的`Image`对象显示图形</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="n">Image</span><span class="p">(</span><span class="s1">'sin_cos.png'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>用markdown语法显示图形</p>
<p><img src="/images/copied_from_nb/3.data-viz/sin_cos.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="绘图接口">绘图接口<a class="anchor-link" href="#绘图接口"> </a></h2><ol>
<li>图形结构:Artist(Figure、Axes、Axis)</li>
<li>MATLAB风格接口:通过pyplot(<code>plt</code>)接口绘图,与MATLAB语法类似,plt是<strong>有状态的</strong>(stateful),会持续跟踪“当前的”图形和坐标轴,控制子图时比较麻烦</li>
<li>面向对象接口:通过<code>Figure</code>和<code>Axes</code><strong>方法</strong>控制图形,可以按照行列控制子图,操作非常灵活<ul>
<li>figure:<code>plt.Figure</code>类的一个实例,是一个能够容纳各种坐标轴、图形、文字和标签的容器</li>
<li>axes:<code>plt.Axes</code>类的一个实例,是一个带有刻度和标签的矩形,包含所有可视化的图形元素</li>
</ul>
</li>
</ol>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/3.data-viz/matplotlib-anatomy.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="MATLAB风格接口">MATLAB风格接口<a class="anchor-link" href="#MATLAB风格接口"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span> <span class="c1"># 创建图形</span>
<span class="c1"># 创建两个子图中的第一个,设置坐标轴</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># (行、列、子图编号)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="c1"># 创建两个子图中的第二个,设置坐标轴</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="面向对象接口">面向对象接口<a class="anchor-link" href="#面向对象接口"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 先创建图形网格</span>
<span class="c1"># ax是一个包含两个Axes对象的数组</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># 在每个对象上调用`plot()`方法</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="图形配置">图形配置<a class="anchor-link" href="#图形配置"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="图形样式">图形样式<a class="anchor-link" href="#图形样式"> </a></h3><p><code>plt.plot()</code>函数设置颜色(<code>color</code>参数)与风格(<code>linestyle</code>参数)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">0</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">)</span> <span class="c1"># 标准颜色名称</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span> <span class="c1"># 缩写颜色代码(rgbcmyk)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">2</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'0.75'</span><span class="p">)</span> <span class="c1"># 范围在0~1之间的灰度值</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">3</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#FFDD44'</span><span class="p">)</span> <span class="c1"># 十六进制(RRGGBB,00~FF)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">0</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">)</span> <span class="c1"># 实线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span> <span class="c1"># 虚线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">2</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-.'</span><span class="p">)</span> <span class="c1"># 点划线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">3</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">':'</span><span class="p">);</span> <span class="c1"># 实点线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>可以将<code>linestyle</code>和<code>color</code>编码组合起来,作为<code>plt.plot()</code>函数的一个参数使用:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">0</span><span class="p">),</span> <span class="s1">'-g'</span><span class="p">)</span> <span class="c1"># 绿色实线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">'--c'</span><span class="p">)</span> <span class="c1"># 青色虚线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">2</span><span class="p">),</span> <span class="s1">'-.k'</span><span class="p">)</span> <span class="c1"># 黑色点划线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="mi">3</span><span class="p">),</span> <span class="s1">':r'</span><span class="p">);</span> <span class="c1"># 红色实点线</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="图形标签">图形标签<a class="anchor-link" href="#图形标签"> </a></h3><ol>
<li>图标题</li>
<li>轴标题</li>
<li>图例</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">'-g'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'sin(x)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">':b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'cos(x)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"正弦余弦曲线"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"x值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"三角函数值"</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="画散点图">画散点图<a class="anchor-link" href="#画散点图"> </a></h2><p>创建散点图可以用<code>plt.plot</code>和<code>plt.scatter</code>。后者功能更强大,可以让每个散点具有不同的属性(大小、表面颜色、边框颜色等),实现多维度可视化,例如<code>alpha</code>参数来调整透明度:</p>
<blockquote><p><code>plt.plot</code>性能优于<code>plt.scatter</code>:由于<code>plt.scatter</code>会对每个散点进行单独渲染,因此渲染器会消耗更多的资源。而在<code>plt.plot</code>中,散点基本都彼此复制,因此整个数据集中所有点的颜色、尺寸只需要配置一次,因此处理几千个点的数据集时,<code>plt.plot</code>方法比<code>plt.scatter</code>方法性能好。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">sizes</span> <span class="o">=</span> <span class="mi">1000</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">sizes</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"viridis"</span><span class="p">)</span>
<span class="c1"># 显示颜色条</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">();</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAioAAAI9CAYAAAAU41OQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdSYzcV57g9+/777FHRu5M7luSoqi1SqWqLtXW3XZ3zxgY2IcZ2PDB8ME++eCbMQZ8GcCAD74YcAMNeAwf2vthbI89013VPd1dpZJaVSxJlEgqSSbJZO57xh7/9fkQXESJTGYmI/6ZSf4+ulCMyP/vRTAj4he/93vvKa01QgghhBAHkbHfAxBCCCGEeBZJVIQQQghxYEmiIoQQQogDSxIVIYQQQhxYkqgIIYQQ4sCSREUIIYQQB5bV7wCfffaZdl2332EODd/3keejd+T57B15LntHnsveOozPZ6vVWnv33XeH04p3a+pjHUUqlVitjvUX77777h+lEowUEhXXdbl48WK/wxwaN27ckOejh+T57B15LntHnsveOozP55UrV2bSjBdFigtnmqnE+t210lAqgR7oe6IihBBCiP7Suo0OP08p2o9SitMliYoQQghx2KkM2G/s9yj6QhIVIYQQ4rDTbZLUKio/TilOlyQqQgghxGGnMij7zf0eRV9IoiKEEEIcdrpNEnyWUrCfpBSnSxIVIYQQ4rBTGQxHKipCCCGEOIh0mzhIq0flJynF6ZJERQghhDjspKIihBBCiINK6xZx8GlK0X6WUpwuSVSEEEKIQ06pLIbz1n4Poy8kURFCCCEOuW5FJa1VP1JREUIIIcQuKJXFfEkrKsZ+D0AIIYQQ4lmkoiKEEEIcclq3CGXqRwghhBAHkVJZrJd06kcSFSGEEOKQ61ZU0lqe/PspxemSREUIIYQ47KSiIoQQQoiDSusWoZ9Wj4pUVIQ49OqtDsubDfwwIuc5jFWKeI683IQQ/SE9KkKIHekEEVduzjK/XkWhsEyDKNEorZk8PsKlE2MYhtrvYQohXjLp9qj8QUpxuiRREaJHwijmwy/vUm22GSnnUepxQpIkmuv3lgmjhHfOTezjKIUQLyOlstjO2/s9jL6QREWIHrm/sslarcVYJf+t2wxDMVLJc3thlVPjFQbymX0YoRDiZaV1C18qKkKIZ9FaMzW7ykDBe+Z9DKWwLZO7i+sMnDua4uiEEC87pbI4UlERQjxLGMU0/ZCRTG7b++U8h/VaK6VRCSFeFVJREUJsSymF0vq599NaSzOtEKLnpKIihNiWbZlUijkabZ98xn3m/eotn0snx1IcmRDiVZDoFh2pqAghtjN5bIhffnGPnOc8seLnoSiO0RqOjwzsw+iEEC8zQ2VxpaIihNjOeKXEmSOD3WbZYhbHMh/d1vZDqs0O3508Rs5z9nGUQoiXkVRUhBDPZRiKd88dpZhz+er+ClthjFKgNZRyHh9cPsWRwdJ+D1MI8RJSUlERQuyEYSgmj45wZnyIrUabOElwbItyznvqdJAQQojtSaIiRB9YpsFQafulykII0Stapn6EEEIIcVAplcWTqR8hhBBCHERat2j7UlERQryitNa02gGdICLRGtNQZFwHz7Wk90aIA0CpLJ4rFRUhxCskThLWNhtMz66xutEgThIUX09KNJZlMj5U5NTRISqlrCQtQuyTRLdo+Z+lFO33U4rTJYmKEOIJWmsWVqp8PjVP2w/JuDalQgbTML513yhOWFyrM7OwQbGQ4e0LRxka+Pbp0UKI/jJUloxUVIQQL7tOEHL1q3lmFjcZKGYp5J59GjR0VzeVCxkgQ6sd8De/ucX5kyO8dnoM62sb3gkh+ivRLZqpVVR+llKcLklUhBAAdIKIX16ZptkOGB0s7HoaJ5tx8FybWzOrVOtt3n/zFLYkK0KkwlBZss5b+z2MvpBERQiBH0RcvbXC4NAIgy+w/4thKEYqBda3mnxydYb33zr51CkjIURvJYlUVIQQL7FrtxZpdSJOF7I9ud5gOcfSepU799c4d3KkJ9cUQjybYWTJulJREUK8hJbWakzPrVHK9/awxEopzxe3FhgZKlDKZ3p6bSHEk+KkRdP/PKVoP00pTpckKkK8wrTWfHFrgWLeY6PT26XFlmlg2xY37y7z3csne3ptIcSTTCNLTioqQoiXzWatRa3RZqRSZKMP1y/mPWaXtrh0LiDr9bZiI4R4LE5aNFLrUZGKihAiJfcXNrGt/r0NGA9WDi2v1Tl1dLBvcYR41RlSURFCvIxWNurkMv2tdHiuzeqGJCpC9FOStKmn1qPyk5TidEmiIsQrKoximp3ghZYj74Tn2qxuNfoaQ4hXnWFkyKdUUVlNJcpjssGBEK+oIIhA0/fzeWzLxA8ikkT3NY4Q4uUkFRUhXlGppg1akWiNgRxaKEQ/pDv18+OU4nRJoiLEK8ow0kkatNagHjfWCiF6rzv182YqsdZSifKYJCpCvKI8x8YwFEmi+5q0+EFEqeCllhgJ8SqKkzY1/2pK0X6UUpwuSVSEeEUZhmKgmKXdCcn2ceVPxw85caTSt+sLIcA0MhRSqqj0Y8+l7UiiIsQrbHykxJc3F/qaqIRRzMhgoW/XF0J0Kyr11CoqH6QUp0sSFSFeYUdHynxxc75vK3LCMMaxLYYr+b5cXwjR1a2ovJFKLKmoCCFSk/FsToxXmF+p9uX6W/U2l8+NYxqyE4IQ/RQnbar+FylF+2FKcbokURHiFffamXHmV6qEUdzT6zZbPvmsIzvSCpEC08hSTKmispVKlMckURHiFZfxbN6+eJR/8RdzPVsBFEYxjbbPz753HssyezBKIcR2oqTFVmo9KlJREUKk7OhomdMTJVY26gxX8i80VRNGMetbTd67fIJKn7fnF0J0mUaWUkoVlVoqUR6TREUIgVKKU0fKGF6FL28tUsx7ZLzdrwSqNzu0/ZDvXT7BcVmSLERq4qTNVmo9Kr+XUpwuSVSEeIlprYkTDVpjGMa20zpKKS6cHqNSznHl2ixrmw2KeQ/Hfv7bRMcPqTXblAtZfvDWKcrFbC8fhhDiOUwjk1pFpZ5KlMckURGHUqLbJEkNpeYIQhulTAxVwDCKKNW/PUF6LdEJtbBBI2qyFdQIdYhC4ZkuZadEzsyQt3K7Ojiw1uywtFZjbaPBWrVJFMWAAqUpZD2GB/KMDBYYGcg/tX9kpFLg998/z8zCBjfvrbLVaGMqA8+xcBwLpRQ60fhhRCcISRJNIevy7msnODpWxjJlhY8QaYuTNpudtCoqP0gpTpckKuLQ0FoTx4v44TWiaKH72Wsu0fYXQetHH+a2dQbHnsQ0h/Z5xM8WxAEL7WVmWnMESQiAbdiYDw403wy2mG0toNHkrRyncscZ8QYx1bMbU1c3G3x1d4mVjQaGUmRcm2LOw3yQOGitCcKY2eUtpmfXsG2Tc8eHOXNs6FtVE8e2OHdihNPHhljfarJVa7G60aDe8h813JaLGYYrw1SKWQaK2b6fwiyEeDbTyFB2L6cSq5lKlMf2lKhMTk6WgP8VMOmO+R9PTU0FvRyYEF+XJE3a/sdE8QxKFTDNse4Ho46wzLFH99M6IYzuE4Q3cZzX8Jy3UMrdx5F/20pnjWvVKSIdUbDy5K3tG047sc/VresU7QKXSpMU7Sc3TwvCiOvTS9y+v0ou6zI8kH9q0qCUwnUsXKf7sg+jmBt3lrk7v853Lh1/amzTMBipFBipFDh/cnSPj1gI0W/dHpUvU4r2/ZTidO21ovIfAP/t1NTUzycnJ/8U+CPg/+7dsIR4LI7XaLZ/DmhMY2Lbb+5KGZjmYDdhCaaI4yWy3h9iGPvfM5HohJu1O9xrzVK0CxSNnW0r75kununSjFp8vH6F10uTHMl0k7Nmy+dXn92h1Q4ZHizs6oRi2zIZruRpdwL+7re3yds+Fy5oqYwIcQhJReUbpqam/vuv/e8wsNKb4QjxpDjZotn+C5TKYBg734ZdKQPTGiOO12l1fk42829jKK+PI92e1pqp2jQzrTmGnMqekoGclcVNHK5u3UBrKKsKf/e7aeIkYWhg78uAM56DY1t8cWOJU/dWmDz17cpJp+nTqLWobTSorjWIwhilwPEcBkaL5ItZ8uUstiOzyUKI3nqhd5XJycnvAwNTU1Mf92g8QjyidUy78yEoe1dJyteZ5iBxvIzvf0rGS7dc+XXz7aUXSlIesgyLAbvEF9WviO+X0ZFJqQcrbEzTYKDgcfXWAsW8x/hwiSRJWF+qcu/aHCtza2B1UJbGdi0MZUPskUQwe2sRUBim4sTkEY6dG6PwAomTEGL3oqTNRmpTP++nFKdrz4nK5ORkBfjvgH9vu/v5vs+NGzf2Gual0+l05PnYIaXuocyroEd41oK4IAyZm519zpUSUH9DEieg09/bo6N9vgxuklUeHdXqyTVnVtdZWrrOkfoE7UZEHMZoDcoAL2eTzbs4noWbsXecGMVxRLO2wf/zV2u8PlHm3ucLtJo1MpU62dFVlB2iNISABogdkmCCJBmExCbxEz7+m0U+/HnCyPEBTr4+juPZPXm8h428zntLns/ns4wMA+7rqcRqpxLlsb020zrA/wH8F1NTUzPb3dd1XS5evLiXMC+lGzduyPOxA1onNFpfgrq07ZTN3OwsR48de+714qSIaQbkvPSf+y+2bjARTFCydtaT8iwaTbPeYW52g/lbbcgnNPApZcsYj1b2QBRGtDcT2kS4nmLseIXycAHL3n4r+7nZWY4cmWDqs3tc/dUsZ94IcQZXQINKxlD6yWXfWgXooS1QW5jts5jBSRQKrTVbKzWWrtd584NJxo4f3NVX/fIqvs61TkA30DoAFEplQGV60vN0GJ/PK1eupBovStpsdK6lFO17KcXp2mtF5T8G3gH+6eTk5D8F/nRqaup/692wxKsuTtZIdBPLKPXkeoYqEYWzJE4r1cbaTuyz2FmhYpdf6DpBEDJzZ43N9Tq1lo+XcXA8j9hr4dSHUTz+MHDcxy/rMIiYubnM7PQqJ86PMjBSeOYHRxIl3Pt0hsb8Otnj85jlCBUNoXj6vihKO6i4giYmzkyB0cbsXEApg4HREn474JO//II3fnCek69NvNDjFweT1hqdrJKEN4nj+yiddKttKJRKABfDPo9pnUYZxf0d7EvOMjIMeOlUVBZTifLYXptp/xT40x6PRYhH4nid7ur33lCq+2Gb6CoG6SUqq/46Cl7oW+X6Wo17t7v96vmix3K1ievaGFrhW20C08eNn151sh0L27GIwpjpa/NU1oocPzf6rabXJE5YnFrGCh2Gzm+RuKu0ascoFZ6/eZvChGiI2L0P2sHyzwLgZhwGx8t8/uFNlGlwYnJ8z8+BOHh00iQKfkMSzaCMDMoYRCmDr/+max0Sh9dJwi8w7Dcw7UsoJQ3X/RAlbdaloiJEeuJkBdXrVToK4riGZab3gbkebOIae9/HZWlhk3t3VsjnM1i2QcePiOME92HVREO4TaLykGWbFCs5qutNbjZnOffmURz3cf/Iyt0V6ssNTl4awCnO02mUqTd9SoWdJXUKBdEgsXcHIxzDSLrNz6ZlMjhe5uqHU5QqOcrD8q36ZZDE64SdXwAaZY4/MxFXykaZw2gdkQSfk8RL2N6Pe//aFphGhkpKFZWlVKI8JomKOJC09rvf1Huoez2/p9d8nq2gSsbI7OlnV5a3uDe9QrGcfXRGTxCET9zH0CaB1YHg+VNkSinypQytus+tz+c4/9YxbMeiVW0xf32RTMnDLqyANrFMm7YfdJtedlgNUhhobZI4Cxid84/+3rJNcsUMn/7yKz74d959bq+MONh0UiXs/CVKeagdrsZTygJrnCReJez8Lbb3+1JZ6bE46bDeuZ5StO+mFKdLflPEAWXyYG1Jz2g0PKPfoh8SnRAkIQVr90urG40O926vUCxlnjhIsO1HmObj/1faIFTh0y7xTNmCS6Pa5v6tZU5dHGfm6ixO1ib0m1j5ZRI/j2FAHGuiOHnqeUDPouICsTOL6Z9C6ccVm2whw/riJnevz3HuzRO7Gq84OLROCP2PUMrccZLydYY5jI6WiMPrWE46B+i9KkzDo+JdSiXWcipRHpNERRxIplkhjpeAF1sp86QYo0fNuTu1l86UOEm4e2sZ17MfreZ5KPlGhUMBqN0ndLmix8ZyHdcxaVVbFIcKBGYDvpbMKVQ33i4oTLSK0eYWKhp+4rbSUJE7X8xy6rWjUlU5pJJoBp0sYZgv0BxtDpGEV9HWKdQOd2cWzxclHdbaUlERIjWmMYRmd5WC51MYKb4xKhQKg0QnGGrnlZzVpSrtpk9x4Pn9IVppjHj3H/pKKXJFjxuf3GOo0p2aUsY3nm+1t0SrO65v/9tZtkkYxqzObzB+cvgpPyUOMq01cfgFSg280HWUskhQxNFdqar0kKXSq6ikvRW9JCriQDLN4Qc9DzFqmxODdypJWphGEUOlV1FRSlFyCgRxgGfurHkwThIW5jbIFZ5+f8sy0Eny+P4qIh/t7TEpBY2NBgMPEhUMzddTk0TrR6uldkUbaJKn3pQteMxMLUqichjpOiRVlDX2/Ps+hzLKJOFNkESlZyLdYa2T1qZ430kpTpckKuJAMpSHbZ0niG5jmSMvfL1Eb5Fxfpj6gXsDdomZYG7HiUq92iIKYrK5p68Uyrg261/LATTgJM5T7/s8oR9i2xZbGy2K5Sw6tnjYFxTHGts2saw9JCpKo57x1uLlXDZXqiRJgmGk1y8kXpxOaj27llIOCRto3ZEVQD1iqYxUVIRIm+tcJoymuyuA1N6X+CZJDdOoYFsneze4HRr2BrnTvL/j+6+u1LHdZ78s7a81tmoSDBROtLc3+qAVYloGoR8RBBFJ6KAeJCpRlJDN7HX7e41Knj5tZRgGSaJpN3xyxb2thhL7I0laaKX2PB34NFq3JVHpkUi3We18lVK0d1OK0yWJijiwDCOH5/6AduffYJrje5oC0ton0U3y3k/3ZTlk0SpQsPJ04s6OqiqNagt3m/NxXNvCtk3iKCF2fPJ+GWOPy7jDdoBhKhIg9GMiP0ccZlGmTxgrSvnd73miVRsVF1HxNr1AWtNpSaIiVHf5u+gJS2UY8F5LJdZqKlEek0RFHGiOfQqtm3T8TzDNEZTa+TRHkjRJ9BZZ9w8wzcE+jvLZlFKczZ/kd5tf4BrutlNPQRASRgkZc5spEQVD5Rzza1vYLuR3sH/Ks8SJRimFUopOOwBDEdaOYpensMzCnioq2mxhtd5g++/dqntApDhUDMPd9Sqw7SjNrl7PYnuhbrPSmUolluKdVOI8JImKOPBc53WUytDxfw2YGEZl2yZPrSOSZB2lPHKZP8EyR9Mb7FMMe4NMZMdY6qwyYD87sQiCaEdl9XzWIXEDCvUxrGTvpxM/jGVaBn4nws1C3K4QuQbDg+auG2m10UQlGYxwB4cQptwrJHrAKKJfYCXY12kdgbJA5XpwNQFgqwzllCoqa6lEeUwSFXEoOPYZLHMUP/iMIJwG9IO57Q5a+2gdoXUHjY/CwrYv4TmXXqi3pZfO509TDWrUo8YzN4DTOywytM02p0tH2Fqy0AW95wZhyzHRSYKhzEfflFstjafepHhiBk0blexsekYbLbSKcBrvPbOR9onYu9hEThwMShVReC/cMwbd3W1N61Tqze3icJJERRwahpEn4/0Q13mHKF4ijlfQqgokKOVhWcewzFEsc/TAlZQd0+Gdyhv8buMqW2GVklX81pv0896zExKaNClT5lj2GHcHN1jZqFMq7K3Xw826JAmYD/aQi+KEKI45PXYcpzlKlP8dibWJigrPTD40EdqqoRIHu/FdVLKz3UqlP+XwUcrEsF8jDn73QkuUtdagfQzrbA9HJ9Kc+jFk6keI7RlGFsc4DfZpdFQin72430PakYzp8d3KW3xVn2ahvUTRyuOaj7+ZGs9YCqzR+PhERIzrcYYZxsDg1NFBwihms9aimPd2/e3U9mw0miTRGEAniHjn1CjZjAuJi11/n9iZI3bvo1WESlzQDyohKkYrH7Ax22cwwyMo/fxm4TCIyORdnG0ahsXBZdpnScIb6KSFMvZ2CrlO1jCtMxj71Df2srJSnPpZTyXKY5KoCJEix3S4XLrAqDvMrcYd1oNNbGXhGi6O0305ag0oTfjgv0QlFHSBcT1OlscfDqZhcP7EMNNz66xs1Clk3V1NqViOhWWb1GsdhscLnBgtUy4+vr7SHpZ/FtM/SWKto+1VEiMADYZ2UOEIRjS4q8Mjm9UWx8+nd3q16C2lXCzvh0Ttv0ArG6V2l3DqpI7CxnTSXd76Koh0h5X2zVRimbyVSpyHJFERImVKKUYzQ4x4g1TDGsudNdaDTWpRHZ0PqUYBjm2TIUNFVyjpEh5Pr1aYpsm548MMFLPcmVsj6QRkPee5CYvWunvAYdHDagS89dpx2q3Np48XCzMahejFm5KjIOLo2f1tbhYvxjDHMN0PiP1foY3SjisrSbyBQmFn/i3UHk8UF89mKY9yJp3qslRUhHhFKKUoOyXKTnclUKITjm7Oc+v6AsPDpecs8X3yOsMDeUp5j9XNBgsrNZrtAKUUlmVimQYKSBJNGEXESbdxtlLKcezNk3zVmqaQd2m3+vVIu5q1NpXREsXK7k/dFQeLaZ9GGVmizock0RLKHHhmg61OGuikhmFNYDnfk4MI+yTUHZZSqqjYUlER4tVkKIMTx0eYvraITmC3x+w4tsXESJnxoSKNVkCrE1BrtAnCGA3YtmJoIEc+65L1HDzXZnO9wZvfO019vdGXx/RQkiS0am3e/tGFvsYR6THMMezsPyQO75KEX5KwhdIa/SC/VtA9kcEcxHJ/imEd3dvZUWJHbOUx5KVTUdlIJcpjkqgIcYBkcy5jEwOsrzYolffWrGgYBsW8RzHvMTb07N1lk0QTRTG/97NLfPHLr9iY6V9Bd3O5xtk3jlMZTe9QSNF/SrlYzgW0fR50DZ3U0doHpVAqi1IFlCEVtDSESYel9q1UYjm8mUqchyRREeKAOf/aBH/38y+Joriv+41sbTQ4dnKIylCBNz+4wK2pOwSdsOcrcmobDQqlLGffPN7T64qDQykDVBlllPd7KK8s2/AYTKlH5endbP0jiYoQB0yxlOXi5WNc+3yWkbH+VCA6nRDTMrl4+RgA5aECF98/yeadBoVKDjfTm31oaht1LMviu3/4OrYjbzdC9Eu3onI7lVgub6QS5yF55xDiADp5dpTlhS021xsMDPa2dB6GMdXNJt//0eQTByBWRoucP3+e3/7Vl3RaPqXBvTc9JnHC5kqVQjnHd//gdTJ5OSFXiH6yDY9KJp0esK1UojwmiYoQB5BpGnznB2f5+Jc3e5qshEHE5kaDt987zcj4t8v0w0cG+NE/+g5ffnSLldkNipU8bnbn1RWtNc1qm3azw7m3TnD28nEs+/nTV37bJ4kTMnlZtirEXnQrKtOpxPK4nEqchyRREeKAclyb9z84z5WPplldrjIwmH+hnpV6tU3HD3n3/bNMHH/2rqC5Qobv/sHrLN5b5eanM6zOb+J6Npm8i+1+u39FJ5qgE9Kqt4mjhNFjFb7zs9coDz+7kfchv+3z5S9vsHhnBQ2UR0q89dNLFAakAVOI3ZCKihBiXziuzXsfnOf+9ApfXp3FsU0KpSyGsfPt8n0/pLbVYmAwz/s/nqSwg3N2DMNg4vQoR06NsLlSY+72MmuLm1TXGygFCoUGeHCabmEgz5nLx5g4M7qrc3w+/esvWV/YoHJkAKUU9Y0Gf/8vr/CTf/J7WLa8PQmxU0His5BSRSXL66nEeUjeCYQ44EzT4NT5MQZHi9z+apH52Q0UkMt7uJ6FYXx7b4owiGi3Anw/JJt1eePdkxw9MYRp7m4fC6UUldHSo2XFgR/SbnSnaZQC0zbJ5j3MPVR6mtUmK/fXGD76uLpTqORZnVtnY2mLkWNDu76mEK8qx3CpZCZTiVVNJcpjkqgIcUgUS1ne+d4ZXnvjGIuzGywubLK10STRGtXdbLa72ZaGTMZhZLzE0RNDVIbyT01m9sJxbZynTP/sRRTGTz1IUQFxGPckhhCvim5F5U4qsXJcSiXOQ5KoCHHIeBmHU+fHOHV+jCRJaDcDwigGrTEMAy/Tu2Sin/IDORzPxm/5uNnu9utxFIOC0g76W4QQj9mGx0BKFZVaKlEek0RFiEPMMAxyhcO59Nc0Td75gzf45F/9jvpmEwCdJLzxo9fIFg7v6p8oTthq+NxdWGdtq0m10SFOEgwDPNtmaCDHQCFLMeeR9XqzX40QB8nk5OT/ALwG/L9TU1P/7Cm3DwB/DowAV6ampv6T7a4niYoQYt8MHx3kZ//+B6zOrpHECYNHKod2xU+zHTC7vMnN2VVmZ5cYayhcx8KxTQxTgYamH7A20ySONSg4Mljk7LEhhkr5XTVIC/E0er8HAExOTv67gDk1NfX9ycnJfz45OXluamrqm3v7/4fAn09NTf355OTk/zw5Ofmdqamp3z7rmpKoCCH2VSbncfzC0f0exp5FUczN2VWu313GUFAqZBgoeAw/I+F6WEXRWrNRb/G3n04zUMjwnYvHKcs+MuLw+wnwvz/4818CPwS+maisA69PTk6WgWPA7HYXlERFCCH2aKve5pPrM9SaPoPlLOYumpaVUhRzHsWcR63Z4Ref3OTymTHOHRuR6orYtTDpMN+6m0qsZOvI0OTk5NcrIH82NTX1Zw/+nAPmH/x5A3jnKZf4FfAPgP8MuMFzDmSWREUIIfZgeaPOh5/fwXNtRiovNl1VzHnkvITPby1QbXZ4Z/IY1i6XkotXm214HEmpmbZRNtempqa+86ybgYelwTzwtF/k/wr4T6empmqTk5P/OfAfAX/2lPsBkqgIIcSurW42+OXndyjmXDynNyusTNNgdLDAzGL3bNrvXDgulRWxY0HSYbN1L5VYJbY9pfkK3emej4E3gamn3GcAuDw5Ofkx8D3gF9tdUBIVIYTYhWY74MOrdylme5ekPKSUYqSS597iJsWsx4WToz29vnh5OYZHOXs+lViN7W/+F8AvJycnjwB/DPyTycnJfzY1NfVffu0+/zXwPwIngI+A/2W7C0qiIoQQO6S15lG5qH8AACAASURBVNObcyil8Pq0V41SiuFyji+mFxkbLFI+xEu1RXqCpMNGayaVWGWefabQg+mcnwB/CPw3U1NTS8Dn37jPJ7DzXeMkURFCiB2aXd5iYa3K2GB/N6QzTYNsxuE3X93nZ++e21WTrng1OYbHREoVleZzbp+amtrk8cqfFyaJihBC7ECSaL68s8hAIZtKvELWZWWjzupmk7HBQioxxeEVJD5rKVVUKqTTtPuQJCpCCLED69UmzU7AaCW9pCHj2tyaXZFERTyXY7gczZ5LJVYrlSiPSaIihBA7MD2/RiblM5TyWZeVjQaNtk8+46YaWxwuQeKz2rqfSqxB0pliekgSFSHEvtJa04gC6oFPJw4BcE2Lgu2St12Mp5ywnDatNUvrNUopN7YqpUBBtdGRREVsy06xotJOJcpjkqgIIfZFnCTMNra4trXMlv+wmNxNSrQGpSBr2bw+MMbJYgXbMPdtrM1OQJzofWlqNU2DjVqTieFS6rHF4REkPqvNdCoqQ1JREUK87KpBm18vz7DablB2Moxln76KphOF/P3qfa5vLvODsZMMZ/bnwMJGK9iXuNDtU1ndet46C/GqcwyXY1JREUKIF7fabvCL+Zs4hsVEbvsqgWfZjFs2jdDnX89O8ePx0xwvDKQ00seiOEbv09G0lmngB+H+BBeHRpD4rLS2PduvZ4ZJJyF6SBIVIURqakGHv1q4Rd52yVrOjn8ub7vYhsnfLt3hj6zJ1CsrWmsU+5OpKCBO9ilLEoeGY3ipVVQ6qUR5TBIVIUQqEq35eGUGS5m7SlIeck2Lku3x4dI9/uT4RRwzvZ4VpRR6n5p6E62x9rE/RxwOQdJhuTWXSqwRzqYS5yFJVIQQqVhs1Vhu1TnynOme7eRsh8VmjTv1dS6UR3o4uu15rs1+rT0KwphSXrbRF9vrVlTSSSD8VKI8JvsyCyFScW1ziYLtvfB1Kl6W65vLJCk2jRSy7r71qHSCiOHy/jQRC3EQSEVFCNF3fhyx0moyln3xD1zXtNj029SCDmU3nUqDa1tkPZswirGtdKdhdJJQLrx4gidebkHSYSmlqZ9RmfoRQrxsakEHpXR3A7NeXTNML1EBODE2wM37qwyWc6nFjOMEwzQoy9SPeA7H8Dj+kk79SKIihOg7P46gh10ehlK0wnSX7J4Yq3Dj3nJ3BVBKjbXVRoczE0M4trxVi+35SYeF5nwqscY5k0qch+S3XwjRd2l9sPdTPusyNlhks95Kpbk10Zowijkxlv6+MeLwcQ2PEylVVIKUl+pLoiKE6DvHMHtZUCHRmpy9+yXOL+rymXF+/slN4jjBNPu7FmF9q8nZY0Oy4kfsiB/7zKdUUTnC6VTiPCSJihCi74qOh9a6d9MmWlOw0z+kr5TPcPnMGF9MLzJSKfQtTtsPyTg2l06N9S2GeLm4psuJXDpTMqFUVIQQLxvXtDiSLVINOhSdF1vB0olCsrbzwtfZq7PHhllcr7NRbVEpZXt+/TCKqTU6/PTds9KbInbMj33mUqqoTEhFRQjxMrpQHuWvFm6+cIKxGbR5b/g4xj71vZiGwfcvn+TvPpvuebIShBGbtRbff/0UQ7J3itgF13Q5KRUVIV5OflylHW3SjtYJdQMAW+XJWkN4VhnX3PtOquKxsWyBiWyJtU6LQW9vH+710Kdke5wqVno8ut1xbYsfvXWGj67eY3m9zlA598I9K9VGmyCM+b03TnNkWH7nxO74sc9scyGVWEc5lUqchyRREa+sVrTCavs6nXgdUJjKxVA2AIFuUAtnAI1nDjKceY2sld6W7S8jQyneGznO/3f/K5phsOtm2E4c0QwD/ujYJPYBOPvGtS1++NZpbs+u8sX0IhnPppB1d92DE0YxG9UWQ+Uc7759jGJONncTu+ekWFGJSFKJ85AkKuKVE+uQ1fYXbAXT2EbhGQnI45UWQdLkfuNvGXDOMpR5HfNBMiN2L2+7/P7EWX4xfws/jqjssLJSCzq0ooCfjJ9l0Etvw7XnsUyDCydHGRsqcvX2AisbdUzDJIq3fyNPtKbVDmi2A1zH4u3zRzk1UcE05FQTsTdB7DPbXEwl1jFOphLnIUlUxCslTgLmWx/RidbJmiMo9fwPBsfIYasMW8Fd/KTGRPb7mEb6S2NfFoNejj85fpFPlu8z36xStD3ytvPUSkQzDNgKOgx5WX585AwVt/fNq71Qzmf40VtnqDU7zCxt8NHKEqsbjSeWZGsN3YeoUcpgsJjl7fNHGR7IY/V5qbN4+XUrKuk0ucZSURGiP7ROWGz9Bj/eJGMN7+pnlTLIWsN04nWWWr/lSO79HSU54ukKtstPJ86y2KpxbXOJ5Xb9wS0PP9m7zXplN8uPxk5xNF/GOgTVhmLO4/KZI5j+FqfOnKPe6tDxI+IkwTAMLNMgn3HIZ12pnoie8mOf5eZSKrGOcyKVOA9JoiJeGdVghka0SM4a3fM1PHOQerRANZih7KbbUPayMZRiIldiIleiHYXUQ592FAAK17QoOi4Z0z6Uu9oqpci4NhlXpglFOlzT5VRqFZU4lTgPSaIiXglxErDauYpnvvhqEc+ssNr5goJzVPpVeiAMIhbvrHFnaolOJ6A8kOfsa+NkJvKHMkkRYj/4sc9iI52KykmpqAjRe41wkUTHPUksTGXj65BmsEjRPd6D0b26wiDik19OsbFapzSQI1dwabcCPvm7KSYvH2Py8tH9HqIQh4JruJxOqaKSpFxRkUlS8UqoBvewjd6tFrGNPFvB3Z5d71U1N7PGxkqd4dESjmOhlCKbcxkaLXLzyznqW639HqIQYp9JRUW8dGKdEOsEHpwrYykDP9nENXp3Cq2lPPxkE60Taap9AXenlimWv72SxzAMTNNgaX6TwlNuF0I8yU98FhrLqcQ6RbqVZElUxKEXJTFrfpU1v8q6X6MWNbtrRh7s8myohCBaZzxjM+B45KwXX1qslEGiEyLdwVbyQbpXQSd4ZiJi2SaBH6U8IiEOJ9dwOZ1Pp8Ffr0gzrRA70oo63AtWubtUI0wibMPCMx0qTvGJJsxO2GQ9CqnX10nQVJwMx7MlKk7mBc+LUWid7pkXL5vKcJHqVpNCMfOt2wI/ojggSaAQO+EnAfMpVVROcyyVOA9JoiIOnUQnzDRX+HLrDovhGhess1jbbKlumTZZ08I1PbTWtKOQT7cWGXSyXCwOkTH31mCrlMZQ+7+V+2F2+sIYv/7FdTJZB8t6/Fy2mj6OazM60bvpOiFeZq7hcCa1ikq6lU5JVMSh0o59frdxi5XOFhW3QMvMbpukQHeVjqFMEhIMZZC1bLLY1MOAj9bmuFgcYjxT2NU4Eh1hYGMq90UezitvaLTE5e+e4trvZlAKTMsgChMc1+K9H1/AceQtSoid6CQB843ZVGKdkYqKEE/XjDp8tHaNIIkYzez8m7ZSCscsEsQNDOPxFEPBdoiShC+rKwRJzIlcecfXDJMWGWtY9vnogVPnxxidGGB5YZOgE1IoZRkeK2FLkiLEjnkpVlSQiooQ39aOfT5eu06oY8pOftc/n7PG6ETX+PphgwCWYTDgeNxsbGAqxdFsaUfXi3WbspPOngWvgmzO5dS5sf0ehhCHVicJmK/PpRLrDOnubySJijjwtNZc3bxDJw4YcHc3RfOQYxYxjQyR9rG+MV1jKoMB2+Wr+jpF26Nobz+dEyYtbCNP1hra01iEEKLXPMNNr6KyGqYT5wFJVMSBN9daZaGzzqi398ZKQxkMuGdYbX+JaToonpyyMZVBxrC4XlvlOwNHnnkAntYJQVzneOHHsn+KEOLA6CQ+s410KirnmEglzkOSqIgDrRMHXN26w4Czt0rK17lmiYJ9lHo4h2uWvpWsZC2bjaDNXLvGyaf0q2id0IpXGPQukN3l6ctCCNFPruFyNn8ynWBSURHisYXWGjEJjtGbX9WicxxNTCNcwDELGN94CRQtl5nWFkczxSeqKrEO6EQbDLjnGPIu9WQsQgjRK37icz+lHpXzUlERoivRCbca8xTt3m36pZSi5JzCMQpsBXfQaGwji0F3ibNlGESRZiNoMeLliXVIkNRQKI7kvkfBPiYrfYQQB45ruJwtnEwn2JpUVIQAYDNo0I4DCj1MVKCbrGTtYRyzSCtaoREuoHXy8FYMIqYbc+StQQxlUXEnKTknsY1v754qhBAHgZ/4zNTnU4k1yZFU4jwkiYo4sKphE7OPDauW4VJ0jpG3jxAlHaKkTax9ClpTj2KO5b+PZxYwlLxMXlZRnNBs+rRaPnGiUQo8zyaf83BlHxdxiLiGy7nUKipBOnEekFeiOLDW/CruHre33w1DmThmDsfMPfo7nyqJ9iRJeUnV6m1mZte5d3+dRGu01ihAo+j+QTM6XOTMqRGGB/My3ScOvE7sM1NLp6JyQSoqQnRtBnUy5v5sUa80NOMOefvVnO4Jk4h2HBDrBFMZ5Cyvr9WttERxwu3pFb66tYhlmZRKGUzz249La0213ubDj28xPlbmjUtHyWZe/NRtIfrFM1OsqKxLRUUIAMIkJmft04ejgvhR38qroxa2mGmucLexTNKtL5CgcQ2bc/kjHM0NkTEP5we2H0T8/ZU7bGw2qQzmMJ+xVw50+5gKeY98zmV9o8Hf/GqKH7x3hnJJTnMWIm2SqIgDS6P3N77e3/hp0lpzL1jjq6VNTGVScnJPVFCCJOJabYZrtfu8VznHkezgPo5296Io5u+v3KFaazM8tPM9eZRSlMtZWu2AX358i5/83iSFvNfHkQqxN53Y515KzbQXZepHiC5TmQ+SlX3oD9C8FFMdO3W9Nst0sMJld/Kpj9sxLIbcEkEc8tH6FN9n8lAlK7furLCx2dxVkvJ12YxDHCdc+XyGD94/99TpIiH2k2e6nJepHyHSVbZztGKfrLUPfSqKQzvFsVsrnS1u1GapGLnnJmeOaTOg8vxm4xZ/6OTIWge/ulCrt/nq1hKDg7nn33kbhbzHymqdmbl1Tp+QnYnFwdKJfe7WF1KJ9RrjqcR5SBIVcWANuUVu1udST1S07tZxctar0Uh7s75AzvSIVGNH93+4S/Bca53zxXR3qNyLe/fXsW1j256UnSqXMty8vcyJo4NSVREHSreiciKdYFJREaKr5OT3paHVT0KKVhbLMFOPnbZ62Ga1U2XILVLdxc8V7Sy36guczo8d6OcpimLuza5RLvemCdZxLKq1NptbLYYG8z25phC90IkD7tQXU4l1ibFU4jwkiYo4sCpOAVOZREmc6odhI2xzuXw6tXj7qRo2Ab3rfUJswyJMWjSiDmXnxaZU+qnR9Lv9Rj2opjxkKIOtmiQq4mBxTSfFioqfTpwHJFERB5ZtWJzOjXGnuUTFffHTk3ci0QmgOJI5PI2iLyJIItRem4aVPvBLuJstn6THi7dcz2JjowGnRnp7YSFegB8HTNfSqai8LhUVIR47nhvlVmP+0cZj/bYVNDmRG0llR9yDwFLm18452h2FwjjgO7bGsYYeL3M3DUUQxT29pui/JEkIgxidJCjDwLLNl6rPyDUdJtOqqGxIRUWIR/J2hvPFY9yszTLslfsay49DDKWYLB7ra5yDpNuovPtkI9EJGsju087BO2UYqufb32utezqVJPpna73BysImG8s1NtfqJI/KaxqFoljJMThSYvhImcpIAeMQ/7t24oDplHpULktFRYgnnctPsNBeoxm197wSR2tNEMZ0/JB2JySKu1UE01R4ro3nWFSTBu8PXcR7RZYlA1ScPDnLw493d2x7LWxxPDt84CtPntv78flBzMT4q7Ei7DBKkoTluU1uX5tja72JaSoyOZfSYO6JREQnGr8TMnN7ienrc2RyHmcuTXD01DCWfXAbxJ/Fk4qKEPvHMky+M3Cev1u5iqnMXSUSQRCxttlkYbVKGEZAd8pCGd1v2VprEp3QoM2IMcBGJ6Aw0aGQ693+IEEnpL7Vor7VZH25RqfloxONYSoK5RyVkSK5UobiQC71b3SGMjhfOMKVzds7/plEa4Ik4lR+tI8j641crvu7ovXuG4afJQpjBsoHt4H4Vdasd/jyk2mW5zcplLMMj5eeeV9lKLysg5ft/o4EnZAvP7nD3RsLvPWDcwwMp9MX1yudOOB2bSmVWG+Q7mtfEhVxKJScPD8Yfp0P166h0c89rDAMI2YXt1herwGQy7hkvW8nOFprarrBSWOU42qc6ftrTN1dYXy4yBsXJshn9z61Ud1oMHtrifu3ltAJYIDr2Vi2hVIQRZrF+2vcv7kEqnvbmdePMn5yGC/FA/COZoeYba1xPVnl6HPum2jNqr/F+cIEZfvgf1hnPIfBgRzNVkA+9+LTVHGSoAxFZeDgP/ZXzeL9NT791S0s22RkYmDXP+94NkPjJVr1Dr/611e58NYJzr4+cWhOzvZMh8liShWVzU46cR6QREUcGoNukR8Ov87fr92gFflUnKd/49nYajJ9f404jslkTRId4us67TDBQGEoC9OwAUWLgDFzkOPmGIYyGByw0FqzUWvx8w+/4s0LE5ycGMQwdv5m1WkHXPvNNIt317Ack/JQAeMZTXve1xKhwA+5/tu73Lhyj4vvnuT4+fFUmv0sw+S9wfPMzc6y0tmiZOeeOqXTinxqYYuzhXEulY4fmjfws6dH+PUn0z1JVGrVDieOVXAdees8SGbvrPDpr24yMFTAdl/s3yZb8PCyDjc+vUcURlx4+8Sh+F3vxAG3asupxHqTdFe8yatNHCoVp8DPRt/mevUeM61l2snjHRK11txf2ODe4grK6RBaLRq+7vaKaoVSgIaYmA4RJooTxiiDXoavrwxRSlEuZAijmE+vzbK22eSdS8ewdpA0LN1f47Nf34JEMzhe2tUbnOPaDI6ViKOYa5/cYeHeGm/+4Bz5FE7sdU2b1zPHyA0McrM+T7XTxFAGCoXWCQmaAafA94cucCRTORRv3A+NDBUZHS5SrbUpFffeWxKEERrN+TPpNhKK7S3Pb/DZhzepjBSw7N58pBmmwfB4mVtfzmHZFucuP6/WuP880+FC8Xg6waSiIsT2XNPm7co5jmSG+OuVj1npbGEpk8Wlde4szOFlE0xlYSv3US9KojURMaGKMLCZUAMMkANiltrTmMqi4h6hZA892lfEtkxGhgrMLW2SJAnfuXxi22Tl7o15vvj4NuWhAo639yZO0zIZOlKmvtnkV//qc97/w9cpD/Z/vtxSBqfzY5zMjbDu12lEbcIkxjEsyk7+QG/sth3DULz+2gT/8hdX2ag1aYcxrU5A8mAax7MtSvkM+axLMetiWd9upIzjhI2NFu+9c4psitNyYnudls9nH96iVMn3LEl5SBmKobESX302w/B4ifIeD7RMSycOuFlNp6LyllRUhNiZ0cwAb2VOMjpyhL+c+pAb89N4eYvYsIiBAB90t4xiYpBRDqOUyCvva3uyWNiGS6xjVjozNMJNRrzjOGb3m7dSipHBAvPLVVx7nrcvPX3p8r2vFvjio2kGx8uYVm+mawoDOdqNDh//xRf84I/fpJhSX4ShDIa9EsM8uxHxsOgEIfcXNrk5s0Jswt27a2QyLvmci6FMtIYwSlhYqxEnMYYyGB8qMlopkHmwYigIIzY2Wrzx2gTH9tD7IPpDa821KzNorV/oi8F2DNMgV/D49Ne3+eCP3zjQq4E80+FiWhWVLamoCLFjkQ64s/Y5S7c3ead8BsOEkIiE7sGCCrAxsTC3na4wlUnOKuHHLe43b3Ake46s9fgb1PBgnunZNcZHSowNF5/42Y3lKl98PM3geLFnScpDmbxHkmh+89fX+OAfvo3Th+W2LyOtNYurVX57fZY4jinmM5wqDDEyUOD23VWaTZ9czsUyDUzTeNRzkiQJS2s1FlarHB8dIGPZKAPee+fUoUxS4iShHvg0/IAg6S7Jtw2DvONQcF2sQ7xvyOZanYV7qwxts7KnF7IFj7XFKvP3VjlxTqb99sOeE5XJyclR4P+cmpr6oIfjEWLHwsRnPrnOwlc2xUwB+0GSYLL30rxrZomSkPnWFEcy58nZ3aTEUIpyMcOVa/f5gx9cePTBFgYRn/5qinzJw3zKlEEv5IoZ1peq3Lo6y6XvvhpnEL2IKE74fGqeO3NrDBSzTzS+5nIur188wspqjfmlLZpRgmEoHNvsThNqjaEUURhzbXqRk0cH+Ue//+ahWuWjtWa93eb2+jp3NzdJdHd3Xo1Ca02nGRAFMTrRTJRLvHFsjBPDlQO/y/A3zdxcxs3YqfRLFQayTF+b59iZkQO7KVwnDpiqraQS622GU4nz0J4SlcnJyQHgfwIOz6tXvFQSHTNd/5TNWptOPcNgpXeVBsvoXmuxc5tjxkXcB9NAnmtTa3a4v7DBuZPdOdrpL2bptAIGx/r7ra4yUmT6yznGTwxRGSk+/wdeUVGc8JsvZ1hYrTI6WHjqh5hpGoyPlRkZKdJo+DSaHeqNDnGcdBupiy6Fgkch59Jo+/zmxn1+9M5ZMn2aXuilmu/zydwci/U6nmUxlM1iKEUcJ2yuNVma26LdDrv95WiW727y8ZU7HBkt8UfvXuTsxNChaJTutHwW7q5SGU3nteB6NmsbTTZX6wyOHswp0XSnftrpxHlgrxWVGPjHwP/Vw7EIsWOrnVnq0QbrSxa5fO8rGZZhEycRK517TGQnMR70tJQLGW7eW+H08SHiIObOjflUNoZShiKTd5n+co7Kz17re7zD6urUPAurVUYqz/83MQ2DUjGz7UqgAcdiq9bi15/f4cfvnn1qo+1BcXt9nU/m57ANkyOFx48/8COmv1qiUffxsg7F8uPHW6Rbgdmstvjn//Ij3r90in/w/dcO9OMEqG400YpHzfJpsByTtaXqgU1U2ilWVN45DBWVqampGsDk5ORz7+v7Pjdu3NhLmJdSp9OR5+MFBbrFrP6cpJ1lcysANmk1+/OG5esVmgTkjMqjv9usdvjo7wOiWofFhUVaQb0vsb9JJ5qZu/exiiFervcrTw777+baVovPbq0wWPSYa2719Nr3Zzv4jQ1OHdnZeVNpP5e3qlW+2tqk8qDvpPng76Mo4f7tTaIwwcvaBPXWM6/hWAl/8dHn3J2b5Q/fOHGgpji++XzOTK2wurpJJ0zntQfgd0Jqv90kcZrPv/M+yEhFZe9c1+XixYv9DnNo3LhxQ56PF3Sn8TkTwTGqaza2dZfh4f5l97GuECYtjuTGMY3uy6VQblMoD7C1ssTZC6dxU1yumvNqlLKDnL7Y+30dDvPvph9ETH/0FRfOnerL+T7jScLaZoOxiRMMFJ+/r02az+XN9TW2/A5vnTn7rcMS791eJZeJKYztbP+Y4cGEuaVNlhOHn10614/h7sk3n8/N+xHemcITGyb2m040W2t1Jicnd5TEXblyJYVRPdaOA76qrqYS612GUonzkKz6EYdKkHTY8OfJW4PMbFUxrf6Wfk1l4GtNM9qi6HRfnJ5jsbiyRVRt933FwTd5WYe1xSqnXzv4G1ClaX5liyCMKBX6c1igaRh4js2tmRXeu3yyLzH2otrp8Ju5eUayuW8lKWEQsb5cJ1fY+blVhmEwPJDnV1fvcPn0EYZzB7MNsd3yU/2CAN1pJq27Zz057sGpNj2UMR1eK6V08nv1JauoCNFLm/4SKAOlFFtbAY7d/zcM2/DYDJYfJSquY7G8tEVBJ32P/U1uxmFjpdrTQ/YOuyTRTN1d6VuS8lAh7zG3vMXr54KnnhuVtkRrPp6dxbPM/5+992iOK8vyPH/3PuXPtYQkCVCCIsjQmRkZqauyukSPldlYj1mv5hvMN5hlz6a3YzaLHuu2WcxqVtM23V3VVZVWVVmpQmRIMkhQC4DQcIfrp+6dhYMawgECToB8v0UIwP295w73d8/9n3P+B8t4saakutJCa3Y0/gHAdR2ayz6/unqTf/PexQPZwqwixav4+Gs0SuntH/gK6IQ+V2uDUVQ+OEyKyvT09M/26DpiYvqiFszjyN6C1O2GbHB/3nMsadEO64TKx5Q2Qgh8LySMBn/DMkxJFCi8jj9Q2fsgU623aXs+6dT+FjVL0RvDML9c58SRwd6oN2Kp1WKx1XqmcPZpVpdbOO7u0mAJy2RptcnDep1j+f7qcgaJYRpo/QoCBs2Bqt15msQgFZV6rKjExGyI1opmuEbSyD2+SQ1OVRAEysOUvZ20Vj1vildFFA5ezTmorDW7A/MASTg2S9XmgQhUppeXSG5hGx8G0Y7VlEdIKbARXFlaPJCBSirr0mp09tw2fyuiUGGYBpZ9MDuiulHA1bXlgZzrA0oDOc8j4kAl5tDgqQ5a68etwsDAdlUCgRe1cdfdajWvLvUSp3yeZanawB7QNOOE3VMaXjVeGPJgrc7QFjUkUgrULuNZrSGTcFhtd2h4HhnnYKl35eEcKwtrJNP919+8LF7Hp1jJHtjv32AVlc27x/aDOFCJOTREOuBpFSORMFhbG8y5pZCEBADrwZKgj2HK+4JGI1/VyQ8g9WYXZ0A7a9M08IOQMIxeymvE8wLmZmvMzqzgdUOkIUhnXCYmy5TKmW2VkLrngWBLJclN2dRW27tSAJRS2AkLD03D9w9coJItpFADTr122/6BttDvRj5XB9T1EysqMTGboLXuDe9ZJ5+3WV4eXApErxfPen5IoZjGXBrsrgJ6RYRSyl3XHjxPNwyo+x6dMGCm0ySxVsWSkoztkLGdPUmpaK3RahUVLYFaQEXLQAgIhMwijBGkMYSQQwix81uS0hpzgMZfINZt6XeO74dc+26We3eXEQiSSfvxfKjV5QYPZ1ZxkzbnLhzhyNHipsdpeB7bXUJlOMvyws59RqJIYRiSbM5l1etQ7bQ3rYN5VeTLaUxTEoXRvo2ueB6lFJU+fXReBbGiEhNzABBCrE9D7pHPOwTBYHZVGhDrKaeuH3JkrMjC4sulALTWBJEiWl9oN+rceB6v41MYyrxUQV/D97izVuVGodJhtgAAIABJREFUdZlOGAACBMzVllmYNXkUDUohGE1lOFusMJxK7zho0VqjwgdE4bdotQIYCOGCdAEJaLTuooKrRMG3CBykdR7DOoMQ/XfVvIoZNbuR/7vdgE9/f4N6vUuxmH5BNbFtk0zWxfdD/vjJbdotjzNnRzc8VicMMLe5hlTGIeFa+H64o9RYp+UxMtabAm4GkpYf9P3cQWHZJpNTo9y5NkdhaP+DqHajS7GSHdgE893QjXyuDKjr53uxohITszHmc4tXJmOhYSCtuooIS/Ty4Z4fcmSsQOPWEn432PGI+SBSrDY7PKjW6foRj9JZ+aTDeCFLLrm5ktFtBxw7vfHitR113+OLhVkeNNYwhCDvuOScJzn+wHYZeapzRmnNarfNP9y/SdK0eHdojOO5Ql/vtVYtQv8zVHgPIbNIY5NrFiZifWSY1j6R/yUquI6Z+BhpDPf1utJJh1qjgz2A9E8UKSxLYu4w9RaFij9+eptW06NUSm/5WNs2KVXSfHd5BidhMjH5oqFhPx2yQgiOHi9x/co8xvqU6O3wugFSSsojmfVjsD6HvH+01nSVT6gUhhDY0sKUe696HDk5xM0rsz2VcZ9Toa16hwsfHt/Xc7wsCcPmwqAUlS0cjveDOFCJOTTY0kUKidIRUhikUxbZjEGnG5F09/mjrMGWCYIgwrFMKsU03fPjXPn01o4GErb9gCuzy3SDkKRtkVtvMdZa0/JDvp1ZpJxxOTNcemEx1FoTRRHDW6QENkJpza3qCp/Oz2AZkpFkuq9gQwpBzkmQcxJ0w5Bfz97lXr3KhyNHSdubKx4qWiXs/gMahTBG+w4ihbAR5ghaNQk7f4t0PsK0zmz7vEoxzfxynfQA2rU7XkApl9pxYLywUGNlqUGlzyF6hiEpltJc+XaGsfEilvXsQp8wTaI+jpMvppg8WebuzSWSmcQLx3maTttHRYqpi2M468F3pDQJo7/vlq9C5jsr3Gg8pBV2gV4IbgrJifQoR5IVMtbeed2ksy5nLh3hxrcz+zoUdG2lxcixEkNjhX07x14QKyoxMQcAIQQZs0g7apAwervw0RGHhcX9DVR6nUUaWyZYrbW5cGoUQ0pGj5X47tNbKKX6SsV4QcjlmSU0kE8+260ghCBpWyRti9Vml+t6hbOj5WfSA616h8pYgXRuewv3RwQq4vcP73N3rUrFTfWVXtqIhGlyJJ1lsd3mv9y+xi+OnWAo+aIyoFWNoPt3COEg5dbKwWYImUaLBJH3OwRgbBOs5NLurmtGdornBZSP7qw1WWvNzesLO3KIBbAsgyhSLC6sMX7k2eA049iIPl/z0FgOyza4f3uFTtPDdkwsx+x1BUWabicgihTpjMPk6Qpu8kkQGihFMbn9522pu8anK9cIdUTGdCk7TwKyUEXcbD5kujHDqfQY53PHnuncexlOnh9n7t4KrXqH1BbDJXeL7wUopbjwwfED2+3ziIRhcyE/IEWlOVhFJW4diDlUFO1RAtV9/P/ZrEEuZ9Fqhft2zkB7uGaGINBYpsnEeG/RcFybY1Oj1Jb6q1V5UG0QRoqkvXWqKJdMsNzsUms/eZ1aadpNj5MX+rfOD5XiNzN3uV9fYzSV2XWQ8jQl1yVpmvz9vZsstp993VoHBN1/RggLscsg5RFCmAhjiMj7Aypa2fKxxVwSxzIJgn40ht2jtSZSmrHKznbvjXqHWrVNMrlzN9tUyuHm9fkXfp51EjtKyBTKaS5+cIwzb43ipmz8bkC74RH4IaWhNBfeGefc2+PPBCk9NBln6+te6FT57dIVkoZDxcmRMJ59vCkNSnaGsp3hRmOWr2u3UXvk6myYBu/9eIrAi+i0vD055iMCP2RtpcV7Pz4z0DbomBeJFZWYQ0XOHkK2n6R/pBScPZvns8+WSCQMDGPvdz2h8qjYx6ittfn4/ZM4TwUaZy4dY+7uEl7H33L2SBBFzK81yST6S08kLJOZaoNiurdLrC43mJwapTzaf9fBp3MPmGk2GE29XNDwPEnLBgS/un+Lvzpxlqzde01RcBlUA2H2V1uyHUJYaJkm9H6L5f7lph1BhpScmahw5dY85cLevtanabY9hkuZHaeY2m2f3QoIiYTFynITpfQz6lrSsii6Li3fJ7VFGu5ppBTkCklyhf4UOS8McS2brLP5It0Ku3y6Mk3OSuIYWwfgUkiGnBx3m/PkzBQnMrurtXqeTD7JD355gT/8/ZWeMrQHyorX8VlbbfH+T6YYHt9ZqvVV0Yl8rtQGY/j2fQb7nsSBSsyhwpQWFecYi9490mbvy5JOWZw8meXGjTrFor2nEm2oQgxp0aoLThytMPrcbtpOWLzzoyn+8HffUhoxNy3qq3f83tyVPq8tYRmsdTy8IET5EZZlcPbdyb6v+0G9xo3qCmPp/emISFoWXhTyycP7/MnEKYRuEwVXEMbeOrYKmUGHD1HhAwxr82LGoyNFrt5ZwA/CfSmqVVrT6vh8762JnT9X6V2bGD/6LGul4bkuofNDFf7l3v2+A5WdUu12eX98fMvP7P32Igi2DVIeIYSgYGe43phhIj2MsUcpoHwpzcd/fpEvf3Od5fk1CuXM47bvnaC1prbUwDANPvrTC1QOeF3K07gDTf20BnOedeJAJebQMeweZ9mbIVD+458dPZKi24148KBJoeDs2jr8abTWeFETxzvC6HCeS2fHN3xcZazA1LuTXPviLuWx3Ib1KuEOLULF+lyZdttDtUN++BeX+u4u6oQBv597QMl19zWvXki4PGw2uFld5kR6cf2698HTQuaJgm+R5uSmr8dNWLx79giffHuP4VJmz1/3aq3F6YnKrhQb05DP+P/sBKV6HW1yA6VwLJPFNU06QYBr7Y2vziO8MMSQkonc5mmuUEXcbsyRM/uvmQKwpUnNb7LsrTGc2LtAIJNP8vGfX+TW1Ydc//pBzwummOzLZ0UpRaPWxu+GHD05xLn3Jh8XFB8WOpHP5drWadK94gcMNoCLA5WYQ4ctE0ykLnKz+fkzM39OncwipeDuvSbZjIVtv9xurR208JtpLkwc4f23jm3Zknr60lGUUlz/6j7F4cwLM0ikEDteq7xOQKfp8dM/f4dCpb9uEYAb1RWCKCKR2N9pwgAVN8kfFx8ybl3HMvfHDEvIJCqaQ+saQmx+gzw6UmB2cY355fqepoDqrS7JhM35E7tzJc1k3Z6nzHPpm35oNbsMDW9s224ZBj84epRf3b7FqGnuqZ/McqfDjyYmtgyAqn6TUEW7aj1OGDYz7aU9DVSgV7Ny5uJRxibKzNxe5O61ecIwQhoCx7WxbRMhBVprwiCi2/aJQoWUgiMnKhw9OUyhcrDM7frFNWzeyvdfw/ZSxIpKTMz2FOxhKs5RFvj88c+kFJw8kSGXtbg6vUanG5HNmLvaXa/Wm4SB5k/feY+pY2PbLjBCCKbemSCZTnD5k1uYlkG2+MQcKmmbfav/Wilq1RaWZfCzf/0uxXL/QUqoFFdXFikOIEiB3mIZRB5zrQYT+zi8TmgJag3k5gubEIIPLhzjd1/dZqXWpJR/+WCl3uqitebjd0/sOqXkJm3Gx4ssLzfI7rB+otsNOXFqaNPfj2ezTJXL3FhZYXSP0nyLrSYT+RzHt/l7BjrctVJkCYNu5G//wF2SzrqcfWeCk+fHqa00adRaLM/XadU7KKUQQuAmHcYnK+RKKXLFNIktaswOA53I59sBKSofxYpKTMz2CCGYSL3FXW5TD5bJmKX1dImgUnHJZm1u3a6zsNABIcikTcxtctZRpGk2A7pBl1ze4C/e+wVDuf79AoQQHDs9Qmkkx+Xf32TxYY2Ea5HOJUk5NpmETTcISWyy4KlI0Wl6REqRrmT4+Y/O7ShIAZhrNQhUtCcdPhsRRgqvG+B5weOBdypq8rnncySj96WYGUALCxUtIc3JLR9nmQYfvX2czy7fZ25pjWI+hbULi3WlNKtrLZKuzcfvnHhpj5bJExVmHqySTif6VlXabZ9kyqFY2joAeX9snI4fMNtsMJzcucfL0yy2WxTcJD84cnTb44in/rlTNL1Bn/uNZZtURvNURvOcOLdx6vZ1IVZUYmIOIFIYDInTJO2IJX+GlJHHlD2p2nEMzp8rcPx4hoWFDjMzbYKgZxcvBDwqI9F6vdgRgZBQGA45OlrknZEfgLBZ8daIdG8HZkuTlOFuK3WnMi4f/ukFaksN7lx7yPy9lZ53ipDcarYwUi6GIdAKwiAk8CO01hiGZPhYkVQ+BaZk8sjOC1NnGms4fRp09UsY9RSe+bk12u1eC6he/4cQoFWDauBh3FlkfMhhcjJJqWTtSZ3QI4Sw0Lq/m6NtmXz09nHuPlzh6+nZnnFdxu3LmVVpTaPVpdMNmJoc4tyJkV0FOs9TLKU5dWaEm9fnKFey2743nhfQbnn86KdT2z7WlJKPJyb4dHaGW6urlN0kjrn1Z8CghSmaWKxhiQZK+1S7AafSQ1waO48tm2id2zJYsaUF7K7N2FcBZXP/TNreRDqRz+VqrKjExBw4pDCYTF8g65e517qCp8A1so8NpdyEyeREholjabpeRLsd0WmHBIFCA4YhSKVMpO0hbI+MdZRAp/j10hUCFa1biIN41LghIGsmOZ4eYzhRwpYbf4WklBSHcxSHc3RaHvVqi9pyA2t6hqszy7imgWtbJDMu2UIKN+XgZhK0uz5BpPjJ28dxnZ0X8y22WyTNvSkCVEqzsFBjdqbacyhNWGSy7oub6Mgn6ErstKRWC/nDH6q4rsFbb6UZGdkr/wnBTlpnpBScOFJmqJjh1oNlbs8uo5TGtkxcx8K2jMeLcBBEdPyeSgQwNpTj9MQQpdzezXURQnDuwjhKKW7fWCSdTWzoqxJFika9QxRpfvDxaQrF/tJXlmHww6PHGM9m+WRmlmq3SyGReCZgEUQ4Ypm0uIUlqwgNoYa6J9BC8FY5z3C6DeHv0SEgM2jzPMI8uuHspbydxpEOvgrWg5b+8XXE0eSLowFids9AFZVWrKjExOwIIQQlZ5yMWWSue4ul7gygcYwUlnAep4TchImbMKHYk/FDFeCpFpHuYpCiFqRY8luYwiNjJjE3sBt/NMfk6+oNpLjFVGaCyfTolm2WbsrBTTkMHyky9c4Ec8trXLk9T63RQQqBlIJOpOg0OoyVs1w4MUIuvfMaEz+KaPgew8mXX2DbbZ/btxdptbxeumLLlI5EC2hrzZGMRSZj0u1GfPrpGseO+Zw7l8ZxXrYNNUKw8xqCdNLh7alxzh4fZqnaZLnaXP936/EMm2TCZqiQplxIM1TMkNqnWgUpBW9dOkq5kuHm9XmWFutIKTBMA600UdRT7o5Nljhxcpj0Dp1shRBM5gsMp9Lcq9X4bnGRarcLaLJmg2H7MpZo0dVJVsPeZ8QyJEfyOYbTaRLPqTBad8D/BB18iba/jzCOPKOwGEJyOjPGlbW7lJ3+1ZFu5JM2ExTtw1m0elDp1aisDuRcP2SwU6TjQCXmtcE2XCZSbzHmnqbqz7PizdKKqo87g54g0CgcmaRsH6GtbG41F7CEQcXZuiZECIFrOLiGQ6girtTvMNdd5p3CadJ9tmmOlnOMlLJUGx1qjQ5hGOHYJqV8irS7+1oIP4oQ7G6y79OsrbW5Pj2HYZpkc30ETMLGFOA91YKdSBg4juThwy6rqwHf/36eVGr3KRStu2Dsfgfu2CZHhvMcGc6vH0+jtO51Yw3QGl0IwehYgdGxAmu1NisrzfVBgIJUymFoOIftvNxt2bUszlYqnC6VqHZadDtf4Hvf0gldIvJkpeRowiFp26Rte9MgWwgXDLf33nv/jDaPg/3hM+rKkWSFm81ZWmGXlLl9YBVpRS1o8YPSWYQQKK3oRC26UZt6WMOLOmgUEgPXSJK2crhGaoPvcMzzuIbNxYEpKi83OX6nxIFKzGuHJR2GEhMMJSZQOsKL2gTaR6/XmhjCxJEpDGFyrXGf6437lKzsjtssTWkw5OSpBy1+u/QNH5UvkrX6UzOEEBSzSYrZnXlQbIVC77oL4xH1eodr1+ZIJu0NFaUNETYCQaSftbAXQlAs2tTrvXTQD39YwHV3GaxokHLvdnG9z8Grnd2SyyfJ5ffu7/88UmiK5jfgzuAnztDyYa0bUu1G1D0NhCTMiIJrknUM0raBsUE9jBAJtByF6AHaa4HzU4ToBdSOYfH90nl+u3wZFbTJWJu/nkCFrPhNLuaOU3LSzHbuMtu+S6h73T+GsDCFyaM0X9VfQXfuotGsqiqlbo6iXcGSh7s7Z7/oRD7fVAejqHwcKyoxMXuHFAaumWEjXeBWY5brjXtU7PxLDUnLWilaYZffL1/mx5W3Sfaxs9wPJGLXDqjQq9W4Pj2H6+4gSIFeRS1ZpG4DLwZq2axJvR7wxz+u8dFHhR13BmkdrM8POhxW5gcBrTXK+4zVxh1uVtPcWav1PhsaLFM8NrkNlSZS6z83BFOVBMfzCdLOs39/IQSIIXS0hPZ+Dc7PEKJXl5K3U/ykcpHPVq+z6NVwhEXG6k0611rTjXyaYRdTGrxbOIEh2vyx+msEgqSRISU3TgElnrqEKmvcal7hjpAcT52j4ozu2WDD1wXXsLlYGJCi0o4VlZiYfafmN/mufofySwYpj0iZCWpBi29qN/le6fwruYk+qjHQWu84naE1zM3VcZw0lr1z1SOSOZKyscmxNZmMZmlpjdu3u5w44YAwkMJ9vDPf8tpUFcO6uOmsn5gXqTZu8NndKyy20yTMgEpye0O4IFJcWezyzXybU6UE74ykcJ5r6RdGBR3No4OrCPvS459nrCQ/H3qbVb/BvdYCs50V1LqCmTWTvF88TdoyudP6jk7YJGMVd/QdMYVFzioRqpAbjcsse/OcTJ8jYeyfInXY6IQ+365WB3Kujxlsx1b8zY9544i04qvadZKGs2ezRgDyVopFr8pMZ4ljyb0ZzLcTTCnJOwm6UYi7w86farVJvd5lcrJ/35inESJByiqhVRMh0+s1BS1CtYqO6mgi0hm4fFlSKAQk0yC07tW3GMMYxihyg5211h4gMMyTu7quNw2lNdMLD/ji7m9IWEnGsv2nSSxDMpSSKK25W/WZqft8dCTNWPa5YFJWIPwGbYwjjCefl15Re5aSk+U9ThNphaRXA1T1lvmu/jmOTJCzdz8PypQmBbtMK6rzTe0TLuQ+IGXGRbkArmlzsTCglEwnVlRiYvaVZa9GI2hRcfbeCyBvpZmu3+OIW3klqspwKsOt2sqOAhWtYXa2RsIxd1XjorVGC8jYZyH8lEjViKJ50B5ggHCQQiIlGKbm4VySqSm5/tyQMJojCB9gyDyWdeZxwKK1RkfLmM5PEHLvWoVfV0Kl+PT+DDcXvmIoKbF2mYKUQlBJmXQCxa9u1/lwPM3ZypPkqRAGWqfR/h8R7p9tepxHm4C1YJWrjS9IGmks+XLGeY9IGVm6UZvLa59xMfc9kub+Tc0+LHRCn2+rsaISE/NacKsxS9LYnzoSW1qsBS1W/DoVZ7AFZwBHMlmuri7u6Dmtlke75WHtpC7lKToqpGi6WIaDHyYJ/M+QsoiQLy4e2SzcuwcnTmgsSyCEiRC9TiulWnS9T7HME5jmMYiWMMyTSHPzqckxPSKl+MO9B9xbXWY0uYKQO3M03gjXkgwbFp/ONhECpspPBSsy20sBqSpii7EGXtTlWv0r3D0MUh6RMJJ0ohbTja+5lP8+xhueGnydFZW4GinmjaIbeawGa/sWqAA40mK2vbRvx9+KoWSalGXTDcO+n1OtNjG2GS+wFR0VMuG4+P6XKN3CMM8APvq5LiDoGexFoaZWe/E4UqaQMkcQ3sTr/hqMUQznBwNtHz6sfLewxN3VKkPJLkKA2CM1z5SCkbTFZzNN5pvBc7+10OHtTZ+rteZO6xoA9h4HKY9wjRSdqM1M+86+HD/mYPBmh6AxbxzNsAPsr3dGwnBY8df27fhbIYXgrdIQn8zPMNpn7r6+1sG2TbqdnZ/PVxEJIK2n0XgY67trLWyiaAatAeHy9NstDajXNZXKBn8D7SORaJEkIsQawDyYw85Ku803c/MMpVOI6AGwt0GBKQV51+D39xv81Zk89qOgVmYgmgHe3/i6/AWW/Xny5u5rUvohY+aZ6dym5AyTNl9eSTqstEOfbwaU+vlRnPqJidk/GmFn34eh2dKk7jXxVbipxf5+cjxf5OrKEk3fI21vvWgppWm1PdK7cMLVWtMIPC4kagjaSPFEdhYyhyGT6GgepapoLUA4gEkiASsrcPLko+OEoDtAAKQx7PeRMkcYPSQIvsa2N14IY56kfFK2hSElOqiB2PvJ2UnLYNELuLzY4b2xXr2QEDY6WkVr74XuLa01D9q3SMrMvitiUkgs4TDXucfpzMV9PddB5nVO/cSBSswbhR/5GAPIeArxyABt8F8xSxr8cHyCv7k7jWv2FrDNCIOoN8toF29JPfQZtiKKchUhXuwWElgI4yjCGEarGkrVQDcxDUGzIdAqpGfuYSPlEMIYQcjs40DSkCP4/rcYxlEMY2jnF/gGsNhsUWt3GMlm6AV6IULsz+TscspkernDuYqLa61/YASgGmA8G6g0wzU6UZOctb9qyiOSRpolb45jydM4+5jWPch0Qp9vBtSe/ONYUYmJeT14lbbflWSKt8ujfLU4x2g6s6mHhtJ6V/pSK/KxBBy35jCM3JYqlcBGyCGkHEKjQHp0vQjDziOEg2DjDiUhJFJk8bzf4rp/vWd1F68TVxeXSNrr798GNUF7iVyf0Hl/zXtSWKsF8GI91KI3h7HBIMP9oqfaCKr+EiPu0YGd9yDhmjaXigNSVB7GikpMzL5hS+sFq/f9QGt2bMm/11yqjOBHIVdXlxhJbRys7EaVb0Y+aHgnaWCrcNNAYyMEEnQC04yQYvuWUinThNFDlFrEMEZ2frGvMU3PZ77eYCg9uNbtvGtwdbHzTAfQRqz5yyTk3qegtsKWDmvB6hsbqHTCgG9WHw7kXD9msLVAcaAS80aRsZIv4zLfF4EKcQ0HW+7MdG2vEULw/sgRLMPg66V5Ck6CpPXsLtc014OpPt4UpTVrgYdrmLybGcaMvgSxc2fQMFS4yf6DOClcguBaHKg8R73bBfHUEMp9Svk8jWNKat2AdhCRtAwQmueXkVAFdFWH3BZzf/YDWzrUw8GkPg4irmlxqTA+mJPNbexCvV/EgUrMG0XK3P9dXjfyKScGm8PdDCkE7wyNMZLK8NvZ+8y3m5QSLta62mMYEjdhE4abq0xaa9oqpBsFTCbynHALGHh4Qetxl89O6HYVI6P9d6YIkSMM7z+e+RPTY6Xd6c13eowFmOvDN/cxTaah4aleoKKB5/xyPLX/BesbYQiTQAUEyn8jBxf2FJXZgZwrVlRiYvYR13Ao2BnaYXffhgd2lc+4e7CKP0dSGf71ySmurSxxbXUJX0WkTJukZZHNJlhefjbnrLXG1xHdKCJCUzJdLqWGyFu990xFbcQutako1OTzvYDDixTtKETpXhtsyjAwnyv+FaK3HGvVQBjxYMJHLLdauNZzgZvMgWruS+fPI4QQNLyQoRQgXYR49nsUabXvquVmCEBp9YrO/mpxTYtLxYOhqExNTf1H4DzwX6enp//dFo/7P4C/mZ6e/v+2Ol4cqMS8cZxIj/P56tV9CVR8FZKQNiXnYCgqT+MYJm8PjXK+PMTDRp3ba1WWOi0CV7DqdQhUgBF0Hy8yacNiIpFlxEmTMp7doSrVZDd+kUppAq1YMX2+nF+jFUQIoektMRoNlGyb0+kUw4kE5vqYXw0o3UASByqPCKIIQz6nXMgSqBXYcF743iCEJogAtQZWPIPpoNAJA74ekKLyky0Ulampqf8RMKanpz+ampr6T1NTU6enp6dvbPC4HwMj2wUpEAcqMW8gQ4k8adPdF1WlFjR4O396T4cd7jWWNJjIFZjIFdBa0/R9/m7lWxaXF5nIjGIIiWuYmFu+Bm/H04w1mjvLbdYyPlFHkTUthhLPBkBaa9pRxO9Xq2RMkw8KeUqO3dspq+7OX+wbhjDK6PDGvqd/esFsiNhgvIEh5K7VtpdFo1/JjK2DwAFSVH4G/D/r//13wI+AZwKVqakpC/g/gf82NTX119PT0/95qwPGgUrMG4chDN4pnOFflr7GMew9CyrqQYuyk+No8mClfbZCCEHGcXj/3AT//W8XHqd2tmdnC5FCc7Pe4s5am/fOZ8k5m7UkC1KmSco0aYUh/7i4zPvFPMcSAnjzJP0givCiCNc0X/DDsQyDTvB8a7AJcgTUAoj9qSPQWmCJFsgiQr6ocDly/wvWNyJUIbZ03sj6FBhs18+lWlSempr6/Kkf/Yfp6en/sP7fKeCRtLMKvLfBIf5n4Dvg3wP/y9TU1LHp6en/fbPzxYFKzBtJwc4wlT3GdP0eZTu/qc9Iv7TDLhGKt/OnD+WO7ujREpmMQ6PRJZPpJ1gx0Fr31d6s0dxptrm32Ob0iSS5nEWzq+j4CqVBCkhYgnRCPuNimjJNHCn5fLWGyAecdN6c25XSmstL81xZWkRpSBgmH46Ncyz3xCejmHRZbDZJO88uzMI8hvYX0DrcserV37UpMnYXYf98w9+b0iRhpAZe1Bpoj6y19xPRDws9RWVsIOfKe43l6enpDzb5dZMnucc0G+eI36UX3MxPTU3938D/BsSBSkzM85xJHyWIAm41ZynZuV37njTDDr4O+WHp4kC6ivYDw5CcmarwcDYgkbC2naQsZYZwA6OvjVj1Au5XOxRSFlbF5Ku7HRod1TNx6Q0DQqNJOZIjJZNyxnxce2FKSdG2+KLWYCzn7MCx5XBzY3WFL+fmGE5nMKWkG4b88727/OXpM5TcXttvOZUiUhtMyhYOmKchvIqmuCuvnC3RDTLpdxFGZdOHFOwSi92HAw1UfNUla765NUydMODrlcEoKj9lyzlif6SX7vkD8DYwvcFjbgIn1v/7A+CADE3nAAAgAElEQVTeVgeMA5WYNxYhBBdyJ3CNBFcbd3CETWYH3g+RVqz6dTJWku8VzpO1Bme8tR+kUjbvvTfMZ5/fplRKP/FY2RC3rwbUSCuurTQwIkEwZjI95+M6gnzqxWN7geL6Q585N+LcuI2zbtNuS4lEc6XW4KPh3b22w8aVpQXKydTjDqiEaWKbBreqq48DlWyi1+LdU7ae/Wv0xhZUQS2iye9ZsOIFayTsDEl365k6ZXuUh50t1549RWmFQFB0Ng+eXndcw+LtASkqzG9Zo/L/Av8yNTU1BvwF8G+npqb+3fT09P/61GP+I/Cfpqam/i29vvp/s9UB40Al5o1GCMHJzDiVRJ5vajdZ8mrY0iRtJjetXfGigEbURgBnMsc4lRnHGIDZ1iA4crSIUoovvrhLNpckkdhYw5AyCRhoIgSbv/bZVY92N8I94tJVUEhv/ljHkjgWNLsRVx54XDyWwDIFmoCcneJBp8nboUfS3NvpwAeRbhi+MFDSkgadIHj8/xnHYTiTpuF5ZJwX3xNhnUEH+qlgZffRitaAXqPWdXhn8mPkNkpJ2sySMrJ4UXcgs3faUZOyM4otX//PxmZ0ooCvV+YGcq6fsrmr9PT0dH1qaupnwC+Bfz89PT0PfP3cYxrA/9Tv+eJAJSYGyFopPi5fohY0udeaY66zQqTVU02zT27ySTPBhdwJRhMlEsbrV7h3bKJMwrX44o936bQ8coUU8vk2WGFimONE4QxCvtiKHUWa1WpAwwzJn05Q72jyfbrRphMG9bbizpLPmVEHpZrY5hSEgsVug8n0678YTeYL3F+rUU4+Uekavsc7w8+6854bqvCPt+5sGKiARFhT6NCG6AFapxC7WMi1DkA3UKIC1jiThe1lLSEEx5In+a7+BbZ09nWCstKKSAeMucf27RyHAdeweLs0oBqd+fqWv56enq7ypPPnpYkDlZiYdYQQFOwMBTvDpbyiE3m0I49IRwgEljRJm+4rt8YfBENDOX7xiwt8d3WWe3eXMQxJNuc+kw4yjFHC8B4a1ZvhA3ieotEIkRLOn0/hGSELD0Iy7s4KjNOuYHEt5FjZwDYEhlnB0SFLXoPJ9GAm8r5KLg2NsNhqMd9sYEhJEEVMZPPPFNMCDGfSZBMOTc8jvVmwYp4EWUKH0710EMm+ApZegNICDDAvsNpJcqpcfDIEcRsKdoWKM0o1WCFj7p+vUCNY5UjyFClzsG6pMYMjDlRiYjZACknKdA9tcexeYDsm77wzwalTw8w8WOXO7UXCSKG1xjAMpBQEfpkgXEKQQQPJpMHFSxlGRhww4VfXVtGKF43JtkEKgUawuFZjYugMCBvHENT99v682ANG2rb5y1NnmGvUafo+xWSS4VT6he40Q0o+mjjK307fwLWsF1qYHyPzCPsDdLTaU1dU7dER6JUI9LTD3iTk9SJpkQDzNMIo0wnAMiPeHut/3pIQgsnUFLXa7/YtBdQOmyTNDOPJyT0/9mGjl/oZVDHt9gNF95I4UImJidmSdDrB2XNjnD4zQrvt0Wp61BtdAj8EkUHr35DLJkilUrjukxZjL1KsNiOcbTqINsO1fZYaCU6OHXn8M/WqvNlfAbZhMJHfXsovp1K8NTzM5fkFRrNbdWMYvU4dowK6DbqNVk3QjV4RihCAizByPQt+kQIEkVJU2y3+5PQJHHNnS4ZjJDiXfY/La58hhNjTGpJO1AI0U9l3XpsasZfhIKV+9po4UImJiekLw5BkMi6ZjMvI6JOfh2GCbvfvMGQC8dSCYUpBGIJt7Lw+QRNgiJBIH308FThU0QsFpjE93hodpt71mKnXGU730X0mkiCSCLl1Gi1SioVmi/ePjDGW211qJWvleSv3AVfqfyRUAUnz5XbjWmta0RqGMLmQ+wDXGOyU5oNKJwz4ejlWVGJiYmJewDTHcJyf4Xn/hCELiPWBeIYQpE0TL1I4W3QGPY8mAO0jjeMo/ST11okCTjtb+je8sZhS8tHkUX5/7wH3qzWG0qnN00B94oUhK60O7x0Z5dzwy7X9Zq0Cb+d+wM3mZdaCZdJmYVcqSKgCGlGNsj3C8dTZgXQUHRZcc4CKykKsqMQcQLRqoNXq48JJIYsIGS8aMT0saxIhfonn/Rp0EynKCCE4krb5aqlF2t5+UdLQK94UJoZ5Cj+0cJ9KGymtKCfiz9xmWIbBx5PHKLgu387N41rWY6+VnaC1ZrXdQQjBT09OcqyQ3/5JfZA007yV+x5z3fvcb99Aa0ga6b5M4QLtsxasYAiTqfTblJ2Rfe0kOox0woCvBlSj8rNYUYk5SKhokSi4go4egBZoAaK3oiDMoxjWBaRxeGbbxOwfpjmOlH+N739GFN5BiATvj6b5fL75XIP3s2g06A4ahWGUMeQwCINO0GVyuLdItkOPnOVStA+3qd5+Y0jJxdFhxnNZPrn3gPlGg4TZC1i2GxMRKUW10yVUEROFPO+Nj/fd4dMvUkjG3UmGnDFWvAVmO3dpBz3zMImBIUyEEGitCXWwbg2gEUjOpC9RcMoY+zAW4HUgVlRi3kii4AaR93uQLshhhJCPFxutNVqtEHb+BsP5CMM680qv9TCilKK20sTrBERhhGEa2I5JvpzBMA7fvCDoGcE5zk9Q1jmCYJrxzC2OpkKWOx4lN8GTcEWBDmE9hJGyhJAFpOyleiKlMKSklHZRWrPmd/jZ8Nl4F90nxaTLvzp7muVWi+uLy9yv1YHebCbbMNeDFk2kNH4UAb0g50ylxPFikby7vykVS9qMuEcZSozjqQ6dqE0zWMNXXRQKicQ1U6SMDAkjxe3FO5QT/XccvYkMskYlVlQ2wYsCqn6bmtemGXoorUhZDgU7Rd523wi3ykESBfcJvd8ijCGEeHFXJYQAkUOLJKH3O8DBsCYGf6GHEN8LmLu/wq3LM7SaHlKuN11I0Apsx+LkhXHGJsq4qcP3uRZCYBhDGMYQtv0+f3HmDv/XN1/QDj2SpgQEQhgImUIIFykceGqXrLWm1vY4OVzAMCSLnTqnMsOMJvcmBfGmIIVgKJ1mKJ3GC0PqXY9at8tqq02get5ACdOknE6RdmyyjoNlDLZ7RgqJa6RwjRRFe/M6mDhA3Z5YUXmFNIIu02vz3GkurU9aFdiyd9lBOyJSCtAcSRU4mxul5Aw20nsd0VoR+Z8jjOKGQcrTCGGBUSLyP0OaRxGHcHLwIHl4b4mvf3eTKFJk8kkqoy8uvoEfcu2re1z78h7n3pvk+NnRQ3ujljLJ2dHz/A9ekv9y/TJC2mSdzXfrWmuqLY+RfJqxUpqFzhpjyTzvFN9s19GXxTIMFJpQKVzbomgnGUmnN3G0jTmMdMKAr5diRWWgKK241Vjiy9X7GEJSdNKbzl5RWrPcbfL3rSucy41yPj+OtctJuDGg1SJaN5FidPsHA0IkULqKVgsIo7/nvIncnZ7jmz/cpFDOYjmbf/Us26Q0lCMKFZc/u4XX8Tn77sShDVaEEHw8OUnCNPmv17/jXqdGOZkkZT8pooyUotkNUUoxXkxTKbksdRucz41xIT++68nWMXCnWuXruTmavo8hJRJBqBQaOJbL8u7YWBywvAa4psXbxQEpKouxooLSii9W7nGjvkg5kdk26JBCkLOTZHSCq2vzrHptPh46hW0cyJd34FHhHYTcWY5ayAQquI2MA5UNmZ9Z4ZtPblIazmFsOZX4CYYpqYwUuHH5AY5rc+LcgCaj7gNCCD44epQzlTKfzNzjN/fvsNruucxKITGkpJJ3KWZSJByDkpPifP5MrJC+JN/Oz/Pl3Bwl12U082zHlNKahWaLv7l+g1+eOknBfXNdmF8H2mHAVwOqUfl5rKjAt9UZbjQWGXZz21aqP40UkhE3x2K3zh+Wb/OjoVPIOBWxY7Rq0bPV3gk2Wrf243IOPVEY8fXvbpIvZfoOUh4hpKA0lOfqF3cZmyiRSB7unW824fLLU2f52fHTzLfqrHkdGlEHU0pStk3eTpGzXVIHpObMD0La3WA9xQymIUm5DuYhKHZ+UKvx5cM5RjLpDT1VpBAUky4Nz+Of7tzhr6amsAdcoxKzdyRNi3diRWUwLHbrfFebY9jN7ihIeZqhRJbZVpU7zWVOZuLW2R0j5LqpxU7QsANTrzeJpbkagR+SK+5uF2KYEtDM3l3m5Pnxvb24V4RlGBzNFjjKgG6sfaK1ptbocG++yvxynXbXf+H3AkEu4zJWyXJ0pEDaPRhB1dNorflmfoG8m9jW+C3jOMw3Gsyu1Tk+qIUuZs/pKSpzAznXzxmsTcCBClSUVny+fJecnXxpJaScyPDlyn3GkwUSxus/7XYvEbKMiuYQO5D3tO4gzeP7eFWHl1tXZkmmX67dM5tPc/vqLJNTo4e2dfmgM79c58rtOWr1DqZlkHJtKoUXvwNKa3w/5NrdRa7cmmdsKMeF4yPkMgcndbLa6VDtdl5I92xGNpHgyuICk4X8oa2FetNJmjbvFIuDOdni2mDOs86BClSWvSb1oMuI+/IjwS1pEKGZaVU5lY1VlZ1gmJMo/6ve7rGPm5bWGqFDjDhQeYFWvUN1uUF55OVaay3HpLbaoLbcoDT88t+PmCd0/YDLN+e4PbtCLp1gqLT14i6FIOFYJByr5+K61uIfPrnOxVMjnDxWeWnreq0V6AZadwBFz3vGQsgMQvSn3jQ8b1ODvY1IWhYLzSahUgNvUY7ZG9qhP8AalTdYUbnbXNlT9SNrudyoL8SByg4RMoswj6KiBYRR2vbxWlWR5jhCxgvo8/heuOsU5vNIKfC9cE+OtRu8IKTl+URK9fwvbJOks739+UGm3urym69u4/khw6XMjv9WQghyaZfIVXxza575lQbfvzi54+vQ2keFD1HhdZRaRqAe/QYQaA0IjRBppDmBYZ5AyM2DX61hcy/gzVH6DRpP/ZoRKyoDYqFTJ2nu3Y0vIU0Wuw0CFWLJA/VSDzym/T2Czn9HqypCbp631qqKwMS0fzDAqzs8RFG083KfTRGoSG3/sD2k52vS4fbCKrcXVx5fx6PfjRWynBmrUMmkkPJwpQzqrS7//MebGIaknH+5HaJhSIaLaVZqLX739R1Kiaiv52ntEwXfEQVXEToEmUbI0gt+RE8coT2i4BrKv4wwRjDs95DGixOQHdPY0ecuVAopRKymxBxIDszqHaiIduiTsfbOulkIgRDQCn3y9oF5qYcCIVNY7i8Juv+ECucQMgki+3gOB7qOVm2EzGO5P0fIeAbLRpimsYt97SboR4W1gyEIIz699YAHK2vYhqSUTj6T1lBas9ps84+Xb1FIu/xoapJU4nAoLJ4f8tuvbmMYkswedlKV8imWay0W5qu8dUFvGbypaIGw+1vQbYRRQvQxw0YIB2FUeiMsdJ2w89+Q9kUM661nzBnLqRSWlIRKYfaRiqp1ukyVy3um/sUMnjj1MwCUVnt3Q3+OSA92F/q6IGQGy/1LtFogCq6iwln0us+7NEYx3R8i5DBiF+Pa3xQc10Zp3Xe9z1ZoNM6AAgE/DPn1d3eotjqM5DYuqpZCkEsmyCVhrd3lH769wS/eOkXmAHbBPM+V23N0/YByfu/9IMr5FF/NPuTBfJWJsReleK11T0XxP++lWY3hHZ/jyQiLNMq/ggofYiV+3ttQALZhMFUpc2VhkZHM1q8xVIpAKY4X4o6fw0yc+hkAUsg9lMifZTNH25jtEcJAGGNIYwytIyAAzL52fzHgphyGxwusrbZI55K7Po7X8UllEuTL+2+0pLXm05szrLY6DGX72znlkgnW2l3+5dod/vTiKWzz4H4+Flcb3HywzPA2RbMvQy5l88X0DJVimuRzwWUUXFkfUTH80t8jIQwwh1HRCkH377ESf4ZYH+x4rlJhtl5npd2mlNz4sxcqxUKzyXtjY+Rjw7dDTTv0+WpAFvpvrKJiSYOkaeOr8PEsn5dFr+9kU3tY9/Im01NOYvVkp0yeHeMP/3D5pQKV5lqHSx+d3PfWUaUVt1YX+Wz2Jvl0gmUvIG9nMfsI9nPJBAtrTWZW6pwYHtDObhd8d3uebMrZ1zTHI0O4uw9XOH/iiVtzFNxZD1JG9lSJlEapF6x4/4SV+CVCmDimyS9OnOBf7t5jrtHANU2yiQQCCJSi1umgNLw/Nsb5objh4LCTNG3eKQzoe7f0hioqAEOJDHOdNew9qifxVEjGcuNC2phXSmk4SyqdoNPydjUNOfBCpCEYPrK/N6FQR3y+epXfXr9LMwohCFj2aphykTOZY6TM7evHsq7D1dkFJiuFA1lcu9bosFxr7aua8oh8OsGNB8ucOTaEaRpo1ST0f48wyvuSLpVGCR3OEQXXMO23AHAtiz89dZKlVovppSUeNhoopXEti4sjI0wWCqTteCP3OtBTVAZl+Lb7TdduOFAr+GS6zJ3m8p4drx50eK84sWfHi4nZDVJK3v3RFL/7799gmBLb6b8FPwojqit1PvzZuR09bzfcDRYIGiZeQ1JJZ3rqjQGe8rneuM/F/KltlRXXtphfa7LSbFHJHrw5PQ8WagPrbDFNgzBULNVajJQyhP6ngOzbC2VXGBWU/wXKHEeud+tJIRhOpxlO9/4ee1EvFXPwiBWVAVFJpElbDu3Qf+k25UBFCGA8+XJGWzExe0GhkuHDn5/js3+8SjLj9qWs+F7A2kqTt39witFjL7ag7iXdyGchrDKuJ0C0n1nIHGlTD1rU/DplZ/vvkwQaHe9ABioLqw2S7uCcqk1TslJrMVzwUNEDhNzfoZ1CmGjpoPxvkYmfbPKYOEh5HYkVlQEhheTD0nF+NXeVhGG9VA552WvyYWkSN65PiTkgDI0X+eG/usSXv5lmaa6Km3RIZd0XFo52o0ur2cF2LD78xTlGjmxvuveytMIOAFpv/J2zpEk9aPcXqEiBF/bnIzJIwkix1uhQLgyuENC1LZarTdSRRSAxmCBB5ImiexiqFdsGvEHEisoAGXaznMuPcq02z4ib3dUXe6nbYMzNcSKzv7vQmJidUqhk+Nlfv8fqYp071x6yMFtFIJ4y9NIUKhkufHic8khux9OWd8uj75kUG/uZKnTf1vBKaSx58IquO+sDBgepKDiOSW2tShTdQcjKQM4phEQjiMK7mPaFgZwz5tXTDny+WhyQoiLeYEXlEW8XjuBHEbcbi1QSGcw+b3pKK5a8JhUnzUeVky892DAmZj+QUlIeyVMeydNpeXgdnyhSSCmxExapzN6ZHvZLzkphYiAMjdrAJyBUEQWrvwJUhcZ1Dt6tRSm9G1f5l0IKgSEbaKWRAxwmKUQKHT0E4kDlTSFp2byTH5CisvyGKyqwngIqT5C3Xb5afYAlDfK2u2ngobWmEXRpRR7ncqNcyI/FnT4xhwI35eyqE2ivMYTBhDVEXXZIuQYdL8R1TBSaZtgha6VIW9vvosKoN9RuaBOTuFfKKyrNMGXjBUv8fUckUWoZrdXgzx3zSmgHA6xRiRWVHlJIpnIjjLg5rtfnudNcRuueeZu9rrCEWhGo3pC2sWSeH+ZOUk7sf9thTMzryIhV5EShQLNxlat3quSkjRZQtnMcS40g+1jpa+0OZ0YrB3JmjGkYDHrmXhgp0k4VIQd7XxJCgg5Bt0DE98Q3gVhReYXkbJcPy8e5WDhCzW+z6rVoBl00veKhopMibydJma9+VxoTc9iZSI0wdKrIf177FoWmnMr0bcDoBSFKw/Ghg2n2lkxYmIYgUqrvepuXpesFlAqSV3Gr1UKgCV+VkBQzYAZaoyJjRWVDEobFiJtjxM296ks5cESqQaiqhNEKSnuAgSlLmEYOUxZi6TdmR7iWzV9cusDff3OdwFfYfZTMeEFItdXhx+eOH9hZP0IIirkUrY5Pyh1MN2DXC8mlB19z9JhBS0gxr4xYUYk5kATRIm3vW/xolp57hYHAADRdrgFgiDSufYmEdTwOWGL6JpdM8CcXT/PP391msd4in0xgb9CBFClFrdUlVIofnZ1kvHiwNxJjlRxfX58dWKASaUUmnQa8gZzveeLv/JtDOwj4amF+IOf6uTHYuVBxoHII0Tqg5X1NO7iMFGlMObJpy6VSHRrev+CFN8kkPsKQ2QFfbcxhpZBy+bNLZ7i3XOXa7CKrzRDTkBhCEmlFpHoty6dGShwfKpJLvkLloE/Gh3qBilJ63y3+O93/n703aY4rzdL0nu/O1+cJMzgPCA5BxpwZmZGRQ2WWVCWVyVSmhX6AdvoDWki7WrVprUWbWj+gZW1Wpu7qGqTMyurIipkRQTKCDHACSMxwwOfpjp8WDjLIIGa4OwHyPmY0g9Hdv+/iwv36ue855z0e6bhNMj5G6H+PYHAFxlLKrpoiIh+VV4WYpg9OUVmvDGafDaJA5YghpUep8c8Ui3M4tQzlNY9aZQXf60q8mi5IZ3WyQyaJtE4mZ2GoE/hBmUrrH0jbf46mRm69EbsjZupcmBjm3GiBYq1BpdXB9Xx0TSVhGoxmk4d6UvKPsQydk+M55pYr5A4wJHI31Jod3r10AlVtID2/r3s9TwehZhBicC68ERH94uhcYSJotxzu3Pk9d28/IPQTKEod01IxLQU73r07DANJtexRXO4QhmAYCienEoweS6GbTartP5CN/yWKOPx3vxGHB01VGMumGMsefUVu6sQID5fKeH6A3idDvVbHY3jEZmI4vTGAcLC1IjJsournB7pnxIul5XuDK6aNUj8RPyYMQx7eXeHG1zfouHdJZfMYxuYXWEURaLpCPNn903puyL1va9z7rs7UlTTDx+u0nOskrJ8M8leIiDg0xG2Dt6Ym+fy7h4zkkz13qg2CkI4b8M7F42iqAqRAHUaGDYQyqPSPi6KeHNBeEYeBKPUT8cJo1jt889l91os1jMQCsXQaZQ/25LqhkB0y8b2QW19XWJ7XmXrzFtbwa2jK4S58jIjoF8dGsyyX6syvlBnO9c5nJAwlxXKDc5NZMskf7jpV7SKB+0cYQJ2KDJsIJY9QDmebeER/iBSViBdCrdLi03++DQLyI4KG03kyun2vaLpCYcSkVnG59i8tjF9PMzr8Xo+POCLiaKAogrdfmyTwA5bW6xSy8QMNQYWuudtaucHF06MIp/zsfto4gZvYCCL6V+AqpUSGVTTz19GU5FeMSFGJGDj1aptP/nALzVCJJywc7xHdFuSDkcoYNOo2n/zzDf78L6+8kLkyERGHAU1Tee/1k1y/s8CD+TUySRvL3F/xaa3ZodPxeXNqkjPHCnz//bMXciE0NOtneO1/BGH3rW1Yhuso2gkU7Vhf1o84vLR8j29WBqSoaJGi8srjewFf/usdVE0hvmEWFYQVFHpjpBVPWFSr63zxr7f5xW+vDGxCb0TEYUNTFd6+cIzxoRRf3pqj3nJIJSxMfXeXxlbHpd5wyKZtPrh6mnRy6wu4oo6i6hcJvdugjfXqV3iClG1AoBnvRmrKK0hM03mjECkqEQPi7u1FmvUOhZEfOiwC6YDoTUAhhCCR0qhV6tyfXuL8pcmerBsRcVQZK6T585/GmV+tMP1wlWqtjaIoWKaGZWgoioJEbhTK+nRcHxlKsqkYP71ygrGh9K5s+VXjDaSsEgbLKOpIz45fyjaEVXTrd31NLUUcXlqexzfLA1JU9EhReaWplJrcvbVAfmiTAj/ZyzH1kmw+wfTNeUYncqQyg53dEBFx2DANjTOTBU6N5ynVmpRqbdbKDcq1Fl4QIBCYuspwNkEhkyCbij1TMLsbhNDRzA/xnf+C9BdBHd5oX94/MqwhZRvN/C2KOnqgtSKOLjF9gIpKKVJUXmkefL+EZemo6rN3Z6oSxw8qwMGtv6WUgIKmWehGyMzdZa6+e/rA60ZEvAwoiqCQ6QYj548P9Xx9IQw081cEyk1C9yZSSexrurKUPgRroGTRrV+jqFGXz6tM10I/UlQi+ky75bA4t05uEzVFU7J4wQpwcOVDSgdV6fpHpDI28zNrTF2exBrQ/JOI3uG0XZYerbO+XMFzPBRVwbQNxk4UyI9mngt4Iw4HQmhoxpuE6iS+8zGhv4RQbBCpHQttpXSQQRmEQDWuouoXESK6lL/qRIpKxIFxHY96tc3SXBUlXEBRIJG0SSQt4kkLRVFYnishBJvOINGUru29lPLAhXIhbewNMyhFUZBSsrxQ5uTZ3uXMI/pLrdTk4Z1F5u6uIKXEipkoqgJSUl1rMHd3BcPSOXN5konTw1EQekhR1CF0+6+Q4SqBd5swWOgOPBZd5aXb6RcipY8gAClA2KjGe6ja8W5wExEBtD2P6wNSVH4VKSovF/Vqi9m7qzyaWQVgeWkVp9497UEQAhCLm5y9MMbyYhk7vnlnj6LY6OoQflBBFfs3jQqlj0BFV/NP/s+KGayt1KJA5Yiw8GCVrz+aRjc0MoVkN0D5EUnAc32+vzbL7O1F3vuzSySzUZHlYUQIBaGOoqijSNnp1pyEVWRYATxAARFDUXIIJQUiEU1FjngOW9e5GikqEXshDENm7qxw+8YcmqaQzSdQFIWWUyWbfzbQcDoe33z+gJk7y1y8enzLNS3tDPXgM0IZoOyjAE9KSShr2PrFZ4aVWZZOqVjb83oRg2fu/jJf/8s0uZEU2g4ttLqhkR9N06y1+dd/uMHP/+Iqyaho+lAjhIVQLVCHX/ShRBwxIkUlYk8EQcj1Lx4wP7tGrpBE28GnxLR00tk4nhvw/c15zl+aIJ19/gtFUWxsfYq2ewuhZPbcLRDIKro6jP6jzgBNV3Edn07bjVIEh5jSao1vPrqzqyDlaeIpm2atzee//5Zf/LdvYuxgauYFAY7nEwQSRRFYhoauRl47ERGHmUhRidgTt755xMLDdYZG0ruuJwn8ENPSsWMGd79b4OKbx4ltkgYytDEgoO1No8gEirKzCZyUwZMgxdYvsNkhCSEI/HBXxxrxYrh7/RF23NxTkPKYeMpmfbnCytw6x85u3sLadDxuPFzi3tIafigRdGf+Korg7EieU8m6Q6wAACAASURBVMM50vHIyTgi4jDS9jyuD8hH5VdGpKgcaYorVWbuLFPYQ5DyGAHoukpgaMzcWeHClclN6w8MbRJFJGi7t/DDMgqxTQOWUAaEsgFILP08hjq+bW6727YccRhpVFsUF0vkRzP7XiORjnHv5hwTp4dRnjIn84KArx4s8On9JSbGIRO3N6b+dgnCkPvL63y/uMqxQoZ3z0xiaNGlIyLiMGHrOm/kB6SolAerqEQVWT0kDENufjlLKhPbtHNnO4QQSLqBgmXrNBsdSmuNLZ+vqRkS1nvY+kUQAj+s/OhfGSnbmNoJkuZPMbXJHQrwJGKPxxwxOOYfrKJq6oE6vkzboFHrUCnWn/yf5wf86fYMD1crZGMmhVT8mSAFQFUU8qkYI+kEi+s1/vjdAxzP3/dxREREROyF6Laoh5SKDZqNDkMj6T2/Vje6NQCP24/tuMnSfIn8cGrTVA10vRgMbQRDG0FKj1C2kTJECBDYCGFu+dqnCcONAMna30C2iP6zNLtGPHVwuVXTFNaWq+RG0kgp+fz+HMVai+FMnLl6edvXCiEYSsdZqzX5ZPoRH148teeAPCIiImKvRIpKD1leKGMY+4v9NE3FtIwndSKGrtJpeXTa7q5eL4SOqqTQ1AyqkkFRdhekQNfjJZmJvXTDCUMZEsrwpUhpuY6H2oOCVlVT8RwPgLV6k7lihaHU3jqBCqk4y5Uaq9WtFb+IiIiIXhEpKj1kbbWGFdt/10wqE6NcrKHp3S8kIaDT9rAPsOZu6LQ9jp3qvVX4oHEClzVnnXW3TNmr4QQOAAoKKT1BzshQMPOk9eTRmy4r5a4Dz10sBcDdpXUsQ9vXuYhZOtOLRUazSaSUNPwObhgQSommKCQ0C115uQLfiIjDTLeYdnkge/3SHGxRfRSo9AgpJY1ae/Nhgrskm09QXHq2SMnduPvtJ74bMDqR7fs+/aITdLjfeMhiewWJxFJMLMUgYXSVAiklTujysLnA/cZDElqcs4lTDFv5IxOwGKZO4IebFlfvhcAPMEyNluMyt1ZhKL0/E7ikZTJfqnJ7fZF5p0TFa3bP5YatqioUziVHOJEokByw50JExKuIretcfUmLaaNApZcc0N4+mY6hGxq+F3RVFdH/Thyn4xFLmmQL+3e7fVFIKVnzyyyurQOSjJ5C2aRgWAiBpZpYarczqhM4fF25yZg1wmups5jq4feOGT1eYHZ6iewBAmHotsHnRtLU2k53XMM+36+1oM0t7yHNlSrH0lmGrWfrsvww4E59mVu1Rc4lR7mSPYYaualGRPSNtudxfWlAiop1RBSVqampfwdcBP5uenr6b3p3SEcTIQSGqeP7wY4Gb1uhKILRyRyPHqySzsRA8iQN1C9qlRZX3zv1TLvqUUBKyb3GDPeDh7ymnkdXdl8IbKkmpmKw5q7zeanO29krxLTDfdc/eWaYe9/NH2jWk9vxsBMmuZEUi+X6zi/YgpJX53Z7Hl1RyekJYtrzrfGaopI3k4RScre2TCdwea9wJgpWIiL6hK3rXM0NSFGpHAFFZWpq6q8BdXp6+v2pqan/a2pq6tz09PTdHh/bkSM3lKBSapFI7j+4KIykWF0s43Q8JGD30Sm2We+QSscYP1bo2x794kHjIfcbj0iJ5J6ClMcIIcjoaep+k2vlG7ybe+OJ4nIYSWbj5EdTtOqdfXf/1CtNLv3kLIqioCqC/Wh1jaDD7c4CcdWiiYdg+6BJEYJhK8Vcs4StGryRO7GvY4+IiNiegdaoHBFF5VfAv9/4+Z+AD4BXPlAZHs+wvFAhkdz/H1HTVE5NjfHdV7NoutI3S/sgCGk1HX7xu8t9V216TcmtcLcxQ97I0hHNA62V1OJUvCrTtXtcyVw81DUrU1dP8vHfX8eKGXvu0GrVOxiWzviJblBqaBr7iVQeOUUMVHShIvHQtZ0VEiEEQ1aKO/VlziZHSOiRu21ERK+JFJXniQMLGz+XgLe2eqLjONy+fXuf2xwtPDdgeXmZZqeypb+E53rMzc/tvFbQpu0ELCwu9PzLU0pJZb3FyfMFllbnWFrt6fJ9xZcB33l3UBB0RAvXdXd1PrdDSsmcnKe+UCOv7t/5dRDERxS+uz5NOh/fdbDitFycjsfrH5zm/sw9AEIpqa4XqZfXMTfW8VyXubn5rdeRHvflMglMarKG5wdU11Tqu/RSKftt/qX8JafN/M5PPuJ0Op1X5ro3CKLzuTORovI8DeCx/pxgGz8W0zS5cOHCPrc5ehhKhnu3FykMpzZ9fG5+jmOTx7Zdw/MCbCPF8FiG5fkShZF0z4y1wjBkbaXGz355nktvnjjUCsJmLLSWydXWyRvdLqXdnM/d4IQugQyYKkxtWpB7WLhwAc6cXeLGx3cxbYNkOralo3DgB9RKTVIxhfd+d5lM/tlCXDs3whf35xnJdAup5+bmOXZscsu9HzlFCk6OlGZTaTm8NpRhMr/7wG4kDGj4Hc5OnH/pW5dv3779Sl33+s1RPJ/Xrl0b6H6RovI81+imez4FrgLTPTuiI87Z18ZYmivRqLVJ7KOWIAwl5bU6V987zbFTBb6/McfdW4tkcnHMAzrHdtoutUqLc5cmmbo8ceSCFCkls805EureDMp2Q7e4tkTFq5EzDreqcuL8GKlsnJlbiyzNFkERxOLmk9Zl3wtoNx00XeXUxQlOTI1hbzLgciKf5puHS7RdD9vY+b1V9ptYio4XBCjAUGpvnWK6ouKHAS3fIW30/m8YEfEqM9CuH/toKCp/C3w0NTU1DvwF8NPeHdLRRtNV3vngHJ/84Rb1WpvkHoKVMAxZW61z8twIx04VUBSFi2+cID+c4vrnD2g2OqSzcdQ9emkEQUi11ETTNd7/9QWGdhhsV12rUZxfZ32hRKVYw3N9VFUhkY2TH8tSmMyTH8/2xCl1LzSDFq2gRc7oj+eLIXRWOmuHPlAByA6lyP4yRfudUyzOFCkulfE6PoqqEE/aXHj7JEMTOfRtnJJNXeMXF07yh5v3d7WnLwNkKGl3Al4/Poq5jynO3XWiKd0REb0mUlR+xPT0dG1qaupXwO+AfzM9PV3t6VEdcZIpm/d/c5GvPr7L2kqNbCGxY3DRajo0am3OX57g/KWJZ9qFR8az/PK/vsLd7xZ4eH8FJMRTFqalb6mKSClxOh6NWgehCE6dG+HshXEMc+s75+L8Orc/u0NltYaiKlhxk1gqhqIqSCnxOh4Pb81z/5tZjJjB+bfOcPzixMAClpbf3lenym6xVIuys/28m8OGHTc5c3mSM5e3Ttlsx1Aqwa8uneGj2w+othzGwxB1k1b1IAxpdjykhCvHR8km9q+IKDt0CkVEROydtutxfXFAikrsaCgqTE9Pl/mh8yfiRyRTNj//s0vcn17i/vdLBEGIZRv4frgxBFDiOj7tlkvgByTTMT743SVyhc0NvUxL5/LbJzl3aYLlhRKzd1dYX62DoNu98fjaL7vW+1J2LfmvvHuK0YnstgGK63jc/vQOMzcfkcwmGJrcrNhRoMZNrI0Ugut43PzoFvN3Frj668ukcgczItsNNa+OJvoXFOlCo+xXCGSA2sd9DgOu47G+XKXd7GDHLX598TQfdRqUGx0CGaKrCqqiEIYS1w9QhOB4NkM8oZOL78/NVsrufHBDjXwmtyIMJR3HIwhCkKCqAssyouGPETtiGwNUVKpHQFGJ2B2arjJ1eZJT50ZYWaxQXK6ytLREpdRACEEybXPq/Agj41kyufiuakZMS+fEmRFOnBnB83yadYd20yEMu3K6oijEEibxhLWrtuNOy+Gzv7tGvdRk6Fhh1xdEw9QZOlagXm7w0X/4jPf/6m1yo/214fekj9LHOZpCCCSCQIYvdaCyMLPKzY/v4fsBiqoQBiGappIbUfjpuxdYLNWptjq4vo+uqqRjFuO5FOt+nT+t7L8crek7DJlJ4psYxL3KNJoOS8sVVtfqlMtNgjCEp1QnRQgy6RhDQwnGRzOkkofbnDDixdB2B1ijclQUlYjdY5g6x04NcezUEHbG61n1uq5rZHIamdz+7nA91+Pz//wVrVqb/Pj+goxkNkGn6fDx//Mlv/jrn5AubN7t1At2MhfrzR7ypU5MrC9X+eqP35MZSj5Tv+K5Pt9/cZdLly9wcouAc1hNYao6Xhjsq2un6Xd4Mx8Zvj1mbb3O3QerrKxWUYSCHTNIpe3nUm9hKHEcj7v3Vrg9vUQhn+D8mVGGh47gcM2IvhEpKhEvJXe+fEBtvU5+/GBvbituEvgB1/7f63z4P7yPts8iy52wVRNfBn1ZGyCUIUIoL7Wacuf6Q2JJ67kiW93QsOIGd288Ij/6+qav1RSVqdQYNypzjPxots9ONH0HWzMZNvsXyB4VHNfn9vQiD2bWiMUNCvntAw5FEdi28cSlutly+Pize0xMZHn94gS2dfhnVUX0n0hRiXjpKC2Xuff1DIWJ3kTg8XSM4sI6968/ZOqdMz1Z88cktASyj+W0buiS1pOH2kflMVJKZLhG4M8SygagoqhjaNoxhNj8IuI6HqWVKrmRzYMMK26wvlzBdbwta5rOJkdY7dQodmoUrN0FHZ3Apek7/NnoRbSX3D9lJyrVFp9++QDP9RkaTu5rKGQ8ZhKzDVZXa/yhWOfdt04yPBQFgK86L7OicvivyBF94d43s9gJ64n3Ri/IjWS49/UMruP1bM2niWsxJLJvE6VbYeeJkdxhJgzWcNr/Eaf9d/j+XcJwnTBcwXc+odP6v/Hca8jNlKduDfeWd+9CiCfP2QpNUflp4SzDdprldgU39Lc+Tikpu02avsOvRl4jZx69Cd29ZL3U4KOP76KqCrlcYt+Tq6H7t8pm49i2zsef3WN5JWq8jHh5iRSVV5BmrcXygxUKm3b37B9VUwmDkJWHRY6dH+/p2tCdejxk5ql5DRJabw3DpJSEUjJiDfV03V4TBqs4nX8EEUPRfnSOlTRSBvjut4RhA8P8APFUGks3NVL5BO1GBzvxvOritFxGTibQze0vC4aq8X7hLPfrq9ypL1F2m9iqgaGoCAS+DGn5DhLJ8VieC5kJUvqrXQBarbX5+LN7xGIm1gGNG5/GNHXSacGnXz7gF++fI597tYPBV5mBtifHo9RPRJ+prFYRQvSlEM9OWCw9WOlLoAJwIjbBF+XrxKXd0+NvBi0KRoZ4jwOgXiJlB6fzB4RIIpTNj1MIFdRRQn8WXymgG5eeekxw7soxPv/9dxiWgfrUQMHAD2nVO5y7cmxX51VTVKbSY5xNdVNBs40iTd8llCFx1eBMYpjj8Ty2djjqJ6SUlDptZsplyp02uqpwPJVhMpXG6LMPkB+EXPtmFsPUehqkPMYwNBIJky++muHXH17A3MbkL+LlxdZ1rmYHlPqpRcW0EX2mtFxB78MFE8CMmZSWykgp+xIIZY0Mw2aBslshrfcmLx/IACd0eSu5eRHpYSHw50G6CHX7i5EQAtQCvncTTZ9CiB8+5qPHC1x67wy3v5xBUQW6ruF5PmEgOXV5jNHjhT0dkyoUxuwMY3Zv3XxdzyfYSPHpqop2gBSlGwR8PP+I+VoVXVGxdI3QlczVHmGoKr88fpLRRP98gO4/WKVe71DYwiOpF9iWQanV4Pb0Im+8frxv+0QcXtqex40BKSofbqLI9pMoUHkFqRarmHZ/vCw0XcV3fDzHw+hDN4IQggups3y89iVO4B54PSklJbfCVPIsSf1wy+a+dxOh7q7bRgidEIcwWEHVJp557MylSUaP51l+tE6z1iaetBk9kefR/Gwfjnp3uJ7PUqnOcqnGarVJx/U2ama65dPpuMVwOsF4IU0hFd+1308oJR/PPWShUX8uGEmaJh3f4/cz9/mvzpynEOu9mtZsOdy+s0Quuz8Lgb2QycaZebjGiWN5spn+7xdxuLB1nSuRohLxshD44ZYTd3uC6M4X6heWavFG9jJflq7jya2LOXdCSknJqzBuj3IiPrHzC14gUvrIsI6ije76NQKVMKyi8vzvFk/anLm0P9v9XtJ2Pe7MFbm3tE4YhliGhm3opGI/BNJSShwvYHalxJ2FNeKWzoXjI5wcye0YsBSbTR7Vq4wnNlffLE3H1QO+WV7kt6fP9vR3A3g0X0JVxJ7nc+0HRQgMQ2Nmdo3sG1Gg8qoRKSoRLxWqpuA5/fMjkVL2/cKcMzK8k73Cf17+/6j5dVLa3mR1N/SoeXUmYmNcTJ07Ai3J/ZxyNHiklCyuV/nizjxBEJJN2Fu+Z4QQWIaGtVF70XF9Pr/ziIerZd45N0lyG0+HO6U1Ytr2ac6kYbLUaFBzOqTM3l2AfT/gwcwqqX1MUd8vqaTN3EKZi6+N96UeJuLwEtWoRLxUpAspFu4tP5nb00t8L0A3dfRtZgv1ipyZ5ZJxjlBXKHbWiak2MXX7Ilsv9Gn4DRShcCVzkVFr6Ii4e2ogLKR0EWKXKTUZoCiH7846DCU3Z5e4/WiFXDK25ynMlqExZqSoNNr807U7/PzSSUZzmysm6+0WMX378yWEQFEELc/raaBSrrTw/e54gkGhKAKJZL3cYGLs8LfaR/SOtudxY2EwisovkpGiEtFncmNZZr+b78vaTsshN5od2Je/JUymMlOU3AozzTlKbgUBqEJFExoCQUCIF3pIJLqicyZxijF7GEs9OjNnhBBo+iU87ytUdWTH50vpg9BQ1N2nigaBlJLrDxaZni8ykt2f4dljMgkbx/P5Lzcf8OHrpzcNVlSh7M53R/Z+REO13n4hQbCuq6yXmlGg8oph6zpXDugyvmvqkaIS0Wcyw+lukWIfOnPajQ5T7/bHmXYrFKFQMHMUzBwtv03Db1L16rSCNlJ2g5O0niShxUnq8SNrka9pJ/G9r3alqoThGrp+ASEOVzD2YLm0EaQczPDsMaaukUnY/Ot3s/z5O1Mkf1QkfiyV5vZ6kSFt60tdEIYIARmrt3eJ6+sNzBeQfrEtnfX1+sD3jXixDLJGJVJUIvpOPBVj+ESB6lqdZLZ3nS7BxjTekZPDPVtzr8Q0m5hmM2ztrc32KCCUOLrxc1znX1DUwpZBSBgUUUUeTb8y4CPcnkbb4Zt7CxRSsZ4EKY8xdY226nHtzjwfvn76mQLb09ks3xZX8MMQTdm8BqbUbnMul8fcJpjZD7Va+4V4mhiGRqnUGPi+ES+WSFGJeOk499ZpPvoPnxFP777VcyfKyxXOvXNmyzkxEQdH008DCp77MVKWEUoChAmEyLCOlB6qNolh/vzQqSlf319AVRX0PtRsZBI2S6U6c8UKJ0Z+SHmkTIu3Rsf5YmmBgh17JhgJpaTUbhE3DF4f3jmdtleCUGL0s7tuC4QQSLq1QL36bEccfiJFJeKlIzea5cwbJ5j9du7A05Oha8sfz8Q4+8bJgx9cxLZo+klUbZzAnyPw7yJlC1BQtTNo2jmUHQzhXgS1VoelUo3hdP+8arIJi1uPVjg+nHkmpXlxaBhT0/hmeYlyp/3Ma05mMrw9OoG1Q2fQfhAC6NNcqp2QcmP/iFeGSFGJeCk5/85Z1hZKVIs10geYvtppOXQaHT7465+g7bKDwwl8am6Hhu8ShCGKEJiqRlI3SehmT1MDLyNCGGj6GTR9sPVA+2V2pYymqH0tLrUMnZVynfVai0L62W6nM9kcJ9MZiq0mbd9HFYK8HSNu9M/i3zR1/GCwXT/Qtew39P6e64jDx0C7flKRohIxIAxT5yf/zdt89p+usb5YIjua3bNU3Kg0cdouP/2rd8gMbe+a6oUBi80qt8orrHfaPB7Wq3QNSJ/cBapCcD49xOlUnoz5ag+ze1mYXSk9Y+LWLzRVYblcey5QAVAVpa9W+T8ml40zt1DCGnAqtNPxouGEryC2rnNlIlJUIl5C7LjFz/67d/nu42ke3ZonkUsQS+4cHHiOR6VYI11I8pO/fItUfusvACklc40Kn60+ohP4pA2LsfjWz/fDkDvVIt+VVziZzPL20CSxQzLcLmLvtF0Px/VJb2PM1itsU2e1cjgKSfO5ODOzxYHv6zge+dzhngIe0XsiRSXipcawDN78zeuMnx3l+8/uUZxfQ9U0rLiJGTNRVYUwlLgdF6fp4HZcTNvk0s+nOHnxGOo20rYT+Hy++ojZeom8FSdn7TxPRVMUhuxE1720WWOxdYsPRk4ykejt4LuIwdBoO9Bjj5KtsHSdUr11KApJU0mbsE82ANsRBCHZzOGdAh7RHwZao9KIFJWIF8TI8SGGjxWoFGsU59ZYXyxRWa0ReAEogkQ2zuRr4wxP5smP57YNUAA6vsc/L92n4rQZi6X2fLEWQlCw43QCnz8s3uNnIyc5k3752o5fdjw/RAxoBICiCEJkt+5JebF+OcmExVA+SbPlkuiDC/RmOI5HIm5GQwkjXiqiQCXiGYQQZIfTZIfT8Pb+CzX9MOS/LD+g5nQYtg+WL7dUjSE7wccrs1iqFikrEdsj+6tedMcwdKi5LUpuAzfsDsY0FI2sESelx/Bkd5bW2dPDfPrF/YEFKrV6h6uXj71wNSli8LRdjxvlAaV+0lHqJ+Il4FZ5hdV2g7HY/ruJnkZXVHJWnD+tzPJX1sWoZuUIoanKwEYqPrbL78cXddVt8rC5ymxzlUCGgMBQVNSNgZYhkofNFSSCleYyQTnGsVSBZMKm0XT6Hqx0HA/T0JgYjwL5VxHb0LmSiVI/ERG7Yr3T5HppkZEDKik/xlI1Gq7Dl8V5Phw73dO1I/pHbIBdL44XkI6bqFu40O6HduByszLLfGsdXaikjfiT4OQ5Nn7VllJhvrXGTH2Z5LE45W8dbFvv6XE9TSgl1UqLn//0LMYehzxGvBxEikpExB74rryCrepbX8wPQN6K8ahRpuK0o9blHhGGkvV6k0drdcq3Z6k0O/hBgCoUYpbOUCpOLhUnv49JxwBxy0BVFIIw7NsX9WParseJ4d4N41torfNV6T4AQ+bu66wUoZA1uoF6RW1Sy1dpLXY4PzHWl8La0nqD06eGGBne3iIg4uUlUlQiInZJw3N41CgzYvfHr0IIgSZUHtTWeWtosi97vCp0XJ9HxTLfz6/ScX1WV8qcNBOYmoqp6UgJHdfjzkIRf24VRQhOj+Y4PZonk9h9kCiEYKKQYrlU39Pr9oPr+Yxke/Peu1db5JvKDDkjgaHuXxXKGHHsMwbXmwt8OzfH5clJRA8DtvX1BoVCkssXJnq2ZsTRI1JUIiJ2yVKrjkD01Vk2a9pMV4tczY/3bY+XGSkli+tVPr8zjx8EpOMW6ZiFW7eemz6sayoxs1sPFIQhs6tl7i6uceHYCBeOD6Oru+usOTNW4NFqf+/CPD/ANDSGMwdPOd6vL/F1ZYZhK90TZdDUdN64Msk3N+b5dm6eC5OTaOrB1g3CkPX1JqNDKd556+TAHXAjDheRohIRsUtWWjVifZib8jSaohBKScN3+rrPy4gXBHx1d4GZlRK5pI2p717hUBWFfDJGGEqm54vMr1f5+YWTpOM7313lUzGSMYtmxyVu9acQutxo8/qpsQMHACWnzvXyDENmqqfpS0PTePPqMe7MLDO3skY+kSS1C3PFzWg0HVpNhwtTY5w9M3Lg3zni6NN2PW6WBqOofJCJFJWII8xqu0FMH0RHjqTuvtyBiuN4+EEIgKFr6PrB7pg9P+Dj2w9ZrTYYzSb2XSuhKILhTJx62+EP1+/x6ytndkzpCCF459wkv//6Lrah97wrp+W4xEydM2P5A63jhwHXSveJ6xZaH3xYdFXl9MkhGgUHc0WjWKyjGyqppL3jOQmlpNHo0Ol4pFM2v/7wNTLpyNgtokvMGKCFfjNSVCKOKKGUtAOP9ECKXAWdwB/APoMjDCVr5QYP50sUS3Ucz//Bz1WCbRuMDac5Pp4lk9rbF1QYSj6/M8dqpd6T1AhA0jZptB3+ePMBv33jLAl7+/bbQjrOa8eHmZ4vMtKjY4CuE2ut6fCbN8+iHzD9cb+xTMNvM2T1ryjV1gyadoehSzZviuPMPlzj0UK5+6CUGKa2EbQIwjDEc4ONtmvB2FiaMyeHyGbikVdKxDO0XI8b6wNSVLKRohJxRHl8MR0EQnQDo5eF5WKVb24t0Go7WJZBzDaeSwu4rs/s/Dr3ZlfJZxO8eXFy16mDmZUSc8UKY7neFjknbJNyvc1X9xb44NKpHb88L54YodLoUKw2GNpkcOBeCYKQ1WqTN86OM5Q+WPDjhwF364tkjP4P9MsaCWaaK0yNT/Lm1RNcujhBo+HQaHYolVt4bjcI13SFbDpOMmmRiJuYAx5wGHF0iBk6VyYjRSUiYlsUIRB0A4h+FtNCd9KypigEfd2l//h+wI3vF5iZWyedshkubG2QZxgaOaP7ka03Ovz+42lenxrnzImhbdM4jbbD1/cXKOxRhdkt2aTNYqnG7GqJ06Pbp150VeX9C8f55PYjVsp1htL7VwYcz6dUa3H17DhTEwcfwrfaqeCFPvoArPdVoeDLkOV2iePxYQxdI5fVyGXjHJ88WPoq4tUkUlQiInaBEIKcGaMT+H0vqAVJQjc5ylUqnhfw2TczrJWbDBWSewrukgkL2zb45vY8bcfn8vmt/Tmm54uoQhw4LbId+VSM6zNLHB/K7ljYaegaP7t0gtuPVrn9aIWEZeyYNnqaUErK9TaKIvjF66eYKPTGifVRaw1bHZzjcUKzmG0WOR4fHtieES8vkaISEbFLhu0E92rrAwhUIKWbrPd9l/4gpeTr7x5RLDUYyu89HSOlxG12sBB8+dV9vKbD1SvHnxsU2XF9Hqysk0/2t+jS0FQ8P2ClXNtV4KCrKldOjTGeS/H1/QVWynUMTSUZM9E2aXmWUuL6AbWWQyglp0ayXD41hm305n0mpWTNqZHcQxfUQbFUnbLbIJQhSh/MESNeLdqux821wSgqP89FikrEEWY0luJWpb8flpbvkjFjWAMIhvrFwnKFuaUKw4W9BSmBF1AuVlmaXaPTdBBCEIQh/2l6UiW2rwAAIABJREFUibnrj7j09kkmTo9gJ7oXkqVSDSnpuyMsQMIyuLOwtieFo5CO89s3z1FutJlZWmdhvYbjtUGI7sRl+XjusiQZs7h8apTjhQyxHrc4twMXLwz64qa8FYrottk3fWegAVLEy4lt6Lw+KEWlFSkqEUeYETuBrRo4gY+p9uftVXUcPhg92Ze1B4HnBXx9a55sOranFuFOy+Hu17N0Wi5W3CSV+6Ho0+h4LDc76F8/5O7XD3n7zy4zPJljuVzDNgbzMY+ZOqvVJl4Q7NoIDjZShskYuWSMt4G249HsuE+KpTVVIWmbfU1dOaE3oDLw5+kEXhSoRByYSFGJiNglqqJwKTvKl8U5xuK9mZz8NE7go6sK4/GjO9NkqVjF8wOMTQIIKeWmwYvTdpn+8gFS8kyA8piYpVNpdrDSeVQp+ewfr/Ozv3yT1WqThDUY5UlsFFM32i7ZA1jl26aOPeDulhfVQSaAkPCF7B3xcnGYFJWpqal/B1wE/m56evpvtnneCPAP09PTb263XpQYjeg5Z9J5UoZF3ettqauUkvVOk/eGjmPs4Y79sHFvtkgy/mzxaBCGfF9a5V8WHnBtdZ6O7z3z+OyteYJQPknpbIaqCIrVJqZtkMwm+OQfr9NuOZvWfPSTVscd6H69QAAvIlSRgPLCtJyIiN4zNTX114A6PT39PnB6amrq3DZP/9+BHe9qIkUloufoisrPRk/y94++x1K1nrV7FjtNjieynEz2bjruoHE9n1q9TeFHBbSr7QaLzRo5K0bd7fCgWuJifgSAVr1Nbb1BaoeiW0vXKDc6nBgBK2ZQXqtRX60xNjQ49UkCfnj0FAJD0V5IuCAAXYkuwxEHp+163CwOKPWT3zb18yvg32/8/E/AB8DdHz9pamrqN0AT2PGgo09IRF8oWHHeHznBx8uzDMcSBw5W1tpN0obFT0dO7Nv6fb/4YZtAdhAoaEocRez/Y9NsOV23uh/hBQHqRupEV7VnXHfXFsuou7DP11SFWsshCENURSGetpm+v4C8ODnQczbov08viGkmQoiBduBIKZFIEtpg8/0RLye2ofP6scGkfiqLs4Wpqakvn/qvfzs9Pf1vN36OAwsbP5eAt378+qmpKQP434D/HvjbnfaLApWIvnE2XQDgk5WHJA2TpL57r4zHBDJktdUgb8f51diZvhXoboYTlKk6d2h4D3nsuKsIg4w5RdI4jSr23nnieptb1BXsBHONCmWnDcC5/A+mX+vLFaxdDP4TQoAAP5CoCsQTNm7DwW27mLG9n/v9IOBIDshThELOSNAOXGLaYM6VG/qk9FhfZgpFvHq0HY+bqwNSVAqZtenp6Xe2eLjBD+mcBJuXmPwvwP8xPT1dmZqa2nG/KFCJ6Ctn0wXShsUnKw9Z2kht7CbYkFJSdTu0fJfXc2Ncyo0OxDH0MU1viZXWn1CEgaUOITbusgPpst6+TsOdYyz+C1Rl73fDm9VCxHSdd4YnaXgelqYSfyqo870AO7F3lUJVFUxDozPAQEUiSPRpOnK/mYjluV6eHVigUvfaXEwfG8heES8/g1RUaG9bTHuNbrrnU+AqML3Jc34L/GZqaup/Bt6Ympr6P6enp/+nrRaMApWIvjNkJ/iL469xt1rku/IKJaeJoejENB1T1Z44snphQNv3aPkeUkqOJdJcyp2hYB18Jsxe8MIGK62PMZQ0qvLsl5YqDGL6KB1/nWL7GqPxn+9pbU1Vt6zaNDUdcxNvGFVVujMDdkipyI3OFfUpS/qEbeIEg6kZ6Y5OgPhRDVTsPDfKswNJ/4QbaZ/JWKGv+0S8Ogy0RqWw7Q3a3wIfTU1NjQN/AfyPU1NTfzM9Pf2/Pn7C9PT0h49/npqa+uN2QQpEgUrEgNAVlYvZUabSwyy366y06qy06xTbDSTdYYamqjFsx7loJxmPp/eVKuoFdXcW4Lkg5WlMNUfLX8ANahjq7tuwEzGDvfaXxBIWbsfD3MFm3g9DLEN7knoJ/IBsPIY/oH6WesthLJcaiLlcPzBVnROJYeaaRXJmb4c3/pia12IyVhiYehPx8nNYFJXp6ena1NTUr4DfAf9menp6Gbi+zfN/tdN2UaASMVBURWEinmZiwwdFSkmwMcSw34MMd4OUkpp7D1Pd3l21WzCq0vQX9hSomKZOzDZwXB9zl0ZsI8cL3LvxaMdAxXEDCukfrPLr5SYX3zrJDC5+EPS9Tbnt+pwbP9oKwWupSeaaa7iBh6H2x8vFCwN8GURpn4ie0nY9vh2QovKz7RUVpqeny/zQ+XNgokAl4oUihEA7BAHKYyQBofR21dmjCp0gbO95j7Mnhrjx/cKuZ/yk8glUTcX3A7Rt3Fn9MGR4I1AJQ4nv+px+bQKtVuf2fJHhdP9SaC3HI2EbFFKDTdP1Gls1eCt7mk/X7zBipXvewSSlpOTWeTt7hnjU7RPRQw6LotIPokAlIuIpBCoCBSnDJwW0WxFKH4W912NMjGa4eWcRPwh31SGjairHp8a4f/MRqVwCZZPUSsf1iVs6CdvoGuMtlTnx2jipXIJzSYv7yyU6rofVoyF+TxNKSbXZ4ddXzqAohyfo3C8TsTynnRFmmisMm70NVtacGsdiQxyPD/VszYgIGGyNyk6KSq+JApWIiKcQQpAwTtL05jDV7Y3lpPSJ6aN73sMydS6fH+fG7YVdDyUsjGfxXZ9H04vYSRvjKYv5UEo6rs/rp4bxvYDKao3x08Nc+mnXENLUNd47f4w/3rzPaEbreTCxVmtxbqLASLa/dR2DQgjB1ewpQhnysFVkyEwduLg2lJK1TpVxO8db2dPRtOSInhMpKhER+ySUAU7YxglaBNIHBKrQsNQYhmIfygt2yjhFzb1HKAMUsXmqxQ3qGGoGU81v+vhOnJrMs7BcoVJtkXmqrmQ7Rk8OYdo6c3eXqa7X0E0dVVOptV2GkzZupYU0NC68e5rTrx97RnkZy6W4dHyUW49WGMkmelYPVK63SccsXj+x94DtMKMKhTdzZzBVgzv1BZKave/C13bgUnWbnE2OcTl9IvJNiegLbXdwPio/G4oUlYgjjpSSpl9ltTPHuruE3GLomoLCkHWMIXOCmNb7AYb7xVRz5K2rrLW/xlJzz3ilSClxwypIyXD8w32nBVRV4b2rJ/jTF/cpV5pkM7ur7ciOZMgMpWhUWxQXyqyu1RnOxLh8eoxj50cZmsii6Zt/rC+fGCUIQ6bnVxlKJw5kzCalZK3WImmb/OLSKYwt9jzKqELh9cwJxuwsX67fY7VTJanb2Oru0n2dwKXutbFUg18OX2bIOrqDNCMOPwNVVDqRohJxhOkETWYbt6h6a2iKQVxLb6mahDJgrTPPcmeWvDHG8fhrGPswUOsHGfM1NBGj1LmJ468gpNgwp5XEtAly1hUM9WCpDtsy+ODdM3xx4yErazXymfi2xbKPEYqCHjOJjaT57XtneH1qouu1sgOKInjj9Di2qXNjZgnb0EjF9n6+Hc9nvd7m+FCGt89OYu2ye+moUjBT/NnoFZbaJe7UFlntVFAQmKqBqeqoG+/vUIZ0Ao9O4FEOmuSl5M3sacZjeYxons+hoHsT5dL0HYLHvkNCENdM4ppxJMc/PKbtetxcGZCiMhwpKhFHlGJnnpnmt2hCJ23s3KaqCJW4nkFKScUtUvWKnI5fJWsOD+BodyZhHCeuT+IEJXzZRqBgqGl0JdGzPWzL4IN3zvJgbo1vpxfwAod4Qsc0NDShoyo/3L2HUtJuu7TaLpap8cE7pxnd48BBIQSvTQ4zlk3xxd05lsuNjYBl57RGx/WoNh10TeHnF05ybKj3XTGw0RnTaDOzUqLSbOP6AZqqEDMNTo1kGUknB27Trysax+PDHIsNUfGaVNwG606ddadOS3bHIuhCJW8kGbJSjFUN3hu9eihTm68agQxZade5W1tltV3Hl12F9/E797HLkCYUhu0k51LDjNjJJwHoUcE2dF4/HikqERFbstSe4WHzFkk9h7rHoX1CCBJ6Bi90ma5/yVn5BgVrvE9HujeEULC0/nqDhHjkRzucja+zsFxiYcGltdb98rO0FAmtgKHEUIRCPpPg6oUJCrmDfVmn4xa/uXKWYq3JvcUiC+s1ys02VrWJoakoikBKiReEBEGIRJC0Td47f4zxfKovqZ4wlMyvV7i9sEql2UbXVGxDR9O6x1Jutli4XcXQVKbGhzg9kh+4miOEIGskyBoJTiW2rsvpqOUoSHnBBDLkXm2NW5VF2oFHTDXIGPaWhoRBGFJ2Wvxx+Q6WonMpO87ZVOHIBCyRohIRsQ3FzjwPm7dI6fkti093g64YJPUs9xrfYKgmKX1/hapHiZq7wqPWNULpY+pJzp2Y5NwJ8P2QZtOn7Tfw5CpJM8VrQz8hpveus0ZRBCOZBCOZBG3X46vrIcPjY1SabfwgRFEUYqZOPhUnYRkkbbNv0rgXBHx5b56Z1TKZuMlI5vnf09Q1kraJ5wd8O7fMzGqJX1w8Rco+HOnCiMNDxW3z2eoM606TnBkjbdg7vkZVFFKGRQoLN/C5tvaQ2foaPxk+RWYXr3/RRIpKRMQWdIIWM83vSGjZAwUpj1GFRkxLcq9xndfTH6ArR3NuzG6oOAs8bH6JrabR1GdTL5qmkE4bpOleeNpBjdnmx5xJfoCp9t5UzTZ0cgmLcxOD9/cIwpBP7zxkqdRgNB3H90I6Tadbi2M+306tayrD6QS1doc/3LzPb6+cJWFFVvQRXWbr63xSnMFSNUZj+yvSNzZeW3Xb/P38d7w/dIqTyZf/xumwEgUqEQfiYfMWmlDRlN4ZiRmKheO1WWjf5WT8Us/W7SWhDKj7FRp+jbpXwQ89FKEQ11Ik9QwpLbvtOWn7VR61rmGrGbRdBGO2mqIT1JhtfMbZ1Id7Tq8dZm4+XGZ2qYTuSG58u4jv+U8eUzWVkWNZciMZLPvZ85myLaqtDh/dmuG3V8+h93lEQMTh50F9jY9XHlCw4hi7mNK+E2nDJhYG/GnlPoEMOZM6vEZ9UeonImITmn6Nilckrfe+hsMQKe6WZwi8ITRhIABT00gZJrbenxksuyGUASudBRZa93Gli4KCoZgIFEBS96ostmdQhMqodZxx++SmqlCxcx9VGLsKUh5jqSnq/ip1d5WMeThqeA5Ks+Pyr9fu0lyqoSiCWNzCjv+gjgRBwOLsGgsP1hg7UWDiVAHxlMKSjlmsVOosl+scK2w/nyni5WaxWeGT1QcM2Qn0HnrV6IrKkJ3g0+IMlqozET+c77Mo9RMRsQlFZ76nd/aOH7BU7zBbbdJ0fdpBkzvGTdIbtSqCboW+pWmcy+U5lR7Qh3KDdtDkXuNb6l6FhJoipmxSR6F2c9mhDFhsP2TNWeJc8gop/QeXWy9sU/YWSKh7P35TJCg691+aQOVP/3qLpdk1JoYzm44GUFWVZDqOlCGLs0V8z+fE+dFngpWEbXJ7fpXJfH+6kCIOP53A45PiDFkz1tMg5TG6opI1Y3xanOEvzcvY2ou7WdqKtutxc3lAispIpKhEHAGklKx1FohpBy/u9IKQe6UGD8oNABKGSj5mEIQK0GI0cfJHzw+4VVzl5uoKVKocc8+QMPpby9LyG3xX/QKAzC6KfBWhktazOEGHbyuf8VrqbXJmVzauussI2HGW0GYYaoy6X6QT1LEO6OPyopm9u8Sn12cYKaQ2DVKeRgiFVC7O6kIZK2Yy+tSdY9w0WC43KDfb5BK7c/mNeLn4Zn0ePwyx+jTxGsBSdRquw9frc/xs5HTf9tkvA1VUnEhRiTgCOGGbkK0t5ndLqe3yzXKZtheQsXTUp+6UFaHRDurPWdnrqspQPIGUku9WVviPd7/nJxOTnEpnn7uj9oIAgThQK68fenxf+wpFKNh7LGQ1VQtFKNypf80V9X1iWhI3bKCK/V9QBQp+6MARDlTCMOTGtRm0mI6h7e4yJBAk0jEWZ4sMjWdQtR/+pooCa7VmFKi8gpScJvfra4za/f885K04s811pjoj5K3DNSk8UlQiIn6EE7YOvMZCrcVXS2XiGwrKjxGi++XkhQ6m+vwXkBCCtGGSskw+ejRLaajNW6PjKEKwWKpya36VUqN7nMcLGS5MjJCO7/0DNte6hxs6z6Rv9oKuGHihwf3Gd1xKv0soQwQHSVGEW44lgK5DqkSioBzaVEhptUaz6aDpewt0VVXB90KqpQa54R86OjRVwXmqCDfi1eF+bQ1TVQfyXhdCYCgq9+rFQxeoRIpKRMSPCOXWX5S7YbHe4tpSmaylo2+jdkgkcoe9TFVjLJni1tpq1zwu0Ln2YJ503GI4nSCUkqVKnfn1Kr++fJZ8cvd33Z2gxVL7EWn9YBeAmJag4q5R9Upoikm44Wa6PwTKU7VBgQyoeeuUnBVqfgk36AASIRQSWpqMMUTOGMXaJNjbK14QUHMcmp6LH4YIIbBUjaRpEtf1XX9ZPLqzjGHp0O7s+RjsmM7qXOmZQAVAyi1eEPHS0gk87teLFAYYNGQMmwf1Na7mJvqaatorbdfj26XBKCrvj0aKSsSRYX93MHXH46ulyo5Byl72UIRgNJHkq8UF3LLHa8NDTxwoFSHIxm3qbYdr9+f53dVzu/5CLXaWUJTeKBOWGmOhPcOJ2ClWuLOvNUIZIFAwlW7qa91Z4mHrNl7ooSsGhmJhGbGN54a4ocNc6y5zzTvkzTGOx6f2PE8plJLVZoPptTUWajWkkCB/sB5/fG4sVeW1whAnM1niO9QM1astbNuAvccpaIZGp+0+839BKDFfwsGIEdtTclpd9XCA7rGP9yo5LcZjh2fQpG3oXD4RKSoREU/QhMYPX1W7Jwgl11cqmKrYRZACIJ9RD7ZDEQI1FDxoljkr86g8u37SNlmpNKi1nF2ngNbcJWylN3drpmJT88pYSgpDsfFDd0/tyQDtsEbBPIkE7ta/Zt1dJqGlN50+rQgFU7UxVbtrQe+uUnaLnE1c2fU8pXL7/2fvzZrjSLM0vefz3WOPQAR2ggtIgktmMvelqrqqq6sX9WhsRqab/gn6HzL9CV1IZrrThW4km55p657pmpqpqsyqSuZWZJIgCW7Y19jDd/90ESSTSGIJABEgSPpzkZZGRLg7HBH+ne8957zH4fOFx2w5DramM5xO7xq0+VHEt6srfLO6wjvDI1yuDKPtUiQbx5KUpqEKQRTHu9qa74QQ3fc/TxRLCumT7x6a0F+2vM5LsbhXhWDLa5+oQMXxAm4sHZOiMpYoKgmvAId1R11sdKg6PpX0/k6iUnYLRw/iTitk1xr+XmOLq8UXF2MhBEHUW9oljAPcqENOO1xtyk7nBokXu1TM8yx2viOr9D6AMZYRcRyQ1ce407xOJ2yS18s9qT3b5yld5wLvMWTuPqtGSsntjXWuLy+R1nXGMvsXKhqqykg6QxjHfLe6wnyjzs+mzpAzX/xbm6aO5waMZzIsNFvkd3jNbsSR3Fbb4gUhGVOnkjtZNQMJg2fNbWC/hPSLreqsuc1jP+9e2OYxKip+oqgkvAIYiomhmIRx0LMrbSwl97aa5MzePnah9LFU+0BtvLapYwuNVafFdK6E9Zw7ZSwlSEnK7O16QxkA9LlITxBIn6J5irq/RCfaItWDn4qUMe1wkxF7hmX3Me2wSfYQxb26YpDW8txrfoOt/nTH9nIpJd+trvDt6goj6cyuqshuaIrCaCZLzXX4l7m7/M30eXLm9h3Y+NkKN/84x+hQmseNJlLKnu+z0/EYmfzhntXbHu9Pj79gtZ/w+tMKPIwB+Kbsh6FqtALv2M+7F4mikpCwA8PWaRY798gqvS2YVcenE0aUU73tnoPYO/AU5ULaxtA1WqHDo/YiwxlBgANIWp2IieIwkdImlmoPrdWDW/hUoXE68yEPW3+iFa5jq8VdzfOC2MWJ64xYF9FEng3v/pHcgDVFR1dM7rducCX/8Qv3YW5ri29WlhnP5lCOEKQVLJu66/KvD+7z9+cvYj7Xhjw6NcT3f7qPpaqUUzZbjtuTqiKRxJGkPNqV3B0/QNcUJksn0y00YbBEBwhw+4kAwvhoDQX9xjZ13k4UlYSE7QwZYyx07vS8G15puWg97npjGSOEsmPtxV6EeJQrHmur8/y5JnnXzCJQ8fwI29RIlWrcqP8RXTEZt88yYk3sGiDoT5Sig+z290MAuuimsjTF5Gz2UzbcOda9OSIZoAv7SU2OJIx9YhlgqlnOpD4iq4/yXf23pLXcka/H1jLU/Q3qwSZF44f0U8Pz+OPSAiPpzJGClKfkLYvVdotvV1f4eGLy2b9btsHk9AiL99eYGSryzeoarcAno++d5nNaHvmhNFbaxPVDGh2PX719HstIHmVvIqoQyJfQ7iXhwErjoDlOReXTRFFJeFUwVZsRa4o1d55sD+27mx0PS+tNpnWjFmVzElXp7SMay4gtb5V1fxlN1bgyOsnjWhNLSWOoCtOVDKVM6tnDJYh9HrVvs+o85nz2rR3TKKrQsNU0oQyeBRdHQUrZHQHwXJuwKjRG7BnK1jRNf41GsEwgPQSCrF6hYEySUrtGdvVgEy/qkDf6M1vJVFMsOw+2BSrXlxbRFaWvA/4qqTS3N9Y5WyhSSf9QRzLz3hSba3WcusvbwxVurG9Qc12ypom6Q5DkOh5CwNSFUeptFy+M+MWVs5ST2pQ3lrRm0gm9vgwgPAhBHJHWTtbE7qRGJSFhFybsC1T9NbzIeTbnZieiWNL0A0p2d8GPopjQj5BP/D50XUVRu4uTFzlYapqi0duk0igOWXIe0AobpNRsVwlQoZhOMT1WJm+9GGToikFeGcKNHP5c/wMXMu9Q2SHNVDZHWXDuH6igF7qWbI1gizAOSWsZbDWNFzvk9Z2nKqtCo2CO7znDp+6vo/ZxSrWp2DSDKkHsoysGzSBgIfAYS2f6dg7odmOldZ3bG+vbAhXTMvjkV1f58l+/p7HR4kqxxLLTYbHZIpaSlK5jqipRFNFpuii6yvD5CnXfZ7yY4+3To0mnzxvOiJ3l+1qLtH68QUMn9DmdPt5ZY/vheAE3Fo9JURlPFJWEVwhN0TmfeZebjS9QhIKu7PzACOKYKIxpVDvUqm18/8XOG9PUyZQ0rJTGqdRZRA/2/FLGLLmP6EQtMjukiYJ98siWaqMJjTutb1GF/mwez1PK5hjznXvEMu7Zq0EiWXYeUQu2UFBZ82JOpy8QxC5nM5d7OsZONIItjF3u72EQXetfvKiDrhgstlvo2cxAcv450+JxvUbb97d5rKQyFp/+7dvMz61x//tF8i7kzCy10GOx2Wbd91E1laHRPCPjJa5MDXO6UiJrH/0+yLgDsgnSoyvmq6CkQWQRfRy2mTA4Smaa6CWkfqI4pnTSnGlNnbfOJIpKQsKOZPQCl7IfMtv8kkiGWD9qXZZSsrFWZ+HxBllVwzA0Uj+yzJeya8u/uhJQCKZonQ8olsx9F822rEMYktll7k3cwzNMU3Qy5LnX+o53tZ9iqD/sFiw1xZh9hmXnUc/utGEcUA9qpNUcQoAXuaw485zJXHw2CfowOFGnL0Mgn0cg8WW3e2HZ6XCmfPjr2wvlSVBUdZ0XzOAMS2f66gRnLo2xuVKnvtUi8EM+NTQyeZtCJYdhagfyWtkNGbeQ0SMIZyF2kcgnNdOCbf+rjCD0y6CM9BQwJ7wcikYKgTjQRuKoxDIGISgZJytQeZ1JApWEvpA3ylzNf8Zc6880gk0yWgFFqMRRzIN7qyyv1NF1jdQOaZiYmEBxMESKsnEOKRTu3lxidLzA1LkKYpcFyo9canKdcWVyx58L6LkgVFcMvNjhYecOF7PvbPvZpH2OqrdGJ2yR0vZPiwihPOkXigGFIPZQ1RTT6avH6qDZC0/jOC8M6YQh5gBz/ZpQ2Ox0mMztbJKlqgrDE0WGJ/rjW/M8UnrI4AYEt5FCAVFAqPkd+7qklBA3kO6/IpQMGJ8g1LG+X1PC0bE1nTOZEgudGiXzeAZS1nyXs5khbO3k2OfDk9TPwjGlfiaS1E/CK0pay3M1/ykrzkOWnPtEMmR5rkVj02OomEavNomlRBGiO1ZPBMSECBSy0Qi2LHSH9emQK6RZWe7Ki1PTwzsqK7VgEyGUXRd/Cdg9Fu8CpNUcG+4yU6nz2wpeNUXnUv59vq//iXbYIL1PJ5ImNEasCVbcRaI4QBLzUemXPQU5e6ErxpNJ0v0LdgQCVWg4YTDAZuwulqaz6Rx9mOVBkdE60v8dUjqgDO/ry9NNieWAXPc97n9BahcQxvuII0y9ThgMF3LDzDU3+tqdtxtSSrwo5Hyut/q54+RYUz9BkvpJeIVRhcZE6jzD5hRf3f4zK0ur6IWQjcBByoCtQJDWVTRFRY9tLDmMIdMoP7K7VxTI5dOsLNXIFlKUyttTHrGMqPkb6Oxc5BpLiSoE9gGm8wohUBSFDW+ZydT0tp/Zapqr+Y+Za92g5m+Q1nJ7Ftjm9SJxHKIqGldyH1E0j96pk9OLVP31ng32ekFKiaWkaAWSQdtRKIJjryeIwyXwfo0UWcQBXICfIoSNVEyI5sBrgvlzRB86wBL6R8lMcSZTYqlTH/hE4y2vw+lMiSHz5KV9EkUlIaFHah2XB+tb3FlY49bv5zEzIygbAtSAOHbYjF1cDDShU0zbpGwbRd95h6sokM5aPLy3Ri5voz03dM6PXWJiBDu/1wkiirZxYC8QS0lR9ddfCFSgW69yOfch694iC537dIJmt2NJGCgoSCRB7BMToQqNs5nLjNmn+xZY5LQy6+5iX44FEMY+hmJhKBaKcAc+fVhKUAeu2zx3vmi9G6QoBYQ4/INVCAXECDJeA+93T4KVpG7lpCCE4L2hUyx3GrhRMLCJxl4UIhC8P3TqpZjM7UeiqCQk7EOt4/L1o0VW6k10VSWoeuRtg3T2aQrFJiXTNIJN0uhIJJvNNhuNNlnbZDSfxdghTaPrKk7bo7rZpjL6Q22DF+09drcTRlwdPriF53l2AAAgAElEQVRbqS4MmmHtSYrlxetRhMKIdYqKOUErrNMOGzSCGpEMUIRKRsuR0fJk9cKuRnKHpWCUUYSy67UdlE7U4nTqEkIIUrp+iBGTB8ONQk4d0xA3Kb1uukdkjxSkPI9QhpHRIoR3EfqlvhwzoT+kNINPh8/wX5fvMmxn0Ppsqx/GEZtem1+MXCClnUxFLVFUEhJ2IYpj7qxu8O3jZUxNYzSfRUrJnxeWsDLbW0hNoZIXBh0ZYguNlGEAkrYXcG9tg/F8jnzKfiEFYdkGq0vVbYFKIH2UXXbnQRRjqArl1MEfKEIIJJJQhhh7BAOKUMjpRXJ6kTH79IHPcxg0RWfMOsuiO0fuCN1D0FVTVKE+G0xoahq2quFHEUYfzd6eJ4hiyvbxSOYyuIGUzqHSPXuiVMC/jlTHEMrJmZybAJPpIh9XzvCnjYeUrQx6n4KVII7YcFt8VD7DqUz/C737RaKoJCTsQBBF/PH+PI82a1QyaTS1m4bx3ZDIj1AzL3pdjKlpboVVzCdFtSCwdY1IxixUGzhByEg+y/NO+4ap0ah1CMMIrYfi2JoX8NZw/mjtrC/Bm6EXxlJn2fSXcaPOtoLfgyClpBXWuZB9b5vvzYht0/Bcyqn+BxNdV15JwRr8TkzGbQhuQ7+DFEAIDSl0CG4hzE/7fvyEo3ExP4wqBH9Yf0haN8jqR/u8NQOXduDzSeXsiSygfR7HC7gxf0yKymSiqCS8AoRRzBdz8yxWG4zmtpuE+U6waxohreiMKSlW4g655wphVaGQNnU2Wx2klIwWcvx4LJDnBmiZbqCioRPvcJaGFzBkG0zlD7fYPp0b0qt1/3GjCo3z2Xf5vv4FHmJPN+CdkFLSCDYZs84wZIxu+9lkOsO9AQ1aa3geE7kc2R4GDx4VGT1ECuVAU7cPhChCeB9pvNu3tFJC/5jOVSiaKT5fe8CK02DITB9YXQniiC2vTU63+R8mr1A6gcWzP8Y2jlFRCRNFJeEV4MbCCgtbNUbzLxqQxXG85+DhUTVFPfbpyJDUc3UcAtENVtoOhq5RzqSe+9mT4z7BVF9cINwwIowlb48UDj1QL5QBtprue31JP0lrOS7nP2a2cZ1W4JHW8j0V9wWxRztsMGafYepJbcrz5A2DYUOj5rp9VT6klLQCn5+Up/p2zD0J74AY3DRlIRSkkBCuIPQzAztPwuEpmWn+buIKdxqrfF9bIYhD0ppJStu9wD6Wkk7o0w49dEXlndIkF3PDfUshDRrHD7h5TDUqnySKSsJJZ73R4ubyGqPZnX1BlH1SLqpQOK/nuRPU6MQBqee6YgSCtKGxWm+SMQ2sp50+gm3+IT+kLLoKiBtGtP2QT0+VyRiHr/p3I4cRe+LQ7z8uMlqBtws/5VH7NpveMpqiY6vZHT1W/NjFDdtoisFM9gNK5siux/14YpJ/vDNLGBt9mw674XSYLpYY6fMMoZ2Q0oG4g1APNnX74JgQrwFnBnyehMOiKQpXCmNcyA2z0qlzp7HOhtuim4QE8USQlV3TZASCspXhvdIko6n8KxOgPCVRVBISnvA05ZO3TJQf52aeoFvavk2ohlC5qBeYC+o0pE8G/dlORxEKqlBYqjU4Wy4hBMgYTOuHj6um6OS0EjV5n6rrI4BPT5UpHXEGTERI2Xw1XEgNxeJ85hpj9hnWnAU2/WViGbGtGllKbC3LuczbFI3hfVulC5bNe6PjfLm8yFgme2hl6ikt30dTFN4fGz+els64ycANYQBECuLVwZ8n4cjoisqpTIlTmRKxlLQCj1boEctu8lgVChnNJKObR/68v0wcP+DmMdWofHIqUVQSTjAr9QYt32c0t/vu2LR0FFUhjmIUdfdd+dNgZS3qsBR30KSCjYoQAkvXaHk+ThCgCwXD1rf5qEgp0cmx6QWcGTG5MlzAOoAL7U50whZZvUB64Lvx/iGEIKMVyGQLnJFX8GMHP/aQSFShYinpA/u4XKpUaAU+tzfWjxSsNH0PNwz52+nz2PoxObrK7u8++OVGB9kY+FkS+osiBDnDIme8frVFtqHz1tlEUUlI4Pbyxr6pFaEIKpMF1uarpPN7F3uqQmFMy5CPTVZjh2rkggRdKN0hdm2HnNA4dbZCGMc4QYATBkRxzEgmz0+HxhkviyMHKbGM8GOPy+kPTqSZUy8oQsFS0y8MhTz4cQQfjk9gqCrfrS5TMO0XBgnuRSwl6502tq7zd9MXKNoHK/g9GpI9C6T6xqv5GUl4fXG8gJuPj0lRmUoUlYQ+E8fyWSGqqiqHXoibrsdGs8XIDgW0P6Y0nmfl4da+qspTUorOWUVnQk3TiH2a0qepB8w364ylM4ykoOG6VNJpZtJlxjNZCpbNDccl0Gp0wuahJwtLKamHVc5mZkj3eTrxq4oiBO+OjjGezfK7+ccst5rkDHPPgCWMY6quQxjHXCqXeXt4FFM75keMUBm4cx3QHTj5atUwJLze2OYxKipRoqgk9IFao8PSWp3NaputegcZd5/eQhGU8ikqpQxjw3ny2d53uw3H6zn/b6UNxqaHWJ7bJFvq3e/DECpl1aZM97rm17f49z+9xrtvn0YV4oUgSxUa53MfcLP+J1phg8w+AwN/TCRDGmGVCfscY9aZA733TWA4neHfXphhqdng+/V1VlrNJ6Z4P9jhRzLuzkkSgotDQ5wrlihYx6miPIdIH4/WIR1QjmlRSEjoAdcLuLl6PIrKx4miknAU1jeb3Ly7zFa9g6oq2JZOMZd6VvgaxxLHC7jzYI3v761Qyqe4emGMytD+SsJWu32gTpCR0yVaWx1adYfMPimgnWhVO+QrGYqjuT3Pa6kp3sp/zFzre6rBOhl174GB0FVROlGTSIacS19l1DqZ8ztOArqqcrpQ5HShSCcIaHguTc/Hj0IUIUjpBjnTJGuafesUOjQiAwKkjAfnowJPApXzgzt+QsIBsUydq4miknCS8YOQ7++ucO/RGtmMzfAugYeiCFKWQcrqLuTtjsd/++M9LpypcOn8KIa++0ei2nYwD1ALoqgKZ9+Z4MF3izS3OqTzVk9poCiM6dQd8iNZ8mcK1N295/oAmKrNpdx7bHgrzHfu0g4aaELHUCw0oSMQRDIikF632FRKhqwRplIXsI9Y0/EmkdJ1UrrO6OA7jQ+FEBpSGYO4BmJwRdGCCKGebKfShITXhSRQeQ1w3IDPv7pPveUwXM4dqEsjnTKxbYO5+U02a20+e/8clrlzsWwYy309Un6MZqhMvzfJ6qMtluc2UFQFO2PuGLBEYYTT8kFKTl0aYWgyT9P1CaPe3FIVoTBsjVM2R2mGVWr+Jo1gi07URCLRhE5Gy5PXSxSM8qEt6BNONkKfQbr/CgwmUOnOEMqAUh7I8RMSDkOS+kk4sXh+yO+v38fxfCqlwxWCKkJQKWWo1Tt8/tV9fvrh9I7KihAcagaOoiqMnStTHMmxuVRnY75KHMtn/RlPj6hpCqNnS5RGc5hPBgrKJ9d3sN9HIa8PkT/i4L6EVxRlBKFkkHEHoQwgGI1rYHw22NRSQsIBSVI/CSeWm3eWaXZcysWja/GFfIr1rSbf313h3SuTL/w8pevUO/unYXbDShtMXKgwPl3GcwJ8N0DGEqEITFvHsHTEj0zkwjjCPoLTbMKbhxAqGJ+C+y9IafU1oJBxHaFUENqZvh0zIaEfuF7AzZVjUlROJ4pKQo+srDe4P7/BcLl/LbVDxQz3Hq0xPpJ/oc6lnEnzcKMKR2zoEIrAShtY6f29OaJYUky/pA6ShFcWoY4i9UtP5v6M7v+GHpDSR0gHYf1VNxhKSDhBWKbO1XOJopJwgpBScuPOErms1VfbZ0UIchmbG3eW+OWnF7d1wuRS1vFYVPyIrDX4ibsJrx9CfxfiBjJeQyjDRzqWlD4i3gDzFwhlcAMPExIOi+sFfL98PIrKR2cSRSWhB6r1Do2Ww/BQ/wsGU7bB2kaTWsOhmP8hx19IWWiqQhjFaD107xwVNwhJGfqBXFETXj0CP2RtYZNWtUMYRGimSmk4z9BY4cDF288jhA7mX4D3e2Q0D0ql+28HRMZ1hHTA/AWKdkwToBMSDsixKipxoqgk9MDCSg19gK6fmq6wuFrbFqjoqsqF4TJ3VjcoZwbfMVN3PD44M77r8MOEVxun5fLw1hIPv18gDCN0Q0Mo3RlRd4KHWGmT8+9McerC6LY5TwdBCAPMn0N4F/zrSFRQij2lbrqTmGvdmhTrrxIlJeFEkygqP2JmZmYE+H9mZ2f/os/Xk9Aj61stUtbgikxTlsHaZvOFfz9TKfL90hpxLAcaQIRRjBBwqpQsDq8j9c0mf/in7wjDkPxQFnUHfx7fDbjx+7ssPVjjw796C9M+uLImn3SpKfoMUh2D4DaE95DEICwQNvD0uDFIF6TT9UlRsmD8BKGdTmpSEk48iaLyHDMzM0Xg/wISl6yXRBjFNFsuQ8XB/QkMQ2Or1iaKYtTn0jx522JmrMKdlXVG9pigfFTWW20+OD2BdciddMLJpd3o8MV/+hbd1MgN7f4ZMiydymSJ6lqDL//1Bp/87Tv7KiuxDHCCZZxoBS9aI4zaIECgYKhFTHUE2/g5BiHEGxCvgtyi2wivd23x1fPdmhalnLgVJ7wyJIrKdiLgH4D/t8/XktAjYRgBDPQhqgiBlJLwR4EKwFsTIyxs1Wh7Pmmz//UjtY7LcDbN+ZHEB+V15M+/u4NQBKke50wVh3NsLG7x8NYi5985veNrIunT8GZp+rNIIhRhoikpLL0bCEkZE0mXZnCHuu9jKHnyxjukrFd3WnZCwvO80YrKzMzM/w7MPPdP/zo7O/u/zszM7PaWbXiex61btw55ea8fruse+X54fsTyyjJOe7BRba3pcvu2hqG/KHtXpM8fHq+SMXSMA9jq74cThPhhxGenR7kzO7vv6/txP08akpiQLVwxT0QDSQioKFiYchKDYRT6HyAO4l6GUcxWzWF5rYXrhXgtl8VvF5k4XSTfsF4Ignc9ThDx23/+I65ooWrb3xNSxVG+Q+KhkkegAj7wYuryKTFLPGYWXY5iycso9Lez7HX8XL5Mkvu5P64X8P3SMSkqZ0+YojI7O/u/HOUEpmly+fLloxziteLWrVtHvh9hGPFgNaRczAxsNyilxK62uXr1yq4dPmfONfjN7ANyltkXU7am62GHEb+6Mk0p3Vuxbj/u50khliEN/x41b45YuhREDlUZQaAAkkgG+HENQZ2scZaCOYOu9C/91s97GUUx9x6s8WhhjSBUKZUq6LrK0u1l8vkCHV/H24oZqWSYHC/2FLCsL25RKYwycuoHpa3lP2LD/YaSMoV2QBdaKSV+tImqLDKc+sWh72UURrQbDlEQIaVEURUezj94bT6XJ4FX8Xt+/fr1Yz3fsSoq8oQpKgknD01TyaRMgiDCMAbzJ/T9kGza3LMNeayQ41eXp/nvdx/R9nyGMqlDBU5xLNlodUiZOn9z9TyF1Jtn8BbFHmvOH+iEy5hqCVXkX3iNJlQ0xULKiJb/iHYwz1j655jqMT2ceiSMYr789hHLKzVKhRTac4pbc71BrphGMzTiOGZlrU6r43JxehR9H2XOsHQ2FqvPApVOsMSG+3tMtYxyiLZjIQSmVsaPaqx1fsNo6leoSm87xcZWi+X7q6w+3qS51eyOgxCCp3MhlpeXWbvZoDRWYGJ6hMpk6dCdSwkJveB6Ad8vHpOicu6EKSoJJ5NyMcPian1ggUrHC5ga238BrOQy/Jt3Zvjm8RL317awDY2c3ZsJXRxLao6DH0ZcHhvmysRIX9NIrwqxDFjtfI4bbZLS9ndRFULF0oYI4jZLrV8zkflrDPXFwOZlIKXk25vzLK/WdnRMjoII/cnQS0VRyGVtGi2Hew/WmJke2dM3RdVUfM8HIIw7bLifYyilQwUpz2OoBbxwnar3DWX70z1fu764xd2vHrC5XEXVVVJZm9JY8YUAvRO2SOUsamt1lh+soRsa596e4syVCQxre9oujANq/ipr3mO8uI2UEkOxqZinKJqjGD0GTwlvNpapc3U6UVS2MTs7+5d9vI6EAzI5WuDBwubAju/7IROjvS1+lq7x6fQU05USd9c2ebxZQ0owNRXb0DFUFSG68wy9MMQJAvwoRgHOVkqcHxnqOdXzOrLpfocTrZPSDuaeqitpQLLS/i2T2b898oLdD7ZqbR4tbL4wfuEpQlGetQw/JZexqdUdqvUOQ3vMrIrjGPWJd1DV/QYhQVX7U1tiqGVawX1S2hQpffyFn/uuz60/zvHo1gLpXIryRGlf9VDTNTIFjUwhTRiE3PnqAY++X+DdX16lMlFCypgV5wHL7j1iGWOqKSwlAwgiGTDfuc1853sq1hSTqUuoItlXJuyO6wXcOiZF5cNEUUnohaFihpRl4HkBptnfBcr1AjJpk1L+YO3PlVyGSi7DtVNjbLQ6bDTbrDZabHWcZ74rOcvkbKVEJZumnEm/8QMHw9ih6c9hq5VDvV9XMnTCNdrBMlnj5bumPni8gWXquy7iqbyN03BQM9uVM8vUWFqt7xmoBG5AtpQmiOp0wseY6kjfrlsIga7kqXs3sLWxbddfW2vwx3/+ltAPKU8MHco/SNM1yuNF3LbH7//DV5x7e5L0Wz4bwTxZrYTyI58WRZjoikksY9bdeZywxYXsB6jKm/19Sdgdy9S5kigqCScJRRFcvTDGH759yEhZ61tRrZSSWsPh0/fOHtrQLW0apE2D00OJWdt+tILHgDjShF9DyVD3bpPRT73UVlvH9VlYru4ZbAyfrXD3D3NYme07MsvUqTUdOh2PVOpFlSSOJVLC2FSZVnAbgdr331VT0jjBCn5cfVb3s7VS44t//Bo7a5ErHb1w2UqbGLbBt199jbre4dpfXHkhSHkeRShk9SGawRYP2zeYzr535Gt4GURhRH2jSbPaorHRJIoiVE2lUMmRKaTJ7WL6l5AASaDySjM5VmBxtcD6VpPiAdWP3ag1HKbGikyMnIyah9cZKSPq3iyGerSATlNSdMLtC+zLoN3xEYg965OypQyaqRH6IdqP6qsUwPXCHQOVZrXFxLlhrLTJevMB+hHv2W4IoeKGa5hqicZmky/+49ekcjZWun/tyzEh8VAL5yE8sFaY/nR836AroxXZ8pcYDc+R1l6d76bTdpm/vcTct48I/aCrXJk6QhHIWPLwxjwCgWZqTL97mlMzE1g7/P0T9sd1A24tLh/LuT6cPt6GhyRQeYURQnDt8iS/+cNdmi2XbOZoecNGy0XXVN6+NJGYYB0DYdwhkj7GDh0+B0Ug8ML6Sw1Uoije9zWKqjB1ZZK56/fJDmVRnusqk0AUv3gMz/EJg4jz104TxQ5x7KEMKFDRFLvraBuc56tf38S0jb4GKQCtsIoQgsJYhqVbGxQmMpSn9v4MCCFQhc6GN/9KBCpSShbvrfDn/3aLKIzIlXPoexT+B37I7B/vc+/rh1z7y6uMnR1OnkEHxLKOMfVDkvpJOAC2pfPTD8/x2y/nqNbbh1JWuumeDrqm8bMPp7EHOEPoVUZKyWa7Q9sLAMiYBqW0fegHakxId3k+OgKVGK8vxzosvZq3lSaKeM4E8zcXyZQyaM8ZCv5Yjek0HTotj0/+9m2yxTRetAkDdWS28KIt5m49plVtU57o/4O/5q9hKBZCEWTKKe7+bpFcOYWR2vt7Z6s51t2FE19YG8cxN343y/3vHlEczr/Q5bQTuqExNF7Ed33++J++5vy7Z7jy2cUjTc9+00gUlYQTTTZt8YuPL/DNrQWW1uqUcqme25Z9P6Ta6DA+kufdy6eSIGUX5jdrfLe0SqPjbFsoc7bFtclRJosnf5c7aFK2QSwlUsp9g7ex86MYlsHjmwt0ggjTNohjiWlqRGFEq+7guwHZQoqf/dv3KFRyQNcKv1/B3U4IFNo1h7vXH1Aa679qI6UkjD0stVvvolsabtPn8XdrnP90Ys/3KkIBIQljH1U9mY9uKSU3fjfLg+8eUZksH7jOzbAMKpNl5r55iBCCK59dTJSVHkkUlYQTT8o2+Oy9s8wvV7l5Z5la08EydWxLf8FIKwgjHMfH8UJSls7H75xhcqyQPBB24fbKGtcfLlJM2Yzmt7fddvyA38w+4KOzk1wcKR/ouP1sJ46JUMXLNcpL2QZjI3lqtU5PacihyRLFsQKN9SaPZxcRnRC31iE0dUYmS5y+PEFxOLftcykQCDnIz6lk9V4bVVd6VogOenwJ24Ld9JDNyp0tpq6NYNj7PJIH+rsfnaW5Fe4fMkh5iqIIypNl7n3dDRbHzvavu+t1xnUDbh+TovJBoqgkHBYhBFPjJSZGCmxUW8wvVVmvtqg1nG2vs0yN4XKWqfES5WLm0A+UN4H1Zpvrj5YYyWVQd5ChU098Yr58uMBQOsVQpnc/GE2k0JQUUez27Ii6E099ScwB1W0chOnTFf77yr2e66UUVaEwmsfXFD589zSTO5inPY+q2EgxOEXF8zos3/GZHN3ZB+aoCKGgKAqxjJ51+3S/f4LNx3XGZnYfxNn9O8d7dgm9TJy2y3e/uUVxOH/kZ4qiCPKVPN/8+ial0WKfrvD1xk4UlYRXCVVVGCnnGCl35XLPD59NXNY0FXNAbravI7Mr66R0fccg5SmaqmDpGndW1/kss/N0350QQqFgXGLdvU7qCIFKKNvYWuVY3Wl9N6BZbdNqdAi8AFVTSedsUlmb8ZE8q+sNyj2281YbDsVCirHh/L6qnipSCDSkjBADWLC3VmuIKLutbqbf5LUKNX8NS/vh/th5g6Vbm3sGKl7cIauX0ZWT2RWzcGeJKIx6qknpBdM2aFVbLN5bTlaqHnDcgNvzx6SoXEgUlYQ+YxpaEpwcAjcIWajWqWT3L1DO2xaPtmp8cHoCQ+v9Xqf1STbcr4+08AZRm7J1PP4aja0W928ssDi3ytNakWdus7K768+VM6hCsL7ZZGgPxS6Wkmq9Q9o2+eT9s9tmAu2GEAJbn8ANV47c1r0TjfU2ljnW9+M+T9YYouqvdK2anwRmuqXRWG0T+hGasfN98GOHM+m3BnpthyUKI+a+eUTuyeaoX2SHstz96iGTHxwsrfomYps6V84nikpCwhuFF4bAi50oO6EI8aRFONozUOmEPjXPpep1WHNauFFI08viRA8oW2XKlkHW0MkbWk/n9aI6plrAOqD9/kGJopi5Pz9m9qsHGKZOcSS3a0dGu+EQVFsEhsaqF6JbOtm0if5kKF8YRjTaHnEcMzlW5J0rkwcKpHP6eTrBw378WtuIpEdjVZLJDHZRNBWbjJ6nHTafFdV2BxoKnIZPtvzibtWNWlhqhqx+sgZQPqWx1SL0wj1bkA+DYeo0N5t0fpS+TngRx0sUlYSENw5VCDhAql0idwwuYinZcNvM1tZ41KwheKIMaDq6UEjpp/DiDo9aGzxsphF05yRdzKeZTFtYuygNflRHACPpn6IMsF01CiO+/s0tlh6sMzSa39dBtJsCsqhtNBFtj1MXRlnealFtdIAnVt8XRpkYK5KyD54mMNQhDKVIEDfRlf7VkvjRFmG9hDU8+NRKxTyDH83ixW1MpavYSSnx2i8GKm7UJpYRF3Mfn9j6lHatPbA6XynBab7c1vtXgURRSUh4A0kZBpau44Uh5j7pHCcISBsGtr69k6fuOXyx+phVt0VK1Rm2MzsGM6PqZVL+fTrhCppiE8cKf95qcmOryVulLOdyqWfvi6SPH1XRlSyj6b94MpxwcNz84h4rj9apTOxd6Po8QgiKlRyNaou1W0v84t990LfdthCCIftjllv/hCZSfalVCaIGpjqEJpVtJnSDQlN0JlIzrDj3ccIGqmIAkjj6oVDYizp4cQdLTXEx9xm2enQL/0FR32w+U8z6jaardOqdgRz7dSJRVBIS3kAURXBlbJjrjxYZze29SNQ7Hh+fnXxWjxFLyWxtnevrC1iqxnhq79y9ECp54zymWqQdLBCJBgVDJZYGX29UedxscK1sY6seqmJSst4hq59FHXBh5er8Bg9uLTI8uf+04J3IFTNsLle58/VDrn5yvm/XZahFCta7VN2vsbTRI81KimKHWHoMW79AiC/7do370Q1WLuBELWrBGtV4GSeq0wxjkJKUlmcydYm8UT7RBm8AYRAh1MFIKkJRCINgIMd+nUgUlYSEN5QzQwXm1jbZbDsMpXfeRWy2O5QyNqdK3eLOKI75cn2B2doaFTuDrvS44xcCSytjqUMEsoUTrhPETYZtQTOQfL4a8venPmIiMzWQjpcfE8cxNz6/S34oeySPneJInvs35jl9aZxMvvf27f3IGZeIYoeGfxtLGznUPQnjNmHcYiT1Sww1j25oRGE80K6f5xFCIaXlSGk5lFSWi/kZhnNDqELDVPt3rwaNpqvIaDBt4zKO0fTEoXY/HC/g9uNjUlQuJopKQsKJwdA0fjFzjt/de8hyvUXK0Egb3bqKtu/TCUKGM2l+ev40hqYSS/kkSFlnNJXrqSD2BYRAF1l044f6i7IN7cDntyt1/m7Ko2gOfhHbWqnTabpUjmgjrygKqqawcHeFSx+e69PVPUkvWe+hKilq7jeoSgpd7a3rRMoYL9pAU2xG03+NqXbbgguVPK16B00/fvM8TdEpFcuktMF4uAyS/FCW+/5gVI8wCCkUBpvefB1IFJWEhDeYlKHzq0vnWWu2mF3dYKvdzZeXM2k+GylTyaSfpXzu1Na5XVtj7LBByh6kdYMYya8X5/gfT1/GHLCN+ur8JkafRipkixkW7q1uC1SabZdG22Wz3mGz1sZ7stCpqkI+Y1MuZMhnLAo5e1cfGyEEefMStjbKpvslTrCMqphoSvYF518pJbF0CeImIMnpM+Stq9teNzReYHO5Sjp3sECl43g4TkAsJaoi8IPoQO+XUiKAdB8Vp+MkU8wMcLAB2NmjDVx9E3C8gNuPjklRmUkUlYSEE4eiCEbz2Rcs9J+n4btcX7/qJnwAACAASURBVF/YtWC2H2R1k7VOk283lvh4ZGog53jK5lINK9WfGhjd0KhvNOm0XeqOz51Ha2xUWwgh0DQFy9SfzaeKpWS91mJhrYYETF3j4lSFU6NFbHPnwMlQC4ymfoUfV2n593HCJfx4E4QCSAQCkGhKnqJ5jZR+Ck15MSgoVHI9TYF+Sq3eYWmlRr3pbEuPbW1ViaTNxGiBdA/Tl922R76c27ej6qSSLWUwbRPfCzB2+RsdBs/xsTM2di4JVPbDNnWuXEgUlYSEhF2QUvLF6mMMVeu9JuWQlO0Mt2prnM4WGUkNLk3QaTnkhvrXaeJ4Af/597cIFEHKMqiUMrvWvpi6Bk/Ufj8IuTG3zI25Zd65MMG5iaEdTeSEEJhqCdPuPqyj2CWSLpIYBRVVSe/bxt2d9qsT+Ht7gkgpWVyu8XhxC8vUyWftba3svqvTbDl8d6vFhbMjlPe5j61am/d+eXXP15xkVFXh/HtnuPX5HYbG+7dYNrdavP0Xl3CUVt+OmfDqkQQqrzlxHLO13mT+wQadlksYRui6RrGcYfJMmewrKjWfNDbcNqudFmPpwdcXKEKQ1U3+vLky0EDl6QyhoxLHMYtrNW4/WOWts0MMH9C91NA1KsUMYRjxze0FFlarfHhlisw+ao+qWKgcbCeuairT70wx+6f7DI3vPmNmdb3B48UtcllrZ+ddIbBtA0OPuXN/FU1XKOR2/q6FQYSma4ycrhzoWk8akxfGuHv9Pp7jYdpHV+LctodhG4xPjzD3IAlU9sPxAmaPKfXzfpL6SegHURgx/3CDe7eWcNo+pq1hGBqKouB5AQ/vrXLv1hJDwzkuXBmnMvryB9q9yszW1zGPUbbP6ibLToO655A3B/PQsDMWgR9iHmF2SxTHzM1vsF5tkU4Z5I8QGGuaykg5S73l8Osv7/Lz96a7SkafmZgeZfbL+4RBiLaDN0gYRjxe2CKXMfcdvqdqCilb5+HjTa5dtXdUkOrrDaavTfXd1fW4MW2D9/7qLb74D9cpTwwdyY8mimIamw1+8u8/6tvsoNedJPWT8ErhewFffX6PteU6hVKazA6FaHbKQEpJp+Xx+a9vcemdU1y4MnGkNtQ3FTcMeNSoUrGPz5BLCIEmFB42q1wbUKBSHiuyeH/t0IGKjCVz8xtsVFtkbQMf0PtQnJvP2LQdj998NcdffnieXLq/9Qt2xuLqTy7y3X+7zfCpF4cEVmsdYhn3vBAbhka94dBqey9MlXZaLlbaZPramX5c+ktn5HSFix9OM/unOSqThwtWoihmY3GTy59coDK5+5DGhO04rs/so6VjOdf7l45XiU8CldeMMIj402/vUK+2GR7be5quEIJ01sJKGdz6dh6Ai1cnj+MyXytqvgv0NhOon2R0k8VOg2uMD+T4w6dKPPx+4dDvX9qos15tUcjatKttymf6N0MnbZvEscvn3z3gVx9d7Gmg4UGYmhlneW6V+kaTfHl7em11o4F1wIJRTVXZrLa3BSpRFNOstvjpv/volVdTnufSx11jv9kv58iXcwcqyHbbHvXNBpc/ucDFD/rXyv4mYFvGMSoq9WM6T5fX59uRAMCtbx9T22wxNNx7HYCqKpRHctz+bp5CKcPwWJIGOgg1zznISKC+YaoaG06bMI7RdmnfPQpDY0XMlIHn+gdWVTqOx6PlKrm0hYwlURhT3kGdOArZtMX6VovZR2tcne7vxGNFUbj2iyv87v+7Tqve2WZU5/sR+gENyFRVedZ+DRBHMRuLW1z99AJDr9n3TQjB5U8uUBor8M2vb9Kqtp90Be3+GfIcj+ZWCzNl8rP/6WPKR/TueRPpKirHVKOSKCoJh8Xt+DyeW6NYOXgKQlUV0lmLuzcXk0DlgKw7LSytfy2ZvaIIgRSSVuBRGED6R1UVrnxynuv/5SaVA1joSym5t7CJoasoiuiqKaeHsA/oTdILpUKK2w9WmRguUOhzvUoqa/Ppv3mPL/7xKzZWqiiWQbPtsrLZQFUEtmlgWzqWoaFqewcuzw+sDIOIzeUqMx+eY/ra6b5e80liZKrCL//hJyzNrXLv64c0t5pIurVGQlGQcdxtBZcSO2Pz9s+vMD490tf25jeJRFFJeCVYnt8E0d0NHoZ0xmJjpU6z3km6gQ5AJwoHomj0hiCUvft+HJTxs8OsTG+w8mij551/q+PR6rjkMzZuy0UzNCavTAzk+lRFwTA07s2v8+GV/vvKxJqCfq7MN//8HW7dJVvubgJabRfHC4hrEiGgkE1RKqTQd0lBhUFEOmXSrLbxOh5v/2yGs1dPvfY1YYZlcObqKaYuT9CqdWjXOzSrLaIgQtVVssUM6XyKTCF16OdWQhfX9Zl9eDyKynuXE0Ul4RDEcczc7PKRAwxNV5l/sM6Vd1/fnV7fkRJeSvKne+5+tRHvhBCCd342Q+CHbCxVKY3m911Qljcb6Fp34q0Qggs/vYA2wBqMXNri8UqVt6bHDlw7shtxLLn3aI0bd5cxDY2P/v491h+ssXBjgYyq4AC22U1lSCmptxxqLYexco7cjwpmpeymNuKOh13K8Mnfv0uudHInIQ8CRVHIlTLkShnGzg6/7Mt5LbEsg8sXE0Ul4QQT+BGeG5A5orxupQyqG4lnwUHQFBU3Cl/S2QXKESYH94JuaHz4q6vMfvWQue8eY2ctMvnUjmpAEEasrNTQIkl+OMfpa1OYPTizHgVFEUgJK5tNzvTBbCyOJd/eXuD+4w3KpQzqk86VsQtjFEYKrN5fY+3zOzQ6DayUiWpq2KZBFMcsrtYJwwgZS7yOR+AGtJoeoxMlPvv7dxk9M/zseAkJ/SRRVBJOPFEU92XWhqIqBP7B5pS86RRNiw23TVp/GX4PktQx1MdousbVT84zdqbCve8eszq/iRBPBw6q3XqDIKLZ8QCY/vAsxfEiYh+fkX5hmRprW/0JVO4+XOP+4w0q5ewLnVx2zubMu6cpninz5Rd3idsuUcvHb7rdtKuUPH6whm1IKuUyVjHL1Pt5/vbvrpHqgwlaQsJuJIpKwolHUURfkg8ylqjJSPUDUbEzfF9dO/bzBnGEpWrYx1jIWxrJ8/HfvE2n6dKstWlstfCdAE1XyZbSbLRd9JUtSkPHOwHYNnU2au0jH6fW6HDz7jLl0t7zmvKFNJ/87BKzcysEYYyhKOiqgkQShBFLa5tkzo9QLmX45P2zSZCSMHBc12f2wTEpKlcSRSXhEOi6ilAEURQfSVr2/ZBS+dUbM/8yyeomDHR27M50wmCgFvp7kcpapLIWIz9qOV66+RirTxOXD4KuqVQbHfwgxNjBTbZXbs2tYppaT9+hTMbi2tVTVGsdFldqtL0f2o9VXTA2WuCzD86hJamehGMgUVQSTjyqpjJ1dpiFR+sUjlCo53Z8ps692jNHjpu8YZEzLDphcCxpmKc4YcDZ3Mnym/DDCPWY0j0vIojiwweM7Y7H8nqdygG+P5qmUilnKQ9lCMOYOJYoimBxUaXWcp4UWickDB7X9Zm9f0yKytVEUUk4JKfOVXhwd+XQ7/f9EMs2KFUONjTuTUcIwdXiKL9ffXhsgYofRZiqyqh9EtWvlxOoHLXTt9pwEIJDtQwLIdD1H1qTVVUhjCIaLZdSIX20C0tI6AHLMrg8kygqCSecfDFNqZyl2XDIHqL7p77V5up7p5OuhEMwmcmjrav4UYShDn444ZbX5v3KJOoJ857QVIUoHpyvy57Io40x2Ky1d/VBOdTlSGi2vSRQSTgWXNfnztzxKCrvvpUoKglH4J2PzvLbf7mJ0/GxU713oVQ3W5RH8kydSzwODoOpanxcOcXvVh4ynh6sItUKPLKGxYV8/2bn9ItSPsXiWh2OeW0OwwjD0DCP4NfScby+BiqaptBxvb4dLyFhLyzz9VVUTtZ2LOHIZPMpPvnLS7gdn2bD2ff1cSzZXGuQzad4/7PzaPrg1YDXlXO5EqfSebbco3ef7EYkYxq+x09GTqMrJ+9vlUtbyJdQWOz4IeUTqFy87s6zCQnHQaKovIaUyll+9jdX+faP91lfqaMbKtl8altKx/dDmjUHKSWnzlW48u4U+hG6JRK6i9LHI1P80/wsDd8lZ1j7v+kAxFKy0mnyXnmCin0ynU2zaQspu26tx7lIO15ApXi0e5JNm2zU2n0Tg6JQkjrgMMeEhMPieknqJ+EVI5tP8dO/vkq92mH+/hrzD9aJpaTrtiLRDY1L75xifKqEfYAx7Al7k9YN/nriPP+8cJe675LvU7ASyZiVTpO3S2O8XRrtyzEHgW3qjA5lqbUccun+Bmq7EUsJEkaHjpZyK+bT3Hm40aer6pLNHM89SEh4nVM/SaDyGiOEoFBKUyid5fK1KcIgIoq7PiuGqSVDwAZE3rT5u1MX+a9Lc6x0GlTsDOoRbO6bgUcz8Hi/PMlbpZETn044P1Xhv389d2yBSrPtMlHJkbaPpl6U8mlAEkt5pKJcgDCM0XX1hbk/CQmDoquoLB3Lud5963jTrEmg8oag6WpSf3KM5AyLv5+6xM2tFb7bWiGl6uQM60ALoBeFVL1O91inLlGxT14Nxk5UChmyKZO24x85eNiPWEpcL+T81NG9f2xL59RYkZWNBsXc0aTtVsfnkw8qJ64rK+H1JVFUEhISDoyuqLxbnmAyU+D7rVXmWzWEEKQ1A1vTdwxavCikEwZ4UYCt6XxQOcX5/NCJLJzdDUURfHhlil//6Q62qaMM0ABus9ZmerJMudCfmp3L06MsrtQIwujQHUCOF2DoKqcnhvZ/cUJCn+i2Jx+TovJ2oqgkJLxWlK00Px8/RyvweNyqsdius+F0iGTcNSmTAiEksYSMbjKRznEmW2LEzryyO/KhfJqZMyPcebTGcOngpnRuGOKEAW4YEkmJKgS2ppPS9Wc+NR3XxzI0rpzrX81OJmVy7fIE12/MMzyUPXCQFYQRjabD5XPlI7VKJyQclMTwLSEh4chkdJMrxRGuFEeIpaQd+oRxRCx5thCb6uvzlbx8ZoRaw2Gj1upJ8YilZKPT4XGzTtPznljNdgvAuw3P3f8OWSmGrRRKAL/8+GLfA4IzE0N03IDv7y1TLmS2Oc7uheMF1BsOH71zmk59ta/XlJCwH4mikpCQ0FcUIZ4MM3x90TSVT94+zRffPWRtq0W5mN61Rqfl+9zeWqfu+aR0jaK9s7OylJKNVoeHW1V+cWUa0+r/I0wIwZXpUbIpk6+/XwCgkLN3dWwOwohao4Np6Pzi4/NUSlluJYFKwjFjWTqXZ4rHdLbGMZ2nSxKoJLwSSBmC9EBYCPHq1Gu86Ri6xk+uneXbu0vcX9igkLWxzO3zkNY7bf68sYapqgztEqBA15yw2fFI6TrvTo/RkQH/8dYd/urCOYZS/fV1EEIwNV5iqJjh/vw69x9vPGvvV7VuwBIEEQLQdY0r02OcmRxK0j0JLw3XDbh773gUlWvv7K2QzszM/B/AFeAfZ2dn/7cdfp4H/m9ABdrAP8zOzvq7HS/5ViWceGS8Rez+V6R0ECKNYv0SoeRf9mUl9IimqXxw+RTjlRzXb83TaLvkMhaWobPZ6fDd+ho509i1YDiOJR3HJ4xjxit5To0UUFWFDND2ff7znTn+buYCBbv/rcBp2+DtixPMnB2h0XJptFwct/s8TadMcmmLXNZGS+ZjJbxkLEvn0glQVGZmZv5nQJ2dnf1sZmbm/5yZmbkwOzv7/7d3bz9yl/cdxz9z/M3szs6e7fVxd8H2Y8zagO1AHKClNCSmTZuqSKUS6hUX+RsQvYpy1QtukLASqRe9S0IvUiTUNpGitikExV5qzME8LIvP9nrXh/WeZn5z+vXi5yE2sPYeZp457PslIc0O4/k+/BjPfvV5nt/zTHzlZS9Jes1a+xtjzDFJRyW9tdx70qig6VXyv1OgqCKxIQWVWVX83yuWPtroYWGVtgx067knOnVpZlafnZvRxZlbOnVjWhkvoWgQ+XI320olUKlcll8sq1yuKBKJaKi/S5v7utTxldudO5NJlSsVvXPmnL6/d7fidVp8nEzENdCb0cA6d78F6iWfL2piwlGi8sg9/x48I+mXtx//WtJTku5qVKy1b9zx46Ck6Xu9IY0KmloQVBQEC1J0c/hEJCNV3K44R+14ybge2DagkS39+s+PP9O2eFbRsjS36KtUqiiQFItKHSlPQ/0d6upIKduZUjy+fAOSTaU0NbcgO31NDw9xqCY2plQqob173SQqs7PnB4wxJ+546mfW2p/dftwp6dLtxzckHVzufYwxRyT1Wmvfu1c9GhU0tUgkqkhsh4LyeQXRHkXKN6XEnkYPC+u0WCjoZjGvvds33b3TbiBpDduuDGQ69MnUVe0Z7FcixhombDxuE5Wea9baw8v86wVJ1cVmGS1z+LExpk/S65JeuF89GhU0vaj3bVWKnVJ5RkruVzTxcKOHhHU6e3NW0Ujk68cBrHFvuHg0qkK5rCtz89rZ27P+AQItxmWicp+7fsYVTve8J+kRSfarLzDGJCW9KekVa+25+1WjUUHTi0SSiiUPNXoYqKELs7PKeLXdXt9LxHVlboFGBRtSPl/UxGeOEpVH77lG5VeSfmeM2SrpeUl/b4z5ibX2H+94zcsKp4ReNca8KumYtfYXy70hjQoAp0qVimZzeW3K1HbTqI5EQtMLCzV9T6BVpFIJ7X2o8YmKtXbOGPOMpOck/ZO1dkrSB195zTFJx1ZajUYFgFOFUlkRfcO0zzolYjHN53M1fU8Aq2etvak/3vmzbjQqABogaLH3BZpfpE0//uxSBMCpRCyqQOF2+LVULJfVkaztuhcAjUeiAsCpRCym7nRKfqmsVKJ2X0G5Yknbuld/UjPcKxZKmjo3o6tnrymejGv77iH1b+mp+XTgRpLPF/W5vXT/F9bAgcfc/j2jUQHg3PZsVp9OX6tpo5IvlrSli0al2fm5gt7795Oau7Ggjq6UyqWKLtjLGh3boYeP7KZZWaNwMe1WR9XmHdUJ0agAcG6kr1cfTV39ctv89SpXKorHotrSna3B6FBPk6fOa2F2UYPb+r58rtLTqS8+uqAto4Pq3+LqzpX2ks8V9bl1c3vygYMkKgDaXHc6pe093ZqeX1Rf5/InJq/UtcWcHh4aVJJdaZtaEAQ6d/qSegbvbiij0Yi8jqQuTV6lUVmjVJpEBQBq6tD2bXr7k09VKJWUjK/9q2jB95XxEtq3mXN+ml0QBOFBk99weGQ0FlWpWG7AqNpDPlfU5586SlQOkagA2AAyXlJHRnbqvyfPajDTsaYzenLFopYKJX1v7y7O+GkB0WhUQ8ODunF1Vtm+u3c3zS/4GhoeaNDIWl8qndDefSQqAFBTO3t79PQDw3rnzHllvIQynrfiP3tzKadypaLv7nlQ/R0ddRwlamnPYyP637dO6Nb1eXX1ZlQplzU7PafezVlt2tHf6OG1LBIVAKiTkb5edXme3j17XlfmFtST9pROJJZ9/YLva94vaEtXlx7fuV1dqZU3N2i8bH9GT//wsCZOntXU2RlF4zHtfmxEo2M7FK/hXWAbDYkKANRRf2eHju7drQuzt/TJ1RldnQ/P7KmesFwJKqruDzeQ6dThHdu1JdulKLeytqSuvowOPjtWs7u+cDtROe0oUTlMogJgA0rEYnqgv0+jfb2a933N5cN/SpWKkrc3ievyvJqfuozGoUmpnVSKRAUAnIhEIsqmUsqmUo0eCtAy8vmiJk+72Zl2/2G3+xXRqAAA0OJSqYSMs0RlwVGdEI0KAAAtLp8vaPL0TSe19h/udlKnikYFAIAWl0olZfb1OKpGogIAAFYhny9o8mNHicrjJCoAAGAVUqmk9j7cnonK1w9cAAAAaBIkKgAAtDjf4dTPGFM/AABgNbxUUqZNp35oVAAAaHEkKgAAoGmRqAAAgKbl5wqa/PiGk1pjT7hqiEI0KgAAtDgvnZR5eLujaiQqAABgFfxcQZMfOUpUvk2iAgAAVsFLJ2XGtjmqtuioTohGBQCAFufnCpr88LqTWmNHep3UqaJRAQCgxYWJiqs1KiQqAABgFcI1KiQqAACgCZGoAACAphWuUbnmpNbYd/qc1KmiUQEAoMV56aTMfleJypKjOiEaFQAAWpyfK2jylKNE5UkSFQAAsArtnKhEnVYDAABYBRIVAABanNPFtEz9AACA1WjnqR8aFQAAWlyYqMw4qTX2ZL+TOlU0KgAAtLgwUdnhqBqJCgAAWAV/ydfkqWkntcaeGnBSp4pGBQCAFuc2Uck5qhOiUQEAoMWFG745WqPyNIkKAABYBS+dlDlAogIAAJqQnyto8gNHicqfNHGiYozplvRzSTGF5zy/aK0t1GNgAABgZUhU/uglSa9Za39jjDkm6aikt2o/LAAAsFJO16g0c6JirX3jjh8HJbm5FwoAACxrwyYqxpifSjJ3PPVba+2PjTFHJPVaa9+7XwHf93X69Ol1DrN95PN5rkcNcT1rh2tZO1zL2uJ63l+4RsXRPip/OuikTtU9GxVr7Y+++pwxpk/S65JeWEkBz/P00EMPrW10bej06dNcjxrietYO17J2uJa11YrXc3x83Gk9L52UecRVopJ3VCe02sW0SUlvSnrFWnuuPkMCAACr4ecKmjx51UmtsWc2OalTtdrFtC9LOijpVWPMq5KOWWt/UfthAQCAlQoTlZ2OqjVxomKtPSbpWJ3GAgAAcBc2fAMAoMWFi2mnnNQae2azkzpVNCoAALQ4t1M/vqM6IRoVAABanL9U0ORJR4nKn5GoAACAVfA6SFTQZiqVQEtLvhYXfM3P51QqVSRJXiqhrq6UOjs9pdPJBo8SALASYaJyxUmtsWeHnNSpolHZYBYXfV28eENnJqdVLJYVSIrGIopGo1IQqFypKAjC13Z3p7Xrwc3aPNSteDzW0HEDAJbndSRlHh12VI1EBXVQKJT06enLOnN2RrFoVNnu9H2bj1yuoBPjZ5VMRLX/wE5t29arSCTiaMQAgJXyl3xNnrzspNbYs1uc1KmiUdkAZmbm9f74GRWLZfX3dykaXVmzkU4nlU4nVSiUdOL4GV2+dFP7D+xgSggAmozX4TlMVAqO6oRoVNrcxYs3dOLEGWWzaXVl02t6j2QyrsFNXZq5Nqd33pnQkSO71Nnp1XikAIC18pd8Tf6fo0Tlz0lUUCOXL9/UieNfqLcvo0RifWtMIpGIenszmpvL6ffvTujJp/aQrABAkyBRQctZWMhrfPyseno7192k3CmbTWt2dlEnT57TE0/sWvE0EgCgfpyuUSFRwXpVKoFOfXBe8XhUyWTt/xf39HTq6tQtXbx4Qzt39tf8/QEAq0OigpZy5cqspqfntGlzd91q9PZl9OGpCxoa6q5LMwQAWLlwjcpFJ7XGvrvNSZ0qfsO0mSAI9PnnU2teOLtSiURMlXJFV67Manh4oK61AAD35nV4Mo+NOKpWdFQnRKPSZm7dyml2dkmDg9m618pkU5qYuKodO/pZqwIADeQv+Zp831Gi8hyJCtbh2sxcuMusA56X0PWZeS0t+cpkUk5qAgC+zku3b6Li5jcanLl2fUGpVMJZvUDhHUYAANQDiUobCYJAN64v1H19yp1i8ahmby5paKjHWU0AwN38nMPFtEz9YK0qlUClUkWxmLugLB6PKZ93GwMCAO7WzlM/NCptJAgCuV7SGolI5UrFcVUAwJ38JV+T4xec1Br7/nYndapoVNpINBqVIpGwYXF0ynGlEigeZ6kTADSS15GUOTjiqFrJUZ0QjUobiUYj6uxMqlgsO9uErVgoq7u700ktAMA385cKmnzfVaKyw0mdKhqVNtM/kNGVy7fc7RYbBMpmuTUZABopTFRGHVUjUcE69Pd36dzZ605qVSqBFI2whwoANFi4RuW8k1pjR3c6qVNFo9JmNm3KKhKNqFyu/90/C/N5bd3aw1k/ANBgXofnMFEpO6oT4jdMm0km4xoZHtD5C9fV21vftSO+X9Lo6GBdawAA7i9MVK46qTX2PIkK1mn0gUGdOTujUqlStzty5m7lNDCQqXszBAC4P6/DkzlEooIWkcmkNDa2XR9+eKEuhxMWi2UViyU98uhOZ7dBAwCWFyYqU05qjT0/7KROFY1KmxoZGdSVy7OavbmonhqmHpVKoJvXF/TowWEW0QJAkwgTlQccVSNRQQ1EoxEd/taofv/uRM2alXK5omvX5mX2btHw8EANRgkAqAV/ydfkiStOao39xYiTOlU0Km3M8xI68p3dOn78C83MzKuvr3PNdwLlcgXNz+e1b9827dkzxJQPADQRt4mK22NTaFTanOcldOTIbp35Yloff3JJyURcXdm0otGVNRrFYlm3bi0pnUro6aeN+vszdR4xAGC1/EWHicpfjjipU0WjsgHEYlHt2j2kTZu7NTExpUsXbyoSkVLppDwvcdedQUEQqFgsK58ryveL8ry49j20VcMjg0okYg38rwAALMfr9GQOk6igxWWzaR06NKp9+7ZpauqWZqbndP36gkqlsoLbr4kEUmfG05at3Rra3KOBwa66bxwHAMByaFQ2oHQ6qdHRQY2ODioIAhUKpXA7fEnxeIzkBABajL/oa/L4ZSe1xn7gar+WEI3KBheJROR5iUYPAwCwDm6nfoL7v6SGaFQAAGhx4WLaS05qjf3AVUMUolEBAKDFhYnKg46qkagAAIBVyC/mNXncTaKy/69cNUQhGhUAAFpcqiPlMFFxi0YFAIAWl19ymKj8NYkKAABYhVRHSuZbJCoAAKAJ5ZfymvzDRSe19v9wl5M6VTQqAAC0uDBRcdtAuEKjAgBAi8sv5vX5Hy44qXXgb3Y7qVNFowIAQItLdXra+3h7JiqcNgcAAJpWJAjqu8Pc+Pj4jKRzdS0CAEBzGT506NCgq2Lj4+P/IWnAUblrhw4dOuqoVv0bFQAAgLVi6gcAADQtGhUAANC0aFQAAEDTolEBAABNi0YFAAA0LTZ8c8gY0y3pp6hrwwAAAY9JREFU55JikhYlvWitLTR2VK3NGLNZ0r9aa59u9FhamTHmnyXtk/S2tfYnjR5Pq+NzuX58X6KKRMWtlyS9Zq39nqQpSc7uQ29HxpheSf8iqbPRY2llxpi/lRSz1h6R9IAxxu3+2G2Gz2XN8H0JSSQqTllr37jjx0FJ040aS5soS3pR0r81eiAt7hlJv7z9+NeSnpI00bDRtD4+lzXA9yWqaFTqyBjzU0nmjqd+a639sTHmiKRea+17DRpaS7rH9WzUkNpFp6RLtx/fkHSwgWNpedbaOUnic1kbfF+CRqWOrLU/+upzxpg+Sa9LesH9iFrbN11P1MSCpPTtxxkxJYwmwfclJL6QnDLGJCW9KekVay3nH6FZjCuc7pGkRySdbdxQgBDfl6iiUXHrZYWx+qvGmP8yxrzY6AEBkn4l6R+MMa9J+jtJbzd4PIDE9yVu41BCANU7VZ6T9D/W2qlGjwcAqmhUAABA02LqBwAANC0aFQAA0LRoVAAAQNOiUQEAAE2LRgUAADQtGhUAANC0/h9HyvZ62NdiPAAAAABJRU5ErkJggg==
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="MNIST手写数字可视化">MNIST手写数字可视化<a class="anchor-link" href="#MNIST手写数字可视化"> </a></h3><p>MNIST手写数字是机器学习图像识别经典示例,在Scikit-Learn里面,包含近2000份8×8的手写数字缩略图,每个图片都是8x8=64像素,展开成特征矩阵是64维空间。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="n">digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">flat</span><span class="p">):</span>
<span class="n">axi</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'binary'</span><span class="p">)</span>
<span class="n">axi</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>通过scikit-learn<a href="https://scikit-learn.org/stable/modules/manifold.html">流形学习(manifold learning)</a>最早的算法Isomap(Isometric Mapping)将64维空间降成2维平面实现可视化,对比PCA(主成分分析),Isomap可以学习到非线性特征</p>
<blockquote><p>"流形学习"——中国拓扑学家江泽涵院士取自文天祥《正气歌》的“天地有正气,杂然赋流形”,表示“多样体”</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">Isomap</span>
<span class="n">iso</span> <span class="o">=</span> <span class="n">Isomap</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"dark"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">iso</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iso</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">lw</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s2">"cubehelix"</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">"数字值"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">clim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">9.5</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjgAAAI9CAYAAAA3uAJFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdZ3xc1bXw4f9oRr333qtVbRX3boONAccEYoNNMxBuLgmQBO6lpBEIueS+CXADSUgIgRAgVGMDtsG9d1mWLcmS1XvvXSPNvB9kjz2eI9mSVbC0nk+adc7ZZx/9QF6zz957qfR6vR4hhBBCiAnEbLw7IIQQQggx0iTBEUIIIcSEIwmOEEIIISYcSXCEEEIIMeFIgiOEEEKICUcSHCGEEEJMOJrx7sAFOp2Ovj5ZsS6EEGJyMDdXj3cXJrRvTYLT16enqaljvLshhBBCjAl3d/vx7sKEJq+ohBBCCDHhSIIjhBBCiAlHEhwhhBBCTDiS4AghhBBiwpEERwghhBATjiQ4QgghhJhwJMERQgghxIQjCY4QQgghJhxJcIQQQggx4UiCI4QQQogJRxIcIYQQQkw4kuAIIYQQYsKRBEcIIYQQE44kOEIIIYSYcCTBEUIIIcSEoxnqBa2trfzkJz9Bp9NhbW3NK6+8wnPPPUd+fj4LFizgkUceAeDZZ581iQkhhBBCjIUhj+B88cUXrF+/nn/84x+4ubmxZcsWdDodH330EaWlpRQVFbFt2zaTmBBCCCHEWBnyCM66desMPzc2NvLFF19w3333ATB37lxSU1M5e/YsN910k1EsKChoZHoshBBCCHEFw56Dk5aWRnNzM15eXnh6egLg6OhIfX09HR0dJjEhhBBCiLEyrASnqamJF154gd/+9rfY2NjQ1dUFQEdHBzqdTjEmhJjY9Ho9TU3ldHQ2jXdXhBBi6K+oenp6ePzxx3niiSfw9fUlNjaW1NRUpk6dSnZ2NsHBwXh5eZnEhBATV01dATt3vUp55Rk0Gkuio25g8YIfodFYjnfXhBCT1JATnE8//ZSsrCzeeOMN3njjDb773e+yadMmampq2LdvHx9//DEqlYq1a9caxYQQE5Ner+frbS9RU5sLQG9vN6czvsLO1pXZM+8f384JISYtlV6v119rI83NzRw8eJCUlBTc3d0HjA1Gq+2jqanjWrsihBhjFVVZfPDRD03iHu5h3Lv2zXHokRDXB3d3+/HuwrgpLS3lhRdeoK2tjfj4eJ5++ukRv8eIbPTn6OjIihUrjBIZpZgQYuLRqJVfQ6k1FmPcEyHE9eL3v/89jzzyCB988AFVVVUcPXp0xO8hOxkLIa6Jh3sogQHJJvHoyKXj0BshxPWgqKiI6OhoAFxdXWltbR3xe4zIK6qRIK+ohLh+tbU3cODQmxQWHcfS0o64mJtITlyNSqUa764J8a010V9RNTQ08NBDDxk+r1mzhjVr1gDw5z//me7ubhISEnjppZf4/PPPsbW1HdH7S4IjhBBCjIOJnuBcyYkTJ3jrrbeIi4sblZJO8opKCCGEEGNuypQpVFZWsn79+lFpXxIcIYQQQoy5t956i/vvvx9ra+tRaV9eUQkhhBDjYLK/ohptMoIjhBBCiAlHEhwhhBBCTDiS4Aghrllrez15+YeobygZ764IIQQwjFpUQghxqe27XuV0xlfo9X2AivjYW7hh8Y9RqeT7kxBi/MhfICHEsB09/gHpZzadT24A9JzO+JLM7O3j2i8hhJAERwgxLL293Rw78W/FY8XFx8e4N0IIYUwSHCHEkOn1OjZtfo7unjbF4+oBCnAKIcRYkQRHCDFkBYVHKSw6MsBRFcmJd4xpf4QQ4nKS4AghhqyxqWyAIyoWLXgUN9fgMe2PEEJcThIcIcSQeXlGKsZnz1xP0tTbxrg3QghhShIcIcSQ+fnGkxC38rJYAklTbx+nHgkhhDGpRSWEGBa9Xk9J2Smqqs7i6OBNRPh8zMzU490tIa4bUotqdEmCI4QQQowDSXBGl7yiEkIIIcSEIwmOEEIIISYcSXCEEEIIMeFIgiOEEEKICUcSHCGEEEJMOJrx7oAQ4vrX29tNds5u6huKcXcLISJ8IRqN+Xh3SwgxiUmCI4S4Jj3aTj7f9Cyl5acMsexzO1l164uyL44QYtzIKyohxDU5m7PTKLkBKCg6yrncfePUIyGEkARHCHGN6usKFeN1DUVj2xEhhLiEJDhCiGtibe2kGHdzCRzjngghxEUyB0cIMWyNzRWknd5kEg8KnE5E+IJx6JEQYiAd2l5ymtvG7H7T3JS//IwVGcERQgzbmTNf0dFRbxKPnbJcJhgLIcaVJDhCiGFrbatRjHd0No5xT4QQwpgkOEKIYfP2ilaMu7oGj3FPhBDCmCQ4Qohhi4+9GX/fqSbxg4feQqfrG4ceCSFEP0lwhBDDplab095h+jqqoiqT/MLD49AjIYToJwmOEGLYzuXuo6GxWPFYe5vp5GMhhBgrkuAIIYatsvrsgMf8/UxfXQkhxFiRBEcIMWwODl6KcZVKjYWFzRj3RgghLpIERwgxbNGRSxV3Mtbr+8jJ3T0OPRJCiH6S4Aghhs3Kyp6w0HmKx3r7tGPcGyGEuEgSHCHENZkSuQRQGcXUZuaEhswenw4JIQSS4Agx6fT0dJKe8RV7D/yVnNy917xfTYBfAksWPoqtrRsADvZeLFv6X7jLZn9CiHGk0uv1+vHuBIBW20dTU8d4d0OICa2zs4VPN/4X1TXnDLGoiCXcvPxnqFSqQa68Mq22i+aWSpwc/dBozK+1q0JMeO7u9mN6v8lWbFOqiQsxiZzJ3GyU3ABkn9tJXMxNBAYkDbvdsvJ0jp34kMamUtzdwpgx/W483cOutbtCCDFskuAIMYnU1RUqxnPzDhDgnzisUZz6+mI+/+LndPf0fzNsbCqnoiqLe+76G7Y24/sNTggxeckcHCEmEVfXQMX4qTMb+fDTx2ltrR1ym1k5OwzJzQVtbbWcy90znC4KIcSIkARHiEkkLvYWPNzDFY+VV5zh4JG3B72+ta2Wquoco4nJdfXKo0LdPTKnTggxfuQVlRCTiI21I2tuf4Wjqf/m2PH3TY6XlKYpXtfX18uuPX8k4+w2+vq6cXEOYMmix3FxDqSkJNXkfJXKjJDgGSPefyGEuFoygiPEJKPX6wn0nYaZmYXJMVsbZ8VrTqZvID3jS/r6ugFoaCxh67aXyMndjba3y+R8b68YPNxCR7bjQggxBDKCI8QkodfrOXz0XU6mb6CrqwULCxt6enqMzkmIW4ler6e4JJXa+gLcXIIICkyhoPCISXttbbU0NZUp3svNNZDiklSqas7h7OhLWOgczMzUo/JcQgihRBIcISaJnLy9HDr6juFzT08HarUF7m6hWFk7EDtlGZHhC9i89Tdk5+4ynBcZvhBzc2vFNgMDUsjN2097R8MlUTNaW2v45PMnLzkvmVW3vIC5udVIP5YQQiiSBEeISaKg4LBJrK+vh/jYFcTH3gL0J0GXJjcAObl7SJp6B/3lGC7uC+rjHUNYyGxsrB04cOgfVFafxdbGBTs7dwqLjxm1UVxygszs7UyNu3XEn0sIIZRIgiMmhXN5+0hL30h7ez1+fgnMSrkHe3v38e7WmOjt7aa+oQSVmfKUO43G0vBzdfU5xXNUZmbcvOxZ0tI30tHZiL/fNGbPuI+6+iIsLe1ZffvLbNv5B85kbqa5pVKxjerqHJAERwgBNDc38+STT1JfX09sbCzPP//8iN9DEhwx4RUUHuXLLc+j1/cvbW5oLKGutoC7Vr92zeUJRkJ3dxtZ2dtpaq7E0yOcyPBFqNUj879mRtbXHDj0D9raa7GwsKF/XYHOcNzRwYewkLmGz06OPortODn5MiVqKVOilgLQ3FLFlm9+S2n5KQA8PMKpqckdtC/OTr7X9jBCiAlj06ZN3HrrraxcuZInnniCM2fOEBcXN6L3kARHTHgZWV8bkpsLKqoyKS49SdA1lCe4WvX1xVTX5OLqEoCnZ4TRsY7OZj79/ElqavMMsV17/4yNtSP+fvHMvIaRprr6Irbt/AM6XS/QP+cGwNUliO7uNry9o5kz434sLC7Or4mKWMyZjM1UVp81xDw9IpgSscSo7V17XjMkN8AVkxtX12Bio28a1nMIISYeJycncnNzaWlpobKyEm9v7xG/hyQ4YsLr6VEuLtfT0z6q99Xr9ew79CYnUj8+n2CpiIpaQmjgDGysnQkISORMxmaj5Aagq6uJrq4mGhqLqaktYO3qP6JSDX1Hh/zCw4bk5lKBAUksXvAjxWssLKy5fdXvOJO5hbr6QlxdgoiLWYGlpa3hHG1vt8kcm4F4uIcTGb6QuJgV2EjZBiHEeUlJSezdu5d3332X0NBQHB0dR/wekuCICS8oaAZFJSeMYrY2LgT6j+7oTWlZOsdP/PuSiJ7s7B1kZ+8AwM8nDhsbl0HbqKzKpLjkJEGByUO+v8Ulc2supdEMvpLJysqelKQ1Ax43U6mxsLChq6vFOG5mjk6nNXx2dQnku995CTvbwZ9RCDExNTQ08NBDDxk+r1mzhjVr+v+2vP766/z617/Gzs6Ot99+mw0bNhiOjRRJcMSENy1+FQ0NxWRmfUOfToujgzdLFj5qNCoxElpaqsnN3w9AeOhcyivSBz2/rOIMvj7xV2z38jpPVysifCFHTrxPe/vFJdzm5jZERy0Z5KorU6s1xEQvJ/Xkx0ZxRwcvYqJupLW9FicHb2Kil8uojRCTmIuLCxs2bFA81tLSQk5ODlOnTiU9PZ3Zs2eP+P1Ver1ef+XTRp9W20dTk9SuEaOnpaWK1rY6PD0i0WjMR7TtwuLjfLX1Bbq7WwGwtLAjJnoZJ099Nuh1Hu7hqNUaKqvOKh63sXHmgXvexcrKblj9qqrK5vCxf1FTl4+Lsx/Tk9YSGJA4rLYu1dfXy8Yvf0FhsfEGgC7OAdy79m9GK7OEEMrc3e3H9H4d2l5ymof3hWk4prkN/AXn9OnTPPPMM1RUVDB16lRef/11bG1H9kunJDhCXCO9Xs/7Hz1CVXW2UdzDPYK+vh7qG4oGvDYsZA4rlv+MnHN7aGgooaY2l7Ly0/TptDjYe7Fk4WOEBM8kJ3cPxSUnsbF2ICZ6OS7O/qP8VIM7cOgfHDn+L8Vjt9z0S6IiFo1xj4S4/kzmBGcsyCsqIa5RX5+WaoVVRDW1uTxw37/IyNhMbV0+zS2VNDSWGo6r1RZMjb8NC3Nr4mIurjBqaamipa0Or/MjTbv3/onUU58ajp/O2Mztq36Hl2fk6D7YAOobSjh64oMBj/f0dI5hb4QQQpkkOEIMU3d3O8dSP6Sk9CQajQVarfE/7C7O/jg7+jB/7sMA9PZqSTv9OSWlJ7G2ciAu9mbMNVakpX+Ok6MvQYEpqFQqHBy8cHDwAqCxuYJTpzcZtdvZ1cyJtE+4ZfnPR/0Z9Xo9hcXHqKnNw9nJj/DQuRQUHTZZdn+BhYUtwUFSRVwIMf4kwRFimDZ/8yIFhablDwBUKjUzUtYZbSSo0ZiTkrialMTVAOzZ/xdOnPyEC+UPgoNmsnLFr4zqNTU2lNB3ycqkC5qaykfkGfR6PcWlaXR1txDol4i1tYPRsa3b/oes7O2GWFBACn19pv0BUKstiY+7lda2GuztXEekf0IIMVyS4AgxDFXVOYrJjaODF6Ehc4gMX4Cvz8C7cpZXZnLislVIhUVH+HLrb7hx6U+xO7983NMjAgtzG3q0xvPTPDzCr/kZ2jsa+XLzryirOAOAtZUjNy55gvCweef7c9QouQEoKjmO+QDLzM1Uak6kfsiJ1A8JCZrBzct/TmdnM9C/E7IQQowlSXCEGCKdro/cvAOKx2xtXQfcRO9SFZWZivGCwoN89GkJq2/7A/b27tjaujBn1nr27P8Len1/iQUXZ3+mJ901/Ac478jx9wzJDfS/+tqx5zWCAlMwN7eiulZ5d2K12hxtb5dJXNt7MQkrKDrKu//+D5qb++tSBQUkMz3pTsoqTtOn6yM8bB5eHhEmbQghxEiRBEeIIaivL+bfnzxOV3ez4vEA/6tbgu1o7zXgscbGUtIzvmLurPUAJE27A3/faRSVHMfa2oGI8IVYWtgMvfOXKSsz3aenvb2WyqosAvwTcXbyU7zO328aufn7rth+c3OF4eeikuMUl6SiP18H63jqhyxf+t9ET7lhmL0XQojBSYIjJrTu7nby8g/Q29dDSPDsa54bsmnLcwMmNwAOdp5X1U5Y6Bz8/KZSVnZK8XhTU5nRZw+PUDw8QoH+JCu96AhOjt6EhczBzEw96L2Kio9z4uTHNDVX4OUZyczp9+DmGoydnRu1dflG56pUauzt+mtfRYTNJ8A/kZLSk4bj7m5h3LjkCSLC53M2ewdl5Rn0aK9u2an+kiKfOl0vh479k6jIxVfsvxBCDIckOGLCqqnNZ+OXP6OltRoAC4u/suLGpwkLnTvodZ2dLeTk7aG7q5XAwBSjVymNjSWDXpuVs534uJtN4o1N5Rw4/A/Kyk9jb+vKtITbuO2WF9l/6E1Ond5ocr6He5hi+9/s/ANnMr4yfHZ08Gbdmj8PuGNwVXUOG7/6Jb3nXyk1NVdQWZXNvev+zrT471BckmpUr2pK5BKcz++xY2amZtWtvyEzaxu5eftobqlEq+3i4JG3mT3jPoICU/jTX78z6O9jME1N5XR0NGJn5zbsNoQQYiDDTnDq6up47LHH+OCDD6iuruZ73/segYGBAPzf//0fLi4uPPvss+Tn57NgwQIeeeSREeu0EFfj0JG3DckN9BfX3Hvwr4QEzxpw1KChoZTPNj1Fc0v/3JH9h95i0fxHSJp2BwBmZhr6+noGvOfl9Zmgf87OF5t/ZRgtaW+vY+v237HK0o6lix4HMEpyfLxjcXML4UzGFjw9I/Fw7x+5qajIMkpuAJpbKtm193VuuUl5yXhm9jZDcnPpNWfP7iAh/lZu/87vOJO5ma6uFgIDUpiWcBvQX4m8sOgYVlZ2WFjYUFKWxoXVXqdOl1HfUMzKFb8e8PdwNZyd/bGxcb6mNoQQYiDDSnCam5t56qmn6Ozs3/cjPT2dH/zgB6xdu9ZwzrZt29DpdHz00Uc888wzFBUVERQUNCKdFuJqKJU/aGzs/8fZ3S1E8ZpjqR8Ykpt+eg4d+SdRkUuxtXEiKGg6+fnKE4wBAv1Ni2IWFR83eRUEejKztxEWOoclCx8jPHQeFVVZ2Nt5UFB4mA2bngZApTIjKXE1C+f+B+kZXyres6x84JpX2gE23dux5xXSTm9k9ox7ueWmXxgdO3X6C3btfc0wstNfnNN4w/PSsjQaG4txdPCiuaVK8R62Nq50dbcqJoRmKg1zZtwvr6eEEKPGbDgXqdVqXn31Vezs+uvjnDp1ik8//ZTbbruNl19+GYBjx45x0039u7POnTuX1NTUEeqyEFfHwcFbMX46YzN9fb2Kx2pq80xi3T1t1J6P37r8l4SFzkNtZg6oMDO7WNMqJHgms2bcY3J97wAjPr29/XGVSkVgQCKzpt8N6DiXt8dwjl6v40Tqh5RVnMbaVvk1lIXFwPVbQoJmDnisvqGQr75+kZrai8lXV1crBw6/ZfTa6vIRIMO53W2suPFZNGrTulOeHlFMTfjOgKNdyUmriYpcPGDfhBDiWg1rBOdCYnPB/PnzeeSRR7C2tub+++8nOzubjo4OPD37J1w6OjpSXj4yG5MJcbVSklbzxeZfwyWTWwHS0jdwNnsH8+c+THys8XwZF2d/kyRHo7HExTng/M/mrLrlefpLuOnp6+ujvPIMVlb2eLor700TGJCMra077e21RvGw4Fkm55YPsHy8vDyD5Kl3cPLkZ+gu2/gv+fzGgUrCw+Yxa8a9pKZ9Rk9Pu8lxvb6XjKythqXtNbW5iq/ZLmdv54G/31TMza148P73yc7ZQUtrNa4uQQQFpODk5M2uvX8a8Pry8jMDHhNCiJEwrBGcyyUmJmJnZ4darSY6Opri4mJsbGzo6ur/5tfR0YFOp7tCK0KMrIiw+YYyCZfr6m5h+66XKSs/bRRPnrYaaytHo1jitDtwcPAwiqlUKlQqMzQacwL9EwdMbgAsLWy4edkzuLkGAf0jLimJa4iPu8XkXAd7D5MYgL29O3a2rnx35f/g6OgDqLCycmD+7O8TH7tiwHurVCrmzFzPQ/e9T1DgdMVzai8ZwXF28lesBG5v74GZWf/3IScnX5bd8N+GHZft7VxJSVrDkoWPMTV+JU5O/SNnbi6BA/ZLz7eixq8QYgIbkQTnwQcfpKamhs7OTg4ePEh4eDixsbGG11LZ2dn4+spOpmLsubsGD3hMr9dxLm+/UczLK4q1q//ErOn3MDV+FatueZF5sx685n4E+E/jvnVvcf+6f/D9+z9gwbwfoFKZ/u8XO2U5DpftkePlGUVE2HwAggKTuPeuNwkL6V8JdjztU3buec3wumsgNjaOuLuGKh7TWFzcmdje3t0w0fgCS0t7bln+C9bf80/mzHyAQP8kKiozaW5WnntzQVTUEny9lXdzvvA8QggxWkZkmfgPf/hD7r33XszNzbnzzjsJCQnBw8ODtWvXUlNTw759+/j444+v3JAQIywwIAlXlyDqG4oUjytNcnV29mXOrAdGvC8qlRlubgMnXNCfYKy+/Q+cOv0FjU1lODl6o0LN9t2v4usVw5SoG3jvwx/Q2HSxKnla+gba2+tZefNzg7YdHJTC8ZP/Non7+041+jx/zsN4eUZRVHwCK0s7YqJvxMnRh41f/oKikuOG806f+ZI7bvtf3FyD6e7pIDtnJ80t1fh4RREaMhsLc2tuv+13pJ78hNOZW2htrcHa2oH42FtImnb7VfzGhBBi+FT6/skEo6K5uZmDBw+SkpKCu7v7oOdqtX00NXUMeo4Qw1FXX8i2HS9TUZVhcmzZ0qeJi1k2Dr26ssbmCj7+7Ke0XrLU3ccnjooK0/krKpUZ//HgJ9jZuii21dJaw5Hj73Eudw9dXa0X2/OK4fZVL2Fpaad4HZgunb9UXMzNzJv9EJ9u/G9qLintEBu9nOU3PGV0bo+2E7WZOWq1bL8lBIC7u/2Y3q9D20tO89VtzDkSprkpL4wYK6P6l8bR0ZEVKwaeHyDEWHBzDSYifJ5ignPg8JvETFn6rVyunJ6+ySi5ARSTG+h/3dbZ2WyS4Oh0fWTn7GLP/r/Q0dloiKtUamZMX8fM5LWGOTeNTeVkn9tFb28PYcGz8PaOBuDQ0XcUkxu4WFai5rK6VRlZXxMXs8Ko4KiFufVVPrkQQlw7+SolJoWOTuWVQe3t9eQXHib8Crsbj4fGpqtfeWhl5WiYxHxBb28PG7/6BUXFx0zO1+v7aG+rNyQ3pWXpbPzqF3R394/uHDvxAUsX/ZiEuFsHLAwK4O4eRp3JHj/9amrzB62oLoQQo2lEJhkL8W0XOEgRzO5u0+XTOl0fx1M/5oOPH+XDTx4nLX0jw3mbq9frKSg6yq69r3P46D9pGkLS4uVpWm1bpTIjJGi2Sbyvr8do12aA7HO7FJObC7q6L76qOnr8PUNy099vHYeO/guttgt7e+X6Wk6OvqQkrsb1ssTqAlcX5bgQQowFGcERk0JgQCLBgTMpLD5iFLewsCUo0HT34f2H3uJ46sUJuWUVp+np6WBGylqTcwdz8MjbHDn2L8Pnk+mfc/t3foeXZ+QVr50av4qComNUVl0cQUlJuhP0egqKDhmdq9V2ci53H0GBKRw++i7llRlGc22UBAekGH6urSs0Od7eXsveA39lSuRSKquyjDb/Cw6czi03/RJLS1sSYldyLncvdfUX25gSuRR/v4QrPqMQQoyWUZ1kPBQyyViMNr1ex9ZtvyP73C50ul7sbN1ZNP8RIiMWGp3X29vDG2/dYZIgODp48/31H1z1/Vrb6njrn/eY7AQcFbHYpDzCQHp7teTl76eltQY/33h8vKP5YvNznMvba3JuQtxKsrK3o9Uql2e4QKUyIz72FpYsfMww9+jTz/+LopITiufb2DgzZ8Z6KqvPotV2ERI8k+ioG1CpVIZzOjtbyMzeRmtrDd5e0USEzftWzmsS4ttEJhmPLhnBEZOGSmXGimXPMHfWA7S0VuPpEWHYrO5SPdpOurpM/wh0djWj1+uN/mEfTENDsWKZg/qG4qvus0ZjblLSoKWtWvHcsvIzV0xu3NxCuHX5L0xeK81IuZuq6hyj11YXdHQ0Ulp+ypCUtbc3kFdwAEd7Hzw8QiksPs6hI+9QU5uPq0sAPt4xktwIIcadJDhi0nFw8MTBQXleCYCNtSPeXtFGr4agv+TC1SY3AG5uoVha2NHdY5wsubuFDa3Dl2lrrVOMD7TSCforlMdMuZEpUUsVVzP5+yVw911vcOTY+2RkbTE5Xlffn5SdPvMVew68cb7sg4qQ4FmUlKbR29ufWNXU5vLllucpjLmJ5Km3X3HfHyGEGC2S4Ahxmd5eLR0dTSbxiLAFQ2rH1saJWTPuYc/+N7hQjdve3pMZyXddU/+USin0xy0GLIy5bOl/4eoSgE7XR2raZ+QXHkajsSR2yo1EhPc/l5OjD/PmfJ/cvH0KSVkwzS1V7Nz7On193eejegoKD2FKR0bmZrJzdnLrTb8kNMS05pYQYuz1dalozxzD0dWh/ckccZLgCHGZgqLDNLeYrnYqLjnBlCFWwE5OXI2vTzxFxcewsnIgKmIx1tYORudUVueQc2436PWEh83H1ydmwPayc3bR1Ky8EisyYhEZmVtNKnj7+03F1aW/WOjufX8iLf1zw7GCwsMsv+G/iY1eDvQnZTOm382+A3/lQlJmZ+dOSvJdlJSmXZLcXFlvbxdHjr8nCY4QYlxIgiPEZXoUlo0D9PQMbxK8t1cU3l5Risdycvey5esX6TtfITz11GcsW/IksTHLFc/PytmhGPf1jSd2ynLST28yOdbSUgNAe0cjZzK3XnZUz6nTmwwJDsD0pDX4ecdSWHwMKyt7IiMWYWfrSmuL8tyf/t0mlIvp1jcUDWnekhBCjBTZB0eIywQFTcfSwrR0QVBgisLZ1+bo8fcNyQ30b8B35Pi/0On6FM/X9WkV49MT78TOztVQ8ftSF15pdbQ3Kr7Cau9oNIn5+MQwZ9Z6kiBaTVwAACAASURBVKbdgZ2tKwAhwTPw8TYeXbIwt2HWjPtwcVauHO7mGiLJjRBiXEiCI8Rl7GxdWbb0ScMGdxqNFUlT7yAu5qYRvY9er6OhsdQk3tRcMeAeNmEKOy47OvgQGJCEvZ07URGLTI53dbfQ2lqDm1sQbgrV1f19r26/GpXKjFW3/IbpSXfh6xNPYEAKTk4+HD76Dl3drYSGzMHMzNxwvoWFLbNn3ndVbQshxEiTV1RCKIgIX0BI8Eyqa3JxcPDC3s5tWO3o9XoqqrJQqzR4ekYYjWaoVGa4uQRRVZNtdI2Toy91dfl4eUebrHhKiLuV5tZqzpz5kq7uNrw8o1i84FE0GgsAZs24j+xzu4025Wtvr+d46scsXvgjFi98nK3f/JbWtv7XVl6eUcwdQuV0Gxsn5s99mN5eLe+8v96wM3NHRwP5BQdJnrYGCwtrzNTmRIYvwNnJd2i/MCGEGCGS4AgxAI3GEl+f2GFfX19fxJZt/0N1zTkA/HziWLHsZ4Yl6vkFh2lqqbjsKjOaWyr5+PMnsbV1Z8mCHxpWOUF/UrRgzsPMTF5HZ1czjg7eRklTTU2uUXJzQVVNDgABfgk8cO8/KSk7ibnGCn+/qahUQx/ILS5NVSw7cSLtI7y8prBo3iOS3AghxpW8ohJilOza+7ohuQEoqzjDngN/AaCnp5Ntu16mq+vyIqA69Pr+Cbvt7bVs2/kHOhUKhVpa2uLk6GOU3Oj1etLPfKHYF0dHb8PP5uZWhAbPJsA/cVjJzfmbDXioquosX259np6eTkO/amvzqa0tGN69hBBiGGQER0xoTc2V9PZ24+oSOOKTXWtq8w0lEyLCFuDhHmo41tHZTHHpSZNriktS0ev1lFecob1decO+S3V1t1JccoLIiEWcydxyvsyEjoiweUxLWGWUoJSWnaKkLM2kDTMzc5ISvjucRxxQT+/gOya3tdVSUHwUb88otm57ibLydKB/yfpNNzw96EaLQggxEiTBERNSV1cr23a+TG7+fvT6Pry9Yli29AnFSbbDcS53L5u/+R/DvjDHUz/m5mXPGF4nmWusMDe3Mi2doIfUtE+GVGnbysqB4yc/Zt+BNwyxsvJTtHc0Mm/2g4ZYc7PyTsa+PrF4DbBMfah6e7spLjlJWempK55rplKze++fDMkN9Cdhu/f9ie/c8vyI9EcIIQYiCY6YkA4eeYdzeXsMnyurMtm282Xu+t4frziSk31uN5lnt9Gr7SY0ZCaJU283qq2k1+s5dPRdo03v+vq6OXjkHcLD5qNSqcjJ3Y1Wa7oku7unjT37/4KtjQuuLkHUNxQN2hdvrxgCAxLZsftlk2Onz3zJrOn3GCYY99eA0pjMwQkMMK6WrtP1kV9wkLqGYtxdgwkNmT3gqyq9Xk9FRSZdPa1oNJZs2/EHmk3mDZlycvIlwG8qm7e+YHKsoOgYfX1a1GpzhSuFEGJkSIIjJqTCosMmsYrKDOobinG7rNDkpc5kbOabnb83fC4tT6O5pZolCx81xHr7eqirLzS5tr6hiPrGElyc/Dhy7F9c2AlYSXtHA9bWypV2ra2dsLN1xc83nhkp6wCVYumIzq5merSdhgTH1TWQmSl3c+joPw33dnTwMdrEr6+vly82P0d+4UFDLCx0LitXPIdKZUZpWRqdXa0E+E1Dp+/jyy3PU1Z+YbRGNegzmZ9f8eXnm8C82Q9hYWGDhaUNnZ3NRudZWtgOf+6PEEJcJUlwxISk0ZhWCTcz02BhbjPodacUJulmZG1l5vS7sbVx7m9bbYGzcwCNjaZVwU+d+pyZ0++lqfnKoxzd3W2KIy7Tk+4iJWk1Wm0XaemfU1RyAgsLW3oue93l5zsVG2tHo5i3dzQqlRq9vr/N5pYK9h34KyuWPQP0l3q4NLkByMs/wJnMrZzN2WF4nWRlZY+XZ9QlyQ0oJTdmZhqsrRxRazQ42Hvi4xXL1PiVhjk2sVOWc/zkR0bXxEYvk2rjQohRJ1+jxIQUM2WZSSwibD4ODh6DXqe0q69W20nnJSMoKpWK8NB5iteXlKWTk7sbB/srT6J1dw9l7qwHUV+yOV5w0EwS4m8FYMOmp9l38G+UlJ6k7bIJya4uQSxe8COTNtPSPzckNxeczdlBfUMJAFk52xX7kpr2qdFcma6uVopLUq/4DDpdL+0d9bS0VFNWfppjqR/w93/eQ3bOLgDmzfk+c2c9iLtbKB7uYcyd9RBzL5k3JIQQo0VGcMSElJy4GpVKRebZbWh7uwgNmnVVu+oG+E0jK3ubUczdLRTXy15rxcUs50Tqv9HpjUsqNDQWsXvf6wCoMEN/vkaTWm1hVATTytKeGcl34esTR3jYPMrLT+Pg4Im/3zRUKhWlZacpvSThuMDZKZAlix4jwC9BcRSkvb3eJKbX62hqKsPVJWDAelpKK7ouLFcfKp1Oy94DfyUsdA4ajSUzp9/NzOl3D6stIYQYLklwxISkUqlITlxNcuLqIV03d9YDNDaVU1mVCYCjgzdLFz1uMjHZ2cmXaQm3kXrq0wHb0qNjSuQS3N3CiAxfQHNLFQVFR7CwsCEqciku5zfCc3byNdkUL6/ggGKbnV1NBAUkDnhPP58Eo713Lthz4A3s7TxRqy0Ur7O386S7QWmfmsHn3Qykta2Gmtp8fLyjh3ytEEKMBElwhLiEg4Mna1e/Rll5OlptFwH+iYZJvJdbOP8RfH3iKC5NpaY2j8qqLJNz7O08mZ58J9C/2V6A/7Sr6oeLc4Bi3Ob8PKCBTE+5i4qqTJO+NDaWsuGLZ2hrrzW5xsM9gtkz7uPLLc8ZFf6cErUUN5dgUk99RldX6/kCoP2jOt5e0YrPe4GFuY3R5oJCCDHWZA6OEJdRqVT4+00lJHjmgMnNhfMiwudzw+KfMCVyieI5jo5eV33fnp5OenraAYiKXIyVlYPJOTOT1w3ahq2NM4vm/1DxmFJyY2fnzu2rXiIsdDa3r/odUyKXEBSQzPy5/0HStNUcS/2Qjo4GdDotoMPW1pU1332VtatfZ3ryWlQq5cnC06beZpiULYQQ40FGcIQYAdFRN3Amcyu1dXmGmKdHBFEDJD4XNDVXkJa+kbz8g7S0VqOiP2lavOBRbl/5Erv3/4WKygzMzNSYm1uRV3AAD49Qow0Lm1qqsNBYYWPTv+zcwdHr/EqqvoFua9DWVsu+A3+jo7OJuroCXF2DmJ5yNwF+Cezd/1e6u42rmre319PcUoG/KoH5c75PfOwtFBYdp7Iqk8amcmxtnIiKWEykQlVzIYQYS5LgCDECNBorggKS6ehsoq+vFz+fWG5Y8gSWFgMvS69vKOaTDU8arZDSA9nndqFSmXHz8p8xb86DfPzZE+h0vXR3t3Euby81tXncu+5N2lrr2L7rZUrLT6FWWxIVuYglCx+jp7tjSBOEM89+bfi5ta2GyqqzrFvzZ7q6WhXP7+puM/zs5OjNtISVTEtYedX3E0KIsSAJjhAjYPfe10jP+NLwOa/gAAF+U0mcdvuA16Sd+txk+fcF5/L2s6y3h4ysb0xGYpqay8nPP0Ta6Y1UVGYA/TspZ2Z9jY21E16ekQxnYvAF3d2tnM74Clc307IWKpUZwYHTh922EEKMFUlwxKTV0dHEiZMfU1GZib2DJ4nxt+HtPWXI7bR3NHIm62uT+OnMzYMmOE3N5QMeMzNT09nVQn7BQcXjtQ2FhuTmUnkFh4iLuVlxA8GhOJe7F2+F+lVWVg44O/spXnP6zFekZ3xJR2czgf6JzJm5Hnt792H3QQghroUkOGJS0ut1bPrqF5RfSBIqIL/gEGtufwVPj/AhtVVWfub8JFxjnQO84qmuySO/4AC9vabXXBAZsYD0jK/o6moxOWZubkNtTZ7CVaBRm+Pi7EfitDs4kfqhIW5r44K2t9swifkCZ6cAGptKTNppaa2it7fbJN7Z2URdfZFR5XSAzLPb2LbrD4bPGVlbaWquZM3tL494FXchhLgakuCISamg8OjF5Oa8np52Tmd8xQ2LfzK0thTqXgF4uoeZxDKzvuGbnb+/ZHTFdJ8ZC3M76moLycjcqthuePhcsrN3KR6bErUUgAVzHibQbxolZWnY2rgwJWoper2OvIJDmKHC1s4NSwsbfH1iefPttbS0Vpu0ZW3tSEen8c7OlhZ22NuZjspknd1mEisrP0VlVRY+3jGKfRVCiNEkCY6YlDoUSjIMFh9Ma6vp8muA0JDZRp97e7UcPPLOZa+OTOfK9GjbqKrJHvB+xUWpiq+frKwcSDm/saFKpSI4aDrBQcbzZabG3WpyXcyUZRw+9q5RzNkpgFnT72XzNy8azQGaGr8Sa2vT5evaXtPK6f3PohwXQkxuH3zwAVu39n+Ja2lpISEhgeeff35E7yEJjpiUAvynoVZb0tdn/BrG32/qkNvy8Y6mpNS4bpO5uTUhwbOMYs0tFbS0Vg29s5dp7zAtxwAQGbFoWFW6pyffRXNrFdk5u9DpenFzDeHGJU/g4x2NtbUjmdnb6NV2ERIym5ioGxTbCAmeRUVlplHMycmXgGH8PoUQE9/atWtZu3YtAC+88AKrVq0a8XtIgiMmJUdHbxbNf4R9B/5Kj7YDUBEVsZj4WNMRjsHo9Xo0aksszG3Ot9Nfd2r+nO9jb+dmdG5h4bGR6r6JoIAU5sy4f1jXmptbseLGZ5g76wE6O5rx8Ag3zJsJDEgkcJDSEBekJK6htaW6Pxnq7cbDPZyli34sVcOFEIOqrq6mrq6OuLi4EW9bpdfrh7+edARptX00NSkXAhRitLS3N1BemYGjg/eQJxcDHDn2PgcO/90oFj1lGStufNooVl2Ty3sf/sBkfxqVymzYRS2hfyfiVbf+Bi+PiGG3MVI6u1ro0XbS09OBm0uQTC4W4grc3e3H9H6trX2knxy7f2ej47Q89NBDhs9r1qxhzZo1Rue8/PLLzJ49m5kzZ474/WUER0xqtrYuRITNH/b1GVmbTWJ5+Qfo0XZiYW5tiBUVH1dMZMJC54IecvP3Af0bBgL0XjanZXrSXRQWH6O2Lt8oHhE6f9yTm4aGUnbue43i4hNYWdoSE30T8+c8jFotf16EmMxcXFzYsGHDgMd1Oh1Hjx7lJz8Z2sKOqyV/gYS4Bl3d7Saxnp4OtD0dRgmOhaWt4vWuLoHMnfUAzc2VdPd00NxcwabNvzQ5r6WtmhuXPMk3O35HXX0RoCIsZDazZtw7Uo8yZPX1xZzLP8DpjC9pPb8Kq6u7jdS0T7C2dmBmyt3j1jchxLffiRMniI+PH7XRXklwhLgGQQHJZJ8zXbL90Wc/JWnq7STE95cwiAxbyLET/zYkAtC/6in6/KTdC5W36+uLFO+Tn3+IIP9k7lv3FhWVZ7GytMPVNXCEn+bqZWXv4Jvt/2tUffxSuXn7JcERQgzqwIEDpKSkjFr7kuAIcQ3mz/0P2joaKCs7dUlUT0NjCdt3v4qNrQvhoXOxsXHkju+8xJHjH1Bbl4ezsz8piWtwcfY3ai8oMAUbayc6OpuM4treLr7Z+XscHX3w90sYgycbWG+vloOH/zFgcgPI5GIhxBX99Kc/HdX2JcERk051TS5nMjbT0dmEv99U4mNvGfZ8EQd7D9Z892WOpX7I/oN/u+yonuycXYSHzgXA1TWIm5c/O2h71tYOLL/habZu/x2dl22yp9fryM3bN+4JTlNzOc0tlYOeExl+9dXEW1prMDPTYGfrcq1dE0IIA0lwxKRSUZnJpxufMpQsOJe3l8qqs6xY9syw2issPs7Z7O00NJYpHtddVijzaoQEz2BWyjp27Xvd9OC3YGWSo4MXtrZutJsUCjXDwd6d2JibSBqkBtcFzS1VbN/1CkXFx1GbaQgPm8/SRY9jZTW2K0uEEBOTJDhiUjmZvsGkHlNW9nZSktbg7haieE1vbzepaZ9SWHwcSwtb4mJWEBY6h6zs7Wzd9tKgy7yHu0IrPHwBh469a1SLysxMQ2TEwmG1N5LMza2YmbKWnXte48JOzObm1ty64jmCA1OuesJgf3LTvzdQn05L9rmdWFraccPiH49W14UQk4gkOGJSaWkxrbkEepqbKwdMcLbtetmo1lJB0RFuuemXnEz7bJDkxoy5s9YTFbF4WP20t3Pj1hW/4uChf1Bdcw5nZ1+mJ6/D1zt2WO2NtGkJt+HmGkJewUFUqDC3tKW4+DhtbbVERS42WkGmpKWtlqLi4ybxvIKDkuAIIUaEJDhiUvHxijYpKWBpYYf3AAUhm5oqyDq73Sim1+tIP/0Fza01g9xJh7tb6DUtfwz0TyRwTSJ9fb2Ymam/dRvn+fsl4O0VxYYvnqWk9KQhfjZ7B9/9zv9gbm414LUaM3PUZhqTicoatfmo9VcIMbkMvXCNENex5KQ78fKMMnxWqy2YP/dhbG2cTM7V6/XsO/g3lApidnQ24nuFKtnay+pcDZdarfnWJTfQ//vZe+BNo+QGoLT8FDnndg96rY2NExHhC0ziUyKXjmgfhRCTl4zgiEnFztaFtatfp6DwMO2dTQT6J+F0fg+ay+XmH+Bc3l7FY/6+U0mIv5X6hkIam8pNjtvauBEcOF3hyolj974/kZb+meKxuoaiK16/dNGPsbS0I6/gIBq1OVERS8Z140IhxMQiCY6YdMzM1P0lEq6grDxdMa5SmZEQ/x3cXAO5b91b5BUcIit7B+Xlp+nuacPdLZRFC36EpYXNSHf9W6O+vpi09I0DHndzDb5iG5aWtixd9DhLFz0+kl0TQghAEhwxCVRVZXMs9SPqG4pxdwthespaPAaYUHwpWxvlfVn0eh2FhYdxcw1Eo7EkKmIRURGL6O5uo729AWdn/2/lK6WRVFdfiH6AJfBBASlERVz9PjhCCDEaZA6OmNCamiv57IunOZe3h/qGQrLP7WTDpqdpa6+/4rXRU27E0kK5hlRTS5VJzNLSDheXgAmf3AC4u4dhZmb6/SgwMJlVt76IRmM5Dr0SQoiLJMERE9rZnB10djYbxdraaq84CRb6l2ovnPdDxWPeXlGK8cnCxdmP5MQ1RjFnJz9uXPRTNBpZCSWEGH/yikpMaN3dbQPETauAK4mNWU5V9VnSM740xMJD58tqH2De7AcJDEiitOwUtjbOREUsxtraYby7JYQQgCQ4YoILCkjhxMmPL4uqCAq8ugq2KpWKG5b8lClTbqCmJhcXZ38CA5Kv6jVUT08n5/L20tZej79vAr4+ypv06fV6yspP09Rcjo9XzLhWCR8KlUpFoP80Av2njXdXhBDChCQ4YkILCkxmzswHOJ76IT3aDqws7Zk5/W58vKOH1I6fTxx+PnFXfX5bez2fbXyK2rp8Q2xGyt3Mm/2g0Xm9vT1s+eZFzuXtA0ClUjMjeS1zZz8wpP4JIYQwJgmOmPBmzbiH+NibqW8swc0lGBsbx1G/58lTG4ySG4DjqR8SHXUDri4Bhlhm1teG5AZAr+/jyPH3CAudg5dn5Kj3UwghJipJcMSkYGvrgq2t8rLvkVRRmcWJkx9TWHzU5JhO10t1zTlcXQLo7e2mtraAotJUhVb6X1lJgjOy8vLyqKmpJikpGUtLWeUlxEQnCY4QI6ShsYwNXzxjVAHcmAoXZ39yzu1lz4G/0NparbjUGsDe3n30OjrJdHZ28pOf/IhNmzbQ19eHn58/v//9qyxefMN4d00IMYokwREC0On6OHHyY3Lz96PCjCmRS5iasGpIe9pkZW8bJLmB2CnLsLVx4aPPfopW23H+vr0m5/l4xxIeOm/oDyEU/eUvr7FhwyeGz2Vlpfz4xz/i8OGT2Noq73MkhLj+SYIjBLDv4N+MVltVVGXS1dPOrOl3X3UbPYMsPZ8at4rFC39ERuYWQ3JzKWfnAFyc/PHyjGBawm2YmamH9gBiQHv3mu55VFVVyeHDB1i6dNk49EgIMRYkwRGTnlbbzZnMLSbxjMwtQ0pwAgNSOJm+QfFYfUMRG7/6OaA8IhTgl8ANi3961fcSV8/JybRSfH989OdkCSHGj+xkLCa93t4uxY3/urpa0Ov1V91OSPAMQoNnKx4rLT9FQeERCgoPo1IZj86o1RZET7lxaJ0WV23duvtQq41/5wsWLCYpKXmceiSEGAsygiMmHZ2uD5XKzDC/xtrakQD/aZSUnjQ6LzAgmb4+LcdSP6Sg4ND5ROQG4mNvUZybo1KpWHnzr/ly6/Pk5e+/EAWMkyS9vg8Pj3B6uttwcPAmedod+HorbwIort2NNy7nrbfe5Z13/kFdXQ1z5sznJz95clLUDBNiMlPph/IVdRRptX00NZnOTRBipLS21rB7/18oKjqKuYUtsdHLmDNzPWZmaurri9m87UVqanIB8PWJY8WyZzl6/H1OZ3xl1M7i+T8icdrtg96rojKLxqYyyivOmFwPkJT4PRbNe2TkHk4Icd1xd7cf0/u1tvaRfnLs/p2du2Bsn+9yMoIjJjSdro/CouP06bSkpX9OaVkaAD3aTo4efx9zc2tmpqzD1TWQe+78KxWVmZiZqfHyjKKjo4ms7O0mbZ7O3HzFBMfHOxof72isrBwUExw/GbERQowxTUcnzqcyxu6GC2aN3b0USIIjJqyGhlK+2PIr6uoLBzznXO4eZqasA/pfMV1aL6qrp43e3m6Ta7oGKOCpJCRoBtPibyPt9Eb6X1WpiI+9hbDQuVfdhhBCiKGTBEdMWPsP/33Q5AZApxv4Da2Lkx9enlOoqj5rFA8OuLpCndCfNC1Z9BixMTdRW5ePm2uw7FAshBBjQBIcMWGVlZ2+4jkRYQNvqHehkvjWb35rSJQC/BOZc1nBzKvh6RGOp0f4kK+bqAoKD5N2ehPt7fX4+SYwI2UdtjbO490tIcQEIgmOmLDs7Fzp7Goyil1Yom1pYU30lGXMOP96aiCe7mHct+7vlFdmYW5uiae7JCnXqqg4lY1f/dKwi3NNbR41tbmsuf1VWdkkhBgxkuCICSsx4Ta+2fkHLl2mnZJ8JzOS1qJWa9BoLK6qHZXKDD8fmRQ8UjKytpiUqCgrP01pWRoB/onj1CshxEQjCY6YsOJib8bKyoGs7O306rSEhcwhPuZmGSUYZwPV6+rqah3jngghJrJhJzh1dXU89thjfPDBB2i1Wh599FGampq44447uOOOOxRjQoy18LB5hA8yz0aMvYCAZIpKThjFrK0cZfRGCDGihlWqobm5maeeeorOzk4A3nvvPWJiYvjwww/55ptvaGtrU4wJIURiwm1ET7nRMB/K1tadG5c8gZXV+G4KJoSYWIaV4KjVal599VXs7OwAOHr0KDfddBMAKSkpZGRkKMaEEEKjsWDFjc+w/p53WP3dP/DQfe/KKJsQYsQN6xXVhcTmgs7OTjw9PQFwdHSkvr5eMSaEEBe4OPvh4uw33t0QQkxQI1JN3MbGhq6uLgA6OjrQ6XSKMSGEEEKIsTAiCU5MTAypqakAZGdn4+vrqxgTQgghhBgLI7JM/LbbbuPhhx/mxIkT5OXlkZCQgKenp0lMCCGEEGIsqPR6/cDFeIagurqa1NRU5s2bh729/YCxgWi1fTQ1jV0ZdyGEEGI8ubuP7crBzuo2Cj48M2b3i3l8fKuJj1iCc60kwRHXoro2jxMnP6apsQx39zBSktbg7CSvRYUQ316S4Iwu2clYXLe6u3t471+fcvz4AYIjclFr+rf/r6w+S0lZGnev+bPsrSKEEJPUiEwyFmI8vPjiq7z22lu0d581JDcXNDWVkXNu91W3VV2Tx8lTGziXuw+drm+kuyqEEGKMyQiOuC6Vl1eydctOACwtlc/p7L662kaHjvyTQ0f/yYWinL4+cdx264sy+iOEENcxGcER16W6uka02v5Rm6pKpTNUBAUkX7Gd+vpiDh/7F5dWHC+vOMPJ9M9HpqNiUtq/fy+/+tXP+P3vX6KoqHC8uyPEpCQjOOK6FBMTSUCALyUl5ZQVq8g8rScyGjQaMDe3Ydb0e/DyjLxiO5XVZ9HrTV9JVVdnK55fV1dI9rldNDaV4+4WQlzszdjaOF/z84iJ4/XXX+X5539p+Pz3v7/Be+99THLy9HHslRCTjyQ44rqk0aj52c9/zK9++f+oqqoh7YQKS/MofvCfqwgNTcTezvWq2nF09FGMOzh6A6DX60lN+4T0M5tpa69Dq7240i8ndzdHjr/PTTc8TWTEgmt/KHHda2xs4I9/fNko1tDQwB//+ArvvvvvceqVEJOTJDjiujVjRiIbN71N6onT2NnbEh8fjUqlMhwvKCjm/fc+o6CgmPDwEO6593v4+xsnNH4+cYSHzic3f58hZmfrxrS4VQCkZ3zJnv1/GbAPvb1d7Nr3OsHB07Ewtx7hJxTXm3PncmhqajKJ5+bmjENvhJjcJMER1zULCwumz0jE3Nz4P+Xq6lr+8z+forqqFoC0tAwOHz7Bv977E05ODobzVCoVNy//GRlZSVRUZGBn505c3M04nx/Zyc7eecU+tLfXUVWVTYD/tBF8MnE9mjIlGldXN+rr64zi0dGx49QjIb7dnnvuOebPn8/ixYtHvG2ZZCyuSzqdjj+9/jbLbryTeXNX8uMf/5LS0grD8c1fbTckNxeUllbwzde7TNrSaCyYGr+SFcufZf7c7xuSG4A+Xa/J+ZczM9Pg4OB1DU8jJgoHB0eefPJp1Gq1Iebp6cVjj/10HHslxLfTiRMnqKurG5XkBmQER1yn/vWvT3jzzfcMn/fsPkhTUxNvv/1/qFQqGhqaFa+rr28c0n1CQ2ZRWZU16DkxU5bhdH7OjhAPPvgwiYlJ7Ny5DTs7B77zne/i7S3/fQhxKa1Wy89//nMWLFjAjh07WLp06YjfQxIccV3asX2/SexUWiZZWeeIiYkkNDRI8brpM4xfI3V2dmFhYW70jftSKYl30t7eSGbW1/RoO/BwC8fe3oOW1kosLR2IDJ9PQtzKa34eMbFMm5bEtGlJ490NIcZVQ0MDDz30kOHzmjVrWLNmDQAbN24kLCyMncINYAAAIABJREFUhx56iPfee4/KykruueeeEb2/JDjiujRQCbWO9k6eeuo37NxhnABZWlqwfv2dJCdPBSArK4eXX/4baSdP4+7uxp133cZ9933PaJIygFqtYcnCR5kzcz1d3S04DbDqSgghhDEXFxc2bNigeOzs2bOsXr0ad3d3Vq5cySuvvCIJjhAAS5fOIyPDeK+a+Pho9uw9xDdfG5doCAjw489/+R/8/PqTk66uLv77v35DWVn/nJ2qqhpefeWveHi4sGKF8jCplZUdVlZ2o/AkQggx+QQEBFBaWgrAmTNn8PEZ+S+PMslYXJfuufd7rH/gTlxcnNBoNMybP4Pf/OYpDuw/anJuSUkZnR1dhs/79x01JDeX2r59n0lMCCHEyLvjjjs4evQo69at49///jcPPvjgiN9DRnDEdUmtVvP449/nhz98gN7eXqys+gtSWVtbKZ5rbXMxvmWL6UoqAJViVAghxEizs7Pjj3/846jeQxIccd3R6/WGuTIajRqNRs3Jk2f45OMvaGvvNDl/8eK5htdTABkZZxXbnb9g5uh0WExaTU2NvPba/3H48AE8PDxYv/4hFiwYnSWxQghjkuCI68Znn23mvfc+o7qqhqlTY3n0sQeZMiWckyfP8MNHnqaz8+JrKBsba1xdnZk3bwY/+M/7jNrp7TWtPQWwYMGcUe2/mFz0ej3f//569u69OGK4c+d2PvjgM+bNmz+OPRNicpAER1wX9u07zG9eeMWweurQoeOUlJTxyadv8uknXxolNwAdHZ386c8vMW2a6Q6yCxfO4vPPtxrFFiycjbOz4+g9gJh0jh07bJTcAHR3d/Pee+9IgiPEGJBJxuK6sHXrbpOl4WVllRzYf5S6ugbFa+rq6hXjj//4+yxbthCNRoOZmYp582fwzDOPjnifxeQ20H9/dXW1inEhxMiSERxxXei75LWSSqXDxxcwg56ebpKTEzh2LM3ofHt7O5KSEhTbcnJy5Hf/+wtqa+vp6+vDy8tjNLsuJqnZs+fg4uJCQ4NxAj5v3sLx6ZAQk4yM4IjrwpIl8wBwdNJx8ypYdCMsWgpl1e+w9MZ45s+/OEHY3t6Op57+IS4uToO26e7uqpjc6PV6UlPT2bv3CJ2dppOWhbgazs4u/O//voKbmzvQX9j11ltX8fDD/znsNvv6+ij5/+ydd3yN1x/H3zfrZg+RhEQkSIkQatbee1Ortp/RYc/qUtWqVlVRq0WprWZjx0pEzCBIrBBkyt47997fH7duXPdmaUKi5/16ecnzfc5zznm4ee7nOec7Qp6Sm1t4jTSB4L+ORJFfStjXTE6OjMTE9Dc9DUEZRaFQ8Ofm3dwN+gMbuxy1c1Xs6zFk4HJu3bpDdHQsdnY2REfFUrNWDapWdSjWONHRsXw691tu3AgAwM7Ohm+/+5SmTUWlcMGrkZyczNWrl6lUqTJ16rx6VXEPjwMsXvwtjx49xNGxKjNnzmX48FElOFPB68bGxuy1jpcRlUrwrtuvbbw605q/trG0IQSOoNwgl8tYuaYnubIsjXOTP/RAKjVlxfLf2bZtP7m5uejr6zF8xPtMmzZBowRDfny7cBn79h1Rszk7O7Jv/8Z861UJBKVNUNADOnVqrbaiqKuri4fHMZo0EekNyitC4JQuYotKUG6QSHQwNbXWsJsYW6Ovb8TFC1fZvPkv1fJ9Tk4umzft5uIFvyKPcfWqv4btyZNQ7t0LevWJCwT/kmPHDmtsl8pkMo4cOfSGZiQQlH2EwBGUGyQSCQ3q99ewN6jfD11dPa763dR63VU/TdGSH9bWVho2qdSAihU1hZVA8LrQ09MeD5KfXSAQiCgqQTmjUYOBmBhX4F7QWRQKOe/UaEOd2l0A7eIEoGLFCkXuf9Dgvty8GYhcnrdz26tXZ+zsbP7dxAWCf0Hv3n1ZvnwpiYmJKpuRkTH9+g18g7MSCMo2QuAIyh2utTrgWksz3X337h3ZueMA4eHPVDYHh0p061b01Pg9enTAUGqAh8cJUtPSad2qKcOGv18i8xYIXiQuLpZNmzYQEHAbF5d3GDNmHFWqOGpt6+joxB9/bOOnnxZz504ALi41mT59NnXrur/mWQsE5QfhZCx4qwgJCWf79n08eviEGi7ODB/+fqGRVAqUvwISUW5T8JpIT0+nb9/u3LyZl7+pWrXqHDrkia2tyMv0X0E4GZcuYgVH8FZRtaoDn302tcjtU5GTggIZYABYIEEqXNMEpczff+9XEzcAjx8Hs3v3dqZMmfGGZiUQvF0IgSN4q0lNTWfVqo2cPeOLvr4e3bt3YOKHo9DX1yMDOYkoeL5wkw2EpqVxYO1War3jQvfuHdDXF78igpInIiJcqz0sLOw1z0QgeHsRT29BuSU7O4f9+47gd+0m1tZWDBjQg1q1XNTa/PjDrxw65Kk6Xr9+O3KFgilTxpHxgrh5jqGJMc8Sk9n+1Y/4nr/MDz9+WeQcOgJBUWnS5D2t9ho1arzmmQgEby9C4AjKDadO+XDI4wRp6Rm0bv0edwLvc+KEl+r8saOnWbtuCXXq1AIgISEJT08vjX6OHj3DBx/0J1aeg6ldRc2BdJSC5sQJLwYN7kPjxtprWgkEr0rr1m0ZNGgoe/bsUrNv3bqZESPGYGxs/IZmJhC8PQhnA0G54MiRk8yZ/Q3e3hfxu+rPL8t+UxM3AMnJqezaeVB1nJubq7VmT1JiEv36juGnb35BJpOpnYuPiefK2Yuq4ydPQkr2RgQClDmdKlfWdH6/f/8ehw8f1HKFMrHfpk0bGDt2ODNmTMHP70ppT1MgKNeIFRxBuWD3Lg+KEvAXGRmFr+8VdmzfT+SzaKytrYmOjlFrk56uzAh77fxV1n6/igFjBlLJoTL3b95l88o/SElKAZRfQm5utUr+ZgQCID4+Vqs9Li5Oq33OnOls2/an6nj//j1s2/YXrVu3KZX5CQTlHSFwBOWC+PiEIrWzq2TLjOnzyc7OK8hpbGxEZmYmurq6ODk58vDhY9W543uOcGLvUQYO6cPdgPsEBtxTnfvgg364udUsuZsQCF6gVas2aoIFlPWl2rbVzNsUHPyI3bt3qNkyMtLZsGGdmsA5f96HI0f+RkdHl/7936dx46alM3mBoBwgBI6gXNCkybuEhUWq2SpUsCQlJZWcHOU2VP36dUhNTVMTN6Bcsfnqq+m0a9+SUyd9WLx4pdp5hUKBtZUFG/9YxqlT54iIiOLdd+sK3xtBqdKv3/tcuXKJrVs3k5OTg6mpGXPmzMPNrY5G29DQEHJycjTs4eF5UVdbtmxi7twZyOVyAP78cyNr1qynTx/N8iYCwX8BIXAE5YKPPx7N4yeh+N8IAMDc3AyJREJOTi6Wlpb07t2Z6TMm0LPHcK3X6+jqYW1dga7d2vPnlr+IeCHbcYUKlvTs2RmpVErPnp1fy/0IBDo6Ovzww8+MH/8xwcFBuLu/S+XKlbW2bdCgERUr2hAbq77d+jwaKzc3lxUrflaJG4Ds7GxWrFgmBI7gP4twMhaUC2ztbNi0aTkb//iFGTMnkpqaRlycctsqMTGR3bsP8uRxKGlp2rNhOzraA2Bpac7q1Yvp07crtWu70K17B1at/l51XiB43bi4uNClS/d8xQ2Aubk5CxcuwtQ0L/Nt/foNmDpVmRQwLi6O0FBNh/jg4EdF8l0TCN5GxAqOoNwgkUho1Kge585dUntTBWVOnDNnz+PgUJl79x6qndPT06VSpbxw8GrVqrJw4dzXMmeBoKQYOHAoLVq05uDBAxgZGTJq1Fh0dXUBsLGxwc2tDnfuBKpdU69efZHHSaAiNzeeuOjdr3HEN1uqQazgCModBvr6Wu07th9AItH8SOfmyhj3v9ncvfOgtKcmEJQaiYmJfP315yxa9DWffjqTgQN7ExwcDCi3uz77bD5mZuaq9hUqWDNv3pdvaroCwRtHFNsUlDsePnzMqJFTVOHeL2NsbIRcLiczM0vN3qZNM1b+uuh1TFEgKHE+/XQWmzatV7O1adOWvXsPqY5DQ59y9Ohh9PT06dmzD5UqVXrd0xQUg9ddbDMlPIQba5a9tvHaLFr+2sbShtiiEpQ50tMzWLtmM2fP+qKvr0+37u0ZP364ajnexaUaS36az4YN27l3N0hDyOQnfAID75f63AWC0uLEiaMaNh+fczx9+hQnJycAHB2d+PDDSf9qnIiIMHx9fbCxsaNt2/Zii0tQbhFbVIIyx48/ruKgxwmq1amJlb0t69Zu4ffftqrOy+VyFAoFfft2pU/frkXu19FRM3OsQFBeMDIy0rAZGBhgaGhYYmNs2/YnLVo0YdKkDxk8uB8DBvQiISG+xPoXCF4nYgVHUKZISEgiPiOD3zz+wNzKAoB7N+/wx5J1fPzJGIIf32LVqp+4fSuMmGgJ+vn447yMgYE+5hamDBv2Mba2Ngwd2odmzRqX5q0IBCXKoEFD+eGH79RsvXv3w87OrkT6j46OZsGCL0hPT1PZfH19WLduNZ999lWJjCEQvE6EwBGUKbKycxg7a4JK3AC41nejy6CenLuwgctXtmNfFeyrwuNgBRe8s8lvIXL4iAGkp2diYKCP39WbnPO+BMCdwAdcvODH2nU/0rCh++u4LYHgXzN9+mx0dHTZt+8vMjMz6Nq1B59++kWJ9e/re47k5GQN+5UrF7W0FgjKPkLgCMoUlnYVyJVo+r03bF4fz50/8aI7QLXq8CwcHgVp6cfSnPHjR2BlZYGv7xV27/pb7XxWVhYHDxwTAkdQbtDR0WH69FlMnz6rxPuWyWSsX79W67nKlauU+Hivyv37d1m27Cdu3bqBk1N1Jk2aJmpxCfJFCBxBmUIPZemElx0bM9O116KysVUKnIEDe3Hq1DkSE5OpUcOZmbM+xOqfVaCkxBSt1yYmar6tCgTlldDQp6xZs4rbt29So4YLEyd+TJ06RRPwx48fxc/vqoZdKpUybtz4kp7qK5GUlMjw4YMJCXkKwKNHj7hy5RIeHsepW1e8qAg0EQJHUKbQQwcTFKglDFAo8Ni+A0tTzfZpadCiZRO+/GoG02dMJDo6FmdnR3R08ratmr7XADMzU1JSUtWufa9Zw9K5CYHgNZOSksLQoQMJClJGCl65colTpzw5cuQkmZkZ7Nu3h/T0dLp27U6bNu00rn/06KGGDaBDh840alQ2CnYePnxIJW6ek5qawp49u4TAEWhFRFEJyhxW6GChAEMFGCkg5HogR/ZcJDpKvV1qCjwLN+HHH5V+CKamJlSv7qQmbgAqVqzAF19Ow9raClBWbO7TtyuDB/d5LfcjEJQ2hw4dVImb58TERLNkyff07NmFFSt+Zv36tQwa1Je1a1dpXO/uXk9rv+3atS+V+b4KqanaV1xTU7Wv0AoEYgVHUOaQIMEMXZ6nwHKwtkZPzwCvk9nUqg0VKkJKMty/B7NnfYKZWeHJsrp160CrVs24ffsulSrZUq2aY+nehEDwGnm5COdzfHy8SUnJEwYKhYJff/2FESNGq/3etGvXgUGDhrJnzy6VrVWr1gwePKz0Jl1MOnbsynffLSArK+sle5c3Mh9B2UcIHEGZx8mpCh8M68+WP/dw+6bSZmJizNcLZtOlS9si92Nqakzz5o2K1PZxcAj37gVRvYYztWrVeJVpCwSvDW3bToDWHDaxsTE8efJYbdVGIpGwatVv9O//Prdu+VOtWg169uyDgYFBaU252Li4uPDTT8tZtOgboqKeYW5uwcSJH9G9e883PTVBGUWUahCUCxQKBT4+l7l61Z8KFSzp2bMTtrYVC7/wFcZZuXIjW7fsITc3Fx0dCQPe78nnn0/T2PoSCMoS771Xn8ePH6vZJBKJRjVxB4cqXLx4vUQTBL5O0tLSuHs3EGfn6lSsWPLPgNeJKNVQuogVHEG5QCKR0KZNM9q0aVaq41zzu8mmP3aqjuVyBXv3HKZZs0Z06iTCUQVlFwMDTcGiUCiwtLQkMTHxnzYGzJv3ZbkVNwAmJiY0blw2HJ8FZRshcARlDj+/m1y6dA1LS3O6dW1PRRvr1zf2tVta7deu3RICR1CmadGiFffv31WzOTpW5eDBYxw/flgVReXq6vZa55WUlMTu3Tt49OghderUZeDAIRgbG7/WOQj+mwiBIyhTbFi/nVWr/lAdb9u6j1Wrv8fFpVqpjKdQKHgaco3o2IdUsKyCjY2V1nY2FV+fyBK8Ggq5jISbV0gNeYiugSGWdRpiUvW/4z81c+Yc7twJ4PJlZeZhGxtbfvjhZxwdHZkw4ePXOpecnByuX79KlSpOjB07HH//66pzhw4dZOfOfejpia8fQekiPmGCMkNMTBx/vLA9BPDsWTRbt+zlm4VzSnw8hULB8ZM/EHjXU2Wzr9yE6tWrEhwckmezt6NX784lPr6gZIk8c5ikAD/VcXJQIFV6DsWshusbnNXrw86uEh4ex/H19SEpKZHWrdtibm5R+IUlzKJFC1izZhU5Odlaz3t7n+XwYQ/69Rvwmmcm+K8hBI6gzHDlynXS0zM07A+CHpXKeMGPL6uJG4CIyKtMm/kR164kEBQUjLNzVT4Y1r9UHJoFJUd2UjxJd26oG+Uy4m9d/s8IHFD6qrVq9ea2Ui9ePM+KFYU7sT7SVl9FIChhhMARlAlkMhmbN/2l9VyNGs6lMmZU9D2t9pzcZ8ycNa1UxhSUDjmpySCXadhlIglcqREZGcG9e/dwc6ujqmg+ZUrRtsLq1KlbmlMTCAAhcARlBF/fqwQFBWvYDQ2ljB49uFTGtLR0yMduXyrjCUoPIzt79EzNyX0p262Rg9MbmtHbzdKli1m1aiXp6WmYmpoxffpsWrduo1FKQRt9+/anS5fur2GWgv86IrGHoEyQnKz9TfvdBnV5553qpTJmTZd2VHFQT1FvU9GFOq5dS2U8Qemho2eAXbte6BjmRecY2lWhYhMR+VbSnD17hiVLFpOengYoSyUsWrQAD48DBV5Xq5YrW7bs4rffNomcUoLXgljBEZQJmjVrhKmpCampaWr29u1bltqYOjq6NG8yk2dOl0lIekIFS0fq1umBkZF5qY0pKD3MXWpj7OBEWshDdKVGmDjVQCIRX6RFJTc3l7i4OGxsbAoUIN7epzVsCoWCmJjYAvv/5JOpdOvW41/PUyAoKkLgCMoEFStW4Kv5M1j601piYuLQ19ejT5+uDBzYS61dRkYGO7YfUGU0fn9gbxo1Kn4l4fM+l1mxYj1BQY+xtavI2LFD6dapf0ndjuANoWdkjEUt7YUjBfmzbdufLF++lJCQp1SqVAkDA0OSkxOxtq5I7979mDDhY2xsbACwtKygtY+6dd2Ry4ewd+9uNbuxsTFjxoxj6NDhhc4jISGejRt/58aNa1St6sTYsROoWbPWv79BQZkjNzeXTp064eiorAv45ZdfUqtWyf5fi1INgjJFenoGgYH3sLevhINDZY3zM2d+zZnT51XHUqmU1Wu+p3Hjd4s8RnR0LO8PGEdKSqrKJpFIWLV6MS1bNvl3NyAQlDMuXvRlwIBeyGSaTtrPqVHDhb17PXBwqIKn5zHGjRulVvTSwaEKJ054YW1tzd9/78fP7yqVK1fGza0udevWUzkhF4RMJqNv3x5cuXJRZbOzq8Tffx+nevXS2aZ+0/yXSzUEBgZy9OhR5swp+RQgzxHrt4IyhbGxEU2aNNAqbgIDH6iJG4CsrCz+2n2oWGN4nfVVEzegXGI/c9qn+BMWCMo5hw//XaC4AXj06CGbNm0gPDyUGTOmqIkbqVTK8uWrsLW1RVdXlwEDBvH990uYMmUGHTt2LpK4AThx4piauAGIinrG9u2bi3lHgvKAv78/Xl5eDBw4kM8//5zc3NwSH0MIHEGZJjMzkxs3bvPsWXS+e/wxsQXv/b9MfhlUdXV1iz0/gaC8U1SH36Cg+xw4sI+YmGg1e1ZWFv7+eTmIrl69zKJFC1mxYhmhoYVHVT0nIiJcq/3gwf0EBt4ucj+C8oG7uzubNm1i79695Obm4u3tXeJjCB8cQYFkZ0NONhgawYvf/znZkJwMslwwMVX+KWlOnjzH0p/WEBUVg1QqpVevTpibm2lEXDVuVF/1s9fZC+z+6yCxMQk0alyP8eOHU7Gius9Ah46tWbNmM7Gx8Sqbnp4e3bq3L/mbEAjKMBER4fj6Fm3lsnbtuqSkaI92TP0n39D69Wv56qvPkMvlAKxd+yvbt++mUaPCi2NmZ2dqtYeGhtC1a3vWrPmdPn1E9uPyRHx8POPHj1cdDxkyhCFDhgDg6uqKgYEBAHXr1uXp06KL4aIiVnAEWpHLITIcHj+EsBDl3ylJynOZGfD0McRGQ0K88nxsdMH9FZfYmDgWfvMzUVExgPItcd++I7z3XkO1FZi6dV0ZOWoQAGdO+zBjxnwuXrhGUFAwu3YeZM7sb3jZzczS0pxlv3zDe+81xNTUBNfa7/Ddok9p2FA4pwreHgICbnP69Ml8RQkoyyoEBBS+OlKnTl3Gjh1Pp06dkUgkauckEgkdO3YhOTmJZct+UokbgPj4uEIzGz979ozz58/x998H822TnZ3N+PFjmD//c7X+BWWbChUqsH//ftWf5+IGYM6cOdy7dw+ZTMapU6dwdS35jOMlsoKjzRv6xIkTeHt7U69ePb7++uuSGEbwGklKgOSkvGOZDCIjwcgE4uOUxy8SHwcmZqCQg9RQfbXnVbh46ZqGnwyAj89ltb3aJ09CiY6O49y5i3yzYJmGmLlxI4D3B/yPqdMm0K5dC5W9Xj03fvv9p383SYGgDJKamsrkyRM5evQwAAYGBowYMZrFi5dqiJNz5wrfFqhUqRIeHicICnrAjRvX+eCDkRw7dpiEhHisrKyYNm02zZq1wM/vCnFxmtvF165dZciQASQnJ9GuXQcmT56OiYkJAEuX/sCaNb+SmpqiMTdtrFu3CmvrikybNrMo/xSCMsykSZOYNWsWAB06dKBFixaFXFF8SkTg3L9/n549e6q8oQMCArh27Rp79+5l9erVXLhwoVQmLyg90tI0bQo5pKdCdpaWcwoIeaz8WUcHbGwhn2jSImFiYqzVnpmpvoydmprGX7v/5vDhk+Tk5Gi9Jjg4hOnTvmLy5P8xfkLhoaoCQXlm7dpfVeIGlKsff/yxHgsLSz777Cu1tlZWVkRFPSuwv/j4BBYu/JItWzarXiCaN2/FrFlzcXevh5WV8he9Zs1aWFtX1BA5MTExnD17ClCKneDgR/z22x+cPXuaJUu+V7UrakDvvn1/CYHzFlCzZk0OHSpegEhxKZEtqpe9oS9evEiXLl3+KfzWCj8/v8I7EZQp8luB0dEFPYOCr5XLIeoZZGnfUi+U2Nh4UlPSNCKp8nvDe/jwidYinS+zceMOsrO1iyCB4G3h3DkvrfYVK5axdetmsrPzqnyPGjW20P6ys7P4889NagLk4sXz3L17RyVuAHR19XByKrw0hofHAR4+fKgSPcUlM7Pw33WBAEpoBee5N7StrS1z584lKyuLatWqAWBhYUFsMaNcBG8eC0v1LSpQbj3p6UFmEdMVpaYqrykOx46d4ftFK1TbU1ZWlhgaSomPTyArK1vrNfXq1+b69VuF9p2RkUlAwF3hayN4q6lYsaJWu1wuY9asqZw8eYI//9yBRCJh3LgPMTY2Ye/e3WRlZZGTk4W/v3+RxvH3v45CoWDTpg14eOzn4cMgoqMLd8aTyWTExcVgYWFZrPt6Tteuoo6VoGiUiMB52Rs6NzdXlSchPT29yEuPgrKDsQlUqap0Is7NASNjsK4IsTGa/jf58fIqkEKh4M6d+yQmJHPn7gOCHgRjaGRI3bqu9OrVGblcwdKf1qj53iQkJFLDxZnIyCiN/qVSAwYP6cPDoMdFmo+OjgQ7O1ut5+7efUBkRDT1362DtbVV0W5QICiD1K1bjyNH8l/6P378CGfPnqJDB6XD8LBhIxk2bCQAp097MnbsSLVVkqZNm3HlyiWNfqpWdebnn5ewZMmiYs3P2bkajRo1wc6uEhs2/PbSlpYE0P59oaenT//+A5g794tijSf471IiAmfOnDl8/PHHvPPOO5w6dYqmTZty7do1evbsyb1793Bw0F61WVC20Rb+XdRtJz09MH0hSee9u0F8/fVS7t9/qNHW4+8TeHp6M2bMYOLiEjTOhzzVnh/jy69mEB0Vy/nzV4o0pzp1XfHwOEHDhu40bdoAiURCVlYWX321hJOe3igUCkxNTZg560MGDOhZtBsVCMoYQUEPCm3z8GEQHTp0VrNdv36VyZM/UhM3PXr0Yu3ajYwZM1xtS6lqVSeGDx9Jnz7dijU3R8eqLF26Ej09PZydq7Fz515Wr15BUNADateug0Siw759u7W+FHfs2InVq9cXazzBf5sSETgve0N/8sknDBs2jO+++w4fHx82bNhQEsMIygAGUsjUInIqV4HkRGV+HKkRVLBWihxQrtws+OZnreLmOX5X/WnVsim6uroaWVW1OQ9LpQY0bOjOxIlFT/N9+9Zdbt+6C8Co0YOYOfMj9vx1CM8TXqo2qalpLPlxNS1aNKFSJe2rPQJBWaYoGWHd3TVLmyxbtkTDQdjT8zjx8XH88cdWduzYwvXr16la1YmRI0djZ1eJhIR4jX7yY8KEj5k/fyFSqVRle/fdhqxf/ycAv/zyE4sXf5vv9bdu+aNQKIiNjWXDht+4dcufatWqMXbsRN55550izwOU22Te3l4oFDJat26n2oEQvF2UiMDR5g29efNmvLy8GD16tCp8XFD+sbJWRljJXniGVqgI5ubKP9q4f/8R9+4GFdr3uXMX6dmrEx5/nyi07fAR7xMbE094WGRRp67G9m376dOnK35+NzXOZWZmcfWqP717d3mlvgWCN0mPHr3w8DiQ7/nhw0fTrFlzDfu1a9c0bLm5uaxZ8yuLFv3IhAkfa5xv3botnp7HC5xPw4aNmTFjNl275l9JXCaTsXnzxgL7iYyMZNeu7WzatF6nRB4eAAAgAElEQVQtc/LRo4fw8DhO1arOBV7/nAcP7jNhwhju3g0ElHW2fvttE/Xq1S/kSkF5o9QyGRsaGtKtW/GWLwVlH0NDcHLOy2JsbKK+FaUNMzMTrSszL/Po0VPWrltCenoGp06e0zhvbW2FnZ0Nbds2p7ZbTdav3/bK9yGTybh3LyhffxvrCsIPR1A+6d9/ICEhT1m/fh0xMdHUrVuPzp27YGAgpUGDxrRv30FrRKKVlZXWPDbe3mfzHWvBgu+Jiori5s0bWs8bGRlx/PiZQuecnZ1NdLSmn93L7NixVU3cAERERLBjxzbmzfuy0OsBFi78SiVuQFlna/78zzh48GiRrheUH0Sphv8oOdmQmAA5OcoyDBaWRU/Op2+gdDguiJSUNPz8/LG0tODdd+vQqXMbThzP/0EJypUTHR0d+vbtplXgxMUlEBeXwJ07BfsYmJoa06JFUzw9vfJtI5FIqFHDmWrVnDh+4ixpqXmhYQ0b1qNZ80YF36BAUEaRSCRMnz6bjz6aTFxcLPb2DkVKojdy5Bi+/lrTgffRoyAePw6mWjXNit4uLi54enqxdu0qFizQvDa/um8vY2RkRKtWrfH29ipS+5eJjIwoUju5XM7585qlKS5cOE9SUuIrR3YJyiaiVMN/kKxMeByszD6ckgwxURAeCjHREBwED+/Dswh41eKu3t4X6dd3NDOmz2fsmGl89OFcpk4dz+gxgwt80DZv3gh9fT1atmxC8+aNX/HulJlbW7dphp1d/iqsf/8e1K5dkzp1arF27Y/07NmRxo3rM37CcJb9sqDIBQgFgrKKoaEhDg5VkEgkZGRkcOrUCS5duqDVgff+/XukpaWp+cc8RyaTcedOoIb9ORKJhI8/nqy1anjnzkVfxV+4cDE1a+al63/ZL8bU1IxRo/6n9RnSrFnREslKJBJsbTV962xsbDA2NinyXAXlA90FCxYseNOTAJDLFWRmiiRspU1urjLj8MvlXHJzICNdaVcolCIoJyd/v5r8yMzM5JOP56kVsgwPj0RXR5eZsz4i5GkYQVrCut3da/PlVzOwsDBHIpHQoUMrLK0sMDM1JSUllbS0IibfQZnvJjDwHj///DWZGcp0Be71atOieWPq1XNj5KhBjBk7RPWgtLOzoWPH1lS2t+X0KR82bdrFjeu3cXJy1CjUKRCUN86fP8fAgX3ZvHkDO3duw9fXhw4dOmNqqgyRPHr0MEOHDsDb+4zWbWQDAwPmzPmcChXy/12QSCQ0bdoMb+8zJCcnA9CiRSt+/vlX1TiFYWNjy+jR/6Np0+YMHjyU6dNnk5mZhUyWS6NGTVi06Ee6desBKPDzu6Ka68CBg5k9e16RXkokEgk5OTl4ealvm02aNJXWrdsVaZ4liYmJpqAsTbJTknh29eJrG8+p45t1U5EoykiSmpwcGYmJRf8SE7wa0VGQEFf09tXfAX39ore/fPkGH06crWF3dKzMocPbiI6KYf78n7h0SenQWMPFmYkTRtCla7t8V3cWfbecPXuKn9K7V+8uTJs2nuvXbuHrewVjE2N69+5CnTq1NNo+fPiE0aOnqG1V2dnZsGv3b1hZWRR7bIGgLCCTyWjd+j0ePlTf1h016n8sXbochUJBp06tuX07/0SZkyZN4+uv849uepHc3FwuXbqAiYkp777boEhbY6/Cw4cPuXHDj+rVXWjUqHirvQqFgj17dnLgwH7kchk9e/Zh5MgxpTbXgrCxKcSBsYRJCQ/hxpqCi5+WJG0WLX/la+VyOZmZmRgba5btkcvlHD9+nB498ndcB+GD85+juFnOn6/opCQrw8OlBmBqXoC/jkJ7pd+UFOXAtnY2rPttCQ8fPiYnJxdXV5dCHywjRr7PWe8LxEYXQ5kBhw954nPuIklJedWUDx44xi+/LKR5C/WH4pHDp9TEDUBUVAyent4MGdKnWOMKBGWFW7f8NcQNwPnzyiKbmZmZ+W4/TZz4Ce3bd9DIl/Oca9eusmPHNlURzQ8+GIGenh6tWrUpuRvIBxcXF1xcXF7pWolEwuDBwxg8eFgJz0pQkoSHh3P8+HHc3d1Vq4LPUSgUeHh4CIEjUKc4riVGxmBgABFhkJqnEdCPA6dq2kWOjq72AYyN1Ws2uLhUK/I8tnidJtJaF4muGZIcGfrJ2cizi+Yg9KK4AaUj85YtezQETnq69tXD/OwCQXnA1tYWqVSqyiz/nIoVbQCln46ra20CAwPUzru51eHbbxfn+/Jx7pwXI0cOJSND+fvx99/7CQi4xeLFS0vhLgT/RXR1ddHV1WXNmjU0btyY2NhY/Pz8cHNzw9nZuUh9CE/KtxyFArKylD42kPd3YUgkYGMD0c/UxQ0oI7AiwrRf5+bmqjX0umXLJsWYdR43Hjxg16lToK+LwsYUeWVzshxMca5VjQoVrDA0LP4edni4Zu6c1m3e07Dp6+vRpk2zV5q3QFAWcHBwZODAIWo2iUTCmDHjVD/PmjUPIyMj1XkjIyNmzZpX4Mrqb7+tUYmb52zfvoWwsJAiz00ul5OWllZipXwCAwMZPXoY/fr1YPv2LSXSp+DNkJuby/Lly7l27RoxMTG0a9eOxo0bY21tTb169WjXrl2R+hErOG8xGel5Vb0lEmUoeD71KjVQKCDkaf7no54lsX3nVpKSknmvaQP69O2Kjo4OJiZGzJr9MYu+W65yDHZ1dWHQsH7sP+dNekYGbd5tQFUtERfauPvkSd5Beha6IYnoZMt5Ikmkc6e2XLx4tWg39AJ167pq2Fq1eo/Jk8eyZcsekpNTsbGxZtLksdSo4Vzs/gWCssSPPy6jevUanD7tiampGUOHjqBXr7xt1169+lC9eg08PA4CCnr37oebWx38/W9gYCDFzc1No8+wsFANW2ZmJiEhIVSpUrXQOW3fvoWVK5fx5Mlj3n23IV98sYA2bdq+8j16eBxk4sQxyP+Jnrhw4TwnThxly5Zdr9yn4M3SuHFjLly4oPo/BVSiu6j+UsLJ+C1FLleGfL8cFKGn9+rh3y+yY+d6/vbIe3gMHNibL7+arjqOiYnjgu9VzMxNsa9RhakrVhAeEwOAob4B34wfR68WLQsdx/f2bSYu+RFSstB7HM/LH2tdXR1kMu1+P9raVK3qwIqV31GtmvaHcGJiEiEhEbi4OGNsbKS1jUDwNnP//j2mTPkIf//rALRt255Vq37Dzq6Sqs3MmVPZtm2z2nV2dnZcuuSPiUnB4dY+Pt4MGtRX7YurQoUKeHldolKlSgVcqc6dOwGsWPEzt27dJCwsVGMbDuDSpetUr/5qvjqvA+FknD/PfXC8vb3JyMggMzOTmJgYrKysMDMzo0KFCqxbt67APsQKzluIQgGR4dqrfpdEepeIyDBOeHqo2Q4ePMbIUQNxcqoCgI2NNX37KUMEZ/66UiVuADJzsln+1190btwEA319PP4+gc/5SxgaGtKpUxvi4xMID4vErY4rbds2w9XYmoe3AjTEjfJ+dAsVOD17dsLR0R57h0q0b9+qQOFiaWmBpaWImhL8d5k3b5ZK3IAyk/HChfNZvfp3lW3mzNncuOGn8t0xMTFlwYLvCxU3oFxtkb+UpyI+Pp7jx4+ots4KIy4uluHDhxAerrmS9CKnT3uWaYEjyJ/IyEhq167NuHGanwm5XM7EiRML7UMInLeQxERNv5nn6OmDiVnxQsVf5vffl5KZqb7alpubS2hohErgvEjgY828N5Fxcdx6+AjfEz5s3rxbZT986KRau1q1XHh0/6FWcQNgZ1eRZ8+iyc3NvwyEh4cnUqmU9wf2pHv3jgXcmUDw3yY8PAxfX81Mv2fPnlY7rlKlKidOeOHpeYykpCTat++Ivb1DkcbIb3vB3/8afn7uNGrUpNAtCA+Pg4WKG+C1RHQJSp7s7GzWrVuHVCrl0qVLGuflcrnWxJIvIwTOW0haav7nTE2VGYtfBQtLkBpC3Xo1uHvvtto5c3Mzrb4tAI62toS9sIIDgEzOR2Omo8gpuD5VQRXIAcLCIpFIJBgbG5KerqXM+T9kZWWxY/t+atasQb9+okaaQKANU1NTTE3NSH3pDcnCQnNV08DAgF69+hZ7jL59+/Pnnxs1nIt37NjGjh3baNmyNZs379A65nPSCnrI/UObNu2oXbtusecnePMYGBiwYcMGQkNDWbZsGffv3+fzzz/H0lJZSkOhUJCdXbhDqYiiegvJbxvKwlK5glMcrysdHTAzhyqOUMkerCrA+PFDqFkzry6NgYE+06aNx9JSe9rjMT16YvhS2nVJTCqyrFzk8n/vAqZQKEhPz6Ru3VpIpQaqOWnj0kW/fz2eQPC2YmFhyaBBQzXsQ4YM5/ff19CyZRNcXavx4YdjCQ0tIAqhAFq2bI27e718z/v6+jB//ucF9tGpUxf0C8hAKpVKWbXq93zPC8oHjo6O/PLLL0ydOpXKlStTt25d6tati7u7O40aFV4vUDgZv4WkpcLL0ZoGUnB0gtgoSEoqel9m5mCvuetEdnYOZ8+cJz4hiabvNaBGdacC+wkMDsbjgi979h4mJzYFnZSsfLedCkMqNSCrqOFgLzFwUC++/HLGK44sELz9ZGVl8euvv3Ds2GH09Q0YMGAgxsamzJw5Wa1d48ZNOXLk5CtlAHZ3r0lU1LN8z1taWvHgQcECatu2P/nhh+/yrUK+YsVaPvhgeLHn9joRTsYF89xX63kZDi8vL1q1alXkIq5iBectxMQU7B2UVcL19MHMQilSYqKLLm4CQ+4CYGGljLqKj4OoSEiMV0Zo5cplXI58wkqvowz9fiGfrl1LwkvZJl+kTvXqzBs+AovEXHSLIW6at2hM9RfEU7XqTvy45Et0dIr/UDU0lNK3r9ieEggKQiqVMnv2PE6fPs/x42eYOPETDhzYo9HOz+8KV69eBiAsLIRZs6bSrl1zRowYjLf3WbW2jx495NKli+T+E8KZmJhQ4BzS09MKneeIEaO5ePE6Xbpo/51OTPwXjoZaSE1NJSpKu5gSlA4tWrRg8eLFquONGzcWqxCy8MF5SzGzUP55EhnJH54nCI+KY1L3qejrFl5Y6l7YA77b/RM/jV2Ic40qhD1VJgt8TkoKbPfZy+4zeY6Hhy/4kpuby89TpuTbr0QioWu3duzaebDA8cePH0ZmVjZ16tSiSxdlbowrV/xRKBQ0bdoAPT1d2rVvyZnT5wu9F1Cqf/d6rgwd0g9399pFukYgEOSRna09Q2h2djbZ2dmMHDlUFVF1504gPj7nOHDgELVquTFz5hQ8PA4gk8moXr0GS5euQEcnv1ovSnJycomMjKByZft826SmpmJkZMSUKTM4dcpTIzLr4MH9fPTRlH9dYyonJ4cFC75k167tpKam0Lp1G77/fik1a2rWtBOULLVr1+aLL77gk08+YdWqVQBC4AiURMXHM2HJj0TExmJhZIaOpPAPRkhMOD/s+4Ws3GzO37lCUORD2ru3U2uTngbPojWLWp2+fo345GQqFFCCfOrU8QAcO3qGpCTtKz72DpUZMEC9xkjz5ur7rQsWzMbevhKeJ84SXUiNKrlczk3/OwTcvs+p0z589dUMEQr+HyQ3I53Yq96khz5G18gYq3pNMXfRTGIn0KR7955cvnxBzVazpivNm7fk+PEjGqUeMjLS2bFjOzY2Nhw4sFdlDw5+xPTpk2jdug2ensfzHU+hkLNr13ZmzJijce727Vt8/fXnXLhwHhsbW8aOnYClpRXx8erPgRs3rnP//j1cXQt+qUlJSWHv3t1ERUXi7l6fbt16ovtCHZpVq5azfv1a1fG5c95MnvwhJ06cfSMFOv8NSaRyRFK0F8OS4N/GsD3/97169Sr79u0r9r+3EDhvMYd8zxMRGwtAUkYK/sG3aeTyrka71MxUwmIiuBJ0jcNXT5ArVy4jZ2ZnIpdrF0V2FravNCdjYyPmzZuCU1UHfvxxtdY2VavmH27qddaXLVv2EBERhbt7bdq0ac7evYeLNLZMJuP0KR9yc3IwMJDy7Fk09d+tw5gxQ6hYscIr3Y+gfKBQKIg49hdpIY9UtvTQYOg1DPMa2qP/BHl8+OEnPHsWwY4dW0lOTqZp02YsWrQEXV1dkvLZ905KSiAgQLNKeUjIU9VWVUGEhmqGgWdnZ/Phh2N5+DAIgKioZ/zww7daV3qkUikWFpYFjrFv31/MmjVNbUusceMmjBr1P6ysKtCxY2eOHTuicZ2//3Vu3LhOw4aFO7oKXo2AgDzR7OrqyrVr1wgLC2PZsmVIJBLc3Nzo2rVrgX0IgVPOyEiHhATIzVYWw7SyVmYn1kZ0YqLa8XrPzRjoT6Ru1dpIJBIkOvAw4hH+wQHs8N6Lgjx/cz0dXRIzksh+lk3H+po63NZaM6FXh4YNC1y9eZGbN+9otbu6utCokfYIi+vXbzNnzkJycpQPx2fPorG2Lr4w8fbOy6tw+/ZdbvoHsvnPFWpvbYK3i4xnoWriBgCFgqSAa0LgFAFdXV0WLlzMnDmfER+fgJNTnl9cu3btMTEx1Qjdbtu2Ix4eB7T2FxERXuiYHh77GTduAnXquKtsPj5eKnHzIqamphq2vn0HULly5Xz7j4yMYObMqRo1tfz8ruLnpywB4+ZWJ9/ttPwiNQX/nuTkZFasWKE6lkgkTJo0icDAQBo1aoRcLmfx4sV06dKlwFUd4WRcjsjIgNCnkJKk/Dk+DiJC8w/7buKq/uB+x96FarZOqg+EQg41KtXg/RZ9mTNgCpbGeeLE1sKWcwG+nLl1jltP1JefzcxhaNdWDO3YERMDKdKUbBpaOzC13/tFvhdb24pa7RMmjMj3A3vk8EmVuHlOXFw8rq7qmUqtrCwxMSl6mYXbt+9y3udKkdsLyh+yDM0tVQBZlna7II+EhHi+/voLOnRoybhxI7l921/tvL19FX766Re11RKJRMKFC+cZPPiDV97GSU5O5ptv5qvZJPlss1tZVWDmzDnY2VXCwsKSLl268cMPPxfYv7f3WQ1x8zJ37gSibfqtWrWmbt38Q90F/w5zc3PWr1+vZnN0dMTS0pK2bdvSvn17/ve//xW6EigETjkiKUFTzGRk5J/Yr77LO7z7zjsAdKrXlpl9J2FqlLfyEp+SwL4LHizes4zI+GgWj/oGMyNT+rZsRUSCsuJ2dm42C3ctYeWhtegZpWLvCJUdwMTIkH6NmmMVmob8cRy3z/oxfsx0rvlpLkmDcnvoRd4f2FOj6nijRvVo3yGvPlVKSgzxCXnL1BkZ2hP5DRzYix9++JK+/bphYmJMQkIiaWkZWh9M+ZGYWIzYeUGZRaFQkHT/NpGnDhLte5KsBOUWrXEVZ3RNNENyTZxEGv/C+Oij8axd+ysBAbfx8jrLhAlj+OOP9Zw6dYLY2FgCA2/j4XGApKS8FWOFQsHevbsIDQ1h7dqNtGzZGje3OsXOLOzr66OW0K1Nm3bUrKm54nblyiV+/XU5UVHPSEpKxNPzON9+O1+j3YsUtn31nOjoaD7/fD7OztWwsrJiwIBB/Prrb8W6D8Grk52dzTv/fI+9KJaHDh1aYC4kEFtU5Yr8xKq2mlPBERF8+NMSlQ/O+y36arxJVTCzol+zXuz22c9Wr10EhNxl05xF2NtUoIptJbYcP0JKRgY2VpZ0a1WPGtXUl4FXrlxPVFRehuLY2Hh+/XU9W7b+yvOAhpMnz7Fxw3aePAmlZs3qfPzJGJo3b4yTkyO/r1/Knj2HCA9/hrt7bYYO7YuOjg5ZWWmcOvML9x96I5fnUsXenc4dZ9OmbTOOHlVPGW9mZkq7di3Iyc3lu+9+UVUwh6InNNTX1+N2wH1MTU1o36Flsbz0BWWLqHPHSLhxUXWcEOBH1X6jMLJzwL5zPyJPe5CbkgQSCWbv1KVCgxZvcLZlD7lczsWLvmRn59C6dRsCAm5x9uwptTYymYx582YByjdtiURHTdy8iJfXaRYvXkr//oNwdnbmxo3rnD9/rsjzqVChgtqXmJ6eHhs2bOarrz7H2/uMWtucHPVIr23b/mTChE9wcdEuYjt37oq7e31u375Z4BwqV67M9OmzmT59dpHnLSgZAgMD+eabb1i0aBGg/HwqFIoirwoKgVOOMDbRXK3JysnCw/ccI3p0UvtP33L8uErcAJgZae5RA+jq6DCwZR/O37nAjeCbBAQ9QyfHmjbvdCc+IZOnsU/5oFMnOryUNVImk3Hr5l2N/vz976CjI0dXV5+bN+/yxeffq0JMb926y9w537Jr9284OFSiRg1n5s3TDCv3vbSZuw/yhExYxG08T//M0IHLCQ5+yq6dB0lOTqVKFXtmzppIRRtrFi9eSWpq4bkz9PX1aNu2OWfP+iKTydHRkZCTk8vePR7s3eNB7z5dWLhwbrmLjhBAdlIcCTfVtxrlGenEX7+AQ/dBmDrXpMboqWREhqFnYoa0gs0bmmnZ5PHjYCZOHMvNmzcAcHGpybhxBRc0TC4g9xUonYA7dmylWsFt0EC7U66TkzNPnz7RsE+c+InG76KrqxsffzxJQ+C8TE5ODsHBQfkKHD09PbZu3cXPPy/h1KnjSCQSTEzMCAq6r2qjo6PD+PEfFjiOoPS4dOmSmrPxvHnzVJ+H0NBQHB0dC7xeCJxyhKUVZKQrSE5WoCPRITUjjS1nd3Lqphf6hjoM7ZhXSDI4XN2JLzQugloO2n/RDfQMcKlcg2eJ0cQlxwOgryulkfN77PQ6zOU7gSz+8CN6tsh729XR0aFyZVuePFGPdHBwqISZmQkKhQRPTy+N/BkpKamcPnWO4SPeZ9++o1y4cJUqDpXp1bsz6RIZFS0sCH6sHo4KEB5xi3nz5tKieQcOHd5KZGQ0Li7V0NNTOgBe8C1aCYb3B/Zi3rwpBAc/4Y+Nuzh8WL245yEPT/r370HDhu759CAoq2TFx4Jcczkz+4WEbzp6Bpg4VtdoI4Bvv52vEjcADx8+YN++v3BwqEJ4eNgr9RkcrO7YfePGNWrWdOXBg3sqm7GxCTEx0RgYGKi2owwNDZk48RMmT56mtV8bm8KjOM3MzKlfv2GBbeztHfj55zxnVoVCwbZtWzhx4iiGhoYMHDiYbt16FjqWoPSYNGkS3bt3x9HRkXr1lH5PAQEBfPvtt+zevbvAa4XAKUfo6EB0RhDzN/1JRfOK3AsLIjlD+QZ17NJFNYHzjmMV7oeEU6OSMxHxz1h1eB0/jfkOQ6mhRr8yuZyI+Aj0dfWo7VhTZa9euRq9GnflsN8JtnueUBM4EomEUaMG8e23v6gVzZv44TD09PSUy4j55CvOlckYOWIyd+48UNm27dhHTrUK6JoZ8kHDVF7eWZXJwOecHyeOX2fInSA++3yq6lx2dg6xsYVnLXV0dGDu3ElkZWVx0tOHc+c0q9QCBAUF07ChO7dv3eFvjxOkpWXQqmVTevTsKFZ2yjBGtvZI9KUocrLU7FLb/JPFCfLQtnXk53eFtWvX8/333xIaGqLlqvwxN7cgOVnTt612bTcmTPgIf//r+PicIyTkidr54cNHs3jxTxgaaj6rnvM8X83x45oh3KBcnZk797MiVZx+EYlEwsiRoxk5cnSxrhOUPM+3oqpWrcqQIUOIiIjg3LlzfP311xgYGKhFWeWHEDjljFy5jOCopwRHqddpedGJNzcXhjQfxqAmY1AAl+5dZeXhdYxa/hFfDZ1N3ap11L6ovQPOExobzoh2Q6hkpf5A+F/nkUj1pZy/56MxlwHv98TGtiJeZ31QoKBXz4507db2nznIade+JVu27FGbm7GxESkpKWriBgC5Ap2IZGTVdPEPhSYvrTyGPoX0dKVvzF9/eTBs+ACcnJRFsh4+fJyvA/KLREREkp6eztKf1nLwYP5JxlxcqnHhwlWmT/tKtQJ17OhpHjwIZsbMgpfsBW8OPRMzbJp3IPrccfgn5YGeiRXWjVoWfKEAAGvriiS+lFrCysqK7t1707t3f/z8rpCWlsakSRPU2tnb25OSkkpKSt52lUQiwd7eXqvAcXauxujR/6Nx46Zs375F43xg4O0Cxc1zVq/+ndWrV+Dj4421tTWDBw8jMzOD2NhY2rZtR+3adYpz+4Iyxvjx4zExMSElJYWQkBACAgIICAigQ4cOBAUFER8fT6VKlQrsQwicckajmrWoVbUq90PU36Y6Nm6i+jkyHBQyKRIJSIAWtZsSlxLHtUf+nLh+ll3eB+jSsD3OtlWxMNehkj3s+OJHdHO0v+30bNyVdLnSmTgrO5s/jhzB2/8GhgYG9G3dmm+/+xRzcyOVaJLLFaSlZVG/vhvffDOH9eu38fRpGO/UrM6kT8awZctfWsfRzchB5140gUnmONhUx91eTkjIU0KfKrh5Pa+dQqHg4MFjTJs2AQB7ezvMzExJScknnOwfZDI5j4NDOXYs/737Xr0707ChOx8M/Uhje23nzv2MHDVQJAUswxhb1sbcSkZO9jN0dKQYSB3JeZKDVDO/peAlGjd+j0ePHqrZOnXqirGxMQAtWrQCYNeuffz++zqePHnMu+82ZPLkqVSsaMvvv6/hxo1rSKVGtGjRUuWI/CJGRkaMGDEKAJlMe9TEyxGX+WFmZsa8eV8yb96XRb5HQflh5cqVREREsG3bNlauXImNjQ1r1qxBV1eXsLAwPv/8c/78888CV9WFwClHZGdBXKwOi0d8w5PocHZ5H+BexD3eb9uO/m3asM3Tk7CoWPo2HKpRlqFrw050a9iJUze9ufn4Nrt99lPHqQ6TeoyjUb0q6OnBfS259yKfhfMo+D7Nq7Yk5Amcu32V1fv3q5ICXr13j6ycXIZ36YRUqvw4ZWfnoq+vh1Sqy4D3u9OjZ0dSUtIwNzdFIpFw+MgpzYH+QSIH3fBkniW6sezTRXTv9gGRkdEa7fbuOUx8XCLjJwzH0dGeD4b14/ffthX471erlku+lchNTY1Z8M0cOnRohUQiIThYs5JxdnYOoaHhQuCUYTLux6Gvb4W+fl4KgvSgeIzdbPtFeicAACAASURBVNAxEIkcXyYpKZHDhz24ceMaf/21Q+P8nj270NGRsHz5GlUizIYNm7BuXRONtlOnzlT9fPq0p9YcJdWq1cDJqRqg3GZq0uQ9VcHO5/Tq1fdf3ZPg7WDHjh08ffoUKysratWqxejRo+nYsSO9e/cmKCiI2bNnF+oyIAROOUGhgIiw50Uv9XG2cWbewOnYVs7BwFDGhB9/4OajR5gbmdGr/iB09NQFjoGePr/8vQafO3kOvEnpKXRt0B5rm+pY5FOa6VlkGPv2bSE1NZXu3foxoP8IRrSPZ+vZPOeuvV5nGdqxI+npOejqSrCwMEZXVwe5XM758xcICnpCzZrvUL++su7P+wN6cupk/qGiEiArXrkaY2trrVXgpKSk8vffx7kdcJedO9fy8cdjqFHdGR+fyxgaSmnQ0J1Vv25UXVulSmXmzP0EPT09atWqwf376s6PXbq2p1MnZY4OuVxObq72t0hbWxF5U5aRZeSQm5tMVmYIKOQYGDqgjzWyjBwhcF4iIOA2I0cOJTxcsyTCi+zevZO2bTswcOCQIvddr14DzM3NNaKsmjVrrvpZIpHw66+/8cUXc/HyOo25uTlDhw7P17G4uNy/f4+IiAiaNn0PExPNzOuCsk1qaioSiYQbN25gZ2dHbm4urq6u1K5dGy8vL1VunIIQAqeckJqiXtFbiYSMdAM8b5zm5iPlF3ZyRgqXH1yjlVszAOQKOfsvHOL4jVPEpySoXZ2RncExv5M0dvuQHO3FgnFze5dfft5MWloqZ84cJTj4AZ3qt2efrwfp2cossAkvPMSMjAzQ1dUhPT2DCRPm4XU2LyfJsGH9mfvpZJq3aMx3iz5l44YdPA6NAC1iIi4pkaysLKo4OnBTSzj6c4IfPcXr7AUaNa5PeHgkxsZGuNdzpUuXdvTo0RF//0AyMzJVPjWpqWlUqVJZLWJDKjWgVcu8N1IdHR1cXJx48OCx2lgVK1agcuWCoze8vC5w5rQPenp6dOvegaZNGxTYXlCyKAwTSQo5CfxTTy3jHmZ2zdAzfzP+GHKZnKyQJHLiM9AzlWLobIGOtGw8dpcu/aFQcfOcK1cuFUvg2NjYMG/el8yf/7lqJcfV1Y3Jk6ertatevTo7d+4lJSUZqdQQAwMDjb4iIyO5ePE8trZ2NGvWAl1d3QLf3DMzM5k+fTJ//70PmUyGnV0lFi9eSq9efYo8f8Gbp0WLFvj7+3PkyBGaNWvGyZMnuXfvHlZWVowbN45169YxY8aMAvsQGc3KCQq5drtcBk8jI9Vs6z0343XbhxxZDoevHGfHuT0a4uY50UkxSHRAogO6Wp67UqkUABMTU3r3HkxqajJmRqaYm+SVdaj+Qr0XXV3lR2r3rkNq4gZgx44D+Psrcxr06tWFAwc3s2TZfBQvPavkhnrExiWwcOEvODjkX0vmOXfuPuB/Y6ezcuVG/vrLg6++XML8+UuQSCQ0aFCXp0/D2Lp1rypPTlhYpFp21KysbFas2KC29z9lygQMDaWqYz09XWbP+bjAJIBbtuxh+rSv8PDwZP/+o3z80accLWA7TlDypCUH8lzcKFGQnhKAIh9/j9JEoVCQ7BtKysVwMu/Hk3otkoQzT5Bnvv65aMPf/0bhjf7B3j7/Arj5MX78R5w548u33y5m9erfOXHiLFWqaM9bYmZmrlXcbNu2hZYtG/PRR+MYMKAXDg7WNG7szu+/r8l33A0bfmP//r9Uv89RUc+YPXsqCQnxqjZXrlxi/vzP+Oabr9RC4wVlh8uXL6Ovr8+IESN4/Pgxffv2pWrVqtSvX59+/foRGhqKXJ7PF+M/lI1XCUGBZGZAXD5R0Kam4OrkrDrW09HFxNCEVUfW4+Qs5dwdzZwyL1LT4R0S4iAhDoyMIKOQZ28VR2eCIh7xLCFKaVAoSA+L44MPPiIzI5P2HVowd+5HeHtf1nr9rVt3effduuTmyoiIeEb7Fs3oOLQ7p46ehaxcFCYGyM2l6IYlceSlHDX5cfXKTUJC1PP+HDt6mkGDetOwoTve5wr+NwB4+jSMgIB71K+vfNNv3eY9duxcy0lPb2RyOR07tqZWrRr5Xp+dnc3WLXvUbDKZjC1b9tCjZ6ci3Yfg35OdGKNhk6WnkJOUgNS68NwpJTqXiBSyw1LU55KQSUZwAiZub36r09nZmYiIwvPb6OvrI5UaIpPJil2Q1tW1Nq6utQFITk5i//49JCUl0aFDR1xd3fK9LjMzky++mMvWrZvV7AqFgtDQEL78ch42Nnb0769Z/+7cubMatvj4eHx9fejVqy/bt29l9uypKgG0ceNvrFu3kR49ehfr3gSly9SpU0lPTyclJQVdXV1kMhnTp0+nYUNlbqMFCxYUmnVeCJwySmpGOocvXOBxRATWhlVpVqsFUj31NxzLCmBhBd3ee49jly9hoLBkYIu+2FrakJaZipWlCTK59mq+oBQ3fZp2Vx1nZMCmk1vJlufiUtGZjo3aa1xjaGjMRo9VqmNJbDqBt/PegB5v3M01v9vcuqV9W6lKlcocPXqGtWs2Exoajr1DJSZMGM7Ny/5EpaUgyZWj+zQRHXlebp0aNZyYOHEYtWr9n73zDo+jvL7/Z2Z7V+/Vsi3LvXfcC8WGUFxCJwFCDUkgJMAvCV86hJpQQkIgtFACppg4YDDYxr33blmS1etqe535/bHWSqvdlWRibCfReR4evO+8U3a1O3Pee889tw/7Dxzh5T+9TVlZ+425pSV2dKrsWAUjRw5Bo9bE3N4RCoWCpKTI3jR9+uTzk5uu7nZfgIaGZhoaolloVVVNjNn/m5BlmaDDh6hRnnI9jCxLOMoOo9Tq8XkiGygqTQmoLKdfGB5ojcopAxCMM366ceutd7B9+1bcHRqRlpQMYv/+vRHz/H4/v/vdvRw6dJBnnvnjdzrX4cOHueKKyygrC6V9H374fh555AmuuebH+P1+/H5/uFoL4IknHokiN53x8ccfxiQ4iYmx/9ZJSSkEAgGeeeaJiGitx+Ph2Wef6iU4ZyH0en3E96Kjr5HZbI61SwR6Cc5ZhAPl5fzhg3+w4/BhvH4/ng5plFWFG7lv4V0oxNCDQa0Bl1zDE39fQW1jE9OHjmNo1qRwt12D1ojPA+P6j6a8PjLPnmZJ5bqZVzC630gUnRiwXmtg6ZolLEegMLOAPlmF4W2SJPHm6nc4VN1eSqqyeenc8mnnzhjlWMC4cSPIykzn6qtvD5dgV1fV8tCDzzJt2kRWrIj22klLS+bv7/yBnJxQqmrM2GFMOWcc8+f/iObmkBdH//5FUUJkURQoGRgSoZ1/wSxWruw6ijNnzlRyc08+DN+GrKx0+vXvw+FDpRHjQ4bEX6X+L8HX6MKxpZpAswdUIrq+iRiHZ5wS48SAx03VZ+/gqjwWvVEUSR0/A1F5+m91ysTYXi6KhO49Xk4HZs+ey8cfL+ODD97D7XYxe/Z5PPXU43Hnv/vuW9x0060UF0c3u+wOf/jDU2FyAyHS9OijD3Hw4AGWLAlpcKZPn8mDDz5GWlo6f/nLS90eU4rhWg0wcuRoPvnko4jtkyadw4QJE6mvr6OiIrpC8siRwyfV46gX/xnoJThnCZxuNz999hmqOvSP6ogdx3az/uBmJpeExMPVzdXc98zDNLSGjLTSjX0Ylh0drrtkwnxsLhur9qzF4/MwJH8QN8y9huzk2O6uzY5QNERG5omPn+PyqQsZlDsAq9PKv7Z9xYGqvVw6dSr7KyoozMhge91qGpzdr0gTEsw8/cwDvP3Wh1H+MsFgkNS0ZCZMGMX69Vsjr/+S88Lkpg0FhTlcdNFsXnvtH0yfMYlf/eo2Ghqa2bevvYfMD394MSUlIVfmOXOm4nTeyXvvfkJjYzOjRg9j2LCBfPvtBuw2JxMnjeG663ouoIwFQRD4+c9v5J5fP0Jra0h0nZaeyq23XfdvHfe/AVJQwrb2OJLzxN/dL+He34RCr0JfnPJvH79lx4YY5EYgcfhYEkpGok0/dU7GkjeA+5iVoM2LKkmHpsCCqIwdjVJnGNHkW/CWt5vdKVN06Pr0rIv16cCIEaMi+kN9/PGH7Nq1I+bcYDBIaenRkyY4DoeDFSuWR403NzfxyivtXbmXL/+cxsZGnE4n3uiKiijMnx9dTv7007/nscceDL9WqVRceukifve7BxAEgdTUtKhWEQCDBw/uJTf/heglOGcJvtm+PS65aUNlQzWE0tl8tevrMLmBkMNxLKiVam6cex1XTF2Mx+8m2RQ/VO/yuFm7r719QX1rI89+Gi3mu37efPIyMnjlL2/zdVPs9FBnWNKS0Oq0KOO0tzebjLz0pyfYv/8QL77wN779NqThSU6J/TBITQ09GN0uDz6vj1dfe5rly1dTW1PH8BGDGTMm0tnt4ovP5+KLzw+/9jY3cNGU4egychGVsa/pZDFx4hiWfPQqq1evR6FQMH36JEym2E1Ovy+0tc04m27W/lpnO7npAG+lvUcEJ+jy49xfj6+hGVVSIoYBqSjN7WlHd22sSiAZXVrOqSU3ngAtX5cRtIZcsz204D1uwzI1H0GM/rwFQcA8MQdvnhlPWSuSx48yQUvQ6T9rKqk646abbmXlyhUx075KpYqRI0ef9DHvuONmGmPc2wRBiGjzArBtW/c95YxGE9df/xMWLPhhxHh1dRV/+MPTEWN+vx+FQiQ5OfQ9E0WRe+/9DbfcciMuV6joICEhgV/+8r6Tek+9+M/A2fkr+x+EP4YpVmcUZuSiUkNCAjQ5Im8Ya/at54LRc9FrdDH3NWh1GLSxt7VBEECpUAGhnLyAEDb064ij1dXUVdTw/POvRm0TRQFJitxHFuFIwMYfP/iAy+ZM5bVX34lwHdbrdcydG9L7lJT05977fsYv7/o/Dhw4wob127jllmgdzOrVIQK0YcNWfvu73/Paa89y4YVzunx/AEGfj5qvPsZ+aA8gY/PDXhKZuXAB+fm57N61jxde+Bt79x4gJzeba69dGL62niA5OTGCSJ0uSJ4A9m01eKvsCAoBbX4ChuHpiIozXygpKGKTrVikoDMkX5Caj5fjaNyFLLkQFSYMh4eTcfE0lPqQJk1lik2ClZZTGylxl7aEyU0bfDUOvFU2tLmxjaQEQSDQ4sF3PBTVCzS48ZRasUzJQ5NpOqXXdyowYsQoPvvsK+bOnYbDESmQVigUmEwnd83Lli1l6dJPYm5LTU2jvr7upK/R4bCzbNln/PjHN5Ke3m7Vv3fv7jBp6Yj9+yNT5uefP58VK9awbNmnKJUq5s+/kJycvJO+jl6c/Tjzd79eADB1+HASu7h5zBw9misuHEWfvpCUAsP6RnYGP95YxTOfvIBE9z2Z4kGn0TG8z2AAijIKGV0UioJMLBnHTef+iCumLqAwLYeS/DzWrtsc8xidyY2kEAgUJYNRw8ffriY7O4Onnr6fkSMHo9VqSUtL4bIF88nNC+lfvF4vv/j5b9mz5wCBQIAVK9bx4otv4DnRa8rlcvP556v46R3X8sGHf+KGG37Irp37ONRJ+xIPzdvWYj+0m7ZeRWYV9PPWcPONv2TPngP84hf3s2HDVux2J/v3HeK+ex9j27bdJ/1Znm7YN1fjLWsFv4TsCeI+2IRzd7RB4pmAKt2AMilad6LtQarGun0v9voNyFJIOCwF7dgb1mHf3Z6SShw2DlEXaeRm7DMAfVb+v3nlkQja4oiGbT4kXwDnvgZa11Rg315LwBGaK3kCuA52Ep8HZVwHuo7Wnkn07dsXlSp67evzeXE6owlEPMiyzP33x46MCIJAfofqzzYYjT2LeB46dIDXXnslYqykZCA6XfQirri4OGqsqKiI22//OTfffFsvufkvRm8E5yxBktnMEzfdztJvN7Pl4G48ASdjBw6kID2D4vx8Zo4eHSEIXjRjBhv37mXt7tDDVyGKTB7RF41KG2Xa5wv4WLJ+KRsPbkGjVjB1yETOG3EexOj2fct513P5lIWkWpKxOluZUnGASSdMAwEuHj8PpaTokYIdQAzKBFUhjYLT40GSZYYNG4harcHj8eDxeHjj9fc5euQYzzz7IF8uX8X+/YcjjvHwQ89TdqyKRYvnsWfPQa655rLwtgkTRpKekYqyk4hUEELeNcFgkI5WCY7yyF47AClGLSkKHy+9+LeoSqhAIMCSD5cxcuSQHr3fM4Ggy4+30hY17i1vxTS862Z0pwOCIGCelItzVz2+OgeiVoWuXyLa/O4JjrPycIzRIK7qIyQSenBpU9LJv+xHWHdvJuCwoc/OJ2Hw6FOeposrGk7U0Lq6An99e/WWt6KVhFmFyO4A+KO9OoL26HYhZwsEQWD69FksWRJpezBlyjRSU3te3v7RRx9QVlYWc5ssy2zevJGxY8dTUVEeLh1PSUnj9df/2qPjL1v2WUQfqpycPG666VaeeebJ8FhmZhY333x7j6+5F/9d6CU4ZwmaGiBZNZBrpw/k2ukyRlOQrBwlse7RXi+47XoeuvpuSuuPUNF4nFHFxWQnZ1MeI5DxtxVv8/m2FWQlZTI6fyTNNiefbvmYaQNnYda3R42qmqrJTs4iLSGUr040JjCxZFzEsURRQVODTEF+P5KSEsKVTBAy+QsGI2/mskqEE2mSKUOHoRBFli77mvqGzUydCZIEZaWwdu1mvvxyFWvXxo4Mrd+wlSd+fw99CqONwq688gd4PO2RI51OhV6vCafLXC4fbnfogaKIkcKTZBm7J0CgJZokAHzzzRpqaq4lMzN2M9IzDlkmRiYx9tgZgtKkwTIptslbl/sZY5f4K0yRZEObnEbGtAu+07X1FNo+iXiP2/HXt0cxNAUJEJAjyA2A5PTjOdKCflAKgl6J7IpMQauSuk4Xn2n89rcP0NBQz7ffrgJgyJCh3HLLybVQeP/9d7qd09LSwo4d+5EkiebmZmbOnNzj4zc1RUfBfv3r3zBu3ERWrfqG5ORkLrts0XcyKezFfwd6Cc4ZQDAIttaQgZ9KDVotNEb4kwk47ErsNjB3Su07HVB1PPRMA4FEZT/ScvuhlcHaHDk3KEms37+JFTtXMabfSH524S3o1KEHg9vr5o1v3iUjMQ2L3sye8v3srzzICzc9FXGM2KtggUMH67HbnRQW5qFUKunbt4A+RQX86aXXIzwmgmlGEAT6qs0cWbGVGUsuY9CQABOnth8trwA2rZM4eqSM5DiNLOvrmpBlmYTEaK2DyWTE63UgimAwaFCrleHrFkUBo1GD3x8kEAiSMGQUzrKDEfvvrLJyrMnJrYsncOxYRYQvCIDT6eKDfyzl9p9eH/PazjRGTTwv4vUzl9/GoNx+aHJ7FmU7m5EwfATWA1tAbv9OCQoNiWNGnvZrEVUKLNPz8R23EbD7UCXrUGcao1NQJyA5/YhKBcZh6dg3VsEJ7i/qVegHh0wHZVnGW2HDW2VDEAS0hQmoM06vMD0WsrKy+fDDpWzZsolnnvk9q1evZNGiHzB+/ESefPI5+vePTvt0RufO5LGgUCgQRRFRFPnss4+pq6vt8TWaO98cCd2vZsyYxYwZveaa/ylobGzk+uuv5+OPPz7lx+4lOKcZbU0zO2rh4pkxulzRBKexoY3ctMPvJyot1Whr4vEPn+VobUirsGjyxWFyAyG9zexh07jrb78Jj6kUKrYd2cHIvpEVSLGgUWvx+/0cO1bBE7//LXPmhBjLiOGD+OKLlciyzMxZU0jJSePblev5w5Mvn3ivEmmdui8IAvQfCEVFBYwcNYS33vwg6nwOh5O1a7cweXJ0F2OfL4AoQkKCPq6zpUajIBAIYi4qgQsWs3vZJ7Q2NrKn2sonuyqZPn0iV1+zkKamFt59N1oUWV7evePrmcDwYTOjxn7+9+dZ/cobGIaeXufe7wO69Gxy5i2kYf1KfNZmNCnppE2ehcaS2P3O3wNEhYi2IDK1pk7RE0uZokwO/d50hYmoUvR4q+yIKhFNriVsdOja14BzZ7tWylNmxTwhJ+ocZwrLli3lyy+/CL/esGEdv/jFbSxdurzbFGBycgrl5WVdzrnooovD//Z4Ts4Acf78H5zU/F6cnXj88cfxeL67drQr9BKc0wy7PZLcAMRrpxHLFd3XzT1AEEIE6B9rPw6TG7VSTX5atJAuLy0XtVKFLxBiRwNy+jGiaFi37wFg2472Vgxbtmxn8eLzEUWB6TPGM3bc8Ij39My6dudjtQZ0eqJgNovMmj2Fior4RKKqKrriIhAI4nB40GpVXdp2dxQ/m/sNYtIdgygtLce7cy/P3pTF6NHDEASBCy86l/fe+zSqfLV4QN/OhzyrMeX6q9mxc8WZvoxTAlPRQExFZ69hoipFj64kGff+9kiOOsuEtk87CVOaNCgHhNJtUlDCU27Fb/Xg7hz9kcF1oPGsITiff/6vqLFNmzayf/9+Bg7s+m+SkRFb/6VSqUlJSWHBgsXccced4fE5c87j8ccfioigCoLAOedMZd26NeGmnSqVmssvv4pf/OKX3+Ut9eIswvr169HpdCel7ToZ9BKc0wx/HG1hGzFpg6iIjt5AiCB43NHjbVCpQaeFA1XtaZhhhYPx+X3oOulPKhurwuQm2ZTEFVMXxl2ViWKIiDldDr78cinfftveJyo1NTHcZFOrVSEIAjZb+0X6/O36A49boKFOJrWTnGVA8XjS0xNY8dXKmOcXBBg0aAA+XwBJkgkEgvj9EoETnci7Wk0GgxJeb7QPS58++fTpE1lpM3Bgf6648tKIKNKQoSUsXNjbiRjA3+LGW2kHEbS5lgg/mv92WA/sxLprM0GXA31eEanjpqM0hNJJphGZaPMt+BvdKExq1BnGmN9JyR+kdVVFhI6nM4IxPIPa9oVQqux0oWPD2TYoFAr0+u41RLpYKxng0ksX8uyzz0ctSPr27cvTTz/Po4/+HxUVFaSmpnHrrXcwYEDImK+2NpS+8vt92Gy2cCPgXpy9aG5u5vrr21P7ixYtYtGikKmqz+fjxRdf5IUXXuDWW2/9Xs7fS3BOM+JZ0SQlh7Q5Hk+IxCQmQazfb0oaVFVEp6naYDRBahrkpidxvKGawXkDuesHt6PqZGbn9XtpdB3n9nk3IMsCo4qGYTHE9vJQ6+0YLAGSTIlcc/Wv2bu3vc+UxWJiwYJIcadaHcqrt3V6nT5tAls2t7mjCmzdJDN1pohOH9qemlLAvPPvQKVS0Ldf7NLeESOHkp6eTmtrJLsTRQGFQsTvl+hcISrLMh6PH7fbFzdKFgt33nkT06dPYsf2PWRmpTFr1pSYnY7PZnTs33Kq4CmzYttQGdaSuPY2YDnn7PRz6Qn8TS68VXYQBLT5XZM12+G91HzeTnp91ia8zQ3kX3pdmMiokvSokkKfe+cIYBs8x6xdkhuIrtaSvAHsW2vwVrSCBKJRjX5gMrqipO/d0PGyyxazZ0+kTcKcOedRUFAYZ492nHfePD788P2o8XfffYt+/fpz++0/i9p26aULmD//Io4cOcyePbt5+OH7qampjpr30Uf/YPHiK5g+fcZJvJtenG4kJSWxZMmSmNv+/Oc/c/nll/e4Ive7QHH//fff/70d/SQgSaGH0X87VCoIBMDbIeWoN0BaBpjMkJAIJhPEa52jVoPJAkpFKKrSoV0VegOkpsO2Q/tZtn4DLXY7i865hKLM6JvRa1+9zZL1y7hr0eUUpg5Ao4zfH6eyroUrH7sbnVbBL35yFV6vD0mWGDVqKA88eCfDhpVEzBcEAbfbH77JDxpUjNvlprS0HJ/PT1HfgVx3zb0MHjSBoYPn8oML78JsClVu5eZmsnffYY4eae8Xk5KSxDPPPoDFEvlDMBo1mExadDo1KpWIzxdEoRAQhFD1lNPpxeXyRZFBWZbYu2YVzz75Eq+9+QF79x3GbDLyzddr2LBxGwaDgaFDSxg5cgj9+vU56Q7KpxoBlwNBFBFipOD+9Kc3Yu6zecvnp/QaZEmmdc1xZG8Hx2wJgk4fuqLYwvCAy0nrgZ146ipRGswoetD09HTBXdocLu321ztxl7agTNKhNMW+xro1X+C3drIQsFmRrUZcu2x4ylpPRGFlbOsrsW+uDo2JQkTFlPtIC4GWrvUGmhxzBGm0bawKtXs48T2WfUF8VQ4kTwBN9vcrJB89eixarY7a2lo0Gi2XXLKAhx56LG50piP69y9GpVKxdm10j7nS0iPceOMtMQmaQqGgqamRq65ajNUa3yl98ODBjBkzLu72/wQYDKf3N2GzN7BpS/wGzKcas2fcGHfbc889x549e/joo4/Yv38/5eXlzJwZrSn8dyDI8ZYapxl+fxCr1dX9xP8CyHJIQOxxhwiL0UTMcnCH28X7X3/D7qNHyE5JZdGsWeSmRQpHA4FQZZUohAjPFxt2sHrXVtbu24DL5+buS37G+OJoe/VnP3mRo3VlFGf14cZzr0XdBcEB+OVrv+Fo7TEWzpjBgzfcgCRJSJKMTqfCaIzc1+cLREVaAOx2B7ZWG1nZmeEbW6z9PR4vb735EZs27SIjM41LLjmffv3yUKtVoaoTbwBZBrO500pXkmltdSIICgKBQMwoV9DnZdc7r3LXy5/S5Gxnhx0dmBUKBb++53YWLDiz3YXdNZXUrv4XnpoKFHoDicPGkTJ2WtRD4cc/voOtW/aEX38f2pugw0fTp4eiN6hE0hZEazFc1eVUfvYOwROCM1GjI/u8BRgL+p3yaztZSEGJ5qWHkVyRCypVqp7E2X1i7lP2/l9xV5dFjZss56DWdGgHoRHBGxkutEzNQ5NtxlfvwL6jjmBjFzlmwDgqE31xMgDeRiety2M0ET0Bw4h0DCXfj37hVMDpdFJUlB2O5rZBq9Vy9GgVqjitW5566nEef/zhLo89ceIknnji2R5VdJ2tSE09vdHPyqp9/PGlaGf47wuPP9R96w2Aq666ijfffPOUn783RXUGIAhgMIT+iwWvF5qbJO56+Vl2lLbbjP9r4wZev+//kdOB5Lgc0NQY0vbIo1y/KQAAIABJREFUssyg7OEMyh7O/DHn8sB7T7C9dFcUwbE5bcwfcy5FWX0ISlJUR/HOcHndNNpCnhNfbt7M7677EbIcSqm5XH5EUQhrb7zeAE5n7BWqyWQM92aqrKxBFAVyczOjuviuXr2RnTsPYDDomTx5HMXF+ahUCg7sP0x2TiaJiRb8/ujWFqIooFIpcbvjRwKte7awfPXmCHIDkSLkYDDIn156nXPPnYHJFOeP9D1DCvio+tf7+G2hFWzQ5aRx/dcojRYSB0WWSP/1r89979cj6lWIRhWSI/KzVVpir0Dr164IkxsAyeumfu2XGPL7nvE+WUGnL4rcAPhb40dWjIX9ogiOIOhRqjpVqnmjc6HechuSN4h9Q1X3FyeCOrf9oefa1bUbtXN7HQqjOm6riDMNg8HApEnnhP102jBp0pS45AbosmCgDevWrWX27Km8884HTJzYc/+cXpx9+D7IDfS2ajjr4PXC8XJYsXlHBLkBqG1u5r0VX4dLwj0uqKluFy53fHDkpGRz4djz+WrHN3y2+XPcvtDNu7alnr+v/pDCzAKAbskNwPIdX9PqCvWl0arVNDfC0cNw9BBUHIP6eh9NTU4aGx3Y7Z4u9S61tfXcduuvmT/vKubPu4rbbvt/lJXVhFd4L7zwBtddexdLlizj/fc/5cYb7uTpp19h6pSFzJ17NRMnXMLjj78UFjV3hsGg4d13PmLWzAWMHXMuP/7Rzzl48Gh4u7u2Eru7exfZpqYWjpWWdzvv+4Kj7GiY3HSE/ci+GLO/fwiigGFwWqT5tULAMCg6eiDLMp766Ie5t6GGoNsRNX66oTRqEA3RD1dVQvwoZtKICVgGjgip/wGlMRGjeQyi2P0aUZZlnPsaup0HgELAtased4UV+/Ya/LXdt0bwHOlZw9vvgs2bN/DAA7/h0Ucf5MCB7/bde+CBR+jXrz3KMmDAQO6//8Eu9oD58y/GaOw+uuF2u7jkknlx+1314n8bvRGcswTBILQ0gdUKwQA02GKbhx0qa2T56mP4ZCvDioqB+LnwgrRcZGRe/eotPlr/GcnmJKqaarj1ghsQhfjEpqLhOM12K26fm21Hd/L1rtXhbTedfyUNHRaVHk/I16ewKL5uqCOeePwF1qzZfOI9y6xauY5HH3mehx+5B7/fx8t/ejtivt8f4MUXXg+/ttns/OG51zh48ChPPHEvKZ2MAV966S0ee6y9LH3r1l3cfNPd/HPZW+h0OlTmRAZlJfDZ3pour9NsNqFQiiz5cBkpqUlMnjy2R6vKU4VYehsAoYu/26mGLMt4jrbgq3YgqEQ0hRYS5/TBUxkypdPkWWKSAkEQUFuS8DZGmrYpTRYU2lMvfj5ZCKKAYUhayHyvLXCnFNDHIGttEJUqsuZcQsr46QSdDtTJGbQsK42OBCkFCETmRtXZxpCGpifwy3hKrXhKrd3PPQHJ232j3u+CN9/8G3ff/fOwceef//wSr7zyN2bO7L6pbUcMGjSEVavWs3r1SkBg6tRp3era+vbty1/+8hpPPvk4e/fuxmQy09LSQiAQHXmTJImnnnqMefMuPOPRwV6cXeglOGcBZBlqqkJamjaU5PSP2c27vK6Cu0+Y8yUYzPxk7nWMK442vwM43tRefdDitKJSKnn8mvtRigqWb1uBWW9mbPGoCLLjD/j4yxevs/f4gYhjJRqN3PaDqxiSE63nkYLgtEN33msul4vVqzdEja9atZ5gMEhlZQ1NTT1bjX7x+WqqKuv46OM/R5Ssvvzy21Fzm5utrPxmPeedP4OEwaMZtX8nF9W1smxPDX5JRqkQEBVKfL72m+fw4YO47tqfhcdGjR7G00/fHyV0/r5gyC9CnZiKryVy5W8e8O/1xJL8QbzlrfitHpQWDdqChLhlx44ddbj3t9vhe45Z0Q1MwTgsvXuTt1GTqV6+BGSpw9g5COKZFWy3QdcnEWWC5kTJu4A2r2cl72pzIphDX3TzpBwcW2sINHtCqaUsE9p+Sbh21hFo9iDoleiLk9HmJ+A+0BSa9z1Ald7ufCxLMoFmN4JGEVcw3RN4vV6efvr3Ea7kTqeDZ5996qQJDoBSqYxyF965cztr135LSUkJ06fPjtpn5sw5zJw5B0mSEEWRadMmsG/f3pjH379/Hy6XC0O8vH8v/ifRS3DOArjdkeQGoDA9n8smXcSH6z5FOvGQyEhIjyAtVqeNP33xGkMKBqPv5HFT39rAZ5siK2nMehN7yvfz2oq38AdDq76S7P7cdclPqW2po8HWxOdbl3OgKtpi/fYFCxmTNymujw8CNNlaCfhl9KoEVKrIkvj6lha+2rIZUSlCpwWnQqngn+vXMbJ/MYWFuRw7dryLT6sde/Yc5LOlK1i4aF54rNUau59UU1Ooj4UmIYn8hddzS8lmLiyvoMYrMmzWbIJBmWXLVuB0uSkp6cdDDz4TQXi2btnJO+98xE03XdOja/t3ISqUZM9bTP23y3FVlaEyGEkcPh5L/+9OcKSgFOXB4jlmJWF6QRTJCXr8uA9HRxHd+xoJ2r2YJ+YixkkTAlhKhqE0mbEd3A2yhLHfIEz5p0ZgLMsS3qYGVEYzini+Cz1Ax7Lu9mPLBGweJKcfOSCjTjcgamLfJtWpBpQp+hBxkcBXaYeAjGVGQaicW61AEENE0DAsndbV5RCMeajvDHWWEcPAUAWir8GFfWNVuOu5Js+MaVz2d/LNOX68nKqq6N/h/v17ee65p7FYLKjVanQ6HXPmnNclsdiyZTMVFWWMGTOW3Nx8/H4/N9xwNcuW/TM8Jy8vnw8/XBqzw3hb5PQXv7ibG264NmYJflZW9vdijdCL/2z0EpyzAME4EeYfTrmMCQPGcNx6BJMqjfe/XUKtNdLNt9VpY//xA4woGka9tZGy+nIOV5eyas8amh2R0ZBBuSW8teq9MLkB2F91iLte+3+0OGKHxI06LWmJCbz++T/5RLOO88ZcwJii4REreBk/P3nmYXaXthMjjVJDcXYBD/7kR1Q3N3LX83/E6fEg6hUoOi1km1VB7nn5ZURBYObIgTQ0NONwdK89AKipiRRhZudkUHYs0g1ZEATOv6DdL0NjSSR98hzSO+kSb7n1OgD+9a+vY9rG79xxevUv2uQ08n5wZZQI+7vCW94a5cESaHTjKbOi75ccMR50+qNSLW3wHbfjq7ChLezabdeQU4ghJ75fiizLBFu9oBB6HG1wlB2mbtW/8LU0IGp0JA0fR8r4Gafk8/HVOWjdXI1s68DiNQrMY7PQ5lpC5KfZjRyUUaXo8Te58ByKbADnq3XgKW3BMCAy3aXJNJE0vz+t35SH3vMpgGlyDrq80N/A1+zCurIsonO5t8KGaFRHdZSXZZl3332Ld999G1FU8OMf38i8eRdFzCko6ENBQSFlZZEVXDabjYcfvj9iLC0tnYULL2fSpMnMmDEr/Lfwer3ceuuNfPppqCxZpVKRl5ePzWajoSHyd1tRUc5VVy3m3Xc/jNsc88ILL2bnzh388Y/PRG3LyMjqTU/1Igq9BOcsgE7f7hTcEQYjlCQYOb7NzcGa2KFZgARDAqIgUt/awBNLYlfUnDduAvmZSbg2RZeoxiM3AA63B4c7pKUop46dZQe4YsoCLpl4ITaXgzdXvhPW6Fw8bh4zhk1BpVCx+fB2/r76fS6691ckGI04T/QakTLNoBARW1wolCr8JhXSiRC7JMt8eXgvjz50J9tXb+GjJcvif2gnUDJ0AA1uDwFJxiKK3HnnTfzsjt9GdDW//IqLSUoKaXWCXg9NW9fgPF6KUqsnYcgYTH0iy0yzsmJbzGdmnpneTqfqxh2wxX6wxhpXJeoQjWokR+yQnb/F3S3BiQdfrQN3mRVfrSPcZVuTY8Y0NgtRG/+WFHA7qfriAyR3yE5C8rpp3LgSlSWZhIHd909rgyxLNG1Zi+3QLpBkjEUDSBo+mdZ1lcjuTqsNbxDbhkoUFg2OTTVhgqiwaFBlxo5aOHc34Gt0IcoCqjQ92r5JiAoRpV5N4qxCHLvr8VU7kEVQpetRJeiQ7D7cR5vBf4JUqgQElRI5RrVXG9oiG75GF9YVpTGjQ75qB3T6aG677Sf84x/vhl+vXfstN9xwMw8//Hh4TKlU8qtf/T9+9rNb8Hq7JmT19XU8//wzPP/8M8yYMYu//vVNDAYDb731tzC5AfD7/V024DxwYB/jx4/kjjt+wZ13/irmnOHDR8Qcz87u7Rjei2j0EpyzAEolpGdAdXUQAQWSLFHVXIHsaOCRN1+n3hqfgIzpOzJs5DcobwD5qbm4fS4cXider5cEk4mfLljIxVOm8PPnTk058dur/0FRTiavfvEBlR1SZpXN1WQlhTxuLhgzB6POwHNLX8Lq6JB/U4hImWakVAMBZezQ+cHaKu6556fs2L6XY8diVzLJgJxqxJeeTKXTg1IGBSLnnDOBN998ns/++RV2u5P582Yxdlx7WXX1F0twlLY7MTvKDpMz//IIkjNs2EBmzjyHFSvaDcosFjOLFv9nNPeT/EGcu+rxVtkRFCGXXv2gVFSJWmI5sKgSYwiFRQHTqAxa11TEfHAqjN/N2dm5rwHnjuieYt5KG4JWgXls/AeVs+xImNx0RPPGrQSO6lGn6NEPTu02GtS4aTWN69u9grxNdfgaWlG74/ip+GVaN1YiNbSHHoOtXuRgnHJBv4S/IlR16D1uw9fgwjIxF/fRZnw1DkS1AvOE7BCZC8ooLBoEQcBQkopjfz2BBheCVokmx4zSpMG+vYZgU6ewp1JAkRT6u7kPNcVNfYlqESkoEWhxo9CrKa06xgcfvBc179VX/8Jtt91BZma7p8+lly5g0KBBLFv2Tw4dOsCSJf+IfZIO+Prrr3j77Te48cab2bBhXbfzO8PjcfP44w8zfvwkJk2KLv2eOXMOeXn5VFRE3hd+8INLTvpcvfjvRy/BOUtgDzTy878+ToYlixZHC4drStFqNHjirJ6KMgoZWTSci8adFx5rtreQbkljV9kePIHQfk02G3/59BPe++pL9pXHJgsGnQ6nu2vzsc54c+U/qGyKrETafHgbW4/sYHS/0CprwoAxvPH132lxxqgg8QVBIUY6HLr9iK1uDm3cQ/no8Tz22P/x4APPsWfv9ohdBUHEn2tm6MASnrv7MarLqxgwoC+33HwNQ4cOZOCgYgYOin5YueurUasVDL76RpR6Ay1HD1G5+itadm+OiuI88ug9fPjBELZu3UVKajKXXTqPfv1jm8CdbbBvqcZ7rP0zd+6qR5ZlDIPSUGe34quyh7epM4xo8mJ7qGiyzSTN64d1RVmE/40iUfudmkFK3kCX5dLeDtfVEXJQQvIFY3efBWSvjGT34bH78De7STy3KKY+SJZlAk1urLu2Rm2zV+wlMbEQUYxN3KQY7sOSw48yRUegG+M+X4UNa7Acf1U70e9YJaVM0WEal03Q5sVzsDlc2eWvcqDJM0eTG0A3IAWlPnStwThRNgBFgobmzw4jOf2gENhVtyWmhiUYDLBnz64IggOhku4BAwaye/euHhEcgG3bQuZu6emZPZofC6tWfR2T4Oj1el555Q0eeuh3bNy4nqysLG644ZaoFFsvegG9BOeswdK1a6ior6Givp00xCM3ACW5xfxwyqURY8u2LmfTkfabt1qhQqPS0GRzYNIkUpJTzP7KgxH75Kal8fzPfs6C3/4GX6Dn5abHqmOXWVe31AAhgqMQlaiUJx4YstxOZiQZscFB5pAiqk54vQhWN4oKKwKwuX4jV67bwcOP3MOjjz7Jhx8u4eNP3sFqbSY1NYOikYNJz7Pw+Zuf4T2hldmwbguHDx7lvff/HFU6Hv481AoGXn4d4okHpTk3H0NaBqUrvoiaq9FouPyKS7n8ikujtp3NCLr9MUuSPcdaMQ5Jx3JOHt7jNgKtoSoqTa4lLISNBaVBQ9LcItylVoKtHhRmDbqiRET1yQtXAzYv+OKbJAnKSFIiyzLOPfW4Dzcje4Iok7VoEgvxtkTqQjSagvC/g61efFV2tJ1Im+QJ0Lr2OP46JwFXDEIS9COaFBBH+iWolcidfx8KAdOEbFq/Lg8RiC7Qkdx0RqDRjWNLTeg30ol7eCtii+aVHXx8VCn6uCTLc6iDDi8o00fIQqlQEugk/BNFkf79B8S9xiFDhjJixGi2b+/emTY3Nw+Aq6++lg8+eI/m5tiWF10hKSk57rbhw0fwwQef4vf7USqVvdqbXsRFL8E5CyBJYLP3TFTbhs82f055fQXnjpyFIIisO7CRNfvWR8zxBf34gqEb7/7KkM1+x9LztMRE3rn//9h7rPSkyE1XKEwvCP97T/le6lsbGNt3JCafkq93rgJJQmxxU1Lcl1effpSHXn+TLzZuRqpvjDiO1+vlzy+/wdt/n8zixZcwa9Z5NDXVMnBgDilpKp595uUwuWlDU1MLX321msVxUkkJOblhctOG5AGDsNZ17Rb7HwVJDjfD7Ag5EBoUxFDKCnrufCtqlBhKUv7tS1NatDFbGbRB10nT4yltwbW7PeITaPJgMo9HnWjAVVeOIGnRqIpQazqJaP3R+RrH7jr8daHfmFqdgc8bGc3U5xWROK0Y69dlUToc0axG1zcR57bI1Jo6z4znqDXc5TsuRGL+TTrCX+cEVc8f1B2DMPqSFPz1zh6VoWcnZ/LKT/+I7Jeotdbx7rdL2Fm+hyuuuCZmBVNH/PKXv+byyy/rck5WVjZXXRWqNOzffwAffvgpr776CseOHWHNmuieVDGvMTuHiy/u+jxAl07IvegF9BKcMwq3GxpqQ/+fN2IBrVYFH2/8LGKOQhQJxrEG3l2+j4qGSgbnlbCzLFKErFNrw+7FHdHRV6e+pQWr3U5uWnpMz52TxaDcARRn9UWSJA5UHeKDtZ9w2YQLuXzaQgCuufRiNu5cxahhgxk3cjhqtZJX7rsbt8tD//7Top4BR46U0dLkIzVdQ0aWDmivyPH6YoflfV4fVmsrb775ATt37CU9PYVFi3/A0KEDo8gNhAz1kgaPPNXVu2cMCoMaVboh/DBvgybnzHf8FtUKjIPTcGyNNAAUjCr0fZPQdyJR3uPR0QvZLZF6znmo0vQ0LTsSLQpWhPxoOsPXwRHYYByGLHvx+0LXocvKI2P6fCSbL/p4IpjGZ6NO1iMqFXjKrMhBCVWWiUC9C/exbgz8BNAPScO1tyFuVVrbeXoMjYi2Q5NNhU5F4pwivMdbsa2t7GLHEEbnDwv/e+bQqRwy1jH70gu63EeWZRwOBxZLAq2tsTWBs2bN4dFHf09eXkF4bNCgITz1VEj7d+WVi1i+/F8x983JySU1NZUBAwZy660/JT09vdv30YtedIdegnOGIElQU0m47YJCUHP1jMU4PHa+2rkKo07HjfMvZM+xUpZv3hz3OK0uGwUZ+dww91r++M+X2XpkB9CzXi4Atz79FEXZ2d+Z3PzkgsVYEgVGFvdj+uiR+HwykgQDHcMZmBsZ8u5TkMuokVeGX/t8AbxeP1qdhpKSvuzdG9nMMS+3kOYmNXYbZOeBtoMWdvr0ibz37icRegKNRsM5U8Zz553/x9YtO8PjK1et5+WXn2Ts2MGo1ZFf+WBQIvhv/AxaD+2hecu3+Fqb0aZnkzphJvrM3O98vFMB09gs7Bur8Ne7QAhpaYxDz44Hhr44BVWKPiSAVivQ5JnDWpIoxEs9COAutUaTEUBh0tC6pgKlUYOuODncyVvUKJBOSHxEhQ5zwlQCARu6kkSUmiRwqnCXRz64ZVnC56qiZUc9CcMHouubga5vKP3pq3fi3h1fT6RM0qItSkKdbkBp1hC0eSN0UZ0hpmiR6mNHYDSFFvx1LiSXH2WSFsPwdASNAk9FK5IngDrdGEo35lkQttXG/FziQa/WMT53eLdpnvvvv4+XXnq+yzmHDh0kJycv7vYnnniKb775Cr8/Mp03ZsxY3nvvY4xGY5w9e9GL7wbF/ffff/+ZvggINTv0eLrOY/83wWGHWAuhcSVDmD1uGDddfCGfrV3L55s2dnsspahgzogZ9M3sw+fbvkJGRpYkpB40im91OjlW03XbgvnnTOThm36E3eXmSGUVSlGBQlRw3qjZLJp8GZecO5y+udkIgoBSKaBUQm2ND4ddIhgMsmHjKjZtXo2MjcLCnAjy5fUGCAYlUtOS+fLL1eHybrVaw7XX3UZOdj6SFOqabu6QVcnJycJsNnHo0FFcLje5udn86te34vP5eeUvndo9+PyAzMRJ41AqRRQKEUEQCAYl7HZ3RKPNk4GrqpzKpW8TcNiQgwH8rS04yg5iKRmBqPpuVUanAqJGia5PIpo8M7oByej7JUfpW84kFHoV6nQj6hR91yZ0QnQUR2HWYBiRgbfcGjMlI3uCSK4AAasH73EbmmxzqFpJEEJGfBHXoSdYHyRQ78Jb3hoSMp9I5UmSB5t1FV73ITz1ZVh3bUEQRPQ5BQD4m9xx9TEA6hwTxiHpYZNAZaIO95GmKI2NoFUimtVIjfHTS4mz+qAvSUFXnIy+OBnZG8S6qhzPoWZ81Q7cR5oR1CLqFANyMIi/LrLSTJGgQVAqEDQKZF90rFLUq7oUjR8/Xs7tt99MoJs0dmtrK4WFfRg0KLYZpclkxmKxsGbN6rBD8oQJk3jjjXexWM7OZqHfNwyG7+42/V1gszewactH3U88RZg948bTdq5Y6I3gnGWQAmoSlf2prmhm9c7dPdon0Ri6OWUlZVCQlkdpXRkBKUiaJYX61sZu9u4aKoWSpd+uY+m361Ar1dx50W2M7T8KhahAFEV0erDZ3CQk6MMNMAVBIL9Ag8Ph5r57f8P2HaEI1Pv/gGnTJvLU0/dH9KLxegNMnDiO8RNGs33bPubOvYjxY6eSm1sQnuOJrg7m8ssv5pJLzqO6up68vGyUSgVLly6P+T7aWkDYbB4UCgFBEPE4HLRsX4+rpgKV0ULisHHo0rNi7h8LrQd3RZkXBZ0OmnduwFjQH11G9mntHdUZSkv85pH/CdDmJyB5g7gONSG5/KjTDBiGZyAqRFRJOjx03dZD9gZxl7ZgGpGBrk8igkLAUxrSzAgaJf5OVVsdIx9u12GCgY7iWJmG9V+hycjClN8PdboB1GJs0bRaDEd62qA0qtEPSsV1oCm0j1pEX5wMKhHXtuiy+Y4IunyoEnQhYrPmeFT6ETnUVkOda8YwKA1RrcRbEYoWaXLN6PonIwgCAaeP5s8OQ7BTr6z0rtsbHDlyFI+nZ20m9uzZzcKFP4y7/frrb2LWrLmsWfMtGRnpzJgx+7T2eOvF/xZ6Cc4ZgsEIKjVxWx8YNUksmnwJr3z5RsR4TkoKlY2NDMkfiFlv5kh1KXOHzwTAH/QhC+036frWRkQEVAoV3mD3HbRjYXTfEYwvHkt6Yiq5yZnoNJE3Q1kGhUKM6u6tVIrs3rMqTG7asHLlOr76cjVzz50edS6tRoPf72PO7IuwmCNXlPECIlqtlj592sPiY8YMR6fT4nZH3pDHjGl3OwsGZWQ5QNVn7+KqLA2P24/uI/+yH6FN7WF5qxRbudO0cSVNG1eiSckgc/bFJ0Wa/hPhrbaHHqgnBMzq9FOXatD3T0bfPznKzVlTkID3uB1fdezS8jbInvbfgzY/AW1+6Htl21xFrHixKsNAoNVL0NocYytU/fM9ChbdiDY5DfPYLOyba5C9oe+BoFGgyjZiKE5BldjeQkLyBrCtq8RXc6KSSqPANCoTVbKW5qXxje/C+7sCkAD2TSfSjrEQDJXAK3PV4c+sM5QGNaZRmdi314Ydj9X5ZrT9YlcdtmHIkKGYzWZstvgRqzZMnTqt2zkFBYUUFMR3uO5FL04VegnOGYIoQlYONNSBK04B1ci+w+DLyDGXO8ADl9/L4PyBAOFGdADljUfw+CMf7BJyl+QmPzWX8ob4vZ9yU3KYPHB83By9y0ncFM/RI2Uxxw8dLo1JcKZOm8Dy5StZtuwDfrj4+g5bZJKSe1ZhkpGRxt2/upWnnvwTDocTQRCYPWcqCxbOj5jnLD8aQW4AJK+Hll2byZx5YbfnkQI+vPauBabexlpqVnxK4Q9/8h9dyuqttuPcU0+g1YvSosEwOBVNVkjk6jrYGCEa9hxtwTAqA4VWhahWoEo3fOf3Lksy/gYnEHIEboO/xY2v1ok6z4Sm0ILkCiCoBBybolOtqnRDqCWEw4egFFHoQpU38cwAQ6k9C/6vDtC6rzZqu+zz0rJzI5kz5qPNS0CdYcLf4ELUKyNITUc49zW0kxsAbxDHjlqUqd33ThK0ClSpegKtnvjk5gR6Yr6o65uEJtuMv9GFaFTFveaOSElJ4de//g333Xd3TA+dNkybNoMZM6KbZvaiF2cKvQTnDEKrhdx88Hqh7Gj09lRTCsVZfTlYfYQ0Syr1rQ2cP2ZumNxAm5hYpry+grtffSTqGGqlCl8geq2qEpXMHTmT62Zdycrda1i+4xvK6srwBtrJUJoltUty0wZ/HLueor4FMcfz8/JwOECpiGzIef75MzleUcU773xEZVUF06fNYdCgfhQXp6PrIoq+e/d+6uoaGTlyCElJCVx88flMmTKBnTv2kpmVRklJ/6h9Aq7Yq9GAq+uIQBtqvvwEd3n3q29vfRXepnq0KZEiX9vhvdgO7kKWJEx9B2Ip6V7oeSYQsHlDbsYnKoACjW5a1xwnaW5fFCY1zn2dUqAyOLe0EwNlqg7L5Lwwsegp/C1ubOsrCVpDXy5lshbzxFx8VXYc29qPr7BosEzLR2lQI/slnLvqwykYbVEiikQtLV+VEmhwgxiK/JhGZaItTMBTFqnjUaUbUOeaEUSBpFETsR3ZixyjEtFva0+NiWoFmuyuK9T8tdErGNkVIBDDPLAjBI0C05gsRJUCSdG1/kVTYOkRWQEQdUo0uebuJ3bA9df/hIkTJ3PHHTezc+eOiG3Dh4/kjjvu5LzzLjgrv8O9+N9FL8E5C6BWQ0h9KOAL+Hhn9YdPeScoAAAgAElEQVR8u28dgUCAQfnFzEwfTWV9K/WtDZTkxrKTF8hLzWNSyXjW7d+ISqFErVIzJH8g6w+2p4j6ZxVx4djzyUzKwOf3U5zTF4DpQ89h+tBzcHpdfLn9G47WlpKekM7c4TNIscQ33GqDzxc7h37RRbPZtbMKrSYRq7WBbTuWkZycTb++U6mqCL1ntVHGmC2jVYBBELjp5mu44spLqa9vpLAwr8v8vMvl5t57HmHlypAlvMlk5K5f3sJFF80lOTmRGTOjnVDboM8uBIUyqtOpPqsAANuhPVj3bUPy+TAW9Cdp5EREZejn4jx+FNvBXd1+Lm3o3PG6edcm6r5eGn7tKD2A32YldXxkVCvgcmI7tIugx4OhoC/6jNNfneWpaI0ubw7I2DZUYpqU023FTqDBjXNPPeYxoRYMki9IoNmNaFSj7CLi4NhaEyY3EPLAsW+uwd8YSRaCrV5c+xsxj87CUJKKJt9CoNGNwqxGadHS8sXRdhIjgbfUikKnwjgsnYTpBXiOWQnYvSgTtWjzE8IOyNrkNLLnLaZyyet0VgarEpJp2b0FUavFVDgg/L3oCFmWcR9pxldlJ+iOXTyhMqvx2WJHV1UZBiyTcttFykYNmhwz3spOousEDbr+SWgLE+N+lgCe4604d9efiGQp0PRJwDgkrcuO8J0xcOAgPvnkc2655XpWrVqJLMtMmnQOf/7za112E+9FL84UegnOWQBZJqwxePfbJXyy8Z/hbesPbGHWyAloTohQrI7YaRFBEPjpvJv42YU3IwqhKqHVe9eGCU5OUhb3LbwLky7+atOg0fOD8V37YcRCRXmA5mYHOXlakpPbv1J1NUounHcN1XWr2Hf4XSYlNKNU+DhU+jbFfa5FEAR8DgFbo4Q3Q8Yty6QgYTIZMZm613H8/e9LwuQGwG538MQTzzNp0pi4bsZtUFsSSTtnLg1rliOfiHCZigaQOHQM1n07qFn+YXiuu7ocX2szKeOm0rx9Pa0Hek5uVJZEVMbI1XLLjg1R8xq3r0NlTkSXlYsmIRlPYx3HP36DgCP0QGvc8A1p58wledSkmOeRZRnHsYM4Ko6i1OowDxiOJqHrz6BHiJN+DDS5cR9sQpGgJWjtOhLRllpxl7Xg2FaL7AkCMj5q8UmHMPYpJmXMlHDlWdATiJmO8dc7YhrmBZraXXyVejXKvNBx/E3umJVW3kobxmGhCif9gPgGhqa8IlImzaJx3QqQQydWJSRj3bU5rL/SpGSSe+HlqDppxhzba3EfiO/gq841YRichq/O2d5gs+09JGsxjc8Ok5vw9YzNQtAq8FbaEVQi2kILhkFp4aiJ3+rBva8Rv9WD0qxGV5KCOlmPt8aO7dv2NLQcCODZ14jk8pEwMX5Zdyx8+ulHLFvW7tX15Zefc9ddd/DSS6+c1HF60YvTgV6CcxZAFKHWWkdmYiZrO7kRA6zauZmrp/+QXWV7STbFf2ipOqwkt5fuZHjhUEw6E3a3nalDJndJbnqKzmLP0BjYbTIH9roZOlyPyazA4QhSW+PH7Wlgx94nCAZDD6xA0MHh0jewJOSTmToVhVlAUIdu8D4B3DL0dC24dUs00XA6XGzfvovZs6dFjKtUihCh8rVHHJKHj8fUZwDumgpUpgT0WaGbfcuuTVHHbd2/A0fFEYLd6G46I3lctNYo4IyRBvN6QqRKVJAy+hx8NmuY3IQg07hpJZaS4Sj10Z9Q47oVNG5eFX7dvHMTeT+4Cl36v9dlWZNjxrUntt+Lt7wV07hsbOsru2zBIOqUBFw+7JuqO0SDBNRk4nNU0rRpFQGnnazZF4e2KAUkpYzPXk7Q34KoNKLRFiDqteAORpVax9PTCMrY6RJB0fM0SuqYKZj6FOOqLENUa6hd9a8Icbm3sYbGLd+SOaNd4yV5AriPxBApq0SUFg3qTBP6AcmIKgWJs/rgPtJMwO5DoVGiyTOjyTHHTPWIWmWoGenY6ENLngCtq9pbRgStHry1DpLmFEX0veoId2kL26z7QatgypRpKGNEojrjrbdejxr75JMl/OpX9/UKh3tx1uF7rc+79957WbRoES+++OL3eZr/CMhySJDrdJ4gBK1QUQZlpVBfCzuPb8bhdhKIUZkTlCQmDBjDry/5Gf2zi7o9185ju3nty7fRaXTcfsGNpCekYtR2L2jsCbrKscsyNNSHbrCtraH3Ude4IUxuOqLRvRH9ANBmy6g7cLbASRgOJqfEDssX9cklOdmAXq9GFAUSEvQkJOixWHQkJxtQqdq/9mpzApbioWFyAxD0xFB9S8HuyY0gApGfj3XHBoKddBz6rPz4x5CCNG5aiaumInqT14O3KVr46rO30rg9snOz5HbSvG1t19fbA6iSdBjGZMTcJssymiwTyRf0wzgmE9PYLNT5nbQdAuj6JeGvccR08lWpQsduPbALvzMkxBUUAg73epz2jXg8h3A5tmGzfoO20Ii2U/k1ahHdgNhpVEnwICT5o4SxCrMG+45a3KUtSPE6gneANjmdpGHjUBpMyDH8Ctw1kSJ9yROI+V4FhUjSnKJQauiE/48qUYd5TDamkRmo0gzf2a/IU9Ea3Q/LJ4Wcl2N43xyrLWPhk9ex+NoFLF58CVOmjGPXrp1R8zqjpSW6ND8QCNDU9O/ZUfSiF98HvrcIzvLly5Ekiffee4977rmHsrIyCgoKvq/TndXwekOuxW29M5XKkHFdeLsH+mX256d//iVJxkRaHJErrj7p+fzq9fsZ138UY4tHd3u+dQc2U9lczVfbv+G80bMZVjiYBlv3NyC1GgQFeE+usXgEAgGJgwfcNNSF3qBSjO3FolBqYhrVnoxEceHCC/l6xRpcHZonTps2nrHjhiEIAgaDBpVKgaqDmZwoihiNWlpa4lek6HP70NqpTFjU6pA83XwwcvTD0lNfje3gbhKHjAmPpU6eg8/ahK8l/t9EVET/NAWVGnVSWtS4r7kBYgjJvc0n/9BxHi+left6/DYruswcksdMxdAvBX+tC18n0722SINCp0LfL0QyNIUJuJOa8NU4ENUKtH0S0WSZYrZdAJA4oUEJBpB8HoJKJcc//Tv+lkjSEAy04A8ex1Q8kJbD68GtQpbc+B21aFvmoEoqwVtpw1tlRwp6sdWux3n8EMgSKlM6BuMYlPoEBAS8Ze1E1XPMiirbiNTiDRne9U1EEAWCNh/KRG24qai/1YOvwk+sxlJt6Sm/wwayjNJiRpGgidAQQXy/GcfuulCU7AQnUmcaMU/O7doEsfPnGIPEtI2rs4yRVVzAk0tf4GB1u0j+yJHD/Pa39/Dxx8u6PM+0adM5fDiyYW96ejp//OOzJCUlc+WVVzNy5P9n772jJKnPc/9Pha7OYXKe2d3ZnBc2scuyIEAgQFgI2SgiycKWLdmyJMtXsnSuZTn8rOt0LUfJ4VhWsGQwIEQUCJYlLCybl815cp7Oubq+vz+qp8N09wTYxYjbzzkcqasrdU1v11Pv+7zPM/tvVBVVvBW4Yk7GP/rRj7jxxhtZsGAByWSSvr4+Vq5cWXH9d5qTsRCmW3HADxPjUBidVC5aqtZZz5KuFk70XKDeU0s4FgEh2LTkKj733t8gnorz6pn93Lz+BtQyN79CHDx/mPPDFzl04Sh9EwPEElHODJzHY3fhdRY4hkrg8YDHB/UN0NAEXp85hDLbvbwSrFbBxHj+A9ptrQyO7CKt59sysqKxYedv4nQ3lGxvB7Q5Fhabmhq46uo1pFNpamp83PPB9/L1r38eqy3fspAkqaTqJMsyyaReceTV3tRGfHQQPWQSTdnhwr1gGcnx0urJXKDVNeLqzFfeVIcT3+qN2BrN9lFqojTs07t6I0n/eE4fBFC/6Trci0oTn2WLBf+x/ZApvsm5uhbjXlz539x0xEcG6H34e6QmRsjEIiRGB4n1XcS78iq0ZhdGNE0mnDSnkbq8uK9qKak4SLKE1uDEvrAGW6c31z5S3BrJgXCRL40w0kQjhxBGHFtLJ/UbdzDywpNEzhXnqk3B4vYRH+ojfOEA6dQgenoUIx0jPtiLZllIZN8wuj9BsPdV4hNnmGIMRiqK5E3iWbimJNXbiKZJD0XRAwnSYzHiFyaJn5ogcd5P/LwfySITG+1l/Nn9GAEJIxPHyOTJmmTRaLjmRsb2PMvwcz9l8tAeEqODuFetRJ9I5ZyRFZ8N9+bWEl1N2h8nPC0/KhNJIVlVtDmMkefOQ5VInCutrtiW1pAJpYrSxg3D4I8e+HPS0wT2fX293Hffp7HbK09jXXXV1Zw+fYoLF0xy5HK58fsnOXv2DEePHubhhx9ky5atuUTxKmZG1cn4yuKKVXBisVguMM3r9TIwMHClDvW2xOiwSW7mg2tWXMXKlqsAiCbiGCKD226KbW9adwM/fe1Jvrfrx9x7wwexaWZlJBwPo0gKiXQSTbXgsrvYvmIzzxzehSEM9pzcy56Te3HbXNyx8ZbiAwoIhcAloDZb5dfTEJ32sK0oJfdOJAmsVjNLK5Mxq1LtHRqTkzqFT7gWi51N6/6Es/3/QSB4Cpe3hWUbPkBDS5kbrwDLvGo4sGHDGlZtWAUWhTZfqTDZJDHF+zQMgVEhwBRAdbrpuvuTRHrOM/bKsyRH+gmePIRk0RBTzoyShHPhcqIXTs56jvam9mnnZGCk4ri7l2FrbCE+2FOkt9FqG6jfdC01q68iePIImWQCV9diXAuWVDzfhi03MPriz5i6qatuH7Wbrpv13AoROH4w//mySI4PE75wCu/S1Xh3dJJJ6CCBYp3fT4ckS3iv6yR2YozUSIR0ZJSI/zAZfRJrY2vOeyh8vvL1tNhbiF64iCzbMIx820+PBAm/fhoVUzCcSg1OO7iGMaERTQ4jy7OMqxcIfjPxBIPP/BfpVLkoExlbYxfN77oF/9G9hAtIWeTCaSRZofWOXyE1HEFSZLRmF5Jc+t2OHi9fZdPHZ/a8mQ5LraNsyGr81AQZf3ElSdJkGpqa6Om9VLS8vr4Bp3Nmcb/PV8MPfvBfnDhxnGPHjvI7v/OZovej0Qj/8i/f5ppryovhq/ifRTAj8XjkrYuR+T9v2ZHK44oRHIfDkbP3jsViM95Q3mlIxOdPblQVvDX5fCrntNFiV9YI5uLwJf5z9/18ZOc97D65i28/8YPcOrIkYdccgMCY1i7JGAY2a/kvdiRsnrPNDpOT+QDQ3LYZcLshYhaVAPN/p9zb2zsU2jttqKqc094UwuNeyPbb/jfKLA+kDsA6T1lYEoMxBOg6tXoGu5ov6wshSCRSOBzWoipOPJ5itpguSZKI9Z0nOZJ/uhbpFIrDRf22m3G0tGGtbeDij79DcmSw4n5kuxPFnv/gwVNHGd/7HCn/BFpNHfVb30Xn+z/B5KFXSPrHsTW0ULfhGhTNhqLZSkbHK6Hu6u3Y2zqJ9pxHsdrwLF1TVow8EzIVynZGMk8mFNvsPxlCCMLnTxLtv4hqc+BdsR7NW4Pq1PBsaiMdSJDo8WKZ9KG12HEv6879faSytgAKnsYbMHoc2JVV2GqXEo0cIZnImkfJMui23K+ZJKk5JZeieHD7dqAo83dXTsTPVSA3AAZJ/zCqx1uWlIUvnEKINLaOmTOWTCPDMrDNvT01hcx0DQ6UkBsASYePvf+j/Mnf/EnR8k984lfRtPK/EYGAn2984w948snH0DSNu+66m61bry37uz4wUNk4tIoq3kpcMYKzevVqDhw4wPr16zl16hQLF/6/o7BPzi22JQfVAs0tYLdDSysMlblfHrl4jC/d9Tm2Lc+PUKzqXInP6SQQNX8kDSGIJqPYrWZ1p6WmiY/uvAefy8dIYBRlhqdXXTeJTLnMJwCnG+oaTFH0dIRCAjXbqmhqseCfLCY5qlMgT6t6SwI08l9AKxL2eVZvAMKIXIHmQihCq8OO06KCYRCPpUmldNJpA6tVzU1RJZNzS1uO9ZW6L2ZiEaxeL7Y6szrZfvsHGd/zHNGBi6hON47ObiZfe4FceyQepf/xH9P90c+iRyMMPv1Qbgon5Z9g8GcPsfCDn56Te/JscDR3vCmvHGfXYsJnpuWfqRacXYvntZ/RF39WJHD2v76Pzrs+jq2+iURfkNBLfdnLI5EYSqD3XTCNAAW4atcTCO8u2p+zfiMW8kJiSbbgdK8nnRrCMGJ4V2xAS9ZiZD1lrLYFxKPmhJ3NsfwNkRsAPT2zhkmk44z9bB+ijJWNpKhZ0fkM2wuB0Ms/+E1FSswHYh4Pkb/+sU/TuKiNn/zkQQzD4Lbb3svHP/6rFdf/yld+j4ceuj/3+p/+6e9JpZLU1tYyOVmsV9u0aeu8z72KKq4ErhjBuemmm/jwhz/M6OgoL7zwAvfff//sG71DYJuboSgAVhu0tIA1u43HBxYrDPbndaN9Y/0MTQ7zwevuLtq2ydOKy+bNERww7/XvumoDF/om+MaHv5rT66zoKHXzLUQ4ZBKrCvFK2GwVLVFIJfM/rPX1FpatMNPEUymB16fiaxIMpJJm40qYOpsaJOQ3OMRnYBBFkAQKn09ThuBSxGRodULCnt1/Op0hna7wwWaAXNY+WUKx52+YmqeG1lvzf5eh5x9n+hyzEY8SPneCdCxaeoGNDCMvP03nL32sQvXirYNv5XqSo4P4X98PRgbZ7qRpxy1onrnfbJOBCSan+fxkomFGnn0W36KdZqL3dN/AsTg6ZvVIoRmbfTmJ+Knc+5KhlMx7SpKKs3U1ju56atZtJjkYIbSnH3SB3bEcyaKg049FrexzgyqhLfSROlu+3KrIjrJ5VYUwghk0WyfJ+Jmi5Z6la1BmSZSXJAmt0UlqWuin4rPNS38zBWu7h8SZYrKh1NjITHNMVnxWrG0ePvjBj/DBD35k1v1OTEzw+OOPlCx/8skn+cY3/j++/OXfJZbNm9mw4Wp++7c/P+9zr6KKK4ErRnBcLhff//73efnll7nvvvtwu9+8B8svCqw28NVC4SCOopj/paY97SUTMDpqRjZMwW4HjxemhmA6Gtp5f03p9IyqqCxvX4pFEyzr7ESRFd6zdSvb16xh38HArGLkQoRmmICuqTU/kxClE2AATlfx3aeh0UJDo1kt0g2Dc8FIXpUjlYQZzwsCwTiC1AzFnqnq0JtFzZqNxHrPU9jPci9eURK7UHTsCk/RwjDKTo0BxHrOMbz7cVpuyHupJCfHGX/teeLDfVg8Pmo3bMe9cGaS+mYhSTLNN9xB7VXbSQUnsTe1oVjnl0iemhwry5KTY6MkM3Pr29oci0nETzPFhGKBI2h1pcnsjdfegNZskk1buxf1Djup/giSAg3tK5CtKsGXekn2lpngUsGztQ1bp4/0whpSYzEUhwWhSoRf7AUDNHs3iWQfiPJZJKpaj8XShKo2ICGTTPaCDL7V62jYduOcPqtSY4VpBMfa6X5DkQeudU0I3SDZE4SMQGt14drUij4WI3Z6nExMNxPZ1zSW1QNVQiaTITNdhAcYhs4993yYHTuuY/fu56mtreWmm25BUebfXquiiiuBK2r05/V6ue22267kId62aGg0UwDC2d/WTMYkCbpeOkUVi5rEp7D9PW3AAU0tf8uejPgJx2L81W/9dvHxPQ2z6kxmg91pio+nXNj9E6XkxmoFr88gGk3m2kCFyeITiRTxaV4j8zX0K0RiFnKDAC8SymWwePIsXgW3f4jA8f1kEnGcHd3Ubawc/wDg6l5ZYhQoWay4ulcg0ikmDrxcIuQFCBzdh9B13ItXYm9bQP9jPySVZbjpwCSxgV667v7EzB46lwmatwbNO7P1fyVY65vNnuu0sXVVnXsVSJYdpl+BML9shhElI0KoUn4flmYnlmlj16pDQ11a7JNjX1FPciQCBVVGrcONZ1MbclZPZKl3YKk3KybpYCKnkbdYfHh815OKnydjxJBkDYMEigts9e3I423ZKT0Fp3sdTvc6lBobddfnW3pGOkP0mBm2KVkk7N012BeZ5ygMQfJC6ZNFsjeMa3VlEl3xulkUvFvbyWxoBkPk8r9imQQHlfM0L2lh9erSKbzZ0NjYyE033cJTTz1etPy220xC3trazoc+9NF577eKKq40qk7GVwjhUJ7cTKFSajhQ8nRvs8H0n77pLsKv9xzn0Pmj7Fi3rmR/FktptagSylVlALwecGW7MckkjJVOM+P2gkWDWCxFLGYe0O225chOQi/fGio09AsFTHFzKmm29+obwVGhQp+pYARoyRImKxKWy+hf6Vm8As/iFXNe39XVTdMNdzCx/0X0cBBrXRMN227KtXlq1m5m8sBLpRsKQfD4QYLHD+LqXpEjNzlkdIInD+No7UIIgf/oawSOHSCTjOPsXEzjNe9CdZZWSYUwAHNUPhOPocciaLUNZSsEycAEiaF+tNoG7E2tM37OxPgImXgEe0sHcgH51jw+GjbtZOyVn+eWybITu6Nchlp5ZDKRHLkBQLIiy06sC7wYSR1LgxPHktrcZ0j0BEn2h5BkCesCL9aW/HXQ6hymm++lIEZKx9rixtpauZqsOC1IDhURM49vsfiwWK7Ongd4trdj6zT/lv6fXyQ9WvyP2tZZbHQY2jtAqqCCFB6Lm23a7lqMpI4RKyMMjiTLOobPFYUTbj/96cN85StfYnx8DEmSuP32O/m7v/v2vLOj/uIv/i+KIvP000+hqhbe9767+NrX/vANnV8VVbxVqBKcK4RoZPZ1puB0mSSjEB6f6XocKahee7wS/ePDXOwf40T/aZ7Y/zT1Xg+/edddJfvU1TGMRN2MYZW547shOK17oKpQGKEUryA+TsRNTUwck7RYkBDhOEpURpZlDF0v696nZRfGosWi6ngMBvtgQXfpNcltJ0TJPh2Ai7dHabx23RZqVm8kHYtgcblzrRVDTxE8eXiWrSFy8XTZ5UaWsQaOH2BkVz4PKHhsP+lQgK73fzy3TI/HGH3xKXPCR5KxeHykAhOIVBJrfTNN19+Os31Bbv2xV3cx/truXHvJs2K9GZ0gmeZ/IpPB2bEIkckw9PRDuckh1e2j5cY7i0bY67fsxNGxkGjvedJDKeRoPbJc4PehgGSzIGLpEj0OQDJ+sei13bEMGQu6P4HW4sLa7sn5yURPjBI9nGfeiYsB3Ne0YS8In1TdVlxrSlu85SCrCq7VjYT3DRadm9bmxrGyHq0hTwzc17QRPThMcjCMZFGwd9fgWJH3dtLDySJyM4X4eT9aq5tEb8iclkoUPwRYau1F5MbIGGQiKRSnBVmd+3fc75/kf/2vL+REwEIIHnvsEdasWcsXvvB7c94PQFNTM//+7z8kGAwgy8r/U5KDKn5xUSU4VwgzPXy53Kb2Rs+Y9+loBC6eg7p6c1QczMnX1naTAKTTZkXHZofW9maamyGh9PHFxb/Cuzdtos5bPIraOzLCr//VH2NTnXzypntZ3bl8xr64wwFOB0xOQCptaoDqGkzN0BQqxdTIqsEEgmTu8wrsQlBrmH4zDsx2VLLgetgF2LIMpVz6QSYDkZCpY5oODRk3GcIF9jZWAc43MIF1JSEpCpq7+O+SHB8lE5sD8zWMsuZDzgWmBid4stRSP9Z7jvjoEPbGFgBGdj1K6Myx/LELxuOS48MMPvUAi+79HIpmJTbcz/irzxXtL3TyMNa6RkJnjpEcNRmoxVuDo7WraCxaDwcY+vkjdH/ic0WVHEdrJ47WTtL+OIHdPbmKCIBrQwuWVgvBkyfQz6rI5HU+6dQ4ifgpLJYWZMWOZm1Ds5rVpEwwSTyYJH7ej++GBag+K7EygZaxkxPYF9YghCB2cpzExQBGKoNsV5GtKmqNDa3VTWowTCaYRPXZsC+tRbJIhM4dRw8FsK5tREr7kBUFW5cX1VNqyKY6Nbw7OhGGSbhLMtqS5auXmZjO5BPnyr9vlXGuybenEpcCRI6MYETTSHYF58oGHMvywum0P0785AR6MGF+jpX1WLzm9dyzZ0/JhBPASy/tnjfBmYLXO//priqq+J9CleBcIXg8FYS7EjS3mveuS+fzk0npNAwPmTqcqeEdSTKrO9PR2dzMx99Tqm0KRaMMjY/z6J49jAYCQIBv/PjPuHvrnSxrX4LX6aW5pjFnHgimhsbtMY/lnsGyw+kCu6O4kiPLYKsXRKdxi7gECSGwZyel6jGTwqcqPDYkpCwhqSQTSmAQwZy4mq6n8aLgwCApQIGi/b2dYfH4is0CK0DSrDTsuIXJvbvRI0Eki0btus14l68FzKmkcjBSphhWj0UInZvZgFCPhIj2nsezeCWx/otl1/EffS3n5gyQDvoJlilN6pEgsYE+HG2dCMNA0fJkwFJjp+6WxWZWUjqDtdVFOj7KhR/+CCMeQ5Lt2OzdyLKbjO4nET+Hta4Dp7ylRFScPxGD+OkJrIu82WTyYmSyrdLYqXGih0fyy+M6GSA9FCF+cjz35UsNhIn3jBOJvkSiwPeoZv1Wmq+/veI1nEIlwa5aZ0fxWskEp4mUhSghN5JNwbGyHluXL6ed0YNJQq/25zRBIp4hcmAY1WtDa3ahR1MEnr+EiJv70v0JkqMR6t69GNmu0thYfoKsrq7UQbyKKt6JqBKcKwSnG5paYHQkH1Eky6bPjaKYLaFyIuBQKE9w5gohBN955Cd872c/IxiJYLeaN5jVXSuwyBYefPWnuXUbvY18/UNfpqWmCY/PFEPPpdUvSWZFKeA3SY7FYlZYYhWsddIIpqblJSQcFQiIyw3BgABVQAYQMkiClFvgF6YOqV4ySgwALcgo2SSjDAL1F4DgqA4Xteu2MLH/xRnXq998PXVrNlGzYh2JsREsHh+WrL5GGBnS8VIxl2yz5wJDhSHKf7mmQcqW5cppdwD0aBnRWJnMKwD/66/R/9h/IjIZ3N0raLr+ttw5y3YVx7K8j83Afz+HkWXKwogTjx5DtjnwLFmJr/E2vCvWEdwzRLrfJHJCCHTdjySpqKrZN82Ek0QOlo/O0OpMAVfi4gyjgdMuT3TkOIlocWSC/5v23DkAACAASURBVPCreJavx9HcltM9BU8dQeg6roXLqN+8o6hqNR2SJOHe0kb41QEyIZPkaO1uUkOlBFUkMtg6vDlyA5AcCE2PvTI/V38IrdlF8lIwR25y+4nqJHqCOJbXsXHjFm644UZ27Xo2977VauVjH/tkxXOuoop3EqoE5wrCV2P+l0pCWjdbPzlJTKX78Ru4Tz+zbx9/9+CDudddDV382rs/jiorfPHfvlq07mhwlMf3/4yvfvReasqHMFeEqpqZVYWo5BMyV8JhcRu4lgqEBYw06MEMyJAckjAiEqig+wSdDYAk8CeT9AyNMD4+gWqzs2TNUhDgJoMH+W1fyWnYfjPBM68XVUam4FuzCe+KDThaTbM+WdVwtBQb98UGexFlnCQVmzPno2NxuXEtXELkQnktD5jTTq5Oc9rHs2Ql/kN7SIzmXXsVlxtZtZIOTBM7KxbITJuQ8viInDuRex0+ewxhZOh474fLHjs5VuoObCRi1G3amRNj+67tJHEpQPj8JYKnnyOjm9fLorXi8mxCdrjRCwjQVHvIMOKkZD96omlexncZvfwIe2KkH1XyMn5gD8EzeWKaHBsiMTCKp2EbskPFvrgWS22pAZZW76D2tsWkJ2LImoLqtTH51Dn0yeK/oeRQke3FTwuigkBfyk4pGhUMK42UuVySJL7znX/nH/7hb9mz50UaGhr55CfvY8eO+UV4VFHFLyqqBOctgGY1/yuEyw3jY6UB1J4ybaJRv59n9u0jlU5zw1VXsaClpej95w8fyv1/p83Bl+76bWpdNbx84lUywkCWZDx2F8FY2PSRifbPm9xUghOJmBCkC3iFVTAnV2IDU78jsr/rsgW0ekgMgxHJMkEdEuMSfkUQd8YRdisNC9tpWNjOcN8QD3/3v7nrEx8gDFiEwIGEQBAj3xKzl2lhaZqKqkrouiCVmpuz8eVAtOccqtNTQnDs7Qvm5GasVijvWaaJPus276xIcNxLV9Ow7aYcIZJVjY5f+hiTh18lPjKA5qujZv1WYv0XGXnu0aJta9dtxt7Sif/YPox4DEdnd1nhdOTCadKRMKrDidDTyAX/ALSaOhLDxdl0qtubq/iA2faxLfQxtHdvjtwApFODxNMn8XZ2k+oPo6dDJBO9GEYMhG7mUI1niI2dwVP/rpy78WyQZU/Z5ZkxicDxS4Qnjpa8Fx04hSWxBEWxkegJ4nvXglz1qBBTAaRTcKysN00JC/7tO1c1FLW69GiK+MVSEowMti7zR0JrdhEvo0Ga8gYC8Pl8fO1rf1D2s1VRxTsdVYLzPwRNg7YOGB81M52sVqirM6s8hTh09iyf+79/zWTYfFr9+wcf5E8//evcuiVvh24rMNDZvHgjtS5TqdzV1MnN69/FXVvvoLmmkUsjvfxw9/0s7Zy/x0YlyMg0YJgkBzMs0wEzVlIMDJJACoFRZjXVbnarChEKgVJn5ozbFYVkJkNzRwvIEgOX+mlb0E4CgY1S0bNNCOoKqjsejw2rtaAVkEwTCs0tX0MIYU4aycq8x3jHXnmO8b27Spa7ulfStPM9c9qHta4R9+IVhKdpbHyrNha9tje1Ya1rIjkxUrTc0dlN+233lOxXdbpp3H5z0TJbbQOK1W62ZTI67kXLqVm3GUmS8SzJh6WGTpXe/JEkJo++RujEQfRICHtrF007bsXe0k7d1dcx8NQDBWZPEvWbrkOaJoQPHT5HcqQ0pFcXo9i6fESOjiKHrIBOKlmsI4r3X8LqHEBhbqJYd/cGMv0jpAN5wuDsWIYxYkeSQBjliJKBECnABmmDxJlJtGuy7bHxEfO6pVM4FywtMmm0dfqQHRYSPUEwBLYObxEpAYifnURES4m3Y2VDrlKktbhwrKo3hdYZAYqEc3UDWmPlHreR0ImeGic9FkVxaNiX1M64fhVV/CKjSnD+B+F0gnNhduq5wr3ynx5+KEduABLpFN964AFu2rgJNXtDuHP7dh7evZuUrlMoLmiva+XTt3wiNyq+oKmTL77vs9Q1zmDIMw16GmIxk5BViqCQkSmX9pPMRioITH8aJxIJBP4pYlNBJlJOPiKAWqtGq9OOpsjohsFoPMmi7k5CwyO0LWhHBmIUT2wBJCSIZ6s7VqtaRG4ArFYLmqaTTKVJIpAwx9Gnk7TwhdOM7d1FcmwYa20D9Vuux7NkVfkPMQ3pSIjxfS+Ufc/R2jmvOISWm+/C4q0j2nse2WqjZvXGnAB5CpIk07D9Zgae+m9EyiRvitNFwzVzc9idgnfZGrzL1sy4jmfpKiYP7ilaZmtsZfK153Ov44M99D/xYxZ97HN4lqzE4vkUwdOvIwwDd/cKXB2LirZPjUaIHfNjZjQUlzkVzRyjlq0SQraa1ZsyEHoU5khwNJ+HBdd8isDxg6RDfmwtHVjkdmKHxsz3tWaS00iUotahKPmqUyZutu6ifefp++mPEGlTd+M/speGbTdTvznfGtLqnWj15YmFkTFIXChTvYGcOSGYLSjXumZsS2rJBJIoPiuqo7ImSAhB8MVe0mPm9dKJk+wP4btxIVr9/KMhqqji7Y4qwXkboJDcBCMR/uknD/Py66/jcTg509dbsn7vyAh9oyMsbGk1E7NTaW7ftp3Xz5/j4uh5IokILptJOab74Ng1O07r3MKyApNZkXSWcLjd0NxWoCOaAQkMxguCMOMIUkKQgHzVphypExQHTGVRUwedbgdy9mKpskyr086H3nM9Vs1CQs8QjSSYmB6FnkU6y6amQkGnQ1IlRlIGevacNCGoRULNipuTgQkGnrw/NwGVHB9m4Kn/RvPVYmtoKbvPTCJObLAH1ekhk4hVDPoKXzxN3dXby75XDorVTtOOW2Zdz71oGd33/jaRCyeRJAXX4pWo9st/I2vYdiOZVJLgicO5z5gMlo4n6+EgkZ6zeJeswt7Ujr2pveI+E70hFMWBZu0glewpes+76ioARMIkPqqlnlSyD0XxoVlbESJDKtWHc2E3yaNzTL6VzCpW/ead+XO4lCcZDvdaDBEnnTKFzYrixem+uqiKN+WGPLH/pRy5mcLEgZeoWbsRxTb79U9eDCAS5dum5XQ+qkObkdhMITUcyZGbHDKCxLnJKsGp4h2JKsF5m+Gr//wdnj90aMZ1GmtqaKmtwzAMvvLtb/P4K/mn51u3bGXpUgeTY6YJXznMpbOSSsHItCGVcBhsfjO+YTZECsjNFGJQmdQUkB6tAaSIQTJhkgu3G5obLTlyUwirZlZjbKqC5rETmdCJlikNTYme9QrpzaFMJkduwIyTCAqRy7AOnz1WOt6d0en9yfdpuOZd1KwubhEFzxxjeNdjKLoHu3MFqlaD2/cu4tFj6OliS+iZJnHeLCwuDzVrt6DHo4y98iyx3vPINju+1RupWX31ZTmGrGqIVLqIwIkyk14wDw19dkW7czXp1ChCxKcOhsVlVk3UGjupaBirbREIgc2xGEkyq5pOaS1GwAJMIziaZO48VfA9kMDWWSx+i7w+QuxEXmAtyzY8vp3o6QCCDKpaW0xuGp25SbFUoFQXYyTjpAJ+7M0OjJRO5OgoqYEwkipjW+DFsTLvLp2eLP8PV62z50jUfDAyMsKf/unX+fnTT+NU7Lxv823cd9O9eWF2BTJVRRW/6KgSnLcRzvb3s7sMuSns5kjAfXfcgc1q5Zl9rxWRG4Cn9r7KTRs38p6tW4lGob9n2r6kfPzCTKhwfyIWnRvBKfuTOcfJMSGDbiN3bwqHIRYxic5MkGUZr9VCJJkqyquyZhPMAZJJnVRKR9PyX/1EMs14qrTyU/gMLiqMXWeiYYZ//giSrOBbucFclogxvOtRSCm4a65Bkk0SpmkNqOo2gpNPF7VVvCvXz/zBCpAKBYgPD2D11WJrLF85KofBnz1I9NLZ3Ovh4X4kWc6d85uByOiEL8zsuwOAJCHPMbzT1uUlcWaSZPxcntwAGDrDzz+Gs2sxjpUNpEajyCkVm2NpsSZKyKQHypgqpgTOjc0kLwXQJ+MoHmuJQ3FqOELs9bGy56VailtetuW1WFvcaM2u3PFtDS2kp1mDK043Wq05ghjeN0iyJ+9wHD1iEl7nKtNtWXGVJ7z2ZWWcL+eAz33uN9m1y4zOGAe+9fh30FSNj9/wIQAsTXP4Qaiiil9AXL7QnireNELRcrUHWNDSwidvu52PvPvd/MuXv8JH3m22J45dLG/QduziBcB0P57uQCwEDA2VmOTmoOumQWGsQjRDJUfj6XizNQlJLr4S/X16xepLEQyJeiR8ApwCfEKiLms4OIVgME4oFCcWSxIKxQmEYmV1P4X/ODyLV5khkhUQOJ4nptH+Hox4DKu1K0ducvuUrWhWc/Rb0qw033wX3qWrZ/9cwMSBlzn/vb9l8Ikfc/E//5GBnz2IqND2KkR8dKiI3OTO+cTMlcI5Q5KQ5pJcLwQDP3uIkZefoeeh7zL0zMPERwbLrqrVO/Fsa0c3SolGJhohPtRvjmDf0o1tcU15wbdRnpRam13Uvrubhl9ZRd1tS7AvKA4WTQ7PMWdFlXCuaMDaUpz+Xbd5J4qrgI3LCo3bbkLRNDKxdNl080LPHvuiGiRH8fU8PXKO1e9aw803X8cTTzw6ffOKOHXqZI7cFOLJQ+YyrdODfckbC1atooq3O6oVnLcR1i9ZwsKWVi4OFf/o33j1Rr5wT+nkS3tD+XydzkZzeThUPkQzHoVgoLQSEw6absoz2YfM1andjURKiKK2z1whDNDDxRvG4+D3x7BaNaxWBSEEVmvxJFM6nSGd1iuKnguRTOoksyUaBRknGaYXrQoloNbaBtpu/QAjLz6FPj24C4o0F2o2yFAIg2SiH0lWsVia8ueadegVqRSO5so6lKLznRhl9KWni3wFQtnwzZo1G2fYEoxUeR3KdJ3IG4UkK3iWryVwZO+s6xrREJNZsXUMCJ0/SdcHfhVbfXPJurYuH9bWBvSL0/U8Ur5N5bbi2tBMsj9U4mqcESFiwWNo1g6sNpNUau3uXOzCdAdiIQQIkLUKsSYWGdL56+9c3VhkzDcFe2Mriz78WcJnj5FJp3AvXIq1zpxcFLpRVlwvCsi7HkyUaHCGx4cJR0IcOXKY3/iNT/HYY0+zdu3slb9UqvzfOGOVqLl1EZbaqvamincuqhWctxEUWeaPPvVrLGhqM19LMjvXbuVTt/1S2fVv27qV1YuKp09WdHXxnmu2AWb8QyVMTzbPZEzNzWzeaJY5lmYsyDQiUSMkPILKmQxTyL4vCzDGJEgXfzWtNsHFcwZHX4+z79Uo+/fGGRiIk0zq6HqGeDxFKFRBdDQH+JDxCtCE2dLyCQnXtH8ensUr6b73d7D4Snt0UzlRAPbmDqyNbSTiJ4mEXiYc2E3Q/3MyehQhdFLJqdFnQXp65HwFRPsulJomAbGBS7Nu62jtRCtjfOTqWlpm7TcG16KlvBGXSiMRx//6/orv167bDHIx4XAvW4O1ziTxeiBJeiSKfWnx5zOMJJHJfaSSfURCe0ikz2FbUoNnS1vZ48TPTTLx2FnG7j9OojcI1uJjSg4V300LcF3VgmN1A76bF+JcWTnyQHU4qVm3hfqNO3LkBkD1WLE0lpIKrS1f8Ymf85c4GO9YsY3VHWaqfSKR4JFHHq547EKsWbOOq68uJcC33fneKrmp4h2PagXnMuFUTw8/efFFgpEI29es4fZt2+btkwLQ5l3CX37yzzg3eAG33U1LbRMhP3jK6E+cdjvf+dLv8d+7d3Ouv4/Fbe3cteN6jISDscjMt5vpLap4vHLbagoWrTiAczbIyLkqiCBDkUF9obAYM1PKLcAGBJyCxKTIj1spAsNloHjAaQcjJUiNm1leo8NxmlvmTrwqQULCjcJsGcmyotB6y90MP/sIyfERkBU8y9ZQt3FHwWcTGMkYhawuo08SjRxCQsbImKRGcTixt86tgqM4ytekKi0vhCQrtNx8F8PPPmr64mTPubbwnN8kgiePMjuLLQ89Uj5bC8C1YCmdv/Qx/Mf3k4nHcHYupnbDFoQhCL02QHJqnFqVsC+vQ0gQeP014oFTGEa+1ZRMn8V99Z1IsowQgvjZSRIX/Bi6geqxkurPn0NmMgFWGWunh0w4ZYZYrqjH4rOh1bx5UuDe1EZ43wDp0RhIYO3w4Fqbr8Ya8dInE1mWafTWQ192nTm0JsEcJf/Wt/6JL3/5i7z88os4nS7uvvtX+J3f+d03/TmqqOLtjirBuQw4cOoUn/nrvyISNysIP335JY5fvMiXP/rRovViiQQPvfACJy5epKOxkfdfv5OmmmLhYCQEsiSztG1xblk0YlZjLGUkID63m/vuuAMw1+m5aPDoq89w8Pxh7FYbN63fwboFpaXs6cWAcvuejrq6uU1glYMHGUUIElmfGQcQFuTEwBkJAtnBK+EC+2KDTPaeo2TDQLMDMsgaWFsgkTKIRWWGh6Cja+bjT5kLCkwSJb+J4qWjpYOFH/kMidFhFJsdzVusYUiMDZeITAHSmZGcuZ1sc9B0/XtRtLmJbt2LlmNtaisyvpPtDnxznIRytHax8KOfITEyhGJ3lJxzJQghiA1cwkilcLQvRNFKmWQmlSyr8QGwNXeQDgcw0ils9S3Eh3tLyoSznYuzqxtnV3fRsvi5yTy5AdAF8dMT1Lynm4GXDoNR3OLRIyEyqQSqzUH87CSR/fm4iFQ5t+Okgdbqxr7o8ulTUmNRkn0hkMC1oRnJoiApMoqz+B+fpdFpkp8C+CMB9p03HaNVVeX228u7XuuxFJEDw+ihJJYaG861jSxduoyHH36cvr5eXC4XNTVvTKxcRRWXG4FAgOPHj7NixQpqay//97JKcC4DvvvkkzlyM4X7n3uOe99zKy11ZqKvYRh8/m+/xcuvv55b59GXX+Lfv/a1EpLzRuGfgO888QOeOPB0btkrp/bxhTs/w/aVW4vWVVQ409dLPJFkTXc3VquM22PqdqZgCINocpLWJh81PnXeIaCFkJBwIeW0MWkMUtOf+KV8DUC2gDzDZZEkUDwCI2q229JJsFjLr5vOuhtP6YFkAXWUBnjO6/NIMvam1rLvqU6XKUieFkxpa2ylcfuN6PEYzvZFM3rSpMIBFItGYmKEWO9FVKeT1lveT/DEYeLD/WieGmo3bMVWO/dkaEmSsTeXb9EkJkZIBSZxNLfnwjfT4SADT95PfND0YlJdHlpveT/OjmKyETx+ECNRRpVusdBx50fMuIZsXtTEoVcYffmZomszefBl0uEg3hXr8R95lVRgAltjG/VbrsdWX951OzVaZsxPQHo0hqO1k1jfhaK3bM0dKFn/p8T58rlTlwupiRjxk+PooRQWnw37ynr0yTjhvQO5L3j89ASebe3YOktFbY4V5vqpQbMClSbDd1+5n3A8zKJF3XzhC7/Hxo2bS7ZLBxP4nzyXa28lg0mS/SFq37MY1W2lo6Pzin3mKqqYL4LBIL/xG7/Bzp07+eY3v8l//Md/XHaSUyU4lwH9Y6MlyxLpFL0jozmCs+vQoSJyA9AzMsKDu57nM+9/f26Z22sSlUK43HOrsAyO+Xn2yPNFywxh8OTBZ4oIjj8a5JsPf5vXTpnns7yriz/9tV9naUcXVhtc7Jvk6IUz/OzgcxzrPcHitjb+8Yu/S5uzvKj5jUCHNxQsWgi5kNDMwFUCFIudjazHTQPiioRzWlwefKs2EDjyWsFSidr1W3G2L6q4HUBiYpThXY8S779kipELSm0WXx0dd32cpmvffdnOVRgZBp95hNDJw4BA0qw0br+Z2nVbGH31uRy5AbMKMvzcYyz62G/ncqwA4sP9ZfYMniWrUR0mK55q19ZtuAZ7axc9D/xrEckJnz1G+PzJnJdOOugnPtzHoo98pqw5nmQt3yuVrSqN299N/+M/Rg+bFR7F6aZpxy1535c5TONJNgVrm5vUcIT4eT9GQsfSZHrdyJbKfVo9nCT4fA8iaX6OTCBBcjRi8ppCPm9A5NAIyZ4QwhBY2z3YFvlMh2aLgn1VA0YqQyaSxlHv5g//8pt88c//N7W1tSjT+sRCCGInx4geHS1NH9cFsRNjeLbMrRVaRRVvFU6fPs1XvvIV1q9fTygU4vjx4+zYcfna5lAlOJcFqxd1c6avr2hZjdvNiq5836R/tJQEAfRNI0f1DaBnDC70jlPjrKG21kJDmYdYwzDQMxm0AuYTivtJ6qXl9kDcj8NpVjpUFR569f4cuQFTP/RH3/0uP/yDP0C1x/idf/4agQJdxLmBAf71sUf5+q9+auYLMQ9ogCRAvAl+MXXvn40AlpsjSUmQEWLOqefzRfPO29F89UQunUGxaHhXXoV70bIZtxFCMPj0gySnRqen9RHTgQkmD75Myw13XLbzDBw/SOhkflxcpJKM7HqcWP9FwmePl6yf8o+TGB3EXjD9ZanQYvJ0ryy7PB2cLKluASVOz3o4SOjscWrWbCpZ1d5da8YZFEw1KT4bWrsbWfGy6GOfJXLxDEII3AuXoRT472gtLhLh4sksyW7+FIq4jlJrQ2mOM/7KXhj0MMXE0yNRdH8c347K/dDExUCO3ExBxCqkfkfTJKPmdUgNhMnEUrjWNKGHkgR39+TMCNP9YUKTcWpv6S4hN2C266KHy/++gCnErqKKtxs2bzarkPv27ePo0aN89rOfvezHqBKcy4BP33knh8+e5cKgqY/QVJUv/Mo9eJz5ns6a7vJP7qsWLix6/eyB1/jWAw9wcWiIlro6fu3OO/lAyw3822OP8+Qrr2CzWlnQ0sreE8cZCwS4etkyvvTBD7FiwQKuXt1Fa20zg5PFFsRbVi6noysfuXDg74srSQBHzp2lZ3iY4cnJInIzhdcvXChZ9magIOPFICDyjsfzJjwJM329HAEshEqp8aAkruwIoSTL1G24hroN1wAQOnecvkd/hMikcXUvp2b1phIRemJkIE9uKiA5PjLj+/NFtK/c31WUJTcAKGquhTUF3+qNBE8eyVVMABwdi7KTVaVQHXMX6hqp8mngFp8N3w0LiJ+ZIBNJodbZcayoR1bMv6qi2fAuW1t2W9eaRkRcN/UwgFJjw7O1DdVrw9AzjOx+lODzB3G5t2G1Fzscp/rCpP1xLDXFkQlGQid6Yoz4pfIZUtgUSMwsDI6fmcSxop7ExUCx0zImSUr2BnEsqy/ZLtkTLFlWCLWhOi1VxdsTQgieeOIJPB4P6lxN1uaBKsG5DGhvbOT+P/ojfr5/P6FolGvXrqWrudjX46qly/joze/mB8/k9THbVq/m/dfls296hof5/e98h3jWoGVoYoI/+e53efTFFzl07lxuvUNn84LOV48f54t//3c88mffxOmw8L8/fi9f+9fvMBk2f/RWLVjIZ+4yW2BT91O308Gwv/gJ1mbRcNntdDQ2oqlqNriz4DNW8Nx5M3AhY8MgKSCJmVM1VcpXMIXHlWCTZTqbZQx99skdJxLBAiJlHvvNCY3nA/+xAwz//Ce519FLZ9GDQRqvLU7vnsuImnUempu5YC5TWIXwrViPxV1809c8Prp++VcJvL6fVNCPvbkN3+qrkaTy19fRthBHx6Jincy0dhwAsoJrYeWql1bveEMZSrJVxbujEz2cRKQN1BpbjmwmRnoJHj+YPXx5AbgRS0MBwRFCEHihB328sk2BY3md6Y6cqfx9FckMIpnBSJcnQka6fGtNVDA0BHO83bX68n5nqqhirpicnOS+++7Lvb7nnnu4p8DTTZIkvv71r/M3f/M3PPfcc9x2222X9fhVgnOZYLdaee/2mQMTf//ee7ll6xYOnTlLLJFg3eLFWAumUp49cCBHbqZgCFFEbsqhd2SEF44c4aaNG7nuqrU8/hd/zivHj+G02dm2enVJ4Ob7d+7k//zwh0XLbt+2jXqfKXj85RvexQ8LiJjLbufeW2+d8RzeKFRk0hjFOVWSeR+QCybFyb9Fl9uBT7MgSRKZjEEkkiCVqvx07EZGxiCeLWHZssnmbwWEEIy98mzJ8skje6nbdG1O+Apgb2gpvfEXQHV5qM1WhC4XalZfTfDE4VzieEXIMo3bb6ZmfbFYPTk5TnyoB4u3loZtN83JGkGSJNpuu4eJAy8RG7iExemmZu0mYoN9TBzcg0glUJxuGrffjLXWrFgkJ8YIXziBJKu4l6yaV/q6eZ5jTOx/kcTYMFpNPZ6la8wMqvpmJCn/N0iM5qer0ukxLFoxOZCsCpaGYrV9ajBcmdxYZJxrGnAub8DW4c1OUUkkB8Lo04TSaq0N2WHB2uomcaY0rNTaWp6MWjs8pcdXwLa0DtfaplxVq4oq3mrU1tby0EMPlX3vn//5n2lsbOR973sf4XAY92xZPG8AVYLzFsNq0Xhw9/P0DJttpKUdHfzVb/0Wi1rbsMzHZGYGeJxObtm8peL7H7vlVjTVwmN7XiaZTnP9hg3cd8d7c+9/5aMfZdWihbxy7Bhel4u7dlzH8q5Z5rDfBBJlgjmRyjvtN9qt1FjzpFBRZFwuG5OTUXQMYggygBUJO1JOROws8OS5UjD0FJMHXyXScw7FZse3+mpzPDla2vIT6SQ9D34PSZFxdXZTe/V2FM1K6y13M/rS00QunUW2WnG0dKLY7KhON57l69CmVU/eLGz1zXTd9XEmj7xKKuTH3txOamKMaE/x2Ldv9Ubqrr62aNnY3t2M730uN/btWrSctvf8MrJFM71mhgcQehJH2wKkaWZ9qt1RIpZ2di6mZu0W0sEJtLpGFM1UkQdPv87g0w/lRuzH9+2m/Y4PzirYnoIej9H3k++TDpnTU8mxIcJnsm1aWcG3YjN163diqXcUGSLGoydR1Bqs1mzmlybj3thS4nRcKaxScluovWlRzu1YdVtRs+aAWouL4Eu9GNkRdcmh4rqqBUmSsLa6caxtMMM+dQFWGdeaporGfI5l9RiJDPHzk5AysDQ6cF3dUtJGq6KKtxPuuecePv/5z/PAAw+wZMkSrr322tk3mickUSlF8C1GhlR5dwAAIABJREFUOp0hEKgQgPQOwr1/8sccOH26aNn1GzbwD1/8XUYmJ7nrq79PMFoh6bICOhobeeTPvllUDXq7w8AgjcmwQwiicyyoLHDZqbGVzoMPTYYZy2SK2lpOATXMjTQaGASzbTIwfXo8yPOasup/6r8JnzpSvLDMuHg5uBevpP2OD835WFcS6WiYkV2PE75wCmQZ79I1NO18T5FQNz42xKUf/mPJtk3X345zwRKTUGRTtVVvDW23/jKOlo55n4swDM5/71ukA8UVDXv7AhZ8YG6id/+RvQzvemzGddze63C0LcJ9TSt9P/13kmNDqGo9mn0BsmTD1tFM7Y6VKLZSNbseSTH5+Nmy7SfFa8WzrT1HNtKBBIlLAUQ6g6XZhYyEMARaq6tkOsuI6+jhJKrXimyd/VnU0DOItFESHyGEIHEpQHo4imRVsC2qweKbm/9SFVcWDQ2Xv2oxE473nuSev758wyKz4djf7Jl9pSuIagXnLcRkKFRCbgD2nTyJEIKm2lo+fecv8ec/+s857U+SJDYtW86XPvzhXyhyE82SCUMyxb42KHE2LnmdxVgiVZbgBIxMiWYnCjgx0OagtQkgiBVsHwYQBt45EqSkf5zw6aOlb8yB3ACEz50gMT5S0fflrYTF6ab9jg+iJ+JIspyrpBRiZNcTZbeN9F9k/LXdZGJ5F2E96Gfw54/Q/dHPztvdOx3yl5AbgOT4MJlEnPjwABaPL9fKKgc9OXuERzo1gj7eQvTgKJlUCovWhtt7DVLWXVKMmSJg19rSv4/q0nBvbCF8cLhoqgsgE0wSOThMzY0LSQ6HCb7Qa1ZlgMRZP851jbkU8emQ7Sqafe4/0bKqgFr6fQ0fGCpqecXP+/HdsOAN6ZeqqOIXCVWC8xbC5XDgczoJTKvQ1Hm9uR/+7evWwRwJzgd2Xs8ffspk4/5wmH986CFeOX4Mj9PJB66/gffv3DnLHt4YwvEYDz2/m1M9PXQ1N3P3zp001MzN8VXHwF/QkhISxAGHgJQwp500zAmncqqQqJ7BEAK54EbpjydIlCNEEqTF7MnmmSkN0DTEMAlSApF1QJawVCBLejRE2UjyeUCPhuF/gOAIw2Dy4B5C508gSTLupaupXbcF1Va+xTGluykLXS8iN1NIT4zgP7of18Il89LPqC4vqtuLHi6eFFJsDs79x7cw4lGQZLwr1tF8453IZVLNXV1LGd/zHDNFSUiyWdFIDYfRg5O4fdfnyM0UYmcmcCyvLxvGae+uRWtzM/GT0yVeNOmRKEZKJ35iIkduphA9OY59ce2cKjRvBHo4SeLsNIKYNoifGke7tmr8V8U7G1WC8xbimddeI15m7PXDN91EKBolEI3Q3drKso5OTvf1ltlDMQrdk7/6nW/zwpF8e+TIuXOoisKdb6CvKYRpNhiJmJNXHi+MhnsZ8ftZtXAhn//Wt9hfUIl6/JU9/PvvfzUnUp4J8XJ6G0wyIQNuzNaQjlGetAAXglHqbBoWRSaQTDEWT5U3DcySm1gUJsYhlQKrDerqwT4HeYIBjCByo+tBIVDIUIdUUhWyNbWhOJxkpqeYzhGq04O99X/mhjP68jNMHngp9zo+2INIJqnfUp4gJyfHypM5SUaZwZ15ZNdPGdkFtpZOOu/+OIHXD+TG0adI1fQKj6yq1G++nuHnfpo/pqKSDoXycQzCIHjiELbGVmqniaAB7E2tNF53K2Ov7qogprZjs5rXXtJUtJo6FLmMoDdlkImmkLXyXx7ZqiLbLOaUVeFlsSlIqoIeLuNHkzLQw0k0q4qhZ8gEkshOS2mbyRCkBsIYKR1LiwvVMbeKbSacKsvrMuHy4/dVVPFOQpXgvEXwh0L8yff+g+S0iO+bN21icGKCd3/h84TjcdYu6uZr997Lvz3+GPtPnUKSJDavWMmLhw+TnmaEtnXVKsA06iskN1N4ePfuN0RwRkegsCsQjQi+/dQzPHN4Fy67vSSW4sLgID958UXue+97mQ2mpqXczdEkFGFAFgZuFBxkylZWwrpOOKyT29UMXY+JpCDUKxDCXNmwCAbHoLNFwmIxN1SQsZIpMQSUmDbFJUEGGBWCVozcmHk6HGTgqQfeMLmRrDaarn8PyhtIDBVCYKRTyKqlyF14rjB0ncDxAyXL/cf3U7f5urItJVtTK5JFQ6SLb5KepasRennBbSESQ71c/ME/5jQ6YJIqIx6j4Zp3laxfs2YjWm0D4fMnzZaZRSs7mRbtO1+W4ADUXbUN7/J1JMaGkGSFsb3PEx/oAZFBkiRS6TFsahf27ho07QZCewZRlGKyJrksqN7K2hVJkrAvqSF6pNh0z95diyRLWGrtOWO/3DZ2FdVrI9ETIHxo2DQFVCTsS2rNvCpJQo+lCO3uRfdnyZki4d7UOqecLEutHSxySetMrasKkKt456NKcN4i7Dt1ilAZ8fCZ3l6e2bcv9/rohfP8wb/9G49885uoikLGMPjbB+5n78kTpLPEQpYk7r7+Bu7KtqDC8fLi7EhyltFf4LWTJ/n+U08xNDHOusWL+dXb3kvUX6xnkCSJWzbcyDOHd5WQmylMd2SuBDtmyOZMHjdBwMAkc6qgKGYhf1LgE6bOpqLKRYJYUEIICclqYG0TKNnf9dG0oLGg5VSDhF+IHMmxUb5FNrXfsBBMzTON7fm5ebMsB0XF4vGZN3MhwKJhbWjE2b6I0Mmj6OEAIplg5IWfoTo9OLJVnNjwAJHzJ5AUBc+SNVjrSr1Mov0XGX3paRLD/ageH3VXXUvt+srTc+VgpFMYidK/aSYepxJ71Nxe6rfcwNhLTzNFVrXaBhq238zYq7vmdNxCcjOFwLED1G+9vqx/jrOtC2ebOckXvliqYwNQrDNrSlSHE1fXYhLjI8SHekGY3zEhYkTDe3GtXohzTSOS1ISq+Yjum4RMwU+kbhA/N4ljaV2FI4B9RT16MElyKIKEOcLtzCaFO1Y3kJqI5Z2NJXCta8RIZwjtHQBdYBgJEtEeoq/FSESXUH/tZmInxvPkBiAjiBwaxtruKdsuK4RsU3GtbzLDRacKYF4rjpWVNUtVVPFOQZXgvEWo95Yf7w2XIQyXhod47cQJtq1Zw3efeIJ/fax4AmR5Zxdf/+Qnc0/XVy1dxsKWFi4ODRWtd93adTOe07ELF/jsX/0lsaz3zsmeHnqHJ/nSnb9bsq7P6ZlxX2sWzjyym8JAABoStQj8YoY8Kikr8p0JwtTEJBGVCQ557zitOU9uAIQFAkIwRRtUZBowgzml7OtRMlQq5BceM9pb3rem5ea7sS9YSMjpIh7yY6SSSJoVuf8SsaP7i5x/9XCAkReeYsE9v0bwxGGGnsl7R4zve5GO934Y14Il+fXjUfof/y9TgwLooQAjzz+GxevDPYM5XiH0aJikfxx7+wIz+6oArq7uikZ9APUbr8XVsYhI33lUuwv3kpUomhVHWxehEwfndPyS80nEzJHzWXxbXF1LsDV3kBguiEdRLXNOVg+fO5EbOc9DoGcGkSQzXsK5qBNrSzOTT5yHbPSCSGSI7B9CssrYu8pXT2LHxkheCmb3CIlzfix1duzdtVhq7NTduphEbxChG1hbPag+K9GTY6ALMnqUUGA3hmF++xMHzpCOD2HNrCk5jkhmSE/EsLbMPoXjWFKH1uAiORxG1hSsnR5TkFxFFe9wVB2g3iJsWLqUBdPcjQEymfIGddZsuNLTr+0tee9EzyVOXLqUe63IMn/8a7/O4jYzKVpVFO7Ytm3WltFDL+zOkZsp7Dl+iESmVCR69NKJivvZuX59RZPDDAZjZBhFMCYJhjEIkq3KzHWgZnpHS4ATk4S4+f/ZO+/wOOp7X78z25u06tVqlnvvxh2MsY3pprdQchJIQhppJyflwk29SU4ScpJwCCEFQiAQEpoN2GAb44Z7t9zkot5XK23fmfvHrFa72lk1y9iYeZ+HB+k3deWV5rPf9hEQeqntzcrRk1sgolcZguOHSPlwNwZE9JFfC7vatSPE9hWp1Z0IBiO20hEEbQ78AoipaUg1Z/H89bd0vPMqvrrqhGN8dWcJdbRRv+nt+A3hEPUfxK91nDwSFTexuI8l/3eKpXHrOo7/+decefkZvA216O3dAtacV0TWvKW9Hh8O+PE110cdyrs6rVJHT8QxqscDWUUoCabEzix7URmCSpFwwrGiyLBr7yBt8mzMOfnYh4+m6Pq7otGvPhHV33g9BV2owRsVN7G0btpLSCVqKgXCeI4mRqY8Fd35XtGsxzoyA9vYLPROE56jzXTuUew3fN7jUXHThevQLsJyYhcZAujs/U9p6p0mbKMzsZSlaeJG4xODFsEZYvyBAC+te48dFRVkp6Vx6+VXUF5YSHO7i4bW1oT91WbejCgsZOqovj6Fxz95p4wYwb9+9GMOnjpFmsNBYVbf49nbO9RrRqrajjAmdzpd5UJHq4/x/PsvRbfPGjuWz9+0koOVJynJzWP+pElJ23/bkfHHbApH6lgGRKTWxhj50grY0CFHojdGlBEkXW9mn6Ao9+EpNuxGA3KWzP5mV8J1+/ozLxHpY+95nEzcJGTnhGnUr3szbp/UMZMxWG10Rq4qdbTj37gGeqlR0VlthLw+JJVankBzzxTg4Ccxt1dW0LT1vZiT+wkF/OQtvgFjZjaW3MJe27n9rhaqXn2WQEsTAA0b3yJrzhIyZy5A1OkpXH4rnkmzCbQ1YckpJOjtpGb1S3EDD2W/H0FvQI600ZtyCsiev7zfr0Fvc5C7aEXS7VI4hOvQHjzVp9Db7DjHTY+2kqeUj6N5+8a4GiJBb8AxcnyPs6ir25DXQ+uerQn1QpIvmOAhBSB51WOMIbefjl3dqaNwuF11PyE1iOwW4ubsmMvS0DsSRaKGhkY3msAZQmRZ5uu//x3v7tgRXVu1ZQt//NZ/0uRqS4iW9HaeLpbOnMWBysq47VNHjGRsSWnPwxBFkQllSqqow+vhpXXr2HvsOPmZGdx6xWJK8vLi9r9s/HhWb9sat2Y2GpkypoTcdKX7SBChKexjYnkJjW2pzBgzhgdXXIPT4WBajAjbuHcvq7duRZIllkyfweLp04Fe6lgGQSZCnH9UOxLumOdwSFa6sHwyZFtN2I1KFEwQBDItJuq98T9/G11Fz+p4kzzgUoj3sUqfNBvRYMJ1eA9SKIRj+CjSp8wBiLiVy4RrzkKw986VtEmzEI1JbNF73Iq9bDQ6m51wZ2y0TSClZ/QkBikUpHHzWlr2fqi6PejtwJnXd5qnZfvGqLjponHzGjqrTpC7+HpMqelY84uiERWjLDPiwa9x5pU/4YlJhcmhIOa8YeTMX4Ylb9iAZ+T0Ru2af9MeM3Sx7dAeilfehzkzF1N6FoVX30rjtvX4mxswZWSROWMR5oz4eTTGfAcYZAjG31fAf5bwWV1U4MiyjCAI6BwmdE4z4bb4d70hidlloK4zrqVcp3cSDCSardrLS9GNcuI72YrsD2PItWEZnj6gn4eGxicRTeAMIbuPHY0TN6DMp3lh7Voeuv56LCZTgteUGserq6msraUsP5/7rr4at8fDK+9vwO3xMHfCRL5x1129PgwkSeIrTzzB5gMHomurt23jT//57TiRc+OCBRw6Vck/N2wgGAqR7nDwtTvuID9T+aRrj6T3506YwNwJE5BlmYozZ/D6/ThjfENeXreO//PMH6PP4Nc3beJbd93NPcuWIaISsVGrXe2jGwoBPHIkbYSS+kpIpAnQGTmPpUcYPs9qxiAK1HX6CAEmWfGo6o1ktyOqbHGOnYJz7JSEdSvKfYeTWCzoHamYM3OxjxiLc8wU5HAIwWRG7lEgbnDG13zoLVYKV9xBw6Y1eGvPYkxNI33aPOzF5UlfT9PWdbTsSj5ZNHZScW/4GtQdzz1nTlK39t8Ur3wAUB78Lbs207pvG8HODtWhh776GszZ+Qnv55Cng0BbC6as3F67y8LBAB0nK5CDAeylI9HbHHjra+LEDYDk7aR1z1byrrwBAHvpKOylo6LiRA3RoMMy3oZr2ykMhiwkyYPPc5RgoIpgDdSufhuTvpxQqw+d3YRtfCaOabm4Nlche5VInS7FhE1lOCCA3CM6aLGUE/BXI4W7Z/44J87EkpMPoA3m09AYIJrAGUKqm5pU12uam8jJyOD+5Vfzu3//q8/zWE0m0uzKo1wURb5066184eabCYfDGA1JPuHHsGn//jhxA9DQ2srL69bxtTvvjK6Josh377ufTy2/murGRsaVlpJiU3dsOlhZyfef+SOHT51Cr9OxbNYsvn//A1hMJv68enVCrOMvb63mjiVLsOoEXD226onMO4s8V0yRyEtQVnROO6iqi1DMecIQnU8TS9cH4oAkIUkSDT4/7YEQOkEg3WTAqNMRCocR6T16A2BBUHyyYu89UtzchbeuiraDuwj7vNiKh+McNzWulkNEJBMJT+4wQsNHEzhxJLpNMJopXHE7ltzC7jW9gayZi2jY+Fb3RQWBrNmLE+7Pml9EyS0PIkthEMQ+IyDtx/Yn3VZvEnn+yCqObHiC3NRsbpl1MwtGq9dVGVLTk4ucs5X4WxoxpWfhOrI3/nWooDOZ49rbZVmmcfO7tOzZihz0o7elkDXvKpxjEgvmfS2NVL/+NwKtSt2LaDKTd9VNyAkFxAoBV2ItS18/M3NeHu0pB2mp2gpS1O8eQTQjNGcQEpUmgXCbj/ZNVTiXlJKxYgSBWjeCKGLIt6uaXfpOt9G5I74pQNRZSM2+En2JBynkwVJQhL14RMKxGhoa/UMTOEPIlBEjMRkMCbNuQqEQj/zqlwzLyubxBz/NnmPHeG/XTto6Eot5AW5YsIC0lPiuJZ0oouvHnJMPDx/mv198QXVbMgFWlJNDUU7yCbqSJPGdPzzF0bNK10ooHOaNzZvJy8jgkZtvobY58by1zc10er2k2G0IMniQkVDaxB0IhFEmF+tQjDEFBMwonlDtSVJDegRlAGDXQ0ZOFDldo3EavX46g2HaAt3/Fu3B7gefEaLmnBKKaDH3iOhYEZBkmQ4UQWUGUmMMPDvPnuDsq3+L1pG4jx3A11BL3hXxxd0iInbAuuwWWvdtx1Ndid7mwDl+RvTTeSwZ0+ZiTMvAffwwgiiQMmoStmGJKcnoaxb7VzQqq7mXAvri4TxV9S61x5U6nyZ3E0eq/i//NWElly1YickZnw5JnzqHzjMnkFQtEITo/bRXJBdUXTjHTYsTOO1HD9K8fUP0+1BnO7Xvvoo1fxjG1Pj7aNr6XlTcAEh+Hw3vv8WwG+4FnT6hU8qcU4gasizTeeY4oQ431sISjKnpSGEJ97Zq/KdcGCglPXMYHa6dBPynADAaCxHFHhEvGdo2nEYQRIzZVmxJnLylUBj37rqEiceCzUD6vDKMGVqkRkNjKNAEzhBSmJXFl265hV/+4x8EI8WkdouFrYe6O1ty0tL463e+y9fuvJO3tm2ltd1NKBxi34kT+IJBLp8ylXuW9t7Bkox9J07wmZ/+hGCSzqzJI5KnL3pj/4kTUXETy8a9+/jyrbcxubw87jUCTBo+nBSbDQEBOwKJc2GlSBRFwYeEv6unKclUYgGZupjtgkxCaisyz49AWCYgJWkgj0SKYqcUd8gyqYRxxJQeCwg40OFA6bbqGfFp2bU1Km66aDuwUxEoqYk1EqLBSMa0uWRMU4+MxOIoG42jbHSf+w0ER/lYWndviVuzl47kyPACag/FFzGHkFl7aAOFzW5Kbv0Melv3v6A1bxjFt36a2ndfxVdzJuF8xqggUhdUBmc6OpMZR/n4hJ9F5+mjiQeEgnRUHkuY8eOtTXxPBl0tSEE/WZctpnHTO9Hpx6acAjKmzkk8tc9L9Zsv4DkbafXX6cmevxSLpTza7g0goMfumEpbsA5J8ikjvtXwS8hI+M+042/oJO2qMgz2+GLgUKuvexZODIIoaOJGQ2MI0QTOEPOp5VezcPIUPjxymLqmZv73tVfjtte3tvLS+nV85dbbuO2KxLTDufDMG28kFTcjCgvZtG8/2w8fYdnsWVwzp++HbBd2i0V1/rA1Ynr55Vtv40tP/Jr6lhZ0xiDpuWF02TX8fNWvuG3WSooy49t3fUi0RMw2kUHs+roLlXocI4mpK1mIDALsecN91aoK4O0Z/RGgXQYrEjqV2hy1dFYwZo5NFClMwNWqKnCSEUZCQolQDcS9vCf+5kbajx1AliVSysZg7hEdyrpsMVIwQHvFfuRwCEfZKHIWrmDfcfW6nABhgq5W2g7vJnP6/Lht5oxsSm5+kKat62g7vAcp6MdROors+d3i3DF8LJ2njsUdZ3CmU3b3I4h69T89goqxJ4CoYiZrcDgTPKoEoxmD3anM6SkZgefsSXR2B46y0ao+Va17tnaLG4BwiIYP3iFHZRCeIBrQG3IJ+E8R9Fcj2SYgir2kjH1hWlcdx3lFaVz9jM5uBJ2Q4D6us/WdftbQ0Og/msA5D5Tk5VGSl8ezb6vXH9S1JNYCtLa3c7KulvKCQlKT1MH4AwFWbd1CxZmzlOXnseKyOdhiTJUqa9XrIjJSUzlWVcWxqioA1u3eRWu7m3uWLevX6xleWMjCKVNYv3t33Pr185SH3oThw3n1xz/htS3reWHXM3QE3FS1uqlqPcPOyt38/v5fk2ZTimSlWHEDUYuGOFQKkG1Aq2pdzuBQi+3IEXPO/k4JMecNw99UF7ems9qw5BT063gZGVdXsbQABlkmTcXnqj90nDpK1ZsvRlufm7dvJH/JjaTG1K7ojCbyr7yBnIVXgxRGZ1LeO7PLZ2E32ejwx7enT9Ar/2ahjiTty6JI1pzFZM1ZrFqs6xw/jVCnm9Z92wh7OrEUlJCzcHlScQPgHDMZ14GdcZExvSMV0WQhHPChM3anhdKnzqG6rgpiLEzSJ81Eb1XEhDkzJ86d3ddUR6C1GXNOQdTw01OjMoE6FCQc7HJHi8dSXITQ7FWKw4dZCNfrCLf5lV0TO8QhJOM50IBxUUl0SWcxYBmZgfdwTGpXBOvo5BOSNTQ0Bo4mcAbIjsOH+d/XXuXo2bOUFxTwmeuuZ1bEE6onU0eOQhSUOo5YJpfHp4qeevXfPLNqFW6PhzS7nc/ftJI7liyJ2ycYqePZtL+7ruH1TZt48mtfj4qc8sJhHK9OHCDnVpm18/d31/Zb4AD88D8+w2//9Qof7NuHw2rlpoWLWLloUXS7w2pFsLjpCMQPKqt31fPeoQ2snKF0rwTo4e/UC4KsdCHZEdChTB7uWXOjI/JBeKCBD5X9BRkG8hk6c8YCfLVnoyJH0BvInres391IHUh0xNxHUIBmWSZXJR3WF41b18d7Q0lhGre9R8qoCQkeVT27kjIc6Xzruq/zu7d+R427ARs6FhlymaZTolDW/JI+r69WrCsIAlmzLydzxgLCQT96c9/pF0tOAYXX3E7DB2sIdrQj6g2E3C6qX/8bOpudnIUrSI3Mq0kpH4vupk/hOrQHKRTEXjqK1NETE84pSxK1772G68AuQAa9gezZl5MxfT5GR6qq35m5yIHX5Ylz/9ZnWshaciWCsATJH8J3xoVklTBkWAm7fHRsr1U5EwTbEocl2CfnoE814a9xK/NzAN8pFwhCv6YTa2ho9I0mcAZAVWMjX3zi17gixcFNLhcHKyt54bHHE2bMAIwrLeWz11/PU6+9RlhSPt5NGTGCmxZ0uzRv3r+fX7/8cvT71o4Ofvzcs0wsL2dcaXdh6TsffhgnbgB2HT3KG5s3c9tiJdX1zbvuYv3uXfhiHMvVip4BGtvakCQJsZ8GjU6Hg/+691O97uPyuFTX22PWRYhLQfV2DzLgRERAiNTA9Jg7HGkbDwMdPUTO6doTbNj7Dp2+DsaVTmbexMWIPSfqxh4jK/Nt1NJTyTCmOCm5/bN0VFYQ9nuxFZVHIwP9Qa1ENyyAX5bjOrX6QpZl/M31CevBthZCng4M9t5tNgDmjryMWcNnsPvt5xErDmMV9ICAc/w0HCPG9Pteuug4fVyZCxRp33aO65+NQtDtomHLu1HRGBsUCXd2UPfuq9gKS9FblSinrbAUW6F6AXbY76PtwE7aj+2PnxwdCtLwwRpsReU4J86i/fjhuIJpe9lo7KNLMGV48B5rIdwZwJBpxTY2C0EQCLZ4aXv/dHcdjQiOWQWYRqbjP5oYnTWkqAteORAm2OBB9innCeHFf9pFypxCTEWpBOs7Cbb5MKSZMWTbhnROkIbGJwFN4AyAt7ZujYqbLtxeL6u2bOFzN92kesySGTN5Ye1aWiPH7T52jGfefJOHb7wRUAbk9SQsSWzctzdO4JysUU8/najp/sOdnZbG3773fX71jxepiESYPnvd9Tzx8kvsPBpfvDlj9Oh+i5v+Mq10Cv/Y9s+E9amlU6Nfx6Z/3J3t2KyJ5cddxL45fT3rdAAEZahfFjosSPhkGTdQUXWQX/z9+3gDymfzzQfWcbLmKPct/3zc4U6UGlQJpZOrZxdVfxD1elJGqEfw+mKoHleCIGDKyIn3ZkKpd9H38vPtiV6nZ8bV9+KbVY+vvhZTRrZql1dftB89QPXql6JGYB0nDhNobSZnft/F803b38dfr/5eB6VTqvPMCdVITSyyJFH1+t/xVKn7hIFM59mTZEybS/HK+2ndvz3SRVWMc8JMBEHAmG3DmJ2YLu480BBfJCyBe2s1unQzYpoRqTUmkmYQsY5TMUrd14DnYKPabdF5pAl/lRv/6e4PBqYSJymXFWgiR0NjAGheVANALRKirCefUPunVW9GxU0Xz7z5BvUR24bYGppYHOb49RHDhqnuN7IwvvV1dHExT379G6x74jf84ZvfYvqYMXzjrrsZlt09pXV4QQFfve32pPc8WGaUTefuuXdg0ispELPBzP0L7mVSUfd03VgLqk3731U9TzAUjERUuotuk9k7+IEmwnSiRD0E4O1tr0bFTRcbdr9NfauSQtChDKi1IeJARyq6qLgJItFCmAbCtBJLYxA8AAAgAElEQVQmqFpYMTRY1Jy6ZUVsDZSs2YsQYlNPoo6s2YsT0lP9wZyRg3Ps5EGJG4Dm3Vu6XU4jtO7bTsijPhYhFrXOqJ7o+pHqcp+s6EXcRM4TEX/m7DzyFl/HsOvvImPaPHQqBc2xBBtVkloyhJt93eLGKGIenUH60uEYc+JFkizJeE+o+EtFCLf748QNgP9UG4GqPi1oNTQ0YtAiOANg0ZQpPPXaq4RiOpV0osjl05KH30+o1MR4/H6OV1WRk5bGtXPn8ve1a+Jm4uSkpbF0dnxL7JIZM7h86lTW7ep2ap41dhwr+tENNb6sjNd+8lM2H9iPKIjMmTABva7/hnuSJLFhzx5O1dYysmgYc8ZPSFpz8eCi+1gx+WpONZ5ieE4pWSnxn15FRGyE6QD2ntxJYXYJ48viJwDXNFdRnl0aJwBMoD7tWOi2g/BEap2aXHX0JCSFaG6tpSgjH0GSSVfpWAoh0RgTKQoAPlkmO0ln1bliR0SWw3TSFUWKn7MzoHOVjKT0jodoP3YQWZJIKR+DOSsxbfpREOpMLEqWg36CHe19RpQMjlT8jeq1LKAYgdqKe3euBwi2J/q+xWLKzCVlEKk3z9FmZBUDzgQCEqJORJ+i0hUmyb2eQzQbkFQ+NLXvqEHcW48h14Z9fDaiWfvzraHRG9pvyAAYV1rK9x94gN+98gq1zc1kp6Xx0A03MLk8ftpoXUsLv3rxRd7fu4cOb2Klhc1sZmQkIlOcm8tvH32UZ954g5M1NYwaVsR/XHcdmanxtRw6UeRXX/wSa3dsp+LMGcryC1g6c2a/JhsDGA0GFk2Z2veOPQgEgzz62//hvZ07o2vXzp3Ljz7z2aQprlxnNrnObNVtAKmISLJEVmoOf3jjV9x91WeYVD4Dn9/DB/veZXTxRII9alEM6qYP8UR0QVn+aCprj8dtslscFOWOwC8pQ3C8yPRMPnhV0mBhAbyyrDLHZ2jomrMzFJjSs8iatWiIzjZ4rHlFtLfHt9AbnOmYM3P7PDZ9ymw6zpyIs3XQO1LRm21YCorInLEgwfVbDUsSZ3F9ahqO4WPImDIHUd9/N25QBvR1qqWVkhBSKS4GEPQixlw7gdrEiJYu1YSxMAWvynVkb4iwN0S43Y/kDuC8vKTf96Kh8UlEEzgD5Mb5C8hMdfL+3j2MLi7m2svih4fJssw3f/87dhw5kuQM8OlrryXL2S1gJpeP4Ikvf6XPaysWCbNZNmv24F/AAFm1ZUucuAGle2vpzJlcPrV/haM9UYqG4aoZ17Hr2FaeePmHGHQGJFli1rgFXH3ZSiBezngG4EF+zWUrOXr2AGcbTgFg0Bm59fL7sVsdXTdAR6T1PJZkVxiw+3k/8CHhRY64ow+uNfxiJfOyK/A31UcLn0WLldyFV/crXWYbNpzilffhOrCLkM+LrbiMtPEzBpxqs+YWkj5tLi07N3Wfu2QEhStuR+zF26o3wp3BqMdUfwg2e2jfWYN1dCZ6W/w1bVNzkbZUEWpRRJBoM2AZnYGlPB1CEoFqd4JpZyyB2g6CzR4M2mBADY2kaAJngPzo2b/y/Jo10e//+d46fv+1r0XNJw9WVvYqbjJTUzly+jRbDx5kdpL28ouJQ6dOqa4fqKzsl8Dp6nvqSrtISHQi4wUKs4v57r0/Z8Pet2lztzCqaDzzJi7uOpDYR0Iyfyo1Mp05PPbAr9hZsYVOr5vxZVPJSY+vJwmSOJ3YhECHyvTdwdTE9EYHEm3KaGble1kmAwnLJSJyTM4MSu58iPajh+g8fQw5HMJbX4MpOx+Dre94lTWvCFN6Nu6jBwj5vfib6jFnDzzdljN/GY4R4/HVVWFITcNeMnLQRbqyLOP+MDHd3OsxvjC+ihZ8J9uwz8wDv0Sgxo1g0GEuc2KdkE3n3nrCnUF0DiOGNIti7aATMZc46dyTmGqNO3/w/NWHaWhcCmgCB6g4c4Y/vvEGFWdOM7yggAdWXMP4ssQ8//4TJ+LEDcC+kyf4x3vv8ZnrrweUlE5vNLlcvP3hh6zfvZvfPvo1LrvIRU5RjnqqqTin93SDFHmIdyXorCiGms0o8166yEnP49bL74s/OGK+aYh54A90oJ9Bb2T22IUYelwviko7thkBe8R7qmt0swMG1V2VjKjXVo8pym5ZRr3c/ONHZ1UlbQd20nHqKJJPeQe4j0F7xT6Kb34AfR8ix9/axNlXnyPYpvhMNQpryF5wNRlTkkcum3ZvwV2xD1FvIHX8NFJHTUQQBKy5hVhz1T2oBkKgtoNQo1pjP9hm5SGaDXTuqkNyqzQcBCU6NsWLo55FxMG6TtpaT5O+vBzRqMNToe4b14VoN6DP0qI3GgMj6DPQcCSxq+9S5dL4yHgONLa18fDPf8abWzZzvLqatz/8kId+8XPONjQk7HvoVKXqOQ6f7p6GOnnEiGh9TW/4g0H+8a56F9HFxLVz5zG6qDhubcrIkSydObPX41qR8QjKdGBZgE4BWkgiNkCZaSODU4ZsBFJ7zBNO+kaVY/7ridD7VOLEemUBJzpyEEiXBXJV7uNcCaM+6LB3WfzxwX2ygjOv/IX2I3uj4qaLQGsTrQd3Jjmym+YdH0TFDQCyTNPWdwl5EgdWyrLM6Vf+TOOGVfjqqvBUVVL71ssc/8uvkaShi3AEm9XFDYAxzYrsDaqLm4HgVzyswu5Ar6kwXYqJlFmFqkaeGhoa3Xzif0NWb90SbdnuorW9nTc3J/rzlOapt82Wxgz5E0WRnzz0MNNGjuzz2i3t6oPxLibCksSwrMyom/nE4eX8/HOfw9RLK20YSXWIXW9//tMAJzrs6FTrUZJdLQUoQFTfHplxoyZ+9DIYYySOhISLMLWEaUTGj9zrL0cYiSBhZRoxYUL9bCfXI6BTuZ/BVYVcfLTs3RpnndCToKv37iYAf0PiHBzJ78PXmLjeefo4njMnEtZDbc1UrXqxz2v1F509STG/WYc+zUzI5R+aC4VldA4jgjUxuK7PsuC8qpT0q8sTWs81NDQSueRSVEdOn+a5Ne9Q09jIhOHDuXfpMjJSU5Pu7/aofzJr9yTOupgxZgxLZ83i7W3bomvFubnculjx4nnunXd49YONeHw+FkyaxLc/dR96nY5jZ8/y+J//RHsPy4QZY8cO8lV+dPzor39hTUyR8b4Tx3n69df5zn33D/GVeq+NSPZGFXtrqxYiokpWbBi6dIURom3iQSTckVRarA1EJ0oKK6dHnU4oknqLln8KkbPKMulIHDyxi39u/zcNrkbGDRvD3XPuIMfZ7YckIJCCQKvcnaYSIjN/Llb8bc20bH8fb101+hQnGVMvwzZsuOq+Ibe6b1UXluy+fboMaRn4eraK6/QY0xJD655e5uZ0nqzA19KIOf3cQ/KmYSl4jjQTbo0p/BUgZVY+giBgcJpJXhLcT0QwFjgQ9Trs47Nxb6+JedOK2CfmYszUhI2GRn+5pATOyZpqPv3Tn9DqVgZibTt0iA8PHeLZ734v6dyXeRMn8uS//5XgFzVvwoSEfQVB4KcPPczCSZPZe/w4+VlZ3DBvHplOJ8++/TY/ee7Z6L7P1tXR2NrGLx55hPIC5Y/6D//y5+jQv4WTJnPf8uVD8rrPF26Ph7U7E1MK7+zYwX996r6kBZs6RCyEE6I4FhRrn55pKp1Mn/UnNgQ6e3hR6SNeVX0igChDNsocntiC5yZkwkm0RUgAlyzhjElTuZDxJanp+fDMQX7y8mP4Q0qsqrLpFIdrKvjfB/4Hndh9DhsiRiS8stJFZUFA/xEEU/0hP1uOfUgwFGDW8BmkWPu2cJBCAape+xuBFqVt2d9cj+fsSYpveVDVVNRaUESgJTG9C0oXU+rYyX1eM2PKXDrPnIhLcaVPnqVqg2FwJP/wghSm8q9PYC8dSd6SGwc01bknol5H2hUleI63EqzvQDTrkdM7CQbrMYZsmIpS8Z1yEaxPTKP16/x2A7aJ2Riciq2DpTwdfZoZf5Ub9ALmolT0DnWndQ0NDXUuKYHz8rp1UXHTxb4TJ1i3axdLZsxQPWZSeTlfv+NOfv/qv2nv7MRusfDAimuYoyJwAAx6PdfPn8/18+fHrb/2wcaEfdfs2E5VQwOF2dksnz2bORMmsOPwYbKcTib2MNy8WEnyLO+TVASQ44uMnZGoSZuspLBkuofbiX084A2IZCLhlmXCKLU1EtCEjI4wRnpPgYUFcMlK5KYLT2S9Nzwolg6gdF0lr8SA9fvWRMVNFyfqT7Lp6FYWjI4fyGhAHJCx57lyqvE03335MapalGLXFIuD/7z2G8we0Xstlfv4kai46UIOBWk7tFtV4GTOWIi3viaaZhIMRhzDx5Iycjz20hH9m2GTV0jJrZ/BdXg3YZ8HW1E5jnIl2hkOBAi0NmJMTUdntpAycjzNOz8g2Jq8KLej8igNm9eSf+UNfV67N0STHvu4LPx5AtWrX8T/odLlZE0fhaNoJrpUG9ZcG1J7AF+VS7Gmj2CbnIO/qZNQVeLsG/OINBzT8hHE+DejIcOqtYFraJwDl5TAaXSp17Q0tvWe9793+XKumzePo1VVlOXnJQzZ6w8eX2IOPixJdPq6A9epNhuLp08f8Ll7UtfcTF1LC2NLSvo96G8wOKxWlsycmVCPtHTWrD7bbfWIZKBESYQeaaQ0dKQN8F68kfZyCeVN64V4pSUrNgdK+zeqKswDpMWknGTVyuR4VAYnJz3K41O3Iuj0D+5T/VDy5/efjYobgHavm9+/+xQzhk+Liy71RAqoJ156FhB3YUhxUnr7Z+k4dYyw34e9ePigIiem9Eyy5y6JW2s7sJOGLe8S7nQjWqxkTJtP5vR5lNzyIDVrX6XzZPLxDB2VR5NuGygNm97G36iIG4t1PBb9OEI1AUIEQC/gXFiCbXIOvtMu5KCEqcCOId2KvE9SFTg6qzFB3GhoaJw7l5TAmT56NKu2bIlb0+t0zBrbdyu20+Fg5pj40e1bDx7kD6+/xqkaxaLgoRtuZFKSyMuCSZM4VRdfNzChrKxfHVX9JSxJ/PjZv/LKhg34g0HyMzL41j33srgXq4hz5dt334NBp2Ptjh3oRR3LL5vNl2+5td/H9xWZ6Q0ZGQ8y3q6al96eAZF6m3RZ+X9Pd/EuWpDwoVTXJ7V/iCE2dSYgYEWpz1FjeulUth/ZFLdmNVqZWXb+/n36y8GqQwlrZ5rPcqbpDKXZ6m7cALbiEaA3xE0Wjq4nQRBFHGWjBn+zKvga66h97zWIdEZJXg+NH7yNOSsXe3E5RdfdhSzLNO38gKYP3kk4XmccmvSOLMt4qk4p3wgmzNYeP4eQjOdwE85FxdhGZ8ZtMhWl4jncBOEYiawXMBX1nSrU0NAYOJeUwLlpwUJ2VVTw5ubNyIDZaGTKiBH8ZfVqpo8ezTVz5vTbQft4dRVf+vWvolYLda0tHKys5KUf/JCctMT4w8M33URDWytrtm8nLElMKCvjsQc/PaTuvy+vW8ff166Nfl/T3Mx3n/4D00aOjA4aHGqcDgc//Mxno69FN8QO5MmQkGhGxj/AH18ApRurk3BCpEUAvJHzhVEiOmJXp1VPIvU9PYt/UxEQZBmPsgu6yH82BFZOWU5tw2lW7XmLQDhApj2Dh6/8LBmOjIG9iPNAdmo2TR3NcWsOs4NMR+8FuMbUNPKuuI6GD94i7OkEnZ60iTNIHTMJWZLoqDxKyNuBtbAUk/P8vc6OyoqouIlbP3UUe3E54YCP2nf+hfv4YdXjneMGblMCEPIECJx1I8sypsIU9HYjeouNgN+HTm9HFBP738Id6slSg9NM6rxhdB5oJOTyoXeasU3I1mprNDTOE5eUwDHo9fz04c9x77LlbD98iKffeIMtBw8C8M8N69lZUcFjDz7Yr3O9uXlzgo9Uc3s7b2/byr3LEouDU6xWfvGFR6huaqTD42XksGFDKm4ANu7bm7Dm6uhg0/79rJgzR+WIoWMg5pxDgQcGLG5AicwEkFTTSGpryZq7HaA6A0dExEl3XU4cAnxp2ee5bfYt1LvqGJk3EovR3M877x89J0P3l5UzbqSi5ihhubuF+4bp1+Kw9J0+co6djKN8NL6GWgwpaRhTnIQ8HZx97Xl8dZEuJlFHzoLlpE+e1fvJBkkye4UuP6nmHZtwH+8RpRIEzFm5pIyeRPqUgf9++OvcuD44CwHlXdK5r57UucNwTppFw4ZVhIMupLAXURdfIm9IT14ybypIwVSQgizLQ/73QUNDI55LSuB0Ma60lH+8925CwfErG9Zz15IljCxSN+KLxRdQ/xTm9fc+76Ig8/xNibRb1P9w2q2XygzcboJ91ceopJZEWfF18vacFNwbSfYz9uMEYSQCgAHiuqD6MhsdDMqcHiVyBEp0qT/F2V1cMW4hqRYHaw68iz/k57IRl7Fk/BX9vr7OaMZW2J3Kat61uVvcAEhhGja9Q8qIsX1OKh4MKSPH07RjI+HO7t9pwWgiZfREADrPHE88SJbJmnsV9uLBFfR37muIihsAQjIdextIXz4bndlCe8U+goE6TKESut5Ios2AZVym6vli0cSNhsb555IUOABn6hNbVSVZprKutl8C5/IpU3n2rbfiHrMGvZ4rBmkwORTcOH8Bb23bRjDUPeV0bEkJ8yZOumD3dL4w9FLOK8pKBEWWlXqYMMpsGzNQ36/S4d4xycRZOMjIdESG/4EioiSUlnE5cps2wjhjWtABLBYDBoMOSZLx+YKEQoOfrOtCprPHnB5ZlkkfwDmmlU1lWtngUjU98arMn5GDAerWv0nOohX98pwaCHqbg2HX3UXTh+vxNdZhTMskc/p8zBmKkNSZ1UW+zjK4LiRZkgm1JBZSh9t8SIEwzjGTcY5RWt5DbX78tW5Eo4hpWCqi8aONdmpoaKhzyQqcMSXFfHg4PmRtNhgZX1qGq7OTxtZWyvLzk9bkzBw7lm/edTd/eP01mtvbyU3P4JGVKxkxhEXDA2XWuHH89xce4W9r3qG+pYWpo0bx8PU3fGR1MR8lRuTEKI2spKBkFPNNK5AVERVhJGr7itz0ti3mWiEghBwRWeBCoiPmWF/XzKSuNUERHAZZxh5ZdDhMmM3daRWz2UBbm2dQIkeOidzE0rMr7KPE6HCqtsy7jx3E39JA8c0PorcM7VA6S04Bw669S3Wbc/x0Ok8dJ1YU20tHYslWnz7eF4IooEsxJzh6i3ZjgoDRO03onVodjYbGxYYgy/K5fuAdEoLBMG1tan/GB0d9awsP/exnHD2rfNIUBYFHbr6ZTq+XF997D7fHQ3lBAf959z3MHj8+6Xk6vF7ONjRQmpuL2aT9Efuo6ECiTej7rZkiQwo62gnT3t/nfA/hJEsyta3VWE02nHalgNwsQyY6wkhUBjzUNleRk56P1Zz8oW2RIQMdOp1AWpotIQ3h8wVxuwc+71ZGpgYpbshh1+so6BE1+qjw1lVz5l9/RvKrv56chcsHVffS6zUbamjctBZv3VkMDifpU+fijBkc6Dp2kLb925F8HqzDhpMxYwH6JJGd/uA73Ub75qq4QKJjVj6W4QOJm2loJCcr6/w0hyRjz9HjXPXFRz+y6zW89epHdi01LtkITk5aOn///v/hne3baXK1MWvsOCrOnOHXL70U3ed4dTXffPL3vP7T/0eKTf3BZbdYGFNcrLpN4/yR2AOlTieKH1Vy9yN1dLJyzNm6E/xx9f9woqYCg97I3PFXcM+yz4JOEbOv71rFn99/DldnKw5rKtfNvZVls25UPWdXHE0URdUaC3GQs04EBCyQEMWxMPBi46HCkltA8c0PULPm36reUYH2tiG9Xjjgo+qNvxOKnNfvr6N2zSvobfZojU1K+VjMWbnoTBb0g0xNxWIudiJaDfjOuEAGc1EKxuzBT0PW0ND4aLlkBQ6A2WTiunnzAKhrbuK5t99K2KfJ5WLT/n0sn33ZR317Gr2g6q6tUljcJYNMCHQOoPrGCFhliUdfepyGdmVSbzAUYP2et0hzpHPTgrs5UnuU/3nrd9HOI7fHxd/WPE1xTjljSnpMuo5k1CQkWtxuVu9fRUN7M9PKJjF3tDIYMRgcqAzrxomAHDMZ2kJkWvQFxJyVR9bsy6l67W8J2yy5hUN6LffJiqi4iSLLNG5aQ8ueLcrP19WGv7kewWAkdewUchYsQ9QN7E9cs7uZyoZTlOaUkmFPx5hlw5il+T9paHwcuaQFThfvbP+Q7zz1VNxU4VhMSVpQNYaObYcO8sbmLQRDQRZPm57UOqMLVYGj8jy3oIgKEVmJyvTnmS8ok5Bf274qKm5i2VGxhZsW3M3WY9vi2qoVZHZUbE4UOIISYal3NfLT57/D6eYzAPwBuHvBrXxlxRfweHozk+gdMTIZOhxpbNd9BN5V/cGcU4ilsARv1/A7wDFqIikjkqd9B0VYvXbJpxI9koMB2vZuw2B3kDljYb8v8ZeNz/HClpfwBX1YjRbunHM7d829fdC3rKGhcWG55AVOIBjk588/n1TclBcUMH/SpdeFdDHx5ubN/Of/Pkk4Mqjt9U2bePS223ngmmuSHmNAJe0kK+td4seCMmSvDhlJUFy5o4P7+iF0jpzZp7ruD3hxAFajev1GMJRcqKza8XpU3HTx/PsvsWjk5ZTllBJGRmTwE54vFmED4D5xhJo1/0LyKckznT2VnPlLSRk5fsjboO1lIxEtNiRv/20v3Ccr+i1wdlbu5s/vd5vlegJenl7/JyYMG8fEInVfOg0NjYubi+ev5XniRE011U2JRnyiILBs5ix+9aUvY9Bf8jrvgvLn1aui4qaLv7y1Gm8S0QngQCCuxlhWam1y0JGDQC4CKQi0A1LkWSoLka/7+Wy1WdUL/EbljyQFkcvHLsRpVXGw1if3/zrbUJmwJiFzuP44DUjUCTK1yLgID0FD+7kTQqKNME2EaSNMKOnow3ikUJC6DW9GxQ1AuMNF55kT52XGi95io3D5zZgy8wAQ+jFAURD7/3u94+RO1fXtSdY1NDQufi75J3t+ZhYpNhvtnfGf/KaPGs0vHnnkAt3VJ4uaxkSB2eRy0dDWRnFuruoxJkRykPDIisGmGQFzRI8bIv93E+7/QD8VZoyey7pdq+PWjHojDy/+DwQEslKy+NFtj/Pcpuc5XneCpvYmJGTe2/UWhVklzJu4OEHs5GcVsffEjrg1AYH07GKCMULMDehi2sovBGEkGpHj0no+WSYLqc9Ika+hNq4mxi+FWROq5cjuo2Q2befqyctZOGbekN6vraic0ruGE2xXzHNPPvc75GDywZs6s4mg10vH8QP4WxoxZeaSOmp8dPpxLA6Luh9Uivmj7XLR0NAYOs45ghMKhVi0aBH33HMP99xzDxUVFQA88cQTrFy5kscee+ycb/JcSLXZuPuqpXFrJoOBT1199QW6o08eE8qHJ6yNHDaMYdm9T/vVI5KCDie6qLiJJWkHUT8DI1NKp/Lg0s+RkaJMny7KKeWLN38HfUq3p9KY/FH88JbHePGR57h3vjKDJRQO8MyqJ3j8ma8SiolgIMPKaddSmF4Qd51lU5dTlJc4Tdd3gSM4nh7iBiAkoEyC7gO9IxViCnj/FqhkVbCGk8E2Pjy5g8de+QHrDr0/1LeMIAgYU9MxpqaTs2Ap9OKE3nHiCJXP/Ya6d1+jdfcW6tb8i7OvPo8UMyizi8XjFpFhj2//znJkccX4y4f8NWhoaHw0nHMEp6KighUrVvD1r389unbgwAF27tzJyy+/zG9/+1s2b97MnPPsldQbn7vxRkYUFrB+zx4sRiPXzZ3HpBHJ3ZA1hpYvrryZE9XV1ERShal2O1+9/fZ+G58mI9kwwP5gliENWDT9GuZPW46700WqPQ1BEPDLyuyZMDJBuq0YPrXgHkYXjqXZ30laaibDUnLJM1kJygJhFHdyvTOf39z737xz4D2a2psYP2wc00bNovWcXun5IVlPV+LjPxGjIxXn+Km07f2QesnLznBL3HYZmdd2vcnlYxec830mI23CDPztbbRuTy6kYq0dADxnT9B+/CDO0fF1dzmp2fz09h/y4taXONV0hrKsEm6bfSsZ9nSkYIC2g7vw1lVhcDhxTpiGMSXRcFdDQ+Pi4pwFzp49e1i/fj3btm1j5MiRPP7442zfvp2rrroKQRCYN28e77///gUVOIIgcNXMWVw18/wYAWr0ztjSUv71ox/z3s6dBEMhFk6ZTGaqql3lgHCBeoqqH1kfA0qhr4gEog6no/vTu0hkenHXuWRIIYwNgZKyyRTEnL9FhkyEuGiS0+bk1lk3Rb+XkXHLEqEeQsxygdu8jUnsMEz9vK/chSswpWdRdXATcuX+hO3tXte53mKvSOEQntMqHlR9EGhO7JwDGJ5Txrev/2bcmizLVK/6h+JmHsF1eA9FN99/Xt3TNTQ0zp0BC5zvfe97VFZ2F1LOmjWLP/3pT2RnZ/ONb3yDDRs24PF4GBaxNEhNTaVJpchX45OF3WKJziQaCoJIqFZf9FMzhFFSXDaUephYRIizZkCAdlmxbwj2OL9fAK8sY+3lwgICGQi4ZBk/SueXDQHbBa7xtyBgk2U6lZuMeGrF+3D1hiCKpE+azaJx0/jzU/upaa2N2z61eMpQ33Ic7RX7VYcM9oUpM6ff+3aeOREnbgBCHS7a9m8nZ/6yAV9bQ0NDwe1285WvfAVJkrBYLPzyl7/EaBzakS0DFjiPP/543PeBQCB6U+PHj+f06dPYbDb8Eddtj8fDReIGoXEJca7SoKt/KwURUZaiA/RCEB9p6UKAQJK3cQCZvubmGhDp22P6o0VAIA0dNiSCkRZ84yB+sga9gUeXf5n/9+YvqHcpJrezymZwz/w7hviO4/HVVfW63ZxTCKKAL8YY1F46ipQRY/t9jWB7i/q662JMOmpofHx47bXXuP/++5k7dy7f//732bhxI4sXLx7Sa5xziurrX/86Dz/8MCNGjGDt2rU89NBDGAwGVq9ezYoVKzhy5Och/b4AACAASURBVAgFBQV9n0hDYwDoEDETJqHRXGXacW8ICDjQ4aBv/ys96vUpHYAB6YJHZAaLEZFz/dw0tXQyf3noafadOUCK2cGo/JGDOo8UCuJvbsBbX40giDiGj0FvVZ8krE9RT3Nmzl6MvXQk5uw85HCY9mMHCES6qFJGjEXopTC5J5bcIhAExbo+BnPO0E5q1tD4pHHXXd3Gua2trWRkDH3K95wFzuc//3kefVQx77riiiuYM2cOkiTxi1/8gh/84Ads3LiRp59++pxvVEOjJ2mRtI8XRdPYUN7Qrq5hf12oCB61iEtv/leiDKkozzl/z/MJ0CrLmJDQD1LkeJDwRYYAWhEGFUm50Jj0JmaUTRv08c07PqBpx/tIvm6f8sYt71K44jasBSUJ+zvHTMF1cBeB1u4UuD7FiaDXIZpMCIKAoNfjHDM54dj+Ys7KJWP6fJpjCpkthSWkTZw+6HNqaGh0s3v3blwuF5MnD/73NBnnzU3c5/Oxfv16xo0bF63H6Y2hdhPX+GQS6yq+++g2tldsxqAzMHvcQsYUTwBZ0Tu5gA5dtJZHh1Jj41IRQ0ZZEVMGRCQkGpBV01gGWRlQaB2gOHERxt2jADkzZu7PJ4H24wepfuMF1W2W/GJKbv206rag20XrgR14a8/iratCDkQqs/QG8q+8gdTRE4fk/jx11XhrTmNIScNRNgrhHDsANTTg0ncTP/L8n/j0p7t/d2+77TZuu+226PdtbW088MAD/OY3vzkvmZ7zNujPbDazbJlWhPdxoLqxkff37MFmsbB42jRsFnWLgosZPxIBZKXzCVi95RWef7c7crhhz9v8x7VfoaG1lm2HNiLIMnPGzGXp3NsxdnmRqUh9kwwZCFFrBRERA2HVVFVQgBZkwnIYe2T/vty+w13dWrEI4JZl+p7Ve+ngPn446TZvXRVyOIygS0wtGRypZF+2mLOvP98tbgBCQRo2rcFRPgaxl8nT/cWaW4A1V0u1a2gMhPT0dF555RXVbYFAgC996Us8+uij562M5ZKfZKzRO6s2b+Z7z/wRb6QovCgnh99+9auU5X98/pi7CCudUBEtEQwFWLU1/pcqLIX5+9qncXV2T9/9xwcv0Ob3cs/SzyoLPaIodiAVMUGk2BHwynLSWh8XSpu50i0FDpVzdBFCmWystv5Jojd7B2NqGvQRMVEz3Qy52/C3NGLJzj/n+9PQ0BhaXn75ZQ4dOsSTTz7Jk08+yR133MHVQzyAV4uzfoLx+f38/MUXouIG4Ex9PU/++9ULeFcDw48UJ24AWtqbaetM7H6JFTddfLB/LaGwine50FWvHP/gDUZqZcwoKSnVsp2IH1ZYgHYBOnup7TGgfo5zjzl8vHCMnqQU86qQPn1+n/5WhtT0hDXRbNUG8mloXKTceeedbN++nWeffZZnn312yMUNaALnE83Rs2epb0kUAvtPnrgAdzM4giRGUrLScijILE7YVy2K4g/4CIXV4yUiSgqplTA1kf/qkXEL4BNImImTjN6sD9TEjyhDygUeAvhR4yguJ2/JjZgyc0FvQG9LwT58NMNuuo+0cVP7PD5j2jzokYrKmDoHnfnjl27V0NAYGrQU1SeYgqws7BYLHV5v3HphVtYFuqOBo1OZxisKIvctfoAnXv05bp8yxi/DkUle5jAOVO6O23dy+UzMRpWHoKx0M7Ui4ztPWsODlDiNWYYMBjeP5uOOc+wUnGMHNxzQkluIvWg4HaePgyBgLyonfeqFm56uoaFx4dEEzieYjNRU7lxyFU+91p2SMhuNfGr5xWlE2tbZRktnKyVZxYiCIgDMCJhkOa51WyfDovKZTPnMU2w+tgW9zsC8UZfhD/r52Zu/ZPuJnQBMLZnM5676LOZIECgc+c+A0g0FJM7Z6Uk/xE+yycA+legTAgRlxddKo//Ur3uDjpNHot93nDxM49b3yJm3tJejNDQ0LmU0gfMJ54s338yoomG8v2cvNouZ6+fNZ3xZ2YW+rTjCUpjfr32KVXvexhv0UppVwpeWfoFJxROiNgidskwA5Q1tQ0CPSIYjnWunroiex2F28NPbf0hVczVH645RkJZPaUpuQuoqhIQESGoCpAeCDA4gEPneEPnaj3KoHaUoWfXYJOe0WkyI/hCSlGSHi4Cw30fzzo10njmBzmzFOX46KeX9nxA8lIQ8HbQfP5Sw3n70QJzACft9NG9/n45TxxFNJpzjpg46YqShoXHxowmcTziCILBs1myWzZp9oW8lKf/a8Tr/3P7v6PeVjaf40Ws/5S8PPY3ZYEZEpL/TJCobTvOT13/G0bpjAEwsmsC3r/06Oc6cSL2NrERtBCUSpJeTWDdAxIRTwKGSTgojRXqnkiskK0JCDY5BgHSriaAo0tkZSHLkhafm7VfoONnd2t15+jisuP2cRI6EhBvFr0tEEYb9mQUkSxLIKmowHL9W++6ruI8eiH7vrT6FLMv9qvHR0ND4+PHJS/RrfOzYenxbwlpDeyO7etTTxJJsfuVv3vltVNwA7Duzn/99748AuLrqbSKaJCwo7dq6rlPJylycVBlSZMhOIm5AsZLoKW4kJDqR6EAijIQJkWxRj1UnogNSDHpKU+wYRBG9vv92Ah81vsa6OHEDgCzTdmDHoM8pI9McKeAORIq4m5Dx03cYy2BPwV6SaA1hLx8T/TrQ2oT72MGEfdoO7Bz0PWtoaFzcaBEcjYses169IsVsSByFV9lwmqfWPc3eM/tJt6Vx04zruWnGDQA0d7Sw5/S+hGN2VO5ClpMUEwsQliFdBhMCul4+E/gjEYgQSqoqJTL9GCCARBMyUuQa7TJkIGGRdeSnORLaoMPhizc/FfJ2qq6HvYOfRO5HVrXA6JDlftUj5V5xLXXrRToqKxBEHSmjJpA958rue/N5E/ykACS/N2FNQ0Pj0kATOBoXPVdNvJLNx7Yix6RzRuePYnLJpLj9QuEQj73yA043nwGgOuDlN+/8nlRrKovHXY7VaMFmstLhj39A2802xbco2Q0IEJLp1YIhiERjTM1OCAjIMtlI6BBxxYgbAEmANlnGKEmEQmEMhu5fRVmW8Xov3vSUNX8YensqoQ5XdK1NCvC+7xQnn/kCOSnZrJxxA5OK+2+TkEzO9VfmGRypDLv2TkJeD4JORGeMF7/m3AKM6VkEWhrj1m3FI/p9jxoaGh8vtBSVxkXPgtHz+Oa1X2NM3ihyUrJZOuFK/s+N/xXtpOpi+8kdUXETy3sH1wNgMVpYNimxq2b5FMVSRN2zWiFW/CjRGGUuTiNhfEhKLU0PhRQWwIuSfvGTSFAABCEhHSUIAibTxfvZQ9QbybvyevT2VAAkWeYP8lneqtnN0dpjbKzYxLde/C6Hq5PbL/TEhFKwrbY+EPQWa4K4ARAEkbwrb8CYlhldsw8fTeasRQO8goaGxseFi/evqIZGDEsnXsnSiVf2uk+yuhsppgD1ocWfxmZLZeORTeh0emaPW8iiadfgQcKBSFiW6IQ4sSLIYIkshHukmvyAvxffKBkZARE9ifYLogwmo051Sq9OpwNUJixfJNhLRjD8vi/iqT7D9oYKKt/ZHrfdF/Sxas/bjCkYk+QM8egQcSLRJsuKdYUMZpJ3oA0Ga34RZfc8grf2LKLJgjkze8jOraGhcfGhCRyNS4ZpZVPJd+ZR01Ybt75g9Lzo16IosmzOLVw595a4fdyyjBWRNHRYkHDLMkHAiDITRx8JdnohLtUEoDJrEIDthzex88B6QqEA00bOZvaUZQhi98F2IByWkWVZpQYnPLAXfx7wBny8sPUffHh8O1aTleWTlnLl+Cui20W9EXtxOb429cnXLl/7gK5nQ8SMhF9W3N1N5yHALIgi1gJlynXQ04nk9WBMz+zTCkJDQ+PjhyZwNC4ZQqEQC8fMZ+2B92h0N5FmTeOG6deybOJV0X0kZNW27yBd0RalNbm3iIwaAuCQifpibdj1Nn9c9UR0/20nttPkqufWy+8DlIiQFZFwWMLnC2CxdCdjQqEwXu+Fj97896pfsfbguuj3u07tIRQOs2zSkrj9ppVOwWww4wvGj0WcVjLw9msdItYBHhPqaEcwmdAZ+pfQksIh6jesxnVoN3IoiCmngLzLr8GSW0jI4yHQ2ogpI1uzedDQ+JijCRyNS4LWjja+8cK3OV7fHU3ISc1m/eH3eX33KmaUTuXBRfeR7khHLyeKHAPqXlU9MSPg6ukkHrF1sCBiQyIkw7qdryeIodW7VnHPZbdjN8dX+3R0BPD7wxiNuojgufBe4rWttaw79H7C+pt7ViUInOyUbL687Av8/t2ncHna0Qk6lk5cwjVTlp/Xe/Q11lK3bhXemlOIJgvO8dPInrcEQeg98tOyazNt+z6Mfu+vr6bm7X+SMmYKLTs3Ivl9iBYrmTMvJ2PKxTsfSkNDo3c0gaNxSfDG7jfjxA3AkdqK6Ner971Dg7uJn9/5Y1IQaIkVKQMwtzQgkoaES1bqcLomGVsi6RR9pN6m2Z1oYur2ddDa2ZIgcACCwTDB4IVPS3XR5mknLIcRBIHxw8bgDwY4Wnucdq9bdf+lE5cwZ+RsjlRXkJeWR2F6Qdz2+rZ63tz7Fg2uRsYWjGbZpKsw6o2Dvj9Zkqhe/TKBlgZAafdu2fkBepuDjBgPqrAUZs/pvcgyTCmZhE7U4T5ZkXC+QGsTTZvXRL+XvB4aNqzCkjcMa25Bwv4aGhoXP5rA0bgkONlY2ec+Oyt3cbz+JOU5ZRiQ8EaKki2ReTWyLOMNeDEZTOjExEF7h2sq+OO6P3GktoI8Zy43z76ZK8dfrjobZ1LRBDYc2Ri3NiK3nML0wkG+wo+WUfkjmD96Do+s+A/GDhuFJElsPbaDjQe2Jz3GYXYwY/j0hPWa1lq++tw3qW+vB+Dt/WvYUbmLx1Z+d9C1L57qyqi4icV9/HBU4JxqPM3//fePOdmgvDdKM0v4zo3fRNfvIYoynaeOagJHQ+NjiiZwNC4JijOLgY197ucLKoPdDIgYYtZ3nNzF0+v/REXtUfLT8rh99q1cO7XbdLTd0873XnqMpo5mAI7Xn+Rnr/2CHEcWk1XmvXx60f2cbamKPlyzU7L4/JKHLopiVl/Qx3sH13O68Qyl2cVcPm4Rph7DFEVB5Ed3fZc0h9IKLooic0bNZNz/b+++w6Mq04ePf8/UzKR3IASSQGihQwBpIkUEBQtKEMSu61rXV1d21Z/u2lZdC+paVteKBRQLVkCQ3gklgPROKOl1+sx5/xgyZDIzSUBQCPfnurwu8sw5zzknk0xun3LfLbJw2E4uCeGs3O99wU2NJduXsfFAHt1bdwtxVgOCBKAAivbE9/fN+e/4vv8Ae4v28cbPb/NIj/FYDvoHxLrIaFyV5dSlMUjZUyHOVRLgiCbh8p6XsXjrUvYW7Qt5TJukdDoF2bZcXFXCk9/8iwqrd9fP4dIjvPzTq7SIaUavDO9C2SXbl/uCmxpu1c0vWxYEDXBaxqfw9i2vs2bPOpxuB73Te2E6np+lZm1OY9b8nG42p42/z3iMDfs3+trmbV7AMzlP+E0ZKYpCTERUwPkRZhMltuCZjEM5UmdXW42nvn6WoVlDmDxwIpGmxlUTK6wsYtba79hXuI+xJiORVv8MQ1Htve+Fy+1iw76NAeev37+RsGv+QbLTRdmmNbitFsJbZRDduTeHvvsUj+1ENmZteCTR7To39jGFEGcZCXBEkxAbEcvU6//NvM2/cLSsgHbN2rDhwCbmbf4Fu8tOp5QO3D/q3oDkgADLd6zwBTc1VFQWb1/qC3A8avD1MW5P6HUzWo2Wfm2z/dqqa5VzMADRKI3aDu32uHF73L9p3QrAgl8X+wU3ALn71rNw62Iu7lI7z5CKqkLdAadQuYbq0655Jku2LwtoL64u4YvVX3GoJJ9ncp5osJ8KSwUPfvp3DhR5kzluxcDkyA500MegM5mJ7ZxNTFYvwDviFBsew7EK/2msWHMMBp0eU7c+xHXr4/da6uXXUbx2MY7iQoyJzYjvPRhdkCBPCHFukABHNBlRpihf3SmA4V2GccuQG6myVdMyrkXo6aH6ajQc1z+zH+8seJ9Km/8i24HtBzT6/uzHq5XXdOsAilSVZDy+PDt1udwu3lnwHnPy5mF1WOnbtg93X3wHSVGJjb5ubfsK94Vo988Arapgt/tvXwcorahky6HtpCW0JjzIYulgLu95Gat3r2HTwcBilwArd61mz7E9ZCRn1NvPz5t/8QU3ACU4eKUyj79ccjeX9xrjd6xG0TC216W8s+B9v/axvS4Lur4KwNw8FfOYSY15JCHEOUACHNGkxYbHEBseU+8xAzP78575I8osZb42jaJhSMfBvq/jI+N5eOxDvLPwPfYU7CUxIoHx/a7mgsy+jb4XS5ByDqoCNlUlIsQ505Z+yuervvR9vWT7UmxOG89f+3Sjr1tbemJa0Pa0xNYBbVVVDtxuFaNRj6qqLN6ygkenP0GVvZoYcww3Dp7M5b0ua/CakaZIXr7u3yzZvpzX574ZMNWnolJhC747q7a6ozE1CioKg7Zfe0EO0aZoFmz1bncf1GEAY3tc2uB1hBBNgwQ44rwXGxHLP656lPcWfcC2w95Fxjn9rqFHWjdUVCyoOFHpnNmLt9v2oriimNiIWPRafcOdn4KVO1cza933lFWXcrgkcP3K2j25HCg+SKv41JPu+6KsC5m/ZQFr967ztWVn9OKiWsFcbVarE6vVyZrda3lo+iO+9jJLGa/NeYOslh1om9y2wetqNVqGdBzErmO7+GTZdL/XUmJb0CW14bUuHVu0C9repWVW0HZFUbi0xyguPcP5eIQQZycJcIQAurXuwivXv4jb40ajaFAUBRWVYjzYao26VAMJ0QlBt4Y3xIRCdZ0kgYrqTR5YY+2eXP5v5j9xeUIn+wuVTbkxjDojz4x/goVbF7Ov6ADpia25sMMg9Lr6g7VVuwK3h7tVNyt3rmlUgFNjUv8JHC49wqKtS/CoHlrFp/LA6L+EnDaqraw6cJeTghKQc0cIIUACHCH81P5Da0P1C27AWwHcoqrU3vOjqip2lx2jzljvNvCw4wUlK1UVtwJ61bvIuPb6m+83/FRvcFNjxooveODSvwRdNN0QvU7PiC7DUFFxNDJcMhuDF1CoaT9ceoSfNs6huKqErqmdGdFlGMfKj7F691rCjeEMbD8AkyEMk8HEY1c+zKEL86mwVtC+ebtGBTcAGw7kBbSpqKw/kEfL+HMjv5AQ4vcjAY4QIbhC/Ol31Pr3ku3L+HDxNPYU7CM9KY3rB17HhR0HBj0PIAIN4ah4VBUNSsBW8QpLw2tRAH7cOIee6T0ZljWEXcd2M335F+wt3Ed6YmsmXHANbZvVP6riOL7g2Xm8UKgRN7F1gq3aRnQZxje53/ktsk6MSGBopwvZV7ifBz/9G8VV3uzNP22cww8bZrPz6E7sLu93KzW+Jc+Mf5KWcS0Ajo+6NH7kpcJawbb8wAzEAPHhsY3uRwhx/pAAR4gQdCHKhNds1N5TsJenv3nW90d8T8Fe/vXtc7SKb0l6UlrIfhUUtCG2bvVK6876/Rv82sL0YSTHJJLT/wqaxzVn477NfL7sazYf2kLX1CymTH+UkuPBxZ7Cvaw/kMd/b3qNhKiEoNdQUSmrCW68N4QdqFBV4kLcc2p8S/5x1SN8tOQTiiqLyWyeyaQBOcSEx/D+4o98wU2NzYf8d0wdLD7EJ8s+Y8qYB0J+X+rz8dLpFFQGLibu2KIDfdv2CXKGEKIuxepEnxc8L1VTJAGOECGEoWBSVay1YhG9CuHHg5PF25b6gpsadpeDxduW1Bvg1Gdc3yvZXbCXhVsXo6KSEBHP2N6jmXTR1SRHe7eGj+g2hAva92Ht9jzmb1noC25qlFSVMG/LAiZccE3Qa7hQcQSJr2yBTT6zN/7M+4s/pKCikLjwWLqkZtE2uQ0A+aWN+8Dcmr+tUccFUzfoqzHxgvFBp7gOleSTd2ATydFJ9Ezr4Zs6VFUPxbnLqNiWh+pyEtGmIwl9h6CVjMVCNDkS4AgRgoJCHBqsqnetig4FM6A5Po0TarXNqS8B9o7WPHbVw0wumEhRVTGdW3YiOiqc6Ej/nDMDOvQhNaoVnyydEbSfqnq2XWtqblIJ0h7EnoK9vPjjVN/aoJLqUv4z903aJGXQrXUX2jfLJLfWrqxQmsc28/3b5rSxevdaFEUhO6MXYfowv2MrrJXotFrMBu8anxhzdOBzKBpaJbQKaJ++4gveXfiB7377ZGTzj3GPYDKYKF69hMIV83zHluQuxWW1kHLxlQ3evxDi3HLyKxSFOI8oKJjREIOWCDS+4AZgcIdBATWcjDoDF3YY9Juvm56URnZGL0wGE0Z98OzFsZHR9M7oFfS17IzAopc1tGgItmS4JoRylJdgOXwAi7WKj5Z8zN9n/F/AwmcVleU7VwAwrs8VtKuz5ietTuBh0pu4pu84ALYd2cHNb/+Jx798ksdmPsEt79zBjqO7ACioKOLRL/7BVS/nMG7qtfz7+5exOmxc3mtMwILqIR0H0yrBf6v8weJDfsENwOo9a5iV+z0A5VvXBzx3xfY8nNVVQb4jQohzmYzgCHGK0pPSeOSKKXy45GP2FewnLak11w+87pSnp0JxOt2Ehflv41ZVFafTTffW3bjtopv5dPkMqu3VhBvDmdg/h25B6mPVFoOCVlWx4h3IMQNml5v8+d9QsT0PPB4+dB9klS309FNN2Yi4iDheu+Fllmxbyk95P7PpwGb2FR3AqDPQKr41XVKzGNl1OO2aZwLw33nvcKTsqK+fw6VH+O/8d3hx0nO8/NMrrNy1GgC3082PG2cTpg/jnpF/5smrH+PHDXOotFXSK70n4/uNC7inTQe3BN2FtuXQr8f7dAS8htuF6grSLoQ4p0mAI0QdtXPhNGRQ+wEMbNcfp9uJXqtv8BytVsFsNqLTaXC53Fgs3mzB9bHZnDhVO3FR3mkaj8eDxeL0nTexfw6ju41kT+E+0hNaExvR8K4iDRqigdoTP0UbllGx1bvWpchjY009wY3ZYGZY1kW+r8NNJi7s2o+0lBSWb1vNtMUzsLsc7Dq2i7+MussX3NiddjYe2BTQX96BzeSXHGb17rUBry3dsYx7Rv6Z/u0uoH+7C+p9ruQQJSwSj7dHpLenfLP/NUwt0zFEh1peLYQ4V0mAI8Rx+SWHeXP+2+TuXU+0KYrR3S9h8sCJDQYtiqI0qgimRgMxMWY0Gu9Ui06nRa/XUVpaTX01LFfvXsNjM5+ke3pnUuKas35PHiM6X8y1/U8sIo4Jj6FnePeg56uqyoYDeeQd2ExiZDwXdboQk8EEwKKti5mx8kuOlh2jtWJilDuWVG045aoTT7BnUDR0b92Vif0n+Mo76PVaoqNNxChmmscn069dbzKbZ/D3T55AReXH9bOZe7yWVnab3iRExlNYWeTXb2JUIka9EY2iwaP6X1mrafzHVFu3gY6aKLZ6ThRPjdIYGNNjNABJA0bgtlZRtXs7oGJq0Yrmw8Y2un8hxLlDAhwh8AYBT816lm2HvblWbE4b7y/+iChTJFf0Pj1/AI1GvS+4qaHVajAaddhsoZP7fbbiC+wuO6t25vraZqz6nLG9RhNuDF3wUlG8wdcbc99hxqqZvvav18ziuWuf5lBJPk9985xvSqeUUg5SxMOmLqRpIkjESCF2vz6v6DWGe0be6ddmMgWOXF3SczhvzXmf/UUHmZ33M+7j1djnbp5Pz7TuAQHOxZ2H8cuWBbSMS2Ff0X6/1y7qdGHIZ6yrYutGbjO2ZYmrgN3uKmI1Bgbrkki0ea+vM5lJHTMJR1kxHpcLY3xSo0bqhBDnHglwhAC2HNriC25qW7B18WkLcDSa4H9IQ7XXyC/JD2grt1RwuPQomc3a+Nr2Fuznq7XfkF+ST1arDtwwdAKVlipmrv7K79xdBXv4eu0sSqvLAtarFOMgz11CX30iE43pfOI+QJHLAkDv9J5cPzCw2nbdoA1Ar9WRGB3P4dKjON1Ov9e2HNrKfZfcw7q960FRaZfcjq/XfkNJdanvmDC9kYiwSIZlXcSNg66r9/tTm8flIEyjY4ShBSNqtatO/3swxMQ3uk8hxLlJAhwhALcn+ByRx+M+bdew292YTKrfiIGqqtjt9V8js3nbgBGPZtHJtG2WRliYDrcb9hcc5MHP/ubLibN+/0ZW717LhAHjcKuBk03r9uWxt3Bv0OspsfEYDIn0bdmHEb37s7VwHxGmCDq0aB/0eKfThV6v5WhZAS9/9wbLt6/GbDARaYwk2hQVUD3c7rLz0ZKP6dcmm/TENNbsyfULbgBMBhMf/umdkCUiQolIa4/lwB6/Nl1ENOZWGSfVjxDi3CcBjhBAhCkCBSWgMlN9261Phkf18OGi6fTK7MzATv0AsDsd6LU6GpohuWHQZHYe3kVhlTfIMeoMvHbz8yQmRPuCpQ8WLwhI+Lfj8G4KK4oC+gPYc2wPNldgar9wYziX5TxAfOSJRbe9o4KPdlRYKpi9aR5HSg/TPaMz3+X+RO4e7yLlUsqAI7RLzgwIcABKq0v5KW8uQEC5Cu/rZewr2k+nlI5Brx1KXPd+OCtKKNu8DtXlxBCXSLOhl6ENsdVeCNF0SYAjBLBgy4KgZSfLLeWoqnrS6zQ27t/Eoq2L0SgahmQNZsuhX/lgyTSuGTSD1bvW8cr3b7H5wFZSE1KYPPhaMhPacaTsKB1btCfKHOXXV7tmbfnfbW+yaNsSbA4bQ7sMoX2r1n73VGbxjoBkpXbgntG307lVRw4WHWLXkX1B7y9YcAPeauMWRzXxIYs2eJVWlfHAJ1PYW+Tt/5vc74IeF6y8Ql3Bvu9RpkhST6GApqLR0GzIZcT3GYK7ugpjQhLKKRQkFUKc+yTAEQKodliDtn+1dhZFlcU8esXf0Ov0QY+p66eNc3jhh6m+3UCz1n1PfEQcreJTMOqNPPDBo5RVRABmKgAAIABJREFUlwOwv/AgT335vO/cSFMkfx56G6O6j/TrM8ocxZielwIQFqYLCLh6t+3BnI0LeOWWf9EsJhmAmPBoOqV24KtV37J+b+DW7GBKqkv4ZPkM/jbmwZDHrNi5krd/eZd9RQca7K/cUt6o69amoDCp/7VEhkU2fHAIenMEenPEKZ8vhDj3SYAjBNCvTTbfrP026GuLty/l583zGd39EnYc3cWirYvxeDwMaj+ATi39p1A8qodPlk332+rs8rgoqSqluKqYr1Z+5wtugqm0VvLq3DdoGZtCYVUhLWJbBKx9cbtVCsoLWbRlGTqtnmFdL2R0zxG43B5fcFNDq9EyYeC4gAAn0hhJpT14OYcdh3eGvL/5mxfw9Kzngo66BNPY45KiEslKySIhKp7urbqwYX8e1795C2ajmZFdR3BFrzGy20kIcVIkwBEC6Nu2DzcPvoGPl32Ko86uH4Bf87cSaYrkqa+fxeH2Zr2dufprHrz0fkZ2He47zmK3kF96OOB8t+rGo3pYuGVJg/dic9q4/5OHcKtuFBSGdb6IKZc9gE7r/XX9cuU3vLPgfars1XRtnUVCRCy92/bgir6jgvbXM60Hmclt2Hlst68tVHAD0CKuOeAN1lRV9RWzVFWVtxe8V2/Q0iYpg90FezAbzFzcZRgb9ucFbPvWaXQBu7cKKgopqFhI/8x+HC49wrIdy32vbT+ygwpLOZ1TO9O+eSYRYTIyI4RomAQ4Qhw3edBEYszRvDT71YDXkqKT+Xjpp77gBrwjM9OWfcrwzhf5goBwYziZzdqy83htpRrtm7cjIymNuXnzgyazq6smb4yKyrzNv9C9VVe6terKv394ibyDmwFolZDK67c9X2/mYlVVMWnDuX7QJP5v5hMNfg/MBjNje17G1NmvseDXRXhUlcHtBzIgsx/Tln1KQUVByHPbNmvL2zf/h4KKQsKN4USEhbN+3wb+NevfvgXSbZLSCdOHsSV/a9A+lu9cGbT9gyUfAxBtjuaOobdwSbeRQY8TQogaEuCI84rNaWN/0UFSYpsHHQm4pNvFzNn0s98f4BYxzRnZdTifLQ+s3J1fkk+ltZKY8BjAm1jvlgtv4KlvnqXKXg1AlCmKWy+6iZ5p3bn1opvJ3b2Or9d9y7b87SRExlNSXRqQK6aujQc28cuWhb7gBuCy3iODBjcejweNRoOqqjgcLpxON0fKjjXq+5PT/yqW71zpK04J8OPG2czeOAdPA9NNBwoPUFhZSHJ0kq+tR1p3PrjjHdbsXYdRa6Bd80zGvTKhUfcSTLmlnBd/fJXurbsHTMcJIURtEuCI88acvHm8u/ADCisLiTJFktNvPBP7j/c7Rq/T80zOE3yb+z07ju2iZWxLLu95KclRSbRJSg8YeUhPSAvY9dS3bR/evf0tlm5bDorChR0GUlRZzPfrfyItoRXDulzE0M5DfPWr8g5u5ps1szhWUUCUKYpVu9f49WcymLi013Bio6IprSrji+WzWLZ9FZGm4FM1NYn3FEXBYNCh1Sp0aNGuUd+j4qpiFm9dHtDeUHAD4HA72HVsL0lRSX7tZqOZCzsMBGDx1oan6Bri8rj4bMXn3D/qnt/clxCi6ZIAR5wXDhQd5MUfp/pGSiqslbyz4F3aJKfTt02237FRpiiuGzgxoI8bBl/PE1895RuZMeqM3DTkejRBtiEnRSXRMaUjS3cs45Ev/sH2Izt8r43oPJQpYx7EoDPgcDlwuBzkXHANHVq0x+lycu9H/49ttY5/9rrHGNb1RLmCwVn9uffdv7Fs20omX+gfoNWlKApGo45urbswpscovlv/U73Hx0fFodZXGKseBq2B9ITW9R6zdMeKU+q7rrpTgEIIUZcEOOK8sHzXqqDTQCt3rgoIcIIpriohNa4lb9/yBou2LcGjendRhcrVMnvjz7z449SAxbQAP2/+hT4ZvdlxdDffrfsem8tb76l3ek8euXwKz4x/kpmrv2b2xjkkxMQxpPNAv/MNOgPj+1/Jve9O4eOFX3DNgMsx6g1Y7FbMRlPA9cLCDISHh/HEpL+R2TKDl757Peg9x0bEcFW/MVRYK/lyRfAdZfXp2KI9zWOb1XtMsKR+obRLzqRXeg8+W/l5wGsR9dTgEkIIkABHnCfCQmSyDTOE1XvejiM7eXrWcxwoPghAz9bd+etl99MsJvQfcrfHzbSlnwQNbmp8uvxzX5K8Gmv3ruPDJR9z3yV307dtbz5dMZ32LTN9C5hrS4yK49Kel1BYWcRz30yloKSIo+UFvHvXq0SHn5gyU1UVrdY7wqTX6bl52CS2H97FD7lzfMekJbaiR0ZXrrvwGtKTWvHg2LtxOd3M2Tgft8cdNDAMlvV548FNbD20jY4tO4R87iGdBvPz5vmN2j7u9DiZPGgiP2z4iQqb/66vgR36N3i+EOL8Jik+xXlhSIfBJEYk+LWZDSZGZA0Nec7RsqP8ZdqDvuAGYN3+Dbz802v1XquooojDZUfqPWZ/na3Tvv73eUsdLN3h3U20fm8ex8oCdy5pFR3PXf8Y94/5M49d8xB/uuQGCiuKuPfdKSz5dQX5JUfYfWRv0NwxQ7IG0DI2hXtG/Jlnxj/BdQNz+L9r/kr7FpmAtxSEQafH43EzJvsSvvv7dNY+v4DHrv4rqfEpQOj8Nh8umQaAy+3ioyWfctv/7uSO9+7h85Vf4lE9XJDZl/suuZsWMc3RKBq6pnZmymXBkwoWVhZhMph4fNyjpCWmAd5dajl9r+ayHqNDfWuFEAKQERxxnogJj+GpnH8ybekn7Dy6i5ZxLbn2gvFkJIcuwvjd+p+wOgNLGqzes5aiyiISIhOCnAUJUQmkxKUErQIOkBAZT1FlYH0mgOjjC5YjjxeZtDltPPH58zyWM4Xk6EQ8Hg/r9m6ke1pXv/O6tM7i6v5jOVZczKOfPk1xVQnDu17I1Jv/BXh3Vrk9bvQ6PXang/zSwwzrPJRocxQaDWiVE6NEBr2By/uOpriqhMfHP+QbQRo/8EoGdOzHpU+Nx6UGLxB6uOwoAP+d/z9mrvna1779yA6sDis3DL6Oy3tdxpieo3G6nBj1RtweN+8uej/ge5KW0AqAnmndefe2N9lfeID4yDiiTP6LuoUQIhgJcMR5o12ztjx59eONPv5YiK3VGjToNaHLNmg1Wm4aNJnnv3/JlzdHr9XTvnk7urXuwsguI/h/Hz8UtAil1WHD6rAxru9VfLxsOg63g0W/LmfMMxPoldGdkqpS+rfrQ+82PQLO7ZDSju0HfqL4eNHNRb8uZ/fRfSzYvIQZS7+iuKqE3hk9qLBVER4WTpjeCIDRqPdNY9Xont6ZGy6aEDA9lhLfnNTEluwtCD4C1TI+BbvTzuzjhTRrm503lxsGX+f9Hioath7exrIdK9l2eFtAcGPSm5hca6G3RtGQnpQW9JpCiHNTUVER9957L59++ukZ6V8CHCHqKKsuY/rKmWzO/zXo633a9CI6PLrePoZ1voj0pDSWbl+BTqtlSMdBtIht4Xt9wgXj+c/Pbwact+vYbp6d9TyPX/0oj1/1CM9+/wKV1kosdisHCw9zVfaVbDqcF/SaVdZqFm47sQ3b6XJy8+t3U1x5osr48h2rARiXfQXG4wFOYWURERGBi6VbxDb3/dvtcfuCnQhT8AW+CgoTBl6J3eWg2m4JvD97la9w6ddrvuW1uW+EnOoa328cfRqx+FsIcW4qLy9nypQpWK3B6wCeDhLgCFGLqqo8/uWTfgn1amvfLJP/u/LhRvWVkZRORlJ60Ncsx7eaB7N4xzKe+vpfRJuj6ZzSifjIeEZ3G0nHlA78vGk+P2/8he9z53BZrxPZfI+UHGPV7nUBfdUObmrrld6DSmslz//wEvHRMTw98dGAY5rHJbN213pe/fFtfj24ndSEFlzdbyy7j+wBvCNVbo8bjaIhIymN+8bcwUWdB1JRYaV3ek/W7M316y87vTeKomBz2vhk2Wf1LjQ+cnyqSwjRNGm1WqZOncqdd955xq4hAY4Qtazbuz5ocNOpRQf+MupuMptlnpbrWIKs7altwdbFfl+XVJXw3MQn6d42i4fH3c+MpV+zdOtKuqd14WjpMSx2K4eLg6/5CUan1fPRkk9Zun05Wo2WAe37MrrXCADfKEtxZSn3f/AIpVVlgHdR9q/5O2gR35xdR/bi9njX4XhUD0fLjtG2WTpVtmqOVRRxz8g7ee67F3yJEXu07sYdw28D4EjpEYqrgwdeNRIjExv9LEKIs1NJSQm33nqr7+ucnBxycnIAiIg48zXlJMARopbi6tKg7VGmqNMW3AD0a9uHGSu+aHS17bRmLYmJMRMbm0mnVpmM6zeWKdP+yVMzXwAgxhwddMeUQWvwq58F3mKXqclJvDkvF42iwe1x89C0x5m+9CvSk1tx3YU5ZDbPYH7eIkqrytBqtDw98VFG9xyBRqPB6XYxbeF0XvruDV+fVfZqJr9yO3aXi0prJZ1bduKekXehATQaLW2OL+ZWVZXdBXsJ0xl9+X/qSoxIYHT30LWmDhQd5FhFAVkpHTEfX4wthDj7xMXF8dVXX/1h15cARwiguLKYN+a9zYpdq4K+3iOt+2m9XrdWXfjz8Nv5eNmnVFhDV/YG0Gl13Dz0Or8Axqg3csuwSczLWwhAmaU84LxmMUncM/wups55jcLKIl+7y+PiP3P+x/QH3sXusvPTunm89O0brNu7kY37N3PL8MkAvoKgl/a8mMt6nwg49FodNw+7joWbl7Fu70Zfe1HlieBw86FfeWbWc7x3+3/RarQcKzvGxkMbKLOWsXz7qoDgJkxnpG1yG9q3aM/lvS7zW/9Tw+ly8sKPU5m3+Rc8qoe48FjuGvFnhmZdGHCsEEJIgCME8MKPU1m5a3VAu4LC0KwhXNF7TIN9VFgq2F2wl1bxqcRHxjV4/DV9r2JUt4vJO7iZdxa8x75C784kraL1VRMHSI5OoFlsUsD5qQnBsygrKLxx2wtkJrWjstriF9zUWL9nI6qqYjaYmDT4GtweNy/M+g8jug6h1fF+h3YZxKs//JfuaZ2DXqdnRle/AKeuA8UH2bBvI11bdQGjk0kXXQ3Any+5mQ8WfspL33pHgGLMMbxzy+skRAXfdl9j1rrvmLtpnu/rkupSps5+lZ5p3XzFToUQooYEOOK8l19yhFW71gS0J0Ul8sz4J2mTHHyhcI3iymKe+OZfbDqwGRUVg87AtReM58bBkxu8dkRYBP0z+5Gd3ovlu1ZRYa2gU4sOvDHvv76kf5XWakqryoiNiKHKWoXREIZeq2PzwW1B+1RRiTCFE2kOxxwWFjLvzqBHR6PX6rhz1C1MGDCOaweN88uHE2WK5PLsURwOsV2+MRXKS6tLWbJrMTmDrvC1aTQabhhyLT/lzmdr/nbKLGXYXY56evHK3bM+oK3SVkXu3nUM6xw6YaMQ4uw1bdq0M9a3BDhC4AnaatQZ/YIbq8NK7t51GPVh9Erv4Vu/8sAnf2N/8QHfcQ6Xgw+XfEyH5u3pl9mnUXeg1+l9FbcBXpj4LLl71mF1WRmVPZQDxQd58KPHWLNzHbHhMYzuOYL1e4NvF89ITqdrWhYaRYOiKEweksPLtdbLAFjs3q2ZMeHRXJ49mjCDMaCfMEMYU676C7NW/8ChosO0TDixzd3msLH/2IkMz3VHnQAyEjP438IPePCKuwP61mq0dEnryNb87QB8vfZb7r74jnq/R5GmyKDt0eb6t+wLIc5PEuCI815KXAq90nuwdq//NutBtQKODfs38sys533TPe2aZfLPcY+yp2CvX3BT2ws/vkx2Rm/G9bmCtsltTuqeFEWhd5teREQYMei1TPnocXYd3Qt4C39OWzwDgI7N2+H0uNhbuB+3x01m8wweHz/FL0HfzUMn0SK2GT9vXIDN4WDJ1uW+xc3Dul5IbET90zsXtOvLpFdu59qB42idlMq2QzuwOmx8cM/rXDv1NnYe2e0LbgxaAwatnl4ZvWgWk8SMlTM5GGJ318HCE+2hSlfUdmmPS1i0dYnfounOLbPomR6Y9FAIISTAEQJ48NL7+c/cN1m5azVGvZGRXYZzw6BJgHex7auz3/Bby7Lj6E7eW/QR3Vp1DdUlxVUlzM6by4pdq3h18ou0Skg96fvS67Ws3rnOF9zU1iwmmX+Mf4TrX7+d5yb/g5T4FmS1bI9G45+VWFEURvUczqiewzlWVsAD7z+KOczMmt3r0SoNl6MzGgwcKT3qV4U8xhxNZrMMdh7Z7Xesw+3gzyNu44peY/nPHG8iw8+XfcMl3YfRKvHEmqFf8hazYseJacFQ+YJq69aqK8/k/JOv1s6iqLKYrqmdufaCHDSNeAYhxPlHAhwhgOToJJ685nEsdis6rRaD7kT18X2F+wMqfwNsPJDHyC4jGuy73FLO7I1zuX3YLX7tv2xZyE8b52BxWOmb0Zuc/tdg1PlPFbndHjRBtn8DpCe1YuXuldhddl75/i3+ftX9uFUP5dUVFJQV0T6lbcA5SdGJfHz/2wDkFx/hpe9ep9xS4auBFcyanYFrXzyqh52H9wU9/lCRd2TGeHza62jZMW587U6uumAMLWKbkbf/V2at+dF3fIuY5lyVfUXQvurqld6TXuk9G3WsEOL8JgGOELWYjaaAtrjwWMwGExaHf0rx+PA42jXPxKgzYg+R06VGqcU/v87cvPk8+92/fVNFv+ZvJb/sCH8f+1e/4ywWB70yetAhpR3b8nf4vTai2xBsDm/CwIPF+dz5zoOYDCacbifNopN4808vkp7c2u+c2lvNU+Kbc++lf+Klb19n/IAryUrtwOHiI+h1ehKjvTua9hccZO3uwABncPuBpCcEH3Vp0yyDClsFVlcVMeYoyiwVFFQU8dac9+nTJpu2SW3ISExHUaFvu75c1WssUfUEWEIIcSokwBGiATHhMYzqfglfrj5RHVtB4fLeY4k0RXB1nyv4ZPmMevvo1rqb39ffrf8+IMnfL1sWcuOgyTSPbcbOo7uwu+x0SunInvx8Lux0AR7Vw64je3z5aUDhwk6DeWvuh1RYKwDvQmiAQyWHyXnpZkb3vJiHrrg3aOAG0DoxlUprFTkv3ky40YzFYUWraOiT2QuX202ZpZz37nyNsupy5uUtRPXAwA79+dOwWzEZTAzuMIjFtepf9cnIZkzvkURFmXly4iO43C7+O/cD3pzzHgDRpihuG3oTt3FTw994IYT4DSTAEaIR7hx+Oy1imrFi5yrCDCZGdhnGwPYDALhlyE20iE1h+c4VGLRGwvRGFmxdhM1pQ6NoGNXtYkbU2cYcLLmfy+PipZ9e4WDJIY6VFwDQJimdAe3689HSTwKOX7cnj9HdRvLfW6byyuy3WLdvA45a260tdisKwUelajhcTnRab2X0mgKZLtXN8u3enEBX9xtLTEQ0Nw2byKHiw2zYt4kVO1fSJjmN6wZO4ImrH2Hp9pXsObaHVgmtGJI1iNiYE7uddFodd426lV1H9vBz3kKMtab+hBDiTFJUVW1crvgzzOl0U1YWWIFYiHNRQUUB2w7vIDUuhfQgC2hfmf0fvsn9rlF9pcalcLDEfydS51YdeetPLxFzvKq52+2hrNzCjOUzmb9lAQ6nk4Ed+nPHyOuJjvRO/1jtNsIMRr9pqpU71rLl4Db+9/NHVNqq/K7RtnkGr9/6PM1jm3H1Czey4/AuFEXh0asfYFy/sei0OlRVxWJxYLF4AyuzWU94eFjAM0xbNIMXZ73Oi5Oe9Sb+E0KQmBg89cGZsnHjVkaPuuF3u17+4cDkqb8nGcER4gxIikoiKepE9uGSqhI+WTaDjQc3kRARx/DOQzlUkh+wNT2YgyX5hBvDqa5VgfzBsXf7ghsArVZDZEQY1/QdxzV9x/naHTYo91gprD5KUXkJ3dI6o9F48+M4HC56pnWnX7veDO7Yn8+Xf8PmA1sJMxi57Hh5BqPByLo9eew4vAuA4V2GkDPgKl//iqIQHm7E4XDjcrlxu4P//5Kianh6wqMM6tIHt9uDzebE5Qqef0gIIU4HCXCEOMM8qofHv3yKzYe2ALD72G5y967nuQlPccuQG1mzey3vLf6o3j5qghuD1kCftr3pkdEt4Bi9XhvQBhARYSQ6Op22LbwjSaqqoqre4MRmczJ73TyiIsL5IXc2FVbvKM7+woO0TEghO7MHYfoTO7t6tgm8LoDBoMHlcmO3u3A63X734nJ5mDh4nN/2daNRT1lZdciASAghfquTTiBRVFTExIkTfV87nU7uuOMOJkyYwMyZM0O2CXG+Wr93gy+4qeHyuPhhw2w6tGjP6O6jMBtCr5OpzeF2kByZiMcdOPoRLFgIC9Oh1QbmxdFoFPR6LZGRYVzcfQiPfvq0L7gBKKsu57OlM/m/z55h1Y61ZLf1bs0uLA+sa1X32uXlFqqr7djtTiwWOxqNEpCbR6NRMJn0jXpmIYQ4FScV4JSXlzNlyhSs1hPbZT/++GOysrKYPn06c+bMoaqqKmibEOerClvwauFVx9e8xEfGccOgySgEz3dT15drZ/Gfn97BU2v5nKqqWK2B9ZzqBhbBHK0ooMxS4fu6Q0om3/xtGlNv/hdPT3yUi7sPZUjWAMZkX8LSrSs4Wupfg0pVVYxGPVqt9/4VRcHl8lBVZcfjUdFogj+XIgn6hBBn0El9wmi1WqZOnUpERISvbdWqVYwaNQqA7OxsNm/eHLRNiPNVr7QeRAWpo9Q7oyduj5s5eT9zoPgAo7uPZGzPyxrV57u/TOP+d/9OcVkZVquD8nIbNpsz4DiHwx3kbH918/v8v7F3+mVdTolvTvf0Lvxr0mN8NWUazWKTOVx8hF1H9lBaVYaiKBiNOqKizISHG4iLCyc62kRcXDgGQ/BpM/CO4kREGNDpGhfYCSHEyah3Dc5jjz3G3r0nUsT37duXu+/2L5xntVpJTk4GIDo6muLi4qBtQpytrA4rNqeN2PDYkMcUVRSx4UAe8ZHxdG/V1W8nUkOizFE8eOn9vDb7DQqritBpdAzvfBFX9BrLv79/mTmbfvYdGx8eR2ZyG3Ye211Pj17zNy8mM7k9Of2u9mvXajW4j09huVxuqqvtmM2GkPf864Htvn/rNFp6pAeus+ncqiOqqqIoCuWWCuZvXsK0RdPxeFQ+vu8tmsUmo9Np0NXKxKwoCgaD3ndejZqvDQYdoCMszEB5uRWns+FgTAghGqveAOeJJ55osAOz2YzNZiMyMhKLxYLZbA7aJsTZxuV28db8d5id9zMWu4Ve6T24d+SdpMb714z6Yf1sXv/5LaxO70hHXHgsUaZI2iS3YcIF1zSqkOag9gPond6LbYe3kxiVQMu4FLYf2cncTfP8jiuuLqFzahZWp41Dx7eGt05IJdIcyeYDvwb0a6y1ANhkMmA2G9BoFNxuD9XVjuPrYBw4HE6iosx+63FqAo1IcwQKCioqLo+bgvJCWif6fw/cHjcOtwOT0US0OYrJF47n8uxR3P7WX5i1+if+NPLGkM9eN7AK9rXZ7A1yhBDidPnNk+BZWVnk5uYCsG3bNlJSUoK2CXG2+XT5DL5c8w3V9mpUVNbuXcez371A7dRQxZUlvDHvv77gBqCkupR9RQeYv2UBU6Y/SmFFYaOuZzKE0SOtG6nxKSgKHCw+GJDNGKDaYeGDP73D8xOe4aVJzzPtznf55/gpAQuREyLiGdJhEODdQRURYfStd9FqNURGGn3rYlwulZKSasrLrVRWWrHZnL5AY2T3oTw18RG6ts6iZVwLNuzbRN30WAadAVOdhIFR5khuH36Drwipx3Pq277rLoQWQojf6jdvE7/yyiu5/fbbWbt2Lbt27aJbt24kJycHtAlxtlmyfVlA26/529h5dCftmrcDIO/gJiyO0AkoS6pK+HnzAib2H9+oa0ZEGAkL8+4eGp09lA+XTuNQ8WG/Y9o1y0Sr0ZLdphc6nXcNS/Oolrxw/VN8uOgz9hceoG1SG3q3yeazFV9gNpq4/qIcwH+ktGYayGo9sTbH4XABBCTju7zPaC7vM7pRz1Bbh5btcLgdOBwuqqvtREaa0OkCR4lqC9bWmLVCQghxMk4pwJk2bZrv3ykpKbz33nvk5uZy3333odVqg7YJcbbRaoL/XFpsFoqrSoiPiCMpKrHBfvYW7sHmtBGmD8zgW1t4uAGT6USpguiISN647UUuf3aibyQns1lbxmVfjlarEBlp8uWTcTrdZDXvwrPjOwMwfcUXTP3pVV9fcTHR3Dx0InWFSlTubf/ti3t1Wh09W2b7ppfKyy2YzQbCwvQoiuILZGqCGlVVsdmcGI1632iTy+XGaq2/WKkQQpwsKdUgzlufr/ySN+e/7dcWY46hylaFqqpckNmPv1xyFy/Pfo1lO1bU21diZCJ/Hn47F3UaHPKYuLjwgKkYVVVZvXUza3atIS4yngHt+rF+Xx59O3ajeVyS37E2m5PKShsVlgque/NmKmttP09LTGX6A+8RERbua3O7PZSUVBOMyaQnIsI/IAs2stLQazUBi8vlxuFw4fGELtdgsdixWp3Ht46DXq9DVU+MKhkMOsxmA1qtgtPpXRwtiQBFUyalGs4syWQszltX970Sm9PGT3lzqbJVEWEM52j5iRwvS3csQ6PR8Mjlf+ObtbPYcCCP8upy9hUdwO7yH3EorCzkhR9epmtqFvGR8UGvF+r/JdokpZGRmEZBRRH3T5tCflk+yy+YHXCcd9cRHCg+5AtudMdHofYVHuT/PnmGf036BzqdBqfTHTQvDoBWqxAWdmIkqaaelMGgC5kNOVTgoyiKb1TK41GpqLCEzL3j8ah4POrxf4Pd7vK9ptNpiIoK813HaNSg02lDBmhCCNEQCXDEeUujaLh+0CSuHzQJt8fNlS/nBByzfMcKPKqba/vncG1/7+slVSX886tnyDu4ye9Yi8PC6j1rGdVtZNDr2e1O35qa2m01cc8nyz5lx9Ed6LQ6SqvKSIitVLcmAAAQpklEQVTyD5RqgoP0xNY0j23G7SNuYGS3oaDAzxsXsPzXNVRW2hp87oiIML91MjVrdUIFN42l0Xin1UKN9NTcfzA1U1q1abUajEadXyAkhBCNJVsXhMAb7Oi0gfG+TqtDq/j/4Y+LiCM1vmXQfszG8KDtABaLk+pqGy6XB7fbg9XqoLLyxEjQlkNbAe/29S9WzAo432bzjsiEh4Xzyo3Pck3/y4kKjyTKHMm4C8by0JX3NfygBK9ZVTvg+S20Wk3QzMWKohAZGeZbYF1XYzIuCyHEyZARHCHw/gEeljWEmau/9mvvkprFw188zqHiQ2Q2a8vkQZPo0Lwdl3Qdwc+b5uNwn5gGSk9Mo3/bvvVex2JxYrEEZhwGSIiMZ3fBHgA+WPAZpVVlDO96IR1bdEB1a7HbT5zXITUz4PzEmHiKi/3LooSF6TGZ9Gg03uR/Dod3DUzN9vHaz3+meSuPG1CUE9NtdrsLg0GL0Rj4UeTxqL71OUIIcbIkwBHiuFsvugmNRsvCXxfh8Xjo1qoLS7evwO72jrIUVhax7cgO3rnldTqnZvHkNY8xc9XXHKsoIKtlJ64bcC163akXkByXfQUOt52/jLmDHuldKSgvYt3uPGwxHsA/x0yweERRvP/VTHkZDDoiIoy+4EWj0aLXa39TvprfSqPR+C1urgl06nK7PVRWWjk7tkAI0TQ4ndUcPrLmj76N343sohIihP/+8j+mr/gioP3ekXdxZe+xZ+Sa4ZE6zGH+CfXKy60BIxnx8eFBp3VKSy24XN6cMvVNCdntTvR67Vk7NWSx2KmuDr5IWoim4vfeRbV27Vqys7N/t+v90eHF2fnpJsRZwO4InpvF7mx4Ie+pMBgCgxsg6PRNsKkbj8fjC24aotWevcENgMv1x40yCSGahrP3E06IP9gF7foFtOm1ei5oG9j+e6uudvgFAaqqUlXlP+JR3+4jVQ0eQLjdjQ+SGqKq6in9H5zd7pKdU0KI30wCHCFCyM7oxR3DbiPGHANAclQSf730flontjoj13M4XAHBhaqqQf/YezwqpaXVVFRYqaqyUVpa7bcIuaa/qipbwJobj8dDdbU9aPBRXW0/qYW99QUwtTMZN7avigorFRVSdFMI8dvJImMh6pHT72rG9BjN4dIjpCa0xKgzNnzSb1BRYSU83IjBoDu+ldxZb8DR0EiH1erEavWutwkL8/66V1c78HhUKivtREQY0Gg0qKqK1eo43l/ojwVvWQUHWq0Gp9ODw+EiIsKIVutNzBdsi3hj2e1OGbkRQpw2EuAI0QCz0UzbZm1+l2u53SoVFad3jY9WqxARYfQlGdTrdVRWWo8HFN7gx+12UzPQ450i8taLOnFfHux2J1arg7qbsKqqvGuVoqNNQXdFeTyqb9dX7REdb8kGb30q72iT1KMSQpw+EuAI0cSZzUa/DMparYbw8DDfrkWnM3DNTUWFDYPBhU6n4HI1Lh+N1WpHr9cGTEt5t68rAdNZNpsDm82FqvKHbl0XQjRNsgZHiCYuWOZivV4bkOyvLofDhcVS/xSZ//GegHVAcGLUpm7gExZmwO32SHAjhDgjZARHiHOAyaTHaPTWa3I4nCeVI8abubhum6fRlbo1GgWNRmnU1u3KSjtOpwe9XotOpwmovVW33+hoE1VVNqkaLoQ47STAEeIsZzLp/bL/6nRGNBrFr45VfSwWh1+lbvAuPm6MyMgwjEYdiqLgcrmprLTicqnodFr0eg1ud+D0lc3mxOl0ERkZFqLXE7zZlsMoL5edU0KI00sCHCHOcrUX+9Zuq6qyN6qUgcPhorzc6gtU7HZXo6adwsMNfpmQdTotEREmnE4XZvOJ3WQ1/dfwjsyY0WobNwNuMOjQaJR6q40LIcTJkgBHiDNkxc6VfLHqa46WH6VTiw7cMPg6UuNTT7qfYLlkanLMREQYMBj0gIrNFnrqyul0B11MrNUqhIcbj++kqr1VPHidqJqpp9oMBh0mk943KmQy6Rsd3MCpJwQUQoj6SIAjxBmw+dAW/vHl075q40fKjrLt6E7+d+sbhOmDT93odBo8Hk/ANmyHw4muTv4dh8NFeLix1giLgtlsRFXVkNXKg6k90qLRgE4Xhsdjxel0Bx1RUVU1aMBVO6BRlJPbu2C3O6WophDitJNdVEKcAXPz5vmCmxr5Jfks27Ei4FidTkNsrJnY2HDi4iKIjPQPZqqrHdhsDt8oR02G4mA1qoJNZ4ViMOgCRloURfH1a7M5A0ZW6pvaMhp1aDTgcgUeU5NIsOY/p9ONy+XGYrE3ei2REEKcDBnBEeIMsDiCJ+uzOgIX00ZGmnzTPoqi+LZP1x6Jqay0U1VlR1GU44nzTj1jcI2GuvBOVdkICzuxe8ticRIV5R9IeTwqJpMBk6mmJpbdL1GgqqpUV9sbvbBZCCFOBwlwhDgDLmibzfwtv/i1mQ1m+rbJ9mvT6QLXtIA32zD4BwSqeqL2U02NqtqLgKHh0g11j/V4PAFVxR0Ot98xdfusqLBhNLp8Sf1q34N3XZCRkpIqbDYniqKEnO4SQogzSQIcIc6AoVkXsbdwP9/kfke1vZpm0cncMew2EqMS/Y6rWWBbd0SmMYtuq6psgIrRqEdVvYuMLZbG58eBmtpXYccXGXuwWh2N2mFVE/hERZkCXlMUhdjYcDQajS+Rn8vlwWZzNiqXjhBCnA6KepZsX3A63b7U8UI0FeWWco6WHSMjKR29Lvj6mKioML8pH29VbdtJVfX+rYKVUmiIRgNxcRGNni7zeFTKy6txuc6Kjxwh/nCJiZG/6/XWrl1LdnZ2wweeJn90eCEjOEKcQdHmaKLN0fUeU1FhIzzcg16vw+PxjsT8nsENnNoHkcGgO6m1QBqNd32RFNUUQvweJMAR4izgzV9zctNL5yKN5rcvjhZCiMaQbeJCiFPicLhOulCm1NUUQvxeJMARQpwSjwfKy604HK6QU1x128PCdEEzJAshxOkmnzRCiFPmcnkoL7cGFAStUXeNTk0iQYfDhU6nwWw2otdrcLlqdnAFlpMQQohTISM4QojfzGr1VhBvDEXx/hcVZTqe/ViDwaAjKsqEVitrdIQ4Xzz88MPk5OTwxhtvnJH+JcARQpwWjU0yqKoQGRkWtExE3cSFQoimae7cuXg8HmbMmMHBgwfZt2/fab+GBDhCiFNmNOqJiTETFxeOXq8Juhanpoq5x+NBVVXCwvQha2adhgoUQohzwOrVqxk1ahQAAwcOJDc397RfQ9bgCCFOicGgIzLS6Ftno9UacLvdaDQaX+JAm83pq6EVGWmstxio93hZgyNEU1FSUsKtt97q+zonJ4ecnBwALBYLycnJAERHR5Ofn3/ary8BjhDilBiNgYn+tFot5eUWQMHt9uB2e/eFq6qKTqcN2Ze3uKgDl0sCHCGairi4OL766qugr5nNZmw2b1Fii8Vy0iknGkOmqIQQpyRUFmNV9ebIqQluatRMVdXmdnsoKqqipKQam02qjQtxvujcubNvWmrbtm2kpKSc9mvICI4Q4pQ4HC6MRv+PEJfLEzSQAbBa7RgMWl/1clVVqa62/+H1aoQQv7/hw4czceJECgoKWLx4MZ9//vlpv4YEOEKIU2KzOdFqvfWlNBoFp9N9vMJ5cC6XSmlptW9qy2534XZLcCPE+SgiIoJp06axbNkybr31ViIjT3/hUQlwhBCnrLraQXW1A41GweNpOFjxeMBq/X0LiQohzk7R0dGMHj36jPUva3CEEL9ZY4IbIYT4PUmAI4QQQogmRwIcIYQQQjQ5EuAIIYQQosmRAEcIIYQQTY4EOEIIIYRociTAEUIIIUSTIwGOEEIIIZocCXCEEEII0eRIgCOEEEKIJkcCHCGEEEI0ORLgCCGEEKLJkQBHCCGEEE2OBDhCCCGEaHIkwBFCCCFEkyMBjhBCCCGaHAlwhBBCCNHkSIAjhBBCiCZHAhwhhBBCNDkS4AghhBCiyZEARwghhBBNjqKqqvpH34QQQgghxOkkIzhCCCGEaHIkwBFCCCFEkyMBjhBCCCGaHAlwhBBCCNHkSIAjhBBCiCZHAhwhhBBCNDkS4ABFRUVMnDjR9/WxY8cYPHgwkydPZvLkyZSUlADw8MMPk5OTwxtvvPFH3epJq/tsTqeTO+64gwkTJjBz5syQbecSl8vFkCFDfO/X9u3bAXj11VcZN24c//znP//gOzw9zsWfv/oEe9+a0ntW+3evqf3e1X62pvR5WVlZya233srNN9/MXXfdhcPhCPoc5+KznY/O+wCnvLycKVOmYLVafW0bN27kjjvuYNq0aUybNo24uDjmzp2Lx+NhxowZHDx4kH379v1xN91IwZ7t448/Jisri+nTpzNnzhyqqqqCtp1Ltm/fzqWXXup7v9q3b8/mzZvJzc1l5syZxMfHs3z58j/6Nn+Tc/HnryF13zen09lk3rO6v3tN6feu7rM1lc9LgG+//ZabbrqJ9957j4SEBH788ceA5zhXn+18dN4HOFqtlqlTpxIREeFr27BhAzNnzuTKK6/kpZdeAmD16tWMGjUKgIEDB5Kbm/uH3O/JCPZsq1at8j1HdnY2mzdvDtp2LtmwYQMLFy7k6quv5uGHH8blcrFmzRouvvhiFEVh4MCBrF279o++zd/kXPz5a0jd923FihVN5j2r+7vXlH7v6j5bU/m8BJg0aRIDBgwAoLS0lG+//TbgOc7VZzsf6f7oG/i9PfbYY+zdu9f3dd++fbn77rv9jhk8eDB33nknJpOJG2+8kW3btmGxWEhOTgYgOjqa/Pz83/W+G6Mxz2a1Wv2eo7i4OGjb2SzYc77//vskJSXx0EMPsWjRIiwWC6mpqYD3mYqKiv6o2z0tzoWfv5PVpUsXv/fNbreTnp4OnPvvWe3/qYCm8XtXo+6znaufl/VZv3495eXlpKSkBDzHuf5s55PzLsB54oknGjymZ8+eGAwGADp16sT+/fsxm83YbDbA+8fG4/Gc0fs8FY15tprniIyMxGKxYDabg7adzeo+p8Ph8L1fnTt3Zv/+/YSHh2O32wHv+3WuVyQ5F37+TlaHDh383jeXy9Wk3rPamsLvXSjn6udlKGVlZTz55JO89tprvP/++wHPcS4/2/nmvJ+iCuaWW26hoKAAq9XKsmXLyMzMpHPnzr6hyG3btpGSkvIH3+WpycrKCniOYG3nkr/+9a9s27YNt9vNvHnz6NChwzn/THU1lZ+/2uq+bxaLpck9Y42m+HtXoyl9XjocDu677z4eeOABUlJSgj7Hufps56PzbgSnMe666y6uv/569Ho9EyZMICMjg6SkJCZOnEhBQQGLFy/m888//6Nv85RceeWV3H777axdu5Zdu3bRrVs3kpOTA9rOJXfddRcPPPAAAEOHDqV///54PB5efPFFnnrqKZYsWcL//ve/P/guf5vhw4c3iZ+/2uq+b3feeScTJ05sMu9ZbU3x965GU/q8nDlzJr/++itvvfUWb731FldddRWzZs3yew5FUc7JZzsfSTXxk1BeXs6yZcvIzs4mMTHxj76dU3bs2DFyc3MZNGgQkZGRIdvOdTabjYULF5KVleVbj3Muayo/f/Vpau9ZbefL712NpvLzGuw5msqzNXUS4AghhBCiyZE1OEIIIYRociTAEUIIIUSTIwGOEEIIIZocCXCEEEII0eRIgCOEEEKIJkcCHCGEEEI0Of8frH2N+y3/vKgAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="子图">子图<a class="anchor-link" href="#子图"> </a></h2><p>Matplotlib通过<strong>子图</strong>(subplot)的概念,实现多角度数据对比:</p>
<ol>
<li><code>plt.axes</code>:手动创建子图</li>
<li><code>plt.subplot</code>:简易网格子图</li>
<li><code>plt.subplots</code>:用NumPy数组创建网格</li>
<li><code>plt.GridSpec</code>:自由排列网格</li>
</ol>
<h3 id="plt.axes:手动创建子图"><code>plt.axes</code>:手动创建子图<a class="anchor-link" href="#plt.axes:手动创建子图"> </a></h3><p><code>plt.axes</code>函数默认创建一个标准的坐标轴,填满整张图。其可选参数用4个值分别表示图形坐标的 <code>[bottom, left, width, height]</code>(底坐标、左坐标、宽度、高度),数值的取值范围是左下角(原点)为0,右上角为1。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">"white"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"font.sans-serif"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">,</span> <span class="s2">"Arial"</span><span class="p">]})</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span> <span class="c1"># 默认坐标轴</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">([</span><span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>面向对象画图接口中类似的命令有<code>fig.add_axes()</code>。用这个命令创建两个竖直排列的坐标轴:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="n">xticklabels</span><span class="o">=</span><span class="p">[],</span> <span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">1.2</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">))</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">1.2</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">));</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmkAAAJdCAYAAABpmuBpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3iV5f0G8Pus7ORkJ2QPEgIJhJUAIWEoIktQRIaIDAURR+ugtvx+tVbb+mttxTpQkaGCiIgoigKySRgBwgyQvffe84zfHwGrlZWT8bznnPtzXb1Qecm56UUe7vOc9/0+Mr1erwcRERERSYpcdAAiIiIi+jWWNCIiIiIJYkkjIiIikiCWNCIiIiIJYkkjIiIikiCWNCIiIiIJUooOcN2IESPg7e0tOgYRERFRryksLERiYuINf04yJc3b2xs7duwQHYOIiIio18ycOfOmP8ePO4mIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkiCWNiIiISIJY0oiIiIgkyOCSVlFRgYcffvimP9/e3o7ly5dj7ty52L59u6EvQ0RERGSWDCpptbW1eOmll9Dc3HzTazZv3ozw8HBs3boVe/fuRUNDg8EhiYiIiMyN0pBfpFAo8NZbb2HFihU3vSYxMREvvvgiACAqKgrJyckYOXKkYSlJuOY2LSoaWlFW34qKhlaU/+zH8vpWeKqtMHOoDyJ91JDJZKLjEhH1ipZ2LQ6nlqG5XQu5TAaZTAa5DB3/DPzi3+VywNnWkusk3TGDSpqdnd1tr2luboaHhwcAQK1Wo7Ky0pCXIgE0Wh22nSnAN+cLfyphDa2aG17rZKOCq50ljqSV49MTuQh2s8WsYb54YIg3PNVWvZyciKh3VDe2YdPJXHxyPAeVjW2d+rWRPmo8OS4YEwd4Qi5nWaObM6ik3QkbGxu0tLTA3t4eTU1NsLGx6amXom6i1+txKLUMf/shBRllDejfxwER3mq42lnAzd4SrnaWcLO3hNu1H51tLaBSdHxiXtfSjh8uFuOrswX4+54UvLE3BbEhbpg1zAcTB3jASqUQ/LsjIuq6vMomrE/IwrYzBWhu12J8Pzc8FhsEHydr6PR66PQA0PGjTq+HTtfxo14P6KFHcmEd1h7NxPLNZxHkZovlY4Nx/2BvWCj5HB/9Wo+VtPDwcCQlJWHSpElISUnBnDlzeuqlqBskF9bibz9cxfHMSgS62uLDBcMwcYDHHW/JO1ipMDfaD3Oj/ZBT0YgdZwvw1dlCPPv5OdhbKTFtkBdmDfPGUD8nbvMTkdG5kF+DtUezsDu5GAq5DDMGe2PZmCCEeth36usM8nHEnChf7E4uxppDmfjd9otYvS8Nj8cFYW6UL2wte+yvZTJC3fKn4cSJE8jMzMQjjzzy03974IEHsGzZMpw5cwYZGRmIjIzsjpeiblZY04x/7U3FjnOFcLJR4c/Tw/HwCL+fdsgMEeBqi+cn9sNvJ4TiZHYlticV4Jtzhfj8VB4CXW3x8rQBGB/m3o2/CyKi7qfT6XE4rQwfHslCYnYV7K2UWDYmGItiArp0O4dCLsO0QV6YOrAPjqSV4/3DmXht1xW8czAdi2ICsHBUAJxsLbrxd0LGSqbX6/U99cVLS0uRlJSEuLg42Nvf+t3GzJkzsWPHjp6KQv+lrqUd7x/OxPqEbADAY7GBeHJcMBysVD3yeg2tGuy+VIz1CdlILa3HS5PC8MSYIO6qEZEk7b5UjDf3pSG9rAFeaissiQ3EnChf2PfQGpmUW433D2di/9VS2FgoMC/aD0+MCYK7A+/tNXW36j89WtI6gyWtd7Rrdfj8VB7e2p+OqsY2zBzijRfu7QdvR+teef3mNi1e3H4B318sxswh3vjbzIG8X42IJEOv1+Pdgxn41740hHna44mxQZg2yKtLny50RmpJPT48komdF4rgbGuBTxZHY4CXQ6+8Nolxq/7DD7/NSG5lI5Z8fBqZ5Y0YFeSCVVP6Y6CPulczWFso8O68IQjzsMe/9qUhs6IRaxcMgwffLRKRYFqdHq98exmbTuZi5hBv/H3WoF4rZ9f187THm3MG44mxwVi08RTmfHgC6xYOx4ggl17NQdLAx0nMRE5FI+auPYnKxjasXzgcW5aO6PWCdp1MJsMzd4fgg0eGIb20HtPfTcCF/BohWYiIgI55Z09vOYtNJ3PxxJgg/POhyF4vaD/Xz9Me25+MgZuDJR7dcAr7rpQKy0LisKSZgexrBa2lXYstj4/E3f3v/KnNnjQpwhNfPRkDpVyO2R+ewM7zhaIjEZEZqmtpx8INp7A7uQT/O7U//jClvyTml3k7WmP78hiEedpj+eYkbDuTLzoS9TKWNBOXVd6AuWtPoE2rw+fLRkru3ob+fRzw7dOjEenjiN9sPY9/7EmBTieJ2ySJyAyU1rVg9gcncDavGv+eOxiPxwWJjvQLzrYW2LJ0JGKCXfC77RfxwZFM0ZGoF7GkmbCMsgbMXXsSGq0eny8diTBPaRW061zsLLH58RGYF+2LNYczsWzTmZuecEBE1F0yyxswc81x5Fc1YcOiKMwY7C060g3ZWiqxfmEUpg3qg//bnYK//XCVb2bNBEuaicooq8e8j05Cp9fj82Uj0c+zcwMXe5uFUo6/PTAQf54ejkOp5Zi55hjyKptExyIiE3Uurxqz3j+OVo0WW5eNQlyIm+hIt2ShlOPtuUPw6Ch/rD2ahZXbL6JdqxMdi3oYS5oJSi+tx9y1idDrgc+Xjuz0RGxRZDIZFsYE4NMl0Sita8WM9xKQVd4gOhYRmZhDKWV4+KNE2FupsH15jLCHqDpLLpfhz9PD8dyEUHx1tgDLNyWhuU0rOhb1IJY0E5NW2rGDJpMBW5eNRIiRFLSfG93XFV+viIFMJsOyTUmob2kXHYmITMSXZ/Lx+KdnEOxui6+ejEGAq63oSJ0ik8nwmwkheO3+CBxMLcOC9YmobeIaaapY0kxIakk95q09CblMhq3LRqKvu53oSAYLcrPDew8PRXZFI5774gLvvyCiLvssMRcrt1/EqCAXbF02Cm72lqIjGWzBSH+8O28oLhTUYPaHJ1BW3yI6EvUAljQTkVJSh3kfnYRS0VHQgt2Mt6BdNyrYBX+c2h/7r5birQPpouMQkRE7l1eNV769jHH93LBhURTsTOAg86mD+mDjomjkVTXh6S3noOE9aiaHJc0EXCmqw7y1J2GhkGPrslEIMoGCdt3CmAA8NMwHbx9Ix57kYtFxiMgIVTe24ekt5+DhYIW35gyGhdJ0/uqLDXHF32ZG4FR2Fd7clyY6DnUz0/mTaqaqGtuw+ONTsFIpsHXZSAQa2f0VtyOTyfDa/RGI9HXE89suILWkXnQkIjIiOp0ez207j/L6VqyZPxSONhaiI3W7B4b4YG5UxwijQyllouNQN2JJM2J6vR6/234R1Y3tWLdwuNHdAHunrFQKrF0wDLaWSizbdAY1TW2iIxGRkVhzOAOHU8vxx/sGYJCPo+g4PeaV6eEI87THc9vOo7CmWXQc6iYsaUZsc2Ie9l8txUuTwxDuZRyPkBvKw8EKHzwyDEU1zXjmc957QUS3dzyjAm/uS8P0SC88MsJPdJweZaVSYM38odBo9Xh6y1m0abhGmgKWNCOVWlKPv+y6grGhblgcEyA6Tq8Y5u+E12ZEID69Am/sTRUdh4gkrLSuBc9uPYcgNzu8PnOgJM4r7mlBbnb4vwcH4lxeDf6xJ0V0HOoGLGlGqKVdi2c/Pwd7KyX++VCkJA4C7i1zo/2wYKQ/PjyaxQPZieiGNFodntlyDo2tWrw/fyhsTeBJzjs1bZAXFo7yx7qEbOxJLhEdh7qIJc0I/d/uFKSW1uOfD0Ua9ZwfQ7183wBEBzrjd9svIrmwVnQcIpKYN35MxamcKrw+c6BRDvTuqlVT+2OQjxort1/g8XpGjiXNyBy4WoqPj+dgyehAjOvnLjqOECqFHGvmD4WLrQWWfXoGFQ2toiMRkUTsu1KKD49kYf4IP9w/RJoHpvc0S6UC7z08FDIAK7YkoaWdR0cZK5Y0I1JW14KV2y+ifx8HvDS5n+g4QrnaWeLDBcNR2diGFZ+d5UHDRIS8yia8sO08Bnqr8cdpA0THEcrX2Qb/mj0YyYV1+Ov3V0XHIQOxpBkJnU6PF768gKY2Dd6ZNxiWSoXoSMIN9FHj7w8OwqnsKi5CRGaupV2LFVuSAABr5g+FlYpr5D0DPLBsTBA2nczFtxeKRMchA7CkGYl1CVmIT6/Ay9PC0dfd/O6xuJn7h3hjUUwAPj6egzM5VaLjEJEgr+26guTCOvxr9mD4OtuIjiMZK+/th2H+TvjDVxeRWd4gOg51EkuaEbhUUIs39qZiUrgn5kX7io4jOSvv7QdvR2us+voSZwMRmaFvzhXis8Q8PDE2CPcM8BAdR1JUCjnemTcEFko5nvrsLO9PMzIsaRLX2KrBs1vPwcXWEv/3oHnM+uksW0sl/jw9HGmlDViXkCU6DhH1opyKRvxhxyVEBzpj5UTzvlf3ZrwcrbF6zmCklNTj5Z3JouNQJ7CkSdyfv7uMnMpGrJ4z2CTPnOsuEwZ4YFK4J/69P52PnBOZCb1ej5e/vQylXIa35w6BUsG/0m5mXD93PDU+GNvOFOBwKs/3NBb8Ey1huy4WYduZAqwYF4xRwS6i40jeK9PDoVLI8b87k6HX60XHIaIetvdyKY6mleO5e0LhqbYSHUfyfnN3KIJcbfHKt5fRquHHnsaAJU2iCmua8YcdlzDY1xG/nRAqOo5R8FRb4cWJoTiaVo7vLhaLjkNEPaipTYPXdl1BmKc9Hh3lLzqOUbBQyvHK9HDkVDZhXXy26Dh0B1jSJOpPO5Oh0+nx77mDoeIW/h1bMCoAg3zUePW7K6htbhcdh4h6yHuHMlBY04xXZ0TwY85OGBPqhknhnnjnYDoKa5pFx6Hb4J9sCTqSVo79V8vwzN0h8HexFR3HqCjkMvztgYGoamzlAcNEJiqrvAFrj2Zh5lBvRAc6i45jdP54X8eg39e+uyI4Cd0OS5rEtGt1eG3XFfi72GDx6ADRcYxShLcai0cH4rPEPCTlVouOQ0TdSK/X40/fXoaVUoE/TO4vOo5R8na0xtPj+2LP5RIcTSsXHYdugSVNYjafzEVGWQP+d+oAnirQBc/fEwovtRVW7bjEI6OITMie5BLEp1fghYmhcLO3FB3HaC0dE4QAFxs+RCBxLGkSUtnQitX70hAX4ooJ/c3z8PTuYmupxJ9nRCC1tB7rE3iDLJEpaGrT4NVdV9C/jwMeGcmHBbrCUqnAK9PDkVXRyDVSwljSJOTNfWlobNPi5WkDOLS2G9wzwAMTB3jgrf1pyK/i7DQiY/fOwQwU17bgtRnhfFigG4zr546JAzzwzoEMFPEhAknin3KJuFJUh89P5WHBSH+EePBszu7yyvRwKGQy/JGz04iMWkZZA9bFZ2HWMB8MD+DDAt3lj9MGQKfX46/fXxUdhW6AJU0C9Ho9Xt11GWprFZ7jTLRu5eVojRcm9sPh1HL8cKlEdBwiMoBer8cr316GlUqB308OEx3HpPg62+Cp8X3x/aViJKRXiI5D/4UlTQL2JJfgZFYVnp/YD2obleg4JmdhTAAGeqvxyneXUdfC2WlExuaHSyVIyKjAynv7wdWODwt0t2VjguDvYoOXv01Gm4YPWkkJS5pgLe1a/PWHqwjztMe8KF/RcUzS9dlplQ2teGNPqug4RNQJja0dJwuEezlg/gg+LNATrFQK/Om+Acgqb8SGY3yIQEpY0gRbF5+FgupmvDxtAG+E7UEDfdRYGBOAzYm5OJfH2WlExuLtg+koqWvBqzMioJDzgaqecleYByb0d8fbB9JRXMuHCKSCrUCgktoWvHcoE5PCPRHT11V0HJP3wsR+cLOzxN9+uMqHCIiMQEZZPdbHZ2POcF8M83cSHcfkvTwtHBodHyKQEpY0gf6+JwVavR6rpnBqdm+ws1Ti2btDcDqnGodTOWWbSMr0ej1e3nkZtpZK/G5SP9FxzIKfiw2eHBuMXReLcTyTDxFIAUuaIEm51fj6XCGWxgXCz8VGdByzMXu4L/ycbfCPvanQ6bibRiRVHUWhEivv7QcXPizQa54cFwxfZ2v8aedlntYiASxpAuh0erz63WV4OFhixbi+ouOYFQulHM/fE4qrxXX4/lKx6DhEdAMt7Vr89furGOitxrxoP9FxzIqVSoE/TQtHelkDPj6WIzqO2WNJE2DHuUJcKKjF7yeHwdZSKTqO2Zke6YUwT3u8uS+N7xSJJOizxDyU1LVg1ZT+fFhAgAkDPDCunxveOZiO2maOLRKJJa2XNbRq8Pc9KRji54gZkd6i45gluVyGFyb2Q3ZFI7YnFYiOQ0Q/09SmwfuHMzC6rwtGBbuIjmO2Vt7bD3UtGqyPzxIdxayxpPWy9w5loLy+FX+6LxxyvkMUZkJ/dwz1c8S/96ejpV0rOg4RXfPJ8VxUNLTh+Xv4sIBI4V5qTI7wxIZjOahubBMdx2yxpPWi/KomrI/PxoNDfTDY11F0HLMmk8mw8t4wlNS1YPPJXNFxiAhAfUs7PjyaifH93DhyQwKeuycUjW0afHiUu2misKT1ovcOZQCyjm1kEm9UsAviQlzx3qEM1PO4KCLhNiTkoKapnbtoEhHqYY/7Bnnhk+M5KK9vFR3HLLGk9ZL8qiZsTyrAw9F+8FRbiY5D16y8tx+qm9qxLp5HoRCJVNPUhnXxWbg33AMDfdSi49A1v5kQglaNFh8cyRQdxSyxpPWSdw9mQC6X4clxwaKj0M8M8nHE5AhPrIvPQhXvuyASZu3RLDS0afDcPaGio9DPBLvZ4YEhPth8MheldS2i45gdlrRekFfZhK/OduyieThwF01qXpgYiuZ2LdYcyhAdhcgsVTS04uPjOZg2yAthng6i49B/+c3dIdDq9B237FCvYknrBe8d4i6alPV1t8fMoT749GQuimp4sDBRb/vgcCZa2rX47YQQ0VHoBvxcbPDQcF9sPZWPQq6RvYolrYflVTZhO3fRJO+3E0Kg1+vx9oF00VGIzEppXQs2nczFzKE+CHazEx2HbuKZuzpOx3n3INfI3mRwSVu1ahXmzJmDNWvW3PDnNRoNxo0bhwULFmDBggVITU01OKQxe/dQOpTcRZM8HycbzB/hjy+TCpBV3iA6DpHZeO9QBrQ6PX5zN3fRpMzL0Rrzon3x5ZkC5FY2io5jNgwqaT/++CN0Oh2++OIL5OfnIycn51fXpKamYurUqdi0aRM2bdqEfv3M75HqjnvRCvHwCO6iGYOnxveFpVKON/eliY5CZBYKqpvw+ak8zI7yha+zjeg4dBtPje8LhVyGf/MTh15jUEk7deoUJk+eDACIjY1FUlLSr645f/48Dh8+jFmzZmHVqlXQaDRdS2qEftpFG8tdNGPgZm+JJaMDsetiMZILa0XHITJ57x7MgAwyPD2+r+godAfcHaywYKQ/vjlXiIwyfuLQGwwqaU1NTfDw8AAAqNVqVFZW/uqagQMHYuPGjdi+fTs0Gg2OHDnStaRGJrey8addNHfuohmNpWOCoLZW4Z8/mufH80S9JaeiEV8mFeDhEX7wcrQWHYfu0PJxwbBSKbib1ksMKmk2NjZoaemYl9LU1ASdTvera8LCwuDu7g4AiIiIQG6ueR298+7BDO6iGSG1tQrLxwbjcGo5TmVXiY5DZLLePpAOlUKGFeO5RhoTVztLLIwJwK6LRUgtqRcdx+QZVNIiIiJ++ogzJSUF3t7ev7pm5cqVSElJgVarxf79+xEWFta1pEYkt7IRO84VYv4If+6iGaFFMQFwt7fEG3tToNfrRcchMjkZZfX4+nwhFo4KgLs910hjsywuCLYWSqzm/bs9zqCSNmHCBOzcuROvv/46du/ejZCQEKxevfoX1zz11FNYuXIl7r//fgwZMgQxMTHdEtgYXN9FWz42SHQUMoC1hQLP3B2C0znVSMioEB2HyOSs3p8OG5UCT/CTBqPkZGuBJbGB2HO5hPfv9jClIb/Izs4OmzZtwrFjx/D444/Dzc3tVztloaGh+O6777olpDHJqejYRVs4KoC7aEZs9nAfvHMgHWsOZSIuxE10HCKTcaWoDt9fLMYzd/WFs62F6DhkoMdiA/HxsWys3peG9YuiRMcxWQbPSVOr1ZgyZQrc3PgX2M+9e+jaLto47qIZM0ulAo/HBeJEViXO5VWLjkNkMlbvT4ODlRKPx3GNNGZqaxWWjQnCgZQyrpE9iCcOdKOcikZ8fa4Qj4z0530WJuDhEf5QW6vw/uFM0VGITMKF/Brsu1KKpXEdT1GTcVs0OhBONirOluxBLGnd6J1r96I9wXvRTIKdpRILR/njxyulSC/lU0xEXfX2gXQ42aiwODZQdBTqBnaWSiwfG4z49AqczuHT8D2BJa2b5FQ04pvz3EUzNYtGB8JapcD7R7ibRtQVqSX1OJBShkUxgbCzNOh2aJKgR0cFwMXWAu8dyhAdxSSxpHWTdw5mQKXgLpqpcba1wNxoX3x7vggF1U2i4xAZrQ+PZsJapcCjo/xFR6FuZG2hwKKYABxOLcfV4jrRcUwOS1o3+GkXbQR30UzR0ms3OH90NEtwEiLjVFjTjG/PF2FutC+c+ESnyVkwyh82Fgp8yE8cuh1LWje4vou2jLtoJsnL0Rr3D/HG1tP5qGhoFR2HyOisj88GAD7RaaIcbSwwL9oP310sRn4VP3HoTixpXVRQ3YRvzhfi4Wjuopmy5WOD0abV4eNjOaKjEBmV6sY2fH4qD9MjveDNMzpN1mOxgZABWJ+QLTqKSWFJ66INCTmQAXg8jk8rmbK+7na4d4AnPjmRg/qWdtFxiIzGpydy0dyu5ekCJs7L0RozBntj6+k8VDW2iY5jMljSuqC2qR1bT3e8Q/TiO0STt2J8MOpbNPgsMU90FCKj0NymxScncnB3mDv6edqLjkM9bPnYILS06/DJ8RzRUUwGS1oXbE7MRVObFkvH8D4LczDIxxGxfV2xPiEbLe1a0XGIJG/bmXxUNbZh+TjuopmDEA97TOjvjk9P5KCpTSM6jklgSTNQS7sWG4/lYEyoG/r3cRAdh3rJk+OCUV7fiu1JBaKjEEmaRqvDR/FZGObvhKgAZ9FxqJc8MTYY1U3t2HY6X3QUk8CSZqCvzxWioqEVy7mLZlZigl0Q6aPGh0czodHqRMchkqzvLxWjoLoZy3kvmlmJCnDGMH8nfBSfjXaukV3GkmYAnU6Pj+KzEOHtgFHBLqLjUC+SyWR4clxf5Fc14/tLxaLjEEmSXq/HB0eyEOJuh7vD3EXHoV62fGwwCmua8f1FrpFdxZJmgP1XS5FV3ohlY4Ihk8lEx6FeNnGAB/q62+H9w5nQ6/Wi4xBJzpG0junzy8YEQS7nGmlu7g5zR4i7HT44wjWyq1jSDPDh0Sz4OFljSoSn6CgkgFwuw/KxwUgpqceh1DLRcYgk54MjmeijtsKMwd6io5AAcrkMy8YEIaWkHofTykXHMWosaZ10JqcKSbnVeDw2EEoF/+8zVzMGe8FLbYU1h3gMCtHPncurxsmsKjwWGwgLJddIczVjsDf6qK14VFQX8Tuokz48mgVHGxVmR/mKjkICqRRyLB0ThDO51TiVXSU6DpFkfHAkEw5WSsyN9hMdhQSyUMrxWGwgTmZV4Xx+jeg4RoslrRMyyxuw/2opHh3pDxsLpeg4JNjcKD8421rg/cMZoqMQSUJmeQN+vFKKR0cFwM6Sa6S5mxvtBwcrJT44zN00Q7GkdcK6+CxYKOR4NCZAdBSSAGsLBRbHBOBQajmuFNWJjkMk3NojHWvkotEBoqOQBNhZKrFglD/2XilBZnmD6DhGiSXtDpXVt+CrpEI8OMwHrnaWouOQRDw6KgC2Fgq8z/suyMyV1rXg63OFmD3cl2sk/WRRTCBUCjk+OpolOopRYkm7Q58cz0G7ToelcRxeS/+htlFh/kh/fH+xCPlVTaLjEAmzISEbGq6R9F/c7C3x0DAf7DhbiLK6FtFxjA5L2h1obNVg04lc3DvAE4GutqLjkMQsigmATCbjocJktmqb2/FZYh6mDvKCn4uN6DgkMcvGBEGj02HDsRzRUYwOS9od2Ho6H3UtGiwby3eI9GtejtaYMrAPvjidj/qWdtFxiHrdZ4m5aGjV4Akek0c34O9ii8kD++Czk7mo4xrZKSxpt9Gu1WFDQjaiA5wx1M9JdBySqMdiA1HfqsG2Mzx4ncxLS7sWGxJyEBfiighvteg4JFHLxwSjvlWDLYl5oqMYFZa02/jhUjEKa5qxjO8Q6RYG+zoiKsAJG49l8+B1MivfnCtERUMrD1KnWxroo8bovi7YkJCNVo1WdByjwZJ2C9cPCQ52s8VdPCSYbuOx2CAUVDfjxyuloqMQ9Qq9Xo8Nx7LRv48DYoJdRMchiXtiTDDK6lvx7fki0VGMBkvaLSRkVOBqcR2eGBPMQ4Lptu4Z4AE/ZxusT8gWHYWoV8SnVyCttAGPxQZCJuMaSbcWF+KKfh72WJ+QzYPX7xBL2i18eCQL7vaWmDHES3QUMgIKuQyLRwcgKbcaZ/OqRcch6nHrE7LhZm+J+yL7iI5CRkAmk2FJbABSSupxIrNSdByjwJJ2E8mFtUjIqMCi0QGwVCpExyEj8dBwX9hbKbmbRiYvo6weR9LK8ehIf66RdMdmDPaGi60F18g7xJJ2E+vis2BrocD8Ef6io5ARsbNU4uFoP+y+VIyCag63JdO1PiEHlko55o/kGkl3zkqlwPyR/jiQUoYsHhV1WyxpN1Ba14JdF4sxO8oXamuV6DhkZBZyuC2ZuKrGNuw4W4CZQ73hbGshOg4ZmQUj/WGhkGMjh9veFkvaDXx6IgdavR6LeJA6GeD6cNutpzjclkzTZydz0arRYcnoQNFRyAi52Vti+mAvbE8qQE1Tm+g4ksaS9l+a27TYkpiHe/p7wN+FR0CRYTjclkxVq0aLT0/mYkyoG93jOUEAACAASURBVEI87EXHISO1ZHQgmtu1+PxUvugoksaS9l++OV+I6qZ2LInlO0Qy3M+H22p1fNScTMeuC8Uor2/FY1wjqQsGeHXM1vvkeA7aOQD8pljSfkav12NDQjbCvRwwItBZdBwycj8Nt71cIjoKUbfQ6/VYn5CNEHc7jAlxFR2HjNxjsYEoqWvBD5eKRUeRLJa0n4lPr0B6WQOWjOZgRuq668Nt1/FRczIRJ7OqcKW4Dks4vJa6wfh+7ghytcUGDre9KZa0n7k+mHEaBzNSN+BwWzI16xOy4WxrgQeGeIuOQiZAfm2NvFBQi6RcrpE3wpJ2zfXBjAs4mJG6EYfbkqnIqWjEgZRSPDLCD1YqrpHUPR4c5gO1tYpr5E2wpF2z4VgOLJRyPDzCT3QUMiHXh9vuSS7hcFsyahuPZUMll+ORURxeS93HxkKJedF+2Hu5BPlVXCP/G0sagOprgxkfGOwNVztL0XHIxCy8Nm+Pw23JWNU2t+PLpALcF+kFd3sr0XHIxCyM8YdcJsPHXCN/hSUNwJZTeWhp12FxbIDoKGSCvBytMZXDbcmIbT2Vh6Y2LcduUI/oo+4YAP7Faa6R/83sS1q7VodPT+Qgtq8rwjwdRMchE/V4HIfbknHSaHX45HgORgW5YIAX10jqGY/FBqKBa+SvmH1J++FSMUrrWrGEu2jUgwb5cLgtGafdySUoqm3hLhr1qEhfRwz3d8LHx7lG/pxZl7TrgxmDXG0xLtRddBwycdeH2+7lcFsyEnq9HusSshHoaou7wrhGUs96LDYQ+VXN2HelVHQUyTDrkpaUW42LBbVYPDoAcjkHM1LPuj7cdgMfNScjcTavGhfya7hGUq+YGO4JHydrrpE/Y9YlbcOxbDhYKfHgMB/RUcgMKOQyLIoJwJncjr/4iKRufcK1NXIo10jqedfXyFM5VbhUUCs6jiSYbUkrqG7CnuQSzBvhBxsLpeg4ZCYeGu4DO0slNh7jO0WStvyq/6yRtpZcI6l3zInyhZ2lEusTskRHkQSzLWmfHM+BTCbDwlEBoqOQGbG3UmH2cF/suliM0roW0XGIboprJInw8zWypJZrpFmWtIZWDbaezsfkCE94OVqLjkNmZlFMALR6PTadyBUdheiGGlo1+OJ0PqYM7MM1knrd4tEB0On1+PREjugowpllSdt+Jh/1LRo+Uk5C+LnY4J7+HvgsMRct7VrRcYh+ZfuZfNS3co0kMXydbTBxgCc+S8xDc5t5r5FmV9J0Oj02Hs/BED9HDPFzEh2HzNSS2EBUN7Vj5/lC0VGIfkF7bY0c5u+Ewb6OouOQmXosLhC1ze3Ycc68h9saXNJWrVqFOXPmYM2aNV26prcdSClDbmUT3yGSUCMCnTGgjwM2JORAr+fgRpKOg9fWyCWjuUaSOMP9nTDQW40NCdnQmfFwW4NK2o8//gidTocvvvgC+fn5yMnJMegaETYkZMNLbYVJ4Z6io5AZk8lkWDw6AKml9TieWSk6DtFPrq+R94Z7iI5CZkwmk2FJbAAyyxsRn1EhOo4wBpW0U6dOYfLkyQCA2NhYJCUlGXRNbyusacaJrEosjAmAUmF2n/SSxNwX6QVXOwsObiTJuFJUxzWSJGPqQC+42Vua9Rpp0HdhU1MTPDw63mWp1WpUVv56J+BOrultLrYWeO3+CDzKR8pJAqxUCswf4Y8DKWXIrmgUHYcIG49lw1qlwNwoP9FRiGChlOPRkf44klaOjLJ60XGEMKik2djYoKWlY35JU1MTdDqdQdf0NiuVAgtG+sPaQiE6ChEAYP5IP1go5PiYw21JsIqGVuw8X4RZw3ygtlGJjkMEAHh4hB8slHJsPJYjOooQBpW0iIiInz6+TElJgbe3t0HXEJk7d3sr3BfphS+TClDb3C46Dpmxz07moU2rw6LRAaKjEP3Exc4SDwz2xldnC1Dd2CY6Tq8zqKRNmDABO3fuxOuvv47du3cjJCQEq1evvuU148aN6468RCZn8egANLVpse10vugoZKZaNVpsOpmL8f3cEOxmJzoO0S8siQ1ES7sOn5/OEx2l1xlU0uzs7LBp0yZERkbi008/RVhYGJ577rlbXmNvb98tgYlMTYS3GiMCnfHx8RxotOJvCyDz892FYlQ0tGIJRxORBPXztEdsX1d8ejwX7Wa2Rhr8+I5arcaUKVPg5ubWpWuIqOOdYmFNM/ZdKRUdhcyMXq/HhoRshHrYIbavq+g4RDe0JDYAJXUt2J1cIjpKr+Iz1kQSMKG/B3ydrbGBDxBQL0vMrsKV4josGR0ImUwmOg7RDY0LdUegq63ZjeNgSSOSAIVchkUxgTidU42LBTWi45AZ2ZCQDScbFe4fwoe7SLrk8o4B4Ofza3A2r1p0nF7DkkYkEbOH+8DOUmm2j5pT78urbMK+q6WYP8IfViqOJiJpe3CoD+ytlFhvRrtpLGlEEmFvpcJDw32w62IRSutaRMchM/Dx8RwoZDIsGOUvOgrRbdlaKjEv2g97kktQWNMsOk6vYEkjkpBFMQHQ6PTYfDJXdBQycfUt7dh2Jh/TBvWBh4OV6DhEd2RhTAAA4NMTOSJj9BqWNCIJ8XexxYT+HvgsMQ8t7VrRcciEfXmmAA2tGo7dIKPi7WiNSeGe+DwxD01tGtFxehxLGpHELBkdiKrGNuw8Xyg6CpkorU6PjcezMdzfCYN8HEXHIeqUJbEBqGvR4Kuzpr9GsqQRSczIIGf07+OAjcdyoNfrRcchE7T/ainyq5q5i0ZGaaifEyJ91Nh4LBs6nWmvkSxpRBIjk8mwZHQAUkrqcSyjUnQcMkEbErLh7WiNiQM8REch6jSZTIYlsYHIKm/EkfRy0XF6FEsakQRNH+wFVztLrEvIEh2FTExyYS0Ss6uwMMYfSgX/CiDjNDmiDzwcLE1+uC2/Q4kkyFKpwMJR/jicWo700nrRcciEbDyWAxsLBeZE+YmOQmQwC6Ucj44KQHx6BdJMeI1kSSOSqPkj/WGlkmNdvGm/U6TeU1bfgu8uFOGhYT5QW6tExyHqkoej/WCplGOjCR+nx5JGJFHOthZ4cKgPvj5XiPL6VtFxyAR8djIPbVodFo3mAwNk/JxsLTBzqA92nC1EVWOb6Dg9giWNSMIeiw1Eu06HTRxuS13U0q7FppO5uDus46BqIlOwZHQAWjU6bEk0zTWSJY1IwoLc7HB3mAc2n8zlcFvqkq/OFqCqsQ1LxwSJjkLUbUI87DE21A0fHzfNNZIljUjilsZ1DLf96myB6ChkpHQ6PdbFZ2OQjxojAp1FxyHqVsvGBKGiodUkB4CzpBFJXHSgMwZ6q7E+3vQHN1LP2H+1FNkVjVgaFwSZTCY6DlG3igl2wYA+DvjIBNdIljQiiZPJZHg8LhBZFY04mFImOg4ZoY/is+DtaI3JEZ6ioxB1O5lMhmVjgpBR1oDDaaa1RrKkERmBKQP7wEttxeG21Gln86pxOqcaj8UGcngtmaypg/qgj9oKa4+a1hrJ71giI6BSyLF4dCBOZlUhubBWdBwyIuvis+BgpcTsKF/RUYh6jEohx5Jra+TFghrRcboNSxqRkZgT7Qs7SyU+ijetd4rUc/Iqm7AnuQTzR/rDzlIpOg5Rj5ob7Qt7SyU+MqEB4CxpREbCwUqFuVG+2HWxGEU1zaLjkBHYcCwbCrkMi2ICREch6nH2VirMG+GHHy4VI7+qSXScbsGSRmREFo0OAAB8fDxHaA6SvpqmNnxxOh/TI73h4WAlOg5Rr1gUEwAZOs6oNQUsaURGxMfJBpMjPPF5Yh7qW9pFxyEJ+ywxD83tWiwdwyOgyHx4OVrjvkgvbD2dh9om418jWdKIjMzSuCDUt2qw7QyH29KNtWq02HgsB2NC3RDm6SA6DlGvejwuEE1tWmw5lSc6SpexpBEZmUhfR0QHOGNDQjY0Wp3oOCRBO88VoaKhFcvieAQUmZ9wLzVi+7pi47FstGqM+6goljQiI/R4XCAKa5qx53KJ6CgkMTqdHmvjs9C/jwNG93URHYdIiKVjglBW34pvzxeJjtIlLGlERuju/h4IcLHBR/HZ0OtN6xgU6pojaeXIKGvAsjGBPAKKzNaYEFeEedrjo/gso14jWdKIjJBCLsNjsYG4kF+DM7nVouOQhKw9mgVPBytMG+QlOgqRMDKZDEvjgpBW2oAjaeWi4xiMJY3ISM0a5gtHGxXWcbgtXZNcWIsTWZVYPDoAKh4BRWbuvkgveDhYGvUAcH4XExkpawsFHhnhjx+vlCKnolF0HJKAj+KzYGepxLwRfqKjEAlnoew4Tu9YRqXRHqfHkkZkxB6N8YdKLseGY6ZzDAoZprCmGbsuFmNulC8crFSi4xBJwrxoP9haKIz2EweWNCIj5m5vhRmDvbDtTD4qG1pFxyGBNiZ0FPXFsRxeS3Sd2lqFudF++M5Ij9NjSSMyck+MDUarRsfdNDNW29yOz0/lYdqgPvB2tBYdh0hSFl87Tm+jEa6RLGlERq6vux0mR3ji0+O5qG02/mNQqPO2nspDY5sWSzm8luhXfJxsMHVgH3x+Kh91RnacHksakQl4anxf1LdqsOlEjugo1MvaNDpsPJaDUUEuiPBWi45DJElL44LQ0KrBViM7KooljcgEhHupcVeYO9YnZKOxVSM6DvWiby8UoaSuhQepE93CQB81RgW5YENCDto0xnOcHksakYl4anxfVDd13JtE5kGr02PNoQz07+OA8f3cRcchkrRlY4NQUteCb84Vio5yx1jSiEzEMH8njApywdqjWWhpN+5DhenO7LpYhKyKRjx7V18eAUV0G+NC3RDh7YD3DmdAozWO3TSWNCIT8vRdfVFW34rtSQWio1AP0+n0ePdgBkI97HBvuKfoOESSJ5PJ8MxdIcitbMK3F4zj4HWWNCITEhPsgsG+jvjgSCbajeSdIhlmz+USpJc14KnxfSGXcxeN6E7c098DYZ72ePdgBrQ66R+8zpJGZEJkMhmeHt8XBdXN+Pa8cbxTpM7T6fR4+0A6glxteZA6USfI5R27aVkVjfj+UrHoOLfFkkZkYu7u744wT3usOZwBnRG8U6TO23+1FCkl9XhqfF8ouItG1CmTIzwR4m6Hdw6kS36NZEkjMjEymQxPje+LzPJG7LlcIjoOdTO9Xo93DmbAz9kGMwZzF42os+RyGZ6+qy/Syxokv0aypBGZoCkD+yDI1RbvHsyAXi/td4rUOYfTynGpsBZPjQ+GUsElnMgQ0wZ5IcjVFm9LfDeN3+FEJkghl+HJccG4UlyHQ6llouNQN9HrO+5F83a0xgNDfETHITJaCnnHJw4pJfXYf7VUdJybYkkjMlH3D/GGt6M1d9NMyLGMSpzLq8HyccGwUHL5JuqKGYO94Odsg7cPpkt2jeR3OZGJUinkWD42CGfzanAiq1J0HOoGbx9Mh6eDFWYP5y4aUVcpFXI8Pb4vkgul+4kDSxqRCXtouC/c7C3x3qEM0VGoi05mVeJUdhWeGBsES6VCdBwik/DA0I5PHN4+IM1PHFjSiEyYlUqBpXGB1z4mqxYdh7rgnYPpcLWzxLxoP9FRiEyGSiHHivHBOJ9fg/j0CtFxfoUljcjEzR/hD0cbFXfTjFhSbjWOZVTiiTFBsFJxF42oO80a5oM+aiu8fUB696Z1uqStWrUKc+bMwZo1a256jUajwbhx47BgwQIsWLAAqampXQpJRIaztVRicUwg9l8tw9XiOtFxyADvHEyHs60F5o/kLhpRd7NUKvDkuGCcya2W3P27nSppP/74I3Q6Hb744gvk5+cjJyfnhtelpqZi6tSp2LRpEzZt2oR+/fp1R1YiMtCimADYWSq5m2aELhbU4HBqOR6LDYSNhVJ0HCKTNHu4L9ztLfH2gXTRUX6hUyXt1KlTmDx5MgAgNjYWSUlJN7zu/PnzOHz4MGbNmoVVq1ZBo9F0PSkRGUxto8IjI/3x/aViZJY3iI5DnfD2gQyorVV4dJS/6ChEJstKpcATY4NxMqsKp7KrRMf5yS1L2ssvv/zTR5YLFizApk2b4OHhAQBQq9WorLzxtuDAgQOxceNGbN++HRqNBkeOHOn+5ETUKY/HBcJSKcd7B7mbZiyuFNVh/9VSLBkdCHsrleg4RCbt4Wg/uNpZ4J2D0tlNu+Xe+auvvvqLf//LX/6ClpYWAEBTUxN0Ot0Nf11YWBgsLCwAABEREcjNze2OrETUBa52llgYE4C1R7PweFwQBng5iI5Et/HuoXTYWyqxaHSA6ChEJs/aQoGlcUF4fXcKknKrMczfSXSkzn3cGRER8dNHnCkpKfD29r7hdStXrkRKSgq0Wi3279+PsLCwricloi5bMbYvHKxU+PueFNFR6DbSSuvxw6USLBodALU1d9GIesMjI/3hZKOSzG5ap0rahAkTsHPnTrz++uvYvXs3xo0bh4yMDKxevfoX1z311FNYuXIl7r//fgwZMgQxMTHdGpqIDKO2UeGp8cE4klaO4xnSmwlE//HuwQzYWiiwZHSg6ChEZsPWUonH44JwOLUcF/JrRMeBTN/JoSC1tbU4duwYoqKi4Obm1m1BZs6ciR07dnTb1yOiG2tp1+Lufx2Bs60Fdj41GnK5THQk+i8pJXWY8u94LB0ThD9M7i86DpFZqW9pR+zfD2FUkAs+WDCsx1/vVv2n03PS1Go1pkyZ0q0FjYh6j5VKgefvCcWlwlrsulQsOg7dwF+/vwp7KxWeHBssOgqR2bG3UuGV6QMQ4GorOgpPHCAyR/cP8UaYpz3+uTcVbZobPwBEYhxOLUN8egWevTsEjjYWouMQmaUHhvjg95PF30/PkkZkhhRyGX4/OQx5VU34LJFPX0uFRqvD3364igAXGywYybloROaOJY3ITI0NdUNMsAveOZiB+pZ20XEIwLYzBUgrbcDvJ4fBQsnlmcjccRUgMlMymQx/mNwfVY1tWHs0S3Qcs9fQqsGb+1IRFeCEe8M9RcchIglgSSMyYwN91Lgv0gvr4rNRVtciOo5Z++BwJioa2vA/UwdAJuMTt0TEkkZk9l6cGAqNTofV+6UxvNEcFdc246P4LEyP9MJgX0fRcYhIIljSiMycv4st5o/wx7Yz+cgo4+HrIryxNxV6AL+b1E90FCKSEJY0IsIzd/WFtUqBf/C4qF6XXFiLHWcLsWR0IHycbETHISIJYUkjIrjYWeKJMUH48UopzuRUiY5jNvR6Pf7y/RU421pgxXgOriWiX2JJIyIAwGNxgXC3t8Tru1PQydPiyED7r5bhZFYVnpsQAgcrHqJORL/EkkZEAAAbCyV+OyEUSbnV+PFKqeg4Jq9dq8PrP1xFsJst5kX7iY5DRBLEkkZEP5k93AfBbrb4x54UaLQ8LqonbUnMQ1ZFI1ZN6Q+lgksxEf0aVwYi+olSIcfvJoUhs7wRXyYViI5jsmqb2/HW/jSMCnLBXWHuouMQkUSxpBHRL0wc4IFh/k5YvS8NTW0a0XFM0prDGahpbsf/TO3PwbVEdFMsaUT0Cx3HRYWhrL4V/+aA226XX9WEjQk5mDnEBxHeatFxiEjCWNKI6FeGBzhjXrQvPorPwoX8GtFxTMo/9qZCLgdW3svBtUR0ayxpRHRDf5jSH+72Vvjd9oto0/Ahgu5wLq8a310owrK4IHiqrUTHISKJY0kjohtysFLhrw9EILW0Hu8dyhAdx+hptDr86dvLcLWzxBNjObiWiG6PJY2Iburu/h64f7AX3juUgZSSOtFxjNqaw5m4WFCLP08Ph62lUnQcIjICLGlEdEsv3xcOtbUKv9t+kbPTDJRcWIu3D6RjeqQXpg7qIzoOERkJljQiuiVnWwv8eUY4LhbUYl1Ctug4RqdVo8Xz287D2dYCr84IFx2HiIwISxoR3dbUgX1wb7gHVu9LQ1Z5g+g4RuXNfWlIK23A32cNgqONheg4RGREWNKI6LZkMhlemxEBS6UcL311ETodD2C/E0m5VVh7NAvzon0xvh9PFiCizmFJI6I74u5ghT9OG4DTOdXYnJgrOo7kNbVp8Py2C/Bxssb/TB0gOg4RGSGWNCK6Y7OG+WBMqBv+b3cK8quaRMeRtNd/SEFeVRPemBUJOz7NSUQGYEkjojsmk8nw+syBkAFY9fUl6PX82PNG4tPLselkLpaMDsTIIBfRcYjISLGkEVGneDta4/eTwxCfXoEvkwpEx5Gc2uZ2/G77RQS72fLoJyLqEpY0Iuq0+SP8ER3ojNd2XUFpXYvoOJLy5+8uo6y+FW/OHgwrlUJ0HCIyYixpRNRpcrkMf39wENo0OvzvN8n82POavZdLsONsIZ4aF4xIX0fRcYjIyLGkEZFBAl1t8cLEUOy7UopdF4tFxxGusqEV//P1JYR7OeDpu0JExyEiE8CSRkQGWzI6EJE+ary8Mxl5leb7tKder8f/fJ2MumYN3pw9GBZKLq1E1HVcSYjIYEqFHG/NHQI9gEUfn0JNU5voSELsPF+EPZdL8PzEUPTztBcdh4hMBEsaEXVJoKst1i4YjoKqZizblIRWjVZ0pF5VVNOMl3cmY5i/E5bGBYmOQ0QmhCWNiLosOtAZbzw0CKeyq/C77RfN5kGCmqY2LNp4ClqdHv96KBIKuUx0JCIyIRyDTUTdYsZgbxRUN+ONvanwc7bBCxNNe0ZYU5sGiz8+jZyKJmxcHIUAV1vRkYjIxLCkEVG3WTEuGPlVTXjnYAZ8nWwwO8pXdKQe0arR4olNSbiQX4M184didF9X0ZGIyASxpBFRt5HJZHjt/ggU1jRj1deX0MfRCnEhbqJjdSutTo/nv7iA+PQK/P3BgZgU0Ud0JCIyUbwnjYi6lUohx3vzh6Kvux1WbD6L1JJ60ZG6jV6vx/9+k4zvLxXjD5PDMCfKT3QkIjJhLGlE1O0crFTYsCgK1hYKLN54ymSOjnpjbyo+P5WH5WOD8cTYYNFxiMjEsaQRUY/wcrTGhkVRqGlux2OfnEZjq0Z0pC756GgW1hzOxLxoX7w0ybQfiiAiaWBJI6IeE+GtxnsPD8WVojo88/k5aLQ60ZEMsu1MPv76w1VMGeiJv9w/EDIZR20QUc9jSSOiHjU+zB2vzojAwZQy/Pm7K0Y3Q21Pcgl+/9VFxIW4YvWcwZyFRkS9hk93ElGPe2SkP/KrmvDh0Sw42ajw2wmhkBtB2TmeUYFnPz+HQT6O+OCRYbBUKkRHIiIzwpJGRL3ipUlhqGhow9sHM3A2rwZvzo6Eu4OV6Fg3dSG/Bks/PQN/FxtsXBQFW0sul0TUu/hxJxH1Crlchn8+NAivzxyIM7lVmPzveBxKLRMd61f0ej12XSzCwo2n4GRrgU2PjYCTrYXoWERkhljSiKjXyGQyzIv2w3dPx8LN3hKLN57GX3ZdQZtGGg8UlNa1YNmmJDy95Rx8nWzw2eMj4KmW7m4fEZk27t8TUa8L8bDHN0+Nxl+/v4p1CdlIzK7C2/OGIFDQ+Zd6vR7bzuTjL99fRZtGh1VTwrBkdCCUCr6PJSJxuAIRkRBWKgVeuz8CHy4YhryqJkx7Ox5fnyvo9Rx5lU2Yvy4RL311CQP6OGDvb8dg2ZhgFjQiEo47aUQk1L3hnhjorcZvt57Hc9fOxHx1RgTsevhGfa1Oj43HsvHPH1OhlMvx1wciMC/KzyieOiUi88CSRkTCeTlaY8vSEXjnYAbeOZiOc3k1eHvuEAz0UffI66WW1OOlry7ifH4N7gpzx18fiEAftXWPvBYRkaFY0ohIEpQKOZ67JxSjgl3w263nMfP9Yxgb6oa4EDeMCXVDgItNlyf9t2l0WHM4A+8dyoC9lQr/njsY0yO9eIIAEUmSQSWtoqICzz77LLZs2XLTa9rb2/HMM8+gpqYGs2bNwqxZswwOSUTmY2SQC3b/Jg7/PpCOgyll2H+1Y0yHj5M1xoS6YUyIK2L6usLBSnXLr6PR6pBZ3ojLRbW4XFSHy0W1uFJUh7oWDWYM9sLL0wbAxc6yN35LREQG6XRJq62txUsvvYTm5uZbXrd582aEh4fjmWeewdKlSzFp0iTY2dkZHJSIzIeTrQVemR6OV6aHI7eyEUfTynE0vQLfni/ClsQ8KOQyDPZ1xJgQN8SFuiLUwx5ppfW4UlSHy0V1uFJUi5SSerReG+1hqZQjrI8DpkV64d5wT4wNdRP8OyQiur1OlzSFQoG33noLK1asuOV1iYmJePHFFwEAUVFRSE5OxsiRIw1LSURmy9/FFgtG2WLBqAC0a3U4l1eD+PRyHE0rx1sH0rB6f9ovrldbqxDu5YBHR/ljgJcDwr3UCHK15dOaRGR0blvSXn75ZWRnZ//07yNGjMDTTz992y/c3NwMDw8PAIBarUZlZWUXYhIRASqFHNGBzogOdMYLE/uhurENCRkVyK1sRIiHPcK9HODtaM17zIjIJNy2pL366qsGfWEbGxu0tLTA3t4eTU1NsLGxMejrEBHdjJOtBe6L9BIdg4ioR/TY/n94eDiSkpIAACkpKfD29u6plyIiIiIyOd0yguPEiRPIzMzEI4888tN/e+CBB7Bs2TKcOXMGGRkZiIyM7I6XIiIiIjILMr1er++pL15aWoqkpCTExcXB3t7+ltfOnDkTO3bs6KkoRERERJJzq/7To8NsPTw8MGXKlJ58CSIiIiKTxGfSiYiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgljSiIiIiCSIJY2IiIhIgpSiA1xXWFiImTNnio5BRERE1GuuXLly05+TTEnz9vbGjh07RMcgIiIi6jW32qDix51EREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREPPdmxAAAIABJREFUEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEsSSRkRERCRBLGlEREREEmRwSauoqMDDDz98059vb2/H8uXLMXfuXGzfvt3QlyEiIiIySwaVtNraWrz00ktobm6+6TWbN29GeHg4tm7dir1796KhocHgkERERETmRmnIL1IoFHjrrbewYsWKm16TmJiIF198EQAQFRWF5ORkjBw50rCU3aS8vhWzPzwBTwcrRAU4YXiAM4b4OcLeSiU0FxGRKO1aHQqrm5FX1fSf/1U2obCmGUFutpgc0Qfj+rnBSqUQHZXI7BhU0uzs7G57TXNzMzw8PAAAarUalZWVhrxUt3KwVuLecE8kZJTj3UMZ0OkBuQwI83RAVIAThgU4IyrACX3U1qKjEhF1u5SSOhxMKUN+VRNyKzsKWVFNM3T6/1xjoZTD18kaXo7WOJJWjp3ni2BrocBd/T0wdaAnxvVzZ2Ej6iUGlbQ7YWNjg5aWFtjb26OpqQk2NjY99VJ3zFKpwO8nhwEIQ0OrBufzanA6pwpncqvwZVIBPjmRCwDwdrTG8AAn3BXmjumRXpDJZGKDExF1QW1TO97cl4pNJ3Oh0wMuthbwdbbBMH8nPDDEG77ONvB3toGfiw087K0gl3esee1aHU5mVeKHS8XYk1yC7y4UwcZCgbvC3DF1YB+M6+cOawsWNqKe0mMlLTw8HElJSZg0aRJSUlIwZ86cnnopg9hZKhEb4orYEFcAgEarw9XiepzOqUJSbjVOZFZi5/kifHW2EP94cBA81VaCExMRdY5Op8e2M/n4x95U1Px/e/cdHWWZvw38mpn03nvvkITQa0IHKSqCKAhEpIqLCBbWlXX1Z9l1XXdfyyoCUpQAiiIKKG1BSgghkECoSSC9AKkkJJmUae8fqKurlCST3M/MXJ9z9izlgbk8Q+5c85TvrWxF4sBAPDMqHK52lvf0580VciSEuyMh3B1vTIrBifwafH/+GvZdvI7vzl2DtbkCI7t54IEe3hjb3evnckdE+qGXkpaamoq8vDzMmjXr51+bPHkyFi5ciPT0dOTm5iIuLk4fL9VpzBRyxPo5ItbPEXPjg6HT6bDpRBH+ujsL9713FG88FIMH43xExyQiuieZJbV4dccFnC2tQ78gZ7z24AB093Fo999nppD//MH2jUnROFnw38L2/blrGN3NE/96NA6O1rzHl0hfZDqdTnf3w9qnvLwcGRkZSEhIgL29/R2PnTJlCrZv395ZUdotv7IBz315FpkltXggzgdvTIqGk42F6FhERL+ruqEF/9ibg63pJXC3t8SfJ3TDpJ6dd9uGRqvDZ8cL8bfdWfBztsaqxD6I8mp/GSQyNXfqP506zNbT0xMTJky4a0GTshB3O2xbNAgvjI3AnvPXcN97R3HkcqXoWEREv6LWaLExtRAj/nkYX58uxYKEYPzw/DA81Mu3U++rVchlmBsfjM8XDkRjqwaTPzqOHZllnfZ6RKaEOw7cAzOFHE+PDMe3i4fAwcocs9efxMvfnoeyVS06GhERThXW4IEPU/DKjouI9XPEnqUJ+PPE7l06XqhfkAu+XxKPGF8HLP0iE6/tugiVRttlr09kjFjS2iDG1xG7lsRjfnwwNqcVY8L7ycgouiE6FhGZsLXJ+XhkVSrqlK1YObM3Ns0bgHBPMVcvPByssGXBQMwZEoQNKYWY8ckJVNxsFpKFyBiwpLWRlbkCL9/fHVvmD4RKo8Mjq47jnX3ZaFXzEyMRda2NqYV48/ssjI/xwoHnh2FCrLfwkUHmCjlefSAa70/viQtlNzHx38eQXlgjNBORoWJJa6dBoa7YuywBD/f2w0eH8jDvs1MsakTUZb44WYxXdlzE6G6e+OCxXrCx6LSJSu0yqacvvlk8GLYWCkxfcwIbUgrQic+pERkllrQOsLcyxzuPxOHvU2KRfKUKL359DlotFyEi6lxfZ5TipW/OY3ikOz6a2QvmCmku5VFeDtjxdDyGR7rjtV2XsGxrJu/lJWoDaX5lG5jp/QPwwtgIfHOmDG/vyxYdh4iM2K6zV7F821kMDnXFqll9YGkm7Yn/jtbmWJPYFy+MjcDOs1fx6OpUNLSwqBHdC5Y0PVk8IgyJAwOx+kg+NqQUiI5DREZo74XrWLY1E30DXfDJ430NZg9NuVyGp0eGY01iX1y6ehPLvsjkVQeie8CSpicymQz/92A07ov2xOvfXcJ3566KjkRERuRgVjmWfH4acX6OWD+nn+TuQbsXY7p74i/3d8eBrHL8Y1+O6DhEkseSpkcKuQzvT++FPgHOeG7rWaTmVYuORERG4OjlSjy16TS6eTvg07n9YWdpeAXtJ08MDsJj/QOw6kgetp8uFR2HSNJY0vTMylyBtbP7IsDVBgs3piPr2k3RkYjIgKXmVWPBxnSEethh49z+cOjCAbWdQSaT4fVJ0RgY4oI/fX2esyaJ7oAlrRM42Vjgs7n9YWtphic2nERZbZPoSERkgNILazDvs1MIdLXBpnn9jWbfYHOFHB/P7ANvJys8mZTONZLoNljSOomvkzU+ndsPylYNZq8/iVplq+hIRGRAMktq8cSGU/BysMKm+QPgamcpOpJeOdtaYN3svmhRaTH/s3Q08olPot9gSetEUV4O+OTxviiuVmLeZ+loVmlERyIiA1BW24TZ60/CxdYCWxYMhIe9lehInSLMwx4fzOiFnOs38dyXfOKT6H+xpHWygSGueG96T5wuvoFnPj8DDRchIroDjVaH57ZmQq3RYuPc/vByNM6C9pMRkR5YMaEb9l0sx//7z2XRcYgkhSWtC0yI9car93fH/kvleGXHBW6NQkS3tTY5H2kFNXj1wWgEudmKjtMl5sUHY1pff3x4KBc7MstExyGSDMN9jtvAPDEkGNdvtmDVkTx093HAzAGBoiMRkcRcvFqHf+7PwbhoLzzSx090nC4jk8nwxkMxKKhuxPJt5xDgYoNeAc6iYxEJxzNpXejFcZGID3PD377PQkmNUnQcIpKQZpUGy77IhLONBf42JRYymUx0pC5lYSbHqll94OlgiYVJGbhWxyc+iVjSupBMJsPfH44FAPxp+zle9iSin/19TzauVDTgnUfi4GJrHKM22srF1gLrZvdDU6sGCzamczN2MnksaV3Mz9kGKyZ2Q0puNTanFYuOQ0QScORyJT49XognBgdhWIS76DhCRXja44PHeuLi1Zt4ZcdF0XGIhGJJE2BG/wDEh7nhrd287Elk6moaW/HCV2cR7mGHP42PEh1HEkZGeeKpYaHYllGKY1eqRMchEoYlTYCfLnvKZDK8+PU5zgYiMlE6nQ4rtp9HrbIV703vCStzhehIkvHMqHAEudpgxTfn0dTKGZNkmljSBPFztsGKCd1wPK8aW07ysieRKfoqoxR7L17HC2MjEe3jKDqOpFiZK/C3ybEorlHi/YNXRMchEoIlTaDH+vvzsieRiSqqbsRrOy9iYIgL5ieEiI4jSYPD3PBIHz98kpyPi1frRMch6nIsaQLxsieRaVJrtHh2aybkchn+9WhPKOSmNW6jLf48sRucbczx0vbz3LGFTA5LmmC/vOy5mZc9iUzCysN5OF1cizcfioGvk7XoOJLmZGOBVx6IxrnSOmxIKRAdh6hLsaRJwGP9/ZEQzsueRKYgs6QW7x+8gkk9fTCpp6/oOAbhgR7eGBHpjn/tv8w1kkwKS5oE3Lrs2QNymQx/3MbLnkTGqrFFjWVfnIGnvSVenxQjOo7B+GnbKJkM+Av3PyYTwpImEb5O1lgxoRtS83nZk8hY/X1PNopqlPjXoz3haG0uOo5B8XO2wfNjI3E4pxI7z14VHYeoS7CkSQgvexIZr6xrN7E5rQizBwVhUKir6DgG6YnBQYjzc8Truy7hRmOr6DhEnY4lTUJ42ZPIOOl0Orz5/SU4WJtj2ehw0XEMlkIuw1tTeqC2SYW/7s4SHYeo07GkSYyvkzX+PJGXPYmMyYGsCqTkVuPZ0RFwsjHNzdP1pbuPAxYODcG2jFIcz+WWUWTcWNIkaHq//172rLjZLDoOEXVAq1qLv35/CWEedpgxIEB0HKOw9Mcto1765jyaVdwyiowXS5oEyWQyvDEpBq1qLd49wO1QiAzZxtRCFFYr8fLEbjBXcMnVh5+2jCqq5pZRZNy4YkhUkJstZg0MxNZTxbhSXi86DhG1Q01jK94/eAXDI90xPNJDdByj8tOWUWuO5uPS1Zui4xB1CpY0CXtmVDhsLczw9z3ZoqMQUTu8+5/LULZq8PLEbqKjGKUVE7rBydocL20/xy2jyCixpEmYi60FnhoRioPZFUjNqxYdh4jaIOd6PTanFWHWgACEediLjmOUnG0t8MoD3XG2tA5fppeIjkOkdyxpEjd3SDB8HK3w1p4sjuQgMhA/jdywszTDstERouMYtQfjfNA7wAnvHbjMhwjI6LCkSZyVuQLPj43EudI67DrHKdtEhuBQTgWSr1Rh2egIONty5EZnkslkeHFcFMpvtuDT44Wi4xDpFUuaAZjcyxfdvB3wzr4ctKj5SZFIylQaLd78Pgsh7rZIHBQoOo5JGBDiiuGR7lh5KBd1SpXoOER6w5JmAORyGVZMiELpjSZsPF4kOg4R3cGmE0XIr2zEnydw5EZX+uN9UbjZrMbqo3mioxDpDVcQA5EQ7o6hEe749w9XUKvknnVEUnSjsRXvHbiChHA3jIziyI2u1N3HAZN6+mB9SgHKOQScjARLmgF5aXwU6lvU+OhQrugoRPQ73j94BfXNKrw8sTtkMpnoOCbn+TGRUGt0+IADbslIsKQZkG7eDpja2w+fHS9CSY1SdBwi+oXcinoknSjCjAEBiPTiyA0RAlxtMGNAAL44VYKCqkbRcYg6jCXNwDw/NhJyOfDOvhzRUYjoF978Pgs2Fgo8y5EbQi0ZGQ5LMzn+tZ9rJBk+ljQD4+VohfnxIdh59irOldaKjkNEAA7nVOBwTiWWjgqHq52l6Dgmzd3eEvPig/HduWs4X1onOg5Rh7CkGaAnh4XA1dYCf/0+CzodB9wSiaT+ceRGkKsNHh8UJDoOAVgwNATONub4xz5uqUeGjSXNANlbmWPp6HCkFdTgYFaF6DhEJm1reglyKxqwYkI3WJhxSZUCBytzLB4RhuQrVTieWyU6DlG7cUUxUI/1D0CImy3+vjcbao1WdBwik9Sq1mLloTz0CnDCmO6eouPQL8waGAgfRyu8vTebVxzIYLGkGShzhRx/HBeF3IoGfJleKjoOkUnallGKstomLB0VzpEbEmNlrsCyMRE4W1qHvReui45D1C4saQbsvmhP9A10xv/7z2U0tqhFxyEyKSqNFh8dykWcvxOGRbiLjkO/Y0ovX4R52OGd/Tm84kAGiSXNgMlkMqyY2A1VDS34JDlfdBwik7L99K2zaMt4Fk2yzBRyLL8vEvmVjdiWwSsOZHjaXdJWrFiBadOmYeXKlb/7+2q1GsOHD0diYiISExORk8OZNZ2hd4AzxkV7YV1yAeqauLEwUVdQabT48FAuevg5Yngkz6JJ2djunugV4IT3DlxBs0ojOg5Rm7SrpO3fvx9arRZbt25FSUkJCgsLf3NMTk4OJk6ciKSkJCQlJSEyMrKjWek2nh4ZhvoWNZJSC0VHITIJ35wpQ0kN70UzBDKZDC+Oi8L1m83YmFooOg5Rm7SrpJ08eRLjx48HAMTHxyMjI+M3x2RmZuLw4cOYOnUqVqxYAbWa90x1lhhfR4yM8sC6YwW8N42ok6l/vBct9sevO5K+gSGuGBbhjo8O5fGKAxmUdpU0pVIJT89bj5s7Ojqiurr6N8fExsZiw4YN2LZtG9RqNY4cOdKxpHRHT48Mww2lClvSikVHITJq32ZeRVG1Es/wLJpB+eO4SNQ1qbDmaJ7oKET3rF0lzcbGBs3NzQBuFTat9rdPzURFRcHD49anzJiYGBQVFXUgJt1N7wBnxIe5YfXRfN53QdRJ1BotPvzhCqJ9HDC6G8+iGZJoH0c8GOeD9ccKUd3QIjoO0T1pV0mLiYn5+RJndnY2fH19f3PM8uXLkZ2dDY1GgwMHDiAqKqpjSemunh4ZhqqGFmw9VSI6CpFR2nn2Kgp5Fs1gPTMqDM1qDT49Xig6CtE9aVdJGz16NHbs2IG33noLe/bsQXh4ON59991fHbN48WIsX74cDz30EHr16oXBgwfrJTDd3oBgF/QLcsaqI3loVXMmEJE+abQ6fPhDLrp5O2AsdxcwSGEe9rivuxc+PV6I+mbem0bSZ9aeP2RnZ4ekpCSkpKRg/vz5cHd3/82ZsoiICOzatUsvIeneyGQyPD0yHLPXn8T206WY3j9AdCQio7Hr7FXkVzVi1azePItmwP4wIhR7L17H5rRiLBoWKjoO0R21e06ao6MjJkyYAHd3zgiSkqHhbujh54iVh/M4YZtITzRaHT744QqivOwxtruX6DjUAT38nJAQ7oa1yQW8f5ckjzsOGBmZTIanR4ShuEaJnWevio5DZBS+O3cV+ZWNeGZUOORynkUzdH8Yfuv+3a/Sef8uSRtLmhEa3c0TUV72+OhQLjRaneg4RAZNq9Xh3z/kIsLTDuOieRbNGAwMcUHvACesOpIPFa84kISxpBkhuVyGp0eGIa+yEXsvXBcdh8ig7b5wDbkVDVgykmfRjIVMJsPiEWEoq23CLl5xIAljSTNS42O8EeJui3//cAU6Hc+mEbWHVqvDBwevIMzDDhNivUXHIT0aGeWBKC97rDycBy2vOJBEsaQZKYVchsXDw5B9vR4HsypExyEySHsvXsfl8gYsGRkGBc+iGRWZTIanhocit6IB+y+Vi45D9LtY0ozYgz194O9ijX8fyuXZNKI2+uksWqi7Le7v4SM6DnWCibHeCHS1wcrDXCNJmljSjJi5Qo6nhoXhbEktkq9UiY5DZFD2X7qO7Ov1WDIynGfRjJSZQo5Fw0JxrrQOKbm/3YOaSDSWNCP3cB9feDta4cMfckVHITIYOp0O7x/MRYibLR6I41k0Yzalty88HSzx0SGukSQ9LGlGztJMgSeHhuBkYQ3S8vlJkeheHM6pRNa1m1g8gveiGTtLMwUWJIQgNb8ap4tviI5D9CssaSZgev8AuNlZ4EN+UiS6J2uO5sPb0QoP9uRZNFPwWP8AONmYY+WhPNFRiH6FJc0EWJnf+qSYfKUKZ/hJkeiOzpfWITW/GnOHBMNcwSXSFNhamuGJwUE4kFWO7Os3Rcch+hlXIBMxc2AgnGzMed8F0V2sPpoHe0szTO/vLzoKdaEnBgfBxkKBjw/zbBpJB0uaibCzNMPcIcE4kFWBi1frRMchkqSSGiV2n7+GGQMCYG9lLjoOdSEnGwvMGhiIXWevoqi6UXQcIgAsaSZl9uAg2Foo8MnRfNFRiCRp3bECKOQyzBkSLDoKCTA/PhhmcjlWc40kiWBJMyGO1uaY3j8Au85dw9XaJtFxiCSlVtmKradK8GCcL7wcrUTHIQE8HKwwta8ftqWXovxms+g4RCxppmbOkCAAwKfHC4XmIJKaTSeK0KTSYMFQnkUzZYuGhkKt1WJtMs+mkXgsaSbGz9kG42O88HlaMeqbVaLjEElCs0qDT48XYViEO6K8HETHIYECXG3wYJwPNqcV40Zjq+g4ZOJY0kzQwqEhqG9RY+upEtFRiCTh2zNlqGpowZNDQ0RHIQl4angYlK0afJZaKDoKmTiWNBPUw88J/YNdsCGlEGqNVnQcIqG0Wh3WJOcj2scBg0JdRcchCYj0sseoKA9sTC1Cs0ojOg6ZMJY0E7UgIQRltU3YfeG66ChEQv2QXYH8ykYsHBoCmYxbQNEt8xNCUNPYim/OlImOQiaMJc1EjYryQIibLT45mg+dTic6DpEwa47mw9fJGhNjvUVHIQkZGOKCaB8HrDtWAK2WaySJwZJmouRyGeYlBON8WR3SCmpExyES4kzxDZwsrMHc+GCYcQso+gWZTIb5CcHIrWjAkSuVouOQieKqZMIe7u0HF1sLPmpOJmvN0Xw4WJlhej9uAUW/NTHWB14OVlwjSRiWNBNmZa7ArIGBOJBVgdyKBtFxiLpUUXUj9l68jlkDA2FraSY6DkmQhZkcswcHISW3GpeucuN16nosaSbu8UGBsDCTY92xAtFRiLrU2uQCmMvleGJwkOgoJGEz+gfA2lzBNZKEYEkzcW52lni4ty+2ny5FdUOL6DhEXaKmsRVfZZTgoV4+8HDgFlB0e4425ni0rx92ni1DBbeKoi7GkkaYFx+CFrUWSSeKREch6hIbUwvRrNJiQQKH19LdzY0Phlqrw8ZUrpHUtVjSCGEedhzcSCajWaXBxtQijIzyQLinveg4ZAACXW0xtrsnNqUVQdmqFh2HTAhLGgH47+DG7ac5uJGM27aMUtQ0tmIht4CiNpifEIJapQpfc42kLsSSRgBuDW6M8XXA2mP5HNxIRkuj1WFtcj7i/BwxINhFdBwyIH0DnRHn54j1HG5LXYgljQDcGty4ICEE+ZWN+CG7QnQcok7xn0vlKKxWYgG3gKI2kslkmJcQgoIqrpHUdVjS6GcTYr3h42iFTzi4kYzUJ8n58HexxrhoL9FRyABNiPGCr5M11h7jGkldgyWNfmaukGPOkGCkFdTgXGmt6DhEepVZUouMohuYM5hbQFH7mCluzdU7kV+DC2V1ouOQCeBKRb8yvb8/7C3N8EkyBzeScVl/rAD2lmZ4lFtAUQdM6+8PWwsFt4qiLsGSRr9ib2WO6f39sfv8NZTVNomOQ6QX1+qasPv8NTzazx923AKKOsDByhzT+gXgu3PXcK2OayR1LpY0+o0nhgQDADZwGxQyEkmpRdDqdNwCivRizpAgaHU6fHacw22pc7Gk0W/4Olnj/h7e+OJUCW42q0THIeqQplYNtpwsxtjuXvB3sREdh4yAv4sNxsd4Y0taERpbONyWOg9LGv2u+fEhaGhR46v0UtFRiDpk+5lS1CpVmBsfLDoKGZF5CcG42azGtgyukdR5WNLod8X6OaJfkDM+PV4ADQc3koHSanVYf6wAMb4O6BfkLDoOGZHeAc7oHeCEdce4RlLnYUmj25o7JBglNU04kFUuOgpRuxy9Uom8ykbMiw/m8FrSu/kJISiuUeI/l7hGUudgSaPbGtPdE75O1ljPBwjIQK1PKYS7vSUmxvqIjkJG6L5oL/i7WGMdh9tSJ2FJo9v6aXBjWgEHN5LhuVJej6OXK/H4wEBYmHGpI/1TyGWYMzgYpwpvILOEA8BJ/7hy0R092s8fNhYKbEgpFB2FqE02HC+EpZkcMwYEiI5CRuzRfrcGgG9I4RUH0j+WNLojR2tzPNLHD7vOXkVFfbPoOET35EZjK7afLsXkXr5wtbMUHYeMmN2Pu1h8f+4artdxjST9Ykmju3piSDBaNVpsPlEsOgrRPdlyshjNKi3mDOHYDep8Twy+Ndw26USh6ChkZFjS6K6C3WwxKsoDm9OK0KzSiI5DdEcqjRYbUwuREO6GSC970XHIBPi72GBMd09sSStGUyvXSNIfljS6J3Pjg1HV0IpdZ6+KjkJ0R7vPX0P5zRbM5Vk06kJzhwTjhlKFbzPLREchI8KSRvdkcKgrIj3tsT6lEDodBzeSNOl0Oqw7VoAQd1sMi3AXHYdMSP9gF0T7OGD9sQKukaQ3LGl0T2QyGebGByHr2k2cyK8RHYfod50uvoFzpXWYMyQYcjmH11LXkclkmDskGFcqGnAst0p0HDISLGl0zyb19IWLrQXW81Fzkqj1xwrhYGWGh3v7io5CJuj+OG+42VlyADjpDUsa3TMrcwVmDgjAgaxyFFU3io5D9CulN5TYc+EaHhsQABsLM9FxyARZmimQODAQh3IqkVfZIDoOGYF2l7QVK1Zg2rRpWLlyZYeOIcMya2AgzOQyfHq8UHQUol/ZmFoEmUyG2YOCREchEzZzYAAsFHJ8ygHgpAftKmn79++HVqvF1q1bUVJSgsLCwnYdQ4bH08EK9/fwwVfppahvVomOQwQAaGxR4/OTxRgf4wUfJ2vRcciEudlZYlJPH2zLKEWdkmskdUy7StrJkycxfvx4AEB8fDwyMjLadQwZprlDgtHQosaX6aWioxABALZllKK+WY258Ry7QeLNGRKMJpUGX5ziAHDqmHaVNKVSCU9PTwCAo6Mjqqur23UMGaZYP0f0C3LGp8cLoNHyUXMSS6vVYUNKAXr6O6F3gLPoOETo7uOAQSGu2JhaBLVGKzoOGbB2lTQbGxs0N9/ao0ypVEKr/e0/wns5hgzX3CHBKKlpwoGsctFRyMQdyqlAYbUS83gWjSRkbnwwymqbsP8S10hqv3aVtJiYmJ8vX2ZnZ8PX97ePu9/LMWS4xnT3hK+TNR81J+HWHSuAt6MVxsV4iY5C9LORUR4IcLHhGkkd0q6SNnr0aOzYsQNvvfUW9uzZg/DwcLz77rt3PGb48OH6yEsSYaaQ44nBQUgrqMGFsjrRcchEXbp6E8fzqjF7cBDMFZwoRNKhkMvwxOAgpBfdwNmSWtFxyEC1a1Wzs7NDUlIS4uLisHHjRkRFReHZZ5+94zH29tzo2Ng82s8fNhYKbOCj5iTIhpQCWJsr8Fi/ANFRiH7jkb5+sLM0wwYOAKd2avdHT0dHR0yYMAHu7rffH+9ejiHD5Whtjkf6+GHX2auoqG8WHYdMTGV9C3ZkXsXUPn5wtDEXHYfoN+ytzPFoX398d+4aym9yjaS24/UB6pAnhgSjVaPFphN81Jy61qYTRWjVaDFnSJDoKES39cTgIGh0OiSlFomOQgaIJY06JNjNFqOiPLD5RBGaVRrRcchENKs02JxWhFFRHghxtxMdh+i2AlxtMKabJzancY2ktmNJow6bFx+M6sZW7MgsEx2FTMTOs1dR1dDK4bVkEObGB+OGUoVvz3Aby3rJAAAgAElEQVSNpLZhSaMOGxTqim7eDlh3rAA6HYfbUufS6XRYf6wAUV72GBzqKjoO0V0NCHZBd28HrE/hGkltw5JGHSaTyTAvPhiXyxuQfKVKdBwycsfzqpF9vR5z44Mhk8lExyG6K5lMhrk/rpEpudx9h+4dSxrpxQNx3nC3t8Q6Dm6kTrb+WAHc7CzwYJyP6ChE9+yBOG+42VlgPcdxUBuwpJFeWJop8PjAQBy5XIkr5fWi45CRyq9swMHsCswcEAgrc4XoOET3zNJMgVkDA/FDdgXyKhtExyEDwZJGejNzYCAszeT8pEidZkNKISwUcswaGCg6ClGbzRoYCAszObeKonvGkkZ642JrgSm9/bD9dBmqG1pExyEjU6dUYVtGKSb19IG7vaXoOERt5mZniSm9fPH16VLUNLaKjkMGgCWN9GpefBBa1FpsTuNwW9Kvz08Vo0mlwZwhHLtBhmtufDCaVVpsPsHhtnR3LGmkV2Ee9hge6Y6NqUVoUXNwI+mHSqPFZ8cLMTjUFd19HETHIWq3CE97DItwx2dcI+kesKSR3s2LD0ZVQwt2nb0mOgoZiT0XruNaXTPmcXgtGYEFCSGoamjBzsyroqOQxLGkkd7Fh7kh0tMea5PzObiR9GL9sQIEu9liRKSH6ChEHTYkzBVRXvYcAE53xZJGevfTcNvs6/VIzePgRuqYjKIbyCypxZwhQZDLObyWDN8v18hjuRwATrfHkkad4sGePnCzs8BaPmpOHbT+WAEcrMzwcG8/0VGI9ObBH59SXpvMNZJujyWNOoWVOQc3UseV3lBiz4VreGxAAGwtzUTHIdIbSzMFZg+6NQD8MgeA022wpFGn4eBG6qiNqUWQyWSYPShIdBQivbu1c4Yc63g2jW6DJY06jZudJSb3vDW48QYHN1IbNbao8fnJYoyP8YKPk7XoOER652xrgal9/PBNZhkq6zkAnH6LJY061U+DG7ec5HBbapuv0ktQ36zm2A0yanOHBEOl0SKJw23pd7CkUaeK9LJHQrgbPjteiFa1VnQcMhBarQ4bjheiV4ATegU4i45D1GlC3O0wKsoTm04UoVnF4bb0ayxp1OnmxQejor4F35/n4Ea6N//JKkdRtZJn0cgkzE8IRk1jK745UyY6CkkMSxp1umER7gj3sMPaZA5upLvT6XRYdSQPAS42GBftJToOUacbEOyCGF8HrE3Oh1bLNZL+iyWNOp1MJsPc+GBcvHoTaQU1ouOQxJ0qvIEzxbVYkBAMMwWXKDJ+MpkMCxJCkFfZiCOXK0XHIQnhCkhdYnIvX7jYWnBwI93V6iN5cLG1wNQ+/qKjEHWZCbHe8HKwwtpj+aKjkISwpFGXsDJXYNaAABzMLkdhVaPoOCRRl8vrcTC7ArMHBcHaQiE6DlGXMVfI8cSQIKTkVuPi1TrRcUgiWNKoy8waFAhzuRyfJPOTIv2+NUfzYW2uwOODAkVHIepyj/UPgI2FAus4AJx+xJJGXcbD3goP9/HDVxmlHNxIv3Gtrgk7MsswrZ8/nG0tRMch6nKO1uZ4tK8/dp29ivKbzaLjkASwpFGXWjg0BCqNFhtS+EmRfm1DSiG0OnDsBpm0uUOCodHq8NnxQtFRSAJY0qhLBbvZYkKMN5JSi3CzWSU6DklEXZMKW9KKMTHWG/4uNqLjEAkT4GqD+6K9sDmtGMpWteg4JBhLGnW5RcNCUd+ixpY0bhVFt2xJK0ZDixoLh4aIjkIk3PyEYNQ1qfB1RqnoKCQYSxp1uVg/RySEu2HdsQJug0JoUWuwPqUACeFuiPF1FB2HSLjeAc7oFeCET5ILoNZwOz1TxpJGQjw1LBSV9S3YfprboJi6b8+UobK+BYuGhYqOQiQJMpkMTw0LRXGNEt+fvyY6DgnEkkZCDAp1RZyfI1YfzYOG26CYLK1Wh9VH8xHj64DBoa6i4xBJxuhunojwtMPKQ3ncKsqEsaSREDKZDE8ND0VRtRJ7LvCToqn6T1Y58isb8eTQUMhkMtFxiCRDLpdh8Ygw5Pw44JlME0saCTO2uxdC3G3x8eE8brxugn7aSN3fxRrjY7iROtH/mhjrjQAXG3x4KJdrpIliSSNh5HIZFg0NxcWrN3Est0p0HOpi6UU/baQewo3UiX6HmUKORcNCcbakFsfzqkXHIQG4MpJQk3r5wMvBCh8fzhMdhbrY6iN5cLYxxyPcSJ3oth7u4wsPe0t8dChXdBQSgCWNhLI0U2B+QjCO51Ujs6RWdBzqIlfK63EgqwKzB3MjdaI7sTRTYOHQEBzPq8bp4hui41AXY0kj4ab3D4CjtTlW8WyayVhzNB9W5nI8PihIdBQiyXusfwCcbMyx8hDXSFPDkkbC2VmaYfagQOy7dB25FQ2i41Anu17XjG8zyzCtrz9cuJE60V3ZWpphzuBgHMgqR/b1m6LjUBdiSSNJmD04CJZmcqw5yk+Kxm5DSgE0Wh3mJ3ALKKJ7NXtwIGwtFLx/18SwpJEkuNpZYlpff3xzpgzX6ppEx6FOcrNZhc1pxZjYw4cbqRO1gZONBWYNDMSus1dRVN0oOg51EZY0koz5CSHQ6oB1yQWio1An+Wkj9Se5kTpRm82LD4aZQo5VR/JFR6EuwpJGkuHvYoMH43yw5WQxapWtouOQnjW1arA2uQDxYdxInag9PBys8GhfP3ydUYrrdc2i41AXYEkjSXlyWAiUrRpsTC0SHYX0bHNaEaoaWvDMqHDRUYgM1pNDQ6HR6fBJMs+mmQKWNJKUKC8HjIrywIaUAihb1aLjkJ4oW9X4+HAe4sPc0D/YRXQcIoPl72KDSXE+2JJWjJpGXnEwdixpJDlPDQ/FDaUKX54qER2F9GRjahGqG1vx7BieRSPqqKeGh6JJpcGnKbx/19ixpJHk9A1yQb8gZ3ySXACVRis6DnVQQ4saq4/kYWiEO/oE8iwaUUeFe9rjvmhPfHq8EPXNKtFxqBOxpJEk/WF4GMpqm/BVeqnoKNRBnx0vxA2lCs+O5lk0In1ZPCIMN5vV2JxWLDoKdSKWNJKk4ZHu6B3ghH//cAXNKo3oONRO9c0qfJKcjxGR7ugV4Cw6DpHR6OHnhIRwN6xNLuAaacRY0kiSZDIZXrgvEtfqmvlJ0YB9mlKIWqUKz46JEB2FyOgsHhGGqoYWfJXO+3eNVZtL2ooVKzBt2jSsXLnytseo1WoMHz4ciYmJSExMRE5OTodCkmkaHOqGIWGuWHkoF40tfNLT0Nz88Sza6G6e6OHnJDoOkdEZEOyCPoHOWHUkn/fvGqk2lbT9+/dDq9Vi69atKCkpQWFh4e8el5OTg4kTJyIpKQlJSUmIjIzUR1YyQS+MjUR1Yys+PV4oOgq10fpjBbjZrMYy3otG1ClkMhkWjwhFWW0TvjldJjoOdYI2lbSTJ09i/PjxAID4+HhkZGT87nGZmZk4fPgwpk6dihUrVkCt5lkQap9eAc4Y3c0Dq4/koa6JTzEZijqlCuuSC3BftCd3FyDqRCMiPRDn54j3DlzmvWlG6I4l7ZVXXvn5kmViYiKSkpLg6ekJAHB0dER1dfXv/rnY2Fhs2LAB27Ztg1qtxpEjR/SfnEzGc2MicbNZjU+OcsK2oVh3LB/1LWosG8170Yg6k0wmw4vjonC1rhlJ3KnF6Jjd6Tdff/31X/38zTffRHPzrf3ClEoltNrfvwYeFRUFCwsLAEBMTAyKivgPh9qvu48D7u/hjfUpBXhiSBDc7CxFR6I7qFW2Yn1KISbEeqGbt4PoOERGb3CYGxLC3fDR4VxM6+8PBytz0ZFIT9p0uTMmJubnS5zZ2dnw9fX93eOWL1+O7OxsaDQaHDhwAFFRUR1PSibt2TERaFZpsPJQnugodBefJOejsVWNpaN4Fo2oq7w4Lgq1ShVWH+EaaUzaVNJGjx6NHTt24K233sKePXswfPhw5Obm4t133/3VcYsXL8by5cvx0EMPoVevXhg8eLBeQ5PpCXW3w9Q+ftiUVoRrdU2i49Bt1DS2YkNKISbGeiPSy150HCKTEePriAfifLDuWAEqbjaLjkN60qaSZmdnh6SkJMTFxWHjxo2wt7dHWFgYnn322V8dFxERgV27dmHXrl2/+T2i9npmVDh0Oh0+OJgrOgrdxuqjeWhSafhEJ5EAz4+JgFqjw/sHr4iOQnrS5jlpjo6OmDBhAtzd3TsjD9Ft+TnbYEb/AHyVXoKi6kbRceh/VDW0YOPxIkyK80GYB8+iEXW1IDdbPNY/AF+cKkFBFddIY8AdB8igLB4ZBjOFDO8d4CdFqVl9JA8tag2eGcWzaESiLBkVBguFHP/czyHyxoAljQyKh70VZg8OwreZZbhcXi86Dv2oor4ZSSeK8FAvX4S424mOQ2SyPOytsCAhGN+fu4bzpXWi41AHsaSRwVk0NBR2Fmb4f/svi45CP1p1OB8qjQ7PjORZNCLRFgwNgYutBd7emy06CnUQSxoZHGdbC8xLCMbei9f5SVECym82Y1NaEab08kWQm63oOEQmz97KHItHhOFYbhWOXakSHYc6gCWNDNK8+GA425jzvgsJeO/AFWi1OizhWTQiyZg1MAC+TtZ4e282tFqd6DjUTixpZJDsrcyxaFgojlyuxMmCGtFxTNaFsjp8caoYjw8KQoCrjeg4RPQjSzMFnhsTgfNlddh94ZroONROLGlksB4fFAR3e0v8c18OdDp+UuxqOp0Or+68CFdbCywbw7NoRFLzUC9fRHra45/7cqDS/P42jiRtLGlksKwtFHhmZBhOFtbgKO+76HLfZpYho+gG/nhfFPcKJJIghVyGP46LRGG1EltPlYiOQ+3AkkYGbVq/APg5W+PtPdnQ8L6LLtPQosZbu7MR5+eIqX38RMchotsYGeWBfkHOeP/gFShb1aLjUBuxpJFBszCT40/jo3Dp2k1sOlEkOo7J+PcPV1BR34L/ezAacrlMdBwiug2ZTIY/jY9CZX0LNqQUio5DbcSSRgZvYqw3EsLd8M99OdxYuAvkVTZg/bECPNLHD70CnEXHIaK76BPogtHdPLHqcB5uNLaKjkNtwJJGBk8mk+H1STFo0Wjx5vdZouMYNZ1Oh9d3XYKVmQJ/HBclOg4R3aM/jotEY6saKw/nio5CbcCSRkYh2M0WTw0Lxc6zVzm8sRMdzKrAkcuVWDo6HO72lqLjENE9ivC0x5TefvgstQiF3HzdYLCkkdF4angoglxt8MqOC2hRa0THMTrNKg1e/+4SwjzsMHtwkOg4RNRGy++LhKWZHC9tP8+xRQaCJY2MhpW5Aq9PikF+VSPWHMkXHcforDtWgOIaJf7vgWiYK7h0EBkaTwcrvDS+G1Lzq/FVeqnoOHQPuNKSURka4Y6JPbzx4aFcFFcrRccxGldrm/DhD7kYF+2F+HA30XGIqJ2m9/NH/2AX/HV3Firq+aCV1LGkkdH5y8TuMJPL8MrOCzylrydv7cmGVqfDnyd2Ex2FiDpALpfhrSmxaFJp8NquS6Lj0F2wpJHR8XK0wnNjI3E4pxJ7L1wXHcfgncivxq6zV7FoWCj8Xbg/J5GhC3W3w9JR4fj+3DX851K56Dh0ByxpZJRmDwpEd28HvLbrEhpaOGW7vdQaLf5v50X4Ollj0bBQ0XGISE8WDg1BlJc9/vLtBdQ3q0THodtgSSOjZKaQ483JMSivb8b7By6LjmOwtpwsRvb1erw8sRusLRSi4xCRnpgr5Pj7wz1QUd+Mf+zNER2HboMljYxW7wBnTO8XgPUphci6dlN0HINT09iKf+2/jCFhrhgX4yU6DhHpWU9/J8wZEoykE0VIL6wRHYd+B0saGbUXx0XC0docL397AVpuwN4m/9yfg4YWNV59IBoyGffnJDJGz4+NgJ+zNV78+hznS0oQSxoZNScbC7w0PgoZRTfwVUaJ6DgGI6OoBp+fLMbsQUGI8LQXHYeIOomNhRn+OjkWeZWN+OhQnug49D9Y0sjoTe3jh/5BLnhrTzZquLnwXdUqW/HM55nwd7bBsjHhouMQUScbFuGOKb188fHhXORcrxcdh36BJY2Mnkwmw5uTY9DQrMbbe7JFx5E0nU6HP247h4r6Zvz7sV5wsDIXHYmIusDL93eHvZU5Xvz6HDS8NUQyWNLIJER42mNeQjC2ppfgZAFvkL2djalF2H+pHC+Oi0Kcv5PoOETURVxsLfDK/d2RWVKLpNRC0XHoRyxpZDKWjgpHgIsNln5xBlUNLaLjSM6Fsjr89fssjIzywLz4YNFxiKiLTerpg2ER7vjHvhyU1TaJjkNgSSMTYmNhhpUze6OmsRVLtpyBWqMVHUkyGlrUWPL5GbjYWuCfj8TxaU4iEySTyfDXyTEAgJe/Oc9t9SSAJY1MSoyvI/42ORap+dV4Zx8HOAK37kP7y7cXUFTdiPen94SLrYXoSEQkiJ+zDV4YG4lDOZXYefaq6DgmjyWNTM7DffyQODAQq4/mY/f5a6LjCLctoxTfnCnD0lERGBDiKjoOEQk2e3AQevo74eVvLuBKOZ/2FIkljUzSX+7vjt4BTlj+1VnkVpjuIpRbUY9XdlzEoBBXPD0yTHQcIpIAhVyGj2b2hqW5AvM+S+foIoFY0sgkWZjJsXJmH1hbKLAwKcMkNxhuVmnw9JYzsLFQ4L3pPaGQ8z40IrrF18kaax7vg+s3m7EoKYO7EQjCkkYmy8vRCh/O6I2iaiVe+Oqsyd0k+8Z3l5B9vR7/ejQOng5WouMQkcT0DnDGO1N74GRhDf78zQWTWyOlgCWNTNrAEFe8ND4K+y6W4+MjprMlyu7z17A5rRhPDg3B8EgP0XGISKIm9fTF0lHh2JZRitVH80XH6TJNrRpU1DeLjsGSRjQvPhj39/DGP/fl4NiVKtFxOl1JjRIvfn0Ocf5OeH5spOg4RCRxy0aH4/4e3nh7bzb2XbwuOk6nq25owYMfHsPTm8+IjsKSRiSTyfD2wz0Q5mGHJZ+fRukNpehInUal0WLJ57cWng8f6wULMy4BRHRnMpkM/3wkDj38nLDsi0xcKKsTHanT1ClVSFx3EsU1Sjw7JkJ0HJY0IgCwtTTDqll9oNbo8NSm02hWGedNsm/vyUZmSS3efrgH/F1sRMchIgNhZa7AJ4l94GRjjgUb01FxU/ylQH1raFFj9oaTyK1owJrH+2JQqPiRRCxpRD8KcbfDvx6Nw/myOry646LoOHql0+nwzr5srD1WgMcHBWJCrLfoSERkYDwcrLB2dl/UNamwYGO6UX2YbWrVYO6np3C+rA4fzuiFYRHuoiMBYEkj+pWx0V5YPCIUW9NL8PnJYtFx9EKn0+H17y7ho0N5eKy/P159IFp0JCIyUNE+jnhvWk+cK6vD81+dhVZr+E98tqg1WJiUjlOFNXh3Wk+MjfYSHelnLGlE/+O5MZFICHfDqzsuYu8Fw75JVqPV4aXt57EhpRBzhwTjb5NjOQ+NiDpkbLQXXhwXhe/PXcN7B6+IjtMhKo0WizefQfKVKrz9cA88GOcjOtKvsKQR/Q+FXIZ/P9YL3X0c8NTmDKxNzjfI+UAqjRbPbs3EF6dK8MzIMPzl/m7cOJ2I9OLJoSF4pI8fPjh4BTsyy0THaReNVodnt2biQFY53pgUjUf7+ouO9BssaUS/w8nGAl8sHIhx0V548/ssvLLjItQarehY96xZpcEfNp/GzrNX8eK4KDw3NpIFjYj0RiaT4a+TY9E/yAXLt50zuPFFWq0OL359Dt+du4YVE6KQOChIdKTfxZJGdBtW5gp8NKM3nhwagqQTRViwMR0NLWrRse6qqVWDBRvT8Z9L5Xh9UjSeGh4qOhIRGSELMzlWJfZBoIsNHl+fhpWHcw3iHjWdTodXd17EtoxSLBsdjoVDpbtGsqQR3YFcLsNLE7rhr5NjcPRKFR5dlYrrddJ99Ly+WYXZ608iJbcK70ztgccl+umQiIyDi60Fvlk8BONjvfGPvTlYmJSOOqV090LW6XR4a082kk4U4clhIVg6Klx0pDtiSSO6BzMHBGLd7L4oqm7EQx+l4NLVm6Ij/UatshUz16bhdPENfPBYLzwiwfsriMj42Fma4cPHeuHVB7rjcE4l7v8wWbIDb987cAVrjuZj9qBA/GlclORvA2FJI7pHwyM98NWiwQCAR1Ydx6GcCsGJ/quyvgXT15xA9vV6rE7sg/t7SOsJJSIybjKZDHOGBGPrk4Og1ugw5ePj+OJksWQeurpW14Q/bM7A+wev4NG+fnj1gWjJFzSAJY2oTbr7OODbxUMQ5GaL+Z+lY9OJItGRUFytxLTVqSiqVmLDE/0wqpun6EhEZKL6BDrjuyXxGBDsgj9tP4/l286hqVXc0FuVRos1R/Mw6l9HcDCrAi+MjcBbU3pAbiCjiMxEByAyNF6OVvjyyUFY8vkZvPztBRRVN+Kl8d26/Iu+/GYzPjqUiy9OlsDSTI6kef3RN8ilSzMQEf0vVztLfDqnP94/eAUfHLyCC2V1WDWrD4LcbLs0x8mCGrz87XlcLm/A6G4eePWBaIPbDo8ljagdbC3NsCaxD17/7hI+SS5A1rV6PDU8FINDXTv9FHpVQws+PpyHTSeKoNHq8EhfPzw9Mhy+Ttad+rpERPdKIZfhuTER6BXghGe3ZuKBfx/DO4/EYVxM50/zr2powd92Z2H76TL4Olnjk8f7Ykx3w7zCwJJG1E5mCjleezAaYR52ePc/lzFzbRpC3G2RODAQU3r7wdHaXK+vd6OxFauP5uOz44VoUWswuZcflo4KR4CrYX0yJCLTMSLSA98ticfizaexaFMG5sUHY258cKd8qNRoddhyshjv7M1Gk0qDxSNC8fSIcFhbKPT+Wl1FppPIXX1TpkzB9u3bRccgapdmlQa7z1/DxtQiZJbUwtpcgYd6+WDWwEBE+zh26O+ua1Jh3bECrD9WgMZWNR7o4YOlo8MR6m6np/RERJ2rRa3Bm99lIenH+3hjfR0xLsYL90V7IszDvsN//9mSWvxlxwWcK63DkDBXvPZgDMI8DGONvFP/aVdJq6qqwjPPPIMtW7bc9hiVSoUlS5agtrYWU6dOxdSpU9sdksiQnC+tw6YTRdhxtgzNKi36BDojcWAgxsd6wdLs3j/RNbSo8WlKAdYczcfNZjXGx3hh2egIRHp1fEEjIhIhv7IB+y6WY9/F68gsqQUAhLjb4r5oL9wX7YU4P8e73jKi0mhReqMJBVUNyK9sxPmyOuw8exXudpZ4+f7ueKCHt0E8ufkTvZa0uro6PPfcc6ipqcE333xz2+M2bNiAhoYGLFmyBAsWLMC7774LO7vbt1qWNDI2dUoVtp0uxaYTRSioaoSrrQUe7eePEDdbNLao0diqufX/LWo0tPz441b1j7+mwdXaJtS3qDG6mweWjY5AjG/HzsgREUnJ9bpm7L90HfsuXseJ/BpotDp4O1phbHdP3BfthWB3WxRUNd76X2Xjzz8urlFC/YudDZxszDGllx+eHRMOeyv93mbSFe7Uf9p8T5pCocB7772HP/zhD3c8Li0tDS+88AIAoF+/frhw4QIGDhzY1pcjMliONuaYFx+MOYODkJJXhaTUIqw+kodf7ppiJpfB1tIMdpZmsLFQ/PxjNztL9ApwwvT+Aejp7yTuP4KIqJN4OVrh8UFBeHxQEGqVrTiYVYG9F6/ji1Ml+Cz11+ONLM3kCHazRZS3PcbHeiHYzQ7BbrYIcbOFs62FoP+CznfXkvbKK6+goKDg558PGDAATz/99F3/4qamJnh63nqawtHREdXV1R2ISWS45HIZEsLdkRDujuqGFihbNbC1NIOtpQIWCrlBnZYnIuoMTjYWeLiPHx7u4wdlqxpHL1eisr7lVhlzt4W3g5XBzDbTp7uWtNdff71df7GNjQ2am5thb28PpVIJGxs+gUbkamcJV9EhiIgkzMbCDONivEXHkIRO23EgOjoaGRkZAIDs7Gz4+vp21ksRERERGR29zElLTU1FXl4eZs2a9fOvTZ48GQsXLkR6ejpyc3MRFxenj5ciIiIiMgmdOietvLwcGRkZSEhIgL39nccG8OlOIiIiMjV6fbqzLTw9PTFhwoTOfAkiIiIio9Rp96QRERERUfuxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJEEsaERERkQSxpBERERFJkJnoAD8pKyvDlClTRMcgIiIi6jJlZWW3/T2ZTqfTdWEWIiIiIroHvNxJREREJEEsaUREREQSxJJGREREJEEsaUREREQSxJJGREREJEEsaURERqy2thYpKSmoqakRHYWI2sikStqKFSswbdo0rFy5UnQUuo2qqirMmDEDAKBSqbBo0SJMnz4d27ZtE5yMfqm+vh7z58/H3LlzsXjxYrS2tvLrS4Lq6uqwaNEinDt3DrNnz0ZNTQ3fJ4mrqqrCQw89BIDfs6RKrVZj+PDhSExMRGJiInJycvDBBx/g4YcfxmuvvabX1zKZkrZ//35otVps3boVJSUlKCwsFB2J/kddXR1efPFFNDU1AQA2bdqE6OhofPHFF9i3bx8aGhoEJ6Sf7Ny5E3PmzMH69evh5uaG3bt38+tLgnJycvCnP/0JTz31FOLj43HixAm+TxL39ttvo7m5md+zJCwnJwcTJ05EUlISkpKSoFKpkJGRgW3btsHV1RXHjx/X22uZTEk7efIkxo8fDwCIj49HRkaG4ET0vxQKBd577z3Y2dkBANLS0n5+z/r164cLFy6IjEe/MHPmTAwZMgQAcOPGDezcuZNfXxLUv39/9OzZE6dOncK5c+eQnJzM90nCUlNTYW1tDXd3d37PkrDMzEwcPnwYU6dOxYoVK5CamoqxY8dCJpMhPj4e6enpenstkylpSqUSnp6eAABHR1bkS1YAAAI5SURBVEdUV1cLTkT/y87ODvb29j//vKmpie+ZxJ05cwZ1dXXw8vLieyVROp0Ou3fvhoODA2QyGd8niWptbcXKlSvxwgsvAOD3LCmLjY3Fhg0bsG3bNqjVarS0tPzqvaqqqtLba5lMSbOxsUFzczOAW//4tVqt4ER0N3zPpK22thZvvPEG/va3v/G9kjCZTIZXX30VkZGROHPmDN8niVqzZg1mzJgBBwcHAFz/pCwqKgoeHh4AgJiYGNjY2KClpQXArfdKn7ttmkxJi4mJ+fl0cXZ2Nnx9fQUnoruJjo7meyZRra2tWLp0KZ5//nn4+vry60ui1qxZg2+//RbArYc9Fi5cyPdJolJTU7FlyxYkJiYiKysLhw4d4nslUcuXL0d2djY0Gg0OHDgApVLZae+VyWyw3tDQgBkzZmDQoEE4evQovvzyy19dWiPpSExMRFJSEsrKyrBw4UIMGjQIZ86cwZdffgmFQiE6HgHYsmUL3n33XURFRQEApkyZgg0bNvDrS2Lq6uqwbNkytLa2Ijw8HM8//zxmzpzJ90niEhMT8fHHH/N7lkRdvnwZzz//PABg5MiRWLp0KWbMmIGYmBgkJydj7dq18Pf318trmUxJA24tWCkpKejXrx/c3d1Fx6F7UF5ejoyMDCQkJHCBkjh+fRkGvk+Gg++V4Whubsbhw4cRHR2tt4IGmFhJIyIiIjIUJnNPGhEREZEhYUkjIiIikiCWNCIiIiIJYkkjIiIikiCWNCIiIiIJ+v+oYpOWj9j9igAAAABJRU5ErkJggg==
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="plt.subplot:简易网格子图"><code>plt.subplot</code>:简易网格子图<a class="anchor-link" href="#plt.subplot:简易网格子图"> </a></h3><p><code>plt.subplot()</code>在一个网格中创建一个子图。这个命令有3个整型参数——将要创建的网格子图行数、列数和索引值,索引值从1开始,从左上角到右下角依次增大:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="nb">str</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">)),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"center"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>plt.subplots_adjust</code>命令可以调整子图之间的间隔。用面向对象接口的命令<code>fig.add_subplot()</code>可以取得同样的效果:</p>
<blockquote><p>通过<code>plt.subplots_adjust</code>的<code>hspace</code>与<code>wspace</code>参数设置与图形高度与宽度一致的子图间距</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">):</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="nb">str</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">)),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"center"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlwAAAJACAYAAABYCLhQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3dfXRU9Z3H8U9EQWI02hBTjU9bBbKb8LDKg2g0XTblNAGrRDAaYK2WVuRolfqAJypIsVCXtWERKdt2lRpBUikLnMVIqhyRDRggVRZWAkgNRHzAJBCISSDJ3P0jGA2ZMDPhfpN5eL/O8Rx755f5fWd6P72fDuFOlOM4jgAAAGDmrO4eAAAAINxRuAAAAIxRuAAAAIxRuAAAAIxRuAAAAIxRuAAAAIxRuAAAAIz5VbgqKyuVk5PT4eONjY2aMmWK7rzzTq1YscK14YBQRm6AwJAZhDOfhaumpkbTp09XfX19h2teffVVJScna/ny5Vq3bp1qa2tdHRIINeQGCAyZQbg729eCHj16aP78+Zo6dWqHa0pKSvToo49KkoYOHaqdO3fq+uuv73D98OHDlZiY2IlxgbYOHjyokpKS7h6jHXKDYBaMuSEzCGZuZMZn4YqJifH5JPX19UpISJAkxcbGqqqq6rTrExMTtXLlSj9HBDqWlZXV3SN4RW4QzIIxN2QGwcyNzLjyS/PR0dFqaGiQJNXV1cnj8bjxtEBYIzdAYMgMQpkrhSs5OVmlpaWSpLKyMj7CBfxAboDAkBmEMp9/pHiqzZs3a9++fZo4cWLrsbFjx+pnP/uZtm3bpo8++kiDBg1ydUgg1JEbIDBkBuEmynEcx40n+uKLL1RaWqqbbrpJ559//mnXZmVl8efqcEWon0vkBt0hlM8lMoPu4Ma5FPAnXB1JSEhQZmamW08HRARyAwSGzCBUcad5AAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAYxQuAAAAY34VrtzcXGVnZ2vRokVeH6+pqdFPf/pTZWVlacaMGa4OCIQiMgMEjtwgnPksXEVFRfJ4PCooKFBFRYXKy8vbrVm9erVuueUWrVy5Ul999ZV27NhhMSsQEsgMEDhyg3Dns3Bt2bJFGRkZkqTU1FSVlpa2W3PhhRdq7969Onr0qD777DNdcskl7k8KhAgyAwSO3CDc+SxcdXV1SkhIkCTFxsaqqqqq3ZrrrrtOn376qV555RVdffXVio2NdX9SIESQGSBw5Abhzmfhio6OVkNDg6SWQHg8nnZrFi5cqFmzZumBBx7Q9773Pa1cudL9SYEQQWaAwJEbhDufhSslJaX1o92ysjIlJia2W3P06FHt3r1bzc3N2r59u6KiotyfFAgRZAYIHLlBuPNZuNLT07V69WrNnTtXhYWF6tu3r/Ly8tqsue+++zRjxgwNGTJENTU1Gj16tNnAQLAjM0DgyA3C3dm+FsTExCg/P1/FxcWaPHmy4uPjlZSU1GbNwIEDtXbtWrMhgVBCZoDAkRuEO5+FS2r5BcbMzEzrWYCwQWaAwJEbhDPuNA8AAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGCMwgUAAGDMr8KVm5ur7OxsLVq06LTrnnnmGa1fv96VwYBQRmaAwJEbhDOfhauoqEgej0cFBQWqqKhQeXm513Xbtm1TZWWlRo4c6faMQEghM0DgyA3Cnc/CtWXLFmVkZEiSUlNTVVpa2m5NY2OjnnrqKSUmJuqtt95yf0oghJAZIHDkBuHOZ+Gqq6tTQkKCJCk2NlZVVVXt1qxatUrXXHONJk+erB07dig/P9/9SYEQQWaAwJEbhDufhSs6OloNDQ2SWgLh8Xjardm1a5fuuOMOxcfH60c/+pFKSkrcnxQIEWQGCBy5QbjzWbhSUlJaP9otKytTYmJiuzVXXHGFKioqJEk7duzQpZde6vKYQOggM0DgyA3Cnc/ClZ6ertWrV2vu3LkqLCxU3759lZeX12bNuHHjVFJSogkTJui1117TT37yE7OBgWBHZoDAkRuEu7N9LYiJiVF+fr6Ki4s1efJkxcfHKykpqd2aBQsWmA0JhBIyAwSO3CDc+SxcUssvMGZmZlrPAoQNMgMEjtwgnHGneQAAAGMULi+8/e2YSMT7gEBwvrTgfYC/OFdaRMr7QOE6RW1trbKzs3XgwIHuHqVb/eEPf9D8+fO7ewyECHLTorCwUI899lh3j4EQQGZaRFJmKFyneOKJJxQXF6fLLrus22Y4ceKEDhw4oGPHjnXbDP/8z/+sgoIC7uYMv5CbFsOHD9cHH3ygV155pdtmQGiItMx0tFckZYbC9S1/+ctfVFZWpueff15nndXy1ng8Hr344otKS0tTcnKybr75Zi1btsxsht///vcaPny4fvCDH+j6669Xbm6uTpw4YbZfdXW10tLS2t1A8O/+7u/0r//6r5o1a5bq6+vN9kfoi6Tc+Hpd3/nOd/Tiiy9qwYIF+uKLL1zfH+EhkjLja69IygyF66Tm5mb927/9m6ZPn67zzjuv9fgLL7yghQsX6oYbbtDMmTN15ZVXatasWSosLHR9hsLCQuXl5enhhx/W6tWr9eCDD2rlypVasmSJ63tJUkNDgx566CF9/vnnXh9PS0vTgAED9Mc//tFkf4S+SMuNP68rKSlJY8eO1cKFC13fH6Ev0jLjz14RkxmnG4wdO7Y7tj2tkpISZ/To0W2OVVVVOSkpKc6SJUtajzU0NDg33HCDc++997o+Q0ZGhjNnzpw2x+6++27nzjvvdH2v6upqJzs720lNTXX69evnvPfee17X/e///q+Tnp7u+v5uCcZzyUowvtZIyk0gr+vLL790rr32WqehocHVGdwSjOeShWB8nZGUmUD2ioTM8AnXSevXr9eoUaPaHKurq9MDDzygCRMmtB7r1auXrrjiCh0+fNjV/ZubmzV9+vR2d04+55xzWj9ydtOaNWvUo0cP/fa3vz3tugEDBqipqUl79+51fQaEvkjKTSCvq0+fPkpKStLmzZtdnQGhL5IyE8hekZAZCtdJe/fu1aBBg9ocu+yyy3Tffffp7LO/uT9sY2Oj9uzZo2uuucbV/Xv06KG0tDRdfPHFrcf27Nmj9957TzfeeKOre0nSyJEj9corr+jCCy/0uXbQoEEULngVSbkJ9HUNHjxYe/bscXUGhL5Iykyge4V7Zvy603wkqKysbHNSdOT1119XbW2tbrvtNrNZ9u7dqyVLlmjt2rUaMWKEJk+e7Poel19+ud9r+/Tpoy+//NL1GRD6Ii03pzrd64qPj9enn35qPgNCS6Rmxp+9wj0zfMJ10vHjx9WrV6/TrqmurtbChQs1fPhw3XDDDWaz1NTUaPfu3aqvr1ePHj3U2Nhotpc/zj33XB0/frxbZ0BwiuTc+HpdvXr1IjdoJ1Iz489e4Z4ZCtdJcXFxqqysPO2aZ555RvX19Zo9e7bpLEOGDNGKFSuUn5+vTZs26bnnnjPdz5fKykrFxcV16wwITpGcG1+vi9zAm0jNjD97hXtmKFwnffe739X+/fs7fLygoEDr1q3T008/rSuvvLJLZho2bJhGjx6tdevWdcl+Hdm/f78uvfTSbp0BwSlSc+PP69q/f78uueQSsxkQmiI1M/7sFe6ZoXCdlJqaqrffftvrY9u3b9ezzz6r22+/XVlZWSb7V1dXa968eaqqqmpzPDY2Vk1NTSZ7+qO6ulp79uzRtdde220zIHhFYm78eV1NTU3auHGjUlNTTWZA6IqkzASyVyRkhsJ10ve//31t3bq13S/s7d+/X1OmTFHfvn01c+ZMs/1jYmK0fPlyLV68uPVYc3OzNm7c2O5vtHSlFStW6Oabb/b5OweITJGWG39fV1FRka688sqw/n/r6JxIykwge0VCZvhbiidddNFFys7O1q9//WstWLCg9fjjjz+u6upq/fjHP9abb77Z5mduvfVWSVJZWZl2796t0aNHt/lrvYHo2bOn7r//fs2bN0+HDx/Wtddeq7ffflvl5eWaNWtW6zo39vLXl19+qT/84Q9aunSp6T4IXZGWG39eV0NDg+bNm6df/vKXnd4H4SuSMuPvXhGTGRduwBqwYLz7r+M4ztGjR52bbrrJWb58ueM4LXdj79evX4f/fG3BggVOv379nNra2jPa3+PxOPn5+c6oUaOcwYMHOzk5Oc7WrVvbrHFrr69VVFR4vdP88ePHnYkTJzq//OUvXdnHSrCeSxaC9bVGSm78fV2/+MUvnPvuu6/T+3SFYD2X3BasrzNSMuPvXpGSGQrXKbZv3+4MHjzY+fjjj/3+mbq6Omf8+PF2Q3XDXgsWLHDuuOMO5/jx4+Z7nYlgPpfcFsyvldy0+POf/+ykp6c7R44cMd/rTATzueSmYH6dZKZFJGWGP1I8xcCBA/X666/rqquu8vtn3nnnHY0fP95uqG7Ya9KkSbrzzjvVs2dP870Q+shNizFjxmjo0KGKjY013wuhjcy0iKTMULi8CPSrFDIyMowm6b69/PnKH+DbyE3L76wE8i0OiGxkJrIyw99SBAAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMEbhAgAAMOZX4crNzVV2drYWLVp02nWVlZW67bbbXBkMCGVkBggcuUE481m4ioqK5PF4VFBQoIqKCpWXl3e49rnnnlNDQ4Ob8wEhh8wAgSM3CHc+C9eWLVuUkZEhSUpNTVVpaanXdZs3b1bv3r0VHx/v7oRAiCEzQODIDcKdz8JVV1enhIQESVJsbKyqqqrarTlx4oQWLVqkRx991P0JgRBDZoDAkRuEO5+FKzo6uvWj27q6Onk8nnZrfve73yknJ0cXXHCB+xMCIYbMAIEjNwh3PgtXSkpK60e7ZWVlSkxMbLdm8+bNWrZsmSZNmqRdu3bpySefdH9SIESQGSBw5Abh7mxfC9LT05WTk6NDhw7p3XffVV5envLy8jRt2rTWNUuXLm3990mTJulXv/qVzbRACCAzQODIDcKdz8IVExOj/Px8FRcXa/LkyYqPj1dSUlKH6/Pz810dEAg1ZAYIHLlBuPNZuKSWX2DMzMy0ngUIG2QGCBy5QTjjTvMAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGKFwAAADGzvZnUW5urvbt26e0tDRNnTq13ePHjh3TtGnT5PF41Lt3b+Xl5alnz56uDwuECjIDBI7cIJz5/ISrqKhIHo9HBQUFqqioUHl5ebs1a9as0T333KOXXnpJffr00caNGy1mBUICmQECR24Q7nx+wrVlyxZlZGRIklJTU1VaWqqrrrqqzZoJEya0/vvhw4cVFxfn7pRACCEzQODIDcKdz0+46urqlJCQIEmKjY1VVVVVh2vff/991dTUaPDgwe5NCIQYMgMEjtwg3Pn8hCs6OloNDQ2SWgLh8Xi8rjty5Ihmz56tF154wd0JgRBDZoDAkRuEO5+fcKWkpKi0tFSSVFZWpsTExHZrTpw4oYceekiPPPKI18eBSEJmgMCRG4Q7n4UrPT1dq1ev1ty5c1VYWKi+ffsqLy+vzZoVK1boww8/1OLFizVp0iS98cYbZgMDwY7MAIEjNwh3UY7jOL4W1dTUqLi4WEOHDlV8fPwZb5qVlaWVK1ee8fMAwXouuZ0ZKXhfK0JPsJ5LXGsQrNw4l/y6D1dsbKwyMzPPaCMgkpAZIHDkBuGMO80DAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAYo3ABAAAY86tw5ebmKjs7W4sWLTqjNUCkIDNA4MgNwpnPwlVUVCSPx6OCggJVVFSovLy8U2uASEFmgMCRG4Q7n4Vry5YtysjIkCSlpqaqtLS0U2uASEFmgMCRG4S7s30tqKurU0JCgiQpNjZWBw8e7NSabzt48KCysrI6My/Qhq9zrTtYZEYiN3BPpOSGzMAtbmTGZ+GKjo5WQ0ODpJaT3ePxdGrNt5WUlHRmViAkWGRGIjcIb1xrEO58/pFiSkpK68e2ZWVlSkxM7NQaIFKQGSBw5AbhLspxHOd0C2pra5WTk6MRI0bo3XffVV5engoLCzVt2rQO1/zpT3/S+eefbz48EIzIDBA4coNw57NwSVJNTY2Ki4s1dOhQxcfHd3oNECnIDBA4coNw5lfhAgAAQOdxp3kAAABjFC4AAABjPm8L0Rm5ubnat2+f0tLSNHXqVL/X+PNzFvMcO3ZM06ZNk8fjUe/evZWXl6ezzjpL6enpuvzyyyVJTz31lPr3799lMzU1NXndf8GCBdqwYYMGDhyomTNnujaPPzMtW7ZMhYWFkqSjR49q0KBBmjFjhun7VFlZqZ///OdatmyZ18cbGxv14IMP6siRIxo3bpzGjRvn9ViwC7bM+PPc5CY4MyORG19ruNZ8g2tNi67IjOufcHX26xmsvrLBn+dds2aN7rnnHr300kvq06ePNm7cqN27d2v06NHKz89Xfn6+q//F+jOTt/137typ0tJSrVixQnFxcdq0aVOXzpSTk9M6z5AhQzR+/HjT96mmpkbTp09XfX19h2teffVVJScna/ny5Vq3bp1qa2u9HgtmwZYZf2eK9NwEY2YkcuNrDdeatrjWdF1mXC9cnf16BquvbPDneSdMmKAbb7xRknT48GHFxcXpgw8+0DvvvKNx48YpNzdXTU1Nrszj70ze9t+6datGjRqlqKgopaamatu2bV0609e++OILVVZWasCAAabvU48ePTR//nzFxMR0uKakpKR17qFDh2rnzp1ejwWzYMuMvzNFem6CMTMSufG1hmtNW1xrui4zrheuU796oaqqyq81/vyc1Txfe//991VTU6PBgwdrwIABevnll7VixQo1NTVpw4YNrszj70ze9j/15yorK7t0pq8tXbpUd911V4dzuiUmJsbnPXbq6+vbze3tWDALtsz4O9PXIjU3wZgZidz4WsO1pi2uNV2XGdcLV2e/nqEzX3Xi1jySdOTIEc2ePVtz5syRJCUlJeniiy+W1HJ34/3797syj78zedv/vPPO0/Hjx1t/zs07evj7Pnk8HpWUlGj48OEdztmVuvJcshJsmfF3JimycxOqmZHIDdeab3Ct8Y8b55LrhauzX89g9ZUN/jzviRMn9NBDD+mRRx5pffyxxx5TWVmZmpub9dZbbykpKcmVefydydv+ycnJZl9r4e/7v23bNg0cOFBRUVEdztmVvL0nlu+ThWDLjL8zRXpuQjUzErnhWvMNrjX+cSUzjsuOHTvm3HLLLc6cOXOcH/7wh86uXbuc3/zmN6ddc/ToUa/HumqepUuXOkOGDHEmTpzoTJw40Vm7dq2ze/duZ8yYMc6YMWPare+Kmbzt39zc7GRnZzuzZ892Ro0a5Rw4cKBLZ3Icx3n++eeddevWnXZOt02cONFxHMfZtGmTk5+f3+axTz75xMnMzHRmz57tZGVlOU1NTV6PBbNgy4y/M0V6boI5M45Dbryt4VrDteZ0rDPjeuFyHMc5cuSIs3btWufQoUMBrfHn56zm6Wqdnam+vt4pLCx0NQBnOlN3+/zzz521a9e2+R9Ob8eCWbBlxvq5OyvYchOM75G/yA3XmtPhWtPemWaGr/YBAAAwxp3mAQAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjFG4AAAAjPlVuCorK5WTk9Ph442NjZoyZYruvPNOrVixwrXhgFBGboDAkBmEM5+Fq6amRtOnT1d9fX2Ha1599VUlJydr+fLlWrdunWpra10dEgg15AYIDJlBuDvb14IePXpo/vz5mjp1aodrSkpK9Oijj0qShg4dqp07d+r666/vcP3w4cOVmJjYiXGBtg4ePKiSkpLuHqMdcoNgFoy5ITMIZm5kxmfhiomJ8fkk9fX1SkhIkCTFxsaqqqrqtOsTExO1cuVKP0cEOpaVldXdI3hFbhDMgjE3ZAbBzI3MuPJL89HR0WpoaJAk1dXVyePxuPG0QFgjN0BgyAxCmSuFKzk5WaWlpZKksrIyPsIF/EBugMCQGYQyn3+keKrNmzdr3759mjhxYuuxsWPH6mc/+5m2bdumjz76SIMGDXJ1SCDUkRsgMGQG4SbKcRzHjSf64osvVFpaqptuuknnn3/+addmZWXx5+pwRaifS+QG3SGUzyUyg+7gxrkU8CdcHUlISFBmZqZbTwdEBHIDBIbMIFRxp3kAAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjFC4AAABjfhWu3NxcZWdna9GiRV4fr6mp0U9/+lNlZWVpxowZrg4IhCIyAwSO3CCc+SxcRUVF8ng8KigoUEVFhcrLy9utWb16tW655RatXLlSX331lXbs2GExKxASyAwQOHKDcOezcG3ZskUZGRmSpNTUVJWWlrZbc+GFF2rv3r06evSoPvvsM11yySXuTwqECDIDBI7cINz5LFx1dXVKSEiQJMXGxqqqqqrdmuuuu06ffvqpXnnlFV199dWKjY11f1IgRJAZIHDkBuHOZ+GKjo5WQ0ODpJZAeDyedmsWLlyoWbNm6YEHHtD3vvc9rVy50v1JgRBBZoDAkRuEO5+FKyUlpfWj3bKyMiUmJrZbc/ToUe3evVvNzc3avn27oqKi3J8UCBFkBggcuUG481m40tPTtXr1as2dO1eFhYXq27ev8vLy2qy57777NGPGDA0ZMkQ1NTUaPXq02cBAsCMzQODIDcLd2b4WxMTEKD8/X8XFxZo8ebLi4+OVlJTUZs3AgQO1du1asyGBUEJmgMCRG4Q7n4VLavkFxszMTOtZgLBBZoDAkRuEM+40DwAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYMyvwpWbm6vs7GwtWrTotOueeeYZrV+/3pXBgFBGZoDAkRuEM5+Fq6ioSB6PRwUFBaqoqFB5ebnXddu2bVNlZaVGjhzp9oxASCEzQODIDcKdz8K1ZcsWZWRkSJJSU1NVWlrabk1jY6OeeuopJSYm6q233nJ/SiCEkBkgcOQG4c5n4aqrq1NCQoIkKTY2VlVVVe3WrFq1Stdcc40mT56sHTt2KD8/3/1JgRBBZoDAkRuEO5+FKzo6Wg0NDZJaAqku7XUAABR/SURBVOHxeNqt2bVrl+644w7Fx8frRz/6kUpKStyfFAgRZAYIHLlBuPNZuFJSUlo/2i0rK1NiYmK7NVdccYUqKiokSTt27NCll17q8phA6CAzQODIDcKdz8KVnp6u1atXa+7cuSosLFTfvn2Vl5fXZs24ceNUUlKiCRMm6LXXXtNPfvITs4GBYEdmgMCRG4S7s30tiImJUX5+voqLizV58mTFx8crKSmp3ZoFCxaYDQmEEjIDBI7cINz5LFxSyy8wZmZmWs8ChA0yAwSO3CCccad5AAAAYxQuL7z97ZhIxPuAQHC+tOB9gL84V1pEyvtA4TpFbW2tsrOzdeDAge4epVv99a9/1b333qumpqbuHgUhgNy0IDfwF5lpEUmZoXCd4oknnlBcXJwuu+yybpvhxIkTOnDggI4dO9ZtM/zDP/yDamtrNW/evG6bAaGD3LQgN/AXmWkRSZmhcH3LX/7yF5WVlen555/XWWe1vDUej0cvvvii0tLSlJycrJtvvlnLli0zm+H3v/+9hg8frh/84Ae6/vrrlZubqxMnTpjt97WCggL179+/9T+fe+65WrRokVatWqUPP/zQfH+ErkjKzaFDh9S/f/92/zz55JOSyA38E0mZ+bZ3331X/fv31xtvvNF6LKIy43SDsWPHdse2p9XU1OSMGjXKKSoqanN8/vz5TlJSkvPEE084BQUFzsSJE51+/fo5b7zxhuszvPHGG87f//3fO0uWLHF27drl/Pa3v3X69+/v/Md//Ifre33boUOHnCFDhjj9+vVr99jLL7/s3HPPPab7n4lgPJesBONrjbTcvP32205SUpLzX//1X86qVata/3n//ffbrCM3wSEYX2ekZeZrdXV1zsiRI51/+Zd/8fp4JGSGwnVSSUmJM3r06DbHqqqqnJSUFGfJkiWtxxoaGpwbbrjBuffee12fISMjw5kzZ06bY3fffbdz5513ur7Xtz344INO//79vRauEydOOMOGDXM+++wz0xk6KxjPJSvB+FojLTf//u//3u71ekNugkMwvs5Iy8zXnnvuOSc5Odn56KOPvD4eCZnhjxRPWr9+vUaNGtXmWF1dnR544AFNmDCh9VivXr10xRVX6PDhw67u39zcrOnTp7e7c/I555zT+pGzhfXr16uoqEi3336718fPOeccpaWl6e233zabAaEr0nKzc+dO/eM//qPPdeQGHYm0zEgtX9X0xz/+UXfffbeuvvpqr2siITMUrpP27t2rQYMGtTl22WWX6b777tPZZ39zf9jGxkbt2bNH11xzjav79+jRQ2lpabr44otbj+3Zs0fvvfeebrzxRlf3+lptba1mzZqlu+66S9ddd12H6wYPHqw9e/aYzIDQFmm52blzp/7v//5P//RP/6SBAwfqjjvu0IYNG7yuJTfwJtIy4ziOZsyYoejoaF133XXasGFDhyUy3DND4TqpsrKyzQnYkddff121tbW67bbbzGbZu3evnnzySd1xxx0aMWKEJk+ebLJPXl6eoqKi9Mgjj5x2XXx8vCorK01mQGiLpNx88sknqqqq0rFjx3TXXXfpscceU0NDg6ZMmaK//vWv7daTG3gTSZmRpFWrVmn79u06evSocnNz9eCDD2rkyJFas2ZNu7Xhnhm/vtonEhw/fly9evU67Zrq6motXLhQw4cP1w033GA2S01NjXbv3q36+nr16NFDjY2N6tmzp6t7fPDBB1q2bJkWL16smJiY067t1auXjh8/7ur+CA+RlJsePXpo2rRpuuuuuxQbGytJGjt2rEaPHq0lS5bo2muvbbOe3MCbSMqM4zh68cUXdc4557T+Dcza2lrNnDlTTzzxhK6++molJye3rg/3zPAJ10lxcXE+m/Uzzzyj+vp6zZ4923SWIUOGaMWKFcrPz9emTZv03HPPufr8jY2NevrppzVmzBilpaX5XF9ZWam4uDhXZ0B4iKTcXHLJJZoyZUpr2ZJavkw5PT1dJSUl7daTG3gTSZn529/+poqKCt16662t15qYmBjNmjVLHo9H//3f/91mfbhnhsJ10ne/+13t37+/w8cLCgq0bt06Pf3007ryyiu7ZKZhw4Zp9OjRWrdunavP+5//+Z/67LPPdP/996u6ulrV1dWqq6uT1PL/rE69Cd7+/ft1ySWXuDoDwkMk5aYj5557ro4cOaLGxsY2x8kNvImkzNTU1EiShg4d2uZ4TEyMvvOd7+jQoUNtjod7ZihcJ6Wmpnb4tyO2b9+uZ599VrfffruysrJM9q+urta8efNUVVXV5nhsbKzrX3lQXFysY8eOKSMjQyNGjNCIESNa/5/UiBEjNHXq1Dbr169fr9TUVFdnQHiIpNysWrXK692w9+zZo5iYGJ1zzjltjpMbeBNJmYmPj5ckRUVFtTne1NSkmpoaXXTRRW2Oh3tmKFwnff/739fWrVv16aeftjm+f/9+TZkyRX379tXMmTPN9o+JidHy5cu1ePHi1mPNzc3auHFju7/RcqamT5+ul19+uc0/X/8V4ZdfflnTp09vXbt9+3YdPny43e+nAFJk5aa2tlb5+fltvvtu27Zt+p//+Z92Fwlyg45EUmYuv/xyJSYm6s0332xzfNWqVWpqatL111/feiwSMsMvzZ900UUXKTs7W7/+9a+1YMGC1uOPP/64qqur9eMf/7jdSXPrrbdKarnHyO7duzV69Og2f603ED179tT999+vefPmtZ50b7/9tsrLyzVr1qzWdW7slZKS0u7Y559/LkltfkHTcRw9++yzeuCBB0zvBYbQFUm5ue2227R48WJNmDBBo0aN0ldffaU333xTF1xwgR5++OHWdeQGpxNJmZGkhx9+WI8//rimTJmitLQ0/e1vf9Nrr72mgQMHauTIkZIiKDNnfOvUTgjGu/86juMcPXrUuemmm5zly5c7juM41dXVTr9+/Tr852sLFixw+vXr59TW1p7R/h6Px8nPz3dGjRrlDB482MnJyXG2bt3aZo1be53qz3/+c7s7zT///PPOrbfe6jQ1Nbm6l5uC9VyyEKyvNZJy8/HHHzuTJ092Bg0a5AwfPtx59NFHnUOHDrVZQ26CR7C+zkjKjOO0fCVWTk6Oc+211zrDhg1zfvGLXziVlZWtj0dKZihcp9i+fbszePBg5+OPP/b7Z+rq6pzx48fbDdUNe7333nvOsGHDnAMHDpjvdSaC+VxyWzC/VnLTgtwEl2B+nWSmRSRlJow/u+ucgQMH6vXXX9dVV13l98+88847Gj9+vN1Q3bDXsGHDtGzZMl1++eXmeyH0kZsW5Ab+IjMtIikz/A6XF4F+lUJGRobRJN23V1RUVIffeQV4Q27IDQJDZiIrM3zCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYIzCBQAAYMyvwpWbm6vs7GwtWrTotOsqKyt12223uTIYEMrIDBA4coNw5rNwFRUVyePxqKCgQBUVFSovL+9w7XPPPaeGhgY35wNCDpkBAkduEO58Fq4tW7YoIyNDkpSamqrS0lKv6zZv3qzevXsrPj7e3QmBEENmgMCRG4Q7n4Wrrq5OCQkJkqTY2FhVVVW1W3PixAktWrRIjz76qPsTAiGGzACBIzcIdz4LV3R0dOtHt3V1dfJ4PO3W/O53v1NOTo4uuOAC9ycEQgyZAQJHbhDufBaulJSU1o92y8rKlJiY2G7N5s2btWzZMk2aNEm7du3Sk08+6f6kQIggM0DgyA3C3dm+FqSnpysnJ0eHDh3Su+++q7y8POXl5WnatGmta5YuXdr675MmTdKvfvUrm2mBEEBmgMCRG4Q7n4UrJiZG+fn5Ki4u1uTJkxUfH6+kpKQO1+fn57s6IBBqyAwQOHKDcOezcEktv8CYmZlpPQsQNsgMEDhyg3DGneYBAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMUbgAAACMne3PotzcXO3bt09paWmaOnVqu8ePHTumadOmyePxqHfv3srLy1PPnj1dHxYIFWQGCBy5QTjz+QlXUVGRPB6PCgoKVFFRofLy8nZr1qxZo3vuuUcvvfSS+vTpo40bN1rMCoQEMgMEjtwg3Pn8hGvLli3KyMiQJKWmpqq0tFRXXXVVmzUTJkxo/ffDhw8rLi7O3SmBEEJmgMCRG4Q7n59w1dXVKSEhQZIUGxurqqqqDte+//77qqmp0eDBg92bEAgxZAYIHLlBuPP5CVd0dLQaGhoktQTC4/F4XXfkyBHNnj1bL7zwgrsTAiGGzACBIzcIdz4/4UpJSVFpaakkqaysTImJie3WnDhxQg899JAeeeQRr48DkYTMAIEjNwh3PgtXenq6Vq9erblz56qwsFB9+/ZVXl5emzUrVqzQhx9+qMWLF2vSpEl64403zAYGgh2ZAQJHbhDuohzHcXwtqqmpUXFxsYYOHar4+Pgz3jQrK0srV6484+cBgvVccjszUvC+VoSeYD2XuNYgWLlxLvl1H67Y2FhlZmae0UZAJCEzQODIDcIZd5oHAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAwRuECAAAw5lfhys3NVXZ2thYtWnRGa4BIQWaAwJEbhDOfhauoqEgej0cFBQWqqKhQeXl5p9YAkYLMAIEjNwh3PgvXli1blJGRIUlKTU1VaWlpp9YAkYLMAIEjNwh3Z/taUFdXp4SEBElSbGysDh482Kk133bw4EFlZWV1Zl6gDV/nWnewyIxEbuCeSMkNmYFb3MiMz8IVHR2thoYGSS0nu8fj6dSabyspKenMrEBIsMiMRG4Q3rjWINz5/CPFlJSU1o9ty8rKlJiY2Kk1QKQgM0DgyA3CXZTjOM7pFtTW1ionJ0cjRozQu+++q7y8PBUWFmratGkdrvnTn/6k888/33x4IBiRGSBw5AbhzmfhkqSamhoVFxdr6NChio+P7/QaIFKQGSBw5AbhzK/CBQAAgM7jTvMAAADGKFwAAADGfN4WojNyc3O1b98+paWlaerUqX6v8efnLOY5duyYpk2bJo/Ho969eysvL09nnXWW0tPTdfnll0uSnnrqKfXv37/LZmpqavK6/4IFC7RhwwYNHDhQM2fOdG0ef2ZatmyZCgsLJUlHjx7VoEGDNGPGDNP3qbKyUj//+c+1bNkyr483NjbqwQcf1JEjRzRu3DiNGzfO67FgF2yZ8ee5yU1wZkYiN77WcK35BteaFl2RGdc/4ers1zNYfWWDP8+7Zs0a3XPPPXrppZfUp08fbdy4Ubt379bo0aOVn5+v/Px8V/+L9Wcmb/vv3LlTpaWlWrFiheLi4rRp06YunSknJ6d1niFDhmj8+PGm71NNTY2mT5+u+vr6Dte8+uqrSk5O1vLly7Vu3TrV1tZ6PRbMgi0z/s4U6bkJxsxI5MbXGq41bXGt6brMuF64Ovv1DFZf2eDP806YMEE33nijJOnw4cOKi4vTBx98oHfeeUfjxo1Tbm6umpqaXJnH35m87b9161aNGjVKUVFRSk1N1bZt27p0pq998cUXqqys1IABA0zfpx49emj+/PmKiYnpcE1JSUnr3EOHDtXOnTu9HgtmwZYZf2eK9NwEY2YkcuNrDdeatrjWdF1mXC9cp371QlVVlV9r/Pk5q3m+9v7776umpkaDBw/WgAED9PLLL2vFihVqamrShg0bXJnH35m87X/qz1VWVnbpTF9bunSp7rrrrg7ndEtMTIzPe+zU19e3m9vbsWAWbJnxd6avRWpugjEzErnxtYZrTVtca7ouM64Xrs5+PUNnvurErXkk6ciRI5o9e7bmzJkjSUpKStLFF18sqeXuxvv373dlHn9n8rb/eeedp+PHj7f+nJt39PD3ffJ4PCopKdHw4cM7nLMrdeW5ZCXYMuPvTFJk5yZUMyORG6413+Ba4x83ziXXC1dnv57B6isb/HneEydO6KGHHtIjjzzS+vhjjz2msrIyNTc366233lJSUpIr8/g7k7f9k5OTzb7Wwt/3f9u2bRo4cKCioqI6nLMreXtPLN8nC8GWGX9nivTchGpmJHLDteYbXGv840pmHJcdO3bMueWWW5w5c+Y4P/zhD51du3Y5v/nNb0675ujRo16PddU8S5cudYYMGeJMnDjRmThxorN27Vpn9+7dzpgxY5wxY8a0W98VM3nbv7m52cnOznZmz57tjBo1yjlw4ECXzuQ4jvP8888769atO+2cbps4caLjOI6zadMmJz8/v81jn3zyiZOZmenMnj3bycrKcpqamrweC2bBlhl/Z4r03ARzZhyH3Hhbw7WGa83pWGfG9cLlOI5z5MgRZ+3atc6hQ4cCWuPPz1nN09U6O1N9fb1TWFjoagDOdKbu9vnnnztr165t8z+c3o4Fs2DLjPVzd1aw5SYY3yN/kRuuNafDtaa9M80MX+0DAABgjDvNAwAAGKNwAQAAGKNwAQAAGKNwAQAAGKNwAQAAGPt/4WOnwxuOlEgAAAAASUVORK5CYII=
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="plt.subplots:用一行代码创建网格"><code>plt.subplots</code>:用一行代码创建网格<a class="anchor-link" href="#plt.subplots:用一行代码创建网格"> </a></h3><p><code>plt.subplots()</code>实用一行代码创建多个子图,并返回一个包含子图的NumPy数组。参数是行数与列数,以及可选参数<code>sharex</code>与<code>sharey</code>,通过它们可以设置不同子图之间的关联关系。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s2">"col"</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s2">"row"</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
<span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="nb">str</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"center"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="plt.GridSpec`:实现更复杂的排列方式">plt.GridSpec`:实现更复杂的排列方式<a class="anchor-link" href="#plt.GridSpec`:实现更复杂的排列方式"> </a></h3><p><code>plt.GridSpec()</code>可以实现不规则的多行多列子图网格。例如,一个带行列间距的2x3网格的配置代码如下所示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">:])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="用Matplotlib画三维图">用Matplotlib画三维图<a class="anchor-link" href="#用Matplotlib画三维图"> </a></h2><p>可以用<code>ax.plot3D</code>与<code>ax.scatter3D</code>函数来创建三维坐标点构成的线图与散点图,需要用<code>%matplotlib notebook</code>实现交互</p>
<p><a href="https://github.com/maartenbreddels/ipyvolume">ipyvolume</a>:通过WebGL在Jupyter notebook实现3D交互,支持百万散点的页面渲染和交互</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> notebook
<span class="kn">from</span> <span class="nn">mpl_toolkits</span> <span class="kn">import</span> <span class="n">mplot3d</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">"3d"</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="mi">15</span>
<span class="c1"># 三维曲线</span>
<span class="n">zline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="n">xline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">zline</span><span class="p">)</span>
<span class="n">yline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">zline</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="n">xline</span><span class="p">,</span> <span class="n">yline</span><span class="p">,</span> <span class="n">zline</span><span class="p">,</span> <span class="s2">"gray"</span><span class="p">)</span>
<span class="c1"># 三维散点</span>
<span class="n">zdata</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">xdata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">zdata</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">ydata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">zdata</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="n">xdata</span><span class="p">,</span> <span class="n">ydata</span><span class="p">,</span> <span class="n">zdata</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">zdata</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"Greens"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div id="7760863a-a888-4c03-b901-7dc7fa2ccbec"></div>
<div class="output_subarea output_javascript ">
<script type="text/javascript">
var element = $('#7760863a-a888-4c03-b901-7dc7fa2ccbec');
/* Put everything inside the global mpl namespace */
window.mpl = {};
mpl.get_websocket_type = function() {
if (typeof(WebSocket) !== 'undefined') {
return WebSocket;
} else if (typeof(MozWebSocket) !== 'undefined') {
return MozWebSocket;
} else {
alert('Your browser does not have WebSocket support. ' +
'Please try Chrome, Safari or Firefox ≥ 6. ' +
'Firefox 4 and 5 are also supported but you ' +
'have to enable WebSockets in about:config.');
};
}
mpl.figure = function(figure_id, websocket, ondownload, parent_element) {
this.id = figure_id;
this.ws = websocket;
this.supports_binary = (this.ws.binaryType != undefined);
if (!this.supports_binary) {
var warnings = document.getElementById("mpl-warnings");
if (warnings) {
warnings.style.display = 'block';
warnings.textContent = (
"This browser does not support binary websocket messages. " +
"Performance may be slow.");
}
}
this.imageObj = new Image();
this.context = undefined;
this.message = undefined;
this.canvas = undefined;
this.rubberband_canvas = undefined;
this.rubberband_context = undefined;
this.format_dropdown = undefined;
this.image_mode = 'full';
this.root = $('<div/>');
this._root_extra_style(this.root)
this.root.attr('style', 'display: inline-block');
$(parent_element).append(this.root);
this._init_header(this);
this._init_canvas(this);
this._init_toolbar(this);
var fig = this;
this.waiting = false;
this.ws.onopen = function () {
fig.send_message("supports_binary", {value: fig.supports_binary});
fig.send_message("send_image_mode", {});
if (mpl.ratio != 1) {
fig.send_message("set_dpi_ratio", {'dpi_ratio': mpl.ratio});
}
fig.send_message("refresh", {});
}
this.imageObj.onload = function() {
if (fig.image_mode == 'full') {
// Full images could contain transparency (where diff images
// almost always do), so we need to clear the canvas so that
// there is no ghosting.
fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);
}
fig.context.drawImage(fig.imageObj, 0, 0);
};
this.imageObj.onunload = function() {
fig.ws.close();
}
this.ws.onmessage = this._make_on_message_function(this);
this.ondownload = ondownload;
}
mpl.figure.prototype._init_header = function() {
var titlebar = $(
'<div class="ui-dialog-titlebar ui-widget-header ui-corner-all ' +
'ui-helper-clearfix"/>');
var titletext = $(
'<div class="ui-dialog-title" style="width: 100%; ' +
'text-align: center; padding: 3px;"/>');
titlebar.append(titletext)
this.root.append(titlebar);
this.header = titletext[0];
}
mpl.figure.prototype._canvas_extra_style = function(canvas_div) {
}
mpl.figure.prototype._root_extra_style = function(canvas_div) {
}
mpl.figure.prototype._init_canvas = function() {
var fig = this;
var canvas_div = $('<div/>');
canvas_div.attr('style', 'position: relative; clear: both; outline: 0');
function canvas_keyboard_event(event) {
return fig.key_event(event, event['data']);
}
canvas_div.keydown('key_press', canvas_keyboard_event);
canvas_div.keyup('key_release', canvas_keyboard_event);
this.canvas_div = canvas_div
this._canvas_extra_style(canvas_div)
this.root.append(canvas_div);
var canvas = $('<canvas/>');
canvas.addClass('mpl-canvas');
canvas.attr('style', "left: 0; top: 0; z-index: 0; outline: 0")
this.canvas = canvas[0];
this.context = canvas[0].getContext("2d");
var backingStore = this.context.backingStorePixelRatio ||
this.context.webkitBackingStorePixelRatio ||
this.context.mozBackingStorePixelRatio ||
this.context.msBackingStorePixelRatio ||
this.context.oBackingStorePixelRatio ||
this.context.backingStorePixelRatio || 1;
mpl.ratio = (window.devicePixelRatio || 1) / backingStore;
var rubberband = $('<canvas/>');
rubberband.attr('style', "position: absolute; left: 0; top: 0; z-index: 1;")
var pass_mouse_events = true;
canvas_div.resizable({
start: function(event, ui) {
pass_mouse_events = false;
},
resize: function(event, ui) {
fig.request_resize(ui.size.width, ui.size.height);
},
stop: function(event, ui) {
pass_mouse_events = true;
fig.request_resize(ui.size.width, ui.size.height);
},
});
function mouse_event_fn(event) {
if (pass_mouse_events)
return fig.mouse_event(event, event['data']);
}
rubberband.mousedown('button_press', mouse_event_fn);
rubberband.mouseup('button_release', mouse_event_fn);
// Throttle sequential mouse events to 1 every 20ms.
rubberband.mousemove('motion_notify', mouse_event_fn);
rubberband.mouseenter('figure_enter', mouse_event_fn);
rubberband.mouseleave('figure_leave', mouse_event_fn);
canvas_div.on("wheel", function (event) {
event = event.originalEvent;
event['data'] = 'scroll'
if (event.deltaY < 0) {
event.step = 1;
} else {
event.step = -1;
}
mouse_event_fn(event);
});
canvas_div.append(canvas);
canvas_div.append(rubberband);
this.rubberband = rubberband;
this.rubberband_canvas = rubberband[0];
this.rubberband_context = rubberband[0].getContext("2d");
this.rubberband_context.strokeStyle = "#000000";
this._resize_canvas = function(width, height) {
// Keep the size of the canvas, canvas container, and rubber band
// canvas in synch.
canvas_div.css('width', width)
canvas_div.css('height', height)
canvas.attr('width', width * mpl.ratio);
canvas.attr('height', height * mpl.ratio);
canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');
rubberband.attr('width', width);
rubberband.attr('height', height);
}
// Set the figure to an initial 600x600px, this will subsequently be updated
// upon first draw.
this._resize_canvas(600, 600);
// Disable right mouse context menu.
$(this.rubberband_canvas).bind("contextmenu",function(e){
return false;
});
function set_focus () {
canvas.focus();
canvas_div.focus();
}
window.setTimeout(set_focus, 100);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items) {
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) {
// put a spacer in here.
continue;
}
var button = $('<button/>');
button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +
'ui-button-icon-only');
button.attr('role', 'button');
button.attr('aria-disabled', 'false');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
var icon_img = $('<span/>');
icon_img.addClass('ui-button-icon-primary ui-icon');
icon_img.addClass(image);
icon_img.addClass('ui-corner-all');
var tooltip_span = $('<span/>');
tooltip_span.addClass('ui-button-text');
tooltip_span.html(tooltip);
button.append(icon_img);
button.append(tooltip_span);
nav_element.append(button);
}
var fmt_picker_span = $('<span/>');
var fmt_picker = $('<select/>');
fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');
fmt_picker_span.append(fmt_picker);
nav_element.append(fmt_picker_span);
this.format_dropdown = fmt_picker[0];
for (var ind in mpl.extensions) {
var fmt = mpl.extensions[ind];
var option = $(
'<option/>', {selected: fmt === mpl.default_extension}).html(fmt);
fmt_picker.append(option);
}
// Add hover states to the ui-buttons
$( ".ui-button" ).hover(
function() { $(this).addClass("ui-state-hover");},
function() { $(this).removeClass("ui-state-hover");}
);
var status_bar = $('<span class="mpl-message"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
}
mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {
// Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,
// which will in turn request a refresh of the image.
this.send_message('resize', {'width': x_pixels, 'height': y_pixels});
}
mpl.figure.prototype.send_message = function(type, properties) {
properties['type'] = type;
properties['figure_id'] = this.id;
this.ws.send(JSON.stringify(properties));
}
mpl.figure.prototype.send_draw_message = function() {
if (!this.waiting) {
this.waiting = true;
this.ws.send(JSON.stringify({type: "draw", figure_id: this.id}));
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
var format_dropdown = fig.format_dropdown;
var format = format_dropdown.options[format_dropdown.selectedIndex].value;
fig.ondownload(fig, format);
}
mpl.figure.prototype.handle_resize = function(fig, msg) {
var size = msg['size'];
if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {
fig._resize_canvas(size[0], size[1]);
fig.send_message("refresh", {});
};
}
mpl.figure.prototype.handle_rubberband = function(fig, msg) {
var x0 = msg['x0'] / mpl.ratio;
var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;
var x1 = msg['x1'] / mpl.ratio;
var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;
x0 = Math.floor(x0) + 0.5;
y0 = Math.floor(y0) + 0.5;
x1 = Math.floor(x1) + 0.5;
y1 = Math.floor(y1) + 0.5;
var min_x = Math.min(x0, x1);
var min_y = Math.min(y0, y1);
var width = Math.abs(x1 - x0);
var height = Math.abs(y1 - y0);
fig.rubberband_context.clearRect(
0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);
fig.rubberband_context.strokeRect(min_x, min_y, width, height);
}
mpl.figure.prototype.handle_figure_label = function(fig, msg) {
// Updates the figure title.
fig.header.textContent = msg['label'];
}
mpl.figure.prototype.handle_cursor = function(fig, msg) {
var cursor = msg['cursor'];
switch(cursor)
{
case 0:
cursor = 'pointer';
break;
case 1:
cursor = 'default';
break;
case 2:
cursor = 'crosshair';
break;
case 3:
cursor = 'move';
break;
}
fig.rubberband_canvas.style.cursor = cursor;
}
mpl.figure.prototype.handle_message = function(fig, msg) {
fig.message.textContent = msg['message'];
}
mpl.figure.prototype.handle_draw = function(fig, msg) {
// Request the server to send over a new figure.
fig.send_draw_message();
}
mpl.figure.prototype.handle_image_mode = function(fig, msg) {
fig.image_mode = msg['mode'];
}
mpl.figure.prototype.updated_canvas_event = function() {
// Called whenever the canvas gets updated.
this.send_message("ack", {});
}
// A function to construct a web socket function for onmessage handling.
// Called in the figure constructor.
mpl.figure.prototype._make_on_message_function = function(fig) {
return function socket_on_message(evt) {
if (evt.data instanceof Blob) {
/* FIXME: We get "Resource interpreted as Image but
* transferred with MIME type text/plain:" errors on
* Chrome. But how to set the MIME type? It doesn't seem
* to be part of the websocket stream */
evt.data.type = "image/png";
/* Free the memory for the previous frames */
if (fig.imageObj.src) {
(window.URL || window.webkitURL).revokeObjectURL(
fig.imageObj.src);
}
fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(
evt.data);
fig.updated_canvas_event();
fig.waiting = false;
return;
}
else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == "data:image/png;base64") {
fig.imageObj.src = evt.data;
fig.updated_canvas_event();
fig.waiting = false;
return;
}
var msg = JSON.parse(evt.data);
var msg_type = msg['type'];
// Call the "handle_{type}" callback, which takes
// the figure and JSON message as its only arguments.
try {
var callback = fig["handle_" + msg_type];
} catch (e) {
console.log("No handler for the '" + msg_type + "' message type: ", msg);
return;
}
if (callback) {
try {
// console.log("Handling '" + msg_type + "' message: ", msg);
callback(fig, msg);
} catch (e) {
console.log("Exception inside the 'handler_" + msg_type + "' callback:", e, e.stack, msg);
}
}
};
}
// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas
mpl.findpos = function(e) {
//this section is from http://www.quirksmode.org/js/events_properties.html
var targ;
if (!e)
e = window.event;
if (e.target)
targ = e.target;
else if (e.srcElement)
targ = e.srcElement;
if (targ.nodeType == 3) // defeat Safari bug
targ = targ.parentNode;
// jQuery normalizes the pageX and pageY
// pageX,Y are the mouse positions relative to the document
// offset() returns the position of the element relative to the document
var x = e.pageX - $(targ).offset().left;
var y = e.pageY - $(targ).offset().top;
return {"x": x, "y": y};
};
/*
* return a copy of an object with only non-object keys
* we need this to avoid circular references
* http://stackoverflow.com/a/24161582/3208463
*/
function simpleKeys (original) {
return Object.keys(original).reduce(function (obj, key) {
if (typeof original[key] !== 'object')
obj[key] = original[key]
return obj;
}, {});
}
mpl.figure.prototype.mouse_event = function(event, name) {
var canvas_pos = mpl.findpos(event)
if (name === 'button_press')
{
this.canvas.focus();
this.canvas_div.focus();
}
var x = canvas_pos.x * mpl.ratio;
var y = canvas_pos.y * mpl.ratio;
this.send_message(name, {x: x, y: y, button: event.button,
step: event.step,
guiEvent: simpleKeys(event)});
/* This prevents the web browser from automatically changing to
* the text insertion cursor when the button is pressed. We want
* to control all of the cursor setting manually through the
* 'cursor' event from matplotlib */
event.preventDefault();
return false;
}
mpl.figure.prototype._key_event_extra = function(event, name) {
// Handle any extra behaviour associated with a key event
}
mpl.figure.prototype.key_event = function(event, name) {
// Prevent repeat events
if (name == 'key_press')
{
if (event.which === this._key)
return;
else
this._key = event.which;
}
if (name == 'key_release')
this._key = null;
var value = '';
if (event.ctrlKey && event.which != 17)
value += "ctrl+";
if (event.altKey && event.which != 18)
value += "alt+";
if (event.shiftKey && event.which != 16)
value += "shift+";
value += 'k';
value += event.which.toString();
this._key_event_extra(event, name);
this.send_message(name, {key: value,
guiEvent: simpleKeys(event)});
return false;
}
mpl.figure.prototype.toolbar_button_onclick = function(name) {
if (name == 'download') {
this.handle_save(this, null);
} else {
this.send_message("toolbar_button", {name: name});
}
};
mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {
this.message.textContent = tooltip;
};
mpl.toolbar_items = [["Home", "Reset original view", "fa fa-home icon-home", "home"], ["Back", "Back to previous view", "fa fa-arrow-left icon-arrow-left", "back"], ["Forward", "Forward to next view", "fa fa-arrow-right icon-arrow-right", "forward"], ["", "", "", ""], ["Pan", "Pan axes with left mouse, zoom with right", "fa fa-arrows icon-move", "pan"], ["Zoom", "Zoom to rectangle", "fa fa-square-o icon-check-empty", "zoom"], ["", "", "", ""], ["Download", "Download plot", "fa fa-floppy-o icon-save", "download"]];
mpl.extensions = ["eps", "jpeg", "pdf", "png", "ps", "raw", "svg", "tif"];
mpl.default_extension = "png";var comm_websocket_adapter = function(comm) {
// Create a "websocket"-like object which calls the given IPython comm
// object with the appropriate methods. Currently this is a non binary
// socket, so there is still some room for performance tuning.
var ws = {};
ws.close = function() {
comm.close()
};
ws.send = function(m) {
//console.log('sending', m);
comm.send(m);
};
// Register the callback with on_msg.
comm.on_msg(function(msg) {
//console.log('receiving', msg['content']['data'], msg);
// Pass the mpl event to the overridden (by mpl) onmessage function.
ws.onmessage(msg['content']['data'])
});
return ws;
}
mpl.mpl_figure_comm = function(comm, msg) {
// This is the function which gets called when the mpl process
// starts-up an IPython Comm through the "matplotlib" channel.
var id = msg.content.data.id;
// Get hold of the div created by the display call when the Comm
// socket was opened in Python.
var element = $("#" + id);
var ws_proxy = comm_websocket_adapter(comm)
function ondownload(figure, format) {
window.open(figure.imageObj.src);
}
var fig = new mpl.figure(id, ws_proxy,
ondownload,
element.get(0));
// Call onopen now - mpl needs it, as it is assuming we've passed it a real
// web socket which is closed, not our websocket->open comm proxy.
ws_proxy.onopen();
fig.parent_element = element.get(0);
fig.cell_info = mpl.find_output_cell("<div id='" + id + "'></div>");
if (!fig.cell_info) {
console.error("Failed to find cell for figure", id, fig);
return;
}
var output_index = fig.cell_info[2]
var cell = fig.cell_info[0];
};
mpl.figure.prototype.handle_close = function(fig, msg) {
var width = fig.canvas.width/mpl.ratio
fig.root.unbind('remove')
// Update the output cell to use the data from the current canvas.
fig.push_to_output();
var dataURL = fig.canvas.toDataURL();
// Re-enable the keyboard manager in IPython - without this line, in FF,
// the notebook keyboard shortcuts fail.
IPython.keyboard_manager.enable()
$(fig.parent_element).html('<img src="' + dataURL + '" width="' + width + '">');
fig.close_ws(fig, msg);
}
mpl.figure.prototype.close_ws = function(fig, msg){
fig.send_message('closing', msg);
// fig.ws.close()
}
mpl.figure.prototype.push_to_output = function(remove_interactive) {
// Turn the data on the canvas into data in the output cell.
var width = this.canvas.width/mpl.ratio
var dataURL = this.canvas.toDataURL();
this.cell_info[1]['text/html'] = '<img src="' + dataURL + '" width="' + width + '">';
}
mpl.figure.prototype.updated_canvas_event = function() {
// Tell IPython that the notebook contents must change.
IPython.notebook.set_dirty(true);
this.send_message("ack", {});
var fig = this;
// Wait a second, then push the new image to the DOM so
// that it is saved nicely (might be nice to debounce this).
setTimeout(function () { fig.push_to_output() }, 1000);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items){
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) { continue; };
var button = $('<button class="btn btn-default" href="#" title="' + name + '"><i class="fa ' + image + ' fa-lg"></i></button>');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
nav_element.append(button);
}
// Add the status bar.
var status_bar = $('<span class="mpl-message" style="text-align:right; float: right;"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
// Add the close button to the window.
var buttongrp = $('<div class="btn-group inline pull-right"></div>');
var button = $('<button class="btn btn-mini btn-primary" href="#" title="Stop Interaction"><i class="fa fa-power-off icon-remove icon-large"></i></button>');
button.click(function (evt) { fig.handle_close(fig, {}); } );
button.mouseover('Stop Interaction', toolbar_mouse_event);
buttongrp.append(button);
var titlebar = this.root.find($('.ui-dialog-titlebar'));
titlebar.prepend(buttongrp);
}
mpl.figure.prototype._root_extra_style = function(el){
var fig = this
el.on("remove", function(){
fig.close_ws(fig, {});
});
}
mpl.figure.prototype._canvas_extra_style = function(el){
// this is important to make the div 'focusable
el.attr('tabindex', 0)
// reach out to IPython and tell the keyboard manager to turn it's self
// off when our div gets focus
// location in version 3
if (IPython.notebook.keyboard_manager) {
IPython.notebook.keyboard_manager.register_events(el);
}
else {
// location in version 2
IPython.keyboard_manager.register_events(el);
}
}
mpl.figure.prototype._key_event_extra = function(event, name) {
var manager = IPython.notebook.keyboard_manager;
if (!manager)
manager = IPython.keyboard_manager;
// Check for shift+enter
if (event.shiftKey && event.which == 13) {
this.canvas_div.blur();
// select the cell after this one
var index = IPython.notebook.find_cell_index(this.cell_info[0]);
IPython.notebook.select(index + 1);
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
fig.ondownload(fig, null);
}
mpl.find_output_cell = function(html_output) {
// Return the cell and output element which can be found *uniquely* in the notebook.
// Note - this is a bit hacky, but it is done because the "notebook_saving.Notebook"
// IPython event is triggered only after the cells have been serialised, which for
// our purposes (turning an active figure into a static one), is too late.
var cells = IPython.notebook.get_cells();
var ncells = cells.length;
for (var i=0; i<ncells; i++) {
var cell = cells[i];
if (cell.cell_type === 'code'){
for (var j=0; j<cell.output_area.outputs.length; j++) {
var data = cell.output_area.outputs[j];
if (data.data) {
// IPython >= 3 moved mimebundle to data attribute of output
data = data.data;
}
if (data['text/html'] == html_output) {
return [cell, data, j];
}
}
}
}
}
// Register the function which deals with the matplotlib target/channel.
// The kernel may be null if the page has been refreshed.
if (IPython.notebook.kernel != null) {
IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);
}
</script>
</div>
</div>
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAB9AAAAfQCAYAAACaOMR5AAAgAElEQVR4XuzdCXfURtYGYAU7gbDYJiw2e2Ym+8z//ylzEvZ9SViDIWDjyXeuKsWnCIO73VK3lkfn6NgD3SXVU9fOHN6+pc/+/PPPPwsHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYucBnAvSRV4DpEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEApIEBXCAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6AXFXIAAACAASURBVGqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJ6EBXCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQICuBggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBLQga4SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAF0NECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJKADXSUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEBuhogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJQAe6SiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgJ0NUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJKADnSVQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBOhqgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAEd6CqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI0NUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAjrQVQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCgqwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAEdKCrBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIEBXAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIAnoQFcJBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAgK4GCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEtCBrhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAAXQ0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEkoANdJRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQG6GiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAklAB7pKIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECAnQ1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkoAOdJVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQE6GqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkAR3oKoEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjQ1QABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgCOtBVAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKCrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAR0oKsEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgQFcDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgCehAVwkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAS0IGuEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgABdDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSSgA10lECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAboaIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSUAHukogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICdDVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSgA50lUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAToaoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQBHegqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNDVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAI60FUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQoKsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBHSgqwQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBAVwMECBAgQIAAAQIECBAYtsCff/5ZfPbZZ8OepNkRIECAAAECBAgQIECAAAECBBoS0IHeEKRhCBAgQIAAAQIECBAg0CWBCM7fvXtXLC8vC9C7tDDuhQABAgQIECBAgAABAgQIEOi0gAC908vj5ggQIECAAAECBAgQIDC9wP/+979ie3u72NraKg4dOlQsLS0VBw4cmH4g7yBAgAABAgQIECBAgAABAgQIjExAgD6yBTddAgQIECBAgAABAgSGKxBd5zs7O+WZA/TcgR4BegTp+RSoD7cOzIwAAQIECBAgQIAAAQIECBDYv4AAff923kmAAAECBAgQIECAAIHOCOQt2yM8j+/fvHlThugRmMcz0CMwjzM/D70aqNvmvTPL6EYIECBAgAABAgQIECBAgACBBQsI0Be8AC5PgAABAgQIECBAgACBWQViy/Z43nl8jTN3okeAHkF5/O98RoAuUJ9V3PsJECBAgAABAgQIECBAgACBoQoI0Ie6suZFgAABAgQIECBAgMDgBapbtufvc4d5fg567kDPAXoO2AXqgy8PEyRAgAABAgQIECBAgAABAgT2ISBA3weatxAgQIAAAQIECBAgQGDRAtWu8wjD43/nzvJqB3oO0Ov3K1Bf9Aq6PgECBAgQIECAAAECBAgQINBFAQF6F1fFPREgQIAAAQIECBAgQOAjAjksjy3b8/fx0rw1e3yNMD2ehV59BvpeoAL1vYT8PQECBAgQIECAAAECBAgQIDAGAQH6GFbZHAkQIECAAAECBAgQGIRAhNzVZ51Xu87z1u0x0f0E6HWgTwXq8Vz13O2erxt/Ft3uy8vL5dfq/QwC3yQIECBAgAABAgQIECBAgACBUQgI0EexzCZJgAABAgQIECBAgEDfBapbtufnmOcgux5WNxGgC9T7XjHunwABAgQIECBAgAABAgQIENiPgAB9P2reQ4AAAQIECBAgQIAAgTkJ5OeZx5bs1S3bc3i+2220EaDXr6NDfU4F4DIECBAgQIAAAQIECBAgQIDAXAUE6HPldjECBAgQIECAAAECBAhMLpC3bK+G5/Wt03cbbR4Bev26AvXJ19UrCRAgQIAAAQIECBAgQIAAge4KCNC7uzbujAABAgQIECBAgACBEQtEaB7PO692nUd4ns9P0SwiQBeoj7hYTZ0AAQIECBAgQIAAAQIECAxIQIA+oMU0FQIECBAgQIAAAQIE+i9Q3bI9gvA4J+k6r868CwF6E4H68vJysbS0VJ7157z3f6XNgAABAgQIECBAgAABAgQIEOiigAC9i6vinggQIECAAAECBAgQGKVABN/RdR5fc+f5tOF5wHUxQBeoj7KkTZoAAQIECBAgQIAAAQIECPROQIDeuyVzwwQIECBAgAABAgQIDE0gh+X73bK97tGHAF2gPrQqNh8CBAgQIECAAAECBAgQIDAMAQH6MNbRLAgQIECAAAECBAgQ6KlAhOe563y/W7YPIUAXqPe0gN02AQIECBAgQIAAAQIECBAYmIAAfWALajoECBAgQIAAAQIECPRHoLple962/cCBA+Xzvmd55ncfO9D3WrX4oEHu1M9f4z3VLe6zXfx5fO8Z6nup+nsCBAgQIECAAAECBAgQIECgLiBAVxMECBAgQIAAAQIECBCYs0AEwDs7O+VZDYOrAfAst5Q72be2toqlpaWZwvhZ7qPN904bqIdD9ZzlAwptzsvYBAgQIECAAAECBAgQIECAwGIFBOiL9Xd1AgQIECBAgAABAgRGJhDB7/b2dpE7zuNrtYu6CY4xBOh1J4F6E5VjDAIECBAgQIAAAQIECBAgQECArgYIECBAgAABAgQIECAwJ4HoOI/nnde3IJ91y/b67Y8xQBeoz6mIXYYAAQIECBAgQIAAAQIECAxcQIA+8AU2PQIECBAgQIAAAQIEFi8QgXkE5znYbqPrvDpLAfqHa65DffE/B+6AAAECBAgQIECAAAECBAj0QUCA3odVco8ECBAgQIAAAQIECPRWIMLsangeQW5+1nlbz+EWoO9dLgL1vY28ggABAgQIECBAgAABAgQIjFFAgD7GVTdnAgQIECBAgAABAgRaF8jbtO+2ZXsE6G0eEaDHdvHxrPW4VtvXa3Mu8xpboD4vadchQIAAAQIECBAgQIAAAQLdFhCgd3t93B0BAgQIECBAgAABAj0UyFu2R4idg/ToNs+d521PSYA+u/CnAvXNzc1yXY8ePVocPHiwvNjS0tLfzrZ2F5h9ZkYgQIAAAQIECBAgQIAAAQIEPiUgQFcfBAgQIECAAAECBAgQaFBgEVu2129fgN7ggv41VDVQv3XrVtnhf/LkyTJEzx+MqH5AQqDe/BoYkQABAgQIECBAgAABAgQIzENAgD4PZdcgQIAAAQIECBAgQGDwAhGwRqha7TqPSc+r67wKLEBvt9yuXbtWrvPGxkZx+PDh97sMxFWrOw0I1NtdB6MTIECAAAECBAgQIECAAIE2BATobagakwABAgQIECBAgACBUQlUu84XsWV7HVuA3m755QD9zJkzxbFjx8qLeYZ6u+ZGJ0CAAAECBAgQIECAAAEC8xIQoM9L2nUIECBAgAABAgQIEBicQA7L371790EXcnQiL+o52AL0dkvt+vXrRax5NUCvX1Gg3u4aGJ0AAQIECBAgQIAAAQIECLQlIEBvS9a4BAgQIECAAAECBAgMWiAC0ghRI6zOZ3X77kVOXoDern4O0GML95WVlYkulj9sUf0ab7Tl+0R8XkSAAAECBAgQIECAAAECBOYmIECfG7ULESBAgAABAgQIECAwFIHqlu3xfYSi+XnXi+o6r9oK0NuttP0E6PU7Eqi3u0ZGJ0CAAAECBAgQIECAAAEC+xUQoO9XzvsIECBAgAABAgQIEBidQISeOzs75ZkD0EDI4XlXQHJH/NbWVnlvcTqaE2giQK/fjUC9ufUxEgECBAgQIECAAAECBAgQmEVAgD6LnvcSIECAAAECBAgQIDAagbxlezU878qW7fVFEKC3W5ZtBOgC9XbXzOgECBAgQIAAAQIECBAgQGBSAQH6pFJeR4AAAQIECBAgQIDAaAUiNI/nnVe7ziM8z2fXYATo7a7IjRs3iu3t7WKaZ6DPekc61GcV9H4CBAgQIECAAAECBAgQIDCZgAB9MievIkCAAAECBAgQIEBghALVLdtzKN3VrvPq8gjQ2y3WRQTo9RkJ1NtdY6MTIECAAAECBAgQIECAwHgFBOjjXXszJ0CAAAECBAgQIEDgEwIRQkfXeXzNYWUfwvOYkgC93dLuQoAuUG93jY1OgAABAgQIECBAgAABAuMVEKCPd+3NnAABAgQIECBAgACBXQRyWN6nLdvr0xCgt1vaOUBfX18vVldX273YPkfXob5POG8jQIAAAQIECBAgQIAAgdELCNBHXwIACBAgQIAAAQIECBDIAhE65q7zPm3ZXl/BvPV8PKc7d81b5eYE+hCg71YT1d0UokbiqO6qcODAgfJ/x7G0tPS3M/95c4pGIkCAAAECBAgQIECAAAEC3RQQoHdzXdwVAQIECBAgQIAAAQJzFqhu2Z6Dxhwo9i08FKC3Wzx9DNDrIjrU260RoxMgQIAAAQIECBAgQIBAfwUE6P1dO3dOgAABAgQIECBAgEADAjls3tnZef+s8xi22o3bwGXmOoQAvV3umzdvFltbW0WXt3CfVkCgPq2Y1xMgQIAAAQIECBAgQIDAUAUE6ENdWfMiQIAAAQIECBAgQGBPgQgNY5vz6tbW1S2t9xygoy8QoLe7MEMM0OtiAvV2a8joBAgQIECAAAECBAgQINBdAQF6d9fGnREgQIAAAQIECBAg0KJAdJzH885zUBiXivA8ny1euvWhBejtEucA/fTp08Xa2lq7F+vI6AL1jiyE2yBAgAABAgQIECBAgACB1gUE6K0TuwABAgQIECBAgAABAl0SiCAwgvPYgjtC9AjMl5aWer1le91XgN5uxY0xQN+txqo7N0TNxVHdwaH6GIT4Gaue8ToHAQIECBAgQIAAAQIECBDoooAAvYur4p4IECBAgAABAgQIEGhFIAK/CM/j63//+9+y+/zixYvFsWPHyuBvKIcAvd2VFKB/6KtDvd2aMzoBAgQIECBAgAABAgQIzE9AgD4/a1ciQIAAAQIECBAgQGBBAjncq27Z/ssvv5RB+qVLl8oAfUiHAL3d1RSg7+0rUN/byCsIECBAgAABAgQIECBAoJsCAvRurou7IkCAAAECBAgQIECgIYG8ZXts155Dveg2F6A3BDzCYW7dulW8ffu2GNMz0GddZoH6rILeT4AAAQIECBAgQIAAAQLzEhCgz0vadQgQIECAAAECBAgQmLtAdcv2/Lzm/Fzmn3/+uXwGemzhvrKyMvd7a/OCOtDb1C0KAfrsvgL12Q2NQIAAAQIECBAgQIAAAQLtCAjQ23E1KgECBAgQIECAAAECCxTIAXK16zxuJ4fn8X0E6LGluwB9gQvV00vnAP3UqVPF8ePHezqLbt22QL1b6+FuCBAgQIAAAQIECBAgMGYBAfqYV9/cCRAgQIAAAQIECAxQIIK47e3t8vnm1S3bq+F5NUC/cOFCsbq6OigJHejtLqcAvV3fGF2g3r6xKxAgQIAAAQIECBAgQIDA7gICdJVBgAABAgQIECBAgMAgBHLgFl3l+fuYWDzvPJ/VicYz0CNoP3/+fLG2tjYIgzyJHKCHRRzx4QFHcwIC9OYsJx1JoD6plNcRIECAAAECBAgQIECAwKwCAvRZBb2fAAECBAgQIECAAIGFC0S4FmFxdJ3nM0Lzetd59UYF6Atftt7egAB98Uu3V6Cef/bj90AcS0tLfzvzny9+Ju6AAAECBAgQIECAAAECBLomIEDv2oq4HwIECBAgQIAAAQIEphKIwLwankewVg/Pdhvw8uXLxdbWVnHu3LnBPcdaB/pUJTT1i2/fvl28efOm8Az0qelae4NAvTVaAxMgQIAAAQIECBAgQGB0AgL00S25CRMgQIAAAQIECBAYhkAOiXd2dj7Ysn2SLcuvXLlSvH37tjh79mzx1VdfDQPlr1kI0NtdTgF6u75NjC5Qb0LRGAQIECBAgAABAgQIEBingAB9OArJ1gAAIABJREFUnOtu1gQIECBAgAABAgR6LZC3bK+G53tt2V6f8FgC9LCK7asdzQnkAP3kyZOD+/BFc0rdGkmg3q31cDcECBAgQIAAAQIECBDosoAAvcur494IECBAgAABAgQIEPhAIELz2LI9B2LxggjP8zkp2dWrV8ttuM+cOVOcOHFi0rf14nXVDnQBevNLJkBv3nTeIwrU5y3uegQIECBAgAABAgQIEOiPgAC9P2vlTgkQIECAAAECBAiMWqC6ZXs89zzOabvOq4AC9FGX00yTF6DPxNfJN+83UF9eXi7ikRHxu8hBgAABAgQIECBAgAABAsMQEKAPYx3NggABAgQIECBAgMCgBSIsj67z+JqDrlnC88C6du1a8ccffxQbGxtFbMU9pEMHerurKUBv17cLowvUu7AK7oEAAQIECBAgQIAAAQKLERCgL8bdVQkQIECAAAECBAgQmEAgh1hNbNlev9z169eL169fF+vr68WpU6cmuJv+vESA3u5a3blzp/zwhWegt+vcldHj5ymO6gd48p/lD/LkLvTcib60tFTEqUO9K6voPggQIECAAAECBAgQIDC5gAB9ciuvJECAAAECBAgQIEBgjgIRUOWu8ya2bK/fugB9jos5sEsJ0Ae2oFNOR6A+JZiXEyBAgAABAgQIECBAoGcCAvSeLZjbJUCAAAECBAgQIDAGgeqW7bnrs97hOavDjRs3ilevXhWnT58uzyEdOtDbXc0coJ84caKI0zFuAYH6uNff7AkQIECAAAECBAgQGJ6AAH14a2pGBAgQIECAAAECBHorkIPfnZ2d9886j8nk8LzJieUAPbZvj23ch3RUHeMDCLGVtKM5AQF6c5ZDHOlTgXr+XZZfEz+b8Wd5u/f8v/NW8EP0MScCBAgQIECAAAECBAh0XUCA3vUVcn8ECBAgQIAAAQIERiIQgdL29vbfnjOcny/cRph08+bNYnNzs3z+uQB9JEXW0DQF6A1BjmSY3QL1e/fulb/vYgeD1dXV9x8Sqj5DPZ6fLlAfSZGYJgECBAgQIECAAAECnRIQoHdqOdwMAQIECBAgQIAAgXEKRMd5PO88gqbomI4jgqR8tqFy69at4uXLl8XJkyeLjY2NNi6xsDFt4d4uvQC9Xd+hjx4/n/H7Z2trq/z9c/To0fJ3Xxy5Q73+yAod6kOvCvMjQIAAAQIECBAgQKBLAgL0Lq2GeyFAgAABAgQIECAwMoEIjSI4j9A8n212nVd5BegjK7YGp3v37t3i9evXZfewZ6A3CDuioWIHjAjQT58+XXagxxG/A/OHiATqIyoGUyVAgAABAgQIECBAoHMCAvTOLYkbIkCAAAECBAgQIDAOgQiLquF5BEb1rss2JW7fvl38/vvvZQB65syZNi8197F1oLdLLkBv13cMo1cD9LW1tb9NeZJnqFd/V+YPHdnyfQyVY44ECBAgQIAAAQIECMxDQIA+D2XXIECAAAECBAgQIEDgvUDusJz3lu31JYhtuF+8eCFAV5tTC+QA/auvviq34HYQmFYgB+jr6+vvO9A/NoZAfVpdrydAgAABAgQIECBAgMBsAgL02fy8mwABAgQIECBAgACBKQTylu3xzPMcpM9ry/b6beYAPULQs2fPTjGL7r9UB3q7ayRAb9d3DKNPE6DXPQTqY6gQcyRAgAABAgQIECBAYJECAvRF6rs2AQIECBAgQIAAgREJLHrL9jp1hKDPnz8vjh8/Xpw7d25QKyFAb3c5Bejt+o5h9Bs3bhTb29vFJB3oe3kI1PcS8vcECBAgQIAAAQIECBCYTkCAPp2XVxMgQIAAAQIECBAgMKVADnOrXecxRH6G75TDNfZyAXpjlKMbSIA+uiVvfMJNBuj1mxOoN75cBiRAgAABAgQIECBAYGQCAvSRLbjpEiBAgAABAgQIEJinQAQ50WUZ3eeL3rK9Pu979+4Vz549K9bW1orz58/Pk6X1a1U/tBD2S0tLrV9zTBeI2nn16lXhGehjWvVm55oD9I2NjWJlZaXZwWujCdRb5TU4AQIECBAgQIAAAQIDFBCgD3BRTYkAAQIECBAgQIDAogVyWP7u3bv3wXncUzzvPJ+Lvsf79+8XT58+LVZXV4sLFy4s+nYav350/Ie/AL1x2kKA3rzp2EacZ4Bet91PoB4fwsnnoncPGVutmC8BAgQIECBAgAABAvMXEKDP39wVCRAgQIAAAQIECAxaIMKZHNxGeBtnhOZdC10E6IMuw1YnlwP048ePF6dOnWr1WgYfpsAiA3SB+jBryqwIECBAgAABAgQIEGhOQIDenKWRCBAgQIAAAQIECIxeIMLyangeYXoOziNE79Lx4MGD4smTJ+X2yRcvXuzSrTVyLzrQG2HcdRABenu2Yxm5SwF63VyH+liq0DwJECBAgAABAgQIEPiYgABdbRAgQIAAAQIECBAgMLNA9Znbefv2GDR3ns98gRYGePjwYfH48WMBegu2Qx9SgD70FW5/ftevXy8/bDSPZ6DPOhuB+qyC3k+AAAECBAgQIECAQN8EBOh9WzH3S4AAAQIECBAgQKBjAnnL9uh4zuF5F7dsr7PlAP3YsWPFpUuXOqY6++3oQJ/d8GMjCNDbsx3LyH0K0OtrIlAfS5WaJwECBAgQIECAAIHxCgjQx7v2Zk6AAAECBAgQIEBgZoHYsn17e/t9cB4DRniez5kv0OIAjx49Kn777bfi6NGjxddff93ilRYztAC9Pff79+8Xm5ubhWegt2c89JFzgH7mzJkiPsTT50Og3ufVc+8ECBAgQIAAAQIECOwmIEBXFwQIECBAgAABAgQITC1Q3bI9QvQ4+9B1Xp2oAH3qZfeGvwQE6EphVoEhBeh1C4H6rNXh/QQIECBAgAABAgQILFpAgL7oFXB9AgQIECBAgAABAj0TiLA8nt0bX/u0ZXud+ddffy3iPHLkSPGPf/yjZ6uw9+3qQN/baL+vyAH62tpacfr06f0O430jFrh27VoRP6ND6EDfaxkF6nsJ+XsCBAgQIECAAAECBLomIEDv2oq4HwIECBAgQIAAAQIdFchheYTn+fu41b5s2V5nzQH64cOHi3/+858dVd//bQnQ92+31zsF6HsJ+fu9BMYUoNctBOp7VYe/J0CAAAECBAgQIEBg0QIC9EWvgOsTIECAAAECBAgQ6IFABB6567yvW7bXmeP557GNuwC9BwXYsVsUoHdsQXp4O2MO0AXqPSxYt0yAAAECBAgQIEBgZAIC9JEtuOkSIECAAAECBAgQmFagumV73rb9wIED7zvPpx2vK69//Phx8fDhw+LLL78s/vWvf3Xlthq7Dx3ojVF+MNCDBw+Kly9fFrZwb8946CPnAP3s2bPF0aNHhz7dqeZX7VDP/83Jf5b/21P9b1DsgrK0tPT+zH831UW9mAABAgQIECBAgAABAhUBAbpyIECAAAECBAgQIEBgV4EILCKEjbO6ZftQwokxBOh5/SJccjQnIEBvznKsIwnQJ1/5+O9PPgXqk7t5JQECBAgQIECAAAEC+xcQoO/fzjsJECBAgAABAgQIDFYgwort7e0ihxXxNbr8hhKex8I9efKkiCD00KFDxTfffDO4tczheXwVoDe7vDlAX11dLdbX15sd3GijELh69Wr5+1UH+vTLLVCf3sw7CBAgQIAAAQIECBCYTkCAPp2XVxMgQIAAAQIECBAYvEDe+rvadR7heT6HAvD06dMinmV98ODB4ttvvx3KtN7PIwfo8ez65eXlwc1vkRMSoC9SfxjXFqA3t44C9eYsjUSAAAECBAgQIECAQBIQoKsEAgQIECBAgAABAgRKgeqW7dEZOcSu8+pSC9AV/n4FBOj7lfO+LCBAb68WBOrt2RqZAAECBAgQIECAwFgEBOhjWWnzJECAAAECBAgQIPAJgQjLo1M5B+cRQOTt2qPzfIjHs2fPinv37hVffPFF8d133w1uijrQ21tSAXp7tmMZOQfo586dK44cOTKWaS9kngL1hbC7KAECBAgQIECAAIFeCwjQe718bp4AAQIECBAgQIDAbAJ5m/YIz4e+ZXtdKgfon3/+efH999/PBtnBdwvQ21sUAXp7tmMZWYC+uJUWqC/O3pUJECBAgAABAgQI9EVAgN6XlXKfBAgQIECAAAECBBoWiBAhgvMIWnN4Ht3mufO84ct1brjnz58Xd+/eLQTonVuazt/Qw4cPi99//71YXV0t1tfXO3+/brB7AgL07qyJQL07a+FOCBAgQIAAAQIECHRFQIDelZVwHwQIECBAgAABAgTmKDDGLdvrvC9evCju3LlTLC8vFz/88MMc9edzKR3o7TnnAH1lZaXY2Nho70JGHqzAlStXyg8u2cK9e0ssUO/emrgjAgQIECBAgAABAvMWEKDPW9z1CBAgQIAAAQIECCxQIIKBHKxWt2wfS9d5lV6AvsBC7PmlBeg9X8AO3L4AvQOLMOEtCNQnhPIyAgQIECBAgAABAgMSEKAPaDFNhQABAgQIECBAgMCnBCIE2N7eLqL7fIxbttdtYgvu27dvF0tLS8WPP/44uOLRgd7ekgrQ27Mdy8g5QD9//nxx+PDhsUx7EPMUqA9iGU2CAAECBAgQIECAwCcFBOgKhAABAgQIECBAgMAIBCJMjeedV7vO43nn+RwBwQdTfPnyZXHr1q1RBOjxIYFYa0czAgL0ZhzHPIoAfTirL1AfzlqaCQECBAgQIECAAIEsIEBXCwQIECBAgAABAgQGLBD/sB/BeXSd5zOC1DFu2V5f5hygh8VPP/00uCqodqAL0JtdXgF6s55jHE2APtxVF6gPd23NjAABAgQIECBAYDwCAvTxrLWZEiBAgAABAgQIjEwgAvNqeB7/qJ+Dc93IRbG5uVncvHmz7Mz+97//PbjqeP36dfHkyZNyfkeOHCm+/PLLcv0dsws8evSoePHiRbGyslJsbGzMPqARRidw+fLlcs62cB/+0gvUh7/GZkiAAAECBAgQIDA8AQH68NbUjAgQIECAAAECBEYuEP9Yn7uP61u2C1D/vziGGqDHmj979qx48OBBuWV/PiJIP3ToUBmkxzOX43v1sL9fFgL0/bl51/8LCNDHWw0C9fGuvZkTIECAAAECBAj0R0CA3p+1cqcECBAgQIAAAQIE9hTIW7ZHgJ7Dc1u278726tWr4saNG+Vf/uc//9nTtg8viHW/f/9+2R0dx+eff16G5G/fvv3g9qMuIkyvBup2JphslXOAfuzYseLMmTOTvcmrCFQEcoB+4cKF8mfQMV4Bgfp4197MCRAgQIAAAQIEuisgQO/u2rgzAgQIECBAgAABAlMJxJbt29vb74PzeHMEovmcarARvDi2OL9+/Xo509jCve/h8R9//FHcuXOn2NraKucU24ufPn26iLqIub1586aIOcf5sUA9OtNzoH7w4MHem7RVxgL0tmTHM64AfTxrPe1MBerTink9AQIECBAgQIAAgeYFBOjNmxqRAAECBAgQIECAwFwF6lu2RxeyrvO9l2AoAXqs/9OnT4uHDx+WH56ItT979mwR3dERnr97965YWlr6WxgeNRKBew7Uc+heVYvO9RymR7D+xRdfCNT/AhKg7/3z5RWfFhCgq5BJBfYK1GOc/EGpvOtI/M7PZ/wu7/sHxCa18joCBAgQIECAAAECTQkI0JuSNA4BAgQIECBAgACBBQjkgDS+2rJ9ugWIAPnatWvlm3766adePg88gvC7d+8WL1++LOcRXeOxJXQ83zz+Ls7dAvS6VLymGqjHTgb1I8KYaqAeQc1YQxkB+nQ/a179d4H4XX3lypXyD23hrjqmFagH6rGjSDy6I45Lly6VwXkOzfMONAL1aZW9ngABAgQIECBAYOwCAvSxV4D5EyBAgAABAgQI9FIgh+URfObvYyK2bJ98OfseoEf3eGzZnsPu48ePl8/jjuAkjmkC9LpajFkN1KPO6sfy8vIHgfrk+v1+5a+//lo8f/687PL3DPR+r+Ui7l6Avgj14V4zP74jZvj111+XE40aiyMH6QL14a6/mREgQIAAAQIECLQjIEBvx9WoBAgQIECAAAECBFoTiH8Yj0Azus7zacv26bnjmeBXr14t3/jjjz+WXXt9OGL9Hz9+XEQXdA5IYsv2tbW1v93+LAF63SEC9bzde3yNsetHdKRXO9QjYB/qIUAf6srOZ17VAP3ixYvljhEOAvsVqAbo3377bTlM7lLPu9MI1Per630ECBAgQIAAAQJjFRCgj3XlzZsAAQIECBAgQKCXAtUt2/M/jFc7y3o5qQXddGx7m7dR/uGHH4o+BL7xwYnYsn1zc7NUi+AttoCOrdvrR5MBenXsCGKqgXqENx8L1OPZ6XFGsN4H30lLMQfoR48eLZ837yAwjYAAfRotr91LoB6g1x+tsdcz1KsfwLPl+17a/p4AAQIECBAgQGAsAgL0say0eRIgQIAAAQIECPRaIP4BPAei1S3bc3je68kt6Ob7FqBHaB7hed5O/cSJE8X6+vpHn92eP2wxyTPQZ1mCqMetra33HeoR5sS168cXX3xRhuk5UO9Lx/9uNgL0WSrGewXoaqBJgdgVJP7bEMd3332359AfC9RzeF7f7j3+PD9DPT4IlV+354W8gAABAgQIECBAgECPBQToPV48t06AAAECBAgQIDAOgbxlewToOTy3Zfvsax+h7+XLl8uBvv/++yK2IO/iEWsege1vv/1W3l4EGefOnStWVlY+ebsRYkfNRLd4vKfeldjWXON+48MJEerk56jn7YOr14yu+Wqgnp/d3tZ9NTmuAL1JzfGNFT+b+fERtnAf3/o3PeMcoMfv+LyF+zTXEKhPo+W1BAgQIECAAAECYxEQoI9lpc2TAAECBAgQIECglwIRgEYHcbXrPHd/zSsQ7SXcBDddDdCjay86pLt2RPh9586dMoyOIwLn8+fPT3SviwrQ64ZRu/G8+RyoR6i+W6Ae29HnQD2+73KgHh9mePbsWWEL9679xPTjfgTo/Vinvtzlq1evinv37pUfktpPgL7b7+zdnqGuQ70vFeE+CRAgQIAAAQIEmhAQoDehaAwCBAgQIECAAAECDQtUt2yPsCVOXefNIkc4/csvv5SDdjFAf/nyZbktb36++MmTJ8st2yf94ERXAvT6qsV91QP1+mtijjlQj+enxznpvJutkt1HE6DPQ3m416gG6JcuXSpiNwYHgf0K5AA9PnT0zTff7HeYj75Ph3rjpAYkQIAAAQIECBDogYAAvQeL5BYJECBAgAABAgTGJZCfXZ2D8/jH6+ozScel0d5so7P/559/Li8QXXtdCbFivR89elQ8fvy4vLfYfj26zo8dOzYVRlcD9Pok4j6jKz1v9x7hev2I8DxC9Pz89AjXFxmo5wD9yJEj5Xb6DgLTCAjQp9Hy2r0ENjc3i/v375f/P6GNAL1+fYH6Xivi7wkQIECAAAECBIYgIEAfwiqaAwEC/8feeTDXcSvrdqqOg+5TFpUDJZLK1jnv//8PW5GURFk5x1e2Vbfq1YJPy9Bwk9xh8qyu2rVpeQYDLGCwZ/ChuyUgAQlIQAISGASBCNOOsEs4VkSWpaWl5IkboVMH0dCONKKLAjph5QnZjpiMIdAins+Tn70vAnp5OOBxnwvq5FMvG0JRLqiz+aFJQV0BvSM3cU+roYDe047raLWJVvLs2bO02Wptba3xWiqoN47cC0pAAhKQgAQkIAEJNEBAAb0ByF5CAhKQgAQkIAEJSEACuxFgARpBF/GQv2/dupW+Ce87q+fxbtfy//9NANZwxvDaY6NCm/bhw4dvGyeox/Hjx4tjx47NLQz3VUAv9wH9FPnT+WaTQdkQ1MM7nW/y2dcpqCugt3mn9P/ajOmNjY3UEEO4978/225B2wJ6uf3zCuo//PBD2gTghsG2R5TXl4AEJCABCUhAAhKAgAK640ACEpCABCQgAQlIQAItE5gUsv3u3bvJA10Bvb7Oge/NmzfTBfDaw6O5DaMez58/L96+fZsuj4hw7ty55H2+iA1FQC8zYKNJhHtHUCeXfdkQYXJBHQ/+KgV1BfRFRqbnKqA7Bqok8PHjx/Qbwm/H6upqlUVXUpaCeiUYLUQCEpCABCQgAQlIoGECCugNA/dyEpCABCQgAQlIQAISCAIsKiOkhNc5gieGNy25ufn35eXl4sCBA0KrgUAXBHTCkxOyPfJ+E22AnNoIIYvaUAX0MhcE9FxQR2AvGzwj5DvC+jwh8fMyyU/PhgdzoC86Ssd5fi6gX7hwIUVM0CQwL4EQ0JnXVlZW5i2msfMU1BtD7YUkIAEJSEACEpCABBYgoIC+ADxPlYAEJCABCUhAAhKQwLwEWEBG+EPkjNzneMginvONgI4QiCfywYMH572M5+1AAO6//fZbOgKvPYTVJu39+/fF06dP0xjATp48mXLeV+UpPRYBvdxn3Fd4pscHsbJsCE25oD7rhgUF9CbvlOFdSwF9eH3aZotI//HixYu0MagPAnqZlYJ6m6PHa0tAAhKQgAQkIAEJbEdAAd2xIQEJSEACEpCABCQggYYJIJ4gjodwzuUj52eIpwro9XdKWwI6wjbCOQI6hujBRomqBfyxCuj5yImNKiGm46k+SVDHAzgX1AkBv5OFgE6fnT17tv7B6hUGRUABfVDd2XpjQkBnHiOiQd9NQb3vPWj9JSABCUhAAhKQwDAIKKAPox9thQQkIAEJSEACEpBADwiwKIxwjrAZn9zrPG/CnTt3koc64tyhQ4d60Lp+VvHXX39NFcdrb9Gc49MQIFQ7IdsJ3Y4Rnp+Q7bsJttOUXT5GAX0rNe7Bv/7665t3OoJ6RADIj0aIQhyPPOrl/lFAn2dEek4Q4Hfg/v376T8N4e64WJQAm7FevnyZUgEMQUAv81BQX3SEeL4EJCABCUhAAhKQwDwEFNDnoeY5EpCABCQgAQlIQAISmJEAIl0unrMgHOHaJ4Xsvnv3bhL6EFcPHz4849U8fFoChHCnLxAd9u3bN+1pMx/HNd69e1c8e/YsXY8+J2T7kSNHKgvZXq6UAvru3URfsJkh91Dn38r2888/fyeok/+cjx7ouzP2iK0EFNAdFVUS4Lfl1atXBfPU+fPnqyy6k2UpqHeyW6yUBCQgAQlIQAISGBwBBfTBdakNkoAEJCABCUhAAhLoEgEWegnXy6ccsh0BfTu7d+9eEvZOnz6dRFatHgJNCOj0PSHbCbOL4SVIyHZChtdpCuiz0+UeJUpACOr8PUlQJ2c6IiiCFX250708ey08Y+gEcgGd6BekcdAkMC+BsQnoZU4K6vOOHM+TgAQkIAEJSEACEtiJgAK640MCEpCABCQgAQlIQAI1EYiQ7bl4vl3I9nIV1tfXk5B36tSpYmlpqaYaWuzNmzdTCG+89vbv3185EEKEE7KdaAIY4fjp0zpCtpcrH2kCuDbXmxTpoPIGD6xAGIagTl/yKRtc9+zZ881Dnb9lPbCBUHFzFNArBjry4oiGQVoJ5p7l5eWR0yjSpqf4MIfH38zL+TNY/Dff/EayMSp+K53DRz+MBCABCUhAAhKQgAQKBXQHgQQkIAEJSEACEpCABGogwKItOczLXuexYLvbJTc2NpJYR5jvo0eP7na4/39OArdu3UrRAaoW0On3N2/eFC9evPgWsp1oAk2G41dAn3NQ7HAaTLkv6VuE9UnGPU50gcifrqBefT/0vUQF9L73YLfqHwI68w4RMbTvCcwjqIeYrqDuaJKABCQgAQlIQALjJaCAPt6+t+USkIAEJCABCUhAAjUQKIdsR5yd1us8r879+/dTGOkTJ04Ux44dq6GmFgmBENDx2jtw4EAlUBDHnjx5Unz69CmVF2G+EVKbNAX0+mgjoPOhT0mxgKjO/UrahbIR3j0X1BkPejfW1zd9KJnNVQ8ePEhVNYR7H3qs23WM+UgBfbp+UlCfjpNHSUACEpCABCQggbETUEAf+wiw/RKQgAQkIAEJSEAClRFAsEQ8jZChfM8jnlMhxJUvX74Ux48fTx+tHgK3b99OfYbX3sGDBxe+CH32+PHjFH0AQ1wlikAbObIV0Bfuzm0L2E6wYsMMQnoI6hG6Py+IsYB3enio//TTTwrq9XVVJ0tWQO9kt/S2UjEfMaecPXu2t+1oq+IK6m2R97oSkIAEJCABCUig2wQU0LvdP9ZOAhKQgAQkIAEJSKAHBCJMO0LsvCHby80MAR3vc7zQtXoIVCWg0+/koCVkO4ZIeubMmUpE+XlbroA+L7ndz5vW45M5IRfUY2NFfgVCBIeYzvePP/6ooL57F/T6iFxAX11dTbmXNQnMS4DfHsK4K6DPS/D78xTUq+FoKRKQgAQkIAEJSKDvBBTQ+96D1l8CEpCABCQgAQlIoFUCLLSG13kIlvN6necNefjwYfH58+cUvl0Bvb4uvnPnTvIWx2vv0KFDc12I/sfrnP7CIowunsVtGuMRj2jah6Dfhhd8m+2v89rTCujlOtAX4Z2OsM7YKRtiallQr7Mtlt08AQX05pkP+YqvXr0q3r17V+zduzdt3NKqJbCooO5vb7X9YWkSkIAEJCABCUigKQIK6E2R9joSkIAEJCABCUhAAoMjkIdsj7DtLJQioC+a43hzczPl0D569GgKAa7VQ+Du3bsFYbYRHQ4fPjzzRRDNEc9DCF1aWkobHrqwYK6APnN3Tn0C3p54fS6ScxhRpiyos+GhbHik54K63spTd1NnD1RA72zX9LJiIaDv27evOH36dC/b0KdKzyqo8zxApJH4dOH5oE+8rasEJCABCUhAAhJoi4ACelvkva4EJCABCUhAAhKQQG8JsHiK0MUnD9ke4nkVDXv06FHx8ePHAkH21KlTVRRpGRMIzCug0+8vX74sEC4wFsYR4Q8cONAZzgro9XVFFQJ6uXYhqOOZHh/6sGxENkC4jzzqjD2tXwTYtEOUEcwQ7v3quy7Wlt+i9+/fFwro7fSOgno73L2qBCQgAQlIQAIygo3fAAAgAElEQVQSqJuAAnrdhC1fAhKQgAQkIAEJSGBQBCJkey6eVxGyvQzp999/Lz58+FAcOXJEj7IaR9C9e/eKP//8MzGG9TSG9yj9g8iJRd7ZtkO2l+uugD5Nb853TAjoe/bsKZaXl+crZJezmGsQWkNMJ/T7JEH9559//iaoI6wrqNfSHZUWqoBeKc7RFxYC+v79+91w14HRoKDegU6wChKQgAQkIAEJSKACAgroFUC0CAlIQAISkIAEJCCBcRBANCdUd+51HuHaFw3ZXiZIWHA8yggrbk7T+sbXrAI6YfXpmwi1TY7648ePLxyyv44WKqDXQfXvMpsQ0Mu1Z95hs0cuqPNvZUNQD+90BHXDBdc3DuYtORfQ19bW3PQwL0jPSwRevHiRNtwpoHdzQCiod7NfrJUEJCABCUhAAhLYjYAC+m6E/P8SkIAEJCABCUhAAqMnkIdsR5TkU4fXeQ76yZMnxbt374pDhw4VZ8+eHX0f1AVgfX29+OOPP5LXHuHytzP6HJHizZs36RDyUNMvhMztqimg19czbQjo5dYwLzF2Q1Dn70mCOl7yIajzt4J6feNi2pIV0Kcl5XHTEAgBnRQiJ0+enOYUj2mRgIJ6i/C9tAQkIAEJSEACEpiBgAL6DLA8VAISkIAEJCABCUhgfAQQIfE65zs8zyPXedVe5zndp0+fJi/XgwcPFufOnRsf+IZavLGxURAaG9Hh6NGjE6+K2EXIdo7D9u7dm8TzH3/8saFazncZBfT5uE1zVhcE9HI96e+yoF4+hjmrLKjXOY9Nw3KMxyigj7HX62vz8+fPi48fPxYK6PUxrrPkENTjOTP+OyIc5c+csXmTVB3xcVNUnb1j2RKQgAQkIAEJjJmAAvqYe9+2S0ACEpCABCQgAQlsSyDE8qZCtpcr8uzZs+TtzIJ4XTmW7f6i2E1AJywu0QAi9zTh2gnb3gfRUQG9vhFOdIhXr14lMbqr9yf9z6aP8FAn/PskQZ0w7+GhTvj3Pozt+nq2mZLpi83NzXQxQ7g3w3zIV+F5gfQibLg7ceLEkJs6irYpqI+im22kBCQgAQlIQAI9IKCA3oNOsooSkIAEJCABCUhAAs0SYPEyvM6bCtlebqECejN9fv/+/SQwIjogjIfR73j14WmMEbKdSAB4n/fFYuzi7YqHml5q1fVcHwT0cmv/93//95ugjrA+SVBnjOSC+k8//aSgXt2w+VaSAnoNUEdcZAjopHxhk5c2LAIK6sPqT1sjAQlIQAISkEB/CCig96evrKkEJCABCUhAAhKQQAME8pDtEU6ziZDt5aYh3r5+/brYv39/cf78+QZaPs5LhICO6BDCA+IWIdsJh43RB2fOnEkiep9MAb2+3goBHY/tvt6fCOrhnY6gzkaLsjH3hXc636Qt0EN98XGVC+gXL150c8viSEddAilfPn/+XCigj2MYKKiPo59tpQQkIAEJSEAC7RNQQG+/D6yBBCQgAQlIQAISkEAHCLAgiaDEJ8K3U60Qz5uu4osXL1KI6H379hUXLlxo+vKjud6DBw+KL1++JPGcD8Io3nyIzwiFeKYvLS31UjRUQK9vGA9BQC/TIepGLqh//fp1C0By7oagjqe6gvp8Y0wBfT5unjWZQAjohw8f/i6SirzGQUBBfRz9bCslIAEJSEACEmiegAJ688y9ogQkIAEJSEACEpBAxwiw+IhYFB7nIZ62JZ6D5+XLl+lDyPCVlZWOERtOdR4+fJg8944ePZrC9r9//z41DmGQ3NaIhH01BfT6em6IAnqZFnMignrkUef+KBtRGcqCen3Uh1OyAvpw+rILLXny5EnaCKaA3oXeaL8OCurt94E1kIAEJCABCUhgGAQU0IfRj7ZCAhKQgAQkIAEJSGBOAnicIwzlXud4HsdnzmIXPg3vc7zQEadWV1cXLs8CJhMIAR3PWsYCduDAgRSynX/rsymg19d7YxDQc3qxySgX1ON+yY9j4wlzVuRR71vag/pGzPclkx7i0aNH6R8N4d4U9eFeJwT0I0eOpM1gmgTK83c844a4znc85+Zpivg3/pvnn/jw35oEJCABCUhAAhIYIwEF9DH2um2WgAQkIAEJSEACEkiCOcJ5iIxd8DrPu4X85+RBR4haW1uzx2ogwBhYX18v8AbFWDg+depU8uIbQp5nBfQaBs1/iyRSAREi+pwDfRE63DvkTA/vdIR1xlvZfvrpp+8E9b5vSlmEWX6uAnpVJC0HAo8fP07RIhTQHQ/TENBDfRpKHiMBCUhAAhKQgASKQgHdUSABCUhAAhKQgAQkMDoCCD25eM5iYu6B0wUgb968Sbm49+zZkzwUtWoJ4D2L197Hjx9TwQh7hMqH91CMcU07CcUdXmVDaVvb7Ri7gF7mz1hjI0oI6nxPEtTZcBAe6mwOGqugngvoly5dGsSGnbbvyTFf//fff0/33tLSUvpoEpiFwE6C+ocPH1JR+/fvTxvG9FCfhazHSkACEpCABCTQdwIK6H3vQesvAQlIQAISkIAEJDA1gRAUERXLIdu7FqLy7du3xdOnT9OCJQKLVh0BPPUQHBCWw/DcO336dHUX6UBJCuj1dUII6HhYX7hwob4L9bRkxh4icS6o829lY8NKhHvnu2vzcF34FdDrIjvOckNAJ3w7v2WaBBYhkAvqDx48SM/Lx48fT5ufQkDPUx0Z8n0R2p4rAQlIQAISkECXCSigd7l3rJsEJCABCUhAAhKQQGUEImR7Lp7nC4GVXaiighTQKwKZFcMYwLOf3PIRdYANCoh8CujV8x5yiQros/VuCOpsXuGDgDxJUEdED0EdcX2ogroC+mzjx6N3JvDo0aN0TymgO1KqJnDv3r00V7PBkDl5pxzqMV8rqFfdC5YnAQlIQAISkEBbBBTQ2yLvdSUgAQlIQAISkIAEGiNQDtnOhXPvmcYqMsOF3r17l0KM4+F6+fLlGc700EkECNkPz0+fPqX/zULwuXPnUh5rQpQqoDtuZiGggD4Lra3HMicj+OWCevko5uiyoM6/DcHYtIPXMGYI9yH0aLttCAH92LFjxeHDh9utjFcfDAHEcgR0jOcl5mNs2hzqCuqDGQo2RAISkIAEJDBaAgroo+16Gy4BCUhAAhKQgASGT6Acsh3v8y57nec9gkD3+PHj4scffyyuXLky/M6qsYVfvnxJYhUiOoZYfvLkyeTdCmNYIzqcOXOmxlo0X7Qh3OtjroBeLVsE9Qj3jqhOPvWyhaBOGGE+kY+32po0U5oCejOcx3KVzc3NdM8ooI+lx5tpZy6gLy8vp42Hk0xBvZn+8CoSkIAEJCABCTRPQAG9eeZeUQISkIAEJCABCUigAQK513nkO++LeA4evKIRfX/44Yfi6tWrDRAb3iXo91evXiUvcwzBHJH84MGD3xqLVzre/ocOHSrOnj07KAgK6PV1J/cnqQDMgV4PYzY75YL6X3/9teVC3M8R7h1Bnb7oi4d6LqAbYaSeMTSmUkNAJ081v2WaBKogwHP0+vp6Kur8+fNp09I0pqA+DSWPkYAEJCABCUigDwQU0PvQS9ZRAhKQgAQkIAEJSGBqAiGW420cf3Ny10O2lxv48ePHgrCs//rXv4pr165N3X4P/JvA169fk3c53ucYQhshSBHZclNAd8TMQ0ABfR5q85/DfB6COt+TBHXmylxQJ3pHVwV1BfT5x4JnbiXw8OHDdE8ooDs6qiTARqaNjY1U5IULF7Y8P017LQX1aUl5nAQkIAEJSEACXSOggN61HrE+EpCABCQgAQlIQAJzE2CRDqEFr5n49MnrPG84ubrxKsPL8vr163MzGeOJnz9/Tt77LP5iS0tLxYkTJxLLsj19+rR4+/Zt8kpHYB+S6YFeX2+GgI5Iu7KyUt+FLHkiAeb5yJ+OGM2GmbIhqEe4d77pq66YAnpXemIY9Xjw4EG6B/idyyOsDKN1tqItAsyz9+/fT5fnd66qOVRBva0e9boSkIAEJCABCcxKQAF9VmIeLwEJSEACEpCABCTQSQJ5yHb+ZoEOwTQ8zztZ6R0qhQiMVxn1/+WXX/pW/VbqS58Trp2w7RgCGmHZ9+/fv219nj17Vrx580YBvZUe6+9FFdC71XeIh7mgjvBTNtJhhKCOp3pVYtA8JKgrETIwQ7jPQ9BzcgIhoJ88ebI4cOCAcCRQCQHmVcYWtrq6mlIK1WEK6nVQtUwJSEACEpCABKogoIBeBUXLkIAEJCABCUhAAhJojUB42eJtnIdsD/G8tYoteGFCj8fC5Y0bNxYsbfinE74WQQphCkMow6N8N5EsBHREh+Xl5UGB0gO9vu5UQK+P7aIlM+5DUI+w7xGNIi+buSEX1OsShya1RwF90V72/JwAXsJsGlFAd1xUSYDnKjZyYmtra2lTYhM2jaCeP+PzN3WLz6RoQ03U22tIQAISkIAEJDA8Agrow+tTWyQBCUhAAhKQgARGQyBCtufieV9Dtpc7DYElQmfigd7VXL5dGGzkiyeXeYhkx44dS7lgp2H2/Pnz4vXr18lL/fz5811oTmV1UECvDOWWghTQ62NbdcncBwhBzKkhqBOlpGw//fTTd4J6nWJRCOjMUZcuXaq6yZY3MgIhoJ86dWrHiCsjw2JzFyTw559/plRC2MWLFyemwVnwElOdrqA+FSYPkoAEJCABCUigBgIK6DVAtUgJSEACEpCABCQggfoJIJbicZV7nUe49mmE0/pruNgVFNB354cI9uLFixSCHcODlJDt+/bt2/3k/x4xBgE9wlnrlTX1sNj1QDZtMHbMgb4rqs4dwG8GwlCI6XxPEtR//vnnJKgT7p3vKu8fBfTODYteV2hjYyNtIFNA73U3dq7yf/zxR/Ho0aNUrzYF9DIYBfXODRUrJAEJSEACEhgsAQX0wXatDZOABCQgAQlIQALDJJCHbEf04DMUr/O8xxB1WBTHrl+/Xql4M4SRgQBGyHY4YXv37k0h22cNw4wAT850RPcLFy4MAc23NsS9ooBefbcqoFfPtK0SuU8QinJBnX8r2549e74J6ojqiwjqCuht9fYwrxsC+unTp2faQDZMGraqKgLMib///nsqjkgZXd2cqqBeVY9bjgQkIAEJSEACZQIK6I4JCUhAAhKQgAQkIIHeEEAsRwzkOzzPhyie0yEIOuvr66lvrl271ljuyT4MBsJnE7I9vEZPnDhRHD16dK7F3ZcvXxZ8EOBXVlb60Pyp66iAPjWqmQ8MAZ0NG6urqzOf7wndJcC8kgvq/F0W1PndQVAP73T+nkVQ//LlS5rDDOHe3XHQp5rxrMC4VUDvU691v66x0YeaXr58ufsV/m8NFdR701VWVAISkIAEJNB5Agrone8iKygBCUhAAhKQgAQkEGL5kEO2l3sZD+t79+6lf7569erMntVDHDUIBM+ePSvevXuXmkf4bEK2I37Pawro85Ib93kK6OPp/xDUEZP4IKiXDSEcMT0X1Hfy1lRAH8/4aaKlPCvwnHTmzJmFfg+bqKvX6A+BocxTCur9GXPWVAISkIAEJNA1AgroXesR6yMBCUhAAhKQgAQk8B0BFr7C63zIIdvL3f7XX38Vd+/eTf985cqVJBaP2RCtCCXKxgJs//79SSyYNWR7mSHh2wnjTo7joXkS64Fe3x2jgF4f266XzO9QhHtHUI85Ka833ui5oE4+9VxQD2GK48gtrElgEQIhoLOhjN8yTQJVEPj8+XPx9OnTFF1jSPOUgnoVo8MyJCABCUhAAuMgoIA+jn62lRKQgAQkIAEJSKCXBPKQ7RG2nYU8hIiu5mKsCvTXr1+LO3fupOIInfnTTz9VVXTvysHjnEVcFj3p95MnTxZHjhypZAyMRUCH3b/+9a/e9X1XK6yA3tWeab5e//u///udoM7mp7KFoI64yYf5fYjCVPP0vSIEYrOdArrjoUoCnz59SlF/eHZYW1ursuhOlaWg3qnusDISkIAEJCCBThFQQO9Ud1gZCUhAAhKQgAQkIAEIhOcswkSEb+ffQzwfAyW87m/fvp2aeunSpQIPxrEZ/c/i7fv371PT2URw7ty55NlZlb1+/bp4/vx5KnNoC8S5B7oCelUj5u9yQlgwB3q1XIdQGnN37qGOWF42fsvYFMaGoPPnz6cII0PfFDaEvu1iG5jbI91L1b+PXWyvdWqOwFg3iimoNzfGvJIEJCABCUig6wQU0LveQ9ZPAhKQgAQkIAEJjIwAC1cIDuFxHiLDmMRzuhzx+NatW6n3CZ25Z8+eUY0EBChCtoc358GDB4vTp09X7kX95s2bJNLDd0ghShksCuj13TIK6PWxHVrJ/J6FoM73JEGdjRiRPx0P9bGn7BjaGKizPQroddIdd9kfPnxIKW6Yj1ZWVkYLQ0F9tF1vwyUgAQlIQAKFArqDQAISkIAEJCABCUigMwQQjfHey73OI1z72Lzzxiqg0/dv375NXuERsv3UqVPF4cOHa/HQVEDvzO3fq4qMJbRtrzqlJ5VFQCctRUTWmFRtBKtcUEdg1yQwiUAuoC8vL49us52joj4CzFEvX75M0X8uXLhQ34V6VvIigjpz+djeZ3rWvVZXAhKQgAQk8B0BBXQHhAQkIAEJSEACEpBA6wRYjEI4x9s8Piwwjc3rPO8IONy8eTP9E6HFqwxb3nqHb1MBNg08efKkIGwoRth6QtLW6X2PWE8uYq5FqPwhmR7o9fWmAnp9bMdQ8ufPn7/lQEf0/H//7/+lDx7qzINlQ1CP/On8Fiioj2GUTNdGnhXW19fTwaQDGGO6l+lIedSsBNjo8+rVqzSmGFvaZAIK6o4MCUhAAhKQwHAJKKAPt29tmQQkIAEJSEACEugFARZ/c/GchagQzsfspQGH3377LfXh6upqEk+GbIhHhGyP8MaHDh1KIdsZC3WaAnqddIdbtgL6cPu2iZaFgP6vf/0rbZAKY94nbUUuqPMbWTY8QnNBnXK0cRJgw8XGxkZqvAL6OMdAXa0OAZ1NjGz00aYjoKA+HSePkoAEJCABCfSBgAJ6H3rJOkpAAhKQgAQkIIEBEogw7ZNCttctmvYF56+//pqqSu7JvXv39qXaM9WTcUAYdUK2Y/Q9wjkCehPGAjFe7whSly9fbuKSjV1DD/T6UCug18d2DCVvJ6CX2849/Oeff37zTkdY59/KhodoLqj7GzqGUfR3G3MBnTDb/JZpEqiCABsMX79+nSIgEQ1Im4+Agvp83DxLAhKQgAQk0AUCCuhd6AXrIAEJSEACEpCABEZGIEK2s/AbQvrYQ7ZPGgJ4oMOHRfF9+/YNbpSweeLx48cFYhKGlxOLtE2GoCXHJ3UgRPKVK1cGxTgEdO4zvFj1Uq2ue6cVQKu7oiUNicC8GzC4p//4449vgjoh3ycJ6sylIajzt4L6kEbP923hd/T+/fvpHxXQh9vPbbSMzY18mEvOnj3bRhUGeU0F9UF2q42SgAQkIIGBElBAH2jH2iwJSEACEpCABCTQVQKGbJ++Z8iBDi/Csu7fv3/6E3tw5JcvX1LIdhb/sSNHjhQnT55sXOgZg4AeUR4U0Ku7MRTQq2M5xpLmFdDLrPh9KAvq5WPYnBaCOp6kfMacHmVo4y0X0IlWw2YwTQJVEMD7HC90IiCdOXOmiiItYwKBnQT1SGkV35zO3zzP/fDDD+nb+dxhJQEJSEACEqiPgAJ6fWwtWQISkIAEJCABCUggI5B7w4bXeSwEufgzeajcunUrhWcl9+SBAwcGMZ7o+1evXhUvX75M7WEhkIXZgwcPttK+Dx8+JCGfhcirV6+2Uoe6LmoI97rIFilqwtOnT9P4vXjxYn0XsuRBEqhKQC/DQVDHK50P4d4R18vG7y0iOl6lfCOu+xvc32H29evX4sGDB6kBCuj97ccu1pxnNdLcEAGJ1DpaMwQU1Jvh7FUkIAEJSEAC0xBQQJ+GksdIQAISkIAEJCABCSxEIPc6N2T79Chv376dPLQJa96WwDx9bXc/koV+wqXjfY5FXs02c7Z+/PixePToUfLiuXbt2u6N6NERCuj1dZYCen1sx1ByCOhs3FldXa2tyWzAygV18qmXjU0guaBOCg0F9dq6pPKC//rrr+Lhw4epXMYSY0qTQBUE2OhIlB4iIJ06daqKIi1jDgIK6nNA8xQJSEACEpBARQQU0CsCaTESkIAEJCABCUhAAlsJhFgeIaQR0jEW5+Mjt+0JDElARzBCPEfQwY4ePVqcOHGidaFGAd07cB4CCujzUPOcINCUgF4mHoI63ul8EF/LhqAe3ul8s8FJQb27YzcX0NfW1tJmME0CVRB48eJFQZQeIiCRYkfrBgEF9W70g7WQgAQkIIFxEFBAH0c/20oJSEACEpCABCTQOAEWeBDOEc3jwyJ8nsev8Ur17IJ37twp8No+e/ZscejQoZ7V/u/qMg5YhCWXJsbiPu3pSk53hKzNzc00Lq9fv95LxttVWg/0+rpTAb0+tmMomY07z58/T97CdXqg78aS3+gI946gzu9N2Zizc0GdHNsK6ruRbe7/E1WA3zBMAb057mO4EnMUcxURkNjwqHWTgIJ6N/vFWklAAhKQwDAIKKAPox9thQQkIAEJSEACEugUgTxkO3+zuBPCuQvv03fV3bt3k4cgOcIPHz48/YkdOZK643WOMIMhwhCOHgGmK6aA3pWe6Fc9SEPw5MkTc6D3q9s6U9uuCOhlIAjouaCOwF42RP8I+c6c3qX5vDMd3GBFcgH94sWLaU7SJFAFgWfPnhU8I7GB8/jx41UUaRkNEJhHUGdeZ7MUH9/TGugkLyEBCUhAAr0hoIDem66yohKQgAQkIAEJSKD7BMLjlTCxEb6dWut1Pl/f3bt3r2Bx/PTp08WRI0fmK6SlsxCIEBgjZPuxY8fSAmzXFubwJCZ/LPX65ZdfWqJVz2X1QK+HK6UqoNfHdgwlh4CO+LyystLZJiOoR7h3vmM+zytMG3JB3RzczXbnH3/8UTx69ChdVAG9WfZDv9rTp08LnpHYwMkznNZPAgrq/ew3ay0BCUhAAt0goIDejX6wFhKQgAQkIAEJSKD3BCJkey6eG7J9sW5dX18vWBw/depUsbS0tFhhDZ1NxAFCtr958yZdETGFkO379u1rqAazXQYh9MGDB4MW0Lkn6Rdz4842NnY6OgR05rhLly5VV7AljYJAXwT0vDP4jc8FdTzVJwnq5EzPBXXnnXqHdC6gMxd1bZNava239DoJsAmS3zo2cB49erTOS1l2gwQU1BuE7aUkIAEJSKD3BBTQe9+FNkACEpCABCQgAQm0T4BFdEK95l7nLOLGp/0a9rMGGxsbKZzuyZMne7F4ibf877//nkR/DNEc8bzLHokhoFPfGzdu9HOg7FBr7ksF9Oq7VQG9eqZjKrGPAnq5f/i9J01HeKjzW8VGnbIhqBPqPfKoK6hXO9Lhzu8upoBeLduxlxYpeBTQhz0SyoJ6zOP5Jug8khh/G/J92GPC1klAAhKQwD8EFNAdDRKQgAQkIAEJSEACcxPIQ7az4MJHr/O5cW458f79+0mcOHHiROfDZ75//74g3GcsvFFnPJa67g0HXzhjhHDven1nHV2xuUUP9FnJ7Xy8Anq1PMdW2ocPH1Kkjq6HcJ+lX3geYBNVLqjzb2X7+eefvxPUzdk9C+Wtx8IboRO7fPnyYoV5tgQyAmzMYIMGz3J9SyNkR85PQEF9fnaeKQEJSEACwyOggD68PrVFEpCABCQgAQlIoBECCHJ4t/IdnueK59WiJ7Q4Qh25w/l00ej/Z8+eFe/evUvVQxA6d+5cEkj6YCwO4+mPKaD3oce6UccQrQzh3o3+6Fsthiigl/uA5wKikXCvMM/ymSSo79mz55ugzt8K6rONZuei2Xh59PQEHj16lO5hBfTpmQ3xyFkFdaKM5J+hbUwdYh/bJglIQAIS2J6AArqjQwISkIAEJCABCUhgJgIhlhuyfSZscx0cAvqxY8eSF3rXjIVVPJTwOsQOHDhQnDlzple5tnMB/fr164MTb/RAr+euUbSqh+tYSg0BnfDmFy5cGEWz2WxVFtTLDUdoKQvqii87Dw+jYYzi9mmlkZubm+n5jmfQw4cPt1IHL9o9Agrq3esTayQBCUhAAvURUECvj60lS0ACEpCABCQggcERYNEkvM4N2V5/9z58+LD4/PlzWrzskoDOOIiQ7fyNwEGedkJ89k3sQNBZX19PnXnt2rVeif/TjEAF9GkozX6MAvrszDzjHwJjFNDL/c8zBBuYwkOdubhs/J78z//8z7f86YjrffuNqXvch4CO5/7Fixfrvpzlj4gAz6B//fVXioB06NChEbXcps5CQEF9FloeKwEJSEACfSOggN63HrO+EpCABCQgAQlIoCUCecj2CNvOgi2L2S5o19MpeP98+vQphc9EoO6CIciS6xwBCMODkpDtiBx9NAX0PvZa+3U273D7fdDnGiigb+09flsi1Dv3V0Q2yY/kmSMX1MmnPvbnDzbZ8ZusgN7nGaGbdScK0tevX9MGzoMHD3azktaqcwQU1DvXJVZIAhKQgAQWIKCAvgA8T5WABCQgAQlIQAJjIMBCCAvbLGbzzWI1ue1CPB8Dg7baSP7Jjx8/FktLS8WpU6faqsa36yJuELIdjySMBdXTp0/32mubcX3v3r3UnqtXrxY//PBD65yrrIAe6FXS/KcsBfR6uI6lVCJ4vHz5Mm1AGksI91n7lrkrvNP5jt+dvByeQ/7P//k/3zzU4Tk2QT0EdJ7L1tbWZsXs8RLYlsD9+/dT1Ck2cJKiR5PAPAR2E9R5DmcuZ0MU3+ZQn4ey50hAAhKQQF0EFNDrImu5EpCABCQgAQlIYAAEWPTA+wSPcxbSEFAJ5UhI8bEtUrfRnYjVeCoSGh2hui1jHLx9+7Z4/vx5ESHbqQ8hPfs+DhTQ2xpV/b6uAnq/+6/t2iugz94DCHm5oM6zSdkQXhDUw0v9xx9/7P1v1G6kiFLz7NmzJDopoO9Gy/8/C/+osI8AACAASURBVIGNjY20cZYNnPv375/lVI+VwLYEckGd90tSBWCMM+Zu3ivyTdoK6g4mCUhAAhJok4ACepv0vbYEJCABCUhAAhLoMIHwXGWhgwUOwomzeN21fNwdRrhw1R4/fpxyjR8+fLg4c+bMwuXNUwDj4MmTJ8kTHsNDhJDt5KIdguHVePfu3dSUK1euFAguQzI90OvpzYjGQOmXL1+u5yKWOlgCCuiLdy0CeuRQ59kEgb1sRBQpC+qLX7lbJfDbzOY22rq6utqtylmbXhNYX19Pz/9smNy3b1+v22Llu0mA8cU4w3jPIYoI/4blQrqCejf7z1pJQAISGAMBBfQx9LJtlIAEJCABCUhAAjMQQDBnIZoFjPiwiIE3NKFCFdBngLngoQjX7969S57eZ8+eXbC02U9HlKDfw9MPIR8PERayhmK07c6dO6k5CKEs3g3JFNDr6U0F9Hq4jqXUENDZkHT+/PmxNLu2dka0nFxQZ+4rGxukckF9CCk7QkCnbSsrK7UxtuDxESC9DfcWwubevXvHB8AW106A900inGGkM+EZfLeQ73qo194tXkACEpCABDICCugOBwlIQAISkIAEJCCBbwQQzHPxnEWM2PVPPm5ChR49ejTlQ9TqJ/D06dMUOp1c43h9N2X0++vXr4sXL16kSzIGImR7U3Vo6joK6E2RHtZ1FNCH1Z9Nt0YBvV7iIaizCSw+4dWYXxmxJsK9I6wTKrhvRpoXfqsV0PvWc92vb0TnYQMn94cmgaoJ8Az+4MGDVCwbgCZFgVJQr5q65UlAAhKQwCwEFNBnoeWxEpCABCQgAQlIYKAEIkw74nn8TVNjlz9/Rz7upaWl5IWs1U+AvKZv3rwpDhw4UCwvL9d/waJIGygIHU+0AYxQ7Yj3eEoO0Wjv7du3U9MuXbo0uHbihRmfPopDXR1zuYDOuGGu1CQwLQEii7x69SrNN3qgT0tt/uN4riFdR4jp3L+TBHX6IwR1vvswZ4aAzmYAPDg1CVRBgHsGD3SMZ0DuB00CVRNgXo4c6Gtra1PNuQrqVfeC5UlAAhKQwE4EFNAdHxKQgAQkIAEJSGDkBCJkOyJbiOfl8Hgg6kI+7rF1VdMCOqI5/Ry5ZI8cOZKiDQwpZHt5DDHub926lf754sWLg8ntHu2MEO58DyFccVfmAAX0rvREP+uhgN5uv/Gs8+eff34nqPNvZUNQx/M2wr538bcwohkooLc7poZ29Tw3NRs42UypSaBqAn/88UdBhLN4Bp9njo131/yb8syhXnVvWZ4EJCCBcRJQQB9nv9tqCUhAAhKQgAQkkAjsFLK97FHZdj7uMXbZ8+fPUyj1/fv31+qlyKIT3pAvX75MmFnAImQnnu9DtyEL6NzfpAFAYMGTkhymIQRNCpM59L6usn0K6FXSHF9ZCujd6nN+AxFywkOdvycJ6oiIIajz9zxiT9UtNx1A1UQtL94P1tfXEwyiZAw1CpG93S6BOp6lFNTb7VOvLgEJSGBoBBTQh9ajtkcCEpCABCQgAQlMQYDFhQjrnIdsj3znk4poKx/3FM0Z7CHkNUXY3rdvX22hWck/iNf5ly9fEkfCdBKuE2+2MRgi882bN1NTCR85lDCleFeSdgEhaJIhoIcQ1Nfcv22OzzoWfdtsj9dulkAI6IiwTaXnaLaF/b4avwtlQb3cIjYZlgX1NlI5uBmj32Otq7XnHWFjYyNVj9QAY3km7Gp/DLVebFriHYS5k3Q4dZiCeh1ULVMCEpDAeAgooI+nr22pBCQgAQlIQAISSARYSEA0ZYF4p5DtZVxNhxO3u4rkEc4Hz+GVlZXKkXz69CktXLFQih09erQ4ceLEqPI5D1FAJycuESMixy/9mocs3i73b9dDFVd+AyxQYB521BzoC4Ac6akK6P3qeOZMNs2EhzoblMqGABT505lL8dhtQlB/+/ZtilTjZox+jamu15ZUPvfv30/V5PnTqDVd77F+1o/UUWzQZgM3aZSaMAX1Jih7DQlIQALDIaCAPpy+tCUSkIAEJCABCUhgRwKxYMCiWO51zgJvfHYqoKlw4nbjPwTwPscLncX41dXVytDQ/5TLojtGeG9CthMqfmwGi99++y01G8aw7qsh8nCfIqhgLHifOXMmeY5x35MDfdrcv7kQhDDThBDUJ+5V5O3sU3uta7UEFD2r5dl0aWw6C0Gd70mCOoJQPo8yD9cxj8ZYiugxTbPwesMkwEbbBw8efHs24vlBk0DVBNjIywZt3kOIAtWGKai3Qd1rSkACEugPAQX0/vSVNZWABCQgAQlIQAJzE2BxAAENgS0+LOTuFLK9fLEmwonP3cCBnojAjSDKwnhVC0t//fVXCu3Noj+Gdzvi+Zi9i3799dfEAi8rePTR6NdHjx59C9nOZgj6FUPsCQG93DbmhlwIinGRH8dcEbnTm/Ss7HI/KKB3uXe6XzcF9O730Sw1ZI4N73TmUObjsvG8lafN4De3CkH9zZs3BR8F9Fl6zGN3I8AYfvjwYTqM508ETk0CVRP4+PFjes9hPqwj0tY89VVQn4ea50hAAhIYLgEF9OH2rS2TgAQkIAEJSEACiQCCeS6eszAQwvksi7d1eUPbTdsTYFEczww8gKsIbchCFSHbI4T38ePHi2PHjlWyiN/nfuy7gF7u15MnTxZLS0upXxF2dhLQy/02TahiFtJzz8qqhKA+jSEF9D71Vvfqqtdw9/qkyhrxzIWgHpuT8OYtG/NonjZj3nk0BHTKik1TVbbFssZJgKgKm5ubqfE8f/LeoEmgagLv379PqaqI0HHhwoWqi6+kPAX1SjBaiAQkIIHeElBA723XWXEJSEACEpCABCSwMwFe+EM8K4dsn2chrA5vaPtwZwIhoJNLlTzL81o5tDehOM+dO9dbb+t5OWx3HiHcuUdYvNu3b1/VxddWHv1KZAjGCUa/Li8vfxeGflYBvVzZ3LMSQWiSEMR1c8/KMYR6VUCvbViPomAF9FF087dGMm/mgjoCe9nyeZQNStNGheHZjPGkgD6uMVV3a/PfOJ4/Z9lwW3fdLH84BN69e1ewQZv3nPPnz/eiYQrqvegmKykBCUigMgIK6JWhtCAJSEACEpCABCTQHQIRsh3xK170Zw3ZXm5N1d7Q3aHV3ZqwKP706dO0sDSvgI4XESHbWQzFEIjxUhuDyDltz968eTN55fdJQC+H4t+uXxcV0MsMQwiKcMWUXzY8iXLPyiGGftU7b9q7y+MmEVBAH++44JmsLKhPmkcR0PPUGdv9ZoeATvqRM2fOjBesLa+UANETeHbEFNArRWthGYEh/BYqqDukJSABCQybgAL6sPvX1klAAhKQgAQkMEICEa657HWOgL6IB0kVYu4Iu2OhJuOZ8eTJkxTa8PLlyzOXRWhEBPgI2Z6H9p65sAGfEAI63i/kDu+6EbKdcRGiy4kTJ4qjR49OvL9zAR0he5E5oMwlF4JCUI+xlh/LBpBcUJ8nAkbX+kQBvWs90q/6DEE06Bfx7taWeZQNURHunbl00jwaG5MifUZsTMJ7k2cFBfTu9nEfa8Y4JOUPzwzzbuDsY7utc7MEhhhBQ0G92THk1SQgAQnUTUABvW7Cli8BCUhAAhKQgAQaIpCHbGfxlc+iXud51RcVcxvCMKjLIICzgIkn2pUrV6ZuG31P7nT6DON8QrYjYmpbCdy6dSuJ0YQ/P3DgQGcRcY8Tsp0FR2yaUPx1CuhlUNQPYTnEdAQh/i035qQ9e/Z8E9T5u0pRv6nOU0BvivQwrxN5qxFDmZs1CQSBmEdDUOd7p41JiO9fvnxJ0WVOnz4tSAlUQoAxxUY9NryRA12TQB0ExrABSEG9jpFjmRKQgASaI6CA3hxrryQBCUhAAhKQgARqI8DiKjk1+a4qZHu5sh8+fEjhHGcVc2tr9AgKDuYIpVevXp2qxYRqp58Q+DAEYcK6DjGM9lRApjioDwI6IX/pV8RpDG9DhLfdQvE3KaCXUTMfMR5DUI80AvlxiOd5/nS8LPsgqOcC+tramvfXFPeZh/xDQAHd0TAtAZ7pmDtzQb28MYmyEDoPHjz4Lez7ECJ9TMvI46on8Pnz5xTBiGdHfuM0CdRBgE2hvOsQ/enUqVN1XKJzZc4jqPNczPM+nz48I3cOuhWSgAQksAABBfQF4HmqBCQgAQlIQAISaJtAvIQjnlcdsr3cNsJGP3r0KL28Tyvmts2n79cP5ixgXrt2bcfm0P94nON5zt8ssBCy/ciRIy627DIQbt++nTagIEgjQHTNPn36lCIRRMj248ePF8eOHZuqX9sU0MscEdQR00MIik0e+XGM9QhRjLDOhp0uLhYqoHftLulXfUJAZ4yfPXu2X5W3tq0SCEE9j/QxqULMozGXEulDQb3VbuvdxXnu4HlSAb13XderCj9//rzgXYfNvryzjNF2E9R5P2EDLVFG3LA5xhFimyUggbYJKKC33QNeXwISkIAEJCABCcxJgBfu8DqvI2R7uVospm1ubqZF2OvXr89Za0+bhcC0zBFJ8RTCiwPDixcxmMVzbXcCd+7cKfDw7pqAzj3+8uXLghCXGAvZ1JFFtGmtSwJ6uc7MX3neX/qgbGzYyT3Ud/O4n5bLoscpoC9KcNznK6CPu/+rbH0IUPzu83y2XaSPsqDexY1JVXKxrMUIIGoyttjEtrKyslhhni2BbQiwSYN3nUOHDhVsDtWKbxviQ1gnGgSpm3j+/fe//+1mKAeJBCQggYYJKKA3DNzLSUACEpCABCQggSoI5CHbI2w7C6csiNa1KMoL/MOHD1P5v/zySxXNsIxdCEzDHAESzwTyoGJ4UJMH1ZDt0w+vENDxBGURrwuGmIzXOXlIMURkxHMWs2exLgvo5XbQ5vCq5Ds87vPjEIlCUEcQamucc78xH2J6BM0yIj0WAiyGv337No1lPdAdE4sQKHtw8kyYb0yaFOmD57g80sfPP/9c27PjIm3z3PYIsCGT8NoK6O31wRiu/OTJk/Sce/jw4RRZSdtKIDbc8fzL+7fRRBwlEpCABJoloIDeLG+vJgEJSEACEpCABBYiwG70EMTykO0hni9U+C4ns8Dx4MGDdNSNGzfqvJRl/5fATgI6/Y8Aw+J5hGxHOEcArmsTxVA75u7du2kDArniWcRr2+h3xHM8tDEWFfHMmadf+ySg59wZ0/RJHqYYYahshCZGhAzvyqYWFhXQ275L+n19BfR+91+Xah8COpvnTpw4saVq/AbkgnpstssPZN7MBXWEmnl+b7rExbosRuD9+/cpAg5j4cKFC4sV5tkS2IYAz7o855Fu6ujRo3KaQCCeF9johIDu3OwwkYAEJNAsAQX0Znl7NQlIQAISkIAEJDA3AQQlPDTD45xvXqKbEM+pNAsc9+/fT/X3BX7ubpzpxHzTQs4cYRWvDcIeYiyq4J2MmKjNTuDevXsFXnptC+jc44RrZ9Eaw7sa79T9+/fP3qj/ntFXAb3cYNjQR7mgzr/lxnwYgjqiOn/XtdCogD73kPREPdAdAxUSiBDI2wno5UvlqTMQ1icJ6vz25II6Xsh1zaUVorCoCgm8e/cuPY/wfHn+/PkKS7YoCfxDgAhazEOI54jo2lYC3Ifcj8zJ165dcy52kEhAAhJomIACesPAvZwEJCABCUhAAhKYhwAiGIueudd5hGtvalGTBY6NjY1UfXKgN+XpOQ+voZwzadMC/8aCU+SLxmP61KlT9scCnR4COh78bS3gcX/Tr3nIdsRzvL8WsaEI6GUGbCAi128I6pPy/tbpVZkL6Kurqyk3pSaBaQmER9nevXvTxh1NAvMSePr0aUHUknlzCPPbk29MimeLvD4I6pE6g+9ZU4nM2zbPa48AEY6Yp9iItry83F5FvPKgCWxubqbNkURa6kIEqC7CJpUCKRV4Xrhy5YoCehc7yTpJQAKDJqCAPujutXESkIAEJCABCfSdAII5i5uIRfFp0us858cCB0Ijxg74tnIP971PZ6l/vmkB5ixospCCIQ4ivOB1pi1GYH19PYmxbERYWlparLA5zkY0RzyPkO144hCKt4rNMUMV0MuYpwlTnItAePIssjlBAX2Oge4p3wgooDsYqiIQAnpVOYQR0HNBPX6X8vqyYSgEdeZSBfWqerM75UTeZfqXCEeaBOog8PDhwxQFgzRFbALSthKINB379u1LAromAQlIQALNElBAb5a3V5OABCQgAQlIQAJTE0Awz8VzxPQI116FsDZ1Rf57IAsc5IrGrl69qsflrADnOB5RF3EXw/MgvJPxCGJBk9Ca2uIE2hLQuacR0mJTBAIvmyIOHDiweKP+W8JYBPQysDxMMWLQJK/KXARCDJrFi1wBvbIhOsqCIiSrHuij7P5KG006F54NqhLQ88pF6iDm0Mijzm9K2RDQc0F9lrm0UhgWVhmB2ORDvxINR5NAHQQePHiQns9OnjxZ6bNvHXVtq8zYJMW7waVLl9qqhteVgAQkMFoCCuij7XobLgEJSEACEpBAVwlEmPa2Q7aX+bDAcefOnfTPly9fXsh7s6vsu1av3Os/6oaHNN7JhtCvrrdITYA4wAIe3t9NGPf348ePU+hdLLy8FvGKnlTvsQrok+av8Krke5IIBPtcBNopygbzIQu/mCHcm7hjhnUNBfRh9WebrQkBnfQjdf9+8XzK5qFcUGezZ9lmmUvbZOe1tyfgHOXoaIIAz988jxEBav/+/U1csnfXiDkeD/21tbXe1d8KS0ACEug7AQX0vveg9ZeABCQgAQlIYFAEImQ7iwkhpLcVsr0MFsHv9u3b6Z/ZAa/3c71Dj/5/9uxZCtuOIZjjBVSld3K9LehP6U0L6IgPjx49+hayvc5NERHJgvsXQbiN6BVdGwm5CBRC0CQRiEgPIajzd75pRQG9a73ar/ooTvWrv7pcWzZiMY81IaCXOTCXstEvvNP5njSX8rzIXMpGMb7dANjlEfV33V6+fFm8f/++IGz06dOnu19ha9hLAkSAYs5gjDHWtK0E8jl+ZWVFRBKQgAQk0DABBfSGgXs5CUhAAhKQgAQksB2BroVsL9cTUf/WrVvpny9evFggKGn1EECcIyc2i+JhLJoQ7lernsD9+/cTazz7jx07Vv0F/lsiYgN5RclniDWRx555hXuXMaWAPrlr6RfSJYSHOn/zb7mx8QDxJwQgWJK7E+PeNAdwbbfNIAsOAV1xapDd22ijQlxhIxafNi3m0lxQL8+l1C82J8WcqqDeZq9NvjapZT58+JC8gvEO1iRQB4F79+6l5y02CLO5RvueAGx4H+S5lAgj58+fF5EEJCABCTRMQAG9YeBeTgISkIAEJCABCZQJ8HIcYZbD65xjIt95V4ghxN28eTNVhxByLHxq1RP49OlTCu1dDjGt13/1rKNEQnGTQ/b48ePpU4fRn/Qr/Ys1lcdeAX323oRZWVAvl4KgHsIQeetZ+NW7f3bWYz1DAX2sPV99uxFXEKwRV/BC75LFXBqC+nabk/g9jM1J5WgfXWrPmOrCRr+PHz+mqEekt9EkUDUBnqEQ0LFz5875XjkBMIyIWEWkDzb4Li8vV90NlicBCUhAArsQUEB3iEhAAhKQgAQkIIEWCfBijGcoi4xdC9lexkL9fvvtt/TP5PzVU6DagcMYIGTm69evU8F4uBLSkMVxTK//annnpdUtoOPZTD9yr2OIHCxIN+F1p4C++Lhh80MIQPQlOYDLxv0a4d751iN9ce5DLkEBfci922zbEFfCO7FrAnqZxLSbk/JoHwjqbk5qdkxxNVIIseHv4MGDKTqPJoGqCTAfEMIdw7Pa1GBbCfPuvbm5mZ47eW9gw6YmAQlIQALNElBAb5a3V5OABCQgAQlIQAKJQIjl5CXOvc5ZJIxPF1EhoFPfCxcumKuuwg5iYSS8yCiWUO2EM0RgNWx+haC3KYpQ3J8/f07eHVUuFHOvkMMeTy7+pj/ZFHHo0KH6G/XfKyigV4+aeRthARF0O0NAD0EdMeiHH36oviKW2FsC5hfubdd1ruIhoPP7dfjw4c7Vb6cK8fuUb07Cy7Js/G7mgjoim4J6/d0cAjrPK3VF5qm/FV6hywTYnLixsZGqyHvlTz/91OXqtlI33h3Y5MtzJ6kUeIfQJCABCUigWQIK6M3y9moSkIAEJCABCUggCWm8CLNwGB8WA7sWsn1SVxHCnTrjKUBeRG1xAuSYfPLkSeKKsVDJQjhjwrD5i/OdpgS8OxBECYFbVahSFgbpV0KgYiz6E3qxaQ8bBfRpRsDsxzCH379/P53IZheiC0QO9XL6BY5hYTgX1PFY18ZLQAF9vH1fdcv5/Yrwvn0T0Msspon2EYJ6zKfMrQrqVY+qonj69GnaWMiY4plUk0DVBHhuQhzGVlZWjNwzATBrBjxrMjfyrFnlJt+q+9PyJCABCQyVgAL6UHvWdklAAhKQgAQk0EkCiFm5eB5eqV32Os9B4g3NSzxCIHkRtfkJMBbwTMZDGcNDlRyAeJ+HGTZ/fr6znFm1gI5HHREFItQ3C9B4jjQRsr3cbgX0WUbC9MfmAnq+8Ms9S7+HmM5YiM0xeemEJQ4ByJy/03MfypEhoLMRjblBk8C8BEJAZ/Ndk9FN5q3vLOcxz+Ye6pEGJS+DzUiRPz3SZyioz0J58rFsAPzy5UtKOcPmQk0CVRPgWYkIUNja2lpKXaV9T4DnRwR0vnlHNBqEI0QCEpBA8wQU0Jtn7hUlIAEJSEACEhghAUQVhGc+5ZDtbYhq83bB7du30wYAXuLJi6jNRwBvMQRW8pZiiCjktZsU5vnXX39NxyDS5eL6fFf2rEkECIGLp/jS0tJCYhb39rt371LuUP5mEZ9wi216BSqg1zPmcwF9p9CjjAPu8xDU+Zt/y41xkgtAhiiup8+6VKoCepd6o991QYBCiBqigF7uGQT0ENT5niSo8xxVFtT73cPt1P7x48fpd4vnIj6aBKomwLsQG4CwixcvtrLJtOo2VV0ez/CEuY/0ad6LVRO2PAlIQAK7E1BA352RR0hAAhKQgAQkIIGFCETI9lw870vI9nLD79y5kxYsCSM3NE+nhTp5hpNDYGVRhHFAOD4WRLbzmDLv/Axw5zy0CgGd+5uQp4TkxxBB2WiCd3GbpoBeD/1pBfTy1SPnb4hAsYkmP84QxfX0WZdKffHiRZor9EDvUq/0sy4hoPMsMbaNjXnqDMTeSekzfvzxx+8E9UkbFfvZ8/XWmk2e/E7hfY4XuiaBqglEtCbKvXTpkqkYJgDO88Svrq62uiG36v63PAlIQAJ9IaCA3peesp4SkIAEJCABCfSSAGIJC3xlr/O+hGwvQ797927ydMJbuk2v2j4OBsYCAuv79+9T9VnURWAl5OhOZt75+nubhWLELBaJ8Rif1RBBEeEjZDubSyinC9ElFNBn7c3pjp9XQC+XPk3OX8KaRrj3CFE8XS09qqsEFNC72jP9qxc5hHnOPHny5KhT6/CcnQvqiHPbCeoxn+KprqA+eczzTMOzDfnPfd7v37zQhxqz6YVIB7wTI6BrWwnkz5p46Y9tk5RjQgISkEAXCCigd6EXrIMEJCABCUhAAoMjkIdsR8AKb2MEtT7nZlxfX08LaoiDeqRMP2xhhkhLuEKM/PFsQpgm359556fnPO+RLOCxsYFFYvplFiOiABsjImQ7+Ywppyv3uQL6LL05/bFVCejlK1JuhHvnm/8uG5tvFICm76suHqmA3sVe6WedyI/LPDF2Ab3ce/wms6kt5lMEdX4Py/bTTz99N59O81zWz5EyW60Jrc0z6xhSA8xGxqOrIvD58+f0/My7MeKwtpVAnif+8uXLKWqNJgEJSEACzRJQQG+Wt1eTgAQkIAEJSGAEBFigYzGT7/A872vI9nJ3kYeNRUgWagnrqO1MYFJO7FkFVgX0+kfZPAJ6OaIAi/DLy8uth2wv01JAr2f85GE1d8qBvsjVp/WoJF0Agnrk/e1C5INF2j2Gc0NAZzMVv6eaBOYlEAI6zxaKK9tTZD5FEEZQjxQa/FvZYj6NOXWs8+mYUwPMey963mwEPn36VDx79ixtJl5bW5vt5JEcHRGuaO61a9d2jVo2Eiw2UwISkECjBBTQG8XtxSQgAQlIQAISGDKBEMsRz4cSsr3cXyzUsvhIrk3COmrbE0Bge/LkSfHx48d0EAIrIdsRuWax27dvpw0ZnGvovlnITX8s/YQnOaHXz549u+uJ5YgC9AtRGbrouaaAvmt3znVAEwJ6uWJlj0rm4kkC0J49e755VPL3WAWguTq2oZMU0BsCPYLLsLGR+UgBfbbOZu7ktzwEdUR159N/GJoaYLbx5NGzE+D96Pnz5yml1crKyuwFjOCMPE/8L7/80rlNuiPoApsoAQlIoFBAdxBIQAISkIAEJCCBCgiw6BZe50MK2V5Gw4Laly9fUkhHPtpkAix4kD+SfJwYwiyL2/MIrHfu3EnlIOxSjlY9AUJIvn37dioBnVDvHB9pGfAeJZ1BV0K2l+kooFc/XigxF9DPnz9f4LXYtOUCECIQYlBZAGJchmc6HpXUs6tjtWl+bV4P0QDxQA/0NnthGNcOAZ1NXPv27RtGo1poBb+VZUG9XA3mztigxLzKZ6jzqZENWhiEI7skz9MvX75MG4yJ5KNtJcA7N5t8sf/85z9ps4EmAQlIQALNElBAb5a3V5OABCQgAQlIYIAE8pDtEbY9cp0PbWGNkI7krMP7HC907XsCiFdv3rwp8C6MnNgsapMTe167e/duyuNJbu5Fypn3+mM4LwR0PMnx9J9k3NuEmsRTHWMRi5Dts0YUaJpnbOhhDLGBY2hzUtM843pdENDLbaevIzQxgjrhisvGb1PkT+ebceyYaH4UKaA3z3yoV1xfX08buhTQq+3hmE9jTkVcL1u+QYlnAcT1ocynbsyodjxZ2lYCPE+/evUqbexjI6K2lUCEuef//N//+3+LH374QUwSkIAEJNAwAQX0hoF7OQlIQAIS4uYJWgAAIABJREFUkIAEhkMAgRQRhU8esj3E8+G09J+WbG5uFrzMk//cvK3f9zARCPASgA/GQipi7KKeqffu3UtCGIvjeDpr1RNAGGfjA96giOJlg//vv/+evNMwjmNDwzwRBaqv/c4lKqDXQ7yLAnq5pdQxz/fLJoqyMYbLgno9xCw1J6CA7nioikAI6Pwm7d27t6piLadEgPkUMT0E9e02KEXED777HPHDjRneAnUTIPLT69ev00bU7Tav1l2HrpcfYe5ZW8ADvQ/vHV1nav0kIAEJzEpAAX1WYh4vAQlIQAISkIAEiiIJ5gimuXiO18mQxXM6nrDkvMwvLS2lkOTa3wQIsYfAypjAELrZYFBF7mEWMRFu4Q13rXoCOwnoHz58SBsj+hKyvUxHAb368UKJfRDQyy1nfkJQj0/MV/lxeKTngrqLtfWMnxDQiXphNJd6GI+lVDbZ8UxKmhfuXa0ZAiGox3w6aYNSRPwIUZ1Q1X3xUI9x5caMZsbTGK+CeI6IzrzF/KVtJRBh7nkW+/e//62A7iCRgAQk0AIBBfQWoHtJCUhAAhKQgAT6TYBFM4SH3OucBbH49Lt1O9f+8ePHBS/zCMR4RI/dGAMsABGyHWOxlMVGRJGqjDCaeDwhyOP5r1VPADGLfty/f/+3MJIIz/w7i3sYwiIeMn0TKBTQqx8vlAhXNrdgbeVAX6RlzF1fv379TlCnTWXDgzIEdUSgKjYFLVLvoZyrgD6Unmy/HaR5wRTQ2+0L3gvyFBrMr2WLiB8hqHc5hYbjqt3xNIarE76dMO5EzuDdSdtKIMLcM1fcuHHDZzAHiQQkIIEWCCigtwDdS0pAAhKQgAQk0E8Cecj2EKXG4HWe9xaeuLzMHzp0aPTeAiyW4nWO9zkWIQjxMKrS7t+/n0QuvBTJPa9VT4ANECzkhYCOJxnRFiJkO//O4l4fcw8qoFc/Xiix7wJ6mQq/b4QkDm9KhCD+rWzMc3mI4r54U9YzCuYvlagXpPvQA31+hp75N4EQOtngxb2pdYMAAnouqE+K+MEzBRuUckG9C7Vn7scDHXNcdaFHhlmHly9fpk3ZPGMb1WxyH5Neig/vlr/88osC+jBvBVslAQl0nIACesc7yOpJQAISkIAEJNANAoglLH6FGMXiUoRrH5OA8PTp0+SRy6L/mPPVff78ucAbPxZECa2OwF2Hd2YI6MePHy/4aNUTCAF93759KboCfRveuHj+0799vc9pB1EzWMxnfNYxRqvvke6XmAvoy8vLxZ49e7pf6RlqyG8cG0hyQb18OvdECD+IQH3O9zsDmkoOVUCvBOPoC1Ho7M8QKEf84He5bHiZ5nNqW5v2hv771p9RM+yaRiSWAwcOpChb2lYCEeaeZ8zr16/39l3EvpWABCTQZwIK6H3uPesuAQlIQAISkEDtBCJM+1hDtpcB75QruvbO6MAFGA94TOCtjBGOE89kFn/qsgcPHiQvdwX0uggXqU/5sFgdmyL4m00ihJbssymg19N7YxMYaG/uTYm3etki32+EfO9yeOJ6RsX0pSqgT8/KI7cnkAvoQ9zIM9S+L6fQYG6dJKjjdZoL6jxzNmHUhfRBWB9TlDTByGssTsDfwd0Zhpc+88C1a9cU0HdH5hESkIAEKieggF45UguUgAQkIAEJSGAoBFjgQkxjISmE9LGFbC/35aRc0UPp793agfcQIdvxyMQQiRBYEYnqtIcPHxZ4vBO+HS93rXoCsTEkSsYTnXyybXl/VdlCBfQqaf5T1tgE9DJFfhfDO53vSfl+IzxxhCiue66sp6frKTWEA9KhGFmkHsZjKHXs89BQ+ph3DFLH5BE/IgpO3kYE9dighKBWl6DOuw/Rj7ALFy6k8NGaBKomQFQz3m8OHz5siqpt4BIh68OHD2kz79WrV6vuAsuTgAQkIIEpCCigTwHJQyQgAQlIQAISGB8BQ7ZP7vM81DWLamOxjx8/FuR/Dw8hxGxEjybCem9ubqZcuUePHjXEYQ0DDrbkO498z/Qr/dtE39bQnC1FKqDXQ1nh6nuu0+T7RUAP8YfvusSfenq82lIV0KvlOdbS8nlIT+HhjAKeR4jykQvq8YySt5K0GbmgXlWKFuZzoh9hKysrtW8UHU7P2ZJZCJAuiTFO6iTecbStBOJZgTzxly9fFpEEJCABCbRAQAG9BeheUgISkIAEJCCB7hJggQqRNPc6p7aR77y7NW+mZhHqmp3wLKoN3VicZtPAmzdvUlPxqMQzGQ/lpgxxFwGfPNynTp1q6rKDvw73On1LfsEwvKyGtkClgF7PUDZ08vZcy+GJWSCf5E1Zl/hTT49XW6oCerU8x1paHmpbT+HhjgLm1D/++COJjYR75zNJUCdPcgjq/D2voI43PNGPsNXV1UFE4xnu6Ohvy4jqxVhGPEdE17YSCC99UoVdunRJRBKQgAQk0AIBBfQWoHtJCUhAAhKQgAS6SSAW/VnoN2T75D5CbCSMO6Ej19bWutmRFdWKBcRY3KFINg0Qsr3psN7UgfB9LC6dPn26otaNu5hyOH6Ec/p7iONaAb2esa6APj3Xab0p81y/iD9DiQIxiVQsihvCffpx5JFbCRhqe5yjgt/1sqBeJsH8WRbUp51T8X4n+hHGs/6Yo4WMc4Q102o2CDOOifpEGHdtKwGin3358qXgWWHo7932vwQkIIGuElBA72rPWC8JSEACEpCABBolgBcPC5EhnHNxFpri02hlOnwxPLHxnGNR7uLFix2u6WJVQ7Bm0SK8Jsk9jofEtIuPi139+7MJcfj+/fu0uHTmzJkqix5lWYRsh2mE4ydkO15abAwZ4rhWQK9nmOcCOhtrEH+16QjADq+z3JuyfCZzbS6o463exvw7XYtmP0oBfXZmnrGVQC6gG2p7vCOE3/l8TkWU3GlOZW7daZNSLqDzrD+vJ/t4e8SWT0OAKAdsXuU5HIFY20ogNnKziXoMkd8cAxKQgAS6SEABvYu9Yp0kIAEJSEACEmiMAAv5LECy+BQfFukN2T65C96+fVuw8I+YMcRQcowBhFTaieFtjjiG93lbhpD/7t27tLhE+HhtPgLc66QgePXqVSoAjyr6lnD8Q94YooA+33jZ7SwF9N0ITf//c/EHUR3xpmz8Juf508mn3mdBPQR0NkbhfadJYB4C5qqeh9rwz2GDYIR632lOjU1KfOeblDgX4Q7jWb/Pc+3we7u/LXzw4EHBHHby5MmCEOXa9wR4zuQ+ZEMMm7jPnz8vIglIQAISaIGAAnoL0L2kBCQgAQlIQALdIMCifS6e86IawrmLRZP7CCEXQXeIuaIRbWKhgtbv378/eXw3HbK9TB6hBUH/4MGDSfDVZifAAh1e54RBxBDiYIkIhw15Y0hsDMLLh/lNT7LZx8+kMxTQq+E4qRTEH0Sf+HD/lo15ORfU256nZ6WhgD4rMY+fRCAX0M1V7RjZjkDMqeGlzvNA2fJNShGVh2MuX74sWAnUQmBjYyNFgzp16lR659K+J8BzJmHueT/FS993QEeIBCQggXYIKKC3w92rSkACEpCABCTQIgFeSHlh51MO2a64tHPHEEocIRLh8cqVKy32YrWXZmMAggbjgc0ThGxfWlrqhNeNAvpiff358+c0Ztksg+HFQf/mm2SGvDFEAX2x8bPT2Xfv3k3/2xDu9TGmZETCXFCP9Av5VdnUlYd873rO3shrqgd6vWNn6KUjhBIGGVNAH3pvV9c+nocihQbfkzYpxdUQ7tis1PeoH9XRs6SqCKyvr6fob6dPn07RoLTvCfBOyvweXvqm8XKESEACEmiHgAJ6O9y9qgQkIAEJSEACLRGIkO25eG7I9uk7g9zgeGnj7Xf16tXpT+zokYwDcrqzMQBjgXB5eblT+YypHyHGCW9I3bTpCHCvE66dsO0Yghoh8Cd5uSigT8fUo74noIDe/Ijgvi4L6izAl41wxOGhjrDetc1xCujNj50hXjEX0NfW1tLvnCaBWQkwp4Z3OoJ6bDjMy4moH7FRKSL4zHotj5dAELh3717auMyzOb/X2vcEYEOYe+5HNhngqa9JQAISkEDzBBTQm2fuFSUgAQlIQAISaIkAi+wsEpW9zhHQDdk+Xad8+vSp2NzcTIu0165dm+6kjh5FTjlC40UoSwRqdvd3bQGanOyvX79Owq/576YbTCw24XWO9znGgi9ewnipTrKhRlagrXqgTzdm5jlKAX0eatWew+854U3DQx0RiH/Ljd/3PXv2fBPU+bvt33wF9GrHwVhLY+zzTIZdvHixcxtFxtovfW438yebZdl8GBuMJ0X9QEBH9AxBvW9pNPrcR0Oou2lwdu9Fnt8R0Ln/2GRA9CxNAhKQgASaJ6CA3jxzrygBCUhAAhKQQMMEyiHbeRHV63y+TkCQJJwc/H755Zf5Cmn5LMYDHsd4dkfIdnb1E0q3bVFlEpoXL14kT2rCG164cKFlet2/PHnOiZIQHlSE4j958uSOfTu0yAp5Lymg1zdmQ0DXe6o+xrOWzHhnc1QI6vxdNub5PH86G2uanvtDQD9y5EhKK6FJYB4CsRGQcxXQ5yHoOZMIsFmWZ2RE8ZWVlamifvQtjYY93y4BfqsJ4Y4RXYuNbdr3BGB0//79tBEWRseOHRORBCQgAQm0QEABvQXoXlICEpCABCQggeYI8NKJkMZ3eJ4rns/PH3GS3fDYjRs35i+opTPZPIFw8fHjx1QDwvzimdzlhRu8gPjs3bs3LWRqkwlwf+Opz4YDjJDNCJtEFtjNFNB3I+T/n0RAAb3744Lf/jzXLx67ZSPqSJ4/vYlcvwro3R87fahhLqBfunSp8Y0gfWBkHWcnwDMy0Y+YC8vPnTxrEbkpj/qxXRqNmFf57lp0p9mpeEaVBHgf29jYSEWyOXi7CFFVXrNvZeWbDGDEhmBNAhKQgASaJ6CA3jxzrygBCUhAAhKQQAMEQixHPDdke3XAWTBjNzyGB3rTXnuLtIS645lMGH/s0KFDKadc13LjltuI9zmiMB6Tq6uriyAY7Lmzhmwvg2CxmHD+Q0hNUG5bROBg3MfmocEOhIYbpoDeMPAKLseifQg/fMfvQV505PoNL/U6QhOTYoLr64FeQaeOuAhSFvBcgymgj3ggVNx0NhXy3ImouVvko2nSaFA9NqzGnIqg3vVn74qRWlyJAL+9sSGbTRps1tC+J5BvMuD9j0hpmgQkIAEJNE9AAb155l5RAhKQgAQkIIGaCbCYE17nEb5Yr/NqoLNYGx4D169f78UCGOPhzZs3aTGQv1m0i5Dt1VCptxS8qvEEYsFxbW2t3ov1sPTyxgg8NMgTOMvirAJ6Dzu+A1W+d+9emlMM4d6BzpizCizi54L6pFy/iEi58FOFJ6UC+pwd5mnfEcgF9MuXL0tHApUQeP/+fYp8hOh9/vz5mcrkN7GcRoN/KxuRn2Je5e9ZntlmqpAHd5IAUQxICYYhDtexUa2TDZ+hUvkmAzZITRNRa4biPVQCEpCABKYkoIA+JSgPk4AEJCABCUigHwTykO0Rtp1FGQT0PnlLd5U2i2KRs+7atWudD8nIRgpC5ZLPEWORjpDtLAr2xRD/yUVJ3clxqv1NIDZGsLkA4z4/c+ZMcfDgwZkRMT42NzdTGWwMGZLpgV5fbyqg18e2jZJnCU28qCelAnobPTy8a7L5g7GEKaAPr3/batG7d+8Koh/x3Enu5UWMd7GyoF4uj/ezsqDuO9si1Lt/LulUeO7GeLdxA8XWPssZXblypdi3b1/3O9YaSkACEhggAQX0AXaqTZKABCQgAQmMkUCIRHiP5SHbQzwfI5M62szLPKIRdvXq1U57DJCvndCmiOgYoXJPnjzZu0UaBfStI5n7HNGgqo0Rnz9/Tp4wLNiSmmBIpoBeX28qoNfHtgslTxOaeF7hJwR0ImaY17QLvd3POoSAzjjEQ1GTQBUE3r59WxD9iMhHbDqt0hDUiZwQkT94rygb4znyp7NZiU2vCupV9kL7ZZl+Yvc+YOMJ6aUwNq1zL2gSkIAEJNA8AQX05pl7RQlIQAISkIAEKiYQIdtz8dyQ7RVD/m9xhJO7c+dO+i+8nQht2zVjPOA5Q/hJbBHP5C60jYXMp0+fpgVEF8iLtPDKglLkLq5iY4QCehdGev/qEAI6kQ/27t3bvwZY45kITONJye9NLvzwGzlJ+FFAnwm9B29DgI2CRNlh3BmhxmFSFQE2bvJBsCNFSZ3Gu1sI6nxPEtSnnVfrrKdlV0vA6Bm788wZsbmXKA2aBCQgAQk0T0ABvXnmXlECEpCABCQggQoJsPCCh3HudR7h2vVWqBD0f4uC9e3bt9N/IeZ2LRQ6oirCBIvKWHjPdFHon7Z3CKXJAjltGHOIVu5xNhMQsj1y2Z8+fbo4dOjQtCi3PY7x8uDBg/T/b9y4sXB5XSpAD/T6ekMBvT62fSg5F35Y6Cana9nIl44IFaJ6/BYpoPehh7tfRzZ/scFOAb37fdWnGuJ9zvNWEwJ6mQvzaninI6hPmlcZ75FGg+8ff/xRD/U+DbCiSO9pbv7ZudOCEUf95z//SeNck4AEJCCB5gkooDfP3CtKQAISkIAEJFABgTxkO15hfPQ6rwDsLkWwsHXr1q10FN5OXdoNz0IyIdupI3b06NHi+PHjvQvZXu6C9+/fp00BLJyQA2+MRp+y0Pbx48fUfDZukJezqg0cLNbev38/lY2Xx5A23yig13fHKKDXx7aPJbPBLA9NHFEy8rb88MMPSfjhOP6/Idz72NPdqXMI6GzUWFtb607FrEmvCRDFic2bRFYhwkqbxrzKM1rMrZPm1dioFJuVFNTb7LHprk0KpmfPnhXOXdvzCka8kyCg8/ygSUACEpBA8wQU0Jtn7hUlIAEJSEACEliQAGI5Cyp8h+d55DofkvC1IKZaTof5zZs3U9ks1uJV17YxBgjXzoIfxmIMISf379/fdtUquf6HDx/SxgAWTsg7PzaLPInhhXT48OHi1KlTlW6MUEAf26iqpr0K6NVwHGopCD3hScl3bO7K28vvFb9VIfzw35oEpiWgCDUtKY+bhQDP1Gze3LdvX0Gkny5ZzKshqPM+WLbYqJQL6l1qg3Up0oZYIkrRV6urqyKZQCAYscaBgO7zgcNEAhKQQDsEFNDb4e5VJSABCUhAAhKYg0CI5YZsnwNeRafQB7/99lsqjQUPFqfaNETVCIVLPajPuXPnBhXmjgUUcn6zcHLt2rU2cTd6bcYaHlB4qPA3m2NYyEVAr9pYiN3Y2EjFXr9+vVJxvuq6zlqeHuizEpv++PX19bSRyxzo0zMb65Hch/xehaAeaUbKPIjqEqGJ+ZuFc00C2xEIAV0RyjFSJYEXL14UbN5kcw8bFrtqzKtlQX3SRiU80vNUGnrytt+jjC/GGWlNLly40H6FOlgDNrGwmYXxSnopBfQOdpJVkoAERkFAAX0U3WwjJSABCUhAAv0nwCIJwjliIguGvEweOXIkLS7rdd5s/yKg0x8rKyspvGNbxlggrHcslh07diyFbB/aeGC8b25uprGOuDsGo0/J68oCG0aodjZG1JUyQAF9DKOq+jaGgM7GDjz1NAlMS4BNUX/88ce3TWjMQfyu5sZvWVlQH9rv27S8PG4yAb04HRl1EMAzmLF14MCB4uTJk3VcopYyY6NSnkqDTW5lQ7TNBXWFyVq6Y8dC2SBL5DCe78+fP998BXpwxWDEBhAEdDfU9aDTrKIEJDBIAgrog+xWGyUBCUhAAhIYFoE8ZDsv2+zGJnQ4HtAuJjff14Rwp09Y8GgjTDrXxmvhzZs3qfFspiBk+1AFLHKcPnz4MI118nMP3RCVCFn/559/pqYeOnQoeUDVucDJNRFDMbz867xW0/2nB3p9xBXQ62M79JKZ4xB5jh49mjYD8rvGPBQe6vxdNhbPefYJD3VEIJ+Bhj5Sdm5fCOgILGxq1CRQBQEi/7B58+DBg8WJEyeqKLKVMnj+4VkyBHW+JwnqiLghqDPHDukZsBXwU1z07du3xevXr9NvGhtkta0EeM/lw/gcWnQs+1sCEpBAnwgooPept6yrBCQgAQlIYGQEQvjBGzXCt/PCjXiKV9bFixdHRqQbzb1161by+l5eXk7eKU0aC2EIDyEuIJojng85HOOYBHS8LfA8j5DtCOeEbK9bJGJckc8aG6qAHnlC9WCpbsZSQK+O5dhKCg/0ENDL7ec3NveiJPx72RB5QkznGxFVGxcBwyCPq7+bai3PYTx7soGRyE5DMZ4teX/IBfVy5A/ayjtmbFbi2+em6kdAiMP8dvEep20lgNMA70WMRwT0ut+F7AMJSEACEphMQAHdkSEBCUhAAhKQQCcJRF47PAVCPOfFkcVCFnbwvLp8+XIn6z70St2+fTuF08djAO+Upoy+J2R7eI/gFYP4MPQFBXLlPnjwIGHGA32I7aVPua/J94dxfzO+WLhswnIB/erVq4PakBEbkRTQqx9JCujVMx1LibsJ6GUO3L/hnY74Q97fsiGg5x7qQ95YNpZxsls7I0eueYR3I+X/n4UAz9o8e7KBkfRIQ7UQ1PPIH5MEdebVmFsRMxXUFx8RIQ6TCuzMmTOLFzjAEoi4xxzPJgPeTYb4/jfAbrNJEpDAAAkooA+wU22SBCQgAQlIoO8E8LxisTiEc9rDS2MI6I8fP06eVleuXOl7U3tZ/zt37qTFezwG8E6p2xBXCSfJLnyMvkdcZUFhDMbC3v3791NThyigI14jJkXIdjZlkFO6yRCaeHfevXs3MWZeGZInpwJ6fbOEAnp9bIdecgjoiFOIVLMav8Eh+vDNc1PZIs9vhCZuck6dtT0ePx+BENDNIzwfP8+aTID3LOYV0kuwUXUsNk0qDd5Fy4K6wubsIyTEYVKBEW1K20qAiHtsHmeTAQK6JgEJSEAC7RBQQG+Hu1eVgAQkIAEJSGACAYQehHMWMOLDogQ7/WNxgnyPLDyzEEyoZa15AgiNCI54DMyz8D9Ljcv5sFlo4bpj8qzD23BjYyNhG1oOPBb/8TznfuceP3nyZFqwbXoxUgF9lrvSY4MA9yXCJRs+SCehSWBaAosK6Pl1eHZiDss91Cfl+cVzMkK+60U5bU91+zg2FuLJqYDe7X7qW+1IlcSz59LSUvqM1ZhH81QasdEz5xGCesyt3ItNP8P2sX+eP39e8E5PKjCe/bWtBNg8/unTp4J3X6PuOUIkIAEJtEdAAb099l5ZAhKQgAQkIIGMAIsUuXjOgnAI5/lCBC+Sm5ub6f8hJmrNEyBXNItIiEaInXUY/R/iauTDbktcraN9s5TJJgI8XbGh5OeeFFVgeXm5sZDtZf54cxJZAWORCs/NoVjugc7feqFW17MK6NWxHFtJPMfwOzqvB/pOvKYJS5yLPnhTIqgr+vRvFIaATv/xG6pJoAoCs6aYqOKafSiDDXO5oM7GpbLxfpqn0uB50rl1a++GOEzUKVJyaVsJsMH48+fPKV3axYsXRSQBCUhAAi0RUEBvCbyXlYAEJCABCUjgbwIh7rAoUQ7ZPinHHC+SDx8+TIsRhLPWmieAaMQCEiH36vBMYSywaEDYOqzpfNjNE935ikPLz0178G5iYwCG9wlRBdoUdtm8c/v27VSfS5cuJW++oZgCen09GQI6cyEeQpoEpiVQp4BersM0YYkVfabtuW4d9/bt2+L169dpA4QCerf6ps+1aXJ+6jMnnh1DUOd7kqDOs20uqJMiSEG9SO95vNMTyYyNZNpWApFKAUarq6sikoAEJCCBlggooLcE3stKQAISkIAEJPC3eM7iQy6el0O2lzkNPR90H8YF+bjpBzwGql70YAEKcTUWodrIh921PhhSeHE2RTx58iSFbMeIKsAmjLYXExXQuzbq+1EfBfR+9FMXa9mmQDWNFyWiT4Qk5hvRR+segRDQEejOnTvXvQpao14SYKMyz57Hjx8vDh061Ms2tFFpniXzVBpENyqbc+vfREIcJpLZ0aNH2+iuzl8zT6Vw4cKFztfXCkpAAhIYKgEF9KH2rO2SgAQkIAEJdJwAAhoLC2Wvc4S0ncS0IeeD7niXfavegwcPii9fvqSFNT5VGOOAhWBy4kXIdrw62XXftrhaRfsWKWMI4cW53+lb+hhDjGGxH2GmC4agdOvWrVQVwiTizTcU0wO9vp5UQK+P7dBLDgG9CwJVLvog/vDfZWPODkEdsfaHH34Yehf1on1v3rwp+NA3Z8+e7UWdrWT3CYSAzkZZNrJq8xHg+T0X1CfNrcyl+dw6ls1KuThcRzSz+XqsO2fx7B7RutisboSR7vSNNZGABMZHQAF9fH1uiyUgAQlIQAKtEiiHbEe42s3rPK/w0MJZt9oZc16chTXC7vFCX0XeOsYAXskfP35MNSJ8NuLqkETMOVGn0/ruHY0XUyyU0R5CXROyvUsCjAL6IiN0vOcqoI+37xdteZcE9HJbmLMRfSI0MfNj2UitknuoT0q5sygjz9+dAOHb2ZimgL47K4+YngAbZRF/iRJEmh1tcQK8/4agvtPcOpbNSo8ePUqpnHiXZLO09j0BxgvPCREJwggjjhAJSEAC7RFQQG+PvVeWgAQkIAEJjI4AXqiIgXyH5/ks4jnAhhTOuq8DgBf6T58+pZB7LK4tYizSI65GmENCRZ4+fbpwMf4fqn0W0NkUQZjGCNnOhgvGTdeiClC/mzdvJuhra2spX+VQTA/0+nqSdBbcn+ZAr4/xUEvusoCeM2f+CEE9RPWYz/Pj2PAWgjp/+xvezMgNAX3v3r1pY5omgSoI+NtWBcWdy8jn1hDUJ82t+WYlnk0JAT8EM03A7uMDRrwf84zJu7EmAQlIQALtEFBAb4e7V5WABCQgAQmMikCI5QgNs4ZsL4Pqs5g4lE7HawBhlJB7vNTPY4wDwo4S1htjsZ3FAXMtbqXZR+9o+pe+pY8xvM3xnmCRv4tGfX/77bdUtdXV1c6Elq+ClQJ6FRQnl6HIUB/boZfcV/ER2C9xAAAgAElEQVSA+QSvwQhLzN/8W25skELoCUGdqDJd2zQ1lPH16tWr4t27d+m3VQF9KL3afjsiugrP5fv27Wu/QiOoAfMoUdZCTOd7kqDOfMrcGnNsXzcrRZQD0wRMHtyMBxix7sF9OO/79ghuHZsoAQlIoHYCCui1I/YCEpCABCQggXET4AUwvM5ZCOAzq9d5TrCPYuLQRgAe4x8+fCiOHDky1454xgNeyYSBx/BWQ1xlUUjbSqBv3tHlkO0svpKbtUsh28uUxyCgM3cylobivdSFuSIEdMPcdqE3+lWHvgroZcrMKSH4IKojAJUNgScX1PGoVFCvZryGgM7vrB6K1TC1lKJYX19Pzwtsyujqxseh91NsVsoF9fJmpXiHCkGdebYvgrobEHcewdx/COg8u/OOfPz48aEPedsnAQlIoLMEFNA72zVWTAISkIAEJNB/AnnI9gjbzos9C6fzLp4OWejqS4+TrxyPJ7zFEUZnsS9fvqSQ7YjoGCI84lNfFnxmaWtVx/ZpzBPan80RkTOXBR/yG857v1fFcJpyfv3113TYysrKoBaMwwNdAX2aUTDbMQros/Hy6H8IDEVAL/cp80wuqLOhqmxs4snzp5PzV5uPwMuXL4v3798nL2EF9PkYetZWAvfu3UuRJXjG517V2ifAezQRP2J+3S76B5uSY8NSl9NpxCYNoxxMHlv0N5EguA+Xl5fTu5QmAQlIQALtEFBAb4e7V5WABCQgAQkMmkAu2OQh20M8X6TxuZg4NKFrES5Nnvv06dPi7du3xcGDB9Ou+GmMfsNTisVejLGAZwtlaLsT6Lq4S/++ePGiIB8rhrc5C699Cv1JCHfaceHChV7Ve7fRYwj33QjN//8V0OdnN/YzQ0AfevhaNstFuHe+Y/Nc3v8I6LmgbpSM6e8OfneJCLR//35D/E6PzSN3IMAzAwI6xjM+YqzWPQIhqOfpNMq1jHQauaDelQ2tsUnDKAeTxxab0RDQMdY72HCuSUACEpBAOwQU0Nvh7lUlIAEJSEACgyUQIdt58QvxfJGQ7ZNADVXo6sugePbsWcptfeDAgbQrfjf7+vVr8krG+xxjIYdFOcK4atMR6PKYp3+JKsAiHka4T/q3yyHbxzSvKKBPd4/Nc1Tk8DSE+zz0xn3OWAT0vJeZi/i9CMEHT8qIVpIfN5Qcv02M8BDQeR5jHtIksCiBXEDnGR8vZq37BGZJpxGCOnNtG4K6mzR2H0+5gL62tpaivmkSkIAEJNAOAQX0drh7VQlIQAISkMAgCfCyh3dR7nUe4dqrfEG/detWWnRlYYdFQ61ZAs+fP0+exng8nT9/fseLl0N6Hz16tMDjrsrx0Gzr27nazZs3Uz5KeMO9K1buX0IMEra9j/3bVcaL9rUC+qIEtz9fAb0+tkMvOcbO0D3Qd+pH5iZCvOce6jvl+MVLvcshidsYszyPffz4MT0LK6C30QPDuybPmoTXxnjmRGTV+kdgmnQaRAMLMZ35lY3NTTy/52PMTRqTxxa/jWy0wy5duuR6R/9uQWssAQkMiIAC+oA606ZIQAISkIAE2iJQzrHLi3HVXud5227fvp2EerxcDQHefK/j8UQ4dsJzE+56kpVDehOSlZDeXRJ/myc3/xW7tmmE/iUcP+MAo3+5H/sUsr3cGwro84/PsZ6pgD7Wnl+83QroWxnyu0Je3zwkcVlQj5DEEfK9LQ/KxUdANSWEgM6zMJsxNAksSiD3fOUZ32hRixLtxvm8N0f+dOZYooGUjWf5XFAnvUYdgno+xtykMXl8/Pnnn8Xm5mb6n1evXk3RvTQJSEACEmiHgAJ6O9y9qgQkIAEJSGAwBBDLeSnnOzzPI9d5HS/dgLt7927yWiJv2uHDhwfDsi8NQTjlw8s8ednKRt8Q0puFGozjEM9ZiNHmI9AlAb0ckh8hA/G87/3bJcbzjZLJZ+mBXiXN78tSBK2P7dBLdvPF7j08bUjiPH96XYLP7rVt5whS6hAJRgG9Hf5DvCrvdPfv309N4xm/7892Q+yjKtpEP+fpNCYJ6qRiKgvqVV3bMbYzSd6heZfGrl+/nvpBk4AEJCCBdggooLfD3atKQAISkIAEek8gxPImQraXYd27d69gZ/bp06eLI0eO9J5l3xpA+Ha8nli0Xl1d/a76hBIl3zkL3xjhvAnrXddmir6xm7e+XYm68Pnz59S/3PfYkELyB+OhpYZQQJ/3rtv9PAX03Rl5xGQCCuizjwy8FnPBh816ZcODsiyoz36l/pwRAjr5cXne0iSwKAGEVOYnTAF9UZr9OZ9+z9NpMN+Wjc0UuaCOwD6P5eHJeY+ct5x5rt2Xc+gL3rewGzdumEqhLx1nPSUggUESUEAfZLfaKAlIQAISkEC9BBBkwuscobTukO3l1mxsbCTvZvI9IuBpzRJ48+ZNwaItuUgvXryYLs4YILQ7/w9jMQSv8z6H9G6W6s5Xu3PnTgq3CFMWyps27nnCtRN5ABtiSP6ubFKoum8V0Ksm+k95Cuj1sR16yQroi/cwv4l5SOLY2JWXjOCTC+r8dg3Jnj59WrCxTQF9SL3abltycXNtbS0972njIsBzYy6oM89uJ6jH/IqwPq0Qnocnd4xNHlvM68zv2H/+8x8jQYzrFrS1EpBAxwgooHesQ6yOBCQgAQlIoOsE8pDtEba97pDtZSaEfWNnNvke8W7WmiXw9u3b9FJP7tFLly6laACEmSN3KYZojtA77UJKs7Xv59XaTFuAKIEXBIs5GItkhGwfWl7Mtjcp1DUyQ0Bn8ZM528Xw6kg/fPgwpRPht4gQypoEpiXAcwxzKxsBDxw4MO1pHrcNgbLgwzNiRMLJT+G5JRd8eH7tsz158qT48uVLSmfk83Cfe7I7dVfc7E5fdKUmzK886+QRQCbNr7wX5PPrds+bvC8+evQoNY/3SKOUbe1pIroR7Q02COi+U3flbrAeEpDAGAkooI+x122zBCQgAQlIYA4CuQgT4dspJsTzOYqc+xQ8t1gwZLEQ4UJrlsC7d+8KFm1ZKCFkKGJ6LKTQH0QFcDGk2j5pK20B9xmbI8Kzb2lpKQk+Q+zfoQrojES8hz58+JDu2b179w6y/6q946YrTQF9Ok4etZWAAnq9o4LnVITAXPDh38rGhrAIScx3337bQkAnnZERmeodU2MpPRc3iTLV900mY+m3JtuZz68RBWTS/LrdhqU8PPnly5ebrHpvrsUzO5Hd2ITw73//282vvek5KyoBCQyRgAL6EHvVNklAAhKQgAQqJhCePeFx3nTI9nJzNjc3i0+fPqXFQsQ8rVkC79+/Tx7JLDTHgglhUvFKxvNAq57A+vp68vA/depUgYhdt9Gv5Lpn8QZjAZWoAkP2lGzTy7/O/mQRDpElNrlw3yIUIaRzvyKq9000qpPXLGWHgM5GojZSK8xSV4/tFoEQ0JnT9+/f363KDbA2/KbxG5oL6uVmxtwYHpSIP12fG3kWo00K6AMctC01CUGUjZOY3sEtdULPLpvPr4wfPpMEdVJ/Mb8yr5Lyi3eLSAXWsybXXl3etUmbhec5ArobWWpH7gUkIAEJbEtAAd3BIQEJSEACEpDAjgQI+4v3ae51zotvfNrAR9g3QpshJLL4rDVLAGGVsHJhiKpnzpxxd3yN3bCxsZEWpNgwUreXGfc7giubVLChhmwvd1dbXv51DRvmbO5TFikxFt8mhdzEuyXPEcxmGG06Agro03HyqK0EFNDbHRXMhXn+dLzVy8acWZ4buyaoh4DO83ATm+va7TWv3gQBvYOboDzsazC/xoalENS3azHzVkQC6dr82mYvkS6N9202uf7yyy8K6G12hteWgARGT0ABffRDQAASkIAEJCCByQQQXxDSeAmODy+2bYRsL9eQBUN2ZpPzEeFWa4YAYwLuhGwPzwI2MOD55KJHvX2A2MKiJiHy68xzyjXwPPr69WtqEH2LaD8Gz4chCej0H/1If2J4mzN2mMvzsMbRz/noRUDPRSNzpm9/byug1zvvDbl0BfRu9S6bRcM7ne9JcyOegHl+3y5sNmKeR6BSQO/WeOpzbUjdwyZKnuvxQNcksCiB2LAUm5YQ18uWRwBBUMdbfczvlojniOhEQkFAHzOLRcef50tAAhJYlIAC+qIEPV8CEpCABCQwQAK86ObiOWJpCOddeIFjYYc83ITMJay0Vj8BFpcRzgkHHcZY4KVeq59ACOiEiuZTtXGP46kckQW439mccvDgwaov1dnymg6TXxeIct56xgsbIbiHmdtzQRyRKBeNOKZsEXIT4Yi/x7CZYtq+UUCflpTHlQkQVYT7zRDu3RwbzI25hzrPxGXrwmajENCJTMM8r0lgUQKfP39Oz/uG116UpOdvR4B3+FevXqUxxjy6XQQQhHSePfnuQ0qNKnscPnCi7deuXVNArxKuZUlAAhKYkYAC+ozAPFwCEpCABCQwZAKIaCzo8imHbO+SaPLs2bMk9hE6fHl5echd0om2RT7Ev/76K9UHb1ZEOuzGjRudqOPQK/HgwYPEvA4BnfudTSmkRcAQSclnz2LVmCzC5DeVZ75qtuW89QjlbDAiv3Kk4igL6HkdOD/3Tp+UwzL3EGIeGHv+dAX0qkfxeMpTQO9PXzM3ljcbTUqHwW9m7qHexHMzKY3w5lRA78946npNSd/DexbPEGtra12vrvXrIYEQ0Jkzz58/n55R8w1L8b6ZNy1SaoSoPvTnT/KfE/WN35SrV68qoPdwnFtlCUhgOAQU0IfTl7ZEAhKQgAQksBCBCNmei+ddCdlebhhesoQ2QxjixVurhwBjgvBx8OZvxsPp06eTaIagixlWrh725VIR6vAKInw7obirMhasWICPcLWkRUBAbmLhv6o2VFVOk3nmq6pzlMO8TWqL7fLWTyOgl+uU57DES31SyM2x50/f3NxMmw7Y2EJEFE0C0xIIAZ3f1H379k17msd1gMA0m42oZgg9Eb2jjghOMQfxbMDvtyaBRQmwmZLnflIWrK6uLlqc50tgC4HI782G3Ukb4Yn4kQvqk1JqxPNnzLN4stcxx7bVfdyD3Is8H1y5cqWtanhdCUhAAhIoikIB3WEgAQlIQAISkEAK65uHbAcJL6Hx6RoidmXzwQNyZWWla9UbRH3KXsl4CeCVzGIHixos/mPXr18fpdjadCezSI44ipcZOckXtfLmCARzhJwxi4BN5ZlftO/K55cjREzKWz+PgF6+zjQ5grsQ0rhqvjuVp3jVJO1hXUsBfTj9ye9piD188ylbHr0DQb2qcMRu4hnOOOpKS0jV9OLFixRa23esrvTKsOpBFDk+zIXTpGKbJqUGGz4i3DvfjN8+G1EgeO8j2t6lS5f63BTrLgEJSKD3BBTQe9+FNkACEpCABCQwP4HtQrZHvvP5S673TPKCsbjDC7LeEdWzxtuUvJrbeSXjiUq+aIy8bHlO5eprY4kQwEscT4SlpaXkIb6ITdocgQfI2EK2lxn2UUAnDCa5SiNCBHnrJ22CqEJAL/OaJn86Y4qNTkPNn66AvshMNO5zFdCH2/9sSs29Jyfl9+W5KfdQn9d70jQSwx1HbbWMsNFsUiba1IULF9qqhtcdMIHI783zIc+ts9o0z5/Mqfkci8DeJyO1Fqm7eKY3lUKfes66SkACQySggD7EXrVNEpCABCQggSkI5F7nke+8qyHby80hfDuhzXgx9qVyis6e8pByDuXtvJJZDL53714qlbxsfVuUmBJHpw5jQwNeQXgX4yk+r5W9lVmYobwxhmwvM6wzz/y8/bXdeczfeKcgoGN5hIhJ59QhoOfXYe4gZyWbb1jw2y1/epUemFWznaU8BfRZaHlsToBNaNzHhnAf/riYJnpHeE9GDvVpn6tCQCe1y8GDB4cP0xbWTiAE9MhPXfsFvcDoCER+b8KTL/JOAzieP3NBnedP5tyysSEkF9S7vvmbtEw8U5OaQ2eB0d0iNlgCEugYAQX0jnWI1ZGABCQgAQnUTSDEckK2x99cs8sh28tMyJ2G1yWLO4Y1q2bEMB54WSfPNkaodkK2T/JKRii7e/duOo68bH0Pk1cNwXpLoW9Y1GQhZR5vDe51xFZE1zyfvTlT/+m3ENCrzjNf9cjg/iMiQeQkRzRhAXKnxcC6BfRyG6fJn86mDcSi8FDv4zyigF716B5PeQro4+nrckun8Z6cVuzhd4vyFNDHO56qbjnPingIb5efuurrWd74CBBFjk3BhCevIi1VTjDf0IkAjaDOM2nZmGNjwxLCepcEddrAex91J/KYkSDGd4/YYglIoFsEFNC71R/WRgISkIAEJFArAV7I8lznvFD2xes8B8PiDqHNePm9fPlyrczGUDiiOS/qjA2Ml3UWY7fzSua427dvp2PZwDD20N9NjBHGO+Mej/Fp8gXmdUI8ZcMJi1XYbt7KTbSni9fAk497ocsCOmH8uVdjMZBw/kQlYB7fyRgD8WljkZBrsxCIdzoLmpEeIq9zH/OnK6B38U7uR50U0PvRT3XXsuw9+f/ZexPoOq603vfTZFu2BssaLQ+yLMm2PMZ2HCfpdLrTaXpIeqAH+nbDpeFBAw943HvXAx7wWG/Ba1hcxnfhNrAauND33qaB9+iBNHQTOgNJOknHSWzHjjVZ8mxrngdLsiT7rd8O2ykfHx3VOafqVNU537fWWZLlOlW7/nvXrr2///f9P+bHeGQP720n2WPXZ5ZAh4SCjFJTBNJFgCBllL4gFQmkVVMEvEbA1vcmAJT9pp/GHItyGnOrJdT5W6wtN8f62bblzk37CJSl3exJKLOlpggoAoqAIhAcAkqgB4e9XlkRUAQUAUVAEcgoAk7Jdn5nc2Zrna9EvmS0oS4uBhGIpDUSl0iIq6WGAGMAGT0yTTCINbKbV3LCQoZ1dHSY7zQ3N5ssFTV/EYAAx6mJsykZhyZZyjwrtgarm2xlf+8kvGeHDJ2ampKqqirPM2LSvWueVTJ2cGpjzH041CBU3FjQBHpsG91kYFpnJhnqzDFhLDNglQBwcKqag5uRqMdYBCyBzjuXMa6mCICAG7KHNTtzIvM/yjTM7wRTlZaWKoiKQNoIjIyMCB/GV7IBm2lfXE+QEwiwpyFglaDgmpqajN4zcyx7I0umxys5RIPsHMtzkOk1KG1kT4LiFAEG+hxmdIjoxRQBRUARuAsBJdB1UCgCioAioAgoAlmOAJswS544JdsteR7F2ycLE+ICwre1tTWKtxB4myGwIFZxIGDWUUZW/0pGAEZ7e7s5jBr0ZKmo+YtAKgQ6Get8z0q242CH5ItawIy/yL599rAS6Cg+8KySvY1BthFE4bZGLt+x7wDOlcz3MoF9rNwmc1JsdhBj1lm7EnI9DOPYEugEXaAEoKYIuEVACXS3SOX2cW7KYYAQcyLBj6zlWMeFYX7M7Z6L7t0TqEfAJmuNVEoGRffOteWZQgBVLda0rJtYPwVpzjkWMp1PrDmDliyh7uccyxoYVSz26uzd0q0THyS+em1FQBFQBLIBASXQs6EX9R4UAUVAEVAEFIFlELCS7ZAnljyPomR77O0Rtc7GknvZs2eP9n+SCJBliww04wLDeUGEu1tnAGOpra3NfHf79u2us2CTbKYe7kAAuUMygnCQryTlhzMI4pzMNAxnOoSrBjokHlKQoQTnUMIAh1UYDAcj5Lktr0CmM9k6bp9Vew9hJtBjcWZ+wYFp5TZtrXfncbZ+upU0dhP440d/KoHuB6q5cc7u7m6zLtMM9Nzob6/ukve7zZzkp1WXcZ6f4FJnwBHlMZJ9Z3jVXj1P9BBAlYoAzJKSEiXuotd9kWgx61pb35s1d5iMOdauQfkZbw3qd1Ana4Pz58+bfTprBK/rxIcJb22LIqAIKAJRQEAJ9Cj0krZREVAEFAFFQBFIAQE2XZAuzqxzNnz2k8IpQ/MVSCXqPmIQ6OoYdNc1sTLQOFmRhUtF9vPMmTPmoo2NjSo/6w7+tI7q7+838t30VUNDw7Lnwplu6+ZxEIQ7zpcg6l6ndcMBfBmHHuUhyIgJOtuDZ5WACfodS+dZ5ftRItBju97WT7eEOpKWsRZU/XQl0AN4ULPkkkqgZ0lHBnwbdhwRTES2Ip9YQ3XEBhvxM2wqJAFDqJePQYDSTgRgst4MSzChdlJ2IRCltZNdg1pSPV7QEkGdNmiJn+mqJEHiQ6DzkwDoTMvcZ9do07tRBBQBRSB9BJRATx9DPYMioAgoAoqAIhAqBJyS7Wy8+GRD1rkTZDax586dM3/avXt3KGvjhmpQiJg6ajbin7YhzQh5DvGUipGBzljbtm2byVJR8xcB6l+TFQTWYB7PcHiSeW6feTIWIIM1wMRd34SFQMdZh7wl2fAYtRdRHUgnyzrKBHps7yVTPx2yCGemX/XTo+QEdvcU6FGZQsASn7yHGadqikAqCMSWAnAzP/IusYQ686MG2KWCfPZ+h/UmwYQEYGrma/b2c5B3RskkiGiIYeqgR8lYTztVQOIFdTpVkphjky2rwT4OP4fdZ4ctS5/++rVf+zX5u7/7O3nmmWeSrtH+la98RT7/+c/Lxz72Mfnt3/7tKHW/tlURUARyFAEl0HO04/W2FQFFQBFQBLITATZcZJ3zM5sk22N7Czk1nIYYNdDV+Zd4POMIg5BjXGA4LJCCTodY7ejoMFmtZEOnksGenU+gf3dFRhAfAh/I+nca/YrEO5KbGEEREK4q2Z5cf1DWgCAE6sQHVfeTuQ1S1jrkaAsZYOkSwNlEoDt7Nej66TYoSWugJ/es6dEiSqDrKPACgURKBrHzI8Gndh3ovDbZkk5CPd33jRf3pecIDgGUbwjgKy8vN+Wd1BQBrxFARY5gH8YX4yzKht/FSajHUwHBT2EDOvm5UlkN1uw2UYA9H8HQYbI///M/lz/4gz8wTUqWQO/s7JRPfepTJoBCCfQw9aq2RRFQBBIhoAS6jg9FQBFQBBQBRSALELBkebZKtsd2EZsunIbYrl27VI5ymTGMoxRH2OjoqDkC2U6k4CBh0zU2wIw3zhd150e6WGTi+2SfkxWE44W689Z4FiDxbI0+ghnIaNSgkuR7JWgCHfKeQBfmc4JbkJGHQPfCspVAj8UG7HgWKPOBQ9NN/fSVHJmJ8FcC3YvRmZvnUAI9N/vd67tOZhwxP7JmsOUwINT5m9N496B6Ygl1fk8n2NLr+9Xz+Y8AAZlTU1MmM1ilo/3HOxevgDw5e0gUDlA6yCaDQLdy78y13Ges2bIaVvY9Vg2O74AR1tzcHKp99pe+9CX5nd/5ndvvjmQIdPD4xCc+cfvelEDPppGv96IIZDcCSqBnd//q3SkCioAioAjkAAI4v2zWebZKtsd2I5vTrq4u8+cdO3akJW2crUMkllhF+hti1aval+BPP3DOqMnvRbHPqX9OMATOlqamJnMLscoCOKKQ+VNnd2o9DHlNFj/jmXGdKYsNdEHqEQUBiAuvLFcI9Fi83NRPt45MAot4vpKZI5VA92qE5t55zp49a25aJdxzr++9vGM7jghmTFZ1hncPQUaWUF8u4MiSPJDqyUoRe3mveq7MIEApoOnpaRPAh1qVmiLgNQJkV7M+I1A0m8uA4aOJJdS5b2sXL16U3/qt3zIYHDhwQI4cOSIPPPCAefb4P+vnCIPSG0EBv/qrvyrf+ta3jDIWgTZYMgT6L//yL8s3vvENs08FGyXQvX6y9HyKgCLgFwJKoPuFrJ5XEVAEFAFFQBHIAAJOyXYr2470IhuTbCbRCBggAxpraWkR5CfV3kbAWQubv/pBrOK0RWYaqWuvsmS1D5dHYGRkxDgrIFXJQHcqC5C5gPNc6+imN4JwGqPWgKICeGbCeIYsCcv1yMThmfJaQSBXCfTYPrRSmxBGZKk7HZn22GTkjG3fEbgSxhqVmRjDeo3UEFACPTXc9FtvIwABYdWYUiHQY7F0E3AUK0UMoa6WXQgQTMj7UQn07OrXMN1NotITYWqn122xhLoNWnr99dflN37jN+5SAiGwjhJ1e/bskY985COBlZVy3v/v/u7vyl/+5V8aov9P/uRP5KGHHjL/7ZZA/+Y3vym/+Iu/aIKUH330Ufna176mBLrXA0zPpwgoAr4hoAS6b9DqiRUBRUARUAQUAf8QYANmCREr387VLHnu35XDcWaCBdrb201jyMZNNusmHHfhfSvABRIQAh3zk1jF+UGWO9kDYavN5j2ywZ/REug4q3FgkwmAkZUA4ZpM1mzwdxPOFmSaQEciFdl4S+L6EehikXYS6IyfbA6wcju63NQHXknOWAl0t2jrcbEIpJM5rGgqAiDgJNC9Vi3h/AQcOaWI49X2tQoeVvJd1yLRH5usSyD4WNtXVVVF/4b0DkKFgNeBP6G6uSQbAxasBV588UWBTD9z5szt/Z09FetQytUdPXpU7r//frn33nvN3i/T9vu///smUPsnf/InzZ5z586dpgluCHSy6T/+8Y+beeWLX/yivPnmm/LHf/zHSqBnuhP1eoqAIpAyAkqgpwydflERUAQUAUVAEQgGARu9bDPO+cnmKlfIc+s0bGtrMx1ANq5m3oqR4YTMgdTG/MpktaO+p6fHXBMZN8289H8uIDMagtdptbW1xrmpZKg3+JPhT6ACzw5khF/GHD44OCjUtcdwRJE9iIS4X6YE+srI2vrpzvrAsd/iPWuJIn6iBME8qBnoK+OrR9yJgBLoOiLSRYD1P2sxzA8CPbZ9EOh2fuRnPAUPgvzsHElwq9dqKulipt9fGQENDFsZIz0idQT8DvxJvWXBf5M5FhL95ZdfluPHj5tydXZfb1vHOnTv3r23CfVDhw5lxA9C25y12t0S6ChtffrTnxb8Np/73OdMFvoXvvAFJdCDH27aAkVAEUgCASXQkwBLD1UEFAFFQBFQBIJGAGcVGSHOrHMr155rJBobMXDYtm1bVtdPW2nMgQF1myH/+J1xQCYrmSN+jgnq15GZxLU0Q2WlXkrv/+nXS5cumfFEPzUAACAASURBVJqUWCYI1/RaHM1vQ4ZSa95PAp35G+c08qgYpDlSjU6nlB/oKYGePKqQU06yCCdgrNk6jtSvrKmpUSWI5GHO2W8ogZ6zXe/ZjTsJ9IaGhoyWM3Kj4MGNUnbGEur8DvmjFm4ELl++bALDWNurwlS4+yqKrWM9yh4Sy/S8FRW8UKhiX8+eAYNMf+WVV+TkyZOmfJrT2D/s37/fEOoPPvigyVD3c/9vr+2WQP/N3/xN+fKXvywHDx6Uv/7rvzbrZCXQozIStZ2KgCJgEVACXceCIqAIKAKKgCIQAQRwVLGJwllmP7mWdR7bTUi4gwWb7yCkzMIwbHBCkJU8MTFhmkPmjxd1MN3c24ULFwwJCGnER80fBGJrZPPc47RQmVTv8bYEOvMJ84rXBhmLY9o6xHBOoyKQCUeXEujp96azfjp9afvReeZk6qen3yI9Q1QRUAnbqPZcuNodJiIqVsEDApa/OW2lkhjhQjd3W0PAJlmv1dXVpg66miLgJQKsnc6fP29O2djY6HsAqZdtz9S5JicnjcIRAUeQ41bJg+cSEv3YsWOGUD99+vRda9Ff+ZVfkR/90R/1valuCPRnn31Wfvqnf9rUPX/iiSdM0D2mBLrv3aMXUAQUAY8RUALdY0D1dIqAIqAIKAKKgNcIQBI7yXMcUlauPRPEi9f349X5Ojs7DS4QxuXl5V6dNjLnIfubTFYbic7mFDn1TMllUs+MjGgcbJCAat4jEFsjmyvQv62trd5fTM8oAwMDRladbGKULbwy5mwk+HGG2fmbrHMy3TNlSqB7i7QtpXLt2jVB1tJmojuvomSRt5iH+WzzizfkTH+PnB2+JNPz1yU/L0/Ki0tlX12LtFRtlfy8O7NulUAPc29Gp21OAp13FkGUYTH2LpDoVsWD32ONvQwy7zZDnfbn8r4mLH3H+p69BcGx7C3UFAEvEWBsMcYwyrBpQPDd6BIYz56EPd++ffuW3dsTyH7ixInbhDrBL7/+678ujz/+uJddFvdcKxHo7Hk++tGPmiD/P/uzP5N3vetdt8+jBLrv3aMXUAQUAY8RUALdY0D1dIqAIqAIKAKKgFcIWJn2eJLtKoEopi4YxAVEVC45eGLJOJyN9fX1Gc8SYZMOwUsWrY0o92rs5/p56GMcJ8iJYziXkNGkbrYS6P6NDj8I9FiVCCRsCfohUzmTFhYCfeT6hFwavyazC/OGVCxbUyJNFZtlTVFm8fAK+6tXrxqCiCw9Ai9Wqp8OWYRsP4QRsptKFnnVE8Gch7n6lcun5XuXTsn80oKMzU7K3MK8iORJ6eq1UrpqrZQUFcu7G++VrWV1Zs3Cmg4CgXco32duZ47nGXWqDPF77N/sv2Mze513v9yYsn/nJ+8R1pH2p/099u/Of/M77WTcOj+6Hg1m7HHVKGVyMnYJ/LRzZLySGIwxS6bbOTI4dHP3yihMMVcRHJuLAcq52/OZuXOyqHn/YU1NTRkL/M7M3XlzFUqzEdDLu5Z652F8zyYi0JnvP/vZz8rrr78uP/ETPyG/8Au/cAcwSqB7M070LIqAIpA5BJRAzxzWeiVFQBFQBBQBRcA1AlaynQ2IJdJzXbI9Fjzqh+KA27RpU8bJY9cd6fGBOEvJeIS4xiDhIOMg5TJtSFEjMVdZWWky39W8QQCnJcoCOJkxWyOb7C0cTjhRdu/e7c3F9Cx3IECAAh8wR1YyXaPP6EuchRgkK89KEI6woAn0a5MDcrK3U/qnR2RxaVFu3FyQPOrjFq6WwoJC2V6xWe7dtEfWrSpOF/aMft8S6JCgBBNZg/y0ZBEZQvHIIshIJ1mkWVgZ7bplL8azwjNrP/QdzzI/nX/n9ysjfTJxfUpuLNwwZGbezTwpkDwpuJUn+bf4PfvrPUN6xpLqjGWyie3f+Z11CmuW2J/2b3xHA0qSewZYL0B2YlGTQnZTEoPx48xQ1zkyufGR6tHIa9M/BMdmUikn1fbq96KFAO9T9pBYS0uLzvtxum9kZET48O7cs2dPIPuGlUZVIgL9j/7oj+RP//RP5dChQ6b+eezcrQT6Sujq/ysCikDYEFACPWw9ou1RBBQBRUARyHkEVLLd3RDo6ekxTm0IKUjcbDcIVcg4HKZYkGQc16ctyLJBHJEBr5Y+AkjigysEDoY8PhKakAr8H5KH/I4zRc17BMj2IAsdUhNZyXRsfHxcent7TUZpUCoRzvYHSaB3Dl2QFy+dlKn5GemfHpbx2Um5KW/Vxi3KL5TqdRVSW1IlFcWl8oGWh6SiOHPS9un0Md9djkCPPa8bsghHqc1OhzQKItAiXTzC9H2CDyG9bSADP+2H96nz35DhliCPV9s+TPeVrW2BiIdMd5LsTvKUZ8P5b+bpTJWsCSvmTgI96lLIzmeV59Oug5zYM0faoCPGQq73v1/j8ty5cwZ/9lelpaV+XUbPm6MI8HyzdsKUQI8/CFAgo/QTAWcETYcxuCwRgf6e97zHBPwnY88884xRFVRTBBQBRSCMCCiBHsZe0TYpAoqAIqAI5CQCOHstyWGzzgHC1jvPSVAS3DQOHhzgZEg4M/+yDSfGAhtpiD07Hsi6D1pWEecHJCFEPu1RSx0B+pjMZwhcDKcwTgSn41IJ9NTxdftNLwh0CHPq/uH4wsig27p1qyF+grSgCPRL433yVM/3ZGB6WC6O90px4RqpLamUklXFsnTrlozNTsjwzJipGb2zulFq1m2Qj7Y+IsVFmVfVSKV/cBCSYR6bgZ7oXLZ+Ok5kvst7jHETa06yECdqGB2oqWCWznfAjsA5cGNOdP6MJcc5Lh4Jl8719bvhQgCy3aniYH/n3UlJBfsz0yUzMoVSttYStsEvzpIY8eZI5kXb5/yuQUfejDwClMGb4FieIzVFwEsEeG+zdmJNA4GudjcC7EeQcWcd2NraGsr1XyIC/f3vf78JIl7O7J6EOdtmpz/55JPqT9CHQRFQBEKLgBLooe0abZgioAgoAopALiHgzDpXyXZ3PY9sJZtwMnT5ZKORCQdRDVGABVU/OR62bIwhCSHykZFXSw0B+pisc8YyhjMYPCFencb/W6lWMtCVTEsN70TfIlAF8huHFXUZkzXIDPoSIg+DvCEQIgxZckEQ6LzL/v7Md+TyeJ90jVyUjSXVsqW87q6xu7C0KF3DF2Tx5qLsq90hh+pb5cjmvcnCH8jxqRDosQ21pLCb+unMD2TiZlv9dDAgC5x5zpLisQS5/XfUSXFbS5xxgPOYrFocyPzu/Djrk8fWK2f+d74DEtVEj/0/G6zprLFua6s7f8b7GxnPia4VyEPo4qJgHEuqO//N7xwTtfeqk0DP5lrCsXMkwTGx45C+cwYdETQRtf50MZQzckh3d7fBl+BY3jdqioCXCPAuZw/JOy6VtbaXbQnruQiaR+WN5w+iOoxzWSICfSVcVcJ9JYT0/xUBRSBsCCiBHrYe0fYoAoqAIqAI5BQCliyHRHNmnVvnaBg3TGHpIOSs2YQjc11bWxuWZnnWDu4N8tzKySJTz32GJcOmr6/P1GejPiIZtmrJIxDbxygp0MfxnnvINepSYkqgJ4+1m2+kQ6DHyu/Tj/RnWObwIAj0qxMD8mT3S9I+2CN5kietNcsHJcwv3pBT/V2ydf1G2ba+Xj6z/4NSkF/gptsCPcYS6Chx8C7ywpz103nuIZZjLWr101nfQIxPTk7e/kxNTd3xb1uexAsMvToH71srK+78OTg7JgOzo9I3MyJVZRukePUakbw8uUXJ8/y8f/u89Tt/uzY9KNemh+XI1j3y2K53yr66FiHLE2toaDDXiIJZ8p2+SuZja8dDfPK7/QkBHBajDwgItJ/169ff/p11TljWXk68wPLSpUvmT8kS6HML8zI6OykEMK0tWiOVa8tDeY/xxodzjiRgjfEUa/SXk1CPYoBEUM+GJdAJACRoS00R8BIB1gEEq7KOSbdckpftCtO5wAecCO7asWNHmJp2uy1KoIeyW7RRioAi4BMCSqD7BKyeVhFQBBQBRUARWAkBHJGQoziC7AeyRSXbV0Lurf+/fPmy2VxCLFOnL1ssnpw3WSA4cMNkbO4hHNncQwCouUeAPkaeD9l2LJ5ke+zZcBJTtgCjHl4YnfnuEQjnkQSEEBiC0kNzc7OrRsbrSxQEwiZ7alVOeOcw3jJB7D9/4TU53X9WTg90S0tlg2woLk+Iac/IZbm+MCv763bK+5ofMGR62M0PAj32nt3WT3dKWWd6fuA5gBAnYyoeSc7/BZk5zpgHHwg154e/8bzziSXKbWZ4vGflq28+Ja9eOSMdAxfkvi17pKjgTsWQ2D4cuT4unYMX5ejWvfLe5vvlvi17I0mge/08Mi85CXXn75CiTlUGW+6Av2U6C54xwBoMct1JrBM4w79jFWO8xmm58zkJdN5Zbp77axOD8srl0/JmX7cs3ly6feryNaVmXN67ZbesWxVsyZFk8WNuYY1kx0u8wAw7B9h5Mqg+S/beMn08zxYEOsZaJujyM5m+f72e/wiwTiDDmmewsbHR/wtG8Apk6BOYy3snrDL3SqBHcGBpkxUBRSBlBJRATxk6/aIioAgoAoqAIpA6Ak7Jdn7HYWGJ80wQG6m3PDzfRCqZTTi1Z6nTlw1GRhf3hRMQw9FHBgiO/LAZzg9IYIjCbdu2ha15oW1PrCw/zkmclCv1MWSCzVikHl4YZMFDC3KKDaMkAU4riDQ3DqvYvlxOfj/F5nj6NSvXzByTKQL922e/K2/2d0vP6GU5XL9HClfIKB+cGZULY1flvs175Z0Nh2VXdfgdq5kg0J0DwVk/3ZJFmaqfznVw6FKXc3x8/PaHf/MuzjRBztzJ+weJUz6WII9HlHstef+NM8/IsStvSlv/eTm0aZcUF61J+LwOTo9I9/AVub9hn3xfywNy76bdCQn06fnrMjn/VlmPklXFUrZG6xBbgJ1y3k5S3T4P/I2ADcaqLafh6WQa52QQ66xF7YfATsh1v8lH1gUEk2K8sxLtH8DtmZ5X5blzrwmKH32TwzI+OyU3b92UooJCqSmplOp1FVKyulh+6NDj0lAR/gCm5frVTdARc4Il0+knW4fX77ES9vM7CXTUpQguUlMEvESA9QMBxKy1NQA7PrJ2bUmAVlhl7pVA9/Kp0HMpAopA2BFQAj3sPaTtUwQUAUVAEcgqBKz8JY5mp2S7Zp0n383Im7MJZ3MJyRx1w+HLPVkSAjlgaruHNaAC5wcfiAvNIHA3+nDsEyCRiiw/mWY2K2jXrl3q7HUHeVJHJUOgQ8xAXFjZaQiTurq763sn1QAfD44EgT49IhfGr8nRzfvkoYZDSqC7GA9uagMnI2XMOOFdFEuQ8+9MkeQQNrxXIMctQR77k/8PMojouxdOyAvnX5fXrrbLprIa2bK+LmFvtQ+ck8Wbi7J/40751IH3SUP5xtuKIlbCHSLz3MgVOdnbKZfG+u4436byGrln407ZWb0tEqUNXAzdjBzC/AyRzseS6s6f/O4nyQ5B6yTW+Z3SHoxfL9Z2tJ01BbYSgf509zF5ruc1uTjWK2ShF+Tny4a15VKUXygzC7MyMTslhfmFsqumUarWrZfPHf24MO6ibrFBR2AWL9iHIMYgVTzCgjPvgCiWlwgLftqOlRFgrY2CGe96LQEWHy/8AQSF8c7QPfbKY0qPUAQUAUXAbwSUQPcbYT2/IqAIKAKKgCLwbwhYyXYnea6S7akPDzJF2YST+UMGb1QNZxXZ3MhHY5ACYZSAjsWX7HPajcNRa9glHn08+ziLwAuD0CLoIxlZfiRJz549a75P1L/Kj3r/xJNJS9YHjvTlag7SlxyH1LtVDqHEAvNQmC0IAv35C6/L6f6urJZwtzKbXtZAT2ccuamfzjuGjEvIRcgkyHHeP3wY235mkjP3UfaDuS/2w98hyqOQCToxNyVffOWr0j18ScauT8o99btMFm88m5qfkdN93dJStdV8fur+T8qtm7fuINBvFeTJN9v/1RDnHN8/PSzXb8zJLbll6lPXllQKEtv1ZdXysT3vkbURk9hOZ0z7/V0C2iDSKT1AkIgNFrG/+1GnneePIEnnB6Ik2aAQJ4GeqE7ulfF++bNXvioXRyHPB2RrxUapL6sxJLq12YV5E8AxOTcj++qaZduGTfKf3vlDnhD9fvdhMufnvU2fWsWC5UoCQO5ZQp3f3cjjJ9OOsB7L/G/LBaEutZI6UljvQ9sVXgTsesMqcIW3pcG0jDmKwCgURgi40iz9YPpBr6oIKAKKgBMBJdB1PCgCioAioAgoAhlAAIcETjpn1jnkuf1koAlZdwkILDbhOOGjGsGOE49Nss2AIisJYjUK5KitF40DJKzycmEY9Dz3kLI46DGbcZGsU9JJoOMoT/b7YcAi7G2wBDrPn5UmdLYZcpK+hFjBkJ9k7uFn2C0IAv3qxIA82f2StA/2iEie7K5pWhYmJIVP9XeZuufb1tfLZ/Z/MBKZtmEj0GMBxgHb399vPszZjF2ycVHD8KOONM8O7+TlSHKvMm/D8Lx97c2n5Ux/t5zq65bVBUWyo3qbFBe9PReA78TctJwdviRrClfJ3rpmeU/TfXJ/w34TpGBJqs1bt8g/dL5FnncNXZCJuRlz/PriUnObk3PTcn1hXkpXF8uu6u2GRP/MPR+U1YXhK+0Shn7xsg1W4SGWVLfkOs+SVwZ5jpJJLLGeSEIb8pdMRSwRgf7VU0+ZuufHr3YYafbN62vjNhsVhNO93ZKflyf763fIjxz+sLRUN3h1i6E8jxsVD/ZqrHUtoc473wsFgTACwpr1/PnzpmlkvkZhPxJGHLVNyyNAQDEB8KwHCEBVuxMB5iQUrlAeQ4kuykkC2reKgCKgCGQLAkqgZ0tP6n0oAoqAIqAIhBIBp2Q7BAYfzTr3pquiXoMbByxknK1fyyYZx2lUnHLJyF170+PROwvObQIkrMw3GWbIfKeSyYRTs7Oz04CAVGsUSNuo9RgkCWREPAIdR5Z1aHFflI6or69PqS+DwCUIAp3339+f+Y5cmeiTzuGLUldSJVvLN941xy0sLUrX8AUjcb2vdoccqm+VI5v3BgFT0te0BDrjgTk8KKN/CQBBGQTntM3w4j3jNVHO80HGPffMx/k7BFNU3mHp9hVZ6P/z+D/J0PSotA+elxtLC7J+TampV34TpYrZCZmen5WyNWultWa7bKuol0/f8wEjk+0k0PvyJ+WVq6elbaBH5hZvSNOGLVJRXHYbR0PEz0/LueErJmN4b12L3LdlrzzafDTdW9Dvp4kAgW08d1a9gZ+sjXiX2LVdmpcwASmsG+yntrb2NqlpCXSeOdYF8YzM8t9+9q+kZ/iyDEyPyn1b90h+3tuZ57HfGZkZl47BC3Kwfpcc2bJHfvDQY+neQqS+70bFgzUcc50l1QlozJZ5j/XqhQsXTJ+hLhUFRZBIDTBtrCn/xRyJ4gzraLU7EeCdf+nSJaOUwbyvQQY6QhQBRUARCB4BJdCD7wNtgSKgCCgCikCWIoATBtKLnzbzXMlz7zo7qjW4GQ9kA+JkxXBOEV1OJH6UzI3cdZTux8u28rzjSCfIwyuZbyXQveyh+OeCbCTggWeSOvPWnMEuzOEbN240pGGUHOZ+EeiQ3xfGrsrQzJgs3lySVQVFRhp4a/lb9eAvjffJUz3fk4HpYbk03itrCtdIzboNUrJ6rXk3js1Nmu8W5OWZmufV6yrl+1sfkTWOTF7/ez71KwRBoBPMAUkOWc570JLmXkqv8wzwTuIDSU7wDwECXtZvTh318HyTsfv3p78j47NTMjwzJgPTIwJhmSd5ZozXlVZJRXGpUVb42N5Hb2eoWwJ96dZNeXrohFwc75ULo9dkX12L+V48m12Yk1N9Z2Vzea00VW6Wnzr6A5qFHp6hcEdL6F/eG6zz+Fhind9tQF2qTWde5TmEWOG5ZI0Byb5cBjrKBn9x7Gty4mqHlK1ZJ81VWxNemvO9cvlN2VJeK/s2tsjPv+uzqTY1K75HX1q5d9Si4kn6ox7grJ8e5axt7u/ixYum71CXSrasQFZ0ut6ErwiwN2J+RKmG9bTanQgwBxPEwr6PAAPFSEeIIqAIKALBI6AEevB9oC1QBBQBRUARyDIELFmuku3+diwEAkR0lCTEIT5sXTPQwXlAZHkUMzwSZev62/PhPjvOVpQFqKeKIb9KgES6GeOct6Ojw5yzubnZnFfNWwRiCXTmcit9zZVwiiPZzpwTNfOaQIc4P9HbLh1DF+TG0g1DGkIGFuUXyOrC1VKyaq0cqNshe2qbjSz1i5dPmtq6EOljs5NyU24ZCIvyC6V6XYXJTifr9gMtD92Wro4Cxn4S6Iw/5hEIcufHlhDwAh8IcmSj+RAUwr+ZqxgviYgijuM5iDJR5AV+nIOx/0Zvp5zs7TRj3Gl1pZVyeNNu2V27/Y6SBFYm+eJkn5yY7JE3B7qluGiN7KzelrBZ1Kgen5uUQ5t2ywd2vEP2b9zh1W3oeTKAAM80a6fYZ9qWeEm1CZCckCyQ6vyEdLFBmSgXfOn1J+T1K21Sta5Ctm1YOePztSttUlOyQfbWNskvvefHUm1WVn6PZ9dZP51/xxrzopNQjxIJzT6F7Fe71kxFMSkrO15vyjMEWFeztikvLxcUNdTuRID3BGUU2PdR1k0x0hGiCCgCikDwCCiBHnwfaAsUAUVAEVAEsggBNj0261wl2/3tWFuDGyIRQjHsRsY2ddutjD8bYkiLKGWxOjFeLls37P3gZ/vITkLm22aYQUjhzPbCAcm4aW9vN80nKyiKJK6f2Htxbhx69B/ObuYUgl1wlGNITeLIimKwC+33kkCnXvk/n31R+qaHTMbt4MyY8DdrJauKpbakUqrWVkhL5VZ5d+MR6ZsakpN9HdI3NSJLNxdlfmlB8iXPZNAWFRTK9g1b5HD9blm3KlrBCV4R6JZYIzML5zI/CRKDzPDCCNayRDlZq/zOz0SBOM7MS2qmxyOKkC62RBFzUpSIIi9wdZ6D+tF9k8Myc2PW1JAuLy41wSHxzBLoJwY7pWu2V97o6zLkeeXa8oTNoqZ628A5ObBxhzy07aC8t+V+r29DzxcAAqwdrKqEVZZgjZuODDzrD0OkV5bLd3pfk5ND3bKqsFBaa7cnvMPFpUU5dvmMbK/cZAI0/uM7fygARKJxSeZt1ntOQj1enxGU5JwnvVgT+oXQ3NycWQdhlAWI6h7FL3z0vOkj4NW6Kf2WhPMMzCEQ6PwkaJfybmqKgCKgCCgCwSKgBHqw+OvVFQFFQBFQBLIIAadku5Vtx0mC80EdEN53dFQkxCEhIM7JOsLITCEjGWdalM1JNra2tkb5VtJuO05UpFkhvqxkO45rpI+9Ms7b1tZmTkddyqiPH69w8fI8ZAGSeWVLbVhJbKSrcWBFeR73ikBnHH7r7AtyebxPOocumOxbSL/KtRWGCJ9bnJehmVEZn5uWyuJyaa7cIgfqdsrRLftNV43OTgiSwrOLc6YOb9nqdabmM0R6FI25nXGTbA10vuMky5k7vCDLyTplrCLx7CTMIbrTNTLSVyKKIOSdRFGUn5l08Ur0fUugv9rfLt2zvXJ6oNtk+1I/PZFdX5iTN3q7ZF9dszzQcEA+uPMhP5up5w4QAd4/dl3B/MAHkp05OBVbyL8pQ3lTUrKhXG6WFclNpoS8vLtOdW1iUC6N9sq9W/fIw42H5UO7H07lcjn5HfqGedwp+R6vv8I8TxLMQfAgpgR6Tg5j328alS6C8gjiY62idicCrNfPnTtn5vpt27aZtZyaIqAIKAKKQLAIKIEeLP56dUVAEVAEFIEsQIANDo4uPla+nduy5HkW3GIobyEKEuJkcuCIssQIdSqRbM+GLD1LNjLOd+/eHcoxkolG8dyTTWFllck0ImMgXcn2eG0/c+aM+XNjY+NtedZM3GOuXMMGhdj75Tkl2IXs86ibVwT6xbFr8p2e70nH0HmhJvOu6u2ytujucgIQ5T0jl2VjabWp//yZ/R80su7ZZpZATyRFCiFhs8rtT5zH6RjzLs5nyHLnJ1OBNW6IIshzp4wxJL4S6m/1uiXQTw11S9fsNTnR1yGNFZsFyfdENnJ9XLqGLsmhTbtMBvojTfelM4z0uxFDgGxnG3jDXGIzOVO5jZsFIgvr8mVxXZ75ebNIZHbxhpzqPSsbiktlR802+Q8P/aCRcldLDQHmSfYBTkI99kzMiah32LmStWOQ8yRtvXr1qmkDBLqaIuA1AowvxpkN8vP6/FE/H/tKCHSMgGnURNQUAUVAEVAEgkVACfRg8derKwKKgCKgCEQcASvfZzPOrTy3kuf+d2yYJcQZF1aynd9xRCHlzSY4SMeYl70CAXThwgVzP3v27PHy1JE5l83UsbWCyUIl89wveU4y0G1GQjaQumHqaBxWFy9eFPoUw6ENee5F5m4Y7tMrAv1bXS9I1/BFaRs8JzsqG0zd8uXsykS/kXg/WL9LjmzaK/duyr55IpZAZxwNDg4a1RE+kFzp1iyHUIEkt0oI/A55HqZyAowviCLeCzjG42XTE5DiJNRzuX66JdAHr4/JS2Nt5plaWFpcsaZ5+8A5Wbq1JPvqdsgn9r1Xtm/YHIbpRdsQEAIEffHesjXU7bxjy8gk06wbBTdlMG9KxovmpLZ+o+zZ3CL//vDjyZxCj10BAeZJ1hiWUI83T7J+jJ0nM7lvYA4nQ5h2RKE8lg666CFAiQDWC2Sfs5ZRuxMBuz7grzyDBGiqKQKKgCKgCASLgBLoweKvV1cEFAFFQBGIMAI4ytnkOLPOrVx7Jp0dEYYwraaHNQOacYHzCccmBvkBEZeo1mxaQAT0ZRyA1GjDINBzaczHC5CAOPc7S4Aa6DhgGxoahJrGat4gEFu7nrOiquBXIIQ3rU7uLF4Q6GScf/mNf5LzY1dkav667K/dkfC5v7G0IG/0dcq29fXSXLlVPr3/g8k1OgJHI/mPM5j5EKIcwjxerXC3twJxJJaeJAAAIABJREFUUltbK3V1dYYw50OwTNTmV2f9dLCJR+hBoCM5zz3nWv108CAAjXfJyxMd0jN6WdoHL8j2DZukrjS+pO3wzJicHb4sLVVbzefHj3zMlEFQy10EWGcy5/AsoUyDMddTR511qP3wDCZr1TXV0rit0UgIEwCaDcpJyWLg9/Fu5kkCpewcyU+/A4+mp6eNsoES6H73fu6en3UTwSOsb7wsdZUtiIINGGE7duzQ/V62dKzehyKgCEQaASXQI9192nhFQBFQBBSBIBDA4YmDHCeV/diauVFzcgeBn1fXxMlD5k2YMqBxUiLZbskCHAPZ6niEdLQSc9lGNiYaozzzOBdtTXsylJFsz0SAhCXQuR7lANTSR4Aas2TtWaUIW69079696Z88RGfwgkBHPvprbU9L22CPkW1HcnolO91/VsrXlJg65z92+GMrHR7q/2ds8Nzz/NvPyMhIym1m7rBkuf1JYEw2riN4J9rsdN6TjMdYC3Nd4JQ7eZkvWgKd/75Rkif/3P2SUB6hd3LYyLhvLK2S4n8rjTC3OC/9U8PSNzks1SUVJhjlvc33y8FNu7xulp4vYggQtIOku5NAj70FO29BpneeOyuXLl+WvIW7n79Et85cRSAoJD0fDeDzZ6AwLzgz1OMFY9HXzgx1rwMbCE5mTcR5m5qa/LlRPWtOI0DwGGOddY9mV989FMjOJzATa21tNc+7miKgCCgCikCwCCiBHiz+enVFQBFQBBSBiCGA09dJnuOYsnLt2ej0DnP3hCkDmnEAkYIj044JK9keZgzTaRsb/J6entsbfK+deOm0za/vxta0x/FD5nmm7r2jo0PIWFICPf0ejg2EsDLZ1GbEsk1VwQZ7UW6A8ZrK+2rk+oR8re0pQ6AXF65xJR8dZQKdZ405HbIc8glSIZVMTsYTmJNtRWa5JcuzqaRHMk+k2/rpZKXbDPVsq5/uJNAhJF++ckpevXJGeicH5drkkJFzX1NYJCJ5Mrd4QwrzC2RjWZVsKa8zMu/va3kgpWc4mX7SY8OPAAE9lIzg+SBT3I0tLi3KyfPtcrzjlEwPj0ve9KLkLd5y89XbxzCXUZsXgpXfU3mfJHXBHDzYlgizcu/LBR6xdrGEOnNmuso5VtWAzHf6WE0R8BoB1MvwpbAe0mDgu9G15cHsXiQTAdpe97GeTxFQBBSBbENACfRs61G9H0VAEVAEFAFfELAy7SrZ7gu8KZ00LBnQjAnIFVuDko0umTo4tbLZkJjr7u42t7hr165Q1eP1A3dq2kOkBVnTvrOz0zidGF+atZF6LzN2UYogIAKzgRD8PVvLEnhBoJMJ+z9P/qNcGLsqE3PTcqBuZ0LiZH7xhrzR3yXbKzZJS2WDfGrf+1PvtAx8k2cLwpyxQSAFz3uqcuyQ4wTXEEiFk7iysjJjgTYZgMrTS9j66RBEZKnnQv10J4EOSUWAxWtX2+SliycNeT4yOy7Xb8zJLbll1B4q166XooJCuW/LPnlHwz1KWHo6AqN7Mkugs96ktEsqZgNAyXjk41RRcnM+glwsmU5wH8SrmvcIuAk84qqQ6JZQZz+SbHCDG1UD7+9Oz5hLCKBeRoAiayRK1KjdiQDrIPwK2P79+30v26D4KwKKgCKgCKyMgBLoK2OkRygCioAioAjkOAJWsh3nLhlotgZxqll8OQ6nZ7cfBgKXTS7ORkuybNiwwZAl6WaAeAaSjycik/Xs2bPmCjt37szaDT7PO889BDpmpUxxUmbaurq6jOzh5s2btW5giuCTXQU5Sr/iWOZ55bnl97AE5aR4awm/5gWBzgX++eyL0jl0Xs4MnpOWDVtlw9ryZa97abxXqNt8sL5Vjm7eZ36GyZi3qR/MeGAe5zlPhTBnvocgJwMUhzCfIOaHMGGbTlvc1AX2W8Y4nfa7+W4sgW5Jx9mFeTkz0CPtA+dkan5GyAsuWVUsrTXbZV9di6xblfn3jpv70WOCQYB1CfXO0yHQY1vOHEjwEDV4KZPE+d0a6yMUFVpaWsxPv+t1u21XNh7H3tTKvfOTT6yxrnES6oyTlQj1VFQNshFfvSf/ECD4mvG7adMmozKjdicCtowCfz1w4IAGJekAUQQUAUUgBAgogR6CTtAmKAKKgCKgCIQXAadkOwQ6dbswalJlSrY5vOgE27IgCVw2/jgVkc7EIFBwBORSVjBOVjKisR07dhhiOduMIA0ysmxGJFKD9HNQzz4BC4x72kCGq5p7BHhmyS4eHh42X8KxTya/s7agsyzB7t27syoQxisC/dJ4n/xL90vSNXxRpudnZGdVo5Ssvrs+49DMqJwfuyaby2qM7PQPHnjcZNIGaWDAGLCZlpBEkLXJGmPGEuVk+DEf8DwSjKHmPQKQzU4Z43h95qyfzu9hD2JjHoecxMje1axd78dNLpzREuiMebK//TACRSHT2f8wZuMpRMS7LmOaoCLIdMZ42FWZbFDdSgSzHxh7cU7a76yfHq+fmBed9dNZB8Xerx9BGV7cn54jexCwwdcEA2t977v71ZZR4HklAz2oPWf2jDi9E0VAEVAE0kdACfT0MdQzKAKKgCKgCGQhApAtOGn5WPl2HJ7IjmG5IFkd9m51Erg46DLlnMOZT8YiTkWM7A6IuGwkkBONAZ4NanJjzc3Nkm012sjCgWCLl6kc1LNB1gZOUcg7sqbV3CEQ+8wiGYnjLpa0chLo2RYk5RWBzvvwye6X5OL4NekauijTN67L+jVlUrVuvRTlFwoy74MzY+bvtesqZFvFZjlcv1sOb9rtrrM8PIq2QgZA/kCaM2+7JX+czSC7nGeOwBV+EihlSQcy2HF2ElyjBLqHnbfMqdzIGDuzLsluC2P9dCXQ/R8ruXCF0dFRExTmJ4HuxJF1H9LClDrhwzrJjUEAkZHO3iksmenMJedHrsqL505I+8B5mV+4IXn5eVJXWiUPNh6Qe7fukeKAg77cYLvcMW6UPFgDOQl1/p3pMZXOPep3o4cAz50t/0XQT7btHb3oEasCwby5b98+JdC9AFXPoQgoAopAmggogZ4mgPp1RUARUAQUgexDgM0dhAuEgyXPccjijLBR09macRul3gyCwJ2enjYkjJX5hVipra0NfbabH/3K89He3m5O3dTUlDWSxdwXpBhORIwMHZw8YZBk7unpMXW7qavM2FNbGYHYMgvV1dVSU1MTV8Y0DGUhVr6j1I5gXDNn8m4jqyWdDN0bSwsmC/3a5KAMzYzJ4MyIXF+Yv92w8tUlUluyQSqKy2VPTZO8o+Fgao1O4VvM0TbDnJ/8O1ljjBBgYT+Jnn0l0JNF19vjnfXTyVJnfow1nNCxWZfetiL5szkJdN6fmmGWPIb6DblNdtpAzkxiYgOUINIJLrY1e1dqAwEtBF1S+oe1VRBjf2puRv77sX+Qc8NXZXZhTganR+XG4lvvxvI1JVK5br2sKVotnz70QTm4eddKtxSJ/3ej5GGDjVgLZSooIxLgaSM9Q4B1qE1GaGhoyFjwu2c3kIETWRUI9p979+5Na72egebqJRQBRUARyAkElEDPiW7Wm1QEFAFFQBFwg4AlyyFH7e98D/KcD47abM64dYNRmI7JJIHLeECu3daCxOEHuVJaWhomSDLaFjBpa2sz10SeMxtk+HAaUgvZkjD0L/0chIM3XmfidEKik0zXqqqqjPZ31C7G+BwZGTHBEJibZ1YJdPe9vHRzSU71d0nH0HmZuTEn80sEnS1JYUGhyUSvKC6Te+p2SktVg/uTpnCkrdeLtDAfK9GfzKkgzFER4VknyzyZYBkl0JNB2v9j3WRdhqF+uhLo/o+FXLgC7zg+QRDosfgSrEaQH9mlrKN4B69ktJuA5D179phgVKvsAZk9MTdlzlG6Zp2nmeDT89flj57/ilwbH5DuoUsyPjslhfkFpsTIws0lQ6ivKiiSbRvqpbpkg3zm3sfkaMO+lW4lUv9vA8WdpTHYU8Uayl42+Ii+SifwLlIAaWN9Q4A1G0E3GCUeck29zQ2wdl7n+cu2clJu7l+PUQQUAUUgjAgogR7GXtE2KQKKgCKgCGQcAZwJbOqszK2VbcZZYB06mSRsMw5ABC/oJHCRhESq1Q/D0U3WOY4mDGcSZAtO+Fy3M2fOGAj8xD9TGE9MTJgMKutEhKQmyztM9TAtgY6jGdJPLT4CVmYWaW3MbZkFnnWrMkJ2XDY9415moDtRv3nrplAXfXhmTBZvLhniob6sRjaW+hfggbylJcwhasisS8Z4rsl8tKR5OhKi1FRn7lAJ92R6IHPHusm6xEnN+oF3e6bqpyuBnrkxkM1XImAItRzGLgFAYTEC/Viv8D5FCSQeORvbVublum2bpDd/Qk4PnZOlm4vmkDzJk9a6Jnlo+0HZXdeUNon75y9/Vd642ilv9nWbdjVsqDclSPLz8s31Zm7MypXxfhmZGZemqi2ysbxafunRH5O6Mv/eaUH3m7M0Bu9Xq7IV2y7WUpZQZ64M0/o4aAz1+u4Q4J184cIFczDB17GllNydJbuPsvM6zxgEuj5n2d3feneKgCIQDQSUQI9GP2krFQFFQBFQBHxEAAeKkzzHkWCJc+emJVOErY+3mnWnJgOafiGKnbrGXhsEHKQqhByWSP7Z62tH4Xx+458JDHj+IcGI+McgTSHWwphRT9YGgRxIkPNRuxsB1ANw2ENQYdSKJxjCTeYUjr2uri7zvWwr0+EXgZ6JMUi/QJRb0txt3V3bNt4NEOb24+W7Qgn0TIwAb67BWoF5gTmUbFlIvtgsWWf9dN4BkOt+OK9Ru7h06ZK5MZVw96Z/c/EslmghAAT1jDAa72Sy0js7O808vpLdklsyUnBdLsqIDMikrF61WurKKqVk9VpprtoqP/7gJ2TdquKVThP3/wemRuQ/f+e/ydmhiybzfP/GHUaqPdZsffSB6RG5d8seeXfLEfnUwfendM2ofQm1Ld6xkOV8liuNwbzoLI1h5d+jdr/a3swioO++lfG2zyDP165du3xZg6zcCj1CEVAEFAFFwImAEug6HhQBRUARUARyFgEcJBCjfGIl25cjW6j5bDIWGhpyWr47LIMGSX36D2KEDECvLJZUJUKe7B4viRev2hrkeaL+PECm4NCFSMHoX/o5rBkRZG1A/CiBHn/UUzewt7fXzOc4dyEU1q9f7/oRUQLdNVS+Hzg1NWWylAgaISBiuYy4eA3BkU8QDO9p3g0VFRW+OSAtgU65h40bN/qOi17AOwTc1E9nLYgT22aoe6VKoSSCd/2Yy2eirBDvvTAT6M7+mZ6eNlnpkOm2vEqi/puXRbkgQ9K12C8FxUWys2abNFZulv/47n8vqwtXJd31Xz/1tDzd9Yq8fqVdGio2yqby5QMRUVR5/XKb1JdXS3P1Vvn8Yz8bl2xPuhEh/0K8dxpzJUQ6a2V+Mn/FGmVynBnqzJV+BB+FHD5t3goI2CBXDmtubnYV3JproNpnkHkdAl1NEVAEFAFFIHgElEAPvg+0BYqAIqAIKAIBIGAl253kORt9p2R7vGbh9MGRj3O+vLw8gJbrJZ0I+NEfOIaQbLekKhtY+juspGqQI8Li73UAQybuCXUB+tlKiyKLTl3xMDv8yMDFAY0SAu1VewsB+rCvr88QCRgEKmMyWWlu5nbGNNbS0mKyT7PFwp6BzjsZpyGEOR8ycJIxngeUSPhAZLtRHEjm/MsdqwS6FyiG4xysB3nvE6QESRSvNIBX9dOVQA9Hn0e9FZZAJ/ivvr4+UrfD+5r37fFTJ+XG9bmEbScrvf/WpFzOH5Wq+lp5ZOd98sl73pf0/f7eM1+S41fa5fzwVTmyda8UFRQmPAc10qfmr8uhza3ysw9/RlqqtyZ9zah9gcAG1seJypIwVzrrp8ebK9kzOTPUdQ8VtZHgT3t5x1olCtbZYd5z+YPAymdlP0MQKYGZqGGpKQKKgCKgCASPgBLowfeBtkARUAQUAUUgwwhAJLDZj806ZxO30kYOeV++S5ZqMpmNGb7FnLkcmSxkEZNpSpZhuhZbBzsKpGq695zO96P4PFiiDulTDKceARIESoTdkPzFqQLRjyy5mpjnnwxlslownL7MB2RDJWtKoCeLWHrH8y5lTFMrl2xzHPJuDcc8GeYQ5vwMquSCEuhueyx6x7mtn24z1JOpn+4k0DULL3pjIywttlK/USTQwZD12G8++UW5cvWarJpYkob8KimQvITwzuctyvz6fPkPH/9x2VCe3Lr/809+UU73dsm18SE52rBvxW6kFnrf5JDct3Wf/PgDH5d99S0rfifqB1gCnSBxt4GabuZKAhstoU6meiprtKhjq+0XE6BGaTT8LRDoancjgJIWwdI8g6wP1BQBRUARUASCR0AJ9OD7QFugCCgCioAikCEEnJLtkOh83GSdO5tHHT8cn2R6UFtXLVgEvOqP2AxWsswIkogCqRpkD3gdwOD3vUC2knVuibqoqQtAFJMZVFlZqXLRIiaYgP4kGwojqABsVgqEWm6ccR7KQmA4rZLNYPd7/KZz/rBkoJN9BFne09Njapq7lWanT3nvNjY2GsKcMgap9nM6OMZ+Vwl0L9EM77mc9dNt5mU69dOVQA9vX0epZVGff7oHL8kXXviKvNnbbebzfbXNsu56gZTMFMia+fyEXZGXnyetu1rl0KFD5n3gxn7n6b+SE1fa5cLINTnasF8K8hNf49zwFRmfm5LDm3fLTz/0KdlZ2+jmMpE+xma/EiTuFlfnDcfOlbzzrdKT8zgUfpyEeqZUYyLdOVnQeIhhCGL6W8nh+B1KgAGBBiQGbN++PQt6XW9BEVAEFIHoI6AEevT7UO9AEVAEFAFFwAUCbN5x1PPTZp4nS55zGTLlcAZA1JAFqhYsApAwZJ4i2QtxlorhyIaYtDX9kEwjg1XlBldG06sAhpWvlP4RsWQrjkGk0MNAwrm9O2QPUUkgeCdqcq1u79HNcczhZN4hX4t5pSLA+6G9vd2cs6mpydTzzBazQWMEkeC4zKSzmmePuZoPAQ+xxONyGONgJ8McByI/w9gfNgNUa6Bny5Pi7j4Yw7YeMIS6VcBwftvWT7ckERmY1pRAd4ezHpUYgagT6E91fk++ceppefXSm9JUvUVqS99exxct5EnpVIGsmymQgluJs9JZsx88eHDFmsp/c/zb8kLP64ZEb6raesf1YpHmnfn6lTapLq0wx1IDvWT12qwfkjb7FfKONXK6xlzJfGcDj5g34wUfEbBo50p+j9LaPF2Mcun7rAcJ0mDdruRw/J5nr8dzwl6PoFE1RUARUAQUgeARUAI9+D7QFigCioAioAj4iIAlyyHPU5Fsj20amXNEBUO+pRKZ7+Ot5uSpqZWLUwaZwVQcPdRgxFnE2MBZw3nSyWDNtU7wIoDBb8ziSbajLoDkadQM8nF8fNxkJeAwzkVjLse5xDyMeakioAS6dyOKuRWFCuYISB63Zh2GOFYJEgm7zKsS6G57NruPs/XTLUlEoEqsOeunQ66TZYaphHt2jw0/785NvWo/r5/uub915nn55pl/leOX26W1brtUrC2765R5N+WtrPTpAllzI3HGOJLHhw8flj179gjPW6xdGu2V//KvX5b2gfMye2NODtTvkMJl6qAj335lrE8ObmmVh7Yfkh8+8uF0bzcS37fZr7yL/QgUZ51FwJGdK5cLPiJgzhl8pIR6JIbPio0kCJg1Ic+nksN3w8WelT0OzwXPH4pLaoqAIqAIKALBI6AEevB9oC1QBBQBRUAR8AkBNiE26zxVyfbYpiE5i/wYZK3b2nA+3Z6eVsRIAacS0ICzmwh4yEiMjfzWrVtDmeEY5o4OuyIDdRlxRDgl2yHP4zlWw4yzbZsl0JHW5D5yzehH1CKs7DfOJeZhrxyrvDPa2toMrBC4QdXV9qNfM5GBPjo6akhzPsPDw65ug74jGISMfzAnOCRKFvUaxFHCOkptdVMT2N4PgSLMNZlUhYgSltrW5RFIpV51mPB89uwx+dobT8mxi6elsXKTbCxPnPE8P3VdCkcWpTG/WvIT1EqHfEXa/cCBA3eVYvnD574sHf3n5XRvt6wuLJLtlVukdPXa2+uIhaVFuTYxKNcmBmTL+lrZWlEv/+mRH5ZtG+pThu7yWJ90DlyQ6zfmZFVBoWxaXyt7NzZLQX5Byuf064u2zBHBxKkqeyXTNjfBRwTSMUdaUp01vFfrvmTaqsemjwD7btZNKLKgLKR2JwLsQ6wqHokaW7ZsUYgUAUVAEVAEQoCAEugh6ARtgiKgCCgCioD3CDgl261sO85JNtzpbLq1BrH3fZXOGS9dumTqIEOkIavvxpBFg1S1GWJkrEQh09HNvWX6mHQVAPxsL4Eu9LOtj03QS1jqJqd632QGkdnLmM0lpwoOJchZgl4w5nICCMrK7s5WSxVbvqcEevLopUKa4wzHcUrmLaR5GKXZ3SKhBLpbpHL3OK/rp+cuknrnsQjYetWsCaIY1Aux/PvPfEna+8/LwtKC7K/fkXCPdnbwkkzPX5fDG1vlnev3yMil/ttqNPFGB0Tr/v37TVa6VR0anh6TP3zur2VgakQ6By/I3MK8rFtVLGtXFcvi0pJMzE3JLRHZDHm+vk4+sPsh+UDrO1IafG9c7ZSnur4nZL4v3lySxZuLkp+XL6sKiqS8uNRktr935/1StEwWfEoXTfNLVj6afRVZ6Jk2AiSd5TEIRoo15L9tdjo/teRWpnsp9euxh6H8EjL9BK6r3YkA6wWSNRj3+DVyVW1Mx4UioAgoAmFDQAn0sPWItkcRUAQUAUUgLQTYeECY8XFKtlvyPK2Ti5j6rbkuoZwuhl5+P5mABkvCkbFjJdupnU7GYzpBFV7eT9TOFUZFhtj62JB1kK3UKI66UW4AwjKXCHTmcgIHJicnTfdZp5uznrCX/XrmzBlzOqQlkYfPFvMyA50x2NXVZTLNR0ZGXEFEPXPIckhzyPOoqkDE3qwS6K66Xw9yIMA7Chlbxs5yFls/XTMudQjFQ8AS6KjSRLWs1O898yU5fa3LZIU3VW2R2rK366A773lidkra+s/Jtg2bpKW6QT7/+P9mstB5D504cSJhqRDWgRDp9913n3mvQ57/2Ut/LyMz4zI+OymDU2NyY2lB8vPypLy4RGpKKk12+mN7Hpb37rg/6T0Cz/g/tT0v/9LxkozPTkvf5JC5zi2YeRFZu2qN1JVVSU3JBmmp3ir/60OfkuKiNaEY5OyrkI8m6DQMijBu1DxYD1pCnYC8sJd+CUVHB9QI1ox86KdcCgR2CzdzB+p6BJIQ3I+fQk0RUAQUAUUgeASUQA++D7QFioAioAgoAh4hwKaDjbbNOOcnxKhX5DnNzEUCy6Pu8eU0bgMaYkk4yBw27pBxaqkjkIoCQOpXW/mbsfWxcajRz9lC1uEsx/FE5nUuZG7gxMWZa9UicObiTPJT6hgJd94lkLw2Y23lkRf+I9Il0FH6gDTv7OxMSPw5keD5a2lpMaQ5QSzZ6NRWAj38Yz+MLbRKOLSNwBJnTeB49dNtxiXkH8SDZlyGsVcz3yb2JKjtRJlAP36lXf7HsX+Qc8NXZWBqWOrLqo2U++rCVQbQxaVFGZgaNfXIS4tLZHftdkNsf3D3O28Dzjub/cCrr74qrEuXM95ByLofOXJEVq1ZLSeudsiL507IlbH+219ZU7Rajm7bJ+9ovEdqSuOT+Sv1NNL0Xz/1tFwc7ZXeiSFZt2qN1JZWSnHRalm4uSRkwY9en5TSNWultXa7kXP/mYc+7evaZqU22/8Hv/n5eROQwbgKk8WqeTCPsraJNfZ2llDndz/XjGHCJwptobwPQZi8yzS7+u4eYzxDoOO3YN0cRWWRKIxDbaMioAgoAskioAR6sojp8YqAIqAIKAKhRICNBuSZM+vcyrV7mV2cawRWKDvb0SgraZ2oJjR1k5EktDKAHEtUtzpU0u/dZBQA0r9a4jPMzMyYfvarPrbf7XdzftQTcD6RTd/Q0ODmK5E9BqUPnm+rFsEzm4lsqGwl0K06C/OgDSxbaXDgnO7u7paOjg7TF24MpyiE+Y4dO4xzNNvnWaRIkSQl2IIxqqYIuEHASaATZOJcp/IOY93Ch/eaLUPiPC9BgM6My2x/ztxgmovHMC8zRng3kjEcRePdBNn8XPdrcnW8X65NDMnNWzcN6ZwneXL9xqyRVK8u2SDbqzbL3o0t8pMPfnLZdwtBTa+99prJTOfc8YwAFIh0MtIJSJmam5HrC3NGSr18TUlatcmpc/6r3/qvRrb94kivqZ2+sazqriz2qfnrJut+3epi2VPXJD/+wCfk4OZdgXchylIE8UDcoXYUZqN/CT6yku/8jO1z5tZYQt1Lv0CY8Qlj23TNlLhXINDPnTtnxjGB0lGd18M49rRNioAioAikg4AS6Omgp99VBBQBRUARCBwBNhg4G212nR9Z586bHBgYMLW7cJaToagWLAKJAhoYG2TrQjpiOJghOMKWUREsguldHcIaKVrqJAZFHtHPPJNWDpcMI4g7r+tjp4eUN9/OhfmHOZxnlgwVDGlOVAQyVSe7vb3dvE8IUMgG2X878twS6DjOcd6RaU4mWrzsrtjRDGkOCQhpnmvBSdYZrNlU3sxxuXKWRAS6EwM3GZdKEOXKqLn7PrOBQOeuGOdPdrwk/9LxoiwsLcrQ9JjMzF83xDlZ2zWlGwy5fXTbAfl3B98vhS5qhhOEB5FOUNxy7zHWF5DoBw8e9Eyp6F+7X5WvnvyOvH6lXTasLTOy9MvZ6PUJ6Ry4aDLQj2zdIz/3rh8KfDCT/WrrL0dtHU0/O9U8+D3W2AuynrQBSIwBJdQzN+zYx7BvZH2t8uR3484Y7unpMf9BKSn212qKgCKgCCgCwSOgBHrwfaAtUAQUAUVAEUgRATYZTvIcB4yVa/drM4yznM0fG29kN9WCRWC5jFzGBY5FZIcxsg8g4cjaUvMOAbcS+t5d8c4z0c+0AQlTzNbU86s+tl+qCRXVAAAgAElEQVT34fa8BAnwgazDsZJtBnlLUAbkEoaDLZPS37xDII7J+CTzI2rO40TjIRGBzrsU3AkeIOPcqjgkOp8lzXfu3GlIc7/euWEf40qgh72Hwtk+J4FO4IlbsxmXNkPdzpXO72v9dLdoRv841j+MBUiWqqqqyN/Q+OyUvHz+pLx66U0Zuz4pt+SWlK5eJwe3tMo7tx9etj56ohuHrDt27Jh5vy1HpBMU/cADD8iePXvSVk35naf/Sk5e7ZCzg5fknk07Tb3z5Yzn+eS1TnOP1EL/jcd/TirWlgXaj+fPnzdrAMjNqAcRspaz2ek8J/HKYxB0a8l0fmZLyadAB1GCi7Nvn5ycNOoGKk9+N1CMWYJYMfxMmVDeCutY0XYpAoqAIhAmBJRAD1NvaFsUAUVAEVAEXCFgZdozIdke2yAymsl6hpBFplYtWARsRq6TUIyV8saxWFdXl7ZTLNg7DefV3Ujo+9Xy2H6urKw0zphslrLN5gAegl0gA6xcMX0JIeAnMTs1PyNnBnqkY/Cc8DvZbtNjk7JlXY08sudB2Vqzya/hm/HzxiPQyfInOw+JdhuEkqhhBCDx3mttbTWBDdn8rLntICXQ3SKlxzkRSJVAj0URQtCS6csRRLZ+uiWJtH569oxFS6Cz/uGTTcY7ywZGe3FfEOmvvPKKIdKXk3YHw4cffjitAMVf+uZ/kfb+c6bO+eEtu1ds+vmRqzI5N2PI9p9/z49KY2Ww6w7IO9Zh2UCgx4LvLI/BHGxLezmPg0B3ZqjrfLniEE7qgN7eXrPeRA2upqYmqe/mwsEEeVBGAUPZKZsCeXOh//QeFQFFIHsRUAI9e/tW70wRUAQUgaxEwEq2s7m3RLqt5+on0WLBhHBg8weRwMZGLVgEnIQiGblOKW/IHaS8w17DL1gE07u6ldAHYzL8M2HxpPkh83LByUD9c7I3cO41NTVlAm7frxErwY+zkrFEUIyf9srlU3LsymlZvLkkwzNjMrPwVtb7/MyslBeVSOX6DXK4Ya88sv2+tOqh+nkPyZzbEuhk/pBlDolAANJKRnYWY23Xrl2mbIk6k+9ETAn0lUaQ/n88BCC7IT+xZDLQV0LTSRBxjXhqElo/fSUUo/P/VrElGwl0v3oBafeXX37ZqM0sZ2R+vutd70op+/P/eOL/kY6B8zI8PS6Ht7SueBsXRq7J+Ny0HNy0U/739/yIbK/cvOJ3/DwA+WgCc1CWITM/mw0C3RmAZAM4nfeMopUNPmLtzZpILXUEsqXsROoIJP7m/Py8KZ+EofCU7c+gXzjqeRUBRUAR8BoBJdC9RlTPpwgoAoqAIuAbAkFItsfeDI4XnJ5EqLOxUQsWAUsoogiAU4OsZCzbpbyDRf3tqy8noe9X++JJ8yO1na2S7bE4ZhuBHivBj5MS8txvCc3nzr8mb/R1ytWJfumdGjLO4rWriiVPRIYnRkxw1qaKOtlRs022b9giH259t+Tn5fs1rH0/L/dHVtnp06fNz5XqmhOMRg14SHPIcy19sXwXKYHu+/DNygv4RaA7wWIegyBiXcT1yLiMffa1fnq0h5cl0FFr0Vq5yfUlAWTf/e535fLly3G/yJ7i0KFDcvTo0aTWmL/1nb+QN651Sc/QZTm0eZesKVq+dBTP6Knes6bO+86abfJ/P/azUrlufXI34vHRBNiZNdCmTb4HMnrc9LROxz2T/essjxFvrcR+0xLq/K5KPMnBns2qGckhEf/oubm523PS7t27jT9DTRFQBBQBRSB4BJRAD74PtAWKgCKgCCgCKyBgM+ecWed8xdY7zySAZO/hbMGxgoytWrAIWEUAZytwJCL/nAlFgmDvPvirWwl9IuTJTvXTcGrhLLaSi7kozZ9NJSRi+zNTz23X0AX557MvyoXRqzIwPSIby6qlrqRKVheuMsN3YHBAhmfHZPzmdePI3lndKA823CNHt+z3c3j7cu6xsTFDmr/55puuJNqrq6sFhx3Eud8KAL7ccAAnJaiF9xB4QTioKQJuEMgEgR7bDrf103HYM55tPWBdS7np0WCOYT8C4aIEeur4k+35wgsvGAWreMazQDY6QdNunoUnO16SJ958Vo5fbpfqkoqEkuwTs9PS1n9Odtdtl3s275JfeM+Ppn4jHn3z7Nmz5kwoOzEH5KrFzpc8Z7HS/4wHK/fOTwh1N2MkVzHlvnXOStz7zrXBnj17zJhSUwQUAUVAEQgeASXQg+8DbYEioAgoAopAAgRsBg1R4EFItsc2jTq9OFsg7yEa1IJDgPFAX9javQQ14PApLS0NrlE5duXBwUHh46xB7zUE9DMEFdnuth5mrkrzZ0MJiXj9mUkJ/r9541tydviidA5dkO0bNktNyZ11Y0dGht+qw76qQM5PXpNtFfXSWLFZPnfkE5GQcifApKuryxDny2XWOZ9Rnl0Ic95nEOhqySFgCXSIBsaxmiLgBgHrJIdsCaocEOtqstJthjrZl7Gm9dPd9GZwx7AGRvKXubuioiK4hkT8yqxLKGtCRjrPZjwjSPTRRx9dsSwU9cz/r299QS6OXpMrYwOyo6ZBquJklc8uzJta6YUFhbK/fod89r6PyNGGfYEiCQ5koGOoAWn269vdwXwJiW4z1Pk91vANOOuno46lhPqdKOmclfgRx6dBqUBs//79vityBTrh6MUVAUVAEYgQAkqgR6iztKmKgCKgCOQSApYsR+LX/s79sxG1nyDwwNF44cIFc2kig3VjHEQviJHZszJwtgVkh/gt/RzM3Yb3qs4a9NSM9NogMqmXh/IDRiQ+Tr1clZQmoxg8cMp5WTfX635b7nw4IGn/xMREIP05MDUsf3v6n6Vz6LwsLC3KvroddzXVEuhlpWVyZWZQZhauyz0bW+UDOx6SXdWNmYIq6esQYAJp3tbWZgiVREawUXNzsyHNkWpXCdKk4b79BSXQU8cul78ZBgI9Fn839dNtPWACbyCKdO4IdhQrGeUt/rw7jx07JidOnIhb6oSAkgcffNBIuyca+/945jkhEx0Z96HpMakoLpWa0kpZu2qNWXsMz4zJ4NSoUb7ZXdckzdVb5ecf+awh04M01mjUQMcoj6TZr8v3BvsTApAsoR4vAIm1lpV7t4oeQfZvGK598eJFs4dHKa68vDwMTQpVG0jU6OvrM/4lCHTmHDVFQBFQBBSB4BFQAj34PtAWKAKKgCKgCMQgAGGOI4+NvP2wkQhCsj22c9gsU0MWg3xQ52Hmhy9kKiScyRL9N1NFgMz3A1f0syY3zxoZtFayneyqjRs35vQzNz4+bgJHCBQhYCRKhmOa/rTk7vr166W+vj6j/Xn8WptQ//y1q2eksWKT1JZW3QXh6MiILC4tGiWLG3lL0jF4XvbVtcj9Ww7Io833hwpynJAdHR1y8uRJo9CwkvH88N4i+EIzy1ZCy93/K4HuDic96k4EwkigO1to1Z9oJ4Gj8eqnc7wz21LlizM/yi0ZVVNTI7xT1bxBALWf5557TsA3noH3+973PuFnPOP5+Zvj35KXL5ySoelR6Zsclpn52duHriooNIR6fXm1bF5fJz/38A9KeXGJN41P4yzsq+wel+C65YJVl24uyRtXu+T88BWZW5g3dd5bahpkX31LJJR60oBo2a+6CUBi7W4JdebOXCRHz58/b3w8dXV1UlZW5kdXRPqcBBhTHg2/BgQ6QRhqioAioAgoAsEjoAR68H2gLVAEFAFFQBFwIABh7iTPrWRzkFnnzg6C/LHydsje5uLmN6gBy9hgU0kdaAzsKysrzd8YHygCqGUWAT9qcvPMk2lNBD6/07dItqtzWEzmNnXgGfvMP1Ex2k3QC88w/QmRS0BEugoeMzdmpX9qSG4sLUhRQZGRYy9bvW5ZWF6+dFK+e/GEnOztkF3V22V98d3lHkZHR8w7CAI9r6hQ3ujtkN012+XezXvlsZ0PhwJySFtI8zNnzqyYbY6TlrkRR9yGDRtMQIoNSAvFzUS8EUqgR7wDA2o+pDRzYpAS7sncejL10yGIyFCHLEp3jk+mjbl4rGZz+tfrjHmysZ999lkTRBJrEFwPPPCAHDlyJG4gIN9/4dxxeebsKzI6MyFItt9YWpSC/HxZV7RGVhetlvsa9spH9j5iMtPDYKx9IDgxJOtRnHAaxPm/tL8kz549JtRvhzznb4X5BeZ+KtaWyaM775fv2/VARoMjw4Cdsw3OACSbpe4M+rbHWkUPS6rnQlA+ARpgwT5AS67dPXIJlKY0Gvu8vXv3KoEetodb26MIKAI5i4AS6Dnb9XrjioAioAiEEwE2nWTV2Y1m2Bz9tO3s2bMGPLL4Yp0L4UQ1+q0icAHi0NacKykpMfVm+btK6gfXv1ZSnCwVL+q48txT+81KfHNeJNtVRvKtPkZ9gSxuMhJaW1uD63iXV2Y+JzPaBr1AqCALmm72c9/UkCHBe0Yuy81bt263hvcFNcsPbmyVres33tVKMs+fP/+avH6tTZoqt0j1ug13HXObQC8plcWCW9I20CN7a5vl6Jb98n0tD7q8c+8Pw7HNuwfinLlwJWtsbDSkOc8ljjj6gudLCfSVkEvu/5VATw4vPfotBKJGoMf2m62fbuWL45WN0Prp/o921r/M6SqH7B/WjO0XX3xRTp06FfciEIEf+MAHlq1Bz7PS1n9OOgcuyPUbs1JUWCSby2vlSMMeKS4KB3Fub4yxZPdUrCGcZbGQnv/T5/9W3uztlsGpERmYHJHrC2/XAV+3qlhqyyqlumSDHNzSKj/10A8ELknv36hI7szWt2Hny+UUPdjrWDKd37ORUCcohWeCwGgCrdTuRIB9NeXRePYg0LNxDGifKwKKgCIQRQSUQI9ir2mbFQFFQBHIcgQgqSELwiDZHgs17ers7DR/hpjI1VrMmRyCRGNDqrLhxnAUVlVVmcwmnBE2W0Jr0meyV966lpeS4gRHQA7bOoJBSHxnHsHkrkhtPGqeRqFkAY5YiF6eUYxMExxm6ap2nOhtN1nkOKIHpkdkdHZCFm8uSUFevlQUl0ltSaWsW7VW7t28R96x9eAdGZCXx/vk621PS9tAt+Tn5UtrTdNdHTA2OioLiwtCkM7A3JiMzU7Iwfrd8mjTUdm/MfOy+Txjb7zxhqlvbrFcbtQgh7lv3z5DnMfWlrQEOu8wTJ1yyT17yx1NcAgfnN4EdakpAm4QsAQ6z2Fzc7Obr4T6GDfyxbmYbel3p1kCXeWQ/UZazD7kqaeeuh0Q6Lwi65qHH35YDhw4EGnVBdbfVrZ++/btt9drrB/+4qWvyrGLp6Vr4ILJPt+wtlyqSitkdcEqmV+8YaTqx65Pmiz0HTUN8s7mw/Ij93+//x0TwSvEKnqw/+FvTmOP6yyRgb8hGxQ9bBIC6yXWTWp3ImDXlPS3lgrU0aEIKAKKQHgQUAI9PH2hLVEEFAFFQBH4NwRwxPEJ40YREre9vd20tKmpKe1MSu305REAa2S8icbGiMYmG9m54cbpQDQ7Rkau1grL7IjyQlI8nmQ7tbGR+Fa7E4GoEOjT09OGPLdKItQJra6uTntOP91/Vp49d0x6JwflykS/kQ2tWveWAxcZ9+HrY+bnxtJqaVi/0dQtv3/rgdsgMtb+x4knTOY6n3gy7pZAL1hdJOcmr0p9WY00btgsn7v3E7K68E45U7/GJ+0kmOT48eOmZEisYzX2uryL7rnnHvNOWo4YVwLdn95SAt0fXLP9rNlGoDv7yylfbDMubQCk8zitn57+KNd6wuljmMwZWNN873vfk9deey3ue5lgGGqjR1U1iWx7gjTtHtfuqc4NXZHf/s5/k56hSzI6Mym76hqlbM3dNdvHZ6cMwV5dUiHbq7bIrz32M7K5ojYZiHPyWDeKHqztnHMmAUlh9JMk6kDeDbYMHvv5dNWosnGwkH2O3wNs8GtErY+zsU/0nhQBRUARAAEl0HUcKAKKgCKgCIQOAcjzeLXCwtBQNn9tbW2mKcjbqfyYP70CMQ4BZ2VBl8te1Zr0/uDv9qzpSorjNCKrhyxbDIcQEt9RdT66xS3V4yCmyQ7CoYLiQtiM+RHnD/X7MJyvOMnI5k7Xrt+Yk786/nW5NjkgF8auyaayGtlUVmsyya0ZyfjpYSHTHOIbGfcfvufDJlPKGtLvyLh3Dl2QyfkZI/kOCW/Pg4T7yPVxGV6YlHVr1sqe2mY5tGm3PLL9vnRvYcXvk7XP+wXiHBwTGYFEZLvxQa1hJVMCfSWEUvt/S6Dj7GSsqykCbhBgLufdly0Z6Inu2U22ZTaQQ2763etjtJ6w14i6Ox/P7pNPPnl77er8Fkowjz/+uKnxHDVzEugEA9iAvL98+Wvy/NnX5OTVTmms3Gyk2pez3olBuTLWb2Tc37frQfmh+z4UNRgCby8+EFs7nSAkq8zlbBjrayv3zk+n3H7gN7BMA9jz2aD3hoYGVfGLgxP7J/bE9OmuXbuUQA/rYNZ2KQKKQM4hoAR6znW53rAioAgoAuFHIMwEOuhBcOAQ3LZtmyfEUPh7JHMtBFcr2c7vEIVIU27YsCHuJhLCqauryzRQa9Jnrp/sldLJiI4NkkBymsxzVRFYvh/JWrT1KcNWsgCH39WrV4UxgeH8gVD0yqlH/fLvXjwuSLhvKC6X7RuWJyvJTu+bGpSDG3fLfVv2yrsaj9wGlXnlHzufMxno50auGLKcTPbyNaWSlycyMDYscwtzsqFkveyr32mI+E/ue5+sKijy7QFDyeHEiROmxirPRSIjwOTgwYNmvkvmWVEC3Z/uUwLdH1yz/ay5RKDH9qWbbEvmNgJULUGUbumPbB1PSqAH17PsP1544YW4tdEhnt/5znfKoUOHIkWA2VJKoEqZMvZgSzeX5Of+v98SstCpfX5wy24pyH87cDG2Byipc+Jyu2xaXyPN1Vvlj37gVyKFQXAjavkruymRwVrbSagnsz7M1D1zH7bsGj4UgqbV7kRgYGBA2BMQeLxzZ+bLRml/KAKKgCKgCMRHQAl0HRmKgCKgCCgCoUMAIsbWaQ1d40Sko6PDZMhDZJBpoOYNAmBKVgcbR4yN9UoSb1qT3hvsUz1LqhnRyNPR1zZIgkwdJNtVqi5xT5CJYp1PYSLQyZRBchyHMlZZWWkCX7zsz/9+4h+ke/iSnB+9KgfqdsqaotXLgrW4tCgn+jpkc1mtkV//qft+4I5MdRzCT/W8LJ1DF2VuYd5krV9fmDXjcWl+UcoK10l1+QbZsXG7fHjXuxNeK9Vnh2tdu3ZNXn311RVl2pkLqW0OcV5VVZXSJZ0EOr+H0bma0o0F/CUl0APugIhePpcJ9NguYx3HO4QAMd5x8db/Wj89/kAnm5OABIIPvVB6iejjFGizWZORjR4v+A0CDEl3rwIJ/b5RnkPUvzCC9DDqnf/C13/PSLNTpXtXbeOKzWjvOydFBYXSUtMgX/jU/+nLGmrFRmTpAbElMuizeKp91NCGULey78uV98kkTOwRbBAwKn5ReS4yiRGl6whERnnPPoOZvL5eSxFQBBQBRSA+Akqg68hQBBQBRUARCB0CYSfQyXhmE7h582ZX0rmhAziEDbJOGytT5zYbWWvSB9uZyRK6sXXt3QRJBHuH4bo6zwkZZ9ju3buXrXedqVbH1q/HQbdp0ybh+fXSbt66Kf/15a/I+dErMrMwK/tq33LsJjIk2rFd1Y3yuSOfkJJVa+86/NrkoJzu65LukUty8xauYZHx8TGpLCyX+xsPyL0tBzwNAuD8PANnz541xDlBJImMoJLDhw8b8hxnaDqmBHo66C3/3dHRURkeHjZOapVw9wfjbDyrJdAJZGlqasrGW0zpntzWT6fMC+QQWer87mWwVkoND+hLlkDnvaslpQLqBBFDeH372982QXGxVl1dLR/96EcjEXDNmh4lIZ4nMtCx1Aj0HikqKFICPQNDkjmTvTN9Zz/8LdbsnMm8ye9BEOq0kzJUGO89DeS8e4AwhxBMxj6KMgpqioAioAgoAuFAQAn0cPSDtkIRUAQUAUXAgUDYCfTu7m5Tm5uMD6TF1VJHgE0+BER/f39K2cjOmvTbt283DlW1zCGQDKHLM0Nmi83SQb0Bp686UNz3F9jZ+oGtra2BYhdbvx6CF1WOdIneeGgsLC3Kn7zyt0Z2/cbSguyuWZlwghRfvLkordVN8r8c/n4j0b6ccf6ZG9dNdtVQ74DcmJ0XnN61tbXuO2eFI3Ecnj59Wl577bXbKhvLfYW5DOKcn14RQ0qge9aVd5xICXR/cM32syqB7q6HmbdYO1hiiDVHLDnEHOmULiYwz6t5010rgzuK/Qh4KIEeXB/YK7Mmevnll01wXKwRYPXhD3/YBF6H2SDuIPAgVy15d1vCffiKDEyOyKEkJNxbqhvkD3/gl3PmeQxD3zIfsFewcya/x5szbWY6cyfr9kzMmc4SAYyvIEj8MPRRojYQwELfEUDLHkBNEVAEFAFFIBwIKIEejn7QVigCioAioAg4EAg7gQ6BxSYQ2WmkitVSQ4B+xlEzOTlpTsAGngw+IuOTMa1Jnwxa3h7rltBFlp++xsG4Ul17b1uYXWeDSMBhju3atUuCqgsbGwxBpgQOfL+cYTj//vTY3xr59oHpUTlU33qHJHtsL3P8qf4uKVu9ztRK/+mj/05WF7qrtXjp0iWTTYZUOjL06RrnOn78uJw8edIQQcuZlWmHOPcjMEsJ9HR7Mv73lUD3B9dsPyvzAlKtmoGeXE+7rZ9us9MhibJZJtgS6BCzGkCa3Fjy62gkqslGj33fsz5Czh31oLDacoE9X/reN+Rfz74qJ690SGPlJqktW76UTO/EoFwZ65dDW1rl+1oflB868qGw3m5OtMvNnMnYdAYhMWf6Qag7SwSgcODHNaLcqazTIdDBCf8SdeLVFAFFQBFQBMKBgBLo4egHbYUioAgoAoqAAwE2e7aWbhiBod4d0cFkJ5KlqJY8AuBHNrLtZyKtCUhIhYBrb283xGxDQ4OpGaaWOQRWInTpF9QFIJkwnDIESaijN7U+IosZ+W+M2ppBEAMEvODgyXQwxLe7viun+7vkVF+XIcWr11UsC+L43JR0Dp2XPTVNsrumWT6x9/tcA04td+4R5xVzUqqGrPcrr7widn5a7jwoMRw5ckT279/vS/a+va59FnGQE6REvVzIJVWASLWH3/qeJdDBFAUGNUXADQJKoLtBaeVjCMS0mZZkz65UPz3b5jy7HlACfeWxkskjeC888cQTMjY2dtdlH3roIfPODyN5aOclgjOd2a/nh6/Kf/6Xv5CeocsyOjMhO2u3SXnx3futseuTcnbwotSUbJDGqs3y64//jGxa752STyb7MFuv5ZwzIWpt6TTn/bIujCXUvcAjXokAL86bLeeAQGcPwt4a/5KuKbOlZ/U+FAFFIBsQUAI9G3pR70ERUAQUgSxDIOwEOvW7ICG8lvjNsm6MeztsDiGWBgYGzP9DmCOFv379+pRvv6OjQ3AIsNGEjFLLHAKJCN3YLGWCG3DyKmGXev848d6xY4eQtZwp49nlueX5xTIdDHFtYkD+/sx3hNrm1xdmjTR7cdHddcGReG8fPCcF+fmmVvrjOx+WlqoG1zAlS6DP3JiVtsEeuTTeKzcWb8ji5LzMXhyT8d6RhNeEnL/vvvtMIEQqgUOub0jEBCoRsITzMtacMp65XEs4GTydxyqBnipyuf09JdD96f/YWsDsJ2LNWQuY+S+MRKYbdHgnW0UaAhO5F7XwIMAamEx0MtJj7cCBA/LII4/4/u5PFg2CBwl6ZX3X2Nh4++uMtb98+WvyvQunpGvggqmLXrG2TKpKKoy6z/ziDRmaGpXx2Snz9x01DfKuliPyw0c/kmwT9PgMI8D6ECLdBiLFC0JiPDgJ9VT3cVbhwFkiIMO3G+rL8ZyhgsV7jCSNsJd8CDWY2jhFQBFQBDxGQAl0jwHV0ykCioAioAikj0DYCfRkCZb0EcmOM7ApJ3OVDTSGExOnX7o1k7u6ugxBxEYzHSI+O1DO7F2AO/hjTkLXKdnO/yGFTUZvVB3VmUV1+asth7ff7YslYMle5nnLpIQ8jqWvtz1tZNzbB3tk4eai1JVUm0x0HLjUMR+aGZX+6bcIfrLPN5XXymf2PyYF+QWuIYJoZvwio05wz3K2eHNJnr/wmiHrbywuyPTQuBT0zUvhFJXUlzdkKyHOwS8TzwNOUd5Z1ilKkBH9Ga8uZqyMZyYDNFx3UMgOVAI9ZB0SkeYsl+kZkeZHopnZXj9dCfTwD0P2sy+88IKcOHHirsayFnjsscdCFVTK2odASd79sfLRrLG++N3/V05d7ZKh6VFTD50AQmslq9dKbVmlVK2rkHsb9shPvOOTSa29wt+b2d9C5hTWh5ZM52e8ICT27ZZQJ3DHbRCovvcSjyHwJ0mDPiDINtEeJPtHo96hIqAIKALhQkAJ9HD1h7ZGEVAEFAFFQMRs1sIs4e6WYNHOfBsBpDXBzZI4kFOQqm433YmwRMKSaG1qMCMFr5Y5BMj8RwEAwxlIlgLOt5GRt7JvIVhRBlDJdm/6hOens7PzNt7pBp+4aVXss1tTU2PUNzJB/sa2b25h3mShD0wPy5WJfhmeGZOlWzclT/LkltwyddEr15bLlvI648T95N73mzroyZib+R1H8j90PCOXx/pk+Gq/FPUvytr5wmUvQ7YOEu3ItvpR3zzehXHEQe5SZxmjDTjkcHZa+X1n1lG8+uxeZR0lg3/UjkWid2hoyASEqdxm1HovuPYqkZB57Jn3CBzinQYxFG/O80u62I+75X56enrMqZl7mIPUwonA8ePH5fnnn7+rccikf+hDH8poMGIihMbHx2VwcNAENlMWK9aWbi7JU53fk2e7jgly7WSe8zeCFAlkrFxXLo/ufEDeu+v+QNaI4ez96LbKTRASd8e60qoZJVL1sAEasQoH0UXI25aDN4oV7PUgz9MpI+Vty/RsioAioAgoAkqg6xhQBBQBRUARCB0CbK2ReKQAACAASURBVCDi1eQKS0OvXbtm6tqR7azyWol7hb6EXMAhg0GYg5mXUus4EHGKstEky1ktcwjgwKXGM4YDl76GlMOCyFLO3J0HcyVnwEJzc7OvDvPYcgsQCzy7SPEHaThsnzl3TLpHLsni0qJQ73zx5qJx4K5fUyqF+YWyrWKTvLf5filZtTbppqKSgROZYByCcuLZk13flTfbzhip9lU38pe9xlL+TcnbuFaqmjbKZ498v6yJIzmfdANdfIHnkvcUzkrMqn3Qh4wh/j9WghOHnbOWMMfFmpU+XrdunTlnEEEULm4/o4cogZ5RuLPmYkqgB9+VzlrAzH3xAnchepjvCAIMW/10J4EO2ZmJgLrgey26LUCt6cknnzTvYKchlf7hD384FCS6fZ8tR6DbdkOav9nbLeeGrsjc4rwpp9Nc3SB7NzZ7Ehgd3V7O7pazL2C/bdeKdr/nvGvWhc7SQIwlu1a0ARrxFA6yGzl3d8ecfv78ebNGR6GPgGU1RUARUAQUgXAgoAR6OPpBW6EIKAKKgCLgQCDsBDoZfWTYQgJrxtnyQxdnJGQU2T4YG2o2hF7LAp87d86QtmS0V1VV6bOUQQR4Vtva2swVCY6wUn/UbqMvlGDztjOcAQtNTU2+1TzFwcuzC8nj57ObDjqT8zNyZqBbqI1O3fOigiLZWFole2tbpKK4LOVTJwqQAv/X3jguz333Obk1ezfBfPuiq/KloH6d9BQOypzckHs27pKHt90rhzftSbldbr9I8BmS7Tg5MQK9yGThWaRfIcp5bhPVsLTvYJykzN/Mr/zNaTzv1kkKwQTRlIvPu1vCwW3/6XG5gYCtNYxKC1moasEjwJrVZqcvJ10cpvrpzOesfzEl0IMfP25awLv5iSeeuCtYg/77yEc+Yt6jQZotSWL3a0G2Ra8dfgRYE6+kZOQsDcQcy5pJFXvi9y14Mqez3mZOUJ9G+J8BbaEioAjkDgJKoOdOX+udKgKKgCIQGQTCTqD39/fL8PDw/8/em4DXVZ333q8H2ZYtW7JkDZ7xLM8YCGGwgUJKwGEKmDA0CaRNM/WmX2+SDvdL26Tf7ZeUNs3tLU2TS5o0XCABEuACToAECEMwswFjW5LlQfJs2ZIlW54lc5/fTpbY2jpHZ59z9t5n7X3e93n0yMM+e6/1X2uvs9b7f9//62RippK4iw3QITYU4g0CzmRacAiEVA2DYEHuDKcnkdoarR3ioKa4tZtA578hAwiSgFBTCx4BN96QLmFI4+MMQ8bcqIAEWW4heESCv2MqAh2nFoEia9ascZx/6WzIqGEyfMpYGVY3WoYMHSLHTh2XdXs3yayqqTKzcqrcvuy6UNZA0x7WXcbOSLSjykEmPesuc8cvge7tn5E+NoR6Kulj3n3mo8nWHIygD37UCndHJdALh32cn6wEut2j50e62GRamjWP4NAw9rjpkHIT6NSrDjo41e4Rim/rdu/eLQ8//PAApTX2dJDoQZS2yhUdgsP5UQI9VwSL+3N+VD1AiP0hpaCYZ4UOGrFpxNxrOsoUUZV8sgkDbYsioAgoArYioAS6rSOj7VIEFAFFoMgRSOWgtwUS5Mj5wWnGAUftfQRwOlIDmwADc0gOW/a5paVFuru7ncM4JL1aNAiQSQBZB6mGkVGAExciTS08BNavX+/cnLUn6EAFyECcu7zHEAFImJPBXExG/8nCKi8vdzK3DXGO9GQ6GzJmuAyfOlaGVZcOIFAa2raIDBGZXz1LPn7m1VI1Ong8vaUyeAdRR3EHWORDoHv7bZykJluTrHavIdtpiCXWhkKSAmHOXyXQw0Q3ufc2BLrWgo3HGPsJIoq6fjrrLnK/mBLo8ZhHppUomT300EMDSPSFCxfK5ZdfHmkghhs5zm7sf9g7aImyeM0pG1vLOdGdoZ5qr8h3IPPN/BRL8GWq8XIT6JTp4hyipggoAoqAImAHAkqg2zEO2gpFQBFQBBQBDwI2E+g4GMhCJ3IaGWW13yJAxiqEqqmJBnmCAybs6PLW1lZHaposd2Tc1cJHwKswwBMh7IKsbR9+L+L5BAhdyFAc5tSZD8IgB3DomuxqMtkYT4jPYjNw2L9/v+NEbmho6KsjngqHIWUlUjJ9rAytTF8PfHvnHuk41uXIuN+w8PdlSnmwa5RXbh8nJGPnDWQJkkB3Y8F9vdLHXrl3gjHcDtKoMzXDnMNKoIeJbnLvrQR6vMfWT6Zl2MQQ6y4KTBgBdWHvteM9Yva1nnMkJLr3vHvOOefIRRddVJAGs/fhO43zGwGUaopAUAiYAHu++yDJ+bsp++V+BsGXZr+InyWpwZepcMWPQlIANmfOHD1TBzX59D6KgCKgCASAgBLoAYCot1AEFAFFQBEIHgEOEV4nfPBPye2OECtkKXLI44CjJsKBGMl2cxhGSp2M8CjkLCHtu7q6HKkzMkbVwkOAdxL1BZxsmNsJogR6eLi777xx40bnPaN8BGUk8jVvzWyCIHCcFmMWCPP75ZdfljfffNMpC5HOekpFdpd3y5zZczI697Yd3CmHTxyRJXXz5GOLr5CJY6vzHbK+z1PnnJqqRm6/qqrKCSJKte6GRaB7O8Nz3BlHpha7+zrmlslOx1EaZ9UKlAlYE9kPaEmXwKZ24m+kBHqyhhgyGzUe82PKF7l76a6fHoQqhxLo8Z9DlIz52c9+1lfuyvTokksukbPOOivyDvJdxncawZl6nooc/sQ/EIU6zuucXdirEjxi1kz2jan8PpDohlBn3YzCr1CogQAPkgKw+vr6wFXGCtUvfa4ioAgoAklAQAn0JIyi9kERUAQUgQQiYDOBTnQ+Tg+yPebNm5dA9P13CSKPLAqCCrBC1MCGuMfhQ61fzZjwP3bZXumVbMehQb1zJET5P/6scnPZopr99UES6N7AFxxakLBJdlClQhyn3ZYtW+SFF15wyNB0Rk3xIVPHSKu0ScP+bTK/eqaMG5VeBeD0e6fl7T2NUlk6zqmB/kfnrJLRJcFk9bPm8T1E28nQYe0b7P2LikD3YucnUzPOGUdKoGe/huknxCERIBNUwj15syGb+unsowgmykWVw52tSP3sOAciJW8W+O8Re4/HHnusH3nIHuz666+PPCjLTXCy38nWnAC6UyekZNhw50dNEXAjgL+AcweBul7FOHfwJWS6UbNzf573wk2os3dM0nmFPpMUgC1YsMDpq5oioAgoAoqAHQgogW7HOGgrFAFFQBFQBDwI2Eyg4/jkgIOzigjhYjUipcHBZBiSsYBke9ROPHfNYkhcteARoMY8Y22yqlAXQGUAx8WmTZucDFgIPIIY1MJFAFlxxiGfjH+vkkCqmtnh9sKeu5PtAXEOGZ3OyMS68MILBZJi04EWebL5N7Jub5OMGFYi8ybMSOvA29fdLi0Hd8mSurmyuG6ufGTexXl3nLHDCdne3u7cy6/cfqEIdG+HWSvcmZpeCU+3gzRXYilvkLO4gSHQGQfKKqgpAn4QUALdD0rJuMZdP521L50qh7vMhR85djeBTjmpYlSNKcQM6Tx6WJ5rek3W7WqS7hNHHaJ46viJcln9B2V2zfScCD1K8zz11FP9ugM5eOutt0a6rx6M4EyHNfP7rR0N8quNa2T97mbpPX3aubSufIJ8qP58uXjeB6Rs5OhCDJU+0zIEKJFECbCKigrnDDmYMa/cakapSvsRPOpdN+NMqPP9QFIAtnjxYmd/r6YIKAKKgCJgBwJKoNsxDtoKRUARUAQUAQ8CNhPoHP4gXTi4ESFcjAZpAHFtyI/a2lqnBnkhDq4cyCGTiGiHVFQLDgFIN+TaTVYuDlqCJNzS4c3NzY4MHyQjMvpq4SLQ2NgoPT09OWf881mCIYxEOSQlgSdRB76Ei1Lmu7N+QZybeoOpPkGGzIoVKxzi3Kxtvad75YdvPiy7D7dJ0/4WmTBmvJxRMUmGDR3WdwvemwNHD8q2g7tkwugKJ/v8+oW/L1PzrH/uVYHgPeR99EOcGAKd4AvWbT+fyYxiflfQJsgknIbMx8GIJSP5bts8VQI9vzlQrJ82BLoGXhTfDPCjyuGnfrpb7lcJ9PDn0aneHrnnlcfk142vCn/uPHZYTvWekqFDhsq4UWNkZMlImTFhsnz2optkWmX22duUj+HHbeypb7nlFqdESBRmCE7UbDjXZbL9hzvkW7/8T9nesUeOnDgq7Ue6HGyGDhniqPOMH1MupSNGymdXfEzOn3Vmptvp/yccAQJV2esRbE0gdjbmZ91kf+gm1G3bL2bqL8HqnE2wJUuWOAo1aoqAIqAIKAJ2IKAEuh3joK1QBBQBRUAR8CAAUeDNTLMFJA5/27Ztc5qzaNEiW5oVSTsYExwsyNhjHO4g3ziwFsqQHITkJQNeMwCDGwUv0Wok270H+s2bNzvEF3KPyH+rhYtAU1OTI5kPcUoWRzbG2gV5zthibiWBbO4T52tZu55//nkhECGdsZYsXbpUli9fnjIoiKzyxxt/LW1HOmRrx07HWVxVWiGlJaOk53SPtB/tlOM9J6XKIc+nOBnol848L68AI+/Ykb3D+GUTtIQDkrG3hUD34k/7yDiir5DqzHOvQTgaMh15SwLZCmlKoBcS/fg+Wwn0+I5d0C33Uz8dAtWse6Z+uptAnz17dsHXwqBxsel+kMJ3PPkfsn5Xs+zq3CcHug9KD4FoQ4ZI73vvyRARGVdaJlPG10rVmAr5qyv/WObUTM+qCwSUrV69WghKdRtKZytXrszqXrleDHkHiecnQ7i9u1P+9rE7ZU/Xftm6f4ccOXlMSoaVSGnJSOk53StHTx6T4UPJzq+TqrIK+cIlt8iKOWfn2jT9XAIQILuavR2BIQTd52N+1k32i27JdxsCRwfrM/L2qECwr+cMYnt78xk//awioAgoAnFDQAn0uI2YtlcRUAQUgSJBwGYCncMfdZ+xhQsXZkVgxHn4IEkh34yMGhnfyHYX+oBHdjQ/OBdnzJgRZ4itabuXrMPRQTZKKrKO+o2QXmTr5usQsQYAixuSi2Q+jllUGnDMYKmUBCzuciBNY91+6aWX5K233kobnEXWFU4rMrsh0QdbT5oPtMhTm1+SoyePy/4jHU7G+W8zr4ZK+aixUje2SspGjJEFNbPkslnnOf+eizF2HR0dTuBSvmNnO4HuxYd9gCHTGb9Ucu+QSYZYKkQ9TCXQc5nV+hmdNzoHUiHAem/KXLD2sbfi39xmylxADjGPMCXQw51Pd73woDzb+Kps2tfiEMW146qkuqxSRpaMcL6XOo52yd6uA3Ky55TMrZ0uEytq5J9u+IpDqmdjjP39998vBw4c6PexK664IhLFM78ZwszJv3v8O7Ju1yZp3LvV2d8gY19eWtZ3Tjh+6oTs7mxzsDmjarKD2T+t+nOZWJ5d5nE2+Om1diOAD4E1jbNikIplzEcvoZ4qCYM9oslQtyEA0ztaJrCOMxoS7oX2r9g9m7R1ioAioAhEi4AS6NHirU9TBBQBRUAR8ImAzQQ6RDJZt9j8+fMTf8DhYErWJgQOf8Z5B1nK4Teb7EefQ5/1ZTiaIAY5DCNjqZY7Ao709IEDQlY/xuGdIAmCJdIZwSSQWxDs2Ury5d7S4v1ktpL5kKY4RclswCAcKXVQLLX1+C5544035JVXXukL/vHOHshyapwjmQhZzfzHyYZ0+2DWcaxL3trdIE0HtjnkuduQa186sV5mjp+S8zqJA5Cxw6kWxNjFjUB348naRPCWIdRxwnqN9SrbOsL5riRKhOaLYHF+XudNcY57tr32Uz+de/IdZgKJVPY3W5QHv55M6z+9//+X1vbdTub53LoZKWt6U/u7ua1FII4XT54rN39gpXx02Yeybgzf9/fee2+//Qpj+olPfCJr1aFsH+43Q3hz23b5m0f/VZrbWuXYyeMyf+Ispxa81/jebu3YLWC4dMo8uXrp78knz78222bp9QlBgPJ37OM4KyLjHpaZ/SJnU37SBSJxHjJ7Rv5caJ+G2RcgPQ+BXmiFpbDGR++rCCgCikAcEVACPY6jpm1WBBQBRaAIEEBmFme/jUaGAFmg2Lx58xJdo4oxQNLPEDiQbki2Q1bbYmTWQu5z+CULRy03BHjncJ4h34gxxox1JqKVcgaQWkhK86MWLgLZSOYT7LN9+3Ynow3DYYXUfjE4ZSAe1q9fLy+++KIcPnw45aCwZpx//vly1lln9a3jJiDHD4Fubnqi56TsPXxAjvUcdyRMkXOvKB2b10TAyUi2jqkLjqTqpEmT8hq7OBPoXjAZX+Mc5beZ4+7rWLvchHoY816luPOa5kX7YSXQi3bo8+q4u8wF+y5TjsV9Uz/10/NqRJF9+ME3npSH1v5S3tnRJLXlVTK5In1tcPYC7+7cJNOrJsm82hnyP2/+bzJs6LCsEeOMiZy72whcvvnmm/PaA2RqiMkQphzTYCWZvvv8/fKrjWvk3V2bnOzyCWXpydCe3l5Zt6vJyTyfWT1Vvnvr3zqZ+2rFh0BLS4uzV+OsmG0JqnzQ8hOIBHnu3i+yf4yaUCeAlzMIz0bhMIw9az446mcVAUVAEShmBJRAL+bR174rAoqAImAxAjYT6LTN1M+dM2eOIAmWRCNiG2eKISaQN4bAsU1SjAMnJD/jwHioZY8ABBREq3HG4jgjo9zP4Z2MAghKJPlw8KmFi4BfyXwIGrKXjWoE726YGR/h9jq7u+Oke+aZZ2T//v0pP8gads455zjkOSS622xQtOB9Yu3F6YcDj6AHxi5fZ16SCHTvwPqph+muhRlUtpEh0CGttIRIdu9pMV9tCHT2LdOnZ1cruZhx076/jwD7NoIeMcqO8PdUgcdGtpgMdVM/XXH0j8BfPfxtWdu6Uba173KyqEcMLxn0w837Wpwa4GRl//21/4/MrJ7i/2GuK3/5y186QYBu+73f+z1ZtmxZTvfz8yG/Etv/9YF/kHd2NsquzjZZOqVehg0dvEQNNdKPnzopCybNkv9+7Z/K7Jppfpqj1yQMAQKu2atxVhxM2SzsbpsATPwcrJumNJ37uZwT3HtG9nj57sEz9YvzBz4N1uxiKhGYCRf9f0VAEVAEbEBACXQbRkHboAgoAoqAIjAAAZsJdBxUDQ0NTpvJePYSMHEfTlNzF1l0N/lGtHjYh8dcsMMRjRORwy2KAGr+EfDWxoYwR7KdYAm/BvGOPDikO0SfWrgIZCLQcQzx7uKEwchkQLI9aetUKpQpNfHss88KMvfpDKfURRddlHaOF1LRgvcR0r+trc1pPjKOjB1ZMUFYkgl0Nz5+5DtZ60y2EcRSrrLHSqAHMTOL7x5KoBffmAfdY0Ogsy8neNRdP92oc6Srn27WPogaG/f1QWOVz/3+y4//Xtbvbpb9hztk6dT6jLfadXCfI/XOtX/54U/L0qm5nUsgGu+5556+OvdmP3fbbbc5ARNhmF+J7c/e+3Vp2L1F2o90ypIpmfu38+A+6TjaKUsmz5OvrvysLJqswc5hjJ/t9+T8wj6Us2JYczgXDPA5GTKdtZN3z2vsx90Z6vw9aGP/zzkG4p4Sgbo2B42w3k8RUAQUgdwRUAI9d+z0k4qAIqAIKAIhImAzgY5DasOGDU7vqZEbFLkRIpy+bw3uZK0ayWOca8h420y+QWCQNcFhtr4+s3PLNxgJvxAnBoEHZqwZY8Y6W0UFsGcMKisrHYUCtXARGKzmPGoRJoOIVpDhQUCEbaoRQSNE9siaNWucWufpSn+Q5Un2ViaVhEIR6N73EVKX9zFIJ12xEOje+UVQiXGOInucSu7dyB6DO85Lv++MEuhBv83FcT8l0ItjnMPsJWsZ+3VDoHufxVnFTQqZciDu69yBRJxlMpXsCbM/tt77zx74pryzs0n2dO6XZdMyk1rb23dL17FuWTxlrvz1ys/Kgkm5l5ZiP/fTn/60HzQEbl9zzTWhwGUI9EwS2w4mO5pkd1ebnDmlPqNa1dYDO51a6QsnzZb/79ovypwaVd0IZQAtvyklqNiPcS5hr2Wr+VE0cpcIymbPOFifCZ5lb8BajD8jCQQ6fiW+Z/wo2tk6H7RdioAioAiAgBLoOg8UAUVAEVAErETAOPqtbJyIQ6DjnDrjjDOkrKzM1mZm1S6irnHWmMjruNRLhgDG6cPhbMGCBVn1uVgv9o415DfEYi4HXEh4DvzMF5wiauEikK7mPO8BY2EIZMYTVYAkOGDSIYoj7t1335UXXnhBIBNSWXV1tUOcI6/tB4tClITw1qpn3Bg/P+3NZrYVK4HuxQiHHmsgcyad7DEBRTh4cWQOJveuBHo2M1CvNQiQZUa2mUq465zIFQFDoLNvg1TNZKZ+uslOHyyQyGRa+g0kyvTsOP//Pz71A/lN85vSsHebzK2dLuWl6bO/OReu27VJykeNkRkTpsidt/y1VI7xr+iUCqennnqqL2jb/P+qVascdZqgzdSopoTTYEpU//brH8szDS87mfkzqqZIVVlF2qb0nu51AhDqxk2QGdVT5Ht/8DUZVZLM0mdBj0fS7oc6FO/IlClTYpN84FX2ICiJs4fXTKkM1k4I9VzO06iHoeiGX8lGRb2vfe1rcv/99zslshjDdLZ69Wq59957nXKH4MX3COpfn/rUp2TlypVJm9baH0VAESgSBJRAL5KB1m4qAoqAIhA3BGwn0JFwp404MApZxyuIceVwSN2tffv2Obfj0EcmMZLtcbDu7m7B6QPZxAFNLT0CXnn+XCTbvXcnAwoygPky2IFaxyUYBJjrzHmIYZycjClZC6beNxnLZC7bnN0RBBIE+zz99NN965b3njixkGpfsmRJVo6sqAl0d636IN7HwbBVAn0gOsY5ash0nH1e2WPGBYeoIdTdtTBxduL01BroQbzVxXMPJdCLZ6zD6in7gN27dzvfb34IdG87/GRZBkEKhdX/qO77est6+R9P3y0bd292sJ5Xmz4Y78Dhg06t9AUTZ8ny2WfJn3/4D/NuJt9JP/rRjxwiyhh7v1tvvTXwIDu/Naqb9m6Trz/+Hdm0r0VO9Jx06r0PHzosZV+3d+xx5O+XTJkrVy66SP5o+Q15Y6I3iB8C7KtMeSXOKOyp4mh+SgThjyDw0gQiDRaE6caA9Zx1Hb8SZTlssrvuukv++Z//2WnSYAT6X//1X/epZhBYT/AySQ6oe2Gf/vSn5c///M9t6pq2RRFQBBQBXwgoge4LJr1IEVAEFAFFIGoEbCfQiaoli41DYDb1oqPGMdPz6ANZqxzYsFxlvDM9J8z/J5sGWWsMAj3orM0w2x7lvXmnILshfIIcaw78kI68B7wPauEigCOCbPMJEyY4P+73F2cNY5BrPedwWx7M3SE6qXNuymh474qD+5xzzpELLrggp9ITEFu8J8gzzp07N5hGp7gLTjiIV+NUiqJWvRLomYeTzCIUAQyhTnkArxGkYsh0MCWARQn0zNjqFe8joAS6zoZ8EWAfsGfPHie7b9asWXndTuunp4ePDOr/+uA/yNb9Ox3CePyYcpleOVGGD3u/BjL4UQ+8tX23jB89TmZWT82r/rm3NevWrXMCBt121VVXBb5H4SzFuTBTjWr6+9X/8y/SsGerNOzdKiOGlci0yolSNnJ03xnsZM8pR+KdevBTx9dJXXm13HH9l2VqZV1ec1U/HE8E2Fsh4Y5RUinbcmG29trsGY2yR7pSGQQMGEKd/X4qXwVnD/aeBKTnu6YHidd//ud/yh133NEXXJqOQP/xj38sf/d3f+cER/zN3/yNfPSjH3WCjsCEf3/44YedZnHd2WefHWQT9V6KgCKgCISOgBLooUOsD1AEFAFFQBHIBQHbCfRNmzY5dVSRrCbCNo4GaQ75hrMEQzaYrIZcZMcK2X+yMrZs2eI0AQn3uLU/CuzAiIxdI9kZpDw/JCAKBkTMhyEpGQU+cXrG9u3bnSAI8GZcTckFyHTe36QGkOCkWrt2rbz44ouSitRkDHE4XXbZZUJJglyNjHDWRQjRsCQUGTPeRxxu2NixYx31hrDlcpVAz35WGLl34xw135feO/HesR/AcZjUdzB79PQT6RAwBDpBi/q9qfMkFwQMgU5Az8yZM3O5RdrPQJC6A4n81E93K3ME2hgLbrZh92b5hye+75DB1PPGkGYvLRklPad75eDRLjl+6qTzbzMnTJaL554rn73oY4F9F7D/ufvuux21J2Ps42+77bZAzzycpdgnoEKWqTzZ3q4D8reP3Slth9ply4EdcvzUCRk1fKSUjhgpp3p7pfvEURk6ZIhMGV8nNWMr5Q8vvF5+f8EFFoymNqEQCLB3MsHulL+DRE6i+SmVQRAwWCxdutQJ9iVLG+PswV6TM4z5t0JixBnzq1/9qvz85z93gmoI2MJSEei0m3JZnKHIVr/44ov7NZ3xv+SSSxy1tFtuuUW+/vWvF7Jr+mxFQBFQBLJGQAn0rCHTDygCioAioAhEgQDOAkMMRfG8bJ9BFDUOJQ4UEM9xMq/kM6QNjv+4StFDphlZuPnz54dOQsVtrHG4cehl3CF2cIwFGfSB9D8HYpxtOEXUwkXAEOjmKQSMQL7G9f31gxZZGdQBJdM3lbEGQ5wHQSKETaCTXQJ5bohYgh4IfoiCdMWxZ37CJuv9jGvcrvGbpWmyjMhSTzKpFLfxs6m9SqDbNBrxbIspHxEGge5FxA8pRDtY+1j3CCTi70myN1o3yL89e68cOXFM9ncflPbuTjl1uschiceNKnNI4rJRYxzpdsjzYWkkzXPFhMBtagu77eqrrw5U6pmzLedvzoR+ygDt7myTf3rqh7L30AE5dLzbwaSnt0eGDBkq40rLpGpMhYwcXiKfuvB6ubT+g7l2XT+XAATw6VAiAIMcTrJSlnu42OtDRJsgTHD4zGc+06f8x7WU5Fq2bJngw6ivr3eCgcnSL7T94z/+o/zgBz9wiP7vfOc7snz5cqdJqQj09evXy7e//W3nPMPnUhny7QRBX3rppfLd73630N3T5ysCioAikBUCSqBnoiC9OwAAIABJREFUBZderAgoAoqAIhAVArYT6EQOcxiqq6tzDgtxMW/mI84uyLc4R4KTVY1jCSNjtFgO5ZnmHA5P5NW7urqcS5HLQ96bjLcgDVKTH5xtNkTMB9k32+7FmOLgNMFFcSy5kA2mrLHPPfecIF+aypjTOHTOOuuswAJneF8guHH+48gKyiBfKXVgMjggsHkfM2V5BfV87qMEepBoikM0IMHvzgr0PsGQSoZY0sCFYMcgrndjLUC5RTPQ4zqChW+3IdALUT7CrcxBUBjfLV5LYv10sq6f3PCivND8ppNx7baFk2bL5QsulHOmh1NKij3Efffd1y+QEKL7pptuCmwyEozMczgX8p3lx0719shr29bJLze+JJv2tfZ9BCn7S+vPk9+rP9ch0tWKGwHO6i0tLQ4IEMTFuhfi/PbKK6/II488Iu+8807fGd09OwhGX7FihZx//vly7rnnOipVhbBvfetbzjoA4c9e1qhyDVYDfbB2XnPNNdLU1CQE/nBvNUVAEVAE4oSAEuhxGi1tqyKgCCgCRYSA7QQ6UdQ4jWpqapyfOBhyj8iDGUdXUiSfOYxyIMOoWRznYICg5hHqCGQqG8l26qmReR6GvD3Z52Shc8gOIgM4KAySdh/GFGLXyJczz2fPnh3KmBYaOxy4OJYgz1NJx9K+hQsXOnKBQRPQkBK8Ozj3yAYJwvg+I4veBLMY0izqtcoQ6JAfScsODGKccrmHOwuUjCGCPkz99FRy74ZUIuCIeRDGmpxLP/Qz0SKgBHq0eCfxaXyfsPcqBIHuxtOrzEG2Jd95bkNhhfXOqHPw5yhUV8Iad8jz5rZW6T5+VEqGl8jU8bVSOy78YOqGhgZ54okn+nULOWTU0IIwE4xMcB8qAtkauJChXzJsuIwdNSbWY5xt3/X6wREw51KuSurZJds5wDqJ/+Lll1+WN954Q959910nW91tnEUWLVrkkOnnnXeeEzAcVf14/CvupIR8CPTW1la5/PLLna5985vflOuvvz5buPR6RUARUAQKioAS6AWFXx+uCCgCioAikA4B45CxFSEOAhDSkNBkodtsYImTjWwnjMMY2QWFimgOGitIIZxK5lAedIZ10O0N+35kQ5J57pZsh0APy1nJvKIOOs42sgrUgkcARzkErNspzZjyHifNyOh98sknnWCBVMaaixMmrLrBQRPoBDzQFxMIEGYwS6a5oAR6JoSy//90dYhZf3E+GkI9HanEugmZDrFEQEVY63T2PdNPhImAIdAZf8gqNUUgWwQMgc66YVP5HFM/3UgWewkh+kngkCHT+a2lLvyNPt/hSCp3d3f3fYBAvyuvvNLfDQa5inEz5bDYXxX7WSpvQPUG/RBgHTD7+jlz5uhexzM/eP9QGOMdxMe0YcMGWbt2bV8gvLkc8hwSHTIdUp1g4qgCYvMh0L/yla/I448/7sjVP/3007q+6PqgCCgCsUNACfTYDZk2WBFQBBSB4kDAdgKdQyDOq8rKSiez11YjA5m2GgcWjnpItyTJnEMqbty40RkCCNxcsiZsHb9s2gUOEOfUcMZwqkbhBIPwRJYaZxtZBWrBIcCYEvwCxhjvLfMbkre8vDxRxAuOYWQN16xZk1IOlvmMXPvZZ58dqvQjhCjOKxz8CxYsyGswuRfrL+MIMUqW2Pjx4wvmOFQCPa/hTPnhdAS692JDKpns9FTKCgS3GTIdUikqp2jwqOgdMyGgBHomhPT/MyHAXo/yObYR6N528/1nyHR+G2Uk93XuUhe69g0+8q+99pr85je/6buIfeHnPve5vM91jBMEHoaaSlRZrpnmuf5/MhDg3UcFj70wBLpafwTYI1IekH06fpra2lpHcQwSnbMRWepkqHvVPVDh+vznPy/UFw/bciXQqXtu2kd99GuvvTbspur9FQFFQBEIHAEl0AOHVG+oCCgCioAiEAQCthPoHAJxXtmcBerNWkVqnsjfpGW4MVeI1MaowQ0BUWzmlfeGXCWwI4oacxABEPc429QpEtzMI3sVKXET/IJiBE4VHOYQ6uPGjQstCzu4Xvi7E+spWedGJcP7KTKsLr300khUM4Ig0FmTKG3AWGGQAwSz+K0p6g+17K9SAj17zDJ9whDorLXZKHAwFm5Siffda0msIZwJz2L5fyXQi2Wkw+snakN8z7BOQHjGxdz101kDByt1wXcmQYNa6uL90SUI66677nJUpoytXLlS6uvr85oCfCdt2bLFuQeKBlGXmMmr8fph6xFANYGzIu+yBlsPHC6IcQh0fqNKk6o8IPvN119/3SHTIdVNyQXe/UcffTT0OZALgc657rrrrnO+q1APu/POO0Nvpz5AEVAEFIEwEFACPQxU9Z6KgCKgCCgCeSNgO4HOIRAHqI1ZoBy+kNSmfRjkDYexJBPLEOjMGZw+QddEznsyh3wDAjmQ9zaS7VFnufJ8CFCyYMzhOuQuJ/72OJrIXMahiZGJgHQ5wS+82zgkINTj5DRPNWgEfjz//PPy1ltvpRxT1tcrrrjCCYyJysC+paXFwRppxGyNMeN9wNGFse6y/tqQTawEerajmfn6XAl0753JynQT6qlqCEMkGdljSLOkBcNlRjs5VxAExY9KuCdnTKPuiSHQUf8Jq6RJ2H0ypS6MMkcx1E8PAtOHHnrIUcoxxh7pox/9aF63JpABAg9TAj0vKPXDKRAIaq+UVHDZ8xHAYnwZVVVVGbvKWfDtt992zt5RlILJlkBnTbn99tsd0p/vKNYtgr/VFAFFQBGIIwJKoMdx1LTNioAioAgUCQJIV9lqtpJY3nq7kGyTJ0+2grwJcyypgQ45xAGtWA5nHLaRTseJikFg0/+oJexROoDshSDMNwMmzDkSh3t7M5fJasUp4g4KQdKdSH7+zaa6p9nii1QoWefuWp7mHhCD5557rlx44YWRZ0HhyN+2bZvTlEWLFmXVLQICcGqbbGKCHgh+sIXoVAI9q+H0dXEYTmE/NYRZGwyZTpCGDQEavgDTixwElEDXiZAvAkbFIM4EuhcDP2sfGazsc025i2Ksn07ZKvZPxsDkC1/4Ql77JfYtZu8DIZ+kUl/5vmv6+fwRoPQUvhPmVZRBsfm3PJo7uBUgZs6c6ZR7ss2yJdC//vWvy09+8hNnr/rggw+qSp1tA6rtUQQUgawQUAI9K7j0YkVAEVAEFIEoEbCZQEealx8cOLYcBMkEJjPeZK7V1dUJEcy2kDdhzp3GxkZHBhKykazVpJs3UIKgAQIlopBs92KLUwSpcZ6N1LZabggwf8lcNoQyDgfms9eJaePak02PIZmffvppWb9+fcqPQThfeeWVwvpVCMuVQHcrQeDM5n20bS1SAj34GRUGge5tJeNGZiYZ6szPVHLvyO2yZrAnUcnj4Mc56DsaAp0xozSHmiKQLQLFUAaA8wxrn8lQ1/rpv50lnAG++93v9quHTAZ6PudRsEV9B4PA06CsbN9IvX4wBNgjc35hrxLn4N+wRtmtAIHEvW3nB/qdDYF+9913yze+8Q3HN8BadfHFF4cFnd5XEVAEFIFIEFACPRKY9SGKgCKgCCgCuSDAYd5d4y2Xe4T1GTJAyQTF+YmjoZCGgwninMMpBuEG8VboertRYtLU1OSQCpBWNkZtB4mFu7Y9wREQjZWVlQULlAiiZnSQ+MTxXjiICUIwxBiBL4xrquAXm9aebLFGnvCJJ55ImXXOurVixQo555xzClrvFJLSyJgi4Z4pAMlbMgPnIEoQZAXaZkqgBz8ipq5ntjXQ82kJ64Qh0/mdSu6d+WcyNFXuPR+0w/msEujh4FpMdy1GFQM/9dOLJZjogQcecMo3GTv77LPzIqkg5Y0s/KxZswoSkGv60nJglzyx/kXZuHuzHD11XEYNHyFzaqfLFQtXyLy6GRn3ZcW0DsSlr6bkBPuRuJefCgNzdwDL3LlznTJdtplfAv3nP/+5fOUrX3H2pl/72tfk1ltvta0r2h5FQBFQBLJGQAn0rCHTDygCioAioAhEhYDNBDqOK+SzcVITKVwoI5sT+WyTrV/ITORCYcBzm5ubHQwmTZrkkMlJNC9RZ0ugRL41o5M4Vn77RIAQTiXWEv7sJ3OZmnfIIJJlipMzDsY69cwzz8i7776bsrlkTX34wx+WioqKgneHYAaIfiwTgQ6RyfoLiYnh8CKbtBBKEH6AcxPotDFTcICfexb7NYUg0N2Ys27w3WcIdeav11hXTHY6v1Wat/CzVgn0wo9B3FtQ7HPI1E93r33eYCLGmL2SKXfBmS0p33svv/yy8GOMkjGf/OQnc57W7NMI5MQ41/K9EbUdPHpI/ufT/1ve3tEop3pPSdexw9LTe1qGDR0q5aVlMmL4CJldM02+fPmnZGJ5ddTN0+flgUAxKGbkAY+43z/U3GxMgvBDoEOe/8Vf/IWjCviZz3xGvvzlL+cDi35WEVAEFAFrEFAC3Zqh0IYoAoqAIqAIeBGwmUCH9CLyn0wHIoWjNi/xZkMmctQYuJ9HPWUOnxMnTnRk65NmvAs4tugjZlNte7fkdSbCMWnjkk9/vMoRZGWQuczvwSxuBDpkNLU6USrwGn299NJLZcmSJdY4td0E+oIFC9I6kZn3kOc4iTCk53Fg2+ycVwI9nzc29WcNgQ7ZUMhgOtM61hUIJfOTSvIYAt1kp+OkLQRREvxIxOuOrOMQCirhHq9xs6m1Oof6j0Y29dNNQFGc66dzBiUL3W1f/OIXcw6QYu/DngabM2dO5HuZjiNd8v8+/D9kx8G9suvgXuk61u20ZdjQYdJ7ulfek/dk7KgxMqWiVmrGVcnfX/dnMrWyMKV+bFoH4tIWXa8GHyn3+8dZ2kYVq0wE+po1a+TTn/60cNa47rrr5I477ojL9NR2KgKKgCKQEQEl0DNCpBcoAoqAIqAIFAoBsvtSZRMUqj3u5yKjjaOBGnH19fWRNomDCZLttAGDxEeynSyLYjUklyEMILGqq5OVlUCNcWpj21rb3i/hWKxzM1W/yRglIMIoR1DrjvIDfogsW9QvMo0nxB1Z5++8807KS8k6p9Y5qhk2GUEqBORgZIF4s8lx0kN8oRqA8f+sv2VlZTZ1I2VblEAPfohsI9C9PTRy74ZQZw54LakZmsGPdnB3VDIhOCyL9U6UcyGYl2AY9g9q/REw9dPN2mf2W+6rOMOZ7HR+x6nuN2v7nXfe2a/TSCVT/icXAyfOGljUgeHsq/7q4W/L+l2bpHlfq5x+7z2pHVcllWPKHQKdsew8dlj2HTogPad7ZU7NNJleNVn+9eavysiSEbl0Vz8TMQK6Xg0OuPv9W7x4sePbsc0yEeg33HCDrF+/3ml2puCkdIpktvVZ26MIKAKKgEFACXSdC4qAIqAIKALWImAzgQ6pCQEGeQLJEpV5ayUjeUzWta2SwVHhsm3bNiEjtKamxvlJguEw2rdvn0CYYjj2yFC2TdYtE+GYhLEIsg/eGva8v+PHj/ed7QN5SwAN2dtkCdloZEatXr3ace57zcasc3cbB5vPvJP0zQQvkSHCO2mjoyvVvFACPfi3xXYC3d1jt9w7zlr2E/yb24zcuyGV4jK3gx/ZcO9oCHQlP8PFOcl3N4QUwVuUL1IbHIFs6qeb9c9PUGMhcf/hD38onZ2dfU24/PLLZdGiRTk1iTMU+xtUdKLeW67b2SR/++idsrmtVU70nJI5NdNlxPCSAf2APN/S9luCff7EWfLFSz8ul80/L6f+6oeiRYDzLHtnFNQ496j1RwCVLhOYu3TpUiuDeTIR6GTOG1WuTOPb1NSU6RL9f0VAEVAErEJACXSrhkMbowgoAoqAIuBGwGYCPeq6zzi5IVI5gPJnHBw4zCDe1ERaW1sdiWgklHPNvrAJR7J3UTgw9WxxkFJb2cbsGLJ6qEGPocZgYxttGFveW2qXm4CIXGvYF7p8xGBYQjAj4ffSSy8NIOb4nK1Z5+4+pZvP/DvvpCmjQPASa7DtDnZ335RAD34liBOB7u29O0MT8iSd3Ls7Q7PYg/WCmkFKoAeFZPHep62tzSFPlUDPfg6466ebDPU41k9/7LHH+hRzQOGss86SSy65JHtARKSQ32V3PPkf8uvGV6Vx71Yns3z86PTKREdOHJPmthaZWT1Vzpq2QL5141/k1F/9ULQIcP4h+QDVqSSc04NGD2zAiDMFZa10rxU0wno/RUARUATyQ0AJ9Pzw008rAoqAIqAIhIiAzQQ6Dhdkw7Gw6z4TzUtWgKkhTAYnksE21scKcToMemvUADh8Uv887pHt9IPxNlK7ttdWhnTZtGmTMz5Ep0MMq/VHgLUM8pV1A8snIAKHOTKb4GyyAWzAG2L/8ccfd7LjvUZbL7vsMiGrwuYa4bTbPZ9NQAhrL+OHg532Z6saYMP40AYl0IMfCZO1Z0sN9Hx66M7QpF+p5N7Zd5j6wfzZ9vc5HzzC/KwS6GGiWxz3NgS6ZnTmP97e+ukEyqVS53CXu0Cdo9Dr34svviivv/56HwBIr1911VU5AWIyYCHuZs2aldM9cv3QzXd9Sbbu3ykdRzplwaQ5MnTIkLS3Ylya9m2T0pKRDtl+zx/d4dRGV7MbAbKrmWOUrOJcq9YfAc52rOm8f0i4K4GuM0QRUAQUAbsQUALdrvHQ1igCioAioAi4EMCZm8qBawNIUclW48SGLIOAwyorK53I7ThlPUYxXmDE4ZOM/LjWgsQphMIAjnWMTG4CJZB4tdmYm0aKDeedSv72Hy2yepifRtaOEgPV1dU5O16RQITMZX5A8BbamLfr1q2Tp59+um+dcreJLO2rr746NmoZbgKd+UxgAFK55p20sYyC3zmgBLpfpPxflyQC3d1r3mveBYJ+6GM6uXcIJb6jINUz1bz0j2ryr9R6sMkf47B7qJLI4SHsp346BJdZ+wpVP33t2rXy3HPP9QHB+eemm27KCRiTAcvecubMmTndI5cP9fT2yKrv/Zm0tu+Wk8i3107PeJuW9l3C52bXTJd//4O/lUkVySjdlbHjMb6AwHD2EpzTOQOp9UfAnDXYR1GGQf08OkMUAUVAEbALASXQ7RoPbY0ioAgoAoqACwGbCfSwZatxXkOk4iDDOEjhGCFyW20gAhzMOXwiq4zUedzMm6GMUw7yPA5y6LynjY2NDuTUTUQhQU2c7CX3O4yzlTEl+zwfw8mJ4gL3mz9/fj63yvuzBBI98cQTfQEU7huSmbV8+XI5//zzY+UIcgeE8B7i8MPi9E6mG1gl0POe8gNuUMi6scH3Jv0dIZR43+kvpDp7IK/xfeUmlDSDKj2eSqBHOXuT+SxDoKskcvjjyz6XICKz/qWq80vwqLvcRRQEGHvvX/ziF30AEGR9++235wSIIdAh8Ci3E5WxV77+u38q29t3S/eJYzJ/Ymbyfsv+7UKO+szqafKD2/5eqsoqomquPidHBAgkZu/AHKXcmlp/BCjvxQ/rCMqGUawfOgaKgCKgCCgC/hFQAt0/VnqlIqAIKAKKQMQI2Eygh5l1S7/JMDXEDRleEG+a2Zt+AiIb3dHR4QQYgFWcDEk7HAtGbSHfDOWo+067GxoanMfOnj1bSwv8TiqbMTVlF4J8h7lna2ur41xZsGBB1MPd9zzeuUcffVTIiPcaGSZknZN9Hjdzr+2m7Tj7kJwstFxrvlgqgZ4vggM/XywEurfnbrl3nOKpCCWCqQyhzhoY9/cnyNmjBHqQaBbnvbSmcGHG3W/99CjKXRBM+bOf/awPCNbZz3/+8zkBYySkOWueccYZOd0j1w/92f3fkLd3NAqZ5fNqZ0jpiFFpb0Xm+Ybdm2VSRbVTB/1Hn/qmDBs6LNdH6+ciQgC/BkEo7Kch0dX6I2DKurBucLbT/ZLOEEVAEVAE7EJACXS7xkNbowgoAoqAIuBCwGYCPSzS0Cv3TE1viBuNRB781cCRyOGTWpDTp2eW/7PhRcMJR70zIw8dVIZy1H2jHxs2bHAei+wjGTjFbDiIcGqGVXah0AQ64/3aa6/J888/79QE99qZZ54pl156aWwDfsgAoVYjhgMLRYukKH8ogR78ylSsBLobSUMomexMCHVv/WDeJXd2pg31g4OfDf7vaAh0FEniGGjkv6d6ZVgIaE3hsJDN7r6sdShysO7xk6rcRVjrH4GaDz74YF+DId++8IUvZNeB311tCHQCn6I+R/3i3eflrhcelPW7NsuYkaVyRtXktATiroP7pP3IQVk4aY6sOvvDctsF1+XUX/1QtAgQ+Mt7gnw7QbZq/RHAH8A7SBAM6mJKoOsMUQQUAUXALgSUQLdrPLQ1ioAioAgoAi4EcPanymqyAaSgScNUZCqS7UgzqmVGAClLHNI4o6POnMjcuoFXQK7i+DIqAxALZM4jnRg3c78LyD7aXrM9THwpI0BmNpgQ9AIxQlmBII0gm5aWFse5gsxflIZzePXq1bJ169YBj8Vxu3LlSqFueByNYAACcVCyMEa98yStwUqgBz8zlUAfiCnrHySSIZSQfvcacu9uQj0O5UqCnD1KoAeJZnHeyxDo7DFQLlKzAwG/9dNZ/9gvQ5jluvf3EuiQ33/yJ3+SExCmBjN7OfY+UdqRE8fk03f/tezp2u9koVeNqZCJFTUy3JVZDq77DrfLvkMHZFJ5jdSVVzv1z+vKVQ48yrHK9VmcW06ePOmsVUGfi3Jtk02fMyU5WBPq6+ttapq2RRFQBBQBRYDEive84eEKiyKgCCgCioAiYAkCNhPoQLR+/XoHqXxJQ2/9a5wqZD2qZLv/iYgzmsMn2JEFbbN5VQaIxsehEOdoczLQ2VISvJBvjW+bxy5d23DsQZyTPYDhxCQgAkdk0AZht23bNue2ixYtCvr2ae9H9sjjjz8uzF+vsV5dc801sSWbvWuw6V/SShIwTwlK4wfFizivOZFN/AwPgiSGxADLOXPm2NIsq9rBXs6Q6axf6eTeDaHEupl01R2TbaYZ6FZN1Vg1hj0H38dKoNs9bH7WP3f9dAh1vp/9WJAEOsGDKHmZkkN+nh/kNS9tXiv/9NQPpeNIp+zo2Ot8p5aXjpURw0sE2fbOo4el971emVheLbXjJsgfXni9XHPmpUE2Qe8VIgKcW9hr19XVxfasECI8jvIVCmMo6cU1EDlMfPTeioAioAgUGgEl0As9Avp8RUARUAQUgbQI2E6gb9y40ZEwRuqOA08u5q1/nQQyNRcc8v0MTh+yR3H8zJo1K9/bhfJ5CGaIfhznGA4yiMdc504ojczxpkG8Czk+uuAfQ5KQ2n4myxK5bzLP/TpAs+0ARJTJACcDPWwSlHn78ssvy4svvjhAlpm2X3DBBbJ8+fLYEl4QeoyfIfUIZjHvKGsJa0pSjO8rvldxYiqBHsyoKoGeHY7u+sG8e2Sqe0tBsKbx3hlCPYly70qgZzdv9OqBCBgCHTlkzg5q8UCALFwTUMTvVKVwTP101kDWwnT7PLJ6H3744b6Oc/3nPve5nICgfA0/Jog7p5vk+aHnm16XO5+9V46fOiHtRzql8+gh6TndK8OGDJNxpWUyoWy8Q6gj2/7RZR/K82n68SgR2LJli7P/nDhxYiLOvUFjZ9ZzVK80GDNodPV+ioAioAjkj4AS6PljqHdQBBQBRUARCAkBnAqmjnBIj8jrto2NjQ7pQqZptjVyHSm6ffscZwWGfClkajFm7+Y1CL/7MJkTHD7J/LXx4Mk8gaRzS7YnSWWgoaHBcYwkTfI609w8dOiQk33K+4yDk8yKysrKUEltCCccUdiCBQuyIq6P95yQnV37hN8lQ4dL9ZhKqRxdnrabBAUg2b558+YB1yAzePXVV8eiZEKqDkLksW6YeucQyqzlOI8JCMGUQM/0Buj/GwIdJDRrKPv5wHvIOmPqp6eSe+fdNGQ6v5Mg924IdALoIBTUFIFsEdi1a5fz3iiBni1y9lyfb/109ipPPvlkX4eqqqrktttuy6mDBCKzJ2JvRwmxQhk1zn/x7gvy66ZX5ejJ98t/QJyvmH22rFxyscyqnlqo5ulzc0SAcwRnJQKM1dcxEESznqMoYmsiQI5Drx9TBBQBRSARCCiBnohh1E4oAoqAIpBMBGwn0JuamhyCH0cDDiy/RvYBZCpEGIazAjI11xp4fp+b5Ouo3cfhk0w120gMb4brhAkTpLa2NlSSNeqxzieYJOq2BvE8nJ4EwOBwxCB0CB6A3AnbIJgMoT1//nxfme77j3TI6zvXy8a2zXLqdG+/Jk6vmChnT14kc6um95uTEDyPPPKI8G55Dal+yPO41rvnu4X1oqury+maqfnJ+sHYUpIAoxxEFGMa9pwx99cM9OCRVgI9WEwJxGJvZAj1VEGUvKesPSY7M45y70qgBztvivFuhnAhaI99pVr8EeA72h1QhMKR10xAEesf59Df/OY3fZcQBHjjjTfmBAQKWez3bCkrQRZ6y4FdcuTkMRlVMlKmV02SspHh77FzAk8/lBGB5uZmZ3+NvyNJ++qMHfd5gfELsZ5TGlBNEVAEFAFFwC4ElEC3azy0NYqAIqAIKAIuBGwn0DkM4twgmpoDjx+DsMHpZST7kAtGejFsGWY/bYvzNeDK4RMis76+3oqu4CiAYIVoxXB6EWyBPFvSLNdgkjji4FUTgMjBaRlVViRrDmsPxlzP9NyNbVtkddNzcuzUcWk70iHtRzvlVG+PDBsyVMaNGiu1ZZVSNmKMLK6dI1fOXSHDhg6T9evXO1lN3lrFrFMrVqyQ888/P7ZrFvht377dWbsxgp/IAHWTcPQfw4kV1yCBVO+WEujBrzhKoAePqfuOEOiGTE8ld+yWe8cpjwpNHPZT7AvYt2gGerjzJ8l3R/2Gd0IJ9OSOsrt+OmPtDSgiA92U9AEFAoivuuqqnADRoJ6cYNMP+UCA87A5t3BeSlJpJB/dz3gJ+JhSYARDURpQTRFQBBQBRcAuBJRAt2s8tDWKgCKgCCgCLgRsJ9CRUSZTCtnmTNkf9IUa3cjjYZBeHCKTRM4UcvL4KPmyAAAgAElEQVQipQ0pBklNVm6hDeIR52Z3d7fTFJwFjDeZc0m0XIJJ4oiDV02A4BeCYKIkbFCw2LRpkwPfvHnzBlWuaG5vlYc2/ErIQG85uEuGDhkqlaXlUloy0qkr2XG0y5FzrxpdITPGT5GldfOkZPsJWbt27YDhYQ5fe+21sZVsp0NeyX2I81TBT0qgx/HtLEyblUCPDncj927qBxsVH3cL3NmZEOq2KvsogR7dvEnqk0zGIrLd/KglHwFvQNErr7wiEN/GkH5etmyZk+GbqX66Fy2zJhHky7lWTREICgF8IEY5C3KYQDe19xFgb2MCezlT4i9QUwQUAUVAEbALASXQ7RoPbY0ioAgoAoqACwEOFJBFttq2bduczCgOO/ykMzIdTWQx15BxRCZypsxRW/ttY7sOHz4sra2tThYpdaELaTj3GW+TKYJjE8n2OMrM+sURxwiykxCSSXTksha1t7c7QTAYJA0yhLzLUZubQCfbKF1QBgT5v7/6Y9nV1SbNHa0yYfR4mVY+0ckwN+b061inQ67XlJTLtLZxMuRwz4AuMa7XXXedlJenr5eeLw7dJ47KzkN75XjPSRkxrETqxk5wyP4gjH7iZEaiFMskuY+EO59Bqj5JtRo1Az2I2dT/HpC4rPeYbeVDgu+tXXdkPhsynd+p9ousj4ZM4rct38NKoNs1l+LYGkOgE8DrVwUrjv3UNqdGgD3Kf/zHfwjnH2Nnnnmmszc1ZhQ6TMkL1sN0AZ/sbwkyZJ/HmUVNEQgKAZQUSDrA2FcnNZg8V7x4l1taWhy/AcEr+IjUFAFFQBFQBOxCQAl0u8ZDW6MIKAKKgCLgQsB2Ah3CFscFzqt00frUk9uzZ48j2Y7TAqcEBGOUGavFMKkIZCCgAVu0aFFBuuwlWXHUcwgOk3QsSEdTPDQbNQZb2uy3HTh+KLuAYxErtJoADhYk87HBCPQNbZvlsYZn5d29zTJy+AiZ46lx7u5/x/52KdvWKyN73yfXzf/jkP3Qhz4UWsDPzq598urOd6Rpf4ucfu90v2GZWTlVzp2yWGZXTfM7XAOu86pB+JHcRxaVNZtMmUIESeTc2QwfVAI9eGSVQA8e01zvyNpoCHX2BKZUjvt+rN8Q6awDhZR712zPXEdZP2cQIGORwEUl0ItzThCc/Z3vfKdf56l/zhrH+pepfrpXoYOzKmdaJdCLcz6F2Wu+m80ZndJItirDhInBYPfGfwA+nFcoC0jQspoioAgoAoqAXQgogW7XeGhrFAFFQBFQBFwI2E6g47yCVIMQ9x52IN1wRnR2djo94rA4bdo0rfsV0gzHaW7qAC5cuDDyAAXGG8l2kwkyatQoR4KtWGTqwJ4xIEAEWfOkGM5p3nOT2UiWF8EyhcxixMHS2NjoQDxnzpy0c+zH76yWd/c1S9P+bTKveqaMGzkm9bAcOCGypVuG9OeuHcL88ssvlyVLloQ2nK/tfFd+tXmNU599X3e7dBzrkt7TvY7UfEXpOKkZQ3320Q6J/vuzL8j6vYbcZPyMGgREA3M0UwCTEuihDXnibuwm0HkfM82txAFgaYfYP0Ig8b0EmcRazr+5jXXcZKdDqEfp1FcC3dKJE6NmGQKdPdf48eNj1HJtahAIEMT90EMP9d2K754vfvGLfcGOmeqnm7OpWQM5z7JWVlRUDKqqFkTb9R7FhQBnKDKsMcoMoOKl9j4CBPtBoPPOoiChChA6OxQBRUARsA8BJdDtGxNtkSKgCCgCisDvELCdQIcwhSDHceWW2/KSbtST4//1wBje1AZzU1+NGuhRYu2VbLeBZA0P6dR39lvOIOp25fM81CN2797tkC44JskKsMFJjYOloaHB6drs2bOFYI1UdufL90nTgW3SdqRDzqyrH0jsQSbtOCZDdh0b8PExY8vkxhtWhVoH863dDfLzpudl7+H9suPQPhk+dJhUlVbIqOEj5NTpHmk/2iknek5KdVmlnFExWc6fdqZcNus830PK2oxyAOOXrRoE+IIzQU+s30kxzUAPfiSVQA8e0zDuyNxnrCCI0sm9Q6Cb7HSyOMPcRyiBHsYoF9c9IVAJElECvbjG3fT2xRdflNdff72v8wRy33LLLWnBcCt0sAayx0ll7CkJNuR3IYNFi3NUk9lr1inWK3Nu0XnVf5zZnxCIzm/OHUkKRE/mjNZeKQKKQDEioAR6MY669lkRUAQUgRghkEqCzpbmQ651dHQ4cndkG0PUGMl2Q7rh0IB006y0cEeNedLc3Ow8pL6+PjS5aXcvGGPGn7qBhqSDZCV7o9jMlDPg0B/3yHkcGKhH8C5j1OrDoZGOqI56rGkfGdIYmRwQPansX9b8b2lub5WOo12ypG5e/0t63xPZ3C1DOk4O+Oh7Y4fLDdffIHMnzgita0dPHZc7X75Xdh1qk9aDu6RubLVMHlcrQ4cM6Xsm79T+Ix3S2rlH6sZWydTyifLHH7hRasuqBm0X+PBO8m7mOn5KoIc29Im7sRLo8RxSlDxMdno6Mok139QO5s9B7uNMvWECdNKVAIonstrqqBAwBHpNTU1R7jujwtnW59x3331CII6xD37wg3LhhRf6aq5boYP1j+8xr0KHqZ9ugooGq5/u66F6UdEioPukwYeec4tJAqBGPMqGaoqAIqAIKAJ2IaAEul3joa1RBBQBRUAR8CCA7Jf3UG8LSJBs7e3tToYiGebuOslId0Oq20K62YJZWO3wWxc6qOd762Iz3pCsxSLZ7sVxsHIGQWEexX1Yb4wsKs+zUT2C9XDDhg0OHDNnznQyJlPZXa8/KA37tzoENBnoJcOG//ayE70ijYdlyNGB2Ued405K+fw6+eNzb5QJY8KThH1lxzuOdPvbexqlfORYmVk5Je3w7j7UJrsPt8nSunnywalLZeW8i9JeyzqwY8cOhxjLZ/yQyIdgYw0nQCopphnowY+kOoaDxzTqOxq1I5OdnopMcsu9s+ZCJuVjSqDng55+FgSQRGbPogR68c0H9jjf+973+nV81apVzjkkF2NvYMoVUb6H/Y/XUOQwcu/e+um5PFM/UzwIMF9R7SMog1I3av0RwKewZcsW5x8519mgdqZjpAgoAoqAItAfASXQdUYoAoqAIqAIWI2AzQQ6kf/79+93MkBxNpg6u2Qgk4msEmXRTS2/daGDaJEhTExdbA66KA0U83hDWnZ1dQny9cz9OBr163HwGFlLsgLJAggy6zAIXNwE+owZM5wMyVT27NZX5aXWtQ5JPWlsjUwaVyPS3SPSeEiGnOpfC5i/tVZ0Stm08TJ7whny2Q98LNR+f++1B5za7Fs7dsii2rlSWjIyLTTURH97b6PUlU2QM8ZPkS9deNv7wQCuT0F+MQ+N4xclBGRIcxm/pqYmZz2nFmGSFCWUQA/iDex/D1OyhX/VGujB41uIOxq5d5z+/KRSQjJy74ZQylbuXQn0Qoxssp5J6Ry+p/iuS1KgV7JGKZzevP322/Lss8/23RzS+wtf+EJe6lsmeJR9E/PJrH/8Nudbd2/yXQPDQUbvaiMC3d3dTkkszsmUnlLrjwDnFiTczT4ySaWjdKwVAUVAEUgKAkqgJ2UktR+KgCKgCCQUAZsJ9La2NuHHGAdDI9me0OGwtlt+60Ln04FUEv221MXOp19BfBbimZrTBBOgxhAnY1x5jwmGwXBEknmcjpi2oW9koNNupP7KyspSNungsUPyv15/ULZ17HTqiS8omSal207KkNP9L39v2BBprTkkB0ced7K8PzT7fDl3yuLQukm7v/n892XrwR1y+MRRWVybORsFKfrT752WeRNmyJ+cd6uML32/Ljn3QwkEQgqDyGL80uHip2NKoPtBSa8BASXQkz8PjNy7kXxPVTsYtSFDphNUmSlwxxDoEFVxL3uS/BlgZw8NgU6wnxIudo5RWK26//77HULS2Lx58+QjH/lIXo8zJQEoxeTNgPVTPx0FLnfJi2IOKs5rIBL4YQKUUe1jf07pKbX+CLhrxPMu53N+UWwVAUVAEVAEwkFACfRwcNW7KgKKgCKgCASEgK0EOg5VnFcmMwnnKaRNsUp4BzTcOd/Gr6x1rg/AYY6ziixrTCX6+yNJ+QJqhpOtS9ZuXIz3mKxlspcxnH+8x5DoNhs10MmSnD59uowdOzZtUx9vfE7W7W2Sts27pO5AqQyR92uM86GekSLN1R1ydNgpqZ8wQ2rHTpBPn3ODjBqePiM8X1wgwr/x3F3ScnCXUAt9QU1mZxqZ6id6T8n86pnyuXNv6pOXBwPmnnkvWYeRMM1XXnnTpk2ONC7BIEmSUgQvfugbjsxMJF++Y10Mn3cT6GRWKWmQ7FE3cu+GTE8n9w6JbmoHk6npfdeUQE/2PImid2QssodRAj0KtO15BvudH/zgB/0adO211+ZNTPotCeBnDXTXT2cd5Myk+w175lDULTl06JAT5Mp3IcpZav0RcO8j58+fn7Y0l+KmCCgCioAiUDgElEAvHPb6ZEVAEVAEFAEfCBD1jsPfJvNKBeMwr6+vV8d5gQdp/fr1TgsGk7XOpYnmYGsk21WifyCKBBd0dHQ4so8Q0HEwCBAkK43kN7KVZALGwcnnl0A/2XNKvv/Q3XJ424EBQ3Jo5AnZXN0hY0ePlSnjamX86HK5ZclHpLasKvTh++ff/Kds6dghew7vlzPr5suwoUPTPhNn7fq2ZhlTUiozK6fKl5bfLqNLRjnBS4yfCWIKspRCc3Ozc18l0EOfCrF/gBLosR/CvDrA/pQ5YAj1VHLvBGQZMp3fBK+QjUdWnmag5wV/UX/YEOgoXw0WSFfUICWw8y+++KK8/vrrfT2DnP7c5z7nrCv5WK4lAdxrIOsg66HXOCcbhQ4CVSFS1YoHARTKUPoiuBXlLLX+CJga8fzrwoULhWBgNUVAEVAEFAG7EFAC3a7x0NYoAoqAIqAIeBCwiUCHyEHm2ci2Q7TxbzgCkNxSKywCfknFbFpJVjXkMOPMeBuJ/jiQrNn0M99rIQOQ0UZGlAxgm42xhOynzRiOPbLm4ySB2tDQ4NRqB+t07SYwYPXq1dLY2DhgOI5ViHRPHSLjSsfKiGElMqW8Vj4y92KpHF0eydA93vhreW3neic7fnrFJKkZU5n2uYdOdDv10udVz5CFNbPltrOuE7JZKBuA49a8l5WV6e+RbacMgU6JhiDvm207gr5eM9CDRrS/hLtmoAePb9zuyLpsyHR+mwAtdz8gvEwWJ+s3GcRqikC2CGzZssXZByiBni1y8b2eM/Fdd93VFzhIT5YuXSqXXXZZ3p0KStGAOYkyB8HmWj8972FJxA04S+M/4bsP5Sy1/gjwrqCmhS1ZskQDTHSCKAKKgCJgIQJKoFs4KNokRUARUAQUgfcRsIVApx0QNkbqGXlOMh4hV4n6R3JLrbAI+CEV/bYQoomxJWoeI2oeslKjwlMjuG/fPsc5QhaUzc4RrxR/UJLffudVUNdBikPMkO1PBqPXyAB66KGHHHl6r42bVS0jZpTLiGEjpKasUpZNrJfasglBNc3XfXYfapMfvvmwbG7fLhDkSLOXlgzMuDjZe0oa92+ToUOGyKLaOXLt/EulZkh5v3r1vJdkNgVpmzdvdrKoICaqqsLPyA+y7YPdSwn04JF2165UAj14fON8R0hy9o6GSIJM4t+85s5OZ6+hAXpxHvXo2m4IdAK9tGZudLgX8klvv/22PPvss/2acPvttwcS6BdWQIbf+ulmHWRfrqVQCjnLgn82QcsHDhwQfCdxUSkLHoX0dzQ14vnuh0C3vYxYlNjosxQBRUARsAUBJdBtGQlthyKgCCgCikBKBGwg0Lu7ux0iCvINg1BB6hlnKDXjOPAguaVWWASampocZzXZxMis52oQZ4y3kWKFoMRBma88Yq7ticPnUGXgB2lGW+vbecc1SMnvqMdosLkOWfPAAw/0KWWYtuGQ/PCHP+xkK9lgD6x7Qhr2b5Wm/VvleO9JqR1TJdVjKp2M+J7TvXLg6EHZ193uNBWCfdLYarm89oNy7MhR59/CrFevBLoNMyQebVACPR7jZEMrIc/JzGTvSHBeqvJE7DNY24zcsTrSbRg5O9vA9xRzSAl0O8cn6FZxBv3Rj34k1EA3xn77ox/9aCCPimI+ueunsw6mCirS+umBDKdVN4E8h0Tne40zulp/BEyNeM5pEOjqb9AZoggoAoqAfQgogW7fmGiLFAFFQBFQBFwIkGVpiOuogeGgDylIZi3GgYaDn6k1CFFFzTgMAl2zhqIeof7P27Rpk1CnPJ+6xTi1yTw30tBIqyLhrGM7+NjyjpCFjnNk5syZhZ0IKZ7uHVcczhDocbV0c51+Qp4jl+g2ZBOvu+46q4IbjveckPveXi07D+2TXV37HMK89zRBSkNE5D0nA2l8ablTn71ixFi5aPwSGTVkhNOtsOvVk4kF0cX7z7OSYqxrfJ8SaAS+muWV/8gqgZ4/hsV4B1MDne9MygClkzpm7TZkOtl7+s4W42xJ3WdKjXBOYc9L0IVashF455135JlnnunXyVWrVgVWNqkQ88kdVOSnfjprISodavFCgDMi5xLWKdYrtf4ImBrxBMwtWrRICXSdIIqAIqAIWIiAEugWDoo2SRFQBBQBReB9BApFoEPEItnOgR7j0I7sGI5OYxAsEC3YggUL1LFZ4ImbT9YoxBIObUM8Ms5IQ+OwVsuMANkFe/fudfCaNWtW5g9EdAXjSrvIfMCSMq6panTjoII8RzHDbTisPvaxjzmqGbbZyZ5T8mTzb2T9vmbpOd0jXce7nd9DhwyV8lFjZfjQYTK5tFoWjpouo4f/VtYT51sq2fog+0YtUNZ+JdCDRDWZ93IT6Kx9mjmUzHEOulcE6rFWo5ZTU1Pj3J59p8nK5Lc3Q92dmcm6rnLvQY9KvO5nCE8Ce4MuYxIvJJLfWoLefvjDH/aVEaPH7E9uueWWwAJ8bZhPpn66WQdZE73GPt4EFfFbv3Ptn/8kI0ASk4BAaSS1/giYGvHMbQh0DZTTGaIIKAKKgH0IKIFu35hoixQBRUARUARcCBSCQEdKa9euXX2Z79XV1Y6D05uFjOMchwNWX1+vNasKPHNzzRplHJFsR+IbGzdunEPSqVPG/4C2t7c7AQjULqQOsA2G441xJdAFw3GDozkJ4+oNFiHY56c//Wlf2QGDP0TzzTffbH22ffeJo/LWngbZ3rlbTvSekpKhw6WubIJMGV4lvUdOOd2BLCKohTkWthkCnaAD1v+kmGagBz+SSqAHj2kx3DEVge7uN5mZ7EkgklA7MvsT9zV8l7nrp6vcezHMnPf7iBINpgR68sf91VdflZdeeqlfR2+88cbA6kmz3pjzLMHitgQPZ1M/nbVQVTrsfBcIZMa3wvmawA+1/ghwhuYHxRlNyNDZoQgoAoqAnQgogW7nuGirFAFFQBFQBH6HANHokOhRGOQCMtQcYjCckTimysrKUj4egs44sObOnauyclEM0iDPMKQXwQ4moytTk6glSLCEkWyHMKPGvUq2Z0Ku//+T4Q0hwOF/zpw52X04hKsPHz7sKEiY8g+MK1LcSRlXd7AIfX3ooYcGrJMQvzfddFPa9SsE2AO7JU5Tgh+MAkjUQS2U5oC0ymYtCazzId5ICfTgwVUCPXhMi+GOhkCnlIifIB2Tmcm6lE7unSAjQ6grkZTsWWQr4Zls1AvTO84pd999d7893hlnnCHXX399YA1yz6eoAhWzbbzWT88WMXuuNyVLCOq1UQ2r0EgZiXsChCHQk3JWLTSu+nxFQBFQBIJEQAn0INHUeykCioAioAgEjkBUBDpOcAg3k60KaQ55PlhGD8R+Y2Oj02dIQ8hDtcIh0NLS4kii4ozOdEBPJe1N1oXKYOY2fsjPEYiAA59gkkIZDjYcEcgFYpmCYArVznyfawh0iOann366L1DA3BcFBbKTosjWzrcv3s9DEEGem8CpQgQ/KIEe9Kgm934E0vHdg6mEe3LHOeieZUuge5/P2m/I9HRy76z/SL2zr2F/qk75oEexcPdTAr1w2Ef5ZMb5kUce6fuOMc/++Mc/7jtQ2E97OROhbIRNnz49FudZo9Jh1sFUKh1IYbvl3pHI1nXQz4wI9pp8v++CbY19dzMS98xVFA11jto3RtoiRUARUASUQNc5oAgoAoqAImA1AlEQ6O4sZMDwS9jgcNi4cWOf49wWyTurBzTExrW2tgrZuGQaDyYRB+Gxffv2PklUpL0hHFX+NPfB4R2C9ARDDv+FMAhXgmBMDXAcEQRF4DBLmqG2wM/atWsH1MmFxLvuuuti12+coah/IPWIIU/M+KVTAAlzTLMJxgmzHUHfWzPQg0b0t3WrlUAPHtek3zFIQoG1kyBQQySZQFA3hkbu3ZBJSfxeTPqccffPTXjamjFcTOMRVl+bmprk5z//eb/bL126VC677LJAH8lZm8BMjOx2gmHjZn7qp3NGcat06LkvmlHmbEagV2VlpXNGV+uPAOqHnKMJeCvUGVrHRBFQBBQBRWBwBJRA1xmiCCgCioAiYDUCYRLoOKCQFSN7FsOhSNY5Bxg/htNyw4YNzqUzZszw/Tk/99ZrskcAApcDKAf0SZMmpbwBNdg4yDP2GES7SrZnj7X3E+BKUAJO+vnz5+d/wyzvgGOG8ScrD8NBQyBMUqP4X3jhBXn55ZeFNchtYH/VVVfFrs477yPvZePeLbLn6AGR4UNlYk2dTB0/UeZWTZdhQ4dlOSPyu9xvME5+T4n+00qgB4+5EujBY1oMd0SxBcLbr4R7NpjwnvOdaH6Yo14zcu+GUCdTUy0+CMQxYzg+6NrRUtaHe+65p6+UDa3ifHr77bcHniFOACpBmeY8m4QAG/pk1kCwNCWd3KOLModZA7XsRXjznvMZgV2ct/lR64+AkbgnoL+QKm46LoqAIqAIKALpEVACXWeHIqAIKAKKgNUI4CQypFiQDUXqjQMdWTtYrlnIZKDTRiTvuIda4RCAgOvs7HQc0mSUuy1VfXuyW/0GSxSuV/F4Mpn/kI444anfFpVBIFN/naxl/szzGXvq7CXVCNpZvXr1APJ80aJFsnLlSgeDOBlr8a/WvSBr2xqk6+QRGVYyTEaMGumQ5iOHj5CyEaVy1qSFcsHUMyMj0pVAj9MMKmxb3QT6zJkzVcmksMMRm6eHSaB7QWAP7SbUUxFJkEeGSEL6PanBZ7GZIBka6s4YjovkdlKwj6If7GcffvhhZ1/tNgIkwyDYWCMoXYMl8XvMWz8dMtcEUht8WfNY+3QdDH6GG9U3SqxxRlfrj4BRpOHsOnv2bIVHEVAEFAFFwEIElEC3cFC0SYqAIqAIKALvIxA0gc4hGpKVwwp/5sBMFjJZy7k4DBsaGpyodiQUx40bp0NXQAQYU8hUDqCQ48YgOEz0O//mp759AbsRy0cjm46MMe/QwoULI+kDawMkBKoDGJkkvIf8Tqq9++678otf/GIAeY6k5xVXXJHTGlZIrBi7+99cLU2drXLo1BE58t5xOX76/WzJ0uEjpXpMpUwYM15mjJ8sH1t0pYwYFr4kP84+VBXIlJk4cWIhIQr02bwz/LAmEmgRt2CLQMEI6GZKoAcEZJHdJkoC3Q2tkXs3hDpEklfJxF03mCDDJGSjJm16JUFyO2ljEmR/3njjDUFpyG2U57nmmmtC2ecVm5KKqZ/uXge946f104Ob0ZwPmWM1NTVSUVER3I0TcicjcU9wAQEsaoqAIqAIKAL2IaAEun1joi1SBBQBRUARcCEQJIGOwwmS1RBuSFhCtOZTu5z6dETuI/2uh8LCTl0k0KihTCADRCpGZjQHU5NxxeGdCPhcgiUK2zu7n448oslegUAPG1+UIyA5jYIE7x6y/UkmBNevX+9knntt2bJlcvnll4eOeZAzEOdlW1ubPNHwvKzraJY9x9rl6HsnpLx0rEwYPV5GDR8hJ3t7pONYpxw8dkjKRoyWOVXTpb56hty4MPxAASXQgxztZN9LCfRkj29YvTMEeqFrwrLHhkSHSOJ7PJXcOwS6u24wpVrUCouAW3I7rjWrC4ugvU9HUen+++/vlyFNIMsnP/nJvM6rg/WYvbTJdicDNsl76VQ4+Cl7Yeqnmwx1rZ/u/x3ifIivhIQFTTYYiJsJ8mc/QElANUVAEVAEFAH7EFAC3b4x0RYpAoqAIqAIuBAIikDHQcgBxTgHyVKGcMvXEdjc3OyQeNyLg49a4RDYt2+f7N+/35HSh0Dn7wcOHHAahKODIAeyz9WCRwDnu6mfGDaBTgAM5ANrA0Q9GcJE7YdN2gePmv87NjY2yqOPPpqy5nlYGUn+W5fdlTj+CWrZe3C//HTbr6T9VJd09R6VmeOnSOXogZkp3SePSnN7i5SNGOOQ6DcvXimzKt9XmMju6f6u5ruCecaaztqeFNMM9OBHUgn04DEthjvaQqB7sTZ1gyHT+V5PJfduZI4h9VTuvTCzNYk1qwuDpF1PRc3pxz/+sfDbbatWreoLDA6jxZTSIXAQmzNnTqL3037wc9dPZx3k717T+ul+kPztNZwPwZDzmpa7648bAcWcOXgHCfA3CQD+0dUrFQFFQBFQBKJAQAn0KFDWZygCioAioAjkjICpW5brDbw1koMm3DZv3uwcejgUIverVjgEyGjlB0UBxhmnB4aTF6UBzRYIb2x4B3gXMGqgh5G9wrtMZg4qAxhZcTga8lGQCA+R4O5MkM4jjzwyoF4jMn8XXHCBExgSFyOQCSctmShvHmiQhiOt0nxou9SUTZAp42rTdqPjaKdsPbhTFtTMkjPr5suNiz4capch+Cn1QWDG5MmTQ31WlDdXAj14tJNeOzZ4xPSOIGAkWwudgT7YaJj9tyHT08m98x3MPovMTL6XkxzMZsvsda87ZCyqzL4tI5N7OyAYH3zwQWef67YPfOADsmLFitxv7OOTJsicS5VA7w8Y6yDvm3sd9NZP5xOsg1o/PfVk43wIZgSkaiD7wPmF+raj0eMAACAASURBVIORuHeXoPPx6uolioAioAgoAhEhoAR6REDrYxQBRUARUARyQyAfAp3MGbJ8qGWLES3OwYSMmaCMqGqI2traWidyWK1wCJB9Tta52xgTZNvVoRvuuKDCANGL1dfXBx6sgPOKCH0TFFEsdeyRPfzZz342IAsQjKmHCcEbFwL94MGDTgkN1nQCLJ7Y/4ps794r27v2yJK6eYPWNucz6/Y1ScWocXJGxST50oW3y6jh4dW6VwI93PUiSXdXIitJoxldX+JAoHvRgAAhWM4QSaaEivs6AhUNmQ6ZlK/KU3QjEq8nqfJFvMYrU2vZ4zz55JPS0NDQ71L2dzfccEPo7xF7a9YkzkoQ6GrpEfBbP10Di97HkPMhuDGf+V5Qex8BcKFGvJG4T1LQro6zIqAIKAJJQkAJ9CSNpvZFEVAEFIEEIpArgY4zAMKNAwkWVo1kDj1I7UHUQqKrFQYB5gkR3Eb2EKctwRIa6R7NeODM3bRpk/OwefPmBZoNxZjyLhdbHXsytclG8kpHUvMcmXwypFnXbCfQIV3IqOro6HDmx4gRI5x3819ev0e2HdwlSLQvrJmdcaJu7dghJ3tPSX31TPnMOR+T6jHjM34m1wsIvILwjwO+2fRRM9CzQcvftUqg+8NJr+qPQBwJdO8Y+pE5NnLvkCZGHUjnQv4IuAl0guk0UCF/TAt5h5deekleffXVfk2g1Nitt94aicoSQTHse5RAz34WsK8ig98EFplSce47FXP9dM7nJsCavX/SVcOynUHgQ7A036dk6KNoqKYIKAKKgCJgHwJKoNs3JtoiRUARUAQUAQ8CqbJc0oHEQQSJZyOBR6YjBxKIkDAMkosMd+Tb9dATBsKZ7wmBgTMa5wWGA2ju3LmBkriZW1HcVzAGTU1NDghgD0mar/EuU8PeqAoUU1AEmdo/+clP+gKADJaLFy+WlStXyp49exxCGgerzXJ/zAvWSJyL2Lhx4xxJdNblf3jx+w6BfuTkUVngh0A/uFNO9pyMhEAH/zjgm+07xjtFIArjwjoZRqmFbNsU9+uVQI/7CBam/YZAZ++YhPI/JtiV4FXzw7+5jTXHSByTpa5y77nPPc5FBI1iSqDnjqMNn1y7dq0899xz/ZrCu3HLLbfIhAkTImkigarse9gTzJ6dOaAxkkbF9CF+Aos4I7EWsg5CKCd5L0aAgSnxRdmtIFUAYzpF+jUbfCDQ2ZtznkM1T00RUAQUAUXAPgSUQLdvTLRFioAioAgoAh4E/BLoHFpxSposZA5pHEaQbg/LyIzt6uoS6lhC1KtFiwBjzZi7s3SJ9EfiWi06BMC/sbHReSDyj/m+czgSGNfDhw8798TBhOOlGOp8EgB0zz33OFK5bqO2/FVXXeU42iDQuQ5CGlxsNAJaWB/Nu4lCB85gU07h3165T5rbWx0J96V186RkWEnabvxWwn2TVIwqkzMqJocu4a4Euo0zys42KYFu57jY3qqkEehevFmzCZyCTOe7IJ3cu5tQ1yxq/7PWTaBDeCaZgPOPSvyu3LhxoyPd7rVrr73WCYyIythrs6/kHYzyuVH1r1DPMfXTzTrImlhs9dM5z23ZssUZgjPOOCOQAOtCjWcYz2U+gA9zhfOclgMMA2W9pyKgCCgC+SOgBHr+GOodFAFFQBFQBEJGADk0byaL95FesgZCu66uLnSnUlKlfkMe0rxvz3yg5nlbW5tzL5w+1IMmY1kJ9LzhzfoGOEhM7UacuflkGOBgImvZlF8gOw/ytRgcxDgxIc9RtXAbWf04VA3BgMIGc33s2LEyffr0rMcrzA94VUDSKQf8ettr8mLLG/LO3kapLZsgk8elL4HRcbRLth7cIQtqZjlk+8cWXRlmF2IRoJALAJqBngtqg39GCfTgMS2GOxJcxHddUjLQM40ZewRDIvHbW5qEzxN4Z7Iy2UMUw3d+JtzS/T8BduyTMCXQc0WxsJ9D1nr16tUDzrcf+tCHZMmSJZE2jj0n+0rOTzNnzoz02cX0sGzqp5u1MO5KHbpHGnyGuzP0Z8yY4SRkqCkCioAioAjYh4AS6PaNibZIEVAEFAFFwIPAYAS6l0jF4YZEMNLGUVgcMkGjwCHKZ3iVBshORmnAZOQwB8jWVYsOARwAZNJgZK/kUuOOd5m607xT/Dnqdzk6tFI/CYf4fffd5wSGuA2HyqpVq/rVOEXWnuvKysqcjA5bjHlAUBGqHJh5N1NJ+ncePyz//uqPZUfXHtnb3S6zKqfJ+NJxA7py5OQx2dTeImUjSmVO1Rly0+IrZXZluFn3SV3XlUAP/k3h+2jr1q3OjXlXi0ElI3gUi++OxUagu0fYT1YmSiV8fyBxDJHEd4hRLym+2TKwx24CHdUfxSZeswLFpieeeGIAeb58+XI599xzI+8Mezb2lXx/8T2mFg0Cpn66KXuRSamDtZAghzgZPpyWlhanyQRnxK39YWPtztDn/BxWycGw+6H3VwQUAUUg6QgogZ70Edb+KQKKgCKQAATSEeje2teDkTVhwWArkRVWfwt9X6/SANlbKA3gPETOnUM6f164cGGhm1pUz8chvmHDhj4HCU6ebAwnErLZnZ2dzsfIREPKLl8p+GzaUMhrWcseeOABR7bebRMnTnTqYHoJaJQX+IFcsMXZieOPjDjjAEQRgvYPlkX49JaX5ZUd78i2gzul41iXjBtZJhPGjJdRw0fIqd4eaT/a6fz7mBGlMrfqDJlTNV1uXrwydLLA5gz/fOapEuj5oJf6s24CXeVJg8c3qXc0BDplLYo948xkZbK/g0jyli9hDqBkYsj0OJJIQc9j1AuYQ5gS6EGjG+792Cv/8pe/HECen3322XLRRReFvr9J1Tv23uwp2WvaFJQZ7kjYd/ds6qeb8he2K3VouYnB55k7wIC1nNJcaoqAIqAIKAL2IaAEun1joi1SBBQBRUAR8CCQikBH6hiyichdDCckMs9RZ2HYSGQlcQLhYEWymoAFDIfBlClT+h00cbyaTEAI9KjnQhJxz6ZPOAUZJ5xvZEb7NZwrOIKN0xz1iEmTJvXLuPZ7rzheR/DAI488Ish5ug1S5eMf/7iTfec1s+7wfzbIbSL/yXpMX3jvIM79kEKn3zstjzc+J+v3bZIDxzpl/5EOIePc2MjhI6RmTKVUj6mUaeUTHfKcfwvbTGCUjRL5+fSd95PvU945Ms205nA+aP72s0qg549hMd5BCfT0o27k3k1Wpinn4v4ERJ8h1AmetZ1ECnqOg40JuKPEi1o8EFi3bp08/fTTAxqLZPtll11WsHML6k+oGhG0altZoHiMbPCtdCt1mLUwjvXTNdhn8LnhDjCYN29eVufn4Ged3lERUAQUAUUgHQJKoOvcUAQUAUVAEbAeAZxn5tDIgRJyAzIVgwCASIXoKITRDrIVceAhvaUWPAIQFMhCEzSBpVMa4JC+ZcsW5xok3IvNoRo88tndEQl33lOcb37fRy/xipoAxGuxBD+wnpGJ9NZbb/UDmwCET3ziE2lLUeDoZB0s9LpD+yHzjew8pCzlFLJRIOAeb+7e4GSid53olhM9J6XndI/z/pYOGymlJaNk2aQFsnzaWVIyLBrpyqQqi5DhiUoABBUymu6MTiXTs1vvzNVKoOeGW7F/Sgl0/zOAoB9DIPHbSyIZuXeTkQkJmPQ9hCHQ6SdZi17rPd0rTbu2ysHuLikZXiLTqydLbcUE/6DrlYEiwD5nzZo18uqrrw6477Jly+SSSy4p6Jzt6OhwztWjRo1y1J/U7EPAWz+doGP+zW3Ovrm01NmD21L6QoN9Bp9L7gADfBe5lECzb7ZqixQBRUARSB4CSqAnb0y1R4qAIqAIJA4BQ6DjRDNORzrJ4RCyppA1R3E6ID2Nwy6VEytxgxFxhzh4M+YmAwlyFZI1FTmOM2Hz5s1OC+fPn6/ZlRGPVUNDg0PM4XzLJEHnDYTJhXiNuHuhPO7111+XZ555pt+9WUv+4A/+QGpqatI+04bAHYhDMuAonYBBxrIe51rfkGz0rR07pLVztxzvPSkjhpbIxLHVMm/CjMiIcwN40pRFeN/4rqK2ezozTlfGsRgIqKBeaCXQg0KyuO5DIAt7FpVwz27cvSQS5IPXCAYyBBLrWa7fSdm1LNqrCYYisNRLoHceOSQ/XfML52dv5/5+jbqw/my5efnVctGCcwtK1kaLVOGfxr74V7/6lRBk6rVzzjlHVqxYUfDxaG9vF35MgHLhUdMWZELAT/10G0pfmLWKc/vs2bMzdavo/t8dYLBo0aKiKV1WdAOtHVYEFIHYI6AEeuyHUDugCCgCikDyEYA8pT4bziIj2V5dXe0QTIXOMqFdkEgQgEhvqQWDAE5SnDlk92McvCdPnpw2I5drCLDYtGmTcz1jUcjAimBQiNddGhsbHTljSFRk2NMZ7zNBETgNMLKtUZFIopN7sBFEsv2hhx7qdwnOrptuuiljBhDvBmQo2UKFcEhBWkAAmcCWQpXQCOsNSRKBjpOVuYJEKwY5XlVV5ayXvIOMZaosJnd2uq6l6WcaexKjfKI10MN6I5N3XyXQgxlT1jeTnQ5Rk07u3RDq/E6COlEqUmrjjmb5L9//uuw/1C5HTxxzfnpO98oQGSIjS0bImFGjZcTwErnyrEvkv9/yJefPauEigDzz6tWrpbW1dcCDzjvvPDn//PMLfo6lYQRlEmTH+8F+XC1+CHD+Yj/H2sCayN+9RumLqNdC1OPYg3K+UaW+gfOKIGQSMTBKOeh+O37vnrZYEVAEigMBJdCLY5y1l4qAIqAIxBYBnGOQbUayHZKNw302NZbD7HxXV5fTPtpVX18f5qOK5t4QEgQlGMl2SEJIWYifwQzHaVNTk3MJNSFxFKhFhwDYMwa8nxUVFSkfjGOH98U4dmwJhIkOpd8+CYnwe++9d4Cz/9prr3XUEzJZIZUvIGJx9kC6+glsydQXG//fSOTbUmM+V4x4HyHqTJYmgS21tbVOIBrBZ4yfO4uJ9xNi3WvFXm94MPyVQM91dhb355RAD2f8WfMgjwyJlK5msAkQiqvahiFdTFbnlr3b5bZ//bIcOHxQDhzqEBRdRpWMdOTb+a4+dvK4s+6XjhwlVWUV8uFlF8sdn/jLRAQThDOT8r8re6VHH33UIabdxnfvpZdeKkuXLs3/IQHdgT0P7eW9IFhZLd4I+K2fzvmafS7jzp/DSEqgVBfB8PhJZs6cGW9gQ2i9wQfsIdCLLZg8BEj1loqAIqAIhIKAEuihwKo3VQQUAUVAEQgKAZxfZLZCANiYqQrJS2YBTixqV6nlh4A3s3UwyXbvk3AOIiOOkZWLM0AtOgTIqCbbZtKkSU4dc7d5FQXIRIBo91srPbpehP8kHN933313X4CIeeJFF10kF1xwga8G4OhEkQNik2CRKIy1GCeYcQZDPBDYksT3zAaJ/HzH1BusQukL3kvGEZLJEOje5xDcYsgnfqerN2wIKOZgGE7XfPsf1eeVQI8K6WQ9xxDoBJGNHz8+WZ2zpDfsO9iTGEI9ldw7e3dDIPE7Ltl/hkA3WZ2f+JcvyRtb35W2rnan5EllWbkMHza8byTA4ujJ49LZ3eUQ61Xjxssdn/grufKsiy0ZrWQ1Y+vWrfLEE084889tkGMf+chHrMvENQQ652z28GrJQsC9FqZTHmIf585OD2pvR6IBQcPcD5Uetf4IGHxYyxcvXqzl53SCKAKKgCJgKQJKoFs6MNosRUARUAQUgfcRwImPVDqys7Y56mnbtm3bnMYuXLjQuvbFZR6ZGr0QdCazFSdOukzmVP2C6DE1BpGJo5afWnQIUH+emq4TJ0503lVjEEyQvUTZY6bGYjEqBEBc3nfffX2lCQxG1L3Dqep3fYu6dIQ3k5ka92Qp4fBJosWZQGf9JMACyUz+zBgR6IBjnL/zPhoFiExSxsbpagj1VAQUhICbgErqnEg3z90E+vTp0zMqpSTxfdE+ZY+AEujZY5bvJ9xy75BIqdQ2INBNcBB7FVvXM7cs8okRp+Xmb/+ptB86KKd6e6SmokqGDhmaEi5k3TsOd0p1eZWcN/dMuftP/zlfWPXzLgT4znz11VdlzZo1A3BhPl133XXOHtk2g+CEyCOo1cb22YZX3Nvjt3662dsxd3MNLmI/SoAGQbfskdT6I8B5jrJR7KUh0DPtyxU/RUARUAQUgcIgoAR6YXDXpyoCioAioAhkgQDOflP7PIuPRXIphIKpf0oGuh58sofdS7ByyJ42bVrWRASOqw0bNjgNmDFjhuMEVYsOAd4D3gcyXamJjUGoQxQYR3U2igLRtTyaJzE/H3vssT6VBPNUMvFvvvnmrGT7oiwdAXnKGJo1GAlwxtcv2R8NusE+pdA15nPtjbfeOeoArKUmWCVbAt3bDuaAu8ZmqnrDrN+svWFKguaKTxifUwI9DFSTf0+Ui8hO1Qz0wo21kXs3NdRTnTMgjkxWZlgSx7kg4JZFvu/Nn8v9v3lc9hzcLxVjxknZqNFpb8l3wL6uA06WetXY8fLIX/4vmVU3LZcm6Gc8CPDd+NRTTwnZ515jz0SJHsqo2GiGQCc4kj28WnEhwNpn1kH2/Jnqp2cTXIRqFUGpZj9aXMhm7q3Bh306iRjqR8qMmV6hCCgCikAhEFACvRCo6zMVAUVAEVAEskLAnTGX1QcjuBiCkMxbjNrFtmarRABFTo/A4URNbEOwknFO5nmuB0gIdByEyMSRcakWHQI4DXHAQLBCCrhrZUO2krGcjaJAdC2P5klkJf3617/u9zDw+OQnP+k46LMxnOeQ2qw3fmqmZ3Nvc20q2X2TyZzL/eL0GVNjHocf5SDiYKnqnfPOudfSfAl0Lw6s2+56w9zfbTwbR6sh1HPNYLIZfyXQbR4de9umBLpdY+NH4tjIvRtCvZAqOoZAZ039+uP/Ji81vulklk+qrM24fz50tFsOHz8ikytr5R8/+d/kimUX2TUYMWwN5xgk25HW99q8efPk8ssvzzmDNwo4UP9iTkHws4dXK24EzN7OkOreUj6gY+qnsx6yz0sXVEtAKj9cR8CwWn8ECC7gzEHwqSoZ6uxQBBQBRcBeBJRAt3dstGWKgCKgCCgCv0PAZgKdQ+amTZucluIkSSJBEMZE9MoMc/CGOM+3FigS7hz0kYkrxvraYYyV33tSyoDMBchzshcg0DGczGTBJrFWtl9sWlpa5IEHHnCCO4zhLIE8d8vd+72fIdBx6KN8EbTxDiG7T6Y7Vmyy+4ZAZ4zmzJkTNLyB3y9VvfNUJU+CJtDdHeHeBEQZQt1b+5Vr3fLIOFNzDZQKHMA8bsi7YoLoVMI9DyCL7KNKoNs94G6JY9bXdHLv7prBUQbQmrq5rKn/7eFvy+ub18nBI4ccUjyTOkz3sSPSefSwTKmqk7+/9ctyzQc+ZPdgWNw65skrr7ziyLZ7A8gYh+XLl8s555yTcUwK3UVKvlAWgKDOmpqaQjdHn28RAn6Ciwarn458O+dBAikJ6lTrj4DBh3MWAdGZ1m/FTxFQBBQBRaAwCCiBXhjc9amKgCKgCCgCWSBgM4EOUdjY2Oj0BqIFwkVtcAQYz927d/eRc2BGZmsQBCtjwZhwP1ulEpM6PyCJyb6hjpuR/0t6rWw/Y4mj+0c/+pFDLrpt1apVOWc34+iEgAmDQIf4JLvdEKAEtVATMwlkp5/x4hqcfQQQEPwxd+5cvx+L/LrB6p2nakyYBLr3eawBbknQdPLIpt4w3wNxdBwqgR75tE/EAw2BDllVzMoscRlMP+uZyciMonyFIdD5jvrGE3fJc+tflgOHDkptRbWUDB8+KKwHu7vk+KkTMnF8jdz56a/LxQs/GJdhsKqd1C5Gsp29gtcgw1auXBmbms+cydi/s98jCFZNEUiHAHse1PcILGKPlypYkmAiE1zE2YegX4LaOUuo9UeA+uesJeBVX18fy32wjqkioAgoAsWAgBLoxTDK2kdFQBFQBGKOgM0EOgdJsp4xpH6DIIFjPlyDNp9DN1KH5sANyU3meVCZO01NTYKUMTJx6pSOdiaZGujmqdRRTJUFG22rCvs05uK9994r1Jd0G1lJ/ORqODoJWIBwRPIvKMPJtXPnTkfFgXvj7KJufbFZHAj0TPXOU42Zm0Dnz0Gtu5nmB88ig9M4XHGoerP1jMPVEOoE4sTBlECPwyjZ10Yl0O0bE78tMuuZUdtItZ65y1dAjJApHmSAEIQLxAsE+gsta+Vb/+f7svvgPhk9stSpg57OTr93+v+y9yZwdlTVvv9Kz51OpzN1pgY685wgMwEkKsMFRRkUH0hQrgyXv/IX4T7f897rE7z36XW8oiLOildREa9wHUBFpgBBQAIJmedAks6cTqfT8/A+3x13p7pSp/vUOVXnVNVZi8/5dJM+tWvXb+/aVXv91votaTiwV6orhxqy/fG7fiLDh2q5o3THnu8x/itWrJAlS5aY/YbbUFy65JJLYlVGiiAAns+871GvXU0RSBcBZ/101kSve4K2WAMJzvBTPz3dPsT5e7Z8AmXnUDJUUwQUAUVAEYgmAkqgR3NctFeKgCKgCCgCDgRwUKfakOUbKBwp1N3GpkyZ4ruWcb77n8vzu2tiQ86R7RCkU3HDhg2GnIeUL0TiL5fjac/FPYAjFxk6jPGkBj1EWKFaw+G98tKbK2TVC8ulaG9HPxhOmnySXPv+a7Oa9zg6kczH5s2blzXM7jHE0YWKg9/a7Fl3JCINQE4QSAAOUXRopVPv3AvKfBHo7r5YeWRLqHvJI5ORzvyz2ZxRVUBwEuiFXqoiIrdvLLpBABTzXjPQYzFcA3YynYxMAoLsesbPbIOXLIHOOlkzZqRcdPf1srdpvzS1HpHa4aOkvLTsuD4bxZLmQ9La0SbjR46V/3Huu+TT7/9Y/Acgh1dAkOGf/vQno9LjNt59zz33XDnjjDOyer/L4eX0nYr3HchPgl4zKSuUjz7rOaOJAO+n9t2OOZVN/fRoXmGwvbLlE1Bsi0PJqGCvXltTBBQBRSA+CCiBHp+x0p4qAoqAIlCwCESZQGdQINBxTEEaEkGs1h8Bxg95QBx+GBkzkHNEoQdt1KIlyx1yXp1AQaN7fHvImqIogLPEGqoCjG8hWldPt/z3mifktYa1InvapeqN7n4wdJeLtM2ulDMnLZB3zVwkRUOKMoIJp9TmzZvNsWSgZxOEwhjiPCWrHYOwZPzikgGcEYCDHIQ8LvMaDJBUjJKlW+/cq89RIdDdfcPharM5vRyuzvqazM+gszmzGV8l0LNBr3CPVQI9uWPvlHtnPbMlbZxXDPFt1TZ4F/b7DCcglaBF2qmvr5cvPfJd+cnTD8vewweko7NDhlVWSVX5UCkpLjb7k/bODjnc2iztXZ0yetgIqamqlgf/8V6ZOv6k5A5EgFdms86fffZZE/jiNsgvJNsJ3o2j8b6DkgLZ5xp8HMcRjGafuW/YXzC3eJ8mW92tPsTaxxpo10N8BH7Xw2hefXq9suoPqOZNnTo1vYP0W4qAIqAIKAI5R0AJ9JxDridUBBQBRUAR8ItA1An0NWvWmE0h2Wc4UdSOIeCupxy0ZLsba0hFHJbjxo3TOn4hT0RwJgvHOocpX0DwAk4AJPQLzZBGfWD572Tt3s2ya+9umfJmtRT3HiPIu4f0yGu1DTK0pkpOrBkvC8bPkqvn/l1GjiKcUUjmY3PmzBmwPrlxnnd3CP0rLy6T4qLivqGhHcbQKnzgPOXeKSTnldc8jSqBfuDAASFbxcqvE+jgJ2grqgS6cwzoI+uIJdT53W04Yq2zNYhszmzWKiXQs0GvcI9VAr0wxp71zJ2R6UUg2XrB/EyHQOJZsG/fPlM2ir1HZ1enfOwHn5Hn17wih1qapLmNMhk9UlR0lEDn99KSUqkZWi1VFUPlix/8pFx0cuZlZApj9I5eJYEKjz/+uCC17GULFiyQ888/34xbXI33QJ61SGyjDKamCASFgFU3IDCDj/P9brD66bb8RVB9iWI7Fh/uO5QM1RQBRUARUASiiYAS6NEcF+2VIqAIKAKKgAMBW28wqqCsXbvWkIiQGRDEakcRIOOczHNbT5ma2GyewyTnkLUmQxNZVD5qwSPA/bh///4+ZyLSyhDmZDDj1C3UDPRntr4sj29cKpv2bpNpO0ZIVVd/Z2rLpBJpKD8kOw/vlREV1TJl1Aly2cy3y1knLvA9SDigUFvAZs+e7SkHe7C1SZY1rJYVu9fKkY6jJGTRkCEybXS9nD5xrozoreojYxnDuro6Xb/+NhLItOJQRmYXfPNtmdQ79+pzHAh0d7+d9TVZ272yOSGRLKHO72E+Y9z9A1NKh2Aq4Z7vOyU+57cEOgFL+t4Yn3HLtqesFwSuESDEJ1WAkJNQ91KDsQQ6mZtW8QcS/QuPfEf+64U/SFd3l5Fq7+ruNutheUmplJWWybiaMXL3/7hdzp19eraXkvjjyTR/4YUXZNmyZcdlzXLx1dXVctFFFxn1sbjbtm3bTPkrLSkR95GMXv+tuoFXeYB06qejOORcD7MtfxElhJwZ+uCThLUkSvhqXxQBRUARCBIBJdCDRFPbUgQUAUVAEQgFgagT6OvXrzeSfhBQGrkvhjAnSxKJSYzNL8RCGJLt7gmHE+jw4cNGhhDCXi1YBHB2IDcHwYjZ7CcybxhziHVUGBjvQrLunm758vM/ki0HtkvZtg6Z2NJfiaKztkQ66o8S6o1tTbLpwJsyeWSdIbM/vvCDvgk/HJ2WtENi3OlgZ71c+uar8vTWlwRJ+f0tjdLc0SK90itlxaUyunKE9Hb2yIghVXLBhDOkZujR8UIKVu0oAlEi0DOtd+41lnEk0J3XkU42J8EgzlrDPH/CNCXQw0Q3uW0rgZ7csfVzZekECPFstmsa71yscbxr8XES6Pa8ew7tk/964Y/y1MoXTM3zspJSmTT2BLnq7EvkbfPOMio0q7atl18889+y5o2N0tHVISOqauT8+WfJe897oU7LkwAAIABJREFUp4wcVtiBwHZNf+aZZ8x+wssonfO2t70tMe9Nqojh567V7/pBwI+6gS3nYwOMWB/dxhpoCXW7HvrpT5S+y1oDPuzpUH8otL1zlMZC+6IIKAKKwGAIKIE+GEL6d0VAEVAEFIG8IxB1Al3rbh+bImwCiTa3WTWQqQQW5CpinI0o5BeR3NRBVwsOAcYUfG39R4JFwBhnLoa8JZKiZOVQk7OQbNWejfKLFY/Kzq3bZfq+Uf0uvadyiLTOriD9u+/f1+/faiTVZ42ZIh865QqZNtpfwMFABPrTW16S595YJg3Ne2RX836TOTWsrNLUWz/S0Sqt7a1SUVQuE4eOkbqacXLbedebWqlqxxDAaU4wDnMbifx8mbtMAkFBrG2ZZljHnUB3jwPBWqxLZKaDlZccKME9lnyCbLLrVVBj6iTQyQTNRaBYUH3XdvKHAGo5kAWagZ6/MYjamW2AkC1fQaY6a5zTbL1g/o3veRHoA13Xxp1b5X//8HOybONK03Znd5dIr0hR0RApLS6V8tIyue4dV8on3/8RI/leaLZnzx55+umnTd1mL6NE0YUXXpg4osuuR7xjaCmyQpv14V5vpsEZpvxUe3ufWgfrYar66ZZQJ9go0/fjcFHwbp3rYa/Bvpp3gUIsf5YP3PWcioAioAhkgoAS6JmgpscoAoqAIqAI5BSBqBPoWnf76HSgbjDZydbhly3Zk8kkg7ynH0jFT5w4MZMm9BgPBJDjZ2y5F3FOgK1bbWH37t2mViRSypMnTy4oHB9d/4w8s/ZFGbamU0p7j9UYpwQ65Hlv5bFa6ABDVvjWxh2yYPwMuWjquXLB1LN94YWzBeULbMaMGX21N7cc3C4PrPidbD+8S3Y375dxVaNlXNUYKS0uka6uTmk6fFiaOo7InrYDxlE+d/x0OXn8LLly9oW+zp/0L1OOAKcfc51Ms3xYtvXOvfpsCXSyelincxXYlCv8kHe35BM/3dlLlnyyhHo6tYYH67sS6IMhpH/3QkAJdJ0XgyHA2uIMEPKSe6cNghZtCQsvuXd7npVb18niL31MDjQ1Gnn3jq7Ofl2gxEt5ablUllXIovlnyXdv/4KRfS8EIwjr+eefl5UrV3peLs/KM844Q84888x+ij9JwYZ9LM9PJdCTMqLRuY6gnnU2YHKg8hfcp06597AViLJFmTWevQbBdASkq98iW0T1eEVAEVAEwkNACfTwsNWWFQFFQBFQBAJEwCuzLMDms2qq0Otus6kl+xjCB2PDSiYem9hcGyQv0vFkiWgkd/bou8cWwilVnV/Ic0h0xn3KlCnZnzxGLfzXqj/JqieXSdmR/p1ury+TrtqS464ESfV1+7bInLFT5YIpZ8ulM873dbU4W9atW2eOcRLoP3/997Ji93pZu2+znFA9TsYNGyOkl+F4b2lpNd8fQqZZRZlsbHxDRlXWyKQRdXLbWdfJ8PJhvvqQ5C/nk0APqt651/gkLQN9oDnozF6CHCF7yW04Wy3xxLo1EPmU6lxKoCd5JQjv2oIiFcLrobYcNQQICGIdYz1DJcWdnU5/eUeza5pTcePA4Ua55FOLpeHAXjnc0myCwyrKyg1BjjYObbV3dkhbZ7sJuKuuHCaLL7hS/u2Dn4gaDIH2h3epV199VV588UVDYnkZ77wXXHBBokt0bdq0yQScQeIRkKGmCASFgA3OCHpuJaF+Ou+P4MO1oNanpeeCmnXajiKgCCgCwSOgBHrwmGqLioAioAgoAiEgEGUCvZDrbpMJS9a3JSdwvLAJzISICGLa7Ny50xD5NTU1hsRXyxwBr7ElKCFV1iry7QRS4LSdOnVq5ieO4ZE//v3PpeH1bf163lrTK2+Oa5bu3h4jnz60tEJGVFSb3w+0HhKyxeePmyEXTTtHLpy60NdVkym0du1ac8z06dNNHc6DrU1y38s/M5ntTe1HZF7tdEOeH2lpkY72DvPdkpJiGTZsmBQVFcvOw3tkz5H9pg9vm3SmLJp0hq8+JPnLEBQQXNi8efNydqlB1jv36nQhEeju64cgsplLjK8XWcJ95CSf0pECVQI9Z7dHok6kksmJGs6cXwwBiwSLQpjzYW1LJfdOcNBPnnlY7nnk+9J4pMmUsYAg91rfOru65HBrs1SUlkv10GHy/Fd+LeNG1ub8+sI+IYTV66+/bohzngdehpT5W9/6VhOkmM6zIOw+h9k+pciYP2TA8o6opggEhUCu5lY69dOd73hRqJ/OPQeBzk98FmPHjg0Kdm1HEVAEFAFFIGAElEAPGFBtThFQBBQBRSAcBCDz3LWvwjmT/1YLVTacWuPUCbROO+p3jRkzJq+OpkKuw+1/5qY+guwmxtZKIKcjx79//35paGgQnBLTpk0LsjuRbovAgR/+8If9nNftxV2ybNxO6RzSbWqdD5EhUjykSEqKS2R05Qhpbm+R7t5umV07Va5/y3tkxphJvq6RcVmzZo05BqzBfOWeDfLImifktV1rZKyRbh8tzYcPS3f30Rqq5eVlMrSqyvQFa+/qkJV7N8iUkSfKqRPmyHULLvPVhyR/GTICpxaGhHsunOdB1zv3Gp9CJtDdeOBstbXTU5FPVuqdn5BUqcyWU9Aa6EleFYK9NiXQg8Wz0FqjVjeldSA7IT2t3LsNEnIqbnT3dMv13/hH2X5gl5FuH145zLyL/O1V4DjoWtpbpb2jQ0YMGy53XHmT3H7FjYmBl/0KwYcvvPCCKffkZahoIdV+6qmnGkWtQrANGzaYOUQANEFkaopAUAjkY27Z0n+2pE9U66ezHqH+QH/r6+uND0VNEVAEFAFFIJoIKIEezXHRXikCioAioAi4EIgygV5osuFs9CCqIUwxss0hDqLgdLF1uHEqTprkj5TUm07MJh7HLNlNfseWzH8UAIjwJyu6EAznx09+8hMTOOC0l0a8IXtLmqSrp7vvn8n6qigpk2IpkpbONplTO01m1U6Wj5/zQZOV7sc47+rVq80hZPuT9b+sYbVQi/2VhtUysapWKrpLpLcXzXaRqqFDpby8ot8pGOtlu1ZL/YiJcvK4mfL3p1zlpwuJ/m6uCfQw6p17DZAS6N7TNp1awxApllDnfnMqcSiBnujlIJSLUwI9FFgLplFLoKP6hDSy25yKG69tXCU3feuT0tzWYjjzqoqj5ZVMKN2QISZAzBkkxrFkqvO9U6fOk9/96/2xx5U1nkxYiHOCHlMZAXPnnntuwWVh22cYKlP5KL8V+wmmF+CJQFQUetKpn84ezdZPx5+Ri+AZgqEh0LHJkyfLqFGjdCYpAoqAIqAIRBQBJdAjOjDaLUVAEVAEFIH+CESZQC8k2XC3rDdENQ6XfEm2u++TQq7Dne2agSw4WefUf8ZwJBAYka4TATlRgknI1ETyshDs+eefl2effbbfpW4s3yOvlG81EqhVpZVSUlRistDJ+G7papOenm4ZWlopdcPHyW1nfkDOm3Sab6hwSq1atcocR715xupoBvqf5a/bV0p10VCprRgpRcVFxhFsss1cphnoqWEnW8U6tebMmWMkb8OwMOude/VXCfT0RtHW1iRDnY9V4nAeDYluCfU33njD/EnJh/Tw1W+JUbjgmYu6C1LRaoqAHwQIFiWDOhWB7mxryesvyoe+fIchxUtLSsy7iZehTnOUTBfz3fLScpky/iRZ+tVH/HQtUt/lGUsG7F/+8pe+oF+vDpL9ed555wlKWoVmUSE5Cw33Qrhe7j8CV7CTTjrJqGVFwXin4z3fqhB5lfSxQZOWVE9Vviyb6+EdwKpdoSZG+Tk1RUARUAQUgWgioAR6NMdFe6UIKAKKgCLgQoDNjbu+X1RAKhTZcLesN7W6amtrcyJvnO5YF3Id7nQx8voeGbeUIrBOBGTkcCT6ka7GmUsbOB1mzpyZTXdicSwO7B//+Mf91qWWkk55onqNSJGY2uc4pEuLSoRE8K6eLiPb3tPbK5Wl5VJfM1E+tehWmVU7xff1Ogl0shbIltjbfEC+8swPZNuhnYaonzlykgyrPlpz3cu0Bnpq2HNBoIdd79zr6pRA932rGVUOAscGkgK1rY4cOVL4RCWgzP/V6hG5QkAJ9FwhnczzWAKd4AuCMAayv6xZJtd+4TY51AyBXipDKyqRGzIKNaxv/Oc0/o066BDo0ydOlqe+8GDagZRRQdtKtVPjnODOVEb2PsQ5waKFalElOQt1PJJ03c4Ma4JUUCiLoqVbP90GTQZVP513y61btxpICDwnIEpNEVAEFAFFIJoIKIEezXHRXikCioAioAi4EIgygY6UIh9ILMispBnONJx1VvYQcoBMOzJbo2aFWoc703FgbJGPJgiE38m0ZWwzyYhramoSMjGJ0p89e3amXYrFcTgcIc+5L6zhgn597G45XN5usKTuaFtXuyHSseKiYqksKZfOni7p7O6S0yfOlbdMmC2LT353RtdMBjrnoVQBmIP977c9Kxua3pTtbXtk0sg6GTfMu54e2edr9m2SUZU1MmlEndx21nUyvDx693NGwARwUFtbW1/WDHM56MyTXNQ794JBCfTsJwf3PgEWllDHAeo2VDh4H8DZSqZ6WAoG2V+NtpAvBCyBDoGnTvN8jUJ8z8s7G+9c6RDouw7skYV3XiFHWlvMu0dNVfVxwZGGSD/KqEtHd5e0tLXKsIqhcvaMU+TfP/AJQ6CnKmERJRTZK1Le5q9//WvKGuf0d/To0YY4R8HHT6BolK41qL7EheQM6nq1ndwh4Mywxj+SrqJZ7np4/JmcQZO85/Exa6PDWDOsChHrIoEBmawj7e3tsm3bNtPyrFmzIlEKL5/Y67kVAUVAEYgyAkqgR3l0tG+KgCKgCCgCfQhEmUBPctYzuJNVzAYSgxSAYI3qJrgQZcQzXSZwmlF+gMxxjIh6snAyzRBAoQBHAGQRstdJtpdeekmefPLJfpd4qLZbVlXskLljp0tnd6fsOXJADrY19TleioYMkZGVNVI7dKRsadwh1eVVhrz+X+d+WIaWVfqGyxLoqAUQOIKDp6F1nzx7aIXsaTsgu5v3y7iq0TKuaoyU/k3Cne80tjXJm027pLioSGaOniwnj58lV86+0Pf5k3wATi1kX7GgCfRc1Tv3Gh9LoHPvQwQHHRiQ5DmR6tp4RlLPGmPtcyvlWEerJdQh1zNxtBYitkm+ZiXQkzy64V+bJdCR/E1HdvyGL98hTyxfKk0th01t8/LSMs9OHs0+P2LeJ6orq+Tuq2+Xc2ceX2aG90W7pvF7vtc09iivvfaaLF++3AQ4pTKI87POOstke2pg01GUnCQnAZk8o9QUgSAQcGZYE6wSR3Ue1kIbNMk6Q4Ct25z10yHU072HaBcfC8a+GVI+anbXXXfJL37xC3niiSeM/yeV4Qv7xje+IU8//bTZk/LdD3zgA7J48WJda6M2qNofRUARyAgBJdAzgk0PUgQUAUVAEcg1AlEm0JOa9eyWbEeuHdn2fDvKBpp7jY2Npo53ociIZ3ofQhCSscxPbMSIETJx4sSsNrnUTkeKjvkxd+7cTLsW+eMIOPj+97/fJ3dPh3sri2TDSYekuaNF5oyd2ncNyLV39XSb/y8pKhZIdGxr4w5p6+qQWWMmy/93xrUybtho39dtCXR7IIEP1Bh8Yedyee6NZdLQvEd2NR8l1oeVVRop95bONpMBX11WJZNH1smE6rHyoZOvMJLyascQcBLoZIUE4fTLdb3zVOOJs1wJ9GBnO8EW3Gd1dXUmKMHW1fQicphLNpOTnxrEEOxYxKW1TZs2mftQM9DjMmLR6mdDQ4Pwjp4ugf7Ea8/LTfd8Qppbj0hnV6ch0ZFzd77Ps4aReY5CDeT5ibUT5ZkvPiS9Pb391jR3Nmam5FEQiBKQ9sorr5isc+6nVMb+BeJ8+vTpkd7DBIGJ3zacQWBxyRL2e436/fwg4Mywnjp1aiLed2z9dJud7qVClG79dNrAZ4HNnz8/beI9V6P53e9+V77yla+Y0w1EoBOMf+211xo1O4znin1OXHXVVfLv//7vueqynkcRUAQUgdAQUAI9NGi1YUVAEVAEFIEgEbBO/yDbDKqtpGU9s+lBkn7v3r0GIhz8ZCZHUbLdPYaFJCOe6fyFAN6xY4fJlGSTiwOfur3ZBkZAGtlMzHnz5mXavUgfx73xq1/9SiA/nNY1t0o2d++W5vYj/Qj0VBeTLYGOw3PdunV9zSPjask7+rj0zVfl6a0vGfJ+f0ujIfapc1pWXCpjho6UypIKObFmvFw99+9kaGn0Mh7yPQlwiK1fv950Y+bMmVkrbuSj3nkqDHH+8TzVDPTgZpkl0Mm4gRS3Zh2tllBnHriN7E1nXc1s1+HgrkpbChMBJdDDRDf5bUNYELRI8COBrYMZ7wUf//bd8pu/PC7NbUeko6vTlJUp+xuJTsmZDrM+9RpyvbK8Un5wx5dk0fyz+zXtLGEB+WODMJ1fSpc8GqzPqf7OtfCuSca5rR+c6rtk55999tkq1T4A2EnIEs50Lulx4SJAtjbB2lhSg1fSqZ/OmoVyGb6UM888s+89kTWctRxbsGBB1nuNIEfzRz/6kXzhC1/oI8JTEejsJ6688kqzZxo1apR89rOflUWLFhlFujvvvFPWrFkjX/va1+SSSy4JsnvaliKgCCgCOUdACfScQ64nVAQUAUVAEcgEgSgT6BCSSHCRWUa2YpyNjSDR0Dj8MRz7bPiiKtnuxrqQZMT9zjMcn9TsRjEBY0zJWA5KMg5nKrK0GBnoSSSC1q5dK4888kg/6N/ylrfI+hF7Zd2+rfLGoQaZP266IapTGY6UlXs2yvDyKqlHwv28G2VoaUXaw8m9iUPKZluRgQZx58YbqfZlDatl+a61cqTjqOQgGfDTRteb+uuTRxx/TNqdSPgXgyTQ81XvPNUQKYEe/OR1ZqAja+xl3PfW0WoJ9YEyOWknLs/d4BFNfotKoCd/jMO8QkugE/xIdnU61tHZIXd+91/l9y8/KZ1dXdLe2W5+ElyHQg2y7nwqysrlnn+4Wy494+2DNsvezGZisq55ZYHbICH2E7xvZvpuiKIHyjvItNvSQ6k6iBT5aaedZt5xMz3foBefkC84CfSkZAknZGhifxnODGvKJiTdUtVPf/TRR+WnP/1p394bX9Hpp58up556qglgJ1Hh5JNPjkSGPuvsv/zLv8jvf/97E2CP2gmWikD/2c9+Jp/5zGeMgt0DDzxgrskavjGIc4h1pN1VcSnpd4BenyKQbASUQE/2+OrVKQKKgCKQGASiTKAnhbQlEhryHKwxaiuTvREn51MhZEFnclO7M2Crq6sN6RrkZpZNt83MppZb0upLkkmBdDv3iTVIrptvvlleaFguT25+UV7fvV5qq0ZK3fBxKYfpYOsh2Xxwh8weM0VOnjBLrltwWVpDimOG4AcrkWcPYhzJQktlHNfe3SE9vT1SXlxmss7UBkbAmeGP0y/deobuVvNZ7zzVFSqBHvzsT4dAd5/V1tW0ZHqqTE5bZxjyKWlravAjEZ8WLYFO6ZQ4qPvEB9nC6CkqQqwdfgh0kCGQ8vcvPSH3//khWbZxZT+wKkrL5fKFF8uNf3eNTK+b7BvIVOSRsyH2E6xlVnWDIKHB9hgEfpJtTgDjQDLtrI+zZ882xDn7F7X0EHDKbE+bNk2fM+nBpt9KAwHWKNYq7nEy0AvNWBPZO/KOiBQ62diswU7jHY+gc4jm8847L+9BP1/84hflBz/4gSH0v/nNb5o+YakI9He/+90m+/yiiy6Se++997ghvv322+UPf/iDIdcJGlBTBBQBRSCuCCiBHteR034rAoqAIlBgCESZQI977Wc2eMi1I9uOQapCykGyxs0KIQva75gwP4kCt45HgiJwLg7mtPR7HmfdaJyYQZLzfvsSxvdxHrz88sv9mr788suNw7apvVm+uvQ/ZXvTLtnVvE8mj6iTUUNrjuvGkY5W2XBgm1SVDpXpo0+Sa+dfJjPHTBq0u4wdGWc264osLtZEiF6k23GiqwWHANjirMdw+lFf3o9Fpd65V5+VQPczkul9NxMC3d1yOpmc3PeWUGdOBr2Gp3e1+q0gEFACPQgUC7cNS6CT2ZcpWbx++2ZZv2OztHW2y4iqGjl9xgIZUTU8MFB5DkIe8V4OkeYVJIRylyXT+WnfGzmOZ/Drr7/eV04qVcdYCyF7UAPSYBT/w1cIMtv+UdEjgkDASpRzX6NuUOjGHu7FF180n2XLlpm9udvY051zzjnmQ/kJ1vhc2pe//GWzJt9yyy1GWZEyVpgXgc71IEmP/cd//Ie8613vOq6rv/71r+Wf/umf5NZbb5U77rgjl5ei51IEFAFFIFAElEAPFE5tTBFQBBQBRSAsBKzTP6z2s2k3zqQtTnuyzm1WLQ56JNszzbjMBscgjsURtHHjRtNUEklcPxh5BUaEWcs+SNlrP9eZi++S+U1EvjNzYMqUKXL11Vf3kVhPbH5Blmx9RbY17pD9rYdkWNlQGTN0hFSUlEtnT5ccaD0kB1ubpKqsUqaNOklmjZliss8HI8FwOiPZbp3PkOXI6kHA8G9kMObawZILzPN5jmwI9CjVO/fCUAn04GdWEAS6s1c2k9Nmp6Pu4ZZ7xyHtJJ5wdKrFBwHeU3ieaAZ6fMYsSj3lvZ29RzYEeq6vh2ePJdP5adWubD9Y41D0glRylqlJ1U+k6yHNkUPWcheZjybPF0vkJbVOdebo6JHZINDU1GRUs3g/Yc+k1h8BgoSef/55Wb16tclOt4kMzm/hy1i4cKEh1MngDqrsWqqxYA/jXE8HItBXrFhh9sHYU089Zd5n3EbZjauuukouvvhi+cY3vqFTQBFQBBSB2CKgBHpsh047rggoAopAYSEQZQI9rqQtznmcJtaJNXr0aBk/fvyghF6UZ54zCxqnWqGSCowpGUo4I7Fc1LIPSvY6ivProYce6pOnp3+QV0i3O6XTcf7+bv3T8srOVYYo33PkgDR3tPRdDkQ68u5jKkfKpJF1hjwvLykb8HJxPuEoh2iBaMc5YbPNIWBYeyDTuXfVgkOA5w3OLAxJU2q4pmNRq3fu1Wcl0NMZSX/fsWQomUOpaqD7a7H/t7n/nXWGWWvdRhamJdSZryr3ng3i4R+rBHr4GCf5DJZA59kfx+c/70usY+xDII24H9iPsM4NZKxrkLwQ57wPDRaAmOQ5ENS12TrVhSqzHRSO2s7xCJChTAkGgvInTRpcbavQMNy3b59Q6ol3NojyLVu2yNKlSw2p/tJLL5n10WkQ26eccoq8853vlGuuuSYn699ABPqSJUvMXpj3z+XLl3v2hwCKRYsWCaXdHn744UIbYr1eRUARSBACSqAnaDD1UhQBRUARSDICUSbQ40ba4rhi08amFsMhhWT78OHBSTfmay46s6CzqV2cr/4HcV6yScjesSRLrgIjssnaDeK6w2pj8+bN8stf/rJf88jqve1tbzvulNxbrzaskefffFX2txyUju5O6erplqIhRVJRUmak20+vmytvrT9dSgaoRU47OJUprYDhNDnppJP6ZR4ogR7WiIspd+CXQI9ivXMvhJRAD37ehE2gu3tsiSdLqrtramZSZzh4VLTFgRBQAl3nRzYIQDbzrod8exwVaOg7yh1kXlKeZjAj67K+vt6QTLzTEixUqAGyg2Hl9++2TjV7QQIG1RSBoBBobGw0exkIVu5ftf4IsMc7ePCg2duxtjkDgnjPo4QFhPoLL7wgr732Wj/Vjl/96lcyf/780CEdiED/85//LB/96EfNcwjS38t4T4X0RzHkueeeC72/egJFQBFQBMJCQAn0sJDVdhUBRUARUAQCRQAHsVfWVaAnybCxOGXeujOT4y7Z7h6ypJK46UxNSFc24g0NDUbuF2cYGZE1NcfX4k6nPb/fyYR09HuOXH+fa/rhD38oSLhbI8OU2nAD1cUG/y2NO2TLwe3S3tUhpcUlMrG6VmaOmTIgcc453GUVOB/S+25nMRLuOKFRjci0Bmqu8YzL+Xje4NjHqNs4kGRilOude+GtBHrwszCfZChrDUoUVu6d393G2mFrpzvrDAePhLaYLgK5DrpIt1/6vXggQJAk93qcCHTebciw5NnKT3fgjxt5G9xLgC/KO+5sc6u6wZrGM1pVNzKbu1qnOjPc9KjBESCwlIB9MqwJAlbrjwCJDGTp834GUT2QogbveH/9618Noc5ad+edd+akdMVABDqy7dQ2x9fw5JNPeg4vPrJ58+ZJdXW16b+aIqAIKAJxRUAJ9LiOnPZbEVAEFIECQyDKBHpciEOigMlasYEIZK1AviXJ6RSXsQj69uX+IIuHaH8MxyLOioFI3jD6kC7pGPS5w2rv5ZdflieeeKJf80jnLViwIJRTutUDcI6PGzfO06lCZjz3NH8nsl8tOAQgJanbh1G3EQe9l0W93rlXn5VAD26e2JbySaC7r2awOsN8H2e2JdT5XWWQg58Tg7WoBPpgCOnfB0LAEug8+21ZlygiBmm+detWWb9+vfDOgkrUYMbehMxKMjLts5fjrOIGP71UNyDRbRkLJKN1XRsM6aN/p9QTgbdapzo9vPRb6SNA8DEf7ksCYdT6I4C8OaW6IJdRzYuiDUSgkxl/ww03GHn+P/7xj57d550U+XaU1FauXBnFS9Q+KQKKgCKQFgJKoKcFk35JEVAEFAFFIN8IRJlAT5dsyReG9I8NLBs1LNeZybm87qiPRRhYUEKAwAibeUjGOdHguQ6MSBr24Pntb3+7D1fGjoCTD33oQ6E4ZlEPIAjCqgcMVlaBDC4yEsaOHWs+asEhkM5cjkO9cy9ElEAPbp7YlqJEoDuvjnlsiSfWCgJ0+Den8ZywpBM/cXKqhY+AEujhY5zkM2zbtk1494sigZ4JaU4gD0QNpPmECRMGfMeyqhs8g1nXvFQ3iouL+61rKvee+m6AwGN/yNo/efLkJN82em05RsBKlBOwx75UrT8C7PlQgKCE3vTp0yMJz0B/BjmWAAAgAElEQVQEOqWurrjiCrPWvvrqq579R4Vg4cKFJkDHBiZH8kK1U4qAIqAIDIKAEug6RRQBRUARUARigUCUCXQAZFOAUwfnAxvFqBhkyY4dO0yEM4aTCjnoXGYm5xoLG+EctbEIAwek3xhf7g+ybSB4yd7JV+aNvQ+IRh82bFgYl5yzNpcsWWKk8py2ePHiwLMoGDuclzgZsHTVA8jqwvGCA50sdLVgERhoHYlLvXMvRHgm2A8kg1r2CESVQHdfGWsNJLolnryyQcnctIS6yiJnPzdStUD9Z94ZIRWi9M4Y3hVry0EiYAl0gudGjBgRZNMZtQWZT1AfpWX4mU6mOc8fSqRAmvPOmOnziOcZ65otY+FV7kvXtdTDyj4CKWkwYhzUFIGgEKD+Ocpo7AcnTpwYVLOJaYf9O+sWazhrYRRtIAKdsT3rrLNMt8lGx//gNtRH3v3ud5sgAVTd1BQBRUARiCsCSqDHdeS034qAIqAIFBgCNpMqqpeNdDXO6fr6eiPFFQVzy0Ej80hmR64zk3ONRRTHImgMuB8gXW1tbjJHCIxIJTUd9PlTtZcU7HFokH3udMTiRLjyyisDhdItAY6DAUIlHUcyDnSkN5F5J3BCLVgEvIJB4lbv3AsRJdCDnSe0BmkErjiI4xQ4RKaoJdP5yTU4jUAslUUOfr7QohLo4eBaKK0SQAdJnU8CnfcP1j4+qCANVtPcjg3qOpDmZFwS1Bu08V7lXNe85N6dZSwIWsxX0GnQ155Je5BgEJ1KoGeCnh4zEAK2xjd7G92nHI/U9u3bzVoF8RxV9YeBCHSu6IILLhCu43vf+56cf/75x13kQw89JJ/61KeMRP1vf/tbvWEUAUVAEYgtAkqgx3botOOKgCKgCBQWAlEn0NeuXSs4oyExkdDOp4EVGZIQrFYOGsd+FLJUcoELkmIQAdQAZ9OeNMM5iLOSTTcGYYNDMgoSlUnBnrrnzkh5nKs33nijIauDMrLHGUdLWuFcGj16dNqOXGqgoizBMQTGqAWLgJtAj2O9cy9ElEAPdp7QWlwJdCcSvCuQRWqzOAnAcxuBPbZ2Oj/TCfQJHu1ktGgJdJ7d+Q58SwaihXUVlkBHfSZXew7WCAgxm2kO6ZquMc8hUCDNc6m4kO66xj1oP4VWxoLyQUhtE0hAELiaIhAUAg0NDSbQlzVKlbL6o8raZMuvsbeM6r03GIH+6U9/Wh588EETYP75z3/+uKlzxx13yKOPPpry70HNNW1HEVAEFIGwEVACPWyEtX1FQBFQBBSBQBCIOoGORBXZIGSPkumdL3NLtqcrB52v/oZx3nXr1pnM4SgEMwR9fZAbbLgJ1sDIPkLCOyrZM1EKJMkUe0jp73znO/2yMefPny/vete7Mm2y33GsZSgHEOCCQUIxV/1mrjIPkN4kc0GlEQMZmn6NONUUGCMCFux95zfYIfjeZd6iJdC5ligE3WR+JdE50hLoBLJERYEmW3TI2nRmcXrJIvN+YQl1MtWj8hzK9tpzcbwS6LlAObnngMTmnuRZFGagKIE0kPX24xVYkwrlfJHmA426XddY2/gMVMbCEupJV+0i4Hrfvn1GDYDAYzVFICgEbI1vAvjZr6odQ4C9IPsKAhfBhn1gFG0wAn3ZsmVy7bXXCoFHZJujLmKNd+PLL7/cPKvuueceufTSS6N4idonRUARUATSQkAJ9LRg0i8pAoqAIqAI5BuBqBPoOEPZBEFkedWAygV+OLYg1axDiA0r/Um688eNbVSCGYIcc+Y/Di6yfzAIPZyTUSNrbPACfYur4sFjjz0my5cv7xs+7p9bbrklkOtxB7hAOuE0QTrTrymB7hcxf9+3BDrrORla3IOZBjv4O3O431YCPXh8k0igu1HivcIS6rxruGWRWSdZzyyhnsmaFvzIRLdFJdCjOzZR71l7Z4csX7VCpFdk+uSpMnJEcEG73NcE91nC3Ab6pYOJfT5OmzZNpkyZ4jsoMJ1zBP0dK/duCXV3GQvOZ8tYQKhDMictUIiATj72fTRojLW9wkXA1vjmPTpIBa8kIMqeglJcvFsRCEUCRhRtMAKdPkOgQ6QT0P/Zz35WFi5caP7/n//5n4U5wD6XLHR9L4ziCGufFAFFIF0ElEBPFyn9niKgCCgCikDeEYCgjqrhQMepzCYo15tENmEQPEil8TvOHYjzfGbC53OcohDMEOT149CjvhgyeNaZlynpGmS/vNqKe/ACtSDJPuc+snbqqafKxRdfnDV0rF8224DGuD/JWM00wIU5QX9pJ6qOl6xBy2MDlkC3XbDZWXF3ACmBHvykKgQC3Yka6yPvO5ZQ93o3IxsJwskS6pmuc8GPVjRa5FmJqYR7NMYj6r2A2F6y8kW5//GH5M+vPisdnZ2myyOqhss1b3uPfPDC98m0iZN8X4ZVxOHdhA/vFV5Z2akaRoUCsnzq1KkyadKkWBMkVu7dkumscc53QTBgHbOZ6axtSZB7JziXLHSui/VITREICgECfbmPKDXFR+0YAqwtKImgBsVeMKpKYukQ6Dw3Fi9ebPxQbmPP9KMf/UhOP/10HX5FQBFQBGKNgBLosR4+7bwioAgoAoWFAE4dtzMjKgiwCUJeGxmuXMqUQYYgkYaUM4YzC3IVsqdQLZ/BDEFj7lYVIIqfII2okhEbN26UtrY24wyIo7Pkj3/8o7z66qt9w4jE9a233pp1JhWy8DgYcIIHFeBCVD+BM2T6q9Mz2DuPrDTUFKxRv5Eghajed36uXgl0P2il991CI9DdqDCnbO10fg6UxQnpxHtK0rI405spx76lBLpfxAr3+wcON8qHv/o/5S9rlklPb490dXf3KUAUFxVJSXGJuZ8+dvmH5ZPv/8iAzyn2ULyPQGxBmPOTe9aPEbQ3efJkQ5zzXCTzPInG+5ozUMgrsMAZKEQGdxyxoP4575KszRqMmcSZnL9rYo1hT0hiQb7U+fJ39QOfmbV48+bN5n2JPVzca8SjYkENdDLNbbmruXPnyl133SUnn3xyVIdB+6UIKAKKQNoIKIGeNlT6RUVAEVAEFIF8IxBlAh0ZLjKE2SRCcObC2JTi/LLZX5A8RDDH0YETJF5sSMkgYTOKnFhcDYcWwRFWVQDHVtRl0eMcvNDc3Czf+ta3+pE/Z5xxhlxwwQUZTyHGbs+ePYKDEsPZSo1JHK3ZGnODrCHu+6jWzsv2GvNxPGuHs945+OLcSgrhpwR68LOKZ47NIopaWY3gr3ZwpzDvapZQ98ri5B3FmcVJoFKhmSXQWbuDeB4UGn6Fcr1NLc1y+WdulNXb1ktHV4d0E4THf0OGGAgg1M27RXGJlJaUys2XXiv/9sFP9MHDOwjvCQTwEXTHT951/Bj3J+8tZJhDnPNMLERjjbeqG/z0ChQieNkqb8RF7p13VNSMhg0bFtks2EKcb0m4ZnwjtsZ31PevucabAB3eHfnJ+hpnf4UTO9YSSoAQMMF1qSkCioAikBQElEBPykjqdSgCioAiUAAIRJlAh3Ahq4OsW7JvwzY3uco5yQpJCsmTDX5s3HAQshmNY0Q3m2lk0BhjLE6qAnEOXnjqqafkxRdf7Jt6kDxkn2dKiOFsdTqrye6BLAmKLGKOEPE/fPhwdVJks2A4joVosKUw7D9DnifJ8acEekCTxdGMEuipMbVZnJZQ98riROLTSr1DJidB6WGwWaYE+mAI6d9B4OPfvlt+/vR/S3tnu1BZBpKcrHOnGld3T7d0dndJSXGxVJRWyL033S31NRPM+weBdgSx+DX2MhDmfAjeDOq9xW8/ovp98B8sUIh1jPXMrm0EUEZxj7Z7926jYsa7bi72r1EdU+1X8AigzoeiE3vxQg28SYUq70YEnbOWsM7GUbUt+BmjLSoCioAiEF0ElECP7thozxQBRUARUARcCESZQM9VPWI2XDjEiPDFcDxrBlP/iZIPNYCgblbmuJW8o824qQrkq5RBtvij5nDffff1q/2J5Nyll16aUdM4rBlHHEcYyhQ4kIJ0nu7atUuoXYnTs76+PqN+6kFHEXAHrZA5RgAEHyXQdZYMhoAl0FGfIaBFLTUCrInOLE7uPaexRjpJJ95xglw3ozI2SqBHZSSi2499hw7IKR+9RI60t0pXd5dUlJb3BZfY+6a6bKjUVdfK+KGjZWLVGJlQPUZKi/wrOvAeQbag/UD6qqWPAOPBe6QNFLLKYM4WCEKwZDpZ6lFRC+NdkgBwnl25UlBLH1n9ZpwR0Hej1KNHMCsEOkY5DJIg1BQBRUARUASii4AS6NEdG+2ZIqAIKAKKgAsBHK9uZ2tUQMqFnDIOGUg565jB2ZHk+oOZji2y9mRTIB+GpH1czFknmz6TCcI1xIk8iGv2/9KlS2XJkiX9psott9ySUc0+pzoEGUgQsGGQamQNIQ2P7CbZC2qZIcBzhTUDUg+z9c43btxoAipYY5Pk2NIM9MzmyUBHqZM4M0zJvIJ0soQ6v7sN0slKIkeJdMrsio8exXVv2LDB/K4BkNkgmexjv/HfP5L/+4uvS1t7mxQXl8iwskoZO3SUjK8aLROqRkvdsFoZXp4Z0U2QCnMPwpyfqKzE6V0z6iNv5d5Z2/jYmsDOflu5d9Y1xiNf+KO6Qwky3n3iqNoV9blQyP2DIOadk704exW1YwiwJvDuiE2bNk0z9HVyKAKKgCIQcQSUQI/4AGn3FAFFQBFQBI4hEGUCPexsUDLOIekJIMDJQpZA3MjVXM3lXKkBBHU9ONMhQ8kmxmy9SZxqcbM4Zv+zrlD73BKoYD5r1iy54oorfMHvJb2PcxoJ/jCMupV8yGiiLqmafwTc9c5xHqMWwBoLwUWwEo4/1tqkmJNAJwMuX077pODJdSiBHsxoMjct4UQm50CkE+teXGoMu9FRAj2Y+ZLkVgje+vjX/49sffMNGVs5QiZW18roipqM12syzAkGI6CPn7p/yN3ssXLvdm3jp1OCn57wHOad3wYL5VLunb0lZa8Iohg7dmzugNEzJR4B3qOZ66w5qmrRf7jZX7BnxmbMmJFxubDETyK9QEVAEVAEIoKAEugRGQjthiKgCCgCisDgCESZQA8rG9RNyuFUgZQjW0HNG4FcqAEEhT0EAdmvkAVY0HWyg+pnuu2gkEAmPbXc4lJLccWKFfLoo4/2u8QbbrjBl5QlaxPXbmuN5kIdwhLoOFyR/1Pzh4Cz3jlEMll4zgwZJdD94VnI31YCPfjRx+nOumolkb1IJxQ+3KRT8D0JvkUngc77HIEAaoWJAHOBuY2ajP3wbOf5lI0NKSuWeTPn9JHmYajgZNO/Qj6WMedd0SpvpJJ7t2sbP8OsQb9jxw6zzqK0U1tbW8hDo9ceIALO5xyBO3EMCg8QjuOaQnGHfSM2e/ZsxSdMsLVtRUARUAQCQEAJ9ABA1CYUAUVAEVAEcoNAlAl0HF+Q6EGSWThVIFetrCkZJGxCo1I3Lzej7v8syBHu37/fyGbjnI6q4bBifG2WHY4rsj/inBEax+z/H//4x4KChDXqiV977bVpTxsydxhHsicx1CEIIAh7HMNYc9K+6Bh/0aveOesEtZadhoQ7ay+BIIxnUkwz0IMfyS1bthiyV2ugB4+tbTGdGsMEGDprDEOwR9GUQI/iqITfJ971IMZRG3IS5jbwLtMetHV1SMORfbK9abdsO7RLDnQcls/d+En5wNv9qehken49LjsE0lHeQMnIZqcTcBPk2sZ7O2Q+qgQo8KgpAkEgoM+5gVFk3WfviM2dO1cD6YKYdNqGIqAIKAIhIqAEeojgatOKgCKgCCgCwSKA88mSVMG2nH1rELYQtzg2qGWVrVHDm6wAW/M9V6Rctv2OwvFhy+lne404FZgvlrQlIILACAIk4m7MWWqAIwXJNUXdUCv4z//8z37dvOqqq4yc3mDmNY7uLObB2sjm7zjhmUOoUUydOjWbpgrm2FT1zr2c0dRuxMGlBHrBTI+ML1QJ9Iyhy/hAW2PYZqi73w0JYOJ9DEKdDwEyYQc1pXsxSiyki1Q8v8dzBqKcD+96fPidUkxu6W6/V9jd0yP72hqloXm/7Gzea4jz/W1Npt22znYpGlIk5aVl8txXfi3TJk7y27x+P88IWOUNm53OO4jdB9qusY7x3meDhbJd2yDxOA+BgkkKFszzUBb86Xkm8x6NEZgcVjmruALNuwt7ZmzBggVCAKCaIqAIKAKKQHQRUAI9umOjPVMEFAFFQBFwIRBlAh3nGGQcG8Tp06dnPHY4SiDFrHwjGypIOZU+Sx/SKNeGxqHAhhmZcwwHv1f2a/pXG61vxkk+H+R++9vfyqpVq/pARLXg1ltvHTS7xz2OODO5T91ZzGGOTtBBO2H2NQptD1Tv3Kt/lkAneClJWVmagR78bLQE+rhx46Smpib4E2iLAyIA6YRij5N0ch9AoJrN4AxbEnmw4eI9D4ULTCXcj6K1fvtmuf/xh2TZptflSGuLDK+qlrNmnSI3XHi1nDS2bjBIc/535hzqMwQMQozz05LmBMAGYRCle1oOyI7De2VH0x7Z13bIfLp7e/pI1aKiIVTQls7uLuns6pSK0nJ56/wz5b8+9d0guqBt5BkBExjR1tZXysIqkjm7xdrmVN7wK/duCXTec8hCV1MEgkAAnw3lbbDJkycrQewC9fDhwybxAjv55JNDLdMQxHhqG4qAIqAIFDoCSqAX+gzQ61cEFAFFIEYIRJlAx4GGDB6E98yZMzNCtaOjw8h5WTlHMpLr6up0U+UTzahKW9t6Z4wzRr1BsluDlGL0CVXgX4+LfD4XTvT/fffd10/VYtGiRbJw4cIBcYGooW6drVuZr3EMKmgn8EkQwQYHq3fu1WUcfxBykKJJqguqBHrwE1QJ9OAxzaZFCGruXUuokxHsNqckMgFQucxOVwL92GjsOrBH/v9vfVqeWr7U/CPY9EIJDxGTTc24vPvsi+QrN/8fGTkst8Ep9IX3BAhxS5Jbwpz/t+V3spmr9lhITwhMnjV8eO7w/5+8//Py0yd/Le0d7dLbK1JaUiLFRcV9mezg1NndLV3dXVJSXCJlJaXyn5+4Ry4+9fwguqVtRAwBp9w765vX2kYgpyXUWdsG22PwPsv+hHnH+6yaIhAEAsxN3o2wKVOmqC/DBSqB9CRMcH+Sga7l+YKYddqGIqAIKALhIaAEenjYasuKgCKgCCgCASOA4yBIh1WQ3WMjhBOCDdDs2bN9N83xEPBWqs86z3Lp1PXd6YgeEMXMXJyuZGeTTcKYTpw4MZGOqqjL5zun7NKlS2XJkiV9/8S9+9GPfnRAtQfnfZrvcWROoWaAszQdyfmI3q6hdsut6OFH8UEJ9FCHJlGNK4Ee7eHEkW+l3iGdvCSRyUq3GeoEQob57uUk0AtZ2vaNPTvksrv+Xnbs32UC2Xp6e46bSJDoJcXFMvOEqfKbu38oo4cHR/DxPkbAKgQ5z3Z+On/n39xzJYiZjkqFJcvtT8reeM255tYjcuW/3iwrtqyRjq4OQcJ9CP/BnEuv9MCqi0hpcYmUlpTKRy77oHz6uo8H0U1tIwYIEJBrg4VSrW2Q6HZ9I3DIPc+2bdtmAkLHjh1ryi+pKQJBIMCcYm5hlJlSgrg/qgRhoZgHLvPnz1d8gph02oYioAgoAiEioAR6iOBq04qAIqAIKALBIhBlAh0px61btxrHxNy5c9O+cBx4kI6QvhhZKEhBkz2glhkCUcrMdRN4kJ1JlmzdvXu3oAAwbNgwmTQpuvU3ue++853vmKwya/PmzZPLLrvMc9Lxfa6NuuMYBAvjiGMyXxaE6kW++p6L8/qpd+7VH0hRSDecynySYpqBHvxIKoEePKZhtZiOJDLruyWc+Bm0418JdJGOzg55xyevkTVvbjTS41hxUVFf1jnjBKFuCWMyr8+dc7o8ctcP0gpusLL+yOTaD+/pzv/nd9bDsAyiHElsW1ua3/n4rQV86Mhhuflr/0uWvP6iwaSru1t6/xZsUFRULCVFxQa7//m+W+WOK29KC5+wrlnbzR8Cdm2zhLpVM3P2yJaysOsbax17V4h4LUGSv7FL4pmt6hrXRmm7MIPS4ogfQdDsl7kH2X8OphQRx2vUPisCioAikCQElEBP0mjqtSgCioAikHAEokyg47Cwtb4g0NPZKLol2yEdTzjhBJU5y3IeR4VYdI8v9bWR5A/aGZ8lXIEebuvP45xDsi+qRqmEBx54oF/3rr/+ejM+bkP1gu9DpmIEtxDk4rfOZNBYkClHv+jHrFmzgm4+1u35rXfudbE4lSFcrJxurAFxdF4J9OBHUgmI4DHNVYtWEtlmqHupHDkzOFGxSOf9bqD+K4Eu8vDSP8jN9/wv6ezqMpnUyI974QopSH1v/kaW9W/u+qEsqJ9lsm5Zn61MP+Pn/uRCsYrnL7LXZO7y05Ll/A4xEpSBw4trX5X7//yQPPrSk9LW0W6aHjtitFz79ivk+ndcFcla8UFdv7bjH4F0SlkQ1Mt9wnc1A90/xnpEagRYm1HWw5RAPx4nEif4cA/iN1ICXe8mRUARUASijYAS6NEeH+2dIqAIKAKKgMvxnwuHWCagE+m/adMmc+icOXMG3QiR+cLG0ma/4LiAqMnWMZtJ35N2TBSIRff4jh8/3jhWkz6+ZGijqADhgGRfVO2xxx6T5cuX93WPsbnppuMzt7ivKc1g60xyj3KvRmEcsy0bEdWxybZfmdQ79zqnEujZjkThHG8JdCUg4j3mkJRWEhkylvWff3MaTm5bX5hAsUxIUt777PtioUq4v+fuv5elq5dJ6ZBiqSqtkKFllVJZUiZDSyqkkv8vKZehpRVSWXL098qScqky3ynP+SQj6BGC3JLk9ickOXMh1+8DSCOvXLvKBB3MnjHLEDBqisBgCPAea7PTWd+8yhPYYCHmtZfc+2Dn0L8rAhYB5hhlplgfIdDV+iPAfpn9CkF5+I1y/RzR8VAEFAFFQBHwh4AS6P7w0m8rAoqAIqAI5BEBNvuWyMpjNzxPjUNrw4YN5m9kg6bKTnVLQfM9ss7JPlcLBoF8EouML1nYyLJhhSbJH8X68+5ZxRpy7733mpqP1hYtWiQLFy7s91Vn3XpIE+5TVASiYvmc51HBwNkPd7kEnL+QU5mSC9RuJBCGGrUEwCTFNAM9+JFUAj14TKPQImsKJLrNcoZcdxvriyXUIZ+M9HhPj3n2p3KIx5VA59qsEhTBrDxL+YALH/u7+9941jo/pu744SaTUR4FY5x4tvNBdp2P83cCJaJEboAvZSMwlH7yrYYThTHUPvhDwJY4YG2zpYncLfDey9y361smwUL+eqXfThICKITs3LnTqK5FOaA6X5jjK0Axj3sMv1GUnjH5wkTPqwgoAopAlBFQAj3Ko6N9UwQUAUVAEeiHQJQJdBxa69atM/2dMWOGJ2njrsmLUwJSTp0SwU70TOvRZ9sLL6nvQhvfXNafxwF4sK1JjnS0mBqgIypr0spOW716tfzmN7/pN9wf+chH+shx1pmGhgaBQMcgYql37rduabbzabDjIXcheXFykr1QyOZeWyE/uPeykURMKoHO/Gat4oNjU5122d85SqBnj2EcWuCesVLvEE/u+tm8A0JsWqUk7i/ITfvh//mwLkE422x2/m7/Zu9J7ku7fvGT/3d+3GubzZTnp/N3iyv3vfND393/xv87CXLn73bNiMM4ufvIuzZBqtXV1X0/+d1++Fs2z4pcY8LcYc3BlEDPNfrJO9/GjRvNWoASky1p4RUsxF7VGSyU5HJUyRvl3F8RQb4okvF8i3JJr9wjc/SMu3fvFhTzuKe0DFe+RkHPqwgoAopA+ggogZ4+VvpNRUARUAQUgTwjEGUCHefi2rVrDUJIlbnJNkhd6hVbh2uUpKDzPKyBnx4Ht83OSbcefbadcNdcJmt13LhxBUdOQToj2UdWHoEkYVh7V4e8umuNvLRzhew7cpTkxnCAz62dJmfVLZCTaiamPPUvf/lL2bx5c9/fJ02aJNdcc435f4hYJNvJkMOiXLc+X4EiYYxpNm0GUe/c6/zMAxyAOJUnTJiQTRcjdawlyZjrSqAHMzRKoAeDY5xasRmcllDHGb5kyZI4XUKs+0owgc2OtaQeRLjz3/h/Pkkj+pwEOpmdSbu+WE/MGHYe9TTWM4IOuX/su7CVe/cKFuI7Vu6dY5Ch1mC8GA5+iF2GHOa5SODF5MmTQzxTPJsmUJtAaAK5wtovxxMZ7bUioAgoAtFEQAn0aI6L9koRUAQUAUXAA4EoE+j0jcxWDIcWjgXMLemNowsnBRsmtXAQ8FuPPpteML5kXRNlz+9RlPrO5vr8Hosc3fbt243DZObMmX4PH/T7e47sl5+u+I0caGuSQ22H5WDbIeno7pIhIlJVNlTGDB0pFcVlckbdfHnntPOluKi4X5uQHci3O+vaXnbZZTJv3jxxB7lEvW69M1CE/heiueudB7m2EvCEA3DUqFEycWLqgIy44a4EevAjpgR68JjGrUWc4T//+c/j1u3I9Lezu0vautulpbNdWrrapKWzTVq7+P+jvx/uaDF/rx05Rh7+zA/MO3acssaDBBo5fBRSsGnTphUsDkFiWshtrV+/3lz+iSee2Ld3deLhlHuHTGeP5XyH5rtW7h0ynU+mpXMKeRySdu3sB5Ept+WUknZ92V4P8vbsOykZwjqupggoAoqAIhBtBJRAj/b4aO8UAUVAEVAEHAiwYfeSlYsCSPRt1apVpitEWpMR45b0xqmAg0Il28Mdsba2NkGSEJs9e3Zo2TmoCbABhmTDyMBgfKMm9R0u2v1bBwuIRyT7gpakO9B6SL677Jeyr+WgbG3cbojzqtJKqSypkJ7eHmnqaJaunm4ZXTlC6oaPkzMnzpd3z3h7v6yY5cuXy2OPPdbXae7F2267zWQBEASBEeTCOJK5FmXDkWkz6XOltBAVPIKud+51XUqgR2W0o98PyCxIrbFjx8qIESOi32HtYSgIPP3008IzxpoyyuMAACAASURBVC3tHsrJItIoWaeQZTxLnT/dv/N+xLuR/dz/xEPy8yW/kcPtR44GwRURBudtXd1d0tPbK2UlpXL34jvltvfcEJGrz083eMdFIQVTAj0/Y5CUs7J3JQMdS0Wgu6+V9y9IdJuhzrPPbawHlkznp6okJGXGpH8dBLju27fP7I0pg6XWHwHU2giE5p1Ra8Tr7FAEFAFFIPoIKIEe/THSHioCioAioAj8DYEoE+h0kQx0HAv19fWGtCMT19bCLFRJ73xMXoIsbEYFWdBhBCzgwIRgs46jkSNHGpnnQs2IsuMcZl3uH7z6K1m3f6tsOLBVyopK5aThE6SytKJvikGi729tlJ2H98jIiuFGxv0D8y+TWWOm9H3HLd/O/DjttNOMVDdGVhuOnjDmTND3Qi6VFoLuezbthVHv3Ks/rN9k0HBv19XVZdPlSB2rGejBD4cS6MFjGtcWef+AWLI1w/nJmsW/8d7AT77jrj/OO6Otj27JJlvPnO/a390/wclKJzt/ev2bbZ/3FOfH/e/Omu3OGu6pfs9Eunnjzq2y8I7LBXK8u6dHiunTkKO13q1xrfyNZztqMlUVlbL8W3+S0dUj4zo9Aum3k0CnZFQm+AfSEW0k9giwttiAY/aumQQAs8ZZMh1C0CuACBLVlldQuffYT5u0LmD//v3Ch30VwRlq/RHAh8A+DpUrlbjX2aEIKAKKQPQRUAI9+mOkPVQEFAFFQBH4GwJRJ9DXrFljHAfIcdmsZJVsz/30xVm9bt06c2LqigUtJQipRuY5jiccl8g7Q7KpiZGjQ84YXMiKDsoamvfKt17+uWxp3G5kXWeOmnScPLs9FyT6m027ZPqoelkwbqZ86OQrzJ9wOn/9618342btzDPPNFmjWNyCIHKltBDUGAbRTlj1zr36Zgl0skOQhk+KKYEe/EhaAr22tlafBcHDm7gWnRLcvCN6EU6QDra2N6RWEknSL//qO/L5X35Tunu6DVGOHSXRKX8khjjHIM8h2O/5h7tl8QVXJW4++L0gSBfIF0wJdL/o6fedCLD2bNq0yfxTpgS6sz27T+ddDTI9ldy7c30jYDWJ61uhzzSyz8lC5zmWpCDUIMaV+4Q1nH0cCRbce2qKgCKgCCgC0UZACfRoj4/2ThFQBBQBRcCFgJdUXFRAWrt2bV/GOX2yUddBE7hRud6o9gOHEMEMGPKWZDsEYW7ZaJw+ZCvbevdBnCPubYRVl/u365+S599cJqv3bpITqseZWuepDMfEmv2bjbx7fc1E+fjZH5JRlTWmxMJvf/vbvsPIwLv44otNtnkcgyBYC630JnL5ZAcm2cKsd+6FG/KKBw8eNPKKSqAneWZlf21KoGePYSG1QMamLb8xadIkk13OszNVfWFIdpu9yc+krPVc9+cevFe++uvvGQxstrmdCyYrvahYioYMkc/d8L/l5ks/UEjTJOW1Ogl0gkTVFIFMEXCvRUHvV9k3QRLa9c1rD8965lzfVO4909GM1nHUPyfgnHJY7LHUjiHA844yHLb0j2bo6+xQBBQBRSD6CCiBHv0x0h4qAoqAIqAIOBCIKoHuJA7p7ujRo2XcuHEFL+mdj8mLwwY5fYy6YkEQ3EiuWrk12q2urjakmjp6+o9wWHW5qX2+YtdaeaNpl8yvnZ4y+9z2hoz1fa2N5rvXzHunzKmdJg8//HCfMgHfI/P83HPPjW0QRKEQ6Lmod+61TqEyAWmPokiSnFuagR78U0kJ9OAxTXKLqORs2bLFXCLSrc6SIba+sCWcePdwGxnpTjnkuJeOeX7Vy/KdRx+QP7zydD+FGGqev2fhxXLLpdfJqdPmJXlK+Lo23rNQSMGUQPcFnX7ZhcBAa1EYYBHgbLPTbakLr/XNqm+wf9Ps9DBGIvw2d+/ebdT42C9T4kztGAIQ6Ki1cf+NHz9eM/R1cigCioAiEAMElECPwSBpFxUBRUARUASOIYAzkY1HVIy+IFPGRtFa0jIWo4J1uv1gTMg2xqZMmWIczdkYsuSQ51ZmlcAIJNfUqXM8qmHV5f7myw/Iyr0bZWfTHjl53MxBh3NvywHZeXiv+e5751wsc0ZNla997Wv9FCLOOOMMWbRoUWyz+VgL169fb7Cglnsc6rYPOnCuL+Sq3rlXv5RA9ztahft9JdALd+wzuXI/pJWtn24JdWcJEs7NewjvOJZQj7MccsOBPfLa5lVypK1FqiuHyWnT5suYmlGZQJzoYyyBztgj4a6mCGSKAO+REHl2v5RLdQv2aqxvdm1jXrv39871jTWODHnde2U62rk9bteuXdLU1GSCUNk3qx1DgHlOEB0KEGTna4CBzg5FQBFQBKKPgBLo0R8j7aEioAgoAoqAA4EoEehsfJD5PXz4sOkhWUA4N9koUgtVLX8IQKCzQUUeFfm4TIzj9+7dK8jQYWSbk4maaXuZ9CFux4RVl/uHr/6XvLprjWw7tFNmj5ki5cVlA0KzvWmXHOpolrljpsl1C94tvXvb5ZFHHuk7BgfcbbfdZmrzxdVwPK5bt850nyy0oKU3841LLuude11rQ0OD7N+/X4YPH25UCpJimoEe/EgixcnapzXQg8c2iS36IdCd1887CfPMZnDyu9sgwGz2JoSTquQkbwZBOLL3YM9BmSI1RSBTBFAyIgAMQ7Ern+sF6xtBuKxvfFKtbzZgKEnlLDIdvygfRxAqAegkFaD4pXYMAd7DIdAJzEfNTgMMdHYoAoqAIhB9BJRAj/4YaQ8VAUVAEVAEHAhEhUBnc09WMo5QbNSoUaaWFY4tnOi6GcrvtEXCnQ1qfX29kY/zawRHIJHJ5h/DUQN5nsQsX7/YDPT9sGTFn976kvx581JZtXejjK4cIROrUztjunt7ZDXfGzpSTho+Xm6a8155/ulnZdOmTX1dr6urk+uvvz7IS895W8zRtWvXmvOShYasb1Is1/XOvXAjewZ1EdYP1pGkmBLowY+kEujBY5rkFjMl0N2YWDlkS6jzTHBbRUVFH6HO75q9Gf+ZxXsp5JQS6PEfy3xfASQ1zy+MYIwolYNIZ32z5SzYoyH3HqX+53ts831+gnzwiYwcOVKTClyDwXv45s2bjZ+CAF1Nusj3bNXzKwKKgCIwOAJKoA+OkX5DEVAEFAFFIEII5JtAJ0KerEQk2/mdzTpkHBJlOCGQK6P+ucpx5XfSQCziTIb0Zmz8mDs4gvGkRpk6ngdHMays6MMdR+QrL/xI3jy0S/a3HpRpI+tlaGnFcR3intx+eLccaG2UWWOmyKxh9XLGsNmyZMkSc29ao/b5W9/61sEvKMLfwLm4Zs2aPscn5EjczaveOc6lfAQHKIEe99mUu/5bAp3SHgTTqSkCAyHgJNApMxOEbHI6csi8r1qpd35qQGCw83TfoQOyp3GfyeKdOHq8VFeGo3BjCXTOQ9awmiKQKQJkfBMMjhGIGdV9jl3fbLAQ/fYqZwGJbtc4lXvPdFYEcxxB6IwXe2g+ascQYO5u3LjR/ANKeYqPzg5FQBFQBKKPgBLo0R8j7aEioAgoAoqAAwEcj+5Nc64AgrAiotoScRBWELSW3GGz2NjYaBzo1LRSyx8CSFszV5BGQz4uHcNBc/DgQUG62R0ckc7x+h0xQQthZUX/97on5KUdr8umg29Ie1eHjKsaLaMqR0hJUbEZr5bONtndsl+a2pulrnqsDO0pl/dMPF+GSYU8/vjj/YbnuuuuM/dunI11EKUFDCc6jsM4Wz7rnXvhRpAUJRwo2YCDKymmGejBj6QS6MFjmuQWwyDQ3Xhxn5NdausLow7jNggmSzZp9mZmM66zq1N+++Kf5ft/+Lk8t+rlvkZKS0rlPWdfJDdfcq0snH1aoMQkZaN4T1UCPbMx06OOIQDByd4VoxRQXCydchbcH86AoSACleKCTxT6qe9FqUcBf5JVRSOIjix9NUVAEVAEFIFoI6AEerTHR3unCCgCioAi4EIgXwQ60e5sBq1kO5sdssydcnGQ6xCwELYQt2r5Q2DDhg1GUp9AhnQyAnE2M36HDh0ynSYoIl+Zr/lDLfszh5kV3dndKfcvf0S2Nu6QHYd3y8HWoxnlZcWl0tPbI509Xeb3sZWjpLS7SBbVniYzhp9kZPiffvrpvouDNLj99tvzWusxe6TFBA2sWrXKNIUDBkdhXC3f9c69cFMCPa6zKff9Vkdx7jGP8xlRUtq6dWvf2p0LYofgNpu9yU+e1U4j81SzN/3Nqt0H98r7P/cReXXTygEP/MDbr5Cv3/oZKSst83eCFN+2BDrzhme/miKQKQIE2LD34f4nAz2uxnrGPt0GDNm9uvN6ePevqqoy78oaMBT+SG/bts3sw6l/nm4ge/i9isYZnEF03HfDhw+PRse0F4qAIqAIKAIpEVACXSeHIqAIKAKKQKwQyDWBDklFPV7kfG1WMqSs12aQjBDk3dkIQb6q5Q8BpNHIviLIYTBpNDb4ECA2Q4uxZYy1lp7/8Qs7K7qju1MeWftnWblngyHMD7Y1SWd3lwwZIjKsdKiUSYn0dHTLorGnybSaE02WOeT5ypXHHNzUeXzf+97n/+IidoSTQJ88ebKUVZZLkQyR4qLiiPV04O4QdEQ9V66HjCGCj6g7nm/bs2eP8MHhCr5JMe5RPpB44B1VydY44Y0ELs57lXCP06jlr6/5INCdV8tay/uOUw7ZjQZrgyWbIJxyQfLnb0T8n/nA4Ua5+J+vk/U7Nqd18JXnXCI/uvMrgbxXooLFnkQJ9LSg1y8NgIAtB8B+h3fjpBi+Akums855yb2jImfXOIKm9V0o2NEnSIxn3bhx43yXUgu2J9FrjecvAQbYzJkzjdKVmiKgCCgCikC0EVACPdrjo71TBBQBRUARcCFAFo07cyYskDgPxE66WclJzVgMC98w20UaDUKD2uWQGqmMsSX7AucKzhMId9QF1JGS2ejkKit6b8tBeXnn67J232Zp6WyVYimS0q5imVw+QaYOq5PqqmoTxEKN1/vuu69f/fMLL7xQTj/99MwuMEJHgfWTrzwrrzdukL3SJD1Dek3vRlbWyOkT5soZE+dLdXk4NViDgCFK9c69rgf5dtZ0yKMkZfkpgR7E7O3fhhLowWOa5BadBDrlNyCr82msCZBMllD3yt6EYHJmbxb6O9Kt3/gn+dlTj/gatns/8m/ywQuzD96zBDrvN0kK7vIFpn45EAQKoRyAU+6dNY69odus3Dvve3y4t9SyQ2Dz5s2mrBf7cM2w7o8lAf4E7mNz5syJfQmu7GaKHq0IKAKKQDwQUAI9HuOkvVQEFAFFQBH4GwK5ItDt5gZHJ5ZOVrLNWEwa4RLHybdlyxaTfYB0HB+34VAhgwfFAAxnCYRr3OtIR2GskBUHXxy7ONzDNreCAJL9OGzIqCG75t577+3XhQ9/+MOecyLsfgbZfltXu/xi5aPy0ubXTCZ+e3GXdPd2m8CPoaWVMrJiuJQUlcil094q5510WpCnDqStqNU797ooJdADGeqCaEQJ9IIY5sAuMmoEuvvC0snedNYWRhq5kGxv436ZfcvbpaOr09dlz62fIUv/45GsAzQJ/CS4C9wnTZrkqw/6ZUXAiUAhlgNwBgxBqNt9vhMX7i1LpvNTFcn83zcEspOIgKKbZlj3x495t337dvOPc+fOFdQQ1BQBRUARUASijYAS6NEeH+2dIqAIKAKKgAuBsAl0iD8khZFj53cIKTZ/ZCUPZvv27TOkLCQsWUVq+UMA6TjI09raWiMf5zQ3ecfGHtlolSgNZrxWr15tMvrr6+tDl+ImEwsnhFUQcN+r69atk4cffrjvwnCKffzjH4+1MwwZ++8t+6Vsa9wpG/ZsleauVikrLZWqskrp6e2V1s42c33jqkZL7dBRctGUc+Qdk88OZnADaCWK9c69Liup67lmoAcwiV1NWAKdciGDlQwJ/uzaYtwQiDqB7sTTZm9aOWSCS91GAKIl1Hn/zXdGfdjz4Z6Hvy+f/slXMjrN45/7mZw165SMjrUHNTY2mvIiSqBnBaMeLGLUmdi3FrKaAXtCpwKHW+6dicK6Ztc4lXtP79bZsGGD8aPU1dXlJJg6vV5F41u2dAK9WbBggSoeRGNYtBeKgCKgCAyIgBLoOkEUAUVAEVAEYoVAmAQ6m2Yk23FOYTinyEpONzKYWukcz+Z6+vTpscI1aZ1FGg3HEGQGsuzW2LRCdtgyAGSnQ7IXuhxpkOO/Zs0agy/3TliyfThlyMCC5MRSKQg89dRT8uKLL/ZdHtla11xzTZCXm/O2fr32cfnL9tdk08E35Uhri4wur5Fxw8dIeVm56QsE+94jB2R/a6OMrRot44eNkRtPeZ9MG3VSzvvqPqGz3jkkPzXqo1Dv3AsY1CkIpEpaQJQS6MHfBkqgB49pklt01j+NgoS7H6x5tlupd37yTu62pJNNH/ryHfLw0j/4ga3vu1+88V/k1nctzuhYe5Al0NlrEKiopghkioCqGfRHjr0F67Nd4wgY4t+cxrurzU5HZUvl3r1n3/r1680fCFAHL7VjCFjlB3wPEOgawK+zQxFQBBSB6COgBHr0x0h7qAgoAoqAIuBAAOedl8MuW5DYJOMEZ+OM1dTUmMxzP5k0OLXIhmUzPXPmzGy7pMdngQBjiWMIOW/GEQcIZCukK8a4Qt6prFwWIKc4dO3ateYeBV/uo6CNthlfMuIwHFicy8sB8dOf/rRPJo/vnnPOOXL++ecH3aWctdfc0SKff/578uahBkOQjy8dJWVFR7P/3E68PUf2y67mfTJtVL2cMn623PCWK3PWT/eJol7v3AsYS6ATQDVt2rS8YRf0iRkLnqNkXeEIVmnS7BFWAj17DAuphTgT6M5x4r2KbHpLNlFb2ItssrXT+ZkEouB9//cf5E/LlmQ0Ze9efKfcedXNGR1rDyIQjRIjSqBnBaMeLGICxlEz0LnkPR14X2Jds2ucl9x7oSlwpHPj8BwgAx3zk4iQTttJ+I4NXOH9GwLdj68pCdev16AIKAKKQBwRUAI9jqOmfVYEFAFFoIARCINAd2ZFEg1MxjKS7X6zksl4JvMZB+GsWbMKeJTyf+k7duwwUvzUrmc8CWwg4hsjOwrCtdDqduZqVJBNh5wj6wD8gzQcWdxjtI+hHoCKgNe9ylrx1a9+tV/AzdVXXx3r8grPbHtZHt3wjKzZt0lGVdRIlVRIb0+vVA6tlLLS/nVocWCt3b9ZhpVVyUk14+UTC2+UkZXBBzQMNr5e9c6RdIy6wyipiiJKoA82Y/3/necLDnaVcPePXSEe4STQCc5JShBLOmQT712WUOddLI7XfuNXPyEPPfu7jKbuf9zyabnpkmszOtYeZAl0grsgp9QUgUwR0LnkDzkCeJ0KHFbNzNkK96Vd4/jdry/BX4+i+W1woQY6hkoGARpqxxCwgSv4i+bNmxf5/ZCOnSKgCCgCioCIEug6CxQBRUARUARihUCQBDrOPiR6cSBgOPYgVnHqZWIQtNu2bTMOwTlz5mTShB4TEAJI6UOAkWGOs9oSrhAc1ESPo9M2IGhCbwbZPrI0IEkJRAnK3PLfEPQDScRT1/H+++/vd/rbb7894/s7qOvIpp37X3tYXt75umxt3CEzRk+SztYOU/+dNcsrIIQM9H0tB2Te2Bny/jmXyCkTcrsuxaXeudeYKIGezUwtrGOVQC+s8c72apNKoLtxGay2MMQSzy5LNvEMiwPZdP/jv5SPfeuujKbBy1//ncw8YWpGx9qDeDahqKQEelYw6sEiZp/EXLKBxQpK+ghYBQ7UsHjXTaXAYeXe+VkogdsEGmzevNmASemsQrnudGePDVxBvQACXX0S6SKn31MEFAFFIH8IKIGeP+z1zIqAIqAIKAIZIABZZMnQDA7vOwQHJpmsVrIdIi7brEg20Vu2bDHnmDt3biwcgdlgGOVjIU9tfWz6yeaU8Q1DUjzKOOSjb8j2cV8hnY+EfrbmDnQhk4Gsq8EyGlasWCGPPvpo3+nJhr/11luz7U5ej//OKw/Kq7tWy5uHdsm8sdPlSPORAQn0A62HZHvTLpk/doZcPusCWXjCW3LW/zjVO/cChf6jZIHjb8aMGTnDLewTaQZ68AhbAp31bsyYMcGfQFtMFAKFQqA7B83WFnaSTe5BJRsPkskS6lFVKTnS1iIzb1okTS3Nvubl+fPOkt/9a/+gPl8N/O3LlBfho6RnJujpMU4E7FziviMoVS1zBJwKHBDq1r/gbNHKvVtSPaprXOYoHD0SP431h0yZMiURpTuyxcTrvmMfS8KFEuhBoqttKQKKgCIQDgJKoIeDq7aqCCgCioAiEBICQRDoSGeRoUxbZLuMHz/eEH3ZZr4QfW4ly3RDFNIESKNZxpXId+raY2xQURYgW0ctfAS4B7gXkM4n4z8bI5Pd1hemHT+BLk8++aS89NJLfaefPn26vPe9782mO3k/9oev/Vr+uvN12da4U2aOmSwdre3S090jFZUVUl52vETi7iP7hVroEOhXz/k7OXXC3NCvIY71zr1A4TkBMYrDc+bMmaHjlqsToOJC/UUcnBBVmhmUPfJKoGePYSG1wLsJAZxYkiTc/Ywh6xDvCZZQ9wqM5Z3NEupRk0L+zE+/Kl/59Xf9XLI89C/flr87bZGvY7y+rKRn1hBqA39DgEBjstB5FyDIWC04BKzcu5V8TyX3bsl0AmKy9UME1/vsWnIGiU2dOlUlyl1w7t2716gf8lzDX5SUcc9u1ujRioAioAhEGwEl0KM9Pto7RUARUAQUARcC2RDobmIHYgRilc1rEIZTdOPGjaap2bNn64YxCFB9tsGmHcLVkudE95M9mtQof5/w5OTrBC/gMEIqnxrlmVpzc7MZS+t0ItAFQj5dR8ODDz7YlwFBH8455xw5//zzM+1OJI778+YX5E+bn5M1ezfJmKGjpErKUxLoZPyt279FKksrpL5motx59g1SW5W9IsBAQOAwhBhi/DE/AQ+RANjRiSQS6DwDCR7j2qzZmsQ40JPkwM3lfFICPZdox/9cSqD3H0OeVVbu3RLq/JvTyNCzRBNrFe/v+bTOrk55/+c+Ik+89lxa3fjk+z8q/3zNbWl9d7AvWdJTs4YHQ0r/PhgClsij3BWqUWrhIGDl3nk3th/3Gsfexr3GpbvfCafXmbeqz7iBsduzZ495D2e8Z82alfa+NvMR0SMVAUVAEVAEskVACfRsEdTjFQFFQBFQBHKKQKYEuptYra6uNnJ1QRKrZMtS/xkjYzHfDr6cDkwETtbU1GQyRpkj1ojuJsNLLXcIINuHE3zs2LHm49dwKuEg3r17tzmUe5RAFxx8fuyb3/ymHD58uO+Qyy+/3AS2xNkOtR2WLyz9vpFwP9TeJBPLa6Wod4jJYnBL2u9vbZQdTbtlyqgTZcHYmXLzqVeHeulxrnfuBQxZ2gRwICuMgyvu5g5uwDGbyoHrrEkc9+vORf+VQM8Fysk5h5NcQBklriRJWCPCukR2us3cTCWFbNcpSIh8SOC2d3bIx799tzzw1MMpoSgvLZO7F98pH7nsg4GNs2YNhzXzCq9dS+SxJ0Y1Si03CLBP5Tkw0BoXl5IWXoixfvP+jOkz7niE2N+yx+AZloT9RW7uGj2LIqAIKAL5RUAJ9Pzir2dXBBQBRUAR8ImAjeL2cxibFGrZWmLVbyZruueCoFi7dm3fhnGwGs3ptqvfGxgB5gSbUVvznMAFMl+RuVQCPfezZ+vWrUL2ONnnZKH7MbLNuVcJhsDIiKXeud9gFBxT99xzT79T33jjjVllxPu5jjC/+7OVv5NXG1bLhgNvSGdnh4wpGyGjqkb0lSjo7umWfa2Nsrt5n4yqrJETho+XxQveI3NrwwskiXu9c6/xShKBjjNz27ZtwjMK494kIIWsTyujbFU7nFhw3+Hgs9np+SCpwryXgmpbCfSgkCyMdpRA9zfO6Ugh865gCXXevXMZlLBhxxb5wR9/IY8s/aPsbtwnJcXFUj/2BFn8jivl+gveK6OHj/R3wYN822YNq+x2oLAWZGOWyGPPxN5YLT8IsPexZDo/7buaszesa86SFlF+HyOImr0c6zAEulp/BBoaGkyAN4ErqOSpKQKKgCKgCEQfASXQoz9G2kNFQBFQBBQBBwJ+CHQIc5wDEKlY0JLt7oHhfKtXrzb/TM0vHHpq4SIAAUSUu5WMhhRCWYCNKZt35Il1cxruGLhbh6gD/zFjxvhyyJFlhvy3zTYbNWqUOT4TJxFz4oEHHujrGm384z/+Y6CKE7lF9djZjnS0yrdf+YU0HN4jG/Ztk5bONqkoLZPqimHS09sjzR0t0iu9MqZypEyorpVzTjhF3j3j7aEQCkmpd+41lgRxMB9RQIizcgEykayFPDu5D1gfccKimML/2/vL6cDF+emu14kj1ElSsbbmkqTK1/2WznnBF8xYs1j31BSBgRBQAj3z+WH3AFbqnQAgt5IGa7YlmvhJJmeujL6EvS6q7HauRjP559m1a5cJWFUCPTpjbUta2DWO/a2XWlCU38cIoqZcEO+XqgJ3/NwCGzCqqalRfKJz62lPFAFFQBEYEAEl0HWCKAKKgCKgCMQKgXQJdMgBSDSca5glVsN0pNG3VatWmfNNmTIlsNrqsRqgHHYW5wJj7MyqRDIc52US6xfnENqsTgXpiEOOeuXpSkI65fcZP2oxjhyZedbW8uXL5bHHHuu7Dkitm266KavritLBh9uPyI9XPCLrGzYLv7f2tktvkYgMGSJVpRUyqnKElBaVyPn1Z8glU88LxaGfpHrnXmNLEAjBIDgA58yZE6XhT6svbmUOCO/6+noTSEbgg5tAdzbqJKlYZ+1z1PkdnqVOCeUgy6GkdYER+pIl0FmzyO5XUwQGQkDlbYObH6xlzrrCrGtui1PmZjrIqOx2Oijpd9JBwGbCQuT5VYxKp339TvYI8D5G0JUl1L3Ugnj/cr6PhenrSOeKeH9mbtEP/CFq/RGwqkW8Myo+nNYXMwAAIABJREFUOjsUAUVAEYgHAkqgx2OctJeKgCKgCCgCf0MgHQLdXQsbpwAEWthZIXQRAp0+Tpo0yXfNZh3k9BAAX1QFyJzAcByQVYkUmrWkZI+mh0i0vkVQA/LXZGNChA9kXvL7SLZnq96wZMkSWbp0ad+pyYB43/veFy2gsuwNUu1PrVwqL+98XfZ0N/ZJuJeXlMkp42fLWXUny/hh4WTDJq3euddQxJlAJ3uc+5AMF4y1kTWStRLCaTAC3Y2HJalw4PLxkhfNp4RylrdS1ocrgZ41hAXVgBLo4Q03qkTOzE1busmekX0AWek2Q52AolzsDYK8Yiu7rXWrg0S1MNuymbAjRowQApDVoo+AVQuygUOseW6zQUOsc7ybZaLklQ0S7AFZp1hfJ0+enE1TiTzWJngQaI6/SE0RUAQUAUUg+ggogR79MdIeKgKKgCKgCLgQsBLPbmDcZByRzyeeeKKJys6VrVmzxkjfQgIiiacWLAJeNbIZY7IrnRZn8itYxHLfGmQSNbFxyEHapTJIOJwIOLuxIFUifve738nKlSv7Tn3aaafJRRddlHswQj6jzfavHjFcqkfXSPGQIhlWNlSKi4pDO3MS6517gQX5vHXrVkOuzJ07NzQ8g26Y5yOZ8zYTk+AxgsgsSZQJge7so1Ne1Gane0ko22wofiY9O10J9KBncbLbcxLoWmImvLFOJ3MzjkoaWrc6vDlTaC3rsyv+I867nlOFwytoCBLdBg3lovwOKnAoZXAuJYj7zzGeS7ZcGYpF+IvUFAFFQBFQBKKPgBLo0R8j7aEioAgoAoqACwEvAp0NpJXE4us47SFWcy1jtm7dOiEaHOIQAlEtOARs3VBLDA1UIxtiZ8uWLbEjv4JDK38tkdFy4MABU9uNe9DLIBBwINjMCZwIVn4/iJ5T/xxy3to73vEOOfPMM4NoOlJt+Mn2z7bjSa537oVNHAl0AoeYE4wVhHldXd1xz6FsCXQ3VrTH/WyzPr0klCsqKvqct/wet4zPwe4dJSEGQ0j/7kRACfT8zAebuWnXKi8lDdYnG/wT1bVK61bnZ/4k8ax238x+imA7tXgjYIOGLKHuVX6HgEanCkcYfhICbffu3WuUsZQg7j+nGCMb5Mq+N9U+Od4zUXuvCCgCikDyEFACPXljqlekCCgCikDiEcBB78x4gzTACYBzDGNDAiGXDyf9+vXrTeYfxEU2NZwTP4g+L9CZ9ZqKGHI2ifNg8+bN5p/IHs3HXPB5iYn5OnXvkNhHgcHLcQK5zne4h5EVJNgkaLWG++67z9Rht3bFFVfIrFmzEoOxvZBcEehJr3fuNTFsEA5/mzdvXqTnDvfSvn37jGQmhkOUeudepRCCJtDdwAwmocw9bzOhIKrCcN7merCUQM814vE+nxLo+R+/dJQ0nGsVaxZyxFEwS6Br3eoojEa8++CUkkZOWi1ZCOAXcQY4esm9kyVu38mCkntnD8iH9pQg7j+nePagbsVYTJgwYdBSZ8makXo1ioAioAjEFwEl0OM7dtpzRUARUAQKFgFLoLMJQSKMKGdLGkDGIQWdL9u4caOQKc2mSJ0R2Y8CZA9kKwQ6xkYfUpao9oEMh8GmTZvMV+bMmZPz+m/ZX3l8W8C5C5lHfU5IPGvusaRGH2PJzyCN83zpS1/qF2Rzww03yPjx44M8TSTaInAIqUSCdQjaCcMKod65F25xCcJhvkPgUnMSwxHKfZWKmOa5iVMV5x2BRWHWxuRcTuetl3oMazpEOh+crXEMdrJ1ZLkPCd5TUwQGQoC1hbUbUwn3aMwV1lHe3Qmc4uOlpBEG0ZTJ1fNOTOCwEuiZoKfHOBFACYp5T/Y5WehqyUaA9z6eP1aFw0vu3anCwf4sk3cy9oAES/M+OlApr2Sj7X11vBejkEdg8sSJE42/SE0RUAQUAUUg+ggogR79MdIeKgKKgCKgCLgQwLHFh8h5NoIYznc2afnOECHrmT5Rc1Yd6dlNXcbYOndoiSxlSMJ06ulC1GzYsMF0gMzjJGQ5Zodm7o4mC5agFgJZbO07e79aOUEcvzgO0hlLvz0n85wMdKd97GMfM46cpFm69eYzve5CqXfuhU8cCHScoayR9r6CwMUZNxApnksC3Y0rDkPruOWnl/PWmZ2e7+d5uveNJdAp24ICjpoiMBACSqBHf36wVjmJJqtwZXsOqZTrusL23JZA1/Um+vMo6j1ESpr9EvtVVU2L+mgF2z/eBRl7+07mJffOu6QtaeFHhYM9IPsH9oHs9dSOIcB7L74ifpKdr++MOjsUAUVAEYgHAkqgx2OctJeKgCKgCCgCDgSIaoY0sA6toOsnZwM2UcVsRukTJLpaZgi4ZfnJHiajP91IeAhb5PSxmTNn5j2wIjMU4nkUqhB8cLpMnjxZqCVNsIu9X/2OpV8UyIC///77+w7DAfSJT3wi7bnj93z5/H469eYz6R+ONZz0rLVYWGoBmfQtV8dEXcXCrQwAcU4G2WBrZD4JdOfYOWt18swkC85tEOjO7PQws+WzmVdKoGeDXuEdawl07tXp06cXHgAxu2JLNFlCPVVdYUs08TOM4EALmypexGwCRbi7SEmzX4LEIyBDrXARgNC1tdP56aXCYd/JbA31VO9kBFKjioQSmWZY959T4IxCHs8VVNpQf1BTBBQBRUARiD4CSqBHf4y0h4qAIqAIKAIOBIiWXrlypfkXHFRknbNBi4oRzQ/5y4YoiZLRYePsJctPhDYOST9G9tDatWvNITiog5YJ99OXQvsu0n2Q2MgAkmlu6zJzvzKWYZdYIIjlwQcf7IMdRw8Z6Em0werNZ3LNhVjv3AsnCF1KcmCzZ88OlRDxO05OZQC/91VUCHT3NRNgYwkqCPVUGZ+WUMeRO1iwgF9cM/2+EuiZIleYxymBHu9xt0STzdz0qivMO6cl1IMuTYHyDOfWkhHxnkdR6D3vy8xfAr55X1dTBCwCVu7dkurudzK+Z1U42Gex57PvZOwBUQPTMhPHzydwtCXmCDLX0gl6zykCioAiEA8ElECPxzhpLxUBRUARUAQcCJDNSlYrZFzUJF7pG1HXbIhUtszftIW4Az8cgxjOR8Y4E/l1Nqhr1qwx7UybNm3Qmun+eqrfHgiB/fv3m+xlMhOsRDNOFuoy5+J+Xb16tfzmN7/p6yLKBTfffHMiBy1VvflML7ZQ65174RVFAt2tDIDDkvuK2rzpWlQJdGf/6SPZT7YesVfGJ88FZ3Z6mBmfg2GrBPpgCOnfnQgogZ6s+cBa5cxOd5em4F2IdyBLqPtZr72QsgQ6+wzNXkzWXMr11SAlzd6LgG/KZKkpAl4IOFU4WOt4J+PfnMY6ZzPT7bublpk4Hk0ngY5/QgNX9J5TBBQBRSAeCCiBHo9x0l4qAoqAIqAIOBDAWYWDKirZZ87BCbsmcVIngpu4wylIRkSmY8zGftWqVQauKVOmJLL+dVTngq2BbvuHkxfnXK7kl1955RV5/PHH++BBpWLx4sVRhSurfnnVm8+0wUKud+6FGWonGzZsMH+aNWtWRoE8mY6F13HuACOc3XV1db4z4+NAoLuv35nxiWMWLNzmJKjI/sz02ZHJmGlN4kxQK9xjmMO8K6qEe/LmAOsr5JIl1HmOuI1AQogmS6j7fTfavn27aV8J9OTNn1xfEZmwEHrIbEdJzS3XOOj5/CHAO5lznfOSe6dF3sVYp1jv8hnk6O/qwv02WFE6AUMhTwNXwsVbW1cEFAFFICgElEAPCkltRxFQBBQBRSBnCOA895ISy1kHBjiRlVQmopjsabWBEcDZSMYymbQYjkQIzyA2lFbqH4k0vxLwOm6ZIYD6As5dm5kAwYfMaC7tueeeEz7WcFC8973vzWUXcnYud735TE6s9c69UYsSgU42POVBrFQw9Upra2szIonjSKA7R4j+g4MzO92dCYWjNlf1iOmbEuiZrDyFe4wS6IUz9v+PvTcNtuSqzjbXPeOd5xpVUpVqHiSBJEAgIYHAMhg3g8BtwGFsoM1sTAM//KNNEESHw+4fxkFA8BlDt2QbY7AwfBiQm89AA92AABmBhpqkUpVqrlt3Hs98O55d7Ft5s8588uTJPGetiBP3Vt08mTvfvXPn3utd613sVazUe7HSFCBhZZCZs6oJ/rEEOuo6fNQUgXoRoEwNZCiKac0ur1RvG/V7wUcAv4wNGuJnMR8Nikk2cMgp9x78u/O2hewtWM9jBOeqf8JbfPVsioAioAg0CwEl0JuFrJ5XEVAEFAFFoGkIBJlA91pSuWkgBuDEbLDJwqJOGlaPHHG520DKG8fQ9u3bNbOiyf0NgUU2NPXPrSGxjHPAbyP7nCx0a7fccou85jWv8bsZvlzPEug4pVBaqNW03nlpxMgSOX78uDlg3759vpQfKNYa5kcIE+YyLwKMwk6guzGymVCWpCqWCdVsx60l0LXeZ60zUGcebwl0nmckXNU6AwFnaYpSMsgE/1gZZIiVYiWMKHVE9qcS6J0xbpp5l5ZAJ9hVibxmIt0552aegyBmLYbaBvuMYnLvTtUgjvNTNaiVvcHczRyOHTx40ARQqSkCioAioAgEHwEl0IPfR9pCRUARUAQUARcCQSbQvcgI7YQOJ6Py9OnTZoONkaWMhGCtUpblsKIGOiQ9NYK9yGjvhH6p5x7d0tKQVfRvqwj0b3/72/LEE0+s3coLX/hCeeUrX1nPrQX+O5cvXzaBC/UQ6FrvvHz3kuV87Ngxc9DevXtrqjPuxcDB4Uj/8k7BcDASDMTz1Yi1G4HuxsKZnc4YL+a4dWanFyOoasVXCfRaEevs45VA7+z+t3dfTfAP9dLtfAXRwhrZEuiUOkIeWU0RqBcBytTwjkT5i3WkmiLgBQIQ6GRao5REYCF7QhvkWKysBeswO8+1u9w761KCYrGbbrrJqI6oKQKKgCKgCAQfASXQg99H2kJFQBFQBBQBFwJBJtDJwCULHUfXrl27tO+KIDA7O2syz3HaEHGOdGAzZL6PHj1qIt+R0mcDr+Y9AjgCcOZaaWmcJRB8/B+ZVAcOHPD+ohXO+O///u+C+oC1F7/4xfLyl7/c93b4ccF65xutd165d1pJoLvVOXAsMo95Qfa2O4Hu7Flbj7ic4xbnpZUV5b1dTxaUEuiVnyc94ioCSqDraCiGAO8cpwwyBLvTmJuYowg8ZW2rBLqOo0YQ4P0IgY6xvtBM2EbQ1O86EaDGN/PUpk2brtl/W7l35jo+/NttrMucgUP1rMuC2iOLi4ty/vx50zwU0giOVVMEFAFFQBEIPgJKoAe/j7SFioAioAgoAi4EIBeKbbiCABT1vHGmQyKqNOf6HsEZSHDB9PS0+QOZNc102iC/zAa+FXW4gzAWm90G+pGxjhPOKS2NcwDnCQ6PQ4cONbsZ15z/61//+lrmMH+866675O677/a9HX5csNb5RuudV98rvGMIwsH27NnjW5YIc5bN3uHayPRu3ry5LmK32N12EoHuvn/61FmPuBhBZcl0nLfVOjaVQK/+udIjRawDXSXcgzcacoWcFKQgEYlILBJrWQOZp8natIQ6v7uN8UPdaks0EbSopghUi4CTQEepq1F1m2qvq8e1PwLPPvus8dOwdi2nAGfLWlgyvZhqEHtJW9aCn/gOwkyoU5YJXwj38LznPc8Em6spAoqAIqAIBB8BJdCD30faQkVAEVAEFAEXAkEm0MnsJLuaDR6yv2pXEIAUsrKT/HtgYMBIBjZz40hmBVJxZLirzKV3IxHSCcKIsY6RKYDzzcrQQVCdPHnS/A15Or/tq1/9qlDX0do999wjd955p9/N8OV6BDGQyQD2kLzlTOud19YlvGcoA4H5RaBDrDFPcu1mqXNYAt0GoXlZNqM2hFt7dDUEFQQ65BQfK59crNU4Q3GKag301vZpWK6uBHqwegrSfCE3L3PZOckWsmuNi0fiMhQfksH4kES7Wkty8E6wJNPc3FxRACFAbQAQv4eZZArWCGnP1rCWt2tlysOolHR79nMr7urEiRNmHUtpNvb71ZpVDbKBQ6Xk3p2EuhfKTNW2z4vjmL8pvYX/4+abb26qH8SL9uo5FAFFQBFQBK4goAS6jgRFQBFQBBSB0CEQZAKdjREESKvqPwexMxcWFky9L/oNQ9IN6clmO/fYwK+srJgIeK6n1jgC7kAICCMCFJyBEDg+yD7AyEBvdj+77+orX/nKGoHP3+6991654447Gr/5AJ6h2oAdrXdee+c5CXTURJqZnYXT0Co60FLeHwSlNKMmqRLoxceCJagIAOJj31f2aCufbAl1yHU7tymBXvvz1cnfUAI9OL2/lFuUi6mLkl/NSzqfklQhtVZeqDvSLclotyHPN3dvkb5YXyAaToAicu9knzOfF8vaJDDKkky1qGkE4ga1Eb4gwDuOfRK2Y8cOE/itpgh4gQCBGQRooADH/FOvOQOHWJeVknt3Bg4FPSiUMnYTExNmnQ+BHvT21tt3+j1FQBFQBNoNASXQ261H9X4UAUVAEegABNiU2ZrLQbtdyGLkd9kQHTx4MGjN87U9OPYuX75sNooYm0Uk2xvZTNdyA5C4OBYh7KnNrdYYAs7sWM5EYALy0m6CnKAF65TjGfDbOfClL31JTp8+vXazv/VbvyUveMELGrv5gH4bRwzBKZB5+/btK9pKrXdeX+fxnjl8+LD58q5du5pWH9St6ECmM+R5tfLhtd6dEuiVEQMjMp9sFhRzmtt4n1kynfc+H6RKmRfVFIFyCFgCncAz5ha11iCwlFuSCyvnDWk+n52TwmpBEpGERLqiUljNS6aQkUhXxGSgQ6Zv7dkqvQEg0d31hXmHIPFuy1MUy9q0ahqWVPd7XdaaHtarlkMAMtIGu954441NW3NoL3QeApRQw1Ca8yoQlHUZvh/nuqxYGR7W0LasRRDl3gmWnZycNAErBJnrXNx5z4fesSKgCIQTASXQw9lv2mpFQBFQBDoagSAT6K2u/xyUgYFjBmIPPDA20JDnzSKFit03TkauD3kOia5WHwI4LdjsIzmH4fSH4CsVCIEj18pCHjhwwHd5ui9+8Ytm7Fl71ateJbfeemt9Nx/wb5VTvNB65411nh8EultWH0UHMnaa6VBTAr32cUEWFCS6zU4vlgXFWZHAhUAPotO29rvWbzQLASXQm4Vs9ecl4/y5pVOynF+WucysJCJJ6Y/1r6t7jrT7Ym7BEOlDiWHpjfbKjr4bDaneSrMZ6KxreWe4jfnJkkz8LKamgaKKJZmYt/xWCmolfnrtKwhARtpySzt37jRBzmqKQKMIsMakhBrGvh9Cuxlmy/DYwCH2nm5jv2qz0/kZhDHOfhoSnXm3FSptzegLPacioAgoAp2AgBLondDLeo+KgCKgCLQZAkEm0FstXx2ErgYDZOytSgDy6Tj6/HbQoQRAViDX16zA+kYGjtdz586Z+r5YNdmxZD9Z58n+/ft9d1j88z//sxl/1u677z65/fbb6wMg4N+iX8i2x0lEsII1rXfeeMfhnHvqqafMiXAue5VFY1sGIcscZcnYUooOjd/J+jM4CXR+d5Zf8Ppa7Xg+mwVlyXT6kf9zGpja7HTGjWLcjiOh/ntiXXLhwgUzLjQDvX4cG/nmbGZGLqcvy+X0hMS7qHU+XHSNyrM9l52V3GpOxpMbZGNyoyHTW2mWQOedgepFOXOraUAyFZuvgkYytRLfTrk2JZkINMaYh/Q91Sk939z7xEdjg6i3b99uiGI/zCn3jh+imFIhwY02cIj9bDODVUvdM8p8KINxffZtfvtG/OgLvYYioAgoAu2IgBLo7direk+KgCKgCLQ5AkEm0FudfdvKrrd1fKkHy+9sTJFvq+Tga1abIRYhGJEZ37JlS7Mu07bnhQgHQysHOjo6agIRKjkccMpZ+b69e/f6Xlfxy1/+8ppTkM55xSteIS960Yvasp+KEejueucbN240KgzqpKltCDSTQEd6n8AUO0+SpTMwMFBbA+s8Wgn0OoEr8TXWI+fPnzcZn8yNbklRvubM9uR3fRa97YOwnU0J9Nb3GNnn89l5Q46PJcbXZZ67W5ctZGU6MyXD8WEj535D3/aW3gCy2wResa6t9b3B/MRcZTPUi5FMEF6WUIfk0fmqpd3dtIuztieID9u9e3fFtX3TGqInbisEnKUBduzY4fse0ILJXtTOdfwsJfduy1r4pcRBWTv2AFyXIHOdX9tq+OvNKAKKQBsjoAR6G3eu3poioAgoAu2KAAQAG6MgmpM8pCaxn5LlrcSDjSmEEJLSljCAFPIr8rzYvSPjzSZ1ZGTEyCKrVY8A/Uh/0q9s7rdu3WpwrMZwnhw9etQcumfPHt/HwEMPPbRWg502vPzlL5cXv/jF1TQ9dMdAxOAAhbij3rzWO/e2C5988klzQuqDlipZUMsVeXdRCgEJR4z5kXIIfs6TSqDX0mPVHUufMmcSLEbAls1Ox2nrzvbkWbUZUPwMgqRodXepR3mFgBLoXiFZ33modX5i8RmZy85JfjUno4mxiieaykxJrCsmQ/Eh2dW/u6Uy7idOnDCy7PUQ6O4bhUC3EsilSCZLMDFfsadRwqficAnFAQR8EySLKYEeii4LRSOdpQFYOwfBD2Ll3i2hjnKQ26zcu53vmtVukgwIfu7v7xf8RGqKgCKgCCgC4UBACfRw9JO2UhFQBBQBRcCBQJAJdDaOx44dM61tRfZtKwaKO1N5eHjYEK6VMpWb3VYIYAhF2kMmvFplBNwEHw4ECL5aatjh2D1y5MiaU46MSz/ta1/72loGPNe9++675a677vKzCb5di1q6VoIThQDq6mGtIGZ9u2kfL4SEO88EWTQ4uxoxngtKC9BnGJmDzEt+y6Yqgd5ILxb/rpNAd5YLAWtbOx3HrVXzcJ5Fsz2974+gn9ES6ARPUB5CzV8EqG1+culZmc3OiKyKDCcqBwfOZGYk0tVlpN539u+SaFfU30Y7rmYJdNbZjb6XnDdRTU1hxqwNANLyFC0bAp5cmHeTLXdEsKsGRngCa8efJAylAapR4kDu3ZLp/PTKp4FiEfsAAi557tQUAUVAEVAEwoGAEujh6CdtpSKgCCgCioADgSAT6K0mD/0eKO5MZTJiyFQOgiOGGqNTU1NmkwoJrFYeATLHcaaRjYThmIXgqzVDEsfE4cOHzTmoq1gL+e5FH33jG99YI/A535133in33HOPF6cO3DnoK+qhOo3xjuKC38Rs4MDxoEFeEehWKtUqp4yPj8umTZtaMk8qge7BwHCdwhLoBEWUKxfCHGuzPfnplhTFQct8aeunNysDynsE9Iy1IKAEei1oeX+szUCfz84J8uxjyfGKF5lMT0oikpDB+GDLM9CpL8zc4TWB7gbB1hS2cxbzl9u0PEXFoRPYAwjqQqkLI+BbTRHwAoEwKhuQ/OAsbVGsFA9rM1vaohG5d4L7mVMJ7mePHHT76U9/Kg8++KA8/fTTQv12fDzUtn/9618v999/v+41g96B2j5FQBHwDAEl0D2DUk+kCCgCioAi4BcCQSbQm1k31y98q7kO94kMGQQ1Vk+mcjXXaeQY2odUMqQGmz210gjgOIA8t/UwqZlN7ex6AiFa/Qx885vfFIhPa9Q/pw56OxoKCzhjrGm9c297mUAQHGnMH7XWmrUtgSzj2bLlEAhuwHHWKlMC3XvkqyXQnVeuJtvTZkBBqOO89SoDynsE9Iy1IIB8K+sTzUCvBTVvjz2zfNpIuM9mZoyEezwSL3mBTCEjM5lpGUmMyGB8WK7vvd7bxtR4Nkug8y7xorRINZe3+y5bO53s5WLlKSzBxE8NAKoG2dYdA4nH+pF1vmbCtq4f2u3KYVc2YF4j6NU517n7iLVYvXMdQSucGyI66Ao0//Iv/yKf+MQn1uZ67tsZXIC622c/+1nxW2mu3Z4ZvR9FQBEIBwJKoIejn7SVioAioAgoAi4EikmhBgUkr7IWg3I/7nZAskIIsQHEIJZw5NWaqdzs+5uYmBA+OBipw6ZWHAFkv8nWx2nA5pisc7KYGzGva0fX0pbvfOc78thjj6195dZbb5VXvepVtZwiFMc6653T4Ouvv16GhoZC0fawNJJSBGThoWBR6zPB80QAD+QqxvwIEe+3IoMbayXQvR999RDo7lYwzpzZ6fzbaZAc7uz0egKcvL97PWOtCCiBXiti3h9P9vml1CWZTF829cxH4qNFAwaZL2ey02Z9RKb6pu7NJgu9lUYmIO3xk0B33y8kCkSZJZmsuorzOA0AauUoqXxtZKSRk2bdTw10NUXACwSsskG7BGbYuc6uz4rNdQQLWUKddVopBTDmbQh05k6UqIIc3I+62Wtf+1ozJD7+8Y/LfffdZ/ZBrHcJVP/Upz4lqJJ86EMfkve///1eDB09hyKgCCgCgUZACfRAd482ThFQBBQBRaAUAmxg3NkPQUHLi6zFoNyLux04XCDPrXM/yBmvSI2x0WNTG/Qo71b0N04BiHOIWMzLutmtDCL54Q9/KEjOWdu/f7+84Q1vaAXEa9dkrnpu7rz87Pyv5dLSlOQKeemL98iB8V3ygi03SW+8+jrxnIt+s/XO7UUOHDigUnoe93K9BDrPFpldlLjAmIMg4YMQZKQEuseDRMS8Z+jrShLu1V65mgwoW4vY1iPW7PRq0W39cZZAx+muwX2t6Q9k3J9bPiUruRWZyc5IrCsq/bEBI9NuLVNIy2JuUXKreRmJj0hPrEe29+4whHsrzRLoBDvybgmCOctTQKCVCgCyJBPkugYAtbbnbCkJyL4wSEm3Fi29erUItHtghlPuvdhcB06Q6EjZ8/eDBw+uqXGwtjt9+rTJcEfpLcjl5T7zmc/Ipz/9aXnb294mf/EXf3FN9//VX/2VkXa/5ZZb5KGHHqp2eOhxioAioAiEFgEl0EPbddpwRUARUAQ6G4EgE+hHjx41UbntlBHKpg9CmoxuDIcL90ed7KAa8vIQjUiLaXbF+l7i+SEQgigRpT3GAAAgAElEQVR4jMxlspm8ImHqJR69GEs///nP5fvf//7aqYjwf+tb31rTqU2WwMIlWcgsSpd0yUj3kGzqG6vL4Xtu4ZI8dOT/lrPzlySbz8pSdkVw3sejcelP9EoiGpeXbrtNXr3rHolGyjvmmVdwvlj1B8gzW7OeQIEgELQ1AR3wg+uZy3Gu0Uf22UKmkbrYXj1bjUJmCXQIDoj+UpkyjV6nk77vNYHuxo6+YjzxrPMpVovYmZ2u5FSwR58S6MHon1Q+JedWzko6n5b57LzkVrMS7YpKpCsqhdW85FfzEuuKm4zzZDQp23q2STJafbBbs+7y+PHj5tSswVutaFLsHm0AkM3YtO9C57GsVSDTLaGu76FmjZbS51UlDP8x74Qr2sCMTihRYktbOOc6m9zxwQ9+0JS5Y4573vOeJ5QTe+lLX2r2kezBN23aZBTfgmof+9jH5F//9V8NeQ6J7jbIc0j0vXv3mox0NUVAEVAE2h0BJdDbvYf1/hQBRUARaFMEgkyg49yifRCSkCdhN5z3SI6xKcbYDOK4C3p9QzJ0kScks1rr+10dhW4Vgc2bN8vYWH3kcKmxXQ/x6NVz8vjjj8vDDz+8djpUEt75zndWdfpULi2PnPu1/PTcr2Rq5Ur2sLVtA5vkJdueL7dvPiSxSLSq8z07e1b+z8cekrn0gsk8hzxfXRXp6hLzk/OM9gzKeO+o3Lxhj7zt5jeUJNFxQj/33HNr5Bn3hZwe9VAxJdCr6pKaDqp1HBPYAHluCU6I89HR4tLANTXEw4M1A91DMH9zKgLLZmdnPctAL9dC+o8gDUumF6tFDBlFcI3NTldyyvs+b+SMSqA3gp63303nU3IhdUGyhaxkCxlJFdKyulqQrq6IdEeSEo8kTH30Ld1bAkGe8/yTgY4FlUB395AzAIh3JPOX21in2/mKoADNTvd2nBc7G6opBH+pEkbzse6kK3Ty+81Z2gJy+Uc/+tE1Xc/e7eabb5Z77rlHXvOa19RcHsqvsWQz0O+++275whe+cM1l3/72txu1tze+8Y2GSFdTBBQBRaDdEVACvd17WO9PEVAEFIE2RSDIBDqEFtJdkCcQk2E2nPMQQtbhxf1AuIbBuYU0OTLKOIf27dsX5m7wpO3FajLjgMVp6bUdO3bMjBmi64eHh70+fdnz4Vz+t3/7t7VjIJmrqc82uTwjn//VQ8LPufSizKTmJZPPmAz07nhSRroHZSDRJzuGrpN3PO+NRoK9nM2lFuRvfvaAXF6ekdNz503G+XjPsAwm+8050/mMucZMas5kol8/uEVevv1F8to9915zWme9czKZ6TfkopkHbTYaYzzoQS2+DgQPLlbLOCZgB8ULnrMgK3Qoge7BwHCdwhLoKLJs3brV+wuUOaOzPiekejFyChUWS07xexje376C6PPFlLjyGfAKl2NORKqduuipQsooxCDT3h3plqH4kPTF+gPzzDgJdOR/eZ7DZqxbbO109hjMYU5jjWMVNQjYRVFDzXsECPri3QW+O3bs8P4CesaOREDH1dVuJ0Dlxz/+sTzyyCPy2GOPrZVMs0cw1yGBftddd8mdd95pMtWDso8jYJvyZ8zVb3nLW+Qd73iH8f/gV4FQ/9rXvmbeP2Spq4+lIx91vWlFoOMQUAK947pcb1gRUAQUgfZAACex2+kSlDs7ceKEkVtlozE+Ph6UZtXUDpx0kHaWEGKTR0Y9Ut9hMZzUyJQjI0d2bicbGUBseskMwHBO4nxt1ka9lSoMqCV88YtfXOtu7vGjH/1o2e6HMP/0o180WeKQ3Zl8TvoSPYYkN871zLKs5NLSG0/KDYNbZMfwNnnfbW8x8uul7OFnfijfPfkTOT79nPTEkoYgL1Y7lXOfnj8v470jsrV/o3zs7vevkfPueufuOvXMg5C8GDJ66mj29imvZhy7+wiHEs9WUPtCCXRvxwhnayWB7r4bJzmF49PKiTodts7sdC374P14qHRGJdArIdTav/PMBDXIhH2PVZ0JK4Hu7F2wZr9kCXVqA7uNNZyVeudnUMqhtHaUNn519niU5mJdSakjNUXACwTsuLJrYS/O2Q7nYO6mvNl3v/tdQSkNhSv3fMfa7I477jBS7xDqBLa08l306KOPyp//+Z8bFUK37dq1S/7yL/9Sbr311nboHr0HRUARUAQqIqAEekWI9ABFQBFQBBSBICIQZAL95MmTRl4VmS4+YTM2eUifE0WOuUm7sNwPZDHZ82SDHjhwICzN9rydqCEQSGA36khKE9zRTCckWeBcj2xMruenUXPu85///LpLQqCXCxb45ye/Kb+48KQ8O3vG1EDdNrBZkrH1WU9LmRU5u3BR4pGo3Di8TV69626578Y7i95arpCT//3/+29yeu6CXFqalD2jOyQeiZWE4eLiZSPzvnfsRpOBTia6u945mfQEsTjlmDkGJwxGmQKeVTXvEKhEoIM/z5atQ1+sj7xrjTdnUgLdGxydZwkSge5slyWnrNw75LrbVDrZ+/FQ6YxKoFdCSP9eCgEngQ7p2W7vfN6pkOmWUCf4023O7HTuv5UEU5hHKqo5k5OTJouUYAw1RcALBNiD8eE5RS1L7SoCzN8kWbA2Y3986tQp+clPfmKy1J966qlroGLPB5EOoX7fffet2//5gesPfvADUwOdQBu3HTp0SN71rnfJ7/zO7/jRFL2GIqAIKAItR0AJ9JZ3gTZAEVAEFAFFoB4EgkygI3tFvXCyzyEqw2SQnhBCkK4YGeds4JpJtjYLH2p9sznFucZGrxMNRz2Z52zawYEN+8jISNOhaGUZA0iiT37yk+vukU1+qXIK8+lF+csf/52cW5iQ2fSC7B65QWIlyG6y0E/OnpEtfeOyffg6+d/ufG/RmuUnZk7Lf/uvLws/yVIn+7ycIef+zPRpuX5wszxv035556H7r6l3vmHDhmscxTiXyWjAdu/eHUo516YPxgYuUC4QhDmSud5KZhMsVayPGrh8U76qBLr3sAaVQHffKeSUJdMhqKyKT05ykuvKSVdEpKe7VwZ7B2Wkf6RpCiXe90C4zmgJdJVODle/BaG1vPMhYLB2JNCdGPOuYj3HnMV8Raa6W1GDgEJndroqalQ/SpXorB4rPbJ6BAjKIDiD55IyXmpXEXDO3zt37ly3HwczaopDpvO5ePHiOuje8573yEc+8hHf4PzZz35mZNttEBPZ8Pi08BHhV7D2gQ98QP7sz/7Mt3bphRQBRUARaBUCSqC3Cnm9riKgCCgCikBDCASZQGdzgYMUwo466GExMraR6bJkKxslsofDmt2B0w01AAwCPaz3Uc/4wclI7TUcGRjZ12SYkBHgh7W6jMGnPvUp42y19vu///uCs6KYff/Uz+Rbz/xAjk+flNHuYdnYN1YWojPzFySTz8qukRvkHbfcL4c27L7m+McvHZN/fOIbcnTqWRnvGTHy7JXsyOQJc+2dA9fJGze83DiKnfXOi32fZ/Xw4cPmT8jp+dW/le6lXf5uA0HcSgrOuZI+wklI9nkYTAl073uJ7BxkS1tRA73eu2EcTC9Ny+TyZVlKLxqialVEusynS6ISlf7ooIz2jpr66cwtYQykqxefZn5PCfRmotve53YSMBAaQS0V0oxeYL3Dus4S6sUUNcDDlqggs1rnrNI9oURnM0apnjOM6yG/eo0gxmeffdZcjqDnUmXxWJ9xHEQ6GeqoYX34wx+W1772tX411dQ/J0AbX9Df/M3fyAte8AJzbdr2n//5n/Kxj33MKBXiX/je974nmzZt8q1teiFFQBFQBFqBgBLorUBdr6kIKAKKgCLQMAJsQopJ+zV8Yg9OAAnNpoJMX7K3g27FyFZk14geD7PhaLOZOgcPHuwYR5pbVhpSB4LPz8ycVpcxeOCBB0wAgbVXv/rV8vznP7/ocP6Xp74t/++Z/5KTs+dk18j10h0rL4M+l1qQswuXZP/YDvmf9twrr9zx4mvOe/jyM/J//fprcmzqpIx0D1Yk5XkGj0ydkLHEkNzQvVneesOrqiqdwPes7B8BAmF/ZoM237iVFMAb5yAZxxiOI7IAcdSHxZRA976nrMMY4iYM73wQmM5MyUJuXjKFrKTzK5LKpySby0oum5PV3KpE8zGJrcYksZqU3kKfRLoihkS35BRjv5OC0rwcNawPmUM0A91LVDvjXE4C5sYbb+xolQgCqa3Uu1NRw44E5ifWRDZDXees9c9IGN9bnfGUh/su2XsRJDYwMBCqJAI/UCfoB2U8bO/evQajIBoZ5q94xStM0/7xH//R1GV3G+T+O9/5TvPff/3Xfy33339/EG9F26QIKAKKgGcIKIHuGZR6IkVAEVAEFAE/EQgygU79cKS4iCwOev0vHFBkzON8wlpBtjZr3CCxDAGGUQPdWTu6Wdds9XnpR/rTykojKY20tN9EBw4CJPS5fiui0r/2ta+ZiH1rL3nJS+RlL3tZ0e75pye+IT85+ys5NXde9oxuN5Lr5WwxsyTPzV2QfWM75Hd23S2v2vnSaw6/vDwt/8dPvmBqpqdyadk1fEPZPoCUf272nFzXs0EODe6S39/36mvqnRdrk5NAx5kOuaXmHQJOJQUConAqkX2OgTXzu5+BKV7cmSXQCUAjo68T5kUvcCt3jrAREbPZGZnLzspSblFWCisS64pJMtJtSPLV1YIh09O5tHTlIhLNRiWSjhoS3WmQUU7pZM30rH6UKYFePVZ65HoEWNtZZaVOJ9CdyPBeY81vCXVbhsp5DO9q55zV6e8+zRTW2aUZCCA9zjoZVaawlbFrBh7OczIvnT59es0vEdSg51/+8pfy1re+1fiEHn300ZL719tvv93s9T/60Y/Ku9/97mbDp+dXBBQBRaClCCiB3lL49eKKgCKgCCgC9SIQZAKdzSPSeEQWk50YVEMGEbIVLLGw1PCtFk8ivS2Jun///tARXdXepz2OoI0LFy6sSX+3Ulaa2tALCwsyPj7eEgcKcnK/+MUv1iBEwr+U9N2/Hf0f8oPTP5dnps+YGuSDyf6y0E8uz8jl5SnZP7ZT7t/3W/LS628vevxn/+tL8uTE0yazvdx58/mcPDOFQ2VVtvZskHfc9Ea5Y/etVQc9PPnkk+b6yLni7FDzDgFLoDOOGc/pdNqcnPIcOAb9Dkzx6s4gz5n3lUD3BtEwEei5Qk7Op87KUn5RlvPL0h/tl+7otaU90oW0LOYWpDvSLT2RXhkqDEt25UoNdbtmcKLnzE4nszqsz4Y3I6L8WZRA9wPlcFxjMbcos5lZyRaypsHxSFyGE8PSHyv+LncS6KjOhC2Ay69e4R0HmW4J9WJzFsoxllDn906bs1DBYC7STGG/RmVnXId9KOvl4eFh41dQu4oAynj4XTCU8YJaduvo0aPy+te/3vQhtdBL2W233WbWhJ/4xCfkLW95i3a1IqAIKAJtjYAS6G3dvXpzioAioAi0LwKWAAjiHSJfhkOdDEUyRIJmZGpA8FuJa7IwIFuDKiVWL344Go8dO2a+jlRau9aKhATDYUENXiyZTJp65/xslRFhTwYCROOWLVt8bwbkOSS6Ncb3H/7hHxZtx+FJ5Na/Ls/OnpFoV1S2D20t2V6enWdmnpPeeLdcN7BZ/vwl/4ts6B0tevyvLh2VLz7x73J6/rwsZVbkuoFNMpDoW+ekTaVTcnb+oiznU7Kle1x2b9ghf3HP+2py5CLhTruUQPd+mFGDEAc8jnUw5if10MlGD7Mpge5t74WJQJ/JTAsZ6DPZaemO9EhfrLRqxUp+2ZDsI/FRGYwPyVhi3DwHBKdZYgqHMP/nNNYUVuodgqrTMz3do80S6Lyjgxxk6e1TomdzIrCQXZALKxcklV+R3GrOfDDUIPgQ1LKlZ4sMxNdL/DolgJVAr25MMT+xH7C105m73HMWChpuuffqzh7eo6zUtmYKh7cPg9hylJp41lgno0KmdhUB5h7K/GE333xzYP0SrOuoeU7g0Y9+9KOiSnIEb7/pTW8y9/Ktb31L9uzZo12tCCgCikBbI6AEelt3r96cIqAIKALti0CQCXSc6TgmcMbg4AqSgRubN6LDMaKfkSFuR3KZjR9R1Bgbu1YSys0aAzhTiWZns4tRNoAavK2W06VN1MAbHR01hKPfhnT/V7/61bXLkl30oQ99qCgxXVgtyF/95O9Npvi5hQm5bmCjDHcPXtNkHK4Ty1MytTwrN45sk+dv3C/vuvV/LnlrHP8PT/x3eeLSMTmzcFEW0svSHUuYDHekkhdXlmQuc+U53NwzLpuGxuV9L/gD2Tm8rSa4Dh8+bDKJIWLaLQimJiA8Ppj+e/rppw1ZiJHpR2BKUCUXa7l9JdBrQavysWEi0M+unDGZ5cv5JUOMMxeVMuZGiPaeaI/0RwdkW8+1pSiYe3j/4DDnY8uHOM/J/AuhzrPTiZmebnyVQK/8TLXzEQSxnFk+K+l8ShZyC5LOX1E2sZaMJmUgNiDJaLdc37tNRhJXg/ScBPquXbs0OKWOgcKchZSyJdStsozzVOyJLKHOz1avqeu4zYpfsVLb7BtaUWqpYgP1gFAigI8Bopj9H+pNalcRwPdCwDt2yy23CKVwgmof+chH5Nvf/rb8wR/8gXz84x9f10zWfO94xzvksccek7vvvlu+8IUvBPU2tF2KgCKgCHiGgBLonkGpJ1IEFAFFQBHwE4EgE+hTU1Nmg4SjePfu3X7CUvZaVjrMEkJsbpEhbkfHEEDgJINcxHA0BlUqrd4BQt0xiGqeBYy+JOM7CDKUOFAgCZB/I/vbb+Paf/d3f7fusn/6p39aUuL8kXO/kq8e/U85vzAhM6l5Ge4ekLGeYemOJU2m0nI2JVMrM7KQWZbN/WMy3jMi77ntzbJ75Iayt5bN5+Sfn/qmkXJfyi7L9MqcLGWWhf+PSkQGYr0y0jMko/3D8rZb3iAHxmsPuDly5IgZA5C7ZBKpNY6AW9UB8pw5JMjOrlruWgn0WtCqfCyKLpTQgCQmgCmoxlx2euWUkP2al7wMx4crNnU+OyciXTIYH5Treq432bHlzJmdXirT05md3okS1KjFEHShGegVh1/bHYBk+7OLz8pSdklms7OS6IobFQhIcwwyfSm3JJnVrAzHh6Qv3i87+3ZKf/yKpDtkLyVy7LpW1R0aHyIE21pFDX7aNbU9M2tq9g9W7r1dSlSo1HbjY0fPcC0CNqgb8hw/g9pVBFBmI3AFvwsEepDnb9a0b3/7242SH9nyd911l5kDURj4zne+Y/b47O+/9KUvaQCODnJFQBHoCASUQO+IbtabVAQUAUWg/RAIMoGOc5QNBk4WpMODYLTp/PnzazLEOPkhN9vZIAuQt8ZQAmiHzFHuxS3BDwGBigCkRFCMscbmm8wW2ua3gdEnP/nJddmQb37zm0uWVOD4/378e/Ljs48ZonxqZVay+bxEIxGDd2F1VZKxuJFrH0oOyBv2vlJeev1tVd0W33/s0hH5yZlfyrMzZ2R5GcnjgvnuQE+/3LXjdrnnhhfKWG99zyMqCziAwRm81RpDACwpQYAj3RqOQAJU2sWUQPe2J8NCoJNRfmblOVnIzktBCjJUFYE+z1vHSLhf1329xCLlCXQnss5MT7I9bfCe8xhIZJudDkkVhAAwb0fHtWezBDprROZt3hFWUtr+Xs3/gRWOeD7O3+2/OwHLZveV1+c/sXBCZjIzcjl9WfqivTIUH7pmzNP3c9k5Wcovy4bkBhlJjMiugV2mKU4CnQDddg2A9Rr3as8H9mDsLFHh/q4tUWEz1MMaBMQ6nUBcldqudnTocdUgQIAPzxDy7WEvd1TN/dZyDMpsKBQyh0BKB5lAt++bhx56yBDmKHKRQc+ajXXLvffeawj2dvcl1dK/eqwioAi0NwJKoLd3/+rdKQKKgCLQtgjgmC0mFRqEGyYqlwxcshX37dvX0ia5Mylx2JKpSnZ8J1i71YeG+CI4gyh2DMKB/gxaZiyZLSgxkBFN+1ph//AP/7Amlcf1X/GKV8iLXvSikk3Bcfr/PPdz+d6pn0oql5HF7JJk8llzPJnoffFe6U/0yOv3vlJu3XSg5luCNHn85GGZTs/JateqbN96gxzaus/IujdiZAcwF5IJoI6MRpAUI0WN8w8SHcNRZB2B7SRxqgR6Y+PE/W1LoEOotEJxo5a7gUBHwn0lvyKj8fKKJcyJSLgnI0npj12RcC8n+V6pHcxTzkxP1idOC0odYlszGcKfNvPT/bvz3xzDnGGfK366f3f+2x7rvv9K+NX6d0uqu8l1cIb0Y93Az1p+Z07kw1qSdSQ/2yUjt1Z8az0+lU/J8fljMp2ZkWwhIxuTG0sGjJiSMekJiUcSMpoYkb2D+6Q72m2kxwnwwihNpEEStfZCbcfzjDJn2Xmr2L4zrEFAtla1Sm3XNib06PIInDp1yrwvN27cqHsSF1Q2eI5370033aQBUPowKQKKgCIQIgSUQA9RZ2lTFQFFQBFQBK4iEGQCHXITBxeRxQcO1E60edXPbGBpBw43DDKTzPOgRzx7df+cp53qQ1vHaRgk+JGog1SiJje1uVthDz/8sDz++ONrl0Yu7zWveU3FpqRyafmvi4fl15eOykJmyTioR5KD8oIth+TmjXtrysDkYjjCwYOAAgxnK0EF/PTCjh8/bpxVPNua7VE/ogQ+4VCmvyCYyLBARYGMC81Arx/XTvhmmAj0qcykIMsOMd4X7ZPuaE/JLkoX0rKQm5fh+IgMxgZlQ3KTZ93Jc2brEJOdXqoOsTM7vZZsW0uCcw37IUDG+W9+d/4fbbCEuWc32gEn4h0JiW7J9WI/rQS2/UmwCQR8LX0adigvpS7JheULcjF1wZRE6I9dkWUvZci9z2fnZXP3ZtnSu1U2dW8y4xWJZEwJdP9HBPODrZ0Oqe4OguFZsFLv/OS5CKrZWtWUfuKjpgh4gcDJkyfNexTVJi0rtR5R9oF8mBcOHTrUUe8/L8aWnkMRUAQUgVYioAR6K9HXaysCioAioAjUjUCQCXQk8YjAxpHCBqkVBomPc8Q6d4JUH9tPPNpF3hrZN8g9+pNxtXXr1kCTpUjUUeO1v79fduzY4WeXr13r5z//uXz/+99f+/eWLVvkj//4j31ti1sOvBlBLMjqQfwwJrTeYO3dC9HGeIUExZwBDgQgMZfiXGb8tItpBrq3PRkmAh1S/GLqvJFxz6xmZDA2JPFI/BpAcqs5Q7RHu2JGZnpjcrP0lCHbG0W0mjrETsLVWWvdElr8dBLi7lrGjbZRv+89Am5i3UmwQ0CyhiAQj9/DTrafWz5nyPOJ1ISRZk9EypOrmULGSL1v7N4om7u3yHW9160j0INSIsr7URGOMzqDgCDTbbCys/VkmlpCnbEdpABmrVUdjnEWtlaeOHHCKLCwZmbuVruKgF0rspY5ePCgKojo4FAEFAFFIEQIKIEeos7SpioCioAioAhcRQDHRbF6mkHACEfKs88+a5oCge6nxKKbDEKak2zXdqn/XWv/hl3e2t2fOOPoTxxxQbaJiQnhQ/bgjTfe2JKmkgXxla98Ze3aPAsf/vCHfXNgQuRAwFrJT+QMqQno9XxgCXScVZpFVNtQw8mHE5mgJwxnHxLc1smtBHpteIb56GwhK9nVzBUFgq6IkS2PdEWruqUwEejc0OX0hCyR3ZqbE4hy7jUZ6ZZoV1QKq3lJFdKSLqTMvyHYe6N9sjG5yfO5y4JLYBjkN2oPfHgeCVzhw+/MpQQJFctSr6qD9KDQI8B7k/UEc7Ql1fnp/j1IBKUb9PPL5+RC6qJMpC7JeGJcktHyKjTpfFomM5OysXuTbOneLFt7rzNS4gTHYkqgB2tYs55wlqiwpWCcrbQBIoxlgvW8Xg/WgohVKNNa1bWgpsdWQuCZZ54xwd4E9TI/q11FgH0xalfMAygUtvL5135RBBQBRUARqA0BJdBrw0uPVgQUAUVAEQgIAkEm0HH2EoGNEWHsV9YMzhrIIBzRGA4aZIghDjvVwpyd6+5PHBGQe2HoT7LPyeolcGPnzp0tGX48h5/61KfWXZsMdD8yialzd/78+XVy4M3KxMBZReaTEui1DTPIOOqd20AsZNqpc+50aDGfov5AZj/OwHYxzUC/2pPL+SVZyC0YwthpEYkY4nggNmDqEJczK8sZhhro3EdhtSCX05dMHfSVwoqk8ynJS37tFrl36i33RHolGe025Dlkej3GWo1nzZLj/IQYd/4bkrzZ9cDrabv9Dms4W+ubILZiv/Nehry1P/m92L/5P0g2MMCJzjudOcfOO8V+L/V305eFwtoHrMv9m79xDGuLYh9by52/2d+d/2drwdOfQQ1g5RlE6WVoaMjU3+Wn/bCG8ms9Xmy8TaYn5dzyWbmYuiS90R6j7FDO5rJzspxfkc3dm0z2+XhywxqBzphAwl0tmAjYPaol1FmP8n/r3jGRiNmnMWb5MLf4aax/eJaVQPcT9fa/Fvtuxjrvtk4N3i/Vy+yL2VPw3O/fv7/9B4PeoSKgCCgCbYSAEuht1Jl6K4qAIqAIdBICQSbQcUiwgcTYIPlBeEKaQ/bYjAccImS8dnp0c1jJRZxuZIeEtT8hlC5cuGBqnO7evbtlU9Pf//3fmzrW1u677z65/fbbm9aeZtc7L9ZwgnVwzlKmARJYrTICkHfMl7YkAvXjIVvcpgR6ZSzDegTP6nR2UpbyS0L2eaqQMj9FViUiXZKIdBsSGeJ4LDFuyPRSFjYCnfvg/mezM7KYXzDPQXY1K6uyKl3SJfGuuFk79McGTP1zMvLLGSQrWVV8cA7bnzaT3KpwtGqscC+8i+wH4tr5b/s7GaGWHHeS5F6v4XgnoVrAdVGUCaMxZiDRLZlOEJf9t/N3/m5rzbOu4dMqJQHI82LkOnM/n2YTmLlCTo7MH5HZzIws55ZNTfNSzxZBLtRM7431ynBiRA4MHpBYJGYCZCnnowR6uJ4anhfWaZZQLxaAwpxjCXXmqGYHe1BqjHawV1vVXSUAACAASURBVCy2/gkXwtraICDAusL6PwjgD7pamt+YsS9m/0FAtSqI+I2+Xk8RUAQUgcYQUAK9Mfz024qAIqAIKAItQiDIBDrOYqTDMTZIOEWaZeCA8/7ixYvmEmQ3EfXdrGzXZt1Hs84bRnIR5zqbbCMlHImY/sTpGybjHsjAhpBoZZbUt771LXnyySfXoEMR4nWve11ToPSj3nmxhlMuAqcs2dMEzqiVRoBnCuKKLBCsUkkEpHIhA0dGRgSSvV2MDHT7CbLkcTPxnspMGhnzhfyCZAppQ5QnkG2XLsmt5s3/YX3RfkOkjyc2lqwBbgl0nMU4jcNg+dW8LOUIHsiY4AGk27l/i0NfrG8t69zW+rUkuZsot6o3ft035KHN2uQn7xmeZd6XkN02S9z+tFLfQZBNBiNLoIdpvHjZt8w9kImWUOT9Vex3xpWf6gSsm1Eb4UM5FPs7/eRVMOrppdNCJvpk+rJ5vkYTo9eoO/BsTmemhZ9knY8nx+WGviuBFuDB2oqx3srgRC/HQyeei32iDShhnLsVOBhvjDtLqLOP9GoMWrwpc0Q7WDui0qCmCDSKAOOYwHVs+/bt5t2sdhUB5m7mcPb0rdwba58oAoqAIqAI1I6AEui1Y6bfUAQUAUVAEQgIAq3KYql0+zgHjxw5Yg7DwUWWUTOM65CJghQoZrOZmknYN+M+mnlOHEQ4p8iw4BNkw/EAcY78N4bjgey0MDoguAfGJmOxlVH2jz32mHznO99Z63aybN773vd6Pgz8qnderOFhGuOeA1/DCXm+GJNkyGIQbzxf5bJLOZ6xzLghkKVdrNMJdKTLL2cuyaKRbk9Lf3TgmnrEZIAi754pZEwmNnLmW7uvK0pihIlAhzCfIfM8t2ik3Mk6xwgcKORWpbBQkNxCXmZnZg3RazPK/ZDsJpjD1rSGOLLkkf29muxM2umsQ+yWTeYaPPv2XF5nl1czR3Q6gV4NRvYY+o/+hHTgQ/ae86f9vVi96VquU+5Y1taWTHf+hHSsldRkPnlm4RlZyS3LVGbKPIM90R5JRq4QTcxHzE9kpo8mxkwG+u6B3ZL4TRkJS6Azjnft2uXVLep5WoiALXPBXsUGk7ibwzzlnLe8CHwj+JLnBvWisAXptrC79NJlEGBtaUvY7dixo6kJBGHsCPYUPOfsKXT+DmMPapsVAUWgkxFQAr2Te1/vXRFQBBSBkCMQVAIdoubw4cMGXTZIzZAwQxITiW/r1CZDkhrIzZb8C9uQQaIQhyOZuWRZBNXoR+SiIWIxHLNkvIa1P8lQJHuXDMB9+/a1DHYyjR944IF11//gBz9oyBOvzM9658XaHJYx7hXe9ZyHLCvmS/t8VTtftjuBjvO8FQRiPX3o5XcupydM3fO53Mxahnmx80NsLOTmpbC6KsOJYRlPbCgq5R4WAh1i7mLqvCwtL8nk9JTMTc/K8uyKLM+tSGpuRdJLGS9hvuZczLtk+doPpA2EOT/5PwiiWgnJcg1mLcZaCYc1n2JBAASoOYl5L69fqm1KoHs7zCwBaUsGEPTh/jSjjADrG8qmsL7kw+98KgU98hyeXDwpmXxalvLLspxbktxqzoAS7YoJ6g990V5JRJNyY/+N65QvCCAg0FIJdG/HUJDOZhUaLKFebOwS1GEJdX6vZ96C6ORa7B1VtSxIIyC8bWGsEtSL3XjjjU0vixE2pNgXEyRDIBb4qCkCioAioAiEBwEl0MPTV9pSRUARUAQUARcCOEPd2UVBAIk2PfXUU2sbSC/JOk4KOQmxw3VwmmzdutVIDKtdiwCkGRn6yHHiJAqi4fSFPMeRhZENQnvrcYgF5f5wXnNPkHP79+9vWbMgUP72b//WyFRau//++z0h9VtR77wYkM8995zJysNxz9hRW48AzirmAZuhyDyA86qa5wu5RcguAlrCIs1dTf/bDPROJNDzqzk5lzpr5NvJBqXGd7mxQMb2fG5eBmNDMhAblI3JawOxgkiIMj/xboHcp32XJyfk4uQlmZ+dl2zq6nxYzXip9hjIcJ4Vsqv4aYlxS5R7kTVZbVuKHWdlky0x5ZZNJmDNSsOzbmtWTeywBFw0gnWQvsuzQPDUpalLcmH6vCHXl+aXZHlhRVYWVmRpccnT5jL2LbFO4CYfng2nZQtZuZyakOnMjCmfgBYEhhJExEi7j8iG7o0Sj8TXfc8S6Kytdu7c6Wm79WTBRKCSqka98xZS28yB7CHd4zOYSGirgo4AY5WgXowEgla/84OEF+8h9sUE9fF+QOJeTRFQBBQBRSA8CCiBHp6+0pYqAoqAIqAIuBAIKoFOM8lAxzHBBsmryH7OR61znOEY8thIEDdLIr4dBhybVZylEGY4iYJk7nrMOEQh6bwOuGjFPRO0AGmJ8+TAgQOtaMLaNb/85S+vOXT4z1tvvVVe9apXNdSmVtU7L9boMASJNAR2A19mriRbj2eNscjzVYujmO9CdkEAMte2i3UygZ7Kp2Qic1FmMzOGnOqLrSe2ivXxdGZKeiI9MhAflK3d10r5t5pAhxienJw0n8uXL6/9jqPWS4Oo4VmwBDk/7e+Qhs0inL28B3suW9fdZqcXUzRijVWNbHyt7VMCvVbEGjseufRzK+eMNDqZ3pDXlC6gBrmRTs+LJFaSIkuyFnDCM426jA1sbKwFYsYRRDqlhJykOnXOF7ILklu9EtQS64rLQHzgmrro9vqsrdgHKIHeaI+E8/s2GMSWqSg2bzEP23mLgKBSSlZPP/20WRuhdtUO+45w9mh7tdqq43FXlLALq4paM3qFZ439Gs8s74F2CsptBl56TkVAEVAEgoaAEuhB6xFtjyKgCCgCikDVCASZQD969KjJeIR08aK2nFvim3Pi9NDo7vLDJagSzO769cj8M1bCRECUQ54sKTKjcZ4cPHiw6me6GQc+8sgj8oMf/GDt1I3WQW9lvfNi+CiBfi0qOKogv22wEUFGPF8QYrWYEui1oBWOY1P5FZnIXDIEOnWFe2OVyznMZKalO9It/bFBua6ndQS6kZRfWJCJiQlDlFuyHFUaLy3Zn5T+4X4ZGR2WGzZsl5HhEUOUEwzYrg5x1mvO2ulu4hSVAnd2ejUqFsX6xRLonG/btmvHk5d92ennWswtyqmlU5LJp2Q+tyDpfHodJLGuqJkD+mP9MpoYlet6r/YHQasQ1rxH+NBvkOr89KKEFKQliij2A7FeaQ1oCXSOUwngTh/dYvaZzFt27ioW8MH+whLqlBew89bx48cNgMxBzEVqikCjCLA/InAd27NnT1VKT41eMyzfZ/3Gvhh/Dmph+HDUFAFFQBFQBMKDgBLo4ekrbakioAgoAoqAC4EgE+jHjh0zstE4JnA8N2I4zKmb1U4S343gUct3gyjB7K5fT3Y8m+l2IibI6rN18G666aZauszzY8nWevDBB9ed973vfW9dz2Wr650XAyfIKgued2YVJ8ShDCaMQayRYCPGDlm9EIftJLfYyRnoyLZfTJ+XueycdDE+4kNlR1VhtSAz2Wnpj/YbAn1L97VKJjYD3QZqVDFMKx6CsxViHLL80qVLhiznp1dZ5bxv+ob6ZGCkXwaGBwxhzu/9Q/2yLEtG3n40MS6bujfLQGygYnvb6QBbU9tKvUMKuA0C09YgLpflWQwXJdD9GS2M4acXjstybllmMjMmq7s/1ifd0R7pki4zxpfzyyYzvSfaIyOJEdncvUU2dG8o20DGB2PDKj7YYBbWB+6yALXcKcQmtdSdpDr7B2egBopKzANKoNeCbGccy7hkX+yct9xlzgi6toFAjCOMTFhIdjVFoFEECOTAX8GcBYGudhUBnkX2xexRUMQLalk57TNFQBFQBBSB4ggoga4jQxFQBBQBRSC0CEBQN+KsauaNI41HhgqbJAjSeozNls004/vtJPFdDx71fCdoGaQ4P8mKZ9y2c/16nCjPPvus6bJDhw61NAuB5+jTn/60ydCx9upXv1qe//znVz2kglLvvFiDcVZBtI2MjHR8RgPkIhketuY9MokQEvVmiiqBXvUjEpoDjTpB+pws5ZZkMb9gaqBDrJUyCDay1kfiozIUHzbHu61RAt2S5byvIDXsex8ypFFDdWFgZEC6h5ISHYjKxtENMjQ6JL0DpaV9ydKdy83JWGJcNiQ3yEiivjVMo20PyvcJOHFmp+MAdxrzCwSUJdTBvNycA/HKmNEM9Ob28PmVczKRmjAf1CbIMC/WLxDoEOyD8UEZig/J/sEDEumK1Nw4xoWdC5yBL408x4wRMhX5EJDLPoDxwxjbsWNHzW3UL3QOAuwzCP6xhHqpcUiAICU4CABrp0Dezunp4Nzp4uKiELjOOELCXe0qAjyPEOisJ5jLURxRUwQUAUVAEQgPAkqgh6evtKWKgCKgCCgCLgSCTKCfOHHCOC7ILB4fH6+573DEQYyxGcVwopElUEneseYLtfkXbOYedY9b6WyEIKEtOD4x+hFJ6XbN+mDs8wxgSLi32in3jW98Q44cObI22vft2yf3339/VaPfy4zmqi5Y40FBLVNQ4200fDjStsyZOKkYbzioGi2fEZT5o2FwXCfo5Ax0oJjPzslsdlpmsjOGKBuIDRYlzKiVvJCbl6SRb++XLcnrJBaJXdMdtRLovNcJzrAfxlmjktAQgwTRsN4gaISffHgGJtKXZDo7JbOZWRlLjJUNGODm0oW0ydCHQB9PjstoYszrIRja81WT5QnJ6cxOd5faUQK9+d1PbfGj80dkLjMnyLhv6t5UlhSHQM8WMrIhuVG29V1vyHYvjPFCZroNjLE/6yXVWTuSlU5wGOo+7DG0lJMXPdX+52DP7AwEcgege1mmov3R1DsshgCKeQQCMift2rVLQXIgwPNGYDk/2f+zTlNTBBQBRUARCA8CSqCHp6+0pYqAIqAIKAIuBIJMoLNJwlGBk4tPLcb3qGtss5xwghOpXG8WZS3XbrdjyQLiQ/2/VtWLdBOwkPk2k6jd8Lb3QybwM888Y/554MCBljt4H3/8cXn44YfX4KYO5Ic+9KGKxH7Q6p0XGy9BLFPg57iGoEBCl+ccg2BAbp1sqkYtCPNHo/dQ7PudTqAXVvNyIXVB0gXqIs8ZYq070mOyVPk9v5qTVD4tqcKKxLriMhQblP7YgJE0L2blCHTWKRDlOJUtYW4D4+rtW+Yvq65gyXKUbkoF2E2mL8t0ZkqmMlMyGBswEtblDMKRex9LbDCEIlm5asURwBnurEFs1S+cRzMXsQbhQ98h4a4Z6M0dUfPZeXlu6ZRMpC5JIpKU4UT5UkrIuU+mJ2UsOSbjyQ2yo6952d2WVLfzAnMD7zC33HY1CPHMk50OIWNJGd0rVINcZx/DvsSqRDEnFQvgIhDI1k4nIEgDNTp7zFRz9wSyMq9piYlr0WKtYPfFBPSPjWlgYjVjSo9RBBQBRSAoCCiBHpSe0HYoAoqAIqAI1IwADgBbF7zmLzf5C6dOnTLZ4zi3q5XpwnmGU5XNJ797lUXZ5FsN9OnJ9AJPMr1bEQ3vDoZgPEB8tLuDE2ccZQyw/fv3G9nRVhpOnc9+9rPrmvDWt761bF3rINY7L4Zh0MoU+NnPzP9k4NO/GM5elDq8Gm+dQKDjFG/3+ajYmMwU0jKRnjBZp8v5JcmuZmVVVtcOjUjEZJ73RnukO9orGxLF523I9vPT5+Xy7GWJxWMy2DMgsxNzMn1pxpDmjKF6iDHbEIgL3hmsI+x6gqzyWvoMCfpzK2dlNjsrq6uFspLstHUqOyndkW4ZiA/KDT07imbd+/mch+laZBY7szyL1SDmmeM4lXBvXs9OZ6bl3PJZOb9y3gSA9MX6yl7MlHZIXZChxLCMJ8Zl94C/8sPOQBvmDT7OsjPVIkWwBu9APgSSoUqhpgi4EWC8ISeNEVzMftMZCOQuU8FxNhCIeYvfa3kHaQ90BgKUk2LNoyUmru1v9itWmW3nzp06N3fGI6F3qQgoAm2EgBLobdSZeiuKgCKgCHQaAkEm0Mkgh9QhwnjLli0Vu4aNFZmk1Mi2jgocYGQGqNWPAJleOCJx9vhZj81mGHHtTgyGgBw4fvy46Tjk0oNQeuCBBx4wMqrWbrvtNvnt3/7tawZXkOudF3sS2rVOd6WnnjFGvXObOcVci5ytl05dsgIZMziMcXi1izkz0DuVQKcvIc+RNkeynOwgSHSRVenqikiiK25+9kcHTN1z97hCInpq5bKcvXROLpy+IJcvXJbF6UXJpDhHfUYACGPYEub8RLHECzuzfFoWcwuGRO+N9hpJercx983n5iWzmpHR39R839S92YvLd+Q5GFOosVCDmE8x2W7WeDY7XUkp74YJkuxnl8/IhZXzJhCk2Hh3Xq2wWpCLqYsmU50M9F39rZUf5lmEjCJAjA/lSez+oBaUqG1NtiMkqZaBqgW59j7WSaCztnEGHTL2+LutnQ6x7g4EgnB3lqkIwhq/vXssHHdH4DHrZt5rBPCoXUXAqfqwZ8+ehktMKbaKgCKgCCgC/iKgBLq/eOvVFAFFQBFQBDxEIMgEOs4unF9kfyCvWM5wsJ45c2aNCKK+4datWyvKS3sIZdueis08zkei4ffu3evLfeI0JxiC/sdwJCCt2UnBEDybR48eNfePoyAI9/6Tn/xEfvSjH62NgYGBAXn/+9+/jhgLer3zYgO4Xet0l3tYUfdgzoQIhthkvmxGpp1VsGgFgY7DelUKJieabGgvAwOUQF8/ushGR7Y8u5q5EvDUFTUZ2H2x/nX1wiEVCIo6c/aMnDpzUiYvTUk+l6/rvcI7iaxyCHP7gSz3sp+dDVvKLcnF1HlZzq8YIj3eFZOeaK/EI3FzGEEEZKrnpSCDsUEj876tZ5uRv1bzBgFbgxiloWJS70pKeYMzZ1nOLcuJxWeMLDs2nixefsFekeMJLtmY3CgbujfItt7rvWuMR2fivXfs2DETOMbatlZCnWAp9iMQ6lZCuFnzjUe3rKdpEgIE86CUhqHOVU6e3RkIBJleTO6d95kl1FH8Yi5T6zwEbDkbxgABO2pXEeC5Ye7GCCz3KjhSMVYEFAFFQBHwBwEl0P3BWa+iCCgCioAi0AQEgkyg27rEkOHUuy5lOMAgeHFQ4MgiW51apmreIACJTTAD2RFsWJttOKVQHyAoAiP7B4dlpzmTIOiOHDliMCDz34t61I32HWToF77whXWn+aM/+iNDvmJhqHdeDANLoJPFSJZZO5stcwGJiZE1RXAKjttmWCtKQGQLWUNwLucWhazIK9YlvTGyhgckGW28trsS6NWNFhyevMt5h/CeRu2Bd3WtxrudbHKIct7x/CTgw2/yai47J5PpCaHe83J+2fy01tUlkuhKmnEGab45uVl6K8he14qDHn8FATL0IEB5LzJ3kelZipSyNYiVlKp99BxfOCazmVkhG30sMSbJaPFgEN4rk+nLEumKyFhyXHb276oo+V57a7z5BqpKfBgPqK4QSMaak582aLPaK7E+JfsYApV1qta4rha58B/nJPNYo9eyR2Hv7SxT4S6lxnuN8WkJdch1v9914e+hcN4Ba2ZIdC1Pcm3/4RdgrsYOHDjQtH1LOEeOtloRUAQUgeAjoAR68PtIW6gIKAKKgCJQAgE27cXqtAUBsEp1iXHCQ3zhCMMgeCGCcDqoeYcAMvpsWHEMsmFtpjmzYrkOJAkOzk50HDG+Dx8+bODGORuUcf35z39+7ZmjbXfccYfce++9hsyAKAuj3H671ul2P6uMKeZV+gpjTDFnNlM61M8SEIy9ueyMLGTnpSAFSedTklvNoShuMqIhzmNkRkd7ZCy5wZA99ZoS6MWRIwAKohwyik+99csTybiMbBqR3g09Mr5pXLZu2SrbB6glfiXbu5VGJvp0ZkrIukeGng8W7YpJtCtyZXwlxqXbg0CNVt5nkK9tCXTIcatQVA0pBSnhJKWCfI9BaNtUelLOLZ+TycykGeejiVFJRBLrmkaQEpnnzLejyXEZig/KngF/1IrqwagcQcV61xLqZDrWUkMdlSCy0iHUCcQLQtBjPfjod6pDwEnmoRJV7z6FdQtkvCXUCUR1G/svGwjETw3UqK6PwnhUsXdbGO+jGW3mGSEYEzt06JDOsc0AWc+pCCgCikATEVACvYng6qkVAUVAEVAEmotAkAn0crLKSHdC6lpHA1LSZKmrU8H78bKwsGAk08iuOHjwoPcXoGIu2UuTk2v1tcmKRboOR1GnGpg89dRT5vZxyDYrQ7hWfH/4wx/KT3/607WvoRDxute9zmRMYGGU22/XOt3OvoVcYs60hIBfyg5+EujT6SlZzM0b6WGktLvIsP8N4QqRzjMF+dMXHzDk5sbkJlOjux5TAv0KaowrgjJsBme9Geb9Q33SN9wnwxuGZPvu7dI/dEWKnSzvlfyyjMRHZTgxYn4GxVaMlPui5AnSYKx1xWXAKByoZHuz+8iSDEi4WgUU5zUtKWVrEBcjpQgcctYgriWDtNn3F5TzQ46fXDopC9kFEzSCugdzKPNnRLokU8gKzwE2khiRnmiPyT5HhSGoZgl0Z/BFsbYyhhhnyHTzIUCwWvUM5i6C0yBWyU4OyvotqH0SxnYxpxBsgTVCoLvvnTHmzE4vVqqCdbZTWaNe8j6MuLd7m/F9oKyHXwO1HbWrCPA+J0ATu+WWW5oa/Ku4KwKKgCKgCHiPgBLo3mOqZ1QEFAFFQBHwCYEgE+ilSC13ljI1UMfHx+uO/vcJ6tBeBrxxHuKgIeLba2MMsiEm8wfD0Qh53sysWK/voVnne/LJJ82pyWYKSjABBNmDDz647pZf+tKXCkR6WANZWiEz3qwxU+y8OHoJgrFqI34qOxBYAfGAwxcnc7NsKbco0+lJWcgtSCaflt5or8k4t1nmhlArpGU5vyRRicpAYkgG40MykqiPkO1UAt0qv1jCnLnbLT9bsY+7RAbHBmR485Bsve46Gd88ZrJbeQfE4jEZH7taaxkCbyY7beSg+6MDcl2P1gStiG8HHFCJQHdDwBh1klJu5SWVTC49aHg2Ty2dkqXsoqQKKUGBIVvIIOwh0a6omWshzOORuNzQu10G4gOBHoH1ZniSJQxhynr45MmTQnBpNcbYQiVh7969hkzXur3VoBb8Y2w2LP3bzLUNqi7O7HR3EAeBP6gJWUIduXe18CLAHou10ODgoFFhU7uKAHMuAZs8cxDoBNurKQKKgCKgCIQHASXQw9NX2lJFQBFQBBQBFwJsxItFtwcBKDepZbNBkITFyDaHaFVnVHN7C8fNs88+ay4Cge5lpoOVQMRBhFG7HoeBZoJd6VMy0Bn3yIIGZZzTns997nPraoVu375dXvnKV8qGDRs8HR/NHdlXz+5nlrRf92SvQ01XSE4rrc+cSaCDX4ZcPNdvNoF+ceW8QKIj394f6y9Z5zxXyMlcdlZ6or3SF++XrT3b6pJy7yQCHaclpBFBGBDnzNu1GO9q5nVIpE1bN0nX6Kosdi1KvCtu+grjPWMI9FjMBMQ5DQI93pUw2d039O6o5dJ6bJsiwDqQua1UBnq522YuZM3hzE7n/5zGOHRmp3e6uhGBLNOZaUHlg0AkDMxYDxKkNBwfkfHkeCjUF2oNvig2lqxq0okTJ4QPWaPVmiXT9+3bp5np1YIWwONsNiz7FQIj/DDGHe9frs2HoA63qbKGHz3RvGtAELPmQiWKBAG1qwiwRiTAgGcOAr3T38s6NhQBRUARCBsCSqCHrce0vYqAIqAIKAJrCASZQHdmLpKBS90rsqExzVL2bxCTvYqDEEPC3StyG4k6iDXGoM3QIYtZ7SoC1EAHHwhqP0nPSn3w3e9+Vx599NG1wyBHP/jBD4Y2G8CvLOlKuHr5dxytOPUJRMJaJa1vCXSyosjAa4al82mZSF2Q+eycIXWGEuXnkcXcgpEiRhJ8NDEu/XVkTLYzgU5QHe9bS5rb8gzV9h3vCKRHCdbgw+82U4i64RdS503NZOrR98euBHNUItATXUmT2Xp9z/Zqm6HHtTECjRDoblisZLLN8iwWVEo9azI8+TCXehlIGLZuIgOd55i5NhqJmSAYMtHDYl6OHXvP7E0INOVDoFE1qhyMIdZ2+/fvNwSsZg6HZQRdaSd9jroOJN6uXbta0ninsgaEerFxR3a6DQbq9LmrJZ1U40XZF9OXIyMjJihZ7SoCBM0xf7OevOmmm5RA18GhCCgCikDIEFACPWQdps1VBBQBRUARuIpAkAl0Nko48XFO4JC3Ts2xsTGTzdbJDkw/xzAZDk8//bS5JI6+RiXT3MQe2RLUisTJo7YegaNHjxrZbUgoshFabfQd0f/ImH7/+99f15w3vOENZnyE0fwgef3EBScqfWQDjloprW/ncZ5zMu6aYWSdU593Jj1lpL67o+XnEshzyHYk3IcSIzKWXJ/xXE0b24lA57nmGUCWmE89suxkSjFPMZdTk7oUGYQc9NmV00IQQ6aQMTXNTa3zEhno1K6fzc7IQGzQkO1bu6+rpnv0mDZHoBkkqIXMmZ3OuHRnp7MmdWanN7omavOuCtztNbvGMHsV5lHWzRDq1ah8MYYgYVlDoTikmZWBGzbXNMjKSdN3O3fubHmDrbKGU+691Nxl5y+du1rebdc0AL8HfYgim1uNJ3it9bdFrFNREGE/AYHuVUC/v3ehV1MEFAFFoHMRUAK9c/te71wRUAQUgdAjEGQCnQxlSCBrbJSQPgwCkRj6jq/hBnAmHz9+3HwDAqyR2uSQwfQp0fUY8qvbtm1rmJSv4XZCdeixY8eM8xWMWp2d7+67n//85yYTwBoZVL/3e78XKnxtY/0gef0ChoAXMuBsWQQccJCbrQo48gPbucyszGamZSYzLQPxQUlEytcANXW1f3PsUHxYxrs31tw9YSfQeZ5x1ELwQPbwvq3FcO5ClvNhfiJDt1qbSF+Shdy8Icapa94d7S5JoFPTnnrLEO2oBQzGB6u9jB7XxghYAp3gIBQOmmWskVHhnBNavQAAIABJREFUsaSUnVed1yOr02an8xy0aq5tFgbtdt5mE+hOvFi/8T5mDc1cW2z8uPElmPTAgQOmZJJmoAZ39Fk5afZEqKQFzaqZuwh0s7XTGXdKSLa+F9kj885h7c46S+0qApTb4sM710tFPMVYEVAEFAFFwB8ElED3B2e9iiKgCCgCikATELAR6004dUOnZOOP08kSra2SH27oJtrkyxAtZEJje/bsMRvXegwHNPVzOR+GY3Djxo3qbC4DJk5XHK4EjiDn1yrDmUPf2Uwq+o06ff/xH/+x1iQcbx/4wAeMMy5sZoN1yMYJaxY9mJMRhfMtSGUR/MC2FRnoYMxcxodswTCQZowPyHKIHOdcXM3zyryP3LD9DA7WT2Sv5FdkIn3RkOhkoZNdnkvl1tVAZ22yUliR5fySkYjuifTKtp7rJRIiqehqcNVj6kPALwLd3TregZZM5yfzgNN4Dzqz0xsJOKwPGf1WJQRQ0YH8ZA5Dzcov413BvobAyGeeeWZtLVzu+gS/QaQTvKoqTX71VHXXYW1DMEZQCfRSc5edv9xzF2sYxpgl1CHXw7Cuqa63wnMUcwSBsOyRW7nvCyJilKSipBCBahDoOj6D2EvaJkVAEVAESiOgBLqODkVAEVAEFIHQIhBEAp2NI859flojG0MlDVszzMi0PHLkiLk4Wca1ZBryHSsPDOHK7ziYyVhshIBpDRL+XxUJUJ4DJJFblYlABjGSzu6+o12f+cxn1smT3nvvvXLHHXf4D1SDV/SD5G2wiWW/Tt/gWMKZiwWpLAJEBfM58zfzeDOsFTXQcT4zN0KoBZVAZ1wgdwlZc+LECfN7tYZjknnHEuYQOV5mp11OT8hSblEW8ldI9EK2IOnFjMSjMRkcGZJ0ISXIvfdGe6U32meyzwd+Uy+92nvQ49oXAT+ziEuhyPOVSqVMoCcf55rVfsdmeEJKsXby8hlq395t7p1ZAh01K+a1VhiBkczJBKdCmLnJTHebeMew/r755ptNqQwljlrRa+uvaesx84wjux8ms3MXZDofgmTdRkCpMxhI9+D+9PCpU6dM4DSByq1WHvPnjqu/ig2cY1wS7KzzYPXY6ZGKgCKgCAQBASXQg9AL2gZFQBFQBBSBuhAIGoEOkQVZ53YmqVRXXd3ryZfoi8OHD5tzUaOxliwYvnv+/HnB0YSpkkBtXQLxhYMeidqxsbHavtzg0bbeOXJ5pfrum9/8pjz11FNrV8Ih/Z73vCd0JIEfJG+D3VHy6zxjzJlWghvHErLaQalt6Re2F1fOXyFks/MmYzkZLS4pnivkZC47Kz3RXumL98vWnm0S6YrU3D1BJdAh9RkPljQn67xag+RDipYPY6hetZFqrre6WpDLmcuynFuSzGpG5lZmZWF5QaKRqCDLnYgkjbR7vCsuw/ERQWpfTRGwCASBQHf3BhnGzux0nkWn4ex3ElIQb2r+I0AwJ/NiKwl0511DXqI2RKAq6+VKRlbq8573PBOQVst6vNJ59e+1IWDrMVt1ltq+Hayjmassmc5PqzblbCUBQHb+0lIVzes/VILAH3UMDTRfj7N977NWDbNaWPNGj55ZEVAEFIFgI6AEerD7R1unCCgCioAiUAaBoBDobrKODEo2j7YGumagt24Y0zeWJIVYqVaimwh6Mk8hgDGclUiRawZW9X1JhhLOVZ4F6uH5Ze565xBaqAa4M1Ag6v7pn/5pXbPe9KY3Gan/MBnOdLLAGJsE64TFbH1V+4zhWCfYIkjPmF/YQp5PpyeFmtmZfNpkLkOiW3KceSxdSBtJ8KhEZSAxJIPxIRlJ1FdjMkgEOnMtWUuQ5jhfi2XClhrTjJedO3ca0hzJUD8zeuiTxfyikXKfW5o1QSDRWFQ2jF1pR3e0RwZjQ9IT7QnLI6nt9AmBIBLozls38006vZadbudo5zGsc61cMsRUkOZtn7qxJZexBDrZnWR5tsIWsgsyn52X/GpOuroi0hvtkeHEiMzPzpv1Nh9bwqpU+1iPIe0Omc4a0c+5uxWYBe2aSEmj/AOZTMBZuxhzF2tLxp8l1fk/p9lSFZZQ11IV3vU++z4CGlibsfdSu4qAnbvBZe/evQqNIqAIKAKKQMgQUAI9ZB2mzVUEFAFFQBFYj0AtzvZmYMdGHaKcjTrW399vyDoICrIyMJxEukFvBvrVnRNnHg4UZArpn0q2uLho+tRmYOEIQIJcHXyVkFv/d2oV81zg5PXL0Vus3nkpYo0x8eCDD65Jh9N6iLg3v/nNtd1oi49mvEJAMj6pNxoGY1w461gH9Rnzi0Cnz6bTU7KYm5fl3LKk8ivSJSKxSNx0Z241Z+awRCQhffEBk928MbnJkBf1WKsJdAg5HK2UeSD4w53tWuqeyJZjHoc052dQMhgvz07IhcmLEotF5fptN0h3pFviv+m7evpHv9PeCFgC3e861vWiajM8LSlFkJrTbP1hS0hp/eF6ka78PbK8eee3gkCfSk/JpdRFSeVTklvNS2E1b9Ydsa64xLqiMpock63dW6VLusy8ztrbEmrl7gwp+ltvvdXslVRqu/IY8OII1Jn48A5FVr9djXUT+wI7dxXzGWgwkHe9TyAk60tK6FSz3/buysE/k527CcinpIWaIqAIKAKKQLgQUAI9XP2lrVUEFAFFQBFwIdBKAt1NtEISWrIOByP1ATEyWpspJ6uDojwCSEviACbLopyknLsWMzLSOJaqzVrXfliPAKQuzwjPhB+1OkvVOy/XL48//rg8/PDD6w5517ve5bvkfCNjB8cgmbvYTTfd1MipfPkumU9kYvC84SznGQuqo83P4ATwmMvOGBn3ghQkbUiKnMiqSKQrajLSISnIbB5LbqhLut12cCsIdJzYOFchzQmeqFQ317bVOhspwYFTttZM18JqQbKrWUF2HRyRVfc6GIrsc0hRHPEE4agpAuUQCBuB7rwXq/zkzPB036vWH27e+Ec5B+xRbGFt5ZedXT4rE6lLspJfkUVKVxTSa5eOdkWlL9onfbF+6Y31yp6BPWsBRMz7rMFZa/HuL2estZ///OfLLbfcEpjgKL/w9fs6ZJ/THwS9EPTdKVZNqQqy8q26Bnt3r9cL7Yw16zveEYwpxpbaVQTOnj1rgsqZuwkCVVMEFAFFQBEIFwJKoIerv7S1ioAioAgoAi4EkH91y7M1GyQ30VqMBGqk9naz299p5yeQAacJRB1kTDGDYGdza2vusvHneFUOqH+0kIEEnsi3I9HZLKum3nmpa6Mg8dnPftZkqFi7/fbb5b777mtWcz0/Lw4Zsv0xMtCD6uyjnyDOrRPdSocGuZZuK4ITsoWMLOYWZTm3KJC/V6zLEBP9sYGS9dFrGVh+EejgZ0lzVD2qfVcTcEOGDqT52NhYXWM6U8gYefWl/KKIQ8IVEh0c+cS6YrXAVvLY+fl5uXjxohLonqDZ/idhrDBmwpKBXq5HmEuctdOL1R8my9Vmpysh1dj4bgWBfjF1Uc4vn5PZzKws5ZeuKKFE+yQWiZl3FKQ6n2hXTMaSYzIYH5S9A3vXBXkx97PGhkiHZCsXQMWeitJXt912m6/lfxrrmXB9+/Lly0IddIhiylN1ojmDgViroIzjXqMwFu3cxU+Cg9SKIwB2PNsY++egKAQFpb9YA7PXRNFOAy2D0ivaDkVAEVAEqkdACfTqsdIjFQFFQBFQBAKIgN8EuptotfJ3bhLIWXubSGONxG7d4EFKn3GCk4jIb7fhNCEjkmMwNrcQvrVmOrbuDoN5ZTCFJIAAQ6K7GVZtvfNy1/7BD34gjzzyyNohPMvvf//7TW3IMBgOGWRSMWqgB3HcuvsJ4ogMlSC21dnnrSDQ7fV5h6xKgQR0iUikLhK51PhtJoHOeMSJeuzYMUOYVEOaMw5wuEKa875stHbmXHbWZPPnVwuSLqQEMp1UfqSFyeRPRJJC1uR4YoP0RBvPklICPQwzZXDa2E4EuhtV1lHl6g8rIdXYOLRZjKxTCU5stuUKOXli7glT83w+OyfD8WHpi/Vdc9lsIStTmSlDmm9MbpTtfTsMmV7MGB/Iu0OmM3eWM94HL3rRi4z6iJp3CExMTAiqTaj/KLZXcLXBQDYgqFgwEAFAllBn/x/UgFXvRkr1ZwI/AiYxFN/Csoeq/g7rP5J1MAQ6/gbm7e3bt9d/Mv2mIqAIKAKKQEsQUAK9JbDrRRUBRUARUAS8QsBPAt1dX7kS0Vpr7W2vMNHzrEcAMgepf5xE9JnTkN4lo4eNP44QSHZqS6o1jgDOAvAF82Y46Gqpd17ubnAifu5zn1tH9L3sZS+Tl7zkJY2D4MMZcMhYpxVZW0GrIUr7UCOwzkhnqQsf4GnoEmHJ7q/1Jnlv4kDHqYcDHYdwI45gzkcQB2of9HU18uyMU+qYU+IEksQrZyskz2x2WpbzyyYrklryZExC7ORX84ZM5177ogOSjCRNLXlk8RsxJdAbQa/zvtvOBLqzN5kHeE9bQsoGKTqPgZAiC5YPc0Aj81AnjCRLoBOYyKcaY56vF1dk288sn5GLqUvSE+mW4UTp9XE6n5bJzKQhzkcTY7J/cH/Z5jE+eG/88pe/NOvwcsbaHCKdd0a991INVp1yjC0jQbBaswJcw44la1ZnMJB7XcM4ZO1kCXUUyzp5bJJgYIN5eU6DrC7l99hkDmZtzDuQPRABo2qKgCKgCCgC4UJACfRw9Ze2VhFQBBQBRcCFABvcapz1jQKH1N358+cN4UC2HIRgJaK12trbjbZNv18eATb0OHHJKrcZO27ZbxwfRMyr5Jx3owlHL+Q0Wf9eS0TWU++83J19/etfNxmz1nCIve997wuFhH+QCXSIRcYBczTzJlnnZJ+HxcKQ3V8rlu6ABr5vHcGWyKqmdAWqAidPnjTPDSUE+HclQ/4U6UpLmnvtYEX+/kLq3G/I82WTXd4TWZ8lhuQwsu6ZQlYGYoPSHe2W67qvb8jxbQl07k9rW1YaBfp3S6BTUoZyBZ1ilQgp3hFOueRq5qFOwc7ep5UBLkegM8chtz6RnjDlQFZXCyaAiLIVG7s3mizyaom+o/NHZSo9KdOZaZNZHo/Ey0I+kZqQaCQqY4kxOTh0yMyv1RiELkQ675NyezrqvkOk7927t+p7qOb6nXZMpwTxeNWv7BlZO1lCnd/dxvvf1k5nHgtaMKtXWJQ6D/M7a0KMdZ7O31eRYvycOnXKBBLji/B6T9zsvtXzKwKKgCKgCIgoga6jQBFQBBQBRSDUCDSbQMeRA3EOYYeRLUPkcDXZcjiCaB+kUSWyPdSdEPDGQ+6QAYWjGuebW06aDEz6tNOcHc3uNp4b6l0z9nkGvLBG6p2Xuz7OxAcffHDdIdRBpx560A11BVt3cP/+/YGo0Ug/UWOTLGcMRxqShdXMm0HCu90I9IWFBSMjaRU3ILAZP27j/y2Z7pQptTKUBIcx5opllLrPRd9DKkOaN9upOp2ZkvncrMxkZqQn2i290Wulhmkf90F99IIUZDg+ImOJDdIX66976CmBXjd0HfnFTiXQnZ3NM+jMTq80D/HuCHrJDz8GsyXQCQZ1Kypx/aXckjyz8LRR2kgX0kaFA0I90hWVnmiPJCMJSUaTsrt/j/TGKpeveGL2cZlKT5mgpM3dmyve4lx2TlL5lGzq3iR7BvbKQHyg4necBywuLsqvf/1r+dWvflX03WSPJYAAlSDeK9UGA9TUkDY/+MKFC8J6oNOCeLzqVrKtrbIGP4sFEDJnWUK9E9Q1WA9CEmOs+bRe/NXRxvuO4ALGCQkYqvrg1ZOo51EEFAFFwD8ElED3D2u9kiKgCCgCikATEGgmgY5Dz9asouk4Gtj4VEu0WunwUrW3mwCHnrIIAmxayRqAPEeukNrc1tnB/yGnpg4474cODrqpqSmTcUx2f6PmRb3zcm34yle+spY9wXG0+z3veU/Vz3uj91fv93FaHT9+3Hx93759Lc/6wLGIHKutbYoDkQCVMDrTgpzdX8t4wXnHswhxh0Fq816yhBQOYOsMpv+cxjE8eygJMJfidK9kvCNxoDIem02a27Zwj2dTp2Uptyip/Iohxsm6LGXU7J3PzclgbEj64wOyKbml5LGcO7+aM/XoqZ3uPq8S6JVGhP7diYCSV9eOB+YYOwexXisll2wJKa/VK8IyQlm/2jq6bgKdbPNj80dlJZ+S2cyMMMcxX/GhfAUfMshHEiNGnQOJ9Uokut8Euu0H1jVPPPGEPProo2b9XsoIJIBI3717t67jaxjEBLgSrECAK3sgtfoRYH3AeLXzF4FB/J97HdXu6hr4TJApx3geNeDp6gjgfcb6mfU1+yF95up/3vSbioAioAi0CgEl0FuFvF5XEVAEFAFFwBMEmkWgO6WHIVeR3MJZVQvRSl1iHF1EGldbq9ATUPQk6xBgQw/pg+MVB4eV4WcTC6Gu1hwEIOsmJycNxmQfN2Je1Tsv1wYc01/60pfWHfK7v/u7cvPNNzfS9KZ/lznQys8ja9pKYgEnIs+bzSZk3mPurGXebDpgNVygHQh0t4oKTlwUIegTxg5kt+0f5kb6DsIC9QjUOyDO5+bmKqLGOXjOUUHYtWuXUWvx06x8OxmQka4uI89eyWay05KIJI208fU9185RZHAuZOdlKb8gq4Y+v2I9kV4ZiA+an9w37xdIUZVwr4S4/h0ElEAvPw6c8xBzUTG5ZIKAnHLJnULW2PcrwZ+Ux7EGOQ7ZDYk+mZ6UeFdcBuNXSlRgBtNCWpgfc6t52ZAcN4FDNw/dXDbQqBEJ90NDN5ls90aMwAoUT37xi1+sKYEVOx+E1J133mkCtsK63mgEp1q/S5AjzxZjiLGk5h0CrLmc6hrFlHpYp1tCHZWfdpi/uGeSDjBVhlg/nhgTlJNjHiagXJ857543PZMioAgoAn4hoAS6X0jrdRQBRUARUASaggDOFXfGXCMXYnNDLT6IPwyHOJsdNrq1mls6vNbv6/HeIGAJdHs2pPQgz/0meLy5m/CchecIGW8k8nfs2FF3w72ud16qITz7X/ziF032tDWCZv7kT/4k0M4t5sCjR4+uOa1aNa7JZsJ5xnyMAxu1DqeDv+4B0MIvBlEevxY4GBsEhhA4hNEfVjqSfnIT6PwfTj4IC7JlytWite3AEUjgxoEDB1pa3z5TSMvF1HmZzc5KvCtWlSQ7x8YicemP9ssNvVfnKOaC6eykkXnPF/KSKqQkt3qlxjvZnMlIt8QjMfNzY3KzLC8uK4Fey8Ds8GOVQK9tADjlkiH93Gt+3jeQUM7s9HYlUUsR6NQeP7V0Si6uXJRoV0Q2JDcUJZKRc7+cvmw6AJn1nf27ZCw5VrJDOO+Z5TNyMXVRuiNJk71eytL5tExmJmUsOS5jiVHZN7i/to4uczTvIoKSf/rTnxo1lVJGcNjdd9+tEskVkCcwjnUBa1yy+NWahwDrMFs7HcxLzV+WUIdcD+P8xb0xrmg7BLraVQTs/MX/EORTrPyG4qUIKAKKgCIQbASUQA92/2jrFAFFQBFQBCog4CWB7paIblR6mFpgkEoQDNTfVvMfASL/IYSswwIZfqd0sf8t6pwrUv+aD88RDoNarVn1zsu1AwftV7/61XWHBD0LnbEN4Ykhm1iuzngmn5X/uviUHJ48IUvZZUMCbu3fKHduu1U29I7W2kXmeEM2Tk8bAhFrJOiorgY08UtBk8ev5VbJ2oRsgSTHUAJAEQDnJs48PtwfGegQEk8++aQZR2QRVTLKGzCPEiQBcWWtVO30Sufz4u+5QlbOp84aWXaSxQfjQ2VPy7glA7072mMy0Lf1XC0zcTk9IYu5BVnKL0qqsCJd0mUyOsEuV8hJXvIS70rIQGzAkOj92UG5dPGSwZLsezVFoBwCSqDXPz6sXDKEFJ9i8xXvIGd2erVll+pvlX/fZF/BvE3GNfLb1p6ae9LUKuezsXujJCKJko2iRjlZ6uPdG2Q8OS4HBg+UPJYAoifmHpe57LzMZ+dkOD4sfbG+a45HLn4qM2VqrW9MbpDtfTvKEvP1Ikb/U7LmkUceKUukE9R11113hT6Ir16cKn3PEuisCVQhrRJa3v3dqa4B4Vxs/mK+svMXP8MyfzEfE4BMNj17EbWrCNjgVP6HNaJz7lacFAFFQBFQBMKBgBLo4egnbaUioAgoAopACQS8ItDZ+JE96WVtbJv5THQ/5IWavwggq4uTyJLnED3U5Q1jZL+/yHlzNbLPyUInqwLca7Fm1zsv1RacWw888IAh/q0RdPHud787sE4siNDDhw+b5uKYcRKa9h7IOvsfJ38s3zv1iCHOV3JpyRXyEpEu6Y4lJRqJyE3je+TNB18jYz1XnfKV+oxrQwbNzMyYQ7k2ih3I67aDhZVAd5YgwZnpLldhJUafeuopM3ZsbfRyfcZzTJY5H4LCOIclsYplVXFdP2t+8uxeSJ8zNdAhv6mBTrZ4KUvnU7KYXzTHQbaPJa7I2PLdycyEkW7PrGakN9or3ZGedTL3/D/X6ZKIDMWHJJZNSGoiXRWBjozyUm7ByChjsd9kyycjjUkdt8Pz1in3YAl0rT/ceI8zD9naw/y0AUPOM/NesnMRCi1hXgNaAp2gXNYmGEE9j838UqYy00IgEZnllYyMcuTVR+Ij8oLRF5bF5FLqkpxbPiuz2Tkz70HOMy9ST521xUp+RZbzyxLrihtCHun4vQN7m4oz/Q6RTka6XX+475l3ECV4kHYvti6qhFE7/539LuQt+1PNhm1dT1czfzFnWUKdcRzU+cuWstFAwmvHE3sJ5m6M7HyCUNUUAUVAEVAEwoWAEujh6i9trSKgCCgCioALgUYJdJzuZN9ZAoGNHxKAXtTGxkFB7VicE2TqqfmDAH0KeWtJUJwN/B8bVsg9NX8QoAwCzxUOn1qyMv2od14OgWJZ6Pfdd5/cfvvt/gBX41UY2xChGIEK7nITOLgffOLr8uiFJ2U2NS+zqQXJFq7IUWM8HwOJPhntHpLRnmH5X1/4NtnSv7FiK9zy4O2o7hCk+vIVO+Q3agA8dwSuYGSEM+dZVQLGCu+lX//613Ls2LG1gLFS5yaTk2f34MGDpr55qTqd1dQs9iM7nQxJssr5xCRq6qAXczZTL5hMdQj2wdiQbEpukeRvagWfXzkri/kF8/eB6GDJGr5Ius9lZw25HsvFZHUyIvFovORcl8qvyHR2SpA5LqySw36FQI9K1GRtcv3R+KjJiFdrbwSUQG9O/zIPMWc75ZL5P6exxncG9jDHhckorcE9Ogl05pTHZ38tl9OTJiiunCS7vVdk3CMSMcfeOnKbxCLlcTi3fE4upS4K2evUWScQyFrUBAH1rX129+825LofBgFJCZsf//jHAoFXzCAgyUa/5ZZbAl2Oxw+87DUo7YJKDcFwYS+18/+z995BdlzXue+amTM5I0cRicggwSBmEgwSBIlJAkWRtmSWTEtlyX73lv3fc3h6z7Kfb+mWqyRbksNToGiZshnEHEEzQcwEA4hIZDEgYzARk2de/Ta0B316+sxJ3ed091mrampATofd3969e+/1rfWtQuIW9L0gWm1AEHshdxkd1mDuchVBtynT6xO4yZ6PANpcVMcyvU8Uj6McFEkV2JIlS0xpMzVFQBFQBBSBaCGgBHq0+ktbqwgoAoqAIuBCgOximzWeLTici9wYmz6MTSmZejj6/TCuTWYEWUaQ8mrBI0CfknVuHWk4SiGPkJgmKAISSK0wCID5wYMHDf6ZyvkVqt75RAjgcL/nnnvMOLJG9scf//Ef+zY3+NkD6Qj0R3Y9J8/sf1kOdR+T7oFThixvrm6QqkSVcc51DfRIR3+XCTKZ3TRdZtZPlb+87FtSV1mTspk49nAG2bnXKQ/u57MV+1rO+vJIwvr1bQjiuehLvjkEbWHOEiQ4ZLds2SLvvfdeymw9Z5uQZ1+xYoXJlIF8yNaYhzPNTqedfpFYEOOH+j4Rssu7hjsNOV1LpuTv5NcN0T/SL70jp2vCN1W2mExKCHSM88hih4gfkRGTnT6RkY3J9WqH62Xw5JDUlzV4Eugcd2zgiAyMDErf8CkZGBlIuiwZnTUVdVJVXilTq6ZnVL892z7R48ODAN9FyvtoBnqwfWKVNiwhBTnlNpvdyTzEWiWs2Z223ZZA55trsxjHMtD7TwhzIBLu6YysckjuSVWT0mag22u19beZWugEAw2PjphAICmjvEVCINGnVE+WGbUzJ1T+SNeuXP/Ot5rv2xtvvCGQVV5GtvU111xj9nmlbqzfwMldCqDUcQnT87NeYa1t5y+vcQ1Z7QwIShXkWIjnYv9G4Drr5Hnz5hXilpG5B8EqBK1gBKSqIkZkuk4bqggoAorAGAJKoOtgUAQUAUVAEYg0ArkS6HYzYx1qZInjkPJz80mWEdntmvlcmCGWqk/JyMynFndhWh+/uxA8AqGHMwXycSIrRr3zidpDli4kutOuuuoqIwUaRqN+NUbWB0SANQjzv3zp+3Kk57i09XbIzIap0lBVN+4RqHP6SfdR4fe85tmyfsla+ex872d1Bzm45cHDiE+ubXIS6LmSybneO5vzyEjEOWfradrvGe8fpAIZeraURarrMm4gzfnxMyONd5u52WaF8m+3+SlRCgl+1JDVA3JqqFvIFKeGeXlZuZEbJh8Vorou0SDIpkOeW6l3iPMTA8fkxOBxqa9okNo02eAQV+1DJ6VmuFYG2oekWVrGEehkbB7u/+R3mZtd5l5krVf+rkbx4MiAqbMO8UUt9pqKGplZPXssIz6bcaDHRgMBJdCL00/u7HSv7M5Clp3IBYV9+/aZwDUngc51NrdvlpMDJ+Vkf5vMqJkxYUY5c+PRvqMm+3xS9WRZ2bwyq6Z0D3ZL51CnDI8MmXkV1YzWqlbz72Ib35c333xT3n333ZTfPNaja9as8UVprNjPm+v9vUoB5HotPa8wCNjAREuoe63pnNnphS5XwZ4P9Tfuq8HqyWOCPrNB2StXrswpMLUwo0x8mKsWAAAgAElEQVTvoggoAoqAIpAKASXQdWwoAoqAIqAIRBqBXAh0Nnk4MHHsk22CvLqfhIEFFCkzyFvNfA5+iJF1CVmEQ5Q+JYOS7C4sn1rcwbc8vneAaMVhQIYEknWprFj1ztMhf9999wnOams4hb71rW+FMnMACXfmM7I+nNKAz+5/VR78YIPsa/9YmqrrZWrdpJSPPTA8KL/tOCjT66fIgpY58v9c+X8kOcS5PtLgzGkYeCAPnkuGcjrsw/J3vi87duwwzUFFwUqhh6V9tMOtBgB5TvAWBILtq1TtJWCMoAvqxDJ2/Awgm+h9tw5gSPWJSKxcs9MHRvrl+MAxUw8YAv10xveoqVleVV4tFb8jfah77qyT3j54UtoGjhupdeTf09Ulh5Dn2OrhGhk0BHrrOAL9cN9B6RrqNHLv1eU1hph3Z7nybvUMk83eJ82VrebeM2pOZ8WrxQ8BS6Cz7kRCWa3wCGSS3eksO8HcX4j5MR0Se/fuNcTwzJkzkwjgQ72H5KNTHwq/mbdSybibslUDJ2RwZNBki8+rn5dRxnq6doXt7yiL/eY3vzGlSryMdSmy7qtXrw5FvxYaPy8lg0K3Qe+XOwK8xyQA2MBE1oHFLleB6hhrTqvml/vTxe9MFGf47mOUkmD+UVMEFAFFQBGIFgJKoEerv7S1ioAioAgoAi4EcL6TVZKJcSzEAgQ65q4Pm8k1sjmGrGfNfM4GseyPdWcusymF1HPKo+Vaizv71ugZTgQIaiCTG3nmpUuXeoJT7HrnE/UYZPFdd92VdAh10KmHHjbbvn27ISLJ+iBgx9r/fv2nsuXYLjncfVzOap4lVRUTO20+6ToiI6OjMrdphvyfl35TPtU0y1wKhz19iRMI4x6UpaCebJwt7AQ67xhBKsyDEAYEC+3evTttWZPJkycLWTBk1RNwUSxiyJmdjiPYS6I0V4llc+2RXuke6jIE+qiMmoAQSOzGRKMh0t12OgP9+O8y0OuM/PtEBgnVMdQ+loHeNNqcVK6C+37S+5F0D3XK4OiQtCRaU0pE016y2ZGbJxN9Tu3csSz1OL9jpfhsSqCHr9cJ5JsosIegF2d2erHKeaQi0JmL3m9/39Qnb+s/YdQzmiubkzLRUczoGOyQ3uFemVw9RRoqG+TclnN9lVynHjv11XuGeoziR6K8wgQETamekrbOehCjgu/j888/nzKYjFryrOmQMi8ls0oG7kCMUsIgTs9qy1VYQt2rXEXQAUHstSHRmSe1bF3y6KKsHP4nviMQ6H6VLYrTGNZnUQQUAUUg7AgogR72HtL2KQKKgCKgCEyIQKYEOptJJG6tfCyy6mQpB0kAKXEb7OB1Zy5DBCEn7e5TW4sbIgbCSK0wCEDo8c7RH8uWLRt30zDUO0+HxCOPPDKWgcyxOD/uvPPO0GUNkiUN2UvwiK2LSnv/r43/KLvbfivt/Z2yoCV93c8Tve3S2d8t81vmyJ+e//uyYurZhtSkXqZ1yFFHFKdz2GvFpuvbTP7O94XgBGzhwoWhUR+AbIUsR+UEpxxyrDYwLNVz8R4SyHLeeeeZ7EX77YQ8LxaB7m5rOhKLdloSK9fs9In6ncz1g30fS+dgp4zIcNoa6KfJ+UGpG6mTwbZhqZW6JAKdjHYI+ZMDJ6Suoj4tId87fEpODZ8ydYknVU1Je/9MxrAeEz4EUMuB6NAM9PD1DS2ygT2WUPcqO0GwJnMQ8xE/hZpD9+zZY+ZulLOcajO0Gwn3PV275dRwr7QPnDQEdnUFihsVpkQE5DZBRMit1ybqZHHjYkOy+2EEC33Y81vTBu7bP9JvgvFQ+yAjnvtOrZkmc+vmFlzqHby2bNkir7zyytge0PnMrGUIjrz00ktLJjPUBmJ4jSM/xoNeo7gIkFhg5y9+u5V+GPNW7p15jPks3zU9a1LWoVwP/4raGQQIdiUom+8EBHqQvifFXRFQBBQBRSAYBJRADwZXvaoioAgoAopAgRDIhEAn8pcsBFsvjNqBZODlu1lM94hK3KZDKPe/4xCAnIVwwcgeQQrVq0+zqcWde4v0TDcCvHcQrzgMli9fPvZnt2oAWRFkTodRChyS/6c//WlSRi9S17fddlvg80c2I4oa17wLBJA0N59xiP/fv/mh7Go7IBDjC1vmpm3z8VMnpWugxxDo/+OCr8mc6mkm89yrNEI27YvqsYxV5PGxsBDo9AVZ5rSL98sra9uJNyQdMrXItEP0YFyD7yFO1jAR6M52B5mdPtF4PNT3iXQNdUnnUPuEddCRh+8Y6jCkeGIoIaPHyqSiPJFEoFNP/eRAm0CkNyVapLJ8YgUIski5b0tlq7RWTZbJVVOi+upouydAQAn0aA0P5kpndrq79rCTjGKOZU0T1P7CEugQVBBVboPA3te9V4ZGh+XU0CnpG+41hDYEdm2iVuqYr8oSsqBhobRUnS5zlK9BzO/s3GmCf7oGO81vp5w0965PNBjlj+aqJlncuKTgJDrPiOIRsu5bt271fGTKPq1bt84EJ8TdJgrEiPuzl9rzZRIQREa0U2EjF4IXxT32TChUEaSpdgYBcAEfcEb9KRd8FU9FQBFQBBSB4iKgBHpx8de7KwKKgCKgCOSJwEQEOptGNixERWNsXCCYvJxOeTbD83Qlbv1HlT4FV7Iu+TfkD33qlK123zUTKXH/W6pXRO6bzFgcyStWrDCAhLXe+US9tXHjRnn11VeTDlm/fr0sXrw4NJ1MnU/IUGQTcQJb++Gm/5B3Dm+Xj7sOy+zG6VJXWZOyzbxPv+08JNUVlTKzYar8z+VflaGOPnO8V2mE0Dx8gA1xEugLFiwYI6ADvOWElyZo6OWXXzZBDe56l84TmRdR2yDbnOAUN5kTBQLdDUShstORHj42cNhIISMBX1teKzUVtWMyx6el4fvk1HCPIaIaE81SPVQtPUd6zfdo0aJFY0232edKoBfrjQnnfZVAD2e/ZNIqZ+1hVAQgZd3GXsOZne4nWULwFG1IRaDTFrLBj/cfk2N9x8y/rVG2YlrNNCOnni6YJxMsOAZyfnvHNukc7DL3HBWRhsRptQ0y3wdHBw2Rf2qox9yTe/OzsPHMPJnpvfw6joDqZ5991lO1hW/lhRdeaLLR4yyzbMcRa0YbWOcXvnqdcCNgA4JsUJANRHe2uqamZoxQ59+ZBAShiITyGCpYJCqonUHA1ocnuIr9cKEUS7QPFAFFQBFQBPxDQAl0/7DUKykCioAioAgUAQHrzHLf2k3U4cyCaC2kQ0SJW38HBKQPtUOJ5MbY1NOn6TKX00mJ+9tKvZpFAOcMdRYxHAbIoEIAQvRiE6kGhAlFpMt/8pOfCBn11iCpv/GNbxR0PpkIk127dhmJdZzqZBxb23Roq/z8/V/LbzsOmlqksxqmpXSEdQ+ckkPdxwzRvqButqyfdrW5DM5VpOELOXeGqf9tttr8+fMLFnzlfH6cnSgMvPnmm0YCciIjkIhs83PPPXecvK/zvCgS6M72B52d3jZwwmSCk0mJrLoIUsQJKZMyGRodMvXUq8uqpT7RaIiihsFGE9TlJtA7BtvHJNwh4ZFxn8gg7/tHeseyz5sr/ckQDdP7pG0RsQT6pEmThJIYatFFgLnUmZ3uRUZZqWS+paxXMyGjUiGSDfFp9kcjAzIiI1Iu5VJV7n9m/PH+47Kve58c7TtiiP0pNVM9a6oPDA/I8YHjUldRKy1VrbKqZZWp014so5/4pr711ltj6mTOtqBS9vnPfz62tdFZM2JKoBdrBIbjvryz7Mls7XTmMndwpi2dYzPUCaj1MtZA7JNQwaLMk9oZBGx9eOZ/9sP5fAMUV0VAEVAEFIHiIKAEenFw17sqAoqAIqAI+ISAF4HulvcuVs3eVBLWPj16SV3GXcOeDTpkYSZR3F6Z0CUFXpEelsws6ixi9BXBD1Y1AKeds1Z3kZqY8W2pg/3oo48mHb9mzRqTpRQGw6mOlDfSo5Ay1oZGhuSvN/6jHOw6asjx5ppGmVrbOs55c2qwz/y9uqJKJlc2yRemXy7LGucbMh4pxkzeszDgEEQbikWgE3CyefNm2bRpU1LwhtczEkhEDVdUETLpq6gT6G4MIEOsA5jf7nqfYEIQnc0KzSQYhKzx9sE2U8e3f6RPhkcgzkUqysuluqxGKsorpK6iQaZUTZXeU72GFHUT6Lx/H/f91mSzI3OMNDtyxl5GJif3pGZxQ6JB5taeZUh7tfghoAR6/PqUJ3KSUTY73U1GkY1u5yF+Z5udHjbic3vHdjnRf1wg0qdUTzXzVyrrGuwyEu8zamfIzNpZclb9WUUfCCdOnJANGzaYACi3MZ9ffvnlJiM9ToQXY5I1I8bagQAPNUUABFg7sfa06ymvEkFkUUOm2x+75mSPx36bfQMl1dTOIGDrw/OuLVu2LFbzifazIqAIKAKlgoAS6KXS0/qcioAioAjEFAEngc6/kcmyjhA2dcUk6tiA7t+/P0nCOqbdEOhjuWvYQ+hBEmbq0LL9QCM18jvQrkq6OE4Y6iw6Lcz1zidChrnlnnvuEaQ/rZGF8Ud/9EdJkumFQzf5TqkIdI7afGSn/H/v3Sft/Z1ytKfNEH9NVfVSnaiS4ZER6R7okd6hfqmpqJLWRJMsqJ8t62ddK7N/R8Zn+p4V69mDvi+1xun/efPmTZjV7Vc7UNiANH///feNqkAqgwRevny5Ic6zzfaJG4HuxMjP7HTqnFMPvXu4S4ZHh8xtyqTc1EZvrGyS6vLTZBHfGEhR3hWk8512tP+wdA52SsfgSUOINyaaxpHokOddQ53mHs2VrdJc2SxTqzWDy693KmzX4TtCoKdmoIetZ/xtD/MsgYSWjPKaz7ORSg4b8Ul2+3sn35O2/hNGmWNazcRzFvPc4d5D0lTZJJOqJ8vq1tX+Ap7j1egnvrmU6nEHX3FJvv3URo+L1DnPaNfmqAsxBtUUAS8EnKVz+GahiOQ01jyQwrwbkOfs+/S7Nh5JWx8enJYuXZqx/0JHpSKgCCgCikB4EFACPTx9oS1RBBQBRUARyBEBIqTZ1BH9jGw6hkMAxwCEXbHMLWFd6kRUtv2As5CobTaeWK417J2Z0BBOmWRoZttWPX48As7xz1+RlyagJduMq7BgS32/X/ziF0nNQdb7K1/5StGdIThDcVwRXIL0qNte++Q9uWfbY9I31C/t/V3S1d9japditQkyXuukcrRC5tXNkvVzr5WFZy0oCFkclr6dqB2oD+BwppY4Yzgo4/uFpCz17Ceqb45yw/nnn29k2nPNHON5+IHQYT6M85xos9MtiZVrdvrpPhmVMo8M8okI9MGRATnY94nJYif7EqsurxmrQTwwMmj+VsYcWdksNeU1MrNmtm81ioMar3rd3BFQAj137KJ8plsq2WsusjLJZKe7lTKcBHoYiE9qm2/t2CpH+46a+aq16kz5mFT9hNQ7gUfIuH968qdD1Z3sN55++mmz73Ab/YGkO7hH3dgvW3Uo1jXpymBF/Xm1/f4gwPyDv8WWrGBv7WWMJ7LQmcsyUfvxp3XhvoqtD9/Q0CBLliwJd2O1dYqAIqAIKAKeCCiBrgNDEVAEFAFFIPII2FrjVmosLLLDzgxcJLuiShwWY4Dg4MHJbOtOsxFHajBV7bWJ2qj9UPgexLHy29/+Vmw9UEjdGTNmFJ1ozhcJpD7feeedpMvceOONRtmgmIYzFMzBOFVN3QPtn8izB16VzUd3GvIcaWpqOg8ODEhjeZ2c17JELp6+SuafNb+ogUfFxNHr3kES6Dgk6bvXX389Sd3Aqx2UQfj0pz+dsUz7RDiWEoHuxMHP7HTndSci0Dmuf7hPjvQfFsj0vpFe6Rvul1E5HcBCRntNRY0hzivLq2R69QyprtCMwLDNA362Rwl0P9GM5rWYi/hmWzIqlVSyLT1BsBTnhClzuHeoV7Z0bJFjfUeNusak6jPlY1L1ypHew2a+a62eJBdOujB0ncfe47XXXjPBbF52ySWXmNI9UQ6IZl2+b98+83hk1xcz0Dx0A0AblDECrCOZv+wcRoCQ2yDTbckK5rAovzcZA+NxoK0PTwCsW6Uon+vquYqAIqAIKAKFQ0AJ9MJhrXdSBBQBRUARCAABCFYcSmzk2JhRAxgCPQxGdp+tV0jEcS7kbxieo9BtgPD+8MMPx+SL8yVfcUzaen9Ip2lEfLA9igQ1csbOLFpqM8fBScfY/OlPf2qkCq0R3PHNb34z52xgP3oDZyhOLKS809UebO/rlJ0n9ktXf7d0tLVLw0iNzK6dJs3NzUYhIM7ZyLlgvWPHDqNwQuYZzi8/jO8V14U498p2c95jwYIFpg4rBLpfVqoEuhs/v7LT0xHo3HdwZNDIuHcPd8soASy/I9DLpdxktTdUNBj5djI51eKNgBLo8e7fXJ7OKZXMfOLOTmd/w1qDv2FhyBweHh2Wd9vekZMD7XJquEdm1MyckCBjDiQDfVLVJJlcPUVWtqzMBaqCnMMe5KmnnhrD23lTlIfIRo+q9DlEJ+XFMJ5F96YFGVKxvwljirEFac7vVHOYJdQZd6VCqLMnZu5uaWmRhQsXxn4s6AMqAoqAIhBHBJRAj2Ov6jMpAoqAIlBCCOB0goiArIPgyFXSNgjIaNvOnTvNpYk4Vpm89CijJsBG0wZEQBqx4czH2Mgji4zFhcjNB4+gzuUdRKbuxIkT5hYQ5rbm56JFiyLrbHTjRVDMgw8+mPS/V61aJddff31Q0Ka9Lo4rnDPTpk0zP+mMQAAUAmzGCOdAvJeKMysdPs6/+0mg802gtvkbb7wxVm7Eqy2olUCQXHnllUaW329TAn08ovlkpxO8AinqVQPdfSdIJ8gmfmMVZRVSX1Ev5WUVfnezXi+kCFgCneBAr5IbIW22NqtACNi5yGZ28r12G4GgyAFDRrHvKVbg296uPXKk76gc6TsszVUt0pBoSInSyYE26Rvuk5k1M+VT9WfJjNoZBUI0t9uAPyQ6ayW3sS+5+eabI/n+si4/cOCAeSQC9DSoOLfxoWclI2AJdAJ5CTZl3rKlc7zmMAh0W7KCOSzOKn32m0+CB++cmiKgCCgCikD0EFACPXp9pi1WBBQBRUARcCFAFjobr7BtvshahHzB4kQgBjEA3eQrG2sIJD8yPDSQIYgeS74mGH/00Udj2TrUikYNwgYuEHEfpuCWfBGBQLfqEvZat99+u5HDLIbhDCUrHhIc59VE1tnZacg+SFSc7mSd+5VZXYxnD/qeBEExvikhQZZ+LoYKxrvvvitvvfWWZ0abvSbzHWOIQB++GUGpNiiBnr4Xs8lOhzhHojMTAj39nfWIuCOgBHrce9jf52MvAZnLN96WNXLegXmH9ZXN7OS7UahguK7BLtnRuUPaB04KNdGpbV6XqEsCgPV911CXdA12mr83VjbK6pbVkihP+AtUAFej7Zs2bZKXX345SVWJW7FPWbduXeQkmVmP2KAA1uZh2zsH0I16yQIggBIW6yaCPtkDOs3OYZZQt+W9nMcwh1lCnYSDQs1hQUPDHMI3n5IdBMwVa58Y9HPq9RUBRUARiDsCSqDHvYf1+RQBRUARKAEEiKZ3ykWH5ZFp07Zt20xziDhmY6g2HgE20sgl4iDEyKqBrPLLqQNZRB1jLG5EbhjGE04B+s+dzUzb4jr+cWIj5e6sWwq5eueddxZFaQJnKG2i/jl10L2M+Qi58KNHj5o/+xmkEoZxGFQbCAJhbBNokK0aBu8GpPnbb7+dNFbcbSWAASlVq7gRtJS+EujZjZZM6hXbK/LtIhAiLs7f7JDSozNBgGAz60zXDPRMENNjQMBZu5pMRrI6GUduI6PYkunsO/xaS6fqhf3d+00d9LaBNukd7jVlKGor6oy6xtDooCHWUdxoqmw25Pm8+nkyrSa9Uk6Yep139vHHH/fE+4orrpBPf/rTkZnzbZks8CVQr1jqBWHqX21L/gjYcnoET7OPT2Wsp/DbWIUN5jC3D4cx6ZzDolxmgGdjj8x+kSBn1BLVFAFFQBFQBKKHgBLo0eszbbEioAgoAoqACwGvWlthAQkCkc0TEccTbSjD0t5Ct4MNNBtLG40ehJS0BjIE16vOeude2cxbt241N4ccxBkSJ3vnnXdkw4YNSY90zjnnyBe+8IWCPybvEJnlkDFekt9kf1AagWMw+gKiT6U703dVLgQ685olzm0ZA687EezAt4F+g3DFucYcGDT5ypzImODbyb3UgZ5+HDiPSJedDmFlM6l414ImsLJrvR5dbASUQC92D0Tz/l61qwmGskQU2Z2pMjstGRVEZiffk/09++R4/wnpH+6T7qFu6RvuF5FR832pq6iT+kS9VJZXyafqPhV66fZUo4MgxUcffVSOHDky7hDWftdee20kvqUQlsxBGOXFgl5vRPNt01Zni8Du3buNv4MA0GwSBpjDGJN2HvNaM6OqYeewYpasyBYTjgcTgpx5LhTCwEdNEVAEFAFFIHoIKIEevT7TFisCioAioAi4EAgzgW7r5yJH7pY0K+WOZEPZ1tZmambzbwgcCL2gMNJABn9Hm1e9c8Y4zlmnxRl3MPjVr3415oi0z33LLbcUXNJzIgIdpw3OG5stD1kLcatO08zeCaT6wZDscLL+JjIcgG+++abJOLeKDF7HQ5qvXr3ayOjzQ1/Y7PPMWpXfUUqg54ef82ywJJAIdYdURkY6zl9+giCw/HsavVIhELAEOoohkyZNKsQt9R4xQMBJoHvVrmYu4hiIdH68MjsJ5rFElN/BPSf6T8iRviOGQMdoD9+2MhFpqWqR6TUzpKmyKdI9Ab7PPffcmKqV82EIFL3++usDK73iF3CsU5CUxigXo6YI5IsA7zoEOsZePp+SXTZA0RLqrJGdVsySFbngBDaU2WLuIMCZDH01RUARUAQUgeghoAR69PpMW6wIKAKKgCLgQiDMBLof9XPj1uFshg8ePGhIBwxygQ23m3z187mRcOe+GsiQP6pe9c6JqPfKsow77ozhn//854ZgtUbmxTe+8Y2sMjDy7RUImY6ODkPGOJ0z1Ezlb2Qb43Tib+lI4HzbErfzcQoSfAB2qcguyAqIc1QJJiLOcVZfeumlZq5jDsRQAUDSMZuMnXwxVgI9XwSTz3cSEpAo1vHLb7fzV7PT/cU+ildTAj2KvVb8NrPOgIjBvAh0dwttZqetO+yV2cn626pl+FV6Asn2nqEeGZERI+PemGiU6ork4Mrio5l7C/h+EiS3cePGcRdBQWb9+vUF/Z5n+ySMBxSJWBOSga6mCOSLAHMNEu4Y61nmEj+Md431t53DUpWssHNYIUpWZPtcPAP14dmHESibqsxWttfV4xUBRUARUAQKi4AS6IXFW++mCCgCioAiEAACYSbQs8leDACa0F0SBx7ZstTgw6gbzYYyaAlhqwTAxp56w2q5IeCud55OcroUcN+8ebM89dRTSYDilMSJWqgsb7KJIPMhx3mfrMLDoUOHTLuKQdLmNsLCd9ZEBDoE6euvvy7vvvtuSuKcMbB8+XJDnJP9T5+gvoGRpcOcVOj6jkqg+zvOnJK4zow+WzvdZoSmIrA0O93f/gj71ZRAD3sPhbN9TgJ94cKFWZeGcGan8+2aqO4wc5KWeJl4HLC/Y+0HMeY0Au1QIgpKUSvf0UlgJQF87Luoga6mCOSLAO/A3r17zWVQWEJyPQjjPqy3LKHuFbAaRFBQPs9CcAEEOr9JFiDIRk0RUAQUAUUgeggogR69PtMWKwKKgCKgCLgQICPW7cAIC0hEZEMWI9sFeVLKRv1AiD7bV2CCo6kQJGMudYxLua+8nj1dvXOvc0oBd5zQv/71r8eyLywO1EKnLmYhjGyikydPSktLi8mUhqTlv7FikbSFeO5C3MNrDmdOJ+N806ZNSeoDzvZY4vyyyy4zcz/zHsFDOP6wQgUPeWGkBLq/IycVge6+C85ezU73F/soXs0GEaqEexR7r3htJhOTcixYLgS6s+XO4B7mJFvixXkMSik2s5N1RCHW6sVDN7c7s/Z65JFHxoKC7VX4vn/5y1823/mwmSXQUUNhHKkpAvki4CwvgQpPoYJCCSpyrqm8goKYw+w8Vqh2OfGEOCe4gLahgsd3X00RUAQUAUUgeggogR69PtMWKwKKgCKgCLgQCDOBzqYJ5zqSXaW6aWLTSH3Yo0ePmp4jq4UobDJcCmWqBJA70pnWO/e6Q6ngDin6s5/9zDhyrJGB8fWvf70gNW7JJiKrmYwniFrbjmKStLmPuHCd6STQwReZdrLOrYqGu7WQDCtWrBCIcyv5bokPm4FMBgrqDcUiJJRA93eMOQl01Ccy6VfNTve3D6J0NSXQo9Rb4Wmrk0Anc9hP5aZM6g5bEoq1ezGIqPD0RHJLCFYkiLKzszPpDw0NDYZET1X6pVjPQzAzQZbsxSgFoKYI5ItAtuUl8r2f1/l2TWUJda+gIOYt5i9Lqvs5h6Z6Jmd2PsEFYZsPgugLvaYioAgoAnFEQAn0OPaqPpMioAgoAiWGQJgJ9P3795uMQwiTUpTtYuNI1jkOG4xNK+R5oZ1vqgSQ26SQTb1zrztkUj86t5aF7yyy7R966KGkhvHO33HHHYFLoeIMPXHihCHubAYGQTtkPmdC5oUPzfC0iCAoMrZQYHj//ffHMsjdLQTnlStXGuLcWWeeuQ/JZrJQOGbOnDlFz0pTAt3f8ZULge5uQTp5ZbIFreOX3/y3WjQRsAQ6QTTOuSKaT6OtLhQCBG0xdjC/CXTnM/B94F6WiPIKFrNEFHMR2emFIKIKhXMu9+E7/8ADD4wp/9hrgM9tt91m1IHCYhD9hw8fNvswCD01RSBfBIIM7sm1bexfmcPsPOalVMjcZddVKG4EsV+iHUi423k7jKoUuWKs5ykCioAiUEoIKIFeSr2tz6oIKAKKQEwRYFPEBiWMhtwijhWyzyG0Ssmss89mXULmgUEQG9R0uKoSQDqExv8923rnXncotcCFJ554QrZs2ZIExWT/vxwAACAASURBVPnnny9r167NvgOyOOPAgQOG5MVwZBOkEtb6m1k8VtEPhfR+4YUXZOvWrUZJJJVBnF9++eVJZJi7Dj3Oauqd47ArtimB7m8P+EGgO1uk2en+9k/YrqYEeth6JBrt8XueyfSpraoNwcD8uIko1vSWiLLZ6cVY52f6PEEdBzZkoh8/fjzpFk1NTYZED8uarKOjQ44cOWLqVFOvWk0RyBeBYs1NmbabNRW+CFs7nfa65d4JSrQqG/xGocEPc2bnL168ODTzgB/PptdQBBQBRaCUEFACvZR6W59VEVAEFIGYIhBmAp3MQ5wVSHZRm7hUjGcm85wNKo602bNnFzUDo9SVALIdd7nUO/e6R6kFLuAoufvuu002uNO++MUvytKlS7PthrTH837hCLUOW941MtPIpFDLHQFwZey++OKL45zhzqsuWbJErrzyynHlOSDew1yHXgn03MeG15lBO48zzU63mVSane5v//p9NQIrydjTDHS/kY339YKeZzJBz0lEQUZ5BZZBPDEX2ez0UpqPwOPBBx806zKnkYEOiV7I0lWp+pP1PSW1WCdSk1lNEcgXAbK82fNjkMRhN9bovKuWULeB/s52835YQr2mpiZnlQ1ndj77wDDMAWHvH22fIqAIKAJhREAJ9DD2irZJEVAEFAFFICsEwkygs6HEWYFMJyRy3M2rXjZZl2w+i2k2QxeH9fTp04vZlFDfO596514PhmwdjhUwB/tSsGPHjhkS3amKgSOGeuh+yvUy7xGgYzPPwRZnj9a0zG+UQXyTdW6lcr2uBsZXXXWVp6oI/c65tg49jnOCp8IkcasEen5jxH22U1o50xroubZAs9NzRS485ymBHp6+iFJLwkhSsQ6xRBRklJcaWCFkksPUj3wPkHOHpHYaKlyQ6MXeD1GznXUq7WB/pqYI5IsA7/4nn3xi1rkE8UbNCFK0Uu/8hmB3GsHJtm56tiobzsCn5cuXh0KFKpP+Ya155513yquvvioXXnih/PKXvwzVPiaTZ9BjFAFFQBHwEwEl0P1EU6+lCCgCioAiUBQEwkygHzx4UNra2kzNW2SV42xu4gi5Qur9hiH7BEKLun84sGbOnBnnbsj52fKtd+51Y5v5X2qBC5s3b5annnoqCRLKF3zta1/zRRbQZjTYrAkcOzh9cOxoTcvcXgECnV566SXZsWNHygswn61ZsyblXI7jHHIMZxxG4AjlO8ImZ6sEem5jJNVZhSTQ3W3Q7HR/+7IQV1MCvRAox+8elkDne0KgTtiM74pzPkolk2yVMvgdhv1BEDjy7Pfdd984NSL2gevXry/qc7MnRbWIwIa470uD6Fu95ngEKFVH8Cnv88KFCyMNEfMYazpLqPNvt1mVDUuqTzSPOQOfVq1aZUonRMEgzP/u7/7OBA488sgjGmwThU7TNioCikCgCCiBHii8enFFQBFQBBSBQiBApLAlLApxv2zucfjwYeOogEyOs1QeG0RIapt9Mm3aNJNxHBbiqFSl9DMdq37UO/e6F0QBjhVIRAjkUjEcMI8//rhs27Yt6ZFXr14t69atywsG8GQ8M+/xfkHqQqQjGaoZ6NlDy9gnw+Ltt98el3Vir0YN04svvlioZ59qTnP2C1k49AvnhdGUQPe3V5wEOtlXxVIbYE6gLbZWsZcsKaSJlSVFGSMs32h/eyTcV7MEOuskFCrUFIFMELBZnmEl0N3P4JRJpu1e+zSyoJ0yyXGaj1AHuvfee00ZL6etWLFC1q5dW7S5lxJD/IA76xQ1RSBfBAhQx98BsRw3FSySNPBxWELdS2WDecxKs7Pur6ysHIOUeYBkCuycc85J+lu+uAd1PupxX/rSl8x68jvf+Y589atfDepWel1FQBFQBCKDgBLokekqbagioAgoAopAKgTCTKAj4cdPXDNDIWLIZmDjzL8hDshoIGAgTGal9HFWq8MouWf8qnfu1d+lnPkPefWLX/zCvB9Og0CHSM/WeL8IxrG1NXHQIL8JIcb/5x3k31HP/sgWl1yPxykGaf7KK6+YesRehiMMyUWUKwgI8goCoV9wRoM/Rr8QLFVsmdaJcLEEunUEFovwzbXvwnZeWAh0Ny6anR62kXK6PUqgh7Nfwt6qOMgk2+AeyCi+Q07jO+TMToeMi7pBnv/Xf/2XCWpy2mWXXSaXXHJJUR6P9SLrUrAuhdJiRQG5xG7KOGdvQnb1vHnzYvv0TpUNS6rbeWzLli3yv/7X/zLv1bnnnmveb0o9sY9gf0BwEP8/7Kob7At+7/d+T95//3259NJL5a677ipasE9sB5I+mCKgCEQSASXQI9lt2mhFQBFQBBQBJwJhJtCpMxfXzFBwJ6oaAhaDMII8J6stbFZKUvqZYs+mn7GJMw3D8eF3vfpSz/wneObf//3fk+qC4iT+/d///awCOXjXqC9oM5nIHKKvrIMZAhf5RN7BKNYfzHTM+nEc437v3r3y/PPPjwtusNdnDsPBfcEFFxjcU6kouOdAd7/40d4grqEEur+ohpVAd6+TUFuwWVSpstMtgaXZ6f6OEefVlEAPDts4X9lmMka1zrCzb/gG2drpzEleQWzMQTY7neDAqGans84mE92duXrDDTfI4sWLCz5k2ZdSB10J9IJDH9sbMp4YV7yzcVbbc3egU/Xnrbfeku9+97vj3vNZs2aZzHPI86985SvS0NAQ6nHwox/9SH74wx+adj722GNC+9UUAUVAEVAERJRA11GgCCgCioAiEHkEcMR4OYPD8GBxJbbAm+xiWxuMGu9kMoQ1kxFykb4gEhzisdQtiHrnXpjazP/W1taSzXTZvn27PProo0nw4Lj8+te/npFSA1mkEC72XQPLmTNnJr1rZBMRJILzKoy1UcPyvhEs8txzz8n+/fs9m8T8BWkOeY7DHkulosA7xN9w/mNe/RKW53a3Qwl0f3smCgS6+4k1O93fMZDN1Q4cOGDWrCrhng1qemyc6gy7e5PvKVnaNsAHYsppfJv5JrN24scpkRyFkbFnz55x60CeAWnkSZMmFfQRCOwk8BmlMNaSaopAvgiwB2F9TRBvKe+xCZb5zW9+I6+99pq8++67grS90whUv/DCC+Xyyy+XK664QpYsWRKqwKCtW7fK7bffbspt/P3f/73ccsst+Q4NPV8RUAQUgdggoAR6bLpSH0QRUAQUgdJFIMwEOlHZZDCyaSpGpkEQowInHsQoEsgYDhgcQGHODmFTS3Q8EdVxlpfLpL+DqnfudW/GPu9AqUvnv/DCC/LGG28kQcR7g/N0IplSnMmQtDZzKdW7Fsd5JpOxnOkxjPmXX35Z3nnnnXGysfYay5YtkzVr1oyrSWxVFJBxt85m9zuEtDt/D/Mc6MRKCfRMR05mx5E9SZALVswa6Jm1dvxRzlrFzDkTZadDXrGeicpYzxWTIM9TAj1IdON7bUugx7HOsPv7RFCSJdRt8KDzGOYgZ3Z6WIN3nW1m/fHiiy8mDVDWDSgSFTIggP0QakZKoMd3rij0kxGgzg/vpJZJO40+PhIIafZ/lIvatWvXmN/E9g+loSDS+SFwt9DBNM5xwrqPuucE+9CHX/va18bq2pNBf80115i1n5oioAgoAqWKgBLopdrz+tyKgCKgCMQIgTAT6DgpIGBweC1dujTSqIMzJDTZCxjPhGQ7DvWwW9xr0WeKf5D1zr3aoNL5p1GBoLr//vvHZT6vWrVKvvCFL3iSUWR0oJzAe0fNPLI6Ur1r9CtBLThhyWhQO4P7e++9Jxs3bhzL4HdjAyn+mc98JqVCgrsMARklYE2f4rRnDsQRHSVzEuh2fEWp/WFra9QJdDee6bLT+fZb8orfYa/pGbbxogR62HokGu3h20Mt3bgT6O7egIhyZqfb4F17HME87uz0MAb48K195plnBFUipxG8t27duoIFJTGGGEsochH8p6YI5IuAlgVIjSCZ+ezn2DPw3r3yyismoNcGXTrnseXLl8uVV15pCPXVq1cXNLDmxz/+sfzTP/2TaQ5KApDmzKs7d+405d5IPviHf/gHYd+qpggoAopAKSKgBHop9ro+syKgCCgCMUMgzAQ6myUySHEw4ySJquGwgjQiAwbDaQ5xVMisiXywYwOL04jN4MKFC/O5VCTPLUS9cy9gwBzsIRhLqS6eFxZkLd99991GOtNpRPVffPHFY/+LvoI4x+FiHRmQ5xNF/scpUMevFwz1A5zVNuDHfV3UKK6++mpZsWLFhI5rW4YAFQX6wF6PfzOmkc2PmimB7m+POQl0vi9xIpSd2emQWJDrbnOSV5qdnn5sWQJ9+vTpQvkbNUUgEwQsgc66e/78+ZmcErtj+HYx31qpd9ZVbiPAwEq9s1cJU3Y68+d//ud/mnWx0yDQIc8KYawv2csx9zAHqSkC+SJgywKwrtaa2clo2uAC1kn4gWxwD8G5EOn8IPnO+spplOOi/Fch5i8I8s997nPCfEoZK+qg22x4GwD+t3/7t8bnc++998ZGUTHfca/nKwKKQGkhoAR6afW3Pq0ioAgoArFFAIdKGK27u1twlrJhgqiJotn6rlbWFclBshbCmOGRCt+41qLPZDwVqt65V1tUOj8ZFZxMv/zlL8eRUF/84heNQoW7r8gQQkovnQNFCfQzOONYRyb1/fff93w9cK5fdNFFcskll2QkR2gJdBxHljzEOU8A0UTy+5m8m8U6Rgl0f5GPM4HuRop1gCWv+M1Ycppmp6cfW0qgp8dIjxiPAN951lSlTKC7USG4F9IH8okfW+7GeVzYAnwouXPPPfcklcogEO+OO+4oiJoNylDsTQkKnDZtmr5qikDeCNiyAKpqMB5Kiw37hlQ1z9lbbN68eYxQR/qd4JYNGzYUJEj3+9//vvzrv/6r2dM8//zznoE13/ve9+TnP/+5rF27Vn74wx/mPWb0AoqAIqAIRA0BJdCj1mPaXkVAEVAEFAFPBMJKoONg3rdvn2lzukzHMHYtDjsIJJzkEOazZ88eVyM4jO12t4lsXpxGOKmI6i4VK2S9cy9MVTp/PCrI4T388MNJf8BpccsttxiHqiVpcWxSHy+TQJW4KF3k816SJYED6qWXXkop106QAlnnOI4zNSvhbo8nKwPZ90z6JdN7FPo4JdD9RbyUCHQncpqdnts42r9/v5nnNQM9N/xK9SxLoKPygJyuWjICfNec5SdY/7oDfFAHcWanF0sthHrIjz/+eNIDoGizfv36wNcWqPMQbNDa2mrWmGqKQL4IqKpBagRtyQSy8zMtsYVCBPN8oRSubr/9dnn33Xfluuuuk3/+53/2fJhNmzbJV7/6VdMu9lrpArvzHVN6viKgCCgCYUNACfSw9Yi2RxFQBBQBRSAnBCCe3I6SnC7k80lkb+/Zs8dcFXm+qGw4wJJNH5nbGBsmZKSpixVFK8Ua0YWud+41LpCuI/oeGc0FCxZEcegE0mbk+iB6ncY7Rt07nCxknZPJkanhbKGeHvNLoWRAM21bIY7DeYdcO3OWl+Ek/uxnP2vmsGwMBzwBUPbbAnGOAkfUTQl0f3uwVAl0N4qsw2yt4lTZ6ZBXtn56VNZD/o4WESXQ/Ua0NK7Hmo6gRCXQM+vvTAJ82NPYOYl/FzIw7sknnzT1hZ32mc98xtQeDtIIimZ+JhhwypQpQd5Kr10iCFhVAw3KGN/hFhv2dGEN4Ee+HWWcP//zP5dvfetbnqOWv3Mchuy8Bt+UyMutj6kIKAJjCCiBroNBEVAEFAFFIBYIhJVAx7G+e/dugzHZj1GQ/EUCkbrtOFgw6ldD6BUrU8OPAVpKEtfFqnfu1U+lXns+1dilj55++mkTxe803jUi/LPJkOb8OJSKyOU9J0CJQAQyJ7zMBiVQ0y/b+cupvsG1cbLHpe6sEui5jLbU57D+wLmIxa0Geq5IZUJehU1aOddnzfY8S6BTCiebQKls76PHxwsBpL8JSiQrkWxltewQcGanewX4ENDjDPAJer/G+uXuu+9Oqn3MmuUP//APTTuCMlR1CA4kGDAOAYFB4aTXzRwBq2qgQRnjMbPYsK9jfRhG+/KXvyxbtmyR73znO2YP6mXIyqOUhr399tsm2FtNEVAEFIFSQkAJ9FLqbX1WRUARUARijEBYCXQcNh988IFBfvHixRnV3C1mN+FUgjy3dQSzkZEuZrvT3btUMnSLWe/cqw9KufZ8ujHJnPWrX/1qXNY0WdK33XZbVoQvWZ+QMtjKlSvT3ToWf2deffbZZ03wgJctW7ZMrr322qxrikIuk+UHUYHhVIcMhOjKNoM9rEArge5vzyiBnh5Pm53OXOUlrQxZ5ZRWjnN2uhLo6ceLHjEeAUugkykdl29RsfqZb6Czdjrzk9sIVLBqGQT7BJGdzlzw0EMPJd2acl820zMIfNjjQd6TfQ7hqaYI5IuABmWkRtCp+BDWINy/+Iu/kAcffFD+4A/+QP76r//a82Huuece+e53v2tKz2zcuDHfIaPnKwKKgCIQOQSUQI9cl2mDFQFFQBFQBLwQCCuBPjw8LDt27DBNXrRoUWgl0HEmUSccCWT+jfN67ty5WZNPYR2dpZChW+x65159X6q159O9B8xXSK4zLl999VUhwMNpqFXcdNNNGZd8IPAFqXEM52sQjt50z1Sov4PVhg0bxpQ93Pclowq59lxqxEKU4+yipjxGhkVlZaVAXMSRQOf7xDNnm51fqL6Oyn2cBDqlKoLOXIwKLqnaWerZ6UqgR30EF6f9rKdQ9VEC3X/8CT4luMeWoGCOchp7IqdiBusCv+ypp54a2yfaa1KTeNasWX7dIuk6rD1RR0OCGcltNUUgXwRsUIaOqWQk8acQXGADVsKqHMIc9Gd/9mdmz0NpCUhyp7FXXb9+vdm3Mjf9zd/8Tb5DRs9XBBQBRSByCCiBHrku0wYrAoqAIqAIeCFAprfb4REGpGjT9u3bTVOQ7sIBEzajjdToor4iZp1zSAnGxeJOMIah3rnXWCnF2vPp3hkcEThUIC8hupFtf+SRR5JkPLnG6tWrTRZSJmQ4wRN79+41t6YGehyzN3FEIdX+4osvile2GA7tyy67TC666KKcCGGuaZ2A4AgRj8TykSNHDGlBP4XV+ZVuzLn/bjPQlUDPFjnv45VAzw/HUstOJ9gJwk4l3PMbN6V2tiXQ2UcQ4KoWDAJ8HyG8LJnOv93G/siZnZ7Pmos14S9+8YukdQ3qX0gpZ7L+yxYFyo0w53KPbMsFZXsvPb40ENAx5d3PzCXsK2zASliVQ/ADIc+Ov4o67X/1V38lF154oZl/3nrrLfne975ngnxQ5XjiiSf0+1Mar7U+pSKgCLgQUAJdh4QioAgoAopALBAIK4HO5mnbtm0GY6S7gqxrl0tHukmj5uZmmT17duwIuLgSjGGqd+41/kqp9ny698+qPBw6dMgcSpYqzhScsCg/II/HPOY0COGrrroq3aWNs3fPnj3mOKTL45ZRjJw6NeOpJehlZP2uXbs2Z2ews3QFDqOZM2eOSZtCoHN/MjNyyWpP23lFOEAl3P0FXQl0//DEkcv7yA8ElntO5P2EQLTkFURWECSTf080/kpKoAeJbnyvTUkcfpRAL2wfE2hmyXR+899Os3OSLUFBMF+2cxI1hV966aWk615//fWyZMkS3x/WKmCQZcqeT00RyBcBHVPeCLLWJmubNSIBc/hXwmoEdv/Jn/yJ7Nq1yzTRzmE8A8a+8gc/+IHZa6kpAoqAIlCKCCiBXoq9rs+sCCgCikAMEQgrgQ7URPTiFCZ7kSzGsBhSyMgVW2eQJY2ydfyE5XkmagfR37t37zaHII8dB4ndsNU798IfKWyi79l4Q+yWqvH+Q5wjBY7hAIc8d8qAksFx3333jVPSuO666+TTn/70hNDFlUBnbnr99dfllVde8VQYgUQDH7Luc523nOoNjFP6xRnopAR6qb61mT+3EuiZY5XNkThuWdtZaeW41E63BDprrjCtCbPpGz228AhYAp3v3pw5cwrfAL2jKXHFfsIG+DAnuY11nQ3w4Xcm2emsdX75y1+aUlrWqE9+xx13ZHR+Nl2jJSSyQUuPzQQB/aZ5o8R8wfvGfp3vfVBlGTLpo0yOYT577LHH5PHHHxf2pMxH7IfOO+88+eY3vykXXHBBJpfRYxQBRUARiCUCSqDHslv1oRQBRUARKD0E2Jy4swLCgsLOnTvN5gnJxTBE+7OhI6Py6NGjBiLIZNoWtux4P/sPgsNGVZPR4Wf9Qj/bmem1wljv3KvtBGkQfY8DEZKzFI13nyACHK4Ykpk4UbycqswVyLnbiH+LF5lIq1atSglfHANEmJ+QCoTA9jLwuPbaa3Mui+FWb0CakCAnd+kK2sEP8yMqInEwzUD3txcheXGSYloD3V9snVez2ek2GzRVdjrvKsRVWLPTlWwIbozE+cqUEoHQUAI9PL3MvtOpmMF6z23O2ukTzUnsUSCunLZu3Trf186U+6HdGsATnnEU9ZaggMX3mb0Nak1qpxFgrc33nveNoCd3bXHFSRFQBBQBRSA6CCiBHp2+0pYqAoqAIqAITIBAmAn0Dz74wGRRsXkqdr05NnFknUNsYjjiIM+jTiinezkYH5CTGPW9IMuiamGtd+6FJ7UdiWInO3jFihVRhTzndhPoQACBdaoi4Udt7Ymypd977z0jV+40jl+/fr0Zu14WpwCRdFnnzKE4lfORU3fPg2SBMj97Sd8rgZ7z8C+ZE50EOkEWcf+ehqFjo5ydrgR6GEZQ9NpA4CsqNgSIhFkKOHrI+tNi5iTWYs7sdHcwJGsMK/XO/su55uDY//iP/zABztZY73z961/3NQvdEuhKdvrT73oVMQpvjF/mpTgH42fb1wQV8L3nN+pWU6dOzfYSerwioAgoAopASBBQAj0kHaHNUAQUAUVAEcgPgTAT6GwsyRDFWYEkX7EMmWcyYXHwYBB5EHq5Sh8X6zlyuS+E2Y4dO8ypixYtkpqamlwuU9Rzwl7v3AscHIk4DzAI9FIYaxYHd6ADgSqZyvW+9tpr4+ph4miFRF+4cOE4qCHwCNTBFi9ePC6LuqgDN4ub4zgmA8sr65yxc9FFF8kVV1yRF0HJ/EdQA3MyNmXKFJMVkmps0ibag7Ob7OI4mGag+9uLSqD7i2cuV8smOx0Hfy51inNpl9c5mgHqF5KldR1LoJPhGXYp4NLqGe+nZU4iiNKWoHArZnAWexGrmMG/WS+jQuS0m2++2XPdlyvGNltYyc5cEdTznAiwnrQl0tjnoLigdhoB5gC+92BE0C9+FzVFQBFQBBSBaCKgBHo0+01brQgoAoqAIuBCIMwEOpsnnCiQ1ZA1xTAnmQdRhOOk2NnwhcSBTSy16DEIyKht8KNQ79yrPxn3jH8MCfdMakEWclwEcS8vaXAyD7JRPeAaL7zwgrz55ptJTYREv+WWW8YRuVFXWOD9pNb5yy+/7FnrnHkTGXskR/MxHNkEERFQwzwICdHa2jrhJZHNPXz4sJkzvIIX8mlPsc5VAt1f5JVA9xfPfK+WSXZ6LnWK822XPV8JdL+QLK3roIbCWl4J9Gj2uzM7neBSd3Y662MC9Z599lmh3r01Ssuw7vPLbLYwqjvcT00RyAcB1u8EZWDsdaIYoJ7P8090LnsNuwcmADfdfiOoduh1FQFFQBFQBPJHQAn0/DHUKygCioAioAiEAAE2KV6150LQNFMbFeJm2rRp5qeQhoMG8sc6Y6i/V4obXHDYtm2bgR6J3ShJzKEcQMaszV5BAo5xFIVsbtpuHSvLli3zlMgu5PsQ9L2Yhz766CNBuh6bSBo8XVsYs0899ZS8//77SYcmEgn58pe/nCRhHmWFBWq6PvbYY3Lo0KFxkDDGL774YpN1znPnY0jfHjx40DituRbzYCbOYyXQ80G9NM5VAj3c/ezMTmct5l4rMs846xQHnZ2uBHq4x0tYW2cJdNYV+QaThfUZS6VdzEmsj212ulUG4/lZQ27evDkJijvuuMOXAGzNFi6VEVa453SSxAR7ZBMsXLhWFudOrDWsChvqd83NzcVpiN5VEVAEFAFFIG8ElEDPG0K9gCKgCCgCikAYEAgzgU4NaAg1iE+kggtlOPVxxJDpgOVD5hWqzUHeBwLdyqiRwRMF6+joMDXraTfZKWSMNDU1RaHppo3IZFtpv6VLl+ZNgob5wXlWAh2sIzSdNHgmz0K/P/HEE7J169akw90kehQVFng2nMTPPffcWHCI8yGROiTrPF+pWncQEdkxkOcEE2ViBB9B7nMeDrA4mM1A57vJ2PGq/R6H5yzUMyiBXiik879PJnWKg85O1xrE+fdjKV6BUiKsCZVAj1/v8w2xtdO7urpkw4YNSesiAn8JJiToz5agyAUFJ4FeisHUuWCm50yMgJMkZpzy/VQ7jQD7QXxAGOW1Mi3jpfgpAoqAIqAIhA8BJdDD1yfaIkVAEVAEFIEcEAgzgY5kcGdnp6l9VaisERwx3NdmWpGxDIEfhazlHLo/o1Oogc44wWkUdhI6ivXOvToB58GuXbvMn5YsWRJbxwoOT4JVICN5xwh08CvTgGtColsFBYszTqpbb73VjGenwgIygZlkVmf00gR0EPPTk08+OaZO4LyNn1nnbkUA3nv6JptSAmTIk7keJwIdvO03Uwn0/Ae5OpDzx7BYVyhGdroS6MXq7Wjf1xLofMcoCaUWTwRYz7344ovy7rvvjj0gAX+f+cxnxtYu/Lcl01HPyHRN45Tb1mzheI6fQj+VkyRm/5GvWlSh2x/k/VCZwBeDocIW9r1ZkFjotRUBRUARiDoCSqBHvQe1/YqAIqAIKAIGAZwCVuI6bJCQQUzdQmpfUXs8SMPxAuGDbLvNWp47d65GPYvIzp07TUABePhFbgbRl1Gtd+6FBe/kBx98YP5E9H2mWb9B4BrENXnHkPjGsY1BakNo49D005jfkDknCMRp3A85d+5pCfawE+iQR5DnSJe6bdKkSXLDDTfknXXOy/ncuQAAIABJREFUdVEEwHHFbyzX0geWQEeW8uyzz/azW4t6LSXQ/YNfCXT/sCzmlWx2OnMTQT5Wvcc955IBiiOcn0yJK+c1KGvCnI66RlTUcIrZL3rv0wiwricYVwn0+I8ISs7cddddSQ969dVXm70L326nOUtQ1NTVGAIzUZ7wDJh2ym3Pmzcvdmvy+I+M8D2hVd+iZQsXLlRFI0cX9fb2muBqbMWKFVofPnzDV1ukCCgCikDGCCiBnjFUeqAioAgoAopAmBEIM4FO9iIkDI4PyNugDAy4F2Q9lq1UcVDtCst1yYQmUp4gBoIZwmhRrnfuhSfEEoELGORjnGrj8b598sknRlIVg0yByA4q+4L7Pfroo2N4Wry53/r168fIHiQUIXjCZgRTvPDCC/LOO+94Nu28886Ta665xheHLiUzcFrhLM5XEQBHNv1M8AdBIHExJdD960knga6khH+4FvtK2WanZxogpgR6sXs2mvenlAhqN+wlClkOKppoRb/V99xzz1hwJk8DAbd27VoTFGiDfCDo+kb75djIMTk+clyGZMgE9VQnqmVOwxw5u3WRtNac2e9osFf0x0XYnsCZZc0+r5SV7tx9w3vK/gE755xzYqvCFrYxqe1RBBQBRSAIBJRADwJVvaYioAgoAopAwREIM4GO04s6umSNQLAFYRDDZFuykcVaWlpMdlMu2VFBtC8M16QWN44ncCHTNWwW9XrnXnhGsTZ3JuMCMph65/Z9IyCD8gxBv2+Qno888siYLL5tK/c9//zzjawrBF7YshqPHTtm2k22vtsIPPjCF77gW31xmzHOfQguQKY0H0UAJdAzeSNK+xgl0OPf/87sdJziEFduQxGE4CV+JpJVVgI9/uMliCe0BDrre8oyqcUbgU2bNsnGjRvHHpIA1G9/+9tj60zmpO3t22VX+24ZGByQrsFuGRwZNMdXSIXUlNVIRVm5TK+eLue2niNNDU2G3LQ1mbVedbzHT6GeDqUWlPawOAWZ+oEfAU/M29i5554bWIC1H23VaygCioAioAhMjIAS6DpCFAFFQBFQBGKBQJgJdOSdIZAgtSC3/DY2aGxerawfRB4EsUaBJyON0xrCE3yoRx8Wi0u9cy88o1abO5MxgbOIYBVIM6zQ44n3nEx0K41v28z7joPm8ssvD03JBvp/8+bN8t///d9jeDkxRu4R8tyPjHnuhaMKAh2DwCJgCVIrH0PRg/mV6yxZsiSfS4XqXM1A9687lED3D8uoXCmf7HRLoKOG48fcFxXMtJ35IYDCFOoqSqDnh2NUzmZv95Of/CSpubfffrsJAma9s6V9q/y2+0PpGGyXrsEuGR0VqSyvlJHhEekf7peR0RGplmppLGuQ5vJmObtikVQmKsf2iqqWEpWREO522ixr9iBxKnPkB+qU3KD0BkHOZKBXVFT4cVm9hiKgCCgCikAREFACvQig6y0VAUVAEVAE/EfAZgf5f+X8rwh5DolOpiX1if0ynplrHz161FySbEsII+6jNh6Bffv2GZlrpC+phxwGi1O9cy88nQR6WKXFsxkHkLOQtDwXjhDet2IQIJA3TzzxxFjdc+czrFmzRi699NJsHiuQY1F7ePrpp8fVbbdz1XXXXSerV6/2JdAHMpigBltXHYlbyCk/FAFQhkAOnvl16dKlgWBVjIsqge4f6lpX1j8so3ilbLPTIUI5Rwn0KPZ28dpsCXQUb8Kyhi0eGqVx57vvvtsomFm77LLL5JJLLpGPez6W905ulhP9J6Rn6JQ0VzZJQ2WDVJSdJuggz7sHe+Rk/0mTjd402igzymbInIrZScBR6suqZpDhroHXpTGu/HxKgnqYm9gTERSrdgYBAnDx0YDNqlWrlEDXwaEIKAKKQIQRUAI9wp2nTVcEFAFFQBE4g0CYCXScH5BuZET6tbnEYU9WJBkKGKQ59dXzzbaM85jav3+/IdiQvgyD/GXc6p2nGjvbtm0zZEEYpcUzHe/u7GacjpDnmda8zfQ+2RxHm5555hl57733xp127bXXykUXXZTN5Xw9loyLhx9+WHAeuY0Alptuusk3FQiIeuT0KWOB8W5DLvjlCFYC3dehEcuLKYEey27N+aHSZafbCxPoAxlazO9Izg+pJxYcAWrpsoZVAr3g0Bfthi+88IK8++67Y/efM2eO3HrrrbLx6G/kSO9ROd5/TCZXT5b6RL1nG8lEP9J3VFoqm6WlskXOrz5fOts7PI+F5GMvCaHOb4IG1RSBdAjYLGvGi59JAunuG4W/UwKKRAd8MytXrvQlqDcKz61tVAQUAUUgjggogR7HXtVnUgQUAUWgBBEIM4Fua/IS3e+HvBnEK9mWljBCjpzax34RRnEdPpBsBBxMmTLF4FVMc9c7JxMNZ3ocbfv27QKhQC3qxsbGyD2iWyWgqalJcGL6kd2cLxjMe88//7y89dZb4y5FFvpVV11V0HmB9rz99tumTfS52y644AK55pprfHPM8j6THc69mP/oF7/fI5yDzLc4l5ctW5Zvl4XmfM1A968rlED3D8u4XSnb7PSJaqfHDRt9nuwQsAQ6JZpYx6rFH4G9e/fKI488MvagrEN+/5tflTfa3pCjvcdkREZkRu30CYEgSx0ifWbtLFlWv1RGjg+b49l3oMpFUIbdTzovxJ7VkunMS7rHjP94y+UJ2c+isgdJjNKY2hkESKDghyC5FStWhGLfqP2jCCgCioAikBsCSqDnhpuepQgoAoqAIhAyBMJMoPtZQ5dr4UTjeXFm4AChHqJaegQgwSDDCDigbnUxLM71zlPhuWPHDlNzkYxtyOcomVslwO/sZj+wYEy9/PLL8sorr4y7HJKB69atK4hsIJngyMrv2rVrXDtwxFLr3K8a4jyzldPnZjjuGF84ef02JdD9RjR+13MS6AQKMd7VFAEvBJxjBTKM/3Ya6zpnFqhmp+s4sgigOgXhqQR66YwJ1lU//vGPkx54zZfWyCeJg/LxqY+ltWqSNFY2TAhI33CfHO07KtNrZsinqudKc+fpdfjixYvHzhscHBwj0xlj7gBIAkaZl+zcpGpnpTMG0z2plSln3cP6R+0MAsePHzd7FVTLli9frkEoOjgUAUVAEYgwAkqgR7jztOmKgCKgCCgCyQjgaAij+UHAQBghi2xr4eFUhTBiU6aWGQI4H9noI39J4EGhjUxm2kC9OKyhocHI7uNEj7Pt3LlTeHae1e/s4CBx472lv3Ak4jwkuznMAQCPP/64bN26dRwklI24+eabA5UJRqLwoYceMo4it82aNctItvsV6EN/UBIDaUQM0hynXVByo2S5o17BGMABFhfTDHT/elIJdP+wLIUr7d69e6wGOvMWGaD89Pb2jnt8iCpbo1iz00thdKR+RtRWGCMEgfKjVhoI/OxnPxOyfK2dc+U50j29Rw72HpSp1VOlNjFx4ODQyNDvjp0mc6pmyeTuyYbIS6WIxn6T4FE7L3ntrdmDOrPTw6DIVBqjIXxPybofotiWtgpfC4vXIuqf43cg8GTp0qVKoBevK/TOioAioAjkjYAS6HlDqBdQBBQBRUARCAsCYSXQ8yVgyAzAcUZWAIYMNmRe3IlXv8cVmfuQbhB54FdIK5V6516YkpGMPCRBCwQvhN1wHkII4/jAohKsAinDz5YtWww54zQUF6ibiRPHb0Nh4MknnxTmKbddfPHFRkY+m7lqcGRAOgc7pXe4R0ZGT0uzV5ZVSWNlk1SNVifNhbzLEPRBOm8JeDlw4IBpBxKMcTEl0P3rSSXQ/cOyFK5kCXTWIc45mXFkJZUhrybKToe80izQUhgtZ57REujIt5OFrlYaCDz66KOyZ8+esYddsHKBlC+rMBnok6omSUOaDPTTddCPmAz0OVWzpbWrxayZFi1alBGABMDaeYnfqeYlZ3a6yr1nBG0sDrIy5fR/offWYQcQaXuCX/heQ6CrKQKKgCKgCEQXASXQo9t32nJFQBFQBBQBFwKQdG7iKAwg4Qjdv3+/aQoETDaOBZwVSI/jwMDCKCEdBowzaQNZq2z0ySIme79QVkr1zr0whSwguAWiM+xOXxyDBFqQfY7h9CBzPqjsZj/HIA5WAjWYB6lBbucMew+CF2677TbfMsHB6oUXXpBNmzaNewwyUW688UYh+z1Tgyw/3n9UThnifFgGRgYMgS5SJpXllSLDIu1t7VLZVy2JkUqZPn26qQObzXyaaVucx8WZQKcP+ckmwCEXDON+jhLoce9hf5/PlrlwE+jOu2jtdH8xj8PV2AvwjVcCPQ69mfkzvPrqq/L666+PnTBzzkxpvKxJjvQeMeuj6bXTJrzYif42QcZ9Vu0sObtmoZSfLDff/GzWZ/YGzEus59nXsj9NpZrhLEMRZIBj5ijqkUEhYGXK2S8VQ90tqOfy47r4HUiiIPHBWTLBj2vrNRQBRUARUAQKi4AS6IXFW++mCCgCioAiECACYSXQcTDs3bvXPDkSwJk4E2yNX2Tb+TfODpytbMLUckMALNnog2Eh6rSVYr1zr56xxC5Z0GGWHWX+QKrbKlnQ1hkzZgRO0OY2msefxRzDXEObcbI/8MAD5rfTcHCRic4x+Rik8sMPP2wk7t0Gsf2lL30pK6Ieovxw30HpG+41BPrA8IBI2aiUS4WMyqgMDPZL36l+SQwnpEISsmDyIpnWMj2fR8j4XGcA1MqVKzM+L+wHWvJcCfT8e4qyAjZDUGug549n3K+QCYHuxkCz0+M+KtI/nyXQp06dGgk1n/RPpEdkggBlkFD5sUYg6lnr5snRvmNyov+4TKmeInUJb3Uh1lJknzdVNsnk6slyUcNFcvzIMRMUumDBgkxuP+ExznkJQt0duMnJlJ6wZShQdAo66DHvh9ILZIWAlSmnLBmB0mpnEDh48KAp20b5skwVHxQ/RUARUAQUgXAioAR6OPtFW6UIKAKKgCKQAwJhJdAh5MjCxZDwSpfNijOeTRd1szBbVwzHg1ruCCClhjQ3jpz58+fnfqEMzizVeude0DiJXTKnwmg4OJBHxRmIcw8nUBTk5p1Y7tu3z2QEQWDjYCdY5L777hvLprfHIvtLdniu2RBk6D/44IMmA8lt55xzjqxduzbtHOc+71j/Eeke7JLOoXaTdV5TUSfV5dVSJmXmPh3d7TJUPiRSITK1YarUVzfIrNq5pzPTAzYwBVssWwWRgJuW1+Utgc5cle6blNeNSuBkJdBLoJN9fMRcCHTn7TPJTtcaxT52WEguZQP8lEAPSYcUqBmsTe+///6xu/Fu3/j1G+X9k1vleP9xE3jYXNUiDYl6KS8rN8cxR5waPiUn+09Kojwh02qmycLGhTKnbLawF2Id6Pc+yDkv2ex0tyocweCWTCdLXdVvCjSIAryNlSlH3S3f4NwAm1mUS7NfYg9DualcFB+K0mi9qSKgCCgCioAnAkqg68BQBBQBRUARiA0C1ODFkR02g9i3DtMlS5ZMWLeSY22WCc9RiBq/YcMrqPZAnrPRx2njR+ZFqnaWcr1zL0zcxG5Q/ZvLda3SAzJ7GEQi8v5B1ArPpX3ZnEOZCBw1lHngB0M6EBKdse+2q6++WqhRnk02EPXVn3766XE1MHGCQpyfe+652TTZHDs4Miif9H4oPUPdMjDSL42JZuPwpW8of2AlQnH4VjdWyqAMSUtVqzRXtsrk6uADMpRAz7pLS+4EJ4HO/EHQm5oikAoBux6kPAjZmfmaZqfni2A0zrcEOt939gZqpYHAyZMn5a677kp62D/90z+Vbd3b5JNTB6Vt4KT0DPVIuZRJdUW1kXWn7vmIDEtNRa1MqZ5sss8vmXqxdHV0CRnDkPDz5s0LFEC+i6yfbP109uhu41tpCfXq6uqs1qOBNl4vnjECVqacLGsCeNXOIEDwC3sYVCP8DlhRnBUBRUARUAQKi4AS6IXFW++mCCgCioAiECACYSXQyfBDgg87++yzBSeBl0F2IYlss2CJ5GbTlQ3BFSC8kb80GbnIuOOwDioSvNTrnXsNEi9iNwyDCecejh+ckxjjAvILojaKduDAASMVSHaa04lFQAcZ4wTmuI2M8c997nNps4DA6sUXX5Q333xz3DXIOlm/fn3OmScnB07IyYE2aR84KbWJWqmtqJOR4WFpO3lSrMO1rrZWmpqbjZx7+2Cb1JbXSX2iQebUnTWWcRVUn+VSgiOotvh5Xc1A9w9NJdD9w7IUruQ3ge7EzNYotqSVV41im50OcQWBlUlZoVLol7A/I994gmyVQA97T/nbPvr8Rz/6UdJFb/uD26Q90SG7u/ZIW3+bUethrzgip4PIE2UJaaxsMCo9U2umyvmTzjP/Zr1LQCX70EKUsnI2muew8xK/vbLTnbXTVRnH33EU1NWsTDlBPTZ4N6h7Rem6jG8IdPZgqK8V+n2LElbaVkVAEVAEooCAEuhR6CVtoyKgCCgCikBGCISVQMe5vn37dvMMELfujCM2WTg0yAowjo8IZ8Fm1FFFOqitrc1I4+M4IpDBT9N656nRJGuK4BAcCGGR9yOoBUIZJx4WB6WHiXCGLCVznAxytxE0QM3yVJmQOH8effTRMRlz5/lkMN188815ZVEe7P1YOgc75NRQt7RUTZLhoWHj5KXNWGNjo8lQsoFE3UNdMjwyJM1VrTKjZpbJsArSlEAPEt14XFsJ9Hj0YyGegrWCLenjVwb6RO3W7PRC9Gph7kEwIvscAuTI9lQrDQSYM77//e8nPez062ZJoikhg6NDRsVnYLhf+ob7pKaiRibXtEptRa3MqJ0uZ9WfZbLP7fqJfRDBxLY0WLEQ5JvJ2hLVJH4g193GXs1mp9NeDSYvVm9NfF8rU07Af1jLdBUDOd5b9pmU8SOwgO+9miKgCCgCikB0EVACPbp9py1XBBQBRUARcCEQVgKdTdS2bdtMa5HwwiFgDecmWecQjBjR92yyopoFG+ZBCSnHRp/sq1zrP3s9n9Y7n7jXcSB0dnbK5MmTZebMmUUfIhCikM30GwapT9ui7pyzBHoqnJmH3njjDZNJ7jbqvd96661G8cJpOFsfeOAB4bfbLrzwQrn22mvzzl78+NSH0jXUKX3Dp6R2pH5MEYD+ILDBLYfdO3xK+oZ6pbV6skytniH1iTPzaRCDCyfvnj17zKWXLVuWNls/iDYEcU3NQPcPVSXQ/cMy7lcqNIHuxNNmp1vSirnNbc7sdIKqov5djNN4sgQ6axaUX9RKB4Ef/OAHSSXKqq+ok776AaPKg5Wxf0ygzFMvNRXVcunUi2Vu/ZxxAFkCnXc7TIQe+3dndrq7HBsqGc7sdN0jh2fsW5ly9h78qJ1GgO8tqiGMbebs2bNnKzSKgCKgCCgCEUZACfQId542XRFQBBQBRSAZAQgxm7UYNmwg0NlMkbHZ0NBgmofzEnLRRt6z8WSTpQ7LYHoPeXU2+mT4L1261JebaL3z9DCCOdhDzs6aNSv9CQEe0d7eboIoeBdxyOFAJMM5DpZpoMIHH3wgjz322FgAgX12sn3IJl+wYIH5Xzh+HnroIZM94TRwQ/Y9l3rnXjh/0vuRdA50SPupNhntMUKkhqRmvHhJeJ4a6pH+kX5prZok06tnSm2iLtDu4/ltxijzRlxkRZVA92/YOElRrYHuH65xvFIxCXQ3npqdHq0Rtm/fPvPdVgI9Wv3mR2v/6Yf/JEODp4M+sbJLEtI4pUlqK2pO7ydH+qRrsFuGR4dNzfOGyka5ZsZVMqm6Nen2ZJ9DokNGz5kznmD3o635XoM50pmd7l6Dcn0b6MNzEAygZSjyRT3389l7WJlydxBu7leN/pmMY4KemLPZ+4YhgDz6qOoTKAKKgCJQPASUQC8e9npnRUARUAQUAZ8RCDOBvmPHDkPu41wnc8RJ5EGYE5lMtqVacAiQBc1GH3KOTNJ8TeudZ4YgCguMd7KcixWB75bYhyzmXeR3XCybQIXDhw+bzHJqprttzZo1xrn6zDPPJGU8cRz/n3rnfjpej/Yelo9PfCidQx2SGK6U2so6aW1pkfKKinFtox87Bk9KorxKGqiBXnuWJMoTgXahEuiBwhuLi4eJFI0FoDF+iLAGW2h2evgHnSXQIWLiEvgXftTD0cJ//OE/yvDg6bI22NSrpkvNlOTyNbzDJ/rb5NTwKSPfPqdujqyZcUXSA1gCHSW0Yq3Hs0WUvb0zO90dKM8emrWpzVCHXFcrHAKoX1mZcvVjnMEdFQUIdMYreyZKb6gpAoqAIqAIRBcBJdCj23fackVAEVAEFAEXAmEm0Mn6RMYLhwWR2idOnDCtZ6OvGWuFGcrI5LPRJ1Nh+fLlOd9U651nBx0Z38jn41jxk3jNtBU4LyCXLVmM45l2EEgRJ8s2UIGAkl//+tdy5MiRjGCght8tt9zia+1V5sR9H+2VtpHjMlDRL1XVVTK9cUbKbKK+4V4hA72pqkUaE00yvSb4kgAohOzatctgtGTJktiU19AM9IyGfUYHKYGeEUx60O9kXa2iRZjXfpqdHr7hunfvXkPGKIEevr4JskU9gz3ybz/8t6RbTL92llS1jCeK+RYd7D0k1RXVJhN93ey10lh5WvUMO3bsmFmPo4RWbEWoXDBzBvpAqlOSyW3Iuzvl3jU7PRekMz/HlpaAIG5ubs78xJgfCYFO0BO/+dZPnTo15k+sj6cIKAKKQLwRUAI93v2rT6cIKAKKQEkhEGYCHYcpEdoQ5layPa5EXlgHHXU/2ehjK1asyEkqX+udZ9+7Bw8eNJKROFYKXXORd46gCfvOTZkyxWQBxLFMQi6BCuDyxBNPCAE+ExnE8fXXX2/mL7/MWYu+p6pLaptrZKhy0GSU1ycapKLsTICDcZqO9BnyvLqixvwd8ry2Ilj5dp61FAh0gkni+E74NVbTXUcJ9HQI6d8tAjjT9+zZY/4zzAS6s8c0Oz0c45dxw/iB+LSloMLRMm1FkAhsO7ZDnvnlU0m3mPm52ZKor/S8bedgp7QPdMqculmyqnWlLG85U7Lq6NGjRhGK/WccJKWdgT4Q6uzRnMa6Bol3Z3a6rnX8Ha1aWsIbT+e3nvJ9Wh/e33GnV1MEFAFFoNAIKIFeaMT1foqAIqAIKAKBIcBG2r15DuxmWV6YDEZL4nEq2ZxEI+tGPksg8zgc5wobfSwXAl3rnecG/qFDh4ziAqULIAwKZSgOkHmOE4P3jKxzv7MjRkZHpGeoW04N95jak2UikiirNAQv5G4h3+9cAxUgR1566SV5/fXXPbuGeerOO+/09Vmc5Q8gb2fMni6dFR0yMNIv3UNdMjo6IonySkmUJWRURs3/p52Q53UV9dJU2SyTqwuTzUGWvA0wWLx4sa9BBIV6F7zu48xAVwI9v55QAj0//Erp7CgS6O7+Ye4gINHKKrsllcn4dGaAkhGqlj8CSqDnj2EUr/D6R2/Kq/e/nNT02TfMlfIqbxWl/uEBOdx3RGbWTpelzUvkgsnnjZ2L4hDrL9bjM2bMiCIcKdvMd5h9tp2bCNLk/zktkUgkzU1xU6IqRodaZQwN7ElGn+8i2GALFiwwZczUFAFFQBFQBKKLgBLo0e07bbkioAgoAoqAC4EwEuhs3sm+hUTEINQgEbV+YeGHLwS4zfyiBno2jhOtd557f+GwQzaSjCmi8IM23jnqPFppcpz3vHNkofhpnYPt0jHYLpDogyODvyPQy0zmNFnUkL+tVVOkLhF8ljTPlWugAtlI999//1hZCS+MrrzySrnsssvyJtHpGzKgGA+YsxZ9/3CfHO0/LEMjQ4Yw7zek+YgQlVBZVmnI84qyhDQlmqW1anLebcl0LBCUtXPnTnP42WefbdocByPAhL5gHuTdpCZqNnNiHDDw8xmszD8qG37PNX62U69VXAScBPpZZ50V+flEs9MLN55QsgJvSkExX6uVBgK/2f2KvPXYG0kPO+dLZ6VcA/UP98vhvqOGQF/WvFTOn7x67NzDhw8L5XsIJo17TWbmWoJ8bKAPwZBuq6mpMe8SP6ztChn0GpfRq/OSd08y3qzqHXsHglbUFAFFQBFQBKKLgBLo0e07bbkioAgoAoqAC4GwEehs3pFVhny1NmnSpEjWnYvDYEPO29YeXbp0qZCJkM603nk6hNL/HZKOHxxU8+fPT39CHke43zmy4CDPM+nrbG7b1n9cuoY6pW+4TyB+R2SY8Bgq3JrLQPTWVNRKVXm1TK6eIg2Jxmwun9OxOEYJHCA4B2ImE6Nf7r33XpOxk87ouxtvvNFk7+Ri9A112nHeYpC2kI1O0pYsfjLQuwY7ZGg0WYqzvqJBGiubpaaiJpfb53xOHAl0gib4NrmzsyB+rTMZuX51Jmc+bJRAzxyrUj4ybgS6uy9tdrrNAtXsdP9GuyWqUNPJ9TvsX2v0SoVC4KXNG+Xt5zaN3a6itkJmfX5uytt3DHRIx2CXkXA/Z9IqWda8ZOxYAi0JnmtpaTFKaKVkzux0SHX3+oe1qFM5w+99Qxyxdqrv6LyU3MO2hBj/lzJYWnYjjm+APpMioAiUEgJKoJdSb+uzKgKKgCIQcwRwTHpFmBfjsdmof/jhh0LWM0YWLG2jBlYc6s4VA9N875mtFLPWO88X8dPnk21MNjiOKWTsgjL6l3rn9p1DLo93DTlZP61rsFPaBo5Lz1CPDIz0GZK8urzGZJ3jTBoaHTTEOr/rKhoM4Uu9bjKogzRLoGea6Y+8/QMPPCA4eZxGFg59dfLkyXHNhZy/6aabsq5l754PmQeRD01F0ILj4OigjBhZ/DIj5+6siR4kju5rQwDt2LHD/O9FixYJGUtRNbcCACQ5TuJUUqeWTGc8+P0eRRXDVO22BLo6kePWs/4+T9wJdCdamp3u79jROcZfPKNytVdff1Vef/VMiZ2qKdUy/aqZns1HEelg72GpraiRydWT5Quz10p95Rm1Akr9dHd3GzlpyvOUqjEPs+6x2enOEmsWE9bCdg3Euk8DCsePFieBTrBylNfHfr8L7EXxA2Go3mn94NKYAAAgAElEQVTQk98I6/UUAUVAESgsAkqgFxZvvZsioAgoAopAgAiEhUB3116GKCIbh8xLzUAPcACkuXQ2maRa79y/fiIrGnKX7NaFCxf6d2HHlXCC4aigjzGIc0havw1n0Se9H8mpoR45NdxtCPLqivGS3hzXO3zKyJBTr7sx0SRTa6b73Zyk61mp/Ewy/Sll8PDDD4/hZS+EQ/XWW281WexvvPGGqY3uztLBibhmzRq5+OKLM3IoOvuGc+kb5sGoGN+V7du3m+ZGmUD3UgDg28T/p1+Y8/hO8eMOROPvNjOL8aU1jcePXiW3ovJGF7edzrqocZBwzwbNbLLTmWc0A/QMuk6iSstEZDPqon/shg0bZOvWrWMPUj63QmZeOFvKy5KDQyHPj/efkL7hfiPf/qn6uXLF9MuSAEB5hm88a7ApU6ZEHxyfnoA1jyXT+c26yGkEEDqz03UNdBqdUv6epRt6jCNUt7AVK1ZocEE6wPTvioAioAiEHAEl0EPeQdo8RUARUAQUgcwRKDaBjoOLbFtkkTGcf0Rks+nGaUFGJ7J5ZKipFR4BJxEGkZuqTq3WO/e3b06cOGHqc5OZAAHpt7W1tZnr8/4hwcg7F1R9UIjzY/1HpHOwwzgvJ5Jmpz3USK8sr5L6RIPMrp1rstSDskyl8rds2SJPPvnkOGIcp/wtt9yS5OQhKOHRRx81GUtu4x264YYbJqz3zJxHxlMh+iYoXDOdN4K6vx/XxTlMX5JxhRFcQrAEz0bQCe+Nza4y2f+Dg8bJTr/bc5ztIHPdZmYxj2pmlogS6H6M1PhfQwmH033MPEPQjiWtrHKMcwRYNRTmmlKfZ5RAj//ckOoJ77nnHqPiNGZnl0vZggqTWU6mOYbqUfdQj4yMjsrUminSWNkg18xYIy1VzUmXhdDjnVMCPfV4snOTDSh0qzRxpnMNxN6mVBV6WD/u27fPgDlv3jyDi9ppBFg/s//BzjnnHA081YGhCCgCikDEEVACPeIdqM1XBBQBRUAROINAMQl0nKI4Jsg+xyDNIaRslDoEH0RiU1OTIfjUCo8ATpFt27aZGyMl7pZT03rnwfQJBDdOBJzhZ599tm83ob94r7g+hhOLdytIB86J/mOGFO8cbDfkOeT4REYWOo7N1qpJMqkKp2aTb8/vvlAmUvlvvvmmPP/88+PaQGDDzTff7OngwYkIiY48vtuYzzhv9uzZSX9yv0v0PdmWQfZNUMCmmzeCuq9f14UAp++sOsOsWbOM85xvFj9uAt19X46xJBdjYaKaxqWcNaoEul8jNt7XUQLdu3+Zh5zzzEQZoKU4zzil/1UqOd5zhPPpCGb78Y9/nJQRPenyKTLQOiTdg90yIqPm8HIpk/pEvTRWNkptokYun3qpTKsdL9FO6R7WBGSfR0kJqJg97pybmKPcayCnQg/7yiiuc3PFl/G5f//+sX21qoacQRJ/EHtUxgcEumKT6yjT8xQBRUARCAcCSqCHox+0FYqAIqAIKAI+IADR4VXHzIdLT3gJW+fK3turvq+VV0YaGSJJrTgIQKAzToiUp1a0Na13Hlx/kIWMAgPBJEuWLPHlRvQXjkAIPQwiF2WHoLNAjvYdkY7Bk9Iz1CXNla3jJDTdDzcwMnD6WEOgT5bmypacn/9g91HZ2/6h9A71S22iWha1niUz6884SCeSymfMb9y4UV577bVx91+1apV8/vOfnxA7nPevvPKK+XEbzqErrrhCLr30UnMNdzARcx59Q5ZzFC3KBDplQwjsov/oG4gXO+9lSqA7+yyTmsalWjd09+7d5tuiNdCj+JYXrs1OAl0z9rxxd2aAQlilyk4vpfrETgK91KT/C/d2hu9OrJ3vvffepIZdfPslcmjwqIyMDsvwyLCh0CvKKqSivEJm186UFS3LpKnKO1gTJRreJyXQc+vrTNZA7HWYmyDT+Ql6X5Lbk/hzFtn5NrgWVaqorvP9QSP5KqjZ4fuh/yHQFZsgUNZrKgKKgCJQOASUQC8c1nonRUARUAQUgYARKAaB3t7ebshB7g2RRCYmMu1uy1ReOWCISv7y1DLGEYkDEmIP03rnwQ4LnAiQ3UTfL126NO+buftr2rRpRo66EDLSyLd3DLRL91CnNFW2GKflRNY/3G9qpbf8LgOdeujZ2jtHtsszB16WD9pOZ3mMyqiUSZn59/LJC2XtvCtk9bSlRuHCSyqfuYms87feemvcrS+66CK55pprMsaOTJPHHnvMZAq6DeJw3bp1plSFlbzESTt9+vSMr58tNoU63tYfnT9/fmDlAfx8Fvqc8XD48GFzWTKi3KRLLgS6u43OmsYEs7izRnEY4kCGtOd3nB2ISqD7OYLjey0l0LPvW81O11rD2Y+aeJyxadMmE/xojTXVHXfcYZSNPu75RE4N95r9Z12iVubUzZbaRO2EDw7ZyfqMNXNra2s8QCriUzgVelgXW6Uf2yT2JZSfsPXTWYsVYq9SKEhsAgH3Q2EsTs+WL4b4h/D9sPdduXJlrNe/+WKl5ysCioAiEAUElECPQi9pGxUBRUARUAQyQqCQBDr3gpyApLAExUSyihNlh2b0cHqQLwjs2LHDZMjSV2QtQ+4SAGEzNAmAaG7OnuT0pXExvQhZsGS9QJ4tW7Ysr6d0Z9RC2tKPhbL2gZPSPtgm/K6pqJXaiomdld1DXTIyOiIQ59NqZkhtRV3GTeW8/9r5pGw48IoMDA9I92Cv9A31jxHoZKHXV9ZJVUWlfH7+VfKZqRcZAt0plc889eyzz8o777wz7r6XX3W5XH7p5Wmz6N0nIkuIpDtBEW7DUUSmBe8RUuFxcdCmUq7IuDMLeCBzGeOAQAYMxy3znVs+0g8C3flYmdQNxZFss0bj5khWAr2AgzzCt9Kasfl1XibzTBxVMDTwIr9xE9Wz77///qS1FopBn/3sZ3N+nAMHDhilNgJPvYK9c76wnmgCGcCWYELIdKTy+X9OYx1myfQ4BBXyjHYvoAR68kvAGpzSWigSQKDHWYlAX39FQBFQBEoBASXQS6GX9RkVAUVAESghBGzmY5CPTM0vNow2CzMTieKg6kAH+ZxxvPYHH3wg9B8EH2OFwAYMMkfrSgbT4xCuZL3gPFi+fHlON8EJhSOCaP5i9tfQyKB80vuR9Az1yODIgCHGy8vKPZ9paGRIuoY6pK6iwdSmnFU7N6vsjF/v2iCP7X1B2vu7pGfwlCTKK0yWUUVZuQyPjsipoV4ZGhmWhso6aa5ulM/NvkxWlc83Y3nx4sXGcff000/L5s2bx7XvwmsukIUr5kt5WYU0JhpNNn2iPJFx30DSvvrqq0bS3e0g5CIESpCNDpERB4sKge4ubYCDnEAGL8cdfcjx6Wqg59p/zLM4kq0z2cuRbMn0OMicWgKdbwvPpaYIeCGgBLq/46JUstN13Pg7bqJwNfYo//Iv/5Kk7HLjjTeaTN9cDRUhvs0oA2mwcK4oZnYeayz8BHYNBO5uc2ans16OWgY3z0YQOu3OZ1xmhmi0jiLBgh/6lb2vEujR6j9trSKgCCgCbgSUQNcxoQgoAoqAIhArBIIm0NksQp5bmbZM5aODqAMdq44r0MPs2rXLZAiwobVjBXnhuXPnqrxaQH3Q3d0tZL3gYFmxYkXWdyHzCgcN2ecY5BT95c6ozfrCOZ5wtO+wdA91S9dghyHP6xMN46TcB0cGpWeo2/wdkh0J92zqnx/qOSZ/+ZvvS0d/l3QN9EhLdaMhz53ONQjJnqFec0xTVYM0JGrlm7O+KFPrJhlH1pNPPilWenzsUctEVl29TGacPcOQ59Xl1VJdUS2JsoRMr5lpsuqzMebCRx55ROhjt0He3nTTTYbAjbp5lX4I2zPZWpTMbxgOcuReUzlkce7ybuHURR0iSMct9yJTyRLqbkeylTl1ZqeHDd907VECPR1C+ncQUCI0uHEQ5+x057ihlAhZjWrxRoCA3yeeeGLsIflOf/vb3zZBkrnavn37zBw0Y8aMgqo35dreOJ3nzE6HWHcHFdqSN7Z+erH2ONlgztr/4MGDZg1JDXS1MwgQ9I3vp6amxhDoQa6xFXdFQBFQBBSB4BFQAj14jPUOioAioAgoAgVEgA2qV0Zkvk3gmmSRI42LsVlEPtrW0U53fb/rQKe7n/7dGwFLoNu/UgeQIAjd2AY3YiDNyHrBINCzwZr32dZs5PzJkycbx1821/D7ychCP9x3UAZGBqRrsEtGZVgqy6sMCY1YI5npw6NDkiirlPrKRqktrzXy7aky1b3a9587npCn9m+Uwz3HpaGqzhDkqQwCvWewV6bVTpKLG1fIdVMvEsg8yhU4raxM5JzrVslZZ881NdSHRofMM2CNiSapqqiWWTWzze9MDPIVAh0HEUQ9QQ5uo58uvfRSufzyyyMdoBJ2Ah0nJn1Bn4A5ASbpShsUkkB3jwvrSGZu8JI5hRxyZqcX833P5F3gGCXQM0WqtI9TIrRw/Z9Jdrolq/gdZsKKoCO7jlICvXBjqJh3gjyHRLc2b948Wb9+fV5N2rt3r1knzJw5M+P9a1431JM9EbBBhTZD3QY+Og+OQikKApspZ8fcuWDBAu1tBwIoplEHHYWlpUuXFnXfqh2jCCgCioAikD8CSqDnj6FeQRFQBBQBRSBECARBoLPRhRyCBMeIJkbuO5ssAD9krEMEcySbYoMYaDyEDAEQKmEYfFfiICLrBcuGQHeTgmGqqQ3xTCb60OigqU/eP9Jnap1jSKFXlVdLVXmVVJfXyNSa6eMy1CdCnev8z+f+Xzl86ph09vfI9PopRrY9lQ2PDMvhUyekqbJOWsobZU37UpMR4rSy8jK5aO2FMm/RWUn/n3tRpx0yvSnRLPWVDTKrdk7aQUG2M3XtrYoDgShkW2zYsMEoPLiNv99www0mKzqKRjACTmfm/XTEdKGfz5YHMWMvkZCzzjpLkAVNZ8Uk0J1tc8qcQqhbdRd7DHM1DkiUQvgd1szLPXv2GKldlXBPN/JK++9KoBen/6Oenc53FSUfDKIqzGR/cXo4Xnft6+uTf/u3fzPrDmvXXXednHvuuXk9qP1OsZ7mm6oWDgQIkHHKvbOWcBry387a6WFZB7GvPnLkiFmXEdijdgYBcAEfgrMg0NUUAUVAEVAEoo2AEujR7j9tvSKgCCgCioALAb8JdK4HUYQzA5uopuxEneHMwl25cqX2WwERwHHKRtbWO+fWkyZNioW0dAFhzPlWvDs47TBqY6PeMJG51R5wFENc4jwKkw2PDkvXYKchoMk4dxrkeUOiSRoSjVlnHSDZ/j+e+ztp62uXkdFRmVLbmvaxj/W2SflouSw7Mk0mdSaTp+UVZXLx5y6STy2Y63kd8O4YbDckf2NlkyHQqytqUt7THdjgDEQh2+Kxxx7zzEbHAXjFFVfIJZdcErlagDt37jTELpnd/z97bwIlWXWda+7MiMh5riFrAKqKmieKqahiRoAQGGQBEpaQJWxrMrL87NfLXl5yPz27/eT13O1uy8+2updkIxshNCKQkLCEmMUMBWKoeS6gpswacqwcIyJ7fSc5WTdvxRw3Iu6N2FsrV6LKO5zzn3PPPXf/e//bL0E3jBuZP9RYxAjsgjzP1LHqFwLdOdHokzs73T0R/ZqVpQR62mVKD0ChRDOJfTEPWM9tbWJ+JyKsnCoYpSasnQQ6Usnp9lG+AFkbkTMCb775pjz11FNT5zPeX/jCFzIKjkt1U1VKyXlIinZiJsE+BPDb9Ym9X6nqa7PnJ9OafRn7T7XTCKBYSPIESoXLli1TaBQBRUARUAQCjoAS6AEfQG2+IqAIKAKKwHQEvCTQ+fBBFhfHGllwSEdDvOYiJ4tMLdJ5WDZZuDq++SFgZaZtjWacUPwbGbFBzYbND5Hin02WMk47jCj8VI5onjWcDsiCY2TSQp5nSgoWv3diSkaQgQ6hjjS6zUDPtS29owPyX5/6n3JipFfQhJ9R35b2UseHemRJ1wyZ09887djqULVc+KHzZXEaacWR2IgMxQalNdIubTXtMrN2dsJ7QtbaMhbJsp0Zw5deekleeOGFM0gJLop06M0332zqcwfF/Eags4YdPHjQOOcwsuIJZMjGiepHAt09H+inrZtOdpYzG49j6a91IvO7lKSSZvYF5WkubTuVQC8t/onunglhVerAHfZRlLPBlED33xzyukX333+/ISatQcCh4pOvUcYKY7/gt6DUfPtWruenK0VhVXpsOYps1PHyxQwFJILTrTJfvtcrp/NRAsP3QNDtkiVLyqlr2hdFQBFQBCoSASXQK3LYtdOKgCKgCJQvAjgn3Zkk2fYWZxpyxNZ54UUGbLZZuNm2WY8/EwEwRz3ASkpD2PFvfNDy3wREqBUeAfC3Trvly5cnJcN5dglYgSjDclV7KHyPCnuHaDwqf/jYX8vJkT4Zio7InIaZKYN2JuIT0vyuyFl904l2yPPzP7RWzlpwltSHUkt6I+XeO35SGkPN0lrTJnPr50/rJGsixDnOMiyTwAZUHx555BGzlroNovOqq66S9evXZ0X6Fhb55FenDinzE6cz87KU5lZFIRho9uzZWQd2BYFAd+KcCcmFE9cS6hBeuQS75Tq2SqDnilxlnacEuv/H22an2wx1P2SnOwl0yJhsgqX8j7i20IkAxNsPfvCDaaBQ+5wa6PkY71AbzIqaTialXvK5n57rPQImYHd0dCqw0KrjOe9EwLEl0wmSKORaQVAtP8wl5pTaaQQIcuV7tr29XevD68RQBBQBRaAMEFACvQwGUbugCCgCioAicBqBfAl0d2YfH59kwOYr35gpiahj6Q0C1B2jbj2OT5wH1KUlChyClr+phLs3OGdyFZ5JCEiMLJpE2REoNJBdZesfE9wwY8aMohJgmfSlWMf8y2/ul5ePvCXdQyeko65V6sNJJNUnRNoPhaXzROO0pjHnL/+tS6VuXkQaQk1Sl0KSnRNxyvWMnzDHQqA766AzJjw3kAkYzxHPUyZOOc4lE/3ll18293AbdThvuukmowjhZ7MEOv3GGVYqwxlHUBC4Qg6DX67tCRqB7sY8HclFkIYzOz2T+ZrPuCqBng96lXOuk0DXWtb+H/dMAneKkZ1uA0JBTAl0/8+bfFr48MMPTymWcR0koD/72c9mtOdKdV/e+bacEt+1BJypBRsBfBa2djp7dLdKD/tEyG1ndrqXgYVknxNYy/XZH6udRoDvJr5t+ZbNN/hFcVUEFAFFQBEoPQJKoJd+DLQFioAioAgoAh4ikA+B7s5Y5qMHIs+Lj02c/cgAY0uXLjX1wtS8R8Bd7xyy1ukoIiKcmm2QTvqx7z3+ia6Ybu4zHgQ7MHaQXGQx4DCsZNt6fLf835v+XY4P98h4PGrqoEeqw2dA0n4kIp3d02vDs17d9OEbpfmcRukZOynUY28MN6WEMxofl/5onzSHW6Ql0iZz6ueZ423Wm1VxINMZsjvbNZGMKrLRbQa7szGM+aWXXmp+8g1UKtScQUEBDEpJoBP4w/rFcwI5zLqG0zJXCzqB7uw3mOCotHLvdr46jyEYzhLqZGhlO4fT4awEejqE9O8goAR6sOdBusAdZ1kJ1hyv3mlOAp1vCK/Xr2CPSvm0nmzeb3/729M6dPXVV8tFF12UdychV20pMepV63do3pD66gLsg9j72H0QeyK3sR45s9PzLXuDUh/fcE1NTSagU20SAcaCYFe+ofhmYr+upggoAoqAIhBsBJRAD/b4aesVAUVAEVAEXAjkSqC7STybsewVwDgutm/fbi5H9ohG/nuF7OnruOud80EPGet0EEDkQeKRRatyc96PQaIrJpv77mAHnHk4GdSpJ4Kk+v/16j2y/cReQ6JH4zFpjNRLQ6ReQlUhU2+9pSskC7rPlBO/8MIL5drrPiAHR96VU9FBGY2NSFukI6XDfTA6IOPxcVP/fFbtbGmOtJr62mRQQLTirOd5odZ2rsba/Oyzz8qmTZsSXoKAJbLRkUn3myF7iiMMByHqFcU0d0kRng+c3/nWuSwnAt09HjiRycqiXAdOZLf6gZU4xZFMdpYX2ekQE6x1zBHePWqKQCIEmJsHDhwwf9IM9GDPERu4YzNAeUe4jb2+Dd7hv3MlvlnHeB9jSqAHe96kav2jjz4q27ZtmzqE9/3nP//5vN/3XNBJoJMRm+8eonxHoTx6xh7Prk38Zg/uNvY/dn3KpewNpZoI7iToee7cueUBnAe94N2AqhrvewKP1d/gAah6CUVAEVAESoyAEuglHgC9vSKgCCgCioC3CJAd4pYwS3UHPnKOHj1qanhh7oxlr1rHfbZu3Wouh9OUD1Y17xBIVO+8s7PzDGcldZwZa4hAjQj3Dv9UV8KJYx2CixcvNoSVO9gB5wvEab7ZEMXpUXHuMjB2Sv7+1Xvk3YEj0j86aOqhQ6xjnQPNsqb7TGfV+eefb3BctWqVnBg/Ln1jvdI33jOZhR5qSui8H4uPCgR6fahRGsKNcnb9Auk52WPWRQyykWfFq3qZZGX88pe/lJ6enoRAEgBAxpWfAincBHpsIi6oBOzte09GY6NSF6qVpe0LZGXHYqmuqvZsgvDsoM6AgxJLFBSU683KmUB3YuJ0IpOZZctE2GMgtNzZ6blgqgR6LqhV3jlKoJfvmKfLTmd/41xrstnvQIChQIJRCket/BBADvs73/nOtICvDRs2yOWXX+5JZ5mf+/btM9datGiR2dupVQYC+CAg0NkDsZbw4w4sdK5Pmapn8J3Q399vgtL55labRABsCZQDcwILNDtfZ4YioAgoAsFHQAn04I+h9kARUAQUAUXAgUA2BDofNmR08CGJFZrEg0Dno4rIf81Q827aJqt3nugORMsfO3bM4K81ybwbg1RXcgaP4LRDQtBG5nPezJkzjeMl18ys4vSiNHcZGh+R7+94RF48/KZE41EZi41LY39YFr/bJlVSNa1R11133RTJvXLlSolXxeTQMATviCHIQ1VhqQ/VS6SqxmDN9UbjI+anprrWyLy3hTtk+MTIFLmNEw3y3CsZWttg1t4XX3wxaW10AlxuuOEGo9bhB0OemyCdzjmd8sapXfLkuy8ZZQCI9IkJMvSrJVRVLbMbZsgHF1wm15x1Sd5EOu8ygg3s+4nMdxxxXj0nlUKgO+dPJhKnBNFZqXeCRjLF2xLojFGll6DwwzPr1zYoge7XkfG2Xc6yEqzhybLTrZxyuux0S6CzHpGBrlZ+CPzkJz+R/fv3T3WMfdfnPvc5zwKutXxE+c2ZXHvE/g9VC5uhnqjsDUGsdi+UbH1C1Q2ln7a2NpNprTaJAOs/zzL7eMhzzc7XmaEIKAKKQPARUAI9+GOoPVAEFAFFQBFwIJApgU4UNuS5zUbLtbZvNuAj4U7mLYRUPlLI2dyznI9NV+88Ud+p18YPTgHIXLXiIGCDR3jOyLKxsuBkS5O5oJYaATLQnz34mux+Z4+MvnZcZDIRfcrI2CZzm0xpbMWKFYb0Hoqekq6RozIeH5Oh2JDEJqyEI+T7hFRJtdSF6qQuVC8NVY1yqmtYhocm6ybiEMPx44W8dbLeEdBCNrrNdncfR6bd9ddfX/L1EnJ0YGhQHh/cJNsH9stwdEROjQ+Z+vTWaqojRma/PlwnG+auk8+u+Zgh1XMxyHqCTKzkJs43JO69tEok0N348T62DmT2BG71mmzqGSuB7uXsLN9rOQl0FFmyyUIuX1TKv2es5U45ZdZfp6XLTmd9Qo1ECfTynCsEy/34xz+e1rmNGzfKZZdd5lmHde3xDMqyu5A7O929PrEXsuoZ/LbqBaxJrE3t7e2m1rfaJALgB4HOnhL5dg0u0JmhCCgCikDwEVACPfhjqD1QBBQBRUARcCDAx4pbotUJEKQrNbCR8sZwWkHiFSNjbMeOHaZtfEwpaZjftM2k3nmiO0DeQtaRWYjzWq04CFgC3d7Na1nw4vSitHeh9MD9999vskacdvHFFwvZ5zjAdu3aZf60fPnyKQfXSGxYjo92mxrn0YmoyTzHkBqPVEekuiok9RP10nOoT6Ljk3+bM2eOIWwzzb7NBxkcTdRFf+655xKu3cyVK664QuhnqcgmyNEH3n1Mto8ckL5x6sqPSW2oRhrCdaYmfXQiZiT2x2JjUheulfbaVrnunI3yuys+nDU0ztrzOC15XxTi/aQE+vShYW9AlijOYH4IYnAbWVg2I8tdL1QJ9KynekWeoCRWRQ77tE47s9NZaxJlf7rXGsh3yCreCX5RZtGR9AYB3sXf/e53jTqWNUjKz3zmM57WKef9RmAexhwqZHCkN8joVUqBgF2fbMBPIvUMq9Rj1TX4XvA6yLMUfffqnjzT7AnBkqQJDS7wClm9jiKgCCgCpUNACfTSYa93VgQUAUVAESgAAqkIdHc9WRxUfNjwIVgMg9zCUTZ//nwTra2WGwKZ1jtPdHVISIInGHt1QuaGf7Zn8dyhvmDr7RVKFjzbdgXpeEhVamNSa9Bpq1evlltuucUQ3RDoO3fuNH8mc9u9rg1Hh2Qg2m+I9AmZMMQvsu3xoQk5fOiwyZjAoUpAUSkUMqiJ/uijj045eN3jg9Q/su6s2cW257a+LN/c92M5FR+W0fi4tNe1Sl3ozPfGcHRUekb7TCZ6a02z/O3l/1XmNmaelWPXJ/pH4MCCBQvMWlUIY7z54Z1EYEIxgiUK0Y9CXZNgN5zDyJPyO1HGqCXTWdOod8n+QyXcCzUi5XFdJ4mlGejlMab59iKT7HTe5wTPaQZ6vmj77/zXXntNnn322WkNQ3nnvPPO87Sx9tuJi1IGQN/5nsJbthezeyEbXOjeC9Fx1idUq9gLFcun4mfA2QtCoGOo3VGCSU0RUAQUAUUg2AgogR7s8dPWKwKKgCKgCLgQSEag47REst1mlRVDntg9OLaObiHkeCtlImRT7zwRJqgPULON7EGtI1n4WYNjmIwX+9xBOEEKauZL5thDcJKdhNy503DKfOxjH5vKysbJhcqFdU+W2vgAACAASURBVI4yx1MZAQ0oMtjrFpqwzaTHtGnz5s3y9NNPn5Fpb89fs2aNfOADHzCZwMWyf3j+Hnnt5DbpiQ5IW22TNEYakt56YOyUDIyfkjkNM+WGhVfInctvTttM+k1gD+sTVowgEyXQ0w7L1AGZZIzagwn0IEBOyYnM8a2kI5VAr6TRzr6vmaw1qZQwsr+jnlFKBHp7e+W+++6bpr5DJu+nP/1pz/fJBGDwHWz3iPqOKuXIB/PemSj18C3B/px9LD+V+L3nJNAJ1lfVwWDOd221IqAIKAJOBJRA1/mgCCgCioAiUFYIJCLQnZK4OAwgsEvh4N63b5/JZEMeGSe7WuYI5FLvPNHVcVYdPHjQZHcic61WOASY69R1dJZUUPWF7PCG5HzooYeE4BunsYbdeeed0zI9WPvI9Mdw2KTKXOa6BJLwPGDFIGyz6TmO3l//+tfy5ptvJjyN4ADqvp9//vkFd87F4jH5/KP/XXrG+mRkYkzmNsxKSY7GJ+LSNXRCmmsapbNhhvzTNf8t5fHuchQ42nhOCu10VAI9mxk5/VhbL5SMLNY5q65hjwqHw1NS75XqQM4d3fI+Uwn08h5fr3tn1xqCRxNJKaMeYskqfpeqzInX/a6E6/HeePDBB80+2Wmf+MQnZN68eZ5DwLuK7x9VMfAc2oq9IHW+WaP43uA3+1mnMdcomWbXKLLTKyFww1mqhWD9Uqh6Veyk1I4rAoqAIlAgBJRALxCwellFQBFQBBSB0iAAKcBHHIZzgppy3d3d5v/j1Eb+F4d2KYwPTRzus2fPNj9qmSGQa73zRFfHCUkGBnNhxYoVmTVAj8oaATJpyajlGcShCxnIc4lTUKXsMofzySefNPXBnUbwD9lJ7nWMtW/btm3mUKSBcVolMgIaUAWwtdS5HoR8oQnbzHt9+khqvj722GNnZN/bI6griNQpqgaFst7RfvmTJ/5Wesb6DUYz69OX3zg+3COh6pC017bI/3fdX5t66YkMJxtjYYkR3gv0qRgORiXQvZkx4MizRECKm0jnDowlz6qVeyd4S61yEdA6xJU79vn0nPItR48eNe8ggqwyrZ1ejHdJPv2q5HPfeustYY/nNIICr7322oLAwpxhT8Uc0hJWBYG44i5KYgDfFCQGNDc3m5JAVurdfmM4QbHBhTY7vVwDfpzveXwNxVTMqrhJqB1WBBQBRaBICCiBXiSg9TaKgCKgCCgCxUHAEuiQrkTak32O+SHDEqKE9pB9zsemWnoE8ql3nujq4M844EBatWpV+gboEVkh4JaiJiuBoBUybBhLLV+QOZy/+c1vDHnsNPC86667EgYhZEKg49BiLGyQEeNBQIOfnez0Cyyee+65hBl44IOaBLLulObw2npG+uVPn8ydQP9/r/0rqQufKaePk5Gx4F0F/tSeL6bMoxLo3s4U60jm/c542ux0913IwLJkOkEufn72vEVIrwYCSqDrPMgFAUugE4BD+RYsnRKGzU632Z/lSlblgmepz6F8DqV5nBm7EJC/93u/V7Aa0oODgybQi3lAkKWaIpAvAtT5Zg4THN3U1DTtcuwxUT2whLpTjcwe6MxOR1mqXPZDznIJ+BqSBTTni7+erwgoAoqAIlA8BJRALx7WeidFQBFQBBSBIiDAB5slSS1JRD05COtSf5iR+UwGNIRVIeT5igBvUW+Rb73zRI3FgXTgwAEzF1avXl3U/pT7zXCOMMdxlmBI1kEKEqyAkwWHgpYvSDwLCDyITSB9OCHVVSE5sP+APPDAA9MyWsERaU8CEhIZ19i6dav507nnnntGhjrPE0FFHMe1zj77bJMxEhTj2X3qqaemsuzd7cYpvGHDBtm4caOnDujxeFS+8OhXpHdsQEYnxk1t81TvEivh3lTTILPrZ8i/fOArZxzf09MzlbFcKmUU3pU4PnlPMh/8qEAQlLlJO52ZWFauE3ydDmS3vCmYO7PTmQtq5Y2AEujlPb6F6h3v766uLlN+yBLozntp7fRCIe/9dXnnfu9735MTJ05Mu/htt92WcGy9agHfxihDKYHuFaJ6HcpLsZek7FCqLGvWJ2fAD9+DbsWecipHYcslMEPWrFkjBAeoKQKKgCKgCAQbASXQgz1+2npFQBFQBBQBFwJIR0OQWpKIj7piZvWlGhDIK2oOkyUJsaiWGAGv6p0nujoftRAdGAR6qYMqymUOkF1OZr8NWnFLUYM52Hd2dhqJarVJBEZjozIY7Zeh2KkpZ1LvyT55/IEnZXxsshSFtZtvvlnWrl2bErotW7aYv+Ngt84sdykLMmCRPA+qQ4d59sQTT5jyHImMoIBrrrnGKEx49Xz/Py/8m7x+Ypv0RAeMLHtDJLE8Pu0ZHB+S/rFB6WyYKdeds1E+vfIjU81kLCgpYttuFRoYk2KbEujeIp6IQHfegbGHPLXZWKyZbuOZtNnpzA2v5q+3PdWr5YOAVdXhGsgoa+BKPmhWzrmWQOddsXDhwrQd1+z0tBCV7IDHH39cNm/ePO3+F1xwgVHRycUI2jt46rDsHdgvg9HJANbGcIMsaT5XzmqcJ9VV1ebfrIoBgVoEWaopAvkisGvXLnMJfBrZlMezpW+sUg/S725jD2QDDIO2H7JqD/TpvPPOM4FPaoqAIqAIKALBRkAJ9GCPn7ZeEVAEFAFFwIUABDXZrjiZyNTko8svhnQeBD+EPtmfamci4GW980T4EvXO/MAg2NR5nf8sxClHcAgOEfDEkWIzMO3V9+/fb4gjiHV+Kt0g006OHZdT0UGJT8RkND5qMtDHR8fl2QdflFN9k05Qa5dddplcddVVaWEjA51r42BHTpExoeYlzncMco51MehSrvSL+qHPPvvsVC13NzgET+GQ9iJY6bntL8s39/xYBuNDMhaPyoy6NqkJnekQG42NycmRPqkP10pbbYv8zaX/Rc5qnizXQZt5TnheMIh+2laqsXAT6KPxMXm1e7O8cWy7DIwPGqf7zLp22di5TtbMWCah953waSdhhR7AGgdphcqGe/1LBAnvOkum85vxcBrzwpmdXqp5UqHDWbBuK4FeMGjL+sJ82xB8lSmB7gQjk+x0JIbtelNOUsp+mxQQ5xDoTiOo9M4775RcFEgODL4rrxx7XQbGB2QsNi7jE5OBl5GqiNmjNIWbZMOsi2RR8wKzD0ylYuA3rLQ9/kaAdWX37t2mkfn6W2zAD4HW/Lj3Q0FT67HBKgRBrlu3rmT7fH/PIG2dIqAIKALBQkAJ9GCNl7ZWEVAEFAFFIA0CNtsS8shvDuejR48Kde8gTsgAVZuOgNf1zhPhyz2QnMNWrlzpuzkSpDmRKLM5mRMFVQgi8nEUkoVeyQZux8eOyVB0UIZiELKjUiVVEpoIyau/fE263p2eWb1o2UL52K13SKg6lBa2bdu2GccT6wvBQ9TYJmgEo3QENc/LKauVvj3//POmRrpbDtKCRX30q6++OmHd+LSAvn8AON67+6eye/Sg9EcHjaO6PlQr9eE6My6xeEyGoiMyEhuV2lCNdNS1yuXzLpTPrvmYuQLOQTLnbdYxdbJ5Dko5FpZAJ/Pn8UMvyuMHXxRIdIIAovGYmZORUFgi1RHpqG2RO5feLKs7lmYKWcUdly2B7gSIucvcsIQ6mepus7VCCYKBRCvl3Km4wfWww04CfenSpTqOHmJbzpeyBDrkdr7fD5qdXpqZQjAjpXmc5CCZqb/7u7+b0/5ke+8ueaH7ZRmOjkg/BHp8ehZvTXWNtESazT7l0lnrZX7V3JyDMEqDmN7VzwgQBGgD0r1UtbIBP7b8TaL9EHsgq9bD3shv+yEbrIIfCuUwv/mj/DyvtG2KgCKgCPgVASXQ/Toy2i5FQBFQBBSBnBDgwyuRFFhOF/P4JLJH+OGjL1ENQ49vF6jLFaLeeSIAmBtWcg5iTWXVcpsmOE5wBtpsWuY0qgrJMmggIDl2xowZhsTNxXi2/eYkyaUfA+P972efn5LoxLjUV9dLTXWtvP3KFtm6adu0S7bOapUbPnaddNTPlPaajrS32759u6lrDTlLfU3q0mPz5s3LyUGb9oY+OYDAJGTdCdRIZGSvnH/++XLFFVdkJTNpr/Xee+/J8Z4T8rPe5+SdkSNyanxYhqLDhmi2Fq4OS2OkXhrC9bJu1nL5o3WflEh12AQwQJ77bSxw4tOm7+x4WF7qelNORYfNT2wiLlXvd2qCTLbqsDRHGqQ+VCe/t+JWWT87dRkBn0yJojfDEug8e/mWjUlHcLHOWucxWaOqpFL04c75hkqg5wxdRZ/Y09NjSn94QaA7geQ94AzeSfT9ZIN3WGs0Oz23acj+97vf/e4Zijk33nijUcPK1g4NHZFfHnzCZJ73jvVJbahWmsON5jfBbwTzDUYHze+2mlZpjjTLpc0XS2Qg5Pkcyrbtenx5IMD+0ZZEQ/WqUKWIuI8l0xOp9fBd6FTr8cN3vQ14Yq8Gga57tPKY89oLRUARqGwElECv7PHX3isCioAiUJYIJIpW9kNHIXnIQudDT+vPTY5IIeudJxpziImdO3eaPy1btqxgH/x+mG+FagMOVghB+5xBiiNbnIrchoAkSIIsaMjcTG0kNiwD0X7hN3UeTVZsdY00hZulMdw0Vdsx0+uV+jjm+5GRQyb7/FT0lJHXpD/v7Tkoz/3yhWnNq62vlcs+ukHaW9ukPtQo8+vPTtvfHTt2GFKUseBeZD0Q2IAiR7kb/UVd4qmnnhLIhkSGg2/jxo2yfv36rIJnkF7HIdbc2iJvjO6SZw6+KgNjpyQaj0pcJsy4hKtC0lrbLNedc6ncuPBKI3mO05y578exgDh57J3n5cd7fiW9YwMyHBuVhlCdqe8OaW6C0eLjpqb7eHxcWmuapSnSIH9+/mdkQXPmz3C5zzvbPy8JdCdmzlqhOI95hzmNZ92dnV4pmAexnwTUsCZgmoEexBEsTZsp/8Q3BMoyKP0UytIF77CncAbvaGZl+pFgr/yjH/3IBEA47aKLLjLqOLnYI+/9St4ZfE+6R45Jc6RJWiMtZ+zBeYf3jQ8Ykn1W3UyZFZohF8iags+hXPqj5wQPAdYK9j0YPo1cShBk22ur1mMJdavq5LwOBLozO70U5LVdr/nmWL16tRLo2Q60Hq8IKAKKgA8RUALdh4OiTVIEFAFFQBHIDwG/EuhkhB45csQ4L5YsWZJfJ8vg7ELXO08EEfckSxdjDBgLtcwRQIYd5z84QtxAhre3t6e9gCUgOZba1OkMYvL4aLeMxkdMbXCkKSdkkkAPV0WMrHR1VUhm1syS+nBDusv55u8EAnSPHDVBAfSFQIC+k33yqx89IdHxyWxxrKq6Sq699Rpp7myUcRmXlnCrzKidZY5PZjiWmNtWHpRMMRzt/K4kI4DgjTfekBdeeGFKMt3df8poXHnllbJmzZqMHFuoLUDKt7W1mbrl4/GovN61Vfb1vWcyvJBIXdq2QC6YtdJIuhuZ/uPHTb1RDCealxKXXoznaHRMvvzCP0jX0HHpGx+UtppJqVe30Zf+sUEZjo2YmugbOs+Tz66clKZXO41AoQh0N8YEMFmpd8hYd+kCPziPdV4kR0AJdJ0duSBgCRmCZQiKK4bZ4B1LVqXKTtfSEolHhP3IQw89JOyBnUbG7q233prR/sN95ZOjPfLQOz+XEyMnTc3zzrrZSQNYTZDyyDEJV4WlpbpZ1ofWyZymzqLNoWLMU71HaRDA10IwNbZ48eKSyJTzLerMTuf/O80ZYEjyQrHK37D/Z83m+wsCvRzU00ozy/SuioAioAj4BwEl0P0zFtoSRUARUAQUAY8QwMmTrB6uR7fI6TIQMBAxfMCR/VzJVox654nwxSFInWj7wY8zUi09AjxPOAMIAMHINICcxSGRibkJyFTnQJ53jR6RsdioDMXI8h2Xqqpqk+VLO+ITMfPfZGWTvT2rdrY0hBszaUbJj+kb75WesRPSN9Zr2lwdrZZHf/i4DPQOTGvbRVddIMvXLTN9H4gOSHOkRVoj7TKjdmbCPjCvcdBaSX2cNmSEVHJ2GGvMyy+/LJs2bTIBH4mMWuQQ6azHqRxc2cxfxuLw4cMmYx1LV96gVJPy5SNvybe2PiDdwyclXB2S9trWpE3huesePmEI9vbaFvnbS/7UZNurnUagWAS6E3PmmtN5bMsE2GP8KG1a6XNGCfRKnwG59Z8AXH6KSaC7W6rZ6dmNHevzI488YpRxnEYg6Sc/+cmcgxvfOPG2bDr+Gzk0dFTaalpMBnoqGxw/JT1jvTIrPEMWVp0j57etMYGAaopAPgg4y5EQkF6KTG9n+20JP2eAobt/tvwN3678FOobCbUJfD6s1ytXrlQCPZ+JpucqAoqAIuATBJRA98lAaDMUAUVAEVAEvEPArwQ6EtZk7/IBt2LFCu86HLArOeud4+DHkZNvzdhMIeADe+vWreZw6tBDbqmlRgAnIMS5lcXGIQB5nk2dOQhFCHjGOV32VNfIERmKnpLBaL9pWF2oQSJVkSkHBAT7SJz601FDQteG6mRe3XwJV0d8P5S9Yz3SC4E+3ieNoSbZ9MTr8s6ud6e1e9GKhbLx+ktMf8m+7x/vk6ZIs7RG2mRm7ewz+sh6R415p5Rhudc8z2agWW+ee+452bJlS9LTKEFw6RWXyrG6QUMUUwe8rbZZLupcLR11bYYQz2T+QmAyFpCaGI5yxsKP2Sf/tuVH8vzh1+X4cK901LWY2qmpbGBsUIaiI9LZMFPuWv4R2di5LpthKPtjDxw4IDyLXtRAzwWsTJzHBNZYaVPUV/w4L3Ppe5DOcRLolR5IGaRxK3VbLYEO6eMH8jNdaQnwcpeWqKT1hvX4iSeekM2bN0+bOozfJz7xCaNmk6u92P2qvHVysxwd7pbZdbOkNlST8lJjsTGThd4Rapezq+bJho6LMlKCyrV9el5lIOD3YLB0AYbONYrnkv2RV2tUd3e3CaLluvh7vLpuZcws7aUioAgoAv5EQAl0f46LtkoRUAQUAUUgDwT8SqCTHQq5QsQzEcmVZiaLsbt7qg4gBCyyxsWWUYdApy1IKFZCbeh85hkZRwR9WEIQpx+EYLaZBhDwOIBbWlpS1u8cjY3K0ZFDcio6KNGJqDSHWxLW/Wb8yE5HvhJimezs9pqOfLpalHPJQO8dOykQ6V07j8lvnnlz2n3bZ7XLBz927VQtQWpPD76fgd4WaZcOVwY648KaYuues7bw30qgnzmcR48elWeeeUYgOpPZqcZxOdJ5SviNygF1zC/sXCWXtZwnDSORlAEgVs7SytxCys+YMcO3jrN/fONe+U33Vjk50i+dDR2mJEIqG46OmFrpc+pnyEcXf0iuP+vSojwzQblJqQl0N05W2pSyG6wTbhUG1nBLpvO7UJlYQRm/YrVTCfRiIV1e97GSwH4h0N3opstOJ3CYtrPWFDLz0w+jzv6UvQalZJyG+tjv/M7vyOzZZwZCZtPul4+9JmShHx3ukll1M6QulLoUFfvq7pHjhkBfUHWWXDLjQrNHVFME8kGATG/UmSCHly5dms+lCn4uz6RzjUpU/oY9kF2f8t0T8b2Bzwcfw/LlywveP72BIqAIKAKKQOERUAK98BjrHRQBRUARUASKjAAfSbYOcJFvnfJ2OLJxsvOxSU2sSrJE9c7JosGpVmyjTjTtIYsaQlctMQI4GKhvZ2WB8yEEcSbgAKb2NEETyezE6DGTcc1PfaghZVZsfCIu/VGOqzdy7vPrz05ItvtpfEdjI0KG/eGuQ/Lsgy9KPBafal6kJiI3feIGaWo9LcdJIEFMYlM10BvDp/+GIgCZ0TiGcPwwnwlUIBN97ty5hrxVm44AWCG1jXObYJ6k87DhlOybcVyijVVSH6mT8ERIPjLzKtkwb13CABDWdgIZeO9ATLK2+X1t+fqb98srXW/LyZE+mVXfLuHq1GvxUHRY+sYGZW7DTPn4kt+Sq+et1+nlQMAS6JAj+WQXFgJU5j3rgpU2JdjDbQSyWULdy0ysQvQnyNckmMHWQ9YM9CCPZHHbbgl0ntH58+cX9+ZZ3q2Ss9NZa5966il56623pqHGHu32229Pq8CUCdTbe3fJ810vyZHhLqkPUVYldTZ771ifnIoOyYxQhyyrPlfWdqwye0Q1RSAfBNj38g3CnhcJ9yCZc43inWwDX519YE9kg36yVezhW2xgYMB8B/g9uCDRuFHq7uMf/7jMmjXLrGdqioAioAgoAiJKoOssUAQUAUVAESg7BPxKoPORtm/fPoM3BHqlSHqVqt55som9Y8cOQwojJV4s6figPWRIz5FZgDMQ5whYQX7napCV/OD8RTo/mR0efs9kXA/HhqU13Jb2GYFghkinRvicuvlpZahzbb+X573bd0B+/oNHZKBncNplr7zpcjl7yem6lNQ/H4wNmEACfubXn2PwYEy6urpMQAIG2UVQAtlNe/fuFQIfCHagvrdaYgTA8M0tb8l/PvlLCY9UJYWpu3FQ9nccl6qmsNRIRP5wyR3ygdWXTTseaXeciBiqGgQyIF3rd3tw96/kFwd+LV3DJ6Qp0iBNkdTlLE6O9MqETMiMunb50/M+Lcvbkj/Hfu97IdrnZwLd3V/ef5ZMZ1/iDji0mViWUM9WcaQQ+JbLNZVAL5eRLG4/bE3dIBDobmQgp5j3rDn85v3rtGLVJS70iNGvJ598Ut5+++1pt2Lfdsstt3hGpI3GxuT7+34sJ0ZPysD4oMyt75RQdWIFGcoAHR3qlsZIozRO1MuVNRtkZutMs0dUUwTyQQCCGKKY5/fcc8/N51IlPzedgoZV7LGEerrgf76fWe8Iply8eHHJ+5dNA2g3wT7saQnWUgI9G/T0WEVAEShnBJRAL+fR1b4pAoqAIlChCPiVQIdI3rNnjxkVJNwrQTK1lPXOk03/nTt3Gik3PgypUax2GoFE5CyEICRtPobzF9IX50MqR8vBoXeMfPtYfFRaIulrRA7HhgSZ85ZIq3TWzZW6kP+Jy5/+/KeyY+uOaXAuXbtE1l9zkfm3iYm4jMbHTJ33cFVYyDpvq2k3MvUoJ5C9iOMKI6iBbGe7lhCgg4OaOsxkDqglR+CHO38hP9vzlES6YrLwZIfURSNJD+5uHJDumUMye+Zs+YfrvzwVyICyAqUJMEhznhVI9CDYkcFj8lcv/5P0jPbLWHxcZta1J1VwGIuNy4nRXmmraZZzmubKX6//ku/VHoo9BkEi0J3YsOYTdGMJ9USZWNZpDHnH/K6U4L9CzCEl0AuBavlf09bURRI4yPLb5ZqdTr+oeb5ly5Zpk5G18qabbjJ1kL2057pekm29O6VruNu8i2fUdkjEpSITjUfl+OhJE2TaWT9L5lXNkeWy2AQOs0dUUwTyQYDve77r2BOkCozO5x6lONe5J+J9nUixh29iuy9i7+/eE/Gdxrn4GIIWXPAXf/EX8vDDDxvolUAvxQzUeyoCioBfEVAC3a8jo+1SBBQBRUARyBkBvxLofITt3r3b9AtnSroI5pwB8MGJfql3nggKxoCx0DrR09Fxy+y7ydl8phXZ0pCNOBpSReMfej8DfSTDDPSh6Ckhw2YyA32e1KapBZlPH7w4F+fqI488Mu1SLTOb5dLbLpGacI35d/pDflZNdY00vJ99Pqu20wR9IKlvnTlkmOMEdTpukCeHCENGOt86m17016/XGI9H5U+e+lvpGjohA2OnZFZNm8zsbZRZ3fUSiVYnbXZf04h89EO3ygXnrp0WyIBMI4EMQcrUxeH/v978trx9fKecGOmVcHVIWmtazG9rrOOQ672j/ebfZ9S1yUfPvUGu0/rnZ8yRoBLo7o6ky8TCWW4z01nPgzTn/bAeWQI9CHVj/YCXtmESAUugsy8rJ/ltAnZsZnqq7HRbm9iP6w1r5i9+8QujAOQ0nvGbb75ZClGqYTg6LD999xeCPPuxkRMSm4gaOfe6EMGuVTISGzVKTqGqkMyqm2ne7RtrLpLo0LjJitX9oa4s+SKAUhnrEupXCxcuzPdyvj3fKvZYFQ2nYs+3v/1teemll0xSxMaNG+Xqq682im0Q6AQmUkorSNg89NBD8pd/+ZdTQcJKoPt2WmrDFAFFoAQIKIFeAtD1loqAIqAIKAKFRYCPHchAvxlOFrKfMRwqfHSWo/mp3nkifFEB0DrR05GBlIWctdmHicjZfOYqWbpI/VFHLlWtvGOj3TIw3md+GsJNhkROZpB7fdFeqauuk/pwo5xVf46vs2LJ1vj3f//3adkM4UhYPvSJD0pdS41EJybXrFBVtdRU10q1VJvs846amSaTgRrbPFs4ZXFqJKqzDIlHXUKyzzXDKPmM/U3XVvna6/dOEsdVIWmtnSxPUBUXmXG8TmYeq5dwLDmRjvwpgSBkl4A1zuigZeXy/JCF/vev/5vJQucnLnGpDdVIpDpi5NpHo2MyPhGVmuqIdNS2yJLWBfIn5336jEy3fNaGcjnXBrf4sQZ6rhjjKLZOY0gu9lZOY867s9NzvVelnKcEeqWMtLf9JNOTPUS5EehOlIKYnQ5JRramLeNi+wPRD3leyPrHfWP98uihJ4XfQ9EhGYyeMopMGO/wpnCjNIQbpCXSLDfOv05OHR80wQp23+LtDNWrVRoCPT09groY33WoL1WCsW/Gf2D3RV/96lfljTfemNZ1gvPXrVsn5513nlx55ZUFXQO8xBwFs49+9KOG+P+DP/gD872qBLqXCOu1FAFFIOgIKIEe9BHU9isCioAioAicgYBfCXTIr+3bt5v2QiLy0Vlu5rd654nwVZnr6aggB/7ee++ZOrgQImTSel0b3taJRvYulUORzPOukSOmDrqpbR5uSUpMIt8+GkPqvdX8zKj1r2Q5Tpfvf//7hgR32i0fvlnOWjrfyNaTeY4hx9kQbpSmcIsJIAA7gg+4BqoVOKogrRIZJB7jSQCE1rhMvrr+6sDzct+2n0r30ElpqWmU+vD0tbg6VmWIdH5SEelkmlxzzTXGyRQ0H7GWFgAAIABJREFUYz7xTtp38l355vYfSe/YgAzHRmQoOiKxeEyoDB8JRaQhXC+1oYgsbV0of7j649Lgwipo/S5Ue8uRQHdiZdQI3s8WhYTByeo2ggKd2elBCyop1NxwXlcJ9GKgXH73sAQ6aieV8m53rzfJaqeXKjudvRYZm7aMi511lNSBPE8VLOrVDGUPvLV3h+zo3SVDsffXZCSMqsRkpK9oXSar21dIXahObF3mjo4Os0dUUwTyQYB5zw9KNOyFK9HYC1G6gSz01157TcjKdxp7oksuucQQ6fwg5+7HfRFr7R133CE7duyQz372syaT/q677lICvRIntfZZEVAEkiKgBLpODkVAEVAEFIGyQ8CvBDrOn61btxq8yV7ko7OczI/1zhPhqzLXk6gwH5FWxzGLIc0LOVuIeUmmAs47nAnp5CyPDB800pOD0X6plpDUhxtMLXBrEOuj8REZjY1IXbjBZKDPrT8rZbZ6qZ+zV199VZ566qlpzVizZo3ccsstU/9mncPWucL/d9bYtlkeqZQrlEDPbKR/uf9ZuX/7z94n0JukPozs6ZlmiPQTdTLjWHoi/dJLLzV1IP3oHEvUNwJmbMbf4PiQvNj9hrzY9ab0jQ9Met/fN2qeXz1vvVwye62EHPLumSFdOUdZAh1FAjL8yt0IvnBmp7tVf8jAJNCHms38LueSNdmMNQ533oUq4Z4Nanose4H+/n6pJALdOep+y04nqPFnP/uZyeh2GkGiH/nIR0wgajGNAMyjQ10yGB0yt20M18vchjlGwt2arcuMrDQ/aopAPgjw/UiAL+/3Ys/3fNpdqHNZo7Zt2ybPPvusvPzyy0Zx0L0vItj2iiuukKuuuspIvrM/8oP9zd/8jXzve9+TCy64QO6//355/fXXlUD3w8BoGxQBRcBXCCiB7qvh0MYoAoqAIqAIeIGAXwl0+kYNZAyihayJcjA/1ztPhK+SjGKIM5z4BD1gOEAgzwtFcnAfsty5/ooVK1JO+7H4mHSNHDa1l4fez0QPVYWNIxCJ6ejEpIxwfaheaqvrpL1mhslA96shcXjvvfdOc6TgBP/MZz6TVIXCXQYh0xrbZLjjZMc5Wk51Ur0e25ePvCVff+N+OT7cIzWhiLTUpHZikW3bdqJWFvZ1SCR22iHtbhfy3RDpy5cv921taNZrflgDmGeUFrEWjUXl3VNHZGD8lCHLZzV0yNnNc72GvyyvV2kEunMQmU+UAYFM4gclGrcRAGSz0yGZghJo4vVkVQLda0Qr43qWQEcdSMuzyBlqGMXMTocke/zxx88gx1jfbr/9dlPWxY/GHpy9DNnnZKGrKQL5IMC3DcHRkMDIlqtNIsBahNId5bRYtzdv3izPPfec+eZ2Gt/DF1544VR2Ot/GpdgXsZb98R//sSkLRjkKFE5eeeUVJdB1QisCioAi4EJACXSdEoqAIqAIKAJlhwCkgLtWp186ieMF4mLBggWmlmHQze/1zhPhW+kkI4QZZI8lOciWhGwlY7BQBqkL7khbrly5Mu1txuKjcmyky5Dl1HQcmxiV+MTEpKx0dcTUCK+Sammv6fA1ec7zcd99901l+duO33nnnWYNSGQQUWDFbyybGts4SAlWwDmqDq3k02w4Oir/5an/ISeGe+XU+LDMrG830vmJjOelb3zQ/Kkt1CQfb75O3tu232TfJjMcURs2bJC1a9cWLCgl7UOU4AAnec5/857kfYS5CQinIgJOPdYH+zuXe5f7OZVMoLvHlnnlzE63c8wex3vAkukEb/H/K8Usgc7zVAyJ50rBtdz7ScYzkuFKoJ850qwvrDd2zXEGhXE07y2UlazUOyo+uRBV3Of55583Us1uY88FeU6wo1+NfSX7/kpRSfHrOJRLu2xZCXwZGrB7elRZJyDQ+Y20PYG17KdRv4NI5wdVMvuNZ88kMOof//Ef5aKLLiraFDl8+LDceuutJvD6m9/8ppFux5RAL9oQ6I0UAUUgQAgogR6gwdKmKgKKgCKgCGSGgJ8JdOpL4Vzmo8rrOtOZoePdUUGod56ot5VMMuK8p/82wASnRzGkHHH8QjBBGqxatSqjSYhUO7XBkXInK91adVVImsJNpkY4ZLqfDSm/F198cVoT169fL9ddd13CZpOxwPiwhuVSjx6JTmrwERQRxLrcxRzLe7c+JI8deEFOjPRKuCokrbUtUl11WrqctoyNj8lwdESG42PSFK6XeXWz5Os3/rXEojF54403jBOMMUtmOOwZb2QRybotpdmsc5x6lki3pLjzb/bv7rZyDHPS/nBuIYNuSolVLvdWAj0xaswbsh5tdjq1Nt1myS2el1zJrVzGrBTnKIFeCtSDf0+IDt41BGdByKglX28g0O16w9ozEBuUgfigCcKsqYrI7NqZ0tLUMkWoZ/Ie4zqPPvqoIcHcxvcc5XgKUf7Iy3G27yjmD/NITRHIB4FKLyuRDDv20Hv37jX77IULFyb8xsZ/smnTJkOm851o15U/+7M/ky984Qv5DEvG5+IH+NSnPmW+ZT7/+c/Ln//5n0+dqwR6xjDqgYqAIlBBCCiBXkGDrV1VBBQBRaBSEPAzgb5r1y4jPQi5FeQ6qUGpd55oziOjhuwcDqRKqttGrTqymPioJ+MPyfZilRHA8XvgwAFDvq1evTrrpSg2ERUIdbLOkXLPJXso65vmeUJ3d7eRbndmXyKd+fu///sJs5JPnDhhxgdD2o8M9WwdspU6t3MZqmPDJ+W/v/BP0jPSJz2jA0bdoCFcJ7WhGpmYEBmNjspIbEyiEzFp5N+rauST59wkt15449TtcEBt3brVZGvwfCUzSMF169bJxRdfXJLAKUuQO38nI8AtgW5rpFuy3dk3S6bzb5aEr/TsdM3uy+wpdJJbZIwWU3o5sxYW9ijehZChmoFeWJzL7eqWQOe7wa8S4X7CnHVlz8A+efvkNjk0eERisbjE47FJEl3Cck5kviyKnCONoYap7HT2w5FI5Iz9JfuyRx55xCgAuO388883WZtBUNFgD873J5muQQ/g9tNcq9S2WFUMDeqZPgPwQUGgY+eee25Gvh6CnyHRL7nkkqIF237ta18zWefIyH/nO9+Z9l2qBHqlPtXab0VAEUiFgBLoOj8UAUVAEVAEyg4BHP9uCT+/dHL37t1Gtgt55SDWoAtavfNE444jErILBxKZI+VujBmODkvwUYsW8hxSr1hGNpCNsIdADwIBng82rEE4JCwhbonGu+66y9SXc5p7fCDNGR8cudlapc3tbPFxH7+n9x35+03fkv6xQRkaH5aR6KhM8L+JySMj1WFpjNRLTXVErm27WK7uvFiWLl16xm0Zb4KjXn75ZVPzMJkx76mPjpOsWBL7bvLczsVMn0FbK92Zue7unzM7vVKl3pVAz/5pZE45s9OTSS9TY9WSW9nfxV9nKIHur/EISmsIjmMfpQR6+hGLxqPy+OFnZN/AOzIaH5NT46fMbxM8WhWSuupaqYFGnwjJ+tp1Mis8Y+qiBC/a8hLsxd566y2TIeouQ8F77tprr5XzzjsvfYN8cgR7cNZY9qB+lpr3CVzajDQI6JqUGCACa5FwxyjT4sdglZdeekk+85nPmLb99Kc/PeO7VAl0ffwVAUVAETgTASXQdVYoAoqAIqAIlB0CfibQiUrGYYwDg2zUIFkQ650nwheC6/jx46YGfbI61EEal1Rt5UMeSXAcrxhOM7LuM5Gs9BIDMg2tQ6ESCHSk+Z588slpEF522WVy1VVXTfs39/jgzECdItfxgbAnk51xhoRXS4/AocEu+f6O/5S3ju2Q8ei4jEXHDYmOo702UiPntMyTD829VGaONJvMkEQEur0LDnpkUnFO8TuVMc7Iuy9btizn8U7XO3c2ubOWebpzE/3dXo93QTqpd863We65zudc2liqcyyBzns9iMFxpcLN+ey4s9PdbSLoy0luZRoEUuq+Oe+vBLqfRiM4bbFkFWtL0L4dioky7+BHDz0pewcOyMnRHhmJjUq4Kiz14TqpkioZj0dlODZs1Iw6atukvqpOrmm9TOrGaqZKG9FeMrXffvvthAFxEOsf/vCHA6dgxR6cPSelm/j+UVME8kGArGm+7SgDVoxSYPm0tZjnsnag9oCxv/fjs/blL39ZfvKTn2QFy9/93d/J7bffntU5erAioAgoAuWEgBLo5TSa2hdFQBFQBBQBg4CfCXQyACAzqUEXpDqGQa13nuiR6OrqkmPHjglZbdQnK1djzCDxbFYf8w3pz1KQDgSNWEk7aqCXM6FGDfJvfetb01QwcHoT7U92kzX3M4WsJo7xfMankoJDvHxucbpvObBDnn3vVekZ75fqcEjOmjlPNsxdJ8vaF5q68hAYEHg4xDIxghnISN+5c2fKwwmaQNqdTDav6qRb2XWbNQfhnS95nqgT6bLTrTy3s256uUq9K4GeyVOR+THMLVvHmN/MYafxDmloaJgi1J1ra+Z3Kf6RlkBH8nnx4sXFb4DeMZAIWLJKCfTUw7e7f688dugZOTF6UkZjY9Je0yZ1odpp+6rYREx6xvpkLDYmM+tmyNyGTvmdBR8x5DJrDUQzGZjs0dzGPvqDH/yg2UsHbR/LHpx1VAn0QC4Bvms0wdl822nQ4PShsd92/OvKlSvNPsVv9pWvfEUefvjhpM1i7259B1at7qtf/arceuutfuuKtkcRUAQUgaIhoAR60aDWGykCioAioAgUCwE/E+gQmtTRw/kCYRYEC3K980T4Qp5DovNRS32ycjTnmOHkI+u8lJKNlC2gfAG2YsWKhDXAy2EccDo88MADU9n2tk+f/OQnp2WEswbgfGKt8nJ8lEDPfhbhUIb8tCoN1HNEXt3pHIdAh8BAVh8J9mysp6dHXnvtNZPNlqq0COT5mjVrTD3CfLJ5rGS7Jc/tHLMkdjZtz+ZYWzPdWTvdfb67dnqyOuzZ3NcvxyqBXriRYN7wDrGEeiJii+fHZqdTpiSfQKTC9URECfRColu+19Zsz8zG9sEDj8g7p96V4yMnpaOm3WSeJzLWlGOjx6W6KiQzazvktgU3y6zIDHn++efljTfeSHgOcsxWMYb1hUx0u+Ykqp2eWYuLd9SePXvMnpP9DQHEaopAPgjgz+C9jD+D0hJqkwgQVMD3HUbAOOtE0Ewl3IM2YtpeRUARKAYCSqAXA2W9hyKgCCgCikBREcAxgoSWHw0ne39/vyFIyALws5VDvfNE+CLfDtHIR225ZYC5x4zIcaS8IRRKaTyP1IjGICBzqe9dyvZneu+tW7fKz3/+82mHn3/++XLjjTeaf2N8kFi3dbLBgfHxysFSKeoKmY5HuuNw/uEEtO+LZCoA+RDotg2QftRThUwngCKVLVq0yBDprE/ZZLk5yXObhV4KktotHW/b4uxzuWWnK4Ge7mnz7u8EvTiz0931icnudman8//9Yjz7qFNoBrpfRiQY7dBsz/TjhGT79/c9JCdGeyQ6EZXZtakVfYaiw9Iz1iud9bNkUfwcOflal5w8efKMG7F/vv766w1RyLoDQWbfX/Zg9nKQ6aw7/GTz3k7fM2+OIIiVdlM+hraqKQL5IIBMOXtn9s1+rPOdT9/yORdZewKesLVr1xrlqqCZEuhBGzFtryKgCBQDASXQi4Gy3kMRUAQUAUWgqAj4mUDnowoyhmhtnBh+tXKpd54IXxxkhw8fTlvP2K9jk6xdjBky0wRoYDjIzj77bF9ke5N5a6WsyeAJokMh3XyAjP3Xf/3XqUxmjifL53Of+5wJYIDkgTghIxnDyQp57qX0cHd3t/DD2EPCqiVHAEc4pKeVN0elIZkTEEUHCAzGCgWFfIz78Sy8+uqrCeurOq9NeyDSkXdPF2RhyXPnb0g6P2TippN6p882O90pNe9HEiLZ2CvBlc9Tkfu5zBuCUyyhzjrsNmemKO+eUj4TSqDnPtaVfKYG6KQffeqeP3rwSTky3CWN4QZpiaSu883acfjUUek42CTVB+IiE2fegz3ahz70oWl1jHmfQZLZNQfpd6e5s9P9st+1QazsdfwoK51+hPUIPyFAqQPm/pw5c0qqcOYnTGiLfcfz3+zdgxgwrgS632aVtkcRUAT8gIAS6H4YBW2DIqAIKAKKgKcI+JlAh7iFwIUYgdz0o5VTvfNE+EJgZlvP2I/j5GwTWQBWTo9/R+EAp0YpiQJn+3Cy7Nixw/zT0qVLPav17Kdxefrpp03dTKfddtttJuOe/uMAx+mKJZIJ96IvlVCewAucWANYi3lXQIovWLAgJUFNUArj5wWBbtvPvQmogki35Q2S9Y37IgUJmc5z7TY3ec7fIZ/98vw722uz0wkksP+dqD+27TaD3u9kuhLoXjyZ+V+DYC3WWaTS+e3OFOVZsrLLpcgUVQI9/zGuxCtYAl3lkpOPvq1/fnjoqCHPmyKps6zjJ6MyvHlAwkPVZ1yU4LMrr7xSLrjggpTvUfu9CZnOemP3eM4L2ux01h2CeUrxLqOddp/Bt2e6gLxKfMa0z9khsHfvXhOAippec3PqYJXsrhzso/leQGWM5xwC3U8KOJkiqwR6pkjpcYqAIlBJCCiBXkmjrX1VBBQBRaBCEPAzgU4GKhLO1KMms8FvxocfpA7EBgRGqqxMv7U90/Z4mU2a6T0LeRxEAeSNzaSlvqHf6tHRtu3btxsYqCNZakl5r8eDZ/pb3/qWeW6snXvuuXLHHXeYGoEEN9j61xCgBDgUgtws5/IEXowZ7wZk7sEJYx5CnqfLELEEOo6wlStXetGUaddAlYS6q0i8J6rv7DyY55uyAGTCk9nmlkt3ZnB73tACXDBddnpQpN4tgc6znU8N+wJAXLGXZG4ht2wzRe0abAEpRaaoEugVOx3z6rjWG04P33unDsnP3n1UukaOSaQqIh21bQlPmhifkOiuEYm/m7jU1+zZs+Wmm27KaR33a3Y67aIGOuaHsk7pR1OP8DsCzCfmlZYEmD5S+Bj4zuB7AQn3IBLofp972j5FQBFQBEqBgBLopUBd76kIKAKKgCJQcAQSyXgW/KYZ3MCvNYrLtd55oiEpNBmWwTTw5BDGDDUDgjIwMutwjPlRmhEny7Zt20w7qetcTtkvjMMDDzwgyBlaI/MA6XbGxAak8G9k/hQyUwMin/kAMUyggtppBJiDkJy2/jjjwHhkkg3GORAYHEsmeKEMgo9Ak9dff9044FIZ5Pnq1atNhsvMmTOngp78mnmeCWZBzk5XAj2TES7tMSi1WDI9VR3jQmaKWgKddwNBVmqKQCYIWAIdchcFG7UzEYjGo3Lvnh/I8ZETMjA+KJ31syVUdTq73Ci1HI1KdPuwyOiZeu28O9evXy8bN270hPRyZqfb2unuVhcrO50gVjKGMYIGa2trdQopAnkhoCUBEsOHwhVqYDzba9asyegbI6+B0JMVAUVAEVAEioKAEuhFgVlvoggoAoqAIlBsBPxKoPtRYrmc650nmnfFIsMKOefd9bQhpCHP02XSFrJNqa6NI3Hr1q3mEEgDP5L8uWKDLOaDDz447fQNGzYYx4klQRkXnJaFzrwnoAJpcpyjSOWrTSIAMQ0BYbO7IZw7OzszVgEo9ppharMePmyIdEofOJUNEo0pqgbr1q0z5QLKxTHuzqwHE7cct5+y05VAD9Zqk0mmKO8pK/fu1btVCfRgzRO/tPbAgQNCAIgS6KlH5Pmul+WNE5vl6HC31IZqpKOm3bzn4wMxQ5xPnIglvMDsztnyoRs+JEjkF8oyWXPYy9s1x8va6ZQRskGeCxcuNOo1aopArghoSYDkyBHIzI8Ncs0kSDfXcdDzFAFFQBFQBIqHgBLoxcNa76QIKAKKgCJQRARwNLmd7UW8fdJb+S1DtNzrnScaCDJB9u/fb/4EyRk0gwyErCl0PW2vcYFA55nEedfU1OT15UtyPZyS99xzjyDBbQ3n5y233GIyHTFIGIIbyDgstJH5cOjQIeO4WbZsWaFvF4jrk2kKec5Y4UjPpcQBZRIgMDifrO9iGvd+8803zQ//ncoYdzLkIdMhWsrJrNS7s967u3/8jTFyytgXy3mpBHpwZ1smmaIEplhii0CoXEtw2PqomoEe3PlSipZbAp3Ar9bW1lI0oSD37Bvrl629O+TQqcMyGh+VcFVEZtXNkFVtK2RO/eysnzMyz3+w7yfSPz4gJ0d7JBwPS/M7tVL9XlzkzKRzkZDIgvMXyW1XfqSomaKZrDleZqfz3WC/exYtWuTbYNuCTDK9qOcIOEsCqKLBdHgpEUUwM/sE9uO57hU8HzS9oCKgCCgCikBeCCiBnhd8erIioAgoAoqAXxHwK4HupwzRSqh3nmh+QjzbTAzIsCB93DrJQPpWyHraXj/bSLjjdMHZUkgZc6/bnep6r7zyijz99NPTDrnkkkumyEtq0c+dO7dojllLoON4JRu50o1ahEjo46ymDiGBDJBg2VopCXTbVuuwhEh3lgtI1hcIdAKEqNleTooP9DdXqXfOLRShzjzj3aI10LN9uvx3PKo8jKWVe+f/O405ZMl0fmdT41QJdP+NdxBaBPkJCcqer6WlJQhNTtnG0diYPHP0Odk7cEDiE3EZiY2Y31VSJbWhWglXh2VW7Qy5bt7V0lHbnlV/qYX+n+89LkPvDkp816hUj1UlPL9qZlgWXbJEfnv5jVLtkHrP6mYeHWzXHLvuEPDnNL5TeI9bVYxsM8j5JiYIA0MFqhgBnR5Bo5fxIQKqaJB8UFAa5FsMNQn230HyMfhwqmmTFAFFQBHwDQJKoPtmKLQhioAioAgoAl4i4FcCnUxVHO2lJLgqqd55ojlF1v2ePXvMn/i4zcb57eUczfZazB2yixm/YtTTzrZ96Y6ntjNOQkjMcnAAM4++8Y1vTMmC0/+Ojg659NJLjcME4pz/X0zniR/Wl3TzoBh/5xkhC8RK6ONszidLxg+qFc7Ma5xzW7ZsMWUR0mWls1YsWbLEkOlknhWKQC7GuCa7h81Ot8R6KaTeLYHOM0+JALXyQIC5xFpvyfRE5YHINLOEOpnqqdZ8S6CzB+R5VFMEMkGAoClIq3Ig0Edjo/Lwu7+Q7pHj0j82IMOxYZmQCamWavObRPHa6hpprWmRxnCD/PY5v2Wy0jM1iOKnf/209JzoSXxKXZXUrmqUdSvXyWWz15ecPHc3shDZ6axbKPFgixcvDsx3T6ZjrscVFwFVNEiON98dBO+yJ1ixYkVxB0bvpggoAoqAIlAwBJRALxi0emFFQBFQBBSBUiLAx126urGlaB/O03fffdc4LyBvi22VVu88Eb44kqhbjfFx6/dMDJxpfJBDCGI46CGhg1brmFrOOIDPPvvsspAg/fWvfy0vvfTStCl2+eWXmwxU+liKLHucNkhJM6cr1XHDuk/9cCurjxOL5yWfQJlSqlbY2t+WQKd/VqKcf9u7d6+8/fbbGWWlgwWSkmvXrjXztBwt1+z0fAMLlEAvx9l0Zp94h1kynXXBvc9knXFmp7vnlRLolTFPvO6lJdAJzCvF3sLL/vzivcdk3+A7cnzkpMQnYtIUaZT6cL2EqkImQHQ4NiKD44MSm4jLzNoOaattlU8s+qipaZ7KyPx89tlnp4jiM46tFmldNkPWXbhO1sxaIXWhOi+7VbBrORUxWHOSZafbdYfgHLfZcl38OwF1+b7vCtZZvXAgEHAqGmhAxvQhO3LkiAwMDJh1WktpBWI6ayMVAUVAEcgIASXQM4JJD1IEFAFFQBEIGgJ+JdBLKQVcifXOE81b5sbOnTvNn/i4zVYKsZjPgjvggQ/ys846Ky8ysJjtd94LzMGe9re1tZWqGZ7cF+fIN7/5zWmOTDLDLrvssrwynfNtXKkDdPJtf77n41gmQAknM4aEPjXP81UBKBWBbslzSwo7yXN3nwie2Lx5s8lMZ36mM4gYSlgg9V9uEu+275bcZB0tdHa6EujpZlz5/d2QfcPDU4Q6pILbkHFtamoypDrEFs/m0aNHzX9rBnr5zYlC9YhAKdaxoBPokOY/OvAT6RntFTLRZ9bNMHLtbkPOnRrmsYmYdNbNkss7N8q6jjUJ4WXf8+KLLwplgpIZz9o111xj9gRBtlyz01mnCK7Eli5dmveeKMgYatvzR0ADMpJjSAAvvh6U1njW1BQBRUARUATKAwEl0MtjHLUXioAioAgoAi4E/EqgO6WAi1l/u1LrnSd6MCDZyIa2jiS/ZnJbyUXrlEcWuLOzM7COr127dgl9gdBE5jjI9uijjwq1qK1BZt54441GJjufTOd8MYGcQaaT7CIyjSvJcOjRd9Z+jIAGsqzzJc+5Fs5nCAwMXIuRveXMOLdEOvelP6n6BFFMEAFEOs+cu36ze05wTcgF+kUmUaLstXKZR1bq3SmH7+4bf7MY20z/TMZbCfRymSW594O1x5md7i4lwLOFOgjrCb+pRaymCGSCgCXQ2T8RkBFUe+boC7Ll5FY5OnJMWiPN0hhpTNqV8fi4HBs5Lu017TK3oVPuXPTRae8+9juvvvqqCRxLpjjGHuDKK68s22fNmZ3O2uN+39va6aw9VpVHs2KD+vT4p90akJF8LCi1xrNIoDh7ajVFQBFQBBSB8kBACfTyGEfthSKgCCgCioALAb8S6MUmYhLVO0fOmKyoSjUcTNTjxpAypH6p3wzHINkiNuOUrO3W1la/NTOr9lB3HpKTDKogy0efOHFC7rnnHiM1ao0sg9tvv90TsjYrUF0Hl1LhIp9253su/YY05nmB7PRaQp95y/zFKL1R6CCJZOR5JkSuE0vaTbAQZDqykukMNQ6c6/SR90S290t3fT/9PVepd/qQCBcl0P00uqVvC/ML5QpLqLtll2mhU+q9nANXSj8awW8B7x/mVNAJ9Pv2/ECODnfLwPiAdNbPTlt//PjICamSKplR1yG/e+4dpi4673tLnCcLEOPZQhGIQOVyfo85Z3Ym2ekcD7EHPnwHVgo2wV8B/NUD3m3seQjQ0Czr6WPj3Au152ZkAAAgAElEQVSqyoy/5q22RhFQBBSBfBBQAj0f9PRcRUARUAQUAd8igLMyXeZdKRpfzPrb9J8POSvni8MEYsnvNb8LPS44mbZu3WpuQwaYn+SLaRu1zql5juFUL5eABzKoCCAhM5hs+qDagw8+KLt3755qPmTq3Xff7Yu6pE6FC7LhK8EIaLDkMM/LggULPA+KKSaB7syOtll1NvM8n/FkXYFIZ+2zEveprsf7AiKdzPTZs2eXPDgkn75ncq7NTs9V6t1mHaGuEeT1LROs9JjsEHASW5RasCoZzqsQvGIJdYgtL5QzsmulHu1nBCyBPn/+fDNPgmr/tvPbcnz0pJFvn10/K203+sb6ZSw2JrPqZ8pvzbpe3tlywKj/JPu+Yw+wfv16ueiii8paTSUtcCIGIxvEQ9CBO0vfZqenqp2eyX30mMpDgPmEVDl7UwLR1SYR4F1P8DvfDOwD+R5RUwQUAUVAESgPBJRAL49x1F4oAoqAIqAIuBDwK4FerPrbWu889SMBkYQRHe4XZyTOLUgYHOwYxD7kebkEPOzbt88485ChnzUrvePUj4va/v375Yc//OG0pl166aVy9dVX+6K5parVXYrO46iCOD958qS5PaQTzqpCPC/FCnxy1jpPVe88H7y5LvOYerGQMokyY93Xp27sihUrTL10nILlTu7lkp1OXWueP7AK6vqWz7zSczNDgPc7AXKsU7zjE8kuQ0rwN0tsFWJNy6y1epRfECBoj3ceakR+CvrMFp9/332/dA8fl+HYsMyum5X2XUKt9PhQTFoONcjEoajEY/GEtySQ8bzzzpMNGzYEGp9s8cz0eAKp2S/x7kZ1i2BWt9kgHuaXZqdnimxlHmfnE8+dypSfngOs0ahh8c1A4ClJC2qKgCKgCCgC5YGAEujlMY7aC0VAEVAEFAEXAn4l0ItRf1vrnad/HCCPIEkg3Jqbm9OfUOAjqA3ORzeBDxgkDFLn5SSvCGkHWYBTgZ8gmS2F8Nhjj5kgB2u1tbXyxS9+0fOM51yxKXaJiFzbme95ZFaR5UEWDEZ5AzLzCvW8FJpAtzXOnQS6rb9dSLLa9ov1kPrxmRjlFyDSIdSDXIohk77aYzLJTu/u7jakBHPRBhkUYj6eGh+SvrFBQ4S01TRLfdh/JUiywbbSjqUOMXMFsmrhwoWGFOU5tFLvdg/gxIX3DHWvIdT570KuCZU2HkHp765du0xTg06g//Sd/5T9g+/IidGTMrN2htSEapIOQbR/XE7tGpCaYyGpOl0xZ9rxrLFr166VSy65xBd7eb/OJ0t4EoyD8lamtdM1O92vI1raduFnIGgQxQeVKT89FrzP2UvzTY/aGt8laoqAIqAIKALlgYAS6OUxjtoLRUARUAQUARcCfiXQccRDVmBEbXtZi1zrnWf+GFADHQcSGd4tLS2Zn1iAI3GcQwbaTNCg1whPBtGBAwcM4Ul2JlnoQTGeWUohkL3z9NNPT2s2NTavuuoq33SlEgh0HFM4qCCdMIIxmFOFJJW4pyUwII+9rFdsyXMrSWvruNOfQvbJPWl5NlkXeT9B8GViEMUQ6fwQ9FMJxvg4fxg/SE8w498h0Kkxa8fPBkLkQ6bHJuKy5eQu+fWRTbK9Z+8UzFz7vI7lctXc9bKy7dyizpdKGOtC9NFNoLvvwT7AWTvdLbtMxp8ltcgU5f+rlT8C9v1DRqOX3w3FRm5n3x558vCvpXvkmISqQtJR2z5t3TLvw56YRPeNSrw7mrR5rKfUNyfjvNR7+GJjmMv9UhGeziAe1p5U2em2dnox9ya59FfPKSwC6d5jhb27f6/Os0SwOO/xefPmmUB4NUVAEVAEFIHyQEAJ9PIYR+2FIqAIKAKKgAsByIhMpGmLDZyz/raX8uFa7zy7kdy5c6epQ0o2D2RHqQz5aYhZ5gWOcAh9v0jKe40JpCdZMJBuROYHwZzKAG+99ZYJdLAGiUr2uZ/kVItZq7sU44dzl3nEeocDl+yOYjy/hSq9YeudO+tu23rnpXRQUy/dkuk43jMxAhmWLVsmS5cuNZnppWx/Ju314hjGzRITdg1nbXMGWPDvFgvG1hLpmRLqZJx/c/sPZXffOzIej8pIbFSiEzHT/EhVWOrCtRKuCsm6GSvkD5bfLrUpMjq96LNeIz8EsiEemDuQWTY7nfeR2yBTLaFOVnslPHf5jUDwzmYeIOGOBZ1Aj8ajcv/eH8nJ0V45OdYj9aE6aa1pkaqJKokfGZfo/jGZ6J9c3xIZ6+aqVatMxnkx3v3Bmy2JW2xLR1jli1T90uz0chn1wvWjp6dHjh07ZtS3+G5Vm0SAtZpyZTxD+BeCFCyuY6gIKAKKgCKQGgEl0HWGKAKKgCKgCJQlAn4l0AHba/lwrXee/RQmmwdnNARcKTIn3fWbrRMC51a5GhL1kE2Qa0GIyneStZAYZJ87swFx4F577bW+Gq5CS42XsrMQT8jnW6KS8gvFCl4oBIFuyXPnbyfBWkqs7b3tOrVjxw6TgU8ATCbGmmrJdAjlciT1wIYAKJutj7S2LSPgDIhw4sU5mM1Ot8ESycj00diYfO3te+XA4CHpHxuU8fi4VFdVS011RLgS/z8+EZea6hppqWmUVe1L5EurPimhas1KzmSeluKYbAh0d/tYhyyZzvvJzid7HPLMzuz0TIM0SoGD3jNzBJwEOmQV+8Ug2zuD78kvDj4uQ9Eh6Rvsk9rDYak7HJaqsaqk3SIoiRrnF154oUq15zD4dt3hPcXeKVPLpMSErZ2u2emZohr849j7EGhJAJfW+T49nuz9IND5zVqNOpaaIqAIKAKKQHkgoAR6eYyj9kIRUAQUAUXAhYCfCXQv5cO13nluU3/Pnj1GdrcUcukoI5DJjCMcQ36SSPVyd3Yjg44TD3LN73XhyK44fPjwFFmLQ2Tz5s1Tkw2i4u677zZ1af1khZQaL1U/3aUprAO4mMEmPLOQyBgZ1rQhH3OT51zLkqn5XLeQ59JmAhhQ7+DHrl/p7tnc3CxLliwxhHq5rHNgQf1P1jOMQA765pTTtiQ6exH7326sEmWnO98DD+x7VJ489JL0jg4Yorwp0mDIcxuQYMiN+JgMjg9JuDosrTXNcvui6+WGs65INyz69xIhkCuR5W4uc8qZnQ657jTmiDs7vURd1tvmiQBjzZ4VKwcCnX68vv8NeeG1FyV6aFQknhygcE1YLr7wYrngggsCLV2f5xTI+3SvMoY1Oz3voSiLC0CeQ6LbvU9ZdMqDTrBW792713w7Lly40ASMqykCioAioAiUBwJKoJfHOGovFAFFQBFQBFwI8BHjdij6BSQv5MO13nl+o8kHLs5nsiORFC+WQdojQW3nZjHqNxerb+nuA/mGEw/ZTcgmPxrPVVdXl8mswCBKGaN77rln2npy0UUXyQc/+EHfdaEQmdKl7KStP29lxCFj3URlMdqH05jAJyxfAt2SqbaOtq2RHaQsbdrO82wz08mGzcQg9BYvXmwIdbLgihkEkUn7MjnGzkkbQEDNcwKx0o0f5znJdHf2sDs7fXwiKv/7a/8oPaN9cio6Iu01LRJOkllOJnrv2IA0RxplXsNs+R/r/1RCVdWZdEePKTICXhHo7mYTPOXMTnf/XbNEizzQHt7OSaCzbuYbwOVh07K6lN2fUA6HfVYqizTWyJp1q+XyCy8P5HsiK2CKcHAhMoY1O70IA+fTWyDfzvccQcTU+labRIA9Hv4F7Nxzzy2Jwp2OhSKgCCgCikBhEFACvTC46lUVAUVAEVAESoyAnwl0ahkitcxHZ0dHR9ZIab3zrCE74wQyiiF9qE9WLIk1ahBCOjE3yTKECCT7vFKMjG6ceBBOfpT8cz9XlqzdtGmTPPPMM1PDxNiRfe7HsfM6U7qUcxNnO7L/BLpgZHKUSg7cSaBD/uYioYuz2Z15HkTy3D0nLKGMxDvvtsHBwYymDdnaZFNCqPPjx+fJ3RHmJOohvL8xgq/4SUeeJ8LMBlAky05/sesN+cH+X8jJsT6TdQ45nsr6xshSn5D22hb5o9WflLUdyzIaBz2ouAjYTNBspZSzaSVziv0NzyKkOuuX03iHkTlo5d5RVFHzLwJOUoasxqAFHp04cULefvttU77Krp3J0J41d5aRaV+5dGXZqzIVc8YxBvwUMmM4XXa6XXfs2oMsv1owESAAhm9avpOCUJKrWCjzDYZ/ASPYNgj72mJho/dRBBQBRSDoCCiBHvQR1PYrAoqAIqAIJETAzwS6zX7ORT5c6517M+EPHDhgnMuQ55DohTS3WgDOz3KR4cwGtyNHjhgHHg4F+u8nI3sPZQAnMca8YB35xje+Ma3286pVq+S3f/u3/dT8qbZ4QfT6oWOQ5pDnVqkh12Ajr/rCPMD5j0H2kkmdjVninOtgNojG1sLO5lp+PtZKm0OkQ6hDFmZqrMWWTM8kozvT63p1HO9eyHMcpBhtRE0jX3NKvdt5wjV/uP+X8uujm6R3rF9aI80SqX6f5KyqkkSVgkdiYzIwPigzatvkwwuulVsWXJNv0/T8AiBQDALd2exMskQh8y2ZTnBQtgEhBYBJL+lAwEnKBIVAt+Vktm7dagJHUxnE6ooVK4xMe6H345U6sYotuZ3JuqOqGMGdjZSwQRmK7zkCW9UmEeAbkm9JjLJFBBioKQKKgCKgCJQHAkqgl8c4ai8UAUVAEVAEXAj4mUDPNfvZXe+cOtJeOPArcfLwgTswMGCyBwv58Q+hifPQSlDjpCb7uhIzvnC44MTDoYAMqV+MDD3IWsYK4sD5XCHb/fDDD09r6l133eVbycJ8iV4/jIlzncOxTrBFqWvN54NrMvLcWevaD7h73Qb6TcAMZDo/6SR7nfcnQw0yfdGiRWatyCXj36v+9I0OyNtdO+Rg92EJS0jm1c2S8xetMYRjIcxKvf/Hzofkxe43hMzy9kiLhBLJtzvI9LH4uDm2o7ZNbjz7Srl9kf9KTBQCr6Bds9gEuhsf3nNW6p3fNqjHHocyhDM7nf+vVloEnAQ6a6JfM3dZ8w8ePCiQ5gRQ2WCjZOjxXl+7dq35KfU7vrQjXPi7W8lt3lvscYttznUHdYxUqhisP36d48XGza/3IyCab2h8EJS5UptEwCY58N8rV64071I1RUARUAQUgfJAQAn08hhH7YUioAgoAoqACwEcOWRA+NGyzX7WeufejyKZhMjPIaFfqPpt7qzmUkpQe49g9leEQMOJhwMPJ7AfDEl5pOUxghoga50Oj/vuu2/q7xyD4/HTn/60H5qesA35EL2l7pQlXAm0wMhO8ku9V9oGKYBR1zBTp5hbsp3/DyFViRmerLcQ6SiwQLK4ibtk8w+sWKPBnXUDZ20x8Nvff1B+9d5zsqlri4xHx0QmRKSqyqwTy9oWynVnXSoXz1pTsLZ8f/cj8sTBF+XkaJ+0RJqkJlQjMkEjEttIbFQGo0Myo7Zdblt0vSHR1fyHgCXQCQoptRIL6xEOf0uoJ5LXpp2Qm7y3WZOL8ez5b9RK2yKUWPbv328a4UcCnbUdhRZ++O90Rhb9unXrTF/KPZAsHRbF+nt3d7f09vb6oma1ZqcXa9QLdx8Cw3lvtLe3F60MWuF6492VUc/Cv4CtXr26pMGf3vVKr6QIKAKKgCIAAkqg6zxQBBQBRUARKEsE/Eygk+1KliWEarraYVrvvDDTEwIHZxIf/4XIxkAeno9om9UMAcS9Ktlw4PED+QgZVkqzUtNkyGKQBJC1zqwXHETf+c53pjXz1ltvNVKjfrVcid5S94d2E8hgJb+ZI5BLflFqyAVXN3kOxpAFSkBNZukQSLZnzx5DDPH/MzXmBgQM5Au/Mw1myPT6HPfMoVfk/l0/k9HouIxER2R8IioTMmHGjnrkdeFaI6l+2ZwL5feX35Y4OzybGyY49vVjW+Wb234oPSN9Zt601jRPEehTNLqDUO8dGzDta400yf+2+vfk3Jazp+abklR5DoaHp/uJQHd3i4xhZ3Y6a5jTWI+t1DvPnc4rDydGiks5CXT2Tn54LzJPyDLfsWOHkI2azpgva9asMdnmra2t6Q7Xv3uMgJ9rVmeTnc7644f57/HwBO5yfEOjJEAQOkpuapMIsC7akhXnnXeeKinoxFAEFAFFoIwQUAK9jAZTu6IIKAKKgCJwGgE/E+iZZj9rvfPCzWjIOrKPceQhqe6VMe+4rnUoJspq9upeQbsO8u1kF1M/GnnmUhnOOp5BghwwavidddZZZ5ABP//5z6eyju1xd999t+9Jgy1btph+QTAWSmbay7GDtGE8cDxhSEIScOI3ciZTXFkD7A9Z1vxAbCp5nnjWgA/rMWQ62emsn9kYJTiY6wRcMG/yda6/dPRNuWfbj+TU+LAMx0aEiuOQ5pFQWOIyIaOxMYlNxKW2ukaaIg1yzfxL5K7lt2bT5IyOjcVj8pevfE2ODh2TgfFT0lLTLLVkoVtzkJvDZJ9zTKRJFjWfJX+x5rNmzjEP+W1/mIN+e64yAqOMDmJ+8y70QwZ6KliZO2TTsS7zroTEdRpzine5JdTJTlcrDAKoGRFwhJWSQEehgHUa0pxAYHeAhbv3zBHWZjIxabeWAyjM/MjkqkGpWZ1Jdnptbe1UmQnWIA1KzGQGeHsMe3beD5DnkOhqkwgga8/3P3MSAj3f/ajiqggoAoqAIuAfBJRA989YaEsUAUVAEVAEPEYgkRylx7fI6XJEJ5OFBFEEcZfItN55TtBmfBIfuGQfQ556JaMKEcR1bRYtjh2urbX8JocFvMEH4mDJkiUZj5WXB7ImvPPOO1PlHZCDnjVr1hkOOIJXvv71r0+r4XnNNdfIxo0bvWxOQa6F1DhOSLJz/V5X1D0enZ2dxiHnR4doJrg6iXObga7keXbTnPUTIp3MdKvikekVcFbyTkVNgp9s5d5HoqPyZy/+n3JyuE+GYsOGOG8I1U1zgprgvPi4DI4PGUIbEv3LF/yhLG1bkGkzMz7usfeelx/v+5X0jw3KaGxcGsJ1Uh+uk+qqanMNSHZI/uHoiNSF6qQ50iB3LfmIXDxzzRnkliXTOc8Gc9i5mXGD9MC8EQgKge7uKCSuzU6HOHGTp+xzLJnO3kcDNfKeKlMXcBLoBB8Wk4jmHb1v3z5TfoM12V27OlEvUdeCNKcGcBCC+LwbKf9eydasJmiYfVZQTLPT/TlSfEexNvD9VOnqas4RwndDsArvPwj0Yq7V/pwp2ipFQBFQBMoHASXQy2cstSeKgCKgCCgCLgT8SqCnIm+13nlxpjEfuGSBNTc3G6IlXyM7C7IHSTvMr1m0+fYzn/NtvXGyR5YuXZrPpXI61y2rD9GWTEr0N7/5jTz22GNT98EZ8qUvfSkQzmDqkBLMwbxmfvvV3OOBEgQBLX41i2uywARLmIO9JdKdRKVf++Xndtl1FeKGHxuclGmbbZavJdRZl1MFZzzxzgty386HpT86KOGqkDSFGyQUDiW83XB0VE5Fh6W9pkUum3uBfGHVxzNtVsbHxSfi8q3tP5ZNxzYbwh6CH1Ft2oakPJnwVSLSEK6Xhki9XDt/o3x88U1m/kE8OOei+6bO7HQ7T5X0zHhocj7QEuiQzF6q3+TcoBxOZF6x17GEOioiTuMZQ7LbEuoaRJgDyI5TbKAZ/0TwYaGfUwIkCGKCNCfTPBPSnLV2+fLlhjiHoPVjEFx+oxDss1F5Yc/FO5DAsiAa7yyCW+3ak6j0C98XrDusP5qdXrhRRhGDwB7mEnNKbRIBSsNRqgzinHIVSqDrzFAEFAFFoHwQUAK9fMZSe6IIKAKKgCLgQoCPu3QSg6UAjVp0x44dM9mhkDHWtN558UbD1uPG0YLEZD6Gs5FofOtERlKYDBx1IE5HFfIL9QWkXpctW5YP5Fmfa7PfOZEsVQg1nGvJ7D/+4z+E59QajuHbbrst6/uW4oTt27cbhzfqB34lpG0wRabjUQoc3fdMFZiQjDwvNNHhB1yK2QbWEBy3kOkQO25Z6XRt4XmAtOSHABonoc46/jevfl3eGT4sQ/ERaYk0S004nPSSjHnPaL/JQm+paZL/dcV/MxniXhsk+QN7filPH35FINRHjIR8zNwGIr02VCvh6mq5+Zxr5OYF15zx3rEkupNQd7fRnZ2uUu9ej+Lp65UDge5Ex5ZLcmanu9Hjnc9+l/0WRKvujbKbX7acE2cVikCHXLWkOcGgmXw7ERhBRvyKFSvMnkrJouzGtZhHs/fmGSVbmKzhcjC+uZyBPLzrnMZ7zBnIo3La3o06ezD2X3zv+vU7w7veZn4l9qj4d1gb16xZU/Bgp8xbpkcqAoqAIqAI5IuAEuj5IqjnKwKKgCKgCPgWAb8S6InIW613XtxpxAcuBCnOFWoz5mpEm+OYwtmIswZixs9Zv7n204vzwOrgwYPGsQAhXQxjXFB8sHWVM5HVR53g3nvvnda8O+64o6R127PBivqkOBaZi8ky7LO5npfHMh48d6g/YJApON6DkKGYLDDBkufO3xAJShJ5OXPOvBZznLUXQp0AJmfAS6Z3htTjOYFQYMy+cfIn0h8flPGJqHTUtaa9zMD4kJFRb6ttlv9j/Z/I2U1z0p6T6wHHh3vk2SObZFP3Fukd65dqqTJt3NB5vlwx50Jpq81MvQGSIVV2uiXOnHXTVeo911E787xyI9DdPWRuOUktd/Yy+ySbmc5vJV3Tzy0ngY56jxfvFqt2hTw7P5mun4wfQafs4SDPg/DuTo9w+R/B3pvnknrVlMkpN7PZ6TaQJ5ECnc1O10Ce/EefYBvW9rlz5+o3rwNOgrX5Ya6tWrVKCfT8p5peQRFQBBQB3yCgBLpvhkIboggoAoqAIuA1An4l0CGPIOkg83BAab1zr0c+/fXyrcftJgL5WCbjl99qiRFgnpM1isOc2piFNgg2MqlwqGGQyfPnz0/r0EC6HQl3awREfPGLX0x7XqH7k+n1d+7caTJDbIZtpucV+ji3wgZZK7QxKFnaiQh0m93Lb3603nmhZ1Hy65NBzvoCmc7vbOXeuXI0HJf+xlHpbRwSaQ/LWF1cjEZ6EkNWPfo+gf6Vi/5IFrWcVToAcriznbf2d6KsU6fUu53fQXlmc4Ck4KdYB3uQJdwzBSkTUosgKkuos3/yghzOtH1BOY61jb0Mlg+Bzr6AtdGS5nZvlA4HnncC3ch+5yeVek+6a+nfS4MA84d5hDoWP+Vump1e2BHes2eP2fPOmzfPqIuoTSKAf4cgOd5rEOj6PtOZoQgoAopA+SCgBHr5jKX2RBFQBBQBRcCFgF8JdCtfjKwlpB7Z0BiZHJCw6pwq/FTOpx43RCDOKCQvMQhWiEDNpEo9bgMDA4bcwhmLY6GQ5lZ0oCYnWTfpnBk43f7lX/5FnNkrl19+uVx55ZWFbK6n17YEOsECyHX6wViLcdzbmpVk/FI7Md14+KHttg3OzH7If3fmuZLnfhotMYFprDf2h+y7bC0WistwY0yGm6KTP41RmXCURO8dHZDqqmppqWmUv7/0L2RGXXBrgTql3pnb9seJmVvq3Zmlni22lXp8JRHo7jHm/WozRHke3ZLL7KEsmY46kO6pJhF0EujZlL/heWW+sQai1EEWcib1zLkncteUmIKwR6VJg0ODvWLZ/Rf7YLLQK8kyCeTR7PTsZsTu3bvNHoFvX9ZqtUkEUBhEbQ1MKG0RpG8cHUNFQBFQBBSB1Agoga4zRBFQBBQBRaBsESDbwu2g80NnrZQ1H1Y24wunIVKyWqOtOCOUq5w4xCrOSAhBLIhEYHEQPvMuBBzgxGXer169umDNgKgnwIFnH7IeB0+mNfp27dolDz300LS23X333aZWclCMPjA//UKgQ5TgvIU8YezJWPELsZ/NmFoC3c4nZ/a5kufZIFn8Y3nPkhnEugCJxO9cCPUJmZCRhklCfbBhTI7W9kpdQ50saV0gf3Xxl8rKWZpO6p1RdGans9aq1Hv6uW0JdBzsrCWVaswdiGFLqNs9lRMPgknZG5PhSIBppZIRrFWsW/QfQjuVgakNGmK/lWmWOdeERLSkOTLtKs9ePk8nc4LvF75Zgrj/8nIkMslOZ92x9dP1u3w6+qzdEOgYfgsN+j+ND6Uw+vr6zHsLAl1NEVAEFAFFoHwQUAK9fMZSe6IIKAKKgCLgQsCvBLqVcLfNJSOADNlKdQ6WYuLygQuJgmMk049cJzHLWOH89luN6VJgmek9ceTu37/fHL5mzZpMT8v4OJttRXkEDOcvsqNI6WVqP/vZz2Tbtm1ThzPGn/rUpzI93RfH4djCUQpRXepMI54zHP+MDdmEKGzgWAqi2cx+cEV1wmboWuJQ1+9gjCrOcwI6rNQm6zoqMNkQTc6ejtfEpXNOp6xdtMrUA+VdjrpMOZkzO93+t7t/7ux0nguVej9zFiiBnvjJYL/uzE53lxPgfW6z0yFsKmlupSLQCTw4dOiQ2c/yk2ktczsKBAeSYc4PQXea9V9OK/fpvhBMwVxB+SdIAaGFHg3NTs8eYfYASLhjfGOpOsVpDI8cOSLsKflGyEYtJPtR0DMUAUVAEVAEio2AEujFRlzvpwgoAoqAIlA0BPxIoCMri5PLOgf9Vqe4aINT4htlIydusxetY1Kl9nMbPJzA1N7EyED3knDEoYPjwtY9JnMEsjabzBHWi3/+53829cOt3XDDDXLhhRfm1uESnYVjC6l0yLxS1brkmYGURM4Qw8HGeATZ0WYz++fMmWMUDeijkoQlmuQ53pbAEt6/9hnnGYFMYCxRJeFvT21+ToaOD0hkzKHVnsX9WNcIiuPa/DBf+P/lRPily063+xunxLtmp09OIhu4UekZ6KkeKeYXmdSo1kCqE/TiNOaSzQ6FVC/3TGkwgCS3tcgtYU5wGgGD7mCDVNiCHUS5Jc1LHWSXxdKqh+aBAMGrvPcI8NLA3+RAZpqdbjPUs/nGyGP4fHUqZSD27t1r2oRiRbkFDOYD9v/P3ptA2XFVd7+7b48auiVZs2VjSReGybwAACAASURBVK3JkjBDMMYQ2xCThCH+GEKwyeeFA04MfM9gzEvy3nrJW9/LW2QgBEPMaAiTCZCEsOxFXgD7C8QBTBwwGGNLskbLYFuTZc2tnu69b/1Oa7dOl273rbq3qm5V3b217rrd6hrO+Z9Tp6r2f+//fvrpp919i2tszZo1zRzK9jUEDAFDwBDIGAJGoGdsQKw5hoAhYAgYAvEhwItw2Hp/8Z219pFwcEEmab1z3SpuIjHpfhTl+GHlxHHk4qwkkxZrhJgtCmbN9gOHuDpdqIEeF6GkGaUqyQwhRpZw1OMj0X333XdPdhNH880335y7jGkwBmsl7podt6j7B68ZHI2Q53nObFPJSjK4UDSAQCfDxByHUWdH67aHhIJw0tIOkEhIQwdtpDwqH374C7Ln4BPSebwqfSc7pX+oV+ae7pEO6WioAxB8EBdck3z4GRndOIOIGmpYDDuBp/+pReb5Uu9KpEddn2NoaiYOYQR6tGFg7rDuanY697agsQ772elFuK60j/SXwEMC4wgQJNAnamkq7lUQXWSLci+OosoTbbRs66wioAS6BgBmtZ1Zapdlp9ceDQIxVE3MSj1MxQh/Afcqnu8IUjIzBAwBQ8AQKA4CRqAXZyytJ4aAIWAIGAIBBLJCoEPi47gn6xlDflKdgHESiTYBwiMQJhsapy1Sv2TzYrwQk1HYro7/8OjW3hIcVfbv4osvjoVQ5ZjUdtSMUpyDZF034kC/6667BJluNZzNb3nLW5rtdur742xnfkPSUe8yTQsGM5DdxjXTyHik2e6ZzqWS1f4artuTUQ8JC0EBKZHnfmYF71rtODU2JPfvf0h2Htsrw+UR6ensltX9F8rLlv2KzO/tr9t0AqDIDMLIGKNu50wk0lh5TP5x97fk+08/KGOVcRmrjImUqzJ7qEcGhnpl9slumTvUIzJerXvu6TZQUp3rFFld1i6ulzzfX/RaUUJdyxz4GASl3v0s9YbBzNGORqA3N1g8T3N/U0I9GCTL9eNnp+cpQ5RrQ9cqiBjWLCT/o5qucTzDQJwXJVgnKg62/VkEeC7k+YznMZ5XzKIjAH6sO7r+BANZWHs0kIc1KE9rTxQ0eDemJAA2ODgYy7tclPNneVtUjPDv8CxHcIGZIWAIGAKGQHEQMAK9OGNpPTEEDAFDwBAIIJAFAh3JWAg+XjgxpFyR9tJM3LiIRBv8aAjUy4bGScKLsEqHZqGedLQeZm9rrgFksLH169c3LbtKOQQ/oxRSrFHHINfpRz/60SlSsa961avk+c9/fvaArNMiMkOYv5ByfNKyYDBDKyXk4+izkn/qJPWlPVGwCBI3ZNhDpvPBiZrnjPs48IvjGGSD//Oee+T+/T+R0cqYjFfKTq64o0Okq6NLukqdcuniS+S6Na+VOd2zzjmllt+AtMQIeGCdCCv5fHLslPxg309l25HdMjR+WnpKPXJR//ly5fmXytJZCx25RekIiC6klFGYiSKnHGwwDneCXiDT+ebDM0NelQ7qSb3Tfz87HQKi6FLvRqDHsTJMHIO5w71byXQNdvTPwDXPesy6zM9ZCnKi7ZQGYu3gwzqiSjpRUOK6Yc1gbePD82pRybsouNi2ZxHgnZNnFuZGLeUVwyoaAu2cna5+DRBDpjzPQX/RRn3mrZkT+A24D/HcRgCTmSFgCBgChkBxEDACvThjaT0xBAwBQ8AQCCDQagLdJ/i07iDy0nETiTbw0RGYKRv62WefdaQIL8OQYEhe4oA1aw4BssQ1w3vdunUNk0K1atLjqGhGljQo345D6N3vfrdTi8ibkRkCwQv5RnZrGoa6Bo4jlcduJpghjfbWO4eS50qSa780W1adp+DMpxZxo6QNzuq8EqD1cEry76fHh+Ujj3xRdh/7hZwujwhkerVamTwlY9Hb2SOzOvtk+Zwl8kfPu1EGes5KsjNGrONafoPxQLY9ycAG1jgIMc7LB1JMz98MVmSQKqGu3wTiZYkMrNc/zU7nmtKfg/sEs9NZh4vmnFcCXedjPdzs7+ERqFe/mGvfzxBNci0Itpp5z9grWc53I9nlHJfrPkiYhw0KCo+mbVkkBFB/Yt3lHmjvM/GPrGana4Z6kbPTCUDneR9bu3Ztrp5D4h/5s0fk+UUTJghe5j3IzBAwBAwBQ6A4CBiBXpyxtJ4YAoaAIWAIBBDAYaUZxGmCE6x3jmMLElbJONoEYacvn2TFmKWLQK0gBiVcINAxCFnGzciveMYmjnlfq742Topms63+9V//VR555JHJjiJ7et1118XT8ZSPggMHQpsMCJzsSZtm4XIe1rpmgxmSbm+947MOBCWo6xF5EKdKpuNADWYhs4ao1Dv3gTwRn/XwSuLv4PexLX8vDz/zmBwfOyXlaln6OntcBnhnR0kq1YqMVMZkpDwiJSlJf88cWTPvIvnj5/+B+zv3fq1FSfsIXONaaAXuzAfIMgh1yHU+/F+zxrWmGepc65Su4BtypBX9jNqfetnpeg35Eu9FyE43Aj3qTGlsez/IietNVaD8o7EWK6HOGh3XdcP9gHE+ePDg5Iffg6olYXsG0U/ADNf3pk2bHAlqz6Vh0bPtQGDnzp3uueSCCy5wJQ7MkkMgTHY675daaiJv5X9QyUD9i/USAt1sAgHGnQBm1n+eN1mnzQwBQ8AQMASKg4AR6MUZS+uJIWAIGAKGQACBVhDowXrnOOeCBB+O461bt7rWIn/WTOasDXpjCASzoSHIiKhXYmNgYMA5moqW/dYYWvHsxbWxbdu2huc9Y0ZNerIfMLIykaNs1umN0wP5dl869ZWvfKW86EUviqfjKR8lLQI9GHCCM5CAk2aDGVKGa8rplDz3vyEvoswx1nfmEkEMkOrBIC7WFJV65zvNLMhWYhvl3I8f/6X8xUN3yMmxIVd/vL97rpNrDxrE+onRU1LqKLns85s3Xy8bBwbdWo7MKEYmEPUoo4xhlLY2si3zQsl0/Wa+xGEE5CmZzrd+KG+RJQz8vmrAih+4EsSiKNnpyPwfOXLEEbfmYI9jxoc7Bs8PKvXO+hwMcuK+5Wenh33241hBspzxbaaUA8Q+c4PnGz68I3AOgmasrm648batziLAXIRAx3gfzaOyUp7HM0p2eh7K//D8QrkJ1kh8GGYTCHCdUUKL8WbdpoyVmSFgCBgChkBxEDACvThjaT0xBAwBQ8AQCCCQNoEerHeO47pW1hsvWVu2bHGtXb16tWUDtGDm+mQuDiUyBHGwYhAuZPZllWxoAVyxnNIPHBkcHIzkxIM0hxhWMhLHRFykGJmhX/ziF6f08R3veIcj6PNoBBlQPoL1JykHDtcPJCWONIzsOBz+YUmHLOIaJM9po9ZkbrS9WqNXs9M1+MM/Hs5syE2Verd1R+Rzj33d1T0/OnJCZnfPctnn09loeUyoVT7Q0y+b56+Va+b8qsv0BEecmARD5cEg4iDTyVrlW4nWZog4v9+Qb6wJrGusnf53lrJZVd49qALh9yVIpvtZ6lkfayPQWz9CzC3WYiXU9dlPW8Z8Cman83yP2gpENh/9udaaHqWH3GN43uRdgQ9rFooZ/n2AMhCsCVynqOOYGQJREPAJdIIcLWg7CnrxbstYsGZwv2f90UA//yyMD0Q6QalZzE4n2I/3JoI/eZczm0CA+woEOs+fBOCnVULL8DcEDAFDwBBIBwEj0NPB2c5iCBgChoAh0AIE0iTQp6t3Pl23IdB5kcYZBnFili4CPpmLo5KxwJHJS29eCJd0EWv+bI0GjuA8/uWTv5SKVNwYPeeC58hAf3yk2Pe//325//77JzsIuXTTTTc13+EWHQFiG8zoB874uA0pXIIZ/AzfvAecBEk7lYqOm8wmAMSXeg/WyYTk1Ox0nKd5DkhodN6xTrzn/vfLkZFjMlwekfk9AzMGM7H9sdET0lXqkj7pkfcu/12Z1d1XCKlaiD2IOkhX/9MsaRccG5z1SqorsQ6JR2BMq2sr15N6py9KqPvXbVavHSPQG10ZktuPexpkFmvz0aNHnXqI/j5T8FMjLeL6UrKcb+6d9VRbaBOBNUagN4K47cMaSg10jBI7VjYsO3OiXnY6JLVKvWclOx1/B0HnpogxdR5xne3evds9jxCowtpuZggYAoaAIVAcBIxAL85YWk8MAUPAEDAEAgjwMhPMLIkbJF6UcGzhFMWC9c6nOx9S1hD8vGQZYRv3qNQ/nk+gszWOScvMqI9bs1tECRzh2nr60FNy4Nh+KXePS2dnSQbmzZOuzk6Z1Tlb+rvmue9mSc4vfOELzhmkdumll8rVV1/dbFdbtj+1CXG446iPWyIYUoEMd83wJeAEki2vxhwLZp4nRZ4HMeK8ZCFB0EDYBO9VEIA4TJVQr0ey5HUMgu0mo/x/+8GfOfl2ap0jzV7PkHFn29mlPrn5guvkktWbClsjmHnDdcgzB88eEOz6SeJ5h/nHNa6Euv+ddr11DXRh/dGfa11Xek/gGtJPvTmU1t+VQAfXJAKc0upHns8DYc49kg9y688++6z78HOtWumN9JU5yD0YRSP/00j2rxLoEJ8QoGaGQBQEWC8h9jCCtrOkOBKlH0XfVrPTub/zbDhTdjr3XtaDZt9/GsHUAnpqo+YHqlBqg6BEM0PAEDAEDIHiIGAEenHG0npiCBgChoAhEEAgaQI9TL3z6Qblsccec3LUyIfnmYDK46Rj3J566iknc43h0MSp1C4EVSvHbOvWrY74wAmMbPV0xhjt3P+YnBg7LtWOqpR6StI3q1ekQ6Szo1O6S73S1dEpfZ2zZHHvMvd/jRiOKuqf+3bdddflWiaVuQ0RANEFwR2XcUzqHuLk41oh4ITMmLyaEueaBc63Sran7ZSkLRA3mu2I8zRorFMq9d4qx2kaY12ulOWd3/+fcmpsSMarFZlXh0BnrTg5PoHXnM5Z8tcv+WNZPKf9HJfMIe5pPqGuUtNxkYLB8Wcd4PmFIED9MEf1Z0hiMuiSsnrZ6Sp/70u8a4BMUm2qd1wj0Osh1PzfNcgEogc1luB33AoOzHHIEiR7+ZB5yCcu9QbuvcwbI9CbnxvteATeNffs2eO6bgR6fmYAAXEq9c53ULGoVdnpth7VnkN+oArl+fJaBiw/V4i11BAwBAyBdBEwAj1dvO1shoAhYAgYAikikCSBHrbe+XTd3bFjhyNMyBC1l6z0JkVQfpozmwpAeviHUV5gjLbv2ybD1SEpd5als7dDenogzLukQzpkvDouValIZ0eXzO6cK32dfbKsb4WUOkqRO0Igy9133z25H5k5t9xyS6KkT+RGRtwBkptsOogtAnSataDKBkQu10yes5imI8+zIvvsSOGTJyc/QccpxKVmppOJlJV2NzvXdP//44G/kX1DB+XU2GmZ39s/7bUNTg6rypD0lnrkvFnz5W9f+ifSWUqOtI2rj2kdh7nOXIJMx/GtGbd8Qy4maZDVzFMl1fnWIBB//sYRvKY10/3a6cG+BWuntyI7HdUACF3LQG985kEIotpBwEitb/6PdSFuY75wX2UOEzymc5ufNTCD9Vg/cQWPKGGl9964+2XHKzYCELHUZsbIjI0rsKPYqGWrd352OoG/tYLitHZ60tnpvF/wPDFr1qxY3jGyhXTjrWFM9u7d6w6wdu1aUxdsHErb0xAwBAyBTCJgBHomh8UaZQgYAoaAIRAHAprVF8ex/GNErXde6/zUoxseHpbly5fLwoUL426iHa8GApAI1IdW+WkcnjjbTQUgvelST3mBLIvdT++S4a5TUu4al+6+bpnb0y89pZ5JqUKu6/HqmJwun3b/N7ezXwa658vC3uj15u6991756U9/OgkAWQNvfvOb0wMkgTPt27fPZaGSBQrR3YxxfSAJr2oNEAdktcdFDDTTtkb3DUq28zv9STvrPGz71XGqUu9Bxynt9qXei+Ac/8be78o39n5Hjo4ed9f+nO5ZU+ACE9ZxvkcqozJaHZP5vQPyivMvk+vXvS4stG2/HUSkL2PtE+y1VBCSAgxHvD+HWWf4XWu/8s0nrPKCyrv7ZLpmo2sfWpWdbgT69LOI8dKMS9Y7vw65/3PcGeTBFnE/ILCVrHL9XrRokftZgz2YPzzD0y4+M8ktQ7QTcNboPcYIq6RWnvY4rk/s8YwbR8BSeyCX3V6GyU7376FxPrNDnrMmcU+OU+Uqu2iHa5kmVrD1+vXrXYCVmSFgCBgChkBxEDACvThjaT0xBAwBQ8AQCCAQN4HeaL3zWgODnB6OQpV7tMFLDgHGjZd9iEVM5ach03FC4ABA7toseQS2b98+LeYQOU8+9aQMzzol1e6ydM4uSX/PPOkudddsWLmKdPMJ6S31ulroF85eKaWIUu6f/exnnTSq2lVXXSWXX3558kAkeAbquePggoRqpl4q1wb1zpWsINBn2bJlDZMACXY59KGD5Dk7qmx76IO0eMOg1HuQGIRk1MxIMpIaJW1a2c2jI8fl//yvv5GTY6fl9Phpmd01S3o7JwgoJc+dwkx1XEaqo9LX1eu2+Z+/crNcMHdZK5temHMzz2rJX6scdhIZvvXA41pVMt0n1yHg+TDfg9/c7+tJvXNezU7XTOKkpN6VQGd9JoCyyOYCXEZG3D2E593gd63/C65nSeLDOhkkyvmd4LOo6ybBKEqm8x3sB/NQM9OZu1FUQ7Qsg2V8Jjkbintsn9gbHBzMdQBkcUep8Z6lnZ2uZUhYz1DRM5tAgIAq3pmwjRs3umcRM0PAEDAEDIHiIGAEenHG0npiCBgChoAhEEAgTgK9mXrntQYGOT2cbEuWLHEfs2QQwHEOcU5mHcYLLVm5ZGnu3LnTOXfPP/98l2lkljwCtUoX+IEpSLaX545K99ySdJRKMrdr+jrptPZ0eUjGKmPS3zXPZaAPdM8L3Qmc97fffvuU7a+//vrcZ1TEQaBDdDzxxBMCKYDl/RphjulHs1J9oiz0pMnYhtyX/MzMIKlJ1pEvlR1nFlLSUHz7F9+Trz9+rwyND8vw+LCTcScbXapVqVSrjjynlAPE+pzu2fLrF7xM3jz46qSbZcc/QzaTIawEu8poo1ShstoE4GTBIC59Up1MYD48A/jf+v8EoPh/09/junbyQqA7pZfxcScV7H8YV/2d5ydIA/2u9XMr5wBjhuQ6AZJ868/8DkmeVCZuPUKLe4+qLkBC1SuHogS6n/FZrpRl/+lDMlwelq5SlyzpWyizuoywaeV8y+q5fWJvzZo1kYI3stona9f0CLBG81zIO850tdP9YJ6o97a83MPSniNgjWIXtnnzZqeYY2YIGAKGgCFQHASMQC/OWFpPDAFDwBAwBAIIxEWgN1vvvNbAQE7hdEYWkqxOs/gRwIlAlrlK0eI0hQjUzB+T0Y8f83pHDAYtQPg99dRTkxLh3QOd0rWgU06Wj7ms8p7SzA6I8cq4nCqfkDld/TKve4Es7l1arwmTfycb/q677pr8HWf6rbfemvvsnAMHDriseojTlStXhsZDN4QA47pRaXNKHORZitAnzjUDvQjkeXBgVVJYpd5rSQr7Mtn1SJvIEyfmHejPN574jvx/T9wnXOfD4yMyWhmTqoh0iDhlCjLPu0tdcuXyS+W/r71m2lrpMTfNDlcHAZ2LPqGuxDrzUz8aoJMHQCHVuUfwrR/9nW9IiDAf+k6AEgQq2c6qgMGaVGtd0kzmmb41KIj7aa2fNQMfvMN8lCRPMxu8kTkAQUEmP2R4rW/uW1EzyRtpR719CDjQ7HTGPogr80kJLeZFMDvdl0zuXzJPfnzoZ/LTw4/IqfGhyVN3dpRk4/x1ctniF8qFc8+v1yT7exshwJzjmQ6jNnMWrok2gr+lXa0XzEPjotZOJ0iX+znrrvkvzg4v9/ann37a/ccll1zinhPMDAFDwBAwBIqDgBHoxRlL64khYAgYAoZADQRqkQhRgIqj3nmt8+HMIHuLzGdIXbN4EQhm0PKSjwS17zgyGf14MQ9ztN27dzvygPHA+YLcHdkxGMEkved1y5HRw3Ji/JjM7pw7rXy7nqtSrUxuO697vizpCy+J+2//9m/y4IMPTjYbufO3vOUtYbqR6W3IDuGDQ37VqlWh24qjDUc9BDwGwQomec6iUMJcayLzu09YhQYnhxtCgilRWUtSmPGFdIJkgrTJqlP90cM75F/2fFe2H3t8YhQ6RLo6u1x71wxcJFdfcLn8yqJNmW1/DqdOKk1WeW+fUPd/9jPoICDN2gcBrm0N9qn1zbrFJ4/3Jr/OO3M8GERC37U8AX2HhFEC/XDnMfnuiR/KSHlUhssj7hsVDhbFHgKKOnuls9QpVyx9sVx9/hW2JrbPJTNjT/3M2HXr1hkqbYyAZqfr/TUYzEMQWL3sdFTdSABA0YMydGYTCIAJ2LCGQ6AnpXBieBsChoAhYAi0BgEj0FuDu53VEDAEDAFDICUEGiXQeakkixMiCsOJhfR3XDWtyLpFVpysaGpwm8WHADVawVfJMjJocbYGzWT048M87JE0aIHMO4JTyJjD2UAQifu/saNyeOSQHB8/Jn2lPunt7Jvx0OOVMTlVPuky0Od3L5BFETLQv/jFLzpnh9oVV1whL3vZy8J2JbPbsW5BguOEX716dah24tQnc4JrB8OBxnWTZwfQdOR5lNqzocDLwUaMry/1HiRtwESl3vmOKumZFASMIdlOzMtnx4/LMx1HZdbAHJnV3Ser+i+weudJAZ+x4+L0Vzla/1tJAIKyCMTiw8+NPvdlrNuFag73Eq1fz3M0P+u3/uwTN1kN6IlzUFQly89ODx6fQCfW570nfin/69T9Mt4xLifGTklVqo40hzB3xymPSkWqMquzz5W0uGLpZfLKFVfE2Vw7Vk4RYH7xTsQ1RQa6mSEAAo1mpzOXmFO8sy1evNjAPIMASRG8e7FeQ6Bn5TnaBsgQMAQMAUMgHgSMQI8HRzuKIWAIGAKGQEYRIHMpqgxl3PXOa0EDWfXss8+6CG6IKrPmEWCceXklWwcjO4mgh+mylExGv3nMox5BgxZ0PxwMjBGOc2ykPCL7hn8pp8ZPSkUqMrezf8YsqqHyKSlXxmVu1zxZ3LdE5nYNhGoS1/htt93mCHy1a6+9NlLGdqgTtWAj5j+kI+TE4OBg3RZApqIEoKUOcIotX74813UylTz3v5lr7UDK1BvwYOYvhGPQILSUUIfAaQVuWt4BRy3GvZJ52Yq21MPU/p4tBAgYgUQPEutao5u/8WzId62fLeP93PHkutM68f438r/64VmLn/Xb/5vJ2da/RljzuA8roa7PJyjt/POxb8ux8nEZqpx2yjz93XOkVOqcPKgjw8rDMjR+epJEv2n99bJijpWIqo98sbdQaWmIPWqgmxkCtRAIm53OfZR7JAp6KIeZTSBAoCdJFwSLUQPdCHSbGYaAIWAIFAsBI9CLNZ7WG0PAEDAEDIEAAlEJ9CTqndcaFAguiC4yo5FJNmsOARyNyOLjKMLAlcz+mV5gIQ3JgkbaHWLGLFkEcPDu2LFDcNJgONchz4O1mPedftLVNYdEn6kOutY/7+uc5ba7YNbK0DWQCbT4/Oc/P6XDt9xyS2wKE8kiOfPRDx8+7DLrwbeeszS43tUqddDKvjRybpVr11rARax33ggu0+1DAIUv9Q5uvkF8QaazpkKsp0Fgs0awnmsmMVlOwRIccWJgxzIEfAS4BpiDwQ/XitYH59lSP/w/zyBag1x/9n/XOuTsw/aQWTyfcF/Uj19qgv/TuujaNr32gt96LL79nzm+/h/fOPbDfLjmfZJc67+nce3bTJxAwA90+tnBLfKd4/fLyfIpNydmd0yU3NB7W6mjJB2lDrcfBDqf83rnyQsXXiJvWPlqg7TNEVBpadaDMEGVbQ6XdV9EuBdBlGswT62gMu4lqOjxXEjQVLvfH0iKwK/DvXPTpk25DkK2i8AQMAQMAUPgXASMQLdZYQgYAoaAIVBoBKIQ6EnVO68FcKN1igs9WA12LkgCQrYsWbKk7su81aFvEPAGdguqOuBgwJFXK8AB4vzQyH45XR6S0cqI9CDlXuqdJMedXGllRIYrw9LZ0SlzOvtlfs8CWdCzMHTLHn74YfnWt741uT3Zre9617tC75/lDXHioHCBQ2smuU5IU4JIcJRBrhBwQl36vFqQhKJfRp5HG00wJAMShzvzQ4Nd9CjME61JDKmehMQ/TlvWZkhGxo/gJq5PM0MgqwgoOe4T4H5bVQWJ+UyAE9cXASkWuJfVEc1Wu+74+Z2y+9QTcrIyJHM750iXlKQaaCL0eUep5NbMo2PHXT30eT398kfP/R/S19WbrQ5Za1JFgHdbgra5X4ct65NqA+1kmUfAz05XVSC/0WFqp2e+k002kHs7H969Nm7caAR6k3ja7oaAIWAIZA0BI9CzNiLWHkPAEDAEDIFYEeClL5hRFzxB0vXOa3UoqsxyrKAU6GA4oiFblCyDBAxLtlgd+nQmAkEsyOX7dWmR/qPu+XT27Ogzcmz0qIxUht1HpOrIcpEOKVcnZNeRMZ1Vmi2zuubI0t5o0s733nuv/PSnP508/fr16+UNb3hDOoAkfBYl0AlSWLduXc2z6Tb8EacqKhhIvufVlDyflLw9ExQQzODMa/9a0W6tz6vZ6Srx77eFOaNS73FkIHEu1mUN6qC8CdlNZoZAXhBQEl2zz4MlhAie5FriukH+1s8Qz0sfrZ3pIvAXP7ldnhk5LKMyJuf1LnAkebVSkUqlKsi7B+fYUGVYqlKRge5+uXHtW2TlvAvrBpSm2yM7W5oIaG1mlCRWrVqV5qntXAVEgFJc+FZ4/uM+Vys7nb8RbMmnVWWA0ob+0KFDcuTIEYfLxRdfbGtu2gNg5zMEDAFDIGEEjEBPGGA7vCFgCBgChkBrEahHoKdR77wWAlFklluLYDbPjsOQIASkuDEcQ8iBRyEBrQ598mNLpgJZzlxnOH2RFacubT0CnfE9NnZEjo4dkWq1IqPV0TPEeVVK0ik9pR6XkT6nKuDkVwAAIABJREFUq18W9SyWjo5SpM7ceeedLktb7aqrrpLLL7880jGyujEOHEhIrgkCA3wDVzKRWH8wrheumzzXp9U65372J6SUkefxzlCuYSXT+Q4GphGIoWQ6TlPGIIoxb5mbup5DnkPKmxkCeUVArxGVdCdwCTILW7BggQv287PTfcWMqNdPXjEqWrufGX5WfnHySRkuj7ggv/NnL5XzZy9rikz5fx+8TY6MHpNxGZfz+hacA5kLIPMI9eHKiIxVx6W/c468uv8quXD2+S4QifWZe77NraLNupn7o7WZITJXrlzZXp233saOgBLolHxCtSps7XSeC1mHilobnOA4rjX6uGHDhqbW/NgHzQ5oCBgChoAh0DQCRqA3DaEdwBAwBAwBQyDLCMxEoKdV77wWPkpyzZQlmmVcW9k2nNIQhOqI5mUVEjCqnLDVoU92FINZzowR854PdfNQC6hn1Dk/OX7cfcar427zkpNtnyv93QPSU4pOsDF/brvtNicRrXbttdcWJjMHB86TTz55DoEeDBbC8cUY5NmZruS5/60ZnfXmlv29cQTAm0AYlXoPZiBBBPpS7zMFaKgCjAZ1EGQDeR51PW+8N7anIZAsAszxffv2TT6zcG2QfV6rZqxf+3yyxnXEYJRke2NHr4XAruOPy38e/InsPfkLBHOkKlXpkA5Ec2Rp32J58eIXyCXnbZwsRRMFxb/+6cflwMghGa6MOgK91DFR77ymVUWOjR53559Tmi2vG/h1WdB5tjSLq6E+e/Zkdmieg+eiYNjO2/LMTXYsAWmoDZkZAs0gsHv3bhcUTQkSSpH4xvsVz4YET6O00k7Z6QT045cgUCkYvNwM3ravIWAIGAKGQDYQMAI9G+NgrTAEDAFDwBBICIHpCPQ0653X6hovWUiPQxIQqWwWDgFexslopk4uRhYXL/GNkIC87OJU4mXXsjLC4R9mq2CWM4QYTjsctc1k/fuZemHaMd02OBPvuOOOKX9+97vf7RzKRbBaa0tQRn/x4sWyZMmSXGdIBMlzxk4zz4swjnnqA/NLs9Nr1cfEcY+jlbWW9UCJQ5ytEIvcjzG2obRDI+t5nvCytrYPAsxxApr0uuCZZenSpS7znA9EhK5lQVSUTNe1Ta8Luz6yM38Yo+8feED+Y/9/ylhlXIbHh2W0MjpZo7yn1O3qkaOas2nBenndRa86U44mfB++uvUuefjYVjlROSlzuufIrK6+aXcuV8ouW31u9xxZ2rdI/se6G2To1JCbfxBbQSOIV6WWyU6vFdQRvqW2ZRYRUAKdey+BrGaGQDMI7Nq1yykQ8azGM91MVi87HR+IBvTkPTudZ1mCSnmOna58VjO4276GgCFgCBgCrUXACPTW4m9nNwQMAUPAEEgYAbJMtS4up2pFvfNaXeQli7rQSJlRK8usPgI4AAk60MxhXt6RAm/UIM8h0XlpX716daOHsf08BLjWCHBQsiCY5YyDgUxT/r9VjjyyJ772ta9Nthqn4i233FIYxzFkJGOgawtZIPzOdYNzfMWKFU4BIM+mcu188/Glj/PcryK0nTWA618Jdf/+S/+YlzhdWXdx7GswFGt53oM6ijB+1of4EGDN5ZlF5ziBSwsXLqx5r2Ed8+umB+tam9R7fOMS55H+69BP5d6n7pOh8SEZGh+Wzo6S9Hb2OpKc+uQjlVFBSae31CP93XPleQs3yX97zm9GasKjT2yVL++7W05Xh10pm3k9A9JZ6jznGMyR42Mn3DYLeubLr6+4Uq5YdtnkdswvngdYn/kE12YCM/zsdFMBiTRMmd0YNShKXhEggbqLmSHQDAI7d+50vhQUrFgvwpqfnc76A7ketDzXTkcZj35RmmXNmjVhYbHtDAFDwBAwBHKCgBHoORkoa6YhYAgYAoZAYwj4BHqr6p3Xajnkwt69e50jddOmTY11ro32wgEE+cpLOwQM5GuzGcM4lJBx54V9cHCwjdBMpqvBkgiQYRAGfkZTFmTzf/SjH8l3v/vdSRAIxHjrW9+aDCgtOKoG5+AMp284deK8blrQpclTatamT6Abed7KEZn53IwX5KFKvbNG1DIydlgvyIY0MwSKgACqDJDnKmGLUk7YwCVd33xCPYhJrex0y0xPd+acGhuS27d+Rk6MnZJT40Myp2uW9HWeVdjQ1oyUR+Xk2ElHrJMZ/ntrrpUL564I3ViUe+7ed488Wd4vJ8tDjpif3TVLejt7nCQ8c2G0Mianx0878nygp1/mdffLzRvfLnO6axNc7MN6rGS6Bnn4jUI5RLPTfeWQ0A23DTOBAEGrfCA7w5ROykSjrRGZRIB1AwIdIxiD9+dGjXujBvTwHQway1t2Okoz9AOVGQvKb3RW2H6GgCFgCGQXASPQszs21jJDwBAwBAyBGBBQAh1HEVmY6sAnC2jZsmUtyzrlJWvPnj2uhxDoJptYe7C1digEOqYShHEQLTiUIOU5pkWLN3exERDC9aXZwDjpiMIPWhZk8++55x556KGHJpu2efNm+a3f+q3mAMjQ3hqc4zdJa1/Gcd20qqsqc8wcw/hWyXZbP1s1KtHOS8YRa7mu58G9mZ8q9W5ywtGwta2zgwBS2ZDnEOCsTdwP60ndztR6y07Pztj6LfnhgR/Lvz39PTkyclR6OnscOT6dnR4fdiT7gp558rzzNskbVr4mdKcIgnv2xBH5t+EfypHyUTk5PiSj5QmZeCXQqXneXeqSOV2zncT7W9f8jjwnAkmvyiFKqOt9VhtJ4KqS6XmXWg4NfEE2JFiYey7jhwKRmSHQKAKsC0i4YwSy8/4ch+U9O533Ewh07v34l6wsXByzwo5hCBgChkC2EDACPVvjYa0xBAwBQ8AQiBkBCHRkYnmxUXIvCxLGZHvoSygS7jinzKYioPKn08mBN4sX8wLHJKSN1StrDE2cBqoOwBHIGKDe+XRZCQcPHhQ+rZTN/8pXvuLIfrUrr7xSXvrSlzYGQAb30gx0bRrEDZkieV5jpiPPLeMygxNwhiZRXoBsSsaTsVu0aJHL0CXoQ0tz6O5K2DB/+eR5/uZrlKy1zSDA8wrPmxCS+8vPyL6eZ2WoMuyIzoV9C+SyJc+T5bOXNHwKLVuh38GsPQ6s2emQ96rQYWtlw5BPu+Mnt31Bnhra5zLQIcZryarrzowJRDtZ6GSI/+HmdznSPYypNPDseXPk+yd/LI8d2+Wy0CHRK1KVDhHpLnVLV6lLFvUukDeufK2smLMszKFrbqPKIVqKQ1UU/I3zLLXcMDA53ZFyVbzvcB9FlcjMEGgUAe5rlMHCIImTCsplzWH9Idlguux0P6Cn1fc31kxN0kB5rVUlyhodV9vPEDAEDAFDoD4CRqDXx8i2MAQMAUPAEMgpArzQ4KxHNhrr7u52LzXNSI7FBQUvhzt27HCH27BhgyMezc4iQIABNeK1RlotOfBm8Tp69KhzdDMv1q9f3+zh2m7/oDoA1xXXF3hOZ1mQzf/Yxz7mCDu117/+9e4aLIJBQj7++OOTShvUlUY6OM8Z2kqe+9+QqXnuUxHmWpQ+aKANwTMYTleCOtT5qnLCXJcEgNSSEyboRsl09rPxjzICtm0aCBw7dsw9c+4ZeVJ+eOpn8mzluJAZDNmJUR+7o6Mk6+atkt9e9Sq5cO7ypprlS73r+hg8YFDq3SfVmzp5m+8Mrn/589vl2OhxGauMy4LecxV3ghCdGDvp5gI1zN+14fdkUd95oVBUaWDu5wQdHRo+LD8+9LDsOfGEkNlO5vmy2UvkRYsukdX9F7lgjTiN53DNTM8DmRVn34twLO67vO+g7sLzoJkh0CgCrAW8Y2CrVq2a8X2v0XME98tDdjr3A3wW+HaWLl1qpRLiGnw7jiFgCBgCGULACPQMDYY1xRAwBAwBQyBeBHih4UWPyHsilXHYZ4Wo5iV0+/btrsNkPycVxR0voukcDSc0GTcq0Yz86cDAQOwnJxuSiHHIOFQAzMIjEFQHQK4dZYd6WQCtls3nuvvQhz40paNvf/vbXf3lvFsw6IT+bNy4se6YZLnfQfKctqpse5bbbW07iwBjSOkG7sOY1mGdKaOc9QUynQ/ETVBOmCAdlXrneEam24xrNQLc2yCqfjb0mNx34kGpdFRlpDIq45XxyaZppjC1q2d39ck7Nv6ubJg/GFvTTeo9NijrHoh17S8e/ls5PnbCjfH8MwS6H7AQPAgEerlakfk9A/LO9W+VxbMW1T0PGyiBjjQwn1ZaPTKLtTiYnd7K9tq5xd1/ea/iPYrSZWaGQKMIQBDv3bvX7U6d71b4VDQ7nWdDJNNr1U5POzudNoAL75gEqZjSQ6MzzPYzBAwBQyC7CBiBnt2xsZYZAoaAIWAIxIAALzM4NXE6ZcnJjgzatm3bXA+pvx1XHbEYIGvZIXgBZayQG8QIKoizxto5zswTJ1zEOIQcRKNZOAQgagk8UFlPou3JigpzfSH3ToYeNbnXrl0b7oQxbgWJd8cdd0w54q233urak2cja5eauxp0ooRjXgl01gL9qFSxShGHmWd5HssitZ2xIxhKFR8ItImqiMAxyHpUQl1VSRQn1m+cpZqd3gqHbpHGzPoSDQF9buHetnv4l/Ivx/5DxmVcTpdHnKR3b6lHukqdrl41ROsItaurFZnTPdvVzP7j590ky2YvjnbSEFv72en6c3C3YHY611K9ILgQp26bTT7y6KflwOlDcnLslJQ6umQYSfUqI42kepfM7Z4lc7r7JuuUHx095mTW+7vnyv+++V0yu2tWKKy4t2tt3VYT6MEG1yOzCHbS9Rli3e7foYY81o1QYSNgmPsvz+tmhkCjCIyMjLj3ZgzfRavvF/UCemhnGuUmNGGD4E+CyS1QpdEZZvsZAoaAIZBdBIxAz+7YWMsMAUPAEDAEYkCAl6ugwz2GwzZ9CF62tmzZ4o5DFDdZdO1sBBRAtODkwdJQDCB6XaXoNm/e3M7wh+57kKiNqg7Q6rrzEP/UQFcjSON973tf6P5nbUPWETIf/TIVEJRa4x1lhbzVjvaJc81AN/I8azOvfnu475I5qXLsBNmEDbSZ7ujMB62ZzloEqRQ0nKVKphMYY4RN/bGyLRpDgOfLffv2uecW5uZXjn1LDowdllNjQ0KWeV/nufOP7YbGT8t4pSz9PXPk8iUvkBvW/3ZjDYiwl2WnRwAr5Kbf+MW9cu+T35Oh8WHpcJXIxQVKYE4qH8WUjpKc1zsg3aVOl60+r6df1vSvkreufXPIs4gLjmOtY/1Exj2rpsFOKvcOmeQbmGhmKN8W7JTOSLJGcb80Aj0dvIt8FtYh1iOMIOisPV/VC+hhzUkiO521b8+ePS6IGbXDIqiaFXkeW98MAUPAEGgEASPQG0HN9jEEDAFDwBDIDQJZJdABEAIdZyp1xHiha1fjhZeIdiLbMTJsiN5O+sWcrEZeeLFNmzYlfr48j28tovaiiy6KrJzQ6rrzW7dulW984xuTQ4Ez+qabbsrl0ARr0BOEg2IDwSg7d+50faK2e56c1EqYa8Ykv6tke9LrQS4nQUYbzVpOEIcSKAR1zJ8/P/bWMtc1M53voNQ7cx8yHbl3ro9WZ0vFDoAdsGUIMPdUWptGHO87LV988m45OT7kaNQ5XdOXFmBdOz52UnpK3dLfPUf+4sV/6LLR0zJV9dDvoAQu7dDsdL9mul0/Z0eI2uMfefSzsvvEE06WfTorOSK9Q/o6u6S3q8fJt7/xotfKpgXrQw83aymBSIsXL5YFCxaE3q+VG2qwk5LptYKdCHBSMgsVLrvHJzNiqD5xf2TuMIfMDIFGEeC9mfseRvm5LFu97HQtN8GzIesQAdWNrkGca/fu3e6+ybsxwU55MX0OyNO7Yl6wtXYaAoZAsRAwAr1Y42m9MQQMAUPAEAggkGUCHTKP9vGyhYO/HQ2nDtHsOKN5caVuWFoOQhySu3btcrDnMVM3rfnCHMUBB/mNKVHbyMs2dRgZb/aF3E3b/uu//kv+/d//ffK0XHtvectb0m5G0+fjesGpjnMag5zk2oHgICBlx44d7v/Xr18vSKjmwaYjz420ycPonW0jcxIHq5YTQKUijQAx5g8kDZl23Fe0xIS2TLMfNTs9L9dFvka/PVpLYAjrrwb9IYv8v478UP796Qfk+OhJJ83d0znzugsBO1oZk3nd/XL92tfLS5e9sCXg+VLvfukMvzFBqXefVG9JozNw0jt3fl1+dnirHBs9LhWpumxzss+VMHdYun/i/gaJvmz2QlnVf6G8bd110tnRGboXGmCaJwI92DmeWZRM5zsY7KSlOJRQz5tyTujBbMGGqHuBuRHoLQC/YKdkHjGfuF6RcM+TJZmdzvoGgY6RFJFlpRDa+PDDD7tyZj/5yU8m3+0JwH7Tm94kb3vb21wwgZkhYAgYAobAVASMQLcZYQgYAoaAIVBoBLJMoD/22GMuQw+5L6T12slwLlIzFGlBDEKVl7c0pexxfuc1UzetuaJEAVkHmE/UNtIGpG4hHlpVd/473/mO/PjHP55sOsoD11xzTSNdadk+mt3rkze+NDbS2du3b3ftI0MkD44QJc/9bxzojWaDtGxw2vzEBNkE13SyDFthKvUOma6BJn47yHhUMt2yH1sxQvk8J+suQWBaGojAJZ7fPr3tH+TBQz+Xk2NDMtAz10l3z2RjlTE5NXbaSXq/buUr5VUXXpUJQOpJvdNIPztdFULaKdDp8PBR+cuHPyanxk+7IIiujk4pV8sThLnTcJ8Qclc5d37u7CjJ4r4F8n89/92uBnoUUwIdWeAklDyitCWObZk/BLAqoa7PMsH1Wcl0K8XRHOqqlAGpl6fM2OZ6bXsngQABijzj8Xw+ODiYxClSOaZfboL322CpP81O1zWIgMuZ3kd4V1ZFOwILsuzTueuuu+RP/uRPXOICz74Es+OP0dJfL3rRi+Tzn/98Lt4dU5ksdhJDwBAwBM4gYAS6TQVDwBAwBAyBQiOgMoJZ7CRZojj5V6xYkVrWdRZw0Lqh1MPGqFkLeZ52RmBeM3XTGkMcnDhu1bGArD7y+s2QmpBZe/fudceAvE7b7r77biFwRe0lL3mJvPzlL0+7GQ2fD4czTg5VbCC7N+iowZGjfaRGYasIzLCd1AxIlRG0eudhkcvOdtxnn3nmGffBcMoRGNaISkUSvdLsR5V753ffcAYrmc53O5GBSeBd1GNq/VfmD3PEV1f4u23/KD869LAj0Pt75jrCdCYbLY+5WugQ6K9f+evymxdemTnY/Ox0/TnYyGB2OrgU/fr51198V77z9P1ybOyEq3Pf19kjw+VRGS2PnkOdu6AwqUpXR5ec1ztP/p8X3iqzuvoijTXPTDyvFoVAD3aeZxYl0yGzgtnprM8QWazNVooj0tRxGxPww9rF8zsfM0OgUQQIgt6/f797XyfTugjGGs17rl9uIljWpF7tdNZn1mmMwOWsqgrybkiWOf19+9vfLjfffPOkQtRXv/pV+bM/+zMXIHfrrbfKO9/5ziIMr/XBEDAEDIHYEDACPTYo7UCGgCFgCBgCWUQgywQ62c9kXpDBlHW5r7jGlpc2nDlxZTQ30668EY3N9DXqvjhJfBlmyLA4HAI4KB5//HHXnM2bN0dtVtPb4yAgKEDt6quvlksvvbTp46ZxAAJOkNJnTcOZg/w8wSdBg9zZtm2b+28yISAzs2gqF+wT6EaeZ3GkZm4T40hGEuUZMEgOgsKySqJp9qNKvQezH5mDkDRKqOdBwSF/syZ/LSb4gnsi8wdCj6A/f2296/F75d4nv+ck3Pu6+qS3c2YJVMjz8UpZBrrnyts3/I68aPFzMw9Kvex0JR18iXdd0zPfuQgNvH3L52XnscddAMRAT/+k2oAjYipjMl4lQGdCvB2pdsa5XB132960/i2yfn60zE0l0CkVkOXMxggQTrupluJQMitYioMdtWax1i2O47xFPgZBlwTEkn3eLu+aRR7PVvaN57wDBw647OSVK1e2simJnXum7HSuo4997GMuyIcA7CuvvNK9Z+Hb0HdLMrrTKFvUCAA33nij/OAHP5Df//3flz/6oz865xDvec975J577nHqAt/85jcbOYXtYwgYAoZAYREwAr2wQ2sdMwQMAUPAEACBLBPo1MsiK4DM3naQ1aOvvGBCXGNxZDQ3M8vzQjQ208eo+2omKQ4SDCdJkCiIekx/ewInVOaODPRmstkbaccXvvAFlz2h9prXvEYuueSSRg6V2j6MCePhZ/dCnk+n2ODPa5wgtUj21Bo/zYmMPG/1CMRzfuaaysNyRGqsQvKkfV030xscn5qZDmkTzDxCwUHJdK6lPPWtGVxs37MI+KUJuCcSUBYMrHj61AF5/0Mfl6Gx045A7e+eM+1cqVQrcgKivbNX5vfOk7988R9KTx3CPWvjoYoh+h28bvT5W8l0JdKzGlgTBd8PPPxJ+eWpp+V0eUTm9wzU3XW4PCIj5RGZ1zMgb13zRnnewo119/E3IOiQdaodCPQgMPXqFvMcpDLLrM9FmF+RJkeIjbUEwOLFi9tK7SwENLZJRAQI5D106JBTtuI9pOgWzE5nLX7f+9435TmR64pA7I0bN7rPC1/4wky+d/H+DWnO++Sdd95Zs2Tepz/9afnQhz7knnmpj25mCBgChoAhcBYBI9BtNhgChoAhYAgUGoEsE+i8iOGwR5aRT5ENB/RTTz3lXjpxcMWV0dwMZrRly5Yt7hCrV69Otf56M+1Oal8c4YyRZpLilIxbhpkgCgJHMBwNaTs777jjDtHSAbThDW94g6xfvz4pSJs+LmMCQYkiAIYKALLBZEBOZ+yzdetW9+csEuha51wzz3VNUKKladDsAKkgALGBmohmCELuQKDnmWBmTnJPVkJdg70UUJUS5jpkfZzpOkxlEOwkiSLA2nT48GFHGGD1ShN8+Oefk8eO7paTY6ekq9Qls7vODbiAPD81NuTykyHZr17xUnnT6lcn2o+kD+5LvWtwVJBQD0q9+1nqSbcv7uN/+NG/k93HfyGnnQT/QN017/T4sKuVjlz/jeuulY0L1kZqkhLoBJ0ODNQn7CMdPEcb+5mhrNPB9VnVQ/y6xTnqXmJNLXoJgMSAswOfgwC1sgnmJViF98N2M9agBx54QL797W/Lj3/848lnA8WBgB5qiFMajOx0ZO7z9Ez8/ve/X770pS+5YK3vfe977Ta81l9DwBAwBGZEwAh0myCGgCFgCBgChUcgKNGalQ6TFYCMLNnnOMaKaMHsWaLWyWjOSl3mRx991MHOS25WJdfSmBdkNyHzCMGNIfO4fPny2F/8kb/btWuXO8fFF1+cOgF1++23T5YPoA3XXXddZmUIVRIQzDDWiTDZvVkODJmOPE87kCKNa6rI5/BrQeMcpAxJ0Ygd5irPDir1rtehP66+1HtW7mlFnndp9k2fXTTgKkxpgl+cfFpu+/ln5dT4aZeJLh0iPaVu6Sp1OkXvseq4jJXHpKOjJHO6ZsniWQvlj593kwz0zE2za4mfq57UOw1QQt0v25GX+8DXHv+m/PDAg3J89ITM6po1o1w//Tw+dlK6S10yp2u2/N8veI8j0qMYqj2QxTyTxVFKJ8q5s7qtBkf7dYuDbUUlws9OzxORFSfu7axgECeOdixx5DkkOs8+BPO2s3Gf4332P/7jP+Q///M/XeByMKgHjCDSr7rqKrnssssymZmuY4ii1Ctf+UpXKozg8r/6q79q5+G1vhsChoAhcA4CRqDbpDAEDAFDwBAoPAJZJdAhLMksXbhwoXOMFc14GSNDkWw+LEz2bNoY8MLLSzBSdO3qmAxK6zMXIdCTcDZyLe7cudMNM3XiqOWdpn3wgx8U5qXaDTfckMlrzx8TJSjJ7g1rWQwMUfJcv+kLhEkS8ywsTrZddAQglFVNhAxsspCyWCYges9m3gPHqC/1zn3DN8galXrHuWzzOu4RSO94jC1OZOY6Rt3psAFl247skk9v+wc5XR6WkfKoyzzWbOxSR0l6S91Orn1h3wJ596a3ytLZi9LrWAvO5Gen68/BZgSz07kvZJlMf+rUfrnt0b9zSgLlalnmds+ZrIMe7Bvy7cPjw9LfM1eed95Gedu634k8Cqj28NxiBPr00IEPEsVKqPvPefqsoWQ663Paz56RBz3GHTQAo90VDGKEtG0PhRoLQWVcSytWrGhbHIIdx5eD0gOqdqiGUWec5+TgM+KLX/xiueKKKxyhTg35LD0nfu1rX5M//dM/dW266667XJC7mSFgCBgChsBZBIxAt9lgCBgChoAhUHgEkJitVZ+x1R3nJQtpc4ixor2IQpSSYa/yvtQIQ6Y+Sy+LjP+2bducY5Ks+KJlUIaZ38i1Mw9VRhscIIGSMubDjh073OHXrVt3Th3ZpM7LcSHA/uZv/mbKKW666SYXLJAl88cEgpIxiaqOgBOHMcVBk+R4hsVNiROtl+tnHYY9hm3XegTIPKJ+IjZdLejWtzL5FqiUsBLqqEX4BvnHNauEejuRNcmjn+wZeB7gnggZhxHgyPNLlGeXfUMH5du//J789JktUq6MS9UJtne4f7O6euUlS18gv3nBlYXLPA8zMvWy0/VZ3Zd41/tFmOOntc3Ht35Rdh1/Qk6MnXLjOrurTzo7OifniVOwgDwvj0hvZ6/M6uqTd264XtbOWxm5iUqgo/SRhft55A6kvIOqh2g5jlpB1JRjUEId9ZAo13fK3Wn6dBaA0TSEdoAzCBw8eND5LQg4L2Lgf6MDzXsbz8a8sz33uc91AWAErpCdjhT6gw8+KMHnRIJPr732WvmDP/iDRk8b236M6zXXXOPG9k1vepP8+Z//eWzHtgMZAoaAIVAUBIxAL8pIWj8MAUPAEDAEpkUgqwQ6GU4QEmQ3FamWGFlbZJ7jKMUphYQZfcyiPfbYY45YBf+stjEJ3HAwkknASzMGGUYWftIyxGAN5tjatWsTP5+PHYQIEu6+3XzzzZlxSDMmyCP6BGWjY5IFpk+vAAAgAElEQVQVZQWth6vZupBTRp4ncUUne8ygnLXWv7Qa4BNS1DxjQKZz79MyGP6IgJeS6UUna5KdickeHQc3zy5KuJGxGUX5I9i646Mn5ZFnH5NjoyddhvLCvvlyyXkbZpT8TraH2Tq6BlTpd61AV1/qXe8dWchOPzx8VG7f+nk5NnrcZaJT2x4CvbPU6dYE5PqJm+jt7HHk+SuWXy6/9ZyrGxoAZILByAj0huBzz/iamc5zYFA9hPuYn51etPuazZ/G5o3tdS4C+/fvd8p5BJwXtfRcI+MO8cz7NMGSSqD7x2H9oXY6ZDof/D8Y97SHHnqopSpOrIdve9vbXPvw19x9991tq8jXyNjbPoaAIdA+CBiB3j5jbT01BAwBQ6BtEcgqgc6LKIQZkdwQZXm3IAHY3d3tsmezLO9LNjTzAwWAZhzleRo7XpbJsMMJgkHsEECQhtMQApWsf2zNmjVCFlBaBrn18Y9/fMrp3vve96bahun6qpLBOGEwnLlcO42OSRaUFZQ8VylV+qiS7UXO9kprPqd1HsYNKUotxYHjlMyjLJBYaWEQ5TxK1mh2epCswcHKPZ91FylhwzEKusltC2lOWR3GT8tmtKMqTXIIz3xkX+pd7x1BQj0o9e5nqbei3QdPPyN/t/0f5fDIERmrjMtoeVQqUpUOEenq6JKezm4XOPHrK35VfnPFVQ1nOSsBynNqVDWaVuCS5XMyhwhyUkJdVbL8NvPOooQ6waV5f16hbBL9tvmT5ZmZj7bt27fPBQoScL506dJ8NDqFVh4+fFj4sF5s2rRpxuc6rkVUIe6//373LP0bv/EbKbRw+lN8+MMflk996lMuoP3LX/6yCwAwMwQMAUPAEDgXASPQbVYYAoaAIWAIFB4BsoqCTuwsdJpMU7KAcaQjtZxnU5IFGTMMYgACMOvStTiWcJyT2ZM1Ke8k5gPXAtL6w8PD7vDI05JFkJaDkHlCdjQ2ODiYanAFAQOf+MQnpsB66623OqcBDo2h8pCMVyekmHF+z+qcPW1d0zjHBsIG4kYlgwnkYD42MyatVlbQOudKimiJACU84sTPjpUcAsxNMnL99SKqnHVyrcv+kd26MjQ0WTs9SNZwPfhS7wSdmaWPAGOkqjkENBBQxjOMWesQqCf1Tsv87HQNzko7IGW0PCYPHd4i9x94UJ4a2j8JWG+pR164aLO8bOmLZPnsJU0BaQRoU/DNuDPPxH52ejBog3cYPzs97fkVR8+1bBLZpbauxYFo+x6DzGmCA3lP4VnQbAIBkiFQFOR9EgK9mfe3NDH95je/KbwHYx/4wAfk9a9/fZqnt3MZAoaAIZArBIxAz9VwWWMNAUPAEDAEGkEgqwQ65DkkOg6N1atXN9K1TOwDKQABqCQLL9Z5yVAkCpxsFEjkRYsWZQLPpBoBSaAZdpyjFUEDOCepz40x59N05hHc8clPfnIKvLfceosMyUk5NnZ0kjzXDTo7umRe93yZ33OeI9STMK4ZAhq0Nh7zkKCGZp0vSqDjMJ0/f34STZ/2mEqe+99k0jfbp1Q7YSdzgUWQiv7cbBeVjqSG35d614AZ/1wocqjUOz/bNZPUSJw9Ltl0KCywXkGWEfiXdCmT5HtVrDP42en6c7CHwex0iM60yU7qoSPn3lXqlHk9A9Jdiue5QQl0I0CTndfMLT87PVizmPWYZ1Yl1PMQ8MR1wfzBCAzKsiJYsqNrR48DAdTLeHYh4Lzo78xR8MKfc+TIEXd9XXzxxbl4dvvZz34mN9xwg/PdvOMd75D3ve99Ubps2xoChoAh0HYIGIHedkNuHTYEDAFDoP0QyCqBjtwXcmg4ypGzzqORuQHJQqYi1gpSthnc9uzZ45wBSNEVOZoeaXAlCSAzIQlaJQX66KOPuiFbtWpVqm0AA2TqfPvtd71BKqWyjFZG3KdcLbs/U8u0p9QrPaUe6S71yIpZz5HeUm8zU+2cfcniIKBBpc1xjsclGbx9+3ZHfKZdmiBIntNpzQyMFTw7WKIIsK7jKNW5yTyC2DWLDwFKG4CzSr1rqQM9A+s0mCP3zlqdNhkYX0+zeyQc3pTSwZBe5b6YB1Isu4im07J62emaRexLvGv99HRaGO9ZNIPYCNB4cZ3paK6O/ZnsdNZoiPWgsWYomQ5xlsWAJ64VSgBgrG9plk1Kb7TsTGkhwPs+1wKBvnzMJhAgGYIgbdaD9evXZ3It8MeKNeH66693pP9rXvMaue222zLfZptrhoAhYAi0GgEj0Fs9AnZ+Q8AQMAQMgcQRyCqBzosLpCbZTmvXrk0ch7hPgFwZAQA4mlpNyjbat7179zoCA/K8iPXcGJuDBw+6UgEYc+2iiy5yZEGrDAl3nHq0A3IoLWO+fvrTn55yumve8Ro5LUNSqZalu6NbukoTEsrjlTEZq45JSUoyp2uu9Jb65MLZK6X7zN+bbbMGz3AcCBuwiNOxicOdbNc0CXTNDOSbjxIWWXQqNzt+Rd4fJyAynRgZuZA2cc7NImPXaN+0Lq+S6WT/+6aZj5qd3sr1u9E+Zmk/8EZylQ8G+cU85znGLF8I6P1Gv4MS3PTGl3rX+1JeAlIsgzgb85EAJ4JtVe49GPDEfPKz07NSvson0HnONHWNbMynvLZC1eZ4ZzZForOjSCAeZcJ4RoNAz7KhSPG2t73N+QVe/OIXy2c/+9mW+gSyjJW1zRAwBAyBKe/j1VpvGYaRIWAIGAKGgCFQIATIjg46O7LQPTJiyfKDQMv6C5ePF48OEOcQkhjkCpkNeXTqI5+NhCtSdMhnF8mY88wv+odBVpPl3GqSYNu2be56ZM7ElXEdZtwgrT/zmc9M2fTX/uBK6cTx2TnHZZ37Bql+qnxKKtWq9Hf1y/yeBbKsb0WYU027DdcOjhbagkHcJJH1iIMEEi4NRQj6FMw8N/K8qWnSkp0ZQ+alH2wDqWgZuekPB0F/kOms3ZA2wdd1SBAl07Oa+Zg+auHOqM8vBIrofZF1Mi+EarhetudWwSAuvTcFn181qEvVUbKcne4T6JZBnI15zZjwfKVkupav8lvHGq3Z6a0sx8GzNqWqsJUrV+byPS0bo26tAAGCzgnOXbJkSerlobI8AlobnnfaLCdEEDT4ute9zgUPrlu3Tr7yla+kGsie5TG0thkChoAhUA8By0Cvh5D93RAwBAwBQyD3CGSVQCdamWhuCE1qZuXBwBIJNxxHGC+LkLJ5dT7TFxzp1HPDiV4UC9alJ0CADPssZANrfW7IuXnz5qUGeS0C/RW//6syr2e+lDpKNdtRqVbkxPhxJ+c+u2uOrJqzpuF66DgymW8QYxh9J0M8iWsHeT6cusuXL09UZlGJc4gLTCW/VTo3tcG1EzWFQJBUxPHP3Gx1sE1TnSrIzlxTvtS7lkvR7qnUO4Q642ZjNv3AgyWqP7oGz58/3wXOZeG+WJDpmqlu1JN6p7F+droS6knckxsFxiS4G0Uuvf20HIcS6vo85K/RZKezRvOd5hrN/YJSVRhlkywgLr15UcQzPf744660Ae+Tab6/ZR1Lniu4/nmmGBwczGxzP/CBD8jnPvc51z5UMma617HdpZdemtm+WMMMAUPAEEgbASPQ00bczmcIGAKGgCGQOgJZJdBx4hLNjfN206ZNqeMS9YQQcmRs8/KMEYGOjFuenc9kaKMEgBQdhFERjJd4AjNw6jE2BAZkSWpP63MTeIGzIS0jUOKTn/zklNO98u0vl4FZAzM24XT5tKuPPtA9X5b0LpMFPedFbjIBDVw7Ks3MdcP1k9S1Q8YRdQohhwieSMKmI8+zRD4k0e+iHZN1Qp1/9M1IxeyOMNcc92GVeq+V+ahEDWSNyfWeHUueA7nfay1j1mBquCa1Bmd3FrVnyzQ7nfVOfw4ioWQ6/899TD+tRMwkuFuJfvRz6xqtZHqwHAdHRDVEs9NR7kpyDeJ9DdITW716tSPNzAyBRhEgGIN7KcG5aZbgarS9ae3HswVqQQTjE6iSVUO6/Yc//GGo5t15551y2WWXhdrWNjIEDAFDoB0QMAK9HUbZ+mgIGAKGQJsjgMMsmLWVBUh42dLMAAj0JJ0ozfYX8hGSRTNMIT/TlN9utv3T7Y/sGlL0kEb0Ke925MgRV79Y69JT8xBCJUvWivrc9B+H5kc/+tEpUPzGDb/msoJmsvHKuJwsn5D+rgE5r2eRLO1bHglOrnPIcw1oIFAj6cAB1hXOS5YIRFHcFpRs1/mW5TUsbgyKcDyc66giqJOfoA4cgDaO+RhdnmuUTOc7KPUOOaNS79wH2nVcCWBinvON4fxPeg3Oxwxq31bWy07Xa0nVVFqVne5LcFsN6/zNV+6xSqbXKscBoa1kOmt03AGIrHkEamNkxqaZ/Z6/0bIW10MAdSvWTgKz67071TtWUf7OvYLnCwIaCVhmnTYzBAwBQ8AQKB4CRqAXb0ytR4aAIWAIGAIBBLJKoJMJpbXpNm7cGLvjJI6JwIvhwYMHJ+vi4pAvUh1Garkj7U0wAP3KqzFOBw4ccHXNsCzXpU+zPrc/npCEH/7wh6cM8Sv/+8tlYP7MGejl6ricGG+MQEfdgMCTtAMayDjCaQshyidOC5LnHFvJhTjPY8dKFgHuP2TNQMKqUkURgqKSRS27R8epDUGjhLoqxWiLuUYhasga47tdMhFxauPc1nlOAJNlzmV3HreiZVw7/icYiEKbWpWdbjWsWzEjkjknc4z7rhLqwTWa+3AwO73ZlvDcSwAntmbNmky+ZzbbR9s/PQR4f2Mt5D7Kc4TZxL0B1TeuNQKW8+xLsPE0BAwBQ8AQmB4BI9BtdhgChoAhYAgUHoGsEui8bPEyim3YsCFzDm2V9qVWO8bLMnWri+R4379/vyOdcajnNWo8WFsbEizL9YvTqs8dXNhwXv71X//1lP++8tqX1pU4H6mMyOnykAx0zZdFvYtlUW99QjoYeIKUMvOLAJQ0TAl0nDlkocdh9Ek/Sjbg8DXyPA500z3GiRMnpgR2oL6RNaWKdBEp1tm4Tsk8ZJwh1FW23O8lRI1mp7M+FTE7HaKKIBHWKzIveX6h32aGwHQIqLy73uP0nudvHyTT/Sz1uJG1GtZxI5qd47FGK5nOGh0M3KBeuZ+d3sgaTQAR5B62du3aQq7z2RnRYreE+ak+C7uXnh1rcCFIheuZsllFKQdX7NlsvTMEDAFDIDoCRqBHx8z2MAQMAUPAEMgZAlkl0Mk+oB40tm7dutTItTDDF6zZTK1QXgwbceCEOV+rtiG7ng9OqizXLZsOH4IwNPKdbZKurR3HOCUtLz5TGyHQcYyrvfiNL5QVy1ZMO69xjCDfXpKSzOmaK8+ZvUr6OmcmYDg+WeeUPcAgqXA2pSmdiWQnxBlygly3zZpPnGsGupHnzaLamv0p80DgEIaDnmyZtAI7WtNjOyskHESNZqf7a6DOA1/qPW4Z4VaMAIF/Ws6Eec4abDXhWzES+T5nPal3eqeEun9PjOsa8mtY84zKXDYrHgKqIKKEerDsGHOLIDfWab7DzgOIeRQ4MCPQizdv0uwRc5QAaKxISnTNYsj6T9Cy1oZH3t7MEDAEDAFDoHgIGIFevDG1HhkChoAhYAgEEOClLyiVlwWQIPa3bds26djIinMXJzsOF63ZzMvgggULsgBZ7G04dOiQkz7HIbV69erYj5/kAYPjlEZt7Tj6k6S8eL32IeGu9Z7Z9oX/7RJZev4S6SvNOodExykyUhmW4cqwzOnql/6ufkegz2SsMwQ0aMZnqwJPyIYg+zQOAl0Jc83M43fNOi9aQE29+ZPnv6sqwrPPPuu6QSYumedFUhTJ8/ik1XbmgS/1rnXB9fxc0wSUKaEelqhJq/1hzsMc576O8VwFeZ7HfoTpq22THgJ6D+TZWH8Onj1uqXefQOcZ1dbr9Ma7VWdSBRE/Oz3YFtY1zU6nZNN0z2Ks9ahw8HcIdDNDoFEErJxEbeS4XgkMBx+eqeNS/Wp0nGw/Q8AQMAQMgWQQMAI9GVztqIaAIWAIGAIZQiCrBDrt2rp1q0NqcHCw5dKivATieKYuOIajjijzIkv7Uv+c/uKAoj5gXoxxIrsuj+Ok2dFxyouHHbdPfOIToiUJ2OdFr36BDFw4V7o6uqS31CtdHRPZXePVcUG6fbw6JrM6Z0lvqU/On3WhzO3qn/ZUSGVCXGuwDoEn5513XtimxbodJD79hMBfvnx5w8eejjyPK7uu4YbZjpEQ4F7DekFQBUbJCuanjWMkGAu5sS/1DtkSNO6NSqbPRNRkARzWK4LiuK9jPLvg0E5T/SMLOFgb0kGgXna6SnL7Eu+apR62hVyfPDNhRqCHRa1Y20HMKZnOGs3vvnEfVzKdb3+9Yz8UkdgmT+84xRrBYvTGyknUHkfuAxDofBOst2RJ/TJfxZgR1gtDwBAwBNoLASPQ22u8rbeGgCFgCLQlAlkl0HGubdmyxY0J0ow4PlplYASRjLwvRnYi5HnRs7aUiCabIw/ZGcwZxkmzSCE0qK2dp3GKMzs66vXyhS98YVK+mn1f+Irny4qLl8tIediR5r51dnS6zPTuUrcs7l0qC3oWTns6yGqttYujkmsH0qlVhoIEEvIQ+I3KCSp57n/jmLWs81aNamPnxenJ3FRVBOYEDj4bx8bwLPJeStSo1HuQqCGoTsl0nleyFICh90YtnTEwMOCCh7LUxiLPnXbvm9ZM92unBzFpJDvdJ9AJtLVgkPaeacwhgjWVUPcVlRQZ3guUUCegk3cGI9Dbe97E0XsL5qmNImv+7t27XSmPlStXusBlM0PAEDAEDIHiIWAEevHG1HpkCBgChoAhEEAgqwQ6zSQDnfZBgpIV2ArDwQLhptln8+fPb5vsxKNHjzpyCQJ6/fr1rYA/9DkhM8gsxnGGQRCQXZc3giCu7OjQwHkb/tM//ZPLFFC7/IqXyHOef4GMVUalXC27DwZ5zqcL8rxnqfR3D9Q8HQ4Tsh21pjS1pLmWW12OgTnN3Kb0AtL+UU3laZUMsHrnURHMxvY4PLneVBVh2bJlhS3HkQ3Ei9MK1jaCLpRMDxI1fk1enl1aGcTFvZEsS703WpBIceZhHnsSvH9yLWk2uvYnbHY61x1Bh5gR6HmcDcm2mQA5Pzuduecb7wf8H98oGOTtfSFZ9OzoURDw1yLUDGwuTaDnS9tzjRW15F2UuWLbGgKGgCFQRASMQC/iqFqfDAFDwBAwBKYgoPXksggLNdB5+SJjFUI0bcNBjnMOJwwGwUL0dLtkJ5KtRvAAmXUbNmxIG/7Q51PHhdarJYMUCfQ8jlMc2dGhgQts+C//8i+Tqg/86dJLL5Vf+7Vfk6HyKTk+dsxJtmNIuvd3z5M5nXOnxZh1BVlsVW1ALpjrOAs1SiGTaBfBMARZhDV19PsEgJHnYdHL1nZa+5T7C2PIPGilKkK20LHWREWAIAxKAECoM7eChCBBQ5qdjoJNWvcmnl24p5CViXFvtAywqKNr2yeJQD2pd86t2en+/RaCKnbSqsPraTXJXtux00ZAg56UUNf3Bb8dPKdqdjpBT2mt02ljYeeLHwH8BdxrMRTbbO5MYOxL2xNYMG/evPjBtyMaAoaAIWAItBwBI9BbPgTWAEPAEDAEDIGkEcgygb59+3aXHQi5AdmVppGhCtEGPjjqqN3Vqiz4NPvtnwvpbTI0kcW8+OKLW9WMGc8LYUEbccIqEZbnF3TNjo5K7sYxON/97nflRz/60eShNm3aJNdcc03kQwfVALKm2gCxj8w/84TrOowpea6yzZq1pPVbwxzDtskGAqxrzAHGlIAO5gCyrmaGQBwIsDZA0mh2ugbg6bG5n/pS70nJTgcVFihXked7YxxjY8fINgIanMZ9Vn8OttiXeuf9gOd0rGHSqkOkA+LcJ885YBXifuLbrHgIMHeeeeYZF/hUyyDQlUwn6Mkyios3B+LskQZlcsx169bFeehcH8uXtgeXdvOj5HrwrPGGgCFgCERAwAj0CGDZpoaAIWAIGAL5RCDLBPrOnTtdhgmOX2RH0zDwOHDggHOsYGSOkTnbatnpNPoePAcEwN69ex0xDZmaJWOcIEGpX4hBhCEPjqMrz9YIuRtXfx944AG57777Jg+3atUqufbaayMdPqgGsHTpUlm0aFGmsjGYM0jLo2rBtV3PtM65OvQ1qMbI83rIZevvWlLg0KFDk2s75Hkr5bWzhZC1Jm4EtCavkumaCe6fh6xHCHUcy5S5iMM0Gw4iEuKHUhWmsBAHsnaMNBGol53O9aQlYqivSzCKZqmHaWdH6SxxPoVEV/KcgxiRHgbKXG5DoPTBgwfdMwCqVZqdHgx60pIcfnZ6LjtsjU4MAeYOwTzcb8m0NptAgDWaIHeMQHyed8wMAUPAEDAEioeAEejFG1PrkSFgCBgChkANBIL1O7MC0u7du12dUaTTIeGSNpzNSLDh7MZwaJP9nlSGWNL9afb4OAQef/xxdxgI9KxI0gXlwSHNIUKLQIRFJXebHWN//5///OfyzW9+c/K/cCjeeOONoU/BdcP1o7LYkJOtKL1Qr8FRMFby3P/GQWbZSPVQztbfGT+IFhzmGI5wSMV2XduzNTrt0xqyHrmvkvXId1DqHQJds9NxNDdyz2UdRsmEYzO/WYfzHljWPjMknZ5CavBsR3Ygkv4EqGad2Ahmp0NyQn7y/sL9mGdAPztd79PT3auVPFfifEq2OVnpZ4ZC/786tXx2OgNlZ0kUAUr5EFBHgDQBuJgGlSuZzjto0Fin/ez0RtbpRDtmB08dAe7pvFtwzx0cHEz9/Fk9oS9tjx/B1J6yOlLWLkPAEDAEmkPACPTm8LO9DQFDwBAwBHKCQFYJ9D179rh6omSxQuYlacHMWc5HvdB2dozw4ksQA7Zx48ZMkIZa0xXnFoYkLURYUQhNSD7UDwjeUIdekvPePzZqA//wD/8w+V84CW+99dZQ1wBqAGTPY1lXAwiLcZA8p2/Ms3ZeE9Kai3Geh4AOMoP8NWP58uU2jnGCbMeKjACEIM83EN4434NZj6w1vtQ762o9O3bs2OQ6TEAZpGJcWe31zm1/zwcCrIdf/vKXndKSbwRZKJnOt354FsnaPQ/in2A9rWPN8zqEpm8anKJKMXrvds+KEOR8nWHJHUley84Q6Uai52NuR20lz608b0PqTadGxPXCOq2Eupbx0XMxn5RMJwglzDodtZ22ffYRoDQQ7xaM/+rVq7Pf4JRaqJn5nO6SSy4pRKB7StDZaQwBQ8AQyBUCRqDnarissYaAIWAIGAKNIoATKpgJ1eix4twPQg/nMs4xSPSkDOc1zrii1NGOCyeylHbt2uUOh/Raq7M1ac8TTzwhZPFhWZQHbxZ7nNpkxECcIEmappGd+6lPfWrKKW+55ZYZsxeDJQ9wREL8Z1kNIAzGmu3Gt64LRp6nORvjORdrBWu7BolxL4EYyhohFE9v7Sh5RYB1lDmqUu+1sh4hOH2pd38Oa0kTMnIx1mEyz43MyeuMSK7dlC/53Oc+F/oEBGCwbuoHNSh+blVZI95XkARmbecaIIASAhNi0w96C3ZQs9PZp7NrQkWm1NUhdTPLPRK9Wg4Nm22YAwS4FviwtrJe1jNdp5VMr1WSg7VXCXWuEXvWqIdqMf5O8BrvFqyXab+7ZRlBzcynjc973vPsmSTLg2VtMwQMAUOgCQSMQG8CPNvVEDAEDAFDID8IZJVAx0lGVDeEBxmDcRvOELIPNBNHM7ZM7nQCaebFjh073M/r169vKSnqBzng+ERaP4vy4M3OUQgQPjjgqEGepkEUf/CDH5wSTHPDDTdMe+3hsEYqmLHBGA/GJetqADNhzJoQzDzXmqrmCE1zNjZ/LpzbkOdk9jJ23ENQrDAzBLKOAHPWl3pnbfaNZxXNTud5haAr5Igx7h15WIezPgZFbR/E82c+85lJRY5G+8laCpmOUhPBjHySzlbXero8e+hzYDDzPCj1Xis4uOQI9A6XgV6tdkjpTCp6R0mF26eiQra6I9qph25S7o1Omcztx/sfWehkjrNmRjXWaT87PbhOE3TsZ6e3Ogg5av9s+/AI1CoHEH7v4m6pmfms12Sg2zVQ3LG2nhkChkB7I2AEenuPv/XeEDAEDIG2QSCrBDrkHFmx1Gc8//zzYx0PHB3I+hI1juFAQcLPMrbOwoyjdfv27e4/1q1b1xIpWJyfZIggjYdBHJDhXNQ6ahAhBHQwH1shA/jJT35y8poA79e97nVOfSBozA3UADQDB0c6DvQ8kMzTYazEuTpB+das8zz0K9YFMucHI5OX9V3HEOd4kGjJeRet+W2CAOuSSr0zr1W2ulb3ITDJyLX1qk0mR4Pd5JnqRz/6kXuugvgJylI3eFiXyatkun5DtMcxH7kGVCkKEoaM4TDBrtwD6J8S61WpSmdnSTo6SyKVqlTO6LdDnavcu/v2yHQj0BudEdneTwOPeDZg3WzGWKdRDtHs9FrrNPNVCXUyleO4Lppps+0bHwJhygHEd7b8HAkfDkHLrNnPfe5zjUDPz9BZSw0BQ8AQiISAEeiR4LKNDQFDwBAwBPKKQFYJdAgQnHvz589vKDtguvFQCUgl/xYsWOCyE7OeOZv2/MLpuG3bNnfaNWvWpE5a4/CkrjYv4Fg7BDng2N63b5/DGszTtq9+9auOGFd7+ctfLi95yUumNANHNuoQmtlLcAvXUF6MrCOIA5yZg4ODrtnTkee2JuRlVM+2k3uGH3AD0dIqueH8oWctzjoCKvWO8kctqXfuHZqdzs9G0mR9RFvbPp6zCCRVOWu+IYP4nilYI2yrmYM8X/PhWYHvqAGQBI4QUMt9miBXgl0bWdPPkugVEbLKyxOKM77xm+ahdyDz3tEhpc4OsTroYUc8P9tB7PF+wXoZd5A2QaZKpvPMHJxnzGM/O92eNfMzb2q1VNfPRtUM8t376VuvmfkEv2/evNn8LEUdaOuXIWAItD0CRqC3/RQwAAwBQ8AQaA8EeNEPStnxrZsAACAASURBVM9loecQibyUIg2NwywOw6Ghsr4cD6cJGe5m5yLAnNi6dav7A0RjmGyfuHCEnIWkxfGEEUTBWBXdyYTjmqABnMNr166NC87Qx/nWt74lDz/88OT21Kx79atfPfk7jnZ1ZJNRwHWZt8zeYJBCULKd3+mbEU+hp00mNmTcyChjfDGrA52JYbFGJIAAz2w8x0Cm61zn/4KZxJA0SqazThf9/pkA1G17SNZTgjQIOGNd1Q/PKM2+L/DM7RPq1FSfbm4iAUwwLUbWLs8ckDFNGeS5VJwke6VcmchCP1O+xT+uUutkrE8QoB3CbnYdNYV+ZnZG7YlnWpQ7kigTph3levGz01mrfeNZE+JVCfWm53dmEG6fhsSpZlAk1DSwgLV706ZNtnYWaXCtL4aAIWAIeAgYgW7TwRAwBAwBQ6AtEMgqgY5zg5dSHMArV65seixw/EHKK0GWR/KvaRAiHACctmzZ4vZAThwHTxqGMgBZ0OpkWrZsmSxcuLAtCE2i9XEW42xANj9te+CBB+S+++6bPC3S19dff727ZrgWydjBIPgbzQJLu0/B8/lBCmT5a1aakgIq297qdtr5wyPA2LG2Q7Zg3DOQZDWiIzyGtmU+EIA0hzzn/gjxQmAZQYYqIUy2Lh8l17VXStJwbUAYGUmTj/HOWisJbuQeqs8DPBPwrB6cb1HazfMO85jnDT6QmQR/+GoisQZEOb32M58zLHm1MkGk8821pBnDSLlz7UC0l8sTpLtKvXN/sXtMlJHO1rYo1fDMwPrJe0YaxrwKZqcHz8v1oGQ6gcsWzJnGyDR3jiTVDJprWWv3JgCL+wXr98aNG20ut3Y47OyGgCFgCCSGgBHoiUFrBzYEDAFDwBDIEgJZJdB5IeXTbD1oHBaQK7zEYbzIQf7hpDCbGQEIdPAjgAHHe9KGM4sMZ61djPwyzv52MeQk6T/kxvr161Pv9q5du+Sf//mfJ88LUf6e97zHZcWTqYPh2OP6IUs7j6YEOtc/ygrMNT44KY08z9+IQuhwzaicNdmNS5YsMUdd/obSWlwHARRZmOtkmrNWQTZOpwCC/LaS6bUkhFnbNTvdSBqbes0gwDMizwcQ6ZCSfPPRMklRj82zBUGTPPuxnjPPV61aFd8zh5LnNIyfp6q4T5Do5KiXK64Wuguyq1SlPF6Z7Ap9VmLTJ9KNUI862q3bnvdCVBbmzZsnS5cubUlDmFsq9c53UEWE+eRnpxNYYpY9BFTNIM1gjOyhcG6LNLCAZ4yLL77YnsvzMGjWRkPAEDAEGkDACPQGQLNdDAFDwBAwBPKHAARE8KU9C72oVas4arvoG9laOCYwXm5xxpmTKxySSLjj4LnooosSJbJxRjLeOCEwleqMWiszXK+yuxVOaOYrTrINGzak3lACGD7xiU9MOe9rX/vaSaeHSq/mOSNGs/wJUkBZwcjz1KdZbCeEJOR60Vq9OMGtJEds8NqBMoQARA/qJFoHmuCysPdHJWkg1DlO8HkPwtKXes9rcFSGhqvtm8I85XkCkpIAPL55vmvkXYPnDbLSCdzjQ7Z60woKdUh0N4BnyHUI9fJ42WWg+9npOsiT2eodE9nqGohn7xnZvgyUQKdEFEF3rTbmEUoOSqjXCkAh8Emz01n/8/ws3mq84zx/FoIx4uxPXMfSwALmbCveaePqhx3HEDAEDAFDYGYEjEC3GWIIGAKGgCHQFghklUBvth50UAocBwm1Fs3hEH5aP/bYY8L8wFlPlkYShnMfYsDPcOZ87ZhpgcOZ2u8QGETrp2048D7ykY9MkWO99NJLXXYODmzIyTxfP/QPAh2HPkaGGx+cO+0439KeX3Gej4xzyHMIGeYkku3tpFYRJ5Z2rGwj4EtZN1sHmjWQZyPNTq9F0pDxyLUEqW5KPdmeG3lqHWs1GYk+qY7qTlTj+Yj1Xgl1nk0aIquDJLrfED8rnZ/P/K4lX+iL/hxsf63s9IbaFxUY2z4SAjwHsg4uWLDAvRtmzZhjfna6lhnSdnId+NnpFvjUuhHUuZSVYIzWITH1zBpYwPNEK8qSZQUHa4chYAgYAkVHwAj0oo+w9c8QMAQMAUPAIZBVAr0ZOWvIWEhZlQIn65zsc7NoCGzfvt3V6wM/HANxG8eGMPbll3GG5pmkbQYjsgOp/46zlXpxrbC///u/dzLBamQNXH311bknJ3Fqsx4w18BYs8bop9UHbsVMa/ycBJrgsGQMcRoTcINEpJkhUCQEVJkFdRaMOc69OM5gH+7BSqZD1vjrIueEQNfsdMiadr03F2leZakvzD2e1QmG2rt3rwtwi2pk4qKShMw7n0jlhnwSPXhiJdEDEu/+ZjxT+GR68Pqx7PSoo5ne9sw71jwCQxctWpTeiRs4kwY+KaFOpnrQuD9odjrrtq3VDQDd4C55mksNdrGh3TSwAP/L2rVrGzqG7WQIGAKGgCGQfQSMQM/+GFkLDQFDwBAwBGJAIKsEumbjRpGzxslAhsuhQ4ccMu0qBR7DtHCH2Llzp8tGRjIzbmlkJTKZfxjEOXUv29lwjj3++OMOgs2bN7cEim984xuCdL8aWQNvfOMbW9KWuE6q5Ll+Qxox/3De1yKNkMnUDEyTyYxrFJo/DuOHMglrvK7vkOeWJds8tnaEbCHAXKeetGboQgqSdZtkJitkIPXSVepd782KDOf2pd7jJPKzhb61Jk0EmHcE7XEv5t4MIc0c5P8gYIKZt/XaBhmqZDoBJ6EycyHSfZuBNJ/u/LTT/wTJdPbT7HTITZV7T/KarodVO/+d+cV6x3tH3t49uE50reY7ONdYm5VMJ/DJ5liyM50AIN4p8jiXkkRGAwsIwB8cHEzyVHZsQ8AQMAQMgRYiYAR6C8G3UxsChoAhYAikhwDOqqCjNL2zT3+mqNm49IOXNYh3DOdBu0qBxzV+u3btcnKvcZPbKATgvMLpg2MHKc5IWUNxdTBjx8ERtmfPHteqTZs2pZpBwlggt/fQQw/JI488MokMgRM33XRTxpAK3xwlzdWx7dco5Sj16gPjiPRJI3NEhsc+zi0ZR+opaoYiTuHQ5EicDbFjGQIJI6BlTSARMZzPy5YtS/1+QPCcZqerSozfdTIeWRsJNrKMx4QnRUEPz3M7KkRaSoByMX6wJkQhz/Vsg3IM94BaxPR08DAvIdPXrFnjvtNSKvGl3vUZJNjGoNS7T6oXdLgz1S0lPQm4iDtAOM2OqrKSZqdzzfjGvApmp6fZvnY4l65hlAKgJIDZBAJ6jXF9sf6aGQKGgCFgCBQTASPQizmu1itDwBAwBAyBAAJZJdD9bNx6ZOLo6KhzrqmsHVHgaTucizixdu/e7aLqwTIOiUMchqgD+BmkSG+S8WsmDmswx5BwT4usZQ3A0QFZQsbjD37wgynDccstt6TmeI5rHjDXgpnnQfI8eC6VySR4ByyCMpnsT2COZqdbBmZcozXzcYKE4rx581xQj0mUpoO/nSU9BHQtVsKa+y6fVs91gix9qfdgVnB3d/cUqfe07l3pjYydKW4EIPp47tD7LEpHrO0zGUQ7wZcQVki+Hz58OHSzuIYIuiITEkI9TaKrntQ7nfCz07l+9HkldAdtw0gIFJX05H1YyXTuI8GAE9ZqzU6HWLe1OtK0qbmx+h+WLFmSSLmz5luY/hGYd6zvrNk8w/Cub2YIGAKGgCFQTASMQC/muFqvDAFDwBAwBAIIZJVAD0sm4tTlJY1+4HDCCZemY6zIEwo5cRwxOAX4NGMq06kKAWSuoRAQSl6zmRPnaF8cDWT9YxdffHEq2ASDT3B0UAfdV6V405ve5BzOebFGyPNafQMbnzQKbmMZmMnPCEgWCBPNUMwKoZh8z+0M7YYAcx1Sh3UHI3Ati88yrK++1HutjEdV7eDbAo3abSbX7y9znLnO3OG5nfIEBKZFNZ4nIdJ5VvWDaMMch0BbStTwIXM0rSAVPztdfw62N5idDslpRGeYUQ2/TTuQnlqWQwn1oNoccx41HyXUIdfNoiPA+sNahoJGvSCg6EfP5x6sYazxBEjhP+B938wQMAQMAUOgmAgYgV7McbVeGQKGgCFgCAQQyCqBXo9M1Hq4yE5jOGmRAscZYBYPAjgmIRBxLuIYaNRwLOCsUhLMFAJqI4mjgbrz2IYNGxInHnCq4eDQ4BOys3D+fPnLX3ZBKWqXX365XHXVVY0Of6r7+ZLtSqRrNlczDnIwAi/NTp8uAxMSgDWomXOlCliGT8b1wPxUpy9Z58hZmxkCRUOAeyNrLnO9GUKxFbj4Uu8Q60Hr6+ublHpHbcbWxlaMUnbOyVzX5w7uzRArcTy3c0+mZroS6vv37w/daQJVlEznWTfNOVovO10ziH2Jd8tODz20027IPCGQo12yhplHwez0IDisz0qms26neR00P6KtOwKlt7h384zaSCBQ61qe3JmZb1xjvP8TDEiQlJkhYAgYAoZAMREwAr2Y42q9MgQMAUPAEAgggPMmmEGUBZB40d+xY4dryvr168WPjKfNEOdaD5dMUMhzi56Pd+RwcpLhA+GNY6ARw6Huk2AoBOS53mAjGITdx5/zOHOp35mUce3gbMbJEQw+ue++++SBBx6YPDXX1u/+7u8m1ZTYjjsdeR535la9DEzO52dgmspC9CEmWIHMc9Z68CS4A8eumSFQNAS4R0Ke61yPi1BsBU5+oBHXML/7xr1G10au57jX5lb02c4ZHgF/rnNfZK4nUZe8KlU5eeqk7NmzW3bv2i1P7D0biFWvtQQR8vyFChDkapokImuA/6lV792Xelci3a6jeqN67t81axhyb2BgIPoBcr4Ha7MqifAdXKuZU0qm823PsdMPOMphXLe833J/M5soScE1RmABuDTqQzAsDQFDwBAwBLKPgBHo2R8ja6EhYAgYAoZADAhklUDnpeuxxx5zPVy7du1knWyVOdUaoWQk8nJmDqQYJkPgEDj1jx075ghvMI5q1NN+6qmn3Is0zheIWCPBpkdxujkfFfeZtmcsqEFPLXqMLBPGxSfryYL/+te/PnkYAlNuvfXWTF9jwXrnNF4zz+PEL3gszepBqYHsdF2X/O3IrsOpRmZKkkERSfYzzWOzbgSVRciMMjMEioYAAWrBQKaizHXWRtZDLYOhta51DFU+mHWR9dECIIs2u6f2h3lAUJQftJfEXIc8lw4R6ahONKCj6oKEf7H3F7Jr527ZvXuPDJ06VymhFvo8+0Kk80m7nIIv9a5qOkFCPSj17mepF3s2Nd87zRpuVwLdR5B5hDKESr0H12p9V1BC3ZREps4/3pnAkCxre8edwIb1CwKdwAwCYJtRsWv+arcjGAKGgCFgCCSJgBHoSaJrxzYEDAFDwBDIDAJZJdBp19atWx1Og4ODLksFZyxS4Crpi+OD7Og0M0QyM3ApNATym0xlghR4AQ5rOBIOHDggzzzzjNsFZ8tFF11k5GEdAHE0bNu2zW1FzXHI7TgtWIce4oJxDWaWkI1y++23Tzn17/3e7zkZviyaOpo1c0uzslqxLrA2KWHEd9DhDYGuhBFrWivamMUxpE1gxZqh6wbzn/lpxFpWR8za1QwCzz77rLtP6j2SbNwiz3UUVnRt5B4TXBt5TtBAI5MPbmZmZW9fAkV4nsS4ByapGFWFOPfIc/j0CUZ9wqrVijz91D7ZsWOn7Ny+06kshTEyKCHSUcRqRZZpPal3vYdmXuqdoTgT2xAG96S22b17tyP3THb7XIR5jlUynbU6WLKIdwY/O72dA9i5j2npraQUNZK6BpI8LnOGIBW+We8pBWdmCBgChoAhUEwEjEAv5rharwwBQ8AQMAQCCGgGZdaAoV1btmxxzVq9erWr3abZzFo30WqNJTtqZMbh5EfSEsdAGMMhRZYR2bjYdCRtmGO12za1gkbiwiCo3FCvDv0dd9wxWSKBNrziFa+Qyy67LK7mxHIczcpS557Wck8j8zxMB2iXSmRyPWjgj+6LE9KXem93JyRZ5yheYOBCNk87YxJmjtk2+UOAdQsFkMOHD7vGo1BRK5Apfz0L32LWRgga1kVI9aB8sL82mnxweFyzuCVBmFqPnMAIniWR8k/CXPZ56WzmeZA8n3pOtq7KgX0HZcf2nbJj+w5B/aSeQVCvWrVKNm/e7AIdWyFt7Wen68/Bdgez07mXtux+CmleqoEsQ1Wph3gyfzfZ7XC4qpKIEuq8iweNe5gS6gSBtVNgKNcfcwmDKI478DncKGVvKx+XlStXumQHM0PAEDAEDIFiImAEejHH1XplCBgChoAhEEAgqwQ6zYRAp33Up9MsEbKUeElNQvrRJsdUBHB6kg0KCU4GeT3DsUK9c6QAsUWLFjnZtnZyptTDaKa/B4NGcErFYUHlhjB16O+55x556KGHJk+PA+S6666LozmxHEPJcyVetH6wZl/FcpIYD0J7kcVUwkivET1FO8sZa9ANwQYYUrm2bsQ4+exQmUGAdcAPFOHe2u4laFQ+WLPTg2sjgwc5o8FGVgYjM9N5xoYwrgSJaLmYNAJFJrPPyUAPZJ7XbKzbbiI7vVoWOXTwkGzfvt2Vj9Jgrpk6iYoMWemQ6a2UKK6Xna5qD6lnpweJ87NCAFOz0CHRU85KN9ntxtYRgnH97PSgkggEupLpXB8tC9porHuR9+L5FTUDjPdk801MQOjjQhJE2iUwIg+k7WAIGAKGgCHQMAJGoDcMne1oCBgChoAhkCcEskygI+HuS8dZNnO6Mwt5WZyfOK4hUGcyHCqQ55oFDClgL8zRx+vRRx91O5HhFEctPQJPUARQgpngkzDyoziR77rrrskOkGX13ve+NxMSw1rvXDOv+F2zzvMSrIETUgkjrp2gE5IsFiWMiixnTNDNL3/5S6cwgi1ZskSoO5uXcYx+hdse7YqAltDgescsUKT2TKi3NkKg69oIKWtrRfauKO5nBw8edApGWFqKIpEJdK2XDole6RD+YbQfBSZK6kCmE4RYz7h3Pfe5z5WNGze2NAtVS9nod/DZQvunZLqWvImd6PTJc7LPlSDnW4l0vjUDPWUSfceOHW5IUf+IK1i13hwp2t9VZUkJ9aDKkgaG+tnpRcOAPiNVru9tRS7DEmXsuI9TAx1bu3atS4QwMwQMAUPAECgmAkagF3NcrVeGgCFgCBgCAQSySqCTral1xWgy9bNwUJmzNL0pjAOUD84PCN3pDIlOnI3MJYhWovDNIdXYOKnqAgELYYju6c6i2V8qnQrpECU7gizAv/3bv51C7L75zW925RRaaUqe+9/MuTyvC/XkjJG71drAXFexO7pbNKCQEpDnftCNOdlaNBh22kQRwMnOXNfsap5nkDTN87qVKGBnDq4EjSp3BAka1kK/DEYr5LTTwCFP5wiqLFACiDrTacz1uAh0H2/uTwSHEtDLOwnE0EwGgbZhwwZ5/vOfL8uWLWvp0PlS76raEyTUg1LvfpZ6U42HNFcSHdJ8ugxztlMSvdzUGUPvTJ+tbnVouEJtqL4EJdNrBZ3wHuJnp6exJoRqfBMbEfy5d+9edwTej5IqT9FEE1uyKz6cJ554wp17/fr1Tb3PtqQDdlJDwBAwBAyB0AgYgR4aKtvQEDAEDAFDIO8I8KKTJcNZirNZs8/J1KIerlm6CCDfDgGLDN/g4OA5J8dhwt+1liuZsmQ4m8Rq4+Okqgvg2CiZyHWDTDCBDRgOq0bqjn7pS1+Sp556arIzl156qVx99dWNd67JPYPkOYfLSr3zJrs2ubvWm9Ts9ODajMPRJ4zy6qxjjWduadAN85N1xswQKBoCQZUFyMT58+cXrZuJ90fLYOjaWIugYQ1BqYg1kueQIhA0iQMb4wl49iCYkvUdS1tloSECXdOhvQz06SDhWiZz+ZFHHnHKPvUMWXeIdAj1LDwX15N6pz9KqGtmun7X6+uUvytxrpnmM8mzs41moqeUhW51qyONZkMbE3hCWR4l1LXckh6MZ3cCQlmr+c7rs6xPFPOebEFcEyNMsCCBRxhlLiyovqHLyHYyBAwBQyAXCBiBnothskYaAoaAIWAIxIFAVgh0HDeQtkiH+4bDmWwts3QRgBiHiIUYX7NmzZST4wwhyAFnNgbZS5CDOQ+aGyPkQsmyg1AkcyuqaaajLxPM9dNI1vIPfvAD4aNG1uSNN94YtUmxbK+ZVCpL6jt3YzlBRg+Cw55rDEJCa4T7TYUw0uz0vBBGyPrqGk+bmetZIBcyOgWsWTlGQJ3IqrKAXHAzyiI5hiL2pnOv88tg+OV+OBmZwL7UeyP3wNgbXeADMschlfU+tWjRIuGTZhBDlTTnUi2t8FrAVyeI2zM10H0J9zDDRIAiJXdQDdKAgen24/6GvPsLXvCCzJQ28rPT9edg+4PZ6VxDoa4jP/tcs8tnAtWNw5lPmO3DDNAM21jd6iYBjLi7Bj8pma5KLP5heM/U7HTqiKe5bkTszpTNfaIYqfK8tLuZPofZl/uABhlt2rSppWUtwrTXtjEEDAFDwBBoHAEj0BvHzvY0BAwBQ8AQyBkCkDS16uSl2Q0cOGQkHjt2zJ2WaGXNxiSLA/LOLF0EcBAyJjj/1q37/9k71yA7qyr97+5cRGWAhJAEUHODXAGBgAIKIgooYACjguBd1NIZxw9aUzVfnBlraqrm01jzH0tnHFBHEQUUUBAvAbkJcg1oQkLucbjkRhJQcUjS3f/67c467N453ec957z38yyr7ZCc8757P3u/+917PWs9a27j5gRcEFlugReS109vXKg9jkQoREu7WYqWCWH1pJEP7UYmmLEnCz20z372sx0R+50iZLKjIYHeK+R5jBlOXyOM+N2MMLLsyzLWBo7r4kL+M8+rmnnU6ZzW93oDAcgCHMg8pwSWSWUhu3FnbcFhb8FGscw2pB/kjBHqWnPSHYu4RAF79smTJ6d7kwRX8wS6z2jm99B+2XBLg44vEBDoQ32uz4j0BPcJP8LzjVTx7373O7du3boD3svx5QhGXbx4sV8PykS2tcpOtzNiKPE+anb6uP29NmK8FaZhpnoOMu5h3WrKJSmAr9UApfvv4B9mp8d7Wd6XRqazly1zYHZIFIfn5HQRq97V2P+YgtkJJ5zgA9pkQkAICAEhUE8ERKDXc1zVKyEgBISAEGiCQNEEOveHkLWodGQfyZrl73CIQtDikJPli8Du3bs9AcDBlxpmGOMR1i0m67xdojffXlTrbsiD8jyAK89BUmNceF5wREEWQEx2KgFv9+Ra//7v/954Lvl7JNyRcs/DTLLdnGvWN3Pg5tGGst4jCWFkmek4Iot2QMbSvszNTpURyjomapcQMAQIBETKGuP9qdIm+c0Nq8VrwUbNlDvIdrT1sUrZjvmhmPxOBCuw97DAvaOOOirXILu4pQ0Zd/6hQaL7/9j/0f0Z6l1kn4+GDnMOefcnnniiZVY655pTTjnFzZ8/z42fMH7kJRuS56OR/8nHp5NPmtKP/W4WYB1KvRuR3shOLzmBzpzduHGjh2bWrFki9zqZJCl9xwLlLTvd1pHw8gRbGqFeNqUlI4p5BshAlw0jgCoHCnbgAoGuoDXNDCEgBIRAfREQgV7fsVXPhIAQEAJCIEKgSAKdwyeELBHpGM43y1whq4NDGDKQZNPK8kXgxRdf9I5RyDdqmCG9bKQAh2FIAdU1S3dM1q5d6zP7w+eg1R3icZkxY0Zq9aRvu+027xA2g5j/0Ic+1KpJXf/7aOR5IvnQru9erQsYYcRaiQO/WW1gntOwNnCePYyzE1FFgDwoU/ZdnnjoXvVGgNIn27Zt852EnOU9KedxcWPeSrmDsbHMdEgavWOSj5UFv0JIsp4T+Md7pkgbkYVuDYFIDy3MNk9Q+7zd/kA8Q9A+/vjjbsOGDWN+nXfz4lNOdiee9MZI5rjvFVnzBvnfbku6/3xcPsdUgUbAOTTUeJ97mffxfa6vn5/+/SoALdqRcwY683bTpk2+UbNnz9b63P00Se0KrCVGphP8FAdvsF6H2elFr9fsuTkXc06mBrpsGAGCCCnVxPhAoBcdxKtxEQJCQAgIgewQEIGeHba6shAQAkJACJQMAQ6ssYRaHk2E+CNCmQMyhysczRyMzSDWOYRBqEMoyvJFAEKOIAYco2RDM14Y2QCMlSTZ0h8PJEBRYiA7F6JxLOO52bJli4OwyWpcaM+NN944ohl/8zd/k2kdXyPPw9+sDyJck823sDYwzr3YAQmpZ9mXZGJmiSvBIKzjJqlMIFQ7ygrJeqxPCYHiEYhLFLCXgVCU47j4sbEWWLajZadbGRr7d9bCUOpde5zRx85q/xKgAEmCJHlZAiqbkuhxVyDRvYp7tlneKDktX77cS7w3y661Zk181URPokOmH/zag19p7f52vpJBX+zz1Erq3beu37n+cf2uf5zX0x9W1IdMH834J2qfE+eQQw10K3dEcyA9tUYXO6dGuztzjYBQI9Tj0hys15xH2c+ybhexXhNozjkMYp9gDNkwAqx7BBKCy3HHHadnTBNDCAgBIVBjBESg13hw1TUhIASEgBAYiUDeBDpOTIhzI2QhcZpJnFI/izrcSIST+SrLFwGczJalYXc+9NBDPSlQdNR/vkjkd7f169d7hxFEI8oLoxlOa+T1CXLAkMTmGUl7XCBjkXEPHb/nn3++O+mkkzIBJSbPuQl9ypLkzaQjJbkoDkiyeCw73ZQ+rHk4jkOp9zTnD/eFPDfpfdYN7iUTAnVDgHWLLDSc6bYeE/SndavcI817LZR6LzLYqNxIjWxduLZb8Cv7+LJZg0iPG9ZQcc+WPA9vy1xbsWKFe/TRRz25NJqB53HHL3KnvvkUN+mw/WV8PImeX1uTjqNlp7MftT/77/Y51z++3/Xt58yH9pPi/X19fk0cQaaH2edGoidtQIefCwl0atKnue/psEn6WgsEWJvj7PT4K8i7W3Y6xHoe71/LtIa8pxyAbBgBfDfbt2/3QQ0Q6HrGNDOEgBAQAvVFQAR6fcdWPRMCBG6IAQAAIABJREFUQkAICIEIgTwJdJPzJaIcG4v4g2QnuxbSluwWWb4IWA10u+vUqVMlvZzxECD3iXMarPlpZiabSgYYhhw2n83KWXTLLbe4VatWNZoyc+ZMd/nll6eKhMmChtLtVlczq36l2oEKXAxsmTMQRhDqWWZf4lQ0dREyUFi/y0iwVGDY1MSSIwB5RLCf7WlQzMlyPS45HJVtHuPIGBqhzn+HZsFGlu3Yq1mr4EPwHu8TK+WDqoksGQJDQ4Nuw8YN7pFHHnV/2PyHUb/EvmfBwvnujDNOd5MmT96fmV0+Ej3sQJidTha6J9JRce/rc25wyA0NWuQCRPpwlvpwj/ZLveeQfc7dCFIluA+jbrX2mMnmbpk+ZcGhrEes2/F6DWGLIoYR6lmVUbFMa9ZAymfJhhHAd8MPuCxcuFAEuiaGEBACQqDGCIhAr/HgqmtCQAgIASEwEoG8CHTIGyTBTYatFSGLLNqOHTt8TUUdTPOdtTglqH9u0v5kN6MEIMsWATL+wR5SfNq0aQfcDHKdcSEQxWqOZj0uq1evdjfffHOjLTimPv/5z6dWZ93I87DWpsjzbOcZV2cdNjK9Wa1JCG/LTscJlsTJzFjiNCPzBON7kOdFSGtmj6Du0OsIsA6zHlswCnuaVqU3eh2zKvS/VbARfQil3sl87AUjMAqlBYw+q5RPB6Pu9cy9drzb8txW99BvH3JPPbVm1Avx3l24aIE7/fTT3aTDJndww2K+Mjg44AYdGgCQ5sP9xfivRtI5pPr+fxoaGFYbyiNTNSTQ586dWwxAumtqCLBe8w42qXcLLg5vwF7UyPQ0SxehpIefwpT0UutUxS/EGYAsdHCBQE9yfqh4l9V8ISAEhEDPIiACvWeHXh0XAkJACPQeAjiB4+jttFHA8UaWlsn5QsiSfT6WUT+LHw69kkZLe0SaXw9HhNWmDz8xf/58n20kyxYBAkzIDka+HRn30HiGLPPLZFN5NrI2Mt6RcQ/lv8877zx38sknd33rMOPciHSTbJfDpWt4E1+gVfYlzz6BTBDqZPU0c3JbaQ7mKaYa0Inh1wcriAAOe7IYLSAQyXbUcmT1Q8CCjSzbMZZ6h0y2YKO8pIPzRhkyhKBWDFKEwCjtCdsdhSGfbN0glPfLsu/aucs99NDDbuWKlaOexdgPLVq0yBPpWQdNttur0T5vEvpk3Xsand9DkObDxLm3wSE3ODD8d7bnC/eAWRDqBAyyl+Z+ZKDL6oWA7WeNULdAcOsl56cwO70bNRHLtGbdl1LeK/MI3w3Z+eCM/0DnuXo9Y+qNEBACQiBEQAS65oMQEAJCQAj0DAJZEug4RThIWUaiZa0kkfMlqhuHHQfTOXPm9Mx4FNVRnAzILuMoNSepRfLPmzdPWaQ5DAzZjNTRJYvxyCOP9HfkGeL54TnCTCowz6w3MtDJRDejbR/96Ee7QmQ08jwLh2lXDe2xLzMuZGhZdjoBFKExPmH2JSRKLGONg58AEDnNemzy9Eh3LYORec/zQEBgHsFMPQJvqbtp0sEEurFGhoFlNJz5AJluP92QM2UAIlYVgRBhvle9X8VgGxDovgEjJdn/9Mc/uUceecQ9vvyJRmBO3E7m1wknnODOOOOMyqw5cS16iPSBgUE3uO8VQj3upxHqoRpRWntDiFUCurkeNdBl9UXA1ESMTI9LF9FzfAyWnc65qp19K34Kgs4tYLS+SLbXs61btzqCacEFAl0mBISAEBAC9UVABHp9x1Y9EwJCQAgIgQiBrAh0I1UgBDEOUu1krXAoRS4Ssl1OjmynrUnRkpmBQYAhR7tmzbC0JFkaqnOZ7RhwdTIacTpQR5eMRpz1OPosqxenPM9Q3s7r9evXuxtuuGEEAJ/61Kc6lis28jz8TZ/acVxlPxq6AwhAoBtZZOtDiAzrMxmapmJC+QECQDSWmj91RCCsAW1KIEkCAuuIRa/3yaSDrW46gRWxQTgbmd4uOVM0vhYAy14cox9HH310LjLbRfc9m/uPTaDbPXnPPvzQw275Y8vd3r37mjaFsiinnHKKe9Ob3uTl9KtqVrqH/YP9Oe5LnJ3erdQ7zytnS9ZvBWdXdeZ01m72qjxfRqjHaiIEhBqZPpraUnhny7RmbeTMJhtGgGB8zg0oV6lMgmaFEBACQqDeCIhAr/f4qndCQAgIASEQIJAFgQ7pghy1RXtDqLSbkUgmNOQhziEdwLKbsnFtempvIyGOM2vVqlX+xgQwiCTIbgzsyshKIns3adIkXwOdZ8ic8pDqZH4XQUwyF772ta95p5PZaaed5s4+++y2QQlrnfNn1TtvG8LCvoCT28gifjeTxkTGGqdZXaWMCwNfNy4cAdZmHMMY+xKCmapMXhUOaM0awF7a1kfelfH6COlpUu+QM0W8y5NCHpfkYF0vav+RtM3l/1ws4U6LR2ahh3146aU/e2n3xx97fFQinXmErPsb3/jG3AMrs8CbZyYk02OC0/6bZ6fT7HSIPdZxyNLZs2dn0Q1dswIIMNc4/1ppDivHYk1nfsXZ6XG3LNOaknRx2a0KQJBZEwlQAVfeG0qAyAxmXVgICAEhUAoERKCXYhjUCCEgBISAEMgDAZwVsQxlN/fl0EQmLdflAEpUNoRgu0bWLdfBySEJsHbRS/Z5HElg3Kw2PY6qlStX+gvhZMJRJ8sWAZwOZHvhZCf4xBw6OK4JQinS7rjjDvfwww83mgBJ+tnPfjZxNprVvgwJdJHnRY5od/dm7SDgYzQju4tMHuYJv/NWTeiud/q2EHgFgVjGWjWgNTtaIcCcIdPR1DticqZZKYxW18zr33lHsxeh7RjBeygSlZnwzwubru/TN+Sc/xkuhT4Wgb7/A+7Pf37JPfTbh93jjz8+6lkN1ai3v/3tPqO6LuPEPAx/YjId9DrJTkcVjfJgBLTMmjWr6yHVBeqBAIH/lplO4HI835gvlp0Osc4azjxiPkEUE/QsG0aAswHvP3w/ClLRrBACQkAI1BsBEej1Hl/1TggIASEgBAIE0iLQOWxC/lmGFsT3G97who6JV5x3ZOBySF24cKHGLEUEjBDg8I/hGJgxY8aILPOQQMfJpBqvKQ7AKJfi2Xn++ecb/8rcJ8sRErJoQ6rwmmuuGdGMyy67LJED0shzk/m2gA3LIiq6b7p/ewigDhKuHdTExaxuOlk9sVnddOYy641MCFQBAdYussyY8xjzmPmeVj3eKmCgNnaHAHMIcsay00crhcHaSPAc5XKKIkF5Rxv5Qa9VkqO7sT/w20my0D2z/grJPtTnufQXX/yju//++92KFSsOIPfsPjNnznTnnHNO4QGXaaMWKxfZnjK8T9LsdIKzWdNRDwEvmRCIEWC+hVLvcZIB6zNB5fw9wc5W9kxIDiNAYD5BCAR+6xnTrBACQkAI1BsBEej1Hl/1TggIASEgBAIE0iDQOWxC/pmTmehsyPNuiBKcjZs2bfKOxEWLFmnMUkLAsouQo8VwAjBWBDzE9uSTT/oMEA7AOHZl2SGA84/5bjLpzYIasrt7sitDoEOkm6EMcckll4z5Zatzbg5Q/hvySeR5MszL9CnGbvv27Y0gD9Z5yMR47TApY4KgmM9xJg8EkUkZk81bFFlUJmzVlvIhEGfiSsa6fGNUxRa1KoXBemp10wnYyCtYg3Ub4sMCoMioJPtcljICloXOZU3BfT9n7u804u/6nBsM/9L5d/C9997r1q9f37RhzJeTTjrJnXHGGbUtvdRK6h1gLDs9lnrn7MM+ln0IgcMyITAWAhYAFWanx59HYYn9Aet1r+9pwcveIwRg4V+QCQEhIASEQH0REIFe37FVz4SAEBACQiBCAEdELC/ZDkh89w9/+EOjVjOR2Mi2d+v0I/p7w4YNvikQ6CJZ2hmV5p/FQcpYWQYU8mrIg482VtRAx9nLAZgab7JsELB6oyg4YIzH3LlzmwY1ZNOCZFdFwh0pdzPaiYz7aBnyRp6Hv/lOt2tDstbqU2kiEJOJjHmSdZ7v4Xi07Ms4kwfHo2Ve5kkWpYmNrlU/BOJMXDKpcAZrH1K/sS6yR7wbydQz9Q4y1UNjvpl6B6R6N0GpY/XT9vF2f9Z2CCFZFgjsl3D3RHmQbR785/BdDyTPw9agFHDXXXc1VL/ilhLg9ra3vc0dd9xxtV63LDgzrJ0eYxFKvRPYh9KTCPQs5nb9r8k8s+x05Ntjs/IcJvfea+WLeNZQD+RdQukPVNRkQkAICAEhUF8ERKDXd2zVMyEgBISAEIgQ6IZAx/HHQclIkenTp3vJrjSczGTBrFu3zrd2wYIFqqHb5cxlrCDPLVgiyVitXr3ajy0HYDlTuxyAUb6OM4ZxscxzPoYqQBnrxvFMfu1rXxsRcPOWt7zFnXnmmQf0LibP+YBlnmeDpK6aFQKsATjrWUOwTmviMieYQ0YWIX0ZWkgWQao3U8XIqo+6rhAwBHhHkkFl81OZuJobeSEQSr2HewK7P6SfBRyllenIPW1vyBp89NFHl6JsTF6YF3efkEinFZDp+9PP+WPLGunDWdYoRd1zzz3+vdrM2L+fe+65tZN1H23cWmWnk4HOD8+SBQFalnpxc0F3riIC+D/YJxCswryL97T0iXXayPQiy3Pkha+pqbGPIkCfZ0wmBISAEBAC9UVABHp9x1Y9EwJCQAgIgQiBTgl0HBDPPPOMd+BkUasZp96aNWt8a+fNm5dZ5k0vTAii5CHArPZ00rra4M844FAlW12WLgI4W3Bcm9OFDDOcoGUl0On9z3/+c/f44483gMAx9LnPfW5EgEtcrzKU0EwXQV0tawRCcoV7pUkm4mAjG4w534wswvFYhrrAWWOs65cHAVuTCRph3cL5K/WV8oxPL7WE4LpQvYP/Do3MxlDqvZNMRwKa2INwbdvHs/+Q5YlAqN9u9zUd92Tt4D394IMPOlSC4nnCFRjbN7/5ze60007rqcC0ODud+U79c/6eeU6GbJidbgpJUklKNu96/VNGoDOPUN9j32BS72SpM8/iNdvI9LoqLvE8bdy40WPB/gkSXSYEhIAQEAL1RUAEen3HVj0TAkJACAiBCIF2CXQORzggduzY4a9ERDUS3/xO0zh8kQGNIWc9ceLENC/fE9eymsVWtxoMGSuIqSS2du1aT+5yCFYtzCSIJf8MThZzXEPUUEsaQnHLli0+m2HOnDnJL5bjJ5lL1EIPbcmSJW7hwoXeERlnnos8z3FwUr4VDkACb3DIZ52Z2IosQro4JIvSUDlJGS5druIIMN/JPA8DzUQmVnxQa9J8U++wgKNm6h3MVVsjk+yXw/kO+d7O3rAmsNauGwQ2I+vO3r2ZEQh73nnn9WRdYgL1CPpmfWe+Q+zFKjc8Zxj7i7h2eu0mizrUNQIQxZzbCCyNVdqsPIcR6nF5Dm7OWY81GzKdPW4d9rU8X+DCnp5gfYILZEJACAgBIVBfBESg13ds1TMhIASEgBCIEOCQ1+xg1wwoDkQ4mE0qkOxAiL9OMl9aDQT3ogY3dswxxyQmfVtdt1f+nUMszqIXXnjBd5kDOofZdmSR169f72WbkXufMmVKr0CXeT937drlnn32WU82Mx4zZszwjhRqoPP3BKMce+yxmbej0xt873vf88SqGWvAlVde6R2TlnFhJJQ5Iju5V+jM7OT7+k7nCKBaEc5Rxpg5moeZ49HIovj9ZDUmLTs9i/dPHv3UPcqDAHPNFHVYk3lXJg00K08v1JJeQQDShn24qXfYu9L6D4Fu6yPrdkzM8D3e4XwP4gbyPAnp3iv4Vr2fGzZscMuWLWvs/+P+nHTSSe6ss87qmTGHxOTsGs531nnOmWHQZ4yTZaeLTK/6E5FN+3nOCPbnjNxKqYY1O8xOj9ds1mHLTmfNrqoKAmc/fAf0j/fKEUcckQ34uqoQEAJCQAiUAgER6KUYBjVCCAgBISAE8kAgKYFOxgtyZUZmcCgisjiriGnatXLlSg8B9aCVCZZ8NnBQJ7s5rFlMtkW7Y4VzgCwlout1CE6O/2ifjNUbIGggz3GcYBDrkDg4slFdKKsR2HLLLbeMaN6HP/zhRqaBkeedOID2Du51fxp40f154I9uz+DLbsgNuj7X717d/1o3acLh7qBx+ZC4ZcU+63YxRwnkMNUKgjkgE22OZn3/Ztfn3WNkEetRbJZ5CWEkEqiIEar2PVl3Uf7ATKWlyPlebTTV+rwR4H0bSr1D6ITGe9gy061MDMFRmu95j1S+9+MccP/993tZ95isoyVkzL773e/27/c6WxgcNdr6Hku9N8OrmdR7NwGidca8V/oGUUwQBiptrK1JjfnG+dwCoOI1m3nFvtYI9SrtR8ADXLBZs2ZJvS7ppNDnhIAQEAIVRUAEekUHTs0WAkJACAiB9hFIQqDjgDBpU5ObjuXK2r9z629AoNO+mTNntnU4bX3l+n6CQzmBDnYghzg//PDDO+rwpk2b/AEf8hwSXdY5AjhMyPgisxdrpt6A/CafwVkyb968zm+W8TdxkHz9619vKFFwO9p74YUX+ueVjOB2gzX43u59O92L+3a7PYP/5/YM7XFDQ1Y/sM/19/W7PtfnXjvuYDd94uvdxHEq6ZD2MDMGEInMQwzn3dFHH52Jwkinbbcak7yTII3iGpM4yHFk8nw1y7zs9L76Xv0QYL5TisbK0TBfIJOkaFC/se6VHjGnLeCINZKaz6OZgkV6Y1YQDPfLX/7SPffcc007vHjxYnfmmWcWGiSX1Uiw3yYoFbNyY0lUuNhXsM81Yj0m1CX1ntWIVeu6lEpgLrBPZr/ciZkPxrLTLfA9vBZrdZid3u75qpN2dfqdkECnFBm14WVCQAgIASFQXwREoNd3bNUzISAEhIAQiBAYi0A3BzM1zzGTesxLyvfJJ5/0DgyydCFEZGMjgFy7yXJCAkAGtBMVH18dIh4nLPLtSNTJOkOATCCwNGc2eBKQEDtBGD8CVXDwzZ8/v7Ob5fQtMpvuueeext3oy8c+9jEfrNGuc8dnPe/d4f64b7f78+Cf3MDQgHNuyA1Xoxz+f6zf9btxfePdhL4J7vUHzVY2eopjjdMLRzNOPIwAqU5UK1JsUstLMW8s85J1Ks7iYQ0M66aLGG0Jac98IA4WYX9BFlknqhk9A5o6WjkEWBNNvYPfzWSDLeCIjMd2392VA6RHG8y4P/bYY37PFr8ngWTy5MnuoosuqlWgLPtpU1pA7andElY2VTrNTte7pP4P25o1a3wnKXGUlkoee3GUloxQ579DY16F2elJAkLyHAlUCgm+xyhF1kraPs+26V5CQAgIASGQPgIi0NPHVFcUAkJACAiBEiNAxkpscQ1tDmzUs8rzsLZ69Wrv7MHxkUfGe4mHaMym4Rwjy2T79u3+c0SrE3RAxkU3hgw8GRyQopBpsvYRiBUByFSYNGlS0wuBNZhD9C1YsKD9m+X4DRw83/jGNxolHbj1CSec4M4///y2W/GnfS+6HXu3uT/ve9ENuMEGdT7ODWeyM7+Rcud/zvW58X3j3UH9r3ZvOGi2m9CvTPS2AY++QIAHgRv2HiDAg58qkSmtMi9NEtPIoipJYnY7vvr+SARsbwOhiJEhRYBYlea7xlQIJEXA9oeU5sBs7WPdDw1ihixHCzrKc6+ftC/6XHcIUK7i9ttvb2Rlh1dj3/m2t73NnXzyyZVfC1HRsYz7NJVFTPFG2endzcM6fJt1lQx0DN8IQRppm+1rjUxvpijCOZ81m7WbPxe9j7Fyf2BBIHinmflpY6nrCQEhIASEQDYIiEDPBlddVQgIASEgBEqKQEygE0EMkWeHNQg/CNS8I+qJ7qYtY5GOJYU0t2bF0uAcpNOSoSWbHUcU488YyNpDAEIcYtJkzVspApBFS6Y6z9nChQvbu1lOn/Zk9tCQV4a4++67fX1NMxywn/70p9tSPfCZoHuedi/u3e1eHqLm+dD+TPNxXrI9tMGhQbfP7fN/Sxb65AlT3bRXHZVTz+t5G9Z45qhlpZGFW4dgJcghy7zE+RhnXuJoJOuY9RLHZ9FOx3rOrvL1innOe81kUilP0olqRvl6phYJgQMRYN2DSCQbFzNlEf7M3trWSALiYoN4NDK9DMSMxjcdBNi7Pfroo+6+++5rmo0+e/ZsXxs9rYzadFqd/CoEiphqGn1g353V2dWk3m1PHO8zaDV/Z7XS+U1bsmpPcpT0yW4RCKXK0whYT9Ie9i9hdnpcwogzmEm9M/eLUF1ib8WZAuMcm5diYRL89BkhIASEgBBIHwER6OljqisKASEgBIRAiRHAkWYHf8gGyHOTDYNQQd6vCFu3bp0n8YtsQxH9TnrPWBocIiDNTDoknclYIUMPiTpZMgTi0gdJFQFwZiN9h5Nt0aJFyW6W46fMSWiSlhD+11xzTWOtoCmnnnqqO/vssxO36v8G/uK27nnWy7eTe46RYR6T53bBfUP7fCb6uL5x7tX9r3UzXj3Hf17WPgLMN55xxhOHLs94HbNF6B/vNeYrfY4lMcm0DKXe5dxufy5V4Ru8L9nbsN/BCApUfc4qjJza2AkCsdIC+/ipU6c2DRZiTQyl3mNixtZIgo4gZrRGdjIi5frO888/72677bYG2Ry2jn0Aku5k1lbJ6BNqXBh9YE+T11ztVOqdtubVxiqNZdnbCpm9YcMG38yZM2d65bc8jfMY/hHWbfa3tq8J2wB5bYQ67csjUBSCnyBF7LjjjutaCS9PTHUvISAEhIAQaB8BEejtY6ZvCAEhIASEQIURMAKdyH2yVSxjFudJkYTK+vXrfaYYpDCSwrJXEOCQChnAIZ5DMWRA2oEOzAUcUtQwq5ojrai5ghONuotk7psTD+ySZAIwpuaQgUDPw9mRFKeYPOe/cfotW7bMPfHEE43LIA/7mc98JnHWwQt7d7kde7e4lwb+7Al0ZNshx0czstAH3D7/zwf1v8ZnoB86vrkkftK+9eLnQolTxowsrW5LPlQBR3M6Gpkeq6/wzPHOs+x0yRhXYVRbtzFUWmCMUVRhjGVCoI4IQIhDYlhmeTtKC6yR7LttjYyJGVsjLehI5TCqO4OYJ/fee+8IJSHrDeN81lln+aDIMu1Fm6FtQas7duzw/8zcZI0vkpi27HQj1uPsdPtvy06nrZahXt0Z1TstJyBv48aNvsOoNhS9V6Q9JvXOuh/PN9oXZqdn9WxA6HMGxijrpfdD7zwT6qkQEAK9iYAI9N4cd/VaCAgBIdCzCEAicOCxGolI2kL65R1RHQ8Ah1MOhGTN8CMbRgDyi8zRrAMdtmzZ4nBIQTQgUScbGwGCGQhqMKc10veoJyR1PuK0JmgEQ/ouKwdHu+MYylOaM5CAAPqFQsHVV189wllz2mmnuTPPPDPRbXbtfd7t2LPF/WXwL77O+fi+CV7CfTSjLci4OzfkJvYf5I6YON0dPuGIRPfSh4blRLdv3+4DYzDWesjzop1/RY1NKGPMuyY2kzFmDcwrg6coLOp6X8YVMpG1i3WL+S5Z0bqOtvrFPgQJXSvBRAAqe5FOrdUaqXIYnSJbnu9x1vrZz37W2LuGLZs7d66XdC/6PDjWnjDc0xDw286+O49R6DQ7vSxngDwwqto9wlrfc+bMSRQknVcfmW+cJ41Qh1wPjbNbnJ2eVtsoXYbvgHtAoPfq2SItPHUdISAEhEDZERCBXvYRUvuEgBAQAkIgNQRwtiGVbuQBzoc8Ze/G6gj1oMmCIfscJ2CvG+QXtf0sywLHJcR2Vo4tpBD5IWp91qxZvQ7/mP03Z4plazFf262ti8ObZxFbsGBBKRwyYeY5ThnLkAmDAn7605+61atXN/Ah4+BTn/pUIvUKMtC37Xm2DQKdOugDnkB/Vf+r3ZSJU93hExRck+ThZPxQlcDBhZUhSytJu/P6DNl4odR7LGPMvAYzkzFOGhiTV/t1nwMRYK4THMg61ktKC5oLvYlAXKYAIpG652mZrZEm9x6XwyBAxTLT+S0CMC3ks78O777bb7+9kVUb3pG97MUXX+z3tGUyOxMRyIkx11HjKvO72fYVPDvKTi/TbGqvLZzXCJjGjjnmmFKvdZxLw+z0uKf4ECw7HWK9m+fnhRde8H4K3gXHH398Kc6x7Y2sPi0EhIAQEALtICACvR209FkhIASEgBCoNAI43FatWuX4TZY3Uo/dHJ7SBIMsGg5jOG1wivSymSQnAQUYJA6BDkmkwTvFjawODsLUvESiTtYcAZzJOFLCWtIEorRrkPBr1671X5s/f36hkfs4JuPM82bkOW1FuYJa6KFk4OLFi90555zTEoK/DLzknn35D/sl3AdbSrgPDA34GugQ6K8Zd7CbMmGaO2zC5Jb36fUPxJK+ZCROmzatNGt92caHuYySBM82a26cwQMxFBJFWa7DZcOmKu1hXeL9hRFsRua55ESrMnpqZ7sIQJIQdGplfdgfskZlZUnKYbB3tHUyq0DPrPrXi9dlTB944AH3m9/85oDuM35LliwpTTAtbSUgkDMidthhh/lA67KcX5POH84N4U8svc11+DuTerd9uIJTkiKczefI8MZHgR177LGVmXfMNfa2RqjzvgiNecW6bYR6uxnkKOQReM/3INDLPE/Z13/rW99yN998sz/Ds4agtvG3f/u3KvGTzWOjqwoBIVBDBESg13BQ1SUhIASEgBAYHQGytCDvOiH9ssQV2VUOY5A91LPrVTPHqNXrJSM/D/ILmWccVMg8E2EvOxABSBqr9wY5gyIAeHVijPOaNWv8V+fNm1cY2dMOeW79JHNpxYoVjW5DKF511VUt1xTu9czLm90L+3a5gaF9bsg5N6FvgutzfQdAGMq397tx7uBxh7ijDnq9m9j/qk7g7pnvMK9w9Jk6AmvH5MkKOkg6AZh3JmMMmY7jNDYjikzqPem19bn0EYjLFDA2WQebpd8LXVEIJEfAsiEJlIKwIFiEeZ+nQUZYZjrkTEwEEsRiZHq3WY559qsX77VhwwZ32223NcoAGAaQt+94xzvcSSdBnR8MAAAgAElEQVSdVCgszC323aamw36GAPCqkecxiKHUuwWwxp8xMp2/51k3grLMRGWhkyWjm7PGUUqNOQeBXkUL97b0x8p+hH1h3TYynbNtq2eMMzEqeQTcLFq0qLQEOnv6z3zmM+7+++/33aVf9s4igP3aa6/NNACtivNFbRYCQkAINENABLrmhRAQAkJACPQUAji+YrnaMgBgddmR5cMh2IvGoZbIaByjHPCQ5OymnmU7GBo5zAG6qg6Cdvrbzmc5aFPnzWpJ4xB+wxve0BXpzXP41FNP+WaAN7jnbaFkuxHpOOYs+2W09pAF9N///d8j1hHq351//vktu/Divt1uy8tPu5cHX3aDbsDXQIcg7+8broXu2+ErpA/43xjy7YeOn+Smv6p3A2taAuucJ3shz239IBAJklfWOQJk7BhRxO+YKMJxCMaQRSKKOse5k2/GWYkEBaJeI3KhEzT1nSogQDYhazx7eALX2Id0GsSXVn9pC3tXWyfjLEfaCSljhLoUPNJCPr3rELx8yy23+GzS2FAYOvvsswtZV33Q5TPPeGUYDIWyMimnpTcCzj/TknpPE9H0rsXaho+CtYsa6HUwK9Fh2emxX4i+htnpzdZtyHN8B5xfIdBbEe5F4fblL3/Z/fCHP/Tj96Uvfcl98IMf9AEE//Iv/+J+8pOfuCuvvNLxGZkQEAJCQAiMjYAIdM0QISAEhIAQ6CkEcG7FtQzLAAAEJYcxyAgye3vNqOtn9Vs55IFBnllF3B9HFYTQ3Llzew3+UfvLs4LDGgcKRoAHxGS3JA3XpZwCRsZ/3k7w0cjzpP1atmyZW758eQM3HCef/OQnWwZ8+GCEPU+73Xt3un1D+zxR7lyf6/dZ6PwME+hm4/smePn26ROPdgeNe7Xm5SgIhPWfWT8IQoLQlaWHgMlhmtR7M6JINYHTw3usKzEWqNbg/MXqkpWYD3q6SxURgERkj8Y7FAUcyPOySaXTNtSTaCvrZLMsx1jqvaykSxXnSDdtJqjz5z//uVu9evUBl6Gs03ve855c51u8xkOco8jVCxZmp9uf4343y05Pun/vBQzT7CPrGQptSJXXscSZlegwMt0U8AxD/DMQzbNmzXJve9vb3MKFC/0ZmNJv+A44ayxYsKCUBDql0ihHwXP0xS9+0X36059uTA3WPNa1TZs2uVtvvVXqe2k+NLqWEBACtURABHoth1WdEgJCQAgIgdEQKCuBTv1SDmMQEDNnzuyZAYyzmyFSi3CMklUMUYyDAEkzmfNSztQZNWcCDry0pCM5zD/55JMeZjIa8iQ7jTwPZSMt8zzpuOMc/+Y3v+lrsJqRSX/JJZe0vMTQ0KDb8vIzbve+nb7GObXOyTkftmFBd/LSIc8PGvcaX/v84PHKpG4GLGNIBohljkGoQJ6XjVhpOSkq9oFWRBGkEESRZaerHnd6A8yaw7vKyDnWZAh0EXHpYawrlQsB9mdWPoa1vVsFnLx6Fyp4QM7EWY6si7ZGsl7qGc5rZJrfh/caNdGpjR4b6h5Lly7NZa/KPGGNR3EB6/VSNMpOL/a5YP3FR8F6BYlcd2PdNjKd3zfeeKP70Y9+1Og2+61TTz3VnXzyyV5Bjf/Gb1DG9fuf//mf3Xe/+10ffHPXXXcdoByHfPtXvvIV94UvfMF97nOfq/vQqn9CQAgIga4QEIHeFXz6shAQAkJACFQNgbIS6JDnHFBxotUxwrvZPImzm5GgpX5rEVkEZLAiH0/2KpHkvW447iDPQznsww47LDVYcFSuXLnSX4/5npfagGWz8JsfHB7tkucGwt133+0eeuihEZhcfvnliUow0P8/Dbzotu/Z6l4e/Mv+vPMhT56P6xvvJvRNdAf1v9pNmjDFvXpcvvVdUxvkjC8EhqyZZIBgqv+cMeBjXN5qApOpxNoRS70TGGXZ6UlqSxbXk3LfmaAm3lPgjVHmBFUQmRCoKwIESLHOY6wdBEgR6Fg1CxU8CMCzZ9j6wT4klHqvYh+rNiajtZe96S9+8YsD1Mogod73vvdlWhrGzkWUpMGmT5/eUtmoLrgn6Yft3e13vNfgGpadbuWYwvrpSe6hz4xEgBIHBKkiVd5rCnnMJfwzV199tQ+ssUAuQ4h1Gvn2Cy64wJd6KFsCxMUXX+xVNS677DJPlMeGktE73vEOd9JJJ7kf/OAHmvpCQAgIASEwBgIi0DU9hIAQEAJCoKcQwDkRS8+WAQDqSyORhoMQSeu6G1nNEAFZZDd3gh0OTWTMcLhwGO5lw1liUqlZyumvWLHCw0xGA47jLM1qnFsGmAUGdEqe01YyQKmFbo5O/o4s/Y985CNtBYG8PPB/7o8DL7iBoX1ezH18/wT32nEHu4l9ryplRkOW45T02owjc9RKC6j+c1Lksv+c1QQ2GeO4ZAoOR8h0Mi8JeigiYCp7FNK/A+sMWYm2dhFsBo4yIVBHBHhnsy+GvMDqFCBF3wiGsXIY4R7CxhJVHgs6grgqY3ZjHeed9Qli6eabbx6xv+PfCFh6//vfnwmpzdrOucjURch6TzNwtW7jFUq9h4pSYT9jqfeQVK8bHln1hyAmSsyZQlxW96nCdTds2OAInoZM//3vf39AIBQBBsi88/OmN72pcDUsiHGCWv/1X/91VIW0xYsX+6C0Bx98sApDoDYKASEgBApDQAR6YdDrxkJACAgBIVAEAmUl0HupBjdOw5gIKDqLDpm2jRs3+ikJgd6LzkocTWQZmMPasg2yksMmy4d7ErGfJRFk5LkReTjdjDjvdpypg0499NDOP/98d8IJJxSxvPXEPWMJa7LC+Ol2LHsCvJw7ybMHOcSaz09cW5IxC+umK+uy+QCBHYQOeBLURBZunmUvcp42ul2PI2B7EYgbjGAb1BbqGmxjksG2TsZS7wo6KuaBIIDjhhtu8DXtQyOYg4zONGuSMwfCoOKjjz7aERgoS45AK6l3rhRmp9s5oK7rSnLkxv4kzwE/7DnYe8iGEVi3bp175JFHfDkyzoIkQYQGXqeffrrPTD/rrLMcATF5GvttO4v+8Ic/dCeeeGLT25977rl+7Xn00UczPYvn2XfdSwgIASGQBQIi0LNAVdcUAkJACAiB0iJQVgLdanBTY2zevHmlxa/bhuEQNQk0nILUssxLvnustkPyrF+/3n9k4cKFtXXUjoaBZfQyDzFILRwlkDVZGU4H7kvEPg7yLMyyUixThf9OizynvVz3O9/5js/OMGM+X3XVVV7uUJYuAjiECL4x+VtlaKWLb9ZXC7Murb5reE/LumQ9IHBHQRHOhfWf2R/wzswqqCnr8df1hUArBHhHQ0TYXoTgStb5XlkL6D9ro5HprJmhgUMo9c6aIMsOAco7XX/99Y1SMeE+Ly0Snf0MBBZjzfhCnme1J84OqXJdOcxOtz/HLYyz0yX13nwMOd9wdmfdYW7KhhEgqJG1etKkSV5Jbe3atT47nZ/HHnvsgBIQ+HYg0z/5yU/mUnqHwJ9TTjnFt/VnP/uZmzNnTtOhW7p0qUMR7vbbb++ZEoKaw0JACAiBThAQgd4JavqOEBACQkAIVBaBshLoda/BbU5RyyiCKIEIKIvzD8lEoskxaqBnSRyX7eEx553JiB5++OG+7mLWDutVq1Z5BwNEfRYKBEaeh78Z17T7hfQ/WUqhnXrqqd5RIksPAVQicFiZggAS1llL/6fXel0pRoBn30gifsdZl7wbIBEI5iEoJe3ntuwjwrrF+xJVEKzK9Z/LjrXaVw4E4tIckydPdlOnTu25Zz8cDQs6Yo3kHRgb64KpePDnXlsn85i5kGQ33nij27p164jb8V66/PLLHXvmTo399+bNm31QIGOn0hydIjn291plp1st9VDinT8rO935PQilvVhnUAKRDSsZcB7h3MzzH9c+x6dz3333uXvuucf/kMFvxprxT//0T5nDyPvi5JNP9ve58847Rw1+oD1k0FOyAv+HTAgIASEgBJojIAJdM0MICAEhIAR6CgEO0Za9WKaO17kGt9X1M+cfZClR7GVyTJDZSvQ4Nn/+fF8PrBeMwAFz3tFfnCM4rfOwp556yj+LOAzTrvMYk+f0p5t6563wuOmmmxoBGHavj3/847lh2ap9Vf93nHcmj8izSdAFZIGsHgiEWZdkzcTvaJ5dq5tO0ETdA5xiCWv6zDpZpndmPWaeelEWBNgnWkYfbTriiCM8MSFC+JURAiP20RZ4ZGVp7BOsi+E6qfUivdnNGeFHP/qRe+aZZ0ZclLUZAqqTfTPBEey/kW9nrBQUmN54jXUl/ADhj5Hn4XdCqXcj0nv1eSJwBEUQAhrzliHPZ0a0fxfmh5Vc4F1FQsBoxlwjwxsincBxzoaWGd7+nZN/g/cDinoY9542bVrTL19xxRVevv26665rEO7J76JPCgEhIAR6BwER6L0z1uqpEBACQkAI7JdcLiOBTobDhg0b/BjVqQY3TiccRCZDSTYRh82yOUWZExC62Ny5c3tCIpcI+TCjFwdAlrXI4wVozZo1fl4QTIEEXlpmco3mIDPnV5ZzbteuXe6aa64ZkUULyYvEZ5b3TQuzsl4HJxXykSaRD2mOk7ksyhVlxa3K7WLMWRcg0iGKTBkj7BOZf5adXjc5c9YtgkVYn7Fek7Cu8txV2ztDAAKR0hwE9GEo4KS5J+isVeX+FuskeNk6yV47NPYdrJOWnV63dbKI0eG9hNqQlaGyNoDxlVde2VbNcsYLAs7Ic/bfKHPJ8kUglHrnmbKfsBWx1HuYpZ5va4u525YtW/x+hL3IaCRsMS0r7q7MCfNtgAnnkjIaRD3vCBQ0jj/++KZNvOiii3wCwfe//323ePHiMnZDbRICQkAIlAIBEeilGAY1QggIASEgBPJCoKwZ6HWswc2hDacomJs0YRZS3WnMHZxYq1ev9pc69thja12/moM/cnI4RTDISOTn8q7ZzYEdJ2JaWe/m+AoJ9DzIc5t/d911l3v44YdHTMd3v/vd7rjjjktjivbcNeJauDipy6Zc0XODUkCHWZtDqfc4W4x1y7Iuqy5hHGfhkoFbxoCzAqaBbllTBML6z3SR/UBZ94llHgJwZJ1k301A7mjrJGslRK0C+zobTfaskOimiGNXYa3+4Ac/mIgEJ/AB8pz1HtUAgi1Fnnc2Hml/q5XUO/cLs9NN2arO2ekEjLC2oBRGELxseA5Qvot1l6z8skrbv+c973EEq3/1q191nEeb2WmnneYIAr/lllu8Ap9MCAgBISAEmiMgAl0zQwgIASEgBHoKgbIS6HWSELfareZggqAte3YF8+LJJ5/0z8KcOXNq68xibHCGcFjGyFBibIqQrKfmPI5EnA/d1JA0h5ZlnPPfVifbMkXyWOTITvrWt77VyBzlnjhFP/GJT3icZckRiIlEHHdkJcrpnxzDOn6S5xpyyLIuIddDq7KEMY5YAs4sk5Sspk5kges47upTPREIs3BV/zm9MWadDKXem62TSI+j4tELJTHSQ3b4SqOR6JBoH/jAB8ZUyCFYGvKcMeJ9xf5b5WjSHqF0rhdmp9uf4yvH2ekQ6XUj0ylbwHqCKggBfbJhAh3VQM4qBPZyPimj/f3f/7378Y9/7D71qU+5L33pSwc0kSz68847z//9gw8+mHo5tTJiojYJASEgBDpFQAR6p8jpe0JACAgBIVBJBEwetmyNr4uEuMnPGkELgThjxoxCCNp2xph5sXLlSv+V2bNn15LwjGvRQ0ri8CvK2bN+/Xovz4zjYcqUKe0M14jPWr1z5h5m5HkR/aJPOCtCoyTDBRdc0HH/eu2LcUYiGS8QiSLPe20mjN1fkzC2rMtmEsaQQyZhXGbZf9oOec7cZ56zLh9yyCEacCFQWwR49zPn2ZfwriYLV4Fm6Q93uE6yVppMfninUOo9byWi9HuczxWZv9QMRs0pNAJwL7nkkqb7aoK/TJWLoFXIc+Gdz3ilcZdW2emm+hBKvJsKVhr3L+oalPpi7hLo3G2wc1F9SPu+zAUIdH7z7iprZv6tt97qvvjFL3qJ+V/96lcHrEusYf/4j//ogwDuvPPOtGHS9YSAEBACtUJABHqthlOdEQJCQAgIgVYIlJVAx4m4atUq3/xjjjmmkhkJZLmQWcFBGyuaoG01F+J/h0BnfiBnnmct8Hbb2cnn41r0ZDdCWhdJSuJ8YK7geOjU+WDkefibrJ4i+4UMHpJ5oVELHWepbGwEQlJFRKJmSzsItJIwJsvPpN4hLYpcI8J+xUQijk6If5kQqCsCIZGoLNx8R7lVSQxqpVvQEcR6WdbJfFFKdjfqQlM3GEWU0KgjfM4554z4OzJ4Ic/Zq5oql+rSJ8O5jJ8yxSv7HZdMoM2h1LsR6UUE9naLH+d6Am84M0oVZxhNxp2AafMZlDWwgP3lWWed5ZXRvvzlL7srr7yyMR3wCyxZssRL0V9xxRXuH/7hH7qdKvq+EBACQqDWCIhAr/XwqnNCQAgIASEQI1BWAr3qEuIcrpECg8TAykDQtjv7kXBnHMiYR9qyLobjzuot4sQhWr4M2Y0bN270soBIAjJf2rWYPOf7Vo+w3Wul+XmyvK6++mqHpLsZ0ocf/ehHx5T2TLMNVbwWTmikIhlXSBWIRGUkVnEki28zAWmhhDH/HRrZf6zxEEXMsaKc2uGcp02szZLzLX7+qAXZIRDOeRGJ2eGc5MpWEoM9Cz+2f7fvsi6GUu9FlPpJ0o8iP7Njxw5PoscKKO9617vc8ccf75sGtmTxsreBNCeYssyKKEXiWcV7h1LvjLH9hH2Jpd7DLPWy95mzPfObsxpnGZnzyikQ6NisWbNKHVjwb//2b+4b3/iGVwL8u7/7O7d06VK3bds295WvfMU98MAD/u9/8pOf+PJ1MiEgBISAEBgdARHomh1CQAgIASHQcwjEjo4yABBKiHMYq1IGGpHNOIdMOhviqwwEbbvjunr1akd2DiTGoYce2u7XS/l5pPSpec784pBMcACy+mUwnDI408lqaKd+XOigsuwPy+4oS7bU8uXL3bJly0bA3CwrqQzjUIY27Ny5023dutU3BQczz6Cys8owMtVvA+sFWTgm9R4GttC7okii3bt3u+eee64x50WqVH+uqQdjI/DCCy/4/Yit85rz5ZkxFlzMnoy1kjUzNvaOlp1eJhWPolHk/HP99dd7Us2MIECUhwjUIjAQAzPmvAIRih6xbO/fSuqdu4fZ6Rb4W1QgXys0yFBm30Sgc13Oxq363Orf8RWgooahGlhmXBi7T3/6054sb2bURqdGukwICAEhIATGRkAEumaIEBACQkAI9BwCZSTQGYSqZUDjACD7wogvMiogaKuaQffUU0/5DBwCAJCfr7IxNowL44MxJoxNmbJeyIon+ALpuyOPPDIR3EaeW8YHv8tGntMR2kVWkhFk1rnLL7/ck8OyYQQYTzIhINAxHPQ8f3Iwa4ZkhQDORCOJrNxIeC/moGWnZ1GfljlP3dzt27drzmc1yLpu6RAIg6TYj/Ae1DpfumFqNAiCKFTxYE8TWllUPMqC4IoVK9ztt98+ojm8S8444wy/r9GcL8tI5duOMDvd/hy3IM5Oh0gvE5kOUcx6QKBzFYPjsxhx9pEEFmBz584tvWodvo3//M//dN/+9rcbJSdQFKA++qWXXpoFRLqmEBACQqB2CIhAr92QqkNCQAgIASHQCgEOPs3qlbX6Xtb/bhnQZCiU/ZCKI4BMIrLoMGRwq55ZsXbtWi9Td/TRR1dapo4sGDJirC4jcwlSskwOGeYM9SDJSKOm3lFHHdXy8TLJdnNC8d+WuVGWzPOwE5Bk3/nOd0ZkJZGl8LGPfUzZ1fuDDFhDwnlKIEXZ5mnLiakPVBYB1kqTL+Z3TBJZPWAIdUiQbtcZ1qwtW7Y03ptkc/K+0Zyv7BRSw1sgYIGWFszHXhHyXHO+OlOHMSTYyNbKWMWDdRHVLMtOL1OgZp4o33nnne7RRx8dcUv2t9RD53xEVrqstxFolZ1uvolQ4t2ChItCDqly9krsz+tU3qwbPClbRxA4Nn/+/MqoBuLjWLNmjQ+mh/jXe7ibWaDvCgEh0GsIiEDvtRFXf4WAEBACQsBLkZWRQK9KBjSR6BwcLXuPmmh1IL7WrVvnOBTTF7Kiq2hEmSONTj8w5NGR3euW+MkCC0h+AjCYP5BIY9lo5HnZD/8PP/ywu+uuu0Z07Y1vfKM777zzsoC0MtdkDSGAwuYpzxvZEGWcp5UBVQ3tCgEjiSw7Pa4HDPkR1gNulwyxoDMLGEHlhIwuzfmuhk1fLjECpoRDKRkM8oVgubK/t0sMaSmaxhnKSmI0U/Eg29rIdP7cK2sca/yNN97o9+ChnX766e6tb31rKcZOjSgPAlaCyn4380uEUu9GpOe9fnI2po2c06pUXi7LkabEBWcYbOHChaUpjZZln3VtISAEhECvIyACvddngPovBISAEOhBBMpKoFsGNA5GshbKaBwaIc+NXIAAgPyqg4OMKHv6R58gnqtmtB3HHeQk48E8gpwuq1EXEsc6RBIZ8qOZkefhb8irKsw5nE4/+MEPGjUwrY/ve9/73KxZs8o6NJm2iwwIHE/hGlLmeZopGLp4KRFgrWGeGklkgR5hYy3jElKwVcYl2VvMeasrzPuFnyqsYaUcIDWq9AjwDFHCBJUZDPUVghM150s/dG01kLUtlHoP64BzIaTejUxnzcyb/GurMyl8GFWdH//4xyNqyDPnqYeu8j0pAFzTS4RlqUKVrbC7sdR7mKWeJSxkLGOc01AQkTmfQEAQOHb88cdLVUyTQggIASHQAwiIQO+BQVYXhYAQEAJCYCQCZSXQy07gUq8aEsCks3EG1UnObePGjd4ROHXqVP9TJcNJzWGesYFcRi6y7JkCOBqpi4rEPO1tZrFTqYz1zlvNE4IEkHIPM1pxKH/84x/3dTF7yXA6sYYwrjjSyWgBC5kQKDMCBCUZmc47Is4Uo1Y685j3YZxxyXNP0JnJHhOgpYCRMo+22tYtAqzvBMjxzGAEhLKnEnneLbLl/j7rIkFCJvVOEFJojD8EnK2VrQKPyt3bka0LSxWw57v//vtHvCfoM+V7KAUiEwKtEGgl9c73w+x0K2eVdoAK9yC4H+Oc1mtnltHGCSUhAsSwE044oWUQZavx1r8LASEgBIRA+REQgV7+MVILhYAQEAJCIGUEcGjHtU5TvkVHl9uwYYOPai4bgcsBevv27W7btm2+X9SFnTFjhoM0qJNt2rTJO/6Qkkb2vAoWjw1jgpOjCmNDLWDqokI6MZ9Co19x5nkVyXPr0/Lly92yZctG9JH6c0uWLOkZUoEgDxxOjCtZaQTgyBlXhVVGbQwRYO8QZlxCrodGABNrGoQJf4ZINFUQAkbqFHSmmSEEYgTIQCaYz6S92U/VRaVIo90eApy1rCQG82G0wCPWSojlqgZY2D78+eef9wARFErQ1H333TcCMKSeL7zwwvZA1Kd7HgELJGZttT/HoMTZ6RDpaZDp3A8Jd6yO5/5OJxcJBZxhwRgCvd2SPp3eV98TAkJACAiB4hAQgV4c9rqzEBACQkAIFIRAWQn0MhK4lklkMpxkNUN8QYDVzXB4cSjG2YvUaNnNaupSRxxjbCDPq3KQ37p1qw/MwHk6c+bMBtxGnpsUqGUrm1xh2celWfvo0w033HBAbcxzzz3XnXjiiVXsUuI203ccy4w1RnAHa0idss8Sg6EP1goB5jby7padHmdcWmdZu0Se12ro1ZkmCBAogsKIlTyQ2oKmiSGQJPAolHqvyj6WdwB7WbLOMStVYHs+zhWhLV261M2ePVsTQwh0jECr7HQLVAkl3i0Aud2bsqYT3I9xTiOAXuZ8aRKee9YpJNyrsl5p7ISAEBACQqBzBESgd46dvikEhIAQEAIVRaCsBHrZCFyTnrW6rchw1rmGJY5fDsX0k/rhZTacGswXy/Kq4tigaMAPxL/VA7es87AGoEkTVjU7yeYRmVhIudvzxN/jdPnQhz5UuZIBSZ+NuA4uYw2RKGdTUgT1uSohYBmXBDU1I9NRXLDsdAJJqr6mVWls1NZsEYhLFbDOk4krEwIxAmHgEcFHFnARfi6Uei8raUc/yEK1INbDDjvMETRi6zp7vm9961sj3gWs/5TvqYJKlGZu+RHgrBT+xCoP9CCUejciPWl2Ous65c0wAj/qGDzfySgTMENQMIHAxx13XCrZ/p20Q98RAkJACAiB/BAQgZ4f1rqTEBACQkAIlAQByEfLbi1Jk3wzkL3EEUN9VJyPRRok3+bNm730LAZxTmZ2nc3wxwn2ute9rrRdxdnI2FhNbcYGAr1qZAzOByL4cZTimIkl2/nvtGQIyzKYSCHedNNNI5rDcwWJXlYncafYscYiX43cNRY7lzu9rr4nBMqMwM6dO/26huFsRpqYQKd4z4Hj1WoBswZWbf0u8xiobfkiQLAIAX1WqoD9E3NbJgSSIMBeln0ChDO/YxKQvZFlp5dlraSNzz77rFetwtiDU34rXsd/97vfuV/84hcjYEB1CPUhmRBIEwELPDZC3dS8wnvEUu9hlnqztrC2c97E5syZo+DX/SChqsUPa9OiRYtEoKc5kXUtISAEhEBJERCBXtKBUbOEgBAQAkIgOwTKSqDjjMH5XjSBSxY2ZDIHbTJFkVvuBWeo4Y8EI30uo+FgJFPeZM1pZ1Vr6lL/nOwdCCYI9ND5A/aWeV7GceimTXfeead79NFHR1wCCcB3vetd3Vy2VN/FIc48tSxc1cEt1fCoMRkgENfBheiBSOQdyr8RlGb1gPfs2TOiBax1qDOEtdMzaKIuKQRSR4B5zVpPgAjzmD0Jc18mBDpBgH0gAUdkpvNjgaJ2rXCtZM0sIiOW9ZzgQNZzjCBI9jjNgqD47PXXX+8DTEL7yEc+4qZNm9YJRPqOEEiEQCupdy4SZqfbmSvMTidg2+buMcccI6J4P/KcX/HXoCq0cOFCBUAmmpH6kBAQAkKg2giIQG+VJcoAACAASURBVK/2+Kn1QkAICAEh0AECZSXQn3vuOR/RjOwltazzNg7SSGqHtYppR69IDRaNf6vxZm7QRozsxRkzZvjDe1XN+sP8MgIdh49JDNY1I5P159prr/XPWmgXXXSRW7BgQVWHs9FuHG4QKpaNiEICQSkyIVBXBOJSBRDhlAEZTSaVwBIjiKwMR4hNFeSL6zqW6ldyBMgWJtiS9zaBIuwXq7wnSd5zfTIPBFhXw7UyLH9j9ycA07LT8yiLwVxnzpuyDsT5lClTxoQDZbFvf/vbI4IBCDS57LLLRLzlMZF0j0aAMoFOFqwcwxJnp7N/4fljP48de+yxmq/7QcNPgow76w/ntrqeV/XoCAEhIASEwCsIiEDXbBACQkAICIGeQ6CsBDqyrxzKcAbNnDkz13Exp5DJEdIGHDy9VKu4SPzHGmwjZ4h2xziwQ54XkXmT5qSkP2T9I4FHDfReIM8NP/r+P//zPyMcqgRFIOXeyhmb5hikfS1IQTKzjFAhA1fZiGmjrOuVCQHmOnOeuY9RgoXMwqQOVfYjoXwx1wvN5Ish5Vn7k163TBipLfVDgOxb5j37E95dkOd1K0NSv1Grdo9srbTgo3ittLIYnF/YdySt85wUFe4HmWhBT6zzSLcnsQceeMDdd999Iz56ySWXeFJSJgTyRqBVdrqVUSAglrMx+w4k3NN+pvLud1r3AxPU+lDBmDdvnvZlaQGr6wgBISAESoyACPQSD46aJgSEgBAQAtkgQAS21fbO5g6dXTWuCd3ZVdr/FnKySLRxUMaQI5w+fXrPHQjJCOaHAzGEbhmMuYrDzsgZMnmPPvroyjsxcM4QvQ+BjmOGfkEQZeH0LMM4NmvDypUr3c9+9rMR/wT59uEPf7iSqg+MJ5L8GI5sAnB6Rb2irHNM7coWAfYRrM/27uy2VEEoXwxBGe9TCGizbEveU70U4JbtSOrq7SAAccC7G2ONZ61nzZcJgbwQYA8ZSr3HZTHYV1pZDH53Oz9tL25Z8JyR2K8lNaTor7766obsO9+jXNfHP/7xygfDJsVAnysnAlYzPaydbi0lqJ6AX4hzU8bjz/ZTzh5l3yrOOmDDuXXu3LnZ31B3EAJCQAgIgcIREIFe+BCoAUJACAgBIZA3AmUl0MOa0ER652E4oCDPQ7nlpBkVebQvz3sUgf9Y/cMhuHnz5kYd6alTp45aZzFPnLq9F45PI4roX2g4ZSCIrBZw3bMdbr/9drdixYoRGFBnkMykqmSaxrWfyZIl87zqCgndznN9v94IsD5DnhtxQ6kCCJG0LJQvhkw3kt6uz/oQSr13SxCl1W5dp94IQKaQfYch1w6pokCOeo95FXrXqiwGc9WCj/hzO/srzoxhkHGna/2qVavcrbfeOgLOc88915144olVgFht7AEETN6d3+w7bK1nX4/iAmbZ6TxDYcmtup/XwuEngIzAdkruSUWiBx4MdVEICAEh4JwTga5pIASEgBAQAj2HQFkJdJO0JqMnjwOZZQBzGLb6lWRq9KpZTW6ca5CYRRqSvjjsmKs4KCAk61BH2shz+w35RF9xRMT1LY0ggkznp46ELFlJ1113XcNJZXPurW99qzv99NOLnIKJ7o2TDUcSjjasVe3nRBfVh4RAyRGAzI7XZ8iZLI21wqSLWTPNiW33ZN9ggUftEkRZtlvXrgcCzDeCDPnB2CuyL+kl0qQeI1n/XrBvtn0layb/HRp7yVDJY6w5THAxaz0EPYYCFKRZJ8Yz9P3vf7+h3sA1uNZVV12lIJROANV3MkOA7GpKdGDsLVBc4DmJ9x38u9VO7yUyHWxYYwiazCvhIbPB1oWFgBAQAkIgEQIi0BPBpA8JASEgBIRAnRAoK4G+e/du9/TTT3upQWpqZWUcdokqN0coh2Nqavd6/UoCCjgUg0ORkmzMA6stiqOPDK861JGOyXPmNw4ZywTCUYmzEzK2GUFEBoRlpzNG7WQQZfUspXFd5HC/+93vHhBAsHTpUjd79uw0bpHJNRgv1isLfEC5ApWEuoxLJqDpopVHgLWJeU/wCIFnyFezNuVp3NvqpndLEOXZbt2rmgjYnpE9EqZAqWqOYy+2mrnLHsWCj4wINyzGUvIgaAnynEBPPgd5ztzvxlBduv7660dc4l3vepc7/vjju7msvisEUkMgJM9DlRHLTseHYn+Ob2pkup3v6ir1zh4QBT/OPWUp+ZbaBNCFhIAQEAJCoCkCItA1MYSAEBACQqDnEODgh2OkbMahFWcNTvkFCxZk0jwOvhz8woxRsogkwelcXgEMow0sjgdqsG/fvt1/xBwXdQhsCGUB+XOYqdAMj1YEEZgYmQ55VXXSdtOmTe7GG28ckd1BYAv10Nups5nJotHkorF8NdKOvVr6IS/MdZ/iEQhrPxPoRnBT0esz7w0y4nmnQxI1I4isFjBrZh2VPIqfGfVtAfPrueeec8x9DCUcJKyr/s6t74ipZ2MhwNnP1koIsGZKHqyT7L8JNLbyVpyT0lAZ4X6oDll2rz1TZKFLzUFzt2gERiPPRzunhWR6/CzVVeqdflG+h33XlClTfAKCTAgIASEgBOqPgAj0+o+xeigEhIAQEAIRAmUl0HHqkJ2AE2XhwoWpj1tcU5uDH8SXHKHDUFsAAwTD/PnzU8d/rAsyJwlsoA0YWS51CGzA0RBnnrciz2OcLIOI54OfOPiF4A8j0yGKquqEfPDBB90999wzovuHH364u+KKK7wztyyG05m5auUF0sjKKkvf1A4hMBoClPggwAkjuAXyvIxkNO/5UOo97k9dlTw0c9NHgH0JRB/zCZPKSPoY64rFIWCBmqZ8FEu9W8s4KzH30wo03rhxow+YDG3JkiWZKo8Vh7LuXBUE2iHP4z71UnY6Z1Ir64DqFipEMiEgBISAEKg/AiLQ6z/G6qEQEAJCQAhECJSVQEeSFccKtmjRolSJ7bim9lFHHVXKzNYiJ2vWAQyj9c1kIk0KG9KUenNVD2ww4pznDeO3SbZ32jeuCUFkZDoZAKFx3apmW9K3n/zkJ27NmjUj+kR2w/ve975SBAbgYKPmOW0tSr66yDVC9+49BEwZZOfOnb7zrC8EjaRFpmSJqNUCtoxLW4vtnmTRW/ARZUI6XZez7IOuXQwCplZEwBR2xBFH+Gw7mRCoIwKm5IESFT/NjDUyLCPUKQ7c69prr/XKDmYEZF122WWdXlLfEwJdIdANeR7fOFYcs0Dq8HNVzk6n7SQ7cBblrM5+UCYEhIAQEAL1R0AEev3HWD0UAkJACAiBCAEj4coGDATq+vXrfbPIQE8rkxbHP44a+l2nmtppjx9ZKEhpQyIQwJCHMeYcxJGJxAhsqIMUdkiem/Mki1p4BB9Y9lAzOU6yLcnm56doqeUk8wmHDI7VHTt2jPj4iSee6N75zncWRnAxhqwjYQYuWRcQcDIhUFcEmPcEjJgyyCGHHOLX6CoSzfSFNdLWy1jJg/UZcsh+qhAgUNd5V3S/2I+YRC1tgSQoYymRonHS/euFAAGZZJYSPMJ6yJynJAYByLE8tZURYr3sJPjoySefdLfddtsIAD/xiU84AmhlQiBPBNIkz5u1G0KdZypUI4s/Z7XTQ4WytHwgaWNJW0l24D3JfpCSJjIhIASEgBCoPwIi0Os/xuqhEBACQkAIRAiUlUDHUbN27VrfWmqgd+vApp9btmxxSM9idaqpncWkhlzYsGGDv3TaCgDN2ovTAilsy8wmAyWNGotZYNPONWPJdstWzpp0apVtieyyZQ/xLGTdnnYwCz9Lrdnvfe97nuwK7ZxzznGLFy/u9LIdf8/WEcvKwllch/ICHQOiL/YEAqwnyFdDnmB1kq+2PZCR6aZ+Eg4szzmBR6yZVQg+6olJmUMnTRGHYC6M7DoCR2RCoM4IsAZCnrMf5+zFftxK5/B3YfCRBbwaHmHwEQolSUp7cI1vfOMbLlx72d+xz5MJgbwQyJo8j/vRqdQ71ykLoc7+CV8Be0TOQpTCkwkBISAEhED9ERCBXv8xVg+FgBAQAkIgQqCsBDoOS5NvnjdvXlfZnRzsyCCy2pU4QDnoleUAWsZJmZUCQNxX5h9BDQQ3YJATyHRD8FbdwsxzHCXt1jtPq/+WbWlS77HDEwdnKF1ctucC4u6HP/yhd9CYgeWll17q5syZkxZMLa/DGBLkYSTioYce6rMtyhp80LJD+oAQSIBAnIFLncs6ZwbSX6ubzu9m2ZZGpqPqoec/wSSq4EcI4oREZD4wxuwZ6xDUV8GhUJNzRABynPMS+x1T6RptP87ayHNi62Wz4CPWyFDqfbT18u6773YPPfRQo6cQ9p/73Oe6Dp7OETrdqsIIhOQ5cxZVqW4D99uFw7LTjViP9x5llHqnrRDo/CbQhvImMiEgBISAEKg/AiLQ6z/G6qEQEAJCQAhECJSVQMdpuXr1at/aY489tmNC1Zyg/MY43EEAyOk99qMQKgDMnz8/URZJuw8XB27k9Hft2uW/SpYfB/AkGSvt3ivPz5tMe1j7rijyPO63OTyNTLfnwj4HeR7WTc/bgTTaOK1atcrdeuutI/4ZyfQrrrjCP89ZG5mIOJUNL+rf8qN1JGvkdf0iESCQDRLRJM6R6CRwpFfMsi2tbnocfMT6GEq9ly34qFfGKe1+QgSy3pt8NWQK+xOZEKgzAgQHMu/ZJ7K/Yj/ejuIG6yPXYL3kN+tnaFzT1kuep3C9RNXnm9/85ojPL1261M2ePbvOkKtvJUCgDOR5DEOn2el570FoJ+X2WDNmzpxZ6+DKEkxVNUEICAEhUBoERKCXZijUECEgBISAEMgTgZhEy/Peo92LQxl18TCyTIkIb9fIijAnqGUQ9ZLzv128ws+nqQDQrB2W1WjZvIcddpivn5b34b8bjJp9t8zkebP2Ms5h3fT4MyZdTMZl0TW+77//fveb3/xmRBNxxkKiZ/lcUwuUdcTIs14jEdN+RnS9aiAQk4hk4BJc06sWBh+xZrIuhMYeI5R6L3q97NVx6rbf7EmsnEwsX93ttfV9IVBWBFjTmPesc5DmkOfdrGGmfGT7SwvCsv6zXobBRwTOXnvtte7ZZ59tQET5qAsuuKCskKldNUCgjOR5DKsFohDQVbbsdNoEgY4R7DJp0qQazAp1QQgIASEgBFohIAK9FUL6dyEgBISAEKglAmUk0HG+rFy5snEoazf7B1lwspsxHDPIgndCwtdywBN0Ki0FgGa3Yr5t3rzZWV3R6dOn+6j1qmfzhpLtRqQTEEC/qtA3HCHm7GyWPYSMJ0Q6P/w57z6B6W233ebIRg+Neswf/OAHM8kQDMkUxrLXScQES4c+UgMEQjJFJGLzAYUQCoOPYrlVJIiNIOLPea+XNZiGuXeBzFlKhnSagZt7g3VDIZACAiGJyN4uCyWoUOodmfjYWCM5Fzz88MONf6ItyLhXXZUqhSHSJTJAoArkebNum9S7nTnjvQff4e/s7GkKaFkEqOMrQMIdO+aYYzINZs5gCuiSQkAICAEh0CECItA7BE5fEwJCQAgIgWojAJHZ7ABWdK8g0E0WLGntST4Pcb5z507ffEjzbjMpisahiPtDphpRyaEY51YaFqsCII1KTfqq22jkeRYOizywaiVdbFKckOkEt+RFDuGsuf766z3JERq1yD/wgQ+0JTfaCkckRS0Ih/4yV0erBdrqWvp3IVAVBMJ5TyYi874dGd+q9DPNdrJemnQx7zjen6FBAFkd4Fi6OM126FqdI/DCCy80sl9Z55n33WTgdt4SfVMI5IdAOO/Z5zPvsyasWR9DqXdbL1E9ueOOO0Z0/r3vfa9XIZMJgTQRqCp5HmPQqdQ710njfGoB8Vxv7ty5PsBaJgSEgBAQAvVHQAR6/cdYPRQCQkAICIEmCJSVQIfAxbECAZ6EZI1lwZF1Pvroo1M5JPbaxElDQj/GjKAGk2eskypAmAVgUnuWeV6HeUP/kCu2uummHGB9o69GDiHxnHXddJysP/jBD9yOHTtGwDtr1ix36aWXdn1/+rt9+3aHigWWl1O5DnNFfaguAsx75jxzX/O+83EER9YoiHR+YoWfZtLFnd9N30wDAfYmW7du9Zci6BISMev3WBrt1jWEQDcIhMFSRc37eL2888473a5duxrdQhb61FNP9cQc+0wFtXQz4vouCIRBI0XN+6xGwrLT85J652z4hz/8wXdnwYIFmSiBZYWVrisEhIAQEAKdIyACvXPs9E0hIASEgBCoMAJlJdCfeuoph0wqssnUyB7LYlnwqVOnuiOOOCK3zNgKD3/TpocS+hCT3dS+5VpbtmxpEJJ1UgUwJwW/+TGpvLwysouYd6wXRqZDFIWWVx1g7k+9TH6HtnDhQl8zs1P8GUOyzslOwXDYKginiFmme+aJAGs0BKIRF6z3vHfTyFDKsx9lvBfrZSj1HreR96EFIJHp3+naVca+l71NzHsCsSwYS/O+7COm9qWFQBg0gioG630Zgkbuuece9+CDDza6SfD0WWed1fhv1CFsvVRpjLRmQ+9cp87keTyKnWant7Pv4wz4v//7v/7WixYtSk2trndmpHoqBISAEKgmAiLQqzluarUQEAJCQAh0iQAktWXOdnmpVL++du1an70FgTVp0qRRrw2JxgHOCMy6yIKnCmYHF+tEQj++DQoCTz/9dIPoxBlWB2LGapyHBHovkOfx+KL6ENZNb1YH2Oqmp00OkS37/e9/32fHh3bKKae4s88+u20iyuaq1edkzZk2bVrb1+ngUdNXhEBhCLCGoQxiwSgot1ASQURu+kPCGmOZ6fyO911kV1qmZZ6lMdLvafmvGAeNgPtRRx2loJHyD51a2CUC7J22bdvmr1K2oBHOC9ddd92IHi5dutSx14xLY0D4Q6bzk4f6UZew6+sFI9BL5HkMtZ1ZeYbSzE6nFIOV1DrhhBOkEFHwHNfthYAQEAJ5ISACPS+kdR8hIASEgBAoFQJlJdDXrVvnyTGc+YcffvgBmJnkLNnNGM5n5N7J6JJ1j0C7EvrxHcm8Q9rNCE4UAVAGqDoxEzoi6DPOCJNsr3rfupk1reoAGzkEUcEzmgZWOG6oiY5zNbQzzjjDveUtb0ncHeYqQTgmTw9xPnny5MTf1weFQBURiINGeM9KuSWfkeQ9QrCOBSCxDwst79IY+fS6HHcBe4JGTGkEhaPp06en8k4qRw/VCiFwIAKx4kIZFXZ4J/2///f/vPqY2ZIlS3x9Zc4SFoAUB07yWYKOLACJgE2ZEDAEepk8bzYLTDXNfsfBz3yHv+OcZj/sSeLsdAIvUe3C3vjGNzrKs8mEgBAQAkKg/giIQK//GKuHQkAICAEh0ASBshLoGzZs8A5myCyc+qGZ1LJJzuI4gTzX4S29Kb569WpPTJLRT1ZiO8a4QZ7zfQ7fZHaNpSLQzrWL/KzVOw8j+EWeHzgiVtfSpN5jcsgyh3B2kjnUjmRgfLf169e7m266yTt7QkP2881vfnPL6WIShDhumasoXtAumRCoMwI8kwSNWI1uBY0UN9qsXVYaA4IoLo1By0QOpTM+vLsJvAJnTEEj6eCqq5QbAdaY7du3N0opscdhr5NGIGPaPScocvPmzY3Lso8LZdz5B95fRqaTBRvv/yDQLTtdah5pj1C1rifyfOzxCqXe7Ywbf8PIdP4+PPPyDJLEwN+RgV6GMhDVmp1qrRAQAkKgmgiIQK/muKnVQkAICAEh0CUCZSXQN23a5B0kkOc4980gZSFnTWqZ7CFJb3Y5CZp8fc2aNd6p30pCP/4qzgpkGDlwc5gmsKGbGurp96yzK5pjIfxN/8rogOysh9l8KySHINTjzCHwY35Y5lAnQTArVqxwt99++wEdOOecc9zixYtH7RgZiGQi2lwlWEQKFtnMA121PAhAmscBTpTXkJUDgValMVQHuLNxihUX2FtOmTKls4vpW0KgIgjE5QrKXqbj7rvvdg899FAD3dmzZztk3EczCMBQzSNWJDI1D5N672SPWZGhVjMjBESetz8leJ6SSr0TvEJgDmfh448/XgR6+3DrG0JACAiBSiIgAr2Sw6ZGCwEhIASEQLcINKst1+010/g+Dn4ILhycyGtikG9kJlhGK39PBpFIzDQQH3kNq0FPcEISOWvLcLHaijj5Z8yY4eogpRiT5yBlUfjpI1/vK1rmEGQ6Ts84cwgCO6ybnhSN5cuXu2XLlh3w8fPOO89LC8a2c+dOt3XrVv/XzFHI8zrM1aR46XO9iQDPHJnnVnqCeU+GnqycCBg5xHpJQGFMDqWp5lFOBNJplQVemuICe8c6qOKkg46uUlcE2F+RIbp7927fxSqUK6B81K233toYEojvz372s4mGiP7yjFt2ejM1D9tjcl32fDo/JoK2ch8Sed79kIXZ6fbn8KqoAIIz+5D58+c7zv16nrrHXVcQAkJACJQdARHoZR8htU8ICAEhIAQyQaCsBDpOfg5mkLeQuJDpZDab4/91r3udU9ZcJlPCXxRpbJxPOJpbZWmZLCrjheGYgpipg5ybOQ2sVhzOAZHn6cw7shzIYDByCIxDw7lpZPpBBx3U0jHzyCOPuF//+tcHNO6CCy5wixYt8n8fZ2NBHrKW1GGupjMqukpdEeA5Q76aZ4AsPNZonitZNRBg3KwOMGNpZLC13tQ8TLp4woQJ1ehYxq0kaIuATBR1MFR1tHfMGHRdvnAEWC9Q2OHshHGWmjp1ast9VNENf/75590111wzohl//dd/3VGgl6l5QKiz14z3mKyRodR7N+WEisZN938FgZg8RwlNY9v9DAmz0/EPEJzDOgO2nLEUhNw9xrqCEBACQqAKCIhAr8IoqY1CQAgIASGQOgJlJdBx9BPdjNwgTn7LFsXhQWazHP+pT4URFxyrBn34QeYPqgCW6YGT7sgjjyy9k64VejgF4sxzkeetUOv838Ga7Fgj0+O66RB+ODoh1CG9R3OG/fa3v3X33nvviIYwbhdeeKGbN2/eiPq3kCjMVTnWOh83fbMaCPAuxdmJ4eTEoSyCtRpjN1orW6l5sEeyNbNXM8PicgUES4GJTAjUGQH2U5yh2E9hKHVRsqAK2aGQdF/96le9jLTZlVde6QOpuzHbY0Kmg0u8xwQbI9P5Lan3btAu7rsiz7PHPlQyIviYZ7Mq60v26OgOQkAICIH6IyACvf5jrB4KASEgBIRAEwTKSqA/99xzjkwEnPzm6IA4w/Evx0b2U3njxo0+Y4OMFX6aWSypDxmJo67qZsS5ZauY6gEOtio4IOuAP8QHTs5mmZYQ3mHd9Dh7/L777nMPPPDACBgYt1NOOcVNmzatcg7lqo+n2l8cAqxlO3bs8D8Y8rV1UQcpDtXy3dnUPEy6OCSfaC17JoKPIIbGCkAqX886bxFBfSgZgQXvDJUr6BxLfbM6CJgiFGsBBrHVSkWqbL37r//6L69AZrZkyRIfAJmW8V5EkcLWSwjB2BSAlBba+V0nJM95z7HmK0A2XfxD8px9xZw5c/x5DNP5OF2sdTUhIASEQFkREIFe1pFRu4SAEBACQiBTBHAuxnU1M71hwosjPUidYjPqVSpbNCF4KXyMrHLIy7AGfXhZ/i2upYuDvuo2GnkuJ0xxI0sAjZHpzRydOMpM6p2AG8bw7rvvdg8//PABjT7hhBPcm9/8Zl8LVCYE6oxAXP8W8hT5aq1ldR714TIVkMeWaWnS5dbrMAAJx3cdAxIJ/rOSPwRYEXgp1aJ6z3v1znmJcuY98x8jYBBVqKrZdddd5/th9va3v90HQGZlnIONTAe7ZgFIlp3Omql3aFYj0fl1RZ53jl3Sb3L+4rnk+eC9eswxx0jRJSl4+pwQEAJCoEYIiECv0WCqK0JACAgBIZAcgTIS6Dh/yYC2DGCIc5xAim5OPq7dfpKaodROJKMc/M1wzhPYgEIAVidJ/Viynf/GSaB51+1sSu/75uiEUG9W0xKpYsu0vP/++91jjz12wM3f+c53upNOOim9RulKQqBkCMRZiASMTJ8+XWtZycYpj+ZAoFtpjGYBSKgS2JrJ+ll1o6/IV/P+Zn8Cea7arFUfVbW/FQLsjQhqtXJKrPcEHlfRbr31Vrdq1apG0yHPIdHzsDAACVIdNaTQOA9AohuhrlIoeYzK2PfYvXt340yqzPNsxiNUdOFcTOZ5HYLms0FLVxUCQkAI1BsBEej1Hl/1TggIASEgBEZBoGwEOlHkRDjjxMBwfM6dO1fjlzMCjAFOCRxwZC1ijAnEuSkD1ElSPybP6S9ZJiLPc554bdwOkjCsmx4raTB+K1ascBs2bDjgquecc45bvHhxG3fTR4VANRDgOWD9NiIFFRF+tJZVY/yybGWYaQk5ZEGKdk/2W1Y3HWK9anMmJFIIBkDCVwRXljNK1y4DAjzXBL1SVgkj6LVolZ0hZ/8b9G3qa/yvvyVkv/71r90jjzzS+NzChQvdhRde2PJ7WXwglnq3s6ndi3XG1kxULqq2ZmaBWZ7XFHmePdqsK6wvlnku8jx7zHUHISAEhECZERCBXubRUduEgBAQAkIgMwRwoFqN8cxukuDCOCW2b9/utm3b5j9NhDOHNRwSyITJ8kWADK5du3Z5J9zrXvc6PxYcoE0a8tBDD62FHDDzzn54FvjBASbyPN/51u3dGEOcPCb1brLF/P1TTz3l1q1bd8AtzjrrLC/nLhMCdUGAdznrtM3/Kmch1mVMytoP1sYwACneB7IHI9OSLDN+899lNgL7tm7d6psI+Q95XvY2lxlPta0aCBAwxZpvmdIEvB5yyCGFNX6YNh90g30D/k+x9bl+1z/U7/rd6OvJfffd5x544IHGVwmivvjiiwvrk92YcxBnIJN7j6XeWW9CqXetP9kOmcjzbPHl6jF5Pnv27ELXl+x7rDsIASEgBIRAKwREoLdCSP8uBISAEBACtUSgDAS6yc2SfY7hrMUBRLazMtCLmXZg//zzz/txoI5i6KCbOnWqO+KIIyqfaRESBO9SMQAAIABJREFU55aBLvK8mPmW1l1NJcHWEgIhWF/WrFnjf2KDQD/zzDMrP5fTwk/XqS4CODqR8IVQYR2DSJHEZnXHM8+Ws25CwFnddMtkDdtgssXMqTJlddP2HTt2+B/bPxL0pzrFec4g3asIBMKAqTKs+RDmA337PIHuifQ+CPRXSHSy0N3QcC56/9A4T6L7v4vswQcfdPfcc0/jbyHtli5dWgTEo97TgjZtzYyl3m0tMkJdZSTSHT6R5+ni2exq4Z6SYBCR59ljrjsIASEgBKqAgAj0KoyS2igEhIAQEAKpI1A0gW4OIJObpdY58oPU34YMGD9+vJs/f37q/dYFx0Zgy5Yt3iFNJhfZjGRa4KDDMU32edXNCHPmvxHplnUuCcZqji5zFOlqq/NrdZ9Ntvihhx5yTzzxxAGdwykEic68xsmp8a/m+Pdyq8mKY+6znuHoJPuWtVsmBDpBgCAMI4aYW2WVLaZdZJ2jloNB7h911FEizzsZdH2nUghwdtq8ebNXELO9OWRtUebzzvv2kXvuBvuQbR9qkOXDbRrOTYcv7/Mker8bNzS+aSb6o48+6u68885GV3ifXX755UV1LdF9GQfLTG+2Zla9PEYiEHL6kMjz7IEmIITAefYCnI1Ntl3no+yx1x2EgBAQAmVHQAR62UdI7RMCQkAICIFMECiSQIfosgMancPxCYGOIcWMc4iDG/XvZPkigFMaSX0zAhne8IY3OOqeV91GI8+VsVbdkcV5ScCNZQGhksBaEjt7Hn74YXfXXXcd0FGCdk488URfMgIShp8q1gCu7giq5Z0iQLDZs88+60lOMoMhG6jLKhMCaSDAHjGULcahHhoBG6yXkHdkqef1HmW+M++Z/5gFTMnBn8ao6xplRoCgVs5HpjbCms+zV6RBnA/07XVDfcNBqRDkcXb5sKT7cGZ6n5dxh0SfcMDnli9f7pYtW9boDmoqV1xxRZHda+verJlheYx4zWSNtMx0fkvqPTm8Is+TY9XpJ2PyfNasWT7AWO/WThHV94SAEBAC9UJABHq9xlO9EQJCQAgIgYQI4OiweqkJv5LKx5BYJmOO+1vGXJg9gcN248aN/sC2aNGiVO6piyRDgDEBe8vkhVSEPK+DBKGR5+Fv5p8cA8nmRhk/hXoF5LmpJBCIM1YNULLQf/nLXx7QlSlTprhTTjnFq15gVs/SagDnRQyVEWO1qZwIhHWfIc0hUsokr11O1NSqThEw2WICHMm2jGWLeY+GUu+2lnZ6v9G+Z2V/aAN2+OGH16KsTNo46Xr1QyAmt1jzyxDYCnk+6AZ89jnkeDNpdhsNL/HeN+Rl3JtloT/yyCPu17/+dWPwOH9cdtlllRzMsDwG65WprYWdIVjTgpCkgjT6MIs8z/4RYH3hPEVQMmcekefZY647CAEhIASqhoAI9KqNmNorBISAEBACqSCQN4HO/bZt29bIbsbpj3MkzpiDvN2wYYPvIwS6CM5UhrvlRXBME9hgWV0coOfNm1eLDAmTa+c3P6p33nI6lP4DEDnPPPNMIxCHEgNJnMlPPfWUu+222zzpHhok+hlnnOHnR2hGDJmTMytiqPSAq4GlQID3KAohzz//vG8Pc565r0y2UgxPzzQCJ7uR6QQ9xmaKHgRHssdLYx/Hmo2D34go1EYg0GVCoO4IUJMY1S6egTKV6iCzfF/fnkbt82bZ5+HYDNdHHyba+d/4oYkjhu63v/2tu/feext/h3z0e9/73loMr5XHgExnzYz3mgTAWXY673UFbg4Pu8jz7Kc/yRSsL0aez5w50yu7pPHezr71uoMQEAJCQAjkhYAI9LyQ1n2EgBAQAkKgVAjkSaDH5CxOArInmjn9cRStW7fOY7VgwQIRAznMGqupCPZmZEPMnTs3h7tndwurcR4S6CLPs8M7ryuH2bc4HdtVSUAC9aabbvLOotAmTZrkLr30Uu+4hBwimCeuARxmDEkuO68R131AgLn43HPPOVRcMNV91rwoAwKQeqHUexycZMQQ8xViqBOnPOQTDn7LfJ8+fbpjvZYJgbojQMAIc599LGcm9jsEqJTByCjfRwa6J8Upc97fsllkq++vhH4AgX7fffe5Bx54oHENgniXLFnS8ppV+wDv8lDqPd6Lsgc1RQ/Oy70auCnyPPuZHZLnvJvJPBd5nj3uuoMQEAJCoIoIiECv4qipzUJACAgBIdA1AnkR6HY4M3KWjCGcn6M5Ufn8mjVrfP9wnkiWtuuhHvMCOOespiIf5OCM06LqBLqR5+bMx/mIU4p514kDP9tR0NWTIGAqFhDoGGQ22bedOBe3bNnibrzxxgNkNXFakvHEGsWcIVuIHwj1OGOIZ8TqpuPQbnde7Rva6waGBsjJcuP6xjv+1+41kuCmz1QfAQtCs2xfyMNp06ZpvlR/aGvVA9Zo9hSWnR6XCTJiyBQ9kignQC5BINq1qIs8VqmOWgGqzvQ0ApCsqC6w/rPPaabaVSRAaRPod9xxh3vssccaXTruuOPcu9/97iK7mPm97Sxue00roRXeOAtFj8w71uUNRJ53CWCCr4s8TwCSPiIEhIAQEAINBESgazIIASEgBIRAzyIQ17FMGwgcATg+yR6CGKJGcausIT67evVq3xQyoOtQfzttXNO6HnLtOOfCevQmk4qzbv78+WndKtfrWJ1zyzznv0We5zoEqd+MsXz22Wc9MYOlkX0LEX/DDTc0yhZYownaueiii9wxxxzT6EerjCGIICOFIOFHk9/0BNPgn92fBv7oXh56RfGBG03se5U7eNxfudf0HyxiNPUZVN0L8k5knbYgNKSrJ0+erDlS3SHtmZazxxyLGCIjnQxL1s5me72w7jN7SAKm+LxMCNQdAYKlbH/eidJOHvg0JNz7Bv05olsJ91tuuaURQE37TzvtNHfmmWfm0ZXS3IMzmK2Z/I4DNzmbmdT7WHvN0nSog4bs2rXLEeSK0UfWfUnadwDkGF+BPGd94TfvVmTb8c8oiDddnHU1ISAEhECdEBCBXqfRVF+EgBAQAkKgLQSyJNA5AEN4GTlL5gQH4VaG82DVqlX+YxBYZZEqbNXuKv07Y7Jjxw63detW32wc1zNmzPC1SiHVCXqAEERCv2pm5Hn4G8eLnC9VG8lX2guB+PTTTzeyxVGxOOKII1Jx9EDIk4nO8xAaTqRzzjnHnXzyyQcAx9xi7bQsy7D0AR/mu0YK8duyLAeHBtyOvds8cc6f9w3t87VD/XeoB9o33vW7cW5i/0R3+IRp/r9lvY1AmCEEEgShHXroob0NinpfSQRYxyEFWTeb1QA2RQ/WTNRFWFdx8LMn5P3NHpK/lwmBuiMAccqeh70GzwVzv6xqXF7C3Q00apsjzz6asd8Z6hty/UPj/M84N3KP873vfc+XKTF75zvf6U466aS6D/foeO1X9DAVpFjRg71mKPVe1jnSzgCKPG8Hrc4+K/K8M9z0LSEgBIRAryMgAr3XZ4D6LwSEgBDoYQQ4RMU1fruFg+tBzBohBSkLOZs0k5zvr1y50jdj9uzZvmamLD0ELJMXeTwM5wvOOSP5cNRs2rTJk4CLFi1K78Y5XCkmz7mlZZ7ncHvdIgMEIKohUaxGZBa1byFqyHwicCS2xYsXu7PPPnvMAAzaZmS6SWyH1/FZln91sNvz6r+4fX173MtDL7vBoUHX3wdtPs5/dJD/+b/r95nokOhTJxzp+vuG/13WewgghW0EorJve2/869xj9iEoFBkxBLkeGu9ty7wsW93nOo+L+lY8AgSxPvPMM74hnJ/Yn3dSpiavnkCeD/Tt83XQ3RhZ6GSrD/la6cMV0McPTfS10EP7+te/7tcEM8rpzJkzJ6+ulP4+nNktO73ZXpP5YsGbnZQVKhoAkefZj0BYEoV9JesLQcnKPM8ee91BCAgBIVB1BESgV30E1X4hIASEgBDoGIG0CXQyhciaCGWWkV5LUucy7AQEOmQokmKS6+x4eA/4Ik5qSEKrsYdcGxmN4cEZp8zGjRv9dyHQq3KoNrl2fvNDu0Wepzd3irgS85T1xDIQqX2b1XrAPX7xi180gnfC/qKEceGFFyYKAhpVfvM1A869dtC5iQR19HmCfFzfBNfX/4oDmaz0PUN7vEt5Yt9B7q/GHeImTZhSBPS6Z8EI4CSHRGEt4/35+te/Xtm3BY+Jbp8NAqboYWR6rOjBXS3LEqn3OmRZZoOkrlp1BF544QWv3IVBgLLul5k8p50Q4xDoZJcP9g3wF/uJ8Vf2NnzG9SHx3uf6hvqbZp9D7H31q18dMYQf+chH3LRp06o+rJm0n70m5zUj1Pnv0Ng3hFLv7Z7DM2n0GBcVeZ494jxjBGUSmCzyPHu8dQchIASEQN0QEIFetxFVf4SAEBACQiAxAmkS6Fxr8+bN/mCGTZkyxTs+OiFgkXDHGUDmOg5TWfcIMC6Mj0kAksnbLOqcrMf169f7G1aBQMf5Hmeeizzvfr4UfQUcyUh5MrY4kHEkZ13OgXs98MAD7je/+c0B3ed5ueSSS9paj7geDs4X//iie3H8Tjc0btC58UPO7cXr3Of6+vp93/jxzs0+57PQ9wz9nxvvJrgJ/RPdURNfryz0oidjzvcPSZSy1r7NGRLdrkcQQBnHJJwJgLP3e9h9sizZF0IOVTHLskeGUt1sE4Fw7lOqgD1P2UlP6+Iwib7Xk+km0z6y+xDnnj4flm934w7IPue5R8I9tC984QuJAhfbhLp2H2edJPDIgpCalWcLpd6TKsLlBZTI8+yRDslz7kbmOX6aTnw02bdWdxACQkAICIEyIiACvYyjojYJASEgBIRALghwoDKZzG5uCElEZjOkN4cxMkUPO+ywji+5evVqR7Y0DiTVe+0YxsYXcaowPow1TmlUAQ455JCmF8YJs27dOv9v1EAvswPPiHObw9Y/5qCcAt3PmyKuwJg+//zzbvv27f72kCWsA3lmHaKA8fOf//yAtREHJCQ6qg3t2F8G/uxrn//f4F/c4MCgc3v73ODgyGwh2HMj0wfHDXgi/aD+V7tJEw53B49r/qy20wZ9tvwIMPd37tzptm3b5htblQzE8iOrFlYBAeY+5X8wIxB5j1vddPYxcZYla6ZJFlMqg/2NTAhUDYFw7jOPO1HuKrrPw9T5cC10n3G+//9Npt3Ic+Tbm9VJf+KJJ9wvf/nLRjdQyLrqqquK7lYl78/ZPpR6j0u1QaDbuslaW+R5SeR59lNM5Hn2GOsOQkAICIFeQEAEei+MsvooBISAEBACTRFIg0DH8RNmihLV3G3d8jVr1vhMaYh4nCiyzhFgfEwS0rIZcZiMZmQurF271v/z/PnzSysfORp5Lgd653Ol6G8yplu2bHFkYmEQ1qwBRQRxEHBy8803NxQ1DBvact5557njjjsuMVwv7Nvl+IFAH5ZuH++GBgfd3n373MC+ATcwMLL+L1KnbmKfm+gmukMnTHJTDpKEaWKwK/pB5j7EOeu1zX1IFK1nFR1QNTsxAsx9AqYInBpr7luWJSWCIIfiLEtIIN4Zlp1edunrxADpg7VGgHlvQVPM36qv+40sdE+iD5uR5s2Ic/vMsmXL3PLlyxvfmTdvnluyZEmtxz6PzhFYHEq9E5weGnsMk3rnd577bZHn2c8AxhvZdiuNQkDyEUccUWjQRPa91h2EgBAQAkIgCwREoGeBqq4pBISAEBAClUCgGwLdyC5zepItB3mehjQcGdAc9o488kgvMy5rH4F4fCDNGZ9WmbzMiaeeesrfcO7cuamMZ/utH/sbsWQ7/43Tp8gsirT72GvXI7OQms84+jAULJBNL3JMWdt+/OMfNwj9cEwWL17szj777EQE5+59O92L+3bvJ9Bf5cb1jRsxvEODQ27fAGT6Prdv34BzfYPOTXA+U939pd8dtPcVUoiM/CIx6bV5mUd/cXAThPbiiy/626G6wrtP45wH+rpHkQjw7ibrHCIFg/wmaCrJ3CfIMsyyjPvBnseyLNmXJrlmkVjo3r2FAHN/x44d/gdjrjL3ezVoCvl2K98AHmeeeaY77bTTemtSZNxb5hyBRyb1bqRqeFsC4I1Qz3LdFHme8WA755X8RJ5nj7PuIASEgBDoFQREoPfKSKufQkAICAEhcAACnRLokF0cyjiEY8iBp5k1QQ1uanFDoFGjS9YeAozP008/7cjUand8OHAjoY8de+yxXkK7TBaT57QNh6Oc42UapfbaEssLkh1B4EwZxpR16Kc//anbvHnzAZ2aMWOGz5BqVZsd8nw4A/0lN75/ghvfBzs+ig05t2fgZbd3aI8bernPuZf6nPvTK4Q7ATCWYYmjswwYtTfa+nSIgK3VL730kv9r5r2ygzRHegEB3uWo41jgSDdBUzxHodR7XJqIdTOUete62QszrLx9jFUX2gkcKW+vOm8ZRO5//Md/uFBq/P3vf7+bOXNm5xfVN1siwHkvDELKa90Ued5yaLr+QEye46OZOnWqzgxdI6sLCAEhIAR6FwER6L079uq5EBACQqDnEeCAFdeUbAUK0evIG5t8Js7+tA9lGzdu9M5QrsuPLDkCZGVB9nU6PjhQnnzySX/DOXPm+FqkZTHaFkq34wQXeV6W0emsHThOCcZhLWI8ybwlA7dMxry766673KOPPnpAsyB9Lr300jEDffYMvuy27n3W8Rt504l9Y2eR2+de1XeQO3jwULf3T/t8MAzPdmioLoTSm72auVamudJOW+LAkWnTprnJkye3cwl9VghUEgHWVBRHLAgzzcAR9ggEpFiWJc9ZaEVKFldysNToVBGIVRekOOIcqmM33XRTA2f2Np///OdbKmalOjA9fjHWZFs3WTubrZuUGLA9Z6clMkSeZz/RLMmBAGBM5Hn2mOsOQkAICIFeQEAEei+MsvooBISAEBACTRFol0DnUA3ZxeEMsotDWRZkFwQwhBHZ52Shy5IhgPMD7Gx8kIOE4GvHcO6tXLnSf2X27Nld17Nv596jfZY2xZnnIs/TQLbYa7CeQKLguIPUoDYfWdVltd///vfuV7/61QFBR2Q3nn/++W7BggWjNn3bnmfdXwZfci8PvuwmjJGFPjC0z+0Z3OMm9r/Kvar/IDd9wityxgTFWP1fc4zZDa3+r2VZdurcLCv2dWsXY8m7FCc1Y3fUUUd5JReZEKg7ArFznyDJrEr1sG8wqXfWznjdBGveOabqkUYJorqPn/rXOQJWWmn37t3+It2oLnTeivJ984477nCPPfZYo2GUm7rsssvK19AeaVG4brJPN4WcsPtWIoM9Z9LSQjt37vQlOzDI+DSV63pkaFp2M36/4gcgOFOqKy2h0weEgBAQAkKgBQIi0DVFhIAQEAJCoGcRaIdApx6w1aeDnEG+OKvsZIiFF154wWfjQSzIWiOAQw4y0uqBMz6dkpEQ6FwH+UScI0VamHFOO4xsxRkgh0CRI9PdvcMsFAhoyPOylQto1kOesZtvvrmpQ/Hkk0/2ddHJnortLwMvuR37trp9g3vd3qG9vg46Uu79ff3+o4NDgw7yfN/QPje+b7yb0D/RTRp/uDt4XHNSlbXbyHTUOkLpU67H2mykUBVw7W42VevbkHgWiEbgCE5knMkyIVB3BFi3QgUjFEfaDfLrBqNQshhiKF43IdBt3WQN1R6jG7T13RCBuGQB55u01buqiDi4XH311Y49odlb3/pWd/rpp1exO7VsM6SsSb3zO5Z6xydgmensZZqpIYk8z35qxCWB8J+QhKD3WPbY6w5CQAgIgV5AQAR6L4yy+igEhIAQEAJNEeCwhUNxLMO5AXHO4RfDqUh2AKRXVkb9bgjhSZMmOaKnZaMjwPhs27bNbd++3X8IsgzyvJtMKiTccZBwHZzJRVlInlsWOo4ZSVUXNSLd3zeu/Un9cMjzKmVMQ1wjN2qZNCEqEEIXX3xx0+fmxX273P9n702D7biqs//V55w7aLCudKWrwbItWfIg2dgGGw94CLYJmJjhBUL4500gIeRNKpVAVT7kQ1KkKlRRIXPlA0lVKkVICISAk1AhNhhsB4MxBmyMsS3Jmi3Zmser8U5n+NdvH62rvn3PuWfq7tPdZy3V8ZV1+3Tv/ezdu3evZ61nnSqNOqJ8qjzl5NzVsQUunniOPIdYX5i/RBYXmqsDz73qd24Gy3IYKdT5vA3rDMwdDXRizjP3uQfMDIGsI4DaAgo5SVFdUMliDUQK7oWtREbWZ2R8/eP5zrrPXMPCLFkQXy+iuRLvL1/4whdmnPzDH/6wK+djljwEmMsEAeq6GSwt5FdDglTHV2DkefTjGCTPuX/4GHkePfZ2BUPAEDAEegUBI9B7ZaStn4aAIWAIGAKzEGhEoONQJFOODEcMuXYI7agJzAMHDrgXbq4HwWBWGwGtI0q2PoazArxqZcC2guErr7ziZKoJlOiWrHBQsl0z680Z0MpIJutY5ivBOKdPn3YNIziDDImo15MoUIAEQnYUWfegEWT0nve8xwWgBO1s6bScKp50GedlKbmfmOflJC955+xalF8sl+SH2nJ8Nar/y9qgGZb1MoWiwMvOKS4oTFVcCGpgre4k0MkwNQTSggAlC8g8Z0/JGsfcT5LqAuumv0TG+Pj4DGhps1/qPcoA0rSMqbWzOQR0n06QGzYyMuLKU5lVEXjqqafkxz/+8TQcvPf91m/9Vlv7H8M0fgS0RAbzW30F/lYQKKjBSayhrP1p3PPHj2zzV2SNIfFA8TfyvHns7EhDwBAwBAyB5hEwAr15rOxIQ8AQMAQMgYwhMBeBjgMRh6dGlyM1iOMnDgLz0KFDcuzYMUf01CKhMjYMbXUHAo/x0ZqeZLSEJdW2bds2lyWGtHCc8qoKhD/zHMeA1Ttva4ok6ks40HDw6HzNinwpBPoTTzwxS8mDOYsM6e233z5rzYQ0P18+K+dLZ6VYKYlIRfJeQebnFsj8/EIn7x6GNUMKqewmP9OkAhAGPnGdg3GgBIqqhBBgwdpqeMc1AnadbiIQLFlAYF5U5X/C6if7Hwghsiyp/xuUekc1gjWTPWqz9X/DapudJz0IBIkt3qPYq/e6ob7j/lQq8vnPfd4Fl6nddttt8ta3vrXXIUpl//EpQOKqIlJQDcmv6kEAVafB3qkEKeRGB9cY/AAEJsfhqwm5K3Y6Q8AQMAQMgYQjYAR6wgfImmcIGAKGgCEQHQL1CHSchmSea71pnP1xZiIj6ceHF+wrr7wyOgBSemaCG1QKlS4QbR6mU2779u0ucAK1AWT04zKVaWfeKYlu5Hlc6Ed3HeYS64kG46xYsUIg0MMwaoZPVSZFkED3cjLgDbifcRpS7l//+tdFlSD812b9+oVf+IVEZFtCCvnrpgcx8mdYWmZ0ODOIdYyAMCUIIN3iUHEJp/V2FkOgMwQgUwic4plOwAjkOYRzmoy20w9dO4OkkNb/hUxnDbXsyjSNbnRtZZ6w79GgQYitOPfT0fWs/TOjulOSovtgB/cfkv/88tdmnPAjH/mICwY2SzcCBA3yHo+xJgbrpquqhwZx2p6z9fEOkue8W7G/NPK8dSztG4aAIWAIGAKNETACvTFGdoQhYAgYAoZARhHg5QtSRU0z5XD4Y8hUdiNbiEw9SCmckevWrcso+u11KxjcwPjggAjTdu7cKZD0YRPzc7XRyPMwRzA55yJ7DwIFZzJOHZw7EA2dGHNlvDImZ4un3U+pXDxbzsvJgvxCWZhf5GqJx2XcL9/85jdl165dsy5JINCDDz4oa9eujas5Da/DePjrpgedm5BcmmFJtqU55BpCOusAMKUcida9RZrWalK2jqN9I50IMO+p+8x6rXvJtJMkWv9Xs9Nr1f81VY90ztcwW83zFYUoLQXAut8NNacw+9TJucg2L8rkBeK8IuULGehPfON/ZfuWndOnHlo8JP/v//2m5EJS4emkzfbd9hHw1zzXoEH/npOApKCqB88GLS+EQontOefGP1gawsjz9uerfdMQMAQMAUOgOQSMQG8OJzvKEDAEDAFDIIMI+Al0rU988uRJ11PIa8jZbsjMErlOrViIm6uuuiqDyLfepVrBDcjbg1HYBglI1gxZIHHUavRLtiuRTsYCDhRzooQ9uvGdj1rnEIhav57ah51K93Kuk8Xjcq50xtUQJwO9WkecDHRP8lJwcugQ6Uv7lsu83PzYOkzbnn32Wfn+978/yzlII5Bzv+uuuxInW0m7VXYT0kvrVSpwPAP8ddPtnmw8pYLZh6yjfAy7xtjZEelHAMUF9nAYwTjd2ktGjaTW/1Wp9+D1eN5pIBIEkd3/UY9I98/P8xPyfGJiwjWGoME4Fby6j8DMFvjJc/Zs0OcinoyfH5d/+YcvSbnE/1ftrrfeIbfc9mbpl0HxxEtaV6w9TSBQizwPqnKoqocGcQb3nEi7E3iqwUgm9T4T+CB5TmkIlALt+dLEBLVDDAFDwBAwBNpGwAj0tqGzLxoChoAhYAikHQEl0NXhQ7YoRqYENbS6JUUJiU/mEg7Ha665Ju0wd9x+CC6IyLiCG1599VVHqPFSzidKq0eed2vuRdnXXjk3Y4oTTeUbuY8hUMhC7MSUPD9bOu1k20uVknOyUjOcn2UodVdTXKQ/1++I9JG+lTKYm9fJZVv+Ls7zRx55xN1DQSMT7T3veY+QjZxEc9n94+PT9X+VBNC2cl/6MyzNsTl7FFF1QbpXsTPp3iTOdGtTVAj4pXshkAmc6oV1opGqB88/DUQiQNXIjqhmYPfOy9rP85/AirAUd7rXm3CujFz7lEy4gEfIdE9ybr/20x+/IM889aPpi7BG/Nrv/KosnL9QclKQfklXqYdw0Er3WZohz4M9ZM/JXklVPVS1wX8c66Vf6r2X186gspGR5+m+Z6z1hoAhYAikCQEj0NM0WtZWQ8AQMAQMgVAR4EWMzBl/PW2c/dTT7uYLKrWEISDIfNywYUOofU7byVQKUsm4OIIb9uzZ45wZIyMjgixcVKbkuf7kOpp5HtU17bxjJ2U0AAAgAElEQVTRIsBYUn7BH+xBZkQYBMp4eUyOTh6SycqEI8r7vH7JS37GWsX1p5AKrZSkPzcg/d6ArOqPPzODYKRHH31Udu/ePQtwMjIfeOABufbaa6MdjBDO3ijDUrOEIIY6DZAIobldPwWOYAgUgtKMQOn6cFgDYkSAtZfyOxDoGGsDa38vBsOBBc8AJYX8pZJ0n6OZ6eAUxvMxxqG2S9VAgDHWdynWfuZ+2OWV0gj8pIy5yudlKRLuKDnJSalYkn/93Jfk7JmLQYbXXn+NvO3Bex25npO8DMg89zezdCDQDnleq2fsnTQznffeYHmhXg5EUqVA1L0w3tEJUOumvyYds9NaaQgYAoaAIRAGAkagh4GincMQMAQMAUMglQggs0m2MS9lODl5Eeu0PnEYQPByCAmBU3Hjxo1hnDKV54CMwSGndTYhs+OQAQZ7xoBrEVARhTHn/NnnOACMPI8C6fjOGZQVDLvm87Gpw3KudFYmyuOOPC94hZqdY15BsotXkQFvnizrWyHz8vFJuWujaMfzzz8v3/ve92Y5ATnmhhtukPvvv98pbaTBcGzi0CToCgdnsIYl5SSUFCJIoNecehBmBH75n6dkTpkZAllHgLXg0KFDwp4SQ7IaFaNeWwNqjbN7Hk1OTq+blMcJGuuEZqen5XmQ9TndSv8YX/bNkOjMed6lCIzodUMVSAl0yuxAjEOQb/rZZnny8e/NgOeXfvUXZfmly1yWOqV4CtLn8tDNko9AWOR5sKfspTQQiT1nrUAkv9R7N0rOxTU6QfKc93PWmV4MUIsLc7uOIWAIGAKGwEwEjEC3GWEIGAKGgCHQswiQJUqGJBHdUdXTbgdcXpTJgsYRdf3117dzitR/B6IKhxwZ6JrNEpfsMyQQKgDDw8POCR6maY1zP4Fu5HmYCHfnXEHZ6rBrPpNRfmDyNZksT0q5UpIBb3BOcobjIdEHcgMyP3+JjPRFp6TQCHGIpYcffniaXPIfzz394IMPumy1NJm/hiWEOuuU33otS4iAI8pssL7hxKVkAUEEZoZA1hHQEjOaFYdKDoF3Rp7XHnnNsGTdZJ8XDERi3dBAJIKSDMdk30F+1RENRLbAqeqYqXx7SaYuZp+XSvLFz/2bnDl9dnpgV19+qXzgl9/nyHMy1SHa89LnaqGbJRuBIHkeVS1uDURSVY9agUiUDFGp9ywFcbLf5j2C93LMyPNk3xPWOkPAEDAEsoqAEehZHVnrlyFgCBgChkBDBHghpU4xL5xJitwm4lyljyHQe82BSGCDn4whuAHHQFy2b98+R/YtWbJEVq9eHdpllTxXsk0zNRnfXhvj0EBNwImoWUjQBcQARp1vSJQwTeXbxytjVdeqN3dmkqvlXRmTPq/P1UBfNXB5mM1p+VxkqD3++OOyZcuWmt+97bbb5K677krUOtxsJ8EaZ6Y6NlUxQ7+Pkoi/bnrWMmZYr3FuYjhtyQoyOftmZ48dl2YEeIazX9ASM5T/QVbWnufNjapmWKqqhz5Dg2sn2elkWmZt7WwOpeQexd5HA115zrH2x7lX9yMD+Qz9zH8xMr213ni3EKT6eZGSOj4CffNLW+Q73/7ujCa9///7P3LZFauNQO/WQLV5Xcp14EPA2ONFRZ7Xah7vkSr1zs+g1Ds+DQ1EIqAlrWtnkDznGUuAZlr70+ZUs68ZAoaAIWAIJAABI9ATMAjWBEPAEDAEDIHuIUD2RNIMp9TOnTtds6677rqeeVHU+tHHjh1zfSf7CPI8bjIG8p6sArJjcQiGYX65diXSVbLdnO1hINydc0CcQKBoMAQOtCikS8fL5+Xo5GGBSM97jQl00NBjIdAvHbiiOwAFrrpp0yZ54oknZklRchjE07ve9S73M83GM6VelhD3ur9uepICt1rFPFjzGeKE9dLqGbeKpB2fRgQgMAic0kzA5cuXC859s/YQcEFf4+PTa2dwb+5fOyGG4t4Xtter7H6LeQ95zt6HNR9Siz173FalzakxXpKKV6XRMehzLFch5BDp9PjriV/MQKf+uSelqbJ86Z++PKP2+arVK+UX/+/7XdDNxQz0gpNxtwz0uGdT89frJnkebCVrp1/qPRjEmda1k7Xl8OHD0+pVqMLhEzDyvPl5akcaAoaAIWAIhIeAEejhYWlnMgQMAUPAEEghAkkk0GnTjh07HJobNmxIZVZmq1OBF2Wc0WQiYdQQhYzsxovywYMHBecIbcAp2Kkpee7/icPRiPNOke3u91EpYK5gOPMhD6OSrZ4sT8jhyQMyURl3jth+b2557OkM9FyfzMvNl5X9yZFIB7dvfvObsn///lkDyH1x9913y5vf/Oau3Pthz6hGcsUquUmGZVRzJ+w+cT7mF3NfJTVpP+UuurFeR9E/O6chMBcC3NeQh7p/jEJ1pNdHgLIompkOORSUeoes1QzLLMkVp2HcGQ/26+zbu1myA8KZDO+yV+apNP1HMcw5Ep3/elKo9Dlp9DgNan9CxqrkvlTkJz/4qTz7zE9mNOG9H3y3rLmy+p6hx1VroPe7OuhmyUMgSeR5LXQg0DU7XdVR/Mfp2sn6mdQyGbXIc97HLUAzefeDtcgQMAQMgV5BwAj0Xhlp66chYAgYAoZATQR40Qw65roNFY7Dbdu2uWZcc8010t8/t1xzt9vb6fXp7969e132EUZ9sxUrVnSNYCbi/ejRo845u3bt2o66FyTPOZlmnnd0Yvty1xBgTFFJ8CslQJ5HmU3sCMvJ1x2BPlWekkFXA71+RtVUZUqKlSkn376osFgWF4a7hletC+Mce+655+Tpp5+eJT3J8QTPvPOd73RlFLJi9NkvuRmsm846r4QQxHpSA2zoB8EP9AVjjLq5Xmdlflg/0oEAe0bIc/Yt3KMEjhBsZxYdAqyVEEG6fgbXTp69BPGwfqZZrjg6BMM7M+MAec6ehMBBSK1uvKP4yXOVbq/Ktlczz8lD59/dntuR6DkpVPrd3+O0SRl3tdBPnxmVL3/uP6ZL/bh9zhWr5X0feq8v+7zk2smfAZk/3Zc422vXmhuBpJPnwdY3WjuTWGKIPSbS+JQH0j0mmedGntvdaQgYAoaAIdBNBIxA7yb6dm1DwBAwBAyBriOQRAKdF95XXnnFYXPVVVd1RRYxroFBBhLynIwudUZ3mzTjxZ0PUstXXnll21DgBPBLt9M/I8/bhjMRX2RMybw9ffq0aw8O+9WrV8eSeXuqeFL4TJTHnJOVLPRaJGu5UnZEe94rSH+uX1b1XyYFL5mZTNxn3/jGN6aDEfyDDClyzz33yM033xwLvnFOMK2bToYlH8g4v6lTM2m1f1mnIU802Am5fWSrk0r2xzmmdq3sI0DGOeS57lcInIqiZEf2kWy/h7p2apmMoFwxeyzGRNfOKAPb2u9FOr8J5pSsYQwgzSHPuyWlDyld9KamSfJa9c79ddHJPs9VctInc6v3MDL6Pc0I19GqUtuFlohtzgGJ/u1vPC7bt1SVxTCemb/8a78kS5cvnb4e/072eV76pE+yHTidxjvAT56zvrD3T9PeR8tkqLJHrTIZBCDxXsOnG4ExQfJ88eLFLpDdyPM03jHWZkPAEDAEsoWAEejZGk/rjSFgCBgChkCLCCSRQOcld/Pmza4n69evF7IRs2jI/6ozTmsoJsEZTWbxoUOHHO7g36ppjXM/gW7keasoJu94AluYr8iXYnFn3pYqJTk0uU/ILkfSnZwqiHEcw65+ZqUsRXKdKlOS86oE+4L8JbK0L9k1xSGjyEQnI72WkeFJNnpW6wuzXvjrpis57Xe0q0OTn90ihHhWQp4rYWWy1clbo6xF0SFAsB/zn+cA+xXI86zuzaJDMfwzsx75pd6DV2CMNDs9TWUywkeqszMSNKhlV8AR8rxbz6KL2efUPi9X90AXMs+DvVQynH/nuEZZ6JyvJFO+eurVmupVq2a4V+uqN0+k7967S/7roa/NaNp1N22Ue99xz4V/q1zIPM+7NkLy1+tPZ6No324XgbST57X6TeCmBiLVKpPBfa57zzhUkdgLE1R74sQJ11wjz9udrfY9Q8AQMAQMgSgQMAI9ClTtnIaAIWAIGAKpQSCJBDrgbdq0yWFIBnQSSOUwB1QlsJFKx9QZlxTnJo4SsoypDYcCQCvmzzjne5DomnWepkyFVvrcC8cGyUMkqyHQ4x7TifK4HJ06JKVK0RHpZJtjiJaqm7fgFRyxPpgblGV9Kx2ZngaDnHr00Uen62r72wxhdeedd8ptt92WuWz04Nj4nZq16ldqhhCkUFwZQpD6ZN5CHjLnkdjHsWpmCPQCAn7Z6m7WfO4FrDvpI+uTv0wG+y+/paVMRicYRPFdgl0PHDgwY7/eLfLc7asJFPSm3E+sUW1zpNyVaM9XqC9eW5FHz+vPXA/ieYFCd9ns1ClvRHSzd/yXf/mXGfsa5uGHf+v/yrz5g273prLyWvu80TmjGGM7Z30EskieB3vLWukvk0Fga3AP7lf2CDsjPEieDw0NOf9H2NexeW4IGAKGgCFgCLSLgBHo7SJn3zMEDAFDwBDIBAKQFUEnWxI6tmXLFtcu6n5BlGTF6BOOuNHRUdclXsjJZEnSSzLR77QRQv/qq69uGvp65DkEull6EfBnHiah5i3Z5yeKR10tdOcWrlSdyOJ5Ql4UbVyQWyhLCkhrp2vu4Wx+6qmn5IUXXqg5YQhcIBt9+fLl6Z1QLbRc61dqhmXwWaUZQjwjCPiJIqADpyrKC1zbMm9bGDw7NBMIkHnLfqDbNZ8zAWaMnWC8yKrUtbNWmYwoCaEYuxrppdirE1CKkYWK8kK39+sq3w7hrfXN5wIBQlyPzVWgz2fLo3PMlDfhjuTPxXrq1b9VSfXqmZREn4uM1/Z85zvfkeeff35G8976tp+TG29+Q3Xb5gh0dm6oCaVrvxbpxEvIyXuBPA9CrapIunYGVZE43i/13mnwO9c7evSogDVm5HlCJr81wxAwBAwBQ2AGAkag24QwBAwBQ8AQ6GkEkkqgb9261dXZxFnFy2QWjP6QxagS2MPDw4IMcBSkTyd44TCEMKK247XXXtvUqZQ89//EyZi0vjXVGTtoGgEcSMiWMq5JIg+dg6syLudKZ2SqMunal/PyMpib52TbyUJPs5GN/q1vfWs60MbfFwJSbr/9drnjjju6JiHbDWz9hBDzMpghREYgWeFa+zeMtcefech6yPOoU2dpN7CzaxoC7SDgJw+7LVvdTvvtO1UEGpXJ4BjIdF0/u1XXO2njRTCpKkVBmKE80m3yHIyiINCRbS96RUe0K0FeKxNcyXglvvsq9eXW2Tt++ctfnjGsYPjLv/zL1bI7gnQ7+kFmSUSgF8nzWuPAXtOv7MF66jdV9mD9ZJ1oZe8ZJM8XLVok69atS8Q6k8Q5aW0yBAwBQ8AQ6B4CRqB3D3u7siFgCBgChkACEEgqgb5t2zahbThbqAOWdiOCfe/eva5PGMQ5BHorL9pxYQBpBIEHIbVhw4aGlw2S53xBZdsbftkOSCwCfucxDiLIw7gksxMLSowNY634wQ9+ID/5yU8cARI01sW3v/3tsnbt2hhblYxLgQdrqtavpIa631h/NLsSp2Y7pId//ht5mIxxt1bEh4CfPElK5m18vc/2lZQQIhAJhY3g8yUOZY+kI+yf/zxLeBdJiprSTAn3ixLo9TC9mIGel3wFofSZGejB7PO5aqpzDTLUyUSv1lQvuOzxoPF8/td//dcZ0u28U/z6r/+6e/cxSzYCx44dc1nRGEGJq1evTuT7atwookREELwS6kFlD917au30uUo9aDk3sMaMPI97NO16hoAhYAgYAq0gYAR6K2jZsYaAIWAIGAKZQwBHGjK5SbMdO3YIpMill16aemcLTkoIaa0HDhGZZFl62gvZjyPguuuuqzs1ePnXD33jQ0CAkedJu5taaw9jStbVyZMn3ReTlHnVWk+ycTTyyWSjq7xjsFcbN26U++67zxHGvWpI3yuZrgoffixaya4M1qK0+d+rs6o3+x3MiEsaediboxJdr5up/ct+FUKIuZAUEjkqRIKkFv2GPExSv1snvC/WSi9UkErPz4APQrzoTbZQU90vCQ99PjDjfGD48MMPC4HQfnvrW98qt912W1RDZ+cNCQEjz5sDknnO3lOl3il35Tfeo7/xjW+4Umj333+/e5/WdYTvsqf3BymsX7++rWDP5lprRxkChoAhYAgYAp0hYAR6Z/jZtw0BQ8AQMARSjkBSCfRdu3YJL6MrV66UZcuWpRZlXpC1fiKymNR0p1Zvkg0ias+ePY4Mv/7662s21U+cawa6kedJHtXm2oYzHdlN5gBGRgRqCUlyHjfXk2wdxTr9ox/9yH1qZaOTMYhz+sYbb+z5LCECwpRM52cQL9ZflSoGN78KCMcSsEDdZ53/BHElUSkkWzPcepMEBJj/hw4dmi4dwfpv8z8JIxNPG1TZQwmhoLIH66A/GGmu7Mp4WhzuVYLBI0nOvC3KlJSaklyv1jTXWum1JNc1ox1p+Go+e+Na5BzrjqzMJtB/9rOfyeOPPz5jcAhCQLrd9pLhztmwz2bkefuIsk9H0UOz07/4xS86Al1t6dKlrvQSe3XIdN1nss4g25619bR9JO2bhoAhYAgYAklEwAj0JI6KtckQMAQMAUMgNgSSSqC/+uqr7kV0+fLl7pM2wxEHcY4MMIYEKuR5Gl6QyeDcvXu3azcEepA8UsIcslWJdM06N6IpbTP1YntZC1BKQHoTw9kzMjJi5GGChpRslccee8yRvLUMsusd73iHGzczcaoYrGcQQnyCaisENfnJdHDluYMhM8uzx9Y0m0m9gEAweIQSEQQw2vzvhdGv3UfkiZVM13XRfyTBSJqdHgxGShtqQeWdoaEhFzyY1PlPDviUN+mk1PlTrVtOPfGLNcX5ndYZJ+s87+TWC7OGpn1J+NkE+pEjR+Tf/u3fhP2kGvME6XYCcsySi4CR5+GNjQajff7zn5dnnnlm1p6d9fINb3iDI9Q/+MEPukA1M0PAEDAEDAFDIMkIGIGe5NGxthkChoAhYAhEjkBSCXQyoInihghasWJF5DiEeQFIGohIzeLFEZc0Cci5+kvmPwoAmF9yjv+vR55bVkmYMyj+c5FpxpzVen44jiFQzJKHAPfgiy++KE899ZQrcxE07sVbbrlF7rzzTqtZ7wPHn10JKYT0Zj1D9cSCEJI3961F0SBAoMm+ffumg0cseCoanNN8Vva1/uzKuYKRKHuRVOK51hgElRfSEjyixDckuZLl/v4pqe6yyit5R55XSfaZppLwnA/j+FrH6beUtK+S8hdrqhOs9qUvfWlG3XO+8/73v1+uuuqqNE//zLfdyPNoh3jnzp3yne98R55++mnZsmXLrGBOgtUpxXTvvfe6wHV7p452POzshoAhYAgYAq0jYAR665jZNwwBQ8AQMAQyhABOMH+mQFK69tprrzl5Mxy5kHlpMUgZ6p4psUUGY9qyeGk7NeixDRs2TGfNK3nu/5nP51PlKE3LPIqznTjFIU8gUXDaEOxBVq5ZshFg3J588kl55ZVXajYUqV2VikwTmREX6lq7kueMqi7otcELEkizK8lUNzMEsoiABvxp/Vb2LOy7zAyBegiwB2S+aHZ6MBiJfQTPH10/2Scm1YLKC2lTHiH7vCRTUvGqNLq4D6YZ6VI389w/JkWZlJJXcmdTufdaY6ZkPb+DQNea6qwjDz30kNtL+u3mm2+Wt73tbUkdfmuXiBh5Hv00YJ1BkQ6FBgJNtm/fLtu2bXOBsCdPnpzRAHwG7N0h1N/ylre4tdTMEDAEDAFDwBDoNgJGoHd7BOz6hoAhYAgYAl1FIKkEOk6Y0dFRWbJkiSP00mC8FEOegykEDO1OYxYvzlBe7rFrr71WII9Urp2ffKzeeRpmZOM2njp1alpakPICl19+uSC3aZYeBCh38cQTT0zXLQ62nHUIB3balDziGAFIIJQXdM1m7kOm16qbDhnEp7+/34KG4hgcu0bkCBA8SbCiBvyZ8kjkkGfyAswfFJcg1DUQw99RgpG0VAbrZ1KMdX7//v2u3VialReqQu5kkFdpdPLMIbgbZZPrWPhl3DlDtQ76RRKe4/yZ7pw7V8lJQfodZ09pmZdeemnG0LKeUPc8DaWrkjIn426HkefRI846A0l++PBhdzEI8fXr17t3a/ae3Dff/e53XUAspLrfWC9vv/12R6Yj9Y70u5khYAgYAoaAIdANBIxA7wbqdk1DwBAwBAyBxCCQVAKdWrREayN/DqmXdIPsxxHHizLOoiuuuMJlMKbRcKpv3brVNf3qq6+eRaAbeZ7GUZ3ZZuYpjjM+GE4Z7jPLtE3n2CK9/6Mf/UieffZZF+ASNO7Zm266Se6++26ZN29eOjsZcqshfAjU4l4gQ5L5DzbgR3a/ZlcGpYpxaCoZxPGW3R/ywNjpYkGAQDnIc9YO5jA1WK1GcSzQZ/oi7B91/eRn8HnE+qmZ6d1cP2kXe3YttUTWJ6U7etUgx0tSlJJXnCEHDwFfNc1wh5hH4j0nhUq/+/vzzz/v5Kn9xhh/+MMfNjWjBE8oI8+jH5wgeY5fgHIG9d618H1ApvP54Q9/OKPU0K/92q/JJz/5yegbbVcwBAwBQ8AQMARqIGAEuk0LQ8AQMAQMgZ5GIKkE+sGDB+X48ePOoQsZnVTj5RhJtqNHj7omQkSuWbMm1bWHmRMqC71u3TrXF5VtN/I8qTOx+XYxltxfZJ9jZEOQpZxkmdXme9fbRxJ0hCObrPRaRob1PffcIzfeeGNiayyWKkU5Vz4r4+XzUqqUXSZd3ivI/NxCmZ+jtq469NsfawKeuAcw1jfI81qZkX6pYgh1yEa/cc8omc59ZHUr2x8T+2Z8CJAxDHkO2cmcveyyy0wmNj74e+ZKkNQoM2l2erBclK6frKGsn3HtQWgXwVMQ/JiVLahOSSXRy15pOttc/13rqWtWupLnvCs88sgjM+Y0QcS/8iu/Yqo3Cb7T/eQ579kEUFkwYLgDxv6RveahQ4fciQkYgjxvVoWDtRMSHTJ98+bN8ru/+7vy8z//8+E20s5mCBgChoAhYAg0iYAR6E0CZYcZAoaAIWAIZBMBHElBUiAJPUXqDFIax9ratWuT0KRZbVAnHDV0MTIucETH5QSMChT6tWXLFnd6ggEICsARgKMdB4s5WaJCPvrzEhxB1pU6jikxsHLlShvT6KGP7Qrcq7t27XJEugZJBC9Ott29996bqLWVdp8unZSz5TPVgB2pOvExstzcHy8vQ/lhmZ9vryYk5yUwSwOeCCiAPG9GYpbvat10yPRaddP9dX+bOWdsk8IuZAhcQMBftsCvvGAAGQJRIsD66Zd6r7V++qXeo1LDYQ9E2Q6Vmmf/Q6kosyoCKtPO87fszVazyVfy5J47cfc9e/bIf/3Xf81SGXjPe94jGzZsMEgTigD7H1WfMvI8mkHqlDyPplV2VkPAEDAEDAFDoH0EjEBvHzv7piFgCBgChkAGEEgqgc4LPiQ6DjWyoJNmBB2QwaVOOGonZoWI9BPow8PDTkafiHnLrkzaLGytPcxZHMda75asK8bXAiJawzEtR5Pt99xzzzlp92Dmn/aBtRUinfWrm+ZkLkvHZKx0TorIyFaQka3muqlTn78XvD7Je3lZnB+WBflLWmoy1+CZQi1KDLKbgKd21zXuJ82sJFMoWDedbCN/3fRGjS1WijJZHneVbLWv/d6A3Z+NgLPfN40AgVM8A/ylZqymatPw2YEhIqDrJ2so8zK4fjIvVeqdQKcw9imQ5+zblbynRjdBhGa1EaiS6TyJ9Wl8sSY6Ci5f/epXZwVgUybmLW95i0GaUASMPI9+YFjLCF5VlaNWM8+jb6FdwRAwBAwBQ8AQaB0BI9Bbx8y+YQgYAoaAIZAhBJJKoJMlyMsnL57r169PFOKQ5nv37p0mpZC+g4jMgqlU+44dO2aQbq2SQVnAIkt98GcdWr3bLI1s476gkIEE5LZt22oerPXR77zzzq7JOJ8ujcqZ4qhMVSbJe5OCV5A8WW6e54iV6fqslZL0ORK9IMsKK2QgN9gYABGXIUdtSTLHMYKCIE/CIGU4H8SMv256sO4vZJBKvQfJoInyuJwrnZGJyvh0xj3nJHSAgIGqdP3C0NraFGB2UOYQYB3gHuB+IruX0jjNSslmDgzrUKIQYL30r5+sp35DzUPXT4Jq2wl6IogM8lwDCClbQ/atWesIQMJCnmsAsZ7h5ptvlvvvv9+eVa1DGss3jDyPHmanpHThWcvV2O9dffXV9qyNHnq7giFgCBgChkDECBiBHjHAdnpDwBAwBAyBZCOgsrRJayW1fHH2Qjzw8pkU48WY2ok4/HDi4YTGsZcFo09KoJNRSQQ92UG1yCDNrGR8wiKhsoBhEvsAaYhsO2OLZC9ZtzihzXoLAYJ+kHVX6c5g7yHT7rjjDrnllluakjQPC71ypSyHp/bLZGVSipUp6ff6nVR70Ji/JfLTK0UZ8AZkXn6BLC0sb9gMyBjWbNY0jGx7JOyjWrdoJ9fivuMTzP73k0GVwZKcLZ8WMKBv5Qqy9VVDsp5AgZyXk0FvniwpLA2l/ntDwOyAzCFAHVbNhuOZzb7FSgxkbpgz0SHWTzLEWTvZfyrhrZ1j3W61VIYqRlGCg+9DnrOHNWsdgSNHjshDDz00izxHsv3d7353ZM/V1ltq3/AjYOR59POhFnlOzXNTeYkee7uCIWAIGAKGQPQIGIEePcZ2BUPAEDAEDIEEI5BUAh2HL6QHmVLXXntt1xHU2rmHDh1ybYFs0vrgXW9chw1wGZ7UHC5X6x1COOFk1CyfucggxkfJdLLUoyKlOuxiz36dQBRkqzHLOuzZaTDdce7xl19+WZ5++ulpQjmICll5P/dzP+dqmMZxP58rnZXR4jGZqEw4srjP6687UO555TtuRd9ql61ez4JlC1asWBGrWojW/VUy3U8GeYMi+digxkkAACAASURBVCGRfF9OpCCu78jTV2XrK1KqIOZedkR6X65fBnPzZEl+WSxjYndKdhBAzQfSC+MZffnll7tAKjNDIA0IQHpDpKvUe7DNzGnNTmdfHnxm8QwgeIyf/I4AwqwEvcY9fvXIc96FfvEXf7HldaX6hCu6/1547LnnHTXW+a9ZOAgYeR4OjnOdhb2eBitznAb/G3kePfZ2BUPAEDAEDIF4EDACPR6c7SqGgCFgCBgCCUUgqQQ6md7ILeLo3bhxY1fRg3Qie0tr55K9m5UMLiXOlTzXzHocjUFHpD8zCEcBjk2/MVZKprcrs9nVgc7QxRkrnJ0Q6BhOZhzHlnWYoUHuoCvcu88++6yrkV6vPvry5csdkb527dpISdsTxaNOwpwMdGp+QyTPZWSpUyN9wBuUxYVlsiBfWwEEsppnCP1LStkCSBzWztNnTsvkgjGB+/f6RCpFIpdE8vmc5AsFKeQLkst5LiN9qgLxk3OZ+UsKI45INzMEGiHAMwDiBAIdI2uXZ0A78teNrmW/NwTiQKBRqQyCBP1S75p5ruQ5wSPcB2atI1CPPGdNgTxvpRwEJVmKMillj6Ddi5XWCR2rBpB5kqtAo/df+P/W22vfqCJg5Hn0M0HJcy2RYuR59JjbFQwBQ8AQMATiR8AI9PgxtysaAoaAIWAIJAiBpBLoZJvs2bPHOXuvu+66riEG+fL666+7+ozY4sWLhZrnWXBC+8lzzUKnX832DYJKMyuR3PQb51BHJg5Ly3iLbwoH6z0T1JCVORsfir1xJQKVvv/978uWLVvqdhjS4Z577nGyt1HYsanDcr58ztU/hxRvlPVecqRy9dihwrBckp9dxxbVDBRMIFxYi3DyJ4k4Oe+y7o8L9c8rpYqUJlW4/SLCtLtQyItX8KQkJUegz8vNl+G+xrL1UYyTnTM9CPA8Ry0HJR8MVQmeAY3urfT00Fra6whoqQzeFdiHQpIH96C6r2XeE/RqpWvamzWUAPra177mpPX91i55PuVNSMXlnZcdfR40SHQy0PlvX2XASPT2hs3I8zZxa+VrrDGsQVomC/Ic2XZqn5sZAoaAIWAIGAJZQsAI9CyNpvXFEDAEDAFDoC0EgjUG2zpJyF+CsH711VfdWa+//vquOH7BBelHzbRG/nfZsmxI6Cp57v8Jyd2ug10zK2vJbHJOHJeanW5Z0CHfLL7TEfABcTg2Nub+Nep6z9H1xM4cJwIHDu6X73z3STm472Ddy+IUhEhnDQzTjhePCIQyGejUNifbei4j+5ys7EGXgb5UFuRn1rJVGU3WNtYaAgCS5swkaGC8POYCASDGIQog+4ulopSKJVdSw2+o2pOZ35/rl5UDl0shV1+2PsyxsXOlDwHmDplwBMdgS5YsEfYu7T7b04eAtbjXENBAYCXTdf/jx0H3oAR2tpIt3WtYBvu7a9cu+Z//+Z9ZSjVtk+cyIRUP6rzkqHOefRDm/NFcdMh18tBzkhevkpM+MRK91XlomeetItb68UHynHWFfTKKX2aGgCFgCBgChkDWEDACPWsjav0xBAwBQ8AQaBmBJBLoOMBw3GBkoDebFd1y5+t8AUccmedaDxwShiyuLFiQPKdP4BuWgx3M1JHJzyAZhHNByXRzZIY3o7iPmbOaibVy5UpHnpgZAvUQcNKT5VNyvnxGUC54bfc+efGZl+X0iTN1QSOg6a677pKhoaFQgD1dGpXTxVGZrIxL3itIAU3zOWyyPOF+258bkGWFFTKQu5jpQ5kNMm/d7/v7XdYhsr5Js8OT+10tdydF72s/7YRUKJfKjrAolYpSLldcjXTPE6lMiZRPerJw8BK3hpq6R9JGtrvt4R4mgEoVcyyAqrvjYVePHwHeHQh81X0n+9rgHpRnA+snZDr70bD2vvH3Ntorbtq0Sb71rW/Nwq8d8pyWQppPeZOu7jnPOUeQO6p8pkGkc2yVRC9IX6XfHWvWGAHm+rFjx9wHM/WRxpi1c4SR5+2gZt8xBAwBQ8AQSDMCRqCnefSs7YaAIWAIGAKhIECGddDBFMqJOzgJZOCOHTvcGTZs2BBr7WbqRpPBhZHBuGbNmsxElONg90u34zgMkzwPDjnXw5lPVihkOuS635C7UzKdv5sjs72bxi9ZDYY4OHEOmxkC9RBgHRgtnZDx8nkpCnXFq/dmpSyyd+trsvnHr8j5M1Ulg6CxZtx4441yxx13uPu3EytWinJ4ar/Lxi5Xyq4Oer11gJrgZKr3ef2OeF5eWOWODTqNIUUIekpq6YiLBHpJBnIDc8IHgT5ZmnCEArEDpRNVIh3zq3twvycxWKCTuWHfbR4Bnq0EUGn2LVnnw8PDzZ/AjjQEUo4A+yDuAfad7N01gIp/1z0ogUl+4xnB2qmfuIN1kwg5z9Nnn31WnnrqqVnNW7dunbz3ve9t61kzJRNS8kruWaYS7XX3Jxck3iHO85W8y0I3mxuB4D6IIMdVq6p7JLPwEABn3msJVuPvlnkeHrZ2JkPAEDAEDIHkImAEenLHxlpmCBgChoAhEBMCSSTQadP27dsdAtdee21bzppW4dO6ocePH3dfhYRJagZjO30LZp5HTZ4H26g1K7VuetCRCfmjZLplBTU/wkj1EvCRZMnq5ntjR8aFwNnSaTlTOlUlrqUseS8veSlMO1tRMtj+8g7Z/OxWmRyfrNksyAcl0msFbDAni1IUiG+s4BVclnnQkHEfK513Wdm4egtev5MsV3NrF3lrlUkn+Yrsucq3B+s90w7qtSeZCDk6dUgmnIT71AXZ+rkd3NWse0/6vD4ZGFso58+cdw7cYOAbUvV+dQ9znMd1N3X3OjxLX3vtNVE1IUiTxYsXd7dRdnVDIEYEWA8hz1kT2Uuydw8qHPE77hEl04M1vS0gSVyQ6+OPPy4vv/zyrNFDfeaBBx5oKzCNrPJJb/yCdHulbvb59DN/OgudJ35e+iuDVgt9jvvJyPN4Fpsgec5ac/XVV2cmyD4eFO0qhoAhYAgYAmlEwAj0NI6atdkQMAQMAUMgVASSSKDjEN66davrJy+nZCdHaTiNiCbHsYYhe0cWb5JJmGbx8Gec8x2yczTrvFsEC23CealkutaZ1z5BzCkRRO3KLIxDs+PV7HFgSLAHtQ4x7hGybi0LtVkEe/c45s6R4kFHnhcrU46YzXm1JVLHxsfklZ9ule0v7JLi1MzsPf/9etNNN8ntt9/uMvnIJD9fPifnymcc6e23AW+eLMxfIvzU9Qcpc0hl2kKGOc52MtQg0Z2keYV6qRXJI+jq9cu83HwZLow4smT//v1O3QKDNKR0QbfWtWZnFIELfCbLyNbn55StB0t/1j2y9fSvkbqHBSQ1OxrpPo5nJ+Q5AS/MC4JHOlWFSDci1vpeQ4D1358N2mzgK/eMlhsiS71WQBLPM+6nXlBIAoOvf/3rDsug3XrrrfLWt7617Wern0Dn3M1IspOprscagV7/rmbe8h6gwd+WeR7NCqhB4Nwf7L/YY1HznHdUM0PAEDAEDAFDIOsIGIGe9RG2/hkChoAhYAg0RAAnEi+DSTLas2XLFtek9evXRxrdrQ5ozUYZGRmR5cuXt+0oShKOfvKcv/OBjE4aIa1ZQRDqwawg2qpOTKv5W51dwaxbcIE4SapkdZLuCWuLyFj5nJNvJ7MZ0g1J9HrGXINonzw/JVuf3y47Xto1qxSDfhfZ3BtuukHW37xW+uYVXLYZ0vA4z50j3HOCrO7nYG6+LMkvm840hzwnE71YnkKs3NUHn/m9woXvzXPfq5QrMySrly1bJnySTp6DA5gcmTrgAgboJ3XfIdKDptiDHjXfh/LDsiA/uzQDxyHdrQFJPNP9pjLFWjc9aeu/3ZPtIcCzkqxbAg4ZU4L+eBaYGQK9ggAKPARRYZDckOc8h1q1RgFJnFP3oVkM6qRm9te+9jU5derULOjuvfdegUDvxIxA7wS9+t818jwaXGvtxbRUlpHn8WBuVzEEDAFDwBBIFgJGoCdrPKw1hoAhYAgYAl1AIIkEOk6BzZs3OzSouRdVhDcvxGRv4YCGeLn00ktlyZIlXRiF8C8ZlGzn/yFSkk4wMR9VYhNZTr/5JTYhg9pxlIaPdLxnRC0Bh7FiY9km8eKfhauNFk/I+fKZC5nN9bPPta+QvNQqH/AGxTtfkJef2yQvvfRS3cCrfCEvV75hjVz9pnWyYOF88S7IsWsmOdnufXIxk1zXpJmZ6zNJYK69IH+JDHrz3HrNuq3KFWSdp23dPl08KWfLZ6Zrv+ddcEHBydRCNiBZD+5It/fn+l2Qw9LCihnS9rXmIus8uCiZXkumGJKV9RNCqBfX0CzcwwRMcA/gzOe5jvoIpU/MDIFeQQCyl/I1WCfkeRAvDUjS7PSgQhLPK3/d9LSvoZTLevTRR6efp4oHGbYPPvigXHPNNR1PKZ5pU96EC6rj2cbTjmddPas+A6u10jm2rzJgEu4BsIw873haNn0CrXnO85b7ncxzC1ZrGj470BAwBAwBQyADCBiBnoFBtC4YAoaAIWAIdIZAEgl0ekQGOi+ra9eudc6qsA3nm8o+4oAmcyUrL8R+8hzCNe5652GNFW1XJyY/gxKbEAb+mr9hXTep5+FeJeNQa92ilrB06dLEB0UkFc9ebdfJ4jE5Xz7ranD3ewMN5w/Etx67KD8kC/NDQubfj370I1crtZ6CCVmx665bK9e9+VpZOLTQ3b/VWuZTrpY5pPCSwlJHjPuN4+rVTvdn3aZZspo+jpaOy1j5vJOoJ0BBM+7BAmqBQAPqxhekT4b7lru/t2qNZIp7bQ1tFb8kHu+XrMaZz94l6jI3ScTB2tS7CIyOjsrBgwcdAKxhBJBEpcADge6Xeg+irmso7ynUXU96kKq2n+f2U089Jc8999ysicS++v3vf7+sWLEitElWlCkpeSjMlBwZPpeMO+R5tWxLXvKVPvcMNLuIgJHn8c0GAu1571LyHFW8KHwS8fXIrmQIGAKGgCFgCLSOgBHorWNm3zAEDAFDwBDIGAJJJdBfeeUVJxWMc5ia5GGZOh6OHDniTonjec2aNc7xlXZTmXa/dHtayfPgWDSS2GQclUzPYr3KIHG4atUqIfvczBBoFYHR4nFHoGttbSTV5zIkxyG9B7wBuSS/WBbmL67HBCJBpG/atKkukc4atObay+W6N2+QxUuHnIQ5svAQ6AO5ebK8sKop0sGfBQRZgmR1VOokrWLazvGs09SJP1c640iFSqXsxO4hz8nah2QYzM2TRfklNSXeW70mz1MwVIWPYOADz0BdQwcHB5sak1bbYMd3hoBfsprxarbec2dXtW8bAslB4MSJE3L48GHXINZ/ngNRkefBXmtQJ4Q6n+AaSta2qnvQtqSS6bT94YcfrlnvnL3l+973vtBJQpVxr4bRlR2FzjPOn4nOMdU/ZKnz+5xY/fOZs9DI8/jWIiPP48ParmQIGAKGgCGQbASMQE/2+FjrDAFDwBAwBGJAADlcnEJJs23btgnkPs6xxYsXh9I8nF3IX2udP6LIo8xcCaXRTZ5EyXP6qAR6VsjzIAT0D8eGyhQzh/2mTkwcmWQHJdWJ2eTQOkct85axtVq3zaJmx9VD4GzptJwpnZLJyviFLOe5s7umypMiUpG+3IAM50dkIDc469T7TrwmP/7hs/LqK3tmKUX4D75s/Wq5/tYNcskItZqr8uTLCitrntP/PYhD5Hq597OWdeskg8vn3XggY896RV30+bkFkm8j67yZme9fQ1lfgnXTs17ztxmMknaMP+s2TMnqpPXT2mMI1EPg+PHjosGvKEbxfsCeqBuma6hmpwfXUNqlddNpa1wkfyMsKP0Aec4eOmgbN26UBx54QNhDR2EltGW8KUegQ5JjVQKdT5U6r/5bVby9UOlzpU3MqggYeR7fTNCa5/hHuHfJPOed0swQMAQMAUPAEOhFBIxA78VRtz4bAoaAIWAIzEAgqQT6jh07nFQ1dcmHh4c7HjWtm6tOI85JpkXayVV1qkCuKnmOkwXnHX3LQv/mGnz6Sna2kunBepU4PjSrkoygbjlb253AJ0+elEOHDrmv49Qk4MPkettF074HAkiGHykedFngZIPPJeMOoVvNFu9zx0F211pTjk8dcdnUo6MnZedP98irr+yVcqnqIK9lK65YLtfcsk5WX3aZLOlbJgsDMu7+7/gzDpn73ANROfjDnCFklI9VxmSsfM5l8EMOQAoMePNkfn6By8BPgrGG8qzVNVRLRGjbWDP9ddOTQgQlAbu42uAnDqOWrI6rT3YdQ6BZBFijjh075j4YxPTq1asTs5+jfew9Vd1jbGxsVtfYf2p2ejcUryACn3nmGacYEzTW+Pvvv1/e+MY3Rv7OoCQ6hHlVc+Wi8opcyDuHQjfyfOYoGXne7GrR+XHcv8i2G3neOZZ2BkPAEDAEDIFsIGAEejbG0XphCBgChoAh0AECSSXQd+7c6YhRSG7qPHdinGfv3r3TWXZhnLOT9oT5Xb9cu2ah4wxLG1EcFiZ+Iohx91tSM4Jq9T3oLENSGeKQzFAzQ6BTBEaLJ2Tsgow75yLj2YmpemSCVTOdtF45pC9k76L84ln1yrUdx6YOX6irPikD3qCMnRuXrT/dLjte3iWlYn2Fk+HlS+TmN98sN21846w1K3gPxC3X2wnG4+UxOVU64TLKVbK2ej5P8heka5GvH8oPSyMJ/U7a0c53yaRUIgjJ96ApEQQZlIZAhnYwSMp3gvdA0ojDpOBk7cguAsF7gHUH8jzJwaG8V2lmOmsoffAbgWCanR5HuQwCMR955JHpYEx/WyiR9d73vte9a8Vl7C0g0sve7L1BrkLl84Kj0s2qCBh5Ht9M4L0RlQYjz+PD3K5kCBgChoAhkHwEjEBP/hhZCw0BQ8AQMAQiRiCpBPru3budxOCKFStkZGSkbRQgAogkV/lrSMisyLApea4/AUkzz9sGLENfnIsIwvnqJ4KSREwzVw8ePCjIVmPMV5QYejUoIkNTMjFdIQv9ePGITFWmpFiZdDlgEOhK5laJ32rGNNnnkL1L8svqkhYnikddLe/JyoTLVNfzjI9NyPaf7ZBtL+6UqYmpuv3HiX/LLbfIjTfeKGTnsaZxD2i5jTTdA2ScnyqerFIEFzLPVaZWJWqdPK3X5wIThgsjiSPRdaC05i/PUYigYM1fiCBV+ODvSSa1EnPzNdkQ7gHUR5Bux7hHeA4Yxk0CaIelHgHuAeqdQwBjQ0NDqVOOYs3UkkOQ6sGSQyh6+KXew9zngd/LL78s3/nOd2aV6QDPK6+8Ut71rne5UkfdMK13TnhBVci9Whfd7CICRp7HNxuC5Pm6devcc9fMEDAEDAFDwBDodQSMQO/1GWD9NwQMAUPAEHBR1kGHThJg2bNnj8vggDyHRG/HkD2FhMHIlFuzZo2Q7ZEF89c65+9ZrXce1lj5iSDmVTAjCAeiEkHdkNfUfnIv7tu3T1QClFIDy5cvN9IkrIlg55lGAPn2k8WjjkR3GWGVoqtDiuHIznt58bycDHqNM6Uhz08WjzsCHfIc0t1vkOdko299YbuMn5+oOwqQsJDoK1eunL5HlyxZ4p4BaSAOIcyPFQ87LIuVoqMDXDadV82mm87sl6IjCiDQ5+Xmy+JCZyorcUzrRkQQQUj+chlpGK84cGvnGsyT/fv3OyUALE33QDv9te8YAkEEggEkixcvds+FNK8rWnJIs9OD5TLoG+UyINT5dKLwwTUef/xxQc0raJD0d999t9x2222pxjPrd42R5/GNMOQ5wfa8gxHUYuR5fNjblQwBQ8AQMASSj4AR6MkfI2uhIWAIGAKGQMQIJJVAR0KNDNxly5Y5p1krptmL1M7FyDS+4oorMiF/rTLtfgLdyPNWZoe4LEqyKVWmmHvAb93KqqSGJg4creMOaQiBbmYIRIWAc+hXzsu58llX63zGfeANyvzcQifJ3oi0IGP90NQ+dw6I436vT3Jeflazp6aKsnPzLtn+wk45d/p83W5xPWR6yUq/+uqrG14/KnxaPS+Z5+edNP6Ek2qHPK+FnastL1PixGq9PhnpWyl5Lz3lGZQI0jW0Vt10f1al1U1vfibxfCKISuXz2QPxaXQPNn8FO9IQSDYCQQWSrAYS+lWSyFIPBnYS8KvraLMKH5xj8+bN8uSTT7oyWEEDy3e/+91tByYne+Zkp3VGnsc3luxf8DlAnhNcouS5PXPjGwO7kiFgCBgChkCyETACPdnjY60zBAwBQ8AQiAGBpBLoEInI9+LsQba0WaM/fJfsCwzJR4iYMGURm21L2Mcpea6Er8rS85JvL/rtoQ2mKq8JGRRUYyADSLMqyVKPCmcyzpm3jK2Sh1kpNdDeyNi34kaArGmy0qtS7nmXgd6KnS6NyuniqCPRyWgvcA4fgQxpTN1TrpGrFOTQ7sOy44Xdcvjg4TkvQ9mNm2++Wa666qpEr+P072jxoOs/feyX/jnXCwINSlJykvcL84vkkvxQK3An6liCfpRMZz31m79cRqdZlYnqdASN0f2LKpBYEFUEINspE41AUH1h6dKlTokqqr1XUsDg3idohncXPsHATlX4YA0lKLjWOw1Bx4899pi8+uqrNbt10003yX333ddRZntS8MpyO7gHjhw5IhoEngX1haSOV5A8p6wBfoOsrzdJHQ9rlyFgCBgChkAyETACPZnjYq0yBAwBQ8AQiBEBSFiyIJJmyJdS9xDHwWWXXdZU83Di7927VzQbDunrrDjetM65Zp7z/1rv3F70m5oeDQ/yZ1VCBmkmuH6RLEq/RHFYQRk4PQ8cOOCyj7gGhGG3alI2BMkOMATqIMD8pRb6WPm8FGXKkciYVjSl3inS8AWv4Mj5ofywLMhd4qSqf/zjH8vu3bvnxJZalG984xudxHsS74+J8riTxJ+sTDrpdjLL5zLwmpQJd9xgbr4sLSzPxNwiCEkliiGEamVVso5CBDWbVZkJYBp0gn0YQVS6f1m1apXb/5gZAr2CAPtbngcaAMv+HfWFXjPWTIJodB0N7kXZeyL1zjrKT/aNL774onzve9+btW8FOwj3Bx54wAWhmSUbASPP4xsfnrU8c3n2ck8ZeR4f9nYlQ8AQMAQMgXQhYAR6usbLWmsIGAKGgCEQAQJJJdCpXU4Nc0gT5NcbGY56JNg0gxfSnSjyLJiS5/6fvOyHReBmAaMo+oBzBSKdT1AKE+w7lSh2hOOJEy7TBKP2OuR5N2uwR4GjnbN3EGBOnyqdcJLw1XrfJSk74hwi3VVWd/XAF+eHZX5+oQMGkgDShECSPXv2TCsx1EONTLzrrrtO3vSmNwlBUkmx8fJ5GS0el4nKhBQQZ29Ckt0d6xWk3xt0Mu5Zs0blMuJS+Eg6rhBk7F9w5JsCSdJHy9oXBQLB0gWs7WSfm4kjxespfKDUhWS7ZisH8dq4caPcf//9jkQ3SzYCRp7HNz7+Zy7vc2vXrnUBaxaQHt8Y2JUMAUPAEDAE0oOAEejpGStrqSFgCBgChkBECCSVQD98+LAcPXrUZVisWbNmzt6Tqa4ZvJArEO5ZcRYFyXOA0MzziKaEnbYGAv5alVqbVg/zSxQzX5mDjYxxZY4zdzHmK0EfViu4EXL2+zQggBw8JPpY+ZwgbY5BFFNTfV5ugSPRMZz/rN0YZCprNxnMP/vZz+SFF15w5RXmMgJOINKpk97tgKKJ8picLB5z9c9d9fMGBLo/A33AmyfL+lakYWjbbqNmVSoRVEvhwx+U1O3xbLujLX6R4Cyy4LT+Ks8BskrNDIFeQSBYumDlypWyZMmSXul+S/0EK4LOCDD+6U9/WleunT3lO97xDvdsNEs+AkaexzdGwYA1Ms+NPI8Pf7uSIWAIGAKGQPoQMAI9fWNmLTYEDAFDwBAIGYGkEuhk5fLBkczLbS1TEvLYsWPu14ODg46AyUoGr8q185MPRK2R5yHfAG2cTh2YSgQFJYqRl1ap91pzMShTilICcr2W+dDGYNhXUosABICqL7B2Q4b7g08gFLdv3y7PP/+8HDp0aM5+QrzecMMNTt4d1ZJuGJL1R6cOSrEy5TLv+xrUQOf4ohSl3+t3gQVDheFuNLtr11SFD8ggrfmtjWEtVIlixraZoKSudaSDC9NvMs95Jlj5jg6AtK+mFgH2U9wDqvJjpQvmHkr2m1u3bpUnn3zS1UyvZTxLyTxnL8r6yUel3lM7UTLccCPP4xtcyHMC1vjJPoPMc4J17P0rvjGwKxkChoAhYAikDwEj0NM3ZtZiQ8AQMAQMgZARcFlwk5Mhn7Xz00GKQ5rgAFq/fv2sE6rcI7K/GIRlVjJ4GZNg5rmR553PqSjO0EiimBq/Sqbzd0jBffv2TTuLqe/Jx5w3UYyOnTOJCASdxTj2V69ePaf6AlnqZNtt27bNkY31jPto3bp1rlY6jtG4s5jJQKcG/JSTcafWe201CjCYkkknat/n9cnSwgrpy/UncbhiaRProgYk1aqbrkFJWjc9lkZFfBECB3gWMBdUOYdnhJkh0CsIcN9DnhNMg/Ec6FYAVBow550I4px1o5aRRXvbbbc5DINlhzieZ62qfKD4YtZ9BIw8j28MjDyPD2u7kiFgCBgChkC2EDACPVvjab0xBAwBQ8AQaAOBpBLo1PODNMGhHJQgRE577969M0jIFStWZIKEVOJcSSJ+ata5kaxtTPAYv8LYITmtddNxDvsNkoRsK81Yt0yrGAfHLpUIBFjPDh486OqdYzj6L7300qbXbkjHF1980Um8N5J359w33XSTvOENb3CkQRw2UR6Xk8WjLrMcGft8tRr6jP4hac/vK1Jx2ef9uUFZWkhOLfc4cJrrGswRxpkPa2kwYAJVDw1KQrkgjc9F5v/+/fsdDPQH5RwjtLo98+z6cSLAPh7yXDNBIc+5r81mI8A6+P3vf9/VOq9l7C3vuOMOufXWW6fVOth/6hpaKyiJdysl09O6jqZ9rhh5Ht8IBtcbyzyPD3u7BEVwqAAAIABJREFUkiFgCBgChkD6ETACPf1jaD0wBAwBQ8AQ6BCBpBLo1IbGwYxz+ZprrpnuJZKnkOdKTkK+DA9nQ/q2HnkedxZlh1PKvi7iSHIygJRMD6o8MKYQfDiMqVVpY2zTJusIEDxC5pwS30uXLpWRkZG2CFCVdycrHUJ+LuPeuuqqq5y8+5o1ayK/106XRuVc6Ywg0I5MO5aTnHgiUna0eVk88aTg9bs66cOF5Q3rpWd9btTrnz8oCTIIJ7jfkD1nDVWJ4jSso+xttCQBJBbkeVYl6nt13lq/50ZAg2D5SQAM6lFxBTmlaWzYNz733HPuE1z7tB8orrztbW9zNZzrmSolKaHOs9hvrD9+qfc0rKNpGsdabTXyPL4RDJLnPHPZf6Yx+C4+1OxKhoAhYAgYAobARQSMQLfZYAgYAoaAIdDzCCSVQD916pSrU4ZjZ8OGDW6cyNri32iz1gvNitMtKNmufbQX/PTfoqOjo3OSfDgrNRPI6lSmf7ytB7MRwIHJ2q1SvSiGhBX4RB11stLJzqtHMmiLCFq5/vrrXVb6XIRDJ2PI2n22fFrOlU47urwkJSHrHIM4z3t5J90Oeb64sMxJuJs1RgBc/XXTgxLFPCt1HeUne4Sk2fHjx4X5iiFLT63iJLYzabhZe7KDAKQwmedKnnMPsO8xu4gABPemTZvkmWeecVnktWxoaEjuvfdep9AVfE+oPneKUpIp9wyqmufUUPKVgkyMT06XzNBnsl6DczEeGphkwT3hz0wjz8PHtN4Zg3tPgiiNPI8Pf7uSIWAIGAKGQDYQMAI9G+NovTAEDAFDwBDoEIGgA6XD04XydTJ3yTSHXNy4caNQE/3w4cPu3GSl8xKclXqhQfKcPqpseyhg2km6ggDjyrzlgyGTSaYV5q/3628czksy0lWi2JyXXRk6u2iICPB8gTxXwgTVkCjq3ELMvPLKK07eXUnKubpBFtINN9zgCIgo5LOnKpNyvnRWxivnp8s20J6C1yfzcwtlMDdfcl4uRKR761TMp3rrKEjoOgqZzp6hm8az4OjRowKBjtEmJKst07Obo2LXjhsBngWQ5yiIMPchz7lPzaoIsE5s3bpVfvCDHwhKFbWMtQy59ltuuaWmckVRpqQok444r/65GLxF4JZ7BkmfFKTfBXQ1WkfZtyqZzjuXBfV2NlsZY95ldXwJ5Fu5cqXh2hmsNb8dJM/Z8y1btsywjgBrO6UhYAgYAoZAthEwAj3b42u9MwQMAUPAEGgSAYgHrcvc5FciP4yafa+++qq7Dg4GsngxMiNwumWBWARz/SCxyAfnlJHnkU+vyC/AuCItjZICVo8wIdNIZTX5GbwPyVJUMr3bJFDkoNkFMocAJTcgz5nnrGsEkESdbcg9hEQ2WekQ6lruox64kAIEaUGmkxkfNkFA9jlZ6CIVJ+WeC9REz9ygd6FDuo5q7fRg3XTGWEmguOv96nzUPQzBIwSRhD3PugC7XdIQaBoBFCMgz7lXVUGK/Y1ZlTjfvXu3q3NOoE0tY72gDMldd91V9xkKcT7lyPOy+1PzPE7/xOWiS58MOhJdjbHh3UvX0aDUO4FmqvJB4IOtYa3N3iB5vmTJkkj2HK21KptHG3mezXG1XhkChoAhYAh0BwEj0LuDu13VEDAEDAFDIGEIJJFAh3jZtWvXDKRwNmTF8ewnzjUD3cjzhN0YbTYnWOu5WSeZ1qnUrMqg81JJIIggywRqc3Dsa7EhwDzev3//dMkNsn8gL+M0SJstW7bISy+9VJeY8LeH7KTrrrvOfbjPzNKHAM9TSCBdR4MBFATf+eumR0kCsaYfOHDAtQVr9lmQPtStxYZAfQT8gVSQ5914FiRxfFirUNoi45x1op6huHXffffJyMhI3WMI0pqUsWnyHGIcqlwJcn9GevU3eZeF3ie11TloG+Om6yjviX4jIM4v9W6lKOaeYUaex3cH8swncFPLvBB0z70T5bM+rt79yZ/8iXzlK1+R//3f/51WNAvz2gQ5ffazn3VrEvf++vXr5WMf+5i8973vDfMydi5DwBAwBAyBlCFgBHrKBsyaawgYAoaAIRANAkkk0Kl3zoucGhJ3WalbpoQ5znUl0jXrPAsv+NHM0nSclXsJx406G5cvX+5qPbc6rsyL8+fPOwcGnyAJRCaQZqaTxdXq+dOBprUyrQiQbYsCA4ZyAg7MbisoIJv68ssvu6z0YP3sWjhD8lAv/Zprrul62+OeB6w/SNAjB8zfkZrv8wZc3fY0GW1nrJUECpar4bmrGZWQQWGSQDzf9+3b58h8jOAMk49N0+yxtoaBAPsY9kTcDwSvsK5mpfxSu/hoxvkPf/jD6edkrXOtWrVK7rnnHleyqpFBnhelKOidQI/zp5ZVRd1L02ooAzJ/RhZ6veuwdqpaEsR60MhI17W028/6RljF/Xsjz+NDPMvk+T/+4z/K3/zN3zgwoyDQCTb9yEc+4u5zjPdKVUX7xCc+IR//+MfjG0i7kiFgCBgChkCiEDACPVHDYY0xBAwBQ8AQ6BYCSSPQeXmDPFcZVmqFkrmVBatHnlst1PSPrj/LCsdDWLWe/SQQRFAwEwjSR8l0nJg2l9I/l9LaA+YqdZ5VhpaM86SV3MDBunPnTkem79mzpyHUkD5XXXWVI9PXrl2b6fvLZR1WzslY+ZyUKkWp+NBB6LffG3T12/tzAw1xS+IBrJ1KpkPsBQ0SXUkggpTaNdRDIA2VaKI0AIFUZoZALyFA8Aj3AesK9xPkeS+Tq+CwY8cOgTg/cuRI3alAoM3dd9/tnjvNBEci1z4h5x0xDkHuyoT4pNmDF9LjyEHvkwH331aMZ6iqfPAzWDKDMVaVj14P8DTyvJWZ1dmxzEuC1vS5S8kggpibuYc6u3L03/7nf/5n+Yu/+ItpQjtsAp2khQcffNDt3dmz//mf/7ncfPPNsmnTJvn93/99F+hD5vtNN90UfWftCoaAIWAIGAKJQ8AI9MQNiTXIEDAEDAFDoBsIUCss6ADpRju45okTJ2ZJGV599dWZyFhR8tz/E/IzCy/33ZovSbluUK4aB0RU9T3JBNLM9GAmbZQZlUnB2tqRTASCjmKISIKfkhzQgdMQByGfU6dONQSWAJUNGzY4iXdUUbK0djN+p0onZKIy7ogYareTq6gGKZPz8k4UeGF+yBHpaTac7Vrrl5+aaaV9IvhDSaBWSmYEa6+SRbp48eI0Q2VtNwRaRoB7CjKL+wpCFfK8k6CUlhuQoC8QULN161Z59tln5dixY3VbNjQ05Gqcb9y4saXnJkohUzIhJSnOmX2uF76YhV6l0Pul/dIqvDsSjKTZ6UG1JN5x2AvwCVvlI0FDXLMpRp7HN0LBoDX2ngSupX2PRjDAJz/5SfnGN74h7CVU2SlsAv2v/uqv5HOf+5x7b3344Ycdia72k5/8RH71V3/VkecPPfRQfINqVzIEDAFDwBBIDAJGoCdmKKwhhoAhYAgYAt1EIAkEOo6GQ4cOuexFDOe1koNkYcRdOzfs8VC5dn7ysXrnYSPcvfMR9IE8NBa3XDX3rmZUqlSwIsEcg/DT7HQyac0MgSgQCNZ6hgjA2ZcW5yXPH8iezZs3y7Zt22apPNTCjD5Cpl977bWpz3Lyk+fFypQjzlUGmMxzCJeSI9MrrnJu3svLJfklMi83P4rpFPs5mb+sn0oC4Yz3m5bMgARiTa03r8lwRz2HdZljcOKz/poZAr2EAIFJ+/fvd10m+ATyvBf3HwQ7vvjii/LTn/7U7dPqGc+SO+64w6mctFNGoiiTMiWTjkBHup21ey7zE+is5wMyL5TpyXNEAzxZS4MBnronDUPlI5QGR3gSI88jBDdw6qyS53TzL//yL+Wf/umfHHn993//906ZAguTQCfo5c4773RBpB/96Eflj/7oj2YN3i/90i/JSy+9JN/97nfd3t7MEDAEDAFDoLcQMAK9t8bbemsIGAKGgCFQB4FuE+i8/EJeqINp0aJFzvFMrVps3bp1zmmdRtMa534C3cjzNI7k7DYztshwQqBjRO4Ttd+OAzQMRLiPlACqlVFJ+5RM72UZ1TCwtnNcREDXb5XETnutZ56Hu3fvdmT6q6++2pQ6CyVGINP50P+02UR5zGWfT10gz8lKpO6531jvSo5GL0neK7jMxaWFFbOOS1vfg+11MvZjY9Nraa2SGZpRyU9VWIAsgjznfuDfkI8l49LMEOglBCBhDhw44Lrcq+Q5AQTPP/+8I5yC64d/LvDcgDhH0aQTpZZuZqDPNbd5lqrKBwFKQZUP5oeqfBAknZaAu0b3s5HnjRAK7/fB/Sels7KkDvTXf/3Xzv/x27/92y4IiYBNLEwCHV/L+973Pnfer371q/LGN75x1gB99rOflb/7u7+TT3/60/KhD30ovAG0MxkChoAhYAikAgEj0FMxTNZIQ8AQMAQMgagR6CaBjnNp7969LmsBGxkZmc7mg8DAEUHdWRzVaTMjz9M2Ys23l4AIMqxwDmIEfRCV34kTtPmrNz5SMyo1Oz2YUamOS5yXrcgTN76yHdFLCATlqnFcQgpkxQgKQHp3y5Yt09KZjfoGga5kelqwGC0ek/HKuBQrkwI1jlR7PSNDHXH3Pq9fFuUXy7xctkli9iYamKS1VRUbCB9IcgKSTp486fYrBFBFWcKj0fyz3xsC3UJgdHR0ep2EECXzvFsBhd3AAHlliHNUTOYqi8UzAuIcMiyMPSNBTRMy5oKbMMptzGXUTEdlhOzzgvQ7EfeordGeFHJQM9MhDMPAJeo+1Tq/kefxoR4kz3kHS5PyUTNIscf2l76IgkB/9NFHXZ1zrvOzn/2splrIY489Jp/4xCfkN37jN+QP//APm2m6HWMIGAKGgCGQIQSMQM/QYFpXDAFDwBAwBNpHAPmuIMHW/tma/ybkBOQ511a5U3+tUKKi+R1OOAjKNJnWOdfMc/4fhxD9zEqWRZrGI8y2cr+8/vrr0/KUS5cudYEfSR1X5h73mtZND9aoVHliyHSy1JPajzDH0M7VOQIQi2TcMp+YM2T+pG2dbgUFlCYg0iHUIUubMdaFa665Rq6++mqXmZ7Ee6tUKcnx4iEpVooCsdInfXO2k9roZDwWvD7p9wZluDDSDBSZOIa57i+ZEcyopJPDw8Ou5jmBSWaGQK8g4C9lAwGKAkMvkOesCTwTXnjhBVeGai5DWevWW28VylKF+SxAkn1SxqYVQuaScVf5ds8JvedlUOY3lHwPew6zbqLYoWupBlDrdTQwSbPT0yL/b+R52DOl/vl4t0a5TktXZZE8r9X7KAj0f//3f5dPfepTcuWVV8q3vvWtmqBrHfS3v/3tLhPdzBAwBAwBQ6C3EDACvbfG23prCBgChoAhUAeBbhDoZKqQwasZW2vWrJkl045TiraRzUWNwLSYkuf+n5Dnac2oSAvucbQTRx/kOVkBWNoybv2OS5yXteSJVeY9zVlAccyFXr4GARncBzgxWddYo9NaZqPVcdTSDTyf+CDX24yRja5kepIkRqcqk3KyeFQmK5Oufm7BKzTsjh7bnxuQZYWVDY/P4gHM/aNHj06X8Aj2kax0JYAsMCmLM8D6pAgcP37clbPBUGSAPM/6fheperI1X375ZVfyYS5j3X/zm9/sSlNFZSrjThY6JDlruQg0uTd9SSXP+Ye8+0Pu+WBUTWr6vOxDVeVDS8H4v8z6qdnprKthBh803cgGB7IvIICCd1uM5/2KFSsS2daw+tyt8wTJc/ZTBHAmcV6EjVEUBPoXvvAF+cxnPuPqrD/00EM1m7xjxw5597vf7eTdkXk3MwQMAUPAEOgtBIxA763xtt4aAoaAIWAI1EEgTgIdJwNOZ3W2kaUFeV6rJvP27dsdwYfTKS1SuEHyHMg189wmYLoRINOBjAclDZmXaSwt4B8FAgI0M52MIL8xb9VpiVO8F7LJ0j1D42k980WDn8gMQyGkV7NtWe+R7YVIR7ZXSzo0GgmIVbLSIVZYR7pJNk2WJ2S0dKwtAh0Z95G+VY26m8nfo0KgGafMfxQGINK4PzTASjvO2qlkOmtpN8c7k4NhneoKAqx/x44dcx+M/UK317MogWDvt3v3blfbnJ+1FCj0+jwbb7jhBrnllltieX+BHJ+SCSkJSiJVEh1TAv3i//NveUewD8i82LPPG40PqmPstTU7PSiFj2KSX+o9CaRpkDxHhWT58uU9Qeg2Gs+wfx8sn0WQAmtOEuZB2H2tdb4oCPQvfelLrrb5bbfdJl/84hdrdmPPnj3ywAMPuPJEX//61+Poql3DEDAEDAFDIEEIGIGeoMGwphgChoAhYAh0DwEcFkFZ5yhaoy++ZG6os43MxXrE3M6dO53MH9JsyGQn3VSunZ98eKE38jzpo9Zc+5izBw4ccAfjGGXeUuMzSwbp45cn9veNuUyGsWanp0VSM0vjk4S+BElD7gN/fcYktLFbbcCJTmCBkum1MulqtY37at26dbJ+/XpZu3ZtzWCyKPuEdPuJ4mGZqqCqUa1tPpfRzymZlLxXcBLuSwvLo2xeIs8NYUggIBaUqwYff930YGASaykEkH5sLe3+EJcq1fxd6kJjmpvreWTxmtVCQINhyT7H2Btklcjiubdp0yaXba6S0fVmBWVMyNKEPI9blcVPovP36nyuEulVKj134b856ZPBC1nqyZ3fWn5Is9ODgUka5KlraTeCPI08j2/+9Dp5DtJREOj/+Z//KZ/85Cflrrvuks9//vM1B5RyTci3s0f99re/Hd+g25UMAUPAEDAEEoGAEeiJGAZrhCFgCBgChkC3EYiDQIegp965Sh1CiDeSsSXDAxKC48juSqrhQNHMc9qoNd2NPE/qiDXfLsYVB7GSJWQa9gJpyBxWpyU/g5lWSGoqmV5LPaJ5hO3INCAQzDRk/OcKfkpDn6Jsozp6kb1ESYXAlGYMAoCMfsh0PnHUlGdsT5aQcJ+QYmVKCtInuTmIw2qt9JL0eQOyMHeJLMgvaqZrmTgmSBo2k3E7V2ASoECy+eWJMwFUSjrBfB+vnJeiFB3RWM3Srebs8l/mODWi57ofUtLVUJsZrPVMiSUCXbOUBco7C+s32eaQR40MJa03velNbt3upsKEyrSTic7Hb2SdI9uel8IMafdGfUvC75lzfqn3WrL5GuTJehrHvtTI8/hmRpA8J8OfUhFZWnOaQTMKAv2JJ56Q3/u933OqSI888kjNZhA89MEPftApBj722GPNNNWOMQQMAUPAEMgQAkagZ2gwrSuGgCFgCBgC7SMQNYFOBhbkuWYPNJtR/uqrr7psD16U+STRlDhXmUGV9+alvtde7JM4Pp20SeWZVTEB6V0yrLqR5dJJPzr9LnPaL6nJeuE3ggqUTOfvNu87RTxZ3w/eB4w19Sa7SRIkC6G5W6NkE0Q6H7IZmzWee0qmNwo4a/ac/uPKlbKMVc7JudJpmahMOGLcEYfS77ITg/cyx5OrCw1DrfSlhZWS9/LtXDp13wmSJQQ3tFp3tZE8MeunkukonNhaGt00maiMu7lPlq7+0atBoDPHydglG32Bt6hn5nkjxIP3weLFixsGwzY6Z1J+ryoiW7ZscSU5guoRwXZC0l5//fWOOE+iStbFLHRaXq2H7q+JnhTc22kHAQ4Ed+onGOTJ2GjZDAL+wl5LjTxvZ9Ta+w7vICiAaSBir5LnoBcFgb5582b5wAc+4IL5XnjhhZqD9OSTT8rv/M7vyMaNG+W///u/2xtI+5YhYAgYAoZAahEwAj21Q2cNNwQMAUPAEAgTgSgJdF54X3/99em60WQt4tRoxiDd+T7Z55AHSTM/ea5Z6JBKRiwlbaRabw/3BHLMKteZJSdx62hc/IZKamrd9GDpB+S8lUyPwmnZSdvtu60jEMz6WbJkiVBzMmxndOstS+c3VNECIp3sxiNHjjTdEQJ4rrzySvdBRrOTEhK042z5lJwrn60K/VbIVfRn4UIgIvPb5yhEzWqEaIRULHh9Mj+3UC7JDzXd/jQfGHTgh3EfMAY8X1TpI7iWIu3ur5tu91x4M2iqMinnKmdcwAh/quLWVXlrctCVVIdIr2bs5uUSb0h6XdI9GEwVxn0Q3qi2fyaCmiCRIM41YHKusxEEjEQ7ZFIcmc7t96w3vsn6jFqZliAKrqUEvWpgEs/RTt/RjDyPb14xtgcPHpTTp0+7i46MjDj1o159HkZBoKPscOutt7qAoW9+85suaDNof/u3fyv/8A//IPfdd5/7aWYIGAKGgCHQWwgYgd5b4229NQQMAUPAEKiDAC+owdpynYKlRMGhQ4fcqSDWkP5qxekP8Y4za3h42GV6JcmUPPf/xEnTqy/1SRqbTtvCvcDco44tRrYDc9DGdiayzH0cLkqm44TxG/eDkulkNnTqtOx0XO37rSGAE3rfvn3TZTdwXJJlZ/dBazjOdfTo6Kjs2rVLdu7c6bBWJZNGV2AMIHGUUG8lO537drR0XMYrY0LtZ0hErZKrNaD1+n5ikQxGss0h1Adz82RRbklPzAXGhLHRYCoC+viEeR/oWqpkuj57dBySUOu30ZxMy+/B+kxl1Mlb8wfivJptDl1+0QgaqUpgV1xZg0Fvvvv0qml2tmaB8izgmRDmfRAnttzPZJlDmkPQNTLeXa677jpHnCdVEatRH3rh98xT1k8l04MqAsxXv9Q776atmJHnraDV2bFB8pznLuR5L79LREGgM0q/+Zu/KU8//bR8/OMfl0984hOzBu5DH/qQvPjii3V/39lI27cNAUPAEDAEko6AEehJHyFrnyFgCBgChkAsCIRNoONgQG5NpWpxVlDXlYyqVowMYM5B9i+1zpJiQfKcdlm986SMTmftwNkGeQ55iKONwI046hB31upkfFudljgug05LJYAg1MkA6jUZ/GSMUPOtIBiC+0CDIiBrWYfNokOAe2bPnj2OTKd8SSP5YH9LUHsgK12z07nH6tmZ0ik5Wz7t6p2TUV4VqUauPSeVCxLt/Dumkr9O0t2R5zmXec4nrcRZKyOIEgn1j3UsUF8gmCpq89f6JbMyaIyvZlS2SgBF3fakn1+zzylFgM1VDxoSneO4QwpSkEu83ggaCY5hUIkE4hwyK20GaY7yB8Q5z7eg7Het/vDucuONN7r6wK2+w6QNnyy2l4BYDUxiLQ2OOYERupY2KkFk5Hl8M4Q1hwB8VYQw8ryKfVQE+sMPPyx/8Ad/4N53qYPOXkfthz/8oXz0ox91//sf//Efbj00MwQMAUPAEOgtBIxA763xtt4aAoaAIWAI1EEgTAJdHc5+6et26+VCwp84cUKGhoZc1HkSDKz80u2QCEaeJ2FkOm8DTjYyDRlfCF6CNgj+MGsdAZyWmgGka4GexZ8BBKFuTunW8Y3yG8EgktWrVzdddiPKdvXSuZWwgkwnQ72VuungRIYkii98GD+VGaaG+ZHiAUeeI11NZm3Oy82C9iKRXnHkYb83IIvyS2TQC7+WbFLHNahEwj6GvUjcRjAXa6iup50QQHG03d++JAZZnK+cEeqfk30OLU5QyFxGFjpEOvfKAu8S6fP644AxMdcIKjCwtiSx3nc9wCBNIc23bt3aNGlOkAzZ5nxq3fNa0oKfWFWpY7aKQWIG0RriEGAu++um877qN/aiSqYHVZOMPI9vEgXJc+5H9jK9nHmu6Dci0Ek8eOc73+kO//SnPy3ve9/7mho4gvYefPBBt0YSiPmnf/qn8oY3vEGeeuop+eM//mNBLemWW26RL3/5y02dzw4yBAwBQ8AQyBYCRqBnazytN4aAIWAIGAJtIhAWgU4GKnXLNWuRCOZOpE6JPj927Jgjb3h57qZpjXM/gW7keTdHJNxrQ1D5yw2QdWS1LcPBGCelZgDxM0gAkT2rUu+GeTiYt3sWiDqVEieIhMAlxsesuwgQSEZWOh9VyGi2RTidIdF5hi6/fJkMjvRLyZuarmVe7zzcp1My6Qj0Pq9PRgqX1iTbm21Hmo5jD0PmOSQ6z/mkBJGw//DXTQ8SQGSj+wmguAhsAjMgpScoC+BkzzFPUC5A7p+fcbWl0Tw7Wz4lkzIxHUASlG4Pfl/rpNOH+d4C6fcGG10iM79nfrHejI2NuT5RKoK650k36iUTfARx3mymOYTphg0b5Prrr3fZl7XmK4Q5a2JRJl1Qhd+g0AmycHM9UA4g6Xj1Yvt4vjGvdW8aLEHEc1OVPvjJuygkIgahSyBJUta0LI0fz7jDhw/PwNrI84sj3IhAZ//+tre9zX3hz/7sz+QDH/hA09Pj5Zdflo997GPT9eb9XySQ6Ctf+YqsW7eu6fPZgYaAIWAIGALZQcAI9OyMpfXEEDAEDAFDoAMEcCQEnQetng6nLg5nHG44Fcje7TRb68iRI8IH5wUR0d0yf8Y5beAFX7POzYHSrVEJ57qMLXMMggqDLGTuWlZ0OPgGz6IEkGZTBgkgJDSVTG8kpxlNC3v3rJAOqH5wT0DEQZ4zBmbJQgBSFyepEuq6djXbyr7+PhlZvVRWXbFSVl6+UhYvHapLBJCpDlFE1u2S/DJHhmbdUGDQvQzPeZ4Hc0nidwuPRgQQATCQ6fqJKnsP4vxc+bSUK9W83Ivy/1LNyvU8RywuzA1J3st3C67p67ZPoPfJfG9hzxDowfIFSS7jwb1w9OjRadKcPV0zxnPuqquucpnmkHRzlZYhkGJcxtz8Zp5XPxcz0CHQ+fBnUOY3VDZopn12THwINCqboS3hvZZAkqjW0/h6nLwrBclzgnUa3ZfJ60W6W8Te5zOf+Yx897vfnQ52vv322+VTn/qUkefpHlprvSFgCBgCHSFgBHpH8NmXDQFDwBAwBLKCQKcEOtm7SrxAPPLCG0bWIhH/ZAWTGdKtqOd65Lk5T9I/+3HWMG8hczGI23bLDaQfjfh7wL2FvCr480Gq2G84t5VMZz2xYJXoxggSlqwfDNIc8txqK0eHd5hnJiuO2ukQ6ijAQLC3YgOD/bJ89YgI4mAMAAAgAElEQVQsX71cll82IkuWLZ4mB0oVhN6LTsJ9cX5Y5uXq11Zv5ZpJPZb1iIxVng1pU2BAAUizKTVjWHFm7fTXTQ8rQGy8PCbnKpDn0ItInVflrD0RRy7y/9CKSKXnvYIM5ZZIrssk+rnKGZk0Cfc5b0GexRApzCls1aWrZMHQfFcPXonjasY1f/q7QhbTRgKJdu/e7YhzrZXcaG3hubZ+/XpXS5jA3Gaec5Dm43LOkeesiMzuKmHOTNe5XnazH8UOAkfmyXx3jFn6EPCrJun7Qa29KcFJvJ/a3rTzMeaZS+CLlqsx8rxzTDs5AwFJrK8EThEwYmYIGAKGgCHQ2wgYgd7b42+9NwQMAUPAELiAQLsEOt+DdIHoxgYHBx153oxDqhnwjx8/LgcPHnTnJUskblPy3P8Tp7o5S+IeifCvp85XJRpMkjF8jFs5I/cYmZ9KpgcVMbjvlEwP1qZs5Tp27EwENHOPtRYDWzJu58rEMwyTiwCOf4KCINL58PwMlkxo1Hoy1KuE+ogsWz0si0cWyUBhnizOL5V5ufmNvp7a30M+4zAGLwhmynikVYGB55uS6agD1SqboVLv7faxVCnKaPm4lC6Q52TeQh7q/siVvZHKtKQ7WegEYizKd1cCfKoyKZDokMEY5H49yW3az3H0i+MWeUsyv/8jAAfynGcwY3npFaskP9+7EBABSXzRqiSyOBK9XwYjly6HJIcwJ1hISyw0s+DwTkIQLqQ5P1t9R5mQMSfdDnlOf2vVO79YF505lZc+GZAB6R25/2bGIU3HsH7x/NTADN5Deb4GA9QIpta1lCAl2zu1PspB8nzx4sWydu1aw7J1KO0bhoAhYAgYAoZAJAgYgR4JrHZSQ8AQMAQMgTQioJkmzbadF14ytfzZu2QthpmZTST6/v37XS3qa665ptmmhXJckDznpCrbHsoF7CRdQ8Bf35ZGpKWuZ9cA68KFWY+UTIdY95s5LMMZENY4yFak27FFixY5BQYLEAoH3ySchfsIUlgJdQ12a6Vthb68jKxaJmsuWyuXr77CzRGeyVky7gH2Ghh9gzxvlWRLKh7s1SDT9RMsm0F/lQBqRenjXPmMjJXPyZRMuQxkP3nux4J1BhLaZSy7LPRhKXh9XYOL9pypjLo2QYjSctofJNEvkv8VJ0E/6M13nyzbLPJ8zSqhasNFaf6qxkDVVG+gKlsORgMyb04SXUnmi1nc1Tx25k4t82eZQ5q3Uq4CwpNMc4JvIePaXbPo+3k563LPEW1387xOjfPqnGFOVe+I+bIw8qCCLM/HbvUtSJ4vXbpURkZGXHN4f9ASREGlD35PECLBnqyp7c65bvW7G9cNltEy8rwbo2DXNAQMAUPAEDAE5kbACHSbIYaAIWAIGAKGwAUEWiHQcbLhkFdia9myZbJixYrQiRci/yHpyQbbsGFDLGPlsqYufHA884FQMvI8FvgjvwgSvRBKkAiMK9m2OLrMkosA6406LMmm9BtjqA5LnJZhSRMnF41wWsb8hzBUPE2BIRxck36Wk2dOyCt7NsuB1w7JkX1H5Nzp8y03mXuOZ/7q1avdB0KdurBpDbwgUI9SMRikG4GAWV1HtG66BicFsym1bjprKdmU9QIiK5WynCwfk2Kl6IhFyNO5xr9aCqAkfRDRufmyMLeo5XkX5hcmKxNyvlIlRatZxdXq1UqMap1rrunk56UgC70hyXnZleT2Bxa6vdEVq8WbX61pD05a4xtMwKmqL1CtBV5FLy/9MuAy0YOmmfxkcftrh+u5+G4fUvDlvFO10oAfAryCpV3mmgesQxDmfNjbhRHQS5snZNwpKVSrnNcm+7VdzCf6y5whA51+maUHgXrkea31rZHSB+oeGpzEsyWtz8ioRi+ogMT9S1kFy+KPCnE7ryFgCBgChoAh0B4CRqC3h5t9yxAwBAwBQyCDCOA8a0bqlYh7nFs4Dpy846WXCrXKojCcvFyLl+mNGzdGcYkZ5/QT55qBbuR55LDHdgGyDHHIqkQvDlYy7szSg4C/NiUZlbWkiVXq3bJ/ao8razeBSRoAtXz5ciHDyiz7CHC/HC8dcTWgpypTMnZ6XI7tPyFH9h+VI/uPyJnRs22BANmqZDo1MwmoS3oGt8Pi+HGh1ifWa+UL6L+/bnpQ6UPrpms2pT+oABn00+WTbg5BppJZPpdpFrojWb0BVw6g2zZRGZOxyvkL2dXV7GI1cqyVFCY/fYG3yNVwz6oxD5BE59kA6ezUpOaLFJ1seXGaPK+VeV3NTtdMfrKuL5mRdQ2ZjAR6tX66ku5VrJkXp06clgN7D8r+vQfkwOsHp+uuN4s16w2y7JDmZAmHTVJCnk/JhMNhLrl/bS94QKJzbL2Agmb7ZsfFi0Ar5HmwZQRbE6CrwZ7BwI9mg5Pi7XH3rhYkz1FA4j428rx7Y2JXNgQMAUPAEDAE6iFgBLrNDUPAEDAEDAFD4AICzRDoZIRrjVBecpE5xXEelUGQ7dmzxznErr/++qguM+3I04xzJdI16zxsh1ykHbGTz0IgSJSQFYKDOOkEjw3l3Ahwv5JBrQ7LoDQx46xkOn+3+7gqP+qvHavZwzbXegcByM/jxSNSrkAJQWpR07eafXv+7Jgc3n9Ejuw7KscOHJfTJ860BQz3GmQW5TEguPhJ1noYGaFtNSjwpaBsLFmCBAAkpX1h9LHVc5CN7q+bHvw+AQaaTSl9FTlTHhXmkhNv9+bOyuVcemyf1ydL8lU55G4bbRqvnHeEJ9RuVZa8mmHNf/s98qnnS66J/nW7L+1en8AJngk8P9nXszcamDcgY3JmGpdaNb/914Myvph1PW8669pPnrtM9kpJTh49KYf2HZGD+w7KwX2H5Py5sZaaTtAjkuyQbfxkXkZpF+ufN0ugI+NePdbqoEc5MuGeuxPyPNgSzsV9petpUOFNg5NYT/n02rsI+FBORkvKGHke7ly2sxkChoAhYAgYAmEjYAR62Ija+QwBQ8AQMARSi8BcBLq+7CKtiJHZuWbNGoGUitKI5t+9e7e7BAR6VASYZptDyPnJ8152pkc5rnGem/FEnnd0dNRdVjMlLcshzlGI/lqMs2b/QKgHs39wUCqZ3kqd3+hbHt8VUA8h8xyihLUNwtDKF8SHf5KuNFmekJOlY9W6vpVqxuRF8lDrWeekMlaR0wfOO+UOPuwBeE62Y9yDZKYrqc7fqXca1XO9XhuDRAmysZD8cbejHQzj+g5rhD84KTjmgwsHZHBZn0hBnKx5GjPQFUvmQzWUhGCS6tymzjXkZ5Yl2+mn/5mgQbFITZN5Pi5jF7LPG8uWV/PKq6QxedcEHWCTpQl5/fBrsv/1/XJw/yE5tO+wTE5MtjyNuT+RdoY0Z92Ic28OEoi4VyXc0SOYW8ZfJe+rovRI2kf7ntQymPaFWQiESZ7Xgtdfhoh9alA5iXtOlT6yHuxp5LndgIaAIWAIGAKGQPoQMAI9fWNmLTYEDAFDwBCICIF6BDqOUxznfgKSzPM4CEgi+Hfu3Ol6jIR7FNdU8lx/ci2rdx7RJIv5tME6z0aUxDwAXbqcZv9onV/WNr+xjiiZTvZanM74LkHiMqH86iFkGVr5gm6NRjKuW6oU5Xz5rJwvn3MizH4j83Z+bqEMevNnEMsQAZDo+/fvd/sCfkLCtWuQBZQQ4AMxxmd4eDiye1L3M6wNGOVnuKaR5/VHsGZwkicyf+WA5Po88fKe5Mqe5PN9bo/mkbwdsKragdZAnycLc0PtThn7XkgIQOQRUMU9gTw/+3oNikWyvFr3e8qRxlDoc5kS6GNnx+XYgRPuc/DgQRe82EoNc73GkuElsnbNWtcmPhCM3TJm7rhUVQoIMporG7+KA8d5LghjnixoWDO9W/2y61YRiJo8D+KswUnsyfgElZO4F5VMz9r+NKgGRj/Xr18fybu9zW9DwBAwBAwBQ8AQCA8BI9DDw9LOZAgYAoaAIZByBHCMB7OMcHwh7YijDcPZjORvXM5mZO927Njhrr1hwwbn5AvTNONcpdut3nmY6Hb3XMxnnMMqnYicMHWe45q73e29Xd2PAHNAyfRgnV/Ic5UlRp0giiCdbo8GpTcgOzGygCEkrD58t0clOdfHqT1ZmXAkutazLnh9TTWQ7548eXKaTIc0Q5Y1mGHX1MkuHMRznvVaSXV+Iv/eqcwtz3mCSMisxjgnH3smND86Wjed9fS8nBVvoCK5/pyUSxWpFCuOPGcNLeQLki9ApntuLpDfXZ1bfbIoNyzIuJt1DwHuAfZHjE2tZ8KUy7kmA51xq511XZwqyrEjx+TwgSNy6OBh9/Pcmeq91aotWLhAVq+5VC69YqVctuZyGbpkSObLwlZPE8nxkOLjcs4FgJBlXy8LXeu7s46Sic8HAt0suQjETZ4HkeD6BKCp1Hsw2JP9KftSiGZ+hv0OHOfIBMlz9t2Q52nuU5z42bUMAUPAEDAEDIFuImAEejfRt2sbAv8/e2cCXddR3/+RvMeObVmSLdnOvhCgQBZ6TijQPyGBAi2EspRCoCwtoYRCWMJayhYKBdoGToEAXQJlSzicEggQWiCkUELCYS1bSEjxJsu2LG/xbkv6n8+8/JTx+Elvu/e+u3x/OS+S7ffunfnM3Ln3zfe3iIAIiECuCMQCOkLT+vXrHX+PkXY1awGSc//617/25z/77LMTE30sTXsooEs8z9V07KgxzF02hy3ySXWeO8JZqg+HqTRNSLMOsgYQ8WPR6UXf2GOd27Fjh9u2bZvvIlF8RJ4XvV+lmpAl7AwiAHMOMd2iUHHi6NRw4EPwRly3n6SAbyaDBPcC7gnmQGOR7p22qcqfPzp1xO08Ou6OTh7xzheTR3nVaoibIaL7KPWeHi+az+uZ75b29slpoYsTJ8xGgnhOOabYOYWo6wOOzBS12uaTR5wb37bdjW2977Vlu9s5vrNtR5mly5a64bVDbnjtsFu9dsgt61vm5wTntejtE9yJ99Wi7yKs+06NAwgOBZaenYj8XkeqBUu3QOR5Lf7cBHbS2COiy/JJoNvieT0q3DtxTuIaNcf18H1kDQpTveeT7PGtip9FJZ4XZeTUThEQAREQARGoEZCArpkgAiIgAiIgAvcRCAV0vsBbakc2pxFd+NKetZHa7le/+pU/7ZlnnplIGkcTzy1tHiK6pWxXJFrWI5z8+dh4Iq2wjevatWt95IZMBGICrAEW+cPPOGrWNitZ+4oWsU1fSLVNdDDGNcC10IzYqJkiAkkTQAwgnbMJ6vzsJPW7tQ9nEIsktywjOPpxzdr9PM5GkrVDla0rZXy+ODC5z+2f2usmpkhwPeGmJqf8i/uvT+XO/6YQYKdcz1SPW3BksVu6ZKlPFV5GHklfN0kfj2d7sjBg3NPIRhKL51yXY9vH3Mi2TW7b1m1ubOuY2zW+u22xnHP1DfS51V4sH/ai+ZIT6z+T5VVApw/E5COj10R0Sl4c6yxChoWasN7rq8BTAV2WTwKxeJ7HbCTh8ynOnnGGOK5fy57Es2pe19NYPOdZlO/zcuTM57WhVomACIiACIhAPQIS0DUvREAEREAEROA+AkRo8YV9fHzcb3JjFp3SrfqDfPH+xS9+4dtCqrdOa/ZanXOLPOfPEs/Lcwns2rXrmLmL44fV9CxPL9WTNAiwJrBJadE/cV1K5pFFpudd/InrPC9btswNDw/ndoM1jfHUMfNNgHsvTis4efAiYp2fVpu809bz7EItdaLWucebM8xZZ53liFpP24jOPjC53x2eImq1JrQhrC3sXeQW9ix2c3rmpN2EzI5PP/dP3YtOPh2xjLg4iZg+MeWOHpnw0ekHx4/4NO/2bGniD1k/8ir+ZAYxgxOFpTy4h61Zs8bt2bPHjY2NHfPiuuzEODb3G14rVi93A8P9buGiBbPWDud8xG8joBPBzX9EoOfNSG2PiF5L1177D6uJ57X/EM/nOpUoyNvYWXuKIJ7H7GgzTmj2fGqZ4ex9lM1gPeWVp1JEVuKFeztG2/gu32k5lrzOLbVLBERABERABMpKQAJ6WUdW/RIBERABEWiZAF/IiUwh5S/GhjOpHbvtJf7zn//ct+e0007rKJLYxPPwJxvrishseark7gOMKZvAOH9gSlWduyEqVIPCzUo2LK0UgHWCzT8T0/MW+YPwzzpu6T+JxiUyVwJVoaZgZRvLvDUx3X5aFoUkoHC/R0BHXOfF74jsvBAfOr1OWDvundzlDk0d9BG7SIIWp2rxqURln9C7xJ3Q0/n5kmCSxDGIQD80dcAdnDrg01ibISQu6FnkJg9Nub337vUCUL06vyam50n8SYJLHo7B/YtyTOvWrZtODU0pA57146jWdtq7YqDPrVq9yg2vXuVOXn2aG1gxMH0dkQYeUdyEcZxIZjJLj07a83lugVvgFrbTnNQ/Y0I/ad1DAZ1W03auc1k+CbAmb9682TuOYHmMPG9Ejj4cOnRoOnuSlSUJP8c6amtqt8TqWDzHUYrI8261pxFX/bsIiIAIiIAIiMDMBCSga3aIgAiIgAiIgHN+E+03v/nNdPQXEYtEp+RBXP7lL3/p24eY324a+Vg8Z9At8lwToNgEmBtkTLANMTaN8jJ3i01WrYcAawcblAg/9cQfIn9MTGeDsJtrZpyqWnWeNYfLQADBFeeo7du3T0fK8nu9GrGd9BdnwVBQt9/5yX2l0bXNWrFncoc7PHnIpzOnfrTFpvq1JIhEJ8J2Ue9it7h3aceifSd9TvqzNaeBWjVon86653jB1Or8sp7GafxxYGAdtTq/EluaGyG4k0GFLDw4nHC98EIkJ/I8Lk/S3FGPfRdjs7x/mRtYNeAGV/W7wVWDbmBlv5s/n+jyXkfNb6LHQ7Pa4QjoOJIw7+sJzMwZrhj+zV8bbok/pkwEkiJQTzzHubDohoOMlSJiDYivdTJCmJiOc3GnTmLN8KINrEWUbcEknjdDTe8RAREQAREQgfwSkICe37FRy0RABERABDIkwJfd//u///NfeFeuXJmriMU777zTR4CSjhthv1WzdO38rNXl7JF43irEnL6faNuNGzdOb8ITUcj8zWKDKKdI1KyUCRD5Y2J6HPmDwNatSEratWHDBr9WMv+p87x06dKUaejwItA9AogFJqrbT0TDOL1tEi3k2uZ64hmEV/g7fybij1rg+yfudUe9WDh1n1jYO30/8o58Xiac9OLgnJ65bmnvcregd1ESTSzkMRqJPwg+5qBEzd8q39t9doN7750WyU0s5yevJOc9jmE8T+GEtXLVSje4asD1rVzmeuf1HFP5m3lMre+5bv4MwviUO+j23xeFjkMJpQxquRhqZu4WiOe1+uEcb4Gr7jVRyAs5540uq3geY7dSRCaox6WIcBILU703cgprZ1hj8ZwsTUSes37LREAEREAEREAEiklAAnoxx02tFgEREAERSIEAm29EqrQb5Z1Ck/wh77rrLp/yk6hi0qw2az4Sig3rycnpnxLPm6WX//cxJxDPLR2som3zP2ZlayFrptWkRMwLLYykZE1NsxQGkbikbWezlA3RtWvXdlTuomzjpP5UhwDPMPfcc4+PyMPBxdLIIqwnHbEeUkVwXLx0sVt84iK3aMkit3jJYrfkxMXuhCUnuMUn8vcnuPkLagLw5BTx6Ud9oup5vQtc35yB6gzQLD018cfW1Fj8QYAxB6W8lc5IYgDpL30nmw4v5rL9bj+TSLketxVnECJxSWfNT148azOnY8P5A8kbRxAcRfiNhOW1yPG5XkiPI8d5f01Ep5xB7dMI5/dbTTrnGLXk7YuUBj2JCaVjeAJVEc/j4Q6zJ3E/xMkyfkbF8cuyfSTxjCrxXBedCIiACIiACJSTgAT0co6reiUCIiACItAGATbv4lq/bRwm8Y/cfffd/os/EZVExDRjJpzbZiM/LWV7lSOYmmFXhPeQ9hXxnDnLeOJckTfHjyJwVBuTI8BctKgffsZpNBF8wkjKpM6M4DIyMuLPxwYomTqI2pSJQNUIkLraUsZyDXAthKIAgrqltSa1Ne/nRfRuFs8+c+bOcYuXnOBF9UVLFrqFixe6JYsXu/4TB93SxbUodl5ZpdnN8/xgPeM+b9k+4uhqxN0w20cakZRJ8rG09dwb7GWOAuHfJZFqfaZ2c//hGbq/v9+/rP5zK/cLxO9Dbr8Xz2tFCe4XwpHBEdMR0WMRnPcddofcUXf4mM/Q1lpK9x4fec5LNcSTnHnVPlZVxfN6oz6bwyfvt2wfrKukfW/1u7IvYbJnj68xb8c766yzFHle7UtQvRcBERABESgJAQnoJRlIdUMEREAERKBzAnkV0IkmYyN1aGjIb/g1spnE87xvsDbql/69RsA2aBhnNtERSRAnZSKQFwKNIinZnDQxvZ2NSutnKBgSnXnyySc71QzOyyxQO7IiwL0AYXxsbMyfknqrZGGoF0Fbr018HhHTUmKHwjq/ZyGuh+3iWcXEdH7SH+5xvMLf7e86WUOyGqNOzsP4hHXT49IZCD1JR1I2aq9Fd5LVwF5kIQn/bL8zt5JMrz5b28yxgLmBEEZJm9NPP90L5p2mUCbq/IDb6yPILRL92LYgfSOjU8W8Vsc8FsMR0qmLzrH43SLXiV6XcN5o1unfWyEg8XxmWnzfZ73CiYefcbYPniPNQYl7TiMxXeJ5KzNT7xUBERABERCB4hGQgF68MVOLRUAEREAEUiKQVwH9t7/9rf+Cz0Ygr9ksTtluImujL/8pIdVhEyTAWBI1uG3bNn9UNoMRzzvdFE6wiTqUCBxHgHmLkGKRlLEYx0alienNpiXmmNR85oXxOQTDJFJwaghFoFsEJqcm3cGp/e7g5AEvsGGIcQt7T3CLeha5nh6rm3x/C7kWuCdwb8DY9CcjSacOc14QmNzhDk0ecvv273N799zr9u7Z7/bt3uf27dnn7t3D39V+n5wgJXX3jOebUExHUCeakJ/2iv/MfZMX6w8/m3U26F4v7z8zYrRl+2BtbSfbB05OHAdhngxH/LQXf0akb/TqFgvGbPny5f5FqnX7uWzZMv+sTOp3jL/H8TSJ51/E7v3uXn9d8h9WizgnerxmtWrmpHTnX0jnPs8tcou7hUnnrTABiefND36Y7YN11cpi2RG4l/L33GcvvPDC40qp1RPPqXnOvUcmAiIgAiIgAiJQDgIS0MsxjuqFCIiACIhAAgRsQzGBQyV6iPXr13vxibqM1LmeyWLxnPdZ2vZEG6SDZU6Asd26datPt4u1GmGYeYN1QhGoQyCsScmaFm9UImKZmM4crycCcgzSVBMtiyUlGGrARKBbBPwG/tQ+t3dit5fgpqb8/31zfExrT61C8pI5y9wJvUumm8nnRkdHpwVDBMTh4eFEBMP9k/e6/ZN7fapq2kJEbS2+tiYY1pJXT7mjU0fdwX2H3F4vrJOee587uPeQ27/3gNt37z63/9797sD+g91C2/R5WWtMTA9/8jvrEs45s/3k3ziGPXPN9rs1ql668vDv+B3HTp5N7RX+md95IXQjpPMTByX7ez5jx7NjmUieVUR40wMQvBHmzGVqk9sr/DPR9rEoHl8LCOs8LychntO0I+6QO+gO+Mrn/tn6vush7l+txvlRL6HznhPcif7akYlAVgQknndGGgcic1Ai+xv2pje9ya1bt86v7w960IPcox71KHfJJZf47BY8y5K2He6I5qRtl3je2Rjo0yIgAiIgAiKQNwIS0PM2ImqPCIiACIhA1wjkVUDfsGGDT9tNCko2x4/bsJtiw732sk1WNg0lnndtKiV6YsaUGs9s6GBsKDMPOo0wTLSROpgItEGAjUqLTI/TEjO/wxq/CFTxtZBkhGEbzddHRCARAgjneyfvdZNTE9OpnZHOa1ZL84wY19szxy3pXeqWzFnqrwU27bl+sCQFQ54ldk5u8+I4giGxtLSgntXkwqOuZ6rXp7XGvHTYM3f67YePHvKRwfvu3e+FdS+q7zvoDu4/6A76n4f8nw8fOpwITx0kvwTIBMC6Xu9lYjnvaUX4Zr7yjGTXAs/KOJy2cozZiCGKk7rdUq8TXT5buvVaevcJR1r2eW6BW+hOyO+AqGWlIhCL51wHzZT+KhWEBDuDQxL3rn//9393n/nMZ7yTUmhkezn//PP968EPfrA755xzJJ4nyF+HEgEREAEREIG8EJCAnpeRUDtEQAREQAS6TiCvAvqmTZt8tCUb5HxZDy0Uzk1Al3je9amUWAOIEmP8bdOGjTBeSW0MJ9ZQHUgEOiTAXEcAwVGEDcvQmO9EpBM9aZGTuhY6BK6P54LAwcn9btfEDjcxddSL514oJ978vnTtRKPX/qv9G8L00p7lbmxk3EcdY0lfC4cmD7h7J3d5wdBqNM8Gy0ep+6j5WsQ8gqMX3Xt6vWPfUXd4OqK+dhycA2rvJrIemZHPHD562B3Yf8Ad2X/UTR5wbuLAlF8LiALkRX/td35mXZs9FxMmh40gBb7VqmedDl/29yaYE12epMVOVWkIhlx9+9yeaeeWmaLPrV8WhW610Be7ZUl2WccSgboEJJ6nOzH4Hnbbbbe5W2+91X3ve9+bLiFkZ+U7+kUXXeRfj3zkI/2aKBMBERABERABESgHAQno5RhH9UIEREAERCABAmw+xCmFEzhsx4cgyoz6pqSwpOb19CZdEHFuQrpFnUtg7Rh71w9AdC7ZB0wkIOqciFuZCJSdACmILYUmP+NUx4gwbFaS7h3xRiYCRSTAvB6f2OaOTB7yYjURq0SZ17MJH51+1L/n8L6j7sCWWrQ2aapXrFiRaPf3Te65L337fW2aIfrcTlqT94lCRxhHQK+J6aSuromJE4GAbuJ5TWyv9bkW3V4T22vnnNsz162YM3sKbpxpQkGd37lv2gvBI/yz1fbmp8T3Y6cMGT5YS0k9zE+iwKkrz5/5yZ/rvRDLSW/fDUM8x8HQHK5WrlzpMzUlbczt/W6vv/5qTh+NU7L768HP7zluidNzW9JjouMdSy6YtYkAACAASURBVEDieXYzwmqeI6L/6Ec/8q977rnnmAbwjPqIRzzCi+mPfexj3dDQUHYN1JlEQAREQAREQAQSJyABPXGkOqAIiIAIiEBRCeRVQKfe7/bt233q7pNPPnl6o9nStYfiudJ6F3X2HdtuNoTZGGaMGdO1a9cqmqEcQ6tetEiAqNONGzf6a6GeIfBY3XR+l/NQi4D19q4RODx5yO2YGHNHp474NsztmT0698jUYTc5MekmD0+6Q2MTbnhwtXesS9r2Tu52Byb3eTF7tvTtdl5L4z5niqjzHh9cXqudfn8t97iNtbT0SJFz789W75xPG88BYLF8zoCb15OOg4xlHEKEt8wW4e/h3+HQY7XF45/hv1nN8fDZrN7vxiJcq+r9btmErJ56WGc9/jtEbF4IN/Z7vZ+I4/w9/bA+2nmsXUVYU+HOM5JlYUjDkcR44ACyz93bpoA+1y1RBHrSS5SOFxCISxikkYVBwGsEYG3fz/id9fTMM8/0Zdb++7//291yyy0+Sh0nrdCom46Qzovf9ZyqGSUCIiACIiACxSIgAb1Y46XWioAIiIAIpEggrwL61q1b3djYmK8Zeeqpp07XOreU7fxkY1VfyFOcHBkemnT9o6Oj/oxsdJN1gMgvmQhUjUDoSMIaZyUsrG56HEWKeGRiOlGTWhOrNmOK1d97J3Y7or0RxmeLPq9t3E+6Q0cOu57eKTd5xLkT5yx1A4tXpdLhfZP3uv2T994fFd9kBPpcN98t6jnBi+f0iTYjwhOFbma1oy299XSp9/veUIu0n3Tzeua5pb0r3IJe3ftSGeT7DorAjwht5TPiNZVnEFtTiTbPw5qKeE52Hittk3Z2HubvPrfbTfhMCpOuUQ30MIU75QlOcCemOYSJHduyRRzx12zNYQ03l3lunnd1ma3ue2KN0IFaIiDxvCVcHb15JvGcZ83QyISCiI6Y/q1vfcuNj48f8++sV9dcc40777zzOmqPPiwCIiACIiACIpAdAQno2bHWmURABERABHJOIK8COuI5Ijqbl6GAzsan6p3nfFK10DzmH5kGeGGI5kSeJ10ztIUm6a0i0DUCRPRQvoLrAhGH7BtERprx9wgoJqbH5TcQ3EPhR9k5ujaUOvEMBPZM7HSI1USgI7RZ3fP47VOTkz6iDYGrd36Pm9sz3504d6lbOqcvFbaHpw65PRM7vPiNhEYU+myGYI7xvqVzVrh5br4X0A9M7fNCfC2he01E944CvsY7qdyPt1BAX9bb7+b33n/Np9JZHbTpNZU1FEdO1lXq+7LGZm0I/IjnFuG5enU6WRjifh10+90Rd6hWqsDN8XO4nnGNWuYF5voCt8jNd/mfw1zrh91B77wSOrzQR8sWMd8t9OuULB8EJJ5nNw6xeM73srPOOsuXt5jN+J7+s5/9zIvpvO666y7/9le+8pXupS99aXYd0JlEQAREQAREQAQ6IiABvSN8+rAIiIAIiEDZCMRp1/LQP7zXiUhGUA0FdInneRidZNrA5gxjvHv3bn9ANqmJtpXolwxfHaVYBHbs2OGdhjBEc7IwNHIkYe02Md0iE63XeRB+ijUCam0WBPZM7LpPQCcCfd50LfDw3JOTE+7QocOEoLue3h43Z36vm9s73y3pPdGdOCed2srcj3ZO1lLLIxia6F2PidU/5z2kW1/eO3CMOL776A53cGr//ce5r955vWNx3ppA2evm9MzxNdD5KesOAZySbE0lqjI0nj9x6kRM53ml0fqcRA9IsY94bs5SOBhy/izs/jrotSh0RHSE5TAq+37xnCh1RPY5brFbmvvIbaTzQ+7AfdI/kef3Z4yosa2VW6CvOATgICPrLgGJ59nxhzVZOqysFmsdadtZ/1o1yhH95je/8fXRlVmsVXp6vwiIgAiIgAh0j4AE9O6x15lFQAREQARySCCPAjpiEpGY1Fo77bTTfC1giec5nDxtNimu5dnX1+eo55mHVKltdkkfE4G2CLBRScYNS3nJBiUiSauRjggtlpKYNPChhcIP4gvR7TIR6AaB/ZP7HFHoR6cO3ycaHzsXJycm3KHD94nnPT1u7gLqhVMffL5bNqfPLepdnFqzqYFOenmrZV4TBGsiGlaLs63J51bLfHHvMreo91hRgTrvuyfHp2ubI7TPdG+bnCJJ9oSPcl3Qu8gtm7Mitf7pwK0RIPJ77969fl1lTWWtDg0xyDJ+8Kya9PNLKJ5zbO4LCPdZGiIzUjNzvpbi3AR0romafM7PWrrzXrfQnZB7sRnHgANu3/TVXBPL6Y9liKjF09f6xb/0ukVusXeqkXWHgMTz7LgnKZ5n12qdSQREQAREQAREIGkCEtCTJqrjiYAIiIAIFJoAkS3xxmA3O+QjwXbu9AI6Zukz2ahsVVTqZj907voEmG9EJFhE1cqVK92KFSsS33wWfxHIO4E4CwNrHOl5O83CgIOKCT/8jNd3UnCGwk/eOal95SEwOTXpxo6Ouompo9PCsQmPE0ePTt8Xenp73fwF892EO+KFq7k9c93g3OEZU74nQYjrZO/Ubndo8oBvG6IhFgro/NlqmSN4L+lZdty9yz/DTNSi2UkT7aPLERiDFO68B5mO83gxvmeu6p8nMYgpHQMnTkR0c1JijQ2NCE1bU1lfOxXTeT4i8hwRnWORkYQU8lkbsxQRnYhtBOX7050jMNfE9Fpq9x630Edq5z91O+I51yVXn/UgrnUepqXn6sXBBRFdlj0BiefZMifynO9orHk4WxJ53o21J9te62wiIAIiIAIiIAIxAQnomhMiIAIiIAIiEBDIk4DORglf2klHvH79ev+7GZuIfIm3TUqJ6cWbxqREZWOGzWfGE7Fw6dKlxeuIWiwCHRJgbSM9pkWLp5WFoZHwQ7p4W1P5vVPhp0Ms+ngFCBCBvn9yrxeYkeGoIz4xMemOEHmOQN3b6+YtmOfFZWJC5/bMc4tTTN8eIvfRd1P3OqLRazGo99dHvl9scz4S/oSeE2e8XqiHvnti3OEwUKuXPnVfnKtFs9eOa5HsC3tPcEt6jxfjKzAdCtdF5gjPMiammzOgdYRn07BueqsOUWSFQjwnAp7PIp63kzo5KbDMU6K2qYdem8v3m78+3TwvnBchQps15YDbe19EvUXOW+T5scTMxaXmJDDHLXJLvCOMLDsCEs+zY82ZYvH8jDPOyDzrRbY91tlEQAREQAREQARmIiABXXNDBERABERABAICeRHQTTy3n4isfJkngrJeFGUopislcf6nNJvNIyMjPhqWDWY2hYnUkolA1QggjOBIYnXLBwcHXX9/f+ritaXmtBq/tCO0pKMoqzau6m9zBBCVd0xsq0VoTx3xIvPkxJRzk9Q873Vz5vZ6cRnhCvGc9O0r5gzWrZfe3BlbfxcR8genDrhDUwccadax3p45bkHPIrewZ5GPGG9kh6cO+XT1U/TvvtTvlgQc4dynh+7pcQt7JJ43YpnXf2dNDeum25pu7Q0dPxHVGz2r8nnEc55/8/icVMuZcH9mBitzkNfxidt12B30KelxCKjFzhM9P7PVIu4nvXPAfLfAzXcLi9LVwrczFs/JVsVzkiwdAlbznLWHdUrieTqcdVQREAEREAERKAoBCehFGSm1UwREQAREIBMCpIgMI70zOWl0Es5vwnm9euf8XZiSOG4v0TkWRYkIJMsXAWrab9261TeKWqGI5/yUiUDVCMQlDIaHh93y5cszx8B6i1hjYnq9KEpbU1lfW42izLxDOmGhCExMTbidR8fcgSP73VEykswh8rzHC+gI57WU571uXs88t3zOgJvDG7pkVgKhnewM9PPg5D53cGq/l9Dvtx43v2eBF8/52c6xu4RDp52FAM/T9qyKINVK+YwwQw/i+cknn+yosy5LjkBY071Wt71+9Lmd0aLQeS8C+gInp8/kRmPmI0k8z4Ly/ecIxXPWHsRznv9kIiACIiACIiAC1SUgAb26Y6+ei4AIiIAI1CHQTQHd1wG9L207TbPU3og1M20oN0pJzIYjX/xJDS6RtrtTnrHdtm2bQ0DHiDhfu3Ztwyis7rZaZxeBdAiE0YWsb1wLRCXmwUgbbGJ6HEXJehymJFb5jDyMWLHbwL1h8+hmt+/IvW7Okl43b+HcY+7XCOekSV/Us7gU4jL99XWXpyZrDgI9c7vqFFDs2VOM1vM8G9ZNjx0/eT41JyUr6WF1hxHPKakhS5aABPRkeaZxNInnaVCd+Zix447E82z562wiIAIiIAIikFcCEtDzOjJqlwiIgAiIQFcIdEtAN/GcTUaMjUMTzpuNxmqUklj1fbsypabHc/PmzV6Uw3BoINpWkazdGxOduXsEEFKoec46l8fUvCEZ7glW39dqtNu/szaHGT8apSTuHnGdOa8EuAYo50GkLta3os/1D66YjtAm2pyUyc0+B+S1n2qXCBiB8FmVec8aW88UeZ7unKGO+yF3sK0U7gvcQl/rXZYeAYnn6bGtd+S4ZITE82z562wiIAIiIAIikGcCEtDzPDpqmwiIgAiIQOYEuiGgh+naTUhHWO1EXOU4eNJbFGW8QRlG+xClrs359KZaXOOZuoXUeRbz9JjryPklsHv3boczCUaJCaILi5IdAwensHxGKymJ8zsialm3CDCfcCQhZSzGfYH7g+4N3RoRnTdrAqyhlvGDe0P8rMpz8OLFi310Opk/lPEjuRGinvk+t9dRy53fZ0vjbunba7XS57jFbomvmy5LhwDXBfcGc6xSzfN0ONtREc83btzo+L7GGnP66ad7R2eZCIiACIiACIiACEBAArrmgQiIgAiIgAgEBPjybFHgWYAx8Tz8yZf3JDfQG9X3JWrS0ryTVjzJc2fBMM/nYGOYTRnbFB4aGnJ9fX15brLaJgKpERgfH/dlDDAcd0466aTCljBoVD5DGT9Sm0alOHDsWLVq1Sq3YsWKUvRNnRCBVgng7IlgiPEMjFMVTqCxhRk/cMCSdUbgoNvvjlBOwR1la9DL43Et9Jp4Pumcm3K9bq6b5+a5he6Ezk6sT89IQOJ5tpOD72kbNmzw4jkOO0SeSzzPdgx0NhEQAREQARHIOwEJ6HkfIbVPBERABEQgUwJZCuixeE5HZ6t3nhQIi/bZs2ePj/wJjY1Lq0NJ1I/E9PapE1WIeG7p+NesWZObGs/t90qfFIHWCbDWIZzv2LHDf5i1heuhLNGEjcpnIPTYuionpdbnT9k+gUMVG/aHDx/2XVu9erVbtmxZ2bqp/ohAUwR4FqWMAYbjEVlJcOwMM35QPiOumy4npabwzvomhPEDbp+PQueF1QR0Xhjy+ZT/jchzXovcYi+0y5InIPE8eaazHTEWzy3yXN99sx0HnU0EREAEREAE8k5AAnreR0jtEwEREAERyJRAVgI6G4Fh6na+rGchnscw2cC3NO9xtA/tCcX0TlLKZzqIOTgZqUhHR0f9GLMRTKQtEbcyEagaAa4BUrYjkmBE9iAYlnWDslHGj9BJiWhKravVuiK45yKeI6JzDaxdu1aOVdWaAuptQGDXrl3+WQnjGQnxvJ5jFc/MOCXyvEpaa57VQ7NMSjyzsq6W9f6SxuSZcBOOSPRanPmk/xlaLS6dyPReH3k+x81JoxmVP6bE82ynQCyen3baad6RTWtHtuOgs4mACIiACIhAEQhIQC/CKKmNIiACIiACmRFIW0C3GuehgN4t8TyGyoa+ielWk9XeQxupP4n4RfRoWSJHk55YjC9pqsfGxvyhiZBCPFeq0aRJ63hFIEAEIZGFRA9ipKimlmeVNigt4wdrK3U2Q0M8Z11F9NG6WoQZ3VkbGX/Ec64Lxp57A2KfTASqSGDnzp1uy5YtvutcBziTNPNsGTopIabHmZS0rrY+mxDOj7jD7qg7PB1xPv3873rcXDffzXPzFXneOtqmPiHxvClMib0pLK/FeiHxPDG0OpAIiIAIiIAIlJKABPRSDqs6JQIiIAIi0C4BNrbjyJZ2jxV/Low4598stTdiUt4EJRiwMUnUqIlf05tpPT1e7LHo9GY2PJNimOfjML5EUhF9jpUtTXWe2att+SPAGoJYaOIGwnl/f3/+Gpphi8xJibW13roa1vclolJWHgJhSQ/umUTaKitJecZXPWmNAI6GlPWwZyXE83azcVgmJdbV2PnTjs/zKs5KcmacfZxI1z7hjk5HoRN1PsfNPa4uemujrXfPRkDiebbzI8wCw5pz6qmnuuXLl+fue3i2VHQ2ERABERABERCB2QhIQNf8EAEREAEREIGAQFoC+kziebsbhlkOWliHkg1K+hJaKKZXVfSJI23ZjBkaGtKGTJYTVefKDYEwuodGqcbz8UPTaF2lVro5Kc2fPz83Y6uGtE6A++amTZv8vRMBD/FcY9o6R32iHAS2b98+naUHUXvNmjVti+cxEXP+5Jqr97yK04qJ6WQIypvzajlGWL1olgCO1GTpYa5iq1at8pl6ZOkQiEuoEHku8Twd1jqqCIiACIiACJSJgAT0Mo2m+iICIiACItAxgTQEdBPPw59EoBVx447NHjZ6rA4lfw4N0Yc072xQViXSh6jSjRs3TkfaDg4O+kjbIo5vxxeQDlB5AgcOHPDXg6WpJrIQJxvZzARYR4lIt3UVdqEh9JiYLtGnWDOJjCSbN2/2jUY0Rzyvyr2xWCOl1qZNgGdgytsQfY6xpiGep/Ws1Ghd5Tq0dZVn17TakTZXHb+YBCSeZztuiOc8m/KTa53I876+Pl332Q6DziYCIiACIiAChSQgAb2Qw6ZGi4AIiIAIpEWADQ0E0aQsFs85LlHnZdiogxXpMknzjqgeiz4W6cMGJaJPGY2atmzIEPXEmBJpiwOBTASqSCCMtFWa6vZmAPcM1lXEdF5xSRGJPu1x7canduzY4bZu3epPzf2QmudVzdLSDf46Z34IsK6Rsp1rAlu2bJkbHh7O7FmY8+PcZU5KiGihcb8iGt5eRcgOlZ/RVUtaJSDxvFVinb1f4nln/PRpERABERABEag6AQnoVZ8B6r8IiIAIiMAxBJIS0NmssxfH5IXAWhbxPJ42jUSfMkZQIhaSepGxZfOVSFtqGMtEoIoEdu3a5UZHR33XibRFLFSa6s5mAusqTjomptcTfSyCkrVHok9nvJP6NONGlC3Rthhjw/2B+4RMBKpGgOthy5YtjnsElocSN5QZMTEdYT00ntWtNBGCupxeqjZj0+2vxPN0+cZHxyl+w4YN05Hnp5xyik+TXwZH9mxJ6mwiIAIiIAIiUF0CEtCrO/bquQiIgAiIQB0CSQjooXBuEehlFs9jjGGkDxuUcUQ/EZSW5p2ovCJuYuzcudNvCGP0B7GwrFH2WihEYDYCsVioSNv05ouJPqyrCOuhIZ4j9iCoI/5IrE1vHBpdD2GkbdI1nrvTK51VBNojwP0BxypKGWCkTKbOc56e+8jyYWI6pTRoc2ikdw/rprdHQp8SAecdblXzPLuZEJbYYs2hhIpKbGXHX2cSAREQAREQgbIQkIBelpFUP0RABERABBIh0KmAboI5xzEh3aLO87RhmAisJg4CA0Qf0ryzQRlHUBLZE0ZQ5p1RXMOTjVUiCxWh1MRk0FtKR4DrgRTVOJRgCLdcD4qETn+o2RgORZ/wjKyjRD3b2qr1Kf3x4AyxWJh1mupseqmziEBzBLgeEAtZpzCEq8HBwVyJ53FPeHYnuxAv2s2fQyOrionpqpve3DzQu2oEJJ5nOxNC8ZwzE3ku8TzbMdDZREAEREAERKAsBCSgl2Uk1Q8REAEREIFECLDhF4u8zR54JvFcYtL9BC2CEkGd30MjYtIEH4S4vInpbH5t3rx5ejOYtlLzXOPb7BWi95WJQHw9SCzs3uhOTExMCz4IPzNFULJmKa1+OuMUiyOkiF25cmXu7mPp9F5HFYFjCcTXA8J50cSrsDQR62qcTcmeWckywTOrngV1FcxEgOth06ZNjgwHGFkYuEfI0iEQi+dEng8MDOh+nA5uHVUEREAEREAESk9AAnrph1gdFAEREAERaIVAuwK6iefhTzbX8iYCt8Ii7ffiqGC1feMalGxEhmJ6tzcmSfHJ5pe1U+JI2rNDx88zAQRbrof9+/f7ZhYhsjDPPJNsGxv1bNLb2hpHUFJqwtZWftc9qnP68fVQRLGwcwo6ggjUCMRiIY4k3COKbJZNybJ+xCU0WEethAY/VUKjyKOdbNslnifLs9HR+L62cePG6TI3Es8bEdO/i4AIiIAIiIAINCIgAb0RIf27CIiACIhApQi0I6BbunZ+8qpSvfOkJoelI2Zz0kQ5O7ZtTFI3vRu1fRH6N2zYMB19pMiRpEZdxykigTiyZ2hoyNe1leWPQBhBydrKxnJo8+bNmxbTlY64vfGLN+t1PbTHUZ8qB4HYmaSsz0uzldBgJK2EBmK6sn6UY2630wuJ5+1Qa/8z8f34pJNOyn3ZiPZ7q0+KgAiIgAiIgAhkRUACelakdR4REAEREIFCEGhFQLca56GALvG882FmA4R0maR5t3SHdlT4IqJbBGXaUT6I+UTasinMudesWePPLROBKhKg7ALOJFyjXA+UMMCxRZZ/AtyviJq0yPS4VElYQgPxp9tZP/JP1HmnKq4HY8n1QCkDmQhUkQDPSVwPFp09PDzsli9fXnoUVkLDaqfPlPUDMX3hwoXK+lH6GVHroMTzbAda4nm2vHU2ERABERABEagSAQnoVRpt9VUEREAERKApAnFt7nofMvGcjTPbKEFwQFRSStymMDf1pka1fUMxfe7cuU0ds9k3IeBT85yxRlwikoEoTZkIVJEAziSkxWRTmLWO6wGhVVZMAtznTEyP0xEzvpaOuBtZP4pANHYmWbt2rWcmE4EqEkC8Qjy35+eqOpPwvIjjJ2J6vawfPKfihGl10/V9oZxXi8TzbMc1LrPF/ZjSEbq+sh0HnU0EREAEREAEykpAAnpZR1b9EgEREAERaJsA0WRsgs1kVufcIs/5s8TztnE3/UF426YkP+MoH8RtNiaJiCU1cbvGeO7YscNt27bNH4I6wYiFnRyz3bbocyKQBwIIASMjI35dRACgpiTXhawcBGZLR8wGtKUjZn1N2lGpiARxOEAsxMFLziRFHEG1OUkCsXiOeKVMPc7fLy3rB8+ssXOuOSohpqtuepIzsrvHisVzlfVIdzy4D+PceeDAAX8iMoVROkLiebrcdXQREAEREAERqBIBCehVGm31VQREQAREoCkCswnoJp6HP9kEU7rbptAm9iaL8rEISssEYCcgTaaleW9F6OO4W7Zscbt27fKHQjhiMzjtVPGJgdGBRCBhAjt37vTXBEYtV8RzOZMkDDlHh2uU9cMclVhfq1jbN8zEwH2B64H7jUwEqkggLGOAYKVMDDPPAr5b8MyKmM46EhvZPizzh+6xxbyaJJ5nO248r1Bmy64niefZ8tfZREAEREAERKAqBCSgV2Wk1U8REAEREIGmCcwkoMfiOQe0yPOmD643Jk6AcWHzxMR0oqFCQ0APxfSZohLijS9q2VLDU1EMiQ+ZDlgAAlxX27dv9y8M4ZRMDHImKcDgJdRE1kTSEdvaOlNtX9ZX1tmyr5VhJgYELsTzKjoRJDS9dJiCE+BZmUwMiOhc+9wfEIFljQnwnGo10/kZZ70yJ1AE9SqsrY2J5f8dEs+zHaNYPOf7mr6zZTsGOpsIiIAIiIAIVIWABPSqjLT6KQIiIAIi0DSBegK6pWvnJy82CyWeN400szeyCUkaPxN82NgNDdHD0ryzQWmCD+8jBaCl2BwYGHC8yi4IZTYwOlGhCMSZGLhmqGmrTBuFGsZEG9vIUcnWVuYKzhZlWzt3797tNm/e7JkqE0OiU0sHKyABnpUQzxGCVcagswE0RyUrURRnVLK1FTGdrEhlW1s7o5ePT0s8z3Yc4M13Nos8l3ieLX+dTQREQAREQASqRkACetVGXP0VAREQARFoSAAx1SLtEA3iyHOJ5w0R5uINjBubvHv27PGCOo4RoVHL1yInx8bGfD1bDKGQ6HOZCFSRAGsf9c7ZzMeWL1/uqOGpTfsqzob6fQ5r+9ZbW8lSYFk/EHyK7nixY8cOt3XrVg8DxysibVULXtdDVQlQ1xvxnGcmrnWuB5xmZJ0TMCdQE9Pj51Z4W810fhZ9be2cWPePIPE82zGox5vvbXpGzXYcdDYREAEREAERqBIBCehVGm31VQREQAREoCkCJqCbcG5iOj8t6lxf1JtCmas3IaZbZDobwPVscHDQ9ff3ayMmVyOnxmRFgGhC6kmSxQHT9ZAV+WKfZ7a1lXum1fUlvXORSgDEZQxwBqDGc5H6UOyZpdbnjQD3BiI/TTynjAFOJbJ0CLC2mphu92U7E99DwrrpcupJZwxmO6rE82yZSzzPlrfOJgIiIAIiIAIiUCMgAV0zQQREQAREQAQiAgjobA5aunb+2cRzRXuUY7oQ1bNt2zYvqMdmgs/SpUv95qTGvBxjrl7MToB1j6hCi3gjJSbR5zIRaIUA84h1FdGH+umhIfggQlt0ep4FH8Rzos537tzpu6AyBq3MAr23jARIl4x4zvMw1y7iOfW5ZdkQwMEtXFvjuulkATBnJY1L+mMi8Tx9xuEZ4uxIq1atcmvWrJHDc7bDoLOJgAiIgAiIQCUJSECv5LCr0yIgAiIgArMRQEBioypM3U7EmaLOyzFvGFdSto+Pj/sOsdGIWI7YY/X0rKeMuW1I8lORh+WYA+rFsQTIyIAwwrrHnGdTEsFQJgKdEMARzaIn+VlP8DExnbrieTHaOTo66qh7jlHSA4cSPQPkZYTUjqwJ8HzEPYJrg5rciOd5umaz5tHt8yEmsqbaK66bztiwtvLcirCutSvZEZN4nizPRkeTeN6IkP5dBERABERABEQgTQIS0NOkq2OLgAiIgAgUjgDRcwirbD4RYaN654UbwlkbzCYMwgh10TE2FxELLcocAdEEHzaMQ8HH0mXapmSeoyfLNWrqTZoEfHGvQgAAIABJREFUmOekbefaUD3bNElX+9jML+aaldGw0ihGBUcmE9P5vVuCT7xRv2LFCrdy5cqutafas0a9zwMBnom4R5h4fsopp3gRXZYPAowLzp/27Mr3mNC4r9tzq7IqdT5mEs87Z9jKEeC9efPm6Yxh3I8ppdKtZ4RW2q73ioAIiIAIiIAIlIOABPRyjKN6IQIiIAIikBCBkZERt2XLFn80RHQEVqKTqfGoL+sJQe7SYYjQYRPYosz7+vocKQBnGtdG0ZOkImZuMEe0mdylQdVpOyKAIwkbkyaMnHTSSUrJ2xFRfbgZAib4mJiO41JorKcmpmcZPRnfIwYHB11/f7/u/c0Mqt5TSgJcozwXc83yTEzkuZ538jvUjFNYN53sMqFZViWeW3nJEbS1sUTMJRODfY8YGhpyfJeQpUMgFs+5J/OcWvTv4zi5XHfdde7GG2/0pZMol/TEJz7RveIVr0g0+9OnPvUpd/XVV886OJ/+9Kfdwx/+8HQGUEcVAREQAREQgZIQkIBekoFUN0RABERABJIhQATHPffc41MZh2ZiOpv6iOmqi50M76yOQlp+Nr2svjPCOZGFzZpFTyI4Mkfi6ElEnjymIm62f3pf9Qjs2LHD13jGiPhlU1LCSPXmQbd7jOCDyGNiuq3R1i6LnmR9xWkprXsv93zuESY4SRjp9szQ+btNgOcdxHO7RyCeS3Dt9qi0dn6EurBuevxp1lQrU6SU/LOzjcVzynogfMrSIRBnDCuLeM4zzkte8hJ32223eXA4A1i2s3POOcchaHNNJmFXXXWVu+mmm/yz/UxOB5/4xCfc+eefn8TpdAwREAEREAERKC0BCeilHVp1TAREQAREoF0CFnm8c+dOn+o7TofIF1HbcEI4TWtDv93263PHEjhw4IAXRhjXJOo7s9ERpiKOa0/iYBGmItZ4iECeCDB/x8bGfKkKjA100mEiVMpEoNsEiJ40MT2OnuRea/deUhEnNWe5xxMFZuI9ZT3ILiITgaoS2LVrly93g/FMg3ie1PVWVabd7jfPqvbsWs8RFEc6W1+VdevY0ZJ4nu3sjcXzgYEB7+RZhu/bb3nLW9wNN9zg11ME7mc/+9nece9d73qX+9KXvuQuu+wyx3uSsMc//vFu/fr17vOf/7x7yEMeksQhdQwREAEREAERqCQBCeiVHHZ1WgREQAREoFkCtuFkYnocHUc0jm04IUQh0BY9tVyzbIrwvjBFdRr1nRulIiaiByEGQb2bdX2LMFZqY/oEmK+IIrt37/YnY24SRVWGTcn06ekMWROYLXqS+yz3XHNWajcyFsEe8ZwIdI6JM0lS0V9Z89L5RCAJAjzvWikjnEQRriSeJ0E2P8cwR1Crmx5n3WI9DeumV/l7jcTzbOctvFl/7DmVMio48JThOfXuu+92T3nKU3wWs9e85jXu8ssvn4bL886Tn/xkt27dOvflL3/ZnXnmmR2BZx2/8MILffT5j370I1+CQyYCIiACIiACItAeAQno7XHTp0RABERABCpIwNJ4m5jOxntobDCG0XES07s3SdgcJEX1tm3bfCPYOGATOM0NBEtFjGhPBGW9zAVsSCJaKrqne3Ojqmdm/dq0aZOPQMMoYbBy5Uo5/FR1QhSs35YZxtIRW8pT60Y7ZTTC7CRsznOPQJSXiUBVCZCZxJ6byPKAQ0kZhKuqjmcz/bZnVxPT4+82lvnD6qZXyZlC4nkzMyi598Cb0kJkwLDn1FNOOaU0a9A73/lO98lPftIRUX/rrbceVzaJ9O3veMc73JVXXumuuOKKjsByfFLFP+xhD3Of+9znOjqWPiwCIiACIiACVScgAb3qM0D9FwEREAERaIsAX/L379/vTEyPU82ywcTmI4IpP9mAqnIER1uQ2/wQm4FswDA2WDdSVNOGMBVxvCFp0T1W11dzo83B1seaIhDXd0Y4R0DXvGsKn96UMwLmzGap3vlzaGT7CMto1Jvn3L8p7cFnuV8T4YZjk0wEqkpg+/btvrwHhlhKKQOJ59WbDWTaMjGddTI2vtOYszDRrWU1iefZjmw98bxspSMuvfRSd+edd7pnPetZXiiPDSfXiy++2J133nnu+uuv72gArrnmGveRj3zEvfjFL/ap4mUiIAIiIAIiIALtE5CA3j47fVIEREAEREAEPAHEUiLZEGxJOcfvobEBGYrpbNZLuEpn8rABMzIy4jf/sLykqJ6trq9lLqCtzBPNjXTmRlWPymY4KaotI8Lq1avdsmXLqopD/S4ZgUZlNBB4TEwnSp31FeGd+wSf5d/ZpE8zO0nJkKs7JSPAdYBwTvQ5xvWCeK5nkZINdBvdaZT5A6cjE9PLVKZI4nkbk6WDj8CbzBfm+NzX1+eIPC9btgOEcZxS3vOe97inPvWpdYldcMEFDifrO+64owOizj3/+c93t99+u3vCE57gcI76zW9+4/cnKNv0//7f/3MvfOEL/e8yERABERABERCBxgQkoDdmpHeIgAiIgAiIQNMELBWiielx9AYbkiams+kkMb1ptA3fGEfZkiKPV942gREyLc17PWcL24zkp6K/Gg673jALgThFNaKI6jtrypSVgN1/LTId55HQuN8i8th9GdEc8bzMUZRlHWv1KxkCXDMIV5S8wXDkw8kqb89NyfRWR+mEgGXesvUVcT001lF7fiXzU1HnkMTzTmZJ65+tiniOI/VDH/pQD+iGG25w5557bl1Yj3vc47zT6w9/+MO2n9e5Nh/+8IdPP+ssX77cnXXWWY7vyb/61a8cWfNwKLz22mvdIx7xiNYHTZ8QAREQAREQgYoRkIBesQFXd0VABERABLIjYGm8TUy32sPWAjaX2GQi2odNJzzOi7rhlB3V+mdiY4J0vBZli1c9GwZ5N9prqTLrzY9QTC9bJEbex6bo7WNekQ6SdYi5Q31nNsxkIlAVArNl/oAB6yvZGHBq0/palVmhfhoB7g1btmyZrjfMM9PQ0JCeQzVFGhJoxlnJaqYXyRlU4nnDoU/0DbEDD2vQqaeeWsr7MY4niNrYV7/6VXfGGWfUZfn0pz/d/fznP3c333yzO/3009vizec5Dk7Yr3/9691znvOc6Sw7OEu96U1vct/61rcckf7f+MY32hbq22qcPiQCIiACIiACBSQgAb2Ag6Ymi4AIiIAIFI+Aiem7du3yad4RS/k7M4RzxC1LNSsxvfkxhiVCIRtfbBasXbvWCyJFMyIGLLKn3vzA2YLoMHO2KFr/1N7sCLDGbN682Z9QKaqz464z5ZMA99qtW7dOp4eNWxk6s3EP5v4rE4EyE+CaGB0d9c+jGELKqlWrJJ6XedBT7BvOSuYMGmdWKsr6KvE8xQlS59CxeI4z22mnnVZK8Zzu873u/PPP9yRuueUWXyajnv3pn/6p+/GPf+xuvPFG98AHPrCtQWFdR0Qn446J9uGByM5DandK2fzN3/yNe+5zn9vWefQhERABERABEagKAQnoVRlp9VMEREAERCA3BNg0IPLYItPZdArFdBqKWGrRxwhgikyvP3w4JLAJjCF6EGVLTcaiW6O6k5a5ALFH6YeLPtrJtZ91hOgSUvJiXAtcExIEk2OsIxWLQCyes2YiFJLGHYelevff0JlNtdGLNd5qbWMCXBMIJ8x/rL+/3w0ODuo5szE6vaMJAqSJDjMrxd9vWF/DuulNHDL1t0g8Tx3xMSdgToyNjbnx8XH/92UXz+kj3+se9KAH+f5++9vf9s8h9YxocdK3f/azn50W3NMYnfe///0+hftjHvMY99GPfjSNU+iYIiACIiACIlAaAhLQSzOU6ogIiIAIiEARCbCJwGYTQjAvNp3YyAnNNpuIPpaYXiMDt+3bt/sXhlBI5HkZxWTmA5ELFp1eb35Y5gKJPUVcBZJpcxzNQxYGrgmyMshEoIoE4ihbNukp7xE6pDVaX4ngsvWV3+XMVsWZVJ4+M98Rz3nWxAYGBvxL87o8Y5ynnoTrK3MurpvOM6uJ6XzX6cY8lHie7YyJxXO+25KqvAplVIgG57vc5z//efeQhzykLvg/+qM/cnfffbf7zGc+4y644ILUBuf66693b33rW30qeVLKy0RABERABERABGYmIAFds0MEREAEREAEckLAxHRSryGm8yU7FksRitlsYsOBjadubDZ1G1csisCDVHhVEArpeyimx5uRCDzMDQQffpdVgwDrBJkY9uzZ4ztcTyisBgn1UgRqBGKhcMWKFW7lypWz3jNZXy0ynfsvzm2h4aBlYnq3xB6Nrwi0S4BrgnI3PENgXA9En8tEIAsCrK+kdzdnUDJxhYaAamI6DoBZPNPH4vnq1av985MsHQKx83OVxHOIPvnJT3Z33XWXI/r7iU98Yl3IF154oc9Q98UvftGdc845bQ8Eadpnc7r/8Ic/7D7wgQ+4M888033lK19p+zz6oAiIgAiIgAhUgYAE9CqMsvooAiIgAiJQOAJsMiCOIqbzQhirJ5aamF6VyDgYsAGMyIEtX77cDQ0NVdaRYDaxBwcLxB42qKoyPwp3oSfQ4PiaUDreBKDqEIUmEF8TpKfmumjF4Yx78MGDB6fFHjajY7HHxHRKamQh9hR6UNT4rhKIrwnSB+NUIhOBbhBgfQ3rprPWhsZajYjOGsv3nDTK0CCeb9iwwYv6mMTzdGdCLJ4ztkQ/VyHy3Mi+8Y1vdP/xH//hXvziF7urrrrqOODr1693j3/84/3f33HHHf47bjvGeW6++WZ33XXXufPOO6/uIZ7//Oe722+/3V1yySXuQx/6UDun0WdEQAREQAREoDIEJKBXZqjVUREQAREQgaISMDEdEd3E9DgyLkyDSJR6GTfziVZhs8uEDKKn2ABuRRQp6hxo1O5Q7GGexJE9ipxsRLCY/844b9y40W9EYxJFijmOanVyBLg3ck2YIIODVV9fX8cn4BqzyMlY7OF+G0ZOVkkQ6BisDpA6AcRzrgkTCilj0K4wk3pjdYJKEuBZJqybHkPAScnW2CRKFUk8z3aa8R2FeufUPceqKJ7T7y9/+cvuNa95jS+v9PWvf/247+rUPX/b297ms6rdcsstbQ/SW97yFnfDDTc40sH/wz/8w3HH+cEPfuCe+9zn+nJo73nPe9xTn/rUts+lD4qACIiACIhAFQhIQK/CKKuPIiACIiACpSHAl102ftjIJ8UbP+uJpWFNwTKI6Wz8sgHMRjCCOZEiRFbLjidgkT0m9pi4au8kkieMnJQDQjFnEePKNcH1r2uimGOoVidLIHayYhM6jfsE57H11bKhWE+4FhF7bI1NI3IyWWo6WpkJ4FCC46E9ByjKtsyjXY6+8ZxvpYoQ1eNSVmRUsu84OAy3+gwr8TzbeRKL54wdkedVvDfyXfb3f//3fVY5RO7LLrtsejBYo5/ylKe4devWuec85zm+Pnm7ds8997hLL73Ufz8gGv15z3vedKT///zP/3gRn1JxD3zgA3099iqORbts9TkREAEREIFqEpCAXs1xV69FQAREQARKQoCNJjaY+CLMF/I4zSxfim2jiU19Nppa3WzqNiqEipGREe8pT2Qfnvv0RdYcgdkiJ63mJCKT0hA3xzMP7wodSnCQ4Zog3alMBKpKgHUOoRDBkHsc1wT3vrTN7sHcp7gXc58KjVrpJqYnETmZdn90/PIQiMXztBxKykNMPckbAdbT2UoVxd9xGjkMx9kY5FCS7ogzfjt27HDbtm3zJ6qyeG6kr7nmGveRj3zEi9ave93r3NOf/nTP5x3veIf73ve+5//+S1/6kncywPj++4QnPMH/fvXVVzcdLX799de7t7/97d4BhWxtrP843lMGDTv//PN9DXSyuclEQAREQAREQARmJyABXTNEBERABERABEpCgI0hNpr4goyYXi/ymM0LXohtRRDT2XjZunWrHyHEh5NOOsn/lLVHwCInmR+WztWOFKYhZo402ohsrwX6VKcEQocSNtq4JojCkolAVQnEDiVcE91wsmKj2iInuU7rRU6amE4UZdGc2ao6v4rY7zAbQ5YOJUVkpTYXg0Cj7Eo8s4Z10+NSGhLPsx3nWDxnbM4888zKRzvj6H755Zd7sbyeURudGulmCN4XX3yx/+O73/1u97SnPa3pgfzJT37iPvaxj7nvf//7vqwNQvpDHvIQH+n+uMc9Tt/zmiapN4qACIiACFSdgAT0qs8A9V8EREAERKCUBNi4D8X0uGarRR6bmM7GU54289l4wSMfAR0jio+IQqWZS266Ep1maYgRfUJjLljmAn6qpm9y3Ds5EpkmRkdH/SFwJDn55JMd9e1lIlBVAqxdbDBzz2Od4prIg0NJo8hJrlsT07m/5en+W9W5VJZ+I9CQjcHKe+BQogwlZRld9cMIMM+tbnpcSoP3hKU0+I5DyRtzHFXkebrziPsfztzmAM36Q0S1nldr3FmbP/rRj7qPf/zj/nsYNjg46FOr//Ef/3G6g6Oji4AIiIAIiIAItExAAnrLyPQBERABERABESgWAdvIt8j0epHHFrXBT0SIbm7mI4Rs3rx5elOB9OLDw8PylE9x2hGZE4rpcRpi5gXjgJguJ4YUB2KGQ8c1JOVQkv0Y6Iz5IxBmY2BjHvE8jxlKuH5xYrM1Ni61wj3XxHSV0sjfPCtSi8JSBoiG3crGUCRmamvxCYSlNHCqirN/8J3Gnmv5PrF8+fLidzqnPYjFc+5pRJ5LPD9+wFiv77rrLs/m7LPP1vfcnM5pNUsEREAEREAEJKBrDoiACIiACIhAhQjYRj5i+u7du32UemhsuFrUhkUeZymmExVNlIhFzPf393uv/CzbUKHpULerbESyAUma93o1fcOoHm2IpT9buGa3bNniiD7HuC6pZagU++mz1xnyS4D7F45WGOnQEQqLsh6xaW5iepwdJiylYQ5t+R0FtSxPBJhLRJ5zD2ce4VCCs5VMBKpEwDJw2RrL9RAa9wnLsMTzrL5fJDc7eF7lWZVnVgy+RJ7n0bEtuV7rSCIgAiIgAiIgAmUnIAG97COs/omACIiACIjADARMTGezAzGiXhrvuJ5gmhtNiAqI56S2w4aGhlxfX5/Gr4sEGtX0ZXPeIie1QZb8QMF/ZGTEOzJgRE1xXaR5HSbfCx1RBJIlQGkPSw1LunaEwqKWmeB+Z0JP7NDGdR46LCn7R7LzqExHI7MQz0+IhXkqZVAmxupLsQhwLeBQYk5KrJ846YYmh6XkxjQWz/l+QOS5vhskx1hHEgEREAEREAER6A4BCejd4a6zioAIiIAIiECuCLDxgYAdiulhGu9wI9/SeCcp4iEcsPmLYMiGFhG2nEeWHwLMB5wsZorqIQqUNO8I6vwu64wAm79hzc6BgQHHK8nrrrMW6tMikC0B1qDt27f7F4aD19q1a0uTjSFMQ1wv+4cclrKdb0U5W/j8hEiIQ4nuwUUZPbUzDQLx85PVPKd8hj3DxuWs4u85Rclokga/Vo/JvRlH7NHRUf9RieetEtT7RUAEREAEREAE8kxAAnqeR0dtEwEREAEREIEuEGAjhE0mE9NnSuNtKRDZZOpE1LNNF87L5i+peIkqlOWXAGPF5iNp3tmMjKN6iDixyHTGspP5kV8K6bWMqFQip6xWsrIxpMdaRy4GAdYcos4pP4KxviCKlLWUQaPsHwiktsbyu9bYYszjpFuJUxuOVlwfPIshniviM2nKOl6RCMTiOQ65OHfGxnMr3294huU6Cp2GeS/PrmGGJa2x9WcB3PguYCVV4HbWWWdpHSrSRaO2ioAIiIAIiIAIzEpAAromiAiIgAiIgAiIwIwE2BhBzEPkRlBns4mN/dCINEBMZ4OqFTGd4/z0pz/1ojmvotWx1bSpEbBSAGxCsolmKfiND3PCNiGZK9qEnH3mkG4UQYTNXVix+Qs/mQhUlQBrDJvzrC/YsmXL3PDwcGXWEvpPlLFFTsYOS1pjq3ll8Dy2adOmafH8lFNO8c9gMhGoKoFmxfOYT+iwxHUV103HKcWchvUcez+9euI5aduVAaOqV6D6LQIiIAIiIALlJCABvZzjql6JgAiIgAiIQOIE2Chh497EdDbzYzGdyAMT09lwmkksJbL2rW99q/va177mHv3oR7urrrrKC4VFrWObOOyCHtBKAZjQQ1mA0HCUMDGd2r4S048d6LiUAdkY4CQTgaoS4B4zMjLinbew/v5+Nzg4WNm1I3RYYp21LBU2P7iHhmtsWSP0q3o9WL8Ze64L5gPPWkSeSzyv+qyodv/bFc9japZhyZ5jY6dQ1lgT0ykjUtU1Fk62DsGQ738Sz6t9Dar3IiACIiACIlBWAhLQyzqy6pcIiIAIiIAIpEjAxHQiAolMZxMljtggAsHE9DDFLAL8q171KvfjH//Yt/AFL3iBe8UrXlFZQSTFYer6oRF3LM07kdWhaRPy2OGxFJhWykB1bLs+fdWALhPgnkKELY4lGML5wMBAl1uVr9PjpGRCT7zGIuyEQo8c1PI1du22hnsF4jnGsxX3CpzTZCJQVQJJiecxPytpNdMaixMoIjpOS6y1VbkOTTwnMwy/sw6Rtl2R51W9AtVvERABERABESg3AQno5R5f9U4EREAEREAEUifA5gmbV6GYXq8mNptLbEIRbb5+/Xrfrj//8z93V1xxRWUjOFIfnBydgCge24Q0QcyaFwo9zJOqRfTs2LHD13c2QYTIc0UT5mjyqimZE+AeQikDE4WHhoZcX19f5u0o0glnW2MReshmYdHpVRF6ijR+zbQVh8XR0VH/ViI+Ec/lGNEMOb2nrAT4/rFhw4bpe8VMNc+T6D9rrNVN5zk2rptOevewbnoS58zbMegzDCwDhsTzvI2Q2iMCIiACIiACIpA0AQnoSRPV8URABERABESgwgTYWCHlLkLpzp07vahuYvq6devc+973Pv/3bN6/9rWvdc94xjMqJ5ZWeHpMd505YWL6vn37jkGC0GNRk/wsszjA9TI2NubGx8c9AwSutWvXlrrPmv8i0IgAIgWCiKUnT1MQadSWov47opIJPfysotBT1LGbqd08O23ZssX/M0IdjlZlvj+WbfzUn+QJZCmex63n3Dy/8izLGhuXtKK0gonpOLuUoWRRPfGctO30TyYCIiACIiACIiACZSUgAb2sI6t+iYAIiIAIiECXCZiYzsbS17/+dXf11Vf7CBFE0Ve/+tXunHPO8VG2/JmX1cQuwyZTl9EX6vRsQoZieiz0WHpMNiLLFDVJP4kkpKQBRv9Wr14th5JCzV41NmkCpCRHPMfJhnsBDiXcH2TtE0DYMaGHtTYWeoggNKEnLLfS/hn1yaQJ4GS1bds2f1juiVwXVcvUkjRTHa/YBLopnsfkeJ4jIt2eZeMsXDy7mmMo33WKeO3G4jkOAojnOPPIREAEREAEREAERKDMBCSgl3l01TcREAEREAERyAGBG264wb397W/3ad6JJERIX7ZsmUMoCS3eYEI8kZiegwHMsAkIO2HUZCz0hCmIi5zinH5R29mi70lNvWrVKs33DOeaTpU/AgcOHPBp27lXIDAQYcs1L0uOQCOhh3XVxHSEEd2Dk2Pf7pG2b9/uM5VgiHA8RxVRgGu3//qcCMQE8iSex21jjeX7jYnp8Xcdrt2wbnoRskjQJ55XeW7ld4nnuiZFQAREQAREQASqREACepVGW30VAREQAREQgQwJIBJec8017mMf+5g/67nnnuuuvfZat2LFCh8Bx2YM9TyJwI03mNhQiqM1tJGf4eDl4FSNoiaLWmsyru08ODjo+vv7JVTlYM6pCd0jYJvzXPes/9R2VlrYdMcDIYSsMCb0WMp8OyvjYGJ6UaMm0yWY7tHjEh+MBeK5noXS5a6j55tAnsXzeuQoSWJrLFHqseXdMVTieb6vB7VOBERABERABEQgfQIS0NNnrDOIgAiIgAiIQOUIIIi/4Q1vcF/96ld93//gD/7Avfe9760riCCYsKmEmE7NdKIQQyNaw8R0ojb4szaQqzWlbAPPNiHZQA3NUhAvXbrUR8bkdX4gUBFha0LV8PCwW758ebUGU70VgYgA1/XIyIiPbCMCGvGc61iWLYEwahJhfbb7cBGiJrOll+zZuBZI2b5jxw5/YO5tlPjI670t2d7raCJQn0DRxPO4F7TfsizhNJb3chr27M39mbZyfyZtuzLD6AoVAREQAREQARGoEgEJ6FUabfVVBERABERABDIgsHPnTnfFFVe4H/3oR/5sL3rRi9xrX/vaplKOslmDgG6R6XG0Rpj6EDGdTXxtKGcwqDk6hc0RRDccLuJakwhvFjVJBGte5geCFLWd2UBVbeccTSg1pasEyECyefNm3wYcYUjbXuTyDF2FmeDJZ4uaZP0KoyYpvyJLjgD3uC1btvjnIAwnq6Ghodzcy5LrqY4kAs0TKLp4HvfUnId5lkVUr1c3PcwAkvWzrJX7IG27xPPm56neKQIiIAIiIAIiUD4CEtDLN6bqkQiIgAiIgAh0jQDRtX/xF3/h1q1b5wXzN7/5ze6yyy5rqz2WXtbEdKsXbQdjMymuI5j1BlNbHdOHEiPQKAVxXur5xumpEQlJQS8TgSoTILp269atHgHXA9eFIpvzNyPCqEmEHtbd0IpaTiN/pJ1nOzo66kvbYH19fW7VqlUSz/M4WGpTZgTKJp7H4MJnWdbYenXTw0xcWdwnw+dWnKTOOussRZ5nNuN1IhEQAREQAREQgTwRkICep9FQW0RABERABESg4ASIPP/mN7/pxRDqn1900UWJ9IjNJTaUENN51RPTLSKOTSY2eySmJ4K+MAexOWJp3uMNSKvnSypc5kpW8yOMsFV66sJMJzU0RQJcq9u3b/cvDEeotWvXNpWlJMVm6dBNECASkfuvrbN5T0HcRJdy8xauC1Ilwxbr7+93g4ODmd2rcgNCDRGBgEDZxfN6g02ZH1tj47JWYQYQvu+kkbGF7F84RLO+832KtO3cp2UiIAIiIAIiIAIiUEUCEtCrOOonu5pOAAAgAElEQVTqswiIgAiIgAikROCmm25yN954o3vVq17lfud3fieVs7DJzOaSRabXi4hDIGVjCbFUYnoqw5D7g9oGJGne43q+iOlhNA/ZEtKw8fFxX8cWIz01tZ2V7jgN0jpmUQiwfhN1TqkPjBS11HZO6xosCpcittNS/JrQE6cgzksGkCKwRahCPOd5BhsYGPCvrBy9isBIbawegSqK5/Eos66GddPjDCCUKrJU75Qw6nTNiMXzM844wz8vy0RABERABERABESgqgQkoFd15NVvERABERABESgBATaSqNVKlC+COptMcUQc0fAmprOh3+nmUgmwVa4Ls9XzRbgzMZ2fSQh5zEuEc1JUY0TurFmzRumpKzfz1OGQANcF9c5xasFU27k886NROQ3LAILQg4NbEutsWejxzEKdYcuss3LlSh99LhOBKhOIxXOylLB+VNnCDCB834FRaJ06LSGesxZxXJw9JZ5Xebap7yIgAiIgAiIgAkZAArrmggiIgAiIgAiIQCkIsIFPpIaJ6UTFxWJ60pEapQBXsU4wRyxisl4pAMRuMhcgprdTZzIWCTkWEbZy3KjYRFN3jyEQR9gqPXW5JwglNGydjTOAhE5LrLftrLNloYdQhWCFcIVR73zFihVl6Z76IQJtEZB43hgbz5qkd2edRUwn61JorWZa4likbYc9n0U8r7rDQuNR0DtEQAREQAREQASqQEACehVGWX0UAREQAREQgYoRMDGdSEci09lgiiM1SKltaQ/5XQJnxSaJc35OsPHIPEFMj1NjIu7YHGkm9TrHIw2vCfMIIUQTam5Vb26px/cTiEVCRdhWa3bMlgEkrOfLWtvMOlsWelwXCFZW43h4eNhnZZCJQJUJSDxvffSttNVMTktkQ/r617/uzj77bHfJJZf459LQcHLasGGDxPPW0esTIiACIiACIiACFSAgAb0Cg6wuioAIiIAIiECVCbCxxIacien8rCemWxpvotQleFZvxhAha3Um65UCIO2wiemkyYyNyHbEEIu2lEhYvTmkHh9PgOuCjXkikrGhoSHX19cnVBUlwL3XIiZZZ2OnJUquhPV8y4opvi7IUrJs2bKydlf9EoGmCLA+rF+/fvp+obTtTWE77k04LdnzLNktbrjhBnfjjTf69/H95gEPeIB79KMf7R7/+Mc7GEs8b4+zPiUCIiACIiACIlANAhLQqzHO6qUIiIAIiIAIiIBzfrMeoTQU09nIDm3+/PnTNbER01WrtXpTp1GdSeYFqdkRepgvpM5kA5JNS0xiSPXmjHp8PAGuB64LSy27Zs0af93IRAAC4Tpbr+RKWbPExOK5rgtdDyJQywgk8Tz5mWAR/R/84AfdHXfcMZ0hyc7E+nPBBRe43/3d3/WCurJgJD8GOqIIiIAIiIAIiECxCUhAL/b4qfUiIAIiIAIiIAJtEjAxnSiNnTt3elHdBFA7JJHGFplOZByRG4pObxN4QT/GPCGCh/lRrxQAcwRBhPcxN0466SRH6neZCFSZABHniOdcG1wXRLmxlspEoB4BW2ctBXHs2MY6a5Hpdi8uIsnQqUTXRRFHUG1Og4DE8zSoHn9MnNluv/12d8stt7jbbrvNjY2NHfOmwcFB99jHPtZdfPHF7sILL3Q4MclEQAREQAREQAREoOoEJKBXfQao/yIgAiIgAiIgAp4AG3jUrqZm+u7du6ejJg0PtVlNTCedt8T06k0cRB7q1ZrIU8/hwiLTVQqgevNDPa4R4BqhnAFrKhk8cCphzZSJQDMEWGcphWHrrGUwsM/OmTNnWkxnXhUlS0yYqUTOVs3MBL2nCgTijAxK257+qOPgtm7dOvfb3/7W/fCHP3Q/+9nP3K9//etjTszaSpp3xPTHPOYxKjGR/rDoDCIgAiIgAiIgAjklIAE9pwOjZomACIiACIiACHSPgKWWNTHd6veGG/gmphNtLDG9e2PVrTOTtWDLli0znh6HCxPTixwx2S2+Om8xCeCEtGnTJp+em2sA8RxnEpkItEuA+6+J6QjroSGeh/dixPU8WpiRQU4leRwhtakbBCSeZ089XotOO+00L46Pjo76yPRvfvOb7vvf/77PHhN+53n4wx/uLrnkEi+ok/ZdJgIiIAIiIAIiIAJVISABvSojrX6KgAiIgAiIgAi0RQAhiBTeJqY32sBnc1xp3ttCXYgPER05Pj4+nfoScZCIKeaJpXmv53Bh6YfN4aIQnVUjRaAFAoicIyMjvpwBKbdPPvlkN3/+/BaOoLeKwOwEyPphYjr35dC47xI1aWstDhx5MJ4ZKGdgGRm4LnCqkolAlQmE4jnXLqIs164sPQJhFgy+q5h4Hn9n4Vn229/+tvvGN77hf+IYFxoR6ddee21hsn+kR1RHFgEREAEREAERqAIBCehVGGX1UQREQAREQAREIBEClsKb6GPSvJOqODQ2pBBI2QTkJ9FwEtMTQZ+LgzD+W7dudYw/xhgjnscphNmkNJGn3hwJxfSipB/OxQCoEbklgIMREWwYdVOJPEdEl4lAWgQQpFln9+7d61+sz6EhUtta2y1HjrCcAc8DiOfKyJDWjNBxi0JA4nn2IxWXkEA8X758ecPvKHzujjvu8JHpRKjzDMyz73e/+105AmU/jDqjCIiACIiACIhAFwhIQO8CdJ1SBERABERABESg+ASsTquJ6fWi4UxMJ8WsxPRijzkR5ps3b/aCDUbKy+Hh4Yabj7NFTIbph5kjEtOLPUeq2vodO3b4TXUM0RLxPK+ptKs6RmXvt5VdMccl/hwaTh0mpvN7Fo5tPBNs3LhxupwB4jnnlolAlQlIPM9+9NsVz+OW8r3nzjvv9GUzuM/LREAEREAEREAERKAKBCSgV2GU1UcREAEREAEREIFUCbCpRNpuE9PjdIdhalk2nkgtm8UGfqqdrtDBiXSkrrM5SfT397vBwcGWx5CNYxN46s0Rc7hA6JEAWaEJVtCusu5t377dv7CZMjIUtHtqdkEJMC9Zq22tDWv50iUyI5iYjsNHGvdi1nfEc9rC/f6UU05ROYOCzic1OzkCsXhOBh+eiWXpEUA8Zy3iJ2vdqaee6vr6+lJZ99LrhY4sAiIgAiIgAiIgAt0jIAG9e+x1ZhEQAREQAREQgRISYMOcjapQTI9Ty8Z1WtPYwC8h2q50iQhyNh+trvmqVavcihUrOm4Lojxphy0FcTxHQjE9L7V8O+60DlAaAnE5AwRJathqLSvNEJeiI5YpxsR07s2h4ahkYjr35SSygLCu43DFuRHrEc9VzqAU00md6ICAxPMO4LX5UYnnbYLTx0RABERABERABEQgICABXdNBBERABERABERABFIiwAY6Aiz1gamZzib+bHVa2WSXAJXSYLRxWETzDRs2ODZ+GZfVq1e7pUuXtnGk2T9CuuFQTI/TD4cOFxJiEsevA7ZIgDWMcgZ79uzxn6SO6tDQkNauFjnq7dkTYE03Mf3gwYPHNCAsqYEDUztZQDj2yMiIv89Td5207Vqzsx9nnTFfBCSeZz8efPfg+dUiz3HkwflT3zGyHwudUQREQAREQAREoNgEJKAXe/zUehEQAREQAREQgYIQYEOdTcRQTI+F0oULF05Hw7H5ro2u7g1uWL8WYYV6jwjZaVtYyxdRnUj10GyOIOQzR2QikCUB5icCIXMTa7ecQZZt1rlEoB4BBCYT0608h70vLLtChHozWUBwKOHawKh1jnjezOc0OiJQZgISz7Mf3TBzEmsZaxH3an2nyH4sdEYREAEREAEREIHiE5CAXvwxVA9EQAREQAREQAQKRsDEdKLSLTK9nlBKbUgTSrXxld0gh1GECCCI5wjXWVujWr6INJZ+mN81R7IeoWqdjzWKcgYHDhzwHV+5cqXflJeJQNEJMLetnAbOIbNliqnnuIRj3OjoqMfAvQLBqp0I9qJzVPtFICQg8Tz7+RCXHWItGhgY0PNh9kOhM4qACIiACIiACJSEgAT0kgykuiECIiACIiACIlBMAmzUs3lP9Bqb8PyMxXTEUcR0xFI25yWUpjfW1K7fsmWLP0GeUvAyTxAuLWKSTdLQaKuJ6Zoj6c2Pqh45FEJgQMr2vr6+quJQv0tMIMwCwnobZ4qJHZe4b9s9Y9GiRd7hSuJ5iSeIutYUAYnnTWFK9E0SzxPFqYOJgAiIgAiIgAiIgCcgAV0TQQREQAREQAREQARyQsDEdDbtTUxnEzI0hNJQTCe9uKxzArDfvn27f2F5FkJoK/V7TUynxmVoRM0jppO9gH7I4aLz+VHlI4S1VOGwZs0aP7dkIlB2Ao2ygCCUm8MbJT4Qz3VPLvusUP8aEZB43ohQ8v8OczLE8GyIsRYNDg7q+S951DqiCIiACIiACIhAxQhIQK/YgKu7IiACIiACIiACxSDAxj2Rb6STJSqayPQ46njevHnTYjpCqTbu2xtbWBNBiNMChoMCImFReB46dMiL6cwRfg8Ngcci0xcvXqzN1PamSGU/xXzasGGDY3MeR4y1a9f660MmAlUj0MhxKVxrEdOLcv+o2jiqv+kSkHieLt96R5d4nj1znVEEREAEREAERKA6BCSgV2es1VMREAEREAEREIECEyDKbd++fdNier2oY4tMZ/MesUuRx40HHCeFkZER76iALV++3KenLio75oVFplutaqOAoBOK6RJ4Gs+PKr+D+UNEG2sPc4VaqjjqyESgygQQ0sfGxtz4+LjHwLURp3nn7+x+jOOSUrpXecZUp+8Sz7Mf61g8x8lt5cqVhX2GzZ6gzigCIiACIiACIiACsxOQgK4ZIgIiIAIiIAIiIAIFI2A1Wi0yvV7Ucbh5LzG9/gCz8bhp0yZfWxwj3WV/f39pNh7JWGBi+v79+4+BwJxgjpCKWwJPwRaADJqLsw7XBmsNJQFIB7tw4cIMzqxTiEB+CSCeb9u2ze3YscM3kvVz9erVPkPDbGstTm3mvMT1JBOBshGQeJ79iMbPsBLPsx8DnVEEREAEREAERKD8BCSgl3+M1UMREAEREAEREIESE0DgQhw1Md3qH1qXiXxDIGXznp9ExhU1ujrJYYzrOhN13tfXl+QpcnUsNlqJsifNO+JoaMwHmyPME0VL5mroMm8MQiBZGRALKRNB5Pn8+fMzb4dOKAJ5IhCX+pgpWwkZG7iGWG958bnQyOJgYrquqzyNsNrSLgGeL9avX+/IgKNSH+1SbO1zrDNkiDEHUMoOrVq1Ss/3rWHUu0VABERABERABESgIQEJ6A0R6Q0iIAIiIAIiIAIiUAwCbNSzmYaYvnv37umNNWs94nkopiOUVlFMx8mAjUer68zGI4JGVYyNV4QdE3ligScU0xUtWZVZUevnrl273OjoqP99wYIFPvIcEV0mAlUmwBrJdcF9FcPZqhmxyrLFWHR6nOqda8zEdH6v4v24yvOqDH2XeJ79KPIMR4YYyywk8Tz7MdAZRUAEREAEREAEqkNAAnp1xlo9FQEREAEREAERqBABNvwRik1Mr5fC24RSUnlXRUwPU1PTZwTCKtd1RtAJxfRY4IENaYoReSSklnsBIS311q1bfScZd64NZSMo95ird40JcC/dvHmzz96BUeaDch+tit0ch/uwiekIj6GxvpqYzvXX6vEb90TvEIFkCcTiOfcMnitl6RGIxfPh4WHHS+tFesx1ZBEQAREQAREQgWoTkIBe7fFX70VABERABERABCpAgI176qSbmF4vhXdYo7WsYjoCCEKIpaZms5eoP1mNgJUDgBOiOhu1oVEDO4yWFLdyEOB6GBsbc+Pj475DCCDUUiVjhUwEqkyANZFyBqyH2MDAgH91KlaZg5uJ6aS+Do17sK213Jt1LVZ5Fuaz7xLPsx8X1iOyJ5lDrMTz7MdAZxQBERABERABEageAQno1Rtz9VgEREAEREAERKDCBExMJ1Uz6WgR08MU3ggDYY1WUnh3KhbkAXcYXavU1I1HpFG0pFIPN2ZYhHcwzkSd41yDkW1g9erVpbjmi8BfbcwvAcQq0iSbw9nKlSt99HkahoObielkjgkN8ZwsMQjqOLcoK0QaI6BjtkJA4nkrtJJ5b7weDQ0N6V6dDFodRQREQAREQAREQARmJSABXRNEBERABERABERABCpKAPHsyJEj05HpRNnF9bCJfrPNe1LMFk1Mj6Nr6Q/RtRIhmp/0MDxw4MC0wMOcCY15YWneiVIv2hxpnkS53hmnpl6+fLljU17jV65xVm9aJxCnSabe+YoVK1o/UBufYH01Mb1e6ZUwWwwObjIRyJKAxPMsadfOJfE8e+Y6owiIgAiIgAiIgAgYAQnomgsiIAIiIAIiIAIiIAJeOGdjlMh0Xojp9ephI6YjlhZBTKdPo6OjPtIeo92kvFQ63PYnvGUwIM07Ik+cehhBJ0w9LDG2fdZpfjLekG+3rnOabdSxRaAbBBDPSZOM0xDGPQPnkm4YbWGd5X5cz8EtzBYzf/78bjRR56wQAYnn2Q92XEYCZ541a9bI0S37odAZRUAEREAEREAEKkpAAnpFB17dFgEREAEREAEREIGZCJiYjvCMmM4GfiymE2lsYjob93kTSmOBsK+vz7HxmLd2Fn0WzpZ6OKzjS+phsc/HaMcCYZqpqfPRY7VCBJojgECIeG5p1ClnsGzZsuY+nPK7uKeRTt6i0+N7sspqpDwAFT88mRE2bNjgnea4l5900km+pIAsPQKxeM69mgxKepZKj7mOLAIiIAIiIAIiIAIxAQnomhMiIAIiIAIiIAIiIAIzEkBMR3BDTOdF5DF/Do2NexPT+b3bm3uxCMKmI+l3u92usk8zNtZN3LHoTeszUf8Wmc6mu7IAdGc2cG0gguD4gHUzurY7BHRWEahPIL42iPIka0kejfsy6d1tvaXtoZEhxtZbotR178vjKBanTRLPsx8rxPPNmzf7axyTeJ79GOiMIiACIiACIiACIgABCeiaByIgAiIgAiIgAiIgAk0RMDEdEd3E9Hjjnmh0q5lOlHrWQikiLgKh1enOUwRhU5BL8qZGdXzN4QIxXfXosxn08NpAUOPayKtAmA0RnUUEagRigZAoT9aoIhj3ZSLmTUyPy2qEmUCon571PbkIDNXGmQlIPM9+dsTi+eDgoI/4lyNM9mOhM4qACIiACIiACIiABHTNAREQAREQAREQAREQgZYJsGnPJh+b9jt37vQ/TbS2gxEFZ2I6UXBpb9wT9Uz6XSLk2WgskgjS8gAU6AM4WVC/lzlCCmLmjhnjhIhu0ZIS09MZWCLOcSxhLJR+Nx3GOmoxCcSOJUVPTT1bWQ3uwXZPlvNSMedrlq2WeJ4l7dq5eK4eHR312Z4wiefZj4HOKAIiIAIiIAIiIAIhAQnomg8iIAIiIAIiIAIiIAIdE0C0RiSlZjobf3EU3Ny5c6c37omCQ8RLMpqGc4+MjPjNR0RYRBBEe1m+CNg8QUxnzEIxnZaGYjpzRtY5gdCxBAHt5JNP1rXROVYdoQQEQscSrg3uG9yfymKNMoHQV3Ne0npbllFPph8Sz5Ph2MpRYvF8YGDAr0lpO5+20ka9VwREQAREQAREQASqRkACetVGXP0VAREQAREQAREQgZQJIJJSn5XIdMR0q7dspzUxnUg4BNNOxXTSyVMrEiPqHYGQVPKyfBNgs9gi0/nJn0PDAQJxhzTjjKusdQJE/G/atMmz5brj2liwYEHrB9InRKBkBEh7TlYG7ldVcCyhn+a4VM95ydZb1lzdP0s22VvsjsTzFoEl8Hbu0Vu2bPHlkbD+/n5/v5Z4ngBcHUIEREAEREAEREAEOiAgAb0DePqoCIiACIiACIiACIjA7ATYFAzFdESL0IgWR0g3MZ3NwmYj04le/u1vf+vrv/IZaq4TraNIuuLNSsYSsdfq+CL2hMbYWqSkBODmxheWZGWArRxLmmOmd1WDQJiVgXsQQhVrTFWM+3K43sbOS6yx4Xrb7D25KvzK3E+J59mPLtff1q1bfQYnbMWKFe6UU06ReJ79UOiMIiACIiACIiACInAcAQnomhQiIAIiIAIiIAIiIAKZEEDIC8V0RIzQEM8thbfVZ51p4x6B9W//9m/dF77wBXfppZe6F73oRb7muaJ1MhnKVE9i88TEdOp2hyZxpzF+NuKpo4rBC4FQjiWNuekd5SfAfYfIc2VlqI11o/UW5xsT04lSl5he3mtE4nn2Y1tPPOd+jWOPTAREQAREQAREQAREoPsEJKB3fwzUAhEQAREQAREQARGoHAE27YkcJ807KSsR1kNDCLf6rESns5loG/d87nWve537zne+4z/y/Oc/31155ZXa2C/hLLJ5QikABHU2+EMzcYc070SQStxxbnx83G3bts1jQvAiK4M240t4cahLLROISxoQ5al05fdjtPXWnJcOHz58DGPWERPTuT/LYa3lKZjbD0g8z35oEM+5V/McjPX19fnIc92vsx8LnVEEREAEREAEREAEZiIgAV1zQwREQAREQAREQAREoKsEbNOeqFnEdESO0BBFLTKdDcdXvvKV7mc/+5l/yzOf+Uz3+te/XhuOXR3BbE7OPDl06JAX0hHUY3GHCOtQ3KmamA6fsbExL6BjXDPKypDN3NRZ8k+Aut+bNm2aLmmAUIUDjmxmArbesubG5VcQz3FuY821jDFiWUwCsXhOBDQOErL0CEg8T4+tjiwCIiACIiACIiACSRKQgJ4kTR1LBERABERABApKgHTYV1xxhbv11lvdr3/969R68Ytf/MJ9+MMfdj/4wQ/8ZuwDH/hA97KXvcw9+tGPTu2cOnCxCJhIGorp/B1GlM573/tet379eh9p/Jd/+ZfuxS9+saKOizXEibV2NnEnjJRE3Cm7mM41smXLlukaqkTkr169uvT9Tmwy6UClJoAAPDIy4sVzIs4RCCWetzbkiKwWmR5njGF9tYwxCOoqF9Ea226+m3HlmYqfjKPE8/RHg3WIyPMdO3b4ky1fvtydeuqpcgRNH73OIAIiIAIiIAIiIAItE5CA3jIyfUAEREAEREAEykfgbW97m/vsZz/rO5aWgP7tb3/bi/SWgpmNOjaR+Pmud73LPe1pTysfWPWoIwLMD6KMEdNxvnjLW97iI2zZnH/pS1/qHvGIR/hNe4uCQxApu1DaEdASf5h5YuIONY5Ds0hJRGXE9LKlHeY62bx5s4/Kt834oaEhXQslnu/qWvMEuC4Qz7EFCxZ4gVACb/P86r0Tp0vWW6L6eZmTm72X0hGWDUQp8jtjneanJZ6nSbf+sWPxfNmyZe60006TeJ79UOiMIiACIiACIiACItAUAQnoTWHSm0RABERABESgnATYBCWi9+Mf//h0B9MQ0Dds2OAuvfRSX+f6wQ9+sBfMzz77bHfbbbe5V7/61V5Uv+mmm3y6YZkIxAR++tOfussvv9wL6Qigb3rTm9wZZ5zhSIEZGpv2iOkIpRLTqzuPWE8QdRDO6kVKmsMFP4tea5RrgLTUVvagv7/fDQ4OSjyv7vRXzwMC3DNGR0f93yxcuNCL50W/5vM2wKxBrD/mwBTfl3FaMDGd3+Xklo8RlHie/TjEZVYknmc/BjqjCIiACIiACIiACLRKQAJ6q8T0fhEQAREQAREoCQFSB77qVa9yt99+uxseHp7eZE5DQKdm9c033+xWrlzphXLSFZrdeOONvob1k570JHfNNdeUhK66kRQBygpceeWVPuU/wuC//Mu/uAc84AHu6NGjvl46Agkb9/GmPWKJielEwGnTPqkRKdZxmCeI6cwRRJ4wUpI5gUMG4g5zpWhRqThAbdy40VnEPesrArpMBESgVvKDsgYYzlUnnXSSxPOUJwbrK05LJqaz/oaGY5uJ6YyJ7sspD8gMh5d4nj33WDzH0fP000/XmpT9UOiMIiACIiACIiACItASAQnoLeHSm0VABERABESgPAQQJb/2ta+5iy66yAvpT3nKU3znkhbQEeof9ahHOcSev/7rv3Z/9md/dgxENlhpAyLXHXfc4euTykQAAp///Od92nbmDikuEc/jLAVsSjKHiDY2MZ33h0bUm4npioCr7txiXpiYXi/tcFjDN+/1kZnzZPagDjyGE1TomFTdUVbPRcD52sJbt271KLiuEc/LVroh7+PMvRnHNxPTKbMRGpkATExnjDQ+2YxoKJ7DnGsD/rL0CHAtbN++3b8wiefpsdaRRUAEREAEREAERCBpAhLQkyaq44mACIiACIhAQQgQ9X3eeee5Zz3rWb4+6MUXX+xbnrSA/o1vfMO97GUv88f+zne+46PQY3vDG97gvvCFL7h/+7d/c4985CMLQlDNTIsAm40f+chH3Pvf/35/inPPPddde+21bsWKFbOeks8hkoZiehwBh4OGRRwrAi6tEcz/cS3tMHMFMb1eOYC81vBFiEI8RwghgnP16tV+Q14mAiLgvEg1NjbmUeA4tWbNGomzOZgYOPuYmI6wHhpCrpXWICuI0uynM2ASz9PhOttRY/Gc5wpKEGmOZz8WOqMIiIAIiIAIiIAItENAAno71PQZERABERABESgBATbSLMqSGrppCehEDb/vfe/zkcPf/OY365K77rrr3N/93d+5N7/5ze55z3teCeiqC+0SQAB/5zvf6T7zmc/4Q5CdgNT+iN2tGJuWiKJs2JPKF6F0tnSypHxXBFwrhMvzXuZKWMM3zmDA3Ahr+Haz54hQiOfMZcRzogcRnGQiUHUCcYpkrlnEc6UJz9/M4PnTxHRSvofGeIXZQIpWWiN/tGstknie/ciwJo2Pj0879JRJPGc+8d2NMlw8k5AB54lPfKJ7xSte4Z+XkjSO/0//9E/uu9/9rl83cEB40YteNJ05Lclz6VgiIAIiIAIiIAIiEBOQgK45IQIiIAIiIAIi4NIU0P/+7//e/fM//7NP4/6v//qvdWl/8YtfdK973evcC17wAvfGN75RI1JhAkSaW+T5M57xDPf2t7+949rUJqYTaUyad2qns/kXGjbidUQAACAASURBVM4kRMDxYvOeTXwJL9WbiI1q+JLBgGhvNoizLgdArXNqniPwE72GeN6qY0n1RlQ9rgIBrttt27b51O0Y1yiZGbSG53/0Wc9MTMeRibEMjTUur9lA8k+31kKJ59mPVCye82yJ8FsGhxCy4LzkJS9xt912mwfLOmvX7TnnnOM+/elP+2fpJOyXv/yld6zm+T0+18tf/nL3V3/1V0mcRscQAREQAREQAREQgRkJSEDX5BABERABERCBAhP48Ic/7FNbN2tsKP/nf/7ncW9PU0B/17ve5T7xiU+4Jz3pST6SuJ7deuutfjPmD//wD90//uM/Ntsdva+EBD7+8Y87nC6YD2yMpSGAsGHPRj1iOpHpVkfacLLBaelkJaaXcJI12aWwhi/zpJ7TBcIOYh1R6mnMVWsq8xXxnDYxP08++WQv4MtEoOoEuCa2bNni13OMSMihoaFUr8eqM0+r/1ZawwT1uLQGa16YDSTNNTetPmZ9XInnWRN3/j6NMw9OPViZxHP685a3vMXdcMMN3pHvqquucs9+9rMdZRn4vvelL33JXXbZZf49nRrPXXx3pCQHDoNkKjv//PPdz3/+c/fKV77SjY6Ouuuvv9497GEP6/RU+rwIiIAIiIAIiIAIzEhAAromhwiIgAiIgAgUmAAp7T74wQ823QPSmd5yyy3HvT9NAf0973mPr23+tKc9zb373e+u21bS8pGOjzTyOAXIqk2A6BYifbMw27C3yPRYTGeDMBTTSfOuTfssRiZf52BDPKzhW8/pwoQdc7pIqgdsIm/evNlvypMpAfE8q+sjqT7oOCKQBgGuCUQUsopgfX19btWqVVqj04Cd8TEbZQNhLbQ1lyh13ZePHyCJ5xlPWne8eE6JFSLPrWRW9i1K9ox33323T53Os/NrXvMad/nll0+fgPn25Cc/2a1bt859+ctfdmeeeWZHJ6f8F2XAuL5vuukmL6Kb/eAHP/BCPeL55z73uY7Oow+LgAiIgAiIgAiIwGwEJKBrfoiACIiACIiACKSawv0DH/iAF8X/5E/+xF199dV1aX/ve9/z6dsf+chHerFdJgLdIMCGIPVYTUwnoiY0xHMT09kUlZjejVHKxzlDMT2eJ+Z0QWQ686QTYYe5iECIEX2JeF6GFLD5GEW1osgEEFhxLMHBBOvv73eDg4MdXW9F5lHmtofZQIhOx8kuNNbc0IGJe3PVTeJ59jOAebpz5063detWf/Kyief06Z3vfKf75Cc/6QYGBhzZw2LHANK3v+Md73BXXnmlu+KKK9oehKNHj7rf+73f885RM5X3euYzn+n+93//17djeHi47XPpgyIgAiIgAiIgAiIwGwEJ6JofIiACIiACIiACqQropOQm8vwxj3mM++hHP1qX9te+9jW/2cJmyXXXXacREYGuE2AjlJrTJqYjrIfGBj2bo2za85MN/E6E0q53WA1omwBCBaIOQh5zJp4n5nTBz1aEnfHx8ekUsERgEX3FPJOJQNUJ4Ow0MjIyXRcXMYeX1uBqzIzZHJhiR7cqrpkSz7O/DmLxnEw0RGCXJfLciF566aXuzjvvdM961rO8UB6bZTQ777zzfHr1du1Xv/qVe+pTn+o/Trr4c88997hDWRY2nLNx0paJgAiIgAiIgAiIQBoEJKCnQVXHFAEREAEREIGCEUgzhft//dd/uZe//OV+I+krX/lKXTKf+tSnfHT6JZdc4j70oQ8VjJ6aW3YCFv1mYjr1qENDtDExHZFUYnrZZ8TM/SNqyur31psnoZg+k7DDfKPmJwI6xtxau3ZtS+J7dUdAPS87AcRznlns+lq5cqWPPpdVk4A5MLHuxo5u3JsRMi06vQrZO2LxnKwlOGDJ0iPAPZvnwy1btviTMOdI217GUisI41xnlOcygTsme8EFF/hMOXfccUfb0G+++WZf5xwHhJ/85Cd1M+/Y98sXvvCF7g1veEPb59IHRUAEREAEREAERGA2AhLQNT9EQAREQAREQARSjUAnxepFF13kN0Fuv/12nwI7tquuusrXt3ve857n3vzmN2tERCC3BKwWNpulvOqJpLZhz1xnE1FRkbkdzlQbNjExcYyYztwxM2GHNO82T/g33sMmPHML499Xr16tOZTqSOngRSHANYV4bkIp9c5XrFhRlOarnSkTmG3N5dQIySaml1HcJLX9hg0bHCI6kfgSz1OecPfds0PxnDmGw3AZ5xeZHx760Id6qDNFhf9/9u4DXKrqXv/4L/SSSAfhUKRolIgKiJjwjyWICigqeo0VDCp4Y4i5ikTRKEJEEQkajYJGsIvCtYCxEiSIIvbEFttz6eUA54hBOsn/eZdZJ/ts5nCmz56Z73oengMyu33Wmj04716/pb/r16+fG4fvvPNOzP/ni6dXHnvsMRs7dqx17NjRVKUsVvProOt4d955Zzy75TUIIIAAAggggEDCAgToCZOxAQIIIIAAAoUnkMkZ6NI64YQTbOnSpa6U++DBgysB6gtPlXcvLS2N+feFp80VFYqAwk59YR0M04Mhqa5TYboCUgWhhOmF0vOJX4fuc3rYQmXeN2/e7ILyYPMPXeg1+nu1xo0b27777kt4njg3WxSggN5DK1asqFgmQWve6j1CQyCWgCoV6H7qK4Loz8FWt27dijBdv8/3B90Iz7P/PtDnuNboXrNmjTt4IYfnuj69lw4//HB3rc8995ybZR+rnX766fbhhx+aZpF36tQpqY554IEHbMKECXbooYfaE088EXMfn3/+uZ100kmuvLsCfRoCCCCAAAIIIJAJAQL0TKiyTwQQQAABBPJMINMB+tSpU23KlCmuDPGcOXNcSWLfZs+ebddcc40re71gwQJTOVYaAvkmoC9SNetLX6YqUNcXjeGQVF+u+jBdFRny/Qv7fOujqJxvdcGOzlOBusJzBTs0BIpdQEsjKDzftm2bo1BVhkaNGhU7C9cfp4A+i1W1wIfpGk/Bps9jPzNdn9P59tlMeB7nQEjjyzSm9ECcqmyp1atXz/bff/+CnHnu2fRASo8ePdwf58+fbyUlJTFFzzrrLHvvvffs6aeftoMOOigpdb+01xFHHGEPPfRQzH3owWw9oH3ggQfaM888k9Rx2AgBBBBAAAEEEKhOgAC9OiH+HgEEEEAAgSIQiDdAf+utt2zYsGFOZPr06darV6+4dMrKytyXHPqySTMFbrjhBldaUmuia+1zlQUcNGiQTZo0Ka798SIEoiygL1b1BX0wTA/PftOXrcFSsvn2hX2U/fPp3DRWFOqobLtm2IabAnRVL9BYIUzPp57lXNMloHupygHr3wlqCm30nqAhkIyA7rl6EMOH6Qqfg00Pc/rPZj3IpFLoUW6E59nvnVjhucq2F/pntP6N0rVrVwe+cOFC0xIasdo555zjyrerDLsP3BPtJf9wdZ8+fdz/b8Zq+lxQ+fb99tvPXnzxxUQPwesRQAABBBBAAIG4BAjQ42LiRQgggAACCBS2QLwB+pIlS2zIkCEO48EHH7TevXvHDaPZ5SNHjnQlr8NNX4jPmjXLmjVrFvf+eCEC+SDgw3Q9POJnpoeD0kIrJZsP/RKVcwyHg1rP2Yfq4VmSWlNVwY7Cw0IoORyVPuA8oiugqh4KSfTvBj1kpCo2quJBQyBdAnoww4fpvsKB37fCc4033XdVOUnhepQa4Xn2e8N/Pq9atcodXJ/Fmnle6OG5l1YJd71fFHB369YtZgeorLrKqz/66KPWs2fPpDpp3rx5dumllzrbZ599NuY+PvjgAzvjjDOsQ4cO9tJLLyV1HDZCAAEEEEAAAQSqEyBAr06Iv0cAAQQQQACBtAl89NFHduONN7qZCWr6Qvy4446z6667jtLtaVNmR1EV0BevCs99mK6fscJ0/4W9ZqkzMz2qvZn6eQXDD/WzylL7mbXBWZIaJwoSgy3fSw6nrsceCl0g/P5o165dpeVfCv36ub7sC+g+68N0lXwPNt2jFaL7z+datWpl/wQDR9T7Y9myZa7ajYJ+VXVS+Xla5gR8eK6y7fp9sYXnkj355JPts88+s9tuu8369+8fE/vII4+08vJyV1Zd5dWTafr/xcGDB7vlbFQOPlZ75ZVX7JJLLnFl4lUunoYAAggggAACCGRCgAA9E6rsEwEEEEAAAQT2KqAvn9atW+e+8GPWOYOlGAX05avKugfD9FgzjoNhetRLyRZjPyZ7zZr1qJm1PvzQzFqFM7GaxkpwlqQvZe1fqyAnWHKYhy6S7RW2i4pA+P2h8FxBCg2BbAno4TYfpmvtZ92Hg01hdXAZlmydl45DeJ5N7W+Ppf7fvHmzaea5D89Vtl0POhZTu/rqq+3JJ5+0iy++2EaNGrXHpeuhjuOPP979d1Uta9y4cVI8GuNaJkxVIZ577jnr3LnzHvuZMmWKTZ061Y499lj3k4YAAggggAACCGRCgAA9E6rsEwEEEEAAAQQQQACBOAV8mK4vZzVrp6oZxz5M1xf3CkkJSuMEjtjLtm7daitWrHDVB1QSWOFgIjMH91ZyWPvTONFM9nxYvzdiXcPpREBAgYkeLtH7g5m1EegQTsE97KYQ3Qfq+nOwZXMZFsLz7A/IcHiu5VQUnifyuZ39s87MEVVO/YorrnDLabz88svuHh1sWvd87NixpqW55s+fn9JJXHjhhbZo0SL7xS9+4ZYAC7czzzzT/vrXv1b59ykdnI0RQAABBBBAAIF/CxCgMxQQQAABBBBAAAEEEIiQgIIjfVnvw3R9YR5smnHsw3SFpITpEeq8ak5F/arwXF/Iqx9VhSOVtVN9yWE9dKFgPtiC6/dqvFDBIH/GSbGeaTA818Mgen8U2wzPYu37fLlu3btV3t2H6eHKMZlcXoPwPPujhPC8srn+nXHUUUe5Bz21/Na5555b8QI93Ddo0CBbunSpnXPOOXb99den1GFz5851s9z1QKCC+1atWlXsb/HixXbBBRe4P8+aNcsOOeSQlI7FxggggAACCCCAQFUCBOiMDQQQQAABBBBAAAEEIirgZ7599dVXtmnTJlfKO9j8jGOVklUJcML0iHakmfvC2a+dqpClQ4cOpp/pagpygiWHg/vVuPAPXeinxg0NgSgJKJjRzHPd89LxcEmUro1zKUwBhat66MPfd8MPu+k+G1xeI5WHmAjPsz+G1L966G3lypXuobdinnke1Pel03WfHj16tJ1++ulWWlpq48aNMwXb+u9z5sypKLuusvcnnnii28X48ePt1FNPjaszNeYHDBjgHjrs2LGj3XjjjXbwwQfbwoUL7dprrzX9u7hnz5726KOPxrU/XoQAAggggAACCCQjQICejBrbIIAAAggggAACCCCQZQEFS5r55sN0fXEfbPqyXiG6D9P1ZT1l3rPcSVUcTn22Zs0a97eaca6ZtfqSOVOtuvV7NU40q0theibPI1PXx34LS8CHVD4818MlCqtoCOSTwN6W1whWBNH9N5GHmAjPsz8KwuG5Hnbbf//9i7Jse1hf43H48OEuLI/VNGtca6T7pgcQ+vbt6/5400032eDBg+Pu0A8++MCGDRvmHkAMt0aNGtnMmTOtU6dOce+PFyKAAAIIIIAAAokKEKAnKsbrEUAAAQQQQAABBBDIsYC+3NWMTZV518z0WOW7g2G6vqwnTM9Np23cuNHNzlLTmqla8zyR8CTVs/ZLAugL6M2bN7uZdMGmZQD8LMl0zohP9bzZvjgENCb9DM9MVGYoDkWuMmoCfnkNzU7Xg2/Bps9ifT77qiB7e4iJ8Dz7PevL9Ou+pId6dF/Smuf6rKR9K6DxPW3aNLv//vtdBQa1Fi1auPXRTzvttLQyqTLJhAkTbMGCBRX/fundu7dba53wPK3U7AwBBBBAAAEEYggQoDMsEEAAAQQQQAABBBDIYwFfRtaH6VV9Wa+Q1JfvJkzPfIerX9avX28K0NUUmLRt2zana5H7JQF8yWH9OdgU8PswnRnAmR8jxX4EjUOV9/XlkVWZgYc4in1UFN71V1cRpKr7LuF59scC4Xli5qq68Nlnn7n79gEHHJDRf9/o31N6qKF169a27777JnaivBoBBBBAAAEEEEhSgAA9STg2QwABBBBAAAEEEEAgagL68ldfaPowXaWRg03BuZ9x7Mt3E6anvxfVD2vXrnXl9tVULr1NmzaRqgLgS9T6MF0hT7Cp1LzOW4G6fk9DIJ0Cqoig8FwtG8sapPPc2RcCyQpU9xCT3gu659arV88t+6H7ssq/6+ESBe20zAqEl5NQ2XZmnmfWnL0jgAACCCCAAAJRFiBAj3LvcG4IIIAAAggggAACCCQpoIBUM9iCYfreynerjCxhepLYgc1krGDQlzVt0qSJtWrVKtK2fkkAhZo67127dlWC0Gx0PzNdwQ7jJPVxUsx70IMlCgfVNJ4UDmZzWYNitufaoyPgZzv7h5jC911/pppt27hxY+67Ge46Ve9ZsWKFK9uufw917tzZVe2hIYAAAggggAACCBSvAAF68fY9V44AAggggAACCCBQJAL6ol5rViq40prp+sI+HKYHy8iqHCchaeKDQ1+8q8Son/nfrFkzty5oPln6JQF8qKOHMIJNY8OH6Roz+XRtifcoW6RbQA/0qDqDmsZPu3btCM/Tjcz+8k7A33f1Ge0rlwQvQg+Y+PuuZkRrVjotfQKE5+mzZE8IIIAAAggggEAhCRCgF1Jvci0IIIAAAggggAACCFQjoC/qNdMtGKaH18LWrNDgWtiEpNUPK5Xa1ey1rVu3uhdr1nnTpk2r3zDCr/BLAvgwXcsDBJtm6QVDHcZJhDszAqdWVlZm69atc2eiEFDhOUFgBDqGU4iEQHDNc91LNetcnyfbtm2rdH56z2hmtO69DRs25AGUFHtP4bkefNNnuB5U0Mxz2dIQQAABBBBAAAEEECBAZwwggAACCCCAAAIIIFCkAj5M16x0PzM9vBa2wnR9Wa/1sFXKm5B0z8Gi2f0Kz33A3Lp1axd+FFpTwKMwXaXew6GOgodgqEMwWmi9n9r1bNiwwdavX+92onFSUlJCeJ4aKVsXkEAwPNe9VMsa6LNXTZ8v/iEmhb3Bps9jhej+3quHmmjxC+gBBX12E57Hb8YrEUAAAQQQQACBYhIgQC+m3uZaEUAAAQQQQAABBBCoQkBhur5EVjiq2en6GQ7T69atW/FFPWthfwup4GP58uUu5FCYoWCwGGav7S3UCc6QVLBDmF68tx3dVxScb9y40SHovaH3CA/iFO+Y4MorC+jBK32GqDJMODwPW+kz2YfpWipkb0ux6IE3WtUCeghM7oTnjBIEEEAAAQQQQACBqgQI0BkbCCCAAAIIIIAAAgggUEnAh+n6ot6H6fpyP9j05byf9aYwvRhDUn0Br9lrstH1t23b1s0GLLam6w+GOsHr9zMkVcFA40UBEa04BHQfKS0tNZVuV9MYaNOmDeF5cXQ/VxmHQCLheXh3Wnpl8+bN7pfuv+GlWPTAm19iQ7/noZX/CIbD806dOrn7Ew0BBBBAAAEEEEAAgUrfZ/wr/MgqPggggAACCCCAAAIIIIDAvwX0vwv+i/ry8nI3M12zj4Otdu3aFWF6/fr1iyJMV+lXzV6TjUJhreesay/2Vt0MST1g4EMdyg0X7mjRfWPt2rXuARw1LWmw7777EuIVbpdzZQkKpBKehw+l95vKu/sHmcIPvOkz2t939TlVzGF68ME3fXYTnic4cHk5AggggAACCCBQRALMQC+izuZSEUAAAQQQQAABBBBIVUABqUrH+jBdJcyDTaGon5neoEED90V9oX1Zrxl/K1eudOVzdb1ar1Yz/GiVBfyDFwp1ZBaeIanx4UMdBTy0whDQ+2LNmjW2adMmd0FNmjSxVq1aFdx9oDB6i6vIhUA6w/Pw+ev9p5DYh+nhz2iFxv6+q3twMVWPCbrrun14Xmj/RsnFmOaYCCCAAAIIIIBAIQoQoBdir3JNCCCAAAIIIIAAAghkQUBhuma9+TBdX04Hm76o92G6Zh4XQpiuGfirVq1yl6ky9grPCX+rH2wKz/XghQ91wmG6ZkX6UIe1e6v3jOorFN6tXr3aVapQa9asmbVo0YLwPKodxnllXSCT4Xmsi9Hx/H1XwXqwKUQOfkYX8hIb4fC8Y8eO1qhRI+5NWX8HcEAEEEAAAQQQQCB/BAjQ86evOFMEEEAAAQQQQAABBCIroEA0GKaHv6jXF/O+fLd+6ov7fJv1pQcFVJZaTTPOFZ5ThjzxIenLDStkVbCjBzGCza/dqzVpFabn2zhJXKQwttA9QA+XqNqAWvPmzd0v+q8w+perSF0g2+F5+Iy1/IoP0/V5HWx6n+qz2QfqhfTZJvcVK1a45Wf0bw/C89THMntAAAEEEEAAAQSKQYAAvRh6mWtEAAEEEEAAAQQQQCCLAgpItUZ4WVmZm4mq3webvsAOhukK16Mesm3cuNFKS0vdZWi2tNY8L+TZetkaLn6sKNTRWAmv3asA3c9Mr1evXuTHSbbconYcheda1kBVBtRatmzpZp/TEEDgWwGFuMuWLXMPDOmzQw9g6Z6Wq6bz8GG63re6FwdboVQFUQn75cuXV4Tn++23nzVu3JjPklwNPI6LAAIIIIAAAgjkkQABeh51FqeKAAIIIIAAAggggEC+Cfj1WDV7W2sih2e9KUz3a2Fr5lvUwnSd//r1600BuprOsaSkpKjWjc3WmKtu7V6VyvdhusKdqD90kS23XB9H4blmd/r3ttY7b9q0aa5Pi+MjEBmBqIXnYRi9h1U5Qr8UqoeX2PBVQXT/1e/z5d4bDM91zpp5TngembcFJ4IAAggggAACCERegAA98l3ECSKAAAIIIIAAAgggUBgCCkgVJPgw3c9W9VenL7h9mK4v6nMdput8VbL9q6++cqeokuJt2rTJm/Agn0eN7BV++DLvGjfBprGhMaI+0ZjJl0Ann/sk1rlrFqvCc19lonXr1i6goiGAwLcCUQ/Pw/3kl9jws9PDVUHy5UEmwnPegQgggAACCCCAAAKpChCgpyrI9ggggAACCCCAAAIIIJCwgA/TFU5rZnq4hKwC0WAJWa3Hms2QVOen9ZwVIqg1adLENLM2m+eQMGoBb6AwxJd537ZtW6UrVZju1+3V0gCqakDLvICCNYXnvj/0cEmjRo0yf2COgECeCORbeB5mra4qiH+QSQ8z6UGmqNx79Xmhe5N+6jNbZdv1Gc7nd568cThNBBBAAAEEEEAgIgIE6BHpCE4DAQQQQAABBBBAAIFiFdCX9Dt37qyYma4ysuH1WPXlvA9JNQMuk1+Eh9dzbt68uelXJo9ZrH2fzHVrrPjZkbGWBPDjRD+jEugkc51R3kbhudYV9pUBtKyBqgHQEEDgW4F8D89j9aOuyd97ww8y6V4bfJBJ4XouGuF5LtQ5JgIIIIAAAgggUJgCBOiF2a9cFQIIIIAAAggggAACeSmg4FzhnGam65fC9PB6rJqZri/qFdilO0wPl6RmPedoDyONFR/oxFoSQDPSNU40XnIV6ERbMPGz0wMMCs/97M62bds6XxoCCHwrUIjhebhv9/Ygkx42073XB+qqIJONFr43tW/f3po1a8bDb9nA5xgIIIAAAggggEABChCgF2CnckkIIIAAAggggAACCBSCgA/TVeJdYbqC0nCYXq9evYowvU6dOil9Ua4v31X21c+qZT3n/BpFevhBD1xo3fTwkgC6EgU6KjWsX9kKdPJLsPqzDa8r3K5dO+dKQwCBbwWKITwP97XuvcEHmcIVZILLsehzOhMt+PmtAJ/wPBPK7BMBBBBAAAEEECguAQL04upvrhYBBBBAAAEEEEAAgbwU0Bfy+pI+GKbrz8FWt27dijBdv0+k5LpC108++cTNVtZ2KkmtoJWWnwJ60EJhukKdWFUMtCSAD9NVxYBWvYCCQc0816x/lWtWeC5HGgIIfCtQjOF5uO/9vdfff8MPvemz2d97E/2crmqchR9+U3jOsiu8KxFAAAEEEEAAAQRSFSBAT1WQ7RFAAAEEEEAAAQQQQCCrAj5MV+itQF0/FeoFm2a5+fKxmv22tzD9o48+spEjR7p93XbbbdajRw9m1Wa1RzN7MAU4mpHuw/TwgxeqYqAHJxTqZGp2ZGavMPN713rHCs9lp/BcAZXeVzQEEPhWIByed+jQwRQQF3PTZ/WWLVsqZqeHP6f18JIP06v7nK7KkfC8mEcY144AAggggAACCGRWgAA9s77sHQEEEEAAAQQQQAABBDIooC/oFZAqHC0vL3c/9YV6sOlL+mCYrgDQtzfffNMuv/xyF7Dqi/zZs2dby5YtM3jG7DqXAj7Q0UMXGiuxqhike3ZkLq83HccOhudaR17huR46oCGAwLcChOfVjwTde3Uv8aXetRxEsOne4u+9qmwR/Jyuau8K5LXsivarpqoYLVq0SKj6TPVnzisQQAABBBBAAAEEilWAAL1Ye57rRgABBBBAAAEE0iCgEp1Tp061F154wdauXeuCx9NOO81GjBiR9pmcEydOtOnTp+/1rP/yl7/Yvvvum4YrYxf5KuDXwdaa6QpJw1/Sa+1rfUmvQF3h+ZgxY1z40axZM7vzzjvtwAMPzNdL57wTFFCgs3Xr1opAJ/zghWaj+0BHgXEiSwIkeCqRfbl8NPNcD6novaPwvNhn1Ua2szixnAgQnifHLjcfpvsA3O9J4bnut/p35WGHHeYqhIQb4Xly7myFAAIIIIAAAgggEL8AAXr8VrwSAQQQQAABBBBAICCgcPK8886zTz/91P1XfdmpQEqtT58+du+995pmFKWrnX322fbuu++aZhNXFWTNmzfPWrVqla5Dsp88F1CYrvKxmpmu8aov7H1bsmSJC8z1mtatW9tdd91lKrlbjCFpnndzWk6/utmRCo99mfdkSw2n5USzuBO9dzS704fnen9Q4j6LHcChIi8Qrs5A2fbkukwPL/kwXfcdNX0mL1q0yP2b79BDD7VjVl8CgAAAIABJREFUjjnG+vXr5x7UDIfnbdu2df+dz+/k/NkKAQQQQAABBBBAILYAATojAwEEEEAAAQQQQCApgYsuusheffVVtw7u9ddfbwMHDrSysjI3o/e1116z0aNH24UXXpjUvsMb6cvSnj17ujKdixcvtqZNm6Zlv+ykeAQUAvow/X//939d5QSFpgo8rrrqKjemNCtdvxo2bOjKx/JlfPGMj+CValyocoEv8x588EKvC5Ya1lgpxHGi6iIrV6507xEFWHqf6CcNAQS+FSA8z8xI0ENtCtOfeuop9zkdrCKje+1BBx1kvXr1sh49erhy7YTnmekH9ooAAggggAACCCBgRoDOKEAAAQQQQAABBBBIWECl0ocPH+62mzJlig0YMKBiH/ris2/fvm62r17XuHHjhPcf3uCjjz6ywYMHW5s2beyVV15JeX/soHgFVBnh1ltvdQDdunVzD3qEKyUoPFcwqvLd+qm/L8SQtHhHQWJXrgDHz45USfNg01jxZd79gxeJ7T16r9a1rlq1yoXnmnGusu2E59HrJ84odwKE59mx/+abb9y/+fRLVWP052Dr0qWL9e/f34477jj7/ve/z+d0drqFoyCAAAIIIIAAAkUjQIBeNF3NhSKAAAIIIIAAAukTuOSSS9wXml27dnWzhMLtlltusfvuu8/085RTTkn5wI888oiNGzfOBfUK7GkIJCqgMHDy5MluaQE1lYO9/fbb3XrOCkW1ZvqmTZvcLPVgU0DaoEEDV76bMD1R9cJ7faxSw/4qNVZUwUCBun7qz/nWNOte4bma3hsKz1W+noYAAt8KEJ7nZiTI/YUXXrDXX3/d3nnnHVfxKNh0r1KQrl/du3fPy/tvbmQ5KgIIIIAAAggggEBVAgTojA0EEEAAAQQQQACBhAVUPlNByxVXXFExEz24E80UGjJkiJ100kkutEy1jRo1yubOnWtjx441rYVOQyARAZWE1dh54okn3GYalzfffPMes2r9Otg+TA/PdtMsdD8zXQEpM9MT6YXCe62WlvAz0/c2VhSoh6scRFFDD5CsXr3anVq9evVceJ4P5x1FS86pMAUIz3PTr/oM15IS/gG3Vq1a2caNG23evHn28ssv2//93/9VOrHmzZu7Skj69cMf/tBV0qAhgAACCCCAAAIIIJCoAAF6omK8HgEEEEAAAQQQKHIBzfrRF5JqDz/8sFuLMtwUQPbu3dt+8IMf2JNPPpmymGYUrVixwpVx//zzz23ZsmWm8EprX+oL0qFDh1qTJk1SPg47KDwBld++8sor3cw1tfPOO8+uueaaamenKUzXMgTBMF3/zTeF6ZqZ7mcba5YuZd4Lb/zEe0UKeLRuuAJ1/QyOFe3DP3ih8RLFGd3l5eW2du1ad7n169e3du3aEZ7H2/m8rigECM9z083//Oc/XXjuH1Jq3bq16Vfw8/bLL7+sCNM/+OCDSieqh92OPvpoO+uss+yII47IzUVwVAQQQAABBBBAAIG8FCBAz8tu46QRQAABBBBAAIHcCeiLSr/m+aJFi6xFixYxT+bggw92AeObb76Z0slu2LDB+vTpU7EPHa9Tp05uJtLf//53U0llrbP+4IMPujUwaQh4AY2RkSNHmsap2qWXXur+nGjQrTBUQbwP02MFpBrrvny31otO9Bj0WuEIKPAJhun6c7D5By8UpkdhbXE9FLVu3Tp3ijo3hef5WH6+cEYQVxI1AcLz3PRIODzfd999rU2bNnv9fNWDQJqZrl/696ceblLTPU1/1n2XhgACCCCAAAIIIIBAPAIE6PEo8RoEEEAAAQQQQKBABe666y67++674746fXGpdaP9uuZ/+9vf3Dq5sdqRRx5pmtWo2UCplM988cUX7Ze//KUrKTx+/HgbOHBgxcxIrdX7q1/9ynQeXbp0cWXeCX7i7s6Cf6ECc32JrjZmzBhXqSDVpjBdD22o3LUCdQWl4YBUM3gVpmvddML0VMXze3uNDc2c9DPTfZjjr0r3NQU6Giup3CeTVdIDSuvXr3eba5a8qnpwD01Wk+0KUYDwPDe9Gg7PVba9pKQkoYfT9Bm9YMECmz9/vnvYU/8OYFmK3PQnR0UAAQQQQAABBPJRgAA9H3uNc0YAAQQQQAABBNIkcMcdd9idd94Z99705eXUqVPt5JNPdl9iagZ4Ve3HP/6xlZaW2ltvveXCoWSbwp2PP/7YffnZtWvXPXaj2ZMq8a6QSud27LHHJnsotiswgTPOOMM++eQTu/HGG+3UU09N+9UpTNdSAj5MV0gaDtMVkPowXQEpM9PT3g15s0ONF1VF8Ouma+wEmx5GUpiuX/p9JseKzkX3Vq0jrKZjJhpO5Q08J4pAkgLB8FxLL7Rv377KhwaTPASbxRDQ56gekNQDamrJhOfAIoAAAggggAACCCCQqgABeqqCbI8AAggggAACCBSZgNYiV2CtmbUffvhhlVevNSdVSnPhwoXuy89MNq1xPWfOHDv33HPtuuuuy+Sh2HceCSj80IMVzZo1y/hZ+zD966+/djPTFZKGZxsrFPVheqYD0oxfMAdISUDjZevWrRVhuqoaBJsetvBhuh7CSGeYrmPr4SY9fKSmB5yqK4uc0sWyMQJ5KEB4nptOC4fnLVu2dJUx0nkPzM2VcVQEEEAAAQQQQACBfBMgQM+3HuN8EUAAAQQQQACBHAsoIOzVq5c7i8WLF1vTpk1jnlH37t3dbMtsBOiTJ0+2e+65x4466ii79957cyzE4YtdQAGlwvNgmB6ebewDUgXqKvlOOFC8o0bjZfv27W686MGLHTt2VMLQzFdf5j3VsaJj6cEmPeSh1qhRI2vdujXjr3iHH1ceQ4DwPDfDQuH56tWr3X1QjfA8N/3AURFAAAEEEEAAAQS+FSBAZyQggAACCCCAAAIIJCzQo0cPN7N31qxZdsghh+yxvf5Or1F7++23XfiTSlO4VNVa69rvNddcY7NnzzbNeleQTkMgKgIKLBUKKBAoLy93IWk4TFc1h+BsY9agjkrv5eY8dL/TeNFY0e+DTev3+rGiNcsTefBCY3HNmjVuyQG1Jk2auOogiewjNyIcFYHsCRCeZ886eCR9Tur+pPuempbtadeuHfen3HQHR0UAAQQQQAABBBAgQGcMIIAAAggggAACCCQjMGTIEFuyZImNHTvWzj777D128frrr9vPfvYzU8Dz7rvvJnMIt40CnwsvvNCtoz5v3rwqS8H369fPli9fbkOHDrUxY8YkfTw2RCCTAj5M17qumgGsIDNculthumal61eDBg1ceEDAmcleifa+NRvdr5muku/BpgctgmH63h680NjTzE4fTmlZAwVUjK1o9z9nl10BwvPsevujhcPz5s2bu/Cch8ly0x8cFQEEEEAAAQQQQOBbAWagMxIQQAABBBBAAAEEEhaYNm2a/e53v7MjjjjCHnrooT22v/XWW10p9ar+PpEDKkBftGiRjRgxwi6//PI9Nn3mmWds9OjR7r8/8sgjdvjhhyeye16LQM4EVOZd1RoUpseabazS3QrSFZISpuesmyJzYD1s4cN0LY8RbArCNVa0nrkeXNJMdd/CawornNIvwvPIdC0nEgGBcHjeoUMH01IbtMwK6P6kZSV8ZQzC88x6s3cEEEAAAQQQQACB+AUI0OO34pUIIIAAAggggAAC/xZQmc2+ffu6dZ4Vph9zzDEVNmVlZda/f38XCl555ZV20UUXpeTmZ7NrZu4tt9xiAwYMqNifwvPf/OY3rszxsccea1OnTk3pWGyMQK4EFCL4MF1BQqzS3cEwXTPzCEBz1Vu5P66WAVAlAz14oXETbBoXCtH14IV+6n7tX6M1hTX7nIYAAv8RUHi+bNkyt9yGHlwiPM/O6AiH57o3tW/fnpnn2eHnKAgggAACCCCAAALVCBCgM0QQQAABBBBAAAEEkhIYNWqUzZ07182MveGGG+z444+3L774wgXaH3/8sQtvXn75ZbfOrm8qxT5s2DD3x+nTp1uvXr3iOvaUKVMqwnGt2atfmrFUWlrqtv/JT35ikyZNcjMwaQjku4BCBc0w9mXeFe4Em8JzH6YrICVMz/ceT+389SCTwnTNTtdPlWuP1TSzU2XbaQgg8B8BwvPcjAZ9zq1bt859zqk1bdrUPbhA2fbc9AdHRQABBBBAAAEEENhTgACdUYEAAggggAACCCCQlIBmyZ5//vn26aef7rG9ZkBOnjzZBg4cWOnvtG661k9Xe/DBB613795xH3vhwoU2Y8YMe//9993Md4VB3bt3t8GDB1ufPn3i3g8vRCCfBBSGau1rhQz6FWsd7OBsY5XuZmZ6PvVwes9VoZSfma5APdzq16/vyrzrASdV9aAhUMwCup8uX76cmedZHgTh8FwPWio8Dy49keVT4nAIIIAAAggggAACCOz53ea/qno8HSwEEEAAAQQQQAABBKoRUFlgzQ6fNWuW+Vmy++23n40ZM8aOPvpo/BBAII0C+l83vc/8zPS9le7WDHXC9DTi59GuVN59xYoVFfdkVQnRkgB68CjY6tWr54J0/apbt24eXSGnikDqAoTnqRsmsweF56oeVF5e7jYnPE9GkW0QQAABBBBAAAEEsiHADPRsKHMMBBBAAAEEEECgwAU04/HLL790ZaU7d+5c4FfL5SGQewGF6QpF/cz0WGG6glOFo3pfal1fZqbnvt8yfQYKzzWjVmNDraSkxM0413jRsgCala5fel2wKUAPhumMlUz3FPvPpQDheW70w+F548aNTQ9dMvM8N/3BURFAAAEEEEAAAQT2LkCAzghBAAEEEEAAAQQQQAABBPJYQOHojh07KoXp4UJjCtMVpCtMJUzP487ey6nv3LnThecaCwrAFZ4rFA83vyyAD9O1XbCptLsv865Z6oTphTleivWqCM9z0/O672jmeVlZmTsBwvPc9ANHRQABBBBAAAEEEIhfgAA9fiteiQACCCCAAAIIIIAAAghEWkAhhQLRTZs2uUBdIWk4TNc62D5MV1hKQBrpLo3r5BSaKzxX36s/27VrZw0bNqx2W1/J4Ouvv3ZjRfsJNj1s4Wem6yEMxkq1pLwgwgKE57npnHB43qhRI+vYsSMzz3PTHRwVAQQQQAABBBBAIE4BAvQ4oXgZAggggAACCCCAAAIIIJBPAgotVKo7GKarhG6wBdfBrlOnDgFpPnXwv89VofeyZctcX9eoUcOF5wq7k2kq/e5npm/btq3SLlRm2YfpCucJ05MRZptcCRCe50Zen0Pr16+3jRs3uhMgPM9NP3BUBBBAAAEEEEAAgcQFCNATN2MLBBBAAAEEEEAAAQQQQCCvBHyYrpnGfmb67t27K10D62DnVZe6k1XgrfBcfanwvH379qYKA+loCuZ9mK7wMdh0rGCYrj/TEIiqAOF5bnpGnzsbNmxwv9S0NESnTp2YeZ6b7uCoCCCAAAIIIIAAAgkKEKAnCMbLEUAAAQQQQAABBBBAAIF8FlCoocDVh+n6GStMV5l3haSsgx3N3tYMcZVtV99pdrjCc/VVJppKw/swfcuWLZUOoZnofqzop86FhkBUBAjPc9MThOe5ceeoCCCAAAIIIIAAAukTIEBPnyV7QgABBBBAAAEEEEAAAQTySkAhh8q6B8N0lQIPNpV2D4bpzDbOfReHQ0GF56ogkI2m8bF582YXqH/zzTemMeSbwnSVd/ez0wnTs9EjHKMqAcLz3IwN3RNUsl2l29V0P+jcuTMP1+SmOzgqAggggAACCCCAQJICBOhJwrEZAggggAACCCCAAAIIIFBIAj5MVzhaXl7uQnXNPA622rVrV4TpKhWuwJS1sLM7CjQDfMWKFe7Bh1q1almHDh1MDznkomn2uw/T9TMYput8gmG6zpWGQLYECM+zJV35OLHCc5Vt5/2fm/7gqAgggAACCCCAAALJCxCgJ2/HlggggAACCCCAAAIIIIBAwQooHNUMYx+ma03sYFMg4memN2jQgDA9CyNBIfXKlStdUK2HGRSe62cUmgL9YJiuPwebHrjQTFStgxyVc46CG+eQfoFgeK6xpgoNuXrIJP1XF9096r5UVlZmpaWl7iT1+aCZ54Tn0e0zzgwBBBBAAAEEEECgagECdEYHAggggAACCCCAAAIIIIDAXgUUhipM/+qrr2zTpk22ffv2Sq9XqW4fpmvWMTPT0z+gVDJ91apVLjxXGKhQMKpBtM5R48Wvm66HMYJNa7X7Mu/ZKj2f/h5hj1EUIDzPTa8QnufGnaMigAACCCCAAAIIZE6AAD1ztuwZAQQQQAABBBBAAAEEECg4AYXpKiPuw/Rt27ZVukaF6b50t35qzXTKvKc2DFROX+G5mgJnhef5MqtTwZrGiw/TtYZ6sOl6gmE6YyW1sVLMWxOe56b39R5XpZJ169a5E9B9XzPPo/qAT26UOCoCCCCAAAIIIIBAvgkQoOdbj3G+CCCAAAIIIIAAAggggEBEBBScKLRSeKKZ6fp9sCk8D4bpCtcJSBPrPLmuXr3abaSZ2wrP5ZiPTeNFD1zogQAF6jt37qx0GQrcfJl3XStjJR97OTfnTHieG/dweK7lPLp06UJ4npvu4KgIIIAAAggggAACaRQgQE8jJrtCAAEEEEAAAQQQQAABBIpVwIejPkzXrONgUxjqw3SVeydMr36kyHLt2rXuhVpDvF27dnkbnoevVuNFSwH4menhZQE0w97PTFcoR5he/Xgp1lcQnuem5wnPc+POURFAAAEEEEAAAQSyI0CAnh1njoIAAggggAACCCCAAAIIFI2AD0d9mK71sINNYahCUQWkCtMVlhKQVh4eZWVlFSWRZaXwXDP6C7UFw/RYywL4MF0PYTBWCnUUJH5d4fC8Q4cOzH5OnDHhLXSP1zIewQd8NPO8Tp06Ce+LDRBAAAEEEEAAAQQQiKIAAXoUe4VzQgABBBBAAAEEEEAAAQQKREBBy44dOyrKvCtM138LNh+mKyQlTDfbsGGDrV+/3hEpMG7btm1Bh+fhoa7x4memx1oWQA9d7LPPPs6mkB8qKJBbQMYug/A8Y7R73THheW7cOSoCCCCAAAIIIIBAdgUI0LPrzdEQQAABBBBAAAEEEEAAgaIVUPCida81c1FreyskDYfpKlXuZxtrTeximm0sC4Xn+qUmh5KSkqIyCL85NF42b97sxkqsSgYK030lg3xdG75obwgpXLiWiFixYoX985//dDPOmXmeAmYCm+oepXv3mjVr3Fa6XzPzPAFAXooAAggggAACCCCQNwIE6HnTVZwoAggggAACCCCAAAIIIFA4Agpidu3aVSlMVxgWbPXq1asI01UauJDDdHmUlpaaSreraYZ1mzZtCvqaEx3NGi/BMD348IXGhmakB5cFSHT/vD4/BAjPc9NPer99/fXXtnr1ancCuj/vv//+lG3PTXdwVAQQQAABBBBAAIEMCxCgZxiY3SOAAAIIIIAAAggggAACCOxdwIfpmtnoZ6bv3r270kYKa3zp7kIL03X969atc2Xu1Ro1amStW7cmPN/LsNH48GG6fu5tWQDNUKYVhgDheW76MVZ4rpnndevWzc0JcVQEEEAAAQQQQAABBDIsQICeYWB2jwACCCCAAAIIIIAAAgggEL+AghqFo5rpqFLv+hkO0xXa+NLdCtbzeWa6rlflkPXggFqTJk2sVatWeX1N8fd2el6pygUq7+7XTQ9XMgguC6CHL2j5KUB4npt+0z1K7y3NPNfvdf/VzHPC89z0B0dFAAEEEEAAAQQQyI4AAXp2nDkKAggggAACCCCAAAIIIIBAggI+TFd448N0lfEONgWiwTC9Ro0aCR4ldy/X9SmU0kMCak2bNrWWLVsSnqfQJTINhumxKhmozLt+EQCmAJ3lTQnPswz+78MRnufGnaMigAACCCCAAAII5F6AAD33fcAZIIAAAggggAACCCCAAAIIVCOgIEczi1WuW6XOFTrv3Lmz0lYq1e3DdM06jnKYrmtZtWqVux615s2bu1/5PJs+aoNYY0bBq5+ZHuvhCwXpWm9eYTr2UevBb8+H8Dw3/aL3j+5Puk/5mecq266qHzQEEEAAAQQQQAABBApdgAC90HuY60MAAQQQQAABBBBAAAEEClBAM4s109iH6Tt27Kh0lbVq1aoI0xs0aODC0agEpArPV65c6c5fTbPOmzVrVoC9FJ1LUgC4bds2F6ZX9fCFD9PzfVmA6KinfiaE56kbJrOHcHiuSh8Kz/VgEg0BBBBAAAEEEEAAgWIQIEAvhl7mGhFAAAEEEEAAAQQQQACBAhZQmK6gzYfp27dvr3S1NWvWrAjTGzZsmNMwXeH5ihUr3Pmqab1zlW6nZU9A4aDGiJ+ZHh4vevjCl3n3D19k7+w4khfQe2T58uVu9rOqS3To0MH9pGVWwC+DoId89HvC88x6s3cEEEAAAQQQQACBaAoQoEezXzgrBBBAAAEEEEAAAQQQQACBJAQUUAfDdM06DjaF6QrRFZDqp8q8Z2tmuoJ+hedbt251p7TvvvtakyZNkrhKNkmnQDBMjzVetCyAyrz7hy/SeWz2FVuA8Dw3I4PwPDfuHBUBBBBAAAEEEEAgegIE6NHrE84IAQQQQAABBBBAAAEEEEAgDQIKgxRWl5WVubLdPrj2u1Z4HgzTFa5nKkzX+tsKz31A26ZNG2vUqFEarpJdpFNg586dFWXeY40Xhel6+EI/NX5o6RcgPE+/aTx7DIfnmu2///77F3zZdq3zPnXqVHvhhRds7dq1bkmN0047zUaMGOFm36ezTZw40aZPn77XXf7lL39xD1fREEAAAQQQQAABBHIrQICeW3+OjgACCCCAAAIIIIAAAgggkAUBvwa2yrxv2rSpooS6P7TCUJXr9uFoOsN0hecqRe1LhZeUlLgZzbRoC6jffJl3v169P2M9aBEM0zVeaKkLEJ6nbpjMHnR/lL3KtquKh8JzrXmue2IhNz1Ydd5559mnn37qLlPva1mo9enTx+69915L53v77LPPtnfffdf5VvWw1rx589zSHjQEEEAAAQQQQACB3AoQoOfWn6MjgAACCCCAAAIIIIAAAghkWcCH6V999ZUL02OFoz5MV6CeSpiuGc0Kz3fs2OECE4Xn2ictvwRUfj8YpvuQzYduGi96KEKhutZQpyUuQHieuFk6tijW8Fx2F110kb366qtulv31119vAwcOdBVLxowZY6+99pqNHj3aLrzwwnQwmx7I6dmzp6tCsnjxYmvatGla9stOEEAAAQQQQAABBDIjQICeGVf2igACCCCAAAIIIIAAAgggkAcCCo80MzwYpscKR/3MdIWj8ZZ5X7dunb300kt26KGHulLA7dq1cyXjafktoDBdD11o9qrKPwfHi64s+PCFZprSqhcIhud6r7Rv397N0qVlXkBj2c881/1NM8+L4T6lUunDhw93wFOmTLEBAwZUYOthmb59+7rPBr2ucePGKXfERx99ZIMHDzYt3/HKK6+kvD92gAACCCCAAAIIIJBZAQL0zPqydwQQQAABBBBAAAEEEEAAgTwRUBCqGeO+zHtV4agv3b23MrxffPGFXXLJJbZx40YX0gwdOrTgyyHnSTen9TRV7loBpJ+drj8Hm2a26uEL/Ur3esppvZAc7kx+K1ascA8iEJ5ntyP04ILsNW4Vnnfu3NlVUSiGpvuzguyuXbvaU089tccl33LLLXbfffeZfp5yyikpkzzyyCM2btw4F9QrsKchgAACCCCAAAIIRFuAAD3a/cPZIYAAAggggAACCCCAAAII5EBAYZ5K7mpmun4pTI8VjipsUunuYJj+97//3f77v//bbaeZnNOnT7cDDjggB1fBIbMpoDETDNM1Uz3Y6tat68aKwnT9nmbOi/A8NyPBr3mucVps4bnEe/Xq5apIXHHFFRUz0YM9sWTJEhsyZIiddNJJNnny5JQ7adSoUTZ37lwbO3asaS10GgIIIIAAAggggEC0BQjQo90/nB0CCCCAAAIIIIAAAgUnoBm+M2bMsKefftqtDa3SqP3797df/vKXaV8bWvu/44473FqmmiGq2XXDhg2zQYMGFZwrF5Q5AR+ma710heIaS+EwvV69em7m5rJly+zyyy93r1FQeuedd9ohhxySuZNjz5EU0JjZunWrC+g0FvQwRrBpprXGhwJ1henxLgsQyYtN8qQIz5OES8NmwfC8Zs2a7rNR47FYmtY5/+EPf+gu9+GHH3ZherjpXt+7d2/7wQ9+YE8++WTKNMcdd5x7WERl3D///HP3WaH7Qtu2bV25eFUpadKkScrHYQcIIIAAAggggAAC6REgQE+PI3tBAAEEEEAAAQQQQACBOAR27NhhI0aMsNdff929WqGRXz/4wAMPNJU4TVf52I8//tjOP/98N3M4fKyRI0faL37xizjOmJcgUFlA41UzNoNhup9prLLtN998symc0oMht99+u3Xr1q0ow1HGzX8ENGa2bdvmgnQF6nqIKNhUvcCXeVfJ92II0wnPc/cO0YMdCnJ13yrG8FzyX375ZcWa54sWLbIWLVrE7JCDDz7YLb3x5ptvptRhGzZssD59+lTsQ8fr1KmT+6xQxRLdE/SZ8eCDD9r3v//9lI7FxggggAACCCCAAALpESBAT48je0EAAQQQQAABBBBAAIE4BK677jp7/PHH3Zf2KmeqMqYKliZMmGBz5syxc8891/SaVJtCKq0zun79emvXrp0LNXv06GEffvih/epXv7I1a9bYzJkz7dBDD031UGxfxAI+TNd4e/XVV93Y1Xhu1qyZjRkzxlq3bu3WdPZrphdLOFrEQ6LaS9eY2b59e8Wa6fp9sKmUtg/TFdwVYphOeF7tMMnYC3R/UmWWQgrP77rrLrv77rvjNmvTpo17uMmva/63v/2tyiUVjjzySCsvL7cPPvjA3cuTbS+++KKrsqNKJePHj7eBAwe6fweprVq1yv27ROfRpUsXV+a9Ro0ayR6K7RBAAAEEEEAAAQTSTGcaAAAgAElEQVTSJECAniZIdoMAAggggAACCCCAAAJ7F1DJUpVOV+nr8Jqjmn118skn29KlS+3ZZ591XyKn0iZNmmR//OMfTYGlvoxWiO7b22+/7YJ6hedPPPFEKodhWwScgJYI+PnPf+7C85KSErvxxhvd2uexZhoHw3RCEgZQMEzX+Ak2BWwaLyrzrjC9EMYL4Xnuxnw4PNcMaI2tfG9apkVLZcTbdI+eOnWq+zeHHlDRDPCq2o9//GMrLS21t956KyUrPcynqjiaed61a9c9DqeS8irxrveHzu3YY4+N93J4HQIIIIAAAggggECGBAjQMwTLbhFAAAEEEEAAAQQQQKCywG9/+1t76KGHrHnz5rZgwQJT2eJgU/n2cePG2WWXXebCyGSb1hT90Y9+5EpsX3DBBXb11Vfvsav/+q//crO9dB6aJUxDIFmB+fPnu5mFCssVSN1///3WsmVL96CIlg/QOrqaoa7lC4LNzzRWQOpnGhfibONkXYtxO40hX+ZdZbaDTeG5f/hCP/MxTCc8z92oVniusu36fNTY0ZrnhRCeJysqCwXW+neIKtNU1Y4++mhbu3atLVy40Fq1apXs4eLa7sorr0xrJZ64DsqLEEAAAQQQQAABBKoUIEBncCCAAAIIIIAAAggggEBWBFQuVTO9fvrTn7qgPNxWrlxpffv2te7du7vy6sm2Tz75xE499VS3ucrFH3bYYXvsys9YUynVM888M9lDsV2RCzz//PNuKQKFUlq3dsaMGa58e7ipXLLWulUpYIXpscp2KxTVL81cV5BOmF7cg0tjSmG6fil4DjaNjWCY7ktBR1mM8Dx3vaP7jcq2+/Dczzwv5nuM7sO9evVynbJ48WJr2rRpzA7Sv0d0785GgD558mS755577KijjrJ77703dwOGIyOAAAIIIIAAAgg4AQJ0BgICCCCAAAIIIIAAAghkRcB/ET1x4sSKgDt84J49e5pm5i5ZsiTpc1KoqfVENbPs/fffd/sLt5deeslGjhxpP/vZz+yqq65K+lhsWLwCTz/9tKtuoJnmBx98sN13333WuHHjakH0eoWJfmZ6VWW7fZiu2aLFHHRVC1oEL9ADGMEwXeuoB5seutBsYo2ZWPe7XBOFw/MOHTpE8jxz7ZSJ44fD844dO1qjRo24p5hZjx493L141qxZdsghh+zBr7/Ta9S09Mv3vve9lLpIfVG3bt0q93HNNdfY7NmzTbPeFaTTEEAAAQQQQAABBHIrQICeW3+OjgACCCCAAAIIIIBAUQjoi2P/BXVVs8IF0a9fPzdT7p133nFhUDLtscces7Fjx5qCghdeeCHmLvw66DpeImunJnM+bFN4AhrD119/vSnI1IMhmi2YTLii7YMz02OV7VY4qn37st2E6YU3nhK5Ir80gAJ1LRGgPweblgPQeNGv8DIZiRwnXa8lPE+XZOL70eeuSpVraQA9iEN4XtlwyJAh7mE9/Xvh7LPP3gP49ddfdw/Z6R787rvvJt4B/95C9/kLL7zQraM+b968KkvB+3//DB061MaMGZP08dgQAQQQQAABBBBAID0CBOjpcWQvCCCAAAIIIIAAAgggsBcBhT2HH364e8Vzzz3n1l+N1U4//XS3HqlmkavMbDLtgQcesAkTJtihhx5qTzzxRMxdfP7553bSSSe58u4KQ2kIxCvwl7/8xYYPH+5e3rt3b7v77rtdwJJqU8iiAF0z0zdt2uSC9WBTAObDdP1U2W7C9FTV83t7X83Az04Ph+n169evCNPr1KmT9YslPM86ecUBd+zY4R5G8+H5fvvt5ypkcM/4T59MmzbNfve739kRRxxhDz300B6ddeutt7qHo6r6+0R6VwH6okWLbMSIEXb55Zfvsekzzzxjo0ePdv/9kUceqfj3UiLH4LUIIIAAAggggAAC6RUgQE+vJ3tDAAEEEEAAAQQQQACBGALBUqjz58+3kpKSmE5nnXWWvffee6by2AcddFBSlg8//LBpbfO9fem9dOlSO+GEE+zAAw80fXFNQyBegaeeesqV/VeZ3d///vdWr169eDeN+3UK01Xa3YfpsdbADs5MJ0yPm7ZgX6gxo3Hiw3SVfQ82lY5WmXfNTN9bGel0ARGep0sy8f0Ew3MF5pp5Tni+p+OaNWusb9++pveKwvRjjjmm4kVlZWXWv39/dw++8sor7aKLLkq8IwJb+Nnsqgpxyy232IABAyr+Vv8G+c1vfmOqGHDsscfa1KlTUzoWGyOAAAIIIIAAAgikR4AAPT2O7AUBBBBAAAEEEEAAAQT2IqAvqLt27epesXDhwipLmJ5zzjmufLvKsPu1RxOF1RqiWku0T58+Nn369Jiba2aeyqVqVt6LL76Y6CF4fZELrF271o3hbMzmVDCqYCUYpgfXwNY5+LLdfg3sbJxXkQ+BSF++r2agMP3rr7+2Xbt2VTpfzUb3Zd71AEi6xwvhee6Gh8JzlW3XT/WrPuOaNGmS9j7O3RWm98ijRo2yuXPnunvoDTfcYMcff7x98cUXLtD++OOP3fvk5Zdfdoa+qRT7sGHD3B/1b4xevXrFdVJTpkypCMf1+aFf+iwpLS112//kJz+xSZMmJb18TVwnwYsQQAABBBBAAAEE4hYgQI+bihcigAACCCCAAAIIIIBAKgIq4a5ARwF3t27dYu5KZdVVXv3RRx+1nj17JnU4rTF66aWX2v7772/PPvtszH188MEHdsYZZ1iHDh3spZdeSuo4bIRAtgUUjCoY82G61sAOhuk6HwVBCtL9GtjpDkezfc0cLzUBX83Az0zX+Ak2zYj1YbpKvqc6XjQmV65c6calgnrdY2vVqpXaRbB1XAKE53ExVXqRlss4//zz7dNPP91jY70XJk+ebAMHDqz0d1o3Xeunqz344INuKY94mx4gnDFjhr3//vtu5nvz5s2te/fuNnjwYPfQHw0BBBBAAAEEEEAgOgIE6NHpC84EAQQQQAABBBBAAIGCFjj55JPts88+s9tuu82VRo3VjjzySCsvL3dl1VVePZn20UcfuS+jFSSqHHys9sorr9gll1ziysSrXDwNgXwTUECp9Y0VAClQV3AZaw1shekq3a2gNNVwNN+MON/KAr6agQ/TVdkg2BR0+zBd989Exwvhee5GnO4FqqziZ57rwYWmTZsm3Ie5u4LcHVkVEzQ7fNasWW7pDDXN3B8zZoxbqoOGAAIIIIAAAgggUJwCBOjF2e9cNQIIIIAAAggggAACWRe4+uqr7cknn7SLL77YVDY13JYtW+bKp6pphpfWbE2mKUBQSVV9Ef7cc89Z586d99iNL6XKeqPJCLNN1AQUjKpMtw/TFZCGw3SV6vZhumYGJxqORu2aOZ/UBXSv9GXefXDo91qzZs2KSgYNGza0GjVq7PWAhOep90eyewiH5+3bt7dmzZrxHk8QVGP4yy+/dOM+1r8bEtwdL0cAAQQQQAABBBDIcwEC9DzvQE4fAQQQQAABBBBAAIF8EVA59SuuuMLatm3r1hQNBzJa93zs2LFWUlJi8+fPT+myLrzwQlu0aJH94he/sJEjR+6xrzPPPNP++te/Vvn3KR2cjRHIoYAP07X2tWamKyBVqeBgq1u3bkWYrt8TpuewwyJyaIWwfmb6li1bKp2V7tV+WQD9DN+7g+G5xpMCXMq2Z6dj1W9a89xXE9DMc8Lz7NhzFAQQQAABBBBAAIHCFiBAL+z+5eoQQAABBBBAAAEEEIiMwNatW+2oo44yBXvXXXednXvuuRXnpi//Bw0aZEuXLrVzzjnHrr/++pTOe+7cuW6Wu0pXK7hv1apVxf4WL15sF1xwgfuzSrYecsghKR2LjRGIqoDCdIXnwTBdM9WDTbPRVbZbwWg61sCOqgXnFb+AxogP01XeOtj0sIVmpOveqjGj+7pf85zwPH7jdLwyHJ7rwQWtqc0DMenQZR8IIIAAAggggAACxS5AgF7sI4DrRwABBBBAAAEEEEAgiwK+dLpmJ44ePdpOP/10Ky0ttXHjxpmCbf33OXPmVJRPXbVqlZ144onuDMePH2+nnnpqXGer0sQDBgxwM/M6duxoN954ox188MG2cOFCu/baa93M3J49e9qjjz4a1/54EQL5LqAwXWXdFYyWl5e7UD0cpmuddL8Gtkq+V1e2O99NOP/qBfQAhmaYa7woTNc4itX0IIZmPzPzvHrTdLyC8DwdiuwDAQQQQAABBBBAAIGqBQjQGR0IIIAAAggggAACCCCQNQEF28OHD3dheaymWeNaI903zWzs27ev++NNN91kgwcPjvtcP/jgAxs2bJgLfsKtUaNGNnPmTOvUqVPc++OFCBSKgA/TFYzqYRKtna5ALtgUpvuy3X5mOjNbC2UEJHcdegBDY8bPTg+H6Q0aNKh4AEPjh5YZAT34oofD/Lr17dq1sxYtWjDzPDPc7BUBBBBAAAEEEECgSAUI0Iu047lsBBBAAAEEEEAAAQRyJaCgbtq0aXb//fe7IEZNX/5rffTTTjstrae1fPlymzBhgi1YsKBi5mTv3r3dWuuE52mlZmd5LKBZxppd7MN0PegSbJpV7MN0haQK0gnT87jDUzx1hegKcNV8lQKF68Gmhy58NQPNTqelR4DwPD2O7AUBBBBAAAEEEEAAgeoECNCrE+LvEUAAAQQQQAABBBBAICMCWvf8s88+M81UPOCAAzJaLnr9+vVund7WrVvbvvvum5HrYacIFIKAgtBgmK73abDVrFmzUpiuAJUwvRB6Pr5rUHgeXvNcY2LLli2u2oceitIDGcGmtdEVpmvddIXpjJf4rMOvUngue607r9a2bVtr2bIlnslxshUCCCCAAAIIIIAAAnsVIEBngCCAAAIIIIAAAggggAACCCCAwB4CCtMVjPqZ6b5ktH+hwnM/M71hw4buIRjC0cIdSLHC8/Ca5yrrroBXQboCdYW+waYA3c9Mr1evHuMlzuGihxI069+H5yUlJdaqVSv84vTjZQgggAACCCCAAAIIJCpAgJ6oGK9HAAEEEEAAAQQQQAABBBBAoMgEfDCqMF2/fJDnGRSeK0RXOKqfmpVMmF44gySe8Dx8tRozeujCr5keXhpA1Ud8mK6S74yX2OOF8Lxw3kdcCQIIIIAAAggggED+CBCg509fcaYIIIAAAggggAACCCCAAAII5FzAB6M+TNcs9WBTEOrDdM1QJ0zPeZeldALJhOfhA2rMKED3Zd5jLQ3gy7w3aNCAMP3fgArPVbbdv8fatGnjliHhYYOUhjQbI4AAAggggAACCCBQrQABerVEvAABBBBAAAEEEEAAAQQQQAABBGIJKBhVGOrDdK2fHmwK+hSIKhxVmK6S34R/+TOWNHt81apVpn7WWubt27d3fZhqU5juy7yHlwbQAxfhpQFSPV4+bq8lFFS23YfnrVu3Nv3i/ZOPvck5I4AAAggggAACCOSbAAF6vvUY54sAAggggAACCCCAAAIIIIBABAX8LONgmK7/FmwK0xWO7rPPPoTpEezD4CllKjwPX/bOnTsryryHqxloaQAfpuun/lwMTeG5Zp77B1I061yzzwnPi6H3uUYEEEAAAQQQQACBKAgQoEehFzgHBBBAAAEEEEAAAQQQQAABBApIQMG5glGF6Zs2bXIBaThM17rXPkzXetiEg9EZANkKz8NXvGvXroowPVY1Ay0NoIcv/NIA0RFL35kQnqfPkj0hgAACCCCAAAIIIJCsAAF6snJshwACCCCAAAIIIIAAAggggAAC1QooOFcwqiBdgbrCWYWEwVavXj1X5l2/6tSpQ5herWrmXqD+0exntXSWbU/0jLX+t9Zf17rpCtPDD2AoTPdjJh1l5RM9v0y8Xu8LlczXdau1atXKSkpKeD9kApt9IoAAAggggAACCCCwFwECdIYHAggggAACCCCAAAIIIIAAAghkRcCH6QpFfZiuoDTYFNr6YFS/Z2Z6VrrGHSQq4Xn4ihUsK1TW+eln+AEMLQ3gx4yqGeRjIzzPx17jnBFAAAEEEEAAAQQKVYAAvVB7lutCAAEEEEAAAQQQQAABBBBAIMICCtMVnvswXT9jhel+DWzNUidMz1yHRjU8jxWma0a6D9PDY0bjRGXefTWDzImlb88Kz1evXu2uSa1ly5bWtm1bxnv6iNkTAggggAACCCCAAAIJCRCgJ8TFixFAAAEEEEAAAQQQQAABBBBAIN0CCtMVIgbDdJV9DzaVdg+G6TVq1Ej3aRTt/sLheYcOHaxmzZqR99C42bJlS8W66eExkw/VDMLheYsWLaxdu3aE55EffZwgAggggAACCCCAQCELEKAXcu9ybQgggAACCCCAAAIIIIAAAgjkmYAP01Wqu7y83IXqO3furHQVKtPtw/T69eu7sJHZ6cl1dL6G5+Gr1bjZunVrRZgeHjN6AMOXeY9KNQOF52vWrHFjXI3wPLkxzFYIIIAAAggggAACCKRbgAA93aLsDwEEEEAAAQQQQAABBBBAAAEE0iagEt0q2e3D9B07dlTad61atSrCdK2FTZgeP30wPFeo3L59+7yYeV7dFSpM37ZtW0WYHmvM+DLv/gGM6vaZ7r8Ph+fNmzd3M8+prJBuafaHAAIIIIAAAggggEDiAgToiZuxBQIIIIAAAggggAACCCCAAAII5EBAoWMwTN++fXuls1DZcT8zvWHDhoTpe+mjQg3PY12yxolmeeuaY40ZPzPdj5lMD22N47Vr19qmTZvcoZo1a+YeXiA8z7Q8+0cAAQQQQAABBBBAID4BAvT4nHgVAggggAACCCCAAAIIIIAAAghESEAhpNa//uqrr1wQqRnHwaYwXYGowlH9VDhJmfdvhYopPA8PWc1G1/Xrl0q+B5vGSDBMz0SgHQ7PmzZtalpzPhPHitDblVNBAAEEEEAAAQQQQCCvBAjQ86q7OFkEEEAAAQQQQAABBBBAAAEEEAgL+PWvVeZdYXqsYDQYpitcL9YwvZjD8/C40TrpPkzXwxjhMN1XM9DPdATcCs/XrVvnHvpQU3heKGXzuSshgAACCCCAAAIIIFBIAgTohdSbXAsCCCCAAAIIIIAAAggggAACRS7g17/2YXo4GFVw7sN0BaPFFKarjPmqVavcCCmkNc/TMeR37dplmzdvdqXetUxAsAXHjGaoa8wk2sLheZMmTdzM82T2leixeT0CCCCAAAIIIIAAAggkJkCAnpgXr0YAAQQQQAABBBBAAAEEEEAAgTwRUJiuNa99mB4rGG3QoIEr260wvVatWgU7M53wPP5Bu3v3bhema3a6fmocBZt/AEPjRmOmuqbwvLS01I1DNcLz6sT4ewQQQAABBBBAAAEEcitAgJ5bf46OAAIIIIAAAggggAACCCCAAAJZEFAIqvWvg2F6OBj1YboPRgulzDvhefIDTOF3MEzXn4Otdu3a9sUXX9jBBx9sbdu23eNAev369eutrKzM/V3jxo1tv/32Y+Z58l3ClggggAACCCCAAAIIZFyAAD3jxBwAAQQQQAABBBBAAAEEEEAAAQSiJKDgXOtfay1qrZmumcbhML1+/fpuZrp+KSTN1zCd8Dx9I09huJYEkKlCdc1Uf/bZZ+3RRx91B+nSpYsdffTR1r9/f+vcubMbU5p5Tnievj5gTwgggAACCCCAAAIIZEOAAD0byhwDAQQQQAABBBBAAAEEEEAAAQQiKaCQU+tfB8P08Cxjhekq8a4wvU6dOnkTphOeZ27IadwoTH/77bdt3LhxFSG5P2L79u3tRz/6kR122GHWrl07N/O8Y8eOzDzPXJewZwQQQAABBBBAAAEE0iZAgJ42SnaEAAIIIIAAAggggAACCCCAAAL5LODDdM1K9zPTNcs42OrVq+fC9H322SfSYTrhefZGosaIgvSXX37ZFi5c6Eq2B1ubNm1swIABdsIJJ1i3bt3y5gGM7AlyJAQQQAABBBBAAAEEoiVAgB6t/uBsEEAAAQQQQAABBBBAAAEEEEAgAgIK0xWMKojW7HT9DIfpdevWrZiZrmA9KmXeCc9zN4A0Rl5//XWbP3++vfnmm7Z69epKJ9O6dWvr16+fC9O7d+/OjPTcdRVHRgABBBBAAAEEEECgSgECdAYHAggggAACCCCAAAIIIIAAAgggsBcBH6ZrrXQfpqvse7CptLsv864wvUaNGjkxJTzPCbs7qMbJhg0b3C81lfxXmzdvnpud/vHHH1c6uebNm1vfvn1dmH7EEUdY7dq1c3fyHBkBBBBAAAEEEEAAAQQqBAjQGQwIIIAAAggggAACCCCAAAIIIIBAnAIKSbVGejBM37lzZ6WtFYT6MF3rp2crTCc8j7MTM/AyjYuNGzdWlG9XeN65c+dKM8xXrFhhL730kgvT33vvvUpn0ahRI/vJT35iJ554oh199NGRqWaQASp2iQACCCCAAAIIIIBA5AUI0CPfRZwgAggggAACCCCAAAIIIIAAAghEVUAlu7/55hsrLy93Zd537NhR6VRr1apVEaY3aNDABaOZKPVOeJ67ERIOz/XwhMJz9X1Vbd26dW5m+osvvmhvvfWWeyjDtxtvvNHOOOOM3F0QR0YAAQQQQAABBBBAoMgFCNCLfABw+QgggAACCCCAAAIIIIAAAgggkB4BhelbtmypCNO3b99eacc1a9asCNMbNmyYtjA9GJ5rxnu7du1YWzs9XVrtXhSel5WVWWlpqXttPOF5eKfa/s9//rObmf7ll1/axIkT7fDDD6/22LwAAQQQQAABBBBAAAEEMiNAgJ4ZV/aKAAIIIIAAAggggAACCCCAAAJFLKAZxcEwfdu2bZU0FKYrRFepb/1UmfdkZqYTnudukIXDc/Vjly5d9jrzPHdny5ERQAABBBBAAAEEEEAgXgEC9HileB0CCCCAAAIIIIAAAggggAACCCCQhICC1q1bt7qZygq89ftgU3geDNMVrscTpms97T/96U925plnWklJCTPPk+ibZDdRn6psv0qxq6n/VLa9du3aye6S7RBAAAEEEEAAAQQQQCAiAgToEekITgMBBBBAAAEEEEAAAQQQQAABBApfQMGrZqMrfN20aZObpR5sCtO1VrpmpqsceFVh+tNPP23jx493a2ePHDnShg4dStn2LA2fcHiu/tLMc8LzLHUAh0EAAQQQQAABBBBAIMMCBOgZBmb3CCCAAAIIIIAAAggggAACmRfYuXOnzZgxwxQqLl++3Bo3bmz9+/e3X/7yly6ITFd7+OGHXWi5t/bII4+wfnG6wAt8Pz5M/+qrr1yY/s0331S6Ys1C92G6xrEP05966ik3DrX9gQceaPfcc09ax3mBs6d0eTJXf61du9btR/2jmed16tRJab9sjAACCCCAAAIIIIAAAtERIECPTl9wJggggAACCCCAAAIIIIAAAkkI7Nixw0aMGGGvv/6621qho0IuNYWLCrQ1kzcdbdSoUTZ37lw307SqEtsPPPCA9ejRIx2HYx9FJKAxu337dhfO+jDdj2M/rhXWvvbaazZp0qSK8HzatGm2zz77FJFU7i41HJ7Xr1/fzTwnPM9dn3BkBBBAAAEEEEAAAQQyIUCAnglV9okAAggggAACCCCAAAIIIJA1geuuu84ef/xxNztXAffZZ5/tSmRPmDDB5syZY+eee67pNeloxx9/vC1btsxmz55t3bp1S8cu2QcCewgoqNWDIT5M37x5swvM9ZDIH/7wB/f773//+zZx4kRr3br1Xh/ogDc9AoTn6XFkLwgggAACCCCAAAII5IMAAXo+9BLniAACCCCAAAIIIIAAAgggEFPg888/t0GDBrl1oK+44gobPnx4xetU1v3kk0+2pUuX2rPPPutmiqbStGb1kUce6cLKd999l1mnqWCybdwCCm537dplTzzxhP32t791Y13h+ejRo00zoNX0U1UWNBN9b9UR4j4oL6wkoD74+uuvbfXq1e6/16tXz/bff3/uAYwTBBBAAAEEEEAAAQQKVIAAvUA7lstCAAEEEEAAAQQQQAABBIpBQIHiQw89ZM2bN7cFCxa48DDYVL593Lhxdtlll9nPf/7zlEi0f5WKP/TQQ12YSUMgWwJaNkCBucJzLQ9w8803mx4Q+cc//uH+W7Ap3PVhukqLV7XUQLbOPd+PQ3ie7z3I+SOAAAIIIIAAAgggkLgAAXriZmyBAAIIIIAAAggggAACCCAQEYFTTjnF/v73v9tPf/pTF5SH28qVK61v377WvXt3mzlzZkpnPWXKFJs6dapdfPHFrlQ8DYFsCGgZgl//+tcuKO/Zs6fdc889LiBXsLt79263XrpKvStM15+DrW7duhVhun5PmJ5Yj8lYrqtWrXIb6uEEVbKQJQ0BBBBAAAEEEEAAAQQKV4AAvXD7litDAAEEEEAAAQQQQAABBApeQMH4li1b3FrQp556aszrVehYq1YtW7JkSUoeQ4cOtTfeeMNOPPFE27Bhg33xxRe2detWtwb10UcfbT/72c/c72kIpEvgmWeesauuuqoiPL/33nutYcOGe+zeh+kqM+7DdJV9DzbNRlfw/r3vfc+VfCdM33sv+fBcZdv1e4XmKttOeJ6u0c1+EEAAAQQQQAABBBCIrgABenT7hjNDAAEEEEAAAQQQQAABBBDYi8D27dvtkEMOca94/PHH7bDDDov56n79+tny5cvtnXfecQFiMk0zew8//HAX1qs1btzYhWkKKT/55BPbtm2bCyXvvvtu++EPf5jMIdgGgUoCwfBcY08zz2OF52E2hb2ara6Z0+Xl5e6nyr0Hm5Y6CIbpNWrUQD8gQHjOcEAAAQQQQAABBBBAoLgFCNCLu/+5egQQQAABBBBAAAEEEEAgbwUUDCpYVHvuueesc+fOMa/l9NNPtw8//NCef/5569SpU1LXq+21HwWNKqd9zjnnmGb0qpWVldmYMWPslVdesSZNmti8efOSDuqTOjk2KjgBhecaZwpyEwnPwxA+TN+8ebObma4Z6jt27Kj0MlVn0Kx0BeoNGjRwM6042ZkAACAASURBVNOLeXa6zOSlsu1+5rnKtqt8Ow0BBBBAAAEEEEAAAQSKQ4AAvTj6matEAAEEEEAAAQQQQAABBApO4JtvvrEePXq465o/f76VlJTEvMazzjrL3nvvPXv66aftoIMOSspB60wrRFf5Zh/aB3ekUFKl3RW6/eY3v7HzzjsvqeOwEQKffvqpnXLKKS687dWrl02bNi2umefxyKmSgt43PkxXFYdgU5iuIF2/NNu92ML0cHiuh2QUnqu6BA0BBBBAAAEEEEAAAQSKR4AAvXj6mitFAAEEEEAAAQQQQAABBApKQGFg165d3TUtXLjQWrVqFfP6NFtc5dsfe+yxisA9ExC33XabK+F+zDHHuNCThkAyAp9//rmdeeaZbqz+/ve/T1t4Hj4XlXkPhulahiDYatasWVHmXTPTVX2hkGemKzyXx8qVK93DC4TnyYxetkEAAQQQQAABBBBAoDAECNALox+5CgQQQAABBBBAAAEEEECgKAU0G1yl3GfPnm3dunWLaXDSSSeZQslHH33UevbsmTGnmTNn2vXXX+9KyaukPA2BZAVU0cAvEZDsPhLZToHxli1b3JrpKvO+devWSpsrPNeMdF/qvdDCdMLzREYLr0UAAQQQQAABBBBAoPAFCNALv4+5QgQQQAABBBBAAAEEEECgYAVOPvlk++yzz0yzv/v37x/zOo888kgXDGpd6QMPPDBpC4WatWvXrnIW7l133WW33367K/n8pz/9KenjsCECuRRQmKwAXWXetXSBgvVgC4bpCtU1Uz2fZ6b78FzLL2hWvt7jeg9r1j0NAQQQQAABBBBAAAEEilOAAL04+52rRgABBBBAAAEEEEAAAQQKQuDqq6+2J5980i6++GIbNWrUHte0bNkyO/74491/X7JkiTVu3Dip69Zxnn/+eZsxY4Z179495j6GDh1qb7zxhh133HH2hz/8IanjsBECURJQuKzS7j5MV4nzYFNwHpyZnm9hup95r7LthOdRGnmcCwIIIIAAAggggAACuRUgQM+tP0dHAAEEEEAAAQQQQAABBBBIQeDZZ5+1K664wtq2bWsvv/yyW6c52LTu+dixY62kpMTmz5+f9JGuu+46e/zxx03l4CdPnrzHft5++20777zz3NrJEydOtFNPPTXpY7EhAlEU0Njevn17pTBd/803hemate3LvNeqVSvSM9MJz6M4yjgnBBBAAAEEEEAAAQSiIUCAHo1+4CwQQAABBBBAAAEEEEAAAQSSEFCp6aOOOsqt26yQ+9xzz63Yi8K+QYMG2dKlS+2cc85x65Mn27788ks75ZRTbOfOnabZ6Oeff74rXa22aNEiF+Jrlu5BBx3k1mNXeEhDoFAFFD5rSQM/M33z5s3u4ZFgU5j+3e9+1wXqe1v6IFdGKk2/YsUKN/Nc71eVbddsehoCCCCAAAIIIIAAAgggQIDOGEAAAQQQQAABBBBAAAEEEMhrgSlTptjUqVNdCDZ69Gg7/fTTrbS01MaNG2eLFy92/33OnDnWuXNnd51a6/jEE090vx8/fnzcs8VnzpxpN9xwgwvcmjZt6ma1a211lX9W69Gjh1sDvWXLlnntyckjkIiAgnM9WKL10hWoK0zXeyTY6tev78L0ffbZJxJhejg8171B50dDAAEEEEAAAQQQQAABBCRAgM44QAABBBBAAAEEEEAAAQQQyGsBzYQdPny4C8tjNa2NrjXSfVPg3bdvX/fHm266yQYPHhz39b///vt2zz332JtvvunWhlaQ3q1bNzfTvV+/fnuUkI97x7wQgQIQUJi+a9euijD9H//4xx5her169SrC9Dp16mS9zLvCc90Ddu/e7R6uITwvgIHHJSCAAAIIIIAAAgggkGYBAvQ0g7I7BBBAAAEEEEAAAQQQQACB7AtoBuy0adPs/vvvN4V2ai1atHCl1U877bTsnxBHRKDIBXyYruUVNDNd70uF1sFWt27dijBdv9c66plsWvJBZdt1HlqCQeG5SszTEEAAAQQQQAABBBBAAIGgAAE64wEBBBBAAAEEEEAAAQQQQKBgBLTu+WeffebKRB9wwAHMCC+YnuVC8llAYbpC62CYrpnqwabZ6AqzVUpdJd/THaYTnufzCOLcEUAAAQQQQAABBBDIrgABena9ORoCCCCAAAIIIIAAAggggAACCCBQtAIK07VGumakl5eXu1A9VpiuIF2Bukq+16hRIyUvLbewfPlyZp6npMjGCCCAAAIIIIAAAggUjwABevH0NVeKAAIIIIAAAggggAACCCCAAAIIREbAh+mbN292Zd43bdpkWo4h2FRNwofpfmZ6IrPTFZ6rbLtCepVt79Spk+2zzz6RMeBEEEAAAQQQQAABBBBAIHoCBOjR6xPOCAEEEEAAAQQQQAABBBBAAAEEECg6AZV5/+abbyrC9B07dlQyqFWrVkWY3qBBA1fmfW9hupZ00Mxzheeaxe7XPE8kgC+6TuCCEUAAAQQQQAABBBBAwAjQGQQIIIAAAggggAACCCCAAAIIIIAAApESUJn3YJiuMDzYNJvcz0xXmK6APBiMh8NzP/Oc8DxS3czJIIAAAggggAACCCAQSQEC9Eh2CyeFAAIIIIAAAggggAACCCCAAAIIICABhelbtmypmJmusuzBpvDch+kNGzZ0M86DM887duxojRo12utsdaQRQAABBBBAAAEEEEAAAS9AgM5YQAABBBBAAAEEEEAAAQQQQAABBBDICwGtm75161YrLy93a6br98HmZ5jrdQrWCc/zols5SQQQQAABBBBAAAEEIiVAgB6p7uBkEEAAAQQQQAABBBBAAAEEEEAAAQTiEVBIrtnoPkzXLPVgU9n2xo0bM/M8HkxegwACCCCAAAIIIIAAAhUCBOgMBgQQQAABBBBAAAEEEEAAAQQQQACBvBZQmK51zzds2OAC9VatWlmLFi0Iz/O6Vzl5BBBAAAEEEEAAAQRyI0CAnht3jooAAggggAACCCCAAAIIIIAAAgggkAEBhelqvpx7Bg7BLhFAAAEEEEAAAQQQQKCABQjQC7hzuTQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgFCNDjt+KVCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIFLECAXsCdy6UhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMQvQIAevxWvRAABBBBAAAEEEEAAAQQQQAABBBBAIOcCu3fvtp///Oe2YMEC+/TTTzN2Ph999JHddddd9vbbb9u2bdvsoIMOsksvvdR+/OMfZ+yY7BgBBBBAAAEEEEAAgVwLEKDnugc4PgIIIIAAAggggAACCCCAAAIIIIAAAgkIjB071h577DG3RaYC9IULF7qQfufOne443/nOd+xf//qX+zlhwgQbPHhwAmfMSxFAAAEEEEAAAQQQyB8BAvT86SvOFAEEEEAAAQQQQAABBBBAAAEEEECgiAU08/yWW26x+++/v0IhEwH68uXL7ZRTTrEtW7bYD37wAxeYH3DAAfb666/b5Zdf7kL1uXPnWtu2bYu4N7h0BBBAAAEEEEAAgUIVIEAv1J7luhBAAAEEEEAAAQQQQAABBBBAAAEECkagrKzM/ud//sfeeOMNa926ta1Zs8ZdWyYC9F/96lf2/PPPW8uWLV1Q3rhx4wrHp59+2n7961/bgAEDbMqUKQXjy4UggAACCCCAAAIIIOAFCNAZCwgggAACCCCAAAIIIIAAAggggAACCERc4LLLLrMXXnjBjj32WBekDxo0yJ1xugN0BfX/7//9P9Ns92uuucaGDBlSSWbXrl3uHDZv3mxLliyxOnXqRFyO00MAAQQQQAABBBBAIDEBAvTEvHg1AggggAACCCCAAAIIIIAAAggggAACWRfQrO/u3bvbT3/6U1u1apX17dvXnUO6A/R58+bZpZde6vb96quvulno4XbVVVfZU089ZdOnT7c+ffpk3YIDIoAAAggggAACCCCQSQEC9Ezqsm8EEEAAAQQQQAABBBBAAAEEEEAAAQTSIKB1x2vXru32tHLlyowF6H/84x9t0qRJbn3zP//5zzHPfMaMGXbzzTfbtddea+eff34aro5dIIAAAggggAACCCAQHQEC9Oj0BWeCAAIIIIAAAggggAACCCCAAAIIIIBAtQKZDNBvvfVWu/fee10Z9/vuuy/muTzzzDM2evRou+CCC+zqq6+u9nx5AQIIIIAAAggggAAC+SRAgJ5PvcW5IoAAAggggAACCCCAAAIIIIAAAgjkrcBdd91ld999d9zn36ZNG3vxxRf3eH0mA/QJEybYAw88YAMGDLApU6bEPNcFCxbYiBEjbODAgfa73/0u7uvhhQgggAACCCCAAAII5IMAAXo+9BLniAACCCCAAAIIIIAAAggggAACCCCQ9wJ33HGH3XnnnXFfR0lJic2fP3+P12cyQJ84caJb23zw4MF20003xTzX1157zYYNG+bKyOuhABoCCCCAAAIIIIAAAoUkQIBeSL3JtSCAAAIIIIAAAggggAACCCCAAAIIFLxAJgP022+/3YXiZ555po0fPz6m5eLFi1359j59+riwnYYAAggggAACCCCAQCEJEKAXUm9yLQgggAACCCCAAAIIIIAAAggggAACBS+QyQD9/vvvdzPPjznmGJs2bVpMyxdeeMEuu+wy+9GPfmQzZswoeG8uEAEEEEAAAQQQQKC4BAjQi6u/uVoEEEAAAQQQQAABBBBAAAEEEEAAgTwXyGSA/tJLL9nIkSOtS5cu9qc//Smm1MMPP+xmpx933HH2hz/8Ic81OX0EEEAAAQQQQAABBCoLEKAzIhBAAAEEEEAAAQQQQAABBBBAAAEEEMgjgUwG6KtXr7Zjjz3WateubW+88YZ997vf3UNm1KhRNnfuXDv//PPt2muvzSM5ThUBBBBAAAEEEEAAgeoFCNCrN+IVCCCAAAIIIIAAAggggAACCCCAAAIIREYgkwG6LvKEE06wpUuXulLugwcPrnTdu3fvduXdS0tLY/59ZJA4EQQQQAABBBBAAAEEkhQgQE8Sjs0QQAABBBBAAAEEEEAAAQQQQAABBBDIhUCmA/SpU6falClTrG3btjZnzhxr2LBhxWXOnj3brrnmGqtZs6YtWLDAWrZsmQsCjokAAggggAACCCCAQMYECNAzRsuOEUAAAQQQQAABBBBAAAEEEEAAAQQQSL9AvAH6W2+9ZcOGDXMnMH36dOvVq1dcJ1NWVuZmoX/99dd22GGH2Q033GDt27d3a6Jr7fPt27fboEGDbNKkSXHtjxchgAACCCCAAAIIIJBPAgTo+dRbnCsCCCCAAAIIIIAAAggggAACCCCAQNELxBugL1myxIYMGeK8HnzwQevdu3fcdppdPnLkSNuxY8ce25SUlNisWbOsWbNmce+PFyKAAAIIIIAAAgggkC8CBOj50lOcJwIIIIAAAggggAACCCCAAAIIIIAAAlkU+Oijj+zGG2+0d955xx31O9/5jh133HF23XXXUbo9i/3AoRBAAAEEEEAAAQSyK0CAnl1vjoYAAggggAACCCCAAAIIIIAAAnkucP3119vMmTPtz3/+s1sjOt1t+fLldscdd9hrr71m//jHP6xz586uDLdKZtMQyIXA6tWrbd26da6MO7POc9EDHBMBBBBAAAEEEEAgmwIE6NnU5lgIIIAAAggg8P/bu5dQq+s1jOO/gQ1yYMNyEGUhFkRFUUE1iYhCGoiUkwisIIQUGjQobCBIdpUGGUR0IYou1qCwqwRJBE2CLnYZNAkrGjgpCQoLPOwFCefoOelz1rLl42dNNNrvWv/38zr7svcmQIAAAQIECBA4rgWefPLJsXXr1skOswjoX3/99bj55pvHr7/+OvmMhe/4PXDgwOTvCz9Oe/369ce1n4cnQIAAAQIECBAgQIDAvAsI6PN+Ic9HgAABAgQIECBAgAABAgQIzIXAs88+Ox588MGDQXvaAX3fvn1j5cqVY+/eveP0008fDzzwwLjooovGl19+Oe68887x008/Tb7z/YILLpgLDw9BgAABAgQIECBAgACBRgEBvfGqdiJAgAABAgQIECBAgAABAgSmJvDbb7+NjRs3jrfeemssXbp0ErIXXtMO6A8//PB46qmnxsknnzx27Ngxieh/vT755JNx0003TeL59u3bp7abNyJAgAABAgQIECBAgACBfxcQ0P2LIECAAAECBAgQIECAAAECBAj8D4GHHnpoPP3005N4/fjjj48rr7xy8tXTDOh//vnnuPzyy8cvv/wy1q5dO+65555DnujGG28cX3zxxdi1a9ck5HsRIECAAAECBAgQIECAwPQFBPTpm3pHAgQIECBAgAABAgQIECBAoEjgkUceGYsXLx633377WLRo0VixYsVku2kG9G+++WasWrVq8r6vvPLKuPDCCw8RfOyxx8a2bdvG5s2bx5o1a4qErUKAAAECBAgQIECAAIH5ERDQ5+cWnoQAAQIECBAgQIAAAQIECBCYQ4E//vhjnHTSSQefbBYB/Z133pn8nvOFz/nss88mof4/Xzt37hwbNmwYt9xyy7j77rvnUMojESBAgAABAgQIECBA4PgXENCP/xvagAABAgQIECBAgAABAgQIEDiGArMI6C+99NLYtGnTWLZs2Xj33XcPu81fvwf9mmuumXwnuhcBAgQIECBAgAABAgQITF9AQJ++qXckQIAAAQIECBAgQIAAAQIEigVmEdCfe+65sWXLlsnvWd++ffth9b799ttx/fXXT368+8KPefciQIAAAQIECBAgQIAAgekLCOjTN/WOBAgQIECAAAECBAgQIECAQLHALAL6Cy+8MPnd5pdeeul4/vnnD6v33XffjWuvvXacc84544033igWthoBAgQIECBAgAABAgT+OQEB/Z+z98kECBAgQIAAAQIECBAgQIDAcSgwi4D+2muvjY0bN44rrrhiPPPMM4dV2bNnz1j48e1nnnnmeO+9945DOY9MgAABAgQIECBAgACB+RcQ0Of/Rp6QAAECBAgQIECAAAECBAgQmCOBWQT0999/f9xxxx1j+fLl48033zzstrt37x433HDDOOOMM8bOnTvnSMSjECBAgAABAgQIECBAoEdAQO+5pU0IECBAgAABAgQIECBAgACBYyAwi4D+1VdfjdWrV4/FixePTz/99LBbfPDBB2PdunXj3HPPHa+//vox2NRHECBAgAABAgQIECBA4MQTENBPvJvbmAABAgQIECBAgAABAgQIEPg/BGYR0Pfv3z8uueSS8fvvv4+33357nH322Yc84aOPPjqeeOKJcdVVV03+9CJAgAABAgQIECBAgACB6QsI6NM39Y4ECBAgQIAAAQIECBAgQIBAscAsAvoC12233TY++uijsX79+rFhw4ZDBNesWTM+//zz//r/i8mtRoAAAQIECBAgQIAAgWMmIKAfM2ofRIAAAQIECBAgQIAAAQIECDQIzCqg79ixY9x1111jyZIlk9+Dfuqppx7k+vjjj8fatWsn//3qq6+O888/v4HSDgQIECBAgAABAgQIEJg7AQF97k7igQgQIECAAAECBAgQIECAAIF5Fvi7gP7jjz+O6667brLC5s2bx6pVq45onYUf475y5crx/fffj2XLlo377rtvnHfeeePDDz8c99577/j555/HxRdfPF588cUjej9fRIAAAQIECBAgQIAAAQJHLyCgH72ZCQIECBAgQIAAAQIECBAgQOAEFvi7gP7DDz+Mq6++eiJ0//33j9WrVx+x1u7du8ett9469u3bd8jMKaecMl5++eVx1llnHfH7+UICBAgQIECAAAECBAgQODoBAf3ovHw1AQIECBAgQIAAAQIECBAgQGCmAnv27BlbtmwZu3btGgcOHJh81mWXXTY2bdokns9U3psTIECAAAECBAgQIEBgDAHdvwICBAgQIECAAAECBAgQIECAwBwK7N27dyx8N/vSpUvHaaedNodP6JEIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA3rqeuwAAIABJREFUgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9AQO+7qY0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ0AM0IwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQJyCg993URgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQCAjoAZoRAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgTEND7bmojAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgEBPQAzQgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9AkI6H03tREBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBAICeoBmhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6BAT0vpvaiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQCAQE9QDNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0CAnrfTW1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoGAgB6gGSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPgEBve+mNiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQEBAD9CMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfgIDed1MbESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAgIKAHaEYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoE9KOdTqAAAABklEQVTgXxmtWtioHGNGAAAAAElFTkSuQmCC" width="1000" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>用<code>ax.plot_surface</code>演示一个三维正弦函数画的三维等高曲面图,要求X,Y,Z都是二维网格数据的形式:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">Y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">"3d"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"viridis"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"none"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"x"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"y"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s2">"z"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div id="b7468461-6fe2-4099-a21c-0f0ca9f769d8"></div>
<div class="output_subarea output_javascript ">
<script type="text/javascript">
var element = $('#b7468461-6fe2-4099-a21c-0f0ca9f769d8');
/* Put everything inside the global mpl namespace */
window.mpl = {};
mpl.get_websocket_type = function() {
if (typeof(WebSocket) !== 'undefined') {
return WebSocket;
} else if (typeof(MozWebSocket) !== 'undefined') {
return MozWebSocket;
} else {
alert('Your browser does not have WebSocket support. ' +
'Please try Chrome, Safari or Firefox ≥ 6. ' +
'Firefox 4 and 5 are also supported but you ' +
'have to enable WebSockets in about:config.');
};
}
mpl.figure = function(figure_id, websocket, ondownload, parent_element) {
this.id = figure_id;
this.ws = websocket;
this.supports_binary = (this.ws.binaryType != undefined);
if (!this.supports_binary) {
var warnings = document.getElementById("mpl-warnings");
if (warnings) {
warnings.style.display = 'block';
warnings.textContent = (
"This browser does not support binary websocket messages. " +
"Performance may be slow.");
}
}
this.imageObj = new Image();
this.context = undefined;
this.message = undefined;
this.canvas = undefined;
this.rubberband_canvas = undefined;
this.rubberband_context = undefined;
this.format_dropdown = undefined;
this.image_mode = 'full';
this.root = $('<div/>');
this._root_extra_style(this.root)
this.root.attr('style', 'display: inline-block');
$(parent_element).append(this.root);
this._init_header(this);
this._init_canvas(this);
this._init_toolbar(this);
var fig = this;
this.waiting = false;
this.ws.onopen = function () {
fig.send_message("supports_binary", {value: fig.supports_binary});
fig.send_message("send_image_mode", {});
if (mpl.ratio != 1) {
fig.send_message("set_dpi_ratio", {'dpi_ratio': mpl.ratio});
}
fig.send_message("refresh", {});
}
this.imageObj.onload = function() {
if (fig.image_mode == 'full') {
// Full images could contain transparency (where diff images
// almost always do), so we need to clear the canvas so that
// there is no ghosting.
fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);
}
fig.context.drawImage(fig.imageObj, 0, 0);
};
this.imageObj.onunload = function() {
fig.ws.close();
}
this.ws.onmessage = this._make_on_message_function(this);
this.ondownload = ondownload;
}
mpl.figure.prototype._init_header = function() {
var titlebar = $(
'<div class="ui-dialog-titlebar ui-widget-header ui-corner-all ' +
'ui-helper-clearfix"/>');
var titletext = $(
'<div class="ui-dialog-title" style="width: 100%; ' +
'text-align: center; padding: 3px;"/>');
titlebar.append(titletext)
this.root.append(titlebar);
this.header = titletext[0];
}
mpl.figure.prototype._canvas_extra_style = function(canvas_div) {
}
mpl.figure.prototype._root_extra_style = function(canvas_div) {
}
mpl.figure.prototype._init_canvas = function() {
var fig = this;
var canvas_div = $('<div/>');
canvas_div.attr('style', 'position: relative; clear: both; outline: 0');
function canvas_keyboard_event(event) {
return fig.key_event(event, event['data']);
}
canvas_div.keydown('key_press', canvas_keyboard_event);
canvas_div.keyup('key_release', canvas_keyboard_event);
this.canvas_div = canvas_div
this._canvas_extra_style(canvas_div)
this.root.append(canvas_div);
var canvas = $('<canvas/>');
canvas.addClass('mpl-canvas');
canvas.attr('style', "left: 0; top: 0; z-index: 0; outline: 0")
this.canvas = canvas[0];
this.context = canvas[0].getContext("2d");
var backingStore = this.context.backingStorePixelRatio ||
this.context.webkitBackingStorePixelRatio ||
this.context.mozBackingStorePixelRatio ||
this.context.msBackingStorePixelRatio ||
this.context.oBackingStorePixelRatio ||
this.context.backingStorePixelRatio || 1;
mpl.ratio = (window.devicePixelRatio || 1) / backingStore;
var rubberband = $('<canvas/>');
rubberband.attr('style', "position: absolute; left: 0; top: 0; z-index: 1;")
var pass_mouse_events = true;
canvas_div.resizable({
start: function(event, ui) {
pass_mouse_events = false;
},
resize: function(event, ui) {
fig.request_resize(ui.size.width, ui.size.height);
},
stop: function(event, ui) {
pass_mouse_events = true;
fig.request_resize(ui.size.width, ui.size.height);
},
});
function mouse_event_fn(event) {
if (pass_mouse_events)
return fig.mouse_event(event, event['data']);
}
rubberband.mousedown('button_press', mouse_event_fn);
rubberband.mouseup('button_release', mouse_event_fn);
// Throttle sequential mouse events to 1 every 20ms.
rubberband.mousemove('motion_notify', mouse_event_fn);
rubberband.mouseenter('figure_enter', mouse_event_fn);
rubberband.mouseleave('figure_leave', mouse_event_fn);
canvas_div.on("wheel", function (event) {
event = event.originalEvent;
event['data'] = 'scroll'
if (event.deltaY < 0) {
event.step = 1;
} else {
event.step = -1;
}
mouse_event_fn(event);
});
canvas_div.append(canvas);
canvas_div.append(rubberband);
this.rubberband = rubberband;
this.rubberband_canvas = rubberband[0];
this.rubberband_context = rubberband[0].getContext("2d");
this.rubberband_context.strokeStyle = "#000000";
this._resize_canvas = function(width, height) {
// Keep the size of the canvas, canvas container, and rubber band
// canvas in synch.
canvas_div.css('width', width)
canvas_div.css('height', height)
canvas.attr('width', width * mpl.ratio);
canvas.attr('height', height * mpl.ratio);
canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');
rubberband.attr('width', width);
rubberband.attr('height', height);
}
// Set the figure to an initial 600x600px, this will subsequently be updated
// upon first draw.
this._resize_canvas(600, 600);
// Disable right mouse context menu.
$(this.rubberband_canvas).bind("contextmenu",function(e){
return false;
});
function set_focus () {
canvas.focus();
canvas_div.focus();
}
window.setTimeout(set_focus, 100);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items) {
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) {
// put a spacer in here.
continue;
}
var button = $('<button/>');
button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +
'ui-button-icon-only');
button.attr('role', 'button');
button.attr('aria-disabled', 'false');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
var icon_img = $('<span/>');
icon_img.addClass('ui-button-icon-primary ui-icon');
icon_img.addClass(image);
icon_img.addClass('ui-corner-all');
var tooltip_span = $('<span/>');
tooltip_span.addClass('ui-button-text');
tooltip_span.html(tooltip);
button.append(icon_img);
button.append(tooltip_span);
nav_element.append(button);
}
var fmt_picker_span = $('<span/>');
var fmt_picker = $('<select/>');
fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');
fmt_picker_span.append(fmt_picker);
nav_element.append(fmt_picker_span);
this.format_dropdown = fmt_picker[0];
for (var ind in mpl.extensions) {
var fmt = mpl.extensions[ind];
var option = $(
'<option/>', {selected: fmt === mpl.default_extension}).html(fmt);
fmt_picker.append(option);
}
// Add hover states to the ui-buttons
$( ".ui-button" ).hover(
function() { $(this).addClass("ui-state-hover");},
function() { $(this).removeClass("ui-state-hover");}
);
var status_bar = $('<span class="mpl-message"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
}
mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {
// Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,
// which will in turn request a refresh of the image.
this.send_message('resize', {'width': x_pixels, 'height': y_pixels});
}
mpl.figure.prototype.send_message = function(type, properties) {
properties['type'] = type;
properties['figure_id'] = this.id;
this.ws.send(JSON.stringify(properties));
}
mpl.figure.prototype.send_draw_message = function() {
if (!this.waiting) {
this.waiting = true;
this.ws.send(JSON.stringify({type: "draw", figure_id: this.id}));
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
var format_dropdown = fig.format_dropdown;
var format = format_dropdown.options[format_dropdown.selectedIndex].value;
fig.ondownload(fig, format);
}
mpl.figure.prototype.handle_resize = function(fig, msg) {
var size = msg['size'];
if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {
fig._resize_canvas(size[0], size[1]);
fig.send_message("refresh", {});
};
}
mpl.figure.prototype.handle_rubberband = function(fig, msg) {
var x0 = msg['x0'] / mpl.ratio;
var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;
var x1 = msg['x1'] / mpl.ratio;
var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;
x0 = Math.floor(x0) + 0.5;
y0 = Math.floor(y0) + 0.5;
x1 = Math.floor(x1) + 0.5;
y1 = Math.floor(y1) + 0.5;
var min_x = Math.min(x0, x1);
var min_y = Math.min(y0, y1);
var width = Math.abs(x1 - x0);
var height = Math.abs(y1 - y0);
fig.rubberband_context.clearRect(
0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);
fig.rubberband_context.strokeRect(min_x, min_y, width, height);
}
mpl.figure.prototype.handle_figure_label = function(fig, msg) {
// Updates the figure title.
fig.header.textContent = msg['label'];
}
mpl.figure.prototype.handle_cursor = function(fig, msg) {
var cursor = msg['cursor'];
switch(cursor)
{
case 0:
cursor = 'pointer';
break;
case 1:
cursor = 'default';
break;
case 2:
cursor = 'crosshair';
break;
case 3:
cursor = 'move';
break;
}
fig.rubberband_canvas.style.cursor = cursor;
}
mpl.figure.prototype.handle_message = function(fig, msg) {
fig.message.textContent = msg['message'];
}
mpl.figure.prototype.handle_draw = function(fig, msg) {
// Request the server to send over a new figure.
fig.send_draw_message();
}
mpl.figure.prototype.handle_image_mode = function(fig, msg) {
fig.image_mode = msg['mode'];
}
mpl.figure.prototype.updated_canvas_event = function() {
// Called whenever the canvas gets updated.
this.send_message("ack", {});
}
// A function to construct a web socket function for onmessage handling.
// Called in the figure constructor.
mpl.figure.prototype._make_on_message_function = function(fig) {
return function socket_on_message(evt) {
if (evt.data instanceof Blob) {
/* FIXME: We get "Resource interpreted as Image but
* transferred with MIME type text/plain:" errors on
* Chrome. But how to set the MIME type? It doesn't seem
* to be part of the websocket stream */
evt.data.type = "image/png";
/* Free the memory for the previous frames */
if (fig.imageObj.src) {
(window.URL || window.webkitURL).revokeObjectURL(
fig.imageObj.src);
}
fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(
evt.data);
fig.updated_canvas_event();
fig.waiting = false;
return;
}
else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == "data:image/png;base64") {
fig.imageObj.src = evt.data;
fig.updated_canvas_event();
fig.waiting = false;
return;
}
var msg = JSON.parse(evt.data);
var msg_type = msg['type'];
// Call the "handle_{type}" callback, which takes
// the figure and JSON message as its only arguments.
try {
var callback = fig["handle_" + msg_type];
} catch (e) {
console.log("No handler for the '" + msg_type + "' message type: ", msg);
return;
}
if (callback) {
try {
// console.log("Handling '" + msg_type + "' message: ", msg);
callback(fig, msg);
} catch (e) {
console.log("Exception inside the 'handler_" + msg_type + "' callback:", e, e.stack, msg);
}
}
};
}
// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas
mpl.findpos = function(e) {
//this section is from http://www.quirksmode.org/js/events_properties.html
var targ;
if (!e)
e = window.event;
if (e.target)
targ = e.target;
else if (e.srcElement)
targ = e.srcElement;
if (targ.nodeType == 3) // defeat Safari bug
targ = targ.parentNode;
// jQuery normalizes the pageX and pageY
// pageX,Y are the mouse positions relative to the document
// offset() returns the position of the element relative to the document
var x = e.pageX - $(targ).offset().left;
var y = e.pageY - $(targ).offset().top;
return {"x": x, "y": y};
};
/*
* return a copy of an object with only non-object keys
* we need this to avoid circular references
* http://stackoverflow.com/a/24161582/3208463
*/
function simpleKeys (original) {
return Object.keys(original).reduce(function (obj, key) {
if (typeof original[key] !== 'object')
obj[key] = original[key]
return obj;
}, {});
}
mpl.figure.prototype.mouse_event = function(event, name) {
var canvas_pos = mpl.findpos(event)
if (name === 'button_press')
{
this.canvas.focus();
this.canvas_div.focus();
}
var x = canvas_pos.x * mpl.ratio;
var y = canvas_pos.y * mpl.ratio;
this.send_message(name, {x: x, y: y, button: event.button,
step: event.step,
guiEvent: simpleKeys(event)});
/* This prevents the web browser from automatically changing to
* the text insertion cursor when the button is pressed. We want
* to control all of the cursor setting manually through the
* 'cursor' event from matplotlib */
event.preventDefault();
return false;
}
mpl.figure.prototype._key_event_extra = function(event, name) {
// Handle any extra behaviour associated with a key event
}
mpl.figure.prototype.key_event = function(event, name) {
// Prevent repeat events
if (name == 'key_press')
{
if (event.which === this._key)
return;
else
this._key = event.which;
}
if (name == 'key_release')
this._key = null;
var value = '';
if (event.ctrlKey && event.which != 17)
value += "ctrl+";
if (event.altKey && event.which != 18)
value += "alt+";
if (event.shiftKey && event.which != 16)
value += "shift+";
value += 'k';
value += event.which.toString();
this._key_event_extra(event, name);
this.send_message(name, {key: value,
guiEvent: simpleKeys(event)});
return false;
}
mpl.figure.prototype.toolbar_button_onclick = function(name) {
if (name == 'download') {
this.handle_save(this, null);
} else {
this.send_message("toolbar_button", {name: name});
}
};
mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {
this.message.textContent = tooltip;
};
mpl.toolbar_items = [["Home", "Reset original view", "fa fa-home icon-home", "home"], ["Back", "Back to previous view", "fa fa-arrow-left icon-arrow-left", "back"], ["Forward", "Forward to next view", "fa fa-arrow-right icon-arrow-right", "forward"], ["", "", "", ""], ["Pan", "Pan axes with left mouse, zoom with right", "fa fa-arrows icon-move", "pan"], ["Zoom", "Zoom to rectangle", "fa fa-square-o icon-check-empty", "zoom"], ["", "", "", ""], ["Download", "Download plot", "fa fa-floppy-o icon-save", "download"]];
mpl.extensions = ["eps", "jpeg", "pdf", "png", "ps", "raw", "svg", "tif"];
mpl.default_extension = "png";var comm_websocket_adapter = function(comm) {
// Create a "websocket"-like object which calls the given IPython comm
// object with the appropriate methods. Currently this is a non binary
// socket, so there is still some room for performance tuning.
var ws = {};
ws.close = function() {
comm.close()
};
ws.send = function(m) {
//console.log('sending', m);
comm.send(m);
};
// Register the callback with on_msg.
comm.on_msg(function(msg) {
//console.log('receiving', msg['content']['data'], msg);
// Pass the mpl event to the overridden (by mpl) onmessage function.
ws.onmessage(msg['content']['data'])
});
return ws;
}
mpl.mpl_figure_comm = function(comm, msg) {
// This is the function which gets called when the mpl process
// starts-up an IPython Comm through the "matplotlib" channel.
var id = msg.content.data.id;
// Get hold of the div created by the display call when the Comm
// socket was opened in Python.
var element = $("#" + id);
var ws_proxy = comm_websocket_adapter(comm)
function ondownload(figure, format) {
window.open(figure.imageObj.src);
}
var fig = new mpl.figure(id, ws_proxy,
ondownload,
element.get(0));
// Call onopen now - mpl needs it, as it is assuming we've passed it a real
// web socket which is closed, not our websocket->open comm proxy.
ws_proxy.onopen();
fig.parent_element = element.get(0);
fig.cell_info = mpl.find_output_cell("<div id='" + id + "'></div>");
if (!fig.cell_info) {
console.error("Failed to find cell for figure", id, fig);
return;
}
var output_index = fig.cell_info[2]
var cell = fig.cell_info[0];
};
mpl.figure.prototype.handle_close = function(fig, msg) {
var width = fig.canvas.width/mpl.ratio
fig.root.unbind('remove')
// Update the output cell to use the data from the current canvas.
fig.push_to_output();
var dataURL = fig.canvas.toDataURL();
// Re-enable the keyboard manager in IPython - without this line, in FF,
// the notebook keyboard shortcuts fail.
IPython.keyboard_manager.enable()
$(fig.parent_element).html('<img src="' + dataURL + '" width="' + width + '">');
fig.close_ws(fig, msg);
}
mpl.figure.prototype.close_ws = function(fig, msg){
fig.send_message('closing', msg);
// fig.ws.close()
}
mpl.figure.prototype.push_to_output = function(remove_interactive) {
// Turn the data on the canvas into data in the output cell.
var width = this.canvas.width/mpl.ratio
var dataURL = this.canvas.toDataURL();
this.cell_info[1]['text/html'] = '<img src="' + dataURL + '" width="' + width + '">';
}
mpl.figure.prototype.updated_canvas_event = function() {
// Tell IPython that the notebook contents must change.
IPython.notebook.set_dirty(true);
this.send_message("ack", {});
var fig = this;
// Wait a second, then push the new image to the DOM so
// that it is saved nicely (might be nice to debounce this).
setTimeout(function () { fig.push_to_output() }, 1000);
}
mpl.figure.prototype._init_toolbar = function() {
var fig = this;
var nav_element = $('<div/>');
nav_element.attr('style', 'width: 100%');
this.root.append(nav_element);
// Define a callback function for later on.
function toolbar_event(event) {
return fig.toolbar_button_onclick(event['data']);
}
function toolbar_mouse_event(event) {
return fig.toolbar_button_onmouseover(event['data']);
}
for(var toolbar_ind in mpl.toolbar_items){
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) { continue; };
var button = $('<button class="btn btn-default" href="#" title="' + name + '"><i class="fa ' + image + ' fa-lg"></i></button>');
button.click(method_name, toolbar_event);
button.mouseover(tooltip, toolbar_mouse_event);
nav_element.append(button);
}
// Add the status bar.
var status_bar = $('<span class="mpl-message" style="text-align:right; float: right;"/>');
nav_element.append(status_bar);
this.message = status_bar[0];
// Add the close button to the window.
var buttongrp = $('<div class="btn-group inline pull-right"></div>');
var button = $('<button class="btn btn-mini btn-primary" href="#" title="Stop Interaction"><i class="fa fa-power-off icon-remove icon-large"></i></button>');
button.click(function (evt) { fig.handle_close(fig, {}); } );
button.mouseover('Stop Interaction', toolbar_mouse_event);
buttongrp.append(button);
var titlebar = this.root.find($('.ui-dialog-titlebar'));
titlebar.prepend(buttongrp);
}
mpl.figure.prototype._root_extra_style = function(el){
var fig = this
el.on("remove", function(){
fig.close_ws(fig, {});
});
}
mpl.figure.prototype._canvas_extra_style = function(el){
// this is important to make the div 'focusable
el.attr('tabindex', 0)
// reach out to IPython and tell the keyboard manager to turn it's self
// off when our div gets focus
// location in version 3
if (IPython.notebook.keyboard_manager) {
IPython.notebook.keyboard_manager.register_events(el);
}
else {
// location in version 2
IPython.keyboard_manager.register_events(el);
}
}
mpl.figure.prototype._key_event_extra = function(event, name) {
var manager = IPython.notebook.keyboard_manager;
if (!manager)
manager = IPython.keyboard_manager;
// Check for shift+enter
if (event.shiftKey && event.which == 13) {
this.canvas_div.blur();
// select the cell after this one
var index = IPython.notebook.find_cell_index(this.cell_info[0]);
IPython.notebook.select(index + 1);
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
fig.ondownload(fig, null);
}
mpl.find_output_cell = function(html_output) {
// Return the cell and output element which can be found *uniquely* in the notebook.
// Note - this is a bit hacky, but it is done because the "notebook_saving.Notebook"
// IPython event is triggered only after the cells have been serialised, which for
// our purposes (turning an active figure into a static one), is too late.
var cells = IPython.notebook.get_cells();
var ncells = cells.length;
for (var i=0; i<ncells; i++) {
var cell = cells[i];
if (cell.cell_type === 'code'){
for (var j=0; j<cell.output_area.outputs.length; j++) {
var data = cell.output_area.outputs[j];
if (data.data) {
// IPython >= 3 moved mimebundle to data attribute of output
data = data.data;
}
if (data['text/html'] == html_output) {
return [cell, data, j];
}
}
}
}
}
// Register the function which deals with the matplotlib target/channel.
// The kernel may be null if the page has been refreshed.
if (IPython.notebook.kernel != null) {
IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);
}
</script>
</div>
</div>
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<img src="data:image/png;base64,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" width="1000" />
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Text(0.5, 0, 'z')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="pandas-plot与pandas-alive"><a href="">pandas plot</a>与<a href="https://github.com/JackMcKew/pandas_alive">pandas-alive</a><a class="anchor-link" href="#pandas-plot与pandas-alive"> </a></h2><p>Pandas以Matplotlib实现了plot接口(Matlab风格),可以快速实现Serise与Datafram的可视化,pandas-alive增加了时间序列的动态图效果</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>head -n <span class="m">20</span> timeseries.json
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>{
"Afghanistan": [
{
"date": "2020-1-22",
"confirmed": 0,
"deaths": 0,
"recovered": 0
},
{
"date": "2020-1-23",
"confirmed": 0,
"deaths": 0,
"recovered": 0
},
{
"date": "2020-1-24",
"confirmed": 0,
"deaths": 0,
"recovered": 0
},
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_covid</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">spain</span> <span class="o">=</span> <span class="n">df_covid</span><span class="p">[</span><span class="s1">'Spain'</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span>
<span class="n">spain</span><span class="p">[</span><span class="n">spain</span><span class="o"><</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>日期
2020-04-24 -10034.0
Name: Spain, dtype: float64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_covid</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"2020-04-20"</span><span class="p">:</span><span class="s2">"2020-04-30"</span><span class="p">,</span> <span class="s2">"Spain"</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>日期
2020-04-20 200210.0
2020-04-21 204178.0
2020-04-22 208389.0
2020-04-23 213024.0
2020-04-24 202990.0
2020-04-25 205905.0
2020-04-26 207634.0
2020-04-27 209465.0
2020-04-28 210773.0
2020-04-29 212917.0
2020-04-30 213435.0
Name: Spain, dtype: float64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">current_total</span><span class="p">(</span><span class="n">values</span><span class="p">):</span>
<span class="n">total</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">s</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"总数 : </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">total</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
<span class="k">return</span> <span class="p">{</span><span class="s2">"x"</span><span class="p">:</span> <span class="mf">0.85</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span> <span class="s2">"s"</span><span class="p">:</span> <span class="n">s</span><span class="p">,</span> <span class="s2">"ha"</span><span class="p">:</span> <span class="s2">"right"</span><span class="p">,</span> <span class="s2">"size"</span><span class="p">:</span> <span class="mi">11</span><span class="p">}</span>
<span class="n">animated_html</span> <span class="o">=</span> <span class="n">df_covid</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">60</span><span class="p">)</span><span class="o">.</span><span class="n">plot_animated</span><span class="p">(</span><span class="n">period_summary_func</span><span class="o">=</span><span class="n">current_total</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Generating BarChartRace, plotting ['Netherlands', 'Pakistan', 'Belgium', 'Chile', 'Mexico', 'Saudi Arabia', 'Canada', 'China', 'Peru', 'India', 'Iran', 'Turkey', 'Germany', 'France', 'Italy', 'Spain', 'United Kingdom', 'Brazil', 'Russia', 'US']
</pre>
</div>
</div>
<div class="output_area">
<div class="output_subarea output_stream output_stderr output_text">
<pre>/Users/toddtao/opt/anaconda3/lib/python3.7/site-packages/pandas_alive/charts.py:70: UserWarning: Plotting too many bars may result in undesirable output, use `n_visible=5 to limit number of bars
"Plotting too many bars may result in undesirable output, use `n_visible=5 to limit number of bars"
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Video</span>
<span class="n">display</span><span class="p">(</span><span class="n">Video</span><span class="p">(</span><span class="s1">'3.data-viz/covid19.mp4'</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<video src="3.data-viz/covid19.mp4" controls="">
Your browser does not support the <code>video</code> element.
</video>
</div>
</div>
</div>
</div>
</div>
</div>Python数据科学分享——2.数据处理2020-05-15T00:00:00-05:002020-05-15T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/05/15/data-etl<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-05-15-data-etl.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/markmap.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span>
<span class="n">seaborn</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"font.sans-serif"</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"SimHei"</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
<span class="kn">from</span> <span class="nn">tqdm.notebook</span> <span class="kn">import</span> <span class="n">tqdm</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/data2info.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/data_type.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<center>python数值计算历史</center><table>
<thead><tr>
<th style="text-align:center">首发年份</th>
<th style="text-align:center">名称</th>
<th style="text-align:center">场景</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">1991</td>
<td style="text-align:center">Python</td>
<td style="text-align:center">编程语言</td>
</tr>
<tr>
<td style="text-align:center">2001</td>
<td style="text-align:center">ipython</td>
<td style="text-align:center">增强shell</td>
</tr>
<tr>
<td style="text-align:center">2001</td>
<td style="text-align:center">SciPy</td>
<td style="text-align:center">算法库</td>
</tr>
<tr>
<td style="text-align:center">2006</td>
<td style="text-align:center">Numpy</td>
<td style="text-align:center">数组运算</td>
</tr>
<tr>
<td style="text-align:center">2007</td>
<td style="text-align:center">Cython</td>
<td style="text-align:center">AOT静态编译</td>
</tr>
<tr>
<td style="text-align:center">2008</td>
<td style="text-align:center">Pandas</td>
<td style="text-align:center">标签数组运算</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">scikit-learn</td>
<td style="text-align:center">机器学习</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">ipython notebook</td>
<td style="text-align:center">计算环境</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">anaconda</td>
<td style="text-align:center">管理工具</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">Numba</td>
<td style="text-align:center">llvm实现JIT编译器</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">pyspark</td>
<td style="text-align:center">集群运算</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">jupyter</td>
<td style="text-align:center">多语言支持</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">TensorFlow</td>
<td style="text-align:center">深度学习</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">jax</td>
<td style="text-align:center">Numpy+autogrd+JIT+GPU+TPU</td>
</tr>
</tbody>
</table>
<center>With great power comes great complexity(越强大越复杂)</center>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Numpy">Numpy<a class="anchor-link" href="#Numpy"> </a></h1><h2 id="神经网络示例">神经网络示例<a class="anchor-link" href="#神经网络示例"> </a></h2><h3 id="背景">背景<a class="anchor-link" href="#背景"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Video</span>
<span class="c1"># https://github.com/Sentdex/NNfSiX</span>
<span class="c1"># Video("2.data-elt/cat_neural_network.mp4", embed=True)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<table>
<thead><tr>
<th style="text-align:center">x1</th>
<th style="text-align:center">x2</th>
<th style="text-align:center">x3</th>
<th style="text-align:center">Y</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
</tr>
<tr>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
<td style="text-align:center">1</td>
<td style="text-align:center">1</td>
</tr>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
<td style="text-align:center">1</td>
</tr>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">1</td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="网络图">网络图<a class="anchor-link" href="#网络图"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/two_layer_nn.png" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="数学描述">数学描述<a class="anchor-link" href="#数学描述"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>公式1 $$ \hat y = \sigma(W_2\sigma(W_1x+ b_1) + b_2) $$</p>
<p>公式2(sigmoid) $$ \sigma = \frac {1} {1 + e^{-x}} $$</p>
<p>公式3(sigmoid导数) $$ \sigma' = \sigma(x) \times (1 - \sigma(x)) $$</p>
<p><img src="/images/copied_from_nb/2.data-elt/sigmoid.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="反向传播">反向传播<a class="anchor-link" href="#反向传播"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/nn_flow.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>公式4 $$ Loss(Sum\ of\ Squares\ Error) = \sum_{i=1}^n(y-\hat y)^2 $$</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="numpy实现">numpy实现<a class="anchor-link" href="#numpy实现"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">σ</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">σ_dvt</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">σ</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">σ</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">class</span> <span class="nc">NeuralNetwork</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">y</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">feedforward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layer1</span> <span class="o">=</span> <span class="n">σ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">w1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">yhat</span> <span class="o">=</span> <span class="n">σ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layer1</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">backprop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">gd</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">yhat</span><span class="p">)</span> <span class="o">*</span> <span class="n">σ_dvt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">yhat</span><span class="p">)</span>
<span class="n">d_w2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer1</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">gd</span>
<span class="n">d_w1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="p">(</span><span class="n">gd</span> <span class="o">@</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">*</span> <span class="n">σ_dvt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layer1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w1</span> <span class="o">+=</span> <span class="n">d_w1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w2</span> <span class="o">+=</span> <span class="n">d_w2</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nn</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">train</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10000</span><span class="p">)):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">feedforward</span><span class="p">()</span>
<span class="n">nn</span><span class="o">.</span><span class="n">backprop</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">((</span><span class="n">_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">_</span><span class="p">[</span><span class="mi">1</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">yhat</span><span class="p">))</span>
<span class="n">train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">yhat</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>
[[0.00644673]
[0.9909493 ]
[0.99080728]
[0.00803459]]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
<span class="n">x</span><span class="p">,</span>
<span class="n">y</span><span class="p">,</span>
<span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"Sum of Squares Error"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"训练次数"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"训练损失"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"训练损失随次数增加而递减"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train</span><span class="p">)),</span> <span class="n">train</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">4000</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">train</span><span class="p">)),</span> <span class="n">train</span><span class="p">[</span><span class="mi">4000</span><span class="p">:])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="数据结构">数据结构<a class="anchor-link" href="#数据结构"> </a></h2><p>NumPy在C语言的基础上开发<code>ndarray</code>对象,其数据类型也是在C语言基础上进行扩充。</p>
<p>CPython的整型对象是一个PyObject_HEAD是C语言结构体,包含引用计数、类型编码和数据大小等信息,相比C语言的整型增加了很多开销,Numpy进行了优化。</p>
<p><img src="/images/copied_from_nb/2.data-elt/pyds_02in02.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="数组初始化">数组初始化<a class="anchor-link" href="#数组初始化"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个长度为10的数组,数组的值都是0</span>
<span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个3x5的浮点型数组,数组的值都是1</span>
<span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个3x5的浮点型数组,数组的值都是3.14</span>
<span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="mf">3.14</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[3.14, 3.14, 3.14, 3.14, 3.14],
[3.14, 3.14, 3.14, 3.14, 3.14],
[3.14, 3.14, 3.14, 3.14, 3.14]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个线性序列数组,从0开始,到20结束,步长为2(它和内置的range()函数类似)</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个5个元素的数组,这5个数均匀地分配到0~1区间</span>
<span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([0. , 0.25, 0.5 , 0.75, 1. ])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># NumPy的随机数生成器设置一组种子值,以确保每次程序执行时都可以生成同样的随机数组:</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1024</span><span class="p">)</span>
<span class="c1"># 创建一个3x3的、0~1之间均匀分布的随机数组成的数组</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[0.64769123, 0.99691358, 0.51880326],
[0.65811273, 0.59906347, 0.75306733],
[0.13624713, 0.00411712, 0.14950888]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个3x3的、均值为0、标准差为1的正态分布的随机数数组</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[ 0.7729004 , 1.64294992, -0.12721717],
[ 0.91598327, 0.52267255, -0.22634267],
[ 1.41873344, -0.16232799, 0.53831355]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个3x3的、[0, 10)区间的随机整型数组</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[6, 4, 4],
[1, 0, 1],
[8, 7, 0]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个3x3的单位矩阵</span>
<span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 创建一个由3个整型数组成的未初始化的数组,数组的值是内存空间中的任意值</span>
<span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([1., 1., 1.])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>属性:确定数组的大小、形状、存储大小、数据类型</li>
<li>读写:数组保存与加载文件</li>
<li>数学运算:加减乘除、指数与平方根、三角函数、聚合比较等基本运算</li>
<li>复制与排序:数组深浅copy、快速排序、归并排序和堆排序</li>
<li>索引:获取和设置数组各个元素的值</li>
<li>切分:在数组中获取或设置子数组</li>
<li>变形:改变给定数组的形状</li>
<li>连接和分裂:将多个数组合并为一个,或者将一个数组分裂成多个</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/numpy.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="通用函数(universal-functions,-ufunc)">通用函数(universal functions, ufunc)<a class="anchor-link" href="#通用函数(universal-functions,-ufunc)"> </a></h2><p>NumPy实现一种静态类型、可编译程序接口(<strong>ufunc</strong>),实现<strong>向量化(vectorize)</strong>操作,避免for循环,提高效率,节约内存。</p>
<p>通用函数有两种存在形式:</p>
<ol>
<li><strong>一元通用函数</strong>(unary ufunc)对单个输入操作</li>
<li><strong>二元通用函数</strong>(binary ufunc)对两个输入操作</li>
</ol>
<h3 id="数组的运算">数组的运算<a class="anchor-link" href="#数组的运算"> </a></h3><p>NumPy通用函数的使用方式非常自然,因为它用到了Python原生的算术运算符(加、减、乘、除)、绝对值、三角函数、指数与对数、布尔/位运算符。</p>
<table>
<thead><tr>
<th>运算符</th>
<th>对应的通用函数</th>
<th>描述 </th>
</tr>
</thead>
<tbody>
<tr>
<td><code>+</code></td>
<td><code>np.add</code></td>
<td>加法运算(即<code>1 + 1 = 2</code>) </td>
</tr>
<tr>
<td><code>-</code></td>
<td><code>np.subtract</code></td>
<td>减法运算(即<code>3 - 2 = 1</code>)</td>
</tr>
<tr>
<td><code>-</code></td>
<td><code>np.negative</code></td>
<td>负数运算 (即<code>-2</code>)</td>
</tr>
<tr>
<td><code>*</code></td>
<td><code>np.multiply</code></td>
<td>乘法运算 (即<code>2 * 3 = 6</code>)</td>
</tr>
<tr>
<td><code>/</code></td>
<td><code>np.divide</code></td>
<td>除法运算 (即<code>3 / 2 = 1.5</code>)</td>
</tr>
<tr>
<td><code>//</code></td>
<td><code>np.floor_divide</code></td>
<td>地板除法运算(floor division,即<code>3 // 2 = 1</code>)</td>
</tr>
<tr>
<td><code>**</code></td>
<td><code>np.power</code></td>
<td>指数运算 (即<code>2 ** 3 = 8</code>)</td>
</tr>
<tr>
<td><code>%</code></td>
<td><code>np.mod</code></td>
<td>模/余数 (即<code>9 % 4 = 1</code>)</td>
</tr>
<tr>
<td></td>
<td><code>np.abs</code></td>
<td>绝对值</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x ="</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x + 5 ="</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x - 5 ="</span><span class="p">,</span> <span class="n">x</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x * 2 ="</span><span class="p">,</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x / 2 ="</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x // 2 ="</span><span class="p">,</span> <span class="n">x</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># 整除运算</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>x = [0 1 2 3]
x + 5 = [5 6 7 8]
x - 5 = [-5 -4 -3 -2]
x * 2 = [0 2 4 6]
x / 2 = [0. 0.5 1. 1.5]
x // 2 = [0 0 1 1]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x ="</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"e^x ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"2^x ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"3^x ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x ="</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"ln(x) ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"log2(x) ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"log10(x) ="</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>x = [1, 2, 3]
e^x = [ 2.71828183 7.3890561 20.08553692]
2^x = [2. 4. 8.]
3^x = [ 3 9 27]
x = [1, 2, 3]
ln(x) = [0. 0.69314718 1.09861229]
log2(x) = [0. 1. 1.5849625]
log10(x) = [0. 0.30103 0.47712125]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="特殊ufunc">特殊ufunc<a class="anchor-link" href="#特殊ufunc"> </a></h3><p><code>scipy.special</code>提供了大量统计学函数。例如,Γ函数和β函数</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"gamma(x) ="</span><span class="p">,</span> <span class="n">special</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"ln|gamma(x)| ="</span><span class="p">,</span> <span class="n">special</span><span class="o">.</span><span class="n">gammaln</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"beta(x, 2) ="</span><span class="p">,</span> <span class="n">special</span><span class="o">.</span><span class="n">beta</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>gamma(x) = [1.0000e+00 2.4000e+01 3.6288e+05]
ln|gamma(x)| = [ 0. 3.17805383 12.80182748]
beta(x, 2) = [0.5 0.03333333 0.00909091]
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="高级特性">高级特性<a class="anchor-link" href="#高级特性"> </a></h3><h3 id="累计">累计<a class="anchor-link" href="#累计"> </a></h3><p>二元通用函数的<code>reduce</code>方法可以对给定元素和操作重复执行,直至得到一个汇总结果。<code>accumulate</code>方法实现截至每一个元素的累积结果</p>
<p>例如,对<code>add</code>通用函数调用<code>reduce</code>方法会返回数组中所有元素的和:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>15</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>同样,对<code>multiply</code>通用函数调用<code>reduce</code>方法会返回数组中所有元素的乘积:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>120</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 1, 3, 6, 10, 15])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 1, 2, 6, 24, 120])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>NumPy提供了专用的函数(<code>np.sum</code>、<code>np.prod</code>、<code>np.cumsum</code>、<code>np.cumprod</code> ),它们也可以实现<code>reduce</code>的功能</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="外积">外积<a class="anchor-link" href="#外积"> </a></h3><p>任何通用函数都可以用<code>outer</code>方法获得两个不同输入数组所有元素对的函数运算结果。用一行代码实现一个99乘法表:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9],
[ 2, 4, 6, 8, 10, 12, 14, 16, 18],
[ 3, 6, 9, 12, 15, 18, 21, 24, 27],
[ 4, 8, 12, 16, 20, 24, 28, 32, 36],
[ 5, 10, 15, 20, 25, 30, 35, 40, 45],
[ 6, 12, 18, 24, 30, 36, 42, 48, 54],
[ 7, 14, 21, 28, 35, 42, 49, 56, 63],
[ 8, 16, 24, 32, 40, 48, 56, 64, 72],
[ 9, 18, 27, 36, 45, 54, 63, 72, 81]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="广播(Broadcasting)">广播(Broadcasting)<a class="anchor-link" href="#广播(Broadcasting)"> </a></h2><p>NumPy也可以通过广播实现向量化操作。广播可以用于不同大小数组的二元通用函数(加、减、乘等)的一组规则:</p>
<ul>
<li>规则1:如果两个数组的维度数不相同,那么小维度数组的形状将会在最左边补1。</li>
<li>规则2:如果两个数组的形状在任何一个维度上都不匹配,那么数组的形状会沿着维度为1的维度扩展以匹配另外一个数组的形状。</li>
<li>规则3:如果两个数组的形状在任何一个维度上都不匹配并且没有任何一个维度等于1,那么会引发异常。</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">a</span> <span class="o">+</span> <span class="mi">5</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([5, 6, 7])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="n">a</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[1., 2., 3.],
[1., 2., 3.],
[1., 2., 3.]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>根据规则1,数组<code>a</code>的维度数更小,所以在其左边补1:</p>
<p><code>b.shape -> (3, 3)</code></p>
<p><code>a.shape -> (1, 3)</code></p>
<p>根据规则2,第一个维度不匹配,因此扩展这个维度以匹配数组:</p>
<p><code>b.shape -> (3, 3)</code></p>
<p><code>a.shape -> (3, 3)</code></p>
<p>现在两个数组的形状匹配了,可以看到它们的最终形状都为<code>(3, 3)</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
<span class="n">b</span> <span class="o">+</span> <span class="n">a</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>根据规则1,数组<code>a</code>的维度数更小,所以在其左边补1:</p>
<p><code>b.shape -> (3, 1)</code></p>
<p><code>a.shape -> (1, 3)</code></p>
<p>根据规则2,两个维度都不匹配,因此扩展这个维度以匹配数组:</p>
<p><code>b.shape -> (3, 3)</code></p>
<p><code>a.shape -> (3, 3)</code></p>
<p>现在两个数组的形状匹配了,可以看到它们的最终形状都为<code>(3, 3)</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="https://raw.githubusercontent.com/muxuezi/pdsh/master/images/pyds_02in04.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Scipy稀疏矩阵">Scipy稀疏矩阵<a class="anchor-link" href="#Scipy稀疏矩阵"> </a></h1><p>地球70多亿人的社交网络中,大部分人直接认识的人数不超过10000,因此这个矩阵中,大部分的值都是0(稀疏)</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/social_network.jpg" alt="" style="max-width: 500pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">SN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">SN</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[ 8, 0, 0, 0, 9, 0, 0, 0, 0, 3],
[ 0, 0, 9, 0, 7, 0, 0, 0, 0, 4],
[ 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],
[ 0, 0, 0, 9, 0, 0, 0, 4, 0, 0],
[ 0, 5, 0, 0, 0, 7, 0, 0, 0, 0],
[ 0, 8, 8, 1, 0, 1, 4, 0, 0, 0],
[ 0, 18, 0, 0, 0, 0, 0, 0, 0, 4],
[ 0, 0, 4, 0, 0, 0, 5, 0, 0, 0],
[ 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],
[ 0, 0, 0, 9, 0, 0, 4, 0, 0, 0]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">SN</span><span class="p">)</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">SN</span><span class="p">[</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">]</span>
<span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">vals</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(array([0, 0, 0, 1, 1, 1, 2, 3, 3, 4, 4, 5, 5, 5, 5, 5, 6, 6, 7, 7, 8, 9,
9]),
array([0, 4, 9, 2, 4, 9, 8, 3, 7, 1, 5, 1, 2, 3, 5, 6, 1, 9, 2, 6, 4, 3,
6]),
array([ 8, 9, 3, 9, 7, 4, 8, 9, 4, 5, 7, 8, 8, 1, 1, 4, 18,
4, 4, 5, 5, 9, 4]))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="稀疏矩阵初始化">稀疏矩阵初始化<a class="anchor-link" href="#稀疏矩阵初始化"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">(</span><span class="n">SN</span><span class="p">)</span>
<span class="n">X</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><10x10 sparse matrix of type '<class 'numpy.int64'>'
with 23 stored elements in COOrdinate format></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> (0, 0) 8
(0, 4) 9
(0, 9) 3
(1, 2) 9
(1, 4) 7
(1, 9) 4
(2, 8) 8
(3, 3) 9
(3, 7) 4
(4, 1) 5
(4, 5) 7
(5, 1) 8
(5, 2) 8
(5, 3) 1
(5, 5) 1
(5, 6) 4
(6, 1) 18
(6, 9) 4
(7, 2) 4
(7, 6) 5
(8, 4) 5
(9, 3) 9
(9, 6) 4
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="按坐标创建稀疏矩阵">按坐标创建稀疏矩阵<a class="anchor-link" href="#按坐标创建稀疏矩阵"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X2</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="n">vals</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)))</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X2</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>matrix([[ 8, 0, 0, 0, 9, 0, 0, 0, 0, 3],
[ 0, 0, 9, 0, 7, 0, 0, 0, 0, 4],
[ 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],
[ 0, 0, 0, 9, 0, 0, 0, 4, 0, 0],
[ 0, 5, 0, 0, 0, 7, 0, 0, 0, 0],
[ 0, 8, 8, 1, 0, 1, 4, 0, 0, 0],
[ 0, 18, 0, 0, 0, 0, 0, 0, 0, 4],
[ 0, 0, 4, 0, 0, 0, 5, 0, 0, 0],
[ 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],
[ 0, 0, 0, 9, 0, 0, 4, 0, 0, 0]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="数据压缩">数据压缩<a class="anchor-link" href="#数据压缩"> </a></h2><p>将稀疏矩阵保存为 CSR(Compressed Sparse Row)/CSC(Compressed Sparse Column) 格式</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[0, 0, 0, 1, 1, 1, 2, 3, 3, 4, 4, 5, 5, 5, 5, 5, 6, 6, 7, 7, 8, 9,
9],
[0, 4, 9, 2, 4, 9, 8, 3, 7, 1, 5, 1, 2, 3, 5, 6, 1, 9, 2, 6, 4, 3,
6]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">indptr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">rows</span><span class="p">)),</span> <span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">)]</span>
<span class="n">indptr</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([ 0, 3, 6, 7, 9, 11, 16, 18, 20, 21, 23])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X3</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">vals</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">indptr</span><span class="p">))</span>
<span class="n">X3</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><10x10 sparse matrix of type '<class 'numpy.int64'>'
with 23 stored elements in Compressed Sparse Row format></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> (0, 0) 8
(0, 4) 9
(0, 9) 3
(1, 2) 9
(1, 4) 7
(1, 9) 4
(2, 8) 8
(3, 3) 9
(3, 7) 4
(4, 1) 5
(4, 5) 7
(5, 1) 8
(5, 2) 8
(5, 3) 1
(5, 5) 1
(5, 6) 4
(6, 1) 18
(6, 9) 4
(7, 2) 4
(7, 6) 5
(8, 4) 5
(9, 3) 9
(9, 6) 4
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X3</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>matrix([[ 8, 0, 0, 0, 9, 0, 0, 0, 0, 3],
[ 0, 0, 9, 0, 7, 0, 0, 0, 0, 4],
[ 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],
[ 0, 0, 0, 9, 0, 0, 0, 4, 0, 0],
[ 0, 5, 0, 0, 0, 7, 0, 0, 0, 0],
[ 0, 8, 8, 1, 0, 1, 4, 0, 0, 0],
[ 0, 18, 0, 0, 0, 0, 0, 0, 0, 4],
[ 0, 0, 4, 0, 0, 0, 5, 0, 0, 0],
[ 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],
[ 0, 0, 0, 9, 0, 0, 4, 0, 0, 0]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X4</span> <span class="o">=</span> <span class="n">X2</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
<span class="n">X4</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><10x10 sparse matrix of type '<class 'numpy.longlong'>'
with 23 stored elements in Compressed Sparse Row format></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X4</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> (0, 0) 8
(0, 4) 9
(0, 9) 3
(1, 2) 9
(1, 4) 7
(1, 9) 4
(2, 8) 8
(3, 3) 9
(3, 7) 4
(4, 1) 5
(4, 5) 7
(5, 1) 8
(5, 2) 8
(5, 3) 1
(5, 5) 1
(5, 6) 4
(6, 1) 18
(6, 9) 4
(7, 2) 4
(7, 6) 5
(8, 4) 5
(9, 3) 9
(9, 6) 4
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X5</span> <span class="o">=</span> <span class="n">X2</span><span class="o">.</span><span class="n">tocsc</span><span class="p">()</span>
<span class="n">X5</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre><10x10 sparse matrix of type '<class 'numpy.longlong'>'
with 23 stored elements in Compressed Sparse Column format></pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> (0, 0) 8
(4, 1) 5
(5, 1) 8
(6, 1) 18
(1, 2) 9
(5, 2) 8
(7, 2) 4
(3, 3) 9
(5, 3) 1
(9, 3) 9
(0, 4) 9
(1, 4) 7
(8, 4) 5
(4, 5) 7
(5, 5) 1
(5, 6) 4
(7, 6) 5
(9, 6) 4
(3, 7) 4
(2, 8) 8
(0, 9) 3
(1, 9) 4
(6, 9) 4
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="COO合计转换">COO合计转换<a class="anchor-link" href="#COO合计转换"> </a></h2><p>coo_matrix会默认将重复元素求和,适合构造多分类模型的混淆矩阵</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">vals</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(array([0, 0, 0, 0, 1, 1, 1, 1]),
array([0, 0, 0, 0, 1, 1, 1, 1]),
array([0, 1, 2, 3, 4, 5, 6, 7]))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X6</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="n">vals</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)))</span>
<span class="n">X6</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>matrix([[ 6, 0],
[ 0, 22]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="2X2混淆矩阵">2X2混淆矩阵<a class="anchor-link" href="#2X2混淆矩阵"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"int"</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(array([0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,
1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0]))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vals</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_true</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>((100,), (100,), (100,))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X7</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="n">vals</span><span class="p">,</span> <span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)))</span>
<span class="n">X7</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>matrix([[21, 23],
[30, 26]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[21, 23],
[30, 26]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"bird"</span><span class="p">]</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">]</span>
<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">"ant"</span><span class="p">,</span> <span class="s2">"bird"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>array([[2, 0, 0],
[0, 0, 1],
[1, 0, 2]])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Pandas"><a href="https://pandas.pydata.org/">Pandas</a><a class="anchor-link" href="#Pandas"> </a></h1><h2 id="Series-与-Dataframe">Series 与 Dataframe<a class="anchor-link" href="#Series-与-Dataframe"> </a></h2><ol>
<li>Series:键值对形成的二序序列,有标签的numpy一维数组</li>
<li>Dataframe:行列值三元序列(类似excel表),有标签的numpy二维数组</li>
<li>Input/output</li>
<li>General functions</li>
<li>Pandas arrays</li>
<li>Index objects</li>
<li>Date offsets</li>
<li>Window</li>
<li>GroupBy</li>
<li>Resampling</li>
<li>Style</li>
<li>Plotting</li>
<li>General utility functions</li>
<li>Extensions</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/pandas.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="向量化字符串操作"><a href="https://pandas.pydata.org/docs/reference/series.html#string-handling">向量化字符串操作</a><a class="anchor-link" href="#向量化字符串操作"> </a></h2><p>Pandas提供了一系列<strong>向量化字符串操作</strong>(vectorized string operation),极大地提高了字符串清洗效率。Pandas为包含字符串的<code>Series</code>和<code>Index</code>对象提供了<code>str</code>属性,既可以处理字符串,又可以处理缺失值。</p>
<h3 id="字符串方法">字符串方法<a class="anchor-link" href="#字符串方法"> </a></h3><p>所有Python内置的字符串方法都被复制到Pandas的向量化字符串方法中:</p>
<p><code>len()</code> | <code>lower()</code> | <code>translate()</code> | <code>islower()</code><br />
<code>ljust()</code> | <code>upper()</code> | <code>startswith()</code> | <code>isupper()</code><br />
<code>rjust()</code> | <code>find()</code> | <code>endswith()</code> | <code>isnumeric()</code><br />
<code>center()</code> | <code>rfind()</code> | <code>isalnum()</code> | <code>isdecimal()</code><br />
<code>zfill()</code> | <code>index()</code> | <code>isalpha()</code> | <code>split()</code><br />
<code>strip()</code> | <code>rindex()</code> | <code>isdigit()</code> | <code>rsplit()</code><br />
<code>rstrip()</code> | <code>capitalize()</code> | <code>isspace()</code> | <code>partition()</code><br />
<code>lstrip()</code> | <code>swapcase()</code> | <code>istitle()</code> | <code>rpartition()</code></p>
<blockquote><p>这些方法的返回值并不完全相同,例如<code>lower()</code>方法返回字符串,<code>len()</code>方法返回数值,<code>startswith('T')</code>返回布尔值,<code>split()</code>方法返回列表</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
<span class="p">(</span>
<span class="s2">"Gerald R. Ford"</span><span class="p">,</span>
<span class="s2">"James Carter"</span><span class="p">,</span>
<span class="s2">"Ronald Reagan"</span><span class="p">,</span>
<span class="s2">"George H. W. Bush"</span><span class="p">,</span>
<span class="s2">"William J. Clinton"</span><span class="p">,</span>
<span class="s2">"George W. Bush"</span><span class="p">,</span>
<span class="s2">"Barack Obama"</span><span class="p">,</span>
<span class="s2">"Donald J. Trump"</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">len</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 14
1 12
2 13
3 17
4 18
5 14
6 12
7 15
dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="正则表达式">正则表达式<a class="anchor-link" href="#正则表达式"> </a></h3><p>有一些方法支持正则表达式处理字符串。下面是Pandas根据Python标准库的<code>re</code>模块函数实现的API:</p>
<table>
<thead><tr>
<th>方法</th>
<th>描述 </th>
</tr>
</thead>
<tbody>
<tr>
<td><code>match()</code></td>
<td>对每个元素调用<code>re.match()</code>,返回布尔类型值</td>
</tr>
<tr>
<td><code>extract()</code></td>
<td>对每个元素调用<code>re.match()</code>,返回匹配的字符串组(<code>groups</code>)</td>
</tr>
<tr>
<td><code>findall()</code></td>
<td>对每个元素调用<code>re.findall()</code></td>
</tr>
<tr>
<td><code>replace()</code></td>
<td>用正则模式替换字符串</td>
</tr>
<tr>
<td><code>contains()</code></td>
<td>对每个元素调用<code>re.search()</code>,返回布尔类型值</td>
</tr>
<tr>
<td><code>count()</code></td>
<td>计算符合正则模式的字符串的数量</td>
</tr>
<tr>
<td><code>split()</code></td>
<td>等价于<code>str.split()</code>,支持正则表达式</td>
</tr>
<tr>
<td><code>rsplit()</code></td>
<td>等价于<code>str.rsplit()</code>,支持正则表达式</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="s1">'([A-Za-z]+)'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Gerald</td>
</tr>
<tr>
<th>1</th>
<td>James</td>
</tr>
<tr>
<th>2</th>
<td>Ronald</td>
</tr>
<tr>
<th>3</th>
<td>George</td>
</tr>
<tr>
<th>4</th>
<td>William</td>
</tr>
<tr>
<th>5</th>
<td>George</td>
</tr>
<tr>
<th>6</th>
<td>Barack</td>
</tr>
<tr>
<th>7</th>
<td>Donald</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>找出所有开头和结尾都是辅音字母的名字——这可以用正则表达式中的开始符号(<code>^</code>)与结尾符号(<code>$</code>)来实现:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s1">'^[^AEIOU].*[^aeiou]$'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 [Gerald R. Ford]
1 [James Carter]
2 [Ronald Reagan]
3 [George H. W. Bush]
4 [William J. Clinton]
5 [George W. Bush]
6 []
7 [Donald J. Trump]
dtype: object</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="其他字符串方法">其他字符串方法<a class="anchor-link" href="#其他字符串方法"> </a></h3><p>还有其他一些方法可以实现更方便的操作</p>
<table>
<thead><tr>
<th>方法</th>
<th>描述 </th>
</tr>
</thead>
<tbody>
<tr>
<td><code>get()</code></td>
<td>获取元素索引位置上的值,索引从0开始</td>
</tr>
<tr>
<td><code>slice()</code></td>
<td>对元素进行切片取值</td>
</tr>
<tr>
<td><code>slice_replace()</code></td>
<td>对元素进行切片替换</td>
</tr>
<tr>
<td><code>cat()</code></td>
<td>连接字符串(此功能比较复杂,建议阅读文档)</td>
</tr>
<tr>
<td><code>repeat()</code></td>
<td>重复元素</td>
</tr>
<tr>
<td><code>normalize()</code></td>
<td>将字符串转换为Unicode规范形式</td>
</tr>
<tr>
<td><code>pad()</code></td>
<td>在字符串的左边、右边或两边增加空格</td>
</tr>
<tr>
<td><code>wrap()</code></td>
<td>将字符串按照指定的宽度换行</td>
</tr>
<tr>
<td><code>join()</code></td>
<td>用分隔符连接<code>Series</code>的每个元素</td>
</tr>
<tr>
<td><code>get_dummies()</code></td>
<td>按照分隔符提取每个元素的<code>dummy</code>变量,转换为独热(one-hot)编码的<code>DataFrame</code></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="向量化字符串的取值与切片操作">向量化字符串的取值与切片操作<a class="anchor-link" href="#向量化字符串的取值与切片操作"> </a></h3><p><code>get()</code>与<code>slice()</code>操作可以从每个字符串数组中获取向量化元素。例如,我们可以通过<code>str.slice(0, 3)</code>获取每个字符串数组的前3个字符,<code>df.str.slice(0, 3)</code>=<code>df.str[0:3]</code>,<code>df.str.get(i)</code>=<code>df.str[i]</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span><span class="o">.</span><span class="n">str</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 Ger
1 Jam
2 Ron
3 Geo
4 Wil
5 Geo
6 Bar
7 Don
dtype: object</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>get()</code>与<code>slice()</code>操作还可以在<code>split()</code>操作之后使用。例如,要获取每个姓名的姓(last name),可以结合使用<code>split()</code>与<code>get()</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">monte</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">()</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0 Ford
1 Carter
2 Reagan
3 Bush
4 Clinton
5 Bush
6 Obama
7 Trump
dtype: object</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="指标变量">指标变量<a class="anchor-link" href="#指标变量"> </a></h3><p><code>get_dummies()</code>方法可以快速将指标变量分割成一个独热编码的<code>DataFrame</code>(每个元素都是0或1),如A=出生在美国、B=出生在英国、C=喜欢奶酪、D=喜欢午餐肉:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">full_monte</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
<span class="p">{</span>
<span class="s2">"name"</span><span class="p">:</span> <span class="n">monte</span><span class="p">,</span>
<span class="s2">"info"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"B|C|D"</span><span class="p">,</span> <span class="s2">"B|D"</span><span class="p">,</span> <span class="s2">"A|C"</span><span class="p">,</span> <span class="s2">"B|D"</span><span class="p">,</span> <span class="s2">"B|C"</span><span class="p">,</span> <span class="s2">"A|C"</span><span class="p">,</span> <span class="s2">"B|D"</span><span class="p">,</span> <span class="s2">"B|C|D"</span><span class="p">],</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">full_monte</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>name</th>
<th>info</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Gerald R. Ford</td>
<td>B|C|D</td>
</tr>
<tr>
<th>1</th>
<td>James Carter</td>
<td>B|D</td>
</tr>
<tr>
<th>2</th>
<td>Ronald Reagan</td>
<td>A|C</td>
</tr>
<tr>
<th>3</th>
<td>George H. W. Bush</td>
<td>B|D</td>
</tr>
<tr>
<th>4</th>
<td>William J. Clinton</td>
<td>B|C</td>
</tr>
<tr>
<th>5</th>
<td>George W. Bush</td>
<td>A|C</td>
</tr>
<tr>
<th>6</th>
<td>Barack Obama</td>
<td>B|D</td>
</tr>
<tr>
<th>7</th>
<td>Donald J. Trump</td>
<td>B|C|D</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">full_monte</span><span class="p">[</span><span class="s1">'info'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="s1">'|'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>0</td>
<td>1</td>
<td>0</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td>0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>0</td>
<td>1</td>
<td>0</td>
<td>1</td>
</tr>
<tr>
<th>4</th>
<td>0</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>5</th>
<td>1</td>
<td>0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>6</th>
<td>0</td>
<td>1</td>
<td>0</td>
<td>1</td>
</tr>
<tr>
<th>7</th>
<td>0</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="处理时间序列">处理时间序列<a class="anchor-link" href="#处理时间序列"> </a></h2><p>由于Pandas最初是为金融模型而创建的,因此日期时间数据处理功能非常强大</p>
<h3 id="Pandas的日期与时间工具">Pandas的日期与时间工具<a class="anchor-link" href="#Pandas的日期与时间工具"> </a></h3><p>Pandas所有关于日期与时间的处理方法全部都是通过<code>Timestamp</code>对象实现的,可以作为<code>Series</code>或<code>DataFrame</code>的索引<code>DatetimeIndex</code>。例如,可以用Pandas的方式演示前面介绍的日期与时间功能。我们可以灵活处理不同格式的日期与时间字符串,获取某一天是星期几:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">date</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">"4th of May, 2020"</span><span class="p">)</span>
<span class="n">date</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Timestamp('2020-05-04 00:00:00')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">date</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">'%A'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'Monday'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>可以直接进行NumPy类型的向量化运算:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">date</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_timedelta</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">12</span><span class="p">),</span> <span class="s1">'D'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-05-04', '2020-05-05', '2020-05-06', '2020-05-07',
'2020-05-08', '2020-05-09', '2020-05-10', '2020-05-11',
'2020-05-12', '2020-05-13', '2020-05-14', '2020-05-15'],
dtype='datetime64[ns]', freq=None)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Pandas时间序列:用时间作索引">Pandas时间序列:用时间作索引<a class="anchor-link" href="#Pandas时间序列:用时间作索引"> </a></h3><p>Pandas时间序列工具非常适合用来处理<strong>带时间戳的索引数据</strong>。</p>
<p>通过一个时间索引数据创建一个<code>Series</code>对象:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">([</span><span class="s2">"2019-01-04"</span><span class="p">,</span> <span class="s2">"2019-02-04"</span><span class="p">,</span> <span class="s2">"2020-03-04"</span><span class="p">,</span> <span class="s2">"2020-04-04"</span><span class="p">])</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
<span class="n">data</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>2019-01-04 0
2019-02-04 1
2020-03-04 2
2020-04-04 3
dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>直接用日期进行切片取值:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="p">[</span><span class="s1">'2020-02-04'</span><span class="p">:</span><span class="s1">'2020-04-04'</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>2020-03-04 2
2020-04-04 3
dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>直接通过年份切片获取该年的数据:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="p">[</span><span class="s1">'2020'</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>2020-03-04 2
2020-04-04 3
dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Pandas时间序列数据结构">Pandas时间序列数据结构<a class="anchor-link" href="#Pandas时间序列数据结构"> </a></h3><p>本节将介绍Pandas用来处理时间序列的基础数据类型。</p>
<ul>
<li><strong>时间戳</strong>,Pandas提供了<code>Timestamp</code>类型。本质上是Python的原生<code>datetime</code>类型的替代品,但是在性能更好的<code>numpy.datetime64</code>类型的基础上创建。对应的索引数据结构是<code>DatetimeIndex</code>。</li>
<li><strong>时间周期</strong>,Pandas提供了<code>Period</code>类型。这是利用<code>numpy.datetime64</code>类型将固定频率的时间间隔进行编码。对应的索引数据结构是<code>PeriodIndex</code>。</li>
<li><strong>时间增量</strong>或<strong>持续时间</strong>,Pandas提供了<code>Timedelta</code>类型。<code>Timedelta</code>是一种代替Python原生<code>datetime.timedelta</code>类型的高性能数据结构,同样是基于<code>numpy.timedelta64</code>类型。对应的索引数据结构是<code>TimedeltaIndex</code>。</li>
</ul>
<p>最基础的日期/时间对象是<code>Timestamp</code>和<code>DatetimeIndex</code>,对<code>pd.to_datetime()</code>传递一个日期会返回一个<code>Timestamp</code>类型,传递一个时间序列会返回一个<code>DatetimeIndex</code>类型:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span>
<span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">"4th of July, 2020"</span><span class="p">,</span> <span class="s2">"2020-Jul-6"</span><span class="p">,</span> <span class="s2">"07-07-2020"</span><span class="p">,</span> <span class="s2">"20200708"</span><span class="p">]</span>
<span class="p">)</span>
<span class="n">dates</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-07-03', '2020-07-04', '2020-07-06', '2020-07-07',
'2020-07-08'],
dtype='datetime64[ns]', freq=None)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>任何<code>DatetimeIndex</code>类型都可以通过<code>to_period()</code>方法和一个频率代码转换成<code>PeriodIndex</code>类型。</p>
<p>用<code>'D'</code>将数据转换成单日的时间序列:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dates</span><span class="o">.</span><span class="n">to_period</span><span class="p">(</span><span class="s1">'D'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>PeriodIndex(['2020-07-03', '2020-07-04', '2020-07-06', '2020-07-07',
'2020-07-08'],
dtype='period[D]', freq='D')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>当用一个日期减去另一个日期时,返回的结果是<code>TimedeltaIndex</code>类型:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dates</span> <span class="o">-</span> <span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="有规律的时间序列:pd.date_range()">有规律的时间序列:<code>pd.date_range()</code><a class="anchor-link" href="#有规律的时间序列:pd.date_range()"> </a></h3><p>为了能更简便地创建有规律的时间序列,Pandas提供了一些方法:<code>pd.date_range()</code>可以处理时间戳、<code>pd.period_range()</code>可以处理周期、<code>pd.timedelta_range()</code>可以处理时间间隔。通过开始日期、结束日期和频率代码(同样是可选的)创建一个有规律的日期序列,默认的频率是天:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'2020-07-03'</span><span class="p">,</span> <span class="s1">'2020-07-10'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-07-03', '2020-07-04', '2020-07-05', '2020-07-06',
'2020-07-07', '2020-07-08', '2020-07-09', '2020-07-10'],
dtype='datetime64[ns]', freq='D')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>日期范围不一定非是开始时间与结束时间,也可以是开始时间与周期数<code>periods</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'2020-07-03'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-07-03', '2020-07-04', '2020-07-05', '2020-07-06',
'2020-07-07', '2020-07-08', '2020-07-09', '2020-07-10'],
dtype='datetime64[ns]', freq='D')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>通过<code>freq</code>参数改变时间间隔,默认值是<code>D</code>。例如,可以创建一个按小时变化的时间戳:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'2020-07-03'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'H'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-07-03 00:00:00', '2020-07-03 01:00:00',
'2020-07-03 02:00:00', '2020-07-03 03:00:00',
'2020-07-03 04:00:00', '2020-07-03 05:00:00',
'2020-07-03 06:00:00', '2020-07-03 07:00:00'],
dtype='datetime64[ns]', freq='H')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>如果要创建一个有规律的周期或时间间隔序列,有类似的函数<code>pd.period_range()</code>和<code>pd.timedelta_range()</code>。下面是一个以月为周期的示例:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">period_range</span><span class="p">(</span><span class="s1">'2020-07'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'M'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>PeriodIndex(['2020-07', '2020-08', '2020-09', '2020-10', '2020-11', '2020-12',
'2021-01', '2021-02'],
dtype='period[M]', freq='M')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>以小时递增:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">timedelta_range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'H'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',
'05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],
dtype='timedelta64[ns]', freq='H')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="时间频率与偏移量">时间频率与偏移量<a class="anchor-link" href="#时间频率与偏移量"> </a></h3><p>Pandas时间序列工具的基础是时间频率或偏移量(offset)代码。就像之前见过的<code>D</code>(day)和<code>H</code>(hour)代码,可以设置任意需要的时间间隔</p>
<table>
<thead><tr>
<th>代码</th>
<th>描述</th>
<th>代码</th>
<th>描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>D</code></td>
<td>天(calendar day,按日历算,含双休日)</td>
<td><code>B</code></td>
<td>天(business day,仅含工作日)</td>
</tr>
<tr>
<td><code>W</code></td>
<td>周(weekly)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>M</code></td>
<td>月末(month end)</td>
<td><code>BM</code></td>
<td>月末(business month end,仅含工作日)</td>
</tr>
<tr>
<td><code>Q</code></td>
<td>季节末(quarter end)</td>
<td><code>BQ</code></td>
<td>季节末(business quarter end,仅含工作日)</td>
</tr>
<tr>
<td><code>A</code></td>
<td>年末(year end)</td>
<td><code>BA</code></td>
<td>年末(business year end,仅含工作日)</td>
</tr>
<tr>
<td><code>H</code></td>
<td>小时(hours)</td>
<td><code>BH</code></td>
<td>小时(business hours,工作时间)</td>
</tr>
<tr>
<td><code>T</code></td>
<td>分钟(minutes)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>S</code></td>
<td>秒(seconds)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>L</code></td>
<td>毫秒(milliseonds)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>U</code></td>
<td>微秒(microseconds)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>N</code></td>
<td>纳秒(nanoseconds)</td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<p>月、季、年频率都是具体周期的结束时间(月末、季末、年末),而有一些以<code>S</code>(start,开始) 为后缀的代码表示日期开始。</p>
<table>
<thead><tr>
<th>代码</th>
<th>频率 </th>
</tr>
</thead>
<tbody>
<tr>
<td><code>MS</code></td>
<td>月初(month start)</td>
</tr>
<tr>
<td><code>BMS</code></td>
<td>月初(business month start,仅含工作日)</td>
</tr>
<tr>
<td><code>QS</code></td>
<td>季初(quarter start)</td>
</tr>
<tr>
<td><code>BQS</code></td>
<td>季初(business quarter start,仅含工作日)</td>
</tr>
<tr>
<td><code>AS</code></td>
<td>年初(year start)</td>
</tr>
<tr>
<td><code>BAS</code></td>
<td>年初(business year start,仅含工作日)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>另外,可以在频率代码后面加三位月份缩写字母来改变季、年频率的开始时间:</p>
<ul>
<li><code>Q-JAN</code>、<code>BQ-FEB</code>、<code>QS-MAR</code>、<code>BQS-APR</code>等</li>
<li><code>A-JAN</code>、<code>BA-FEB</code>、<code>AS-MAR</code>、<code>BAS-APR</code>等</li>
</ul>
<p>也可以在后面加三位星期缩写字母来改变一周的开始时间:</p>
<ul>
<li><code>W-SUN</code>、<code>W-MON</code>、<code>W-TUE</code>、<code>W-WED</code>等</li>
</ul>
<p>还可以将频率组合起来创建的新的周期。例如,可以用小时(<code>H</code>)和分钟(<code>T</code>)的组合来实现2小时30分钟:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">timedelta_range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">"2H30T"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',
'12:30:00', '15:00:00', '17:30:00', '20:00:00'],
dtype='timedelta64[ns]', freq='150T')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>所有这些频率代码都对应Pandas时间序列的偏移量,具体内容可以在<code>pd.tseries.offsets</code>模块中找到。例如,可以用下面的方法直接创建一个工作日偏移序列:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pandas.tseries.offsets</span> <span class="kn">import</span> <span class="n">BDay</span>
<span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'2020-07-01'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="n">BDay</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DatetimeIndex(['2020-07-01', '2020-07-02', '2020-07-03', '2020-07-06',
'2020-07-07'],
dtype='datetime64[ns]', freq='B')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="重采样、时间迁移和窗口函数">重采样、时间迁移和窗口函数<a class="anchor-link" href="#重采样、时间迁移和窗口函数"> </a></h3><p>下面用贵州茅台的历史股票价格演示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> snowball.py
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">import</span> <span class="nn">requests</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="k">def</span> <span class="nf">get_stock</span><span class="p">(</span><span class="n">code</span><span class="p">):</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
<span class="s2">"https://stock.xueqiu.com/v5/stock/chart/kline.json"</span><span class="p">,</span>
<span class="n">headers</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"User-Agent"</span><span class="p">:</span> <span class="s2">"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36"</span>
<span class="p">},</span>
<span class="n">params</span><span class="o">=</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"symbol"</span><span class="p">,</span> <span class="n">code</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"begin"</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">timestamp</span><span class="p">()</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"period"</span><span class="p">,</span> <span class="s2">"day"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"type"</span><span class="p">,</span> <span class="s2">"before"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"count"</span><span class="p">,</span> <span class="s2">"-5000"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"indicator"</span><span class="p">,</span> <span class="s2">"kline"</span><span class="p">),</span>
<span class="p">),</span>
<span class="n">cookies</span><span class="o">=</span><span class="p">{</span><span class="s2">"xq_a_token"</span><span class="p">:</span> <span class="s2">"328f8bbf7903261db206d83de7b85c58e4486dda"</span><span class="p">,},</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">response</span><span class="o">.</span><span class="n">ok</span><span class="p">:</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">()[</span><span class="s2">"data"</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">d</span><span class="p">[</span><span class="s2">"item"</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="n">d</span><span class="p">[</span><span class="s2">"column"</span><span class="p">])</span>
<span class="n">data</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">timestamp</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s2">"ms"</span><span class="p">,</span> <span class="n">tz</span><span class="o">=</span><span class="s2">"Asia/Shanghai"</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">data</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"stock error"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing snowball.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">snowball</span> <span class="kn">import</span> <span class="n">get_stock</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">get_stock</span><span class="p">(</span><span class="s2">"SH600519"</span><span class="p">)</span> <span class="c1"># 贵州茅台</span>
<span class="n">data</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>timestamp</th>
<th>volume</th>
<th>open</th>
<th>high</th>
<th>low</th>
<th>close</th>
<th>chg</th>
<th>percent</th>
<th>turnoverrate</th>
<th>amount</th>
<th>volume_post</th>
<th>amount_post</th>
</tr>
<tr>
<th>timestamp</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020-05-08 00:00:00+08:00</th>
<td>1588867200000</td>
<td>2907868</td>
<td>1317.0</td>
<td>1338.00</td>
<td>1308.51</td>
<td>1314.61</td>
<td>2.61</td>
<td>0.20</td>
<td>0.23</td>
<td>3.839218e+09</td>
<td>None</td>
<td>None</td>
</tr>
<tr>
<th>2020-05-11 00:00:00+08:00</th>
<td>1589126400000</td>
<td>2367119</td>
<td>1320.0</td>
<td>1335.00</td>
<td>1313.67</td>
<td>1323.01</td>
<td>8.40</td>
<td>0.64</td>
<td>0.19</td>
<td>3.135991e+09</td>
<td>None</td>
<td>None</td>
</tr>
<tr>
<th>2020-05-12 00:00:00+08:00</th>
<td>1589212800000</td>
<td>1972181</td>
<td>1318.0</td>
<td>1334.99</td>
<td>1316.00</td>
<td>1333.00</td>
<td>9.99</td>
<td>0.76</td>
<td>0.16</td>
<td>2.621825e+09</td>
<td>None</td>
<td>None</td>
</tr>
<tr>
<th>2020-05-13 00:00:00+08:00</th>
<td>1589299200000</td>
<td>2201431</td>
<td>1333.0</td>
<td>1337.99</td>
<td>1322.88</td>
<td>1335.95</td>
<td>2.95</td>
<td>0.22</td>
<td>0.18</td>
<td>2.931465e+09</td>
<td>None</td>
<td>None</td>
</tr>
<tr>
<th>2020-05-14 00:00:00+08:00</th>
<td>1589385600000</td>
<td>1857492</td>
<td>1330.0</td>
<td>1334.88</td>
<td>1325.11</td>
<td>1326.59</td>
<td>-9.36</td>
<td>-0.70</td>
<td>0.15</td>
<td>2.467976e+09</td>
<td>None</td>
<td>None</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gzmt</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'close'</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gzmt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">8</span><span class="p">));</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'贵州茅台历史收盘价'</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="重采样与频率转换">重采样与频率转换<a class="anchor-link" href="#重采样与频率转换"> </a></h3><p>按照新的频率(更高频率、更低频率)对数据进行重采样,可以通过<code>resample()</code>方法、<code>asfreq()</code>方法。</p>
<ol>
<li><code>resample()</code>方法是以<strong>数据累计</strong>(data aggregation)为基础</li>
<li><code>asfreq()</code>方法是以<strong>数据选择</strong>(data selection)为基础。</li>
</ol>
<p>用两种方法对数据进行下采样(down-sample,减少采样频率,从日到月)。用每年末(<code>'BA'</code>,最后一个工作日)对数据进行重采样:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s2">"-"</span><span class="p">)</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s2">"BA"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">":"</span><span class="p">)</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">asfreq</span><span class="p">(</span><span class="s2">"BA"</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">"--"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'贵州茅台历史收盘价年末采样'</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"input"</span><span class="p">,</span> <span class="s2">"resample"</span><span class="p">,</span> <span class="s2">"asfreq"</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s2">"upper left"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>在每个数据点上,<code>resample</code>反映的是<strong>上一年的均值</strong>,而<code>asfreq</code>反映的是<strong>上一年最后一个工作日的收盘价</strong>。</p>
<p>在进行上采样(up-sampling,增加采样频率,从月到日)时,<code>resample()</code>与<code>asfreq()</code>的用法大体相同,</p>
</blockquote>
<p>两种方法都默认将采样作为缺失值<code>NaN</code>,与<code>pd.fillna()</code>函数类似,<code>asfreq()</code>有一个<code>method</code>参数可以设置填充缺失值的方式。对数据按天进行重采样(包含周末),<code>asfreq()</code>向前填充与向后填充缺失值的结果对比:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">gzmt</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">14</span><span class="p">:]</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"贵州茅台近两周收盘价"</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">asfreq</span><span class="p">(</span><span class="s2">"D"</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"采样缺失值填充方法对比"</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">asfreq</span><span class="p">(</span><span class="s2">"D"</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"bfill"</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="s2">"-o"</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">asfreq</span><span class="p">(</span><span class="s2">"D"</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"ffill"</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">style</span><span class="o">=</span><span class="s2">"--o"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"back-fill"</span><span class="p">,</span> <span class="s2">"forward-fill"</span><span class="p">]);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="时间迁移">时间迁移<a class="anchor-link" href="#时间迁移"> </a></h3><p>Pandas提供<code>shift()</code>方法<strong>迁移数据</strong>,<code>tshift()</code>方法<strong>迁移索引</strong>。两种方法都是按照频率代码进行迁移。</p>
<p>用<code>shift()</code>和<code>tshift()</code>这两种方法让数据迁移900天:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="c1"># 对数据应用时间频率,用向后填充解决缺失值</span>
<span class="n">gzmt</span> <span class="o">=</span> <span class="n">gzmt</span><span class="o">.</span><span class="n">asfreq</span><span class="p">(</span><span class="s2">"D"</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"pad"</span><span class="p">)</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">900</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">gzmt</span><span class="o">.</span><span class="n">tshift</span><span class="p">(</span><span class="mi">900</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="c1"># 设置图例与标签</span>
<span class="n">local_max</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">"2010-01-01"</span><span class="p">)</span>
<span class="n">offset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="mi">900</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"input"</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">()[</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="s2">"heavy"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">local_max</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"shift(900)"</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">()[</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="s2">"heavy"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">local_max</span> <span class="o">+</span> <span class="n">offset</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"tshift(900)"</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="s2">"heavy"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">local_max</span> <span class="o">+</span> <span class="n">offset</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><code>shift(900)</code>将<strong>数据</strong>向前推进了900天,这样图形中的一段就消失了(最左侧就变成了缺失值),而<code>tshift(900)</code>方法是将<strong>时间索引值</strong>向前推进了900天。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>可以用迁移后的值来计算gzmtle股票一年期的投资回报率:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ROI</span> <span class="o">=</span> <span class="p">(</span><span class="n">gzmt</span><span class="o">.</span><span class="n">tshift</span><span class="p">(</span><span class="o">-</span><span class="mi">365</span><span class="p">)</span> <span class="o">/</span> <span class="n">gzmt</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
<span class="n">ROI</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"贵州茅台年度ROI"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="移动时间窗口">移动时间窗口<a class="anchor-link" href="#移动时间窗口"> </a></h3><p>Pandas处理时间序列数据的第3种操作是移动统计值(rolling statistics)。通过<code>Series</code>和<code>DataFrame</code>的<code>rolling()</code>属性实现,它会返回与<code>groupby</code>操作类似的结果。</p>
<p>计算茅台股票收盘价的一年期移动平均值和标准差:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rolling</span> <span class="o">=</span> <span class="n">gzmt</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">365</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
<span class="p">{</span>
<span class="s2">"input"</span><span class="p">:</span> <span class="n">gzmt</span><span class="p">,</span>
<span class="s2">"one-year rolling_mean"</span><span class="p">:</span> <span class="n">rolling</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
<span class="s2">"one-year rolling_std"</span><span class="p">:</span> <span class="n">rolling</span><span class="o">.</span><span class="n">std</span><span class="p">(),</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s2">"-"</span><span class="p">,</span> <span class="s2">"--"</span><span class="p">,</span> <span class="s2">":"</span><span class="p">],</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_alpha</span><span class="p">(</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"贵州茅台一年期移动平均值和标准差"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="高性能Pandas:eval()与query()">高性能Pandas:<code>eval()</code>与<code>query()</code><a class="anchor-link" href="#高性能Pandas:eval()与query()"> </a></h2><p>Pandas在处理复合代数式时(compound expression),每段中间过程都需要占用内存。Pandas从0.13版开始(2014年1月)基于<code>Numexpr</code>程序包实现了<code>query()</code>与<code>eval()</code>,可以避免中间过程直接运算,借助NumPy风格的<strong>字符串</strong>实现,可以比普通方法快一倍(而且内存消耗更少)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span> <span class="o">=</span> <span class="mi">100000</span><span class="p">,</span> <span class="mi">100</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">df3</span><span class="p">,</span> <span class="n">df4</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> df1 + df2 + df3 + df4
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>46.6 ms ± 394 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> pd.eval('df1 + df2 + df3 + df4')
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>28.3 ms ± 424 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="算术运算符">算术运算符<a class="anchor-link" href="#算术运算符"> </a></h3><p><code>pd.eval()</code>支持所有的算术运算符:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="o">-</span><span class="n">df1</span> <span class="o">*</span> <span class="n">df2</span> <span class="o">/</span> <span class="p">(</span><span class="n">df3</span> <span class="o">+</span> <span class="n">df4</span><span class="p">)</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'-df1 * df2 / (df3 + df4)'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="比较运算符">比较运算符<a class="anchor-link" href="#比较运算符"> </a></h3><p><code>pd.eval()</code>支持所有的比较运算符,包括链式代数式(chained expression):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="p">(</span><span class="n">df1</span> <span class="o"><</span> <span class="n">df2</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df2</span> <span class="o"><=</span> <span class="n">df3</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df3</span> <span class="o">!=</span> <span class="n">df4</span><span class="p">)</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'df1 < df2 <= df3 != df4'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="位运算符">位运算符<a class="anchor-link" href="#位运算符"> </a></h3><p><code>pd.eval()</code>支持<code>&</code>(与)和<code>|</code>(或)等位运算符:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="p">(</span><span class="n">df1</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df2</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df3</span> <span class="o"><</span> <span class="n">df4</span><span class="p">)</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>还可以在布尔类型的代数式中使用<code>and</code>和<code>or</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="对象属性与索引">对象属性与索引<a class="anchor-link" href="#对象属性与索引"> </a></h3><p><code>pd.eval()</code>可以通过<code>obj.attr</code>语法获取对象属性,通过<code>obj[index]</code>语法获取对象索引:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">df3</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'df2.T[0] + df3.iloc[1]'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="用DataFrame.eval()实现列间运算">用<code>DataFrame.eval()</code>实现列间运算<a class="anchor-link" href="#用DataFrame.eval()实现列间运算"> </a></h3><p>由于<code>pd.eval()</code>是Pandas的顶层函数,因此<code>DataFrame</code>有一个<code>eval()</code>方法可以做类似的运算。使用<code>eval()</code>方法的好处是可以借助<strong>列名称</strong>进行运算,示例如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"B"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">])</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.615875</td>
<td>0.525167</td>
<td>0.047354</td>
</tr>
<tr>
<th>1</th>
<td>0.330858</td>
<td>0.412879</td>
<td>0.441564</td>
</tr>
<tr>
<th>2</th>
<td>0.689047</td>
<td>0.559068</td>
<td>0.230350</td>
</tr>
<tr>
<th>3</th>
<td>0.290486</td>
<td>0.695479</td>
<td>0.852587</td>
</tr>
<tr>
<th>4</th>
<td>0.424280</td>
<td>0.534344</td>
<td>0.245216</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s1">'B'</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'C'</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s2">"(df.A + df.B) / (df.C - 1)"</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result3</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'(A + B) / (C - 1)'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="用DataFrame.eval()新增或修改列">用<code>DataFrame.eval()</code>新增或修改列<a class="anchor-link" href="#用DataFrame.eval()新增或修改列"> </a></h3><p>除了前面介绍的运算功能,<code>DataFrame.eval()</code>还可以创建新的列,创建一个新的列<code>'D'</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'D = (A + B) / C'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.615875</td>
<td>0.525167</td>
<td>0.047354</td>
<td>24.095868</td>
</tr>
<tr>
<th>1</th>
<td>0.330858</td>
<td>0.412879</td>
<td>0.441564</td>
<td>1.684325</td>
</tr>
<tr>
<th>2</th>
<td>0.689047</td>
<td>0.559068</td>
<td>0.230350</td>
<td>5.418335</td>
</tr>
<tr>
<th>3</th>
<td>0.290486</td>
<td>0.695479</td>
<td>0.852587</td>
<td>1.156439</td>
</tr>
<tr>
<th>4</th>
<td>0.424280</td>
<td>0.534344</td>
<td>0.245216</td>
<td>3.909296</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>还可以修改已有的列:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'D = (A - B) / C'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.615875</td>
<td>0.525167</td>
<td>0.047354</td>
<td>1.915527</td>
</tr>
<tr>
<th>1</th>
<td>0.330858</td>
<td>0.412879</td>
<td>0.441564</td>
<td>-0.185752</td>
</tr>
<tr>
<th>2</th>
<td>0.689047</td>
<td>0.559068</td>
<td>0.230350</td>
<td>0.564268</td>
</tr>
<tr>
<th>3</th>
<td>0.290486</td>
<td>0.695479</td>
<td>0.852587</td>
<td>-0.475016</td>
</tr>
<tr>
<th>4</th>
<td>0.424280</td>
<td>0.534344</td>
<td>0.245216</td>
<td>-0.448844</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="DataFrame.eval()使用局部变量"><code>DataFrame.eval()</code>使用局部变量<a class="anchor-link" href="#DataFrame.eval()使用局部变量"> </a></h3><p><code>DataFrame.eval()</code>方法还支持通过<code>@</code>符号使用Python的局部变量,如下所示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">column_mean</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">result1</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">+</span> <span class="n">column_mean</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'A + @column_mean'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><code>@</code>符号表示<strong>变量名称</strong>(Python对象的命名空间)而非<strong>列名称</strong>(DataFrame列名称的命名空间)。需要注意的是,<code>@</code>符号只能在<code>DataFrame.eval()</code><strong>方法</strong>中使用,而不能在<code>pandas.eval()</code><strong>函数</strong>中使用,因为<code>pandas.eval()</code>函数只能获取一个(Python)命名空间的内容。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="DataFrame.query()方法"><code>DataFrame.query()</code>方法<a class="anchor-link" href="#DataFrame.query()方法"> </a></h3><p>用<code>query()</code>方法进行过滤运算:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result1</span> <span class="o">=</span> <span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">B</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">)]</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">'df[(df.A < 0.5) & (df.B < 0.5)]'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result3</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'A < 0.5 and B < 0.5'</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">result1</span><span class="p">,</span> <span class="n">result3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Cython与Numba">Cython与Numba<a class="anchor-link" href="#Cython与Numba"> </a></h1><h2 id="Cython"><a href="http://cython.org/">Cython</a><a class="anchor-link" href="#Cython"> </a></h2><p>直接将Python代码编译成C/C++,然后编译成Python模块:</p>
<ul>
<li>用Python代码调用原生C/C++</li>
<li>用静态类型声明让Python代码达到C语言的性能</li>
<li>代码变得更啰嗦,会破坏可维护性和可读性</li>
</ul>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/py_cy.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> Cython
<span class="c1"># %reload_ext Cython</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span>
<span class="k">cdef</span> <span class="kt">int</span> <span class="nf">a</span> <span class="o">=</span> <span class="mf">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mf">10</span><span class="p">):</span>
<span class="n">a</span> <span class="o">+=</span> <span class="n">i</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>45
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>用Cython把<code>.pyx</code>文件编译(翻译)成<code>.c</code>文件。这些文件里的源代码,基本都是纯Python代码加上一些Cython代码</li>
<li><code>.c</code>文件被C语言编译器编译成<code>.so</code>库,这个库之后可以导入Python</li>
<li>编译代码有3种方法:<ol>
<li>创建一个<code>distutils</code>模块配置文件,生成自定义的C语言编译文件。</li>
<li>运行<code>cython</code>命令将<code>.pyx</code>文件编译成<code>.c</code>文件,然后用C语言编译器(gcc)把C代码手动编译成库文件。</li>
<li>用<code>pyximport</code>,像导入<code>.py</code>文件一样导入<code>.pyx</code>直接使用。</li>
</ol>
</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="创建Cython模块">创建Cython模块<a class="anchor-link" href="#创建Cython模块"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>%%bash
<span class="nb">pwd</span>
rm -rf test_cython
mkdir test_cython
ls
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>/home/junjiet/data_science2020/2.数据处理
2.数据处理.ipynb
cat_neural_network.mp4
cpp.ipynb
data2info.png
data_type.png
markmap.png
matlab_numpy.png
nn_flow.png
numpy.png
pandas.png
py_cy.png
pysparkdf.png
python_visual.png
rdd.png
sigmoid.png
social_network.jpg
test_cython
two_layer_nn.png
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cd</span> <span class="n">test_cython</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>/home/junjiet/data_science2020/2.数据处理/test_cython
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pwd</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'/home/junjiet/data_science2020/2.数据处理/test_cython'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> test.pyx
<span class="k">def</span> <span class="nf">join_n_print</span><span class="p">(</span><span class="n">parts</span><span class="p">):</span>
<span class="sd">"""merge string list with space"""</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing test.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>test.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="pyximport自动编译">pyximport自动编译<a class="anchor-link" href="#pyximport自动编译"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span>
<span class="k">import</span> <span class="nn">pyximport</span><span class="p">;</span> <span class="n">pyximport</span><span class="o">.</span><span class="n">install</span><span class="p">()</span>
<span class="k">from</span> <span class="nn">test_cython.test</span> <span class="k">import</span> <span class="n">join_n_print</span>
<span class="n">join_n_print</span><span class="p">([</span><span class="s">"This"</span><span class="p">,</span> <span class="s">"is"</span><span class="p">,</span> <span class="s">"a"</span><span class="p">,</span> <span class="s">"test"</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>This is a test
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="setup.py手动编译">setup.py手动编译<a class="anchor-link" href="#setup.py手动编译"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> setup.py
<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
<span class="kn">from</span> <span class="nn">Cython.Build</span> <span class="kn">import</span> <span class="n">cythonize</span>
<span class="n">setup</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"Test app"</span><span class="p">,</span> <span class="n">ext_modules</span><span class="o">=</span><span class="n">cythonize</span><span class="p">(</span><span class="s2">"test.pyx"</span><span class="p">),</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing setup.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>python setup.py build_ext --inplace
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>running build_ext
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre><span class="ansi-blue-intense-fg ansi-bold">build</span>/ setup.py test.c <span class="ansi-green-intense-fg ansi-bold">test.cpython-37m-x86_64-linux-gnu.so</span>* test.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>tree build/
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>build/
└── temp.linux-x86_64-3.7
└── test.o
1 directory, 1 file
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Cython通常都需要导入两类文件:</p>
<ul>
<li><strong>定义文件</strong>:文件扩展名.pxd,是Cython文件要使用的变量、类型、函数名称的C语言声明。</li>
<li><strong>实现文件</strong>:文件扩展名.pyx,包括在<code>.pxd</code>文件中已经定义好的函数实现。</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> dishes.pxd
<span class="n">cdef</span> <span class="n">enum</span> <span class="n">otherstuff</span><span class="p">:</span>
<span class="n">sausage</span><span class="p">,</span> <span class="n">eggs</span><span class="p">,</span> <span class="n">lettuce</span>
<span class="n">cdef</span> <span class="n">struct</span> <span class="n">spamdish</span><span class="p">:</span>
<span class="nb">int</span> <span class="n">oz_of_spam</span>
<span class="n">otherstuff</span> <span class="n">filler</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing dishes.pxd
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> restaurant.pyx
<span class="n">cimport</span> <span class="n">dishes</span>
<span class="kn">from</span> <span class="nn">dishes</span> <span class="n">cimport</span> <span class="n">spamdish</span>
<span class="n">cdef</span> <span class="n">void</span> <span class="n">prepare</span><span class="p">(</span><span class="n">spamdish</span> <span class="o">*</span> <span class="n">d</span><span class="p">):</span>
<span class="n">d</span><span class="o">.</span><span class="n">oz_of_spam</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">d</span><span class="o">.</span><span class="n">filler</span> <span class="o">=</span> <span class="n">dishes</span><span class="o">.</span><span class="n">sausage</span>
<span class="k">def</span> <span class="nf">serve</span><span class="p">():</span>
<span class="n">cdef</span> <span class="n">spamdish</span> <span class="n">d</span>
<span class="n">prepare</span><span class="p">(</span> <span class="o">&</span> <span class="n">d</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">d</span><span class="o">.</span><span class="n">oz_of_spam</span><span class="si">}</span><span class="s2"> oz spam, filler no. </span><span class="si">{</span><span class="n">d</span><span class="o">.</span><span class="n">filler</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing restaurant.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="调用Cython模块">调用Cython模块<a class="anchor-link" href="#调用Cython模块"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre><span class="ansi-blue-intense-fg ansi-bold">build</span>/ setup.py test.c <span class="ansi-green-intense-fg ansi-bold">test.cpython-37m-x86_64-linux-gnu.so</span>* test.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">test_cython.test</span> <span class="kn">import</span> <span class="n">join_n_print</span>
<span class="n">join_n_print</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>a b c
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="定义函数类型">定义函数类型<a class="anchor-link" href="#定义函数类型"> </a></h2><p>Cython除了可以调用标准C语言函数,还可以定义两种函数:</p>
<ul>
<li><strong>标准Python函数</strong>:与纯Python代码中声明的函数完全一样,用<code>cdef</code>关键字定义。接受Python对象作为参数,也返回Python对象</li>
<li><strong>C函数</strong>:是标准函数的优化版,用Python对象或C语言类型作为参数,返回值也可以是两种类型。要定义这种函数,用<code>cpdef</code>关键字定义</li>
</ul>
<blockquote><p>虽然这两种函数都可以通过Cython模块调用。但是从Python代码(.py)中调用函数,必须是标准Python函数,或者<code>cpdef</code>关键字定义函数。这个关键字会创建一个函数的封装对象。当用Cython调用函数时,它用C语言对象;当从Python代码中调用函数时,它用纯Python函数。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面是一个纯Python函数,因此Cython会让这个函数返回并接收一个Python对象,而不是C语言原生类型。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span>
<span class="k">cdef</span> <span class="nf">full_python_function</span> <span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">**</span><span class="mf">2</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这个函数使用了<code>cpdef</code>关键字,所以它既是一个标准函数,也是一个优化过的C语言函数。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span>
<span class="k">cpdef</span> <span class="kt">int</span> <span class="nf">c_function</span><span class="p">(</span><span class="nb">int</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">**</span><span class="mf">2</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="优化示例">优化示例<a class="anchor-link" href="#优化示例"> </a></h2><p>两经纬度地理距离,A点经纬度(110.0123, 23.32435),B点经纬度(129.1344,25.5465)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="纯Python">纯Python<a class="anchor-link" href="#纯Python"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span> <span class="o">=</span> <span class="mf">110.0123</span><span class="p">,</span> <span class="mf">23.32435</span><span class="p">,</span> <span class="mf">129.1344</span><span class="p">,</span> <span class="mf">25.5465</span>
<span class="n">num</span> <span class="o">=</span> <span class="mi">5000000</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> great_circle_py.py
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span>
<span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">):</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mi">6371</span> <span class="c1"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span> <span class="o">-</span> <span class="n">lat1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span> <span class="o">-</span> <span class="n">lat2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span> <span class="o">-</span> <span class="n">lon1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Overwriting great_circle_py.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">great_circle_py</span> <span class="kn">import</span> <span class="n">great_circle</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num</span><span class="p">):</span>
<span class="n">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span> <span class="o">-</span><span class="n">a</span>
<span class="k">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span>
<span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">):</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">/</span> <span class="mf">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span> <span class="o">-</span> <span class="n">lat1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span> <span class="o">-</span> <span class="n">lat2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span> <span class="o">-</span> <span class="n">lon1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>building '_cython_magic_510139e97843e1ad4066ec2ca94da783' extension
/home/junjiet/conda/bin/x86_64-conda_cos6-linux-gnu-cc -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include -fPIC -I/home/junjiet/conda/include/python3.7m -c /home/junjiet/.cache/ipython/cython/_cython_magic_510139e97843e1ad4066ec2ca94da783.c -o /home/junjiet/.cache/ipython/cython/home/junjiet/.cache/ipython/cython/_cython_magic_510139e97843e1ad4066ec2ca94da783.o
x86_64-conda_cos6-linux-gnu-gcc -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/junjiet/conda/lib -Wl,-rpath-link,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include /home/junjiet/.cache/ipython/cython/home/junjiet/.cache/ipython/cython/_cython_magic_510139e97843e1ad4066ec2ca94da783.o -o /home/junjiet/.cache/ipython/cython/_cython_magic_510139e97843e1ad4066ec2ca94da783.cpython-37m-x86_64-linux-gnu.so
</pre>
</div>
</div>
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<!DOCTYPE html>
<!-- Generated by Cython 0.29.15 -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Cython: _cython_magic_510139e97843e1ad4066ec2ca94da783.pyx</title>
<style type="text/css">
body.cython { font-family: courier; font-size: 12; }
.cython.tag { }
.cython.line { margin: 0em }
.cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }
.cython.line .run { background-color: #B0FFB0; }
.cython.line .mis { background-color: #FFB0B0; }
.cython.code.run { border-left: 8px solid #B0FFB0; }
.cython.code.mis { border-left: 8px solid #FFB0B0; }
.cython.code .py_c_api { color: red; }
.cython.code .py_macro_api { color: #FF7000; }
.cython.code .pyx_c_api { color: #FF3000; }
.cython.code .pyx_macro_api { color: #FF7000; }
.cython.code .refnanny { color: #FFA000; }
.cython.code .trace { color: #FFA000; }
.cython.code .error_goto { color: #FFA000; }
.cython.code .coerce { color: #008000; border: 1px dotted #008000 }
.cython.code .py_attr { color: #FF0000; font-weight: bold; }
.cython.code .c_attr { color: #0000FF; }
.cython.code .py_call { color: #FF0000; font-weight: bold; }
.cython.code .c_call { color: #0000FF; }
.cython.score-0 {background-color: #FFFFff;}
.cython.score-1 {background-color: #FFFFe7;}
.cython.score-2 {background-color: #FFFFd4;}
.cython.score-3 {background-color: #FFFFc4;}
.cython.score-4 {background-color: #FFFFb6;}
.cython.score-5 {background-color: #FFFFaa;}
.cython.score-6 {background-color: #FFFF9f;}
.cython.score-7 {background-color: #FFFF96;}
.cython.score-8 {background-color: #FFFF8d;}
.cython.score-9 {background-color: #FFFF86;}
.cython.score-10 {background-color: #FFFF7f;}
.cython.score-11 {background-color: #FFFF79;}
.cython.score-12 {background-color: #FFFF73;}
.cython.score-13 {background-color: #FFFF6e;}
.cython.score-14 {background-color: #FFFF6a;}
.cython.score-15 {background-color: #FFFF66;}
.cython.score-16 {background-color: #FFFF62;}
.cython.score-17 {background-color: #FFFF5e;}
.cython.score-18 {background-color: #FFFF5b;}
.cython.score-19 {background-color: #FFFF57;}
.cython.score-20 {background-color: #FFFF55;}
.cython.score-21 {background-color: #FFFF52;}
.cython.score-22 {background-color: #FFFF4f;}
.cython.score-23 {background-color: #FFFF4d;}
.cython.score-24 {background-color: #FFFF4b;}
.cython.score-25 {background-color: #FFFF48;}
.cython.score-26 {background-color: #FFFF46;}
.cython.score-27 {background-color: #FFFF44;}
.cython.score-28 {background-color: #FFFF43;}
.cython.score-29 {background-color: #FFFF41;}
.cython.score-30 {background-color: #FFFF3f;}
.cython.score-31 {background-color: #FFFF3e;}
.cython.score-32 {background-color: #FFFF3c;}
.cython.score-33 {background-color: #FFFF3b;}
.cython.score-34 {background-color: #FFFF39;}
.cython.score-35 {background-color: #FFFF38;}
.cython.score-36 {background-color: #FFFF37;}
.cython.score-37 {background-color: #FFFF36;}
.cython.score-38 {background-color: #FFFF35;}
.cython.score-39 {background-color: #FFFF34;}
.cython.score-40 {background-color: #FFFF33;}
.cython.score-41 {background-color: #FFFF32;}
.cython.score-42 {background-color: #FFFF31;}
.cython.score-43 {background-color: #FFFF30;}
.cython.score-44 {background-color: #FFFF2f;}
.cython.score-45 {background-color: #FFFF2e;}
.cython.score-46 {background-color: #FFFF2d;}
.cython.score-47 {background-color: #FFFF2c;}
.cython.score-48 {background-color: #FFFF2b;}
.cython.score-49 {background-color: #FFFF2b;}
.cython.score-50 {background-color: #FFFF2a;}
.cython.score-51 {background-color: #FFFF29;}
.cython.score-52 {background-color: #FFFF29;}
.cython.score-53 {background-color: #FFFF28;}
.cython.score-54 {background-color: #FFFF27;}
.cython.score-55 {background-color: #FFFF27;}
.cython.score-56 {background-color: #FFFF26;}
.cython.score-57 {background-color: #FFFF26;}
.cython.score-58 {background-color: #FFFF25;}
.cython.score-59 {background-color: #FFFF24;}
.cython.score-60 {background-color: #FFFF24;}
.cython.score-61 {background-color: #FFFF23;}
.cython.score-62 {background-color: #FFFF23;}
.cython.score-63 {background-color: #FFFF22;}
.cython.score-64 {background-color: #FFFF22;}
.cython.score-65 {background-color: #FFFF22;}
.cython.score-66 {background-color: #FFFF21;}
.cython.score-67 {background-color: #FFFF21;}
.cython.score-68 {background-color: #FFFF20;}
.cython.score-69 {background-color: #FFFF20;}
.cython.score-70 {background-color: #FFFF1f;}
.cython.score-71 {background-color: #FFFF1f;}
.cython.score-72 {background-color: #FFFF1f;}
.cython.score-73 {background-color: #FFFF1e;}
.cython.score-74 {background-color: #FFFF1e;}
.cython.score-75 {background-color: #FFFF1e;}
.cython.score-76 {background-color: #FFFF1d;}
.cython.score-77 {background-color: #FFFF1d;}
.cython.score-78 {background-color: #FFFF1c;}
.cython.score-79 {background-color: #FFFF1c;}
.cython.score-80 {background-color: #FFFF1c;}
.cython.score-81 {background-color: #FFFF1c;}
.cython.score-82 {background-color: #FFFF1b;}
.cython.score-83 {background-color: #FFFF1b;}
.cython.score-84 {background-color: #FFFF1b;}
.cython.score-85 {background-color: #FFFF1a;}
.cython.score-86 {background-color: #FFFF1a;}
.cython.score-87 {background-color: #FFFF1a;}
.cython.score-88 {background-color: #FFFF1a;}
.cython.score-89 {background-color: #FFFF19;}
.cython.score-90 {background-color: #FFFF19;}
.cython.score-91 {background-color: #FFFF19;}
.cython.score-92 {background-color: #FFFF19;}
.cython.score-93 {background-color: #FFFF18;}
.cython.score-94 {background-color: #FFFF18;}
.cython.score-95 {background-color: #FFFF18;}
.cython.score-96 {background-color: #FFFF18;}
.cython.score-97 {background-color: #FFFF17;}
.cython.score-98 {background-color: #FFFF17;}
.cython.score-99 {background-color: #FFFF17;}
.cython.score-100 {background-color: #FFFF17;}
.cython.score-101 {background-color: #FFFF16;}
.cython.score-102 {background-color: #FFFF16;}
.cython.score-103 {background-color: #FFFF16;}
.cython.score-104 {background-color: #FFFF16;}
.cython.score-105 {background-color: #FFFF16;}
.cython.score-106 {background-color: #FFFF15;}
.cython.score-107 {background-color: #FFFF15;}
.cython.score-108 {background-color: #FFFF15;}
.cython.score-109 {background-color: #FFFF15;}
.cython.score-110 {background-color: #FFFF15;}
.cython.score-111 {background-color: #FFFF15;}
.cython.score-112 {background-color: #FFFF14;}
.cython.score-113 {background-color: #FFFF14;}
.cython.score-114 {background-color: #FFFF14;}
.cython.score-115 {background-color: #FFFF14;}
.cython.score-116 {background-color: #FFFF14;}
.cython.score-117 {background-color: #FFFF14;}
.cython.score-118 {background-color: #FFFF13;}
.cython.score-119 {background-color: #FFFF13;}
.cython.score-120 {background-color: #FFFF13;}
.cython.score-121 {background-color: #FFFF13;}
.cython.score-122 {background-color: #FFFF13;}
.cython.score-123 {background-color: #FFFF13;}
.cython.score-124 {background-color: #FFFF13;}
.cython.score-125 {background-color: #FFFF12;}
.cython.score-126 {background-color: #FFFF12;}
.cython.score-127 {background-color: #FFFF12;}
.cython.score-128 {background-color: #FFFF12;}
.cython.score-129 {background-color: #FFFF12;}
.cython.score-130 {background-color: #FFFF12;}
.cython.score-131 {background-color: #FFFF12;}
.cython.score-132 {background-color: #FFFF11;}
.cython.score-133 {background-color: #FFFF11;}
.cython.score-134 {background-color: #FFFF11;}
.cython.score-135 {background-color: #FFFF11;}
.cython.score-136 {background-color: #FFFF11;}
.cython.score-137 {background-color: #FFFF11;}
.cython.score-138 {background-color: #FFFF11;}
.cython.score-139 {background-color: #FFFF11;}
.cython.score-140 {background-color: #FFFF11;}
.cython.score-141 {background-color: #FFFF10;}
.cython.score-142 {background-color: #FFFF10;}
.cython.score-143 {background-color: #FFFF10;}
.cython.score-144 {background-color: #FFFF10;}
.cython.score-145 {background-color: #FFFF10;}
.cython.score-146 {background-color: #FFFF10;}
.cython.score-147 {background-color: #FFFF10;}
.cython.score-148 {background-color: #FFFF10;}
.cython.score-149 {background-color: #FFFF10;}
.cython.score-150 {background-color: #FFFF0f;}
.cython.score-151 {background-color: #FFFF0f;}
.cython.score-152 {background-color: #FFFF0f;}
.cython.score-153 {background-color: #FFFF0f;}
.cython.score-154 {background-color: #FFFF0f;}
.cython.score-155 {background-color: #FFFF0f;}
.cython.score-156 {background-color: #FFFF0f;}
.cython.score-157 {background-color: #FFFF0f;}
.cython.score-158 {background-color: #FFFF0f;}
.cython.score-159 {background-color: #FFFF0f;}
.cython.score-160 {background-color: #FFFF0f;}
.cython.score-161 {background-color: #FFFF0e;}
.cython.score-162 {background-color: #FFFF0e;}
.cython.score-163 {background-color: #FFFF0e;}
.cython.score-164 {background-color: #FFFF0e;}
.cython.score-165 {background-color: #FFFF0e;}
.cython.score-166 {background-color: #FFFF0e;}
.cython.score-167 {background-color: #FFFF0e;}
.cython.score-168 {background-color: #FFFF0e;}
.cython.score-169 {background-color: #FFFF0e;}
.cython.score-170 {background-color: #FFFF0e;}
.cython.score-171 {background-color: #FFFF0e;}
.cython.score-172 {background-color: #FFFF0e;}
.cython.score-173 {background-color: #FFFF0d;}
.cython.score-174 {background-color: #FFFF0d;}
.cython.score-175 {background-color: #FFFF0d;}
.cython.score-176 {background-color: #FFFF0d;}
.cython.score-177 {background-color: #FFFF0d;}
.cython.score-178 {background-color: #FFFF0d;}
.cython.score-179 {background-color: #FFFF0d;}
.cython.score-180 {background-color: #FFFF0d;}
.cython.score-181 {background-color: #FFFF0d;}
.cython.score-182 {background-color: #FFFF0d;}
.cython.score-183 {background-color: #FFFF0d;}
.cython.score-184 {background-color: #FFFF0d;}
.cython.score-185 {background-color: #FFFF0d;}
.cython.score-186 {background-color: #FFFF0d;}
.cython.score-187 {background-color: #FFFF0c;}
.cython.score-188 {background-color: #FFFF0c;}
.cython.score-189 {background-color: #FFFF0c;}
.cython.score-190 {background-color: #FFFF0c;}
.cython.score-191 {background-color: #FFFF0c;}
.cython.score-192 {background-color: #FFFF0c;}
.cython.score-193 {background-color: #FFFF0c;}
.cython.score-194 {background-color: #FFFF0c;}
.cython.score-195 {background-color: #FFFF0c;}
.cython.score-196 {background-color: #FFFF0c;}
.cython.score-197 {background-color: #FFFF0c;}
.cython.score-198 {background-color: #FFFF0c;}
.cython.score-199 {background-color: #FFFF0c;}
.cython.score-200 {background-color: #FFFF0c;}
.cython.score-201 {background-color: #FFFF0c;}
.cython.score-202 {background-color: #FFFF0c;}
.cython.score-203 {background-color: #FFFF0b;}
.cython.score-204 {background-color: #FFFF0b;}
.cython.score-205 {background-color: #FFFF0b;}
.cython.score-206 {background-color: #FFFF0b;}
.cython.score-207 {background-color: #FFFF0b;}
.cython.score-208 {background-color: #FFFF0b;}
.cython.score-209 {background-color: #FFFF0b;}
.cython.score-210 {background-color: #FFFF0b;}
.cython.score-211 {background-color: #FFFF0b;}
.cython.score-212 {background-color: #FFFF0b;}
.cython.score-213 {background-color: #FFFF0b;}
.cython.score-214 {background-color: #FFFF0b;}
.cython.score-215 {background-color: #FFFF0b;}
.cython.score-216 {background-color: #FFFF0b;}
.cython.score-217 {background-color: #FFFF0b;}
.cython.score-218 {background-color: #FFFF0b;}
.cython.score-219 {background-color: #FFFF0b;}
.cython.score-220 {background-color: #FFFF0b;}
.cython.score-221 {background-color: #FFFF0b;}
.cython.score-222 {background-color: #FFFF0a;}
.cython.score-223 {background-color: #FFFF0a;}
.cython.score-224 {background-color: #FFFF0a;}
.cython.score-225 {background-color: #FFFF0a;}
.cython.score-226 {background-color: #FFFF0a;}
.cython.score-227 {background-color: #FFFF0a;}
.cython.score-228 {background-color: #FFFF0a;}
.cython.score-229 {background-color: #FFFF0a;}
.cython.score-230 {background-color: #FFFF0a;}
.cython.score-231 {background-color: #FFFF0a;}
.cython.score-232 {background-color: #FFFF0a;}
.cython.score-233 {background-color: #FFFF0a;}
.cython.score-234 {background-color: #FFFF0a;}
.cython.score-235 {background-color: #FFFF0a;}
.cython.score-236 {background-color: #FFFF0a;}
.cython.score-237 {background-color: #FFFF0a;}
.cython.score-238 {background-color: #FFFF0a;}
.cython.score-239 {background-color: #FFFF0a;}
.cython.score-240 {background-color: #FFFF0a;}
.cython.score-241 {background-color: #FFFF0a;}
.cython.score-242 {background-color: #FFFF0a;}
.cython.score-243 {background-color: #FFFF0a;}
.cython.score-244 {background-color: #FFFF0a;}
.cython.score-245 {background-color: #FFFF0a;}
.cython.score-246 {background-color: #FFFF09;}
.cython.score-247 {background-color: #FFFF09;}
.cython.score-248 {background-color: #FFFF09;}
.cython.score-249 {background-color: #FFFF09;}
.cython.score-250 {background-color: #FFFF09;}
.cython.score-251 {background-color: #FFFF09;}
.cython.score-252 {background-color: #FFFF09;}
.cython.score-253 {background-color: #FFFF09;}
.cython.score-254 {background-color: #FFFF09;}
.cython .hll { background-color: #ffffcc }
.cython { background: #f8f8f8; }
.cython .c { color: #408080; font-style: italic } /* Comment */
.cython .err { border: 1px solid #FF0000 } /* Error */
.cython .k { color: #008000; font-weight: bold } /* Keyword */
.cython .o { color: #666666 } /* Operator */
.cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.cython .cp { color: #BC7A00 } /* Comment.Preproc */
.cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.cython .c1 { color: #408080; font-style: italic } /* Comment.Single */
.cython .cs { color: #408080; font-style: italic } /* Comment.Special */
.cython .gd { color: #A00000 } /* Generic.Deleted */
.cython .ge { font-style: italic } /* Generic.Emph */
.cython .gr { color: #FF0000 } /* Generic.Error */
.cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.cython .gi { color: #00A000 } /* Generic.Inserted */
.cython .go { color: #888888 } /* Generic.Output */
.cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.cython .gs { font-weight: bold } /* Generic.Strong */
.cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.cython .gt { color: #0044DD } /* Generic.Traceback */
.cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.cython .kp { color: #008000 } /* Keyword.Pseudo */
.cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.cython .kt { color: #B00040 } /* Keyword.Type */
.cython .m { color: #666666 } /* Literal.Number */
.cython .s { color: #BA2121 } /* Literal.String */
.cython .na { color: #7D9029 } /* Name.Attribute */
.cython .nb { color: #008000 } /* Name.Builtin */
.cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.cython .no { color: #880000 } /* Name.Constant */
.cython .nd { color: #AA22FF } /* Name.Decorator */
.cython .ni { color: #999999; font-weight: bold } /* Name.Entity */
.cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.cython .nf { color: #0000FF } /* Name.Function */
.cython .nl { color: #A0A000 } /* Name.Label */
.cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.cython .nt { color: #008000; font-weight: bold } /* Name.Tag */
.cython .nv { color: #19177C } /* Name.Variable */
.cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.cython .w { color: #bbbbbb } /* Text.Whitespace */
.cython .mb { color: #666666 } /* Literal.Number.Bin */
.cython .mf { color: #666666 } /* Literal.Number.Float */
.cython .mh { color: #666666 } /* Literal.Number.Hex */
.cython .mi { color: #666666 } /* Literal.Number.Integer */
.cython .mo { color: #666666 } /* Literal.Number.Oct */
.cython .sa { color: #BA2121 } /* Literal.String.Affix */
.cython .sb { color: #BA2121 } /* Literal.String.Backtick */
.cython .sc { color: #BA2121 } /* Literal.String.Char */
.cython .dl { color: #BA2121 } /* Literal.String.Delimiter */
.cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.cython .s2 { color: #BA2121 } /* Literal.String.Double */
.cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.cython .sh { color: #BA2121 } /* Literal.String.Heredoc */
.cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.cython .sx { color: #008000 } /* Literal.String.Other */
.cython .sr { color: #BB6688 } /* Literal.String.Regex */
.cython .s1 { color: #BA2121 } /* Literal.String.Single */
.cython .ss { color: #19177C } /* Literal.String.Symbol */
.cython .bp { color: #008000 } /* Name.Builtin.Pseudo */
.cython .fm { color: #0000FF } /* Name.Function.Magic */
.cython .vc { color: #19177C } /* Name.Variable.Class */
.cython .vg { color: #19177C } /* Name.Variable.Global */
.cython .vi { color: #19177C } /* Name.Variable.Instance */
.cython .vm { color: #19177C } /* Name.Variable.Magic */
.cython .il { color: #666666 } /* Literal.Number.Integer.Long */
</style>
</head>
<body class="cython">
<p><span style="border-bottom: solid 1px grey;">Generated by Cython 0.29.15</span></p>
<p>
<span style="background-color: #FFFF00">Yellow lines</span> hint at Python interaction.<br />
Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it.
</p>
<div class="cython"><pre class="cython line score-0"> <span class="">01</span>: </pre>
<pre class="cython line score-49" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">02</span>: <span class="k">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span></pre>
<pre class="cython code score-49 "> __pyx_t_1 = <span class="py_c_api">PyList_New</span>(4);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_pi);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_pi);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 0, __pyx_n_s_pi);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_acos);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_acos);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 1, __pyx_n_s_acos);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_cos);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_cos);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 2, __pyx_n_s_cos);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_sin);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_sin);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 3, __pyx_n_s_sin);
__pyx_t_2 = <span class="pyx_c_api">__Pyx_Import</span>(__pyx_n_s_math, __pyx_t_1, 0);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_pi);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_pi, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_acos);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_acos, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_cos, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_sin, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
</pre><pre class="cython line score-0"> <span class="">03</span>: </pre>
<pre class="cython line score-0"> <span class="">04</span>: </pre>
<pre class="cython line score-62" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">05</span>: <span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">):</span></pre>
<pre class="cython code score-62 ">/* Python wrapper */
static PyObject *__pyx_pw_46_cython_magic_510139e97843e1ad4066ec2ca94da783_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyMethodDef __pyx_mdef_46_cython_magic_510139e97843e1ad4066ec2ca94da783_1great_circle = {"great_circle", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_46_cython_magic_510139e97843e1ad4066ec2ca94da783_1great_circle, METH_VARARGS|METH_KEYWORDS, 0};
static PyObject *__pyx_pw_46_cython_magic_510139e97843e1ad4066ec2ca94da783_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_lon1 = 0;
PyObject *__pyx_v_lat1 = 0;
PyObject *__pyx_v_lon2 = 0;
PyObject *__pyx_v_lat2 = 0;
PyObject *__pyx_r = 0;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle (wrapper)", 0);
{
static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_lon1,&__pyx_n_s_lat1,&__pyx_n_s_lon2,&__pyx_n_s_lat2,0};
PyObject* values[4] = {0,0,0,0};
if (unlikely(__pyx_kwds)) {
Py_ssize_t kw_args;
const Py_ssize_t pos_args = <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args);
switch (pos_args) {
case 4: values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
CYTHON_FALLTHROUGH;
case 3: values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
CYTHON_FALLTHROUGH;
case 2: values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
CYTHON_FALLTHROUGH;
case 1: values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
CYTHON_FALLTHROUGH;
case 0: break;
default: goto __pyx_L5_argtuple_error;
}
kw_args = <span class="py_c_api">PyDict_Size</span>(__pyx_kwds);
switch (pos_args) {
case 0:
if (likely((values[0] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon1)) != 0)) kw_args--;
else goto __pyx_L5_argtuple_error;
CYTHON_FALLTHROUGH;
case 1:
if (likely((values[1] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat1)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 1); <span class="error_goto">__PYX_ERR(0, 5, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 2:
if (likely((values[2] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 2); <span class="error_goto">__PYX_ERR(0, 5, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 3:
if (likely((values[3] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 3); <span class="error_goto">__PYX_ERR(0, 5, __pyx_L3_error)</span>
}
}
if (unlikely(kw_args > 0)) {
if (unlikely(<span class="pyx_c_api">__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "great_circle") < 0)) <span class="error_goto">__PYX_ERR(0, 5, __pyx_L3_error)</span>
}
} else if (<span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args) != 4) {
goto __pyx_L5_argtuple_error;
} else {
values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
}
__pyx_v_lon1 = values[0];
__pyx_v_lat1 = values[1];
__pyx_v_lon2 = values[2];
__pyx_v_lat2 = values[3];
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args)); <span class="error_goto">__PYX_ERR(0, 5, __pyx_L3_error)</span>
__pyx_L3_error:;
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_510139e97843e1ad4066ec2ca94da783.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return NULL;
__pyx_L4_argument_unpacking_done:;
__pyx_r = __pyx_pf_46_cython_magic_510139e97843e1ad4066ec2ca94da783_great_circle(__pyx_self, __pyx_v_lon1, __pyx_v_lat1, __pyx_v_lon2, __pyx_v_lat2);
/* function exit code */
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
static PyObject *__pyx_pf_46_cython_magic_510139e97843e1ad4066ec2ca94da783_great_circle(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_lon1, PyObject *__pyx_v_lat1, PyObject *__pyx_v_lon2, PyObject *__pyx_v_lat2) {
PyObject *__pyx_v_radius = NULL;
PyObject *__pyx_v_x = NULL;
PyObject *__pyx_v_a = NULL;
PyObject *__pyx_v_b = NULL;
PyObject *__pyx_v_theta = NULL;
PyObject *__pyx_v_c = NULL;
PyObject *__pyx_r = NULL;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle", 0);
/* … */
/* function exit code */
__pyx_L1_error:;
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_7);
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_510139e97843e1ad4066ec2ca94da783.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
__pyx_r = NULL;
__pyx_L0:;
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_radius);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_x);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_a);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_b);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_theta);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_v_c);
<span class="refnanny">__Pyx_XGIVEREF</span>(__pyx_r);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
/* … */
__pyx_tuple_ = <span class="py_c_api">PyTuple_Pack</span>(10, __pyx_n_s_lon1, __pyx_n_s_lat1, __pyx_n_s_lon2, __pyx_n_s_lat2, __pyx_n_s_radius, __pyx_n_s_x, __pyx_n_s_a, __pyx_n_s_b, __pyx_n_s_theta, __pyx_n_s_c);<span class="error_goto"> if (unlikely(!__pyx_tuple_)) __PYX_ERR(0, 5, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_tuple_);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_tuple_);
/* … */
__pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_46_cython_magic_510139e97843e1ad4066ec2ca94da783_1great_circle, NULL, __pyx_n_s_cython_magic_510139e97843e1ad40);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 5, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_great_circle, __pyx_t_2) < 0) <span class="error_goto">__PYX_ERR(0, 5, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
</pre><pre class="cython line score-1" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">06</span>: <span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span></pre>
<pre class="cython code score-1 "> <span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_int_6371);
__pyx_v_radius = __pyx_int_6371;
</pre><pre class="cython line score-5" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">07</span>: <span class="n">x</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">/</span> <span class="mf">180</span></pre>
<pre class="cython code score-5 "> <span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_1, __pyx_n_s_pi);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
__pyx_t_2 = <span class="pyx_c_api">__Pyx_PyInt_TrueDivideObjC</span>(__pyx_t_1, __pyx_int_180, 0xB4, 0, 0);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 7, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_v_x = __pyx_t_2;
__pyx_t_2 = 0;
</pre><pre class="cython line score-0"> <span class="">08</span>: </pre>
<pre class="cython line score-8" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">09</span>: <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span> <span class="o">-</span> <span class="n">lat1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-8 "> __pyx_t_2 = <span class="pyx_c_api">__Pyx_PyInt_SubtractCObj</span>(__pyx_int_90, __pyx_v_lat1, 90, 0, 0);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 9, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
__pyx_t_1 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_2, __pyx_v_x);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
__pyx_v_a = __pyx_t_1;
__pyx_t_1 = 0;
</pre><pre class="cython line score-8" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">10</span>: <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span> <span class="o">-</span> <span class="n">lat2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-8 "> __pyx_t_1 = <span class="pyx_c_api">__Pyx_PyInt_SubtractCObj</span>(__pyx_int_90, __pyx_v_lat2, 90, 0, 0);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 10, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
__pyx_t_2 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_1, __pyx_v_x);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 10, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_v_b = __pyx_t_2;
__pyx_t_2 = 0;
</pre><pre class="cython line score-11" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">11</span>: <span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span> <span class="o">-</span> <span class="n">lon1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-11 "> __pyx_t_2 = <span class="py_c_api">PyNumber_Subtract</span>(__pyx_v_lon2, __pyx_v_lon1);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 11, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
__pyx_t_1 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_2, __pyx_v_x);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 11, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
__pyx_v_theta = __pyx_t_1;
__pyx_t_1 = 0;
</pre><pre class="cython line score-125" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">12</span>: <span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span></pre>
<pre class="cython code score-125 "> <span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_2, __pyx_n_s_acos);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_4, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
__pyx_t_5 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_4))) {
__pyx_t_5 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_4);
if (likely(__pyx_t_5)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_4, function);
}
}
__pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_v_a) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_4, __pyx_v_a);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
if (unlikely(!__pyx_t_3)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_5, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
__pyx_t_6 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_5))) {
__pyx_t_6 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_5);
if (likely(__pyx_t_6)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_5, function);
}
}
__pyx_t_4 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_v_b) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_5, __pyx_v_b);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
if (unlikely(!__pyx_t_4)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
__pyx_t_5 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_3, __pyx_t_4);<span class="error_goto"> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_3, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_3);
__pyx_t_6 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_3))) {
__pyx_t_6 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_3);
if (likely(__pyx_t_6)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_3, function);
}
}
__pyx_t_4 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_6, __pyx_v_a) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_3, __pyx_v_a);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
if (unlikely(!__pyx_t_4)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_6, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_6);
__pyx_t_7 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_6))) {
__pyx_t_7 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_6);
if (likely(__pyx_t_7)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_6, function);
}
}
__pyx_t_3 = (__pyx_t_7) ? __Pyx_PyObject_Call2Args(__pyx_t_6, __pyx_t_7, __pyx_v_b) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_6, __pyx_v_b);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
if (unlikely(!__pyx_t_3)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
__pyx_t_6 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_4, __pyx_t_3);<span class="error_goto"> if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_4, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
__pyx_t_7 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_4))) {
__pyx_t_7 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_4);
if (likely(__pyx_t_7)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_4, function);
}
}
__pyx_t_3 = (__pyx_t_7) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_7, __pyx_v_theta) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_4, __pyx_v_theta);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
if (unlikely(!__pyx_t_3)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
__pyx_t_4 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_6, __pyx_t_3);<span class="error_goto"> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;
__pyx_t_3 = <span class="py_c_api">PyNumber_Add</span>(__pyx_t_5, __pyx_t_4);<span class="error_goto"> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_3);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
__pyx_t_4 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_2))) {
__pyx_t_4 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_2);
if (likely(__pyx_t_4)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_2, function);
}
}
__pyx_t_1 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_t_3) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_2, __pyx_t_3);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;
if (unlikely(!__pyx_t_1)) <span class="error_goto">__PYX_ERR(0, 12, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
__pyx_v_c = __pyx_t_1;
__pyx_t_1 = 0;
</pre><pre class="cython line score-6" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">13</span>: <span class="k">return</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">c</span></pre>
<pre class="cython code score-6 "> <span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_r);
__pyx_t_1 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_v_radius, __pyx_v_c);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
__pyx_r = __pyx_t_1;
__pyx_t_1 = 0;
goto __pyx_L0;
</pre></div></body></html>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Cython编译">Cython编译<a class="anchor-link" href="#Cython编译"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> great_circle_cy_v1.pyx
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span>
<span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span>
<span class="n">cdef</span> <span class="n">double</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">radius</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mi">6371</span> <span class="c1"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing great_circle_cy_v1.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> great_circle_setup_v1.py
<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
<span class="kn">from</span> <span class="nn">Cython.Build</span> <span class="kn">import</span> <span class="n">cythonize</span>
<span class="n">setup</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">'Great Circle module v1'</span><span class="p">,</span>
<span class="n">ext_modules</span><span class="o">=</span><span class="n">cythonize</span><span class="p">(</span><span class="s2">"great_circle_cy_v1.pyx"</span><span class="p">),</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing great_circle_setup_v1.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>python great_circle_setup_v1.py build_ext --inplace
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Compiling great_circle_cy_v1.pyx because it changed.
[1/1] Cythonizing great_circle_cy_v1.pyx
/home/junjiet/conda/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /home/junjiet/data_science2020/2.数据处理/test_cython/great_circle_cy_v1.pyx
tree = Parsing.p_module(s, pxd, full_module_name)
running build_ext
building 'great_circle_cy_v1' extension
/home/junjiet/conda/bin/x86_64-conda_cos6-linux-gnu-cc -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include -fPIC -I/home/junjiet/conda/include/python3.7m -c great_circle_cy_v1.c -o build/temp.linux-x86_64-3.7/great_circle_cy_v1.o
x86_64-conda_cos6-linux-gnu-gcc -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/junjiet/conda/lib -Wl,-rpath-link,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include build/temp.linux-x86_64-3.7/great_circle_cy_v1.o -o /home/junjiet/data_science2020/2.数据处理/test_cython/great_circle_cy_v1.cpython-37m-x86_64-linux-gnu.so
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre><span class="ansi-blue-intense-fg ansi-bold">build</span>/
great_circle_cy_v1.c
<span class="ansi-green-intense-fg ansi-bold">great_circle_cy_v1.cpython-37m-x86_64-linux-gnu.so</span>*
great_circle_cy_v1.pyx
great_circle_py.py
great_circle_setup_v1.py
<span class="ansi-blue-intense-fg ansi-bold">__pycache__</span>/
setup.py
test.c
<span class="ansi-green-intense-fg ansi-bold">test.cpython-37m-x86_64-linux-gnu.so</span>*
test.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">great_circle_cy_v1</span> <span class="kn">import</span> <span class="n">great_circle</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num</span><span class="p">):</span>
<span class="n">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span> <span class="o">-</span><span class="n">a</span>
<span class="k">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span>
<span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span>
<span class="k">cdef</span> <span class="kt">double</span> <span class="nf">a</span><span class="p">,</span> <span class="nf">b</span><span class="p">,</span> <span class="nf">theta</span><span class="p">,</span> <span class="nf">c</span><span class="p">,</span> <span class="nf">x</span><span class="p">,</span> <span class="nf">radius</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mf">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<!DOCTYPE html>
<!-- Generated by Cython 0.29.15 -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Cython: _cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55.pyx</title>
<style type="text/css">
body.cython { font-family: courier; font-size: 12; }
.cython.tag { }
.cython.line { margin: 0em }
.cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }
.cython.line .run { background-color: #B0FFB0; }
.cython.line .mis { background-color: #FFB0B0; }
.cython.code.run { border-left: 8px solid #B0FFB0; }
.cython.code.mis { border-left: 8px solid #FFB0B0; }
.cython.code .py_c_api { color: red; }
.cython.code .py_macro_api { color: #FF7000; }
.cython.code .pyx_c_api { color: #FF3000; }
.cython.code .pyx_macro_api { color: #FF7000; }
.cython.code .refnanny { color: #FFA000; }
.cython.code .trace { color: #FFA000; }
.cython.code .error_goto { color: #FFA000; }
.cython.code .coerce { color: #008000; border: 1px dotted #008000 }
.cython.code .py_attr { color: #FF0000; font-weight: bold; }
.cython.code .c_attr { color: #0000FF; }
.cython.code .py_call { color: #FF0000; font-weight: bold; }
.cython.code .c_call { color: #0000FF; }
.cython.score-0 {background-color: #FFFFff;}
.cython.score-1 {background-color: #FFFFe7;}
.cython.score-2 {background-color: #FFFFd4;}
.cython.score-3 {background-color: #FFFFc4;}
.cython.score-4 {background-color: #FFFFb6;}
.cython.score-5 {background-color: #FFFFaa;}
.cython.score-6 {background-color: #FFFF9f;}
.cython.score-7 {background-color: #FFFF96;}
.cython.score-8 {background-color: #FFFF8d;}
.cython.score-9 {background-color: #FFFF86;}
.cython.score-10 {background-color: #FFFF7f;}
.cython.score-11 {background-color: #FFFF79;}
.cython.score-12 {background-color: #FFFF73;}
.cython.score-13 {background-color: #FFFF6e;}
.cython.score-14 {background-color: #FFFF6a;}
.cython.score-15 {background-color: #FFFF66;}
.cython.score-16 {background-color: #FFFF62;}
.cython.score-17 {background-color: #FFFF5e;}
.cython.score-18 {background-color: #FFFF5b;}
.cython.score-19 {background-color: #FFFF57;}
.cython.score-20 {background-color: #FFFF55;}
.cython.score-21 {background-color: #FFFF52;}
.cython.score-22 {background-color: #FFFF4f;}
.cython.score-23 {background-color: #FFFF4d;}
.cython.score-24 {background-color: #FFFF4b;}
.cython.score-25 {background-color: #FFFF48;}
.cython.score-26 {background-color: #FFFF46;}
.cython.score-27 {background-color: #FFFF44;}
.cython.score-28 {background-color: #FFFF43;}
.cython.score-29 {background-color: #FFFF41;}
.cython.score-30 {background-color: #FFFF3f;}
.cython.score-31 {background-color: #FFFF3e;}
.cython.score-32 {background-color: #FFFF3c;}
.cython.score-33 {background-color: #FFFF3b;}
.cython.score-34 {background-color: #FFFF39;}
.cython.score-35 {background-color: #FFFF38;}
.cython.score-36 {background-color: #FFFF37;}
.cython.score-37 {background-color: #FFFF36;}
.cython.score-38 {background-color: #FFFF35;}
.cython.score-39 {background-color: #FFFF34;}
.cython.score-40 {background-color: #FFFF33;}
.cython.score-41 {background-color: #FFFF32;}
.cython.score-42 {background-color: #FFFF31;}
.cython.score-43 {background-color: #FFFF30;}
.cython.score-44 {background-color: #FFFF2f;}
.cython.score-45 {background-color: #FFFF2e;}
.cython.score-46 {background-color: #FFFF2d;}
.cython.score-47 {background-color: #FFFF2c;}
.cython.score-48 {background-color: #FFFF2b;}
.cython.score-49 {background-color: #FFFF2b;}
.cython.score-50 {background-color: #FFFF2a;}
.cython.score-51 {background-color: #FFFF29;}
.cython.score-52 {background-color: #FFFF29;}
.cython.score-53 {background-color: #FFFF28;}
.cython.score-54 {background-color: #FFFF27;}
.cython.score-55 {background-color: #FFFF27;}
.cython.score-56 {background-color: #FFFF26;}
.cython.score-57 {background-color: #FFFF26;}
.cython.score-58 {background-color: #FFFF25;}
.cython.score-59 {background-color: #FFFF24;}
.cython.score-60 {background-color: #FFFF24;}
.cython.score-61 {background-color: #FFFF23;}
.cython.score-62 {background-color: #FFFF23;}
.cython.score-63 {background-color: #FFFF22;}
.cython.score-64 {background-color: #FFFF22;}
.cython.score-65 {background-color: #FFFF22;}
.cython.score-66 {background-color: #FFFF21;}
.cython.score-67 {background-color: #FFFF21;}
.cython.score-68 {background-color: #FFFF20;}
.cython.score-69 {background-color: #FFFF20;}
.cython.score-70 {background-color: #FFFF1f;}
.cython.score-71 {background-color: #FFFF1f;}
.cython.score-72 {background-color: #FFFF1f;}
.cython.score-73 {background-color: #FFFF1e;}
.cython.score-74 {background-color: #FFFF1e;}
.cython.score-75 {background-color: #FFFF1e;}
.cython.score-76 {background-color: #FFFF1d;}
.cython.score-77 {background-color: #FFFF1d;}
.cython.score-78 {background-color: #FFFF1c;}
.cython.score-79 {background-color: #FFFF1c;}
.cython.score-80 {background-color: #FFFF1c;}
.cython.score-81 {background-color: #FFFF1c;}
.cython.score-82 {background-color: #FFFF1b;}
.cython.score-83 {background-color: #FFFF1b;}
.cython.score-84 {background-color: #FFFF1b;}
.cython.score-85 {background-color: #FFFF1a;}
.cython.score-86 {background-color: #FFFF1a;}
.cython.score-87 {background-color: #FFFF1a;}
.cython.score-88 {background-color: #FFFF1a;}
.cython.score-89 {background-color: #FFFF19;}
.cython.score-90 {background-color: #FFFF19;}
.cython.score-91 {background-color: #FFFF19;}
.cython.score-92 {background-color: #FFFF19;}
.cython.score-93 {background-color: #FFFF18;}
.cython.score-94 {background-color: #FFFF18;}
.cython.score-95 {background-color: #FFFF18;}
.cython.score-96 {background-color: #FFFF18;}
.cython.score-97 {background-color: #FFFF17;}
.cython.score-98 {background-color: #FFFF17;}
.cython.score-99 {background-color: #FFFF17;}
.cython.score-100 {background-color: #FFFF17;}
.cython.score-101 {background-color: #FFFF16;}
.cython.score-102 {background-color: #FFFF16;}
.cython.score-103 {background-color: #FFFF16;}
.cython.score-104 {background-color: #FFFF16;}
.cython.score-105 {background-color: #FFFF16;}
.cython.score-106 {background-color: #FFFF15;}
.cython.score-107 {background-color: #FFFF15;}
.cython.score-108 {background-color: #FFFF15;}
.cython.score-109 {background-color: #FFFF15;}
.cython.score-110 {background-color: #FFFF15;}
.cython.score-111 {background-color: #FFFF15;}
.cython.score-112 {background-color: #FFFF14;}
.cython.score-113 {background-color: #FFFF14;}
.cython.score-114 {background-color: #FFFF14;}
.cython.score-115 {background-color: #FFFF14;}
.cython.score-116 {background-color: #FFFF14;}
.cython.score-117 {background-color: #FFFF14;}
.cython.score-118 {background-color: #FFFF13;}
.cython.score-119 {background-color: #FFFF13;}
.cython.score-120 {background-color: #FFFF13;}
.cython.score-121 {background-color: #FFFF13;}
.cython.score-122 {background-color: #FFFF13;}
.cython.score-123 {background-color: #FFFF13;}
.cython.score-124 {background-color: #FFFF13;}
.cython.score-125 {background-color: #FFFF12;}
.cython.score-126 {background-color: #FFFF12;}
.cython.score-127 {background-color: #FFFF12;}
.cython.score-128 {background-color: #FFFF12;}
.cython.score-129 {background-color: #FFFF12;}
.cython.score-130 {background-color: #FFFF12;}
.cython.score-131 {background-color: #FFFF12;}
.cython.score-132 {background-color: #FFFF11;}
.cython.score-133 {background-color: #FFFF11;}
.cython.score-134 {background-color: #FFFF11;}
.cython.score-135 {background-color: #FFFF11;}
.cython.score-136 {background-color: #FFFF11;}
.cython.score-137 {background-color: #FFFF11;}
.cython.score-138 {background-color: #FFFF11;}
.cython.score-139 {background-color: #FFFF11;}
.cython.score-140 {background-color: #FFFF11;}
.cython.score-141 {background-color: #FFFF10;}
.cython.score-142 {background-color: #FFFF10;}
.cython.score-143 {background-color: #FFFF10;}
.cython.score-144 {background-color: #FFFF10;}
.cython.score-145 {background-color: #FFFF10;}
.cython.score-146 {background-color: #FFFF10;}
.cython.score-147 {background-color: #FFFF10;}
.cython.score-148 {background-color: #FFFF10;}
.cython.score-149 {background-color: #FFFF10;}
.cython.score-150 {background-color: #FFFF0f;}
.cython.score-151 {background-color: #FFFF0f;}
.cython.score-152 {background-color: #FFFF0f;}
.cython.score-153 {background-color: #FFFF0f;}
.cython.score-154 {background-color: #FFFF0f;}
.cython.score-155 {background-color: #FFFF0f;}
.cython.score-156 {background-color: #FFFF0f;}
.cython.score-157 {background-color: #FFFF0f;}
.cython.score-158 {background-color: #FFFF0f;}
.cython.score-159 {background-color: #FFFF0f;}
.cython.score-160 {background-color: #FFFF0f;}
.cython.score-161 {background-color: #FFFF0e;}
.cython.score-162 {background-color: #FFFF0e;}
.cython.score-163 {background-color: #FFFF0e;}
.cython.score-164 {background-color: #FFFF0e;}
.cython.score-165 {background-color: #FFFF0e;}
.cython.score-166 {background-color: #FFFF0e;}
.cython.score-167 {background-color: #FFFF0e;}
.cython.score-168 {background-color: #FFFF0e;}
.cython.score-169 {background-color: #FFFF0e;}
.cython.score-170 {background-color: #FFFF0e;}
.cython.score-171 {background-color: #FFFF0e;}
.cython.score-172 {background-color: #FFFF0e;}
.cython.score-173 {background-color: #FFFF0d;}
.cython.score-174 {background-color: #FFFF0d;}
.cython.score-175 {background-color: #FFFF0d;}
.cython.score-176 {background-color: #FFFF0d;}
.cython.score-177 {background-color: #FFFF0d;}
.cython.score-178 {background-color: #FFFF0d;}
.cython.score-179 {background-color: #FFFF0d;}
.cython.score-180 {background-color: #FFFF0d;}
.cython.score-181 {background-color: #FFFF0d;}
.cython.score-182 {background-color: #FFFF0d;}
.cython.score-183 {background-color: #FFFF0d;}
.cython.score-184 {background-color: #FFFF0d;}
.cython.score-185 {background-color: #FFFF0d;}
.cython.score-186 {background-color: #FFFF0d;}
.cython.score-187 {background-color: #FFFF0c;}
.cython.score-188 {background-color: #FFFF0c;}
.cython.score-189 {background-color: #FFFF0c;}
.cython.score-190 {background-color: #FFFF0c;}
.cython.score-191 {background-color: #FFFF0c;}
.cython.score-192 {background-color: #FFFF0c;}
.cython.score-193 {background-color: #FFFF0c;}
.cython.score-194 {background-color: #FFFF0c;}
.cython.score-195 {background-color: #FFFF0c;}
.cython.score-196 {background-color: #FFFF0c;}
.cython.score-197 {background-color: #FFFF0c;}
.cython.score-198 {background-color: #FFFF0c;}
.cython.score-199 {background-color: #FFFF0c;}
.cython.score-200 {background-color: #FFFF0c;}
.cython.score-201 {background-color: #FFFF0c;}
.cython.score-202 {background-color: #FFFF0c;}
.cython.score-203 {background-color: #FFFF0b;}
.cython.score-204 {background-color: #FFFF0b;}
.cython.score-205 {background-color: #FFFF0b;}
.cython.score-206 {background-color: #FFFF0b;}
.cython.score-207 {background-color: #FFFF0b;}
.cython.score-208 {background-color: #FFFF0b;}
.cython.score-209 {background-color: #FFFF0b;}
.cython.score-210 {background-color: #FFFF0b;}
.cython.score-211 {background-color: #FFFF0b;}
.cython.score-212 {background-color: #FFFF0b;}
.cython.score-213 {background-color: #FFFF0b;}
.cython.score-214 {background-color: #FFFF0b;}
.cython.score-215 {background-color: #FFFF0b;}
.cython.score-216 {background-color: #FFFF0b;}
.cython.score-217 {background-color: #FFFF0b;}
.cython.score-218 {background-color: #FFFF0b;}
.cython.score-219 {background-color: #FFFF0b;}
.cython.score-220 {background-color: #FFFF0b;}
.cython.score-221 {background-color: #FFFF0b;}
.cython.score-222 {background-color: #FFFF0a;}
.cython.score-223 {background-color: #FFFF0a;}
.cython.score-224 {background-color: #FFFF0a;}
.cython.score-225 {background-color: #FFFF0a;}
.cython.score-226 {background-color: #FFFF0a;}
.cython.score-227 {background-color: #FFFF0a;}
.cython.score-228 {background-color: #FFFF0a;}
.cython.score-229 {background-color: #FFFF0a;}
.cython.score-230 {background-color: #FFFF0a;}
.cython.score-231 {background-color: #FFFF0a;}
.cython.score-232 {background-color: #FFFF0a;}
.cython.score-233 {background-color: #FFFF0a;}
.cython.score-234 {background-color: #FFFF0a;}
.cython.score-235 {background-color: #FFFF0a;}
.cython.score-236 {background-color: #FFFF0a;}
.cython.score-237 {background-color: #FFFF0a;}
.cython.score-238 {background-color: #FFFF0a;}
.cython.score-239 {background-color: #FFFF0a;}
.cython.score-240 {background-color: #FFFF0a;}
.cython.score-241 {background-color: #FFFF0a;}
.cython.score-242 {background-color: #FFFF0a;}
.cython.score-243 {background-color: #FFFF0a;}
.cython.score-244 {background-color: #FFFF0a;}
.cython.score-245 {background-color: #FFFF0a;}
.cython.score-246 {background-color: #FFFF09;}
.cython.score-247 {background-color: #FFFF09;}
.cython.score-248 {background-color: #FFFF09;}
.cython.score-249 {background-color: #FFFF09;}
.cython.score-250 {background-color: #FFFF09;}
.cython.score-251 {background-color: #FFFF09;}
.cython.score-252 {background-color: #FFFF09;}
.cython.score-253 {background-color: #FFFF09;}
.cython.score-254 {background-color: #FFFF09;}
.cython .hll { background-color: #ffffcc }
.cython { background: #f8f8f8; }
.cython .c { color: #408080; font-style: italic } /* Comment */
.cython .err { border: 1px solid #FF0000 } /* Error */
.cython .k { color: #008000; font-weight: bold } /* Keyword */
.cython .o { color: #666666 } /* Operator */
.cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.cython .cp { color: #BC7A00 } /* Comment.Preproc */
.cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.cython .c1 { color: #408080; font-style: italic } /* Comment.Single */
.cython .cs { color: #408080; font-style: italic } /* Comment.Special */
.cython .gd { color: #A00000 } /* Generic.Deleted */
.cython .ge { font-style: italic } /* Generic.Emph */
.cython .gr { color: #FF0000 } /* Generic.Error */
.cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.cython .gi { color: #00A000 } /* Generic.Inserted */
.cython .go { color: #888888 } /* Generic.Output */
.cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.cython .gs { font-weight: bold } /* Generic.Strong */
.cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.cython .gt { color: #0044DD } /* Generic.Traceback */
.cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.cython .kp { color: #008000 } /* Keyword.Pseudo */
.cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.cython .kt { color: #B00040 } /* Keyword.Type */
.cython .m { color: #666666 } /* Literal.Number */
.cython .s { color: #BA2121 } /* Literal.String */
.cython .na { color: #7D9029 } /* Name.Attribute */
.cython .nb { color: #008000 } /* Name.Builtin */
.cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.cython .no { color: #880000 } /* Name.Constant */
.cython .nd { color: #AA22FF } /* Name.Decorator */
.cython .ni { color: #999999; font-weight: bold } /* Name.Entity */
.cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.cython .nf { color: #0000FF } /* Name.Function */
.cython .nl { color: #A0A000 } /* Name.Label */
.cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.cython .nt { color: #008000; font-weight: bold } /* Name.Tag */
.cython .nv { color: #19177C } /* Name.Variable */
.cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.cython .w { color: #bbbbbb } /* Text.Whitespace */
.cython .mb { color: #666666 } /* Literal.Number.Bin */
.cython .mf { color: #666666 } /* Literal.Number.Float */
.cython .mh { color: #666666 } /* Literal.Number.Hex */
.cython .mi { color: #666666 } /* Literal.Number.Integer */
.cython .mo { color: #666666 } /* Literal.Number.Oct */
.cython .sa { color: #BA2121 } /* Literal.String.Affix */
.cython .sb { color: #BA2121 } /* Literal.String.Backtick */
.cython .sc { color: #BA2121 } /* Literal.String.Char */
.cython .dl { color: #BA2121 } /* Literal.String.Delimiter */
.cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.cython .s2 { color: #BA2121 } /* Literal.String.Double */
.cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.cython .sh { color: #BA2121 } /* Literal.String.Heredoc */
.cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.cython .sx { color: #008000 } /* Literal.String.Other */
.cython .sr { color: #BB6688 } /* Literal.String.Regex */
.cython .s1 { color: #BA2121 } /* Literal.String.Single */
.cython .ss { color: #19177C } /* Literal.String.Symbol */
.cython .bp { color: #008000 } /* Name.Builtin.Pseudo */
.cython .fm { color: #0000FF } /* Name.Function.Magic */
.cython .vc { color: #19177C } /* Name.Variable.Class */
.cython .vg { color: #19177C } /* Name.Variable.Global */
.cython .vi { color: #19177C } /* Name.Variable.Instance */
.cython .vm { color: #19177C } /* Name.Variable.Magic */
.cython .il { color: #666666 } /* Literal.Number.Integer.Long */
</style>
</head>
<body class="cython">
<p><span style="border-bottom: solid 1px grey;">Generated by Cython 0.29.15</span></p>
<p>
<span style="background-color: #FFFF00">Yellow lines</span> hint at Python interaction.<br />
Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it.
</p>
<div class="cython"><pre class="cython line score-0"> <span class="">01</span>: </pre>
<pre class="cython line score-49" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">02</span>: <span class="k">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">pi</span><span class="p">,</span> <span class="n">acos</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span></pre>
<pre class="cython code score-49 "> __pyx_t_1 = <span class="py_c_api">PyList_New</span>(4);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_pi);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_pi);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 0, __pyx_n_s_pi);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_acos);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_acos);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 1, __pyx_n_s_acos);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_cos);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_cos);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 2, __pyx_n_s_cos);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_n_s_sin);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_n_s_sin);
<span class="py_macro_api">PyList_SET_ITEM</span>(__pyx_t_1, 3, __pyx_n_s_sin);
__pyx_t_2 = <span class="pyx_c_api">__Pyx_Import</span>(__pyx_n_s_math, __pyx_t_1, 0);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_pi);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_pi, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_acos);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_acos, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_cos, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_1 = <span class="pyx_c_api">__Pyx_ImportFrom</span>(__pyx_t_2, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_sin, __pyx_t_1) < 0) <span class="error_goto">__PYX_ERR(0, 2, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
</pre><pre class="cython line score-0"> <span class="">03</span>: </pre>
<pre class="cython line score-97" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">04</span>: <span class="k">def</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span></pre>
<pre class="cython code score-97 ">/* Python wrapper */
static PyObject *__pyx_pw_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyMethodDef __pyx_mdef_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_1great_circle = {"great_circle", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_1great_circle, METH_VARARGS|METH_KEYWORDS, 0};
static PyObject *__pyx_pw_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
double __pyx_v_lon1;
double __pyx_v_lat1;
double __pyx_v_lon2;
double __pyx_v_lat2;
PyObject *__pyx_r = 0;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle (wrapper)", 0);
{
static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_lon1,&__pyx_n_s_lat1,&__pyx_n_s_lon2,&__pyx_n_s_lat2,0};
PyObject* values[4] = {0,0,0,0};
if (unlikely(__pyx_kwds)) {
Py_ssize_t kw_args;
const Py_ssize_t pos_args = <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args);
switch (pos_args) {
case 4: values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
CYTHON_FALLTHROUGH;
case 3: values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
CYTHON_FALLTHROUGH;
case 2: values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
CYTHON_FALLTHROUGH;
case 1: values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
CYTHON_FALLTHROUGH;
case 0: break;
default: goto __pyx_L5_argtuple_error;
}
kw_args = <span class="py_c_api">PyDict_Size</span>(__pyx_kwds);
switch (pos_args) {
case 0:
if (likely((values[0] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon1)) != 0)) kw_args--;
else goto __pyx_L5_argtuple_error;
CYTHON_FALLTHROUGH;
case 1:
if (likely((values[1] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat1)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 1); <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 2:
if (likely((values[2] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 2); <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 3:
if (likely((values[3] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 3); <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
}
}
if (unlikely(kw_args > 0)) {
if (unlikely(<span class="pyx_c_api">__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "great_circle") < 0)) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
}
} else if (<span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args) != 4) {
goto __pyx_L5_argtuple_error;
} else {
values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
}
__pyx_v_lon1 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[0]); if (unlikely((__pyx_v_lon1 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
__pyx_v_lat1 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_lat1 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
__pyx_v_lon2 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_lon2 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
__pyx_v_lat2 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[3]); if (unlikely((__pyx_v_lat2 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args)); <span class="error_goto">__PYX_ERR(0, 4, __pyx_L3_error)</span>
__pyx_L3_error:;
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return NULL;
__pyx_L4_argument_unpacking_done:;
__pyx_r = __pyx_pf_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_great_circle(__pyx_self, __pyx_v_lon1, __pyx_v_lat1, __pyx_v_lon2, __pyx_v_lat2);
/* function exit code */
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
static PyObject *__pyx_pf_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_great_circle(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_lon1, double __pyx_v_lat1, double __pyx_v_lon2, double __pyx_v_lat2) {
double __pyx_v_a;
double __pyx_v_b;
double __pyx_v_theta;
double __pyx_v_c;
double __pyx_v_x;
double __pyx_v_radius;
PyObject *__pyx_r = NULL;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle", 0);
/* … */
/* function exit code */
__pyx_L1_error:;
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_8);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_9);
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
__pyx_r = NULL;
__pyx_L0:;
<span class="refnanny">__Pyx_XGIVEREF</span>(__pyx_r);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
/* … */
__pyx_tuple_ = <span class="py_c_api">PyTuple_Pack</span>(10, __pyx_n_s_lon1, __pyx_n_s_lat1, __pyx_n_s_lon2, __pyx_n_s_lat2, __pyx_n_s_a, __pyx_n_s_b, __pyx_n_s_theta, __pyx_n_s_c, __pyx_n_s_x, __pyx_n_s_radius);<span class="error_goto"> if (unlikely(!__pyx_tuple_)) __PYX_ERR(0, 4, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_tuple_);
<span class="refnanny">__Pyx_GIVEREF</span>(__pyx_tuple_);
/* … */
__pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_46_cython_magic_a8c9eb2e14c0c5fef8bdedbf1ab48c55_1great_circle, NULL, __pyx_n_s_cython_magic_a8c9eb2e14c0c5fef8);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 4, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
if (<span class="py_c_api">PyDict_SetItem</span>(__pyx_d, __pyx_n_s_great_circle, __pyx_t_2) < 0) <span class="error_goto">__PYX_ERR(0, 4, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
</pre><pre class="cython line score-0"> <span class="">05</span>: <span class="k">cdef</span> <span class="kt">double</span> <span class="nf">a</span><span class="p">,</span> <span class="nf">b</span><span class="p">,</span> <span class="nf">theta</span><span class="p">,</span> <span class="nf">c</span><span class="p">,</span> <span class="nf">x</span><span class="p">,</span> <span class="nf">radius</span></pre>
<pre class="cython line score-0"> <span class="">06</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">07</span>: <span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span></pre>
<pre class="cython code score-0 "> __pyx_v_radius = 6371.0;
</pre><pre class="cython line score-16" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">08</span>: <span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mf">180</span></pre>
<pre class="cython code score-16 "> <span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_1, __pyx_n_s_pi);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 8, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
__pyx_t_2 = <span class="pyx_c_api">__Pyx_PyInt_TrueDivideObjC</span>(__pyx_t_1, __pyx_int_180, 0xB4, 0, 0);<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 8, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_3 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(__pyx_t_2); if (unlikely((__pyx_t_3 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 8, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
__pyx_v_x = __pyx_t_3;
</pre><pre class="cython line score-0"> <span class="">09</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">10</span>: <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_a = ((90.0 - __pyx_v_lat1) * __pyx_v_x);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">11</span>: <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_b = ((90.0 - __pyx_v_lat2) * __pyx_v_x);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">12</span>: <span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_theta = ((__pyx_v_lon2 - __pyx_v_lon1) * __pyx_v_x);
</pre><pre class="cython line score-166" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">13</span>: <span class="n">c</span> <span class="o">=</span> <span class="n">acos</span><span class="p">((</span><span class="n">cos</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sin</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span></pre>
<pre class="cython code score-166 "> <span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_1, __pyx_n_s_acos);<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_5, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
__pyx_t_6 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_v_a);<span class="error_goto"> if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_6);
__pyx_t_7 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_5))) {
__pyx_t_7 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_5);
if (likely(__pyx_t_7)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_5, function);
}
}
__pyx_t_4 = (__pyx_t_7) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_7, __pyx_t_6) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_5, __pyx_t_6);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
if (unlikely(!__pyx_t_4)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_6, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_6);
__pyx_t_7 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_v_b);<span class="error_goto"> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_7);
__pyx_t_8 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_6))) {
__pyx_t_8 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_6);
if (likely(__pyx_t_8)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_8);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_6, function);
}
}
__pyx_t_5 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_6, __pyx_t_8, __pyx_t_7) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_6, __pyx_t_7);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_8); __pyx_t_8 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
if (unlikely(!__pyx_t_5)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
__pyx_t_6 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_4, __pyx_t_5);<span class="error_goto"> if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_6);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_4, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
__pyx_t_7 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_v_a);<span class="error_goto"> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_7);
__pyx_t_8 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_4))) {
__pyx_t_8 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_4);
if (likely(__pyx_t_8)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_8);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_4, function);
}
}
__pyx_t_5 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_8, __pyx_t_7) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_4, __pyx_t_7);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_8); __pyx_t_8 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
if (unlikely(!__pyx_t_5)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_7, __pyx_n_s_sin);<span class="error_goto"> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_7);
__pyx_t_8 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_v_b);<span class="error_goto"> if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_8);
__pyx_t_9 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_7))) {
__pyx_t_9 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_7);
if (likely(__pyx_t_9)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_9);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_7, function);
}
}
__pyx_t_4 = (__pyx_t_9) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_9, __pyx_t_8) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_7, __pyx_t_8);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;
if (unlikely(!__pyx_t_4)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
__pyx_t_7 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_5, __pyx_t_4);<span class="error_goto"> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_7);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
<span class="pyx_c_api">__Pyx_GetModuleGlobalName</span>(__pyx_t_5, __pyx_n_s_cos);<span class="error_goto"> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
__pyx_t_8 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_v_theta);<span class="error_goto"> if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_8);
__pyx_t_9 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_5))) {
__pyx_t_9 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_5);
if (likely(__pyx_t_9)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_9);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_5, function);
}
}
__pyx_t_4 = (__pyx_t_9) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_9, __pyx_t_8) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_5, __pyx_t_8);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;
if (unlikely(!__pyx_t_4)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
__pyx_t_5 = <span class="py_c_api">PyNumber_Multiply</span>(__pyx_t_7, __pyx_t_4);<span class="error_goto"> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_7); __pyx_t_7 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
__pyx_t_4 = <span class="py_c_api">PyNumber_Add</span>(__pyx_t_6, __pyx_t_5);<span class="error_goto"> if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_4);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
__pyx_t_5 = NULL;
if (CYTHON_UNPACK_METHODS && unlikely(<span class="py_c_api">PyMethod_Check</span>(__pyx_t_1))) {
__pyx_t_5 = <span class="py_macro_api">PyMethod_GET_SELF</span>(__pyx_t_1);
if (likely(__pyx_t_5)) {
PyObject* function = <span class="py_macro_api">PyMethod_GET_FUNCTION</span>(__pyx_t_1);
<span class="pyx_macro_api">__Pyx_INCREF</span>(__pyx_t_5);
<span class="pyx_macro_api">__Pyx_INCREF</span>(function);
<span class="pyx_macro_api">__Pyx_DECREF_SET</span>(__pyx_t_1, function);
}
}
__pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_1, __pyx_t_5, __pyx_t_4) : <span class="pyx_c_api">__Pyx_PyObject_CallOneArg</span>(__pyx_t_1, __pyx_t_4);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_5); __pyx_t_5 = 0;
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;
if (unlikely(!__pyx_t_2)) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
__pyx_t_3 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(__pyx_t_2); if (unlikely((__pyx_t_3 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 13, __pyx_L1_error)</span>
<span class="pyx_macro_api">__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;
__pyx_v_c = __pyx_t_3;
</pre><pre class="cython line score-6" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">14</span>: <span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span></pre>
<pre class="cython code score-6 "> <span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_r);
__pyx_t_2 = <span class="py_c_api">PyFloat_FromDouble</span>((__pyx_v_radius * __pyx_v_c));<span class="error_goto"> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 14, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_2);
__pyx_r = __pyx_t_2;
__pyx_t_2 = 0;
goto __pyx_L0;
</pre></div></body></html>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="C标准库函数">C标准库函数<a class="anchor-link" href="#C标准库函数"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> great_circle_cy_v2.pyx
<span class="n">cdef</span> <span class="n">extern</span> <span class="kn">from</span> <span class="s2">"math.h"</span><span class="p">:</span>
<span class="nb">float</span> <span class="n">cosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="nb">float</span> <span class="n">sinf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="nb">float</span> <span class="n">acosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="n">cpdef</span> <span class="n">double</span> <span class="n">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span>
<span class="n">cdef</span> <span class="n">double</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">radius</span>
<span class="n">cdef</span> <span class="n">double</span> <span class="n">pi</span> <span class="o">=</span> <span class="mf">3.141592653589793</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mi">6371</span> <span class="c1"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mi">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acosf</span><span class="p">((</span><span class="n">cosf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sinf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sinf</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing great_circle_cy_v2.pyx
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> great_circle_setup_v2.py
<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
<span class="kn">from</span> <span class="nn">Cython.Build</span> <span class="kn">import</span> <span class="n">cythonize</span>
<span class="n">setup</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"Great Circle module v2"</span><span class="p">,</span> <span class="n">ext_modules</span><span class="o">=</span><span class="n">cythonize</span><span class="p">(</span><span class="s2">"great_circle_cy_v2.pyx"</span><span class="p">),</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Writing great_circle_setup_v2.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>python great_circle_setup_v2.py build_ext --inplace
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Compiling great_circle_cy_v2.pyx because it changed.
[1/1] Cythonizing great_circle_cy_v2.pyx
/home/junjiet/conda/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /home/junjiet/data_science2020/2.数据处理/test_cython/great_circle_cy_v2.pyx
tree = Parsing.p_module(s, pxd, full_module_name)
running build_ext
building 'great_circle_cy_v2' extension
/home/junjiet/conda/bin/x86_64-conda_cos6-linux-gnu-cc -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include -fPIC -I/home/junjiet/conda/include/python3.7m -c great_circle_cy_v2.c -o build/temp.linux-x86_64-3.7/great_circle_cy_v2.o
x86_64-conda_cos6-linux-gnu-gcc -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/junjiet/conda/lib -Wl,-rpath-link,/home/junjiet/conda/lib -L/home/junjiet/conda/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/junjiet/conda/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/junjiet/conda/include build/temp.linux-x86_64-3.7/great_circle_cy_v2.o -o /home/junjiet/data_science2020/2.数据处理/test_cython/great_circle_cy_v2.cpython-37m-x86_64-linux-gnu.so
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">great_circle_cy_v2</span> <span class="kn">import</span> <span class="n">great_circle</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num</span><span class="p">):</span>
<span class="n">great_circle</span><span class="p">(</span><span class="n">lon1</span><span class="p">,</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">lat2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="n">cython</span> <span class="o">-</span><span class="n">a</span>
<span class="k">cdef</span> <span class="kr">extern</span> <span class="k">from</span> <span class="s">"math.h"</span><span class="p">:</span>
<span class="nb">float</span> <span class="n">cosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="nb">float</span> <span class="n">sinf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="nb">float</span> <span class="n">acosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span>
<span class="k">cpdef</span> <span class="kt">double</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span>
<span class="k">cdef</span> <span class="kt">double</span> <span class="nf">a</span><span class="p">,</span> <span class="nf">b</span><span class="p">,</span> <span class="nf">theta</span><span class="p">,</span> <span class="nf">c</span><span class="p">,</span> <span class="nf">x</span><span class="p">,</span> <span class="nf">radius</span>
<span class="k">cdef</span> <span class="kt">double</span> <span class="nf">pi</span> <span class="o">=</span> <span class="mf">3.141592653589793</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mf">180</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">acosf</span><span class="p">((</span><span class="n">cosf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sinf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sinf</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<!DOCTYPE html>
<!-- Generated by Cython 0.29.15 -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Cython: _cython_magic_001d315da99fd0491a49a895f572c5f0.pyx</title>
<style type="text/css">
body.cython { font-family: courier; font-size: 12; }
.cython.tag { }
.cython.line { margin: 0em }
.cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }
.cython.line .run { background-color: #B0FFB0; }
.cython.line .mis { background-color: #FFB0B0; }
.cython.code.run { border-left: 8px solid #B0FFB0; }
.cython.code.mis { border-left: 8px solid #FFB0B0; }
.cython.code .py_c_api { color: red; }
.cython.code .py_macro_api { color: #FF7000; }
.cython.code .pyx_c_api { color: #FF3000; }
.cython.code .pyx_macro_api { color: #FF7000; }
.cython.code .refnanny { color: #FFA000; }
.cython.code .trace { color: #FFA000; }
.cython.code .error_goto { color: #FFA000; }
.cython.code .coerce { color: #008000; border: 1px dotted #008000 }
.cython.code .py_attr { color: #FF0000; font-weight: bold; }
.cython.code .c_attr { color: #0000FF; }
.cython.code .py_call { color: #FF0000; font-weight: bold; }
.cython.code .c_call { color: #0000FF; }
.cython.score-0 {background-color: #FFFFff;}
.cython.score-1 {background-color: #FFFFe7;}
.cython.score-2 {background-color: #FFFFd4;}
.cython.score-3 {background-color: #FFFFc4;}
.cython.score-4 {background-color: #FFFFb6;}
.cython.score-5 {background-color: #FFFFaa;}
.cython.score-6 {background-color: #FFFF9f;}
.cython.score-7 {background-color: #FFFF96;}
.cython.score-8 {background-color: #FFFF8d;}
.cython.score-9 {background-color: #FFFF86;}
.cython.score-10 {background-color: #FFFF7f;}
.cython.score-11 {background-color: #FFFF79;}
.cython.score-12 {background-color: #FFFF73;}
.cython.score-13 {background-color: #FFFF6e;}
.cython.score-14 {background-color: #FFFF6a;}
.cython.score-15 {background-color: #FFFF66;}
.cython.score-16 {background-color: #FFFF62;}
.cython.score-17 {background-color: #FFFF5e;}
.cython.score-18 {background-color: #FFFF5b;}
.cython.score-19 {background-color: #FFFF57;}
.cython.score-20 {background-color: #FFFF55;}
.cython.score-21 {background-color: #FFFF52;}
.cython.score-22 {background-color: #FFFF4f;}
.cython.score-23 {background-color: #FFFF4d;}
.cython.score-24 {background-color: #FFFF4b;}
.cython.score-25 {background-color: #FFFF48;}
.cython.score-26 {background-color: #FFFF46;}
.cython.score-27 {background-color: #FFFF44;}
.cython.score-28 {background-color: #FFFF43;}
.cython.score-29 {background-color: #FFFF41;}
.cython.score-30 {background-color: #FFFF3f;}
.cython.score-31 {background-color: #FFFF3e;}
.cython.score-32 {background-color: #FFFF3c;}
.cython.score-33 {background-color: #FFFF3b;}
.cython.score-34 {background-color: #FFFF39;}
.cython.score-35 {background-color: #FFFF38;}
.cython.score-36 {background-color: #FFFF37;}
.cython.score-37 {background-color: #FFFF36;}
.cython.score-38 {background-color: #FFFF35;}
.cython.score-39 {background-color: #FFFF34;}
.cython.score-40 {background-color: #FFFF33;}
.cython.score-41 {background-color: #FFFF32;}
.cython.score-42 {background-color: #FFFF31;}
.cython.score-43 {background-color: #FFFF30;}
.cython.score-44 {background-color: #FFFF2f;}
.cython.score-45 {background-color: #FFFF2e;}
.cython.score-46 {background-color: #FFFF2d;}
.cython.score-47 {background-color: #FFFF2c;}
.cython.score-48 {background-color: #FFFF2b;}
.cython.score-49 {background-color: #FFFF2b;}
.cython.score-50 {background-color: #FFFF2a;}
.cython.score-51 {background-color: #FFFF29;}
.cython.score-52 {background-color: #FFFF29;}
.cython.score-53 {background-color: #FFFF28;}
.cython.score-54 {background-color: #FFFF27;}
.cython.score-55 {background-color: #FFFF27;}
.cython.score-56 {background-color: #FFFF26;}
.cython.score-57 {background-color: #FFFF26;}
.cython.score-58 {background-color: #FFFF25;}
.cython.score-59 {background-color: #FFFF24;}
.cython.score-60 {background-color: #FFFF24;}
.cython.score-61 {background-color: #FFFF23;}
.cython.score-62 {background-color: #FFFF23;}
.cython.score-63 {background-color: #FFFF22;}
.cython.score-64 {background-color: #FFFF22;}
.cython.score-65 {background-color: #FFFF22;}
.cython.score-66 {background-color: #FFFF21;}
.cython.score-67 {background-color: #FFFF21;}
.cython.score-68 {background-color: #FFFF20;}
.cython.score-69 {background-color: #FFFF20;}
.cython.score-70 {background-color: #FFFF1f;}
.cython.score-71 {background-color: #FFFF1f;}
.cython.score-72 {background-color: #FFFF1f;}
.cython.score-73 {background-color: #FFFF1e;}
.cython.score-74 {background-color: #FFFF1e;}
.cython.score-75 {background-color: #FFFF1e;}
.cython.score-76 {background-color: #FFFF1d;}
.cython.score-77 {background-color: #FFFF1d;}
.cython.score-78 {background-color: #FFFF1c;}
.cython.score-79 {background-color: #FFFF1c;}
.cython.score-80 {background-color: #FFFF1c;}
.cython.score-81 {background-color: #FFFF1c;}
.cython.score-82 {background-color: #FFFF1b;}
.cython.score-83 {background-color: #FFFF1b;}
.cython.score-84 {background-color: #FFFF1b;}
.cython.score-85 {background-color: #FFFF1a;}
.cython.score-86 {background-color: #FFFF1a;}
.cython.score-87 {background-color: #FFFF1a;}
.cython.score-88 {background-color: #FFFF1a;}
.cython.score-89 {background-color: #FFFF19;}
.cython.score-90 {background-color: #FFFF19;}
.cython.score-91 {background-color: #FFFF19;}
.cython.score-92 {background-color: #FFFF19;}
.cython.score-93 {background-color: #FFFF18;}
.cython.score-94 {background-color: #FFFF18;}
.cython.score-95 {background-color: #FFFF18;}
.cython.score-96 {background-color: #FFFF18;}
.cython.score-97 {background-color: #FFFF17;}
.cython.score-98 {background-color: #FFFF17;}
.cython.score-99 {background-color: #FFFF17;}
.cython.score-100 {background-color: #FFFF17;}
.cython.score-101 {background-color: #FFFF16;}
.cython.score-102 {background-color: #FFFF16;}
.cython.score-103 {background-color: #FFFF16;}
.cython.score-104 {background-color: #FFFF16;}
.cython.score-105 {background-color: #FFFF16;}
.cython.score-106 {background-color: #FFFF15;}
.cython.score-107 {background-color: #FFFF15;}
.cython.score-108 {background-color: #FFFF15;}
.cython.score-109 {background-color: #FFFF15;}
.cython.score-110 {background-color: #FFFF15;}
.cython.score-111 {background-color: #FFFF15;}
.cython.score-112 {background-color: #FFFF14;}
.cython.score-113 {background-color: #FFFF14;}
.cython.score-114 {background-color: #FFFF14;}
.cython.score-115 {background-color: #FFFF14;}
.cython.score-116 {background-color: #FFFF14;}
.cython.score-117 {background-color: #FFFF14;}
.cython.score-118 {background-color: #FFFF13;}
.cython.score-119 {background-color: #FFFF13;}
.cython.score-120 {background-color: #FFFF13;}
.cython.score-121 {background-color: #FFFF13;}
.cython.score-122 {background-color: #FFFF13;}
.cython.score-123 {background-color: #FFFF13;}
.cython.score-124 {background-color: #FFFF13;}
.cython.score-125 {background-color: #FFFF12;}
.cython.score-126 {background-color: #FFFF12;}
.cython.score-127 {background-color: #FFFF12;}
.cython.score-128 {background-color: #FFFF12;}
.cython.score-129 {background-color: #FFFF12;}
.cython.score-130 {background-color: #FFFF12;}
.cython.score-131 {background-color: #FFFF12;}
.cython.score-132 {background-color: #FFFF11;}
.cython.score-133 {background-color: #FFFF11;}
.cython.score-134 {background-color: #FFFF11;}
.cython.score-135 {background-color: #FFFF11;}
.cython.score-136 {background-color: #FFFF11;}
.cython.score-137 {background-color: #FFFF11;}
.cython.score-138 {background-color: #FFFF11;}
.cython.score-139 {background-color: #FFFF11;}
.cython.score-140 {background-color: #FFFF11;}
.cython.score-141 {background-color: #FFFF10;}
.cython.score-142 {background-color: #FFFF10;}
.cython.score-143 {background-color: #FFFF10;}
.cython.score-144 {background-color: #FFFF10;}
.cython.score-145 {background-color: #FFFF10;}
.cython.score-146 {background-color: #FFFF10;}
.cython.score-147 {background-color: #FFFF10;}
.cython.score-148 {background-color: #FFFF10;}
.cython.score-149 {background-color: #FFFF10;}
.cython.score-150 {background-color: #FFFF0f;}
.cython.score-151 {background-color: #FFFF0f;}
.cython.score-152 {background-color: #FFFF0f;}
.cython.score-153 {background-color: #FFFF0f;}
.cython.score-154 {background-color: #FFFF0f;}
.cython.score-155 {background-color: #FFFF0f;}
.cython.score-156 {background-color: #FFFF0f;}
.cython.score-157 {background-color: #FFFF0f;}
.cython.score-158 {background-color: #FFFF0f;}
.cython.score-159 {background-color: #FFFF0f;}
.cython.score-160 {background-color: #FFFF0f;}
.cython.score-161 {background-color: #FFFF0e;}
.cython.score-162 {background-color: #FFFF0e;}
.cython.score-163 {background-color: #FFFF0e;}
.cython.score-164 {background-color: #FFFF0e;}
.cython.score-165 {background-color: #FFFF0e;}
.cython.score-166 {background-color: #FFFF0e;}
.cython.score-167 {background-color: #FFFF0e;}
.cython.score-168 {background-color: #FFFF0e;}
.cython.score-169 {background-color: #FFFF0e;}
.cython.score-170 {background-color: #FFFF0e;}
.cython.score-171 {background-color: #FFFF0e;}
.cython.score-172 {background-color: #FFFF0e;}
.cython.score-173 {background-color: #FFFF0d;}
.cython.score-174 {background-color: #FFFF0d;}
.cython.score-175 {background-color: #FFFF0d;}
.cython.score-176 {background-color: #FFFF0d;}
.cython.score-177 {background-color: #FFFF0d;}
.cython.score-178 {background-color: #FFFF0d;}
.cython.score-179 {background-color: #FFFF0d;}
.cython.score-180 {background-color: #FFFF0d;}
.cython.score-181 {background-color: #FFFF0d;}
.cython.score-182 {background-color: #FFFF0d;}
.cython.score-183 {background-color: #FFFF0d;}
.cython.score-184 {background-color: #FFFF0d;}
.cython.score-185 {background-color: #FFFF0d;}
.cython.score-186 {background-color: #FFFF0d;}
.cython.score-187 {background-color: #FFFF0c;}
.cython.score-188 {background-color: #FFFF0c;}
.cython.score-189 {background-color: #FFFF0c;}
.cython.score-190 {background-color: #FFFF0c;}
.cython.score-191 {background-color: #FFFF0c;}
.cython.score-192 {background-color: #FFFF0c;}
.cython.score-193 {background-color: #FFFF0c;}
.cython.score-194 {background-color: #FFFF0c;}
.cython.score-195 {background-color: #FFFF0c;}
.cython.score-196 {background-color: #FFFF0c;}
.cython.score-197 {background-color: #FFFF0c;}
.cython.score-198 {background-color: #FFFF0c;}
.cython.score-199 {background-color: #FFFF0c;}
.cython.score-200 {background-color: #FFFF0c;}
.cython.score-201 {background-color: #FFFF0c;}
.cython.score-202 {background-color: #FFFF0c;}
.cython.score-203 {background-color: #FFFF0b;}
.cython.score-204 {background-color: #FFFF0b;}
.cython.score-205 {background-color: #FFFF0b;}
.cython.score-206 {background-color: #FFFF0b;}
.cython.score-207 {background-color: #FFFF0b;}
.cython.score-208 {background-color: #FFFF0b;}
.cython.score-209 {background-color: #FFFF0b;}
.cython.score-210 {background-color: #FFFF0b;}
.cython.score-211 {background-color: #FFFF0b;}
.cython.score-212 {background-color: #FFFF0b;}
.cython.score-213 {background-color: #FFFF0b;}
.cython.score-214 {background-color: #FFFF0b;}
.cython.score-215 {background-color: #FFFF0b;}
.cython.score-216 {background-color: #FFFF0b;}
.cython.score-217 {background-color: #FFFF0b;}
.cython.score-218 {background-color: #FFFF0b;}
.cython.score-219 {background-color: #FFFF0b;}
.cython.score-220 {background-color: #FFFF0b;}
.cython.score-221 {background-color: #FFFF0b;}
.cython.score-222 {background-color: #FFFF0a;}
.cython.score-223 {background-color: #FFFF0a;}
.cython.score-224 {background-color: #FFFF0a;}
.cython.score-225 {background-color: #FFFF0a;}
.cython.score-226 {background-color: #FFFF0a;}
.cython.score-227 {background-color: #FFFF0a;}
.cython.score-228 {background-color: #FFFF0a;}
.cython.score-229 {background-color: #FFFF0a;}
.cython.score-230 {background-color: #FFFF0a;}
.cython.score-231 {background-color: #FFFF0a;}
.cython.score-232 {background-color: #FFFF0a;}
.cython.score-233 {background-color: #FFFF0a;}
.cython.score-234 {background-color: #FFFF0a;}
.cython.score-235 {background-color: #FFFF0a;}
.cython.score-236 {background-color: #FFFF0a;}
.cython.score-237 {background-color: #FFFF0a;}
.cython.score-238 {background-color: #FFFF0a;}
.cython.score-239 {background-color: #FFFF0a;}
.cython.score-240 {background-color: #FFFF0a;}
.cython.score-241 {background-color: #FFFF0a;}
.cython.score-242 {background-color: #FFFF0a;}
.cython.score-243 {background-color: #FFFF0a;}
.cython.score-244 {background-color: #FFFF0a;}
.cython.score-245 {background-color: #FFFF0a;}
.cython.score-246 {background-color: #FFFF09;}
.cython.score-247 {background-color: #FFFF09;}
.cython.score-248 {background-color: #FFFF09;}
.cython.score-249 {background-color: #FFFF09;}
.cython.score-250 {background-color: #FFFF09;}
.cython.score-251 {background-color: #FFFF09;}
.cython.score-252 {background-color: #FFFF09;}
.cython.score-253 {background-color: #FFFF09;}
.cython.score-254 {background-color: #FFFF09;}
.cython .hll { background-color: #ffffcc }
.cython { background: #f8f8f8; }
.cython .c { color: #408080; font-style: italic } /* Comment */
.cython .err { border: 1px solid #FF0000 } /* Error */
.cython .k { color: #008000; font-weight: bold } /* Keyword */
.cython .o { color: #666666 } /* Operator */
.cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.cython .cp { color: #BC7A00 } /* Comment.Preproc */
.cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.cython .c1 { color: #408080; font-style: italic } /* Comment.Single */
.cython .cs { color: #408080; font-style: italic } /* Comment.Special */
.cython .gd { color: #A00000 } /* Generic.Deleted */
.cython .ge { font-style: italic } /* Generic.Emph */
.cython .gr { color: #FF0000 } /* Generic.Error */
.cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.cython .gi { color: #00A000 } /* Generic.Inserted */
.cython .go { color: #888888 } /* Generic.Output */
.cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.cython .gs { font-weight: bold } /* Generic.Strong */
.cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.cython .gt { color: #0044DD } /* Generic.Traceback */
.cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.cython .kp { color: #008000 } /* Keyword.Pseudo */
.cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.cython .kt { color: #B00040 } /* Keyword.Type */
.cython .m { color: #666666 } /* Literal.Number */
.cython .s { color: #BA2121 } /* Literal.String */
.cython .na { color: #7D9029 } /* Name.Attribute */
.cython .nb { color: #008000 } /* Name.Builtin */
.cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.cython .no { color: #880000 } /* Name.Constant */
.cython .nd { color: #AA22FF } /* Name.Decorator */
.cython .ni { color: #999999; font-weight: bold } /* Name.Entity */
.cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.cython .nf { color: #0000FF } /* Name.Function */
.cython .nl { color: #A0A000 } /* Name.Label */
.cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.cython .nt { color: #008000; font-weight: bold } /* Name.Tag */
.cython .nv { color: #19177C } /* Name.Variable */
.cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.cython .w { color: #bbbbbb } /* Text.Whitespace */
.cython .mb { color: #666666 } /* Literal.Number.Bin */
.cython .mf { color: #666666 } /* Literal.Number.Float */
.cython .mh { color: #666666 } /* Literal.Number.Hex */
.cython .mi { color: #666666 } /* Literal.Number.Integer */
.cython .mo { color: #666666 } /* Literal.Number.Oct */
.cython .sa { color: #BA2121 } /* Literal.String.Affix */
.cython .sb { color: #BA2121 } /* Literal.String.Backtick */
.cython .sc { color: #BA2121 } /* Literal.String.Char */
.cython .dl { color: #BA2121 } /* Literal.String.Delimiter */
.cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.cython .s2 { color: #BA2121 } /* Literal.String.Double */
.cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.cython .sh { color: #BA2121 } /* Literal.String.Heredoc */
.cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.cython .sx { color: #008000 } /* Literal.String.Other */
.cython .sr { color: #BB6688 } /* Literal.String.Regex */
.cython .s1 { color: #BA2121 } /* Literal.String.Single */
.cython .ss { color: #19177C } /* Literal.String.Symbol */
.cython .bp { color: #008000 } /* Name.Builtin.Pseudo */
.cython .fm { color: #0000FF } /* Name.Function.Magic */
.cython .vc { color: #19177C } /* Name.Variable.Class */
.cython .vg { color: #19177C } /* Name.Variable.Global */
.cython .vi { color: #19177C } /* Name.Variable.Instance */
.cython .vm { color: #19177C } /* Name.Variable.Magic */
.cython .il { color: #666666 } /* Literal.Number.Integer.Long */
</style>
</head>
<body class="cython">
<p><span style="border-bottom: solid 1px grey;">Generated by Cython 0.29.15</span></p>
<p>
<span style="background-color: #FFFF00">Yellow lines</span> hint at Python interaction.<br />
Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it.
</p>
<div class="cython"><pre class="cython line score-0"> <span class="">01</span>: </pre>
<pre class="cython line score-0"> <span class="">02</span>: <span class="k">cdef</span> <span class="kr">extern</span> <span class="k">from</span> <span class="s">"math.h"</span><span class="p">:</span></pre>
<pre class="cython line score-0"> <span class="">03</span>: <span class="nb">float</span> <span class="n">cosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span></pre>
<pre class="cython line score-0"> <span class="">04</span>: <span class="nb">float</span> <span class="n">sinf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span></pre>
<pre class="cython line score-0"> <span class="">05</span>: <span class="nb">float</span> <span class="n">acosf</span><span class="p">(</span><span class="nb">float</span> <span class="n">theta</span><span class="p">)</span></pre>
<pre class="cython line score-0"> <span class="">06</span>: </pre>
<pre class="cython line score-85" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">07</span>: <span class="k">cpdef</span> <span class="kt">double</span> <span class="nf">great_circle</span><span class="p">(</span><span class="n">double</span> <span class="n">lon1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat1</span><span class="p">,</span> <span class="n">double</span> <span class="n">lon2</span><span class="p">,</span> <span class="n">double</span> <span class="n">lat2</span><span class="p">):</span></pre>
<pre class="cython code score-85 ">static PyObject *__pyx_pw_46_cython_magic_001d315da99fd0491a49a895f572c5f0_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static double __pyx_f_46_cython_magic_001d315da99fd0491a49a895f572c5f0_great_circle(double __pyx_v_lon1, double __pyx_v_lat1, double __pyx_v_lon2, double __pyx_v_lat2, CYTHON_UNUSED int __pyx_skip_dispatch) {
double __pyx_v_a;
double __pyx_v_b;
double __pyx_v_theta;
double __pyx_v_c;
double __pyx_v_x;
double __pyx_v_radius;
double __pyx_v_pi;
double __pyx_r;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle", 0);
/* … */
/* function exit code */
__pyx_L0:;
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
/* Python wrapper */
static PyObject *__pyx_pw_46_cython_magic_001d315da99fd0491a49a895f572c5f0_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyObject *__pyx_pw_46_cython_magic_001d315da99fd0491a49a895f572c5f0_1great_circle(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
double __pyx_v_lon1;
double __pyx_v_lat1;
double __pyx_v_lon2;
double __pyx_v_lat2;
PyObject *__pyx_r = 0;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle (wrapper)", 0);
{
static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_lon1,&__pyx_n_s_lat1,&__pyx_n_s_lon2,&__pyx_n_s_lat2,0};
PyObject* values[4] = {0,0,0,0};
if (unlikely(__pyx_kwds)) {
Py_ssize_t kw_args;
const Py_ssize_t pos_args = <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args);
switch (pos_args) {
case 4: values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
CYTHON_FALLTHROUGH;
case 3: values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
CYTHON_FALLTHROUGH;
case 2: values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
CYTHON_FALLTHROUGH;
case 1: values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
CYTHON_FALLTHROUGH;
case 0: break;
default: goto __pyx_L5_argtuple_error;
}
kw_args = <span class="py_c_api">PyDict_Size</span>(__pyx_kwds);
switch (pos_args) {
case 0:
if (likely((values[0] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon1)) != 0)) kw_args--;
else goto __pyx_L5_argtuple_error;
CYTHON_FALLTHROUGH;
case 1:
if (likely((values[1] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat1)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 1); <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 2:
if (likely((values[2] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lon2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 2); <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
}
CYTHON_FALLTHROUGH;
case 3:
if (likely((values[3] = <span class="pyx_c_api">__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_lat2)) != 0)) kw_args--;
else {
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, 3); <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
}
}
if (unlikely(kw_args > 0)) {
if (unlikely(<span class="pyx_c_api">__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "great_circle") < 0)) <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
}
} else if (<span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args) != 4) {
goto __pyx_L5_argtuple_error;
} else {
values[0] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 0);
values[1] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 1);
values[2] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 2);
values[3] = <span class="py_macro_api">PyTuple_GET_ITEM</span>(__pyx_args, 3);
}
__pyx_v_lon1 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[0]); if (unlikely((__pyx_v_lon1 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
__pyx_v_lat1 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_lat1 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
__pyx_v_lon2 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_lon2 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
__pyx_v_lat2 = __pyx_<span class="py_c_api">PyFloat_AsDouble</span>(values[3]); if (unlikely((__pyx_v_lat2 == (double)-1) && <span class="py_c_api">PyErr_Occurred</span>())) <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
<span class="pyx_c_api">__Pyx_RaiseArgtupleInvalid</span>("great_circle", 1, 4, 4, <span class="py_macro_api">PyTuple_GET_SIZE</span>(__pyx_args)); <span class="error_goto">__PYX_ERR(0, 7, __pyx_L3_error)</span>
__pyx_L3_error:;
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_001d315da99fd0491a49a895f572c5f0.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return NULL;
__pyx_L4_argument_unpacking_done:;
__pyx_r = __pyx_pf_46_cython_magic_001d315da99fd0491a49a895f572c5f0_great_circle(__pyx_self, __pyx_v_lon1, __pyx_v_lat1, __pyx_v_lon2, __pyx_v_lat2);
/* function exit code */
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
static PyObject *__pyx_pf_46_cython_magic_001d315da99fd0491a49a895f572c5f0_great_circle(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_lon1, double __pyx_v_lat1, double __pyx_v_lon2, double __pyx_v_lat2) {
PyObject *__pyx_r = NULL;
<span class="refnanny">__Pyx_RefNannyDeclarations</span>
<span class="refnanny">__Pyx_RefNannySetupContext</span>("great_circle", 0);
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_r);
__pyx_t_1 = <span class="py_c_api">PyFloat_FromDouble</span>(__pyx_f_46_cython_magic_001d315da99fd0491a49a895f572c5f0_great_circle(__pyx_v_lon1, __pyx_v_lat1, __pyx_v_lon2, __pyx_v_lat2, 0));<span class="error_goto"> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)</span>
<span class="refnanny">__Pyx_GOTREF</span>(__pyx_t_1);
__pyx_r = __pyx_t_1;
__pyx_t_1 = 0;
goto __pyx_L0;
/* function exit code */
__pyx_L1_error:;
<span class="pyx_macro_api">__Pyx_XDECREF</span>(__pyx_t_1);
<span class="pyx_c_api">__Pyx_AddTraceback</span>("_cython_magic_001d315da99fd0491a49a895f572c5f0.great_circle", __pyx_clineno, __pyx_lineno, __pyx_filename);
__pyx_r = NULL;
__pyx_L0:;
<span class="refnanny">__Pyx_XGIVEREF</span>(__pyx_r);
<span class="refnanny">__Pyx_RefNannyFinishContext</span>();
return __pyx_r;
}
</pre><pre class="cython line score-0"> <span class="">08</span>: <span class="k">cdef</span> <span class="kt">double</span> <span class="nf">a</span><span class="p">,</span> <span class="nf">b</span><span class="p">,</span> <span class="nf">theta</span><span class="p">,</span> <span class="nf">c</span><span class="p">,</span> <span class="nf">x</span><span class="p">,</span> <span class="nf">radius</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">09</span>: <span class="k">cdef</span> <span class="kt">double</span> <span class="nf">pi</span> <span class="o">=</span> <span class="mf">3.141592653589793</span></pre>
<pre class="cython code score-0 "> __pyx_v_pi = 3.141592653589793;
</pre><pre class="cython line score-0"> <span class="">10</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">11</span>: <span class="n">radius</span> <span class="o">=</span> <span class="mf">6371</span> <span class="c"># 公里</span></pre>
<pre class="cython code score-0 "> __pyx_v_radius = 6371.0;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">12</span>: <span class="n">x</span> <span class="o">=</span> <span class="n">pi</span><span class="o">/</span><span class="mf">180</span></pre>
<pre class="cython code score-0 "> __pyx_v_x = (__pyx_v_pi / 180.0);
</pre><pre class="cython line score-0"> <span class="">13</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">14</span>: <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_a = ((90.0 - __pyx_v_lat1) * __pyx_v_x);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">15</span>: <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="mf">90</span><span class="o">-</span><span class="n">lat2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_b = ((90.0 - __pyx_v_lat2) * __pyx_v_x);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">16</span>: <span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">lon2</span><span class="o">-</span><span class="n">lon1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre>
<pre class="cython code score-0 "> __pyx_v_theta = ((__pyx_v_lon2 - __pyx_v_lon1) * __pyx_v_x);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">17</span>: <span class="n">c</span> <span class="o">=</span> <span class="n">acosf</span><span class="p">((</span><span class="n">cosf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="o">+</span> <span class="p">(</span><span class="n">sinf</span><span class="p">(</span><span class="n">a</span><span class="p">)</span><span class="o">*</span><span class="n">sinf</span><span class="p">(</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="n">cosf</span><span class="p">(</span><span class="n">theta</span><span class="p">)))</span></pre>
<pre class="cython code score-0 "> __pyx_v_c = acosf(((cosf(__pyx_v_a) * cosf(__pyx_v_b)) + ((sinf(__pyx_v_a) * sinf(__pyx_v_b)) * cosf(__pyx_v_theta))));
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">18</span>: <span class="k">return</span> <span class="n">radius</span><span class="o">*</span><span class="n">c</span></pre>
<pre class="cython code score-0 "> __pyx_r = (__pyx_v_radius * __pyx_v_c);
goto __pyx_L0;
</pre></div></body></html>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Numba"><a href="http://numba.pydata.org">Numba</a><a class="anchor-link" href="#Numba"> </a></h2><p>通过装饰器控制Python解释器把函数转变成机器码,实现了与C和Cython同样的性能,但是不需要用新的解释器或者写C代码。可以按需生成优化(JIT)的机器码,甚至可以编译成CPU或GPU可执行代码。</p>
<ul>
<li>JIT即时代码生成(On-the-fly code generation)</li>
<li>CPU和GPU原生代码生成</li>
<li>与Numpy相关包交互</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="@jit装饰器"><code>@jit</code>装饰器<a class="anchor-link" href="#@jit装饰器"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">sum2d</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="o">%</span><span class="k">timeit</span> -r3 -n10 sum2d(a)
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>275 ms ± 13.1 ms per loop (mean ± std. dev. of 3 runs, 10 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="延迟编译(Lazy-compilation)">延迟编译(Lazy compilation)<a class="anchor-link" href="#延迟编译(Lazy-compilation)"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
<span class="c1"># jit装饰器告诉Numba编译函数,当函数被调用时,Numba再引入参数类型</span>
<span class="nd">@jit</span>
<span class="k">def</span> <span class="nf">sum2d</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="o">%</span><span class="k">timeit</span> sum2d(a)
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>1.28 ms ± 48.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="即时编译(Eager-compilation)">即时编译(Eager compilation)<a class="anchor-link" href="#即时编译(Eager-compilation)"> </a></h4><p>由于python支持动态类型,因此<code>@jit</code>装饰器可以设置函数的接收类型(返回类型),按照配置参数进行优化,适合进行浮点数精度控制float32、float64。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span><span class="p">,</span> <span class="n">float64</span>
<span class="nd">@jit</span><span class="p">(</span><span class="n">float64</span><span class="p">(</span><span class="n">float64</span><span class="p">[:,</span> <span class="p">:]))</span>
<span class="k">def</span> <span class="nf">sum2d</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="o">%</span><span class="k">timeit</span> sum2d(a)
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>949 µs ± 1.69 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>@jit配置函数签名的常用类型如下。</p>
<ul>
<li><code>void</code>:函数返回值类型,表示不返回任何结果。</li>
<li><code>intp</code>和<code>uintp</code>:指针大小的整数,分别表示签名和无签名类型。</li>
<li><code>intc</code>和<code>uintc</code>:相当于C语言的整型和无符号整型。</li>
<li><code>int8</code>、<code>int16</code>、<code>int32</code>和<code>int64</code>:固定宽度整型(无符号整型前面加<code>u</code>,比如<code>uint8</code>)。</li>
<li><code>float32</code>和<code>float64</code>:单精度和双精度浮点数类型。</li>
<li><code>complex64</code>和<code>complex128</code>:单精度和双精度复数类型。</li>
<li>数组可以用任何带索引的数值类型表示,比如<code>float32[:]</code>就是一维浮点数数组类型,<code>int32[:,:]</code>就是二维整型数组。</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="编译选项">编译选项<a class="anchor-link" href="#编译选项"> </a></h3><ol>
<li>非GIL模式:把<code>nogil=True</code>属性传到装饰器,就可以不受GIL的限制,多线程系统的常见问题(一致性、数据同步、竞态条件等)就可以解决。</li>
<li><a href="https://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#numba-troubleshooting">无Python模式</a>:可以通过<code>nopython</code>参数设置Numba的编译模式:<ol>
<li><code>object</code>模式:默认模式,产生的代码可以处理所有Python对象,并用C API完成Python对象上的操作;</li>
<li><code>nopython</code>模式:可以不调用C API而生成更高效的代码,不过只有一部分函数和方法可以使用:<ul>
<li>函数中表示数值的所有原生类型都可以被引用</li>
<li>函数中不可以分配新内存</li>
</ul>
</li>
</ol>
</li>
<li>缓存模式:避免重复调用,通过<code>cache=True</code>将结果保证在缓存文件中</li>
<li><a href="https://numba.pydata.org/numba-doc/latest/user/parallel.html#numba-parallel">并行模式</a>:通过<code>parallel=True</code>并行计算,必须配合<code>nopython=True</code>使用</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sum2d</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="o">%</span><span class="k">timeit</span> sum2d(a)
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>962 µs ± 29.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">prange</span>
<span class="nd">@jit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sum2d</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">M</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="o">%</span><span class="k">timeit</span> sum2d(a)
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>@njit:@jit(nopython=True)的</li>
<li>@vectorize与@guvectorize:支持NumPy的通用函数(ufunc)</li>
<li>@stencil:定义一个核函数实现stencil(模版)类操作</li>
<li>@jitclass:jit编译python类</li>
<li>@cfunc:定义可以被C/C++直接调用的函数</li>
<li>@overload:注册一个在nopython模式使用自定义函数</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="pyspark"><a href="https://spark.apache.org/docs/latest/api/python/">pyspark</a><a class="anchor-link" href="#pyspark"> </a></h1><p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/spark_apply.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="安装">安装<a class="anchor-link" href="#安装"> </a></h2><p>直接用connda安装即可,自动配置</p>
<div class="highlight"><pre><span></span>conda install pyspark -c conda-forge
pip install findspark
</pre></div>
<h2 id="初始化">初始化<a class="anchor-link" href="#初始化"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">findspark</span>
<span class="n">findspark</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">spark_home</span><span class="o">=</span><span class="s2">"/home/junjiet/conda/lib/python3.7/site-packages/pyspark"</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span><span class="p">,</span> <span class="n">dataframe</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkConf</span><span class="p">,</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">functions</span> <span class="k">as</span> <span class="n">F</span>
<span class="n">sparkConf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">()</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.sql.execution.arrow.enabled"</span><span class="p">,</span> <span class="s2">"false"</span><span class="p">)</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="n">conf</span><span class="o">=</span><span class="n">sparkConf</span><span class="p">)</span><span class="o">.</span><span class="n">enableHiveSupport</span><span class="p">()</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="RDD简介"><a href="https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD">RDD简介</a><a class="anchor-link" href="#RDD简介"> </a></h2><p>RDD(Resilient Distributed DataSet,弹性分布式数据集),是Spark中最基本的数据抽象是,具有分区,不可变,并行操作特点</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/rdd_compute.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdd</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="RDD常用转换(Transformation)API">RDD常用转换(Transformation)API<a class="anchor-link" href="#RDD常用转换(Transformation)API"> </a></h3><p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/spark_transform_api.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdd</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[2, 2, 4]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="RDD常用动作(Action)API">RDD常用动作(Action)API<a class="anchor-link" href="#RDD常用动作(Action)API"> </a></h3><p><figure>
<img class="docimage" src="/images/copied_from_nb/2.data-elt/spark_action_api.png" alt="" style="max-width: 800pxpx" />
</figure>
</img></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdd</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>7</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdd</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[1, 2, 3, 4, 5]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="RDD与DataFrame基本操作">RDD与DataFrame基本操作<a class="anchor-link" href="#RDD与DataFrame基本操作"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/rdd.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/2.data-elt/pysparkdf.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Dataframe">Dataframe<a class="anchor-link" href="#Dataframe"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">schema</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">StructType</span><span class="p">()</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"user_id"</span><span class="p">,</span> <span class="s2">"string"</span><span class="p">)</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"country"</span><span class="p">,</span> <span class="s2">"string"</span><span class="p">)</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"browser"</span><span class="p">,</span> <span class="s2">"string"</span><span class="p">)</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"OS"</span><span class="p">,</span> <span class="s2">"string"</span><span class="p">)</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"age"</span><span class="p">,</span> <span class="s2">"integer"</span><span class="p">)</span>
<span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"salary"</span><span class="p">,</span> <span class="s2">"double"</span><span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="p">[</span>
<span class="p">(</span><span class="s2">"A203"</span><span class="p">,</span> <span class="s2">"India"</span><span class="p">,</span> <span class="s2">"Chrome"</span><span class="p">,</span> <span class="s2">"WIN"</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mf">12.34</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"A201"</span><span class="p">,</span> <span class="s2">"China"</span><span class="p">,</span> <span class="s2">"Safari"</span><span class="p">,</span> <span class="s2">"MacOS"</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mf">14.56</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"A205"</span><span class="p">,</span> <span class="s2">"UK"</span><span class="p">,</span> <span class="s2">"Mozilla"</span><span class="p">,</span> <span class="s2">"Linux"</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mf">16.78</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"A206"</span><span class="p">,</span> <span class="s2">"China"</span><span class="p">,</span> <span class="s2">"Chrome"</span><span class="p">,</span> <span class="s2">"MacOS"</span><span class="p">,</span> <span class="mi">68</span><span class="p">,</span> <span class="mf">23.45</span><span class="p">),</span>
<span class="p">],</span>
<span class="n">schema</span><span class="o">=</span><span class="n">schema</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>root
|-- user_id: string (nullable = true)
|-- country: string (nullable = true)
|-- browser: string (nullable = true)
|-- OS: string (nullable = true)
|-- age: integer (nullable = true)
|-- salary: double (nullable = true)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>+-------+-------+-------+-----+---+------+
|user_id|country|browser| OS|age|salary|
+-------+-------+-------+-----+---+------+
| A203| India| Chrome| WIN| 33| 12.34|
| A201| China| Safari|MacOS| 45| 14.56|
| A205| UK|Mozilla|Linux| 25| 16.78|
| A206| China| Chrome|MacOS| 68| 23.45|
+-------+-------+-------+-----+---+------+
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"age"</span><span class="p">]</span> <span class="o">></span> <span class="mi">30</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>DataFrame[user_id: string, country: string, browser: string, OS: string, age: int, salary: double]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"age"</span><span class="p">]</span> <span class="o">></span> <span class="mi">30</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>3</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">df</span><span class="p">[</span><span class="s2">"age"</span><span class="p">]</span> <span class="o">></span> <span class="mi">30</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"China"</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>+-------+-------+-------+-----+---+------+
|user_id|country|browser| OS|age|salary|
+-------+-------+-------+-----+---+------+
| A201| China| Safari|MacOS| 45| 14.56|
| A206| China| Chrome|MacOS| 68| 23.45|
+-------+-------+-------+-----+---+------+
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>user_id</th>
<th>country</th>
<th>browser</th>
<th>OS</th>
<th>age</th>
<th>salary</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>A203</td>
<td>India</td>
<td>Chrome</td>
<td>WIN</td>
<td>33</td>
<td>12.34</td>
</tr>
<tr>
<th>1</th>
<td>A201</td>
<td>China</td>
<td>Safari</td>
<td>MacOS</td>
<td>45</td>
<td>14.56</td>
</tr>
<tr>
<th>2</th>
<td>A205</td>
<td>UK</td>
<td>Mozilla</td>
<td>Linux</td>
<td>25</td>
<td>16.78</td>
</tr>
<tr>
<th>3</th>
<td>A206</td>
<td>China</td>
<td>Chrome</td>
<td>MacOS</td>
<td>68</td>
<td>23.45</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="自定义函数">自定义函数<a class="anchor-link" href="#自定义函数"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="DataFrame属性">DataFrame属性<a class="anchor-link" href="#DataFrame属性"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">dataframe</span>
<span class="k">def</span> <span class="nf">spark_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">(),</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">dataframe</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">spark_shape</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(4, 6)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="UDF">UDF<a class="anchor-link" href="#UDF"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udf</span>
<span class="k">def</span> <span class="nf">age_category</span><span class="p">(</span><span class="n">age</span><span class="p">):</span>
<span class="k">if</span> <span class="mi">18</span> <span class="o"><=</span> <span class="n">age</span> <span class="o"><</span> <span class="mi">30</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"A"</span>
<span class="k">elif</span> <span class="n">age</span> <span class="o"><</span> <span class="mi">60</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"B"</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"C"</span>
<span class="n">age_udf</span> <span class="o">=</span> <span class="n">udf</span><span class="p">(</span><span class="n">age_category</span><span class="p">,</span> <span class="n">StringType</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">"age_category"</span><span class="p">,</span> <span class="n">age_udf</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"age"</span><span class="p">]))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>+-------+-------+-------+-----+---+------+------------+
|user_id|country|browser| OS|age|salary|age_category|
+-------+-------+-------+-----+---+------+------------+
| A203| India| Chrome| WIN| 33| 12.34| B|
| A201| China| Safari|MacOS| 45| 14.56| B|
| A205| UK|Mozilla|Linux| 25| 16.78| A|
| A206| China| Chrome|MacOS| 68| 23.45| C|
+-------+-------+-------+-----+---+------+------------+
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Pandas-UDF">Pandas UDF<a class="anchor-link" href="#Pandas-UDF"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">min_sal</span><span class="p">,</span> <span class="n">max_sal</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="s2">"salary"</span><span class="p">),</span> <span class="n">F</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="s2">"salary"</span><span class="p">))</span><span class="o">.</span><span class="n">collect</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">min_sal</span><span class="p">,</span> <span class="n">max_sal</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(12.34, 23.45)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">pandas_udf</span>
<span class="k">def</span> <span class="nf">scaled_salary</span><span class="p">(</span><span class="n">salary</span><span class="p">):</span>
<span class="n">scaled_sal</span> <span class="o">=</span> <span class="p">(</span><span class="n">salary</span> <span class="o">-</span> <span class="n">min_sal</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">max_sal</span> <span class="o">-</span> <span class="n">min_sal</span><span class="p">)</span>
<span class="k">return</span> <span class="n">scaled_sal</span>
<span class="n">scaling_udf</span> <span class="o">=</span> <span class="n">pandas_udf</span><span class="p">(</span><span class="n">scaled_salary</span><span class="p">,</span> <span class="n">DoubleType</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"salary"</span><span class="p">],</span> <span class="n">scaling_udf</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"salary"</span><span class="p">]))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="http://dblab.xmu.edu.cn/wp-content/themes/labstyle/images/branding.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li><a href="https://pythonhunter.org/">捕蛇者说</a> 中文python播客,有趣有料</li>
<li><a href="https://github.com/pandas-profiling/pandas-profiling">pandas_profiling</a> EDA可视化报表,支持导出html格式</li>
<li><a href="https://github.com/nalepae/pandarallel">pandarallel</a> CPU并行加速,apply、map、groupby与rolling等应用场景</li>
<li><a href="https://github.com/google/jax">jax</a> NumPy的GPU加速——谷歌开源,jakavdp参与开发</li>
<li><a href="https://github.com/rapidsai/cudf">cudf</a> Datafame的GPU加速</li>
<li><a href="https://koalas.readthedocs.io/en/latest/index.html">koalas</a> Databricks按照pandas实现的pyspark接口</li>
</ol>
</div>
</div>
</div>
</div>Python数据科学分享——1.jupyter和python2020-05-08T00:00:00-05:002020-05-08T00:00:00-05:00https://asyncfor.com/jupyter/python/data%20science/2020/05/08/jupyter-python<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-05-08-jupyter-python.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/1.jupyter-python/map.png" alt="map" style="max-width: 700px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="数据科学(统计学+计算机+行业经验)">数据科学(统计学+计算机+行业经验)<a class="anchor-link" href="#数据科学(统计学+计算机+行业经验)"> </a></h1><p><figure>
<img class="docimage" src="/images/copied_from_nb/1.jupyter-python/drewconway.png" alt="drewconway" style="max-width: 500px" />
</figure>
</p>
<ol>
<li>统计学家的能力——建立模型和聚合(数量不断增大的)数据</li>
<li>计算机科学家的能力——设计并使用算法对数据进行高效存储、分析和可视化</li>
<li>专业领域的能力——在细分领域中经过专业的训练,既可以提出正确的问题,又可以作出专业的解答</li>
</ol>
<p>郁彬2020年2月发表论文《Verdical data science(靠谱的数据科学)》提出的数据科学三原则(PCS):</p>
<ol>
<li>可预测性(predictability)</li>
<li>可计算性(computability)</li>
<li>稳定性(stability )</li>
</ol>
<p>B. Yu and K. Kumbier (2020) <a href="https://www.pnas.org/content/117/8/3920">Verdical data science</a> PNAS. 117 (8), 3920-3929. QnAs with Bin Yu.</p>
<blockquote><p>郁彬:统计学家,美国艺术与科学学院院士、美国国家科学院院士,加州大学伯克利分校统计系和电子工程与计算机科学系终身教授</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="REPL-与魔法函数"><a href="https://jupyter.org/">REPL 与魔法函数</a><a class="anchor-link" href="#REPL-与魔法函数"> </a></h1><center><img src="jupyter-python/jupyter.png" alt="" width="700" /></center>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li><p>Jupyter是一个非营利组织,旨在“为数十种编程语言的交互式计算开发开源软件,开放标准和服务”。2014年由Fernando Pérez从IPython中衍生出来。jupyter-console==IPython</p>
</li>
<li><p>Jupyter主要包括三种交互式计算产品:Jupyter Notebook、JupyterHub和JupyterLab(Jupyter Notebook的下一代版本)。</p>
</li>
<li><p>Jupyter Notebook(前身是IPython Notebook)是一个基于Web的交互式计算环境,用于创建Jupyter Notebook文档(JSON文档),由一组有序的输入/输出单元格列表构成,包含代码、文本(支持Markdown)、数学公式(Mathjax)、图表和富媒体,通常以“.ipynb”结尾扩展。</p>
</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>$\begin{align*}
y = y(x,t) &= A e^{i\theta} \\
&= A (\cos \theta + i \sin \theta) \\
&= A (\cos(kx - \omega t) + i \sin(kx - \omega t)) \\
&= A\cos(kx - \omega t) + i A\sin(kx - \omega t) \\
&= A\cos \Big(\frac{2\pi}{\lambda}x - \frac{2\pi v}{\lambda} t \Big) + i A\sin \Big(\frac{2\pi}{\lambda}x - \frac{2\pi v}{\lambda} t \Big) \\
&= A\cos \frac{2\pi}{\lambda} (x - v t) + i A\sin \frac{2\pi}{\lambda} (x - v t)
\end{align*}$</p>
<p>其中,$ \theta = kx - \omega t $</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">altair</span> <span class="k">as</span> <span class="nn">alt</span>
<span class="kn">from</span> <span class="nn">vega_datasets</span> <span class="kn">import</span> <span class="n">data</span>
<span class="n">source</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cars</span><span class="p">()</span>
<span class="n">brush</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">selection</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"interval"</span><span class="p">)</span>
<span class="n">points</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="o">.</span><span class="n">mark_point</span><span class="p">()</span>
<span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="s2">"Horsepower:Q"</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s2">"Miles_per_Gallon:Q"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">brush</span><span class="p">,</span> <span class="s2">"Origin:N"</span><span class="p">,</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="s2">"lightgray"</span><span class="p">)),</span>
<span class="p">)</span>
<span class="o">.</span><span class="n">add_selection</span><span class="p">(</span><span class="n">brush</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">bars</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="o">.</span><span class="n">mark_bar</span><span class="p">()</span>
<span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"Origin:N"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"Origin:N"</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"count(Origin):Q"</span><span class="p">)</span>
<span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">brush</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">points</span> <span class="o">&</span> <span class="n">bars</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-08104d82927b413c8eb7a1c777ecdb76"></div>
<script type="text/javascript">
(function(spec, embedOpt){
let outputDiv = document.currentScript.previousElementSibling;
if (outputDiv.id !== "altair-viz-08104d82927b413c8eb7a1c777ecdb76") {
outputDiv = document.getElementById("altair-viz-08104d82927b413c8eb7a1c777ecdb76");
}
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "vconcat": [{"mark": "point", "encoding": {"color": {"condition": {"type": "nominal", "field": "Origin", "selection": "selector001"}, "value": "lightgray"}, "x": {"type": "quantitative", "field": "Horsepower"}, "y": {"type": "quantitative", "field": "Miles_per_Gallon"}}, "selection": {"selector001": {"type": "interval"}}}, {"mark": "bar", "encoding": {"color": {"type": "nominal", "field": "Origin"}, "x": {"type": "quantitative", "aggregate": "count", "field": "Origin"}, "y": {"type": "nominal", "field": "Origin"}}, "transform": [{"filter": {"selection": "selector001"}}]}], "data": {"name": "data-f02450ab61490a1363517a0190416235"}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", "datasets": {"data-f02450ab61490a1363517a0190416235": [{"Name": "chevrolet chevelle malibu", "Miles_per_Gallon": 18.0, "Cylinders": 8, "Displacement": 307.0, "Horsepower": 130.0, "Weight_in_lbs": 3504, "Acceleration": 12.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick skylark 320", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 165.0, "Weight_in_lbs": 3693, "Acceleration": 11.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth satellite", "Miles_per_Gallon": 18.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 3436, "Acceleration": 11.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc rebel sst", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 150.0, "Weight_in_lbs": 3433, "Acceleration": 12.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford torino", "Miles_per_Gallon": 17.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 140.0, "Weight_in_lbs": 3449, "Acceleration": 10.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford galaxie 500", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 429.0, "Horsepower": 198.0, "Weight_in_lbs": 4341, "Acceleration": 10.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet impala", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 454.0, "Horsepower": 220.0, "Weight_in_lbs": 4354, "Acceleration": 9.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth fury iii", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 440.0, "Horsepower": 215.0, "Weight_in_lbs": 4312, "Acceleration": 8.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac catalina", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 455.0, "Horsepower": 225.0, "Weight_in_lbs": 4425, "Acceleration": 10.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc ambassador dpl", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 390.0, "Horsepower": 190.0, "Weight_in_lbs": 3850, "Acceleration": 8.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "citroen ds-21 pallas", "Miles_per_Gallon": null, "Cylinders": 4, "Displacement": 133.0, "Horsepower": 115.0, "Weight_in_lbs": 3090, "Acceleration": 17.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "chevrolet chevelle concours (sw)", "Miles_per_Gallon": null, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 165.0, "Weight_in_lbs": 4142, "Acceleration": 11.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford torino (sw)", "Miles_per_Gallon": null, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 153.0, "Weight_in_lbs": 4034, "Acceleration": 11.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth satellite (sw)", "Miles_per_Gallon": null, "Cylinders": 8, "Displacement": 383.0, "Horsepower": 175.0, "Weight_in_lbs": 4166, "Acceleration": 10.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc rebel sst (sw)", "Miles_per_Gallon": null, "Cylinders": 8, "Displacement": 360.0, "Horsepower": 175.0, "Weight_in_lbs": 3850, "Acceleration": 11.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge challenger se", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 383.0, "Horsepower": 170.0, "Weight_in_lbs": 3563, "Acceleration": 10.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth 'cuda 340", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 340.0, "Horsepower": 160.0, "Weight_in_lbs": 3609, "Acceleration": 8.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford mustang boss 302", "Miles_per_Gallon": null, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 140.0, "Weight_in_lbs": 3353, "Acceleration": 8.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet monte carlo", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 150.0, "Weight_in_lbs": 3761, "Acceleration": 9.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick estate wagon (sw)", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 455.0, "Horsepower": 225.0, "Weight_in_lbs": 3086, "Acceleration": 10.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corona mark ii", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 113.0, "Horsepower": 95.0, "Weight_in_lbs": 2372, "Acceleration": 15.0, "Year": "1970-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth duster", "Miles_per_Gallon": 22.0, "Cylinders": 6, "Displacement": 198.0, "Horsepower": 95.0, "Weight_in_lbs": 2833, "Acceleration": 15.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc hornet", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 199.0, "Horsepower": 97.0, "Weight_in_lbs": 2774, "Acceleration": 15.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford maverick", "Miles_per_Gallon": 21.0, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 85.0, "Weight_in_lbs": 2587, "Acceleration": 16.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun pl510", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 88.0, "Weight_in_lbs": 2130, "Acceleration": 14.5, "Year": "1970-01-01T00:00:00", "Origin": "Japan"}, {"Name": "volkswagen 1131 deluxe sedan", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 46.0, "Weight_in_lbs": 1835, "Acceleration": 20.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "peugeot 504", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 110.0, "Horsepower": 87.0, "Weight_in_lbs": 2672, "Acceleration": 17.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "audi 100 ls", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 107.0, "Horsepower": 90.0, "Weight_in_lbs": 2430, "Acceleration": 14.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "saab 99e", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 104.0, "Horsepower": 95.0, "Weight_in_lbs": 2375, "Acceleration": 17.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "bmw 2002", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 113.0, "Weight_in_lbs": 2234, "Acceleration": 12.5, "Year": "1970-01-01T00:00:00", "Origin": "Europe"}, {"Name": "amc gremlin", "Miles_per_Gallon": 21.0, "Cylinders": 6, "Displacement": 199.0, "Horsepower": 90.0, "Weight_in_lbs": 2648, "Acceleration": 15.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford f250", "Miles_per_Gallon": 10.0, "Cylinders": 8, "Displacement": 360.0, "Horsepower": 215.0, "Weight_in_lbs": 4615, "Acceleration": 14.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevy c20", "Miles_per_Gallon": 10.0, "Cylinders": 8, "Displacement": 307.0, "Horsepower": 200.0, "Weight_in_lbs": 4376, "Acceleration": 15.0, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge d200", "Miles_per_Gallon": 11.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 210.0, "Weight_in_lbs": 4382, "Acceleration": 13.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "hi 1200d", "Miles_per_Gallon": 9.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 193.0, "Weight_in_lbs": 4732, "Acceleration": 18.5, "Year": "1970-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun pl510", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 88.0, "Weight_in_lbs": 2130, "Acceleration": 14.5, "Year": "1971-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet vega 2300", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 90.0, "Weight_in_lbs": 2264, "Acceleration": 15.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corona", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 113.0, "Horsepower": 95.0, "Weight_in_lbs": 2228, "Acceleration": 14.0, "Year": "1971-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": null, "Weight_in_lbs": 2046, "Acceleration": 19.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen super beetle 117", "Miles_per_Gallon": null, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 48.0, "Weight_in_lbs": 1978, "Acceleration": 20.0, "Year": "1971-01-01T00:00:00", "Origin": "Europe"}, {"Name": "amc gremlin", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 2634, "Acceleration": 13.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth satellite custom", "Miles_per_Gallon": 16.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 105.0, "Weight_in_lbs": 3439, "Acceleration": 15.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevelle malibu", "Miles_per_Gallon": 17.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 100.0, "Weight_in_lbs": 3329, "Acceleration": 15.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford torino 500", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 88.0, "Weight_in_lbs": 3302, "Acceleration": 15.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 3288, "Acceleration": 15.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet impala", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 165.0, "Weight_in_lbs": 4209, "Acceleration": 12.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac catalina brougham", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 175.0, "Weight_in_lbs": 4464, "Acceleration": 11.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford galaxie 500", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 153.0, "Weight_in_lbs": 4154, "Acceleration": 13.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth fury iii", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4096, "Acceleration": 13.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge monaco (sw)", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 383.0, "Horsepower": 180.0, "Weight_in_lbs": 4955, "Acceleration": 11.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford country squire (sw)", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 170.0, "Weight_in_lbs": 4746, "Acceleration": 12.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac safari (sw)", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 175.0, "Weight_in_lbs": 5140, "Acceleration": 12.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc hornet sportabout (sw)", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 258.0, "Horsepower": 110.0, "Weight_in_lbs": 2962, "Acceleration": 13.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet vega (sw)", "Miles_per_Gallon": 22.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 72.0, "Weight_in_lbs": 2408, "Acceleration": 19.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac firebird", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 100.0, "Weight_in_lbs": 3282, "Acceleration": 15.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford mustang", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 88.0, "Weight_in_lbs": 3139, "Acceleration": 14.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury capri 2000", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 86.0, "Weight_in_lbs": 2220, "Acceleration": 14.0, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "opel 1900", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 116.0, "Horsepower": 90.0, "Weight_in_lbs": 2123, "Acceleration": 14.0, "Year": "1971-01-01T00:00:00", "Origin": "Europe"}, {"Name": "peugeot 304", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 70.0, "Weight_in_lbs": 2074, "Acceleration": 19.5, "Year": "1971-01-01T00:00:00", "Origin": "Europe"}, {"Name": "fiat 124b", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 88.0, "Horsepower": 76.0, "Weight_in_lbs": 2065, "Acceleration": 14.5, "Year": "1971-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota corolla 1200", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 71.0, "Horsepower": 65.0, "Weight_in_lbs": 1773, "Acceleration": 19.0, "Year": "1971-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 1200", "Miles_per_Gallon": 35.0, "Cylinders": 4, "Displacement": 72.0, "Horsepower": 69.0, "Weight_in_lbs": 1613, "Acceleration": 18.0, "Year": "1971-01-01T00:00:00", "Origin": "Japan"}, {"Name": "volkswagen model 111", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 60.0, "Weight_in_lbs": 1834, "Acceleration": 19.0, "Year": "1971-01-01T00:00:00", "Origin": "Europe"}, {"Name": "plymouth cricket", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 70.0, "Weight_in_lbs": 1955, "Acceleration": 20.5, "Year": "1971-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corona hardtop", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 113.0, "Horsepower": 95.0, "Weight_in_lbs": 2278, "Acceleration": 15.5, "Year": "1972-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge colt hardtop", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 97.5, "Horsepower": 80.0, "Weight_in_lbs": 2126, "Acceleration": 17.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen type 3", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 54.0, "Weight_in_lbs": 2254, "Acceleration": 23.5, "Year": "1972-01-01T00:00:00", "Origin": "Europe"}, {"Name": "chevrolet vega", "Miles_per_Gallon": 20.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 90.0, "Weight_in_lbs": 2408, "Acceleration": 19.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford pinto runabout", "Miles_per_Gallon": 21.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 86.0, "Weight_in_lbs": 2226, "Acceleration": 16.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet impala", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 165.0, "Weight_in_lbs": 4274, "Acceleration": 12.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac catalina", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 175.0, "Weight_in_lbs": 4385, "Acceleration": 12.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth fury iii", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4135, "Acceleration": 13.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford galaxie 500", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 153.0, "Weight_in_lbs": 4129, "Acceleration": 13.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc ambassador sst", "Miles_per_Gallon": 17.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 150.0, "Weight_in_lbs": 3672, "Acceleration": 11.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury marquis", "Miles_per_Gallon": 11.0, "Cylinders": 8, "Displacement": 429.0, "Horsepower": 208.0, "Weight_in_lbs": 4633, "Acceleration": 11.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick lesabre custom", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 155.0, "Weight_in_lbs": 4502, "Acceleration": 13.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile delta 88 royale", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 160.0, "Weight_in_lbs": 4456, "Acceleration": 13.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler newport royal", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 190.0, "Weight_in_lbs": 4422, "Acceleration": 12.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "mazda rx2 coupe", "Miles_per_Gallon": 19.0, "Cylinders": 3, "Displacement": 70.0, "Horsepower": 97.0, "Weight_in_lbs": 2330, "Acceleration": 13.5, "Year": "1972-01-01T00:00:00", "Origin": "Japan"}, {"Name": "amc matador (sw)", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 150.0, "Weight_in_lbs": 3892, "Acceleration": 12.5, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevelle concours (sw)", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 307.0, "Horsepower": 130.0, "Weight_in_lbs": 4098, "Acceleration": 14.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford gran torino (sw)", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 140.0, "Weight_in_lbs": 4294, "Acceleration": 16.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth satellite custom (sw)", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4077, "Acceleration": 14.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "volvo 145e (sw)", "Miles_per_Gallon": 18.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 112.0, "Weight_in_lbs": 2933, "Acceleration": 14.5, "Year": "1972-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volkswagen 411 (sw)", "Miles_per_Gallon": 22.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 76.0, "Weight_in_lbs": 2511, "Acceleration": 18.0, "Year": "1972-01-01T00:00:00", "Origin": "Europe"}, {"Name": "peugeot 504 (sw)", "Miles_per_Gallon": 21.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 87.0, "Weight_in_lbs": 2979, "Acceleration": 19.5, "Year": "1972-01-01T00:00:00", "Origin": "Europe"}, {"Name": "renault 12 (sw)", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 96.0, "Horsepower": 69.0, "Weight_in_lbs": 2189, "Acceleration": 18.0, "Year": "1972-01-01T00:00:00", "Origin": "Europe"}, {"Name": "ford pinto (sw)", "Miles_per_Gallon": 22.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 86.0, "Weight_in_lbs": 2395, "Acceleration": 16.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 510 (sw)", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 92.0, "Weight_in_lbs": 2288, "Acceleration": 17.0, "Year": "1972-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyouta corona mark ii (sw)", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 97.0, "Weight_in_lbs": 2506, "Acceleration": 14.5, "Year": "1972-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge colt (sw)", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 80.0, "Weight_in_lbs": 2164, "Acceleration": 15.0, "Year": "1972-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corolla 1600 (sw)", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 88.0, "Weight_in_lbs": 2100, "Acceleration": 16.5, "Year": "1972-01-01T00:00:00", "Origin": "Japan"}, {"Name": "buick century 350", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 175.0, "Weight_in_lbs": 4100, "Acceleration": 13.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 150.0, "Weight_in_lbs": 3672, "Acceleration": 11.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet malibu", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 145.0, "Weight_in_lbs": 3988, "Acceleration": 13.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford gran torino", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 137.0, "Weight_in_lbs": 4042, "Acceleration": 14.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge coronet custom", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 3777, "Acceleration": 12.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury marquis brougham", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 429.0, "Horsepower": 198.0, "Weight_in_lbs": 4952, "Acceleration": 11.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet caprice classic", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 150.0, "Weight_in_lbs": 4464, "Acceleration": 12.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford ltd", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 158.0, "Weight_in_lbs": 4363, "Acceleration": 13.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth fury gran sedan", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4237, "Acceleration": 14.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler new yorker brougham", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 440.0, "Horsepower": 215.0, "Weight_in_lbs": 4735, "Acceleration": 11.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick electra 225 custom", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 455.0, "Horsepower": 225.0, "Weight_in_lbs": 4951, "Acceleration": 11.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc ambassador brougham", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 360.0, "Horsepower": 175.0, "Weight_in_lbs": 3821, "Acceleration": 11.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth valiant", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 105.0, "Weight_in_lbs": 3121, "Acceleration": 16.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet nova custom", "Miles_per_Gallon": 16.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 100.0, "Weight_in_lbs": 3278, "Acceleration": 18.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc hornet", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 2945, "Acceleration": 16.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford maverick", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 88.0, "Weight_in_lbs": 3021, "Acceleration": 16.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth duster", "Miles_per_Gallon": 23.0, "Cylinders": 6, "Displacement": 198.0, "Horsepower": 95.0, "Weight_in_lbs": 2904, "Acceleration": 16.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen super beetle", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 46.0, "Weight_in_lbs": 1950, "Acceleration": 21.0, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "chevrolet impala", "Miles_per_Gallon": 11.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 150.0, "Weight_in_lbs": 4997, "Acceleration": 14.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford country", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 167.0, "Weight_in_lbs": 4906, "Acceleration": 12.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth custom suburb", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 360.0, "Horsepower": 170.0, "Weight_in_lbs": 4654, "Acceleration": 13.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile vista cruiser", "Miles_per_Gallon": 12.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 180.0, "Weight_in_lbs": 4499, "Acceleration": 12.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc gremlin", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 2789, "Acceleration": 15.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota carina", "Miles_per_Gallon": 20.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 88.0, "Weight_in_lbs": 2279, "Acceleration": 19.0, "Year": "1973-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet vega", "Miles_per_Gallon": 21.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 72.0, "Weight_in_lbs": 2401, "Acceleration": 19.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 610", "Miles_per_Gallon": 22.0, "Cylinders": 4, "Displacement": 108.0, "Horsepower": 94.0, "Weight_in_lbs": 2379, "Acceleration": 16.5, "Year": "1973-01-01T00:00:00", "Origin": "Japan"}, {"Name": "maxda rx3", "Miles_per_Gallon": 18.0, "Cylinders": 3, "Displacement": 70.0, "Horsepower": 90.0, "Weight_in_lbs": 2124, "Acceleration": 13.5, "Year": "1973-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 19.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 85.0, "Weight_in_lbs": 2310, "Acceleration": 18.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury capri v6", "Miles_per_Gallon": 21.0, "Cylinders": 6, "Displacement": 155.0, "Horsepower": 107.0, "Weight_in_lbs": 2472, "Acceleration": 14.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "fiat 124 sport coupe", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 90.0, "Weight_in_lbs": 2265, "Acceleration": 15.5, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "chevrolet monte carlo s", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 145.0, "Weight_in_lbs": 4082, "Acceleration": 13.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac grand prix", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 230.0, "Weight_in_lbs": 4278, "Acceleration": 9.5, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "fiat 128", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 68.0, "Horsepower": 49.0, "Weight_in_lbs": 1867, "Acceleration": 19.5, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "opel manta", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 116.0, "Horsepower": 75.0, "Weight_in_lbs": 2158, "Acceleration": 15.5, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "audi 100ls", "Miles_per_Gallon": 20.0, "Cylinders": 4, "Displacement": 114.0, "Horsepower": 91.0, "Weight_in_lbs": 2582, "Acceleration": 14.0, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volvo 144ea", "Miles_per_Gallon": 19.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 112.0, "Weight_in_lbs": 2868, "Acceleration": 15.5, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "dodge dart custom", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 3399, "Acceleration": 11.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "saab 99le", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 110.0, "Weight_in_lbs": 2660, "Acceleration": 14.0, "Year": "1973-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota mark ii", "Miles_per_Gallon": 20.0, "Cylinders": 6, "Displacement": 156.0, "Horsepower": 122.0, "Weight_in_lbs": 2807, "Acceleration": 13.5, "Year": "1973-01-01T00:00:00", "Origin": "Japan"}, {"Name": "oldsmobile omega", "Miles_per_Gallon": 11.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 180.0, "Weight_in_lbs": 3664, "Acceleration": 11.0, "Year": "1973-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth duster", "Miles_per_Gallon": 20.0, "Cylinders": 6, "Displacement": 198.0, "Horsepower": 95.0, "Weight_in_lbs": 3102, "Acceleration": 16.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford maverick", "Miles_per_Gallon": 21.0, "Cylinders": 6, "Displacement": 200.0, "Horsepower": null, "Weight_in_lbs": 2875, "Acceleration": 17.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc hornet", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 2901, "Acceleration": 16.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet nova", "Miles_per_Gallon": 15.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 100.0, "Weight_in_lbs": 3336, "Acceleration": 17.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun b210", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 67.0, "Weight_in_lbs": 1950, "Acceleration": 19.0, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 80.0, "Weight_in_lbs": 2451, "Acceleration": 16.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corolla 1200", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 71.0, "Horsepower": 65.0, "Weight_in_lbs": 1836, "Acceleration": 21.0, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet vega", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 75.0, "Weight_in_lbs": 2542, "Acceleration": 17.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevelle malibu classic", "Miles_per_Gallon": 16.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 100.0, "Weight_in_lbs": 3781, "Acceleration": 17.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador", "Miles_per_Gallon": 16.0, "Cylinders": 6, "Displacement": 258.0, "Horsepower": 110.0, "Weight_in_lbs": 3632, "Acceleration": 18.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth satellite sebring", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 105.0, "Weight_in_lbs": 3613, "Acceleration": 16.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford gran torino", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 140.0, "Weight_in_lbs": 4141, "Acceleration": 14.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick century luxus (sw)", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 150.0, "Weight_in_lbs": 4699, "Acceleration": 14.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge coronet custom (sw)", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4457, "Acceleration": 13.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford gran torino (sw)", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 140.0, "Weight_in_lbs": 4638, "Acceleration": 16.0, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador (sw)", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 150.0, "Weight_in_lbs": 4257, "Acceleration": 15.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "audi fox", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 83.0, "Weight_in_lbs": 2219, "Acceleration": 16.5, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volkswagen dasher", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 67.0, "Weight_in_lbs": 1963, "Acceleration": 15.5, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "opel manta", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 78.0, "Weight_in_lbs": 2300, "Acceleration": 14.5, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota corona", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 76.0, "Horsepower": 52.0, "Weight_in_lbs": 1649, "Acceleration": 16.5, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 710", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 83.0, "Horsepower": 61.0, "Weight_in_lbs": 2003, "Acceleration": 19.0, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge colt", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 75.0, "Weight_in_lbs": 2125, "Acceleration": 14.5, "Year": "1974-01-01T00:00:00", "Origin": "USA"}, {"Name": "fiat 128", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 75.0, "Weight_in_lbs": 2108, "Acceleration": 15.5, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "fiat 124 tc", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 116.0, "Horsepower": 75.0, "Weight_in_lbs": 2246, "Acceleration": 14.0, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda civic", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 97.0, "Weight_in_lbs": 2489, "Acceleration": 15.0, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "subaru", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 108.0, "Horsepower": 93.0, "Weight_in_lbs": 2391, "Acceleration": 15.5, "Year": "1974-01-01T00:00:00", "Origin": "Japan"}, {"Name": "fiat x1.9", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 67.0, "Weight_in_lbs": 2000, "Acceleration": 16.0, "Year": "1974-01-01T00:00:00", "Origin": "Europe"}, {"Name": "plymouth valiant custom", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 95.0, "Weight_in_lbs": 3264, "Acceleration": 16.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet nova", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 105.0, "Weight_in_lbs": 3459, "Acceleration": 16.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury monarch", "Miles_per_Gallon": 15.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 72.0, "Weight_in_lbs": 3432, "Acceleration": 21.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford maverick", "Miles_per_Gallon": 15.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 72.0, "Weight_in_lbs": 3158, "Acceleration": 19.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac catalina", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 170.0, "Weight_in_lbs": 4668, "Acceleration": 11.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet bel air", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 145.0, "Weight_in_lbs": 4440, "Acceleration": 14.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth grand fury", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4498, "Acceleration": 14.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford ltd", "Miles_per_Gallon": 14.0, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 148.0, "Weight_in_lbs": 4657, "Acceleration": 13.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick century", "Miles_per_Gallon": 17.0, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 110.0, "Weight_in_lbs": 3907, "Acceleration": 21.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevroelt chevelle malibu", "Miles_per_Gallon": 16.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 105.0, "Weight_in_lbs": 3897, "Acceleration": 18.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador", "Miles_per_Gallon": 15.0, "Cylinders": 6, "Displacement": 258.0, "Horsepower": 110.0, "Weight_in_lbs": 3730, "Acceleration": 19.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth fury", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 95.0, "Weight_in_lbs": 3785, "Acceleration": 19.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick skyhawk", "Miles_per_Gallon": 21.0, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 110.0, "Weight_in_lbs": 3039, "Acceleration": 15.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet monza 2+2", "Miles_per_Gallon": 20.0, "Cylinders": 8, "Displacement": 262.0, "Horsepower": 110.0, "Weight_in_lbs": 3221, "Acceleration": 13.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford mustang ii", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 129.0, "Weight_in_lbs": 3169, "Acceleration": 12.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corolla", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 75.0, "Weight_in_lbs": 2171, "Acceleration": 16.0, "Year": "1975-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 83.0, "Weight_in_lbs": 2639, "Acceleration": 17.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc gremlin", "Miles_per_Gallon": 20.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 100.0, "Weight_in_lbs": 2914, "Acceleration": 16.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac astro", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 78.0, "Weight_in_lbs": 2592, "Acceleration": 18.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corona", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 134.0, "Horsepower": 96.0, "Weight_in_lbs": 2702, "Acceleration": 13.5, "Year": "1975-01-01T00:00:00", "Origin": "Japan"}, {"Name": "volkswagen dasher", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 71.0, "Weight_in_lbs": 2223, "Acceleration": 16.5, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "datsun 710", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 97.0, "Weight_in_lbs": 2545, "Acceleration": 17.0, "Year": "1975-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 171.0, "Horsepower": 97.0, "Weight_in_lbs": 2984, "Acceleration": 14.5, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen rabbit", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 70.0, "Weight_in_lbs": 1937, "Acceleration": 14.0, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "amc pacer", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 90.0, "Weight_in_lbs": 3211, "Acceleration": 17.0, "Year": "1975-01-01T00:00:00", "Origin": "USA"}, {"Name": "audi 100ls", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 115.0, "Horsepower": 95.0, "Weight_in_lbs": 2694, "Acceleration": 15.0, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "peugeot 504", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 88.0, "Weight_in_lbs": 2957, "Acceleration": 17.0, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volvo 244dl", "Miles_per_Gallon": 22.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 98.0, "Weight_in_lbs": 2945, "Acceleration": 14.5, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "saab 99le", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 115.0, "Weight_in_lbs": 2671, "Acceleration": 13.5, "Year": "1975-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda civic cvcc", "Miles_per_Gallon": 33.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 53.0, "Weight_in_lbs": 1795, "Acceleration": 17.5, "Year": "1975-01-01T00:00:00", "Origin": "Japan"}, {"Name": "fiat 131", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 107.0, "Horsepower": 86.0, "Weight_in_lbs": 2464, "Acceleration": 15.5, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "opel 1900", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 116.0, "Horsepower": 81.0, "Weight_in_lbs": 2220, "Acceleration": 16.9, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "capri ii", "Miles_per_Gallon": 25.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 92.0, "Weight_in_lbs": 2572, "Acceleration": 14.9, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge colt", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 79.0, "Weight_in_lbs": 2255, "Acceleration": 17.7, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "renault 12tl", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 101.0, "Horsepower": 83.0, "Weight_in_lbs": 2202, "Acceleration": 15.3, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "chevrolet chevelle malibu classic", "Miles_per_Gallon": 17.5, "Cylinders": 8, "Displacement": 305.0, "Horsepower": 140.0, "Weight_in_lbs": 4215, "Acceleration": 13.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge coronet brougham", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 4190, "Acceleration": 13.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc matador", "Miles_per_Gallon": 15.5, "Cylinders": 8, "Displacement": 304.0, "Horsepower": 120.0, "Weight_in_lbs": 3962, "Acceleration": 13.9, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford gran torino", "Miles_per_Gallon": 14.5, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 152.0, "Weight_in_lbs": 4215, "Acceleration": 12.8, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth valiant", "Miles_per_Gallon": 22.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 100.0, "Weight_in_lbs": 3233, "Acceleration": 15.4, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet nova", "Miles_per_Gallon": 22.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 105.0, "Weight_in_lbs": 3353, "Acceleration": 14.5, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford maverick", "Miles_per_Gallon": 24.0, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 81.0, "Weight_in_lbs": 3012, "Acceleration": 17.6, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc hornet", "Miles_per_Gallon": 22.5, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 90.0, "Weight_in_lbs": 3085, "Acceleration": 17.6, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevette", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 52.0, "Weight_in_lbs": 2035, "Acceleration": 22.2, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet woody", "Miles_per_Gallon": 24.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 60.0, "Weight_in_lbs": 2164, "Acceleration": 22.1, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "vw rabbit", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 70.0, "Weight_in_lbs": 1937, "Acceleration": 14.2, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda civic", "Miles_per_Gallon": 33.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 53.0, "Weight_in_lbs": 1795, "Acceleration": 17.4, "Year": "1976-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge aspen se", "Miles_per_Gallon": 20.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 100.0, "Weight_in_lbs": 3651, "Acceleration": 17.7, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford granada ghia", "Miles_per_Gallon": 18.0, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 78.0, "Weight_in_lbs": 3574, "Acceleration": 21.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac ventura sj", "Miles_per_Gallon": 18.5, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 110.0, "Weight_in_lbs": 3645, "Acceleration": 16.2, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc pacer d/l", "Miles_per_Gallon": 17.5, "Cylinders": 6, "Displacement": 258.0, "Horsepower": 95.0, "Weight_in_lbs": 3193, "Acceleration": 17.8, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen rabbit", "Miles_per_Gallon": 29.5, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 71.0, "Weight_in_lbs": 1825, "Acceleration": 12.2, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "datsun b-210", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 70.0, "Weight_in_lbs": 1990, "Acceleration": 17.0, "Year": "1976-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyota corolla", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 75.0, "Weight_in_lbs": 2155, "Acceleration": 16.4, "Year": "1976-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford pinto", "Miles_per_Gallon": 26.5, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 72.0, "Weight_in_lbs": 2565, "Acceleration": 13.6, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "volvo 245", "Miles_per_Gallon": 20.0, "Cylinders": 4, "Displacement": 130.0, "Horsepower": 102.0, "Weight_in_lbs": 3150, "Acceleration": 15.7, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "plymouth volare premier v8", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 3940, "Acceleration": 13.2, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "peugeot 504", "Miles_per_Gallon": 19.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 88.0, "Weight_in_lbs": 3270, "Acceleration": 21.9, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota mark ii", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 156.0, "Horsepower": 108.0, "Weight_in_lbs": 2930, "Acceleration": 15.5, "Year": "1976-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mercedes-benz 280s", "Miles_per_Gallon": 16.5, "Cylinders": 6, "Displacement": 168.0, "Horsepower": 120.0, "Weight_in_lbs": 3820, "Acceleration": 16.7, "Year": "1976-01-01T00:00:00", "Origin": "Europe"}, {"Name": "cadillac seville", "Miles_per_Gallon": 16.5, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 180.0, "Weight_in_lbs": 4380, "Acceleration": 12.1, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevy c10", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 145.0, "Weight_in_lbs": 4055, "Acceleration": 12.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford f108", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 130.0, "Weight_in_lbs": 3870, "Acceleration": 15.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge d100", "Miles_per_Gallon": 13.0, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 150.0, "Weight_in_lbs": 3755, "Acceleration": 14.0, "Year": "1976-01-01T00:00:00", "Origin": "USA"}, {"Name": "honda Accelerationord cvcc", "Miles_per_Gallon": 31.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 68.0, "Weight_in_lbs": 2045, "Acceleration": 18.5, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "buick opel isuzu deluxe", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 111.0, "Horsepower": 80.0, "Weight_in_lbs": 2155, "Acceleration": 14.8, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "renault 5 gtl", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 58.0, "Weight_in_lbs": 1825, "Acceleration": 18.6, "Year": "1977-01-01T00:00:00", "Origin": "Europe"}, {"Name": "plymouth arrow gs", "Miles_per_Gallon": 25.5, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 96.0, "Weight_in_lbs": 2300, "Acceleration": 15.5, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun f-10 hatchback", "Miles_per_Gallon": 33.5, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 70.0, "Weight_in_lbs": 1945, "Acceleration": 16.8, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet caprice classic", "Miles_per_Gallon": 17.5, "Cylinders": 8, "Displacement": 305.0, "Horsepower": 145.0, "Weight_in_lbs": 3880, "Acceleration": 12.5, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile cutlass supreme", "Miles_per_Gallon": 17.0, "Cylinders": 8, "Displacement": 260.0, "Horsepower": 110.0, "Weight_in_lbs": 4060, "Acceleration": 19.0, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge monaco brougham", "Miles_per_Gallon": 15.5, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 145.0, "Weight_in_lbs": 4140, "Acceleration": 13.7, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury cougar brougham", "Miles_per_Gallon": 15.0, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 130.0, "Weight_in_lbs": 4295, "Acceleration": 14.9, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet concours", "Miles_per_Gallon": 17.5, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 110.0, "Weight_in_lbs": 3520, "Acceleration": 16.4, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick skylark", "Miles_per_Gallon": 20.5, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 105.0, "Weight_in_lbs": 3425, "Acceleration": 16.9, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth volare custom", "Miles_per_Gallon": 19.0, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 100.0, "Weight_in_lbs": 3630, "Acceleration": 17.7, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford granada", "Miles_per_Gallon": 18.5, "Cylinders": 6, "Displacement": 250.0, "Horsepower": 98.0, "Weight_in_lbs": 3525, "Acceleration": 19.0, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac grand prix lj", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 180.0, "Weight_in_lbs": 4220, "Acceleration": 11.1, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet monte carlo landau", "Miles_per_Gallon": 15.5, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 170.0, "Weight_in_lbs": 4165, "Acceleration": 11.4, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler cordoba", "Miles_per_Gallon": 15.5, "Cylinders": 8, "Displacement": 400.0, "Horsepower": 190.0, "Weight_in_lbs": 4325, "Acceleration": 12.2, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford thunderbird", "Miles_per_Gallon": 16.0, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 149.0, "Weight_in_lbs": 4335, "Acceleration": 14.5, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen rabbit custom", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 78.0, "Weight_in_lbs": 1940, "Acceleration": 14.5, "Year": "1977-01-01T00:00:00", "Origin": "Europe"}, {"Name": "pontiac sunbird coupe", "Miles_per_Gallon": 24.5, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 88.0, "Weight_in_lbs": 2740, "Acceleration": 16.0, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corolla liftback", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 75.0, "Weight_in_lbs": 2265, "Acceleration": 18.2, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "ford mustang ii 2+2", "Miles_per_Gallon": 25.5, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 89.0, "Weight_in_lbs": 2755, "Acceleration": 15.8, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevette", "Miles_per_Gallon": 30.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 63.0, "Weight_in_lbs": 2051, "Acceleration": 17.0, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge colt m/m", "Miles_per_Gallon": 33.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 83.0, "Weight_in_lbs": 2075, "Acceleration": 15.9, "Year": "1977-01-01T00:00:00", "Origin": "USA"}, {"Name": "subaru dl", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 67.0, "Weight_in_lbs": 1985, "Acceleration": 16.4, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "volkswagen dasher", "Miles_per_Gallon": 30.5, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 78.0, "Weight_in_lbs": 2190, "Acceleration": 14.1, "Year": "1977-01-01T00:00:00", "Origin": "Europe"}, {"Name": "datsun 810", "Miles_per_Gallon": 22.0, "Cylinders": 6, "Displacement": 146.0, "Horsepower": 97.0, "Weight_in_lbs": 2815, "Acceleration": 14.5, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "bmw 320i", "Miles_per_Gallon": 21.5, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 110.0, "Weight_in_lbs": 2600, "Acceleration": 12.8, "Year": "1977-01-01T00:00:00", "Origin": "Europe"}, {"Name": "mazda rx-4", "Miles_per_Gallon": 21.5, "Cylinders": 3, "Displacement": 80.0, "Horsepower": 110.0, "Weight_in_lbs": 2720, "Acceleration": 13.5, "Year": "1977-01-01T00:00:00", "Origin": "Japan"}, {"Name": "volkswagen rabbit custom diesel", "Miles_per_Gallon": 43.1, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 48.0, "Weight_in_lbs": 1985, "Acceleration": 21.5, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "ford fiesta", "Miles_per_Gallon": 36.1, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 66.0, "Weight_in_lbs": 1800, "Acceleration": 14.4, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "mazda glc deluxe", "Miles_per_Gallon": 32.8, "Cylinders": 4, "Displacement": 78.0, "Horsepower": 52.0, "Weight_in_lbs": 1985, "Acceleration": 19.4, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun b210 gx", "Miles_per_Gallon": 39.4, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 70.0, "Weight_in_lbs": 2070, "Acceleration": 18.6, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "honda civic cvcc", "Miles_per_Gallon": 36.1, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 60.0, "Weight_in_lbs": 1800, "Acceleration": 16.4, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "oldsmobile cutlass salon brougham", "Miles_per_Gallon": 19.9, "Cylinders": 8, "Displacement": 260.0, "Horsepower": 110.0, "Weight_in_lbs": 3365, "Acceleration": 15.5, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge diplomat", "Miles_per_Gallon": 19.4, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 140.0, "Weight_in_lbs": 3735, "Acceleration": 13.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury monarch ghia", "Miles_per_Gallon": 20.2, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 139.0, "Weight_in_lbs": 3570, "Acceleration": 12.8, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac phoenix lj", "Miles_per_Gallon": 19.2, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 105.0, "Weight_in_lbs": 3535, "Acceleration": 19.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet malibu", "Miles_per_Gallon": 20.5, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 95.0, "Weight_in_lbs": 3155, "Acceleration": 18.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford fairmont (auto)", "Miles_per_Gallon": 20.2, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 85.0, "Weight_in_lbs": 2965, "Acceleration": 15.8, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford fairmont (man)", "Miles_per_Gallon": 25.1, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 88.0, "Weight_in_lbs": 2720, "Acceleration": 15.4, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth volare", "Miles_per_Gallon": 20.5, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 100.0, "Weight_in_lbs": 3430, "Acceleration": 17.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc concord", "Miles_per_Gallon": 19.4, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 90.0, "Weight_in_lbs": 3210, "Acceleration": 17.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick century special", "Miles_per_Gallon": 20.6, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 105.0, "Weight_in_lbs": 3380, "Acceleration": 15.8, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury zephyr", "Miles_per_Gallon": 20.8, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 85.0, "Weight_in_lbs": 3070, "Acceleration": 16.7, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge aspen", "Miles_per_Gallon": 18.6, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 110.0, "Weight_in_lbs": 3620, "Acceleration": 18.7, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc concord d/l", "Miles_per_Gallon": 18.1, "Cylinders": 6, "Displacement": 258.0, "Horsepower": 120.0, "Weight_in_lbs": 3410, "Acceleration": 15.1, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet monte carlo landau", "Miles_per_Gallon": 19.2, "Cylinders": 8, "Displacement": 305.0, "Horsepower": 145.0, "Weight_in_lbs": 3425, "Acceleration": 13.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick regal sport coupe (turbo)", "Miles_per_Gallon": 17.7, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 165.0, "Weight_in_lbs": 3445, "Acceleration": 13.4, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford futura", "Miles_per_Gallon": 18.1, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 139.0, "Weight_in_lbs": 3205, "Acceleration": 11.2, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge magnum xe", "Miles_per_Gallon": 17.5, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 140.0, "Weight_in_lbs": 4080, "Acceleration": 13.7, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet chevette", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 68.0, "Weight_in_lbs": 2155, "Acceleration": 16.5, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota corona", "Miles_per_Gallon": 27.5, "Cylinders": 4, "Displacement": 134.0, "Horsepower": 95.0, "Weight_in_lbs": 2560, "Acceleration": 14.2, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 510", "Miles_per_Gallon": 27.2, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 97.0, "Weight_in_lbs": 2300, "Acceleration": 14.7, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge omni", "Miles_per_Gallon": 30.9, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 75.0, "Weight_in_lbs": 2230, "Acceleration": 14.5, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota celica gt liftback", "Miles_per_Gallon": 21.1, "Cylinders": 4, "Displacement": 134.0, "Horsepower": 95.0, "Weight_in_lbs": 2515, "Acceleration": 14.8, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth sapporo", "Miles_per_Gallon": 23.2, "Cylinders": 4, "Displacement": 156.0, "Horsepower": 105.0, "Weight_in_lbs": 2745, "Acceleration": 16.7, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile starfire sx", "Miles_per_Gallon": 23.8, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 85.0, "Weight_in_lbs": 2855, "Acceleration": 17.6, "Year": "1978-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 200-sx", "Miles_per_Gallon": 23.9, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 97.0, "Weight_in_lbs": 2405, "Acceleration": 14.9, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "audi 5000", "Miles_per_Gallon": 20.3, "Cylinders": 5, "Displacement": 131.0, "Horsepower": 103.0, "Weight_in_lbs": 2830, "Acceleration": 15.9, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volvo 264gl", "Miles_per_Gallon": 17.0, "Cylinders": 6, "Displacement": 163.0, "Horsepower": 125.0, "Weight_in_lbs": 3140, "Acceleration": 13.6, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "saab 99gle", "Miles_per_Gallon": 21.6, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 115.0, "Weight_in_lbs": 2795, "Acceleration": 15.7, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "peugeot 604sl", "Miles_per_Gallon": 16.2, "Cylinders": 6, "Displacement": 163.0, "Horsepower": 133.0, "Weight_in_lbs": 3410, "Acceleration": 15.8, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volkswagen scirocco", "Miles_per_Gallon": 31.5, "Cylinders": 4, "Displacement": 89.0, "Horsepower": 71.0, "Weight_in_lbs": 1990, "Acceleration": 14.9, "Year": "1978-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda Accelerationord lx", "Miles_per_Gallon": 29.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 68.0, "Weight_in_lbs": 2135, "Acceleration": 16.6, "Year": "1978-01-01T00:00:00", "Origin": "Japan"}, {"Name": "pontiac lemans v6", "Miles_per_Gallon": 21.5, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 115.0, "Weight_in_lbs": 3245, "Acceleration": 15.4, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury zephyr 6", "Miles_per_Gallon": 19.8, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 85.0, "Weight_in_lbs": 2990, "Acceleration": 18.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford fairmont 4", "Miles_per_Gallon": 22.3, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 88.0, "Weight_in_lbs": 2890, "Acceleration": 17.3, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc concord dl 6", "Miles_per_Gallon": 20.2, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 90.0, "Weight_in_lbs": 3265, "Acceleration": 18.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge aspen 6", "Miles_per_Gallon": 20.6, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 110.0, "Weight_in_lbs": 3360, "Acceleration": 16.6, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet caprice classic", "Miles_per_Gallon": 17.0, "Cylinders": 8, "Displacement": 305.0, "Horsepower": 130.0, "Weight_in_lbs": 3840, "Acceleration": 15.4, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford ltd landau", "Miles_per_Gallon": 17.6, "Cylinders": 8, "Displacement": 302.0, "Horsepower": 129.0, "Weight_in_lbs": 3725, "Acceleration": 13.4, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury grand marquis", "Miles_per_Gallon": 16.5, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 138.0, "Weight_in_lbs": 3955, "Acceleration": 13.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge st. regis", "Miles_per_Gallon": 18.2, "Cylinders": 8, "Displacement": 318.0, "Horsepower": 135.0, "Weight_in_lbs": 3830, "Acceleration": 15.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick estate wagon (sw)", "Miles_per_Gallon": 16.9, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 155.0, "Weight_in_lbs": 4360, "Acceleration": 14.9, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford country squire (sw)", "Miles_per_Gallon": 15.5, "Cylinders": 8, "Displacement": 351.0, "Horsepower": 142.0, "Weight_in_lbs": 4054, "Acceleration": 14.3, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet malibu classic (sw)", "Miles_per_Gallon": 19.2, "Cylinders": 8, "Displacement": 267.0, "Horsepower": 125.0, "Weight_in_lbs": 3605, "Acceleration": 15.0, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler lebaron town @ country (sw)", "Miles_per_Gallon": 18.5, "Cylinders": 8, "Displacement": 360.0, "Horsepower": 150.0, "Weight_in_lbs": 3940, "Acceleration": 13.0, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "vw rabbit custom", "Miles_per_Gallon": 31.9, "Cylinders": 4, "Displacement": 89.0, "Horsepower": 71.0, "Weight_in_lbs": 1925, "Acceleration": 14.0, "Year": "1979-01-01T00:00:00", "Origin": "Europe"}, {"Name": "maxda glc deluxe", "Miles_per_Gallon": 34.1, "Cylinders": 4, "Displacement": 86.0, "Horsepower": 65.0, "Weight_in_lbs": 1975, "Acceleration": 15.2, "Year": "1979-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge colt hatchback custom", "Miles_per_Gallon": 35.7, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 80.0, "Weight_in_lbs": 1915, "Acceleration": 14.4, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc spirit dl", "Miles_per_Gallon": 27.4, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 80.0, "Weight_in_lbs": 2670, "Acceleration": 15.0, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercedes benz 300d", "Miles_per_Gallon": 25.4, "Cylinders": 5, "Displacement": 183.0, "Horsepower": 77.0, "Weight_in_lbs": 3530, "Acceleration": 20.1, "Year": "1979-01-01T00:00:00", "Origin": "Europe"}, {"Name": "cadillac eldorado", "Miles_per_Gallon": 23.0, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 125.0, "Weight_in_lbs": 3900, "Acceleration": 17.4, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "peugeot 504", "Miles_per_Gallon": 27.2, "Cylinders": 4, "Displacement": 141.0, "Horsepower": 71.0, "Weight_in_lbs": 3190, "Acceleration": 24.8, "Year": "1979-01-01T00:00:00", "Origin": "Europe"}, {"Name": "oldsmobile cutlass salon brougham", "Miles_per_Gallon": 23.9, "Cylinders": 8, "Displacement": 260.0, "Horsepower": 90.0, "Weight_in_lbs": 3420, "Acceleration": 22.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth horizon", "Miles_per_Gallon": 34.2, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 70.0, "Weight_in_lbs": 2200, "Acceleration": 13.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth horizon tc3", "Miles_per_Gallon": 34.5, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 70.0, "Weight_in_lbs": 2150, "Acceleration": 14.9, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 210", "Miles_per_Gallon": 31.8, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 65.0, "Weight_in_lbs": 2020, "Acceleration": 19.2, "Year": "1979-01-01T00:00:00", "Origin": "Japan"}, {"Name": "fiat strada custom", "Miles_per_Gallon": 37.3, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 69.0, "Weight_in_lbs": 2130, "Acceleration": 14.7, "Year": "1979-01-01T00:00:00", "Origin": "Europe"}, {"Name": "buick skylark limited", "Miles_per_Gallon": 28.4, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 2670, "Acceleration": 16.0, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet citation", "Miles_per_Gallon": 28.8, "Cylinders": 6, "Displacement": 173.0, "Horsepower": 115.0, "Weight_in_lbs": 2595, "Acceleration": 11.3, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile omega brougham", "Miles_per_Gallon": 26.8, "Cylinders": 6, "Displacement": 173.0, "Horsepower": 115.0, "Weight_in_lbs": 2700, "Acceleration": 12.9, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac phoenix", "Miles_per_Gallon": 33.5, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 2556, "Acceleration": 13.2, "Year": "1979-01-01T00:00:00", "Origin": "USA"}, {"Name": "vw rabbit", "Miles_per_Gallon": 41.5, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 76.0, "Weight_in_lbs": 2144, "Acceleration": 14.7, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota corolla tercel", "Miles_per_Gallon": 38.1, "Cylinders": 4, "Displacement": 89.0, "Horsepower": 60.0, "Weight_in_lbs": 1968, "Acceleration": 18.8, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet chevette", "Miles_per_Gallon": 32.1, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 70.0, "Weight_in_lbs": 2120, "Acceleration": 15.5, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 310", "Miles_per_Gallon": 37.2, "Cylinders": 4, "Displacement": 86.0, "Horsepower": 65.0, "Weight_in_lbs": 2019, "Acceleration": 16.4, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "chevrolet citation", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 2678, "Acceleration": 16.5, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford fairmont", "Miles_per_Gallon": 26.4, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 88.0, "Weight_in_lbs": 2870, "Acceleration": 18.1, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc concord", "Miles_per_Gallon": 24.3, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 3003, "Acceleration": 20.1, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge aspen", "Miles_per_Gallon": 19.1, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 90.0, "Weight_in_lbs": 3381, "Acceleration": 18.7, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "audi 4000", "Miles_per_Gallon": 34.3, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 78.0, "Weight_in_lbs": 2188, "Acceleration": 15.8, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota corona liftback", "Miles_per_Gallon": 29.8, "Cylinders": 4, "Displacement": 134.0, "Horsepower": 90.0, "Weight_in_lbs": 2711, "Acceleration": 15.5, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda 626", "Miles_per_Gallon": 31.3, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 75.0, "Weight_in_lbs": 2542, "Acceleration": 17.5, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 510 hatchback", "Miles_per_Gallon": 37.0, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 92.0, "Weight_in_lbs": 2434, "Acceleration": 15.0, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyota corolla", "Miles_per_Gallon": 32.2, "Cylinders": 4, "Displacement": 108.0, "Horsepower": 75.0, "Weight_in_lbs": 2265, "Acceleration": 15.2, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda glc", "Miles_per_Gallon": 46.6, "Cylinders": 4, "Displacement": 86.0, "Horsepower": 65.0, "Weight_in_lbs": 2110, "Acceleration": 17.9, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge colt", "Miles_per_Gallon": 27.9, "Cylinders": 4, "Displacement": 156.0, "Horsepower": 105.0, "Weight_in_lbs": 2800, "Acceleration": 14.4, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "datsun 210", "Miles_per_Gallon": 40.8, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 65.0, "Weight_in_lbs": 2110, "Acceleration": 19.2, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "vw rabbit c (diesel)", "Miles_per_Gallon": 44.3, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 48.0, "Weight_in_lbs": 2085, "Acceleration": 21.7, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "vw dasher (diesel)", "Miles_per_Gallon": 43.4, "Cylinders": 4, "Displacement": 90.0, "Horsepower": 48.0, "Weight_in_lbs": 2335, "Acceleration": 23.7, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "audi 5000s (diesel)", "Miles_per_Gallon": 36.4, "Cylinders": 5, "Displacement": 121.0, "Horsepower": 67.0, "Weight_in_lbs": 2950, "Acceleration": 19.9, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "mercedes-benz 240d", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 146.0, "Horsepower": 67.0, "Weight_in_lbs": 3250, "Acceleration": 21.8, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda civic 1500 gl", "Miles_per_Gallon": 44.6, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 67.0, "Weight_in_lbs": 1850, "Acceleration": 13.8, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "renault lecar deluxe", "Miles_per_Gallon": 40.9, "Cylinders": 4, "Displacement": 85.0, "Horsepower": null, "Weight_in_lbs": 1835, "Acceleration": 17.3, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "subaru dl", "Miles_per_Gallon": 33.8, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 67.0, "Weight_in_lbs": 2145, "Acceleration": 18.0, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "vokswagen rabbit", "Miles_per_Gallon": 29.8, "Cylinders": 4, "Displacement": 89.0, "Horsepower": 62.0, "Weight_in_lbs": 1845, "Acceleration": 15.3, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "datsun 280-zx", "Miles_per_Gallon": 32.7, "Cylinders": 6, "Displacement": 168.0, "Horsepower": 132.0, "Weight_in_lbs": 2910, "Acceleration": 11.4, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda rx-7 gs", "Miles_per_Gallon": 23.7, "Cylinders": 3, "Displacement": 70.0, "Horsepower": 100.0, "Weight_in_lbs": 2420, "Acceleration": 12.5, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "triumph tr7 coupe", "Miles_per_Gallon": 35.0, "Cylinders": 4, "Displacement": 122.0, "Horsepower": 88.0, "Weight_in_lbs": 2500, "Acceleration": 15.1, "Year": "1980-01-01T00:00:00", "Origin": "Europe"}, {"Name": "ford mustang cobra", "Miles_per_Gallon": 23.6, "Cylinders": 4, "Displacement": 140.0, "Horsepower": null, "Weight_in_lbs": 2905, "Acceleration": 14.3, "Year": "1980-01-01T00:00:00", "Origin": "USA"}, {"Name": "honda Accelerationord", "Miles_per_Gallon": 32.4, "Cylinders": 4, "Displacement": 107.0, "Horsepower": 72.0, "Weight_in_lbs": 2290, "Acceleration": 17.0, "Year": "1980-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth reliant", "Miles_per_Gallon": 27.2, "Cylinders": 4, "Displacement": 135.0, "Horsepower": 84.0, "Weight_in_lbs": 2490, "Acceleration": 15.7, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "buick skylark", "Miles_per_Gallon": 26.6, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 84.0, "Weight_in_lbs": 2635, "Acceleration": 16.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge aries wagon (sw)", "Miles_per_Gallon": 25.8, "Cylinders": 4, "Displacement": 156.0, "Horsepower": 92.0, "Weight_in_lbs": 2620, "Acceleration": 14.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet citation", "Miles_per_Gallon": 23.5, "Cylinders": 6, "Displacement": 173.0, "Horsepower": 110.0, "Weight_in_lbs": 2725, "Acceleration": 12.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "plymouth reliant", "Miles_per_Gallon": 30.0, "Cylinders": 4, "Displacement": 135.0, "Horsepower": 84.0, "Weight_in_lbs": 2385, "Acceleration": 12.9, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota starlet", "Miles_per_Gallon": 39.1, "Cylinders": 4, "Displacement": 79.0, "Horsepower": 58.0, "Weight_in_lbs": 1755, "Acceleration": 16.9, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth champ", "Miles_per_Gallon": 39.0, "Cylinders": 4, "Displacement": 86.0, "Horsepower": 64.0, "Weight_in_lbs": 1875, "Acceleration": 16.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "honda civic 1300", "Miles_per_Gallon": 35.1, "Cylinders": 4, "Displacement": 81.0, "Horsepower": 60.0, "Weight_in_lbs": 1760, "Acceleration": 16.1, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "subaru", "Miles_per_Gallon": 32.3, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 67.0, "Weight_in_lbs": 2065, "Acceleration": 17.8, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 210", "Miles_per_Gallon": 37.0, "Cylinders": 4, "Displacement": 85.0, "Horsepower": 65.0, "Weight_in_lbs": 1975, "Acceleration": 19.4, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyota tercel", "Miles_per_Gallon": 37.7, "Cylinders": 4, "Displacement": 89.0, "Horsepower": 62.0, "Weight_in_lbs": 2050, "Acceleration": 17.3, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda glc 4", "Miles_per_Gallon": 34.1, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 68.0, "Weight_in_lbs": 1985, "Acceleration": 16.0, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth horizon 4", "Miles_per_Gallon": 34.7, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 63.0, "Weight_in_lbs": 2215, "Acceleration": 14.9, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford escort 4w", "Miles_per_Gallon": 34.4, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 65.0, "Weight_in_lbs": 2045, "Acceleration": 16.2, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford escort 2h", "Miles_per_Gallon": 29.9, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 65.0, "Weight_in_lbs": 2380, "Acceleration": 20.7, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen jetta", "Miles_per_Gallon": 33.0, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 74.0, "Weight_in_lbs": 2190, "Acceleration": 14.2, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "renault 18i", "Miles_per_Gallon": 34.5, "Cylinders": 4, "Displacement": 100.0, "Horsepower": null, "Weight_in_lbs": 2320, "Acceleration": 15.8, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "honda prelude", "Miles_per_Gallon": 33.7, "Cylinders": 4, "Displacement": 107.0, "Horsepower": 75.0, "Weight_in_lbs": 2210, "Acceleration": 14.4, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyota corolla", "Miles_per_Gallon": 32.4, "Cylinders": 4, "Displacement": 108.0, "Horsepower": 75.0, "Weight_in_lbs": 2350, "Acceleration": 16.8, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 200sx", "Miles_per_Gallon": 32.9, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 100.0, "Weight_in_lbs": 2615, "Acceleration": 14.8, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda 626", "Miles_per_Gallon": 31.6, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 74.0, "Weight_in_lbs": 2635, "Acceleration": 18.3, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "peugeot 505s turbo diesel", "Miles_per_Gallon": 28.1, "Cylinders": 4, "Displacement": 141.0, "Horsepower": 80.0, "Weight_in_lbs": 3230, "Acceleration": 20.4, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "saab 900s", "Miles_per_Gallon": null, "Cylinders": 4, "Displacement": 121.0, "Horsepower": 110.0, "Weight_in_lbs": 2800, "Acceleration": 15.4, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "volvo diesel", "Miles_per_Gallon": 30.7, "Cylinders": 6, "Displacement": 145.0, "Horsepower": 76.0, "Weight_in_lbs": 3160, "Acceleration": 19.6, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "toyota cressida", "Miles_per_Gallon": 25.4, "Cylinders": 6, "Displacement": 168.0, "Horsepower": 116.0, "Weight_in_lbs": 2900, "Acceleration": 12.6, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 810 maxima", "Miles_per_Gallon": 24.2, "Cylinders": 6, "Displacement": 146.0, "Horsepower": 120.0, "Weight_in_lbs": 2930, "Acceleration": 13.8, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "buick century", "Miles_per_Gallon": 22.4, "Cylinders": 6, "Displacement": 231.0, "Horsepower": 110.0, "Weight_in_lbs": 3415, "Acceleration": 15.8, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile cutlass ls", "Miles_per_Gallon": 26.6, "Cylinders": 8, "Displacement": 350.0, "Horsepower": 105.0, "Weight_in_lbs": 3725, "Acceleration": 19.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford granada gl", "Miles_per_Gallon": 20.2, "Cylinders": 6, "Displacement": 200.0, "Horsepower": 88.0, "Weight_in_lbs": 3060, "Acceleration": 17.1, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler lebaron salon", "Miles_per_Gallon": 17.6, "Cylinders": 6, "Displacement": 225.0, "Horsepower": 85.0, "Weight_in_lbs": 3465, "Acceleration": 16.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet cavalier", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 112.0, "Horsepower": 88.0, "Weight_in_lbs": 2605, "Acceleration": 19.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet cavalier wagon", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 112.0, "Horsepower": 88.0, "Weight_in_lbs": 2640, "Acceleration": 18.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet cavalier 2-door", "Miles_per_Gallon": 34.0, "Cylinders": 4, "Displacement": 112.0, "Horsepower": 88.0, "Weight_in_lbs": 2395, "Acceleration": 18.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac j2000 se hatchback", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 112.0, "Horsepower": 85.0, "Weight_in_lbs": 2575, "Acceleration": 16.2, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "dodge aries se", "Miles_per_Gallon": 29.0, "Cylinders": 4, "Displacement": 135.0, "Horsepower": 84.0, "Weight_in_lbs": 2525, "Acceleration": 16.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "pontiac phoenix", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 2735, "Acceleration": 18.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford fairmont futura", "Miles_per_Gallon": 24.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 92.0, "Weight_in_lbs": 2865, "Acceleration": 16.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "amc concord dl", "Miles_per_Gallon": 23.0, "Cylinders": 4, "Displacement": 151.0, "Horsepower": null, "Weight_in_lbs": 3035, "Acceleration": 20.5, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "volkswagen rabbit l", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 74.0, "Weight_in_lbs": 1980, "Acceleration": 15.3, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "mazda glc custom l", "Miles_per_Gallon": 37.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 68.0, "Weight_in_lbs": 2025, "Acceleration": 18.2, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "mazda glc custom", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 68.0, "Weight_in_lbs": 1970, "Acceleration": 17.6, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "plymouth horizon miser", "Miles_per_Gallon": 38.0, "Cylinders": 4, "Displacement": 105.0, "Horsepower": 63.0, "Weight_in_lbs": 2125, "Acceleration": 14.7, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "mercury lynx l", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 98.0, "Horsepower": 70.0, "Weight_in_lbs": 2125, "Acceleration": 17.3, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "nissan stanza xe", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 88.0, "Weight_in_lbs": 2160, "Acceleration": 14.5, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "honda Accelerationord", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 107.0, "Horsepower": 75.0, "Weight_in_lbs": 2205, "Acceleration": 14.5, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "toyota corolla", "Miles_per_Gallon": 34.0, "Cylinders": 4, "Displacement": 108.0, "Horsepower": 70.0, "Weight_in_lbs": 2245, "Acceleration": 16.9, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "honda civic", "Miles_per_Gallon": 38.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 67.0, "Weight_in_lbs": 1965, "Acceleration": 15.0, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "honda civic (auto)", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 67.0, "Weight_in_lbs": 1965, "Acceleration": 15.7, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "datsun 310 gx", "Miles_per_Gallon": 38.0, "Cylinders": 4, "Displacement": 91.0, "Horsepower": 67.0, "Weight_in_lbs": 1995, "Acceleration": 16.2, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "buick century limited", "Miles_per_Gallon": 25.0, "Cylinders": 6, "Displacement": 181.0, "Horsepower": 110.0, "Weight_in_lbs": 2945, "Acceleration": 16.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "oldsmobile cutlass ciera (diesel)", "Miles_per_Gallon": 38.0, "Cylinders": 6, "Displacement": 262.0, "Horsepower": 85.0, "Weight_in_lbs": 3015, "Acceleration": 17.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chrysler lebaron medallion", "Miles_per_Gallon": 26.0, "Cylinders": 4, "Displacement": 156.0, "Horsepower": 92.0, "Weight_in_lbs": 2585, "Acceleration": 14.5, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford granada l", "Miles_per_Gallon": 22.0, "Cylinders": 6, "Displacement": 232.0, "Horsepower": 112.0, "Weight_in_lbs": 2835, "Acceleration": 14.7, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "toyota celica gt", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 144.0, "Horsepower": 96.0, "Weight_in_lbs": 2665, "Acceleration": 13.9, "Year": "1982-01-01T00:00:00", "Origin": "Japan"}, {"Name": "dodge charger 2.2", "Miles_per_Gallon": 36.0, "Cylinders": 4, "Displacement": 135.0, "Horsepower": 84.0, "Weight_in_lbs": 2370, "Acceleration": 13.0, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevrolet camaro", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 151.0, "Horsepower": 90.0, "Weight_in_lbs": 2950, "Acceleration": 17.3, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford mustang gl", "Miles_per_Gallon": 27.0, "Cylinders": 4, "Displacement": 140.0, "Horsepower": 86.0, "Weight_in_lbs": 2790, "Acceleration": 15.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "vw pickup", "Miles_per_Gallon": 44.0, "Cylinders": 4, "Displacement": 97.0, "Horsepower": 52.0, "Weight_in_lbs": 2130, "Acceleration": 24.6, "Year": "1982-01-01T00:00:00", "Origin": "Europe"}, {"Name": "dodge rampage", "Miles_per_Gallon": 32.0, "Cylinders": 4, "Displacement": 135.0, "Horsepower": 84.0, "Weight_in_lbs": 2295, "Acceleration": 11.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "ford ranger", "Miles_per_Gallon": 28.0, "Cylinders": 4, "Displacement": 120.0, "Horsepower": 79.0, "Weight_in_lbs": 2625, "Acceleration": 18.6, "Year": "1982-01-01T00:00:00", "Origin": "USA"}, {"Name": "chevy s-10", "Miles_per_Gallon": 31.0, "Cylinders": 4, "Displacement": 119.0, "Horsepower": 82.0, "Weight_in_lbs": 2720, "Acceleration": 19.4, "Year": "1982-01-01T00:00:00", "Origin": "USA"}]}}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">HBox</span><span class="p">,</span> <span class="n">VBox</span><span class="p">,</span> <span class="n">IntSlider</span><span class="p">,</span> <span class="n">interactive_output</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">IntSlider</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">IntSlider</span><span class="p">()</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">interactive_output</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2"> * </span><span class="si">{</span><span class="n">b</span><span class="si">}</span><span class="s2"> = </span><span class="si">{</span><span class="n">a</span><span class="o">*</span><span class="n">b</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> <span class="p">{</span><span class="s2">"a"</span><span class="p">:</span> <span class="n">a</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">:</span> <span class="n">b</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">HBox</span><span class="p">([</span><span class="n">VBox</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">]),</span> <span class="n">out</span><span class="p">]))</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="用符号?获取帮助文档">用符号<code>?</code>获取帮助文档<a class="anchor-link" href="#用符号?获取帮助文档"> </a></h2><p>Python内置的<code>help()</code>函数可以获取这些信息,并且能打印输出结果。例如,如果要查看内置的<code>sorted</code>函数的文档,可以按照以下步骤操作:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="nb">sorted</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Help on built-in function sorted in module builtins:
sorted(iterable, /, *, key=None, reverse=False)
Return a new list containing all items from the iterable in ascending order.
A custom key function can be supplied to customize the sort order, and the
reverse flag can be set to request the result in descending order.
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>IPython引入了<code>?</code>符号作为获取这个文档和其他相关信息的缩写:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>sorted<span class="o">?</span>
<span class="c1"># Signature: sorted(iterable, /, *, key=None, reverse=False)</span>
<span class="c1"># Docstring:</span>
<span class="c1"># Return a new list containing all items from the iterable in ascending order.</span>
<span class="c1"># A custom key function can be supplied to customize the sort order, and the</span>
<span class="c1"># reverse flag can be set to request the result in descending order.</span>
<span class="c1"># Type: builtin_function_or_method</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>也适用于自定义函数或者其他对象!下面定义一个带有docstring的小函数:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">square</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="sd">"""返回a的平方"""</span>
<span class="k">return</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>square<span class="o">?</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="通过符号??获取源代码">通过符号<code>??</code>获取源代码<a class="anchor-link" href="#通过符号??获取源代码"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Jupyter提供了获取源代码的快捷方式(使用两个问号<code>??</code>):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span> square<span class="o">??</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Object `square` not found.
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>当查询C语言或其他编译扩展语言实现的函数时,<code>??</code>后缀=<code>?</code>后缀</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>sorted<span class="o">??</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="用Tab补全的方式探索模块">用Tab补全的方式探索模块<a class="anchor-link" href="#用Tab补全的方式探索模块"> </a></h2><p>Jupyter支持用Tab键自动补全和探索对象、模块及命名空间的内容(下面用<code><TAB></code>来表示Tab键)</p>
<ol>
<li><p>对象内容的Tab自动补全</p>
</li>
<li><p>导入时的Tab自动补全</p>
</li>
<li>通配符*匹配</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="对象内容的Tab自动补全">对象内容的Tab自动补全<a class="anchor-link" href="#对象内容的Tab自动补全"> </a></h3><p>每一个Python对象都包含各种属性和方法。Python有一个内置的<code>dir</code>函数,可以返回一个属性和方法的列表。Tab自动补全接口更简便:输入这个对象的名称,再加上一个句点(<code>.</code>)和Tab键:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">L</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">L</span><span class="o">.</span><span class="n">append</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_error">
<pre>
<span class="ansi-cyan-fg"> File </span><span class="ansi-green-fg">"<ipython-input-9-9b66bc403048>"</span><span class="ansi-cyan-fg">, line </span><span class="ansi-green-fg">1</span>
<span class="ansi-red-fg"> L.</span>
^
<span class="ansi-red-fg">SyntaxError</span><span class="ansi-red-fg">:</span> invalid syntax
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>为了进一步缩小整个列表,可以输入属性或方法名称的第一个或前几个字符,然后Tab键将会查找匹配的属性或方法:</p>
<pre><code>L.c<TAB>
L.clear L.copy L.count
L.co<TAB>
L.copy L.count</code></pre>
<p>如果只有一个选项,按下Tab键将会把名称自动补全。例如,下面示例中的内容将会马上被<code>L.count</code>替换:</p>
<pre><code>L.cou<TAB></code></pre>
<p>Python一般用前置下划线表示私有属性或方法。可以通过明确地输入一条下划线来把这些私有的属性或方法列出来:</p>
<pre><code>L._<TAB>
L.__add__ L.__gt__ L.__reduce__
L.__class__ L.__hash__ L.__reduce_ex__</code></pre>
<p>为了简洁起见,这里只展示了输出的前两行,大部分是Python特殊的双下划线方法(昵称叫作“dunder方法”)。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="导入时的Tab自动补全">导入时的Tab自动补全<a class="anchor-link" href="#导入时的Tab自动补全"> </a></h3><p>Tab自动补全在从包中导入对象时也非常有用。下面用这种方法来查找<code>itertools</code>包中以<code>co</code>开头的所有可导入的对象:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">co</span><span class="o"><</span><span class="n">TAB</span><span class="o">></span>
<span class="n">combinations</span> <span class="n">compress</span> <span class="n">combinations_with_replacement</span> <span class="n">count</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">同样</span><span class="err">,</span><span class="n">你也可以用Tab自动补全查看系统中所有可导入的包</span><span class="err">:</span>
<span class="kn">import</span> <span class="o"><</span><span class="n">TAB</span><span class="o">></span>
<span class="kn">import</span> <span class="nn">h</span><span class="o"><</span><span class="n">TAB</span><span class="o">></span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="通配符*匹配">通配符*匹配<a class="anchor-link" href="#通配符*匹配"> </a></h3><p>Jupyter还提供了用<code>*</code>符号来实现的依赖中间或者末尾几个字符查询的通配符匹配方法。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>str.*find*<span class="o">?</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>假设寻找一个字符串方法,它的名称中包含<code>find</code>,则可以这样做:</p>
<pre><code>str.*find*?
str.find
str.rfind</code></pre>
<blockquote><p>这里的<code>*</code>符号匹配任意字符串,包括空字符串。</p>
</blockquote>
<p><strong>在实际应用过程中,灵活的通配符对于找命令非常有用。</strong></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Jupyter-shell中的快捷键">Jupyter shell中的快捷键<a class="anchor-link" href="#Jupyter-shell中的快捷键"> </a></h2><p>Jupyter通过GNU Readline库实现了四类shell快捷键,在IPython中提高工作效率</p>
<ol>
<li><p>导航快捷键</p>
</li>
<li><p>文本输入快捷键</p>
</li>
<li>命令历史快捷键</li>
<li>其他快捷键</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="导航快捷键">导航快捷键<a class="anchor-link" href="#导航快捷键"> </a></h3><table>
<thead><tr>
<th>快捷键</th>
<th>动作 </th>
</tr>
</thead>
<tbody>
<tr>
<td>Ctrl + a</td>
<td>将光标移到本行的开始处</td>
</tr>
<tr>
<td>Ctrl + e</td>
<td>将光标移到本行的结尾处 </td>
</tr>
<tr>
<td>Ctrl + b(或左箭头键)</td>
<td>将光标回退一个字符 </td>
</tr>
<tr>
<td>Ctrl + f(或右箭头键)</td>
<td>将光标前进一个字符 </td>
</tr>
</tbody>
</table>
<h3 id="文本输入快捷键">文本输入快捷键<a class="anchor-link" href="#文本输入快捷键"> </a></h3><table>
<thead><tr>
<th>快捷键</th>
<th>动作 </th>
</tr>
</thead>
<tbody>
<tr>
<td>Backspace键</td>
<td>删除前一个字符 </td>
</tr>
<tr>
<td>Ctrl + d</td>
<td>删除下一个字符 </td>
</tr>
<tr>
<td>Ctrl + k</td>
<td>从光标开始剪切至行的末尾 </td>
</tr>
<tr>
<td>Ctrl + u</td>
<td>从行的开头剪切至光标</td>
</tr>
<tr>
<td>Ctrl + y</td>
<td>Yank(即粘贴)之前剪切的文本 </td>
</tr>
<tr>
<td>Ctrl + t</td>
<td>Transpose(即交换)前两个字符 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="命令历史快捷键">命令历史快捷键<a class="anchor-link" href="#命令历史快捷键"> </a></h3><p>Jupyter的命令历史都保存在配置文件路径下的SQLite数据库中,下面的快捷键实现历史搜索:</p>
<table>
<thead><tr>
<th>快捷键</th>
<th>动作 </th>
</tr>
</thead>
<tbody>
<tr>
<td>Ctrl + p(或向上箭头)</td>
<td>获取前一个历史命令 </td>
</tr>
<tr>
<td>Ctrl + n(或向下箭头)</td>
<td>获取后一个历史命令 </td>
</tr>
<tr>
<td>Ctrl + r</td>
<td>对历史命令的反向搜索 </td>
</tr>
</tbody>
</table>
<h3 id="其他快捷键">其他快捷键<a class="anchor-link" href="#其他快捷键"> </a></h3><table>
<thead><tr>
<th>快捷键</th>
<th>动作 </th>
</tr>
</thead>
<tbody>
<tr>
<td>Ctrl + l</td>
<td>清除终端屏幕的内容 </td>
</tr>
<tr>
<td>Ctrl + c</td>
<td>中断当前的Python命令 </td>
</tr>
<tr>
<td>Ctrl + d</td>
<td>退出Jupyter会话</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Jupyter魔法命令">Jupyter魔法命令<a class="anchor-link" href="#Jupyter魔法命令"> </a></h2><p>Jupyter在普通Python语法基础之上的增强功能,被称作Jupyter<strong>魔法命令</strong>,都以<code>%</code>符号作为前缀。这些魔法命令设计用于解决数据分析中的各种常见问题。魔法命令有两种形式:</p>
<ol>
<li>行魔法(line magic):以单个<code>%</code>字符作为前缀,作用于单行输入;</li>
<li>单元魔法(cell magic):以两个<code>%%</code>作为前缀,作用于多行输入。</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="粘贴代码块:%paste和%cpaste">粘贴代码块:<code>%paste</code>和<code>%cpaste</code><a class="anchor-link" href="#粘贴代码块:%paste和%cpaste"> </a></h3><p>当你使用Jupyter解释器时,粘贴多行代码块可能会导致错误,尤其是其中包含缩进和解释符号时。Jupyter的<code>%paste</code>魔法函数可以解决这个包含符号的多行输入问题:</p>
<pre><code>In [1]: %paste
</code></pre>
<blockquote><blockquote><blockquote><p>def donothing(x):... return x</p>
<pre><code>## -- End pasted text --</code></pre>
</blockquote>
</blockquote>
</blockquote>
<p><code>%paste</code>命令同时输入并执行该代码,所以你可以看到这个函数现在被应用了:</p>
<pre><code>In [2]: donothing(10)
Out[2]: 10
</code></pre>
<p>另外一个作用类似的命令是<code>%cpaste</code>。该命令打开一个交互式多行输入提示,你可以在这个提示下粘贴并执行一个或多个代码块:</p>
<pre><code>In [3]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:>>> def donothing(x):
:... return x
:--</code></pre>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="执行外部代码:%run">执行外部代码:<code>%run</code><a class="anchor-link" href="#执行外部代码:%run"> </a></h3><p>在Jupyter会话中运行代码文件非常方便,不用在另一个新窗口中运行这些程序代码。通过<code>%run</code>魔法命令来实现。</p>
<p>假设你创建了一个myscript.py文件,该文件包含以下内容:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%file</span> myscript.py
<span class="k">def</span> <span class="nf">square</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="sd">"""求平方"""</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">N</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="s2">"squared is"</span><span class="p">,</span> <span class="n">square</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Overwriting myscript.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ls</span> <span class="n">myscript</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>myscript.py
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">run</span> myscript.py
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>1 squared is 1
2 squared is 4
3 squared is 9
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>当你运行了这段代码之后,该代码中包含的所有函数都可以在Jupyter会话中使用:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">square</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>25</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="计算代码运行时间:%timeit">计算代码运行时间:<code>%timeit</code><a class="anchor-link" href="#计算代码运行时间:%timeit"> </a></h3><p><code>%timeit</code>会自动计算接下来一行的Python语句的执行时间。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> L = [n ** 2 for n in range(1000)]
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>238 µs ± 24.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>%timeit</code>会自动多次执行命令以获得更稳定的结果。</p>
<p>对于多行语句,可以加入第二个<code>%</code>符号将其转变成单元魔法,以处理多行输入。例如,下面是<code>for</code>循环的同等结构:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>it
<span class="n">L</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
<span class="n">L</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">n</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>321 µs ± 37.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>从以上结果可以立刻看出,列表解析式比同等的<code>for</code>循环结构快约20%。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">magic</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="魔法函数的帮助文档?、%magic和%lsmagic">魔法函数的帮助文档<code>?</code>、<code>%magic</code>和<code>%lsmagic</code><a class="anchor-link" href="#魔法函数的帮助文档?、%magic和%lsmagic"> </a></h3><p>和普通的Python函数一样,Jupyter魔法函数也有文档字符串,输入以下命令即可查询:</p>
<pre><code>In [10]: %timeit?
</code></pre>
<p>查询魔法函数的描述以及示例,可以输入命令:</p>
<pre><code>In [11]: %magic
</code></pre>
<p>获得所有可用魔法函数的列表,可以输入命令:</p>
<pre><code>In [12]: %lsmagic
</code></pre>
<p>也可以自定义魔法函数,详情查看官方文档。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="输入和输出历史">输入和输出历史<a class="anchor-link" href="#输入和输出历史"> </a></h2><p>Jupyter在shell和Notebook中都提供了几种获得历史命令的输出方式:</p>
<ol>
<li>Jupyter的输入(In)和输出(Out)对象</li>
<li>下划线快捷键和历史输出</li>
<li><p>禁止输出,末尾加分号</p>
</li>
<li><p><code>%history</code>、<code>%rerun</code>和<code>%save</code></p>
</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Jupyter的输入和输出对象">Jupyter的输入和输出对象<a class="anchor-link" href="#Jupyter的输入和输出对象"> </a></h3><p>Jupyter的<code>In[1]:</code>/<code>Out[1]:</code>形式,并不仅仅是好看的装饰形式,还可以获取输入和输出历史:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.9092974268256817</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>-0.4161468365471424</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Jupyter实际上创建了叫作<code>In</code>和<code>Out</code>的Python变量,这些变量自动更新以反映命令历史:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># In</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Out</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>In</code>对象是一个列表,按照顺序记录所有的命令(列表中的第一项是一个占位符,以便<code>In[1]</code>可以表示第一条命令):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">In</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>import math
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>Out</code>对象不是一个列表,而是一个字典。它将输入数字映射到相应的输出(如果有的话):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">Out</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0.9092974268256817
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>不是所有操作都有输出,例如<code>import</code>语句和<code>print</code>语句不会加入Out。因为<code>print</code>函数的返回值是<code>None</code>。任何返回值是<code>None</code>的命令都不会加到<code>Out</code>变量中。</p>
</blockquote>
<p>如果想利用之前的结果,理解以上内容将大有用处。例如,利用之前的计算结果检查<code>sin(2) ** 2</code>和<code>cos(2) ** 2</code>的和,结果如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Out</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">Out</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>1.0</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="下划线快捷键和历史输出">下划线快捷键和历史输出<a class="anchor-link" href="#下划线快捷键和历史输出"> </a></h3><p>标准的Python shell用变量<code>_</code>(单下划线)可以获得前一个命令输出结果,在Jupyter中也适用:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">_</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>1.0
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Jupyter可以用两条下划线获得倒数第二个历史输出,用三条下划线获得倒数第三个历史输出(跳过任何没有输出的命令):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">__</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In', 'Out', 'print(In[1])', 'print(Out[2])', 'Out[2] ** 2 + Out[3] ** 2', 'print(_)', 'print(__)']
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">___</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>-0.4161468365471424
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>另外,<code>Out[X]</code>可以简写成<code>_X</code>(即一条下划线加行号):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Out</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.9092974268256817</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>0.9092974268256817</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="禁止输出">禁止输出<a class="anchor-link" href="#禁止输出"> </a></h3><p>要禁止一个命令的输出,在行末尾处添加一个分号:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>这个结果被计算后,输出结果既不会显示在屏幕上,也不会存储在<code>Out</code>中:</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="mi">25</span> <span class="ow">in</span> <span class="n">Out</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>False</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="其他历史魔法命令">其他历史魔法命令<a class="anchor-link" href="#其他历史魔法命令"> </a></h3><p>如果想一次性获取此前所有的输入历史,<code>%history</code>魔法命令会非常有用。在下面的示例中可以看到如何打印前4条输入命令:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">history</span> -n 1-4
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> 1: %load ../README.md
2:
from ipywidgets import HBox, VBox, IntSlider, interactive_output
from IPython.display import display
a = IntSlider()
b = IntSlider()
out = interactive_output(lambda a, b: print(f"{a} * {b} = {a*b}"), {"a": a, "b": b})
display(HBox([VBox([a, b]), out]))
3:
m = 5.1
if m < 5:
print(f"{m}小于5")
elif m % 2 == 1:
print(f"{m}是奇数")
elif m % 2 == 0:
print(f"{m}是偶数")
else:
print("不是整数")
4: 5.1 % 2
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>按照惯例,可以输入<code>%history?</code>来查看更多相关信息以及可用选项的详细描述。其他类似的魔法命令还有<code>%rerun</code>(该命令将重新执行部分历史命令)和<code>%save</code>(该命令将部分历史命令保存到一个文件中)。如果想获取更多相关信息,建议你使用<code>?</code>帮助功能(详情请参见1.2节)。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Jupyter和shell命令">Jupyter和shell命令<a class="anchor-link" href="#Jupyter和shell命令"> </a></h2><ol>
<li>在Jupyter中使用shell命令</li>
<li>在shell中传入或传出值</li>
<li>部分自动魔法</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="在Jupyter中使用shell命令">在Jupyter中使用shell命令<a class="anchor-link" href="#在Jupyter中使用shell命令"> </a></h3><p>Jupyter终端直接执行shell命令的语法,在shell命令前加<code>!</code>,将不会通过Python内核运行,而是通过系统命令运行。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>ls
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre><span class="ansi-cyan-intense-fg ansi-bold">jupyter-python</span>
<span class="ansi-cyan-intense-fg ansi-bold">2.data-elt</span>
2019-01-01-kivy-perface.ipynb
2019-02-01-kivy-ch1-clock-app.ipynb
2019-03-01-kivy-ch2-paint-app.ipynb
2019-04-01-kivy-ch3-sound-recorder-for-android.ipynb
2019-05-01-kivy-ch4-chat-app.ipynb
2019-06-01-kivy-ch5-remote-desktop-app.ipynb
2019-07-01-kivy-ch6-2048-app.ipynb
2019-08-01-kivy-ch7-flappy-bird-app.ipynb
2019-09-01-kivy-ch8-shaders-app.ipynb
2019-10-01-kivy-ch9-shmup-app.ipynb
2020-02-20-test.ipynb
2020-05-08-jupyter-python.ipynb
2020-05-15-data-etl.ipynb
2020-05-22-data-viz-1.ipynb
2020-05-29-data-viz-2.ipynb
2020-06-08-data-stats.ipynb
<span class="ansi-cyan-intense-fg ansi-bold">3.data-viz</span>
<span class="ansi-cyan-intense-fg ansi-bold">4.data-stats</span>
README.md
<span class="ansi-cyan-intense-fg ansi-bold">kbpic</span>
<span class="ansi-cyan-intense-fg ansi-bold">my_icons</span>
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pwd
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>/Users/toddtao/Documents/air/_notebooks
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span><span class="nb">echo</span> <span class="s2">"数据科学"</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>数据科学
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="在shell中传入或传出值">在shell中传入或传出值<a class="anchor-link" href="#在shell中传入或传出值"> </a></h3><p>shell命令不仅可以从Jupyter中调用,还可以和Jupyter命名空间进行交互。例如,通过一个赋值操纵符将任何shell命令的输出保存到一个Python列表:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">contents</span> <span class="o">=</span> <span class="o">!</span>ls
<span class="n">contents</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['1.jupyter和python.ipynb',
'drewconway.png',
'enumerate.png',
'harry_potter',
'HW',
'HW1.jupyter和python.ipynb',
'jupyter.png',
'map.png',
'myscript2.py',
'myscript.py',
'tale-of-two-cities.txt',
'zip.png',
'猜数字.png']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">directory</span> <span class="o">=</span> <span class="o">!</span><span class="nb">pwd</span>
<span class="n">directory</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['/home/junjiet/data_science2020/1.jupyter和python']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这些结果并不以列表的形式返回,虽然可以像列表一样操作,但是这种类型还有其他功能,例如<code>grep</code>和<code>fields</code>方法以及<code>s</code>、<code>n</code>和<code>p</code>属性,允许你轻松地搜索、过滤和显示结果。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">directory</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>IPython.utils.text.SList</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>另一个方向的交互,即将Python变量传入shell,可以通过<code>{varname}</code>语法实现:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">message</span> <span class="o">=</span> <span class="s2">"美妙的Python"</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span><span class="nb">echo</span> <span class="o">{</span>message<span class="o">}</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>美妙的Python
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>变量名包含在大括号内,在shell命令中用实际的变量替代。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="与shell相关的魔法命令">与shell相关的魔法命令<a class="anchor-link" href="#与shell相关的魔法命令"> </a></h2><p>不能通过<code>!cd</code>来切换目录,原因是Notebook中的shell命令是在一个临时的shell中执行的。如果你希望以一种更持久的方式更改工作路径,可以使用<code>%cd</code>魔法命令:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span> <span class="o">%</span><span class="k">cd</span> ..
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>/home/junjiet/data_science2020
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>其实可以直接用cd实现该功能:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cd</span> <span class="o">..</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>/home/junjiet
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这种方式称作自动魔法(<code>automagic</code>)函数,可以通过<code>%automagic</code>魔法函数进行调整,默认开启。</p>
<p>除了<code>%cd</code>,其他可用的类似shell的魔法函数还有<code>%cat</code>、<code>%cp</code>、<code>%env</code>、<code>%ls</code>、<code>%man</code>、<code>%mkdir</code>、<code>%more</code>、<code>%mv</code>、<code>%pwd</code>、<code>%rm</code>和<code>%rmdir</code>,默认都可以省略<code>%</code>符号。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="错误和调试">错误和调试<a class="anchor-link" href="#错误和调试"> </a></h2><p>代码开发和数据分析经常需要一些调试,Jupyter调试代码工具:</p>
<ol>
<li><code>%xmode</code>:控制打印信息进行traceback</li>
<li><code>%debug</code>:基于ipdb(pdb增强版)专用的调试器进行调试</li>
<li><code>%run -d</code>:交互式模式运行脚本,用<code>next</code>命令单步向下交互地运行代码</li>
</ol>
<p>常用调试命令如下:</p>
<table>
<thead><tr>
<th>命令</th>
<th>描述 </th>
</tr>
</thead>
<tbody>
<tr>
<td><code>list</code></td>
<td>显示文件的当前路径 </td>
</tr>
<tr>
<td><code>h(elp)</code></td>
<td>显示命令列表,或查找特定命令的帮助信息 </td>
</tr>
<tr>
<td><code>q(uit)</code></td>
<td>退出调试器和程序 </td>
</tr>
<tr>
<td><code>c(ontinue)</code></td>
<td>退出调试器,继续运行程序 </td>
</tr>
<tr>
<td><code>n(ext)</code></td>
<td>跳到程序的下一步 </td>
</tr>
<tr>
<td><code><enter></code></td>
<td>重复前一个命令 </td>
</tr>
<tr>
<td><code>p(rint)</code></td>
<td>打印变量</td>
</tr>
<tr>
<td><code>s(tep)</code></td>
<td>步入子进程 </td>
</tr>
<tr>
<td><code>r(eturn)</code></td>
<td>从子进程跳出</td>
</tr>
</tbody>
</table>
<p>在调试器中使用<code>help</code>命令,或者查看<a href="https://github.com/gotcha/ipdb"><code>ipdb</code>的在线文档</a>获取更多的相关信息</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="代码分析与优化">代码分析与优化<a class="anchor-link" href="#代码分析与优化"> </a></h2><p>“过早优化是一切罪恶的根源。”——高德纳</p>
<p>Jupyter提供了很多执行这些代码计时和分析的操作函数。</p>
<ol>
<li><p><code>%time</code>:对单个语句的执行时间进行计时</p>
</li>
<li><p><code>%timeit</code>:对单个语句的重复执行进行计时,以获得更高的准确度</p>
</li>
<li><p><code>%prun</code>:利用分析器运行代码</p>
</li>
<li><p><code>%lprun</code>:利用逐行分析器运行代码,需要先安装<code>pip install line_profiler</code>,再导入<code>%load_ext line_profiler</code></p>
</li>
<li><p><code>%memit</code>:测量单个语句的内存使用,需要先安装<code>pip install memory_profiler</code>,再导入<code>%load_ext memory_profiler</code></p>
</li>
</ol>
<ol>
<li><code>%mprun</code>:通过逐行的内存分析器运行代码</li>
</ol>
<p>详情请参考<a href="https://www.ituring.com.cn/book/tupubarticle/19702">Python数据科学手册 第 1 章 IPython:超越 Python</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Python-基础"><a href="https://docs.python.org/zh-cn/3/index.html">Python 基础</a><a class="anchor-link" href="#Python-基础"> </a></h1><p>通过“猜数字”游戏介绍Python编程基础,涉及输入输出、模块、函数、控制流、数据结构等概念。程序流程图如下:</p>
<p><figure>
<img class="docimage" src="/images/copied_from_nb/1.jupyter-python/guess.png" alt="" style="max-width: 500px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="游戏代码">游戏代码<a class="anchor-link" href="#游戏代码"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 猜数字游戏</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">5</span> <span class="c1"># 最大值和猜测次数</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
<span class="n">name</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span><span class="s2">"Hello! 你是谁?"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"欢迎你,</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">同学,我在1到</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">之间选了一个整数,共</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">次机会,你猜猜看?"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">guess</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">input</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">同学猜是:"</span><span class="p">))</span>
<span class="k">if</span> <span class="n">guess</span> <span class="o">==</span> <span class="n">x</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"猜中啦,</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">同学!就是</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">,赶紧去买注大乐透吧!"</span><span class="p">)</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"猜大了~"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">if</span> <span class="n">guess</span> <span class="o">></span> <span class="n">x</span> <span class="k">else</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"猜小了~"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"还有</span><span class="si">{</span><span class="n">n</span><span class="o">-</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s2">次机会,再猜猜看:"</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="o"><</span> <span class="n">n</span> <span class="k">else</span> <span class="nb">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">">:<没猜中,我想的是</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">啊!"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Hello! 你是谁?tutu
欢迎你,tutu同学,我在1到30之间选了一个整数,共5次机会,你猜猜看?
tutu同学猜是:15
猜中啦,tutu同学!就是15,赶紧去买注大乐透吧!
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="import语句与random模块">import语句与random模块<a class="anchor-link" href="#import语句与random模块"> </a></h2><p>第1行是Python的注释,以#开头,解释器运行时会忽略 #后面的字符</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 猜数字游戏</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>第2行是import语句,导入了一个random模块(module)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><strong>语句(statement)是执行操作的若干指令,而表达式(expression)会计算并返回值</strong></p>
</blockquote>
<p>第4行是赋值(assignment)语句,将30、5保存在变量(variable)m、n中</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">5</span> <span class="c1"># 最大值和猜测次数</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="字面值(Literals)">字面值(Literals)<a class="anchor-link" href="#字面值(Literals)"> </a></h2><p>字面值用于表示一些内置类型的常量。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="数字">数字<a class="anchor-link" href="#数字"> </a></h3><p>整型(int)、浮点数(float)和复数(complex),与计算器一样,输入一个表达式就会有答案</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="mi">3</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">/</span> <span class="mi">6</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>6.333333333333334</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="mi">20</span> <span class="o">//</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span> <span class="o">%</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span> <span class="o">**</span> <span class="mi">3</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(6, 2, 8000)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sys</span><span class="o">.</span><span class="n">float_info</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="字符串">字符串<a class="anchor-link" href="#字符串"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>字符串有多种形式,可以使用单引号(''),双引号(""),反斜杠 \ 表示转义字符:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"C:\some</span><span class="se">\n</span><span class="s2">ame"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>C:\some
ame
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>字符串跨行连续输入。用三重引号:"""...""" 或 '''...'''。字符串中的回车换行会自动包含到字符串中,如果不想包含,在行尾添加一个 \ 即可。如下例:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"""</span><span class="se">\</span>
<span class="s2">Usage: thingy [OPTIONS]</span>
<span class="s2"> -h Display this usage message</span>
<span class="s2"> -H hostname Hostname to connect to</span>
<span class="s2">"""</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>字符串可以用 + 进行连接(粘到一起),也可以用 * 进行重复</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="mi">3</span> <span class="o">*</span> <span class="s2">"un"</span> <span class="o">+</span> <span class="s2">"ium"</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'unununium'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>很长的字符串用括号包裹多个片段即可:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">text</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'很长的字符串用括号'</span>
<span class="s1">'包裹多个片段即可'</span><span class="p">)</span>
<span class="n">text</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'很长的字符串用括号包裹多个片段即可'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="函数">函数<a class="anchor-link" href="#函数"> </a></h2><p>关键字<code>def</code>创建函数,后面函数名称和带括号的形式参数列表</p>
<p>第5行调用了random模块的randrange()函数(function),这个函数会生成一个随机数<code>[1,n]</code>范围的整数x</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 定义 Fibonacci数列</span>
<span class="k">def</span> <span class="nf">fib</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="sd">"""Print a Fibonacci series up to n."""</span>
<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span>
<span class="k">while</span> <span class="n">a</span> <span class="o"><</span> <span class="n">n</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">" "</span><span class="p">)</span>
<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="nb">print</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 调用函数</span>
<span class="n">fib</span><span class="p">(</span><span class="mi">2000</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>函数通过<code>return</code>语句返回值,即使没有 return 语句的函数也会返回一个值None</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 定义 Fibonacci数列</span>
<span class="k">def</span> <span class="nf">fib_return</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="sd">"""Print a Fibonacci series up to n."""</span>
<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span>
<span class="n">fib</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">while</span> <span class="n">a</span> <span class="o"><</span> <span class="n">n</span><span class="p">:</span>
<span class="n">fib</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="k">return</span> <span class="n">fib</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fib2000</span> <span class="o">=</span> <span class="n">fib_return</span><span class="p">(</span><span class="mi">2000</span><span class="p">)</span>
<span class="n">fib2000</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>random.randrange<span class="o">??</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="输入输出">输入输出<a class="anchor-link" href="#输入输出"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>第6行input()函数获取用户输入</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">name</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span><span class="s2">"Hello! 你是谁?"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>Hello! 你是谁?tutu
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">name</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'tutu'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>第7行print()函数打印输出,f字符串可以在{ 和 } 字符之间引用变量或表达式(Python3.6新特性,另外还有C语言%字符串与.format字符串)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"欢迎你,</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">同学,我在1到</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">之间选了一个整数,共</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">次机会,你猜猜看?"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>欢迎你,tutu同学,我在1到30之间选了一个整数,共5次机会,你猜猜看?
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">mod</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">):</span>
<span class="k">return</span> <span class="n">a</span> <span class="o">%</span> <span class="n">b</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">dis</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dis</span><span class="o">.</span><span class="n">dis</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre> 2 0 LOAD_FAST 0 (a)
2 LOAD_FAST 1 (b)
4 BINARY_MODULO
6 RETURN_VALUE
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mod</span><span class="p">(</span><span class="s1">'hello</span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span><span class="s1">'world'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'helloworld'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="mi">3</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>7
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>更常见的还有文件读写操作:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"myscript.py"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">txt</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>def square(x):
"""求平方"""
return x ** 2
for N in range(1, 4):
print(N, "squared is", square(N))
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"myscript2.py"</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cat</span> <span class="n">myscript2</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>def square(x):
"""求平方"""
return x ** 2
for N in range(1, 4):
print(N, "squared is", square(N))
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"myscript.py"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">txt</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
<span class="n">txt</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['def square(x):\n',
' """求平方"""\n',
' return x ** 2\n',
'\n',
'for N in range(1, 4):\n',
' print(N, "squared is", square(N))\n']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"myscript2.py"</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cat</span> <span class="n">myscript2</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>def square(x):
"""求平方"""
return x ** 2
for N in range(1, 4):
print(N, "squared is", square(N))
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="控制流">控制流<a class="anchor-link" href="#控制流"> </a></h2><ol>
<li>for...[else]、break、contiue</li>
<li>if...else</li>
<li>while...[else]</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="for">for<a class="anchor-link" href="#for"> </a></h3><p>第8行-17行是一块for...else循环语句,对序列或其他可迭代对象的每个元素进行迭代:</p>
<div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="o">...</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">">:<没猜中,我想的是</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">啊!"</span><span class="p">)</span>
<span class="o">...</span>
</pre></div>
<p>else子句是可选语句,当元素被迭代结束后,else 子句才会被执行</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"game over"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0
1
4
9
16
25
36
49
64
81
game over
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">></span> <span class="mi">5</span><span class="p">:</span>
<span class="k">break</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"game over"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0
1
4
9
16
25
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">></span> <span class="mi">5</span><span class="p">:</span>
<span class="k">continue</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"game over"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0
1
4
9
16
25
game over
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="if...else...">if...else...<a class="anchor-link" href="#if...else..."> </a></h3><p>第10行-13行是if...else条件语句</p>
<div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">guess</span> <span class="o">==</span> <span class="n">x</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"猜中啦,</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">同学!就是</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">,赶紧去买注大乐透吧!"</span><span class="p">)</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="mf">5.1</span>
<span class="k">if</span> <span class="n">m</span> <span class="o"><</span> <span class="mi">5</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">小于5"</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">m</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">是奇数"</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">m</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">是偶数"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"不是整数"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>不是整数
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="mf">5.1</span> <span class="o">%</span> <span class="mi">2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>1.0999999999999996</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>浮点数精度问题,建议参考decimal标准库</p>
<blockquote><p>鲜为人知的Python特性 <a href="https://github.com/satwikkansal/wtfpython">https://github.com/satwikkansal/wtfpython</a></p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="while">while<a class="anchor-link" href="#while"> </a></h3><p>另一种循环语句,支持else可选子句</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span> <span class="o"><</span> <span class="mi">10</span><span class="p">:</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">></span> <span class="mi">5</span><span class="p">:</span>
<span class="k">break</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"game over"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>0
1
4
9
16
25
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="运算符(Operators)">运算符(Operators)<a class="anchor-link" href="#运算符(Operators)"> </a></h2>
<pre><code>+ - * ** / // % @(Python3.5新特性)
<< >> & | ^ ~ :=(Python3.8新特性)
< > <= >= == !=</code></pre>
<p>运算符优先级:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<table class="docutils align-default">
<colgroup>
<col style="width: 56%" />
<col style="width: 44%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>运算符</p></th>
<th class="head"><p>描述</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">:=</span></code></p></td>
<td><p>赋值表达式</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#lambda"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">lambda</span></code></a></p></td>
<td><p>lambda 表达式</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#if-expr"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">if</span></code></a> -- <code class="xref std std-keyword docutils literal notranslate"><span class="pre">else</span></code></p></td>
<td><p>条件表达式</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#or"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">or</span></code></a></p></td>
<td><p>布尔逻辑或 OR</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#and"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">and</span></code></a></p></td>
<td><p>布尔逻辑与 AND</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#not"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">not</span></code></a> <code class="docutils literal notranslate"><span class="pre">x</span></code></p></td>
<td><p>布尔逻辑非 NOT</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#in"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">in</span></code></a>, <a class="reference internal" href="#not-in"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">not</span> <span class="pre">in</span></code></a>,
<a class="reference internal" href="#is"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">is</span></code></a>, <a class="reference internal" href="#is-not"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">is</span> <span class="pre">not</span></code></a>, <code class="docutils literal notranslate"><span class="pre"><</span></code>,
<code class="docutils literal notranslate"><span class="pre"><=</span></code>, <code class="docutils literal notranslate"><span class="pre">></span></code>, <code class="docutils literal notranslate"><span class="pre">>=</span></code>, <code class="docutils literal notranslate"><span class="pre">!=</span></code>, <code class="docutils literal notranslate"><span class="pre">==</span></code></p></td>
<td><p>比较运算,包括成员检测和标识号检测</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">|</span></code></p></td>
<td><p>按位或 OR</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">^</span></code></p></td>
<td><p>按位异或 XOR</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&</span></code></p></td>
<td><p>按位与 AND</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre"><<</span></code>, <code class="docutils literal notranslate"><span class="pre">>></span></code></p></td>
<td><p>移位</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">+</span></code>, <code class="docutils literal notranslate"><span class="pre">-</span></code></p></td>
<td><p>加和减</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">*</span></code>, <code class="docutils literal notranslate"><span class="pre">@</span></code>, <code class="docutils literal notranslate"><span class="pre">/</span></code>, <code class="docutils literal notranslate"><span class="pre">//</span></code>, <code class="docutils literal notranslate"><span class="pre">%</span></code></p></td>
<td><p>乘,矩阵乘,除,整除,取余</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">+x</span></code>, <code class="docutils literal notranslate"><span class="pre">-x</span></code>, <code class="docutils literal notranslate"><span class="pre">~x</span></code></p></td>
<td><p>正,负,按位非 NOT</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">**</span></code></p></td>
<td><p>乘方</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#await"><code class="xref std std-keyword docutils literal notranslate"><span class="pre">await</span></code></a> <code class="docutils literal notranslate"><span class="pre">x</span></code></p></td>
<td><p>await 表达式</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">x[index]</span></code>, <code class="docutils literal notranslate"><span class="pre">x[index:index]</span></code>,
<code class="docutils literal notranslate"><span class="pre">x(arguments...)</span></code>, <code class="docutils literal notranslate"><span class="pre">x.attribute</span></code></p></td>
<td><p>抽取,切片,调用,属性引用</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">(expressions...)</span></code>,</p>
<p><code class="docutils literal notranslate"><span class="pre">[expressions...]</span></code>,
<code class="docutils literal notranslate"><span class="pre">{key:</span> <span class="pre">value...}</span></code>,
<code class="docutils literal notranslate"><span class="pre">{expressions...}</span></code></p>
</td>
<td><p>绑定或加圆括号的表达式,列表显示,字典显示,集合显示</p></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>条件表达式也称三元运算符(ternary operator):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">cmp</span> <span class="o">=</span> <span class="s2">"2 > 1"</span> <span class="k">if</span> <span class="mi">2</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">"2 < 1"</span>
<span class="nb">cmp</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'2 > 1'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>第14、15行均使用了条件表达式:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"猜大了~"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">if</span> <span class="n">guess</span> <span class="o">></span> <span class="n">x</span> <span class="k">else</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"猜小了~"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"还有</span><span class="si">{</span><span class="n">n</span><span class="o">-</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s2">次机会,再猜猜看:"</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="o"><</span> <span class="n">n</span> <span class="k">else</span> <span class="nb">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>猜小了~
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="数据结构"><a href="https://docs.python.org/zh-cn/3/library/stdtypes.html">数据结构</a><a class="anchor-link" href="#数据结构"> </a></h2><ol>
<li>range</li>
<li>list(列表)</li>
<li>tuple(元组)</li>
<li>set(集合)</li>
<li>dict(字典)</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="range">range<a class="anchor-link" href="#range"> </a></h3><p>第8行中有一个range()函数,返回值是一个range对象,属于Python的基本序列类型,另外两个是list和tuple。range(n)包含<code>[0,n-1]</code>范围内所有整数。<code>range(start, stop[, step])</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>range(0, 10)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>range</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="列表list">列表list<a class="anchor-link" href="#列表list"> </a></h3><p>列表是可变序列,通常用于存放同类项目的集合</p>
<ol>
<li>使用一对方括号来表示空列表: []</li>
<li>使用方括号,其中的项以逗号分隔: [a], [a, b, c]</li>
<li>使用列表推导式: [x for x in iterable]</li>
<li>使用类型的构造器: list() 或 list(iterable)</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"魔法石"</span><span class="p">,</span>
<span class="s2">"密室"</span><span class="p">,</span>
<span class="s2">"阿兹卡班囚徒"</span><span class="p">,</span>
<span class="s2">"火焰杯"</span><span class="p">,</span>
<span class="s2">"凤凰社"</span><span class="p">,</span>
<span class="s2">"混血王子"</span><span class="p">,</span>
<span class="s2">"死亡圣器"</span><span class="p">,</span>
<span class="s2">"被诅咒的孩子"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">harry_potter</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['魔法石', '密室', '阿兹卡班囚徒', '火焰杯', '凤凰社', '混血王子', '死亡圣器', '被诅咒的孩子']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">harry_potter</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>list</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">harry_potter</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>8</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s2">"凤凰社"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>4</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'凤凰社'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'被诅咒的孩子'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['魔法石', '密室', '阿兹卡班囚徒', '火焰杯', '凤凰社', '混血王子', '死亡圣器']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"被诅咒的孩子"</span><span class="p">)</span>
<span class="n">harry_potter</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['魔法石', '密室', '阿兹卡班囚徒', '火焰杯', '凤凰社', '混血王子', '死亡圣器', '被诅咒的孩子']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">book</span> <span class="ow">in</span> <span class="n">harry_potter</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"《哈利·波特与</span><span class="si">{</span><span class="n">book</span><span class="si">}</span><span class="s2">》"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>《哈利·波特与魔法石》
《哈利·波特与密室》
《哈利·波特与阿兹卡班囚徒》
《哈利·波特与火焰杯》
《哈利·波特与凤凰社》
《哈利·波特与混血王子》
《哈利·波特与死亡圣器》
《哈利·波特与被诅咒的孩子》
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="p">[</span><span class="sa">f</span><span class="s2">"《哈利·波特与</span><span class="si">{</span><span class="n">book</span><span class="si">}</span><span class="s2">》"</span> <span class="k">for</span> <span class="n">book</span> <span class="ow">in</span> <span class="n">harry_potter</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>['《哈利·波特与魔法石》',
'《哈利·波特与密室》',
'《哈利·波特与阿兹卡班囚徒》',
'《哈利·波特与火焰杯》',
'《哈利·波特与凤凰社》',
'《哈利·波特与混血王子》',
'《哈利·波特与死亡圣器》',
'《哈利·波特与被诅咒的孩子》']</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="tuple(元组)">tuple(元组)<a class="anchor-link" href="#tuple(元组)"> </a></h3><p>元组是不可变序列,通常用于储存异构数据集、同构数据集的不可变序列</p>
<ol>
<li>使用一对圆括号来表示空元组: ()</li>
<li>使用一个后缀的逗号来表示单元组: a, 或 (a,)</li>
<li>使用以逗号分隔的多个项: a, b, c or (a, b, c)</li>
<li>使用内置的 tuple(): tuple() 或 tuple(iterable)</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"魔法石"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"密室"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"阿兹卡班囚徒"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"火焰杯"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"凤凰社"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"混血王子"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"死亡圣器"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"哈利·波特"</span><span class="p">,</span> <span class="s2">"被诅咒的孩子"</span><span class="p">),</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">book</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">harry_potter</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"第</span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">本《</span><span class="si">{</span><span class="s1">'与'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">book</span><span class="p">)</span><span class="si">}</span><span class="s2">》"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>第1本《哈利·波特与魔法石》
第2本《哈利·波特与密室》
第3本《哈利·波特与阿兹卡班囚徒》
第4本《哈利·波特与火焰杯》
第5本《哈利·波特与凤凰社》
第6本《哈利·波特与混血王子》
第7本《哈利·波特与死亡圣器》
第8本《哈利·波特与被诅咒的孩子》
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/1.jupyter-python/enumerate.png" alt="" style="max-width: 500px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="set(集合)">set(集合)<a class="anchor-link" href="#set(集合)"> </a></h3><p>由不重复哈希对象构成的无序容器。用途包括</p>
<ol>
<li>成员检测</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="s2">"哈利"</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">"哈利"</span><span class="p">,</span> <span class="s2">"波特"</span><span class="p">}</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>从序列中去除重复项</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">set</span><span class="p">((</span><span class="s2">"哈利"</span><span class="p">,</span> <span class="s2">"波特"</span><span class="p">,</span> <span class="s2">"哈利"</span><span class="p">,</span> <span class="s2">"波特"</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'哈利', '波特'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>数学集合交、并、补等运算</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter_books</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">x</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">harry_potter</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">y</span><span class="p">)</span>
<span class="n">harry_potter_books</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'凤凰社', '哈利·波特', '密室', '死亡圣器', '混血王子', '火焰杯', '被诅咒的孩子', '阿兹卡班囚徒', '魔法石'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic</span><span class="p">,</span> <span class="n">secret</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">harry_potter</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">set</span><span class="p">(</span><span class="n">harry_potter</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">magic</span><span class="p">,</span> <span class="n">secret</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>({'哈利·波特', '魔法石'}, {'哈利·波特', '密室'})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic</span> <span class="o">&</span> <span class="n">harry_potter_books</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'哈利·波特', '魔法石'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter_books</span> <span class="o">-</span> <span class="n">magic</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'凤凰社', '密室', '死亡圣器', '混血王子', '火焰杯', '被诅咒的孩子', '阿兹卡班囚徒'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic</span> <span class="o">|</span> <span class="n">secret</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'哈利·波特', '密室', '魔法石'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic</span> <span class="o">^</span> <span class="n">secret</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'密室', '魔法石'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"死亡圣器"</span><span class="p">)</span>
<span class="n">magic</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'哈利·波特', '死亡圣器', '魔法石'}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>frozenset不可变对象,不支持add、remove、pop和update等操作</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic2</span> <span class="o">=</span> <span class="nb">frozenset</span><span class="p">(</span><span class="n">magic</span><span class="p">)</span>
<span class="n">magic2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>frozenset({'哈利·波特', '死亡圣器', '魔法石'})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">magic2</span><span class="o">.</span><span class="n">add</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AttributeError</span> Traceback (most recent call last)
<span class="ansi-green-fg"><ipython-input-97-02238ce56a10></span> in <span class="ansi-cyan-fg"><module></span>
<span class="ansi-green-fg">----> 1</span><span class="ansi-red-fg"> </span>magic2<span class="ansi-blue-fg">.</span>add
<span class="ansi-red-fg">AttributeError</span>: 'frozenset' object has no attribute 'add'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="dict(字典)">dict(字典)<a class="anchor-link" href="#dict(字典)"> </a></h3><p>映射类型,形式为若干键:值对。键不可重复,列表、字典或其他可变类型不可作为键。</p>
<p>Python3.6改写字典算法,保持顺序不变。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter1</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">魔法石</span><span class="o">=</span><span class="mi">1997</span><span class="p">,</span> <span class="n">密室</span><span class="o">=</span><span class="mi">1998</span><span class="p">,</span> <span class="n">阿兹卡班囚徒</span><span class="o">=</span><span class="mi">1999</span><span class="p">,</span> <span class="n">火焰杯</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">凤凰社</span><span class="o">=</span><span class="mi">2003</span><span class="p">,</span> <span class="n">混血王子</span><span class="o">=</span><span class="mi">2005</span><span class="p">,</span> <span class="n">死亡圣器</span><span class="o">=</span><span class="mi">2007</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter2</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"魔法石"</span><span class="p">:</span> <span class="mi">1997</span><span class="p">,</span>
<span class="s2">"密室"</span><span class="p">:</span> <span class="mi">1998</span><span class="p">,</span>
<span class="s2">"阿兹卡班囚徒"</span><span class="p">:</span> <span class="mi">1999</span><span class="p">,</span>
<span class="s2">"火焰杯"</span><span class="p">:</span> <span class="mi">2000</span><span class="p">,</span>
<span class="s2">"凤凰社"</span><span class="p">:</span> <span class="mi">2003</span><span class="p">,</span>
<span class="s2">"混血王子"</span><span class="p">:</span> <span class="mi">2005</span><span class="p">,</span>
<span class="s2">"死亡圣器"</span><span class="p">:</span> <span class="mi">2007</span><span class="p">,</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter1</span> <span class="o">==</span> <span class="n">harry_potter2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter3</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="nb">zip</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"魔法石"</span><span class="p">,</span> <span class="s2">"密室"</span><span class="p">,</span> <span class="s2">"阿兹卡班囚徒"</span><span class="p">,</span> <span class="s2">"火焰杯"</span><span class="p">,</span> <span class="s2">"凤凰社"</span><span class="p">,</span> <span class="s2">"混血王子"</span><span class="p">,</span> <span class="s2">"死亡圣器"</span><span class="p">,),</span>
<span class="p">(</span><span class="mi">1997</span><span class="p">,</span> <span class="mi">1998</span><span class="p">,</span> <span class="mi">1999</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="mi">2003</span><span class="p">,</span> <span class="mi">2005</span><span class="p">,</span> <span class="mi">2007</span><span class="p">,),</span>
<span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><figure>
<img class="docimage" src="/images/copied_from_nb/1.jupyter-python/zip.png" alt="" style="max-width: 500px" />
</figure>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter1</span> <span class="o">==</span> <span class="n">harry_potter2</span> <span class="o">==</span> <span class="n">harry_potter3</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter2</span><span class="p">[</span><span class="s2">"凤凰社"</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>2003</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">book</span><span class="p">,</span> <span class="n">year</span> <span class="ow">in</span> <span class="n">harry_potter2</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"《哈利·波特与</span><span class="si">{</span><span class="n">book</span><span class="si">}</span><span class="s2">》出版于</span><span class="si">{</span><span class="n">year</span><span class="si">}</span><span class="s2">年"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>《哈利·波特与魔法石》出版于1997年
《哈利·波特与密室》出版于1998年
《哈利·波特与阿兹卡班囚徒》出版于1999年
《哈利·波特与火焰杯》出版于2000年
《哈利·波特与凤凰社》出版于2003年
《哈利·波特与混血王子》出版于2005年
《哈利·波特与死亡圣器》出版于2007年
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter2</span><span class="p">[</span><span class="s2">"被诅咒的孩子"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2016</span>
<span class="n">harry_potter2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'魔法石': 1997,
'密室': 1998,
'阿兹卡班囚徒': 1999,
'火焰杯': 2000,
'凤凰社': 2003,
'混血王子': 2005,
'死亡圣器': 2007,
'被诅咒的孩子': 2016}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter2</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"被诅咒的孩子"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>2016</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">harry_potter2</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'魔法石': 1997,
'密室': 1998,
'阿兹卡班囚徒': 1999,
'火焰杯': 2000,
'凤凰社': 2003,
'混血王子': 2005,
'死亡圣器': 2007}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="函数式编程">函数式编程<a class="anchor-link" href="#函数式编程"> </a></h1><p>借助Python的不可变对象和高阶函数,进行更简洁的表达,实现快速并行计算。</p>
<ol>
<li>可变与不可变对象</li>
<li>max、min、sum、any、all、sorted、reversed</li>
<li>filter、map、reduce</li>
<li>multiprocessing与concurrent.futures</li>
<li>第三方高阶函数包:cytoolz、fn与fancy</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="可变与不可变对象">可变与不可变对象<a class="anchor-link" href="#可变与不可变对象"> </a></h2><ol>
<li>Python中一切皆对象,对象有两种:可变(mutable )与不可变(immutable)</li>
<li>对象被实例化(instantiate)之后获得唯一id,可变对象可以改变状态或内容,而不可变不能修改</li>
<li>不可变对象:int、float、bool、string/unicode、tuple*、nametuple、frozenset,读取速度快,节省内存</li>
<li>可变对象:list、dict、set、自定义类</li>
<li>tuple例外</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tup</span> <span class="o">=</span> <span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s1">'two'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tup</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tup</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>([3, 4, 1], 'two')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="collections.nametuple">collections.nametuple<a class="anchor-link" href="#collections.nametuple"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>symbol</th>
<th>name</th>
<th>current</th>
<th>percent</th>
<th>pe_ttm</th>
<th>current_year_percent</th>
<th>volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>SH601398</td>
<td>工商银行</td>
<td>5.17</td>
<td>0.39</td>
<td>5.855</td>
<td>-12.07</td>
<td>104063910</td>
</tr>
<tr>
<th>1</th>
<td>SH601939</td>
<td>建设银行</td>
<td>6.43</td>
<td>0.16</td>
<td>5.939</td>
<td>-11.07</td>
<td>51089825</td>
</tr>
<tr>
<th>2</th>
<td>SH600519</td>
<td>贵州茅台</td>
<td>1265.70</td>
<td>-0.72</td>
<td>36.908</td>
<td>6.99</td>
<td>2466087</td>
</tr>
<tr>
<th>3</th>
<td>SH601318</td>
<td>中国平安</td>
<td>74.46</td>
<td>0.62</td>
<td>10.474</td>
<td>-12.87</td>
<td>48625473</td>
</tr>
<tr>
<th>4</th>
<td>SH601288</td>
<td>农业银行</td>
<td>3.46</td>
<td>0.58</td>
<td>5.631</td>
<td>-6.23</td>
<td>118473509</td>
</tr>
<tr>
<th>5</th>
<td>SH601988</td>
<td>中国银行</td>
<td>3.48</td>
<td>0.58</td>
<td>5.420</td>
<td>-5.69</td>
<td>79563490</td>
</tr>
<tr>
<th>6</th>
<td>SH600036</td>
<td>招商银行</td>
<td>35.09</td>
<td>0.20</td>
<td>9.274</td>
<td>-6.63</td>
<td>71533247</td>
</tr>
<tr>
<th>7</th>
<td>SH601857</td>
<td>中国石油</td>
<td>4.44</td>
<td>1.37</td>
<td>42.328</td>
<td>-23.84</td>
<td>85973295</td>
</tr>
<tr>
<th>8</th>
<td>SH601628</td>
<td>中国人寿</td>
<td>28.54</td>
<td>-0.73</td>
<td>16.348</td>
<td>-18.15</td>
<td>16644522</td>
</tr>
<tr>
<th>9</th>
<td>SH600028</td>
<td>中国石化</td>
<td>4.46</td>
<td>0.45</td>
<td>23.430</td>
<td>-12.72</td>
<td>119640204</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601398"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"工商银行"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">5.17</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.39</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">5.855</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">12.07</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">104063910</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"银行"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601939"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"建设银行"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">6.43</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.16</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">5.939</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">11.07</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">51089825</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"银行"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH600519"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"贵州茅台"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">1265.7</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.72</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">36.908</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="mf">6.99</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">2466087</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"白酒"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601318"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"中国平安"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">74.46</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.62</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">10.474</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">12.87</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">48625473</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"保险"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601288"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"农业银行"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">3.46</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.58</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">5.631</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">6.23</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">118473509</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"银行"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601988"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"中国银行"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">3.48</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.58</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">5.42</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">5.69</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">79563490</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"银行"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH600036"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"招商银行"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">35.09</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">9.274</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">6.63</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">71533247</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"银行"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601857"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"中国石油"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">4.44</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">1.37</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">42.328</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">23.84</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">85973295</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"石化"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH601628"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"中国人寿"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">28.54</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.73</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">16.348</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">18.15</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">16644522</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"保险"</span><span class="p">,},</span>
<span class="p">{</span><span class="s2">"symbol"</span><span class="p">:</span> <span class="s2">"SH600028"</span><span class="p">,</span><span class="s2">"name"</span><span class="p">:</span> <span class="s2">"中国石化"</span><span class="p">,</span><span class="s2">"current"</span><span class="p">:</span> <span class="mf">4.46</span><span class="p">,</span><span class="s2">"percent"</span><span class="p">:</span> <span class="mf">0.45</span><span class="p">,</span><span class="s2">"pe_ttm"</span><span class="p">:</span> <span class="mf">23.43</span><span class="p">,</span><span class="s2">"current_year_percent"</span><span class="p">:</span> <span class="o">-</span><span class="mf">12.72</span><span class="p">,</span><span class="s2">"volume"</span><span class="p">:</span> <span class="mi">119640204</span><span class="p">,</span><span class="s2">"industry"</span><span class="p">:</span> <span class="s2">"石化"</span><span class="p">,}]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Stocks</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span>
<span class="s2">"Stocks"</span><span class="p">,</span>
<span class="p">[</span>
<span class="s2">"symbol"</span><span class="p">,</span>
<span class="s2">"name"</span><span class="p">,</span>
<span class="s2">"current"</span><span class="p">,</span>
<span class="s2">"percent"</span><span class="p">,</span>
<span class="s2">"pe_ttm"</span><span class="p">,</span>
<span class="s2">"current_year_percent"</span><span class="p">,</span>
<span class="s2">"volume"</span><span class="p">,</span>
<span class="s2">"industry"</span><span class="p">,</span>
<span class="p">],</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Stocks</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>__main__.Stocks</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">icbc</span> <span class="o">=</span> <span class="n">Stocks</span><span class="p">(</span>
<span class="n">symbol</span><span class="o">=</span><span class="s2">"SH601398"</span><span class="p">,</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"工商银行"</span><span class="p">,</span>
<span class="n">current</span><span class="o">=</span><span class="mf">5.17</span><span class="p">,</span>
<span class="n">percent</span><span class="o">=</span><span class="mf">0.39</span><span class="p">,</span>
<span class="n">pe_ttm</span><span class="o">=</span><span class="mf">5.855</span><span class="p">,</span>
<span class="n">current_year_percent</span><span class="o">=-</span><span class="mf">12.07</span><span class="p">,</span>
<span class="n">volume</span><span class="o">=</span><span class="mi">104063910</span><span class="p">,</span>
<span class="n">industry</span><span class="o">=</span><span class="s2">"银行"</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">icbc</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">icbc</span><span class="o">.</span><span class="n">name</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'工商银行'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">icbc</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'dd'</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AttributeError</span> Traceback (most recent call last)
<span class="ansi-green-fg"><ipython-input-118-74e3a93185c6></span> in <span class="ansi-cyan-fg"><module></span>
<span class="ansi-green-fg">----> 1</span><span class="ansi-red-fg"> </span>icbc<span class="ansi-blue-fg">.</span>name <span class="ansi-blue-fg">=</span> <span class="ansi-blue-fg">'dd'</span>
<span class="ansi-red-fg">AttributeError</span>: can't set attribute</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Stocks</span><span class="p">(</span><span class="o">**</span><span class="n">_</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行'),
Stocks(symbol='SH601939', name='建设银行', current=6.43, percent=0.16, pe_ttm=5.939, current_year_percent=-11.07, volume=51089825, industry='银行'),
Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒'),
Stocks(symbol='SH601318', name='中国平安', current=74.46, percent=0.62, pe_ttm=10.474, current_year_percent=-12.87, volume=48625473, industry='保险'),
Stocks(symbol='SH601288', name='农业银行', current=3.46, percent=0.58, pe_ttm=5.631, current_year_percent=-6.23, volume=118473509, industry='银行'),
Stocks(symbol='SH601988', name='中国银行', current=3.48, percent=0.58, pe_ttm=5.42, current_year_percent=-5.69, volume=79563490, industry='银行'),
Stocks(symbol='SH600036', name='招商银行', current=35.09, percent=0.2, pe_ttm=9.274, current_year_percent=-6.63, volume=71533247, industry='银行'),
Stocks(symbol='SH601857', name='中国石油', current=4.44, percent=1.37, pe_ttm=42.328, current_year_percent=-23.84, volume=85973295, industry='石化'),
Stocks(symbol='SH601628', name='中国人寿', current=28.54, percent=-0.73, pe_ttm=16.348, current_year_percent=-18.15, volume=16644522, industry='保险'),
Stocks(symbol='SH600028', name='中国石化', current=4.46, percent=0.45, pe_ttm=23.43, current_year_percent=-12.72, volume=119640204, industry='石化'))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'工商银行'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="max、min、sum、any、all、sorted、reversed">max、min、sum、any、all、sorted、reversed<a class="anchor-link" href="#max、min、sum、any、all、sorted、reversed"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">max</span><span class="p">(</span><span class="n">stocks</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Stocks(symbol='SH601988', name='中国银行', current=3.48, percent=0.58, pe_ttm=5.42, current_year_percent=-5.69, volume=79563490, industry='银行')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">max</span><span class="p">(</span><span class="n">stocks</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">_</span><span class="o">.</span><span class="n">current</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="n">stocks</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Stocks(symbol='SH600028', name='中国石化', current=4.46, percent=0.45, pe_ttm=23.43, current_year_percent=-12.72, volume=119640204, industry='石化')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="n">stocks</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">_</span><span class="o">.</span><span class="n">current</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Stocks(symbol='SH601288', name='农业银行', current=3.46, percent=0.58, pe_ttm=5.631, current_year_percent=-6.23, volume=118473509, industry='银行')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">sum</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>6</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">any</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">all</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>False</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">sorted</span><span class="p">(</span><span class="n">stocks</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">_</span><span class="p">:</span> <span class="n">_</span><span class="o">.</span><span class="n">current</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[Stocks(symbol='SH601288', name='农业银行', current=3.46, percent=0.58, pe_ttm=5.631, current_year_percent=-6.23, volume=118473509, industry='银行'),
Stocks(symbol='SH601988', name='中国银行', current=3.48, percent=0.58, pe_ttm=5.42, current_year_percent=-5.69, volume=79563490, industry='银行'),
Stocks(symbol='SH601857', name='中国石油', current=4.44, percent=1.37, pe_ttm=42.328, current_year_percent=-23.84, volume=85973295, industry='石化'),
Stocks(symbol='SH600028', name='中国石化', current=4.46, percent=0.45, pe_ttm=23.43, current_year_percent=-12.72, volume=119640204, industry='石化'),
Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行'),
Stocks(symbol='SH601939', name='建设银行', current=6.43, percent=0.16, pe_ttm=5.939, current_year_percent=-11.07, volume=51089825, industry='银行'),
Stocks(symbol='SH601628', name='中国人寿', current=28.54, percent=-0.73, pe_ttm=16.348, current_year_percent=-18.15, volume=16644522, industry='保险'),
Stocks(symbol='SH600036', name='招商银行', current=35.09, percent=0.2, pe_ttm=9.274, current_year_percent=-6.63, volume=71533247, industry='银行'),
Stocks(symbol='SH601318', name='中国平安', current=74.46, percent=0.62, pe_ttm=10.474, current_year_percent=-12.87, volume=48625473, industry='保险'),
Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒')]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">stocks</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Stocks(symbol='SH600028', name='中国石化', current=4.46, percent=0.45, pe_ttm=23.43, current_year_percent=-12.72, volume=119640204, industry='石化'),
Stocks(symbol='SH601628', name='中国人寿', current=28.54, percent=-0.73, pe_ttm=16.348, current_year_percent=-18.15, volume=16644522, industry='保险'),
Stocks(symbol='SH601857', name='中国石油', current=4.44, percent=1.37, pe_ttm=42.328, current_year_percent=-23.84, volume=85973295, industry='石化'),
Stocks(symbol='SH600036', name='招商银行', current=35.09, percent=0.2, pe_ttm=9.274, current_year_percent=-6.63, volume=71533247, industry='银行'),
Stocks(symbol='SH601988', name='中国银行', current=3.48, percent=0.58, pe_ttm=5.42, current_year_percent=-5.69, volume=79563490, industry='银行'),
Stocks(symbol='SH601288', name='农业银行', current=3.46, percent=0.58, pe_ttm=5.631, current_year_percent=-6.23, volume=118473509, industry='银行'),
Stocks(symbol='SH601318', name='中国平安', current=74.46, percent=0.62, pe_ttm=10.474, current_year_percent=-12.87, volume=48625473, industry='保险'),
Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒'),
Stocks(symbol='SH601939', name='建设银行', current=6.43, percent=0.16, pe_ttm=5.939, current_year_percent=-11.07, volume=51089825, industry='银行'),
Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行'))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="filter、map、reduce">filter、map、reduce<a class="anchor-link" href="#filter、map、reduce"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">current</span> <span class="o">></span> <span class="mi">5</span><span class="p">,</span> <span class="n">stocks</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行'),
Stocks(symbol='SH601939', name='建设银行', current=6.43, percent=0.16, pe_ttm=5.939, current_year_percent=-11.07, volume=51089825, industry='银行'),
Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒'),
Stocks(symbol='SH601318', name='中国平安', current=74.46, percent=0.62, pe_ttm=10.474, current_year_percent=-12.87, volume=48625473, industry='保险'),
Stocks(symbol='SH600036', name='招商银行', current=35.09, percent=0.2, pe_ttm=9.274, current_year_percent=-6.63, volume=71533247, industry='银行'),
Stocks(symbol='SH601628', name='中国人寿', current=28.54, percent=-0.73, pe_ttm=16.348, current_year_percent=-18.15, volume=16644522, industry='保险'))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stocks</span> <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">current</span> <span class="o">></span> <span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Stocks(symbol='SH601398', name='工商银行', current=5.17, percent=0.39, pe_ttm=5.855, current_year_percent=-12.07, volume=104063910, industry='银行'),
Stocks(symbol='SH601939', name='建设银行', current=6.43, percent=0.16, pe_ttm=5.939, current_year_percent=-11.07, volume=51089825, industry='银行'),
Stocks(symbol='SH600519', name='贵州茅台', current=1265.7, percent=-0.72, pe_ttm=36.908, current_year_percent=6.99, volume=2466087, industry='白酒'),
Stocks(symbol='SH601318', name='中国平安', current=74.46, percent=0.62, pe_ttm=10.474, current_year_percent=-12.87, volume=48625473, industry='保险'),
Stocks(symbol='SH600036', name='招商银行', current=35.09, percent=0.2, pe_ttm=9.274, current_year_percent=-6.63, volume=71533247, industry='银行'),
Stocks(symbol='SH601628', name='中国人寿', current=28.54, percent=-0.73, pe_ttm=16.348, current_year_percent=-18.15, volume=16644522, industry='保险'))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">volume</span> <span class="o">/</span> <span class="mi">10000</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">stocks</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(('工商银行', 10406.4),
('建设银行', 5109.0),
('贵州茅台', 246.6),
('中国平安', 4862.5),
('农业银行', 11847.4),
('中国银行', 7956.3),
('招商银行', 7153.3),
('中国石油', 8597.3),
('中国人寿', 1664.5),
('中国石化', 11964.0))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">((</span><span class="n">s</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">volume</span> <span class="o">/</span> <span class="mi">10000</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stocks</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(('工商银行', 10406.4),
('建设银行', 5109.0),
('贵州茅台', 246.6),
('中国平安', 4862.5),
('农业银行', 11847.4),
('中国银行', 7956.3),
('招商银行', 7153.3),
('中国石油', 8597.3),
('中国人寿', 1664.5),
('中国石化', 11964.0))</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>reduce<span class="o">?</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">acc</span><span class="p">,</span> <span class="n">v</span><span class="p">:</span> <span class="n">acc</span> <span class="o">+</span> <span class="n">v</span><span class="o">.</span><span class="n">volume</span><span class="p">,</span> <span class="n">stocks</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>698073562</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">accumulate</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">tuple</span><span class="p">(</span><span class="n">accumulate</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">volume</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stocks</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(104063910,
155153735,
157619822,
206245295,
324718804,
404282294,
475815541,
561788836,
578433358,
698073562)</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="groupby">groupby<a class="anchor-link" href="#groupby"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">box</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span>
<span class="n">acc</span><span class="p">[</span><span class="n">v</span><span class="o">.</span><span class="n">industry</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">acc</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks_industry</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">box</span><span class="p">,</span> <span class="n">stocks</span><span class="p">,</span> <span class="p">{</span><span class="s2">"银行"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"保险"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"白酒"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"石化"</span><span class="p">:</span> <span class="p">[]})</span>
<span class="n">stocks_industry</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'银行': ['工商银行', '建设银行', '农业银行', '中国银行', '招商银行'],
'保险': ['中国平安', '中国人寿'],
'白酒': ['贵州茅台'],
'石化': ['中国石油', '中国石化']}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks_industry</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="n">box</span><span class="p">,</span> <span class="n">stocks</span><span class="p">,</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">))</span>
<span class="n">stocks_industry</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>defaultdict(list,
{'银行': ['工商银行', '建设银行', '农业银行', '中国银行', '招商银行'],
'白酒': ['贵州茅台'],
'保险': ['中国平安', '中国人寿'],
'石化': ['中国石油', '中国石化']})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stocks_industry</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">acc</span><span class="p">,</span> <span class="n">v</span><span class="p">:</span> <span class="p">{</span><span class="o">**</span><span class="n">acc</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span><span class="n">v</span><span class="o">.</span><span class="n">industry</span><span class="p">:</span> <span class="n">acc</span><span class="p">[</span><span class="n">v</span><span class="o">.</span><span class="n">industry</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">]}},</span>
<span class="n">stocks</span><span class="p">,</span>
<span class="p">{</span><span class="s2">"银行"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"白酒"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"保险"</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">"石化"</span><span class="p">:</span> <span class="p">[]},</span>
<span class="p">)</span>
<span class="n">stocks_industry</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
<span class="ansi-green-fg"><ipython-input-53-1a6f22b9366d></span> in <span class="ansi-cyan-fg"><module></span>
<span class="ansi-green-intense-fg ansi-bold"> 1</span> stocks_industry = reduce(
<span class="ansi-green-intense-fg ansi-bold"> 2</span> <span class="ansi-green-fg">lambda</span> acc<span class="ansi-blue-fg">,</span> v<span class="ansi-blue-fg">:</span> <span class="ansi-blue-fg">{</span><span class="ansi-blue-fg">**</span>acc<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">**</span><span class="ansi-blue-fg">{</span>v<span class="ansi-blue-fg">.</span>industry<span class="ansi-blue-fg">:</span> acc<span class="ansi-blue-fg">[</span>v<span class="ansi-blue-fg">.</span>industry<span class="ansi-blue-fg">]</span> <span class="ansi-blue-fg">+</span> <span class="ansi-blue-fg">[</span>v<span class="ansi-blue-fg">.</span>name<span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">}</span><span class="ansi-blue-fg">}</span><span class="ansi-blue-fg">,</span>
<span class="ansi-green-fg">----> 3</span><span class="ansi-red-fg"> </span>stocks<span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 4</span> <span class="ansi-blue-fg">{</span><span class="ansi-blue-fg">"银行"</span><span class="ansi-blue-fg">:</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">"白酒"</span><span class="ansi-blue-fg">:</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">"保险"</span><span class="ansi-blue-fg">:</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">"石化"</span><span class="ansi-blue-fg">:</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">}</span><span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold"> 5</span> )
<span class="ansi-red-fg">NameError</span>: name 'stocks' is not defined</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">groupby</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="p">{</span>
<span class="n">k</span><span class="p">:</span> <span class="p">[</span><span class="n">_</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">s</span><span class="p">]</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">groupby</span><span class="p">(</span>
<span class="nb">sorted</span><span class="p">(</span><span class="n">stocks</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">industry</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">industry</span>
<span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'保险': ['中国平安', '中国人寿'],
'白酒': ['贵州茅台'],
'石化': ['中国石油', '中国石化'],
'银行': ['工商银行', '建设银行', '农业银行', '中国银行', '招商银行']}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="multiprocessing与concurrent.futures">multiprocessing与concurrent.futures<a class="anchor-link" href="#multiprocessing与concurrent.futures"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">cmpt_open</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"进程:</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">getpid</span><span class="p">()</span><span class="si">}</span><span class="s2"> 正在计算 </span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">rst</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"name"</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s2">"open"</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">current</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">percent</span> <span class="o">*</span> <span class="mf">0.01</span><span class="p">)}</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"进程:</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">getpid</span><span class="p">()</span><span class="si">}</span><span class="s2"> 完成计算 </span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">rst</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">cmpt_open</span><span class="p">,</span> <span class="n">stocks</span><span class="p">))</span>
<span class="n">result</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>进程:81395 正在计算 工商银行
进程:81395 完成计算 工商银行
进程:81395 正在计算 建设银行
进程:81395 完成计算 建设银行
进程:81395 正在计算 贵州茅台
进程:81395 完成计算 贵州茅台
进程:81395 正在计算 中国平安
进程:81395 完成计算 中国平安
进程:81395 正在计算 农业银行
进程:81395 完成计算 农业银行
进程:81395 正在计算 中国银行
进程:81395 完成计算 中国银行
进程:81395 正在计算 招商银行
进程:81395 完成计算 招商银行
进程:81395 正在计算 中国石油
进程:81395 完成计算 中国石油
进程:81395 正在计算 中国人寿
进程:81395 完成计算 中国人寿
进程:81395 正在计算 中国石化
进程:81395 完成计算 中国石化
CPU times: user 18.9 ms, sys: 2.09 ms, total: 21 ms
Wall time: 10 s
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>({'name': '工商银行', 'open': 5.149915330212172},
{'name': '建设银行', 'open': 6.419728434504791},
{'name': '贵州茅台', 'open': 1274.8791297340854},
{'name': '中国平安', 'open': 74.00119260584377},
{'name': '农业银行', 'open': 3.4400477232054083},
{'name': '中国银行', 'open': 3.459932392125671},
{'name': '招商银行', 'open': 35.019960079840324},
{'name': '中国石油', 'open': 4.379994081089079},
{'name': '中国人寿', 'open': 28.749874080789763},
{'name': '中国石化', 'open': 4.440019910403186})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="n">pool</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Pool</span><span class="p">()</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">cmpt_open</span><span class="p">,</span> <span class="n">stocks</span><span class="p">)</span>
<span class="n">result</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>进程:47440 正在计算 建设银行
进程:47443 正在计算 农业银行
进程:47447 正在计算 招商银行
进程:47444 正在计算 中国银行
进程:47451 正在计算 中国人寿
进程:47442 正在计算 中国平安
进程:47452 正在计算 中国石化
进程:47439 正在计算 工商银行
进程:47450 正在计算 中国石油
进程:47441 正在计算 贵州茅台
进程:47440 完成计算 建设银行
进程:47443 完成计算 农业银行
进程:47447 完成计算 招商银行
进程:47442 完成计算 中国平安
进程:47439 完成计算 工商银行
进程:47444 完成计算 中国银行
进程:47451 完成计算 中国人寿
CPU times: user 40 ms, sys: 159 ms, total: 199 ms
Wall time: 1.19 s
进程:47450 完成计算 中国石油
进程:47441 完成计算 贵州茅台
进程:47452 完成计算 中国石化
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[{'name': '工商银行', 'open': 5.149915330212172},
{'name': '建设银行', 'open': 6.419728434504791},
{'name': '贵州茅台', 'open': 1274.8791297340854},
{'name': '中国平安', 'open': 74.00119260584377},
{'name': '农业银行', 'open': 3.4400477232054083},
{'name': '中国银行', 'open': 3.459932392125671},
{'name': '招商银行', 'open': 35.019960079840324},
{'name': '中国石油', 'open': 4.379994081089079},
{'name': '中国人寿', 'open': 28.749874080789763},
{'name': '中国石化', 'open': 4.440019910403186}]</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">concurrent.futures</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="k">with</span> <span class="n">concurrent</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">ProcessPoolExecutor</span><span class="p">()</span> <span class="k">as</span> <span class="n">pool</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">cmpt_open</span><span class="p">,</span> <span class="n">stocks</span><span class="p">)</span>
<span class="nb">tuple</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>进程:48625 正在计算 工商银行
进程:48626 正在计算 建设银行
进程:48628 正在计算 中国平安
进程:48627 正在计算 贵州茅台
进程:48631 正在计算 招商银行
进程:48629 正在计算 农业银行
进程:48634 正在计算 中国石化
进程:48630 正在计算 中国银行
进程:48633 正在计算 中国人寿
进程:48632 正在计算 中国石油
进程:48625 完成计算 工商银行
进程:48626 完成计算 建设银行
进程:48628 完成计算 中国平安
进程:48627 完成计算 贵州茅台
进程:48629 完成计算 农业银行
进程:48631 完成计算 招商银行
进程:48632 完成计算 中国石油
进程:48634 完成计算 中国石化
进程:48633 完成计算 中国人寿
进程:48630 完成计算 中国银行
CPU times: user 32.1 ms, sys: 192 ms, total: 224 ms
Wall time: 1.22 s
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>({'name': '工商银行', 'open': 5.149915330212172},
{'name': '建设银行', 'open': 6.419728434504791},
{'name': '贵州茅台', 'open': 1274.8791297340854},
{'name': '中国平安', 'open': 74.00119260584377},
{'name': '农业银行', 'open': 3.4400477232054083},
{'name': '中国银行', 'open': 3.459932392125671},
{'name': '招商银行', 'open': 35.019960079840324},
{'name': '中国石油', 'open': 4.379994081089079},
{'name': '中国人寿', 'open': 28.749874080789763},
{'name': '中国石化', 'open': 4.440019910403186})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="k">with</span> <span class="n">concurrent</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">ThreadPoolExecutor</span><span class="p">()</span> <span class="k">as</span> <span class="n">pool</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pool</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">cmpt_open</span><span class="p">,</span> <span class="n">stocks</span><span class="p">)</span>
<span class="nb">tuple</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>进程:81395 正在计算 工商银行
进程:81395 正在计算 建设银行
进程:81395 正在计算 贵州茅台
进程:81395 正在计算 中国平安
进程:81395 正在计算 农业银行
进程:81395 正在计算 中国银行
进程:81395 正在计算 招商银行
进程:81395 正在计算 中国石油
进程:81395 正在计算 中国人寿进程:81395 正在计算 中国石化
进程:81395 完成计算 工商银行
进程:81395 完成计算 建设银行
进程:81395 完成计算 贵州茅台
进程:81395 完成计算 中国平安
进程:81395 完成计算 农业银行
进程:81395 完成计算 中国银行进程:81395 完成计算 招商银行
进程:81395 完成计算 中国石油
进程:81395 完成计算 中国人寿进程:81395 完成计算 中国石化
CPU times: user 19.2 ms, sys: 8.83 ms, total: 28 ms
Wall time: 1.01 s
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>({'name': '工商银行', 'open': 5.149915330212172},
{'name': '建设银行', 'open': 6.419728434504791},
{'name': '贵州茅台', 'open': 1274.8791297340854},
{'name': '中国平安', 'open': 74.00119260584377},
{'name': '农业银行', 'open': 3.4400477232054083},
{'name': '中国银行', 'open': 3.459932392125671},
{'name': '招商银行', 'open': 35.019960079840324},
{'name': '中国石油', 'open': 4.379994081089079},
{'name': '中国人寿', 'open': 28.749874080789763},
{'name': '中国石化', 'open': 4.440019910403186})</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="cytoolz、fn-与fancy"><a href="https://github.com/pytoolz/cytoolz">cytoolz</a>、<a href="https://github.com/fnpy/fn.py">fn</a> 与<a href="https://github.com/Suor/funcy">fancy</a><a class="anchor-link" href="#cytoolz、fn-与fancy"> </a></h2><p>借助第三方包实现大量高阶函数,可以增强Python函数式编程功能,包括纯函数、curry柯里化(偏函数)、lazy惰性计算、并行化...</p>
<p>下面以cytoolz为例演示双城记词频统计:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">cytoolz</span> <span class="kn">import</span> <span class="nb">map</span><span class="p">,</span> <span class="n">concat</span><span class="p">,</span> <span class="n">frequencies</span> <span class="c1"># cytoolz 的 map 默认惰性计算</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="n">frequencies</span><span class="p">(</span>
<span class="n">concat</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"tale-of-two-cities.txt"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8-sig"</span><span class="p">)))</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>CPU times: user 79.4 ms, sys: 2.53 ms, total: 82 ms
Wall time: 89.1 ms
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>{'T': 54050,
'H': 38974,
'E': 74839,
' ': 126957,
'P': 9960,
'R': 37212,
'O': 46537,
'J': 714,
'C': 13899,
'G': 12547,
'U': 16738,
'N': 42385,
'B': 8422,
'K': 4787,
'F': 13563,
'A': 48167,
'L': 22048,
'W': 14121,
'I': 41016,
'S': 37587,
',': 13274,
'Y': 12185,
'D': 28046,
'\n': 16271,
'M': 15296,
'V': 5204,
'.': 6815,
'-': 2431,
':': 269,
'1': 63,
'9': 18,
'4': 10,
'[': 2,
'#': 1,
'8': 16,
']': 2,
'2': 14,
'0': 20,
'7': 14,
'3': 13,
'*': 90,
'X': 723,
"'": 1269,
'Q': 666,
';': 1108,
'Z': 215,
'(': 151,
')': 151,
'"': 5681,
'!': 955,
'?': 913,
'_': 182,
'É': 2,
'6': 9,
'5': 13,
'/': 24,
'%': 1,
'@': 2,
'$': 2}</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%time</span>
<span class="n">Counter</span><span class="p">(</span>
<span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"tale-of-two-cities.txt"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8-sig"</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>CPU times: user 190 ms, sys: 2.68 ms, total: 193 ms
Wall time: 190 ms
</pre>
</div>
</div>
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>Counter({'T': 54050,
'H': 38974,
'E': 74839,
' ': 126957,
'P': 9960,
'R': 37212,
'O': 46537,
'J': 714,
'C': 13899,
'G': 12547,
'U': 16738,
'N': 42385,
'B': 8422,
'K': 4787,
'F': 13563,
'A': 48167,
'L': 22048,
'W': 14121,
'I': 41016,
'S': 37587,
',': 13274,
'Y': 12185,
'D': 28046,
'\n': 16271,
'M': 15296,
'V': 5204,
'.': 6815,
'-': 2431,
':': 269,
'1': 63,
'9': 18,
'4': 10,
'[': 2,
'#': 1,
'8': 16,
']': 2,
'2': 14,
'0': 20,
'7': 14,
'3': 13,
'*': 90,
'X': 723,
"'": 1269,
'Q': 666,
';': 1108,
'Z': 215,
'(': 151,
')': 151,
'"': 5681,
'!': 955,
'?': 913,
'_': 182,
'É': 2,
'6': 9,
'5': 13,
'/': 24,
'%': 1,
'@': 2,
'$': 2})</pre>
</div>
</div>
</div>
</div>
</div>
</div>Fastpages Notebook 博客示例2020-02-20T00:00:00-06:002020-02-20T00:00:00-06:00https://asyncfor.com/jupyter/2020/02/20/test<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2020-02-20-test.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="简介">简介<a class="anchor-link" href="#简介"> </a></h1><p>以下内容演示<a href="https://github.com/fastai/fastpages">fastpages</a>处理notebook的能力</p>
<p>在你的<code>fastpages</code>代码仓库中,将jupyter notebook保存到<code>_notebooks</code>文件夹并提交到master分支,就可以借助GitHub的actions自己生成Jekyll博客啦!</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Front-matter模板设置">Front matter模板设置<a class="anchor-link" href="#Front-matter模板设置"> </a></h2><p>Jupyter Notebook第一个单元格,或markdown文件的最前面需要配置metadata。示例如下:</p>
<pre><code># "My Title"
> "Awesome summary"
- toc:true- branch: master- badges: true- comments: true
- author: Hamel Husain & Jeremy Howard
- categories: [fastpages, jupyter]</code></pre>
<ul>
<li><code>toc: true</code> 自动生成目录</li>
<li><code>badges: true</code> 自动配置带徽章的GitHub、ninder和Google Colab链接</li>
<li><code>comments: true</code> 支持github issue评论,借助<a href="https://github.com/utterance/utterances">utterances</a>实现</li>
</ul>
<p>如果标题中包含特殊字符(如冒号),可以用双引号括起来。关于Front matter的更多详情参考<a href="https://github.com/fastai/fastpages#front-matter-related-options">front matter section</a>的README文件。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Markdown快捷方式">Markdown快捷方式<a class="anchor-link" href="#Markdown快捷方式"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在单元格上方加<code>#hide</code>,<strong>可以在博客中同时隐藏输入和输出</strong></p>
<p>在单元格上方加<code>#hide_input</code><strong>在博客中仅隐藏输入</strong></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_subarea output_stream output_stdout output_text">
<pre>The comment #hide_input was used to hide the code that produced this.
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在单元格上方加<code>#collapse-hide</code>可以实现默认<strong>隐藏</strong>内容的控件,点击按钮可以显示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<details class="description">
<summary class="btn btn-sm" data-open="Hide Code" data-close="Show Code"></summary>
<p><div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">altair</span> <span class="k">as</span> <span class="nn">alt</span>
</pre></div>
</div>
</div>
</div>
</p>
</details>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在单元格上方加<code>#collapse-show</code>可以实现默认<strong>显示</strong>内容的控件,点击按钮可以隐藏:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<details class="description" open="">
<summary class="btn btn-sm" data-open="Hide Code" data-close="Show Code"></summary>
<p><div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-show</span>
<span class="n">cars</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/cars.json'</span>
<span class="n">movies</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/movies.json'</span>
<span class="n">sp500</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/sp500.csv'</span>
<span class="n">stocks</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/stocks.csv'</span>
<span class="n">flights</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/flights-5k.json'</span>
</pre></div>
</div>
</div>
</div>
</p>
</details>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="与Altair交互">与Altair交互<a class="anchor-link" href="#与Altair交互"> </a></h2><p>Altair支持交互图形,下面示例取自<a href="https://github.com/uwdata/visualization-curriculum">项目</a>,都在<a href="https://github.com/uwdata/visualization-curriculum/blob/master/altair_interaction.ipynb">这个notebook</a>里面。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="示例-1:-DropDown">示例 1: DropDown<a class="anchor-link" href="#示例-1:-DropDown"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># single-value selection over [Major_Genre, MPAA_Rating] pairs</span>
<span class="c1"># use specific hard-wired values as the initial selected values</span>
<span class="n">selection</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">selection_single</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">'Select'</span><span class="p">,</span>
<span class="n">fields</span><span class="o">=</span><span class="p">[</span><span class="s1">'Major_Genre'</span><span class="p">,</span> <span class="s1">'MPAA_Rating'</span><span class="p">],</span>
<span class="n">init</span><span class="o">=</span><span class="p">{</span><span class="s1">'Major_Genre'</span><span class="p">:</span> <span class="s1">'Drama'</span><span class="p">,</span> <span class="s1">'MPAA_Rating'</span><span class="p">:</span> <span class="s1">'R'</span><span class="p">},</span>
<span class="n">bind</span><span class="o">=</span><span class="p">{</span><span class="s1">'Major_Genre'</span><span class="p">:</span> <span class="n">alt</span><span class="o">.</span><span class="n">binding_select</span><span class="p">(</span><span class="n">options</span><span class="o">=</span><span class="n">genres</span><span class="p">),</span> <span class="s1">'MPAA_Rating'</span><span class="p">:</span> <span class="n">alt</span><span class="o">.</span><span class="n">binding_radio</span><span class="p">(</span><span class="n">options</span><span class="o">=</span><span class="n">mpaa</span><span class="p">)}</span>
<span class="p">)</span>
<span class="c1"># scatter plot, modify opacity based on selection</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">movies</span><span class="p">)</span><span class="o">.</span><span class="n">mark_circle</span><span class="p">()</span><span class="o">.</span><span class="n">add_selection</span><span class="p">(</span>
<span class="n">selection</span>
<span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'Rotten_Tomatoes_Rating:Q'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'IMDB_Rating:Q'</span><span class="p">,</span>
<span class="n">tooltip</span><span class="o">=</span><span class="s1">'Title:N'</span><span class="p">,</span>
<span class="n">opacity</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">selection</span><span class="p">,</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mf">0.75</span><span class="p">),</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mf">0.05</span><span class="p">))</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-1a49e83878ce4d678d7b162f3d6b510f"></div>
<script type="text/javascript">
(function(spec, embedOpt){
const outputDiv = document.getElementById("altair-viz-1a49e83878ce4d678d7b162f3d6b510f");
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "data": {"url": "https://vega.github.io/vega-datasets/data/movies.json"}, "mark": "circle", "encoding": {"opacity": {"condition": {"value": 0.75, "selection": "Select"}, "value": 0.05}, "tooltip": {"type": "nominal", "field": "Title"}, "x": {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}, "y": {"type": "quantitative", "field": "IMDB_Rating"}}, "selection": {"Select": {"type": "single", "fields": ["Major_Genre", "MPAA_Rating"], "init": {"Major_Genre": "Drama", "MPAA_Rating": "R"}, "bind": {"Major_Genre": {"input": "select", "options": ["Action", "Adventure", "Black Comedy", "Comedy", "Concert/Performance", "Documentary", "Drama", "Horror", "Musical", "Romantic Comedy", "Thriller/Suspense", "Western"]}, "MPAA_Rating": {"input": "radio", "options": ["G", "PG", "PG-13", "R", "NC-17", "Not Rated"]}}}}, "$schema": "https://vega.github.io/schema/vega-lite/v4.0.2.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="示例-2:-Tooltips">示例 2: Tooltips<a class="anchor-link" href="#示例-2:-Tooltips"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">movies</span><span class="p">)</span><span class="o">.</span><span class="n">mark_circle</span><span class="p">()</span><span class="o">.</span><span class="n">add_selection</span><span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">selection_interval</span><span class="p">(</span><span class="n">bind</span><span class="o">=</span><span class="s1">'scales'</span><span class="p">,</span> <span class="n">encodings</span><span class="o">=</span><span class="p">[</span><span class="s1">'x'</span><span class="p">])</span>
<span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'Rotten_Tomatoes_Rating:Q'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="s1">'IMDB_Rating:Q'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Axis</span><span class="p">(</span><span class="n">minExtent</span><span class="o">=</span><span class="mi">30</span><span class="p">)),</span> <span class="c1"># use min extent to stabilize axis title placement</span>
<span class="n">tooltip</span><span class="o">=</span><span class="p">[</span><span class="s1">'Title:N'</span><span class="p">,</span> <span class="s1">'Release_Date:N'</span><span class="p">,</span> <span class="s1">'IMDB_Rating:Q'</span><span class="p">,</span> <span class="s1">'Rotten_Tomatoes_Rating:Q'</span><span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">properties</span><span class="p">(</span>
<span class="n">width</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">400</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-c022b476f4fb482ca6f609bf6ed082d2"></div>
<script type="text/javascript">
(function(spec, embedOpt){
const outputDiv = document.getElementById("altair-viz-c022b476f4fb482ca6f609bf6ed082d2");
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "data": {"url": "https://vega.github.io/vega-datasets/data/movies.json"}, "mark": "circle", "encoding": {"tooltip": [{"type": "nominal", "field": "Title"}, {"type": "nominal", "field": "Release_Date"}, {"type": "quantitative", "field": "IMDB_Rating"}, {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}], "x": {"type": "quantitative", "field": "Rotten_Tomatoes_Rating"}, "y": {"type": "quantitative", "axis": {"minExtent": 30}, "field": "IMDB_Rating"}}, "height": 400, "selection": {"selector001": {"type": "interval", "bind": "scales", "encodings": ["x"]}}, "width": 600, "$schema": "https://vega.github.io/schema/vega-lite/v4.0.2.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="示例-3:-More-Tooltips">示例 3: More Tooltips<a class="anchor-link" href="#示例-3:-More-Tooltips"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># select a point for which to provide details-on-demand</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">selection_single</span><span class="p">(</span>
<span class="n">encodings</span><span class="o">=</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="c1"># limit selection to x-axis value</span>
<span class="n">on</span><span class="o">=</span><span class="s1">'mouseover'</span><span class="p">,</span> <span class="c1"># select on mouseover events</span>
<span class="n">nearest</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># select data point nearest the cursor</span>
<span class="n">empty</span><span class="o">=</span><span class="s1">'none'</span> <span class="c1"># empty selection includes no data points</span>
<span class="p">)</span>
<span class="c1"># define our base line chart of stock prices</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">()</span><span class="o">.</span><span class="n">mark_line</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="s1">'date:T'</span><span class="p">),</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="s1">'price:Q'</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Scale</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">'log'</span><span class="p">)),</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Color</span><span class="p">(</span><span class="s1">'symbol:N'</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">alt</span><span class="o">.</span><span class="n">layer</span><span class="p">(</span>
<span class="n">base</span><span class="p">,</span> <span class="c1"># base line chart</span>
<span class="c1"># add a rule mark to serve as a guide line</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">()</span><span class="o">.</span><span class="n">mark_rule</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#aaa'</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'date:T'</span>
<span class="p">)</span><span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">label</span><span class="p">),</span>
<span class="c1"># add circle marks for selected time points, hide unselected points</span>
<span class="n">base</span><span class="o">.</span><span class="n">mark_circle</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">opacity</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="p">)</span><span class="o">.</span><span class="n">add_selection</span><span class="p">(</span><span class="n">label</span><span class="p">),</span>
<span class="c1"># add white stroked text to provide a legible background for labels</span>
<span class="n">base</span><span class="o">.</span><span class="n">mark_text</span><span class="p">(</span><span class="n">align</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">dy</span><span class="o">=-</span><span class="mi">5</span><span class="p">,</span> <span class="n">stroke</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">strokeWidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">text</span><span class="o">=</span><span class="s1">'price:Q'</span>
<span class="p">)</span><span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">label</span><span class="p">),</span>
<span class="c1"># add text labels for stock prices</span>
<span class="n">base</span><span class="o">.</span><span class="n">mark_text</span><span class="p">(</span><span class="n">align</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">dy</span><span class="o">=-</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">text</span><span class="o">=</span><span class="s1">'price:Q'</span>
<span class="p">)</span><span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">label</span><span class="p">),</span>
<span class="n">data</span><span class="o">=</span><span class="n">stocks</span>
<span class="p">)</span><span class="o">.</span><span class="n">properties</span><span class="p">(</span>
<span class="n">width</span><span class="o">=</span><span class="mi">700</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">400</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div id="altair-viz-9283d3681fd24aafa3d1e2f9ad193ecf"></div>
<script type="text/javascript">
(function(spec, embedOpt){
const outputDiv = document.getElementById("altair-viz-9283d3681fd24aafa3d1e2f9ad193ecf");
const paths = {
"vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
"vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
"vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext",
"vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
};
function loadScript(lib) {
return new Promise(function(resolve, reject) {
var s = document.createElement('script');
s.src = paths[lib];
s.async = true;
s.onload = () => resolve(paths[lib]);
s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
document.getElementsByTagName("head")[0].appendChild(s);
});
}
function showError(err) {
outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
throw err;
}
function displayChart(vegaEmbed) {
vegaEmbed(outputDiv, spec, embedOpt)
.catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}
if(typeof define === "function" && define.amd) {
requirejs.config({paths});
require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else if (typeof vegaEmbed === "function") {
displayChart(vegaEmbed);
} else {
loadScript("vega")
.then(() => loadScript("vega-lite"))
.then(() => loadScript("vega-embed"))
.catch(showError)
.then(() => displayChart(vegaEmbed));
}
})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "layer": [{"mark": "line", "encoding": {"color": {"type": "nominal", "field": "symbol"}, "x": {"type": "temporal", "field": "date"}, "y": {"type": "quantitative", "field": "price", "scale": {"type": "log"}}}}, {"mark": {"type": "rule", "color": "#aaa"}, "encoding": {"x": {"type": "temporal", "field": "date"}}, "transform": [{"filter": {"selection": "selector002"}}]}, {"mark": "circle", "encoding": {"color": {"type": "nominal", "field": "symbol"}, "opacity": {"condition": {"value": 1, "selection": "selector002"}, "value": 0}, "x": {"type": "temporal", "field": "date"}, "y": {"type": "quantitative", "field": "price", "scale": {"type": "log"}}}, "selection": {"selector002": {"type": "single", "encodings": ["x"], "on": "mouseover", "nearest": true, "empty": "none"}}}, {"mark": {"type": "text", "align": "left", "dx": 5, "dy": -5, "stroke": "white", "strokeWidth": 2}, "encoding": {"color": {"type": "nominal", "field": "symbol"}, "text": {"type": "quantitative", "field": "price"}, "x": {"type": "temporal", "field": "date"}, "y": {"type": "quantitative", "field": "price", "scale": {"type": "log"}}}, "transform": [{"filter": {"selection": "selector002"}}]}, {"mark": {"type": "text", "align": "left", "dx": 5, "dy": -5}, "encoding": {"color": {"type": "nominal", "field": "symbol"}, "text": {"type": "quantitative", "field": "price"}, "x": {"type": "temporal", "field": "date"}, "y": {"type": "quantitative", "field": "price", "scale": {"type": "log"}}}, "transform": [{"filter": {"selection": "selector002"}}]}], "data": {"url": "https://vega.github.io/vega-datasets/data/stocks.csv"}, "height": 400, "width": 700, "$schema": "https://vega.github.io/schema/vega-lite/v4.0.2.json"}, {"mode": "vega-lite"});
</script>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="表格">表格<a class="anchor-link" href="#表格"> </a></h2><p>notebook代码生成表格可以直接在博客中显示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">movies</span> <span class="o">=</span> <span class="s1">'https://vega.github.io/vega-datasets/data/movies.json'</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">movies</span><span class="p">)</span>
<span class="c1"># display table with pandas</span>
<span class="n">df</span><span class="p">[[</span><span class="s1">'Title'</span><span class="p">,</span> <span class="s1">'Worldwide_Gross'</span><span class="p">,</span>
<span class="s1">'Production_Budget'</span><span class="p">,</span> <span class="s1">'Distributor'</span><span class="p">,</span> <span class="s1">'MPAA_Rating'</span><span class="p">,</span> <span class="s1">'IMDB_Rating'</span><span class="p">,</span> <span class="s1">'Rotten_Tomatoes_Rating'</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Title</th>
<th>Worldwide_Gross</th>
<th>Production_Budget</th>
<th>Distributor</th>
<th>MPAA_Rating</th>
<th>IMDB_Rating</th>
<th>Rotten_Tomatoes_Rating</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>The Land Girls</td>
<td>146083.0</td>
<td>8000000.0</td>
<td>Gramercy</td>
<td>R</td>
<td>6.1</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>First Love, Last Rites</td>
<td>10876.0</td>
<td>300000.0</td>
<td>Strand</td>
<td>R</td>
<td>6.9</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>I Married a Strange Person</td>
<td>203134.0</td>
<td>250000.0</td>
<td>Lionsgate</td>
<td>None</td>
<td>6.8</td>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>Let's Talk About Sex</td>
<td>373615.0</td>
<td>300000.0</td>
<td>Fine Line</td>
<td>None</td>
<td>NaN</td>
<td>13.0</td>
</tr>
<tr>
<th>4</th>
<td>Slam</td>
<td>1087521.0</td>
<td>1000000.0</td>
<td>Trimark</td>
<td>R</td>
<td>3.4</td>
<td>62.0</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="图像">图像<a class="anchor-link" href="#图像"> </a></h2><h3 id="本地图像">本地图像<a class="anchor-link" href="#本地图像"> </a></h3><p>可以引用本地图片,它们会被自动复制并在博客中渲染。可以用markdown实现:</p>
<p><code>![](my_icons/fastai_logo.png)</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/my_icons/fastai_logo.png" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="远程图片">远程图片<a class="anchor-link" href="#远程图片"> </a></h3><p>远程图片语法如下:</p>
<p><code>![](https://image.flaticon.com/icons/svg/36/36686.svg)</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="https://image.flaticon.com/icons/svg/36/36686.svg" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Gif动画">Gif动画<a class="anchor-link" href="#Gif动画"> </a></h3><p>Gif动画,当日没问题!</p>
<p><code>![](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif)</code></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif" alt="" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="图片说明(Caption)">图片说明(Caption)<a class="anchor-link" href="#图片说明(Caption)"> </a></h3><p>图片说明可以用下面markdown语法实现:</p>
<pre><code>![](https://www.fast.ai/images/fastai_paper/show_batch.png "Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/")</code></pre>
<p><img src="https://www.fast.ai/images/fastai_paper/show_batch.png" alt="" title="Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="其他元素">其他元素<a class="anchor-link" href="#其他元素"> </a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="GitHub-Flavored-Emojis">GitHub Flavored Emojis<a class="anchor-link" href="#GitHub-Flavored-Emojis"> </a></h2><p>输入 <code>I give this post two :+1:!</code> 渲染颜文字:</p>
<p>I give this post two :+1:!</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Tweet卡片">Tweet卡片<a class="anchor-link" href="#Tweet卡片"> </a></h2><p>输入 <code>> twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20</code> 渲染推文:
<center>
<div class="jekyll-twitter-plugin"><blockquote class="twitter-tweet"><p lang="en" dir="ltr">Altair 4.0 is released! <a href="https://t.co/PCyrIOTcvv">https://t.co/PCyrIOTcvv</a><br />Try it with:<br /><br /> pip install -U altair<br /><br />The full list of changes is at <a href="https://t.co/roXmzcsT58">https://t.co/roXmzcsT58</a> ...read on for some highlights. <a href="https://t.co/vWJ0ZveKbZ">pic.twitter.com/vWJ0ZveKbZ</a></p>— Jake VanderPlas (@jakevdp) <a href="https://twitter.com/jakevdp/status/1204765621767901185?ref_src=twsrc%5Etfw">December 11, 2019</a></blockquote>
<script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
</div>
</center>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Youtube视频">Youtube视频<a class="anchor-link" href="#Youtube视频"> </a></h2><p>输入 <code>> youtube: https://youtu.be/XfoYk_Z5AkI</code> 渲染视频:
<center>
<iframe width="560" height="315" src="https://www.youtube.com/embed/XfoYk_Z5AkI" frameborder="0" allowfullscreen=""></iframe>
</center>
</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="提示文字(Boxes-/-Callouts-)">提示文字(Boxes / Callouts )<a class="anchor-link" href="#提示文字(Boxes-/-Callouts-)"> </a></h2><p>输入 <code>> Warning: There will be no second warning!</code> 效果:
<div class="flash flash-error">
<svg class="octicon octicon-alert octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 000 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 00.01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"></path></svg>
<strong>Warning: </strong>There will be no second warning!
</div></p>
<p>输入 <code>> Important: Pay attention! It's important.</code> 效果:
<div class="flash flash-warn">
<svg class="octicon octicon-zap" viewBox="0 0 10 16" version="1.1" width="10" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M10 7H6l3-7-9 9h4l-3 7 9-9z"></path></svg>
<strong>Important: </strong>Pay attention! It’s important.
</div></p>
<p>输入 <code>> Tip: This is my tip.</code> 效果:
<div class="flash flash-success">
<svg class="octicon octicon-checklist octicon octicon-checklist" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M16 8.5l-6 6-3-3L8.5 10l1.5 1.5L14.5 7 16 8.5zM5.7 12.2l.8.8H2c-.55 0-1-.45-1-1V3c0-.55.45-1 1-1h7c.55 0 1 .45 1 1v6.5l-.8-.8c-.39-.39-1.03-.39-1.42 0L5.7 10.8a.996.996 0 000 1.41v-.01zM4 4h5V3H4v1zm0 2h5V5H4v1zm0 2h3V7H4v1zM3 9H2v1h1V9zm0-2H2v1h1V7zm0-2H2v1h1V5zm0-2H2v1h1V3z"></path></svg>
<strong>Tip: </strong>This is my tip.
</div></p>
<p>输入 <code>> Note: Take note of this.</code> 效果:
<div class="flash">
<svg class="octicon octicon-info" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 01-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 01-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg>
<strong>Note: </strong>Take note of this.
</div></p>
<p>输入 <code>> Note: A doc link to [an example website: fast.ai](https://www.fast.ai/) should also work fine.</code> 效果:
<div class="flash">
<svg class="octicon octicon-info octicon octicon-info" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 01-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 01-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg>
<strong>Note: </strong>A doc link to <a href="https://www.fast.ai/">an example website: fast.ai</a> should also work fine.
</div></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="尾注">尾注<a class="anchor-link" href="#尾注"> </a></h2><p>可以在notebook里加尾注,但不是用markdown语法实现,<a href="https://github.com/fastai/fastpages/blob/master/_fastpages_docs/NOTEBOOK_FOOTNOTES.md">具体语法请看说明</a>。</p>
<pre><code>For example, here is a footnote {% fn 1 %}.
And another {% fn 2 %}
{{ 'This is the footnote.' | fndetail: 1 }}
{{ 'This is the other footnote. You can even have a [link](www.github.com)!' | fndetail: 2 }}</code></pre>
<p>For example, here is a footnote <sup id="fnref-1" class="footnote-ref"><a href="#fn-1">1</a></sup>.</p>
<p>And another <sup id="fnref-2" class="footnote-ref"><a href="#fn-2">2</a></sup></p>
<p><div class="footnotes"><p id="fn-1">1. This is the footnote.<a href="#fnref-1" class="footnote footnotes">↩</a></p></div>
<div class="footnotes"><p id="fn-2">2. This is the other footnote. You can even have a <a href="www.github.com">link</a>!<a href="#fnref-2" class="footnote footnotes">↩</a></p></div></p>
</div>
</div>
</div>
</div>Markdown文章示例2020-01-14T00:00:00-06:002020-01-14T00:00:00-06:00https://asyncfor.com/markdown/2020/01/14/test-markdown-post<h1 id="markdown文章示例">Markdown文章示例</h1>
<h2 id="基本设置">基本设置</h2>
<p>Jekyll要求文件名称进行如下配置:</p>
<p><code class="highlighter-rouge">YEAR-MONTH-DAY-filename.md</code></p>
<p>其中<code class="highlighter-rouge">YEAR</code>是4位年份(2020),<code class="highlighter-rouge">MONTH</code>(06) 和 <code class="highlighter-rouge">DAY</code>(04) 都是两位数字,<code class="highlighter-rouge">filename</code>文件名随便取,方便查询即可。<code class="highlighter-rouge">.md</code> 是markdown文件后缀。</p>
<p>文件正文第一行标题需要用<code class="highlighter-rouge">#</code>开始,后面跟空格,在增加标题内容。这样就实现了markdown的<em>一级标题</em>。然后也可以用多个<code class="highlighter-rouge">#</code>创建2、3级等标题,例如上面的二级标题<code class="highlighter-rouge">## 基本设置</code>。</p>
<h2 id="基本样式">基本样式</h2>
<p>可以实现 <em>斜体</em>, <strong>加粗</strong>, <code class="highlighter-rouge">代码字体</code>,还有<a href="https://www.markdownguide.org/cheat-sheet/">链接</a>,以及尾注<sup id="fnref:1"><a href="#fn:1" class="footnote">1</a></sup>和水平线:</p>
<hr />
<h2 id="列表">列表</h2>
<p>一个列表</p>
<ul>
<li>item 1</li>
<li>item 2</li>
</ul>
<p>带序号列表</p>
<ol>
<li>item 1</li>
<li>item 2</li>
</ol>
<h2 id="提示文字boxes-and-stuff">提示文字(Boxes and stuff)</h2>
<blockquote>
<p>这是提示文字</p>
</blockquote>
<div class="Toast Toast--warning googoo">
<span class="Toast-icon"><svg class="octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 000 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 00.01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"></path></svg></span>
<span class="Toast-content">You can include alert boxes</span>
</div>
<p>…以及…</p>
<div class="Toast">
<span class="Toast-icon"><svg class="octicon octicon-info" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 01-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 01-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg></span>
<span class="Toast-content">You can include info boxes</span>
</div>
<h2 id="图像">图像</h2>
<p><img src="/images/logo.png" alt="" title="fast.ai's logo" /></p>
<h2 id="代码">代码</h2>
<p>可以配置文字和代码格式</p>
<p>一般文字:</p>
<div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code># Do a thing
do_thing()
</code></pre></div></div>
<p>Python代码和输出:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1"># Prints '2'
</span><span class="k">print</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
</code></pre></div></div>
<div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code>2
</code></pre></div></div>
<p>shell命令格式:</p>
<div class="language-shell highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nb">echo</span> <span class="s2">"hello world"</span>
./some_script.sh <span class="nt">--option</span> <span class="s2">"value"</span>
wget https://example.com/cat_photo1.png
</code></pre></div></div>
<p>YAML格式:</p>
<div class="language-yaml highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="na">key</span><span class="pi">:</span> <span class="s">value</span>
<span class="pi">-</span> <span class="na">another_key</span><span class="pi">:</span> <span class="s2">"</span><span class="s">another</span><span class="nv"> </span><span class="s">value"</span>
</code></pre></div></div>
<h2 id="表格">表格</h2>
<table>
<thead>
<tr>
<th>Column 1</th>
<th>Column 2</th>
</tr>
</thead>
<tbody>
<tr>
<td>A thing</td>
<td>Another thing</td>
</tr>
</tbody>
</table>
<h2 id="tweet卡片">Tweet卡片</h2>
<div class="jekyll-twitter-plugin"><blockquote class="twitter-tweet"><p lang="en" dir="ltr">Altair 4.0 is released! <a href="https://t.co/PCyrIOTcvv">https://t.co/PCyrIOTcvv</a><br />Try it with:<br /><br /> pip install -U altair<br /><br />The full list of changes is at <a href="https://t.co/roXmzcsT58">https://t.co/roXmzcsT58</a> ...read on for some highlights. <a href="https://t.co/vWJ0ZveKbZ">pic.twitter.com/vWJ0ZveKbZ</a></p>— Jake VanderPlas (@jakevdp) <a href="https://twitter.com/jakevdp/status/1204765621767901185?ref_src=twsrc%5Etfw">December 11, 2019</a></blockquote>
<script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
</div>
<h2 id="尾注">尾注</h2>
<div class="footnotes">
<ol>
<li id="fn:1">
<p>This is the footnote. <a href="#fnref:1" class="reversefootnote">↩</a></p>
</li>
</ol>
</div>Markdown文章示例Kivy指南-9-射击app2019-10-01T00:00:00-05:002019-10-01T00:00:00-05:00https://asyncfor.com/jupyter/kivy/android/ios/2019/10/01/kivy-ch9-shmup-app<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2019-10-01-kivy-ch9-shmup-app.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>前面提到过,在这一章我们来做射击(shoot-em-up,简写shmup)app,一个快节奏的射击游戏,比魂斗罗简单许多。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><img src="/images/copied_from_nb/kbpic/9.1.shmup.png" alt="shmup" /></p>
<p>做一个在屏幕上同时移动不同内容的游戏,需要大量的渲染来实现,在移动端(或多平台支持)也是如此。这一章我们就来做这些事情,上一章的知识和源代码已经带我们入了门。</p>
<p>教学大纲如下:</p>
<ul>
<li>用Kivy的纹理图集(Texture atlases)完成本来需要用底层代码实现的纹理坐标值的设置工作</li>
<li>继续用GLSL开发一个质点原型,然后用这个原型做不同的游戏角色</li>
<li>实现二维射击游戏的——一个控件,鼠标和触摸屏,基本冲突发现子弹</li>
</ul>
<p>后面会涉及到大量细节,如果看不明白就运行一下文末的源代码。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="项目的限制">项目的限制<a class="anchor-link" href="#项目的限制"> </a></h2><p>我们做的app比较简单,功能有限,至少有以下限制:</p>
<ul>
<li>为了简化,忽略了奖惩机制,2048里面也是这样</li>
<li>这个游戏只有一个敌人角色,简单模式</li>
<li>许多优化被忽略了,可以少写一些代码</li>
</ul>
<p>如果感兴趣可以自己做。下面我们来看一下Kivy的纹理处理相关内容,后面会用到。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="纹理图集简介">纹理图集简介<a class="anchor-link" href="#纹理图集简介"> </a></h2><p>纹理图集(也叫sprite sheets)是一种应用开发中把图象组合成更大纹理的方法。与只是把一堆单个图象载入应用相比,这么做有些好处:</p>
<ul>
<li>应用打开更快,读一个大文件比读许多小文件要快。如果你有几百个这样的图片,用这种方法性能提升会很明显——网页上更是如此:图片太多会严重占用HTTP请求资源,在移动设备上这点更加明显</li>
<li>一次性渲染也会很更快。用纹理映射可以只改变需要变化的纹理坐标,而不需要引起其他内容的变化</li>
<li>当有一个大的模型时,像GLSL类的渲染,用纹理图集方法更适合。另外,纹理的坐标值更容易获取,也不需要二次绑定纹理</li>
</ul>
<blockquote><p>在HTML和CSS里面常用类似的方法,叫CSS图片合并(CSS sprites)。原理是一样的。网页app通常是获取网络资源,如果大量图片存在会占用HTTP请求数,用CSS图片合并可以很好的降低HTTP请求占用。</p>
</blockquote>
<p>这一章,我们要介绍以下内容:</p>
<ul>
<li>用Kivy的CLI工具创建纹理映射</li>
<li>文件格式化和<code>.atlas</code>文件结构</li>
<li>Kivy应用纹理图集的用法</li>
</ul>
<p>如果你已经掌握了相关内容,可以直接跳到<em>GLSL使用纹理图集</em>一节。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="创建一个图集">创建一个图集<a class="anchor-link" href="#创建一个图集"> </a></h3><p>和网页开发不同,那里没有标准工具处理这个任务,Kivy框架用一个命令行工具处理图集映射。</p>
<div class="highlight"><pre><span></span>python –m kivy.atlas <atlas_name> <texture_size> <images…>
</pre></div>
<p>在Mac系统上,把<code>python</code>替换成<code>kivy</code>,因为安装的时候<code>Kivy.app</code>会调用Python解释器。</p>
<p>这样会创建至少两个文件,由所有的图像是否满足一个设定大小的纹理来决定。本章假设<code>texture_size</code>的值足够包含所有图像。</p>
<p>所有输出文件都是<code>atlas_name</code>开头的参数:</p>
<ul>
<li>图集的索引称作<code><atlas_name>.atlas</code></li>
<li>纹理有一个后缀<code><atlas_name>-0.png</code>(这个文件总是存在的),<code><atlas_name>-1.png</code>等等</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="图集结构">图集结构<a class="anchor-link" href="#图集结构"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>.atlas</code>是JSON格式的文件,用来描述纹理映射的位置。</p>
<div class="highlight"><pre><span></span><span class="p">{</span>
<span class="nt">"game-0.png"</span><span class="p">:</span> <span class="p">{</span>
<span class="nt">"player"</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">170</span><span class="p">,</span> <span class="mi">78</span><span class="p">,</span> <span class="mi">84</span><span class="p">],</span>
<span class="nt">"bullet"</span><span class="p">:</span> <span class="p">[</span><span class="mi">82</span><span class="p">,</span> <span class="mi">184</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
<p>纹理的名称就是其源文件的文件名,没有后缀,<code>foo.png</code>就是<code>foo</code>。后面的数值对应的是<code>[x, y, width, height]</code>,所有值都是像素。</p>
<p>组合纹理就是把一堆图片合并起来获得想要的内容,如下图所示。通常,为了利用空间会紧密排在一起。</p>
<blockquote><p>创建图集的时候,Kivy谨慎会处理每个图集的边框,同时考虑图片可能因渲染后效果引起尺寸改变的情况。这就是为什么你需要为图片组合边距留出充分的像素。这样做效果并不可见,但是很有必要。</p>
</blockquote>
<p><img src="/images/copied_from_nb/kbpic/9.2.textureatlas.png" alt="textureatlas" /></p>
<p>在Kivy代码里面用图集的方法和<code>http</code>方式类似,<code>atlas://</code>后面跟图集的路径。如下所示:</p>
<div class="highlight"><pre><span></span><span class="nt">Image</span><span class="p">:</span>
<span class="nt">source</span><span class="p">:</span> <span class="s">'flags/Israel.png'</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="nt">Image</span><span class="p">:</span>
<span class="nt">source</span><span class="p">:</span> <span class="s">'atlas://flags/Israel'</span>
</pre></div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Kivy使用图集的简易方法">Kivy使用图集的简易方法<a class="anchor-link" href="#Kivy使用图集的简易方法"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>要演示上面的方法,我们用前面用过的图标<code>icon_clock.png</code>和<code>icon_paint.png</code>来试试:</p>
<p><img src="/images/copied_from_nb/kbpic/9.3.kivyatlas.png" alt="kivyatlas" /></p>
<p>要创建图集,我们用下面的命令:</p>
<div class="highlight"><pre><span></span>kivy -m kivy.atlas icons <span class="m">512</span> icon_clock.png icon_paint.png
</pre></div>
<p>如果不是Mac系统,用<code>python</code>命令。运行之后会出现如下提示:</p>
<pre><code>[INFO] Kivy v1.9.1
[INFO] [Atlas] create an 512x512 rgba image
('Atlas created at', 'icons.atlas')
1 image have been created</code></pre>
<p>之后就会出现两个文件<code>icons.atlas</code>和<code>icons-0.png</code>。</p>
<blockquote><p>现在可以删除源图片文件。不过最好还是保留,有可能后面更新图集的时候还会用到。</p>
</blockquote>
<p>图集准备好以后,我们来做一个简单的app。<code>basic.py</code>文件代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.app</span> <span class="kn">import</span> <span class="n">App</span>
<span class="k">class</span> <span class="nc">BasicApp</span><span class="p">(</span><span class="n">App</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">BasicApp</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在<code>basic.kv</code>文件里面加载布局很简单:</p>
<div class="highlight"><pre><span></span><span class="nt">BoxLayout</span><span class="p">:</span>
<span class="nt">orientation</span><span class="p">:</span> <span class="s">'horizontal'</span>
<span class="nt">Image</span><span class="p">:</span>
<span class="nt">source</span><span class="p">:</span> <span class="s">'atlas://icons/icon_clock'</span>
<span class="nt">Image</span><span class="p">:</span>
<span class="nt">source</span><span class="p">:</span> <span class="s">'atlas://icons/icon_paint'</span>
</pre></div>
<p>运行代码,效果如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/9.4.kivyatlaseasy.png" alt="kivyatlaseasy" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="在GLSL代码使用图集">在GLSL代码使用图集<a class="anchor-link" href="#在GLSL代码使用图集"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Kivy对图集的支持非常简单,但是GLSL应用里面没这么容易。好在<code>.atlas</code>是JSON格式,所以我们可以用Python的<code>json</code>模块来处理。然后,我们可以将像素坐标值转换成OpenGL的UV坐标值。</p>
<p>由于我们知道每个纹理的绝对尺寸,我们可以计算出每个图片组合的顶点与中心的相对位置。这样就可以实现对图集按照其原始形式进行渲染,保持等比例变化。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="UV映射的数据结构">UV映射的数据结构<a class="anchor-link" href="#UV映射的数据结构"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>每个图集都有很多数据,为了方便管理数据,我们需要一个数据结构:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="n">UVMapping</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">"UVMapping"</span><span class="p">,</span> <span class="s2">"u0 v0 u1 v1 su sv"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这个数据类型和C语言的结构体类似,和下面的代码差不多:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">UVMapping</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u0</span><span class="p">,</span> <span class="n">v0</span><span class="p">,</span> <span class="n">u1</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">su</span><span class="p">,</span> <span class="n">sv</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">u0</span> <span class="o">=</span> <span class="n">u0</span> <span class="c1"># top left corner</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v0</span> <span class="o">=</span> <span class="n">v0</span> <span class="c1"># ---</span>
<span class="bp">self</span><span class="o">.</span><span class="n">u1</span> <span class="o">=</span> <span class="n">u1</span> <span class="c1"># bottom right corner</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v1</span> <span class="o">=</span> <span class="n">v1</span> <span class="c1"># ---</span>
<span class="bp">self</span><span class="o">.</span><span class="n">su</span> <span class="o">=</span> <span class="n">su</span> <span class="c1"># equals to 0.5 * width</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sv</span> <span class="o">=</span> <span class="n">sv</span> <span class="c1"># equals to 0.5 * height</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>注意,这些代码只是演示命名数组的原理,并不是完全相同。每个属性的定义如下:</p>
<table>
<thead><tr>
<th style="text-align:center">属性</th>
<th style="text-align:center">定义</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><code>u0, v0</code></td>
<td style="text-align:center">图集左上角的UV坐标</td>
</tr>
<tr>
<td style="text-align:center"><code>u1, v1</code></td>
<td style="text-align:center">图集右下角的UV坐标</td>
</tr>
<tr>
<td style="text-align:center"><code>su</code></td>
<td style="text-align:center">图集宽度一半,在建立顶点数组的时候用</td>
</tr>
<tr>
<td style="text-align:center"><code>sv</code></td>
<td style="text-align:center">图集高度一半,在建立顶点数组的时候用</td>
</tr>
</tbody>
</table>
<p>这样做让代码可读性更好,原来的<code>tup[3]</code>就可以用<code>tup.v1</code>表示。同时,<code>UVMapping</code>是元组类型,一种不可变的、内存结构合理的数据结构,可以通过索引连接所有属性。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="图集加载器">图集加载器<a class="anchor-link" href="#图集加载器"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>现在,让我们写一个函数来描述图集加载的过程,包括处理JSON,确定坐标值等等。这个函数在程序的最后使用:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">json</span>
<span class="kn">from</span> <span class="nn">kivy.core.image</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="k">def</span> <span class="nf">load_atlas</span><span class="p">(</span><span class="n">atlas_name</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">atlas_name</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">atlas</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">"utf-8"</span><span class="p">))</span>
<span class="n">tex_name</span><span class="p">,</span> <span class="n">mapping</span> <span class="o">=</span> <span class="n">atlas</span><span class="o">.</span><span class="n">popitem</span><span class="p">()</span>
<span class="n">tex</span> <span class="o">=</span> <span class="n">Image</span><span class="p">(</span><span class="n">tex_name</span><span class="p">)</span><span class="o">.</span><span class="n">texture</span>
<span class="n">tex_width</span><span class="p">,</span> <span class="n">tex_height</span> <span class="o">=</span> <span class="n">tex</span><span class="o">.</span><span class="n">size</span>
<span class="n">uvmap</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">val</span>
<span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">x0</span> <span class="o">+</span> <span class="n">w</span><span class="p">,</span> <span class="n">y0</span> <span class="o">+</span> <span class="n">h</span>
<span class="n">uvmap</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">UVMapping</span><span class="p">(</span>
<span class="n">x0</span> <span class="o">/</span> <span class="n">tex_width</span><span class="p">,</span>
<span class="mi">1</span> <span class="o">-</span> <span class="n">y1</span> <span class="o">/</span> <span class="n">tex_height</span><span class="p">,</span>
<span class="n">x1</span> <span class="o">/</span> <span class="n">tex_width</span><span class="p">,</span>
<span class="mi">1</span> <span class="o">-</span> <span class="n">y0</span> <span class="o">/</span> <span class="n">tex_height</span><span class="p">,</span>
<span class="mf">0.5</span> <span class="o">*</span> <span class="n">w</span><span class="p">,</span>
<span class="mf">0.5</span> <span class="o">*</span> <span class="n">h</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">tex</span><span class="p">,</span> <span class="n">uvmap</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>记住我们现在处理的是最简单的情况:一个图集由一个纹理构成。这也可能是最有效的配置方式,所以这个限制应该不会影响我们的代码,尤其是因为图集的生成完全在我们控制之下。</p>
</blockquote>
<p>因为坐标值是通过Kivy的坐标系统实现的,所有我们需要把纵坐标调整一下,用OpenGL的左上角为原点的坐标系统。否则,图集就会颠倒(不过,在我们的小游戏里面这不是什么大问题。这种bug可能要在代码里长期存在,虽然没被注意到,也没什么大碍)。</p>
<p><code>load_atlas('icons.atlas')</code>函数返回的是<code>icons-0.png</code>加载的纹理,和图集里每个纹理的UV描述,类似下面的结果:</p>
<div class="highlight"><pre><span></span>>>> load_atlas<span class="o">(</span><span class="s1">'icons.atlas'</span><span class="o">)</span>
<span class="o">(</span><Texture <span class="nv">size</span><span class="o">=(</span><span class="m">512</span>, <span class="m">512</span><span class="o">)</span>...>,
<span class="o">{</span><span class="s1">'icon_paint'</span>:UVMapping<span class="o">(</span><span class="nv">u0</span><span class="o">=</span><span class="m">0</span>.2578125, <span class="nv">v0</span><span class="o">=</span><span class="m">0</span>.00390625, <span class="nv">u1</span><span class="o">=</span><span class="m">0</span>.5078125, <span class="nv">v1</span><span class="o">=</span><span class="m">0</span>.25390625,
<span class="nv">su</span><span class="o">=</span><span class="m">64</span>.0, <span class="nv">sv</span><span class="o">=</span><span class="m">64</span>.0<span class="o">)</span>,
<span class="s1">'icon_clock'</span>: UVMapping<span class="o">(</span>...<span class="o">)})</span>
</pre></div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>有了这个数据格式,我们就可以从纹理中挑出每个合并图形然后渲染到屏幕上,下面就来实现。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="从图集中渲染合并图形">从图集中渲染合并图形<a class="anchor-link" href="#从图集中渲染合并图形"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>把上面的内容放到一起,我们用类似前面的GLSL纹理映射例子来实现一个新版本。</p>
<p>这里的<code>tex_atlas.py</code>文件与上一章的内容类似。通过<code>load_atlas()</code>函数来生成订单数组:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.graphics</span> <span class="kn">import</span> <span class="n">Mesh</span>
<span class="kn">from</span> <span class="nn">kivy.graphics.instructions</span> <span class="kn">import</span> <span class="n">RenderContext</span>
<span class="kn">from</span> <span class="nn">kivy.uix.widget</span> <span class="kn">import</span> <span class="n">Widget</span>
<span class="c1"># ......</span>
<span class="k">class</span> <span class="nc">GlslDemo</span><span class="p">(</span><span class="n">Widget</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">Widget</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span> <span class="o">=</span> <span class="n">RenderContext</span><span class="p">(</span><span class="n">use_parent_projection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">shader</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="s2">"tex_atlas.glsl"</span>
<span class="n">fmt</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vCenter"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vTexCoords0"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">texture</span><span class="p">,</span> <span class="n">uvmap</span> <span class="o">=</span> <span class="n">load_atlas</span><span class="p">(</span><span class="s2">"icons.atlas"</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">uvmap</span><span class="p">[</span><span class="s2">"icon_clock"</span><span class="p">]</span>
<span class="n">vertices</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">128</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="o">-</span><span class="n">a</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">a</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">a</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="mi">128</span><span class="p">,</span>
<span class="o">-</span><span class="n">a</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">a</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">uvmap</span><span class="p">[</span><span class="s2">"icon_paint"</span><span class="p">]</span>
<span class="n">vertices</span> <span class="o">+=</span> <span class="p">(</span>
<span class="mi">256</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="o">-</span><span class="n">b</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">b</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">b</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="mi">256</span><span class="p">,</span>
<span class="o">-</span><span class="n">b</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">b</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">indices</span> <span class="o">+=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="p">:</span>
<span class="n">Mesh</span><span class="p">(</span>
<span class="n">fmt</span><span class="o">=</span><span class="n">fmt</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"triangles"</span><span class="p">,</span>
<span class="n">vertices</span><span class="o">=</span><span class="n">vertices</span><span class="p">,</span>
<span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span>
<span class="n">texture</span><span class="o">=</span><span class="n">texture</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这点代码除了通常的GLSL初始化过程,就是把<code>load_atlas()</code>结果复制到<code>vertices</code>数组。我们选了两个不同的记录:<code>icon_clock</code>(用变量<code>a</code>表示)和<code>icon_paint</code>(用变量<code>b</code>表示),然后把它们放到顶点数组里。</p>
<p>顶点数据格式包含以下内容:</p>
<ul>
<li><strong>vCenter</strong>:这是合并图片在屏幕上的位置,应该和指定合并图片的所有顶点有相同的值</li>
<li><strong>vPosition</strong>:顶点与合并图片中心的相对位置,与<code>vCenter</code>无关</li>
<li><strong>vTexCoords0</strong>:每个顶点的UV坐标值,决定纹理要渲染的部分</li>
</ul>
<p>只有合并图片的位置(数组的前两个数值)不能从UV映射关系中找到,其他数值都可以从<code>load_atlas()</code>获得。</p>
<p><code>tex_atlas.glsl</code>相关文件着色器代码如下:</p>
<div class="highlight"><pre><span></span><span class="o">---</span><span class="n">vertex</span>
<span class="err">$</span><span class="n">HEADER</span><span class="err">$</span>
<span class="n">attribute</span> <span class="n">vec2</span> <span class="n">vCenter</span><span class="p">;</span>
<span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">tex_coord0</span> <span class="o">=</span> <span class="n">vTexCoords0</span><span class="p">;</span>
<span class="n">mat4</span> <span class="n">move_mat</span> <span class="o">=</span> <span class="n">mat4</span>
<span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">vCenter</span><span class="p">.</span><span class="n">x</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">vCenter</span><span class="p">.</span><span class="n">y</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">vec4</span> <span class="n">pos</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vPosition</span><span class="p">.</span><span class="n">xy</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">move_mat</span><span class="p">;</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="n">projection_mat</span> <span class="o">*</span> <span class="n">modelview_mat</span> <span class="o">*</span> <span class="n">pos</span><span class="p">;</span>
<span class="p">}</span>
<span class="o">---</span><span class="n">fragment</span>
<span class="err">$</span><span class="n">HEADER</span><span class="err">$</span>
<span class="kt">void</span> <span class="n">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">texture2D</span><span class="p">(</span><span class="n">texture0</span><span class="p">,</span> <span class="n">tex_coord0</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
<p>这里只有最简单的功能——定位和显示纹理。类似的着色器可以用在游戏最后,那时将增加一个控制相对大小的属性,<code>vScale</code>。</p>
<blockquote><p>如果你不理解这段代码,请看看上一章的内容。</p>
</blockquote>
<p>最后运行程序的效果如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/9.5.atlasglsl.png" alt="atlasglsl" /></p>
<p>下面,我们来开发一个粒子系统作为整个游戏其他对象的基础。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="设计重用粒子系统">设计重用粒子系统<a class="anchor-link" href="#设计重用粒子系统"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>当我们有很多类似的对象时,写一个具有简单功能的粒子系统是通用的做法。后面我们的飞船、子弹等等都用这个粒子系统来扩展。</p>
<p>其实,上一章的屏保程序整个就是一个很好的粒子系统,不过还缺少一个配置能力,也不能轻易重用。因此,这里我们要改变这些GLSL代码。</p>
<blockquote><p>值得一提的是,这里用的方法——每个粒子的四周用纹理渲染——并非底层渲染的最佳方案。但是,这么做非常直截了当,容易理解,而且与任何支持GLSL语言OpenGL的实现兼容。</p>
<p>如果你打算更系统的学习OpenGL,你可能会用把纹理的四周渲染改为点渲染,或者类似的概念,这已经超出的本书的范围。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="类继续关系">类继续关系<a class="anchor-link" href="#类继续关系"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>粒子系统的API由两个类构成:<code>PSWidget</code>执行渲染,<code>Particle</code>类表示每个粒子。</p>
<p>这两个类将与设计紧密耦合,在通常的OOP理论中这么做很有问题,但是可以改善我们app的性能:粒子可以直接连接渲染模块的顶点数组,来改变mesh网格——复制次数更少,考虑到需要同时处理很多粒子,这么做可以大大提升性能。</p>
<p>粒子系统部件的实现和GLSL部件没什么不同,除了现在它是一个子类。<code>PSWidget</code>和<code>Particle</code>类都是抽象基类,也就是说,它们不能直接通过调用<code>PSWidget()</code>来实例化。</p>
<p>增强这个现在有很多不同的方法。我们可以用Python标准模块<code>abc</code>来创建<code>true</code>抽象类(<code>abc</code>其实就是抽象类)。虽然这对Java程序员很有用,但是对Python程序员来说并不常用。</p>
<p>为了简化,我们为所有需要改写的方法添加<code>NotImplementedError</code>异常处理。这使得基类没有元类和复杂的继承关系就不能使用,就像<code>abc</code>模块说明的那样。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="PSWidget渲染类">PSWidget渲染类<a class="anchor-link" href="#PSWidget渲染类"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面是<code>PSWidget</code>类代码:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">PSWidget</span><span class="p">(</span><span class="n">Widget</span><span class="p">):</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">vertices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">particles</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">Widget</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span> <span class="o">=</span> <span class="n">RenderContext</span><span class="p">(</span><span class="n">use_parent_projection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">shader</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">glsl</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vCenter"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vScale"</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vTexCoords0"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vsize</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">texture</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uvmap</span> <span class="o">=</span> <span class="n">load_atlas</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">atlas</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这和前面的GLSL初始化类似,有一些属性尚未定义。<code>self.glsl</code>属性将加载着色器的文件名,<code>self.atlas</code>是纹理映射的文件名,被当作是纹理为渲染实例提供的唯一来源。</p>
<p>这么我们还没有生成顶点数组:这件事留给子类去做。但是,我们应该提供一个简单的方式为派生类处理内部数据结构。因此,<code>make_particles</code>方法可以容易的加入大量类似粒子:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_particles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Cls</span><span class="p">,</span> <span class="n">num</span><span class="p">):</span>
<span class="n">count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">particles</span><span class="p">)</span>
<span class="n">uv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">uvmap</span><span class="p">[</span><span class="n">Cls</span><span class="o">.</span><span class="n">tex_name</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">count</span> <span class="o">+</span> <span class="n">num</span><span class="p">):</span>
<span class="n">j</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">i</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">extend</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">3</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
<span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="o">-</span><span class="n">uv</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">uv</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="o">-</span><span class="n">uv</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">v1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">u1</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="o">-</span><span class="n">uv</span><span class="o">.</span><span class="n">su</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">sv</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">u0</span><span class="p">,</span>
<span class="n">uv</span><span class="o">.</span><span class="n">v0</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Cls</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">particles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这<code>Cls</code>类的质点数量(<code>num</code>),把它们增加到部件的<code>self.particles</code>列表,然后同时生成<code>self.vertices</code>。每个粒子类型应该显示一个<code>tex_name</code>属性,用来在UV映射中查找出正确的合并图片,这个数据结构由前面的图集(<code>PSWidget.uvmap</code>)派生出来。</p>
<p>其实,这个辅助函数是可选的,但是很有用。部件的具体类的在渲染之前的初始化阶段调用这个函数。</p>
<p>这个部件基类的最后部分就是渲染函数:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">particles</span><span class="p">:</span>
<span class="n">p</span><span class="o">.</span><span class="n">advance</span><span class="p">(</span><span class="n">nap</span><span class="p">)</span>
<span class="n">p</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="p">:</span>
<span class="n">Mesh</span><span class="p">(</span>
<span class="n">fmt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"triangles"</span><span class="p">,</span>
<span class="n">indices</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span>
<span class="n">vertices</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">,</span>
<span class="n">texture</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">texture</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>从<code>canvas.clear()</code>调用开始的代码和前面的GLSL例子类似。前面这段代码是在迭代所有粒子时调用两个方法:<code>advance()</code>方法计算粒子的新状态(由粒子决定),<code>update()</code>保持顶点数组中必要的数据的同步。</p>
<blockquote><p>这里主要是为了代码的可读性,并没有过多考虑性能,如果需要优化性能,有如下建议:</p>
<ul>
<li>循环部分可以并行处理</li>
<li>代码还可以完全用另一个线程,不用每一帧都升级(优化可能应用到粒子选择的类,比如,不影响主程序背景色的填充物)</li>
</ul>
</blockquote>
<p>这个方法更多的实现细节会在后面介绍。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Particle类"><code>Particle</code>类<a class="anchor-link" href="#Particle类"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面的代码就是Particle类,表示每个合并图片。源自满天星app的<code>Star</code>类,没有运动部分(后面的子类会实现):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Particle</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">y</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parent</span> <span class="o">=</span> <span class="n">parent</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vsize</span> <span class="o">=</span> <span class="n">parent</span><span class="o">.</span><span class="n">vsize</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_i</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">i</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">vsize</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">(</span><span class="n">created</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_i</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">vsize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">vsize</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">advance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>self.parent</code>保存一个引用到父类<code>PSWidget</code>中,方便后面的信息交互。前面也出现过的<code>update()</code>方法,让多边形四个顶点的变化与粒子位置和比例保持同步(<code>x</code>,<code>y</code>和<code>size</code>属性)。</p>
<p>这里还有一个方法没有出现在<code>Star</code>里面,就是<code>advance()</code>,它应该被改写,因为没有为屏幕改变设置默认的动作,完全由粒子决定如何变化。后面你会看到,粒子系统可以用来创建不同的效果。</p>
<p><code>reset()</code>方法是在粒子的生命周期的最后重新初始化粒子(比如,已经离开屏幕或用完TTL的粒子)。虽然这里都是粒子系统,但是任何系统都会有一些要被恢复到原始状态的粒子。另外,这里也没有设置默认的行为让我们调用,所有这个函数什么也没有。</p>
<blockquote><p>从一个虚拟方法触发<code>NotImplementedError</code>错误是提醒开发者,可以在派生类里定义该方法的内容。我们也可以忽略后面两个方法,但是这样做有可能引发<code>AttributeError</code>错误。保留方法的定义,即使没有实现,也是很好的做法,可以减少其他开发者的猜测(或者过段时间再看代码的时候,会感觉一头雾水,不知道自己怎么写的)。</p>
</blockquote>
<p><code>reset()</code>方法里面的<code>created</code>参数。一些粒子系统在第一次生成的时候可能需要额外的(或不同的)初始化过程。前面也有过类似的情况,如满天星app里面,星星在屏幕的右手边生成。如果我们不考虑<em>已生成</em>状态,所有的星星都会出现的屏幕的最右侧,而且有同样的横坐标<code>x</code>,看起来就是一条直线。这样的结果肯定不是我们想要的,所以我们让通过将<code>created</code>变量设置成<code>True</code>使得星星的位置完全随机,这样就会看到漂亮的初始分布了。</p>
<p>调用<code>reset()</code>方法意味着后面重生的粒子会比第一次生成的多很多,所以把<code>created</code>变量设置成<code>False</code>。</p>
<p>现在基类的工作都完成了。后面你会看到,游戏的实现会变得很简单。下面我们就用粒子系统来创建游戏的角色。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="制作游戏">制作游戏<a class="anchor-link" href="#制作游戏"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>我们的app将用前面做好的模块来构建:根部件是<code>PSWidget</code>的子类叫<code>Game</code>,所有的游戏角色都由粒子系统<code>Particle</code>类派生出来。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.base</span> <span class="kn">import</span> <span class="n">EventLoop</span>
<span class="kn">from</span> <span class="nn">kivy.clock</span> <span class="kn">import</span> <span class="n">Clock</span>
<span class="k">class</span> <span class="nc">Game</span><span class="p">(</span><span class="n">PSWidget</span><span class="p">):</span>
<span class="n">glsl</span> <span class="o">=</span> <span class="s2">"game.glsl"</span>
<span class="n">atlas</span> <span class="o">=</span> <span class="s2">"game.atlas"</span>
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">GameApp</span><span class="p">(</span><span class="n">App</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">EventLoop</span><span class="o">.</span><span class="n">ensure_window</span><span class="p">()</span>
<span class="k">return</span> <span class="n">Game</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">on_start</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">root</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="n">Clock</span><span class="o">.</span><span class="n">schedule_interval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">root</span><span class="o">.</span><span class="n">update_glsl</span><span class="p">,</span> <span class="mi">60</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这里用的两个文件解释如下:</p>
<ul>
<li><code>game.glsl</code>着色器和<code>starfield.glsl</code>是一样的</li>
<li><code>game.atlas</code>纹理映射包含下列纹理:<ul>
<li><code>star</code>:和上一章的星星一样</li>
<li><code>player</code>:朝向右边的飞船</li>
<li><code>trail</code>:飞船发射的火球</li>
<li><code>bullet</code>:飞船发射的炮弹</li>
<li><code>ufo</code>:外星人朝向左边</li>
</ul>
</li>
</ul>
<p>上面的代码还没有在屏幕上显示出来,因为我们还没有生成顶点数组,下面我们来实现它们。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实现星星">实现星星<a class="anchor-link" href="#实现星星"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>现在我再建一个简单的星空。这次它从右向左运动,和前面的Kivy Bird游戏一样。</p>
<p>要创建一个简单的平行视差效果,我们把星星分成三个平面,然后让它们有不同的速度。一个平面上的星星比较多也比较大,快速移动,另一个比较少慢速移动。一旦星星飞出屏幕就在左边的随机位置重生。</p>
<p>下面我们来实现:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">random</span> <span class="kn">import</span> <span class="n">randint</span><span class="p">,</span> <span class="n">random</span>
<span class="k">class</span> <span class="nc">Star</span><span class="p">(</span><span class="n">Particle</span><span class="p">):</span>
<span class="n">plane</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">tex_name</span> <span class="o">=</span> <span class="s2">"star"</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">plane</span> <span class="o">=</span> <span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="k">if</span> <span class="n">created</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">random</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">width</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">width</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">random</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">height</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">plane</span>
<span class="k">def</span> <span class="nf">advance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">-=</span> <span class="mi">20</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">plane</span> <span class="o">*</span> <span class="n">nap</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>tex_name</code>是必须有的,引用<code>game.atlas</code>里面的纹理。</p>
<p>随机生成一个星星的位置和所属的平面,无论初始化(<code>created=True</code>)是否被调用。</p>
<p><code>advance()</code>方法就是一旦星星飞出屏幕就重生。</p>
<p>为了使用粒子系统,我们需要用<code>PSWidget</code>类的<code>make_particles()</code>方法来增加一些星星。在<code>Game.initialize()</code>里面:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Star</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面就可以看到效果图:</p>
<p><img src="/images/copied_from_nb/kbpic/9.6.starfield.png" alt="starfield" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实现宇宙飞船">实现宇宙飞船<a class="anchor-link" href="#实现宇宙飞船"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>我们只需要一个宇宙飞船(单人模式),用一个粒子就可以实现了。这么做是为了和后面的代码统一,这个对象的构建和其他对象没什么区别。</p>
<p>飞船一直粘在鼠标位置,要实现这个效果,我们把鼠标的位置储存到<code>Game</code>属性里,用<code>player_x</code>和<code>player_y</code>来表示,然后把飞船图片加载到里面。代码如下所示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.core.window</span> <span class="kn">import</span> <span class="n">Window</span>
<span class="k">class</span> <span class="nc">Game</span><span class="p">(</span><span class="n">PSWidget</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">player_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">player_y</span> <span class="o">=</span> <span class="n">Window</span><span class="o">.</span><span class="n">mouse_pos</span>
<span class="n">PSWidget</span><span class="o">.</span><span class="n">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>由于飞船实在用户的控制之下,没有其他逻辑要实现,只要把图片移动到鼠标位置就可以了:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Player</span><span class="p">(</span><span class="n">Particle</span><span class="p">):</span>
<span class="n">tex_name</span> <span class="o">=</span> <span class="s2">"player"</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_x</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_y</span>
<span class="n">advance</span> <span class="o">=</span> <span class="n">reset</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>你会发现<code>reset()</code>和<code>advance()</code>方法是一样的。还有飞船的初始化:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Star</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Player</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面就是效果图:</p>
<p><img src="/images/copied_from_nb/kbpic/9.7.spaceship.png" alt="spaceship" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实现飞船的尾巴或火焰">实现飞船的尾巴或火焰<a class="anchor-link" href="#实现飞船的尾巴或火焰"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>科幻小说里面的飞船都跟着一个尾巴。这个尾巴用下面的算法实现:</p>
<ol>
<li>粒子在引擎附件生成,尺寸是随机的。粒子的尺寸也是它的存活时间(time to live,TTL)</li>
<li>它以一个恒定的速度飞离飞船,尺寸不断减小</li>
<li>最终粒子的尺寸会比原来小10%</li>
</ol>
<p>当有很多粒子来时,这个效果会很好看。不过截屏是看不出来了,你可以运行一下代码试试。代码如下所示:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Trail</span><span class="p">(</span><span class="n">Particle</span><span class="p">):</span>
<span class="n">tex_name</span> <span class="o">=</span> <span class="s2">"trail"</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_x</span> <span class="o">+</span> <span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="o">-</span><span class="mi">20</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_y</span> <span class="o">+</span> <span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="k">if</span> <span class="n">created</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">random</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.6</span>
<span class="k">def</span> <span class="nf">advance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">-=</span> <span class="n">nap</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o"><=</span> <span class="mf">0.1</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">-=</span> <span class="mi">120</span> <span class="o">*</span> <span class="n">nap</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>其实现方式很简单,用同样的<code>player_x</code>和<code>player_y</code>属性来决定飞船的位置。在初始化阶段,添加许多粒子来实现效果:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Star</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Trail</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Player</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>截图效果如下所示:</p>
<p><img src="/images/copied_from_nb/kbpic/9.8.spaceshiptail.png" alt="spaceshiptail" /></p>
<p>还有敌人和子弹两个粒子系统没有实现。和前面看到的角色不同,它们都是在某个时间立刻出现,而敌人和子弹不是立刻出现的,两者都需要等一个特定的事件发生,然后逐渐增加数量,发射一颗子弹或者生成一个敌人。</p>
<p>但是,之前我们需要分配固定数量的粒子,因为顶点数组的增减会让代码变得复杂,这不是我们想要的。</p>
<p>方法是给粒子增加一个新的布尔变量属性,决定粒子是否属于激活状态,然后激活需要的粒子。这个方法后面会提到。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实现子弹">实现子弹<a class="anchor-link" href="#实现子弹"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>我们想让飞船的大炮在我们单击鼠标或触摸屏幕的时候能够发射子弹。用<code>firing</code>属性就可以实现:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Game</span><span class="p">(</span><span class="n">PSWidget</span><span class="p">):</span>
<span class="n">firing</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">fire_delay</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">on_touch_down</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">touch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">firing</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fire_delay</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">on_touch_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">touch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">firing</span> <span class="o">=</span> <span class="kc">False</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>要在两次射击之间增加延迟,我们引入一个变量<code>fire_delay</code>。这个变量会按帧递减到0,然后一个新的子弹生成,<code>fire_delay</code>开始增大。在<code>firing</code>变量为<code>True</code>的时候循环:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">player_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">player_y</span> <span class="o">=</span> <span class="n">Window</span><span class="o">.</span><span class="n">mouse_pos</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">firing</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fire_delay</span> <span class="o">-=</span> <span class="n">nap</span>
<span class="n">PSWidget</span><span class="o">.</span><span class="n">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>现在,让我们看看这个粒子的状态。开始的时候,所有的子弹都没激活(<code>active=False</code>),移出屏幕(坐标值<code>x=-100, y=-100</code>设置子弹位置,可以在渲染的时候不让它们出现)。代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Bullet</span><span class="p">(</span><span class="n">Particle</span><span class="p">):</span>
<span class="n">active</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">tex_name</span> <span class="o">=</span> <span class="s2">"bullet"</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">active</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>当遍历所有子弹之后,我们跳过那些没激活的子弹,保留<code>firing_delay</code>不是0的子弹。这时,我们激活一个子弹,然后把它放到玩家面前,启动<code>firing_delay</code>变量到倒计时。</p>
<p>激活的子弹想星星一样移动,与星星方向相反。不像星星,子弹飞出屏幕后不会重生。它们回到不激活状态,从屏幕上消失。代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">advance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">active</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+=</span> <span class="mi">250</span> <span class="o">*</span> <span class="n">nap</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">width</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">firing</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">fire_delay</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">active</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_x</span> <span class="o">+</span> <span class="mi">40</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_y</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">fire_delay</span> <span class="o">+=</span> <span class="mf">0.3333</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>fire_delay</code>属性设置为1/3秒,子弹发射的频率是每秒三发(3 rounds per second,RPS)。效果如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/9.9.spaceshipguns.png" alt="spaceshipguns" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="实现敌人">实现敌人<a class="anchor-link" href="#实现敌人"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>敌人的概念和子弹类似,但是它们是连续出现的,我们不需要<code>firing</code>这样的标记,用一个<code>spawn_delay</code>就够了。代码实现如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Game</span><span class="p">(</span><span class="n">PSWidget</span><span class="p">):</span>
<span class="n">spawn_delay</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="nf">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">player_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">player_y</span> <span class="o">=</span> <span class="n">Window</span><span class="o">.</span><span class="n">mouse_pos</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">firing</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fire_delay</span> <span class="o">-=</span> <span class="n">nap</span>
<span class="bp">self</span><span class="o">.</span><span class="n">spawn_delay</span> <span class="o">-=</span> <span class="n">nap</span>
<span class="n">PSWidget</span><span class="o">.</span><span class="n">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在初始化阶段,我们创建了一个预定义数量的敌人,开始不激活。为了实现后面对子弹的碰撞检测,我们需要存储一个子弹列表(<code>Game.particles</code>):</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Star</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Trail</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Player</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Enemy</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">make_particles</span><span class="p">(</span><span class="n">Bullet</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bullets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">particles</span><span class="p">[</span><span class="o">-</span><span class="mi">25</span><span class="p">:]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这些代码看着很复杂,因为这里涉及到很多不同的运动状态。为了固定<code>x</code>方向的速度,每个敌人还带一个随机垂直运动矢量<code>v</code>。当这样的粒子从屏幕边上的顶部到底部离开屏幕时,粒子的<code>v</code>属性不断改变,在屏幕上看到的是敌人又回到屏幕的效果。</p>
<p>其他的规则与子弹类似:当敌人到达屏幕的底部,它重置然后消失再重生。代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Enemy</span><span class="p">(</span><span class="n">Particle</span><span class="p">):</span>
<span class="n">active</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">tex_name</span> <span class="o">=</span> <span class="s2">"ufo"</span>
<span class="n">v</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">created</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">active</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">advance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">active</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_hit</span><span class="p">():</span>
<span class="n">snd_hit</span><span class="o">.</span><span class="n">play</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">-=</span> <span class="mi">200</span> <span class="o">*</span> <span class="n">nap</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o"><</span> <span class="o">-</span><span class="mi">50</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">*</span> <span class="n">nap</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">v</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">height</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">=</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">v</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">spawn_delay</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">active</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">width</span> <span class="o">+</span> <span class="mi">50</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">height</span> <span class="o">*</span> <span class="n">random</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">v</span> <span class="o">=</span> <span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">spawn_delay</span> <span class="o">+=</span> <span class="mi">1</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这段代码的设计思路很简单:</p>
<ol>
<li>检查是否被一个子弹击中,或者要重置</li>
<li>水平移动,检查是否离开了屏幕,然后重置</li>
<li>垂直移动,检查是否离开了屏幕,改变速度矢量</li>
<li>如果<code>spawn_delay</code>已经到0,就重生一个敌人,然后启动<code>spawn_delay</code></li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="碰撞检测">碰撞检测<a class="anchor-link" href="#碰撞检测"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>我们还没实现的<code>Enemy</code>类的另一个有趣功能是<code>check_hit()</code>方法。有两种情况敌人会撞到:飞船和子弹。为了简化,我们设定玩家是无敌的,敌人碰到任何物体都会被消灭:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">check_hit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">player_y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="o"><</span> <span class="mi">60</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">bullets</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">b</span><span class="o">.</span><span class="n">active</span><span class="p">:</span>
<span class="k">continue</span>
<span class="k">if</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="o">.</span><span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="o"><</span> <span class="mi">30</span><span class="p">:</span>
<span class="n">b</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">return</span> <span class="kc">True</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>math.hypot()</code>是计算两物体中心距离,我们假设所有的物体都以这个方法监测。不能用没激活的子弹碰撞(<code>if not b.active</code>),因为没激活的子弹在屏幕上是看不到的。因此,它们不会在屏幕上撞击任何物体。</p>
<p>这样游戏就完成了。</p>
<p><img src="/images/copied_from_nb/kbpic/9.10.fullgame.png" alt="fullgame" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="改善功能">改善功能<a class="anchor-link" href="#改善功能"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>游戏有很多地方可以改进,尤其是游戏玩法上。当然这只是一个原型,不是商品,可以慢慢改进。</p>
<p>如果你感兴趣,下面的建议留给你完成:</p>
<ul>
<li>游戏需要一个“Game Over”状态。胜利的状态不一定有,失败必须有,和上一章的类似</li>
<li>增加角色,实现多种敌人,更多攻击的方式,也可以让敌人攻击飞船,增加关卡的难度,照着街机雷电游戏去做就行</li>
<li>增加声音效果,可以仿照Kivy Bird那一章的内容。<code>MultiAudio</code>类也可以重用</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="总结">总结<a class="anchor-link" href="#总结"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这章的重点是用粒子系统来实现不同的游戏角色。这可能不是你了解的最好方法,但还是让我们把细节放下,来总结一下整本书的重点。</p>
<p>一路走来,我们基本上学完了Python和Kivy游戏开发的过程,能胜任的领域很多:</p>
<ul>
<li>桌面和移动应用开发</li>
<li>文字、图像、声音合成内容等应用的开发</li>
<li>网络聊天应用的开发,以及社交网络,远程控制等程序</li>
<li>视频游戏开发</li>
</ul>
<p>通过这本书,我们还为如何有效使用新技术提供了一些基本原则:</p>
<ul>
<li>把其他领域的经验迁移过来。Kivy虽然不一样,但并非完全不同,很多其他领域的方法都可以在这里重用</li>
<li>努力探索实现的过程。理解框架工作的内部原理可以为调试提供极大帮助。</li>
<li>如果文档缺失,请读源代码。毕竟,它是Python。</li>
<li>遇到问题请用搜索引擎。你遇到的问题别人也遇到过。</li>
</ul>
<p>总之,我们衷心希望你能喜欢这次旅程。</p>
</div>
</div>
</div>
</div>Kivy指南-8-着色器app2019-09-01T00:00:00-05:002019-09-01T00:00:00-05:00https://asyncfor.com/jupyter/kivy/android/ios/2019/09/01/kivy-ch8-shaders-app<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: _notebooks/2019-09-01-kivy-ch8-shaders-app.ipynb
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>恭喜你Hold到现在!最后两章我们准备讨论一些OpenGL的底层细节,与Kivy完全不同的内容,比如<strong>OpenGL着色器语言(OpenGL Shading Language,GLSL)</strong>。这个语言可以让我们轻松写出高性能的代码。</p>
<p>我们将通过一个屏幕保护程序来介绍OpenGL的特性,然后做一个射击游戏(shoot-em-up game,shmup)app。本章的代码是准备工作,和其他项目都是一章不同,射击游戏在下一章做。</p>
<!-- TEASER_END-->
<p>本章会讨论很多复杂的问题,不过由于篇幅不能面面俱到,建议阅读OpenGL最新文档,因为OpenGL是一个标准,更新很快,新特性不断被加入,希望你在阅读的时候及时留意OpenGL相关的进展。</p>
<p>首先要讨论的是高性能着色器方法,尽管和普通的Kivy代码差别不大,而且两者大部分都相互兼容,在很多部件中都可以互相替代。因此,这种方法主要是用GLSL实现于资源消耗很大、性能要求很高的部分,消除性能的瓶颈。</p>
<h2 id="OpenGL简介">OpenGL简介<a class="anchor-link" href="#OpenGL简介"> </a></h2><p>现在让我们简单介绍下OpenGL。OpenGL是一个底层的图像API,其标准被广泛采用。桌面系统和移动系统均支持(在移动系统上是OpenGL ES,<strong>Embedded Systems,嵌入式系统</strong>)。现代浏览器也支持OpenGL ES的一个分支版本,就是大名鼎鼎的WebGL。</p>
<p>OpenGL的广泛采用使其具有良好的跨平台能力,尤其在视频游戏和图形编程方面。Kivy的跨平台着色器同样依赖于OpenGL。</p>
<h3 id="并行方式">并行方式<a class="anchor-link" href="#并行方式"> </a></h3><p>OpenGL可以操纵基本的图形元素,如单个的点和屏幕上的每个像素。我们可以画三个点,然后构成一个三角形,计算每个像素的应该填充的颜色。你可能认为这种做法是非常麻烦的。这正是像kivy这样的高级图形框架出现的原因:它们都隐藏了OpenGL的管线(pipeline)的一堆细节,提供组件和布局来简化操作。复杂底层的管线功能如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.1.pipeline.png" alt="pipeline" /></p>
<p>这个复杂的图形包括以下四个方面:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ul>
<li>应用给OpenGL一个点数组(vertices),可以重用这些点的索引数组(indices),以及其他数值(uniforms)</li>
<li>对每个点进行点着色器(vertex shader),根据需要做一系列计算。然后输出到片段着色器(fragment shader)</li>
<li>对每个像素进行分段着色器(也叫像素着色器,pixel shader),计算像素的颜色。有时除了点着色器的输出结果,还要考虑一个颜色常量</li>
<li>像素被着色器到屏幕上,其他的一些任务被完成,这些任务我们暂不讨论</li>
</ul>
<blockquote><p>上面使用的一组点称为模型(model)或网格(mesh)。它们不一定是连续的,也可能是离散的多边形;这些模型的理论基础后面会介绍。</p>
</blockquote>
<p>OpenGL超快速度的背后是大量并行计算方法的使用。上面提到的点着色器和像素着色器可能不会自动飞快运行,但是当通过GPU加速的瞬间,着色器造成的延迟并不会随着着色复杂度的增加呈指数级增长;在正常的硬件上其增速基本是线性的。</p>
<p>对应到现实中,我们讨论的,就是今天用2-16核CPU的电脑进行多任务、并行编程的个人电脑。一般的中档显卡,也有几千个GPU核心,可以并行处理更多的计算。</p>
<p>每个任务都是独立运行的,不像一般程序里面的线程,着色器不能等待其他任务完成再运行,这样会出现阻塞且严重影响性能,除了使用管线命令的时候(就像前面提到的,先进行点着色器,再运行像素着色器)。这种限制在你刚刚接触GLSL的时候,可能有点难理解。</p>
<p>这就是为什么有些算法可以在GPU上面非常高效。现代加密算法像<strong>bcrypt</strong>就是用来限制这种高并发性能的——通过限制暴力破解能力来保障安全性。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="性能得失">性能得失<a class="anchor-link" href="#性能得失"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>并非只有原始的OpenGL才可以获得极快的性能。很多时候,像Kivy这样的高级框架也可以如此。比如,当你在屏幕上着色器多边形的时候,下面的一系列动作就会发生:</p>
<ul>
<li>用python定义多边形的形状和位置</li>
<li>点,索引数组和相关的资源(如花纹)都上传到图像接口</li>
<li>调用点着色器功能,进行定位、着色、缩放等变换</li>
<li>最后,调用像素着色器功能,返回一个栅格图像显示到屏幕上</li>
</ul>
<p>无论你是要Kivy部件还是用原始的OpenGL命令和GLSL着色器,两种方式的性能都是一样的。这是因为Kivy底层和OpenGL类似。</p>
<p>也就是说,底层优化其实没多大必要,这就是由几个矩形构成的<em>Kivy Bird</em>游戏,可以直接通过高级接口实现的原因。基本上,我们可以在<em>Kivy Bird</em>里优化一两个部件,但是其性能很难被察觉。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="改善性能">改善性能<a class="anchor-link" href="#改善性能"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>那么,性能到底如何改善呢?答案就是减少Python代码,精简着色器内容。假如我们要着色9,000个相似的多边形(比如秋天的落叶或漫天的行程)。如果每个多边形都用一个Kivy部件,那我们就要做很多个Python对象,要被单独序列化成OpenGL指令。另外,每个部件都有点集合相关的图像接口,必然产生大量的API调用,还有许多类似的匹配(mesh)。</p>
<p>我们可以做两件事来优化:</p>
<ul>
<li>避免大量Python类实例,把它们放在一个数值中即可。如果储存成一个适合OpenGL处理的格式中,就可以免掉序列化的步骤</li>
<li>把所有的图形组合到一起作为一个单独模型处理,这样可以减少API调用。批处理通常是很好的优化方法,因为它让OpenGL以并行方式更好的运行。</li>
</ul>
<p>在本章结束的时候我们会实现这些方法。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="GLSL介绍">GLSL介绍<a class="anchor-link" href="#GLSL介绍"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>GLSL作为一门语言,与C语法类似,是一个静态强类型。如果你不熟悉C语言,下面是参考。C与Python不同,不在乎缩进,句末要用分号,逻辑代码块要用括号。</p>
<p>GLSL同时支持C和C++的注释方式:</p>
<div class="highlight"><pre><span></span><span class="cm">/* ANSI C-style comment */</span>
<span class="c1">// C++ one-line comment</span>
</pre></div>
<p>变量声明方式为<code>[type] [name] [= optional value];</code>:</p>
<div class="highlight"><pre><span></span><span class="kt">float</span> <span class="n">a</span><span class="p">;</span> <span class="c1">// this has no direct Python equivalent</span>
<span class="kt">int</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
</pre></div>
<p>函数定义方式为<code>[type] [name] ([arguments]) { [body of function] }</code>:</p>
<div class="highlight"><pre><span></span><span class="kt">float</span> <span class="nf">pow2</span><span class="p">(</span><span class="kt">float</span> <span class="n">x</span><span class="p">)</span>
<span class="p">{</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">x</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>控制结构形式为:</p>
<div class="highlight"><pre><span></span><span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o"><</span> <span class="mf">9.0</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">x</span> <span class="o">=</span> <span class="mf">9.0</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>差不多就这些了,即使没有学过C,你现在也可以读GLSL代码了。</p>
<p>着色器代码的起点是<code>main()</code>函数。后面,我们把点着色器和像素着色器放在一个文件中,因此,在一个文件里面会有两个<code>main()</code>函数。其结构如下:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="c1">// code</span>
<span class="p">}</span>
</pre></div>
<p>这里的<code>void</code>类型表示函数没有返回值,和Python的<code>NoneType</code>不一样,你不能把变量声明为<code>void</code>类型。这里的两个<code>main()</code>函数返回值和参数都被忽略了,所以写成<code>void main(void)</code>。着色器会根据需要把结果写到内部变量,<code>gl_Position</code>,<code>gl_FragColor</code>和其他变量里面,不需要返回数据结果,输入参数为空也是同理。</p>
<p>GLSL类型系统完全反映了它的作用。不像C语言,它为点和矩阵准备了专门的类型,这些类型支持数学运算(所以你可以直接对矩阵变量进行乘法<code>mat1 * mat2</code>,多简单啊!C一个试试就知道C多麻烦了)。在计算机图形学里,矩阵计算不可或缺,后面会介绍。</p>
<p>下面,我们就来写几个GLSL的例子。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="在Kivy中自定义着色器">在Kivy中自定义着色器<a class="anchor-link" href="#在Kivy中自定义着色器"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>首先我们需要用Python代码实现窗口、加载着色器等等。代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.app</span> <span class="kn">import</span> <span class="n">App</span>
<span class="kn">from</span> <span class="nn">kivy.base</span> <span class="kn">import</span> <span class="n">EventLoop</span>
<span class="kn">from</span> <span class="nn">kivy.graphics</span> <span class="kn">import</span> <span class="n">Mesh</span>
<span class="kn">from</span> <span class="nn">kivy.graphics.instructions</span> <span class="kn">import</span> <span class="n">RenderContext</span>
<span class="kn">from</span> <span class="nn">kivy.uix.widget</span> <span class="kn">import</span> <span class="n">Widget</span>
<span class="k">class</span> <span class="nc">GlslDemo</span><span class="p">(</span><span class="n">Widget</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">Widget</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span> <span class="o">=</span> <span class="n">RenderContext</span><span class="p">(</span><span class="n">use_parent_projection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">shader</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="s2">"basic.glsl"</span>
<span class="c1"># Set up geometry here.</span>
<span class="k">class</span> <span class="nc">GlslApp</span><span class="p">(</span><span class="n">App</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">EventLoop</span><span class="o">.</span><span class="n">ensure_window</span><span class="p">()</span>
<span class="k">return</span> <span class="n">GlslDemo</span><span class="p">()</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">GlslApp</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这么我们创建了一个部件<code>GlslDemo</code>;用来管理所有的渲染。<code>RenderContext</code>是<code>Canvas</code>的子类用来轻松替换着色器。<code>basic.glsl</code>文件包含点着色器和像素着色器,后面会实现。</p>
<p>注意我们没用Kivy语言,因为没有布局要使用,所以这里不是<code>glsl.kv</code>文件,我们通过<code>GlslApp.build()</code>方法配置根部件。</p>
<p><code>EventLoop.ensure_window()</code>是必须的,因为我们需要接入OpenGL特性,包括在运行<code>GlslDemo.__init__()</code>方法时,调用GLSL编译器。如果此时没有应用窗口(更重要的是,没有相关的OpenGL内容),程序就会崩溃。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="建立几何图形">建立几何图形<a class="anchor-link" href="#建立几何图形"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>写着色器之前,我们还需要渲染一些东西——一系列点模型。我们用两个等斜边的直角三角形构成一个简单的矩形(这么分是因为基本多边形是三角形)。</p>
<blockquote><p>Kivy更偏向于二维图形,所以在二维设计中不会强加任何限制。而OpenGL是源自三维图形,所以你可以用生动的模型来创建视频游戏,也可以结合Kivy的部件来创建UI。本书不做介绍,但是两者底层的机制是一样的。</p>
</blockquote>
<p>这里需要升级<code>GlslDemo</code>部件里的<code>__init__()</code>方法:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">Widget</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span> <span class="o">=</span> <span class="n">RenderContext</span><span class="p">(</span><span class="n">use_parent_projection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">shader</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="s2">"basic.glsl"</span>
<span class="n">fmt</span> <span class="o">=</span> <span class="p">((</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),)</span> <span class="c1"># Step 1</span>
<span class="n">vertices</span> <span class="o">=</span> <span class="p">(</span> <span class="c1"># Step 2</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># Step 3</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="p">:</span>
<span class="n">Mesh</span><span class="p">(</span><span class="n">fmt</span><span class="o">=</span><span class="n">fmt</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"triangles"</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span> <span class="n">vertices</span><span class="o">=</span><span class="n">vertices</span><span class="p">)</span> <span class="c1"># Step 4</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>方法解释如下:</p>
<ul>
<li>写<code>OpenGL</code>代码时需要注意点对象没有标准的格式,因此,我们需要定义一个。简单的做法就是用点的位置<code>vPosition</code>。我们的矩形是二维的,因此我们就传递两个坐标,默认是浮点型数值。因此,定义是<code>(b'vPosition', 2, 'float')</code></li>
<li>确定了点的格式之后,我们把这些点放到一个数组里,如<code>vertices = (...)</code>行所示。这个元组只有一层,后面会单独定义记录的格式,然后再把所有值聚合在一起,没有用分隔符或其他类似好记的名称。这是C语言结构体的经典方式</li>
<li>索引数组用来复用那些顶点。通常,一个顶点被多个三角形同时使用。我们使用索引数组里的index来重复使用这些顶点——这样顶点数增加时内存占用相对更少</li>
<li>有了这些数据结构之后,我们就用类似Kivy画布指令的<code>Mesh</code>把它们组合起来。它按照通常部件渲染的方式进行渲染,和其他Kivy部件有很好的兼容性。GLSL代码可以用来连接所有的部分</li>
</ul>
<blockquote><p>这章提到了一个C语言概念,<code>array(数组)</code>——存放同类数据的连续内存区域。Python数据结构里也有,不过,Python通常用<code>tuple(元组)</code>或<code>list(列表)</code>。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="解释索引">解释索引<a class="anchor-link" href="#解释索引"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>要理解OpenGL的索引(index),让我们举个例子。用前面代码里面的顶点,是按照<code>(x, y)</code>存放的:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vertices</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>一个索引就是顶点列表里面的顶点数据,原点是<code>(0, 0)</code>。如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.2.openglindex.png" alt="openglindex" /></p>
<p>现在顶点还没有连起来,所以显示出来就是一些点,而不是一个多边形。我们定义一个<code>indices</code>列表来组合它们。三个点两组构成两个三角形:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">indices</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">2</span><span class="p">,</span> <span class="c1"># 三个点构成一个三角形</span>
<span class="mi">2</span><span class="p">,</span>
<span class="mi">3</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span> <span class="c1"># 另一个三角形</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这两个三角形中,第一个是由顶点0,1,2构成,第二个是由订单2,3,0构成。如下图所示,颜色是展示用的,我们还没有设置颜色,后面会补上。</p>
<p><img src="/images/copied_from_nb/kbpic/8.3.triangles.png" alt="triangles" /></p>
<p>OpenGL代码里面的索引就是这样使用的。</p>
<blockquote><p>OpenGL数据结构内存优化方法中很少是专门用来减少RAM的——很多时候视频接口吞吐量是性能的瓶颈,所以优化的目标都是每帧传递更多的内容,而不是通过压缩数据来节省内存。这点自始至终没有改变过。</p>
</blockquote>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="写GLSL代码">写GLSL代码<a class="anchor-link" href="#写GLSL代码"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面我们就来写可以在GPU上执行的GLSL代码,它和C语言一样快。</p>
<p>Kviy要求点着色器和像素着色器代码在一个文件里,代码用<code>'---vertex'</code>和<code>'---fragment'</code>分割,<code>$HEADER$</code>语句由Kivy指定,这并非任何标准,只在这里用:</p>
<div class="highlight"><pre><span></span><span class="o">---</span><span class="n">vertex</span>
<span class="err">$</span><span class="n">HEADER</span><span class="err">$</span>
<span class="kt">void</span> <span class="n">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="c1">// vertex shader</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="p">...</span>
<span class="p">}</span>
<span class="o">---</span><span class="n">fragment</span>
<span class="err">$</span><span class="n">HEADER</span><span class="err">$</span>
<span class="kt">void</span> <span class="n">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="c1">// fragment shader</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="p">...</span>
<span class="p">}</span>
</pre></div>
<p>为了节省篇幅,后面的代码会忽略这些样本代码,希望你看代码的时候记得它们是存在的。</p>
<p><code>$HEADER$</code>宏变量是上下文相关的,表示不同类型的着色器。</p>
<p>在点着色器里面,<code>$HEADER$</code>是下面代码:</p>
<div class="highlight"><pre><span></span><span class="n">varying</span> <span class="n">vec4</span> <span class="n">frag_color</span><span class="p">;</span>
<span class="n">varying</span> <span class="n">vec2</span> <span class="n">tex_coord0</span><span class="p">;</span>
<span class="n">attribute</span> <span class="n">vec2</span> <span class="n">vPosition</span><span class="p">;</span>
<span class="n">attribute</span> <span class="n">vec2</span> <span class="n">vTexCoords0</span><span class="p">;</span>
<span class="n">uniform</span> <span class="n">mat4</span> <span class="n">modelview_mat</span><span class="p">;</span>
<span class="n">uniform</span> <span class="n">mat4</span> <span class="n">projection_mat</span><span class="p">;</span>
<span class="n">uniform</span> <span class="n">vec4</span> <span class="n">color</span><span class="p">;</span>
<span class="n">uniform</span> <span class="kt">float</span> <span class="n">opacity</span><span class="p">;</span>
</pre></div>
<p>在像素着色器里面,<code>$HEADER$</code>是下面代码:</p>
<div class="highlight"><pre><span></span><span class="n">varying</span> <span class="n">vec4</span> <span class="n">frag_color</span><span class="p">;</span>
<span class="n">varying</span> <span class="n">vec2</span> <span class="n">tex_coord0</span><span class="p">;</span>
<span class="n">uniform</span> <span class="n">sampler2D</span> <span class="n">texture0</span><span class="p">;</span>
</pre></div>
<p>有点啰嗦,以后的Kivy版本应该会简化它们。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="存储类与类型">存储类与类型<a class="anchor-link" href="#存储类与类型"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>在前面的代码里,不仅有数据类型,还有一个存储类:</p>
<ul>
<li>存储类(Storage classes)<ul>
<li><code>attribute</code>:由点数据格式指定对应每个点的属性,由应用传递</li>
<li><code>uniform</code>:GLSL全局变量,同样由应用传递,但是不会随点变化</li>
<li><code>varying</code>:有点着色器传递到像素着色器</li>
</ul>
</li>
<li>常用数据类型(Commonly used data types)<ul>
<li><code>float</code>:浮点类型</li>
<li><code>vec2, vec3, vec4</code>:长度为2,3,4的元组类型,内部值为浮点类型。可以表示点、颜色等等</li>
<li><code>mat2, mat3, mat4</code>:规模为 2 × 2,3 × 3,4 × 4的矩阵类型,</li>
<li><code>sampler2D</code>:表示一个用来装饰的纹理(texture)类型</li>
</ul>
</li>
</ul>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="基本着色器">基本着色器<a class="anchor-link" href="#基本着色器"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>现在,让我们来写一个简单的着色器:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">vec4</span> <span class="n">pos</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vPosition</span><span class="p">.</span><span class="n">xy</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="n">projection_mat</span> <span class="o">*</span> <span class="n">modelview_mat</span> <span class="o">*</span> <span class="n">pos</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>把每个点的坐标值转换成Kivy系统的坐标值,其原点在左下角。</p>
<blockquote><p>这里不演示坐标变换的细节,作为入门教程有点复杂。其实也没必要完全理解这些细节,或者读完整本书。
如果你很感兴趣,可以去看看<a href="http://www.learnopengles.com/understanding-opengls-matrices/">OpenGL的坐标空间和矩阵用法</a>
最简单的像素着色器就是一个返回固定颜色的函数:</p>
</blockquote>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
<p>这里为每个点返回一个RGBA颜色值<code>#FF007F</code>。</p>
<p>如果你运行程序,你会看到输出结果如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.4.basicshaders.png" alt="basicshaders" /></p>
<p>聊胜于无,折腾这么久,终于有点儿成果了,下面我们调整一下看看变化。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="程序的颜色">程序的颜色<a class="anchor-link" href="#程序的颜色"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>除了显示一种颜色外,颜色还有可以通过另一个方法让像素着色器显示出来。</p>
<p>假设我们想要计算每个像素的RGB颜色:</p>
<ul>
<li><code>R</code>通道值与<code>x</code>轴坐标成正比</li>
<li><code>G</code>通道值与<code>y</code>轴坐标成正比</li>
<li><code>B</code>通道值等于<code>R</code>与<code>G</code>均值</li>
</ul>
<p>对应最简单像素着色器的算法如下:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="kt">float</span> <span class="n">r</span> <span class="o">=</span> <span class="n">gl_FragCoord</span><span class="p">.</span><span class="n">x</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">;</span>
<span class="kt">float</span> <span class="n">g</span> <span class="o">=</span> <span class="n">gl_FragCoord</span><span class="p">.</span><span class="n">y</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">;</span>
<span class="kt">float</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="n">g</span><span class="p">);</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
<p><code>gl_FragCoord</code>变量包括相对于应用窗口的像素坐标值(并非实际屏幕的坐标值)。这里除以<code>255.0</code>是为了简化计算,让所有的值都落在[0,1]区间内。</p>
<p>效果图像如下:</p>
<p><img src="/images/copied_from_nb/kbpic/8.5.computingcolor.png" alt="computingcolor" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="彩色顶点">彩色顶点<a class="anchor-link" href="#彩色顶点"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>类似的彩色效果可以通过给点增加颜色数据来实现。因此,我们需要扩展点的数据格式,增加一个带颜色的属性<code>vColor</code>:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fmt</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vColor"</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">vertices</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mf">0.462</span><span class="p">,</span>
<span class="mf">0.839</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mf">0.831</span><span class="p">,</span>
<span class="mf">0.984</span><span class="p">,</span>
<span class="mf">0.474</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mf">0.541</span><span class="p">,</span>
<span class="mf">0.847</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mf">0.988</span><span class="p">,</span>
<span class="mf">0.474</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>更新之后,每个点都有5个维度,后面三个是RGB颜色值。对应的<code>vertices</code>也要做修改。</p>
<p><code>vColor</code>属性是一个RGB颜色值,而点着色器用的是RGBA颜色,这里并没有每个点的alpha通道值,我们需要对点着色器做一些调整:</p>
<div class="highlight"><pre><span></span><span class="n">attribute</span> <span class="n">vec3</span> <span class="n">vColor</span><span class="p">;</span>
<span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">frag_color</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vColor</span><span class="p">.</span><span class="n">rgb</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">vec4</span> <span class="n">pos</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vPosition</span><span class="p">.</span><span class="n">xy</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="n">projection_mat</span> <span class="o">*</span> <span class="n">modelview_mat</span> <span class="o">*</span> <span class="n">pos</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<blockquote><p>在GLSL中,<code>vColor.rgb</code>和<code>vPosition.xy</code>表示法叫混合(swizzling)。它们可以有效的获取矢量的一部分,类似于Python的切片(slice)。</p>
<p>这里的<code>vColor.rgb</code>表示“取矢量的前三个值”,在Python里面就是<code>vColor[:3]</code>。还可以颠倒顺序,如<code>vColor.bgr</code>,甚至重复,如<code>vColor.ggg</code>(取三个<code>G</code>通道值),样式很灵活。
同理,可以取矢量的四个值,如<code>.xyzw</code>,<code>.rgba</code>或<code>.stpq</code>。</p>
</blockquote>
<p>那么,像素着色器就很简单了:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">frag_color</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>让点与点之间的颜色插值计算,就呈现平滑渐变的效果了,这就是OpenGL的工作方式,如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.6.passingcolor.png" alt="passingcolor" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="纹理映射">纹理映射<a class="anchor-link" href="#纹理映射"> </a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>下面再给我们的矩形增加一些纹理。我们还要扩展点数据格式的定义,给每个点增加纹理坐标值:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fmt</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vTexCoords0"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">vertices</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">255</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>纹理坐标值通常都在[0,1]区间内,原点在左上角,这和Kivy的左下角原点是不同的。使用过程中,需要注意这个差别。</p>
<p>下面是Python加载纹理并传递给渲染的过程:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.core.image</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="p">:</span>
<span class="n">Mesh</span><span class="p">(</span>
<span class="n">fmt</span><span class="o">=</span><span class="n">fmt</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"triangles"</span><span class="p">,</span>
<span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span>
<span class="n">vertices</span><span class="o">=</span><span class="n">vertices</span><span class="p">,</span>
<span class="n">texture</span><span class="o">=</span><span class="n">Image</span><span class="p">(</span><span class="s2">"kivy.png"</span><span class="p">)</span><span class="o">.</span><span class="n">texture</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这样就把当前文件夹内的<code>kivy.png</code>文件转换成纹理了。如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.7.texture.png" alt="texture" /></p>
<p>这和前面的着色器没啥不同。点着色器传递纹理的坐标值:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">tex_coord0</span> <span class="o">=</span> <span class="n">vTexCoords0</span><span class="p">;</span>
<span class="n">vec4</span> <span class="n">pos</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vPosition</span><span class="p">.</span><span class="n">xy</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="n">projection_mat</span> <span class="o">*</span> <span class="n">modelview_mat</span> <span class="o">*</span> <span class="n">pos</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>像素着色器用修改过的<code>tex_coord0</code>坐标值来装饰在<code>texture0</code>位置的纹理,于是返回对应的颜色:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">texture2D</span><span class="p">(</span><span class="n">texture0</span><span class="p">,</span> <span class="n">tex_coord0</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
<p>把代码放到一起就看可以到如下效果:</p>
<p><img src="/images/copied_from_nb/kbpic/8.8.texturemapping.png" alt="texturemapping" /></p>
<p>通过着色器的实现经历,你可以去做一些相关程序了。如果有的地方不明白不用郁闷,GLSL确实比较复杂,系统的学习它得费一番功夫。</p>
<p>但是,它可以让你更清楚底层的工作细节。即使你日常工作中没写过底层代码,你也可以掌握这些知识,以避免性能瓶颈,改善程序的架构。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="做满天星app">做满天星app<a class="anchor-link" href="#做满天星app"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>学习了一堆GLSL的知识后,我们来做一个满天星屏保的app,星星会从屏幕的中央向四边飞来,给人一种穿越星空的快感。</p>
<blockquote><p>动态效果用图片没法展示,你可以直接运行实例代码看看效果。</p>
</blockquote>
<p>每颗星都会做如下动作:</p>
<ol>
<li>在屏幕中央随机生成</li>
<li>从屏幕中央向四周飞去直到看不见为止</li>
<li>然后从屏幕中央重新生成</li>
</ol>
<p>我们还会让星星的尺寸在飞行中不断变大。如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.9.screenshot.png" alt="screenshot" /></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="应用架构">应用架构<a class="anchor-link" href="#应用架构"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>新应用类的架构借用前面章节的内容即可。这里同样不用Kivy语言描述部件层级,所以没有<code>starfield.kv</code>。Python代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.base</span> <span class="kn">import</span> <span class="n">EventLoop</span>
<span class="kn">from</span> <span class="nn">kivy.clock</span> <span class="kn">import</span> <span class="n">Clock</span>
<span class="k">class</span> <span class="nc">StarfieldApp</span><span class="p">(</span><span class="n">App</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">EventLoop</span><span class="o">.</span><span class="n">ensure_window</span><span class="p">()</span>
<span class="k">return</span> <span class="n">Starfield</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">on_start</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">Clock</span><span class="o">.</span><span class="n">schedule_interval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">root</span><span class="o">.</span><span class="n">update_glsl</span><span class="p">,</span> <span class="mi">60</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这里<code>build()</code>方法创建并返回根部件<code>Starfield</code>;它将控制所有的数学和渲染应用中的内容。</p>
<p><code>on_start()</code>事件handler在程序启动后,让根部件通过<code>update_glsl()</code>方法实现每秒更新60次。</p>
<p><code>Starfield</code>类还分成两部分:<code>__init__()</code>方法用来创建数据结构,<code>update_glsl()</code>方法呈现图像(计算每颗星的位置)并渲染星星。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="数据结构与初始化">数据结构与初始化<a class="anchor-link" href="#数据结构与初始化"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>初始化代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.core.image</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">kivy.graphics.instructions</span> <span class="kn">import</span> <span class="n">RenderContext</span>
<span class="kn">from</span> <span class="nn">kivy.uix.widget</span> <span class="kn">import</span> <span class="n">Widget</span>
<span class="n">NSTARS</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="k">class</span> <span class="nc">Starfield</span><span class="p">(</span><span class="n">Widget</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">Widget</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span> <span class="o">=</span> <span class="n">RenderContext</span><span class="p">(</span><span class="n">use_parent_projection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">shader</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="s2">"starfield.glsl"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vCenter"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vScale"</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vPosition"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">b</span><span class="s2">"vTexCoords0"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"float"</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vsize</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">NSTARS</span><span class="p">,</span> <span class="mi">4</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">extend</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vertices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NSTARS</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
<span class="p">(</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="o">-</span><span class="mi">24</span><span class="p">,</span>
<span class="o">-</span><span class="mi">24</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">24</span><span class="p">,</span>
<span class="o">-</span><span class="mi">24</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">24</span><span class="p">,</span>
<span class="mi">24</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">1</span><span class="p">,</span>
<span class="o">-</span><span class="mi">24</span><span class="p">,</span>
<span class="mi">24</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">texture</span> <span class="o">=</span> <span class="n">Image</span><span class="p">(</span><span class="s2">"star.png"</span><span class="p">)</span><span class="o">.</span><span class="n">texture</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stars</span> <span class="o">=</span> <span class="p">[</span><span class="n">Star</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NSTARS</span><span class="p">)]</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>NSTARS</code>是星星的总数,调整它会改变星星的密度。一个带Intel集成显卡的中档电脑可以轻松支持几千个星星。再高档点的显卡带几万个星星也没问题。</p>
<p>和前面的例子不同,这次我们不在最后实现索引和点的数组。在初始化阶段我们就设置好,方便后面<code>update_glsl()</code>方法更新。</p>
<p><code>vfmt</code>顶点格式包括四个属性,如下表所示:</p>
<table>
<thead><tr>
<th style="text-align:center"><strong>属性</strong></th>
<th style="text-align:center"><strong>功能</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><code>vCenter</code></td>
<td style="text-align:center">星星中点在屏幕上的坐标值</td>
</tr>
<tr>
<td style="text-align:center"><code>vScale</code></td>
<td style="text-align:center">星星的尺寸,1表示(48 × 48 像素)</td>
</tr>
<tr>
<td style="text-align:center"><code>vPosition</code></td>
<td style="text-align:center">每个顶点与星星中点的相对位置</td>
</tr>
<tr>
<td style="text-align:center"><code>vTexCoords0</code></td>
<td style="text-align:center">纹理坐标值</td>
</tr>
</tbody>
</table>
<p>还有个属性<code>vsize</code>是顶点数组<code>vfmt</code>里的单个顶点的总长度。它计算顶点格式<code>vfmt</code>中间列的总和。</p>
<p><code>vertices</code>列表包含需要的数据;但它没有层次,不方便操作。这里就做了一个<code>Star</code>辅助类来实现,它把细节封装起来,在顶点数组外加了一个类,这样可以省不少事儿。</p>
<p><code>Star</code>类还会持续跟踪不属于顶点数据格式的一些属性,极坐标(以中心为原点的<code>angle</code>角度和<code>distance</code>距离)和不断增大的<code>size</code>。</p>
<p><code>Star</code>类定义如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">random</span> <span class="kn">import</span> <span class="n">random</span>
<span class="k">class</span> <span class="nc">Star</span><span class="p">:</span>
<span class="n">angle</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">distance</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">size</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sf</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sf</span> <span class="o">=</span> <span class="n">sf</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_idx</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">i</span> <span class="o">*</span> <span class="n">sf</span><span class="o">.</span><span class="n">vsize</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">angle</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">random</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">=</span> <span class="mi">90</span> <span class="o">*</span> <span class="n">random</span><span class="p">()</span> <span class="o">+</span> <span class="mi">10</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="n">random</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.05</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>base_idx</code>是星星点数组的第一个顶点,还要加一个引用<code>sf</code>,方便<code>Starfield</code>实例连接<code>vertices</code>。</p>
<p><code>reset()</code>调用之后把星星的属性复原。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="推动图像">推动图像<a class="anchor-link" href="#推动图像"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>Starfield.update_glsl()</code>方式实现了星星运动的算法,被<code>on_start()</code>事件handler的Kivy时钟程序频繁调用。源代码如下:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">kivy.graphics</span> <span class="kn">import</span> <span class="n">Mesh</span>
<span class="k">def</span> <span class="nf">update_glsl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nap</span><span class="p">):</span>
<span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">center</span>
<span class="n">max_distance</span> <span class="o">=</span> <span class="mf">1.1</span> <span class="o">*</span> <span class="nb">max</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">star</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">stars</span><span class="p">:</span>
<span class="n">star</span><span class="o">.</span><span class="n">distance</span> <span class="o">*=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">nap</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">star</span><span class="o">.</span><span class="n">size</span> <span class="o">+=</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="n">nap</span>
<span class="k">if</span> <span class="n">star</span><span class="o">.</span><span class="n">distance</span> <span class="o">></span> <span class="n">max_distance</span><span class="p">:</span>
<span class="n">star</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">star</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">canvas</span><span class="p">:</span>
<span class="n">Mesh</span><span class="p">(</span>
<span class="n">fmt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">vfmt</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"triangles"</span><span class="p">,</span>
<span class="n">indices</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span>
<span class="n">vertices</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">,</span>
<span class="n">texture</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">texture</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>首先星星从屏幕中央产生之后,我们计算最大距离<code>max_distance</code>。然后,我们重复星星列表,让它们运动起来,然后不断放大。超出屏幕的星星被重置。</p>
<p>函数的最后看着很熟悉,那是前面讲过的渲染技巧。调用<code>canvas.clear()</code>是必须的,但是每次调用都增加一个新的匹配(Mesh),极快的推送到显卡上进行处理。</p>
<p>代码的最后内容是<code>Star.update()</code>方法。它更像星星的四个顶点,把新的坐标值写到<code>vertices</code>数值中:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">iterate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">j</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sf</span><span class="o">.</span><span class="n">vsize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sf</span><span class="o">.</span><span class="n">vsize</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x0</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y0</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">iterate</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sf</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><code>iterate()</code>辅助函数用来提高代码可读性,看着有点多,其实读起来更方便。</p>
<p>为了完成程序迭代,整个内存映射进程会把序列化每一帧里大量的对象作为一个重要目标来处理,这会提高性能。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="写GLSL代码">写GLSL代码<a class="anchor-link" href="#写GLSL代码"> </a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>着色器代码和前面类似。点着色器代码:</p>
<div class="highlight"><pre><span></span><span class="n">attribute</span> <span class="n">vec2</span> <span class="n">vCenter</span><span class="p">;</span>
<span class="n">attribute</span> <span class="kt">float</span> <span class="n">vScale</span><span class="p">;</span>
<span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">tex_coord0</span> <span class="o">=</span> <span class="n">vTexCoords0</span><span class="p">;</span>
<span class="n">mat4</span> <span class="n">move_mat</span> <span class="o">=</span> <span class="n">mat4</span>
<span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">vCenter</span><span class="p">.</span><span class="n">x</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">vCenter</span><span class="p">.</span><span class="n">y</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">);</span>
<span class="n">vec4</span> <span class="n">pos</span> <span class="o">=</span> <span class="n">vec4</span><span class="p">(</span><span class="n">vPosition</span><span class="p">.</span><span class="n">xy</span> <span class="o">*</span> <span class="n">vScale</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">move_mat</span><span class="p">;</span>
<span class="n">gl_Position</span> <span class="o">=</span> <span class="n">projection_mat</span> <span class="o">*</span> <span class="n">modelview_mat</span> <span class="o">*</span> <span class="n">pos</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
<p>我们通过<code>vScale</code>因子与顶点坐标值相乘来等比例放大,然后用<code>vCenter</code>属性把它们变换成位置。<code>move_mat</code>矩阵是变换矩阵,线性代数里的一种放射变换方法。</p>
<p>像素着色器代码:</p>
<div class="highlight"><pre><span></span><span class="kt">void</span> <span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">gl_FragColor</span> <span class="o">=</span> <span class="n">texture2D</span><span class="p">(</span><span class="n">texture0</span><span class="p">,</span> <span class="n">tex_coord0</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
<p>最终的效果如下图所示:</p>
<p><img src="/images/copied_from_nb/kbpic/8.10.star.png" alt="star" /></p>
<p>这样,满天星app就完成了,感受一下穿越的乐趣吧。</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="总结">总结<a class="anchor-link" href="#总结"> </a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>这一章我们介绍了底层的OpenGL硬件加速方式,用GLSL实现了点,索引和着色器。</p>
<p>GPU直接编程是一个极其强大的概念,这种强大也需要大量付出。着色器比普通的Python代码要难得多,调试工作就更复杂了,也没有一个像Python的REPL那样方便的交互开发环境。也就是说,做应用的时候,写原始的GLSL代码是否必要并没有答案,只能因地制宜。</p>
<p>这章的例子只是一个简单教程,并不是能力测试。因为 GLSL非常难学,大量的书籍和在线教程都已经介绍过它,这里的内容不足以完整的介绍OpenGL的内容,若感兴趣可以看看。</p>
<p>现在,我们算是入门了。下一章我们利用这章的代码来做一个更好玩的应用,一个酷炫的射击游戏。</p>
</div>
</div>
</div>
</div>