<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<channel>
<title><![CDATA[Unified Python Planet]]></title>
<description><![CDATA[A uniqued union of the Official and Unofficial Python planet feeds.  Generated by Atomisator FTW!]]></description>
<link>http://feeds2.feedburner.com/UnifiedPythonPlanet</link>
<language>en</language>
<copyright>Copyright 2008, Atomisator</copyright>
<pubDate>Sat, 15 Mar 2008 00:15:05 +0200</pubDate>
<lastBuildDate>Sat, 15 Mar 2008 00:15:05 +0200</lastBuildDate>
  <item>
    <title><![CDATA[Peter Bengtsson: premailer now excludes pseudo selectors by default]]></title>
    <description><![CDATA[<p>Thanks to Igor who emailed me and made me aware, you can't put pseudo classes in style attributes in HTML. I.e. this does not work:<br />
</p>
<div class="highlight">

<pre><span class="nt">&lt;a</span> <span class="na">href=</span><span class="s">&quot;#&quot;</span> <span class="na">style=</span><span class="s">&quot;color:pink :hover{color:red}&quot;</span><span class="nt">&gt;</span>Sample Link<span class="nt">&lt;/a&gt;</span>
</pre></div>

<p>See for yourself: <a href="http://www.peterbe.com/oc-Python/rss.xml">Sample Link</a><br />
</p>
<p>Note how it does not become red when you hover over the link above.<br />
This is what <a href="https://pypi.python.org/pypi/premailer">premailer</a> <strong>used</strong> to do. Until yesterday.<br />
</p>
<p>BEFORE:<br />
<div class="highlight"></div></p>
<pre><span class="o">&gt;&gt;&gt;</span> <span class="kn">from</span> <span class="nn">premailer</span> <span class="kn">import</span> <span class="n">transform</span>
<span class="o">&gt;&gt;&gt;</span> <span class="k">print</span> <span class="n">transform</span><span class="p">(</span><span class="s">'''</span>
<span class="s">... &lt;html&gt;</span>
<span class="s">... &lt;style&gt;</span>
<span class="s">... a { color: pink }</span>
<span class="s">... a:hover { color: red }</span>
<span class="s">... &lt;/style&gt;</span>
<span class="s">... &lt;a href=&quot;#&quot;&gt;Sample Link&lt;/a&gt;</span>
<span class="s">... &lt;/html&gt;</span>
<span class="s">... '''</span><span class="p">)</span>
<span class="o">&lt;</span><span class="n">html</span><span class="o">&gt;&lt;</span><span class="n">head</span><span class="o">&gt;&lt;</span><span class="n">a</span> <span class="n">href</span><span class="o">=</span><span class="s">&quot;#&quot;</span> <span class="n">style</span><span class="o">=</span><span class="s">&quot;{color:pink} :hover{color:red}&quot;</span><span class="o">&gt;</span><span class="n">Sample</span> <span class="n">Link</span><span class="o">&lt;/</span><span class="n">a</span><span class="o">&gt;&lt;/</span><span class="n">head</span><span class="o">&gt;&lt;/</span><span class="n">html</span><span class="o">&gt;</span>
</pre>

<p></p><br />

<p>AFTER:<br />
<div class="highlight"></div></p>
<pre><span class="o">&gt;&gt;&gt;</span> <span class="kn">from</span> <span class="nn">premailer</span> <span class="kn">import</span> <span class="n">transform</span>
<span class="o">&gt;&gt;&gt;</span> <span class="k">print</span> <span class="n">transform</span><span class="p">(</span><span class="s">'''</span>
<span class="s">... &lt;html&gt;</span>
<span class="s">... &lt;style&gt;</span>
<span class="s">... a { color: pink }</span>
<span class="s">... a:hover { color: red }</span>
<span class="s">... &lt;/style&gt;</span>
<span class="s">... &lt;a href=&quot;#&quot;&gt;Sample Link&lt;/a&gt;</span>
<span class="s">... &lt;/html&gt;</span>
<span class="s">... '''</span><span class="p">)</span>
<span class="o">&lt;</span><span class="n">html</span><span class="o">&gt;&lt;</span><span class="n">head</span><span class="o">&gt;</span>
<span class="o">&lt;</span><span class="n">style</span><span class="o">&gt;</span><span class="n">a</span><span class="p">:</span><span class="n">hover</span> <span class="p">{</span><span class="n">color</span><span class="p">:</span><span class="n">red</span><span class="p">}</span><span class="o">&lt;/</span><span class="n">style</span><span class="o">&gt;</span>
<span class="o">&lt;</span><span class="n">a</span> <span class="n">href</span><span class="o">=</span><span class="s">&quot;#&quot;</span> <span class="n">style</span><span class="o">=</span><span class="s">&quot;color:pink&quot;</span><span class="o">&gt;</span><span class="n">Sample</span> <span class="n">Link</span><span class="o">&lt;/</span><span class="n">a</span><span class="o">&gt;</span>
<span class="o">&lt;/</span><span class="n">head</span><span class="o">&gt;&lt;/</span><span class="n">html</span><span class="o">&gt;</span>
</pre>

<p></p><br />

<p>That's because the new default is <a href="https://github.com/peterbe/premailer/blob/129ae59fadf55a268b03b74ac300c2cdcb0ceb1e/premailer/premailer.py#L100"><strong>exclude</strong> pseudo classes by default</a>.<br />
</p>
<p>Thanks Igor for making me aware! </p>]]></description>
    <link><![CDATA[http://www.peterbe.com/plog/premailer-now-excludes-pseudo-selectors-by-default]]></link>
    <pubDate>2013-05-27 00:47:56</pubDate>
  </item>
  <item>
    <title><![CDATA[Vasudev Ram: Different ways to read the comp.lang.python newsgroup]]></title>
    <description><![CDATA[<p><a href="http://wiki.python.org/moin/CompLangPython">CompLangPython - Python Wiki</a></p><p>Saw this via a chain of links from a comp.lang.python post.</p><p>This will be known to many people but not to many others, so I'm blogging about it:</p><p>The Python Wiki page linked above, describes multiple ways of reading the Usenet newsgroup comp.lang.python, which is also gatewayed bi-directionally with the python-list mailing list hosted at http://python.org.</p><p>Usenet newsreaders, email, Gmane.org and Google Groups are four of the ways described.</p><p>- Vasudev Ram<br />http://dancingbison.com</p><div class="blogger-post-footer"><a href="http://www.dancingbison.com">Vasudev Ram</a>
<br /></div>]]></description>
    <link><![CDATA[http://jugad2.blogspot.com/2013/05/different-ways-to-read-complangpython.html]]></link>
    <pubDate>2013-05-26 23:44:15</pubDate>
  </item>
  <item>
    <title><![CDATA[Jacob Perkins: Instant PyGame Book Review]]></title>
    <description><![CDATA[<p><a href="http://link.packtpub.com/2Vrs5V"><img class="alignleft" alt="Pygame for Python Game Development How-to" src="http://www.packtpub.com/sites/default/files/2865OScov.jpg" width="130" height="160" /></a>This is a review of the book <a href="http://link.packtpub.com/2Vrs5V">Instant Pygame for Python Game Development How-to</a>, by <a href="http://ivanidris.net/wordpress/">Ivan Idris</a>. <a href="http://www.packtpub.com/">Packt</a> asked me to review the book, and I agreed because like many developers, I've thought about writing my own game, and I've been curious about the capabilities of <a href="http://www.pygame.org/">pygame</a>. It's a short book, ~120 pages, so this is a short review.</p>
<p>The book covers <a href="http://www.pygame.org/">pygame</a> basics like drawing images, rendering text, playing sounds, creating animations, and altering the mouse cursor. The author has helpfully posted some <a href="http://www.youtube.com/user/ivanidris">video demos</a> of some of the exercises, which are linked from the book. I think this is a great way to show what's possible, while also giving the reader a clear idea of what they are creating &amp; what should happen. After the basic intro exercises, I think the best content was how to manipulate pixel arrays with <a href="http://www.numpy.org/">numpy</a> (the author has also written two books on numpy: <a href="http://www.amazon.com/gp/product/B00CITNP76/ref=as_li_ss_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B00CITNP76&linkCode=as2&tag=streamhacker-20">NumPy Beginner's Guide</a> &amp; <a href="http://www.amazon.com/gp/product/B009X5KIH8/ref=as_li_ss_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=B009X5KIH8&linkCode=as2&tag=streamhacker-20">NumPy Cookbook</a>), how to create &amp; use sprites, and how to make your own version of the <a href="http://www.youtube.com/watch?v=NNsU-yWTkXM">game of life</a>.</p>
<p>There were 3 chapters whose content puzzled me. When you've got such a short book on a specific topic, why bring up <a href="http://matplotlib.org/">matplotlib</a>, profiling, and debugging? These chapters seemed off-topic and just thrown in there randomly. The organization of the book could have been much better too, leading the reader from the basics all the way to a full-fledged game, with each chapter adding to the previous chapters. Instead, the chapters sometimes felt like unrelated low-level examples.</p>
<p>Overall, the book was a quick &amp; easy read, that rapidly introduces you to basic pygame functionality, and leads you on to more complex activities. My main takeaway is that pygame provides an easy to use &amp; low-level framework for building simple games, and can be used to create more complex games (but probably not <a href="http://en.wikipedia.org/wiki/First-person_shooter">FPS</a> or similar graphically intensive games). The ideal games would probably be puzzle based and/or dialogue heavy, and only require simple interactions from the user. So if you're interested in building such a game in Python, you should definitely get a copy of <a href="http://link.packtpub.com/2Vrs5V">Instant Pygame for Python Game Development How-to</a>.</p>
<img src="http://feeds.feedburner.com/~r/StreamHackerPython/~4/me5L-vP5M5I" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/StreamHackerPython/~3/me5L-vP5M5I/]]></link>
    <pubDate>2013-05-26 19:44:06</pubDate>
  </item>
  <item>
    <title><![CDATA[Doug Hellmann: Smiley 0.1.0 — Python Application Tracer]]></title>
    <description><![CDATA[What is smiley? Smiley spies on your Python app while it runs. Smiley includes several subcommands for running Python programs and monitoring all of the internal details for recording and reporting. For more details, see the README on PyPI. What&#8217;s New? This is the first public release of Smiley, and it is very early in [...]]]></description>
    <link><![CDATA[http://doughellmann.com/2013/05/smiley-0-1-0-python-application-tracer.html]]></link>
    <pubDate>2013-05-26 16:32:42</pubDate>
  </item>
  <item>
    <title><![CDATA[Brett Cannon: Practicality beats purity when it comes to backwards-compatibility]]></title>
    <description><![CDATA[This past week I was on a 5 hour train ride where I was doing some API work on <a href="http://docs.python.org/3/library/importlib.html#module-importlib">importlib</a> to make it easier to customize imports. The route I have been taking to accomplish that is to define a couple key methods in the appropriate ABC so that subclasses can do as little custom work as possible and use sensible defaults as defined by the ABCs. That way gnarly details are taken care of in a consistent way by importlib and users can simply focus on what is different/special about their importers.<br />
<br />
Part of that includes dealing with __path__. For those of you who might not be aware, the existence of __path__ on a module is the <a href="http://docs.python.org/3/reference/import.html#packages">official definition of a package</a> in Python. And <a href="http://docs.python.org/3/reference/import.html#loaders">if __path__ is defined it must be an iterable that only returns strings</a>&nbsp;(if it returns anything at all; this all means [] is a legit value for __path__ to define that something is a package). Relatively straight-forward in the common case of some directory on your file system representing a package.<br />
<br />
But what about situations like zipfiles? They are kind of like a file system, but not exactly; at least they have the innate concept of a path. Or how about a database that stores your code? In that situation a file path isn't really there unless you choose to make it happen; you could easily have a primary key of just module names and completely forgo the idea of paths. Since I'm trying to minimize the work anyone has to do for a custom importer I wanted to make sure that the only thing a user of importlib might have to do for __path__ is provide its custom value, I tried to come up with a way to allow that.<br />
<br />
This all came up while I was designing a method to handle the setting of the various attributes on modules. As of right now that's all handled in&nbsp;<a href="http://docs.python.org/3/library/importlib.html#importlib.abc.Loader.load_module">importlib.abc.Loader.load_module()</a>&nbsp; which is not a good point to do it at since you need to set it on a module and all load_module() takes is a name as a string (i.e. there is no hook in the API to tweak attributes on a module before it is executed). By providing a method that does the right thing using the pre-existing APIs allows for a convenient location to tweak values, such as __path__. The method is going to be called init_module_attrs() and will set things like __loader__, __package__, __file__, __cached__, and hopefully __path__ (the trigger for this post).<br />
<br />
As it stands now, the only API for packages in importlib is <a href="http://docs.python.org/3/library/importlib.html#importlib.abc.InspectLoader.is_package">importlib.abc.InspectLoader.is_package()</a>&nbsp;on loaders which I inherited from <a href="http://python.org/dev/peps/pep-0302/">PEP 302</a>. The problem with this method is that all is_package() does is return a boolean value to say whether something is a package or not. There was no provided API to return what __path__ should be. Basically there was no hook point in the API for overloading a function to provide __path__ for a module until after import which is not where you want it to happen.<br />
<br />
My initial reaction was, "I'll define a new method!" From an API perspective that the purest solution. I could define a new method called package_paths() that either returns what __path__ should be set to or even subsume is_package() by returning None when a module isn't a package and all other values signify a package and what __path__ should be (this is to allow [] as a value for __path__). Nice and straight-forward.<br />
<br />
But upon reflecting on the idea, I realized it wasn't practical. While databases and such might not need to rely on paths, in the massive majority of cases __path__ will end up with at least path-like entries (if for any other reason than most code that mucks with __path__ just assumes it can use os.path), so that suggests using what <a href="http://docs.python.org/3/library/importlib.html#importlib.abc.ExecutionLoader.get_filename">importlib.abc.ExecutionLoader.get_filename()</a> returns is reasonable. There is also the point that users would need to define this new method just to get any help with setting __path__ which is always a problem since code would only be usable in 3.4 without users writing their own shim for 3.3 and earlier. All of this led to me to the realization that it would be better to just set __path__ in init_module_attrs() as best as I could (e.g. <span>os.path.dirname(self.get_filename())</span>or []) and that if the default didn't work then people could override the method, call <span>super().init_module_attrs()</span>, and then reset __path__ to what they want. That still provides a way to make overriding simple while still giving a reasonable default for the majority of cases. Practicality beats purity, especially when you are trying to provide a solution that works in a backwards-compatible fashion.<br />
<br />
And just to really hit this point home, I had another "brilliant" idea that I decided was too much of me thinking about API purity and not what was practical. With the advent of __package__, an alternative way to tell if a module is a package is to see if __name__ == __package__. That's nice and simple and is a clearer definition since both of those attributes always exist (at least starting in Python 3.3) and it doesn't require having a dummy empty list entry on packages where __path__ must exist but lacks any other reasonable value (which is what I was trying to avoid since you don't see an empty list for __path__ very often and I didn't want to confuse users by doing that or that __path__ had to have a true value). Good idea I thought!<br />
<br />
Except in a practical sense, there is too much code out there relying on __path__ being the way to tell if a module is a package. Now making older code work with this __name__ == __package__ idea wouldn't be hard since it's a check for that and if __package__ isn't set or is None then fall back on looking for __path__. I could even provide an importlib.util.is_package() whose code could be copied easily for code that needed to stay compatible with 3.3 and earlier. But what benefit would it get me? Not confusing users when they see an empty list for __path__? That's pretty minor since the vast majority of code should be doing a <span>hasattr(module, '__path__')</span> check to tell if something is a package instead of checking for the attribute <b>and</b>&nbsp;making sure the value is true. Plus how much code ever changes __path__, let alone even cares if it exists? My bet is hardly anyone cares, and so it would be better to not try to clean up the semantics to be more straight-forward and easier to explain for all future versions of Python how to tell if a module is a package and just stick with what is seemingly working now for all pre-existing code when the status quo isn't that bad.<br />
<br />
So practicality beats purity when dealing with backwards-compatibility. When the itch strikes you to expand an API to try to have a more pure solution to something, take a moment before you scratch that itch and see if there is a more backwards-compatible way that might not be as pure or simple, but still allows for the same outcome. This has happened to me more than once with importlib which is why I always mull over any API changes for at least a week before actually committing any code.<img src="http://feeds.feedburner.com/~r/CoderWhoSaysPy/~4/lwMDG2KzAzE" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/CoderWhoSaysPy/~3/lwMDG2KzAzE/practicality-beats-purity-when-it-comes.html]]></link>
    <pubDate>2013-05-26 15:02:01</pubDate>
  </item>
  <item>
    <title><![CDATA[Vasudev Ram: Convert multiple text files to PDF with xtopdf]]></title>
    <description><![CDATA[<p>This Python program, batch_text_to_pdf.py, can create a PDF file from a batch of text files.</p><p>It uses my xtopdf toolkit, ReportLab and Python.</p><p>It also uses the fileinput module from Python's standard library.</p><p>The fileinput module is quite useful for creating Unix-style command-line programs in Python, that process a batch of text files, like awk, sed, and other similar Unix tools can.</p><p>The basic pattern for these commands is:</p><p>command_name file1.txt file2.txt&#160; ...</p><p># batch_text_to_pdf.py</p><p>import string<br />import fileinput<br />from PDFWriter import PDFWriter</p><p>def main():</p><p>&#160;&#160;&#160; # Hard-coded for now,<br />&#160;&#160;&#160; # should take PDF name from<br />&#160;&#160;&#160; # command line, by skip-<br />&#160;&#160;&#160; # ing first name for fileinput.<br />&#160;&#160;&#160; pw = PDFWriter('output.pdf')</p><p>&#160;&#160;&#160; pw.setFont('Courier', 12)<br />&#160;&#160;&#160; pw.setHeader('Batch text files to PDF')<br />&#160;&#160;&#160; pw.setFooter('Created by xtopdf')</p><p>&#160;&#160;&#160; for line in fileinput.input():<br />&#160;&#160;&#160;&#160;&#160;&#160;&#160; pw.writeLine(line.strip('\n'))</p><p>&#160;&#160;&#160; pw.savePage()<br />&#160;&#160;&#160; pw.close()</p><p>main()</p><p>The program has no error checking and can be improved some (e.g. see the comment in the code); this is just a quick demo.</p><p>Run it like this:</p><p>python batch_text_to_pdf.py file1.txt&#160; ...</p><p>where the&#160; ... means (optionally)&#160; more text file names.</p><p>The combined output should then be in file output.pdf.</p><p>xtopdf: https://bitbucket.org/vasudevram/xtopdf<br />ReportLab: http://www.reportlab.com/ftp<br />Use ReportLab v1.21, not v2.x.</p><p>Posted via mobile, sorry for narrow post width.</p><p>- Vasudev Ram<br />dancingbison.com</p><div class="blogger-post-footer"><a href="http://www.dancingbison.com">Vasudev Ram</a>
<br /></div>]]></description>
    <link><![CDATA[http://jugad2.blogspot.com/2013/05/convert-multiple-text-files-to-pdf-with.html]]></link>
    <pubDate>2013-05-26 05:05:25</pubDate>
  </item>
  <item>
    <title><![CDATA[Python Diary: Django bootstrap theme is ready!]]></title>
    <description><![CDATA[<p>It's finally ready for developer consumption, my <a href="http://www.pythondiary.com/packages/django-bootstrap-theme.html">Django bootstrap theme</a><a href="https://bitbucket.org/kveroneau/django-bootstrap-theme" rel="tooltip" title="Direct link"><i class="icon-bookmark"></i></a> Django app.  With this super simple app in your Django project, you can easily apply a site-wide Bootstrap theme in a matter of minutes.  Here's an example <b>base.html</b> you would use in your project's templates directory:</p>
<div class="highlight"><pre><span class="cp">{%</span> <span class="k">extends</span> <span class="s1">'bootstrap_theme/starter.html'</span> <span class="cp">%}</span>

<span class="cp">{%</span> <span class="k">block</span> <span class="nv">title</span> <span class="cp">%}</span>Bootstrap Django site<span class="cp">{%</span> <span class="k">endblock</span> <span class="cp">%}</span>

<span class="cp">{%</span> <span class="k">block</span> <span class="nv">branding</span> <span class="cp">%}</span>Django Project<span class="cp">{%</span> <span class="k">endblock</span> <span class="cp">%}</span>

<span class="cp">{%</span> <span class="k">block</span> <span class="nv">content</span> <span class="cp">%}</span>
Yes Folks, this is how easy it is to implement bootstrap in Django now.  Be sure to look at the other templates available to see what <span class="nt">&lt;b&gt;</span>blocks<span class="nt">&lt;/b&gt;</span> you can override.
<span class="cp">{%</span> <span class="k">endblock</span> <span class="cp">%}</span>
</pre></div>

<p>Be sure to add <b>bootstrap_theme</b> to your <b>INSTALLED_APPS</b>, and this will work like a charm.  Here is a list of available base templates you can extend:</p>
<dl>
<dt>base.html</dt>
<dd>This is the main template which the ones below extend to create the Bootstrap experience.</dd>
<dt>starter.html</dt>
<dd>This is the exact starter template from the Bootstrap documentation, it's the most basic and easiest to extend.</dd>
<dt>fluid.html</dt>
<dd>Similar to starter, except it allows users to sign in and out, and has a left navigation bar.</dd>
<dt>marketing.html</dt>
<dd>The marketing template directly from the bootstrap examples page tailored to work with Django.</dd>
<dt>marketing_narrow.html</dt>
<dd>From the bootstrap example page, another version of a marketing page you can try out.</dd>
<dt>marketing_clean.html</dt>
<dd>Yet another marketing page example, also from the bootstrap examples page.</dd>
<dt>carousel.html</dt>
<dd>The bootstrap carousel example made for Django, the bonus is that the Carousel is driven from the database, so it's easy to add and edit the big Carousel banner.</dd>
</dl>
<p>Wait, there's more...  There's a large amount of <b>templatetags</b> at your disposal as well in the <b>bootstrap</b> library which you can load into any template you need it in.  Here's what's included:</p>
<dl>
<dt>buttonlink <b>url icon</b></dt>
<dd>You can create bootstrap themed buttons with this tag.</dd>
<dt>emphasis filter</dt>
<dd>This filter will add special styling to the text, such as <i>muted</i> which is the default, or anything in the <i>text-</i> CSS components.</dd>
<dt>abbrev filter</dt>
<dd>This handy filter will create an abbreviation text.</dd>
<dt>yesnoicon filter</dt>
<dd>Depending if the value is <b>True</b> or <b>False</b> a different icon is displayed.</dd>
<dt>ratingicon filter</dt>
<dd>This super handy filter will create a specific amount of <b>star</b> icons depending on the value.</dd>
<dt>link filter</dt>
<dd>Pass in any model object which has a <b>get_absolute_url</b> and it will automatically make it click-able.</dd>
<dt>carousel <b>group_name</b></dt>
<dd>This is used to display a database driven Carousel.  The <b>group_name</b> parameter is which group in the database model to display for this instance.</dd>
<dt>navbar_gradient <b>first_color last_color</b></dt>
<dd>This super handy tag will automatically generate the required CSS style needed for the navbar gradient.</dd>
<dt>modal <b>title modal_id form_action</b></dt>
<dd>This block tag is used to generate a modal dialog box, the last 2 options are optional.  The first is the title of the dialog box, the second is the unique div ID for the modal, and the last one is the URL which the form inside the modal will be posted to, if the user saves the changes.</dd>
<dt>icon <b>slug</b></dt>
<dd>This is used to display bootstrap icons easily.</dd>
<dt>modal_button <b>title modal_id icon</b></dt>
<dd>This is used to generate the required <b>A</b> tag to display a modal dialog box generated above.  The first option is what to display on the button itself, the second is the div ID chosen, and the last one is an icon slug to place on the button.  The last two options are optional and have reasonable defaults.</dd>
</dl>
<p>Don't leave yet, there's still one last thing...  An include-able template which can display a Django form very elegantly in bootstrap!  Finally you can make your Django generated forms look great and functional with very little effort.  Here's an example contact form to get you started:</p>
<div class="highlight"><pre>          <span class="nt">&lt;form</span> <span class="na">action=</span><span class="s">&quot;</span><span class="cp">{%</span> <span class="k">url</span> <span class="s1">'contact-form'</span> <span class="cp">%}</span><span class="s">&quot;</span> <span class="na">method=</span><span class="s">&quot;post&quot;</span><span class="nt">&gt;</span><span class="cp">{%</span> <span class="k">csrf_token</span> <span class="cp">%}</span>
            <span class="nt">&lt;fieldset&gt;</span>
              <span class="nt">&lt;legend&gt;</span>Contact form<span class="nt">&lt;/legend&gt;</span>
              <span class="cp">{%</span> <span class="k">include</span> <span class="s2">&quot;bootstrap_form.html&quot;</span> <span class="cp">%}</span>
              <span class="nt">&lt;button</span> <span class="na">type=</span><span class="s">&quot;submit&quot;</span> <span class="na">class=</span><span class="s">&quot;btn btn-primary&quot;</span><span class="nt">&gt;</span>Send Message<span class="nt">&lt;/button&gt;</span>
            <span class="nt">&lt;/fieldset&gt;</span>
          <span class="nt">&lt;/form&gt;</span>
</pre></div>

<p>Wow, that's a lot to take in, in the next release I will be adding a bunch of ready made templates for <a href="http://www.pythondiary.com/packages/django-registration.html">django-registration</a><a href="https://bitbucket.org/ubernostrum/django-registration/" rel="tooltip" title="Direct link"><i class="icon-bookmark"></i></a> so that you can get a new Django website up and running in a matter of hours.</p>]]></description>
    <link><![CDATA[http://www.pythondiary.com/blog/May.25,2013/django-bootstrap-theme-ready.html]]></link>
    <pubDate>2013-05-25 23:41:06</pubDate>
  </item>
  <item>
    <title><![CDATA[Catherine Devlin: Review of Learning IPython for Interactive Computing and Data Visualization]]></title>
    <description><![CDATA[<div id="hreview-valuable-but-traditional" class="hreview"><h2 class="summary">valuable but traditional</h2><abbr title="2013-05-25T21:56-04:00" class="dtreviewed">May 25, 2013</abbr>  by <span class="reviewer vcard"><span class="fn">Catherine Devlin</span></span><span class="type">product</span><img class="photo" src="http://dgdsbygo8mp3h.cloudfront.net/sites/default/files/imagecache/productview_larger/9932OS.jpg" alt="photo of 'Learning IPython for Interactive Computing and Data Visualization'" /><div class="item"><a href="http://catherinedevlin.blogspot.com/feeds/posts/default/-/www.packtpub.com/learning-ipython-for-interactive-computing-and-data-visualization/book" class="fn url">Learning IPython for Interactive Computing and Data Visualization</a></div> <span><span class="rating">4</span> stars (of 5)</span> <div class="description"> <p><a>Packt Publishing</a> recently asked if I could review their new title, <a href="http://www.packtpub.com/learning-ipython-for-interactive-computing-and-data-visualization/book">Learning IPython for Interactive Computing and Data Visualization</a>.   (I got the e-book free for doing the review, but they don't put any conditions on what I say about it.) I don't often do reviews like that, but I couldn't pass one this up because I'm so excited about the IPython Notebook.</p> <p>It's a mini title, but it does contain a lot of information I was very pleased to see.   First and foremost, this is the first book to focus on the IPython Notebook.  That's huge.  Also:</p> <ul>  <li>The installation section is thorough and goes well beyond the obvious, discussing options like  using prepackaged all-in-one Python distributions like <a href="https://store.continuum.io/">Anaconda</a>.</li><li>Some of the improvements IPython can make to a programming workflow are nicely introduced, like the ease of debugging, source code inspection, and profiling with the appropriate magics.</li><li>The section on writing new IPython extensions is extremely valuable - it contains more complete  examples than the official documentation does and would have saved me lots of time and excess code if I'd had it when I was writing  <a href="https://pypi.python.org/pypi/ipython-sql">ipython-sql</a>. </li><li>There are introductions to all the classic uses that scientists doing numerical simulations value IPython for: convenience in array handling, Pandas integration, plotting, parallel computing, image processing, Cython for faster CPU-bound operations, etc.  The book makes no claim to go deeply into any of these, but it gives introductory examples that at least give an idea of how the problems are approached and why IPython excels at them.</li></ul> <p>So what don't I like?  Well, I wish for more.  It's not fair to ask for more bulk in a small book that was brought to market swiftly, but I can wish for a more forward-looking, imaginative treatment.  The IPython Notebook is ready to go far  beyond IPython's traditional core usership in the SciPy community, but this book doesn't really make that pitch.  It only touches lightly on how easily and beautifully IPython can replace shell scripting. It doesn't get much into the unexplored possibilities that  IPython Notebook's rich display capabilities open up.  (I'm thinking of  <a href="https://pypi.python.org/pypi/ipythonblocks/">IPython Blocks</a> as a great example of things we can do with IPython Notebook that we never imagined at first glance).  This book is a good introduction to IPython's uses as traditionally understood, but it's not the manifesto for the upcoming IPython Notebook Revolution.</p> <p>The power of hybrid documentation/programs for learning and individual and group productivity is one more of IPython Notebook's emerging possibilities that this book only  mentions in passing, and passes up a great chance to demonstrate. The sample code is downloadable as IPython Notebook <var>.ipynb</var> files, but the bare code is alone  in the cells, with no use of Markdown cells to annotate or clarify.  Perhaps this is just because Packt was afraid that more complete Notebook files would be pirated, but it's a shame. </p>  <p>Overall, this is a short book that achieves its modest goal: a technical introduction to IPython in its traditional uses.  You should get it, because IPython Notebook is too important to sit around waiting for the ultimate book - you should be using the Notebook <em>today</em>.   But save space on your bookshelf for future books, because there's much more to be said on the topic, some of which hasn't even been imagined yet.</p> </div><p>This <a href="http://microformats.org/wiki/hreview">hReview</a> brought to you by the <a href="http://microformats.org/code/hreview/creator">hReview Creator</a>.</p></div>]]></description>
    <link><![CDATA[http://catherinedevlin.blogspot.com/2013/05/review-of-learning-ipython-for.html]]></link>
    <pubDate>2013-05-25 20:27:42</pubDate>
  </item>
  <item>
    <title><![CDATA[Go Deh: Greedy Ranking Algorithm in Python]]></title>
    <description><![CDATA[<br />I mentioned in <a href="http://paddy3118.blogspot.co.uk/2008/07/ranking-modelsim-coverage-results-using.html" target="_blank">an earlier post</a> that I had written my own ranker and thought I'd revisit this with some code.<br /><br />I verify and ensure the safety of microprocessors for my day job. One way that very complex CPU's are tested is to create another model of the chip which can be used to generate pseudo-random instruction streams to run on CPU. The so-called ISG can create thousands (millions!) of these tests in very little time, and the ISG is written in such a way that it can be 'tweaked' to give some control or steering to what the instruction streams will exercise on the CPU.<br /><br />Now simulating these instruction streams and gathering information on just what parts of the CPU are exercised, called covered, by each individual test takes time, and multiple ISG generated tests may cover the same regions of the CPU. To increase the overall coverage of of the CPU we run what is called a regression - all the tests are run and their coverage and the time they take to simulate are stored. at the end of the regression run you may have several thousands of tests that cover only part of the CPU.<br /><br />If you take the regression results and <b><i>rank </i></b>them you can find that subset of the tests that give all the coverage. Usually thousands of pseudo-random tests might be ranked and generate a sub-list of only hundreds of tests that when run would give the same coverage. What we then usually do is look at what isn't covered and generate some more tests by the ISG or other methods to try and fill the gaps; run the new regression and rank again in a loop to fully exercise the CPU and hit some target coverage goal.<br /><br />Ranking tests is an important part of the regression flow described above, and when it works well you forget about it. Unfortunately sometimes I want to to rank other data, for which the stock ranking program from the CAD tool vendors does not fit. So here is the guts of a ranking program that will scale to handling hundreds of thousands of tests and coverage points.<br /><br /><h3>Input</h3>Normally I have to parse my input from text or HTML files of results generated by other CAD programs - it is tedious work that I will skip by providing idealised inputs in the form of a Python dict. (Sometimes the code for parsing input files can be as large or larger than the ranking algorithm).<br /><br />Let us assume that each ISG test has a name, runs for a certain 'time' and when simulated is shown to 'cover' a set of numbered features of the design. after the parsing, the gathered input data is represented by the <span>results </span>dict in the program.<br /><br /><pre><span class="lnr">  1 </span><br /><span class="lnr">  2 </span>results = {<br /><span class="lnr">  3 </span><span class="Comment">#    'TEST': (  TIME, set([COVERED_POINT ...])),</span><br /><span class="lnr">  4 </span>  <span class="Constant">'test_00'</span>: (  <span class="Constant">2.08</span>, <span class="Identifier">set</span>([<span class="Constant">2</span>, <span class="Constant">3</span>, <span class="Constant">5</span>, <span class="Constant">11</span>, <span class="Constant">12</span>, <span class="Constant">16</span>, <span class="Constant">19</span>, <span class="Constant">23</span>, <span class="Constant">25</span>, <span class="Constant">26</span>, <span class="Constant">29</span>, <span class="Constant">36</span>, <span class="Constant">38</span>, <span class="Constant">40</span>])),<br /><span class="lnr">  5 </span>  <span class="Constant">'test_01'</span>: ( <span class="Constant">58.04</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">10</span>, <span class="Constant">13</span>, <span class="Constant">15</span>, <span class="Constant">17</span>, <span class="Constant">19</span>, <span class="Constant">20</span>, <span class="Constant">22</span>, <span class="Constant">27</span>, <span class="Constant">30</span>, <span class="Constant">31</span>, <span class="Constant">33</span>, <span class="Constant">34</span>])),<br /><span class="lnr">  6 </span>  <span class="Constant">'test_02'</span>: ( <span class="Constant">34.82</span>, <span class="Identifier">set</span>([<span class="Constant">3</span>, <span class="Constant">4</span>, <span class="Constant">6</span>, <span class="Constant">12</span>, <span class="Constant">15</span>, <span class="Constant">21</span>, <span class="Constant">23</span>, <span class="Constant">25</span>, <span class="Constant">26</span>, <span class="Constant">33</span>, <span class="Constant">34</span>, <span class="Constant">40</span>])),<br /><span class="lnr">  7 </span>  <span class="Constant">'test_03'</span>: ( <span class="Constant">32.74</span>, <span class="Identifier">set</span>([<span class="Constant">4</span>, <span class="Constant">5</span>, <span class="Constant">10</span>, <span class="Constant">16</span>, <span class="Constant">21</span>, <span class="Constant">22</span>, <span class="Constant">26</span>, <span class="Constant">39</span>])),<br /><span class="lnr">  8 </span>  <span class="Constant">'test_04'</span>: (<span class="Constant">100.00</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">1</span>, <span class="Constant">4</span>, <span class="Constant">6</span>, <span class="Constant">7</span>, <span class="Constant">8</span>, <span class="Constant">9</span>, <span class="Constant">11</span>, <span class="Constant">12</span>, <span class="Constant">18</span>, <span class="Constant">26</span>, <span class="Constant">27</span>, <span class="Constant">31</span>, <span class="Constant">36</span>])),<br /><span class="lnr">  9 </span>  <span class="Constant">'test_05'</span>: (  <span class="Constant">4.46</span>, <span class="Identifier">set</span>([<span class="Constant">1</span>, <span class="Constant">2</span>, <span class="Constant">6</span>, <span class="Constant">11</span>, <span class="Constant">14</span>, <span class="Constant">16</span>, <span class="Constant">17</span>, <span class="Constant">21</span>, <span class="Constant">22</span>, <span class="Constant">23</span>, <span class="Constant">30</span>, <span class="Constant">31</span>])),<br /><span class="lnr"> 10 </span>  <span class="Constant">'test_06'</span>: ( <span class="Constant">69.57</span>, <span class="Identifier">set</span>([<span class="Constant">10</span>, <span class="Constant">11</span>, <span class="Constant">15</span>, <span class="Constant">17</span>, <span class="Constant">19</span>, <span class="Constant">22</span>, <span class="Constant">26</span>, <span class="Constant">27</span>, <span class="Constant">30</span>, <span class="Constant">32</span>, <span class="Constant">38</span>])),<br /><span class="lnr"> 11 </span>  <span class="Constant">'test_07'</span>: ( <span class="Constant">85.71</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">2</span>, <span class="Constant">4</span>, <span class="Constant">5</span>, <span class="Constant">9</span>, <span class="Constant">10</span>, <span class="Constant">14</span>, <span class="Constant">17</span>, <span class="Constant">24</span>, <span class="Constant">34</span>, <span class="Constant">36</span>, <span class="Constant">39</span>])),<br /><span class="lnr"> 12 </span>  <span class="Constant">'test_08'</span>: (  <span class="Constant">5.73</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">3</span>, <span class="Constant">8</span>, <span class="Constant">9</span>, <span class="Constant">13</span>, <span class="Constant">19</span>, <span class="Constant">23</span>, <span class="Constant">25</span>, <span class="Constant">28</span>, <span class="Constant">36</span>, <span class="Constant">38</span>])),<br /><span class="lnr"> 13 </span>  <span class="Constant">'test_09'</span>: ( <span class="Constant">15.55</span>, <span class="Identifier">set</span>([<span class="Constant">7</span>, <span class="Constant">15</span>, <span class="Constant">17</span>, <span class="Constant">25</span>, <span class="Constant">26</span>, <span class="Constant">30</span>, <span class="Constant">31</span>, <span class="Constant">33</span>, <span class="Constant">36</span>, <span class="Constant">38</span>, <span class="Constant">39</span>])),<br /><span class="lnr"> 14 </span>  <span class="Constant">'test_10'</span>: ( <span class="Constant">12.05</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">4</span>, <span class="Constant">13</span>, <span class="Constant">14</span>, <span class="Constant">15</span>, <span class="Constant">24</span>, <span class="Constant">31</span>, <span class="Constant">35</span>, <span class="Constant">39</span>])),<br /><span class="lnr"> 15 </span>  <span class="Constant">'test_11'</span>: ( <span class="Constant">52.23</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">3</span>, <span class="Constant">6</span>, <span class="Constant">10</span>, <span class="Constant">11</span>, <span class="Constant">13</span>, <span class="Constant">23</span>, <span class="Constant">34</span>, <span class="Constant">40</span>])),<br /><span class="lnr"> 16 </span>  <span class="Constant">'test_12'</span>: ( <span class="Constant">26.79</span>, <span class="Identifier">set</span>([<span class="Constant">0</span>, <span class="Constant">1</span>, <span class="Constant">4</span>, <span class="Constant">5</span>, <span class="Constant">7</span>, <span class="Constant">8</span>, <span class="Constant">10</span>, <span class="Constant">12</span>, <span class="Constant">13</span>, <span class="Constant">31</span>, <span class="Constant">32</span>, <span class="Constant">40</span>])),<br /><span class="lnr"> 17 </span>  <span class="Constant">'test_13'</span>: ( <span class="Constant">16.07</span>, <span class="Identifier">set</span>([<span class="Constant">2</span>, <span class="Constant">6</span>, <span class="Constant">9</span>, <span class="Constant">11</span>, <span class="Constant">13</span>, <span class="Constant">15</span>, <span class="Constant">17</span>, <span class="Constant">18</span>, <span class="Constant">34</span>])),<br /><span class="lnr"> 18 </span>  <span class="Constant">'test_14'</span>: ( <span class="Constant">40.62</span>, <span class="Identifier">set</span>([<span class="Constant">1</span>, <span class="Constant">2</span>, <span class="Constant">8</span>, <span class="Constant">15</span>, <span class="Constant">16</span>, <span class="Constant">19</span>, <span class="Constant">22</span>, <span class="Constant">26</span>, <span class="Constant">29</span>, <span class="Constant">31</span>, <span class="Constant">33</span>, <span class="Constant">34</span>, <span class="Constant">38</span>])),<br /><span class="lnr"> 19 </span> }<br /><span class="lnr"> 20 </span><br /></pre><div><span class="lnr"><br /></span></div><h3>Greedy ranking algorithm</h3>The object of the algorithm is to select and order a subset of the tests that:<br /><br /><ol><li>Cover as many of the coverage points as possible by at least one test.</li><li>After the above, reduce the number of tests needed to achieve that maximum coverage by as much as is possible.</li><li>Generate a ranking of the tests selected to allow an even smaller set of tests to be selected if necessary.</li><li>After all the above having increasing importance, it would be good to also reduce the total 'time' accrued by the ranking tests .</li><li>Of course it needs to work for large sets of tests and points to cover.</li></ol>The greedy algorithm works by first choosing the test giving most coverage to be the test of highest rank, then the test giving the most incremental additional coverage as the next highest ranking test, and so on...<br />If there are more than one test giving the same incremental additional coverage at any stage then the test taking the least 'time' is picked.<br /><br /><h4>The following function implements the algorithm:</h4><br /><pre><span class="lnr"> 21 </span><span class="Statement">def</span> <span class="Identifier">greedyranker</span>(results):<br /><span class="lnr"> 22 </span>    results = results.copy()<br /><span class="lnr"> 23 </span>    ranked, coveredsofar, costsofar, <span class="Identifier">round</span> = [], <span class="Identifier">set</span>(), <span class="Constant">0</span>, <span class="Constant">0</span><br /><span class="lnr"> 24 </span>    noncontributing = []<br /><span class="lnr"> 25 </span>    <span class="Statement">while</span> results:<br /><span class="lnr"> 26 </span>        <span class="Identifier">round</span> += <span class="Constant">1</span><br /><span class="lnr"> 27 </span>        <span class="Comment"># What each test can contribute to the pool of what is covered so far</span><br /><span class="lnr"> 28 </span>        contributions = [(<span class="Identifier">len</span>(cover - coveredsofar), -cost, test)<br /><span class="lnr"> 29 </span>                         <span class="Statement">for</span> test, (cost, cover) <span class="Statement">in</span> <span class="Identifier">sorted</span>(results.items()) ]<br /><span class="lnr"> 30 </span>        <span class="Comment"># Greedy ranking by taking the next greatest contributor                 </span><br /><span class="lnr"> 31 </span>        delta_cover, benefit, test = <span class="Identifier">max</span>( contributions )<br /><span class="lnr"> 32 </span>        <span class="Statement">if</span> delta_cover &gt; <span class="Constant">0</span>:<br /><span class="lnr"> 33 </span>            ranked.append((test, delta_cover))<br /><span class="lnr"> 34 </span>            cost, cover = results.pop(test)<br /><span class="lnr"> 35 </span>            coveredsofar.update(cover)<br /><span class="lnr"> 36 </span>            costsofar += cost<br /><span class="lnr"> 37 </span>        <span class="Statement">for</span> delta_cover, benefit, test <span class="Statement">in</span> contributions:<br /><span class="lnr"> 38 </span>            <span class="Statement">if</span> delta_cover == <span class="Constant">0</span>:<br /><span class="lnr"> 39 </span>                <span class="Comment"># this test cannot contribute anything</span><br /><span class="lnr"> 40 </span>                noncontributing.append( (test, <span class="Identifier">round</span>) )<br /><span class="lnr"> 41 </span>                results.pop(test)<br /><span class="lnr"> 42 </span>    <span class="Statement">return</span> coveredsofar, ranked, costsofar, noncontributing<br /><span class="lnr"> 43 </span><br /></pre><div><span class="lnr"><br /></span></div>Each time through the while loop (line 25), the next best test is appended to the ranking and tests that can nolonger contribute any extra coverage are discarded (lines 37-41)<br /><br />The function above is a bit dry so I took a bit of time to annotate it with a tutor capability that when run prints out just what it is doing along the way:<br /><br /><h4>The function with tutor</h4>It implements the same thing but does it noisily:<br /><br /><pre><span class="lnr"> 44 </span><span class="Statement">def</span><span> </span><span class="Identifier">greedyranker</span><span>(results, tutor=</span><span class="Identifier">True</span><span>):<br /></span><span class="lnr"> 45 </span><span>    results = results.copy()<br /></span><span class="lnr"> 46 </span><span>    ranked, coveredsofar, costsofar, </span><span class="Identifier">round</span><span> = [], </span><span class="Identifier">set</span><span>(), </span><span class="Constant">0</span><span>, </span><span class="Constant">0</span><span><br /></span><span class="lnr"> 47 </span><span>    noncontributing = []<br /></span><span class="lnr"> 48 </span><span>    </span><span class="Statement">while</span><span> results:<br /></span><span class="lnr"> 49 </span><span>        </span><span class="Identifier">round</span><span> += </span><span class="Constant">1</span><span><br /></span><span class="lnr"> 50 </span><span>        </span><span class="Comment"># What each test can contribute to the pool of what is covered so far</span><span><br /></span><span class="lnr"> 51 </span><span>        contributions = [(</span><span class="Identifier">len</span><span>(cover - coveredsofar), -cost, test)<br /></span><span class="lnr"> 52 </span><span>                         </span><span class="Statement">for</span><span> test, (cost, cover) </span><span class="Statement">in</span><span> </span><span class="Identifier">sorted</span><span>(results.items()) ]<br /></span><span><span class="lnr"> 53 </span>        <span class="Statement">if</span> tutor:<br /><span class="lnr"> 54 </span>            <span class="Identifier">print</span>(<span class="Constant">'</span><span class="Special">\n</span><span class="Constant">## Round %i'</span> % <span class="Identifier">round</span>)<br /><span class="lnr"> 55 </span>            <span class="Identifier">print</span>(<span class="Constant">'  Covered so far: %2i points: '</span> % <span class="Identifier">len</span>(coveredsofar))<br /><span class="lnr"> 56 </span>            <span class="Identifier">print</span>(<span class="Constant">'  Ranked so far: '</span> + <span class="Identifier">repr</span>([t <span class="Statement">for</span> t, d <span class="Statement">in</span> ranked]))<br /><span class="lnr"> 57 </span>            <span class="Identifier">print</span>(<span class="Constant">'  What the remaining tests can contribute, largest contributors first:'</span>)<br /><span class="lnr"> 58 </span>            <span class="Identifier">print</span>(<span class="Constant">'    # DELTA, BENEFIT, TEST'</span>)<br /><span class="lnr"> 59 </span>            deltas = <span class="Identifier">sorted</span>(contributions, reverse=<span class="Identifier">True</span>)<br /><span class="lnr"> 60 </span>            <span class="Statement">for</span> delta_cover, benefit, test <span class="Statement">in</span> deltas:<br /><span class="lnr"> 61 </span>                <span class="Identifier">print</span>(<span class="Constant">'     %2i,    %7.2f,    %s'</span> % (delta_cover, benefit, test))<br /><span class="lnr"> 62 </span>            <span class="Statement">if</span> <span class="Identifier">len</span>(deltas)&gt;=<span class="Constant">2</span> <span class="Statement">and</span> deltas[<span class="Constant">0</span>][<span class="Constant">0</span>] == deltas[<span class="Constant">1</span>][<span class="Constant">0</span>]:<br /><span class="lnr"> 63 </span>                <span class="Identifier">print</span>(<span class="Constant">'  Note: This time around, more than one test gives the same'</span>)<br /><span class="lnr"> 64 </span>                <span class="Identifier">print</span>(<span class="Constant">'        maximum delta contribution of %i to the coverage so far'</span><br /><span class="lnr"> 65 </span>                       % deltas[<span class="Constant">0</span>][<span class="Constant">0</span>])<br /><span class="lnr"> 66 </span>                <span class="Statement">if</span> deltas[<span class="Constant">0</span>][<span class="Constant">1</span>] != deltas[<span class="Constant">1</span>][<span class="Constant">1</span>]:<br /><span class="lnr"> 67 </span>                    <span class="Identifier">print</span>(<span class="Constant">'        we order based on the next field of minimum cost'</span>)<br /><span class="lnr"> 68 </span>                    <span class="Identifier">print</span>(<span class="Constant">'        (equivalent to maximum negative cost).'</span>)<br /><span class="lnr"> 69 </span>                <span class="Statement">else</span>:<br /><span class="lnr"> 70 </span>                    <span class="Identifier">print</span>(<span class="Constant">'        the next field of minimum cost is the same so'</span>)<br /><span class="lnr"> 71 </span>                    <span class="Identifier">print</span>(<span class="Constant">'        we arbitrarily order by test name.'</span>)<br /><span class="lnr"> 72 </span>            zeroes = [test <span class="Statement">for</span> delta_cover, benefit, test <span class="Statement">in</span> deltas<br /><span class="lnr"> 73 </span>                     <span class="Statement">if</span> delta_cover == <span class="Constant">0</span>]<br /><span class="lnr"> 74 </span>            <span class="Statement">if</span> zeroes:<br /><span class="lnr"> 75 </span>                <span class="Identifier">print</span>(<span class="Constant">'  The following test(s) cannot contribute more to coverage'</span>)<br /><span class="lnr"> 76 </span>                <span class="Identifier">print</span>(<span class="Constant">'  and will be dropped:'</span>)<br /><span class="lnr"> 77 </span>                <span class="Identifier">print</span>(<span class="Constant">'    '</span> + <span class="Constant">', '</span>.join(zeroes))<br /></span><span class="lnr"> 78 </span><span><br /></span><span class="lnr"> 79 </span><span>        </span><span class="Comment"># Greedy ranking by taking the next greatest contributor                 </span><span><br /></span><span class="lnr"> 80 </span><span>        delta_cover, benefit, test = </span><span class="Identifier">max</span><span>( contributions )<br /></span><span class="lnr"> 81 </span><span>        </span><span class="Statement">if</span><span> delta_cover &gt; </span><span class="Constant">0</span><span>:<br /></span><span class="lnr"> 82 </span><span>            ranked.append((test, delta_cover))<br /></span><span class="lnr"> 83 </span><span>            cost, cover = results.pop(test)<br /></span><span><span class="lnr"> 84 </span>            <span class="Statement">if</span> tutor:<br /><span class="lnr"> 85 </span>                <span class="Identifier">print</span>(<span class="Constant">'  Ranking %s in round %2i giving extra coverage of: %r'</span><br /><span class="lnr"> 86 </span>                       % (test, <span class="Identifier">round</span>, <span class="Identifier">sorted</span>(cover - coveredsofar)))<br /></span><span class="lnr"> 87 </span><span>            coveredsofar.update(cover)<br /></span><span class="lnr"> 88 </span><span>            costsofar += cost<br /></span><span class="lnr"> 89 </span><span><br /></span><span class="lnr"> 90 </span><span>        </span><span class="Statement">for</span><span> delta_cover, benefit, test </span><span class="Statement">in</span><span> contributions:<br /></span><span class="lnr"> 91 </span><span>            </span><span class="Statement">if</span><span> delta_cover == </span><span class="Constant">0</span><span>:<br /></span><span class="lnr"> 92 </span><span>                </span><span class="Comment"># this test cannot contribute anything</span><span><br /></span><span class="lnr"> 93 </span><span>                noncontributing.append( (test, </span><span class="Identifier">round</span><span>) )<br /></span><span class="lnr"> 94 </span><span>                results.pop(test)<br /></span><span><span class="lnr"> 95 </span>    <span class="Statement">if</span> tutor:<br /><span class="lnr"> 96 </span>        <span class="Identifier">print</span>(<span class="Constant">'</span><span class="Special">\n</span><span class="Constant">## ALL TESTS NOW RANKED OR DISCARDED</span><span class="Special">\n</span><span class="Constant">'</span>)<br /></span><span class="lnr"> 97 </span><span>    </span><span class="Statement">return</span><span> coveredsofar, ranked, costsofar, noncontributing<br /></span></pre><div><br /></div>Every block starting <span>if tutor:</span>&nbsp;above has the added code.<br /><br /><h3>Sample output</h3>The code to call the ranker and print the results is:<br /><br /><pre><span class="lnr"> 98 </span><br /><span class="lnr"> 99 </span><br /><span class="lnr">100 </span>totalcoverage, ranking, totalcost, nonranked = greedyranker(results)<br /><span class="lnr">101 </span><span class="Identifier">print</span>(<span class="Constant">'''</span><br /><span class="lnr">102 </span><span class="Constant">A total of %i points were covered, </span><br /><span class="lnr">103 </span><span class="Constant">using only %i of the initial %i tests,</span><br /><span class="lnr">104 </span><span class="Constant">and should take %g time units to run.</span><br /><span class="lnr">105 </span><br /><span class="lnr">106 </span><span class="Constant">The tests in order of coverage added:</span><br /><span class="lnr">107 </span><span class="Constant">    </span><br /><span class="lnr">108 </span><span class="Constant">    TEST  DELTA-COVERAGE'''</span><br /><span class="lnr">109 </span> % (<span class="Identifier">len</span>(totalcoverage), <span class="Identifier">len</span>(ranking), <span class="Identifier">len</span>(results), totalcost))<br /><span class="lnr">110 </span><span class="Identifier">print</span>(<span class="Constant">'</span><span class="Special">\n</span><span class="Constant">'</span>.join(<span class="Constant">'  %6s  %i'</span> % r <span class="Statement">for</span> r <span class="Statement">in</span> ranking))</pre><br />The output has a lot of stuff from the tutor followed by the <span>result at the end</span>.<br /><br />For this pseudo randomly generate test case of 15 tests it shows that only seven are needed to generate the maximum total coverage. (And if you were willing to loose the coverage of three tests that each cover only one additional point then 4 out of 15 tests would give 92.5% of the maximum coverage possible).<br /><br /><span>## Round 1</span><br /><span>&nbsp; Covered so far: &nbsp;0 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: []</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp;14, &nbsp; &nbsp; &nbsp;-2.08, &nbsp; &nbsp;test_00</span><br /><span>&nbsp; &nbsp; &nbsp;14, &nbsp; &nbsp;-100.00, &nbsp; &nbsp;test_04</span><br /><span>&nbsp; &nbsp; &nbsp;13, &nbsp; &nbsp; -40.62, &nbsp; &nbsp;test_14</span><br /><span>&nbsp; &nbsp; &nbsp;13, &nbsp; &nbsp; -58.04, &nbsp; &nbsp;test_01</span><br /><span>&nbsp; &nbsp; &nbsp;12, &nbsp; &nbsp; &nbsp;-4.46, &nbsp; &nbsp;test_05</span><br /><span>&nbsp; &nbsp; &nbsp;12, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp;12, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; &nbsp; &nbsp;12, &nbsp; &nbsp; -85.71, &nbsp; &nbsp;test_07</span><br /><span>&nbsp; &nbsp; &nbsp;11, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp;11, &nbsp; &nbsp; -15.55, &nbsp; &nbsp;test_09</span><br /><span>&nbsp; &nbsp; &nbsp;11, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -12.05, &nbsp; &nbsp;test_10</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -16.07, &nbsp; &nbsp;test_13</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -52.23, &nbsp; &nbsp;test_11</span><br /><span>&nbsp; &nbsp; &nbsp; 8, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; Note: This time around, more than one test gives the same</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; maximum delta contribution of 14 to the coverage so far</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; we order based on the next field of minimum cost</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; (equivalent to maximum negative cost).</span><br /><span>&nbsp; Ranking test_00 in round &nbsp;1 giving extra coverage of: [2, 3, 5, 11, 12, 16, 19, 23, 25, 26, 29, 36, 38, 40]</span><br /><span><br /></span><span>## Round 2</span><br /><span>&nbsp; Covered so far: 14 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp;12, &nbsp; &nbsp; -58.04, &nbsp; &nbsp;test_01</span><br /><span>&nbsp; &nbsp; &nbsp;10, &nbsp; &nbsp;-100.00, &nbsp; &nbsp;test_04</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -12.05, &nbsp; &nbsp;test_10</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 9, &nbsp; &nbsp; -85.71, &nbsp; &nbsp;test_07</span><br /><span>&nbsp; &nbsp; &nbsp; 8, &nbsp; &nbsp; &nbsp;-4.46, &nbsp; &nbsp;test_05</span><br /><span>&nbsp; &nbsp; &nbsp; 7, &nbsp; &nbsp; -15.55, &nbsp; &nbsp;test_09</span><br /><span>&nbsp; &nbsp; &nbsp; 7, &nbsp; &nbsp; -16.07, &nbsp; &nbsp;test_13</span><br /><span>&nbsp; &nbsp; &nbsp; 7, &nbsp; &nbsp; -40.62, &nbsp; &nbsp;test_14</span><br /><span>&nbsp; &nbsp; &nbsp; 7, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; &nbsp; &nbsp; 6, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; -52.23, &nbsp; &nbsp;test_11</span><br /><span>&nbsp; Ranking test_01 in round &nbsp;2 giving extra coverage of: [0, 10, 13, 15, 17, 20, 22, 27, 30, 31, 33, 34]</span><br /><span><br /></span><span>## Round 3</span><br /><span>&nbsp; Covered so far: 26 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 7, &nbsp; &nbsp;-100.00, &nbsp; &nbsp;test_04</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; -12.05, &nbsp; &nbsp;test_10</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 5, &nbsp; &nbsp; -85.71, &nbsp; &nbsp;test_07</span><br /><span>&nbsp; &nbsp; &nbsp; 4, &nbsp; &nbsp; &nbsp;-4.46, &nbsp; &nbsp;test_05</span><br /><span>&nbsp; &nbsp; &nbsp; 3, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp; 3, &nbsp; &nbsp; -16.07, &nbsp; &nbsp;test_13</span><br /><span>&nbsp; &nbsp; &nbsp; 3, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; &nbsp; &nbsp; 3, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; &nbsp; &nbsp; 2, &nbsp; &nbsp; -15.55, &nbsp; &nbsp;test_09</span><br /><span>&nbsp; &nbsp; &nbsp; 2, &nbsp; &nbsp; -40.62, &nbsp; &nbsp;test_14</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -52.23, &nbsp; &nbsp;test_11</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; Ranking test_04 in round &nbsp;3 giving extra coverage of: [1, 4, 6, 7, 8, 9, 18]</span><br /><span><br /></span><span>## Round 4</span><br /><span>&nbsp; Covered so far: 33 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01', 'test_04']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 4, &nbsp; &nbsp; -12.05, &nbsp; &nbsp;test_10</span><br /><span>&nbsp; &nbsp; &nbsp; 3, &nbsp; &nbsp; -85.71, &nbsp; &nbsp;test_07</span><br /><span>&nbsp; &nbsp; &nbsp; 2, &nbsp; &nbsp; &nbsp;-4.46, &nbsp; &nbsp;test_05</span><br /><span>&nbsp; &nbsp; &nbsp; 2, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -15.55, &nbsp; &nbsp;test_09</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -16.07, &nbsp; &nbsp;test_13</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -40.62, &nbsp; &nbsp;test_14</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -52.23, &nbsp; &nbsp;test_11</span><br /><span>&nbsp; The following test(s) cannot contribute more to coverage</span><br /><span>&nbsp; and will be dropped:</span><br /><span>&nbsp; &nbsp; test_13, test_14, test_11</span><br /><span>&nbsp; Ranking test_10 in round &nbsp;4 giving extra coverage of: [14, 24, 35, 39]</span><br /><span><br /></span><span>## Round 5</span><br /><span>&nbsp; Covered so far: 37 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01', 'test_04', 'test_10']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; &nbsp;-4.46, &nbsp; &nbsp;test_05</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -15.55, &nbsp; &nbsp;test_09</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -85.71, &nbsp; &nbsp;test_07</span><br /><span>&nbsp; Note: This time around, more than one test gives the same</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; maximum delta contribution of 1 to the coverage so far</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; we order based on the next field of minimum cost</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; (equivalent to maximum negative cost).</span><br /><span>&nbsp; The following test(s) cannot contribute more to coverage</span><br /><span>&nbsp; and will be dropped:</span><br /><span>&nbsp; &nbsp; test_09, test_07</span><br /><span>&nbsp; Ranking test_05 in round &nbsp;5 giving extra coverage of: [21]</span><br /><span><br /></span><span>## Round 6</span><br /><span>&nbsp; Covered so far: 38 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01', 'test_04', 'test_10', 'test_05']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; &nbsp;-5.73, &nbsp; &nbsp;test_08</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -32.74, &nbsp; &nbsp;test_03</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -34.82, &nbsp; &nbsp;test_02</span><br /><span>&nbsp; Note: This time around, more than one test gives the same</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; maximum delta contribution of 1 to the coverage so far</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; we order based on the next field of minimum cost</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; (equivalent to maximum negative cost).</span><br /><span>&nbsp; The following test(s) cannot contribute more to coverage</span><br /><span>&nbsp; and will be dropped:</span><br /><span>&nbsp; &nbsp; test_03, test_02</span><br /><span>&nbsp; Ranking test_08 in round &nbsp;6 giving extra coverage of: [28]</span><br /><span><br /></span><span>## Round 7</span><br /><span>&nbsp; Covered so far: 39 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01', 'test_04', 'test_10', 'test_05', 'test_08']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -26.79, &nbsp; &nbsp;test_12</span><br /><span>&nbsp; &nbsp; &nbsp; 1, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; Note: This time around, more than one test gives the same</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; maximum delta contribution of 1 to the coverage so far</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; we order based on the next field of minimum cost</span><br /><span>&nbsp; &nbsp; &nbsp; &nbsp; (equivalent to maximum negative cost).</span><br /><span>&nbsp; Ranking test_12 in round &nbsp;7 giving extra coverage of: [32]</span><br /><span><br /></span><span>## Round 8</span><br /><span>&nbsp; Covered so far: 40 points:&nbsp;</span><br /><span>&nbsp; Ranked so far: ['test_00', 'test_01', 'test_04', 'test_10', 'test_05', 'test_08', 'test_12']</span><br /><span>&nbsp; What the remaining tests can contribute, largest contributors first:</span><br /><span>&nbsp; &nbsp; # DELTA, BENEFIT, TEST</span><br /><span>&nbsp; &nbsp; &nbsp; 0, &nbsp; &nbsp; -69.57, &nbsp; &nbsp;test_06</span><br /><span>&nbsp; The following test(s) cannot contribute more to coverage</span><br /><span>&nbsp; and will be dropped:</span><br /><span>&nbsp; &nbsp; test_06</span><br /><span><br /></span><span>## ALL TESTS NOW RANKED OR DISCARDED</span><br /><span><br /></span><span><br /></span><span><b>A total of 40 points were covered,&nbsp;</b></span><br /><span><b>using only 7 of the initial 15 tests,</b></span><br /><span><b>and should take 209.15 time units to run.</b></span><br /><span><b><br /></b></span><span><b>The tests in order of coverage added:</b></span><br /><span><b>&nbsp; &nbsp;&nbsp;</b></span><br /><span><b>&nbsp; &nbsp; TEST &nbsp;DELTA-COVERAGE</b></span><br /><span><b>&nbsp; test_00 &nbsp;14</b></span><br /><span><b>&nbsp; test_01 &nbsp;12</b></span><br /><span><b>&nbsp; test_04 &nbsp;7</b></span><br /><span><b>&nbsp; test_10 &nbsp;4</b></span><br /><span><b>&nbsp; test_05 &nbsp;1</b></span><br /><span><b>&nbsp; test_08 &nbsp;1</b></span><br /><span><b>&nbsp; test_12 &nbsp;1</b></span><br /><br /><h3>What should be next</h3>There is a new&nbsp;<a href="http://www.accellera.org/resources/videos/ucisintro/" target="_blank">Unified Coverage Interoperability Standard</a> for a database for storing test coverage data ideally the greedy ranker should be hooked up to that UCIS DB to get its inputs via its C-interface or maybe its XML output instead of parsing text files.<br /><br /><b>END.</b><br /><br /><br /><img src="http://feeds.feedburner.com/~r/GoDeh/~4/UuWRvt9T9VE" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/GoDeh/~3/UuWRvt9T9VE/greedy-ranking-algorithm-in-python.html]]></link>
    <pubDate>2013-05-25 19:40:48</pubDate>
  </item>
  <item>
    <title><![CDATA[Amit Saha: Book Review: Learning IPython for Interactive Computing and Data Visualization]]></title>
    <description><![CDATA[I received a review copy of Packt Publishing’s Learning IPython for Interactive Computing and Data Visualization by Cyrille Rossant. Although the book title mentions only IPython, the book looks into using a number of other Python tools and libraries of potential use to the intended audience. Here is my review. (The book uses Python 2). Chapters The book [&#8230;]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=echorand.me&blog=899010&post=2507&subd=amitksaha&ref=&feed=1" width="1" height="1" />]]></description>
    <link><![CDATA[]]></link>
    <pubDate>2013-05-25 03:17:39</pubDate>
  </item>
  <item>
    <title><![CDATA[Armin Ronacher: My Slides Workflow]]></title>
    <description><![CDATA[<p>I was asked multiple times in the past in regards to how I do my slide
design, how my workflow looks like and what applications and tools I use
to get them done.  I'm very glad to hear that people seem to like my
slides and I'm more than happy to share how I do that stuff.</p>
<p>I think the most disappointing answer is probably: trail and error, a lot
of time and practice.  There is no silver bullet in this blog post that
which will instantly create a nice slide.  The reason for this is that I
generally do things by hand and stay away from templates.</p>
<div class="section" id="the-workflow">
<h2>The Workflow</h2>
<p>I think my workflow of doing slides is a bit strange but it seems to work
for me.  At first I think about what the presentation is going to be about
and roughly how to structure it.  I usually write that stuff down in a
text file or moleskine and do some revisions on it.</p>
<p>After that I think what kind of visual identity can go with the slides and
if they should be structured as bullet points or more abstract.  When I
have decided on something I pick colors and fonts.</p>
<p>Once I am done with that I spend quite some time making the slides with
Keynote and putting things into place.  For some presentations I use
Inkscape to make some vector art that goes with it.</p>
<p>Lastly (and in fact after I have done my slides) I will roughly go through
the timing of the slides.  Due to how I do my presentations these timings
are never accurate so I don't waste too much time on it.  Times will be
off when presenting anyways.  Primarily because what I say to the slides
might be different depending on how people in the audience seem to
respond.</p>
</div>
<div class="section" id="colors">
<h2>Colors</h2>
<p>This is the part where I waste most of the time.  I have yet to find
something that makes my life easier there but in many ways finding colors
is trial and error for me.  For many presentations I just surrendered at
one point and picked black/white + one or two colors.  When I do
presentations with strong background colors however this might take me
multiple hours to feel happy with the end result.</p>
<p>I generally use <a class="reference external" href="http://www.colourlovers.com/palettes">Colourlovers</a> for
a basic inspiration for colors that work together.  The initial color I
generally pick from the topic of the presentation.  For instance for my
MongoDB presentation I decided on an earthy brown due to the official
MongoDB art guidelines.  But my color choices sometimes just don't seem to
make much sense for anyone but myself.  For instance for my “Happiness
Through Ignorance” presentation I had two things to guide me.  One was the
location of where I gave the presentation (Japan), the other one was the
general topic of the presentation which was discussing the idea of
“ignorance is bliss”.  Both of these things made me go with pink as color
due to the association of pink and light blue with young children and a
blossoming cherry tree which I then also used as abstract art on the
slides.</p>
<p>Nobody really cares about that stuff but it helps me personally with
achieving some sort of consistent appearance.  The only reason my slides
look like something is because I have an external guideline to go by.
I am not an artist and I don't have the time to create something new,
instead I get inspired by my surroundings.</p>
</div>
<div class="section" id="fonts">
<h2>Fonts</h2>
<p>Once I have the colors I try to find fonts that go with it.  Unlike
colors, fonts are usually not free.  Because my only use for fonts are
presentations I typically try to stay within the choices provided by
<a class="reference external" href="http://www.google.com/webfonts">Google Webfonts</a> and
<a class="reference external" href="http://www.fontsquirrel.com/">Font Squirrel</a>.</p>
<p>Before I pick fonts I go through my notes and decide on how many I want
and of which category.  At the bare minimum I pick one for headlines and
one for text.  If there is code on the slides I also pick a monospace
font.  If there are random notes on there I also decide on if I want a
separate font for that.  Sometimes I do slides which have chapter
markings, then I pick one for that as well.</p>
<p>In some rare cases I go crazy with fonts and build slides out of different
fonts and put huge words in.  If there is code on the slides I also pick a
monospace font.  If there are random notes on there I also decide on if I
want a separate font for that.  Sometimes I do slides which have chapter
markings, then I pick one for that as well.</p>
<p>In some rare cases I go crazy with fonts and build chapter slides with
different fonts.  In those cases it's hard to give a general
recommendation.  I usually pick those fonts by the text that goes with
them.</p>
<p>First question I ask myself for the fonts is the obvious sans-serif vs
serif choice.  Fonts for headlines are easy because even if they don't
look that nice out of the box, there is a bit of flexibility you get by
changing the tracking or capitalization.  On body text you don't have that
much choice really because of how the text is laid out.  Sometimes it
happens you find a nice font on Google Webfonts but once you set text in
it, it shows kerning problems or just otherwise does not look good.</p>
<p>This can be quite a frustrating experience unfortunately.  Depending on
how you value your time it might be a better idea to just get Linotype
FontExplorer X which from what I gathered, lets you preview fonts
directly in the app and then purchase them.  Personally I am quite happy
with the selection of good free fonts.  Since I value Open Source a lot I
like sticking to that.</p>
<p>Some fonts I used on recent presentations:</p>
<ul class="simple">
<li><a class="reference external" href="http://www.google.com/fonts/specimen/Dosis">Dosis</a> as a general
sans-serif headline and body font.  Comes in different weights from
very light to very heavy.</li>
<li><a class="reference external" href="http://www.dafont.com/monofur.font">Monofur</a> as a monospace font.
It's very light but also only comes in one weight.</li>
<li><a class="reference external" href="http://www.whatfontis.com/Nanum-Pen-Script-OTF.font">Nanum Pen Script</a> as a font for
notes.  It looks like nice but messy handwriting.</li>
<li><a class="reference external" href="http://www.fontsquirrel.com/fonts/overlock">Overlook</a> as a sans
serif font for headlines.</li>
</ul>
</div>
<div class="section" id="applications">
<h2>Applications</h2>
<p>So which applications do I use?  This mostly depends on your choice I
suppose.  For me Keynote is irreplaceable.  It's not at all aimed towards
programmers and it's impossible to script and limited in so many ways, but
it has a few things over all alternatives:</p>
<ul>
<li><p class="first">What you see is actually what you get.  I can't tell you how much
HTML5 presentation tools annoy me because depending on where you open
them, your formatting is all over the place.  Presentations are not
websites.  You pick an aspect ratio and then you put your stuff in.
Reflowing is something you don't want in your slides.</p>
<p>Yes, you can get the same thing with Powerpoint and OpenOffice
Whatever as well, but every once in a while your presentation will
just look different.  The worst offender is Powerpoint in that regard
that just randomly moves elements around (OS X version).</p>
</li>
<li><p class="first">Excellent font rendering.  I love fonts and no other application shows
fonts this nicely on OS X.  It also beats the PDF rendering from
Preview.app.  When I export my slides to PDF I can easily see the
degradation in font quality.</p>
</li>
<li><p class="first">Nice presenter display.  Being able to see notes, the next slide and
current clock on your secondary display is very useful.  From all of
the alternatives I have used so far, nothing came close to Keynote.</p>
</li>
<li><p class="first">Lag free.  Keynote has an indicator in the presenter display when the
next slide has loaded.  This is huge.  You press the button and the
slide loads.  I am a person that gets out of flow really quickly if
things don't go as intended and a hanging slide kills that flow
quickly.  (Did I just press that button?  Let's press again.  Oh, I
just skipped a slide, let's go back etc.)</p>
</li>
</ul>
<p>Aside from Keynote I use Inkscape and Gimp.  Inkscape is awful on OS X
because it requires X11 but still the best vector editor I have used so
far.  Gimp is horrible but the prize for Photoshop is a dealbreaker.</p>
</div>
<div class="section" id="source-code-highlighting">
<h2>Source Code Highlighting</h2>
<p>I now try to keep the amount of source on the slides to the absolute
minimum.  In that case I usually just hand colorize them or just set
keywords in bold face.  If you want to automate this you can try the
<cite>pygmentize</cite> command from Pygments and let it generate RTF.  That's
something you can copy/paste into keynote and it will show up correctly.</p>
</div>
<div class="section" id="artwork">
<h2>Artwork</h2>
<p>Where to get artwork?  Images are simple as flickr has a <a class="reference external" href="http://www.flickr.com/search/?l=cc&mt=all&adv=1&w=all&q=searchword+here&m=text">Creative Commons
Search</a>.
Vector art is harder.  One of the reasons I stick to lineart for many
presentations and websites is that that one is easy to create.  Inkscape
has awesome vectorization support.  Just draw something and make a
photograph or prepare a picture with gimp, then vectorize it and fix up
some issues.  Sometimes I just trace photos, gets the job done just as
well.</p>
</div>]]></description>
    <link><![CDATA[http://lucumr.pocoo.org/2013/5/25/my-slides-workflow]]></link>
    <pubDate>2013-05-25 00:00:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Future Foundries: Announcing Crochet 0.7: Easily use Twisted from threaded applications]]></title>
    <description><![CDATA[<div>Crochet is an MIT-licensed library that makes it easier for threaded applications like Flask or Django to use the Twisted networking framework. Features include:<br /><ul><li>Runs Twisted's reactor in a thread it manages.</li><li>Hooks up Twisted's log system to the Python standard library <tt>logging</tt>framework. Unlike Twisted's built-in <tt>logging</tt> bridge, this includes support for blocking logging.Handler instances.</li><li>Provides a blocking API to eventual results (i.e. <tt>Deferred</tt> instances).</li></ul>This release includes better documentation and API improvements, as well as better error reporting.</div>You can see some examples, read the documentation, and download the package at:<br /><br /><a href="https://pypi.python.org/pypi/crochet" target="_blank">https://pypi.python.org/pypi/crochet</a><br /><br />For those of you who have seen Crochet before, I'd like to feature a new example. In the following code you can see how Twisted and Crochet allow you download information in the background every few seconds and then cache it, so that the request handler for your web application is not slowed down retrieving the information:<br /><br /><pre>"""<br />An example of scheduling time-based events in the background.<br /><br />Download the latest EUR/USD exchange rate from Yahoo every 30<br />seconds in the background; the rendered Flask web page can use<br />the latest value without having to do the request itself.<br /><br />Note this is example is for demonstration purposes only, and<br />is not actually used in the real world. You should not do this<br />in a real application without reading Yahoo's terms-of-service<br />and following them.<br />"""<br /><br />from flask import Flask<br /><br />from twisted.internet.task import LoopingCall<br />from twisted.web.client import getPage<br />from twisted.python import log<br /><br />from crochet import run_in_reactor, setup<br />setup()<br /><br /><br />class ExchangeRate(object):<br />    """<br />    Download an exchange rate from Yahoo Finance using Twisted.<br />    """<br /><br />    def __init__(self, name):<br />        self._value = None<br />        self._name = name<br /><br />    # External API:<br />    def latest_value(self):<br />        """<br />        Return the latest exchange rate value.<br /><br />        May be None if no value is available.<br />        """<br />        return self._value<br /><br />    @run_in_reactor<br />    def start(self):<br />        """<br />        Start the background process.<br />        """<br />        self._lc = LoopingCall(self._download)<br />        # Run immediately, and then every 30 seconds:<br />        self._lc.start(30, now=True)<br /><br />    def _download(self):<br />        """<br />        Do an actual download, runs in Twisted thread.<br />        """<br />        print "Downloading!"<br />        def parse(result):<br />            print("Got %r back from Yahoo." % (result,))<br />            values = result.strip().split(",")<br />            self._value = float(values[1])<br />        d = getPage(<br />            "http://download.finance.yahoo.com/d/quotes.csv?e=.csv&amp;f=c4l1&amp;s=%s=X"<br />            % (self._name,))<br />        d.addCallback(parse)<br />        d.addErrback(log.err)<br />        return d<br /><br /><br /># Start background download:<br />EURUSD = ExchangeRate("EURUSD")<br />EURUSD.start()<br /><br /><br /># Flask application:<br />app = Flask(__name__)<br /><br />@app.route('/')<br />def index():<br />    rate = EURUSD.latest_value()<br />    if rate is None:<br />        rate = "unavailable, please refresh the page"<br />    return "Current EUR/USD exchange rate is %s." % (rate,)<br /><br /><br />if __name__ == '__main__':<br />    import sys, logging<br />    logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)<br />    app.run()<br /></pre>]]></description>
    <link><![CDATA[http://blog.futurefoundries.com/2013/05/announcing-crochet-07-easily-use.html]]></link>
    <pubDate>2013-05-24 07:59:14</pubDate>
  </item>
  <item>
    <title><![CDATA[Ned Batchelder: What made you feel competent in Python?]]></title>
    <description><![CDATA[<p>I'm trying to come up with more project ideas for intermediate learners,
        somewhat along the lines of <a href="https://openhatch.org/wiki/Intermediate_Python_Workshop/Projects">Intermediate Python Workshop Projects</a>.</p><p>So here's a question for people who remember coming up from beginner: as
        you moved from exercises like those in Learn Python the Hard Way, up to
        your own self-guided work on small projects, what project were you
        working on that made you feel independent and skilled?  What program
        first felt like your own work rather than an exercise the teacher had
        assigned?</p><p>I don't want anything too large, but big enough that there's room for
        design, and multiple approaches, etc.</p><p>Any and all ideas welcome. Thanks in advance!</p>]]></description>
    <link><![CDATA[http://nedbatchelder.com/blog/201305/what_made_you_feel_competent_in_python.html]]></link>
    <pubDate>2013-05-24 02:12:06</pubDate>
  </item>
  <item>
    <title><![CDATA[Ralph Bean: Writing plugins for Mailman 3]]></title>
    <description><![CDATA[<div class="document">
<p>GNU Mailman 3 (the long-awaited revamp of the widely-used, widely-despised
GNU Mailman 2) is <a class="reference external" href="http://pyvideo.org/video/688/mailman-3">on the way</a>, and
Fedora's <a class="reference external" href="http://aurelien.bompard.org/">Aurélien Bompard</a> has been working
on a new frontend for it (here's a <a class="reference external" href="http://threebean.org/hyperkitty-screencast.webm">before and after screencast</a> showing some of
it off).
I set out to write a <a class="reference external" href="http://fedmsg.com">fedmsg</a> plugin for mm3 so we
can add it to <a class="reference external" href="http://ralph.fedorapeople.org/so-i-turned-the-fedmsg-data-into-a-git-log-and.webm">cool visualizations</a>,
gather more data on non-development contributions to Fedora (which is always
hard to quantify), and to support future fedmsg use cases we haven't yet
thought of.</p>
<p>I searched for documentation, but didn't find anything directly on how to
write plugins.  I found <a class="reference external" href="http://www.aosabook.org/en/mailman.html">Barry's chapter in AOSA</a> to be a really helpful guide
before diving into the mailman3 source itself.  This blog post is meant to
relay my findings:  two (of many) different ways to write plugins for
mailman3.</p>
<div class="section" id="adding-a-new-handler-to-the-pipeline">
<h1>Adding a new Handler to the Pipeline</h1>
<p>At the end of the day, all we want to do is publish a <a class="reference external" href="https://zeromq.org">ØMQ</a> message on a <tt class="docutils literal">zmq.PUB</tt> socket for every
mail that makes its way through mailman.</p>
<p>I learned that at its core mailman is composed of two sequential
processing steps.  First, a <em>chain</em> of <em>rules</em> that make <em>moderation</em>
decisions about a message (should it be posted to the list? rejected?
discarded?).  Second, a <em>pipeline</em> of <em>handlers</em> that perform <em>manipulation</em>
operations on a message (should special text be added to the end?  headers?
should it be archived?  added to the digest?).</p>
<p>I came up with this template while trying to figure out how to add another
handler to that second pipeline.  It works! (but its not the approach we
ended up using.  read further!):</p>


<div class="pygments_monokai"><pre><span class="sd">&quot;&quot;&quot; An example template for setting up a custom pipeline for Mailman 3.</span>

<span class="sd">Message processing in mailman 3 is split into two phases, &quot;moderation&quot;</span>
<span class="sd">and &quot;modification&quot;.  This pipeline is for the second phase which</span>
<span class="sd">will only be invoked *after* a message has been cleared for delivery.</span>

<span class="sd">In order to have this module imported and setup by mailman, our ``initialize``</span>
<span class="sd">function needs to be called.  This can be accomplished with the mailman 3</span>
<span class="sd">``post_hook`` in the config file::</span>

<span class="sd">    [mailman]</span>
<span class="sd">    post_hook: mm3_custom_handler_template.initialize</span>

<span class="sd">After our ``initialize`` function has been called, the</span>
<span class="sd">'custom-posting-pipeline' should be available internally to mailman.</span>
<span class="sd">In mailman 3, each mailing list can have its *own* pipeline; precisely</span>
<span class="sd">which pipeline gets used at runtime is configured in the database --</span>
<span class="sd">through postorious.</span>

<span class="sd">:Author: Ralph Bean &lt;rbean@redhat.com&gt;</span>

<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>

<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">from</span> <span class="nn">zope.interface</span> <span class="kn">import</span> <span class="n">implementer</span>
<span class="kn">from</span> <span class="nn">zope.interface.verify</span> <span class="kn">import</span> <span class="n">verifyObject</span>

<span class="kn">from</span> <span class="nn">mailman.config</span> <span class="kn">import</span> <span class="n">config</span>
<span class="kn">from</span> <span class="nn">mailman.core.i18n</span> <span class="kn">import</span> <span class="n">_</span>
<span class="kn">from</span> <span class="nn">mailman.core.pipelines</span> <span class="kn">import</span> <span class="n">PostingPipeline</span>
<span class="kn">from</span> <span class="nn">mailman.interfaces.handler</span> <span class="kn">import</span> <span class="n">IHandler</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s">'CustomHandler'</span><span class="p">,</span>
    <span class="s">'CustomPostingPipeline'</span><span class="p">,</span>
    <span class="s">'initialize'</span><span class="p">,</span>
<span class="p">]</span>

<span class="n">elog</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s">&quot;mailman.error&quot;</span><span class="p">)</span>


<span class="nd">@implementer</span><span class="p">(</span><span class="n">IHandler</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CustomHandler</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot; Do something.. anything with the message. &quot;&quot;&quot;</span>

    <span class="n">name</span> <span class="o">=</span> <span class="s">'my-custom-handler'</span>
    <span class="n">description</span> <span class="o">=</span> <span class="n">_</span><span class="p">(</span><span class="s">'Do something.. anything with the message.'</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mlist</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">msgdata</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; See `IHandler` &quot;&quot;&quot;</span>
        <span class="n">elog</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s">&quot;CUSTOM HANDLER: </span><span class="si">%r</span><span class="s"> </span><span class="si">%r</span><span class="s"> </span><span class="si">%r</span><span class="s">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mlist</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">msgdata</span><span class="p">))</span>


<span class="k">class</span> <span class="nc">CustomPostingPipeline</span><span class="p">(</span><span class="n">PostingPipeline</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; A custom posting pipeline that adds our custom handler &quot;&quot;&quot;</span>

    <span class="n">name</span> <span class="o">=</span> <span class="s">'my-custom-posting-pipeline'</span>
    <span class="n">description</span> <span class="o">=</span> <span class="n">_</span><span class="p">(</span><span class="s">'My custom posting pipeline'</span><span class="p">)</span>

    <span class="n">_default_handlers</span> <span class="o">=</span> <span class="n">PostingPipeline</span><span class="o">.</span><span class="n">_default_handlers</span> <span class="o">+</span> <span class="p">(</span>
        <span class="s">'my-custom-handler'</span><span class="p">,</span>
    <span class="p">)</span>


<span class="k">def</span> <span class="nf">initialize</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot; Initialize our custom objects.</span>

<span class="sd">    This should be called as the `config.mailman.post_hook` callable</span>
<span class="sd">    during the third phase of initialization, *after* the other default</span>
<span class="sd">    pipelines have been set up.</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c"># Initialize our handler and make it available</span>
    <span class="n">handler</span> <span class="o">=</span> <span class="n">CustomHandler</span><span class="p">()</span>
    <span class="n">verifyObject</span><span class="p">(</span><span class="n">IHandler</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span>
    <span class="k">assert</span> <span class="n">handler</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">handlers</span><span class="p">,</span> <span class="p">(</span>
        <span class="s">'Duplicate handler &quot;{0}&quot; found in {1}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">handler</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">CustomHandler</span><span class="p">))</span>
    <span class="n">config</span><span class="o">.</span><span class="n">handlers</span><span class="p">[</span><span class="n">handler</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">handler</span>

    <span class="c"># Initialize our pipeline and make it available</span>
    <span class="n">pipeline</span> <span class="o">=</span> <span class="n">CustomPostingPipeline</span><span class="p">()</span>
    <span class="n">config</span><span class="o">.</span><span class="n">pipelines</span><span class="p">[</span><span class="n">pipeline</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">pipeline</span>
</pre></div>



<p>The above approach works, but it involves a lot of hacking to get mailman
to <em>load</em> our code into the pipeline.  We have to occupy the mailman
<tt class="docutils literal">post_hook</tt> and then kind-of hot-patch our pipeline into the list of
existing pipelines.</p>
<p>A benefit of this approach is that we could use <tt class="docutils literal">postorious</tt>
(the DB) to control <em>which</em> mailing lists included our plugin and which
didn't.  The <em>site</em> administrator can leave some decisions up to the <em>list</em>
administrators.</p>
<p>I ended up abandoning the above approach and instead landed on...</p>
</div>
<div class="section" id="adding-a-second-archiver">
<h1>Adding a second Archiver</h1>
<p>One of the Handlers in the default pipeline is the <tt class="docutils literal"><span class="pre">to-archive</span></tt> Handler.
It has a somewhat nicer API for defining multiple destinations for archival.
One of those is typically HyperKitty (or... kittystore)... but you can
add as many as you like.</p>
<p>I wrote this &quot;archiver&quot; (and threw it up on
<a class="reference external" href="https://github.com/fedora-infra/mailman3-fedmsg-plugin">github</a>,
<a class="reference external" href="https://pypi.python.org/pypi/mailman3-fedmsg-plugin">pypi</a>, and
<a class="reference external" href="https://bugzilla.redhat.com/show_bug.cgi?id=966732">fedora</a>).
Barring tweaks and modifications, I think its the approach we'll end
up using down the road:</p>


<div class="pygments_monokai"><pre><span class="sd">&quot;&quot;&quot; Publish notifications about mails to the fedmsg bus.</span>

<span class="sd">Enable this by adding the following to your mailman.cfg file::</span>

<span class="sd">    [archiver.fedmsg]</span>
<span class="sd">    # The class implementing the IArchiver interface.</span>
<span class="sd">    class: mailman3_fedmsg_plugin.Archiver</span>
<span class="sd">    enable: yes</span>

<span class="sd">You can exclude certain lists from fedmsg publication by</span>
<span class="sd">adding them to a 'mailman.excluded_lists' list in /etc/fedmsg.d/::</span>

<span class="sd">    config = {</span>
<span class="sd">        'mailman.excluded_lists': ['bugzilla', 'commits'],</span>
<span class="sd">    }</span>

<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">from</span> <span class="nn">zope.interface</span> <span class="kn">import</span> <span class="n">implements</span>
<span class="kn">from</span> <span class="nn">mailman.interfaces.archiver</span> <span class="kn">import</span> <span class="n">IArchiver</span>

<span class="kn">import</span> <span class="nn">socket</span>
<span class="kn">import</span> <span class="nn">fedmsg</span>
<span class="kn">import</span> <span class="nn">fedmsg.config</span>


<span class="k">class</span> <span class="nc">Archiver</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; A mailman 3 archiver that forwards messages to the fedmsg bus. &quot;&quot;&quot;</span>

    <span class="n">implements</span><span class="p">(</span><span class="n">IArchiver</span><span class="p">)</span>
    <span class="n">name</span> <span class="o">=</span> <span class="s">&quot;fedmsg&quot;</span>

    <span class="c"># This is a list of the headers we're interested in publishing.</span>
    <span class="n">keys</span> <span class="o">=</span> <span class="p">[</span>
        <span class="s">&quot;archived-at&quot;</span><span class="p">,</span>
        <span class="s">&quot;delivered-to&quot;</span><span class="p">,</span>
        <span class="s">&quot;from&quot;</span><span class="p">,</span>
        <span class="s">&quot;cc&quot;</span><span class="p">,</span>
        <span class="s">&quot;to&quot;</span><span class="p">,</span>
        <span class="s">&quot;in-reply-to&quot;</span><span class="p">,</span>
        <span class="s">&quot;message-id&quot;</span><span class="p">,</span>
        <span class="s">&quot;subject&quot;</span><span class="p">,</span>
        <span class="s">&quot;x-message-id-hash&quot;</span><span class="p">,</span>
        <span class="s">&quot;references&quot;</span><span class="p">,</span>
        <span class="s">&quot;x-mailman-rule-hits&quot;</span><span class="p">,</span>
        <span class="s">&quot;x-mailman-rule-misses&quot;</span><span class="p">,</span>
    <span class="p">]</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Just initialize fedmsg. &quot;&quot;&quot;</span>
        <span class="n">hostname</span> <span class="o">=</span> <span class="n">socket</span><span class="o">.</span><span class="n">gethostname</span><span class="p">()</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">fedmsg</span><span class="p">,</span> <span class="s">'__local'</span><span class="p">,</span> <span class="bp">None</span><span class="p">),</span> <span class="s">'__context'</span><span class="p">,</span> <span class="bp">None</span><span class="p">):</span>
            <span class="n">fedmsg</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">&quot;mailman.</span><span class="si">%s</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">hostname</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">fedmsg</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">load_config</span><span class="p">()</span>


    <span class="k">def</span> <span class="nf">archive_message</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mlist</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Send the message to the &quot;archiver&quot;.</span>

<span class="sd">        In our case, we just publish it to the fedmsg bus.</span>

<span class="sd">        :param mlist: The IMailingList object.</span>
<span class="sd">        :param msg: The message object.</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">if</span> <span class="n">mlist</span><span class="o">.</span><span class="n">list_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'mailman.excluded_lists'</span><span class="p">,</span> <span class="p">[]):</span>
            <span class="k">return</span>

        <span class="n">format</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">value</span><span class="p">:</span> <span class="n">value</span> <span class="ow">and</span> <span class="nb">unicode</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
        <span class="n">msg_metadata</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">k</span><span class="p">,</span> <span class="n">format</span><span class="p">(</span><span class="n">msg</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">k</span><span class="p">)))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">])</span>
        <span class="n">lst_metadata</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">list_name</span><span class="o">=</span><span class="n">mlist</span><span class="o">.</span><span class="n">list_name</span><span class="p">)</span>

        <span class="n">fedmsg</span><span class="o">.</span><span class="n">publish</span><span class="p">(</span><span class="n">topic</span><span class="o">=</span><span class="s">'receive'</span><span class="p">,</span> <span class="n">modname</span><span class="o">=</span><span class="s">'mailman'</span><span class="p">,</span>
                       <span class="n">msg</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="n">msg_metadata</span><span class="p">,</span> <span class="n">mlist</span><span class="o">=</span><span class="n">lst_metadata</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">list_url</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mlist</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; This doesn't make sense for fedmsg.</span>
<span class="sd">        But we must implement for IArchiver.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">None</span>

    <span class="k">def</span> <span class="nf">permalink</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mlist</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; This doesn't make sense for fedmsg.</span>
<span class="sd">        But we must implement for IArchiver.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">None</span>
</pre></div>



</div>
</div>]]></description>
    <link><![CDATA[http://threebean.org/blog/plugins-for-mailman3]]></link>
    <pubDate>2013-05-23 23:17:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Dariusz Suchojad: A no-nonsense introduction to ESB and SOA]]></title>
    <description><![CDATA[<p>After <a title="Zato 1.0 release announcement" href="http://www.gefira.pl/blog/2013/05/18/zato-1-0-the-next-generation-esb-and-application-server-open-source-in-python/">releasing</a> <a title="Zato ESB and app server" href="https://zato.io/">Zato</a> 1.0, several people asked me in emails to write an explanatory material on what ESB and SOA are anyway.</p>
<p>I figured it would be best to add it to <a href="https://zato.io/docs/">Zato docs</a> directly so here it is &#8211; <a title="A no-nosense intro to ESB and SOA" href="https://zato.io/docs/intro/esb-soa.html">a no-nonsense introduction to ESB and SOA</a>.</p>
<p>The concepts are so overloaded, so often misused, mistrusted and misunderstood that I feel such introductions are needed indeed.</p>
<p>Feel free to comment it here or <a title="Zato support" href="https://zato.io/support">there</a> if you&#8217;d like add something to the matter though please note that it&#8217;s an intro only and it purposefully leaves out advanced stuff, some of which is listed at the end.</p>
<p>You can also <a title="Upvote the intro on Dzone" href="http://www.dzone.com/links/nononsense_plain_english_introduction_to_esb_and.html">upvote it on DZone</a> if you like what you&#8217;ve read. Cheers!</p>
<p><a title="Zato on Twitter" href="https://twitter.com/zatosource">@zatosource</a></p>
<p><a title="Me on Twitter" href="https://twitter.com/zatosource">@fourthrealm</a></p>
<p><a class="a2a_dd a2a_target addtoany_share_save" href="http://www.addtoany.com/share_save#url=http%3A%2F%2Fwww.gefira.pl%2Fblog%2F2013%2F05%2F23%2Fa-no-nonsense-introduction-to-esb-and-soa%2F&title=A%20no-nonsense%20introduction%20to%20ESB%20and%20SOA" id="wpa2a_2"><img src="http://www.gefira.pl/blog/wp-content/plugins/add-to-any/share_save_171_16.png" width="171" height="16" alt="Share" /></a></p>]]></description>
    <link><![CDATA[http://www.gefira.pl/blog/2013/05/23/a-no-nonsense-introduction-to-esb-and-soa/]]></link>
    <pubDate>2013-05-23 21:30:16</pubDate>
  </item>
  <item>
    <title><![CDATA[Kristján Valur Jónsson: Surprising Python]]></title>
    <description><![CDATA[I haven&#8217;t written anything on Python here in a good while.  But that doesn&#8217;t mean I haven&#8217;t been busy wrestling with it.  I&#8217;ll need to take a look at my Perforce changelists over the last months and take stock.  In the meantime, I&#8217;d like to rant a bit about a most curious peculiarity of Python [&#8230;]<img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=cosmicpercolator.com&blog=45513407&post=333&subd=cosmicpercolator&ref=&feed=1" width="1" height="1" />]]></description>
    <link><![CDATA[http://cosmicpercolator.com/2013/05/23/surprising-python/]]></link>
    <pubDate>2013-05-23 15:43:05</pubDate>
  </item>
  <item>
    <title><![CDATA[Kate Editor: A rich python console and more in Kate editor]]></title>
    <description><![CDATA[<!-- Start Shareaholic LikeButtonSetTop Automatic --><!-- End Shareaholic LikeButtonSetTop Automatic --><p>I have done some improvements in the plugins: python_console_ipython, python_autocomplete, python_utils, js_utils, xml_pretty and django_utils. <a href="http://kate-editor.org/2013/02/18/new-plugins-to-the-kate-utils-to-python-javascript-django-and-xml/" target="_blank">These plugins</a> I added a month and a half ago (except python_console_ipython) to the kate repository. I have done two improvements and a new feature:</p>
<ul>
<li>Now they work with Python2 and <a href="http://docs.python.org/3/howto/pyporting.html" target="_blank">Python3</a> (except python_autocomplete, this only works with Python2, due <a title="pysmell" href="https://pypi.python.org/pypi/pysmell" target="_blank">pysmell</a> dependence)</li>
<li>Now they have a configuration page (except python_autocomplete, this should not have configuration parameters)</li>
</ul>
<p><a href="http://kate-editor.org/wp-content/uploads/2013/04/config.png"><img class="aligncenter  wp-image-2456" src="http://kate-editor.org/wp-content/uploads/2013/04/config.png" alt="" width="459" height="331" /></a></p>
<h6><em>Page Config Plugins</em></h6>
<p>The new feature is the integration of the python_autocomplete and python_console_ipython plugins with the <a title="Using the projects plugin in Kate" href="http://kate-editor.org/2012/11/02/using-the-projects-plugin-in-kate/" target="_blank">project plugin</a>. The behaviour of these plugins depends of the loaded project. That is to say, the python_autocomplete plugin autocompletes with our project modules.<span> Currently the kate community is working to add a new<a href="https://git.reviewboard.kde.org/r/109326/" target="_blank"> python autocomplete</a>  using <a title="jedi" href="https://github.com/davidhalter/jedi" target="_blank">jedi</a> (this will work with Python2 and Python3).</span></p>
<p><a href="http://kate-editor.org/wp-content/uploads/2013/04/autocomplete.png"><img class="aligncenter  wp-image-2460" src="http://kate-editor.org/wp-content/uploads/2013/04/autocomplete.png" alt="" width="472" height="281" /></a></p>
<h6><em>Python Autocomplete (with our modules)</em></h6>
<p>And the python_console_ipython plugin has something that we can name the project console. A ipython shell with the python path of our project and with the environments variables that the project plugin needs.</p>
<p><a href="http://kate-editor.org/wp-content/uploads/2013/04/ipython_console.png"><img class="aligncenter  wp-image-2463" src="http://kate-editor.org/wp-content/uploads/2013/04/ipython_console.png" alt="" width="698" height="388" /></a></p>
<h6><em>IPython Console (converted in a django shell)</em></h6>
<p>To use this we need to add in the project file (.kateproject) a new attribute named &#8220;python&#8221;, with this structure:</p>
<pre> {
  "name": "MyProject",
  "files": [ { "git": 1 } ],
  "python": {
        "extraPath": ["/python/path/1",
                      "/python/path/2",
                      "/python/path/n"
        ],
        "environs": {"key": "value"},
        "version": "3.2"
    }
 }</pre>
<p>I am a django developer, as we say in <a title="Django project" href="http://www.djangoproject.com/" target="_blank">Django</a> we can come to have a <a title="django shell" href="https://docs.djangoproject.com/en/dev/intro/tutorial01/#playing-with-the-api" target="_blank">django shell</a> (python manage.py shell) integrated in our editor. But this feature is a generic feature not tied to Django. This feature can be used by other Python developers. I have added only a specific feature to the django developers. If we set in the project file (.kateproject) the attribute projectType with the value Django, the ipython shell automatically loads the models and the settings like a <a title="Django extensions" href="https://pypi.python.org/pypi/django-extensions" target="_blank">shell_plus</a> does.</p>
<pre>  {
   "name": "MyProject",
   "files": [ { "hg": 1 } ],
   "python": {
        "extraPath": ["/python/path/1",
                      "/python/path/2",
                      "/python/path/n"
        ],
        "environs": {"DJANGO_SETTINGS_MODULE": "myproject.settings"},
        "version": "2.7.3",
        "projectType": "django"
    }
  }</pre>
<p>I hope you like it <img src="http://kate-editor.org/wp-includes/images/smilies/icon_smile.gif" alt=":-)" class="wp-smiley" /> </p>
<div class="shr-publisher-2455"></div><!-- Start Shareaholic LikeButtonSetBottom Automatic --><div></div><div class="shareaholic-like-buttonset"><a class="shareaholic-googleplusone"></a><a class="shareaholic-fblike"></a><a class="shareaholic-fbsend"></a></div><div></div><!-- End Shareaholic LikeButtonSetBottom Automatic -->]]></description>
    <link><![CDATA[http://kate-editor.org/2013/05/23/a-rich-python-console-and-more-in-kate-editor/]]></link>
    <pubDate>2013-05-23 11:38:01</pubDate>
  </item>
  <item>
    <title><![CDATA[Juho Vepsäläinen: Thoughts on Kasvu Open Forum and Djangocon Finland 2012]]></title>
    <description><![CDATA[Two days, two conferences. The first was a local, business oriented one, <a href="http://www.kasvuopen.fi/paivamaarat/kasvu-open-forum-25-5/">Kasvu Open Forum</a>. The second, <a href="http://djangocon.fi/">Djangocon Finland 2012</a>, focused mainly on technology. I gave two talks in the latter one. It was very nice to experience both events and meet some new people and a few old acquaintances.<br /><h2><br /></h2><h2> Kasvu Open Forum</h2><div><a href="http://kasvuopen.fi/">Kasvu Open</a> is a competition of sorts aimed for Finnish growth ventures. This is the second year they are organizing it so things are just about to get rolling. They have two series, one for ideas and one for established companies. Me and my business partner participated in the former one this year with an entry.<br /><br />We didn't make it to the finals and weren't impressed by the quality of the feedback given. This event totally made up for it. This is definitely something they can improve on the next year. The last thing you want to do is to discourage some potential idea or company. After all Kasvu Open is in the business of creating new business.</div><div><br /></div><div>You might expect business conferences such as this to be really boring. This wasn't the case here. Each talk given gave some unique view to growth venturing. For instance it was particularly interesting to see how different the mindsets of a venture capitalist and a business angel can be. Former focuses on profit while the latter thinks in more long term and uses a different kind of investment strategy.</div><div><br /></div><div>I also enjoyed the talk of Jouni Hynynen. He represented <a href="http://www.keksintosaatio.fi/en/Home/">The Foundation of Finnish Inventions</a> and explained how <a href="http://nixtu.blogspot.com/2011/12/brief-primer-to-immaterial-rights.html">immaterial rights</a> relate to business and what is their worth in practice. Even though I'm somewhat categorically opposed to concepts such as software patents, the talk gave some nice insight to the subject.</div><div><br /></div><div>Overall it seems like there is some positive buzz going on in the Jyväskylä area. It might not be the Silicon Valley and we might be missing the scale benefits. I wouldn't be surprised if something really interesting emerged from the area within the next decades.<br /><br /></div><h2> Djangocon Finland 2012</h2><div>This was the first time I visited the Finnish version of <a href="http://djangocon.fi/">Djangocon</a>. I think there were around&nbsp;forty&nbsp;people or so participating the event. The talks were primarily technically oriented. There were a couple of longer talks and several lightning talks.</div><div><br /></div><div>I actually met a reader of this blog (apparently there are those) at the conference. That was quite a pleasant surprise to be honest. It's small things such as this that make it all worth it.<br /><br /><h3>Thoughts on "Kaleva.fi - how we replaced 10 years of legacy code in one year"</h3>It was particularly interesting to see what Kaleva.fi looks like from "outside" in terms of <a href="http://en.wikipedia.org/wiki/DevOps">DevOps</a>. I participated in the project as a software designer during the past year for a period of a few months. So I got to know certain bits of it quite well. I never really looked into the overall infrastructure (too busy staring at my code :) ), though.<br /><br />There were many interesting tidbits in Markus' talk. Especially the bits on scaling the service were interesting. It's quite different to develop a service used by hundreds of thousands than something that has only a few users. You get a lot of new problems to solve.<br /><br /><h3>Thoughts on other talks</h3><div>There was this one guy that made Django act like PHP. <a href="https://github.com/mjtorn/dhp">Django Home Pages</a> is a terrible abomination that simply should not exist. I guess that was kind of the point, though. He created it just in order to see if it can be done.</div><div><br /></div><div>Leo Honkanen discussed about classy Django applications. I think the main gist here was that with some effort you can provide namespaced url lookups for your templates. Essentially you have to deal with routing using a proxy class that implements urls using a property. The proxy class contains the name of the namespace as a class level attribute. I believe it is possible to implement this as a class method so you can avoid instantiating the whole thing at your url definition.</div><div><br /></div><div>I'm not entirely sure if one should abuse classes this way. There might be a neater functional solution around to be found. If I ever need to namespace my urls somehow, I'll keep this in mind.</div><div><br /></div><div>There were a couple of lightning talks as well. The <a href="http://en.wikipedia.org/wiki/Behavior_Driven_Development">BDD</a> one was semi-interesting. I couldn't see myself writing that amount of code anytime soon, though. There must be some nicer way to describe stories.</div><div><br /></div><h3>bongaus.fi - Spotting Service Powered by Django</h3></div><div>My first talk had to do with a service me and my business partner developed during the Spring. We did the development of <a href="http://bongaus.fi/">bongaus.fi</a> in a few distinct phases. The idea of the talk was to give some insight how we created the service and what kind of lessons we learned while doing it. I hope the people got something out of it! You can examine the slides below (probably not visible in RSS):</div><div id="__ss_13090811"><strong><a href="http://www.slideshare.net/bebraw/bongausfi-spotting-service-powered-by-django" target="_blank" title="bongaus.fi - Spotting Service Powered by Django">bongaus.fi - Spotting Service Powered by Django</a></strong>  <br /><div>View more <a href="http://www.slideshare.net/" target="_blank">presentations</a> from <a href="http://www.slideshare.net/bebraw" target="_blank">Juho Vepsäläinen</a></div></div><div>If there is something you should pick out from the talk I believe it is the importance of developing a <a href="http://en.wikipedia.org/wiki/Minimum_viable_product">Minimum Viable Product</a> (MVP). The only way to know if your product is on the right track is to give it for your users to test. Developing a MVP is an effective way to do this. Besides, it is really fast to get one done since you don't need to get stuck on the details.<br /><br />In development of bongaus.fi we noticed <a href="http://en.wikipedia.org/wiki/Pareto_principle">Pareto principle</a> applies quite well here. It takes only perhaps 20% or so time to get the relevant bits done. The rest is just tweaking and dealing the corner cases. And boy that sure can take time.<br /><br />Another important thing to pick out is the value of pivots. Even if you are doing something and going to a certain direction, doesn't mean you should be going there indefinitely. It can pay off to adjust the trajectory and try something perhaps a bit different. I believe the willingness to pivot is one of the key attributes of startups that become successful.<br /><br /><h3>Speccer</h3></div><div>My second, really brief talk, had to do with <a href="https://github.com/bebraw/speccer">Speccer</a>. It is a small testing tool I developed ages ago to make using <a href="http://docs.python.org/library/unittest.html">unittest</a> bearable. To quote myself "unittest provides testing for masochists while Speccer is meant for the rest of us". I hope the slides below give you the gist of it:</div><div id="__ss_13090828"><strong><a href="http://www.slideshare.net/bebraw/speccer" target="_blank" title="Speccer">Speccer</a></strong>  <br /><div>View more <a href="http://www.slideshare.net/" target="_blank">presentations</a> from <a href="http://www.slideshare.net/bebraw" target="_blank">Juho Vepsäläinen</a> </div></div><div>Essentially the tool just transforms the light Pythonish syntax (you can mix Python with it) to code using unittest. This means you get to enjoy from the benefits of the both. You get the robust output provided by unittest's test runner while get to use a lighter syntax.<br /><br /></div><h2> Conclusion</h2><div>At times I feel like I'm slowly drifting away from a pure development role and more into business. These kind of conferences seem to confirm this. It takes a lot more than just technical skill to make things work in a real world.</div><div><br /></div><div>Overall it seems like a good idea to be active. You get a lot of new contacts that in turn might prove to be valuable longer term (applies both ways). In addition these sort of events give you a nice extra burst of motivation and helps you to validate some of the work you have done. Sometimes it is a good idea to step out of your role a bit and try something different (ie. a business conf :) ).</div>]]></description>
    <link><![CDATA[http://www.nixtu.info/2012/05/thoughts-on-kasvu-open-forum-and.html]]></link>
    <pubDate>2013-05-22 08:45:39</pubDate>
  </item>
  <item>
    <title><![CDATA[Vasudev Ram: Zato, an open source ESB in Python]]></title>
    <description><![CDATA[<p><a href="https://zato.io/">Zato | home page</a></p><p>Zato is an ESB (Enterprise Service Bus) which is written in Python. It is free and open source (LGPL).</p><p>http://en.m.wikipedia.org/wiki/Enterprise_service_bus</p><p>Tibco was one of the first ESB products and Mule is an open source one.</p><p>According to the Zato site:</p><p>It supports HTTP, JSON, SOAP, Redis, JMS WebSphere MQ, ZeroMQ, FTP, SQL.</p><p>It has a web admin GUI, a CLI and an API.</p><p>Documentation and commercial support are available.</p><p>I got to know about Zato recently from the main&nbsp; developer, Dariusz Suchojad, who had earlier written to me&#160; regarding my blog post about PyMQI:</p><p>PyMQI, Python interface to IBM WebSphereMQ (formerly IBM MQSeries):</p><p>http://jugad2.blogspot.com/2013/02/pymqi-python-interface-to-ibm.html</p><p>Dariusz was a maintainer of PyMQI and also a developer on Spring Python, which is sort of a port of the Java Spring framework to Python.</p><p>Zato docs (quite detailed):</p><p>https://zato.io/docs/index.html</p><p>Part 1 of a basic Zato tutorial:</p><p>https://zato.io/docs/tutorial/01.html</p><p>I took a look at the tutorial. Broadly, it shows how to install Zato, create a simple Zato service in Python, that talks to PostgreSQL and Redis, and deploy it. Two servers get created, behind a load-balancer, and the service gets hot-deployed to the servers. Then curl is used to access the service. (This tutorial does not create a real client; curl is used to simulate one.)</p><p>Zato looks interesting and powerful (and somewhat complex, but that is to be expected for a product like an ESB).</p><p>I will check it out more and then report on my findings.</p><p>- Vasudev Ram<br />dancingbison.com<br /></p><div class="blogger-post-footer"><a href="http://www.dancingbison.com">Vasudev Ram</a>
<br /></div>]]></description>
    <link><![CDATA[http://jugad2.blogspot.com/2013/05/zato-open-source-esb-in-python.html]]></link>
    <pubDate>2013-05-22 05:22:22</pubDate>
  </item>
  <item>
    <title><![CDATA[PyPy Development: PyPy 2.0.2 - Fermi Panini]]></title>
    <description><![CDATA[<p>We're pleased to announce PyPy 2.0.2.  This is a stable bugfix release
over <a class="reference external" href="http://morepypy.blogspot.ch/2013/05/pypy-20-einstein-sandwich.html">2.0</a> and <a class="reference external" href="http://morepypy.blogspot.ch/2013/05/pypy-201-bohr-smrrebrd.html">2.0.1</a>.  You can download it here:</p>
<blockquote>
<a class="reference external" href="http://pypy.org/download.html">http://pypy.org/download.html</a></blockquote>
<p>It fixes a crash in the JIT when calling external C functions (with
ctypes/cffi) in a multithreaded context.</p>
<div class="section" id="what-is-pypy">
<h1>What is PyPy?</h1>
<p>PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7. It's fast (<a class="reference external" href="http://speed.pypy.org">pypy 2.0 and cpython 2.7.3</a> performance comparison)
due to its integrated tracing JIT compiler.</p>
<p>This release supports x86 machines running Linux 32/64, Mac OS X 64 or
Windows 32.  Support for ARM is progressing but not bug-free yet.</p>
</div>
<div class="section" id="highlights">
<h1>Highlights</h1>
<p>This release contains only the fix described above.  A crash (or wrong
results) used to occur if all these conditions were true:</p>
<ul class="simple">
<li>your program is multithreaded;</li>
<li>it runs on a single-core machine or a heavily-loaded multi-core one;</li>
<li>it uses ctypes or cffi to issue external calls to C functions.</li>
</ul>
<p>This was fixed in the branch <a class="reference external" href="https://bitbucket.org/pypy/pypy/commits/7c80121abbf4">emit-call-x86</a> (see the example file
<tt class="docutils literal">bug1.py</tt>).</p>
<p>Cheers,
arigo et. al. for the PyPy team</p>
</div><img src="http://feeds.feedburner.com/~r/PyPyStatusBlog/~4/9XCePSY5TiI" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/PyPyStatusBlog/~3/9XCePSY5TiI/pypy-202-fermi-panini.html]]></link>
    <pubDate>2013-05-21 17:42:59</pubDate>
  </item>
  <item>
    <title><![CDATA[Brian Okken: How not to test, part 1]]></title>
    <description><![CDATA[<p>or Complete coverage testing or More is Better testing or Pathological approach to testing The setup For the sake of this post, let&#8217;s say I&#8217;ve got a Python package that needs testing. It&#8217;s written completely in Python It has a specification fully describing the API The specification is so complete that it also covers behaviors [...]</p><p>The post <a href="http://pythontesting.net/strategy/complete-coverage-testing/">How not to test, part 1</a> appeared first on <a href="http://pythontesting.net">Python Testing</a>.</p><img src="http://feeds.feedburner.com/~r/PythonTesting/~4/nYDBUxFjkG4" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/PythonTesting/~3/nYDBUxFjkG4/]]></link>
    <pubDate>2013-05-21 14:14:49</pubDate>
  </item>
  <item>
    <title><![CDATA[Reinout van Rees: Advanced Python through Django: metaclasses - Peter Inglesby]]></title>
    <description><![CDATA[<div class="document">
<p>Metaclasses are a handy feature of Python and Django makes good use of them.</p>
<p>When you create certain kinds of classes in Django, a metaclass will do
something to the class before it is created. For forms, the various attributes of the
class are converted into a <tt class="docutils literal">base_fields</tt> dictionary on the class.</p>
<p>Similarly, a subclass of <tt class="docutils literal">Model</tt> also fires up a metaclass that does some
registering. A foreignkey to another model adds a relation back on that other
model, for instance.</p>
<p>As a recap, a class is something that can be instantiated into an object. It
can have an <tt class="docutils literal">__init__()</tt> method that does something upon
instantiation. <tt class="docutils literal">type(your_instance)</tt> will return the class.</p>
<p>Did you know that you can create classes dynamically? See for yourself:</p>
<pre class="literal-block">
&gt;&gt;&gt; name = 'ExampleClass'
&gt;&gt;&gt; bases = (object,)
&gt;&gt;&gt; attrs = {'__init__': lambda self: print('Hello from __init__')}
&gt;&gt;&gt; ExampleClass = type(name, bases, attrs)
&gt;&gt;&gt; example = ExampleClass()
Hello From __init__
&gt;&gt;&gt; type(example)
&lt;class '__console__.ExampleClass'&gt;
</pre>
<p>So... we can actually control how classes are created! You could create a
<tt class="docutils literal">create_class()</tt> method that calls type but that modifies, for instance, the name.
Or we could take all the attributes and add them to a <tt class="docutils literal">base_fields</tt>
dictionary on the instance. Hey, that's what we saw in the first Django form example!</p>
<p>Now, what is <tt class="docutils literal">type</tt> exactly? It is a class that creates classes.</p>
<p>This also means we can subclass it! The most useful thing to override in our
subclass is the <tt class="docutils literal">__new__()</tt> method. The <tt class="docutils literal">__init__()</tt> method creates
instances from the class, the <tt class="docutils literal">__new__()</tt> creates classes. (<strong>Update</strong>: it
is a little bit different, actually, see <a class="reference external" href="http://reinout.vanrees.org/#comment-900194349">Venelin's comment</a> below). So again we can modify the name and/or the
attributes.</p>
<p>How do you use it in practice? Normally you'd set a <tt class="docutils literal">__metaclass__</tt>
attribute on a class. This tells python to use that metaclass for creating the
class. The same for subclasses. This is how our Django form classes use the
metaclass specified in Django's base <tt class="docutils literal">Form</tt> class.</p>
<p>Django uses metaclasses in five places: admin media, models, forms,
formfields, form widgets. Grep for <tt class="docutils literal">metaclass</tt> in your local django
source code once to get a better feel for how Django uses it.</p>
<p>Note on python 3: it uses a slightly different syntax for specifying
metaclasses. So Django 1.5 uses <a class="reference external" href="https://pypi.python.org/pypi/six">six</a> to
support both ways in a single codebase.</p>
<p><strong>Warning:</strong> don't overuse metaclasses. They can make code difficult to debug
and follow. Use Django as a good example of how to use metaclasses. Django
saves you a lot of work by using metaclasses in a few locations.</p>
<p>See <a class="reference external" href="https://github.com/inglesp/Metaclasses">https://github.com/inglesp/Metaclasses</a></p>
<p>Nice way of giving a presentation, btw. Some sort of semi-interactive python
prompt. The software is online at <a class="reference external" href="https://github.com/inglesp/prescons">https://github.com/inglesp/prescons</a></p>
</div>]]></description>
    <link><![CDATA[http://reinout.vanrees.org/weblog/2013/05/16/advanced-python-metaclasses.html]]></link>
    <pubDate>2013-05-21 06:38:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Python 4 Kids: Comprehending Lists and Tuples]]></title>
    <description><![CDATA[<p><a href="http://www.ibras.dk/montypython/episode05.htm"><em> SUPERIMPOSED CAPTION: &#8216;SUBURBAN LOUNGE NEAR ESHER&#8217;<br />
Elderly couple, Mr A and Mrs B are staring through french windows at a cat that is sitting in the middle of their lawn motionless and facing away from them. A car is heard drawing up. </em></a></p>
<p>The last couple of tutorials have been a bit heavy, so this week&#8217;s tutorials are going to balance that out by being a little light.  We&#8217;re having a look at a couple of different aspects of the Python language. </p>
<p><strong>List Comprehensions</strong><br />
The first is comprehensions:</p>
<pre class="brush: python; collapse: false; title: ; notranslate">
&gt;&gt;&gt; a_list = [1,2,3,4,5]
&gt;&gt;&gt; b_list= [element*2 for element in a_list]
&gt;&gt;&gt; b_list
[2, 4, 6, 8, 10]
</pre>
<p>In this example, we have automatically generated a new list <em>b_list</em> from an existing <em>list</em>* <em>a_list</em> by using a <em>list comprehension</em>.  The structure of the comprehension is, I hope comprehensible.  If not, it is a little hard to explain in steps.   The comprehension above is equivalent to: </p>
<pre class="brush: python; collapse: false; title: ; notranslate">
&gt;&gt;&gt; b_list = []
&gt;&gt;&gt; for element in a_list:
...    b_list.append(element*2)
... 
&gt;&gt;&gt; b_list
[2, 4, 6, 8, 10]
</pre>
<p>We could call the <em>variable</em> <em>element</em> anything we liked &#8211; it is just another <em>variable</em>:</p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; b_list = [baloney*2 for baloney in a_list]
&gt;&gt;&gt; b_list
[2, 4, 6, 8, 10]
&gt;&gt;&gt; 
</pre>
<p>You can also add conditions.  Let&#8217;s say you only wanted the even elements in <em>a_list:<br />
</em>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; b_list = [element for element in a_list if element%2 ==0] #ie remainder is 0 when dividing by two
&gt;&gt;&gt; b_list
[2, 4]
&gt;&gt;&gt; 
</pre>
<p>The condition here is <em>if element%2 ==0</em>, but you can substitute other conditions (as long as they resolve to either <em>True</em> or <em>False</em>).  You can have a comprehension which relies on multiple <em>variables</em> (this example is from the <a href="http://docs.python.org/2/tutorial/datastructures.html">Python documentation</a>):</p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
</pre>
<p>Unwinding this is a little tricky &#8211; for each element in [1,2,3] it runs through each element in [3,1,4] (ie 9 comparisons in all) and appends the <em>tuple</em> if the elements are not equal. </p>
<p>List comprehensions provide an easy shorthand for constructing lists.  Moreover, they are often more readable than writing out the for loops explicitly. </p>
<p><strong>Tuples</strong><br />
The second topic is tuples.<br />
The other thing we&#8217;re going to look at is tuples (pronounced, variously, &#8220;tupp-lls&#8221;, &#8220;two-pls&#8221; and, to some people, &#8220;tyou-pls&#8221; &#8211; my preference is tupp-lls, rhymes with couples).  Tuples are a little like lists, except that they are immutable.  That is, once they are made they cannot be changed.  Tuples are made by putting the elements in round braces [actually, adding commas between them and, sometimes, also adding braces]: </p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; my_tuple = (1,2,3)
&gt;&gt;&gt; my_tuple
(1, 2, 3)
&gt;&gt;&gt; my_list = [1,2,3]
&gt;&gt;&gt; my_list
[1, 2, 3]
</pre>
<p>Elements of the tuple are referenced in the same way as you&#8217;d reference a list but, unlike lists, these elements cannot be changed (tuples are &#8220;immutable&#8221;):</p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt;my_tuple[0]
1               
&gt;&gt;&gt;my_tuple[0]=2
Traceback (most recent call last):                                         
  File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;                                      
TypeError: 'tuple' object does not support item assignment    
</pre>
<p>The TypeError is telling us that you can&#8217;t change the elements of a tuple.  To make a tuple with a single element is a little difficult because the interpreter could just interpret the parentheses as determining order of operation (think (1+2)*3).  Instead, we put a comma after the single element to indicate that we are creating a tuple:</p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; my_tuple = (1)  # no comma, so Python just thinks it's a number
&gt;&gt;&gt; my_tuple  
1             
&gt;&gt;&gt; my_tuple[0]  # just a number, so has no elements
Traceback (most recent call last):                                                                           
  File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt; 
TypeError: 'int' object has no attribute '__getitem__'
&gt;&gt;&gt; my_tuple= (1,)
&gt;&gt;&gt; my_tuple
(1,)                                                                                                                 
&gt;&gt;&gt;my_tuple[0]                                                                                                       
1              
</pre>
<p>So, if a tuple does what a list can do, only less, why bother with a tuple? Why not use a list?  Well, tuples are easier for the Python interpreter to deal with and therefore might end up being faster.  Tuples might also indicate that you have an ordered list where the ordering has some meaning (for example, a date tuple might store year, month and day (in that order) in tuple).  The fact that you&#8217;re using a tuple then flags that each of the entries has a distinct meaning or use and that their order is significant.  Another <a href="http://en.wiktionary.org/wiki/pragmatic">pragmatic</a> reason to use a tuple is when you have data which you know shouldn&#8217;t change (like a constant).  So, if you accidentally try to change one of the tuple&#8217;s entries, you will have an error thrown.   </p>
<p>Finally, since tuples are immutable they can be used as keys in dictionaries, unlike lists: </p>
<pre class="brush: python; collapse: false; title: ; notranslate"> 
&gt;&gt;&gt; my_dict={}
&gt;&gt;&gt; my_tuple=(&quot;A Name&quot;,23,&quot;A location&quot;)
&gt;&gt;&gt; my_list= list(my_tuple)
&gt;&gt;&gt; my_dict[my_tuple]=&quot;Client 1&quot;
&gt;&gt;&gt; my_dict[my_list]=&quot;Client 2&quot;
Traceback (most recent call last):
  File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;
TypeError: unhashable type: 'list'
</pre>
<p><strong>Notes:</strong><br />
* as per Don&#8217;s comment, you can use any <em>iterator</em>, but we haven&#8217;t talked about them yet, so they don&#8217;t yet exist. </p>
<br />  <a rel="nofollow" href="http://feeds.wordpress.com/1.0/gocomments/python4kids.wordpress.com/661/"><img alt="" border="0" src="http://feeds.wordpress.com/1.0/comments/python4kids.wordpress.com/661/" /></a> <img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=python4kids.wordpress.com&blog=14472219&post=661&subd=python4kids&ref=&feed=1" width="1" height="1" /></p>]]></description>
    <link><![CDATA[http://python4kids.wordpress.com/2013/05/19/661/]]></link>
    <pubDate>2013-05-21 06:18:22</pubDate>
  </item>
  <item>
    <title><![CDATA[Reinout van Rees: The imaginative programmer - Zed Shaw]]></title>
    <description><![CDATA[<div class="document">
<p><strong>His goal</strong>: teach programmers to be more creative.</p>
<p>He's got a love/hate relationship with creativity. The first part of his talk
was impossible to summarize. You'll have to watch the video later on :-)</p>
<ul class="simple">
<li>Artists tell him he's not artistic because he works on developing technical
skills.</li>
<li>Guitarists tell him he's not a real guitarist as he doesn't play in a
band. And 'cause he builds his own guitars he's a programmer, not a Real
Guitarist.</li>
<li>Writers tell him he's not a writer because he writes technical books.</li>
<li>Programmers tell him he's not a programmer because he doesn't work on their
project. And by the way, he's a (technical) writer now, so he's not a
programmer.</li>
</ul>
<p>He's not creative enough. Or so the others say. Or he's not acceptable. How to
deal with creativity? In a way, you can re-phrase creativity. Programmers are
always making <em>something</em> from <em>nothing</em>, right? Isn't that the pinnacle of
creativity?</p>
<p>Here are four hypothetical persons:</p>
<ul class="simple">
<li>Technique, no imagination: a stereotypical programmer.</li>
<li>Imagination, no technique: stereotypical biz dude.</li>
<li>No imagination, no technique. Probably doesn't exist.</li>
<li>Both imagination and technique. Zed's goal.</li>
</ul>
<p>Zed's imaginative programmer process. Everyone has a process (if they're
good), here's the one he proposes to help you be more creative:</p>
<ul class="simple">
<li>You start with an idea.</li>
<li>You establish a concept that helps form the idea. It gives your idea
clothes.</li>
<li>Research techniques or tools. Do some research or you'll pay for it later
on.</li>
<li>Refine the concept through composition. So put a box around the vast world
of available possibilities. Just mark which features are inside and outside
the concept.</li>
<li>Explore through prototypes. Throw away code or use paper prototypes for
instance. <strong>This saves you so much time later</strong>.</li>
<li>You make it <strong>real</strong>.</li>
</ul>
<p>We are programmers, so we should iterate this process.</p>
<p>He tried this process with <a class="reference external" href="http://projectzorn.com/">http://projectzorn.com/</a>, going through all the
stages. And he's probably going to work on it during the sprints this weekend,
so join him if you want.</p>
</div>]]></description>
    <link><![CDATA[http://reinout.vanrees.org/weblog/2013/05/15/imaginative-programmer.html]]></link>
    <pubDate>2013-05-21 06:13:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Vasudev Ram: A partial crossword solver in Python]]></title>
    <description><![CDATA[<p><a href="http://blog.robindeits.com/2013/02/11/a-cryptic-crossword-clue-solver/">A Cryptic Crossword Clue Solver &#8592;</a></p><p>Saw this via Twitter.</p><p>It is a partial crossword solver, because it only helps solve a particular category of crossword clues - those in which the clue (which is usually a sentence or phrase) contains both a "definition" of the answer as well a hint of some kind that leads to the same answer. This solver tries to compute the answer using both the definition and the hint, and checks whether the results match. Ingenious.</p><p>I found it interesting because this is a somewhat difficult problem, and yet the author managed to create a solution (involving NLTK and parsing) that works in many, if not all cases.</p><p>Also, long ago, in college days, I had written another kind of partial crossword solver (in BASIC); it was much simpler, using a brute force method - what it did was help solve the kind of crossword clues in which the answer is a permutation of a substring of the characters comprising the clue sentence or phrase. The program would generate and display on the screen, all possible permutations of all possible substrings of the sentence, that were of the same length as the answer. Then you had to view those permutations and guess whether any of them was the right answer, based on the clue.</p><p>I wrote the permutation-generation code by hand, but saw recently that the Python itertools module has methods to generate permutations (as well as combinations) from sequences:</p><p>http://docs.python.org/2/library/itertools.html</p><p>http://en.m.wikipedia.org/wiki/Permutation</p><p>http://en.wikipedia.org/wiki/Crossword</p><p>- Vasudev Ram<br />dancingbison.com</p><div class="blogger-post-footer"><a href="http://www.dancingbison.com">Vasudev Ram</a>
<br /></div>]]></description>
    <link><![CDATA[http://jugad2.blogspot.com/2013/05/a-partial-crossword-solver-in-python.html]]></link>
    <pubDate>2013-05-21 03:14:06</pubDate>
  </item>
  <item>
    <title><![CDATA[Montreal Python User Group: Python Projects Night V]]></title>
    <description><![CDATA[<p>Montréal-Python would like to invite you all to the next Python Project Night, on Thursday, the 30th of May, 2013 at the offices of Caravan.</p>
<p>Like on previous nights, it’s an informal meetup where people work on different projects and generally mess around with Python code. Everyone is welcome, from the grizzled python hacker to the absolute beginner who just finished their first workshop. We will encourage people to help each other and we will also have dedicated helpers to help people get started.</p>
<p>As per usual, beer and pizza are provided, so just bring your laptop computer.</p>
<p>If you have any projects you would like to work on, please post them on the mailing list: <a href="mailto:montrealpython@googlegroups.com">montrealpython@googlegroups.com</a>. If you don’t have any ideas, don’t worry, we will find you a project that needs help.</p>
<p>When: Thursday, May 30th 2013 from 7 PM to 9:30 PM</p>
<p>Where: <a href="http://goo.gl/maps/OAmyb">Caravan, 5334 de Gaspé, office #1204 (Montreal)</a></p>
<p>Where to sign up: <a href="http://www.eventbrite.ca/event/6778218835">Please sign up on our Eventbright event</a></p>
<p>We would like to thank <a href="http://caravan.coop/"><img src="http://montrealpython.org/partners/CaravanLogo.png" alt="Caravan" /></a> for hosting our event!</p>]]></description>
    <link><![CDATA[http://montrealpython.org/2013/05/python-projects-night-v/]]></link>
    <pubDate>2013-05-21 01:15:27</pubDate>
  </item>
  <item>
    <title><![CDATA[Armin Ronacher: Porting to Python 3 Redux]]></title>
    <description><![CDATA[<p>After a very painful porting experience with Jinja2 to Python 3 I
basically left the project idling around for a while because I was too
afraid of breaking Python 3 support.  The approach I used was one codebase
that was written in Python 2 and translated with 2to3 to Python 3 on
installation.  The unfortunate side-effect of that is that any change you
do requires about a minute of translation which destroys all your
iteration speeds.  Thankfully it turns out that if you target the right
versions of Python you can do much better.</p>
<p>Thomas Waldmann from the MoinMoin project started running Jinja2 through
my <a class="reference external" href="https://github.com/mitsuhiko/python-modernize">python-modernize</a>
with the right parameters and ended up with a unified codebase that runs
on 2.6, 2.7 and 3.3.  With a bit of cleanup afterwards we were able to
come up with a nice codebase that runs in all versions of Python and also
looks like regular Python code for the most time.</p>
<p>Motivated by the results from this I went through the code a few more
times and also started migrating some other code over to experiment more
with unified codebases.</p>
<p>This is a selection of some tips and tricks I can now share in regards to
accomplishing something similar.</p>
<div class="section" id="drop-2-5-3-1-and-3-2">
<h2>Drop 2.5, 3.1 and 3.2</h2>
<p>This is the most important tip.  Dropping 2.5 by now is more than possible
since very few people are still on it and dropping 3.1 and 3.2 are no
brainers anyways considering the low adoption of Python 3 so far.  But why
would you drop those versions?  The basic answer is that 2.6 and 3.3 have
a lot of overlapping syntax and features that allow for code that works
well with both:</p>
<ul>
<li><p class="first">Compatible string literals.  2.6 and 3.3 support the same syntax for
strings.  You can use <tt class="docutils literal">'foo'</tt> to refer to a native string (byte
string on 2.x and a Unicode string on 3.x), <tt class="docutils literal">u'foo'</tt> to refer to a
Unicode string and <tt class="docutils literal">b'foo'</tt> to refer to a bytestring or bytes
object.</p>
</li>
<li><p class="first">Compatible print syntax.  In case you have a few print statements
sitting around you can <tt class="docutils literal">from __future__ import print_function</tt> and
start using the print function without having to bind it to a
different name or suffering from other inconsistencies.</p>
</li>
<li><p class="first">Compatible exception catching syntax.  Python 2.6 introduced <tt class="docutils literal">except
Exception as e</tt> which is also the syntax used on 3.x to catch down
exceptions.</p>
</li>
<li><p class="first">Class decorators are available.  They are incredible useful to
automatically correct moved interfaces without leaving a footprint in
the class structure.  For instance they can be used to automatically
rename the iteration method from <tt class="docutils literal">next</tt> to <tt class="docutils literal">__next__</tt> or
<tt class="docutils literal">__str__</tt> to <tt class="docutils literal">__unicode__</tt> for Python 2.x.</p>
</li>
<li><p class="first">Builtin <tt class="docutils literal">next()</tt> function to invoke <tt class="docutils literal">__next__</tt> or <tt class="docutils literal">next</tt>.  This
is helpful because they are performing about the same speed as calling
the method directly so you don't pay much of a performance penalty
compared to putting runtime checks into places or making a wrapper
function yourself.</p>
</li>
<li><p class="first">Python 2.6 added the <tt class="docutils literal">bytearray</tt> type which has the same interface
in that version of Python as the one in 3.3.  This is useful because
while Python 2.6 lacks the Python 3.3 <tt class="docutils literal">bytes</tt> object it does have
a builtin with that name but that's just another name for <tt class="docutils literal">str</tt>
which has vastly different behavior.</p>
</li>
<li><p class="first">Python 3.3 reintroduces bytes-to-bytes and string-to-string codecs
that were broken in 3.1 and 3.2.  Unfortunately the interface for them
is clunkier now and the aliases are missing, but it's much closer to
what we had in 2.x than before.</p>
<p>This is particularly useful if you did stream based encoding and
decoding.  That functionality was plain missing between 3.0 up until
3.3.</p>
</li>
</ul>
<p>Yes, the <cite>six</cite> module can get you quite far, but don't underestimate the
impact of looking at nice code.  With the Python 3 port I basically lost
interest in maintaining Jinja2 because the codebase started to frustrate
me.  Back then a unified codebase was looking ugly and had a performance
impact (<tt class="docutils literal"><span class="pre">six.b('foo')</span></tt> and <tt class="docutils literal"><span class="pre">six.u('foo')</span></tt> everywhere) or was plagued
under the bad iteration speeds of 2to3.  Not having to deal with any of
that brings the joy back.  Jinja2's codebase now looks like very clean and
you have to find the Python 2/3 compatibility support.  Very few paths in
the code actually do something like <tt class="docutils literal">if PY2:</tt>.</p>
<p>The rest of this article assumes that these are the Python versions you
want to support.  Also attempting to support Python 2.5 is a very painful
thing to do and I strongly recommend against it.  Supporting 3.2 is
possible if you are willing to wrap all your strings in function calls
which I don't recommend doing for aesthetic and performance reasons.</p>
</div>
<div class="section" id="skip-six">
<h2>Skip Six</h2>
<p>Six is a pretty neat library and this is where the Jinja2 port started out
with.  There however at the end of the day there is not much in six that
actually is required for getting a Python 3 port running and a few things
are missing.  Six is definitely required if you want to support Python 2.5
but from 2.6 or later there really is not much of a reason to use six.
Jinja2 ships a <cite>_compat</cite> module that contains the few helpers required.
Including a few lines of non Python 3 code the whole compatibility module
is less than 80 lines of code.</p>
<p>This saves you from the troubles where users might expect a different
version of six because of another library or pulling in another dependency
into your project.</p>
</div>
<div class="section" id="start-with-modernize">
<h2>Start with Modernize</h2>
<p>To start with the port the <a class="reference external" href="https://github.com/mitsuhiko/python-modernize">python-modernize</a> library is a good start.
It is a version of 2to3 that produces code that runs in either.  While
it's pretty buggy still and the default options are not particularly
great, it can get you quite far with regards to doing the boring parts for
you.  You will still need to go over the result and clean up some imports
and ugly results.</p>
</div>
<div class="section" id="fix-your-tests">
<h2>Fix your Tests</h2>
<p>Before you do anything else walk through your tests and make sure that
they still make sense.  A lot of the problems in the Python standard
library in 3.0 and 3.1 came from the fact that the behavior of the
testsuite changed through the conversion to Python 3 in unintended ways.</p>
</div>
<div class="section" id="write-a-compatibility-module">
<h2>Write a Compatibility Module</h2>
<p>So you're going to skip six, can you live without helpers?  The answer is
“no”.  You will still need a small compatibility module but that is small
enough that you can just keep it in your package.  Here are some basic
examples of what such a compatibility module could look like:</p>
<div class="highlight"><pre><span class="kn">import</span> <span class="nn">sys</span>
<span class="n">PY2</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</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">if</span> <span class="ow">not</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="n">text_type</span> <span class="o">=</span> <span class="nb">str</span>
    <span class="n">string_types</span> <span class="o">=</span> <span class="p">(</span><span class="nb">str</span><span class="p">,)</span>
    <span class="nb">unichr</span> <span class="o">=</span> <span class="nb">chr</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">text_type</span> <span class="o">=</span> <span class="nb">unicode</span>
    <span class="n">string_types</span> <span class="o">=</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">unicode</span><span class="p">)</span>
    <span class="nb">unichr</span> <span class="o">=</span> <span class="nb">unichr</span>
</pre></div>
<p>The exact contents of that module will depend on how much actually changed
for you.  In case of Jinja2 I put a whole bunch of functions in there.
For instance it contains <cite>ifilter</cite>, <cite>imap</cite> and similar itertools functions
that became builtins in 3.x.  (I stuck with the Python 2.x functions to
make it clear for the reader of the code that the iterator behavior is
intended and not a bug).</p>
</div>
<div class="section" id="test-for-2-x-not-3-x">
<h2>Test for 2.x not 3.x</h2>
<p>At one point there will be the requirement to check if you are executing
on 2.x or 3.x.  In that cases I would recommend checking for Python 2
first and putting Python 3 into your else branch instead of the other way
round.  That way you will have less ugly surprises when a Python 4 comes
around at one point.</p>
<p>Good:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">__str__</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">__unicode__</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s">'utf-8'</span><span class="p">)</span>
</pre></div>
<p>Less ideal:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="ow">not</span> <span class="n">PY3</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">__str__</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">__unicode__</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s">'utf-8'</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="string-handling">
<h2>String Handling</h2>
<p>The biggest change in Python 3 is without doubt the changes on the Unicode
interface.  Unfortunately these changes are very painful in some places
and also inconsistently handled throughout the standard library.  The
majority of the time porting will clearly be wasted on this topic.  This
topic is a whole article by itself but here is a quick cheat sheet for
porting that Jinja2 and Werkzeug follow:</p>
<ul>
<li><p class="first"><tt class="docutils literal">'foo'</tt> always refers to what we call the native string of the
implementation.  This is the string used for identifiers, sourcecode,
filenames and other low-level functions.  Additionally in 2.x it's
permissible as a literal in Unicode strings for as long as it's
limited to ASCII only characters.</p>
<p>This property is very useful for unified codebases because the general
trend with Python 3 is to introduce Unicode in some interfaces that
previously did not support it, but never the inverse.  Since native
string literals “upgrade” to Unicode but still somewhat support
Unicode in 2.x this string literal is very flexible.</p>
<p>For instance the <tt class="docutils literal">datetime.strftime</tt> function strictly does not
support Unicode in Python 2 but is Unicode only in 3.x.  Because in
most cases the return value on 2.x however was ASCII only things like
this work really well in 2.x and 3.x:</p>
<div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="s">u'&lt;p&gt;Current time: </span><span class="si">%s</span><span class="s">'</span> <span class="o">%</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s">'%H:%M'</span><span class="p">)</span>
<span class="go">u'&lt;p&gt;Current time: 23:52'</span>
</pre></div>
<p>The string passed to <cite>strftime</cite> is native (so bytes in 2.x and Unicode
in 3.x).  The return value is a native string again and ASCII only.
As such both on 2.x and 3.x it will be a Unicode string once string
formatted.</p>
</li>
<li><p class="first"><tt class="docutils literal">u'foo'</tt> always refers to a Unicode string.  Many libraries already
had pretty excellent Unicode support in 2.x so that literal should not
be surprising to many.</p>
</li>
<li><p class="first"><tt class="docutils literal">b'foo'</tt> always refers to something that can hold arbitrary bytes.
Since 2.6 does not actually have a <tt class="docutils literal">bytes</tt> object like Python 3.3
has and Python 3.3 lacks an actual bytestring the usefulness of this
literal is indeed a bit limited.  It becomes immediately more useful
when paired with the <tt class="docutils literal">bytearray</tt> object which has the same interface
on 2.x and 3.x:</p>
<div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="nb">bytearray</span><span class="p">(</span><span class="n">b</span><span class="s">' foo '</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
<span class="go">bytearray(b'foo')</span>
</pre></div>
<p>Since it's also mutable it's quite efficient at modifying raw bytes
and you can trivially convert it to something more conventional by
wrapping the final result in <tt class="docutils literal">bytes()</tt> again.</p>
</li>
</ul>
<p>In addition to these basic rules I also add <tt class="docutils literal">text_type</tt>, <tt class="docutils literal">unichr</tt>
and <tt class="docutils literal">string_types</tt> variables to my compatibility module as shown above.
With those available the big changes are:</p>
<ul class="simple">
<li><tt class="docutils literal">isinstance(x, basestring)</tt> becomes <tt class="docutils literal">isinstance(x, string_types)</tt>.</li>
<li><tt class="docutils literal">isinstance(x, unicode)</tt> becomes <tt class="docutils literal">isinstance(x, text_type)</tt>.</li>
<li><tt class="docutils literal">isinstance(x, str)</tt> with the intention of catching bytes becomes
<tt class="docutils literal">isinstance(x, bytes)</tt> or <tt class="docutils literal">isinstance(x, (bytes, bytearray))</tt>.</li>
</ul>
<p>I also created a <tt class="docutils literal">implements_to_string</tt> class decorator that helps
implementing classes with <tt class="docutils literal">__unicode__</tt> or <tt class="docutils literal">__str__</tt> methods:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">implements_to_string</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">__unicode__</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">__str__</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">__str__</span> <span class="o">=</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="n">__unicode__</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s">'utf-8'</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">cls</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">implements_to_string</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
</pre></div>
<p>The idea is that you just implement <tt class="docutils literal">__str__</tt> on both 2.x and 3.x and
let it return Unicode strings (yes, looks a bit odd in 2.x) and the
decorator automatically renames it to <tt class="docutils literal">__unicode__</tt> for 2.x and adds a
<tt class="docutils literal">__str__</tt> that invokes <tt class="docutils literal">__unicode__</tt> and encodes the return value to
utf-8.  This pattern has been pretty common in the past with 2.x modules.
For instance Jinja2 and Django use it.</p>
<p>Here is an example for the usage:</p>
<div class="highlight"><pre><span class="nd">@implements_to_string</span>
<span class="k">class</span> <span class="nc">User</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">username</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">username</span> <span class="o">=</span> <span class="n">username</span>
    <span class="k">def</span> <span class="nf">__str__</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">username</span>
</pre></div>
</div>
<div class="section" id="metaclass-syntax-changes">
<h2>Metaclass Syntax Changes</h2>
<p>Since Python 3 changed the syntax for defining the metaclass to use in an
incompatible way this makes porting a bit harder than it should be.  Six
has a <tt class="docutils literal">with_metaclass</tt> function that can work around this issue but it
generates a dummy class that shows up in the inheritance tree.  For Jinja2
I was not happy enough with that solution and modified it a bit.  The
external API is the same but the implementation uses a temporary class
to hook in the metaclass.  The benefit is that you don't have to pay a
performance penalty for using it and your inheritance tree stays nice.</p>
<p>The code is a bit hard to understand.  The basic idea is exploiting the
idea that metaclasses can customize class creation and are picked by by
the parent class.  This particular implementation uses a metaclass to
remove its own parent from the inheritance tree on subclassing.  The end
result is that the function creates a dummy class with a dummy metaclass.
Once subclassed the dummy classes metaclass is used which has a
constructor that basically instances a new class from the original parent
and the actually intended metaclass.  That way the dummy class and dummy
metaclass never show up.</p>
<p>This is what it looks like:</p>
<div class="highlight"><pre><span class="k">def</span> <span class="nf">with_metaclass</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="o">*</span><span class="n">bases</span><span class="p">):</span>
    <span class="k">class</span> <span class="nc">metaclass</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
        <span class="n">__call__</span> <span class="o">=</span> <span class="nb">type</span><span class="o">.</span><span class="n">__call__</span>
        <span class="n">__init__</span> <span class="o">=</span> <span class="nb">type</span><span class="o">.</span><span class="n">__init__</span>
        <span class="k">def</span> <span class="nf">__new__</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">this_bases</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">this_bases</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
                <span class="k">return</span> <span class="nb">type</span><span class="o">.</span><span class="n">__new__</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="p">(),</span> <span class="n">d</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">meta</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">metaclass</span><span class="p">(</span><span class="s">'temporary_class'</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="p">{})</span>
</pre></div>
<p>And here is how you use it:</p>
<div class="highlight"><pre><span class="k">class</span> <span class="nc">BaseForm</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="k">pass</span>

<span class="k">class</span> <span class="nc">FormType</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
    <span class="k">pass</span>

<span class="k">class</span> <span class="nc">Form</span><span class="p">(</span><span class="n">with_metaclass</span><span class="p">(</span><span class="n">FormType</span><span class="p">,</span> <span class="n">BaseForm</span><span class="p">)):</span>
    <span class="k">pass</span>
</pre></div>
</div>
<div class="section" id="dictionaries">
<h2>Dictionaries</h2>
<p>One of the more annoying changes in Python 3 are the changes on the
dictionary iterator protocols.  In Python 2 all dictionaries had
<tt class="docutils literal">keys()</tt>, <tt class="docutils literal">values()</tt> and <tt class="docutils literal">items()</tt> that returned lists and
<tt class="docutils literal">iterkeys()</tt>, <tt class="docutils literal">itervalues()</tt> and <tt class="docutils literal">iteritems()</tt> that returned
iterators.  In Python 3 none of that exists any more.  Instead they were
replaced with new methods that return view objects.</p>
<p><tt class="docutils literal">keys()</tt> returns a key view which behaves like some sort of read-only
set, <tt class="docutils literal">values()</tt> which returns a read-only container and iterable (not an
iterator!) and <tt class="docutils literal">items()</tt> which returns some sort of read-only set-like
object.  Unlike regular sets it however can also point to mutable objects
in which case some methods will fail at runtime.</p>
<p>On the positive side a lot of people missed that views are not iterators
so in many cases you can just ignore that.  Werkzeug and Django implement
a bunch of custom dictionary objects and in both cases the decision was
made to just ignore the existence of view objects and let <tt class="docutils literal">keys()</tt> and
friends return iterators.</p>
<p>This is currently the only sensible thing to do due to limitations of the
Python interpreter.  There are a few problems with it:</p>
<ul class="simple">
<li>The fact that the views are not iterators by themselves mean that in
the average case you create a temporary object for no good reason.</li>
<li>The set-like behavior of the builtin dictionary views cannot be
replicated in pure Python due to <a class="reference external" href="http://bugs.python.org/issue2226">limitations in the interpreter</a>.</li>
<li>Implementing views for 3.x and iterators for 2.x would mean a lot of
code duplication.</li>
</ul>
<p>This is what the Jinja2 codebase went with for iterating over
dictionaries:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="n">iterkeys</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">iterkeys</span><span class="p">()</span>
    <span class="n">itervalues</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">itervalues</span><span class="p">()</span>
    <span class="n">iteritems</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">iteritems</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">iterkeys</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
    <span class="n">itervalues</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
    <span class="n">iteritems</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
</pre></div>
<p>For implementing dictionary like objects a class decorator can become
useful again:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">implements_dict_iteration</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">iterkeys</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">keys</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">itervalues</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">values</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">iteritems</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">items</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">keys</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">iterkeys</span><span class="p">())</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">itervalues</span><span class="p">())</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">items</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">iteritems</span><span class="p">())</span>
        <span class="k">return</span> <span class="n">cls</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">implements_dict_iteration</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
</pre></div>
<p>In that case all you need to do is to implement the <tt class="docutils literal">keys()</tt> and friends
method as iterators and the rest happens automatically:</p>
<div class="highlight"><pre><span class="nd">@implements_dict_iteration</span>
<span class="k">class</span> <span class="nc">MyDict</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="o">...</span>

    <span class="k">def</span> <span class="nf">keys</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">iteritems</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
            <span class="k">yield</span> <span class="n">key</span>

    <span class="k">def</span> <span class="nf">values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">iteritems</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
            <span class="k">yield</span> <span class="n">value</span>

    <span class="k">def</span> <span class="nf">items</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="o">...</span>
</pre></div>
</div>
<div class="section" id="general-iterator-changes">
<h2>General Iterator Changes</h2>
<p>Since iterators changed in general a bit of help is needed to make this
painless.  The only change really is the transition from <tt class="docutils literal">next()</tt> to
<tt class="docutils literal">__next__</tt>.  Thankfully this is already transparently handled.  The only
thing you really need to change is to go from <tt class="docutils literal">x.next()</tt> to <tt class="docutils literal">next(x)</tt>
and the language does the rest.</p>
<p>If you plan on defining iterators a class decorator again becomes helpful:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">implements_iterator</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
        <span class="n">cls</span><span class="o">.</span><span class="n">next</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">__next__</span>
        <span class="k">del</span> <span class="n">cls</span><span class="o">.</span><span class="n">__next__</span>
        <span class="k">return</span> <span class="n">cls</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">implements_iterator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
</pre></div>
<p>For implementing this class just name the iteration step method
<tt class="docutils literal">__next__</tt> in all versions:</p>
<div class="highlight"><pre><span class="nd">@implements_iterator</span>
<span class="k">class</span> <span class="nc">UppercasingIterator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterable</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
    <span class="k">def</span> <span class="nf">__iter__</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="k">def</span> <span class="nf">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_iter</span><span class="p">)</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
</pre></div>
</div>
<div class="section" id="transformation-codecs">
<h2>Transformation Codecs</h2>
<p>One of the nice features of the Python 2 encoding protocol was that it was
independent of types.  You could register an encoding that would transform
a csv file into a numpy array if you would have preferred that.  This
feature however was not well known since the primary exposed interface of
encodings was attached to string objects.  Since they got stricter in 3.x
a lot of that functionality was removed in 3.0 but later reintroduced in
3.3 again since it proved useful.  Basically all codecs that did not
convert between Unicode and bytes or the other way round were unavailable
until 3.3.  Among those codecs are the hex and base64 codec.</p>
<p>There are two use cases for those codecs: operations on strings and
operations on streams.  The former is well known as <tt class="docutils literal">str.encode()</tt> in
2.x but now looks different if you want to support 2.x and 3.x due to the
changed string API:</p>
<div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">codecs</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">codecs</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">b</span><span class="s">'Hey!'</span><span class="p">,</span> <span class="s">'base64_codec'</span><span class="p">)</span>
<span class="go">'SGV5IQ==\n'</span>
</pre></div>
<p>You will also notice that the codecs are missing the aliases in 3.3 which
requires you to write <tt class="docutils literal">'base64_codec'</tt> instead of <tt class="docutils literal">'base64'</tt>.</p>
<p>(These codecs are preferable over the functions in the <cite>binascii</cite> module
because they support operations on streams through the <a class="reference external" href="http://docs.python.org/3/library/codecs.html#incrementalencoder-objects">incremental
encoding and decoding support</a>.)</p>
</div>
<div class="section" id="other-notes">
<h2>Other Notes</h2>
<p>There are still a few places where I don't have nice solutions for yet or
are generally annoying to deal with but they are getting fewer.  Some of
them are unfortunately now part of the Python 3 API are hard to discover
until you trigger an edge case.</p>
<ul>
<li><p class="first">Filesystem and file IO access continues to be annoying to deal with on
Linux due to it not being based on Unicode.  The <tt class="docutils literal">open()</tt> function
and the filesystem layer have dangerous platform specific defaults.
If I SSH into a <tt class="docutils literal">en_US</tt> machine from an <tt class="docutils literal">de_AT</tt> one for instance
Python loves falling back to ASCII encoding for both file system and
file operations.</p>
<p>Generally I noticed the most reliable way to do text on Python 3 that
also works okay on 2.x is just to open files in binary mode and
explicitly decode.  Alternatively you can use the <tt class="docutils literal">codecs.open</tt> or
<tt class="docutils literal">io.open</tt> function on 2.x and the builtin <tt class="docutils literal">open</tt> on Python 3 with
an explicit encoding.</p>
</li>
<li><p class="first">URLs in the standard library are represented incorrectly as Unicode
which causes some URLs to not be dealt with correctly on 3.x.</p>
</li>
<li><p class="first">Raising exceptions with a traceback object requires a helper function
since the syntax changed.  This is very uncommon in general and easy
enough to wrap.  Since the syntax changed this is one of the
situations where you will have to move code into an exec block:</p>
<div class="highlight"><pre><span class="k">if</span> <span class="n">PY2</span><span class="p">:</span>
    <span class="k">exec</span><span class="p">(</span><span class="s">'def reraise(tp, value, tb):</span><span class="se">\n</span><span class="s"> raise tp, value, tb'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">reraise</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">tb</span><span class="p">):</span>
        <span class="k">raise</span> <span class="n">value</span><span class="o">.</span><span class="n">with_traceback</span><span class="p">(</span><span class="n">tb</span><span class="p">)</span>
</pre></div>
</li>
<li><p class="first">The previous <tt class="docutils literal">exec</tt> trick is useful in general if you have some code
that depends on different syntax.  Since exec itself has a different
syntax now you won't be able to use it to execute something against an
arbitrary namespace.  This is not a huge deal because <tt class="docutils literal">eval</tt> with
<tt class="docutils literal">compile</tt> can be used as a drop-in that works on both versions.
Alternatively you can bootstrap an <tt class="docutils literal">exec_</tt> function through <tt class="docutils literal">exec</tt>
itself.</p>
<div class="highlight"><pre><span class="n">exec_</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">s</span><span class="p">,</span> <span class="o">*</span><span class="n">a</span><span class="p">:</span> <span class="nb">eval</span><span class="p">(</span><span class="nb">compile</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="s">'&lt;string&gt;'</span><span class="p">,</span> <span class="s">'exec'</span><span class="p">),</span> <span class="o">*</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</li>
<li><p class="first">If you have a C module written on top of the Python C API: shoot
yourself.  There is no tooling available for that yet from what I know
and so much stuff changed.  Take this as an opportunity to ditch the
way you build modules and redo it on top of <a class="reference external" href="https://cffi.readthedocs.org/en/release-0.6/">cffi</a> or <cite>ctypes</cite>.  If
that's not an option because you're something like numpy then you will
just have to accept the pain.  Maybe try writing some abomination on
top of the C-preprocessor that makes porting easier.</p>
</li>
<li><p class="first">Use <a class="reference external" href="https://bitbucket.org/hpk42/tox">tox</a> for local testing.  Being
able to run your tests against all python versions at once is very
helpful and will find you a lot of issues.</p>
</li>
</ul>
</div>
<div class="section" id="outlook">
<h2>Outlook</h2>
<p>Unified codebases for 2.x and 3.x are definitely within reach now.  The
majority of the porting time will still be spend trying to figure out how
APIs are going to behave with regards to Unicode and interoperability with
other modules that might have changed their API.  In any case if you want
to consider porting libraries don't bother with versions outside below
2.5, 3.0-3.2 and it will not hurt as much.</p>
</div>]]></description>
    <link><![CDATA[http://lucumr.pocoo.org/2013/5/21/porting-to-python-3-redux]]></link>
    <pubDate>2013-05-21 00:00:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Tarek Ziade: A step-by-step introduction to Circus]]></title>
    <description><![CDATA[<div class="note">
<p class="first admonition-title">Note</p>
<p class="last">Circus is a process &amp; socket manager. See <a class="reference external" href="https://circus.readthedocs.org">https://circus.readthedocs.org</a></p>
</div>
<div class="figure">
<a class="reference external image-reference" href="https://secure.flickr.com/photos/kennethreitz/8751753401/in/pool-2174519@N25/"><img alt="https://farm9.staticflickr.com/8420/8751753401_0760d37279.jpg" src="https://farm9.staticflickr.com/8420/8751753401_0760d37279.jpg" /></a>
<p class="caption">Photo by kennethreitz</p>
</div>
<p>During Django Con, I was asked how to use Circus to run &amp; monitor a Python
web application. The documentation has no single page step-by-step tutorial
yet, so here goes... this blog post will be integrated into the documentation
for the next release.</p>
<div class="section" id="installation">
<h2>Installation</h2>
<p>Circus is tested under Mac OS X and Linux, on the latest Python 2.6 and 2.7.
To run a full Circus, you will also need <strong>libzmq</strong>, <strong>libevent</strong> &amp;
<strong>virtualenv</strong>.</p>
<p>Under Debuntu:</p>
<pre class="literal-block">
$ sudo apt-get install libzmq-dev libevent python-virtualenv
</pre>
<p>Create a virtualenv and install <em>circus</em>, <em>circus-web</em> and <em>chaussette</em>
in it</p>
<pre class="literal-block">
$ virtualenv /tmp/circus
$ cd /tmp/circus
$ bin/pip install circus
$ bin/pip install circus-web
$ bin/pip install chaussette
</pre>
<p>Once this is done, you'll find a plethora of commands in the local bin dir.</p>
</div>
<div class="section" id="usage">
<h2>Usage</h2>
<p><em>Chaussette</em> comes with a default Hello world app, try to run it:</p>
<pre class="literal-block">
$ bin/chaussette
</pre>
<p>You should be able to visit <a class="reference external" href="http://localhost:8080">http://localhost:8080</a> and see <em>hello world</em>.</p>
<p>Stop Chaussette and add a circus.ini file in the directory containing:</p>
<pre class="literal-block">
[circus]
stats_endpoint = tcp://127.0.0.1:5557
httpd = 1

[watcher:webapp]
cmd = bin/chaussette --fd $(circus.sockets.web)
numprocesses = 3
use_sockets = True

[socket:web]
host = 127.0.0.1
port = 9999
</pre>
<p>This config file tells Circus to bind a socket on port <em>9999</em> and run
3 chaussettes workers against it. It also activates the Circus web
dashboard and the statistics module.</p>
<p>Save it &amp; run it using <strong>circusd</strong>:</p>
<pre class="literal-block">
$ bin/circusd --daemon circus.ini
</pre>
<p>Now visit <a class="reference external" href="http://127.0.0.1:9999">http://127.0.0.1:9999</a>, you should see the hello world app.</p>
<p>You can also visit <a class="reference external" href="http://localhost:8080/">http://localhost:8080/</a> and enjoy the Circus web dashboard.</p>
</div>
<div class="section" id="interaction">
<h2>Interaction</h2>
<p>Let's use the circusctl shell while the system is running:</p>
<pre class="literal-block">
$ bin/circusctl
circusctl 0.7.1
circusd-stats: active
circushttpd: active
webapp: active
(circusctl)
</pre>
<p>You get into an interactive shell. Type <strong>help</strong> to get all commands:</p>
<pre class="literal-block">
(circusctl) help

Documented commands (type help &lt;topic&gt;):
========================================
add     get            list         numprocesses  quit     rm      start   stop
decr    globaloptions  listen       numwatchers   reload   set     stats
dstats  incr           listsockets  options       restart  signal  status

Undocumented commands:
======================
EOF  help
</pre>
<p>Let's try basic things. Let's list the web workers processes and add a
new one:</p>
<pre class="literal-block">
(circusctl) list webapp
13712,13713,13714
(circusctl) incr webapp
4
(circusctl) list webapp
13712,13713,13714,13973
</pre>
<p>Congrats, you've interacted with your Circus! Get off the shell
with Ctrl+D and now run circus-top:</p>
<pre class="literal-block">
$ bin/circus-top
</pre>
<p>This is a top-like command to watch all your processes' memory and CPU
usage in real time.</p>
<p>Hit Ctrl+C and now let's quit Circus completely via circus-ctl:</p>
<pre class="literal-block">
$ bin/circusctl quit
ok
</pre>
</div>
<div class="section" id="next-steps">
<h2>Next steps</h2>
<p>You can plug your own WSGI application instead of Chaussette's hello
world simply by pointing the application callable.</p>
<p>Chaussette also comes with many backends like Gevent or Meinheld.</p>
<p>Read <a class="reference external" href="https://chaussette.readthedocs.org/">https://chaussette.readthedocs.org/</a> for all options.</p>
</div>]]></description>
    <link><![CDATA[http://blog.ziade.org/2013/05/20/a-step-by-step-introduction-to-circus/]]></link>
    <pubDate>2013-05-20 16:30:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Enthought: Enthought awarded $1M DOE SBIR grant to develop open-source Python HPC framework]]></title>
    <description><![CDATA[We are excited to announce that Enthought is undertaking a multi-year project to bring the strengths of NumPy to high-performance distributed computing.  The goal is to provide a more intuitive and user-friendly interface to both distributed array computing and to high-performance parallel libraries.  We will release the project as open source, providing another tool in [...]]]></description>
    <link><![CDATA[http://blog.enthought.com/general/enthought-awarded-1m-doe-sbir-grant-to-develop-open-source-python-hpc-framework/]]></link>
    <pubDate>2013-05-20 16:11:26</pubDate>
  </item>
  <item>
    <title><![CDATA[Morten W Petersen: Jep, Jython and CPython compared, all in a nice little script]]></title>
    <description><![CDATA[So, in relation to the work with Jep ( <a href="http://www.nidelven-it.no/weblogs/hosting/blog_entry?id=1368634033X37">http://www.nidelven-it.no/weblogs/hosting/blog_entry?id=1368...</a> ) - I've been building a script that can build and setup Jep, Jython and CPython in a directory, and then have a compare.sh script that runs the same code on the 3 different systems.<br /><br />The installation script is here: <br /><br /><a href="https://raw.github.com/morphex/PythonCompare/master/install.py">https://raw.github.com/morphex/PythonCompare/master/install....</a><br /><br />Here's the output from the generated compare.sh file:<br /><br />morphex@copyleft-laptop:~/projects/self/jython_jepp_installer6$ ./compare.sh <br />Starting comparison of Jython, Jepp and CPython..<br />Starting with Jep..<br />0.39471411705<br />0.341079950333<br />0.333596944809<br />0.34021282196<br />0.322955131531<br />0.322613954544<br />0.322247982025<br />0.323844909668<br />0.319895029068<br />0.324920892715<br />Ran in milliseconds: 3878.0<br />Now Jython..<br />1.25800013542<br />0.579999923706<br />0.436999797821<br />0.361000061035<br />0.43799996376<br />0.287999868393<br />0.289000034332<br />0.910000085831<br />0.289000034332<br />0.290999889374<br />Ran in milliseconds: 7576<br />Now CPython..<br />0.321110010147<br />0.312225103378<br />0.312016010284<br />0.309517145157<br />0.30745100975<br />0.313014984131<br />0.312664985657<br />0.312491893768<br />0.311804056168<br />0.312137126923<br />Ran in milliseconds: 3165<br /><br />As you can see, the Jep and CPython scripts are fairly stable in terms of execution speed while Jython varies a bit but gets faster (probably due to the HotSpot technology).  If you want to change the test to something else, you can try "./jdk1.7.0_21/bin/java -jar usr/lib/jython-standalone-2.5.3.jar -m compileall -l ." and mytest.py will be recompiled, along with the other .py files.<br /><br />Now, feel free to use the install.py script as you like, I think it's a good example of how to setup the whole thing in a specific directory..  there was some hair-pulling to get the entire build process setup so that linking and dependencies were setup the right way.<br /><br />Fun to work with a mix if Python, Java and Bash scripting for a change.  I think being able to use Python to script for example prototypes or even production code is a big win in terms of productivity..  weak typing has its place in rapid application development. :)]]></description>
    <link><![CDATA[http://www.nidelven-it.no/weblogs/hosting/blog_entry?id=1369051179X45]]></link>
    <pubDate>2013-05-20 14:31:01</pubDate>
  </item>
  <item>
    <title><![CDATA[John Cook: Need a 12-digit prime?]]></title>
    <description><![CDATA[<p>You may have seen the joke &#8220;Enter any 12-digit prime number to continue.&#8221; I&#8217;ve seen it floating around as the punchline in several contexts.</p>
<p>So what do you do if you need a 12-digit prime? Here&#8217;s how to find the smallest one using Python.</p>
<pre>&gt;&gt;&gt; from sympy import nextprime
&gt;&gt;&gt; nextprime(10**11)
100000000003L</pre>
<p>The function <code>nextprime</code> gives the smallest prime larger than its argument. (Note that the smallest 12-digit number is 10<sup>11</sup>, not 10<sup>12</sup>. Great opportunity for an off-by-one mistake.)</p>
<p>Optionally you can provide an addition argument to <code>nextprime</code> to get primes further down the list. For example, this gives the second prime larger than 10<sup>11</sup>.</p>
<pre>&gt;&gt;&gt; nextprime(10**11, 2)
100000000019L</pre>
<p>What if you wanted the largest 12-digit prime rather than the smallest?</p>
<pre>&gt;&gt;&gt; from sympy import prevprime
&gt;&gt;&gt; prevprime(10**12)
999999999989L</pre>
<p>Finally, suppose you want to know how many 12-digit primes there are. SymPy has a function <code>primepi</code> that returns the number of primes less than its argument. Unfortunately, it fails for large arguments. It works for arguments as big as <code>2**27</code> but throws a memory error for <code>2**28</code>.</p>
<p>The number of primes less than <em>n</em> is approximately <em>n</em> / log <em>n, </em> so there are about 32 billion primes between 10<sup>11</sup> and 10<sup>12</sup>. According to Wolfram Alpha, the exact number of 12-digit primes is 33,489,857,205. So if you try 12-digit numbers at random, your chances are about 1 in 30 of getting a prime. If you&#8217;re clever enough to just pick odd numbers, your chances go up to 1 in 15.</p>]]></description>
    <link><![CDATA[http://www.johndcook.com/blog/2013/05/20/need-a-12-digit-prime/]]></link>
    <pubDate>2013-05-20 11:50:29</pubDate>
  </item>
  <item>
    <title><![CDATA[Python Diary: Looking for advertising proposals]]></title>
    <description><![CDATA[<p>As some of you may have noticed, the AdSense bar no longer exists on my blog, this is due to Google recently revoking my AdSense account, I am quite sure it is in regards to mentioning it on a page.  Currently users who have an account are able to opt'd out of either being tracked by Analytics or have no ads served to them.  I will be removing this feature soon, as I am planning on self-hosting ads from prospect publishers.  If you have a Python or Django related project which you would like to adverse on this blog, please <a href="https://www.veroneau.net/Contact">contact me</a>.  Having a way to fund this website will allow me to publish more quality articles and tutorials.  When a new article is posted, this blog receives over 1,000 hits in that single day.  These are 1,000 prospect users which use Python and maybe Django who will see your advertisement.  These can also be users who are just learning Python, so books and courses are also welcome.</p>
<p>Currently the ad serving system has yet to be implemented, so at this time I am only asking prospect advertisers to provide me with a proposal on how the ads should be served to users and costs they might be willing to pay.  For the record this blog has been online for well over a year now and receives many returning users due to the quality of the content which is provided.  This website is also much more than a blog, as it has other features which bring users back for more.</p>
<p>Thank you for your time in reading this.</p>]]></description>
    <link><![CDATA[http://www.pythondiary.com/blog/May.19,2013/looking-advertising-proposals.html]]></link>
    <pubDate>2013-05-19 23:41:20</pubDate>
  </item>
  <item>
    <title><![CDATA[codeboje: Post to Tumblr with python]]></title>
    <description><![CDATA[<p>I have an art blog over at tumblr where i post my (almost) daily doodles. Usually i post them with the Tumblr UI, but lately i i got annoyed but that way and hacked something together to post images directly from my windows exlorer.</p>
<ol>
<li><a href="http://www.tumblr.com/oauth/apps">Register an app at tumblr</a></li>
<li>Install <a href="https://github.com/simplegeo/python-oauth2">oauth2</a> and <a href="https://github.com/tumblr/pytumblr">pytumblr</a></li>
<li>Modify the "Twitter Three-legged OAuth Example" Script from <a href="https://github.com/simplegeo/python-oauth2">python-oauth2</a> to use tumblr endpoints and insert your consumer key and secret</li>
<li>Run the script and note the oauth token and secret the script outputs</li>
<li>
<p>My actual Poster Script (pretty less coe :-) ), add your keys and blog url here</p>
<div class="codehilite"><pre><span class="kn">import</span> <span class="nn">pytumblr</span>
<span class="kn">import</span> <span class="nn">sys</span>

<span class="n">client</span> <span class="o">=</span> <span class="n">pytumblr</span><span class="o">.</span><span class="n">TumblrRestClient</span><span class="p">(</span>
    <span class="s">'&lt;consumer_key&gt;'</span><span class="p">,</span>
    <span class="s">'&lt;consumer_secret&gt;'</span><span class="p">,</span>
    <span class="s">'&lt;oauth_token&gt;'</span><span class="p">,</span>
    <span class="s">'&lt;oauth_secret&gt;'</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">client</span><span class="o">.</span><span class="n">create_photo</span><span class="p">(</span><span class="s">&quot;your blog url&quot;</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="s">&quot;published&quot;</span> <span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
</pre></div>


</li>
<li>
<p>Add Script to Windows Explorer context menu follwoing <a href="http://www.howtogeek.com/107965/how-to-add-any-application-shortcut-to-windows-explorers-context-menu/">this tutorial</a> </p>
<div class="codehilite"><pre><span class="o">&lt;</span><span class="n">path</span><span class="o">-</span><span class="n">to</span><span class="o">-</span><span class="n">python</span><span class="o">&gt;</span><span class="n">python</span><span class="p">.</span><span class="n">exe</span> <span class="o">&lt;</span><span class="n">path_to_script</span><span class="o">&gt;</span><span class="n">poster</span><span class="p">.</span><span class="n">py</span> &quot;<span class="c">%1&quot;</span>
</pre></div>


</li>
<li>
<p>Enjoy :-)</p>
</li>
</ol>]]></description>
    <link><![CDATA[http://codeboje.de/tumblr-with-python/]]></link>
    <pubDate>2013-05-19 21:31:05</pubDate>
  </item>
  <item>
    <title><![CDATA[PyCon: PyCon 2014 Begins! Call For Launch Day Sponsors]]></title>
    <description><![CDATA[<span><i>French translation:&nbsp;<a href="http://montrealpython.org/fr/2013/05/pycon-day-sponsors/">http://montrealpython.org/fr/2013/05/pycon-day-sponsors/</a></i></span><br /><span><br /></span><pre><span>It's that time again! Planning for PyCon 2014 is well underway, and we're currently preparing for the launch of our new site. With the launch comes a unique opportunity: Is your organization interested in being a launch day sponsor?<br /><br />PyCon 2013 <a href="https://us.pycon.org/2013/about/press/20120709_launch_release/">launched</a> on July 9, 2012 with 21 sponsors pledging support, leading the charge that drew over 150 organizations to pitch in to the biggest and best Python conference yet. We're planning a similar go-live date for the 2014 site, and we're building up our cadre of supporters for the April 9-17 conference taking place in Montreal, Quebec, Canada.<br /><br />Your organization's support enables PyCon to do the awesome things that it does. 2013 introduced a number of new events that we've heard great feedback on, so we'd like to keep doing those things and more. For example, the inaugural <a href="http://pycon.blogspot.com/2013/03/how-kids-stole-show-young-coders.html">Young Coders tutorial</a> was such a hit that there are already plans around the world for user groups to replicate it, and we're looking forward to doing a PyCon 2014 rendition. Programs like Financial Aid, which saw its budget increased and then quickly doubled to reach $100,000 USD, are greatly enhanced by the giving of sponsors.<br /><br />Along with benefiting the community, sponsorship of PyCon brings many benefits to its supporters. We hear it year after year that there is no better place to hire Python developers than at PyCon. We offer sponsors a place on our site to promote their open positions, and we run a job fair on-site that has been a huge success. The Expo Hall is a great place to market your latest projects and to network with 2,000 eager Python developers. The value is unparalleled in the conference scene, especially after considering our flexibility to work with each and every organization. We even offer a 50% discount to organizations under 25 people. See <a href="https://us.pycon.org/2013/sponsors/whysponsor/">https://us.pycon.org/2013/sponsors/whysponsor/</a> for more thoughts.<br /><br />While we're still finalizing the sponsorship prospectus, it will be very similar to the one we used in 2013 at https://us.pycon.org/2013/sponsors/prospectus/. We'll share the details as soon as we complete them, and any questions can be forwarded to our Sponsorship Chair, <a href="mailto:jnoller@gmail.com">Jesse Noller</a>.<br /><br />For 2014, PyCon will have a maximum capacity of 2,000 attendees. We've sold out the last two conferences and we're expecting a third, so mark your calendars for April 9-17, 2014. Other dates to remember are our Call for Proposals in July, and we're looking forward to opening registration in September. We're planning for the conference schedule to be laid out in December, just in time for the holidays.<br /><br />If you don't have a passport, don't forget that Canada requires one. US residents should see <a href="http://travel.state.gov/passport/">http://travel.state.gov/passport/</a> for details.</span></pre>]]></description>
    <link><![CDATA[http://pycon.blogspot.com/2013/05/pycon-2014-begins-call-for-launch-day.html]]></link>
    <pubDate>2013-05-19 18:20:42</pubDate>
  </item>
  <item>
    <title><![CDATA[Python Diary: DVD Collection source code now available]]></title>
    <description><![CDATA[<p>For those who were wanting a copy of the DVD Collection software made in Python, I have now open sourced it and it is live on BitBucket!</p>
<p><a href="http://www.pythondiary.com/bookmarks/dvd-collection">DVD Collection software</a></p>]]></description>
    <link><![CDATA[http://www.pythondiary.com/blog/May.19,2013/dvd-collection-source-code-now-available.html]]></link>
    <pubDate>2013-05-19 17:41:21</pubDate>
  </item>
  <item>
    <title><![CDATA[Python Diary: Python Script to encode Django templates]]></title>
    <description><![CDATA[<p>Do you need to display raw <a href="http://www.pythondiary.com/packages/django.html">Django</a><a href="https://www.djangoproject.com/" rel="tooltip" title="Direct link"><i class="icon-bookmark"></i></a> template code in your Django 1.4 project?  Look no further than this script!  It's rather crude, but gets the job done.  I haven't yet updated a few Django websites to Django 1.5, which has a new template tag to do this for you, so I created this script to use in legacy Django sites, and it works like a charm!</p>
<div class="highlight"><pre><span class="c">#!/usr/bin/python</span>

<span class="kn">import</span> <span class="nn">sys</span>

<span class="k">try</span><span class="p">:</span>
  <span class="n">filename</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
  <span class="k">print</span> <span class="s">&quot;This command needs exactly 1 parameter!&quot;</span>
  <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">()</span>

<span class="n">data</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s">'r'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'{%'</span><span class="p">,</span> <span class="s">'{! templatetag openblock !}'</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'%}'</span><span class="p">,</span> <span class="s">'{! templatetag closeblock !}'</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'{{'</span><span class="p">,</span> <span class="s">'{% templatetag openvariable %}'</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'}}'</span><span class="p">,</span> <span class="s">'{% templatetag closevariable %}'</span><span class="p">)</span>

<span class="k">print</span> <span class="n">data</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'{!'</span><span class="p">,</span> <span class="s">'{%'</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">'!}'</span><span class="p">,</span> <span class="s">'%}'</span><span class="p">)</span>
</pre></div>

<p>You should <a href="http://www.pythondiary.com/packages/pygments.html">Pygments</a><a href="http://pygments.org/" rel="tooltip" title="Direct link"><i class="icon-bookmark"></i></a> to highlight the syntax like I do on this blog of course.  If you are using Django 1.5 or greater, you should use the <b>verbatim</b> template tag over this.</p>]]></description>
    <link><![CDATA[http://www.pythondiary.com/blog/May.19,2013/python-script-encode-django-templates.html]]></link>
    <pubDate>2013-05-19 17:41:21</pubDate>
  </item>
  <item>
    <title><![CDATA[Holger Krekel: PEP438 is live: speed up python package installs now!]]></title>
    <description><![CDATA[<p>My &#8220;speed up pypi installs&#8221; <a href="http://www.python.org/dev/peps/pep-0438/">PEP438</a> has been accepted and transition phase 1 is live: <strong>as a package maintainer you can speed up the installation for your packages for all your users now, with the click of a button</strong>: Login to <a href="https://pypi.python.org">https://pypi.python.org</a> and then go to <tt>urls</tt> for each of your packages, and specify that all release files are hosted from pypi.python.org. Or add explicit download urls with an MD5. Tools such as <tt>pip</tt> or <tt>easy_install</tt> will thus avoid any slow crawling of third party sites.</p>
<p>Many thanks to <strong>Carl Meyer</strong> who helped me write the PEP, and <strong>Donald Stufft</strong> for implementing most of it, and <strong>Richard Jones</strong> who accepted it today!   And thanks also to the distutils-sig discussion participants, in particular Phillip Eby and Marc-Andre Lemburg. </p>
<p> </p>
<br />  <a rel="nofollow" href="http://feeds.wordpress.com/1.0/gocomments/tetamap.wordpress.com/584/"><img alt="" border="0" src="http://feeds.wordpress.com/1.0/comments/tetamap.wordpress.com/584/" /></a> <img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=holgerkrekel.net&blog=6679328&post=584&subd=tetamap&ref=&feed=1" width="1" height="1" />]]></description>
    <link><![CDATA[http://holgerkrekel.net/2013/05/19/pep438-is-live-speed-up-python-package-installs-now/]]></link>
    <pubDate>2013-05-19 08:49:44</pubDate>
  </item>
  <item>
    <title><![CDATA[Mathieu Virbel: Kivy 1.7.0 is out]]></title>
    <description><![CDATA[<p>If you missed it, <a href="http://kivy.org/">Kivy 1.7.0</a> is out last Monday. It&#8217;s quite a big release that include some big fixes we did during the <a href="https://us.pycon.org/2013/" title="Pycon US"></a>:</p>
<ul>
<li><a href="http://developer.android.com/tools/devices/emulator.html">Android Emulator</a> is now supported</li>
<li><a href="http://kivy.org/docs/api-kivy.uix.scrollview.html">ScrollView</a> has been re-factored with new physics calculations. To be exact, the scrollview doesn&#8217;t calculate anything now, it just pass the touch position to an <a href="http://kivy.org/docs/api-kivy.effects.scroll.html">Scroll effect</a>. This class calculate the movement&#8217;s velocity, and the over-scroll&#8217;s distance (means how far you scrolled out of the bounds). Then, we implemented 2 visual effects that use the over-scroll to make the scrollview act as a <a href="http://kivy.org/docs/api-kivy.effects.dampedscroll.html">Damped spring<a>, and/or to <a href="http://kivy.org/docs/api-kivy.effects.opacityscroll.html">fade out</a> the scrollview if you over-scroll too much.</a></a></li>
<li><a href="http://kivy-garden.github.io/">Garden</a>: a separated organization for centralize user&#8217;s widgets and addons. You can create garden packages very easily, and import it in the source code with just &#8220;kivy.garden.<em>packagename</em>&#8220;. <a href="https://github.com/kivy-garden/">Few garden packages</a> are already available!</li>
<li>And more, <a href="https://groups.google.com/forum/?fromgroups#!topic/kivy-users/TyOzyLtYKMw">Check the announcement </a> <img src="http://txzone.net/wp-includes/images/smilies/icon_smile.gif" alt=":)" class="wp-smiley" /> </li>
<p>If you like my work, <a href="https://www.gittip.com/tito/">tip me!</a> 
</p></ul>]]></description>
    <link><![CDATA[http://txzone.net/2013/05/kivy-1-7-0-is-out/]]></link>
    <pubDate>2013-05-19 07:48:23</pubDate>
  </item>
  <item>
    <title><![CDATA[Twisted Matrix Labs: Migration Report]]></title>
    <description><![CDATA[<p>I have completed the migration <a href="https://github.com/twisted-infra/braid">scripts</a> for deploying the <a href="https://github.com/twisted-infra">services</a>
currently running on cube. They have been run against our new
machine, dornkirk which is currently running with a snapshot of data.
</p>
<p>
It can currently be accessed by putting
</p>


<pre class="example">66.35.39.66     twistedmatrix.com speed.twistedmatrix.com
</pre>

<p>
in <code>/etc/hosts</code>. Please tests it, and verify that things appear to be
working, but be aware that any changes will be lost, when the
transition occurs.
</p>
<p>
At some point Monday or Tuesday, there will be some downtime for mail
and the mailing lists, as mail-service is migrated to the new
machine. For those that have accounts on cube, your data will be
copied to the new machine at this point.
</p>
<p>
On Wednesday, at about 10 MDT (16 UTC), there will be downtime of all
twisted services, as live data is transfered over. This may last
up-to a couple of hours.
</p>
<p>
This work is made possible by the sponsorship of individuals and
organizations which have donated to the Twisted project, part of the
Software Freedom Conservancy, a not-for-profit organization that helps
promote, improve, and develop open source software.  Thanks!
</p>
<img src="http://feeds.feedburner.com/~r/TwistedMatrixLaboratories/~4/f1Hs3pETcpc" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/TwistedMatrixLaboratories/~3/f1Hs3pETcpc/migration-report.html]]></link>
    <pubDate>2013-05-19 00:45:55</pubDate>
  </item>
  <item>
    <title><![CDATA[Dariusz Suchojad: Zato 1.0. The next generation ESB and application server. Open-source. In Python.]]></title>
    <description><![CDATA[<p>&nbsp;</p>
<p>(This is a re-post of what I sent to python-announce@ but with several screenshots attached)</p>
<p>I&#8217;m very happy to announce the first release of Zato, the next generation ESB and application server, available under a commercial-friendly open-source LGPL license.</p>
<p><a href="https://zato.io">https://zato.io</a></p>
<p><span>What can you expect out of the box?</span></p>
<ul>
<li>HTTP, JSON, SOAP, Redis, AMQP, JMS WebSphere MQ, ZeroMQ, FTP, SQL, hot-deployment, job scheduling, statistics, high-availability load balancing and more</li>
<li>Incredible productivity with Python</li>
<li>Painless rollouts with less downtime</li>
<li>Slick web admin GUI, CLI and API</li>
<li>Awesome documentation (several hundred A4 pages)</li>
<li>24&#215;7 commercial support and training</li>
</ul>
<p>Project&#8217;s site: <a href="https://zato.io">https://zato.io</a><br />
Download: <a href="https://zato.io/download/zato-1.0.tar.bz2">https://zato.io/download/zato-1.0.tar.bz2</a><br />
Support: <a href="https://zato.io/support">https://zato.io/support</a><br />
Docs: <a href="https://zato.io/docs">https://zato.io/docs</a><br />
Architecture: <a href="https://zato.io/docs/architecture/overview.html">https://zato.io/docs/architecture/overview.html</a><br />
Tutorial: <a href="https://zato.io/docs/tutorial/01.html">https://zato.io/docs/tutorial/01.html</a><br />
GitHub: <a href="https://github.com/zatosource">https://github.com/zatosource</a><br />
Mailing list: <a href="https://mailman-mail5.webfaction.com/listinfo/zato-discuss">https://mailman-mail5.webfaction.com/listinfo/zato-discuss</a><br />
IRC: <a href="irc://irc.freenode.net/zato">irc://irc.freenode.net/zato</a><br />
Twitter: <a href="https://twitter.com/zatosource">https://twitter.com/zatosource</a><br />
LinkedIn: <a href="https://www.linkedin.com/groups?gid=5015554">https://www.linkedin.com/groups?gid=5015554</a><br />
Diversity statement: <a href="https://zato.io/docs/project/diversity.html">https://zato.io/docs/project/diversity.html</a></p>
<p>Spread the news and enjoy <img src="http://www.gefira.pl/blog/wp-includes/images/smilies/icon_smile.gif" alt=":-)" class="wp-smiley" /> </p>
<p>Cheers!</p>
<p><img src="data:;base64,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" alt="" /><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,iVBORw0KGgoAAAANSUhEUgAABM4AAAHnCAIAAADPcIZbAAAAi3pUWHRSYXcgcHJvZmlsZSB0eXBlIGV4aWYAAHjaVY7LDcQwCETvVJES+BlMOZFlS+lgy18cZ9fKO8BohIaB/rkGHBNCBi1eLcww0dDgM0XFhSASI82dc/FsoVS8bRBewqI66j7Ux/9RxKoNV3cr1qxxpnMXEs6Zj2Cm4qwRO6T1OzLfvv3u/wKrxRdUUCy2dFNCZAAACe1pVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+Cjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDQuNC4wLUV4aXYyIj4KIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIgogICAgeG1sbnM6dGlmZj0iaHR0cDovL25zLmFkb2JlLmNvbS90aWZmLzEuMC8iCiAgIGV4aWY6UGl4ZWxYRGltZW5zaW9uPSIxMjMwIgogICBleGlmOlBpeGVsWURpbWVuc2lvbj0iNDg3IgogICB0aWZmOkltYWdlV2lkdGg9IjEiCiAgIHRpZmY6SW1hZ2VIZWlnaHQ9IjQ4NyIvPgogPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgIAo8P3hwYWNrZXQgZW5kPSJ3Ij8+L77h6gAAAANzQklUCAgI2+FP4AAAIABJREFUeNrs3WVcVFkDBvAzzDCUl5AOi6FDEFQUaQVsRcFY1sK1u9dWxFy7c+1eA8VC1BUDAxAEUUSlEUTKgSkm3g9gAHcUXXZf133+H+an47mn7rnjfebemWG0bduWAAAAAAAAADQcBUwBAAAAAAAAIGoCAAAAAAAAoiYAAAAAAAAgagIAAAAAAAAgagIAAAAAAACiJgAAAAAAACBqAgAAAAAAANQH48SJE5gFAAAAAAAAaEC4qgkAAAAAAACImgAAAAAAAPB9Y3C5XMwCAAAAAAAANCBc1QQAAAAAAABETQAAAAAAAEDUBAAAAAAAAERNAAAAAAAAAERNAAAAAAAAQNQEAAAAAAAARE0AAAAAAAAARE0AAAAAAABA1AQAAAAAAABETQAAAAAAAABETQAAAAAAAEDU/FuJSzOyuFKsAwAAAAAAgL8vakrLn55cNKRzOwcHB4dW7XwDx6yKKpD85UYq03f1chlyuVT2vY2ed39qh4Dd6aJ/rEHhk1AH48BI7j+9bcNWQruPC+7snhXs187BwcGhtU+/SZuisgV/6/7+bhcVAAAAAAAQwvr0L7Ly+8tGLUvsFLZvrQ9HVViY+ST2CWHU81xeKhbJWGwm3T8xtV1HTm9ho8L43kYvLS98V6nxDzao2CRo9U6/lir/9LZ/M0nBpV/7/RrfclLYsa2tjZhFSRc2z58RlDTrxOYBTdl/aQf9CxcVAAAAAADUuqopfpP8pNy4e7CvlbaKooqmkVWHnn076LEIIURSGr9/Zn9vZwcHh3ZdR2+IfiMmhBDhs3VdXII3b5kZHNCtS6/Rk7u27bsvU1xdGz8xzLf92CvFUiIpurtz9cEUflVolZTE7ps1wKe1g4ODs0fvKUfTRXLrl5Tc3za+h6uTg6OjUzvfQese8+uEnDe3dq/7/e7bGpdehSkr3QwaV9GiKIpqNiamoiJ571gfS30NiqL0bHssulYozNwdNDiiNHO9b+vR0RWEVOZeXNDbXp+iKB3rzjNPpwtqNcVPOzjaw0xPg1I3aNl78dUCCSGSwpsr+zsZUhSla9tjweW8SkIEj+e3sg4c08OqsYZ+Ex3Kdm5CVT3irJ2ehl5b7x6dPnJ1Ip8QIn4duSTQyYiiKF3bXqFRbyR0tX2qMvtk1bbCJ6HOloGzRnZqaWlqZGjbe9ntYnHhmQCjWm3tyuJ/ZkTSsphFHQxtR5/NrfxCu1+abUL4CdtWRqkN3bwqpENzDSV2IyPn/ou3Tm76YP2a26WirH2B9KuiYRcVTVWykshR7XtseSEihPDjF3g4OA45XyglRFp8+Zf2AXsyxDj8AQAAAAD+kaipaODcRjt7/6KV+y7eTcnlfjwVr8w6OmHsPlHfLdfj4u+dmGR4Zda049lV/ywTJF/mB27548KVC7tmh1jknIrIqLofteLxiShB+4HttWreo1uZfmj8mD3crmsvxyY8unN6ZbAdxZRXvzBt3+KDvOBDdx4lPHpwdc+MjgaKtfsvLok/tf90YlmN8KNkM+t2fnFxcXFxQcLmLtraXZfPaZm5fvCvib6HUou5JemnArI2ztyfafjLyQPdNZtNvhq73UNNlLoxaMgJ3dk3c4sLHqywihzVOyye90mdsqIr02fd9TyUVlyWf31c5e4Zm5Mrsg4O6b9VOvpSVlHBg2UtzgwL3vmqkhAiyrn+vOPh5KcPonZ25Z49/pRPCBHnXDj4wn54N6Pqy8jirP1DBu1XnXw1t7jg9gzFXcOmXEw4QFsbLVHejUf262KevXxxOThrzdSDmRpeIzxqteVbvlXeiGTchPVBQcctN17a1Fv/9cF6t0s720SYcTOmxKRrd/NPLrgqNukUYMGPjUyTGPsNoF8VDbmoaKtiqNt1bF54K/athIiybiXIDBulRz2rIIT/LOq5RjsXAxYOfwAAAACAfyRqElWnmYd2TLB9e23rrJ+6ujl7BC88lcqTEVF6+JFnnPG/9rXRYDFVmvqOG2uRFn6r+kOcLJthPztrMgkhTAPPgfaF58+mCQiRlcUdu0k8Bzip17zBUfjy7LFnnHHzgh11FBkKyjqWre11mPLqZ7CUWJVvXz5Jy6+QsqimLVvq1UkHSpaTLiVEjOHQ3aYp4z5c1m/ai4GHdw5oosoZdebe2amt1StLXucLNXUUuTlln17VEqWHH37pMH/JACt1RVXTnguXeRaePJzy6UVUlpoG83XU/n3hDwpMQs5nJiyxK7q8+4HJpGXDHLXYqi16zJ1rl7L/XKaYEKLkPCrYyciomWWnET688KMpfFKZHXE4o9UvfgbV94JKCq4fTGg+cdFAW3VFVcufd1w6O8foNk1tcrOmsvPIgXZqDIaquVdbrZK0QrGW2y812/LiR9CMSECITPTy98FdF7+bemZbUFNFSd7l+rdLP9tSbj6XoWGkUeM+V6a6AcUoL3xXKW9VNOCiklcVU9fZQy/zWtK7ysLYeyK3UcGGz6+95PNf/fmY0dLTVBlHPwAAAADAPxQ1CWHrtwv+dcPBiFvx9y7tClG/Gjp+4xO+uCijWJT8W6CXu7u7u7u7Z8DSFJm4uFxMCCEMRU3dRtUhQ0GnQ3Ab7uVTT/nSknvH7qn497NXq1W/pCSnTLlJC80asURe/WzOsLULPAsOTevl2sar35Rtt958xT2P4tw/xvbfqb34xKIOGgqESIrvbvy5lUHjJq26j990/bVIKq3xGVRxSdY7laZNqKr5YCjrm6pzs0s+aY6h0XHD6eXtCw5P8W1pbOTYb+n1vMLMImHqQqfGFEVRlIbZiHu8svSiSkIYytqNqz5CqN5uuH9lxJEn77LOHclu+4uP3vvpFpdmFxNtU+2qq7QsbTtnDvN1MW1t9LuNra6uVJW3mIoKMomsblvaZXQjqiREJnhxW2Cp/eLE2TQBIZXFckbxFWuIMqBkZXk1r3VK3uVzZY101RXlrYoGXFRyq1I06tC2Ueq1lLyEG2/N/Tt4uigm3nqZcfc+17KjpQoOfgAAAACAv4/cmwgVVIza9hviun108qtyhoWJlorz+LO7u+nUjKZCQghhMD7JY60HuolmH48Lbn8sXqv7pLrn80wtEw3hg8xSiYsq89Mn6esnxKzHtI09pskE+bEHpo+bubzl1XUdGtVjWLKK+N/6j4/tfODWL2ZKhBBx9uEREy45Hn50zs9IiVF2sY/FBFIjarI0TSj+tRyulKgpECITvH5ZRjXX+mR2pBVZL0Xt55/8Za20OOXCsoHDJm63D9FUdZ796NpgIyYhRMLNyijTbKqSEUEYH6akkfOwbiTk4AXtB/muCzy1GaSgujkNEy1SnF4sJhosQkTpJ9ccL5CpqTpvqVWb6lfsylptsQroRqRICKORz45z+xstaBs0bkefq+M0jWhG8TXtEqXmnu219l+MSBs+xfb9Dq/MjjrzXKX1aHMleatC7k7/pkUlb/2Yerckiy9eKs807myj3aKRPe+3q5dJQbMAe3V8nRAAAAAAwN+oxqm5JP/qhnWH/3ySU8wXV3JzYo7suc03crZQV+H0HsBJWbfyj+SiShmR8Ite3b92v4DuCiODatW/k+KfG37b8cSwT09TpbqxhNO7v/mLzcuPPS6ulEkFRc9jk4okSnLqF2Vfv3D3ValQylDS0NXVUGQyGbUDAu0X1Ujywyf0W68488RqP52qQCsVlHLFbC1tis0Q5l1bE3aNVymSEMJQVGWWZ73MKRIomvb+yTQhbNHJ5+WVvPTzofNuNu49wEZFWvbkSvj1lzwZkb6NnB4QOP9iloClYWSoocTWNLTsNtj26dJ5R5+UicXFDzb0beM542ZZrd/oVG05uBfj+Oz1rz2Gd9D62Hmmgc8gh/RNS048Lxfz0k4u+HXHk6Z9Qmhq+9iBL6vZFpt2RMqEMBQU2YqaHovWdctYNn5ftl6XL4/is7NNiIrjmFmdKvZNmLX3TmaZqLIiL/7EorHrs9pMnuqmqSBvVSg16KKSV5WqVUfzooidDzS8HBszlc19OJn79qXqurfWYeLYBwAAAAD4p6ImQ0VHs/jmxomBPu2cW7v1nHVBMWjFttGWSkSxxc9btg1RCp/SpbWjQ2uvvlP33Hsjpk8/KjaBnTWex+Sa9uvSVJHm3xVbDNq8eTD77PhOrR1bdeg960gyVyKvfmnFq8urf/Fr28rBsUPwXjJg+UyXOvdO0nxRjST3/OqTBbzYRa766lSV9mfdlk80PORnYmDqFLSL9ct0+8rnL0okytaB3TTCB7ceeLqAZTXp+O99ckM7GDbWd5md6rs1fFFrVSLKODplyOyLBWLCajZo51rvpEkOuuqapj9da716T4iF5Ygjhwa9W93RREurWcBh7RnHNnetc1lNxTY4SKdIpfPwduqfPs1qNvTAgZ+Kl7kZaum7rRKOObyhe+uRNLV97EA91GyLTTuiDztep/OylW7JCyadUhz25VF8ZrYJIYSp32XF8S0DVaIWBXm0ae3aferJcv+VJzb2b8b+zKpo0EUlryoFTYeOHCLRbt9Wn0UYjWw7cgjRbONmzMahDwAAAADwd2JwuVzMAgAAAAAAADSgH/MHHyiKwq79K/AGBAAAAAAA/BW4qgkAAAAAAAANTAFTAAAAAAAAAIiaAAAAAAAAgKgJAAAAAAAAiJoAAAAAAAAAiJoAAAAAAACAqAkAAAAAAACImgAAAAAAAACImgAAAAAAAICoCQAAAAAAAIiaAAAAAAAAAIiaAAAAAAAA8DdHzSVLlhBC8IhHPOIRj3jEIx7xiEc84hGPeGyoRwaXy0XgBgAAAAAAgAaEG2gBAAAAAAAAURMAAAAAAAAQNQEAAAAAAABREwAAAAAAAABREwAAAAAAABA1AQAAAAAAAFETAAAAAAAA4Juipoz3/I95/dpztCmKorRN2/WbdzK1QtbQrYqerXCkKOu5CYIGqY4XO9XKdMTtir9SBz9uuq3drHj+X++N+M2NVcGuHB2KorSMHbpNOZBcLvuOVwAvZoxV62VPhfXeQJJ3sKN+u03plYQQQoQpYS0pyvL9zImzdrgb+R5+LaKdBEn2bjfqI32bTmN2xpVKCSFElLbGWd//bMn/f4Z5MWMsWy1OFn6HuypxZVDI+SIZIYTI3p4O8lz4WPCverWh33Hf74QDAAAAQINFzcrMfQPdh224VGAxcFbokjlDHN5d2hDSoc+2tAY+DWTpd5qxbOnsbsaKP9yEyooujx+8W3Xy5Ywy7rv8uK1eSbP6zImp+IFGyNRzC+BkXYwtlRJCKrOvXJa4uzGiwl8ICSGy0ofnM8wC3RQi5U6CqtPm1FIul8stK3i4uVP6wq7B+zIrMcP1IUy/fieLX5U0S+6dKnDtaa6MQwMAAAAA/gVRs+LhisXXeSqddt6O2DxvyuTZa09FH+ujIbwbtujoOh91Si8w/K2MEMnrI120KL3As4VSaVns5iFtjSiKUm/abvCG+yVSQvhx080pynZgQEsdqvnA7b9502woKoj6bc7c5RdyK+vWIEpb05qiWq1MFZGK26OaUpTBz1HvSOWL9W0oyu7z1z74cdPt7YNH9vX19mxrY+s7+0qBRFZ0vl+LdmvTRIQQIs7a6c3pe6ZQXPpw0xBXSzMLCzNrz1G7E7mfXBOTltxe6MnxmHfjrURWnrRntJedtY2NlW2HwRsflEoJ4T2YaGsXGOjSXN8q5EoJzbU0mai0WES1MDdSUyAMJYMOE/fsC+2iTWREStPop9dzPlxdrNFEUWns5mHu1uZWVuY2PlNOZYkIXa8+6X5Z7fJ07RJJUfSKQCczc2tbl4EbH1dfE/x8zZ+8UWDUsbthakRSBSHivKjwMu9pM/wEly9miAgpTzr3zKi3j4FUziTUWHqqTX1m7FnZMnbdvtSveStD7gxX5l2c17ttSwcHW05TS59p5/LEVUvCbsDw3l7u7e04jv1X7lr4czdfDwdz56EH00WEHzfdzqrngG5+3Xr4dvD4ecPDshpD/uyE1NhNxVyakpKiW6uCWlvbOjp16Ds60NZl2VMh/R6nbUhS9Gdo7zYOjg62VnY+E49n8HKOTZoXw70/p+eIU6/FZQ9PZrXu3TzpC32oXYm8IdMtEpqjia7Cei8b+Tvu4+qt0w3R0+XOZsNucAmRFZ7qoa3d4/RbGSHvrg219NqRKa73vvh/zQNNMQAAAID/ZNSszH8QW0SIY7CvIbP6X3XcBrRRJNyk7Nbjuqjyo7ZdeS0R55zbflvcuM/Ejjrvrk0NmH1Ja9yhS5cOjGh0aV7QzJvvquvKilEZvnXP+tkDh06ru6Hu+1ZlpXVrEDTrMcCCvDgblVv+/MLNEkIq7l1I5b6+9scz0iyor7nS58cjzHlpEXruxs171xcoHVxwNEOs7TXOt/jgkedCQkTpJ/fmeY71Zv05bcAmxZnXUp4/T7446t2yfrNuVYdNydubC3r+EtvzeESot47g3vzgrVqh0ckpKU9urzDaM2hmNJcQQgRZmW32pOQmbuukxaCZUIOAdUvs/+jczKxdj6HTlu+OKnXq08VSjZRel9cojQ9NuL5cPGhd5fgrT549TTzWJWFp2M2cGPpeVZ9zxy6qWf7PjGt125WW3ZwVcsBwecyzpwlnhzfKKxMTQkjFvc/V/Cml5v6d1B5FpvElBTdPF7bp27ZNn3al5y5nV/KfX4zX6OrXVIl+EupOF1Pbsb1OwYNXX3PfspwZZvCSdq570Hrr3YTEJ89j5rOPhh3PqKxaEq+sl126dff2VpubYXs05p66Gn17t9OdNVUBV1Twxmn52YjzkRfDdHeFhD3iffLOy5cm5MNuap+6oE5J2bvoWSH7jVfefpzw4HQIeZQl/15XuoYkeWcX7iFzrj1KTI471JsffTNfb8CGsPaUy7Jzu/oa8uP/eOnQ107lC32oU0mOiG7IMnmLs/bRVElTYf2XjcIXVgVdN4QtevRUf3D2KY+8iz/7TIWVHJ5YTioSTzw07OdvzKrvvvh/zQN9uwAAAAD/Eawaf2MQQojs0wQkq/obQ9t78gC9i79vD3/pUL7zEWk6ZWxbih9/KqqU8O+u+LnLiqrS8Xezhe0IIYQYD5wxql8rZUIIp+6GH2rnp9DUIJvRo7/54iV/RD0sj8xV5pgrvPwz8oHt9UTSbPoXkyZRNPb0NlUhhGhwLDWEb8slhNN2VIB4yP6kGUuUDh3mdtvenv1iToxyz4P+JmxCSIve430XjLyQJmhDiCTn4KAgHjXs4ghndQUieHEhMj1dMMYzQoEQIqssExglvBa1JYTd1MeXo8qS1xOGmv3IffHDSl7E3r515/b1FT3mLh547PKCRhE0jbaWU8f7JkQ5UdEiz3Xdm7IJYbeccSOeCBLnTa7bKy+KTQghRJRZuzzv4eTJddotVY24y/bf767LJMSw08jezRIIkTPe9zXXpGLVs0PlzNu5rw1OZrcc16pRI9KvVcHK6By/4tsM780cJcJQopuE5R51q5JKZAwmi/E1a5Z+hpd7Oy0M3x1z+fjGq+kvn9xOqRC0F0irloSHdwsVwmDot9Az0e7QTJkQhrYJJSriSwiTKFsFBVipMgjRcA5w4U+9miWyfh9evjgh73eTIJ2mJJe6FMP2399em0mIvs+Ivi3i5b6xQNeQpF37AKv5I907nvDv5NtlclhHUzb58EHWisQ/ntoGtVQjTz/fB5pKKuPqDvmdNIL+iKhzNDEta1coffJ7vZeNnB3n8v7VII2mG7LFvbsqDYt4USwOT7GdMb54T8Szt41O3tUO/LVG0vw+54FuJwIAAAD8J6OmooGTkxZ5lnDwSm5AsAmLECIpuHHwQSVp5NDOpLHz6BFmvy/dtmKZ8DlxWDHMWplUSiUSQszmPbw/y4pVkZtdrtVEX1WBH0cIIZS+evWVUbW6GxLRx6BBUwMR9exntmTpplX56areu2crTgo59NvGnHolTcJgKlbnFgaDUZWZVeyH/awWsPtWF9XTzAHHHFRJIpFzOVGl1bwDfaKGj1kcdGe1O1sqIZqdt8cc8VUnhEgr8vL4WkZsWSxhsFXZcr+4V1oUtWD6Td91oZ5m7XuYte8xbMLQ5e28Vt+auJC+UQZDJpFWffpOJKh8X+R9EzKpWErexzAZLzctXyoQ0/TqwxsDdcpLBDIZXb6Tyt4XYyiwmQxCiIx2vHKGqWYX4FS86cZV5RdWwa01GAzi3N8uZ9+VKzyu60IrFXmT8KuHU616xG9ibxcadzVVqf+KlVf5TEe1BW7DkjzGD/F2G+jnVBITWj1yBkv5Q5RlMOvsOZm0eoJklQIxYXz6D1+akI+7iaak4lMGQyqR1l6WNHucviFlhcmXXgQ+uhEZde3Sr96hdnse7nerbpeX9Mdj877LqS/2gaaSHUY0Q5bJOSLqHk3KNrUqjJ5c32Ujd1Wc1Hs/43TdULbo4yebcD6KF6sbMKNX1uER4Zekt7QCJ5vU/Jz39zkPNDuxhy6+9RsAAAD+I2qc9qi5zJrrriy4Mdq9+/iw9RuWTQ7wGBzBVWz76zx3dcK2CJ7kopB+8nwe23tiYDNFQpStA7wo8mJb6J5rt45O9bYzs5t4p/x9vR/P6etu+PE0kr4GtmmvfhySfjtHwTHAwzOwrWLOnfT63D0rB9ts4HCjqNnTw7WHBVsoERWLbu0E57ZfyRERIsw8tzVS2t6Po0wIYWo7dHAL2bzM/MTIuddLlMy6uLOv/7Y/mScjlVnHQ9p5zLn35S86VWhk1Chlx5ipe2KyeVIiKU//88SVAsPWLRrTNsqk9JXeJqYUS0hl3o2j0W/EtWpTat7JTTF6b2RuJZHxkrf085t8W8v/M72iKa/TtW67mpa9PMWX9tzIFxPJ2zuHzmUICSEqXzVedee+NmmbVse3CGzXmEEIo3G7ILOnm7dmOfVtqSZ3EmoGSik/99bG8YteuE37yewrLvbIq5yZHnWj1GXG0pnD+3XiFEbFvBGJJfX4XlrB0yOH4sqkRPjq9LYYzc5+zT50pf4TQltS2aKHF7m882puJZEUxxw6+1JICCF0e5x2c2HqGi+HsfEmnUNmrd463132/HmpWIHFIpUVQt7TM3HNAtuof7kPdJXQDFld3hFRh6hOhcLm9Z2lL64KOQemilWgT9nupWcV/TuYmHXu8G7PwtOqfbo2kfONYt/VPNC3CwAAAPBfjJqEbTryjzt7J3cxeH5k5fx5y/clqvuO23ErfKKlEiGEadJzajc1QjR6TfLXUyCEKGh33vhHaE/d6Jm9u46+qBWw9FhY+0Y0bdTZ8GPrcmpgm/YKMiWE2PZqr6vdtq8DIX8haRLCMuk1yjor02JEQFNFQhiaHdceHsdf4WNtaWnXZZvKjBNrvDU+XM1iNRmwaYVN+OhZkULXVScWmBwLsDG3tPJZw5h8eKWn+pdv9FSymXr6YDB/T7CDvgalaew++5HrplO/2qvQNqpk9cvioLKF7Tkc+8Bj6p5mdS7uqbULOzxJusrH2tquzZBb3lu2BVp4fq5XdcoHWfvWbVdBw2vl4bHcxe7W1i07rytspscmhBDK7SvGy9By6WWcnmUS4F51kUZB1723fmaxbaATJX8SlAkhhBc/3lKToihKw6DN2MjmS68c+qlJ9bV13t1BTT/8EorluLu8r5lhTdsRs/wzJ7m0cvX0+yVcr6s5LzWX9+WsyVJv9Hiup52Vbdc9OguOzHH8ZBfUf0JoSzbqsOLEr6y1fg72bbqvTldrxCSEENo9Tre5ktnQxf1L57ta2djZe61gTVgfwmGrWPdqmznJ2sDrd+0gF03Gl/tAU4kizZA/f0TUfNemToXa9Z6lz60KQuQfmKo2/XwYrxS9vE0UVa16tGUU6vfp1kzud1d/T/NAtxPxfw4AAAD8ZzC4XC5mAf6b+HHT24QoH74f5vA3/2gIL2aM04QmZ+7MsVb6rwwZux4AAAAAUfNj1KQo6jvvLm0w/n91+7vqzA85xv/CDP+QsOO+amYAAAAAfvyoCQAAAAAAAPDX4dsQAQAAAAAAAFETAAAAAAAAEDUBAAAAAAAAURMAAAAAAAAAURMAAAAAAAAQNQEAAAAAAABREwAAAAAAAABREwAAAAAAABA1AQAAAAAAAFETAAAAAAAAAFETAAAAAAAAEDUBAAAAAADg34XVUBWlpqZiNgEAAAAAAH5UlpaW/4eoSQjR0tLC7AMAAAAAAPx4SkpKvqo8bqAFAAAAAACABoaoCQAAAAAAAIiaAAAAAAAAgKgJAAAAAAAAiJoAAAAAAAAAiJoAAAAAAACAqAkAAAAAAACImt83fsJCd4/FiQLsUQAAAAAAgP9A1JTxnp9ePLijA4fD4dh06Dv994clkoZvhW3SdcqULsaK2KMAAAAAAAA/fNQUvTowvM+CBOtpxx8kP310eaVvybZB/X+LL5d9Y30SsZT2eaaOS5+AtjpM7FEAAAAAAIAfPGrKSqNXrU1yWrJ9ZlcrbRW2elO3Ues39X23L+xsRuoWf8fBF95WB0few9mu7Wfd5xEiyo1cMcjLnsPh2HoNW3+nSEII7+EMV/eJa+b+5O/l6j/z6Cq6DT+5gbZODdKCU0FO/c8USgkRJIV14FgMvlQiI4QfP9fNKywJ99wCAAAAAAD8q6Im/8XVWKF9kIfeh6uNDKplb89Gzy6nNu7Sx/DxkTtFUkIIqXh8/Abp2N9eVZCycdi0aNvFkSnPky/N0D8zcealNxJCiDDvRrJdWPiNu1Fr+gfRbfihSZoa3mo4eOm+inpaQSRvHj4U6KulXXvOJ5W5MbEie68WylgEAAAAAAAA/6aoKeW+4SnpaKswPnmOpWFAScreiow7BzZ5cjS6UEII99GxaEX/IGtV/tMjp4u8Z4/3NFRiqjTtPHGUyaOTCVxCCFOr06hepsoMQhisJjQbfgy3dDUIjNu3ZiffSOeVJd0scxkTrPPkdha/OOHGG9OOlqpYAwAAAAAAAA2M9fcGWUpPVZhVLJCs6wvjAAAgAElEQVQR1Q9pU1yWz1XQ0FZjm/gFNdt07M/C3l1Sj91W7brfSoWIijLflj6c7OuqxCCEEJmIy7B8J5BqERZloP7+yiiLZkPCf187bQ1sFx+b8jX30zumZpp09uoYt29pXKbjrQwDbwct/NoLAAAAAADAvytqqpj5OitNPnHzTZcA/aqkKCtPOnuz3HKsHcVgUp36c9aeuJaqGR2j3v2QuRIhUi0THX2TjVHLW396sZH3kJBPr4syjeps+HE8tDUQCenYLO/4+fM8Dc/xBmYaloU7z5/LUm072pCFJQAAAAAAANDQ/t6LegxNjxkTrOPmj1518VmxQMTNvrNz0vg/1AbNDTBhEsLU9+lvkbZj6dqYxr16mbEJISo2P/VWuxS66UY2T0okvMLUW1GPiuv+NErdDT+GW/oamI2d3HWe7Dld4dTWQFHNqqNB4q6IUmtvU3xQEwAAAAAA4N8WNQlhm4Xs/WORw5Pf+rWxtXb0nX5ZY+SBE7+2oRiEEMLU8xxol3/vhWFA9+ZVv4ipYj9l/2qP1KU9HMw5lm26T9obVyqmqbXuhh+zJn0NbBM3J0qqbO9tqkwUGjt6GEqIpa91IywAAAAAAACAhsfgcrkNUlFqaqqWlhYmFAAAAAAA4MdTUlJiaWlZ//L4VhwAAAAAAABoYIiaAAAAAAAAgKgJAAAAAAAAiJoAAAAAAACAqAkAAAAAAACAqAkAAAAAAACImgAAAAAAAICoCQAAAAAAAFAvrAasS09PDxMKAAAAAADw4ykpKfmq8riqCQAAAAAAAA0MURMAAAAAAAAQNQEAAAAAAABREwAAAAAAABA1AQAAAAAAABA1AQAAAAAAAFETAAAAAAAAEDUBAAAAAAAAEDUBAAAAAAAAURMAAAAAAAAQNQEAAAAAAABqY/3D7b158waTDvBvoaenh4MaAAAAAGd93wBXNQEAAAAAAABREwAAAAAAABA1AQAAAAAAAFETAAAAAAAAAFETAAAAAAAAEDUBAAAAAAAAURMAAAAAAADgPxg1+QkL3T0WJwoasiQANBTewxmuPitThN/ja8K3NvBovrvn4seCf3pC6tEuXuUAAADgPx81JUV3t4zp4mzO4XDMW7p2H7H2fqmUEMKPn+vq/oXzpFpl2CZdp0zpYqz4l0oCwF8n472MWDGia1srDofDsXMPnLY9uqAS0wIAAADwn8H6v/dAWhQ5c/R2/uidfw5x1q58kxZ7K+WbAyBTx6VPQAOXBICvJnq1f3ivFaW9Fm2J7GyrQ4pSo0/sOXOv2MkIU/M9koilTBY+TQEAAAAN6/9/dlGZn/C80vbnn9obq7GVNU3sOw3s76SpQMqjZ449VpB34Of2Tk4dQk7nSyRFN1cM7uhsxeFwbNyDV0QVSOqW+XDDmCT/atgAVxszjpmFbbvAVY8L5ZYkRJh9adlgL3sOh2Ph1HnyqWwxqbM5HwsFoP5kpdGr1iW1WrY3dECbppqqqppNWvWctnldT30FQoi05P6mYR42HI611+j9z3gyQgihObqrbi51n7xuwZAefl4uzh3HvC8s73lCRLmRKwZ52XM4HFuvYevvFEk+TVOfPai/qgNEyk3cNcrblmNu7z5ofUyJhC680TRH81JD5EwI/VjktMu9PsSx+77sqr/y4ma7ei1/UuuWXJraeA9nuLpPXDP3J38vV/+5D3lYtgAAAPDDRU22cbtWKnEr5244dTMxo0RUfZ5FGnms2jpA32jwoZj4+Du/9zFgEqJi+dP6i/HPnyeeHUf9MX3+tRK1umWq8BM3LbrcbMmfz16+eHrvxPzOhlryShL+43VDpt8wnR2RlJb2+Mq6YOtGjLqb4z5bgK/Af3E1Vmjfz1OfWeNpBiGECLMvXFQafSox9cEu76e/zY8oqEpIdY5uGSGECPNuprYKOxX5Z/SxoOx1C94Xpn9ekLJx2LRo28WRKc+TL83QPzNx5qU3ErmvCbUP6q/oQPnD5aM3Fg84mvDs0akR4jNXaG4MpmmO7qVG7oTQjqUe7dKTNzPCvBvJdmHhN+5GrXBRxbIFAACAHy5qMjS9VpzaEqTyYOeM/h1bW7cNXHQ+i+5LMpjabQM62+urMpmNLHqP7amRcjND7sc4GUy2Ai/72dOMkkoFqqlDS1259wnzU46dLfOZP82/maqCgrKudRsbLeZXbA4AdUm5b3hKOtoqDLoDWct3WoiLriJLu01ff53cuBzhZ45ulq7/L/5N2IQoNfVwb5xTVVjO8/ynR04Xec8e72moxFRp2nniKJNHJxO49XxN+JoO8J6euibxnz6oJcVk6bmPndhGuT4vQbQvNfImhHYsb+rRLv2rnLyZYWp1GtXLVJlR/S4AAAAAQIP6HkIUQ7WF3/jVfuOJTFjwYO/UX6aNb9by9LBmtc9e3yUeClu270ZqUaWCgoT7jtlFIJNXo7L9pI2TV61eErQunZh1GjI7dIKnOn1JcUl2hWZrQ+UvbV7r8gwAyKdA6akKs4oFMqJaJ8Ow1PXVq152GGwVRUmB+HNHN1NVS7Xq0GOw2EyJQPw+GdI8Ly7KfFv6cLKvqxKDEEJkIi7D8p1Aqlevg/prOiApy+NpuuooVr96GTXXYNXjJciJ7qVG3oTQjqW85Mvt0r/K0c+MFmFRBup4ZQMAAIC/7Zzwe+oMQ0nfJfiX1qysR3miqieI7H2cFD7bMWV9brfNUXGJCfH3jg41eX+W9UmZT4al2WrwssNRj54+2Neft3fqylienJIsrSaNSl/mC+qxOQDUj4qZb2ulpBM339T8FKPc94bkHt1fg6VloqMfsOPa3SoxsUlxhwMNFOp1UH9VB5gahmoV+e+qY6+opKCc5sOadZoT0b7UfM1YjLXktMtgKTEllZLqz5FWlNZ+E07+zOBiJgAAAPzQUbMy68xv607EpOW/E1Vys28f2BkrNXNpokQIs5GuCjcjp1xadQIlLBMqG3FM1JlE8vbOwfM5IkJql/l44ph+5cKDbK5YxqK0dTXYLAWGvJIqNgN6aVwPWx+VzZdKhW+fxaaUSug2B4B6Y2h6zJxiFz93+OKTcdllfH5ZzqPzaydMPVcgpS9Pf3R/bb61+am32qXQTTeyeVIi4RWm3op6VCz5zGvCt3ZAxTrQW3Z1/61CCSGi9PA9dyrqZui6zdG+1HzVWJTktavcpI1B/vV7+WJCRJkRv98qk3zNzAAAAAD8sFFTQVVXJfOP2UFuraytHDtOPk8N27ypnwmTEHaLvqM8c2a3M+fY9z+Wx7YfM9srdbpfx+4B/ade0XIxUiKkdpkPJ08SbsrxX3s5W5qZ2XVZze2/cqazqpySRKXllH0r3Z4u7mxnbt7Sf/LhZ1wZ3eZYKQBfgW065PezK9zy9ozq6GRn59J7xtGiNv3ba8t5vVGhPbq/OmvaT9m/2iN1aQ8Hc45lm+6T9saVislnXhO+tQOMRm1mbxnKW9WrY9eeAxY+te+gW/d7w2iao3up+bqxyGuX1ax/6HD21l4enboGLUq1c9Fhfc3MAAAAAPxNGFwut0EqSk1NtbS0/GKxN2/eYNIB/i309PRwUAMAAADgrK/+ie8D/Go3AAAAAAAANDBETQAAAAAAAEDUBAAAAAAAAERNAAAAAAAAQNQEAAAAAAAAQNQEAAAAAAAARE0AAAAAAABA1AQAAAAAAACoF9Y/3F59fhsUAP5FcFADAAAAQF24qgkAAAAAAACImgAAAAAAAICoCQAAAAAAAIiaAAAAAAAAAIiaAAAAAAAAgKgJAAAAAAAAiJoAAAAAAAAAiJr/XrzYqTa20+P4mAn4r6xqrHkAAAAARM0GJkxebKfj+0eR7FtOXu9PsLT5EU5Paw2E3bTP3LkBTRSxQD9MUPxvwd4OTSiKMhoWXfH+WVnF4x1ju7Zqok5RlK6135SjaTwZ5op8Hwc19Z5xq76hkfniL2zyVWu+zotGaXhnHZt5CTknOlH15Xv45qKv7SQAAAAA/Jui5j9AUin9d3WYpecW/FMHPRYW6AcqJh2GLtm9obPGp5Mi4795q91j6em4Zy8eh0/SPjMq8LdkIebq+6DksvtFccnb3MRd/hlrhky5WvL3r3mGTuDFwsLCwsLCjDN9tTR6n8ooLCwsLCzIL6j7ZOGFPtqMup381rcqeDFjrFove/ql1cePm25rNyue3zDFGrYS3oOJNg7zEgXfx/LhJa4MCjlf9E+8dfTNs43dBAAA8O+MmpLCyHk9HZtqUxSla9Nt3sU8MSFEnBvxq7+lrjqlrqln5rMgriBqdPC+vOwdXc2bNLHqezhXUuM/5LujLWyGLp7QtY2dZeuJdyqIMOv8vB52+hRF6dn1WXqjUFK3Qh7h3R1tYT143thu7u2drC1chuxIqqg615FVJP0+yq25JkVpNuswcvfjcll1EyFhU3p1cLbjNHP8aVty1TW1utUSutY/4tYeyIebCXl3R1vYjt4wt7ulNkUZu0+LyHx5aopXC02Kaua7+FZJdYL+bOU/BlXrgWOH9HS3bFzjqpeCTqe5S8Z0bWNurN/CdeicIU1ybz19V/vktD5zSLfL4C9iMFkslpK6qe+IwRaCp4kFlZ89umusebrDqn4vaGzlKkpMwmAqVf1ZVU217pPKbAXaTopeHV+ycH/q33Qmr2w780LEdBvlhinWwO/m2M+7ED7ZSun7WD3C9Ot3svj/yE0K3zzb2E0AAAD/zqhJiJrt8N/vZ5eW5t2cqX5oxORLRTJe3Irp4abrk4q4796+uPpbL5PGnbYfHmrUZNTFtOzsZ6eCjZm1qhBkX0potSE6KfXRVneFxOUBI6Ic1sYVlhbcDzU4MnTMmfzy2hUqEkKIIOdKssvmazHxiVcnlC/7aUk8jxBSHjMncPbTLodfFBe/ONotdW7gnDtcGSFEkB35pM3G63HJTy4Pzgib8keehBBe3Wr5NK1/kgepzw1EmBV+SXtBTP6bhGU6x4b6BV9w2PL47evbk4TbpuxKExHypcr/K8RvYu++1W9nQTFoTlu/MIc82pUADUFanhJx7pViCysdxc8d3bWO3LqH1T/Vycqcy9u3ncuorFWiMu/ivN5tWzo42HKaWvpMO5cnJoRIiqJXBDqZmVvbugzc+LhcRggh/Ljp9nYDhvf2cm9vx3Hsv3LXwp+7+Xo4mDsPPZguIoInq7p1X50iIPy46fb2wSP7+np7trWx9Z19peDTMX6umKzofL8W7damiQghRJy105vT90yhuPThpiGulmYWFmbWnqN2J3I/mVBpye2FnhyPeTfeSmTlSXtGe9lZ29hY2XYYvPFB6ae3e/CTwrr1Wv9MSCRFf4b2buPg6GBrZecz8XiGiBApTf2fH4K8BPl0Qx8XJycnJycnR0tdirKdlyCoO7finGOT5sVw78/pOeJUbpH8odF1lW6MvAcTbe0CA12a61v2Gu/bvNbsZSdVzzaRlsVuHuZubW5lZW7jM+VUloh8dsZ+3N0EAADwY0dNpm6Hgb2cDFWZTMqm//R+mklXX/IZLGWF8sykx6+KREz1Fs7O+l/KAyzt7lP6m6swCGHwkn4/8tZ/6Sw/E2Wmaotes6c2u3/w4TvaCplanSf1bcEmhN0sYFKninPHn/IJ7/GB88JuoZM66Cgq6rhOXNKz8vyBRD4hRFG/58TezZQIUW7RsaNOVkyWkJC6/aRvvb5TodN97qi2jRVVTLsPsmWw/CYPsKVYjWwDgpq/jskQEPKXKv9RyHiPN4XMyw1cN6Wl8jfM4VcvLahHqLgXYqmvp6Np6LaC33/D2i46nz26a2xKd1jR1D+Mo924Wouf7wgbpJMMNY+9uW9O+VM1y/GSdq570Hrr3YTEJ89j5rOPhh3PqJSV3ZwVcsBwecyzpwlnhzfKK3v/SU9hzivrZZdu3b291eZm2B6NuaeuRt/e7XRnzb7UGn0U5ry0CD134+a96wuUDi44Wifdyikm1vYa51t88MhzISGi9JN78zzHerP+nDZgk+LMaynPnydfHPVuWb9Zt6pTjOTtzQU9f4nteTwi1FtHcG9+8Fat0OjklJQnt1cY7Rk0M5pbt0FJ3tmFe8ica48Sk+MO9eZH38wRll6nr7+eQ/iEkvWk0/fj4+Pjbu8b2lzDccbemQ7SunMrMxmwIaw95bLs3M6OT2fKGRptV0UVcsYoyMpssycl9/GJsHl+NWfPR+f9f4O82EWD1lWOv/Lk2dPEY10SlobdzImpz4z9eLsJAACgYXyvnwaUlsXtnDV725UnhSIFBcm7MmYAX6biPmf/vPmLZnUMe0Gsuo4JWz/LT+Pzg1M30qi+QCh++6qw+G6Ik4USgxBCZKIyhm2ZrCVdhSx1Y83qaWFqGqtx00olRFyaU6HZQZ9d9TRb31yL92dJJdEmTDVttarzFIaiElMsqJQRotqqdrXt6FrnSYgWsz67SN2gahQMRRUlRU2jqs4xFFUVpQKRTM7Q6lv5j4GfuvvnXpu1l0Wu8dNW+JY5VKmzy/wM8UHZv4jttDb6WA8d5UaNNdVYDEII73NHd823BmgOq7r1t1p943C3xlWrvixykMfqhuikHKpOC8N3x1w+vvFq+ssnt1MqBO0FUn5axF22/353XSYhhp1G9m6W8D4pG3t4t1AhDIZ+Cz0T7Q7NlAlhaJtQoiK+hHxyVCoae3qbqhBCNDiWGsK35RJC6N7iqFuM03ZUgHjI/qQZS5QOHeZ2296e/WJOjHLPg/4mbEJIi97jfReMvJAmaEOIJOfgoCAeNeziCGd1BSJ4cSEyPV0wxjNCgRAiqywTGCW8FnlR7Nrv87UPsJo/0r3jCf9Ovl0mh3U0lTzaSF9/PYdQhyjzyPDAXc3WRM1pq84gNHP7oaQgLYKmaQ8nFTldlT75nWaMbQlhN/Xx5aiylIhm7dnTYDx936uoaJHnuu5N2YSwW864EU8EifMm12PGftTdBAAA8INGTUHy2pCl2WMiHl100GQJHs12DnhNCFHQajNq84VRm0WFcbtCeoyY75GykU0YRCb38zwM8v7kkdW4qa5h0/0JW9qr1ihhXLtCJqksSssXyIgygxDh67TSRiaaTMLSNFErffFGRKzZhBBRQVqJqomW3P+s6/Qz9hf61mt1VvZNH0ySN7T/CkHa3sFdlkp/vbR3MOebPzRVd2ntdlfDy8NfwmBr6hvoazPqeXR/ff1K2sYmJtX1N9JWZjRoJ2u99VUSNcFtWJLH+CHebgP9nEpiQmUyQqQSqez9lgwFNvPDn1nKH2Irgyn3zhEGU7G6GIPBkH/s0xRTsR/2s1rA7ltdVE8zBxxzUCWJRM72Kq3mHegTNXzM4qA7q93ZUgnR7Lw95oivOiFEWpGXx9cyoklNyjaTL70IfHQjMurapV+9Q+32RI+TU389h1CTpPDK9B5zuVMu/97LkCVvbj+Qkc/VW7erk+nGKIslDLZq1adz687e+2Ark4qlH/7LkPFy0/KlAnG9ZuxH3E0AAAAN4Hu5gVYqEnwgrJRKBaVClSaWzTVYRPzmz50ns4SEEMGLc6fuZLyrlCmq6+prKrGYCgwmZaBS9jKD+8UvmFVtOXxAo7Mzl1/J5EmJhFfw5NrFh295dSskhMjKr4VtvFskFhfHbAq9pt6jv7UKUW35czd2xMIt94rF4uL7WxaeY3Ud5KAiL/vUqVaNrvUaH5ip90DqObQf79M4MrFQIBAIxTIief8HQojw1cFh/nOKR5/cM7iFglAgEIgk33ImJaBdCfA3Hex0R/d3g/ZrgYTpUTdKXWYsnTm8XydOYVTMG5FYIlOx7OUpvrTnRr6YSN7eOXQu46+PQyYozMzI533xZYBtNnC4UdTs6eHaw4ItlIiKRbd2gnPbr+SICBFmntsaKW3vx1EmhDC1HTq4hWxeZn5i5NzrJUpmXdzZ13/bn8yTkcqs4yHtPObcK5fVaVSUusbLYWy8SeeQWau3zneXPX8uMJVT/2dVRA8z1ut7pea9nzLuwxWBIx/2PHRolJWK/LklRIHFIpUVQjb90Kr7LKjTVWFz2jF+bvY+UGreyU0xem9kbiWR8ZK39PObfFvLvz4z9q/eTQAAAD9+1BQ+GGWlp1vN5OfrklbTwvyejHR2dPXy+yVc272JMiFEWvZ4/1j3Zo3V1fXbLn43ZOtiF1Uls+CpfpnjzTQoA7+92Z/LV6pO88J3dUz51dVQg9Jq7jpsa0yxWFK3QkKIUtOggHehrsbazXqfMl18eIGzKiGkUYflJ5eYhfcz1dJqERRuGnpyhRslL43Q9JOu9Rrb1H8g9RvaD7dQBYkLHXV1m/U6UVRypk9zXV372fF8QkRZZ9ZHFJY/WOLTvHr5uG/9lg8iSWlXAvw9VOmO7u8G7dcCqdiOmOWfOcmllaun3y/hel3Neam5PKLhtfLwWO5id2vrlp3XFTbTY//lxvnx8z3bTbhT8cWCLJNeo6yzMi1GBDRVJISh2XHt4XH8FT7WlpZ2XbapzDixxlvjw+sTq8mATStswkfPihS6rjqxwORYgI25pZXPGsbkwys91Rl1GmWbDV3cv3S+q5WNnb3XCtaE9SFmep+rX95Bxc3OZVj7mNfYu+Lso1NWxJfkHB/mbGZqampqatsvvHFI3bmVERXrXm0zJ7Xy2mm4nKbp6j5X1ukqR9uNboyfm72P1NqFHZ4kXeVjbW3XZsgt7y3bAi086zNj/+bdBAAA8HdicLncBqkoNTXV0tLyXz8fvLujHcfo/nF/SUu8GQwA/yHlNwa3nGl36c5MS3ZDVPfu6hDfPwZd2d5JE2nnO95NAAAAf2fiw3efAAB8AUVR/42BnmmtveSvbP/xvUt13/33ff/DM/kv2U0AAAB/J0RNAACcmmMmAQAAAFHzb6Xquv15IqYBAAAAAADgr1DAFAAAAAAAAACiJgAAAAAAACBqAsB/i6Twz1U/uTTToChKw9DSNWjJrZLqHwSUVaQcntHT0YiiKErXynvk5rtFn/y4jzhzuwdF6Q+8VPL+txCFyYvtqPeMW/UNjcwXY34BAAAAEDUB4D9HWnh+zMC1JUEHkl6/fZN+/+jcLk2rf5hBmLa9r/fUh/YLI18VvM15sLVH8Zoefgvvc6uDpSj91O8pOmaKN3fceCv9WJ+Sy+4XxSVvcxN3+WesGTLl6occ+o14iSuDQs4Xyf6BqeDHTbe1mxXP/+c2bPBKAAAAABA1AeC7IMp9mFLpMDLEs2kjJZXGTZ26hQxx0VIgRFZ8bcGSRy7rj4YG2OmqKmm08Jny+/6fyrbNOpYtJoQQ4fMj+zKdF/w2Qi9mR2T+Jxc7GUwWi6Wkbuo7YrCF4GliQaXo1fElC/enCr6tf8L063ey+P9E0iTKtjMvREy3Uf7nNgQAAABA1ASAH5NSM4+2KvfmTVx2ODLuZZHwQ6bjp56PEbYa1MmA+SFDqrce6Es9CX/0TkYIP/ng0XzHIZ1dgwIN4ndG5Na+UVZanhJx7pViCysdxcqcy9u3ncuorJkgn27o4+Lk5OTk5ORoqUtRtvMSBJV5F+f1btvSwcGW09TSZ9q5PLE459ikeTHc+3N6jjiVW/Rw0xBXSzMLCzNrz1G7E7mfxk9J0Z+hvds4ODrYWtn5TDyeISJEVp60Z7SXnbWNjZVth8EbH5RKCeE9mGhrFxjo0lzfstd43+bt1qaJCCFEnLXTm9P3THbSqm7dV6cICJGWxW4e5m5tbmVlbuMz5VSWiLa2DwRPqjfkx023tw8e2dfX27Otja3v7CsFElnR+X4tajVUKC6VPxZpye2FnhyPeTfeSj7bKAAAAACiJgB8vxhafltvHBqsdmfdaD/H5jqmHaedTBcQQiTvCiqU9HRVGZ+UZWoaq0tK35RLSUX87yeLnIb46Kty+vQzStpz+kOSFN4LsdTX09E0dFvB779hbRcdhprH3tw3p/ypmgnXetLp+/Hx8XG39w1truE4Y+9MB2nSznUPWm+9m5D45HnMfPbRsOMZMpMBG8LaUy7Lzu3s+HTmgE2KM6+lPH+efHHUu2X9Zt36GNAkeWcX7iFzrj1KTI471JsffTNHVHFvfvBWrdDo5JSUJ7dXGO0ZNDOaSwghgqzMNntSch+fCJvnV3zwyHMhIaL0k3vzPMf66Lx/ieXFLhq0rnL8lSfPniYe65KwNOxmTgx9bXUJc15ahJ67cfPe9QVKBxcczRBre43zrdmQN+vPaXLGInl7c0HPX2J7Ho8I9dYR3KtvowAAAAB/EX5XEwAaPmyqmvWYtaPHLCIT5N3ZOiJwxGBT5+vjDNT11YTpb/kyovYhbUpKc98paOo2YpTF7Ap/57zKR0+BKHAC+jVZtfd42pi5NoQQwnZaG32sh45yo8aaaizGF5oWZR4ZHrir2ZqoOW3VGcRpYfjumMvHN15Nf/nkdkqFoL3g41U8QVpEjHLPg/4mbEJIi97jfReMvJAm8HBSqcrAuu0DrOaPdO94wr+Tb5fJYR1NpU9+j0xPF4zxjFAghMgqywRGCa9FbQlhN/Xx5aiylIhm21EB4iH7k2YsUTp0mNtte3sNxtP3vYqKFnmu696UTQi75Ywb8USQOG9y3dq8KHbdISkae3qbqhBCNDiWGsK35RLCqd0Q+8UcmrG0IUSSc3BQEI8adnGEs7oCEby4EFnPRgEAAAAQNQHg+82cykZuv0x0XR7yMFtEWlh2b6cUcuBqfsBAw6p7aGXcuKNXubYzWlGlt3ZeKhPyxtkbTGYQIhPypdJDh1KmLrMhhDDYmvoG+tqMLzcnKbwyvcdc7pTLv/cyZBEiLYma4DYsyWP8EG+3gX5OJTGhsk9vkZWRz31eU9lm8qUXgY9uREZdu/Srd6jdnujJEqLZeXvMEV91Qoi0Ii+Pr2XElsUSBluVXXX1UsV+2M9qAbtvdVE9zRxwzEGVvA+2MqlYSt4PQMbLTcuXCsQ0tdFPIlOxOmEzGAwZfUOJ8sai0mregT5Rw8csDrqz2p0tldS3UQAAAIC/CJFYv9gAACAASURBVDfQAkADE6UfXRh24ObT3DKh6F3G9e3r7kgt3VooEUbjTot/tb83eeCCM8lv+cJ3GTfWDxt8uNHI5QNNiqO232D474pNSXr8+PHjx0lP/lxsn39iXyJPTgt0Xwsk4z5cETjyYc9Dh0ZZVV2ZFKZH3Sh1mbF05vB+nTiFUTFvRGKJjBAFFotUVgjZFt3aCc5tv5IjIkSYeW5rpLS9H0eZyASFmRn5PEHqGi+HsfEmnUNmrd463132/LmweRd39vXf9ifzZKQy63hIO48598prBTy22cDhRlGzp4drDwu2UPr4vFLzTm6K0XsjcyuJjJe8pZ/f5Nta/nS1Vbf+xU9Q1mpIhX4shDC1HTq4hWxeZn5i5NzrJUpmXx4CAAAAAKImAHyXmGr6qq8Oju9kbaKjbewY8of6uIMHBjdlEUKULMefubbGOXGhbws9HWPnkeGak8OvLnFVe31pxz3NAdN7WBoaVDFp9fM0r/LwPQ/oP0lI97VA4uyjU1bEl+QcH+ZsZmpqampq2y+8ccgs/8xJLq1cPf1+Cdfras5LzeXJiIp1r7aZk1p57TRcfngcf4WPtaWlXZdtKjNOrPHWYBB+/HzPdhPuVJoNXdy/dL6rlY2dvdcK1oT1IRxtt1UnFpgcC7Axt7TyWcOYfHilp3rtS60sk16jrLMyLUYENFX89Hm1dmGHJ0lX+Vhb27UZcst7y7ZAC0+62qpbr/jiJNdsiKHZcS3NWN6XbTJg0wqb8NGzIoWuXx4CAAAAQINgcLkN86UQqamplpaWmFAAAAAAAIAfz9cmPnxWEwB+EBRFYRK+QUO94QgAAADwL4mavNiprYcoHIxdbf2k+g/OKl8u/JkyAIDIBAAAAAD/iO/ms5rizO0eFKU/8FJJ/b6jgnd/gqXN9Dh+9V/ZTfvMnRvQRBF79McmerHZS4OivA7lV31tijj7xBgvG2MtiqIofduuM068fL8iiKw8ef/4jhbaFEXpWviFxvO+UB4AAAAAAH68qClKP/V7io6Z4s0dN95Kv2F7lp5b8E8d9HA/8A9NnHVsxjZhS42P7ygoaLQMXnokOjk9Kz3p7HjVo2NGncqTEEKIOPf4sK7zMnw33X6ennbvyExvfdZnywMAAAAAwA8YNYXPj+zLdF7w2wi9mB2R+V8+9+dGjQ7el5e9o6t5kyZWfQ/nSnixU21sp8fxCe/uaAvb0RvmdrfUpihj92kRmS9PTfFqoUlRzXwX3yqpjrHCrPPzetjpUxSlZ9dn6Y3CWi2+u9zL2HVrppgQQgjv3jhL+7kJAkKIODfiV39LXXVKXVPPzGdBHI9ICiPn9XRsqk1RlK5Nt3kX88SEECIte7i+n70epWlg23Pu6kGWtjOqL79+oV34HElBxNy1ZEpoZ92Pq1ZB3cqtgyPHWEdLx9iUY0ypalFsBiFEkLJj6e2Wv+2a4W9tqGPAad3J3Zj9ufLwo+E9Wujq+dsz4d/djihtnVe7uXE8zDgAAADAdxk1+ckHj+Y7DunsGhRoEL8zIlf8pQ2oTtsPDzVqMupiWnb2s1PBxswauTUr/JL2gpj8NwnLdI4N9Qu+4LDl8dvXtycJt03ZlSYihPATlweMiHJYG1dYWnA/1ODI0DFn8usT+nhxK6aHm65PKuL+j737DGgiedgAPksSQoIbQgm9o6AC0iwniKei2Chn7w0bevbeG9a/5fRexXr2eoqKDQ97AzkOlWJBFCkCIr2FhLT3Q7DRBM+C5/P7BHF3ZnZ2E3kyuzOF2c8urfU1ZhFC1G1H7o5Izc9PvzGLd3D0lJAchaLozryB/ysaE5qWk3p9OuPEufTy4/nUeoEQIs+7tmRJ2og1/U0qDl0L/57pbKDD12rY/59eh7Z766gQIsuOvJahYxI94ydDmta0cB/7R2yJoobt4YuofFe8OG6pHU3TLTYklJW/Inmx2ZWm6cZz75cSQoii5NGhmT6OhjRN04LG7cdsDsuRvbejkpFTr2Whr2r6lJC/Ohdw3GDSMGv22311fA6/GcDOD+6i57z2adnnOEZVq8GTjU8uOYXBcQAAAID6GDVL7u0+nuM8rIMe16pnX8PYP05+uFxeXTF0vOaPbanF4lh6DbGlmJ5T+tvSzAa2PfqYZ4QniQgRxu4+nN15xWxPYzUG18J37jSziAORhbUomGKqqRQnx8Yk5pQxeBYuLnoswhC4DfB1NuAyGHTTfjP68mMvPS8tjTl0UeG7cKQjj8EStJ08x52rHDf75HqBKIr/Xj3nvu+60daVByE5Tov+uv8g6sqO4dJd4wJuFygIkRW9LhInHQtrvDYyMzN2u1vkzL6LI4XVbw9fQnV3xTcwyz5yLEE53liWeOJQuilfRXlWxQnberWfFmm/ODQxM/vl34Heueu9PRdHFClPEbvVrme5edlp0Ts7J60fNvVS9Y91S1ODA6Pt/Dq8N/5NqNsrfosq+QIfoTrtRzk82hqULMEZBwAAAKhnUVNREL4zuNBlaAddFcK26tHX5MmeN3+HfhomT1+DQQghFIvDZvEN+UzlL1yWXFSmIESanZiVe87P2dra2traukmHgHhKWiCsxaAEx2nevgWWN2Z7WGibtOy3IjRDSuQFUdv8PR0sjAyNTBp1355SWFiqkOa/LNaw0lEOvlEcfVO+ctD1k+uF0tiN0y66rpnmxK3qGwAWLdA3tmo5IGDDL8JjW/8pIUSFo8EhnE7zJrcz4nKNO06d6ZT1V0iiuNrt4Quo7q54ju2QHoqgw49FhBBx/JGjIu9hzbgUIUSRe2VRwP1WG48s62En4LI1LDpM3b1vYMHW2UdTlSOYFIPJZLJ5lp1GD7UWPY7OlJQlHgtYvC9eVKFmedbt409NvZ003n0twWk1czTn0IKjKRXGQhUlsbvHtjHn0zTfzG3MrphiBSHCMH/rpn7Lp/q6udhZmTkO3BonLA+1Vd4AT/EcfSwSg25m4q0MAAAAUL+ipiLv1o6QAvGdX+31dXV1TX7+X6I88eDBR7WYGJQiik8akGJqmQoM+h+NfqqUkJSZcn7wB/fgUgw2QyaRKkuXFecJy8dkVDRbjN18/v7L7MTgYcItoxeG58Rt8FuR2nP//eT0tNRnIeNNWYQQJt+4QcHzbOXftIrSVyn5slrWC1WT5USGxibu791IT1fXqPXyZ+Kocfa2o64XV7qWCCEUIYQwBfa2mgxK5e2VUs21V8O/wb/9eqDau+LVrPv3UzuzL0ZISuMOnGL0HtxEuUJRafzZcLHTkI76b98TFK/5gE70w+D7he+90+XFj86dSWRZNNZhSV5e3Lb1TKWbIETJ4cnqjRvy3/90U2sydpnH0zX/Cyt6/0OjOHxe77mPux56lpv77Ej3+Pm9590pUhBCRKmhD1v8fjUq7uHFoUnLp55Il5Hqb4BX4TW0o1PCksQ46QAAAAD1KWrKX1/edo3qvPOfR7ExMTExMbEPry+1f/Xn3uiPzLPBoPU5Bc+Tij5hulpus5H9G5yeteqvZKGcyISZD69ciMz+YERCzcLNKC3kVoaEEHFi0OYr+cpaRM/OBN1JKpQoWDyBHp/NZKgoRPlijomNuQaTSF9f33E8RUwI4dgP6ESCl++OLpJLsm79vuZW+ZjIx+uFak624cDTzxKexMbExMREnZlsxmq26sq19a7qRJ57d+/ei/devC4syIg5GTDrhKT9AAcuIUTdeXQ/rRsrA+9kisWvbvy+IVq3azdLdvXbw+dW013xbKteg7RCd0e9urfnAj2wX0N2+TcKhZklbF0B9/30z+Ab8WR5mcVyQoj4rp+Nnq4O36DN6tJ+mzZ01aHU2+5Jex3Uma741URxVqmqFufDDzeGTseFk3SCFu578e4ZTWHM/rPi7ssmu+mwWDqukwJ8JGf3R5cSQlh6PpN+MWMTombh4aGTEp4irukGeAZXU7U0qwTvZQAAAID6FDVl6SHb7/L7z/C2MdBXMnYaPL1dcfAff9e8GDu74aBpnskTGmrQ+p57Uuv2Rx7XeUHwTo9Hc1wNNGhNc9cRgeG5H95Wx7Ic/ttE9lp3O6efOs2Ic3QvX0RFXhCzb7y7mRaPp9dyaeGwwKWtdJymL/d8OMbF0bWd56hgbXcTNUIIxXNffWg6d2tHQy2Tn9dKenQ1UGWqULWpF6q7TDlaeuXXh76AZqmw+Lq6GqoUIVRZ4sk5vzhbGRlbe8yLbLYyOLC7cpof9ZZL/1xpfXZAQx0dmxE3XNadWNycS2rYHj6rj9wVzzL1GWpw47fff7siGNLDXPVNGOTpqYtfZ5e+P+woy08rpDQE6iqEEFXnDTejYx8lv8pLDwscYMGu/qsJjiZbUiiu+DWUaiO/ZV4vN6y4lvvmX6T5L0v4jfTKG6Cq10hT+DJPQghhqGurK68LisVmSEWSGm+8l4uLpGy+Gi4kAAAAgPdRRUVFn6Wg+Ph4GxsbdGilv7mzT3o7rOt25+Z4cyz6CT/KVZ97rl+TASFCphqHRRGiEJeK5MYTb95b2fT5UpcOkf97dq5zyaHuTf3vNt/y8OJQ3pXe1lMaXYhaaXZ1gJ2fcP2DUwMMlPfQKopuTXLyfTD93jW/4hUu7W4viQ/trf3xW55lqbvatzo+IfpiXwFFCBHHlVfajSdJ3NLJbb/bFO6uY91u3Z1p/I+/3UDJnrg/fm5ACCm+Odp+GHXg4UaVaY7jBCciApqpESJ+tLy19/ONMXvaUnd/dRxK9j3Y0rrCOLgi52R3+3VeYXiPAwAAwH9bXRMfvoj/EiTpty5GvRIrFOK00LXLI41/6WCIv0Hhh1GLu+IZ+j22Xzz7187ehu8eVqa0Oi6dY393yoBFp+KyS8WFSdc2jhh6UH3UqoFm1bx9qpkWiKHn7m2cGBJX6WFewrIYuqxXXuD6f5Qt4TYb3F313OItd3Ol0tyILYvPMLsNceBUc1TV3gBfEnchwaB7OwO8xwEAAAAQNb80RcnDXUMd9DT5gmbjI1qv3z/BRhWdAj+KWt0VT3HNWrVrZf7Bg5mEbTPh1JX1LtGLO1no6hjZ+yzPHhl8eWUbjeoGMquZFoioWvUdZRaxLzy/0rxhFO06a3bzt09rNnBbdTygYXBfS01Niz7BlsuOr25DVztqWvUN8IqCiP1hRiMGNGLjzAMAAAB88JcXbqAFgPqnLOnAIPcZedOvnJlsx63zPMGKwhsT269xOnVmpOmXHWyUpu79xffulKtbO/IxmTEAAAD8t9U18eGeLwCoh1TNB+8+rxJ4IepxYVMXjbrefkHx2q4LbVTI+eK3bTB0um0P7W+InAkAAACAqAkA3wWKbjZodrNP3ltN21DtKzSSo2vEwbkCAAAAqAzPagIAAAAAAACiJgAAAAAAACBqAgB8bcIwf2uHhTGi+lLOVzzy+4tdf177RPyl6ylL+K3dT/OjhLjWAAAAAFETAL4KcdxSO51OJ3IU5T/TNN1iQ8KbNUYkLza70jTdeO79UkJkWdf/N7CVmQZN0xoGNq59Am7lyT8MNE9WO+l2OJihfLX0/tzGNP3+r030ugdlKerQuI/W+JU7qobOIURR8ujQTB9HQ5qmaUHj9mM2h+XIai5S/upcwHGDScOs2W/L1/E5nF6+V35wFz3ntU/LPkfjVa0GTzY+ueRUugxXPAAAACBqAsC30MAs+8ixBOU4W1niiUPppnwVihAizzo7bsCGvD77YzOyX7+IODK/q2nFNWhVTdu50vHnY4sJIUSaGR4mNhUkh7z99XqWeRfnOkwAW4saPxOZRP7vOocQccK2Xu2nRdovDk3MzH75d6B37npvz8URRTUka2lqcGC0nV8HwXuf7dTtFb9FlXyB/z502o9yeLQ1KFmCKxwAAAAQNQHg6+PYDumhCDr8WEQIEccfOSryHtaMSxFCytIiH0kcxvj9bNqAzdEyde7uN6yVZoVPJY5NVwf5/UsJpYQoCh5cymoxxd/k0ZtfLybqtHPVZ4lTzi7wttOjaVrXrueKa1nl42zS16ELu9joafD0XYZtjy1R1FSjMMzfusnQBeO7u7d2bmLd6s32ytSYc2t1D1sBTevYD9gaJyx/vYpKhWH+1k2HL53YrYWdTfNJd0pINQ2rVecocq8sCrjfauORZT3sBFy2hkWHqbv3DSzYOvtoqpSUJR4LWLwvXlQpSd8+/tTU20njXfjmtJo5mnNowdEU6YebKkpid49tY86nab6Z25hdMcWK8vb7LZ/q6+ZiZ2XmOLDGgyWE4jn6WCQG3czEuCYAAAAgagLAN6Bm3b+f2pl9MUJSGnfgFKP34CbKFULYZm1bcu4umLTyUGjU8xxxlaN1FK9Z14Y5N29nSEjpk4sJxt4eHq4qEeW/xrJcOjUi0at6jL7ssCEqKz8zYpn+4eHjTr2SEULEKSeCNRaGpeUkn+71YlH/gHvCmmsUvfwrrtXmK+H3oi9NLF45MOBe+WOI4uSgU2rTr6blJv7ZOWbJlBPpMkJIaTWVilJDHjhtuhkbfz/QXaWabWrXOaXxZ8PFTkM66jPedUXzAZ3oh8H3CxWSlxe3bT2TVHE8UZQcnqzeuCH//U92tSZjl3k8XfO/sA9GQ4vD5/We+7jroWe5uc+OdI+f33venSIFIUSUGvqwxe9Xo+IeXhyatHxqzQerwmtoR6eEJYlxiQMAAACiJgB8A2yrXoO0QndHvbq35wI9sF9Ddnl20vQMvHZwqPqd3/w9Hc11LD2mH39RaQIehu5PHfSS/rqXX5p07b76zy5G1h2aZl++l1+adC28pHE3O0bs7sPZnVfM9jRWY3AtfOdOM4s4EFlICFGhOy2Y6KbNZGr+NGFRp+Izxx6X1lgjQ7PL5F4WqoSomvWY3LHkzLHHpcrXtb0XTWijp8oSuA7yFaSEp4gJEVZXKVPba2q/RhyKEKrabWrXObLCzBK2roD7/t3BDL4RT5aXWSxXb7sn7XVQZ7pCUbLirFJVLc6HH+wMnY4LJ+kELdz34t0zmsKY/WfF3ZdNdtNhsXRcJwX4SM7ujy4lhLD0fCb9YsYmRM3Cw0PnIwdLGFxN1dKsEoxqAgAAQGVMdAEAfHksU5+hBlt++z37sWDITHPV+DevU9yG3rO3e88mClH6ncDRvUcPtXS5+qsl6/191Sw9WqntDYlNFd4odlpizlbX7GKSGBKbKrySYdytuZbseWJWbpifszWbIoQQRVkBZVsglGkTppalbnlsU9W14BU9y5dVXyMhhMkz4jPfRjr1ooR8GSEqhDA1DMpfp9hcVVmGREGILLu6SnmGGuXDkNKqtyGajFp1DoOnpy5+kV2qIOpv06YsP62Q0hCoV/clIYOjyZYUiis+JarayG+Z15YxK6518yp/RZr/soTvplf+nKqqXiNN4fU8CdEmDHXt8tIpFpshFdVwsESTIRcXSdl8NXxnCQAAAIiaAPCNPmuMuo0wm+F/ufmWTcZMEl/p3yk1wzajJrmu8otMLSMfRk3CbdLVVrQ4+Ezey4ZDGnOICtO5Hb0h+EzOU57bahNVZp6pwMB034Mtrbnv7SMMI9LcxNdiQtQIIWWvXxQ2MOYzqq+RIkSSk/BKpCBqFCHijIR85fbVTMHD1KqmUkIRqsZtat05HBuvn9h++y+96jHAQNlwRVHUkUuFTae//yjmh9imzY2KjyeXKFp9MBhKKP7Pc6eZui36w7q8KUy+sXr+s9dlpIkqIaQsMyGPa6zJInU5WEKIQpj8qMDQy4yN6xsAAAAqwZfRAPBVMPR7bL949q+dvQ3fBb6yF0cWL99/43FagbisMOnqtt/uyG3aWFQKLhTfuYt5yt4NYdqdHDUoQlTN2jrmH9pwXebY1YZLuM1G9m9wetaqv5KFciITZj68ciEyW0YIkRddXrnlbq5Ulv/31oBLDbz7NeHUWKOi+Mry38NypNLc8P9bdoXn3a/8ocmqVFtpHbepoXMorY5L59jfnTJg0am47FJxYdK1jSOGHlQftWqgGbO6aYEYeu7exokhccWVamBZDF3WKy9w/T/C8rYN7q56bvGWu7lSaW7ElsVnmN2GOHDqerAlcRcSDLq3M8B3lgAAAPBdRU3hP9Oa2s6IKn33Q202/vrNA4CKFApCqIojbxTXrFW7VubvD7cx1PW4iQcmdGxirKNt5Oh3gvfrgf1DTSsHF5aBWztDeRHPtbU+kxBCOI27NJUVyWy6NaMJIVznBcE7PR7NcTXQoDXNXUcEhudKCSGEbdrLK3dxayMtE++jpksOLXLh1lwj27RPj8JlrkbaZr8EWS5Vbl991qym0rpuU33nEMK2mXDqynqX6MWdLHR1jOx9lmePDL68so0GRUg10wIRVau+o8wi9oXnVxqNpWjXWbObv31as4HbquMBDYP7WmpqWvQJtlx2fHUbmqrbwSoKIvaHGY0Y0AiDmgAAAFDV3zdFRUWfpaD4+HgbG5tP31+avK2D3cx4rz+fHO6qSZVnuebDVA78s67Jw/IfXN77zl0YMdFpBPtwVPmL0te3j12mOvV10/1aX6/Xrkbpi83tvR6tiQx05X7u+r9YyfWTPD/id3//1eefldCNey7YsXmc09u/i8u74i/PpU2GhL23i/aQ0EeBrbk17ksIKXu22dNlbpTT1oSrg/VVCMk73dm06nLe1fX30lQfy1F/v9lCvcfZZ/vbNcDnybu35yR7n5eBCSc7876fNof5O44TnIgIaKZWL9tXlnRgkPuMvOlXzky249a0jqii8MbE9mucTp0ZafplPw2lqXt/8b075erWjnVY1xQAAAC+X3VNfPVlVLPsRdDuRzoNWTe2X8uWf8L+TN02gwZ+vZxZ2xplmbeCs1x62n3+NPjlSq6XFHlXpvdfnTf8fEpW0qk+yUv7zb1VqKjQFcY+Z7PKZT7+o4O6oOtAe+5H9iVEmnJ05lZxM413T6lpVlNOxW5Xd9nyMFO5XfIfbZEz334pUFaccv3gX7kWPzfkoDc+H1XzwbvPb+gkinpcWPNHJMVruy50l7fOF/9wZ+h02x660QM5EwAAAOp11BQ/Pbw32WXR2tG64dtDX3184vyiy/6D9qanbu/WyMSkca9DabJ3d9uG+Vvb+m+a72WjTdNG7tPPJT8PmtrOgk/TZp2W3sor/xvtI+uq16aQD2qsetFzIs+9eyLFro8zXZsCZVmhC3wcTbVpmhY07b7gQrqUEHH87x2MnBdEFisIkaTs8zVu/OvlXHldS67yeKuqrqZj+bYK7u4IYfRdOtpFU0271fhlvpLTOyOLK3QyUVFVU2IV3Nxzl997lEuDj+xLZJnn5m8gU5d1Ebz3TqimnA/qIoRQKqzy7dTYLDz0/Pa9eXWIle3Qq/YLN4+wZKE7PieKbjZo9pxhLhofu9ooNW1DXe4XvyYpjq6RjhqCJgAAANTrqFkad+DIK8dhXVz79Na/t+NcmvRjO9Adtx0abmgy9kJCauqToEFGH8wrKU4JDtFeFP7q9YOVOkeHew4677AlJjvj9mTx1qk7E8pI9cuR16mQ91W56DkhisJ/jsdb9W7Jp2pXoLrtyN0Rqfn56Tdm8Q6OnhKSo2Db+O9apLvXb8mdrGd7xsx73m/nSg8tlbqWXM3xVq6uhmP5tsoyHiRIzFwtlaNkHKvWJuInsZmSSp2sJE07t+Mf3QFD7Tgf2Veed23JkrQRa/qbVDk4/WE5leoSRs5qZaJv0qTNgGXnU7CE/bv35rHMouyHJ6Y1531fIYTruu1pdH29exYAAAAAUfOTlNzbfTzHeVgHPa5Vz76GsX+crDzXRV0wdLzmj22pxeJYeg2xpZieU/rb0swGtj36mGeEJ4lqWo68DoV8oKpFzwkhJdFB0YY9XZU3sn20QIbAbYCvswGXwaCb9pvRlx976XkpIaqWfjtWWB7r79F1YebwP5a2US5yUKeSqz7eqqur9li+LVlpvohweOUDKAyOhhopzS+VV+oKQgghkqSTu+LMRwy0Zte8r6L479Vz7vuuG22tWmUmqlDOB3VR6o6TdwWdDrl58/Ran7LdA7stiRTi0wQAAAAAoD5FTUVB+M7gQpehHXRVCNuqR1+TJ3uOJfybfMPk6SuXUKdYHDaLb6hcfJ1icVlyUZmifF31c37O1tbW1tbWTToExFPSAqGsboV8mEurWPScEOGjk3e1fNuVr4j30QLlBVHb/D0dLIwMjUwadd+eUlhYqiCEEJaJ1xh36YtXTSf6N2+gzER1K7nq45VUU13Vx/KNMTh8NVJaJFaUh8dCMeHwOSqVuoIQQsRPj+xJbDqql4VqzfuWxm6cdtF1zTSnah53rVjOh3VxbXz6eLo0trJ28Zqxda3r65N/PsZkxN8z4f3Frj+vffLFv1gpS/it3U/zo/DFBAAAACBqfoWkmXdrR0iB+M6v9vq6uromP/8vUZ548OCjWvzdThHFJ8UgppapwKD/0einSglJmSnnB394D+7nIEo4e0vNs6NJLZ9XE8Vt8FuR2nP//eT0tNRnIeNNy/eT515ZOP1m495tEgPmnlHe51vHkqs8Xu3HVVdXP6kaODZkpd5NUl4Vohd3U9Qa2+uxquqK0rj9hzOcx3obM2veV5YTGRqbuL93Iz1dXaPWy5+Jo8bZ2466XlxtOdV2O8VgsSi5RK7Ax8kbhRe8eHRFDad+ppFfcdxSO5qmW2x4eye75MVmV5qmG8+9X0oIIYqSR4dm+jga0jRNCxq3H7M5LOcjt4HLX50LOG4waZg1+235Oj6H39w8nh/cRc957dOyz3IpWw2ebHxyyal6cWM6AAAAwH86aspfX952jeq8859HsTExMTExsQ+vL7V/9efe6I/8Vcqg9TkFz5OKPmG62jqsq/4vlKWEXpa287Kq7YpzclG+mGNiY67BJNLX13ccV964Ks+6OMv/fLN1h7Yf2N4pYsqkE2nSOpdc5fFKq6yu3tL4aUxXybGle2OKJAVRO5eeZvmObtGgqk4uubf7RJ7bWE99xkf2ZRgOPP0sbit8YgAAIABJREFU4UlsTExMTNSZyWasZquuXFvvql5tOe/XJS98cOLEzSdpuUX5KRF7Z8+9rt6xhw2mW32L1+VU1juv4nb5aHNcxo+0/fh8yTJJLd/RDcyyj7y5+6Es8cShdFO+inLMX5ywrVf7aZH2i0MTM7Nf/h3onbve23NxRFENXwVIU4MDo+38Orw/ORR1e8VvUSVf4CNXp/0oh0dbg5IluE4AAAAAUfNLkqWHbL/L7z/D28ZAX8nYafD0dsXBf/xd83qf7IaDpnkmT2ioQet77kmtW1Csw7rqn0yacfV8QetfGtc6f3Cdpi/3fDjGxdG1neeoYG13EzVCZK+Cp/565aeNv/cxUtXxXLPNO3bmhKNJqXUsucrj5VRRXT1GaXZcf3Sm+o5OhlrGXgf1Fx5d5c6jKneyoiBsx1mxh3+H9xd6qHpfosLR0iu/5vQFNEuFxdfV1Sh/arOKcj6sS5IdsXVs+yZmhia2PutSO285u64tDzNxvve5wmK/mZ2XJO8dPfmO2+ZDk+y4pMrJkIVh/tZNhy+d2K2FnU3zSXdKFCWxu8e2MefTNN/MbcyumOIqQiLHdkgPRdDhxyJCiDj+yFGR97BmXIoQosi9sijgfquNR5b1sBNw2RoWHabu3jewYOvso6lSUpZ4LGDxvviKz1rLs24ff2rq7aTx7hRyWs0czTm04GhKhU+GqtpW/bzNVc50TfEcfSwSg25mYlwTAAAA/tuooqKiz1JQXRf0/I+TZxzs2j5kesQhT43vpmR0Mrr9s3dbwe15P/e42OXUjZVtNFQIKY1e0qbzxa77Tyzw0MoInuE19fXSiGPdEn9t3PmUy6bbh0c04hBSHD7Zpcf9oSdPzmpJItf26rHXIShqUxv6XQwUxy116RAZcO7n/41M3xTxm/3jWT+N09ix8P4vsxtdiFoomtbU68XaJyF9BG/3EIaNa+qbuin+bMc4P+vehbufB3Wm32+l8O4EhyGy/dFbW3Pflb/q7vg/O0zR2B/1f21lZ7rYLPW4dXemdVlYFW1zjh3XuPM5t+139g40Uzze0KFjiP/fF4caMao82N76DCL8e4pTf+EfcTvacHGJAAAAwPejrokPywF+GdIy2mvOjNa876lkdDK6/TN3WlrQ+MF7dRcfW9JGuRRktZM/M7W9pvZrxKEIoYQx+8+Kuy+b7KbDYum4TgrwkZzdH13Fo9tsq16DtEJ3R726t+cCPbBfw/KbqGWFmSVsXQH3/TFmBt+IJ8vLLJart92T9rpCziSEyIqzSlW1OB9+GDJ0Oi6cpBO0cN+Ld89oVtu2quZtrn6mawZXU7U0qwSjmgAAAPDfxkQXfBGqZr4Th39nJaOT0e2fU+nD/xs84W777XfG2by5O1uanZiVG+bnbM2mCCFEUVZA2RYIZdqEyTPUKH8qVpr/soTvplc+76+qXiNN4fW8qp5rZJn6DDXY8tvv2Y8FQ2aaq8aXJ0Senrr4RXapgqi/TZuy/LRCSkOgXt0XawyOJltSKK74lKhqI79lXlvGrLjWzYvU2DbtKudtllV9sESTIRcXSdl8NXzPBwAAAIiaAAB1Is+7Nr/fqqIxZ3/3fW+GJqaWqcDAdN+DLa3fv3NUGEYIRd4EQybfWD3/2esy0kSVEFKWmZDHNdascn5kplG3EWYz/C8337LJmEnKoybHxusntt/+S696DChfAUdRFHXkUmHT6e8/ivkhtmlzo+LjySWKVh8MhhKK//PcaaZui/6w5ta1bdUeLCFEIUx+VGDoZcbGZQIAAAD/afhiHQA+M0nKkTFDDxku3DvTgSUWvVEmr83kz9xmg7urnlu85W6uVJobsWXxGWa3IQ5Vz4HF0O+x/eLZv3b2NnyXZimtjkvn2N+dMmDRqbjsUnFh0rWNI4YeVB+1aqAZs7ppgRh67t7GiSFxxZVqYFkMXdYrL3D9P8K6tq2Gma5L4i4kGHRvZ4Dv+QAAAABR8yv41+unY2F0gHqTNJODNl7MF4bPa20keI/X6VxOLSZ/buC26nhAw+C+lpqaFn2CLZcdX/3+nEAfoLhmrdq1Mv9wLJJtM+HUlfUu0Ys7WejqGNn7LM8eGXx5ZRsNihDJy4vbtp5JqnQ7rqpV31FmEfvC8yvNdUvRrrNmNy/7hLZVN9O1oiBif5jRiAGNMKgJAAAA/3H1YgZa+as/e3c4Nuj68V66nxx95Vmn+rbd3ePK6UHvjXAAwI+sLOnAIPcZedOvnJlsx61pNRpF4Y2J7dc4nToz0vTLDjZKU/f+4nt3ytWtHflYHQcAAAC+L9/jDLRVrZ9eZ1gYHQAqUDUfvPv8hk6iqMeF8ho3pHht14Xu8tb54h+IDJ1u20M3eiBnAgAAwH9fPYiaFdZPF4b5W9v6b5rvZaNN00bu088lPw+a2s6CT9NmnZbeypMTQog07dyczjYCHs3j6zbssChKSLAwOgBUkSHpZoNmzxnmovGxjzpKTdtQl/vFPxApjq6RjhqCJgAAACBqfg2i5PBk9cYN+e+aIk4JDtFeFP7q9YOVOkeHew4677AlJjvj9mTx1qk7E8oIEUatnhFsuTE2p6gw+9mltb7GLEIIUeE1tKNTwpLEOKsAAAAAAAA/etSsvH46Q8dr/tiWWiyOpdcQW4rpOaW/Lc1sYNujj3lGeJKIEIqpplKcHBuTmFPG4Fm4uOgplxvAwugAAAAAAACImuXBstL66UyevnI9d4rFYbP4hnym8hcuSy4qUxDCcZq3b4HljdkeFtomLfutCM1QTmKJhdEBvkPCMH9rh4Uxok/YM3JKU9uZUaU1bfLPtKa2M2rcpJZ1/dtZsmsJk2kDAAAAouZnwzZtblT8NLlEUYdWa7YYu/n8/ZfZicHDhFtGLwwvIW8WRm+BhdEBvjlZ1vX/DWxlpkHTtIaBjWufgPLHrL9X8lfnAo4bTBpmzSaEiOOW2tG0js/h9PJbKPKDu+g5r31a9jlqUrUaPNn45JJT6bg9AwAAABA1/63q10+vjujZmaA7SYUSBYsn0OOzmQwVimBhdIB6E8yyzo4bsCGvz/7YjOzXLyKOzO9qqlr/07Gk+jBc1SzZ1O0Vv0WVfIHPZEymDQAAAIian0v166dX94dsQcy+8e5mWjyeXsulhcMCl7biYmF0gHqjLC3ykcRhjN/Ppg3YHC1T5+5+w1ppKj9rREmn5/nY69M0rWnS0u/Qmzwly7m1uoetgKZ17AdsjROWfxSIU84u8LbTo2la167nimtZMkKIvDBqU/9murSGXlPvFTdy3oz9FV70NXINTFbeSy+8+6uN/fwHFW7JraI0YZi/ddPhSyd2a2Fn03zSnZKyxGMBi/fFiypF5w9mySaEEE6rmaM5hxYcTZF+uKmiJHb32DbmfJrmm7mN2RVTrCivxW/5VF83FzsrM8eBNR8gJtMGAAAARM3Ph2naf2GH2I1BqVJCCNd129PogGZqhBBCeF3Ppt/xVy6qzjAeeSsjxFuDEK7LgjPRqXlFRUX5r2KDl3TWZxLpy1O/3W+7YLAFC+cU4Btjm7Vtybm7YNLKQ6FRz3PE775DEkYt9x1zsdGKsMyCgvR//hhl10D5CSRODjqlNv1qWm7in51jlkw5kS4jhJRGr+ox+rLDhqis/MyIZfqHh4879UpWFDav/6rsESFpuS+vTpYePpMurWWbqiyNECJKDXngtOlmbPz9QHd1ycuL27aeSao4nlh5lmxC1JqMXebxdM3/wore/4qsOHxe77mPux56lpv77Ej3+Pm9590pUhBCRKmhD1v8fjUq7uHFoUnLp9ZwgASTaQMAAACi5mf079dPx8LoAPUGpekZeO3gUPU7v/l7OprrWHpMP/5CRAgRxuw5mtflf4t9LLkqKhw9e1cHbYby/avtvWhCGz1VlsB1kK8gJTxFTIgwdvfh7M4rZnsaqzG4Fr5zp5lFHIjMiD0YIvNZPMaFx2Dpe8yY68qpZZOqLK2QEMLU9prarxGHIoQiRL3tnrTXQZ3pCjtXniWbEMLQ6bhwkk7Qwn0v3j2jKYzZf1bcfdlkNx0WS8d1UoCP5Oz+6FJCCEvPZ9IvZmxC1Cw8PHRqOMBCQjCZNgAAAPw31JcHGyk1bUO1f7M/R9eIg9MJUE/e0NyG3rO3e88mClH6ncDRvUcPtXS5+qtObnKRVmujyu90poaBcp5pQrG5qrIMiYIQWXZiVm6Yn7M1myKEEEVZAWVbUJTzskSzXfnyRpS6iZVmLT/CpFWVJpRpEybPUDnfdQ0qz5JNCCFEtZHfMq8tY1Zc6+b1ppb8lyV8N73yB1NV9RppCq/nSYg2YahrqyuTKsViM6Siag9QKCOaDEymDQAAAIiaAAA1Zk41wzajJrmu8otMLSP6Wma8vPh0EWlSi1mCmFqmAgPTfQ+2tOa+e1EYdrtBSVqBlBBVQkhZ7qtCmYWyHgabIZNIlTezyorzhHL+x0sjwjBCKPLReyHYps2Nio8nlyhacT/YluL/PHeaqduiP6zLC2XyjdXzn70uUx5gWWZCHtdYk1WXAyTkzWTaXphMGwAAAL5v+N4cAD6zshdHFi/ff+NxWoG4rDDp6rbf7sht2liwCbfZiH78i3NWXEgSyuWi13HhMbnV3yTKbTayf4PTs1b9lSyUE5kw8+GVC5HZqvaDOivObb2SKSNE/OzY79eKy4ca1SzcjNJCbmVICBEnBm2+ki+vRWmVKq9mWqDqZ8lmWQxd1isvcP0/wvJaBndXPbd4y91cqTQ3YsviM8xuQxw4dTlAGcFk2gAAAICoCQBQFYa6HjfxwISOTYx1tI0c/U7wfj2wf6gpkxCuy4LTgR1iZ7TU09AwbO63K7aohtU2uc4Lgnd6PJrjaqBBa5q7jggMz5VStNvKA+OLF7k7/OTeeVqMc/vye2kJy3L4bxPZa93tnH7qNCPO0V2XWYvSKlVZzbRANcySTdGus2Y3f/u0ZgO3VccDGgb3tdTUtOgTbLns+Oo2NFWXAySYTBsAAAD+K6iioqLPUlB8fLyNjQ06FAD+gxSFNya2X+N06sxI0y872ChN3fuL790pV7d2xCRnAAAAUL/UNfHhFi0AgI+heG3XhTYq5Hzx+0AYOt22h/Y3RM4EAACA7x6iJgBALcLmv5wlu7bVYDJtAAAA+I/As5oAAAAAAACAqAkAAAAAAACImgAAAAAAAICoCQAAAAAAAICoCQAAAAAAAIiaAAAAAAAAgKgJAAAAAAAAgKgJAAAAAAAAiJofEIb5WzssjBHhJAEAAAAAACBq1jlT3ls7qL2DCU3ThiNuluCcQB0uko9cPGXPNrfToOl2B1/J0YcAAAAAAD9W1CQcY7fhAbs2ddFg4oRAHS+Smi4eacrRmVvFzTRY6D4AAAAAgB8vanKbDBg/zMfdRqtyIpDl3Frdw1ZA0zr2A7bGCRUV/73woq+Ra2CylBBCiPDurzb28x+ICCHStHNzOtsIeDSPr9uww6IoIZFlhS7wcTTVpmla0LT7ggvpUkIIkRdEbuxrr0vz9W195q8bYmM7M6qUEEKIOOXsAm87PZqmde16rriWJcOlUj8vkpounsxz8zeQqcu6CPBEMgAAAADADxg1qydODjqlNv1qWm7in51jlkw5kV7LxCeMWj0j2HJjbE5RYfazS2t9jVmEEHXbkbsjUvPz02/M4h0cPSUkR6EoujNv4P+KxoSm5aRen844cU6ZPwkpjV7VY/Rlhw1RWfmZEcv0Dw8fd+oVwub3RZ53bcmStBFr+ptgrBwAAAAAAFHzAwxt70UT2uipsgSug3wFKeEp4trtRzHVVIqTY2MSc8oYPAsXFz0WYQjcBvg6G3AZDLppvxl9+bGXnpeWxhy6qPBdONKRx2AJ2k6e486llEk1dvfh7M4rZnsaqzG4Fr5zp5lFHIgsxMXyHVEU/716zn3fdaOtVSn0BgAAAAAAouYHmBoGfOWYFMXmqspEEkXt9uM4zdu3wPLGbA8LbZOW/VaEZkiJvCBqm7+ng4WRoZFJo+7bUwoLSxXS/JfFGlY65TVw9E35DEIIIdLsxKzcc37O1tbW1tbWTToExFPSAiGGNb8fpbEbp110XTPNiYu+AAAAAAD4JmHu+24+xWAzZBKpMoHKivOEcr4yQWu2GLv5/NjNZVlRO/28Ry9se2/SZb8VqePO3b/gwGeK7s916ZFBCJNv3KDgebaUWKsSoih9lZIvsyGEEKaWqcDAdN+DLa2RVL5LspzI0NjEmN6NjlJEIS0VScg4e9vrxyN3tWuAzgEAAAAA+BrqxaimQioWiURiqYLI3vxQS2oWbkZpIbcyJISIE4M2X8lXLmkhenYm6E5SoUTB4gn0+GwmQ0UhyhdzTGzMNZhE+vr6juMpYkIIx35AJxK8fHd0kVySdev3NbfK5x3iNhvZv8HpWav+ShbKiUyY+fDKhchsDGrWz4ukqtcZhgNPP0t4EhsTExMTdWayGavZqivX1ruqoxcBAAAAAH6gqCmKXuwoEJj5/pmTd6qnuUBgP/deaS13ZVkO/20ie627ndNPnWbEObrrKkdp5QUx+8a7m2nxeHotlxYOC1zaSsdp+nLPh2NcHF3beY4K1nY3USOEUDz31Yemc7d2NNQy+XmtpEdXA1WmCkUI4TovCN7p8WiOq4EGrWnuOiIwPFeKa6VeXiRVv67C0dLTLyegWSosvq6uBp7aBAAAAAD4aqiioqLPUlB8fLyNjc133BOK7JPeDuu63bk53hxTlgIAAAAAAPybxPeDrzgoSb91MeqVWKEQp4WuXR5p/EsHQ+RMAAAAAACAf+kHD1aKkoe7hvbunyaSM3Sc+6/fP8FGFdcEAAAAAAAAoua/odrI/8RDf1wGAAAAAAAAn5MKugAAAAAAAAAQNQEAAAAAAABREwAAAAAAABA1AQAAAAAAABA1AQAAAAAAAFETAAAAAAAAEDUBAAAAAAAAEDUBAAAAAAAAURMAAAAAAAAQNQEAAAAAAAAQNQEAAAAAAABREwAAAAAAABA1AQAAAAAAAFGzXhKG+Vs7LIwR/ety/pnW1HZGVOkPeX6F9xe7/rz2ifiTCyhL+K3dT/OjhHirAAAAAADA9xU1hffWDmrvYELTtOGImyWfvXhV057z5/cwYdW8lfTFZnfb8WFfIFJ9uZI/Sv7qXMBxg0nDrNmf3ntWgycbn1xyKl32Ta8RRdYxD/od/aHXiyv28J3XMdvHd3My4dE0LWjiOfVIglBRvnNxzK4xbcx4NK1h2W7ioYQ33zpU9/qblP1sczsNmm538JW88tn8whctAAAAAACi5r/HMXYbHrBrUxcN5pconanbZtBAN92ay5Zl3grOculpx/3s1X+5kj8eclODA6Pt/DoI/s1ZVtFpP8rh0dagZMm3vkzUXbY8zMzKysrKykr+o22Dij1sq/I6W9t7xcmoJ89igidrnxrbe22cmBBCiu7M6TX7wc874zJexe5oGzW9z8r7pTW9Xt55KUdnbhU302BVcza/7EULAAAAAICo+a9xmwwYP8zH3Uar8sCjLOfW6h62AprWsR+wNe7NKNU7wjB/a1v/TfO9bLRp2sh9+rnk50FT21nwadqs09JbeXJC3ruBVhjmb93Ub/lUXzcXOyszx4HvFSjPvXsixa6PM12bAmVZoQt8HE21aZoWNO2+4EK6lBBx/O8djJwXRBYrCJGk7PM1bvzr5Vx5XUsmRJxydoG3nR5N07p2PVdcy5JVXV1Nx/KWPOv28aem3k4aVK37Spp2bk5nGwGP5vF1G3ZYFCUkhFA8Rx+LxKCbmbJvfJ1QKiy1cmzWuwv3TQ9r6HScHzCuW4tGRnoWrsPnDTNJu/W4UEFI6dPgS/mt5kzrZNpA3bTj1Hk/ZRzeHSOs/vXySHlu/gYydVmXD2P627NZ00ULAAAAAAD1fFogcXLQKbXpV9NyE//sHLNkyomq7uIUpwSHaC8Kf/X6wUqdo8M9B5132BKTnXF7snjr1J0JZRU2FqWGPmzx+9WouIcXhyYtn/qmQEXhP8fjrXq35FO1K1DdduTuiNT8/PQbs3gHR08JyVGwbfx3LdLd67fkTtazPWPmPe+3c6WHlkpdSy6NXtVj9GWHDVFZ+ZkRy/QPDx936pWsqupqOJb3DjY5PFm9cUO+Sq37Shi1ekaw5cbYnKLC7GeX1voaswghRIXX0I5OCUsSf+OrQRg5q5WJvkmTNgOWnU9525gPevgN6et/wrL1frKmKUIIUSgIRZX/M0VRktfRj/JkNbxO5HnXlixJG7Gmv8mHQ5ZV1gUAAAAAAN9b1GRoey+a0EZPlSVwHeQrSAlPqSLtMHS85o9tqcXiWHoNsaWYnlP629LMBrY9+phnhCdVnFOIpecz6RczNiFqFh4eOm8LLIkOijbs6aqjUqsCGQK3Ab7OBlwGg27ab0Zffuyl56WEqFr67Vhheay/R9eFmcP/WNpGOZZYp5KFsbsPZ3deMdvTWI3BtfCdO80s4kBkYdXVVXss78iKs0pVtTgqte8riqmmUpwcG5OYU8bgWbi46CmH7BhcTdXSrJJvOapJqTtO3hV0OuTmzdNrfcp2D+y2JLJ8APKDHlYGQmHM//ktSOv929RmaoQQtUZeHei7/9t4Lb20NOP6plXXJKS0oFRe7etEUfz36jn3fdeNtlatECkr1wUAAAAAAN9h1GRqGPCV40oUm6sqE0kUVWzD09dgEEIIxeKwWXxD5Q4Ui8uSi8oqbs9Q11ZXHjLFYjOk5QUKH528q+XbzoBRuwLlBVHb/D0dLIwMjUwadd+eUlhYqiCEEJaJ1xh36YtXTSf6N29QftNqnUqWZidm5Z7zc7a2tra2tm7SISCekhYIJdVUV/WxvH+wHE22pFAsr31fcZzm7VtgeWO2h4W2Sct+K0IzpIQQQuTiIimbr/ZNrxWujU8fT5fGVtYuXjO2rnV9ffLPx6WVe5gQUhq/a7DvZu2VZ9d7aqsQQgjFa7vmeEDjyyPt9Yx+mvvcraexGi1owKj29dLYjdMuuq6Z5lTp6dpKdQEAAAAAwHcZNb8OUcLZW2qeHU1q+dSdKG6D34rUnvvvJ6enpT4LGW9avp8898rC6Tcb926TGDD3zCvZJ5TM1DIVGPQ/Gv1UKSEpM+X8YO3HVVf3cWzT5kbFT5NLFHW4HDRbjN18/v7L7MTgYcItoxeGlxBCFMLkRwWGLczY9eR8UQwWi5JL5IrKPSxK2DO06wr5nHN7hlqpvTsqDZfxe8Je5BTmJt9eZfcyz7ijk6ZKda/LciJDYxP3926kp6tr1Hr5M3HUOHvbUdeL63w2AQAAAAAQNb8xhVQsEonEUgWRvfnhKypLCb0sbedlVdskJRflizkmNuYaTCJ9fX3HceWNq/Ksi7P8zzdbd2j7ge2dIqZMOpEmrXPJ3GYj+zc4PWvVX8lCOZEJMx9euRCZLa2yutpg6Ll7GyeGxBXXPnQ/OxN0J6lQomDxBHp8NpOhQhFCSuIuJBh0b2fwDadalRc+OHHi5pO03KL8lIi9s+deV+/Yw4ZT4dyJEw+M6Dwv1//4H0MtVMQikahMpryQxKlhtx+m5RVlJ1xaP3ZefPv5I2zY1b7OMBx4+lnCk9iYmJiYqDOTzVjNVl25tt5VveLZ/LYXLQAAAAAAoubHA070YkeBwMz3z5y8Uz3NBQL7ufdKv17t0oyr5wta/9KYU9sduE7Tl3s+HOPi6NrOc1SwtruJGiGyV8FTf73y08bf+xip6niu2eYdO3PC0aTUOpZMuM4Lgnd6PJrjaqBBa5q7jggMz5VyqqiullSt+o4yi9gXnl/bFCQviNk33t1Mi8fTa7m0cFjg0lZcoiiI2B9mNGJAo286qCnJjtg6tn0TM0MTW591qZ23nF3Xlkd9eO7KUk5tPJdV/HdAB3NdgUAgEAjcA5MkhBAiST09p2tTU0OLVr+GWq64sKtX+S2wVb+uwtHS0y8noFkqLL6uroYqVeE6+bYXLQAAAABAfUcVFRV9loLi4+NtbGy+vw6QZxzs2j5kesQhT43vpuTaUxTemNh+jdOpMyNNP3FMUpq69xffu1Oubu1Y/6Zd/Zo9XB/OJgAAAADAt1PXxPfDrz8vLaO95sxozfueSq7DNwm8tutCGxVyPn3wmqHTbXtof8N6ubzH1+zh+nA2AQAAAAC+Hz/8qCYAAAAAAAB87sSHGWgBAAAAAADgM0PUBAAAAAAAAERNAAAAAAAAQNQEAAAAAAAARE0AAAAAAAAARE0AAAAAAABA1AQAAAAAAABETQAAAAAAAABETQAAAAAAAEDUBAAAAAAAAERNAAAAAAAAAERNAAAAAAAAQNQEAAAAAAAARE0AAAAAAABA1KyfhGH+1g4LY0Q4SQAAAAAAAIiadc6U99YOau9gQtO04YibJV+3bumLze6248OEuBLqO3l+xMb+Tvo0TRu2GBZ4v0hR622kqX+Oa9fUSJOmaVrPttvMP5+XojsBAAAAAP77UZNwjN2GB+za1EWD+W9KkUnkdd8n81ZwlktPOy6uhPpNkXdlev/VecPPp2QlneqTvLTf3FuFilpuo6LRbNCKwzfjXqS8iD09gXtk3NigdBm6FAAAAADgvx41uU0GjB/m426jxaocBXNure5hK6BpHfsBW+OElUayhGH+1k2HL53YrYWdTfNJd0qIOOXsAm87PZqmde16rriWJSOEyAujNvVvpkvz9W195q8bYmM7M6p8WEuee/dEil0fZ1qadm5OZxsBj+bxdRt2WBSFUc56puDujhBG36WjXTTVtFuNX+YrOb0zsriW26jwGrdxc7Qy0tHUMbK0MqK5mrQqhS4FAAAAAPivR83qiZODTqlNv5qWm/hn55glU05UNRglSg154LTpZmz8/UB3lehVPUZfdtgQlZWfGbFM//DwcadeyYrC5g1YnTfyr7Sc1KtTFEfPpEvf7Koo/Od4vFXvlvzSqNUzgi03xuYUFWY/u7TW15iFK6NeKct4kCAxc7U2RSV+AAAgAElEQVTkEEII4Vi1NhE/ic2U1H4b4d8znQ10+FoN+//T69B2bx1MhwUAAAAA8ANHTYa296IJbfRUWQLXQb6ClPAUceVtmNpeU/s14lCEUMLY3YezO6+Y7WmsxuBa+M6dZhZxIDIj9mCI/JfFo514DJZe+2nz3N/dLFsSHRRt2NNVR4ViqqkUJ8fGJOaUMXgWLi56iJr1i6w0X0Q4PDXlYCSDo6FGSvNL5bXfhuO06K/7D6Ku7Bgu3TUu4HaBAn0KAAAAAPDjRk2mhgFf+fwmxeaqykSSKhICk2eowVD+KM1OzMo95+dsbW1tbW3dpENAPCUtKMp5WaxhpVNeDNfQjP/miVDho5N3tXzbGTAIx2nevgWWN2Z7WGibtOy3IjRDiiujfn3nwOGrkdIisaI8VBaKCYfPUan9NhSLFugbW7UcELDhF+Gxrf+UoE8BAAAAAH7cqFkrFHnz4B1Ty1Rg0P9o9FOlhKTMlPODzbSNGxQ8z1aGR0VpRkp++V24ooSzt9Q8O5qwCCEqmi3Gbj5//2V2YvAw4ZbRC8MRReoVVQPHhqzUu0nKZ2xFL+6mqDW2rzD2XJttCCEKQgie1AQAAAAA+BGipkIqFolEYqmCyN788Em4zUb2b3B61qq/koVyIhNmPrxyITJb1X5QFyp4+e7oIrnk9Y3fVt0sn1yoLCX0srSdlxWbECJ6diboTlKhRMHiCfT4bCZDBWGkftH4aUxXybGle2OKJAVRO5eeZvmObtGAEKIourd14f9C0qXVbyPPvbt378V7L14XFmTEnAyYdULSfoADphwGAAAAAPjPR01R9GJHgcDM98+cvFM9zQUC+7n3PnHhQ67zguCdHo/muBpo0JrmriMCw3OlFO228tBM9W0dDbVM2q2X9/ExZBJCiDTj6vmC1r805hBCiLwgZt94dzMtHk+v5dLCYYFLWyGK1C+UZsf1R2eq7+hkqGXsdVB/4dFV7jyKEKIoeXRsy57br6U1bEOVJZ6c84uzlZGxtce8yGYrgwO7Y1ogAAAAAIAv/Cd8UVHRZykoPj7exsam3h+vMHJK8+GsA/+sMQzq2j5kesQhTw1cBAAAAAAAAJ838f2oozvSMtprzozWPFwxAAAAAAAAnx3zBz1uVTPficNx+gEAAAAAABA1/z1ui42PHuK0AwAAAAAAfEmYHgUAAAAAAAAQNQEAAAAAAABREwAAAAAAABA1AQAAAAAAABA1AQAAAAAAAFETAAAAAAAAEDUBAAAAAAAAEDUBAAAAAAAAURMAAAAAAAAQNQEAAAAAAAAQNQEAAAAAAOALY6ILACpRMJ/+SQiRWvclhEJ3AAAAAAAgagL863dF/DG1y6MJISJCpNb90CEAAAAAAHVVj2+gFYb5WzssjBH9GCdCGDmlqe3MqFJckvXJjh07R48eLZFIarl9cXFxPTyKyq2qn+0EAAAAAETNz5yy7q0d1N7BhKZpwxE3S+p7JIyYaNN0BiLhf5rUpl+q4+rDRd391l0yNjZ++fJlLXe8ePFiPTycyq2qn+0EAAAAAETNz4xj7DY8YNemLhrf9+28MokcF9R/BPWgrGmOXmdCKFNT06SkpNrsIxaLr1y5IhaLv2ZDxWLxmDFj/N4YM2ZMhQZUbtU3aScAAAAAIGp+fdwmA8YP83G30WJVTm85t1b3sBXQtI79gK1xQkXFfxeG+Vvb+m+a72WjTdNG7tPPJT8PmtrOgk/TZp2W3sorj37ilLMLvO30aJrWteu54lqWjBBZVugCH0dTbZqmBU27L7iQLi0vranf8qm+bi52VmaOAyvXWHTZf9De9NTt3RqZmDTudShNJgzzt246fOnEbi3sbJpPulNSVV3VFisvjNrUv5kuraHX1HvFjRwZLsf6IykpydzcnBBibm5ey6j5/PlzsVj8/Pnzr9nO58+fW1hY7H7DwsKiQgMqt+qbtBMAAAAAEDXrE3Fy0Cm16VfTchP/7ByzZMqJ9CrSmDglOER7Ufir1w9W6hwd7jnovMOWmOyM25PFW6fuTCgjhJRGr+ox+rLDhqis/MyIZfqHh4879UpGiLrtyN0Rqfn56Tdm8Q6OnhKSoyCEEFFq6MMWv1+Nint4cWjS8qkVa6Q7bjs03NBk7IWE1NQnQYOMGIQQUWrIA6dNN2Pj7we6q1RdV9XFFoXN678qe0RIWu7Lq5Olh8+kS3E91hspKSlvo2ZKSkptdomPj1dTU4uPj/+a7YyPj2/cuPHbXxs3blyhAZVb9U3aCQAAAACImvUJQ9t70YQ2eqosgesgX0FKeEoV9/wxdLzmj22pxeJYeg2xpZieU/rb0swGtj36mGeEJ4kIEcbuPpzdecVsT2M1BtfCd+40s4gDkYUMgdsAX2cDLoNBN+03oy8/9tLzUkIIYen5TPrFjE2ImoWHh07VNVbA1Paa2q8RhyKEqrquaooVxh4MkfksHuPCY7D0PWbMdeXgcqwnJBJJenq6sbExIcTY2Dg9Pb02MwM9efLEw8PjyZMnX7OpT548sbGxefurjY1NhQZUbtU3aScAAAAAIGrWJ0wNA77y+U2KzVWViSSKKrbh6WswCCGEYnHYLL6hcgeKxWXJRWUKQqTZiVm55/ycra2tra2tm3QIiKekBUJJQdQ2f08HCyNDI5NG3benFBaWKgghhKGura7sE4rFZkirrLFSAwyVDaiuLlnVxUrzX5ZoWusp7xqm1E2sNLHyTD3x8uVLQ0NDFotFCGGxWIaGhh+dGUgsFqekpHTu3DklJeWrPQaprNTKyurtK1ZWVu83oHKrvkk7AQAAAOAH9N9PN0wtU4GB6b4HW1pz370oilncbUXquHP3LzjwmaL7c116ZNS+RIooFB/+TtVUFxGGVd0wvnGDkrQCKSGqhJCy3FeFMgtckPVCUlKSqanp21+VMwNZWNR0ep4/f25qatqgQQNTU9Pnz583bdr0K7RTWSmbzX77CpvNfr8BlVv1TdoJAAAAAD+gejGqqZCKRSKRWKogsjc/fD7cZiP7Nzg9a9VfyUI5kQkzH165EJktFeWLOSY25hpMIn19fcfxlFoP7zBofU7B86Qiea3rqm62H479oM6Kc1uvZMoIET879vu1YkxgW8M1wnx6jPn0GCGKr1DZ2zmBlGozM9DbZyYrPy355VR4UFPp/QZUbtU3aScAAAAAIGp+E6LoxY4CgZnvnzl5p3qaCwT2c+99zmUruc4Lgnd6PJrjaqBBa5q7jggMz5VynKYv93w4xsXRtZ3nqGBtdxO12pbGbjhommfyhIYatL7nnlRZLeqqriSKdlt5YHzxIneHn9w7T4txbq/HwvVYDWb8MbVLo9QujWI+/fMrVPd2TqC3UfOjMwO9fWay8tOSX06FBzWV3m9A5VZ9k3YCAAAAwA+IKioq+iwFxcfHV/6rF+AzRc2japdHE0J+f9Hqdq7pl66OwWAEBgYqn9UkhEgkkvHjx8tkNa1Gw2Qy/+///o/NZovF4okTJ0qlX2M64beVvv/i+w2o3Krq2tmtW7fevXvjSgMAAACAz5X4EDXhu6BgPj1OCJFa9yFvH40FAAAAAID6GjUx6Sl8FyipdV/0AgAAAADA90IFXQAAAAAAAACImgAAAAAAAICoCQAAAAAAAIiaAAAAAAAAAIiaAAAAAAAAgKgJAAAAAAAAiJoAAAAAAAAAiJoAAAAAAACAqAkAAAAAAACImgAAAAAAAAD/3959xzV1rnEAf0MSIMGwkQ0iAgICKk4Ur60KTnBWK617oK2zal3Vq9ZxO6xa666jVeuse++FCxyAAyeCgOwRCAEy7h9BCOEccgJh+vv+cT/Vm/OO513nSU4iUk0AAAAAAABAqgkAAAAAAABINQGgRsg5L/YXPtpBiByxAAAAAIA6hYMQANTX1RuzX//ieH1CxHy+xHUoAgIAAAAAdUcd/lRTFBbq6vNDpLg+h1d0f7qH5+yIfEw0qE4SiRRBAAAAAACkmqr52IOfQz7zsRcIBDajr+fVThPuTnHzmIWUEOpXhuk2NL7lqnVv2z8qdNfowsLCQkQPAAAAABp8qkl4dp1GLdu2tqdRlR7nlRbJqqNx1VQsNERyeQ1/Z5L1qNDjXm6zqKhoxZ8jIyOZXHb58mUMFgAAAAA0+FST7/7l5JFB/m6m3PJ5XvqNVQM8LQQCc68vN0aLyt3Hi8JCXT1GLZnSu20LtzZTb+WRgrgTC/u1sBQIBI1bDFx+JVVKCJHlRKwd5t1YYGzlGbTgl6/dyj3RKrwYGrIzMX5zbxd7++aD9iRImRQrCgt19Rjz44zgTr4tnB1bDi9p3sfqjCw9+i2/lo4nGz8ht27dquEaY2Nju3Tposgws7Ozt2zZUlBQUPElBQUFx48fV/syAAAAAID6nmpWcFP87vAR/e8uJ2S8ORAY+d/phxIp8jZx/JlHrdZej4p5uMFf5/HKAeMv+qyOSM1KvrvUau+oSUc+SIVh879clTn2XEJ6/OXp8n3HEyWqRQi6b9ozysZ+4umX8fHPD4fYspkUSwgRx59/0nbd5YjoJ2dHxP44Q9E8Ydj8YSvTRp9JyHh/eZpkL0V10DCJxeK9e/eKxTX67eK4uLiOHTsSQpKSks6ePZufn//s2bOKL3n27JlYLFb7MgAAAACAhptqss36Lfq2s6Uu18IvJNgi7nYcxQcxHLO+M4a68FiEsERR2/emBS7/PsBOn813Cp430/Hu3/eTonafkfVfPL6VIZtr+dnM+f58RlWrKzaHEMK1DJra31GPEH2nbt3MFc0TRe0+Iw1aPMHXkM216jZrnh8Pk+wT8fDhQ7FY/PDhwxqrsaioKDEx0c7Oztvb+8aNGzdv3gwMDIyKiqr4qqioKAsLC7UvAwAAAACoirr9j51wjKyNFS1k6fF1pUlFFN+E4xjaGLEV/ylJe5OaETamtaseixBC5IXZLM9sYfr7XKOu5sXF8G0cjRn1WV2xIqkZYRuYGShydRZXjy0RF8kJkWa9zzPpaql4FphlYO9sgn9P5hMRHh7u7e0dHh6u+JixBrx//97GxobL5Xp5ef3+++8BAQH+/v6rV69WeVlGRoapqWnJHyMjI4cPH753714MGQAAAABUH5363wUWYX1MD00dLKyH7Xv8QuFlbHLcqa8czewaZb9OUzzGKs9PisuS0hRT9idd1BRry6ZJUY3tGuUlZBc/NVuY8SEHX9asj5KTk0NDQ8eMGRMaGpqcnKz29YpHUkeMGKF4PLVmGhkbG+vg4EAIcXd35/F4PXv2tLa2JoQkJSUpd2Tp0qUlXVD8X61atVJ5GQAAAABAA0w15ZICsVhcIJET6cf/qBS+99hhjY7OWXnunUhGpKLkJ5dO30/T9QrpyTr24/bHQllRyrXfVl4XUZTOFljxsl/HCmWMi6VLIHleIYHykxsvJUsJKXi1f92VXPyArVbmCOfFfs6L/YTUxE+8yuXy7du3Dxo0qOR/1f607MOHD93d3U1NTd3d3WvsGdrY2NgmTZoQQvT09CZMmGBkZEQI8fb2fvz4sXJHrKysSrrw+PFjb29vlZcBAAAAADTIVFP8eHFLCwvH4APpmUcGNrGw8Jr3oJL/wCW/9cJjW7s9netnbSQwaeI3esPtDAlL0GnFntkGm7rbmNp3/VU2JMiG4olWvWYhMwPefdvMSGAVsCNeyqBYuiawBJ1W/D05d5G/Twf/wJmRrT+z5GKWVRknZr/+hXH6F8ZxXhyogeouXrxICOnevXvJ/yr+pgLh4eFt2rQhhLRp0yY8PLxmwhIXF6dINRWpo+I/vLy8oqOjlTsye/bskv+Ojo728vJSeRkAAAAAgNaxhEKhVgqKiYlxc3Or8/0V3Z/eZhT37/CfffFzPfUq1dynf3E8IWTd2/Y3MxyquzpdXd0lS5ZYWloq/picnLx48eLCwsIKLuFyuWvXrtXX1xeLxdOmTSsqKqqBsLDZ7A0bNnC5Zd7NKCgomDp1qqIBJR0p6QKXy123bp2enp7yywghvXv3Hjx4MGYaAAAAAGgr40OqCfWCnPPiICFE4jqElHyJFgAAAAAA6mqqiZ9HhXqBJXH9AlEAAAAAAKgvPrVUk992zdMnGHYAAAAAAIDqpIMQAAAAAAAAAFJNAAAAAAAAQKoJAAAAAAAASDUBAAAAAAAAkGoCAAAAAAAAUk0AAAAAAABAqgkAAAAAAACAVBMAAAAAAACQagIAAAAAAABSTQAAAAAAAACkmgAAAAAAAIBUEwAAAAAAAJBqAgAAAAAAAFJNAAAAAAAAAKSaAAAAAAAAgFQTAAAAAAAAkGoCAAAAAAAAVAZHi2WlpKQgoAAAAAAAAIBPNQEAAAAAAACpJgAAAAAAACDVBAAAAAAAAKSaAAAAAAAAAEg1AQAAAAAAAKkmAAAAAAAAINUEAAAAAAAAaEipZv6jxf5dljwW19/mFbz559vPvVyatRi4450E8w4AAAAAAJBqVl7h81+7On/k0S5g+JzN1xIK6meo8h8s8POnzid17XrPmNHLlkt/sejxxnWRXXY8ehn972hHDuYdAAAAAAAg1awa3dar78S8eB4VcW7bTL/MnWP7Tvk3Udqwosg2bz9wQDtzdgU5d3p8npmHPY+FKQcAAAAAAEg1tYHF5rDZXL6xvXfPb9fvnGZx5efNUWJCSGHC+VVfd/Vydnb27Dp6za10KSGi+7P9Ok9ZNTckqE+Pzn69pv71TCQvW5hc9OyfWf3auDo7u/r2m7XnaZ686PXGnq2+PpEmU7xAdG9uR79590Si+7P9/jNn68qv/Jo7O7cM+u+F+NhTiwa2dXV29v3i17tZxa+maYP/9N8WjewX0LW9b7dJu56L5ITkXp8zeV9y4l9fdWzdutOYfz+USZZLHqClvFaWfnpi75n38yIXBbTp8MWmVwXluoB5CAAAAAAASDWrQM8psLd9+p37HyTip+tGf3fdc8n5py+iz8y2PDJ1zpkUKSGkIOnK81YrD5+6cGX/2Ny1k1ZH5itfnxe+Yuzyl9023ImJubOp+6uV41bcK3DsO8w+es+1VCkhhAgf7L3MCRzqxSeEFLw/e9nku9PRTy7NMzs6dejkCx4rrzyLPDG+YOeiPW+KCCG0bUi8FtPqx8Pnr17fNyT+t0Unk6WkUZefNgyztBmx+/aDB7e2D7Si/QSz/LU6Zr03nf6ljYH3svPhdw6EWkeW60Iukk0AAAAAAECqWQVsQysBS5SWl/ts77/pn8379j/WemyeQ8+pE+0eHnwkJIToGH8+vq8DlxCufe8JXUTnjr1U+nak6OnB84Xdvx/fzpTDMW07bm5g0fmDTwptAkNcXvxzOVlK5Fn399zg9x3iziOEELZpj+kjWhlz9B0DBruxuF0nDHBrxDZw6x1knxweLyYkn64NHIvAcYH2uoToOXTxN30f8V6Dr5equ5ayC/mYiQAAAAAA0IDU+C/USHM+COV8cwN5+ru0rPvTe/jpsQghRF4oZLnliGUmhCOwMuJ8TEut+XlvsqWlGbE0OzHfqJ2FruJPuuZNjfNvZUvYlp9/1eKnPy8kDu73ZPdds4EzXfQIERHCETQWsAkhhMXR1+UaWhkqyuXyOLKCIjkhEro2sPkmfMXHliyOLlsq1uQnY9VdS90FzEQAAAAAAECqWWmF7y6ciTfr0NZKX2pnbmm37uLKNnyl/1t0nxRlvE0Wy1vosQgpSHmTw7c2YhPy8QFTtpENL/ttWiFx4RJCCtPeZPFsjDhER9Dla99la04+aBT20HrID05chp03oWkDHRaRV/lRV5ouAAAAAAAANBw18QCtXCqRSovysxKiz2+YOvK3lM9mT/TS53kM729wZunvV+JFMiIVpcbcuPgwQ0oIkeddX/Pn/UypNCv8z19vNAoIbqZfWhTfY1AP3Ys/74jIkkqzHuz4+Tyn+xBPHiEso/ZfdUzd9sNPjxyH9nRgmrjRtoE6R2xkwRPGvs+VVS0adF0AAAAAAABAqslc4YOZHdxcm7fw7TH21zDjUdtPrR9owyaE5zVj1y9dYpb383Fxdmvbd9qOiCwJIYTo2fXrJVzdx6e57+hTjnM2fuej/IkjMWg3f9ucJmcntHN1bTv+rMPsbQvaNWIRQliGbUI+Y71Obz48wIbNuGl0baCk6zRo4n/ez+vg4uw1dF8V/rkWui4AAAAAAAA0FCyhUKiVgmJiYkxMTKpaiuj+7O7fm287872HnqaXSmK39Ot/PfTCX8EWOhhXAAAAAAAALcrMzHRzc2P++oaSlcmFEbv2pPmP/Y858kwAAAAAAIBa1hASM1naia+9W4684jNvjr8xHkUFAAAAAACobXXsAVoAAAAAAACoez7VB2gBAAAAAACgzkCqCQAAAAAAAEg1AQAAAAAAAKkmAAAAAAAAINUEAAAAAAAAqDyOFstq3LgxAgoAAAAAANDwZGZmavR6fKoJAAAAAAAAWoZUEwAAAAAAAJBqAgAAAAAAAFJNAAAAAAAAQKoJAAAAAAAAgFQTAAAAAAAAkGoCAAAAAAAAUk0AAAAAAAAApJoAAAAAAACAVBMAAAAAAACQagIAAAAAAAAg1QQAAAAAAACkmgAAAAAAAIBUs4GRZ99e1qu5jbVL742vixAOUEMUFurq80OkGJGABjzL70/38JwdkV+P1osofKaH5yxtNBkACCHil9u/9rE0MrTs+sebotpagDW5rmv5cK8/uy42W/jEUk3Rg59DPvOxFwgENqOv51WmhLzw37fE9T/x+uXpSc7cCvfdxwtbt136pKCaO3R3iptHLa/hSve05MKql1BrE6qa469Sfv0NFCGSt+v9PSeHiernFG2oE0zT/tZWQKo+giqR1HUYuGDBAHtufRosxqMjz4vcPLl3K3tDgUBg4R4w45+XInnxS2RZd9cMa2UlEAhs2o7c8FAor/BklMQfmNTVw9ZEIBAILD17zz7wmqpz1GXKU/d3E5SyGnE1l/G1RC56ujP0P02NBQKBqcvnU/e8zCfytAPdBRR6HEqXMi5EBV2gKgggg9uJytdLCCHy3Ohd33ZzNRMIBBauAUsfiCoIlLbK0cYOKYr4deWD7kcTspOvftOUW1s7ZOXWtYb7XvFZlldz+18tnp5aqLoSg8JsL9WsbXXhbhnqTaopLZJV4UKeXadRy7at7WnEqWz1okyJqUtjvRpufMMbjrrZ/oY3QHVllKXJN46l+g5swf/Eplk9mmCf1OTnNO4cMrxTY06D7Jw8PyXNrN/yfyOev4o8Ns3syMTBP0cXEEKIPPPSd8NWZY46FZcae2TIuyVD593IUeQa1CejjpF3yPK916Pfxr2NOvot/59JEw8nSlUroy2TEAPfP54kp6ampqamvvuzS6PyDaW5tuD52q+mX2nx26PkzA/3ljc9NWXUtjcS88GnFUXFHhlkYtT/cKziT6cG6FxmXEgRs0DRBrAs6qBVoV5CJAn7R/deGNvj95sv3r68s3fOZ5acCgKlrXK0oSD1ba6FjyOf1eDX9cezzKDmtuYqnZ41VjXdCVJdg1KLYcF5Wr9STUnCybmBbhaGAkPjxs0+XxQhIoQUxJ1Y2K+FpUAgaNxi4PIrqVLF5/seo5ZM6d22hVubSdsXtbENOpxSPIx5Yd+4uky6maf+wqm38vjuX04eGeTvZlrpN92kEpmcpaMUqJyzwbZ+G95JFG+b3PnGzWvBozIPIqi2ga6dnqFrF/R1MxMIbP2/O/nu9eEZXZ2MBQLHHktuZCq6Ks+L2j6xcxNjgcDYsdOEbZG5cuHF0JCdifGbe7vY2zcftCdBShmE8ujaXJnhmHqr9L29wjf7ly3eFSNmWB1zDEoQxx6dH+RlJRAITOzbjdnzroj+KpX2pzAcII8xP84I7uTbwtmx5fCN0SI5IRTxp5wz6TdWDfC0EAjMvb5UXEg5muXjn0xffiVDXRCz7nPb1gvv58oJKYrbFWzX/JuLGTIGo3w5gvGFVLOIyDLuHIprMaS1bm01oKFOsBpey6VkORFrh3k3FhhZevRbfi1dSrul08eWIqS066WKI1gukiXPdDHae5lMs3KrVZp6fmFQSwczgUBg4dFn4elECWG21YvCQl3dRyyc3Me/Y2t31/YjN0flqQRBzejomHdfsGxS77YutpZOfqPmj7RPuPEsR04Iyb6z5Qz7iyXjfU30zdpPXhpcdHTr/VxCCKE5GXUMm3fu1NLZ1tzE3Laps62AbyLQVU0laMskhLB0uPrF9LgUtxd01xa+f5hk1uPrHk34HINmfUZ2FSREJhYSHd2PZbEJi61X/AddoUaFlIkeXaBoA1gm8jRBo62XwQCJn25eftP7562zA92tza2c23T3t9WtIFDaKqeq60uWemRYh3FhuREz2jg267E6+q0mOzDVUUi5OVAsKPp1TVks9TarwcpSPssEGh3uld5nytRY47tuSdV0m5LqCULZ99IHaCk7WK5J5Ya1wpsK2bkvnRsrWJgKBIK2a16LGUwVRvdgIurRZHj051FHm9FdOlJNLRJFrJp1rOmaqHRhTtqrCz8H23FJ/uOVA8Zf9FkdkZqVfHep1d5Rk458kBJCxPFnHrVaez0q5uHWkSNC7CK2X0mVEUJIbsTOc6zeo1obqL9wg3+V34WSZTy8+FbX3t6QrfalXPsBixb2s+USlTbo0LSzIO7YGbNFtz+kPFphvm9UQMgpnz8i05JuTivYOGPry0JCSO7t+YPnPeu151VGxqt/+sQsGDz/Fum2ac8oG/uJp1/Gxz8/HGLLpgtCNQ7HBn/jkp4WvT+7aePxWKbfYS0JkXKsNFJyoSjix+AJZ12WhyVnZyeG/zmuRaOK57Jy+xsxGyBx/Pknbdddjoh+cnZE7I8zDiVKiaC7Svyp3+19d/iI/neXEzLeHAiM/O/0Q4lS6tEUlou/qWr53KqGWs8tdNuixjvH/PdW6qsdE+a/Hrp1RTdTHQaj3N2X6YW55WcRIfKc8IMxzoPbGevXSgMa9gSryeAMyDIAACAASURBVLVcEhBh2PxhK9NGn0nIeH95mmTv8UQJXb30TaQOKeV6qeoImlYUSbV7L6NpRlGFgefY7Xfjs7ISr80x3D1++pl0OaPqCCHi9+ei26+/dPvB4wtTclcMX/ZAVCYI6kZH+e3clPCwNMsOrgIWIYVJj14WOfo15Sk+k3PuaF/wPCpZzSYiuje7tbW5sWmzYeGD9mzuZ64y8SssU3R/Tnt7K3v3zl8uPRVX/mNB2mv5LYb4y678ffGdSCJ6fXrXTU6X4OY8mvZpXgh19JQDRf/3DDZeTRqvUr407f6VJHP7x7M62AgEJk7+E/9U3NAzGbgqlVPF9WVvM+Cf21s6NvJdE/Hu1YXQvNVMNwGao1BOszmUW1CNaNc1ZbHU26xmK6vkLGNpcrjLK7vPSJVrrOFdV6WzlJuSygkip+m7AmUHKZqkevAVVHhTYRz4z+uUlJSUlA/P941wNO40qpctl8FUYXQPxqUdTSZHP4sq2lW7S0eqWRksjr5O7ruoyDfphWxDJ19fS64oavvetMDl3wfY6bP5TsHzZjre/ft+DiGEY9Z3xlAXHosQFtcx+Ksmj7ZfTJYSknNv50XdoBFefCYXVvH5kOe/fWbt2H+f/cyFnQXqX8429e0f3MpYh5RtA2072eZ9F0xsZ8rlNe37tSeLEzB9mKeA08hzwJAmSbdjxYSIIv86UdBn6bRO5lyuud/UZUFFJ/56rPLUOW3h1TkcSj016LIjIeVwoIBhfSUXKsdKIx8vFEXu2JfZ86fFQU35Ojo8Sy8/H7OK78pVZgWTAeJaBk3t76hHiL5Tt27mcbfjmH6vhW3Wb9G3nS11uRZ+IcEWigupR7Nc/OknVWVDTXSbjtmyvOn+Yd16/ZA86s8lnY1YhNnaYXwhVS/yHh9+bDPQz7yWGtCwJ1hNruWSgETtPiMNWjzB15DNteo2a54fj65e2nseupBSrZeqj2CFr1G39zKe52WLtej0ZXBraz6bLfAYOusL46gLim86qquOEELYJj2nDXLSJUTXccC07nnH9z/LJ8xHp/QDI1Hk72MWJgz+bYa3PiFEmp8lJjxDfcVRyOYZ6ZP8rHw1j3nxWi069/BRxKUtoyTbJi27ma1ym0VbJsug5bRth4+euX796M9BhduH9/7vfdVPt2ivZVsH/7Kq3cWQFpYmli3HhHX5aUWgOd0Qal4IRfRUAkX/9ww2Xk0arzpAwhRhQez+sOY/309Ojtrc6f7sLxbfFzEYuKqWo9X1xXwTIDRHYTrl5kC3oBgXm0+3zWqyspTOMk0O90rvM0o11vSuq9pZqk1J5XSr8DaVsoMfGJytLDU3FYr3nGL/GjfmdIcNu0Ld9BlMFYb3YExuvOmOfspoV/EuHalmZfBazd+1sOm177s5mdm3G7r8fJJEkvYmNePkmNaurq6urq7uny+LYUmyRVJCOIY2Rh8nINchaIRz9I7zHyTZd3Zc5g8I8eQTRhdWiV7zGVcSYv7q9fqP9Q81/Iq0UhsqaKeV4jUsLk+Pa2xjzFH8gc+ViQvlhEiy3ucZu1jqKorRtXQxEb3PVH1flq7wah2OOkCS8U5o2txWvzIjwnSA2AZmBooFwuLqsSXiIqZfceEYWSsGk7D0+LpScRHdaHLKxb9agsW17zvBX/L2g8eU0DaNWBXPyTJRYnah3Lt8L0RP/71jGtzVml1bDWjQE6w21rIk632eiWvxLQ3LwN7ZhENTr8YhpVov1Uvt3lu5aSbLjtgUGuDjZGtja+/SZ3NcTk6+nEl1itfYFgeBsI1tDYTxWZV43zs/ZttXwevNVpz4NcBMca/IM9Yn+cICeXGOllNAeMY8Ncc+iyuwsLJzbvflstX9Rfs3hucR6fs//Yt/k6fTlkQuXZl8t6AhAb7NnV19+87a+LNfyr8HnuUzu1YuDJvb67v3486+TMtKeX7iyxdT+i2hfQqTrlMaFFIuUGr+vkJVqVeHZ8QjvB7zp3W15fPtus+Y3Sr13Jk3BeoGTlvlVNt9nZqdpNxRKKLcHOgWFONii0jVt9myZxnjw72osvuMSFpaY43vuiqdpdmUlE+3Cm9TKTuYk6r+bKXqYNm2yYX3Vwxd8GH8P2v7WrIZTRWG92BMbrxp7/apok0VhIyXW0p3xTgJafBq/LuaOiZtJ64/9fB92ptjI0V/jP/hdoGpg4X1sH2PXyi8jE2OO/WVLZsQwlL6VJJj13uk67OdZ55c+/Oa0eBh7vqEcBhdWOV7EuvOQc2ET17nlL6fyGLrsaVFkuK9OzdTRPUecWkb6NuprmpjO4OsVynFz1cVJr/M5NuZcAkhLCKXa1g4XZsrORxqP2FhEqIqlcAxdTTMjEkUM79Kpf1VGCCl+GswkShHs1z88zQtn0GoZRmXfvjuevPBnd8sm3f8g5QwXjtML+SW64X45Ykb+gHdi3+BrhYa0IAnWA2vZaUJ3CgvIbv4UCzM+JAjpak3j6aF1CGt/Me4DDaZSi1VzaaZUhXi6NVjlscP/Ovhu8SE+FdnJjto8nRiUfrLD2JFSQVJL7Ma2Rlr+m6A+OWOEb2Wy+ae3DHC+eO9jq51y2bc+DuxijflxW/vxOk397Jk3C45UXxIz7Ybe0OocGuCsz2DMllsLpclK5LJmV1bGHv+fLL7xNF+Vnpsnq3/6JHN4s9eS6C5v6PrFNNCqAJV0d+rU5V6ORZeniZslk7JhGUwcNoqR4uHuI4GOzDlUcin2hxoFxTVuqa9X6qismeZZrdqldtnikprrOldV6WztJuS0ulWYd8pO+hoQdMk5WHVqfimQpp0dOrwnfar/vne14DFcKowvAcroHwZs6OfMtpUQRjZbkLprujQMH+rrlZTTfGr44dvxeYUybmGFpbGehy2joH32GGNjs5Zee6dSEakouQnl07fTyv/fi7bpudoj+e/zV963XzoUDc9Qgif2YVySYFYLC6QyIn0439odkOjy+OypMqX6Tt1sk04cyOpiJCCN4fXX8qqOI9i2E6qC7/qo3ty8R93MiSSjLt/LD7O6f21D48tsOJlv44VyjQqnKbNlR6OkkOW+iv1GoQo7/po28aDzgk1LIHvPXqo8dm5y0/HimQycUr07cgMqcZDU5kBKht/5ihHk1Uu/iza8isbalnq2Tmhp7x/2bP578097k6feihBwqzLjC8UletFUdz5i5KufZ31aqsBrAY8wWp4LZe8zewVEig/ufFSspSQglf7113JpauXRdNC6pCqV8kRrPRSpRsykbrVKhNnFfDs3ZoYcYgk5eqWgxo9EC3PvfTjurB0iSTj9u9LLxn2G+rO02B0SMGbv0cHzs8IPfjnCCedArFYXCiVE0KIUYcJvYr2L9kZKSzKjti65Cg3eHzbRvQnoyzjzs6dZx+8TcnJTor8d9mcQ0Wffemj+nOPNGXKch4dOnT9eUKGMCvu7s7v51016D7AjcfsWq6Vjys7ctvu+2lF8sLk23/teqrX3NOC7g5M40KUokcXKLq/Lxt5yqDR1isXPtj4w09nEiUVlG/QevxQ02srNtxKLij4cG3d6seNe/Vuqkc/cNoqp+rrq8L7OpaGR6EZ1eZAvaBo1jX1/RKDNwoqXlmFymeZRrdqld1n8pVqrOFdV7WzajYl9X2n7KAeZZPKDqu4wpuK/Kfrv/o2ot+OrSGOxSklk6nC8B7MgLJHzJYDZbQrnQIg1aw8WXbkrsn+jqaGhpbtluSM3LCkPZ/feuGxrd2ezvWzNhKYNPEbveF2BtXHyWzrHqNbJlyPsR0+2Fnx0TajC8WPF7e0sHAMPpCeeWRgEwsLr3kPNP1HdnR0iFyqNK+4TUf9NkXvZ/8WrTr0mBXd0l/dLzoz7GB5jTqtPLis2bEvmpqYOA051nTpwVWdBSy9ZiEzA95928xIYBWwI17KsHCaNld+OD6+FU/zlXrGIZIJ4xNY7p+76GtaAt934dENn0fNamdpZGTTZsy2KKFM86GpxACpxJ/5NKIaTXn5+NOWX7lQSz8cm/HNpQ5r1g2x1TUP+N+mflGzv90XL1HfZQ0ulKr2wjf78qnsjv2b82qrAe35DXiC1fBaLnnPTdBpxd+Tcxf5+3TwD5wZ2fqz4o9GKOqlixJlSNWfGZUdwUovVZohk6pbrXqtvvsx4MkE35Z+XQPGHTPzt9fkozE9hyEDcpb62Zo59j/cdMmeRb58TUanMO7ImpOpufeWfd6ksYWFhYWFhf+G2CJCCMuk+6/7Zhts6WFjatd3t9UP+1b6G7IqOBlZhW/+ndu/tbOtnWu3+fe9Vxzb0Kfc1w7pyixKu7tx4mfujjb2nkG/xAf+ceKXLoYsZtfqWPRdv2sSf2d/J1NDM5dhhxvP/Gt1N2O6fEXjQkqjRxco2gCWiTx10Gjrlec93f/HjpspkorKN2i35MAK1xNfNjM3dxt9zfeXQ4vb8On7qK1ytLC+Kr6v0/TGhmpz4FMuKLp1TVksg8cJKlxZkqTSs0zDHlVynxEr11izu265zqrZlBj0nfIMomxS2WEtquimouDtv3/ey4ndPdzD1tra2trauc+2VC8GU4XhPRhljxguB8quVToFaEBYQqFQKwXFxMS4ubk1xBCJbk/y/lq2OXwT/ckHlZZzYWSPQ1+f29QdwW0wZEm7e3125ru7ewKMMMEAI8jgkAkLbTnJ4tDdZd76GHDA+vp0z7JaPD1Vqq7KpiS6N9V3NG93+P98efU+LKCNjI+DkKnDbzltXpexk71dneedPTbJmYuIaJNhj113eyAMDYqkUNB37qyOhphggBEEwPrCWVYPTk/tVS3JiHmeYzrYgtsQwgLagFRTPV7zsdtvja0vrS16u3VA4NIyv83M81l07sh4JyTJCHWN0HUMnjIK0wMTDACwcdXjjavmz7JaPD21VLUofHbbHntMxu0YYMtpCGEBbcADtAAAAAAAAKDljE8HIQMAAAAAAADtQqpZX4juT/fwnB2Rj0iA9idX+EwPz1mVnlxVvLzW21/3O1hd/QoLdfX5IVJcP/rbUEcB2wK2hepby4DgVEtk6uodaT04Jgpi1gQ4W9t4BP0UkSv/RCYaUs2qz+y7U9w8mMxsydv1/p6Tw0TV2BbNqmDc8lpU9aBVU0xqYDRrrmpdh4ELFgyw51ayJRJ1l9fsTKs78dEa8eOFrdsufVJQQxdWor8qQ1zpBtfZkNaH3VLLqjjtdevWtlDn4lMQvaSFeY9D6fL6spnUSrF1Yc5oa3MrubCG9/PqDmAtdqcGDmttRay0s3pu08+/fnW4R8zGDQ9r4w6yNuBngWqMNPnGsVTf71vw63UVmraoSMbm6lT+QmY9qqiWaopJFYqtmZhoVDWnceeQ4ZUPgmFjw8pcXg0hqqahqWR86u+xUFv9leR8yNGzNNX7NP/ZharuDNqut4rTQOuzqIrdrGvxqX2FWUkivrWxbh2c0gD1dFUynPN6Vi6mRZdEsk9kuLAJ0Mo+96VzYwULU4FA0HbNa3Hq+YVBLR3MBAKBhUefhacTJUR4MTRkZ2L85t4u9vbNB+1JkBJ5XtT2iZ2bGAsExo6dJmyLLP6EXJZx51BciyGtBYSIY4/OD/KyEggEJvbtxux5V0QI9VWynIi1w7wbC4wsPfotv5Ze+o8VF8SdWNivhaVAIGjcYuDyK6nSMlXItNvy0vvAhJNzA90sDAWGxo2bfb4oQkTdElFYqKvHqCVTerdt4dZm0vZFbWyDDqcUL6i8sG9cXSbdzFN/4dRbeUpBK191uRdTRbXaYlJBw2oxJpWuWumZE8owVjzfBCWXi8JCXT3G/DgjuJNvC2fHlsM3RovkhCKqDHqaQlkUIdLyo1ZW3YlP6duiYaGu7iMWTu7j37G1u2v7kZuj8uSEFL74tZ1dv4Mldd2a7OL6za08mirUyDkbbOu34Z0iGKI737h5LXhU9qEoutUtSTn/Q083SyNDK9/ihpV5Bomya+VaSDHEJbezb/YvW7wrpuIntCTZz89tnju0vaPz0ANJ1FUon+VUc6D8cFNNgGrf52txt9R6vVWd9nVpWyB1Lz6qW0T5fhXG/EQ5Hyqee7LMsB8/d2oz/VSSpOI2yDJOj3Jz8B00a/2p6IwipjdGareacjOTJryeoWsX9HUzEwhs/b87+e714RldnYwFAsceS25kymi2LGZzhiYDSL+xaoCnhUBg7vXlx2lDGUnVGZKs8eamfjfWSpDVBrB8cCg3asaRob8jrWpA6s6qVD/6VJFhMufLJV9slkwq/VQeoCVCLQkPDxc2UNnvjo51NO60Kjw1683ZLbuvvUjOykq8+3tfM8Nee2NzhMkXR9nYT7yaUvzqpHNjbPit5p59m5Hx9ty81nzr0WcSc4TCnPgDfWz8t77KFiZfndaU5zJhz+Pk7OyUV2HnbsZm0VyVeGaklUH7pVcTsjJeHpnsokNsQq+mCIXClJvfuRp4Tjv8LDUrOXLX1w6mgTteZilXodWWl0i+OMrW7qsDLzOEwsyEyKtXX2VQtyT5XIgJ4Xdf+yAlRyjMyXi41MOwyzZFq5JOfWlpM+ZiMpMLhUo9oqha5cWUUa22mFTUsFqMSaWrTr4y3t5h4tUU6jCqnW+ll58LMSFGfTdHpwmFqfeWeBl2+ON5lmLqKEeVSU/piqIetY8NqEvxUVo550JMCL/b+sh0oTA9emOAaZNvriYLhZlR/2tp5LcpJksoFAoTjg2xsJ94JZm6iqx3V//efT0+m3aTSjj4uaHX/6IzFfVdGGHTZOqNVKHShZQzOflciCnRbfn92djMzLgL81rxHb65miwUlvaXOmJULVQZ4tIGfzg12JAXcCiRstlZCRH/rp7a18tYh+fUdeR/d159kZYjFAqpg1DSKqo5UH64KSZAmapVQ1rfd0vt11vVaV+HtoW6GJ+027Mc9TrsiM35uEWU7xd1vXRr2brp9JsfXh2a5GnWavqJ2Cy6kCrP/PQ3N/b8OK57Mz5L0LznpP8dvBuXqe5+SO1WozJkdOE1JY06Lbn0LiPl0fruhno2nkN+v5OYmXRzSSsDtx/C0+mWGJM5U77NyedCTAm//YIzr9Iz3hyb6MBvr5g2VFVQ3WxouLmpDVHV93MmAaSOfNm+aBIZ2jvSKgek7qxK9aNPt/rUznmVQc+J2xdoaD/hdAWzoC7TNOPDp5pqnzGJ/WvcmNMdNuwKddNnW3T6Mri1NZ/NFngMnfWFcdSF16oPcIsi/zpR0GfptE7mXK6539RlQUUn/nqcT0je48OPbQb6meuIInfsy+z50+KgpnwdHZ6ll5+PGZv6qrSo3WekQYsn+BqyuVbdZs3z4xVXEbV9b1rg8u8D7PTZfKfgeTMd7/59P0epCu22vASLo6+T+y4q8k16IdvQydfXkkvXEsIx6ztjqAuPRQiL6xj8VZNH2y8mSwnJubfzom7QCC8+kwuJUo/KV03KvpgyqqTaYlJBw2oxJpWuWqm/VJOT4XxT4FoGTe3vqEeIvlO3buZxt+PKfw+DYU+pi1I3anUnPmWwTXpOG+SkS4iu44Bp3fOO73+WTzj2QePcn/55NlFK5Jlh2y4ZDBrRgk89k9mmvv2DWxlrvl1/vJB2desIeiyc0smMwzHp8O2iHrnH9z/LVzNSH2jWGmW9hBh02ZGQcjhQoPICee6DdaM7N3Xwn3Uyt93sYy/iH59Y/90gX2s9Fl2clQqnmAPlh5t606BoYQPZLbVerxamfZ3ZFup6fGj6RT0f6OaevDD+8Dc9QiMHHTq5pKsZmy6kyjNf16Jl0LTfjjx8/+bs4i7SSwu7OTXpEPJzWGYlHutTLlb5aKaLA9u874KJ7Uy5vKZ9v/ZkcQKmD/MUcBp5DhjSJOl2rJjBEqtozlA0z6zfom87W+pyLfxCgi0U4aWuouJ9g8nmpjZEVd7PCZMAMgwO48jQ3ZFWPSB1Z1Wy1I0+7bRUO+dVBp1l8tkPU8y397a3DdjwuqjBJ1JINSskF95fMXTBh/H/rO1rySZElh2xKTTAx8nWxtbepc/muJycfNWPvyVZ7/OMXSyLv/2ga+liInqfWURET/+9Yxrc1ZpNJBnvhKbNbfXVX5Wf9T7PxLV4qrMM7J1NFN+slaS9Sc04Oaa1q6urq6ur++fLYliSbJG0tArttTzt7Z/+AoVOW1K95u9a2PTa992czOzbDV1+PklC0xJCOIY2Rh/vDbkOQSOco3ec/yDJvrPjMn9AiCefMLpQKWi8VqpVE1LmxZRRVS6hmkazfMNqMSaVrrq0v5STk8l8Kz08DMwMFLsKi6vHloiLyj8gwqynNEWpGbW6Ex+VL5AY2hoXfzGebWxrIIzPkhLCtuk5vtXrnSfjC9Ovb71hPnx4c326mVxVNDOZEI5p08Z6xX/b2MlQ0bAKu5aTqpUWFmW+efoys5GLj09LHy8X8zJfz1QTBKo5UH64qTeN6t3nS9X4zqD9erUw7evKtlDn40PXL8r5QDf3JAmn1h8u6DdnbGtDHQ3bwDV1buHTsqVPc5OcV09fpxdW8bE+5aOZPrxWitewuDw9rrGNYn9kcflcmbhQzmCJaRZkjpF18Q7M0uPrSsVFdFVwmO8btUttACmDI6t8ZOjuSKuuVldlxsstFd3lMj5JGcz5sk+U3l2zKXHIgdfvz0925pKGDj8LVAFp0tGpw3far7r2va8BixAijl49Znn8pJMPT/sYc8QP5/kOSCp+f4LIP27NHGM7g6xXKYXEXZcQUpj8MpNvZ8IVvzxxQz9gvD2XkCJTR8PMmESx4gUVXcUztmuUl5AtIUSXEFKY8SFH6kQIIRxTBwtrh12P/uio/MMn4sebPlahvZabO429IRxbWonDxPWnJq4vTI3YOqbf+B+6hI+jaokojBCW0vvsHLveI12X7jzzxOTCNaPBp9z1CZGZMrhQKWhEx6Rt2aqfrmMrv5hDFdUyJVTPaJZvWC3GpNJVl/5/lJNT/XxjQDmqTHpKg3bU6lp8VBOr9JcfxHKizyKkIOllViM7YzYhRKdx9wntv19++J7h1bt2I/7XTJcQCeVMVh9eth5bWiRRhFiamymSGZfd6ClnMiFEkvEmpYAQfUJIYcrbHEXDKhwp0Z071C1UGmIGDTbptuZu/IKHp/fs2D6j04ysZr1CRoz6+otu7iYcuuVc8Rwov0Vs8y//NwbVus+XeRe3ZneGaqhXC9O+jmwL9Tc+lPOBbu5xncb9syBr1oiBq/49NredkQ6TNkhzXl499NeuXXtOPOV1HDJq8oHn/drY6LOqsNUUjytL7bCq6zldN9XOGebBpayC4mZDV8PNjUmIqrkE2sjfZdIXje5ItdCdcjGPqNFVOVI4gfYuV2X06Xd+dXNedeVlvnpZ4DqnY2P2J5FN4VNNWvlP13/1bUS/HVtDHItvIWTirAKevVsTIw6RpFzdclDxEBBbYMXLfh0rVLxbxPf+qo/uycV/3MmQSDLu/rH4OKf31z7suPMXJV37OusRQvjeo4can527/HSsSCYTp0TfjsyQUl9l5hUSKD+58VKylJCCV/vXXcn9WMXYYY2Ozll57p1IRqSi5CeXTt9Py1eqQost5ykf56+OH74Vm1Mk5xpaWBrrcdg6BlQtKf+WItum52iP57/NX3rdfOhQNz26LqhcWKjUo/JVq5yFlFEtrJ6YsCtsWC3GpNJVVxxGtfONwXMyKlFl1E7qD7QoR63uxUcqFz7Y+MNPZz7+PIk899KP68LSJZKM278vvWTYb6g7T5Fw+U/4T/K6aYvuOY/q34RLV4WSvOujbRsPOidUabS+UyfbhDM3kooIKXhzeP2lLJnqAqFZ3TLhxRV/3MmQSLPubVx2odHHhhH6kdKjbGHZIVYekwp+FkjXolX/6euOP45/cXRWO+HJ2f9xaPbFvgSpmiBQzoHyw12obtPQ+j7PUxr0Gt0ZqqdeLUz7urEt1Nf40M4H2pOaYx207sxal92DBv0SIZSrC6ks7dQod7sOUw+mtfhm35P46NMbZg9pW5pnVnKrIao7TyWHlbqb2pszdFWwyu8bmm5uGoRIO0HWIPK0fVEfGbo70qoHpPyq5NfkqqywJSwN75OZTku5rFCqo6f3qaRgSDXpFLz99897ObG7h3vYWltbW1s799mW6vXdjwFPJvi29OsaMO6Ymb+9PiGE6DULmRnw7ttmRgKrgB3x0kadVh5c1uzYF01NTJyGHGu69OCqzrwPl09ld+zfXDEf+b4Lj274PGpWO0sjI5s2Y7ZFCWWEUFwl0BF0WvH35NxF/j4d/ANnRrb+7ONj4/zWC49t7fZ0rp+1kcCkid/oDbczxEmlVWix5QLlmzNZduSuyf6OpoaGlu2W5IzcsKQ9n6IlVGco27rH6JYJ12Nshw921qXrgsqFEqUeUVWtujWWj6qkemJSZjTrVkwqXXVFYVQ73xhQiSrDdlIega2oRq3k46m6Eh8Jkec93f/HjpspxRXoOQwZkLPUz9bMsf/hpkv2LPItnsAso47jAlkvUr3G9rXj0FdRugiF8Qks989dVJ8t5TYd9dsUvZ/9W7Tq0GNWdEv/xqrPq9Ctbj2HQX0zFne0NbXvt8/hv6UNI/RzkrKFKkOs9Hnu+7ObNh6PrfDLKLqNWw2Y8fuJx/Gvjs9qY8RSEwTKOVB+uPXUbhpE27ul0qDX6M5QTfVWfdrXiW2h3saHrt4KT2qu3aA/zv5s92f/IWse5fIq7BqL7zl5/7OE6DMb53zRrtxHmZXealTHprLDStlN7c0Zuirk5WeIppsb8xBpK8jMI0/bF/WRob0jrXJAanlVVtwSDe+Tmc55uUwi12GTT+Xf9WIJhUKtFBQTE+Pm5oYMlWo3Sdrd67Mz393dE2BUj6uod0GrppjUYqjrzijXzflWZ1eBKCy05SSLQ3eXeVN8+7Do9ZrOXS7OfHB8qCWDN/5yLozscejrc5u6G1fnESW6N9V3NG93+P98eQQAPkE1s9UgyAjyp5kWpJ8d03aG6YEHIQh4ogAAAcZJREFUq9vUy0NW04wP39WsfpJCQd+5szoa1u8q6l3QqikmtRjqujPKdXO+1cdVIM+5u3FrSrfVAY2ZPWBi2GPX3R7VHsiMmOc5poMtGv5vFQBALW41CDKC/OkpiFnTr8/qOJte83/w+lTezMWnmgAA1YnmU01ZysFg7zFh5oPWnSr9omDtNzZ8dtsee0zG7TiyKtCCjcEDgHqj6O3WAYFLy/yjKDyfReeOjHfCG2cAWqNpxodUEwAAAAAAALSc8eFngQAAAAAAAEDLkGoCAAAAAAAAUk0AAAAAAABAqgkAAAAAAABINQEAAAAAAACQagIAAAAAAABSTQAAAAAAAECqCQAAAAAAAIBUEwAAAAAAAJBqAgAAAAAAAFJNAAAAAAAAAFUcLZYVExODgAIAAAAAAABLKBQiCgAAAAAAAKBFeIAWAAAAAAAAkGoCAAAAAAAAUk0AAAAAAABAqgkAAAAAAACAVBMAAAAAAACQagIAAAAAAABSTQAAAAAAAACkmgAAAAAAAIBUEwAAAAAAAJBqAgAAAAAAACDVBAAAAAAAAKSaAAAAAAAAgFQTAAAAAAAAkGoCAAAAAAAAINUEAAAAAACAOuv/ZcJln1/vXH4AAAAASUVORK5CYII=" alt="" /></p>
<p><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,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" alt="" /></p>
<p><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,iVBORw0KGgoAAAANSUhEUgAAAlsAAALPCAIAAADaUJJzAAAAAXNSR0IArs4c6QAAAAlwSFlzAAALEwAACxMBAJqcGAAAAAd0SU1FB90CFQAeLnfkY4wAACAASURBVHja7N11XBTpHwfw78wmSy7dCChIKB5goigidneecbbYnt15nnV2nO3Z7dktdoKoGEinwALLwubE749FJBbFu5+e4vf9B6/ZYefZ53lmmM8+z8wuRJ06dQAhhBD64ZHYBQghhBAmIkIIIYSJiBBCCGEiIoQQQpiICCGEkA7EoUOHsBcQQgghHCMihBBCmIgIIYTQe4RMJsNeQAghhHCMiBBCCGEiIoQQQpiICCGEECYiQgghhImIEEIIYSIihBBCmIgIIYQQJiJCCCGEiYgQQghhIiKEEEKYiAghhBAmIkIIIfQvE5HJf3l4bv+W9Xx8fHx+qhfSdcTvl9/R5WyqerWqVb2RN/P/2Qsro5a3bDjujrz8Z2ji/uxQt//5XPafvUBFNv93TUAIIVSZcIs/YPPvLx62+GmzhTtXNnUVqTITXjx6AQT7nTSFodQsl895/5Bj1mDoJGdPPQJ3MkIIoc8dI1IZz1/k27XtE1LdTI+nZ2JbPaB9lwBLLgAAnfNo55SeTf19fHz8AjuO3x+nLhzqJZye36fJTz4+/q1CdzzL16Ynnftk1+QeQX4+Pj71Wg9fHZZBvX/y3/N6Bf7k49ug9ciND3MKB5+KJ7OaBs8OVxQ+5/lvIQE6xo66y1S9WtWqbp916yf36dSmVdfF7wsBAKAld7Ys3xOl0FZJnXp5xZDW9Xx8fHwb95i297mMKXqiPPrI9O6N6/j41Gs/fldkHgMAdM79jaHtGvj61KrlWy+k36pIBR4pCCH0QyUiz9qvtlnSrrlLd569E5Uio4p+oYn7K3TENlnrlecfRYTfPra0j7chBwCAVTze+7zWnFMPH15a1Shm3ewD8RoATeL+0SN3qrusv/r4yb1DY20uTJl4MIkCUL36c/TcO26zzzx4dH17d8XhMxlUhatZXpkAwCqfn1d0XX/kzIUTc/31dG+uer1lxOSzZiP23wt/fGlF44TVw2dfy9ZmIqsI/+tF3d/O33sStqlt5sbQRbelrCp657w98j5/3Q6PCH9waduvwdY8PFIQQuiHSkQQ+U7+a/Nor6wrG6b0bt3QL7DPnKOv5SyoYk4ceOU6amafWuY8ghSau/vXMNfOTnI9B49u42bE5VvW7draKu1hghLUcSf3vXINndrF05jL0XMMGTXSLfrkzXe0KubMmXTvUaFNbQUk37rxiNF1+BWuZXllFtZhYF8/E85HNlfFnP47xWPk+NbOeiTXzH/gpFbc2/sfSrWjR9Kl/+g2Lnokx7hWvwnNOTcOPs0nuAKuJivmRXR6AcM1dKxZ05KLRwpCCFV2pU/1fKt6fabW6wPAKFIfHVwwbn6onvupMbJkqdDBuWzqEDyxpb52LcEX8UGtYYCSxGerny/r2uSPwit4lJK1qZ1P0dIUmcjR3kCbwYSenYuYk17BWpZXJpgDEDwTCwPORzenpSl5Alt7o8Jn8SycTZnn6fk0iAAIvrmTaWEv8MxdTZjojAJOo4ErZyvW/DWxw0yJvnvjHqOnDWmEoYgQQj9YIn4YPOrZ1unev8Gm4c9j8wk3e2PVg4Rcuq6I88kSOWJ7sZ5f6ImtbcxLDEBVUbaGyphcNQt6BABQuel5hRcSuQIOXfB+BpWV5yiZipYJKgAA4lM3z3CMbY1U4Sl5NOhxAECTFZ9Niq21McpqJPHZFBhwAUCTFZtLmlnocwi9qu0mrmk3kVWmP9o9adTkJTUvrQowwKMFIYQqsxIBQ6dfWr1q7/UXydkKSiNLvrtv2y2FrZ+bkZ5rxx7V3q5bciAyW8MySsmbR88k5X0mAwSuHXu6Rq1aeuS5RMMCrZDE3r9y/x0FApc2LS0itp+IVrAAythT224V3vQisPWtooy4+rKABVYZf2bHjTymomVWkMClbVvblxvXXkxUMFT2k90rzlH1e/gba4OUjtmz/ly8gqGlkfv+uEA36uajr066euZObK6KIQTGFhbGPA6HwDtWEULohxojEnrmJtmH14xZG5ulYIFj7FKv228bh7sLgHDut24dtXh5aLPfpTTXyKXxyFU1a5Q3ZuI5912/kVy+fHyrRRkqUih28gn5ZU4ggLD6sDVTc6aHtj9iYm5k6uHfyDyaBQAgzYNnTLk1cUKb1mILU7vaAQ3MIpmKlllBwupDNyxRzPmjW72pSo6xW7NRG2YGm72fvv2pT/U7v7Za8CaHdGgydPXMRiakOj32/PKF82IkKuAYONXvuWRyXX08VBBCqJIjZDJZ5WyZJmZT5973x1zcEWKMuxkhhNAnVdpvcWPyYqNyDKpY8HEfI4QQ+nETUfF0YatmM+MDxvzipYf7GCGEUEVU3llThBBCCMeICCGEECYiQgghhImIEEIIYSIihBBCmIgIIYQQJiJCCCGEiYgQQghhIiKEEEKYiAghhBAmIkIIIfRFEnHBggUAgD/xJ/7En/gTf/7gP/F7TRFCCCGcNUUIIYQwERFCCCFMRIQQQggTESGEEMJERAghhDAREUIIIUxEhBBCCBMRIYQQwkRECCGEMBERQgghTESEEEIIExEhhBDCREQIIYT+KW7xB0+ePLl58yZ2CvoWjB07tvhDjUYjlUoZhqnM709J0sjIiM/n495H6L9PRACYMWMGdgr6z4WFhRV/qFQqU1JSHB0dDQ0NORxOpWwyTdMymSwpKcnOzk4gEOAxgNB/n4gIfYMSExNNTEyMjIwAoLIOEwmCMDIyMjAwSEhIcHNzw52OECYiqpwKCgo++Zzo6OhatWrp/JVUKnVzc6vcU6ZaVlZWiYmJeMAghImIflzR0dGHDh0qLxFpmubz+SzLVvp+4PF4FEXh8YAQJiKq5NLT08v71aFDhz6+7Y8wQEQIYSJ+NjovIYm1r2LMKbVcaVTKRv1LmIgIoS/t8z+PqAz/1dmk3fm8ip/KUrfXIUoStDlXke3VUfPd9INOSkuuld8f59t0xWt16eUK0UQvq+O/+JW62HJexBQX44pV6OMd874c3dWuuE80ism6MLxa9cnhyvLWqBNP/NrMRZ8gCELs3fX3WxKmnJWs/NWuYfVteARB6FcJGrv/rfLbTsSPUqbe2PRrvzZNAgMDA0O6jvztxJsCmmHUcTv6BAb22PxWyfwfqKI3dg8M7L0zXqWzBlGrOgUG9j+Yov53r4JnJYS+n0T8/Jew7nk6KVErIer4MGeDn6bNbWT4zwdQ+e9y1DqWK4JKuXhC3aKtM7/U8rc1Qiy/UUze022DGrTc/FbBlrdGE7ule++9VnPu56gVKcd7pM5rN/S0RK1rpfL5bx2H36i/PVZByV+urX58YI8/Y7/dK1jsR9EpJ2fN2veAqdt/3OQJgxsJnp9dOXHdUwVBEAQAAEF8soSK4Fk0HBg6+ud65pwypQGA9rUIkvyXr4VnJYS+8URUJxwb39heQBCGnr02vSjQ/tHSkptLu9UQkwTBtw0YufeNgsk60c7EfuQ9ufZcnXGwuXHV6U9VpMjS3sHBwcHB3iR5y8R9VgsOTq9tQFQ0wzIuzWzuJCAIrnXD8adSlQlbO3Y9nJ28LtC107HwP4uWT2WrouZ7WTYa1LVOdU/Pqi6+vdY8zmMAVJEzXDjuC15qE4Z+d/WItEnHaoJSy0UvlnZ+dlt3I4IgeNZ1Bm15KmMBgEq/OK+jl7aZDcccjlcDAJv/bNvQhk6GXIIgeNb1RhyIV3+02lQ5hSujdw+pa6fPIQiRU5NxRxLUQBVr4KlstkT9FQ8n+jVeSf+yoKFB4X4ruwbUkmzn3osX9PY24Qltm4yaWFv15HaSTNdK8Jp+L+nB0pYOQqCUSg2hb2MpIr7ZI/UTA6uCTCmAQZVa9Zq26jJo5oq5Q/p1ciNUdGE3xZ7+bXCH4KDgLmM3P8imGUYjebh79qD2wUFBQW1+nrbtXqYice+AoKB+u+PVrDxiSfugoNZzHuazmuQDA4OCem2NKRwTajJv7Vi3ds99CcVSpUrIUDMAQABII3ZN7d0yKKhlnxl7I/NoOi9i97S+rYOCgoKCWvebtisil8IxIkLfdSKq36zt0u+oy/IX+cqYLf5R1zJVAEAl7u7Reol04Pl3SnnsjoDbw1rPfKTXZFRz5ckd4QUAwLy7uPm+3YB+HkWRo3rxx7Atol//HF7tM4Zl6qi/5QPPpcmSLwzIWT9ySaTl4BNHupnah4bFHO/805Ci5famBIAm83Z4jXUPXkS9DJvCLOk44bqU5VXpu/nY+u72XO3s4s1D6Q26euqVWi5q5ur2nbYIJ4RJVPLYXc0ix4cMOytRJ+3u3WUtM/JyploRvcb1yIC+OxMpVdTK7qG3Gu2JU7JU7t1x9I6xCx4UfKzaTxS6Cs/KOj92zNWG+xLVjDxygXhf6IybeVynwcUbVaL+fPfx15Ii94zwN3l/gbHsGtCvPXv/1gEuPAAAOv3q3md6/oGOprpWCkihibkR+2ZZbQMj9+77nCYvaGP1xa9cXrlypWpJV65c+ZeJyLIsz6VLaEsb2c3lw9oHBbUfvOIR4da0pZcRaIdcuc/Tqg2cv6C/W27kgc1X36mTjkyfsuMm1WDk7LnjmvEf/jVt0n55g+ZOkBz2KEuZcidcBqB4fidRIXl8NR5smgc5CLSvUjSAoxIPly5h91vtmyLJi4L6Y5cuGlwj987WWRvC4y+u23FP3mT+7gM7FnZypGIfv5XSLCYiQt9vImrij297Xn36vG5V9QWWDcf/PsCGD0CnnFl302rMilF1LQR69i1mLG0u2bf1KT9gZFvm9I4n+UCnnd/82Hlwz6rv04+VXJy7KrXNb6O8hJ9TQf5PU+d09zQ1sGvYt6tTblSa6iPPJQyDZ4bWNiKAZ99+Qmc4seVxAWnkEdKhmbshCQBszt398X7da+qXWv7QzO3PPOesGFzLlK/n0GLmmn6cv9ffent16z3bMb8N8TPjCZ06rb56fX1rcw6/6sizz67ObmRK56alyPSt9PJTpfRHq52vq/C7BfrGnJQL27ceCksy7Xk0Pe2vYKPS+6dY/YFj5GhvWCKzyq4pFiHZN+d1GHSv2fpVLUyJ8lfyq024L1elXh4nmxMy5EzWlz4fBwcHb968uejh5s2bg4OD/+WsKcMwNGEeNGP/+b1/zBzVu6krE3V6y6yBE44mqbURZtZsYNdA34CQADuAAoks5da5N6xR66m/9ggJ7jZuakdzSLx4g2rQ3BFirj16/eBBJt/RSZj9+OGrh5ejwaZ5sD2v5HwmQaXfOlu6hEtXE9QAAI7dhndq4Nu417A25pD3NFzu5GEOOedmDxnz298S58AObWsak4Czpgh9x4koicvhWjsWDkP4Vu6WPACNJCZLHbOgplB7r4xJq+O5ufGZKsPawzpxzm5/nJtyZsuz6sO6VOG9PxWnn1t5Tr/PxCDTMhNz+df7mhbecmP28438kr/kGtsYcQAACC6fw1LUx84XXGN7SyFRGBU2InlKprL40/Me7n/l1dPPsPTy+2ZmJ+QK7F1MCwOGb+VmTmUkJcVJWPOq5tpmcEyq1/axFxEEk3N3TS9PI76RS6O+C07GyBkW2I9WW62r8DSyyfozqxpn7h3b1N1M5BQ88Wii+v+zW1n5610D6rQ9VHPrnT097bkfWQlAckiCbxM8eWGI8tyep/Ivf8wVhWLF4/BTs6bqtMsrxg6f9JfUvXmP4TNX7PxruifQL65G5tEsCwB8EZdSqzUsSQKwQHBJAgAYmlKpVGo1BQBAcLg2jUIcIOrQrlMJwrqDfqknSjq75+BzsGnWxI5D0+8Ho0V/NjpKIABYALVMoV3JAgDJM/KbtGf30on9W7jzUm7uXTlx8NKHeTSOERH6fhORZ+ZiSqXF5VCFwRGfTQFwTRxMhD5r4jSFdzbkJ0W/3NfSBPR/+qW73sWtpw5ue11zRPui8y6bfXv3Q5P2fWqKypYvqv3b7adatxb7l3kCUSpCCQBC1zIAlRufUniNU5nyQqJfxVqv2G/zww88rdqzrglRarmomWJHY1VybE7hYE+V9jKTY+ng4CQGSaxEo32B1DOLZu98nR+/s8/Aw07Ln+Urs+Oe/D2nnhFR9p19yWrrLNyKTI5WN5j/d2SGKufVyf75G4ZMvZ1fulGfj8kOm9W03rSc0VcfbO5ehV/eSibzeFdXv/nPtKNullIoWYGR8Ov8MxRtKFY8Dj81a8oR2+unvnj015Spy3YeOLh7zZLNUQAWXq5Ghc1hGYamaVobNjzrBsFOkHdh+R9Hrlw9tn75ySxwbtXMTmDdpJkDpERkkNWDfvJt5snNiEgBm+aNbUiKKrz2V7iTWa5NQJkSWoc48gEA0o/9sevSnSv7N5/NArM6vvl7hncf8sdLh3ajpk/92ZMEpUxBE9pxLSYiQt9lIjp3GeEfvXjavlcyteThxqlbk1QAXId2Q33fLpq0LTybovOebe3j491zfzIFIPQe0MfoxMS5L/1HtrIpmtCTvzr3lPRr7a6nqw769h41tTzs9T9ZI4InImVxr+Iy5EyJZQA2//rMeacSFMqUi4smndTrNsxXn5G+OHf0wksZA/Jnh+7b9WhoRgCUWC7WzE4DvKLm/7ozMlejSLq4aNxeTathDaoGD66bsnbW7mdSSpFwel7o0qtKAanMkVF8sYUJn9Bk3l4762CWUq1hPtGHOgqvo7k0oWXrScfjFBwjW3szAdfAwoBTsoEf6l9Rmrit3duu1Z95ZHUnS2lKUlJSUlquWq1jJWVSK9gmauWUbU9zVdKX+6dMvmn785Baoq915H1WHMKn7t4UuA9aNa9HXXHs6e0b1m458kzo12Xmyl/ceEXvVD7MR7IM4dRzxZK+9cnbm5csXntFXWfA8qW9q5AUYRXY1B4AXJt4GIg8g90AwCYkyJqkii4istpRIkmULWFZH2cuywKAa3uP2J3zF+2JsWk+8ffhPh6dR/VtILq1eEjP3qHbs3y6zZ5c1xhnTRH6RlXoE/o81+FHjqT3H/6TUX/WudXgzi5PcgB4LsOOHZcMHRNkNlwKIueQ0YePDHflAYDAve8ApwXznUc2syxKNyY/LkpqGeKo93+osp5nzy42Hfp7t8qJvjf6w3JYCADPtW72/FqijjLLugPXnVsWYAjqyH2juh8Z+PzZr/KjYWZdp1iSAKB8+WG5GL77+JOHcgaPbyAeXMC3qdd35aVV7Sz45IADR5N+GVXfZIjc0LXl2AMHf6kiIoesnXKxf4h4Hd/CpX6/8fMDwzc/SVY5f6zWugq3MVLv3Py43+jqom5qrmmNzguPzvfTA2CKNXB4QWH9Z3lU7HYkxdPV86/I8mBSgOukwlVWw68eEpRdeTd245Ajx1MHjKwvHqUQODUdtffSwjr63+yR+vHBk1rNsWw4dFnQKOL9By00Go1arQa7XnuuD6BpWqVSkbbdd13vR9M0paZMag9Y0mCI9skURanVKjVN8+z67Av7hWEYtVrNab42rA2PoiilUkkzDADQ726fPHTrHUA1Qx6thlIlaDRKwmXEsRvjAABGjSQIgqZpjUZNC927z97akyw81rS1omkaTz0IfYMImUxW9ODJkyeBgYHfaUvUUfO9a19blnqtgzHu1m+O9pu+P/Itbtu2bQOAxYsXax+GhYX5+voW/fbevXseHh4fO44JgiRJkiSLElE7A6ldr31YapnD4WifTNO0dhRIkiSHwyn+5KJ7WQmCyTo3bdjGV4RTq8m/jatnqGFZtlQJ2jLh/QcTGYbRJh+HwyHff0ixqMCPtOXly5f16tXDYwahb3SMiNA3PkbUxtJnFVj2C7UZhinvW7ZJkrRpv+pidyEAKBQKtZpiWbbsk3VujiNChDARESrh8uXL9+/f/2fbkiSp0Wi43P/scNVOpdI0zbKsdkT4hV6IoiiSJPFoQQgT8V/he85+UzAb92jli0MAEIlEUqlULBb/h02gaVqj0XzpV5FKpSKRCA8YhDARUaXVoUOHDh06/OPNnZyc7t27l5OTY2NjIxAIKmUXqVSqtLS0vLy8unXr4gGDECYiQroZGhrWq1fv0aNHd+/eraz/UJfL5Zqbm9euXdvQ0BD3OEL/iRL3mmZnZ2OPoG9BfHx88XtNEULoayciQggh9MPCu9oQQgghTESEEEIIExEhhBDCREQIIYQwERFCCCFMRIQQQggTESGEEMJERAghhDAREUIIIUxEhBBCCBMRIYQQwkRECCGEMBERQgghTESEEEIIExEhhBDCRPzX5I8meHpNeqwo57d3hrv5zIpUYj8hhNCPlIis/M2Rmd3quZgZGhoaWnkGD1l5OVX9z5Pm/mh3z3KT5tvBd+w8Y0YnBx4eEAghhImopYre2LnRsEs2w/96lPDuXeKDncPsw/ffzKKLPYXWMJWvG7iWDfv0DrDkfnuD16dLuw36W8IqHk/y8p7y5Mu8tfhE4e/r8H9r090R7j/Ne64qufLBGE+fmU+/zDhcfndEdf/FL1Xf3G6tYOcghP6DRGSzr8xeGF5nzbFVAxo4m4pEYqc63efs2dbdVnVnuJvngHmjW9f2dvcfc7sAVIl/z2znbWVoaGjp3XnRtUwagM68OLN9LUczQ0NDC882M8+mUiC7PLzPztSkza2rOThU77I3hQZdGxano5BSJ4w7w908By0c3yHAz9vVqVbvjc/lhaeVMiUzaXubOTTf/44BUDyZWt1Q3P6EhAWQ3x/t4T211On/w6wpW/Bs+7CGVUwMDU2cAoZujcwvLJ7KuDirpbuVsZG1X//Nzwq0a9WxBxfM2fX6C06nquKu3k5UsCD0mnzm9CRP4Rd5kU8U/r4O6P++WxFC324iKl7/fVf108/NbTklnkMAACiTzkX8tDrs2evwDY3Ip0s6Dbnss/JxZu67+/Ot9w0YcTydBtD3+mX7/aTc3NQbk43+GjLunMSg2aa9A2wdhp2NTkp6dbSPHUehe8PiyhRS5rShTLr4ovaaq4+fvzj/c/zC8UdSaQDQUXKGiX8L6+gzz/KBSr9zR2Gr//LcSzmok27cU/k2r6anuz/y707vOu1lq71vs7Pf7m/zekbX6bdlLACoEo+cNJ51J0WScKJL3OyeC57IAQA0yec3bTwVrylVCJP7cG3/Bu5V3dyqejQetvWpjAVQPJ5Uo0afoV1CghrX8fQKmXbhHQ0AbP6zbcObeHt4elb3Cvh5zYPcEuNvKvnA2Jl3Zfentx+y7/KSNm2XRylB8XhSDe+ev3Rs0qi+t2utHkv/nNO3TUigTzW/AXvi1J8qsBzKF79/KLxUJYvV4WiqtGzh8gdjvLy7dq1bxcq9Q2hIlXoro9UAAFTiliDXLsczValnZ3asU9PHx8vV0b3pxFNl3uKU6La7Szr5eXl6eDXot+aRlAEAjY7Nacn1+R1r+9Ty8aru3XTMwfjyWk1Lwn7r6lu1modX3V5rit7ZfAojfbRuYCOPatWrV/NsOv5oovpDA6sPuiDJqeieLf42r1SF5e+7tF23Ea2qeE/Tvjtj0g+0d2t/IL1oh/2jXYkQ+r8lIp33rkBgaSEidM0rmrUd36OaHgFAyJ9t35fVYtGU5vZCjsi5w7QJTvf3PMzjWAT06uBrI+JwDD17TOpu8uxSTOlpON0bFn9GBQoBnlX7MR2dBABC5+Bg88S7iapySlY6BNYXhF+IlueGX8puOHGwVcSVOLnk4YW0aq299MuZzYrc/beqzfyxAeY8nnmDMQvaa/7e/VQBAKRhyMzRAWZcrrhe6OyQ/FMHXyoAQD9wR0rG0RaGJcpgc69O7LmWN/lK1Js3z88Oy1vcfcpNbaomx7jNP3Xtxr2rswV7Zu+P10DBvVl9Nojnhz2Pinpx6zfbbf0mh8mK97l9z9UL6xvWXXzqz842H96lqJJjPRafu3nn1gbPGwu3Gc84eins1lbf2yt2vlZ9osCKjF5KVZL9UIeWCXN1Fq5MTKi9LSol8tDCmc2z9+x7owJQxx3ekdp4ZFP9F1tWPfDfcCfi6Ys3d2fx9y88WObtwwea7HTbiRcio56eG0Ot+nneIznIn5XdnE49MWcbTL8S/vT54786KsJuJKt1tZqV3pgyaLfNkruvXkac+MUgVUpVbDrz0dx+qzShF168evn0QKuIRQtv5LLvG/h0g3/4pAru2eJ/VaUrnG5Z2KV/H165qBdxdPNDGYAm4eimNw1Gt7R6/xf573clQuhzlbhyxjGy0lfFZSlY0C8TilwjW+PCkzKVFZuZfWeQr5uAAABg1VLCSyrXSGO3TZm28cKLTDVJ0nlSTqcys0K6N6RBzPnwDv3xlk8UAhx9M33taYPgCTiUUsMC0DpLFjRqVSN/3q03rV7EVunQovW9DdPuxda5EmPX0t+0nHtsqdzkApMAK772Ed+qmlh+PUcDZsA1dbEUFK61dDaSvc2ly+tSRfTpu8L2e1rY8wHAuWNoyOyhZ6KVtQF4do2DXPQAwNjV3ViVlU8r3565GBenHNH4NAkArEaqtI1IUzcx5H98n/HsAoOc9YAgrJwt7c0CnIQAhJm9oVqikP+zAksWXqqSH0aSOguvA8B3bBriKuIKwKTOsE5U/13Pfl0g+GuvrM2m+sYi0ZyTW++eP7jmUlzMi1tRBcr6yvJHOkLPn4cFmnMAHNsObTht4uUEVR3fsptz7Ot3qj5raKPgQy2ahbQatzDYhXmxvWzF8gxO3+G32NXIggNg02xoR6eIirRenXA5TN14VVtHPgC/5q/XngDIH/z9voHy2IruWQBesXd5pSvMh5zC3+nVGDzYsvn6G5LaVfbsyG63KcCEePqx3v6sXYkQ+leJqOferr5g4O6LaZ16F584ZQvnTt+nJNfU0cLGcVfE+vqiYjNvkXNaL0oacTr8rI8JVxk+za9T2vs5V5b92IYlpu+erxyks5BPNkN3yTS0cU7aeeRwgThkik11E6/01YcPxekHTLQr76ZSrom9fu7bDDV48AFA/S46R2Qv5gEAlR2boQIQAoA6Iy7PwN6EU25lkBdAhgAAIABJREFUWNA9Q0dweFxtHxIEwQIAy9Bg0nLT3X0hRgDAFKSmKsS2qss97DqdVQKAsPXx2E26yuEKuURRmcWzXWeBn3cOLVPJTxTOPgKCL+KThSf4gX31O2292Up0jNPzgI+Iybk8uuHAZ4Gh/YMa9mrum3N3PlusSNnlHi5FLX0+EYDkFjaGIDkkl8dhcy6Hlt1c6Dnu3Nuu4dcuXr5ybmrQfO9tYeN0VIz3jGbYol4i+RyiIq1nGYopOs5ZeUp0usCS/dDACu/ZEk2LPViqwg93NSx6A+LcI9Rr3aaTl1yP8PodrikC6v+3KxFC/2rWlDANnj+z1v2xXSftuZeQK5fnJj48PP/nwYdSS46H9GoM6mlwYvKS8/EFNEsVpD+/fOZBpkaRq9Kzd3M0JBl1+rXNhxNVLMswhL6VnvRtnJRiGIZhhN4DexicmLz4fFw+xWjy055dPvMgQ8N8QClydBRSAssCfFjHArBs+SUTpv7BlhFr9+XXaWDNFXm1snu0+ki2d3NXAcPoLlbo3ac1//ScdXez1Oqse+vmnOK26ltDwLAsI7u8eN3dLLUm+/6GBZcM2nZ3FzAMo3x7YP7snS/lJYoSVG1dV3lq4/lEJcMo4k6uv8jUC3Hma+tavN4MI3Bp2ZB/ddnOyHyaUcUfGFgvcNrdPFHT/e+kUqlUKn23v6k+cLigyVdQxTYvVs775r9f5OsqkC5WN9mNAXaWnc9LyzRfR+FFy4V14OkuvMQO4br0GGR7edqkk2YDelXlMYqYS9dy60xaMGlg16bOGZfuZqg1Grqos/VLtJRlVW9OnY9XMowmI2zvA6OQpna0rs2VL5c38Rnx2Lb5gF9/XzezIfvmtcJRR8X41doHUue2Xk1VM5qMW3tOxatKHUk6u4LnGBzAC9t+IUnF0PmR67o3H3sxiy5qYMX3bPGmNUwuXeFs9YfdChYtxgS+mT3hb7uh3V24H45Ewad2JULoM7Dl+NgYEUBQbcTxMPPFM5b3rDVSQvHNqtZtN2zaUAtOLADA++1ZvZ+mH99CjJ8WYNstj9WzrN6w17y1zZqMmx/Sb1jtn8ztzMw96jd0ELIMw/Bceo0LOTLaTTxIVO+Pe6f715p2bBMxYWpDu5g8Vs/SvWHPeWubMUzR23dhzfE6CmGKTbQxDACrTQEAhmVZAG3Y6i6Z59ikjvHvqbWauQoYEPsF29J3Tdp465coUltsYbiI6i08MG/iqB5VF+WwYu8ucw8sqq/PKhgQOHZuLZkT4HA/FZw7zN49o5aQYRhQJZ3fvCnPf3zfasXfvRs1WbZ7xLgJzbymEiTHKnDi/t8DDVkVyxZVnHm/rF9/yf6ZE8d18V5DcsDAf8zuxQ0NWIYptpME7u1qJ4zza5KwrBaAiNWeMQvLYViWfX8qLlz8RIFMXmIKUb29K79kB2jPwiUL/1BJ5n0dMi4d3T9zWqnClcyHTQCAtG03pPqMUdSqDvYchuF4/PJr897j6vsusTE192zUsqr8dXI+XbX4Piy2B3hi4fVx7c+xlIzxnPbXlJoCgVrH5pwW/WZ3Gzquoec0PT6h5zVqVX9nsXHZVgPZaPHu4WPGNPb6lW/kXMvRklfyFcvpCr3a83aPHj+mmdcMrkDo0mHN+k6WKXeKalvxPVusUK5LmQpzBURhl165OMmjzqDmehfeDWttSxQe7BU8NhBCnzUBRhA6l4uvJGSyT1yvL0rRwjgsysViP4svfJcUjybUG6K38+6in4SV+YiQXRnc+njv02ubGhM/+h/HN9MVTP7jxa2GSVeELasjwnMWQl8hEbULRT9LPPx4IpZNwbJDzuJZ+J3mIpWyv3OTHR2vnh3k8H/4kL61tfU31br09PTvq8LYFf+4/gihiowOiTI+PUYsm4JlZ2MLJ+zKDBm/p/FhxJzgzodN+q3bPbupOQcPHoQQqoSJWPSTJMlScfhhTXmJWDwOtbFXdMGq1OXKopiESjOJihBCqLIMDYuPC7XhRxZT9JAgCG5F4rAo/GiapmmaYZiihbK38WAcIoQQ+qZGh8WHg1ocDofD4WgXtLFFkmS5l81KxaE2BSmK0v6kKIphGO3DsrmIoYgQQuibisPiWcjlckmS5HK5XK72Q0+sNhe5Hx8gFoWiNgU1Gk2phY+EIkIIIfTfhqLOONTi8Xg0TXO5XG1scbncT8yaFk2WalNQrVZrf6rVaoqitD+Lhoxlp08RQgih/zYOixKxKAv5fL72J5//4TPJ5V5HLIrDosnSoiBUqVQqlapoofh48cNnxXHWFCGE0H+diPD+5tKiOOTxeFwuVyAQCAQC7QRnUVp97M6aItqbaLTJp1Kptm/fjh2NEELoe9e5c2dtHGpTk8PhcD+ShaUuImrHhQDw+PFj7EqEEELfLz8/P7lcXjwOdSRiqWgsO2uK/YgQQqgSUCgUH7KQyy03EYuPEbUzrdphonaMiBBCCFWCRNRmIY/H094K84lPX+icNUUIIYS+d0qlksfj8Xg87U2nFEWR5Y0RAaDoS9qKPpVPURR2IkIIocqRiNpPTBR9tr6i31lTdDUROxEhhFAlkJycbGhomJuba2BgIBKJRCLRJ/75Udm5U+xEhBBClYB2vlQoFOrp6RkYGOjr65PlpSCU+SI3bShiJyKEEKoEyn4XKVk2Dj8yTCz6thuEEELou1b8S7m1982Q5T2V1QUTESGEUGVKxOIZR1ZkM/z+boQQQpVM8ZGeNt3IimwDxS4uIoQQQpUmEaHY5UKygnGIoYgQQqjyJWLxXCSxU8rFte++9ez6Fmbkt1wTvlO/nWf/aGJMfPlacKw7bz67sZ0l+dm99BUriRBC/9gXOttzTLzaj1m67fDZS5cuXTp/dPvScZ19TDj/tlSeQ+8dZ9c0M/lKEcXkPtm7ZvPfd15mab6BKrGa1OevdNeEVSc/e539VT4Yw1JpL15lqtnP66WvXEmEEPpmEpE0aTBh4x+DXd78OaKZt7O9k3fLsXvT3AJtVAWaYmdSgsvn/IMxA6tJef5K8lW+OofJf3vtzL18U0sDzv+zSv+w4d+qivYSQgh967j//yIF7n3HNieO9O4w6pLa0sHOxUXEy328c/pVSYZM3Hj23i0Nbx+I827sbilKWTt0VqRDx1GhPRu7mfLUkqiL25ZvupykYkHPrffk8e38XMz1SLXk5ZUdK9ZfTFAJfScs7mfP5/5++4mcoGJ2Tpp1NkPk0WFk2c2L01kUC3ynfls2BNzYG+0d6GlhJuYlXfhjwZ/3c0p8uIRr333TlsaH+42+IGF0P19Zq4JV4jv127LhfcMNmSS1tcWlUcN2x6sLx5mbNje9PHTE/mSu7tp+ssur9lw2emxHD1PIenLsj993PpDQ5TWcNK7Ve+LYLnXs9ElWI42/umLKqnu5DJCGunuSb9Nk6NRRbT2N1JkvLh59peNwKd5LADyL+v3GD+3gby9i85PuH1+/au/jHLr8SiKEUKUeI/Lt69c1zfh7yzWZedWq9mYGAi5Jcnh6Rub2VZ3FXAC+SyNiz8gOTXxrddyeWnf27/2Mzo1r5Gpt6zP4vGPo/O62wAABdObdNQMaVrOxcmo0Ndxn7LQWxgyjeLJyyrY41aPx9d3d3Gu0WXIpRthirq7NSwzIdBZVWFPXBupdI7sE1/UJmp/ZYlw3a7WGLTv++zBXWfb5eY8/o0pFDa/RYOBv4ebBvoxEwQIAz7FlK+uYvzafjUhXseXX9mNd7jk48PnctjWdqob8Lmk1a1KAAcuW03Cec7eJPU2O9vdxsDS3rtZk6OrzEfFSimvTRme1+VV6LJjSKGV5azc754bTX9dp58hndI+Stb3Ed+qxaE4b5Z4+NextqnbYpOo4f2ZTY2DLrST+/SGEKncikvoW+pATm8kxNRfpmkejY7atOfdKburi8VOLUcNrpGyesOTcO30Xd7v867vvG7YIEkgKaEXMpXOPU1nzqp7VjBLPHom3b2Sfm6lggcPnEXweqe/o6ePj49u4Ww9PXZsXP2mz5RUFANTrrRuvxChNXauZZTxJMarlosrI/1j86Hx+xav0oeEeVqkXIk269XWSKxgQVmsTLH6282As39KUryy/th+buEzeOffPa6kCB0fei31H3vl09VLmKuU6iwJaRXEMrezcavr5+rhZgEpkaSYU2DXroqvaGrvg5rZv183YEsHYutkrb+6+KmUpTfldxHNo1sIxftOUP27LLVyrEOG7D6V7dfGjcpRsOZVUYSYihCr1rClTkJkP3rZiPp/SebWMzkvJ41pYGfEIrqmjmOs69NjT/iSXJAAIUqjHyPVkkhxbtzbDhvcI9LIxIGkahIaCJDOurKDUCK7czbPNPnxfKyGwazKgnKIYuUTOt7Qx5BIsrWGAb8iRyzWsoaC8i3y6n1+hKmWyxRsOTG7EiYf8yQPcVy3N92gfKHw4/VSayMKLT36ktoXtMW7yx5EZXgAA8HplrzHn8gCAkkRnEBb2FiIOoclLk4FZFWHOacK7+8iyRRXEH5mzmOo6cO2xn+yoxAfn92zccT2ZU061pUJrfVX8s3S+jbNYQAKVm5yjqfKR91BcI2tDKjkilWvjaKbHITS5KVLW1FkkzVGWU8m/1SaWArwBFSFUaRNRnXz3oaRXh141tu3KB/hwuiMIKDqxc/lcAoCWpkrVkb83Df4rk3x/nmUZmjBr0nbWpEaRU/r8cuxZppxwGHzpzgCKYWgWAFgAorDM8jY3cv0QIFzbNuUWBQBAcLgkUTTBCizziUGLrudXpEoebPGGA7Cy56duM/N+qbXuYod6cHPEhUwDS2/ep2oLAKz0+rigY6+fxuUDAIDIyd8BgGtWzVp0lyAAgCd2NCHyUnMpq34zJzV6VrYoVpF4c+/ik8tCpRzXLquOLAwNvzXxSTnVNm+Vls+ramYgTOISAEAaWBpyiY/cFUTlvZNxazqbid5yCQDgmTqLSVlytlLNZ3VWUqnis4CJiBCqvIkIqtd/rTlbd95fa8mVf92IjJdoRDbVG7Tp4vN8+W/3SmZn4sVjrzrOXB4q3XIvWcZwjWw9ajpkP7ybITYSgiQ87BVl5eZjX2/4KG+B+iQBLNDy7Hww93IQ3lR9ZPNk1YeXIHmGuov6f6lglcp+7lPx6sy1gpXTplexV1zqfSPP2NqRW9Hack3d/UzfP+ABAGn/84zuN1Y8KhA4Nf+lk9XrdeeSOTYmerqK4ts2CLZPffKUKzTjWoiAZtm8xPh0zQXd1ZZcv5TWYfQAj+HnlMC3aNAz2IxM+si8sibpysXELuPntH+54VmBXtU2wzpbvdl4Ip7hexI6Kwl8N4xDhFClTkRgcu6sHjbkUee+vRf0nyPmApOX8PDc3o3nI2LYgJJjipRTcybk9Ro4f+9sez1Qy9KiHxz7/bI0T3Vq9YGqoy8+HCSRKhVJj+7FqWtqpzlz7u060WzS6ddDeapXG4aOP65zcyXfWEgWhW45Rf3/2lqhKhnoGEvHnTv/rkv/qkkbR9xTip0MOP+8tuqo7be95uydU9OclEQcmfzL2mieheD06gPuOooS2jcaPGa8s5mAYBTpz87Nn3DyHWNukVpOT8YemL1MMG7jkZ5KqbIg4fH1JFUV+iMVUscdmDmbGhq657yzPitPvn9oXP+VLznmnoJyKunJx0RECH07CJlMVuIMzzAMw9A0TVGUWq1WKBQKhUImk8lkstzc3Nzc3IiIiMePH1egZJbKz0xNzczJV1IsEFyhkamVtY2ZAUeZ+uKVzLaGu+n7MKYLMpOT07Pz1QwQXIG+kYWDk5WIoPJSYxOylCSfz+EIjfQK0vPMvDytBQRL5aXExGfka1hS38HT3VLA6NqcLFER3UWBokRNqOzXz1KNPLxshMXO0qwqPSoq18q7ujkP2PKeDxWqEqEo3XAAVpH6IipNxbf29LbTIz5e22I1Kd3TitQXL6Vmdnq573LkGpZvaOngbGfCI8opiqdMj4vLyFNoGACSb2hu52RnKiDL2xEkMKrs5LiUXIrD43L1DAX57+RW3tUtirKM59B78yb/FbXa3rB0M+cBAKvOTU1IzshTMcARGlvaO9kY84hyK4l/ggih/4Kfnx9JkiYmJiYmJsbGxsbGxgYGBl8uEdEPgRQ3nLVtnHRgzeGxztXNeNghCKHvNxG52C/oHxN4jt2xqoXm5vKf7ykE9vidNQih7xsmIvrnVFGrewZMfPFaQojtq+rjt8YjhDAR0Q+MNHCs4eeI/YAQqgwnNOwChBBCCBMRIYQQwkRECCGEMBERQgihUvDOGoQQQj8iExMTU1NTMzMzsVgsFouNjIxwjIgQQggB4KwpQgghhImIEEIIYSIihBBCJf2TO2v8/Pyw4xBCCGEiwtChQ7HjEEIIfb+2bNlSdiXOmiKEEEKYiAghhBAmIkIIoR/ZqpMJ6qIHdMb5eT9jIiKEEPoRESWGh/pubcdhIiKEEMJ41Hfx88VERAghhABAiYmIEEIIAYAAExEhhBACAAITESGEEALA/4+IEELoB8XSGoqiGBYA2Lznx6/QOEZECCH0I1K+OjRv+vQpB+LVAEBoki+txDEiQgihH9HUqVNNTU3NzMz4AIThT6M3HMcxIkIIIQSA1xHR5yooKLh58yb2A0LoP9SyZUtMRPRNmDFjBnYCQui/EhYW9oVKxllThBBCCBMRIYQQwkRECCGEMBERQgghTESEEEIIYPna3WeTNJiI6FtE5yXES2nsB4TQ1zG6X0gNE/IrJqI6ekN7f/8GI89kMNoVeVeHNuyyPV6D+6KSUEfNd9MPOin91wXJ74/zbbritbrUamXEFBfjNufy/pPGMVkXhlerPjlcifsZoUpIYGLjYMj5umNEAoAI33o8ncMh3r+mob2DmEfg7qgMuPbd1u6dW1v070eI+e9y1N9Qw5i8p9sGNWi5+a2Cxb2MUKW09ejNV3nM101EEPiO/0XvwOLL+ZwSIchk31k9snPT+v7+/g3aDl8dlkEDgCJ8bqv283b/MaJ1fX//Jn1/v56aePG3/iF1/P2Df9nw+H3V1WnXVo9s38jf3z+g/ZjND7IZ3LP/GSr58Og+cx/KQR0138smZNwvjd0crE0MbRtO+DuNAnXUfC/LRoO61qnu6VnVxbfXmsd5TMlhnzL8V2eTdqefbe3Y9XB28rpA106nssuJICrt/Oy27kYEQfCs6wza8lTGAoA66cTEJvZCgtBzbj0u1NOg4YEsVvdKWnJzabcaYpIg+LYBI/e+UbCgejbTw67FkDbOAo5tz1NZxV5X8XCiX+OV9C8LGhrghQWEKikfzaMDZ+JUXzcRQeA+eHrD1yu2RBMlM1FYrfvyi1Gp6fHXplqdnbsqXE0SAKBOvfqwyvQzUTH3F9qenz50+m3PeRffRF8dQxxccT6XywFQvf5z9Jy7HnOuxKYm31nofGHGb3fzCRxxfgM06VdvOS97mJD+7sk0etOIxU8UAKDJvB1eY92DF1Evw6YwSzpOuC7VEXhcp8EnjnQztQ8Nizne3lTnzlS/Wd2+0xbhhDCJSh67q1nk+JBhZyWqtxu69j3s9HukTJm0P+j5zpcFAKDRtZJK3N2j9RLpwPPvlPLYHQG3h7We+VAOAOrUy0/8/nweG7asafHX5buPv5YUuWeEvwkH9ytClZSBkEOSBLCKmDN79tzJor/SnTUcsyahP5scX3Qmj/Ph9UhT3zbBnmJKkiYR1unZ0vDFHamhiAPAETefMqyRHSHn1WjuSvCa/zq0oRWtMPRvapv2QKJvxFO9Pno6p9GsWV1diNwsqNH7Z9vHx+IF+hzMxP+ewG/kIH9jEgSOjZva5L56pwYAwjB4ZmhtIwJ49u0ndIYTWx4X/KO0jT++/ZnnnBWDa5ny9RxazFzTj/P3+hvhh7c8qzFvSU83A4F5vTErRlcRAGhidaykU86su2k1ZsWouhYCPfsWM5Y2l+zbGiEHAL7viGFB1ZyqOpQYDHKMHO0NMQwRqtRuKH26t6oiAHVC2KnTT3Por/a9pjznTqGN985d87x+vferGNmLQytXHbj1NltDkky+jGwt54rMCOAYWhkT8hypiuHweVx9S2NSLs1Xs1wBh5ErSCGPzk3OloaPD/DkMgwLwGryoXoOIxKS+QV4n+J/jKNvpq/NFQ6PZBkWAIBrbG8pJApjxkYkj8xUlhgksmzpMWP+9b6OQXtzAABM+50/aaNNxOyEXIG9i2lhSvGt3MypRwlxr7OF9g6G2tfkWbpb8m+COvNt2ZUaSUyWOmZBTeGCD1MUIZkqAIJnYmXAKfWi1xN2NzbA3YlQJTe8Z2NTIxII46ZLD3cRi/lf75u+CSP/wQOs+y06VoWnXaGK3jVzc3rfPTdG1LMRUZGz6ndP55Bk4dt0hmI+nDBpunjSEVxjG1NL152P19URFD2JBHVWCgGA90B8e6jc+JQCFvQJAGXKC4l+FWsRh0tQSrV271HStDwKQHsHlnYLUe3fbj+drAEA4JnaKdZqk03saKy6H5tDgyEHAFRpLzM5lo7O1cTK00kyBgxJAE3W2ywNAM/cpexKromDidBn0stHo6twAYApSI7NFDqYpD0EQjvjXuJFXUW43xD6EX292wa49u1GN5dsWR+pJAgAYFV5aqGjr7epKiM+/NDqQ4lKDV2hQBO6d2kjOj1t6aknr+MS4l49uXJwz+kncWkyCuPwm8TmX58571SCQplycdGkk3rdhvmaWFS3o59sP/ZSmp98ffW8Uzk0AADBE5GyuFdxGXLQt/eoqeVhXzjmBJ5zpwFeUfN/3RmZq1EkXVw0bq+m1bDAWl0HVo+cP/toTIE6J3zrtHWxSgC+i46VXId2Q33fLpq0LTybovOebe3j491zfzJV7A9B14sihDARv9QwUb/WgME1NAAkhwAQeg4c2+h1aP2GbXsOmnHNxN9GyK3gpUCh57D1iwKiFnYLrONft1mvGfsjC3giAV5H/FbxXOtmz68l0nPqd6P2unPLAgxJ605rl7dKmFrTxLDmpLg2Y2sZAADoefbsYnOpv3erP+N0flqV7z7+5KEB0iUNxHxR1QHXfFZe2tbOQug+9uj21i9C3Q0Etl3/tgiwFPL4HIGulXyXYceOj9T8EWTG4xrX+00+5PCR4a483DsIoeIxJZPJij9mGIZhGJqmKYpSq9UKhUKhUMhkMplMlpubm5ubGxERMXTo0M95Bb6pvZ0gOyGtgAEAgmtk42AupHKSknM0wDUwtzITkTTF0GolrWdAZCa+UwosHC3o9ORsFQtAimwcxfLkVCnFAsE1srXXz01MK2AACK6+2NzUSMQjgaXVCll2Zo6cxlHiV1BQUBAYGFjBJ6uj5nvXvrYs9VoH4y9SGU3q3VvpVRr8ZCMgAOT3Rrq1y94dt6f6s7IrDzTFC4MIVRJhYWH6+vr/spAtW7aYmpqampqamZmJxWKxWGxkZPTlryOy6uykuA+PqLzUuKIvIKHyM1PyPzxVAgAAysyEpKKAlqfFyz9smhKfV/SgIDu9IBsPjR+bJnF335AXk26cGu1Dvtq75JCs9gYvkSZOx0rsK4TQJ+EFE/QdE9VesHey8cbGYh7XNPAPcuzRHV2sSJ0rsa8QQp/ExS5AXw7fc/abgtlf8AU45k1m/f1mVgVWIoQQjhERQgghHCMihBBCuhUUFPD5fB6Pp/1YMkVRmIjo81SpUiUxMRH7ASGEY0T0o8vMzMROQAj9h/79Ry/Kg9cREUIIIUxEhBBCCBMRIYQQwkRECCGEMBERQgghTESEEEIIExEhhBD6YPOFZHXRAzrr6vJQTESEEEI//IiQ0HNtPhQTESGEEMajvlPNmt9wIiqfL23b7vcXKtxTCCGEvkLqfJVEpFMP9vP3bzQpLO+j/+VeGbmoddsPEcizDRkR2tXf2UxA4I5CCCH0hQm+xveaapIvHo0ROwsf/x3Hbe5LK+mPPJfQs3YwE2RKVCxwTH3bttbjygrkuJ++sdkFaYbeuQ1k+ltgmDK/IxmbavKWIxUkL+PtS8ef6mN3IYS+E8RXSER1/JkTad4Ttzfe/suft1RbGwJNA4A65cr6RWuOP0iRc4xcmo3/Y17ThLm/Hs+QQKcaJ7hCrwk7/miWsazrNGLp/m73Bg58POTYuuamJADIwxd0mcYuOjbbl5t2beOCVUcepCoEtg1+njl3SB1TvCr6VfDObRQLueZtfia4XAKABSAAWJYlgGAYShL3SvPw5PmIGEtXD0xEhND39Hb/i7+C6u2p05kevds1atfSPHzblQIBhwBQRm0MnX3LZcrZ6LTMpAc7RzepamUdOG95V2v7IadexsW9OBXqb2vAASD0rFxqtWln/fJMLE+PAwDyqOO3iKYDWngYxv85es5djzlXYlOT7yx0vjDjt7v5BE6vfhWalLdm1WowqdF04gsq4QWdEEUnRDFJUXTiCyYnnV+v7Zlbj/h6oppte2FfIYS+XQxNURRFswDAyF6ePXvviyei/MXRC7nefVrYkhZBLayebz+bI+AQytfHzkobz1/Qx4OXm5qWL7IxoQrUDLA0Q6tpSpqaEB+fmJ5fOLvKtWvazjZyxy0VnwP5z47f44X08RdlPdp/OqfRrFldXYjcLKjR+2fbx8fiBfoczMSvQE0zoFGx+kYMyWMoiqU0NKVhKIrli/Ltvbdv2UTRdL2+oRweD/sKIfTNUr49tXzx4oUnkzQAQNCpNzZ94VlTVvb0yNX8mvNaWGtkyXbNWthu230io0dfQpoqFzdwEcmz3hVoWACg1Uoayrvthmsb1MFh6+4bymC/yGP39Vru8xPmJ71LypaGjw/w5DIMC8Bq8qF6DiMSkvkFNO7nL52ILEEIRGl6VoaWAlHsI1ZZACyAgUmBU83tf+2T5kqbeFTRMxZjRyGEvmWjR48Wi8VisZgHQBp4D16688smIpv3+HCYTK2c7OcwHViW1agYZu+h5AEDTGz1pTHv5GrjUilIAKsjFzlQCscfAAAgAElEQVTWgR2qbNxzNZl75qFhm8k+vIJsxtjG1NJ15+N1dQRFN3eQoM5K0V7XQl+ShiXA1Obwzn0EQQzs3lnv7QPgcOVVfHbsPSiRSLp06ULHRGAvIYS+O1901pTJvnvwHtl4zd3IiPAn4eHhERGXZnlnHNkXp1+jcyujyzOW3UhTMYw6OzrijZQBIPXNhbL4BFnZRCMtG3V0fb16/pqHpu17eHMLZGqBe5c2otPTlp568jouIe7VkysH95x+EpcmozAOv8IYkQGG0gCwEolkx6Gjcld/uYvfjv2HJRJJg7p1a1obZ8e9xl5CCGEiFg/EzLBDT43aj+/oZkSoVSqVSkWbNu5XN//U7ld6viPXzm/wfHaHBnXqBHadceStmi/gCBzb9m+YNMHbpUajwcfSS9zXT5o36FQ97Va0XY9eHtz8fA0LQs9h6xcFRC3sFljHv26zXjP2RxbwRAK8jviVxohMenyntm2EAoFEkr3z8PGdB49IJNmuLs7BdWtRL+9rWNwPCKHvDyGTyUqkGMMwDEPTNEVRarVaoVAoFAqZTCaTyXJzc3NzcyMiIoYOHVrh4rmGtg6mqrTELGXR4I2jb+NoTqcnZSg5+qbmZkZ6XIKl1QXZ7zJlGpbgGVpYmxvwCFaZmZSWzzFzsGLfJUlULABwDG0cLfSo7KTkXA1bWLy+2NzUSMQjgaXVCll2Zo6cxlHilxa/sH8rn2qshX26gf3eQ0eUKhUAmIrFQ/r14kReIyn1uafRVWbuwo5CCH2ztmzZIhAItNcRjY2NTUxMDA0Nv/SdNZQsJU5Wch1dkBZXoI3fAklagaTkBhpZRpIs4/1DKisx8cOGsrRSZbFUQXZ6QTbu2688RiQIjUYNqbHWVnSf7l1Pnj0PAD26dOREP2AU+TQAhf9TBSH0HeJiF6DPxber9joj093SkM1IsCFgZPd2AMBkxDE5GSwLb7KkfIdq2EsIIUxEVPnZdByWcHzL8+cxLMsAJAHcKvoVQZAGjtUcOgzGXkIIYSKiH2CMaGLuMnA69gNC6Lu2ae1aDdfEI7jX8G61TWjJ7Y2zMRERQgj9iPqODLUm067u27XN2W2K49l1N+3wDgiEEEI/IgMOwTP1aNHU9O3NGDmHRyrScYyIEELoR7R9+3aSJAlGRTjIwbnnzAH7MRERQgj9iAYNGvTh84hcA8MOE3DWFCGE0I8oT8MCo85LexURK1Om3rsemYZjRIQQQj+iA5vXqYBraFWtYc/qdeRx51ZvwEREn6egoODmzZtfqPCWLVtiDyOEvo6hoaOLZk1FJr2W7hyKiYg+24wZM75EsWFhYdi3CKH/EF5HRAghhDAREUIIIUxEhBBCCBMRIYQQ+n4TURE+t1WnNW/UuM8QQgh9d4moif2zc8DAU5kMAACoXq5s7e9f/GGbhsMvZn/Ov7wnRDb2pgICdxtCCKHvKxF5NnV8DeIf5hkbcgDorIgIja1Zerjs/cOHOfYtmtW01efgbkAIIVS5ExGEzg3dmYhLCaSAZGWv7mTXHDrYPupivPbhzWRxQCM7yE24snpk+0b+/v4B7cdsfpDNAACo066VXfkekxe+aWBItyU3Mmk64/rKIa0b1PavXSegxcC1UUrcowghhCpCX1/fwMDAyMjIxMRELBabmpp+2UQkDNwbOWbfupvN46libybYhIQE1Wbv3ZbweKrYm9Hcms3d6Uerhs++6zHnSmxq8p2Fzhdm/HY3n1C//nP0nNIrtXOlLJV5c8XQX2/7LTu8+mdfo5ity644zr0Zn5mV/vriyn5+jjinihBC6FscIwJp5lPXPOFCpArePXipV6+Oo3NdV8mVp0p49+Cp3LWlD/lk98mcRrNmdXUhcrOgRu+fbR8fi2cSj58us1KgTwKrTjkxvtuMNy02HVzcwU6dkZqj4RGKd4mxb9/EJGaxpqaUtIDCfYoQQuif+NLf4sa3r1dDcPLc62zfRwqvKVV5JCfAOu7cq2z/+xk2IXXNFM/is6Xh4wM8uQzDArCafKieo8pPzpY+LrWS0bMg6HcXN54Ud903OshckZ6Wo2L4HsN+C928dWanpTGsS+OeY6cObmCBVyURQgh9i4kIev9j704DY7reBoA/d/YlmWQm+ypCqbWWkX8FsQSNSO17W6UtWoLSxRK7UBRFUVJUq1S19qVoLU1spUItQZBlMkkmmWT29c5d3g8JskwsbaO8nt+nzM09zzn33DvzzDl3mciYeo61+47qNHX6NpM6ndLGbaTf7Tuiy/GM6hTBoe4GK/zrbb64Okp4/1Qhx376tMK/bpWFYDgMvLC3vp1hm/7BsM/WrhjWUMIB4Mia9pv61cglnuStrWP7z07p/OushkCzuF8RQgg9qVq/H5GQNeoQot6y6oJPlzYKyubiBSlfNv745WmmaY/GQgfU79dTcmDa4n3pt7Jzs2+mH/txy4F0jW+v6guzNTYWgBcUN3fT7Do/jftoe6adBVJ1/Gi6qrS0WGt0EHwel+vp5yPCE4kIIYSexYwIPP9WbfwZs2f7TnUIG8mAKLJ9PdpMv/R6a5nTRgobj1mzoF1G8sCYKOX/ug5N+uGKlS/xavZ+9YXC8qZy/TpO+Xpa4A9jP/kpy2rO3Jv8Vuyrbdq0H7LG3G/BNKWExQEiQgihvzWEM5vNFV8zDMMwDE3TFEWRJGm32+12u9lsNpvNBoPBYDBcvnx59OjRT1qL0Cc8xAuM+apSJwsAXGlQeICYLFEVmCgWgOBJ5b4KmYTPAZYm7WadVm+jofpCO9833I/WqHVOluB5+If4co2FRXaJv7+XiM8hWNblMJYW6204a1qbrFZrTExMbUROTU2VSqXYwwihpyAlJUWhUCgUCh8fn7JfSZTJZE/n9xFZZ2luVumD17S1MDurwr8pq05j1VUp5GYhrc3Nu1fEUpRrAQAAZ2GeHvcuQgihfwif9I0QQuhF9MXe3AdPyqaLD88djhkRIYTQi6jSZZgcaYOEDzEjIoQQwvQojWzdCjMiQgghBAAOHvYBeiIREREqlQr7ASH0/44QMyJ6MlqttpYi460XCKH/FIGzpgghhBDAU3iuKUIIIfQsYmkXRVEMCwCs6druYzSOERFCCL2IHDd3zJ0+fcr2HBIACJf61+U4RkQIIfQimjp1atlT3AQAhGfL8Wt34xgRIYQQAsCnuCGEEEKYERFCCKEH8DwiejJWqzUtLa2WgsfFxWEPI4QwI6LnRlJSUm2ETU1Nxb5FCP2HcNYUIYQQwoyIEEII3YOzpgghhF5EyxYtcvEVTXuM/Gh4OzmtPblsMmZEhBBCL6JRH08J4ap/2bj+y/qN50fuXnIsDGdNEUIIvYhkPILv06x3D5/MY5k2roCw59fyGJG8vXbA0E0FZS8Eiohm7V5/e/TQ6CAB7guEEEL/pdWrV3M4HIJxQoSVrT9i0fuban/WlBC0WnvllwHeLlPhrdM7Fn868Y3rC3Ys6u6Lo1OEEEL/ncTExLLnmsrlcjnPUzZ45tPISwSXQ+kKi4yMd/MhC1Mm+p9cuaOIz2N1Z1aO7delrVKpjE54f2VqMQ2unG8GdR57RMeUlbOlz4vrMT/dRhefXD4qPrqNsk1Uu9dGfpnhwB2JEELoHzK4WGBIo/r6hUyTQ512ND3/6Y3UWJZlaRf4te0cWnIq3eEhIEQvDVp6NKNAk3NiasChOV9cIoXh3fuF3tx3iyvmAoDl6s5TvG4j4yLyN31+LHxOWo62RHPr6PK3WocrhATuS4QQQv/EN8sXfzpt7pz1v94004zlzp55bz71a015nn5SwqKxCST+rXvGCj2o0kKTKGpInOeUM0bPjqGdB0R+vfmkLbYLobn08zlxz+1KkTWVR9jVqqw7mYSXkKNQUEYrg7sSIYTQPzJpytT7s6YS+ci1eyY+9bN5lFlrYSW+HlyO9fr2WW92jW7bvmPn18b8kG/S2XgSgX/7gS/f/fZXk8D8508X5X2GNedbqJdGL0qsc2pG3/ZRnQd/tO5Ugc1Js7gvEUII/buedkYk1Sd/y1e8Gh1MX9+QtF7TY/XvGVnZWTf3jwkX8rkcDoejeHXgK+ot+2+d33k1YMDgxjyLhSRkTftN/erw1ezrO9+B7bNT7gIXZ00RQgg9hxmRpWmKdjlMhTdObJqeuK6kw6eTWwusJiMpCm/VVOEszrm0Y+UOlcNFswBAyFoPaFO8dsrSK+FD+jcgTBaXU3X8aLqqtLRYa3QQfB6X6+nnI8KUiBBC6N/1FM4jkuljG9UBABAoIlt0HL7p10m9w0mt+qUREztMS2zb3tvP06teS2WQiFc28iM8X+nfDkbvb/rFgHqsuZBiaUvm3uTkmSoTzZGEtBm6YLFSwpbinkMIIfRcZUTBS2P3XZkRGurNvzdcdDls5kK10U5xg7pP/76fb4CPhENTDE3OpcUS0AIACHwjFJ5t3+kRQpnUFAvixu+vPTgt0N9LxOcQLOtyGLXFTjyRiBBC6HkbI7KkLi9L5/5flEWbb3nwuhQAgLVc2f6zru3czj5Ok5oqy3ysU1+Yp8e9hRBC6HnOiE+C0R1J7JN0ybv73O3xPg6jmsYdhBBC6IXMiBzFa2tP9w8K9xdR1hKNHRMiQgihFzQjAgDQlsJsC+4YhBBCtcpqtQoEAj6fTxAEAFAUhc/bRgghhACe/h36CCGE0LOJh12AnkhERIRKpcJ+QAhhRkQvOq1WW0uRpVIpdi9C6D+Es6YIIYQQZkSEEEIIMyJCCCGEGREhhBDCjIgQQggBrD+iJu+/oEuOL03EjIgQQuiFHxES4nrdR2NGRAghhOlRWqd5c7wfET0Zq9WalpZWS8Hj4uKwhxFC/xEHZkT0xJKSkmojbGpqKvYtQui/I8RZU4QQQggACMyICCGEEAA+1xQhhNALiqEpiqJoFgAY841DaQyOERFCCL2IHHf2LV24MHlvngsACLrg93U4RkQIIfQiGj9+vFwul8vlfACOR9P3Fm+u5TEieXttr+h3jxrYewtMx0dHJ6y8ReK+QAgh9Gyp/VlTAng+4cEe3ApLPILDfITE3w3IUAzuN4QQQs9fRqwRa8vcNfuNrlFKZVTssFk/Z9pYAEvauE7DfiigAQDA/tf8+N4rbpIAYL80p0fC9LULxgzqFT9g4SVL8cnlo+Kj2yjbRLV7beSXGQ7ckQghhJ7bjGi7vOLD5Vmd1/ypKrixrW/BysnrbrkIAABCFhLixS8fQhLioNCy8aRTcyo7etmBM5cupPSwbPn8WPictBxtiebW0eVvtQ5X/P0hJ0IIIQTwVO6+IC+OaRTBI8pOJbIURQf9DwDst/adJLt8OSUuyFWoCUwYG/v1tH3FMyfXnKF58m4ThjTzsqvVJZZShrAXqbLuZBJeQo5CQRmtOJGKEELoyVDqgytXS975bGSzpzZG5DddePzipfT09PT09Isnl7UScLkEAG3S2L0ahgusOqODZnnyMJk1T8/34NcYhusR5EVYDWYXC8JGYxYl1jk1o2/7qM6DP1p3qsDmpFnctwghhP6Jp5ARCaFPkK+IYBiGYViWJYChWQCuLFBsyil1uGgWAFyluUZxoJeQxxNyGVd5emNsJkeFREcAce+iGo6sab+pXx2+mn195zuwfXbKXeDirClCCKFnPSMCALA0VY5hgQUAEDd8vSP/yLyv/zIztPHKttUnuF2GvCLkh7T01/x6SkMBkHlHt5410QRRPdeRquNH01WlpcVao4Pg87hcTz8fEaZEhBBCj2/znnPZtnvDLrr09FeT/rs79CUtJ62YvHzBuzGL9KyswWuTNn0W62kvCugzbfifs17rvMpHHtzy1Ta+hXw3mY62ZO5NTp6pMtEcSUiboQsWKyVsKe5dhBBCj29gK3bvPhPdHgDAeXvH6rSQWs6IgpfG7vtTEepD6K1lC2RdUs69HlxH6CwAQtKgz6ztw1f5yaUCLkGTFr2mxA6ezUd+eXhygL9MwNIUZbezHgKXBsQt5xw+5BcupDUAAOLG7689OC3Q30vE5xAs63IYtcVOPJGIEELo8UnDlR3Drx0DAACCy+fY82t/jMiSurzsigsYa0G29d7fTlOx2lSlBOPQF6r0916V6srjOLS5eQ+iOvWFeXrcowghhP4uvkwhKTt3KKg3ZMaIH/BJ3wghhF5MtN3sKL93j+PRqPdkzIgIIYReyHxouHU2u/wp266CcyevFGJGRAgh9CLaeVIbXLf8odu0LfuX5WPx16AQQgi9iAb1iZXL5YPlcj6AqP7QxZtHY0ZETyYiIkKlUmE/IIT+/8GMiJ6MVqutpchSqRS7FyH0H8LziAghhBBmRIQQQggzIkIIIYQZESGEEMKMiBBCCGFGRAghhGqCd18ghBB6EUmlUg8PD5lM5u3tLZfLZTIZZkT0ZKxWa1paWi0Fj4uLwx5GCOEYET03kpKSaiNsamoq9i1C6D+E5xERQgghzIgIIYQQZkSEEEIIMyJCCCH0TGVEx7XFCa8vue7E3YAQQuj/e0Ykb6/tpVQqB23OdZUvodTbhimVyvjlN5zAD+72QeIAZV0fIfGEqfTKgvgETKUIIYSeqzEiAdJw06/nnBI+AQCuvKP7i4O8OHyvkDAfiU+rhPgmPLOV+jtxxYFhT5xKEUIIIQAA+GJvLnn/BV18eO7wpzBrKmw4tDe7a0c+n08AmX3oEBk7tKmEAABwXFvcM37uuRKHJX1Oj4SkdYvGDRvQq3tsv0+237azAACWtHGdhv1QQAMAgP2v+fG9V9wkwXp2zie7iwu/7dusRbv4CQeKGQCy8MTKsb06KJXKdr0mrD+vYwDo4pPLR8VHt1G2iWr32sgvMxy4+xFCCFUYr1UcHkobJHz4NM4jCusP7Ms/sDWbJyBv7/uN22NoMzHLUOy9oV5AmI+QAKfmbEGn5XtPXkjf/1ZxyhfnHLzyxhKykBAv/r0X4qBQH0X0nKUDAkNH7buRnX19X6IymH/n6/GzzzaafSyrQH0mue6RpEVnLY7rXy85Fj4nLUdborl1dPlbrcMVOKBECCHkPj1KI1u3eipX1ggje/Xx/O27W/bM3amSXoOaCClgq67D840b26ehh7OklNdY6Z170ejhwas5g7E0Q5M0ZSzIzclR5VzccUDfYebMAZGEoQSaDRsefHFXjshDyLEXqbLuZN5VlbAKBWX8W3OzCCGEXhCOp/MUN35otz6+b676pviGT/+PG3Jv0dVX4UoUUrAaLS4WeHyC0pE80eO2jTKodcZLk9o15jEMC8C6LPCyXvC/D5ckfpEyo+/iu2xkxyETp74X7cfFHY4QQqim0dtTuvuCFxDTNzj1q9SwN/rW41gdjPu1GPr+0JEFIAgguEIu4ypfythMjgdDSwLYey94XkEK/wFbLmZcz8jIyMi4cVt1Z//wIJ68ef+pXx2+mn195zuwfXbKXeDirClCCKGaEE/rSd9c3y6zN9aBZl3rsiZ3Q8QaEnZoC3/Nr6c0b8RyyLyjW8+a6JcIAgA4Ul+R+VaumQ0BABA17N9TMm7a4u6fD27iI3CWZv113d6wjdeltJKG0c0pB+Mg+Dwu19PPR1RSbGdxpyOEEHI7eHtquVcc3LQZX2jUaB2Pfz6PF9p32vCLs17rvMpHHtzy1Ta+hWWX2AjCE95u/9vkppFWcYvpO1L6NR6zZsF3y5MHxtw1sUJFpLLXpAUdRLf3JifPVJlojiSkzdAFi5USthT3N0IIoXIs7aIoimEBgDVd232MJsxmc8UVGIZhGIamaYqiSJK02+12u91sNpvNZoPBYDAYLl++PHr06CdJhQJFaIhQl1torTBXypEEhvu6CvNKSYFvWABblFdKCv3C/WiNWudkAQiBPDSIX6IqtrMAHJE8wF8mYGmKsttZD6lLoy51skDwPf0CfT34BOvQ5hWaKeBJ5b4KmYTPAZYm7Wad1kDLAvy9RHwOwbIuh7G0WG+jcYj4T1mt1piYmNqInJqaKpVKsYcRQk9BSkpKSkoKAHjHzlg3rpU/L+e7BStqf4zIkrq87KoLGZsmRwUAAM4SVdkfDm1u3oMy+rzc++s69IUq/b1Xpbp767jMxXnm4gcxKatOY9VVqkZfmKfHHY8QQqi6qVOnKhQKHx8fAQDh2XL82t34pG+EEEIIAH/7AiGEEMKMiBBCCGFGRAghhCrjYRegJxIREaEqvxgKIYQwI6IXmFarraXIeOsFQui/hbOmCCGEEI4REUIIvaiWLVrk4iua9hj50fB2clp7ctlkzIgIIYReRKM+nhLCVf+ycf2X9RvPj9y95FgYzpoihBB6Ecl4BN+nWe8ePpnHMm1cAWHPxzEiQgihF9Hq1as5HA7BOCHCytYfsej9TZgREUIIvYgSExPLnmsql8vlPE/Z4Jk4a4oQQuhFZHCxwJBG9fULmSaHOu1oOs6aoidktVrT0tJqKXhcXBz2MELo6fhm+WIn8GVBL8eObNLecmfPwqWYEdETS0pKqo2wqamp2LcIoadm0pSp92dNJfKRa/dMxFlThBBCCACfWYMQQghhRkQIIYQwIyKEEEKYERFCCKEXKiM6rn6W0OvzDCfuZIQQQs9ARmR05zd+Mjg2SqlURsXED5v01UUT8zS3TxwQ5iMkcD/XIjJjXgPiAdmQ4xbsFITQ86h270dkdCfmfvytY1TK6fGd64D2xrljV7z85WKX0U5j1/8/wejSfy1sPOfUgXfCeQAAXKk//vAvQgjHiNVQxVfvUo1GjesRDoZiE+3dJLZrfQHDAgCQhSdWju3VQalUtus1Yf15HQMAQOYf+2Js7xilUvm/LoOS9hdQAMDaMnfNfqNrlFIZFTts1s+ZNhbAfmlOj4SkdYvGDRvQq3tsv0+237azZR/PlutbPurTThnVIWHsuguGe8NRuvjk8lHx0W2UbaLavTbyywwH7vh/je3GLzcJwR8zEl5tFRU3+ssLTimOyRFCzwOr1WqxWEwmk8Fg0Ov1Op2udjMiP1jZXHRpwUcLvz38Z5bOydCU02oyO2hw3vp6/OyzjWYfyypQn0mueyRp0VkL4cz4KnHWqcgph24XavPObx7fqX6AJ99+ecWHy7M6r/lTVXBjW9+ClZPX3XIRAODUnC3otHzvyQvp+98qTvninINHAFgvr/joa8PAbRnq7BOfSo+eKKYJAgAc179ecix8TlqOtkRz6+jyt1qHK/BT+19CqtL+LOUHd5yyPTV162jxdwNjp52zYrcghHCMWBUhazfzuxWDJec3z3mvX9fo7u8sOaImAcBxa+cBfYeZMwdEEoYSaDZsePDFXTlM7u5Dxo7z5r/RiG8oKLRIgrwpK2m9te8k2WXGlLggl84WmDA2ljyyr1gi5gDPN25sn4YezpJSXmOld+5Fo4cHz5G5P5XpOn1iJ2+b1h4x4N3WIg6fSwAQXAHHXqTKupN5V1XCKhSU0Urhrv93CBrPvGrO2zslrlm9Rl0mrF/TvXjzqvN4IhEhhBnRTU4Uh0YPnZ7yy8Vs1ZV9470Ozpp5WEcQlEGtMx6f1K5x86iO3eLi316dBU6901ZokzeOlNhKdFYXC8DSpMNJmjR2r4bhAqvO6KBZnjxMZs3T86V84EoUUrAaLS4WeHyCspI8EY82axxeDcP4Fr3RyYDYP0RGUCwACBuNWZRY59SMvu2jOg/+aN2pApuTZnHf/1u7WCCVlJ+OJjwjGits6hK8vhchhBnRPdZl1Ws1xWRw7MCWvJyrNi+Z2DtI4T9gy8WM6xkZGRkZN26r7uwfHu4b5mG8W2QjK2Yrrmeg2JRT6nDRLAC4SnON4kAvIY9DAAAwD9ZkAQiC6xkgtheZXS4KAIA0ai00w7AAwJE17Tf1q8NXs6/vfAe2z065C1ycNf1XMNpd/eu1mXulLAeylpzrekWTMDF2DEIIM2IVLvWh1ev2XsgqNpMuS/4f2zdfZCKj6niIPBr27yk5MG3xvvRb2bnZN9OP/bjlQLrG9/U42W9Jn/9e6GQYUnf7cqaRAXHD1zvyj8z7+i8zQxuvbFt9gttlyCtCinE7xBM1eL0Dc3j9ST0AkKrD35+3sQQBAKTq+NF0VWlpsdboIPg8LtfTz0eEKfFfOX7kLbsGXVsxbeNfRtJ8c+vkSWn1ExNbSrBjEELPodq9+4Ir8RHnb/zs3WSVmQWOd70Ow1cvGxHOmvNEjcesWfDd8uSBMXdNrFARqew1aUF3r2Zjv5zHWzyrd7TawZNFdv14jbIZl9ty0orJyxe8G7NIz8oavDZp02exnvYcl9vqCGnLiUveXDD/9QSuXObdoOWrfloehwCgLZl7k5Nnqkw0RxLSZuiCxUoJW4r7/t85gOqO+mmXesS4tt7j7KKIbh/+eOCTJkLsFoTQ84gwm80VXzMMwzAMTdMURZEkabfb7Xa72Ww2m80Gg8FgMFy+fHn06NFPUAFX7O0j95CI+BwAlibtJn2JwUqxAEDwpHJfhUzC5wBLk3azTqu30cCXKnx9ZGIewdKkVVekNbtY4Ahlvn5yqYBL0KTFoC0xOhlC5BfuR2vUOicLQAjkoUH8ElWxnQXgCL39/b34LMXQpIOVSF2avFInCOWB/l4iPodgWZfDWFqst+GpxL/FarXGxMTURuTU1FSpFO9lRAg9DSkpKUKhUC6Xy+VyLy8vb29vT0/PWv/FYJa264vterf/oqw6jVVXZanLWlporTKCY5ymYrWpcmGHNjfv/gtSn5f7YGWDJs9wf8XyUE59YZ4eDwKEEEI1wSd9I4QQehGtL7sdsAxdcnxpImZEhBBCL/yIkBDX6z4aMyJCCCFMj9I6zZtjRkQIIYQAwMHDPkBPJCIiQqVSYT8ghP7fEWJGRE9Gq9XWUmS89QIh9J8icNYUIYQQAqjtZ9YghBBCzyiGpiiKolkAYMw3DqUxOEZECCH0InLc2bd04cLkvXkuACDogt/X4RgRIYTQi2j8+PFlT3HjA3A8mr63eDOOERFCCCEAfIobQgghhCUnKF4AACAASURBVBkRIYQQegDPI6InY7Va09LSsB9eWHFxcdgJCDMiQuWSkpKwE15Mqamp2Ano/xFKfXDlask7n41sVvb6/9+sqePqZwm9Ps9w/setuLY44fUl1514xCGE0POitjMiozu/8ZPBsVFKpTIqJn7YpK8umhgAcFxZEJ/wZAnjiYqIA8J8hMQ/j1O+Ecb0lPGvRyuVynbx7yw/b2IeHZYf3O2DxAHKujW1AiGE0IuVERndibkff2tKWHP6Tn5B5pkfZvVpGuwvF3MBAIAQB4Y9bsJgKOZJizzMk8Uhs35I/lbX7/ur6oKre+b2bR7qW74FDwnLVbRKiG/CM1spPMYQQuhZtHnPuWwbW/6CLj391aTazYhU8dW7VKNR43qEg6HYRHs3ie1aX8Cw1rNzPtldXPht32Yt2sVPOFDMMLozK8f269JWqVRGJ7y/MrWYBgD7pTk9EqavXTBmUK/4AdPXTqlcpHLCtFzf8lGfdsqoDglj110wMPcTcrWw1aoGIAtPrBzbq4NSqWzXa8L687rqI0CGZsVhbVr4OHVWYXh9mctOsVXWqBbWcW1xz/i550oclvQ5PXrN/W7FB/FtlcpOby45WaA6uujtblFKZey7ay/eG24+ug0IIYT+VQNbsVdummgAAHDe3rE6LaR2MyI/WNlcdGnBRwu/Pfxnls7J0JTTajI7RG3nLB0QGDpq343s7Ov7EpXBHjzxS4OWHs0o0OScmBpwaM4Xl0gOAQBOzans6GUHzly6sO3zrRuGVCziyXswxLNeXvHR14aB2zLU2Sc+lR49UUwT5f8UVQ3rGV2lav6dr8fPPtto9rGsAvWZ5LpHkhadtRCVR4/8oDatub9NHP/9HSvNsjRpt7uqZixptS3iAhDlc7dkwfELEdMPZtz9Izn48PTR0083nns08/bxCcSPyw4beFwA561HtgEhhNC/Sxqu7BhefnkpweVz7JrazYiErN3M71YMlpzfPOe9fl2ju7+z5IiaBAAAlmZokqaMBbk5OSqNTdayZ2xjOVVaWCqKGhLnef2M0VPCBeDJu00Y0syLLFGrc9V6Z8Ui5gfjNHvm/lSm6/SJnbxtWnvEgHdbizh8LgEAHEUrN2ErVZ1zcccBfYeZMwdEEoYSaDZsePDFXTlCKfdBPqKLDs6ccaFd0ijX5yPnHytyAYDz+oq33t10l6y0rZW3yEI/+A9X3n3KmA4hhI3frHs9gt/9k9HtA2i7p7JLcOH5UqmM77y18xFteKaRqj2fdI2UEgRByJsOWHKq9MHXBabkyPsvvfzpJUf1UqzlytqhTb0IghDXfW3GkSL6EaEeUpfbUO4awNpufjumbRCfIAhpROeJP9xxPFar/t62gDNn18exERKCIKT1E5J/L6Ufp9RDGoAQ+vdHbTKFpCwLCuoNmTEiuLavrCHEodFDp6f8cjFbdWXfeK+Ds2Ye1lUf/jDm69tnvdk1um37jp1fG/NDvkln40l4BHA9grwIq8HsYh9WB23WOLwahvEteqOTAbF/iIwoS5fuw1YqSxnUOuPxSe0aN4/q2C0u/u3VWeDUMxLR/X4hc3ZvSn/p43kTPl698S3tZ6M+O6l1FF38oyQkumWEXPBYSYvrGeBF2PRGJ8MV8HlSfy+OzWghWZ6Qy9jsHBGfflQbnmWurJRBw7YGzP5DT9rzdw8umPv66AOlLAAwpr82vhMdt/6O3d3Os5yd1mtqzuCDGpv+fJLnhsHDf8inawz18LrchQJ3DXBeW9Tn/d/bbsqyU7YbX768e+Tgr7OoR7YK/ta2gP3y/B5vHWi86obNWXJijGnxwEm/mx5dqqYGIIRqBW03O8q/eHM8GvWe/DQ+dVmXVa/VFJPBsQNb8nKu2rxkfAKAAPbexwt559sZ6zU9Vv+ekZWddXP/mHAhn8vhcACAAKL8ohqASkWqZByxvcjsclEAAKRRa6EZhn1I2ApxeF5BCv8BWy5mXM/IyMjIuHFbdWf/8CAu537WZu1GB887yIvSO4P7L17VJ3v28MRZPzB9P+ggpxyVzyfW0LyynP9gK1iWpit+zBGPbMMzPUIs1dUdtnD+sKbefFFwp3EftXGmn85zgv3CR607Lqffnd/ew90hZr284SdrQvKH7QPE3s1GLJ4Sfnr1L4V296EeUZfOXSjaXQOETaafyzu/OC5MBJTD4SKkQf4S4pGtchsKnFeSIrkN598gayhlurhqo2HgVwt61RELfKImbD+x88OXBewjS7lvAEKodvKh4dbZ7PLJPlfBuZNXCms3I7rUh1av23shq9hMuiz5f2zffJGJjKrjIeJxpL4ic06uuSyDMA4TKQpv1VThLM65tGPlDpXDRVfLLZWLVCRq8HoH5vD6k3oAIFWHvz9vYwmixrAcj4pxRA3795QcmLZ4X/qt7Nzsm+nHftxyID278MGcrDCye4zk1KI1Z0oo4Hi81GNYe9fla1TTqEieyeBgHqd5j/TINjzLpG1m/bBhRCQfAIDWHN96VayMCReCoOGkE3lXtnyg9HZ3WS5juHXNFNQmQlz2rcS/aQN+7nkVz32oh9cVancXyum2ARyRt6+Mzfy8jYes4aBtdT6d3zOA+8hWuQ/Fj3hz/a41g0J57ktlqf68aApvkrO4V/MQH//63edf8W4QJCIeUSqnyH0DEEK1YudJbXBdj7L3NW3L/mX52Np9Zg1X4iPO3/jZu8kqMwsc73odhq9eNiKcNecJwhPebv/b5KaRVnGL6TtSeo2c2GFaYtv23n6eXvVaKoNEvOon0aoU6Rd4P5kT0pYTl7y5YP7rCVy5zLtBy1f9tDwOASBq7C5s1TiNx6xZ8N3y5IExd02sUBGp7DVpQXch1+m8l5MlLSZ9NXvDsk97b7TweSCM6Dx21cKMz8YPmL/uq3ebeBA1NC/+iVLio9rwPGB0aXN7v3Oua8r51xQEgCw8FABMNc1UGOysSHZvXpgj8hSQpgffL6qEemhdXo6VbkNxa2qA4KXJf9gmFh2fE9+z26gmN7cm+HIe0Sp3oTiyRt16NwIAcLkrZTEWmW3nF6xruybl2GL63Bfvju46LvTm5u7yh5ayWh/aLQihf9egPrFyuXywXM4HENUfunjz6NrNiBzF/95b2OljH7mHRMTnALA0aTcVF5kplh/Sa8GeN1ICfT34BOvQ5r02/ft+vgE+Eg5NMTQ5lxZLQNtyzuFDfuFCWlP+tbxKkQqDKI5n02FL9oz19/fisxRDkw5WInRpgBfU3U3YanEg+r1l3T7xVcgkfA6wNGk367QVvpgTwrAuiWt6zfKVewg4LABLWo3dBoh9ffgWTWGp/d6kVtWwUw8e8QkTsUUVt8IjZm1aXFA4z6YG4AT033p6ZLAXGIwA/ICHt+EZx9puffd+zwnnO284s2ZQqPtjynLyzfDOW/UAAIph388QEU6zkym7IZZxmkm+t5DjJlTFUm+dzP2uo0eVFWi1rKZQNR2UXA5wg2I/Te626o0tf9kSYj3uDyGfNFSNpcRCMQ+C3ly18I3/SQBeXrrq0E9Dv/3L2r3TQ+sSS/5OAxBC/5paf64pS9v1xXa9u/+4zMV55uIHH5nafMuD/5YCAIBDm5v3kCKVxg1OgybPUKU8sJS7sFA1DmXVaay6h2yFy1JaaCmtsMRkNT1qi6gSlaraVjC2whzbvfUpU36O6f6LR7Th2R0cps6K773Jb87x8+Nby2r8/Ja0WXT6r09dAAB8RWDRDM9F6SoHRHoAUNprmc7QsWFCN6Eqlaonqb4CV96wiaemeig37dTuHvRqcvM9Z2Y1EwKwlN3BCgMrXr70+KEqzYO4K1WvTlA9D9Z179sSweNxOZXOC7stFRH4dxqAEPr3RnHYBeifcGVvGJTwpXTGzyv7+hvz8/Ly8goNpLvJXo40tFHzMo1C/Vu/25e/e+qytCKH+ebW6Yuzo96PD2bchKIkFUoJct3UJWnpJpS7k5cceYvYoIzlUzb+ZXAab/ww5dO04OGjWkgqrCF93FAAjPH6LzuP3DAzNZTy7TChN7Pt0y9OaV1kUeoXSfuFvUe2kDyqlOzxG4AQwoyInjH2v1bOO2Y2Hf+4Xb064WVaTku3P7KcZ7vFu+eH70gIFMuaJWne3LZtRLjrUaFqqMtNKPczH7y6o37ePZ5Z2lYu8m45Pafv1l+To6SPalUNkyiu3G3jBk34WU3VUIpQxH11ZOlLe/oHC4TBfXfXX3Lwi85exKNKPUEDEEK1gDCbzZW++zIMwzA0TVMURZKk3W632+1ms9lsNhsMBoPBcPny5dGjR2PHvbCsVmtMTAz2w4spNTVVKpViP6D/B1JSUoRCoVwul8vlXl5e3t7enp6eOEZECCGEAHDWFCGEECpT+9ea6s+t32LtNy7W/59dIUBpz2zbuPdSgYMR+LbqP/qNtgE8quBoytdHrxSYI9/5/JP/ySrducboL+1Y9+MlAysM7z763S6hvJJqxR8SuVpxEV21LtZ6Y9vyb/7UUywrjuj4xju9G1doQNXiQluNK7upnTVl7Nmw9UwhCYSsca/Rb7Wv0Nbqm1y1LoElfdOGovjEHsF8PLwRQugZGiO68tN+p9t3bhLqxf+HzyTjKlq8seCbn3ft+Dxev2f3XYZLcDwadHtv5rQuAVL/OiEeFRMua83Y+XNedNL671ImBKb9mG7nEdWL1xwZqhevVhchrpvw0fLvfty589tJdS7t/tP64KEC1Wvn1Lxy9doZ7bmdf/q9s3LLj1uX9tAePFrCf9B11ZpRvS6uZ5MY3yunDEI+/noGQgjVTCqVenh4yGQyb29vuVyuUChqOSNSReev8KNahXDNFhfjyDvx9fzpU6ZOmb7kh3RDxUdxsObrPy+Z8vHUmUs2bV08Y8NNN78vwFO8/EpdT7K0QEP5hIiMRqGPhC+LaBDuxXHRZJUHjAJZeDHbu3PXRhKbRdainv6PXJ53QKOqxbk1RuYVVS3uIfSqWhdH5CVlDBpVTnaBiRXJfPxl91JQ9do9hOJqKwPldLoYN7VLpXI5HwRCV3FOTiEpknv7KURE2crAqbrJburii8Jb+9z8VS0S4ZQ4Qgg9gdqdNWXNOXfpiC4BYC2hXeqj267U/2D59Fd8bGcWJf1SqHwrlCn/WM85sP1m04++eq8VnJr//nWhT6i/uLS42u8MsJTdRjHWu6eu8JrF+/B5pQTQtNPp5ilXlFlHewd7MWa9i+vhJ7KoLaIoodlcvTjrNjJrdVu8al2s6eLq5I2XDWxEn3nj/eHes3Xc124q+LPSynm5uxZskSVO7+HPqVK7AFoO6nzuszeGMQQl6/TxorocqijnwcpspWa4rUvgX0+YdVkn6iW02//1Z4BFRESoyp88gBBCmBEff4hoLbUL/ORckmQY452r+QWc7xdf3ky6KJtT3krgXZ8ssTMAjOlurrD1hFe87dqCwNZ1JX/xOTVN+NElf3z/fUH0+A8a8mzFD38SNsHl0OVrsDTD4XMJoKmai1eMnE27L161Bs+WHyz9RsHP3Dh9w7720zp7sg+pnVNlZWlgp3ff8Wsa5qUvMLjYirUX5p/c/JvnO2t/7Bmc/8MnC7dfjBwQWGnlR24pV+wttF01cURBhJ38tx+NqtVq8W3zIs8yYScgzIh/E8ETcBiapSkGABiO96sTP//wJdbFAgDB4dLGQgKY8k9snpCxWUkWWJahSMbtxzhjufHT2l9Eg+cMV8qsxQXVPusp9cFlq0+W0MIGQwcpuMYSGxMBQJmL7eJmMi4wlYursg4sLVt5xPRRjZhKkRkPN8VrSNFWHe3TNMz0S4lHHx9z2dNaeJ41FK+0MsvWCeUbSywUW2W7LJprJcG9Y+qwpQVExEvEiUzWq574/spV95+7uljGRXH4XB4fH3eCEEKPr3ZPNfFkwQqnqsTFAeB4N2zEubDnTG5xYUG+KuOP8zdzNUbdnb8y9S6OLLIOmX5W5WQZw/WzWXYAAMZ0969MvetBKJZU/7Z+iyZmUmKPOqDTGJzVkyYvtOfU5Ru27tiS3CemS339r6laYK13Tmd6tG4qZy25v1Yqzr2/co8I2++VIwuCWtetXByq/TwVY8pTGVxs2S9s3eaE+UrF/PLOrF6cLsnOrbIy1553PUNtcricVbeL7xUsLLySpTfZHNobN0xeATKJwKm+nqE2OaqPit021WXIt3vV8+WxLB7gCCH0jIwRQRTR2vfgnxroKwReSI/RCT9/P+MTI8uyosgeH4ypK8w7tOWX9otmtIlMGPzSutmJhwS+desHeIrEXMKpKvuXH1GeBpy39+7LLIW8ue/uZhluYLfJn7wepDu+4vO92SaH6+w7QwNeHTdtaD0hqS/I1QNASN8BwV9PH7/dJYqIS+xVl1N6bHfV4qG8spUd17/ZWfVfjQcMyKhY3FqgOb5sScW6Bopv7Fmz/I6FIXiKxvFj33+ZR5aUjyMJadXi5vTre1YurbRyYdHZH3/2njw3trDadoX3eKPN5uVjEm0sR9584OSOPqz+xpkff/aePLenIP/I8iqbXK2pRbQ5+y6/RZwv6yzBAxwhhB5bbT/FjbVe+XadesCsvp5WJwtACGR+ft5iLrDAOA1FxSZaHBjmbVGrDU6eLCDIF66tmfx9+PxFXaFAS5b9q8By7/wdRxxQJ0gKLMOyAKzLWFigd/G8goIVQi5BsCxDmTXqEsf9cRHBk/oE+Ep5wDgNxUVGJ1G9+L2JVzeRSahSnCX4VeoqZbwCA7xFPAIYymYo1horXnhTtXYQeFddmRD6hPrRmnwTz7/adnHEcn8/TwFBAG3VFZWYXVC+soGqvsmVm6rPP75qm+CD5EEBbs86IoQQgpSUFIVCoVAofHx8yp7lJpPJav+5plTJlT+tyrgGLncTneVobdr6r34tpmmSE9593EdvNCOKNM/HT8g/exhb1pkrkq59WgpKNCbsQ4QQeuyMWPtP1uf5Nm8byGe4DAE1fj5z/TqMm9/TP8BLQADBksbiYvwo/7s4ksj27UR8ayl+pUAIoYf4Ym/uzJGKewOz4sNzE5/Kb82wlNVEPWodl6lIbcJd9K+gHZVH/gghhKqqdJsfR9og4UN8rAlCCCFMj9LI1q0wIyKEEEIA4MCMiBBCCAGAEDMiQgghBAAEZkSEEEII4Cn8YjBCCCH0LGJpF0VRDAsArOna7mM0jhERQgi9iBw3d8ydPn3K9hwSAAiX+tflOEZET8ZqtaalpdVS8Li4OOxhhNDTMXXq1LJn1ggACM+W49fuxoyInlhSUlJthE1NTcW+RQj9h3DWFCGEEMKMiBBCCGFGRAghhDAjIoQQQv8/MqL90pwefVdlkrUUnszdNaVvh6g27d/elkfhMYIQQpgR/43ccnttL6UyeuzB4vKflzcdH92+/6Yc1z8PTUiCQhVCojby7bVvUjLabbpemJ82I6aOF594mjvEcWVBfMKS687n5ghibTe/HdM2iE8QhDSi88Qf7jgAAFjLlbVDm3oRBCGu+9qMI0U0vtUQQjhGBAKAuLRht4bLvZdZOJ6hYfKnm2fuYSjm0Su59Pk2n1ZN5I7CnKzsfKOL/Ztx/n6XiQPDfITEc3EAOa8t6vP+7203Zdkp240vX949cvDXWRRYzk7rNTVn8EGNTX8+yXPD4OE/5GNORAg9W5YtWvRp0qIv9mYYGQBae3LZe09h1lTYatK74u0Lf7NwK33GW9LGdRr2Q0HZB6X9r/nxvVfcJKFsRrTX3O9WfBDfVqns9OaSkwWqo4ve7halVMa+u/ai6X4morW/zRsS2y6qTcyAaTsybeVpiyw8sXJsrw5KpbJdrwnrz+uYsoAJ09cuGDOoV/yAhZfsRKUBTuau2W90jVIqo2KHzfo508bofvtoyMxLtmtJ7Roq39yUVWFitlqcGurqOXXVvPffHNq/Z4/BD28Yozuzcmy/Lm2VSmV0wvsrU4tpsJ6d88nu4sJv+zZr0S5+woFihi4+uXxUfHQbZZuodq+N/DLD8awdUsIm08/lnV8cFyYCyuFwEdIgfwlhvbzhJ2tC8oftA8TezUYsnhJ+evUvhZgSEULPlFEfT1kwuYf3mfVfntHbbn635FjY0ziPKGz43vT2t5al3Ca4VcY9hCwk5P6sJCEOCi0bGZEFxy9ETD+YcfeP5ODD00dPP9147tHM28cnED8uO2zgccsyTP6+/V4fH7yWc+vnwdrVU75TMQSA89bX42efbTT7WFaB+kxy3SNJi85aCALAqTmVHb3swJlLF1L6NfAV32+F7fKKD5dndV7zp6rgxra+BSsnr8v07LZsx6I2Hi2X/3H31m9zOkd4VxzMVowTXvSN+7qKTud1WrHvtzMXDo5nNkx7aMNELw1aejSjQJNzYmrAoTlfXCI9o+csHRAYOmrfjezs6/sSlfLsDZ8fC5+TlqMt0dw6uvyt1uGKZ23syBF5+8rYzM/beMgaDtpW59P5PQMIw61rpqA2EWIAAOD5N23Azz2vcuL7DyH0LJHxCL5Ps949fDKPZdq4AsKe/1SurOH6dEoc7r17wUET97Hq48q7TxnTIYSw8Zt1r0fwu38yun0AbfdUdgkuPF8qlfEJAOB4dJ6aGBtKmKwB8aPamX85opcInbd2HtB3mDlzQCRhKIFmw4YHX9yVI5RygCfvNmFIMy+yRK3OzdPa702D2m/tO0l2mTElLsilswUmjI0lj+wrlohYp4ulXIyzJC8nt8BQcdK0QpxbxzbscVsX1yt23IBGUkeJltNq0KuWXw7X2DBPv9Y9YxvLqdLCUlHUkDjP62eMnhIuSzM0SVPGgtycHFWxk0/Yi1RZdzLvqkpYhYIyWp/JS30EL03+w+Ys+O1D8+xuow4WWQ12ViQTce7lTE8BaXIw+P5DCD1LVq9enZy8MHm3BmgrW3/EovfDntJT3Ph1+yZ23Dpn1bW2rz5ORvQM8CJseqOT4Qr4PKm/F8dmtJAsT8hlbHaOiE8AANerjr/Api9y0CxfHiQ15xqFHjKDWme8NKldYx7DsACsywIv6xmxH8H1CPIirAZzlVOCtFlj92ofLrDqihw0y5OHyayX9XwP3xpHYRXiUDXXFejNsRmsLhZ4nn4is6rmhrkyNs5ftv3UHZ2Lw2EsZk68jSfxrTy8bjRmUeL6DTP6Lr7LRnYcMnHqe9F+3GfxyOJwOcANiv00uduqN7Zcnd1VRDjNTqbsPDXjNJN8b/wpToTQsyUxMbHsuaZyuVzO85QNnvm0nmtKyJTvjQh8a8GuCP69JVwhl3HRLPAAgLGZHKx/xQIVrl1hWZqueBKKAACgjblaKxXAAgBZqrZIwrwEPIF3kMK/3uaLq6OE9wtzwHAYgADCzcUwXM9AsSmn1OHyYgHAVZprFAd6CXmcmuclH8ThedVQF2XI0lhdASwAOLW5ZkmdmhrmvDRt+nrNm1t+/+DVIAl1ZWbbQRouh8MBIIC9n7k5sqb9pn41cokneWvr2P6zUzr/Oqsh0Owzc0Qx2t2DXk1uvufMrGZCAJayO1hhoNSnYRNPTbrKAZEeAJT2WqYzdGyYEN9/CKFnicHFKhjSqL5+R9s8JuTaOUeLp/fFnRf6+vjupSlrrjgIAgBAGNrCX/PrKQ0FQOYd3XrWRBPEE5wiYywnF2+4ZGYY05XtX52VdO3/soASNOzfU3Jg2uJ96beyc7Nvph/7ccuB9GyNraYEIm74ekf+kXlf/2VmaOOVbatPcLsMeUVY9mNZjyKqqS7W+vui9ZdMDG28vHXNWWnX/o1qaFiWuoQUhbdqqnAW51zasXKHyuGiWeB4+IrMObnmsjaQquNH01WlpcVao4Pg87hcTz8f0bN0IpEjbxEblLF8ysa/DE7jjR+mfJoWPHxUC7+W7/bl7566LK3IYb65dfri7Kj344O5+P5DCD1Lvlm++NNpc+es//WmmWYsd/bMe/Mp/vYFIW0x4r1mexde4nAJAOCF9p02/OKs1zqv8pEHt3y1jW/hE92QIQh5PV7/eb+YPzQQ1nXi+tnRYmthibDxmDULvluePDDmrokVKiKVvSYt6P6Q6TpJy0krJi9f8G7MIj0ra/DapE2fxXraNbmPNQQT1VCXIKR3b/PS/mUNm7x+drTIWqh127B2733Y4dPEtu29/Ty96rVUBol4XAIE4Qlvt/9tctNIq7jF9B0rX87cm5w8U2WiOZKQNkMXLFZK2NJn6pDi1R318+6CEWPbysfZhXW6jNv6a3KUFKDd4t3zR41ICJxj4oV0/HDbthHh+CsrCKFny6QpU+/PmkrkI9fumUiYzeZKQy+GYRiGpmmKokiStNvtdrvdbDabzWaDwWAwGC5fvjx69OgnSYQCRWiIUJdbaGUAgODJgsJ8RZQ+T613scARyQP8ZQKWpii7nfWQujTqUidLiPzC/WiNWudkATiSoHC5TV1gpFggeLLgUKlBVWhlCJFfuD+YrHyZTMQFl1VXrDU6GQAAgieV+ypkEj4HWJq0m3VavZ3v+yBgtWGOUObrJ5cKuARNWgzaEqOTAa40OMzbnJdvrnzLQKWGuasr/8iEuLmhO05PC6P4Xo/TMELqG+Aj4dAUQ5MOWuxBaFVFduB7+gX6evAJ1qHN05CeAf5eIj6HYFmXw1harLf9p7OmVqs1JiamNiKnpqZKpVJ8lyKEnoKUlBSFQvHgPKJcLpPJav+bO0vq8rIfvKJMBdmmBwnYoS9U6e+9KtWVr+TQ5ubdX8VWmGN7UDo/x3R/HRUAgL7qmImlrDqNVVdpGV0hYFWM01SsNlVeRlsLcqxutsVRJU7VuuxlpwlZ2q7TGh6nYWDR5lsevCov4jIX55mL7y3UF+bp8fBFCKHahhcAIoQQQgAAeHbnXyVuOefwIb9wIa3BvkAIIcyIL7hqM6sIIYSeCzhrihBCCOEYET25iIgIlUqF/YAQwoyIXnRarbaWIuOtFwghzIgIIYTQ02a1WgUCAZ/PL3tgGkVReB4RIYQQAsAraxBC0TXjzgAAIABJREFUCCHMiAghhBBmRIQQQggzIkIIIYQZESGEEHIP775AT8ZqtaalpWE/IIT+Q3FxcZgR0TMhKSkJOwEh9F9JTU2tpcg4a4oQQuhFtP6Imrz/gi45vjQRMyJCCKEXUaX8R4jrdR+NGREhhBCmR2md5s0xIyKEEEIA4HjeMqLj2uKE15dcd+Kuex7RptwcI42bjxB6JglrOyMyuvMbPxkcG6VUKqNi4odN+uqiifkn8fjB3T5IHKCs6yMkcO89Z2x/fNiqy7Jb5OOsS2bMayDtvNf4GN+RLk+J9Or5i6m2Wv34Lfl7m1/b7UcIPTaidu++YHQn5n78rWNUyunxneuA9sa5Y1e8/OVil9H+qG/KDMVweG7SNVfRKiFezDNbbbjznrshkqVITwY8b63mhQ78cmvHZpIXdPMReqHU7hiRKr56l2o0alyPcDAUm2jvJrFd6wsYFgCALDyxcmyvDkqlsl2vCevP6xgA+6U5PRKmr10wZlCv+AFzd60e2GnsUV35iNJ2aX6PuHnpNse1xT3j554rcdAsmX/si7G9Y5RK5f+6DEraX0DVEBbVIvLGojYyQRk+hyAIXrc9egDWcnXj6PZ1PHkEQfADX/1ge44td0OfAT/p1Ktj6vXdp2OBKjw8K6GhjCAIfmDUOyl/mVk3R8+vM7rXERIEL7D9pH0FFADQpWmLBzaTcwhCENxu7NZMO1tpMNfEv8M7A6Jebty4fmSroavKpiPcFHFendEo5LVRPesKucFDdpz9dtT/QqRcgpDU6fThz7lVx3CU+qfxb8y5YAMyY16ToG4fvtuxQVigt2dw+8n7Cyn3lVYa9jkufVLX+/UDVytvvvu3i7s+IfP2fNQpVEQQ4rrxHyY29mi/vYR1v/BRW7qvhMUDFqFKQy+aoiiKZgGAMd84dOhc7WZEfrCyuejSgo8Wfnv4zyydk6Epp9VkdtDgvPX1+NlnG80+llWgPpNc90jSorMWggBwak5lRy87cObShW8mjB4SfONgFl/MBQBbxu5TRJcRrzXyFQIQ4oAwHzbjq8RZpyKnHLpdqM07v3l8p/oBnnyyhrCotggaTb1gIkmSJO2qn94K9Wq/ZMVrcnBmLB+UeKrDlmwHSxnOfkh/M3F+uu97e34eqAhNTL27u5fClbmyV98U0eTUUqct69uuVyZ1G3OotOq3FzJjv23kL4Vm9ZER+jVjP0u3U6rvBsd/Zhx5uMhhy/qm3ekx8TMuVJorcGlPX2q2+vz1jBupU5jP+kw+aXTVUIQs+C299dfXsn6fRs6deLz9NhXJ2K7Ml29LTEp7yPylS3P8VN3PL+RqitKn0es+WJhud1upm8TDq1Nx890ekqS7PnHeWTvgzZ/qLLliduT90Pna5htWAHC5W0g9YktTP++iwLcCQhU57uxbunBh8t48FwAQdMHv62o3IxKydjO/WzFYcn7znPf6dY3u/s6SshsiHbd2HtB3mDlzQCRhKIFmw4YHX9yVI5RygCfvNmFIMy+yRK0u4ET1DL7yzSmngAuWq7vP8bu9oZRYLeVf4e03dh0ydpw3/41GfENBoUUS5E1ZSZv7sFz8IKhtrO2vz/sOP9bl258nNhEDCOqPPXT1+KwOCtpQmG+WBogtBZWuKHHl7N50tfHsZe+1UAjEYa/NWPUWd/+aM1VP1QlaTp09qLHCI6T9mwPqGDIKbfkHV6cFTFg27n9+QnHoa0mLu5du23C5YkokPGNnJLaREcAP7TW5H+xJOZ9ZUxFBqw/GdH6pzkt1A+Tc/CObNuxIzVMM2akp/D5W9pDtFLYe+47SiwPC8I5dggw3i0h3lV60/p0udNsnv1/6KeVqs7mfDWngIfR9dcKy8RFCAFeWm4V0/iO2tH6YB15YjlAl48ePnzVr1px+4XwAjkfT9xZvru03CSEOjR46PeWXi9mqK/vGex2cNfOwjiAog1pnPD6pXePmUR27xcW/vToLnHpGLCS4HkFehNVgdrEAvODOvcMyvvvdIbRd2fWHOO6N1iKL2Vn+/ZsyFNjkjSMlthKd1cUCsDTpcDr17sJKRPhJULtozZ73eyZTn+5f2yuACwBAMPqzq4Y2lglkkR3enL/3ro1hoeLAyaXLNQhDIxXc8tQX0MCXKi60VDm5zPMKkpWF4wm4LEWRpXdLyLvzm4sIgiAIwrvHboMhR+usVCLUX1T2/YcrC5LY8gvy77gvQvC9Azy4ACCLXXPwi47arRO7NPSR1In9aKfqYVf+cKU+0rKjicvnsGXz/9Uq1ToqDRJZtuqY0XLyTUVZgwif4b9bHtInudm3dKLQMM+yOvn+Df0FAKT2TvWFrpo65/6WIoQe6WkkC9Zl1Ws1xWRw7MCWvJyrNi+Z2DtI4T9gy8WM6xkZGRkZN26r7uwfHsQlAAggGKp8+owbGNM74uaW4+q/dl3w7Dn0Fb7VQt77cOF5B0uNd4tsJM1W+gh1F5aDE6e1uXetl5f0eetw9IY9U1tKyzqaytn8xsif6iy9anHostP3z35VRrAsAAFQviP48nAvpzpLX54CnYU3tFz/IM9qn9uVdxzPO8xb9MqqbBfLsizL0pa82ze2xXlXWIMy5ORbyw4IR/71UmlEaFB4DUWIstiMNfc2GT1v/5Vip/7m3rcta0dNPW15sg6oVmmghMsjKAdZdhhTxkITBVBx8yVtFp3+q8yphUrJQ/okvO5Lcoc6z1wWylVyp8QFwPeNrL6w5s4h8PBH6NnIiC71odXr9l7IKjaTLkv+H9s3X2Qio+p4iDwa9u8pOTBt8b70W9m52TfTj/245UB6tsZW5cs0x79Dn3q3Vs5bdUHRa3BTntV8PyGCuFG/HrLfkj7/vdDJMKTu9uVMIwMit2ELzRReUVBrw8PC3e/3TCYn7984OOT+dcuMQ2+mBHI/bwHh0p7+cuaPJQ7SxQDBl3DM2Tezi23cun1HNMmY98nmKwaXPe/ogg+3unqMeVXGmDIO7zycUdP9Obyw10e3urPg442XdBRturrhjVeaDvlBTVXMzpaTM+buy7U78o8u+HiveOCYqJcfUYQp/XVyXPzHu7PtXFlwqI+Q5+HnwQV4REsqfyWoWmkrb7+XQ+j0TbtuGC3qkyvn7ivLcg82H6ShjZqXaRRaPuYEvrs+iWkxYOTLV+bN2nnXSuovbZi2OssBIIh0s/DRnYMQ+o8zIlfiI84/+Nm7PTtHt+3UN+mo5/DVG0eEs3aXqPGYNQvaZSQPjIlS/q/r0KQfrlj5kuo3R3J8o/u+XHjqdsjgoY14FourQmYTNfngy3nR12b1jo6KihmQ9PMdUiDkit2GxfOItZcQ1buTvy+wXZr/qoxTPg8YMOYs9dKoL6fU3dFNLvQMaT9XO2RejKc6Xe0UNx7SP+jXt5v2+DqbaDhp744Rxs+i5QJJ/REnXln+68bX/TiuvB3jByVuV7lqqI0fOWbX7rGuFZ19+DyvVxfZRv308/v1+JXWqPc/3bwWEnGdt35vs/qXz9t5PqoIL3z45vUJN8a/LOFwFV2+CUneOa+1GOBRLYGHVsoJ7Pvl0h65U5t7ezb/OLvnxBYeAAAVNt9tWIG7PhE1nLhzU/z1xIYewuAB+/3a+Yv4Aq7Q3ULBIzsHIfQohNlsrviaYRiGYWiapiiKJEm73W63281ms9lsNhgMBoPh8uXLo0ePfoIK/q+9O4+Pqrr/P/65syeTZSaBLCwBobIIWFFwoYLUDXCtWxWlVanEqiA/xYqKC2JVVNS6FCEoghWwVkUW0WIFZHMF8kXZ0UASEkgg22Qy673398clw2QBRCAkzOv5Rx4zN/eec+dOmDefM/feY45zpboT4h1Wk4iuBn1V5XsrvGFdRBSL090qJSneahJdDfo8ZaXlPmurrNbq7sKywIHwMydmZrWOC5cVFFaEdBFR7K3ap+t7CvYFdMXqTGmVmhRnUXQ16C3bU+oJ6Y01W6NSJR4rXq93wIABzXDHghsn9Oy79IWipVcnnzydhoq+Wrm7Y7/emXZFpObru7tcWfZO3r+6/dBw4XsXJvDHiRixfPlyp9N5lI3k5OTY7Xa32+12u5OTk10uV2Ji4nGfH1FXfeUlvvJGfxX2lu32ltUtOkp3FtSvQzzFedGprQf25ufvfxjy7iv27jt8s0CLFMp/Z9glGx74cv6o35o2z3r2fU/fyT3iQ3mNLORYAUeP0zCB5iu+71OzHkx+4wK31ZIy4B+m0R++fV26qdGFHCvg6B33UVOcZJrtqCmAGHGsRk3fzskJWVzdLxr61xv6tk9W1895xcLBBQDEoGF3j8wwFS+ZPfOtU7qMzVr0+oq2DLYAAGJRglmxpnQfdGHK9hU/1ZitJt9uakQAQCyaPn26yWRStIDSvkZOuenR2+aQiDgyHTt2zK891xcAWq7hw4cfuPrCkpB49f0kIo5MaWkpBwHACXT0p9UYqkK6WwtWFW/O23fauRmb1wR6kogAgFj03tTXA2JJTD/1/Ju6nV2T9+krk0lEAEAsyh45KjJqGu8a+tyMbM41BQBAhHvWAABAIgIAQCICAEAiAgDQEOea4sh4vd4VK1ZwHACcQIMHDyYR0SyMGzeOgwDgRFm+fPlxaplRUwAAqBEBALHK6XQmJCQkJSW5XC63252UlESNCACACKOmAACQiAAAkIg4VgLrx3VS6nDdsqz6ICvXrM5u12HUt75j0K9eMqtf4tnTi7Rj0lHVp5fHu25aUs37CcQyzqzB0XP0n7V53pAk44lisicktLRXkHD+61+vMHWK570EqBGBo6FYE1zuWq7keItopQtvzYw769kffCKiVywb2Tn1kinrFgwbMm1X/uvnxCuK7fS/bwxWL7slvd3QiY9e16uVSen2+LqtM67v4rYpiqLYMvoOn76pQYmn12x6e8R57eIUxdru0ok/+EREwjsbbFX133odNbJOXb7cJwddMWljQNS9y/5+dbdkRVGs7k79R84tCvP2AiQi8Iv5llztigyaWjuMWOk1tb78lTl3lE24+dm1nr3/HXPrR32mvjOi95XvfjqibdbIb2p0Pbj+0dNsIhLY9Z+3N3W7+41PvphxW+f08/82+9tCj89Xlff+5WsfuOuD4rpjot7vHrti1LfnvfZ9UcmOxRMuTrOIiJhSGmyVMKheR42sc5DR1u8e+9Mr8vB31aGawlU5t52qhHTeXiBmMGqKo2c/d+o3sy5O3F8wWpIy40UU1wXP/vu+cwZefcWnoeIr53xzbaa50U073jv73afPihMRkXBJYPX42+9a/P3WokqvL5C1sSQkmfYDgbhu6uyaG/494ZoeCSIZ5/ZJt64SEVNcUsOtkuv9x6+xdaJaPlCEBrx+b/G2LTsrOnY/7eK/nMZ7C1AjAkf0VxSf0bFTrVOyWjkUEREloe+DL11ZtTz3tMfHXeA+/F9acMtLQwZPLBn03KINu6v3/u/mdJOm16nQgnu2e1J71Auyw271C9cxOH83ceaYzPnDe7W2O9r1u+PNDV5qROBk9fK8ncHIE7Xksyf/TCLieNG9a18eu7TTZT3WjH14UakxSqkoih7WGk0ZvXLt/E0d7336rgu7tIpr9O/SktzaXrmzPPxLtoru6PAtR/XR5rKn5q0vDdUUrRzfbv6ou+bu0XgngZOTUuc/9s4uV/w/EhHHIPsClXtLa+0tqw6JiF7x5UM3vdH2hfkfLnj1t/NuHfHerrCINbVT0t6vlmwq8+wtKvXXDUYlLrOTc+fC+d/lF/+8+u0H7/9wT/2TWpynD+3nefexycs2b1v32RvjXljjPehWdToKOA7X8oF8/XLCIzlLNpcEbClt2qTGxbmSbArvLxAL8ejsdNaZJCKOnn/Vn3+TVqt1pztXVaslC0bd8u+er04b1t7Rfti0yf2X3/mnaT8FrZ3//Oztpol9UpM63DC7oF4uJZw/8a1s9fnfdWjz29s/sF7Qx1X/i0dT2tVTZvyp8tnfd+9+6dilrnM6OA66Vd2OHIdrOfJvwupO/GnKLb9Nj7c4utyz7cYZk4ekkIhAzHyUKR6PJ3qBpmmapqmqGg6Hg8Ggz+fz+Xwej8fj8VRUVFRUVOTm5mZnZ3PkYpbX6x0wYADHAcCJsnz5cqfTeZSN5OTkvLPC8+jtvTNTU40rx5KSEqkRAQAQEYVEBABAhOsRAQAxSldD4bBxUrpe9ePcL1RqRABALPJvfv/JRx4Z+96OoIgoocLPX6JGRPMQyF+64Dtzv6sGtLFyMAA0gYceeiglJSU1NdUmoiT2HjV5LjUimkkibnz59uycrQGOBIAThUTEUSbZQedH1MsW3pDa+pbPK6OvxG90oYiILb1b+6wembba59VLhyYfaNPU8+lNQQ42ABIRzZyj/6wdZbXKd047P0FEREm56MFbnQtfWBR9I7RGF4qIOHo/v3Hdw10jiRh/1tjnbzmtzTmj3tvi1XTtx3HdbRxoACQimrmG8yMa4s64e3Snr178947ou9M0XHhg1kNLerffxFnOnl6kiSnpjOycj57p8P5N3c8c8V5e4OBrAgCJiOai4fyI+38RKs/fW73mlTc3+KPWrr/wwKyHe35e/PRlGRYREb0698Uh3S59J/Px/8z+49a7B961qFRrfE0AOEb4TMHRa2x+RBHR9i5+ZpZ2fu/yGa9+8/BbFyQ2vrDurIf9z8m0fi0S2jb5tgkVf1u7YlRnq4T77PtvjzFTHzhvYCNrAgA1IprRX1Gj8yOGdsx+ZnnX8W9Ovs02d9Ln+6eDariw0VkPPesXbWtz1SXtrSIilszfXZResPznysbWBAASEc1czbpXX86/dNz1Xc68M7vNsklzC8ONL2xs1kMxxznNNXu9+78kDHtKfHGtnPbG1gSAX+fFiRMfHDfx5XkbKzURtXTZi3eQiDh6jcyPqO1d/Mws6+1jB7oV26nDRp/+w8sztwYbXdjYrIeS2Oe2C6pmPDbt+32h0N5vpjzxgTb41jPSGlsTAH6dEQ+Mffr+Ia7VU19bXV6z+Z3nv2hPIuLoNZwfMZQ36+nlXR/4a0+HiJjb/eH+C4pz/vn1j40sXONrbNZDU/q1b3/8gH3yRa1sttZD3kmfsPD1S9zmxtYEgF8nyaJYU3tdPSR16xdba8w2xbeL+RFxZJgfEcCJdazmR5w9e7bJZFK0gNpxxPSXL635fDrnmgIAYtHIkSON+5q63W63JTHpxscYNQUAxKKKkC5asLJww3dbq/yFKxav3UWNCACIRW+/9FxArEmZ3S66vcf51ds/fmYSiQgAiEX3jX0oMmoa77598sejGTUFAECEK/QBACARAQAgEQEAIBEBACARAQAgEQEAIBEBADg0rtAHAMQir9drs9msVquiKCISDoepEQEAEGHUFAAAEhEAABIRAAASEQAAEhEAABIRAAASEQAAEhEAgPqm/rcwGHmi7l0yaSSJCACI+YpQiet8aTaJCAAgHp0dTj+dRAQAQET8JCIAACJiJxEBABARhUQEAECE+REBADFKU8PhcFjVRUTzbFq0QqNGBADEIv/2+ZOeeebv8wpCIqKoRV9OoUYEAMSiUaNGud1ut9ttFTEl9LzjuRnUiAAAiHAXNwAASEQAAEhEAABIRAAASEQAAEhEAAAOjusRAQCxaMprr4Usru4XDf3rDX1d6r5VbzxOIgIAYtGwu0dmmIqXzJ751ildxmYten1FW0ZNAQCxKMGsWFO6D7owZfuKn2rMVpNvNzUiACAWTZ8+3WQyKVpAaV8jp9z06G1zSEQAQCwaPny4cV/T5ORklyUh8er7GTUFAMSiqpAuWrCqeHPuzx5/0dfL1hdTIwIAYtF7U18PiCUx/dTzb+p2dk3ep69MJhEBALEoe+SoyKhpvGvoczOyGTUFAECEe9YAAEAiAgBAIgIAQCICABA7iej/4dkrrnphY4B3GADQLBIxuG3yVX3qGP5pmX6wFFv/9GVXPL/h2KVYXHr7VLvSNH0BAFq44389omI787W1C69P3Z9MitWu7925q1o9yNpxGe1T7aX7AnVjUwtrJsuxTu+D9AUAIBGPWybapGpP0YEQ1AN57955+9zer/1rVK94tejj0bfkZDzz5kXz/ja3ZJ9c0+tji6PH/W//46JdE659LHjlefuWfVMQ7vPUv0YF3hw3ad66/KqQLaPPjQ9OGDkgzVw3Nqs3zHpi3JQvi0wZfa67NlGTRGNx2erXHq27ofPb8XX7uszydf116jUOADipeDweRVFEJBwOh0KhmpqaJvoeUa9DbKfc+NTItI/Hz/zJW/DR+H8UXvbixJvOvfq5l27IaDdi/qa8vA3zR/Zpk2iWwO6Vef1eXLh63Xc5156amtzlj5MWbyzavWPpQ+mLxr+8LmiqMyLqzf3HmGkVN8zeWJi39EHn4qUlqrL/945T622Y2G/8pOuj+0qwxJ16yMYBACeXlJSUtLS0jIyMtm3btm/fvkOHDk1QIwbX3Nm9o0XRRUSsWcPefHdkV5tY2/3h8ftWDLvz1gUB/doZ7w1O8Zbs8qlhTQ2q4cqi4n0BXXyqWNyX3HtTr2RfYeHekC49LrvIkRDeV1zlOPumwYljV1cmXpBR6a2tPH1bFyzXLn5j9EBXTdHujtf/5ayZr1rNioiYUs68vMGGLl2N7kuSel9+qMYBACe9JkhEa89n/jf7SrfoIiKWxDS3XlpUFtAtGf2vPeuFMUt6vHDfhcnVJbt8WsNNzQmZyYq3whPSRTTPhv+89PJ7K7eXhUwmrdpjuqzGEm9RvOr+bwFVz25/8sD21uryfQFN4tLaJilhXeQgG7aq29HhGgcAnPSaYNRUsadmtnIomqZpmhas3L27IqiLaJVfv/rcd50uOzfvucc/3l4eMPJQEb1OBimiaGFNRCS4feajU3cPef3LjT/n/bx5wZ1ZdqvZZIrafXNiepxvjycUCouIBCtLq1VN0w+xYVRfh20cAEAiHgN6MOCvqeULhjVdtLKVk8Z/2X3cmzkz/zHgm4cmrKwSRUzOVg7Pjp2eRusyzV8VdGSd2TMlULJj3fuvvJ/vD9Ut4BxdruyvfTZ1WbmIBPM/e/fbGl1RDrqhKSG6r8M2DgA46TXF94hrR/XqFHnWPvv990ckfTnxqa/PGP/5bZ2lwjZq/IW3jJvw5UeTBmZdcev5/7u/Zydv3BmPvJ8zpE4rjtNuH93/4ZHnne9qnZjcuXefTIfFXOfcF8XZe/Tzw55+6sorzO4kV5fe57YutZiUg21oq9vXVYdpHABw0lM8Hk+dUkzTNE1TVTUcDgeDQZ/P5/P5PB6Px+OpqKioqKjIzc3Nzs4+kh5sKe3auaxRS0LlhYUVanxau1ayt7DEq+pijmvdNs1Svmu3R7Ukts5olWBVdH9pQXG1pVVWa3V3YZlxyaBiSWiVnhpvUsOaGvSrcQlKaf4eX51izmR3paUlW/Wwpgb9erwztLtgX0BvfEOx1ulLnIdrHCLi9XoHDBjAcQBwoixfvtzpdB5lIzk5OQkJCSkpKSkpKS6Xy+VyJSYmHv8aUQ+WFfxc1sgn656d3trHqq80f+f+uPSUFHhKan8RLt1ZENVUuLp0V/WB5/satqoFKnYXVNRf4yAb1u1LDts4AOAkxtkjAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAkIgAAJCIAACQiAAAgEQEAIBEBACARAQAgEQEALQgmqbput4EHVk41gCAZl26mZqoeKNGBAA0U0ZpmJubW1ZWRiICAGI9ETVNa5oykVFTAEAzZQThmWee2UTdccQBAM2KpmkisnPnzg8++EBE5s6dW1BQQI0IAIjR0rBDhw4dOnTQdf2aa66hRgQAxKK8vDwRCYVCO3fuVBSlrKyspqaGRAQAxApd143x0jVr1ohIZWXlli1bjKdVVVVNsAOMmgIAmgVFURRFEZHrr79eRFq1anXppZf6/f5LLrmkaXaAGhEAcIIZpWFubu7atWtF5KOPPhKRsrKyhQsXOhyOVatWFRUVUSMCAGKiOhSRDh06GA/69u0rIk6ns0+fPiLSuXNnl8tFIgIAYiUR3W638bR9+/YiYrfbMzIyRMT42QQYNQUAgBoRR65jx475+fkcBwAkImJdaWkpBwHACeR0Oo9Ty4yaAgBAIgIAQCICAEAiAgBAIgIAQCICAEAiAgBwKFyPCABN9YFrsTidTmM63ONN13Wv1xsKhU7gPjTZiyIRAaAlsVqtuq6npqYmJiaazebj2peqqh6Px+v1Wq3W6Pxoyn1oshdFIgJACxMKhdxud1JSktROfnT8KIqSlJSUkJBQXl5+ovahyV4UiQgALUxVVVXXrl2bMofS09Pz8/MTExOj96Fbt266rrfcw9jwRZGIANDCqKpqs9maMo2sVms4HK63D1artcVVh4d+USQiALQ8zSGKWnQcHm8kIgDEUBq16CFTEhEAYiURdX/+splT3/3s25/KQ2J1dzp78LA7b/19lkMr+vdfbn6j6HeT5j3TN04PFC548q4XV3nSLn3itQd/n245tvsgur9wxexpsxZ9tbk0IOJo3eWs3187fPjg38Qpx/XgKDWrRg95ZJ10vHv29BvbnpjzYElEAGgW9Zmi7vnk0exJ3/tTel81fGh7Zc+ahR/OmfDdxvLpk64xKYqIKCaR4K5PJtz94ipPuz88+8/7+7n0I607D7d+MP+DB0b884eGIbDBAAAJtklEQVSgiNhad2hrK8/buur9WV0uv7hT1vFNqdrWFeNAnZBalkQEgGZRIwY3zpz2vd9+7hNvTxrUStEVZej154z/44P/e2v6+kEjjFVCuz59aswLKz2n3PjS66P6JGrhsHqkyXGY9StWTZ72Q1DEdfH4nMcGZ9lFq962cOoHYZtJgj+/9/gT768vLvepIo7WXftdc9e9fzw9yVyz+r4rHs2V1AHXnFGydMnmCqV13+FPPD70NKciIuHSb+ZMmfnZ99uKPaottcv5Nz/y4DVZSslXs16fPv/rnyrC4sw677p7xvypb4rZCMPanTwhichd3ACg6RLxYHQ9VLZxU6VIt8vOTgoH/X6/3x9M6HVxD4vUbPmxJCwiElz30qiJyytF2vS7+DSnGgwGVVXVDumI9kHTtMD2JblBEeky/K6L0iXg9/tD1qzBo8YMyVBEq95dZu903uU33HjdxT1spVuW5Dz2xg++2rJO9i3/sii5Wze3aKXfvTnpk+KQrmtVa14b+fDbSzfvSeh9yeUXnx63Y+Xq4pB37eujxr2zcs8pf7jnvr8MdOZ/9c4jT36y+0C066If6YuiRgSAFuYQdY+i7A8WLayGw2Fd00TEFNaMDRTjd949eq8rLylf8Pmsx6f1zck+za4c630IVFYERMSekenUQqGQruuKsr8Tk73XA29O3rP1x415xWUJfdZtWLLPs6OgWu+dYKzRaeSUnFvaB74bd9W9S2r2bNsbkoyaNXMWlYokDZo044lzEnRRK7dtCQTXPLWoVKTrXx8cMaiVMjD+m2VP//jDZ7kVV10QXSAKo6YAcJLXiAf/pSm1W9ck2bHlk9Ul512YqmiKou/9ftGPYYnvcnpryzYRkd/8NeeVP7X9OXnjHe9++HTO2VPu+a3zWO6Doij25GSbSDCwu6A8eHpKnTXV8q9eGfPEvHxNzEmZbezlIiK6diBidS0cCgTU+NZOkRoJi0i4urhUFZF2fbKsQb9fFbFndTYXfVuqisiWSUMHTYo07imtUaNCW9P4HhEAYjURdV23dxt2R++lL62ZeM/Y9Zef3ca8e82nC9d5Lb3uHn66Q9siIraMrqlK0Nz55nHXLb77wwUTp543ZXTvREU/VvugKIqtY/+e5lVr1a3/mr6yz73ntbbour9wxZz5/suye38zfV6+Jr8ZPWfaDVn+ZSOHPLquNr32R6YaDoXU2m8DdRFzYpsUkxRoRbl5noFOW1jRqwt2hN3pKSYp0NKufnzcpWlmXVEUUYPBlDSzXhA5FJqmkYgAcDI7xKe8rushU9pVz77l+tfUmYsWz1ofFlPSKefeeOddfxnYRrQi3SjDQqFQQDefdvvYK5eOWbD42cnnvvm3cxOVIwmPQyeNntL/3uEfD5+2ufzzJ/+8Mq1Dpq1qR2G51u7WQWZ7vF1EpPB/s2dU2rd/8X8NW6t7PoymSeJZNw9O+b9FZZ88fM+eAd0TqrZ9+2PKw/8Zf/OQlP/7pGTeS6+V9uuWqniK837c7LjrnZfP0SPtiDBqCgCxWiOKSCgUEmta/zueGnin2fj2TtO0YDAYDATMade+vWyopml+vz+kaYqt1wNzv3xAxO/3h8PhIzrZ5HArmzsMfemdzHenzvnvN9tKdv4s9tbdBw66YUCatVXGmDG5f3/jiw0L/lPd/4oz20vBzv014v4i0ajtojtS43qPmTIxc/JbH636fkmhmFydB1zRzuLMun/KxIypMxesXr14u4jEZ/b83cVZdl3To9s5IXczUDweT72DpWmaqqrhcDgYDPp8Pp/P5/F4PB5PRUVFRUVFbm5udnY2f9kAcETKy8u7d+9+mE9kRTGZTGazWVGUSDAYp7eYzWZd11VV1XXdWCfyiX2IcmrTpk1utzt6H3r06HHoHTCbzTabzWq1RroIhULBYFBEHA6HxWIxejSbzZqm+Xw+Y7miKD6fT1VVu91u3HrU7/frum61Wu12u8lkUhTFCPhwOGwymWw2W2Q6KlVVA4FAdDvGST0H28kNGzZEv6hfJycnJyEhISUlJSUlxeVyuVyuxMREakQAaBY1YiQeGp38L/r21pqm/eq7XR92H4yiKBAIRM4yNa7xMB4YUw0bIW0ktLHc+GmkeCAQiIS38XKMRDSeGk1FL2zYzom63R2JCABNwWQyhUIhi6XpPnWNauzXpfIRLY+O54brNLpVowuP36QWJCIANCNxcXGVlZVHP9z3y1VWVsbFxZ3YVG6amCcRAaAlcTgcO3bsKC8vz8zMtNvtx7WvQCBQXFxcVVWVlZUV/YVc06dyE8Q8iQgALYymaW3atCkqKtqxY8fxHh60WCwul6tNmzb1zk9pylRuspgnEQGg5bFYLFlZWVlZWbGQyk0W8yQiAKBFpnJzxtwXAACQiAAAkIgAABxBIhr3LIj8jNzCAACAFk2JYjw1/ZJtiEMAwEmZiNEPTL98M0IRAHDSMG6sGs30S1LwwNomvncEAJwMzGazyWSKzkVTwyBsNBpNtTiIAICTIxEjoWjkoulg1WF0mRjZoEXfHxYAgAir1WqxWCwWi+kX1nzRiWhsyUEEAJwELBZL/VD8JVlozNdsbMxBBACcBOx2u9VqNUJx//DpweJQas/DicQhNSIA4KThcDjsdnsk3cxms+lgcdhwvNRms7W42UMAADhEItrtdpvNdqiaL/qcGqOWNFY2EjEnJ4dDCQBo0eLi4qLLRJPJpHg8nug1dF3XdV3TNE3TVFUNh8OhUCgQCAQCAV+tmpoa44Hf7/f7/YFAwFhNVVVVVTVNMxrhcAMATpTous5sNhvfF9rtdofD4XA44uLi4uLi4uPj42rZ7XZLwyakwXUXFotFVVWbzWaknYgYfVitVpvNFgqFQqFQOByOjkMSEQBwwhMx+mwY4zwaY6TUUesIRk0VRTGbzZqmWSyWSBwaC43WHQ6HEYfhcNioLI11SEQAwIlNRONn9AkxkUrRCELjQeR0U8sh2jKZTJF4iyRcJG+NAjFcKxKH1IgAgOZTI9YLReNEUeOnzWYzsmz/hYkHa0jX9Ugoms3mSMJFys9wOGwkojFYWi8ReTMAAM0kFOtdWx9dL0YeNF4jRuIwUhGKiMViiQyiGjRNO0QcEooAgBMbh4cIxUjJaDzdf1XFoduKvoS/3leUqqpardZGB0uJQwBAcwvFerlopKDxwPjVYUZNI6GoKIqmacZTTdOMGtHIwujLLSJZSCgCAE54HEqD285E52L0U0VRDlUjRoei8dhYaDyNXLkYnYUEIQCgeVaKUvtVYMOpf40HlkO3EgnFSMDqdUWfdEMiAgCabbEojY2jRk+A+P8Bxf6boJPGmvIAAAAASUVORK5CYII=" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,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" alt="" /></p>
<p>&nbsp;</p>
<p><img src="data:;base64,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" alt="" /></p>
<p><a class="a2a_dd a2a_target addtoany_share_save" href="http://www.addtoany.com/share_save#url=http%3A%2F%2Fwww.gefira.pl%2Fblog%2F2013%2F05%2F18%2Fzato-1-0-the-next-generation-esb-and-application-server-open-source-in-python%2F&title=Zato%201.0.%20The%20next%20generation%20ESB%20and%20application%20server.%20Open-source.%20In%20Python." id="wpa2a_2"><img src="http://www.gefira.pl/blog/wp-content/plugins/add-to-any/share_save_171_16.png" width="171" height="16" alt="Share" /></a></p>]]></description>
    <link><![CDATA[http://www.gefira.pl/blog/2013/05/18/zato-1-0-the-next-generation-esb-and-application-server-open-source-in-python/]]></link>
    <pubDate>2013-05-18 22:58:51</pubDate>
  </item>
  <item>
    <title><![CDATA[kdev-python: kdev-python 1.5.1 released]]></title>
    <description><![CDATA[<i>kdev-python provides Python language support for the KDevelop integrated development environment. For more information, please look at the <a href="http://scummos.blogspot.de/2012/11/kdev-python-14-stable-released.html">1.4</a> and <a href="http://scummos.blogspot.de/2013/05/kdev-python-15-released.html">1.5</a> release announcements, and especially make sure to watch <a href="https://www.youtube.com/watch?v=WLhigzo00iM">this short demonstration video</a>!</i><br /><br />The 1.5.1 release fixes a few issues with 1.5.0. Especially, it<br /><ul><li>fixes a crash bug which could happen when PEP8 checking was enabled</li><li>fixes capitalization in the coding line completion</li><li>displays the correct plugin version in the About dialog</li></ul>This is a rather minor update and I recommend it to everyone running 1.5.<br /><br />There's one thing I forgot to announce in the 1.5 release: <b>PEP8 checking support</b> (which was actually a good thing, since it was rather crashy until now ;)).<br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container"><tbody><tr><td><a href="http://4.bp.blogspot.com/-AEj2xMyOjuQ/UZd3OvTLWtI/AAAAAAAAAbo/BqKEo_LI4ek/s1600/pep8.png"><img border="0" height="225" src="http://4.bp.blogspot.com/-AEj2xMyOjuQ/UZd3OvTLWtI/AAAAAAAAAbo/BqKEo_LI4ek/s400/pep8.png" width="400" /></a></td></tr><tr><td class="tr-caption">PEP8 checking suppot in kdev-python 1.5.1</td></tr></tbody></table>It will run the PEP8 style checker whenever an open file is modified and display all errors inline (if you have inline error display enabled) and in the Problems toolview.<br />By default it is disabled though, since it displays a lot of errors for people who don't follow PEP8, so if you want to use it, go to Settings -- Configure KDevelop -- PEP8 style checking (the change will only apply to newly opened or modified files).<br /><br />If there's any issues, please make sure to let me know on the <a href="https://bugs.kde.org/enter_bug.cgi?product=kdev-python&format=guided">bug tracker</a>.<br /><br />The tarball can be found <a href="http://download.kde.org/stable/kdevelop/kdev-python/1.5.1/src/kdev-python-v1.5.1.tar.bz2.mirrorlist">here</a>, sha256 checksum is&nbsp; <span><span>d9b68bd2dd9361961e264254d2acfebc9ce0ea4b47ea2689d2f01a3ed81f7c47</span></span>]]></description>
    <link><![CDATA[http://scummos.blogspot.com/2013/05/kdev-python-151-released.html]]></link>
    <pubDate>2013-05-18 15:55:07</pubDate>
  </item>
  <item>
    <title><![CDATA[Vern Ceder: Another Quick Python Deal]]></title>
    <description><![CDATA[<p>Just a quick note that the Quick Python Book, 2nd edition will be Manning Publications deal of the day Saturday, May 18.&nbsp;</p>
<p>Here&#8217;s the official scoop:</p>
<p>Deal of the Day : Half off my book <em>The Quick Python Book, Second Edition</em>. Use code dotd0518au at <a title="Quick Python Book, 2nd Edition" href="http://www.manning.com/affiliate/idevaffiliate.php?id=1119_168" target="_blank">http://www.manning.com/ceder/</a>.</p>
<p>Also on the same code is <em>Hello! Python</em> (<a href="http://www.manning.com/briggs/" rel="nofollow">http://www.manning.com/briggs/</a>) and <em>Extending jQuery</em> (<a href="http://www.manning.com/wood/" rel="nofollow">http://www.manning.com/wood/</a>)</p>
<p>And for anyone in Europe and the Americas, you should know that the deals usually run until past the end of day everywhere in the world. So if you don&#8217;t get it done on Saturday, the code might still work on Sunday morning. Just sayin&#8217;&#8230;</p>
<br />Filed under: <a href="http://learnpython.wordpress.com/category/python/">Python</a>  <a rel="nofollow" href="http://feeds.wordpress.com/1.0/gocomments/learnpython.wordpress.com/188/"><img alt="" border="0" src="http://feeds.wordpress.com/1.0/comments/learnpython.wordpress.com/188/" /></a> <img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=learnpython.wordpress.com&blog=1158170&post=188&subd=learnpython&ref=&feed=1" width="1" height="1" />]]></description>
    <link><![CDATA[http://learnpython.wordpress.com/2013/01/19/another-quick-python-deal/]]></link>
    <pubDate>2013-05-18 13:49:55</pubDate>
  </item>
  <item>
    <title><![CDATA[Kay Hayen: Nuitka Release 0.4.3]]></title>
    <description><![CDATA[<p>This is to inform you about the new stable release of <a class="reference external" href="http://nuitka.net">Nuitka</a>. Please see the page <a class="reference external" href="http://nuitka.net/pages/overview.html">"What is Nuitka?"</a> for clarification of what it is now and what it wants to be.</p>
<p>This release expands the reach of Nuitka substantially, as new platforms and
compilers are now supported. A lot of polish has been applied. Under the hood
there is the continued and in-progress effort to implement SSA form in Nuitka.</p>
<div class="section" id="new-features">
<h2>New Features</h2>
<ul><li><p class="first">Support for new compiler: Microsoft Visual C++.</p>
<p>You can now use Visual Studio 2008 or Visual Studio 2010 for compiling under
Windows.</p>
</li>
<li><p class="first">Support for NetBSD.</p>
<p>Nuitka works for at least NetBSD 6.0, older versions may or may not work. This
required fixing bugs in the generic "fibers" implementation.</p>
</li>
<li><p class="first">Support for Python3 under Windows too.</p>
<p>Nuitka uses Scons to build the generated C++ files. Unfortunately it requires
Python2 to execute, which is not readily available to call from Python3. It
now guesses the default installation paths of CPython 2.7 or CPython 2.6 and
it will use it for running Scons instead. You have to install it to"
C:Python26" or "C:Python27" for Nuitka to be able to find it.</p>
</li>
<li><p class="first">Enhanced Python 3.3 compatibility.</p>
<p>The support the newest version of Python has been extended, improving
compatibility for many minor corner cases.</p>
</li>
<li><p class="first">Added warning when a user compiles a module and executes it immediately when
that references <tt class="docutils literal">__name__</tt>.</p>
<p>Because very likely the intention was to create an executable. And esp. if
there is code like this:</p>
<pre class="code python literal-block">
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">"__main__"</span><span class="p">:</span>
   <span class="n">main</span><span class="p">()</span>
</pre>
<p>In module mode, Nuitka will optimize it away, and nothing will happen on
execution. This is because the command</p>
<pre class="code sh literal-block">
nuitka --execute module
</pre>
<p>is behavioral more like</p>
<blockquote>
<p>python -c "import module"</p>
</blockquote>
<p>and that was a trap for new users.</p>
</li>
<li><p class="first">All Linux architectures are now supported. Due to changes in how evaluation
order is enforced, we don't have to implement for specific architectures
anymore.</p>
</li>
</ul></div>
<div class="section" id="bug-fixes">
<h2>Bug Fixes</h2>
<ul><li><p class="first">Dictionary creation was not fully compatible.</p>
<p>As revealed by using Nuitka with CPython3.3, the order in which dictionaries
are to be populated needs to be reversed, i.e. CPython adds the last item
first. We didn't observe this before, and it's likely the new dictionary
implementation that finds it.</p>
<p>Given that hash randomization makes dictionaries item order undetermined
anyway, this is more an issue of testing.</p>
</li>
<li><p class="first">Evaluation order for arguments of calls was not effectively enforced. It is
now done in a standards compliant and therefore fully portable way. The
compilers and platforms so far supported were not affected, but the newly
supported Visual Studio C++ compiler was.</p>
</li>
<li><p class="first">Using a <tt class="docutils literal">__future__</tt> import inside a function was giving an assertion,
instead of the proper syntax error.</p>
</li>
<li><p class="first">Python3: Do not set the attributes <tt class="docutils literal">sys.exc_type</tt>, <tt class="docutils literal">sys.exc_value</tt>,
<tt class="docutils literal">sys.exc_traceback</tt>.</p>
</li>
<li><p class="first">Python3: Annotations of function worked only as long as their definition was
not referring to local variables.</p>
</li>
</ul></div>
<div class="section" id="new-optimization">
<h2>New Optimization</h2>
<ul><li><p class="first">Calls with no positional arguments are now using the faster call methods.</p>
<p>The generated C++ code was using the <tt class="docutils literal">()</tt> constant at call site, when doing
calls that use no positional arguments, which is of course useless.</p>
</li>
<li><p class="first">For Windows now uses OS "Fibers" for Nuitka "Fibers".</p>
<p>Using threads for fibers was causing only overhead and with this API, MSVC had
less issues too.</p>
</li>
</ul></div>
<div class="section" id="organizational">
<h2>Organizational</h2>
<ul class="simple"><li>Accepting <a class="reference external" href="http://nuitka.net/pages/donations.html">Donations</a> via Paypal,
please support funding travels, website, etc.</li>
<li>The <a class="reference external" href="http://nuitka.net/doc/user-manual.html">User Manual</a> has been updated
with new content. We now do support Visual Studio, documented the required
LLVM version for clang, Win64 and modules may include modules too, etc. Lots
of information was no longer accurate and has been updated.</li>
<li>The Changelog has been improved for consistency, wordings, and styles.</li>
<li>Nuitka is now available on the social code platforms as well<ul><li><a class="reference external" href="https://bitbucket.org/kayhayen/nuitka">Bitbucket</a></li>
<li><a class="reference external" href="https://github.com/kayhayen/Nuitka">Github</a></li>
<li><a class="reference external" href="https://gitorious.org/nuitka/nuitka">Gitorious</a></li>
<li><a class="reference external" href="https://code.google.com/p/nuitka/">Google Code</a></li>
</ul></li>
<li>Removed "clean-up.sh", which is practically useless, as tests now clean up
after themselves reasonably, and with <tt class="docutils literal">git clean <span class="pre">-dfx</span></tt> working better.</li>
<li>Removed "create-environment.sh" script, which was only setting the <tt class="docutils literal">PATH</tt>
variable, which is not necessary.</li>
<li>Added <tt class="docutils literal"><span class="pre">check-with-pylint</span> <span class="pre">--emacs</span></tt> option to make output its work with Emacs
compilation mode, to allow easier fixing of warnings from PyLint.</li>
<li>Documentation is formatted for 80 columns now, source code will gradually aim
at it too. So far 90 columns were used, and up to 100 tolerated.</li>
</ul></div>
<div class="section" id="cleanups">
<h2>Cleanups</h2>
<ul><li><p class="first">Removed useless manifest and resource file creation under Windows.</p>
<p>Turns out this is no longer needed at all. Either CPython, MinGW, or Windows
improved to no longer need it.</p>
</li>
<li><p class="first">PyLint massive cleanups and annotations bringing down the number of warnings
by a lot.</p>
</li>
<li><p class="first">Avoid use of strings and built-ins as run time pre-computed constants that are
not needed for specific Python versions, or Nuitka modes.</p>
</li>
<li><p class="first">Do not track needed tuple, list, and dict creation code variants in context,
but e.g. in <tt class="docutils literal">nuitka.codegen.TupleCodes</tt> module instead.</p>
</li>
<li><p class="first">Introduced an "internal" module to host the complex call helper functions,
instead of just adding it to any module that first uses it.</p>
</li>
</ul></div>
<div class="section" id="new-tests">
<h2>New Tests</h2>
<ul class="simple"><li>Added basic tests for order evaluation, where there currently were None.</li>
<li>Added support for "2to3" execution under Windows too, so we can run tests for
Python3 installations too.</li>
</ul></div>
<div class="section" id="summary">
<h2>Summary</h2>
<p>The release is clearly major step ahead. The new platform support triggered a
whole range of improvements, and means this is truly complete now.</p>
<p>Also there is very much polish in this release, reducing the number of warnings,
updated documentation, the only thing really missing is visible progress with
optimization.</p>
</div>]]></description>
    <link><![CDATA[http://nuitka.net/posts/nuitka-release-043.html]]></link>
    <pubDate>2013-05-18 10:17:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Vasudev Ram: Python's inspect module is powerful]]></title>
    <description><![CDATA[<p><a href="http://docs.python.org/2/library/inspect.html">27.13. inspect &#8212; Inspect live objects &#8212; Python v2.7.5 documentation</a></p><p>The inspect module comes as part of the standard library of Python. It allows you to inspect live objects at run time by using its methods.</p><p>Here is an example:</p><p>import inspect<br />class Bar( object):<br />&#160;&#160;&#160; def foo( self ):<br />&#160;&#160;&#160;&#160;&#160;&#160;&#160; print "in Bar.foo 4"<br />&#160;&#160;&#160;&#160;&#160;&#160;&#160; self .a = 1<br />&#160;&#160;&#160;&#160;&#160;&#160;&#160; self .di = { 'b' : 2, 'c' : 3 }<br />&#160;&#160;&#160;&#160;&#160;&#160;&#160; self .li = [&#160; 4, 5, 6 ]</p><p>bar = Bar()<br />bar.foo()<br />for member in inspect.getmembers(bar):<br />&#160;&#160;&#160; print member</p><p>Running the above code gives this as (partial) output:</p><p>in Bar.foo 4</p><p>('__dict__', {'a': 1, 'li': [4, 5, 6], 'di': {'c': 3, 'b': 2}})<br />('__doc__', None)<br />('a', 1)<br />('di', {'c': 3, 'b': 2})<br />('foo', bound method Bar.foo of __main__.Bar object at 0x4035bfac)<br />('li', [4, 5, 6])</p><p>This shows both the bound methods and the member variables of the instance bar.</p><p>The inspect module can do a lot of other things too. Check the docs for it, linked at the top of this post.</p><p>You can also modify and run the above code snippet at this codepad.org URL:</p><p>http://codepad.org/dMLmkhQ6</p><p>It may not last there for too long, though, since they probably delete pastes to make room for new ones.</p><p>- Vasudev Ram<br />dancingbison.com<br /></p><div class="blogger-post-footer"><a href="http://www.dancingbison.com">Vasudev Ram</a>
<br /></div>]]></description>
    <link><![CDATA[http://jugad2.blogspot.com/2013/05/python-inspect-module-is-powerful.html]]></link>
    <pubDate>2013-05-18 01:55:13</pubDate>
  </item>
  <item>
    <title><![CDATA[Joe Abbate: Pyrseas contributions solicited]]></title>
    <description><![CDATA[<p>Do you use <a href="http://www.postgresql.org/">PostgreSQL</a> and truly believe it&#8217;s &#8220;the world&#8217;s most advanced open source database&#8221; and that its upcoming 9.3 release will make it even more awesome?</p>
<p>Do you also use <a href="http://www.python.org/">Python</a> and believe it&#8217;s &#8220;an easy to learn, powerful programming language&#8221; with &#8220;elegant syntax&#8221; that makes it an ideal language for developing applications and tools around PostgreSQL, such as Pyrseas?</p>
<p>Then we could use your help. For starters, we want to add support for the <a href="https://github.com/jmafc/Pyrseas/issues/55">MATERIALIZED VIEWs</a> and <a href="https://github.com/jmafc/Pyrseas/issues/56">EVENT TRIGGERs</a> coming up in PG 9.3.</p>
<p>We have also been requested to add the capability to load and maintain &#8220;static data&#8221; (relatively small, unchanging tables) as part of <a href="http://pyrseas.readthedocs.org/en/latest/yamltodb.html">yamltodb</a>, so that it can be integrated more easily into database version control workflows.</p>
<p>And for the next release, Pyrseas 0.7, we&#8217;d like to include the first version of the <a href="https://github.com/jmafc/Pyrseas/issues/17">database augmentation tool</a> which will support declarative implementation of business logic in the database&#8211;starting off with audit trail columns. Some work has been done on this already, but it needs integration with the current code and tests.</p>
<p>Or perhaps coding is not your forte, but you&#8217;re really good at explaining and documenting technical &#8220;stuff&#8221;. Then you could give us a hand with revamping the <a href="http://pyrseas.readthedocs.org/en/latest/">docs</a>, maybe writing a tutorial so that users have a smooth ride using our tools.</p>
<p>Or maybe you have your own ideas as to how improve the PostgreSQL version control experience. We&#8217;d love to hear those too.</p>
<p>If you&#8217;d like to help, you can <a href="https://github.com/jmafc/Pyrseas">fork the code on GitHub</a>, join the <a href="http://lists.pgfoundry.org/mailman/listinfo/pyrseas-general">mailing list</a> and introduce yourself, or leave a comment below.</p>
<br />Filed under: <a href="http://pyrseas.wordpress.com/category/postgresql-2/">PostgreSQL</a>, <a href="http://pyrseas.wordpress.com/category/python/">Python</a>, <a href="http://pyrseas.wordpress.com/category/version-control/">Version control</a>  <img alt="" border="0" src="http://stats.wordpress.com/b.gif?host=pyrseas.wordpress.com&blog=19437126&post=2019&subd=pyrseas&ref=&feed=1" width="1" height="1" />]]></description>
    <link><![CDATA[http://pyrseas.wordpress.com/2013/05/17/pyrseas-contributions-solicited/]]></link>
    <pubDate>2013-05-17 18:48:31</pubDate>
  </item>
  <item>
    <title><![CDATA[Montreal Python User Group: PyCon 2014 Call For Launch Day Sponsors]]></title>
    <description><![CDATA[<p>It&#8217;s that time again! Planning for PyCon 2014 is well underway, and we&#8217;re currently preparing for the launch of our new site. With the launch comes a unique opportunity: Is your organization interested in being a launch day sponsor?</p>
<p>PyCon 2013 <a href="https://us.pycon.org/2013/about/press/20120709_launch_release/">launched</a> on July 9, 2012 with 21 sponsors pledging support, leading the charge that drew over 150 organizations to pitch in to the biggest and best Python conference yet. We&#8217;re planning a similar go-live date for the 2014 site, and we&#8217;re building up our cadre of supporters for the April 9-17 conference taking place in Montreal, Quebec, Canada.</p>
<p>Your organization&#8217;s support enables PyCon to do the awesome things. 2013 introduced a number of new events suggested by the community, so we&#8217;d like to keep doing those things and more. For example, the inaugural <a href="http://pycon.blogspot.com/2013/03/how-kids-stole-show-young-coders.html">Young Coders tutorial</a> was such a hit that there are already plans around the world for user groups to replicate it, and we&#8217;re looking forward to doing a PyCon 2014 rendition. Programs like Financial Aid, which saw its budget increased and then quickly doubled to reach $100,000 USD, are greatly enhanced by the giving of sponsors.</p>
<p>Along with benefiting the community, sponsorship of PyCon brings many benefits to its supporters. We hear it year after year that there is no better place to hire Python developers than at PyCon. We offer sponsors a place on our site to promote available positions, and we run a job fair on-site that has been a huge success. The Expo Hall is a great place to market your latest projects and to network with 2,000 eager Python developers. The value is unparalleled in the conference scene, especially after considering our flexibility to work with each and every organization. We even offer a 50% discount to organizations under 25 people. See <a href="https://us.pycon.org/2013/sponsors/whysponsor/">https://us.pycon.org/2013/sponsors/whysponsor/</a> for more thoughts.</p>
<p>While we&#8217;re still finalizing the sponsorship prospectus, it will be very similar to the one we used in 2013 at <a href="https://us.pycon.org/2013/sponsors/prospectus/">https://us.pycon.org/2013/sponsors/prospectus/</a>. We&#8217;ll share the details as soon as we complete them, and any questions can be forwarded to our Sponsorship Chair, <a href="mailto:jnoller@gmail.comjnoller@gmail.com">Jesse Noller</a>.</p>
<p>For 2014, PyCon will have a maximum capacity of 2,000 attendees. We&#8217;ve sold out the last two conferences and we&#8217;re expecting a third, so mark your calendars for April 9-17, 2014. Other dates to remember are our Call for Proposals in July, and we&#8217;re looking forward to opening registration in September. We&#8217;re planning for the conference schedule to be laid out in December, just in time for the holidays.</p>
<p>If you don&#8217;t have a passport, don&#8217;t forget that entering Canada requires one. US residents should see <a href="http://travel.state.gov/passport/">http://travel.state.gov/passport/</a> for details.</p>]]></description>
    <link><![CDATA[http://montrealpython.org/2013/05/pycon-day-sponsors/]]></link>
    <pubDate>2013-05-17 14:32:32</pubDate>
  </item>
  <item>
    <title><![CDATA[Reinout van Rees: Principle philosophy - Swift]]></title>
    <description><![CDATA[<div class="document">
<p>Principle philosophy: a way to discuss our rules and beliefs that govern our
actions. He tells it from his personal experience.</p>
<p>His parents wanted to raise him as a good person. So they thought him good
principles (like don't be a quitter, don't steal, etc). This is quite
black/white though. We are all more gray/gray.</p>
<p>What about the question &quot;how can I be a good programmer&quot;? Programmers use
logic, which sounds black/white again: write tests, don't repeat
yourself. Sigh.</p>
<p>Talking about things like this is impossible without <a class="reference external" href="https://en.wikipedia.org/wiki/Immanuel_Kant">Immanuel Kant</a>. He differentiates between
reason and instinct. If &quot;be happy&quot; were our life goal, we'd just follow our
instincts. So what is reason for, then, apart for doing good? Reason has to do
with moral. There are three ways of looking at &quot;doing good&quot;:</p>
<ul class="simple">
<li>Duty. Good things can come from duty. Duty can also lead to non-good things,
though. Hm, so this is not it.</li>
<li>Make a difference between the goal and the outcome. The outcome might be bad
even though the goal could be worthy.</li>
<li>Universal lawfullness. Only do something if you know that everybody thinks
it is a good idea.</li>
</ul>
<p>Does this help with a question like &quot;is testing good&quot;?</p>
<p>Gandhi said that a man is the sum of his actions.</p>
<p>In a sense we are the sum of our experiences. So increase the amount of
experience that you have. Either have the experiences yourself, or share them
<strong>like on this conference</strong>. Everything looks different from the trenches:
learn from eachother.</p>
<p>Some lessons he learned from a <em>little baseball league</em> experience (where he
sucked) as a kid:</p>
<ul class="simple">
<li>Swing for the fences. Aim for a home run. It allows you take great risks
(because you have great goals). It motivates you.</li>
<li>Set reasonable goals, too. Incremental intermediate goals. Those
intermediate goals help you progress.</li>
<li>You suck... and that's totally OK. You're not good at everything. It gives
you a different perspective. And you <em>can</em> still give it your best. Also to
that almost-unused old project that you get a bug report for.</li>
</ul>
<p><strong>Some take-aways</strong>:</p>
<ul class="simple">
<li>Build a strong foundation of principles.</li>
<li>One size doesn't fit all</li>
<li>Learn from your experiences and share them.</li>
<li>Build a great network.</li>
<li>Ask all the right questions.</li>
</ul>
</div>]]></description>
    <link><![CDATA[http://reinout.vanrees.org/weblog/2013/05/17/priniciple-philosophy.html]]></link>
    <pubDate>2013-05-17 09:23:00</pubDate>
  </item>
  <item>
    <title><![CDATA[PyPy Development: PyPy 2.0.1 - Bohr Smørrebrød]]></title>
    <description><![CDATA[<p>We're pleased to announce PyPy 2.0.1.  This is a stable bugfix release
over <a class="reference external" href="http://morepypy.blogspot.ch/2013/05/pypy-20-einstein-sandwich.html">2.0</a>.  You can download it here:</p>
<blockquote>
<a class="reference external" href="http://pypy.org/download.html">http://pypy.org/download.html</a></blockquote>
<p>The fixes are mainly about fatal errors or crashes in our stdlib.  See
below for more details.</p>
<div class="section" id="what-is-pypy">
<h1>What is PyPy?</h1>
<p>PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7. It's fast (<a class="reference external" href="http://speed.pypy.org">pypy 2.0 and cpython 2.7.3</a> performance comparison)
due to its integrated tracing JIT compiler.</p>
<p>This release supports x86 machines running Linux 32/64, Mac OS X 64 or
Windows 32.  Support for ARM is progressing but not bug-free yet.</p>
</div>
<div class="section" id="highlights">
<h1>Highlights</h1>
<ul class="simple">
<li>fix an occasional crash in the JIT that ends in <a class="reference external" href="https://bugs.pypy.org/issue1482">RPython Fatal error:
NotImplementedError</a>.</li>
<li><cite>id(x)</cite> is now always a positive number (except on int/float/long/complex).
This fixes an issue in <tt class="docutils literal">_sqlite.py</tt> (mostly for 32-bit Linux).</li>
<li>fix crashes of callback-from-C-functions (with cffi) when used together
with Stackless features, on asmgcc (i.e. Linux only).  Now <a class="reference external" href="http://mail.python.org/pipermail/pypy-dev/2013-May/011362.html">gevent should
work better</a>.</li>
<li>work around an eventlet issue with <a class="reference external" href="https://bugs.pypy.org/issue1468">socket._decref_socketios()</a>.</li>
</ul>
<p>Cheers,
arigo et. al. for the PyPy team</p>
</div><img src="http://feeds.feedburner.com/~r/PyPyStatusBlog/~4/q2ijxAdBLcQ" height="1" width="1" />]]></description>
    <link><![CDATA[http://feedproxy.google.com/~r/PyPyStatusBlog/~3/q2ijxAdBLcQ/pypy-201-bohr-smrrebrd.html]]></link>
    <pubDate>2013-05-16 20:10:08</pubDate>
  </item>
  <item>
    <title><![CDATA[Reinout van Rees: The web of stuff - Zack Voase]]></title>
    <description><![CDATA[<div class="document">
<p>A plane flew over (noisily) at the start of his presentation. He put our work
in perspective by saying that that was a 80 ton plane and that we're just
building websites :-)</p>
<div class="section" id="possibilities">
<h1>Possibilities</h1>
<p>Computers used to take up whole rooms, now you have a smartphone. Big data is
<em>really big</em> data now. Moore's lawworks both ways, though, so you have really small
computers now. An arduino for instance.</p>
<p>He often makes comparison to the human body. All over our body, sensors give
off signals that go into the central nervous system. The brain processes it
and gives signals back to muscles if necessary. Sensing, feedback,
understanding, reaction.</p>
<p>Stuff can talk to the cloud. Like a sensor in your body talks to your mind,
stuff can treat the cloud as a brain. The cloud is what allows small tools to
be smart.</p>
<p>Stuff <em>does</em> often need a human to interact with it. Like a
smartphone. There's all sorts of people thinking about how to &quot;liberate the
computers from their human overlords&quot;. Why cannot computers sense and act on
their own account?</p>
<p>So how do you bridge the gap betwen <strong>sensing</strong> and <strong>acting</strong> of stuff? How
do you use Django for it? There's a lot available online about sensing and
about acting, but not the communication in between.</p>
<p>The communication medium itself is a bit of a problem. You don't want to have
a telephone data contract for every single small piece of stuff. A physical
connection isn't always handy either.</p>
<p>His preferred communication medium is <a class="reference external" href="http://www.twilio.com/">Twilio</a> for
sending SMSs. The stuff has low memory, so the message length limit is fine.</p>
<p>He showed a demo with a card reader that read his London transport card and
sended an SMS to his Django site. The card reader was a combination between an
arduino, a 'shield' sms sender and an RFID reader. The django app then submits
it to foursquare. (The last part didn't work, probably due to a local
foursquare problem, but the django app <em>did</em> have all the data he send from
his card reader). Nice.</p>
<p><strong>SUCCESS</strong>: after the lightning talks he did it again and now it worked!</p>
</div>
<div class="section" id="personal-development">
<h1>Personal development</h1>
<p>He had never done any hardware work until four months ago. No compiling for
arduino. It sounded a bit scary to him.</p>
<p>It is normal, if you start as a beginner, you're slowly getting better if you
keep at something. Then you automatically learn more and thus learn that
there's a lot you don't know. That's the dip in the middle. Those are the
people we need to keep on board so that they push through to the expert stage.</p>
<p>When you're in the middle, you <em>know</em> how bad you are (or how good you aren't
yet). That's the risky phase were people quit.</p>
<p>Likewise documentation. Tutorials are useful for beginners. Reference material
is useful for experts. There's not a lot in the middle and you're bound to be
a bit frustrated in that stage.</p>
<p>So if you're going to start experimenting with electronics, you're bound to
hit a wall, for instance when calculating complex electronic schemas. Push on
anyway: the first time you make a phone call with your own device is <strong>totally
worth it</strong>.</p>
<p>Two books he recommends to get you started:</p>
<ul class="simple">
<li>Getting started with Arduino.</li>
<li>The art of electronics.</li>
</ul>
</div>
</div>]]></description>
    <link><![CDATA[http://reinout.vanrees.org/weblog/2013/05/16/web-of-stuff.html]]></link>
    <pubDate>2013-05-16 15:47:00</pubDate>
  </item>
  <item>
    <title><![CDATA[Reinout van Rees: Growing open source seeds - Kenneth Reitz]]></title>
    <description><![CDATA[<div class="document">
<p>He shows us three kinds of (more or less) open source projects.</p>
<div class="section" id="type-1-public-source">
<h1>Type 1: public source</h1>
<p>Once upon a time there was an &quot;open source project&quot; called the <em>facebook
SDK</em>. Basically it just stopped working one day and nobody could help, despite
offers for help on the issue tracker. Hacker news got wind of it and it was on
the front page for a while. Facebook's reaction? Disabling the issue
tracker... (Later on they fixed it).</p>
<p>That's not open source, that's <strong>public source</strong>. Often it is abandoned due to
loack of interest, change of focus or so. The motivation for having it as open
source simply is not clear.</p>
</div>
<div class="section" id="type-2-shared-investment">
<h1>Type 2: shared investment</h1>
<p>A different example: <a class="reference external" href="https://www.gittip.com/">gittip</a>. They aim to be the
world's first open company. There's a github issue for everything, even the
company name. Major decisions are voted for on github. The code is open
source, of course. All interviews with journalists are filmed and
live-streamed. And all otherwise-often-backdoor-cooperation-agreements are
fully open.</p>
<p>Projects like gittip are <strong>shared investment</strong> projects. Shared ownership,
extreme transparency. There is very little questioning of motivations. The
motivation is clear and public. There's a documented process for new
contributers. The advantage? It is low risk. There's a high bus
factor.</p>
</div>
<div class="section" id="type-3-dictatorship-project">
<h1>Type 3: dictatorship project</h1>
<p>Kenneth is the author of <a class="reference external" href="https://pypi.python.org/pypi/requests">requests</a>. An open source project, very
succesful. But all the decisions are made by Kenneth.</p>
<p>That's really more of an <strong>dictatorship project</strong>. A totalitarian BDFL that
owns everything. The dictator is responsible for all decisions. Requests'
values lie in its extreme opinions. If he'd involve more people, the value
would be dilluted. There are drawbacks. A low bus factor. High risk of
burnout: Kenneth is the single point of failure.</p>
</div>
<div class="section" id="lessons-learned">
<h1>Lessons learned</h1>
<ul>
<li><p class="first"><strong>Be cordial or be on your way</strong>. As a user, you need to keep all your
interactions with the maintainer as respectful as possible. The maintainer
put a lot of work in it and they don't owe you any of their time.</p>
<p>As a maintainer, you also must be cordial. Be thankful to all
contributions. Feedback is the liveblood of your project, even the
negative. You'll need to ignore non-constructive comments. Be careful with
the words you choose, sometimes contributors take what you say VERY
personally. You might have to educate your users. And: a bit of kindness
goes a long way.</p>
</li>
<li><p class="first"><strong>Sustainability</strong> is almost the biggest challenge. Don't burn out. Try to
get others to help.</p>
<p>He quotes Wes Beary: &quot;open source provides a unique opportunity for the
trifecta of purpose, mastery and autonomy&quot;. Pay equal attention to all of
these three. Learn to do less, focus more on your purpose, for instance.</p>
</li>
<li><p class="first"><strong>Learn to say no</strong>. People ask for crazy features. Or they submit quite
sane pull requests that, if you allow them all in, makes your project slow
and unfocused. <strong>Kenneth wants as few lines of codes in his
project</strong>. Negative diffs are the best diffs!</p>
</li>
<li><p class="first">Open source makes the world a better place. Don't make it complicated!</p>
</li>
</ul>
</div>
</div>]]></description>
    <link><![CDATA[http://reinout.vanrees.org/weblog/2013/05/16/planting-open-source-seeds.html]]></link>
    <pubDate>2013-05-16 12:02:00</pubDate>
  </item>
</channel>
</rss>

