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	<title>Martin Laprise &#187; Python</title>
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	<link>http://www.martinlaprise.info</link>
	<description>physics, computing and other stuffs.</description>
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		<title>Solving the nonlinear Schrödinger equation on GPU</title>
		<link>http://www.martinlaprise.info/2010/08/26/solving-the-nonlinear-schrodinger-equation-on-gpu/</link>
		<comments>http://www.martinlaprise.info/2010/08/26/solving-the-nonlinear-schrodinger-equation-on-gpu/#comments</comments>
		<pubDate>Thu, 26 Aug 2010 20:38:48 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[Physics]]></category>
		<category><![CDATA[PyOFTK]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=730</guid>
		<description><![CDATA[PyOFTK can now perform the Split-Step Fourier algorithm on the GPU via the PyOFTK.ssfgpu() function. The first draft of the code do not take into accound the Raman response time. The implementation was made very easy with the pyfft python package done by Bogdan Opanchuk which is the Apple&#8217;s FFT implementation ported for PyCuda and [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>githubGraph + ubigraph server = pure awesomeness</title>
		<link>http://www.martinlaprise.info/2010/06/18/githubgraph-ubigraph-server-awesome/</link>
		<comments>http://www.martinlaprise.info/2010/06/18/githubgraph-ubigraph-server-awesome/#comments</comments>
		<pubDate>Fri, 18 Jun 2010 18:43:56 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[githubGraph]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=708</guid>
		<description><![CDATA[Example of a GitHub user&#8217;s social graph. This undirected graph of users which mutually follow each other, contains 7243 nodes and 14484 edges. The video shows the convergence of the Force-based algorithm used to visualize the graph. httpvhd://www.youtube.com/watch?v=HAmfZ58C6Qo]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Weekend project: compute a 68 gigapixels fractal image: Part II</title>
		<link>http://www.martinlaprise.info/2010/04/15/weekend-project-compute-a-68-gigapixels-fractal-image-part-ii/</link>
		<comments>http://www.martinlaprise.info/2010/04/15/weekend-project-compute-a-68-gigapixels-fractal-image-part-ii/#comments</comments>
		<pubDate>Thu, 15 Apr 2010 16:59:43 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[C/C++]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=641</guid>
		<description><![CDATA[So, as promised, here is the source code for the gigapixels fractal project: gmpano.tar.gz. The archive contain the following files: mandelbrot.cpp: The actual C++ code for the computation numpy.i and PyMandelbrot.i: The SWIG descriptions files used for the function wrapper (C++ to Python) gigaMandelbrot.py: This one contain the Python class used for the image tile rendering gmPano.py: [...]]]></description>
		<wfw:commentRss>http://www.martinlaprise.info/2010/04/15/weekend-project-compute-a-68-gigapixels-fractal-image-part-ii/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<item>
		<title>Weekend project: compute a 68 gigapixels fractal image</title>
		<link>http://www.martinlaprise.info/2010/03/27/compute-a-68-gigapixels-fractal-images/</link>
		<comments>http://www.martinlaprise.info/2010/03/27/compute-a-68-gigapixels-fractal-images/#comments</comments>
		<pubDate>Sun, 28 Mar 2010 01:07:13 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[C/C++]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=603</guid>
		<description><![CDATA[Here is a project I made in order to test HDView SL. I&#8217;m not a big fan of the Flash/Silverlight thing, but I have to admit that the Seadragon zooming technique include in Silverlight is pretty neat. Put it in Fullscreen and have fun ! Quick facts: - The final image size is around 3.2 [...]]]></description>
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		<slash:comments>10</slash:comments>
		</item>
		<item>
		<title>Fractal &amp; Python: Part II</title>
		<link>http://www.martinlaprise.info/2010/03/02/fractal-python-part-ii/</link>
		<comments>http://www.martinlaprise.info/2010/03/02/fractal-python-part-ii/#comments</comments>
		<pubDate>Wed, 03 Mar 2010 02:36:19 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=526</guid>
		<description><![CDATA[After the Lorenz attractor, here is a little code for exploring the Mandelbrot Set. Of course, you could compute the same thing in CUDA with a 1000X speedup&#8230; but  it wouldn&#8217;t be pythonish as this ! from numpy import * import matplotlib.pylab as pl &#160; maxIteration = 128 z_min = -2-1j z_max = 1+1j &#160; # [...]]]></description>
		<wfw:commentRss>http://www.martinlaprise.info/2010/03/02/fractal-python-part-ii/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<item>
		<title>Visualizing the Lorenz attractor in 3D with VTK</title>
		<link>http://www.martinlaprise.info/2010/02/28/visualizing-the-lorentz-attractor-with-vtk/</link>
		<comments>http://www.martinlaprise.info/2010/02/28/visualizing-the-lorentz-attractor-with-vtk/#comments</comments>
		<pubDate>Sun, 28 Feb 2010 16:11:24 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=497</guid>
		<description><![CDATA[I just finished my nth reading of the James Gleick&#8217;s classic Chaos: Making a New Science and I realized that the famous Lorenz attractor should be much more exciting to explore in 3D. So here is a very simple Python code that resolve the nonlinear differential equation describing Lorenz oscillator and show the solution in [...]]]></description>
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		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>GitHub Social Graph &#8211; Part II</title>
		<link>http://www.martinlaprise.info/2010/02/22/github-social-graph-part-ii/</link>
		<comments>http://www.martinlaprise.info/2010/02/22/github-social-graph-part-ii/#comments</comments>
		<pubDate>Mon, 22 Feb 2010 17:03:22 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[githubGraph]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=473</guid>
		<description><![CDATA[Here is a little Python class I made to retrieve a social graph from github. The ffDigraph() method generate a directed graph of a user by examining his followers list and his following list. The ffGraph() method is the most interesting one since it generate a undirected graph by adding only the nodes which mutually [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Visualize your own twitter graph &#8211; Part 2</title>
		<link>http://www.martinlaprise.info/2010/02/15/visualize-your-own-twitter-graph-part-2/</link>
		<comments>http://www.martinlaprise.info/2010/02/15/visualize-your-own-twitter-graph-part-2/#comments</comments>
		<pubDate>Tue, 16 Feb 2010 02:03:13 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=438</guid>
		<description><![CDATA[Here is real (less shameful) version of my twitter graph generator. In fact, the last one was just a little test. This version actually generate a real graph with all the nodes adjacents to the twitter user. The final graph containt around 6200+ nodes. You can get a sample (4597 × 4699) of the image here. The real [...]]]></description>
		<wfw:commentRss>http://www.martinlaprise.info/2010/02/15/visualize-your-own-twitter-graph-part-2/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Now, let&#8217;s take a look at your GitHub neighborhood.</title>
		<link>http://www.martinlaprise.info/2010/02/11/visualize-your-own-github-graph/</link>
		<comments>http://www.martinlaprise.info/2010/02/11/visualize-your-own-github-graph/#comments</comments>
		<pubDate>Thu, 11 Feb 2010 18:26:25 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[githubGraph]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=400</guid>
		<description><![CDATA[The GitHub social graph is an other cool thing to look at. The graph are made exactly the same way as the twitter one. I suspect the &#8220;commit&#8221; graph to be much more interesting. import time # Import pygraph from pygraph.classes.graph import graph from pygraph.classes.digraph import digraph from pygraph.algorithms.searching import breadth_first_search from pygraph.algorithms.traversal import traversal [...]]]></description>
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		<slash:comments>0</slash:comments>
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		<item>
		<title>Visualize your own twitter graph</title>
		<link>http://www.martinlaprise.info/2010/02/09/construct-your-own-twitter-graph/</link>
		<comments>http://www.martinlaprise.info/2010/02/09/construct-your-own-twitter-graph/#comments</comments>
		<pubDate>Tue, 09 Feb 2010 19:36:03 +0000</pubDate>
		<dc:creator>mlaprise</dc:creator>
				<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">http://www.martinlaprise.info/?p=363</guid>
		<description><![CDATA[Are you wondering how your twitter neighborhood looks like ? Here is a simple way to construct a &#8220;following&#8221; graph with python-graph, pygraphviz and twython. Don&#8221;t try this at home with a 10000+ nodes graph &#8230; this is a bad idea ! Also, the Twitter API clients only allow users to make a limited number of [...]]]></description>
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