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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="Docutils 0.14: http://docutils.sourceforge.net/" />
<title>From Python to Numpy</title>
<meta content="An open-source book about numpy vectorization techniques, based on experience, practice and descriptive examples" name="description" />
<meta content="width=device-width, initial-scale=1, maximum-scale=1" name="viewport" />
<link rel="stylesheet" href="book.css" type="text/css" />
<link rel="stylesheet" href="math.css" type="text/css" />
</head>
<body>
<div class="document" id="from-python-to-numpy">
<h1 class="title">From Python to Numpy</h1>
<h2 class="subtitle" id="copyright-c-2017-nicolas-p-rougier-nicolas-rougier-inria-fr">Copyright (c) 2017 - Nicolas P. Rougier <<a class="reference external" href="mailto:Nicolas.Rougier%40inria.fr">Nicolas<span>.</span>Rougier<span>@</span>inria<span>.</span>fr</a>></h2>
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<!-- Title: From Python to Numpy -->
<!-- Author: Nicolas P. Rougier -->
<!-- Date: January 2017 -->
<!-- License: Creative Commons Share-Alike Non-Commercial International 4.0 -->
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<div class="title-logos docutils container">
<img alt="data/cc.large.png" src="data/cc.large.png" style="width: 40px;" />
<img alt="data/by.large.png" src="data/by.large.png" style="width: 40px;" />
<img alt="data/sa.large.png" src="data/sa.large.png" style="width: 40px;" />
<img alt="data/nc.large.png" src="data/nc.large.png" style="width: 40px;" />
<div class="line-block">
<div class="line"><br /></div>
<div class="line">Latest version - January 2019</div>
<div class="line">DOI: <a class="reference external" href="http://doi.org/10.5281/zenodo.225783">10.5281/zenodo.225783</a></div>
</div>
</div>
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<p>There are already a fair number of books about Numpy (see <a class="reference internal" href="#bibliography">Bibliography</a>) and a
legitimate question is to wonder if another book is really necessary. As you
may have guessed by reading these lines, my personal answer is yes, mostly
because I think there is room for a different approach concentrating on the
migration from Python to Numpy through vectorization. There are a lot of
techniques that you don't find in books and such techniques are mostly learned
through experience. The goal of this book is to explain some of these
techniques and to provide an opportunity for making this experience in the
process.</p>
<p><strong>Website:</strong> <a class="reference external" href="http://www.labri.fr/perso/nrougier/from-python-to-numpy">http://www.labri.fr/perso/nrougier/from-python-to-numpy</a></p>
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<div class="contents main-content topic" id="table-of-contents">
<p class="topic-title first"><strong>Table of Contents</strong></p>
<ul class="simple">
<li><a class="reference internal" href="#preface" id="id62">Preface</a><ul>
<li><a class="reference internal" href="#about-the-author" id="id63">About the author</a></li>
<li><a class="reference internal" href="#about-this-book" id="id64">About this book</a></li>
<li><a class="reference internal" href="#license" id="id65">License</a></li>
</ul>
</li>
<li><a class="reference internal" href="#introduction" id="id66">Introduction</a><ul>
<li><a class="reference internal" href="#simple-example" id="id67">Simple example</a></li>
<li><a class="reference internal" href="#readability-vs-speed" id="id68">Readability vs speed</a></li>
</ul>
</li>
<li><a class="reference internal" href="#anatomy-of-an-array" id="id69">Anatomy of an array</a><ul>
<li><a class="reference internal" href="#id5" id="id70">Introduction</a></li>
<li><a class="reference internal" href="#memory-layout" id="id71">Memory layout</a></li>
<li><a class="reference internal" href="#views-and-copies" id="id72">Views and copies</a></li>
<li><a class="reference internal" href="#conclusion" id="id73">Conclusion</a></li>
</ul>
</li>
<li><a class="reference internal" href="#code-vectorization" id="id74">Code vectorization</a><ul>
<li><a class="reference internal" href="#id7" id="id75">Introduction</a></li>
<li><a class="reference internal" href="#uniform-vectorization" id="id76">Uniform vectorization</a></li>
<li><a class="reference internal" href="#temporal-vectorization" id="id77">Temporal vectorization</a></li>
<li><a class="reference internal" href="#spatial-vectorization" id="id78">Spatial vectorization</a></li>
<li><a class="reference internal" href="#id19" id="id79">Conclusion</a></li>
</ul>
</li>
<li><a class="reference internal" href="#problem-vectorization" id="id80">Problem vectorization</a><ul>
<li><a class="reference internal" href="#id21" id="id81">Introduction</a></li>
<li><a class="reference internal" href="#path-finding" id="id82">Path finding</a></li>
<li><a class="reference internal" href="#fluid-dynamics" id="id83">Fluid Dynamics</a></li>
<li><a class="reference internal" href="#blue-noise-sampling" id="id84">Blue noise sampling</a></li>
<li><a class="reference internal" href="#id29" id="id85">Conclusion</a></li>
</ul>
</li>
<li><a class="reference internal" href="#custom-vectorization" id="id86">Custom vectorization</a><ul>
<li><a class="reference internal" href="#id31" id="id87">Introduction</a></li>
<li><a class="reference internal" href="#typed-list" id="id88">Typed list</a></li>
<li><a class="reference internal" href="#memory-aware-array" id="id89">Memory aware array</a></li>
<li><a class="reference internal" href="#id38" id="id90">Conclusion</a></li>
</ul>
</li>
<li><a class="reference internal" href="#beyond-numpy" id="id91">Beyond NumPy</a><ul>
<li><a class="reference internal" href="#back-to-python" id="id92">Back to Python</a></li>
<li><a class="reference internal" href="#numpy-co" id="id93">NumPy & co</a></li>
<li><a class="reference internal" href="#scipy-co" id="id94">Scipy & co</a></li>
<li><a class="reference internal" href="#id54" id="id95">Conclusion</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id55" id="id96">Conclusion</a></li>
<li><a class="reference internal" href="#quick-references" id="id97">Quick References</a><ul>
<li><a class="reference internal" href="#id58" id="id98">Data type</a></li>
<li><a class="reference internal" href="#id59" id="id99">Creation</a></li>
<li><a class="reference internal" href="#id60" id="id100">Indexing</a></li>
<li><a class="reference internal" href="#reshaping" id="id101">Reshaping</a></li>
<li><a class="reference internal" href="#broadcasting" id="id102">Broadcasting</a></li>
</ul>
</li>
<li><a class="reference internal" href="#bibliography" id="id103">Bibliography</a><ul>
<li><a class="reference internal" href="#tutorials" id="id104">Tutorials</a></li>
<li><a class="reference internal" href="#articles" id="id105">Articles</a></li>
<li><a class="reference internal" href="#books" id="id106">Books</a></li>
</ul>
</li>
</ul>
</div>
<div class="line-block">
<div class="line"><br /></div>
<div class="line"><br /></div>
</div>
<p><strong>Disclaimer:</strong> All external pictures should have associated credits. If there
are missing credits, please tell me, I will correct it. Similarly, all excerpts
should be sourced (wikipedia mostly). If not, this is an error and I will
correct it as soon as you tell me.</p>
<p>The book is open-access (you're reading it) but <strong>if you insist on buying it</strong>,
my advice would be to read it first and then decide if you still want to buy it
(!?). If this is the case, you can do it via <a class="reference external" href="https://www.paypal.me/NicolasPRougier/">Paypal</a>, price is free (<a class="reference external" href="https://www.paypal.me/NicolasPRougier/5">5 euros</a>, <a class="reference external" href="https://www.paypal.me/NicolasPRougier/10">10 euros</a>, <a class="reference external" href="https://www.paypal.me/NicolasPRougier/25">25 euros</a>). You won't get anything extra but
it might help me with the writing of the upcoming <strong>Python and OpenGL for
Scientific Visualization</strong> (May 2018).</p>
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<div class="section" id="preface">
<h1><a class="toc-backref" href="#table-of-contents">Preface</a></h1>
<div class="contents local topic" id="contents">
<p class="topic-title first"><strong>Contents</strong></p>
<ul class="simple">
<li><a class="reference internal" href="#about-the-author" id="id107">About the author</a></li>
<li><a class="reference internal" href="#about-this-book" id="id108">About this book</a><ul>
<li><a class="reference internal" href="#prerequisites" id="id109">Prerequisites</a></li>
<li><a class="reference internal" href="#conventions" id="id110">Conventions</a></li>
<li><a class="reference internal" href="#how-to-contribute" id="id111">How to contribute</a></li>
<li><a class="reference internal" href="#publishing" id="id112">Publishing</a></li>
</ul>
</li>
<li><a class="reference internal" href="#license" id="id113">License</a></li>
</ul>
</div>
<div class="section" id="about-the-author">
<h2><a class="toc-backref" href="#contents">About the author</a></h2>
<p><a class="reference external" href="http://www.labri.fr/perso/nrougier/">Nicolas P. Rougier</a> is a full-time research scientist at <a class="reference external" href="http://www.inria.fr/en">Inria</a> which is the
French national institute for research in computer science and control. This is
a public scientific and technological establishment (EPST) under the double
supervision of the Research & Education Ministry, and the Ministry of Economy
Finance and Industry. Nicolas P. Rougier is working within the <a class="reference external" href="http://www.inria.fr/en/teams/mnemosyne">Mnemosyne</a>
project which lies at the frontier between integrative and computational
neuroscience in association with the <a class="reference external" href="http://www.imn-bordeaux.org/en/">Institute of Neurodegenerative
Diseases</a>, the Bordeaux laboratory for research in computer science
(<a class="reference external" href="https://www.labri.fr/">LaBRI</a>), the <a class="reference external" href="http://www.u-bordeaux.com/">University of Bordeaux</a> and the national center for scientific
research (<a class="reference external" href="http://www.cnrs.fr/index.php">CNRS</a>).</p>
<p>He has been using Python for more than 15 years and NumPy for more than 10
years for modeling in neuroscience, machine learning and for advanced
visualization (OpenGL). Nicolas P. Rougier is the author of several online
resources and tutorials (Matplotlib, NumPy, OpenGL) and he's teaching Python,
NumPy and scientific visualization at the University of Bordeaux and in various
conferences and schools worldwide (SciPy, EuroScipy, etc). He's also the author
of the popular article <a class="reference external" href="http://dx.doi.org/10.1371/journal.pcbi.1003833">Ten Simple Rules for Better Figures</a> and a popular
<a class="reference external" href="http://www.labri.fr/perso/nrougier/teaching/matplotlib/matplotlib.html">matplotlib tutorial</a>.</p>
</div>
<div class="section" id="about-this-book">
<h2><a class="toc-backref" href="#contents">About this book</a></h2>
<p>This book has been written in <a class="reference external" href="http://docutils.sourceforge.net/rst.html">restructured text</a> format and generated using the
<code>rst2html.py</code> command line available from the <a class="reference external" href="http://docutils.sourceforge.net/">docutils</a> python package.</p>
<p>If you want to rebuild the html output, from the top directory, type:</p>
<pre class="code literal-block">
$ rst2html.py --link-stylesheet --cloak-email-addresses \
--toc-top-backlinks --stylesheet=book.css \
--stylesheet-dirs=. book.rst book.html
</pre>
<p>The sources are available from <a class="reference external" href="https://github.com/rougier/from-python-to-numpy">https://github.com/rougier/from-python-to-numpy</a>.</p>
<div class="section" id="prerequisites">
<h3><a class="toc-backref" href="#contents">Prerequisites</a></h3>
<p>This is not a Python beginner guide and you should have an intermediate level in
Python and ideally a beginner level in NumPy. If this is not the case, have
a look at the <a class="reference internal" href="#bibliography">bibliography</a> for a curated list of resources.</p>
</div>
<div class="section" id="conventions">
<h3><a class="toc-backref" href="#contents">Conventions</a></h3>
<p>We will use usual naming conventions. If not stated explicitly, each script
should import NumPy, scipy and matplotlib as:</p>
<pre class="code python literal-block">
<span class="keyword namespace">import</span> <span class="name namespace">numpy</span> <span class="keyword namespace">as</span> <span class="name namespace">np</span>
<span class="keyword namespace">import</span> <span class="name namespace">scipy</span> <span class="keyword namespace">as</span> <span class="name namespace">sp</span>
<span class="keyword namespace">import</span> <span class="name namespace">matplotlib.pyplot</span> <span class="keyword namespace">as</span> <span class="name namespace">plt</span>
</pre>
<p>We'll use up-to-date versions (at the date of writing, i.e. January, 2017) of the
different packages:</p>
<table border="1" class="docutils">
<colgroup>
<col width="55%" />
<col width="45%" />
</colgroup>
<thead valign="bottom">
<tr><th class="head">Packages</th>
<th class="head">Version</th>
</tr>
</thead>
<tbody valign="top">
<tr><td>Python</td>
<td>3.6.0</td>
</tr>
<tr><td>NumPy</td>
<td>1.12.0</td>
</tr>
<tr><td>Scipy</td>
<td>0.18.1</td>
</tr>
<tr><td>Matplotlib</td>
<td>2.0.0</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="how-to-contribute">
<h3><a class="toc-backref" href="#contents">How to contribute</a></h3>
<p>If you want to contribute to this book, you can:</p>
<ul class="simple">
<li>Review chapters (please contact me)</li>
<li>Report issues (<a class="reference external" href="https://github.com/rougier/from-python-to-numpy/issues">https://github.com/rougier/from-python-to-numpy/issues</a>)</li>
<li>Suggest improvements (<a class="reference external" href="https://github.com/rougier/from-python-to-numpy/pulls">https://github.com/rougier/from-python-to-numpy/pulls</a>)</li>
<li>Correct English (<a class="reference external" href="https://github.com/rougier/from-python-to-numpy/issues">https://github.com/rougier/from-python-to-numpy/issues</a>)</li>
<li>Design a better and more responsive html template for the book.</li>
<li>Star the project (<a class="reference external" href="https://github.com/rougier/from-python-to-numpy">https://github.com/rougier/from-python-to-numpy</a>)</li>
</ul>
</div>
<div class="section" id="publishing">
<h3><a class="toc-backref" href="#contents">Publishing</a></h3>
<p>If you're an editor interested in publishing this book, you can <a class="reference external" href="mailto:Nicolas.Rougier%40inria.fr">contact me</a> if you agree to have this version and all
subsequent versions open access (i.e. online at <a class="reference external" href="http://www.labri.fr/perso/nrougier/from-python-to-numpy">this address</a>), you know how to
deal with <a class="reference external" href="http://docutils.sourceforge.net/rst.html">restructured text</a> (Word
is not an option), you provide a real added-value as well as supporting
services, and more importantly, you have a truly amazing latex book template
(and be warned that I'm a bit picky about typography & design: <a class="reference external" href="https://www.edwardtufte.com/tufte/">Edward Tufte</a> is my hero). Still here?</p>
</div>
</div>
<div class="section" id="license">
<h2><a class="toc-backref" href="#contents">License</a></h2>
<p><strong>Book</strong></p>
<p>This work is licensed under a <a class="reference external" href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-Non Commercial-Share
Alike 4.0 International License</a>. You are free to:</p>
<ul class="simple">
<li><strong>Share</strong> — copy and redistribute the material in any medium or format</li>
<li><strong>Adapt</strong> — remix, transform, and build upon the material</li>
</ul>
<p>The licensor cannot revoke these freedoms as long as you follow the license terms.</p>
<p><strong>Code</strong></p>
<p>The code is licensed under the <a class="reference external" href="LICENSE-code.txt">OSI-approved BSD 2-Clause License</a>.</p>
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</div>
<div class="section" id="introduction">
<h1><a class="toc-backref" href="#table-of-contents">Introduction</a></h1>
<div class="contents local topic" id="id3">
<p class="topic-title first"><strong>Contents</strong></p>
<ul class="simple">
<li><a class="reference internal" href="#simple-example" id="id114">Simple example</a></li>
<li><a class="reference internal" href="#readability-vs-speed" id="id115">Readability vs speed</a></li>
</ul>
</div>
<div class="section" id="simple-example">
<h2><a class="toc-backref" href="#id3">Simple example</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">You can execute any code below from the <a class="reference external" href="code">code</a> folder using the
regular python shell or from inside an IPython session or Jupyter notebook. In
such a case, you might want to use the magic command <code>%timeit</code> instead of the
<a class="reference external" href="code/tools.py">custom one</a> I wrote.</p>
</div>
<p>NumPy is all about vectorization. If you are familiar with Python, this is the
main difficulty you'll face because you'll need to change your way of thinking
and your new friends (among others) are named "vectors", "arrays", "views" or
"ufuncs".</p>
<p>Let's take a very simple example, random walk. One possible object oriented
approach would be to define a <code>RandomWalker</code> class and write a walk
method that would return the current position after each (random) step. It's nice,
it's readable, but it is slow:</p>
<p><strong>Object oriented approach</strong></p>
<pre class="code python literal-block">
<span class="keyword">class</span> <span class="name class">RandomWalker</span><span class="punctuation">:</span>
<span class="keyword">def</span> <span class="name function magic">__init__</span><span class="punctuation">(</span><span class="name builtin pseudo">self</span><span class="punctuation">):</span>
<span class="name builtin pseudo">self</span><span class="operator">.</span><span class="name">position</span> <span class="operator">=</span> <span class="literal number integer">0</span>
<span class="keyword">def</span> <span class="name function">walk</span><span class="punctuation">(</span><span class="name builtin pseudo">self</span><span class="punctuation">,</span> <span class="name">n</span><span class="punctuation">):</span>
<span class="name builtin pseudo">self</span><span class="operator">.</span><span class="name">position</span> <span class="operator">=</span> <span class="literal number integer">0</span>
<span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name">n</span><span class="punctuation">):</span>
<span class="keyword">yield</span> <span class="name builtin pseudo">self</span><span class="operator">.</span><span class="name">position</span>
<span class="name builtin pseudo">self</span><span class="operator">.</span><span class="name">position</span> <span class="operator">+=</span> <span class="literal number integer">2</span><span class="operator">*</span><span class="name">random</span><span class="operator">.</span><span class="name">randint</span><span class="punctuation">(</span><span class="literal number integer">0</span><span class="punctuation">,</span> <span class="literal number integer">1</span><span class="punctuation">)</span> <span class="operator">-</span> <span class="literal number integer">1</span>
<span class="name">walker</span> <span class="operator">=</span> <span class="name">RandomWalker</span><span class="punctuation">()</span>
<span class="name">walk</span> <span class="operator">=</span> <span class="punctuation">[</span><span class="name">position</span> <span class="keyword">for</span> <span class="name">position</span> <span class="operator word">in</span> <span class="name">walker</span><span class="operator">.</span><span class="name">walk</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">)]</span>
</pre>
<p>Benchmarking gives us:</p>
<pre class="code pycon literal-block">
<span class="keyword namespace"></span><span class="generic prompt">>>> </span><span class="keyword namespace">from</span> <span class="name namespace">tools</span> <span class="keyword namespace">import</span> <span class="name">timeit</span>
<span class="generic prompt">>>> </span><span class="name">walker</span> <span class="operator">=</span> <span class="name">RandomWalker</span><span class="punctuation">()</span>
<span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"[position for position in walker.walk(n=10000)]"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">10 loops, best of 3: 15.7 msec per loop</span>
</pre>
<p><strong>Procedural approach</strong></p>
<p>For such a simple problem, we can probably save the class definition and
concentrate only on the walk method that computes successive positions after
each random step.</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">random_walk</span><span class="punctuation">(</span><span class="name">n</span><span class="punctuation">):</span>
<span class="name">position</span> <span class="operator">=</span> <span class="literal number integer">0</span>
<span class="name">walk</span> <span class="operator">=</span> <span class="punctuation">[</span><span class="name">position</span><span class="punctuation">]</span>
<span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name">n</span><span class="punctuation">):</span>
<span class="name">position</span> <span class="operator">+=</span> <span class="literal number integer">2</span><span class="operator">*</span><span class="name">random</span><span class="operator">.</span><span class="name">randint</span><span class="punctuation">(</span><span class="literal number integer">0</span><span class="punctuation">,</span> <span class="literal number integer">1</span><span class="punctuation">)</span><span class="operator">-</span><span class="literal number integer">1</span>
<span class="name">walk</span><span class="operator">.</span><span class="name">append</span><span class="punctuation">(</span><span class="name">position</span><span class="punctuation">)</span>
<span class="keyword">return</span> <span class="name">walk</span>
<span class="name">walk</span> <span class="operator">=</span> <span class="name">random_walk</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">)</span>
</pre>
<p>This new method saves some CPU cycles but not that much because this function
is pretty much the same as in the object-oriented approach and the few cycles
we saved probably come from the inner Python object-oriented machinery.</p>
<pre class="code pycon literal-block">
<span class="keyword namespace"></span><span class="generic prompt">>>> </span><span class="keyword namespace">from</span> <span class="name namespace">tools</span> <span class="keyword namespace">import</span> <span class="name">timeit</span>
<span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"random_walk(n=10000)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">10 loops, best of 3: 15.6 msec per loop</span>
</pre>
<p><strong>Vectorized approach</strong></p>
<p>But we can do better using the <a class="reference external" href="https://docs.python.org/3.6/library/itertools.html">itertools</a> Python module that
offers <em>a set of functions creating iterators for efficient looping</em>. If we
observe that a random walk is an accumulation of steps, we can rewrite the
function by first generating all the steps and accumulate them without any
loop:</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">random_walk_faster</span><span class="punctuation">(</span><span class="name">n</span><span class="operator">=</span><span class="literal number integer">1000</span><span class="punctuation">):</span>
<span class="keyword namespace">from</span> <span class="name namespace">itertools</span> <span class="keyword namespace">import</span> <span class="name">accumulate</span>
<span class="comment single"># Only available from Python 3.6</span>
<span class="name">steps</span> <span class="operator">=</span> <span class="name">random</span><span class="operator">.</span><span class="name">choices</span><span class="punctuation">([</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">],</span> <span class="name">k</span><span class="operator">=</span><span class="name">n</span><span class="punctuation">)</span>
<span class="keyword">return</span> <span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">]</span><span class="operator">+</span><span class="name builtin">list</span><span class="punctuation">(</span><span class="name">accumulate</span><span class="punctuation">(</span><span class="name">steps</span><span class="punctuation">))</span>
<span class="name">walk</span> <span class="operator">=</span> <span class="name">random_walk_faster</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">)</span>
</pre>
<p>In fact, we've just <em>vectorized</em> our function. Instead of looping for picking
sequential steps and add them to the current position, we first generated all the
steps at once and used the <a class="reference external" href="https://docs.python.org/3.6/library/itertools.html#itertools.accumulate">accumulate</a>
function to compute all the positions. We got rid of the loop and this makes
things faster:</p>
<pre class="code pycon literal-block">
<span class="keyword namespace"></span><span class="generic prompt">>>> </span><span class="keyword namespace">from</span> <span class="name namespace">tools</span> <span class="keyword namespace">import</span> <span class="name">timeit</span>
<span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"random_walk_faster(n=10000)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">10 loops, best of 3: 2.21 msec per loop</span>
</pre>
<p>We gained 85% of computation-time compared to the previous version, not so
bad. But the advantage of this new version is that it makes NumPy vectorization
super simple. We just have to translate itertools call into NumPy ones.</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">random_walk_fastest</span><span class="punctuation">(</span><span class="name">n</span><span class="operator">=</span><span class="literal number integer">1000</span><span class="punctuation">):</span>
<span class="comment single"># No 's' in NumPy choice (Python offers choice & choices)</span>
<span class="name">steps</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">random</span><span class="operator">.</span><span class="name">choice</span><span class="punctuation">([</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">],</span> <span class="name">n</span><span class="punctuation">)</span>
<span class="keyword">return</span> <span class="name">np</span><span class="operator">.</span><span class="name">cumsum</span><span class="punctuation">(</span><span class="name">steps</span><span class="punctuation">)</span>
<span class="name">walk</span> <span class="operator">=</span> <span class="name">random_walk_fastest</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">)</span>
</pre>
<p>Not too difficult, but we gained a factor 500x using NumPy:</p>
<pre class="code pycon literal-block">
<span class="keyword namespace"></span><span class="generic prompt">>>> </span><span class="keyword namespace">from</span> <span class="name namespace">tools</span> <span class="keyword namespace">import</span> <span class="name">timeit</span>
<span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"random_walk_fastest(n=10000)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">1000 loops, best of 3: 14 usec per loop</span>
</pre>
<p>This book is about vectorization, be it at the code or problem level. We'll
see this difference is important before looking at custom vectorization.</p>
</div>
<div class="section" id="readability-vs-speed">
<h2><a class="toc-backref" href="#id3">Readability vs speed</a></h2>
<p>Before heading to the next chapter, I would like to warn you about a potential
problem you may encounter once you'll have become familiar with NumPy. It is a
very powerful library and you can make wonders with it but, most of the time,
this comes at the price of readability. If you don't comment your code at the
time of writing, you won't be able to tell what a function is doing after a few
weeks (or possibly days). For example, can you tell what the two functions
below are doing? Probably you can tell for the first one, but unlikely for the
second (or your name is <a class="reference external" href="http://stackoverflow.com/questions/7100242/python-numpy-first-occurrence-of-subarray">Jaime Fernández del Río</a>
and you don't need to read this book).</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">function_1</span><span class="punctuation">(</span><span class="name">seq</span><span class="punctuation">,</span> <span class="name">sub</span><span class="punctuation">):</span>
<span class="keyword">return</span> <span class="punctuation">[</span><span class="name">i</span> <span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name builtin">len</span><span class="punctuation">(</span><span class="name">seq</span><span class="punctuation">)</span> <span class="operator">-</span> <span class="name builtin">len</span><span class="punctuation">(</span><span class="name">sub</span><span class="punctuation">)</span> <span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">)</span> <span class="keyword">if</span> <span class="name">seq</span><span class="punctuation">[</span><span class="name">i</span><span class="punctuation">:</span><span class="name">i</span><span class="operator">+</span><span class="name builtin">len</span><span class="punctuation">(</span><span class="name">sub</span><span class="punctuation">)]</span> <span class="operator">==</span> <span class="name">sub</span><span class="punctuation">]</span>
<span class="keyword">def</span> <span class="name function">function_2</span><span class="punctuation">(</span><span class="name">seq</span><span class="punctuation">,</span> <span class="name">sub</span><span class="punctuation">):</span>
<span class="name">target</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">dot</span><span class="punctuation">(</span><span class="name">sub</span><span class="punctuation">,</span> <span class="name">sub</span><span class="punctuation">)</span>
<span class="name">candidates</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">where</span><span class="punctuation">(</span><span class="name">np</span><span class="operator">.</span><span class="name">correlate</span><span class="punctuation">(</span><span class="name">seq</span><span class="punctuation">,</span> <span class="name">sub</span><span class="punctuation">,</span> <span class="name">mode</span><span class="operator">=</span><span class="literal string single">'valid'</span><span class="punctuation">)</span> <span class="operator">==</span> <span class="name">target</span><span class="punctuation">)[</span><span class="literal number integer">0</span><span class="punctuation">]</span>
<span class="name">check</span> <span class="operator">=</span> <span class="name">candidates</span><span class="punctuation">[:,</span> <span class="name">np</span><span class="operator">.</span><span class="name">newaxis</span><span class="punctuation">]</span> <span class="operator">+</span> <span class="name">np</span><span class="operator">.</span><span class="name">arange</span><span class="punctuation">(</span><span class="name builtin">len</span><span class="punctuation">(</span><span class="name">sub</span><span class="punctuation">))</span>
<span class="name">mask</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">all</span><span class="punctuation">((</span><span class="name">np</span><span class="operator">.</span><span class="name">take</span><span class="punctuation">(</span><span class="name">seq</span><span class="punctuation">,</span> <span class="name">check</span><span class="punctuation">)</span> <span class="operator">==</span> <span class="name">sub</span><span class="punctuation">),</span> <span class="name">axis</span><span class="operator">=-</span><span class="literal number integer">1</span><span class="punctuation">)</span>
<span class="keyword">return</span> <span class="name">candidates</span><span class="punctuation">[</span><span class="name">mask</span><span class="punctuation">]</span>
</pre>
<p>As you may have guessed, the second function is the
vectorized-optimized-faster-NumPy version of the first function. It is 10 times
faster than the pure Python version, but it is hardly readable.</p>
</div>
</div>
<div class="section" id="anatomy-of-an-array">
<h1><a class="toc-backref" href="#table-of-contents">Anatomy of an array</a></h1>
<div class="contents local topic" id="id4">
<p class="topic-title first"><strong>Contents</strong></p>
<ul class="simple">
<li><a class="reference internal" href="#id5" id="id116">Introduction</a></li>
<li><a class="reference internal" href="#memory-layout" id="id117">Memory layout</a></li>
<li><a class="reference internal" href="#views-and-copies" id="id118">Views and copies</a><ul>
<li><a class="reference internal" href="#direct-and-indirect-access" id="id119">Direct and indirect access</a></li>
<li><a class="reference internal" href="#temporary-copy" id="id120">Temporary copy</a></li>
</ul>
</li>
<li><a class="reference internal" href="#conclusion" id="id121">Conclusion</a></li>
</ul>
</div>
<div class="section" id="id5">
<h2><a class="toc-backref" href="#id4">Introduction</a></h2>
<p>As explained in the <a class="reference internal" href="#preface">Preface</a>, you should have a basic experience with NumPy to
read this book. If this is not the case, you'd better start with a beginner
tutorial before coming back here. Consequently I'll only give here a quick
reminder on the basic anatomy of NumPy arrays, especially regarding the memory
layout, view, copy and the data type. They are critical notions to
understand if you want your computation to benefit from NumPy philosophy.</p>
<p>Let's consider a simple example where we want to clear all the values from an
array which has the dtype <code>np.float32</code>. How does one write it to maximize speed? The
below syntax is rather obvious (at least for those familiar with NumPy) but the
above question asks to find the fastest operation.</p>
<pre class="code python literal-block">
<span class="operator">>>></span> <span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">4</span><span class="operator">*</span><span class="literal number integer">1000000</span><span class="punctuation">,</span> <span class="name">np</span><span class="operator">.</span><span class="name">float32</span><span class="punctuation">)</span>
<span class="operator">>>></span> <span class="name">Z</span><span class="punctuation">[</span><span class="operator">...</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="literal number integer">0</span>
</pre>
<p>If you look more closely at both the dtype and the size of the array, you can
observe that this array can be casted (i.e. viewed) into many other
"compatible" data types. By compatible, I mean that <code>Z.size * Z.itemsize</code> can
be divided by the new dtype itemsize.</p>
<pre class="code python literal-block">
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.float16)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number float">2.72</span> <span class="name">msec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.int16)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number float">2.77</span> <span class="name">msec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.int32)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number float">1.29</span> <span class="name">msec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.float32)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number float">1.33</span> <span class="name">msec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.int64)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number integer">874</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.float64)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number integer">865</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.complex128)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number integer">841</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"Z.view(np.int8)[...] = 0"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">100</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number integer">630</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
</pre>
<p>Interestingly enough, the obvious way of clearing all the values is not the
fastest. By casting the array into a larger data type such as <code>np.float64</code>, we
gained a 25% speed factor. But, by viewing the array as a byte array
(<code>np.int8</code>), we gained a 50% factor. The reason for such speedup are to be
found in the internal NumPy machinery and the compiler optimization. This
simple example illustrates the philosophy of NumPy as we'll see in the next
section below.</p>
</div>
<div class="section" id="memory-layout">
<h2><a class="toc-backref" href="#id4">Memory layout</a></h2>
<p>The <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html">NumPy documentation</a> defines the
ndarray class very clearly:</p>
<blockquote>
<em>An instance of class ndarray consists of a contiguous one-dimensional segment
of computer memory (owned by the array, or by some other object), combined
with an indexing scheme that maps N integers into the location of an item in
the block.</em></blockquote>
<p>Said differently, an array is mostly a contiguous block of memory whose parts
can be accessed using an indexing scheme. Such indexing scheme is in turn
defined by a <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html#numpy.ndarray.shape">shape</a>
and a <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html">data type</a> and this is
precisely what is needed when you define a new array:</p>
<pre class="code python literal-block">
<span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">arange</span><span class="punctuation">(</span><span class="literal number integer">9</span><span class="punctuation">)</span><span class="operator">.</span><span class="name">reshape</span><span class="punctuation">(</span><span class="literal number integer">3</span><span class="punctuation">,</span><span class="literal number integer">3</span><span class="punctuation">)</span><span class="operator">.</span><span class="name">astype</span><span class="punctuation">(</span><span class="name">np</span><span class="operator">.</span><span class="name">int16</span><span class="punctuation">)</span>
</pre>
<p>Here, we know that Z itemsize is 2 bytes (<code>int16</code>), the shape is (3,3) and
the number of dimensions is 2 (<code>len(Z.shape)</code>).</p>
<pre class="code pycon literal-block">
<span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">itemsize</span><span class="punctuation">)</span>
<span class="generic output">2
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">shape</span><span class="punctuation">)</span>
<span class="generic output">(3, 3)
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">ndim</span><span class="punctuation">)</span>
<span class="generic output">2</span>
</pre>
<p>Furthermore and because Z is not a view, we can deduce the
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.strides.html#numpy.ndarray.strides">strides</a> of the array that define the number of bytes to step in each dimension when traversing the array.</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">strides</span> <span class="operator">=</span> <span class="name">Z</span><span class="operator">.</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">*</span><span class="name">Z</span><span class="operator">.</span><span class="name">itemsize</span><span class="punctuation">,</span> <span class="name">Z</span><span class="operator">.</span><span class="name">itemsize</span>
<span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">strides</span><span class="punctuation">)</span>
<span class="generic output">(6, 2)
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">strides</span><span class="punctuation">)</span>
<span class="generic output">(6, 2)</span>
</pre>
<p>With all these information, we know how to access a specific item (designed by
an index tuple) and more precisely, how to compute the start and end offsets:</p>
<pre class="code python literal-block">
<span class="name">offset_start</span> <span class="operator">=</span> <span class="literal number integer">0</span>
<span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">ndim</span><span class="punctuation">):</span>
<span class="name">offset_start</span> <span class="operator">+=</span> <span class="name">Z</span><span class="operator">.</span><span class="name">strides</span><span class="punctuation">[</span><span class="name">i</span><span class="punctuation">]</span> <span class="operator">*</span> <span class="name">index</span><span class="punctuation">[</span><span class="name">i</span><span class="punctuation">]</span>
<span class="name">offset_end</span> <span class="operator">=</span> <span class="name">offset_start</span> <span class="operator">+</span> <span class="name">Z</span><span class="operator">.</span><span class="name">itemsize</span>
</pre>
<p>Let's see if this is correct using the <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tobytes.html">tobytes</a>
conversion method:</p>
<pre class="code python literal-block">
<span class="operator">>>></span> <span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">arange</span><span class="punctuation">(</span><span class="literal number integer">9</span><span class="punctuation">)</span><span class="operator">.</span><span class="name">reshape</span><span class="punctuation">(</span><span class="literal number integer">3</span><span class="punctuation">,</span> <span class="literal number integer">3</span><span class="punctuation">)</span><span class="operator">.</span><span class="name">astype</span><span class="punctuation">(</span><span class="name">np</span><span class="operator">.</span><span class="name">int16</span><span class="punctuation">)</span>
<span class="operator">>>></span> <span class="name">index</span> <span class="operator">=</span> <span class="literal number integer">1</span><span class="punctuation">,</span> <span class="literal number integer">1</span>
<span class="operator">>>></span> <span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">index</span><span class="punctuation">]</span><span class="operator">.</span><span class="name">tobytes</span><span class="punctuation">())</span>
<span class="literal string affix">b</span><span class="literal string single">'</span><span class="literal string escape">\x04\x00</span><span class="literal string single">'</span>
<span class="operator">>>></span> <span class="name">offset_start</span> <span class="operator">=</span> <span class="literal number integer">0</span>
<span class="operator">>>></span> <span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">ndim</span><span class="punctuation">):</span>
<span class="operator">...</span> <span class="name">offset_start</span> <span class="operator">+=</span> <span class="name">Z</span><span class="operator">.</span><span class="name">strides</span><span class="punctuation">[</span><span class="name">i</span><span class="punctuation">]</span> <span class="operator">*</span> <span class="name">index</span><span class="punctuation">[</span><span class="name">i</span><span class="punctuation">]</span>
<span class="operator">>>></span> <span class="name">offset_end</span> <span class="operator">=</span> <span class="name">offset_start</span> <span class="operator">+</span> <span class="name">Z</span><span class="operator">.</span><span class="name">itemsize</span>
<span class="operator">>>></span> <span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="operator">.</span><span class="name">tobytes</span><span class="punctuation">()[</span><span class="name">offset_start</span><span class="punctuation">:</span><span class="name">offset_end</span><span class="punctuation">]</span>
<span class="literal string affix">b</span><span class="literal string single">'</span><span class="literal string escape">\x04\x00</span><span class="literal string single">'</span>
</pre>
<p>This array can be actually considered from different perspectives (i.e. layouts):</p>
<p><strong>Item layout</strong></p>
<pre class="code output literal-block">
shape[1]
(=3)
┌───────────┐
┌ ┌───┬───┬───┐ ┐
│ │ 0 │ 1 │ 2 │ │
│ ├───┼───┼───┤ │
shape[0] │ │ 3 │ 4 │ 5 │ │ len(Z)
(=3) │ ├───┼───┼───┤ │ (=3)
│ │ 6 │ 7 │ 8 │ │
└ └───┴───┴───┘ ┘
</pre>
<p><strong>Flattened item layout</strong></p>
<pre class="code output literal-block">
┌───┬───┬───┬───┬───┬───┬───┬───┬───┐
│ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 │ 8 │
└───┴───┴───┴───┴───┴───┴───┴───┴───┘
└───────────────────────────────────┘
Z.size
(=9)
</pre>
<p><strong>Memory layout (C order, big endian)</strong></p>
<pre class="code output literal-block">
strides[1]
(=2)
┌─────────────────────┐
┌ ┌──────────┬──────────┐ ┐
│ p+00: │ 00000000 │ 00000000 │ │
│ ├──────────┼──────────┤ │
│ p+02: │ 00000000 │ 00000001 │ │ strides[0]
│ ├──────────┼──────────┤ │ (=2x3)
│ p+04 │ 00000000 │ 00000010 │ │
│ ├──────────┼──────────┤ ┘
│ p+06 │ 00000000 │ 00000011 │
│ ├──────────┼──────────┤
Z.nbytes │ p+08: │ 00000000 │ 00000100 │
(=3x3x2) │ ├──────────┼──────────┤
│ p+10: │ 00000000 │ 00000101 │
│ ├──────────┼──────────┤
│ p+12: │ 00000000 │ 00000110 │
│ ├──────────┼──────────┤
│ p+14: │ 00000000 │ 00000111 │
│ ├──────────┼──────────┤
│ p+16: │ 00000000 │ 00001000 │
└ └──────────┴──────────┘
└─────────────────────┘
Z.itemsize
Z.dtype.itemsize
(=2)
</pre>
<p>If we now take a slice of <code>Z</code>, the result is a view of the base array <code>Z</code>:</p>
<pre class="code python literal-block">
<span class="name">V</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[::</span><span class="literal number integer">2</span><span class="punctuation">,::</span><span class="literal number integer">2</span><span class="punctuation">]</span>
</pre>
<p>Such view is specified using a shape, a dtype <strong>and</strong> strides because strides
cannot be deduced anymore from the dtype and shape only:</p>
<p><strong>Item layout</strong></p>
<pre class="code output literal-block">
shape[1]
(=2)
┌───────────┐
┌ ┌───┬╌╌╌┬───┐ ┐
│ │ 0 │ │ 2 │ │ ┌───┬───┐
│ ├───┼╌╌╌┼───┤ │ │ 0 │ 2 │
shape[0] │ ╎ ╎ ╎ ╎ │ len(Z) → ├───┼───┤
(=2) │ ├───┼╌╌╌┼───┤ │ (=2) │ 6 │ 8 │
│ │ 6 │ │ 8 │ │ └───┴───┘
└ └───┴╌╌╌┴───┘ ┘
</pre>
<p><strong>Flattened item layout</strong></p>
<pre class="code output literal-block">
┌───┬╌╌╌┬───┬╌╌╌┬╌╌╌┬╌╌╌┬───┬╌╌╌┬───┐ ┌───┬───┬───┬───┐
│ 0 │ │ 2 │ ╎ ╎ │ 6 │ │ 8 │ → │ 0 │ 2 │ 6 │ 8 │
└───┴╌╌╌┴───┴╌╌╌┴╌╌╌┴╌╌╌┴───┴╌╌╌┴───┘ └───┴───┴───┴───┘
└─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘
└───┬───┘ └───┬───┘
└───────────┬───────────┘
Z.size
(=4)
</pre>
<p><strong>Memory layout (C order, big endian)</strong></p>
<pre class="code output literal-block">
┌ ┌──────────┬──────────┐ ┐ ┐
┌─┤ p+00: │ 00000000 │ 00000000 │ │ │
│ └ ├──────────┼──────────┤ │ strides[1] │
┌─┤ p+02: │ │ │ │ (=4) │
│ │ ┌ ├──────────┼──────────┤ ┘ │
│ └─┤ p+04 │ 00000000 │ 00000010 │ │
│ └ ├──────────┼──────────┤ │ strides[0]
│ p+06: │ │ │ │ (=12)
│ ├──────────┼──────────┤ │
Z.nbytes ─┤ p+08: │ │ │ │
(=8) │ ├──────────┼──────────┤ │
│ p+10: │ │ │ │
│ ┌ ├──────────┼──────────┤ ┘
│ ┌─┤ p+12: │ 00000000 │ 00000110 │
│ │ └ ├──────────┼──────────┤
└─┤ p+14: │ │ │
│ ┌ ├──────────┼──────────┤
└─┤ p+16: │ 00000000 │ 00001000 │
└ └──────────┴──────────┘
└─────────────────────┘
Z.itemsize
Z.dtype.itemsize
(=2)
</pre>
</div>
<div class="section" id="views-and-copies">
<h2><a class="toc-backref" href="#id4">Views and copies</a></h2>
<p>Views and copies are important concepts for the optimization of your numerical
computations. Even if we've already manipulated them in the previous section,
the whole story is a bit more complex.</p>
<div class="section" id="direct-and-indirect-access">
<h3><a class="toc-backref" href="#id4">Direct and indirect access</a></h3>
<p>First, we have to distinguish between <a class="reference external" href="https://docs.scipy.org/doc/numpy/user/basics.indexing.html#">indexing</a> and <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing">fancy
indexing</a>. The first will always return a view while the second will return a
copy. This difference is important because in the first case, modifying the view
modifies the base array while this is not true in the second case:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">zeros</span><span class="punctuation">(</span><span class="literal number integer">9</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">Z_view</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[:</span><span class="literal number integer">3</span><span class="punctuation">]</span>
<span class="generic prompt">>>> </span><span class="name">Z_view</span><span class="punctuation">[</span><span class="operator">...</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="literal number integer">1</span>
<span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">)</span>
<span class="generic output">[ 1. 1. 1. 0. 0. 0. 0. 0. 0.]
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">zeros</span><span class="punctuation">(</span><span class="literal number integer">9</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">Z_copy</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">2</span><span class="punctuation">]]</span>
<span class="generic prompt">>>> </span><span class="name">Z_copy</span><span class="punctuation">[</span><span class="operator">...</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="literal number integer">1</span>
<span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">)</span>
<span class="generic output">[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]</span>
</pre>
<p>Thus, if you need fancy indexing, it's better to keep a copy of your fancy index
(especially if it was complex to compute it) and to work with it:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">zeros</span><span class="punctuation">(</span><span class="literal number integer">9</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">index</span> <span class="operator">=</span> <span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">2</span><span class="punctuation">]</span>
<span class="generic prompt">>>> </span><span class="name">Z</span><span class="punctuation">[</span><span class="name">index</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="literal number integer">1</span>
<span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">)</span>
<span class="generic output">[ 1. 1. 1. 0. 0. 0. 0. 0. 0.]</span>
</pre>
<p>If you are unsure if the result of your indexing is a view or a copy, you can
check what is the <code>base</code> of your result. If it is <code>None</code>, then you result is a
copy:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">random</span><span class="operator">.</span><span class="name">uniform</span><span class="punctuation">(</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,(</span><span class="literal number integer">5</span><span class="punctuation">,</span><span class="literal number integer">5</span><span class="punctuation">))</span>
<span class="generic prompt">>>> </span><span class="name">Z1</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[:</span><span class="literal number integer">3</span><span class="punctuation">,:]</span>
<span class="generic prompt">>>> </span><span class="name">Z2</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">2</span><span class="punctuation">],</span> <span class="punctuation">:]</span>
<span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">np</span><span class="operator">.</span><span class="name">allclose</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span><span class="name">Z2</span><span class="punctuation">))</span>
<span class="generic output">True
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z1</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name">Z</span><span class="punctuation">)</span>
<span class="generic output">True
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z2</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name">Z</span><span class="punctuation">)</span>
<span class="generic output">False
</span><span class="keyword"></span><span class="generic prompt">>>> </span><span class="keyword">print</span><span class="punctuation">(</span><span class="name">Z2</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name builtin pseudo">None</span><span class="punctuation">)</span>
<span class="generic output">True</span>
</pre>
<p>Note that some NumPy functions return a view when possible (e.g. <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html">ravel</a>)
while some others always return a copy (e.g. <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.html#numpy.ndarray.flatten">flatten</a>):</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">zeros</span><span class="punctuation">((</span><span class="literal number integer">5</span><span class="punctuation">,</span><span class="literal number integer">5</span><span class="punctuation">))</span>
<span class="generic prompt">>>> </span><span class="name">Z</span><span class="operator">.</span><span class="name">ravel</span><span class="punctuation">()</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name">Z</span>
<span class="generic output">True
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span><span class="punctuation">[::</span><span class="literal number integer">2</span><span class="punctuation">,::</span><span class="literal number integer">2</span><span class="punctuation">]</span><span class="operator">.</span><span class="name">ravel</span><span class="punctuation">()</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name">Z</span>
<span class="generic output">False
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z</span><span class="operator">.</span><span class="name">flatten</span><span class="punctuation">()</span><span class="operator">.</span><span class="name">base</span> <span class="operator word">is</span> <span class="name">Z</span>
<span class="generic output">False</span>
</pre>
</div>
<div class="section" id="temporary-copy">
<h3><a class="toc-backref" href="#id4">Temporary copy</a></h3>
<p>Copies can be made explicitly like in the previous section, but the most
general case is the implicit creation of intermediate copies. This is the case
when you are doing some arithmetic with arrays:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">X</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">10</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">Y</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">10</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">A</span> <span class="operator">=</span> <span class="literal number integer">2</span><span class="operator">*</span><span class="name">X</span> <span class="operator">+</span> <span class="literal number integer">2</span><span class="operator">*</span><span class="name">Y</span>
</pre>
<p>In the example above, three intermediate arrays have been created. One for
holding the result of <code>2*X</code>, one for holding the result of <code>2*Y</code> and the last
one for holding the result of <code>2*X+2*Y</code>. In this specific case, the arrays are
small enough and this does not really make a difference. However, if your
arrays are big, then you have to be careful with such expressions and wonder if
you can do it differently. For example, if only the final result matters and
you don't need <code>X</code> nor <code>Y</code> afterwards, an alternate solution would be:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">X</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">10</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">Y</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">10</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">np</span><span class="operator">.</span><span class="name">multiply</span><span class="punctuation">(</span><span class="name">X</span><span class="punctuation">,</span> <span class="literal number integer">2</span><span class="punctuation">,</span> <span class="name">out</span><span class="operator">=</span><span class="name">X</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">np</span><span class="operator">.</span><span class="name">multiply</span><span class="punctuation">(</span><span class="name">Y</span><span class="punctuation">,</span> <span class="literal number integer">2</span><span class="punctuation">,</span> <span class="name">out</span><span class="operator">=</span><span class="name">Y</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">np</span><span class="operator">.</span><span class="name">add</span><span class="punctuation">(</span><span class="name">X</span><span class="punctuation">,</span> <span class="name">Y</span><span class="punctuation">,</span> <span class="name">out</span><span class="operator">=</span><span class="name">X</span><span class="punctuation">)</span>
</pre>
<p>Using this alternate solution, no temporary array has been created. Problem is
that there are many other cases where such copies needs to be created and this
impact the performance like demonstrated on the example below:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">X</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">1000000000</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">Y</span> <span class="operator">=</span> <span class="name">np</span><span class="operator">.</span><span class="name">ones</span><span class="punctuation">(</span><span class="literal number integer">1000000000</span><span class="punctuation">,</span> <span class="name">dtype</span><span class="operator">=</span><span class="name">np</span><span class="operator">.</span><span class="name">int</span><span class="punctuation">)</span>
<span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"X = X + 2.0*Y"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">100 loops, best of 3: 3.61 ms per loop
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"X = X + 2*Y"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">100 loops, best of 3: 3.47 ms per loop
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"X += 2*Y"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">100 loops, best of 3: 2.79 ms per loop
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"np.add(X, Y, out=X); np.add(X, Y, out=X)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="generic output">1000 loops, best of 3: 1.57 ms per loop</span>
</pre>
</div>
</div>
<div class="section" id="conclusion">
<h2><a class="toc-backref" href="#id4">Conclusion</a></h2>
<p>As a conclusion, we'll make an exercise. Given two vectors <code>Z1</code> and <code>Z2</code>. We
would like to know if <code>Z2</code> is a view of <code>Z1</code> and if yes, what is this view ?</p>
<pre class="code literal-block">
>>> Z1 = np.arange(10)
>>> Z2 = Z1[1:-1:2]
</pre>
<pre class="code output literal-block">
╌╌╌┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬╌╌
Z1 │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 │ 8 │ 9 │
╌╌╌┴───┴───┴───┴───┴───┴───┴───┴───┴───┴───┴╌╌
╌╌╌╌╌╌╌┬───┬╌╌╌┬───┬╌╌╌┬───┬╌╌╌┬───┬╌╌╌╌╌╌╌╌╌╌
Z2 │ 1 │ │ 3 │ │ 5 │ │ 7 │
╌╌╌╌╌╌╌┴───┴╌╌╌┴───┴╌╌╌┴───┴╌╌╌┴───┴╌╌╌╌╌╌╌╌╌╌
</pre>
<p>First, we need to check if <code>Z1</code> is the base of <code>Z2</code></p>
<pre class="code literal-block">
>>> print(Z2.base is Z1)
True
</pre>
<p>At this point, we know <code>Z2</code> is a view of <code>Z1</code>, meaning <code>Z2</code> can be expressed as
<code>Z1[start:stop:step]</code>. The difficulty is to find <code>start</code>, <code>stop</code> and
<code>step</code>. For the <code>step</code>, we can use the <code>strides</code> property of any array that
gives the number of bytes to go from one element to the other in each
dimension. In our case, and because both arrays are one-dimensional, we can
directly compare the first stride only:</p>
<pre class="code literal-block">
>>> step = Z2.strides[0] // Z1.strides[0]
>>> print(step)
2
</pre>
<p>Next difficulty is to find the <code>start</code> and the <code>stop</code> indices. To do this, we
can take advantage of the <code>byte_bounds</code> method that returns a pointer to the
end-points of an array.</p>
<pre class="code output literal-block">
byte_bounds(Z1)[0] byte_bounds(Z1)[-1]
↓ ↓
╌╌╌┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬╌╌
Z1 │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 │ 8 │ 9 │
╌╌╌┴───┴───┴───┴───┴───┴───┴───┴───┴───┴───┴╌╌
byte_bounds(Z2)[0] byte_bounds(Z2)[-1]
↓ ↓
╌╌╌╌╌╌╌┬───┬╌╌╌┬───┬╌╌╌┬───┬╌╌╌┬───┬╌╌╌╌╌╌╌╌╌╌
Z2 │ 1 │ │ 3 │ │ 5 │ │ 7 │
╌╌╌╌╌╌╌┴───┴╌╌╌┴───┴╌╌╌┴───┴╌╌╌┴───┴╌╌╌╌╌╌╌╌╌╌
</pre>
<pre class="code literal-block">
>>> offset_start = np.byte_bounds(Z2)[0] - np.byte_bounds(Z1)[0]
>>> print(offset_start) # bytes
8
>>> offset_stop = np.byte_bounds(Z2)[-1] - np.byte_bounds(Z1)[-1]
>>> print(offset_stop) # bytes
-16
</pre>
<p>Converting these offsets into indices is straightforward using the <code>itemsize</code>
and taking into account that the <code>offset_stop</code> is negative (end-bound of <code>Z2</code>
is logically smaller than end-bound of <code>Z1</code> array). We thus need to add the
items size of Z1 to get the right end index.</p>
<pre class="code literal-block">
>>> start = offset_start // Z1.itemsize
>>> stop = Z1.size + offset_stop // Z1.itemsize
>>> print(start, stop, step)
1, 8, 2
</pre>
<p>Last we test our results:</p>
<pre class="code literal-block">
>>> print(np.allclose(Z1[start:stop:step], Z2))
True
</pre>
<p>As an exercise, you can improve this first and very simple implementation by
taking into account:</p>
<ul class="simple">
<li>Negative steps</li>
<li>Multi-dimensional arrays</li>
</ul>
<p><a class="reference external" href="code/find_index.py">Solution</a> to the exercise.</p>
</div>
</div>
<div class="section" id="code-vectorization">
<h1><a class="toc-backref" href="#table-of-contents">Code vectorization</a></h1>
<div class="contents local topic" id="id6">
<p class="topic-title first"><strong>Contents</strong></p>
<ul class="simple">
<li><a class="reference internal" href="#id7" id="id122">Introduction</a></li>
<li><a class="reference internal" href="#uniform-vectorization" id="id123">Uniform vectorization</a><ul>
<li><a class="reference internal" href="#the-game-of-life" id="id124">The Game of Life</a></li>
<li><a class="reference internal" href="#python-implementation" id="id125">Python implementation</a></li>
<li><a class="reference internal" href="#numpy-implementation" id="id126">NumPy implementation</a></li>
<li><a class="reference internal" href="#exercise" id="id127">Exercise</a></li>
<li><a class="reference internal" href="#sources" id="id128">Sources</a></li>
<li><a class="reference internal" href="#references" id="id129">References</a></li>
</ul>
</li>
<li><a class="reference internal" href="#temporal-vectorization" id="id130">Temporal vectorization</a><ul>
<li><a class="reference internal" href="#id8" id="id131">Python implementation</a></li>
<li><a class="reference internal" href="#id9" id="id132">NumPy implementation</a></li>
<li><a class="reference internal" href="#faster-numpy-implementation" id="id133">Faster NumPy implementation</a></li>
<li><a class="reference internal" href="#visualization" id="id134">Visualization</a></li>
<li><a class="reference internal" href="#id10" id="id135">Exercise</a></li>
<li><a class="reference internal" href="#id11" id="id136">Sources</a></li>
<li><a class="reference internal" href="#id12" id="id137">References</a></li>
</ul>
</li>
<li><a class="reference internal" href="#spatial-vectorization" id="id138">Spatial vectorization</a><ul>
<li><a class="reference internal" href="#boids" id="id139">Boids</a></li>
<li><a class="reference internal" href="#id14" id="id140">Python implementation</a></li>
<li><a class="reference internal" href="#id15" id="id141">NumPy implementation</a></li>
<li><a class="reference internal" href="#id16" id="id142">Exercise</a></li>
<li><a class="reference internal" href="#id17" id="id143">Sources</a></li>
<li><a class="reference internal" href="#id18" id="id144">References</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id19" id="id145">Conclusion</a></li>
</ul>
</div>
<div class="section" id="id7">
<h2><a class="toc-backref" href="#id6">Introduction</a></h2>
<p>Code vectorization means that the problem you're trying to solve is inherently
vectorizable and only requires a few NumPy tricks to make it faster. Of course
it does not mean it is easy or straightforward, but at least it does not
necessitate totally rethinking your problem (as it will be the case in the
<a class="reference internal" href="#problem-vectorization">Problem vectorization</a> chapter). Still, it may require some experience to see
where code can be vectorized. Let's illustrate this through a simple example
where we want to sum up two lists of integers. One simple way using pure Python
is:</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">add_python</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span><span class="name">Z2</span><span class="punctuation">):</span>
<span class="keyword">return</span> <span class="punctuation">[</span><span class="name">z1</span><span class="operator">+</span><span class="name">z2</span> <span class="keyword">for</span> <span class="punctuation">(</span><span class="name">z1</span><span class="punctuation">,</span><span class="name">z2</span><span class="punctuation">)</span> <span class="operator word">in</span> <span class="name builtin">zip</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span><span class="name">Z2</span><span class="punctuation">)]</span>
</pre>
<p>This first naive solution can be vectorized very easily using NumPy:</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">add_numpy</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span><span class="name">Z2</span><span class="punctuation">):</span>
<span class="keyword">return</span> <span class="name">np</span><span class="operator">.</span><span class="name">add</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span><span class="name">Z2</span><span class="punctuation">)</span>
</pre>
<p>Without any surprise, benchmarking the two approaches shows the second method
is the fastest with one order of magnitude.</p>
<pre class="code python literal-block">
<span class="operator">>>></span> <span class="name">Z1</span> <span class="operator">=</span> <span class="name">random</span><span class="operator">.</span><span class="name">sample</span><span class="punctuation">(</span><span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">),</span> <span class="literal number integer">100</span><span class="punctuation">)</span>
<span class="operator">>>></span> <span class="name">Z2</span> <span class="operator">=</span> <span class="name">random</span><span class="operator">.</span><span class="name">sample</span><span class="punctuation">(</span><span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1000</span><span class="punctuation">),</span> <span class="literal number integer">100</span><span class="punctuation">)</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"add_python(Z1, Z2)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">1000</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number integer">68</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
<span class="operator">>>></span> <span class="name">timeit</span><span class="punctuation">(</span><span class="literal string double">"add_numpy(Z1, Z2)"</span><span class="punctuation">,</span> <span class="name builtin">globals</span><span class="punctuation">())</span>
<span class="literal number integer">10000</span> <span class="name">loops</span><span class="punctuation">,</span> <span class="name">best</span> <span class="name">of</span> <span class="literal number integer">3</span><span class="punctuation">:</span> <span class="literal number float">1.14</span> <span class="name">usec</span> <span class="name">per</span> <span class="name">loop</span>
</pre>
<p>Not only is the second approach faster, but it also naturally adapts to the
shape of <code>Z1</code> and <code>Z2</code>. This is the reason why we did not write <code>Z1 + Z2</code>
because it would not work if <code>Z1</code> and <code>Z2</code> were both lists. In the first Python
method, the inner <code>+</code> is interpreted differently depending on the nature of the
two objects such that if we consider two nested lists, we get the following
outputs:</p>
<pre class="code pycon literal-block">
<span class="name"></span><span class="generic prompt">>>> </span><span class="name">Z1</span> <span class="operator">=</span> <span class="punctuation">[[</span><span class="literal number integer">1</span><span class="punctuation">,</span> <span class="literal number integer">2</span><span class="punctuation">],</span> <span class="punctuation">[</span><span class="literal number integer">3</span><span class="punctuation">,</span> <span class="literal number integer">4</span><span class="punctuation">]]</span>
<span class="generic prompt">>>> </span><span class="name">Z2</span> <span class="operator">=</span> <span class="punctuation">[[</span><span class="literal number integer">5</span><span class="punctuation">,</span> <span class="literal number integer">6</span><span class="punctuation">],</span> <span class="punctuation">[</span><span class="literal number integer">7</span><span class="punctuation">,</span> <span class="literal number integer">8</span><span class="punctuation">]]</span>
<span class="generic prompt">>>> </span><span class="name">Z1</span> <span class="operator">+</span> <span class="name">Z2</span>
<span class="generic output">[[1, 2], [3, 4], [5, 6], [7, 8]]
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">add_python</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span> <span class="name">Z2</span><span class="punctuation">)</span>
<span class="generic output">[[1, 2, 5, 6], [3, 4, 7, 8]]
</span><span class="name"></span><span class="generic prompt">>>> </span><span class="name">add_numpy</span><span class="punctuation">(</span><span class="name">Z1</span><span class="punctuation">,</span> <span class="name">Z2</span><span class="punctuation">)</span>
<span class="generic output">[[ 6 8]
[10 12]]</span>
</pre>
<p>The first method concatenates the two lists together, the second method
concatenates the internal lists together and the last one computes what is
(numerically) expected. As an exercise, you can rewrite the Python version
such that it accepts nested lists of any depth.</p>
</div>
<div class="section" id="uniform-vectorization">
<h2><a class="toc-backref" href="#id6">Uniform vectorization</a></h2>
<p>Uniform vectorization is the simplest form of vectorization where all the
elements share the same computation at every time step with no specific
processing for any element. One stereotypical case is the Game of Life that has
been invented by John Conway (see below) and is one of the earliest examples of
cellular automata. Those cellular automata can be conveniently regarded as
an array of cells that are connected together with the notion of neighbours and
their vectorization is straightforward. Let me first define the game and we'll
see how to vectorize it.</p>
<div class="admonition legend">
<p class="first admonition-title"><strong>Figure 4.1</strong></p>
<p class="last">Conus textile snail exhibits a cellular automaton pattern on its shell.
Image by <a class="reference external" href="https://commons.wikimedia.org/wiki/File:Textile_cone.JPG">Richard Ling</a>, 2005.</p>
</div>
<img alt="data/Textile-Cone-cropped.jpg" class="bordered" src="data/Textile-Cone-cropped.jpg" style="width: 100%;" />
<div class="section" id="the-game-of-life">
<h3><a class="toc-backref" href="#id6">The Game of Life</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Excerpt from the Wikipedia entry on the
<a class="reference external" href="https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life">Game of Life</a></p>
</div>
<p>The Game of Life is a cellular automaton devised by the British mathematician
John Horton Conway in 1970. It is the best-known example of a cellular
automaton. The "game" is actually a zero-player game, meaning that its
evolution is determined by its initial state, needing no input from human
players. One interacts with the Game of Life by creating an initial
configuration and observing how it evolves.</p>
<p>The universe of the Game of Life is an infinite two-dimensional orthogonal grid
of square cells, each of which is in one of two possible states, live or
dead. Every cell interacts with its eight neighbours, which are the cells that
are directly horizontally, vertically, or diagonally adjacent. At each step in
time, the following transitions occur:</p>
<ol class="arabic simple">
<li>Any live cell with fewer than two live neighbours dies, as if by needs
caused by underpopulation.</li>
<li>Any live cell with more than three live neighbours dies, as if by
overcrowding.</li>
<li>Any live cell with two or three live neighbours lives, unchanged, to the
next generation.</li>
<li>Any dead cell with exactly three live neighbours becomes a live cell.</li>
</ol>
<p>The initial pattern constitutes the 'seed' of the system. The first generation
is created by applying the above rules simultaneously to every cell in the seed
– births and deaths happen simultaneously, and the discrete moment at which
this happens is sometimes called a tick. (In other words, each generation is a
pure function of the one before.) The rules continue to be applied repeatedly
to create further generations.</p>
</div>
<div class="section" id="python-implementation">
<h3><a class="toc-backref" href="#id6">Python implementation</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">We could have used the more efficient python <a class="reference external" href="http://docs.python.org/3/library/array.html">array interface</a> but it is more convenient to
use the familiar list object.</p>
</div>
<p>In pure Python, we can code the Game of Life using a list of lists representing
the board where cells are supposed to evolve. Such a board will be equipped with
border of 0 that allows to accelerate things a bit by avoiding having specific
tests for borders when counting the number of neighbours.</p>
<pre class="code python literal-block">
<span class="name">Z</span> <span class="operator">=</span> <span class="punctuation">[[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">],</span>
<span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">],</span>
<span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">],</span>
<span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">],</span>
<span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">],</span>
<span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">,</span><span class="literal number integer">0</span><span class="punctuation">]]</span>
</pre>
<p>Taking the border into account, counting neighbours then is straightforward:</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">compute_neighbours</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">):</span>
<span class="name">shape</span> <span class="operator">=</span> <span class="name builtin">len</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">),</span> <span class="name builtin">len</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">])</span>
<span class="name">N</span> <span class="operator">=</span> <span class="punctuation">[[</span><span class="literal number integer">0</span><span class="punctuation">,]</span><span class="operator">*</span><span class="punctuation">(</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">])</span> <span class="keyword">for</span> <span class="name">i</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">1</span><span class="punctuation">])]</span>
<span class="keyword">for</span> <span class="name">x</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">]</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">):</span>
<span class="keyword">for</span> <span class="name">y</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">):</span>
<span class="name">N</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">+</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">+</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">]</span> \
<span class="operator">+</span> <span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator">+</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> \
<span class="operator">+</span> <span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">+</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">+</span><span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">][</span><span class="name">y</span><span class="operator">+</span><span class="literal number integer">1</span><span class="punctuation">]</span>
<span class="keyword">return</span> <span class="name">N</span>
</pre>
<p>To iterate one step in time, we then simply count the number of neighbours for
each internal cell and we update the whole board according to the four
aforementioned rules:</p>
<pre class="code python literal-block">
<span class="keyword">def</span> <span class="name function">iterate</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">):</span>
<span class="name">N</span> <span class="operator">=</span> <span class="name">compute_neighbours</span><span class="punctuation">(</span><span class="name">Z</span><span class="punctuation">)</span>
<span class="keyword">for</span> <span class="name">x</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">0</span><span class="punctuation">]</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">):</span>
<span class="keyword">for</span> <span class="name">y</span> <span class="operator word">in</span> <span class="name builtin">range</span><span class="punctuation">(</span><span class="literal number integer">1</span><span class="punctuation">,</span><span class="name">shape</span><span class="punctuation">[</span><span class="literal number integer">1</span><span class="punctuation">]</span><span class="operator">-</span><span class="literal number integer">1</span><span class="punctuation">):</span>
<span class="keyword">if</span> <span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator">==</span> <span class="literal number integer">1</span> <span class="operator word">and</span> <span class="punctuation">(</span><span class="name">N</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator"><</span> <span class="literal number integer">2</span> <span class="operator word">or</span> <span class="name">N</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator">></span> <span class="literal number integer">3</span><span class="punctuation">):</span>
<span class="name">Z</span><span class="punctuation">[</span><span class="name">x</span><span class="punctuation">][</span><span class="name">y</span><span class="punctuation">]</span> <span class="operator">=</span> <span class="literal number integer">0</span>