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<div class="section" id="introduction-nilearn-in-a-nutshell">
<h1><span class="section-number">1. </span>Introduction: nilearn in a nutshell<a class="headerlink" href="#introduction-nilearn-in-a-nutshell" title="Permalink to this headline">¶</a></h1>
<div class="contents local topic" id="contents">
<p class="topic-title"><strong>Contents</strong></p>
<ul class="simple">
<li><p><a class="reference internal" href="#what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity" id="id4">What is nilearn: MVPA, decoding, predictive models, functional connectivity</a></p></li>
<li><p><a class="reference internal" href="#installing-nilearn" id="id5">Installing nilearn</a></p></li>
<li><p><a class="reference internal" href="#python-for-neuroimaging-a-quick-start" id="id6">Python for NeuroImaging, a quick start</a></p></li>
</ul>
</div>
<div class="section" id="what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity">
<h2><a class="toc-backref" href="#id4"><span class="section-number">1.1. </span>What is nilearn: MVPA, decoding, predictive models, functional connectivity</a><a class="headerlink" href="#what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity" title="Permalink to this headline">¶</a></h2>
<div class="topic">
<p class="topic-title"><strong>Why use nilearn?</strong></p>
<p>Nilearn makes it easy to use many advanced <strong>machine learning</strong>,
<strong>pattern recognition</strong> and <strong>multivariate statistical</strong> techniques on
neuroimaging data for applications such as <strong>MVPA</strong> (Mutli-Voxel
Pattern Analysis),
<a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">decoding</span></a>,
<a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">predictive modelling</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">functional connectivity</span></a>,
<a class="reference internal" href="connectivity/parcellating.html#parcellating-brain"><span class="std std-ref">brain parcellations</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">connectomes</span></a>.</p>
<p>Nilearn can readily be used on <a class="reference internal" href="decoding/decoding_intro.html#decoding-intro"><span class="std std-ref">task fMRI</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">resting-state</span></a>, or
<a class="reference internal" href="auto_examples/02_decoding/plot_oasis_vbm.html#sphx-glr-auto-examples-02-decoding-plot-oasis-vbm-py"><span class="std std-ref">VBM</span></a> data.</p>
<p>For a machine-learning expert, the value of nilearn can be seen as
domain-specific <strong>feature engineering</strong> construction, that is, shaping
neuroimaging data into a feature matrix well suited to statistical
learning, or vice versa.</p>
</div>
<div class="section" id="why-is-machine-learning-relevant-to-neuroimaging-a-few-examples">
<h3><span class="section-number">1.1.1. </span>Why is machine learning relevant to NeuroImaging? A few examples!<a class="headerlink" href="#why-is-machine-learning-relevant-to-neuroimaging-a-few-examples" title="Permalink to this headline">¶</a></h3>
<dl class="field-list simple">
<dt class="field-odd">Diagnosis and prognosis</dt>
<dd class="field-odd"><p>Predicting a clinical score or even treatment response
from brain imaging with <a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">supervised
learning</span></a> e.g. <a class="reference external" href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0029482">[Mourao-Miranda 2012]</a></p>
</dd>
<dt class="field-even">Measuring generalization scores</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>Information mapping</strong>: using the prediction accuracy of a classifier
to characterize relationships between brain images and stimuli. (e.g.
<a class="reference internal" href="decoding/searchlight.html#searchlight"><span class="std std-ref">searchlight</span></a>) <a class="reference external" href="http://www.pnas.org/content/103/10/3863.short">[Kriegeskorte 2005]</a></p></li>
<li><p><strong>Transfer learning</strong>: measuring how much an estimator trained on one
specific psychological process/task can predict the neural activity
underlying another specific psychological process/task
(e.g. discriminating left from
right eye movements also discriminates additions from subtractions
<a class="reference external" href="http://www.sciencemag.org/content/324/5934/1583.short">[Knops 2009]</a>)</p></li>
</ul>
</dd>
<dt class="field-odd">High-dimensional multivariate statistics</dt>
<dd class="field-odd"><p>From a statistical point of view, machine learning implements
statistical estimation of models with a large number of parameters.
Tricks pulled in machine learning (e.g. regularization) can
make this estimation possible despite the usually
small number of observations in the neuroimaging domain
<a class="reference external" href="http://icml.cc/2012/papers/688.pdf">[Varoquaux 2012]</a>. This
usage of machine learning requires some understanding of the models.</p>
</dd>
<dt class="field-even">Data mining / exploration</dt>
<dd class="field-even"><p>Data-driven exploration of brain images. This includes the extraction of
the major brain networks from resting-state data (“resting-state networks”)
or movie-watching data as well as the discovery of connectionally coherent
functional modules (“connectivity-based parcellation”).
For example,
<a class="reference internal" href="connectivity/resting_state_networks.html#extracting-rsn"><span class="std std-ref">Extracting functional brain networks: ICA and related</span></a> or <a class="reference internal" href="connectivity/parcellating.html#parcellating-brain"><span class="std std-ref">Clustering to parcellate the brain in regions</span></a> with clustering.</p>
</dd>
</dl>
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<div class="section" id="installing-nilearn">
<span id="installation"></span><h2><a class="toc-backref" href="#id5"><span class="section-number">1.2. </span>Installing nilearn</a><a class="headerlink" href="#installing-nilearn" title="Permalink to this headline">¶</a></h2>
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<href="https://store.continuum.io/cshop/anaconda/"
<target="_blank">Anaconda</a></p>
<p>We recommend that you <strong>install a complete
<span style="color:red">64 bit</span> scientific Python
distribution like <a class="reference external" href="
https://www.anaconda.com/download/" target="_blank">Anaconda</a>
</strong>. Since it meets all the requirements of nilearn, it will save
you time and trouble. You could also check <a class="reference external"
href="http://python-xy.github.io/" target="_blank">PythonXY</a>
as an alternative.</p>
<p>Nilearn requires a Python installation and the following
dependencies: ipython, scipy, scikit-learn, joblib, matplotlib,
nibabel and pandas.</p>
<p class="emphasize">Second: open a Command Prompt</p>
<p><strong>(Press "Win-R", type "cmd" and press "Enter". This will open
the program cmd.exe, which is the command prompt)</strong><br/>
Then type the following line and press "Enter"</p>
<div class="code"><pre>pip install -U --user nilearn</pre></div>
<p class="emphasize">Third: open IPython</p>
<p><strong>(You can open it by writing "ipython" in the command prompt
and pressing "Enter")</strong><br/>
Then type in the following line and press "Enter":</p>
<div class="code"><pre>In [1]: import nilearn</pre></div>
<p>If no error occurs, you have installed nilearn correctly.</p>
</li>
<li>
<p class="emphasize">First: download and install 64 bit <a class="
reference external" href="https://www.anaconda.com/download/"
target="_blank">Anaconda</a></p>
<p>We recommend that you <strong>install a complete
<span style="color:red">64 bit</span> scientific
Python distribution like <a class="reference external"
href="https://www.anaconda.com/download/" target="_blank">
Anaconda</a></strong>. Since it meets all the requirements of nilearn,
it will save you time and trouble.</p>
<p>Nilearn requires a Python installation and the following
dependencies: ipython, scipy, scikit-learn, joblib,
matplotlib, nibabel and pandas.</p>
<p class="emphasize">Second: open a Terminal</p>
<p><strong>(Navigate to /Applications/Utilities and double-click on
Terminal)</strong><br/>
Then type the following line and press "Enter"</p>
<div class="code"><pre>pip install -U --user nilearn</pre></div>
<p class="emphasize">Third: open IPython</p>
<p><strong>(You can open it by writing "ipython" in the terminal and
pressing "Enter")</strong><br/>
Then type in the following line and press "Enter":</p>
<div class="code"><pre>In [1]: import nilearn</pre></div>
<p>If no error occurs, you have installed nilearn correctly.</p>
</li>
<li>
<div class="contents"><p>If you are using <strong>Ubuntu or Debian</strong>
and you have access to <a class="reference external"
href="http://neuro.debian.net/" target="_blank">
Neurodebian</a>, then simply install the
<a class="reference external"
href="http://neuro.debian.net/pkgs/python-nilearn.html" target="_blank">python-nilearn package</a> through Neurodebian.</p></div>
<p class="emphasize">First: Install dependencies</p>
<p>Install or ask your system administrator to install the following
packages using the distribution package manager: <strong>ipython</strong>
, <strong>scipy</strong>, <strong>scikit-learn</strong> (sometimes called <cite>sklearn</cite>,
or <cite>python-sklearn</cite>), <strong>joblib</strong>,
<strong>matplotlib</strong> (sometimes
called <cite>python-matplotlib</cite>), <strong>nibabel</strong>
(sometimes called <cite>python-nibabel</cite>) and <strong>pandas</strong>
(sometimes called <cite>python-pandas</cite>).</p>
<p><strong>If you do not have access to the package manager we recommend
that you install a complete <span style="color:red">64 bit</span>
scientific Python distribution like
<a class="reference external"
href="https://www.anaconda.com/download/" target="_blank">
Anaconda</a></strong>. Since it meets all the requirements of nilearn,
it will save you time and trouble.</p>
<p class="emphasize">Second: open a Terminal</p>
<p><strong>(Press ctrl+alt+t and a Terminal console will pop up)</strong><br/>
Then type the following line and press "Enter"</p>
<div class="code"><pre>pip install -U --user nilearn</pre></div>
<p class="emphasize">Third: open IPython</p>
<p><strong>(You can open it by writing "ipython" in the terminal and
pressing "Enter")</strong><br/>
Then type in the following line and press "Enter":</p>
<div class="code"><pre>In [1]: import nilearn</pre></div>
<p>If no error occurs, you have installed nilearn correctly.</p>
</li>
<li>
<p class="emphasize">To Install the development version:</p>
<p><strong>Use git as an alternative to using pip, to get the latest
nilearn version</strong></p>
<p class="first">Simply run the following command (as a shell command,
not a Python command):</p>
<div class="code"><pre>git clone https://github.com/nilearn/nilearn.git</pre>
</div>
<p>In the future, you can readily update your copy of nilearn by
executing “git pull” in the nilearn root directory (as a
shell command).</p>
<p>If you really do not want to use git, you may still download the
latest development snapshot from the following link (unziping required):
<a class="reference external"
href="https://github.com/nilearn/nilearn/archive/master.zip">
https://github.com/nilearn/nilearn/archive/master.zip</a></p>
<p><strong>Install in the nilearn directory created by the previous
steps, run (again, as a shell command):</strong></p>
<div class="code"><pre>python setup.py develop --user</pre></div>
<p><strong>Now to test everything is set up correctly, open IPython and
type in the following line:</strong></p>
<div class="code"><pre>In [1]: import nilearn</pre></div>
<p>If no error occurs, you have installed nilearn correctly.</p>
</li>
</ul>
</div>
</div>
<div class="section" id="python-for-neuroimaging-a-quick-start">
<span id="quick-start"></span><h2><a class="toc-backref" href="#id6"><span class="section-number">1.3. </span>Python for NeuroImaging, a quick start</a><a class="headerlink" href="#python-for-neuroimaging-a-quick-start" title="Permalink to this headline">¶</a></h2>
<p>If you don’t know Python, <strong>Don’t panic. Python is easy</strong>. It is important
to realize that most things you will do in nilearn require only a few or a
few dozen lines of Python code.
Here, we give
the basics to help you get started. For a very quick start into the programming
language, you can <a class="reference external" href="http://www.learnpython.org/">learn it online</a>.
For a full-blown introduction to
using Python for science, see the <a class="reference external" href="http://scipy-lectures.github.io/">scipy lecture notes</a>.</p>
<p>We will be using <a class="reference external" href="http://jupyter.org">Jupyter</a> for notebooks, or
<a class="reference external" href="http://ipython.org">IPython</a>, which provides an interactive scientific
environment that facilitates many everyday data-manipulation steps (e.g.
interactive debugging, easy visualization). You can choose notebooks or
terminal:</p>
<dl class="field-list">
<dt class="field-odd">Notebooks</dt>
<dd class="field-odd"><p>Start the Jupter notebook either with the application menu, or by
typing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">jupyter</span> <span class="n">notebook</span>
</pre></div>
</div>
</dd>
<dt class="field-even">Terminal</dt>
<dd class="field-even"><p>Start ipython by typing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ipython</span> <span class="o">--</span><span class="n">matplotlib</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">--matplotlib</span></code> flag, which configures matplotlib for
interactive use inside IPython.</p>
</div>
</dd>
</dl>
<p>These will give you a <em>prompt</em> in which you can execute commands:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="mi">3</span>
<span class="n">Out</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="mi">7</span>
</pre></div>
</div>
<div class="topic">
<p class="topic-title"><cite>>>></cite> <strong>Prompt</strong></p>
<p>Below we’ll be using <cite>>>></cite> to indicate input lines. If you wish to copy and
paste these input lines directly, click on the <cite>>>></cite> located
at the top right of the code block to toggle these prompt signs</p>
</div>
<div class="section" id="your-first-steps-with-nilearn">
<h3><span class="section-number">1.3.1. </span>Your first steps with nilearn<a class="headerlink" href="#your-first-steps-with-nilearn" title="Permalink to this headline">¶</a></h3>
<p>First things first, nilearn does not have a graphical user interface.
But you will soon realize that you don’t really need one.
It is typically used interactively in IPython or in an automated way by Python
code.
Most importantly, nilearn functions that process neuroimaging data accept
either a filename (i.e., a string variable) or a <a class="reference external" href="http://nipy.org/nibabel/nibabel_images.html">NiftiImage object</a>. We call the latter
“niimg-like”.</p>
<p>Suppose for instance that you have a Tmap image saved in the Nifti file
“t_map000.nii” in the directory “/home/user”. To visualize that image, you will
first have to import the <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">plotting</span></a> functionality by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">nilearn</span> <span class="kn">import</span> <span class="n">plotting</span>
</pre></div>
</div>
<p>Then you can call the function that creates a “glass brain” by giving it
the file name:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">plotting</span><span class="o">.</span><span class="n">plot_glass_brain</span><span class="p">(</span><span class="s2">"/home/user/t_map000.nii"</span><span class="p">)</span>
</pre></div>
</div>
<div class="sidebar">
<p class="sidebar-title">File name matchings</p>
<p>The filename could be given as “~/t_map000.nii’ as nilearn expands “~” to
the home directory.
<a class="reference internal" href="manipulating_images/input_output.html#filename-matching"><span class="std std-ref">See more on file name matchings</span></a>.</p>
</div>
<a class="reference external image-reference" href="auto_examples/01_plotting/plot_demo_glass_brain.html"><img alt="_images/sphx_glr_plot_demo_glass_brain_001.png" class="align-center" src="_images/sphx_glr_plot_demo_glass_brain_001.png" style="width: 396.0px; height: 156.0px;" /></a>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>There are many other plotting functions. Take your time to have a look
at the <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">different options</span></a>.</p>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>For simple functions/operations on images, many functions exist, such as in
the <a class="reference internal" href="modules/reference.html#module-nilearn.image" title="nilearn.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.image</span></code></a> module for image manipulation, e.g.
<a class="reference internal" href="modules/generated/nilearn.image.smooth_img.html#nilearn.image.smooth_img" title="nilearn.image.smooth_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.smooth_img</span></code></a> for smoothing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">nilearn</span> <span class="kn">import</span> <span class="n">image</span>
<span class="gp">>>> </span><span class="n">smoothed_img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">smooth_img</span><span class="p">(</span><span class="s2">"/home/user/t_map000.nii"</span><span class="p">,</span> <span class="n">fwhm</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<p>The returned value <cite>smoothed_img</cite> is a <a class="reference external" href="http://nipy.org/nibabel/nibabel_images.html">NiftiImage object</a>. It can either be passed
to other nilearn functions operating on niimgs (neuroimaging images) or
saved to disk with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">smoothed_img</span><span class="o">.</span><span class="n">to_filename</span><span class="p">(</span><span class="s2">"/home/user/t_map000_smoothed.nii"</span><span class="p">)</span>
</pre></div>
</div>
<p>Finally, nilearn deals with Nifti images that come in two flavors: 3D
images, which represent a brain volume, and 4D images, which represent a
series of brain volumes. To extract the n-th 3D image from a 4D image, you can
use the <a class="reference internal" href="modules/generated/nilearn.image.index_img.html#nilearn.image.index_img" title="nilearn.image.index_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.index_img</span></code></a> function (keep in mind that array indexing
always starts at 0 in the Python language):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">first_volume</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">index_img</span><span class="p">(</span><span class="s2">"/home/user/fmri_volumes.nii"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>To loop over each individual volume of a 4D image, use <a class="reference internal" href="modules/generated/nilearn.image.iter_img.html#nilearn.image.iter_img" title="nilearn.image.iter_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.iter_img</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">volume</span> <span class="ow">in</span> <span class="n">image</span><span class="o">.</span><span class="n">iter_img</span><span class="p">(</span><span class="s2">"/home/user/fmri_volumes.nii"</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">smoothed_img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">smooth_img</span><span class="p">(</span><span class="n">volume</span><span class="p">,</span> <span class="n">fwhm</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<div class="green topic">
<p class="topic-title"><strong>Exercise: varying the amount of smoothing</strong></p>
<p>Want to sharpen your skills with nilearn?
Compute the mean EPI for first subject of the brain development
dataset downloaded with <a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a> and
smooth it with an FWHM varying from 0mm to 20mm in increments of 5mm</p>
<p><strong>Hints:</strong></p>
<blockquote>
<div><ul class="simple">
<li><p>Inspect the ‘.keys()’ of the object returned by
<a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a></p></li>
<li><p>Look at the “reference” section of the documentation: there is a
function to compute the mean of a 4D image</p></li>
<li><p>To perform a for loop in Python, you can use the “range” function</p></li>
<li><p>The solution can be found <a class="reference internal" href="auto_examples/06_manipulating_images/plot_smooth_mean_image.html#sphx-glr-auto-examples-06-manipulating-images-plot-smooth-mean-image-py"><span class="std std-ref">here</span></a></p></li>
</ul>
</div></blockquote>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="topic">
<p class="topic-title"><strong>Tutorials to learn the basics</strong></p>
<p>The two following tutorials may be useful to get familiar with data
representation in nilearn:</p>
<ul class="simple">
<li><p><a class="reference internal" href="auto_examples/plot_nilearn_101.html#sphx-glr-auto-examples-plot-nilearn-101-py"><span class="std std-ref">Basic nilearn example: manipulating and looking at data</span></a></p></li>
<li><p><a class="reference internal" href="auto_examples/plot_3d_and_4d_niimg.html#sphx-glr-auto-examples-plot-3d-and-4d-niimg-py"><span class="std std-ref">3D and 4D niimgs: handling and visualizing</span></a></p></li>
</ul>
<p>More tutorials can be found <a class="reference internal" href="auto_examples/index.html#tutorial-examples"><span class="std std-ref">here</span></a></p>
</div>
<hr class="docutils" />
<p>Now, if you want out-of-the-box methods to process neuroimaging data, jump
directly to the section you need:</p>
<ul class="simple">
<li><p><a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">Decoding and MVPA: predicting from brain images</span></a></p></li>
<li><p><a class="reference internal" href="connectivity/index.html#functional-connectivity"><span class="std std-ref">Functional connectivity and resting state</span></a></p></li>
</ul>
<div class="line-block">
<div class="line"><br /></div>
</div>
</div>
<div class="section" id="scientific-computing-with-python">
<h3><span class="section-number">1.3.2. </span>Scientific computing with Python<a class="headerlink" href="#scientific-computing-with-python" title="Permalink to this headline">¶</a></h3>
<p>In case you plan to become a casual nilearn user, note that you will not need
to deal with number and array manipulation directly in Python.
However, if you plan to go beyond that, here are a few pointers.</p>
<div class="section" id="basic-numerics">
<h4><span class="section-number">1.3.2.1. </span>Basic numerics<a class="headerlink" href="#basic-numerics" title="Permalink to this headline">¶</a></h4>
<dl class="field-list">
<dt class="field-odd">Numerical arrays</dt>
<dd class="field-odd"><p>The numerical data (e.g. matrices) are stored in numpy arrays:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span> <span class="c1"># 2000 points between 1 and 10</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">array([ 1. , 1.00450225, 1.0090045 , ..., 9.9909955 ,</span>
<span class="go"> 9.99549775, 10. ])</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">/</span> <span class="mi">2</span>
<span class="go">array([ 0.5 , 0.50225113, 0.50450225, ..., 4.99549775,</span>
<span class="go"> 4.99774887, 5. ])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="c1"># Operations on arrays are defined in the numpy module</span>
<span class="go">array([ 0.54030231, 0.53650833, 0.53270348, ..., -0.84393609,</span>
<span class="go"> -0.84151234, -0.83907153])</span>
<span class="gp">>>> </span><span class="n">t</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># In Python indexing is done with [] and starts at zero</span>
<span class="go">array([ 1. , 1.00450225, 1.0090045 ])</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/numpy/index.html">More documentation …</a></p>
</dd>
<dt class="field-even">Plotting and figures</dt>
<dd class="field-even"><div class="figure align-right">
<a class="reference external image-reference" href="auto_examples/plot_python_101.html"><img alt="_images/sphx_glr_plot_python_101_001.png" src="_images/sphx_glr_plot_python_101_001.png" style="width: 192.0px; height: 144.0px;" /></a>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">t</span><span class="p">))</span>
<span class="go">[<matplotlib.lines.Line2D object at ...>]</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html">More documentation …</a></p>
</dd>
<dt class="field-odd">Image processing</dt>
<dd class="field-odd"><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">ndimage</span>
<span class="gp">>>> </span><span class="n">t_smooth</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/advanced/image_processing/index.html">More documentation …</a></p>
</dd>
<dt class="field-even">Signal processing</dt>
<dd class="field-even"><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span>
<span class="gp">>>> </span><span class="n">t_detrended</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">detrend</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/scipy.html#signal-processing-scipy-signal">More documentation …</a></p>
</dd>
<dt class="field-odd">Much more</dt>
<dd class="field-odd"><table class="hlist"><tr><td><ul>
<li><p>Simple statistics:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
</pre></div>
</div>
</li>
</ul>
</td><td><ul>
<li><p>Linear algebra:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span>
</pre></div>
</div>
</li>
</ul>
</td></tr></table>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/scipy.html">More documentation…</a></p>
</dd>
</dl>
</div>
<div class="section" id="scikit-learn-machine-learning-in-python">
<h4><span class="section-number">1.3.2.2. </span>Scikit-learn: machine learning in Python<a class="headerlink" href="#scikit-learn-machine-learning-in-python" title="Permalink to this headline">¶</a></h4>
<div class="topic">
<p class="topic-title"><strong>What is scikit-learn?</strong></p>
<p><a class="reference external" href="http://scikit-learn.org">Scikit-learn</a> is a Python library for machine
learning. Its strong points are:</p>
<ul class="simple">
<li><p>Easy to use and well documented</p></li>
<li><p>Computationally efficient</p></li>
<li><p>Provides a wide variety of standard machine learning methods for non-experts</p></li>
</ul>
</div>
<p>The core concept in <a class="reference external" href="http://scikit-learn.org">scikit-learn</a> is the
estimator object, for instance an SVC (<a class="reference external" href="http://scikit-learn.org/stable/modules/svm.html">support vector classifier</a>).
It is first created with the relevant parameters:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">sklearn</span><span class="p">;</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">print_changed_only</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="gp">>>> </span><span class="n">svc</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
</pre></div>
</div>
<p>These parameters are detailed in the documentation of
the object: in IPython or Jupter you can do:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>In [3]: SVC?
...
Parameters
----------
C : float or None, optional (default=None)
Penalty parameter C of the error term. If None then C is set
to n_samples.
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'.
If none is given, 'rbf' will be used.
...
</pre></div>
</div>
<p>Once the object is created, you can fit it on data. For instance, here we
use a hand-written digits dataset, which comes with scikit-learn:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="gp">>>> </span><span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span>
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
</pre></div>
</div>
<p>Let’s use all but the last 10 samples to train the SVC:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">svc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">[:</span><span class="o">-</span><span class="mi">10</span><span class="p">],</span> <span class="n">labels</span><span class="p">[:</span><span class="o">-</span><span class="mi">10</span><span class="p">])</span>
<span class="go">SVC(C=1.0, ...)</span>
</pre></div>
</div>
<p>and try predicting the labels on the left-out data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">svc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">10</span><span class="p">:])</span>
<span class="go">array([5, 4, 8, 8, 4, 9, 0, 8, 9, 8])</span>
<span class="gp">>>> </span><span class="n">labels</span><span class="p">[</span><span class="o">-</span><span class="mi">10</span><span class="p">:]</span> <span class="c1"># The actual labels</span>
<span class="go">array([5, 4, 8, 8, 4, 9, 0, 8, 9, 8])</span>
</pre></div>
</div>
<p>To find out more, try the <a class="reference external" href="http://scikit-learn.org/stable/tutorial/index.html">scikit-learn tutorials</a>.</p>
</div>
</div>
<div class="section" id="finding-help">
<h3><span class="section-number">1.3.3. </span>Finding help<a class="headerlink" href="#finding-help" title="Permalink to this headline">¶</a></h3>
<dl class="field-list simple">
<dt class="field-odd">Reference material</dt>
<dd class="field-odd"><ul class="simple">
<li><p>A quick and gentle introduction to scientific computing with Python can
be found in the
<a class="reference external" href="http://scipy-lectures.github.io/">scipy lecture notes</a>.</p></li>
<li><p>The documentation of scikit-learn explains each method with tips on
practical use and examples:
<a class="reference external" href="http://scikit-learn.org/">http://scikit-learn.org/</a>.
While not specific to neuroimaging, it is often a recommended read.
Be careful to consult the documentation of the scikit-learn version
that you are using.</p></li>
</ul>
</dd>
<dt class="field-even">Mailing lists and forums</dt>
<dd class="field-even"><ul class="simple">
<li><p>Don’t hesitate to ask questions about nilearn on <a class="reference external" href="https://neurostars.org/tag/nilearn">neurostars</a>.</p></li>
<li><p>You can find help with neuroimaging in Python (file I/O,
neuroimaging-specific questions) via the nipy user group:
<a class="reference external" href="https://groups.google.com/forum/?fromgroups#!forum/nipy-user">https://groups.google.com/forum/?fromgroups#!forum/nipy-user</a></p></li>
<li><p>For machine-learning and scikit-learn questions, expertise can be
found on the scikit-learn mailing list:
<a class="reference external" href="https://mail.python.org/mailman/listinfo/scikit-learn">https://mail.python.org/mailman/listinfo/scikit-learn</a></p></li>
</ul>
</dd>
</dl>
</div>
</div>
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<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<h4> Giving credit </h4>
<ul class="simple">
<li><p>Please consider <a href="authors.html#citing">citing the
papers</a>.</p></li>
</ul>
<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">1. Introduction: nilearn in a nutshell</a><ul>
<li><a class="reference internal" href="#what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity">1.1. What is nilearn: MVPA, decoding, predictive models, functional connectivity</a><ul>
<li><a class="reference internal" href="#why-is-machine-learning-relevant-to-neuroimaging-a-few-examples">1.1.1. Why is machine learning relevant to NeuroImaging? A few examples!</a></li>
</ul>
</li>
<li><a class="reference internal" href="#installing-nilearn">1.2. Installing nilearn</a></li>
<li><a class="reference internal" href="#python-for-neuroimaging-a-quick-start">1.3. Python for NeuroImaging, a quick start</a><ul>
<li><a class="reference internal" href="#your-first-steps-with-nilearn">1.3.1. Your first steps with nilearn</a></li>
<li><a class="reference internal" href="#scientific-computing-with-python">1.3.2. Scientific computing with Python</a><ul>
<li><a class="reference internal" href="#basic-numerics">1.3.2.1. Basic numerics</a></li>
<li><a class="reference internal" href="#scikit-learn-machine-learning-in-python">1.3.2.2. Scikit-learn: machine learning in Python</a></li>
</ul>
</li>
<li><a class="reference internal" href="#finding-help">1.3.3. Finding help</a></li>
</ul>
</li>
</ul>
</li>
</ul>
<h4>Previous topic</h4>
<p class="topless"><a href="user_guide.html"
title="previous chapter">User guide: table of contents</a></p>
<h4>Next topic</h4>
<p class="topless"><a href="decoding/index.html"
title="next chapter"><span class="section-number">2. </span>Decoding and MVPA: predicting from brain images</a></p>
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