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<!doctype html>
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<li class="toctree-l1"><a class="reference internal" href="quickstart.html">Quickstart</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_3d_and_4d_niimg.html">3D and 4D niimgs: handling and visualizing</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_decoding_tutorial.html">A introduction tutorial to fMRI decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_single_subject_single_run.html">Intro to GLM Analysis: a single-session, single-subject fMRI dataset</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="auto_examples/01_plotting/index.html">Visualization of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_glass_brain.html">Glass brain plotting in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_visualize_megatrawls_netmats.html">Visualizing Megatrawls Network Matrices from Human Connectome Project</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_atlas.html">Basic Atlas plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_multiscale_parcellations.html">Visualizing multiscale functional brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_colormaps.html">Matplotlib colormaps in Nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_overlay.html">Visualizing a probabilistic atlas: the default mode in the MSDL atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_overlay.html#visualizing-a-probabilistic-atlas-with-plot-prob-atlas">Visualizing a probabilistic atlas with plot_prob_atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_dim_plotting.html">Controlling the contrast of the background when plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_visualization.html">NeuroImaging volumes visualization</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_carpet.html">Visualizing global patterns with a carpet plot</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_haxby_masks.html">Plot Haxby masks</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surface_projection_strategies.html">Technical point: Illustration of the volume to surface sampling schemes</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_plotting.html">Plotting tools in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_prob_atlas.html">Visualizing 4D probabilistic atlas maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surf_stat_map.html">Seed-based connectivity on the surface</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surf_atlas.html">Loading and plotting of a cortical surface atlas</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_glass_brain_extensive.html">Glass brain plotting in nilearn (all options)</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_stimuli.html">Show stimuli of Haxby et al. dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_mixed_gambles_frem.html">FREM on Jimura et al “mixed gambles” dataset.</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_frem.html">Decoding with FREM: face vs house object recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_oasis_vbm_space_net.html">Voxel-Based Morphometry on Oasis dataset with Space-Net prior</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_searchlight_surface.html">Cortical surface-based searchlight decoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_grid_search.html">Setting a parameter by cross-validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_full_analysis.html">ROI-based decoding analysis in Haxby et al. dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_miyawaki_encoding.html">Encoding models for visual stimuli from Miyawaki et al. 2008</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/04_glm_first_level/plot_write_events_file.html">Generate an events.tsv file for the NeuroSpin localizer task</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_association_test.html">Example of generic design in second-level models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_nifti_simple.html">Simple example of NiftiMasker use</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps.</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_advanced_decoding_scikit.html">Advanced decoding using scikit learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_beta_series.html">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User guide</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2"><a class="reference internal" href="introduction.html">Introduction</a></li>
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<li class="toctree-l3"><a class="reference internal" href="decoding/estimator_choice.html">Choosing the right predictive model for neuroimaging</a></li>
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<li class="toctree-l3"><a class="reference internal" href="connectivity/parcellating.html">Clustering to parcellate the brain in regions</a></li>
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<li class="toctree-l3"><a class="reference internal" href="glm/glm_intro.html">An introduction to GLMs in fMRI statistical analysis</a></li>
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<li class="toctree-l3"><a class="reference internal" href="manipulating_images/masker_objects.html">From neuroimaging volumes to data matrices: the masker objects</a></li>
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<li class="toctree-l3"><a class="reference internal" href="building_blocks/manual_pipeline.html">Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="building_blocks/neurovault.html">Downloading statistical maps from the Neurovault repository</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="modules/connectome.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code>: Functional Connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="modules/regions.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.regions</span></code>: Operating on Regions</a><input class="toctree-checkbox" id="toctree-checkbox-29" name="toctree-checkbox-29" role="switch" type="checkbox"/><label for="toctree-checkbox-29"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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<section id="contributing">
<span id="development-process"></span><h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this heading">#</a></h1>
<section id="how-to-get-help">
<span id="id1"></span><h2>How to get help?<a class="headerlink" href="#how-to-get-help" title="Permalink to this heading">#</a></h2>
<p>If you have issues when using Nilearn, or if you have questions on how to use it, please don’t hesitate to reach out!</p>
<p>There are currently three ways to interact with the Nilearn team: through the <a class="reference external" href="https://neurostars.org/tag/nilearn/">neurostars</a> forum, our <a class="reference external" href="https://github.com/nilearn/nilearn/">github</a> issues, and through our weekly <a class="reference external" href="https://github.com/nilearn/nilearn/issues/2882">office hours</a>, usually <strong>every Friday from 4pm to 5pm UTC</strong>.</p>
<p>If you have a <em>usage question</em>, that is if you need help troubleshooting scripts using Nilearn, we would appreciate it if you either ask it during office hours or create a topic on <a class="reference external" href="https://neurostars.org/tag/nilearn/">neurostars</a> with the “nilearn” tag.
Asking questions or reporting issues is always valuable because it will help other users having the same problem. So, please don’t hold onto a burning question!</p>
<p>We ask that you <em>don’t</em> open an issue on <a class="reference external" href="https://github.com/nilearn/nilearn/">GitHub</a> for usage questions. We use our <a class="reference external" href="https://github.com/nilearn/nilearn/">GitHub</a> issue board for bug reports, feature requests, and documentation changes only.</p>
</section>
<section id="how-to-help-the-project">
<h2>How to help the project?<a class="headerlink" href="#how-to-help-the-project" title="Permalink to this heading">#</a></h2>
<p>If you are interested in contributing to the Nilearn project, we thank you very much. Note that there are multiple ways to help us, and not all of them require writing code.</p>
<section id="report-bugs-or-discuss-enhancement-ideas">
<h3>Report bugs or discuss enhancement ideas<a class="headerlink" href="#report-bugs-or-discuss-enhancement-ideas" title="Permalink to this heading">#</a></h3>
<p>We welcome open discussion around improvements—both to the documentation as well as to the code base—through our GitHub issue board!</p>
<blockquote>
<div><ul class="simple">
<li><p>If you think you have discovered a bug, please start by searching through the existing <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a> to make sure it has not already been reported. If the bug has not been reported yet, create an <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">new issue</a> including a <a class="reference external" href="https://stackoverflow.com/help/minimal-reproducible-example">minimal runnable example</a> to showcase it (using Nilearn data) as well as your OS and Nilearn version.</p></li>
<li><p>If you have an idea for a new feature, check if it is in the <a class="reference internal" href="#nilearn-scope"><span class="std std-ref">Scope of the project</span></a> and feel free to open a <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">new issue</a> to discuss it.</p></li>
<li><p>If you think the documentation can be improved, please open a <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">new issue</a> to discuss what you would like to change! This helps to confirm that your proposed improvements don’t overlap with any ongoing work.</p></li>
</ul>
</div></blockquote>
</section>
<section id="answer-questions">
<h3>Answer questions<a class="headerlink" href="#answer-questions" title="Permalink to this heading">#</a></h3>
<p>Another way to help the project is to answer questions on <a class="reference external" href="https://neurostars.org/tag/nilearn/">neurostars</a>, or comment on github <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a>.
Some <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a> are used to gather user opinions on various questions, and any input from the community is valuable to us.</p>
</section>
<section id="review-pull-requests">
<h3>Review Pull Requests<a class="headerlink" href="#review-pull-requests" title="Permalink to this heading">#</a></h3>
<p>Any addition to the Nilearn’s code base has to be reviewed and approved by several people including at least two <a class="reference internal" href="authors.html#core-devs"><span class="std std-ref">Core developers</span></a>.
This can put a heavy burden on <a class="reference internal" href="authors.html#core-devs"><span class="std std-ref">Core developers</span></a> when a lot of
<a class="reference external" href="https://github.com/nilearn/nilearn/pulls">pull requests</a> are opened at the same time.
We welcome help in reviewing <a class="reference external" href="https://github.com/nilearn/nilearn/pulls">pull requests</a> from any
community member.
We do not expect community members to be experts in all changes included in
<a class="reference external" href="https://github.com/nilearn/nilearn/pulls">pull requests</a>, and we encourage you to concentrate on those code changes that you feel comfortable with.
As always, more eyes on a code change means that the code is more likely to work in a wide variety of contexts!</p>
</section>
<section id="join-the-triage-team">
<h3>Join the triage team<a class="headerlink" href="#join-the-triage-team" title="Permalink to this heading">#</a></h3>
<p>The <a class="reference internal" href="authors.html#triage"><span class="std std-ref">Triage team</span></a> is composed of community members who have permission on
<a class="reference external" href="https://github.com/nilearn/nilearn/">github</a> to label and close <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a>.
Their work is crucial to improve the communication in the project and limit the crowding of the issue tracker.
The <a class="reference internal" href="maintenance.html#issue-labels"><span class="std std-ref">Labels</span></a> and <a class="reference internal" href="maintenance.html#closing-policy"><span class="std std-ref">Closing policy</span></a> of the project is defined in more details in the <a class="reference internal" href="maintenance.html#maintenance-process"><span class="std std-ref">Maintenance</span></a> page.</p>
<p>Any community member may become a member of the Nilearn <a class="reference internal" href="authors.html#triage"><span class="std std-ref">Triage team</span></a>.
If you are interested in joining the <a class="reference internal" href="authors.html#triage"><span class="std std-ref">Triage team</span></a>, please read the <a class="reference internal" href="maintenance.html#maintenance-process"><span class="std std-ref">Maintenance</span></a> page and contact one of the <a class="reference internal" href="authors.html#core-devs"><span class="std std-ref">Core developers</span></a> to express your interest!</p>
</section>
<section id="contribute-code">
<h3>Contribute code<a class="headerlink" href="#contribute-code" title="Permalink to this heading">#</a></h3>
<p>If you want to contribute code:</p>
<blockquote>
<div><ul class="simple">
<li><p>For new features, please be sure to create a <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">new issue</a> first, to discuss whether it can be included and its specifications.</p></li>
<li><p>To help with known <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a>, please check <a class="reference external" href="https://github.com/nilearn/nilearn/labels/Good%20first%20issue">good first issues</a> to get started, <a class="reference external" href="https://github.com/nilearn/nilearn/labels/Bug">known bugs</a>, or <a class="reference external" href="https://github.com/nilearn/nilearn/labels/Enhancement">proposed enhancements</a>.</p></li>
</ul>
</div></blockquote>
<p>Please see the <a class="reference internal" href="#contributing-code"><span class="std std-ref">How to contribute to nilearn</span></a> section for more detailed information, including
instructions for <a class="reference internal" href="#setting-up-your-environment">Setting up your environment</a> and a description of the <a class="reference internal" href="#id2">Contribution Guidelines</a>.</p>
</section>
</section>
<section id="how-do-we-decide-what-code-goes-in">
<h2>How do we decide what code goes in?<a class="headerlink" href="#how-do-we-decide-what-code-goes-in" title="Permalink to this heading">#</a></h2>
<p>The following sections explain the <a class="reference internal" href="#nilearn-scope"><span class="std std-ref">Scope of the project</span></a> and <a class="reference internal" href="#nilearn-governance"><span class="std std-ref">Who makes decisions</span></a>, which jointly determine whether potential contributions will be accepted into the project.</p>
<section id="scope-of-the-project">
<span id="nilearn-scope"></span><h3>Scope of the project<a class="headerlink" href="#scope-of-the-project" title="Permalink to this heading">#</a></h3>
<p>Nilearn is an Open-source Python package for visualizing and analyzing human
brain MRI data.
It provides statistical and machine-learning tools for brain mapping,
connectivity estimation and predictive modelling.
It brings visualization tools with instructive documentation & open community.</p>
<p>Nilearn targets ease of use, but as Python code.
In other words, we will not add graphical user interfaces, but we want our
code to be as easy to understand as possible, with easy prototyping and
debugging, even for beginners in Python.</p>
<p>We are parsimonious in the way we add features to the project, as it
puts on weight.
To assess new features, our main concern is their usefulness to a number of
our users.
To make Nilearn high-quality and sustainable we also weigh their benefits
(i.e., new features, ease of use) with their cost (i.e., complexity of the code,
runtime of the examples). As a rule of thumb:</p>
<ul class="simple">
<li><p>To be accepted, new features must be <strong>in the scope of the project</strong> and
correspond to an <strong>established practice</strong> (typically as used in scientific
publications).</p></li>
<li><p>It must have a concrete use case, illustrated with a <strong>simple example</strong> in the
Nilearn documentation to teach it easily to end-users.</p></li>
<li><p>It must be <strong>thoroughly tested</strong>, and respect <strong>coding conventions</strong> of the
existing codebase.</p></li>
<li><p>Features introducing new dependencies will generally not be accepted.</p></li>
<li><p>Downloaders for new atlases are welcome if they comes with an example.</p></li>
<li><p>Downloaders for new datasets are usually discouraged. We will consider adding
fetchers only for light datasets which are needed to demo and teach features.</p></li>
</ul>
<p>Exhaustive criteria used in the review process are detailed in the <strong>contribution
guide below</strong>.
Be sure to read and follow them so that your code can be accepted quickly.</p>
</section>
<section id="who-makes-decisions">
<span id="nilearn-governance"></span><h3>Who makes decisions<a class="headerlink" href="#who-makes-decisions" title="Permalink to this heading">#</a></h3>
<p>We strongly aim to be a community oriented project where decisions are
made based on consensus according to the criteria described above.
Discussions are public, held on <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a> and
<a class="reference external" href="https://github.com/nilearn/nilearn/pulls">pull requests</a> in Github.
All modifications of the codebase are ultimately checked during a reviewing
process, where maintainers or contributors make sure they respect the
<a class="reference internal" href="#contribution-guidelines"><span class="std std-ref">Contribution Guidelines</span></a>.
To be merged, a pull request usually needs to be accepted by two maintainers.
In case a consensus does not emerge easily, the decisions are made by the
<a class="reference internal" href="authors.html#core-devs"><span class="std std-ref">Core developers</span></a>, i.e., people with write access to the repository, as
listed <a class="reference internal" href="authors.html#core-devs"><span class="std std-ref">here</span></a>.</p>
</section>
</section>
<section id="how-to-contribute-to-nilearn">
<span id="contributing-code"></span><h2>How to contribute to nilearn<a class="headerlink" href="#how-to-contribute-to-nilearn" title="Permalink to this heading">#</a></h2>
<p>This project, hosted on <a class="reference external" href="https://github.com/nilearn/nilearn/">https://github.com/nilearn/nilearn/</a>, is a community
effort, and everyone is welcome to contribute.
We value very much your feedback and opinion on features that should be
improved or added.
All discussions are public and held on relevant <a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a> or
<a class="reference external" href="https://github.com/nilearn/nilearn/pulls">pull requests</a>.
To discuss your matter, please comment on a relevant
<a class="reference external" href="https://github.com/nilearn/nilearn/issues">issue</a> or open a new one.</p>
<p>The best way to contribute and to help the project is to start working on known
<a class="reference external" href="https://github.com/nilearn/nilearn/issues">issues</a> such as
<a class="reference external" href="https://github.com/nilearn/nilearn/labels/Good%20first%20issue">good first issues</a>,
<a class="reference external" href="https://github.com/nilearn/nilearn/labels/Bug">known bugs</a> or
<a class="reference external" href="https://github.com/nilearn/nilearn/labels/Enhancement">proposed enhancements</a>.
If an issue does not already exist for a potential contribution, we ask that
you first open a <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">new issue</a> before sending a
<a class="reference internal" href="maintenance.html#pull-request"><span class="std std-ref">Pull Requests</span></a> to discuss scope and potential design choices in advance.</p>
<section id="contribution-guidelines">
<span id="id2"></span><h3>Contribution Guidelines<a class="headerlink" href="#contribution-guidelines" title="Permalink to this heading">#</a></h3>
<p>When modifying the codebase, we ask every contributor to respect common
guidelines.
Those are inspired from <a class="reference external" href="https://scikit-learn.org/stable/developers/contributing.html#contributing-code">scikit-learn</a>
and ensure Nilearn remains simple to understand, efficient and maintainable.
For example, code needs to be tested and those tests need to run quickly in order
not to burden the development process.
To keep continuous integration efficient with our limited infrastructure,
running all the examples must lead to downloading a limited amount of data
(gigabytes) and execute in a reasonable amount of time (less than an hour).
Those guidelines will hence be enforced during the reviewing process.
The section <a class="reference internal" href="#setting-up-your-environment">Setting up your environment</a> will help you to quickly get familiar
with the tools we use for development and deployment.</p>
<div class="table-wrapper docutils container">
<table class="docutils align-default">
<colgroup>
<col style="width: 23%" />
<col style="width: 17%" />
<col style="width: 60%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"><p>Which PR ?</p></th>
<th class="head"><p>Guidelines</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="#pr-structure">PR Structure</a></p></td>
<td><p>Any</p></td>
<td><ul class="simple">
<li><p>Clear name</p></li>
<li><p>Link issue through mention :”Closes #XXXX”</p></li>
<li><p>Clearly outline goals and changes proposed</p></li>
<li><p>Doesn’t include “unrelated” code change</p></li>
<li><p>Add entry in “doc/changes/latest.rst”</p></li>
</ul>
</td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#coding-style">Coding Style</a></p></td>
<td><p>Any</p></td>
<td><ul class="simple">
<li><p>Variables, functions, arguments have clear names</p></li>
<li><p>Easy to read, <a class="reference external" href="https://www.python.org/dev/peps/pep-0008/">PEP8</a> compliant</p></li>
<li><p>Public functions have docstring (<a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html">numpydoc</a> format)</p></li>
<li><p>Low redundancy</p></li>
<li><p>No new dependency</p></li>
<li><p>Backward compatibility</p></li>
<li><p>All internal imports are absolute, not relative</p></li>
<li><p>Impacted docstrings have versionadded and/or
versionchanged directives as needed.
These should use the current dev version.</p></li>
</ul>
</td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#tests">Tests</a></p></td>
<td><ul class="simple">
<li><p>Bugfixes</p></li>
<li><p>New features</p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Test type is adapted to function behavior</p></li>
<li><p>Tests pass continuous integration</p></li>
<li><p>Coverage doesn’t decrease</p></li>
<li><p>Fast, using small mocked data</p></li>
<li><p>Atomic (one per function) and seeded</p></li>
<li><p>For Bugfixes: non-regression test</p></li>
</ul>
</td>
</tr>
<tr class="row-odd"><td><p>Examples</p></td>
<td><p>New features</p></td>
<td><ul class="simple">
<li><p>Clearly showcase benefits</p></li>
<li><p>Run in a few seconds</p></li>
<li><p>Use light data (generated or from Nilearn)</p></li>
<li><p>Renders well after build</p></li>
</ul>
</td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#documentation">Documentation</a></p></td>
<td><p>Any</p></td>
<td><ul class="simple">
<li><p>Simple and didactic</p></li>
<li><p>Links to relevant examples</p></li>
<li><p>Renders well after build</p></li>
<li><p>Doesn’t include code</p></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="pr-structure">
<h3>PR Structure<a class="headerlink" href="#pr-structure" title="Permalink to this heading">#</a></h3>
<p>A new pull request must have a clear scope, conveyed through its name, a
reference to the issue it targets (through the exact mention “Closes #XXXX”),
and a synthetic summary of its goals and main steps.
When working on big contributions, we advise contributors to split them into
several PRs when possible.
This has the benefit of making code changes clearer, making PRs easier to review,
and overall smoothening the whole process.
No changes unrelated to the PR should be included.</p>
<p>When relevant, PR names should also include tags if they fall in various
categories.
When opening a PR, the authors should include the [WIP] tag in its name, or use
github draft mode. When ready for review, they should switch the tag to [MRG] or
can switch it back to normal mode.
Other tags can describe the PR content : [FIX] for a bugfix, [DOC] for a
change in documentation or examples, [ENH] for a new feature and [MAINT] for
maintenance changes.</p>
</section>
<section id="coding-style">
<h3>Coding Style<a class="headerlink" href="#coding-style" title="Permalink to this heading">#</a></h3>
<p>The nilearn codebase follows <a class="reference external" href="https://www.python.org/dev/peps/pep-0008/">PEP8</a> styling.
The main conventions we enforce are :</p>
<ul class="simple">
<li><p>line length < 80</p></li>
<li><p>spaces around operators</p></li>
<li><p>meaningful variable names</p></li>
<li><p>function names are underscore separated (e.g., <code class="docutils literal notranslate"><span class="pre">a_nice_function</span></code>) and as short as possible</p></li>
<li><p>public functions exposed in their parent module’s init file</p></li>
<li><p>private function names preceded with a “_” and very explicit</p></li>
<li><p>classes in CamelCase</p></li>
<li><p>2 empty lines between functions or classes</p></li>
</ul>
<p>Each function and class must come with a “docstring” at the top of the function code,
using <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html">numpydoc</a> formatting.
The docstring must summarize what the function does and document every parameter.</p>
<p>Additionally, we consider it best practice to write modular functions;
i.e., functions should preferably be relatively short and do <em>one</em> thing.
This is also useful for writing unit tests.</p>
<p>Writing small functions is not always possible, and we do not recommend trying to reorganize larger,
but well-tested, older functions in the codebase, unless there is a strong reason to do so (e.g., when adding a new feature).</p>
</section>
<section id="tests">
<h3>Tests<a class="headerlink" href="#tests" title="Permalink to this heading">#</a></h3>
<p>When fixing a bug, the first step is to write a minimal test that fails because
of it, and then write the bugfix to make this test pass.
For new code you should have roughly one test function per function covering
every line and testing the logic of the function.
They should run on small mocked data, cover a representative range of parameters.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>It is easier to write good unit tests for short, self-contained functions.
Try to keep this in mind when you write new functions.
For more information about this coding approach,
see <a class="reference external" href="https://en.wikipedia.org/wiki/Test-driven_development">test-driven development</a>.</p>
</div>
<p>Tests must be seeded to avoid random failures.
For objects using random seeds (e.g. scikit-learn estimators), pass either
a <cite>np.random.RandomState</cite> or an <cite>int</cite> as the seed.
When your test use random numbers, those must be generated through:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">my_number</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">normal</span><span class="p">()</span>
</pre></div>
</div>
<p>To check your changes worked and didn’t break anything run <cite>pytest nilearn</cite>.
To do quicker checks it’s possible to run only a subset of tests:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="o">-</span><span class="n">v</span> <span class="n">test_module</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
</section>
<section id="documentation">
<h3>Documentation<a class="headerlink" href="#documentation" title="Permalink to this heading">#</a></h3>
<p>Documentation must be understandable by people from different backgrounds.
The “narrative” documentation should be an introduction to the concepts of
the library.
It includes very little code and should first help the user figure out which
parts of the library he needs and then how to use it.
It must be full of links, of easily-understandable titles, colorful boxes and
figures.</p>
<p>Examples take a hands-on approach focused on a generic usecase from which users
will be able to adapt code to solve their own problems.
They include plain text for explanations, python code and its output and
most importantly figures to depict its results.
Each example should take only a few seconds to run.</p>
<p>To build our documentation, we are using
<a class="reference external" href="https://www.sphinx-doc.org/en/master/usage/quickstart.html">sphinx</a> for the
main documentation and
<a class="reference external" href="https://sphinx-gallery.github.io/stable/index.html">sphinx-gallery</a> for the
example tutorials. If you want to work on those, check out next section to
learn how to use those tools to build documentation.</p>
</section>
</section>
<section id="setting-up-your-environment">
<span id="git-repo"></span><h2>Setting up your environment<a class="headerlink" href="#setting-up-your-environment" title="Permalink to this heading">#</a></h2>
<section id="installing">
<h3>Installing<a class="headerlink" href="#installing" title="Permalink to this heading">#</a></h3>
<p>Here are the key steps you need to go through to copy the repo before contributing:</p>
<ol class="arabic">
<li><p>fork the repo from github (fork button in the top right corner of our <a class="reference external" href="https://github.com/nilearn/nilearn/">main github page</a>) and clone your fork locally:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">git</span><span class="nd">@github</span><span class="o">.</span><span class="n">com</span><span class="p">:</span><span class="o"><</span><span class="n">your_username</span><span class="o">>/</span><span class="n">nilearn</span><span class="o">.</span><span class="n">git</span>
</pre></div>
</div>
</li>
<li><p>(optional but highly recommended) set up a conda environment to work on and activate it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">nilearn</span>
<span class="n">conda</span> <span class="n">activate</span> <span class="n">nilearn</span>
</pre></div>
</div>
</li>