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---
title: Home
description: MNE Annual Coding Sprint 2019
image: /images/background.jpg
bottom_image: /images/all-logo.png
map:
latitude: 48.869516
longitude: 2.338116
address: "Facebook, Paris, France"
zoom: 13
---
<section class="hero" style="background-image: url({% include relative-src.html src=page.image %})">
<div class="inner-hero text-container">
<div class="hero-text-container">
<center>
<h1 class="editable">MNE Code Sprint 2019</h1>
<p class="subtext editable"><b>April 22nd - 26th, Paris</b></p>
</center>
</div>
</div>
<div class="learn-more">
<a href="#why-what">
<center><p class="subtext editable">Learn more</p>
<img src="https://mne-tools.github.io/sprint2019/images/arrow.gif">
</center></a></div>
</section>
<div class="content" class="container-fluid">
<section class="info" id="why-what">
<div class="container flex">
<div class="text">
<h2 class="editable">Why a code sprint?</h2>
<p class="editable">
Alone you go fast and together we go far!
</p>
<p class="editable">
<a href="https://github.com/mne-tools/mne-python">MNE</a>
is the most popular Python software for making sense of neural signals
such as EEG or MEG. <a href="https://github.com/mne-tools/mne-python">MNE</a>
is developped by a community of developers scattered around the world.
The annual MNE code sprint is the moment for the MNE developers to exchange,
share a vision and make fast progress.
</p>
<p class="editable">
The primary workhorse of this success has been free open source software (FOSS). With its strong emphasis on API design, the FOSS culture has made it less effortful to plan, develop in teams, re-use, distribute, teach, optimize & scale data analysis efforts. Coding sprints are a way to focus development efforts and share best practices that generalize across a range of application domains.
</p>
<h2 class="editable">What?</h2>
<p class="editable">
The aim of the event is to gather established experts in the processing of neural time series data. Together, we will work on the software stack for all aspects of the processing chain: from pre-precossing to advanced machine learning use cases, including integration experiments using <a href="https://pytorch.org">PyTorch</a>.
</p>
</div>
</div>
</section>
<section class="pad" id="get-ready">
<div class="container">
<h2 class="editable">Get Ready!</h2>
<p class="editable">
The detailed Pull Requests / Issues tackled during the sprint are described on the <a href="https://github.com/mne-tools/mne-python/projects/4">MNE github project page</a>.
During the sprint, we'll chat on <a href="https://gitter.im/mne-tools/mne-python">gitter</a>.
<p class="editable">
<!-- If you are not a core developer, you will need to:
</p>
<ul>
<li> contact me (jeanremi.king [at] gmail.com) to sign-up to the coding sprint and be allowed in the building.</li>
<li> ensure that you have installed the master (dev) branch of each package.</li>
<li> you have the tools to contribute to FOSS (<a href='http://mne-tools.github.io/stable/contributing.html?highlight=contribute'>See MNE recommendation</a>).</li>
<li> identify a specific pull request.</li>
<li> contact the assigned core developer(s) <i>in advance</i> to discuss the requirements.</li>
</ul>
<p class="editable"> Else: </p>
<ul>
<li> open issues that need to be addressed in the coding sprint.</li>
<li> ensure they contain explicit descriptions, a difficulty level and an assignee.</li>
</ul>
</p> -->
</div>
</section>
<section class="pad" id="who">
<div class="container">
<h2 class="editable">Who?</h2>
<ul class="staff">
{% for person in site.staff_members %}
<li>
<div class="square-image" style="background-image: url({% include relative-src.html src=person.image_path %})"></div>
<div class="name"><a target="_blank" href="{{ person.website }}">{{ person.name }}</a></div>
<div class="position">{{ person.blurb }}</div>
</li>
{% endfor %}
</ul>
<!-- <p class="editable">
The current developers focus on <i>neural</i> time series. However, we are particularly interested in productive exchange with other scientific fields that encounter similarly structured signals (finance, musicology, speech processing, climate science, seismology, kinect and radar analyses, etc).
</p>
--> </div>
</section>
<section class="info" id="where-when">
<div class="container">
<div class="text">
<h2 class="editable">Where?</h2>
<p class="editable">Facebook, Paris</p>
</div>
</div>
<div class="container">
<div class="text">
<h2 class="editable">When?</h2>
<p class="editable">The sprint will take place Monday April 22<sup>nd</sup> - Friday April 26<sup>th</sup>, 2019. The day-by-day schedule is TBD.</p>
</div>
</div>
</section>
<section class="contact", id="contact">
<div class="container">
<h2 class="editable">Contact</h2>
<p>jeanremi.king [at] gmail.com</p>
<p>alexandre.gramfort [at] gmail.com</p>
</div>
</section>
<!-- <section class="achievements", id="achievements">
<div class="container">
<h2 class="editable">Main enhancements</h2>
<p><a href="https://github.com/mne-tools/mne-python/pull/4144">MNE: Add SPoC object to decode continuous targets from oscillatory activity</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/3563">MNE: Merge the code of Xdawn and rERP to extract evoked responses from continuous raw signals</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4119">MNE: Use frequency domain for XC/AC optimization in ReceptiveField.</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4139">MNE: Add example of Time-Frequency summary with Global Field Power.</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4115">MNE: Add example of Time-Frequency Decoding based on Common Spatial Pattern.</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4103">MNE: Add scikit-learn compatible SlidingEstimator, GeneralizingEstimator and cross_val_multiscore.</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4110">MNE: Generalize real-time clients and Add LSL real-time client.</a></p>
<p><a href="https://twitter.com/choldgraf/status/848224615960805376">MNE: 3D vizualization in notebook.</a></p>
<p><a href="https://github.com/mne-tools/mne-python/pull/4097">MNE: Improved resolution of favicon ;)</a></p>
<p><a href="https://github.com/glm-tools/pyglmnet/issues/23">pyGLMnet: Add predict_proba method compatible with scikit-learn.</a></p>
<p><a href="https://github.com/glm-tools/pyglmnet/issues/97">pyGLMnet: Add scorers metrics.</a></p>
<p><a href="https://github.com/glm-tools/pyglmnet/issues/158">pyGLMnet: Add GLM and GLMCV estimators.</a></p>
</div>
</section>
-->
<section class="logos", id="logo">
<div class="container">
<div class="text">
<h2 class="editable">Supported by</h2>
</div>
<div class="image">
<img class="editable" src=images/support-logo.png alt="Suported by" />
</div>
<div class="text">
<h2 class="editable">To contribute to</h2>
</div>
<div class="image">
<img class="editable" src=images/contribute-logo.png alt="Suported by" />
</div>
</div>
</section>
</div>