<div dir="ltr">Great, thanks for all the hard work.</div><br><div class="gmail_quote"><div dir="ltr">On Tue, Nov 20, 2018 at 6:32 AM serafim loukas <<a href="mailto:seralouk@hotmail.com">seralouk@hotmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Amazing news. Thank you for this amazing Python module.<br>
<br>
Serafeim<br>
<br>
> On 19 Nov 2018, at 17:42, Gael Varoquaux <<a href="mailto:gael.varoquaux@normalesup.org" target="_blank">gael.varoquaux@normalesup.org</a>> wrote:<br>
> <br>
> Hi everyone,<br>
> <br>
> We are happy to announce the release of version 0.5 of nilearn, a tool<br>
> for multivariate analysis of brain imaging data in Python.<br>
> <br>
> Maybe the most exciting addition in this release is interactive plotting<br>
> that naturally embeds in the Jupyter notebook, to plot volumes, surfaces,<br>
> or connectomes:<br>
> <a href="http://nilearn.github.io/plotting/index.html#interactive-plotting" rel="noreferrer" target="_blank">http://nilearn.github.io/plotting/index.html#interactive-plotting</a><br>
> <br>
> Nilearn can be used for decoding or biomarker extraction, resting-state<br>
> fMRI analysis, or more general manipulation of brain images in Python:<br>
> <a href="http://nilearn.github.io/user_guide.html" rel="noreferrer" target="_blank">http://nilearn.github.io/user_guide.html</a><br>
> <br>
> A detailed changelog can be found at:<br>
> <a href="http://nilearn.github.io/whats_new.html" rel="noreferrer" target="_blank">http://nilearn.github.io/whats_new.html</a><br>
> <br>
> Nilearn is developed by a variety of contributors around the world:<br>
> <a href="https://github.com/nilearn/nilearn/graphs/contributors" rel="noreferrer" target="_blank">https://github.com/nilearn/nilearn/graphs/contributors</a><br>
> <br>
> Gaël<br>
> <br>
> _______________________________________________<br>
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<br>
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</blockquote></div>