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<h2>Statistics for NeuroImaging in Python</h2>
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<h1>0.3.1<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h1>
<p><strong>Released June 2017</strong></p>
<p>This is a minor release for BrainHack.</p>
<div class="section" id="highlights">
<h2>Highlights<a class="headerlink" href="#highlights" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><strong>Dropped support for scikit-learn older than 0.14.1</strong> Minimum supported version is now <code class="docutils literal notranslate"><span class="pre">0.15</span></code>.</p></li>
</ul>
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<div class="section" id="changes">
<h2>Changes<a class="headerlink" href="#changes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>The function <code class="docutils literal notranslate"><span class="pre">sym_to_vec</span></code> is deprecated and will be removed in release <code class="docutils literal notranslate"><span class="pre">0.4</span></code>. Use function <a class="reference internal" href="../modules/generated/nilearn.connectome.sym_matrix_to_vec.html#nilearn.connectome.sym_matrix_to_vec" title="nilearn.connectome.sym_matrix_to_vec"><code class="xref py py-func docutils literal notranslate"><span class="pre">sym_matrix_to_vec</span></code></a> instead.</p></li>
<li><p>Added argument <code class="docutils literal notranslate"><span class="pre">smoothing_fwhm</span></code> to <a class="reference internal" href="../modules/generated/nilearn.regions.RegionExtractor.html#nilearn.regions.RegionExtractor" title="nilearn.regions.RegionExtractor"><code class="xref py py-class docutils literal notranslate"><span class="pre">RegionExtractor</span></code></a> to control smoothing according to the resolution of atlas images.</p></li>
</ul>
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<div class="section" id="fixes">
<h2>Fixes<a class="headerlink" href="#fixes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>The helper function <code class="docutils literal notranslate"><span class="pre">largest_connected_component</span></code> should now work with inputs of non-native data <code class="docutils literal notranslate"><span class="pre">dtypes</span></code>.</p></li>
<li><p>Fix plotting issues when non-finite values are present in background anatomical image.</p></li>
<li><p>A workaround to handle non-native endianness in <a class="reference external" href="https://nipy.org/nibabel/reference/nibabel.nifti1.html#nibabel.nifti1.Nifti1Image" title="(in NiBabel v3.2.2+146.ga20f13ad)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nifti1Image</span></code></a> passed to resampling the image.</p></li>
</ul>
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<div class="section" id="enhancements">
<h2>Enhancements<a class="headerlink" href="#enhancements" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>New functions <a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault.html#nilearn.datasets.fetch_neurovault" title="nilearn.datasets.fetch_neurovault"><code class="xref py py-func docutils literal notranslate"><span class="pre">fetch_neurovault</span></code></a> and <a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_neurovault_ids.html#nilearn.datasets.fetch_neurovault_ids" title="nilearn.datasets.fetch_neurovault_ids"><code class="xref py py-func docutils literal notranslate"><span class="pre">fetch_neurovault_ids</span></code></a> help you download statistical maps from the <a class="reference external" href="http://neurovault.org">Neurovault</a> platform.</p></li>
<li><p>New function <a class="reference internal" href="../modules/generated/nilearn.connectome.vec_to_sym_matrix.html#nilearn.connectome.vec_to_sym_matrix" title="nilearn.connectome.vec_to_sym_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">vec_to_sym_matrix</span></code></a> reshapes vectors to symmetric matrices. It acts as the reverse of function <a class="reference internal" href="../modules/generated/nilearn.connectome.sym_matrix_to_vec.html#nilearn.connectome.sym_matrix_to_vec" title="nilearn.connectome.sym_matrix_to_vec"><code class="xref py py-func docutils literal notranslate"><span class="pre">sym_matrix_to_vec</span></code></a>.</p></li>
<li><p>Add an option allowing to vectorize connectivity matrices returned by the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method of class <a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConnectivityMeasure</span></code></a>.</p></li>
<li><p>Class <a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConnectivityMeasure</span></code></a> now exposes an <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method, useful for going back from vectorized connectivity coefficients to connectivity matrices. Also, it allows to recover the covariance matrices for the “tangent” kind.</p></li>
<li><p>Reworking and renaming of connectivity measures example. Renamed from <code class="docutils literal notranslate"><span class="pre">plot_connectivity_measures</span></code> to <code class="docutils literal notranslate"><span class="pre">plot_group_level_connectivity</span></code>.</p></li>
<li><p>Tighter bounding boxes when using <code class="docutils literal notranslate"><span class="pre">add_contours</span></code> for plotting.</p></li>
<li><p>Function <a class="reference internal" href="../modules/generated/nilearn.image.largest_connected_component_img.html#nilearn.image.largest_connected_component_img" title="nilearn.image.largest_connected_component_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">largest_connected_component_img</span></code></a> to directly extract the largest connected component from <a class="reference external" href="https://nipy.org/nibabel/reference/nibabel.nifti1.html#nibabel.nifti1.Nifti1Image" title="(in NiBabel v3.2.2+146.ga20f13ad)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nifti1Image</span></code></a>.</p></li>
<li><p>Improvements in plotting, decoding and functional connectivity examples.</p></li>
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<li><p>Please consider <a href="../authors.html#citing">citing the
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<h3><a href="../index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">0.3.1</a><ul>
<li><a class="reference internal" href="#highlights">Highlights</a></li>
<li><a class="reference internal" href="#changes">Changes</a></li>
<li><a class="reference internal" href="#fixes">Fixes</a></li>
<li><a class="reference internal" href="#enhancements">Enhancements</a></li>
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