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<!DOCTYPE html>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Getting Started with pactools</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a></li>
<li class="toctree-l2"><a class="reference internal" href="#phase-amplitude-coupling-pac">Phase-amplitude coupling (PAC)</a></li>
<li class="toctree-l2"><a class="reference internal" href="#driven-auto-regressive-dar-models">Driven auto-regressive (DAR) models</a></li>
<li class="toctree-l2"><a class="reference internal" href="#acknowledgment">Acknowledgment</a></li>
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<div class="section" id="getting-started-with-pactools">
<h1>Getting Started with pactools<a class="headerlink" href="#getting-started-with-pactools" title="Permalink to this headline">¶</a></h1>
<a class="reference external image-reference" href="https://travis-ci.org/pactools/pactools"><img alt="Build Status" src="https://travis-ci.org/pactools/pactools.svg?branch=master" /></a>
<a class="reference external image-reference" href="https://codecov.io/gh/pactools/pactools"><img alt="Test coverage" src="https://codecov.io/gh/pactools/pactools/branch/master/graph/badge.svg" /></a>
<a class="reference external image-reference" href="https://github.com/pactools/pactools"><img alt="Python27" src="https://img.shields.io/badge/python-2.7-blue.svg" /></a>
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<p>This package provides tools to estimate <strong>phase-amplitude coupling (PAC)</strong>
in neural time series.</p>
<p>In particular, it implements the <strong>driven auto-regressive (DAR)</strong>
models presented in the reference below [<a class="reference external" href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893">Dupre la Tour et al. 2017</a>].</p>
<p>Read more in the <a class="reference external" href="https://pactools.github.io">documentation</a>.</p>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
<p>To install <code class="docutils literal notranslate"><span class="pre">pactools</span></code>, you first need to install its dependencies:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">numpy</span> <span class="n">scipy</span> <span class="n">matplotlib</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span>
</pre></div>
</div>
<p>To enable all features, you will also need to install optional packages:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">mne</span> <span class="n">h5py</span>
</pre></div>
</div>
<p>Then install <code class="docutils literal notranslate"><span class="pre">pactools</span></code> with one of the following two commands:</p>
<ul>
<li><p>Development version:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pactools</span><span class="o">/</span><span class="n">pactools</span><span class="o">.</span><span class="n">git</span><span class="c1">#egg=pactools</span>
</pre></div>
</div>
</li>
<li><p>Latest stable version:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pactools</span>
</pre></div>
</div>
</li>
</ul>
<p>To upgrade, use the <code class="docutils literal notranslate"><span class="pre">--upgrade</span></code> flag provided by <code class="docutils literal notranslate"><span class="pre">pip</span></code>.</p>
<p>To check if everything worked fine, you can do:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">c</span> <span class="s1">'import pactools'</span>
</pre></div>
</div>
<p>and it should not give any error messages.</p>
</div>
<div class="section" id="phase-amplitude-coupling-pac">
<h2>Phase-amplitude coupling (PAC)<a class="headerlink" href="#phase-amplitude-coupling-pac" title="Permalink to this headline">¶</a></h2>
<p>Among the different classes of cross-frequency couplings,
phase-amplitude coupling (PAC) - i.e. high frequency activity time-locked
to a specific phase of slow frequency oscillations - is by far the most
acknowledged.
PAC is typically represented with a comodulogram, which shows the strenght of
the coupling over a grid of frequencies.
Comodulograms can be computed in <cite>pactools</cite> with more
than 10 different methods.</p>
<div class="section" id="examples-using-pactools-comodulogram">
<h3>Examples using <code class="docutils literal notranslate"><span class="pre">pactools.Comodulogram</span></code><a class="headerlink" href="#examples-using-pactools-comodulogram" title="Permalink to this headline">¶</a></h3>
<div class="sphx-glr-thumbcontainer" tooltip="A comodulogram shows the estimated PAC metric on a grid of frequency bands."><div class="figure align-default" id="id1">
<img alt="_images/sphx_glr_plot_comodulogram_thumb.png" src="_images/sphx_glr_plot_comodulogram_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_comodulogram.html#sphx-glr-auto-examples-plot-comodulogram-py"><span class="std std-ref">Comodulogram</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comodulogram shows the estimated PAC metric on a grid of frequency bands. In absence of PAC, ..."><div class="figure align-default" id="id2">
<img alt="_images/sphx_glr_plot_surrogate_analysis_thumb.png" src="_images/sphx_glr_plot_surrogate_analysis_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_surrogate_analysis.html#sphx-glr-auto-examples-plot-surrogate-analysis-py"><span class="std std-ref">Surrogate analysis</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="It also shows the comodulogram computed with a DAR model."><div class="figure align-default" id="id3">
<img alt="_images/sphx_glr_plot_dar_model_thumb.png" src="_images/sphx_glr_plot_dar_model_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_dar_model.html#sphx-glr-auto-examples-plot-dar-model-py"><span class="std std-ref">Fitting a DAR model</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="It relies on the function raw_to_mask, which takes as input a MNE.Raw instance and an events ar..."><div class="figure align-default" id="id4">
<img alt="_images/sphx_glr_plot_mne_epoch_masking_thumb.png" src="_images/sphx_glr_plot_mne_epoch_masking_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_mne_epoch_masking.html#sphx-glr-auto-examples-plot-mne-epoch-masking-py"><span class="std std-ref">Interface with MNE-python</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how a spike train can generate a significant amount of phase-amplitud..."><div class="figure align-default" id="id5">
<img alt="_images/sphx_glr_plot_spurious_pac_thumb.png" src="_images/sphx_glr_plot_spurious_pac_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_spurious_pac.html#sphx-glr-auto-examples-plot-spurious-pac-py"><span class="std std-ref">Spurious PAC with spikes</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div style='clear:both'></div></div>
</div>
<div class="section" id="driven-auto-regressive-dar-models">
<h2>Driven auto-regressive (DAR) models<a class="headerlink" href="#driven-auto-regressive-dar-models" title="Permalink to this headline">¶</a></h2>
<p>One of the method is based on driven auto-regressive (DAR) models.
As this method models the entire spectrum simultaneously, it avoids the
pitfalls related to incorrect filtering or the use of the Hilbert transform
on wide-band signals. As the model is probabilistic, it also provides a
score of the model <strong>goodness of fit</strong> via the likelihood, enabling easy
and legitimate model selection and parameter comparison;
this data-driven feature is unique to such model-based approach.</p>
<p>We recommend using DAR models to estimate PAC in neural time-series.
More detail in [<a class="reference external" href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893">Dupre la Tour et al. 2017</a>].</p>
<div class="section" id="examples-using-pactools-dar-model-dar">
<h3>Examples using <code class="docutils literal notranslate"><span class="pre">pactools.dar_model.DAR</span></code><a class="headerlink" href="#examples-using-pactools-dar-model-dar" title="Permalink to this headline">¶</a></h3>
<div class="sphx-glr-thumbcontainer" tooltip="It also shows the comodulogram computed with a DAR model."><div class="figure align-default" id="id6">
<img alt="_images/sphx_glr_plot_dar_model_thumb.png" src="_images/sphx_glr_plot_dar_model_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_dar_model.html#sphx-glr-auto-examples-plot-dar-model-py"><span class="std std-ref">Fitting a DAR model</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="It creates an artificial signal with phase-amplitude coupling (PAC), fits a DAR model over a gr..."><div class="figure align-default" id="id7">
<img alt="_images/sphx_glr_plot_grid_search_thumb.png" src="_images/sphx_glr_plot_grid_search_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_grid_search.html#sphx-glr-auto-examples-plot-grid-search-py"><span class="std std-ref">Grid-search and cross-validation</span></a></span><a class="headerlink" href="#id7" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example disantangles the two distinct notions of phase shift and temporal delay in phase-a..."><div class="figure align-default" id="id8">
<img alt="_images/sphx_glr_plot_phase_delay_thumb.png" src="_images/sphx_glr_plot_phase_delay_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="auto_examples/plot_phase_delay.html#sphx-glr-auto-examples-plot-phase-delay-py"><span class="std std-ref">Phase shift and temporal delay in PAC</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
</div>
</div><div style='clear:both'></div></div>
</div>
<div class="section" id="acknowledgment">
<h2>Acknowledgment<a class="headerlink" href="#acknowledgment" title="Permalink to this headline">¶</a></h2>
<p>This work was supported by the ERC Starting Grant SLAB ERC-YStG-676943 to
Alexandre Gramfort, the ERC Starting Grant MindTime ERC-YStG-263584 to Virginie
van Wassenhove, the ANR-16-CE37-0004-04 AutoTime to Valerie Doyere and Virginie
van Wassenhove, and the Paris-Saclay IDEX NoTime to Valerie Doyere, Alexandre
Gramfort and Virginie van Wassenhove,</p>
</div>
<div class="section" id="cite-this-work">
<h2>Cite this work<a class="headerlink" href="#cite-this-work" title="Permalink to this headline">¶</a></h2>
<p>If you use this code in your project, please cite
[<a class="reference external" href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893">Dupre la Tour et al. 2017</a>]:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@article</span><span class="p">{</span><span class="n">duprelatour2017nonlinear</span><span class="p">,</span>
<span class="n">author</span> <span class="o">=</span> <span class="p">{</span><span class="n">Dupr</span><span class="p">{</span>\<span class="s1">'e} la Tour, Tom and Tallot, Lucille and Grabot, Laetitia and Doy{\`e}re, Val{</span><span class="se">\'</span><span class="s1">e}rie and van Wassenhove, Virginie and Grenier, Yves and Gramfort, Alexandre},</span>
<span class="n">journal</span> <span class="o">=</span> <span class="p">{</span><span class="n">PLOS</span> <span class="n">Computational</span> <span class="n">Biology</span><span class="p">},</span>
<span class="n">publisher</span> <span class="o">=</span> <span class="p">{</span><span class="n">Public</span> <span class="n">Library</span> <span class="n">of</span> <span class="n">Science</span><span class="p">},</span>
<span class="n">title</span> <span class="o">=</span> <span class="p">{</span><span class="n">Non</span><span class="o">-</span><span class="n">linear</span> <span class="n">auto</span><span class="o">-</span><span class="n">regressive</span> <span class="n">models</span> <span class="k">for</span> <span class="n">cross</span><span class="o">-</span><span class="n">frequency</span> <span class="n">coupling</span> <span class="ow">in</span> <span class="n">neural</span> <span class="n">time</span> <span class="n">series</span><span class="p">},</span>
<span class="n">year</span> <span class="o">=</span> <span class="p">{</span><span class="mi">2017</span><span class="p">},</span>
<span class="n">month</span> <span class="o">=</span> <span class="p">{</span><span class="mi">12</span><span class="p">},</span>
<span class="n">volume</span> <span class="o">=</span> <span class="p">{</span><span class="mi">13</span><span class="p">},</span>
<span class="n">url</span> <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">doi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="mf">10.1371</span><span class="o">/</span><span class="n">journal</span><span class="o">.</span><span class="n">pcbi</span><span class="o">.</span><span class="mi">1005893</span><span class="p">},</span>
<span class="n">pages</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="o">-</span><span class="mi">32</span><span class="p">},</span>
<span class="n">number</span> <span class="o">=</span> <span class="p">{</span><span class="mi">12</span><span class="p">},</span>
<span class="n">doi</span> <span class="o">=</span> <span class="p">{</span><span class="mf">10.1371</span><span class="o">/</span><span class="n">journal</span><span class="o">.</span><span class="n">pcbi</span><span class="o">.</span><span class="mi">1005893</span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
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