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<li><a class="reference internal" href="#">Documentation</a></li>
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<span class="target" id="module-neo"></span><a class="reference internal image-reference" href="_images/neologo.png"><img alt="_images/neologo.png" src="_images/neologo.png" style="width: 600px;" /></a>
<p>Neo is a package for representing electrophysiology data in Python, together
with support for reading a wide range of neurophysiology file formats, including
Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for
writing to a subset of these formats plus non-proprietary formats including HDF5.</p>
<p>The goal of Neo is to improve interoperability between Python tools for
analyzing, visualizing and generating electrophysiology data (such as
<a class="reference external" href="https://github.com/OpenElectrophy/OpenElectrophy">OpenElectrophy</a>, <a class="reference external" href="http://neuralensemble.org/NeuroTools">NeuroTools</a>, <a class="reference external" href="http://www.g-node.org/">G-node</a>, <a class="reference external" href="https://www.dbunic.cnrs-gif.fr/documentation/helmholtz/">Helmholtz</a>, <a class="reference external" href="http://neuralensemble.org/PyNN">PyNN</a>) by providing a common,
shared object model. In order to be as lightweight a dependency as possible,
Neo is deliberately limited to represention of data, with no functions for data
analysis or visualization.</p>
<p>Neo implements a hierarchical data model well adapted to intracellular and
extracellular electrophysiology and EEG data with support for multi-electrodes
(for example tetrodes). Neo’s data objects build on the <a class="reference external" href="http://pypi.python.org/pypi/quantities">quantities</a> package,
which in turn builds on NumPy by adding support for physical dimensions. Thus
Neo objects behave just like normal NumPy arrays, but with additional metadata,
checks for dimensional consistency and automatic unit conversion.</p>
<p>A project with similar aims but for neuroimaging file formats is <a class="reference external" href="http://nipy.sourceforge.net/nibabel/">NiBabel</a>.</p>
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<h1>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="install.html">Download and Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="core.html">Neo core</a></li>
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<h1>License<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h1>
<p>Neo is distributed under a 3-clause Revised BSD licence (BSD-3-Clause).</p>
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<div class="section" id="contributing">
<h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline">¶</a></h1>
<p>The people behind the project (see <a class="reference internal" href="authors.html"><em>Authors and contributors</em></a>) are very open to discussion.
Any feedback is gladly received and highly appreciated!
Discussion of Neo takes place on the
<a class="reference external" href="http://groups.google.com/group/neuralensemble">NeuralEnsemble mailing list</a>.</p>
<p><a class="reference external" href="https://github.com/NeuralEnsemble/python-neo">Source code</a> is on GitHub.</p>
<p>The <a class="reference external" href="https://github.com/NeuralEnsemble/python-neo/issues">bug tracker</a> is at:</p>
<div class="highlight-python"><div class="highlight"><pre>https://github.com/NeuralEnsemble/python-neo/issues
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