Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats
Neo is a Python package for working with 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.
The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data 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.
Neo is used by a number of other software tools, including OpenElectrophy and SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing) and PyNN (simulations).
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 quantities 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.
A project with similar aims but for neuroimaging file formats is NiBabel.
- Home page: http://neuralensemble.org/neo
- Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble
- Documentation: http://packages.python.org/neo/
- Bug reports: https://github.com/NeuralEnsemble/python-neo/issues
For installation instructions, see doc/source/install.rst
|copyright:||Copyright 2010-2016 by the Neo team, see doc/source/authors.rst.|
|license:||3-Clause Revised BSD License, see LICENSE.txt for details.|