./wip/py-neo, Python package for representing electrophysiology data

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Branch: CURRENT, Version: 0.3.3, Package name: py27-neo-0.3.3, Maintainer: jihbed.research

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.

The goal of Neo is to improve interoperability between Python tools for
analyzing, visualizing and generating electrophysiology data (such as
OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) 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 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.


Required to run:
[math/py-scipy] [math/py-numpy]

Master sites:

SHA1: 6b77f46899eae7dc0a12a0f2495c8d5df2551c3a
RMD160: 9c20f781a3d51ea997dcd9eb901235b8567236ac
Filesize: 1373.154 KB

Version history: (Expand)


CVS history: (Expand)


   2014-05-12 23:42:00 by Kamel Derouiche | Files touched by this commit (1) | Package updated
Log message:
update DESCR

   2014-05-12 23:40:56 by Kamel Derouiche | Files touched by this commit (4)
Log message:
Import py27-neo-0.3.3 as wip/py-neo.

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.

The goal of Neo is to improve interoperability between Python tools for
analyzing, visualizing and generating electrophysiology data (such as
OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) 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 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.
or installation instructions