arf 2.6.7

Description:

arf 2.6.7

The Advanced Recording Format ARF
is an open standard for storing data from neuronal, acoustic, and
behavioral experiments in a portable, high-performance, archival format.
The goal is to enable labs to share data and tools, and to allow
valuable data to be accessed and analyzed for many years in the future.
ARF is built on the the HDF5
format, and all arf files are accessible through standard HDF5 tools,
including interfaces to HDF5 written for other languages (e.g. MATLAB,
Python, etc). ARF comprises a set of specifications on how different
kinds of data are stored. The organization of ARF files is based around
the concept of an entry, a collection of data channels associated with
a particular point in time. An entry might contain one or more of the
following:

raw extracellular neural signals recorded from a multichannel probe
spike times extracted from neural data
acoustic signals from a microphone
times when an animal interacted with a behavioral apparatus
the times when a real-time signal analyzer detected vocalization

Entries and datasets have metadata attributes describing how the data
were collected. Datasets and entries retain these attributes when copied
or moved between arf files, helping to prevent data from becoming
orphaned and uninterpretable.
This repository contains:

The specification for arf (in specification.md). This is also hosted
at https://meliza.org/spec:1/arf/.
A fast, type-safe C++ interface for reading and writing arf files
A python interface for reading and writing arf files (based on h5py).


contributing
ARF is under active development and we welcome comments and
contributions from neuroscientists and behavioral biologists interested
in using it. We’re particularly interested in use cases that don’t fit
the current specification. Please post issues or contact Dan Meliza (dan
at meliza.org) directly.
The MATLAB interface is out of date and could use some work.


installation
ARF files require HDF5>=1.8 (http://www.hdfgroup.org/HDF5).
The python interface requires Python 3.7 or greater, numpy>=1.19, and
h5py>=2.10. The last version to support Python 2 was 2.5.1. To
install the module:
pip install arf
To use the C++ interface, you need boost>=1.42 (http://boost.org). In
addition, if writing multithreaded code, HDF5 needs to be compiled with
--enable-threadsafe. The interface is header-only and does not need
to be compiled. To install:
make install


version information
The specification and implementations provided in this project use a
form of semantic versioning (http://semver.org). Specifications receive
a major and minor version number. Changes to minor version numbers must
be backwards compatible (i.e., only added requirements). The current
released version of the ARF specification is 2.1.
Implementation versions are synchronized with the major version of the
specification but otherwise evolve independently. For example, the
python arf package version 2.1.0 is compatible with any ARF
version 2.x.
There was no public release of ARF prior to 2.0.


access ARF files with HDF5 tools
This section describes how to inspect ARF files using standard tools, in
the event that the interfaces described here cease to function.
The structure of an ARF file can be explored using the h5ls tool.
For example, to list entries:
$ h5ls file.arf
test_0001 Group
test_0002 Group
test_0003 Group
test_0004 Group
Each entry appears as a Group. To list the contents of an entry, use
path notation:
$ h5ls file.arf/test_0001
pcm Dataset {609914}
This shows that the data in test_0001 is stored in a single node,
pcm}, with 609914 data points. Typically each channel will have its
own dataset.
The h5dump command can be used to output data in binary format. See
the HDF5 documentation for details on how to structure the output. For
example, to extract sampled data to a 16-bit little-endian file (i.e.,
PCM format):
h5dump -d /test_0001/pcm -b LE -o test_0001.pcm file.arf


related projects

arfx is a commandline tool
for manipulating ARF files.


open data formats

neurodata without borders has similar goals
and also uses HDF5 for storage. The data schema is considerably more
complex, but it does seem to be achieving growing adoption.
pandora is also under active
development



i/o libraries

neo is a Python
package for working with electrophysiology data in Python, together
with support for reading a wide range of neurophysiology file
formats.
neuroshare is a set of routines for
reading and writing data in various proprietary and open formats.