Package: arf
The Advanced Recording FormatARFis 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.ARFis built on the theHDF5format, and all arf files are accessible through standard HDF5 tools,
including interfaces to HDF5 written for other languages (e.g. MATLAB,
Python, etc).ARFcomprises a set of specifications on how different
kinds of data are stored. The organization of ARF files is based around
the concept of anentry, 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 probespike times extracted from neural dataacoustic signals from a microphonetimes when an animal interacted with a behavioral apparatusthe times when a real-time signal analyzer detected vocalizationEntries 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
athttps://meliza.org/spec:1/arf/.A fast, type-safe C++ interface for reading and writing arf filesA python interface for reading and writing arf files (based on h5py).contributingARF 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at meliza.org) directly.The MATLAB interface is out of date and could use some work.installationARF files require HDF5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 was2.5.1. To
install the module:pipinstallarfTo 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:makeinstallversion informationThe 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 is2.1.Implementation versions are synchronized with the major version of the
specification but otherwise evolve independently. For example, the
pythonarfpackage version2.1.0is compatible with any ARF
version2.x.There was no public release of ARF prior to2.0.access ARF files with HDF5 toolsThis 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 theh5lstool.
For example, to list entries:$h5lsfile.arftest_0001Grouptest_0002Grouptest_0003Grouptest_0004GroupEach entry appears as a Group. To list the contents of an entry, use
path notation:$h5lsfile.arf/test_0001pcmDataset{609914}This shows that the data intest_0001is stored in a single node,pcm}, with 609914 data points. Typically each channel will have its
own dataset.Theh5dumpcommand 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-bLE-otest_0001.pcmfile.arfrelated projectsarfxis a commandline tool
for manipulating ARF files.open data formatsneurodata without bordershas similar goals
and also uses HDF5 for storage. The data schema is considerably more
complex, but it does seem to be achieving growing adoption.pandorais also under active
developmenti/o librariesneois a Python
package for working with electrophysiology data in Python, together
with support for reading a wide range of neurophysiology file
formats.neuroshareis a set of routines for
reading and writing data in various proprietary and open formats.