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arf-2.6.4
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Advanced Recording Format for acoustic, behavioral, and physiological data
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | arf-2.6.4 |
| نام | arf |
| نسخه کتابخانه | 2.6.4 |
| نگهدارنده | ['Dan Meliza'] |
| ایمیل نگهدارنده | ['dan@meliza.org'] |
| نویسنده | Dan Meliza |
| ایمیل نویسنده | dan@meliza.org |
| آدرس صفحه اصلی | https://github.com/melizalab/arf |
| آدرس اینترنتی | https://pypi.org/project/arf/ |
| مجوز | BSD 3-Clause License |
## arf
[](https://travis-ci.org/melizalab/arf)
The Advanced Recording Format [ARF](https://meliza.org/spec:1/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](http://www.hdfgroup.org/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 3.6 or greater, numpy>=1.19, and h5py>=2.10. The last version to support Python 2 was `2.5.1`. To install the module:
```bash
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:
```bash
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:
```bash
$ 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:
```bash
$ 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):
```bash
h5dump -d /test_0001/pcm -b LE -o test_0001.pcm file.arf
```
### related projects
- [arfx](https://github.com/melizalab/arfx) is a commandline tool for manipulating ARF files.
#### open data formats
- [neurodata without borders](http://www.nwb.org) 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](https://github.com/G-Node/pandora) is also under active development
#### i/o libraries
- [neo](https://github.com/NeuralEnsemble/python-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](http://neuroshare.org) is a set of routines for reading and
writing data in various proprietary and open formats.
نیازمندی
| مقدار | نام |
|---|---|
| !=3.3.*,>=2.8 | h5py |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl arf-2.6.4:
pip install arf-2.6.4.whl
نصب پکیج tar.gz arf-2.6.4:
pip install arf-2.6.4.tar.gz