Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/axopy/axopy

A Python Library for Implementing Human-Computer Interface Experiments
https://github.com/axopy/axopy

brain-computer-interface electrophysiology experiment-design gui human-computer-interaction prosthetics qt

Last synced: 4 months ago
JSON representation

A Python Library for Implementing Human-Computer Interface Experiments

Awesome Lists containing this project

README 

        
.. image:: https://github.com/axopy/axopy/raw/master/docs/_static/axopy.png

   :alt: AxoPy Logo



|



.. image:: http://joss.theoj.org/papers/10.21105/joss.01191/status.svg

    :target: https://doi.org/10.21105/joss.01191

    :alt: JOSS Paper



.. image:: https://github.com/axopy/axopy/actions/workflows/ci.yml/badge.svg

    :target: https://github.com/axopy/axopy/actions/workflows/ci.yml

    :alt: GitHub Actions CI testing status



.. image:: https://readthedocs.org/projects/axopy/badge/?version=latest

    :target: http://axopy.readthedocs.io/en/latest/?badge=latest

    :alt: Documentation Status



.. image:: https://codecov.io/gh/axopy/axopy/branch/master/graph/badge.svg

    :target: https://codecov.io/gh/axopy/axopy

    :alt: Codecov test coverage



.. image:: https://img.shields.io/pypi/v/axopy.svg

    :target: https://pypi.org/project/axopy/

    :alt: PyPI package



.. image:: https://img.shields.io/conda/vn/conda-forge/axopy.svg

    :target: https://anaconda.org/conda-forge/axopy

    :alt: Anaconda package



|



**Documentation**: https://axopy.readthedocs.io



Axo-Pythonic synapses are those in which an axon synapses upon a Python

program. AxoPy aims to facilitate such connections between electrophysiolgical

signals and machines by making it easy for researchers to develop

human-computer interface experiments. If you've ever found yourself spending

more time thinking about how to implement your experiments than thinking about

*what the experiment should be*, AxoPy may be able to help.



AxoPy consists of:



Graphical interface

    Central to AxoPy is the graphical user interface providing visual feedback

    to the subject and controlling the flow of the experiment. The GUI is

    backed by PyQt5, and you're free to implement customized graphical elements

    if those built in to AxoPy don't suit your needs.

Data acquisition

    AxoPy establishes a fairly simple API for communicating with input

    hardware, so all that's usually needed is a bit of middleware to get going.

    Check out pytrigno_ or pymcc_ to see what this is like. A couple input

    devices are built in (keyboard, noise generator), so examples run without

    needing special hardware.

Data storage

    Data is stored in a file structure with common file formats (CSV and HDF5)

    so you can a) start working with data as soon as an experiment session is

    over and b) you don't need anything but standard tools (pandas, h5py) to do

    so. A high-level interface to the storage structure is also provided to

    make traversing a dataset simple.

Pipeline processing

    Estimating intentions of the user from raw electrophysiological signals

    often involves a large number of processing operations. AxoPy facilitates

    flexible construction of pipelines that can be reused in different parts of

    an experiment and re-used for offline post-processing, etc.



Quickstart

==========



Installation

------------



pip

^^^



AxoPy is available on `PyPI`_, so the following should get it installed if

you're using a standard Python installation with ``pip``::



    $ pip install axopy



*Note*: if you have Python < 3.5, ``pip`` will not be able to install the

``pyqt5`` package for you because wheels for ``pyqt5`` are only provided for

Python >= 3.5. If you are stuck on an older version of Python, consider using

``conda`` (described below, works for any Python version) or installing Qt5 and

PyQt5 yourself before running the command above.



See the `development documentation`_ for information on setting up

a development environment to work on AxoPy itself.



conda

^^^^^



AxoPy is also available on `conda-forge`_, so if you're using (Ana)conda with

any Python version, you can install it with::



    $ conda install -c conda-forge axopy



Hello, AxoPy

------------



Here's a minimal example to display some randomly generated signals in an

"oscilloscope":



.. code-block:: python



    import axopy



    daq = axopy.daq.NoiseGenerator(rate=1000, num_channels=4, read_size=100)

    exp = axopy.experiment.Experiment(daq=daq)

    exp.run(axopy.task.Oscilloscope())



Next Steps

----------



Check out the documentation_ for more information on creating experiments. Some

`examples`_ are also available.



Citing

======



If you use AxoPy in your research and want to acknowledge us, see our

instructions for `citing AxoPy`_.



Contributing

============



Please feel free to share any thoughts or opinions about the design and

implementation of this software by `opening an issue on GitHub

`_. Constructive feedback is

welcomed and appreciated.



GitHub issues also serve as the support channel, at least for now. Questions

about how to do something are usually great opportunities to improve

documentation, so you may be asked about your thoughts on where the answers

should go.



If you want to contribute code, open a pull request. Bug fix pull requests are

always welcome. For feature additions, breaking changes, etc. check if there is

an open issue discussing the change and reference it in the pull request. If

there isn't one, it is recommended to open one with your rationale for the

change before spending significant time preparing the pull request.



Ideally, new/changed functionality should come with tests and documentation. If

you are new to contributing, it is perfectly fine to open a work-in-progress

pull request and have it iteratively reviewed. See the `development

documentation`_ for instructions on setting up a development environment,

running tests, and building the documentation.



.. _pytrigno: https://github.com/axopy/pytrigno

.. _pymcc: https://github.com/axopy/pymcc

.. _documentation: https://axopy.readthedocs.io

.. _examples: https://github.com/axopy/axopy/tree/master/examples

.. _PyPI: https://pypi.org/

.. _conda-forge: https://conda-forge.org/

.. _conda: https://conda.io/docs/

.. _citing AxoPy: https://axopy.readthedocs.io/en/latest/about.html#citing-axopy

.. _development documentation: http://axopy.readthedocs.io/en/latest/development.html