Metadata-Version: 2.1
Name: bispy-polar
Version: 0.9.4.dev0
Summary: An open-source python framework for processing     bivariate signals.
Home-page: https://github.com/jflamant/bispy
Author: Julien Flamant
Author-email: julien.flamant@cnrs.fr
License: CeCIll
Keywords: signal processing
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: numpy-quaternion
Provides-Extra: dev
Requires-Dist: check-manifest; extra == "dev"
Provides-Extra: test
Requires-Dist: coverage; extra == "test"

# BiSPy : Bivariate Signal Processing with Python

[![docs-page](https://img.shields.io/badge/docs-latest-blue)](https://jflamant.github.io/bispy/)
[![PyPI version](https://badge.fury.io/py/bispy-polar.svg)](https://badge.fury.io/py/bispy-polar)

BiSPy is an open-source python framework for processing bivariate signals. It supports our papers on time-frequency analysis [1], spectral analysis [2] and linear time-invariant filtering [3] of bivariate signals.

> [1] Julien Flamant, Nicolas Le Bihan, Pierre Chainais: “Time-frequency analysis of bivariate signals”, Applied and Computational Harmonic Analysis, 2017; [arXiv:1609.0246](http://arxiv.org/abs/1609.02463), [doi:10.1016/j.acha.2017.05.007](https://doi.org/10.1016/j.acha.2017.05.007)

> [2] Julien Flamant, Nicolas Le Bihan, Pierre Chainais: “Spectral analysis of stationary random bivariate signals”, 2017, IEEE Transactions on Signal Processing; [arXiv:1703.06417](http://arxiv.org/abs/1703.06417), [doi:10.1109/TSP.2017.2736494](https://doi.org/10.1109/TSP.2017.2736494)

> [3] Julien Flamant, Pierre Chainais, Nicolas Le Bihan: “A complete framework for linear filtering of bivariate signals”, 2018; IEEE Transactions on Signal Processing; [arXiv:1802.02469](https://arxiv.org/abs/1802.02469), [doi:10.1109/TSP.2018.2855659](https://doi.org/10.1109/TSP.2018.2855659)

These papers contains theoretical results and several applications that can be reproduced with this toolbox.


This python toolbox is currently under development and is hosted on GitHub. If you encounter a bug or something unexpected please let me know by [raising an issue](https://github.com/jflamant/bispy/issues) on the project page.

### Install from PyPi

Due to name conflict the available version on PyPi is named ``bispy-polar''. To install from PyPi, simply type

```
pip install bispy-polar
```

It will automatically install dependencies (see also below). 

To get started, simply use 
```
import bispy as bsp
```

Requirements
============
BiSPy works with python 3.5+.

Dependencies:
 -   [NumPy](http://www.numpy.org)
 -   [SciPy](https://www.scipy.org)
 -   [Matplotlib](http://matplotlib.org)
 -   [numpy-quaternion](https://github.com/moble/quaternion)

To install dependencies:
```shell
pip install numpy scipy matplotlib numpy-quaternion
```

[numpy-quaternion](https://github.com/moble/quaternion) add quaternion dtype support to numpy. Implementation by [moble]. Since this python toolbox relies extensively on this module, you can check out first the nice introduction [here](https://github.com/moble).


License
=======
This software is distributed under the [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.

Cite this work
==============
If you use this package for your own work, please consider citing it with this piece of BibTeX:

```bibtex
@misc{BiSPy,
    title =   {{BiSPy: an Open-Source Python project for processing bivariate signals}},
    author =  {Julien Flamant},
    year =    {2018},
    url =     {https://github.com/jflamant/bispy/},
    howpublished = {Online at: \url{github.com/jflamant/bispy/}},
    note =    {Code at https://github.com/jflamant/bispy/, documentation at https://bispy.readthedocs.io/}
}
```
