SPECTRUM : Spectral Analysis in Python

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:contributions: Please join https://github.com/cokelaer/spectrum :contributors: https://github.com/cokelaer/spectrum/graphs/contributors :issues: Please use https://github.com/cokelaer/spectrum/issues :documentation: http://pyspectrum.readthedocs.io/ :Citation: Cokelaer et al, (2017), 'Spectrum': Spectral Analysis in Python, Journal of Open Source Software, 2(18), 348, doi:10.21105/joss.00348

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Spectrum contains tools to estimate Power Spectral Densities using methods based on Fourier transform, Parametric methods or eigenvalues analysis:

* The Fourier methods are based upon correlogram, periodogram and Welch estimates. Standard tapering windows (Hann, Hamming, Blackman) and more exotic ones are available (DPSS, Taylor, ...). 
* The parametric methods are based on Yule-Walker, BURG, MA and ARMA, covariance and modified covariance methods.
* Non-parametric methods based on eigen analysis (e.g., MUSIC) and minimum variance analysis are also implemented.
* Multitapering is also available

The targetted audience is diverse. Although the use of power spectrum of a signal is fundamental in electrical engineering (e.g. radio communications, radar), it has a wide range of applications from cosmology (e.g., detection of gravitational waves in 2016), to music (pattern detection) or biology (mass spectroscopy).

Quick Installation

spectrum is available on Pypi::

pip install spectrum

and conda::

conda config --append channels conda-forge
conda install spectrum

To install the conda executable itself, please see https://www.continuum.io/downloads .

Contributions

Please see github <http://github.com/cokelaer/spectrum>_ for any issues/bugs/comments/contributions.

Some notebooks (external contributions)

• http://nbviewer.ipython.org/gist/juhasch/5182528