README.rst

SPECTRUM : Spectral Analysis in Python

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

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 for any issues/bugs/comments/contributions.

Changelog (summary)

release	description
0.8.1	move CI to github actions include python 3.9 support include PR from tikuma-lshhsc contributor to speedup eigenfre module fix deprecated warnings

Some notebooks (external contributions)

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