Spectrum Analysis Tools
Project description
SPECTRUM : Spectral Analysis in Python
- contributions:
Please join https://github.com/cokelaer/spectrum
- contributors:
- issues:
Please use https://github.com/cokelaer/spectrum/issues
- documentation:
- 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 |
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0.9.0 |
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0.8.1 |
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