Spectrum: a Spectral Analysis Library in Python

Citation:Cokelaer et al., (2017). ‘Spectrum’: Spectral Analysis in Python. Journal of Open Source Software, 2(18), 348, doi:10.21105/joss.00348 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/

Spectrum is a Python library that contains tools to estimate Power Spectral Densities 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. Finally, Multitapering combines several orthogonal tapering windows.

Quick installation

conda install spectrum

Examples

Visit our example gallery or jump to the main documentation

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Targetted audience

The targetted audience is diverse: 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).

Documentation

• 1. Installation
  • 1.1. Using pip
  • 1.2. Conda installation
  • 1.3. From source and notes for developers
• 2. Quick Start
  • 2.1. QuickStart (Periodogram example)
  • 2.2. The object approach versus functional approach (ARMA example)
• 3. Overview of available PSD methods
  • 3.1. Non-parametric classes
  • 3.2. Autoregressive spectral estimation
• 4. Tutorials
  • 4.1. Yule Walker example
  • 4.2. PBURG example
  • 4.3. Variance outputs
  • 4.4. Windowing
  • 4.5. All PSD methods
  • 4.6. What is the Spectrum object ?
  • 4.7. Criteria for Parametric methods
• 5. Reference Guide
  • 5.1. Fourier Methods
  • 5.2. Multitapering
  • 5.3. Parametric methods
  • 5.4. Other Power Spectral Density estimates
  • 5.5. Tools and classes
• 6. ChangeLog
  • 6.1. Version 0.8 (2020)
  • 6.2. Version 0.7 (2019)
  • 6.3. Version 0.6
  • 6.4. Sept 2012
  • 6.5. March 2012
  • 6.6. February 2012
  • 6.7. January 2012
  • 6.8. October 2011
  • 6.9. May 2011
  • 6.10. April 2011
  • 6.11. March 2011
• 7. Contributions
  • 7.1. Contributors
  • 7.2. How to contribute ?
  • 7.3. Some notebooks

License

Spectrum is released under a BSD3 license

Indices and tables

• Index
• Module Index
• Search Page