Spectrum: a Spectral Analysis Library in Python
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
- 1. Installation
- 2. Quick Start
- 3. Overview of available PSD methods
- 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. todo list
- 7. ChangeLog Summary
Spectrum is released under a LGPL License.