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: https://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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Overview

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

• Fourier-based methods: correlogram, periodogram and Welch estimates. Standard tapering windows (Hann, Hamming, Blackman) and more exotic ones are available (DPSS, Taylor, ...).
• Parametric methods: Yule-Walker, BURG, MA and ARMA, covariance and modified covariance methods.
• Non-parametric (eigenanalysis) methods: MUSIC and minimum variance analysis.
• Multitapering (MTM).

The targeted 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), to music (pattern detection) or biology (mass spectroscopy).

Quick Start

The example below creates a cosine signal buried in white noise and estimates its power spectral density using a simple periodogram:

.. code-block:: python

from spectrum import Periodogram, data_cosine

# generate a 1024-sample cosine at 200 Hz (amplitude 0.1) buried in white noise
data = data_cosine(N=1024, A=0.1, sampling=1024, freq=200)

# create the periodogram object and plot
p = Periodogram(data, sampling=1024)
p.plot(marker='o')

All PSD classes share the same interface: instantiate the object, run the estimation (or let it run lazily on first access), then call p.plot(). The functional API is also available for all methods when a quick result is needed without a full object.

See the documentation <https://pyspectrum.readthedocs.io/>_ for a complete tutorial, API reference and gallery of examples.

Installation

spectrum is available on PyPI::

pip install spectrum

and on conda-forge::

conda install -c conda-forge spectrum

To install conda itself, see https://docs.conda.io/en/latest/miniconda.html.

Contributions

Please see GitHub <https://github.com/cokelaer/spectrum>_ for any issues, bugs, comments or contributions.

Changelog (summary)

========== ============================================================ release description ========== ============================================================ 0.10.0 * add python 3.12 and 3.13 support * add sos2ss, sos2tf, etc. fix #85, #88 0.9.0 * handles new numpy API (keeping back compatibility). * included https://github.com/cokelaer/spectrum/pull/73 thanks to @butala contribution to speed up fft. * fix rho calculation in burg algo thanks to https://github.com/cokelaer/spectrum/pull/82 from @cl445 * remove warnings/deprecation related to pkgresources, numpy and scipy. * ran black through entire code. 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