Lomb-Scargle Periodograms (lombscargle)

The Lomb-Scargle Periodogram (after Lomb [1], and Scargle [2]) is a commonly-used statistical tool designed to detect periodic signals in unevenly-spaced observations. The lombscargle package contains a unified interface to several implementations of the Lomb-Scargle periodogram, including a fast O[NlogN] implementation following the algorithm presented by Press & Rybicki [3].

The code here is adapted from the gatspy package, described in [4] and [5].

• Quick Start Guide
• Periodogram Implementations
• Example

Literature References

[1]	Lomb, N.R. “Least-squares frequency analysis of unequally spaced data”. Ap&SS 39 pp. 447-462 (1976)

[2]	Scargle, J. D. “Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data”. ApJ 1:263 pp. 835-853 (1982)

[3]	Press W.H. and Rybicki, G.B, “Fast algorithm for spectral analysis of unevenly sampled data”. ApJ 1:338, p. 277 (1989)

[4]	VanderPlas, J. “Gatspy: General Tools for Astronomical Time Series in Python” (2015) http://dx.doi.org/10.5281/zenodo.14833

[5]	VanderPlas, J. & Ivezic, Z. “Periodograms for Multiband Astronomical Time Series”. ApJ 812.1:18 (2015)

API Reference

lombscargle Package

lombscargle

AstroPy-compatible implementation of the Lomb-Scargle periodogram.

Classes

LombScargle(t, y[, dy, fit_bias, center_data])

Compute the Lomb-Scargle Periodogram

Class Inheritance Diagram

lombscargle.implementations Package

Various implementations of the Lomb-Scargle Periodogram

Functions

lombscargle(t, y[, dy, frequency, method, ...])	Compute the Lomb-scargle Periodogram with a given method.
lombscargle_chi2(t, y, dy, frequency[, ...])	Lomb-Scargle Periodogram
lombscargle_fast(t, y, dy, f0, df, Nf[, ...])	Fast Lomb-Scargle Periodogram
lombscargle_fastchi2(t, y, dy, f0, df, Nf[, ...])	Lomb-Scargle Periodogram
lombscargle_scipy(t, y, frequency[, ...])	Lomb-Scargle Periodogram
lombscargle_slow(t, y, dy, frequency[, ...])	Lomb-Scargle Periodogram

lombscargle.heuristics Module

Functions

baseline_heuristic(n_samples, baseline[, ...])	Use a heuristic to compute a frequency grid.
get_heuristic(heuristic)