RedNoiseFALs calculates red-noise-based false alarm levels for Lomb–Scargle periodograms of unevenly sampled astrophysical time series. The Python package provides a core module and example Jupyter notebooks that demonstrate how to compute and interpret frequency-dependent false alarm thresholds for specific data sets. Users import the module from the source directory and can adapt the notebook workflows to apply the method to their own time series.

NWelch uses Welch's method to estimate the power spectra, complex cross-spectrum, magnitude-squared coherence, and phase spectrum of bivariate time series with nonuniform observing cadence. For univariate time series, users can apply the Welch's power spectrum estimator or compute a nonuniform fast Fourier transform-based periodogram. Options include tapering in the time domain and computing bootstrap false alarm levels. Users may choose standard 50%-overlapping Welch's segments or apply a custom-made segmentation scheme. NWelch was designed for Doppler planet searches but may be applied to any type of time series.