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GWFAST forecasts the signal-to-noise ratios and parameter estimation capabilities of networks of gravitational-wave detectors, based on the Fisher information matrix approximation. It is designed for applications to third-generation gravitational-wave detectors. It is based on Automatic Differentiation, which makes use of the library JAX (ascl:2111.002). This allows efficient parallelization and numerical accuracy. The code includes a module for parallel computation on clusters.
WF4Py implements frequency-domain gravitational wave waveform models in pure Python, thus enabling parallelization over multiple events at a time. Waveforms in WF4Py are built as classes; the functions take dictionaries containing the parameters of the events to analyze as input and provide Fourier domain waveform models. All the waveforms are accurately checked with their implementation in LALSuite (ascl:2012.021) and are a core element of GWFAST (ascl:2212.001).