The deep learning-based framework DeepExtractor reconstructs power excesses, including both signals and glitches, from Gravitational Wave (GW) data. The package includes scripts for generating training data, training models, and for evaluating performance, and contains a tutorial notebook for using DeepExtractor to reconstruct real glitches from O3 data at the LIGO detectors. DeepExtractor facilitates easy reconstruction of glitches and visualization of the results.

LeR calculates detectable rates of gravitational waves events (both lensed and un-lensed events). Written in Python, it performs statistical simulation and forecasting of gravitational wave (GW) events and their rates. The code samples gravitational wave source properties and lens galaxies attributes and source redshifts, and can generate image properties such as source position, magnification, and time delay. The package also calculates detectable merger rates per year. Key features of LeR include efficient sampling, optimized SNR calculations, and systematic archiving of results. LeR is tailored to support both GW population study groups and GW lensing research groups by providing a comprehensive suite of tools for GW event analysis.