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.

gwsnr calculates gravitational-wave (GW) Signal-to-Noise Ratio (SNR), essential for population simulations and hierarchical Bayesian inference with selection effects. The package eliminates computational bottlenecks through sophisticated interpolation techniques, Just-in-Time (JIT) compilation, and parallel processing. It offers multiple optimized backends tailored for different hardware configurations: a numba backend for multi-threaded CPU performance and JAX (ascl:2111.002) and mlx backends for GPU acceleration. gwsnr integrates easily into existing workflows. It is used by the LeR package (ascl:2503.040) for simulating lensed and unlensed GWs, allowing researchers to incorporate fast SNR computations with minimal overhead.