nbodykit provides algorithms for analyzing cosmological datasets from N-body simulations and large-scale structure surveys, and takes advantage of the abundance and availability of large-scale computing resources. The package provides a unified treatment of simulation and observational datasets by insulating algorithms from data containers, and reduces wall-clock time by scaling to thousands of cores. All algorithms are parallel and run with Message Passing Interface (MPI); the code is designed to be deployed on large super-computing facilities. nbodykit offers an interactive user interface that performs as well in a Jupyter notebook as on super-computing machines.

pyRSD computes the theoretical predictions of the redshift-space power spectrum of galaxies. It also includes functionality for fitting data measurements and finding the optimal model parameters, using both MCMC and nonlinear optimization techniques.

kdcount provides a simple API for brute-force spatial pair-counting in low-dimensional data sets using a KD-tree to prune the search space. For a given distance threshold D, kdcount invokes a user-supplied callback for each pair of points whose separation falls within D, enabling custom counting or correlation statistics. The Python interface additionally supports clustering via a Friend-of-Friend algorithm and can exploit shared-memory parallelism with installation of an optional package. Periodic boundary conditions are supported, making kdcount suitable for analyses in periodic domains. While the implementation admits internal “smarter” algorithms, only the standard brute-force mode is tested and documented; the design emphasizes simplicity and flexibility over asymptotic optimality.