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.

MADLens produces non-Gaussian cosmic shear maps at arbitrary source redshifts. A MADLens simulation with only 256^3 particles produces convergence maps whose power agree with theoretical lensing power spectra up to scales of L=10000. The code is based on a highly parallelizable particle-mesh algorithm and employs a sub-evolution scheme in the lensing projection and a machine-learning inspired sharpening step to achieve these high accuracies.