Tempo analyzes pulsar timing data. Pulse times of arrival (TOAs), pulsar model parameters, and coded instructions are read from one or more input files. The TOAs are fitted by a pulse timing model incorporating transformation to the solar-system barycenter, pulsar rotation and spin-down and, where necessary, one of several binary models. Program output includes parameter values and uncertainties, residual pulse arrival times, chi-squared statistics, and the covariance matrix of the model. In prediction mode, ephemerides of pulse phase behavior (in the form of polynomial expansions) are calculated from input timing models. Tempo is the basis for the Tempo2 (ascl:1210.015) code.

PlasmaPy provides core functionality and a common framework for data visualization and analysis for plasma physics. It has modules for basic plasma physics calculations, running desktop-scale simulations to test preliminary ideas such as one-dimensional MHD/PIC or test particles, or comparing data from two different sources, such as simulations and spacecraft.

The VOLK2 (VLBI Observation for transient Localization Keen Searcher) pipeline conducts single pulse searches and localization in regular VLBI observations as well as single pulse detections from known sources in dedicated observations. In VOLKS2, the search and localization are two independent steps. The search step takes the idea of geodetic VLBI post processing, which fully utilizes the cross spectrum fringe phase information to maximize the signal power. Compared with auto spectrum based method, it is able to extract single pulses from highly RFI contaminated data. The localization uses the geodetic VLBI solving methods, which derives the single pulse location by solving a set of linear equations given the relation between the residual delay and the offset to a priori position.

FacetClumps extracts and analyses clumpy structure in molecular clouds. Written in Python and based on the Gaussian Facet model, FacetClumps extracts signal regions using morphology, and segments the signal regions into local regions with a gradient-based method. It then applies a connectivity-based minimum distance clustering method to cluster the local regions to the clump centers. FacetClumps automatically adjusts its parameters to local situations to improve adaptability, and is optimized to detect faint and overlapping clumps.