IQRM implements the Inter-Quartile Range Mitigation (IQRM) interference flagging algorithm for radio pulsar and transient searches. This module provides only the algorithm that infers a channel mask from some spectral statistic that measures the level of RFI contamination in a time-frequency data block. It should be useful as a reference implementation to developers who wish to integrate IQRM into an existing pipeline or search code.

riptide implements the Fast Folding Algorithm (FFA) to identify periodic signals from time series data. In order to identify faint pulsars, the code provides access to a library of functions and classes for processing dedispersed radio signals. The FFA approaches the theoretical optimum for sensitivity to periodic signals regardless of pulse period and duty cycle.

clfd (clean folded data) implements GPU-accelerated smart interference removal algorithms to be used on folded pulsar search and pulsar timing data. The code converts each source profile to a small set of representative features, flagging outliers in the resulting feature space. clfd further visualizes the outlier flagging process, as well as the resulting two-dimensional time-frequency mask that is applied to the clean archive. The code provides access to cleaning algorithms that were initially developed for the High Time Resolution Universe (HTRU) survey which found several pulsars.

ionFR calculates the amount of ionospheric Faraday rotation for a specific epoch, geographic location, and line-of-sight. The code uses a number of publicly available, GPS-derived total electron content maps and the most recent release of the International Geomagnetic Reference Field. ionFR can be used for the calibration of radio polarimetric observations; its accuracy had been demonstrated using LOFAR pulsar observations.

SeeKAT is a Python implementation of a novel maximum-likelihood estimation approach to localizing transients and pulsars detected in multiple MeerKAT tied-array beams at once to (sub-)arcsecond precision. It reads in list of detections (RA, Dec, S/N) and the beam PSF and computes a covariance matrix of the S/N value ratios, assuming 1-sigma Gaussian errors on each measurement. It models the aggregate beam response by arranging beam PSFs appropriately relative to each other and calculates a likelihood distribution of obtaining the observed S/N in each beam according to the modeled response. In addition, SeeKAT can plot the likelihood function over RA and Dec with 1-sigma uncertainty, overlaid on the beam coordinates and sizes.