SFoF is a friends-of-friends galaxy cluster detection algorithm that operates in either spectroscopic or photometric redshift space. The linking parameters, both transverse and along the line-of-sight, change as a function of redshift to account for selection effects.

PySAP (Python Sparse data Analysis Package) provides a common API for astronomical and neuroimaging datasets and access to iSAP's (ascl:1303.029) Sparse2D executables with both wrappers and bindings. It also offers a graphical user interface for exploring the provided functions and access to application specific plugins.

ShapePipe processes single-exposure images and stacked images. Input images have to be calibrated beforehand for astrometry and photometry. The code can handle different image and file types, such as single-exposure mosaic, single-exposure single-CCD, stacked images, database catalog files, and PSF files, some of which are created by the pipeline during the analysis, among others. The end product of ShapePipe is a final catalog containing information for each galaxy, including its shape parameters and the ellipticity components :math:e_1 and :math:e_2. This catalog also contains shapes of artificially sheared images. This information is used in post-processing to compute calibrated shear estimates via metacalibration.

sf_deconvolve performs PSF deconvolution using a low-rank approximation and sparsity. It can handle a fixed PSF for the entire field or a stack of PSFs for each galaxy position. The code accepts Numpy binary files or FITS as input, takes the observed (i.e. with PSF effects and noise) stack of galaxy images and a known PSF, and attempts to reconstruct the original images. sf_deconvolve can be run in a terminal or in an active Python session, and includes options for initialization, optimization, low-Rank approximation, sparsity, PSF estimation, and other attributes.