PURIFY is a collection of routines written in C that implements different tools for radio-interferometric imaging including file handling (for both visibilities and fits files), implementation of the measurement operator and set-up of the different optimization problems used for image deconvolution. The code calls the generic Sparse OPTimization (SOPT) (ascl:1307.020) package to solve the imaging optimization problems.

sic (Sparse Inpainting Code) generates Gaussian, isotropic CMB realizations, masks them, and recovers the large-scale masked data using sparse inpainting; it is written in Fortran90.

COMB supports the simulation on the sphere of compact objects embedded in a stochastic background process of specified power spectrum. Support is provided to add additional white noise and convolve with beam functions. Functionality to support functions defined on the sphere is provided by the S2 code (ascl:1606.008); HEALPix (ascl:1107.018) and CFITSIO (ascl:1010.001) are also required.

FastCSWT performs a directional continuous wavelet transform on the sphere. The transform is based on the construction of the continuous spherical wavelet transform (CSWT) developed by Antoine and Vandergheynst (1999). A fast implementation of the CSWT (based on the fast spherical convolution developed by Wandelt and Gorski 2001) is also provided.

SSHT performs fast and exact spin spherical harmonic transforms; functionality is also provided to perform fast and exact adjoint transforms, forward and inverse transforms, and spherical harmonic transforms for a number of alternative sampling schemes. The code can interface with DUCC (ascl:2008.023) and use it as a backend for spherical harmonic transforms and rotations.

DarkMappy reconstructs maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem in order to implement hybrid Bayesian dark-matter reconstruction techniques on the plane and on the celestial sphere. These convergence maps support principled uncertainty quantification and provide hypothesis testing of structure, from which it is possible to distinguish between physical objects and artifacts of the reconstruction.

harmonic learns an approximate harmonic mean estimator (referred to as a "learnt harmonic mean estimator") from posterior distribution samples to compute the marginal likelihood required for Bayesian model selection. Using a large number of independent Markov chain Monte Carlo (MCMC) chains from another package such as emcee (ascl:1303.002), harmonic uses importance sampling to learn a new target distribution in order to optimize an approximate harmonic estimator while minimizing its variance.

FLAGLET computes flaglet transforms with arbitrary spin direction, probing the angular features of this generic wavelet transform for rapid analysis of signals from wavelet coefficients. The code enables the decomposition of a band-limited signal into a set of flaglet maps that capture all information contained in the initial band-limited map, and it can reconstruct the individual flaglets at varying resolutions. FLAGLET relies upon the SSHT (ascl:2207.034), S2LET (ascl:1211.001), and SO3 codes to provide angular transforms and sampling theorems, as well as the FFTW (ascl:1201.015) code to compute Fourier transforms.

QuantifAI reconstructs radio interferometric images using scalable Bayesian uncertainty quantification relying on data-driven (learned) priors. It relies on the convex accelerated optimization algorithms in CRR (ascl:2401.016) and is built on top of PyTorch. QuantifAI also includes MCMC algorithms for posterior sampling.

stringgen creates emulations of cosmic string maps with statistics similar to those of a single (or small ensemble) of reference simulations. It uses wavelet phase harmonics to calculate a compressed representation of these reference simulations, which may then be used to synthesize new realizations with accurate statistical properties, e.g., 2 and 3 point correlations, skewness, kurtosis, and Minkowski functionals.

S2FFT computes Fourier transforms on the sphere and rotation group using JAX (ascl:2111.002) or PyTorch. It leverages autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g., GPUs and TPUs). More specifically, S2FFT provides support for spin spherical harmonic and Wigner transforms (for both real and complex signals), with support for adjoint transformations where needed, and comes with different optimisations (precompute or not) that one may select depending on available resources and desired angular resolution L.