AniCosmo provides functionality to perform a Bayesian analysis of anisotropic cosmologies. It has been used to study Bianchi models of universal rotation in a number of papers.
Bayesian analysis of anisotropic cosmologies: Bianchi VII_h and WMAP; Planck 2013 results: Background geometry and topology of the Universe; Planck 2015 results: Background geometry and topology of the Universe; A framework for testing isotropy with the cosmic microwave background; How isotropic is the universe?

The BIANCHI2 code provides functionality to support the simulation of Bianchi Type $\text{VII}_h$ induced temperature fluctuations in CMB maps of a universe with shear and rotation.
The implementation is based on the solutions to the Bianchi models derived by Anthony Lasenby (not published) that incorporate a cosmological constant. Functionality is provided to compute the induced fluctuations on the sphere directly in either real or harmonic space.
Functionality to support functions defined on the sphere is provided by the S2 code.

The BIANCHI code provides functionality to support the simulation of Bianchi Type $\text{VII}_h$ induced temperature fluctuations in CMB maps of a universe with shear and rotation.
The implementation is based on the solutions to the Bianchi models derived by Barrow et al. (1985), which do not incorporate any dark energy component. Functionality is provided to compute the induced fluctuations on the sphere directly in either real or harmonic space. For a description of the equations implemented to perform the Bianchi simulations see the Appendix of our paper: Non-Gaussianity detections in the Bianchi $\text{VII}_h$ corrected WMAP 1-year data made with directional spherical wavelets.

COMB provides functionality to support 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.
COMB is typically used with the S2FIL code to generate template functions for the construction of optimal filters, such as the directional matched filter (MF) and scale adaptive filter (SAF) on the sphere, and to general simulated data with which to test object decection algorithms.

FastCSWT provides functionality to perform a fast 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). Since the transform is based on a continuous framework, while it is possible to compute a wavelet transform of a signal of interest, it is not possible to reconstruct that signal from its wavelet coefficients. It order to consider exact synthesis, please see our S2LET code, which is based on an alternative scale-discretised wavelet framework on the sphere.

FLAG provides a fast implementation of the Fourier-Laguerre transform, a novel 3D transform exploiting an exact quadrature rule on the ball to construct an exact harmonic transform in 3D spherical coordinates.
The angular part of the Fourier-Laguerre transform uses the MW sampling theorem and the exact spherical harmonic transform implemented in the SSHT code.
The radial sampling scheme arises from an exact quadrature on the radial half-line using damped Laguerre polynomials.

FLAGLET provides efficient routines for the wavelet analysis of signals on the ball, the space formed by augmenting the sphere with the radial half-line.
FLAGLET exploits the S2LET, FLAG and SSHT codes to assist with wavelet construction and support harmonic transforms on the ball and sphere, respectively.
The flaglet transform is theoretically exact, i.e. the original signal can be synthesised from its wavelet coefficients exactly since the wavelet coefficients capture all the information of band-limited signals.

Today’s AI techniques have shown remarkable performance and are changing the world in which we live. However, the deep learning techniques that are leading to this AI revolution simply do not work with spherical data.
fourpiAI unlocks the remarkable potential of deep learning for problems involving spherical data, in virtual reality and beyond.
For further details on some of the techniques implemented in fourpiAI see our related paper: Efficient generalized spherical CNNs.

The Generalised Lensing and Shear Spectra (GLaSS) code provides functionality to compute spherical Bessel, tomographic and generalised lensing spectra for weak gravitational lensing (i.e. cosmic shear) studies.
GLaSS provides a module for Cosmosis and is called by Cosmosis. Run-mode options are specified in a Cosmosis ini file. GLaSS can either be run like any other Cosmosis module or interactively in Python. See the jupyter notebooks to see how to run interactively.

massmappy provides functionality to recover convergence mass maps on the celestial sphere from weak lensing cosmic shear observations. At present the spherical Kaiser-Squires estimator is implemented. Additional estimators will be added in future.
massmappy also supports numerous projection operator to project the sphere to planar regions for comparison with the spherical Kaiser-Squires estimator.
massmappy relies on the SSHT (built on the MW sampling theorem) and HEALPIX codes to handle sampled data on the sphere.

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