Until recently a person's ability to make progress in observational cosmology and extra-galactic observations has been limited primarily  by data access.  For this reason, collaborations have often formed around data access. Examples of this include Sloan, COSMOS, DES and many other. 

Looking forward though, this is changing. With ever increasing high quality archives and the flood of data we expect to see in the coming years (with LSST, Euclid, DESI, WFRIST etc) the rate limiting step is access to methods  rather than access to data.

SkyPy is therefore envisioned to be a collaboration built around methods. Two exciting  and related types of method have  recently emerged (i) Forward Modeling/Likelihood-free inference and (ii) Machine Learning.

The  key to enabling both of these  is the ability to generate realistic simulations. The vision for SkyPy is to build an open-source off-project high quality python package that will contain the functionality needed to make end-to-end simulations that can enable both Forward Modelling and Machine Learning method.

Unequalpy is a package that contains functions to obtain the unequal-time matter power spectrum at one loop in perturbation theory and effective field theory. It also provides functions to reproduce our analysis in de la Bella et al. 2020:

• Approximations to the unequal-time matter power spectrum: the geometric and the midpoint approximations.
• Weak lensing functions: filters and lensing efficiency.
• Tests: using DES-Y1 and cosmoSIS.

                                                                                                                                 The full list of features can be found in the unequalPy Documentation.

The corfu library computes exact equal-time or unequal-time angular correlation functions and power spectra by projecting three-dimensional correlation functions or power spectra onto the sphere in real space. 

The full list of features can be found in the corfu Documentation.