Lilit

Now LiLit is installable through pip. It is sufficient to do

pip install lilit

Also, I have implemented a new computation for lmin, lmax and fsky for the cross-correlations.
Files have been reorganized and I added the file containing the sensitivities of some experiment and the _Planck_ 2018 inifile.

Here is the first release of the Likelihood for LiteBIRD (LiLit)!

This repository is meant to be an easy-to-use tool for those who want to start running their MCMCs.

The current version encodes an exact and a Gaussian likelihood (power spectrum based) under LiLit.
The desired likelihood can be defined dynamically on an arbitrary number of fields, with arbitrary lmax and fsky for each.
The fiducial power spectra are automatically computed based on _Planck_ 2018 results. The noise is computed via inverse noise weighting of the channels of the desired experiment. Both can also be passed to the likelihood.

In Template, you can find a verbose version of LiLit and two elementary examples to get used to the Cobaya framework.

In Example, you can find some examples of MCMC runs on BB, TTTEEE, and TTTEEEBB. The Cobaya dictionaries that you can find here are designed to get _Planck_ 2018 compatible results, given that the fiducial spectra are computed considering those results.