What's Changed

• Update README.md by @ggalloni in #34
• Bug fix by @ggalloni in #35
• Update init.py by @ggalloni in #36
• Bug fix by @ggalloni in #37
• Bug fix and added long description by @ggalloni in #38
• Bug fix and added custom mapping by @ggalloni in #39
• Update docs by @ggalloni in #40
• Update docs by @ggalloni in #41
• Develop by @ggalloni in #42
• Making some function public, first commit by @ggalloni in #43
• Other functions made public by @ggalloni in #44
• Update init by @ggalloni in #45
• txt 2 dict conversion handled externally by @ggalloni in #46
• Update samplingTTTEEE.py by @ggalloni in #47
• Update docs by @ggalloni in #50
• Bug fix and added exclusion of specific probes by @ggalloni in #51
• Update version by @ggalloni in #52
• Update docs by @ggalloni in #53
• Bug fix by @ggalloni in #54
• Bug fix and made data vector computation more vectorial by @ggalloni in #55
• Public functions, biased fiducials, type hinting by @ggalloni in #56
• Hotfix: added missing fsky in exact and added correlated Gaussian case by @ggalloni in #58
• Update README.md by @ggalloni in #59
• Typos in README by @ggalloni in #60
• Typo README by @ggalloni in #61
• Update docs by @ggalloni in #62
• Update docs by @ggalloni in #63
• Merge new likelihoods to master branch by @ggalloni in #64
• update develop by @ggalloni in #66
• Add binning algebra by @ggalloni in #67
• Modernization of the codebase by @ggalloni in #65

Full Changelog: v1.1.0...v1.3.0

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.