pcl_like module

Contains functions relating to the exact pseudo-Cl likelihood.

pcl_like.marginal_alm_cov(cl_path, w0_path, lmax_in, lmin_in, lmax_out, lmin_out, save_path)

Calculate and save the marginal pseudo-alm covariance for each l from lmin_out to lmax_out, including mixing to/from all l from lmin_in to lmax_in.

Parameters

  • cl_path (str) – Path to theory power spectrum.

  • w0_path (str) – Path to W0 object output by pseudo_cl_likelihood.mask_to_w.combine_w_files.

  • lmax_in (int) – Maximum l to include mixing to/from.

  • lmin_in (int) – Minimum l to include mixing to/from.

  • lmax_out (int) – Maximum l to save marginal pseudo-alm covariance for.

  • lmin_out (int) – Minimum l to save marginal pseudo-alm covariance for.

  • save_path (str) – Path to save all covariances to.

pcl_like.marginal_cl_like(l, alm_cov, steps=100000)

Returns the exact cut-sky marginal likelihood distribution for a single l, given a pseudo-alm covariance matrix.

Parameters

  • l (int) – The l to calculate the marginal likelihood for.

  • alm_cov (2D numpy array) – Pseudo-alm covariance for this l.

  • steps (int, optional) – Resolution of the characteristic function, which translates to range in the likelihood (default 100000).

pcl_like.marginal_cl_likes(covs_path, save_path)

Wrapper for marginal_cl_like to generate and save the marginal pseudo-Cl likelihood for each l, using the pseudo-alm covariances output by marginal_alm_cov.

Parameters

  • covs_path (str) – Path to pseudo-alm covariance matrices as output by marginal_alm_cov.

  • save_path (str) – Path to save output.