plancklens.qest

Quadratic estimation implementation module.

qest.eval_qe(qe_key, lmax_ivf, cls_weight, get_alm, nside, lmax_qlm, verbose=True, get_alm2=None, transf=None)[source]

Evaluates a quadratic estimator gradient and curl terms.

(see ‘library’ below for QE estimation coupled to CMB inverse-variance filtered simulation libraries, whose implementation can be faster for some estimators.)

Parameters:

  • qe_key – QE key defining the estimator (as defined in the qresp module), e.g. ‘ptt’ for lensing TT estimator

  • lmax_ivf – CMB multipoles up to lmax are used in the QE

  • cls_weight – set of CMB spectra entering the QE estimator weights

  • get_alm – callable with ‘t’, ‘e’, ‘b’ arguments, returning the corresponding inverse-variance filtered CMB map

  • nside – the estimator are calculated in position space at healpy resolution nside.

  • lmax_qlm – gradient and curl terms are obtained up to multipole lmax_qlm.

  • get_alm2 – maps for second leg if different from first. The estimator is symmetrized

Returns:

glm and clm healpy arrays (gradient and curl terms of the QE estimate)

class qest.library(lib_dir, ivfs1, ivfs2, nside, clte=None, lmax_qlm=None, resplib=None)[source]

From two inverse-variance filtered CMB simulation libraries returns a QE evaluation library.

Parameters:

  • lib_dir – QE estimates will be stored there.

  • ivfs1 – inverse-variance filtering instance of the QE 1st leg.

  • ivfs2 – inverse-variance filtering instance of the QE 2nd leg.

  • nside – QE estimates are obtained from real-space healpy maps at resolution nside

  • clte (optional) – TE CMB spectrum weight. If set this is used to build \(X^{\rm WF}\) from \(\bar X\). Defaults to None, which is adequate for the MV estimator if T and P maps are jointly filtered.

  • lmax_qlm (optional) – QE estimates are computed up to multipole lmax_qlm (defaults to 3 * nside -1).

  • resplib (optional) – response library with get_response methods. Only used for bias_hardened estimators.

get_sim_qlm(k, idx, lmax=None)[source]

Returns a QE estimate, by computing and caching it if not done previously.

Parameters:

  • k – quadratic estimator key

  • idx – simulation index

  • lmax – optionally reduces the lmax of the output healpy array.

get_sim_qlm_mf(k, mc_sims, lmax=None)[source]

Returns a QE mean-field estimate, by averaging QE estimates from a set simulations (caches the result).

Parameters:

  • k – quadratic estimator key

  • mc_sims – simulation indices to use for the estimate.

  • lmax – optionally reduces the lmax of the output healpy array.