def get_nhl(qe_key1, qe_key2, cls_weights, cls_ivfs, lmax_ivf1, lmax_ivf2,
lmax_out=None, lmax_ivf12=None, lmax_ivf22=None, cls_ivfs_bb=None, cls_ivfs_ab=None):
"""(Semi-)Analytical noise level calculation for the cross-spectrum of two QE keys.
Args:
qe_key1: QE key 1
qe_key2: QE key 2
cls_weights: dictionary with the CMB spectra entering the QE weights.
(expected are 'tt', 'te', 'ee' when/if relevant)
cls_ivfs: dictionary with the inverse-variance filtered CMB spectra.
(expected are 'tt', 'te', 'ee', 'bb', 'tb', 'eb' when/if relevant)
lmax_ivf1: QE 1 uses CMB multipoles down to lmax_ivf1.
lmax_ivf2: QE 2 uses CMB multipoles down to lmax_ivf2.
lmax_out(optional): outputs are calculated down to lmax_out. Defaults to lmax_ivf1 + lmax_ivf2
Outputs:
4-tuple of gradient (G) and curl (C) mode Gaussian noise co-variances GG, CC, GC, CG.
"""
if lmax_ivf12 is None: lmax_ivf12 = lmax_ivf1
if lmax_ivf22 is None: lmax_ivf22 = lmax_ivf2
qes1 = qresp.get_qes(qe_key1, lmax_ivf1, cls_weights, lmax2=lmax_ivf12)
qes2 = qresp.get_qes(qe_key2, lmax_ivf2, cls_weights, lmax2=lmax_ivf22)
if lmax_out is None:
lmax_out = max(lmax_ivf1, lmax_ivf12) + max(lmax_ivf2, lmax_ivf22)
return _get_nhl(qes1, qes2, cls_ivfs, lmax_out, cls_ivfs_bb=cls_ivfs_bb, cls_ivfs_ab=cls_ivfs_ab)
def eval_qe(qe_key,
lmax_ivf,
cls_weight,
get_alm,
nside,
lmax_qlm,
verbose=True):
"""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.)
Args:
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.
Returns:
glm and clm healpy arrays (gradient and curl terms of the QE estimate)
"""
qe_list = qresp.get_qes(qe_key, lmax_ivf, cls_weight)
return uqe.qe_eval(qe_list, nside, get_alm, lmax_qlm, verbose=verbose)