plancklens.filt.filt_cinv

conjugate gradient solver CMB filtering module.

class plancklens.filt.filt_cinv.cinv_p(lib_dir, lmax, nside, cl, transf, ninv, pcf='default', chain_descr=None, transf_blm=None, marge_qmaps=(), marge_umaps=())

Polarization-only inverse-variance (or Wiener-)filtering instance.

Parameters:

• lib_dir – mask and other things will be cached there

• lmax – filtered alm’s are reconstructed up to lmax

• nside – healpy resolution of maps to filter

• cl – fiducial CMB spectra used to filter the data (dict with ‘tt’ key)

• transf – CMB E-mode polarization transfer function (array)

• ninv – inverse pixel variance maps. Must be a list of either 3 (QQ, QU, UU) or 1 (QQ = UU noise) elements. These element are themselves list of paths or of healpy maps with consistent nside.

• transf_blm (optional) – B-polarization transfer function (if different from E-mode one)

Note

This implementation now supports template projection

class plancklens.filt.filt_cinv.cinv_t(lib_dir, lmax, nside, cl, transf, ninv, rescal_cl='default', marge_monopole=True, marge_dipole=True, marge_maps=(), pcf='default', chain_descr=None)

Temperature-only inverse-variance (or Wiener-)filtering instance.

Parameters:

• lib_dir – mask and other things will be cached there

• lmax – filtered alm’s are reconstructed up to lmax

• nside – healpy resolution of maps to filter

• cl – fiducial CMB spectra used to filter the data (dict with ‘tt’ key)

• transf – CMB maps transfer function (array)

• ninv – inverse pixel variance map. Must be a list of paths or of healpy maps with consistent nside.

• rescal_cl – isotropic rescaling of the map prior the cg inversion. This just makes the convergence criterium change a bit

Note

The only difference of the original plancklens filter is the rescaling of the maps. In effect, the modes of :math’D_ell rather than :math’C_ell are reconstructed This changes nothing to the iterations, but gives the dot product testing for convergence more sensitvity to relevant scales

class plancklens.filt.filt_cinv.library_cinv_jTP(lib_dir: str, sim_lib, cinv_jtp: cinv_tp, cl_weights: dict, soltn_lib=None)

Library to perform inverse-variance filtering of a simulation library.

Suitable for separate temperature and polarization filtering.

Parameters:

• lib_dir (str) – a place to cache the maps

• sim_lib – simulation library instance (requires get_sim_tmap, get_sim_pmap methods)

• cinv_jtp – temperature and pol joint filtering library

• cl_weights – spectra used to build the Wiener filtered leg from the inverse-variance maps

• soltn_lib (optional) – simulation libary providing starting guesses for the filtering.

get_fal(lmax=None)

Isotropic matrix approximation to inverse variance filtering

\(F_\ell \sim (C_\ell + N_\ell / b_\ell^2)^{-1}\)

Output is dictionary with the usual ‘tt’, ‘ee’, ‘te’, ‘bb’, … keys.

class plancklens.filt.filt_cinv.library_cinv_sepTP(lib_dir, sim_lib, cinvt: cinv_t, cinvp: cinv_p, cl_weights: dict, soltn_lib=None)

Library to perform inverse-variance filtering of a simulation library.

Suitable for separate temperature and polarization filtering.

Parameters:

• lib_dir (str) – a

• sim_lib – simulation library instance (requires get_sim_tmap, get_sim_pmap methods)

• cinvt – temperature-only filtering library

• cinvp – poalrization-only filtering library

• soltn_lib (optional) – simulation libary providing starting guesses for the filtering.

get_fbl(lmax=None)

Isotropic approximation to B-polarization inverse variance filtering.

\(F^{B}_\ell = (C_\ell^{BB} + N^{B}_\ell / b_\ell^2)^{-1}\)

get_fel(lmax=None)

Isotropic approximation to E-polarization inverse variance filtering.

\(F^{E}_\ell = (C_\ell^{EE} + N^{E}_\ell / b_\ell^2)^{-1}\)

get_ftl(lmax=None)

Isotropic approximation to temperature inverse variance filtering.

\(F^{T}_\ell = (C_\ell^{TT} + N^{T}_\ell / b_\ell^2)^{-1}\)