tjpcov.covariance_fourier_ssc#

Module Contents#

Classes#

FourierSSCHaloModel

Class to compute the CellxCell Halo Model Super Sample Covariance.
class tjpcov.covariance_fourier_ssc.FourierSSCHaloModel(config)[source]#

Bases: tjpcov.covariance_builder.CovarianceFourier

Class to compute the CellxCell Halo Model Super Sample Covariance.

The SSC is computed in CCL with the “linear bias” approximation using pyccl.halos.halo_model.halomod_Tk3D_SSC_linear_bias().

Initialize the class with a config file or dictionary.

Parameters:

config (dict or str) – If dict, it returns the configuration dictionary directly. If string, it asumes a YAML file and parses it.

cov_type = 'SSC'[source]#
get_covariance_block(tracer_comb1, tracer_comb2, integration_method=None, include_b_modes=True)[source]#

Compute a single SSC covariance matrix for a given pair of C_ell.

If outdir is set, it will save the covariance to a file called ssc_tr1_tr2_tr3_tr4.npz. This file will be read and its output returned if found.

Blocks of the B-modes are assumed 0 so far.

Parameters:

  • tracer_comb1 (list) – List of the pair of tracer names of C_ell^1

  • tracer_comb2 (list) – List of the pair of tracer names of C_ell^2

  • integration_method (str, optional) – integration method to be used for the Limber integrals. Possibilities: ‘qag_quad’ (GSL’s qag method backed up by quad when it fails) and ‘spline’ (the integrand is splined and then integrated analytically). If given, it will take priority over the specified in the configuration file through config[‘SSC’][‘integration_method’]. Elsewise, it will use ‘qag_quad’.

  • include_b_modes (bool, optional) – If True, return the full SSC with zeros in for B-modes (if any). If False, return the non-zero block. This option cannot be modified through the configuration file to avoid breaking the compatibility with the NaMaster covariance. Defaults to True.

Returns:

Super sample covariance matrix for a pair of C_ell.

Return type:

array