tjpcov.covariance_cluster_counts_gaussian#

Module Contents#

Classes#

ClusterCountsGaussian

Implementation of gaussian covariance term.
class tjpcov.covariance_cluster_counts_gaussian.ClusterCountsGaussian(config)[source]#

Bases: tjpcov.covariance_cluster_counts.CovarianceClusterCounts

Implementation of gaussian covariance term.

This class calculates the gaussian (shot-noise) contribution to the autocorrelation of cluster counts (NxN).

Class to calculate the gaussian covariance of cluster counts

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 = 'gauss'[source]#
_get_covariance_block_for_sacc(tracer_comb1, tracer_comb2, **kwargs)[source]#

Compute a single covariance entry ‘clusters_redshift_richness’

Parameters:

  • tracer_comb1 (tuple of str) – e.g. (‘survey’, ‘bin_richness_1’, ‘bin_z_0’)

  • tracer_comb2 (tuple of str) – e.g. (‘survey’, ‘bin_richness_0’, ‘bin_z_0’)

Returns:

Covariance for a single block

Return type:

array_like

_get_covariance_gaussian(tracer_comb1, tracer_comb2)[source]#

Compute a single covariance entry ‘clusters_redshift_richness’

Parameters:

  • tracer_comb1 (tuple of str) – e.g. (‘survey’, ‘bin_richness_1’, ‘bin_z_0’)

  • tracer_comb2 (tuple of str) – e.g. (‘survey’, ‘bin_richness_0’, ‘bin_z_0’)

Returns:

Covariance for a single block

Return type:

float

get_covariance_block(tracer_comb1, tracer_comb2, **kwargs)[source]#

Compute a single covariance entry ‘clusters_redshift_richness’

Parameters:

  • tracer_comb1 (tuple of str) – e.g. (‘clusters_0_0’,)

  • tracer_comb2 (tuple of str) – e.g. (‘clusters_0_1’,)

Returns:

Covariance for a single block

Return type:

array_like

shot_noise(z_i, lbd_i)[source]#

Returns the cluster shot noise contribution to the covariance.

The covariance of number counts is a sum of a super sample covariance (SSC) term plus a gaussian diagonal term. The diagonal term is also referred to as “shot noise” which we compute here.

Parameters:

  • z_i (int) – redshift bin i

  • lbd_i (int) – richness bin i

Returns:

Gaussian covariance contribution

Return type:

float