tjpcov.covariance_cluster_mass

Classes

ClusterMass

Calculate the covariance of cluster mass measurements.

Module Contents

class tjpcov.covariance_cluster_mass.ClusterMass(config, min_halo_mass=10000000000000.0)

Bases: tjpcov.covariance_builder.CovarianceBuilder

Calculate the covariance of cluster mass measurements.

This class is able to compute the covariance for _tracers_types = (“cluster_mean_log_mass”, “cluster_mean_log_mass”)

Class to calculate the covariance of cluster mass measurements.

Parameters:

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

• min_halo_mass (float, optional) – Minimum halo mass.

space_type = 'Fourier'

cov_type = 'gauss'

_tracer_types

Tuple with the tracer types (e.g. (“cl”, “cl”)).

This is used to decide if the block covariance should be computed or is zero. For instance, if the class is meant to produce the covariance for Cells and the tracer types are clusters, the class should return 0.

overdensity_delta = 200

h0

fft_helper

covariance_block_data_type

The covariance block data type for your builder.

Returns:

Covariance block sacc data type

Return type:

str

load_from_cosmology(cosmo)

Load parameters from a CCL cosmology object.

Derived attributes from the cosmology are set here.

Parameters:

cosmo (pyccl.Cosmology) – Input cosmology

load_from_sacc(sacc_file, min_halo_mass)

Load and set class attributes based on data from a SACC file.

Cluster covariance has special parameters set in the SACC file. This informs the code that the data to calculate the cluster covariance is there. We set extract those values from the sacc file here, and set the attributes here.

Parameters:

• ( (sacc_file) – obj: sacc.sacc.Sacc): SACC file object, already

• loaded. –

_get_covariance_block_for_sacc(tracer_comb1, tracer_comb2, **kwargs)

Compute a single covariance entry ‘cluster_mean_log_mass’

Parameters:

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

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

Returns:

Covariance for a single block

Return type:

array_like

get_covariance_block(tracer_comb1, tracer_comb2, **kwargs)

Compute a single covariance entry ‘cluster_mean_log_mass’

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)

Compute a single covariance entry ‘cluster_mean_log_mass’

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