firecrown.models.cluster.abundance.ClusterAbundance

class firecrown.models.cluster.abundance.ClusterAbundance(min_mass, max_mass, min_z, max_z, halo_mass_function)

Bases: Updatable

The class that calculates the predicted number counts of galaxy clusters.

The abundance is a function of a specific cosmology, a mass and redshift range, an area on the sky, a halo mass function, as well as multiple kernels, where each kernel represents a different distribution involved in the final cluster abundance integrand.

Parameters:

• min_mass (float) –

• max_mass (float) –

• min_z (float) –

• max_z (float) –

• halo_mass_function (MassFunc) –

Public Methods:

__init__(min_mass, max_mass, min_z, max_z, ...)	Updatable initialization.
update_ingredients(cosmo)	Update the cluster abundance calculation with a new cosmology.
comoving_volume(z[, sky_area])	The differential comoving volume given area sky_area at redshift z.
mass_function(mass, z)	The mass function at z and mass.

Inherited from Updatable

__init__([parameter_prefix])	Updatable initialization.
__setattr__(key, value)	Set the attribute named key to the supplied value.
set_parameter(key, value)	Sets the parameter to the given value.
set_internal_parameter(key, value)	Assure this InternalParameter has not already been set, and then set it.
set_sampler_parameter(value)	Assure this SamplerParameter has not already been set, and then set it.
update(params)	Update self by calling to prepare for the next MCMC sample.
is_updated()	Determine if the object has been updated.
reset()	Reset the updatable.
required_parameters()	Returns a RequiredParameters object.
get_derived_parameters()	Returns a collection of derived parameters.

Private Methods:

Inherited from Updatable

_update(params)	Method for auxiliary updates to be made to an updatable.
_reset()	Abstract method implemented by all concrete classes to update self.
_required_parameters()	Return a RequiredParameters object containing the information for this class.
_get_derived_parameters()	Returns the derived parameters of an implementation.

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comoving_volume(z, sky_area=0)

The differential comoving volume given area sky_area at redshift z.

Parameters:

• sky_area (float) – The area of the survey on the sky in square degrees.

• z (ndarray[Any, dtype[float64]]) –

Return type:

ndarray[Any, dtype[float64]]

property cosmo: pyccl.cosmology.Cosmology

The cosmology used to predict the cluster number count.

mass_function(mass, z)

The mass function at z and mass.

Parameters:

• mass (ndarray[Any, dtype[float64]]) –

• z (ndarray[Any, dtype[float64]]) –

Return type:

ndarray[Any, dtype[float64]]

update_ingredients(cosmo)

Update the cluster abundance calculation with a new cosmology.

Parameters:

cosmo (Cosmology) –

Return type:

None