firecrown.likelihood.gauss_family.statistic.binned_cluster_number_counts.BinnedClusterNumberCounts

class firecrown.likelihood.gauss_family.statistic.binned_cluster_number_counts.BinnedClusterNumberCounts(cluster_properties, survey_name, cluster_recipe, systematics=None)

Bases: Statistic

The Binned Cluster Number Counts statistic.

This class will make a prediction for the number of clusters in a z, mass bin and compare that prediction to the data provided in the sacc file.

Parameters:

• cluster_properties (ClusterProperty) –

• survey_name (str) –

• cluster_recipe (ClusterRecipe) –

• systematics (None | list[SourceSystematic]) –

Public Methods:

__init__(cluster_properties, survey_name, ...)	Updatable initialization.
read(sacc_data)	Read the data for this statistic and mark it as ready for use.
get_data_vector()	Gets the statistic data vector.
get_binned_cluster_property(tools, ...)	Computes the mean mass of clusters in each bin.
get_binned_cluster_counts(tools)	Computes the number of clusters in each bin.

Inherited from Statistic

__init__([parameter_prefix])	Updatable initialization.
read(_)	Read the data for this statistic and mark it as ready for use.
get_data_vector()	Gets the statistic data vector.
compute_theory_vector(tools)	Compute a statistic from sources, applying any systematics.
get_theory_vector()	Returns the last computed theory vector.

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:

_compute_theory_vector(tools)

Compute a statistic from sources, concrete implementation.

Inherited from Statistic

_reset()	Reset this statistic.
_compute_theory_vector(tools)	Compute a statistic from sources, concrete implementation.

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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_compute_theory_vector(tools)

Compute a statistic from sources, concrete implementation.

Parameters:

tools (ModelingTools) –

Return type:

TheoryVector

get_binned_cluster_counts(tools)

Computes the number of clusters in each bin.

Using the data from the sacc file, this function evaluates the likelihood for a single point of the parameter space, and returns the predicted number of clusters in each bin.

Parameters:

tools (ModelingTools) –

Return type:

list[float]

get_binned_cluster_property(tools, cluster_counts, cluster_properties)

Computes the mean mass of clusters in each bin.

Using the data from the sacc file, this function evaluates the likelihood for a single point of the parameter space, and returns the predicted mean mass of the clusters in each bin.

Parameters:

• tools (ModelingTools) –

• cluster_counts (list[float]) –

• cluster_properties (ClusterProperty) –

Return type:

list[float]

get_data_vector()

Gets the statistic data vector.

Return type:

DataVector

read(sacc_data)

Read the data for this statistic and mark it as ready for use.

Parameters:

sacc_data (Sacc) –

Return type:

None