firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe

class firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe

Bases: ClusterRecipe

Cluster recipe with Murata19 mass-richness and spec-zs.

This recipe uses the Murata 2019 binned mass-richness relation and assumes perfectly measured spec-zs.

Public Methods:

__init__()	Updatable initialization.
get_theory_prediction(cluster_theory[, ...])	Get a callable that evaluates a cluster theory prediction.
get_function_to_integrate(prediction)	Returns a callable function that can be evaluated by an integrator.
evaluate_theory_prediction(cluster_theory, ...)	Evaluate the theory prediction for this cluster recipe.

Inherited from ClusterRecipe

__init__([parameter_prefix])	Updatable initialization.
evaluate_theory_prediction(cluster_theory, ...)	Evaluate the theory prediction for this cluster recipe.

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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evaluate_theory_prediction(cluster_theory, this_bin, sky_area, average_on=None)

Evaluate the theory prediction for this cluster recipe.

Evaluate the theoretical prediction for the observable in the provided bin using the Murata 2019 binned mass-richness relation and assuming perfectly measured redshifts.

Parameters:

• cluster_theory (ClusterAbundance) –

• this_bin (NDimensionalBin) –

• sky_area (float) –

• average_on (None | ClusterProperty) –

Return type:

float

get_function_to_integrate(prediction)

Returns a callable function that can be evaluated by an integrator.

This function is responsible for mapping arguments from the numerical integrator to the arguments of the theoretical prediction function.

Parameters:

prediction (Callable[[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], tuple[float, float], float], ndarray[Any, dtype[float64]]]) –

Return type:

Callable[[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

get_theory_prediction(cluster_theory, average_on=None)

Get a callable that evaluates a cluster theory prediction.

Returns a callable function that accepts mass, redshift, mass proxy limits, and the sky area of your survey and returns the theoretical prediction for the expected number of clusters.

Parameters:

• cluster_theory (ClusterAbundance) –

• average_on (None | ClusterProperty) –

Return type:

Callable[[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], tuple[float, float], float], ndarray[Any, dtype[float64]]]