firecrown.models.cluster.recipes.cluster_recipe.ClusterRecipe

class firecrown.models.cluster.recipes.cluster_recipe.ClusterRecipe(parameter_prefix=None)

Bases: Updatable, ABC

Abstract class defining a cluster recipe.

A cluster recipe is a combination of different cluster theoretrical predictions and models that produces a single prediction for an observable.

Parameters:

parameter_prefix (None | str) –

Public Methods:

__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.

---

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

Evaluate the theory prediction for this cluster recipe.

Parameters:

• cluster_theory (ClusterAbundance) –

• this_bin (SaccBin) –

• sky_area (float) –

• average_on (None | ClusterProperty) –

Return type:

float