firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe#
- class firecrown.models.cluster.recipes.murata_binned_spec_z.MurataBinnedSpecZRecipe[source]#
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
(key, 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.
Determine if the object has been updated.
reset
()Reset the updatable.
Returns a RequiredParameters object.
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.
Return a RequiredParameters object containing the information for this class.
Returns the derived parameters of an implementation.
- evaluate_theory_prediction(cluster_theory, this_bin, sky_area, average_on=None)[source]#
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 (
Optional
[ClusterProperty
]) –
- Return type:
float
- get_function_to_integrate(prediction)[source]#
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)[source]#
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 (
Optional
[ClusterProperty
]) –
- Return type:
Callable
[[ndarray
[Any
,dtype
[float64
]],ndarray
[Any
,dtype
[float64
]],tuple
[float
,float
],float
],ndarray
[Any
,dtype
[float64
]]]