firecrown.models.cluster.recipes.murata_binned_spec_z_deltasigma

Module for defining the classes used in the MurataBinnedSpecZ cluster recipe.

Classes

MurataBinnedSpecZDeltaSigmaRecipe

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

Module Contents

class firecrown.models.cluster.recipes.murata_binned_spec_z_deltasigma.MurataBinnedSpecZDeltaSigmaRecipe[source]

Bases: firecrown.models.cluster.recipes.cluster_recipe.ClusterRecipe

Inheritance diagram of firecrown.models.cluster.recipes.murata_binned_spec_z_deltasigma.MurataBinnedSpecZDeltaSigmaRecipe

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.

integrator
redshift_distribution
mass_distribution
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:
Return type:

Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float, float], numpy.typing.NDArray[numpy.float64]]

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[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float, float], numpy.typing.NDArray[numpy.float64]])

Return type:

Callable[[numpy.typing.NDArray, numpy.typing.NDArray], numpy.typing.NDArray]

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:
Return type:

float

get_theory_prediction_counts(cluster_theory)[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 (firecrown.models.cluster.deltasigma.ClusterDeltaSigma)

Return type:

Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float], numpy.typing.NDArray[numpy.float64]]

get_function_to_integrate_counts(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[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float], numpy.typing.NDArray[numpy.float64]])

Return type:

Callable[[numpy.typing.NDArray, numpy.typing.NDArray], numpy.typing.NDArray]

evaluate_theory_prediction_counts(cluster_theory, this_bin, sky_area)[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:
Return type:

float