crow.recipes.binned_parent module

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

class crow.recipes.binned_parent.BinnedClusterRecipe(cluster_theory, redshift_distribution, mass_distribution, completeness: Completeness = None, purity: Purity = None, mass_interval: tuple[float, float] = (11.0, 17.0), true_z_interval: tuple[float, float] = (0.0, 5.0))

Bases: object

Cluster recipe.

Object used to compute cluster statistics.

property completeness: Completeness | None

The completeness used to predict the cluster number count.

evaluate_theory_prediction_counts(z_edges, mass_proxy_edges, sky_area: float, average_on: None | ClusterProperty = None) → float

Compute predicted cluster number counts in a bin.

This method must be implemented by subclasses. It evaluates the theoretical number of clusters within the specified redshift and mass-proxy bin.

Parameters:

• z_edges (array-like) -- Lower and upper edges of the redshift bin.

• mass_proxy_edges (array-like) -- Lower and upper edges of the mass proxy bin (log10 scale).

• sky_area (float) -- Survey area in square degrees.

• average_on (ClusterProperty, optional) -- Property over which the prediction should be averaged.

Returns:

Predicted number of clusters in the bin.

Return type:

float

Notes

The implementation may include: - Mass–richness relations - Selection effects (completeness, purity) - Cosmological volume integration

evaluate_theory_prediction_lensing_profile(z_edges, mass_proxy_edges, radius_centers, sky_area: float, average_on: None | ClusterProperty = None) → float

Compute predicted stacked lensing profile.

This method must be implemented by subclasses. It evaluates the theoretical lensing signal for clusters within the specified bin.

Parameters:

• z_edges (array-like) -- Redshift bin edges.

• mass_proxy_edges (array-like) -- Mass proxy bin edges (log10 scale).

• radius_centers (array-like) -- Radial positions at which the profile is evaluated.

• sky_area (float) -- Survey area in square degrees.

• average_on (ClusterProperty, optional) -- Property over which the prediction should be averaged.

Returns:

Predicted lensing profile values.

Return type:

float or ndarray

property purity: Purity | None

The purity used to predict the cluster number count.

setup()

Perform any necessary pre-computation before evaluation.

This method is intended to be implemented by subclasses. It should initialize any internal quantities required for evaluating theoretical predictions (e.g., reseting/ precomputing grids, normalization factors, or integrators).

Notes

This method must be called before any evaluation method.