"""Binned cluster number counts statistic support."""
from __future__ import annotations
import sacc
from firecrown.data_types import TheoryVector
from firecrown.modeling_tools import ModelingTools
from firecrown.models.cluster.abundance_data import AbundanceData
from firecrown.models.cluster.properties import ClusterProperty
from firecrown.likelihood.binned_cluster import BinnedCluster
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class BinnedClusterNumberCounts(BinnedCluster):
"""A statistic representing the number of clusters in a z, mass bin."""
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def read(self, sacc_data: sacc.Sacc) -> None:
"""Read the data for this statistic and mark it as ready for use.
:param sacc_data: The data in the sacc format.
"""
# Build the data vector and indices needed for the likelihood
if self.cluster_properties == ClusterProperty.NONE:
raise ValueError("You must specify at least one cluster property.")
cluster_data = AbundanceData(sacc_data)
self._read(cluster_data)
super().read(sacc_data)
def _compute_theory_vector(self, tools: ModelingTools) -> TheoryVector:
"""Compute a statistic from sources, concrete implementation.
:param tools: The modeling tools used to compute the statistic.
:return: The computed statistic.
"""
assert tools.cluster_abundance is not None
theory_vector_list: list[float] = []
cluster_counts = []
cluster_counts = self.get_binned_cluster_counts(tools)
for cl_property in ClusterProperty:
include_prop = cl_property & self.cluster_properties
if not include_prop:
continue
if cl_property == ClusterProperty.COUNTS:
theory_vector_list += cluster_counts
continue
if cl_property == ClusterProperty.DELTASIGMA:
continue
theory_vector_list += self.get_binned_cluster_property(
tools, cluster_counts, cl_property
)
return TheoryVector.from_list(theory_vector_list)
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def get_binned_cluster_property(
self,
tools: ModelingTools,
cluster_counts: list[float],
cluster_properties: ClusterProperty,
) -> list[float]:
"""Computes the mean mass of clusters in each bin.
Using the data from the sacc file, this function evaluates the likelihood for
a single point of the parameter space, and returns the predicted mean mass of
the clusters in each bin.
:param cluster_counts: The number of clusters in each bin.
:param cluster_properties: The cluster observables to use.
"""
assert tools.cluster_abundance is not None
mean_values = []
for this_bin, counts in zip(self.bins, cluster_counts):
total_observable = self.cluster_recipe.evaluate_theory_prediction(
tools.cluster_abundance, this_bin, self.sky_area, cluster_properties
)
mean_observable = total_observable / counts
mean_values.append(mean_observable)
return mean_values
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def get_binned_cluster_counts(self, tools: ModelingTools) -> list[float]:
"""Computes the number of clusters in each bin.
Using the data from the sacc file, this function evaluates the likelihood for
a single point of the parameter space, and returns the predicted number of
clusters in each bin.
:param tools: The modeling tools used to compute the statistic.
:return: The number of clusters in each bin.
"""
assert tools.cluster_abundance is not None
cluster_counts = []
for this_bin in self.bins:
counts = self.cluster_recipe.evaluate_theory_prediction(
tools.cluster_abundance, this_bin, self.sky_area
)
cluster_counts.append(counts)
return cluster_counts