firecrown.models.cluster

Module that contains the cluster model classes.

Classes

ClusterAbundance	The class that calculates the predicted number counts of galaxy clusters.
AbundanceData	The class used to wrap a sacc file and return the cluster abundance data.
NDimensionalBin	Class which defines the interface for an N dimensional bin.
SaccBin	An implementation of the N dimensional bin using sacc tracers.
TupleBin	An implementation of the N dimensional bin using sacc tracers.
ClusterData	The class used to wrap a sacc file and return the cluster abundance data.
ClusterDeltaSigma	The class that calculates the predicted delta sigma of galaxy clusters.
DeltaSigmaData	The class used to wrap a sacc file and return the cluster deltasigma data.
Integrator	The integrator base class.
NumCosmoIntegrator	The NumCosmo implementation of the Integrator base class.
ScipyIntegrator	The scipy implementation of the Integrator base class using nquad.
Completeness	The completeness kernel for the numcosmo simulated survey.
KernelType	The kernels that can be included in the cluster abundance integrand.
Purity	The purity kernel for the numcosmo simulated survey.
SpectroscopicRedshift	The spec-z kernel.
TrueMass	The true mass kernel.
MassRichnessGaussian	The representation of mass richness relations that are of a gaussian form.
MurataBinned	The mass richness relation defined in Murata 19 for a binned data vector.
MurataUnbinned	The mass richness relation defined in Murata 19 for a unbinned data vector.
ClusterProperty	Flag representing cluster observables.
ClusterRecipe	Abstract class defining a cluster recipe.
MurataBinnedSpecZDeltaSigmaRecipe	Cluster recipe with Murata19 mass-richness and spec-zs.
MurataBinnedSpecZRecipe	Cluster recipe with Murata19 mass-richness and spec-zs.

Package Contents

class firecrown.models.cluster.ClusterAbundance(mass_interval, z_interval, halo_mass_function)

Bases: firecrown.updatable.Updatable

The class that calculates the predicted number counts of galaxy clusters.

The abundance is a function of a specific cosmology, a mass and redshift range, an area on the sky, a halo mass function, as well as multiple kernels, where each kernel represents a different distribution involved in the final cluster abundance integrand.

Parameters:

• mass_interval (tuple[float, float])

• z_interval (tuple[float, float])

• halo_mass_function (pyccl.halos.MassFunc)

property cosmo: pyccl.cosmology.Cosmology | None

The cosmology used to predict the cluster number count.

Return type:

pyccl.cosmology.Cosmology | None

kernels: firecrown.updatable.UpdatableCollection

halo_mass_function

min_mass

max_mass

min_z

max_z

update_ingredients(cosmo)

Update the cluster abundance calculation with a new cosmology.

Parameters:

cosmo (pyccl.cosmology.Cosmology)

Return type:

None

comoving_volume(z, sky_area=0)

The differential comoving volume given area sky_area at redshift z.

Parameters:

• sky_area (float) – The area of the survey on the sky in square degrees.

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

mass_function(mass, z)

The mass function at z and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.AbundanceData(sacc_data)

Bases: firecrown.models.cluster._cluster_data.ClusterData

The class used to wrap a sacc file and return the cluster abundance data.

The sacc file is a complicated set of tracers (bins) and surveys. This class manipulates that data and returns only the data relevant for the cluster number count statistic. The data in this class is specific to a single survey name.

Parameters:

sacc_data (sacc.Sacc)

get_observed_data_and_indices_by_survey(survey_nm, properties)

Returns the observed data for the specified survey and properties.

For example if the caller has enabled COUNTS then the observed cluster counts within each N dimensional bin will be returned.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

tuple[list[float], list[int]]

get_bin_edges(survey_nm, properties)

Returns the limits for all z, mass bins for the requested data type.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

list[firecrown.models.cluster._binning.SaccBin]

class firecrown.models.cluster.NDimensionalBin

Bases: abc.ABC

Class which defines the interface for an N dimensional bin.

property z_edges: tuple[float, float]

Abstractmethod:

Return type:

tuple[float, float]

Redshift bin edges.

property mass_proxy_edges: tuple[float, float]

Abstractmethod:

Return type:

tuple[float, float]

Mass proxy bin edges.

property radius_edges: tuple[float, float]

Abstractmethod:

Return type:

tuple[float, float]

Radius bin edges.

property radius_center: float

Abstractmethod:

Return type:

float

Radius bin edges.

__str__()

Returns a string representation of the bin edges.

Return type:

str

class firecrown.models.cluster.SaccBin(coordinate_bins)

Bases: NDimensionalBin

An implementation of the N dimensional bin using sacc tracers.

Parameters:

coordinate_bins (list[sacc.BaseTracer])

coordinate_bins

dimension

property z_edges: tuple[float, float]

Redshift bin edges.

Return type:

tuple[float, float]

property mass_proxy_edges: tuple[float, float]

Mass proxy bin edges.

Return type:

tuple[float, float]

property radius_edges: tuple[float, float]

Radius bin edges.

Return type:

tuple[float, float]

property radius_center: float

Radius bin center.

Return type:

float

__eq__(other)

Two bins are equal if they have the same lower/upper bound.

Parameters:

other (object)

Return type:

bool

__hash__()

One bin’s hash is determined by the dimension and lower/upper bound.

Return type:

int

class firecrown.models.cluster.TupleBin(coordinate_bins)

Bases: NDimensionalBin

An implementation of the N dimensional bin using sacc tracers.

Parameters:

coordinate_bins (list[tuple])

coordinate_bins

property dimension: int

Number of dimensions for this bin.

Return type:

int

property mass_proxy_edges: tuple[float, float]

Mass proxy bin edges.

Return type:

tuple[float, float]

property z_edges: tuple[float, float]

Redshift bin edges.

Return type:

tuple[float, float]

property radius_edges: tuple[float, float]

Radius bin edges.

Return type:

tuple[float, float]

property radius_center: float

Radius bin center.

Return type:

float

__eq__(other)

Two bins are equal if they have the same lower/upper bound.

Parameters:

other (object)

Return type:

bool

__hash__()

One bin’s hash is determined by the dimension and lower/upper bound.

Return type:

int

class firecrown.models.cluster.ClusterData(sacc_data)

The class used to wrap a sacc file and return the cluster abundance data.

The sacc file is a complicated set of tracers (bins) and surveys. This class manipulates that data and returns only the data relevant for the cluster number count statistic. The data in this class is specific to a single survey name.

Parameters:

sacc_data (sacc.Sacc)

sacc_data

get_survey_tracer(survey_nm)

Returns the SurveyTracer for the specified survey name.

Parameters:

survey_nm (str)

Return type:

sacc.tracers.SurveyTracer

abstractmethod get_observed_data_and_indices_by_survey(survey_nm, properties)

Returns the observed data for the specified survey and properties.

For example if the caller has enabled COUNTS then the observed cluster counts within each N dimensional bin will be returned.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

tuple[list[float], list[int]]

abstractmethod get_bin_edges(survey_nm, properties)

Returns the limits for all z, mass bins for the requested data type.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

list[firecrown.models.cluster._binning.SaccBin]

class firecrown.models.cluster.ClusterDeltaSigma(mass_interval, z_interval, halo_mass_function, conc_parameter=False)

Bases: firecrown.models.cluster._abundance.ClusterAbundance

The class that calculates the predicted delta sigma of galaxy clusters.

The excess density surface mass density is a function of a specific cosmology, a mass and redshift range, an area on the sky, as well as multiple kernels, where each kernel represents a different distribution involved in the final cluster shear integrand.

Parameters:

• mass_interval (tuple[float, float])

• z_interval (tuple[float, float])

• halo_mass_function (pyccl.halos.MassFunc)

• conc_parameter (bool)

conc_parameter = False

delta_sigma(log_mass, z, radius_center)

Delta sigma for clusters.

Parameters:

• log_mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

• radius_center (float)

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.DeltaSigmaData(sacc_data)

Bases: firecrown.models.cluster._cluster_data.ClusterData

The class used to wrap a sacc file and return the cluster deltasigma data.

The sacc file is a complicated set of tracers (bins) and surveys. This class manipulates that data and returns only the data relevant for the cluster number count statistic. The data in this class is specific to a single survey name.

Parameters:

sacc_data (sacc.Sacc)

get_observed_data_and_indices_by_survey(survey_nm, properties)

Returns the observed data for the specified survey and properties.

For example if the caller has enabled DELTASIGMA then the observed cluster profile within each N dimensional bin will be returned.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

tuple[list[float], list[int]]

get_bin_edges(survey_nm, properties)

Returns the limits for all z, mass bins for the shear data type.

Parameters:

• survey_nm (str)

• properties (firecrown.models.cluster._properties.ClusterProperty)

Return type:

list[firecrown.models.cluster._binning.SaccBin]

class firecrown.models.cluster.Integrator

Bases: abc.ABC

The integrator base class.

This class acts as an adapter around an integration library.

integral_bounds: list[tuple[float, float]] = []

extra_args: numpy.typing.NDArray[numpy.float64]

abstractmethod integrate(func_to_integrate)

Call this method to integrate the provided integrand argument.

Parameters:

func_to_integrate (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]], numpy.typing.NDArray[numpy.float64]])

Return type:

float

class firecrown.models.cluster.NumCosmoIntegrator(method=None, relative_tolerance=0.0001, absolute_tolerance=1e-12)

Bases: Integrator

The NumCosmo implementation of the Integrator base class.

Parameters:

• method (None | NumCosmoIntegralMethod)

• relative_tolerance (float)

• absolute_tolerance (float)

method

integrate(func_to_integrate)

Integrate the provided integrand argument with NumCosmo.

Parameters:

func_to_integrate (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]], numpy.typing.NDArray[numpy.float64]])

Return type:

float

class firecrown.models.cluster.ScipyIntegrator(relative_tolerance=0.0001, absolute_tolerance=1e-12)

Bases: Integrator

The scipy implementation of the Integrator base class using nquad.

Parameters:

• relative_tolerance (float)

• absolute_tolerance (float)

integrate(func_to_integrate)

Integrate the provided integrand argument with SciPy.

Parameters:

func_to_integrate (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]], numpy.typing.NDArray[numpy.float64]])

Return type:

float

class firecrown.models.cluster.Completeness

Bases: firecrown.updatable.Updatable

The completeness kernel for the numcosmo simulated survey.

This kernel will affect the integrand by accounting for the incompleteness of a cluster selection.

ac_nc

bc_nc

ac_mc

bc_mc

distribution(log_mass, z)

Evaluates and returns the completeness contribution to the integrand.

Parameters:

• log_mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.KernelType(*args, **kwds)

Bases: enum.Enum

The kernels that can be included in the cluster abundance integrand.

MASS = 1

Z = 2

MASS_PROXY = 3

Z_PROXY = 4

COMPLETENESS = 5

PURITY = 6

class firecrown.models.cluster.Purity

Bases: firecrown.updatable.Updatable

The purity kernel for the numcosmo simulated survey.

This kernel will affect the integrand by accounting for the purity of a cluster selection.

ap_nc

bp_nc

ap_rc

bp_rc

distribution(z, mass_proxy, mass_proxy_limits=None)

Evaluates and returns the purity contribution to the integrand.

Parameters:

• z (numpy.typing.NDArray[numpy.float64])

• mass_proxy (numpy.typing.NDArray[numpy.float64])

• mass_proxy_limits (tuple[float, float] | None)

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.SpectroscopicRedshift

The spec-z kernel.

Assuming the spectroscopic redshift has no uncertainties, this is akin to multiplying by 1.

distribution()

Evaluates and returns the z distribution contribution to the integrand.

We have set this to 1.0 (i.e. it does not affect the redshift distribution)

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.TrueMass

The true mass kernel.

Assuming we measure the true mass, this will always be 1.

distribution()

Evaluates and returns the mass distribution contribution to the integrand.

We have set this to 1.0 (i.e. it does not affect the mass distribution)

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.MassRichnessGaussian(parameter_prefix=None)

Bases: firecrown.updatable.Updatable

The representation of mass richness relations that are of a gaussian form.

Parameters:

parameter_prefix (None | str)

static observed_value(p, mass, z, pivot_mass, log1p_pivot_redshift)

Return observed quantity corrected by redshift and mass.

Parameters:

• p (tuple[float, float, float])

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

• pivot_mass (float)

• log1p_pivot_redshift (float)

Return type:

numpy.typing.NDArray[numpy.float64]

abstractmethod get_proxy_mean(mass, z)

Return observed quantity corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

abstractmethod get_proxy_sigma(mass, z)

Return observed scatter corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.MurataBinned(pivot_mass, pivot_redshift)

Bases: MassRichnessGaussian

The mass richness relation defined in Murata 19 for a binned data vector.

Parameters:

• pivot_mass (float)

• pivot_redshift (float)

pivot_redshift

pivot_mass

log1p_pivot_redshift

mu_p0

mu_p1

mu_p2

sigma_p0

sigma_p1

sigma_p2

get_proxy_mean(mass, z)

Return observed quantity corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

get_proxy_sigma(mass, z)

Return observed scatter corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

distribution(mass, z, mass_proxy_limits)

Evaluates and returns the mass-richness contribution to the integrand.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

• mass_proxy_limits (tuple[float, float])

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.MurataUnbinned(pivot_mass, pivot_redshift)

Bases: MassRichnessGaussian

The mass richness relation defined in Murata 19 for a unbinned data vector.

Parameters:

• pivot_mass (float)

• pivot_redshift (float)

pivot_redshift

pivot_mass

log1p_pivot_redshift

mu_p0

mu_p1

mu_p2

sigma_p0

sigma_p1

sigma_p2

get_proxy_mean(mass, z)

Return observed quantity corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

get_proxy_sigma(mass, z)

Return observed scatter corrected by redshift and mass.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

distribution(mass, z, mass_proxy)

Evaluates and returns the mass-richness contribution to the integrand.

Parameters:

• mass (numpy.typing.NDArray[numpy.float64])

• z (numpy.typing.NDArray[numpy.float64])

• mass_proxy (numpy.typing.NDArray[numpy.float64])

Return type:

numpy.typing.NDArray[numpy.float64]

class firecrown.models.cluster.ClusterProperty(*args, **kwds)

Bases: enum.Flag

Flag representing cluster observables.

This flag containing the possible cluster properties we can make a theoretical prediction for.

NONE = 0

COUNTS

MASS

REDSHIFT

SHEAR

DELTASIGMA

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

Bases: firecrown.updatable.Updatable, abc.ABC

Abstract class defining a cluster recipe.

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

Parameters:

parameter_prefix (None | str)

my_updatables: firecrown.updatable.UpdatableCollection

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

Evaluate the theory prediction for this cluster recipe.

Parameters:

• this_bin (firecrown.models.cluster._binning.SaccBin)

• sky_area (float)

• average_on (None | firecrown.models.cluster._properties.ClusterProperty)

Return type:

float

class firecrown.models.cluster.MurataBinnedSpecZDeltaSigmaRecipe

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.

integrator

redshift_distribution

mass_distribution

get_theory_prediction(cluster_theory, average_on=None)

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)

• average_on (None | firecrown.models.cluster._properties.ClusterProperty)

Return type:

collections.abc.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)

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 (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float, float], numpy.typing.NDArray[numpy.float64]])

Return type:

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

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

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 (firecrown.models.cluster._deltasigma.ClusterDeltaSigma)

• this_bin (firecrown.models.cluster._binning.NDimensionalBin)

• sky_area (float)

• average_on (None | firecrown.models.cluster._properties.ClusterProperty)

Return type:

float

get_theory_prediction_counts(cluster_theory)

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:

collections.abc.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)

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 (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float], numpy.typing.NDArray[numpy.float64]])

Return type:

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

evaluate_theory_prediction_counts(cluster_theory, this_bin, sky_area)

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 (firecrown.models.cluster._deltasigma.ClusterDeltaSigma)

• this_bin (firecrown.models.cluster._binning.NDimensionalBin)

• sky_area (float)

Return type:

float

class firecrown.models.cluster.MurataBinnedSpecZRecipe

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.

integrator

redshift_distribution

mass_distribution

get_theory_prediction(cluster_theory, average_on=None)

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._abundance.ClusterAbundance)

• average_on (None | firecrown.models.cluster._properties.ClusterProperty)

Return type:

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

get_function_to_integrate(prediction)

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 (collections.abc.Callable[[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64], tuple[float, float], float], numpy.typing.NDArray[numpy.float64]])

Return type:

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

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

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 (firecrown.models.cluster._abundance.ClusterAbundance)

• this_bin (firecrown.models.cluster._binning.NDimensionalBin)

• sky_area (float)

• average_on (None | firecrown.models.cluster._properties.ClusterProperty)

Return type:

float