firecrown.likelihood.number_counts

Number counts source and systematics.

Attributes

LINEAR_BIAS_DEFAULT_ALPHAZ
LINEAR_BIAS_DEFAULT_ALPHAG
LINEAR_BIAS_DEFAULT_Z_PIV
PT_NON_LINEAR_BIAS_DEFAULT_B_2
PT_NON_LINEAR_BIAS_DEFAULT_B_S
CONSTANT_MAGNIFICATION_BIAS_DEFAULT_MAG_BIAS
NUMBER_COUNTS_DEFAULT_BIAS
NumberCountsSystematicFactory

Classes

NumberCountsArgs	Class for number counts tracer builder argument.
NumberCountsSystematic	Abstract base class for systematics for Number Counts sources.
PhotoZShift	Photo-z shift systematic.
PhotoZShiftandStretch	Photo-z shift systematic.
SelectField	Systematic to select 3D field.
LinearBiasSystematic	Linear bias systematic.
PTNonLinearBiasSystematic	Non-linear bias systematic.
MagnificationBiasSystematic	Magnification bias systematic.
ConstantMagnificationBiasSystematic	Simple constant magnification bias systematic.
NumberCounts	Source class for number counts.
LinearBiasSystematicFactory	Factory class for LinearBiasSystematic objects.
PTNonLinearBiasSystematicFactory	Factory class for PTNonLinearBiasSystematic objects.
MagnificationBiasSystematicFactory	Factory class for MagnificationBiasSystematic objects.
ConstantMagnificationBiasSystematicFactory	Factory class for ConstantMagnificationBiasSystematic objects.
NumberCountsFactory	Factory class for NumberCounts objects.

Module Contents

class firecrown.likelihood.number_counts.NumberCountsArgs

Bases: firecrown.likelihood.source.SourceGalaxyArgs

Class for number counts tracer builder argument.

bias: None | numpy.typing.NDArray[numpy.float64] = None

mag_bias: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

has_pt: bool = False

has_hm: bool = False

b_2: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

b_s: None | tuple[numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]] = None

class firecrown.likelihood.number_counts.NumberCountsSystematic(parameter_prefix=None)

Bases: firecrown.likelihood.source.SourceGalaxySystematic[NumberCountsArgs]

Abstract base class for systematics for Number Counts sources.

Derived classes must implement apply() with the correct signature.

Parameters:

parameter_prefix (None | str)

abstractmethod apply(tools, tracer_arg)

Apply method to include systematics in the tracer_arg.

This does not modify the supplied tracer_arg; it returns a new one that has been updated.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – the Modeling tools used to update the tracer_arg

• tracer_arg (NumberCountsArgs) – the original NumberCountsArgs to which to apply the systematic

Returns:

the updated NumberCountsArgs

Return type:

NumberCountsArgs

class firecrown.likelihood.number_counts.PhotoZShift(sacc_tracer, active=True)

Bases: firecrown.likelihood.source.SourceGalaxyPhotoZShift[NumberCountsArgs]

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.number_counts.PhotoZShiftandStretch(sacc_tracer, active=True)

Bases: firecrown.likelihood.source.SourceGalaxyPhotoZShiftandStretch[NumberCountsArgs]

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.number_counts.SelectField(field='delta_matter')

Bases: firecrown.likelihood.source.SourceGalaxySelectField[NumberCountsArgs]

Systematic to select 3D field.

Parameters:

field (str)

firecrown.likelihood.number_counts.LINEAR_BIAS_DEFAULT_ALPHAZ = 0.0

firecrown.likelihood.number_counts.LINEAR_BIAS_DEFAULT_ALPHAG = 0.0

firecrown.likelihood.number_counts.LINEAR_BIAS_DEFAULT_Z_PIV = 0.5

class firecrown.likelihood.number_counts.LinearBiasSystematic(sacc_tracer)

Bases: NumberCountsSystematic

Linear bias systematic.

This systematic adds a linear bias model which varies with redshift and the growth function.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• alphaz – the redshift exponent of the bias.

• alphag – the growth function exponent of the bias.

• z_piv – the pivot redshift of the bias.

Parameters:

sacc_tracer (str)

alphaz

alphag

z_piv

apply(tools, tracer_arg)

Apply a linear bias systematic.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – the ModelingTools used to update the tracer_arg

• tracer_arg (NumberCountsArgs) – a NumberCountsArgs object with values to be updated

Returns:

the updated NumberCountsArgs object

Return type:

NumberCountsArgs

firecrown.likelihood.number_counts.PT_NON_LINEAR_BIAS_DEFAULT_B_2 = 1.0

firecrown.likelihood.number_counts.PT_NON_LINEAR_BIAS_DEFAULT_B_S = 1.0

class firecrown.likelihood.number_counts.PTNonLinearBiasSystematic(sacc_tracer=None)

Bases: NumberCountsSystematic

Non-linear bias systematic.

This systematic adds a linear bias model which varies with redshift and the growth function.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• b_2 – the quadratic bias.

• b_s – the stochastic bias.

Parameters:

sacc_tracer (None | str)

b_2

b_s

apply(tools, tracer_arg)

Apply a non-linear bias systematic.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – currently unused, but required by interface

• tracer_arg (NumberCountsArgs) – a NumberCountsArgs object with values to be updated

Returns:

the updated NumberCountsArgs object

Return type:

NumberCountsArgs

class firecrown.likelihood.number_counts.MagnificationBiasSystematic(sacc_tracer)

Bases: NumberCountsSystematic

Magnification bias systematic.

This systematic adds a magnification bias model for galaxy number contrast following Joachimi & Bridle (2010), arXiv:0911.2454.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

• r_lim – the limiting magnitude.

• sig_c – the intrinsic dispersion of the source redshift distribution.

• eta – the slope of the luminosity function.

• z_c – the characteristic redshift of the source distribution.

• z_m – the slope of the source redshift distribution.

Parameters:

sacc_tracer (str)

r_lim

sig_c

eta

z_c

z_m

apply(tools, tracer_arg)

Apply a magnification bias systematic.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – currently unused, but required by the interface

• tracer_arg (NumberCountsArgs) – a NumberCountsArgs object with values to be updated

Returns:

an updated NumberCountsArgs object

Return type:

NumberCountsArgs

firecrown.likelihood.number_counts.CONSTANT_MAGNIFICATION_BIAS_DEFAULT_MAG_BIAS = 1.0

class firecrown.likelihood.number_counts.ConstantMagnificationBiasSystematic(sacc_tracer)

Bases: NumberCountsSystematic

Simple constant magnification bias systematic.

This systematic adds a constant magnification bias model for galaxy number contrast.

The following parameters are special Updatable parameters, which means that they can be updated by the sampler, sacc_tracer is going to be used as a prefix for the parameters:

Variables:

mag_bias – the magnification bias.

Parameters:

sacc_tracer (str)

mag_bias

apply(tools, tracer_arg)

Apply a constant magnification bias systematic.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – currently unused, but required by interface

• tracer_arg (NumberCountsArgs) – a NumberCountsArgs object with values to be updated

Returns:

an updated NumberCountsArgs object

Return type:

NumberCountsArgs

firecrown.likelihood.number_counts.NUMBER_COUNTS_DEFAULT_BIAS = 1.5

class firecrown.likelihood.number_counts.NumberCounts(*, sacc_tracer, has_rsd=False, derived_scale=False, scale=1.0, systematics=None)

Bases: firecrown.likelihood.source.SourceGalaxy[NumberCountsArgs]

Source class for number counts.

Parameters:

• sacc_tracer (str)

• has_rsd (bool)

• derived_scale (bool)

• scale (float)

• systematics (None | collections.abc.Sequence[firecrown.likelihood.source.SourceGalaxySystematic[NumberCountsArgs]])

sacc_tracer

has_rsd = False

derived_scale = False

bias

systematics: firecrown.updatable.UpdatableCollection[firecrown.likelihood.source.SourceGalaxySystematic[NumberCountsArgs]]

scale = 1.0

current_tracer_args: None | NumberCountsArgs = None

tracer_args: NumberCountsArgs

classmethod create_ready(inferred_zdist, has_rsd=False, derived_scale=False, scale=1.0, systematics=None)

Create a NumberCounts object with the given tracer name and scale.

This is the recommended way to create a NumberCounts object. It creates a fully initialized object.

Parameters:

• inferred_zdist (firecrown.metadata_types.InferredGalaxyZDist) – the inferred redshift distribution

• has_rsd (bool) – whether to include RSD in the tracer

• derived_scale (bool) – whether to include a derived parameter for the scale of the tracer

• scale (float) – the initial scale of the tracer

• systematics (None | list[firecrown.likelihood.source.SourceGalaxySystematic[NumberCountsArgs]]) – a list of systematics to apply to the tracer

Returns:

a fully initialized NumberCounts object

Return type:

NumberCounts

create_tracers(tools)

Create the tracers for this source.

Parameters:

tools (firecrown.modeling_tools.ModelingTools) – the ModelingTools used to create the tracers

Returns:

a tuple of tracers and the updated tracer_args

Return type:

tuple[list[firecrown.likelihood.source.Tracer], NumberCountsArgs]

get_scale()

Return the scale for this source.

Returns:

the scale for this source.

Return type:

float

class firecrown.likelihood.number_counts.LinearBiasSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for LinearBiasSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['LinearBiasSystematicFactory'], Field(description='The type of the systematic.')] = 'LinearBiasSystematicFactory'

create(bin_name)

Create a LinearBiasSystematic object with the given tracer name.

Parameters:

bin_name (str)

Return type:

LinearBiasSystematic

create_global()

Create a LinearBiasSystematic object with the given tracer name.

Return type:

LinearBiasSystematic

class firecrown.likelihood.number_counts.PTNonLinearBiasSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for PTNonLinearBiasSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['PTNonLinearBiasSystematicFactory'], Field(description='The type of the systematic.')] = 'PTNonLinearBiasSystematicFactory'

create(bin_name)

Create a PTNonLinearBiasSystematic object with the given tracer name.

Parameters:

bin_name (str) – the name of the bin

Returns:

the created PTNonLinearBiasSystematic object

Return type:

PTNonLinearBiasSystematic

create_global()

Create a global PTNonLinearBiasSystematic object.

Return type:

PTNonLinearBiasSystematic

class firecrown.likelihood.number_counts.MagnificationBiasSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for MagnificationBiasSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['MagnificationBiasSystematicFactory'], Field(description='The type of the systematic.')] = 'MagnificationBiasSystematicFactory'

create(bin_name)

Create a MagnificationBiasSystematic object with the given tracer name.

Parameters:

bin_name (str) – the name of the bin

Returns:

the created MagnificationBiasSystematic object

Return type:

MagnificationBiasSystematic

create_global()

Required by the interface, but raises an error.

MagnificationBiasSystematic systematics cannot be global.

Return type:

MagnificationBiasSystematic

class firecrown.likelihood.number_counts.ConstantMagnificationBiasSystematicFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for ConstantMagnificationBiasSystematic objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Annotated[Literal['ConstantMagnificationBiasSystematicFactory'], Field(description='The type of the systematic.')] = 'ConstantMagnificationBiasSystematicFactory'

create(bin_name)

Create a ConstantMagnificationBiasSystematic object.

Parameters:

bin_name (str) – the name of the bin

Returns:

the created ConstantMagnificationBiasSystematic object

Return type:

ConstantMagnificationBiasSystematic

create_global()

Required by the interface, but raises an error.

ConstantMagnificationBiasSystematic systematics cannot be global.

Return type:

ConstantMagnificationBiasSystematic

firecrown.likelihood.number_counts.NumberCountsSystematicFactory

class firecrown.likelihood.number_counts.NumberCountsFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for NumberCounts objects.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type_source: firecrown.metadata_types.TypeSource

per_bin_systematics: collections.abc.Sequence[NumberCountsSystematicFactory] = None

global_systematics: collections.abc.Sequence[NumberCountsSystematicFactory] = None

include_rsd: bool = False

model_post_init(_, /)

Initialize the NumberCountsFactory.

Return type:

None

create(inferred_zdist)

Create a NumberCounts object with the given tracer name and scale.

Parameters:

inferred_zdist (firecrown.metadata_types.InferredGalaxyZDist) – the inferred redshift distribution

Returns:

a fully initialized NumberCounts object

Return type:

NumberCounts

create_from_metadata_only(sacc_tracer)

Create an WeakLensing object with the given tracer name and scale.

Parameters:

sacc_tracer (str) – the name of the tracer

Returns:

a fully initialized NumberCounts object

Return type:

NumberCounts