firecrown.likelihood.source

Abstract base classes for TwoPoint Statistics sources.

Attributes

SOURCE_GALAXY_SYSTEMATIC_DEFAULT_DELTA_Z
SOURCE_GALAXY_SYSTEMATIC_DEFAULT_SIGMA_Z

Classes

SourceSystematic	An abstract systematic class (e.g., shear biases, photo-z shifts, etc.).
Source	The abstract base class for all sources.
Tracer	Extending the pyccl.Tracer object with additional information.
SourceGalaxyArgs	Class for galaxy based sources arguments.
SourceGalaxySystematic	Abstract base class for all galaxy-based source systematics.
SourceGalaxyPhotoZShift	A photo-z shift bias.
PhotoZShift	Photo-z shift systematic.
PhotoZShiftFactory	Factory class for PhotoZShift objects.
SourceGalaxyPhotoZShiftandStretch	A photo-z shift & stretch bias.
PhotoZShiftandStretch	Photo-z shift and stretch systematic.
PhotoZShiftandStretchFactory	Factory class for PhotoZShiftandStretch objects.
SourceGalaxySelectField	The source galaxy select field systematic.
SourceGalaxy	Source class for galaxy based sources.

Functions

dndz_shift_and_stretch_active(z, dndz, delta_z, sigma_z)	Shift and stretch the photo-z distribution using an active transformation.
dndz_shift_and_stretch_passive(z, dndz, delta_z, sigma_z)	Shift and stretch the photo-z distribution using a passive transformation.

Module Contents

class firecrown.likelihood.source.SourceSystematic(parameter_prefix=None)

Bases: firecrown.updatable.Updatable

An abstract systematic class (e.g., shear biases, photo-z shifts, etc.).

This class currently has no methods at all, because the argument types for the apply method of different subclasses are different.

Parameters:

parameter_prefix (None | str)

read(sacc_data)

Call to allow this object to read from the appropriate sacc data.

Parameters:

sacc_data (sacc.Sacc) – The SACC data object to be read

Return type:

None

class firecrown.likelihood.source.Source(sacc_tracer)

Bases: firecrown.updatable.Updatable

The abstract base class for all sources.

Parameters:

sacc_tracer (str)

cosmo_hash: None | int

tracers: Sequence[Tracer]

sacc_tracer

abstractmethod read_systematics(sacc_data)

Abstract method to read the systematics for this source from the SACC file.

Parameters:

sacc_data (sacc.Sacc) – The SACC data object to be read

Return type:

None

read(sacc_data)

Read the data for this source from the SACC file.

Parameters:

sacc_data (sacc.Sacc) – The SACC data object to be read

Return type:

None

abstractmethod get_scale()

Abstract method to return the scale for this Source.

Returns:

the scale

Return type:

float

abstractmethod create_tracers(tools)

Abstract method to create tracers for this Source.

Parameters:

tools (firecrown.modeling_tools.ModelingTools) – The modeling tools used for creating the tracers

get_tracers(tools)

Return the tracer for the given cosmology.

This method caches its result, so if called a second time with the same cosmology, no calculation needs to be done.

Parameters:

tools (firecrown.modeling_tools.ModelingTools) – The modeling tools used for creating the tracers

Returns:

the list of tracers

Return type:

Sequence[Tracer]

class firecrown.likelihood.source.Tracer(tracer, tracer_name=None, field=None, pt_tracer=None, halo_profile=None, halo_2pt=None)

Extending the pyccl.Tracer object with additional information.

Bundles together a pyccl.Tracer object with optional information about the underlying 3D field, or a pyccl.nl_pt.PTTracer and halo profiles.

Parameters:

• tracer (pyccl.Tracer)

• tracer_name (None | str)

• field (None | str)

• pt_tracer (None | pyccl.nl_pt.PTTracer)

• halo_profile (None | pyccl.halos.HaloProfile)

• halo_2pt (None | pyccl.halos.Profile2pt)

static determine_field_name(field, tracer)

Gets a field name for a tracer.

This function encapsulates the policy for determining the value to be assigned to the field attribute of a Tracer.

It is a static method only to keep it grouped with the class for which it is defining the initialization policy.

Parameters:

• field (None | str) – the (stub) name of the field

• tracer (None | str) – the name of the tracer

Returns:

the full name of the field

Return type:

str

ccl_tracer

tracer_name: str

field

pt_tracer = None

halo_profile = None

halo_2pt = None

property has_pt: bool

Answer whether we have a perturbation theory tracer.

Returns:

True if we have a pt_tracer, and False if not.

Return type:

bool

property has_hm: bool

Answer whether we have a halo model profile.

Return True if we have a halo_profile, and False if not.

Return type:

bool

class firecrown.likelihood.source.SourceGalaxyArgs

Class for galaxy based sources arguments.

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

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

scale: float = 1.0

field: str = 'delta_matter'

class firecrown.likelihood.source.SourceGalaxySystematic(parameter_prefix=None)

Bases: SourceSystematic, Generic[_SourceGalaxyArgsT]

Abstract base class for all galaxy-based source systematics.

Parameters:

parameter_prefix (None | str)

abstractmethod apply(tools, tracer_arg)

Apply method to include systematics in the tracer_arg.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – the modeling tools use to update the tracer arg

• tracer_arg (_SourceGalaxyArgsT) – the original source galaxy tracer arg to which we apply the systematic.

Returns:

a new source galaxy tracer arg with the systematic applied

Return type:

_SourceGalaxyArgsT

firecrown.likelihood.source.SOURCE_GALAXY_SYSTEMATIC_DEFAULT_DELTA_Z = 0.0

firecrown.likelihood.source.SOURCE_GALAXY_SYSTEMATIC_DEFAULT_SIGMA_Z = 1.0

class firecrown.likelihood.source.SourceGalaxyPhotoZShift(sacc_tracer, active=True)

Bases: SourceGalaxySystematic[_SourceGalaxyArgsT], Generic[_SourceGalaxyArgsT]

A photo-z shift bias.

This systematic shifts the photo-z distribution by some amount delta_z.

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:

delta_z – the photo-z shift.

Parameters:

• sacc_tracer (str)

• active (bool)

delta_z

apply(tools, tracer_arg)

Apply a shift to the photo-z distribution of a source.

Parameters:

• tools (firecrown.modeling_tools.ModelingTools) – the modeling tools use to update the tracer arg

• tracer_arg (_SourceGalaxyArgsT) – the original source galaxy tracer arg to which we apply the systematic.

Returns:

a new source galaxy tracer arg with the systematic applied

Return type:

_SourceGalaxyArgsT

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

Bases: SourceGalaxyPhotoZShift

Photo-z shift systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.source.PhotoZShiftFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for PhotoZShift objects.

Parameters:

data (Any)

model_config

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

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

create(bin_name)

Create a PhotoZShift object with the given tracer name.

Parameters:

bin_name (str)

Return type:

PhotoZShift

create_global()

Create a PhotoZShift object with the given tracer name.

Return type:

PhotoZShift

firecrown.likelihood.source.dndz_shift_and_stretch_active(z, dndz, delta_z, sigma_z)

Shift and stretch the photo-z distribution using an active transformation.

We use “makima” interpolation, a cubic spline method based on the modified Akima algorithm. This approach prevents overshooting when the data remains constant for more than two consecutive nodes. Additionally, we set extrapolate=False and we set extrapolated values to zero.

The active transformation preserves the redshift array and modifies the dndz array. This transformation introduces an interpolation error on dndz.

Parameters:

• z (numpy.typing.NDArray[numpy.float64]) – the redshifts

• dndz (numpy.typing.NDArray[numpy.float64]) – the dndz

• delta_z (float) – the photo-z shift

• sigma_z (float) – the photo-z stretch

Returns:

the shifted and stretched dndz

Return type:

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

firecrown.likelihood.source.dndz_shift_and_stretch_passive(z, dndz, delta_z, sigma_z)

Shift and stretch the photo-z distribution using a passive transformation.

Parameters:

• z (numpy.typing.NDArray[numpy.float64]) – the redshifts

• dndz (numpy.typing.NDArray[numpy.float64]) – the dndz

• delta_z (float) – the photo-z shift

• sigma_z (float) – the photo-z stretch

Returns:

the shifted and stretched dndz

Return type:

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

class firecrown.likelihood.source.SourceGalaxyPhotoZShiftandStretch(sacc_tracer, active=True)

Bases: SourceGalaxyPhotoZShift[_SourceGalaxyArgsT]

A photo-z shift & stretch bias.

This systematic shifts and widens the photo-z distribution by some amount delta_z.

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:

• delta_z – the photo-z shift.

• sigma_z – the photo-z stretch.

Parameters:

• sacc_tracer (str)

• active (bool)

sigma_z

apply(_, tracer_arg)

Apply a shift & stretch to the photo-z distribution of a source.

Parameters:

• _ (firecrown.modeling_tools.ModelingTools)

• tracer_arg (_SourceGalaxyArgsT)

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

Bases: SourceGalaxyPhotoZShiftandStretch

Photo-z shift and stretch systematic.

Parameters:

• sacc_tracer (str)

• active (bool)

class firecrown.likelihood.source.PhotoZShiftandStretchFactory(/, **data)

Bases: pydantic.BaseModel

Factory class for PhotoZShiftandStretch objects.

Parameters:

data (Any)

model_config

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

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

create(bin_name)

Create a PhotoZShiftandStretch object with the given tracer name.

Parameters:

bin_name (str)

Return type:

PhotoZShiftandStretch

create_global()

Create a PhotoZShiftandStretch object with the given tracer name.

Return type:

PhotoZShiftandStretch

class firecrown.likelihood.source.SourceGalaxySelectField(field='delta_matter')

Bases: SourceGalaxySystematic[_SourceGalaxyArgsT], Generic[_SourceGalaxyArgsT]

The source galaxy select field systematic.

A systematic that allows specifying the 3D field that will be used to select the 3D power spectrum when computing the angular power spectrum.

Parameters:

field (str)

field = 'delta_matter'

apply(tools, tracer_arg)

Apply method to include systematics in the tracer_arg.

Parameters:

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

• tracer_arg (_SourceGalaxyArgsT) – the original source galaxy tracer arg to which we apply the systematics.

Returns:

a new source galaxy tracer arg with the systematic applied

Return type:

_SourceGalaxyArgsT

class firecrown.likelihood.source.SourceGalaxy(*, sacc_tracer, systematics=None)

Bases: Source, Generic[_SourceGalaxyArgsT]

Source class for galaxy based sources.

Parameters:

• sacc_tracer (str)

• systematics (None | Sequence[SourceGalaxySystematic])

sacc_tracer

current_tracer_args: None | _SourceGalaxyArgsT = None

systematics: firecrown.updatable.UpdatableCollection[SourceGalaxySystematic]

tracer_args: _SourceGalaxyArgsT

read_systematics(sacc_data)

Read the systematics for this source from the SACC file.

Parameters:

sacc_data (sacc.Sacc) – The SACC data object to be read

Return type:

None