lib.theory.effect_ap.AnisotropicScaling

class lib.theory.effect_ap.AnisotropicScaling(model=None, basis=None)

Bases: lib.theory.base.BaseModel

Class applying anisotropic scaling of the theory model (correlation function or power spectrum).

Initialize AnisotropicScaling.

Parameters

• model (BaseModel, callable) – Input model.

• basis (ProjectionBasis, default=None) – Projection basis. If None uses BaseModel.basis attribute of input model. If basis.mode is 'multipole', model is first expanded onto Legendre polynomials to get :math:’mu’ dependence.

Methods

copy	Return shallow copy of self.
deepcopy
eval	Evaluate model in rescaled coordinates.
from_state	Instantiate and initalize class with state dictionary.
is_mpi_broadcast
is_mpi_gathered
is_mpi_root
is_mpi_scattered
kmu_scaling	Apply anisotropic scaling to \(k, \mu\) coordinates.
load	Load class in numpy binary format from disk.
load_auto	If different formats are possible, this method should between them based on file name extension.
log_critical
log_debug
log_error
log_info
log_warning
save	Save class to disk.
save_auto	If different formats are possible, this method should between them based on file name extension.
set_scaling	Set scaling parameters, along qpar and perpendicular qperp to the line-of-sight.
smu_scaling	Apply anistropic scaling to \(s, \mu\) coordinates.

Attributes

logger
mpiattrs	MPI attributes
mpicomm
mpiroot
mpistate

__call__(*args, **kwargs)

Evaluate model.

copy()

Return shallow copy of self.

eval(x, mu=0.0, grid=True, **kwargs)

Evaluate model in rescaled coordinates.

Parameters

• x (array) – x-coordinates (\(k\) or \(s\)).

• mu (array) – Angle to the line-of-sight.

• grid (bool) – Whether input x, mu should be interpreted as a grid, in which case the output will be arrays of shape (x.size, mu.size).

• kwargs (dict) – Arguments for input model.

Returns

model

Return type

array

classmethod from_state(state, mpiroot=0, mpicomm=None)

Instantiate and initalize class with state dictionary.

kmu_scaling(k, mu, grid=True)

Apply anisotropic scaling to \(k, \mu\) coordinates.

Parameters

• k (array) – Wavenumbers.

• mu (array) – Angle to the line-of-sight.

• grid (bool) – Whether input k, mu should be interpreted as a grid, in which case the output will be arrays of shape (k.size, mu.size).

Returns

• kap (array) – Wavenumbers after rescaling.

• muap ; array – Angle to the line-of-sight after rescaling.

classmethod load(filename, mpiroot=0, mpicomm=None)

Load class in numpy binary format from disk. If the loaded state contains __class__ and that exists in cls._registry, return instance of cls._registry[__class__] (instead of cls).

load_auto(*args, **kwargs)

If different formats are possible, this method should between them based on file name extension.

property mpiattrs

MPI attributes

save(filename)

Save class to disk.

save_auto(*args, **kwargs)

If different formats are possible, this method should between them based on file name extension.

set_scaling(qpar=1, qperp=1)

Set scaling parameters, along qpar and perpendicular qperp to the line-of-sight.

smu_scaling(s, mu, grid=True)

Apply anistropic scaling to \(s, \mu\) coordinates.

Parameters

• s (array) – Separations.

• mu (array) – Angle to the line-of-sight.

• grid (bool) – Whether input s, mu should be interpreted as a grid, in which case the output will be arrays of shape (s.size, mu.size).

Returns

• sap (array) – Wavenumbers after rescaling.

• muap ; array – Angle to the line-of-sight after rescaling.