lsst.scarlet.lite.models.fit_psf.FittedPsfBlend Class Reference

Inheritance diagram for lsst.scarlet.lite.models.fit_psf.FittedPsfBlend:

Public Member Functions
tuple[int, float]	fit (self, int max_iter, float e_rel=1e-4, int min_iter=1, int resize=10)
	parameterize (self, Callable parameterization)
Public Member Functions inherited from lsst.scarlet.lite.blend.Blend
	__init__ (self, Sequence[Source] sources, Observation observation, dict|None metadata=None)
Image	get_model (self, bool convolve=False, bool use_flux=False)
float	log_likelihood (self)
Blend	fit_spectra (self, bool clip=False)
None	conserve_flux (self, bool mask_footprint=True, Image|None weight_image=None)
ScarletBlendData	to_data (self)
Blend	__getitem__ (self, Any indices)
Blend	__copy__ (self)
Blend	__deepcopy__ (self, dict[int, Any] memo)
Public Member Functions inherited from lsst.scarlet.lite.blend.BlendBase
tuple[int, int, int]	shape (self)
Box	bbox (self)
list[Component]	components (self)
Self	__getitem__ (self, Any indices)
Self	__copy__ (self)
Self	__deepcopy__ (self, dict[int, Any] memo)
Self	copy (self, bool deep=False)

Protected Member Functions
tuple[Image, np.ndarray]	_grad_log_likelihood (self)

Additional Inherited Members
Public Attributes inherited from lsst.scarlet.lite.blend.Blend
	observation = observation
int	it = 0
list	loss = []
Static Public Attributes inherited from lsst.scarlet.lite.blend.BlendBase
Sequence	sources [SourceBase]
dict	metadata | None

Detailed Description

A blend that attempts to fit the PSF along with the source models.

Member Function Documentation

_grad_log_likelihood()

tuple[Image, np.ndarray] lsst.scarlet.lite.models.fit_psf.FittedPsfBlend._grad_log_likelihood ( self )

protected

Gradient of the likelihood wrt the unconvolved model

Reimplemented from lsst.scarlet.lite.blend.Blend.

fit()

tuple[int, float] lsst.scarlet.lite.models.fit_psf.FittedPsfBlend.fit	(		self,
		int	max_iter,
		float	e_rel = 1e-4,
		int	min_iter = 1,
		int	resize = 10 )

Fit all of the parameters

Parameters
----------
max_iter: int
    The maximum number of iterations
e_rel: float
    The relative error to use for determining convergence.
min_iter: int
    The minimum number of iterations.
resize: int
    Number of iterations before attempting to resize the
    resizable components. If `resize` is `None` then
    no resizing is ever attempted.

Reimplemented from lsst.scarlet.lite.blend.Blend.

parameterize()

lsst.scarlet.lite.models.fit_psf.FittedPsfBlend.parameterize	(		self,
		Callable	parameterization )

Convert the component parameter arrays into Parameter instances

Parameters
----------
parameterization:
    A function to use to convert parameters of a given type into
    a `Parameter` in place. It should take a single argument that
    is the `Component` or `Source` that is to be parameterized.

Reimplemented from lsst.scarlet.lite.blend.Blend.

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The documentation for this class was generated from the following file:

• python/lsst/scarlet/lite/models/fit_psf.py