LsstBlend¶

class lsst.meas.extensions.scarlet.LsstBlend(sources, observations)¶

Bases: scarlet.blend.Blend

LSST Blend of sources

It is possible that LSST blends might require different funtionality than those in scarlet, which is being designed for multiresolution blends. So this class exists for any LSST specific changes.

Attributes Summary

`K`	Number of components.
`components`	Flattened tuple of all components in the tree.
`coord`	The coordinate in tree.
`frame`	Frame of the components.
`n_components`	Number of components.
`n_sources`	Number of initial sources or components.
`parameters`	The list of non-fixed parameters
`sources`	Initial list of components or sources that generate the tree.

Methods Summary

`check_parameters`()	Check the all parameters have finite elements
`fit`([max_iter, e_rel, f_rel])	Fit the model for each source to the data
`freeze`()	Fix all parameters
`get_model`(*params)	Get the model of this component tree
`set_frame`(frame)	Set the frame for all components in the tree
`unfreeze`()	Release all parameters

Attributes Documentation

K¶: Number of components.

components¶

Flattened tuple of all components in the tree.

CAUTION: Each component in a tree can only be a leaf of a single node. While one can construct trees that hold the same component multiple times, this method will only return that component at its first encountered location

coord¶

The coordinate in tree.

The coordinate can be used to traverse the tree and for __getitem__.

frame¶: Frame of the components.

n_components¶: Number of components.

n_sources¶

Number of initial sources or components.

This can be different than self.n_components because sources can have multiple components.

Returns:	int: number of initial sources

parameters¶

The list of non-fixed parameters

Returns:	list of parameters available for optimization If `parameter.fixed == True`, the parameter will not returned here.

sources¶

Initial list of components or sources that generate the tree.

This can be different than self.components because sources can have multiple components.

Returns:	The arguments of `__init__`

Methods Documentation

check_parameters()¶

Check the all parameters have finite elements

Raises:	`ArithmeticError`

fit(max_iter=200, e_rel=0.001, f_rel=0.0001, **alg_kwargs)¶

Fit the model for each source to the data

Note that two convergence criteria are specified:

e_rel for the change of the norm of each parameter between two iterations
f_rel for the change of the loss function

Parameters:	max_iter: int Maximum number of iterations if the algorithm doesn’t converge e_rel: float Relative error for parameter convergence f_rel: float Relative error for functional convergence of the loss alg_kwargs: dict Keywords for the `proxmin.adaprox` optimizer

freeze()¶

Fix all parameters

The tree will not provide optimizable parameters anymore.

get_model(*params)¶

Get the model of this component tree

Parameters:	params: tuple of optimization parameters
Returns:	model: array (Bands, Height, Width) data cube

set_frame(frame)¶

Set the frame for all components in the tree

see set_frame for details.

Parameters:	frame: `~scarlet.Frame` Frame to adopt for this component

unfreeze()¶

Release all parameters

The tree will provide all parameters as optimizable parameters. Calling this function overrides previous setting of parameter.fixed for every parameter of this component tree.

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LsstBlend¶