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 that all parameters have finite elements
fit([max_iter, e_rel])	Fit the model for each source to the data
get_model(*params)	Get the model of this component tree
set_frame(frame)	Set the frame for all components in the tree

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 that all parameters have finite elements

Raises:

`ArithmeticError` when non-finite elements are present

fit(max_iter=200, e_rel=0.001, **alg_kwargs)

Fit the model for each source to the data

Parameters:

max_iter: int

Maximum number of iterations if the algorithm doesn’t converge

e_rel: float

Relative error for convergence of the loss function

alg_kwargs: dict

Keywords for the proxmin.adaprox optimizer

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