LsstBlend¶
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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
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K
¶ Number of components.
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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
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coord
¶ The coordinate in tree.
The coordinate can be used to traverse the tree and for
__getitem__
.
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frame
¶ Frame of the components.
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n_components
¶ Number of components.
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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
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parameters
¶ The list of non-fixed parameters
Returns: - list of parameters available for optimization
- If `parameter.fixed == True`, the parameter will not returned here.
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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
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check_parameters
()¶ Check the all parameters have finite elements
Raises: - `ArithmeticError`
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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 iterationsf_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
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freeze
()¶ Fix all parameters
The tree will not provide optimizable parameters anymore.
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get_model
(*params)¶ Get the model of this component tree
Parameters: - params: tuple of optimization parameters
Returns: - model: array
(Bands, Height, Width) data cube
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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
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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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