scarlet.blend

class scarlet.blend.Blend(sources, observations)

Bases: CombinedComponent

The blended scene

The class represents a scene as collection of and provides the functions to fit it to data.

Attributes:

bbox

Bounding box of the blend

children

List of child models

frame

Hyper-spectral characteristics is this model

log_likelihood

Log likelihood at each iteration

parameters

List of parameters, including from the children

Methods

check_parameters()	Check that all parameters have finite elements
fit([max_iter, e_rel, min_iter, noise_factor])	Fit the model for each source to the data
get_model(*parameters[, frame])	Get the model of the entire blend
get_models_of_children(*parameters, **kwargs)	Get realization of all child models
get_parameter(i, *parameters)	Access parameters by list index or by name
model_to_box([bbox, model])	Project a model into a frame
update()	Update internal state or configuration of the model

property bbox

Bounding box of the blend

fit(max_iter=200, e_rel=0.001, min_iter=1, noise_factor=0, **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

min_iter: int

Maximum number of iterations if the algorithm doesn’t converge

alg_kwargs: dict

Keywords for the proxmin.adaprox optimizer

get_model(*parameters, frame=None)

Get the model of the entire blend

Parameters:

parameters: tuple of optimization parameters

frame: `scarlet.Frame`

Alternative Frame to project the model into

Returns:

model: array

(Bands, Height, Width) data cube

property log_likelihood

Log likelihood at each iteration

The fitting method computes and sums the negative log-likelihood for the each observation given the current model as loss function for the optimization.

Returns:

log_likelihood: array of (positive) log-likelihood