scarlet.renderer

class scarlet.renderer.ConvolutionRenderer(data_frame, model_frame, *parameters, convolution_type='fft', padding=10, psf_shift=None)

Bases: Renderer

Attributes:

children

List of child models

convolution_bounds

Build the slices needed for convolution in real space

parameters

List of parameters, including from the children

Methods

__call__(model, *parameters)	Call self as a function.
check_parameters()	Check that all parameters have finite elements
convolve(model[, convolution_type, psf_shift])	Convolve the model in a single band
get_channel_map(data_frame, model_frame)	Compute the mapping between channels in the model frame and this observation
get_model(*parameters)	Get the model realization
get_models_of_children(*parameters, **kwargs)	Get realization of all child models
get_parameter(i, *parameters)	Access parameters by list index or by name
map_channels(model)	Map to model channels onto the observation channels
update()	Update internal state or configuration of the model

property convolution_bounds

Build the slices needed for convolution in real space

convolve(model, convolution_type=None, psf_shift=None)

Convolve the model in a single band

get_model(*parameters)

Get the model realization

Parameters:

parameters: tuple of optimimzation parameters

Returns:

model: array

Realization of the model

class scarlet.renderer.NullRenderer(data_frame, model_frame)

Bases: Renderer

Attributes:

children

List of child models

parameters

List of parameters, including from the children

Methods

__call__(model, *parameters)	Call self as a function.
check_parameters()	Check that all parameters have finite elements
get_channel_map(data_frame, model_frame)	Compute the mapping between channels in the model frame and this observation
get_model()	Get the model realization
get_models_of_children(*parameters, **kwargs)	Get realization of all child models
get_parameter(i, *parameters)	Access parameters by list index or by name
map_channels(model)	Map to model channels onto the observation channels
update()	Update internal state or configuration of the model

get_model()

Get the model realization

Parameters:

parameters: tuple of optimimzation parameters

Returns:

model: array

Realization of the model

class scarlet.renderer.Renderer(data_frame, model_frame, *parameters)

Bases: Model

Attributes:

children

List of child models

parameters

List of parameters, including from the children

Methods

__call__(model, *parameters)	Call self as a function.
check_parameters()	Check that all parameters have finite elements
get_channel_map(data_frame, model_frame)	Compute the mapping between channels in the model frame and this observation
get_model(*parameters, **kwargs)	Get the model realization
get_models_of_children(*parameters, **kwargs)	Get realization of all child models
get_parameter(i, *parameters)	Access parameters by list index or by name
map_channels(model)	Map to model channels onto the observation channels
update()	Update internal state or configuration of the model

get_channel_map(data_frame, model_frame)

Compute the mapping between channels in the model frame and this observation

Parameters:

model_frame: a `scarlet.Frame` instance

The frame to match

Returns:

channel_map: None, slice, or array

None for identical channels in both frames; slice for concatenated channels; array for linear mapping of model channels onto observation channels

map_channels(model)

Map to model channels onto the observation channels

Parameters:

model: array

The hyperspectral model

Returns:

obs_model: array

model mapped onto the observation channels

class scarlet.renderer.ResolutionRenderer(data_frame, model_frame, padding=10)

Bases: Renderer

Attributes:

children

List of child models

parameters

List of parameters, including from the children

Methods

__call__(model, *parameters)	Call self as a function.
build_diffkernel(data_frame, model_frame)	Builds the differential convolution kernel between the observation and the model psf
check_parameters()	Check that all parameters have finite elements
get_channel_map(data_frame, model_frame)	Compute the mapping between channels in the model frame and this observation
get_model(*parameters)	Get the model realization
get_models_of_children(*parameters, **kwargs)	Get realization of all child models
get_parameter(i, *parameters)	Access parameters by list index or by name
map_channels(model)	Map to model channels onto the observation channels
sinc_shift(imgs, shifts, axes)	Performs 2 1D sinc convolutions and shifting along one rotated axis in Fourier space.
update()	Update internal state or configuration of the model

build_diffkernel(data_frame, model_frame)

Builds the differential convolution kernel between the observation and the model psf

Parameters:

model_frame: Frame object

the frame of the model

Returns

——-

diff_psf: array

the differential psf between observation and frame psf.

get_model(*parameters)

Get the model realization

Parameters:

parameters: tuple of optimimzation parameters

Returns:

model: array

Realization of the model

sinc_shift(imgs, shifts, axes)

Performs 2 1D sinc convolutions and shifting along one rotated axis in Fourier space.

Parameters:

imgs: Fourier

a Fourier object of 2D data to sinc convolve and shift to the adequate shape.

shifts: array

an array of the shift values for each line and columns of data in imgs

axes: array

Optional argument that specifies the axes along which to apply sinc convolution.

Returns:

result: array

the shifted and sinc convolved array in configuration space