autolens.FitFluxes#

class FitFluxes[source]#

Bases: FitDataset

Class to fit a masked dataset where the dataset’s data structures are any dimension.

Parameters:

dataset (MaskedDataset) – The masked dataset (data, mask, noise-map, etc.) that is fitted.
model_data – The model data the masked dataset is fitted with.
inversion (Inversion) – If the fit uses an Inversion this is the instance of the object used to perform the fit. This determines if the log_likelihood or log_evidence is used as the figure_of_merit.
use_mask_in_fit – If True, masked data points are omitted from the fit. If False they are not (in most use cases the dataset will have been processed to remove masked points, for example the slim representation).

residual_map#: The residual-map of the fit (data - model_data).

chi_squared_map#: The chi-squared-map of the fit ((data - model_data) / noise_maps ) **2.0

chi_squared#: The overall chi-squared of the model’s fit to the dataset, summed over every data point.

reduced_chi_squared#: The reduced chi-squared of the model’s fit to simulate (chi_squared / number of data points), summed over every data point.

noise_normalization#: The overall normalization term of the noise_map, summed over every data point.

log_likelihood#: The overall log likelihood of the model’s fit to the dataset, summed over evey data point.

Methods

Attributes

`chi_squared`	Returns the chi-squared terms of the model data's fit to an dataset, by summing the chi-squared-map.
`chi_squared_map`	Returns the chi-squared-map between the residual-map and noise-map, where:
`data`	rtype: `Union`[`Array1D`, `Array2D`, `ArrayIrregular`, `Visibilities`]
`deflections_func`	Returns the defleciton function, which given the image-plane positions computes their deflection angles.
`figure_of_merit`	rtype: `float`
`fluxes`	rtype: `ArrayIrregular`
`inversion`	Overwrite this method so it returns the inversion used to fit the dataset.
`log_evidence`	Returns the log evidence of the inversion's fit to a dataset, where the log evidence includes a number of terms which quantify the complexity of an inversion's reconstruction (see the Inversion module):
`log_likelihood`	Returns the log likelihood of each model data point's fit to the dataset, where:
`log_likelihood_with_regularization`	Returns the log likelihood of an inversion's fit to the dataset, including a regularization term which comes from an inversion:
`magnifications`	The magnification of every position in the image-plane, which is computed from the tracer's deflection angle map via the Hessian.
`mask`	Overwrite this method so it returns the mask of the dataset which is fitted to the input data.
`model_data`	The model-fluxes of the tracer at each of the image-plane positions.
`model_fluxes`	rtype: `ArrayIrregular`
`noise_map`
`noise_normalization`	Returns the noise-map normalization term of the noise-map, summing the noise_map value in every pixel as:
`normalized_residual_map`	Returns the normalized residual-map between the masked dataset and model data, where:
`reduced_chi_squared`	rtype: `float`
`residual_flux_fraction_map`	Returns the residual flux fraction map, which shows the fraction of signal in each pixel that is not fitted by the model, therefore where:
`residual_map`	Returns the residual map, over riding the parent method so that the result is converted to a ArrayIrregular object.
`signal_to_noise_map`	The signal-to-noise_map of the dataset and noise-map which are fitted.

property mask#: Overwrite this method so it returns the mask of the dataset which is fitted to the input data.

property deflections_func#

Returns the defleciton function, which given the image-plane positions computes their deflection angles.

For multi-plane ray-tracing with more than 2 planes, the deflection function determines the index of the plane with the last mass profile such that the deflection function does not perform unnecessary computations beyond this plane.

property magnifications#: The magnification of every position in the image-plane, which is computed from the tracer’s deflection angle map via the Hessian.

property model_data#

The model-fluxes of the tracer at each of the image-plane positions.

Only point sources which are a PointFlux type, and therefore which include a model parameter for its flux, are used.

property residual_map: ArrayIrregular#

Returns the residual map, over riding the parent method so that the result is converted to a ArrayIrregular object.

Return type:: ArrayIrregular

property chi_squared: float#

Returns the chi-squared terms of the model data’s fit to an dataset, by summing the chi-squared-map.

Return type:: float