autolens.FitFluxes#

class autolens.FitFluxes(name: str, fluxes: autoarray.structures.values.ValuesIrregular, noise_map: autoarray.structures.values.ValuesIrregular, positions: autoarray.structures.grids.irregular_2d.Grid2DIrregular, tracer: autolens.lens.ray_tracing.Tracer, point_profile: Optional[autogalaxy.profiles.point_sources.Point] = None)[source]#

__init__(name: str, fluxes: autoarray.structures.values.ValuesIrregular, noise_map: autoarray.structures.values.ValuesIrregular, positions: autoarray.structures.grids.irregular_2d.Grid2DIrregular, tracer: autolens.lens.ray_tracing.Tracer, point_profile: Optional[autogalaxy.profiles.point_sources.Point] = None)[source]#

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 (LEq) – If the fit uses an LEq 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

__init__(name, fluxes, noise_map, positions, …)

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

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`
`deflections_func`	Returns the defleciton function, which given the image-plane positions computes their deflection angles.
`figure_of_merit`
`fluxes`
`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 LEq 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`
`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:
`potential_chi_squared_map`	The signal-to-noise_map of the dataset and noise-map which are fitted.
`reduced_chi_squared`
`residual_map`	Returns the residual map, over riding the parent method so that the result is converted to a ValuesIrregular object.
`signal_to_noise_map`	The signal-to-noise_map of the dataset and noise-map which are fitted.
`total_mappers`