autolens.FitPositionsImage#

class autolens.FitPositionsImage(name: str, positions: Grid2DIrregular, noise_map: ValuesIrregular, tracer: Tracer, point_solver: PointSolver, point_profile: Optional[Point] = None)[source]#
__init__(name: str, positions: Grid2DIrregular, noise_map: ValuesIrregular, tracer: Tracer, point_solver: PointSolver, point_profile: Optional[Point] = None)[source]#

A lens position fitter, which takes a set of positions (e.g. from a plane in the tracer) and computes their maximum separation, such that points which tracer closer to one another have a higher log_likelihood.

Parameters
  • positions (Grid2DIrregular) – The (y,x) arc-second coordinates of positions which the maximum distance and log_likelihood is computed using.

  • noise_value – The noise-value assumed when computing the log likelihood.

Methods

__init__(name, positions, noise_map, tracer, ...)

A lens position fitter, which takes a set of positions (e.g.

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

figure_of_merit

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:

mask

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

model_data

Returns the model positions, which are computed via the point solver.

model_positions

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:

positions

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 between the masked dataset and model data, where:

signal_to_noise_map

The signal-to-noise_map of the dataset and noise-map which are fitted.