autolens.FitPositionsImagePair#

class FitPositionsImagePair[source]#

Bases: AbstractFitPositionsImagePair

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:
  • data (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

square_distance

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

Overwrite this method to returns the data of the dataset.

log_likelihood

Returns the log likelihood of each model data point's fit to the dataset, where:

model_data

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

noise_map

Overwrite this method to returns the noise-map of the dataset.

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:

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.

source_plane_coordinate

Returns the centre of the point-source in the source-plane, which is used when computing the model image-plane positions from the tracer.

source_plane_index

Returns the integer plane index containing the point source galaxy, which is used when computing the model image-plane positions from the tracer.

source_plane_redshift

Returns the redshift of the plane containing the point source galaxy, which is used when computing the model image-plane positions from the tracer.

property residual_map: ArrayIrregular#

Returns the residual-map between the masked dataset and model data, where:

Residuals = (Data - Model_Data).