autolens.FitPositionsImage#

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

__init__(name: str, positions: autoarray.structures.grids.irregular_2d.Grid2DIrregular, noise_map: autoarray.structures.values.ValuesIrregular, tracer: autolens.lens.ray_tracing.Tracer, point_solver: autolens.point.point_solver.PointSolver, point_profile: Optional[autogalaxy.profiles.point_sources.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 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:
`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.
`total_mappers`