autolens.FitPositionsImage

class autolens.FitPositionsImage(name: str, positions: autoarray.structures.grids.two_d.grid_2d_irregular.Grid2DIrregular, noise_map: autoarray.structures.arrays.values.ValuesIrregular, tracer: autolens.lens.ray_tracing.Tracer, point_solver: autolens.point.point_solver.PointSolver, point_profile: Optional[autogalaxy.profiles.point_sources.Point] = None)
__init__(name: str, positions: autoarray.structures.grids.two_d.grid_2d_irregular.Grid2DIrregular, noise_map: autoarray.structures.arrays.values.ValuesIrregular, tracer: autolens.lens.ray_tracing.Tracer, point_solver: autolens.point.point_solver.PointSolver, point_profile: Optional[autogalaxy.profiles.point_sources.Point] = None)

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 Chi_Squared = ((Residuals) / (Noise)) ** 2.0 = ((Data - Model)**2.0)/(Variances)
figure_of_merit
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 LinearEqn module):
log_likelihood Log Likelihood = -0.5*[Chi_Squared_Term + Noise_Term] (see functions above for these definitions)
log_likelihood_with_regularization Log Likelihood = -0.5*[Chi_Squared_Term + Regularization_Term + Noise_Term] (see functions above for these definitions)
mask
model_positions
noise_normalization [Noise_Term] = sum(log(2*pi*[Noise]**2.0))
normalized_residual_map Normalized_Residual = (Data - Model_Data) / Noise
positions
potential_chi_squared_map The signal-to-noise_map of the dataset and noise-map which are fitted.
reduced_chi_squared
residual_map Residuals = (Data - Model_Data).
signal_to_noise_map The signal-to-noise_map of the dataset and noise-map which are fitted.
total_mappers
residual_map

Residuals = (Data - Model_Data).

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