autolens.FitPositionsImage#
- class FitPositionsImage[source]#
Bases:
FitDataset
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
Attributes
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
- rtype
figure_of_merit
- rtype
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:
Overwrite this method so it returns the mask of the dataset which is fitted to the input data.
Returns the model positions, which are computed via the point solver.
model_positions
- rtype
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
- rtype
reduced_chi_squared
- rtype
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.
- property mask#
Overwrite this method so it returns the mask of the dataset which is fitted to the input data.
- property model_data: Grid2DIrregular#
Returns the model positions, which are computed via the point solver.
It if common for many more image-plane positions to be computed than actual positions in the dataset. In this case, each data point is paired with its closest model position.
- Return type
- property residual_map: ArrayIrregular#
Returns the residual-map between the masked dataset and model data, where:
Residuals = (Data - Model_Data).
- Return type