autolens.FitFluxes

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

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(name, fluxes, noise_map, positions, …) Initialize self.

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
fluxes
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_fluxes
noise_normalization [Noise_Term] = sum(log(2*pi*[Noise]**2.0))
normalized_residual_map Normalized_Residual = (Data - Model_Data) / Noise
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