autolens.AnalysisPoint#

class autolens.AnalysisPoint(point_dict: autolens.point.point_dataset.PointDict, solver: autolens.point.point_solver.PointSolver, imaging=None, cosmology=FlatLambdaCDM(name="Planck15", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0486), settings_lens=<autolens.analysis.settings.SettingsLens object>)[source]#
__init__(point_dict: autolens.point.point_dataset.PointDict, solver: autolens.point.point_solver.PointSolver, imaging=None, cosmology=FlatLambdaCDM(name="Planck15", H0=67.7 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0486), settings_lens=<autolens.analysis.settings.SettingsLens object>)[source]#

The analysis performed for model-fitting a point-source dataset, for example fitting the point-sources of a multiply imaged lensed quasar or supernovae of many source galaxies of a galaxy cluster.

The analysis brings together the data, model and non-linear search in the classes log_likelihood_function, which is called by every iteration of the non-linear search to compute a likelihood value which samples parameter space.

Parameters

  • point_dict – A dictionary containing the full point source dictionary that is used for model-fitting.

  • solver – The object which is used to determine the image-plane of source-plane positions of a model (via a Tracer).

  • imaging – The imaging of the point-source dataset, which is not used for model-fitting but can be used for visualization.

  • cosmology – The cosmology of the ray-tracing calculation.

  • settings_lens – Settings which control how the model-fit is performed.

Methods

__init__(point_dict, solver[, imaging, …])

The analysis performed for model-fitting a point-source dataset, for example fitting the point-sources of a multiply imaged lensed quasar or supernovae of many source galaxies of a galaxy cluster.

fit_positions_for(instance)

log_likelihood_function(instance)

Determine the fit of the strong lens system of lens galaxies and source galaxies to the point source data.

make_result(samples, model, search)

modify_after_fit(paths, model, result)

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

modify_before_fit(paths, model)

Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.

modify_model(model)

profile_log_likelihood_function(paths, instance)

Overwrite this function for profiling of the log likelihood function to be performed every update of a non-linear search.

save_attributes_for_aggregator(paths)

save_results_for_aggregator(paths, model, …)

tracer_via_instance_from(instance[, …])

Create a Tracer from the galaxies contained in a model instance.

visualize(paths, instance, during_analysis)

with_model(model)

Associate an explicit model with this analysis.