autocti.AnalysisDataset1D#

class autocti.AnalysisDataset1D(dataset: ~autocti.dataset_1d.dataset_1d.dataset_1d.Dataset1D, clocker: ~autocti.clocker.one_d.Clocker1D, settings_cti: ~autocti.model.settings.SettingsCTI1D = <autocti.model.settings.SettingsCTI1D object>, dataset_full: ~typing.Optional[~autocti.dataset_1d.dataset_1d.dataset_1d.Dataset1D] = None)[source]#

Bases: Analysis

Methods

`fit_via_instance_and_dataset_from`
`fit_via_instance_from`
`log_likelihood_function`	Determine the fitness of a particular model
`make_result`
`modify_after_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_before_fit`	PyAutoFit calls this function immediately before the non-linear search begins, therefore it can be used to perform tasks using the final model parameterization.
`modify_model`
`profile_log_likelihood_function`	Overwrite this function for profiling of the log likelihood function to be performed every update of a non-linear search.
`region_list_from`
`save_attributes_for_aggregator`	Before the model-fit via the non-linear search begins, this routine saves attributes of the Analysis object to the pickles folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
`save_results_for_aggregator`
`should_visualize`	Whether a visualize method should be called perform visualization, which depends on the following:
`visualize`
`visualize_before_fit`
`visualize_before_fit_combined`
`visualize_combined`
`with_model`	Associate an explicit model with this analysis.

modify_before_fit(paths: DirectoryPaths, model: Collection)[source]#

PyAutoFit calls this function immediately before the non-linear search begins, therefore it can be used to perform tasks using the final model parameterization.

This function:

Visualizes the 1D dataset, which does not change during the analysis and thus can be done once.

Parameters:

paths – The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored, visualization and the pickled objects used by the aggregator output by this function.
model – The PyAutoFit model object, which includes model components representing the galaxies that are fitted to the imaging data.

log_likelihood_function(instance: ModelInstance) → float[source]#

Determine the fitness of a particular model

Parameters:: instance –
Returns:: fit – How fit the model is and the model
Return type:: Fit

save_attributes_for_aggregator(paths: DirectoryPaths)[source]#

Before the model-fit via the non-linear search begins, this routine saves attributes of the Analysis object to the pickles folder such that they can be loaded after the analysis using PyAutoFit’s database and aggregator tools.

For this analysis the following are output:

The 1D dataset.
The clocker used for modeling / clocking CTI.
The settings used for modeling / clocking CTI.
The full 1D dataset (e.g. unmasked, used for visualizariton).

It is common for these attributes to be loaded by many of the template aggregator functions given in the aggregator modules. For example, when using the database tools to reperform a fit, this will by default load the dataset, settings and other attributes necessary to perform a fit using the attributes output by this function.

Parameters:: paths – The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored, visualization,and the pickled objects used by the aggregator output by this function.