autolens.AnalysisImaging#

class AnalysisImaging[source]#

Bases: AnalysisDataset

Analysis classes are used by PyAutoFit to fit a model to a dataset via a non-linear search.

This abstract Analysis class has attributes and methods for all model-fits which fit the model to a dataset (e.g. imaging or interferometer data).

This class stores the Cosmology used for the analysis and settings that control aspects of the calculation, including how pixelizations, inversions and lensing calculations are performed.

Parameters

  • dataset – The imaging, interferometer or other dataset that the model if fitted too.

  • positions_likelihood (Union[PositionsLHResample, PositionsLHPenalty, None]) – An object which alters the likelihood function to include a term which accounts for whether image-pixel coordinates in arc-seconds corresponding to the multiple images of the lensed source galaxy trace close to one another in the source-plane.

  • cosmology (LensingCosmology) – The AstroPy Cosmology assumed for this analysis.

  • settings_pixelization (Optional[SettingsPixelization]) – settings controlling how a pixelization is fitted during the model-fit, for example if a border is used when creating the pixelization.

  • settings_inversion (Optional[SettingsInversion]) – Settings controlling how an inversion is fitted during the model-fit, for example which linear algebra formalism is used.

  • settings_lens (Optional[SettingsLens]) – Settings controlling the lens calculation, for example how close the lensed source’s multiple images have to trace within one another in the source plane for the model to not be discarded.

  • raise_inversion_positions_likelihood_exception (bool) – If an inversion is used without the positions_likelihood it is likely a systematic solution will be inferred, in which case an Exception is raised before the model-fit begins to inform the user of this. This exception is not raised if this input is False, allowing the user to perform the model-fit anyway.

Methods

check_and_replace_hyper_images

Using a the result of a previous model-fit, a hyper-dataset can be set up which adapts aspects of the model (e.g.

fit_imaging_via_instance_from

Given a model instance create a FitImaging object.

fit_imaging_via_tracer_from

Given a Tracer, which the analysis constructs from a model instance, create a FitImaging object.

hyper_background_noise_via_instance_from

If the model instance contains a HyperBackgroundNoise attribute, which adds a free parameter to the model that scales the background noise, return this attribute.

hyper_image_sky_via_instance_from

If the model instance contains a HyperImageSky attribute, which adds a free parameter to the model that scales the background sky, return this attribute.

instance_with_associated_hyper_images_from

Using the model image and galaxy images that were set up as the hyper dataset, associate the galaxy images of that result with the galaxies in this model fit.

log_likelihood_cap_from

Certain Inversion's have stochasticity in their log likelihood estimate (e.g.

log_likelihood_function

Given an instance of the model, where the model parameters are set via a non-linear search, fit the model instance to the imaging dataset.

log_likelihood_positions_overwrite_from

Call the positions overwrite log likelihood function, which add a penalty term to the likelihood if the positions of the multiple images of the lensed source do not trace close to one another in the source plane.

make_result

After the non-linear search is complete create its Result, which includes:

modify_after_fit

Call functions that perform tasks after a model-fit is completed, for example ensuring the figure of merit has not changed from previous estimates and resetting preloads.

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

output_or_check_figure_of_merit_sanity

Changes to the PyAutoGalaxy source code may inadvertantly change the numerics of how a log likelihood is computed.

plane_via_instance_from

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

profile_log_likelihood_function

This function is optionally called throughout a model-fit to profile the log likelihood function.

raise_exceptions

save_attributes_for_aggregator

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

save_results_for_aggregator

At the end of a model-fit, 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_stochastic_outputs

Certain Inversion's have stochasticity in their log likelihood estimate (e.g.

set_hyper_dataset

Using a the result of a previous model-fit, set the hyper-dataset for this analysis.

set_preloads

It is common for the model to have components whose parameters are all fixed, and thus the way that component fits the data does not change.

stochastic_log_likelihoods_via_instance_from

Certain Inversion's have stochasticity in their log likelihood estimate.

tracer_via_instance_from

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

visualize

Output images of the maximum log likelihood model inferred by the model-fit.

with_model

Associate an explicit model with this analysis.

Attributes

fit_func

fit_maker_cls

imaging

preloads_cls

modify_before_fit(paths, model)[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:

  • Checks that the hyper-dataset is consistent with previous hyper-datasets if the model-fit is being resumed from a previous run.

  • Checks the model and raises exceptions if certain critieria are not met.

Once inherited from it also visualizes objects which do not change throughout the model fit like the dataset.

Parameters

  • paths (DirectoryPaths) – 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 (Collection) – The PyAutoFit model object, which includes model components representing the galaxies that are fitted to the imaging data.

log_likelihood_function(instance)[source]#

Given an instance of the model, where the model parameters are set via a non-linear search, fit the model instance to the imaging dataset.

This function returns a log likelihood which is used by the non-linear search to guide the model-fit.

For this analysis class, this function performs the following steps:

  1. If the analysis has a hyper dataset, associated the model galaxy images of this dataset to the galaxies in the model instance.

  2. Extract attributes which model aspects of the data reductions, like the scaling the background sky and background noise.

  3. Extracts all galaxies from the model instance and set up a Tracer, which includes ordering the galaxies by redshift to set up each Plane.

  4. Use the Tracer and other attributes to create a FitImaging object, which performs steps such as creating model images of every galaxy in the tracer, blurring them with the imaging dataset’s PSF and computing residuals, a chi-squared statistic and the log likelihood.

Certain models will fail to fit the dataset and raise an exception. For example if an Inversion is used, the linear algebra calculation may be invalid and raise an Exception. In such circumstances the model is discarded and its likelihood value is passed to the non-linear search in a way that it ignores it (for example, using a value of -1.0e99).

Parameters

instance (ModelInstance) – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).

Returns

The log likelihood indicating how well this model instance fitted the imaging data.

Return type

float

fit_imaging_via_instance_from(instance, use_hyper_scaling=True, preload_overwrite=None, profiling_dict=None)[source]#

Given a model instance create a FitImaging object.

This function is used in the log_likelihood_function to fit the model to the imaging data and compute the log likelihood.

Parameters

  • instance (ModelInstance) – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).

  • use_hyper_scaling (bool) – If false, the scaling of the background sky and noise are not performed irrespective of the model components themselves.

  • preload_overwrite (Optional[Preloads]) – If a Preload object is input this is used instead of the preloads stored as an attribute in the analysis.

  • check_positions – Whether the multiple image positions of the lensed source should be checked, i.e. whether they trace within the position threshold of one another in the source plane.

  • profiling_dict (Optional[Dict]) – A dictionary which times functions called to fit the model to data, for profiling.

Returns

The fit of the plane to the imaging dataset, which includes the log likelihood.

Return type

FitImaging

fit_imaging_via_tracer_from(tracer, hyper_image_sky, hyper_background_noise, use_hyper_scaling=True, preload_overwrite=None, profiling_dict=None)[source]#

Given a Tracer, which the analysis constructs from a model instance, create a FitImaging object.

This function is used in the log_likelihood_function to fit the model to the imaging data and compute the log likelihood.

Parameters

  • tracer (Tracer) – The tracer of galaxies whose ray-traced model images are used to fit the imaging data.

  • hyper_image_sky (Optional[HyperImageSky]) – A model component which scales the background sky level of the data before computing the log likelihood.

  • hyper_background_noise (Optional[HyperBackgroundNoise]) – A model component which scales the background noise level of the data before computing the log likelihood.

  • use_hyper_scaling (bool) – If false, the scaling of the background sky and noise are not performed irrespective of the model components themselves.

  • preload_overwrite (Optional[Preloads]) – If a Preload object is input this is used instead of the preloads stored as an attribute in the analysis.

  • profiling_dict (Optional[Dict]) – A dictionary which times functions called to fit the model to data, for profiling.

Returns

The fit of the plane to the imaging dataset, which includes the log likelihood.

Return type

FitImaging

profile_log_likelihood_function(instance, paths=None)[source]#

This function is optionally called throughout a model-fit to profile the log likelihood function.

All function calls inside the log_likelihood_function that are decorated with the profile_func are timed with their times stored in a dictionary called the profiling_dict.

An info_dict is also created which stores information no aspects of the model and dataset that dictate run times, so the profiled times can be interpreted with this context.

The results of this profiling are then output to hard-disk in the preloads folder of the model-fit results, which they can be inspected to ensure run-times are as expected.

Parameters

  • instance (ModelInstance) – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).

  • paths (Optional[DirectoryPaths]) – 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.

stochastic_log_likelihoods_via_instance_from(instance)[source]#

Certain Inversion’s have stochasticity in their log likelihood estimate.

For example, the VoronoiBrightnessImage pixelization, which changes the likelihood depending on how different KMeans seeds change the pixel-grid.

A log likelihood cap can be applied to model-fits performed using these Inversion’s to improve error and posterior estimates. This log likelihood cap is estimated from a list of stochastic log likelihoods, where these log likelihoods are computed using the same model but with different KMeans seeds.

This function computes these stochastic log likelihoods by iterating over many model-fits using different KMeans seeds.

Parameters

instance (ModelInstance) – The maximum log likelihood instance of a model that is has finished being fitted to the dataset.

Returns

A log likelihood cap which is applied in a stochastic model-fit to give improved error and posterior estimates.

Return type

float

visualize(paths, instance, during_analysis)[source]#

Output images of the maximum log likelihood model inferred by the model-fit. This function is called throughout the non-linear search at regular intervals, and therefore provides on-the-fly visualization of how well the model-fit is going.

The visualization performed by this function includes:

  • Images of the best-fit Tracer, including the images of each of its galaxies.

  • Images of the best-fit FitImaging, including the model-image, residuals and chi-squared of its fit to the imaging data.

  • The hyper-images of the model-fit showing how the hyper galaxies are used to represent different galaxies in the dataset.

  • If hyper features are used to scale the noise or background sky, a FitImaging with these features turned off may be output, to indicate how much these features are altering the dataset.

The images output by this function are customized using the file config/visualize/plots.ini.

Parameters

  • paths (DirectoryPaths) – 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.

  • instance (ModelInstance) – An instance of the model that is being fitted to the data by this analysis (whose parameters have been set via a non-linear search).

  • during_analysis (bool) – If True the visualization is being performed midway through the non-linear search before it is finished, which may change which images are output.

make_result(samples, model, sigma=1.0, use_errors=True, use_widths=False)[source]#

After the non-linear search is complete create its Result, which includes:

  • The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute the maximum likelihood model, posteriors and other properties.

  • The model used to fit the data, which uses the samples to create specific instances of the model (e.g. an instance of the maximum log likelihood model).

  • The non-linear search used to perform the model fit.

The ResultImaging object contains a number of methods which use the above objects to create the max log likelihood Tracer, FitImaging, hyper-galaxy images,etc.

Parameters

  • samples (SamplesPDF) – A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC run of samples of the nested sampler.

  • model (Collection) – The PyAutoFit model object, which includes model components representing the galaxies that are fitted to the imaging data.

Returns

The result of fitting the model to the imaging dataset, via a non-linear search.

Return type

ResultImaging

save_attributes_for_aggregator(paths)[source]#

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

For this analysis, it uses the AnalysisDataset object’s method to output the following:

  • The dataset’s data.

  • The dataset’s noise-map.

  • The settings associated with the dataset.

  • The settings associated with the inversion.

  • The settings associated with the pixelization.

  • The Cosmology.

  • The hyper dataset’s model image and galaxy images, if used.

This function also outputs attributes specific to an imaging dataset:

  • Its PSF.

  • Its mask.

  • The positions of the brightest pixels in the lensed source which are used to discard mass models.

  • The preloaded image-plane source plane pixelization if used by the analysis. This ensures that differences in the scikit-learn library do not lead to different pixelizations being computed if results are transferred from a HPC to laptop.

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 perform a fit, the default behaviour is for the dataset, settings and other attributes necessary to perform the fit to be loaded via the pickle files output by this function.

Parameters

paths (DirectoryPaths) – 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.