autogalaxy.AnalysisInterferometer#

class AnalysisInterferometer[source]#

Bases: AnalysisDataset

Fits a galaxy model to an interferometer dataset via a non-linear search.

The Analysis class defines the log_likelihood_function which fits the model to the dataset and returns the log likelihood value defining how well the model fitted the data.

It handles many other tasks, such as visualization, outputting results to hard-disk and storing results in a format that can be loaded after the model-fit is complete.

This Analysis class is used for all model-fits which fit galaxies to an interferometer dataset.

This class stores the settings used to perform the model-fit for certain components of the model (e.g. a pixelization or inversion), the Cosmology used for the analysis and adapt images used for certain model classes.

Parameters:

dataset (Interferometer) – The interferometer dataset that the model is fitted too.
adapt_images (Optional[AdaptImages]) – The adapt-model image and galaxies images of a previous result in a model-fitting pipeline, which are used by certain classes for adapting the analysis to the properties of the dataset.
cosmology (LensingCosmology) – The Cosmology assumed for this analysis.
settings (Settings) – Settings controlling how an inversion is fitted, for example which linear algebra formalism is used.
title_prefix (str) – A string that is added before the title of all figures output by visualization, for example to put the name of the dataset and galaxy in the title.

Methods

`adapt_images_via_instance_from`
`compute_latent_samples`	Compute latent variables from a model instance.
`compute_latent_variables`	Override to compute latent variables from the instance.
`dataset_model_via_instance_from`	Create a dataset model from a model instance, which is used to fit the dataset.
`fit_for_visualization`	Build the fit used by the visualizer.
`fit_from`	Given a model instance create a FitInterferometer object.
`galaxies_via_instance_from`	Create a list of galaxies from a model instance, which is used to fit the dataset.
`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 interferometer dataset.
`make_result`	After the non-linear search is complete create its Result, which includes:
`modify_after_fit`	Overwrite this method to modify the attributes of the Analysis class before the non-linear search begins.
`modify_before_fit`	This function is called immediately before the non-linear search begins and performs final tasks and checks before it begins.
`modify_model`
`perform_quick_update`	Perform a quick visualization update during non-linear search fitting.
`print_vram_use`	Print JAX VRAM use for a given batch size.
`save_attributes`	Before the model-fit begins, this routine saves attributes of the Analysis object to the files folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
`save_results`	At the end of a model-fit, this routine saves attributes of the Analysis object to the files folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
`save_results_combined`
`with_model`	Associate an explicit model with this analysis.

Attributes

`LATENT_BATCH_MODE`
`LATENT_KEYS`
`interferometer`
`supports_background_update`	Whether this analysis supports background quick updates.
`supports_jax_visualization`	Whether the visualizer can work directly with JAX arrays.

Result#: alias of ResultInterferometer

Visualizer#: alias of VisualizerInterferometer

property interferometer#

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 interferometer 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:

If the analysis has a adapt image, associated the model galaxy images of this dataset to the galaxies in the model instance.
Extract attributes which model aspects of the data reductions, like scaling the background background noise.
Extracts all galaxies from the model instance.
Use the galaxies and other attributes to create a FitInterferometer object, which performs steps such as creating model images of every galaxy, transforming them to the uv-plane via a Fourier transform 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 interferometer data.
Return type:: float

fit_from(instance)[source]#

Given a model instance create a FitInterferometer object.

This function is used in the log_likelihood_function to fit the model to the interferometer 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).
Returns:: The fit of the galaxies to the interferometer dataset, which includes the log likelihood.
Return type:: FitInterferometer

save_attributes(paths)[source]#

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

It outputs the following attributes of the dataset:

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

The settings associated with the inversion.
The settings associated with the pixelization.
The Cosmology.
The adapt image’s model image and galaxy images, as adapt_images.fits, if used.

The following .fits files are also output via the plotter interface:

The real space mask applied to the dataset, in the PrimaryHDU of dataset.fits.
The interferometer dataset as dataset.fits (data / noise-map / uv_wavelengths).

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 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.