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Feature/adapt loading
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,195 @@ | ||
| import os | ||
| import logging | ||
| from typing import Optional, Union | ||
|
|
||
| from autoconf import conf | ||
| from autoconf.dictable import to_dict, output_to_json | ||
|
|
||
| import autofit as af | ||
| import autoarray as aa | ||
| import autogalaxy as ag | ||
|
|
||
| from autogalaxy.analysis.analysis.dataset import AnalysisDataset as AgAnalysisDataset | ||
|
|
||
| from autolens.analysis.analysis.lens import AnalysisLens | ||
| from autolens.analysis.result import ResultDataset | ||
| from autolens.analysis.maker import FitMaker | ||
| from autolens.analysis.preloads import Preloads | ||
| from autolens.analysis.positions import PositionsLHResample | ||
| from autolens.analysis.positions import PositionsLHPenalty | ||
|
|
||
| from autolens import exc | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
| logger.setLevel(level="INFO") | ||
|
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| class AnalysisDataset(AgAnalysisDataset, AnalysisLens): | ||
| def __init__( | ||
| self, | ||
| dataset, | ||
| positions_likelihood: Optional[ | ||
| Union[PositionsLHResample, PositionsLHPenalty] | ||
| ] = None, | ||
| adapt_image_maker: Optional[ag.AdaptImageMaker] = None, | ||
| cosmology: ag.cosmo.LensingCosmology = ag.cosmo.Planck15(), | ||
| settings_inversion: aa.SettingsInversion = None, | ||
| raise_inversion_positions_likelihood_exception: bool = True, | ||
| ): | ||
| """ | ||
| Fits a lens 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 | ||
| 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. | ||
| adapt_images | ||
| Contains the adapt-images which are used to make a pixelization's mesh and regularization adapt to the | ||
| reconstructed galaxy's morphology. | ||
| cosmology | ||
| The AstroPy Cosmology assumed for this analysis. | ||
| settings_inversion | ||
| Settings controlling how an inversion is fitted during the model-fit, for example which linear algebra | ||
| formalism is used. | ||
| raise_inversion_positions_likelihood_exception | ||
| 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. | ||
| """ | ||
|
|
||
| super().__init__( | ||
| dataset=dataset, | ||
| adapt_image_maker=adapt_image_maker, | ||
| cosmology=cosmology, | ||
| settings_inversion=settings_inversion, | ||
| ) | ||
|
|
||
| AnalysisLens.__init__( | ||
| self=self, | ||
| positions_likelihood=positions_likelihood, | ||
| cosmology=cosmology, | ||
| ) | ||
|
|
||
| self.preloads = self.preloads_cls() | ||
|
|
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| self.raise_inversion_positions_likelihood_exception = ( | ||
| raise_inversion_positions_likelihood_exception | ||
| ) | ||
|
|
||
| if os.environ.get("PYAUTOFIT_TEST_MODE") == "1": | ||
| self.raise_inversion_positions_likelihood_exception = False | ||
|
|
||
| def modify_before_fit(self, paths: af.DirectoryPaths, model: af.Collection): | ||
| """ | ||
| This function is called immediately before the non-linear search begins and performs final tasks and checks | ||
| before it begins. | ||
| This function: | ||
| - Checks that the adapt-dataset is consistent with previous adapt-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 | ||
| 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. | ||
| """ | ||
|
|
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| super().modify_before_fit(paths=paths, model=model) | ||
|
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| self.raise_exceptions(model=model) | ||
|
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| def raise_exceptions(self, model): | ||
| has_pix = model.has_model(cls=(aa.Pixelization,)) or model.has_instance( | ||
| cls=(aa.Pixelization,) | ||
| ) | ||
|
|
||
| if has_pix: | ||
| if ( | ||
| self.positions_likelihood is None | ||
| and self.raise_inversion_positions_likelihood_exception | ||
| and not conf.instance["general"]["test"][ | ||
| "disable_positions_lh_inversion_check" | ||
| ] | ||
| ): | ||
| raise exc.AnalysisException( | ||
| """ | ||
| You have begun a model-fit which reconstructs the source using a pixelization. | ||
| However, you have not input a `positions_likelihood` object. | ||
| It is likely your model-fit will infer an inaccurate solution. | ||
| Please read the following readthedocs page for a description of why this is, and how to set up | ||
| a positions likelihood object: | ||
| https://pyautolens.readthedocs.io/en/latest/general/demagnified_solutions.html | ||
| """ | ||
| ) | ||
|
|
||
| @property | ||
| def preloads_cls(self): | ||
| return Preloads | ||
|
|
||
| @property | ||
| def fit_maker_cls(self): | ||
| return FitMaker | ||
|
|
||
| def save_results(self, paths: af.DirectoryPaths, result: ResultDataset): | ||
| """ | ||
| 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. | ||
| For this analysis it outputs the following: | ||
| - The maximum log likelihood tracer of the fit. | ||
| 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. | ||
| result | ||
| The result of a model fit, including the non-linear search, samples and maximum likelihood tracer. | ||
| """ | ||
| try: | ||
| output_to_json( | ||
| obj=result.max_log_likelihood_tracer, | ||
| file_path=paths._files_path / "tracer.json", | ||
| ) | ||
| except AttributeError: | ||
| pass | ||
|
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| image_mesh_list = [] | ||
|
|
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| for galaxy in result.instance.galaxies: | ||
| pixelization_list = galaxy.cls_list_from(cls=aa.Pixelization) | ||
|
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| for pixelization in pixelization_list: | ||
| if pixelization is not None: | ||
| image_mesh_list.append(pixelization.image_mesh) | ||
|
|
||
| if len(image_mesh_list) > 0: | ||
| paths.save_json( | ||
| name="preload_mesh_grids_of_planes", | ||
| object_dict=to_dict( | ||
| result.max_log_likelihood_fit.tracer_to_inversion.image_plane_mesh_grid_pg_list | ||
| ), | ||
| ) |
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