Feature/remove settings pixelization

autolens/__init__.py

@@ -16,7 +16,6 @@
 from autoarray.inversion.pixelization.mesh.abstract import AbstractMesh
 from autoarray.inversion.regularization.abstract import AbstractRegularization
 from autoarray.inversion.pixelization.pixelization import Pixelization
-from autoarray.inversion.pixelization.settings import SettingsPixelization
 from autoarray.inversion.inversion.settings import SettingsInversion
 from autoarray.inversion.inversion.factory import inversion_from as Inversion
 from autoarray.inversion.inversion.factory import (
@@ -83,7 +82,6 @@
 from . import plot
 from . import aggregator as agg
 from .lens import subhalo
-from .analysis.settings import SettingsLens
 from .lens.ray_tracing import Tracer
 from .lens.to_inversion import TracerToInversion
 from .analysis.positions import PositionsLHResample

autolens/aggregator/fit_imaging.py

@@ -19,7 +19,6 @@ def _fit_imaging_from(
     fit: af.Fit,
     galaxies: List[ag.Galaxy],
     settings_dataset: aa.SettingsImaging = None,
-    settings_pixelization: aa.SettingsPixelization = None,
     settings_inversion: aa.SettingsInversion = None,
     use_preloaded_grid: bool = True,
 ) -> List[FitImaging]:
@@ -30,7 +29,6 @@ def _fit_imaging_from(
 
     - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
     - The mask used to mask the `Imaging` data structure in the fit (`dataset/mask.fits`).
-    - The settings of pixelization used by the fit (`dataset/settings_pixelization.json`).
     - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
     Each individual attribute can be loaded from the database via the `fit.value()` method.
@@ -53,8 +51,6 @@ def _fit_imaging_from(
         A list of galaxies corresponding to a sample of a non-linear search and model-fit.
     settings_dataset
         Optionally overwrite the `SettingsImaging` of the `Imaging` object that is created from the fit.
-    settings_pixelization
-        Optionally overwrite the `SettingsPixelization` of the `Pixelization` object that is created from the fit.
     settings_inversion
         Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
     use_preloaded_grid
@@ -67,9 +63,6 @@ def _fit_imaging_from(
 
     tracer_list = _tracer_from(fit=fit, galaxies=galaxies)
 
-    settings_pixelization = settings_pixelization or fit.value(
-        name="settings_pixelization"
-    )
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
 
     mesh_grids_of_planes_list = agg_util.mesh_grids_of_planes_list_from(
@@ -92,7 +85,6 @@ def _fit_imaging_from(
             FitImaging(
                 dataset=dataset,
                 tracer=tracer,
-                settings_pixelization=settings_pixelization,
                 settings_inversion=settings_inversion,
                 preloads=preloads,
             )
@@ -106,7 +98,6 @@ def __init__(
         self,
         aggregator: af.Aggregator,
         settings_dataset: Optional[aa.SettingsImaging] = None,
-        settings_pixelization: Optional[aa.SettingsPixelization] = None,
         settings_inversion: Optional[aa.SettingsInversion] = None,
         use_preloaded_grid: bool = True,
     ):
@@ -118,7 +109,6 @@ def __init__(
 
         - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
         - The mask used to mask the `Imaging` data structure in the fit (`dataset/mask.fits`).
-        - The settings of pixelization used by the fit (`dataset/settings_pixelization.json`).
         - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
         The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
@@ -143,8 +133,6 @@ def __init__(
             A `PyAutoFit` aggregator object which can load the results of model-fits.
         settings_dataset
             Optionally overwrite the `SettingsImaging` of the `Imaging` object that is created from the fit.
-        settings_pixelization
-            Optionally overwrite the `SettingsPixelization` of the `Pixelization` object that is created from the fit.
         settings_inversion
             Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
         use_preloaded_grid
@@ -155,7 +143,6 @@ def __init__(
         super().__init__(aggregator=aggregator)
 
         self.settings_dataset = settings_dataset
-        self.settings_pixelization = settings_pixelization
         self.settings_inversion = settings_inversion
         self.use_preloaded_grid = use_preloaded_grid
 
@@ -176,7 +163,6 @@ def object_via_gen_from(self, fit, galaxies) -> List[FitImaging]:
             fit=fit,
             galaxies=galaxies,
             settings_dataset=self.settings_dataset,
-            settings_pixelization=self.settings_pixelization,
             settings_inversion=self.settings_inversion,
             use_preloaded_grid=self.use_preloaded_grid,
         )

autolens/aggregator/fit_interferometer.py

@@ -19,7 +19,6 @@ def _fit_interferometer_from(
     galaxies: List[ag.Galaxy],
     real_space_mask: Optional[aa.Mask2D] = None,
     settings_dataset: aa.SettingsInterferometer = None,
-    settings_pixelization: aa.SettingsPixelization = None,
     settings_inversion: aa.SettingsInversion = None,
     use_preloaded_grid: bool = True,
 ) -> List[FitInterferometer]:
@@ -30,7 +29,6 @@ def _fit_interferometer_from(
 
     - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
     - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
-    - The settings of pixelization used by the fit (`dataset/settings_pixelization.json`).
     - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
     Each individual attribute can be loaded from the database via the `fit.value()` method.
@@ -54,8 +52,6 @@ def _fit_interferometer_from(
         A list of galaxies corresponding to a sample of a non-linear search and model-fit.
     settings_dataset
         Optionally overwrite the `SettingsInterferometer` of the `Interferometer` object that is created from the fit.
-    settings_pixelization
-        Optionally overwrite the `SettingsPixelization` of the `Pixelization` object that is created from the fit.
     settings_inversion
         Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
     use_preloaded_grid
@@ -70,9 +66,6 @@ def _fit_interferometer_from(
     )
     tracer_list = _tracer_from(fit=fit, galaxies=galaxies)
 
-    settings_pixelization = settings_pixelization or fit.value(
-        name="settings_pixelization"
-    )
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
 
     mesh_grids_of_planes_list = agg_util.mesh_grids_of_planes_list_from(
@@ -95,7 +88,6 @@ def _fit_interferometer_from(
             FitInterferometer(
                 dataset=dataset,
                 tracer=tracer,
-                settings_pixelization=settings_pixelization,
                 settings_inversion=settings_inversion,
                 preloads=preloads,
             )
@@ -109,7 +101,6 @@ def __init__(
         self,
         aggregator: af.Aggregator,
         settings_dataset: Optional[aa.SettingsInterferometer] = None,
-        settings_pixelization: Optional[aa.SettingsPixelization] = None,
         settings_inversion: Optional[aa.SettingsInversion] = None,
         use_preloaded_grid: bool = True,
         real_space_mask: Optional[aa.Mask2D] = None,
@@ -122,7 +113,6 @@ def __init__(
 
         - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
         - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
-        - The settings of pixelization used by the fit (`dataset/settings_pixelization.json`).
         - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
         The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
@@ -143,8 +133,6 @@ def __init__(
             A `PyAutoFit` aggregator object which can load the results of model-fits.
         settings_dataset
             Optionally overwrite the `SettingsInterferometer` of the `Interferometer` object that is created from the fit.
-        settings_pixelization
-            Optionally overwrite the `SettingsPixelization` of the `Pixelization` object that is created from the fit.
         settings_inversion
             Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
         use_preloaded_grid
@@ -155,7 +143,6 @@ def __init__(
         super().__init__(aggregator=aggregator)
 
         self.settings_dataset = settings_dataset
-        self.settings_pixelization = settings_pixelization
         self.settings_inversion = settings_inversion
         self.use_preloaded_grid = use_preloaded_grid
         self.real_space_mask = real_space_mask
@@ -177,7 +164,6 @@ def object_via_gen_from(self, fit, galaxies) -> FitInterferometer:
             fit=fit,
             galaxies=galaxies,
             settings_dataset=self.settings_dataset,
-            settings_pixelization=self.settings_pixelization,
             settings_inversion=self.settings_inversion,
             use_preloaded_grid=self.use_preloaded_grid,
         )

autolens/aggregator/subhalo.py

@@ -11,7 +11,6 @@ def __init__(
         self,
         aggregator_grid_search: af.GridSearchAggregator,
         settings_dataset: Optional[aa.SettingsImaging] = None,
-        settings_pixelization: Optional[aa.SettingsPixelization] = None,
         settings_inversion: Optional[aa.SettingsInversion] = None,
         use_preloaded_grid: bool = True,
     ):
@@ -22,7 +21,6 @@ def __init__(
 
         self.aggregator_grid_search = aggregator_grid_search
         self.settings_dataset = settings_dataset
-        self.settings_pixelization = settings_pixelization
         self.settings_inversion = settings_inversion
         self.use_preloaded_grid = use_preloaded_grid
 

autolens/analysis/analysis.py

@@ -23,7 +23,6 @@
 from autolens.analysis.positions import PositionsLHPenalty
 from autolens.analysis.visualizer import Visualizer
 from autolens.lens.ray_tracing import Tracer
-from autolens.analysis.settings import SettingsLens
 
 from autolens.lens import ray_tracing_util
 
@@ -40,7 +39,6 @@ def __init__(
         positions_likelihood: Optional[
             Union[PositionsLHResample, PositionsLHPenalty]
         ] = None,
-        settings_lens: SettingsLens = SettingsLens(),
         cosmology: ag.cosmo.LensingCosmology = ag.cosmo.Planck15(),
     ):
         """
@@ -55,14 +53,10 @@ def __init__(
 
         Parameters
         ----------
-        settings_lens
-            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.
         cosmology
             The Cosmology assumed for this analysis.
         """
         self.cosmology = cosmology
-        self.settings_lens = settings_lens or SettingsLens()
         self.positions_likelihood = positions_likelihood
 
     def tracer_via_instance_from(
@@ -162,9 +156,7 @@ def __init__(
         ] = None,
         adapt_result=None,
         cosmology: ag.cosmo.LensingCosmology = ag.cosmo.Planck15(),
-        settings_pixelization: aa.SettingsPixelization = None,
         settings_inversion: aa.SettingsInversion = None,
-        settings_lens: SettingsLens = None,
         raise_inversion_positions_likelihood_exception: bool = True,
     ):
         """
@@ -186,15 +178,9 @@ def __init__(
             trace close to one another in the source-plane.
         cosmology
             The AstroPy Cosmology assumed for this analysis.
-        settings_pixelization
-            settings controlling how a pixelization is fitted during the model-fit, for example if a border is used
-            when creating the pixelization.
         settings_inversion
             Settings controlling how an inversion is fitted during the model-fit, for example which linear algebra
             formalism is used.
-        settings_lens
-            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
             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
@@ -206,19 +192,15 @@ def __init__(
             dataset=dataset,
             adapt_result=adapt_result,
             cosmology=cosmology,
-            settings_pixelization=settings_pixelization,
             settings_inversion=settings_inversion,
         )
 
         AnalysisLensing.__init__(
             self=self,
             positions_likelihood=positions_likelihood,
-            settings_lens=settings_lens,
             cosmology=cosmology,
         )
 
-        self.settings_lens = settings_lens or SettingsLens()
-
         self.preloads = self.preloads_cls()
 
         self.raise_inversion_positions_likelihood_exception = (
@@ -297,9 +279,6 @@ def save_results(self, paths: af.DirectoryPaths, result: ResultDataset):
 
         - The maximum log likelihood tracer of the fit.
 
-        - The stochastic log likelihoods of a pixelization, provided the pixelization has functionality that can
-          compute likelihoods for different KMeans seeds and grids (e.g. `VoronoiBrightnessImage).
-
         Parameters
         ----------
         paths
@@ -332,105 +311,3 @@ def save_results(self, paths: af.DirectoryPaths, result: ResultDataset):
                     result.max_log_likelihood_fit.tracer_to_inversion.image_plane_mesh_grid_pg_list
                 ),
             )
-
-        if conf.instance["general"]["adapt"]["stochastic_outputs"]:
-            if len(image_mesh_list) > 0:
-                for image_mesh in image_mesh_list:
-                    if image_mesh.is_stochastic:
-                        self.save_stochastic_outputs(
-                            paths=paths, samples=result.samples
-                        )
-
-    def log_likelihood_cap_from(
-        self, stochastic_log_likelihoods_json_file: str
-    ) -> float:
-        """
-        Certain `Inversion`'s have stochasticity in their log likelihood estimate (e.g. due to how different KMeans
-        seeds change the pixelization constructed by a `VoronoiBrightnessImage` pixelization).
-
-        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 the log likelihood cap of a model-fit by loading a set of stochastic log likelihoods
-        from a .json file and fitting them with a 1D Gaussian. The cap is the mean value of this Gaussian.
-
-        Parameters
-        ----------
-        stochastic_log_likelihoods_json_file
-            A .json file which loads an ndarray of stochastic log likelihoods, which are likelihoods computed using the
-            same model but with different KMeans seeds.
-
-        Returns
-        -------
-        float
-            A log likelihood cap which is applied in a stochastic model-fit to give improved error and posterior
-            estimates.
-        """
-        try:
-            with open(stochastic_log_likelihoods_json_file, "r") as f:
-                stochastic_log_likelihoods = np.asarray(json.load(f))
-        except FileNotFoundError:
-            raise exc.AnalysisException(
-                "The file 'stochastic_log_likelihoods.json' could not be found in the output of the model-fitting results"
-                "in the analysis before the stochastic analysis. Rerun PyAutoLens with `stochastic_outputs=True` in the"
-                "`general.ini` configuration file."
-            )
-
-        mean, sigma = norm.fit(stochastic_log_likelihoods)
-
-        return mean
-
-    def stochastic_log_likelihoods_via_instance_from(self, instance) -> List[float]:
-        raise NotImplementedError()
-
-    def save_stochastic_outputs(self, paths: af.DirectoryPaths, samples: af.Samples):
-        """
-        Certain `Inversion`'s have stochasticity in their log likelihood estimate (e.g. due to how different KMeans
-        seeds change the pixelization constructed by a `VoronoiBrightnessImage` pixelization).
-
-        This function computes the stochastic log likelihoods of such a model, which are the log likelihoods computed
-        using the same model but with different KMeans seeds.
-
-        It outputs these stochastic likelihoods to a format which can be loaded via PyAutoFit's database tools, and
-        may also be loaded if this analysis is extended with a stochastic model-fit that applies a log likelihood cap.
-
-        This function also outputs visualization showing a histogram of the stochastic likelihood distribution.
-
-        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.
-        samples
-            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.
-        """
-        stochastic_log_likelihoods_json_file = path.join(
-            paths._files_path, "stochastic_log_likelihoods.json"
-        )
-
-        try:
-            with open(stochastic_log_likelihoods_json_file, "r") as f:
-                stochastic_log_likelihoods = np.asarray(json.load(f))
-        except FileNotFoundError:
-            instance = samples.max_log_likelihood()
-            stochastic_log_likelihoods = (
-                self.stochastic_log_likelihoods_via_instance_from(instance=instance)
-            )
-
-        if stochastic_log_likelihoods is None:
-            return
-
-        with open(stochastic_log_likelihoods_json_file, "w") as outfile:
-            json.dump(
-                [float(evidence) for evidence in stochastic_log_likelihoods], outfile
-            )
-
-        visualizer = Visualizer(visualize_path=paths.image_path)
-
-        visualizer.visualize_stochastic_histogram(
-            stochastic_log_likelihoods=stochastic_log_likelihoods,
-            max_log_evidence=np.max(samples.log_likelihood_list),
-            histogram_bins=self.settings_lens.stochastic_histogram_bins,
-        )