Feature/interferometry

autogalaxy/__init__.py

@@ -22,8 +22,6 @@
 from autoarray.inversion.pixelization.border_relocator import BorderRelocator
 from autoarray.mask.mask_1d import Mask1D  # noqa
 from autoarray.mask.mask_2d import Mask2D  # noqa
-from autoarray.operators.convolver import Convolver  # noqa
-from autoarray.operators.convolver import Convolver  # noqa
 from autoarray.operators.transformer import TransformerDFT  # noqa
 from autoarray.operators.transformer import TransformerNUFFT  # noqa
 from autoarray.layout.layout import Layout2D  # noqa
@@ -48,8 +46,6 @@
 
 from .analysis.adapt_images.adapt_images import AdaptImages
 from .analysis.adapt_images.adapt_image_maker import AdaptImageMaker
-from .analysis.maker import FitMaker
-from .analysis.preloads import Preloads
 from . import aggregator as agg
 from . import exc
 from . import plot

autogalaxy/aggregator/imaging/fit_imaging.py

@@ -7,8 +7,6 @@
 import autofit as af
 import autoarray as aa
 
-from autogalaxy.analysis.preloads import Preloads
-
 from autogalaxy.aggregator.imaging.imaging import _imaging_from
 from autogalaxy.aggregator.galaxies import _galaxies_from
 from autogalaxy.aggregator.dataset_model import _dataset_model_from
@@ -19,7 +17,6 @@ def _fit_imaging_from(
     fit: af.Fit,
     instance: Optional[af.ModelInstance] = None,
     settings_inversion: aa.SettingsInversion = None,
-    use_preloaded_grid: bool = True,
 ) -> List[FitImaging]:
     """
     Returns a list of `FitImaging` objects from a `PyAutoFit` loaded directory `Fit` or sqlite database `Fit` object.
@@ -53,10 +50,6 @@ def _fit_imaging_from(
         randomly from the PDF).
     settings_inversion
         Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-    use_preloaded_grid
-        Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This grid
-        may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is used
-        as the fit.
     """
 
     from autogalaxy.imaging.fit_imaging import FitImaging
@@ -71,25 +64,14 @@ def _fit_imaging_from(
 
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
 
-    mesh_grids_of_planes_list = agg_util.mesh_grids_of_planes_list_from(
-        fit=fit, total_fits=len(dataset_list), use_preloaded_grid=use_preloaded_grid
-    )
-
     fit_dataset_list = []
 
-    for dataset, galaxies, dataset_model, adapt_images, mesh_grids_of_planes in zip(
+    for dataset, galaxies, dataset_model, adapt_images in zip(
         dataset_list,
         galaxies_list,
         dataset_model_list,
         adapt_images_list,
-        mesh_grids_of_planes_list,
     ):
-        preloads = agg_util.preloads_from(
-            preloads_cls=Preloads,
-            use_preloaded_grid=use_preloaded_grid,
-            mesh_grids_of_planes=mesh_grids_of_planes,
-            use_w_tilde=False,
-        )
 
         fit_dataset_list.append(
             FitImaging(
@@ -98,7 +80,6 @@ def _fit_imaging_from(
                 dataset_model=dataset_model,
                 adapt_images=adapt_images,
                 settings_inversion=settings_inversion,
-                preloads=preloads,
             )
         )
 
@@ -110,7 +91,6 @@ def __init__(
         self,
         aggregator: af.Aggregator,
         settings_inversion: Optional[aa.SettingsInversion] = None,
-        use_preloaded_grid: bool = True,
     ):
         """
             Interfaces with an `PyAutoFit` aggregator object to create instances of `FitImaging` objects from the results
@@ -145,15 +125,17 @@ def __init__(
                 A `PyAutoFit` aggregator object which can load the results of model-fits.
             settings_inversion
                 Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-            use_preloaded_grid
-                Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
-                grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
-                used as the fit.
+
+        Parameters
+        ----------
+        aggregator
+            A `PyAutoFit` aggregator object which can load the results of model-fits.
+        settings_inversion
+            Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
         """
         super().__init__(aggregator=aggregator)
 
         self.settings_inversion = settings_inversion
-        self.use_preloaded_grid = use_preloaded_grid
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -167,7 +149,7 @@ def object_via_gen_from(
         ----------
         fit
             A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
-        an output directory or from an sqlite database..
+            an output directory or from an sqlite database..
         instance
             A manual instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
             randomly from the PDF).
@@ -176,5 +158,4 @@ def object_via_gen_from(
             fit=fit,
             instance=instance,
             settings_inversion=self.settings_inversion,
-            use_preloaded_grid=self.use_preloaded_grid,
         )

autogalaxy/aggregator/interferometer/fit_interferometer.py

@@ -7,8 +7,6 @@
 import autofit as af
 import autoarray as aa
 
-from autogalaxy.analysis.preloads import Preloads
-
 from autogalaxy.aggregator import agg_util
 from autogalaxy.aggregator.interferometer.interferometer import _interferometer_from
 from autogalaxy.aggregator.dataset_model import _dataset_model_from
@@ -19,7 +17,6 @@ def _fit_interferometer_from(
     fit: af.Fit,
     instance: Optional[af.ModelInstance] = None,
     settings_inversion: aa.SettingsInversion = None,
-    use_preloaded_grid: bool = True,
 ) -> List[FitInterferometer]:
     """
     Returns a list of `FitInterferometer` objects from a `PyAutoFit` loaded directory `Fit` or sqlite database `Fit` object.
@@ -54,10 +51,6 @@ def _fit_interferometer_from(
         randomly from the PDF).
     settings_inversion
         Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-    use_preloaded_grid
-        Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This grid
-        may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is used
-        as the fit.
     """
     from autogalaxy.interferometer.fit_interferometer import FitInterferometer
 
@@ -73,25 +66,14 @@ def _fit_interferometer_from(
 
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
 
-    mesh_grids_of_planes_list = agg_util.mesh_grids_of_planes_list_from(
-        fit=fit, total_fits=len(dataset_list), use_preloaded_grid=use_preloaded_grid
-    )
-
     fit_dataset_list = []
 
-    for dataset, galaxies, dataset_model, adapt_images, mesh_grids_of_planes in zip(
+    for dataset, galaxies, dataset_model, adapt_images in zip(
         dataset_list,
         galaxies_list,
         dataset_model_list,
         adapt_images_list,
-        mesh_grids_of_planes_list,
     ):
-        preloads = agg_util.preloads_from(
-            preloads_cls=Preloads,
-            use_preloaded_grid=use_preloaded_grid,
-            mesh_grids_of_planes=mesh_grids_of_planes,
-            use_w_tilde=False,
-        )
 
         fit_dataset_list.append(
             FitInterferometer(
@@ -100,7 +82,6 @@ def _fit_interferometer_from(
                 dataset_model=dataset_model,
                 adapt_images=adapt_images,
                 settings_inversion=settings_inversion,
-                preloads=preloads,
             )
         )
 
@@ -112,50 +93,51 @@ def __init__(
         self,
         aggregator: af.Aggregator,
         settings_inversion: Optional[aa.SettingsInversion] = None,
-        use_preloaded_grid: bool = True,
     ):
         """
-            Interfaces with an `PyAutoFit` aggregator object to create instances of `FitInterferometer` objects from the
-            results of a model-fit.
+        Interfaces with an `PyAutoFit` aggregator object to create instances of `FitInterferometer` objects from the
+        results of a model-fit.
 
-            The results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+        The results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
         attributes of the fit:
 
-            - 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 inversions used by the fit (`dataset/settings_inversion.json`).
+        - 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 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
+        can load them all at once and create an `FitInterferometer` object via the `_fit_interferometer_from` method.
 
-            The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
-            can load them all at once and create an `FitInterferometer` object via the `_fit_interferometer_from` method.
+        This class's methods returns generators which create the instances of the `FitInterferometer` objects. This ensures
+        that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
+        `FitInterferometer` instances in the memory at once.
 
-            This class's methods returns generators which create the instances of the `FitInterferometer` objects. This ensures
-            that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
-            `FitInterferometer` instances in the memory at once.
+        For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
+        creates instances of the corresponding 3 `FitInterferometer` objects.
 
-            For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
-            creates instances of the corresponding 3 `FitInterferometer` objects.
+        If multiple `Imaging` objects were fitted simultaneously via analysis summing, the `fit.child_values()` method
+        is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
+        `FitImaging` objects.
 
-            If multiple `Imaging` objects were fitted simultaneously via analysis summing, the `fit.child_values()` method
-            is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
-            `FitImaging` objects.
+        This can be done manually, but this object provides a more concise API.
 
-            This can be done manually, but this object provides a more concise API.
+        Parameters
+        ----------
+        aggregator
+            A `PyAutoFit` aggregator object which can load the results of model-fits.
+        settings_inversion
+            Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
 
-            Parameters
-            ----------
-            aggregator
-                A `PyAutoFit` aggregator object which can load the results of model-fits.
-            settings_inversion
-                Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-            use_preloaded_grid
-                Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
-                grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
-                used as the fit.
+        Parameters
+        ----------
+        aggregator
+            A `PyAutoFit` aggregator object which can load the results of model-fits.
+        settings_inversion
+            Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
         """
         super().__init__(aggregator=aggregator)
 
         self.settings_inversion = settings_inversion
-        self.use_preloaded_grid = use_preloaded_grid
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -169,7 +151,7 @@ def object_via_gen_from(
         ----------
         fit
             A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
-        an output directory or from an sqlite database..
+            an output directory or from an sqlite database..
         instance
             A manual instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
             randomly from the PDF).
@@ -178,5 +160,4 @@ def object_via_gen_from(
             fit=fit,
             instance=instance,
             settings_inversion=self.settings_inversion,
-            use_preloaded_grid=self.use_preloaded_grid,
         )

autogalaxy/analysis/analysis/analysis.py

@@ -139,7 +139,7 @@ def profile_log_likelihood_function(
         An `info_dict` is also created which stores information on 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,
+        The results of this profiling are then output to hard-disk in the `profiling` folder of the model-fit results,
         which they can be inspected to ensure run-times are as expected.
 
         Parameters
@@ -192,33 +192,33 @@ def profile_log_likelihood_function(
 
         try:
             info_dict["image_pixels"] = self.dataset.grids.lp.shape_slim
-            info_dict[
-                "sub_total_light_profiles"
-            ] = self.dataset.grids.lp.over_sampler.sub_total
+            info_dict["sub_total_light_profiles"] = (
+                self.dataset.grids.lp.over_sampler.sub_total
+            )
         except AttributeError:
             pass
 
         if fit.model_obj.has(cls=aa.Pixelization):
             info_dict["use_w_tilde"] = fit.inversion.settings.use_w_tilde
             try:
-                info_dict[
-                    "sub_total_pixelization"
-                ] = self.dataset.grids.pixelization.over_sampler.sub_total
+                info_dict["sub_total_pixelization"] = (
+                    self.dataset.grids.pixelization.over_sampler.sub_total
+                )
             except AttributeError:
                 pass
-            info_dict[
-                "use_positive_only_solver"
-            ] = fit.inversion.settings.use_positive_only_solver
-            info_dict[
-                "force_edge_pixels_to_zeros"
-            ] = fit.inversion.settings.force_edge_pixels_to_zeros
+            info_dict["use_positive_only_solver"] = (
+                fit.inversion.settings.use_positive_only_solver
+            )
+            info_dict["force_edge_pixels_to_zeros"] = (
+                fit.inversion.settings.force_edge_pixels_to_zeros
+            )
             info_dict["use_w_tilde_numpy"] = fit.inversion.settings.use_w_tilde_numpy
             info_dict["source_pixels"] = len(fit.inversion.reconstruction)
 
             if hasattr(fit.inversion, "w_tilde"):
-                info_dict[
-                    "w_tilde_curvature_preload_size"
-                ] = fit.inversion.w_tilde.curvature_preload.shape[0]
+                info_dict["w_tilde_curvature_preload_size"] = (
+                    fit.inversion.w_tilde.curvature_preload.shape[0]
+                )
 
         self.output_profiling_info(
             paths=paths, run_time_dict=run_time_dict, info_dict=info_dict