Jax to Main!

README.rst

@@ -20,6 +20,18 @@ PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
 .. image:: https://joss.theoj.org/papers/10.21105/joss.04475/status.svg
    :target: https://doi.org/10.21105/joss.04475
 
+.. image:: https://www.repostatus.org/badges/latest/active.svg
+    :target: https://www.repostatus.org/#active
+    :alt: Project Status: Active
+
+.. image:: https://img.shields.io/pypi/pyversions/autogalaxy
+    :target: https://pypi.org/project/autogalaxy/
+    :alt: Python Versions
+
+.. image:: https://img.shields.io/pypi/v/autogalaxy.svg
+    :target: https://pypi.org/project/autogalaxy/
+    :alt: PyPI Version
+
 `Installation Guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_ |
 `readthedocs <https://pyautogalaxy.readthedocs.io/en/latest/index.html>`_ |
 `Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_ |

autogalaxy/__init__.py

@@ -1,3 +1,8 @@
+from autoconf.dictable import register_parser
+from autofit import conf
+
+conf.instance.register(__file__)
+
 from autoconf.dictable import from_dict, from_json, output_to_json, to_dict
 from autoarray.dataset import preprocess  # noqa
 from autoarray.dataset.imaging.dataset import Imaging  # noqa
@@ -23,8 +28,6 @@
 from autoarray.mask.mask_1d import Mask1D  # noqa
 from autoarray.mask.mask_2d import Mask2D  # noqa
 from autoarray.mask.derive.zoom_2d import Zoom2D
-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
@@ -50,8 +53,6 @@
 
 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
@@ -115,6 +116,4 @@
 from autoconf.fitsable import output_to_fits
 from autoconf.fitsable import hdu_list_for_output_from
 
-conf.instance.register(__file__)
-
 __version__ = "2025.5.10.1"

autogalaxy/abstract_fit.py

@@ -110,9 +110,7 @@ def linear_light_profile_intensity_dict(
             reconstruction = self.inversion.reconstruction_dict[linear_obj_func]
 
             for i, light_profile in enumerate(linear_obj_func.light_profile_list):
-                linear_light_profile_intensity_dict[light_profile] = float(
-                    reconstruction[i]
-                )
+                linear_light_profile_intensity_dict[light_profile] = reconstruction[i]
 
         return linear_light_profile_intensity_dict
 

autogalaxy/aggregator/agg_util.py

@@ -140,7 +140,7 @@ def adapt_images_from(
 
             galaxy_name_image_dict[value.header["EXTNAME"].lower()] = adapt_image
 
-        instance = fit.model.instance_from_prior_medians(ignore_prior_limits=True)
+        instance = fit.model.instance_from_prior_medians(ignore_assertions=True)
 
         adapt_images = AdaptImages(galaxy_name_image_dict=galaxy_name_image_dict)
 

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,
         )