Feature/xp namespace

autogalaxy/__init__.py

@@ -25,6 +25,8 @@
     mapper_from as Mapper,
 )  # noqa
 from autoarray.inversion.pixelization.border_relocator import BorderRelocator
+from autoarray.preloads import Preloads
+from autoarray.preloads import mapper_indices_from
 from autoarray.mask.mask_1d import Mask1D  # noqa
 from autoarray.mask.mask_2d import Mask2D  # noqa
 from autoarray.mask.derive.zoom_2d import Zoom2D
@@ -51,6 +53,7 @@
 from autoarray.structures.visibilities import Visibilities  # noqa
 from autoarray.structures.visibilities import VisibilitiesNoiseMap  # noqa
 
+from .analysis import model_util
 from .analysis.adapt_images.adapt_images import AdaptImages
 from .analysis.adapt_images.adapt_image_maker import AdaptImageMaker
 from . import aggregator as agg
@@ -80,7 +83,6 @@
 from .galaxy.galaxy import Galaxy
 from .galaxy.galaxies import Galaxies
 from .galaxy.redshift import Redshift
-from .galaxy.stellar_dark_decomp import StellarDarkDecomp
 from .galaxy.to_inversion import AbstractToInversion
 from .galaxy.to_inversion import GalaxiesToInversion
 from .profiles.geometry_profiles import EllProfile

autogalaxy/analysis/adapt_images/adapt_images.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-import jax.numpy as jnp
 import numpy as np
 from typing import TYPE_CHECKING, Dict, Optional, Tuple
 
@@ -132,7 +131,7 @@ def from_result(cls, result, use_model_images: bool = False) -> "AdaptImages":
             else:
                 galaxy_image = result.subtracted_signal_to_noise_map_galaxy_dict[path]
 
-            minimum_galaxy_value = adapt_minimum_percent * jnp.max(galaxy_image.array)
+            minimum_galaxy_value = adapt_minimum_percent * xp.max(galaxy_image.array)
             galaxy_image[galaxy_image < minimum_galaxy_value] = minimum_galaxy_value
 
             galaxy_name_image_dict[path] = galaxy_image

autogalaxy/analysis/model_util.py

@@ -0,0 +1,129 @@
+import numpy as np
+from typing import Optional, Tuple
+
+import autofit as af
+
+
+def mge_model_from(
+    mask_radius: float,
+    total_gaussians: int = 30,
+    gaussian_per_basis: int = 1,
+    centre_prior_is_uniform: bool = True,
+    centre: Tuple[float, float] = (0.0, 0.0),
+    centre_fixed: Optional[Tuple[float, float]] = None,
+    use_spherical: bool = False,
+) -> af.Collection:
+    """
+    Construct a Multi-Gaussian Expansion (MGE) for the lens or source galaxy light
+
+    This model is designed as a "start here" configuration for lens modeling:
+
+    - The lens and source light are represented by a Basis object composed of many
+      Gaussian light profiles with fixed logarithmically spaced widths (`sigma`).
+    - All Gaussians within each basis share common centres and ellipticity
+      components, reducing degeneracy while retaining flexibility.
+
+    - Users can combine with a lens mass model of their choiuce.
+
+    The resulting model provides a good balance of speed, flexibility, and accuracy
+    for fitting most galaxy-scale strong lenses.
+
+    This code is mostly to make the API simple for new users, hiding the technical
+    details of setting up an MGE. More advanced users may wish to customize the
+    model further.
+
+    Parameters
+    ----------
+    mask_radius
+        The outer radius (in arcseconds) of the circular mask applied to the data.
+        This determines the maximum Gaussian width (`sigma`) used in the lens MGE.
+    lens_total_gaussians
+        Total number of Gaussian light profiles used in the lens MGE basis.
+    source_total_gaussians
+        Total number of Gaussian light profiles used in the source MGE basis.
+    lens_gaussian_per_basis
+        Number of separate Gaussian bases to include for the lens light profile.
+        Each basis has `lens_total_gaussians` components.
+    source_gaussian_per_basis
+        Number of separate Gaussian bases to include for the source light profile.
+        Each basis has `source_total_gaussians` components.
+
+    Returns
+    -------
+    model : af.Collection
+        An `autofit.Collection` containing:
+        - A lens galaxy at redshift 0.5, with:
+          * bulge light profile: MGE basis of Gaussians
+          * mass profile: Isothermal ellipsoid
+          * external shear
+        - A source galaxy at redshift 1.0, with:
+          * bulge light profile: MGE basis of Gaussians
+
+    Notes
+    -----
+    - Lens light Gaussians have widths (sigma) logarithmically spaced between 0.01"
+      and the mask radius.
+    - Source light Gaussians have widths logarithmically spaced between 0.01" and 1.0".
+    - Gaussian centres are free parameters but tied across all components in each
+      basis to reduce dimensionality.
+    - This function is a convenience utility: it hides the technical setup of MGE
+      composition and provides a ready-to-use lens model for quick experimentation.
+    """
+
+    from autogalaxy.profiles.light.linear import Gaussian, GaussianSph
+    from autogalaxy.profiles.basis import Basis
+
+    # The sigma values of the Gaussians will be fixed to values spanning 0.01 to the mask radius, 3.0".
+    log10_sigma_list = np.linspace(-4, np.log10(mask_radius), total_gaussians)
+
+    # By defining the centre here, it creates two free parameters that are assigned below to all Gaussians.
+
+    if centre_fixed is not None:
+        centre_0 = centre[0]
+        centre_1 = centre[1]
+    elif centre_prior_is_uniform:
+        centre_0 = af.UniformPrior(
+            lower_limit=centre[0] - 0.1, upper_limit=centre[0] + 0.1
+        )
+        centre_1 = af.UniformPrior(
+            lower_limit=centre[1] - 0.1, upper_limit=centre[1] + 0.1
+        )
+    else:
+        centre_0 = af.GaussianPrior(mean=centre[0], sigma=0.3)
+        centre_1 = af.GaussianPrior(mean=centre[1], sigma=0.3)
+
+    if use_spherical:
+        model_cls = GaussianSph
+    else:
+        model_cls = Gaussian
+
+    bulge_gaussian_list = []
+
+    for j in range(gaussian_per_basis):
+        # A list of Gaussian model components whose parameters are customized belows.
+
+        gaussian_list = af.Collection(
+            af.Model(model_cls) for _ in range(total_gaussians)
+        )
+
+        # Iterate over every Gaussian and customize its parameters.
+
+        for i, gaussian in enumerate(gaussian_list):
+            gaussian.centre.centre_0 = centre_0  # All Gaussians have same y centre.
+            gaussian.centre.centre_1 = centre_1  # All Gaussians have same x centre.
+            if not use_spherical:
+                gaussian.ell_comps = gaussian_list[
+                    0
+                ].ell_comps  # All Gaussians have same elliptical components.
+            gaussian.sigma = (
+                10 ** log10_sigma_list[i]
+            )  # All Gaussian sigmas are fixed to values above.
+
+        bulge_gaussian_list += gaussian_list
+
+    # The Basis object groups many light profiles together into a single model component.
+
+    return af.Model(
+        Basis,
+        profile_list=bulge_gaussian_list,
+    )

autogalaxy/analysis/plotter_interface.py

@@ -168,18 +168,6 @@ def should_plot(name):
             mat_plot_1d=mat_plot_1d,
         )
 
-        try:
-            if should_plot("subplot_galaxies_1d"):
-                galaxies_plotter.subplot_galaxies_1d()
-        except OverflowError:
-            pass
-
-        try:
-            if should_plot("subplot_galaxies_1d_decomposed"):
-                galaxies_plotter.subplot_galaxies_1d_decomposed()
-        except OverflowError:
-            pass
-
         if should_plot("fits_galaxy_images"):
 
             image_list = [

autogalaxy/config/visualize/plots.yaml

@@ -30,8 +30,6 @@ fit_imaging: {}                            # Settings for plots of fits to imagi
 galaxies:                                  # Settings for plots of galaxies (e.g. GalaxiesPlotter).
   subplot_galaxies: true                   # Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?
   subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?
-  subplot_galaxies_1d: false               # Plot subplot of all quantities in 1D of each galaxies group (e.g. images, convergence)?
-  subplot_galaxies_1d_decomposed: false    # Plot subplot of all quantities in 1D decomposed of each galaxies group (e.g. images, convergence)?
   fits_galaxy_images: false                # Output a .fits file containing images of every galaxy?
 
 inversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).