Merge branch 'feature/cored_NFW' of https://github.com/Jammy2211/PyAutoGalaxy into feature/cored_NFW

Author: Niek Wielders

CONTRIBUTING.md

@@ -1,3 +1,21 @@
+# Community Guidelines
+
+We strive to maintain a welcoming, respectful, and inclusive community. All contributors—whether opening issues, submitting pull requests, reviewing code, or participating in discussions—are expected to follow these guidelines.
+
+- Be respectful and considerate of others.
+- Assume good intent and be patient, especially with newcomers.
+- Keep feedback constructive and focused on the work, not the person.
+- Communicate clearly and professionally.
+- Respect maintainers’ time and decisions.
+
+Harassment, discrimination, or abusive behavior of any kind will not be tolerated.
+
+This project follows our [Code of Conduct](CODE_OF_CONDUCT.md). By participating, you agree to uphold it.
+
+### Reporting concerns
+If you experience or witness behavior that violates these guidelines or the Code of Conduct, please contact the project maintainers privately.
+
+
 # Contributing
 
 Contributions are welcome and greatly appreciated!
@@ -74,3 +92,4 @@ Before you submit a pull request, check that it meets these guidelines:
 
 1. The pull request should include new tests for all the core routines that have been developed.
 2. If the pull request adds functionality, the docs should be updated accordingly.
+

README.rst

@@ -34,7 +34,7 @@ PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
 
 `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>`_ |
+`Introduction on Colab <https://colab.research.google.com/github/Jammy2211/autogalaxy_workspace/blob/release/start_here.ipynb>`_
 `HowToGalaxy <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_
 
 **PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:
@@ -54,7 +54,7 @@ The following links are useful for new starters:
 
 - `The PyAutoGalaxy readthedocs <https://pyautogalaxy.readthedocs.io/en/latest>`_, which includes `an overview of PyAutoGalaxy's core features <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_.
 
-- `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_, where you can try **PyAutoGalaxy** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Google Colab <https://colab.research.google.com/github/Jammy2211/autogalaxy_workspace/blob/release/start_here.ipynb>`_, where you can try **PyAutoGalaxy** in a web browser (without installation).
 
 - `The autogalaxy_workspace GitHub repository <https://github.com/Jammy2211/autogalaxy_workspace>`_, which includes example scripts and the `HowToGalaxy Jupyter notebook lectures <https://github.com/Jammy2211/autogalaxy_workspace/tree/main/notebooks/howtogalaxy>`_ which give new users a step-by-step introduction to **PyAutoGalaxy**.
 
@@ -63,149 +63,43 @@ Core Aims
 
 **PyAutoGalaxy** has three core aims:
 
-- **Model Complexity**: Fitting complex galaxy morphology models (e.g. Multi Gaussian Expansion, Shapelets, Ellipse Fitting, Irregular Meshes) that go beyond just simple Sersic fitting (which is supported too!).
+- **Big Data**: Scaling automated Sérsic fitting to extremely large datasets, *accelerated with JAX on GPUs and using tools like an SQL database to **build a scalable scientific workflow***.
 
-- **Data Variety**: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.
+- **Model Complexity**: Fitting complex galaxy morphology models (e.g. Multi Gaussian Expansion, Shapelets, Ellipse Fitting, Irregular Meshes) that go beyond just simple Sérsic fitting.
 
-- **Big Data**: Scaling automated analysis to extremely large datasets, using tools like an SQL database to build a scalable scientific workflow.
+- **Data Variety**: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.
 
 A complete overview of the software's aims is provided in our `Journal of Open Source Software paper <https://joss.theoj.org/papers/10.21105/joss.04475>`_.
 
-API Overview
+Community & Support
+-------------------
+
+Support for **PyAutoGalaxy** is available via our Slack workspace, where the community shares updates, discusses
+galaxy modeling and analysis, and helps troubleshoot problems.
+
+Slack is invitation-only. If you’d like to join, please send an email requesting an invite.
+
+For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/Jammy2211/PyAutoGalaxy/issues).
+
+Here’s a clean, **AutoGalaxy**-appropriate rewrite, keeping the same structure and tone but removing lensing-specific language:
+
+HowToGalaxy
+-----------
+
+For users less familiar with galaxy analysis, Bayesian inference, and scientific analysis, you may wish to read through
+the **HowToGalaxy** lectures. These introduce the basic principles of galaxy modeling and Bayesian inference, with
+the material pitched at undergraduate level and above.
+
+A complete overview of the lectures is provided on the `HowToGalaxy readthedocs page <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html`_
+
+Citations
+---------
+
+Information on how to cite **PyAutoGalaxy** in publications can be found `on the citations page <https://github.com/Jammy2211/PyAutoGalaxy/blob/main/CITATIONS.rst>`_.
+
+Contributing
 ------------
 
-Galaxy morphology calculations are performed in **PyAutoGalaaxy** by building a ``Plane`` object from ``LightProfile``
-and ``Galaxy`` objects. We create a simple galaxy system where a redshift 0.5
-``Galaxy`` with an ``Sersic`` ``LightProfile`` representing a bulge and an ``Exponential`` ``LightProfile``
-representing a disk.
-
-.. code-block:: python
-
-    import autogalaxy as ag
-    import autogalaxy.plot as aplt
-
-    """
-    To describe the galaxy emission two-dimensional grids of (y,x) Cartesian
-    coordinates are used.
-    """
-    grid = ag.Grid2D.uniform(
-        shape_native=(50, 50),
-        pixel_scales=0.05,  # <- Conversion from pixel units to arc-seconds.
-    )
-
-    """
-    The galaxy has an elliptical sersic light profile representing its bulge.
-    """
-    bulge=ag.lp.Sersic(
-        centre=(0.0, 0.0),
-        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.9, angle=45.0),
-        intensity=1.0,
-        effective_radius=0.6,
-        sersic_index=3.0,
-    )
-
-    """
-    The galaxy also has an elliptical exponential disk
-    """
-    disk = ag.lp.Exponential(
-        centre=(0.0, 0.0),
-        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.7, angle=30.0),
-        intensity=0.5,
-        effective_radius=1.6,
-    )
-
-    """
-    We combine the above light profiles to compose a galaxy at redshift 1.0.
-    """
-    galaxy = ag.Galaxy(redshift=1.0, bulge=bulge, disk=disk)
-
-    """
-    We create a Plane, which in this example has just one galaxy but can
-    be extended for datasets with many galaxies.
-    """
-    plane = ag.Plane(
-        galaxies=[galaxy],
-    )
-
-    """
-    We can use the Grid2D and Plane to perform many calculations, for example
-    plotting the image of the galaxyed source.
-    """
-    plane_plotter = aplt.GalaxiesPlotter(plane=plane, grid=grid)
-    plane_plotter.figures_2d(image=True)
-
-
-With **PyAutoGalaxy**, you can begin modeling a galaxy in just a couple of minutes. The example below demonstrates a
-simple analysis which fits a galaxy's light.
-
-.. code-block:: python
-
-    import autofit as af
-    import autogalaxy as ag
-
-    import os
-
-    """
-    Load Imaging data of the strong galaxy from the dataset folder of the workspace.
-    """
-    dataset = ag.Imaging.from_fits(
-        data_path="/path/to/dataset/image.fits",
-        noise_map_path="/path/to/dataset/noise_map.fits",
-        psf_path="/path/to/dataset/psf.fits",
-        pixel_scales=0.1,
-    )
-
-    """
-    Create a mask for the data, which we setup as a 3.0" circle.
-    """
-    mask = ag.Mask2D.circular(
-        shape_native=dataset.shape_native,
-        pixel_scales=dataset.pixel_scales,
-        radius=3.0
-    )
-
-    """
-    We model the galaxy using an Sersic LightProfile.
-    """
-    light_profile = ag.lp.Sersic
-
-    """
-    We next setup this profile as model components whose parameters are free & fitted for
-    by setting up a Galaxy as a Model.
-    """
-    galaxy_model = af.Model(ag.Galaxy, redshift=1.0, light=light_profile)
-    model = af.Collection(galaxy=galaxy_model)
-
-    """
-    We define the non-linear search used to fit the model to the data (in this case, Dynesty).
-    """
-    search = af.Nautilus(name="search[example]", n_live=50)
-    
-    """
-    We next set up the `Analysis`, which contains the `log likelihood function` that the
-    non-linear search calls to fit the galaxy model to the data.
-    """
-    analysis = ag.AnalysisImaging(dataset=masked_dataset)
-
-    """
-    To perform the model-fit we pass the model and analysis to the search's fit method. This will
-    output results (e.g., dynesty samples, model parameters, visualization) to hard-disk.
-    """
-    result = search.fit(model=model, analysis=analysis)
-
-    """
-    The results contain information on the fit, for example the maximum likelihood
-    model from the Dynesty parameter space search.
-    """
-    print(result.samples.max_log_likelihood())
-
-
-Support
--------
-
-Support for installation issues, help with galaxy modeling and using **PyAutoGalaxy** is available by
-`raising an issue on the GitHub issues page <https://github.com/Jammy2211/PyAutoGalaxy/issues>`_.
-
-We also offer support on the **PyAutoGalaxy** `Slack channel <https://pyautogalaxy.slack.com/>`_, where we also provide the
-latest updates on **PyAutoGalaxy**. Slack is invitation-only, so if you'd like to join send
-an `email <https://github.com/Jammy2211>`_ requesting an invite.
+Information on how to contribute to **PyAutoGalaxy** can be found `on the contributing page <https://github.com/Jammy2211/PyAutoGalaxy/blob/main/CONTRIBUTING.md>`_.
+
+Hands on support for contributions is available via our Slack workspace, again please email to request an invite.

autogalaxy/__init__.py

@@ -6,6 +6,9 @@
 
 from autoconf.dictable import from_dict, from_json, output_to_json, to_dict
 from autoarray.dataset import preprocess  # noqa
+from autoarray.dataset.interferometer.w_tilde import (
+    load_curvature_preload_if_compatible,
+)
 from autoarray.dataset.imaging.dataset import Imaging  # noqa
 from autoarray.dataset.interferometer.dataset import Interferometer  # noqa
 from autoarray.dataset.dataset_model import DatasetModel
@@ -44,6 +47,9 @@
 from autoarray.structures.grids.irregular_2d import Grid2DIrregular  # noqa
 from autoarray.operators.over_sampling.over_sampler import OverSampler  # noqa
 from autoarray.structures.mesh.rectangular_2d import Mesh2DRectangular  # noqa
+from autoarray.structures.mesh.rectangular_2d_uniform import (
+    Mesh2DRectangularUniform,
+)  # noqa
 from autoarray.structures.mesh.delaunay_2d import Mesh2DDelaunay  # noqa
 from autoarray.structures.vectors.uniform import VectorYX2D  # noqa
 from autoarray.structures.vectors.irregular import VectorYX2DIrregular  # noqa
@@ -119,4 +125,4 @@
 from autoconf.fitsable import output_to_fits
 from autoconf.fitsable import hdu_list_for_output_from
 
-__version__ = "2025.12.21.1"
+__version__ = "2026.1.21.3"

autogalaxy/aggregator/interferometer/interferometer.py

@@ -53,7 +53,7 @@ def _interferometer_from(
         )
         uv_wavelengths = fit.value(name="dataset")[3].data
 
-        transformer_class = fit.value(name="dataset.transformer_class")
+        transformer_class = fit.value(name="transformer_class")
 
         dataset = aa.Interferometer(
             data=data,

autogalaxy/analysis/model_util.py

@@ -127,3 +127,28 @@ def mge_model_from(
         Basis,
         profile_list=bulge_gaussian_list,
     )
+
+
+def simulator_start_here_model_from():
+
+    from autogalaxy.profiles.light.snr import Sersic
+    from autogalaxy.galaxy.galaxy import Galaxy
+
+    bulge = af.Model(Sersic)
+
+    bulge.centre = (0.0, 0.0)
+    bulge.ell_comps.ell_comps_0 = af.TruncatedGaussianPrior(
+        mean=0.0, sigma=0.2, lower_limit=-1.0, upper_limit=1.0
+    )
+    bulge.ell_comps.ell_comps_1 = af.TruncatedGaussianPrior(
+        mean=0.0, sigma=0.2, lower_limit=-1.0, upper_limit=1.0
+    )
+    bulge.signal_to_noise_ratio = af.UniformPrior(lower_limit=20.0, upper_limit=60.0)
+    bulge.effective_radius = af.UniformPrior(lower_limit=1.0, upper_limit=5.0)
+    bulge.sersic_index = af.TruncatedGaussianPrior(
+        mean=4.0, sigma=0.5, lower_limit=0.8, upper_limit=5.0
+    )
+
+    galaxy = af.Model(Galaxy, redshift=0.5, bulge=bulge)
+
+    return galaxy

autogalaxy/config/priors/mesh/rectangular.yaml

@@ -1,4 +1,4 @@
-RectangularMagnification:
+RectangularAdaptDensity:
   shape_0:
     type: Uniform
     lower_limit: 20.0
@@ -19,7 +19,7 @@ RectangularMagnification:
     limits:
       lower: 3.0
       upper: inf
-RectangularSource:
+RectangularAdaptImage:
   shape_0:
     type: Uniform
     lower_limit: 20.0

autogalaxy/exc.py

@@ -39,7 +39,7 @@ class PixelizationException(af.exc.FitException):
     """
     Raises exceptions associated with the `inversion/pixelization` modules and `Pixelization` classes.
 
-    For example if a `RectangularMagnification` mesh has dimensions below 3x3.
+    For example if a `RectangularAdaptDensity` mesh has dimensions below 3x3.
 
     This exception overwrites `autoarray.exc.PixelizationException` in order to add a `FitException`. This means that
     if this exception is raised during a model-fit in the analysis class's `log_likelihood_function` that model

autogalaxy/galaxy/to_inversion.py

@@ -80,7 +80,15 @@ def __init__(
 
         self.preloads = preloads
 
-        self._xp = xp
+        self.use_jax = xp is not np
+
+    @property
+    def _xp(self):
+        if self.use_jax:
+            import jax.numpy as jnp
+
+            return jnp
+        return np
 
     @property
     def psf(self) -> Optional[aa.Kernel2D]:

autogalaxy/interferometer/model/analysis.py

@@ -185,5 +185,6 @@ def save_attributes(self, paths: af.DirectoryPaths):
         super().save_attributes(paths=paths)
 
         paths.save_json(
-            "transformer_class", to_dict(self.dataset.transformer.__class__), "dataset"
+            "transformer_class",
+            to_dict(self.dataset.transformer.__class__),
         )

autogalaxy/plot/visuals/two_d.py

@@ -93,15 +93,15 @@ def plot_via_plotter(self, plotter, grid_indexes=None):
                 plotter.tangential_critical_curves_plot.plot_grid(
                     grid=self.tangential_critical_curves
                 )
-            except TypeError:
+            except (AttributeError, TypeError):
                 pass
 
         if self.radial_critical_curves is not None:
             try:
                 plotter.radial_critical_curves_plot.plot_grid(
                     grid=self.radial_critical_curves
                 )
-            except TypeError:
+            except (AttributeError, TypeError):
                 pass
 
         if self.tangential_caustics is not None:
@@ -111,16 +111,19 @@ def plot_via_plotter(self, plotter, grid_indexes=None):
                         grid=self.tangential_caustics
                     )
                 except (AttributeError, ValueError):
-                    plotter.tangential_caustics_plot.plot_grid(
-                        grid=self.tangential_caustics.array
-                    )
-            except TypeError:
+                    try:
+                        plotter.tangential_caustics_plot.plot_grid(
+                            grid=self.tangential_caustics.array
+                        )
+                    except (AttributeError, TypeError):
+                        pass
+            except (AttributeError, TypeError):
                 pass
 
         if self.radial_caustics is not None:
             try:
                 plotter.radial_caustics_plot.plot_grid(grid=self.radial_caustics)
-            except TypeError:
+            except (AttributeError, TypeError):
                 pass
 
     def add_critical_curves_or_caustics(