feature/remove_mapper_valued

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!

README.rst

@@ -59,19 +59,19 @@ The following links are useful for new starters:
 
 - `The PyAutoLens readthedocs <https://pyautolens.readthedocs.io/en/latest>`_: which includes `an overview of PyAutoLens's core features <https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>`_.
 
-- `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/release?filepath=start_here.ipynb>`_: try **PyAutoLens** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Google Colab <https://colab.research.google.com/github/Jammy2211/autolens_workspace/blob/release/start_here.ipynb>`_: try **PyAutoLens** in a web browser (without installation).
 
 - `The autolens_workspace GitHub repository <https://github.com/Jammy2211/autolens_workspace>`_: example scripts and the HowToLens Jupyter notebook lectures.
 
-Support
--------
+Community & Support
+-------------------
 
-Support for installation issues, help with lens modeling and using **PyAutoLens** is available by
-`raising an issue on the GitHub issues page <https://github.com/Jammy2211/PyAutoLens/issues>`_.
+Support for **PyAutoLens** is available via our Slack workspace, where the community shares updates, discusses
+gravitational lensing analysis, and helps troubleshoot problems.
 
-We also offer support on the **PyAutoLens** `Slack channel <https://pyautolens.slack.com/>`_, where we also provide the
-latest updates on **PyAutoLens**. Slack is invitation-only, so if you'd like to join send
-an `email <https://github.com/Jammy2211>`_ requesting an invite.
+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/PyAutoLens/issues).
 
 HowToLens
 ---------
@@ -80,4 +80,16 @@ For users less familiar with gravitational lensing, Bayesian inference and scien
 you may wish to read through the **HowToLens** lectures. These teach you the basic principles of gravitational lensing
 and Bayesian inference, with the content pitched at undergraduate level and above.
 
-A complete overview of the lectures `is provided on the HowToLens readthedocs page <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>`_
+A complete overview of the lectures `is provided on the HowToLens readthedocs page <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>`_
+
+Citations
+---------
+
+Information on how to cite **PyAutoLens** in publications can be found `on the citations page <https://github.com/Jammy2211/PyAutoLens/blob/main/CITATIONS.rst>`_.
+
+Contributing
+------------
+
+Information on how to contribute to **PyAutoLens** can be found `on the contributing page <https://github.com/Jammy2211/PyAutoLens/blob/main/CONTRIBUTING.md>`_.
+
+Hands on support for contributions is available via our Slack workspace, again please email to request an invite.

autolens/__init__.py

@@ -16,7 +16,6 @@
 from autoarray.mask.derive.zoom_2d import Zoom2D
 from autoarray.operators.over_sampling.over_sampler import OverSampler  # noqa
 from autoarray.inversion.inversion.dataset_interface import DatasetInterface
-from autoarray.inversion.inversion.mapper_valued import MapperValued
 from autoarray.inversion.pixelization import image_mesh
 from autoarray.inversion.pixelization import mesh
 from autoarray.inversion import regularization as reg

autolens/analysis/result.py

@@ -385,11 +385,5 @@ def source_plane_inversion_centre_from(
                     mapper_index = 0
 
                 inversion = self.max_log_likelihood_fit.inversion
-                mapper = inversion.cls_list_from(cls=aa.AbstractMapper)[mapper_index]
 
-                mapper_valued = aa.MapperValued(
-                    values=inversion.reconstruction_dict[mapper],
-                    mapper=mapper,
-                )
-
-                return mapper_valued.max_pixel_centre
+                return inversion.max_pixel_centre(mapper_index=mapper_index)

autolens/imaging/plot/fit_imaging_plotters.py

@@ -182,7 +182,6 @@ def figures_2d_of_planes(
         plane_signal_to_noise_map: bool = False,
         use_source_vmax: bool = False,
         zoom_to_brightest: bool = True,
-        interpolate_to_uniform: bool = False,
         remove_critical_caustic: bool = False,
     ):
         """
@@ -224,9 +223,6 @@ def figures_2d_of_planes(
         zoom_to_brightest
             For images not in the image-plane (e.g. the `plane_image`), whether to automatically zoom the plot to
             the brightest regions of the galaxies being plotted as opposed to the full extent of the grid.
-        interpolate_to_uniform
-            If `True`, the mapper's reconstruction is interpolated to a uniform grid before plotting, for example
-            meaning that an irregular Delaunay grid can be plotted as a uniform grid.
         remove_critical_caustic
             Whether to remove critical curves and caustics from the plot.
         """
@@ -319,7 +315,6 @@ def figures_2d_of_planes(
                         pixelization_index=0,
                         reconstruction=True,
                         zoom_to_brightest=zoom_to_brightest,
-                        interpolate_to_uniform=interpolate_to_uniform,
                     )
 
             if use_source_vmax:
@@ -341,7 +336,6 @@ def figures_2d_of_planes(
                         pixelization_index=0,
                         reconstruction_noise_map=True,
                         zoom_to_brightest=zoom_to_brightest,
-                        interpolate_to_uniform=interpolate_to_uniform,
                     )
 
             if plane_signal_to_noise_map:
@@ -357,7 +351,6 @@ def figures_2d_of_planes(
                         pixelization_index=0,
                         signal_to_noise_map=True,
                         zoom_to_brightest=zoom_to_brightest,
-                        interpolate_to_uniform=interpolate_to_uniform,
                     )
 
     def subplot(
@@ -786,12 +779,8 @@ def subplot_mappings_of_plane(
                 mapper = inversion_plotter.inversion.cls_list_from(
                     cls=aa.AbstractMapper
                 )[0]
-                mapper_valued = aa.MapperValued(
-                    values=inversion_plotter.inversion.reconstruction_dict[mapper],
-                    mapper=mapper,
-                )
 
-                pix_indexes = mapper_valued.max_pixel_list_from(
+                pix_indexes = inversion_plotter.inversion.max_pixel_list_from(
                     total_pixels=total_pixels, filter_neighbors=True
                 )
 

autolens/interferometer/plot/fit_interferometer_plotters.py

@@ -302,7 +302,6 @@ def figures_2d_of_planes(
         plane_noise_map: bool = False,
         plane_signal_to_noise_map: bool = False,
         zoom_to_brightest: bool = True,
-        interpolate_to_uniform: bool = False,
     ):
         """
         Plots images representing each individual `Plane` in the fit's `Tracer` in 2D, which are computed via the
@@ -333,9 +332,6 @@ def figures_2d_of_planes(
         zoom_to_brightest
             For images not in the image-plane (e.g. the `plane_image`), whether to automatically zoom the plot to
             the brightest regions of the galaxies being plotted as opposed to the full extent of the grid.
-        interpolate_to_uniform
-            If `True`, the mapper's reconstruction is interpolated to a uniform grid before plotting, for example
-            meaning that an irregular Delaunay grid can be plotted as a uniform grid.
         """
         if plane_image:
             if not self.tracer.planes[plane_index].has(cls=aa.Pixelization):
@@ -354,7 +350,6 @@ def figures_2d_of_planes(
                     pixelization_index=0,
                     reconstruction=True,
                     zoom_to_brightest=zoom_to_brightest,
-                    interpolate_to_uniform=interpolate_to_uniform,
                 )
 
         if plane_noise_map:
@@ -367,7 +362,6 @@ def figures_2d_of_planes(
                     pixelization_index=0,
                     reconstruction_noise_map=True,
                     zoom_to_brightest=zoom_to_brightest,
-                    interpolate_to_uniform=interpolate_to_uniform,
                 )
 
         if plane_signal_to_noise_map:
@@ -380,7 +374,6 @@ def figures_2d_of_planes(
                     pixelization_index=0,
                     signal_to_noise_map=True,
                     zoom_to_brightest=zoom_to_brightest,
-                    interpolate_to_uniform=interpolate_to_uniform,
                 )
 
     def subplot_fit(self):
@@ -462,12 +455,7 @@ def subplot_mappings_of_plane(
                 "total_mappings_pixels"
             ]
 
-            mapper = inversion_plotter.inversion.cls_list_from(cls=aa.AbstractMapper)[0]
-            mapper_valued = aa.MapperValued(
-                values=inversion_plotter.inversion.reconstruction_dict[mapper],
-                mapper=mapper,
-            )
-            pix_indexes = mapper_valued.max_pixel_list_from(
+            pix_indexes = inversion_plotter.inversion.max_pixel_list_from(
                 total_pixels=total_pixels, filter_neighbors=True
             )
 

docs/installation/overview.rst

@@ -6,7 +6,7 @@ Overview
 **PyAutoLens** requires Python 3.9 - 3.12 and support the Linux, MacOS and Windows operating systems.
 
 **PyAutoLens** can be installed via the Python distribution `Anaconda <https://www.anaconda.com/>`_ or using
-`Pypi <https://pypi.org/>`_ to ``pip install`` **PyAutoLens** into your Python distribution.
+`Pypi <https://pypi.org/>`_ to ``pip install autolens`` into your Python distribution.
 
 We recommend Anaconda as it manages the installation of many major libraries (e.g. numpy, scipy,
 matplotlib, etc.) making installation more straight forward. Windows users must use Anaconda.

test_autolens/analysis/test_result.py

@@ -108,16 +108,10 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
     )
 
     inversion = result.max_log_likelihood_fit.inversion
-    mapper = inversion.cls_list_from(cls=al.AbstractMapper)[0]
-
-    mapper_valued = al.MapperValued(
-        values=inversion.reconstruction_dict[mapper],
-        mapper=mapper,
-    )
 
     assert (
         result.source_plane_inversion_centre_from().in_list[0]
-        == mapper_valued.max_pixel_centre.in_list[0]
+        == inversion.max_pixel_centre().in_list[0]
     )
 
     lens = al.Galaxy(redshift=0.5, light=al.lp.SersicSph(intensity=1.0))