Remove MatPlot1D/MatPlot2D from all PyAutoLens plotters; use output=/cmap=/use_log10= API

Replace mat_plot_2d=/mat_plot_1d= constructor arguments across all plotter classes
with the simplified output=/cmap=/use_log10= API. Eliminates MultiFigurePlotter,
MultiYX1DPlotter dependencies and all MatPlot wrapper objects throughout autolens.

Key changes:
- All Plotter subclasses (TracerPlotter, FitImagingPlotter, FitInterferometerPlotter,
  FitPointDatasetPlotter, PointDatasetPlotter, SubhaloPlotter, SubhaloSensitivityPlotter)
  now accept output=, cmap=, use_log10= directly
- SubhaloPlotter: replaced update_mat_plot_array_overlay with direct array_overlay= arg
  to Array2DPlotter; subplot methods use plt.subplots + _save_subplot pattern
- SubhaloSensitivityPlotter: same pattern, sensitivity_to_fits uses Output directly
- plotter_interface.py files updated: output_from() call (no quick_update arg),
  subplot filename fixed to subplot_fit_combined
- FitInterferometerPlotter: removed invalid subplot delegation to meta plotter
- Fixed variable shadowing bug in figures_2d(ax=None) for point plotters: standalone
  flag captured before positions block to avoid ax rebinding affecting fluxes block
- All test files updated from mat_plot_2d=aplt.MatPlot2D(output=...) to output=...

https://claude.ai/code/session_01CzJBy8KvFXiNchoNdk5i9k

Author: claude

autolens/analysis/plotter_interface.py

@@ -1,184 +1,184 @@
-import ast
-import numpy as np
-from typing import Optional
-
-from autoconf import conf
-from autoconf.fitsable import hdu_list_for_output_from
-
-import autoarray as aa
-import autogalaxy as ag
-import autogalaxy.plot as aplt
-
-from autogalaxy.analysis.plotter_interface import plot_setting
-
-from autogalaxy.analysis.plotter_interface import PlotterInterface as AgPlotterInterface
-
-from autolens.lens.tracer import Tracer
-from autolens.lens.plot.tracer_plotters import TracerPlotter
-
-
-class PlotterInterface(AgPlotterInterface):
-    """
-    Visualizes the maximum log likelihood model of a model-fit, including components of the model and fit objects.
-
-    The methods of the `PlotterInterface` are called throughout a non-linear search using the `Analysis`
-    classes `visualize` method.
-
-    The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml`.
-
-    Parameters
-    ----------
-    image_path
-        The path on the hard-disk to the `image` folder of the non-linear searches results.
-    """
-
-    def tracer(
-        self,
-        tracer: Tracer,
-        grid: aa.type.Grid2DLike,
-    ):
-        """
-        Visualizes a `Tracer` object.
-
-        Images are output to the `image` folder of the `image_path`. When used with a non-linear search the `image_path`
-        points to the search's results folder and this function visualizes the maximum log likelihood `Tracer`
-        inferred by the search so far.
-
-        Visualization includes a subplot of individual images of attributes of the tracer (e.g. its image, convergence,
-        deflection angles) and .fits files containing its attributes grouped together.
-
-        The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml` under
-        the `tracer` header.
-
-        Parameters
-        ----------
-        tracer
-            The maximum log likelihood `Tracer` of the non-linear search.
-        grid
-            A 2D grid of (y,x) arc-second coordinates used to perform ray-tracing, which is the masked grid tied to
-            the dataset.
-        """
-
-        def should_plot(name):
-            return plot_setting(section="tracer", name=name)
-
-        mat_plot_2d = self.mat_plot_2d_from()
-
-        tracer_plotter = TracerPlotter(
-            tracer=tracer,
-            grid=grid,
-            mat_plot_2d=mat_plot_2d,
-        )
-
-        if should_plot("subplot_galaxies_images"):
-            tracer_plotter.subplot_galaxies_images()
-
-        if should_plot("fits_tracer"):
-
-            zoom = aa.Zoom2D(mask=grid.mask)
-            mask = zoom.mask_2d_from(buffer=1)
-            grid_zoom = aa.Grid2D.from_mask(mask=mask)
-
-            image_list = [
-                tracer.convergence_2d_from(grid=grid_zoom).native,
-                tracer.potential_2d_from(grid=grid_zoom).native,
-                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 0],
-                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 1],
-            ]
-
-            hdu_list = hdu_list_for_output_from(
-                values_list=[image_list[0].mask.astype("float")] + image_list,
-                ext_name_list=[
-                    "mask",
-                    "convergence",
-                    "potential",
-                    "deflections_y",
-                    "deflections_x",
-                ],
-                header_dict=grid_zoom.mask.header_dict,
-            )
-
-            hdu_list.writeto(self.image_path / "tracer.fits", overwrite=True)
-
-        if should_plot("fits_source_plane_images"):
-
-            shape_native = conf.instance["visualize"]["plots"]["tracer"][
-                "fits_source_plane_shape"
-            ]
-            shape_native = ast.literal_eval(shape_native)
-
-            zoom = aa.Zoom2D(mask=grid.mask)
-            mask = zoom.mask_2d_from(buffer=1)
-            grid_source_plane = aa.Grid2D.from_extent(
-                extent=mask.geometry.extent, shape_native=tuple(shape_native)
-            )
-
-            image_list = [grid_source_plane.mask.astype("float")]
-            ext_name_list = ["mask"]
-
-            for i, plane in enumerate(tracer.planes[1:]):
-
-                if plane.has(cls=ag.LightProfile):
-
-                    image = plane.image_2d_from(
-                        grid=grid_source_plane,
-                    ).native
-
-                else:
-
-                    image = np.zeros(grid_source_plane.shape_native)
-
-                image_list.append(image)
-                ext_name_list.append(f"source_plane_image_{i+1}")
-
-            hdu_list = hdu_list_for_output_from(
-                values_list=image_list,
-                ext_name_list=ext_name_list,
-                header_dict=grid_source_plane.mask.header_dict,
-            )
-
-            hdu_list.writeto(
-                self.image_path / "source_plane_images.fits", overwrite=True
-            )
-
-    def image_with_positions(self, image: aa.Array2D, positions: aa.Grid2DIrregular):
-        """
-        Visualizes the positions of a model-fit, where these positions are used to penalize lens models where
-        the positions to do trace within an input threshold of one another in the source-plane.
-
-        Images are output to the `image` folder of the `image_path`. When used with a non-linear search the `image_path`
-        is the output folder of the non-linear search.
-
-        The visualization is an image of the strong lens with the positions overlaid.
-
-        The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml` under the
-        `positions` header.
-
-        Parameters
-        ----------
-        imaging
-            The imaging dataset whose image the positions are overlaid.
-        positions
-            The 2D (y,x) arc-second positions used to penalize inaccurate mass models.
-        """
-
-        def should_plot(name):
-            return plot_setting(section=["positions"], name=name)
-
-        mat_plot_2d = self.mat_plot_2d_from()
-
-        if positions is not None:
-            pos_arr = np.array(
-                positions.array if hasattr(positions, "array") else positions
-            )
-
-            image_plotter = aplt.Array2DPlotter(
-                array=image,
-                mat_plot_2d=mat_plot_2d,
-                positions=[pos_arr],
-            )
-
-            image_plotter.set_filename("image_with_positions")
-
-            if should_plot("image_with_positions"):
-                image_plotter.figure_2d()
+import ast
+import numpy as np
+from typing import Optional
+
+from autoconf import conf
+from autoconf.fitsable import hdu_list_for_output_from
+
+import autoarray as aa
+import autogalaxy as ag
+import autogalaxy.plot as aplt
+
+from autogalaxy.analysis.plotter_interface import plot_setting
+
+from autogalaxy.analysis.plotter_interface import PlotterInterface as AgPlotterInterface
+
+from autolens.lens.tracer import Tracer
+from autolens.lens.plot.tracer_plotters import TracerPlotter
+
+
+class PlotterInterface(AgPlotterInterface):
+    """
+    Visualizes the maximum log likelihood model of a model-fit, including components of the model and fit objects.
+
+    The methods of the `PlotterInterface` are called throughout a non-linear search using the `Analysis`
+    classes `visualize` method.
+
+    The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml`.
+
+    Parameters
+    ----------
+    image_path
+        The path on the hard-disk to the `image` folder of the non-linear searches results.
+    """
+
+    def tracer(
+        self,
+        tracer: Tracer,
+        grid: aa.type.Grid2DLike,
+    ):
+        """
+        Visualizes a `Tracer` object.
+
+        Images are output to the `image` folder of the `image_path`. When used with a non-linear search the `image_path`
+        points to the search's results folder and this function visualizes the maximum log likelihood `Tracer`
+        inferred by the search so far.
+
+        Visualization includes a subplot of individual images of attributes of the tracer (e.g. its image, convergence,
+        deflection angles) and .fits files containing its attributes grouped together.
+
+        The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml` under
+        the `tracer` header.
+
+        Parameters
+        ----------
+        tracer
+            The maximum log likelihood `Tracer` of the non-linear search.
+        grid
+            A 2D grid of (y,x) arc-second coordinates used to perform ray-tracing, which is the masked grid tied to
+            the dataset.
+        """
+
+        def should_plot(name):
+            return plot_setting(section="tracer", name=name)
+
+        output = self.output_from()
+
+        tracer_plotter = TracerPlotter(
+            tracer=tracer,
+            grid=grid,
+            output=output,
+        )
+
+        if should_plot("subplot_galaxies_images"):
+            tracer_plotter.subplot_galaxies_images()
+
+        if should_plot("fits_tracer"):
+
+            zoom = aa.Zoom2D(mask=grid.mask)
+            mask = zoom.mask_2d_from(buffer=1)
+            grid_zoom = aa.Grid2D.from_mask(mask=mask)
+
+            image_list = [
+                tracer.convergence_2d_from(grid=grid_zoom).native,
+                tracer.potential_2d_from(grid=grid_zoom).native,
+                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 0],
+                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 1],
+            ]
+
+            hdu_list = hdu_list_for_output_from(
+                values_list=[image_list[0].mask.astype("float")] + image_list,
+                ext_name_list=[
+                    "mask",
+                    "convergence",
+                    "potential",
+                    "deflections_y",
+                    "deflections_x",
+                ],
+                header_dict=grid_zoom.mask.header_dict,
+            )
+
+            hdu_list.writeto(self.image_path / "tracer.fits", overwrite=True)
+
+        if should_plot("fits_source_plane_images"):
+
+            shape_native = conf.instance["visualize"]["plots"]["tracer"][
+                "fits_source_plane_shape"
+            ]
+            shape_native = ast.literal_eval(shape_native)
+
+            zoom = aa.Zoom2D(mask=grid.mask)
+            mask = zoom.mask_2d_from(buffer=1)
+            grid_source_plane = aa.Grid2D.from_extent(
+                extent=mask.geometry.extent, shape_native=tuple(shape_native)
+            )
+
+            image_list = [grid_source_plane.mask.astype("float")]
+            ext_name_list = ["mask"]
+
+            for i, plane in enumerate(tracer.planes[1:]):
+
+                if plane.has(cls=ag.LightProfile):
+
+                    image = plane.image_2d_from(
+                        grid=grid_source_plane,
+                    ).native
+
+                else:
+
+                    image = np.zeros(grid_source_plane.shape_native)
+
+                image_list.append(image)
+                ext_name_list.append(f"source_plane_image_{i+1}")
+
+            hdu_list = hdu_list_for_output_from(
+                values_list=image_list,
+                ext_name_list=ext_name_list,
+                header_dict=grid_source_plane.mask.header_dict,
+            )
+
+            hdu_list.writeto(
+                self.image_path / "source_plane_images.fits", overwrite=True
+            )
+
+    def image_with_positions(self, image: aa.Array2D, positions: aa.Grid2DIrregular):
+        """
+        Visualizes the positions of a model-fit, where these positions are used to penalize lens models where
+        the positions to do trace within an input threshold of one another in the source-plane.
+
+        Images are output to the `image` folder of the `image_path`. When used with a non-linear search the `image_path`
+        is the output folder of the non-linear search.
+
+        The visualization is an image of the strong lens with the positions overlaid.
+
+        The images output by the `PlotterInterface` are customized using the file `config/visualize/plots.yaml` under the
+        `positions` header.
+
+        Parameters
+        ----------
+        imaging
+            The imaging dataset whose image the positions are overlaid.
+        positions
+            The 2D (y,x) arc-second positions used to penalize inaccurate mass models.
+        """
+
+        def should_plot(name):
+            return plot_setting(section=["positions"], name=name)
+
+        output = self.output_from()
+
+        if positions is not None:
+            pos_arr = np.array(
+                positions.array if hasattr(positions, "array") else positions
+            )
+
+            image_plotter = aplt.Array2DPlotter(
+                array=image,
+                output=output,
+                positions=[pos_arr],
+            )
+
+            image_plotter.set_filename("image_with_positions")
+
+            if should_plot("image_with_positions"):
+                image_plotter.figure_2d()