Overhaul 2D plot styling and subplot layout

- Default colormap driven by segmentdata.py via config (autoarray)
- Colorbar: 3 ticks (min/mid/max), unit on middle, configurable
  fraction/pad/rotation/labelsize (separate figure vs subplot sizes)
- Tick labels: arcsecond suffix, inward positioning, 2 d.p. formatting,
  vertical y-axis labels
- Axes box aspect set from data extent (restores old square-panel
  behaviour, eliminates subplot whitespace)
- hide_unused_axes() called in all fixed-grid subplot functions
- subplot_imaging_dataset consolidated to 3x3 grid; subplot_imaging removed
- Position dots default to black
- save_reconstruction_csv() extracted to inversion_plots.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: 2 people

autoarray/config/visualize/general.yaml

@@ -15,13 +15,27 @@ units:
   cb_unit: $\,\,\mathrm{e^{-}}\,\mathrm{s^{-1}}$ # The string or latex unit label used for the colorbar of the image, for example electrons per second.
   scaled_symbol: '"'                    # The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).
   unscaled_symbol: pix                  # The symbol used when plotting spatial coordinates in unscaled pixel units.
+colormap: autoarray               # Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).
+ticks:
+  extent_factor_2d: 0.75  # Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).
+  number_of_ticks_2d: 3   # Number of ticks on each spatial axis of 2D plots.
+colorbar:
+  fraction: 0.047          # Fraction of original axes to use for the colorbar.
+  pad: 0.01                # Padding between colorbar and axes.
+  labelrotation: 90        # Rotation of colorbar tick labels in degrees.
+  labelsize: 22            # Font size of colorbar tick labels for single-panel figures.
+  labelsize_subplot: 24    # Font size of colorbar tick labels for subplot panels.
 mat_plot:
   figure:
     figsize: (7, 7)                     # Default figure size. Override via aplt.Figure(figsize=(...)).
   yticks:
     fontsize: 22                        # Default y-tick font size. Override via aplt.YTicks(fontsize=...).
+  yticks_subplot:
+    fontsize: 22                        # Default y-tick font size for subplot panels.
   xticks:
     fontsize: 22                        # Default x-tick font size. Override via aplt.XTicks(fontsize=...).
+  xticks_subplot:
+    fontsize: 22                        # Default x-tick font size for subplot panels.
   title:
     fontsize: 24                        # Default title font size. Override via aplt.Title(fontsize=...).
   ylabel:

autoarray/dataset/plot/imaging_plots.py

@@ -92,13 +92,15 @@ def subplot_imaging_dataset(
             title="Point Spread Function",
             colormap=colormap,
             use_log10=use_log10,
+            cb_unit="",
         )
         plot_array(
             dataset.psf.kernel,
             ax=axes[4],
             title="PSF (log10)",
             colormap=colormap,
             use_log10=True,
+            cb_unit="",
         )
 
     plot_array(
@@ -107,6 +109,7 @@ def subplot_imaging_dataset(
         title="Signal-To-Noise Map",
         colormap=colormap,
         use_log10=use_log10,
+        cb_unit="",
         grid=grid,
         positions=positions,
         lines=lines,
@@ -120,6 +123,7 @@ def subplot_imaging_dataset(
             title="Over Sample Size (Light Profiles)",
             colormap=colormap,
             use_log10=use_log10,
+            cb_unit="",
         )
 
     over_sample_size_pix = getattr(getattr(dataset, "grids", None), "over_sample_size_pixelization", None)
@@ -130,8 +134,11 @@ def subplot_imaging_dataset(
             title="Over Sample Size (Pixelization)",
             colormap=colormap,
             use_log10=use_log10,
+            cb_unit="",
         )
 
+    from autoarray.plot.utils import hide_unused_axes
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)
 

autoarray/dataset/plot/interferometer_plots.py

@@ -6,7 +6,7 @@
 from autoarray.plot.array import plot_array
 from autoarray.plot.grid import plot_grid
 from autoarray.plot.yx import plot_yx
-from autoarray.plot.utils import subplot_save
+from autoarray.plot.utils import subplot_save, hide_unused_axes
 from autoarray.structures.grids.irregular_2d import Grid2DIrregular
 
 
@@ -84,6 +84,7 @@ def subplot_interferometer_dataset(
         use_log10=use_log10,
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)
 
@@ -138,5 +139,6 @@ def subplot_interferometer_dirty_images(
         use_log10=use_log10,
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)

autoarray/fit/plot/fit_imaging_plots.py

@@ -3,7 +3,7 @@
 import matplotlib.pyplot as plt
 
 from autoarray.plot.array import plot_array
-from autoarray.plot.utils import subplot_save, symmetric_vmin_vmax
+from autoarray.plot.utils import subplot_save, symmetric_vmin_vmax, hide_unused_axes
 
 
 def subplot_fit_imaging(
@@ -118,5 +118,6 @@ def subplot_fit_imaging(
         lines=lines,
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)

autoarray/fit/plot/fit_interferometer_plots.py

@@ -5,7 +5,7 @@
 
 from autoarray.plot.array import plot_array
 from autoarray.plot.yx import plot_yx
-from autoarray.plot.utils import subplot_save, symmetric_vmin_vmax
+from autoarray.plot.utils import subplot_save, symmetric_vmin_vmax, hide_unused_axes
 
 
 def subplot_fit_interferometer(
@@ -98,6 +98,7 @@ def subplot_fit_interferometer(
         plot_axis_type="scatter",
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)
 
@@ -191,5 +192,6 @@ def subplot_fit_interferometer_dirty_images(
         use_log10=use_log10,
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, output_filename, output_format)

autoarray/inversion/plot/inversion_plots.py

@@ -1,13 +1,15 @@
+import csv
 import logging
 import numpy as np
-from typing import Optional
+from pathlib import Path
+from typing import Optional, Union
 
 import matplotlib.pyplot as plt
 from autoconf import conf
 
 from autoarray.inversion.mappers.abstract import Mapper
 from autoarray.plot.array import plot_array
-from autoarray.plot.utils import numpy_grid, numpy_lines, numpy_positions, subplot_save
+from autoarray.plot.utils import numpy_grid, numpy_lines, numpy_positions, subplot_save, hide_unused_axes
 from autoarray.inversion.plot.mapper_plots import plot_mapper
 from autoarray.structures.arrays.uniform_2d import Array2D
 
@@ -215,6 +217,7 @@ def _recon_array():
     except (TypeError, Exception):
         pass
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(fig, output_path, f"{output_filename}_{mapper_index}", output_format)
 
@@ -332,7 +335,40 @@ def subplot_mappings(
         lines=lines,
     )
 
+    hide_unused_axes(axes)
     plt.tight_layout()
     subplot_save(
         fig, output_path, f"{output_filename}_{pixelization_index}", output_format
     )
+
+
+def save_reconstruction_csv(
+    inversion,
+    output_path: Union[str, Path],
+) -> None:
+    """Write a CSV of each mapper's reconstruction and noise map to *output_path*.
+
+    One file is written per mapper: ``source_plane_reconstruction_{i}.csv``,
+    with columns ``y``, ``x``, ``reconstruction``, ``noise_map``.
+
+    Parameters
+    ----------
+    inversion
+        An ``AbstractInversion`` instance.
+    output_path
+        Directory in which to write the CSV files.
+    """
+    output_path = Path(output_path)
+    mapper_list = inversion.cls_list_from(cls=Mapper)
+
+    for i, mapper in enumerate(mapper_list):
+        y = mapper.source_plane_mesh_grid[:, 0]
+        x = mapper.source_plane_mesh_grid[:, 1]
+        reconstruction = inversion.reconstruction_dict[mapper]
+        noise_map = inversion.reconstruction_noise_map_dict[mapper]
+
+        with open(output_path / f"source_plane_reconstruction_{i}.csv", mode="w", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow(["y", "x", "reconstruction", "noise_map"])
+            for j in range(len(x)):
+                writer.writerow([float(y[j]), float(x[j]), float(reconstruction[j]), float(noise_map[j])])

autoarray/plot/array.py

@@ -47,11 +47,11 @@ def plot_array(
     title: str = "",
     xlabel: str = "",
     ylabel: str = "",
-    colormap: str = "jet",
+    colormap: Optional[str] = None,
     vmin: Optional[float] = None,
     vmax: Optional[float] = None,
     use_log10: bool = False,
-    aspect: str = "auto",
+    cb_unit: Optional[str] = None,
     origin_imshow: str = "upper",
     # --- figure control (used only when ax is None) -----------------------------
     figsize: Optional[Tuple[int, int]] = None,
@@ -105,8 +105,6 @@ def plot_array(
         Explicit color scale limits.
     use_log10
         When ``True`` a ``LogNorm`` is applied.
-    aspect
-        Passed directly to ``imshow``.
     origin_imshow
         Passed directly to ``imshow`` (``"upper"`` or ``"lower"``).
     figsize
@@ -135,6 +133,10 @@ def plot_array(
     if array is None or array.size == 0:
         return
 
+    if colormap is None:
+        from autoarray.plot.utils import _default_colormap
+        colormap = _default_colormap()
+
     # convert overlay params (safe for None and already-numpy inputs)
     border = numpy_grid(border)
     origin = numpy_grid(origin)
@@ -180,16 +182,33 @@ def plot_array(
     else:
         norm = None
 
+    # Compute the axes-box aspect ratio from the data extent so that the
+    # physical cell is correctly shaped and tight_layout has no whitespace
+    # to absorb.  This reproduces the old "square" subplot behaviour where
+    # ratio = x_range / y_range was passed to plt.subplot(aspect=ratio).
+    if extent is not None:
+        x_range = abs(extent[1] - extent[0])
+        y_range = abs(extent[3] - extent[2])
+        _box_aspect = (x_range / y_range) if y_range > 0 else 1.0
+    else:
+        h, w = array.shape[:2]
+        _box_aspect = (w / h) if h > 0 else 1.0
+
     im = ax.imshow(
         array,
         cmap=colormap,
         norm=norm,
         extent=extent,
-        aspect=aspect,
+        aspect="auto",  # image fills the axes box; box shape set below
         origin=origin_imshow,
     )
 
-    plt.colorbar(im, ax=ax)
+    # Shape the axes box to match the data so there is no surrounding
+    # whitespace when the panel is embedded in a subplot grid.
+    ax.set_aspect(_box_aspect, adjustable="box")
+
+    from autoarray.plot.utils import _apply_colorbar
+    _apply_colorbar(im, ax, cb_unit=cb_unit, is_subplot=not owns_figure)
 
     # --- overlays --------------------------------------------------------------
     if array_overlay is not None:
@@ -198,7 +217,7 @@ def plot_array(
             cmap="Greys",
             alpha=0.5,
             extent=extent,
-            aspect=aspect,
+            aspect="auto",
             origin=origin_imshow,
         )
 
@@ -223,7 +242,7 @@ def plot_array(
         ax.scatter(mesh_grid[:, 1], mesh_grid[:, 0], s=1, c="w", alpha=0.5)
 
     if positions is not None:
-        colors = ["r", "g", "b", "m", "c", "y"]
+        colors = ["k", "g", "b", "m", "c", "y"]
         for i, pos in enumerate(positions):
             ax.scatter(pos[:, 1], pos[:, 0], s=20, c=colors[i % len(colors)], zorder=5)
 
@@ -263,7 +282,7 @@ def plot_array(
             pass
 
     # --- labels / ticks --------------------------------------------------------
-    apply_labels(ax, title=title, xlabel=xlabel, ylabel=ylabel)
+    apply_labels(ax, title=title, xlabel=xlabel, ylabel=ylabel, is_subplot=not owns_figure)
 
     if extent is not None:
         apply_extent(ax, extent)

autoarray/plot/grid.py

@@ -32,7 +32,7 @@ def plot_grid(
     title: str = "",
     xlabel: str = 'x (")',
     ylabel: str = 'y (")',
-    colormap: str = "jet",
+    colormap: Optional[str] = None,
     buffer: float = 0.1,
     extent: Optional[Tuple[float, float, float, float]] = None,
     force_symmetric_extent: bool = True,
@@ -101,6 +101,10 @@ def plot_grid(
 
     lines = numpy_lines(lines)
 
+    if colormap is None:
+        from autoarray.plot.utils import _default_colormap
+        colormap = _default_colormap()
+
     owns_figure = ax is None
     if owns_figure:
         figsize = figsize or conf_figsize("figures")
@@ -126,7 +130,8 @@ def plot_grid(
                 ecolor=colors,
             )
 
-        plt.colorbar(sc, ax=ax)
+        from autoarray.plot.utils import _apply_colorbar
+        _apply_colorbar(sc, ax)
     else:
         if y_errors is None and x_errors is None:
             ax.scatter(grid[:, 1], grid[:, 0], s=1, c="k")

autoarray/plot/inversion.py

@@ -21,7 +21,7 @@ def plot_inversion_reconstruction(
     title: str = "Reconstruction",
     xlabel: str = 'x (")',
     ylabel: str = 'y (")',
-    colormap: str = "jet",
+    colormap: Optional[str] = None,
     vmin: Optional[float] = None,
     vmax: Optional[float] = None,
     use_log10: bool = False,
@@ -76,6 +76,10 @@ def plot_inversion_reconstruction(
     from autoarray.inversion.mesh.interpolator.delaunay import InterpolatorDelaunay
     from autoarray.inversion.mesh.interpolator.knn import InterpolatorKNearestNeighbor
 
+    if colormap is None:
+        from autoarray.plot.utils import _default_colormap
+        colormap = _default_colormap()
+
     owns_figure = ax is None
     if owns_figure:
         figsize = figsize or conf_figsize("figures")
@@ -192,7 +196,8 @@ def _plot_rectangular(ax, pixel_values, mapper, norm, colormap, extent):
             aspect="auto",
             origin="upper",
         )
-        plt.colorbar(im, ax=ax)
+        from autoarray.plot.utils import _apply_colorbar
+        _apply_colorbar(im, ax)
     else:
         y_edges, x_edges = mapper.mesh_geometry.edges_transformed.T
         Y, X = np.meshgrid(y_edges, x_edges, indexing="ij")
@@ -204,7 +209,8 @@ def _plot_rectangular(ax, pixel_values, mapper, norm, colormap, extent):
             norm=norm,
             cmap=colormap,
         )
-        plt.colorbar(im, ax=ax)
+        from autoarray.plot.utils import _apply_colorbar
+        _apply_colorbar(im, ax)
 
 
 def _plot_delaunay(ax, pixel_values, mapper, norm, colormap):
@@ -240,4 +246,5 @@ def _plot_delaunay(ax, pixel_values, mapper, norm, colormap):
         vals = pixel_values
 
     tc = ax.tripcolor(x, y, vals, cmap=colormap, norm=norm, shading="gouraud")
-    plt.colorbar(tc, ax=ax)
+    from autoarray.plot.utils import _apply_colorbar
+    _apply_colorbar(tc, ax)

autoarray/plot/segmentdata.py

@@ -1042,3 +1042,15 @@
         ]
     ),
 }
+
+COLORMAP_NAME = "autoarray"
+
+
+def register():
+    """Register the autoarray segmentdata colormap with matplotlib (idempotent)."""
+    import matplotlib
+    import matplotlib.colors as mcolors
+
+    if COLORMAP_NAME not in matplotlib.colormaps:
+        cmap = mcolors.LinearSegmentedColormap(COLORMAP_NAME, segmentdata)
+        matplotlib.colormaps.register(cmap)