render: point positions panels (PyAutoLens#786)

AGENTS.md

@@ -70,9 +70,9 @@ everything else. Producers with several sub-datasets write one pair per sub-fold
 (`multi_dataset` per band, the datacube per channel, `cluster` per point-source plane). Many
 of these figures are the galaxy-scale plots and look poor on point / cluster / weak data —
 bespoke figures are a follow-up through the Eyes dashboard. `cluster` calls the two
-`PlotterPoint` methods directly instead of `VisualizerPoint`: the visualizer's critical-curve
-pre-pass costs ~100 s on the 15-galaxy multi-plane lens and the point fit subplot never draws
-the curves (the PNGs are byte-identical).
+`PlotterPoint` methods directly instead of `VisualizerPoint`, skipping the untracked tracer and
+galaxy figures; the "Fit Positions" panel still draws the critical curves, which cost ~2 min per
+dataset on the 15-galaxy multi-plane lens (the PNGs match the visualizer's).
 
 **What is tracked.** PNG figures under `scripts/<domain>/images/**`, `GALLERY.md` and
 `gallery/viz_manifest.yaml`. The FITS /

gallery/viz_manifest.yaml

@@ -14,26 +14,26 @@ figures:
   producer: scripts/cluster/visualization.py
   domain: cluster
   source: point_0
-  bytes: 33388
-  sha256: b49c895cb752c02fe77c56bc0f5850f261b65b4422fda42f08dcb1a968974995
+  bytes: 39187
+  sha256: 65b2b421786401373253a058a997f953beee6818b5bd492e93a69e4e86f046b0
 - file: scripts/cluster/images/visualization/point_0/fit.png
   producer: scripts/cluster/visualization.py
   domain: cluster
   source: point_0
-  bytes: 34546
-  sha256: b5fdd8c2f4242a15b26cc4d517ddc521845b4539679eb0ce48383cbf7a338b0c
+  bytes: 68264
+  sha256: c330570f6373ef9055c8d4dd5b7be3fa8b3f096cdb0117d2aec4b3d5987e43fa
 - file: scripts/cluster/images/visualization/point_1/dataset.png
   producer: scripts/cluster/visualization.py
   domain: cluster
   source: point_1
-  bytes: 23417
-  sha256: 02bf29263d691355b438037ac8cdb239637c5c9bcc14f1f903bf9a9a61bb778d
+  bytes: 36831
+  sha256: 41adea0870d20e15d7b52c040d364f74f81fa2952119e25c4dd52d12032a470f
 - file: scripts/cluster/images/visualization/point_1/fit.png
   producer: scripts/cluster/visualization.py
   domain: cluster
   source: point_1
-  bytes: 24592
-  sha256: c3c4eead8a0bbd536a4a20db89b8e6a4e32292ace3345cfaea65ce6a167a19eb
+  bytes: 65979
+  sha256: aaea68e094f6ff3d142c9a24cb92e750781b43b7768c82f3ff527356150c0f35
 - file: scripts/group/images/visualization/dataset.png
   producer: scripts/group/visualization.py
   domain: group
@@ -362,14 +362,14 @@ figures:
   producer: scripts/point_source_image_chi_squared/visualization.py
   domain: point_source_image_chi_squared
   source: ''
-  bytes: 27586
-  sha256: bcfe90b921dd5b206f457859b22598f89daf1cf75d53a41cf7c668bed8d2ad28
+  bytes: 38680
+  sha256: ad0e575bc1d7aded073a16481b3acdfa3e964364acb01dd3d98d9663e7b9a534
 - file: scripts/point_source_image_chi_squared/images/visualization/fit.png
   producer: scripts/point_source_image_chi_squared/visualization.py
   domain: point_source_image_chi_squared
   source: ''
-  bytes: 28952
-  sha256: 6b52412bb75eab16a3dc4ae4e0a4925d5f23c2ff7ed8bb9a6199f406f201cfed
+  bytes: 59950
+  sha256: dcf7b7a60281c49244264678c6c15ab0a5636be9c66fd4e555bba2b9fdecab40
 - file: scripts/point_source_image_chi_squared/images/visualization/fit_image_with_points.png
   producer: scripts/point_source_image_chi_squared/visualization.py
   domain: point_source_image_chi_squared
@@ -386,14 +386,14 @@ figures:
   producer: scripts/point_source_source_chi_squared/visualization.py
   domain: point_source_source_chi_squared
   source: ''
-  bytes: 27586
-  sha256: bcfe90b921dd5b206f457859b22598f89daf1cf75d53a41cf7c668bed8d2ad28
+  bytes: 38680
+  sha256: ad0e575bc1d7aded073a16481b3acdfa3e964364acb01dd3d98d9663e7b9a534
 - file: scripts/point_source_source_chi_squared/images/visualization/fit.png
   producer: scripts/point_source_source_chi_squared/visualization.py
   domain: point_source_source_chi_squared
   source: ''
-  bytes: 28823
-  sha256: caf49de216f6f80553e6c79b03231fd51ee626bd5939dd6864489e56a134320c
+  bytes: 60925
+  sha256: b9af86e928d6a1e395cc81aca38cffc7b94bb5b3af82484180d37f989b245472
 - file: scripts/point_source_source_chi_squared/images/visualization/fit_image_with_points.png
   producer: scripts/point_source_source_chi_squared/visualization.py
   domain: point_source_source_chi_squared

scripts/cluster/visualization.py

@@ -15,12 +15,12 @@
         point_0/dataset.png, point_0/fit.png   <- PlotterPoint.dataset_point, PlotterPoint.fit_point
         point_1/dataset.png, point_1/fit.png
 
-**Plotter, not Visualizer.** ``VisualizerPoint.visualize`` computes the tracer's critical curves
-and caustics on a 100x100 grid over the dataset extent before plotting, which for this 15-galaxy
-multi-plane lens costs ~100 s per run (measured 35 s + 67 s) — and the point-source fit subplot
-does not draw those curves. So this producer calls the same two ``PlotterPoint`` methods the
-visualizer calls (``dataset_point`` in ``visualize_before_fit``; ``fit_point(quick_update=True)``
-in ``visualize``) with empty curve lists, writing byte-for-byte the figures the visualizer would.
+**Plotter, not Visualizer.** ``VisualizerPoint.visualize`` also renders the tracer and galaxy
+figures this repo does not track, so this producer calls the same two ``PlotterPoint`` methods the
+visualizer calls (``dataset_point(solver=...)`` in ``visualize_before_fit``;
+``fit_point(quick_update=True)`` in ``visualize``), writing the figures the visualizer would. The
+"Fit Positions" panel draws the tracer's critical curves, which ``fit_point`` computes on a 100x100
+grid covering the solver grid: for this 15-galaxy multi-plane lens that costs ~2 min per dataset.
 
 The point plotter writes ``dataset_point.png`` (renamed to ``dataset.png``); every other file
 the visualizer writes is deleted.
@@ -120,14 +120,10 @@ def _repo_root() -> Path:
 
     t0 = time.perf_counter()
     plotter = PlotterPoint(image_path=paths.image_path, title_prefix=analysis.title_prefix)
-    plotter.dataset_point(dataset=analysis.dataset)
+    plotter.dataset_point(dataset=analysis.dataset, solver=analysis.solver)
     plotter.fit_point(
         fit=analysis.fit_for_visualization(instance=instance),
         quick_update=True,
-        image_plane_lines=[],
-        image_plane_line_colors=[],
-        source_plane_lines=[],
-        source_plane_line_colors=[],
     )
     keep_dataset_and_fit(point_path, dataset_png="dataset_point.png")
     print(f"visualize [{dataset.name}]: {time.perf_counter() - t0:.1f}s")