refactor: dispatch visualize_combined per Visualizer type in FactorGraphModel#1340
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FactorGraphModel.visualize_combined (and perform_quick_update) routed EVERY factor into the lead factor's Visualizer.visualize_combined, which breaks mixed-dataset graphs — the first one (AnalysisImaging + AnalysisWeak, the weak series' combined strong+weak example) crashed until PyAutoLens's visualizers grew type filters (PyAutoLabs/PyAutoLens#587). Fix the producer: group (factor, instance) pairs by the analysis Visualizer class (order preserved) and issue one combined call per group. Homogeneous graphs produce exactly one group — byte-identical to the previous behaviour. The PyAutoLens filters remain as defence in depth. Closes #1339. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Summary
FactorGraphModel.visualize_combined/perform_quick_updaterouted every factor into the lead factor'sVisualizer.visualize_combined, crashing mixed-dataset graphs (first hit by AnalysisImaging + AnalysisWeak in the weak series; PyAutoLens papered over it with type filters in PyAutoLabs/PyAutoLens#587). This fixes the producer: factors group by their analysisVisualizerclass (order preserved) and each group gets one combined call with its matching sub-instances. Homogeneous graphs form a single group — byte-identical previous behaviour. Closes #1339.API Changes
None — internal dispatch; the public visualize surface is unchanged.
Test Plan
test_visualize_combined_dispatch.py: homogeneous graph = one call with all factors/instances; mixed graph = one call per visualizer type with order-preserved sub-lists.test_autofit/: 1424 passed, 14 skipped.Validation checklist (--auto run)
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