feat: background thread for on-the-fly visualization#1212
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Run perform_quick_update on a daemon thread so the sampler is not blocked while matplotlib renders plots. Uses a latest-only queue pattern — if a new best-fit arrives before the previous visualization finishes, the stale request is silently replaced. JAX arrays are conditionally converted to numpy before handing to the worker thread. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Run
perform_quick_updateon a background daemon thread so the sampler is not blocked while matplotlib renders and saves plots. Uses a latest-only queue — new best-fits silently replace stale pending requests. Opt-in viaquick_update_background: trueingeneral.yaml.API Changes
BackgroundQuickUpdateclass inautofit.non_linear.quick_updatesupports_background_updateandsupports_jax_visualizationproperties onAnalysisbackground_quick_updateparameter toFitness.__init__quick_update_backgroundconfig option underupdates:ingeneral.yamlNo removals or breaking changes. See full details below.
Test Plan
Full API Changes (for automation & release notes)
Added
autofit.non_linear.quick_update.BackgroundQuickUpdate— daemon thread worker for background visualizationautofit.non_linear.quick_update._convert_jax_to_numpy(instance)— deep-copy with JAX→numpy conversionAnalysis.supports_background_updateproperty (defaultFalse)Analysis.supports_jax_visualizationproperty (defaultFalse)Fitness.__init__(..., background_quick_update=False)parameterFitness.shutdown_quick_update()methodgeneral.yaml→updates.quick_update_background(defaultfalse)Migration
Closes #1211
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