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perf: enable apply_sparse_operator on Delaunay JAX profilers - #65
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Wire the now-unblocked apply_sparse_operator(use_jax=True) path into the two Delaunay JAX profilers so per-fit curvature assembly uses the FFT-based sparse precision-matrix preload instead of dense DFT per source pixel. Unblocked by PyAutoLabs/PyAutoArray#316 (Pmax > 1 extent-indexing fix); the path was previously guarded with NotImplementedError on Delaunay by PR #315. - jax_profiling/jit/interferometer/delaunay.py: new "apply_sparse_operator" timer section right after dataset_load. - jax_profiling/jit/datacube/delaunay.py: chain .apply_sparse_operator(use_jax=True, show_progress=False) onto the per-channel from_fits in the dataset_list comprehension. Validation: - jit/interferometer/delaunay.py: end-to-end run, eager log_likelihood = -3152.876, figure_of_merit = -3167.526, all 11 per-step JIT stages completed. - jit/datacube/delaunay.py: end-to-end run on the hannah 34-channel cube, cube log_evidence = -6964494.694 (regression assertion PASSED), all 8 per-step JIT stages completed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Enables
apply_sparse_operator(use_jax=True)on the two Delaunay JAX profilers injax_profiling/jit/. Per-fit curvature assembly now uses the FFT-based sparse precision-matrix preload instead of dense DFT per source pixel.This was blocked by the
NotImplementedErrorguard on Delaunay added in PyAutoLabs/PyAutoArray#315 and replaced by the actual math fix in PyAutoLabs/PyAutoArray#316.Scripts Changed
jax_profiling/jit/interferometer/delaunay.py— new"apply_sparse_operator"timer section right afterdataset_load, callingdataset.apply_sparse_operator(use_jax=True, show_progress=True). The 11 per-step JIT-profiled stages downstream now operate on the sparse-curvature path.jax_profiling/jit/datacube/delaunay.py— chains.apply_sparse_operator(use_jax=True, show_progress=False)onto each per-channelfrom_fitsin thedataset_listcomprehension. 34-channel per-channel precision-matrix preload.Upstream PR
Test plan
python jax_profiling/jit/interferometer/delaunay.py— end-to-end. Eagerlog_likelihood = -3152.876,figure_of_merit = -3167.526. All 11 per-step JIT stages completed. Results JSON + PNG written.python jax_profiling/jit/datacube/delaunay.py— end-to-end on the hannah 34-channel cube. Eager cubelog_evidence = -6964494.694(regression assertion PASSED — agrees with the existing hardcoded reference, confirming the upstream library fix preserves correctness on the eager numpy path too). All 8 per-step JIT stages completed. Results JSON + PNG written.🤖 Generated with Claude Code