4 Commits

Include renames

Author	SHA1	Message	Date
lars	55332db67a	Bump ruff line-length to 120 and reformat ... Rejoins lines that only wrapped because they exceeded the old 88-char limit; ruff check and the full test suite (725 passed) are unaffected.	2026-08-12 13:33:09 +02:00
larsandClaude Sonnet 5	24445b7427	Add coverage for router-center seeding, geometry batch reader, material topN cache, and setup-cache corruption paths ... Closes the highest-value coverage gaps found via pytest-cov: pipeline.py's EnergyRouter quantile-seeding (the roadmap's flagged fix for the failed MoE rollout benchmark) had zero coverage, geometry.py's real parquet-batch reader was always mocked, the material top-N-map cache-hit branch was untested (only pdg's was), and setup_cache.py was missing malformed-cache-body and unknown-axis error paths. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-10 11:38:04 +02:00
larsandClaude Sonnet 5	3faa272562	Add fast slab lookup for the GeometryOracle, replacing knn as the default ... miniCaloSim's detector is a stack of planar layer slabs along one axis, so material/layer_id are a pure function of depth. The new "slab" method exploits this with an exact O(log #segments) binary search over depth-axis segment boundaries, instead of a nearest-neighbour search over hundreds of thousands of reference points — much cheaper per call, which matters since the oracle is queried on every autoregressive rollout step. "knn"/"svm" remain as fallbacks for non-slab geometries. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-08 11:42:12 +02:00
larsandClaude Opus 4.8	26a176aeaa	Add autoregressive shower rollout driver ... Closes the loop from single-step prediction into full showers: - giant/geometry.py + `dwarf build-geometry-oracle`: learn position -> (material, layer_id) from data (KNN/SVM) to supply the conditioning the surrogate does not predict; flag detector escape by NN distance. - giant/rollout.py: breadth-first batched frontier that steps all active tracks, spawns secondaries as new tracks, and terminates on energy cutoff, per-track max steps, escape, or natural end. Energy is deposited locally on every stop except escape (leakage), so showers conserve energy exactly. - `giant rollout` CLI: seed from real events (argmax pre_E), load checkpoint, write a world-frame steps parquet + YAML sidecar. - giant/analysis.py: compute_rollout_observables + plot_rollout_* for single-sided longitudinal/transverse/total-energy shower profiles; analysis/export_rollout_observables.py driver. - scikit-learn added as an optional `geometry` extra (lazy-imported). - Tests: tests/test_geometry.py, tests/test_rollout.py. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-08 10:10:36 +02:00