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	93b19911f8	v0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode ... - giant/sample.py: fix every sampler's call convention against Stage1Model/Stage2OneShot's actual forward signatures (was still calling model(x, t, cond_cont, cond_cat) positionally); add sample_secondaries_ar (free-running AR loop, unsnapped history feature) and sample_stage1/sample_stage2/resolve_n_sec dispatch helpers that read each stage's generator_kind/decoder off the model instance itself. - giant/particles.py: decode_topn_class (argmax + other_policy) and decode_embedding_nearest (L1-snap + distance) turn a secondary's "onehot"/"embedding" type prediction into a concrete PDG. - giant/rollout.py: decode_secondary_identity routes all three particle_type.target values to real mass/charge; per-stage generator dispatch (drops the single shared `mode` string, adds ddpm support); L1DistCollector accumulates the §11.3 embedding-distance diagnostic. - giant/cli.py: drop the onehot/embedding-target rejection gate (narrowed to the still-unimplemented conditioning.particle/material.type=onehot axis); fix the dead model_cfg.get("mode") bug in predict/rollout. - giant/analysis/: new type_embedding_l1_distance PlotSpec, wired through the rollout YAML sidecar (no live-model call needed, unlike router_gating -- the histogram is already pre-aggregated at rollout time). - Un-xfail every test that was blocked on this step (test_rollout.py, test_flow.py, test_wgan.py, test_phase2.py, test_router.py, test_validate.py); add test_sample.py, test_type_embedding_distance.py. Known follow-up: giant/validate.py still unpacks the training val-batch as a stale 6-tuple and doesn't use the new per-stage dispatch, so marginal validation during training degrades gracefully with a warning rather than working -- not in this step's scope. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-07 10:37:57 +02:00
larsandClaude Sonnet 5	06c9ad8e5f	Fix crashes in physical-property conditioning edge cases ... - make_seed_frontier only resolves particle mass/charge in "physical" mode, so "embedding"-mode rollouts no longer crash on a seed PDG code giant.particles can't resolve (the TERM_UNKNOWN_PDG gate now handles it). - nearest_known_pdg skips unresolvable candidate PDG codes instead of raising and killing the whole rollout/predict run. - predict/rollout fail with a clear message when a checkpoint predates the sec_phys normalizer, instead of a bare KeyError. - validate_marginals' phys_kl degrades to NaN (matching the energy_fraction_kl pattern) instead of crashing when a validated batch has zero secondaries on either side. - Correct CLAUDE.md's stale claim that the materials table is unfilled. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-20 10:36:25 +02:00
larsandClaude Sonnet 5	68fb99bed8	Condition on material/particle physical properties instead of learned embeddings ... Adds model.conditioning = "physical" | "embedding": physical mode routes particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through small MLPs to replace the learned PDG/material embedding tables, so the surrogate generalizes to PDG codes/materials outside the training vocab instead of memorizing it. "embedding" stays available as the comparison baseline (old checkpoints without the key default to it). Stage 2 now regresses a secondary's mass/charge directly against a fixed physics-derived target instead of a learned/snapped embedding, and uses no snapping at inference — the model's raw predicted (mass, charge) is the secondary's physical identity, including for its own further rollout steps. A separate reporting-only nearest-known-PDG lookup (never fed back into the model) populates output pdg columns / the embedding-mode rollout fallback. giant/materials.py's table is populated with Geant4's own built-in NIST constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both by runtime lookup and by searching minicalosim's history) that it's never actually a constructed Geant4 material there, only documentation/UI color-map text. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-17 15:12:54 +02:00