5 Commits

Include renames

Author	SHA1	Message	Date
larsandClaude Sonnet 5	b944bba8fb	Offset event_id per file to avoid cross-file collisions ... Each input parquet file is one Geant4 job (scripts/steps_to_parquet.py), and a job's event_id numbering always restarts from 0 — so loading multiple files together (a directory or .manifest) let same-numbered events from different files collapse into one during the event index scan and train/val split, corrupting both. Every per-file event_id now gets offset by file index * EVENT_ID_FILE_STRIDE (giant/data/loader.py), threaded through the setup-cache event index, the streaming dataset, and predict/rollout seeding. Bumps the setup-cache format version so stale sidecars computed pre-fix are invalidated. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-30 11:36:48 +02:00
larsandClaude Sonnet 4.6	e6e0eb22bf	Implement Phase 2: secondary particle prediction ... Two-stage factorisation: Stage 1 predicts 9D primary kinematics + n_sec classification head (COND_DIM reduced to 8, dropping n_sec/e_sec inputs); Stage 2 (SecondaryDecoder) generates K_MAX=15 secondary slots via masked flow matching over (stick_logit, local_dir, type_emb) conditioned on Stage 1 output. Joint training with combined loss L_s1 + λ_nsec*L_nsec + λ_s2*L_s2. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-29 11:34:31 +02:00
larsandClaude Sonnet 4.6	8cebc4809d	Apply ruff format and document lint/type tooling in CLAUDE.md ... First repo-wide ruff format pass, plus a note in CLAUDE.md to run ruff and ty periodically. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-18 17:40:27 +02:00
larsandClaude Sonnet 4.6	53fd2e4405	Add ruff and ty as dev dependencies, fix lint/type findings ... ruff removed unused imports across analysis.py and several test files. ty caught a wrong dict[int, int] annotation on StreamingStepsDataset's mat_map (materials are strings) and a real bug in steps_to_parquet.py where --compression none passed None to polars' write_parquet, which only accepts the literal "uncompressed". Also narrows a few Optional-typed attributes (ddpm_schedule, Normalizer.mean/std) with asserts and aligns __getitem__'s parameter name with torch's Dataset base class. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-18 17:38:40 +02:00
larsandClaude Sonnet 4.6	9277d79dff	Implement Phase 1: full data pipeline, model, training, and config support ... - Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-17 10:48:03 +02:00