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>

configs/default.toml

@@ -0,0 +1,14 @@
+# git: c3bf3abebfe29a10fe42b9cbafbb3460ab78d243
+
+[train]
+mode         = "flow"
+epochs       = 100
+batch_size   = 4096
+lr           = 3e-4
+val_fraction = 0.1
+num_workers  = 4
+
+[model]
+hidden_dim = 256
+n_blocks   = 6
+emb_dim    = 16