Add giant.analysis module for notebook-based model quality diagnostics

Provides stratified marginal comparisons, joint-structure checks (correlation
matrices, physically-coupled pairwise plots, direction alignment), and
physical-constraint validation (unit-norm directions, non-negative raw
targets) for a trained model's generated samples, building on the aggregate
marginal/KL check already in giant.validate.

Supports two entry points: live sampling against a checkpoint + val data
(load_model_bundle/collect_samples), or loading a precomputed
`giant predict --coord local` parquet directly (load_predicted_local) without
needing the checkpoint at all. Predict output is now tagged with parquet
schema metadata so the loader can verify a file's format and reject
coord=global or untagged files with a clear error instead of guessing from
column names.

Also extends the config git-hash mismatch warning (added for --config
loading) to checkpoint loading: both `giant predict` and
analysis.load_model_bundle now look for a config.toml next to the checkpoint
and warn (without failing) if it was generated from a different git commit.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

pyproject.toml

@@ -21,6 +21,9 @@ convert = [
     "awkward>=2.6",
     "polars>=1.0",
 ]
+analysis = [
+    "matplotlib>=3.8",
+]
 
 [project.scripts]
 giant = "giant.cli:app"