Replace giant/analysis.py's dual numpy-SampleCollection + polars paths with a
single polars-streaming implementation that produces the validation notebook's
plots directly from a `giant predict --coord local` parquet, sized for files
larger than RAM.
- Drop the numpy SampleCollection path (load_predicted_local, marginal_table,
correlation_matrices, direction_alignment, constraint_report, plot_kl_bars)
and the rollout observables; the 5 remaining plotters now take a parquet
path / LazyFrame and stream internally.
- Rewrite compute_event_observables_pl to aggregate in parallel streaming
polars (post-pos reconstruction as expressions) instead of a serial
pyarrow-batch + numpy loop, fixing a pre-existing OOM (holistic median +
323M-row join in the bin-edge sizing). Medians are approximated from a
streaming log-bin histogram with within-bin interpolation.
- Keep every full-file scan narrow (few columns): on a file larger than RAM,
peak mmap memory, not scan count, is the binding constraint. Marginals run
one dim at a time (~15GB peak) rather than a combined all-dims pass (OOM).
- Update analysis/validation.ipynb to the path-based API; delete the
analysis/export_*.py and compare_ode_steps_*.py one-off scripts.
- Rewrite tests/test_analysis.py around parquet fixtures with an inline numpy
oracle; add correlation/streaming-plotter and approx-median coverage.
Verified end-to-end on the 32GB predict file: full notebook completes at
~25GB peak (no OOM); event rollup runs at ~13 cores.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
lars
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